From b25ab02610acbedef06786bb1dadd418a37e45ce Mon Sep 17 00:00:00 2001
From: Tomasz Kalinowski <kalinowskit@gmail.com>
Date: Tue, 21 May 2024 10:12:29 -0400
Subject: [PATCH 01/11] update urls

---
 R/applications.R                           |  4 ++--
 R/losses.R                                 | 10 +++++-----
 R/metrics.R                                |  2 +-
 R/optimizers-schedules.R                   |  2 +-
 man/application_mobilenet_v3_large.Rd      |  2 +-
 man/application_mobilenet_v3_small.Rd      |  2 +-
 man/layer_tfsm.Rd                          |  4 ++--
 man/learning_rate_schedule_cosine_decay.Rd |  2 +-
 man/loss_binary_focal_crossentropy.Rd      |  8 ++++----
 man/loss_categorical_focal_crossentropy.Rd |  2 +-
 man/metric_binary_focal_crossentropy.Rd    |  2 +-
 vignettes/getting_started.Rmd              |  2 +-
 12 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/R/applications.R b/R/applications.R
index 92694a481..50b95e60c 100644
--- a/R/applications.R
+++ b/R/applications.R
@@ -2642,7 +2642,7 @@ function (input_shape = NULL, alpha = 1, include_top = TRUE,
 #'
 #' # Reference
 #' - [Searching for MobileNetV3](
-#'     https://arxiv.org/pdf/1905.02244.pdf) (ICCV 2019)
+#'     https://arxiv.org/pdf/1905.02244) (ICCV 2019)
 #'
 #' The following table describes the performance of MobileNets v3:
 #' ------------------------------------------------------------------------
@@ -2788,7 +2788,7 @@ function (input_shape = NULL, alpha = 1, minimalistic = FALSE,
 #'
 #' # Reference
 #' - [Searching for MobileNetV3](
-#'     https://arxiv.org/pdf/1905.02244.pdf) (ICCV 2019)
+#'     https://arxiv.org/pdf/1905.02244) (ICCV 2019)
 #'
 #' The following table describes the performance of MobileNets v3:
 #' ------------------------------------------------------------------------
diff --git a/R/losses.R b/R/losses.R
index 9435444a6..4981aca12 100644
--- a/R/losses.R
+++ b/R/losses.R
@@ -131,7 +131,7 @@ function (y_true, y_pred, from_logits = FALSE, label_smoothing = 0,
 #' Computes focal cross-entropy loss between true labels and predictions.
 #'
 #' @description
-#' According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf), it
+#' According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002), it
 #' helps to apply a focal factor to down-weight easy examples and focus more on
 #' hard examples. By default, the focal tensor is computed as follows:
 #'
@@ -157,7 +157,7 @@ function (y_true, y_pred, from_logits = FALSE, label_smoothing = 0,
 #'     when `from_logits=TRUE`) or a probability (i.e, value in `[0., 1.]` when
 #'     `from_logits=FALSE`).
 #'
-#' According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf), it
+#' According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002), it
 #' helps to apply a "focal factor" to down-weight easy examples and focus more
 #' on hard examples. By default, the focal tensor is computed as follows:
 #'
@@ -274,13 +274,13 @@ function (y_true, y_pred, from_logits = FALSE, label_smoothing = 0,
 #' @param alpha
 #' A weight balancing factor for class 1, default is `0.25` as
 #' mentioned in reference [Lin et al., 2018](
-#' https://arxiv.org/pdf/1708.02002.pdf).  The weight for class 0 is
+#' https://arxiv.org/pdf/1708.02002).  The weight for class 0 is
 #' `1.0 - alpha`.
 #'
 #' @param gamma
 #' A focusing parameter used to compute the focal factor, default is
 #' `2.0` as mentioned in the reference
-#' [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf).
+#' [Lin et al., 2018](https://arxiv.org/pdf/1708.02002).
 #'
 #' @param from_logits
 #' Whether to interpret `y_pred` as a tensor of
@@ -450,7 +450,7 @@ function (y_true, y_pred, from_logits = FALSE, label_smoothing = 0,
 #' `class_weights`. We expect labels to be provided in a `one_hot`
 #' representation.
 #'
-#' According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf), it
+#' According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002), it
 #' helps to apply a focal factor to down-weight easy examples and focus more on
 #' hard examples. The general formula for the focal loss (FL)
 #' is as follows:
diff --git a/R/metrics.R b/R/metrics.R
index ddcd5d861..2308f4a3c 100644
--- a/R/metrics.R
+++ b/R/metrics.R
@@ -3,7 +3,7 @@
 #' Computes the binary focal crossentropy loss.
 #'
 #' @description
-#' According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf), it
+#' According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002), it
 #' helps to apply a focal factor to down-weight easy examples and focus more on
 #' hard examples. By default, the focal tensor is computed as follows:
 #'
diff --git a/R/optimizers-schedules.R b/R/optimizers-schedules.R
index 231897c25..5af2955fa 100644
--- a/R/optimizers-schedules.R
+++ b/R/optimizers-schedules.R
@@ -7,7 +7,7 @@
 #' SGDR: Stochastic Gradient Descent with Warm Restarts.
 #'
 #' For the idea of a linear warmup of our learning rate,
-#' see [Goyal et al.](https://arxiv.org/pdf/1706.02677.pdf).
+#' see [Goyal et al.](https://arxiv.org/pdf/1706.02677).
 #'
 #' When we begin training a model, we often want an initial increase in our
 #' learning rate followed by a decay. If `warmup_target` is an int, this
diff --git a/man/application_mobilenet_v3_large.Rd b/man/application_mobilenet_v3_large.Rd
index daf255a34..7bc18635b 100644
--- a/man/application_mobilenet_v3_large.Rd
+++ b/man/application_mobilenet_v3_large.Rd
@@ -99,7 +99,7 @@ Instantiates the MobileNetV3Large architecture.
 }
 \section{Reference}{
 \itemize{
-\item \href{https://arxiv.org/pdf/1905.02244.pdf}{Searching for MobileNetV3} (ICCV 2019)
+\item \href{https://arxiv.org/pdf/1905.02244}{Searching for MobileNetV3} (ICCV 2019)
 }
 \subsection{The following table describes the performance of MobileNets v3:}{
 
diff --git a/man/application_mobilenet_v3_small.Rd b/man/application_mobilenet_v3_small.Rd
index a9071052c..ec1950833 100644
--- a/man/application_mobilenet_v3_small.Rd
+++ b/man/application_mobilenet_v3_small.Rd
@@ -99,7 +99,7 @@ Instantiates the MobileNetV3Small architecture.
 }
 \section{Reference}{
 \itemize{
-\item \href{https://arxiv.org/pdf/1905.02244.pdf}{Searching for MobileNetV3} (ICCV 2019)
+\item \href{https://arxiv.org/pdf/1905.02244}{Searching for MobileNetV3} (ICCV 2019)
 }
 \subsection{The following table describes the performance of MobileNets v3:}{
 
diff --git a/man/layer_tfsm.Rd b/man/layer_tfsm.Rd
index 816169c03..4dc8db48d 100644
--- a/man/layer_tfsm.Rd
+++ b/man/layer_tfsm.Rd
@@ -59,8 +59,8 @@ model |> export_savedmodel("path/to/artifact")
 ## Output Type:
 ##   TensorSpec(shape=(None, 10), dtype=tf.float32, name=None)
 ## Captures:
-##   124766388878704: TensorSpec(shape=(), dtype=tf.resource, name=None)
-##   124766388873776: TensorSpec(shape=(), dtype=tf.resource, name=None)
+##   130838090025264: TensorSpec(shape=(), dtype=tf.resource, name=None)
+##   130838090034768: TensorSpec(shape=(), dtype=tf.resource, name=None)
 
 }\if{html}{\out{</div>}}
 
diff --git a/man/learning_rate_schedule_cosine_decay.Rd b/man/learning_rate_schedule_cosine_decay.Rd
index 97631db30..a9211cd0d 100644
--- a/man/learning_rate_schedule_cosine_decay.Rd
+++ b/man/learning_rate_schedule_cosine_decay.Rd
@@ -42,7 +42,7 @@ See \href{https://arxiv.org/abs/1608.03983}{Loshchilov & Hutter, ICLR2016},
 SGDR: Stochastic Gradient Descent with Warm Restarts.
 
 For the idea of a linear warmup of our learning rate,
-see \href{https://arxiv.org/pdf/1706.02677.pdf}{Goyal et al.}.
+see \href{https://arxiv.org/pdf/1706.02677}{Goyal et al.}.
 
 When we begin training a model, we often want an initial increase in our
 learning rate followed by a decay. If \code{warmup_target} is an int, this
diff --git a/man/loss_binary_focal_crossentropy.Rd b/man/loss_binary_focal_crossentropy.Rd
index 121470a77..57dbc83c2 100644
--- a/man/loss_binary_focal_crossentropy.Rd
+++ b/man/loss_binary_focal_crossentropy.Rd
@@ -27,12 +27,12 @@ loss_binary_focal_crossentropy(
 binary classes 0 and 1.}
 
 \item{alpha}{A weight balancing factor for class 1, default is \code{0.25} as
-mentioned in reference \href{https://arxiv.org/pdf/1708.02002.pdf}{Lin et al., 2018}.  The weight for class 0 is
+mentioned in reference \href{https://arxiv.org/pdf/1708.02002}{Lin et al., 2018}.  The weight for class 0 is
 \code{1.0 - alpha}.}
 
 \item{gamma}{A focusing parameter used to compute the focal factor, default is
 \code{2.0} as mentioned in the reference
-\href{https://arxiv.org/pdf/1708.02002.pdf}{Lin et al., 2018}.}
+\href{https://arxiv.org/pdf/1708.02002}{Lin et al., 2018}.}
 
 \item{from_logits}{Whether to interpret \code{y_pred} as a tensor of
 \href{https://en.wikipedia.org/wiki/Logit}{logit} values. By default, we
@@ -60,7 +60,7 @@ Binary focal crossentropy loss value
 with shape = \verb{[batch_size, d0, .. dN-1]}.
 }
 \description{
-According to \href{https://arxiv.org/pdf/1708.02002.pdf}{Lin et al., 2018}, it
+According to \href{https://arxiv.org/pdf/1708.02002}{Lin et al., 2018}, it
 helps to apply a focal factor to down-weight easy examples and focus more on
 hard examples. By default, the focal tensor is computed as follows:
 
@@ -87,7 +87,7 @@ when \code{from_logits=TRUE}) or a probability (i.e, value in \verb{[0., 1.]} wh
 \code{from_logits=FALSE}).
 }
 
-According to \href{https://arxiv.org/pdf/1708.02002.pdf}{Lin et al., 2018}, it
+According to \href{https://arxiv.org/pdf/1708.02002}{Lin et al., 2018}, it
 helps to apply a "focal factor" to down-weight easy examples and focus more
 on hard examples. By default, the focal tensor is computed as follows:
 
diff --git a/man/loss_categorical_focal_crossentropy.Rd b/man/loss_categorical_focal_crossentropy.Rd
index fe05fc361..bdbfa6789 100644
--- a/man/loss_categorical_focal_crossentropy.Rd
+++ b/man/loss_categorical_focal_crossentropy.Rd
@@ -61,7 +61,7 @@ classes and if you want to handle class imbalance without using
 \code{class_weights}. We expect labels to be provided in a \code{one_hot}
 representation.
 
-According to \href{https://arxiv.org/pdf/1708.02002.pdf}{Lin et al., 2018}, it
+According to \href{https://arxiv.org/pdf/1708.02002}{Lin et al., 2018}, it
 helps to apply a focal factor to down-weight easy examples and focus more on
 hard examples. The general formula for the focal loss (FL)
 is as follows:
diff --git a/man/metric_binary_focal_crossentropy.Rd b/man/metric_binary_focal_crossentropy.Rd
index 20fe06b39..5670ad186 100644
--- a/man/metric_binary_focal_crossentropy.Rd
+++ b/man/metric_binary_focal_crossentropy.Rd
@@ -44,7 +44,7 @@ Binary focal crossentropy loss value
 with shape = \verb{[batch_size, d0, .. dN-1]}.
 }
 \description{
-According to \href{https://arxiv.org/pdf/1708.02002.pdf}{Lin et al., 2018}, it
+According to \href{https://arxiv.org/pdf/1708.02002}{Lin et al., 2018}, it
 helps to apply a focal factor to down-weight easy examples and focus more on
 hard examples. By default, the focal tensor is computed as follows:
 
diff --git a/vignettes/getting_started.Rmd b/vignettes/getting_started.Rmd
index 65120bf0d..71e8872fb 100644
--- a/vignettes/getting_started.Rmd
+++ b/vignettes/getting_started.Rmd
@@ -233,7 +233,7 @@ Keras provides a vocabulary for building deep learning models that is simple, el
 
 ### Deep Learning with R Book
 
-If you want a more comprehensive introduction to both Keras and the concepts and practice of deep learning, we recommend the [Deep Learning with R, 2nd Edition](https://rstd.io/dlwr-2e) book from Manning. This book is a collaboration between François Chollet, the creator of (Python) Keras, J.J. Allaire, who wrote the original R interface to Keras, and Tomasz Kalinowski, the maintainer of the R interface to Keras.
+If you want a more comprehensive introduction to both Keras and the concepts and practice of deep learning, we recommend the [Deep Learning with R, 2nd Edition](https://www.manning.com/books/deep-learning-with-r-second-edition) book from Manning. This book is a collaboration between François Chollet, the creator of (Python) Keras, J.J. Allaire, who wrote the original R interface to Keras, and Tomasz Kalinowski, the maintainer of the R interface to Keras.
 
 The book presumes no significant knowledge of machine learning and deep learning, and goes all the way from basic theory to advanced practical applications, all using the R interface to Keras.
 

From f91969b02c5e0e55e9d8af3dac5cfab2f796c488 Mon Sep 17 00:00:00 2001
From: Tomasz Kalinowski <kalinowskit@gmail.com>
Date: Tue, 21 May 2024 10:15:08 -0400
Subject: [PATCH 02/11] Increment version number to 1.0.0

---
 DESCRIPTION | 2 +-
 NEWS.md     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 23dc30827..b37c83ff8 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: keras3
 Type: Package
 Title: R Interface to 'Keras'
-Version: 0.2.0.9000
+Version: 1.0.0
 Authors@R: c(
   person("Tomasz", "Kalinowski", role = c("aut", "cph", "cre"),
          email = "tomasz@posit.co"),
diff --git a/NEWS.md b/NEWS.md
index b3f23133d..1c7100c6e 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,4 @@
-# keras3 (development version)
+# keras3 1.0.0
 
 - Chains of `layer_*` calls with `|>` now instantiate layers in the
   same order as `%>%` pipe chains: left-hand-side first (#1440).

From a0b68224a887a0b8d52f5681ef0d2969d1c2b22c Mon Sep 17 00:00:00 2001
From: Tomasz Kalinowski <kalinowskit@gmail.com>
Date: Tue, 21 May 2024 10:16:42 -0400
Subject: [PATCH 03/11] update build tools

---
 tools/make-cran-pkg.R | 18 ++++++++++++++++++
 tools/make-man.R      |  3 +++
 2 files changed, 21 insertions(+)

diff --git a/tools/make-cran-pkg.R b/tools/make-cran-pkg.R
index e69de29bb..22ecb49e2 100644
--- a/tools/make-cran-pkg.R
+++ b/tools/make-cran-pkg.R
@@ -0,0 +1,18 @@
+
+devtools::build(manual = TRUE)
+
+withr::with_dir("..", {
+  system("R CMD check --as-cran keras3_*.tar.gz")
+})
+
+r"(
+
+tools/make-man.R && \
+tools/knit-vignettes.R && \
+tools/knit-examples.R && \
+tools/make-website.R
+
+
+tools/make-man.R && tools/make-website.R
+
+)"
diff --git a/tools/make-man.R b/tools/make-man.R
index c1bef3d80..1a3bc2bc9 100755
--- a/tools/make-man.R
+++ b/tools/make-man.R
@@ -41,3 +41,6 @@ cr_family <- function(rd_file) {
 Sys.glob("man/*.Rd") %>%
   walk(cr_family)
   # walk(itemize_family)
+
+
+remotes::install_local(force = TRUE)
\ No newline at end of file

From 442bad216f6eb4cc0a28f4b675f65a2599254bea Mon Sep 17 00:00:00 2001
From: Tomasz Kalinowski <kalinowskit@gmail.com>
Date: Tue, 21 May 2024 10:32:38 -0400
Subject: [PATCH 04/11] back to return_dict=TRUE

---
 R/model-training.R | 35 ++++++++++++++++++++---------------
 1 file changed, 20 insertions(+), 15 deletions(-)

diff --git a/R/model-training.R b/R/model-training.R
index a1d3fdb1c..c71537b8f 100644
--- a/R/model-training.R
+++ b/R/model-training.R
@@ -269,17 +269,20 @@ function (object, x = NULL, y = NULL, ..., batch_size = NULL,
                                verbose = as_model_verbose_arg),
                           ignore = "object",
                           force = "verbose")
-    args[["return_dict"]] <- FALSE
+
+    ## return_dict=TRUE because object$metrics_names returns wrong value
+    ## (e.g., "compile_metrics" instead of "mae")
+    args[["return_dict"]] <- TRUE
 
     if(inherits(args$x, "tensorflow.python.data.ops.dataset_ops.DatasetV2") &&
        !is.null(args$batch_size))
       stop("batch_size can not be specified with a TF Dataset")
 
     result <- do.call(object$evaluate, args)
-    if (length(result) > 1L) {
-      result <- as.list(result)
-      names(result) <- object$metrics_names
-    }
+    # if (length(result) > 1L) { ## if return_dict=FALSE
+    #   result <- as.list(result)
+    #   names(result) <- object$metrics_names
+    # }
 
     tfruns::write_run_metadata("evaluation", unlist(result))
 
@@ -761,11 +764,12 @@ function (object, x, y = NULL, sample_weight = NULL, ...)
     result <- object$test_on_batch(as_array(x),
                                    as_array(y),
                                    as_array(sample_weight), ...,
-                                   return_dict = FALSE)
-    if (length(result) > 1L) {
-      result <- as.list(result)
-      names(result) <- object$metrics_names
-    } else if (is_scalar(result)) {
+                                   return_dict = TRUE)
+    # if (length(result) > 1L) {
+    #   result <- as.list(result)
+    #   names(result) <- object$metrics_names
+    # } else
+    if (is_scalar(result)) {
       result <- result[[1L]]
     }
     result
@@ -824,11 +828,12 @@ function (object, x, y = NULL, sample_weight = NULL, class_weight = NULL)
                                     as_array(y),
                                     as_array(sample_weight),
                                     class_weight = as_class_weight(class_weight),
-                                    return_dict = FALSE)
-    if (length(result) > 1L) {
-      result <- as.list(result)
-      names(result) <- object$metrics_names
-    } else if (is_scalar(result)) {
+                                    return_dict = TRUE)
+    # if (length(result) > 1L) {
+    #   result <- as.list(result)
+    #   names(result) <- object$metrics_names
+    # } else
+    if (is_scalar(result)) {
       result <- result[[1L]]
     }
 

From 22b8aff11170c6de22d0953ec29b2e37a56bdbf2 Mon Sep 17 00:00:00 2001
From: Tomasz Kalinowski <kalinowskit@gmail.com>
Date: Tue, 21 May 2024 11:14:15 -0400
Subject: [PATCH 05/11] knit guides and examples

---
 man/layer_tfsm.Rd                             |   4 +-
 vignettes/custom_train_step_in_tensorflow.Rmd |  37 +-
 .../distributed_training_with_tensorflow.Rmd  |  18 +-
 vignettes/distribution.Rmd                    |  30 +-
 vignettes/examples/index.Rmd                  |   2 +-
 ...l_machine_translation_with_transformer.Rmd |   2 +-
 .../imbalanced_classification.Rmd             | 100 +--
 ...data_classification_with_feature_space.Rmd | 104 +--
 .../timeseries_anomaly_detection.Rmd          | 158 ++--
 .../unnamed-chunk-10-1.png                    | Bin 17877 -> 17853 bytes
 .../unnamed-chunk-11-1.png                    | Bin 7077 -> 7046 bytes
 .../unnamed-chunk-12-1.png                    | Bin 31116 -> 31147 bytes
 .../unnamed-chunk-13-2.png                    | Bin 5800 -> 5677 bytes
 .../unnamed-chunk-14-1.png                    | Bin 30523 -> 30517 bytes
 ...timeseries_classification_from_scratch.Rmd | 704 ++++++++++++------
 .../unnamed-chunk-12-1.png                    | Bin 57721 -> 66684 bytes
 .../unnamed-chunk-13-1.png                    | Bin 55312 -> 61201 bytes
 vignettes/examples/vision/autoencoder.Rmd     | 258 +++----
 .../vision/autoencoder/unnamed-chunk-5-1.png  | Bin 54121 -> 54331 bytes
 .../vision/autoencoder/unnamed-chunk-7-1.png  | Bin 100007 -> 99851 bytes
 vignettes/examples/vision/mnist_convnet.Rmd   |  48 +-
 .../examples/vision/mnist_siamese_graph.Rmd   |  72 +-
 .../mnist_siamese_graph/unnamed-chunk-5-1.png | Bin 31043 -> 31048 bytes
 .../mnist_siamese_graph/unnamed-chunk-7-1.png | Bin 8811 -> 8724 bytes
 .../mnist_siamese_graph/unnamed-chunk-7-2.png | Bin 10022 -> 10205 bytes
 .../vision/oxford_pets_image_segmentation.Rmd | 124 +--
 .../unnamed-chunk-9-1.png                     | Bin 36929 -> 36912 bytes
 vignettes/functional_api.Rmd                  |  98 +--
 vignettes/getting_started.Rmd                 |  38 +-
 .../getting_started/unnamed-chunk-12-1.png    | Bin 31998 -> 30397 bytes
 vignettes/intro_to_keras_for_engineers.Rmd    |  58 +-
 ..._new_layers_and_models_via_subclassing.Rmd |  74 +-
 vignettes/sequential_model.Rmd                |  52 +-
 vignettes/serialization_and_saving.Rmd        |  50 +-
 vignettes/training_with_built_in_methods.Rmd  | 156 ++--
 vignettes/transfer_learning.Rmd               |  70 +-
 .../understanding_masking_and_padding.Rmd     |  30 +-
 ...g_a_custom_training_loop_in_tensorflow.Rmd |  40 +-
 vignettes/writing_your_own_callbacks.Rmd      |  22 +-
 39 files changed, 1281 insertions(+), 1068 deletions(-)

diff --git a/man/layer_tfsm.Rd b/man/layer_tfsm.Rd
index 4dc8db48d..44e64ae33 100644
--- a/man/layer_tfsm.Rd
+++ b/man/layer_tfsm.Rd
@@ -59,8 +59,8 @@ model |> export_savedmodel("path/to/artifact")
 ## Output Type:
 ##   TensorSpec(shape=(None, 10), dtype=tf.float32, name=None)
 ## Captures:
-##   130838090025264: TensorSpec(shape=(), dtype=tf.resource, name=None)
-##   130838090034768: TensorSpec(shape=(), dtype=tf.resource, name=None)
+##   128255174032224: TensorSpec(shape=(), dtype=tf.resource, name=None)
+##   128255174017264: TensorSpec(shape=(), dtype=tf.resource, name=None)
 
 }\if{html}{\out{</div>}}
 
diff --git a/vignettes/custom_train_step_in_tensorflow.Rmd b/vignettes/custom_train_step_in_tensorflow.Rmd
index f07bb531a..50d637af2 100644
--- a/vignettes/custom_train_step_in_tensorflow.Rmd
+++ b/vignettes/custom_train_step_in_tensorflow.Rmd
@@ -42,7 +42,7 @@ Let's see how that works.
 ## Setup
 
 
-```r
+``` r
 library(reticulate)
 library(tensorflow, exclude = c("set_random_seed", "shape"))
 library(keras3)
@@ -74,11 +74,10 @@ to update the state of the metrics that were passed in `compile()`,
 and we query results from `self$metrics` at the end to retrieve their current value.
 
 
-```r
+``` r
 CustomModel <- new_model_class(
   "CustomModel",
   train_step = function(data) {
-    # unpack data into x, y, and sample_weight
     c(x, y = NULL, sample_weight = NULL) %<-% data
 
     with(tf$GradientTape() %as% tape, {
@@ -113,7 +112,7 @@ CustomModel <- new_model_class(
 Let's try this out:
 
 
-```r
+``` r
 # Construct and compile an instance of CustomModel
 inputs <- keras_input(shape = 32)
 outputs <- layer_dense(inputs, 1)
@@ -128,7 +127,7 @@ model |> fit(x, y, epochs = 3)
 
 ```
 ## Epoch 1/3
-## 32/32 - 1s - 23ms/step - loss: 2.9118 - mae: 1.3597
+## 32/32 - 1s - 19ms/step - loss: 2.9118 - mae: 1.3597
 ## Epoch 2/3
 ## 32/32 - 0s - 1ms/step - loss: 2.6026 - mae: 1.2856
 ## Epoch 3/3
@@ -154,7 +153,7 @@ on any object listed here at the beginning of each `fit()` epoch or at the begin
 `evaluate()`.
 
 
-```r
+``` r
 CustomModel <- new_model_class(
   "CustomModel",
   initialize = function(...) {
@@ -164,7 +163,6 @@ CustomModel <- new_model_class(
     self$loss_fn <- loss_mean_squared_error()
   },
   train_step = function(data) {
-    # unpack data into x, y, and sample_weight
     c(x, y = NULL, sample_weight = NULL) %<-% data
 
     with(tf$GradientTape() %as% tape, {
@@ -214,11 +212,11 @@ model |> fit(x, y, epochs = 3)
 
 ```
 ## Epoch 1/3
-## 32/32 - 1s - 19ms/step - loss: 2.6540 - mae: 1.2901
+## 32/32 - 1s - 16ms/step - loss: 2.6540 - mae: 1.2901
 ## Epoch 2/3
-## 32/32 - 0s - 2ms/step - loss: 2.4139 - mae: 1.2303
+## 32/32 - 0s - 1ms/step - loss: 2.4139 - mae: 1.2303
 ## Epoch 3/3
-## 32/32 - 0s - 2ms/step - loss: 2.2080 - mae: 1.1761
+## 32/32 - 0s - 1ms/step - loss: 2.2080 - mae: 1.1761
 ```
 
 ## Supporting `sample_weight` & `class_weight`
@@ -233,11 +231,10 @@ it manually if you don't rely on `compile()` for losses & metrics)
 - That's it.
 
 
-```r
+``` r
 CustomModel <- new_model_class(
   "CustomModel",
   train_step = function(data) {
-    # unpack data into x, y, and sample_weight
     c(x, y = NULL, sample_weight = NULL) %<-% data
 
     with(tf$GradientTape() %as% tape, {
@@ -285,11 +282,11 @@ model |> fit(x, y, sample_weight = sw, epochs = 3)
 
 ```
 ## Epoch 1/3
-## 32/32 - 1s - 21ms/step - loss: 0.1607 - mae: 1.3018
+## 32/32 - 1s - 18ms/step - loss: 0.1607 - mae: 1.3018
 ## Epoch 2/3
 ## 32/32 - 0s - 1ms/step - loss: 0.1452 - mae: 1.2999
 ## Epoch 3/3
-## 32/32 - 0s - 2ms/step - loss: 0.1335 - mae: 1.2986
+## 32/32 - 0s - 1ms/step - loss: 0.1335 - mae: 1.2986
 ```
 
 ## Providing your own evaluation step
@@ -298,7 +295,7 @@ What if you want to do the same for calls to `model.evaluate()`? Then you would
 override `test_step` in exactly the same way. Here's what it looks like:
 
 
-```r
+``` r
 CustomModel <- new_model_class(
   "CustomModel",
   test_step = function(data) {
@@ -335,7 +332,7 @@ model |> evaluate(x, y)
 ```
 
 ```
-## 32/32 - 0s - 9ms/step - loss: 0.0000e+00 - mae: 1.3947
+## 32/32 - 0s - 8ms/step - loss: 0.0000e+00 - mae: 1.3947
 ```
 
 ```
@@ -359,7 +356,7 @@ Let's consider:
 - A loss function to train the discriminator.
 
 
-```r
+``` r
 # Create the discriminator
 discriminator <-
   keras_model_sequential(name = "discriminator", input_shape = c(28, 28, 1)) |>
@@ -402,7 +399,7 @@ Here's a feature-complete GAN class, overriding `compile()` to use its own signa
 and implementing the entire GAN algorithm in 17 lines in `train_step`:
 
 
-```r
+``` r
 GAN <- Model(
   classname = "GAN",
 
@@ -480,7 +477,7 @@ GAN <- Model(
 Let's test-drive it:
 
 
-```r
+``` r
 batch_size <- 64
 c(c(x_train, .), c(x_test, .)) %<-% dataset_mnist()
 all_digits <- op_concatenate(list(x_train, x_test))
@@ -510,7 +507,7 @@ gan |> fit(
 ```
 
 ```
-## 100/100 - 5s - 53ms/step - d_loss: 0.0000e+00 - g_loss: 0.0000e+00
+## 100/100 - 5s - 47ms/step - d_loss: 0.0000e+00 - g_loss: 0.0000e+00
 ```
 
 The ideas behind deep learning are simple, so why should their implementation be painful?
diff --git a/vignettes/distributed_training_with_tensorflow.Rmd b/vignettes/distributed_training_with_tensorflow.Rmd
index 01b48c682..0ac083e4c 100644
--- a/vignettes/distributed_training_with_tensorflow.Rmd
+++ b/vignettes/distributed_training_with_tensorflow.Rmd
@@ -40,7 +40,7 @@ industry workflows.
 
 
 
-```r
+``` r
 library(keras3)
 library(tensorflow, exclude = c("shape", "set_random_seed"))
 library(tfdatasets, exclude = "shape")
@@ -90,7 +90,7 @@ in a multi-device or distributed workflow.
 Schematically, it looks like this:
 
 
-```r
+``` r
 # Create a MirroredStrategy.
 strategy <- tf$distribute$MirroredStrategy()
 cat(sprintf('Number of devices: %d\n', strategy$num_replicas_in_sync))
@@ -113,7 +113,7 @@ with(startegy$scope(), {
 Here's a simple end-to-end runnable example:
 
 
-```r
+``` r
 get_compiled_model <- function() {
   inputs <- keras_input(shape = 784)
   outputs <- inputs |>
@@ -173,7 +173,7 @@ cat(sprintf('Number of devices: %d\n', strategy$num_replicas_in_sync))
 ## Number of devices: 2
 ```
 
-```r
+``` r
 # Open a strategy scope.
 with(strategy$scope(), {
   # Everything that creates variables should be under the strategy scope.
@@ -193,10 +193,10 @@ with(strategy$scope(), {
 
 ```
 ## Epoch 1/2
-## 782/782 - 6s - 7ms/step - loss: 3.0622 - sparse_categorical_accuracy: 0.8615 - val_loss: 1.1367 - val_sparse_categorical_accuracy: 0.9006
+## 782/782 - 5s - 7ms/step - loss: 3.0622 - sparse_categorical_accuracy: 0.8615 - val_loss: 1.1367 - val_sparse_categorical_accuracy: 0.9006
 ## Epoch 2/2
 ## 782/782 - 3s - 4ms/step - loss: 0.5774 - sparse_categorical_accuracy: 0.9259 - val_loss: 0.6612 - val_sparse_categorical_accuracy: 0.9210
-## 157/157 - 0s - 3ms/step - loss: 0.6729 - sparse_categorical_accuracy: 0.9150
+## 157/157 - 0s - 2ms/step - loss: 0.6729 - sparse_categorical_accuracy: 0.9150
 ```
 
 ```
@@ -218,7 +218,7 @@ training from your saved model.
 Here's a simple example:
 
 
-```r
+``` r
 # Prepare a directory to store all the checkpoints.
 checkpoint_dir <- "./ckpt"
 if (!dir.exists(checkpoint_dir)) {
@@ -274,10 +274,10 @@ run_training(epochs = 1)
 ```
 
 ```
-## 782/782 - 6s - 7ms/step - loss: 2.9519 - sparse_categorical_accuracy: 0.8655 - val_loss: 1.3110 - val_sparse_categorical_accuracy: 0.8836
+## 782/782 - 4s - 6ms/step - loss: 2.9519 - sparse_categorical_accuracy: 0.8655 - val_loss: 1.3110 - val_sparse_categorical_accuracy: 0.8836
 ```
 
-```r
+``` r
 # Calling the same function again will resume from where we left off
 run_training(epochs = 1)
 ```
diff --git a/vignettes/distribution.Rmd b/vignettes/distribution.Rmd
index 415b960fa..c3dabfdca 100644
--- a/vignettes/distribution.Rmd
+++ b/vignettes/distribution.Rmd
@@ -42,7 +42,7 @@ clients, while preserving its global semantics.
 ## Setup
 
 
-```r
+``` r
 # This guide assumes there are 8 GPUs available for testing. If you don't have
 # 8 gpus available locally, you can set the following envvar to
 # make xla initialize the CPU as 8 devices, to enable local testing
@@ -52,7 +52,7 @@ Sys.setenv("XLA_FLAGS" = "--xla_force_host_platform_device_count=8")
 
 
 
-```r
+``` r
 library(keras3)
 
 # The distribution API is only implemented for the JAX backend for now.
@@ -79,7 +79,7 @@ You can find more detailed concept explainers in the
 [TensorFlow DTensor guide](https://www.tensorflow.org/guide/dtensor_overview#dtensors_model_of_distributed_tensors).
 
 
-```r
+``` r
 # Retrieve the local available gpu devices.
 devices <- jax$devices() # "gpu"
 str(devices)
@@ -97,7 +97,7 @@ str(devices)
 ##  $ :TFRT_CPU_7
 ```
 
-```r
+``` r
 # Define a 2x4 device mesh with data and model parallel axes
 mesh <- keras$distribution$DeviceMesh(
   shape = shape(2, 4),
@@ -140,7 +140,7 @@ portion of the input data.
 
 Here is a sample usage of this class.
 
-```r
+``` r
 # Create DataParallel with list of devices.
 # As a shortcut, the devices can be skipped,
 # and Keras will detect all local available devices.
@@ -184,14 +184,14 @@ model |> fit(dataset, epochs = 3)
 
 ```
 ## Epoch 1/3
-## 8/8 - 0s - 47ms/step - loss: 1.0629
+## 8/8 - 0s - 37ms/step - loss: 1.0629
 ## Epoch 2/3
-## 8/8 - 0s - 7ms/step - loss: 0.9712
+## 8/8 - 0s - 5ms/step - loss: 0.9712
 ## Epoch 3/3
-## 8/8 - 0s - 7ms/step - loss: 0.9322
+## 8/8 - 0s - 5ms/step - loss: 0.9322
 ```
 
-```r
+``` r
 model |> evaluate(dataset)
 ```
 
@@ -235,7 +235,7 @@ multiple matches, a `ValueError` is raised. If no matches are found, `NULL` is
 returned.
 
 
-```r
+``` r
 mesh_2d <- keras$distribution$DeviceMesh(
   shape = shape(2, 4),
   axis_names = c("data", "model"),
@@ -280,17 +280,17 @@ model |> fit(dataset, epochs = 3)
 ## Epoch 1/3
 ## 8/8 - 0s - 29ms/step - loss: 1.0714
 ## Epoch 2/3
-## 8/8 - 0s - 4ms/step - loss: 0.9744
+## 8/8 - 0s - 3ms/step - loss: 0.9744
 ## Epoch 3/3
-## 8/8 - 0s - 5ms/step - loss: 0.9280
+## 8/8 - 0s - 4ms/step - loss: 0.9280
 ```
 
-```r
+``` r
 model |> evaluate(dataset)
 ```
 
 ```
-## 8/8 - 0s - 9ms/step - loss: 0.8802
+## 8/8 - 0s - 7ms/step - loss: 0.8802
 ```
 
 ```
@@ -304,7 +304,7 @@ more data parallel or model parallel. You can do this by adjusting the shape of
 the mesh. And no changes are needed for any other code.
 
 
-```r
+``` r
 full_data_parallel_mesh <- keras$distribution$DeviceMesh(
   shape = shape(8, 1),
   axis_names = list("data", "model"),
diff --git a/vignettes/examples/index.Rmd b/vignettes/examples/index.Rmd
index 069f52d26..bd963a1b8 100644
--- a/vignettes/examples/index.Rmd
+++ b/vignettes/examples/index.Rmd
@@ -1,7 +1,7 @@
 ---
 title: Keras examples
 output: rmarkdown::html_vignette
-date: 'Last Modified: 2023-11-30; Last Rendered: 2024-05-16'
+date: 'Last Modified: 2023-11-30; Last Rendered: 2024-05-21'
 vignette: >
   %\VignetteIndexEntry{Keras examples}
   %\VignetteEngine{knitr::rmarkdown}
diff --git a/vignettes/examples/nlp/neural_machine_translation_with_transformer.Rmd b/vignettes/examples/nlp/neural_machine_translation_with_transformer.Rmd
index 555c8fb8b..9c2330b77 100644
--- a/vignettes/examples/nlp/neural_machine_translation_with_transformer.Rmd
+++ b/vignettes/examples/nlp/neural_machine_translation_with_transformer.Rmd
@@ -583,7 +583,7 @@ transformer |> fit(train_ds, epochs = epochs,
 ```
 
 ```
-## 1297/1297 - 56s - 43ms/step - accuracy: 0.7708 - loss: 1.5755 - val_accuracy: 0.7784 - val_loss: 1.3806
+## 1297/1297 - 58s - 44ms/step - accuracy: 0.7709 - loss: 1.5752 - val_accuracy: 0.7731 - val_loss: 1.4209
 ```
 
 
diff --git a/vignettes/examples/structured_data/imbalanced_classification.Rmd b/vignettes/examples/structured_data/imbalanced_classification.Rmd
index f15111d73..aec1198ac 100644
--- a/vignettes/examples/structured_data/imbalanced_classification.Rmd
+++ b/vignettes/examples/structured_data/imbalanced_classification.Rmd
@@ -14,7 +14,7 @@ vignette: >
 ---
 
 
-```r
+``` r
 library(keras3)
 use_backend("jax")
 ```
@@ -29,7 +29,7 @@ the link, or if you're setup with a kaggle API key at
 `"~/.kaggle/kagle.json"`, you can run the following:
 
 
-```r
+``` r
 reticulate::py_install("kaggle", pip = TRUE)
 reticulate::py_available(TRUE) # ensure 'kaggle' is on the PATH
 system("kaggle datasets download -d mlg-ulb/creditcardfraud")
@@ -39,7 +39,7 @@ zip::unzip("creditcardfraud.zip", files = "creditcard.csv")
 ## First, load the data
 
 
-```r
+``` r
 library(readr)
 df <- read_csv("creditcard.csv", col_types = cols(
   Class = col_integer(),
@@ -87,7 +87,7 @@ tibble::glimpse(df)
 ## Prepare a validation set
 
 
-```r
+``` r
 val_idx <- nrow(df) %>% sample.int(., round( . * 0.2))
 val_df <- df[val_idx, ]
 train_df <- df[-val_idx, ]
@@ -99,7 +99,7 @@ cat("Number of training samples:", nrow(train_df), "\n")
 ## Number of training samples: 227846
 ```
 
-```r
+``` r
 cat("Number of validation samples:", nrow(val_df), "\n")
 ```
 
@@ -110,7 +110,7 @@ cat("Number of validation samples:", nrow(val_df), "\n")
 ## Analyze class imbalance in the targets
 
 
-```r
+``` r
 counts <- table(train_df$Class)
 counts
 ```
@@ -118,19 +118,19 @@ counts
 ```
 ##
 ##      0      1
-## 227463    383
+## 227450    396
 ```
 
-```r
+``` r
 cat(sprintf("Number of positive samples in training data: %i (%.2f%% of total)",
             counts["1"], 100 * counts["1"] / sum(counts)))
 ```
 
 ```
-## Number of positive samples in training data: 383 (0.17% of total)
+## Number of positive samples in training data: 396 (0.17% of total)
 ```
 
-```r
+``` r
 weight_for_0 = 1 / counts["0"]
 weight_for_1 = 1 / counts["1"]
 ```
@@ -138,7 +138,7 @@ weight_for_1 = 1 / counts["1"]
 ## Normalize the data using training set statistics
 
 
-```r
+``` r
 feature_names <- colnames(train_df) %>% setdiff("Class")
 
 train_features <- as.matrix(train_df[feature_names])
@@ -155,7 +155,7 @@ val_features %<>% scale(center = attr(train_features, "scaled:center"),
 ## Build a binary classification model
 
 
-```r
+``` r
 model <-
   keras_model_sequential(input_shape = ncol(train_features)) |>
   layer_dense(256, activation = "relu") |>
@@ -193,7 +193,7 @@ model
 ## Train the model with `class_weight` argument
 
 
-```r
+``` r
 metrics <- list(
   metric_false_negatives(name = "fn"),
   metric_false_positives(name = "fp"),
@@ -227,88 +227,88 @@ model |> fit(
 
 ```
 ## Epoch 1/30
-## 112/112 - 2s - 22ms/step - fn: 42.0000 - fp: 22983.0000 - loss: 2.3302e-06 - precision: 0.0146 - recall: 0.8903 - tn: 204480.0000 - tp: 341.0000 - val_fn: 12.0000 - val_fp: 536.0000 - val_loss: 0.0540 - val_precision: 0.1532 - val_recall: 0.8899 - val_tn: 56316.0000 - val_tp: 97.0000
+## 112/112 - 3s - 27ms/step - fn: 40.0000 - fp: 23705.0000 - loss: 2.2728e-06 - precision: 0.0148 - recall: 0.8990 - tn: 203745.0000 - tp: 356.0000 - val_fn: 8.0000 - val_fp: 1502.0000 - val_loss: 0.1089 - val_precision: 0.0553 - val_recall: 0.9167 - val_tn: 55363.0000 - val_tp: 88.0000
 ## Epoch 2/30
-## 112/112 - 0s - 4ms/step - fn: 36.0000 - fp: 6311.0000 - loss: 1.4468e-06 - precision: 0.0521 - recall: 0.9060 - tn: 221152.0000 - tp: 347.0000 - val_fn: 9.0000 - val_fp: 1081.0000 - val_loss: 0.0956 - val_precision: 0.0847 - val_recall: 0.9174 - val_tn: 55771.0000 - val_tp: 100.0000
+## 112/112 - 1s - 6ms/step - fn: 33.0000 - fp: 8009.0000 - loss: 1.4949e-06 - precision: 0.0434 - recall: 0.9167 - tn: 219441.0000 - tp: 363.0000 - val_fn: 10.0000 - val_fp: 1176.0000 - val_loss: 0.0912 - val_precision: 0.0681 - val_recall: 0.8958 - val_tn: 55689.0000 - val_tp: 86.0000
 ## Epoch 3/30
-## 112/112 - 0s - 2ms/step - fn: 27.0000 - fp: 7704.0000 - loss: 1.2139e-06 - precision: 0.0442 - recall: 0.9295 - tn: 219759.0000 - tp: 356.0000 - val_fn: 11.0000 - val_fp: 631.0000 - val_loss: 0.0443 - val_precision: 0.1344 - val_recall: 0.8991 - val_tn: 56221.0000 - val_tp: 98.0000
+## 112/112 - 0s - 2ms/step - fn: 32.0000 - fp: 7704.0000 - loss: 1.3369e-06 - precision: 0.0451 - recall: 0.9192 - tn: 219746.0000 - tp: 364.0000 - val_fn: 9.0000 - val_fp: 1202.0000 - val_loss: 0.0870 - val_precision: 0.0675 - val_recall: 0.9062 - val_tn: 55663.0000 - val_tp: 87.0000
 ## Epoch 4/30
-## 112/112 - 0s - 1ms/step - fn: 26.0000 - fp: 8933.0000 - loss: 1.1396e-06 - precision: 0.0384 - recall: 0.9321 - tn: 218530.0000 - tp: 357.0000 - val_fn: 4.0000 - val_fp: 3813.0000 - val_loss: 0.1850 - val_precision: 0.0268 - val_recall: 0.9633 - val_tn: 53039.0000 - val_tp: 105.0000
+## 112/112 - 0s - 2ms/step - fn: 28.0000 - fp: 8366.0000 - loss: 1.2269e-06 - precision: 0.0421 - recall: 0.9293 - tn: 219084.0000 - tp: 368.0000 - val_fn: 10.0000 - val_fp: 1560.0000 - val_loss: 0.0967 - val_precision: 0.0522 - val_recall: 0.8958 - val_tn: 55305.0000 - val_tp: 86.0000
 ## Epoch 5/30
-## 112/112 - 0s - 1ms/step - fn: 21.0000 - fp: 6451.0000 - loss: 9.1104e-07 - precision: 0.0531 - recall: 0.9452 - tn: 221012.0000 - tp: 362.0000 - val_fn: 9.0000 - val_fp: 1398.0000 - val_loss: 0.0724 - val_precision: 0.0668 - val_recall: 0.9174 - val_tn: 55454.0000 - val_tp: 100.0000
+## 112/112 - 0s - 2ms/step - fn: 19.0000 - fp: 6217.0000 - loss: 8.5492e-07 - precision: 0.0572 - recall: 0.9520 - tn: 221233.0000 - tp: 377.0000 - val_fn: 9.0000 - val_fp: 2626.0000 - val_loss: 0.1236 - val_precision: 0.0321 - val_recall: 0.9062 - val_tn: 54239.0000 - val_tp: 87.0000
 ## Epoch 6/30
-## 112/112 - 0s - 1ms/step - fn: 16.0000 - fp: 5997.0000 - loss: 7.4602e-07 - precision: 0.0577 - recall: 0.9582 - tn: 221466.0000 - tp: 367.0000 - val_fn: 8.0000 - val_fp: 2379.0000 - val_loss: 0.1421 - val_precision: 0.0407 - val_recall: 0.9266 - val_tn: 54473.0000 - val_tp: 101.0000
+## 112/112 - 0s - 2ms/step - fn: 18.0000 - fp: 6566.0000 - loss: 7.4300e-07 - precision: 0.0544 - recall: 0.9545 - tn: 220884.0000 - tp: 378.0000 - val_fn: 8.0000 - val_fp: 2302.0000 - val_loss: 0.1081 - val_precision: 0.0368 - val_recall: 0.9167 - val_tn: 54563.0000 - val_tp: 88.0000
 ## Epoch 7/30
-## 112/112 - 0s - 1ms/step - fn: 14.0000 - fp: 7075.0000 - loss: 7.6051e-07 - precision: 0.0496 - recall: 0.9634 - tn: 220388.0000 - tp: 369.0000 - val_fn: 10.0000 - val_fp: 888.0000 - val_loss: 0.0525 - val_precision: 0.1003 - val_recall: 0.9083 - val_tn: 55964.0000 - val_tp: 99.0000
+## 112/112 - 0s - 2ms/step - fn: 14.0000 - fp: 6819.0000 - loss: 7.1838e-07 - precision: 0.0530 - recall: 0.9646 - tn: 220631.0000 - tp: 382.0000 - val_fn: 7.0000 - val_fp: 1376.0000 - val_loss: 0.0800 - val_precision: 0.0608 - val_recall: 0.9271 - val_tn: 55489.0000 - val_tp: 89.0000
 ## Epoch 8/30
-## 112/112 - 0s - 2ms/step - fn: 20.0000 - fp: 6333.0000 - loss: 8.2217e-07 - precision: 0.0542 - recall: 0.9478 - tn: 221130.0000 - tp: 363.0000 - val_fn: 6.0000 - val_fp: 3549.0000 - val_loss: 0.1391 - val_precision: 0.0282 - val_recall: 0.9450 - val_tn: 53303.0000 - val_tp: 103.0000
+## 112/112 - 0s - 2ms/step - fn: 19.0000 - fp: 7345.0000 - loss: 8.3921e-07 - precision: 0.0488 - recall: 0.9520 - tn: 220105.0000 - tp: 377.0000 - val_fn: 10.0000 - val_fp: 416.0000 - val_loss: 0.0406 - val_precision: 0.1713 - val_recall: 0.8958 - val_tn: 56449.0000 - val_tp: 86.0000
 ## Epoch 9/30
-## 112/112 - 0s - 2ms/step - fn: 11.0000 - fp: 7070.0000 - loss: 8.2005e-07 - precision: 0.0500 - recall: 0.9713 - tn: 220393.0000 - tp: 372.0000 - val_fn: 11.0000 - val_fp: 1307.0000 - val_loss: 0.0807 - val_precision: 0.0698 - val_recall: 0.8991 - val_tn: 55545.0000 - val_tp: 98.0000
+## 112/112 - 0s - 2ms/step - fn: 13.0000 - fp: 6282.0000 - loss: 8.3488e-07 - precision: 0.0575 - recall: 0.9672 - tn: 221168.0000 - tp: 383.0000 - val_fn: 10.0000 - val_fp: 933.0000 - val_loss: 0.0450 - val_precision: 0.0844 - val_recall: 0.8958 - val_tn: 55932.0000 - val_tp: 86.0000
 ## Epoch 10/30
-## 112/112 - 0s - 2ms/step - fn: 13.0000 - fp: 6641.0000 - loss: 7.9420e-07 - precision: 0.0528 - recall: 0.9661 - tn: 220822.0000 - tp: 370.0000 - val_fn: 8.0000 - val_fp: 1721.0000 - val_loss: 0.0840 - val_precision: 0.0554 - val_recall: 0.9266 - val_tn: 55131.0000 - val_tp: 101.0000
+## 112/112 - 0s - 2ms/step - fn: 15.0000 - fp: 7597.0000 - loss: 1.1098e-06 - precision: 0.0478 - recall: 0.9621 - tn: 219853.0000 - tp: 381.0000 - val_fn: 10.0000 - val_fp: 2417.0000 - val_loss: 0.4261 - val_precision: 0.0344 - val_recall: 0.8958 - val_tn: 54448.0000 - val_tp: 86.0000
 ## Epoch 11/30
-## 112/112 - 0s - 2ms/step - fn: 13.0000 - fp: 7348.0000 - loss: 7.5710e-07 - precision: 0.0479 - recall: 0.9661 - tn: 220115.0000 - tp: 370.0000 - val_fn: 9.0000 - val_fp: 1006.0000 - val_loss: 0.0516 - val_precision: 0.0904 - val_recall: 0.9174 - val_tn: 55846.0000 - val_tp: 100.0000
+## 112/112 - 0s - 2ms/step - fn: 24.0000 - fp: 10269.0000 - loss: 1.9062e-06 - precision: 0.0350 - recall: 0.9394 - tn: 217181.0000 - tp: 372.0000 - val_fn: 9.0000 - val_fp: 1434.0000 - val_loss: 0.1261 - val_precision: 0.0572 - val_recall: 0.9062 - val_tn: 55431.0000 - val_tp: 87.0000
 ## Epoch 12/30
-## 112/112 - 0s - 2ms/step - fn: 11.0000 - fp: 5095.0000 - loss: 5.2767e-07 - precision: 0.0680 - recall: 0.9713 - tn: 222368.0000 - tp: 372.0000 - val_fn: 10.0000 - val_fp: 932.0000 - val_loss: 0.0462 - val_precision: 0.0960 - val_recall: 0.9083 - val_tn: 55920.0000 - val_tp: 99.0000
+## 112/112 - 0s - 2ms/step - fn: 17.0000 - fp: 6634.0000 - loss: 1.0711e-06 - precision: 0.0540 - recall: 0.9571 - tn: 220816.0000 - tp: 379.0000 - val_fn: 8.0000 - val_fp: 2263.0000 - val_loss: 0.1654 - val_precision: 0.0374 - val_recall: 0.9167 - val_tn: 54602.0000 - val_tp: 88.0000
 ## Epoch 13/30
-## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 4173.0000 - loss: 4.2331e-07 - precision: 0.0833 - recall: 0.9896 - tn: 223290.0000 - tp: 379.0000 - val_fn: 11.0000 - val_fp: 442.0000 - val_loss: 0.0245 - val_precision: 0.1815 - val_recall: 0.8991 - val_tn: 56410.0000 - val_tp: 98.0000
+## 112/112 - 0s - 2ms/step - fn: 10.0000 - fp: 5620.0000 - loss: 5.3600e-07 - precision: 0.0643 - recall: 0.9747 - tn: 221830.0000 - tp: 386.0000 - val_fn: 12.0000 - val_fp: 914.0000 - val_loss: 0.0414 - val_precision: 0.0842 - val_recall: 0.8750 - val_tn: 55951.0000 - val_tp: 84.0000
 ## Epoch 14/30
-## 112/112 - 0s - 2ms/step - fn: 9.0000 - fp: 5914.0000 - loss: 6.4355e-07 - precision: 0.0595 - recall: 0.9765 - tn: 221549.0000 - tp: 374.0000 - val_fn: 8.0000 - val_fp: 1851.0000 - val_loss: 0.0791 - val_precision: 0.0517 - val_recall: 0.9266 - val_tn: 55001.0000 - val_tp: 101.0000
+## 112/112 - 0s - 2ms/step - fn: 8.0000 - fp: 5103.0000 - loss: 5.1168e-07 - precision: 0.0707 - recall: 0.9798 - tn: 222347.0000 - tp: 388.0000 - val_fn: 11.0000 - val_fp: 1382.0000 - val_loss: 0.0660 - val_precision: 0.0579 - val_recall: 0.8854 - val_tn: 55483.0000 - val_tp: 85.0000
 ## Epoch 15/30
-## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 6205.0000 - loss: 5.7546e-07 - precision: 0.0571 - recall: 0.9817 - tn: 221258.0000 - tp: 376.0000 - val_fn: 8.0000 - val_fp: 1595.0000 - val_loss: 0.0678 - val_precision: 0.0596 - val_recall: 0.9266 - val_tn: 55257.0000 - val_tp: 101.0000
+## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 4497.0000 - loss: 4.4632e-07 - precision: 0.0796 - recall: 0.9823 - tn: 222953.0000 - tp: 389.0000 - val_fn: 10.0000 - val_fp: 1805.0000 - val_loss: 0.0786 - val_precision: 0.0455 - val_recall: 0.8958 - val_tn: 55060.0000 - val_tp: 86.0000
 ## Epoch 16/30
-## 112/112 - 0s - 2ms/step - fn: 8.0000 - fp: 6424.0000 - loss: 7.3225e-07 - precision: 0.0552 - recall: 0.9791 - tn: 221039.0000 - tp: 375.0000 - val_fn: 11.0000 - val_fp: 904.0000 - val_loss: 0.0412 - val_precision: 0.0978 - val_recall: 0.8991 - val_tn: 55948.0000 - val_tp: 98.0000
+## 112/112 - 0s - 2ms/step - fn: 5.0000 - fp: 5056.0000 - loss: 6.1038e-07 - precision: 0.0718 - recall: 0.9874 - tn: 222394.0000 - tp: 391.0000 - val_fn: 13.0000 - val_fp: 889.0000 - val_loss: 0.0676 - val_precision: 0.0854 - val_recall: 0.8646 - val_tn: 55976.0000 - val_tp: 83.0000
 ## Epoch 17/30
-## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 4041.0000 - loss: 3.7349e-07 - precision: 0.0857 - recall: 0.9896 - tn: 223422.0000 - tp: 379.0000 - val_fn: 9.0000 - val_fp: 1348.0000 - val_loss: 0.0906 - val_precision: 0.0691 - val_recall: 0.9174 - val_tn: 55504.0000 - val_tp: 100.0000
+## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 4639.0000 - loss: 4.5763e-07 - precision: 0.0781 - recall: 0.9924 - tn: 222811.0000 - tp: 393.0000 - val_fn: 11.0000 - val_fp: 1185.0000 - val_loss: 0.0669 - val_precision: 0.0669 - val_recall: 0.8854 - val_tn: 55680.0000 - val_tp: 85.0000
 ## Epoch 18/30
-## 112/112 - 0s - 2ms/step - fn: 6.0000 - fp: 5602.0000 - loss: 6.0889e-07 - precision: 0.0631 - recall: 0.9843 - tn: 221861.0000 - tp: 377.0000 - val_fn: 8.0000 - val_fp: 935.0000 - val_loss: 0.0487 - val_precision: 0.0975 - val_recall: 0.9266 - val_tn: 55917.0000 - val_tp: 101.0000
+## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 4355.0000 - loss: 4.5299e-07 - precision: 0.0826 - recall: 0.9899 - tn: 223095.0000 - tp: 392.0000 - val_fn: 9.0000 - val_fp: 1405.0000 - val_loss: 0.0772 - val_precision: 0.0583 - val_recall: 0.9062 - val_tn: 55460.0000 - val_tp: 87.0000
 ## Epoch 19/30
-## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 5741.0000 - loss: 5.3650e-07 - precision: 0.0615 - recall: 0.9817 - tn: 221722.0000 - tp: 376.0000 - val_fn: 8.0000 - val_fp: 1279.0000 - val_loss: 0.0567 - val_precision: 0.0732 - val_recall: 0.9266 - val_tn: 55573.0000 - val_tp: 101.0000
+## 112/112 - 0s - 2ms/step - fn: 6.0000 - fp: 5779.0000 - loss: 4.8324e-07 - precision: 0.0632 - recall: 0.9848 - tn: 221671.0000 - tp: 390.0000 - val_fn: 10.0000 - val_fp: 1135.0000 - val_loss: 0.0546 - val_precision: 0.0704 - val_recall: 0.8958 - val_tn: 55730.0000 - val_tp: 86.0000
 ## Epoch 20/30
-## 112/112 - 0s - 2ms/step - fn: 1.0000 - fp: 3224.0000 - loss: 2.7791e-07 - precision: 0.1059 - recall: 0.9974 - tn: 224239.0000 - tp: 382.0000 - val_fn: 11.0000 - val_fp: 705.0000 - val_loss: 0.0347 - val_precision: 0.1220 - val_recall: 0.8991 - val_tn: 56147.0000 - val_tp: 98.0000
+## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 4748.0000 - loss: 3.7424e-07 - precision: 0.0764 - recall: 0.9924 - tn: 222702.0000 - tp: 393.0000 - val_fn: 9.0000 - val_fp: 895.0000 - val_loss: 0.0396 - val_precision: 0.0886 - val_recall: 0.9062 - val_tn: 55970.0000 - val_tp: 87.0000
 ## Epoch 21/30
-## 112/112 - 0s - 2ms/step - fn: 2.0000 - fp: 3403.0000 - loss: 2.7057e-07 - precision: 0.1007 - recall: 0.9948 - tn: 224060.0000 - tp: 381.0000 - val_fn: 11.0000 - val_fp: 330.0000 - val_loss: 0.0178 - val_precision: 0.2290 - val_recall: 0.8991 - val_tn: 56522.0000 - val_tp: 98.0000
+## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 3798.0000 - loss: 3.1417e-07 - precision: 0.0938 - recall: 0.9924 - tn: 223652.0000 - tp: 393.0000 - val_fn: 10.0000 - val_fp: 652.0000 - val_loss: 0.0309 - val_precision: 0.1165 - val_recall: 0.8958 - val_tn: 56213.0000 - val_tp: 86.0000
 ## Epoch 22/30
-## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 1904.0000 - loss: 2.0272e-07 - precision: 0.1664 - recall: 0.9922 - tn: 225559.0000 - tp: 380.0000 - val_fn: 10.0000 - val_fp: 1666.0000 - val_loss: 0.1536 - val_precision: 0.0561 - val_recall: 0.9083 - val_tn: 55186.0000 - val_tp: 99.0000
+## 112/112 - 0s - 2ms/step - fn: 0.0000e+00 - fp: 2188.0000 - loss: 1.8112e-07 - precision: 0.1533 - recall: 1.0000 - tn: 225262.0000 - tp: 396.0000 - val_fn: 10.0000 - val_fp: 427.0000 - val_loss: 0.0250 - val_precision: 0.1676 - val_recall: 0.8958 - val_tn: 56438.0000 - val_tp: 86.0000
 ## Epoch 23/30
-## 112/112 - 0s - 2ms/step - fn: 11.0000 - fp: 6652.0000 - loss: 7.3955e-07 - precision: 0.0530 - recall: 0.9713 - tn: 220811.0000 - tp: 372.0000 - val_fn: 9.0000 - val_fp: 1633.0000 - val_loss: 0.0681 - val_precision: 0.0577 - val_recall: 0.9174 - val_tn: 55219.0000 - val_tp: 100.0000
+## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 3066.0000 - loss: 3.4932e-07 - precision: 0.1134 - recall: 0.9899 - tn: 224384.0000 - tp: 392.0000 - val_fn: 11.0000 - val_fp: 1053.0000 - val_loss: 0.0494 - val_precision: 0.0747 - val_recall: 0.8854 - val_tn: 55812.0000 - val_tp: 85.0000
 ## Epoch 24/30
-## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 3458.0000 - loss: 2.8492e-07 - precision: 0.0990 - recall: 0.9922 - tn: 224005.0000 - tp: 380.0000 - val_fn: 12.0000 - val_fp: 635.0000 - val_loss: 0.0299 - val_precision: 0.1325 - val_recall: 0.8899 - val_tn: 56217.0000 - val_tp: 97.0000
+## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 3175.0000 - loss: 5.1994e-07 - precision: 0.1099 - recall: 0.9899 - tn: 224275.0000 - tp: 392.0000 - val_fn: 11.0000 - val_fp: 1012.0000 - val_loss: 0.0997 - val_precision: 0.0775 - val_recall: 0.8854 - val_tn: 55853.0000 - val_tp: 85.0000
 ## Epoch 25/30
-## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 5581.0000 - loss: 6.4007e-07 - precision: 0.0631 - recall: 0.9817 - tn: 221882.0000 - tp: 376.0000 - val_fn: 9.0000 - val_fp: 1782.0000 - val_loss: 0.0677 - val_precision: 0.0531 - val_recall: 0.9174 - val_tn: 55070.0000 - val_tp: 100.0000
+## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 5874.0000 - loss: 6.5862e-07 - precision: 0.0621 - recall: 0.9823 - tn: 221576.0000 - tp: 389.0000 - val_fn: 9.0000 - val_fp: 3090.0000 - val_loss: 0.1296 - val_precision: 0.0274 - val_recall: 0.9062 - val_tn: 53775.0000 - val_tp: 87.0000
 ## Epoch 26/30
-## 112/112 - 0s - 2ms/step - fn: 1.0000 - fp: 3655.0000 - loss: 2.9078e-07 - precision: 0.0946 - recall: 0.9974 - tn: 223808.0000 - tp: 382.0000 - val_fn: 13.0000 - val_fp: 677.0000 - val_loss: 0.0310 - val_precision: 0.1242 - val_recall: 0.8807 - val_tn: 56175.0000 - val_tp: 96.0000
+## 112/112 - 0s - 2ms/step - fn: 6.0000 - fp: 5395.0000 - loss: 5.9253e-07 - precision: 0.0674 - recall: 0.9848 - tn: 222055.0000 - tp: 390.0000 - val_fn: 9.0000 - val_fp: 1320.0000 - val_loss: 0.0928 - val_precision: 0.0618 - val_recall: 0.9062 - val_tn: 55545.0000 - val_tp: 87.0000
 ## Epoch 27/30
-## 112/112 - 0s - 2ms/step - fn: 2.0000 - fp: 3187.0000 - loss: 2.7425e-07 - precision: 0.1068 - recall: 0.9948 - tn: 224276.0000 - tp: 381.0000 - val_fn: 12.0000 - val_fp: 701.0000 - val_loss: 0.0271 - val_precision: 0.1216 - val_recall: 0.8899 - val_tn: 56151.0000 - val_tp: 97.0000
+## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 3887.0000 - loss: 7.1278e-07 - precision: 0.0916 - recall: 0.9899 - tn: 223563.0000 - tp: 392.0000 - val_fn: 12.0000 - val_fp: 572.0000 - val_loss: 0.0352 - val_precision: 0.1280 - val_recall: 0.8750 - val_tn: 56293.0000 - val_tp: 84.0000
 ## Epoch 28/30
-## 112/112 - 0s - 2ms/step - fn: 10.0000 - fp: 4659.0000 - loss: 6.8191e-07 - precision: 0.0741 - recall: 0.9739 - tn: 222804.0000 - tp: 373.0000 - val_fn: 11.0000 - val_fp: 1022.0000 - val_loss: 0.0797 - val_precision: 0.0875 - val_recall: 0.8991 - val_tn: 55830.0000 - val_tp: 98.0000
+## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 2209.0000 - loss: 5.8153e-07 - precision: 0.1510 - recall: 0.9924 - tn: 225241.0000 - tp: 393.0000 - val_fn: 11.0000 - val_fp: 1373.0000 - val_loss: 0.0808 - val_precision: 0.0583 - val_recall: 0.8854 - val_tn: 55492.0000 - val_tp: 85.0000
 ## Epoch 29/30
-## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 4183.0000 - loss: 3.9729e-07 - precision: 0.0831 - recall: 0.9896 - tn: 223280.0000 - tp: 379.0000 - val_fn: 12.0000 - val_fp: 406.0000 - val_loss: 0.0232 - val_precision: 0.1928 - val_recall: 0.8899 - val_tn: 56446.0000 - val_tp: 97.0000
+## 112/112 - 0s - 2ms/step - fn: 1.0000 - fp: 2506.0000 - loss: 2.9031e-07 - precision: 0.1362 - recall: 0.9975 - tn: 224944.0000 - tp: 395.0000 - val_fn: 11.0000 - val_fp: 690.0000 - val_loss: 0.0401 - val_precision: 0.1097 - val_recall: 0.8854 - val_tn: 56175.0000 - val_tp: 85.0000
 ## Epoch 30/30
-## 112/112 - 0s - 2ms/step - fn: 2.0000 - fp: 2432.0000 - loss: 2.3592e-07 - precision: 0.1354 - recall: 0.9948 - tn: 225031.0000 - tp: 381.0000 - val_fn: 13.0000 - val_fp: 371.0000 - val_loss: 0.0200 - val_precision: 0.2056 - val_recall: 0.8807 - val_tn: 56481.0000 - val_tp: 96.0000
+## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 2719.0000 - loss: 6.1097e-07 - precision: 0.1260 - recall: 0.9899 - tn: 224731.0000 - tp: 392.0000 - val_fn: 13.0000 - val_fp: 861.0000 - val_loss: 0.0362 - val_precision: 0.0879 - val_recall: 0.8646 - val_tn: 56004.0000 - val_tp: 83.0000
 ```
 
 
-```r
+``` r
 val_pred <- model %>%
   predict(val_features) %>%
   { as.integer(. > 0.5) }
 ```
 
 ```
-## 1781/1781 - 1s - 286us/step
+## 1781/1781 - 1s - 311us/step
 ```
 
-```r
+``` r
 pred_correct <- val_df$Class == val_pred
 cat(sprintf("Validation accuracy: %.2f", mean(pred_correct)))
 ```
 
 ```
-## Validation accuracy: 0.99
+## Validation accuracy: 0.98
 ```
 
-```r
+``` r
 fraudulent <- val_df$Class == 1
 
 n_fraudulent_detected <- sum(fraudulent & pred_correct)
@@ -323,12 +323,12 @@ At the end of training, out of
 are:
 
 - Correctly identifying
-  96 of them as
+  83 of them as
   fraudulent
 - Missing 13
   fraudulent transactions
 - At the cost of incorrectly flagging
-  371 legitimate
+  861 legitimate
   transactions
 
 In the real world, one would put an even higher weight on class 1,
diff --git a/vignettes/examples/structured_data/structured_data_classification_with_feature_space.Rmd b/vignettes/examples/structured_data/structured_data_classification_with_feature_space.Rmd
index 067df374b..0c5adc16a 100644
--- a/vignettes/examples/structured_data/structured_data_classification_with_feature_space.Rmd
+++ b/vignettes/examples/structured_data/structured_data_classification_with_feature_space.Rmd
@@ -61,7 +61,7 @@ Target | Diagnosis of heart disease (1 = true; 0 = false) | Target
 
 
 
-```r
+``` r
 library(readr)
 library(dplyr, warn.conflicts = FALSE)
 library(keras3)
@@ -83,7 +83,7 @@ use_backend("tensorflow")
 Let's download the data and load it into a Pandas dataframe:
 
 
-```r
+``` r
 file_url <-
   "http://storage.googleapis.com/download.tensorflow.org/data/heart.csv"
 df <- read_csv(file_url, col_types = cols(
@@ -100,7 +100,7 @@ The dataset includes 303 samples with 14 columns per sample
 (13 features, plus the target label)
 
 
-```r
+``` r
 glimpse(df)
 ```
 
@@ -127,7 +127,7 @@ glimpse(df)
 Here's a preview of a few samples:
 
 
-```r
+``` r
 df
 ```
 
@@ -156,7 +156,7 @@ has a heart disease (1) or not (0).
 Let's split the data into a training and validation set:
 
 
-```r
+``` r
 val_idx <- nrow(df) %>% sample.int(., . * 0.2)
 val_df <- df[val_idx, ]
 train_df <- df[-val_idx, ]
@@ -174,7 +174,7 @@ cat(sprintf(
 Let's generate `tf_dataset` objects for each dataframe:
 
 
-```r
+``` r
 dataframe_to_dataset <- function(df) {
   labels <- df |> pull(target) |> as.integer()
   inputs <- df |> select(-target) |> as.list()
@@ -193,7 +193,7 @@ Each `tf_dataset` yields a tuple `(input, target)` where `input` is a dictionary
 and `target` is the value `0` or `1`:
 
 
-```r
+``` r
 c(x, y) %<-% iter_next(as_iterator(train_ds))
 cat("Input: "); str(x)
 cat("Target: "); str(y)
@@ -201,26 +201,26 @@ cat("Target: "); str(y)
 
 ```
 ## Input: List of 13
-##  $ age     :<tf.Tensor: shape=(), dtype=int32, numpy=41>
+##  $ age     :<tf.Tensor: shape=(), dtype=int32, numpy=59>
 ##  $ sex     :<tf.Tensor: shape=(), dtype=int32, numpy=1>
-##  $ cp      :<tf.Tensor: shape=(), dtype=int32, numpy=2>
-##  $ trestbps:<tf.Tensor: shape=(), dtype=int32, numpy=120>
-##  $ chol    :<tf.Tensor: shape=(), dtype=int32, numpy=157>
-##  $ fbs     :<tf.Tensor: shape=(), dtype=int32, numpy=0>
-##  $ restecg :<tf.Tensor: shape=(), dtype=int32, numpy=0>
-##  $ thalach :<tf.Tensor: shape=(), dtype=int32, numpy=182>
+##  $ cp      :<tf.Tensor: shape=(), dtype=int32, numpy=4>
+##  $ trestbps:<tf.Tensor: shape=(), dtype=int32, numpy=164>
+##  $ chol    :<tf.Tensor: shape=(), dtype=int32, numpy=176>
+##  $ fbs     :<tf.Tensor: shape=(), dtype=int32, numpy=1>
+##  $ restecg :<tf.Tensor: shape=(), dtype=int32, numpy=2>
+##  $ thalach :<tf.Tensor: shape=(), dtype=int32, numpy=90>
 ##  $ exang   :<tf.Tensor: shape=(), dtype=int32, numpy=0>
-##  $ oldpeak :<tf.Tensor: shape=(), dtype=float32, numpy=0.0>
-##  $ slope   :<tf.Tensor: shape=(), dtype=int32, numpy=1>
-##  $ ca      :<tf.Tensor: shape=(), dtype=int32, numpy=0>
-##  $ thal    :<tf.Tensor: shape=(), dtype=string, numpy=b'normal'>
-## Target: <tf.Tensor: shape=(), dtype=int32, numpy=0>
+##  $ oldpeak :<tf.Tensor: shape=(), dtype=float32, numpy=1.0>
+##  $ slope   :<tf.Tensor: shape=(), dtype=int32, numpy=2>
+##  $ ca      :<tf.Tensor: shape=(), dtype=int32, numpy=2>
+##  $ thal    :<tf.Tensor: shape=(), dtype=string, numpy=b'fixed'>
+## Target: <tf.Tensor: shape=(), dtype=int32, numpy=1>
 ```
 
 Let's batch the datasets:
 
 
-```r
+``` r
 train_ds <- train_ds |> dataset_batch(32)
 val_ds <- val_ds |> dataset_batch(32)
 ```
@@ -228,7 +228,7 @@ val_ds <- val_ds |> dataset_batch(32)
 ## Configuring a `FeatureSpace`
 
 To configure how each feature should be preprocessed,
-we instantiate a `layer_feature_space`, and we
+we instantiate a `layer_feature_space()`, and we
 pass to it a dictionary (named list with unique names) that maps the name of our features
 to a string that describes the feature type.
 
@@ -250,7 +250,7 @@ the co-occurence space is too large.
 
 
 
-```r
+``` r
 feature_space <- layer_feature_space(
   features = list(
     # Categorical features encoded as integers
@@ -304,7 +304,7 @@ bins for discretizing features of type `"float_discretized"`,
 or the dimensionality of the hashing space for feature crossing.
 
 
-```r
+``` r
 feature_space <- layer_feature_space(
   features = list(
     # Categorical features encoded as integers
@@ -354,7 +354,7 @@ Note that `adapt()` should be called on a `tf_dataset` which yields dicts (named
 of feature values -- no labels.
 
 
-```r
+``` r
 train_ds_with_no_labels <- train_ds |> dataset_map(\(x, y) x)
 feature_space |> adapt(train_ds_with_no_labels)
 ```
@@ -363,7 +363,7 @@ At this point, the `FeatureSpace` can be called on a dict of raw feature values,
 single concatenate vector for each sample, combining encoded features and feature crosses.
 
 
-```r
+``` r
 c(x, y) %<-% iter_next(as_iterator(train_ds))
 preprocessed_x <- feature_space(x)
 preprocessed_x
@@ -371,7 +371,7 @@ preprocessed_x
 
 ```
 ## tf.Tensor(
-## [[0. 0. 0. ... 0. 1. 0.]
+## [[0. 0. 1. ... 0. 0. 0.]
 ##  [0. 0. 0. ... 0. 0. 0.]
 ##  [0. 0. 0. ... 0. 0. 0.]
 ##  ...
@@ -407,7 +407,7 @@ do inference with an end-to-end model that includes the `FeatureSpace`.
 Let's create a training and validation dataset of preprocessed batches:
 
 
-```r
+``` r
 preprocessed_train_ds <- train_ds |>
   dataset_map(\(x, y) list(feature_space(x), y),
               num_parallel_calls = tf$data$AUTOTUNE) |>
@@ -427,7 +427,7 @@ Time to build a model -- or rather two models:
 - An inference model that expects raw features (one sample = dict of raw feature values)
 
 
-```r
+``` r
 dict_inputs <- feature_space$get_inputs()
 encoded_features <- feature_space$get_encoded_features()
 
@@ -452,7 +452,7 @@ Let's train our model for 20 epochs. Note that feature preprocessing is happenin
 as part of the tf.data pipeline, not as part of the model.
 
 
-```r
+``` r
 training_model |> fit(
   preprocessed_train_ds,
   epochs = 20,
@@ -463,45 +463,45 @@ training_model |> fit(
 
 ```
 ## Epoch 1/20
-## 8/8 - 3s - 320ms/step - accuracy: 0.4564 - loss: 0.7517 - val_accuracy: 0.4833 - val_loss: 0.7192
+## 8/8 - 3s - 326ms/step - accuracy: 0.4315 - loss: 0.7427 - val_accuracy: 0.5333 - val_loss: 0.7048
 ## Epoch 2/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.5436 - loss: 0.6978 - val_accuracy: 0.6167 - val_loss: 0.6730
+## 8/8 - 0s - 13ms/step - accuracy: 0.5311 - loss: 0.6984 - val_accuracy: 0.6500 - val_loss: 0.6438
 ## Epoch 3/20
-## 8/8 - 0s - 12ms/step - accuracy: 0.6473 - loss: 0.6429 - val_accuracy: 0.6500 - val_loss: 0.6319
+## 8/8 - 0s - 13ms/step - accuracy: 0.6473 - loss: 0.6316 - val_accuracy: 0.6833 - val_loss: 0.5937
 ## Epoch 4/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.6639 - loss: 0.6126 - val_accuracy: 0.6833 - val_loss: 0.5965
+## 8/8 - 0s - 13ms/step - accuracy: 0.6639 - loss: 0.6134 - val_accuracy: 0.7500 - val_loss: 0.5524
 ## Epoch 5/20
-## 8/8 - 0s - 12ms/step - accuracy: 0.7137 - loss: 0.5854 - val_accuracy: 0.7167 - val_loss: 0.5653
+## 8/8 - 0s - 12ms/step - accuracy: 0.7178 - loss: 0.5820 - val_accuracy: 0.7667 - val_loss: 0.5176
 ## Epoch 6/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.7386 - loss: 0.5565 - val_accuracy: 0.7667 - val_loss: 0.5353
+## 8/8 - 0s - 13ms/step - accuracy: 0.7718 - loss: 0.5573 - val_accuracy: 0.7500 - val_loss: 0.4897
 ## Epoch 7/20
-## 8/8 - 0s - 12ms/step - accuracy: 0.7718 - loss: 0.5237 - val_accuracy: 0.7667 - val_loss: 0.5086
+## 8/8 - 0s - 12ms/step - accuracy: 0.7718 - loss: 0.5200 - val_accuracy: 0.8167 - val_loss: 0.4640
 ## Epoch 8/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.7718 - loss: 0.5098 - val_accuracy: 0.8167 - val_loss: 0.4835
+## 8/8 - 0s - 14ms/step - accuracy: 0.7759 - loss: 0.5068 - val_accuracy: 0.8167 - val_loss: 0.4388
 ## Epoch 9/20
-## 8/8 - 0s - 12ms/step - accuracy: 0.8091 - loss: 0.4921 - val_accuracy: 0.8500 - val_loss: 0.4621
+## 8/8 - 0s - 12ms/step - accuracy: 0.8174 - loss: 0.4724 - val_accuracy: 0.8333 - val_loss: 0.4162
 ## Epoch 10/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.8050 - loss: 0.4562 - val_accuracy: 0.8333 - val_loss: 0.4435
+## 8/8 - 0s - 12ms/step - accuracy: 0.8050 - loss: 0.4545 - val_accuracy: 0.8167 - val_loss: 0.3960
 ## Epoch 11/20
-## 8/8 - 0s - 12ms/step - accuracy: 0.8091 - loss: 0.4398 - val_accuracy: 0.8333 - val_loss: 0.4261
+## 8/8 - 0s - 13ms/step - accuracy: 0.8091 - loss: 0.4514 - val_accuracy: 0.8500 - val_loss: 0.3786
 ## Epoch 12/20
-## 8/8 - 0s - 12ms/step - accuracy: 0.8257 - loss: 0.4272 - val_accuracy: 0.8167 - val_loss: 0.4095
+## 8/8 - 0s - 12ms/step - accuracy: 0.8423 - loss: 0.4291 - val_accuracy: 0.8500 - val_loss: 0.3647
 ## Epoch 13/20
-## 8/8 - 0s - 12ms/step - accuracy: 0.8050 - loss: 0.4381 - val_accuracy: 0.8333 - val_loss: 0.3949
+## 8/8 - 0s - 12ms/step - accuracy: 0.8465 - loss: 0.4028 - val_accuracy: 0.8667 - val_loss: 0.3499
 ## Epoch 14/20
-## 8/8 - 0s - 12ms/step - accuracy: 0.8091 - loss: 0.4118 - val_accuracy: 0.8333 - val_loss: 0.3828
+## 8/8 - 0s - 13ms/step - accuracy: 0.8340 - loss: 0.4037 - val_accuracy: 0.8667 - val_loss: 0.3369
 ## Epoch 15/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.8382 - loss: 0.3797 - val_accuracy: 0.8500 - val_loss: 0.3712
+## 8/8 - 0s - 12ms/step - accuracy: 0.8548 - loss: 0.3928 - val_accuracy: 0.8667 - val_loss: 0.3289
 ## Epoch 16/20
-## 8/8 - 0s - 12ms/step - accuracy: 0.8423 - loss: 0.3884 - val_accuracy: 0.8500 - val_loss: 0.3610
+## 8/8 - 0s - 12ms/step - accuracy: 0.8589 - loss: 0.3745 - val_accuracy: 0.8667 - val_loss: 0.3206
 ## Epoch 17/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.8548 - loss: 0.3654 - val_accuracy: 0.8500 - val_loss: 0.3511
+## 8/8 - 0s - 12ms/step - accuracy: 0.8257 - loss: 0.3820 - val_accuracy: 0.8667 - val_loss: 0.3129
 ## Epoch 18/20
-## 8/8 - 0s - 12ms/step - accuracy: 0.8299 - loss: 0.3726 - val_accuracy: 0.8667 - val_loss: 0.3434
+## 8/8 - 0s - 14ms/step - accuracy: 0.8631 - loss: 0.3650 - val_accuracy: 0.8667 - val_loss: 0.3079
 ## Epoch 19/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.8672 - loss: 0.3472 - val_accuracy: 0.8667 - val_loss: 0.3365
+## 8/8 - 0s - 12ms/step - accuracy: 0.8382 - loss: 0.3635 - val_accuracy: 0.8667 - val_loss: 0.3024
 ## Epoch 20/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.8714 - loss: 0.3380 - val_accuracy: 0.8667 - val_loss: 0.3301
+## 8/8 - 0s - 13ms/step - accuracy: 0.8631 - loss: 0.3524 - val_accuracy: 0.8833 - val_loss: 0.2970
 ```
 
 We quickly get to 80% validation accuracy.
@@ -512,7 +512,7 @@ Now, we can use our inference model (which includes the `FeatureSpace`)
 to make predictions based on dicts of raw features values, as follows:
 
 
-```r
+``` r
 sample <- list(
   age = 60,
   sex = 1,
@@ -537,7 +537,7 @@ predictions <- inference_model |> predict(input_dict)
 ## 1/1 - 0s - 257ms/step
 ```
 
-```r
+``` r
 glue::glue(r"---(
   This particular patient had a {(100 * predictions) |> signif(3)}% probability
   of having a heart disease, as evaluated by our model.
@@ -545,6 +545,6 @@ glue::glue(r"---(
 ```
 
 ```
-## This particular patient had a 48.9% probability
+## This particular patient had a 49.7% probability
 ## of having a heart disease, as evaluated by our model.
 ```
diff --git a/vignettes/examples/timeseries/timeseries_anomaly_detection.Rmd b/vignettes/examples/timeseries/timeseries_anomaly_detection.Rmd
index 8f420c14c..54e420323 100644
--- a/vignettes/examples/timeseries/timeseries_anomaly_detection.Rmd
+++ b/vignettes/examples/timeseries/timeseries_anomaly_detection.Rmd
@@ -22,7 +22,7 @@ autoencoder model to detect anomalies in timeseries data.
 
 
 
-```r
+``` r
 library(dplyr, warn.conflicts = FALSE)
 library(ggplot2)
 theme_set(theme_minimal())
@@ -45,7 +45,7 @@ We will use the `art_daily_small_noise.csv` file for training and the
 allows us to demonstrate anomaly detection.
 
 
-```r
+``` r
 get_data <- function(url_suffix) {
   url_root <- "https://raw.githubusercontent.com/numenta/NAB/master/data/"
   url <- paste0(url_root, url_suffix)
@@ -62,7 +62,7 @@ df_daily_jumpsup <- get_data("artificialWithAnomaly/art_daily_jumpsup.csv")
 ## Quick look at the data
 
 
-```r
+``` r
 df_small_noise
 ```
 
@@ -83,7 +83,7 @@ df_small_noise
 ## # ℹ 4,022 more rows
 ```
 
-```r
+``` r
 df_daily_jumpsup
 ```
 
@@ -110,7 +110,7 @@ df_daily_jumpsup
 We will use the following data for training.
 
 
-```r
+``` r
 plot_ts <- function(df) {
   ggplot(df, aes(x = timestamp, y = value)) + geom_line() +
     scale_x_datetime(date_breaks = "1 day", date_labels = "%b-%d")
@@ -128,7 +128,7 @@ We will use the following data for testing and see if the sudden jump up in the
 data is detected as an anomaly.
 
 
-```r
+``` r
 plot_ts(df_daily_jumpsup) + ggtitle("With Anomaly")
 ```
 
@@ -144,7 +144,7 @@ Get data values from the training timeseries data file and normalize the
 -   288 * 14 = **4032 data points** in total
 
 
-```r
+``` r
 df_train <- df_small_noise |>
   mutate(value = (value - mean(value)) / sd(value))
 
@@ -161,7 +161,7 @@ Create sequences combining `TIME_STEPS` contiguous data values from the
 training data.
 
 
-```r
+``` r
 TIME_STEPS <- 288
 
 as_dataset <- function(df) {
@@ -187,7 +187,7 @@ output of the same shape. In this case, `sequence_length` is 288 and
 `num_features` is 1.
 
 
-```r
+``` r
 model <- keras_model_sequential(input_shape = c(TIME_STEPS, 1)) |>
   layer_conv_1d(
     filters = 32, kernel_size = 7, padding = "same",
@@ -248,7 +248,7 @@ since this is a reconstruction model.
 
 
 
-```r
+``` r
 history = model |> fit(
   x_train, x_train,
   epochs = 50,
@@ -263,13 +263,13 @@ history = model |> fit(
 
 ```
 ## Epoch 1/50
-## 106/106 - 5s - 51ms/step - loss: 0.1863 - val_loss: 0.0316
+## 106/106 - 7s - 62ms/step - loss: 0.1863 - val_loss: 0.0316
 ## Epoch 2/50
 ## 106/106 - 0s - 2ms/step - loss: 0.0333 - val_loss: 0.0233
 ## Epoch 3/50
 ## 106/106 - 0s - 2ms/step - loss: 0.0248 - val_loss: 0.0201
 ## Epoch 4/50
-## 106/106 - 0s - 2ms/step - loss: 0.0209 - val_loss: 0.0187
+## 106/106 - 0s - 2ms/step - loss: 0.0209 - val_loss: 0.0186
 ## Epoch 5/50
 ## 106/106 - 0s - 2ms/step - loss: 0.0179 - val_loss: 0.0146
 ## Epoch 6/50
@@ -279,29 +279,29 @@ history = model |> fit(
 ## Epoch 8/50
 ## 106/106 - 0s - 2ms/step - loss: 0.0109 - val_loss: 0.0088
 ## Epoch 9/50
-## 106/106 - 0s - 2ms/step - loss: 0.0096 - val_loss: 0.0084
+## 106/106 - 0s - 3ms/step - loss: 0.0096 - val_loss: 0.0084
 ## Epoch 10/50
 ## 106/106 - 0s - 2ms/step - loss: 0.0086 - val_loss: 0.0069
 ## Epoch 11/50
-## 106/106 - 0s - 2ms/step - loss: 0.0078 - val_loss: 0.0061
+## 106/106 - 0s - 2ms/step - loss: 0.0078 - val_loss: 0.0062
 ## Epoch 12/50
-## 106/106 - 0s - 2ms/step - loss: 0.0073 - val_loss: 0.0057
+## 106/106 - 0s - 2ms/step - loss: 0.0073 - val_loss: 0.0058
 ## Epoch 13/50
-## 106/106 - 0s - 2ms/step - loss: 0.0068 - val_loss: 0.0053
+## 106/106 - 0s - 2ms/step - loss: 0.0067 - val_loss: 0.0054
 ## Epoch 14/50
-## 106/106 - 0s - 2ms/step - loss: 0.0064 - val_loss: 0.0050
+## 106/106 - 0s - 2ms/step - loss: 0.0063 - val_loss: 0.0051
 ## Epoch 15/50
-## 106/106 - 0s - 2ms/step - loss: 0.0061 - val_loss: 0.0045
+## 106/106 - 0s - 2ms/step - loss: 0.0060 - val_loss: 0.0045
 ## Epoch 16/50
 ## 106/106 - 0s - 2ms/step - loss: 0.0058 - val_loss: 0.0039
 ## Epoch 17/50
-## 106/106 - 0s - 2ms/step - loss: 0.0056 - val_loss: 0.0040
+## 106/106 - 0s - 2ms/step - loss: 0.0055 - val_loss: 0.0041
 ## Epoch 18/50
-## 106/106 - 0s - 2ms/step - loss: 0.0053 - val_loss: 0.0035
+## 106/106 - 0s - 2ms/step - loss: 0.0052 - val_loss: 0.0036
 ## Epoch 19/50
 ## 106/106 - 0s - 2ms/step - loss: 0.0050 - val_loss: 0.0037
 ## Epoch 20/50
-## 106/106 - 0s - 2ms/step - loss: 0.0048 - val_loss: 0.0032
+## 106/106 - 0s - 2ms/step - loss: 0.0048 - val_loss: 0.0033
 ## Epoch 21/50
 ## 106/106 - 0s - 2ms/step - loss: 0.0046 - val_loss: 0.0031
 ## Epoch 22/50
@@ -309,33 +309,33 @@ history = model |> fit(
 ## Epoch 23/50
 ## 106/106 - 0s - 2ms/step - loss: 0.0042 - val_loss: 0.0033
 ## Epoch 24/50
-## 106/106 - 0s - 2ms/step - loss: 0.0041 - val_loss: 0.0031
+## 106/106 - 0s - 2ms/step - loss: 0.0040 - val_loss: 0.0031
 ## Epoch 25/50
-## 106/106 - 0s - 2ms/step - loss: 0.0039 - val_loss: 0.0033
+## 106/106 - 0s - 2ms/step - loss: 0.0038 - val_loss: 0.0032
 ## Epoch 26/50
-## 106/106 - 0s - 2ms/step - loss: 0.0037 - val_loss: 0.0031
+## 106/106 - 0s - 2ms/step - loss: 0.0036 - val_loss: 0.0031
 ## Epoch 27/50
-## 106/106 - 0s - 2ms/step - loss: 0.0035 - val_loss: 0.0032
+## 106/106 - 0s - 2ms/step - loss: 0.0035 - val_loss: 0.0031
 ## Epoch 28/50
-## 106/106 - 0s - 2ms/step - loss: 0.0034 - val_loss: 0.0025
+## 106/106 - 0s - 2ms/step - loss: 0.0033 - val_loss: 0.0025
 ## Epoch 29/50
-## 106/106 - 0s - 2ms/step - loss: 0.0033 - val_loss: 0.0026
+## 106/106 - 0s - 2ms/step - loss: 0.0032 - val_loss: 0.0026
 ## Epoch 30/50
-## 106/106 - 0s - 2ms/step - loss: 0.0032 - val_loss: 0.0028
+## 106/106 - 0s - 2ms/step - loss: 0.0031 - val_loss: 0.0029
 ## Epoch 31/50
 ## 106/106 - 0s - 2ms/step - loss: 0.0030 - val_loss: 0.0024
 ## Epoch 32/50
-## 106/106 - 0s - 2ms/step - loss: 0.0029 - val_loss: 0.0026
+## 106/106 - 0s - 2ms/step - loss: 0.0029 - val_loss: 0.0025
 ## Epoch 33/50
-## 106/106 - 0s - 2ms/step - loss: 0.0028 - val_loss: 0.0026
+## 106/106 - 0s - 2ms/step - loss: 0.0028 - val_loss: 0.0025
 ## Epoch 34/50
 ## 106/106 - 0s - 2ms/step - loss: 0.0027 - val_loss: 0.0022
 ## Epoch 35/50
-## 106/106 - 0s - 2ms/step - loss: 0.0027 - val_loss: 0.0024
+## 106/106 - 0s - 2ms/step - loss: 0.0026 - val_loss: 0.0023
 ## Epoch 36/50
-## 106/106 - 0s - 2ms/step - loss: 0.0026 - val_loss: 0.0026
+## 106/106 - 0s - 2ms/step - loss: 0.0026 - val_loss: 0.0025
 ## Epoch 37/50
-## 106/106 - 0s - 2ms/step - loss: 0.0025 - val_loss: 0.0026
+## 106/106 - 0s - 2ms/step - loss: 0.0025 - val_loss: 0.0025
 ## Epoch 38/50
 ## 106/106 - 0s - 2ms/step - loss: 0.0025 - val_loss: 0.0023
 ## Epoch 39/50
@@ -345,7 +345,7 @@ history = model |> fit(
 Let's plot training and validation loss to see how the training went.
 
 
-```r
+``` r
 plot(history)
 ```
 
@@ -366,7 +366,7 @@ value then we can infer that the model is seeing a pattern that it isn't
 familiar with. We will label this sample as an `anomaly`.
 
 
-```r
+``` r
 # Get train MAE loss.
 x_train_pred <- model |> predict(x_train)
 ```
@@ -375,7 +375,7 @@ x_train_pred <- model |> predict(x_train)
 ## 118/118 - 0s - 3ms/step
 ```
 
-```r
+``` r
 train_mae_loss <- apply(abs(x_train_pred - x_train), 1, mean)
 
 hist(train_mae_loss, breaks = 50)
@@ -383,14 +383,14 @@ hist(train_mae_loss, breaks = 50)
 
 ![plot of chunk unnamed-chunk-11](timeseries_anomaly_detection/unnamed-chunk-11-1.png)
 
-```r
+``` r
 # Get reconstruction loss threshold.
 threshold <- max(train_mae_loss)
 cat("Reconstruction error threshold: ", threshold, "\n")
 ```
 
 ```
-## Reconstruction error threshold:  0.039214
+## Reconstruction error threshold:  0.03930207
 ```
 
 
@@ -400,7 +400,7 @@ Just for fun, let's see how our model has recontructed the first sample.
 This is the 288 timesteps from day 1 of our training dataset.
 
 
-```r
+``` r
 # Checking how the first sequence is learnt
 plot(NULL, NULL, ylab = 'Value',
      xlim = c(0, TIME_STEPS),
@@ -417,7 +417,7 @@ legend("topleft", lty = 1,
 ### Prepare test data
 
 
-```r
+``` r
 df_test <- df_daily_jumpsup |>
   mutate(value =
            (value - mean(df_small_noise$value)) /
@@ -429,7 +429,7 @@ plot_ts(df_test)
 
 ![plot of chunk unnamed-chunk-13](timeseries_anomaly_detection/unnamed-chunk-13-1.png)
 
-```r
+``` r
 # Create sequences from test values.
 x_test <- as_dataset(df_test)
 
@@ -442,7 +442,7 @@ hist(test_mae_loss, breaks = 50, xlab = "test MAE loss", ylab = "No of samples")
 
 ![plot of chunk unnamed-chunk-13](timeseries_anomaly_detection/unnamed-chunk-13-2.png)
 
-```r
+``` r
 # Detect all the samples which are anomalies.
 anomalies <- test_mae_loss > threshold
 cat("Number of anomaly samples:", sum(anomalies), "\n")
@@ -459,43 +459,41 @@ cat("Indices of anomaly samples:", which(anomalies), "\n", fill = TRUE)
 ## 4 2014-04-01 00:15:00 -0.746
 ## 5 2014-04-01 00:20:00 -0.792
 ## 6 2014-04-01 00:25:00 -0.802
-## 118/118 - 0s - 755us/step
-## Number of anomaly samples: 509
-## Indices of anomaly samples: 216 218 219 220 221 396 398 507 793 794 795 797
-## 798 799 861 974 975 1658 1659 1944 1945 1946 1947 1950 2004 2008 2012 2016
-## 2020 2022 2024 2026 2028 2036 2038 2040 2042 2044 2045 2046 2048 2050 2052
-## 2053 2054 2056 2057 2058 2060 2061 2062 2064 2065 2066 2068 2069 2070 2072
-## 2073 2074 2076 2077 2078 2080 2081 2082 2083 2084 2085 2086 2088 2089 2090
-## 2092 2093 2094 2096 2097 2098 2100 2101 2102 2104 2105 2106 2108 2109 2110
-## 2112 2113 2114 2116 2117 2118 2120 2121 2122 2124 2126 2129 2141 2521 2522
-## 2523 2525 2546 2698 2700 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711
-## 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726
-## 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741
-## 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756
-## 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771
-## 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786
-## 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801
-## 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816
-## 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831
-## 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846
-## 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861
-## 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876
-## 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891
-## 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906
-## 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921
-## 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936
-## 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951
-## 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966
-## 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981
-## 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996
-## 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011
-## 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026
-## 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041
-## 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056
-## 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071
-## 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086
-## 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101
-## 3102
+## 118/118 - 0s - 763us/step
+## Number of anomaly samples: 486
+## Indices of anomaly samples: 216 218 220 396 507 793 795 1659 1945 1946 1947
+## 1949 2013 2016 2017 2021 2025 2029 2033 2037 2041 2045 2046 2049 2052 2053
+## 2054 2056 2057 2058 2060 2061 2062 2064 2065 2066 2068 2069 2070 2072 2073
+## 2074 2076 2077 2078 2080 2081 2082 2084 2085 2086 2088 2089 2090 2092 2093
+## 2094 2096 2097 2098 2100 2101 2102 2105 2108 2109 2112 2113 2117 2121 2124
+## 2126 2129 2141 2145 2153 2521 2522 2523 2525 2538 2542 2546 2550 2698 2700
+## 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716
+## 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731
+## 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746
+## 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761
+## 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776
+## 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791
+## 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806
+## 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821
+## 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836
+## 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851
+## 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866
+## 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881
+## 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896
+## 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911
+## 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926
+## 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941
+## 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956
+## 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971
+## 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986
+## 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001
+## 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016
+## 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031
+## 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046
+## 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061
+## 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076
+## 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091
+## 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101
 ```
 
 
@@ -525,7 +523,7 @@ All except the initial and the final time_steps-1 data values, will appear in
 Let's overlay the anomalies on the original test data plot.
 
 
-```r
+``` r
 is_anomaly <- test_mae_loss > threshold
 is_anomaly <- is_anomaly &
   zoo::rollsum(is_anomaly, TIME_STEPS,
diff --git a/vignettes/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-10-1.png b/vignettes/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-10-1.png
index b09a4d2f12c42c3888c575afaec4bd4698e7b55b..2e6ffed7e75307a5c427fee4fc996879a8c98fa8 100644
GIT binary patch
literal 17853
zcmdtK1yq!8-!6)Rgh+#QBc0MM-5|n%G)i}OjDkvoNH+pHG*TihDqSKuq=a<C&~fg;
z|NHIt+xx7w&-%{Zd!2Py3&Dq(=eeKf{^j+%u8DZ8sf2Tv;w}mb3XY1h{1X(E+u<lE
zw?1Ls21h=uqkaNEuw0c5JWx=u36bBoe$515prFv9sL0Fc`ets;_!*P@xx(1p>C4Q5
zz~u>CWpK202#nP<`fV$xdyL?<Ri#rEh|epKYJ`2-8*i>^2v<r^6(>h+-^jE+q1!DL
znO&;*>YOycU?|l?S@{u<RE(1*vu6LczD)i(Kg(tQUVi@D7SUiCFYxll?tq_P&G7rc
z4=Nd6R1_3fw6=TTQ2ZHnA9sox1X5N~m|I+&$7-mr{gQ9lRivhTfv1Y|V7ULWHgVll
znHLFk&%fC)WtTK(uoEI_OtR?RXk{?g5ZQVXC{?C*5;eTGWaD5wH@ai)-8ge`!B$w@
zGpO22=235KsJ};w=7IBNvgs^PYIc37;Z>_|vuU|^OT9f+-Bkaem?`pI=qM<*=KtgR
z2+;V!l9m3ef3Qn!YqB3Lrk8tj&o1k5zn90s1RLD-9orXen+SiimX&g=ES(k#!P2Fu
z57L*131cL;P*6tnQ12}-EG#54$D^w-<jry|9Pi9kcqxE|)Q+Nriqg~1yLwjlcSarb
zYPQxn?0kKRT3e2Zf|C20!kia7WOqJ{Hkc$w!bI@Grj5mE>hov=1K5PgUv?&s9{=d<
zY&}~qG1Ldwac!gcO#p81(dBF8?f=FMAqTHx#Iyz%ils{huMi3OU;T&huVljwZ;Ht*
zG-1_3I{p$g&89q&#+#;KyT}rlR5oP8!^te1>!uuq988q&C}_)T6Qju>j}j}yTfuDw
z?7`8TLD=|4ZFyb@XKyrL@cMkQTa#%8lgdTuBGO<)z`Jb)Oc%tsBN?QPxNz2LxN|8&
z=w7{gHDaw=G43Fg;U&hMESqelpe+KQE-7C3ZghZ0dwt#Vro4x)>HQv@3)m9OjS3e-
zx?Lnovx7_;;W{dEMUt7|mC3j4Ue9?yYV5V{uCIi3by`Am@>y&Y&I9noPU!#lJhA^1
zyYj#A8sXezX2q;M(d7J8xJu8TFMM%ZWRDZ`Tz&lH$vY;M4<A0nNgh3QbmSzE-0x!S
z&k)8_L_R!(+T++%Vdv@8%uETpUj@H@{rdJz_WDuBQ)lPx!?occDO!T0#6(LgD~${x
zr^y;SWh?Ne)?73XQ&UqzLka?L-ami-oMJT0Y|k|Ma6~R#*M9kOGEr#(hr=Dl%W)KO
zkQdQ@tZ6m1<sH>7D{oy5cdk3fNbg@JU{u_ksQi9i(*AsHy51v&+r)YxlbnQPv`7cW
zPiqq`N)<cVrwoC>hS?&GjoI`eNqo!Fw7H@kWCU4uf&FZe3kRoYGCv#e85k6_i52Ey
zZk7hi#bgU>JYjVm^ET8h3<k*%$Y?|4-Dp18uwTT+7uLwDP1~gq!ajO>dQDhDxG}fB
zva)jRu%)Ht36xC4_4n1~IR^$mfyz7G405axIu$~eHcrft;^Nv;iHVE0J{3OsAHZ26
z63m&wB~R?A$|Lu7%=UHg^kWz?Ls%m-N4}FS!pNkxiFw={`UaV0jbyNv6B83NGc$3i
z#g>+qqG==>ChhKXKK)tmv7(tJ_Ge`UR#2cz^8hmh?pWR*J?&gKWIM7JQbkOviu99c
z#r(0?_?Ld%@00tVVt~K4ng6yV?b$%|RP|{s0@u~zi{|^sPo#1N&3Rp`V);9axk;sC
z7TxRBnThnp?;`Xc3nfYh9C5O;rV2S3dU|@w%E~%BJ9~S3C)t%ZtQS8}OIFVmsVprm
z_1T$iV+AQ9cb4E{MPh?3qyPT>`)?SRtBZ>5YYp@B^J}>aZo4S;X~Os)K76tv%Pv9{
z>tV826aXG3Ux${YvEabkFf2xqk!9n{b$D71N0nZQAOpi~rM_Ej?QcLbM7j8{#`Awq
zUH>mr!~b_q^^)(S7Y>B4BU97H>EofVgnzRsas5Q|V_@mbD(}F~6YzC_O@}*<HN3qW
znHQeNiW!1D$`;AcTLlaS9F@w^0-@DJ;f_Hvxy8lEcgP}0u`2IQ3_7h0mSJu;2G#V#
z%a~Xp__HAeG7|}+$zWn(74;{YR*X9eZFm}UzUJ(W%yX@(*N2R5d5>3-Z@ie=5+b{o
zxhd<{YziCh)>;&y!VQje6U+K1>R)h(uxyA}^!;%+;JjakUG#g;x6hiNC4dV-O-iW>
zws}|X-zts&pKKRSTS-NWn8$Kob(lC+EUBsQ`|XW-TV&3*?V5Hz4!MI3DyQ4X?48^4
z&g%3f292_&=0IQHbgcs^*hiY+lX<-|!}iY3Igdff=*sh**4EtI-)7fm<M8|%?pA-X
zGEktPd{I7F8Jw%O(Q<Y!@3-C<Dbi6?>?k*B#H9k^>FceIr-p`xAT(!+dU$V53I-f)
zW{Lagk&m_n1(c=aQXp4TSTR;=!0)~JG_(z$lvLQV3!h>A*N5Iscr_`v@fYCY7M-#6
zGu!+`<Ghg^B1s@VBa&Dn^PUn|5)cr`*Er8KY?L<+EbdLa_j0YRttGLPKO@#hy6s~#
z#LnDxew@_)X`oPs)9L;n`5LeY5I6P~J2U#1sl~m4?J+zd{$rdk!sHr&XcTf3GJ5(n
zRkO1l%g-R0M1hYY#*kE2zM!~x)IKIQ7HQR>W-#Z?gjbFxk8gO1WyQzG8<IY0n|B3O
zgDtRXV0|E>(v(xIfMleYAOD{!flr2IV9D>8M)v}(41#ytFj~*I8&gwLXPX099z4LI
z6mtCb3QIvjfsKuAZ*Om^&c&?J+sWG68m5`WtdXv3HPYa<vHs=v<Re~;dm;7cn9CHd
zNs{1Sx>Sc;2(PfRu_G%e#lG^ecBV(>^`-KCpLb<qW{#y4F5fSd3&VZz-~mV!-vz*~
z#pwM{vglm>M{p{Td)$9yrlh2#rK!l<+1dH|`C(yU$uD^~f^FH_q3|iUyZBq6@$Z{i
z%`f$QC@zlXGf1xGeihy|=k;~o)fQA?2FX?i_y1L!X+x57I`&QjFNhg?A6i7`_<ymd
zQqY#5!2=FTa2hBTEQWPj*OBriHY(3nC<7Jp<dP&&xq52riW}=cW#S(l9b>$}79KJB
z@G130SEl<!fi!Uu*2JB%VJyk+kgACP%JAs>p?|Y=HF<N@;K$<PsjX$0I7e7>)hS4X
zVl?^s-Z*~)gG+OjBuyOLQL$q4Md8*Pcf|Urmc$;K1t$jw2Lr?Ny@hr`^~`tg-X$c+
z$%~4Lg6$!dL|)oLXUaTV!0w5j-dLRUm2KtIqphjqF(a2zUbn$)N$k6KKWb#$r>4$&
z`}S={Mn+ba$$<c=ffRV-_a{>r(FGqrZco(#3%&9IrG#@cVhkkQkdP2_QXf;_uU+-O
zZ!rpfMOCzap#%T>6C)b!v6FD3r2pZU#aMBv%fn${%H0Vpb&l|dg;rp@1T<@LmJO<{
z5u0$2KRrp>+S(y>oQ5^5|N5gO78L=3+g#A`DKuNc?;(qD0cZh@8A?VCO--{C6Lq&H
zd}jS%3Q_kB3=CGN$yjC9klvP?^zGZX6tiHz<-Rm4E34YH;Pa!coWT8v>lJ>jHZhBf
zlRX;@*IHE651*C#5`dwKd9A0J!t3UO&sT@?Lv@M34Q~}S`uxnHPV4RMoeSQN$e65k
z<LK!_NJ}5|@k71D1=D_br(|93ogS>vin!+1)QnhHcUN8Xa8yK7i;p=9b+)%RUmi_W
z+YD+dE3<NOnY0AfPi-YL*#V#4X*w8yqxSanD8|v0u}0Ei@-!bl$&m`;c6PI;>|@c&
z-kfW_ZoR(TdG`H{pv&yV@3v4<VQJ95eBI~r-BIVw2+!V1n7^*4jIUY^<q!KRYuq$H
zWFK-mAigtQWu<0mx%Am-YPmn74pfJr&;#xmOCiWEEq&c*siLCd(sH`YKPYj~%hReW
z?g|VCPwM1%jPUFP@DMeF%t`d>R{f*((UP3tGrPkc&^rZVav5f0SvSrE7w_6eeR|9w
zX33$p{&T*Mt5$ZzZ<}sws*Z=?_3PKohriVBM?DZ0uG`Na3P7wY$b;>?(&x*PbiKa1
zJf8N5qr(KzE~QH3@1AoRf7#vZq*bbL1pQg${Pk7P^2$f~>!9@_YBi}nw)It5PV@0>
zfWraKm6AW%J#>b2L5H!+<2h(MiT<?butpNB=!ko(2i2(7zBcIm=*N#AU?p=Pq>|Qg
zddf6cf#V2p{*q!b^RF+!U!$@?HU4O~X}^b~rAS9XyYvsL79k;Fw%>AE@Z}j|btp9a
z4Ib^)dP&7Vx?u0~O0Y}fwbF7I6BcnX6oU2xnWD@IYfrK-pG>hpHPRkh!K<u<XFNe;
zr<TG^E$Y4my*kK=%67>8-EFoK!U7#d(Yh+Wm3;w0l{hUy_uVHYC^Kn1TIx<9CnsO*
zj5DjURC)SzqCH5<DYPCmvW?zb`_}{_Spy&wZG-(^uqJ<mx$GgsJC4$3j0$>u{64`)
zyhQQi8(G3GrXW8_U+u+3M@L6pWn+ie@Z*uR4J&{!S<Fjz4?Lpl4^VL4)6Y}R8#9f^
zh1&UK7QM+_l>F9x9|kFrFDrE7)ykF-<>&Y4PZyLw{D#egwiCgEjI;35P`{BvZO9uc
zslXGk0Y+e<uByQ8Y?}sX`-qs;3q!@g>t5Is_34{<L8wQ78>8r+*QtS@kEkF0dsV`n
zopuKW1)nv#65QbHSFb!*f6f!O+HX(S4-F0JmDB>iwb>@R`W=d=&OF>NJZJ2udgIwG
z09NSHF{MJ{69QCHIWC8*LuRRRU2B24R#C!pfd{`@J%A6dWLX2hHu~)7x8Kde6XcB}
zoEl{6wZr}1>K#5wz84#~wEVA0g#Dj6+G1a9OCjx}NrVl$&%MgcwIRf293C03IoLzc
zb@li3`0urof(W5f4W}X{JwBRpIcG_g4Dbd4dSxJM2juJBUDuReuazvH`y?bW)Z#~d
ziJ*#*cK!Vb7&$QSfa7gR>prr&sTq**kQ;z}a{uP@S1D>^xoxHvM_j&hb*plJ>TnE6
zk)f<{uY*#*+}J=*Pf`9IlSY=97YGnS8C=rRt^1ZdEhh^^Ag;HQnw^+YdM*5j=+T+=
zTghTnjJBQn%tg=-Y824lwO;J2`TCwjl5m8xva#8X7DG%Ly)XZym<<?dg-SlZKip6#
z8Zbg_WVecVRp$&IsWHaG#++05;+}dc@BGRSOLq|$%YBE6W^e1nc`BYSlwjk*Wo6{^
z6=UvVV+X8#Na4J%2C5~_-Xs=r-(7PMtHQgkuhU@{E7I3%IS+Ywco-P+^ear|iAhNQ
z9Gng`ir^fD8>nu``lE_l>EO|hG~7o)!OR=89!|UFj*32+&&O+RF1{YDi@tF5_Wq-d
z)v-QE50b(@a1XP<z4qPt?>7DEUe}k$gE`WzUK?W&W{0WIW!Jtw2h08Po=1r`ruoyw
zkP@(W1VbG$l$RDftFKdWgi^aL33Z0Um1kNMHUMgvyS<7y10`jHS!-}DRAmnYQgRDW
z&wLNVyWW9bi@Pr=N44*Ru&JB9oMw~lcXhggflIY2y<YavN>=vUN)9v_6od@^C-Y$-
z1<iog)u?Q2qoVanc7t?)DKN;g8V(YTXT!QySBYpq;7+RsbG6$7cEK=4BN?(7n_qgc
zUR-QgHnzHIi>%?j8*$J>ePa)lz4|r~Vp#)SUCPMAt|Gw9Y5?Xlyk;4_hir8{&JOMJ
zhq}7O29O}%{U`;{2tF+v3l9&+7UO>9G5=hSoauRE@2j`??u{dJ$972I?NOP)k~y;q
zp|}qt!yTJyP$OsC{%lic^44Hs5VXPb5UVK%&iMO+!k?F=mN3skS$jlMy!M}Cz96zp
zd=P){A_O2hA{^<|56+U|%94R^;iZP-z)oFhz_Hh>Wc2j$WI8^MqsaYyabSDWrY`?v
z+>_zSXP2=C{F_6>ZJegFSxgZmq?O-pmpOo-P^glRX-zMn?&w2Kz-9y;zIkuDKqTn!
zivrLI*u+2ooSVA^F7h?0gRt+pJqtb4t4vLaY><V0$DW3qR&;te&lDit8D(X}Hso}$
z8os_0kk)LxVF(TtkB5gC*rqZ4=!h>a7O(sJC=kWagv!rIu&pr}3B+BCmpI6+PD4)S
zjpmkPW}V4X9-AXZr0-m0@Pu3oP63b>Jm9XX(qj89Gea8P$UM;ZLl#{j(bo#Cpjduc
z(xFY<zJb`Ujy(tU9z|#COv?|Sa_kWEhiSJ#z1MC5ph<I~3<v%Xpsk~b5i@a039&ld
zD>r>Wb^YD9hxvza>nn;9`rfKM*32e(6C~hb&}&_adQ+MkFQ`(onpWs1TLn`nK_J1A
z&j8?XQWRE#EIQFcUnY1>w;uFsEq*cPPUvPrHmw3^pl;rX#wVcvY^NmOBQ*UJz|9i&
z#$Y${TCpcS_y6cTWd*y@Lt(<JW|!4%`TWRT@Yv_?ZWIsQ;a|gf0xOp<aGcG(d8`n@
z7IS98fI}vvp*Y)uoIfkjW3Flc;D;cif_9zD(9M>$+C?L`+h2>}iO^-S7d?3R&|&3J
zgUzu}ridisk~^n2rm~x&NYN*2o9b(z#PDS34p^D*FXhm*fz1Bd%A_Wmzl-?k8*1Q~
z!~RO??Rfs?<a;kJa554K7v2O`J&8Mxy^3YXY*P3$JTy(YXH_F4mqw~fHS)$-Ulhh;
zRR0uERd?2q?@89Ux#XjfuF$mbA2iLt^Uo;RBFAdM=>*Qqlf`@<RKHHj(epU6y>XFz
zJnLT@ADq{~ts7R{+&ZQ;Ly?U%AsXUqu%2NaaveV>=u=g~V~7$A2`qdGm_Xjc1-H#~
zv?+Jh>Csb#JolsgH^6w`iFuL8%BFpPzYQRK<Qog=QzOEaVML7xItDAoh8MR`UL@o9
zkrebvF=4L1M=d>kb@S%M*XHGPKYm1(P2yYhd^EecD+(U812gbK`2uISe3cf`vUB5Q
zd9M}JHyB>MdI*z)H|zZCpnl$P#9A77<x?2)7Je$zr(P?rkH*Ps_e=NP1uZgga|%YZ
zgE>r%^vErp2xa(g)Xjx0UYVDxuFu4UCsG@w{&k<+AsJr%2OmB0pD!pUk8)-FeKR%M
zfdQJ#`e;gcBF=^~q_!Mb1O=Eg2o@Y>TRyDR5ked{-#qvj?2vjf#r%<43<9>+g%+@y
zLLJQIb(Q7{I+@3K7Xy)TH!}eN25=Co((U;M@wfAj;MSb!QwNI{T8hdty0(5Hjmwr3
zzi)-<qc>r?z=Jp?c$4<c%~n3*&B<U@4Jv11u)5Ux;sORj>*yi%%uf_u9;T)B7jL)S
zoQ)fFla>OilKzD87sGLeDIk=@+zvUFD3H>B$6EF6w&3-roBRJBW-f*IUPY(U!iM2V
zutCYqrtN~IXsrvs2nW$H4N-9OLR1)bM+$r7LyL}kVc+9ZQ$?Jozu03WCnrlCO;}V`
zR>~iCF~$kF%u0|Yp}k5SQB_yh!;WQby((?oW-jXm+x~`=RjH3s*jZgu6WMKnKJ?kM
zXSJwms;X60RiMQJIIlpq4tx;$v-IMKgGLCfScW8#ak;6fsjp9+K~+r+v>j^7$|3=D
z1fb{V6c-og;wpn)UnNNokRA9+)TR+wfE3bGMYXF?SnL!)Uxk%b<tKFU%^y+XO$Y&B
zR?&BwQw?`h=DiyMq8Ku`ViAoMlIuk;FJHx30zl2huBtu=-;ZH4T>Y5*;^J!_gTLbm
zW5HNxBUfaK=O-4-kRjW9x+(zN3;}qu5$An|hZ+6ejm?uU!6N{e$TomH`qKFG^YTFZ
zq<cykC}rkky>vZXn$h1plAm&TDO~d`tHJ^AU4ebnUP<WlfnGC3u8c}t*2r&KyywR`
z0NlM^Hr-%n&vmm0<FT71u!CuYyyu#cg*GukA8~;>E*2KR#N?OU>p_YvrcRBIpQn`q
zD9z>hu|BS~3_eJ|_WX<){nnLFJsWa46(IWe4zJFLIK-{G&rMW!{EmG8=HUue2n3Xt
z{}yv+J#zt+@(=VD%U=?)oT<t3x4X|AhG|j8C~A(mb)@$G*{9vw{c2zP=G80ZQR<qn
zukZG}c?fU`7M8GZcX#(EPo5N0e=%<h0nj{=c5rZTU|=9`4<>J@%;qR?v|4F=;YI-L
z{QQdcAQeDK29F=}0*rrkbrqm5)6>(No15&Y75^hb%=p_g=q#bFOI5Qlz~?5X38>$e
z9#`WI>9@wl#xBX%fy>J?K@n;Dy@vGkL%Y!Ha<Rx(9XP0~k@@Po4*W07^RlG6RwT&;
zHM6JuB>;rN!@m}7zWDuI&LB=%{~nXd^VfIQ+l-MR`2{*=Ti~--;mSo0Vgzgs!9Q=j
z@&$e$srtHfIGT*)t~dwh--EWugW(ZGI^qQJP4_YSL78oixg7PLk~j6DqE<w^^v17p
z=~8*#{CudQZl@L9Egk&V*^md{35Z06k7P|f9df(5@Eg>&!1x~pdL$VA4lUz1mlXk5
z*cl96E)xN{=*1%$Ub7}ujQ*0p!3h)X;6YoGHK->!!EXc!h7oMkuE+%VGQ_+^_t$R+
zkOb;uBBO3zy)KD%@IlPuXX!#NnE!E;hlrwFVDvi<YMw0?-njbZ8*^L~zM`awN`5}Y
zxw4x(ES$sdd-=X`8J$o;%w4(v(&Q!>(a7;)yul~p@Z6G--xuzU;Jfkkv%76nyx(%)
z7YKh^gckl&e~jp3F25l7#>iTYN6<Z2Mh9lOR3J2_;P>4cbwM%U?DKzex}}fIaWDL_
z+Nd5|SC?PYfsk|&D?w9M0*;<xv{9Kq+2ME{u58WIDJOOd<u?OeDyl|BN_qXu@WdSb
z>R*I6FZ4%mqw@XrXQo}xNm+X-srM$CtfwiG_C@ANTwfdjSOj!z0Dr*gkwBC2xpXB&
z^BUxqa6tnPLpdLNS=3`$zSQW_5B9lE+$m1#%=YavLyaw)*B}#vU%O2Kt4unmECnQD
zxKp9$TXjQ4Tj8vdQstJH4Zr)98<zbarWv@!SJ?&aE`tb$qyGJ#b;kt2mA-hcJ!zaV
zwbHe=UWT2!N7x?hEu>0ahUkalQ+xzw621hP;|*i<<-ZL=Wx+G_129NK=Q{Ho>F9rU
zSuO&i$sg_G8K0cX9vR`S0VDiho*mKTGF)=&Q)c0j^eH;0R~fkjU?@y+FKg@$+H3?7
zi<F3|yp|Rdh)}H;x|<(<{fi5*-&<+XnN7~2@(Uos`br;GJsUQh4}*4NtM*8X0)TxM
z#V&(8MChSPJO_o_k~`#L!8Ntt8YhZCM?9h^E1YkxN@4X5$bT!y=`;hw!f9LR>T5QE
zm3~UR6@Y)LPx*U(SA5sj6-%o#c3t4~0F<$<=)UY3;mn$uzAIVK#FUgempM~>G^Z#6
zhK$uWmfajuDf&T-AH4|D743}h0FqyNi-zyK)^;clAfD;4s+^G()%OZ&`+k3Tl5ffu
z*RnQrelvM(=?iN@1+Qx<uVY-~=A#7Dr*4G5eanhiSCZdP>FwIOF|*&qA@$p<<t>`w
z;g+!8r3&dApF$qRC68&Cufvb8^2=|4=8TPkA}Nl-op(^&^rBxSV+?W;GeHkoA4(ug
zNa1FH>1?=z5>gyARR=TKVF$QojRA9rMvuGGG-bV~QQHmkvX(^N4eD3+X-G#x@u6_!
zRaz}*T(D5cBVj2k%BX8Kd|b$@mw~suvT}VDz-yN0_;5{ePBkRgaVnZ-5`f(bp*YLR
zS<co|V;c?L+f)<_j}u|Ie~u>_4#unY)1?t3I$DxGGlCw@wEXrXtOm)Ux)~E6vl8K9
z;Y)+0{O5iElz5@2U|uU8TdEe#XR}q_`p?W=aLcqSx}TfdZ|!NNoHknCR9=Qu&QA$N
zvwp_G$jHd-#`($P3QAdd7suJ`{jZGrW<g$Cy?Qqzp(jqv_XADWp#0s|5FPO%#%t~K
z`Vm5lzAbNu^UU2IVg&+)_=J^a*8k~v$i6-ngVI7BS=4ue@q0{%nsKLs_L5D>2YMRX
zZZ=F=Fct+9DJ0ly023d4B0)qUCiK2N-!CjBF)>)z{_>ZecU1Moyq*g6WNnE=8k5Si
zH<7LTuAwja^m-p&SQ_XNe;CU+$44wr1$xX8A8!e5PCY=ZLe9xY-xL>v`ESTN9(&!C
z6EB(l?CWBFFo|08+#eRf4GYbAo~gO=`sxn`iGIwYS(_;QgGF?`F?UpZo)<ld1QJzV
zwygNjvIw#S24VD@cIqkU+y@H|6;7SRdc|HV@gs@TZdejbMN^Y|=dDoC@R*PHrU5fH
zsppqKf**QLA8v(4C||jgi)&Mj_}xNz6g2cS1n%-Up-U!8>f`>fynLnGwNo;${mzaO
zI|6R{$y_GRhFP%n_+>@Q8M}ahzyx9y-6o>Dta}5&m8vCW_Q?hV3^g5)%?-MCt_}>4
zC>T4ElQY#Xou)j(4z?m?<?~o^$sbyt?9O|6X2}tIS!~lqy(XbfX}!whUIt+z(806q
z;-?(mQ1sg29>(FcPDSF>!*6mX#b?ihpY?onI*275-%l<^Aq4)Z6iv@y^<B(s<+E6}
zIPlGnLw7t>RU?CD23w;W^H-~_v}Ro|JDg2sci#T`X3M(o-?=lm@!4zT__NN++2%rf
z)HR|4ibQ^2HYccp;vKQ6Cf@?AHAC>l9LP}!&3tTYhPzl;V2G<_Y)nOfCGR5;)!)A=
z^+1$Q{+&0RTF!f$dUxtGwYO4|R`d1Md4_2K;@Rk$L4z%Te_nE__{?a?RxdBPpGjqF
zJ>(WbMG-KK;Y%H~j{kAZRM*$PI#zZf!uNS>ZT+*~xzqWP{x<37=k1XZLHtDDzi+Qi
z+Krc++$w)mF+L|0tIn+Vln&Y=^3@C%tPCUMl&7IeXKMK>4((-lDjlbY_T+q>Q`{1@
z)I5Sfdt<DujwFjNFCW$$I`qsaJYsk*P#i0y$||AinaS5yT2Pwb0X@v7x4445?nRZG
zg*bdgdsO&tTrIo?QUCq)+JC>hzRDB9k|P~hvy`N7ilun#xjigWJfrnYT{4q2+5iNR
z75qLZz^_I%Zs!lC4B?g7?tPnk*ub?hK`;2@xqc$)0e0hN|I9Bb1S~tKRm#*8fPi$P
zxs>i@6ZA>M3RfqRdV2Q@>cNTXz=;UZo`E??Z~5lAOrwyM!?^sbFub^k$*ft#_ueOy
zsTx+r{<gpA8g_2^Tb+%p4VMIZ11MB@)^`zr;U{pL9Fyk+XGR@&wTJplxp`8xWCG$r
z8ra2ZyAT%3at6i*hI}}9c*l4IcN!$Qg;?^9&ITQnt>oqXFW2{uTa6z-EoZ&50}KSj
zzRC4Pp~=F#8X@c)zkL8grRctb=e1rsKO`Hu_}=mWJEFBN8VOB<K1P8DlLygLP?WIx
zlb)UDIK6+6sz+<~i&am2zFrP;t_5#OQ>Yk@TFBLlNSU=U3U?`Q^`eT%Yp{sA_}=q^
z6Xf9$Y|Q0J3JKrc>`em~KWb?ur2)Hf<{`_Pv!!mOyYGaYPgw<?m(4&HJd^yI%&0{Y
zv$KQOs)@usN33ud8UA$cB$l<BoB?$5QS;YD2ip2kkV0dr#R;{g^BU9JgLDHIMnj}=
zzZh3XYLZ!B?esJ^PC3t=ZC>CRes&~(3s<>B=iN~GGoJIuDhkw-l}?QZQ1IZ^MXC7A
zm_k{nNoZ3Nh7buOM<%jzP0`e<8t3z4;va=0PS<otXGcscgoTC2x4g|(jXlTlj(vU|
z1xe&2{T_V_>I>SC(|5`tt{#quM~A&BpEaCUSoI@X#O$_OEwtjw#7T)$d?AYR01|F&
zFv$?U<`XNCYXmGDvx$51+ShSzvVqv{qCp4CpL9Owa)K4P%9$hThbwLh>ftp3Df*p*
z)AHt{$(?fqj9H_5W5{P`-Xc0Yo%|kAy71KH!pIu>11W)}#7=(t>mNvL@crCiPfwsr
zzqwEeK2b@|?pu5c@7cJ*e~27B)_x&>iC5Z7&y{}uKLPw~1t^en$ngKh#QN9#)y_m&
z?rZy-s+K7*r8%zL8`)7+-H=y`YuOS(SU#84X)f>D$O<g@ePHAqkazsTEJHqw{GQZY
zds8l;m;H=`KElMddB75QV4cuQzN-KnL=%*gbYf!ZkynBDCn}|%<ma2&WqH=v#MMpJ
zX<%0tzN;xvD0wMaVejTQm)f`^%Edv-hfU9J2tB^Eto*dt8MxE`phEa9a-Kd7hP#gd
zTw3#9Zne8}6%M1(vQLYTmyy4(LPbkk*Qhi;ngTO%5>SrLvd`WPw$zQn$=I3Ashjt#
zzpe&7Q9T$bBN2AcQjSTGzBll=l-_yJEYmN&X~dh87=`5vP)n+EK8)z$HIOb>8r%(*
z5CfoQ8Q5D0kv4mfM)AB}@ZrMLyl~$}0(ZnhGY9m<v-ha5urPfkZ+ZD-7Mek<(8Q?$
zoyzizwdxdHrua?af#VuekN%~@7}eMUm(C8r&W7WzdpAsuB})$;%n&A(l}R<sqyi2G
zMjA(g9CDiFC!?X<K9%Ej4bxYhqXuE=?MiD1Z6OXd=V8##AM`z7S9BaRo~cgAVzus!
zRn6jtt>4e7OVUfWQhE0u45~n>oNmisH<S`UBtj0~p~9Hu>+>+OxX%3HH4#xlIzXsG
z_&1M1bY1I>nG+%s%G`p&6z?@8uxQN&9`wuZNHHqKa*BC#auj#l*ipwGt@D*>xJSxV
zDF2F_xNp&LbTufa6+}#Ot_EN0vEhxSmA=}ln*$>Y$dxRBJuWBq3WiHscMgJfaZPl?
z9)c%J6e79`CZILGU(Nm3T6^Eb_@1r7E>Rcnok8my<EO+IM~BCLlx5&>r;HQsyVd8<
z9nEx8)=9YScHuTIA{iDXW;;Q{W;EllaWJ1_hr&P&DSgEKYi)FzYA&qH8dUhGZB(9j
z?%WwEmn6{c{Q+)8kNo@x&iIP-k?ZNGeo(wOJUo>(f%X0u$>G?mA+O%pd2gRimhAQM
z>z+6lva{t37roQm2`LElJgZ>1sJ``l_MFz_#G)OPr*fFfxs(#Vyj&;Yn#sRx)cE=N
zOQOB%jic^!8CKcOn@8E%b>g-+^MiE#u*Qxki*6!AAZwPJD}QG+E!25pym2SlJ>XCE
zG(a<&j%$UNv%UGVG$nn%o*!lOnVP2b0w}HRg(>_NBNDo`Ii9eL4g$jfkPCaey1oDs
z2m7(%;c4Kw8T8V}q#>T}-t6a7B!%T*k1%Iv6hXPX)6dq(3AwD6R#3}#d+Iq%nV<3T
ztjU5ups&UMMQPP$nYfLybfU55d%MJ|?eo`#WU>{T6z{>9|0hQy`YLGL4|{Owz&9BL
zLT+KR14wMHr+-rBT)*Q0yrJUBI+*h2%tBu%&Z;}<;F4UqK`iCn-K(Iw;Y?|9_nQ#w
zC7XwAJ@AyYu}XP-$EpjX`pfSN@9J4J({-LWvhz<mYQplnQ>PXkA8aOS%+$)M+wBZd
zn)<JO0JsDo=|)H2qnX5Lu5$Pa>9jcPDF>Ny9HqGmH9V#DQwN)bM5ZIOCFPqnHXdGu
z#1UfL493Ruwp|uCAS$f_dc3yaQaZ+?TE?L@*+XI#(8)a|?oHouPxO2mtGiSurRL5~
zRl<kadY2Z5<Bv<1SGM*gryQ+;CfYR6i#_&({@z~xG<87T@TyJ)twO*=<Iod(`}KS$
z!=b3b;a;8V3Ip{z=Na}mt7wRXv^>lrT4A+-uaYn4<7b|=1il#J$!h>l(D~|!w>+^|
zlXhh)8XWaOP@Sk)swb;Z47HTWyzB7ULQ-*h=8B5f%j#y&=Cp++1wD7c<jc)l1u!l_
zXxj6YXfv`0t^2LmRKcT3hYCq;V=yED7*CQm=&PSfS%CsigK}(6*lIs*UQv6~>E}?z
zMo|z3L!Q~V%~7j+V=3|Z(jATG_XQ#d$Tn<wHhd4m?Z%HjXEo|x4b(b}e-`&&Euu#a
zx4jVNxJ%<p$H%wVH$e+{Orv`~Ua>K=hMv!7;~XYGI~jc_$XALp<V-%0$_8~LH${j+
zfSa4!C@!s-r){wPI!MqBlKbXK!vlbaNR^P)Y^y26wr`@ruq=7-N={oZhy8rae?4HM
z2$g$0`%{->QHsv`ABLegBk#i)%pGc)F&2jDHBUu~81D)#o``tn{Nd9&Qb>O)pGEhs
zL%*0w_SQ&^-SF~2R)BS49xEsa1UKPslZ#tY-qV0gpz~o8B-)jwrNf=MR`*}1C|_U8
z@_KOb@l{S{5-GUNe|xR9JzX@sCg#1VEKmQ=Ge~!MBR`#R=&Mo3iQ?S!^x|2Oonz%t
zP4D)El~3R4q6mRtOtkl`$nvv{q`R@79SFsnO&WX5dda3tjjQUGEMcjv<8u}6fdC}1
z>)fZM_9r2@T>O5lx>FBxPuT%AoW&f*&XE&*VY{2i?84J7jz*FldKAUek}3@i2BT8s
zsNNfz!$@$6Bp}36t&Npsz#Us&eY}lgONZZwcnat(#ndz(y43Eh-OaZ<7@W%#q7YV8
zbg`WYmh+qrjvHs^7~$)eHsID?ZHQsJ?@`=7-S%9CFC%bE>>elE2ad2`JtQvCRNANF
z0LX<vf{tK_XXtX_rY?0;92f^Z1}=f3>*x&YqYPKIK66_`&EnN537pu>2jw%3dg{U3
zi!%DjA|{J0>LA$AvL8=ffDw6et3a)XaUcJwlC<HmLsIiwFg*_fgKsd;1R)g|vR6_&
zAfSRTWi7207*r7D>xb__eYZTy&r>%{0Z$GUPvVoIRlY3ePIOCmjSDHCB6_~XPr;o;
z(QI}J%_8cEaNXKTQggt4RE?6p8-`ei|FTu)r7)tvIm%b6Xxx!%Skz>fraoU+>x`p4
zUI9!a3zo=P-=np$;!>B%nlj~~`T6&Gk47&|sO!Y@bi~v1ELa89KfQXayqc&)>(`)#
z7ynA~0ZVJF(`gQ=S%LIUsBvrXW#jRjxP$Vw#h)W6^#wAaR9rv+1MpNdVA*O&Lw|+#
z(sFT`U4ON@x*E*M$q5Mfta<?7H?apvX-MIbc3<l1if0sEOBom~Yxoievii-pD1a_+
zeb)kG&EjGZP?P}sAwro;gaLZRCVqrLz?XHEkPqclU;8o%yEpmSYVw=Hx#TPrjK6bU
zY}MOIT6=*%YbCHs8^WNbY5D4P&Ec1PP5ASyBat|c$&H93|L4uyU20JKI@WhXOk{^v
z-eIfm28UuVUj?yfd9T8aTq^P7&3WH31vlHy{~YU<*L`YgN=7n%Tr5LEE1Yq>3D2lr
zhys+N+J<R(ry8u~@ZlOF#8k^4b<HzQ`q;Q{Cty-uv2XUX2XKCl)`8v)#B9J$b7!z&
zKCf3#XVlr@nofPW4otK8bU6cH(H0%iYojIlNyV2qG(<!m1YtYIR(OBucA(6|1PEo*
zorAgTix0&;71A6VW1n<L>NFZX{dHj6XZN{Yf3cQz<Od@Jf-<w#mK_}}oz5Cfv6<b*
z9qxpJ+bJ7`;{kmz;sm<!g*`e33wme$l8+kCV>(3g`4sUb6gs7hn>(iK%#2UhtaG@H
zkA9=lb7R1_`qt>enoqh25S|9%rnL5j8!Jp*>>g{L^V-^$ymy(2)^IT5Xdu|wlL8Tk
zMe8g9$8iA*Di4j6-=T%Ryw?$tz+yxG>g3Z~{ot94uFU!rlVH&ZLh=}S%UZ+EXz~Q#
zg^76`t2oP6udUi~z3d^tnaPG;`Neetpr{bED|&2;fI<Lf_9noQ2XF%z*m-!4eI>k3
z(S-lSq`|-s5jFpUYc1U|B8M^Jl#w(eUf7e><Bxg?k1l47l;ZJghMvn3YLdlt#w_0s
zUGw#Q)oisN@oAT^`p>HZae9uhQlYT6N3{W;QWOaDJ<pi^O^C9#f5%e4CWt2{ml5}w
z8Dp>1H&lu$Hq>p;V?F=ud@0oWXw}u=)#hl$)4d)^pVvj~1mw-m)p>!Ji19EdA-V8&
z-_5vZ!Z>jv9+~UzB&4KUl9{x#0`Hn;G2<GY+#j9>swa1E&@0q`IVz1{s)r~_<tW8J
zkdbiqscCJ(CfB%<gDM^(R&o$7X>{S=2Jb*UTjo2GBe=RP>0hEHi0=GutFLGZH1Hf5
z8vavORo=Qd8=!)ZmJop!ul^BP!i0&s7#=o!$__%39eT!>tqigsTJ^lap#-9YgI{Vq
zkouSX_O$~U!h>7hb8el~fJ_Ei2i!h@IT(op&~~f0_2A$jNNNu5(_?q1Esz^?8T}qG
zJ_KFr;I7|jP{7Ey+j@bSph6aMKqN%u;ZrEi6UeHUl9BTK=)me<c+ISBW0sa|b^0Ao
zdfI!}{I~?Yc5AkIWQAk@Otq<nFvkU|(tV79`=mEnKKch;X-$<Qb0ikd5Y}x1@;mRF
zYHHr1?Yn&1$Gx{T#HbEq%u~){!u%H3+scZI#h>2)F|_^|mx_#%oV>V84DO(pm~iJ#
zYneg5%jAy*1k)-)uEG03Tn2Ibk{6}qc77OM*qc`r5rX<lUwIU~bUS*8ArF?_>N9@)
zc$$|Nq0F16ObnIwUF~WM{h*QFKy`GV`Jk3j5qP~25mvFLu)|U2E$e0(!jy~P-}B=(
zit>tjnSCsiO#y?MELSrXR#7?Fcf+K`-6mEuIIbOL!Si;IHe!GK)9dH&_9#+)?hfl}
z$-|#=EMwPHGbKBeE=}5hv>cANXR59H3DM@?htcfMF}tE$I0F6>;6DK{X%LiOW>(}T
z+~Z&;odhT<>X*~2^_Rf75hx=Dvc&Z%r2)$UHaz-=pN#;?B)hTybsb*)`8{3-wxZ%|
z;pGD?f63QMnD3v}N??}5eddCMeYU3o*|#&tpS?gKim;+$geB*lRR(A4-gaXE_m1NG
zXNk~)0MGyt5t$UPKY@hhi8RgT!?Y5H<U?EsH0=$EA{dy4{&;qaQDfyjQs4C_l;!+E
z#&`XF>M4B9y}FH#PtjDXkTqSsLmAG(`@DGFQ{>y7cgjEcAS~zUSEpL$Ppqn+?0FP!
z4p&Cze77<<J6L(FFm_qLpH9+VpqF{?XA!6F)rM-T0wg+Vfan!_Pnrv3o-3RMh*-Yp
z6MraZnkKog$TU$lwsdj=_*-q3%-VE$TcWK2s+uRn3fd4pp>06eEEVB+w3M85-OQIS
zQe}5AER@lEa56#)q)@vn{Vv#~JXiC*f0T&c5ymd#Q3rH#U7skmH^ng5jRy=$9@nr{
z*OS!qO3eeC1u`xsYVv#XHU5b8P8VvTX9CH760{Yj0dT7ni1X=txiDw5x>$nq&XWKn
z%G8q&$m(8Ttde6pBUHaS0eDO$5-jGR8O0{$9@#)t04(C?dpV9cb}lY31m0{5#R$Wp
zJOcd0>`Pmpn>cpsl{s#zKUH6wPkEghP`V&7b3k|nwLk#iGL@*iuKs)vv?l@%V~&7a
zT`(-J`#O9Y?9>RUS)g0Xuts;8Z9iTVi&m_P>N|2qhPWQp9<A1^vyI05=UvXUGxa*H
zFHxxhz$#=wpQlgCl_zx>9<Hh?ma2ipWt7Yj*_WyYiOZ3&8(vd}9qX&d>~NQY{V9RA
ze^!C0Cj~#q!_2Ack$$hRN1oW~Bq#rzU=(qyw24LApnX8bOO}{cCoQj-A%4h~jzI>-
z@)AJL-xnWL!*f~Ji*&f2KQE9Lz8%6!BNF)3c`TYK!q_>gS%Qr%f=Pw&gX_nqPM<Gd
zza}P9tI8ACn~-GGDIKW?bXlC(kJ?6k@5NI%gNkiB0L9h#1I>RtDKhZe?W;+pj)|Tm
zjyNjOcS%WJ%SpyICxI>C`2zJ|PLSM)5^a!x_mL4XEwpU#U=5a|*AiS0sN*%z*nEQg
zRr6Un`epvkXWS64l651OX2fRCPNBgCiRL*V|6fwdCl#tEZ+V|r4@xzfYIj@r2Cudq
zL7~g12Rg8FHRXa`*E2*)y_V!?fl7i-)q>d$m_{&j87p^}6@9UluXA>~ZsMCME9Qm(
z0NDjQT`{3s{q-GTW4G5swwyYaNdyF05`OOIdy69lY@$^C{U5u&h#co&t?&ECiJM%R
z+ASTdd5O`|@*s+N{Nbv?CnLBC+1=gUMGx=ZP)JeeQ&G{uZ{@R{#)gLefH?@z$*iFA
zePpf-JkS8+Y`_bLTg1^yJJ%ZWG;UVelP&>99vCItxTFS3&B1bl?W^I6(PTqSeh2Ip
zPC`>!(w8GZXH}h(yWrhO%4Mi)Y8rgdFPw8ZJq9?4Wv0yy9xDT=7`UYm8y(8)mpdun
zB(%$3XP6u!B71T!&G#0d+@`9b6(2PyO}{i9I2})y=uY0|JYUYJTNylVU9+6{^z7;q
z^}5jSU^&SC?t9HFHlE{2oiXw4FW;r7-xtC*kCbM8L(3^-f2@Y>m_ZK-Ts~~HaOjnu
zC_J#Ad4o&6Nl9RO%3K3D>IwnJ+MNQ_7Qb4Wg}rv})6i%}#~0%d)y)OjY>g-oJ{a1H
z6xH5@X5#ATGFbD}xrqC|B_tx(c}?`d&dCtl=1PG~JaF~x6rn6LQBsa`@h~8G6X-<n
zQN|Ci)%Z1$T;ge_2*$<UXXgr5QHdZVc%}=V4>~V5IsXo5nZ-4tH`N%*T~%I=|DIN&
zC*BX5r5wPdLBAQ&C@LxfA?WKDJ`&vQ?EV{Nb;jJ|Gawir@jr$FXa+QmA7L>4`m){~
z<2to^<8qY*<}G+Ni=d!aPa+$Hc>+uuGhABOFu#T3Nlk#5)01y$kS**o3t*~pz<owm
zM^}`OiLF3DP-BwxYd)42E(0t~hDjiBscYQ+m^u;6-TCb`c1h;N(+1BqAdl&{_5eyY
ze}DfZg)e{;4a!%K8L#nXz@tu5;_Fb1r8<RIGVJ@$k_DX0;YsTHZ361OsN+8IT^uKU
z;~KZc4x|*s4(+cW5+|CQg0dgzSwP<<3b+t_79A(!h3u%HbQsKDmIZX2J8j0H=(K$)
zd9gbd*3nLLEi<+PdTYHw)|*55N~l+c#6ToFyfz@kxUk~gIEY9@`ZcKbi4&DUv(?+z
z$COu7Q`6KW0bSK2PtD1J9ti|T^X<&J<VliVC#v!~Yc{(Ah97i?Eua4S>b`UZB-tu0
ze4v)C)Z!m*3}C20_@XNtN;_%0SJEwjc9A!-=ovO6=C}IgLL^fq4FjneIX&&&bBW<`
zTQR~%14^L6ck30oXw@^$$2*;A{eUlTy7ko_Ci)!+zN{6mr0-}79V3;5ECE<o=H*uW
zW4EsF$0R`6kjT!3K&&)2QuPa6A&k-Cf<S<BSQ_33S;UX;T&xG$vUf~q{CC(=<$zv<
z=M3OmHUkk5@~2w@!69_=^713rihwdKAV3VO8eEo6GC|T@sIq&2IE~_<X!Bs9*v*x_
z5giBxIXUmas!$nNDM1TGe4T|3q#-u5TRK+ewZj0&6>tYy%52Gi`^3cR8A4oQVq!qV
z3E+5f@y4F7?5*he0C;%ek8#Y0aW4#3HN??qh(xFWwd9uOKe*6;K`m@7k-nH0k=NB7
zKyY!wrihPLzJ0g?vkF(nN7JP7TIhuE8DFXq*u;FFLI;{tAaH+^A1r+eaOd2YZBk%v
z^?#>dk3XB_Fw#yW=eOpwR-Q#cxucK#TmYb?1S$Yvj&>%vN<b_Mnn;jK(hDELh5-#i
zy<(j5uheACQ%qoh9LTQ#>A=6a1*lVi_tg~_+h$fLrKE(te*LVrxU{C`u(FdTDkkPZ
zo)V286E`<*3=uv85$?Cx5d%Ul5S4(e&Be(%(m=({+u`ov0n^IaJ~+kHIP?MJ@E|}c
zXVFL}CM5+JmY9I1Rf%3%1CXw>XlB}0!*OXO!aGE#0GtZQ-qhdh5k8>D156Gi(hTIK
zfC4=v^74sUs3_oFb8*Fl14Z%v{(cR27`AgA;Gxe%=AA$lh#+IY`GHQ0NZZ!7(6mBn
zQCG;a>n)9>e_~P+(ESz`vI=ODw70h-rTN!3U=84RFz4wV7%*-0=6dgT8`gpjjJfV9
zr@W<5#+G;E*dNz6N1-?}zHd;&h@bM%@s^--;MvF;6eM1tYOu}hNf)sDMg|s{PKS9v
z>Q0PKT5<7C>opRC-o7RQ|Gm*6A8^%8?=QwlZ_hSU#t^Y05ELHD%HhLnObjW#B!q-{
zUZN7RL=Zl}uWOvSn)Vn3&IFbRWYB<W&(pN~jU^|r%5In$978HE9$G2`0o=7d_%B$)
z6)ie0)feb>uz+Un$T;I)X)qN-Pj@%yR)!j=Xx>M#8?^>EGpXQ7Uwneb(ZuM0_4gia
z!sX-v$qgS3oJJGo(H=<xO6|$!fMZZ942dLhJ^f@3o~(RgP7W<CZCnRd;@O$9rh;}=
zb#=3;i?Q)EkYm?!hcObdXa*j^Z8VsP(hIAAf(LqjymJiO7aTVpF45-!W(Blz{5Aui
zx&wqjz_(!~c<)v$;<oUklNNfhnjiNZm{wt-Ezp`oR|1k425sPD2t+{h(a1RR!wPKQ
zu5T1M0w+Yry7_>K|6cYvLb)+sK`UsVI0iv^<oX|){X#}SS@#E^wYcXhuiY;uhI9aT
z1G&XCu&0880xTlt6EjKB=_LZ$(8q!TByZ?SOA-infNTtWVc+MZ%L8CT3^_lupdbPX
z$3r0B+i$&~E2@ZFLMt=_V#tGTmK?{K21vy?sH^rtx_XcYP|RmLv&~@70Idb7S#fvY
z2UD{ahjDI(b$GRnwe`pE^G|{71=uJcM*bQemcBY!1QH)0!=w`xt=GztD$%c?5wLwb
zriPAk>y0^jA-2O<X{BKq079Abz(39tpWZ9Q(S%~)Iggc+6U+pkZ-Fk>3;2>^48<R)
z1YARSrpCHI9W+7lj7k*u-hiP1IJr5P5H|ZGfX0M`z-wpr5>z;o+{7qos3GLJ+)p`^
z0R}a9{T1{|-DKw{i*X8-3x@AZD^fs?K$F?Ui)T`SKn#)rksqpTXIIp@xQ8K%B4GZd
z<KAOZbR3F90MsNUB?0v(@V>JgL@T(=yLW^J$<i0Q-+_PteA)$c?bioD{9g0qHfiwu
z`r;NU`n{)BmJyu2z&3zB>Pr6@$nD-RTEA#`o1HBcd~tGk=o!~}FbKWkmy`?w+X%!?
z)IyF=wY7)9o0-*9%>^is>@09P{2>5j`}Yfu^LBV|P7GCAC;><$=<E<Udszx9N(e4b
zCCbkZ@JRrGLkJ|AND}u~(oCet>o;a>>a7~L^$|9M<Qo+_8p_E(ACRGG_1`|y<Ud*5
aDb(-d<MGrzI~fp$QB)K(<;!KwL;f%P^r3YC

literal 17877
zcmeIaby!wk*Di{RfFhF8p@@`pN{J{bB_$owlF|(dD&0tT^H9<aA_7t(Qqm35(hX-k
z{N8V$cYohL=Z}4TXa9A$E(F)J)?91NIma0HxW_&4e<3G<b(8ca3JMCAl%%L43d&VK
z6qGBUF|NXqtd(n@;m3_PlInISD42N2?<>D1JWo+j?xIMEiYPlLu1~l)4;qf5{iW*|
z=}Kc}W{&!GJ@;-@LzMQ2sMyg99-DVpBnCtz#3(3iu84}hztUX$86!6}<O_YkHJ_XG
zrJLr<UlsR%1+$pQZ+m(rYCLDi`oh;UwtudzqhlFCE3YGGOUGx8_l{j0p5qI;!YcBy
zra4eK;fDlHH~dR3!gdV><xQq5dRfcN3l)`B#(+Eof<9nKg}@+a#Db?Vzgi~R$g`G&
zF<{o7zoKZCqiU?s;YN&$AZ5|Y41zePpP{RCNJVRL%ekCohNEucNAF~HZT@8Qnmzx~
z5q&PAvsb)}*zOUzhN=yTcg<C;bsZr}Z0kK!?XN~wRSEe%)<vZ4%l6x4!KzM!0<l+6
zP|S$`>*Jv|--74I{onn-%$TiMv#QAJ;`}T@R6c=6c2xsj-RcR}E#s;&q4kcYA`AS3
zxz8f%Q^kktRa50f7$_*IrDl3;CdS5X*U?F6{QYXV&0-@W(jCO$!cQvKeE2vxwna^S
zl9o6*w=oH+9*yV7Qx!gh>CG)5CGPgV>%P@WB+L^Omq$9Ou`gR@-1DzN?KOy|iLl6c
z5+8fi4ynLk<DW^L+u-e9c-%w2{UdKZG*}`GZxR%gxA!rO;f#>G|J@%@v$d5Mr7^X|
zPFNLpN3q067Y_$g6ZsM$5FBkZQom|(sHxjx`<J;AdNha>WfTY$2(F9#L>?7WFjVIu
z4ThuLb6bD*TnqE*X}dTKf>K<o&B&|9n5FQ9$EumSPPszhYSQ4U>fpfi@4Y(G&-e>5
z>3#2FT)A@PQ{1n0$Ali^q>#XhFe7yZ0y)P61cE>5$8#QoJ8$@t(0txSKBKQ1v#c6B
zdQG0t(<aaESeL}!CNHGL9pZFt$&kz>h>!esU~9U=y;d!jm=TM``P{79Iem#6FjqSN
zAI;eRiB<V;yhr*@yUZQ2I3c&n9^=4(08&n4nPr9cP^!?7kZ2kP0fCdO7@eD#nD^7S
z=GrAhMGrH;H@#hHWi{y@tF#RC_m6vIqwnarAIE7@e~rW-FE5YN<j1bFplH;mPuzN6
z-jZ+{cgM0{%Y$t@y%3`(Codlw8cLUbczC$GYrpv<r{Cr8w1}8OeUq`Vak79D3j@RJ
zxwhc*T-e{K{M%hwYw79KfvrzPjmv6sbH5AxwBW%e;V_(P^qACTCX`O#u^g?m6fpUb
z7#0>bRHzwq%4g7RT&7|yf<TO}J1Wf2CfKT|kQhv^UWqpd!c~x(GnZ2!2yMTQ;XU*L
z2bUoro&Kh9@ck|h)m9w&6!J+@-xSM4KSTAGm)?!NZO!q7*=jDG-nA?vdPkZK9ToNW
zx+AsiBXZcC3v!XMv476e<Wn9qFfe5K#QD)kA-cU$MZ#$OBlQSTMN|f#zud5%)LYCX
zrq&OViufkQh990zf68@RwbeP4^(fCbzYP~vgg{Jg<yFv@Qcw(iKvgyGq6twNa}%na
zmX?;St!*G7b3|mMFE*9V23J=!vw45E?CMY<CkMyu?CkSUJk(|j9#vxzxtxAet`&Pv
z^+2)*U8U*t;u#97PyNp8qhE9w7hHu;_1NCnVk|h;8L2Pk*eI(k<Pf(J|H~9Z+YQ)=
zAC0t;7~J;tNolirL{1y&pMUMcN*Aj@CyRD%BEN%KTM$WFT3UE`I4rM}l$25S@>td8
z_CnW``%cg4>8aI3?NJ6S@LMy9zdAbw+1uOO1uq}|h%L@Wm|0m_nVRMfeIS@y4;rzm
zLcf0fwYDUCLXTsiwL2nt!pa2R99uP5>BS2@E>gacwg!{#Q}_D`2?=Dx#D1K{DbxSI
zvHlmw?)QbbzxHD(v4LY<1BX(r7E6N;Yk(HH4n$DmNE#_vdOu<VSak~?bC_!}1yKb8
zR1xG+0<mzT5mPQRI2ziXF}zw)lpf2}6~yid;n`-~s)|PH)MBobX{Z0u<&K)1<zWe+
z7ItICS}tcvCHKCIii)ai{2UxEPl5wK%h1zkBQb?XBNO?E6?^`NBS9)(mhDMzp~>A&
zgRv&8mU3(?c*ulX!DQNqh4<bJKAOZzjI>N-Uwre3i7*iMgk6o6aamksM)XQZAjJdl
z$<F`h0^5J13;ll`>TSH<@$RzSVt0*eb&s*zQj!y*F*)HQgzmTT-OKhYnw2uEj8-Vf
zAdYyb9-ZQ`KN1@o`y)w!>d~9Gu#T+tf1g@dShRe^N^#$oYWrPnGbfkCzt_##t0nY&
zb-OF4zZNF$Z9=(eU-~oO+gaJ!iwg@CRulZ}>|0;nU3-1`6;uKacVJ*3RQUW54pMpS
z_MM-dbOo7B*4GS~zqpM=OUkNbJQl><W>L+07}(gGQ%%CS6_#WB8KIAlSMrN*Q$2Ry
z80X9BSLd*~o>A>BJpwpH+drK?pjCvBm{?L2TygEi`H42mupjPyJUqPT@><RGMMyl7
zTDvlsKOCd$xxD2JuHj{&hL|WBuN66)-k0Ac20g|*jUFfCR=nViZ3`91@v6`E>o_i5
zNWNTq>vb3P=sRNUNFec4H1ps5i5)Wjr_FzPscBU;_HT}b5C<pK;ZEzCqrg87F45cj
zYDVAp4xS%KXh|3-w)!++VQ~=+4efb^a%<NoqobOo<ddn3bJZr#Gw@_$5)#;?Tz{5x
zQX1dPJOwWdGk>r#QD-yP28&W<HQ_q@&EH7ea<n`z0JG>8YFY2y*oSZRicnNXeBfWe
zts2+ug1@zfK%7LWKea8&qwicBDYN9!R8}5{WjA;p%I|sVY;RvqBXvgx@bnAwqlF+B
zh^6f8?D%wY-9LUjwp$Exx@*A1&F!$*9Y-0uq|pyU4cL0}89?~l9IxTTZEbJQ$;pur
z7sm_lUlw%!n_6W)UXi_}A2hPQ-^uj-gc*}=N(+H>2S(~h!>`vyx8O+ti-Z418;RhQ
zv%L3{R3K1M0o)YY-XhOl%EZ}rhzv*y1g#6UHl@M%;q7xXIgw#b*6x90)${?zWh1H&
zRCGQ4%ON0J$Do#tXeletQ<(B3_}(JHBP4v;%moiwG2WBN&e_KJnCT`4#^2;^e)16W
zz1#vlQV|@+5$yg;G`HK?7>_{6<wT}z6p-(CLO`lsaSqZMx%1a*I?-aTey`IJ=@v*D
zN<Gp`1v>Xuhnu!L9|o0NxrT;{hUR&)UUf%?oQNnUF;Paeqoc#d#)el23kBtgrP7FA
z@*|tsw^y!tp6#gyw%X2r50`&@l<DnXXTNN_GLQ#Y<o(Bw;^N}j+1a!Y9;gWR$VOws
z3Y01DU%Ah$St$rtbg?##S6YrqP<e3C(k>3<DV?)y)c$=NZr*%NobfGskTOb4>BGyP
z*u-0Dog4H54lBDcO{WX6gAU-<w6gI7)iwrA(br*M0TrIur5{OSS<*4w=0oq_zsK<Y
zyEm+RQo#fkJDOKY^zPcVYZv=fO)g%^T(9xycrzg@7*8J`AOHIG>z_Y=Y};AXKBp>V
zh+jjyRlV^<cT2k10%o}|k<ZR}wEQ(Vm5$!t;_h9ygN?Jr1nYX?7{TlL-@vGfZ%^Bz
zA3rzhjir-Id}P!a6`?b7Um}G3v3qrj88Y(_+?#%UTz22?)vK9PRb7|AmDzG)ab-sb
zH}Gk-HXO}3{HrEB_fl9j%IP7|6c7+#WtER!IGA*^9>`TNF5?pvbU!~m0ym;yXlMxN
z522EuD3?Pcl@G@KS+9BU+Rn}nfR-ZqfV&u*jVD%MK-N|cjAY$>e0&YAd#*<dG3Z#N
z1h}|1YrjhpG$~;^tX-m+S#B8P_pMIgHwZi3Rct)3pC?emur_rrN!0T^-D-DQAH73B
zP;Szj`~j1Q^3fZ;&M1Z?0Vhfx3pH5N4#U2_J}S?{nfEvF&kyH9ZPAN#o6f<u-KO86
z*E*~^KFo&=vcKP{19om+-0)EcJlGfPG=;ecJr+jBTKh%&`{oqX{K+v?6X{|d%oR_+
zJ8axt@5?~PRWBTJn(`RdFUic#j`rko9WK)Lr>CL$cEXsQkDi^GDR?v=>3x@mhGx8T
zMfq~YPX<+W&*rEuo*>t2I$&m#HRe<+^v{{sx!u&ZwO|1MX~l4g>q=f{ck_uPwomgD
zQM;ud6uegB6i(w?(>~bI$=Ty|<<i(e!NC)CPS)T#CL7!&f{1PGR-f_?JQU#Jaa{hD
zg+T0YOb9SCu4EHILNhJA*>I3-(g`Wp!=o>e-@ku{#oPtgqm&~j%H{lbdbvNRH%S0q
zg9$d(wyK}5>vGnsmX97VwX~7TFO$y{N9ep{lLcKCIzM4QK1`*Ndib({nBV@7rgeiu
zzx3W6!J}1;zw^a6r#p1-e2Hvgd`su0f+BxTogPfayEs2jLrWV%A>eqlz3?)BZ*dW3
z+iIv_<lQy&daA2+6O-WOqoSe`G&8a9#TH+0*1CdfcO9qu&J#9Olx7)Gt17pduO9$k
zh6-*=>g&(S_Aa4O7tW_qJJ&*n{e%R~=hb+%P1WO+PP83^P$9tjRq*WWZ7++oj{de_
zPaT&cGln4eG*c>)-)W=%RSO!mD@5DVgKzjqYg;>R7PR>Nnd2E4M2Yj(>_S(}Ne&J2
zlhxzq)7^e~RS^oXttX#uEW(q%{MUcH>ImSV@|Az56J}=P)Zt*`SK_fU6Zt1ko&ZR<
zvU$0)*n^3M)zi~s2{zK<@ioQkyr#p@^-r4bbNRT01Wh%yY6vgClqSiMgAI^VhMFAy
zota9w_T9>>(k262`1J445})JR?^U@nr_D+2I>!bGyj6|W@nqel1(ZTeqM|qVo|dzK
zV>y)^tyX6~JK1TqekViye6+g8@J&8SSDM^<@%awylwz%#Qk@1@@EWoF4iLN>&3F=8
zT3VKRQ#O;PA)p5Y1zGfG_vl_64})cZlWwQ-I{o?qGebO>G0wIxT?{}25sTKJ!F*K+
zc$A)puX6fFU|t_cM>FZvI}ajM<Z_fEGF9?ly6Cc~zau@NC^K26?u|)d5U4WKhV^>0
z{tlf|t;|@~)AKQ|R0J1zAUJtpVZq4IFe=#sd~tF8cIUyA*QST%Kx-h8wEPj6EM#_>
z>Se~FT#(O!(>vR0r*c>w(r$3&#)YViq-8u#_Pt=cI;`fEm~`vs51DP>bJ%2A@uHe3
zBtt>rR@GPvc_3V;Rg{zBd3=ydTslIfx7~EUR~(`6(Y$`z>hE+j-1jCfkSg6~6^$nw
zb%cQrxVcrL7k)*;&Mg2xDms7W)v6RiDKsiz<m&3Gr>FNH);_nT<>X+tMp8CVE3~}F
z#HuRfqJ<=fzJ$id%09T5pd>i#iqS1tA6q@ZeRi><pt!6M-nLzQ53KqT9o^E&W)l@e
z>hT)8_F%Fq0BaB_qT&kReJ6J&z>VSc4Jt*~i}QhLpX{%TmRpYTZ2l&5y=NSZWy}%x
zjj+&9a-vRp6==iMl|OsOb(@WK;0Zr@No{F6O|%kY-nvB&!^2&K?f<+8Y%SGm_Wso`
z6U#OOEY(JTPUt~FRd*Dma8n)!+8P?9xD9{5VglGyFV<<WwX;LNP41krhxy=!E(NTT
zdg05YqEpQ6m6>bFtb`FG`tEZSwQXFFX^_cfV!f!g${@t3pFdv|Vlph+^JAg;I0vsv
zs&ubAj;-&frrw3afETV_y;=?l#(&N6kf~;ieu=5JWC;xO<;xc`e%ANqGx`$CbozlW
zy&t?KcjR918>&EB-gDX8#t}3n4g%DQZ@6KN5&onU-J<#@sMLy)*pV%14$N@Yuxh^#
zU$*}X*7t^?wx%hUS8C~EVH@M2mS{^nodPGEOKaxJhA&0Ij~nhPn55m=UrPwKznXj0
z%rt;aM9NLuHgHx~Q3S>{&2Q!jiTc3fcYaYq0u?N%mxsjI0@v{+2}VBbtgXE+GzVvs
zD_MwPoED-|Uw9ptq0Y%v8xy>Tn3)*z+7eEe6XS;g|2&A2<qhK!Bf&W4ZmX6#zAGqi
zO)4J5O6A0imeWxi9XJ+B!(i3f8u;*9?V`xn;!OC5b?j74@aE%xnM)-XkWTqK<MLG(
zc1|<{tgjk$TUDvrKKE@=II21g*s^TJS``$i<R7!I{&2M`2f=6nOl(T;5v{XsndRK#
z=-YJqQYO{&tOd_3`Tzt1NH-+9BUX<lF*qG)QJJF;n6<4rgzWo54N2J5@iw+vY7vN)
zjjZKIAXq7V)N$TFZzP@ghm9*LHE*une{-=P_+}$0>+=)SpMMF>n0%SK0@B}X<2k6P
zB+1bUTgN(G0`zU)7oI>otUC$_2OE@)95S?7Rs9QUZe8NN$-CuhOvsxq*v9o3OGT8k
zWYR*?uWt1I%U$+L5?ltiEdn-J0)_RP#*E8XD^2L~5SQ~BrLyX+ZZ;8eOZ;V6ady@}
z^RHgP65F9i-<aky%yT)h^!fYOIm3VFVdCcbVzX(6|Fa}%FVXB|B4^3s4>o>XZiI&e
zZF;oS3~8LW|KY;E+%bF27I-5`D&jY+c{ju5npYP@qm{jAXXLS#677_;zXJF@WZ)NE
znWscVt+>BV8>)H97Y7VZ>Je&|;}tk`EWfzDF?@Ls{R-Y#DT3D9{-T-bn8e^DGYHW+
z!=rwGKot?P30g3Gjod*f3e4EX_qb$&LLRh5X^&m*$hW!C=$yFzNws+4wdMkouVKFk
z=B_t0RlJs`<$U;EH4^*s(kF%3#vH_%`!_^0Ev5<_7+^3AW9mF<Cu&8d(9&^o<Qu#l
zz1=MH)W>g@MX%Z#N)hEB;o<QFoam?>mUt8iE>F?GYL=N_ziM3a`Rmoa?;`)>n5d|0
z!$2cGF2h)rsWx0YV1}`%uNXr~gJI@+v#U+%3+#VRIP_HQQSDo})gn%JjQ`|GwxiVp
z^&#X8<YvJUslu@v(J01cupF05HlT>HsA%t6VnB_{py@eW3-K0to@!X_K8?r=c}f!#
zd2{5%42WYaKE$|}roS`hFFCUBi(JSjmhbggq*q+gVV5|u*Iimo>W~Oq%mbb#Z49jY
zp%N1n+Q<b#9@G`XsozXctUQ%NARM>>o!8xs?Yj5C{+`dfg#`qIL^;y<&4^I#T)G!$
zB}}X*GLchwc@~{RlMkwhtWakui`nI~P-84^-<ZK_{hj@dcq8E|%3H*Hy++zRb9rh-
zM@zaG>C1aoK4H_1B%XSSag#RZ+9vWWMjyRK9)+md2@%mwDl(fXcvodCR6DWE9}^vh
z`Ytuj|8eAl3~io)$#yaqY_LNr?<4+9$_7b-!N~SOB@&Hc0Px(%gYRAM>32C(E1v&O
zMf!#pI{>WYSNanC27FF_X9->|9I4z=XdGhzhugkILJ#4cJKR9W+M*kqm^AJFl798-
zl_;0z>7kz=hL`dzzP^PLJEMlsr!<RmS*NLM8ZmHp?l|2au~n91g!uTJHnZQ>*4BXE
zjS^sc_|W{z+bfskt^rza-r56)O)=5V5axONe`d(=f0eE32!;7U5*-s2ML<P0m9JX-
z%L}q{m5o=LLF`Zx=ql}1O)ID-e5!}DplfS2A*$^bE#QxUFOIfT9hnEtU2kmdh-bE(
zi4DB(nrdU<hBI{`5ZnEaN}23bZf)&$0E?xpvga950qLzDdz;Ff^TAlUvoxKX_g&&(
zaotbK9!C#if#hZ)#Kev3Sxiq)w?+T>;X}pi{%p&!N=T<}+_>?2qSj%V&si`TnMd~(
z#7{H~<mK;nk&f1O^f%&5Db}<)tB9}<FED3B^r&4Y5SyWkZD^MVkLu)947|h7M|WPR
z44z00)rRS7ao@(BIG$7^5JTlWVu-zV^(wFuxP0UxZ{NPPMW>~ub@e@1?x%b3Aj_T&
z(_61X<6&89eZ3&mEBcR&m^*7TLd|!qpPoPHDpAfem>rxmQh!PCAyWPm0#Q>_^U0!(
zaT*zWTbL<jPXfl)E63L1DSC__j>vGCRE_=1vl9>yz^ltu+O);C%=}GW|9h90m-p`7
zCybk!*R7&-9it$BhAdh%Dlrj~YSoP|MqSYqg3e)n92^{^JQjaKIo!KVqiXl>QX9?n
ze(Sl1gsyC!Hx~aNm|?X2^)V;N0IVC2j6FR)0X?27eC(AHa*J&K!eWf*@cqZiXy2u5
zN?SeE(McXOD@>Oj5f!B=)=0BB(l#Zxbl%S@%{Z)!q97$jK;7n1eG~-&K_>iGU8@l&
zJ#yzu*4oYaO9E$Bu~Pm`Nk)Nqroahcgnp8hE&G2qJw~&;!TVIbJX!Q@&;DBwJ<Kr{
z$!PbM<-Jq0Y0AoSF5RjbDe4Xu{@1UqNNjb!u6lWCJX*6mXXd0+_(28X2+=?15uon$
z;A9xIX4_;UI*TtZp+l|$PB*U<AWvL|6UQ>9M6lGizyw)G5cg*uX1WN;#a}`BVgl!2
z)Ahjp?JyP-d?SI${mX~C!`v(d(|AbeeH1;N3A~>>X0v<#bGdj1fqxvw=rgt{M)qv~
z-@81DDtD0Azv;s1etdV2>WS#u7ae}DmzVy<^%>jATjGAvdZA?EjFgM~OCQj1FV^E3
zuO5y+byg1IHN&N|7$o){;eG%4O`5g@b;R(wWYBBv+ep3-!#D-^80&6BauQz_<#^8J
z_1bW~IL58aZ&t7*NAC#4R&Bw3YCL5-8KCg^Zt<#yuk%NtMhv*1K|Jq~oNmI2BoBW{
z*se7b8mizg2C=<I+A}GgZaQ33fAy|@%j9xaSJ0c+%w2u#d!JA%dTRFC+LaeU=@LPL
zTDA$0%wM68#%=zh=rR=N8k=de<s<k#j&}jfUK}l@^mcXaba9}H9DI3$hYGv}_j;}_
zA;ds#<wDFwmz_obRZoo(M28RGQd=n1W=<AHFsbx9J}l{$fTD^pGt#RkYY=s7R$90)
zHD1ScupD5s>$g7F46~oQX2?;}y#sFRToW14*tj`kNeotpi!XY;&H=4-20fRQJbb)B
zjJw_xd*@e0-Gv#>ikgGd{(b5rul%?M3s^jq1w>r)DP%7P+Q)lg%wwy&^>e?nq_ZT1
zcDgxD_#H`X4-efL^o9!5J(-`?W>f)4U2|NBsqoasfjbgIwexR7K|#TTr62v~)_Aya
zGC<MJeGk7c-L_(1o2QiXQh|ZLVWTn3I<@#ev;gQrx-80-RuTNPH-kx4`)q+?9Ywq|
z>4thJ`Hxv$^4pOenXlOO-O0B0ofTDjJFS0Wf(;bqqoLX9=}l)gHY`st>`MJgHOTWp
z!}?W8IdQG!yPlzm{6mPpl|Wlg>tfYM$%+CS>3+Jq?CtFx&8&ITJJpi?>5I=rQxn@>
zUSY#$G?i9#sA#V>fk69PfR@be#%(oT4M|>nPHD_QStt&!6idu$%T46+qZAGP)>{mu
zU-1pPtDuZc@gkIbpq3QwL*!i(fDx{sxU_+^e-W0Lp#_-G9Gq?y38&t(rxQ;c8L(Na
z0H>Rau&LgQR6m%0L#<e8-PQ%sE>(-0FV-aK@qvnQG>8gBEJid|!X$$_x?;jxiX8YL
zFH0gN>y}s#8J0_Q=i;llIB!{g&`UP+*6JtDJ^I*%rLO#@OI$WB7mT%N8o5=GE9paj
zn&_0b_oE?FF4<OPqgfL&ELnN^0|O)4GOIuLX)rpAELYO5d1}GZDL>=piS0>$#6WAN
z+U_`UwNOqa$$i^?O#8&MqfU8Cw_&S|jP+nsz~FEi_r5p)?3~eW)r*QbMWQrb7tA9z
zB9P3m;1e5Y48I^W=w2=uXf^R|dBsI4R+8R=`KfQVV2xKksUeeIl#7Puup#%mINI5u
z5)?_#?Y>N;VAm)38L$8D-~QmW3rzjCQ9d$C%Bj})=GAD;(v>YMQkqYMV7XLpOXtxb
zYxmed#~vkGhcI<T(AaF(6NxE6iDtwC8Kr<e*JCr)7K4&d3-MeI<)z31_FkveoM#*H
zj<X;{l?f}}IGH<}!Nt-zsFqV!Uv98Ger1H={h}i&`?7jJJRz4HY$aI#%11Rc<@EGx
z>nA=J^PO#UqJ<^`ior^iu3wStZC7Q*H+!3DGnl$~%QvHu_56%JwY<C@V~0b7<m*yj
zn>~)l=CVTkKk2?O%0-w#QNM#Sr~fq@v6W56-kqv3kBp0I&nyI^a4Q>BFE8^JXP?id
zW7R1xy?KYP$zl39;*gqzu=uxu6h?P2IR6k;`Hc-U?pa2dDh;wS4>>X{v%2p&EWg&*
z*WZq>h;1<FO+WLEsayUi7h~=Hn@g@XgB7=9%B!oC<E)kmfGCTZG@F|Pc7a=aX>Z==
zFuz=#?}>i7@6aQqk1a4Sx!kIw_V;1!DaSLuR0S{dn^yZK?3#!vq^+t<mi4!tPa7T~
z%?VN$YL4Z-sDx)w!nkUXZ)ZE_v~jky6?9eo9<@{i+AU%>_rt%8$Br)v972O=13zd#
zqANa1V~hqW#XaAlSr94)$J4syhPVyqy<Q`jwdg|*c`Lh0CbN!f$xa*IiVNB^5bn;V
zaRK@tQEVbj20Wf@JXeY3xxrGISC`IN%i#%{h1rzr$a<AnToBPja*k4H#aP9rSIuyJ
zmKlWX-p40v-q$e=)%RaX>90B}>htQ$=$zlFi&|>oyN2?lLTS}I&X0*u`svfBl9Cv>
zH$+3<4-3cter0A+t`c#3WbJ_b(YV(|nuk~=hgXGRM?@j#l$#ujEz`wHVb$cerBv+&
z8Pj`SL&t=A3)~~FJ#bpCdBM%@+-3A~ejzgJ<7p<%5A@fT>Q8SA9E8Zo^}NyX6rO|1
z(bP&GAOGhKrFBi%=~L}@{$L$Tw1+k4CU=IZG9PSp!$8M?w2jM0cw|$=y`PB6;6xi6
zb5E5lWf{<w3Q|%B0Ey~0Lyeonz_cwf%yzH-o#!`6JXE9hmqsUqDZNw>h_0jV7^$nP
z6ZGXC#v}>EyNt7_Bsm;w^;_1_B@6Bycs260(;ggdPswK`y8_F4s_^zVBG}$UmpVd~
zSyi6$pq(0K9AbP|Jh!={)2qjplcFaFeZ$fhrekI<`Gm|&i%-Q|PUe?+d?Qr3%}dM=
zzw1sU1DSP{hc+Q}t#y%EN?|@LP)(#UB^n~@_)|7r+JM=rDbEuTiXCOm^E{O)U*8W{
zg;ch=N++$%v+1&J?&9_rPh6)(;tzkE_BPS)R6+fX6bPWOd(V(uxhCUN9Q*s?H=Fm=
zUcEaV#M>SVg!;@|dB~v#3N;TPs;02LdJ0O5hG#S)#1nU!xJwLXD|_ZWY%*!A+|9<<
zIG5I*Z@JL`k3U7Z_j@)wxX}!^;c#KAJ#^}qifJ=mDiwG1uIt`6c9+)SZ&RA$;BUQf
zx<BdTd~i9~&ACzhvr$M{?N2FceXM7C$G-Mv3cqG9J<y8Uq};g%Kgk~Cc}4!w+$>`j
z<@zPe6@e-;Qugd<&+D-|MC)8a7-+CayDstKeAhhTB?-mYkGjIw@B6}*_v>9x4d;iF
zQq)&0n97&<lXyaxa(3~7yQ|-8^)DP--k^{a-DWih;>OzJBcw#k0Lh*}#Q{pN_;>pL
zcKD$!CkfEDa{Kd;U>Yv;2lBZs{;1=u-17?*V|A~+%gESC7Z|y*M(40D71Z&tQ86^@
z*f1I14N#jbN8l`HFln~KMs>BAmR;b;4>gUt+hpzMmNueu`6;B?^qnArv6>Z0hMi75
z=_tSuP;bxP)%Yi46p~?Gj780KqW67RUqkFyK9{Ro;K%g0{4VV-*st54sJQsMlBo3w
z7Z<9x`<qi<+h5#HxPu#JP6|X7F8&awiJ+E^31SKx9}T;Q@B(VMbv6G-lIIRmW|0q-
z`pR4C(Lmr;S9)Ci@Zp2TRa<NGjrF35Zi{2L48KDBsYLx3q{9`9{7JpZ9qKi<3&B59
zv^#g)x3APi(Y^n$n&K&v`^~OhSp~RjEG(>st%@zoOTQ}`9(YaUQeNfb?G3z4Y>#n%
z`f$)o@Bc*U1&e1O)imsF<hOlW_4^>EKz{P>-Ma`H(0a&zX$>i@6#)M>>Y6riL1{L8
z@8tVTDES9@F|N;&`n4@b?#Ekqu!T4{OP2ch9ejesW4av9MzqB+ZVm|_73dXqu^77&
z(uB9n?5F@Zkh>e}are=q*wV59ua4T^JoJD2$KsPWe!X^$jHQoM;2~aW<*^>#nar+o
z@FV3OEu8FraPwYf<9YEMfY+s7S3<AnR#mL3FjDn9z5RE&xw#BNZa>M2`{sEsDDlQC
z%vGroIl~QjgMZYPLLO>VNDla4?K?8A^1vHna~&%h4l&6TNStgEf~;AZwE>wtmzS3Z
z#87hu{k$+@TE;SKyMax46Ak6Xvl#*7l#Z@0&^Ta`a?v|k1War#*I=gTR>#tHiPGRq
zkPsZIy_qID+ejSA2&B2mdQrP9d~#dsAW(kq4{k3Jrli8R=)%2FA-Um0ar7y6Rh0kv
z`y`C(^)H%2^J<q%t%hxB-SFz3c*d57#Emr9God~2l)#|s9*ZBJ7L??MrE*q%rH|sA
zrdDraS{Dsp*XmR<NZ^JV_G}hL+Ft8tG+=tyX0JPL6b#f-e5yLGw=6GZl8&JH51G{L
zma`0PB-D=q4xssnfbtVWoz$}kO3whB8ZG{$kVwue)DcGN#lz+RBrXH-qY*MyCsWa;
zuC6XJKHJQnS*xH6*k;usAH2I(<yaVrFL0V+lkq~zp0p>Bly|*9YjaFq=t4<St>b}J
zv2+XzB;?G(7XiaENVAp@65&sZcmKX~<Q^fUQuh>sssx5{Q-Yw&_OBlk%MEcCjVF!6
zI&M7zpKDrAJ^N$rglg-<93EQ?J1Rs6uK4yc&<Bh)*6k#p+5Jcq*mSg>tanj6sk}IA
z$9y(gF<EJ;x$F|atg$Td7<r$FwIla?E@6;&aie;VY(h~>oLLhWx0R>Hc`G2tY5DrA
z{m*G}tWB{K`6umF-0Os<cl;a)2pYmX)3=7Su_=42Pd+RquMFCaq`E;VL*2MiKQMZ7
zGj?#muy<)^Ios+RIQhxDv1dh4=P5s(QdX19_xkfQ?O9w&DN`{NepYK8<Oi@v#v6M|
zSSWJZ#M{x;2--?XB`z{7WBxq$p<L;^Q0qFxRGp;$6cNF3-hVx%&R>e3o4Hl!;+gS7
z#MeO>zX@uODdB*!qVnA=KsD)ge%h13OGiT!M8;cWZ-OPJfQC(Z`YeMU@BIl!h}Xe{
z(*pzS(X~yhkxG8LoLrIjPJWUD+@CkDrR`DP!n#p9_R-1WqS({PnP9K$S;yv-Tg<fW
z&3kFbO(FPCpWbLFefS<q_)m)0Zl<a_UjO-U%on~*lvRv^v^}EkVgP9PI^FC8g?Pn9
zr8=my#2ip%wx2&Oob3;xYI?{;!_Pk|z&M^7+R=6NC#6_H<Z+S^85vPb`=9czJ-f-Q
z8k1hH1Q+&4f=qt&-LC{x)d;q~|M@)5<j5c|^jS5?e1@IWq$hq4R2xu;v|sxj?%VkD
z&nN9GMf{W*m(Npo_0OmEXZ7x1&FC`vB!39ky%I<=3i*~Jn;;%Jbua(Z_6~l;W3{sR
zoy}6a#;zYD>M!gSapWEA?1XUnlCEf04TQcPt@iM(m40kg!?$r{M>O*3=d+Wgi6O(8
z?c&91t-1D4*0S-PrCzdIA3=-As4fj2=E|emK1B%3ZD@m8*ymz)CZR*pN&F6sLPK*~
zVIwl4h9%V#8;gWH!Wq<6PNr_P<~d%Hq9$_qkOfvwdL-9`4Nm_l5jZ)W8rau%B9#e^
zSnWF`eCab&Be%|L@%x2qa!?7fx>dZpd7^y4vY^8R#+W0Qr1)2j9R($LI!N6B9>|N2
zTSaA%kXa*&gxhuZ4^>X1tk6cPY?ILWEarn2G7_u<PNTNAz1`osetse3A%3C4Ii;37
zCzWUlN~EIKT?C!xbu3(&R7T5kAlJ@XI)bcRB9q+H;E6CbY1wQ7r@SC835hz#K4^=a
zS4$+U@`<7dwY;`wTNM-(jGjN2md=L)G>B+CMj_>YL*lLORyE<MS<`}!P3b<rKL%C(
zPlvz#IF3f4q$^TpTzI@~7qQ^0wO^d<N!_!o5t&fM!@GGPeLZIPnCdH;g7f{AApyx~
zdCJG5LZ7V;zSOR-qRuZZ`JxjX5Z<U#yLt2G^Ik^?_gz%=#UUaoiID=pB!Gq~*D#8y
z3t3!#bKG||X0<ZyKCOJRR`w{!Hku?tDc6UKbz!C4f6{}B<&JlpITC$A>CAKQO_E%d
z?Lwz)0?)F2EeL@yy?>7DTUoI;;gHNI4hp0k)!BVPNkVMoletUfXoi0(qkib?#}q+4
z_1VVtps^pDiYxgMq1ort)y{r<6_vdR=i1cIjjgr}OHd#*A1+#tvYV-?bH(j5nDR{8
zTPp*-3oD5Lxb~wZZXr^M`n~1$IU;vQsANZ<j~(qS6|0qe)i0?(KVCIr8OHB}@_GoB
zFw}exesQ9r<W}Hx4}PMN0>o46TCG4pW}>TgFTWvnX}WDNZ|I9<bDAc#fm0l_qu9i&
zvVMvN+s!{>qU{7(3n4;<g$`C#Y8q&2@o};Wg&Nn-Ym-SOKAhJ(ZhY;1-aBMfHOx(1
znIuG|_Y<8oIOO5fjfS(X-X<#kFh7m=0|~YE%SjV;CF#_PD%7JjUKimXQVLcEGrp`-
zKHhEKYv+9KB%31Swx8B3ygC564yM<I;d-x8(URA82c7WQZe)jHJlAVQcX#(VZdqAb
zA-hExNYTJltkUjXK{@n3iS=02IAB+8$dCwq47|#!k%QXA+Yc?2T$FrIf`G8kXxLPb
zvOb|{3L}j<+pZ=MrwQvBI=64rMW3Xy<PcAg!NPLoX5HM6{WFdms^}>)-s}Nd15weS
z5^0w;{l$8Q$T2^rC&kzmu?1JIWShI^vW%`b%gzPuwY=wlS&E7g{dT$dVkhgz4N-fh
z&0k(CLtX}*1RQZvIDV#Q$9oqp3$1Y;o8K!1#Luf8TRQr)O!k`5sn*UdJQF_u99(XX
zv7CH)oM21+{P}YYsEPv%Gv5(8H#=LTCe#L!8Herpj<jdK(MJyp-#Fy6Y#0=w{PVqk
z5A<b!p)#0>SgEmFs&CFPP7G^0;T7--@r#wa=&9*0c0JIwzV(98Yk$xrg(7mHh37<}
z?*7CKQ>~+kg;03A6@Jf^ccpbAD}Buh;o+6u$3&c~V}UDmiLY2pD8%;F_aa2mGJ4h{
zv{)kLBt+Y{w+%+C07w{eMbP8+^$HiPkl~Ao#_^o^r-s4a#}7~C4U>zBh={1S>hb7_
zpBK>HVVy^A#>pnUZezUnyi-XpSr;TDQ)}%U*R>B<+kO@A*w#(R3+)ZmO!3gmB-QVl
zZwg8qnwWU~ZAJ%;f%95Pdr{-d8OOR%sJZ7VWF%DI2+m&vz6S*<5h(+)h{wk&y~f!N
zT?7K^5+miwXGiG>_fP&knrOV%_ulgoeEs?%RgXIUxP#7`T<|Fc|F1lyYpxT^B|}0B
zjz-P|OBIAl0XiyJ2!zu7=fr}dt-@7JCaj>3Yz>bu)Q-udAM@TO8M@0_)t7!G+;|f5
zBCL0fr*tfK!f|U}YOEFW9a@HZG4FE~kDX*`99f5zFdcd0z|`TCj_xQ<R?)$VPQ<at
zN{Mfq(0yXr%>M96s5ERgT$~FTYdgPP@S$*{O5te`8*`)y@a~h0*l?Ho0s4#44h3f#
zfu%l%%@AVt^j$?Bi{F3GPd2+GLU$=o20*~nIq(_q4~Q~K!0mwVtUI2$1<4bj`}NU^
z{L&?!e`yZAM|^=8ZgF69W6#dcLAW{6``!*a>5S3&gO%CemtvkqbX}p*9W61RSBova
zH=Zd@+~8wh;wv^6Qtrnl945=VBSA(k^PQd_wTftm__2BxUHP@c<cJDlc#3p9?q)vz
z;M@OEcWP~@&^qTmiWpBmLMpufV9nEMz4lAv*{w?ImQL#?_f=+U>X`nM!M$ShVakcJ
zIyRbt_(-9Z5{U*~PR^<Xf(6hZb-3-nX!$sA`{ALjd;ZIL+i(P1j*qgqoJ1@4B>5-y
zP{d8qn%gEVziAEGFd{_7@8iFp@;s~1%=qa2q^{RQ%zhJ>g5rK`4-HNnlSD+MmsTk~
zqKLbFhRa^k`M73DD(buGuJ}0_K3%eI740_*ehp!LU^KFXHRN)3AlH>wKT+~25;E?I
zzk`dbP#X(ks|ODr@Fo2yF=*GBaQO?yDB!qe3L?z7qH~@05Q<2nnsJ4AU==XEYtVpZ
zm*K`(#Ivf3cb62h3ouGHA>vfmu{w&Big=L|f<w&o{v#9v2Ywz4aLSQ={fb|-re4VA
zcz14KmD9MOgSEQKqwc)r;}WA;bh};iddIf291&rXE&noM3gWw3m>eC3H~ouLus+b|
zI1Lm^r;En0h$YAdw|tfHN(Ra=QSgt#cQ+}Qlgbk8TfYL+RU-VpGfKhys08#_Vcg=P
z5Z?cxD=x0KN~)s1X^T#8+vacJb$UX^SMW|@t31ZuX=*U0M%y(sCuFQO@y9hc$Ke~d
zqwV?g+YPu$9n9_s`LwpgcHJkt#)M|Bjfct+=TCpOHnIkDbR8|fGX05Et=kurU&~U(
zbRsYIK8ghr?p4MIM{KO#mq>{hd_e!`_lc?@+uCH6b;nrb==(w8uul`4A7UuUIWi5C
zFRP0a7wRk#Kg)G5K(-g{`5syVHg<Z2Q!0#<Bx5$9=*F<qH`?TN0a6$mDN<oicW7{s
z4%H7T)T*(CrGThG4qCss3P_~PhYCOme0Fls<eH+RtQ-{)fk#E9yJ5UwTeIuxQ8SDe
z5D@SqS!k^MwbIlE`K7q*1jl>C1fj$hGh<saZm}6hkBe(SBc<HVI8YM7nrzhok^O_N
z=j)r;Ub`brn$)C5Zi0NJiA&+E9otUA7q;2z<RrJ0!>EioND5viq>ri$swm#_H5{SF
znDjC&qmM<sM@QFg#o-?z8svX;#N6`LFE0p}{S&c3f=i!X1!*kfZ;N}b+DUIwd=P2`
zpOhc4@~U#h6(A5Mh(EU1m-ieDu^DdSlBl$vE)DOyei~E=(5+#{8~nuD<T=LZX76S7
zHT}86LRazKe$EQHye>*t{VUsd&QQD!LtmKfnDA@A>nS3=bzM~d)vGf!tnTJLzwd4<
zUb}x3I(u2Son<1FO$*1?Lj?Q+Sj!)ghg_%fa(|pliL1_P9FlTc*{Xl<9cpBpUn8i@
z_Aay|`yMf#&TqX773A07{WH6;yvw5E0nyoDyL7$Oy~L(F;(@fN0*y~Nbhr%_>t1yK
zeI+inox9?cP!&}((F7UW7)iLqxpcxIsKlZu_ZDJwL*h^Fweq|^Y~KWBIlbM!Fd#8t
z8M`fbkj@_<3yTmV9i0it<zSu3xJ-ZUF83Fo$4DT3|JpOCPUW>VuXzo-)+uXfjDm{o
zc;cvgNx&%{)UV54oz!ASz1YI;XDOTBE5lR=-JH>%*{Bu$w~gZbvgML$OT+}lM1K~q
zPS#sLDT^*l^fEsBaC+*W;vp^@4*;Ob?<4H2UA78I1^?VH_q}#jiwClqal;Gg6>(PH
zf2<|3%SXs6P~7&7Oi;b5ER=!JC|YZVd}ikp5t_YBMW>P5k$=$Op7S3@V-<Go41qLt
zm7w9r`y9E?-=^b)%g|s_=LLL^Z8oLJJDh{y7GOAHS&5@CF~0V9S}#dX!s~o2>5<LT
z8`jgf4@Txn<`=a#rs!@SeGp?X>3t#Tp=fw^WVbFri;*tr<+<XxG5#9!Mi1Md)X~B4
zg~gaFXn&1vKKmcj1i|vlQn5#OZ*L07jQ#QHKHjD{_PRLmGHN3vBs||AvkoTZHiMq2
zI0fh*O8DDO7~r2)sL3&0bORIfAxQ;fp||Ifvm_)GT}Mq@h0OOYsJC;w$J9NVy{~Vc
zouhLHti`7QjJDsr-Is^Bt-{R67!SHamZW4`+w2e$3a5pqQ(hNm+Oo6X&9VwLv(MI-
zUH4XMNXlSugJRcxXT7T3=NY}Qm+66(f~<Z6I*#bPa@2+8DKlp}?(&}ftaIpl)Xmk;
z%KAs8ifU@Ht{dcoSTrN}`XwOv#tZLMYb>A-P@J~Mqe^L6$u-y6f2nS{HS~Udex4)a
z|2A^?ZF<F{8`26vn^{p1`4&y)QGj~Ym&()6-`@pvm_?JV&`Zhx@L@g#*M$v_ABlYU
zgoKWJ0yozkPl$Bg!y_YiKugN|`d4PRntC)g`6C=6B9jV@uAt8DZkgxL85tR&J+6BY
zn;$Uh1UMP+RQUM#(%4Rq$fGHRmV^BbIr;~xLcNY$EG(FCU5FnlMu7_SC=EL@S5<ek
z=B(k}0Z0$)=drbeWaD|($NEhqbLxaYZ+!E|*AC)47c4#l?XRcVWCS&-!hWj&Hih$I
zoCynS-|}%+`2l?iCa!t8Wfy3d7d?p5l#Fhxs6&1IxL$f7lOc<|L727{@)*(i7b-cO
zP`q#Ci;yXt#bU%iSf?CpaC`Fxhao^hB~aM`^bJQn)-gfijNe1Me>|6wG4x6<Ai8tM
za<od9ibN{lX&e#1v%LF-pn$xr6hjnEhkp1?%$9<ozkh{88;BPc<_&8Tm3hPb=vVc%
z3=O0RmV5KBl8}%*H}y!+NwJ-tKGWnqgL0_RDEU7C4kQ=<slTx1T36Dd_`g)Z|IRP=
z3Qz*af6;I-X;(J^<n-!Tm33T+?`;a$@Jiq#x@c-u&CC!?T_DzH;uHi83TT2K%B4`?
zk%UJ@4Wx?r{4!wGsjoKdz!SlNrZv0094ip33OH@7^<}gR9gHivR;SY=mA1B0@>U0t
zX?u%3397DZU^3#NlxKBa9=fLAp;3i|oLtKlP><%6^IS0Oboav<U!%!N=xWyW*c${z
zHa6AcQ;?QF6bfMHc{BYbh2>6I%lYKrSKy<}mP<g`;yLS2$6=sv-PJWwV&HqgFZvo|
z@g`80-&vdMA&rK#92@o~JR&^I=L2>kBQ#?}LoK16V4(g1b6n+q9BVChXR5uMc&yH;
zxX;uAUZV#8FvA99_BzE){uq}4dF>hWW9;tkf~E#v0g@+I*IHitu3O#TzgN4)<0Wq&
zM`#7JL)R^x>f9YM1(k}3PbUcogz_>p1qP&lFJ(GR4?QT{@id0wndf63WP8mW-jIWR
zggJ8`Rq%bO2n1p!_~6teh#}x>hcKZ*P`><a+>TOr{6dZRyBLXyrmwqLWnVxWh)@V;
zLXVi3*urflCsNinBv0J!UEdy*xVF*Ij&4<TQmgdxiZwe@vhrn+@>`wT0>UQBT=-T+
zfQwUs)*$qsO!`=ZMg_7*l}1El{mXi{In6|W8Yxw$%G?j9c-_!$37x%u<hLa{jB+_(
z`?N`eB2mZqTh^NSmQLs5k{L6Lk!mWBZf7G)5h*&fXoMvsm{(}nFpSZimSzM62JTG0
z4oghb+O|qTUJgt|Dzc%|Y5CUz^mu?#V_;zg2L;K1VS}A>e(o+lrw27d>S(hE;Om(z
zi``BZBDlq6_XmhjfLx=R{oiO!Bnn-uew|KlyN!m)>KTBG`YvDK?C3QKH6H4_NQoq8
z8<ktC7mDB1=l)i<%oN&`v!pHNPRgGHotOLSO%OcLWn;Ttpf}Vuu7d_qMEO5A=hyCc
zz9cZ(UmM{`05@$0y|)@ME#bgUNk~XYN4%$H?6IoqjpJ-zcSJ`+J72f<>R+}uO~i9?
zaVazD9a`N3uG$&)uCj{QoL1rI&p*v7^vf8b1LWV^fPH;^Gi=<`Ym?cnfuyCSHHMC;
zk&zLgI{RwLS=ibXm6YIj==Q>pwa-Gz;30_tF~Kct?5XnC_@p`Ipz5CRIG8|+gmiQ!
z0fhg2q&oKbRST3KF%kwpm6nzwOQHr~9-PLaqN1rb?pd{Hb|A-gcNhFu?~}RqZK6cE
zrDh-WJ7Qbs0kn*TfK0))-A@O5g?4i;xz~UbH(_x-JsW$BF>l-unTraKi#r;#hHe}2
zxv1#4IG2s_n#{~h=w84hZFfSLvw;^>XCkELxZKPzZ`aF41P#CAY#Kdyim#mqcH7+C
zT<5qxTI*o0uTKpxGCi%=_8XoORphf8lWF2FWfkpG!wyhyG&H5#JTFsY0*al4UH@Bg
z)4413Idg>+D-Un}PeL%1!mb(I_}n1C#-PqaiUy$dDOgFv(a}*-^1Hxh{Z0?>l*Lpd
zMX=Inz@3N32b<Ov8keI>%gF4W|ICq30X5y_z?_TK)z$O!hSbmH<U#@i`)kQ5)BK;0
z)w=+$hAu+y13yX_r1R55a6O`;py0<rg;{c}^VkEbdg+G{w1`0Y^5>&0@UCk>){25$
zyzld?XP1LZ*fIu0L`BUz)<GkIL@;TV10T5yJe#i9nL}g;?5p*Dd0|;8sjcj7aNlE4
zN8yt5$0TCuj^mUl9@Z=zLdI9WpO1{R>YZ(%cZId_keVm|7mHT)Uy%88wmp6JEX8eu
zPerBg(PIT|9}w20iX)0*kX;>SH2>ax@y{QjfWPwa!qNg*{NJBnX{cU7Isc#sHjVsS
z2&n<x@!a`Rk+jSj<*|=!sBwYy-c0kQG=msoZx4MQ!s{M;-y!2m;5H{DB02zc<aV(V
z%sc59i2MST5DBg}&{<H>w;Cy3c!n*Mi9meJzlP$es7=*z<3$5Bk<27mH;y#8Rp}H{
z0l+W;OexxQy4^L?;`{9B(+vm=u$pb(zPSUb4t2S^=^&P~-xwb;F0&l3-ugS!0;Oh9
z8UkdVfa%rH(2$XlfetBPAg79Tg$svop}60I_qzkM0Jt$IoxPz(%Ap`0L9?0tu(lJo
z>Cd-*;vwWm@HVY&ZMUJF;l#NVoyz?Ua0N$4N6-h#O?(SLAF!_wKdhmJ!N9=4Ki8l=
z1RAhLiwIB-ufa?)sxd`FTcZn=^J}DD5T55w>+EL4u4@AmRH!h#osb>vh|W?AHsbnc
zrDbK>EHS*_ucV^2ICZ0_r0F(!1`@IU=<Nle_rbVb=z;nL;GwS0&IqHUK-NZN^9b}7
zyev?gvF8VT1)J9u*+=QL37t1xrS8z}^Bz!Nz8gxaaSI9O6BS8GNn!y<i>FV$yB0`=
zT<xJ^0H?d{{$V%$nF>2;cVprN_<i`B6944t%(z0we=?vajnx3`!=q56!nQ^W#HqJ#
z+~{)^Liw`!7Y`G~vKv+(_MlR>OkXVy$}}d65qQxzs3zSgqt*7y5lmgs*seiz6~+Bu
k`|5JX{jdJA#;Nd=y;rOAG(=}mJ4BHZlM^j^s^|TG0qWMCZvX%Q

diff --git a/vignettes/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-11-1.png b/vignettes/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-11-1.png
index a08eb6338088d6b66c2d210a06744ede43246489..4bfcae3ae3f21fb1eb83f91660bf4d610b1d0ee0 100644
GIT binary patch
delta 5965
zcmai22{e@L+n+I($TCv)Wk!o764H=uWFmW%P@%>eV=vjCq2jTW7?M3i*|V0NOhT4O
z_9bJ@ntkV+(fj`2_k8C&|L>fc^PJ~;uKQYk*L_{T=eg5Sfv6X!pc@6Jb)hgvM@NDi
z^d*6j%9Mbk;vs}kB>;f=NFoFRp?AJ&;s${nJx2LMJBR#sArMX(4UEbSkA(RlPoLvf
zNne(z(h$^)aZf-E3<7x<&np~skD5N`4i&>0(U!(}$q+FqS+ho2vBJl6Gw*o$>)+L<
z)c$1Ph6Yi8O0P%ed<sE$M8H*#JfTifF0Dv<8>gEtZ)nQznKj!pG}1n6C7L<rDf+{S
ze(1rY<AHGh<8SpK$VY^GToQzQI(~qab}1*7*InGvaMDOvM<P$jGqoI<=C}x73ru#g
z(_y>T9nRP!$d0vYc*bF5BP`D?HDlV``qZx>Q$`03#mYWn#peECdg75?sM#!ALPd1e
z-AF5&Mv0Rg2WFSkgwL8xgmq0XES8rh`TfjF3XRgAQ$OvlwXV5-zn$G^(hsO(7Ik=Q
z<}x23Zql*Gyi!*T8w^q&GfR1XeMflXLY_=^o|EL9P`gPNzvf(1H6K6EL4{Pi?hL(G
zn?^#W4VU{Be&V^IibqaO$o<a3rA9x^c<I@E!%5zUzqGkKwLKFHG&Hf3>7vBxQ&tnB
zUnN@SGw)1wrd_Bo(?2)cd$|lCuw?MPua1{8Eje__ESaUD7A4i;#$FQGxh|&n@qy)Y
zsp-m60)u}1W%=t4T;pHap9r!D>OI{o>)gM+KIg06C6E~({l>hH`}A`@yRMulCPSoX
z_};ZDxr5U;y)s>wUw2L)7(bT^JRy94#yAOf=g!TQ(V?*}r4ZE{A-#Y>diIo<9aH-J
zR=_gcqAI$iUw7f8;&oe_8M(3dXO|J>Kpyi1{Os8A^zSF><X#&^({X0o<?$Y%xoUd{
zM2Tlk<RyO4nP*EA7Gf4{3Y$3VR0z|gdnncp^#X_0UBFz<0g30XM&7~&Q&qbMFQ$qU
z2i3hYzO{9^HQY18s9FN2ofsXnF7%~Qd0p}RR+9eA)iP7+Nx{a}2&<{Rl6}Hzb6)l!
ze4u=-a?F2P4$acBBfRtoGbz_>nwwIrC?v|VU;l%H60(GDN@8XyV}&3$sr*ZKK?t<j
z+!P<uR}(dU9Y=6IF_2j-Y?G!4e=Dk2c5@3qEmzBfEm#n?0Konv2~prKZZyr@PQEOB
zRhw*PDy%*a4BRX@rs%pl5wmcwKDw@=4>D2C+B)>XMIhZYT#X-;5EZABv46vx4O=2X
z`N0JjI{_<3{ehL9j}04x8O8bE+zk#IyZ@D`R)smM@7Tflbx-%zOYYms*HepP%1d$k
zuIk#8U-?0s1;>tGPD4>;50PHXztIoE$@|ZO9NgTjO75+L^;b4RYt)poWQo`39eHe<
z5&2`5)7#9G_-h5sffVQ}qm1|gWQt45_bFBrD0ZlHurtdlzocP@reAYjrR4|HkO(7;
zuumrq2%<5kr05EE16tqu3o2TG(QXFu!uldb*#XWMg|X6svW}R0_aFXxSxK)V%X7Sd
zOEoO_28T?Odon}Fco@=pd1HUzmDNV(>#J>?SQmqs%HS=oVEM;~YhCioGVV52Ug5Eu
zQ|35cH#*9L5mfMbeDz6?6FfLa!KX;<wm7h6GSJ3w<qng_rnL!YwdpC>ELsI07<ko;
zETM%*RJJJixEbsjXP7gQWexC0j~u%kOLlaCH6B~loc?RI@-wTk2x%_WImI;G^~?7f
zRR%j9D*NAQ=qA%k5hRH(-*Vpm{_D;=6|uNt_4$)%#^5ld`(D=?a#011akpj!mc}Mw
zTD?s?MNrnhSHa$`>LdAg=LSP!x7YCC_z%3%5>zmf10i?}Dk#YMUoQH+s=HJ0CF;I(
zY}uDz(yG-A-eBnWx0yP)_L%S6vcA@hMx!iC%++GkUt+wsFH!FC;8kjnV9g~;9()`?
z1f8Ud6iZEK<c$crg4s)E&%fJ%m7xkC>ZkgJ&k)v#BXHJW@<rGc=|0<}<+iOIQStt<
zhwT}sFjh=^=Pzlsg-^_+nUQnX<EcTU;igJLov$Jjh&+LyWmpQm)4_1NP1yiOL?i9k
z#C!)|u7r0%Sm7GfAk$M1g=fl)UBKAAUn5$lXvl)iXRq$gI{&%rORrpp2thEJv5$@Y
z>b^9e_vci6!C}tVON|$n`PXJy<@rR7Jz6klI>nO%P@$vZB#AC38MLysK8)*pbAn)G
z&{2_+#9qyZ`h=Vj2xx~FK5YTUW_NvV5Zj|g-+*BN5iV6R-I+*~r1D9Zw|+207Bn?V
zd(b{l#8}FyBQWmLEL5Kfo^HA9LW~S4pM@OWFuF{0x&Nk}h*VLYK|xZv5t>b$sRni1
zdV33-Vfslj6G2^Oeobv`Mcjx$>kLH0jf(vY&P9`=q#DD^3)=E}Ep~X$xLCXgXvGI$
zaX^S4%<n=r*>4zf|BU1%>+=J=2ZesBuT<u?hh0lpW|WkEx>0hA^=`FUce1!`^uvVD
z8g7yFD!qHc+l>v9+V%Uwc^0Cnj~tPO$THx5)1C=*rv|N&=aRmD(4)f0O9gt&;jh*p
zwUNyILP;XoJe+WcR!&mD^gHUiGJApNpT6>t22$rrD(qPp!BT6Ix|>S*;@UlirMUJ@
z*SWlY7%Vk67H_QBz2QZ{Ok}i;0TFow4voOzkASDBFz8dP2yPXh6gEIBLKa;tjlK{d
zYlA%S;zaaw5t!jT_iay*?5j4N*y9*MDJo1hJej}J*nu0-d;~78+NA>lnHVU>?!gM)
z;88qq)x!7wAIH1z+o7niSJ^MZ*V;R&h~ExX{`fP2qZ*clzOBzK&OJTS0num2v{U;g
zR>6_n6mP&`OaOfG)>k0E;{lpc4L{9-of_1)%Y{mELQ|E&#Fr~p5)}Dg(MRBAbAS8P
z22lwjnzhhAQi`M10W-DCG#GSL%#`{HYQ+r;e3;aR94dT;6<}MVJ{BjuIz7p9sDj@l
z;n`vAyjYwO&q@##aA<{hL#3=d$IM~>8k!p8Ne|jgaul3skKg565d0$`crRGUyEf20
z5Jpg?R#Fo;lkW)l1v5?mr9VGZ^K9$x4*pLR_)W)8t2ELBl_$QXkfUDi<X0a=*^}5R
zW`>G%DS}S~en<p{iWHde0C)uDV*v5O63~J#MLWzN8U8lu!Z9<HJ=kOY$NwX7*$Joc
zX1PqAsCQ9)5BK|M;81-tDw0mK72o&HPM>+n|4iU*CW?z=@Qr1kdDh#R=j<ZacoLo(
zuO>{(9&p1|<=g8~6JPe`BFZ#PaV?#IlQTQ1`0eK60M{1%;d5w#rSaHms$Y=rynaI0
zX(z0o8cBDLHVDZe!~(+_pF%wMTQ&AtV}toD7+imEIh@KQ^ROFs2qVt4zO%3I=Ki|i
z=5HoMpb-_er}Z3oCDC_YWp$JX&6rP3)G=ZVOYHK>%~u4K$P;`Nh<4B4ia{lN?t$%!
zGfF-yi%;zc)&st-Ygah1LVlj?w7-`HEJ=N+hXU1GUS<8oa;1sU!PA%Lek<e$5n_bK
zkHOtMs@H4lQP9g>v<>0Y=wtBE(9jRnLsNdq6+g*di<K7zT5WF8{wZB0l3#=I55}H*
zd<7f8gtX7R3#H+phlpZRDu$FcjNyFIuQSLqh+G&pi6qhAD+cU;3ZYR@xtaFxQ6~TF
z&R?|$cnx;z*HhfSHn+tpABWE-BdpFiRTE-FEIaox3iI37JWbI-o%qci7R?{*FCO<k
zRd#z+(Ec_OVaiSP%>!8Is61kH)o8G~>*$h;;|Q;px@H+KG|)aL?{&+!-%&)pR=tnL
zy=Oz8ig3>_+l<iP$Y+^5bj?-c(d<O2wUpmd)ChS}Pgs#YU*9KpcvMUohu#(=C7}k1
zq-7q;o((fsm_Si-_|GyDyxp1(*o@a0f>Nrt8@#z`J_EefHJ5`eJ-uTlg$OjH!j4Sl
zUJ7dPwhfP%r-o~sLijZu{exa6Jl}P1!;##F*t_t>Ume%y``L8wpk^}RX+*%E2J~0q
z6+XkcT*cP&%!?f4;oym2SUT3T7p3KFLj&j2e#)7B;+x80hcJ|xGth&hTnH*bGJjDh
z2Y;2)dB>os^KinMl}Kno5~RZ&6z^WTMTJ#_rtZNXl1FJiMVD~xs5k)ur&psmA--0f
zyZE5^)_%C|9p$X2jKq3*kH@O~BpIzOHY@c7hRL-wvqto8xc8Urk|Vw#HlP4MZP<wB
z<-`f$IS<*6Y>pd<kOL>G{UlBt0*AcWrs0r^NpH#PB$E^w-2X*ehmyZo3(gBJDt?(g
z0^UoMB(@mNU@czysX8Mj#U`(id&h>cKsHlrn7A88Ox$Lw^Y85up}74QgN2^${w?GV
ztow9Te=p}msHvS1P#4Smy`7qMV?NgiCwI~m&&kE5%Ig)#SE*8Fc`Z?>bWB|u(fsva
zl=f!;NYO}U^sk{ldyuJ<T=hQ$_J_azWc4rhf>9(bgZ4^A0M9^qRvTbPSrP$oQzH1g
zc&z^v=ebHt<8)?siY#YOgcp9A6XOsNq)gxN@D2apIv94+$=$vWX#=w4ZS(k;321Uf
z;Y%U}drk0YL>L>IQ4>GCNsXUohVvyu;6u>VF>*EK(GPqAloB^<-UG$A?@z6ftwqM(
z?H-x0OzMJRq{tedA^Ho{cw0$ylspu1ixEDd3jx_-n6m%p(VNRT%^Z49_6Wu*ZS;tK
z?h~Mu94YucD)hv70wgsz!vEb+nD1u>S6xU)HzcBk8V~&2#qZV2`vcsXzI(K7?^)nY
z+UE;lKmhK`>;31dq7jc@$;PU;X?#25ks9-nT0pC^di*5i&5YkfXkG7othxV`K#YfP
zlRC4YuTWjwT@gf_76UQa&Gm;Pvz!q5?dUDikF8J_qHl-4>Q05ez=h~1XBG!A!sinq
za2;B3ot)tr7{;6Yc13`uR*@5Jf^-biLpm(+wu@vaq|n8W0;Y%qKOQEFk6t2><J0@y
zaf-et*5|p(aXqRb)@8DBO<k42VaM0EHeNiU0u5Xlu=>5SP>cvvaEreC`Cy%VCw^8(
z3H?S{WyYhV;3T(m70|?i13!}?iX9z0Wz2@-%d2>qwwd0-hugdO=K6bN9`kLXx}fRU
z#-DME=EB><pP1NlAd;46-ZQ$iMNR!syY5G$j0kL@&Oqw6kq%A~ijx6Fq2t4vRB%;m
zDi)&hwAprUkgS^ad3TP3TaP_P#7VUd-A)e%3VE=fk;6tiz?SR1-b)1nB(#fozIns<
zGmY@s1M!z9chS7--hP8&962M?rB*0nnXg)=srN~9cZS<SrF!pEm};J+t+HW4*K`(s
zex2M$gwUHYo02<unB$!p=oA{n-haJ}>!dT)Nic}$5Vdm)Z8<aCSmrIx{4-jT_&2#4
zZZiQ&$OW@D9`0q7#kAgeAO0Hgg=Lujj{4IdWhAwM6NseyB>8WC9g8g3gb8&RFBt3p
z&j-*plM?cI>_x5lnHM!&^>+hAc1vw-ATBQKBTgCnj%E5@V5s`C|FSuwGs2n{i%Tzm
zr?H-6@-nyEB`Lfrxow;$f1w44(Gc<JTfbC2_o5}{zQbmr7L~a*amlK*6es?2sefCn
zp!)0?+n>~Y11l42O(jB&$IWd*_lmv~3)76<;a}HDYL4;eUM3RV3siefxT9O{q@TNJ
zO`{}x*;+c#_-{^cLnJVT^!_t@xOduVt~fJ!@UkKS&;Rp7M&I8!BJS*m;l$G{uVTeL
zYm4JgvGkVKj6;haAHg#a3(*I4L+=Fp7Sl^SGRL8BX|^pn(rCLq18%@(?JGlzAsslp
ztv%lMBDz=xeSwGEh=@2QxC0Zsl@=@|6X4d>CYYnI!7|=Sx=P5i_LsRPzw<Y_CfKAV
ze}45lwB<zPmx^mh&)TBGgG#xH+4Kk|jrG$}%6Anb{6Rr^0`v$c;0h;|=JYN_4=4K$
z=1%B%bKXw$`s|>r!S%aj{8fswSAL9ilB}L5Ov>FWD|%^{*#mbidpO97*>>xqWTG3~
zK2j-%SLw@aQCh3AoJ^Xuyvvrd-g9mti`uH(noFKL)cI?!4Qx{)PG}=S{4LJ*MLb7&
z<?XdD#bb2EKJl;G0XdMyl=H0i+V<R@e^|84C6k2OUd=RAGfL)X+^;b4#n}?GHV=}*
z1XE*gT)dddD(Ytjt|2RTH%SzL-Ky4Unc^j$_6&=@xU`*<`lLW-zBjWQP`-rA2!omm
zTwL?6x35ZAB6>FSyx4nDJ#Y*Iz|L`dZ9J^6A;`X-7$EupZ7K<<j<uRx1GCv88TxFl
zo$SsF3r`ZDD9hr$UliZ{o|-(rKjjMS+0v(uEE=2WE2YH{q2eQFCf%4xsXjdij`|#x
zN@u_7owdZd#ph>DT5EEB`=M-5SraKmK~89M=D0OeY7KP6*|d7>ndSr&1-fK6xU!qC
z#cjZ#Qc1pN47$NKV?FfM*wf<Z!OjmZ=l#nK)fI2HNnUbe!7EXypRcB;tu<HKJz;t6
z&MG+EsK%Xr+C%*6yjy#4IZg4c#vK|<eIav2dA-{oC3M?Mokvm^@B$BpS7udx6=toT
z3AJ4x#^>ei1y4KGy5;sHC->=*@GqTI*;eE>Wl7Il#+MAkWK1QLytQ478q6Fxz5_L3
z(7wubBW9&J6A62Td4-9@*M^`Uomd;lhZ{}Y+hd{R!MATkLs)|GMN%^#{uqG{+~cYm
z;RXKQI&<qrg=l9+cq{wF{_Coc)gA|bK1NUtn%Wd7@@rs6DWa9V*r8(#GSHDYU-fHY
zGl{39)mwVUyl>}wlEZ#zuww7j(6I%vj*At@ysOWu_Bd`zpT4**vczgivH7_gw?8t)
zj2d!VuND!-Or#$LnmbAt0K=Px>zZPHLvzKwW0;o7IQ5|W@uqN=(tb0UEyq&9qSb+y
z2Va#hE&IaBZ8RCsJEM@j9UQF?m4CT%?)X4Vp2JYQv;UrpsD(z*XMQK<yK~;{1AZS7
zZOOI0dOGKDc02hiVN2oDSaMf#5Zl5G_4LY91cG`a3Sb<_49Rt1JekYh+jwqC8&{q^
zhAlcuN?UlOc;AckDS4p8>s<)&V3H?=3lY~!)A4zsDu->(i5DB^1Y*N`gvz?u@5c`T
zg1VM-M60Rr-P&~<u_=MXEj3$O&^q+eY~W_I_*Ap2V?Il2@7UGd?kpiirzhh9n`_bL
zH%0)Vov(O{^dVqm^zS7+5g&&>=ZSrI*j7O=&<)d{tLt_TX<DmHDc><$DeU?fp<J{)
zaI0a&yjA(q%Cm2bl#`Hxd-FAV4&^1SjcotrROY{&*zg=4=j<s5Um5Bw6cEiJpN&H_
MuIOU&F5eCKAHtCHE&u=k

delta 5689
zcmZ`-c|25W_@5zUKPW^PTgsa3I~hrZ%34B=?8`KiouhO)2$#80Ox&m_N@GfvaMeLf
zXp(&kjj>CUWvsuMy7%|_{qy_dobx&FdEWQ=KFjxc-t#8uJ=XhE0l7_7v_s;<kvZ%S
zA3ntMvAMH1H8tV=*(BM?WHSCX8v*p_PR1b+2<~eqFN7fwyuz#t`FSLM3xN=~vq4)p
zMiLfAcrQ76@3byk7r%XIfy_pFoI{@tIi#3#sOsroIileMmo)sn^X~h5mxVA~P6c*^
z<SkUa-qGtmzx&!dYtZcG%f@ga+o9`PZnh5AmS~}GQQKVvnt{C5NByuQDc^fzA7jlb
zat}^jk}fcyJwKXw^o~3lHMPWzy2_8YIG~O<Js=GphEWAqf(omuv((BjcXU;l#=okS
zEsh^hoOcqN?2i+7hyt&gi&g?E-7gFq4by{zKa@tioJ+lQ$7}7%84jpvI0_0clia&}
zqUuDOemD|yl=l5;&H7+3MQ`cT*3qgw7rP~ig4f4~qGxZCNx8(VEg(&~?NWzXTS(l6
z#lBcjaPBDe^J?1ZWAC`;MG_Bixe69~xj)SG0{>ch?-$tn(=V?3%8=@Oa(2uQnPjWx
zJEm8LYzqGlwhMhy<^5@YG~ht0tQ_16Sr2O0dm{oPqT^z8mwat~w?5^}@9$jl#?6S$
z0q-XVX5-Y<J3D-&bWF;<1zXf-dh<rqIkf~_!Lc;k?%?Ukei=nnlFn&fugPH2OUd?r
zlJV+<TWJ5+uogQ*^rWS@mQGH3?4ZQMPl<;=%f`1D?tUn&#WwC7xuKP|n_l5XiN)e`
zL<>qRuWEr$M~!E@hC?-t8j^JwdR<o{=*M#&WqKQ#PgXkfVWnDk4kNSn)8Dw-GtEi@
zkFCaL*iLOptTqtwD<_ANkNO1U<5-40FLa^rf!qCBYRz6*BciiR{)uu@3IFb{3(*^A
zfqW-FBkcXp8n=kWF|KU1;{Lf<y}2T$mVbmWBgK2*lbsQ5#pMHnifdbm>ynlK^tVMn
zT40*-x<h}O#~<42FSE=e`FI<Ge=7!MS3Q;*+7*>-XPCi#&|sc@ev1ZUHdF-`a5lmA
zqXq@8-F96&%+Qg0Bjw!@a@ebDIq37yMNdhi3z^2dvUl!FkpEVBBpa_ym-o<w>K`GM
zsfDE5_*juT0@mv}nGviDIrQTPm6d>Ct}lPM&R+W~25KqjB3T=FZhQdsTI_n|gJs^`
zdm7wg;d0V)>(4*NLfz8R$m>USuNuj~h5}gc`j&0i5>wkCpwKs%^G(n@fiH=XI|xbi
zoc^kHO|suS!|i%^U5L*El{*i9f|colS3Z8xon&G9@upIpzj3yZ0OlOn5i(o3hKZbV
z8|;;rm;3Db(!w~-ckc2cb`?XPvwS6WInj?5n>8Tagwe=pT~$v@2v&-_RmED(mD638
zlY-B9z75f>=4HVy`B!Xk^!8w?$<C#qZn@v@8+R|s3{1_gjV$?A^|avc1xnUB0G#+H
zb{qUxc6oVOj^(oB6v3T>z0X`uTFmP@V}D)-?f3MkJVLFicMtJEKiTltxb&3R(CDJG
zNs-s5_$cAKcD&1<lB$EJPt|kfG9oTQ$@*{%yugt#y^GHRj)30YnIrX%*M}Rsd1nyB
za@E~ctLPf5od+H##ZrOOBH1ew{7@Ji%7)sgID6ZbAfLmLa8k*94_X*0hsr)&D!UkO
z{p!RhC%TqH2lf~7nY&@27{V?$a!YdJlx(>SD!WSyqP{jQNWumjgvQ!65T?%h^iO0H
zxIM}}pZF36Yn;-LN;c~Vz(&%do(KhHLew->dg9)=RZhTZo1N+t_tGaMxF=5FBC&sH
z3Dyr=-g)*#Oo!XFAVrW3{-@0ZzP|jewS~{q-YPCZT)!sSRiZG@EZ*_ra20DNLi5^>
zujj&S$A8@NmJEnB6Qdpgw9EjF%%P)DPhq>ufC-3clm?u>jnko4<ya&Bv8(29=bN5J
z^Cwo5a>c!;(^lt@=d2cGryfkjM3d4uWdd#w9PZ>?-c`8EX)%mT?hmF2Z>}Vi%B=wx
zIArvxC5heEgS}g|PFQ-pr`;JVCZ?!8nnf2kPieMPO@YlCVUoJ!ToDH$6sMiE`7l`v
zTn%j~8`TqVZuo{jaD)$FS6Txq(s?t*-|4l!{aP(MjT)ubuIEik(uxlNV#dnhL&}Xa
z+>r8*Z|CSU?r!U1fwRvA450&2UMqucgbW|^gK@9>)w~j21tH~te?WF#Ludw@>b<C0
zQape%RnHNOSMFsmN<|0(Hs7QtZjOM*TKN+WvxbH(FCCN?Eu&Q<f#zs)$1JYGJYEMv
z1s9sgau5=p9W(M2gfJ$kXL|*mXD!c2n^<&>8(AkzlMzHy_+EtR=hGw^ejx4u%J8P^
zyrU>;<Jhk>DT;xpqV>Y&Kd&TAi(-O5@<-1kOut7^OyDxOL(JtMDnndx$J6H+O8M|%
z*#eQm(`xuQ9%2`M!YRBMdCtOudl~Cqx$-WK7gyt2#g69SP=x!YcqL2_nwQzo<_bcn
z+NAzdvA4Q7;S+lScgr9((J?lRJYGmh0$%}$NEU@cPbZANTp(>n@vy0zI|`hO|4Wel
z`{UXzal8c|3S_OIoY-IdX|XwyAEJid7`NFi*f)VJItGn9EO^}MOIWusBi4?YZO5F4
zxS)1a`w00Us=>^5IDVb|mn4%1djVt?I$<xQz@rTLQs??Q+290~QdG`QVU5c}902=^
zzi+w5R#Z>vv19(>fD%s1{YRj^_+nr`s32>nX6U<dmN|OE1|pruEUA?1MjS@571ah6
z=7FyFM0BD@{6&)4<WSGZoJ7nBJpG6OQ43^vpoCI&?q!C>b1t<}GQ(xT*lZtwJm)Sv
zaW8_r;=OXQB<60P;RWvBVn5$u8UoJUC7CGGZUncy6Q=bJ?S;f{uY(~P%WP}%ND5v7
z<{mI&^b&}jtKwR3Kb#Lf(pIrXpGb(bfGPw5jcaDdj}tbktSW}bd{cacF(0oibYva&
zjX^ujrseHnCQ{@T=uIj$P8hSRu&k2qwKKWYjXuUrq=zhY6m)j3p7bo%(Dl&|L7*Lg
z&3e_G_qz)$0gxMKHUFwvYjC@HK>G3WQ^utA<L2P7VFU~IWEA{>)M~bTm+l_GlPjh=
zbk+fD!t#vqroCX~1}6|lxLqx))SO2$`Tmn1rF&c$Q&<wd*c)2KRnUNhPn<k{Cw#TW
z#c_8gUyb+Y;#RSS(v)q2c(n}_p&HHzRG2f114*7+6Kxkb8Xn{^m=yyLY*wDGRa8_o
z7-F{{Ltx|;WETO0kw132^>oO&vnD(cGJjzv|3{*a;A3`5cI`>*9u#Mg@B}eG@5w&F
ze&5-%#{sU?z%6TYzJ3LGNZR1f^V=}WH|Ai!+P^k&6NCBf$9?4<ED-gLRh_{u96QS*
z1-Kwz2WoD!i#T$9gI|o94R>vzeD!+S+0m*cqmjoOYn_4|XmiKh<x<9nxiF6b?BcJK
z;b^Wnh=l*KKmJkZzTxlqWBmPR#K47!#;2A@A)v=;&4E6n`$T{>+r=H)oIE4NN}U&m
zp7I{u7_j!Yl%lNYg`tq&YmU!Ink!`87MD>>Wem`NEutuEs{86^t_FRoP#@RJ9HXc}
zCYw{UBNI!c6DpIQu)|<}dj?BM1?o_FZXIK@oYJ09?GQs6g0VB-nDkJ_1D@#*wp$1)
zQ~AGvwiHi&P6khmHjWNIWD3mkWXKdSxg%(1%mFEc?!I?WXJ;9_DN_)7;8)Ps$B(!$
zJI^z6B6Daz65?8$jpKljo|kvOJJPbEEfJbuMS!lWd9mK-1NWfX19vo0oUuPV;nu<%
z+xdp>O!6@aLj=n{Tor4y9P(!A%(ej<|8txnOnz;qa^^R&q%ilkywq8wWc9jV95;m1
zUbP+(8*k!4ZD>ki{>u98x&8fSeSDCG2(zL{icM=kLIXujJEkexY-rFzJA7$r34c-^
zp9kpR%b9HFI0QfVL(ZHpk4<D^MbNf~9sawG*(5vTd+N+&;{zi)kec7a1c{*@eLkwH
zxRAS(oGb`6$@)H#S&s+LXPb5YPM80BVTL5Ln)gtpF&h$bM9ufOL>ZLysPZg3)RYqg
z{U17g<$?r94V}S?11L^6Y2$zwM=p8146^KC?4JwiCKpWA`PcY;mcT6!A}}whzZ&A~
zuznzAlPp~Lar!tC^Jz*NGwR<^qF3klUAC1!e*Fv6<op<@{FWf$M|abVyp$e-6&ZO*
zsAte6Rr6n5NVwkEOM_M?{O@OF6F6}4oqW>w4owYKDPV?IDKpQ0Y4Ba6s{>Yt6=k-$
z#+yuiocJ6-0o+Lv*YL>JC>|9o`NR1p64%N`>9N>*Zn44CeYEv?dbqRAesRE)u%0)1
zw}}_$ErSy)G41j5z3Yf%!(~-pOtIj@mAW=?LOy|epk!)E*_8zGc}i0Cm3!;4cbPvQ
z(l`hY_b{YToH48d{=22H$)zB&#9>;p+J43D2|DStvbh2$ZuYBH^!u?D#(?5})cHjH
z3#z_sMJ320SGJ;5=2lAeOddeSe&``1;Ik=*;6zKfCvp#9smKi8uf5V<Aca{x??hUC
z4W`A0=h$#SXjuV?!yZD_o9cxRyw4-$&OtR@<hD2(iF8JpnJr47(9Otpxo3bLlfowU
zitt<r;GLGwL|ITGi~*yZzRlfaCT6rWDpir$lvynZMuq1+)Z+%EWE*5VKi+8p6+2r}
z#NjV@qq`7A^6<{Y$MX5I#Ygy5adecEGdsov3SGM#7v*ct7X&4L^PO*vKO3RBC5+<K
zg}baFu&ZChxwK9^zO4&TdYsmWZG?B8A&U4hZ#-6gOG(xDZ{+IVEVSQO=F5&rlvsP%
zUB(9KLhii=BXRV-I5v~pks%Lbo5qE4n_N+0O_U^0OC2QUq`C2c!`spClike=jp?Fq
zxN^0(B%W~t>>?Abxk_`*wF&w~b^W7m!^TnIgZ_m9aiZZ5`-#;^^L@yoT)<3RAt$pg
zVZAOGQy&}TWgZ)S$2DUQdXvcjs{#wviW`-cvOsPL#08EglyXjdSe&?h;z^1(FSn+p
z=?7h|<x4R**Nvbh4Px7{WjUem^#R_~1UG7R(-9cMkgI6%{M4A1uhFrO{6|=G=r>Rp
zCufK#l7ON(FavRST~7p&hPJiz05g1UaINdxY&G3Ry@8sPj@3F1ca5j2_&1Gf<^`6E
zvKa?Fu0E~$>sw~n@Yj74Blf8-Q45E#N~!sRtD<B{I$YT*jI&2(al%*Z)8;-OR~4<k
zdC#mhWX-BCwP(yXE#Ip!Ot4FH4FHRkSFCJr1$VTcsSn|>Tix-h?H5-Pz3-B072rmk
z7uLw(c88DYI;j>@7d0^y2lcwPD}xc)b!#uj+%xi+8efZ$1*DoQ-7AMpMncxE*TnqT
zj~O7=69V-NQnc$vw2I*(>vGrdQ-4Mf%Z8tqj0~r@8H`dRqjHCy3@$BR19L^F?sn6v
zIWCvh)g$mq)rrQIF5(de?`Pa~JI}TX{3!lFnOx}l6PN2iy>FA_d+1!JiD}1<=?AUP
z4t7sy%AV$Del`4iG6!YtnH5djR0rHDeN`RI-0|<LjQU<$^W<7oQzgGnLm@BExWsp3
zx#P2JXtbO;nn^?E10Tc14W8#%rkK|7%#B^3`qfBrJv*pYmfKDV9a51}J7P`sGgv!3
zF>dMv6z-rY=FwbBXALA|LQhfs%5q8%rk8D7P?H;+4F<!-c4wNnkVP{HirD{me|k$F
zOf0(jQ()wAsCGfbMXKLYeR=3n?SiuFuaX;`3<fifC|lQEeugSIzqz}g%$*tTd?c_L
zZ!|McQ>%Zm`ly|vF5W=8h5ybj0ghS@XA9|6RW8QJtOj(yxkG19l&=^l38{YVu2Ijp
z_TD<iKvr()?8wTgBz%~owgj!@?Ydo38O70^%YYEk*F=At<SMf3L46r@V|25or&Xy$
zi9Yyczmt95uE2w`@r<hV!b$m7?Z{VB!Hq`obJd1(clnfPrGDDaxjXK5*n&Mb{(K)5
zA$8`P4*brsGp)8aYDq@#T6|I4vh96>%aV)kC4W^~S&Uxfs~g?=U<vr(e}T+x<RwoB
zXgl>;2+&4u9L?$ja%o{9{?4@eR4)Q{7S5%P4B-0h+mnn2R+r4iw2PaH{js|Ik*_<W
z72mfRC`+X8W><nE#%Pz~r?mp1`b#eoQ<%ma_1KhbZC@C56RXP+d+9{&u5i{p2&U*{
z>TY9Db|;4Z><w{afzX;N^?5ZYX{LWwx`<r&Y~B#6o9+1xBmMKvi`zX+OW@K+a-E*G
zw5`h`Y0t)W-)z1Jh>w16-5qU2TvXf70s%@aG^Y&nLN87@giFEKM%}qYqWG5`f}Isp
zZj2fY(Qc9kpRf22a{C2r$Vj=o@7Q6dQEfYRoXYhE-#sSmUO%_O_}3wWBje+07pez5
z=}Z5|DQlT|6~nH0vt0Vb<d1r%`@XN%2dd_}I>i4Ds$QXZ7W(G}Zj4*iQ$}soH-kgX
zhN7*0xyF{91`4^SKbbZBF#fhq;LwkqDERpdw^x}yw!x!84}Za^>2!Bd(pGs$wV06=
z-*C@YN~8sioHS2*?xIc5w6?<2d5XxCeePUdEWgsmja2X`ZVTqo00&s0`)D!!vuYR@
zh<_5Q+$4^QzIbmXeVJ6;7_1p_6jjR$n9Lu_`Qg2*bc`_}@O*QA%8j(1(Gbyc6|1eg
zbW$dAK&SBPn@tV%wXcOMsrB3Uhyy%0r@m6R%<p+A=g!%;znP9rnD)Q2mG9g|80mXE
z|1nPF1~lLG8f@3BTD%MfDpzC}Rn=3E)U;Bd4xP;5t3kb}#gwhefOijKoocDAx?^c?
zc539c+0pjhs1pT~PRR^nTk&-$m+;NV;m&g-Od!L{K|ga$X)Q7ipzRX3+XeY2-r)!`
zA<lJA-_vONVps|TLFv|p+6XT8X-g*N=JPF#MLK}jFI>?EX;Q0gCR6Cgeo}ui_t2TH
zV2=&G__cjRD&dmi=mst06CYq=n^;QRT)I=dWsB5oGeFn0s;mVBq@{QCf<mof@jtV}
zHo@__&!a-v?J#RUPN+aMyoCqXw&H95Z8%yM3eDWs^mOc~+!j{gpJ-IytliidVvrIA
zTq{Qbto6TBbd48Jr;nrzR~e{a#_AC08ZNLg2K{0o_o<tVPQ|=L9J%7{)|9U1n#+IQ
uVAi=H*Sf{-{HYP2tGxf7sq%~N$P7rvF%la2-Z0O95jIwK=oiN?-})a^!P5}{

diff --git a/vignettes/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-12-1.png b/vignettes/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-12-1.png
index 70851fd9e29014c9279b21ffa59a78e048da7944..2a620f180e166b6676ed85f43f5d03a433396ba3 100644
GIT binary patch
literal 31147
zcmeFZWmMH~)CGtLh=6oASGv1H;L;`C-Q8UZe;Vmd0qK&K7Lf*#mX>aimY(PO&b!up
znOXB?z71<#Sr_lGo;YWpefGXlZ<S?Up%A0M!NI+flZC3m!NEtu!9Dwo3=jTNy#M?&
z`1R67R@WU44iy{rdN#QnbPoqd2`2{?*YL?XT=ovozibuzvx9czIF!_4J|IU*M-}P-
z{}NGN*Vg8vt!+W4Ekic^a%ajN<25`>H~JSxwjvhX-7tJvcdEC<A3yz<kRXoq%$h%H
zgN99RwsBiv=X#(HZ$6E5vEDK)U?q5ncqbrNE$}{o?zJ5DGdQ?6K_+zV;5VELN(d3S
zk$B1s@a_!x|9|uU=@^L3zak_{A8E22NgHxPgoDeO(MdDx^lOqR$cT)|H|@FqA#~Dl
zz2ZNT#*%#)p7Si|JbN|Z$llmo*CQGZF1EosKhSHX!&kjjIrrb^6`|)l<K4$?2ej>Y
z*6&L5gKo1|?`g;LeV=fi!)>-2wtFcp^{Fa@solgv-~!W5F3$e^|J@svk4!aR%rs8K
zTz-GOzd5HX<A9UP&CN|-{*QZMVS%>&C7kcG9^v;Z0q0{wPVhN1ZSCz(_d8-3ev=!g
zRYskDY71N)i%#v;?d|^c`p?*JFh|W#SDzkMH8nM*i(L06i)G?S6Z7v!j2}527a9u-
z3$vZO1hAvPp@NCBqnTXRU<`KhgS+nYa)rEUDM~w$zw+DJ+xs6>w;+kM2HsuWk9NE8
zlSV<oqvMp7mhSxS2~A!`;eSq<AyZvmzV|}xZ%>I*Hn^>w@%*O)<EN}hbTUDYIYV!7
zxxJ!zQ9<xvCqdWk+QxyYJ2#;(Fc?#Zv5XK*0>XMtyH~|_hmICo?gF~xY{&n6TLV|h
zJJeGA*y(?be)JWN&<<Of|BsI_W2$`Vl+yP;K8Ig_{%*uuyN9`ti(Ffa-OaN*)3dTV
z+1b@E9+Ah(6ucem8;D`+`kPuCP~6;{7Ww#hK+LamW$h&C3IeymM2h|b{=K-$@j_E&
zb2Be_yvolgPjmbzsM}i(>+=}n;D<|a+NBKVqw71#f}a^;=+^ND2<MvGhL@8HY~?fi
z8w2sdHzUR?o&J0SuK5p#)?g*rM)Y!YUF)@b9W6B34_s%3erCS=(~HslcRfZ*O3JU(
z=n+p(Uw>yb^L2MYO%3amHH{qcHn<sk8}<P+4sp!k;bCENm97Wv^z7)HyVa-1t#sp{
zA)55qwFdv|<FW18pziLL7QSqzueuNd!};3It1L=nsK=BNAB%BUM|pX9ak1sq;e5+#
zkU#pd=&_OCUNMV)^WkEP^IVPL)0OiRt=OIme~xnL{oh^;v4EScbagREbm9H)KWn|c
z24Eguz1jxD&8qYH<HwI*kG$efIs-&kCyHbmS|ywqw`XT(Uy<;}9yN&Ovk`xYyO}M^
zF#XR>u>XE7g5q(Vpu4QB49q~#Zb3w|({k2GdptqQq9bqi2kD~m9sjc-I`cY0tiUx?
zW1r*Joj3}yW3Tp;wu74OZL6EH37csf?&HOl+Tvmw>?q-fi>d#)A2RcmX2(U}om}fC
zJrVNpzbgkb+QwsUxD0&B-3F|4G8BO+_xipX@bBO7vY;WrgBzlD!yP?q8`|IBw=m=3
z=FU(oSy~DVFa-0-Jd&24ZtT02+Op&}a&@%G=d|?2q$lL*e*CFpGf4rg^irj4T5lu+
z@DUy!o~crm?uV<zbT(tBmPPBaES^y*+RM2Rl>%96&N8!=uE2KE(G4Sx?x)AQqPCKO
zjhlYHuEbkKYV09#?PmJuk#x4zuE0ARF?u;QcEVBG3{{R^<Snz<n-El1omRKvxq~g=
z$2g&5pNGr27RSYAux9*EI{99BQ+U%RGkCrn_M9`M6VvGqdbqtjP+!d)U5KCv`U^&y
zd`Jp*^H`4I4eI=)=t!YS)EsE9#WuX!$<SrQ#e2SJkKSFMw9Gra4h}XqH$NNq(7nQQ
zXU11(M>b2StnIpW4!&EA#$%H9Jbg<~9c`A@6B|t=4I#igZ$If;oSd9oUhXs>h<o}Q
z|3nGN+sXG?j}kim^7@7M3x0dBPvbViwQPr?1qjjN6kgHo1hNy7-pFKSWg#sKjzr%K
zCcfrd4LJXEvbVQarvpZu<?eSVhO~cR>&Y`buFW^c{TVZMo{o-3>wPg%HXtNbYE>_E
z_`3f%Mt89i@cCn+qN0*U@G_z|EG%ru)JiT%Cyl1iqVK*`>|w7q_`yESo2u}7HTbFH
z{0GU53=*O{H*u#tuDR}}R$$=6&nDB*#+DWW0)hr+`<6wSyYcKmcCgC>^b3)0Y_VrK
zM)nU5=BjkLMkxkuwW;&@UDsvAZYL1<UYVRdT!2e#^L5{}jt8=4$Nbl8+P<6@%TzmO
z&zsFqYzD##BP;8VuBH<(-#cPYH{DMUC$FDm-QC^o*duz^oH-Qj@yQNBfE94;VR^xY
zrIg9JwK<fGA7i;Whk*dUIxd)T>G$w2jL4yGZ;hm5JUtaQG9{gK-5p0c;Jz`!L|Du9
zUb*>3(3P;|2_8&aTieu>3X3LPAy?Sf&7k>qzQKC@;d*u4)c}(C`vxnplADLewrOEA
zK2i4j;Ca+kYDX`jCcCzl7B3IahyBrvH<T{V6YXW$#jcm!35)|yWgok+q*RaV;0Vbd
znKBVX^Hm;$mRj9+3Z-H=J01UvP^YOy$|r93KH()qn^(z63l*48T?Rh{qe5PTWUWd{
zO7qU+dtU8$C}E8F*~J=h9l^ggPH}N@Kk?3dbv4892{_vvf<id%{d1J9TZf=Y)Y_v(
zZ9$Zn^d|Ga5?GYMvKU*v-{!LwJF9jMtSm2QxXj#S7Jg?+nK3Y5c14jP;Jyt@4vMww
zE)@;4`p%tAPRnhlDBBi&O9Z3-S1w|Ybsak@-rn9<J@-E)F_yg+9WUa8tGomaSqZRU
zgG7xQrmoJ29koj+J#02vPl;Umq4VnM%H239lh;8O39J<nkKLcLH5AAQvCvbho~6ab
zy9N8&VwPEbJ=l+0zG~%6{&A?FiNPi$BUgDR5zWoz+V#5o>8^Xuz<umn7^)KO)Xh%*
z>d&8VpfAu#zx?@zdeV7q<cl&2_KuMAO2_*8y63?x&0MkY+2HG~tu2sxhw9G5!^1&d
zRtn15bE(i_=KWULQ`!#>xC>(opt>{R_4GDwao_oE60}F-)*e*`B79RGc{Yf)4|mtb
zo->+_-nFT+PUnW+%T4xkHKw9q20R1}?c~93WJ%=D@?9=(Yim37x|0o=|1~Aa)<NlQ
zi8BFWMDXKze*7lBc)ki#w4~6YzrVlpm$mT5QuQy($Ko=^_Dy<<qv2D-QtaLLXCtTt
ztT`h7R~1y?k)uAF_J&hD?8>mFA0#QT-kha(GmEwQUpplaHJT5gw=8yAjb*JqUbR$L
zSL5N~89&|5MeV}=9S4^j%jTmH_W1)AGm%G+DS9ai0(?;?an8hjkl27L`1JgX22R4p
z{*C7$^HxRID@6->MoC%O+1BHExk`OYOG^PD?+)5i+CBxTLlSe;n*ne$75z0mt)Z)1
zKDC#`fENYjPJ)^nvU+X~CK7R3<y<)$8X6Ak#NG#?CN3RxIj@Sg4pR&tTy}xzJw#E&
zN+6j+c;*cPbL(D7_9Rln$kzVuZUV(fg!Qh6AhRVHfxgXJ00J9oYL1WFPk!|2emdz=
zaPCN)!7EWNCUGWxWQieQOQS1!00<&zGf`$!l%rVfCif&D!g5~Q$k%eBQ0nXo(R<lb
z8)SJ(%wJ&il=zmZ^POE+1Fx%f8)z>>dP$Xe!Yg}-9Ph4<0K^LF@rmW&>g*H@mm@Q;
z@o2Sq4A|deuyYpk*nO*{G<Lc%FyM6xcEHS$7i<9p%EZ116;w@G9AzJcC5ch^=<V%o
zhT>u#nfP!Cs>&I8=$eZTOEmZFPeHd$SrA_DPP+4NH=kn&|LLW8OxXCc$rZS~Y(J;=
z<TVfOVC3Q?01O_;o)_yeY-z!YUzVZNXytn}a-4){%p>Tzi(6jp$vvdV2qkVyOEs(-
zqb_;^S*e|NGZm^z??~Hq8|X(6Us|2puBz$kGKl^5?E+x3x>~7rZsg!~R2e`aAoZ0;
zC?SUS#Kpyhp%M&_YiMe|YWvL|q5fgb&PCeZ$*HEILQU+?o1?ErKRMpgVcUtzR%a-z
z1m}1CmGaw4WiUDc$V$6xW*~xENtitNYTnw|XDtjCA9}0i1bzPe2IDYyEosk`ntEzf
zyLfbU_h4yh8Ppw|5%>jUqy5H({UVuU20ZkL)F=e5+2|Vpaf~~z^p?L48LNmrT(Gn(
zB?<P8Cdei%E6*7z%o$p9W(J5FtE;P*NZ&p@PUrr`Cda^vA~KsXtMlnx2?+_gy}gB{
zwwm_iXz3#S7#gBca5ZTNM`h^?JgywAxRA`98&E9p5_s3o+i>5($0@lniHl<b<Q^Rz
z9nxcR7H=8R>v}n(Eq1e+3{8=x1{q(|0QQrUgD80M=ZkoRIu9?ek^2~rc^B9(f`Wpe
zG!b6-GLdyTK})QvadlOK)KJru`a>*V^zX;R?@>zr*DE@z9QLxShd*+KR)g-2>}xxl
z3ks;z*;@eOO-;?c^<5R!`~1drGje1rslA+sc<gw&o$E+&a%rjKo6{T(_VR-O0cse(
zsztvb$395jo>AXh;we;)#fW42l=Fn=r>2t8w%<^`?Q_-8mD6I1TED2TmyAHS<<3&X
z;e7}S*4Nj!wk{12E!n-q?zJFBOivFdz{Sl0nH2y`YDoGBkN?%-CSk+s37ds(Cw-w=
z_|&(m!@6G}?*J&m7!CWFfts3{p<(XrKAyJ0=`d4mz@MHMbvlP2AD4H~`~^rF20@Bs
z;-w*pba_5zKbccOR3Yx2`5il(j_~0_nvd{^f-S;(45Vbk`3aj8OWk#TMn*=f3{bG>
zhKesI&`3KvIuaPT^u!Wzh3_e(tCR~~o_7Uu2X{%7Uwkp3Q_0KWv>Xm2FybF&cLxhV
zj=AK+09ZQbeo>~s>W~`7ccv(WN|HZW4CnE?N?`sstx8oorCT}iRtgboB9_R#abeEL
z-|<I|fbjUpK^2IK-885Mkh8l7fFQ**x$gp0b6M5<9GdjD>h_P1kFT!0Aa3o;Gcz+o
zRQF(L-+aPDfNe13?Yb`)lM2C4cdKAID`oROT-FAYrNn?N5`4cwF;{DR;=(Wb_xJOg
zvVl}4ZLkwoJ9i6XtbXM9_Tg!xEzFo^^p=yN57^WqSvP`hS6B!U^sRq`Suc#C!Gdf+
znNXFXD+lrYI<rQPE7h`C?b#X&4Lx4>5L8f5aOBnBucBAQ4Zf^2rU?#sdboZ;3SZ|;
zF_u5R=#cnBxz)_5&a4k0aZaA9&d%k-b6c}I3wG4^ZxkyvQNH~OQctto`}(Y!nkJov
zOvG>6fwvUN9i$t)kt@XWM%yVOtBw8-=i6fn$Dz$2vrLOLJ_J2gXjUpCHmv4<){BS>
zVRLUUZ(p=(7$Vp^K2Gxo*j_uA@=sDqN0u%oH$!j?oJEKUdCa$O-@v%E8q|O~afGXR
z`$ulJVABy;GF7#;0CD<)LN)36B)~f=;F-+LH^%r@mwx?{h3@3}AA^Ozr40r#c>Q}6
zn1#|jKY8+tg)gq=3JNi5<)mvfzo&YRy!3U`4zD0-V`pgeA1E<Zmkwwka!FND2m+)~
zC_vZ3Myjek+<f{vUO&GdHa9l|v@9+oi3I;104$E14Olsnb4|&YRi(AEmT=o%{|c#>
z{m!Lt!?mPUSA#x@A@$FlIZ~*dVh`yDX-!7Fz_X|<dfzDk52Rx7i=BKXS5}M|k*S}=
zY!({NWIuIv1wG#146RP@d6d-Hf(XZ?U89JO$KdBL)gJh>!&NSc_4v5t?nK4t`<C0#
z@*K$44T~+>;k`-JL)u(4*jYZLATr^H5K(r^CVkHlmQzUkbxS7@pQ(pg)~JDnf|WSF
zQ>j5;_UXUK!8LX%NNQ>-ZcJt*5@N&D9y0@j+|}4H2#NIyOFQR#m$zN>KUfK>ja8Vm
zFtN-Vh|D+yGNK|ab<4Yr6t~WA6lrmqnl2ZP)*kJQkdbh#8U!u%vE`CDk2Yzd&Gr=n
zvm?WLnMX>rMnNi7uFen=5_0V99EzTwohq8JvC0_Bw{=bro~o&-5$_QOY{4dF9dBX+
zX`HzJM<@iVlYa8?Ve8bVo~ixw=g%!Ib6akiAw3KzVfO>?S5M{!%!YN)deBP4Ovsz?
zn6#+95uasX(kcH8K`b#1y#WQI5{Z8~cgUOK;#+7)B%miMQ--@tnio9_ejXM=vIJ+S
z<w8SR8ycpZT9_z;1LXYu&*ZvS^8*&+$b`~Z^rfMVuCEO}@eIAmME+7@sfxd6NZdU>
zW(_zIach5tB)PDWhfX1Cps%kGlE6;9@i5PZ6dKZ3DF1T1`+kef_-0sp^3e#?>I>T4
zw=<jZ<nh)OYI0j!qysdeH{YXhQ1O{T>Jt(aL|bE@KY~KlOUf1&ojXpYgAQf|#>U)A
z@Bzdo#{xVAD5Qmbvm-r1-bhJ>O80;b<_W!sF&<t1Of{bx_(NcaKUX<@r1<)#LW9wF
zNe~m+rq`6ia*y{7E%LT%WmT0yo5!9-3;Yj<x{{LLS4WchpfEuSeNRkcyRU&bjxm+`
z@bx)HV=2PU+X)*D8K|Y@kO}L~mo5`6dNjMsK;t{&nHgIt*SG93g@bj>4_7Tx3{;!f
zcgc9?1(FflqnVNz!f%{A#GN~(9+(1$?09If3+XxI#gtd?^7_o=+C9BMiFUZ4x9-cC
z`QM}QTa++p3{4Z$U3JUKnO`Ste10+kU<wGdP5`UV;zdM6ni7%6vIpuN>dC>Qqeqy(
z?W@wDm(9Z}eEU}TaknUbGLEcYN6~1@k~f=1PX4hK=Xj4{_c)KwIZt@BN`X3K-ONgs
z{hV1rA#GQLJbpw9`u>f{?Zw{F(NW&9Q`o9;Kf4?I-9r!}R*?oQGfA*NR}2YN5otPE
z@sAK)<vXveORT8y$)hMSvl0{x&)3I9kGO=E<GCl&tJ&JF4iJ@4VtyQ!kK33$9B%bw
z(q%45v0Ve)2rkNyki#_@#m#m16pjkoFW#?qP3IlbMFl#s*im5xZ|zuV0WG$h69Ui3
z^g3DYP{oM>H>qlNddN5egCgj(kATAG_xl3N7QmW-R2U2by{D%qR^q@$`PrTA@sfqM
z7d`Ie!R73ulSO7}r(xoPUT0p2xS-1$rA@ciP4xS-v$M_P+1cjikdr`Xke4We?>Vq+
zabDJZ`j6yGb45#wwh`S>T58|oLZmE1^wf-9=@gN(BVLJ$H#N4K^_SI?9pAjIFGe6K
z2_jp)lcW4kB8AmMJv4J_uOsuC@WxYL|4xSDm3xdVbzv$sBNNv}zP9NH={rU%h#LLw
zFOG_;O<&O_vnwBA{Ft!EKQC^3FdULnm{0D5x~b*dqyYy9iI1@Q@k#vPqDo6>Kr=NR
zXMBn+*movR#Fl9rpbI7K^0N9Qp6ues%`^$B=FjW`{YyW8vO6wtCl7i2%XQ;1kpZIl
zy=yvv$QAYm^k+(&0k^jkhz|t?1tldVw`uy{7@h_N<0BN7Ka`e)qmxpmMr$)}nHYH=
z{zBBUcgx7cKx$oXx2~KSh$B;=#`aU)92x#a)x#X$#8g?CYG?@ch#U9rnXOhXO?Gs)
zPG@C_geKu}4Y<bkB*<P5puAy3;QC6P@XeHUnRW(nv;g?yq9;pBhK1G5&NfnD00N_s
zko@XnqzivlA~hpJ9lN5lv$MB%OKZ!u2K(YsqxJYt6EDGB9$u2BmMKI6Tuw!|$Mo?X
zb$Z-0p-!XPhhB`m`Vpkn`j21x*>^I=G*rdI!lK_J+9v@t(rTcgJA{YJ{?I3ggWllP
zu7|sow+Sou2n;>2u;v|_!P;3p;mcG+kC0@Q1}ndyC7l#KeCKrmCKjN?Ml*Pod^eFj
zJ!!-=aX(y*-6tt!OOK1G#NadFqO>d?c?g#Neb|XM<IH#!g<$BVI~GR<1_>1@R2m|s
zOC;vv>RWGS#mHE-=0c5Kjww|+GPAPcgpSmz+t3(7`NIAC&PJnNzXf)K=nquUOUzNY
z<Tof0ntN9Xpz5NpUyQPhiezJuvmlfLS(w#IH|W<nO79x`0J}w<jsPnrGKgV3SmiT;
zYy@}{HHP3p8E_v{Htp^0vsIuw`6`HrR&w%J#di;nlap$#Q;P?e#9qh3JdHUPO?uR}
z;c9X-Gtlog-0zQ%3#un=xINCOHMJNWc$>SkvtCBrguo;A4(=#*p8pVXW5>{oVNTxh
zqzj#2oa$vtg;H0{aMY$p#*vHo#Tls-B(#bBl~QdSFiUCEpKr9iNbmkxZ`omVa&qV)
zm|G(IiIGqdsX;XHqvt`&Nf!wqAK$-AvLq^U{TWgdImcA40R;gxY{CZc6?nZiiwl5t
zWg#w}R4Z1SZ?TP99GgF?npd1B+d;H`4wjNdB$RuP_L--LhlWdzFm@FqQ!=NAWxKw9
zkzTccqzDUY1jwFAeP&dkng&!b*=MW|z2f8;U=c{0b6e_a0343k`_@;Wt}1CK84m`x
z+1S!U1=cRzpcb63x{7D?%<FQD_2e}*q12gUYmXnSl5gYQ+u0Q|sN?dItUk@XB>2Fn
zNSkK)Ix&(yTDi0#Wn*_EJ~_Ei{T0f#i@%V46L#XKnc<jbkhqxwL>U=5uTOH<z1l}`
zqq`X9>usmXN+Z%(He#arYqcMH`zB|-4u5gncm@h9^OCUW^Ri*??(MDd$2IEBe049`
zaPpRT)MjC7kSPNEl4TTvz8<lIBTH-_Kqci<twMs4vRKG2VPPG8Ats<YpD>!=7~FEp
z<##o!*LOuCwDNzE5s(-ux?E#KMn*=H7)k1|AB`f_R$~G=qY#=kGBjM8<{l;cw)Q!7
zW6*(DZW7?u?U`9lN5@hlFaFIS!&$SaQUri;42a^@eFd->)U5Jv9v<DttOPQ4e1#Pk
z!W89c*rKNw<h70Dpo-$+0fQ9y5$I%D0`9bqOR`&AjP1E6`7isVA#O10&xD=dau~Pm
z#>&o-<#r9D1-(4OvAP<uWx1rH3az;jGx68T4?F@KFnlQQ;f98mCnX4sZ1z^kKNS@f
z{kL&@rJbm$HQBn8G$XH7dHTn+bgKO4L8c4;ikOQNCIV-+?M%g%eoGLb&lULNxU<CZ
z+`nyLZM_7=E={4&{om8RIzsoGtu($Bup2eB`qR?v;z_9>=EBNaOyl*Ww81@lIlevj
z)9NawMU{y<pH0N~-+Lz*L$DessZ|VQVSDm$_hXwN#EcZ(J)A|rpM<2Z5K>dlKpw-u
zNg6k;&5=EZB>s0Q|Iy>A`+=nM?zlXi4BZ!ldBqM-4Za-^K$N&iT-n;?OAmn)rY(0e
zcUOVG&#_c}o!N?=?sx{HaLy?3JC^eZCsPA=e-?}fY;#?FL;a+|h>EKD;9ib<%J0lQ
zj0bA0yQ%?6PtS{|N8_V5&k5giB*RaW2bObH6HU#6ab&L{&F%UE-mF+0g1J9e=W9?A
zcB7d_g}jF;_6`p{WGCO5Z1XSFg+>nA@$_x5<mRGu<|&rc=jFYn|IQw(n>1)f)!<9q
z+ws|X)lDGdh1TfHco|N@$jIpE!@@6@f7bAXRk*CvI`7>QsZ~|6Qd4v=NRcCYOQ0cD
zT5+-hvkza_4qdkq&qEMV^FS>M`iO6|GHg=l&f1qt-bWN5!atjN|NJ714HIkrXd7)u
z2dc04Yuzk=Y_==Vzpp~mc6Ewils1_)kst6D^~1yJT-}jng&|g>ln_hPf{P)#q0(IC
zQY!33CM!mI<jY#Jq$MYF2$l9|I5dPgEw)=<<^>)eR^sFuCJ8Z8r;f34@D9)1pmV|^
ztynF-)TG^3bnC!g$L?=&zZ=U*JDhYwEpCf(M|ELkPfrIash;gI=qHS@zmsYZvkSjg
zGjs&_XrG+zA5Bq(!WGLC6C7OR6hBjypu#Fpiw0e&Dghd&r>BN`!=F$L5VLwr*l`l)
z6|LOmsLv+yL}KNo<crOIm#~_tU=V$PLP<;4Z#MD+zU_H%oA)z^Nzg=pdi&OP&$CkN
zVUtdbA$p+v<2&q^j^Rh6a7pa_XzVXDY#L*BL>_uGpL6pL<I&9@rTGMWy)qKb7ar4b
zaMn&sz0}gardENHLx|Cm)Z|dYl1yv9tfNS2&%HCr<B18nWP+@tqWn$yq1nAUE>rYr
z1{-U~<@?=>sLVWaNACUWJ4A7I6p4NlEk*|X<aAYAd3hUI?Et`09Q~wf`wd_CtfvYa
zVRAx<_3nSQ05+S^tt|yT#;Q~;;+jT3UhNI1wTG2APUC;TPNcz4Rxs77C3yY}vX1jo
zt#Zhz1!st>7X-l>G|4k)wRqJmc5(d)WG6?R4q3T$>f|~bn#hc=R_=i!wekLWVb2?A
z=#^eF;75Y1a@FbKUtq{@ZGn;ER@I=#g+hyqceJ(nczB35c}YIB)sas>v&Mko3KKJq
zTCD>Tetn6gPtP*7b#zKJoK)s175y$XPfrJ;BKMAGwKVH?a&X22h`l>XWu|Q+2Tj<Q
z00Fe=fsVXQoh+A6#fVEo6ZL40$+PyE6F2GlsUFi3&8jlBO6#VeqD)r6MYsvzz{;7)
z*=mo+n^31}Fe-LLaiH;mRhoQsBKsB}uRI)WIO{fa9`qMr)xNuQQNXRblr!7RY|^sP
z&)_q2FshuA`C{`9=y~}SbIHBp?%NuOp>FJzgyAXj5H)?~;YZeko)=ZO4^{KbVz?+M
zRRi(v>ZRalViY+y8bdV+N5}4mBj=se^buGSN*T51-wNTj;Y3vTvZ95=4>V*%;<N1E
z>DK0J83X{Toi=7WzS%*b!xjhY)TyPer2lh3>)GyvVhiSdkX-3&NT*S^&#9T6qY{Wc
zZ{+6}rl!obEAMWtq8jn=rp4GsGh{#4>3rWf_3I1_t+mC&ed~sU5hEq`ndvcEMKqHK
z^l*fK@j<J#nC>DRwrU(y%VTc?>aM&*1qdCQvXE$XneiJUA@dFzdV0)qAo(CZlv<ri
zzYddtYLKB!9L|5zvL`BG%UE2*8!9#n)v4w!WFUSeK&oNrcz(BKvVGH)C>nHokt+Rq
zisGx;Fgq6sn%lz@u7uoBp^b)!J1tf@D$2BaDK=JWTGABg)TT{Oe>4lng4HGPtmN@{
z_|j^nayY)idF~J_y$=4HSoz|%-yx`Z!H>Q-15oSI`tqnqkDiY~`_q#&3L27D+B7dk
zDTbJQ3B#$Qu1-ql8>TwgXfpVmR{~}_d;_14LUVcxPVyI>WvL@K5Yj74OJ#cN_wN3S
z8njC#;K0Gc!zFxSwGT**#?Nh8qgK8JyjV0(+X<n%YLz%a!G)Cyng%%?k)|eXpl9FJ
zjW%sZHb5pC@QYRM>^Fxqr>b);eY83RD~l&4(kCyyy}p-)1;y9IpFVqx8M#<k1^q=`
zm0Iz>jq~+Qz>rCa?G)?Jy-hePUEZwv8v+tE##?{nU(wO1gwlNW_*j|g%nX9p1`t4M
zCHa#HlQYs&7OBPnbO6K^itxPnMM+6M!7_Esn8?!?7daJxnb?K(&%vZJh*|mhDH^e1
zWp23kJF&5{qW5j2m~J-{B40RKRL`l)yr^Tg<+fe1=G>D~T|v0}z_;?{a|tccsD@6)
z66+#nd`xk1;i3p-U3iwzwZ<I_e(zV4M<6^P7%)2{N(_8LXC{g1i<G5`{-hOS=ee}=
zx<x<RA#}!$6nhjUF1g6CT!S2p3ALOBFHYh&3p1F|!TBKGE^en&L<IWj6;f-X$jx+1
z@~%dwQC?DidfII~1;*b82RBtor7>aEl;=9+h$=xTCr3(14fSn)ZPXlY5rD`H%+CBH
zJoJQY#FnXO!M!iVk!Pw*@eYt6>@UCg<kVK-BF(l3b+2Z~z7_8&FD+4-b6n0cQ0w#U
z94n;%Vvs|fDAB{N*r3HUJ6GBnhKmg9;Ow!B+XIgk-@R-zPCS>K)e{1#(_eMw#f3hM
z8IHWZdOpJBd@&Gp_1=pMz=yAe_#Q2&^VRDp7G@Be=Lek0S-QKgj<VhGt$zVsWx1q#
z5_w`J2&PG=QJ~w&Xb5}*eZSh{-#=igJ>OLs1>gVfvG7m9M<s~@CxeIF<g0RK`YkR-
zm6`0O98!NMc~isy%C$$And$QB1!C7$<=k-&Knt1t<ZvX$;*bo{T@KbYbaae0y`{}r
z^<%0wT)Zy%CL!pPKpmx%hKBSS3q2!bjVj|A?K6V-gHja=k2tBr&7qZP4cbEEhiLME
z&-G9K21Lj?^vn)^caF_s6_T2oxz63WkK_fIO!4^o%rMHw$Mm)(Q;`;uKi&emQ-GpV
zRZy_&GoXxKbNO|0txptQUOoYZbmfsN=Q#)H`E};edrFRhg75ENbG^^Mr~Q4p=1$_F
zkU$}2`z8s>k1^1cp&Ex`wXyngg45!AOG-R~cClO8iB<Bjq3<$9Mzt-@5<H0-I)u*l
zI0#e%=((!ieDe;_?^+r<pwhK2ofa!o)YMd@hQxCyph$6VH8NRJ7cw%ZP>>w#`^ENm
zg^DQB${#me{gk}OJxN%y1-)x2EE&$;Kd>519G8X5>d$k&>{x+ZiQsM<iqQ9f6wSk-
zyS1y*i51z&>F<8ZJ<2z!GOYhDWRdBdZ<3H4I`2?ICS)^|bm7dE$vC#H@6@`rSDN2-
zSU*x$yfrO{!K+V|II;zpZhX1Ia`jhHP-ht#H?Lo|#d+ov6BDY3E^i^?3=Ku*#7dtU
zxQIpp0`B=JZgjR7zjHHfa(uLldCL`FTV)YXj(4Y^FI}rmgAKaUJ@wyRq2KZFqR7ZV
zbubjdUj!|5)^*p-6d}f2a^jswN{t>K*;>_m%V=)oEGgLwbVc&e3L9=+j&n;(snw^T
zEGCs)d4=>VPa7_@aaSM-@%hJM$R%|ZN^rR>PD8b|v*v#QnvGvMt_2EF1^I8i*>U30
zhPm@XZF?hrY9(avXlpBL+@ZvMe2m{vp_Yw<IQVudpIq3-Wu`)Nde+PCtXbOp_f0aL
z%58vX5#?YH>A^0clXv2KHLgn_iXy-zm};~wcaF4gny9Ji>U<kXn5&G2r1bpm{6%gs
zo~K~gBa)4sT}7u+UAGlYwH8ZsdG1kw+{>xuPC$<fc=`9&I6%}G83c~?wX~#hZJf5%
zu0ao5X@$f9JhiAXcxpgRM#?73ejXC(6^IME-?*52TEp18THK0Y9syj|zqS9KK03g3
z?PIHI$Veah60#y~`Ak<F`i(jdK!_7dIm}hox!!rdPpMaL4uB@)_wn+Xd2>s8I#;h5
z!0EK+>L6{{p04*THan`9Yv9JH%BwU<7YVp+My{S{*E7!_^+O>O%VRX5e>{?s3!`>H
zu+=Smi?%yJE5GYecYJKoH@fe^0cKUM{Ct`)bvVC1+q<Lab*qM+n0Y@ugjN^jzyq}X
zURilcg-y>~0#${tlj?*EUc-!E#ZGHgRgS42F-dB<c$Zso2a3Vlax+n5KzZ8RGYuJk
zJVfr2YSOzsOW%qhWwhca`^Uym%au=jetzx!DUV##a9fHw%|!BfpP6g8E3?j9+GsVm
zZ;8q~)`a`D@;lB;yZ)zAHyi{}^jpJj%Ytp^Kd^Y15<kB8_Xb^YfeeAT*D-zc$fq@|
zeeA_MAC1*3UtQ&!Pb@7Nthv2Hv6?W6Y&Te20eye&%DFosA-v}UpK+wkoreYV%(Mv>
zpK8BrYq47Bit4U|CsalA@aB4Gt*Nf^n3)R(%EFDM<2BIx7P_=^tuw@wsIIP<-qL(}
zJaNP5{-iY!*!A^vqe;?>gpBH6R|rPHMHhk&g@$mQQ7z}P-r|=rd?8;@B6LfLqfHge
zoH6K!c?gg$>o*U^#~88(&9UBaP=Mz`H?2-n!w4YlpREoJFXZ9X-=NFCU`edkK_R3)
z^r+xuK_(wIlgqpRH9Kns)UNJ7<N^&jiE*5~r+090Q6THD!ryvde>6!$K`CSykl`f9
z$ai>SYa2yhkrcYFSFMM+o|`+A=h9qe<K%h@BuWgeKFlnYmm2Kn9a%d~bs3C?2_xtI
zsrdnJALf|Qg@2lTn%-YYQAU4P@Q)Hoq7IWNcuQ}<klUEETC4m+S~<|&)wQrVY{^M%
z97pwInQU$>1mbkO6xXr>fyh%!gqVm183Y+&#V{nDM@P_|ype!DT?czy&Xm{wk&>!f
zBs<(Q)nxSDO8V_q>}YX|s$X6A*tAe{>{LvNrl&BLF=nQsGJ1eFV+UDB3(r^_Lthd~
zuG7OYG%O9e@KGL{Z`Lzsu2gWcEu1KWa!kzo+CjfMb^G;2$7Z|I7Hf?{<x__XG&n=8
z2Cr8okP*P3Au=zfPF5tUFCNKU9TN4vSUY@gf|P*}_|T_UEo7;!Y{_0G*J4kruP3i;
z-O<4HNef5ysJdmg#wa(S$z-PDZ=MKrbGfOqvWy-deMCR54AJxpF-L08oA+>X*hee5
zZ6}0Ss-B*f5G)7V#8E52WkL5{_yf&~d*>a7k}1eKJ4?L|4hJ&ElIBRRZ#GS|2tO?3
zk#h*-%6COBvwnhp{I;V$;_r=#)%ynzZ(-AtD4YtrtzG44*R~;X3C`*d>)dl@-K4go
zm)0%gzxn0wtUpM<MhMl5ml<Hsy}KR=?{*MOi;jN%a1IAI`452lI%L+(XI-DDh?b*A
zcn%H96_izNwzWx&jlE2cik!Y)>YbgTG0tta`f&XHCFJb>z<4byt5=WZXjuVDEKX>P
z(L4kdr8t_GM8!u#|J>H*&Xd)TA!bpf1zGo9`EmX_KegI}AKH=~302L(LU~1OLJOI7
z+7Ze7Z*>(`J)8g5V}PjP<EhH8)-n=K7kdzX#=|S`V;!lli;Gi#4KJ+1h_~#|_6qH;
z2cM;IYeZ$auC0x=_oZH}GF`Yt*qY1v;r!ZI_UqE>zKFLA4ux|}ud<Bb65xB#65LOH
zLQEqERkd5J&$l<HZgpbG8sjn}Be`d07~|<CXK6oGuAz{{X{8k>(-lz!$+~Y(XODrD
z0sVy4m#8(0ivRPvOD0}Sw2UG<(grj_pksH}Be}h#19)e`AHJAr5fG?9mB+Z#L~W0e
zTp%4t5!j-WMc+LnNlQXelFMTwN%&h84fV48&Wc1m@30`Zy%h9Y6dyMDhmN#LkGpOb
z>@|$?+LnLW+LkC)#tlqqjmBy*g-L|fWjObt^u>|YDZVgA>$S--((qtDipqzB{s&u!
ziLyDCQG*8hBm!Q{0ui!Qa<<weBmfBdbk<#6HO7=jmUUGDlvA|+$Mm7z@|cLLP`^20
zDOKcK^v!YmUQIhac0nrBi*D{fKV9eAO)sX~mb~V=!$Br1L)}c$Q5F`KDf7C5QX56%
z#z@q#4gkfJWuWrO<%+C{>NX?8YTjlXwAkfx!ZxPdBlY_$E6-5dbkdyKqPos=v6?d@
zGtS)dKzoVs7Q1rpx^3AmO|wR4jo*(u&+G1VO_v#!k<sgTNnju2o9=Vi1SQHU8r|P!
zjvExHf!43{-<E7LbXt6zwzdqk9@6eV-+uOgP*hRb%Mn0qu<FasmP_K7E8OHjD{b;%
zcsO~Q{MpH?$w)S?AGx`aN8Q=QpC~K#Hw)doDGPy$Jieu=DHHMi<mHPp>v7TBvk^ck
zBu+Z8(x_pO+RUY-maA{qxF<m~`3|gfXZb&I1xybQ4_e3zF^}zCzW<>drl$MFrGNvI
zP=OstOV|6Q5xCI)-SUu$(H9pd=BxPNd-K07AghRDQnv_A^@L4nRm*epT~lMLmnfE3
zRM=Hj?B6_`L<rfLaclJTd7^|Z2{31l0ab%ikhNJ|%E6^fJo%BG!zQQ0rmpK4PeWV#
zR&-{g9#0}`t1cfF0K@1R#7MoaUU3R{1YhY>(yebdLZ=RVOSznQ3DQ#nM%lk1-%+d0
zOlN3RC<DFMC<wm->1bAIJfqp(7ngHU^!QO|4G|i#mUy|j6=*LAe&mY<ck3;G)Ut0{
zsHmvuo|};(_Z!T!ocR@>$#WndBb9gGx8Sf;woRqa+-G;$6Z&G~1$tytlkIAuLEXAp
zSV+3Leo+Axp8Ni3;;By#QS-Axc}?jQ$36o!2_i4PjSte<i!3QAe}s)|HKJdW_EAK(
zZl#TTdwj}MLrg#c6^s+$juQ#Lj9B?i*i?yu25x2g)y01j1#c7-sLvH4Eo!!Q&U2Q?
z+x7MJ-FDpisHjn86&3plQ#Zk+)?~h7SAT#cf(&J7V>oU>PttOWUQ}OaH*!Gi@vph1
zWT@ZTw~G^j9nw(hnPHsfCcQ$cx?$I$6Ndb$%)!PzFrF{%nqDUo-q8E_p&&l2J{1S|
z*)U^7WJaSv7W9o)k~zf!a7C|MX$lgUEa!0FX#i+>N)^4H-gQxoHP);I62-;*xDh6L
zzOZk7OEpVctMPQTn(IUvgZqkyAZZs7S7cdZ;|0)hKK)*DpC@r5JgQAFG9(L=aMN+)
z)n8DO<Wi&Oo+@n@{rh!t)=E(~o_kk>J2=Q#yID^OExGwMQ0ga8lj(OG5jRfyQL0wY
z#;nsL&2bHV4yJSF*1{>bvx_V<-&Q=i*fVeCAMqmkFx{=o@RbsFo2NjX2N)`FXJ;4A
zO0Tv<;6=t;c)9;uTv*>Lc{U;;U6j8wmT7Is0$AeV6b9&bSJILCxq!>ci(ZPjV}ij?
z&1r-swb4yF()zKC-DjzY1M%8Op-VXeM5~P!a>mu8?4Kskjv{$86}!8yR|fZIUJ#S)
z0D7l)S*4UDLgR;`E$1mTT(=>ztg1?piO>InIt|aP&ef4Pz%O?E0u#>XcCQph2LVDX
z_bOM7pH$^#{9r6mPp(iAZ+3<+PHfrCK2Y~tqHim(b#k{JU4hOqP`IbZl^pd>?v*uP
zY0#b9_(zFdw{ETe4d=e+wYXkU82ZnhKU|qL+wF${6W~dt^PaY+RNQ;FYd%dt2e^-g
zB;-NzLfQU7QwX#i4k5avr?I^Ji@=)GFAk*0+S+r0=i4Hy-I|x|*gYQ;l0=s7ZyJMx
zJ8GOVMsW_W;(R)2-PvmubwdKKm%Zb^ti3+p&JM(gj<MA+AB6%mM-~SiMyIm<ls2f4
zUgFz3!ZyBsW6$fTC*V<h`6kwgB74w8OP5&{;#+$L^iX;{hwrXkbhL<vJP`PqwgfYt
zzkg>`OA%y5^Oa}^CfjcFJgCU?rOh){eOm!#r!PThaP31`4u2K0{KLX%Gr6qnSDvf^
zXSim=2BcMW_x^@E_|Zn}sP$s<?+c39i{){#{B617B$`{`8VO!dNuQgopV+n0;}c|T
zXf-S-Gtq@)i8ep8A{r<#3s-dG=qE*wpa5ztAhIl*u`8M=(_k!B$qxeRpaJ&p@cH;a
zRt0qO6>;d63M00w%>PN44U>;Fqq2VX^ptbrOE-POgs7#YKaKOV1P^z%odmDmu0G{v
z`)5YS$nZG_q~Q7m9(-;XmzFit#lqvVNQg1%I%;0@I$3!D-3q`?4s4tPJ&1LlZ%J3z
zBhUkn^(;Sgqa;Mz=O(tZXHuRoV#J}sO;T%nqg$;-g*-h~G{}yV5ebCuJwzm<eSLi~
z_$<Q{V;*e|1RVK4&Kwu(yCeUraTsI<&WUVZM>{8{*^BFMy3A=nV9H|D@kP)#n(-?U
zD=tcAuW3ak)5$)N`Gtm))5z(yI4x5={gnv?Y_6GeE&m^lLh`1zPt{}jrsixAMtXYo
zxR1%CgBHV$lMLM-nMgQCr1}hujJgE2kjds}tMwf-g^uBVFatx%?&;48O?B#WOIhbD
zP%Yiu9@P5Zj<e$W`4_(*I$~vEaduMnCu|rst3zwhLQ&Rj2zTo8tta7t&q#fhxm9Qu
zbXYe~1B4QH$L+N>H9t()5kh-<BQV+xYJd!t1}O9g-<z<PdAHm4qjgHSR+X2Rl+fM)
zCNbdgW(RE8T7*#X8zK(e!uQz$QY9+d$IIEa=4^t!?e*@_rnDYp=U>+N<uGa6u@Xw%
zutpWqm~9;#mg+4P)YX$0kD7Iu`Br>3hEtg$jN_4AkY0vK?Eg^K>(D8m;-L_dCNNA1
z*fU<eTX}ALzkX0%9n@_cBpnS!(j2`HLG0?j@5^YV1+&=&I<ItPr|d8J(Me+8&<g0W
z_PH+g5g6y(#?Boq1681pQz#s}@3%Gf)miD(R7K6yr=oZ@V@6PU*Q`DrPZpOlZm$?w
z{r*-O(&~<Z#WExuU8Pl-4qA*@ED>^4b<AMpqR~2rCIYo4kNxa_y=#ijFg@-DfM>fy
zKtx#Lg$=)%xNTKY_I5lgmC&b<;>7D9l2jp$Iy4iKC{D+ut(;jeTY33mO8kQh($`J4
zL5ImM<TN&8QAI|a2_QxUP8y-zA)wxF@9YeOEF!XNb#{K5A>J9+Mi<T){IxjpcssWv
z`gGs(m>l`Z#pfrXyOdNIxqJH$M&=j}t3iYa9k^IJuZ70-xAd1pMoBhD3(=Ah5~9Yd
z>WRogpPBTlwTx3ZnP27mp^X=TT?n%E##hq#<N)&Z>2ewdLvfH&O$pb({7GR@1dLll
zA~KhS`xs1jD&rUy@YVH~2?FPkly>vf9_UEh8fS5sOQRFRu}?%7PIOI?DbjXQB8O12
z3GUiBZh=xLX?PfyjO;8s(<X)Npd9*#;k`-tD42UTpa|~<s&^SkVu4V!$yY*AL}18j
zrTm$~a28KG4m2|{eG<@`Tfcq>T1ZK~+U4Wj842i!&Fs;Z*CkMZPEY0*B;y3QG=MT4
zrgR6Sh`<-JH*uio<CH5rbGwm=+X*zPB^?!!iPJgsf~=P)PSAQ!%TfP*pWOYsDm0pi
zb7X?>9B2f$;~yLO-*HK>6E;ShtrBu2ew_H5t9;{na=Kw)%YX$E9@>L7wnAYU=@)I@
z97jR~E{n7xlWkvecm3Z}j9@oQarl<0O4OblVQlLt#I)5XMK(4yZ9it>Cs9*njJtth
z23xkL%Hf>@VyJi#WkMh1i9S<t?Qsal%)h;$Bs_8&5pno&ivz-F^`;Lk6OfAaK!ATy
zr^A^s<PhiOHH;yUJ|b+f_dYV32y--n2*~a9draOAu}|kW8HkH7Z6#5V`1$i_YMyOi
z4kWIs0AmCLZnT^&<L1loojPq^pln1M)S}0g7h(L+GLO)rALJ0#mz4aiO@q<SmMWIx
zIq2bj2FqX`1u3+=r>b$WIFB3>-Dg&w>cN@8(T|pKkJ$T${dw%rj5b|6#Q*s-+AR}$
zC0CA(bF9RvJ;hfhEy{G<yh)UiP~`0fvH>hct3fz`vS=xS72CarFr-@a<poIO)iu5&
z!9%B4CchOsP-8RFN9Oa3C(5Q+VvSl0XpFL>0fHq%7Q6E_{Qj*+yNc2fEoVwPlLQrH
zU%*eJUN3n(!m$}@{u&XUBfMv`wt=+woY4H=;x#vGnesom6uI)rcwCl>jBJ^21TL^k
zQqfU-0=oS8#iOOf*B;BBC$LV3=!mHnFAP1i<HL_94?%$n=lgAd!~QcCD<HI>EOaq1
zs(7+Mg-m9Sy$mmSx~&cL5(>FtjxY%a9+GGM>tEkLpQK6uneB{?b#l%?8mC^%fZIWh
zg~h{naIl}Ga&UQz5p<)V*9CM*u$gD>&!FN0&jsrT{Xj&uIS2YBgwTq@!f8NRK_Hge
z>8`U?o5@I_RD@_r9kD1q0cjij(ztZ6_z331{KA!<x>jNK;)+qdKPf75>x&EjVI=g|
zqhoiZZ0+0&_-CMO#;Sa4az+4~VnS58B;baGLaC$MbYkT)13|}i0JL(x>BP#*C#evl
zAt7)L62|osa{c>s5zs-YN+C8i5@JV9Z>d44?YIX3SzPq??Qs9=ArpKAD$i1Oe$+Y?
z!~(E<Ve=oRRxa#yNH~@uE)z$H^HN@yRSNpD^YQQT@^H$s6uo6E5nMyPDIq|DbtbyJ
z*ik<(_B=KtwJ>FCRRT5H2?K<@Q62`g2Z727E`%r?a2bTJlZQ6hSW8~ZL`D)?eYLmZ
z(l%BBHTO6TUGTHdD2P^;lC<D#96%SbZ7QBXOq?NJuJ~P#S*uiCQ$6{Xn$b%3o1hgT
zQV5YrEHKv{kdTDEc}K>X9MdvyXU?&4IA5+}7E6>wm?s0a6>I`wwKGt_DbuHh$n)&d
zNSUrFLez(kApl5KzD`8GbQXiR=F%$o`hO0Qh+I-6u=%Y3C%{`<TWBfCMO#~$tYk`C
zyX^3ejm;cijw1*}|Mo^6Wh9z8XJ*&w2WR0Hdu@l`DGrcHT!IJzr=P@(5Nw2JTb!6!
zF}m}x-D`3IRW&1o=yeB)_#d}+dfk1TW6{*Cn>J^;1)A-^`_q9|8>&q{t#T;1?LVc#
zsBmbIyj$hYjW7aiNnR1Hg`;5s_wb-&U=v;L@I6Q1L;nHyD+wq3eKa^Vg7_f?ofV;U
z!CSPT&}C)7D*qfe3^?s`?tb9C+Cb1M0QvhF5f}_<C3))aQ_8=IeK0?b+Hm)8oN|k!
zTN9xh{?9#5|L0>sd@#C?%GQA?qws9xoc}vKC+LO6W)a3K91=ctyU$yugHSb6Yyz8c
zRUDis4sF0xF#7=iN`H%pOTRdKFFRPpMQKWXqwI>dwtIh@U;N-l{-5~*r_bpp9)<gF
z$e5iupcO85A=1k7xkdk(aGHWRY|>S6%<4G2EuUky)tl=4b$dq0BZv7j_0{|vZLooj
zS>cMpqlxOjGo&)N)l)r(ogj-E+1!W~@eQEkb^aZ{;rcCTE&(xogZ&2ef9^a()+Cmw
z{@ce9M9!@L*a+eWa8SbBf{ovuCXFGzhJ))J=}R90{#)SxiQN5rx&eg5&3e_e$$Mub
zY$u0@qW~TDlQCMsW$?HDb@M%HnnMt$EX;Olv%NXn0_HWqSO5bZ%oF79;{DE*jt09d
z<C#gwpVq~G*N^|J1?ZTEnJhuc9=hWT@dHLyd3kv^U+;IPA<;x|w90W^2Af6)V9mii
ztC`{9NXbJf+3n9`*)&Gs*j?RDx7>OV391ZQCr#L476{<(q$~v1&T4~JwIDQ?ek`1q
zNF&8?CT5$3On1xPt2BkO)NW^+Kx?c56&@Em{f=WlTQ&55c@7eDt;(6ErlzxvVuf^|
zn*`P>V^Hx3*u#DG!E!x(4X2n93v=lJcZsX(DZ=s>pcn^6RbZW-A~6DvSAd<ZekKDq
zXf0sVKhi#nO`t~pb%P2=E1P8k<e4zX7R=}jax^ejtb>gd2uiR45p>{r{Rekoo(T8t
zQE`>Wx!?x^Ub^>kbRa^5t8yE&!n`oRPyuXnkv5=70PL>ar~M=Y2SM-Oydj=PuhXk;
z_Crn{`Y-p;WU{~v>0MNEiP=h<r@g*D$pGl@oZqVioh361HTJIk0mjY)7|aKjK;jQ)
z!p54Kmov3Nml0z31#0D^+rx0l@aC`qo(yQ^Ck2fUFY4fXV5@FG+Xk*W$Lr%|6_HB~
z&>9dXKiS{++s?EGHc#iy%UK6jRCe9B9M8Xgms95uyWK4UD0wT1Mgcf$x-P4oJ8$<Z
zga7tY)VB@**E+DE@eqHwFX}VXZFZOk_SDMC%7#|vuNFOoTyTmx;E91%RS-}UpaydS
z#$ZHL{KJ*bJ77K--6jVfjch(=@{3Iv+~?KjO-IaNhApr4PsXg44fC_L#$ra-KL1bX
z-Lk{!S%Sj<9P@Jfd|WJeHyP+1Rx(cNEQXT$`}={B8yG%Ymz`qz+%`V&)fluUNsc|U
zrj^4a6fJ%674GZ&>#_6^wxCngoA=amFd^}Nd8rjJ)onTQ$F&0odP4{Dn>>JE;Be@a
z*x7XJsc(FM|JL|+5@<sQU?%9z&zf+)ul|j+@{@Kj5UWnujEbwn{QwaJE`z<4$Q)nF
z6&Snz*~_yL5>BW|?~vheXqDKX4z0x$9wuzKe`IpeX&*h~e-4sfEJ%2t2iVb4w@0=B
z5F7~<g8M;PmO<r@-5VK&Q2JKUOOo;g4SD<Ke8<`kW|gMx;()V8R;3Qemq^pmiX6Zi
zWuJa$@(847+;U@Z{K>LW2t_l>UQEA7X*pvtW3V12L#U7t&Vh$@s>>D_Ofrq&`AMQ6
zAge22kD%230#f%g)hxF3*D`VU``X>0O?ehyr{e(pZ{Yg}Je^3861qS4AX})I=`ti{
z9BRE0l|SEd>wU%NLTcSGFDN7g1HqtVF@8KtZ*?Ym^N1~lFBzJU+g0Q6j%re4^z!mj
zzs2blQYi4~-&}%?Gy{SYFzbP@J1{&Zd67_x036R2F_vLI0re!$Zs^C-V`l6`ybfUo
zXu({k7X8vGAjaM7vV`+RJ%A0%NHnsQEFG9c@Vx^KE^FU_jv07K1FstLd9ud<t~Y@b
z|0-xTh)o+TPWwFM1{l#`l_{C`Qk~zWUBmo+1Nat#;&db6ErRW8izW<83b0!(UL|)^
zCO<P~?J;q=+@H~_zKQ`A8bSBpKG4t(1m9qgSC#f`lg-0Yaux*eOf*9CM_?KJIWcj1
z*f0*#j(tsQNn6`3FiK-x-NO?q;_3o~{Surrv?sZnKTf~ZZR>Ts@Z|I~@Gh~rl3w48
z2Lp-AUh*XWJK->M3MzXr<;k%MqQG`>eK_9$?4$7mp+m-S&w;X~iVFN+k1>$8ScCZk
zQNS2HP@CJ<Fp1L_Q}7q?trcp-U?f2rpWonDd<<zs3TQP5y4iw#?F9j=fzPt%+yl5^
z^WstR4D-7VSkeJ6PNF|s_24W6<o;Fl^=|cg&s|{9ecl|#GkoIuJ+cM+rV5Lu=f4#}
zT(2JhJz^@B!)sJ_J2YOx_f@8$Sc3=vg!;i2D|o^5xw|jn;{yM40QU=fSH@8oDalN!
z&&>4ln*|^I8P-4403$I_!RsA>PCVddUEjTuru=uXDS&_&4ImT#J9c+Cyoy6O-Xy#c
zmVYC_9nhNYu>Aw&cCbsLjpG1l1(V*Znp3}oo%DYX1;y<l2jBw!0R#;5(FBpWUZmFv
zz~N&uts^G^9ud6${ih190GOs1V484X*Myy)e3nC!wiO+?`6%Vo;+#>~yZ#-b`wi5%
z)C4{M_gs%z9qAI8$&zU+ZtQdLSZJU=YyswLV5rLX+b;(OYaij2nVHK`uJP%)xmCY|
zYJo38U%w<%M@h0vdcjJ;&Giw>sS>5Ju`%Kg(vK5nb<F=qaqk_Eb@=`bf9x%pWp6@8
zME2f$L^6|P3n5!ZlAV(5oxLM_ldXtsLN>|D%6;g2fB(4e*YkQk&+o6tUoJk^d7ale
zkK=v3kN06zZ4Y+_m{b3K^bwyVS_QCu)!-7<0qn$c@29jTtc7n@mCN(n2+8xsPnVJ>
z6PLz9k9_CZ_lMCY-di65bTfn0!B%r@%pf@O+-r?X8-A-0ZWHouYw^h*UW=0L#J2%Y
z^c|Ps*YDpy0n!7A2#8XY-*ar(?Sd@2%%1`LZ<v+;lu}f5{1Stm1$nM5ZT}}E0edPj
z2@{FndGP==Se{s2n&`J|_*FOli3yMphVnNqz-{ger!vT-I7|r^eiLa`M?(^xU|W5*
z{2J&921tAMt*dY|tCo&m@v5k;6{d{Tu&hTA7nD{Qp8w)}%0lS_R#YMmQwvW(#z0eT
z1Re;L=at30ytB=TdBrp{?@Sa2_yngkBM2)Bm{ug4XBY@C&b<%_W7)tYBLT_*Fo<P)
z6bP^g?-0TgA$R|aiX@;H$+XfZFr+9?9dBIJc+8=wC-0!Oq{Xyi$KLb~X8DalhCxP+
zja{8dhS9uT>{dN#+br+kz?tJ-@O`XqSjFVi6Ha~k;$9;8n5!`a6&^A&pFhpfu^ls@
z%3u%=>>Y2o9R1wx>2n@x+-dX(C6ejI$MVmJnh*MORYr;M7bX8XHmg-I(QCBLUIm)g
zjtC+dm-N6Z*Q9A${{0?s(`xjpT0b@eGgd-^wjwVdQ*aFR{5;Qp|HvUg8A88AgP|jr
zkZ{>x87Gw+ItjlFb`Q$iR%l`}B<y;I<7*WhT=`k)^cvdg|1>hHt|OM1N&DZ{-QwgJ
zJIVRI9^Uzb_wVg9!SJ~x|84C5eihWLr3r0|1VY=}u>wL5!>>0AlZlvMl=5XAnp-I3
zd1qVjRVRE&YHu~mV}g#Y)8;G5<;_bH%H-9|?_$84UUde9U1u1z`}HSK>qtqSv7syC
z<U8c_<Kg1Mw^{$so9y|rXd!(sC@9FxXcIhGkDb}Apl30REsJ<EGfXy)U3UF>AF{Kv
z<rEd0tMAFk$oTpB?SuxAqSBfXt<%bIb#+8|ftA{8cbW&@<UOvzzP^p5n3(B>e_ePf
z_pF|D{RaGik!Q?Z6L%|`PLdECo1KemvobHn=fUL{DeH{z(z3G3jgHn_Q5IrgXl-wo
z<sv^a)KhBu*Y(S(2*M70BFMWc#wY3XXLw)$j^;4AObI;vo~!@*p$47A4r$v>OzdlG
zD~Nk7azkH7#{zyHJb2zXp@JICgY9-j;u!CMG>|@5U!UCXcUbpq<FK^dr`%ixGc!8m
zyS*S`zdrFZAL%@AY{&@c&r{^ZX_I3gz1MVBlEHBHTebcXt)$=Pj~}Rry&x5q6(^2Z
z6aA?8cMApa5SF)J$+-mt1tq#HT0`(!R%&i)>!K+}YxML7a~c$>tMl>w@28(dUOymh
z8`X1N(;*{!YZZo0&J_@Zb>VNQOJYBX4q#0n>cE5c`M<vJ=KQ-mikSa>eIg4!t^D6n
zsEv5vJw012QISXmlA-s|zKkU^J{{rg?ED%I8dh7eQK-P}gLhodhqVq+&=go$dqpir
zDPS>=ilf7d)a1W?+t<{D0FPYCf^oWzrNPThAcb)6CyJD>Z)1EsIU=R>`DFpe=bl*C
zqP(O3%-#<)1uf0Wswyrnu3$3G`44GQx_hJfy2{GR7#J8j`FirzmHByc6B9VqPVelp
z^LYL(yH;}kb!U(VC{9bbWZZ*d4>Kz(IT_jeq@=!|(R0<s?6-)~1FbT6c_d6}V>6qi
z;ye)S#z)zgV<W)HS@<VjE-4|w8q#MsHoT92KLV5=vP%s4c${|nWJ4tgu(ecGzGjU~
z45jtmFdeO5dh!FZ=10DN|9&(jpQmUZe*NGRgX|0DYW&Sf5EB6e4>H_ezkXd&S!u)u
zcI1=I@25xG1Q+|dcQdpuKYy5_`&~~Q8$^9jq%<@JAy5Bcf4{1#id7@|o~h|9mXTuK
z;|I{{9W=C{-d+_K5hkXGpJN!*SmU}EAq!$+V&WsW^2qpjULG$tp?<8=($yR5c)qa-
zJSKI_u}X+JoW8WQv@l?hqotvt0WQ{BP%+bJJ2NpX+U|+(-<Ot_EFV9H?-=L0xw)B{
zl{HnBfD+Kq{%Upcj~3nfh+Tg)t$3aL7F=Fv0pQNPPe|Bb9=p1JRVNm^zCt_N)x#q?
zGLj4%eRg(si5fyi;F&^RMU@e)8O~qAjjTny2|F0Xo8F?m;DMS>Cx;Fjg|xsdVL%3y
zv$HcFA0OB{Oe>%2>Z(VK6;#rBi1&bb3HgtPT>6fV+d#lYN5IS3+uvvYaMi2+aywEb
zr=;lW=->jUx~S;0FgG_hm7x9VrYQR1<L0Cja4$g+02v+~eUo-6R)Qb`lnx-MfP#YJ
zTjmz*MGouHDIHkqB8_e&?_15$F)-ZpOw%(_eIz^^uQxtC*`}-n5rv>2G+}X<rO$A5
ztgf!&kg)C>BG?DmGF-WE{x!C=w?AwNKr!K(mirKHYrC3|FemTg-J;~><s~H*AVnWz
zfMB1C!v1WugaFGZ_s;zRTid`_xL$=Tff*hzrim*<L$RzL?F+WSP_RH(HbOer*9X3R
zYox$`oz<k#4>{lvd64lu*ShHF=m7t!Nra@^h3Occ*Fh&HAb8NnuRJW8%R^i#MGxcE
zz0e*CCu^__L!`pP$_dw%K>|WTE8c4~zN!$(bI)pwsI06^O*Q4|4%hks+Y*ER(XH~%
zvy%1jjs~xTb<k-5K{9X+EADY|zw29g0?Fq3#rNIjTb@TXby`(U*t;jaN0$a2ivyPi
zY&XpMidaT^f!p%8Z^O}W@Ty6vpMu8}HmaV10h=UD|EK!;D|8ZP-!S=PoKW)L$h41F
zFbFf<<5D(BX~>dl%;ZUpGk0{P1(KuH&zX7;@Fq^pH?R-5d|I$|&^bViMy-D*uj$V2
z1%xhgHPf0N^^sR_CUq}5-${n3JZR7zwZa-S43-PLX~pcJ=itC7YBZ~zZu$kb(^V#g
zrRC+$jt)@gf&Of)u7btYKQdCe;jzh(EyYZ1&&O1&`{gStx52-+D6;+4<8XsyqNV+O
z-4B+fn$oMqK{u_ku~)skNNF`MpJ50kxq#gJgL8$e>wLsGQN0Q--NPfgtsQ4kZM4F{
zbvTNTm@v`{N;Q}Fmf>zAb5ZJVZEf{!)(-Z<Wgstv1@k*MH!JJwG}jKhR%wi^g=vMQ
z@|+WC1})RuA92kw(|qsM^5qyW|JhPvA6%!*2<O;qQINv=_YoZtT1WG$TrrI1x8B3S
zciE%omltz}QVz$Ar#PKVBZ^V~4lfuHZdabys4`$kWqk=o=t+A;iAMMSox!BkXCb6P
z0SX&q?aje54?xdJ*cbd?IGJX{@EUJizB5kmRZw#bqjk0pWRb3i(oycrY^WSV80oX)
zs|kQE;F@((2>mO5dGV<0+;D%yB;WEu=Xu}q_T=3^OFhG1vP5|r{4e~(-3|~32g1;-
zl9IpsYa^iQ))YqKMgb<!<=1L8Q@Z-gMu452{*RA}#Sc5Yow;U7$1jZAZ#hLZl2^w|
z=Rh$51T0SImG*he4FqK_EtBkPfAlz#C47|)-u<nsc6#(E2L!Tu%O9Ti573#`y1*F+
zDk~P@h7T?;^dz{@NrRNs3BW_jD>dsj#}0q>`8{zMv$%Ll^3T0vbZJBi1XzTEP-I_d
z<!Yq#TG-gw5NK$yy0|Wri>#Agb`-~`Ux`miKtOJC(ubeFBuI9)CvK^~(4h3>^fbKF
z0{MQ@ipBRq1Ld+IGk3uvV;n!fk$Xi4oCcIW7me<wsbX1*H0}SU4NV~?Fvi-JTtwK=
z$cVo?vas1XDA`kh(W2|+RS@4$Q&l&4cI3Dif``PsODX0d3dmx*b7xgwFG5B0Lj?6i
zX|;aILKfk|JP-6L?QJBfY)Rz6`@iQCR#syVyY*j$qDjfxtR;$iOdlMe;o_=Xj|j1M
zx3c9xp6Kf25fbt={anTNK59$zrv!Dk?s;uC7#*!6Eo{`e?g-N71@Zb8?t)1Jg03!^
zyU)t(-kJAbn~k{6;eGT%-{3Uo!-kxKt++oS5>doOAXGzPhKC;<Es5#v-JZ+psSqua
zn;m9IPjnglU)qP*;LoA7X3sq<+Gw_S=56imr6%=boo#F|tKZB|Qa82bd{+i$EmT{v
zBIrBQGcsZm6Kx$F2oU8g1qab*yh?Z7ES0b$XdQ%XULRo5<>6n?<Rn(k(b;=}xt=b~
z({%Xvw9Ep-+RpB3Fr2O{S5|$GkB_Md6=$`dCcS3;d3JX8`LmMC$@;fK{>Os?e0;}0
zlbpOPmjUkxkBr>1Scr*<Sp>e2iE2MUiXis{@nO%PRWCINYSsWQN?@RK@80Upj;038
z?}K-AXWplKLuN+c{MW&OGtjRndEw*#=a(bKaiA2Uo7?ieTt6p=lyz^e2qlBI)AE+9
ziwoDyn<(ag-*OgiPahwwY6IkoXq_%o{G4q>q{fHm#e8i?M$t7i9GW0i%KKMTJ|wD-
zH*LM@UcAH|O^g=+b=#S)9hu=J=P@-lF;Um(?&@j+eLbuzw42*1OB%`~uF}%dj(x?b
zwY9YmyCMu5z1$QOx<F^eh(grw>={MceKsO&$JqvDjX99)9HZVb_VPNZ?C7vV=PL{9
zNUpUZdEAaU(cLgTO(Q_*sS+C(r>mtk{I<Bk^e)-cUjc!=`}_NZldGK%9_$OvE?z)<
zetq3GRtX)6jg2)C6DpX0lI(x7_EGWqw?YHWM1lNIpFSP*Z=zud;NjKNSU+S!M{miL
ze{!@P|DHd`%WK9IO-4Mj$CC2ewY>cNSh|l@RWI<b|F*auf+|3XyeulRU}Ee?TKbCy
z##z*?)P=CD(u7@e!StDtuS7)~N)_ikRU(oD4ZoyY1|`NgSFY^*nBst87Id0bqoqdR
z9RE1QnqOFGcfJePJv@&V05b)=f+fJjz%DYGNS4w5+L4@=#zP#|+t-HzYwmfc1xgD%
zJzi|YHgyG=AM1(?GuaT4@cey=UG}V1RNNvDf18*{Nk`}O@S$uqYGFZv<#mhM(R?2X
zI^?&mZnLU-j_+By$;O_3m|WEp24O=Ani(PzCL^81gijUayK5P;pqTWZNY3_&X1o$J
z0Rh1l&r}^*H6*|*|85KUNFxw_JMK09?B>%X`j-G4^BnEo;NU<FC;%6pPGb74|73&f
z%2OQ1(~|>@-j2@BFKheYHEE_vn7CucB_(ZW8TDBoux%HdpEB?gp`s#E9B*}Y-EU+g
z32LlK1KSPwU>|0xzPm<AS;}pD>(-B)oJN6-1=-cAn$p8?9^z;kZ#TdD_a}vhQdw$5
zBBq<3IqzjFJG@E}cI_G;P<KyqTS@*jeJm|4>b^-Y`4<*Jso=A1piqe8h2PN4jk%cz
za4hV$<Ya0@NmKLa;DCnLJfH~WoE!$*I4Qn*ZkSg{$bjQ*L0&^1x{=Y8VWrg;A%1Y0
zKV|@|<f0W98~=-$w`8=!F2QQvV1;>_&I)3M&d<*S+y{5;7MS=55j8c}hPI*ivWlM$
zG4g9?)`40OKQOQ=N5`*kB8QhRmiQ`)j|5^(N@`tY`^%Y5l6&u0g06NsNOUR3v9d5T
zKRsugyW{P?@}rYSqN6L&ep=Dl`FLw8XLomNRL3TtFEneyCGhNMCC$~y>qUsV`hA%P
zjdfnSSRaN=u2NBn4z&G9GOGF9F>&$toCtS04N}auC^mLx)#0|f!=WO`#=}$UCUi%d
z2$TgoJvPQglM;eMLx&P?ZvdW)lsS!a<LT=9HTyj>Y1#xcTth*jttXDxDb+s8K22IF
zN<cv1GhTnnFyw3RPVg&x9~)U%jh$^?7dA06@-`pm-p-W&TH3sA>EKvft8P%zAaVie
z2_^VlpMF?OP!-y?wXbw^{rqSfyLarf*WdqZe;;<@;6qG=`(1rep0%OH57?lf&6t#w
z6b%|K@83_<MJO>B%zoN!L_upxyTixDg^zq483E;n9#$89{rJX4aYPHW7jAIYIL-O(
zVs8Ajy7L6#Hbs{om{9TZx$e6A!&_bbQCJuY3yY%~Ys~pK78X^f8OrxbMxUJMNGZ%F
z%iG&VtNi=+o{F?CJQ2$q-8wjcXD@vY;;`knoaC68r|w%Py`%d;g@DsbGgYjow-*%w
zMUts$W6x7TgviFmw*3VPGBhe6DUxVxv^G)MiLULx$NI9<0;DuaQKe(Tv#Fz)>04%p
zwTS7OS@>P8H3)0leL+&JB2Ftq>yz_h7XZH>)njh6X++qr*$=%J`Ix;-?ER(ptt9`o
z<H*QgDtkxUiMKOtM%ZuiJQO-VKiW_8XZ?_-pzyo6Xnk`NS0l|&ean9KY(4QZg7CL2
zU23@5U*6>wvPw=NT9}_A`6Rvl?)fg(lRJo%rsjv@dkl_lwilLPv$2{lxKE$$uYQST
zQcMgDZrNXp5)=gKulE8ruS-jLc7Cd-9L?HQS94qEZ;jd7!UVRbeWWf>^7Qm%Gbo~;
zo{=^Y7Zw&~VZi{qL%Qr<Q3<n^KayA!UQb3QNQH}wr03_`bVXcm&&(V&G!*wa#J_cG
z(&Vm<hK4_;?CIM>?zAC;T3;jIi3(}8pS!Z5ruIS-vo8iD${mu5zRyu``{)>{eyka?
z+i5P1Y-nik!^iCGUH|@T;WDNJ9?sV*62qLFb|ycEkLKzRHxxx(t<3dEJ)f$ps-8|x
zpLtrbR;ly@9>3&y{0#%IqK7t+(_ns9{PW<8l_4{#Fk|*d7EQiMoTL(FjrRfNo-Qi7
zed7iZB`yhDTmSnqt4KSS-$0b*IX)2)^T~rg8M;<(s`ON1UC}r_ZmerU!y+syETU#7
zmGC_g;OEy3Z4DsfbWchWb($&gJlVIze7HSb8!I6Z6-|pGC2gEc8N%qZkBtcY-aF`y
z!U)LCkfplM&5sAlhYl7xE<e>wjE!qc9N#A<ZuWQ<goK=IZ)0X;U<J$c{@L}ja0Vy%
z(WB*JqnfcILlR<QVoFMgmx+Qjb3{bM@bK`Mbb*+cz{??uU&k#g8+!WjDIyrifv4xP
z$BtuIn6kD3o2f*kgQKHhv**b1J2TsFqa7h3!CHA^(?u@pq_tH)_HAxULbUD6bbL2u
zRW$v&`zHGp9M4^CuK1g=DmjxLJBNF}<~_Z`iAhjewCJ^_DnicZTQ59no5g$*DagB`
zLB~C%PULh*a@@gf(DMB|HtX6|Ep6>`$icf=Qetk8Pv?itX+TIRz|O$10LDwRdKzJB
zYC#az_(zXHzlF9C5XcC5@gj=yHiv*fRc-Bc<PP<qJ-3i=|1|`gfWV~gDP)D?Rwu+V
z{@UA{8n(q(IaO2326nXfPO1(KZ-BlrJ_6*oV-Vo%{A@j&qv}63@z!TozTS@Gj&EJd
zz<?Y*QO$`WM63>zg<Z)v)>_}(5ZU|rO+k(b{Y%I4I&O%_RP~x!vxaFk8uI7RXtJA*
z-=O%G=YC$6xvGlFkHtllbmkk%nWa~ZYA8Q^7!u+?_3Dey@z@z;zY%L<+dnYyXnW4-
zcl#Cd2PpdbS<J7&0^1+eyTH0)ANLxe=j7~@Hlbl;^s%V|xFQ#iLgEHj5gX4%iUdKD
zP&->%fPHpdpmy(KUxw^yQSnEsAwgADT~Jo`*|&Zf5j<1Rz=|m*mZ!J=HI7a3i?#0P
zsCgUui|WIXKhF>%Z1f4;4}{T?>Jt7<2fdV3RCjqyzFwC!wS5pvMPcwJ24Q98^_MVi
zCd8v?t97M|e-{l&6SbbM)($rjy}9~#Hjm_o`q7cB6R+HGJcQTRckK$@YfinMA)))t
zv_F2RaGMYd3*UX8Fgo3&&VB2r%iWY@^Rs8Tp=jY0Q$%t;K4;*?Wa8qLUhMb}4h}-_
zjV+okZLV}~$8`nd47X<L-<?6syu#7?nWJ5j=eDtp-Y5YHN!MaG5>xvS%j4`BdH6Tp
zC*}j+3XA;>y240^@$d>#O;hr1<ki$VBdH7p1*-};tnJZ}$PT$A`S6GjYTD`=tU|+n
zcOP3$>+2ToH(*g9$aUe%8Udm%=yI9A=lnQn1=XsV7t;Lo$Hv=#4p44y7f?r~XFQk5
zk~W{`y88`u(=ls5iO~o~JaIX>Fg7(w(KRsvV^rO<7B@)x%dnQNc7AC-O2EnFeS*7p
z7mK(m=3&wL|NVj2r70<`gx#Nvb9l(>%qV^{QYEJ?2#eSDO=|+*>%$V4M=7F5cFH|z
z7#J4`?`>vk;}T*J#LfR=K#;#aCbo5DkCT&=$E>N}oL6A7-Xc_sE^B11pL?1<ec@-9
zi-*6PL|>0`R>f}+4wk-gqdi3C<x3<WC-jQJ%%?BLrhjccTO+9oW@jzL#WniO*G?Y~
zT9)1?I~fpyoVjaa31DP$;Vy$k{vL}^PgF#QRb$^kJM7gfR3x+Zy~#c6Pk!RdjYndi
zrro>iiO{d|Ia{0*+(m}3(eb!FMJFTsvr%ejW3x+3TeZ9Bkgek~T%yE$;xd?wFfpya
zxq%>5g8eHli;Ii$&d&3Ti+4>45i3v6`*vdiQuA-$=bw*gW)377h={D4G+@gxkdg<c
zq$n}#e6R0(!^ekrw6$nwn`mTs-fm&o)q(x)CK^&!fogcc9Fj>wU3AcjC6xub`DtcN
z_=vYv|FzY5sf)d#if@HRr8j+sQa@Vo*-vJRcvbzHZOr}itgetVFHg#t8XYsRNy7C@
zCS64(14pk8V}4$qwTcR{dSbSTz5)@tEC<Hz+eq23y@#*2ntOY1g<1AiJo@_kckcIl
z1LY4{E{-+Z&rcEhSS0}#;^DD)hx?HOgArk#E=$;bogs(kM=0v*B-=CmxVV8!mc6CT
zFK|LSy$`cf;b_&Ql^h!xCOZBznk;JIw@zwz@{}R%iVTBsgLSzr8M4}Zl=|!M`B-z|
z+Pw6a{WXN%`HA(*$l|CdauLxL5fSpgyNZAyH%YZTeNsjxLA}0CFWEftRcF$)!0wYz
z?f1c`hfWukvkevMNgOV&VJ9aP;nnt62U7{^>bxW*PKMGXo81a$KVv7To%1~`868Wl
zs2SSZ6J;e2vJW;;>o-v!_;GTw`RfTo5&<G(w^7Ca7zuD)#-01Blbvt)`;W7a{U#UT
zV{lx9g9D-n5veD<o6|U=?g1az@(KcTbMtG2HokFlN%;RPHcEW(fQTfEm{P=4HtRx>
zPTIY?JB*|X4GY2qB~XLF`G*`pykH?;yLRoV-8)&Os62y`fQ*I0y#q{qtwong$y58X
zw`9SgXeoq<f`Wn@m7rK<rT6z+>l_QKU$Ua2kQNp7+i-NqJ38jv%qq3>*dZJqaW$?Z
zH8w#(`Uf0ns9y^@5GRY|(2}C#zo<(x+6F^g%jfLbRnL@qGf8!at?c+d+hP;LCNU-Y
zYp>(bkT;`xw_Q>uC*K5NMH+JbnY++4EZQ8^gKGrmxIE*z=PHpFJAzbKE8us<P1U4?
zzgi8a5GpJXWM<>J$q`7x2H^ASt*P*kHdQ~r)V}z;xFKcULVu2dHIIRb>28;xk;XX7
z0lsB3IJs0M%wU25B!w>h$NQO&h>Z?l!)zg{Aa4aG%7EQ?>cNiYn|CDWyQ7m{Bl~OM
z3B!%(U~79a(W}znD=JO&bz}UB;eFb&N)iS6#wy&{m!EqXu^Gb${GKc-M8xS1PE_8w
ze?JHOC;|Ssw{@k3s(N>Cr-)s5bkZb}dwTIV2he7PuC<wZ*B68=*qQWfY%`zp4qkPN
zdN@{9OV~e@HEvSV(<i19BpNTd%J?O`v&Qk8|M4TtIK#Af_h%!y#!fpwVD|1A<|HOs
zv9Oq&4Eeu`rdHk_kLF5x^X83%g^Ut2PZMXX<@%}~XuhF4KRP|E($3ORjQI3-qjdam
zi?gCkX>5F@$W)!Y!OHr(_2U}DyB{q(U%A^3O`VP(Z1jzMwbj%6s*xhHwGdnHKqXBi
zXFAJt^Jc7;61jI~&Vlv$$;R{N4oywHLz*C*xAgRUb0UdNy$%*sY5uY0fo&kY4v6m>
zOCi!k6ck)arWH%u4W1WV_p9b6YJo!`!?CsGq(UTD5g#vi_z)*BMujRc(A~?c*UIwI
zqpke|mi~cv^>wf0GC`mD^z?LPY3XUx{oq+{YI06#Ma7oo>i1?Tv9U#kNzT8SrHT0T
z^=-KH^`AXcY4$^xUIJZIhhh4PdzsbMWd*NZMzdXUMwcc+m3~$1z#TcJWu!_JEWohv
zI)g!)D8H$RLeP;pJiFBR`ohY%Rrqa#5@uVw@bwKks_SnbTPYab`$a2mtIH}%Kfl^d
zM}l77*Q;_eqFGF_+ur6^R<1lY!h{~^`}glRcXx_Vd<KfUdV1)Xn1iX}qLPvqx@>GR
zckjMQT;JL8hhh;zdBPSwuUP;taH@Er!TvD3Ha$I{fyJ!fVy3hRP^7i&vW1SYNnzo9
zPW@~CCpY$Yt{|Hay94=e9fJ1lk3YM@aco1F=_RHF2&qsFidgC+6B^mWRZ-$dOm}x=
zcxhhCb%VUHYSeQ2lb72;#bUQ7IeCT7yZ(W&^z@S;EZD1vmV=Yo%&6sU^MK>B2U^-F
zNKSdVx`INnff|j_pgR386oig$csw-CFA#@B>QIY=r_|4=Mi4yG{fO%XLQb})Cy90S
zl?8<%VyRp6txuhtOrJc-ZxlBi>*)!TB^ti98vAD@4Pc1Izvn0|*8A294;Zr2Tc*3a
zAO7q&<KQ5cJh5(ZYK~<|!>4PKlJDH?rVATG6WN{%Wl}_>UdOc`{$Af%&C@m8xkj4Z
zY>Bh;oe!}#uI92GWv2|1Ca2KOe-n)ERT$;?x;<Ojt+rN67Y%9akc&H-r7CwTq>6t0
z`|l}YF6zO3TQHs;dpAq*g`ZzMr-5yn*qXLsJ{9@l53#z3PYEJ@Cg@8e@Sp88;v-{c
z#}%lcFZ(z|hMh~zz~I9dYX~mo<m8mnSO*PHEeF<lb^O`2O-yw7xgm0=EQ)5a)KK^D
zuMDe)Ov`7U-=ZbCzl~uc;h@QYi-(eai$%UDw`+JLXraAFmKq62Ox%v7@(#W7*v1YG
z=^PkHrtj6&8GRk+u+q;E{@wq0dG*a3Nl1yh|Kg*JO}=^Ot4un-^n;CV8bPj|dFl2S
zm_O%R9Wr(3#1nu2;bRk@NgMjmaF$MdaJ+Y}s$wl7lAb%Zv}Iyoc45D+=Mww;xip07
z9z4hZS@74Xv_Mvh%*=!cp_;>u^Z>ZdS5{Vx3=L6`q=BEi&)#TU`N2_L?T*^2CX!O?
zCoUW+Y=O=xVOpBYT9L=jC28idp-)c|QC%$&X~j$)ly6gCXz{6Dl};i%ywg@h#8_4J
z?Z}7LFWS{rR!g6QA=fMdKj>%<3yH)b`AUG#PWlRxHcVXke;pG<(_XD;Ok3LABuE#v
zCdw;tC%s6Iq*5Fmvvs~B($s`#mE_#Dx0K7!vi~_$!^u6Cd^?;=A?u4H6_qMDtco^c
z=Z8kyFRv~!CZV8pL`EJA3{<<jbCGj`an}i{_+yf+cm5<*K1O>y@I#4ND(V?2_I%~g
z{4m4IR~*PSwZ9%Vwx}&HCn___hyY>2i57bHg>gcKgMgrzR$`P!v|9a_goFfWfD@5p
zM$=IccPM!r`9(z$1YuD<^F|9;AI^w+da|YZ4qrtD|NM+=dd3bX(0Lwt`rvcb_pi4?
zH<_L6?Nh~*u<@pH3f7mGSvaLKw65VRJb3T`@EjC+cJ@_&(3!R~2#3WK{QkXJpKpLe
z8Jk)juYf(ZWRuN6;&W5e4>Z&TVq%Dtya|nfz*j<=-l_5+Kv@gT1U^=sHGOj{&+Lgy
zddIVZ+^M48225;obQGt&{PD^l24Za;Ane*SJm%-$ii)fXIZ!&Eix71bUVO#`2Rtq=
z?&NeQGnOf108w*rXexIO3dKUowzj&5t@Q^7D?*6F<bWlL6crV_>X$SC6HcJh?|JAg
zRx88si7tSBYF5xfEEaxzy{MT>KUj>tEik`Px-cs~lBIaJ#}^MlI&yRI$qTb?uW2Tv
zU+mvV)4)Pr#l`u$S1S}&9{~67z)f%T8fJTEI~syHl#<l!NP_^XE;%+F5XUCJwq{%G
zej7o^$ci&$zZnh6PAO)+A4roVL6TEY=4;;>j4bsa=8NlIYN~z9?N=UQ;P8}w=!<VX
zJLYw_H#B_j*|%m1MO#vJ^WRfa3Q!UtV&dXPrL*nr?I4NL(lXBxE+1249uh29Ibl{l
z@#~AC^V2%R!W9Cj-t&m^`ZxRUWkdZ%2WuR2;QrK36bzK2UAyzdT+WrB2;Jv&%jWJk
z=^#v$fU!8X^b!s7;getO+4JT%A*H8LRMOvZ>UY4<;B`+=aZz`CBv{nL@2<X}R}V2^
z=!n(hCoV1`z9;)YF#!O0&=J5C6GMI(r&yo?9)I-sfYSqVKljMq#wH1(v}8Z*Ha4Q|
ze@sbp)3kn!o}<@mYjWJN&HfvQg01bVkWN)~dU0`g+k)LlsvM(gDJiM1-aBi@$B@p9
z8gTA^VfQ*-Uz3o?^SbZ66p2LhTnJuQn(yJd&epFd^-fJF2o+V*>(|7m@!L`eAf+B|
zZWxH7VmAQa+1bn#6z|3rxHTI`<!4pBmMAajop^Y7IDwZ$Wbm8JE<y~rTb^bM-1)TH
ze6jVUT<^Yej=~j6Iie29)1z>d-~6}k^H79$LM0Z@18=|7)UV|a<02#bO3a+<xtM~i
zYjvhF8pJI2tz9ZWBENevc4YdUzX9-Pf^kSbbC$w~^z`+`-8^aUUGLV;5}Etl&1oQL
zc#kMGb^3-bAV&?2jp0Md$2*^#0WgO8Om~?Sam}KR`^U}9bnZHMe7tO+r)OdB7`E>g
zS4m;t*qBCT&5l^L`{-D%t<ATYX0tA<o%5|4h&KafDJaj#$oPVQYHZsKoq>hnb)=!^
zL}Zqt|JQF@y_C$vVbSqpnhXr3+`WUJU*5WX&L%KAG{gf4k>G_BO$w)}=~l^BdDCBA
zhpoXHb|UQ6r(O^-w;u!~AH*eb0zdg2yO^8Lug5doa>_I)<>Opbb%7w}=Z#+IU65IA
z#Fwbc!R)X;u(iJKHcg1jlGgL)`xIJ0&-d~*(pQUpCL<6R*6<!M<EKi>8A{zM$}{(R
zR%OEK-EGTOR5U+!(?zHLTe{=GJO6hOTn7>MXvx2Mg@sT~Mdk1?jyB@@-egJ07gJOJ
z?^6&9b05Q^+nlbg%+9tPJerGpoL{Vdx)c}i(QZmCJ!pAhx3SW-UfqO_E)AmF5VOYj
zUd9zb0^;MNK0K~0cU~#~C~K6ocf1n+_pkrjM(MpWDP3JxIo0vT{^1N+I&rV1;kT3u
zt2FW(^YXS5m5;9^-6`j^1K>!Tj^C=IPuzi-3M;he{FKh+4#XUsW?GgbC?9>54zC{U
zRVnx|e&|Nc1LFTllKxG$g?K)|>17dKSY5qFM&@>~Y$Qvj&BJnL`jzSCPbmC#q{Mq+
zHNws9e{seyO~e@c8CU|`euc0)GBX3INoEU~iIugqw3L*TlE}AUGMJc{L@yjYJ!|Xh
z>uYL&%2D4V?C9ut_3BlV2}gJLLr4>uG}(ueZxCLvpZ-+de)UUjZAUXD*TAxiue$np
ziSdLsO8LEh%UiW3+dR_P9}BaymvWNmP1yi5m6U9rk-y=8VUm#gx0ESXi5YOpw(-MT
z?$V{z31(t)`wq@fm<IwwcMJ7<L}UEdAV3IS7VFdo*b`kBnfYv2EXTDJR|aoE)EfvY
z@gIv}9Msj+p1Qf!SKyT2JDWT>`UGTX@ZN<}s=}^N-zj^#_O%Hghhel3qDll)^@a8I
z@!TeL&Wj4LvxUsQ6<$*Kh-79)3d0Pq12F)8$HluO>;d`UKmWr0$KU(^`f}-}cUVY#
z{1}idDagnUcA8R?lex&lMg37Kf6wn7Q&Lj?j*`*FMn^q}uTka@&eww?KzgEnJvBO4
z!{iXD4<pMoFJ*{uLjd_KiZXL76oZN!UOO*$<HcX^5kOLf6G$8t&k+^%!wiBmx&-O?
zF-puNtZD$Mzi2?y#s<=ygxCHm3<Beh6loi19@q#>!hBt}dk;9vP(!J1Uq@me6WD|d
z!9+UrmqbNu1`Hq%{y)9!eVYI#xt`ve4K|NJ1@At`_ziJVTnsx|%nSAVS-@}LxI3%O
zjyo-M^1@|?yK~uPmr(3_QRG;epe1&owY~i)5PLl6ai!Twb=XNEKMneX8r=;EPJh^6
z&gL5b=R==-zww+%2KxrlC9^UZgibEGmjACWA1U|a;NY~sAmHcay;orw29aiuJHHp!
z)|74dJbzod<PwkdmzdPUDPl=CRgAKnA20YZBU!Wu;w_;Nu!fQ}Fiudc$0hgc_O-3C
z7cSe^Tx8i{@!C5&`W<c>N~B3X>U}djtmUc5OlM_f1q)b=HoAKe2OU-B*j9m=c;=I{
zEF5C(3o)*f#V9QK@dkE5TD>XRd3i5;EEN<LU2CU@Bu@Lke2LZiL6bEzN&m*BJBBGo
zso($Nyr!~}U)XnNRveN+j=PvBpe&rOZsma5qr&$<D29)M6a_;L+ZxO_+4oJ036`go
zFU!!atSD-XfBUA*{$_IWe$EWv<mG?(mXvPPRREuenueyw25zUoK-9}7A>=MV2_&Vv
z@Tc){At51(H>Fl^hNnvS7C@jQX~dC$UHN0feX8EtIJH!<V@On5da9#CoHPJ%vi{^{
zkF^GAWTW0N@<D~KzFEx{i*leWz3Y4Hq~u$gkpbLGrdzivGBex0d~t>-YQXIgX##eK
zkL)WNZI&R+>V`58WvD8x1}2+)McH?wzBoFtuS78@s_Yhsf*>UnKGblnHKvTr4R#v5
zF5&YBDvkB59c+xlkvs-a??9Cinu^g)mHIf~YM0(C{{hT9$myrOZA;326t!#Xz`hm5
zz@zfT0-TS*{(j~U!qkLG1Nv>_@sLmtG^Oa|+>td{o_Ft(+2zhqYE9Z|52na56RRii
zN`sUs3rk49Y<h>NLjC2UctL$b-hx)h(HNp&YHAU3j5v6BNunMl4NR|tK)j=+MT#(#
z>8*;#_Vn7uMxW)aax0keoMLjA7Z4%gArFUG9QRw5e?n2{EVKWe80Fclp%Wzwi_Gxw
z@Q{#BfGFXKhW6O#vNB$Xw*Eao#lpk{xHy*oZJ8CsH15yQkHV~IeyqwTV3}493<{c}
zCW9<4>_;<vA_ed#z9l#v<jhjg=;5GRz5OQh-zN4l0@4!f9MXT>Wf`UC|3AK5omu$t
z!v%_gjFT6ZmJ0Fmt_D3LlDR;=WL5vCXR9W0@$rJN_aGS)0^g8r(cRGzvj<~q<LEeO
zRS8ikCQhG~!4&Yku2E49CGchC=Kh3>)6~=yp?|9qN<iffWY6O>%-}cIpxIgJqn$ZA
z37=<%h7(YvqQ-s#6@lsa3k3*)A(<xO>$Nd<v*I2sNGU0Bp3n7-jqM*iC<kL5ApBRI
zKj4<!nWzjMumYf@@4$mTJHq-mA;K`acMZP_Qm`Q184MFxd?@o!cognqkEc)310cr<
z9UUEMfe`Y{^t3`WEdjDIR$N(DHoyDK|7<t3xmhwqrq=(W$;Tf<rWXfplpT7(Bq%+K
zgXnR*P1e=d|GB=-VWgr#N=633S7A|65Xz#V@f1r1p95mOO-)S@MohzsB5Lc17_NUQ
zlTr2lp}jp2Bs4W7{mv4Sc?ATLMcmdQw1Y@-k+U%~!%)pNde^|U-o5}NY<x<AyenA>
z*eLV2t%Bdk!B>DPRh1PLK$bk*o>8@O^!L92<E6W^^R|$X|K5NQM91Y_UH3pW0eIbD
z_00j$d4E=$DLVr0!E93S4WN!G0O0&j=B51o{aNF15TNcZEGz(rR8T-5B0O9aO4fl@
zrDW=63gX=m)0UG9Za`C1TGPS~jPbyS1xn-v+_(S15&u8B5B?_)$6TP{8=L8$@5e)a
P0)i;ZYsi(zm<RqZIX*s0

literal 31116
zcmeFZbx@UG^gfEBfJk?PM;e4fA38jwAl=<amvjh7mvn=4gS3Evh={bbbazSTfxG$q
zzQ4J1XYSlT?#%t~o*9vN&%5`#_u6YcYpv%w;Yx3%G11A;k&uuuWo6(hNJx*vkdPjI
zL3<2-Q@Dft1^oBSQAXPZ3F$fB!@ozP^M1ETNHj>YaB(${%)NO}Bl5E>-hYP_Ee^fO
zK8DR%ALpJulm4M!g@YC>;h=;5bmsA_`|QD+?PU8y`{%9uwXV89M(mz*|C1QqFCtKp
zz>+|FgfS$$@-i8la?*Er;HF0_!uCy!-E_P}_ORioOgd??T5NkqLL6;4n9FCOFNN)o
zz&Eg`oft?+N>Vrj;0N<3|NjsFU#`JN_A^ZRsM-C6c3<z(Ltq+1)~x+ih8+gEO7cCu
z!(WX2&L%Vs{Sjvq<(etiQbS1hcZc_P)5Ctr<|B-ck)Yk#UOT1Tk;KEP9Hs(q{X(8{
z;l9$YU7OT(cy*?q`H0tU=CqsLFK%=A+kN;Wq`tWfhjxBn25J7M|1Wk}D7vcsdgoc&
z{k;34eyv$A_dJG}l)t}!sl$uV?Cfln#m8dWVo=5Wj=Qt!!uF@I@@BoNMZ|&oODwOp
zg|pqMlr_I!ZWr_Jt$I}|i%-S2U!*duF5c}gre|bCORY9<W_w@$Grn4)zQ63zuCq{A
zSD*LX%&O6{MO>eis#!1p2%c?n=vWGTzIL%U-^zCa+muBrQc_eL?q}~1T3I@nZ|&#0
z&l|B~C1N?#m(TC)>}+go1OsJ0!WqDyoSwdlcJMLnPkzOgV$&N>*Kyg+9=5gA8FaDW
zeVF`87kq7VU-Z4u?6{tI!`}Au#79>*L#34BVGs>&GR!BBUt%3Bw9Ot~JUj+bv{e3x
zRq3G#SsUVfHlG}dB*oH+g=$c(S*Wt@+OiG1+8E5ZKM}pJ`^3en+qfq!dObqoP*PQO
zu(ww~yAQ6*Rr=lCQ#UMf)2ry`d@$DxKb>8?o7_B!_D#xUc$BI$V#Q5@5d*v2$Z#0S
z668yhe>)cQK7lB*J3@(r_&C)6dMwB9db^mxcUz1G2P>2T;RN$0c$r$=c0L0Z@@=Jt
z|K)OM)0}gEyYCGwruFu?pU-Y)%@I~~u0zgeKljyXbv)bUNwT^BaYByJ&0ZZ?Zm%|j
zJN&oVLd}lL5?7d&m6cZ_EV82{53|V+;*XO-3sln7+&uH@-|uKyQ5(3wjJ@}mP}kIV
zn>J6l+?#di(EnYC93V!E$Fzpwn9L}pdOlgH`&67JPw?QY>0mlfR#w*h{QPqw)~{fO
z5sqg}7jrJdc8l$Z?*YgT?N=L{qW7LxpSzYF1+tD;5jPk2+lu~ucVUbX$3d?{v7(*u
zK9uqZq%Qj1>=(XC(W%sk?cr*eI=(?j$54O~yGv6Q!Q4GhHhO(04=;LFk7NzW2)UwE
zm2Xln4j1q1au5}{xw$PZ*P)`f%Q2LK5uRtID@Ps!7Ih<5r#FZPtHlq0f;;cng{dft
zI1Q!t+dA?%XuDXbs$#nuJo1>c;--Q*er|0QB#N|~sec!hfFxfz;yWPdejs%C?ZlQh
zV`?{R#-{Cb^v%<A2%+V_l-Yg1!?p{~jwA+J5)2~e&`u*9Fd^x<K<#Y4>Cw@6*@}GW
z*mU`#6+K^TU$CJT+Kuh)cj-1&SH~$G-@Gplhz&f9w?_*XZx$}tTK8(3T{ef(dEVG#
zeBDU5u^7ta_dehAK4`WCYwOnPdv|+fnpsfbPvrBY+oT4U@C2NQb))R#2+yf1eSCr9
zsa?bm;`UFzv_z4FxlFnQ-8?pvdkbyedDMxtOa$nJcmpM=#+eZ#JwD69#8vvOz!;B&
z?vtv0)`$J?P9BWVe(v%+GB&~Tj_QyZr@5kd^Vy{CI%?Cr+pJyFd@^E4e)4XOLG%h(
zG-q}K1<cV%;DB`KEwE$)blLcy&?ujut4A;jtnvwgb;1+D!*8m}${cqWIWQ;4375Wx
zM|Y+St@GPJlI82&YX7^d&0*2|+h<uq6j+gYT1L=(laM1usqWhuo9g%O4?aZL``l+!
zaPb8sdz0FGuLjJMMe?h_cWoB3XlDw(7P)@4fm|gkIcpgI;p^N#m!hN7)BG!=Pc;TY
zv8mfnh6H>3hL@H;cbjOpd7Y6veB&57>9@l!NzFxznChv}DBm2)YIR<hg9rdCovPGr
zvYTzp)2Cb-7@eHtg~7CzWPz(OH#aZJ(2l1~HV<c1<q$pki9alKA|IgnB2T~7Ez5nu
z6aV^mm{Cm6>eI>m{r&y?&5-Cnv$MMT`h&F<^Y%Ggq7KWQ6c{M|b`fNW#drgz$qh|S
z4u8KGM^XRgx1^;O^}E$wyq@G<Boo{Ay=YxTtPpox*2MZ&d`f`lE4R*PhqL4iTk>Ww
zm74&+QUUDr*4}-8=KcKpDa7#o?J#)eiHRi20bt*J82F;|kC9AaK9~RAJ-6pi4KFDz
zEzQsGGO3BWzg5K>@M`lLr4Fr=?hW(==45)`!MGW<c5~3`AmD)hnH4SY-%fe;0yr8^
zvn}CJYzz!F4GlbHixxF$>7GQ^vl9?-girsxrsyeDValeSYLc_GEY>I=6pHRPc_nl&
zUK7|{Q&Uq{=OW?#>q$^&2C0QB6r8cK6a%kq55L8*JjNz1q$te$q{I)4hbbM@7r=H+
zx+8oX$Tv6C(&{>;e*rg!f%1A8xwu<wNg_{PwXuJl41L2@(9_b=a=G8dONc61B3Grf
zLSy%GHnh#4dcqnEE|rfGgHJ#DMp!~xTADeD>_+%)vCF<q^~fE<xmzgr1U0A=mEYbK
zdVf~!kGSx^zsRJ%8ck^*pPo+hrR74$Ho`#LVB*a^Lj~UaqTCR{yjcD>bEo3$xSxH|
z=VUM#wXS!)<#w%CzFUZ3z@fd@ci-kXU6d`Q{GZX?GPb`52zCkm4e~g1_I$YSU0tIr
z^Bmpex=lEQ`{kZSMdLkLuCJMzowYeUEbh3a!yh>ExCH@emERvqQjbR`_1;t(5(g}A
zgLSg?zgJys$;3|VxfCUoE&_MpZSjdCmE_m!#2bJJ4uJ(bT^+B}Cu|2kXM=g2GUB#@
z-6Pg_9YMwxqQMUE+;5m7VmHv!*Y`ac<g?r;6uSM_b#`_(I5?Qreggc^g73xr@v2XT
zDji<9Q#Pgur-Ao=1G)Xz+<!(EKa5>kToO4uZ-3$wpy4Rf;?>OV=eswjeoA0ad^k!|
z2HvmQZjWcOsjjkt$6MfA^l^z&4DK?{dYh7^GGe7R3|%o}*$4=D)H*McZ8RCuRc{(P
zm#6@5bw9j2iuD)De)A+qJfF5u3gIFNi5vFW;No{L{^ud|vax;Ax`8AWvsgXK=x!|M
zuHNVB_~~<^3i3iU%^a)@K8L%>ruKEWR(9YXb?uuaMZP*LiX`;bTMkpj_S6}7g&5u)
zr!XjH+ge#&&TWcF!~O4X7X5EmsU2n;L4?}};EK}e2M2F4I!X|}FnQ+4;{c={CrnJt
zU{o&haFQo41XC&L@hM?qT$ax9;}a7NMxB8Y%o|lzRX2wnhS|uns932_UvL%K-kk57
z`h2OYtE;I|E*>*iVJ3=vM;lHrsK4`Xxyxl^fPa4$1qFow{pqtn$<jpKd29+8jpkMU
zl}~#IV&5UH*to;+C0eO6p$jWf>uQXkGfLg;{=x2U0(F0e<(8{}ym`nN^RK1qYQM8`
zFmGU3h8*V*-+i9zenWJDt|(I1n|;#`^Yy-@<L~wU*Tw!`ApEcMauljuVc-A5X6<jk
zT#01SsQ8q^{uLk|ax${O&Lv>ghhJB*JpCh4seK%~LNKQpC`?_-WHT{`tiaB+1FThK
z#&1JUHbI{-?K{A==$~W%HQ@ZPnv4YI@o#A$jY||5QE%35RqMgAhY+xW{Xayjc6Pti
z<k~Gmj12&6<)Bw`egA&Y6(-VXqY0yEoVJA%L`zNN$s}M_c7<XOX9>C=4rNWFcHAQl
z{ZBF$*I)<ZxoQ#0rCZ-ltfttm05m{7MhH@i_*}+GQ#FIwx?cP&HuMFal=?tCEhz>{
zvbl8ZJ{UaB2n!_$CFs$A0anVqTDQqw0-1)v;m(KIQ6g~KXQqjpm$whwBiq5sYHNFY
zWcTd&RtB<ja3JEiBzA70nyXY&UF`r)&~e<x#s=>Hxe)rmTMrIeH?(|0;F;~H$&rR*
zl&4}-6)<FfPGHCg>}*=}^VU|M(9NHGr%0cmD10_fefKXE_>*1JfhlVn8*jwfl%jAf
zd^~XJ$m4N<7`Pt|4v8sc)zHw8MpD`I?qGo$H<@~&$^bYa0O$BxOqBVPv`79bQpjk(
zU1zt%<|vn;Eon|;YyQ}1&yyza*U?c|$F-<{@;G2G=~4x_mS>(ta=NTUAQm*}%+Ai<
zUr%*Bz*zuUOlr&m`Ek$&jHV|S4Acf3btKU(vNv-;&S0Wfnioi4%eJ&;mW)U6B)7P@
zxU7ts0Nvb=RgfZPx4PqQs^g9aw@{@N9Ok-~hnD~sy6fqN(@WlB0jHIp(R>TA?Yd6V
z%Ia!>eunY~Gi;t4z;s@sML5C)Q`JWp)io2kl&7Yf&-R--pe`Gs`8(Zh-WPZK4*oZn
zJ)(Z6TO&T5KF4dl;LfVU-d-h*8o21q^Y+bYI{71evrX)aWiP>6*<hmBF;C=RZFq3^
zJ%7wK9iR-tcXqQ!jUo=`ZY(`scuY(Tfco-K;LGV_@>>LS8Y0>e`$dwGSX4qKBUZ-<
zB=jebg@uI?2*mMS>dF!g?mBrky&`6lPGwIl^<sy=C~ouofue>wrR@E^09Y$I+Ktl-
zfHuZ{l<f8X1?K>y1GEt<ihjol>FMdo$;o?~)gTWdHuO0HYam05_mJbP({(<;qraxH
zl>E*BVF{oAHGL^n8dbwTK|6$ES)WV$_nW6w=i(BX^4gDOqZVR*s#t7<k1MQ9`yALf
z;qn*zae{o1MLlQ#<_M6TX?<aTt6G-V-H>3tM~y@-@P(9=lnM$84<4OlegI%;^_o=r
zw1EP(ADb^_O&G`r{5lM0r%RIx<ol>Z{q%4??isu&9^-#gS#<5C!Yo^uuB=-3es?nX
zIq|U&`*58X09ag4*>Ime4w$a9%wbMP_=THGV=Law)R@-Wqzqal+?46FX*xKt<9Z5Z
zD}L1rIbEtYf?B;CefF5y0`}_3iJ6{LH{t!6;k|j&Y?JNu@c`Fw)bH-=>+8k4^Ckca
zGrE>NR-^gWDyabeym^SQ@o(7D_#761&lNUN4+T!w8AkD+(*<uTD=XVt68C0SU-J8G
zE&l2BTFuM8Mu$N5PO*Ergg`R3C*P%20sgXT#Z|z@#)gHNxmH>-Jn!-(=U(W<6%5eD
z8}KPeyYjq{W#eR6)PM6I)uL@%TU$}5_<IfL^G+kVQnhS*h_eW5|7h-OGTA<PT><B{
zuxZ;Ntc`_*1*w?)YcEKicAZXA-vj7ATIo)E|A*IMA+s&SjRr@n$zI<L%D|dD(zdh#
z54<}w1QS?G_^QX&eAd73rn~Coa=pllgQg{^oFbPCOh*E$C}nQG4)V%5`v)wuj;k9<
z^s0|)#%+*is=lcS8w;x)q-1GW+aQc|w6@;-495p>aWfpBA<SyJ)*M8W1jDxd#@QHZ
z(F5mZD*s!j=CR#iVuP)N0}bY+RUvX8ZVw@<jBW{rx8qKF0p4Qoo&;Fi&^2Rf#M_2H
zQCG*s<)@f5tIf>~*xspy4ul0vgCo$;(D1S`1Op`qq@A`K+rUMnkY0#!6Ab8?7k<LB
z2<n9X642{4i{IXhBE?8cl)FMa%U7mDOSt4(J^CtFm{-LXv&u;<>~*>oLC8`|EVRA9
zU-`CB#y%j2r<^~@+QGrF)=YMT{k=fev`ad2!1tL38!mmx2pRa7S?MQrw6Z$lpB=x-
z609eDowxUlNSxj0mCR38lZ&UlvMo}ROOVg|Z{?5^iDlzcW3U7^DWtDk{YJS1zmHEa
z<tsx#s)s3OPM@l9<Y8+1^6BFZ<<31|4c6A2c6FQ`i#eq8`xiH7UWhLvLkPs0Bb1OZ
z1TKMP0Ws0m7%ITM_3P+0gXFNk<CPvmESfMTa%QM&_s7e`CpyrvD-+Zi9(%R%mM4Xg
zBo9`lYF6o`>2q9MT+Bk^aw9y?_jZhw@<%|x>oKnR?Z5r~`}Z)TY#tj83rH<n8UbYt
zZYM=#H#ht=_Wmm{MtM~?kQvb8IkoD|h<y&(T&G425c{4gQ-c5O30Edjpy!VvklGBC
z(IAbjEQz;Vu0)tQM;ZBT7p6G>GT?2XQL?KPNrFI9`X@@%;=dKt%~xTEZjMi?*i|YM
zNI!A$_wSgu*JMuWHRE)F;-Hx=G+GX4gZj`|>*46j*guGiG4qs>+bcMnH-jV1wOKET
zk}^?-1S2GKR+v)2RmR>D?|qBeH8w$DXX13l5c-BFfM7YdYYZ-T`XFi|#*0OgJa^8#
zEE$v340H53KpNr$lF(^F7em7wkvJMp*1r|nGuhc7;m;CvTng#9*B??VpGa?$bfv+8
zl0_dc*+E7NRaDd`!kkv)WImc|x2(nYVT%rMo9DO;@xLwR8lBG?-L)Y=Z(bB2!Pqh3
zpi-EIVxet3v$3@m0@>CZUt&j#Nbk0Qd}(Rw6wB7p&Y*hRv}CKOqP;@^-;QbWzomrr
zypK<$9B826Q{yO~KnY&)HoS!l=2TM>4&*U9Q%H}LKv?KOF`~#s1R#|k!OypM4U0oi
zTFx|PYJ;hK6T^O6a22ZLF>)><*bp0hM3HoxqthToizFxSHHnM*rM7{M5h_{E)5>(S
zo?>`@<pWlR(ibJR=L8gF9<cqV94suXA$j_D3qr}}TxV87RPHc}m&_ZgT>0tgCHboW
zt>n(P4u*de>hHJZeG{K})r^n!M4oJLr8{!n-Xe<z3JzmPu_OeydHsVE>_=N0oB#s_
zY~=jxY{uTf+kvcIBNF~p<bV(KL<txu53w7~;sc(Q_gWmX`vU*<<)7cx=Wf<^>z;m5
z5!36=(ZY>1WYN}i;X6C7+eU6Oqe@VG4h&OFl+x1h&98Ii&VF|%DPQ%{Ed4q}DX{uk
zY;Sx-%5@aru86aGAO7Ch@ZT2YDvY4e_4M)S=@=nO5c`z9GAcHeP>)qX5&bVSys_R_
z!Cmo#MFZ#$C`A~Qj27?jE~HcD{W|VZMK3!akSPH85O9p5M;HgQ`inAZyOzHgR6F=y
zE{jcyelBj(j3cG*J)vzv-J36u?U}c4n)_e~Y)X5+Wo4~5qT1IE)c6iN?nQ97Rhb$a
zlgUMznDN4YBhO#`k`nNY4~r@tkN23{c2#j;&vJ?3Ns1@$LJmM0u4Ns5h}IHN<irmo
z;m~G{KnnB?e1?+J@G&bNWaO(M#~GuTZWHE^+&;P2ULEMV5b?D6XZ$g5Ge7^5%HFJF
zv8bN1snOc<zYAUPonn(sJ7&W{lZmIbEEwJ_-{1N?;*w~`i#p85!;9B5!XWB-6pqi&
zvJr%eJ+uU~wl*2G;K-U&feol>Sftm+QV9##OtL2Qx`MLpp<MN7((=U(C05V%+3tEx
zf<blD%x1pliB@*3B;>fqawzlN_03{;MPXS(P>|m1uF|?x`LzpGP2y^$-<`LmL8wzY
ztZDWZ=Fs2whxbF3rTlJtTFgnDVGu*Z;TUv_W)qFZdPaRVNIErI#owmkZP#PA6t7-9
zU*@FJRQ?!K!U&Q;(zmFc7)p;-xXXSw$h-%|+N3V=csPZ$x_bDsH@-PMe}uZ*WWI~I
zg$zNJY~E|jiw`U^rQbY4ii?m-UG2{uV*C#jlA86_vaU;f@rDX9+oA5HXK|adc&N!p
za4hI6lCwYWzMU5-WiV<{ZZ<s`f$j|3iZs;o`e!s`bbf=F33y~}y~0W)N4zV~-8Zvu
zt(>SEZ|J$6^akMBFlJ_Esg!x(9L+fSDX^MJ&b-<*s#7e62qtzu4okzOW2yA@^aoq!
z<uyheVtYcR{CX{hi}YK6+ou#83T6$>Sh+HFnFVU+$g7yH?qQ3-zydK6aLUE!<pxlT
zG_aDPeUcD|+xoV>0)<nPvp^%4W-o7Vg*dduKcD3<I6?!(V>WxO2!6G3(PuV*eg_?m
zWd4pGaz4N#>4|b>r7s{=Rd08;d#{{Q`rjw1Fhw_S%_>1N<cX3oQG(tUtFlBC6IHy2
zZ4L63*2JkZKGs$q85^t9!`V9Se>YhnC)-zFi268SW=5xZWOnmpduGP!ROWg4gcP@C
zVcv#?O1XBDwQG@7zH+qGYf8%T@t?Ytwq9*mykzV9ZiI2)7RSr0+nG^M!kT|`dg{5;
zr=p^wCcz8TBFLgC20+o1Kh#*Jc+5=7H!@E+?(@uys%08aWVT=Wkg#T4)kBn2rFCXb
zRb{#!O&Moz1KuU5^K*zXNrr;3k6{1Dsf<ypurO;ImR0a@6YzrV-QD*Bl@Hi~DM^k$
zRn>sqvgV5g7hz)-aVF;ftWAsFn9mJZ8wjqo9UVkn6<^S2>^lZ97h*}5s%2Bsw1{b1
z*-oQ>`%Q}=Z7()(gKb?pm6e&u7r{01y%ZG{^J$geJfGg5RAW}<04sBS?ZuxO+$sLH
z_+^sy8l7FEr`wI>;NbW;99}RMTuU?>k7<WIF(R&@&=(P*3SumiJbjG0X-z6)#M|P!
z#<ncK^SXi0Tei5>Qv*(EM(54*-=to{<E0XgZ^BDDIy!#s7M=|A6{9A53uOxg2U5g<
zB@)gs@QKr?Tp6(FJ#-hyA`|~|enu(iChs@@Np2*QpH`MvoP>hHj6bG+2A8W6pDLE|
zLU(FHogNRAb=Z<jh}hq{x#_Grl~rkwMK3HEKvYm3XU=EO%OHJHjGOGsmauQSxjb4e
zY>(vnPSP(Zw%u(~q*v|*DlO_gCeAE1MTL@C#nWSBv*c6T7VBg>Nx0Ecb`%V#4+U1@
z#zxL*a#?-~HQhI?{nNI+6-I6XS;XC|U_o>$lNno0lADTX@`8hc<cE0bUlB<%!Wk4t
zM+-ru*ELatQVV%FE`0MmJ!m!KWMW~tat&o6j%69J@I2LEB(S!&&g-GdU+rOOzekJ^
zo_xa=t<UxsL0tAYL!Y;`Rm{Pw$9iw*ZV-N}-;wlvYOba_hHkqik8i&Kpc5G{tJ9uo
z%bVZ7?keD!A9r+<upNAhQA(iAT5sGn0m|i_sMHlJuu*GiwSm%3aapX5nHg*G%YR+i
zpdm8wlvwNjA8H28r}-fPiXGFQu-}Pt%96o>o73(Kq^tfsNCNmlS4Tb6JrbI$HLlyE
z#&5*Lw8Ogxv<2DBPS4KLu5Y$87HJ_xMZeN5{e%D1m@cGom19#UyI&1nWn0nyaHL3+
z_Y(J531_&w8WIehu*O2=;l{yrs0+`^O6vl^SEIv_&2ydC;L5|p@n3-N&4O-6b{JWn
za-YFDOVFweL1}&vC~Sm%?duya>9he+@7w9OlQlnWbID*%1S)fa?1Gh<HZM`+72-~+
zEql)1Yb!T-Gy5c(dGM@;<YeDLH^*;=`H+(z`dP0rDom@*KptCB)mzB*Vt&IAA$KPO
z5SxB{$Ws@6%6Ug*;HFP^O_7n&qeBwqGH5}SEX0@<ZdUV$e{;YxbrYLHE1}$k$VWu>
zfD%fAf#I_CC-;O9J)N)m1)lPekQ2r9{x58iFsb|^vv^zHWMj1ieQb&$DoX<>yMD`Q
zO%E05ejyQj4DacJA7xXSo>M8nJB>aOf6S#EwYIkP4CJ325uca{9P>OzTx6WsmZbl?
zdXf|Y3RB9uy0e~K7MWyMl5nih%yzj}eXoo80psGa(vz#T_bgWxMnv=W-rUTZ1J`Ep
ztjXW}buAe{P>lPrjI-$N@G}y?r;AE0@&067T+-;YSKMlWVmg(29N_Fd_x9St&-rXq
z#c_MtlYbG(MV~M}H()!bb#{`2A16YvVr>&1HnO{}KraCTx|5o|{)-6bYBxi}#Y{72
zEhra;@CgQP+@J-nqylV!n@?3?aCGQx@z^^kNl0LGS(yl)r^CIUR4Hw+1c#3@ap$IK
zl++bSnzyWMz<23_?jk*y2&1x!+wt`j|C`yxM*0LbW%1qux7yu+u4!an;P}7)T=hlo
zUMex3h|!7~?ax(x-|#Aqr19I-P}b>>=WyE7(|_;Z-=%S(8v5}s(y55#6indZ4xeZm
z`ZoO77va&22uo%w^uM!&tl05NPCm&NqxsH-;Y5L3W8ng83wg!`D|tivIN$>fnv^&R
zOKQHdr$t>$Q3z>+m>jT;^?%E`0j5%>^o47fylWBD`)=%CBnv<MZ!Un4^G9BLTF6FO
zd6^WZ0v~;XS)H=JzPZp__~a?s67adW;Yu!C>KYny@j{&6IywwlWD{Lli8CrSDtWtC
z&6@gWb3v>WqcI|o<jm+b;Rs&|cVEE83>l$LdK@4Rts_&&t5mSIEtsy;HLb~|w{Jz{
zJ$)`9D2N&UWZKq@dxyS(3YMw<5CXo_;{^%m8(msGla1d9m8iF#_~X*HP^7ZI&~`IU
zJ~LZ5Wyk>8*;VD^dG;m+UAy%~^rz!1cx7@J21=Lk#oTLO-!5j0U=yo@PrU`h5$Fa4
zOl?9$hN=m24h$91Qc1a=zTKrH86Ts#Sd8<<l@&?x`y~97q0O7H-j3$u?97xc&5cM~
zHL1}m$bSu<#E1qWsSxco{x<TLT7)fcS!JbMAzt`Ly0}kojEAfW98<&6yNq?de2}LP
zqV3fm?@C6Hby~RmyT!;?#@a~MlE<>(8TrGG5fpIdMVIjIRJu^?16Rg+Pj6q}&T7w9
z8kcN72SY+>pByBwPmYY_a-H43xuPZxR36BmAsveYxQT=x8(KLjqp`_`>)EzY*t=G0
zL~m_n<*^ii$|-qrQ{CZrTkTH=37)OY;d#?!pVinVyh!)cNQn&xWT|~gEYc`0A=4F#
z(%nl-MlKenaEQ8oRw5OVy$pS`75CoBm5)dr?K-AK#{RgTarwljh8bR5-+!H`C!52?
z?d`cU!Yrv14@d0h$&nFsWd7B}IEHUg-Vg3u+Hm*}Ry5~sy*J}iQ<iV&*F_bKIeRnY
z>*0`kqer+*9)66eH`fGLLCx{z8TC5+spV}3{HdOPCB?BhT$_;mI9b#$sXTX)&w@ES
zStrLUWN5ihHgMzqj^^d?M27vgFAjJV+ErsQL{U5a+idh9YyWRGQsn6=W8f`RNnkOw
zm!Yo=GWqjHciF+lKV%BWW8>dHT(8MOMo}KOkaF!Lv#lSq7rnYfygn;`E4>V*6!j~M
z6UyjKe6|5vZTkEUR6Kmx^JecG*xHU$IFcYCxN`@co#L;$J_&icb7#CdajMmc?kewk
z=-<!H==8akp9p1O@i#EWYp|3}$aaWMPZj^xK{JO#K}<u&CB-mn(4pP_llvMA(rmJ!
zK+j7?C|LuEBiW%>bT}Z`JJ6WfzdD#^mOW!8RM&`~C{v^bP!ZwhPoMhkU@p>CV94YJ
zB-wmboATZkR`JQbn`VWKY=~iWFh>!dM69RO<in2}H~;n+!T{2m#7b#s*EUV+Be@R>
zv?c8ZBqAA%+^~|Ruf``qhs~Sh!onSIhp&u%Ma*d>@~jkSQ+FO-;>rbjEMd5D1n3;j
z(2PNDn9Tk@w}U~<McN>Waw2i(UBBU7ZBdbh6%i+{x`xhZu9bWtyj9<^HI#1Big&g&
zR=4uka@zIX%^~|6`Z`T|V{P6z85~@8*#adQGKC+9#Hc80X-Q(XjU%0z&5sIIJV9hZ
zSEg;v3L~J$$B#yN{HWZA0iY**3KANKKI2ad_ZsYuP3uDEBG9TS__KS{(i#+il#w<L
zwHz8Xi*K4<Babo1EVp<WCl4*x(J2cjxpMDaPf4H{NJ^r`R=Du$wWiBgW{>Dy_Q(B>
z_K>|U#mkz%iA-!TqQ#{XxrJKQVR|oyV166%(7v|IT)fYweWtH}OrRTL9JA}1FD=qv
zpcIzWLzgCBZ>Q__{0d5+kY)^=C}TuV45bS^zc_xvJ|&1hO+Ni;06@NH;Jhqqb^5GE
zyRIj_j<w^hW3=e`3pyKr{?5KB*UY(gU;63@%Y0X!!cF#q#H`Ig-$Hpe0zHz3wsynk
z25~naBxX=4a@&b2mrR$<04ePL(QP7<GYpYEK99*8Bsx5xhwO&En4Y%f6&&W<b#;ca
z=<57DI7prKE$i?Z$|UVHngBG8pN@L0Hr|XPWa|CCz3>)(7YSCDdz#O&zP4M`b4E1H
zv3fXRo9p%tw$P`huOEC!iagoI;A%)UHQw$nzH}_~dU}|*fOuo=b*_DAzW0q=vT>Mk
z7^9GB$ZnFRvq9jqP=5Z@L5rFC@Tv}xi%Re<q+{_Ov|lT=Y9tI2Y_{lH^|S2>(4N?0
zbPZ&dhIjh}1<jFk3Rz;Aw}eJZ;VYjX(~MXp-Y*61HF|mG|I%WmKOBLRMdM&t=POGV
zweX}#WzsI4IBl2Cs;DRJ0{Q}1bCLyDj?ATx2u~LVkI>(J35RMLv_>^%2Wa^OL1bJ%
z9V*w@UoG5l+UuJ_*Ug$a82bfBablmx<RGI7hx&C>qSJzq;l|79Cjp$VccZ0(XpMuI
zNXP?8KNG-5CTGgX+67GWIz)RuGdHAr7*y31n5i^ajfAHdZj33L0cOQg$zY0^JZzws
z!+~=I6sIi|4g@*UcA|f=`(O2E@Y=N;_p$VD3nf*!wPqR5*}K!=e38K$%zP(P2th`k
zeqG)3qc*~on7ubLou4969?Pa&lMxc|z0_#ZTD9}DZ1%C>sU<Y+kzQ@s7*m162OC>?
zN|Awri!8HKFLmam`N_el@h}7efPaI+KJtOuPVsothV}*A*yr|q8SmPbVCnn`^7Ifn
zrle;%hg&20u|mgaxJ;`@^4%4H?lW7@)RQ(<U2IcL*=Lch$%(;i@!yj_p_S#FoGJ&q
z4*5DS-_pgIQKE9?<sHtLBsWp1&+_d^iaAR@I>m5kO`9Vl9PaV3<vJbN$FGo!m0%T<
zhkh5Wc6N5Vla()(X?YP1O3KETsWCBF9g7ZlPtrMF=g9xMxprPKd+Vc7)Y#CVucfbW
zthHWWAcenwNUbxO5jL`2;OJqM?o8I>G~I9B<L%vjI!t^PMfxfzNJ4;2wpcaN#Lcud
zuPEewD=651`(t116VzcAPs=Bv^!hcj@|W_+euKYe2YURePm`FpFQ|pRT!*uT;RUjV
zf8}4IcKx|N;~%>9zr#lKcl@br>KoQdmh8s(&)&i5%`cvaFklz8PGW&XkVbc6izbTl
z?Dnycx3u7G&k0y=kIFz(-!aX9b6U-{n_cVC<1YJzytfj$-G1|{Jtv7p6R@RJ39(eY
znlT`dLX~PYDiI$T4jQ9&GBXQisaoIqiu3%#lXruoOkJVE7?M2l7T>kyV?U^hXlJo4
zpJ;R1<ly4smaARYq2w^!TyxggrSdtDj+Clsm8(AwTwGlqm257miF0ZO%e?riw3M{m
zM1z@$>%+I2nmBV*#Y`oOj{A(o#L%H$!8hmU=h;G_IF%TmXv!5kLd0Gqc<_nZnIbfT
z^jC-x3gaW$_(VB6u3MVb6DDW%_p;?Y&MQyo@MJ?ub-w<25uqZ$<tm_$J*+AndRy-_
zRrUS;#{bxqjYzexQ^-c+;OKAu{$Fuoy-&Vk<r96K15Om$mGt5=rx%;wdguHtCQ(#D
ziwx7=J;unb)in3h&lC91owK`k5#fH7Nhz(sUV>ho+c&Pwe_g`|czCN;U?N=iau1vP
zt%+|;z1qf}O2~N)5TjT(-C_v`G<HkRuR=n0%s5BK^85VmK`-=Gxb=Hx92sCj7oDi9
zx6&k_zX}T2q@SOxMQE>G$!NTMMT}PBT=@v1rbgS+`pyyh`vg8-Jm#c*gWhUmE#!aS
zdiz%dRswSr^Q#E!QGfZ%BWDuuFJ>C1QMm=?mxEA4z3g5Qv2?++hBEom63l9Tj=D5>
zdwH?W&(~XxeBmR(V5;U5fiae-!GDrs7^m3$Y@`V6=Cu9M)YE0@M>;;q`#g}D>!WVI
z@-vCr4pCK=I;@`CzP?%e*63Tmz6RGRoQ!?7*G(GbmfHG|xRc?;p6C<l7}iMm&t*Y{
zX#9M&kY95|7)TE_vUtFPiul?IO`Ui>fEtuu@pI6qtn5i~f>8DcP()fTV^+6V;*N8k
z=DaAc#>2hxk=E9jn&5F2N>hue<I*ap^Gx{Dv_Pgja_rW+cJ#5$K&p~HvYS;lu)na6
z?}f){mgWBKmRoaTJh!tsUeJs6GkRYU@k)Bgf>D^4(90rI4mWRRn;(3Fu#y1`MHiR~
zYk3yOO9WXz-ZNxmJ$m!~bCzT_${ijfsd^=|n(5lzcS#b;g=(Dq{*zYR63`NuXKj)>
zs?>fK57kaz-%E!!o^b3R)5ze#IGO6E{r9vsN+A@J8+}6UjT$?LidYzMHD-u)+x=a$
zwo49k_18e(-B%Fwy}9kuN=ybk=e{!4(m8M5C=1TGVLonF&!1`~>FGMM=<nalTGV}m
zg$NLt{`%W#@-u68UqYa*41(KtE&RfCz+z<c&81H~#eh^k4E7rn+hNF_xQ^cEB;bPp
z{s2bT#KgPM91X`-LhW_-vkO&Pi7xkGvR;e+gNY4h7E4Ep^~{@siHO`!ox2nNzH#mP
zw;plv@lwsSy5muQtV0E6XGU;#hJ5%f!Luwz>MSj|sIFvZm%mAHZvGWXG`s*%J*N3x
zIIG#Tn%K78RD}*e@BQaob7+H(fa8ZS?mXCnksHkF{%(YF01iLdtRCGKSn$gzE%{{0
zrha^@rTZrO61lvLHa0X2uAGUF70cKY9@e1s`;a(LTsi**B)E4?hW6+3rsm{0lxn!N
zxO1s=T{`UV_4OXlckN_C6o9VQ=<`si3oywUrxuio>l;V;H#1xINx3$jOAHxVxC(F(
zIK%UWD=XV61>^A-kM*>bUtA$Z|B4bU?LGL47C5B47Ov*L8hr=&KrUKL5w??`i!l^b
z6Pz8PJFXGUd3m+9$9B;nZu@d=2;Nq!5#t2exmHaP@<N8o>|gHfuSF3|h~t!c`}yA~
zL;RL6lrLHjos*K_Kb_`225PJ7_7}M8>(NH!2ThB(6kYrbSEHf9u2e~v$AqWV=yR&u
z*`3R=wrI(x&EMSg8>&p+@W%k_Hx_<8r7xXxfBP<^L*I>5|NTViY__nsRJL%jcAEiu
zXhR^5;AZA{ws80nMlV&TRi?iBds~$W-b^}>GD;Fh(vgIOSv!2IDXKfEHw}y8vM6~R
z05>!|Fi^}3!o(aQHe8FPZp!pNSgO#jp%kQd6D4|Xw;(UIy0ujB^f_6Oc<-w6?;B=k
zy{A3VqN(rC&MH7*81P74R*v?{)HK`&c_!xP+q0?oQwix+h2u;-yxXg4l+zmU`u}9d
zl4|dU;o|n!rduVFQ|-I<zd9Yui8;v*4|`|3<{@I~ja)v}^UWZ8b0U&>cV<>~^>#a9
z&`f7;yq>sn`V%!UxGj^Kk8@ATCde>D+!Ze<?Mf>W<9o-yi?tg~>iIa`CTZf0Sgl5p
z76m^~NGSQm%xrbC5l1<|UH`ElT88Rhw-vXWIEg=$3vl$3<meMXDGEiFj8|4iLzS!F
zdcNWPu2Q!x?-V{$9eG|j_u1J@MR*6gY@QD}46la5QjY%J+?&s9lYc%PI+$Ui6qU0W
zbEgjrH&r$9B{`&2a<Ce<a}&LWc9A@+b4pcec$jY824eBfZLi6uK}Q@1-lQ96Nq>+>
z$TAl{-dhXE1AxeqH8{8|c(}%}fJjRyE3>YW$gQfRgY?=~$<Q7jT*1-@HHs&w_?>@x
zUqYixOt>7HmqX}$eJkcrD@N3C-i5l?b0sDHs!(A4MbDVjfbkkXi-A|-&S)cA%Er)e
zeEAEzj#)(aM?HP)Vy6Yq^Ow>yGc)KduP<c))FseoO_pKz*1qpmjF@f0SN^dZy}n2x
zVWd5@Kg1W{0XW%$^)s)n8#K!8?Clr6nf2--5HXoS5ezZf^;-f7vTT#2846svy{&|V
z4MK~Plk{yTG2CU8x<!C6`Lh4mnWJ(7FHWG&C``V%UynOYp3`)|3xcOs)^a+jlAx(B
z`6hYICzy}S2H^*~;+t3a=)tg|<@eJB=tb@AY)O3!?$2oeC^hQao33L>(w=t0NGr01
zOmq{oqdobt>{!w&nB8$prpAczw%84BQaCX&A)KosY^W&>2dm2PHuG|5>9bcE*&AZk
zR7$kp%A2Wzntb1~uDGM55>z}-_nY2J$6D9Ac~e+iIal_MGankT-alWfc1Iv1kN#Vi
zmcrp7OJIx{nG|MYebaI8JETb<W!gaVkIND-Zp<oE9*RH0Go=?N-xA_*{p?eK5lSjq
z9;4qy2zd;ph|hJC1&?`xfrC=1KrB@>EUWi+@i<7Xuyd1Z&G5Nu`IMlwby!JbqZ^<}
z@bK_lKhALBB4R6e=#5WWte(`%Z03L4rte~@Rujl7dd@z8z)CJGuQFL9<lrGQA?a%%
zYPyZwbifvNwdRl0gEyp8))laQ`e9}gR$o~O=iYM6v>?(Dp}zSGDOSb#$kfh%S@OH1
zni)y{CrkCWV{EG7@8k{xS)%vc4ufT>;f#8Q)H8L`r7DHT{{5k6*CkUnLyH1!+`^yJ
zS7FKK4$bSy;qwTRje!>pUL{U{O-S#5v!|Wc<>+6H<?K8Spnj>;anY9RWX$@8d1A|G
zQnyl9<o+sBvpO5GSNF>F{&Zy9Bw_1<lCN-D9=_2X8NL-XQEnOSf_sc>%WFW;tz>0=
z2)K<(9LVy~3K_hwN=nea-QT@iWRlF6Js-b6``PSbdUMfpb6s%ToKdfZ68;NqgQe>0
zO5o`+jXU7g5^Cyk#CcQW507n%igFvxT4z~7L9H;}9Dgs>kf))cSWXd$8R%^KPllgM
z$BNu!9e%vOl(ye{#a%yhxt5;NBW+&uHH_lbE4W!Ue?o?B9bByVWs|p@1Ez?;!=wTL
zmkD%Hbva4<>ja$})fj2b-Y1r&yjsonq<;sEit{uvUl(I;VDyJp56<|kw2#>x!;J5b
zs9$5#8*tz&_tct$+EvfZfnG3Aoz)0ttGmN=?RwxCV#(<AdVEV(TUFYex5;IWIXWb)
zs^iXUf|iKn1q2dqUU-Bd{h>~!hGoMD8rHD2l)rhknREYBfsZLrT<-7%M+k1Gt?<2%
zuVwMp(HGMwIyabJ;ODfT71N~+CQs*GhT)<FZQDij#D+o(?j5(zLtU)>24KC45w5B>
zHWTk!@z6g{S|7^YyQT_zyP2A<(klLRf&`KtGfF|FK@akF2^DZ8l|gGPU%6CmC|md@
zH<U^)%G(|ymbSaSou;YXJCIf0t24n*{ept1E(B9>h+mo<L(brkWVNhdV?%#z`n`aG
z&MQD&9>Lc4T2GqtUw*#o>u!u(roh8fw6P&E1mz8usJWZeeJ&dNv(|)D`_gmiWhqIg
z51>*W&Ndx__QT=+z5;y$GBUEF$d#G5_l>^$y!yv-WCC%}Qk2ZE_wiic>Miv?SCW=K
zfAoT4$e=sjeEJD)N*lu{WBSYtvCBT1rxeKf3~Edy<JgME2wPfyZ$B{9nQm#V`a(De
z@?$-L`~(2@G!0l}cdggi4oGGi>+1ob9!U~tI53x?JUpdzOn@1->*cdecYNdE-1<<j
zCc{91;Jw6k^3dNJ-%W9Ir9G*jmVj%NPr$PDi>WE6^j=%S>H3^XQeWhls11Nt(`=(1
zxsxNPk`OjqusqHT|FQgqjUaEw8f2IA^TK;Pu?+v_Tm&2KLA&+%5eHse2sYXA#H0o$
zhShQPym5`zFOqo7;w{(a3uQJwF!kFAWe(Bnt^5aN4z<s}&dEuQpiUh0U@S_3HMds4
zgX@OqQGP~~$o(|AnLl0EYqITmk4WRPv@nRs61wuvKU$uyd%f*hZUSS)Tufr*@oogN
zmg`%>I-pY^`Z_?Hh#ziF#|nMy2?Q*fl{)WNeunSQHo3dG0akZpxAV<@Q?-6;az>uy
z`6D?t3M>|15ocJzwbvowyM$n-rCeiCTHkFeZUXkTuwRD{JWA@FM|Xu@?azzTF{uQm
z#VcJMo%ZXA>bGNqub(+)ei*6#BptWG;X>gK7&jKwM^`>TYLV4vj)Nq}0O%|Be4qtt
zIS@u6kcd!$5ZM@fg=#6s?d%os*o4VMNTn^~=P(nq{(bFgerEt`ifsamI%AIXD=>_^
zKp(lr?=rx0LNLjJI-s4;cH!HVD3Ae3Q-C~6#_i<`G1Bebm!VxSf4`mW;yP@Gm7}8c
z^sb~N39pDcg6PqID_?7BoWeE8hdNpbe8xXBksq^66L+Tb*!+?)hs*%c&aY^4X^L-$
z?Khw}S$xutM9-203I5Sy6?beYD)?S0UL~(&YB#}6@az<WGOF9kgW3h;>%ZKx^p!YX
z`)RR|8!^`?C=gC}z>ou=J-Bgd`h#a@nBdC4%jLm<Tn?J@1s?9t=t8$u9^j7=IwRc4
zN=OqlLu`~3%Q{3$)bMasHH&}mbqF%2hHsS?XEP5~N@$C9V_XsDGtF+hf*x}w`(PSZ
zF&ZBdbiSgOwzr?&g@Qpgkmh#+mXLG|ngti`QFOGASHoL+D$5W)^Db>bP`Dy|nR<U(
zEQ(kJ>91+c-z`GPKgJl;k4CKO)5AvlgZ>Foqdj3IdX9S)Luty)5-u|_ULdcSVzT07
zFVIB)kqfEc1poQ(Vv^3=iE6)whK95Dn}Y|I;E#2r2v!QLsV}Vj6t9N~BR<T&LUs9)
z|5{mH5-yOyRA&=O8xhfKPmD&5OCe2?gy~688tXaV^8QUSE7MQ|crO7uKW+Hvt{dIy
zwUL*F+TNP1ov$#cC8Ka4Bw1zSYrhC5F3I=&6GHJ97x|A`%hC8$qzoK41<=~yOElt^
z8so~3M<7CeNkKSCFdl{EihTgd1ss~Q|ECAz%2<t8EEgOb{-nH99Q01x82+r+)y?zC
z({<r8l^WRpg!r>QMv$U)Vwi#gI{`BVRz`d;XxQ-T)^-^B4P5y&PS>sX*eA&um_8xo
zf^M0H+yW>Vj2<iz;sLs~S4_IfFA9oTcTG6#>?&{n7OM4if=Nr{GWJ$Vq#1coy>XTh
z0cBYU(1J{&RrWGO5AJ$VSj_4Y58qyasdD5qk^f1=G!=Uo8W4jIjmyfaEPO;Da<q7?
z-aGduX%VCf)8GFI0XGh&CokvKfCE7f?V5g6QChi<{0QO<cz}=grnY}3FfdUma$*Et
z*@lW({KoFYfHI{R$Zbb3Y+P8f*OZo5Yd#5rkGrot$!PdNY}yckjJ)Q?pheW_lWgii
z1*`)U(&@Voi_gz<d#GZ~8Y~PrczH@+;zG!T+*ohEb{d7T8pylPIgQ)*ELGQ05Rn9M
zJiLQ0DX(cEo|@C1FTp_nV;BKSkOWTHZ|^m<90RPVuv0IfcW@sA3bl>F*WF+w&un<j
zxceh>bTB>AJlc_V-(%CEP18}A!rEX#!HMl=6}TBU*}C0id@pU_6)V_u<A20!4@fNP
zLWoMhL&>k=-xa}jWnt|R!`m%D_7s}fJ#&UK;5~bCqK$QuYK#Gvx;Ly?#Yz0LM`l=J
z=VxaRXe68#t*BjOwJ#|Rkk?-jpyLLThJ~$?aGSrT0+-9~xWl&EF2ZE+u~^E>>kK1G
z)N0z>uVp@FaWS|6Et%PuRA5Cfz{9?dF9`B#n;W#1p-t`U+tXYX$h!aeESfRiAcx-y
z3Ni$mE>|WJWN@V8&uJ5!q2vj&WFnU==1|zhe!^&^Yai(Jph=v|89Nf05TZZfTI))=
zMnM{qkt=kL?U$?}!A+}cOpbp>uX;M6S-8AEn<ip`gfdo0BdRull+amg%T1L3&?Hlm
z!!xhEm4ur$?99%na9Nm90~Hq1L-c#t5kOd!OnH?*Wk`*{(^Vc>a&<o0w4@%uxO*(7
z${Y0vEWGT=#-N~v{;Pw(oty(<DITr!nesa+?872q5u}`2**&gIzk$1e$^s#rFpccW
z!F;Lmb3>N$vOu`{8{o>})LdqtV@ZE~0J~BElz<J?{PCmK`8xYyDdRjm(xkr*95Dn-
zG<r-pAn~-Nz|on9FMFaWOn(Bk1*3Q!U0tBGeSgot`Lm9mrN92wt5{7nGlx;ugqLZg
z6Q`J#<fk0}C0qCmvUqX5X4gPY_iul`^`_3P>=P*q^Fo@i_vz*naj|9$T2!v|+85XZ
zrYNZ}aU9Z+F#Fbhphj)%vN#Ew+IIsHZBgXf1qe9+-OZ2V0H==}6owymX9cb-$0vP%
zyD6#|t#hhXsNxH-FD0TGU{Dhf)yMq2mCnCVb45K;m`1{9$YgdS2~mzBOUfE9Ofk4_
z>Of%bIz28|GHM}M3j%8ZWlF+h;Q7#R#Z8~kdxXmGqxRx|+2J$%cv&)@6|YRTmMw_w
ztEXuHYxEyK&77SHxp4IXKMT!bDNzm>!hiSSNQ$Q?A9$0(2Dp5o#pixYQC&u|Z~r^W
z#wMIh3XMz|#;G&PC)vxkUB*;gYoTE9FA~?OkI;Mvp!7b?Nb;$PatbXbC^Qg_T%#Hk
zinRcC;tyaJy|Nb^$T%qeovFZc;fF+!qJJ#1B|&c2L!!s<9Z;fC8-Subyq$#Ef)S@!
z>A}Yd{ab$wH3mXGF^pkTa0G)M?!;n7#f5~6+w%foAUvODkG2IznBMK~V|hYZ91!}%
zNicRpzeOCaI|CWiFR=aB57d+@y?1rf^7Las!#c6JXb7;LJU=j3s4uv%_WiDg=grLj
zy>#{c2PTFhm6Fs)M(O{$zx};K+}s28Sjwg+<Qh;zwNzDA2f)1uqxw)jY^xTq6E{z^
z6c!eim%kBlKHXAI=vB)Ee}2Vnj{N<U=~qfW;B|j>+t&%|biBPhdeAY#>w$qc*i7}s
zJ{p3>b3C27_}^Rr8YqS4PAk%f*dh~d^N#Zw8#qB^w}5~EkS*Z?wI<LX*!}&Ii>8U1
z>TLDj=-)&v8gzu+5A?2%fWmtB<f_qb)~%aRlVBeOsR$j#|Izo;Uh;uXBPap7x~8V$
zSTGQf@Y(*wiIzHPTYeCJCMb&VuPgzz=GfoAFomWk(6@Mjq)0^yF-GTJZ8<k{n|Hui
zn;IKMfZ_pL=-+oBTPYI4nyS#|l48amktEjV1`PyU)cbYAyQh%}V!h#|1i7)`2GTKx
zhzH7hCcjHdYiqa_kS^IwRx|@Sb@7f@r;!rbqJ*nXWurMkLhr|?E}z#xb_AxX%yIa)
zCcn5ibQK0!@w@|?X}18(86a>3%E)do)#>SJ(%2r)YzKS$J3wSU8+7nfEmb3Ow)hbL
z=?N%aBgrRFbX)^QCa7R>Dgs@wp%E0QzUKkq-uw0lG(x-+de;EG`{7m#3w~4j?u5=d
zA5_G<F_6X>q`Pl_zl_y$8RRL~sNC7zy#ZQ5ph#i|1mX^+H8R~s+wGm5Q7i6a)&C2;
z|L+FWVCWn$G?0?I@BOU-I{d3_?*r`?*KMF|tZLp!m#9)UH0%JaZ(LMxfiigge)mWY
z<#`m+_xfI8{j$8gmBv~7j4~Uc|3@TV+VVt<BV5bq$$wk)AmNgt_5l(k2H$A)a*ZsQ
zlL>fKpdJD8IFp(=pry8%s#;fa`A&-dus9hMowuMLAn!*b3uF?ZKzDEi)OG|Oq%o^(
zK-m0P@v#y;04@(}0j<_)U_QA75^ls#29ae)*xjn=U4EGRQ>BH6t*Ynu6;113=Ol}k
z{7U$N>*>bB_VFuF*D*$*Vp0630QY!<wEk8?mG^%Qn@^2kyeS>A();RYTyucBgh5lN
z0s_Ym+4zq<A;$F-oj$-=2=7IZhG-ss&4KYU0zzN>-Ks0l6O@jw7TlM<2W)qce&G5@
z^95b9)Zy`vEnXKIh}EQ0<U|9CLPzj$d*mf~$g{_#6NT;1VRf>sEVOu+*v}sR{2<P_
zDgw%n7mII@EMLl}1O|p<yrjhg7cxroU#DVIi`+g)O>oeDE8>U$1d0wIkh)%3AQ8Kg
zpyncYYog7R#C)bN#^1*gd&QM=h0)_gNJt1IN1C8q3e2hFZnuNzeh8^Ah31RT!TXIz
zzuuKFksBpa7_tG@l%U%l_(YF~O92Glwt3L-gD*f_9qI3awEm0`3_x|OzSH`_IWgY^
zNa#RfS2t}72vDm>TdplYyZPU(iC#tk@Aw!z79{mW3I*W_;$pD_$R&Z!?7`ZAW|c2v
z5Euqc1}z#!dTlNgETd0|Z}S|`fdGxyiDvt868MycjKK|R#Fq51@I_|N>J<Rx`eirB
zj1TDeY1b0_Z%Zv`s6;Nf&Zc1|!@gy^D8pW-#-#gI)0|3~ATpya9)QA~z~hQ^U8g<>
z`T&0qNa9p`&5nPOJJ`N^cLnsC!8{#EQbusxPK@9c*eS>Bw=YrsohX{z_Gf_T{%c*`
z3Gh^_u!EM}stiyqkhRD9ECi4;SY;Ai5UbxTAX4?&rgR<J8i2~PdMdKh5UK04Nk|~L
zW{vB(zkBzr{ojx0K#4s7RQ{7ddz1zQrNGAmZ3100{|C-s<!L3@1H$TR*Q;vTDbqCY
zGzY@JrUkEE&{!30oi`|(eavVX#Rvg0<{_yzHe>^*#P@VWnw*^c&3is1OB9f;2EXpc
z@Wgn&;o!4MUf6>Df$Q11X~+*JiVg5Uo~Zth#0)@;G6L)NAAXI(l~fy0VpJRW4{QAR
za!f4$2t4`&%6ryTVw?IH&Ehxc)<B)W4xxuAu+mUJ;rjj&cuMphfM|{Z2$!b-F_5C?
z;VC~p#C8Bi2teZH`GBn667Yiu3c&fFyDL5X3HrZoZc6)LKxjc7|8qx^5T@@=@id9o
z^q(mnkRuidv(S)DWiac9xtP*DT#o)fnCJ(I@>*iw!-r@r|IfWwzI;a`h+6gpX1}Jg
z_aM5ArzO0~LsE@QqGj!U_Vh=I?L#6)5iRBaFNhi_W~3`M$fKnSRUSU9Ae73BF;3>8
z-l2*OM#0LjeGzN&<OF=4NSniAdk?k$x27iWQ5RSk7(!QT3E*Ua5|Sov6&7?|Yrr%l
z@c$_8tD>rGyLKNWq(MNsOOS4mMnEJ4K}A49x<NW5L_)e7q+3M+=@bMBL0Uqj8$?P#
zVo%=p`^Vk~d;H@+*kkN<wDMW^bH~hUUNZ;$VfN1Qb1uDVC@QrQq&zP9w|u`R5wIA#
zVUCx2`7TlKvwzO_Q!s+GBEssLn|;A@0`?A29J#r<m%0(3I)DQEWf;XSI}i%2=0*EL
zL9ibntMv5r@Gx$(k=$}wjREq8-=E3C|LdB@il`*uWWg1U3_e<qLZMc*!_S$CiM8Jz
zoTR;A&ozSvAE9;(j$_8UIFNfm9tGP&?7EdHd)G6la<@9XmNii0{%Zydxm;23jX#nH
zvmbD^rbf<r5`BgVPRNT>JGOLi`1$_nZ9&Sfe}XToX)h5DuU-PdMAd}t@N*jk8HUx0
zQU6bkem?)TuUXQD<>S$!-$zHUM|WKY1dy;757|IOi(MB_0lc?CIi0$Ik%GeK)u&VV
zA$c`5?q2qINVdo234MHwgJh$TzqWv{*BLGGApS%~o|jJHy?;Le=kNGGZ(qlT?e(9x
z|MyQXpRwq{Rc39$a@%c_1NtJCR(6udal}qgj)5c~Wg2iWGr+V%Sp3lyFLxj!gJpDS
zv>9O_Y$*Slm+%Bm0(heT4B&Na{^fKq{qxn!2nhI=aHXOLAk}XC`=)X5^Gj=yImd*s
z5V>jok4qq%%f_dADfK<z#9p2qG^~JEM*j+hOB0s<ni;+-3f#q|$BQNR8CMJZjekz2
zqSEQb;kI~o=$;R96yJ@B@+rZN92*lG^A%<;W3GSL!;RKb*XM7I%k1n%vy}BXXnbwT
zKI%G#doyz9-+MqpsBk})qEIyzD=_9+i?rr$fqw+4k}VJ{B{zt;s~bdlZ~Oe3X)ssq
zTg_q`wlv-BQKV_>h|m~Nl4qn}kj6hL+MyKiqLOH)9}ytRA=K-`%xQiZN|E!rjZ{Fc
zSbgp{`ae(C{jjjG(9zL%qplnftM*|Y;8v&H=b3k^0}RMU#dokFF3D*Ke$y-Xcaa5p
zDNawT8W}4#F>`ToadW@xHSX^2K0ZG7p~Kh2^mr~?Y{ATUO)8G~AgQXViu-RdRCp>e
z_w@E|WW~fxKQ&f_&*d244;=2Qs0<Gd&N<a(CV1{H-gR_zeERfhi$c&MPBagUfq`^E
z$b3YPUqHae$46Yf9{vSNSQ%rBf9A^V2K_S-P7-4nM-V-xIB|SG@g>o~^z?M`gHjE)
z&6?I2>VNK!L7ow&rAdySFv5&K*<q^F=jeUBF$)VzXJ;oAV}DP7%e-?GpfND($UdTn
z8Q{@Ul|W1?yxWR0>v)S35D;&RimoEI!jNWoMD)+y?JV<*=v85U@>d<pko@a@;|AEo
z?ffO|?A!SGNUM3}?@n75*+h2)v&n?nBa-5PNGLS$u=~cOqGGq28a<(mtI+D|X@R`+
zUaRZIllAr1kpw#n_m5OeuLoNYT0_Eh3OUYBQi1jB8Ta#2oR?#|O=FeNoR?cei7)gH
zSFce^;|~`~^78kHdy!z{guIrH<}zn_aXA|pdVR$;5e+_i^*j*f9;~u*T*gIYd?Y!a
zU^ckl)~5kZ2@34bJu|e`wojLr`70}zsQK2({<)?1pCi07LqoeaPP~jN8F&c;0s@B%
zJ<TlIcb8_@b+xsN3JTDfiGa_AU!2NOu#%{C0Y^^qC71cg1}7&cHV)3yDdF!8WZN^6
zpO*>?3rR^xpm4du)<QtQ`>BzJ-{G4lObTZx|1K)gHIlH#JeiA|=H}+G<+AfoJ3A4_
zo!K@ZjLFZ>C%Jw-H8r(6>_hvf*KhT45J-QCiHSMkkR9;-voWJg6r1e97^~pxVP|K5
z^yrbVuP;1-?~IL&RT$ImEvLpQQE8VS{{DSwlq?Z*tb03&Hb2nT77-NOzi_*@emzi0
z{u9ja!+ZDc!7qo_zI^#|o#T<HxOh=vVfEcU#OQ<9N0p~@A56M-*iLxH=t5z0!pf;3
zG3n_W+uQHT%E}4~t`ib!7#PeD=-nxHpHtz~C?6glFMsnU;a-c^K~HygDAcKVG`@Mm
zNkx@D#?tM)(wv-ZK|x7*G9M(tsBo4e9mSK(V+eIa$a~ufexR8O2nfi{WxRSd1fuqb
z?PJ&0^#dQ{euvHdwWsH%h=|CI8(l*~nzy<-J6Yk#sfAPE;?9m6&b6X2Ng3kb&-EQF
zS3max2M_$Z;s-a8=ND&xV1J+0xdwU#$+LBIM0ox963YZk2VYk9x+1A=WG<|)d#w$z
zkcN*|7uSd&-!OBtnB>n2v%D7bo5r9Hy`m=1)WX8X%IaVMMx%f>a~gEg3YQ=zCI)RP
zY2HV^My;(qUj5Z9pZa9sh~I19l8%lJA^~%bQs+k|NUh&Rb98jH3uTTwj+7DF0XlHG
zfINWv1kwfN6`1aFol3=bvJA(o(eE~ojyw=cYwMnYf%5Y5s|c{EP*6}%Q%_&cZ*u5o
z4{hOfR}$<X#k`O9jKgdD6awa7_?Ve_&fFt)9zGm}l)<g7xjCpYpduq%`St6k0}A~J
z2S_0U6Gfl4;bCXkUO%|h7_rc=q=hLPDh4jzwlDWlRab{f>E(sUpw-OFy)5$S(<inc
zGamzQ-i*9{UH9#4oZxezMYoc^;bG!RelN18;S>kgC?gfO54Ui>mX-!2<qWJE7Pjd1
zJ>k~Aefy<6qpJ=-3(FGlUTj&+H%%)w?{pg~UBet5NrQ538uWGIv)Gj5=jS|-!8+QR
z3kU$Z2AKlQ3ERU)&pn6~-YFXXd3c$?nUL|O07e;o{U1_MWI6ZlGQ&1-x?#@y{`0BB
zC|#(SpkP&D;WE_q13FBVHNJPr+|(4DK9kR3*DoE-shaP=7WLI$AfEme!0Yy(HDbDA
zbn!7M(3|rHK74p7<3rDt`jWZzZ*p=z(9(+cHU03I=}wf3ySu|rdc{>5deZn}rv@7$
z26ljWJ=E?M#~!(Hve5Cfua6V9-q$`LEm&V$JNRdldhC&DVy30dr%`%deQ_f1mvM9f
z2w?$>eq0=!csH{>kH(0I2pC02N5}r<56qxIKr!B(P-!Ce<HykYTb_3=f5p6fjY)BF
zY3WC^PeXQgc4=uC5c}xYk92hv{bf2N)TBmpGh;%RfvkL3*uOLB@;pa_ZJ{eV`CgWo
zv*O_iN#Km+k^i-PgpHHbvF`F7vBajIoB-*L-&xE3Krfu;XbSc#Y45lTa%5-jXG^`v
zlOkMa_>dBzKcO_U>gAP^a`ARCCI$TG@e3-2;&G)*$=#h9_5$VOhx?a5U<m1G^!Po0
z{*z6v37w8NfaFD56uu69j5|rO`YfnX+Bl^zqEE29sb03l0NCMj0GIMA=5Yj<IkP*E
z3xOSXIgyvDh5&XFI|32ewvJPtpi6bk;7Bc?i9EQONRLU6>3KE4i;mSi&?`8w+($m_
zg?f&Rx}qu;S^yc3fl?8VVe=DB%^{IzIeys00&<efv9qG)`IwY!B*o<ey}b)=zpfc^
zXunIq3!7;4bk^0?HB<~am%1DaAxz@uZ!!gPK0cjS=_DW^!Hx6lM?-SADUex+GBM?4
zXU`t*tt2KUo)I#iQ0;Wk<<NdgZ;3IEsZI%z^iE<VZBOFLB%6WtZ-4b_ceLzJh6K<&
z9Ro!E;e!YJ>KVmFynIX}w_&@6<-{rn1qMF9rOzQ!vlj=K1WlVz38``?`%rm7A;6}L
z{b=;8^qZHL4AP%~u~nzU8e2Yk$bwHB!N)gv4Ldz8EnncX@iQ;4dY5`YM$%&<vyP%5
zmk8+y1S~LCH$XD!P*60ry|Y!goI+X<>6*BVuC9#I(m#8`a?AeC&q%KIegFQwwN(Zb
zC<4rIufoGo5&V#adqpo_qN--pe(P8qR+knsthskF%HgRPJ%fKap|=?^Z7)yD;5U!0
zO<Q|AJZ|nFKE9#UuDo2i>FIk}QfIqMy&2wrhP1R${M+XM_04O75kV_S&(W)=5}p>5
z{ZNZ(d}mN^P6(DziL<jKf<!ha=(B>IopAbF2b;ex<J?GbSs9MUuIW1^!H>mkW1r@$
z(<bMrM2yb7@Iy>8GVx5ht|VfUVB3C24E^kOdU<xw(Th!BT8l$K3HK;s%c}D<S0#Kk
z-}w8Nii8Zcb!*4bfi=F-?_&JmfH+HXnDW*GNge`ZW^uWcoZR2#^z8cwnH(97Z^(1V
z1!)d-^sVf*{M{B`+Xrp2JdoA$v>k&s`bM(R;jf>5TazE{Nu@k@O|vkDFHEBuy|iO?
zeEO95V8|^VzdsEP5vi(it?ieG5Y)_k40m0|JvzBnU)#saOTYD<?|qIeV{FRkSX?mv
z*`AF`Qfq&vk-4x8+v47_(;2!Cx3si06VvUP`}-+ztjnjTr&>k#?iQJtm_TK^ovrQZ
zSCOClJw1OmC-!#c+U-ft52qZWsl}XtqH&w#T}g=!1aDkj52Rnc8<+4o*$4OU!jUB?
zDCotD7eiLDO3bT=?579i&FdAapc8<-X|nJ6{*;6J6Mb(IQ@x`_o_un0Tt$2PcR@ku
z$m1y5Cf^Rs`SpitHa1B19r@xXwFoIGDf3X7b~y$P+U)Al%J}|%Yai~ai|fsgWLbQg
zpzfD!*gPd>EV6vYzFz0ntzwgsYA$-psasWCTB>~O3<iBSo|Oh@{E9eY#aYz)z`miz
zzjN#BoMU5C)9u^04S@{q^muRV^Sunw!LLPQkQ|=KZiSKdq%CBsKn1J1S|pWFnCBn+
z5Oi8Psf<SV@=X(8w?mWA#r^L1OZ!7lL*rZK+f(ADf`Sq(EE;aFW|KQwBCkb8>Og`&
zc=!-2NDdKoU4I}l5P6edAjIddUGkk5-@bhVsX9MDKPFJ=_G`@gc=(U%U966-+qQ2=
zqeI41Vi}Ei$^T_ZL6ENKvdx*T)deO!3{KJ*rFBY5Z619}WyUiezMAZzwZx?*$;o-(
z3RG565q~0R(is5-L%mW&XoKIj#b&h24Suxv`H2`ZWu~V$z_wazLqP)No`%@P0aj~_
zZed{oNUTt1zov^^5=;VJjFXd7eWh4XZhokkw6q}`Nm6n$s*I11WPCT8Kcwl)4VrYT
z=@6OyV7$<kepid2BK0h&LalVEfAc1y+qBu^HzPYc)Kn=?Vn5$oj^w=EmmvluS=SKk
zEU6z!X{EWP>-Qh*x42>j<=&0Yk9YO{BTOsBsi*lm!IUF5L0<lqd@c~w(Aj@1Rx$oi
z+uYo|V0M+@)5!r1cO4m#=GpHq8t$Q5pQ-b#t>0*}PwJeOm<)e{orZkr?UV33^>9D8
z+RM#5z+x;Kwh!*;fJ%pI@WKI{(9>@uN*(2+hL<;*RolR=A23llx2&e5q9P*1vVY*+
zh!N-on!>1ZA~TsmM^9SH**seYsjjvo71~%GF*83mOOipj7IMV*yAHSh%+F5__(2RQ
zH!d!{!M%5R8U#DQ)a}B6-^xDf<xATXSF+f6C5Y;u6)fR@gm<eQc20q?P5Dk&iB^%a
zo(3Ks9yn-qb#+M0>(|>WD<U2{Mmne`G%)QtDH$2Xg`IPb%tTmqO9g+xj0Ug_1W45L
z>7eG9@R6bVmL-qvW%a>O)s0xCWirxW@mueJvEpET6zJGqSGBl9QoDlo`r+5~j7?j_
zveB=lza3TBj-MDazl0J&i>8fTnD_5OMa<oV1$fk}uNzofKQ)?-=;;Bo8x0+u<;D&7
ziyK`4^EX}Y!zU%V-ZW8v;991opSr&5^!HA5mF_)C2(6e7^pIhq3A?)9H08gr>u#pH
zMCJ#5JzpN<vYHpH_jFDV>uE}wOUW#bj`p*%JgOfR(=sqH*symDpcl5ECO;q5j(U0Z
z7jQBG15&wR3x@xfpT1O<MjGWMI%8YcGK@bQ?XL|JUPr$Z)w=xky_?-;3<I+j6*@zm
zrnYuXRn=x=L~ujH>YA48Et*N!J<P~Rbgt04!a`Q3Kik%ZhQG-ST`H<mqv@Q*Z#~)!
zjmYR-h`h-j+w|e)@uZNKlaoPNyEZ_RV#s)^&CFR@S>29@YoBbBp`+7TS%anSB$@##
z5-b{v^8-OSHj?FcPbgSO9Sj;hARhbAs;#cF*)dv}nc-0iVj@b+ar^8X?%UJXT9_Q(
zKN{-TyiQh>S8%|}kdvR^@%{VJK%JSH3L_)wbc>(Mfi+UQyEpI%bZJnK{yQ!vrgj}Q
zyO&B2G&MCJJXpL*QjwR(<?8z9^u#75^9PvcqSXDvMe3Cm6^Q@93Qu_nd!0K0!o0S&
z&drU`kBkr4-Bl_W9Hb5?Ztx^V9;BsNzO8@x>J2AANW7w=yxe~UCnioTt*E%y0+p4r
z8_`Wd%rTg7*q_3(W3MGQrk$4lHfQR~syJ6Vbw==Vz1=OnX(=WUQ&(dgWV!wzG3k)S
zVWHETtB&`e=XzYu92G}dW2HqyaVERgESJs>h}qtoH_>k0x~`se#ozvEj^XY0?&9K_
zch<e6N^0mx;=YTrW${rP>O>q>rTGs@7p24BU>?)b(k@G%A}UGWr>5@6%frTlBQZK%
z+qS!BQ(bX$bM<*yeZAMk`B{+mJDIZn`o_k_NDghHj~|Vm{sz=$Rh3|<yj)vDMeG$L
z{#1Qdb1wCP5<$wkykMcwuSttbC^A}F!*6sdC8FtC3JR0FmvI}HY#Z{6zXZ{z47~oB
z`t$quFD>5MW<4Z+{Kw!&4W+JYm(%8J`Z<gSH$R*EWK65RId`9gYHCTnx3{;_&Ysn<
z`FUTj4OsP;L>1D{Yr6~^R&%RuQ{{@{d7hAyUT2Z4Z+?F4)RVwIL|Vzom!6WMG|&qO
zCzh?H;D9USBKVKq25f))@i$A8b$MePM=4*VH|Ybne$UshG{)B0611=6dHtm1<!kRK
zc)k|@qhK`qZP65j$5m{!3Jw-@*og$x-+g_+rO?wh*w)e71I^Xfr%D=$kuH38GgkDS
ze(!X{cyt?7x6I5DNbx>Dr$%;POVeR~i%CF3O3~513=EKOvzt%cm%?1^0*;QHSy^G%
z2mzarmDT95rM!xEg!TQ{=AxqC>e6-;5-E$zq|7962*9bIsdu%3lpV0~+Ie`?W@Tjo
zxi9GDPwnk`SMU0rjFi@;4lJO^em6I}PIC(lneOPg<LN1F&~hHoMjscao9tk3UD4?4
z^QAxY38mU&gL{M>i$g;vlHU0$y-~C*$Hy;fL~2+zqeA5qYwRC7Ok*Og3$kSvdw@Oh
zHD*-Nt{MAjm=JdbS(c#nS<^hbN-#6FG~>8KFMSgmsoGe#Z8_B}8vVSGv$lrEMbQNM
z03NpcmX`Jy`ud_yrcn6^u`0mBP7xDbk&?RE-|tHEBa_ViwDz`#ho`0G<ZPRCP>_&_
z2&#;tVz8l|-QcTh2q7(PvuSr6Y%?7m962Y!r=J2LBI0u2@=HvK@rRRF*S3<s+1Y+k
zP(a>;AMzFzL)6QApw?G%W+<-GN-M->Uj7alK9#waA&<|Slo8MYa&mG>R8;Mo6ZO^A
zp%(pwXoDjoiv~I+a|=wbA`=hL<J#NV4WG9?sj_U1zu|yWouALl&W?p(8#d)yT7UXr
z()syw*~5qK4&Mng5*GFKxf({k%sIYs`$Y>Pa(mmzi1%GxU3K;0Qg1TH&`_GQGYY*3
zlZleAloB(j5QxWs&RJVq^Ak4mJw_jU7le)@bEhbLGM>0VqIq+|`TM5;A?L!uGVRYi
z2As`~D24aX-Q829XrAbI_xC5c|FZuw@W+Dx>8VFyu%fQ@O=PETdUC0k<M?#HWoBXg
z+qc$6m%Cl#$r45~ZpzAS3tc3)Z>LC5sHz%G35)u8nR(Wsh6|bgn)#5Ji0mx)XO7p`
zFYdb_yl=`(M8#12Cj+rYlD27_WMBNY$hsC9`k9PdaB@l;mt4bjBgrL}A-ag)6cd+5
zIV$u8fiyKI=?xYZeQ^IUqyOCTsLjjED=XVbEiOJTH`p?+?CtGgWMV2Xgz$v<6Yj1V
zsg2Evd7E*ks*hlyA+4dW{O`u6sG29fwR_kb|1!UVi3wu8?3|AxL+^fmt5=}@dSrEj
zz*?-$l$8Y!V1<q+F0N}A->2*u?y5B8l<Io?qD@Hrz5KOIw?e*}nUC)oSM=3%;Sz7J
zQghQCDoRUJeop=RC;C49Z*=IUrs&QF{LtS1vghDXaj-0waDP{rFQ1z$r+FXPYh7V3
zG_>`~R0`6nI;yQlOS6RS^ZoYlPEQ3+cThKf;B;RV!h=o%6qJ`e0=fk$Bjs{ho4jY2
z%w`vuJdZ1U-f!AXsfyS+^$yfXyIOzBxg8xJhtM-InFPIP6XU<+;DrJb$d*9OnZAD0
zo<44luT0(`*xY16s%ojg5kkXa9^BNVtDxZKR&8=OlsNk8+#JHY)DhbG@@l$x{ZL<Y
zB#T-~c8_VHv=?(GvEP>NexVv9zMs7-4;jqs9xmh>8mt2fkG!KJ6kH5QJa;{?c5x9z
z2#r4|+#mE$<hbkWODkaza^okO>>C0su|K2QNUQgehN;C>670>XmGVM2ZT(EAo$PC=
zoK03hF{G%Ld28fn*8Cg_vU4zwQ(77q7uS;|!S{Tht+<vPndTP|!ly>)ve+jk95)|#
zyZ<~J{}!{ih&3|OI$SjSi^s)f(Z<TPrTT9?KdX7$U|u98r8(-=!s4>OK6`3Qcj(%_
zraLZRaOxdeU<vE}UCH`5{tlfelw6SQ*Q5|qD`-V@joQ|CUVilA%y8qQ!r94&Q!FMd
zN;LHZEW0#c?E3r_1h6Eto4DrYs?sfzIy#Q4F^L+F@82Ixh|?5*mh1M6PnPf|5ms>t
ziocbNf8oBds+n2pW9mkilKjRD2*K6YxL0QC+dY>+UVS!x7>Z`f{z6{UtWv<?=RGmE
zJ|)AfdN;fGk2kYYt;mV4;N#~92h%)!c=t{egkljHU8jn)LRQbdlu4Cj<tSRa48E85
zwNTMy>ph2|&;T()N5kBnwmNJ05`rH}#*K!=zO~brlT%Pt4T_J~FqGcdU8<kd%?rjj
zc-n5O7~wyu`zo4@fRfqswASb0cvt76@82a{T;EcN@h=bNC7gKWE=8@*|7sa`aem2g
z+r`J{z_5k8**V0<60@^Yv9M5keccxiyMl*<!^H(nNB#V*-k*_Cfuqxat*PU&G31-5
zrA8-sNjo=%lPv7-X=!<%oqB2-&%fzYjpoub>LvX)jC%dLs5j}SPqRf?^b*t_5=I|X
zJEO<#{Vdjw_sY?)#1lxPA?x{f1N@P<JS}JeM*8)ptIMS6DddouB_zx)FTa>=L-lDq
z#%baJ1MDU07Yx3dPn5!{dG#lguO%<oc|zJlUZ`Rqw{gOk>zoB}@hX#et_C3fi^Mmg
z@F}h!x&yCM`!lCD=QT&1P|8k{4V!s5sdxYW;&r6|W=Tl0$U_8#^-at-l>vJASdYNy
z!)yI!Tm+&I*9!{~M3q%b@9$j|le~{d6O}cNqVZaqh<`?sqPzQ5BwIu2{{8hE>H(Dk
z3VP}BNf?MUP<`hDTX4Y~3I>LCQj#Yh-%MRyzM=0k;8f+{NPl90Z!sXJird~@Qd%p@
zBmEONQ@gznFHK)t9WZ6U7WQ^Yzvrjf+41D@V@rGcdj*;URFt&Ul_pJqlc?Gn-uV-9
zyocg1=H=3U_Sc<T->JLbZL;O3%e1H(t8+Ve=NA1DQBkCrXu;xctEc<K#buSOs^5Bg
zG`ssoD8vtW%5-b+-L}!RwMz?GZ*WDX#pX!4mPW~FBxQ10iA@Rd@|PwnWxu;eNSBpY
z)@#bIvg#6lC%Uij-ow3}nEb*kGIxEY6x7}xgr&;L&QI0cAt5b&e#)02O>p<Fkr9&2
zEvtn2{7lGdFjv2`v(%((tjRai`H+*(Hl=%WqBNRrn24y4jI)@Zm#yC8e&5ohhX>*x
zA5G9wL1gMK57`1TGJ<p=9OS`|X=W>{dG9~PGc$1$l{VG(YR-<?eOUw$trNPVpZT}@
zMtE8}hwnv`&qEP|mU~dJTkUr-xNiM-bybrrdHxk9;y+%#HNOyfxP^+OrKY-RY56>t
zF!UN<ooU+XqRW4)r_XKB_|#7SVxxTM;WM5m#f~nvuSM_MtlYa!K!9ja2^21auqZxV
zAP7s>%Zpa;cW5c_-H(lK5T9<ssb~p!f%2po*V=k^5iOYq6@jHw<2>)(`pM1?lCvX{
zf+;=c?07}&3X1A$+q3n=!`};Nvedxkbh5vuzpanSp5eSA<90)hgEBbS<%Lnl`B?<)
z;qHX5?xC-E@2U^fdI?>$T%aT8CkMB~!^Ilh%Y=p3Cnqad)sw1ld8$>FbEQ15xVgP|
zVMmZ~g_^2UdB)f$wMLD$6bSF5@@k(Q1i5+dHM8zH%jbp8rH@ZY;!C0<7H67l5@K_1
zvnH>s*eoxvpPZc6MJS?*h#)bz6q??DxDni_u%YOPz(-B!eFqzA{aJtBmA#yu?PIuI
z`>UzzY2}M<G#Z*81kw_1M${POr)PgIgoJ`t27=n!1kC#`iJ^Q<^Lj}l_*s`B%)S<L
zdW&=;L&j)&keefKWu@y&kJowk4e(Ioq`vi^?9B0qWfV_NhVAS)^6+$_pcW4oq9EO0
zzb17Fl$Ye-(VZth<W1#$#)#}}ZQ0mQf4j-fM=NchT0m;hJWVedgogM(CMKRg`(qPD
zeGQ)u9~l)Ej*N^Xy&X&*NvXijiRM2Ph}Il?f8)AVXQZ=@?LHG`3NOn3zR|{`6v@AS
z7UT<EWtmcHI@O%4Y{h(120ZkwBR}+;3WGJZOoW`6BPg`?CM?W^g@-DD6LJ70+w7b0
zv;9>e8X8ct2=C6WX7|AOmmYp>T$iIwfy}_b`n&NMHH*E0d?u$@#*2Oz99BNqjb=EU
zj}c`4^tlRM7K)-G-T0_jLqj^Z&JYPPDuRbYnsKn3Yi+3%E^@ylA%vi0XvLSDT&DNA
z@k}G|G0JdaAZ^1%%jf4ab91a}YOJ!d2(L#sk)ko9EMZ&%396r8<x;Ou6dj$GZqH;L
z@#GWKLQMuik}IfYHW+x3S9BZ>qiA+UwO3a956Gnr4)JVkHtbYcAwDeN!TV6n4*G=v
zfKh+O@?{L;$;Puhp$eH88>^D}<uUW-ccG#BE>*K#bR70SG_9<24Vsko8>(M@zJd&=
z3gC%SBgIkS92Or|>+1<Au`6(EEC!BbUn<5c;lkOWM@RGc?NPwOa+82AZ6J$>%ecw+
zEM;V5HIx`@;v)nXX8!yXIXhx{sP=a3b5Nz<h5w0HaMi|#>#pkR9w&l(pTilD{6`mW
zk!(J<H)6?pPwntrymVyz594nf$KOyP15!KfM~@(AsQdWwqNR1%!n}QFm$;Z1lzweF
zjF??$barH9WdVz+3<8lJ3EF5#5oxAK*rw2ANrxzp9u4^AJCnbDts6~OMk9TmW!6g?
zCSO=lWQa#@q{^BeCcl#u8zjiXqjaw@T)vc(bf6>ucSq<L6Hc$BB;8f?5~=6Kbc6`9
z?&~|jNg66oDd^#NTZ<So=pdRdUUzpji<-I<Wwo}}KdfhJz6R*}!4?jln5ZYdaYd$}
z(EP&-lu-GK$M0KX+Xs^lNfC{b5ouL57LG?U&-n5Suo#(C8XNP&-)wHm<KY4INzcsd
zZsWx1`s*X>sW*NF1#gRcI-R`HDest<c6j#ul$=%y4I3SwnhlLy*f!<O`|**I=^o{y
zt(vYYFBWnYDvqzCV!Wr5H2!G)UO6&Vlu!0HDn3m>J~Mvm=G<J4nY}@^W6s>_Ad+ou
zRnlerL)vFDJHNwmUcPcL&x-LH7h~?(`<TPPM~{w&y0ViJ1T&@8pVYe?tPOv7TYn33
z&6)RjsD+ffz7ZnkeJQ_zQ`N0bH*kha8lU<LF3yHyY-D&Zlb)WnjLdMF&|#k9+4knC
z*QrO0n`lY#qTTdkDq4dnC*+upU&myCHhy|qT6)M5uRYjoYldsi$u3`i7)f}6I)RR^
zD>tViB0`U8X&-FX+ib>P)R%^iMCeqqT}s|6s=wLNzK!MvGrjr^fn0Zr#@-#DJnBzH
zMObf#)K@$%?C-Bo0D37%QX-sOTzk8_Aoq_Td2;Zk@FW@RN1gDAo}SX)x#R4@WlPKA
z8e58QYksNa<?6ciBm6=S-?~-^@@i;lht!yQowdsBSqxY|e1OQ9+StS=6^>btA43lf
zVg07{cW<`@tw|73?#Dapl>>;3o}Tz^1$6sq!e+06$>j-2=D6iAvDREGIW&30YEow|
z5|ONu`fPiL%U97+kg48{*Yzj76K1YRaLu2+>m2lKn4doN54_$Qiz^Tm5>hE1QN@7j
z&nq(N{d5qo21u)q&zZEK;Hydth8tI*w5_~s1LGFWr%!Pu4~8E#T><9pwa(7OpFTC?
zUy2XbtQWFn9prOg;N#mF*Hl)9=W8;spjX?8my`l3N(14Q_Ru(_EBgxyi%Nta>x!Td
zN^4`)NU66)TW4F_!#h!k3{V+ntt@+adRv&9R!u;Tm>LTDY%DCUBErJLmF8d9`_ry@
zg+yTD9qj$_o6;<A4{^7$@_cK6htO?Li4>Z>s%(3CH94C0?U%2v_O9Y9hUAnztfR?1
zH8XYbdF2<$tc00Thn<mN2&csmVq$t&SrqX-%MA;|MsU3@eA@T-Me6If#+9k~J5su|
zn!HA<@``BHj(^|D0$ZpRa`lyORBt^}?&<661Jo3<xwQphoYb_m_38ERWv^bj_dq}v
zpoj+q+WdfA{`5&MfF8IRYrq?O*8&lS``Y7~LdkD+4{nLp#$WmTU|oTXEPy`F_aqcy
zQ@v}|^njU&M$)V5tcKOwTSYNSRY;h-!L#xQYcFPOark==3FD1NYo=;yc+p+)_nhmd
z#|nmqhEC7UK1W8P$p!`nLXNeYbnAW9X@76oUgVpTRi-w7l#VK^auefDfHOkprZ}cM
za*EORTALmHnR`u5=vQPnQj6N#)yqoVYA0g=z%xA!#yD{2;gjN^9jB!Y*xHFlzeP2E
z78Z4HD^uLe)~(~8I^fXIP`faB1qD1hsmAseOgUwUX}XDCfOiPtogX?cA9MOD7QB8^
zReYAaxw$F%=#jhciPfh-EC7F09Ixyj@Tg{dpPL+VaW%BIa&1<S%2Ll81cH21XP`}|
zCL_Cn=M4r7bX@uo+UOik#?uc7q?L`e7=Ed6!$Xa0FupOGw+<g2pNfpz*1E7)dE3a@
zP^6_1d3z(@=)|ijTfcXg9B5((5fKp$o$H+l3-?x3_?8xbi;2QJ>Se{7N_Gfu^H0*!
zxAKch2;L0ffW~#VZk-$-+x_@(`_7%Bswx+wvTIZ9d5W$RA9<~(Yn3?460`i8ZF>?R
zX_1_e(04p{bsS2f&4YsbpENgqMZHz^V_F!GYHZM?yLEhAgq1b;voW57^O8Z>J#8HP
z!pg^)U1KQxVh&#^r)FoJImaN_CL}1RSVg4{d7Is%-zHOT1pM+!o*F-Qo0I(g5rB+r
zzj>j`ddI`_Yo<u~86)V_%}ntuQ&Z>&_Qn-e#C0fN*1>8}n4BtJP)Ph|ZfWT{T=Su>
z{_NBUKmXLsj1&O-tEtpQV=JYUk9_jAb^D;sd^OSNJeb$-B0L2D8X8m#iBM3v@98Zq
zEdk*8hwaA;V&e}Uoe>n5fw^?jEr2?xXWkk;c87@NC1ot^pC1kGDl4^hbqWd!t`-dt
z27;1K*^qF97GFu%^$|e!t}HLRoi43g!+^x~tX}pmaSQVv8Lti1Tqg{b$HmA~Yq=s<
zT%@e5++qCePp`E1A*Zx-OS^(jY%Gh2{pprchw+Ed(7WjiQ$L4`xR#~q7*F1bLNXAN
zXpi1JX80v@UI;l~Xw7nWY6FZ4MTZtW%^anM;2F1VPGVYGDX3M@nL2@<b`W#F^~1Y5
zN{M;h>8#0qxKNFOfg!Uh^baiHqMi02q1><R-Cd{lZ5Kf|Gr)jvjF;C`RGh4oSrb5(
z7?WW{z$Yyw@%KC1KtegCrvPiiWC$d|gtR@pd;#dV|M|;C6P^#>AUwI<@GCbj?_y>=
zOVS%!Pu%iDsob@+wFT%>vO7sw0PsbZlyr+@2hjuVxm-@ql;Q5%2Z3@7$hZ36aSmqm
zgqR=;Q&bBZQDa*mUD7OW+-MTLcp4&gH2V??ftnAf0um=64i4xYu3u~<0gpKk|NLor
zwh-KidLI*-n*smxcPQUjq<JML>NtxeDrZ7%>p)`X`e@l@BNqFn1cEcv?}?Fwa+vId
zfG1!GjJX0>!~fgMCFMR-{#4aR@3N+`ZXVE3agMu#k{%1jJx{Mv1mMOc0!)LX)?uGu
ziIV&ClG8J^*SKSfHjk<J(PRY$1uZQtzlakC!5#gh+LPVsv0rp8@R8#_Cy_VQdiU>t
zif$&z{Lkn3KMV4zf_ZsyadECaWMgB4$DlD~V5L`_`grH}Z-$R45}p+<g+yb00KV7N
z)MWho{V5)s`p2pgyrt>8#wI4285w|If~)}KWEe6_s>BsTKIUYI2Tv0!LsvSGTba^+
z%}`*wK2kC_H@6$5B&DjR=Ire335H+S!rpP<?r4Mt8}LpA2L~%c+}Gt^h)_23=uopT
zX2JB`u&^))ls$Puo!omiAI<}M1B+#b@75bUId*qkG0m`!=VvFbfC^gMhu(Ep@hF*p
zdMv`0u(7nPN-eP!+5<K!QBhHik_S3B*WY6#<m*n32Rm&u!;wzmeH!2bG2kS5#?g@x
zwymhar*>>BQ4DvK2NU%*G>o7LM`m?R4G$YzjDv(zY*v<ffhM>XI-E&aS#%-oC`)A!
z>MULv$bwS&?q!$9sLGmLTQ;}F+~d0AZ$NuBxm%fOX+7qZP#K%(*0uUxIKjCqhN(cQ
z51Rf}{2}H|RRsp_C8s4S&5N?~@<03g5|E!(PzY*jI)k(dip?^YP<GX5ZiYD4%eHTW
zgDC!&;yhJgWhKiG<KHP*n&w+nVmEMwni3C**Dh(jOPVDQSe`paL2C<u%jFMe`JLAV
z1u3|?3XQ44q{7vz#P0%r>JFXHKEyF9Ee4_}g|HL6yDSZ<lp9RGglkr%0CrVCK%ih4
zaAvl`dRT^fNhv8F7w0}}g3#DYgAKUD9}Cco`42q`hiR0{`35CickbNa;!;ZskqLE=
zz`vY<e-=gpOioxmXdfB=c^<f)`_e_!XRdY2>gY_se+%&QGZS?mZcUw?o|5BY{MYz0
z#P6!AMkq0#01*PT#DXr1m}uD@|Fxwgy8muh`a&#neiINV_RW@qj4x#9e>pCiKr#@b
z>(J{7@^cUfi|>E%DUE{HNM-{?4hFmXKqY-r<5Q+7BxLQP!eB)LE4IlU%J5iV24Be&
z%KK%a(u061#(}lJJjhKPfY^aIu+jdJY56k$A7AeOn%mr@CMF&>`vBd5z^e3pb(LXX
z<o^(*=XM6o&(}Z~5fK?%T8cU?et}6>Spk9eqpvUGt2t!#zF>ECUAkbfs=(F&3B42O
zc?PqFj)cndLw7nrMtxZkq0jGt8{Owgh>4lq-ZlUqj*yU$loSJjgz6<#^uql7YiU2<
z4ah8i00VY*b`})g+uGXi)6>tPivSK8H%X{G9IaQcUV&o=@Dryq8ccSk(neGn%mb-g
znE*>LCJGe<wFJwA+6rh1=+$EG;6Q)?z!^9=5JF<&1;}(QEukZj))E#GS^4eJa&hi$
zWJDh-udJjL^Q&A&Neq<As^P1z=5WXnBuFl{A9%N&U0oBo67K;_wxF;OG`O<#<kZxj
zhXk;<Ly!_EfQX6XeF>-;0s#}{$3zr5VjdM6d*9c$+2sU|>k!uM+wE4vg<BJqR}p!}
z`%X?3v9W`ov!UY*KR-Vhi^2FblhOB?0uca?%PAX!krf*otD~a>_*Zx`)JH>3P7Z&O
zk^*yaiPy26uKiF~=LzTpfQ8Len5ls0(wL{HL;-_WVm~@Mf|O|I`1p8#KQ}LLOq&tn
zpPD*xe)gA)OV`xWG6l{YU^Af&opSjtp2zRA>*~aA-c+7>!IbZ!geLn%90N8d<{1_M
m*Z&)J^WQ6@|979pTwG1+T1Crs{T+qC<F3Lz`FC<g0sjlAfHEHd

diff --git a/vignettes/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-13-2.png b/vignettes/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-13-2.png
index 040d25975418d605b5914b1100bf43295b4a95e8..d47de13cd4705025c7a2e036c242e8e7574b2945 100644
GIT binary patch
delta 4489
zcmZu!cU)6hx19mRU`ehZHViQcGD=mEA|RR|STG7m6KRnmD#YkWAV98Ykn5nJMx{s@
zR7MFMMQTDZj37;VNuenUp$H}fA%PdrdEb2R_wvuZci(mP*>|mV_BnZ}ldYX6`SpQ}
zA<abQ45Fo_rM0!St*x!Sy}hHOgO(&ik&~IG9HgnrZUhwvk6Io&9h@}I4fkCCDY<fL
zB}^}JpPYg^US`vZDlGasG;3%3RqharekwMlb0hEpduSKaaq_k1;n2bP;~km5=_!wW
zw|m`v<E4Yvikv@JuE;O{O<|iQPce9*w5P72X~MUw2i=ULcvYlYJZNk(Yra$+r#`Qq
zV7mo4xKAJ}^UG73EwGt(YNZxP<*X)F*r{YYZOm|Spg$ssn*JD_Ynb~?n@+)5ob0bt
zQayvK{RD_+HurP6*5P)kE?^d=SX*Kip{dDwmbwvN%@wYhQWN%y8n6)aiizdC$Gqff
z?8xQ13tGzQ!+b_tnf2V_-@S9(=QFaY4srYd`sI*{?{gyASd*YyKaiX}x<5T2&N@zX
zcMZ?kG{JP+elhb4vhdrbO%B~tqi#oOriz+G-<Z3XyAe7w=ac;Lxv^Fzn+G!_^P=Y5
zwZu?LTgy#{7vY3!wg`$(hYOi_QTVXa=;edy;UMU~-9B&#k%-v*5|_8dE_?B4#s*E)
zvz)Bt2IN;q!oEgb7MS8kB7SS}%k+v;)Fi3f3^t9$TqYG@f7c|vAbLbe)~!B_yX9OD
zpEAETC@(=71r}70bsf_CMi0|(y7xU7!p*DUwvF9q7{8B!Bgn>%rfc-NK4H&=B=;<G
z_thGC=N9d(<OBPgw~zW}ugvID+CkFzImaIdUUz?-01fg-Vx-@L7w|`+UO*IdivC%L
ze?gXf_C)L4L6t(Eh%Izs?l|D;=wb>Qd`gl@-_@DXbSBoJnO``n9-7mAodx7(=t$D2
zb#Aw5#(T@QbCRDTbPP*OPh2W}W}6fBQY%%JcdYQNPtkOI;-!F*=Z1{ux}9X)xCrVr
zpELk<!pHQ+pP_^98VWGltmSl0G>`nS$ND8&UW%mQZkV+oEreFqRxIVcM0N1VUS7Kc
zwnNEL3pHwy(G)6B>~II^F+JJ2P^9r;?m6xwHEEVvz@ojv&Z2EDf$gYnGjS}QmE40I
zm{=cvKEICZk8eZXy<*NXI_)h0l`YOQkHy-(bs9|@r5<NnE~Yoz`uG{#j-1tcWRZ1E
zsjvhG@vb{VEg{VD4+0NeTTMp|+{BTe3BAFBAgNNfPmm1W3jyJA3EN6u9NRf5j4scp
zc+#uWI;nAyKZ8<!*c=mCJV`%CW_G(u{J6k5Sv<P;TiEWbQ*JF4fw)A@ef`^}S1(cD
zQkDzkbXh;+tlq8kXmxg=s$-FXV5k;Njel<x(4HK--pH-<L+th_0Dr8$nGgpK^B3lQ
z0-0rDC3#F!Wb)62rAPZ9s{qz&v$#>O#QOL6Vk~FgQ}lROE?cq^6KqiHvVdL?wECFR
zN!qBZZm?K+lX?qQHDe|-v8jjhTdmIm`f-=L-3TK5mE_dDdNEd<CurZ@6+3P}?cZOL
zx#8V+@ex(z6)XAX#ETm&jK1}dF5YNnc=i{je7hNI4X+!XZWNn3f;Hktz*KlaNf;KY
zbvBG6z}9W9AD&)|Ry)^omWj6X)_ziNM1}4k>!fh>8wz4}jk=|GmTJ9G?ws2W8gM_K
ztlbBwHDtV#j;T)ie;)`-fI30$ES=9g4C%kKU<Gs~iyB^V6O&(g><*DdS8rVe^q7|>
z`f<5oA>W{nYpJlSVyt#|Mx3B!jy+SS0$Vpc8|7ST<;2VszNbAflIEL2Ox=F!-&yZR
zZnEJWg!DCU`ZZ>5bkXzGd3n`hsgfk>ZNLjo%a`2)zDU0bOL`1fyL@IpROvsmP3FG>
z(`zj~zssxTg~_gSW=6oY`wq%ICTzo)>#hnJ%|0NjASL8$SLLqEQQkX0%lIMGAQVpK
zj?jDZ0)7_uk}g<b&*bcpOfOfyXI1{6y>qzybJOw|s>x<*G3YLG%M~UD{)7hy9)0dv
z?A}WZS_UMTUE~!FZ<kbEyf(4JGM;u6G98aAdoU@zm(KOxKJlSWI~hziS6Je}`21Xs
zMom3zoy#W_enB;RqtNUKRJ&slI62P{;v2j%mWJP>X04vA))F@TDv`n-R=pYqa)(d*
zhhOH6*NK$W8V1yDXhg(D8Ue8dtmB|%G_OX%k{Xa|m-mpM%6|f9qY?j?5Se--AmWn>
zR4L`l_`xR?-Bk)h|L|qRY8FP6=$(Gid-IbDwNhbfA}riKb%AC#UNyZI(@*WDyiE(b
zsKq~f-QbTUz*^#GRJ~vre6y3}^<BQ_Gu1e)oUiu=j(kK$<<vxFUBzT26n*^qm&e4>
z!j)ZvSQtKJocF*NA$<oJhd9Js>g7wSRP4vA3TFWs&0MWOHYal~CoN;9|4bMxg63p<
zcOqR>bo|HIq;j0=g~-{3ojXYbXToX^XtFc{^t8*<z6imCSShQ;9+!(Oz&`sYH`Z!c
z#=n*N;|3Csa{4n(>cTO=saR$ytTf*$7Wwe)lA8uXrS2nQ#N~uCUL>PSe#<ebr4^>K
zz}(J#2>2?4hk#d%5}qQ+mAkM?c4_DkwVMzj#1GUi&t{BXII^}Fe|_#VaR@w68}^&j
z{hF-BpmgzxymqP2i|dX00`B}wGpKhaE~rrM=M=?{s-hH^<~Dn_XZDoTTR4aiXA@GX
zc>YI{y(_uIA`PzlS%+U-hN<0w9=+zqI!Gz~!3))|E6CC>laj@{;=p<P-~s}7JPH7f
z(K$Jfe%z~z8{ZS8jQ#)>4!w&(hPn{nyGb(?G%FA*Sx@9Ef@cV=<2BQ33mO!57gXS{
z^Fo5?=FZGRuq2=?t5ZXHW%NIS&z{fBeIYalkNkMg@oQ9ppRN!)C%;m%&JVi@%zF4m
zeo_Icm5ckewEMQnEp)l$J2=)bst%*Qk`}KyT8*{waZZi50*>=4mlhqEtn84k7eD>!
z@kpE|VcU<07E6ZjVsj!nm%Y?4PPS)s@|pZX_cooMEu~rK_WCtl$4#4XOAX>3oLH7K
z%M72%{(c1fdM3F{VI4T!jehJnlU1-{nPK=8@6CzrZ+^(sw>{=)#k=9cnB_H%Kj{c}
z+Io!f?%DlvyzBD1#X)`IVxP<nP88aexEh8XVp(cqa&=cJe$ZzpDP!U2=#qsvsr#%K
zfqu`ZpYjLIcax{$CL(Srs)151lh_lb^SKr?9Y4I-oVZBn40y$a8A^QwchZc6QDN&~
z>(pi!y)|@}+KJd(1NpD3*n5TASDG?ZdD3y4jD<@CENeSP&Xk4DSx(oD)_rZ>{nyE5
zFXKHDMZBJgfzlT#^9Pm+1He9QmBO8pWy3hC3_jkSzPfF^%3%c{W6IL(J$V9rurg=Y
zb)Xwf_q{LhBlpu~U=hecLnpd<G5r@zf5o4TQtXX2l!9u975BmuBv9-p74HNPT2>TY
zu%vTT?NS3ksnr|7e*M`%F00oYon5c^cYc`Je|6mdZ7`^AtQ>|4TR>m2JwmOtu+&|3
z8}R;`o)C>uo8JSwzjO&MkfEIdwn&esJ3*(E8?dC(O=_1%@la((G+O3`?ydg>{?*-M
zyuS~3P~H^yAJ{H!>gWE|^EWSSarSZG&M)sX#`sisQt1W5OFwj8K+|`HS3T4#vZ*jz
zGFH+ZjP}bnO#89;@(92SC$Edn=?d?<$oT6Yc#5my&P_RSK}pHWfE~?=qW@TKn064@
zWn~jJ+f|a{Plap};6zTgMXZT*C*3)OW)9Vmq+75356uBJ%WamMcBh4THq@PzN1cT9
z2NR1J;8;(r+|t7hTEDC^cn|{8@~Op7|4jWnCb6cRu3e9V5<J%Laf4Em2e|J0f}Osu
zT-8&6!skU}6!9_dDNvQ}ssvlef>XSGxN-HZ;2pX%Hpt2EBb<}?$8N|XuaDT2(9h(X
zAjyw&YcmnF{`Nsal+7vO?$r&f<VKe#Y~Uyt4dfIjs8V{$BrNc*zi;A!ckGcm(?LUy
zl2KcGah3h&(8-agB38gABq?<89IxNx)u3;pVo}@2I0k7@IE(Guw5ad?Ehy1qXQS!V
ztBs89W~W9a4;m|(#3Ht@IqUVtNV5>%KNyobBBru7f2%0m%ZoD}qxrCAE9nLa3<;fF
zD41o}B$u<@hCrkBe{*&B^UAmy6l_#68+n|;PF6BxA-;@pb&qxf;1^xq_)zqqp}H9}
zhc}L^@ejIyS{yKLDhtQ#K*hYC^tZiDD!VGiO--H>M?EumhOj;=t~kdTe`?QQzi6zt
zg8cPXDa6~yMq^}#+lQXsyKW+Mww7x^TS8LiBY*(33>vfKR;grLJRo){!e8I*%Jc_w
z$EdH(y|Wljn8RD-e0h?Y;6x_I7>1TV1i4_LLjb-Lzys9yebh~IXy3KV;WjCHf6hRR
zxw<AoLReI(J|s;@Hj09GrRc+3VOUlRw5<4tc#QZZVW2QHnH?J#_$Ria#dI-CpYrk1
z;D?hzx|{N|z2MnGl6^sFmeqRvi4lS*hWvr)BEfkFyxlsNX-Jq%pd7oom3*zGFUZ~T
zbJC@>t-X7g4nc@c2y-@!-dSJDHUe9<^x6Riwz=dK-|a+@AuDa;i8gXze8oH<0)Op_
zZc?-Cx_a(4ucbBQneh_P$>9RydDbC#0K<LH?8q$>L8rTxLd(8yWd6m1E0kGOAfyoG
ze!4s_0xk(m#d)RB8chWQ(UhXQfLn7+;lj;f6Tfe8BUUkXatr&Mi!8Et`dE>?iC{>{
z0R4BleU9*o2&Kl6&Vw>Lcd@lfKXxnWi%au)iO;LiTgHOoBhL%}&~6^C+F}dsd$*Y8
z+aNhpfeNPsfe<h4vye^jzlYT`_UR}xN^RQ~^@i8K_o?*1Yg3o%htdw${bNhtdCJzy
zUAZbW*Dob_?CV{$s>y&X4ysj<p3pU<2c?Z5%k$O?@;vH7n4m*lS<?2GQuKwaxX8w7
z?Uo`Zfr4nAp&fOTjXS+)u|*uL#eAFW>O2sBwpHlH&#6|PP^HGN!>sXsK6<U6!K-m{
z98FY+j1+YfT+vpyT&Hx(@+wymNAIu+AL`IeeD~m<AipEy$JNm+&*|R}SG6XxtwzC+
zPo{<+!HF=dJ7V(F&Y}YsBZG@S60~|zHmitkWfrJS;G?~)&5)(EiWCavrGtAL_ju{T
z$i1Kn1M8rNTU&iRT+s`}>z(Gj%S`J_D%|st#{B)+6@ekeLC{{{Hf(piS-6LnUj5^@
zh=+w`-><7YWqx7AHn%xKO0LQq-hjpXNA?Z|h+K1B9opeUd9`(DdD@HA0_KV=iU*;W
z-X%y-Sv^%!w^y|YlQog&$ba;GUJ09YjnS#B1Mm1}KoMyF4gu$wJ1<`OGK`dX<ncO#
zAmwzfkt>?%lj>PmU(Et4jAver-gerLO^P^T300jvbERLHvO*tgILeLtNJ?@>hO^<e
z2;}o@AU8pkJ2ISKns-?1xIoE6;PNe#YkFtl@6Cg2EY)b9h~K2bDQys2)ZWLxeQ!3Q
R`it~;^f1mc2jh9;e*h7R$8rDw

delta 4589
zcmYjUdpy(o|Nn-v6eh<#mrd!SNRB&G+fWE4_gm2+k%ct3?UNGg6sjfHC?&@|O753x
zQgUbHE@I4ew%Nw;`;2qW@9d8~_IkZvuh;u^dA^>X&kB$Ib^Pu64*rac4C49@ZT`~I
zQlciG^X{&$uI}#co}M0JIv-v@n7MYHsK74;h`XIdpS~EHK0h3GNrGJ4K;w_jz9D=X
zNl1PxaJob(|LSnY)ur88kUuY3xa1Q=kC~V}T`g#LgXZ3`x5+QV30=K6Pz5vjKILq(
zXZ6&MJ#dxV(*65%f!a!<(}JW=z)9?~#R7|NcCu2F(o)EI_ABFnra3>~U#ob&Q@_EG
z0z{OkB=MA}4ADktAMgkVQ?R-Aj<yh~RgEkQQXFx!cS>>^)l>7qx6)r5g$>z$7wX?X
zUWdOs9J`jwqMn?cq#E0k4Xp==;+_+f7-`lGd6c0??UE54nw8@((#iENM$G;tB8BsR
zoz)W@KFY{=d>S~pg4d~B&rQg+V2UKxdyfULmf0rgS26g3+VdgL2R?j9`Rzi-_!nig
zC_v@$5sWBm7(y|{V~K9Go9HrrsKoo0sbfpdl%brTECuKFG!eEU^ZQksfDnQQMO$7A
z9Wt<KE)~))W%|5P>b-r!HC)+I>gdp3jSB@CgWS7Q**SRurl>roXyUa%?s;k{_1oNz
zVuoFJ3$g_WTanaO42hAwrHc=;7&@@hvI1FC(nLoDMaZt<erz@(A1A`>HBphMkWJT7
zriV*wafYfLO(#5;`a(y`x7H%z1nQz@U4g6K!s*)l%7NPRS{%#FmtSW8pb7-5`P>$3
zeL|giMQ=H8`yO$wR$8a>c)-30?}%ue4I@{NIM@y7c59hB$#^nmsRSyN6=IXu7b2I>
zAMSXrclDB7*?O4P#o0rxP#1e?!FnTRi(z)Nc3zuvg01zmFjW`4;8Y~R;bIa(Ga$%?
zd!Uz>{EKPZV3qQmz&CymOO4QO2q09IysZ2#F^uZ$WqqiO=^GN&+kO#9xN5i@-aQV4
zZTc$;m-xMZnUfBEzJms{vgt$kv6aAhyk@jPEEu_!V4gn~<zD%V(k5lpXa9Uj6?>If
z&M>}6!7zo$?)Am-1Nlw5Fe|%y4Fz+tEKMbCQaJ-dKIg1nLHk_s?Rp^bhvuO2+}$ke
z)|8=)FDXSD%_~rk3y;rx;X44Xve5-$kDS=N0IBl(_&4nr59a(Hh~ySF%lB5@A9(g#
zmO;aA4gGc<`P7YHaB?mDKU1PB`tio(Q}yNWAgae&^qQOuSEq1PS~|=Lz@ieqHsr+B
zdU!nP1B%4QHxw1gv!XaM?i);>KONc{hLG<qnrQ!EdC)T8ei|vGU*@t#W~yeTQQUf+
zMspo>{ji7UP5SMsqPk2oNf_zktq<qRTaNfw<!#GPd(*qsSZ3<sij>jdmF3ANRSG@2
zV#LdWzZ0!Rg}}wy1#BRPA`X1E=I?JaAmLaH|J<PPp}-T-K6eWy<;T_4+{^NV`cg|A
z?k)L0Dyu=YjQZ5&Q`2X1?)Xp1du~#{Pj2E)y~z|RO~Dc{5-b^m%dYiVEL5oWS7(H~
zmc#Lg$X>7S`6oABA2mAGML~{@o{F|q8})aZ3css9&qk!U0(B`jW)kDw;}~sjkTWVt
z7!~fka?@lS-I)zB2Stcj9od^RHF)lg^{t4#2heQ$w8=CFl~i+|(1&nPik(cm=gu16
z{waq<-=MyYL=hlX^3OomkGY?hg<){r5cWLph`S--@y9YMnlKg`TQQOMPO4_~m1loS
znm85;zcV2}zVnro@C;+_*%9S^+ZxZ2fZD9g-qH8gL#GLlcr2lDHiQ#Y*e9P<aThAK
ze-WxUquUx<{i3gcBo9VqhmyIp3lpt#6r#c*v_OuC)MYqYAeMMu@OLGod;kWFA<}0S
z4RKWzgWtfR2@Om!7~kItA|NCU*rZL$t-X1q6UHlair&}Itm=t)8$dK*olZC%4H>OL
z?Jx1b7&>XmeNYY*^ho8UV0RCJr9PYQRzJ6uV=<I|G;Z;?J=+y1lg@=Vs1)|G&iq|~
zkA%r`%xvP$;O&|zcJvJ<X0}ZIfRQBuawR?!icQ0?4j@1QtajGQ|3WExmjA5TzODZF
zcC!i&yP6Qb9$th7*OSA2i@{!6E{o7!_dV3{Dn5w5@&rpzFVib_T~si91buuOgaAOZ
zyw@8mIhxMa1qb08y_?v{e}sq;6asEI>>xC_?Ns8!O5BDF+5HdxuR`ZuG!H2P&oD}S
z-*uo-lX8THWnuIs&MtTlh=mTd?nTO0i+EVb_Oi^eqDz|)mg4?n_coGWYz}JNDJW%R
zmqLX+!$A2|NiM-m^)=zt^kpc1Z5NQ@bMMCn{qwx`(?i2@HL^PQ&|mst3;1_vj9(fR
z5+HOR>KNwMT|ZmvAtDck*jCsbcMrTWyFU6x|4tebE@0&zQQmh$0Q7|v)HJL64@58@
zwdTf;y?F$QO6*IPD`ysQ7hfI`{rTq6S3^}&yj@i}qC>6lB?uP45ti}33lhk@?<;%|
zEzl$sXfjA&E?!`JI-_=reJt^~6S+>BPY5`NIm~O^U*hm&*vh+Xt*0RHG)bb@x4k%B
z-2S|{T(ui?BM|N-NbZ$W)pZ=4&g(5ZRZ{n2L603P{aLRKi2nj;@GIb;O=2cb0zgp^
zfvIr{y1acMvIZ@zpy{|yVblE9MYmF-C46{Vzx$ain83<%D>m&_m#jI642kjD!Uxr|
z{|FkhU5eZ4Ba<_KJCd_<v2xe+{~UBt`dk17ZVnV<v|j7qnN@}h*c0Yg`a%VOJ~Oas
z&~~Q_6U8IoHbgfFmDC?K{P>6pDM}zf9btIa<76>#RbYg*v_#_^!)VZ%UI5jx+^-Wb
zH2o<x-jlOS#`~UbXqS2{_vb_7Z2dcva$ppVpcnpcM{uTj%8qrX!5gY03m!C!gjj|M
zeAj_QNsOZk>r%DH(t)mvW^W_!LVpye8XG%u>gi94`V<<Mg^^>p%*0=lX*w9~xSB?<
z0Aw@<vG8u6ox2$QV;jqo)w+_L2K|}F!r7b7^~qV=x5mt$8Ty#yEzq=vP|ZVT8iagb
znMqPvPy%NPYD@`XUvv`e&ThK$b_ka#QFd1J$7|-Q0&w2|oEWXnhT>%MxaMd0A0LOd
zNu<(q?ms&jFEid(RUpZOga+!E+M?ZMb3Yy+yb0xH*JU-b^*sl7S&WPDrn@NC)Eh}c
zJx)j3ZYk2GRLz=2`fhwJ9m!P$`3Qs<W%9U7S%tq(GwLV+q=xeX*%MwBam<s>$v)r0
z$XuqD*(Q9_CiZ;{n6THq9}{j9t;P{={>GEZd{LFxr_MdyTR>RP<7|45>aKM&c*dWH
z&%*e?_cmlS1c5KTAP(jf$-R=T0)l;sC_!kjwpZdac8Y><uk3^`5dl!64@aLs9V`0>
z)Cb>03@|LV%4<uS!j->|P~u2RN-i9|z4sj|vhvEW-X1fMB%)vFljgl1B=y`Bz)GBg
zv_KVo0aUA0*jN!CBuXahnq)4=Tofb}*a_6oWs8t4qsU`dgKf`T$*9jP0EfPpF7jA|
z2NnY~j~N!BP9RchP**s+U`ry8!Gqf%m4IZNM#%%hO4^wUufWGp2{B4YN@Y|F#Vc?G
z7G!&d=g*@BHoL3k9PDuz0{M%#=k5ikh(GydK$24dlt7k$o}<K<q!t5lt&)b{nDHbu
zWIurYtKbpUKTpPiW1}^i-3;QOk?B5oWPa;%tIq?=zIKN6+5acEC63>vNr`U>Vq1(%
zSP!50me>DQej7|8=k2D0a{~Gi>s{MeG8WP(?qXY&C<O|FqedWyUJLqnh2s*oPD5iu
z?av>;RJc2x(Ahf2yK60aY<_TGLw0@U4b35v_>upR)EyC;1HW|$8X|_@DL>Bc8)jFD
z!MSjvf~Y+3SDC}w-kS`L#qbmy-7{c|t;`%-102dLA!22w1ZWf#hD7ZRY#6)HG?R7>
zxB%&bqUJbvs&S3gWyVJY?h6NgqAg(1EWG|Y&qGpfob56^Um4t$sXARv+~+B|TZhD0
zuren&9T<XkR~zJJLV*vFNfUuAEz6mddJkZ#0_c%$I{08D_*nX_cN3|z-ueNx!BJkl
z=N2ZgtyqeyZohoQFe%v!KOg^Ghm7NnqzyGujK|yP1AAG?Yi)s<lw{fhZBNF{OnO8;
zL2s%w*=wpISgpGIdGJ&yD{Lgvq=lwK+OPQ&fn9S*_`*#9$j^h@Y8N#6+Cvgj0K)+s
z;<Hax!sz;o>UHDtO0OIeH@3QB_I^Cq24z`iF!PD3NZoI&+Spmn!~#@XX}b6eFZ^@>
z_NZFmZ|P$Q_lv&}BQ!_sTC$bKU=zt-s%r4Le$`;p7k?><*CT2RV*%Ls{)H!t0ob_V
z0Py=kznY%Dx4{Z6S<gbKYsfraUXH$hd<rvqoOui1mmF3_^2b`$x<TpRrX4>YdU0Y(
z+{SSn|EVe@q}-yaV(`%MHZ<WI_Rvl~huCT&ANYPWd^T#V=d!#Qgh+#k105a6$V-=e
zYo)tt@og>x*%@tvhd6a#Wyqj@E`b?5^4yC`sac&1*I#T&`edXCTSXCMObHuM&ls^R
z<LKne(bJ+ZZ|N;{FX%I#vZ{ya|FNo9tX7yqAKAQCLGil^Q#i9KSM+GEwUoZFha+Vp
z=S;Zz8X^XV!igqAVt}v;sa)`vM`il}{;ErDIy<|rGA9<h)0<UOKbx-OX=aq}FuV9B
z=GH*WK2JMZCM&L{X{aF5sPrYmiffYQQ2!k7$)>DVOw`{>u&R=DnQHe-{&BRRTRRz=
z#Ow^Wvks=`)CME>b{f$%DZaHi-xfOBt8b?EK0vMw$0l!f0O2z;(aF=a66eKhzBn6c
zPr|}|)#fty^ZPc!;#jINEyb~Pe1C*IOeIz8BRjRAVDx_wO;Uxp`f=9_iWIkzV%w{I
zp26)Azqs`aN+fhboaRPCs+U`{K?~H&s>Ww%cRqEZCdMZs8W@Q!?u<_U#oTZ%E-{w7
z95bE4?l?m979}@@`KWh<TfA%TXg5IY^DTt?A@wn+-6hl?^$u~hs9AES)E=IBD6N4%
zW~qIRdtVW!IoYz*?iblmPxv-6JIZOk>s%gg5-XP#F)nlTn)Nh={H{vTC^FbFCvHvj
z9O0`rQEa#P-+!*50Ew~Jnn|lClcuY`9bG+SHEdv7+>|+btO>f#{*3>L_`K^+Xg85a
zod9%<dkKqYD3Ye3i;^L30ftXEU*ZH{t4M-xr419oHXZ%+y*Y%e#o^Rz7;7UR?)4~Q
z=FyiQLNUpKbh5q%snLjT23!};#Xk^;PR`ix_N=^=`UTj-9N(x$w6Bv^r8Jg*9VjdY
zS|GGDo%INR;4q97H$S4MOKa+d`YDZzd4>lu#8Gj}sS9qMOvTu;^=!S;{OPx9oMs5E
z^`mp<(i5hU=is!JGO6Feciry0G8t76MKB}GeCsTyu(bV+gRp0-j|k2~nH)?_#0PC;
zde2e7xW2Jne8Z%MCNhD`FQvkIPmz>Kj9=4cahM&d4zq<GROY&*9t?q>e;6AFRW<PM
z2zOhEVncnbJE{sAfBx&R|A+J2m9at;V;xmKFUxpJE73+BQ(dTRh0rAE&iQW7NU`jh
zPQ{B>RV}if!e~RJWqYHEpvTF%M29M;#oEK=moEIl@X|yZF@C=u9oop4K5hGDhAUaP
ww-aDz6{kwA%$>NFqeYS>g#KrIqLOUDkR1BR{IC$_f8gh=i50pCdFjso0Z8`%u>b%7

diff --git a/vignettes/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-14-1.png b/vignettes/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-14-1.png
index c8572f66c853a17a005406c9229427b07e24b0cd..61609bdd6248bbc13613fcc48c860cf03c73fc43 100644
GIT binary patch
literal 30517
zcmeGEWmJ`K)IE%DK)OLdX{9@*Lq$OvL{hq?r8`7QQluM^k_Hiwln^9DM7kTKrTbm@
zd;ZV+jx)}Ob3UAJXOE#<Vc++C#aeUCHP^a!n2M4NE*3c!0)fDlee&oj0)ZTgKp>T4
zAj3~`|Dcq^8>Yh(Z6^c*o9Oxv(yuAsa|D7GA^YgTGxxNODL2ELL#Ki}+cxq7jSu{P
z7ST3hzw-BgLi`AobBVP;t2`^zvqQh^u0H!su2;C&7;m1TKKKxr9ZZT55JT|rq349i
zHqA<BgV*4wX@5rYlvrkSLLYB`a(eP!n$vQxwrqkK!9$EYk0_k2$o#MWbw$Hqe*FU}
zW*%aACw~~03_l(qkn4R-@Gvx3#h4M^`Ck+9-FZ#${!UyHCcNWjw<}=Wd2NaD|G)JA
z$IBG@U=#V7vWCX+;pW5;C+eNoF*=I*gOSwY<CgrMCJPb?Nj0_;pItY{C+zAKH($Zd
z6*e|qp058$7p<`zXr-Vys%Xc_vY%<fmpm^Y&!v4$@YIV+$f3oHBH!!(%MA{7$XA-X
zINJS+F66d#H7)FhM<?^%PVdp<Y$uEJ{dXUi=&b%Wu7G{C*OX`nay7N|COZf@TwP#b
zK9I#EA|@mbq!J|{3t>?!cjsws<#sL@H)CMUq6WJPz2_&PQT8p}?Y)zQ5M@_)t5bir
z4F1iVx&v#!u&}u8sf|r`H*2iO;tB$<%@E_+v)7>*H_+@RYkAg&G<VPULL)vI)CboJ
zO*;0xIonDVu+@?9k0rWE`cY5gJsx>CSa86rz_;%LOP)_$ts7Lx$_n|AGbuMEyxY@w
zg5l`+US4j)wSKpL+mPFE_I`Db-0uhC;1h&hbN+q7e&=mkPoJvPa4My>pwmnyCWR(&
zyiF3iJU^jv+r&EF_EXKrGw)TYFMdRI?HrG?gq^YmzZxQF<zuTp-E><1#Kzvf+;>al
zQ;Q}4_sWaq)R((=E31Y$*;LW5y@zmV%5ArkCW#N7RNnOI)5VVPli1hq--k02lk>MM
z^*DWT)f+F*cG<UVIKFG%_q4wDe;$Jof<hrMn|ak9Ma7+ZM1d2Ooh|9IIvWvTV^eK>
zxUn&quhzFrY+kjrs=B`!{)xX@Ug7tHcg^7Fp}h+HR<9ofZisvRp)ZV|qK-`!J@|Qt
z;%t4wK7@<}_lqM#R@UsRF-qL~3F;i2s<;Q&(+W+xl_nIhHW+=dWs8sTWN^*7GEJps
z>&<XUWMzcE|K0Sh%e@tvC(6p36J+6}*%r$G9(au<4clFWIFuOL-^QwN`_tdqe3r)R
zRL!#07+&PFsARx=@SqtT^>(^aDm||IrN@W7`98rRPX7NEXO~y|TW_*fHP&F>TpPZc
z&Sx>xs@!#F4y4AmxAwe^w*1{RzO~(!lPYD?4PRXP@hO69W`e(eaIFgyxkj_l#M}>A
zX7ay_+<5Q4H(ej5Y73b#^RRr>PLxGUd!MVvhZ653CpuwyaU<-_A1&Rqneyy$21cv*
z-wj?9%$wc0W8>2|^!>XsmFVGdSu=Us4>oOW)cc_wu_nBcE<(;TK4k1Y@^~~q2G-p^
zTjjcjg{2g90xas2QsIW6qzcY_ak8*GJ9G`qMvR*Aqah_qJ$$&~Dn5&W-~V$hEtVe1
zXU1J>lZg19>NUh}KmUxgK724%Uo$sejTdMX|J4x174Z@kbt9X(FpE|z!D;{Wem>vn
z{)>(2632ir&wmp<dMy3kLOF1Xb8J((=-@$WNqXNfML9}<tCxf!5^ax>tCv4}+gdwa
zbb+4nd-8k4PA^~X4k}BQ7$bop5QLzVH9RG{rS-IIdW6rM68#T~^z27=`TMsF9Yq5D
zJ^ZQ4gI>?JqHc`kxMqim{8s3#GVjx=G!1)$3OMrMshX-*5;37s&yS450ht3NOpF_^
z0-iNaW3HNM<M$#tw;a07p!ys5sn`E}MD>wj*}(7OpgoM(>M_%6g6ucpZ!`Vs^cz+6
z>VL#6-OsvJi}e9nM0*;o>pqJLq8I&(_oZ-(pv;TH@gT-ODoi{Eb&gB9iTXO9EZ?#Y
zfH{6DYidn*a}{+ob<i2tZox|OpKB4lAFDruU5>0|h_8a2mc);l$YpZwUX88zJG<4X
zw~}3Qrv+uZ@koE_i%Ze*=%1k?GA@1H<vypCAJs1gmn%A{_so$Xy4A30?tEuD<KxW`
zcQC{=M8)!d++uDR{WZqE>ViTG=MyqHlC6Os6`#d?!t>u?sUcWl9&@chiYffJd5j-P
zOG}f`PzVAxy<F&S5VjsJ-rsk&Ply6P$a1qUeqJ+oIAJgDkae$|rVBNqrOLnble46Q
zAUE17c2dCGtCRlF5bCG(?T_xU;+Snu)Yx~tA<53p?n@Qq;^4r=#ht2mD?2uZz$}C>
z5!!r7UgLW~I?GIeQH&Tyo<Uh*SVR6vGK!9adWe*(s%k}+SRfljy(c*1-KKA9YRYdr
z{wXW#zV~g|eC9x;8DENt58oNHupMw~$uLle{0X!#(aT945ElMr0h1=3Ds~Mqzo|d9
zK%~d0u;@=V?-M|Dv~U_y(xRyP6V@Uk&>j8r{Woni4z0@y^>qK)myL~$tn7aV^0HL-
zJ2q}Q#s`F&zc9jo`F;UA4f-(Bfq!$r(tX#Sgj#3Jv!fzi!}mm|E-V0q#)Ekk&p#t#
z0+0=f5ciSa1Z4Z;9wR5=h~es559aNz4vbczH+mL6H+_%w57M_&?_paH3+;P~(xPPh
zqcEeg``^a6(c8jLi*XAxjfVHeix;7h|BUaq$v+3HK=$-8f7JfAxH#cHb_=4*AEg}W
zu|FmTiV~7yfSr6oi+Nixt`%8IL7*pu#(5tNwue$2jA~?AG%+Y~$gV9ItN!x-IP}Q3
z?;{A3<6d`COKBxtn`hQF8JgD2$Fupoz@gKQh(;pxzs-zFPfKixX@mCAzv(`bgV9e~
zO7V|?5mx{C?Zr7?Wu-K@GP-`9{$5I1w0Ww(FM^(hED5)La|T~$zMM4Y-)(Cg8S)gq
zW8wEBu#iN5Qq|%hFwo`vwOVL<68#nZ*=1{;WtWhN5B|}#?THGMp2TKRGdW3G!We&#
z7C(Qf7XABZ0!SLNyeRViCRfKH;~flE@3qIS0ZxMr83jowz^P@%A1MaGM}X~&W;%;E
zD;${p+q|A?wZik>_$O6x-h2PnkBcvvw&#gryxHv**DTI*ggZJn3ehZr6NzTr2EW`O
zot5)jwe@I|-lik5%YUOum82nN?A5B-x?6s$7R?*K>;5ej93gsHWI6gm%ot=FuM3y+
zV>*d*mCc9$=Mw7~>S)QWxDO01JXshR2&{mE`{;%$hK9r~NaqM=*uTG+ckik^`*4j%
z{}z(KdiZ>-!o+@7X_mUh^@6&JuvR`H-Jj|SQWKKg`<&gGtM{_R|99-e2+y^(`m^3b
z+``;~&<gm{LQUIl%z~`6df>EqsOjI5MtOcVGg_wxu<W7|4Z-Vm?Lt(-ix&nc(I_HW
z1V2#}k=O{p4h#)H(k~&4)Kg)TW<5ap-%$5DNvLA3+FB8T?Z8)#%Y4H$>xNMN$O^JS
zO5Y0+J)Eo>9Ukvm*nyA#`13=tlf}Z?T6$XiCx~FAla{-N_%S(yw#XSAoR`t;7AW<Z
zKBOWa)2z8f4qp@8ZBOP)o|~INap_&AYSE`fPQqFZK>hlaE#RKHlc=^Y*6P7p{u6mM
zAPD#PPr_m37`V2MYrh`T3J4w7#S-OeI@A0t%9<tr`QgK2NlQysY%CI;jKJlJWQ7?v
zy=)&cas2;Gc)fHoCg!_84;L3UGoliKZHTlL9kxa<`yH(<H8)ofrLHd}Tb7ufsR%*}
z-ZlNb+x|{j`sU3=q?o11mf$Zg(Fi5lo5yaxzFF)tU5;7oGidv|pD>V+%C&xT^863$
zc^LZ!895IT^Wk7%;H8GOav;+EI}X;eVZ@=XLO+reQ2fzC@E5uWPpQIn!ndwNIl<W2
zV@5S=#K3_4sLet6n^$Q+h+1&EV&ifLWn*u$_t>MH{(9P*@|d{FnnPsg+I`tHjVEh2
zVq#X1v}4B?7axt;NaqjYP{z`y4{CR=DZB|gv8XcdM^?%q%#pdC3;Fe2=FP^&v}y^S
ze=gn{KVZE7YfVY!@zSlxo-Z!-Sp0))rn0<Uj%R6U20t<-5vJ94k^TsXFVCd@@tlnv
zBOGcV+s5;W{*&)fP3yW0lRS^haSIll6H*jhX@6Vcx(OMN`LVF5R<%4*zh;Z=J%4^b
zkVe~T9I>gkyRleMV19BkXJm9@WVGhrX4;L3)jhNxu9&xAWMmzX)vFLej+(FbzYfM#
ze`3Bd9ogPK+R$+8ezT94xk`*Mqcm5K9-@)Hqq~xY|M$<jy*d#Was=7g;bRBx`Gp})
z!T(`al|`CwQ?aY+Ubr4s0q(RK^Q1{oC1l$3-ufWngEKd0<mqEL^<@DkxM6j5)_UY;
zDHW&Q)B4H(*k^l>#L10*@sO=4vMI+NJlR+p@A57~hu{!<DzRF6PP}?Ix_cyJV}J&4
z!m+8vMxUwpzhfGxE*f9@AGFj@h<p8V!8F9=XP)8+y{w#UScTRbLEDe1$+u(DBLh$d
zvXC--$-jQBbz~3_){ZsXUk}Ms{C^Doa1+vyA5JSe<C`XCx5+qf;E><^Rh9qp2j#?Q
z#powY4jscw?+>|il2@{gC(CriOvZ;c|Mx}}Yx8+uxV*T!tU}b&-{B#IEwBDKm7kfi
z@}pzB`D*4*s;4*_zQkaah0(>SRbo;>*mc_BKGq%o<Oy?F*b96yLZVkc(%~<Q%*Pj}
zKwH_hUmeY8)`vclvMjNAh_PEaZ~W-7+04t%v-^!%wENr96ZSG!m$eQ^+1a5{t{27h
zvX$&b>QCRJ{$~W9hwW1M_k?`CMIA~DFrI%tpW!e~nSRXrQ0gC0fBiaI)k#EkA;?|(
z=_VubL#ZK7;sMV8%=LNA{O`ia1db3S3lwCBja#Ig&*~K()&Cb|sKm)V7oTX(Cg&;g
z7~c@EZ4E#n@r?n0`tpyc4i^wkd$Mt}Sz{B##3BbGU7g?y*gfjcuu6=uB)n$*?L8Up
z$f*Lt5#MgiF4qdJ9z2l`3^;xK!T7&nyKc0cpCFph_RkKD4s9AP1inQSZY;#oW3p2Y
zP+C<IJ`5H7&g*-It64^lg*}22;5Hc$OXR%v>Tr|oipDv*PD3rfv%zxUJ)>d@@TMs8
zd#rSHQt`aNZZN{h==g7H6y0Orutx|`h`6+xL>KC@zt>}>UnXW!YJdzkRnT5ZNl74C
zfva&p_W1fDTFHyo5jZQhHSq}v=Ez@O-?(ujOF6S!LTO?2QBZqYTpYj0-^I_i6Vi_!
zDI{^<VPTQ|$An^vG<kf_*O)RXpFZQqcZj-$6q57j>Vec)bs8|c{ndfJ<-VTp-%s}X
zCCNB+_>Ed`?3!KkUvfcd<GcQ9FJ7$qJI}ouN4=|)KfYMj7J@H%wo#>7roTCmBNIfU
z)#!OJ-x1!^kerk>T4^S~od{G=$YY5_A?3I8NU8jNW?D1zyJ8LS#2k2{RhY-mtOp_Z
zG?$PRGASg*BqeEGqAYxi1WKu!ENIb}5}%R5`N97+L90}mKFnhFow6@dYXHl|866Ae
z*<rTZcK0PZuGxqq@JGMnxsXi1OJOgc9*&wcNxx~kns6>OU~|6;2dmGvXKpm0%ud+v
zE~g$}dR6y4`XnFJK3S|?g^Eo|Pfx$NveKU_Sf-pQfu@5LLNMR?=8a871w}yW7_*@z
zGZxnGYa1ltou6`>@!6WJ<1zWRxVX5#HFcJ1-$ZDD281r(?`r34TM%(^dVu1eEB~^S
z$|^C;GEqctNEno;WImbrpAbBBY>LJ2-bHor{fp^)K_c#D43r(l5<YyLw%fC^x0f3Q
z)lBjtKe3%6S7Orl)7#`FRQ!({?GI_XuV%lxylC@x()(h#Z&|Fb*U`DA&GN9S6yr{N
z>W_4(sV|X|7yANPJ)doVuJc%`3|$3MCw^S|m#9tuF2B=s+)7Q%hY{M<{ZOBpRi`e1
z0QEX6-_3(0Kf^9M!W*U5c~B8_j8zp9n*3~=f9hC+Y?;hp-ebKl`>2lO0+8Kz^_R^r
z@JA{B^E$z5tDs3;3)98a`<~&X?tf)}ml<?)GRVH4)a@Bq<hs83aLGV{>TxixAF?8F
zc$05g>P4&)oISje<|B^($d-L9J-eQjoMI#LDkYw7t-s)O!QX|rcx%!BoPKj6^J+ev
zDS#td>{VHF@R;sF=L_cggnz4YR~MBsAiyf|)iL7Vhh2(^721Bc{}U(LdlERTUtV4K
zA-j2>^F&dr*YiJG|8LMG2GQxF{akt?h|!CFKjUSE#pREBjPGCk^Ym1WlGgRI9yApH
z*aGGVs_=BtqGKUkH{yQ}P!yn0$h*2yeut@H4a;i|w)fvf!T6m_ndOPnMI=R<3&YQc
zI0y%X{=4EGlT?}gZVQSeLTaV2O`hwHfX4sss{Y|Oij}Lg8SU6lCrgPu?hZqlxgb^j
zL*R6qnwq!|BC@j1fyt8u|2C@9q*JFii3i!zdzAnUw>6p1iY(NU{J&pWWYishMK2?d
zlwnk85u7m`E@qXG2T1*QFSVCE$jCM>e{Mxerz?e+_Ze1wwJV$V`)TRQA@eVsvv3nU
zevANY;<uX=LX_(maTn;azBl@BZq6e68`>6Qm9zc`Vt-A8j?J2)|6Q$~X%88JWb^aC
z*wRstE>4p~H8+vLWan<e*7FA5ar*V4Y~wIm;`GVpdjgvK*Gkj>sIQ^HKNp_<8=WP#
zIH!8gKVhKkomuji)^^3~=l;J}Ug5G5%YTOXDd>M!f8A0;;N|}Z(k^bcu<?HjqE~Hh
zHn{M#I!z?ZV=zxKUDOQ^AHVrv_&G5V(K8m5Yx(y9!0+$hzr7%fzBrmumX}{2%<n(m
zUFb**xYmprNr?7J_3BR#H$|OSf5ybbB<g#v{Vr6itE-z2e{d}r$7EMZ-h>w^>2R4r
zrdOA0jx;WnNL-_Y_pt+r3wQ6{t(AcXJ@i&Z4HFg>J-Y;yDmRj<cil{2*XDKGnq2Bh
z6nydPwPx4s^~(^^43Qw>HXkWu@^hUUW%_!T%3)z)9M3;5suNwWt2OJB-e|GEPe2od
z&v<uIx$SR^RRO5si|_x0SpS2^N?(7PE?UfSvFmN7?`a`Or6j)Q*V+5GNVdIYfWu#d
zgTe_grW+1^8Uo^+kKvA>WN7N>r0AES8*<;$_dPLdLKzoG-Ha05S{*K#s4(e_kf!V8
z0M<&A)tPHmhsEgE@$K8zRK4bjg}1lZOnZatrcV8ogx{ri{@{xWEp}4u&d6)=U83X_
z_1-H~up7#;BF!>3%~G-Rown;a{>qgX4vvVve&OTbJe&UB*N2ObzjEtZ@!eG=*-t{p
zx#a*byt~lZpC(*oJyME}PfaQ8NZ%Q=-E>xZ-ptH){Q!lIX!(xzc7;>{MF&;b5Q3)<
zjJHP03{C`LZPzYZA&UBa9M&28kWfU+1MnZ+7-M#_zczS{8#7IfqfZGRKH+$LlKLzr
z$?M=xZUfBe=rty>v55&8r!I5(vuk;I0BmS7a-{o5Z!ZouHn9qr{him7v(#0CYhvEV
zI{^Hsq+$PrYFJ6Iq>OdRw$@e==T+K|VzA6lN;DoAm)h)FU0>!taYet(srTjCVI2fk
zuV!vw=$WfB?4f6uOgw&o53D4C1P{BevbD#mC*h+?=_2aK*%0&UDkTZ8Z;*qv#gi%Q
zWDasIyf!j5id_0L#7#P*NS<@U?>`89oE2U!RLlQ!aGI2q6cG_I*A`*`A-fG<@}>dr
z^(RO_W_5c1UHlHaP*h&-aJ)08mZxz0p4IOH)tt-SXunV{%IkHUc=ppIDk@6CZA;hL
zc^}NQOs}4llG6KdLwlEDFzfp}CbqJwswxiB@FU};r6mvhh4>1cl2&5|CMGVOPoa(a
zP2L_F8VUYc20jNv#L8k2mF=-Y4@M09d@fH`x+TkW>ngpD?6^8k=R#;M&-av<L=_Yi
z_NC*FkB>pYiuZ5=nlc@ln~<O3`3J5Nci*A)N2l?27^^fBb=^4LteJtFg{v4RmfqvV
zU--bq!O@H#iWcZ<O+IIcbMR;2EC<rXe~UGWx^mNP1t#LzG-G39Uq4<fc-4O1%h&I`
zJ$(VH*+OSD4eVCAQJd8sGF@R|A-MNO^?HY}-{tvjK6B8S-04<N_EyH+wj6|h{rW}i
zwNargdGVVfz*PH3Pft&Ozoh3rGX>SIbR3o~NcixgqcedGYQ8v36(j;PVP|G`@UZ|*
zZGEIn(qXn`s7Q0hbDd+0(8<w}h=>TG#h&mW5G^+^kJV2En8ZIf9)?Z#SpAt@WznA@
z;d5HmFOe$hww3iD5DOc7Z*Pyr_t+>Z;3jaDYkN&oVme#-Ap6d|FXiAHg}pmn0&nNz
zq_=c3c^^K|Gcq#L({GGdub-@B&eXeYXG?|r{rgwnYqPr1>j;Yzp9|K>2TTviW~S)^
zSem5MvI5Ae;I>-L>w)+ZLcZs<g7X>TUamtQ)w?CJv9a-~#cE#+GTOI03Q=}+bik5*
z|Nec0N;^R&in0#Ii%TKkPWQe>JtisB`|o$Pe5J>a9yJ_Id%;|&Veeq^=$lN*D;$@)
z%bHF<mX{Ow4_T>yY$z@+hLxcab?v2iVBo#m0oMEB!v_GdwRlba6Ob9@<>V0i>m%EL
zmwG5sz|g?*&p?4?(<pwTpzth(cDk)J&1HSKFXdj(nlq=)C+0hM?l3WR&N(<lyX?$<
zwVV1<hlg^Lgk;R-^TGOv{lw=!3Qd@_&y4SxEdn^Is}N<W=#sO@iEE(i*5t_|P|@8E
zG63t!F&mgClTw-%{3lxSay61`F94mo7GTfYyW!W@?(g5jqNAI=PyW6kVVm~5@@bmO
zfMLhQ#bI52@;cfAi!9Zxi?wf;3c-W&R^OU)hNRzRCyie(yn1>>VTA|R_P-smD5$Cq
ztPSQnIXQuWl>GkvEPpULIT_ZJx_-(RtN~1TeACsw{o-u9`Dl9vpH@`V3+#$XIpchP
zP}xj7L(Ic5dAV{0vVubxzTL!U+wM4)OrOK#!8HXXB~E7M7_%`M8Zkga<uswUOB8`$
zxnItM6k`z_5`xW@q#;BZ)h}^ovpHVfFSZvkWOcC+E$Qg!h|nq-Zf$+Y6?vgw=h$IY
z-E_89Pa19pzPN$&{FCLt*Ff}>Lv{#5n+`(YZ|pq`|6YJBMt)ylS)!;!g}xboi>A5i
zW-C+3l$bKF5>EdCd!^UpebO&^_3YG$Qky*i3?wf<pKZIVvKKG8ZElu&7kq(}gTpNK
z0*n_)DE&tmF;fPxZSekO>aCr*Hd$F&ZEfvJb_;M*GqZ}D#??C7ss8tt-A}gax8J;Z
zgBZ0DIE?Br7WO%HHZn4Dbo>h#(!xWEsGri;(ZQk>78Vv4zgQi}>Av~f=j>3g&M|1a
zF~e=zgNco;$F4>+{fByy#^y|O^QY_AZ>wsFqe-U?<bCefI?bKzJ%1sQS(u+6?Y%Qx
zccX>#el4YneyM(=i0g({ct`8!%=GjN7%FTT=>>#oaF02X9^(q{y*@$LdLLLl((t9(
zuYt-EC#H6Gc5-r9qAAk_?KcW&c_4h9KqgXR(0mEus>W{W&tS>peqksp^?inMn)M`%
zZ65X{av3zZ^W3>JxA^_tM%g4?hNxRqH8T}nh+|(@Wdpz~48n>{9|BIhWBia+^~5Ha
zalTUe>3ldBf>PYm`Ooh{N)sLk^^nqmp&@+FHf!V)IRVfsbmt?r5Z|JxdcLd$1qHFN
zuxw!OOB?&1?{;pwiWYqx`!@LNmoFq_5fM1UYI77n>}nF@;>ttegNdAa#`cw5d*CR!
zy;S-Crx#$>>r9HJdCCs(rB_W^JpSD~H60zR3a!KKnSP(`G^G{bD1Z0%pwuv$ubc@c
z3AmSX5f)jtvx!M1?)lriDqX~dm4ziN^8GfT*?6@zOlIXrx(SMT>rUTHB|F)_*Tkl4
z{Ztcp%&>9Qn9YVLyQF;!ac*ud?CA<*un@Dx9)p!0ZcSNNjRBa}*o-;Owjf2#!LuO(
zlL=6EkUv$*=MxuCzxUKkd%51N71+U8Up(aL*XdwRw1m}k!wH16I{;#H&LW$xXC2{W
zdw>6G5%;b+Z%x!hr|ZLth2T-mQ@lLgU)y+n3BhO#S6%}aYGne~_~g5B1waE>^Zfig
zFdN&cFC+-BJ|0qkKqW9_=D-*L(Z1f^XS@tETD2KT5WBz@Y%8<?w_(KEIU#2)y#8rv
zl&-<Ep5!Ka3WrmGCmHsSLfTYeNoywjE>A2*iVOFXPJDB_hc{fkpx_Q@a~<|$V~L7}
zJpV4rHiezE24SP)Q5qh3rYo%kVUxLTPp8Mkv~<(<@E%nK#rpv=b}PyWRJMM@>*!T*
z?$~|C;$-<FO}NcfQMPw?celY9$w7!xCYoANNy$9S<6Fe-;y}Ba32ZE^IJzz#&~Tmu
zDUM!Gr0~DII1{DB8#X@}h%(D<?xZ9k83H0YT=E<RB!uvmyDzXsTjWT#okalA>#avP
zUF{g?>DdjM((E5Cy{hv*sdS`ymS83z6KEfuyu?&Al#-mR{p{H@6&1sp2`ao!Ad{LU
zIvu6V`s}1VaM!`X!JZA|vwT76G%eIcXA!lhPkH(H^tQE2b*kT(a01ul;N<+#*H^;L
zlbDc@5Epm8B6&4Of_LB8*jQEdZHIBim<@ov%5=@dCa^42R8;Th)Mbr_DV5c6aW_A^
z*~A5<b9)y_3KZgUwv|+}Z_hOQfiokHr}up}t4BEX8@dj+fERY3m7+9&=}CANIbK~}
zjE#+zZbPikk%?~2&6t}g=<ez=l*#|_p?%IFP3WaDFm`sz8B)Z8gAf)L7SMZ~L?wAu
zRYFlFA3+|?9m+k3dxAEj<>34Rw&M|0qOL%ZAe@jG@J1R|BdmF=tlS{c0m4OcwhqLZ
z;+|DI+b~?@b+}RZOd2s7!-ddJh#!y96hh#IEF>EE3Og=7dG_pP>O)Fkc1zvyim3u-
z78X5~X$c9WWMrKT8qK~J52d7fn)`|PQkhx_DXLMxIL_p_^cxD!4<J!eRaI>;_PID1
zIruXK-qsz+Y3A7rWT5*AI~l}&%=%=<{Z-YG@1m~t7eHaEwAj6gzXN8*kKDd}8y7kP
z4t(+J?+a;cLAa1pZaL72G4uHP3Kjn6Z9M_VvP4?BPN16Xy_^HNkPpCSeN&sx90Gt{
zm*Q<h`5+M@)&Aw5-z<lUo-f{vfepuv$W3)w$tNT=zN}I8$MWn2f;80<fT9eMakeFZ
z%)q-sz`{(IrrSK7NFe>I3-17gRH0#i8_wuov8Y)J?=<qr{-zgx9Zv;T=tfmAS?9C@
z@u~OQzR+|w7*5HK(a%qx_z@sqdG8O%cyu7|d_|{$BFojc18wBev#x0|uS51Sji|Oc
zLlieyjN;*Ah|FUO=1mLlHWY-MmLt}E$2MJE`-7&AXFFUr7VEd0)L0F^-ShF>0>D(s
zxZ<o@x|ReXQDx`ZJLMgVS2sFAK}N29t)hCOr?zYg234^gb4Epa_#PsX3v);H{QRwf
z0~V5{x1I^{KAz0=uc)1DscSj8goU+VtXKT`eo!Gf?7Kn|jw?w@N}Av&b_%w+adUCh
z<j0R6zH7OOaX&Xu>wQhN%`fDm@?FeOGT4ocjPCUOY+y!NF76bwpKd^>c5kWXwvmqb
zZu(a&fxQQKg~`~fbbP!Jsr<o;2N?FnY^~-4RU@T7gaQX&?P{197+&_Wm4<mh1=1{o
zUoC}BmpK^)k?OW~Is!Z*fAIS*t6^bLYmpEutK5sAv(@5_zipQ`o;UqLtSf(zbA0Tc
zKUg@Z?)kh*C(Z%rje?@0I;WKVeiD%C=OY5|dB;*+GPUqKWr+DQ1Lw;0y>}mvtek79
zuYQ>&OC4!r(D3#?2QH?C-gwTgBCtYwPUNJd^aE@mUss@1+<YYZWkXt;l7*QyP6ZYl
z#FT}=-*SrG<Ts#%?yR>JNOezNaKlX7T<x8%S8qT-fUbbP_M;^+XK`uBGswK&0!&tX
z*E-O)s2VHQSWmfUUEA5+)5EqmD`9=%;7u^>p?4y=+pW&Z+H76AU0!wyW<=RJ7neeR
z@A{=KEMF%Xhf2%BvIbkueAP6#<_&RmEJE+<b~huIfQ44!arey95^PXc*ArP;kCk*c
zR<U<+DWRIiKl=OKs5Xlo+A$lvdgD2a$#37roQ3dz9kof6PRO-?E-spmWaE7{XdqQL
zL+R-j`wGg3NV-zhnC`NPeu~xeWokaJxpAyr-5~}GlVG`t0jW}pW5}P>mmO8MEcU3n
zOC7Ayq)<`bfc&ln1c(>gtB%op>CbXfV;o<(!uGX7s;<8$;_6!8SZO3sKV`>#3)-_}
z#LXq!l`44~rANlo7MvAbZ9x@brLY-zAM{t#jQy=9+2^dR7MCHZrEIRU!z8w|ca{0m
z82Qe1y4K!JVabsRRX=n+Jx|Y1_Sh79DeE#^N*8*&;~1Kvr#19mveih|wma&r)}L?U
zZ{cTE-Hsx$Q!5*(_l+?5NinZg6z7pPbx_K8)b71Ge$Ol;>+cRxj@Zd}LW+-t8)cE?
z_vDn7^V>H<SiS%Js8%kVEZ<H0MWrb)+T`Q4RMzdSs=uP0TrH=&(!^??qL9a{naK3<
z_<da13}x9}2HoTU(UJQN!6Tzpvl~Sw;X)#?ByMFVuFb4xyzyK=tAkeRC=Qc6P+PC2
z4GJ7$lI?}^2Zhahlm1!@tsi)fv~T2e`+4ba@?0mG6ZXw!WsN+pdT3r&&6UmMIKlJt
z#xzvj8WzRhG$+fk48ye@!#b2`N0PZp^*ER6SoBwtr|g>NELRi-r`mV#WU|JV#(rve
zn?zE257n!WEsedW)UC5bV;xfd?%u|E-k#oj#zQo-LgH52(~as^TG%twAJ&HtH^$U1
zU3u$Sh4IWZ-4$=+p!q5GcDE6BpY*TyI6I|3i;@~sPg{}BdAUAZqM!a}PW-n?X*Vu$
z)q0L1HS3It0C{A*Pp{LKv*@)DbjUdIEGRx*kjUu-Ys#f7(WS?|#C_ITMDNT4iWt*1
z_6ErclmrAV6%wq?%+Hf4-cg>h3F=w|SJMic%Pmz`a;LF1=~Z;UvE?T}!80r3WvlnD
zFw@phqBi?ZrJq>muU}b2!MF?yL*9KT2+66q%b#aMnRCa8`d=m*r3`*@y&6UFeJ|{%
zzEpoVHCE4fs_a3>Q(qePbz`!ws#nx3qqC#}%X*Vn+&l`Oi%P`V60#YyX`7MK^mR#L
zC3$C&bdroWZ}JpV9WmP{%SR_eD%rf76^sv6myVQT>6cjLWo3;<Xghn-8Vv`98zW0Z
zR!*bV@0f6y$n{<>0~;?5r_u<vj|M&O`P!fOLR=GLV_E?o85x;9ti+6rA9kA1!YIJY
z8ym@0VesU&8&Q>L>b%!RH+Ql^sW|;-9a1uqEuG}{emBSaAVcNk<n%Pml<J9+#%=T0
zP*C3#AX<;a`@-f&)tx{u>#;XlVY2aSVcNtzUfs-{s{U^dOQ{Q^mtxr1Os&V^PRV5W
zn>TS)B?`vod>`D?ElR`cDp&5M^1o<hORFRgR?4>?EjN1d1j9cVm!eXzW70&!!C;`U
z)rppyyBxB$ZproEg$tXmh{Q<ETb$h7F;u+493>wI*R*=*n3*xA`{Uj~vhfo0nM*C`
z#s&A-QnweLnoYBCaHuagPS^-=F_`l-do7B{=YeE{Kt@4fkTo7bE|Rnrpg79(BlRb8
z%4M~&`Lf5OKiB=N+PumG6us$H9yX5(puMG3bdCD`Y`RaM5_x40t^tT1uH|R`Iq)=F
zv-ujP9kxECaGQYOXOSjXE@8{d>Ni+z@iex})ow3dypY2)_dNK6dLLp>I?<#FDPJ5F
zC1tU3bn;npVcH%9NE=of(JWXuiIbu~UNjA@bsA*F@ie5Qd4p@CnBV^>AF}1@wuU_l
zt9!cCpTTQEK@okRzogHYMlXADc620dtbvS*j$Z#6O=#LggOqPz{Xirgmgib5`$)eG
zY}V=DDz?}NTuUJ!`mz_jEM8)pyf-o}Yycr7j{f^So~mitKs4MqYQ9dTjK5};sC4U~
zZX9&F2eAeAy;iY~B-D>nGi&ij+6Z!_o_6XNuk~Hq{n8uHS*%}1R{pE@>Bi`&*05GY
zXsAkQ^+yVHav2!y#v663wDR(DV0Ue~d0Mp&Mi7aE7|e$s6xyiw{BcwakfYVOa$Wry
zApX#n`<6SnSxx)ltG;5@KNxS1lITE>K6=f1p783`D=oGUK;3Qp8dKzTKHGdZTUdhZ
z()Bl^Qd-xv5epyK@4c51(m4Z`i%o7DpY{(II7>TRBRlZ4AsOxVEB+SnOq(sW(a?oV
zxAdMX$qwL&E4{20<P6EA<wxZ00J?uhE9m?okCTgqNPw1TrMNTCt*c^OdQ&t_4xd*@
zD5;t!euz6l+5~89qhK7sf!<2JT<nLzHC?}nH=06AI`Q-@V5lI1ZuBk-#g*8ExX|FD
z1uEsM%~z6{S0!%`YD?!-cvn;3x>fLuz}rbSIXG_2#{8t$fhtj}-~&0w%=m1rTXDy5
z?MstGB}+?YYu9pxoRM|l6TJ+ix^McLc<hh7bekDQcszzp4hQlLNo=-ts;!2;xL6C^
z&rM^Re$tB<64>|HaB7%0eOdM$V})C-n>h{|N>KrYfK37TiPIa;-pjCb@zNuVu|hG2
z+53k-0I3&;sGT4`Iog_fc`<KP-5}UncbF6y7`P2&Z)a!cj#RMo`f%g|5#>fuY>}3y
zxqT(sby>AO<<rv&R`Ga%COoJjP<5UjtUE=Z(u#>q9w;Ux^_f@cm#v&_M5cx%lW8aD
zMU#hD+f9iGP$OClxgi}$FcCU@CGO~SH07pw!Go{veg|O*l3h3{2c%|gM@HOVN2jNt
z)}i;s*+|misd>^HFJqA6%U(8qe&%@(2?+^=v*(~E%<S5Z2vCN1D5<(8unrV<ZfJJX
zn=b_uQ}7}E1o3bs(=WpdHCqh%76pZ<^)S1xWLsPU(u@Nl80U7YetG*O)C;=0x@P>Y
z8ujX30bR9i?O<!&LVPN`)v)DzOP<%DIRGuM5R{UV%6b1@D~nf>b{q4v8$h*#&pU=?
z5cs3gh0y{7whN!;VNW-B?A335De4{UZD_4{8TIq0%Hd!#THqaktAnI?k+)|m0u)()
z2J@9q2o0n9GwMhu4%iwm4ksYBwd5xUd_6l@U&*Pe)NV_mViXX6lYS0vX-wz0;koE;
zUb+3Vy0ds>{d#9$wfEA)5s7jNWg|zc0}FkEGsT)^)D+q`4w5d^&BT`>>w;<v@rbD3
zB)ZpF8&fPj$mY!a{Bct)Z3o#l_Rcp)Z?%Aq>?|?`4BJIS&%uEkVZV{Cl%ER9MZItL
z<kq~)K!%9R$AW@ThUHxOL;<VcU8cMskIs7fd`*#PXzB%_`3kB;2sC`^;)cS(HAaOb
zSaftquDEZ3Tu_T|)(qMkl+QJq_^ZYy!BqY#Q+P$;npK4bfUwF>v0z4JnXY8Po7}+9
zmg-}|_^$9=(``^ua2Ko7*DdX3N%xop6bDvUSHHNd|HWEi)K3&XOD1)V1@n@P9hHc>
z&6fubhk)OH+vRl=qO{THKl?N4KNCtwM=xuvD562sf5a&v0b^{|w40#73I6%>CnQPI
zGBQ=3zf5^mjSr<j#xu9*hhdM2LR>IA$pN9SX2w^vR5^a$5!2`D;>f`Fgu$!tl!ni|
zcNJPdK`Vjz+VCa=i2&ZxxZj04QARE9`6QPA#V`7i_FgmQ3G=Fo8E$-B+@bY@Ky>_4
zP?e*p#rHig#E&NVjN37CJA0RUbiX*SaW$Vm)zyW20p0ouH4T4%gsWei-X{}i=6GJi
zS>}3QU!VH<r(Z7B5PoLBR##UoKPz=W(aoJ<`C1?f0#nM=2bB!ebe2BybfwO3D^LwO
zo(;rz+-Uvd`~~#KRp&3@p^s!_1Z~ITh8i}KRW%m&uvWf2d2DJ*FW(DFBe)Y-uwvt)
z(E03Xj5W+KF0f!^9UYQhD7Apl6Q2301H7Sl8UqgxN=p%uksSwB46LkHQ2mw8c{|em
zM=8JRaX2ZY*xiRny(#zPW9f%Y7u5sJ%*{c`9<mXTygXhwp1ExsOP}1goRyUofP!Ax
ziEha};kN;63QP`hdbHjD=M}&Wa}NK;Lo=>get~Ej$tJ%m-$M~*W@a%lUEx3gvRhEW
zBp}%NA-c_PGaBO+T?aaVoWAMV5U@%MXxcmX>;;__g!mVRhP2ihC>Xf7=}-;;$O3(k
zf{JRY!bGM~-@u%?_8a6>a&mI+?m`sB*m!u*jdyjeydsJ-I~_60MD7X-CSHub|Mes~
zGEyB)XOvcef_m0*0~Ek!!>`D?H079>n71Xog-SK6K_o%DmZm|wbf*q~Yv$1va|aT!
zjbI;8dI4C+Mp2HaoqT;Ith2%NW*mDspr9t0tN=rAgi=G`t5<jF=$I5!o`Hn%ku2dO
zscZ1Hq>k`}Ys(BImn1x)wO3a+4dUCc4T3ak&>x{*GHUf$uI;XfNJ{$Q#`Ghwfl+Ve
z5hE03Axb=fS5r{AU83e&*DN1tND%_rXS4zZVeP;cmb{rbW`l=^w>4I^0`-V%-G$-3
zVO$d1vV#zaYPi6*R9>vF9e5t@Zvv=H0z;0fs!&Q3niGi=cV1P|m_R;0-|L5};k5Uj
zqJ!S|2P}sm8&d<mTK=SE;7L_rTynjc*nSQoILa+vQw*;HO^;?Uf~(_B$!Yh6NEmra
z-!jzXoKASb_kDeRJEN$y?_%QOHm>ER0{26^ANv8!LjtO=Hg*Du4p37r#-0Gd6_yJB
zex`&E(7X1_ZqMFQwV&dq6<S)Ev1apGS+s{Y-znwS10X@&1IvHGc^+ysX=!O_xa1Mk
z;-{c|+$aaGE1+9AvR?1JX3U%1+ScZwUfW&PItfWDC{#EY7~JHMP=^5?c`vn3+;blT
z0|Tb;#0+0ha|AMUD5xQD$oWI5=mZMXmg}7Ky_hOj(m^+ez*N<~;W<zt@Sa{4WCRK^
zcLy-P&P>ghLX;xurBKjDLC0$Vpr5F=W|Dmh)(GWxEw%WCaeNzYdk|h9Ja_;VE(oq7
z!pzK;yYn3!I-f8wG3Q2uiDmmbSFYX1YpX6~K@6{Hl!#I;S|;{`zrR0#-<tCm2nSx!
zxI`IJskdj~;II?Nq9z@2E4zW1oV@!>IVB6>b%6>7wY;>H4l?ywU@*>eDb=zOIHdGy
z3e{oggDf!u^6uq$c{u1qMnO@u*a|h&kf0zEQc^r}{w}L(G-&6#al;05LkPPdnPr5i
zHGOYsX+c6kTRk`hPRU~7^6c5~=k|@&AZF{UO~}d1YadwflT+|p#>B@n$o>LVx&Pyx
z1oM4QA7D`NGkAa7A-`-i?!X$3Hmz^@_7L&_UgP#qxG5lVCI9z|&m9N4M39+%y92<9
z&DF5ePORJut@w0wZ8ic<m~K$CgbFDXYd%{I{i$2Ypn&8FR<TUK5n}tV_m8ocO!U8S
zHpnHI6%FZVX<^)dv4jFuDU|0ml~5+RIvr_7^k#^sYN`lS1op*x%mtAF9su;E2-;u%
zlIN<ZH&GTp_?;%~RNHQxkCoa}h@%qp`t_s7kH!CX(E%nV(yzg>1+b)8E=UaDC4saK
zgMp@tT+yvssd(gCDcN2^LPD?x=qAwD)up7c!-ejF@eSQ5k%=y-u=qj!0zfN4oBg4O
z@oP;{Ly%pcf41RCR)DJPMqQgmi4Fu?g>;dj0~25qk+9OCp%?<@TGKRs=Wo-}dKo6i
zsw@~}L4P%7c`g1<4<O(L^znOQ-OrJZM8mn2b<SPFq?>sI$`9*6gnlg7aogmgY9|2R
zL{W?X@n8pWIx(>`FTO;(YWccU0D>%xQ#1;e(QWc2dS}Q}Ks4~M?kg=71%=+2J5qn3
z#RAmuYmRfy<u(+tjzxmVQ_h_Js9v<(mwL20k*MAd(bEeWI8G$`nnCb|5_xE7=ye-`
zJGTD<2ffj#${|=6Qh9Q8FXX2S3k!M{+llsUBfUjsO<$l=o33;O-F{VW&LUK)IXS(u
z-iKor_Q!lXcHMn_W^_B?o9e>>m>3xDphD?Us7uJDw~N8q3P9AvD_eri7&Bo(K{EdV
z%gJ&h6zi?*$<v{RZ+Tj%gcKALx$<NqR_Sxode_|_4<9}RlkJXY8++W7aQc_%S}HHB
z?2kL)U{vm^^j`awE4tm_1aIA`U%!6s-r;RK8lCfOwi^HRVo7okXij*?yoa%?NO~oE
zK2*a8W5-Zj-T;?`<064G(-q4vpr?lQ+!{7T#N|&rPa5ywE87_L+8OV?$iGMlq9euH
z3mzBxT3W|Y>_}3`17-rp2umQ%iT9}lCL(yQY86%Mom|Pz)~7PEUJ8|2o61k`!E|*~
zsNg1wApWnDA1{IbLwfnL&d5MTr6tc94!Qg|kRa?tLXZ@`OB$0}rPOra3C1SlgmO^5
zQCqN_#v!$9US$XK$KzX_Z})(iT(j(W_1eicB+KDq?IMj5v6&Q6PJ<?I=vxd8>tN&%
zO;<v4BZzO~G4H))-)XF6>}tWD*i+b=M>n~Hbc55v+}wN_G&Lx#e$|;!$KeajjNd$&
zh6F8vF+N_6t<8FSG$y#q;8JC{rf{B*GLwuzwqE`d_^BtroZ0ksC&hG1%aURe_XBOE
z4-dW7Gyx&<6_V#qh9@yCY9=}(ZqqHJNlw(m6&1I&;^(|D>-KKam6?!HQZ_(02{2vv
zrh9^z_3MSjDtCBwA!yS0?5o01WoG*9f32{(X(v$O$tWn`JkOjBra2V5E9!c8yYp<z
z#76MLRJ+)}<3it{i36tJV8^9i(#xe4ikT1DH?FF^+P4JM_E&NS%ea1-Nee31Ji_kS
zooL1!z21kDmhx;Mj>1>dTjocm*OsuEo}E3h2T7;`efK>H+i`v#><{H8Zf%fMTl9Os
ze43H^DK)A3{%FvmdEVm0gApAehs{Kc=SMOLOMjhm8py&M4SNJI9iZG*>vqRs!v)hC
zy|#|UU?Z7@EWPrxpP9Sf^7sbN*{aItna@JC4*9PL(!SKHFr;;jlWrX(c~FVIJsP^j
z(^(3&-}At7{iVw;;j;1J=#a`g0*YTeqLUZQwmQYlo2Pg<+wsjH>C&%TgnF(+j`H`K
zu{*~-H^)p|nRxbW>%wpQs%UiH6pQ0nen#jW@|gRGVy1yH03?*PQ~W2BJ8c;ufmhU`
z1)}f)yO(|Cg`Y3SP0UH`hSVyBi6%D;?M6+OgD!TelC(<u6*Mno^{Yz;EKNyD&&>8-
zA7|Sb754ZvL{Z&{LI>toQ(msJ^Yd+L=Z_U>(fi`dtPZ-CJHeC9Z-g}bT1)SK6*rd#
zyntFsJANT%co(%=UbfBGc+tepT-x%nxl0ol{<uv%m=b=nLLLMoj3U~}%*Z7Vb_BTH
ztAdkv1wTMo!;Y-BofzIztfOOMl1Y^s(=A8scugfLN!?8<#`fGHT3dTc*|ZkiV5Du+
zl9XZj4crXC7n9;Cv6x!m1$k^abroyBz>bc`S@2{-XD=J6*PZ1ifA$_Y9JV=8Lv~K-
z#6i=&{BUQAM|wv^;7t4b#{rek%|^|7cNj_Ql|9-l3~RacIT+JerN-{szc^bjwjq6D
zid_xGw7!y)BbCo9llHhy4For89NjOBQ_)n7OXEbtl1=g=!ompa3U1%Xdskl0eJB;6
z?x)~T6-WmI|8_7l*f`f&yP@~s0n)iM^K))>QbHoJIK{9!0b|K1M#hBH{?@-GlR>$q
zki!Ex!u>J2ID|)_eNi!!NFor&A^Q|+X`PuxA9bK;XUUw3eiTyZzNt6V{B!|#i{AWN
z%`~slHeKT6wa29HM#~ypd#(7+{56Mj*2sF0cnYe22`^(Mnr^K_wkVswW0+Zm76hCY
z^E7qE9b1gI@uHoN*`~z!>RyA6lqkxE<z%yj;iaG|)=Y)9qpPA4Q=U-IS|k$|!7LkF
zzR{r}OfcLN77^imq1VdlP&lbAH#{47H)A`!ZLWM`6S_4ElES=-Ta1U_2FqES)$dV+
zHpr2XlmA&xWN~MnAoJ=4`sxC3g(3R>m1Z>2jEO5Bj}xW^_pL`iJl@iOPk*bgUnC7_
z5cCBJ*iWAa&$;88dDRjgX_si!`m|-pKWfy!`cqVfh0R1HdTKWvIP3UQO@8|S^a9+0
zo)O^ZZF`%w{e9UsNDOmPpcO*K=FrxEyz1G*_-?JVev3D?ul4R1;kZ7wgkf{vm-Mnv
z0ZKm!gmx~CR<yG_rtCwogQh5U_OvHT`Gb9_O${j#Xn|kT2V+h08cvo|J#6Ky`2}e;
zL@~E`pegVQDxQQM7Qan-gF-^W!+=3`y`}4Df9Rqiu;jP_!-1Z|*%Q~=J@4(0@k!!L
z?qc`3wG$>jy%+>GCn&k$(GD$jPz==75+YSK6?)6e<MpOT6;e{YYODB_iMepVG6+!Q
z{Fbzgupugtf!QB}^f{?O-*Cz>H#dhbahxSO=WWM4vF7|46i|q}Fu(6)do}DmbkY&4
z(~b4zp|SK~C&C`jR;a7Es6|zb4++Y0Kt7q$e)vl!{}M&3#xA{F3*w<4)P!CK(anx*
ztb#-g8qXx=ic`2|eY9rD{IaXirFn3<0|L6z2O(+UO#MYG*Zb&(5q|5b;O;ty_2;%Z
zkFOuA-R`#Srpy-hsRfNp-?RLAzlte;g_-A_9OhQy@s>LRDc{0yvdrqDwv#=BD+>Ks
zP_CCcTX(*`VC<!o&K8fCQk7SywT<?oifi@fwsoGzZ|830v~S3JS2!|Jsuz7esI!YX
z)}3eEJ(*bfhT>h?0t)6B-DNsUp|b4c%8Ytde=g^4focaKgSo-O!f-k(*U1=jCV~@V
zp|3aaC7-QN=4EeCyl+=veOxGSeeT7a+FIDLQYxCs=HLP%14b_A%u=|#gJp7mB!{(d
z+6uquNjSOs793s-#b_GEBao#0lQUFMM(mBA9$aZ3N>n9tUXLxmfYj8}+^j&8enZJe
zPJgbQi=XipJb!+sy2P+)H0Z$ovauy}RwDP9YcM)G-90WzHn*U&_?`}Rcb>^gPJ4g%
za%(e1dt8zarn+BKc+?c`i^GJ^T+4{Q!=x9?6VQ|{%ds>gHRd~mmo=xNNGN$CUo}??
zV`6<QHKrp4z1pI-pV{rZWWqNY<$9Z5f0QiA{@w9*2H(uON)?s7dzf0T_ruf6DU-u(
zg|!)!gPpH)Dt*gDG<vaW|L(IPe=eFBL-ETdZ~x5I->~VpHqP+U=Odg?)R_zKgRcfS
z)VmaCoA7C>^?qX>LgStd`(at0NH<<j@$0WHpFLR~PNt~(jUyOrDi`#wALI1l9lo(&
zIySbI4^72^uh6he^mF@(dc<;3AGj6HFT&<E(^N|1H+hE*8x6(0*MFdk;ZuI@d<_*1
zF4L`pB%d(9BY9?*my6*gl)H12lcBW^e&+##Gh%|~y<4K&=b#et*ru#Jun)B&!F^B7
zR~Q#9ATYI<Q}>9zxw*5r&ZCFM=k;=*sPCo6z*0}7oSY#yEeGz)m-j|v&9`~ZE(W!A
z;S^s?3h~U)&*iQ<zoReib-NYN$?dYf1-(jMP{!f6^v5Q1*ho6fo$n)yPKUHxHut>A
zaiz7nljIT6%mcT*ck?11q}X<KsL*Cu-NhRry}0PkqZ=uqJ4X7cj`K~leg=P>;*-7?
zuH}iXb!~kP^W))D^$RB}^+B)M(L{9XUJc|B-qv1-e6iQF*m^-XA8zXdgZiz*Pp#7P
zVmINiC7{;%`wL!F1R)+e5s|}s$-VI0F;Q%OwrhI`(>R__xpk@7Q}ZdP!}x|`^msWE
zSJSPujA@+O_akkVk1Q=C>8u1?PL4bQG>jSJ{|+c+%!1=B&dg=aai**QU$wLSP<yM&
zoGMMw9>HB=r}ybaU<3~S!e5qKBMz;`UoF^pT!{7{y$8)s1A;U(5JD?f!BaFxDTx&M
z^W)D2er`th>fIV9-M39|yPknUx$h~C_>fO0^2NNO{fOn>(MB?t+8rKI$|y*PEucsP
zU0frjdS|OS(e2O*Pg61(=00z3rkwr;o#v616?(dzk`mtOLEzW*J*N&yE%w;x>CvZm
zc%auDj=mUgj8>Reok03{1_1!BKW6K5HDXEI4kk`?$y1avrPryMwyjcRXMgnMNo383
zF|I+fwy^w%#!c?K#-KBduEW=V=ry>jdfyHe^@cc{dVZEy#ub&O$(EMYDC$mN;2TEc
zyGlfUTGsUhj(rT5?71p5H~0EfXsFH!F9WUy>1SHqA}5F5GRWf|_9#l|UaRZ>k3&m@
zx%5^$+gK0j4Jb2zZ#;Yca?{`MiUMEEe;|hsH+ZJ4jmK9^;tVH8ddSAad5}y(G_K^K
zaa2SEJ3G5+8T2>dph-P>OegUFa*S!~;4TrOX$<$*26^&M-yh{28}>fD5G)Z_50B6=
zBmy!rAl?JzcU-rM#}e8zZBoS5y?4K;88O#;>@8dTNHe-Vn=}Py`Dko^6WIP*UZZ!P
zuJqfR;4&KJW;ckk9=Yzlljh+$J~oM<THj2P7^fw@tt19#6#S7;py}|7&zWmTTZQ4*
zS0Dt1bD`{N@?!?kU0w24-`Xn7S1F_Z;${#1!ZTE2p{EBt)(K>7FLUd-1``Wok`6pC
zpq&odM8_cGL`O$&-f6|2@43(4wat|6{-Zf_|KM!=@EZ==`2pGGnSt2hD8=Tu(V46J
zPLSoE1r6%n0H0hsO~v(D0jLi=!&IDH36B?@>uR1r+9KAelP5yM|2F%zQ?ul+Dc>je
z>Q=IT1)bP;mO)&l@f<=fs+qBbS7xV^zdu_A?R9h%U7S6<2F#_SDcW&S#ocZ55c7PQ
zlX<tT8838zEce!JCrn(kHVZblqp*$EUn-wP;lxh~ftbWvxKm#NRhEX3LGdK2U$XwG
zgW37<Lj;5QQ`D<1G{Tw37R$INM}}p7+f;DH=1#}BMwOnH0j2NHzlnvegjNqz)h0ka
zy$12zb5XTlDy|$WGgXxUhW~?>1T+gdiqh5LDlC~jaex_i>=F|Cn|IxVxDHvWDYroS
zgH)4*RB3T~%~_;q=+B(=NA=ljHCe=^yDij?_ZALxY)X0^7Bf){*qA(w{Q#Xr{WS~A
z(;y8(ZsZ{(_yWFq01o>o3sXjc0#y%RZlKi;T0b<QhlZI|jkVuErj>mvEBhUs{q^I0
z-RfYe>taF8v}aGz(o2S(%8-!$i^jqA18}3i^rQ=ywxLjSghKhyUg#F+aG>IXXH(nB
zRAEOzBiYwBt*uN}=^Rzl669Iv6I=3optS(3<_|Om!snnPnw0p5RrQZl!TvpwR;pO~
zO%ON-WN1hT!h<H7!DH+j=DtgRjAyxm{{64=)!038vGgTc74JcW&3NsUsbAI%c{C&e
zOpKJ!c?Nx{Nm|l5g#ICOpBh?1yA)pVCX3PN7A8QA5Za@knm+1>z<%@0_JvU(-%5Wb
z)Dr8<9iVe8gvOVExW|zDCb*E9Sr{g$eZIbzu?+J6S8ZP!PIVh^?I~$TWfzr*GCUd#
z2~oyKGV_qkLq%jJ^VmR0l8luhB2y8PnNS%rXO4}?Q06JKv-We&mvdd`yzhtidVNTy
zz5o0E-}i4=zqQu(_1pd=HkOC;$eqf{O7J07KMZ*P%<TMq+Nf+DvB#YuQ`f5wWU7E&
z)qkG}8r?Ma@3T2{u+dzQOk8DQ5&ct@onRGue-wdt<f8WNE@Qrc-8)|v6lg0QyI*lk
zzLmVpld+$#gZQ<`q3?9N7CpIN-=dvJ?4?VWSZqFR)G=>URlJ%GVN1X+j9dmR1G_hF
zRG3<IZ`h%P1($`)8jQ9(yH@gxTrIZmtJF2&J`=R7+PT3ZkNWH1n6FlezB=prQm+0Z
zqoTpgM3<)J38p&@H|fGu0M>{}@bvW;Bdl*b!Nar25woTpqSK`>P@%+07sZmhHb?;)
zDIth(cTwPUc=QSplW@<tz{2F<-yX+D9!EXh&V5iZSop`LJuQKdI^{E1S>=4^MLd1)
z4V#w49b%$(sVbr`0i^(%La?9bh%ug;j;%F0#lzEAVE@X0U*GO(UdkrA+j+XFpo0X<
zNKhPeIH7lb(QlIZ_wxYxdi>plpTGEsMf+tU^n5c{(mK>WyfX;ns5RzujgIa+bl`wy
ziVAbn<*V;kl-|~w)YaE}V)i569k7<7gVC6nN`1R{Mq0K0Z@H)R3=AjUT>3(Fk22m&
zB0Rcp`b2cYNI}6litAry3l^d?M8o&|Q>}71^^kLwH`pvzq+|w61F%W{n9%$XwVo0_
z4BD^%JY`=s<EMMJT^b>ULe#r<?Tah|BsEYDgg*A2zi=F~+sBU=&Io%2%d-FG3WzyM
z@z1lTiq|tE0ie5|`&_C!2>=-71h4CU6}~^TGJrj7izkIb=eIAomyY7z9!%$u$67dX
z$WznO9*QZFQSmw8rmG71(Xd7YS%QUulka37MwZ8Zb>&%g&q2Ac3!<T17pu8LGRoH5
z$*rF9<~Aacc+6oRYbc;wD7kzMc3{ebv2Fb5B!#f#EkQ<%EH8_X=%xTm!npLH^MEAA
zPe3ZKX|-V9MMqE1!QK1ybh(&m9lfgVxAyd2;E(k;bKjL>nu8Xh?^6;lFh4-(5D6I7
zKf@y~x_fDBN57+~Fc*AyWG|rjd&l>Cr0O23gu<g{D%7oV!w#b<B53I#Ie;sj;=o?3
zh4!Njd|FIWPrQ-9IDZH-BJP|p7xW$vj}Z8uDY0?s$EqBE6W00umPV-cISQ7?&IRQz
zXm#}RZ6*OlM9U9Q6d|2M0Kr%-eKv$FuI)reqFQ-N2je>c_F~=Ad5_5ph7&BdYiIuV
zzrY-D2XfpMqjMA@!0i=(`0#7Knp0%SaC>^hyEZcKBT#>~s_K)z0Sj;1>E*9)A^}vm
zlJ+?G(4c*qpaL;G`{cH>oXf{WKW|B=3i%k+UH<3#$OV(dor8rkcISBMF7_VJsh)5v
z__e=xFY!p@#r8d;w`)I^3nUB0ix2tj7ubnDWkdAL+EvjDYlEJaeRUT5Vs-({mX^L(
z6Ci_(bNd+D<b08_cAj*f6y90MZIjhzuYH1og4SlLXf1o2XQ#Ih{+qD$m#@C2Kbl!|
zzqInEX~zCWf156?C}1{{nJ+;cDqElXia9~L0Wm+IJbAL8{J;f9Y-LOwc=(X2m8&6z
zRDYtq#=35arRBPP1xNoZX`87+|0S%<toTixNDU1QKR>$O+E*{ij#LFo3Rtf9TiSK9
zL%k4|n_SeeH?;gl#eU4myc;%)eVV?cOYJo13h{N2!r#k31tRnJgo5#63q*~6eh@i6
ziPj!y*^_P~Y7F841|+Yd?VfTlWAXwRyRwo;CnxDk&|R%R;gK9Ck@~cctEPM-Puoy)
zy|pi}08a+fR|kk=ez6qK%-Fep5VOv!gis;q&}pQwHtwrV<&l8W%;MZ+O@3di^xai?
z-R&_JTO`&7Ly$`TWWy{U6gi*QAM#OP|3Z7hi>JJ~RZ8`YA2JQ_ywB2T8HmJA#k!Dm
z%Dqd;SN9fj3tWrRIs8G7Wfr6h?XEQK;?8g?>IRP^xJr{oFCzDghgRPkoVs_A=aXfT
zHwV*ycj(#r?UY<n>nr8r=7vDZGWf-#c2hnEiPf=Jz(50cxj<TT@7}iH{t`R#dS7*m
z6?0QpDgZC%MFZzVeuFaSH%to<B)B+Z472*NiGxm;o#5053`Duv5v-B|zoYnf{ahwy
zul?~nZ|$JI%A8e>WPCpek<4+fo1HIdUc|)c581W1La~E|RPB1!jq-eA!b&xg`E5s3
zZO6K#*XsLdau&oq9rU_P`*Ri8Bp<zXFKZu)<cT{z5ygV@0a*Y<L!TGs!EQS~n66g{
z0>k#WMa6W{5BivfXKQ1Qa`islYpOJwj~EK*_FYgYu<$Z`nuIy)$jAs-98N&JAr81!
zuGLlQ;X;8)EQnUyD)C)h8jGa&dS0htM)$#Vr*w?#2p2HoZeSE?7uW1!USP~uv*cs`
z>%!HmR~sq=wV3E~+bwudK=AO0gAs!?p{v^i9c_5eFaR5j9@~2cfV{leDqors%nY=&
zrdXDjKIVJYmttE^;5|dvpv0HkB7SV6ewgTVF+HH8FY$V^Za%MWh2<s7w=^~vOzmmR
zPxD$`PfKMun2=e+R{Rn!S9j~z2G>P|54CV|v$l?I-D2RJuMew_F&n9u|5BHpo!wGh
z?eA%Bi2}&O!$VV3)87v|wpy;}q9TzJ%euVy!FVeApw9pwm6es*LQ@M153_O*2_pJT
zKSxH&yf=y<M8n6MH%iBd|5=69M#SQ<r>FnI-ag2yFW0CfFMkUn9(-6^E32wje>c+A
zb;m#rjL7$Sc?S+0kmq>X+pF(pxhqx$HQ)qz=QD5be)#YKHUqc=*eXLiX7uVt=aY|C
zbL|hr4gFb1qoJjBnE<7mzpwAW=KSiayFP#a6uY&xbwh1!g?+K}b(v)K*%wQn4;g`W
zK4B0PD3b`@WP)12+9eEDiP8pRK~>Y9|9-4PIyIx@ke6jto|&2H4db<xtOEo}5aY)y
z-6%-Az`O7Od6p%_--BTH30FP*^Cww#wagqj^`OvaYZIJ;pVXL4I0M(h_zBA~GIDcH
zCIyRzSMlM)5;6r_=oki3JUl!mv>NX2A|ch_R)^-7p*tZf7=uDWps}{JvEh^~BTrw&
zN3{{DaY1GFL$ZhB80BBJSXf%B#)>SWa>DB<84Aq?Z=bF7oi;b9>YzT`T3eU;Y?YN1
zQ<wRQ+yC%QNil=`OG>H&!^2u_N~r<iCrzU+uKh*MdW^!?JtejfGRn6$MOQ`!s!oZE
zmtM4Rb8{mkeEISPQkoARgxum6{Cq}Bva_=j60V`%gd7Y6iK#}fC#0pj8}D6&HMKBk
zEOkeyl1{Mu@hNbAw8_QE$=U`DunOel@#pM+K$Gd|<pn7gZ>PoL;-a#WQciB}*Sb2)
z$<;M9s;a6mi)TXUVPY!w-dF{=jUg9?QMW8Dp@E%)hV9p{UrL<w81db9cHTre|MOQ=
z$G2}c_@CkWu@Kwt4xo3XImm5(>sD<|jqCdIEE+th&V;PG_?#!YFc35~F~J}iHK{4)
zoyfk3GdKC4Ic(HG^?2Xf$_kqa9fdYG=#TdH_M4lVP@+|M1)>|tN=-cndaaC%jJ5UK
ztgNiyU}YItim_kDG;<Yq|LfvSv@N}_uUx%KmSa^7x_Wpt#fl1_IkPX`=h0C~Z$OM#
zv8$Szv3%Fa!#`wc=mj4im1H@5_@oy1k-e;KU0u=7pSz=KLr{gTXL)ti;am(rivGge
z)vc}S$B3Uke{KMdj42>aCRPIx1L})Ev$H3;xx;^Fi!m}XvW1>Fdv+1zsAYXVQPJ<+
z-G{gSptS++`NN?Q0#HaXd(Z}B&3k)$%a(rx;bA~Os9EcxIu)^so&|XUBiRSFCQ#?N
z!h{Y-j=;*p^A<$~-?Ms{0ANcan9r}QKY#ulWO|&|l9CeqA1YKF)jF2FQSGS4$mylM
zUEE_yNmVtgtjv35?k7%Ac1{iklHoW6j~}Z*5vt9-ch|1$4<E?vIt*}Al9Lb6(b1vi
z^EfB;BDg6zCnpCImZxE1eZ9T1!HoFf(63*Z8f3dnlR$2ADC`A6Gadq9kY2F5B>1e{
zzCDka=_VFeUS2*iIOgaMQUdxWaBjhHyZneD8=V#k3G7t}4BG4Jet`;*te%GLj?}HF
zs2KF<5i}GA@6G=)Hy^`j8EF{4s+QeVa4`_ny>tl@w$r@&1!$+lZ+zLkUjGR_O+itS
zHzvh60np}eL4dnY8as=Gwk$BP7CTcwKmZ<)!y;cR%u*5Hc2f<Zam=^ve;gD<Lm-gr
zP^{b(sZ>u##|=9XbPbrWq@<+${{4Gvc_;)63D_e$KmX6tQsnPK`g6V5ci5`X^jVsl
zM~8-n!s5p1R~vRa^d!MYCF3fLQ`6HCOnUnJ<Ew|jE_{nI3^(_igaoM*^ehsd+Dmr0
z2Z#u7-@e7aD2DBB-xd%Sjz4R86}~n|@uo&0KZr-e;k5u3m*dJ$$Vr%_Yd(E4f%?fx
z=zC2KH1<cu64TQkX1sp>ya&6ox3BM~Zf<=1QE$oFOI5%(nvKl-uzS!YkU3jTLb3#m
zS??+&W6#G)CjfV1^95C;#ZP9;QKan6!psbVh-2zPH5r~k&M7(5(a~Wg#KcO!e}APh
zA11Xk-zZE+M@Alevv+hnBI#Xn_wHZPHMO<3$a1iQG+EEAh9)K^?2^yB&J3V5ctCqp
z0-U(+8>q^j)L(~y6U)l6RRT_zqobh8ktUcnQU-lyBU}gT9RhyT?s)z&Bwkb6%bUMg
zNr&Iz4+;tkTg>0%#X$Bu<Y*^eHPb+21*T<=e;CW=+vH@YpLGYEoECAyI@{WalxL8}
z5j{cUldon3|0r0NX3rjGKI1!&A2%Yxn$qS2y6d%I_jPn~LQiIk>Gf|iA*dC%4mW$q
zM@Lr|BW-tAS5Pm`^6@pdwEUy2yr<vGDEDUY#2~5t3nm(>tEy+u=7Gls?#p2&ChQLq
ziDfzi{n1l#eQj+9Ll}u#f-<^K`qZgYX#NmQ`bs^7B~~$idSv+#v-tBz8o>dXRm9lr
z4lP}G)jmpA@y90UXF%>a6Nh`bgfu~(f)5OQyqOyu96UKWsi&tm>-QWR))3&HQl=f|
zMAfGq?d+OcTd4_znWd#K@me-EnGq2OLaOH%7C>&j?B;g9>?Cwg4Gj&*#BdYY%OZBd
z<IUdtJvCvUafd<$Ha0d&OG^*${sQo4a$+Lss2oOXjLghD9tB|ZC6vp$KC#nSuRR_w
z*AydoYyWxH(sjfhQ(7(L3uC@*+qT(#lS)uSR&kl=vL9<^?bd3QQd4WjqZO~u{aG_R
zFoNK4*e7-QrPmrgt&ze5U*Cd)f||S$$P+ok<TK4sy$`=faRS=X%n|))9&T=;bV11=
z^CeQH=QK&YchUEEB4qmuQrPK?3=CbYXBmSZFtPhS$;imi9~2W8uU%A2yP_YN)Kra^
zSj!pm`TAh4FDeKq;hC9Z{|$)0F?SlafR;q({yu5QD4Cz~gXgBH>2&*cnD;!`&Ze~3
zJZ*z9RIGxwB(WIwvN&(aLhAYA2R=J@>=+a}wBdB7<uCuL3Oa|gf1`RCw=<GO3e(V-
z7k6tJcl2}|P&lb|?I5*Ztc187_W78P3xns_U?UX($`S!~-?G(TT-{Y0NSMoNx<!sB
zyT2wUB%CpCe);4Has9ZC78s?Eh?S!iV_9)}ZZz{AQxar@gD_PIP=P9JnWqkX+*&Q)
zT9XuGwY{dItQ@hI)oUt%c%6>{O_uBH;$qPVh0jM1OUo%L!sJ0Qv(oc-2MQY=ho81b
zEg4=kyjDAbSi?Mm;08u!9Sd1&Z<|FKCc))JTSzS+MTFKrLLvw0<BFyxdfii<3Q1De
zHnVIFj_loVD46vs=2A!4?dZ5liU>I@K$Y(Tt5sKu<Et6se`nB?ee@DdEb)7GEa0qV
zhn(%Zg&u5AJ9fEb_1diSZXO<C?TVzzBP=YNoBNYry%K=k%$(}@_B+xEAOL7BX^XK;
zLlcG^XlQ6?BCyIE>)bJkIGi~_V%gbzQfz6|8ih=h_`&!lRcLqlp8+Ea|9k8^2*$&8
zkB<@bON(SPE4f4EKG^g<EH>jS$$0VNMF^8{V_REzg)!xh9Zm$+4c4iy?2DOgK&3B>
z#FDugP{52km1XEf$i9;I56kB(_xX4}LLoxpeeG@foSIs_Xow1$J*w@xySfIhzd;e2
zm4!&bxzUe?Og_`D^+r-HWB*?^79xE~l3VK<OWG*Jzg6(&xRh6a`BE2Qka95xrUk56
z$3=^4n`V19=a!b1W@bKaDAH5!2>HA?J1eE0rwmTHWxoulh={XSPPL;_2s!yuJx#!@
zQAT~cpIt1c{}pXLy`ex}v1+~5g{ws;We9HW?)xe4l5hR6-y5Wk3&)-aQy<*Fe=hTz
zHtskCU!(JGVl0h-D5O^v4^c|V88vS5?A+;(!#wCQ4Cc_D^X1?43T4dL^=CALCc3OX
zEGWC+=PG*sU=$BgA|;ov&nX%i8HsI8(oFqn?&w%rSZHf$VKv&!Ps+E3sCszksbVyi
zVs}OPOx$*b)qb)qD7n#y6zaadY`-px0Q#dW64eqrJNv<)<cy4<@;I*GUM*32Asxz{
zJCQHp^Wn@;72?l6u*os561BACJih(1p)GBrowGCQKG)p8_XjSV;Byc=d7fk5pfg}v
z`c+F?d;9F^8`?TLrKKOy2y*NX#FmEr2|63c*92B3CZ@tl9c}G%=g$v5#cnu3ui$@x
z&DZXG*I+@F+n|e{tfPn*N)$o@+5O4Ath7}8{6<{RU0%WHf^HuVfnkG?>+8AH^N2z?
z22^{Gq$Vcb^LUDgvUu^~bqb1uRpj>wnAJH>k|gI!&2KwUbO>Ux1O!m72nq>(sh0P@
zXpg#(z<T4x4NqA~$PL8Ey7ajR?7u04WdTA<O3N7-F!$eQ^@z*M%X_B887xaSYr^gy
zcU=7*`;foLZg3mg^&nz|0tyZ!9|m!EL5YveMjO7ZoxBC}`k9rFU|ED`^{l*`A6c@g
zrlG;E6>xeV4qQ!~A%~BL%&V4}29*i0`HDPO_&4v^Z5<avydvLOySTWxVdVnY#MHbn
zaCa|4$n6)RrlO+4!a!_4OeNPp6Be77cXm@vObo(V9ck}X)HTz;!t4NmLfev^UD2Ny
z${ec#4(=*|UZV8t*ROFj&cz^sgoV+)XqbAkh(HV8xG#|ri5zL}Cu3y5U~Ixg;nQV)
zx6I5^($cyI^*^<=?3-3Zy*kH2C@LyKjSrAGp9M5@)PE?1x==f}=>0h+C@8qOnB_D0
z?W*sSvN8!bRb5$B2`Dl*Ayb}RIr3D*;hEA1lDfT`fk7Y6;XMAIo}P|=$KBn%pgWY;
zAqOkO#BWjqhZsjkM?^RHsg#v%f<bRC*aLEB&F6*1#bjDxYQG%M6^HHHw--|Ru#!sy
z_&v|Mr6YCYwZZraml+w^0TL}y8oB(lV(Yl$I_XDWdryyhYm)r1kEyS1^D*BKR^3Qd
zflbNbANaO%Wlh<A7Zw(<NkA)gN2Q3qtEjm6MRYV=MM}}<!!t!wa}nhZxqQ~qc~)Y%
zG(Z1J?SzDc1X-3*uY?K$n3=M2uylgCrRDsJC+>W2TAtmYG)ipL2k@4l@mZ86>E0tU
z!i$0y?=yhz6qpnGsvkcRksvcNnCR$UA$VC^PmYX0QvEtTz34{u-d=xOE35UNQAUvw
z5om){NOmBq!s6#SwEeQOpvsHFVgg<SfNO9U$leG(P8Cf})9BHT?5*zU(dF155fO2i
zfgwO9k=I1nePQgu19UGI=H}@9(h?J;lhs!|%ZiFtA-sp|4;4jEPY=0|we6o89)`y1
ziwQr<8ThD_>|}~iSe%=)u(E<-(_xl&e$%>e>{p|5Z*&~`s;ah_(P9A+@z7{B)z`zo
z>$$&wHPCAmQ(~wz_x2sg0ZqFGmD4pdv+AK)vg1o)Vj>dyuU{6Ps^nw8Omhh@9?&8V
zLq-0KfxiB*bvMjJm|0l7e|*}7=mBGqfq^?s$(-<~nfBRwA^YmQhzK?1J)kbCs!c!$
zJw0*~6WQQgxy0ph3l+8J%)nhgze~o(#^^{*O_NbY<mL+aS3w_$_{7W2{i)yeu%LM}
z#3(p1rD#UbNAa!;E|77@l{wQrdxGog>YyG+#{+Mb!NJhDIB(Pr7)aqZx3{%5k1wqK
znI?mbX#CnbI!KU)9eQkk;6Ma!{fp<%VU>jMqEJO>gut`7sAw;fP<>O=X->}9P}^f`
z0{sA357Q_~EE76tC_vt$1$SAbEvJE-&e^j72R5+@e5J1ecaf8;0W7jlnoLnmNEn3N
z1V#q2(a`}>ovp37c3tSL?pL@j&m?fObBII(b^$3L$47@7Ep-Gy6`%{~6Ma_4Qvm*<
z#n?m<3=bvrL6!HZPtwEPM*~{p77~dx-WBlY<3+}Qmn_#$dJwfYJVbB}T~^NM`A}hp
zJ$(xA1w1jo*XjcBm37g17>$s@_wP5%HxIrbH{nb83N19+F!)Sr`1trF^VDy>@u+Xz
zdsHF@;RXEg@bESgDGp@mpAI-lyY4NMiRiJSov{&;u*||75fC3@F_>Ayv#x+)D=upx
z+5rQEwsN+~6241szY3rFblqN7mWpL}H;KecBtSh+&h7Nn8R_XHLkUF2f&y0<ZK6Uq
zAVft)X{o8*NwKuBs7d%6Q3U=Fa0-EmX*X^k9z}|XS{#vp1fDxa4Mma6Qbcs>DCBUU
zfa&0;;QGGJ%;Y4n9Y3CeWkoMN{1hh|Kctj`j%UcU2y?%4@T6BA9UYzSD;7R~eg%ja
zr4)GnhwvzrQURZ@um7~VcML=^H!;J`>wtzNJ(r(+{rMp;ZyzOk)$qv3s{mUK4L6}A
zLp_d;8hv7!+x&xL0my%Vn*wE8^R$ytn4nz7sMyrhw7sL_p-dvm_VFD(-@limq|Mgu
zZMe{?uC9)LR*p!yS#bwD8Z%^4krNrF##oq(m-k@M=a!ZhtN}S1rKXOly!rG=CP582
z3v>_PplHJXLql6yTkmbbPbCvK6i4pfF_?|v>fz_mop%8$S!5!B*TqO`aT<^-8W?!t
zsDN2dhAk+i*75K1w_%6EK6z@7nE@&2Wl)~u_n^`TGaRv`6b@d9w+<=5J8f-kv);ZH
z$Hqd$yyNnA3NdRRfpWRS6}}5$$5m}eB`b(qSO<VBcpLzKu(fp-ppI{XdrG<52@b*!
zc-7&4pa5H*n#Rcj#D#h5A8@&el=hqJZv5Rgl|!sEjm>9E-9vN|XcK;yp(6!k6#Azg
z??OcJSue+=R?EoC&-T+oflr6{O?>Y{?V{;53AC?R>!UX0-+F1a`kF64FE2SMiHw<%
z;>C90yQ}$Sha*bS#j=ES#*XIwg>P-B_tTG{Y<yW>HoVG<jcDuRB7KcE=iguVo%0TP
z_Vs7k#`+VSh<)s`3$;t+N2h-Ivhv8`!z&n+9rFby1rzi5y1D(5l9F?w%1I#Z(~e#1
z#P8iHp}!K1p&e;+)}8!!juibjsPH*FV2*TSDja<^0CPZ9U3pR1k3bB0#VQjlb#-+a
zc0XXNs$ILC*T+gE6wNqN6Lt=+>YTg8;zB7!@T=TpR6*4YLpI1n%hU}GNkP*}r#ISC
z9CY6qOS!ofR#w8$roFS1i#sMN>d;E=N9uv8smyoU#J!a#&CUThj?YPFzzKsePffev
z;X~>|7GaVyATAxePex_sNrADte=n0j|L^JsUbyx@eOV)!!KaaS<&>uVNv?B8f@L+j
zv~r1P+o?A)nSJluDKI>iPG~k4^z-}O&l7#G>ZAa*6w#}6&%<wRZAr<=I|h#(!Ecc-
z>8;Pz|9pZY3ryr?QlO+3z^9ioFZs6r-@hEb$Wsqoik_NmRMsj0kDyBlYLL<V)NRLB
zLr~D+?!e>=VI^tH3GOgBXe?d5Ht%p!`S~quZKo?Or+W*B#>Q#}?EuJO^=In0;1vX^
zmxzc6sx+v-$6A6T^g_)1cTwI0sKnCqCG+>}#3?Q=zzm5gDWgAruuXfDa^1zw@<v(S
zxbZ3@19%9M^!HaxT=`Qb{!Zp1q};4ih9PDtSCkbbTRLfdrxKwcZytA~qYJ}@h+Sq2
zjehnlEj=ARMI~E#(g{yuypbh=SHmj~hjo2@9oIJFnXT<P^kt8-asy}^OX}1)1s;Dg
z(c&YUDkwa}8@r?+><Gbp5LuQ$xy{I^AJ%Gd#{%Z&9FR2eI7pCiFSLhWKxy7M|Jdw=
z94e^u_bbK*8zMA9%#Mf3O-xQ=VyUZ#*>2yj>FIvG7n?5bVtI(W=@}WvIXTPR7cIc|
zC)ao6M^JH=F;lKy`=hx7D-xbAKQ1gWQ28b;?L3Co4u4SVcEGU^RiXX0#{mJ*v;%Q*
zaCZK(rUtmBxOffrQAe1WYZvVbd*LTIFpi||;^HF4Oe7kk=1oj&&G&Odg240v2obse
zkE3H|7Ke8s+E8>DT3VebZRn=uCJ~I$C%Ud84_|z+o0aeb7*QCfa-{$wm~w&|5Yy!5
z@T3J-XNIV+PWJZW^Oe0r-Q?)_HQ@7)oE(0?5%%P2jbg90522y^;>Z>TWO0^Y{8oGH
zL_p*7$A2a=9(E3OpVSO%we{@sTS4mqK>FitJf929kMyHqTgb3mR!Zuw+6jHw-Q}2m
zCETlNXlQS4CV(0cD8t;*(cJtbGI9xk5XQl$TjzrxJz9Oor^vBiL*pBYE|hG5jfF);
zL>O5er+Uwa%Dulms^Zz@_`C;30HgKK?x!#@GNLpi+;g2DQTFl@k9!8oaMVv@Z#G+*
zy7y83qp1mFr>OY&{(Qee;T%!-h18IcCP27oZ@s*{P%W*o<r>PrMc`;|<`WSqDk<?q
zF2KtE3aTe0e(w%Vdhyt05-*gg`=n}EYdl_PVM0PhTig56(%54JF0PE{&l&Zjv9ocS
z2=`L6vH<Nh!1iVr`K5x&?mD;^ISoEj08LR7CyoBGz6N(2I|ql#Z)iHnIII*AIc-=e
zTf6>xHvG&;w=7UtBJS)e<d9Cy&JJBa9rg7rKR-0SPucj#m26G0CxOvJSEG+s<=+?f
zJ=cs@8n%xK+1X^nTWEg&F_zwHS1kSstdl6co9cemV2S67y8X&}md(w}Pi;g<J{+gi
zZA~u!n&(WGTv6NUa}6G1jg7nl0`o}!04>qXxk^MR?4h9n+zpqvr!0^7j6YJGih%PD
z8Iy(e;c*U*ffW`O7BLnuAiXf#nY=GZVigh+l9Y_E8ninXLl$lqoD=Ks?LDHJnwpC9
zta;_i%SQDZ+1g0P6^b}}X{o8^78Y#2%buI4z@Wb&-)NlDvYOoqq}nNH&4h%70hCXL
zJxW@Xbw>O_4hF7`N}>a2$p+9Cyi7Mkd%zbd11St;wM0C$ud4v<PDz}8^P=0SsL_Qv
zkbDPOlW(c7MwCeijInusRw91FCPHikM7fIhQrbs(H!B)0N1aG&1M^czfKRmQot>ST
zJ91HOV=y!@4j6(lnDbyz?bg!Z0eE^r=4!IAzV3-*m!QVL%nZo$kr}F@@!!A6)9Y_-
z<aag$L?sp*5pmXa#`xYb8Me^e+<6GaL?;u}qYdk9VETpP5+CSVS(DPzP`D+h#%yLp
z<tVDCINRBM6yW0GO1qN&<8+Dp$vh0vU@}W+LP?UOp62Dy(z`Js*Q$Iu<s_HkY10%A
zMAG?dMrYW4p`(k7j&{WyA1(S50mNP>CnwaVE1sKZhc2<T6dT&yx^;NpzFFKyOa-AA
zB@fCgXq|jOp{0k#<&7Knx8CRHn`LW5qe(~r6pu<PP-g$ZgE#T)DB#8!F(I(Dw1gcZ
zDmXZ79OTx%L&Q`HtFg8;879Ypf(9SdU*&X9n}QRCE(9M}^!4kF`S60_&G717l3Zzc
ztiS(dP0e;-?tkx>#}E#U0R7f*kD-D!6yaKU^Pz?Y%xlS{4B!bb8m?-qCrz1krm1LL
zEitT9k$)cUJ#znaJ%$*76tQtIb&G+2%7tWgi~z=lhH|feehBq0ic;rQ2T93t2!PN+
z^|iLn0eVJ9^RQ>8oGj=|&@cz!f{7@B6+Idnb2OwJfB%ho?Vl6jDxDVqhD6`S%|O<G
z6@#xDfB}p`FQSgqx*)>5pzf@Y7?tx19v-~Hf_TG@=687WHMk*A*CgLJRA~72?KTz#
zn@aG^nL@XD&Zw`N7kF)ddppOfKlRFs%F%ernH--$q-^Q@PBBbG3XF+)BX*o9PLs2C
zVR5L9XtI-j>VElka?!C$ms}7}Z8G}nmk2=LGSWlRwr;PNtM(RzobA**4Z86wclO`S
zw8G~L|4W_nf39NwUw;}No1ZT%eF7G(@KzQt)1|nR^Ixxw!u2Q&Wex_RMLsIwIW|1p
zmYXq|hN{z@TK~-Umu+x$K(@YVYip#fU0~6E%2(~$wL$c}FoVG00*!X2QMm-9gIFI-
z<%gmmi9dJl+-?RwV<3sB-UtcEco@XrhHl+I7Ygn1c61q-`)I=+($W%W^eVQ$4-*?F
z=S^g69BN>Z_P1|)KqwbqDoU}SNJ(*_5FrVLH+TUU%k8Jc3EA%3j4|PkZ57dHZeW;*
z(*!5tXG%8#oS<CC$wGwu-qQoh9V{dih%(aBl^Aa$H6fa3zI_XuRd$Cw^lrPUUV+(q
zf}X61L}iBS`0ttR^5yC>PZx?Ymn*BwC6EkB0`vrSO6~V!Y6`s~@E?4jt2Hn%P*A7?
zXo2{@Pa29+glq6OR##WaGkX+C80*A0MH#kZGK{hhQej{w7@Z<p^gp;jAUB3F=jmAA
zgMW?I6bD{gTl>VS0maMsd)PJ-VgRUsEVBat!#c)+oj{aES1HEQgl-!jU%!&wl@P!W
z11AC-^&pRKQdU+uOo2n?WHKKrBtP);Lko%#>>$n0@;!Hn{KCT3bQoUYGEtV6#tBet
zF{nlVHKw(D&mOb-=zpLYg5Y#$_VJPP{Kg;epbP}mj+UGo$4x*W?CZ~`Uy*81A<w#&
zpu47|qN-y^e)DFxDgu<&l`9N~4=;LhQmku8U85S<-rUo3YUn90Q=j(_H0aAzGcd^x
zCO!m1Z6db`6)N@#vJmCAzY2RmEWuhLU`fRLpkx^j8vj$%*r=kU6fVaBq7MQis67T>
zX((odPSjJJ5IuVIC?eir#Su6w;@;pwt<23`$<gk(Dw{7VH$a#YFg8G)i&|-AZ7nP#
zR={j;gaY2G^Cb!o=>9PRM|-oJ{||;uA<UvTjg6lm;2t{UFIxH+h4{9z>fIDiR52X|
znE)-Pku8B@gAQlo-=e>acL&9j5R}pYM^@drD8}+%a*^8uHMZu6J@|ZLAFlG>(_gv!
e|L>QfTRR(_K5s9s)``RuQOI9Xk;#-czV{#RR2nw`

literal 30523
zcmeGEWmMH&)IJJtL_#S=0Yyp)l}5S|6eI)$NhxXR?vxe@=@bzKr9-;AJGXR;bT^#2
zy`Sg&$9Tv2@_snq4r6%Zy<zX)T64{L&1+tBE`#J{CGf73UPmAhc#<!kDIgG60uczb
zeC#Xm%J;wM`S9(U)eALS1OkT;^#^TW(&G$)ph8GKd-~cbX>HO`=l1Xk_tvJFG*|Ug
zFJHn;s%o6~US2P5HC=h2fkA{reEU^iwJp)MjnW5_S@k{!><<ICuYSV!Csn7qDk_S7
zZN+@G$}@>_cjKjAq@b&z!>-$fI#;KFk%M86ZBIPA5v^LxQ|$Y`H_ZEfY9d78Z)cuS
zao6AlywoNc_+MjeT0?mGnb;$?TkxG!G${UtD0cr1sV?6eqJjSMx^(cJ!<Ud9-tzVS
z|M%Aax6OoEj3dv_%gZ}nVy;+u8*KlVGg+3~VNEry|4&HBsjBW&h5hR9VpGfVMdD%{
zII(XRCx5f@Dje2-XDTsRnoYD4vU^>ep9=4^QJciRzajeKyx8;7W79lo<Nx0SZb%jg
zA`q$zpqyJnUb-%?sePmf@yEMK<d>_t-AIrb6ZWJ1@UtHH$8?DyUd_=JcB6*bI&=am
znyUr|FJ#4Ka7!#DG|19r>gSrVaT>Q~O=6>ec)5UIJs3GVR;?y2oes^_oVB8Qh}A0`
zrd2Kf+D<K_^AUYRm2+T7GxzilP5H0*C^vi{FL~|3gBy3s-ZuJ2jcT{zuuwf2@YtuH
zo3k2x$*@sw^!wL4KSW1|yhgTzS`eS{qr@AcSFhuXYf+S#tsEWJwx)lZ8G4+=vn{SQ
z#ee^PTW2scL(%wpi)F1}y;r@@Ff+p|tbK@yXH)^`T=zCAdsUT{hw|@-Qnm(APDLht
z9Lf2V<hEIEJH0t^cWcu#?J0U!Tq^2GC|?K^v0W0$@To_L#LyTy^DcF*$ZLFjnaJwq
z^r_!C!r(N7d#(XvVJTBn`F}Rkak@EwIL&5+&luAd71f4Aa-H@XEiH)*9ml)((y>c+
zn>8FlQ+y7LS}xa$41a2B{LkBb8@=~ew@Yi^o;t*JpW^z@_4QHm+1rSVa}}wxxjJn8
zR9rFKwJt*1EMT7;N{IXarltG)rCl4W_soGSfvY+E{=rnWeZ)|U1wngnip$oQkn{aX
z$3|%>e~lV4F#VKSC?-{BCUNW!|9?mHOa>PWu(u*{dQgmYRsWA2pPH=6nAP*~5^*(g
z5s@I-!@6=?0z<>}!_Bbg!F`pb{~m~flqts}R!jna#`&)Jqaq{P#GIdv)>n;FB5f~M
z2FoLSzkHc~|Nf(F#>)uI>a8mSgWCt=CL{?zd{7AE6wj!)B$KV~_ik{jU*cWP8_HkE
zC-xO1UQ@G=IO3-`Nf*O})D1XtEr*P?8vU2WKh{-lHs~u<PCO$=AyZT5z(8LGCn+c2
z&6}@E3_1@!QLh&Y2t3feCV%_Jm)J!Q{D8CVR_Wax2Z`tQ%uGxl!&EApp1tHa4!$8;
z#Yp3ulUL5ixxbPok#clw<TL2Z`!E9=yFhhECVd+}U}N0U$H2KgRm|DtJe13Ph?(ux
zEtJ<Zb$(5owbB_(Pjp;s+Z^k+V7-mTAJRfKqwwNI;AAcq+3c(uU$SBZ&6mqJmOnLh
zztW+6dd|HwW;Ry)WF2{{)*+`+N7aCJ%xhgl<JC>qM^g0k*JBn1s)hPiJ_^}iuXOmS
zq4SmEzjuB5^k;Hhr&QXZvfsqL&2UwxXdh!u<jbTMwko2{CT@~~s?&;Z`%julY}^m=
z?j{X&^^-34-XE#}{WqIt>rC^aF)(8|P4<RA-fi`2@w!>|pp)<hW?WJal@5gtmf8LL
zJf0Mlj#-+<U-3C_?PjYL#W1RU)kT8@5x!@4n`HU<voRan4EHl@J&`L{ymhEF^Bk|Z
zsEwf8BFOXC&kxaNmQ#kAIbH>r1RXD@cnW!(m8thbWc?UWxJ|5OGGr`sxWTT^d*cyC
zE@H(?Hm3B-6M6jGXuIAPDW0J*Os}+Z9a4k%h9tXUS+qTl7h_47)W1pD!P?h5S*%aj
z1@S2<5<XdD=3k-Hq1mDc|2lezmhE+G3@-|;^`TtLcj>Z2tzw*)L#Yk6U4@LoM>9UQ
z%RS~}`ET(k-&DKo&ou`#R5>54AptA}FOKG?7^&|2ZaEx35jxh_RvxiEJ?urx7P*;^
zJ$7}C>&|ua5>G_1H<yUwm-9qR-6fd`yU5>ubbsaVv$1HD<vZ^#rHO}hbaZ6S>fNG~
ziDP{$>c?%=oe&)#ztTN$Q#6odZ#`JsL(OwFhkJ8+ns^Lr0bP8CS42gthM?@$9jra9
zuDU;-7q&x<1X<t}q(uIOhZ~cXy3HT@`ubv+-o(Vl2Kf7LAd4j2*ueZr0Tc}>p0cW?
z7VK76v9Bs4OuaX~Y$NO_{@|}+_@lde@BDaF94tNbO*HIyHLuo?rLeG2{g?5p*RN@6
zZ~+CP>b)#$g3PDfsQd8Bu210kqDhJbGEQS<W02Lon_-~3{PXHHDGSX$785HZ716aR
zcfR{?1?8`=U8O@jxq)%Vd$+Dygkp;$Kcu_EV;&D4^*YT^H#8)KiGe|}00B?vS?t6}
zO^x*8(7Dcvh3F&1j$YKOV-caAVbS3YAFtqK)@rJ8rzeT~;p?IK;fAR1Sef<NQa3{X
z3w9hXBHT+sherp!2C;$l87;TObkuB3b!{p{$bE?E$tw&z0|45??gT$hQ!<xo@^EkY
zI<zQs;yT=`R}6JXZ(RL|*@Jh_|08ZwOD4*5|DtgDFh>=wT8RI_oRzGs=@%j$gnk{`
zm=|H)RbRA+-ozqdw9-07Z_iy&?($R+DA2Z07Ex*{apcc?tcW)B4m5t9t9EtIMKB}0
z8L1F~GLrT4VbqK=>!P>p|8b&C(>?^(c!6?`u2-u!b{%dMI$=28FHA19`y!rSuz7^_
zu|vsT0lIMh1Ey1tvzxrUX%6cH=y{r1&fTNpMK9|Rh=?eHF3h=ub#x|c)v#A6s<rv1
zoKP`+!8gDNt=WtG&ozxJ2w!vq5k{Tx#o#(|$T1pKKWw~%T>hn=L-Xm-#m?!JCkBXc
z+!u8MGbwdXbqJ`?@1bSU#Cy}$^`CAiR+a(4F+fJF>N0t*Lxp%~MSO(AiXha%)b-Bw
z!kxeruw;!IH!HnTt(+%b>41Xbe+gjDfbN$5-IbL>M)WaEj;q9oO*BK1D`a)gXQ&g`
zpB`i>e*a$PLNs3TQ`6$V3^nJzyR0|Ybhq}*22B-L1EU6Ap9=R823Z6RnnA=6*$ecN
znq#_}OA1Q&Z>SjmZv*ygfe$}hNevLrJd(e1713PB_vIEV4QoE{4B`UE4(-Hk2Qt6B
z!q@-%9XCuz&ic!3wPze=nCl$YDT;2EiNz#)k-tE%!R+{&whigo*|u0(X6R{TEkq4=
z@OO8Ylf=5nx;u3URd4?~3#umF$7rFe`*~yQd1&6w&PT1n`Fa8Wk{1PJAZ+V#^Bndk
zHCH|(>_phUV5uXNsc!V4)z`h2?7*^f;3sAjiKIh;>%V-3&Gt^=Y&feM4abT)pXd>$
z{uf-HtkGrkOSVLIyNWv^mc_aYFHoT(hGNyePJe$!T3bIBK?}e75pmu7Ijlx5V;oyR
zMg2rhtp~$F@Q}>zHSegl-5Nk<;9y2kV`D8kX<YZXx1h*XR-!(1ocBH|S4_K>@C(m4
z`j!XfK7C4cunsaY-XVdN-z5EgHrI?JVrI3qYhWOi_lM$(+^c@2FV1R6esAxm1_nOj
zVHO9#Aa)I3L?mF)(Wm~m;a2kF`}YsgTi(AX`-0tu#^{CNL`9b#O!J)-*D^MC8hha(
z%@=3eZ|RwUQvdO>CPlgDw~2@l;fr{6C;oT@=(1F|k0jo{{lX~GX7hz{`pTZxFKjfl
zd{tDE#6r~<H$@|)u3Sk+gnzT|@3&Sqwe&#~VY9MTi@z1<z$2R|<KsmafIqwS_Jlmx
zB^X6i18<a<)6yxvLG<@qBF#dirQRp?JgLKNi-<}al#IB|*tvP-WXGl};rXqiRVKbI
zR6=D?(QWXZ2oEPgs73r<Tzrl+6VDjLC5xg-9z@oy$OHu)zY%S)^%^v88Y@7Zp#ML{
zp=QBM%-popomJ^rixzHIq4?^Rv-o@=g<vk?8ecM_B&OO%T<qasx$UWUQi=J^8(+W`
zP5wD=EIFTVsu<dyoa_YwRjCPv8Z+CSbP7IwbW)z=MY>3G>|*ss@=(<S?T(HiM%GtE
zC_8KyV&a8I`&Djoy~(+U;H9Te&1V&0%w}a|yk!+9<NWmLv5Wi9b~{E!Go~9+$sufd
zGj^H(cYqmiPX%gLh9{A|k-vfdr_{U?|N4{GrauPd_rAn_Q&r>g^Czuv5FG#V^wx~%
z<pnmrxbD~Ai@*X=q)f|U@%qm0TdvZ!Zg+eyn&+yF{)!n5*KuBA`V8fz+!qj#$s;fu
zvwB}Ie}kOdZJ(acuC6)QBsTGXcp36x%FOX{f6?;SM2p|dk~?Aq(RXlrwzinW0%Dq*
z-;OeW%b?~a884BvwyqZoNcix<YVK-<qsspwW(o?dZ{M$3;UdenGifB>y<;PmUTF!z
zD=I>_v1M~&P2i|!@1^3o6U)+?o-S4R5e>cXtKa`w{73<{&{?1JfthKgdllpS*|FDs
zF;SO8uAX?kd%_g=xW6hfbftuS#68<hS>c{?avGrN`keEB4vLm$CM&N43qJ{CjP_c4
zds}yNrV_c(`OsMh!_j)_<r{a!yw<+d4A(<uiwV_r!dsmGpqK@qSW?n}jg8#<p_pi2
zF@vb+)`YF`#2uC@FIwrXdMp!?PsZ3e(J#Te%IY6}{FoTGI7JxWzU}LcZoQgwhv&v}
zFEz*Q{{GyQXd1~FgJLPEUta(D+~$<i(JWnSs5yF1Oq9;<(q*Ne%wXjUrGKSM6(i4`
zO2;=}(=M<0$HX@3iyHs<ZSdgta}<PBnROB;3B*rV>|@W!h#*2Knj8E~VuOrH{yA3-
zwS*`E<p#dM_*V?^kcO6!vD(@Wf&s{GhyREwlmYykSC<ydFs}>@=&;VDTsHaR{h55T
z9&wZDD+&ypkdbn_^Wp*haaSkqkYH6)+`Y>xUmN&3<lnN92kUXdlyxdz9-CTSpF<p{
zo6D?+-8CX;y#1&X#9nfNd()}4<kik~5Q$lD^HFV;jLR$gTTJ#9=<Q$nY_N@*{@sfM
zMmJRDyxZ2+#-`VzZ(#6K0TbZi?Y6G3e}hS}zu!4VouODPv(uS%aq#nB(tb^DXGh!I
z({8qd=`Vl&{7DzNvKXsPtc`lpLI7oW^kUUw%+Ah^nMiDuuNPu(Rb{gE9a-X!@Hj;U
zg^k+Fi-YxXiD%Dbl6cr?X`j2@MG;vB6|2qajL$A6s*>#jb-X6G$wCt@vG5E=ZRu&2
z3iMig61iDeST@6iFI0aS_csOIYL4}Wit^nEZhJdI)=*#n%E0li2}z)FvPonuJ~`js
zgl(^8xy^T#JS`;;o&CSV!6YoXTZT3^Hmf5!)!KZb*s}}8qpZ$0!O8qAS5mzou(!70
zi6Mrgwwbg`_r?6l`0W0iZq}yoSnI;O770*nvcdNn(Nq$rsWQ}9;a3Pnu?gHybmp48
z<b;l|VPmD^k_ATHUYiWAanCxX<cQMj{mB}2-Y-GvesR3Kw*QiQ(r#mdw{nuX<_=J^
z7qQGLlukO-TNFS7>&GqUD>sLjZ}l@15!#B1HoV8ciHL}}j)Sw@oj97OEsUPyC5FAN
zM@w5WHilOhVs@LA<M#9CC^w6}es{Zve^bEqV6Du09?X7nuztEfsvXRNe&08c-Ad;O
zo#2ia8agJTZs2>_I#+S$vtQE5xqp**ZI`;@)1-{y)R?$NC~khj<Pko;Dt!5Is@i(9
z`kx2?3#6&n8Xs>`f3U20cRPHlgegh3>Np*oKSBJT^RINWY;QF_N5}F<Gpe+294k<*
zuJ8{VEK=|_)C9uKoh%sakC*6vvce=%n)4=gRIe=@%q+;P)cYz76+DlHD74+)h6UZd
z@@!$@%NK-F&V)$d`+vTzxH{6-^-Ds$21BE27L$jJ$0;8^S)S{kNH+CtH0^nCgvI0u
zy7Tt-*sp7Tew=PeEd|&9MO`|Vd3eg^$B%CJ+RTQp%L)3mgqR+c(MU!muJR_FJ%@sL
zsDvqj3s1ZDg5XQDiuT(Ho?q`28(R(3()-t3LQ&QB+{*~x@6s<<R$|eT8@+dzw@2P^
zdL$LvxJ&%oceY-ut+w0t?fm0@iS1z()<n|_9+bPHsO2+X1}|z>`E`XGlp_CrIn@6B
zySRk1|LSREp+OFX(+t5(iRU?vMCcDq(y#Xa2x;O-PGczfatt&1)wPrHX;xdho!wz@
z366hd=F2SQ*=}wFNYZMrZ?}ZChLXd6{VV_5FI^%Y-QeYAM*G#Q;-$-{MTM)p|GkM?
zVlGl{Oge4&!*G`MZuj4qKx4B1EOfdVpf&xs9_tbZH-(#krS{{PD!2y6|G>%{%jDC;
zS|iDUbQSI9zEn=n7+?VZKGME$a!Ngd^xL;SJVP7J|4RH&3Uf}qmPcQZ^*(o=T+NWn
z(xm$O?!O;>c0Xtcpscarp!m#lQ`<8{t;pnv5o(A3miPTTHg-Ozu?`2RJs&wCp-xe~
zMc%AupRt4Be^!3XdV`i0K_~ZF;BCWIM85i4)*l-5U*G;`E<3)xjY%D3;fxpJmKSJH
zIi;DJbpQL@DOWkJc*C>3TjF8j^DRz`M8!C;Ie$fL5C?Gm*V-d%e~k-Y9O)GYi@G6D
zn!*37ucV@3(l=47`^vOe_WFO;Asb`x_xdgFe>v#?S$QQDE&clMEc%HPqBVQ}{bpX8
z8nVe9wb=jt@c(CNm&11{@ZTfn?28kkUxrB!+y13l3c1G(H8H1vz}ZrwrL2^c!gr_{
zMFTN^hQ|#K4sO<*ZV(6^QBhN;Y1eujPPyhCJpqyU{_k(1x>M!0Mwb`o0R)s3goHag
zoh&H=&QFAewNXVYtmMh-!+@_}zs}9gwS?TWCEzveikoc=C>jmGCzk?!BrtG>!Vf-b
z`?(U$(!|6B7KM|}`N=*xpPg!<emg^6RiWGQ&Qf>c?)AH5s4ra57d$^h=73T@>ul`r
za2CVmV_x2J`_+*uDOi!O@k^p0h!RTk(cDS+9m~q~lBsIf!`0Ev*#OFm_0nk`ai&L)
zoc8)92q=Zl8VQ8=Hm7SldjEd_m##Gec8}QT?MQF^{sKO+SNO84t*vi$AAyV6n3Y%4
z;V3e0STyM{ZZ7Du$K$g1qu+R;%mDU*jygw`=7qemu`xl{gGZM#GBOK^aih7KrlsS{
zy(yb^d`!f_&APK?*09{B45fZOt$dyOKmBQ^dn>lbJBtz&&I_<dtqQxxt>HoczP!D?
zJNS=}k7s9R$;4207s~uVvHRtzPtRvT*Ujp~o+REP*TW4ge6l-&E*!1lo3)oUXSI(Q
zP<p=1aF}#+Q<F?0*GsEcs1s3mu1mqHBeKZtxHdL>-T>S5%p6une*2vDfUP%?JFGg~
z;CSe#VrLA~>R3VAn8Hob7lO}^6JLLdg9WBlZH$+0LPpfn(<5Qlc$EJdKG3s&USWS?
zf3%~grw1Pg=a&3!)In%0QxpxZ2-yAU>xyT`AP$0c|E$e?(FKBx{kf5<s%nO8^0Vjw
z)XSsr@-UMY@F?FKmZvFA6FF~6{|Y(&>ASy4;(tT*?ACc8hV}F%&C=!BvM@c7&kfPG
z%TzVw{sg?ltXc8;IF-`#te`^pChECq^c!p`LhddXXUBeierqYpS@s~S%(sS-ykUi(
ze-p}_<6ZixnDKZ2BrYy4J3IRmwS-o=&0<=Z5TW)H)NfE^er<J|YYGy+JS!?Ib6RNo
zq?j&qmmNHpO>d)eQ)t_d8YRmo(z|r^_xBt1ehvu@t+87f7|v4h@$u<R;-#+9C$+mg
zKV-<WwY7!QKGY?;ecQ=$ez-tAr$P67dOEZEufS@p8aHQI*-)=9D4{D7viiZeB~IWz
zoV^F6@D5=X?V2jnQD&CrlTFXd(^g8)N7HX@Yzn!*($LYtxtBB~CUTkYh%*)oobL_7
zT6ewCu@C}#ePVEQ<2CN5d9@cVKmpzwgwwQ3N7i1P?e^AQoZ!@TL^FUvaAx4=6*h|>
zxOJ5bi`AbqubUMinYC+@A|f6zK9GQDG)i$Vfz7GpYLr97LwpPAHU*wk)xB_Waq;jF
za$R?^?nx2$Z25RsO-*eI8}+P%WPY;Hj~_q6V$TNUg!jd%)M9Y1Uk7FpM#kG9E>U&!
zyVN}{vlp_mJslkh!k!nfhQA?AYj%9WA3kVItCd;JLUZF_bJ}yUBieDwIl#54s;cVS
zHyQ+Sut8h+{gsuKjuhDxq4ROeTI-Md@E9@yr!DX=@THTrqFycM#b{ttw@FAyh>3-u
z0YZlUc%y2!C+OBgzGT^h-uB3cy$PJFIi*uzH-pe%)-;JQS!ro$Sy_O*-{rQ;I4o89
zuxB4E0^-}ZtF~M20nxdsL7@dU*nuX?m9Y}@@mq2N)+c)_0|NsXIK=rLqXCp2PHSUq
zmQ8}daWa*%qCAO7NGN%%bmm(^rUDnOc%-DI0YeoP6_NafjA0alE@1xE(Y!<PXO<9i
z!uvyt59L083=E9pcidP*7PW>^UIH!!vc!vL!(%&HY8jcCF?EX7qv=^5J~RS)DG^4o
zFWwic<?@(;fq|BG5w(K#;?bJprNqL*CkS**+%mfr!d4NW%SA@Lpurw}AYzD!jEtrW
zJj`cUj@9;v=QNEOT+sk|P(eXKOY3vv><q<foi{pwnH4$aqsNa6kY>PL$1Ci!xO2y#
zn^b$cQHcPmyI{qm;n=Zax3iA9K2|XQGvo8gTri8n#>BfKB<wScQn&{42msL3J?$kQ
z0wE_@Zd22<?>^D7v9WP+PAdc7i5QgKpm=g?6x{7%fA{X4(#1PK$Me%e?Q$F1=|C(3
ziV1iuK!@YT#No7ON^-KMyu1}yIAd@nWK%H?sH$Ry?Beor?QRc0c!X>QJ3l|b-=st3
z`AoeJgG%nN@%5>|(@|~D_3=^y5etir20t7*X#cEaq;wRzL13yFGrL;A{^SJrhemSV
zWXQztAdOOZlY54ShQ5A%pcdo4S6@U=xcf~qQniNd4sImf%LHCqLno)Qt@rQz8fTBd
z*G6(RPvM(-&d9Xqr63Cnf!DAJ6EpL{z}V(uXKar0D)4#B0#y)lTYB=J2NRR?Sb16Y
z?f|sn2Q-=#cNA(UqrSKdI&L3iNXIgpPuC>tF)mHHZo0!+rpu*R6d{vjmsvng1ET?Z
z5L*eb)-O;6u#b$7|5-)d`Gj8|KfuSe38Ffysw&C+0OH~5j2+*@mkD;@ppMh-v94ue
z0r=m)eOp*qXe&&Lj*gCuY*YvwHZd^2G7>kx2w}wZ_%WJJ5ps;5ICv4(*4*3-UPI||
zbiY*<vF70F?9A&0M(OJ6qLv7Kq$m$&c0JxPs5x|jg;pmekyTD^*zu|4s25vK*FZh6
zvtyGrd;(BYgwxk}!{nTzh_MqQJ}@{~k3B$jnGjKln6(jc2E6S!+MZ`3#u@j3^MUeC
zuXJ2(kKL$;Ovo)xg)z8!2dawk^#g;>7&XQibS!)U2op$w;4f>&E@_0fzI<q!C^j7h
z2dTB5ZyC&xn=Uj6mMbL|T(;vg=Yr<xI9T4Wbeu)A!fv*~@7uR;w;syo*V=?R9jvL1
ze6`~t>)@pB+{x9fWF*GP%5f@p+J4Zfq?|QuQ5qW)^I5=oCzz!MVFln1xx(gS^O#u)
z4hgfqkrDb4tc*pgN>yII&L)Uk7wwL)hrnt&Ku*T@?*%M<_kxat9)8XrU(d!gy4Ty*
zb_D317#-d0LhsgyTt9#SC1zt{S_f{j_VR+_-Rb|%0vs4zT%2yYS$aNNEFE9ZkW2A6
zS;^Sl+3D!)WFqbaJTKc!9$ZO+F2mcmv;KJZPWD&10dOD`3WN)`iE0E>;e=q7kq_n4
zzkSPpwU^BAs8#J!{Oi{qfQnh2%$=I!H!+I<TLS|Mm6H~w<BpqC-Rfpsi6QO*(aU&F
zU#qIlAO#8AEWEI=V6OZ@j^7Gt#*FKYwe>bIu*1#iUN#MQww&;JlYg437@&Zmp<$tT
zCS#}Dp==yIA$w)H4b<weR1fH~F}*2q+i8E;94kYeB%3j5QEN3T`t&K9>3aw>AY_Gu
zS!HEM5U+030O-ldeYNKY_bT7Ef2eo)>sI-cQ5cRLY?U#nrmpS+AOatU(8=R%xsq=&
zS&^O1XmDc(a36_8N~JH{!x<$k>NczdJYXW0e){yOwKbdRo)x7kW_WCDYtIc0X}#>=
z_L^~^9@`;*ta!kr>kg+--v3G9jHY0p&U=kan6&6^Z$BQby|9PO4-d{7cA0CsRugo5
zcD#FfxM_6gk}SJ?yu0kU{yRD<s;Psj^T}b6U$p1uRCUKoDXzkX7X0Qe8N24~*FUX{
zVMpB#FD1Kxy;bR=S@Dobgpx_Vd}$Em4B0Z<2W$`dE{eL1Z2}BCn)4>WH1?hsq;Ebl
z{1L<b{!oMB+Ey|mqUiAOz7q2{h(vBnVpSURw^*t_=U^2u)B%9o-jf4P4N$Wi@<W3)
zmRVC_!>6OLYsq+Povr9hzgWGfs7P8$>cfW*YYvr-v#J$#E0CPVh?;?XLRBaYXXmG>
zbGg?Ncq_{*N!CbRUERT<WL2<JDNEV9ixy(-`}gngUnROFh-zpid0y<MR5hSM@@9to
zpvuUVxDFxQaq3W6ou8lo<42%n=X?nFlzd4{Oiar`Xh>oHEq}}XR=VPPgEFO<MfcMK
zI73{-bYhJqWL7^MlCh~|A$N7>X?w5Is;UVIOC{izEZp3q`{cpRHVbVx%ZAw9?kNGe
zSo;$d9nDJ?3M3BFXM*4S_!S*8e2G`D`XP#D2i`q?{P^wiw@tbw`p<g6s*X{iVL3Gc
zvAsRtYWe%uPk8p6bo$L~Vx5<U%Ig*rW!sQd^*se)ssdCc=Vo=FVsMbr9oUF_I~Y`*
zg$nm}u4C2BTAl3;$^r8U{qR9oF%ZZdijx7{OiXB~#jrnl0#!{}6DhE1V`JmRLp5E)
z-Df<yX$}Z|HAC$c*kM#H1-}C$J^crj?7W(j%vWjRT!}B8`@V~Y@p5u1-3?lVdRgc#
zw_+$6?;M|Vt3|2j0n&&#*w69-GXJg54j%(UmSiMd*~sQw671HF>g;Tpm1|{MIE9QX
zs`+2Cvp-sz1G0Rd5OpAbkQv_VHU#Am*j9YhQI&yAL{c)ylsbaMLH&x(E|ctMf%Y0n
zmX~b&o)=X+>Ob$PnYR-bac^*4mwz6140(VYh!oHy;9i#l(k#Qv*&j{CklBh2>Fi~)
zbQNyLPg+UCyBA-n7b2qG4Rq{@*06J`y6%n@=;fkE^E<2^ZMRY)<0xABIZ!Smgq!f`
z6Piw=KVF#N!JRx+gwWYmqvzQ?Wf_-rojoNo(WobBcDTcuhs<myS2Qesm=w_NabkOy
zqgzDsHNZZHNtVjVo{jSSdz4jWp~-Hi>!nhQon`5Az2^Xu9lO&+B`8@CMG<KYwY2Ff
z3o{u#MMMy>H|R5~OgoaYKYqkLAh)S)Y|x5|jjhE0UZ$);MqMHF{i}dNZ(*Svfq_cD
zUT!b?Xf&{KdUHW#!E&L4*58{|x&^bXaH<_)5>P`VB)=zf?J6%Xha_A`m{6@V8pha}
z?b*KlE7zhf@;T5l!@CJ226ajMaiE+X&iD{i5c_urvc=1MZjCT-27<x(CV#hn)(VCv
zxUgqK?4OS@x*Ys2W@$(l8e2bzin<-@+|0?Ar(Ijq5NRl7>x)Bci_qC+*1S~L&;U}*
ztA)X6ujEW5@%;JY94pG?Xiev_l$T*wpkBPfWgHb32cK(SVPa`I@_}#ziZT>EHuw`l
zu?zWh{<rvRI@h9&TdT82#=1+ktY^d0EY%AqNt9XFHA&8NcU{Vwq-10?3Uo16zo>l1
z=vbO~udUnD*;%>#*)^)OrMPwNge^2Sa&RTvBILgETObiuZsF=bCDnXpfKKIhy6300
ztom0ocAA+)n1=DPvW9oIWCj073dn8!1J$xV3-B}^uDhl}tB_)bO*D%P@eWlnFffQH
zCX8|@LkSxA@_wE@)GC*H9_`q@Pkx}3BIp`3&y%DO`7HzW#bEUKFrW|t7<UFo9zE^I
zmn<i+eKoOQRagx9tbPfXfk=C?vI-6e5CUic)WKTOMiC0PwxznD@XJk0Rtn|ub_^@8
zcrHUtO^ucW+A0vKs)onf)Hn$V34i&9>JNfPzh;{0sKq}l(`&`g7<|W-$l%r`t!U0L
zp=ZdV@#g;Ltnvl3F8O!-E^Yg`m%F{f3>x?y=VmoxNF|GIogca)uMnZ+rjVrGzGWG0
z%GQ`9D~h$jRQ{t`b$M~xVXD>IV^ONY-32@Z53><jWc0IjEnrMiId$}pdZKMoZhLOX
z+?=j~56~xti_DL-JAWI9iHOQ`U497cEe=!I+IuP-4BeZJVvaQp>E&iC>bDt*@9gSI
z687|n4vME9E$XpPQj0HaI_w`$V6a#`z=~DP#c@}uIj=uQv*T(k=rI4g?=l)@jq9RG
zW*U7xtf+Fze(7^uo>p~c*R9tSTDnmWg?V+wIET-R-wme6$Hzy-oy$kdO_m=#j=snB
zS+Qci0~3P#$C*+*OUJ4}R=#r3>V(ST%gFZEvwwG<;b})b*-o5S`sSO(YPC!nIJ!mZ
zN}S|wZ+24xOftK{IlZA`ubsYwQBkrb;5bRao+olNeTt)m|6~^rXj*H?Jr!p>qiU_q
zggE!ti<Z4vf=f_&dF*y`uN8B*<TB{Ut*acW_|(g*tN%?CL;n3ZeDq;e8O@AMnz>-?
z+5+Ehb8+W)0q*8i0hs0*C10<a^}oj>w4f+9pcE0qO<2@WoUW7S>=$W(+}|i0HW75*
z(f2D?(J+iJ7tcOMC(DpnM;2u)QJT(<C)!Q^hS>{-X<0)R+Sb%H`n<<J^)q=vYR){O
zyqC`I@fNv}9oCO3{dXsfIV_Nc%~^_qRRJztoaGj}4(`9&#iCui#M25Y!kl}OEdoFb
z8k^Ej%q%S271zzxNUg*_F0{CtM??Tb$W+)8QVx_WIpIO*SFZ4kNJNtuWLfD&=y@(y
ztajxS)3nO`tWqZSw{0$EP}VF65vQI`HB&M)P+=}%?CfYyV=Ql6eL~VF?O;ENn(mPj
zLtZRFNe?At_AxsdrH+bAS4Z)JToBTvxR7_<e7uA+UM9Ok@$X|x8pBaewm5Qrhu2OS
z4ZCIb$_6=_sjXexv){q$kSsw_0Nu^%{9)_6AlZzf4#ijfU!UVJd&jL3C!WeXL~p9R
z=n~4@nUak7reT00;-m{s@kJzNd>N$$O3ND4)`I%k1~Ng&=_Ee;4*P<}zraM6mzL-?
ztFITom65@1qFeg&$5?Cj2|GJ-{V%F7BQ9RJO?!Y7v?Z(SV5I04rinop*<Kvbq})08
zwL>#2{ODK~-*~dZ-p<a>?~vc_Pu=^S<Rynmp9XQ=>i(yWi+N(m2%5*1P*O)}x=grs
zWfx}R=jo|9b2JzC=;u9VJH5x<OHmW;3aQYhMCnDBng8v)-7ZcxmBT8--KB14KSkxn
zz7@Oe?d|*b?<3~s=aGrJ8*B_^d4FCn^4_>}XAGJliz#@d99s_jUI1^nBzNw#;oohH
zQ665g%U-;B`}WmMBdKN(7|X9_@@l|G4ktu+A6oysz6oiEVRy=$D>0U^C~jKNCYg4y
z*i;1pQeFg2fvNX!3h?gJUSS-OpmKF3F0RSrJ9$<_nd(JI=t<@|rL|@+G=oD{I(*!7
z5zMm!hO_1;n<4$;#St))eV6J&yONov?52H_bX;ugKZ6-z1K2ZV>d&KR>BG2oe^)~K
zqs3x(KiNb3GPp7(du@X)_-+S2KE856Yu9<#>z-q%)3~C;$mJbYtW(aEcB=`rW;L>K
zMmh*^a0XZG)_nE%AD%`lFwZ{w+N6=wVI=2Q6cW<vl4p~5LpI_YFjW_7HU{taK0can
z(8)I07=q*f>;pjt^YB3VphUO}T0;3hE9!71g47EGnu;;QJR>_w8wsYHNqJC8y@*&y
zh*R5y5}QscEMD)S-0>6S`%fXHoSDaZRSimYhN3@;Td-L=em*hoqdE}fCD^Q3W!9+N
zbJARrB2ms8#Zi5y@z}x4jM0=){MoZdqw3uu=pkuM8cYt&^qjJ)s$<oMQ#i2+x={Rq
z3_ubSr-t>tLW#+=lM{p9TlIYWr{co{?BaTz8T)4XX~J<Ft|IZD)X%2$LP3(Ax|O-}
z26QXkoNy?i3eNXUnBPrSaK?tHEZOlPUnO$9BXkY;4ubHDktR0M5b?B6r&9%k1LI+p
zWyIs0#Qgf8qx?84;KNcd$T2&3Kdfn0J~F0M$IZqt{)y(NUTY|kdfk-i4Q}(_zn~^T
z81^JRmQ+wxy`z!ISs&bJ^%Q(!&5B;IZDvMiq~*18BP(eLaOXYX*8RqI$(!_&5waP!
z=jIGrvr(o>5#Q?S>M-$0^Vt*U<x2|5N8CB?;DX#0)TtHMZCE2`bT_1>W7tJU=ZnJA
zt!`Q(QU$}J_(k?cP7U4E1e0Gjratpi1;lTP47<npQ&Us3k@ZhPwDO>HdRpG}Ub+Cu
zy|nHTwpzNtN7w7pC3JE-`~A8mD38BO%sVlEMC~@+3qsCX<(Fe;kE^E+Lfx!?fPdKY
zv0g`q$llGZN;X5U`|)44(}xZRWmDAp89Pw&A^Eu{tb9~lyI#F|1sZu%ad5!!&J_0Y
zZ#{6)8sHxdjg0|#_eRnr2tuW;@=2s}bywo6+qL#q1_L%t2BexZ_g=$j8=Kpal>^y5
zZi_B9Vy~?&t5I$>U9QA#m+H@(Mcfv2hFbb+4uU3(Bf#DoTF?=9aQ&eiu*GfF%9pS}
zse%c@OR4lMR}pXu?}jsnp`m#sgiCj4ys~n$rt(dx#Tu3@J+7j)T$i@{K2h#)j8q;S
z6&2O}`){}Bn%!DUHOH+OiTR%+)iR%DP!wrHrzHBGtXbDp5fCG_oMtgLh<tajb<E7x
zV2ILc9am8u)2$Kh4wu;-*Lp%Y^<*F=p1^BCRI{S&Ff1;Iqn#u_Xx@3r#K=}^K2BUG
zcsS8Gg^Pz50@a5%{h3$Q!?7v*zeDZ&FJt1MT7KiObO#r+CPT`P*$E`y&9eE>>vHV7
z*IQe7Ma9IBaz>=M{&jUCBYMhzpKXXC&Ac+9cM~n)#Vh2xq;58vw>R>}5?FtJzwyV=
zP-qEwh4pk8;+Z<cjqN>h*de(4#5d%uVD-~{e0z=9C`A#t1e^>hSlf~LiUvJfwbpc&
zN?z%-M<tXsX8JdW^bS;p_v6Mvl&N&I)L1>Urd%^1NO=)4^y}SVU!S}KI>D%aVRip2
z9X$lK_(y<UP<w_}?CR_55mg1i?kxTe{aicwX;m3K`rLJAp*<pkFeVP_S`>Cfw%@*a
z)7x|(!}Mh9<O#G<E}>*WyTfPqV<%lUBN3<_7`@A|s9pz@rg@<#RF%MOIwEsC&}C>_
z>99^OY`(_J71d3LDFmXU*~eJ>FZO)N?fN?d*=7eJ@iOTh+LL(P6Lkz^r<2w##q>s}
zKSo#6($YY>YGX)B1k718QXi89_o9{5J>`K45&{kZO&hG%&I#~-=l-=UJ#-J42aTW!
z;#^Uc6qv4TYHEVMnQXbaCF>nH5N*(=A^j=KCCI=4u!k{Bp{}jn6>{ToU5-}KdV8D(
zsk(H^g(3H4^qkE#_rpoY%i~_pG1HMtXgi2c*SPcB|K+gzBOlrRZEywJ?yKJ=EXF)Q
z8Ig|o2D)}7oguRr5|YZfzq7IN53M{(K09L{Ogzv|zK^Z~cILt{Jr*=%)?OO;F6{~0
z639T2?I8JOq`yqWym~eNG;QrGIk$xdRBg`1=zxA>U!!xg$XE7HdXo7iBqWOL>7fNu
z&%hQ&&L6{+Bm~(7j6g#}^QfCNS+=!h85%-IePRUGYYl(xDxl&2$F4#)neVxTM5%HA
z`{Am!_*csFyVsX1UOd;=r;+Y@`}Qpw8XBgL_si-9p0k;ga8o$LC?7C<0Pfu<@F?i%
z1*QCI#*yyIp2Eh*hwc(gn>H60(bChKOq5vz>W+5&1)>6i1prCM0a{lAr*s6(i2j0-
zkAabqCD1f8F5&H_TSwD(%_C^yyO+Lv`BLwLRn&@Q{Ak=0R52rn8w4gkS=-<DaJArZ
z8poQb0ZSPN7woXaa$4ws@6n@20s<PmKJ)YQKJVXOxpD=idLA7)Z3r%b%>CH;)4B?H
z=x5gr-mO)(jBnrEz^;(uvz_s&I5_O!h#{Ym3VXN#fq{tMcX9Vio#C_PO9u7?%{;Sw
zW*(j+OV87BKsK~|4~kCOs=t9}XKn|2!BW(MTwKa`jo<o=nK^lN7L1o#PQ#prm3bW<
z4UIm?l4Z)nq2&C;b&x=zXR_qXC?2y2o4~-tEWCH{>C-0wR((AJAVp9NW&4qmle-VT
z<PIU@9ngPLY}`KwjV%1@*FSvxm?YqA1Dy&}E!9lrvAZbpjs6FEd@#fjb^52EpupN1
zWohXmF)J4<zGM(X46}$H;{}5&JNzzky#?~>+g(A;06c+NE1ofI2Y`H=u=st3F;lIV
zWNI<6YJ4xwvgoF+gHAIpEG#UPfIRR`@?|2|BH=4msV*g39x@2fE5NpX{MrYy<9c$?
za?f&Yx>mRqsS3RiNAeB|h>|RGG&g9Afzw#lo^zGuE_}It3IG-o5<>n&KQJQhC0(Qq
z{|9MZRGSM8_a4_r!J(|G>HN2i(74!yh7YNr%P*u^4Jxa-#e>s;WCbA1_{rDbf6A&6
z4-g4KB{=}eiPCxDCKq?2blJj!{)nIXNH%h!+!k$bb<`3tX)Q0ib&U^WQVc|z{l{u_
zU_xCvwiUYy=v#jQQjg$~ec039-rm`1I`FL>)DcRTKlc%kOAH#LU8-x~FLaTQ1q6~*
z{IbsIyPz8l-ScHHFGO1Z9YVr^T2Id+<OHCqmzNhDrP865t$B`W!2@b)^t$l-Vq<K&
zK72%<`In0wAGl6|EMW-)wbEU=B;=;lQV|@`ol6x9z{JF~IddioXiObkN#Hc)a@v}C
zV_97cZGZ?GPLrWW4<5YdRN~D&ufyDh9s|Yg+e4+6+6Q!5k>KCNELszw5r&7~2sP*V
zH00)Xp?!HWs_8h{Y)yfsZFUX+dn>i1f{2I+_`Dt8WU}KFuYTK&P%&igF$)imQ%BTe
z06>MQYj}7DnyR5ufh^T~3T|$0PoLHU%9IHkx8-x*h8W0F&Kc80sB|ZCWAy<i)+*Nr
z?F~Flidh46IOq(6;0)W%RyKu4iHOvpG}3IfNGREzc@`6KadF8wjKcxo*bP70D1$aq
z4Z3fo<K_})aSpUWlsusj15ycBs3K?tx9ZTdNp;_e=Z%({Ar+3-spP7_AsCdib<SUu
z;FvM~`frN=Ixen&?GmnZB$??OjZRv?B{MS7ywTdDxnO9_F2KAR2%uet?6Fo5#?W+I
z1)l&8^x5;`7^WFWh=}%A294mF0jiJ}TKc})kfpCyqMifY6hxG=kQcyUfjz_cuXnqL
zwU^{|fIQ9^2DkXf7~@H_-TGJT93b^Urvr$}lS=*rVEDc9>;ck@4w?DoNgW8K7cXAC
zemw*Y>fdG7dXf<oo@d)IVRkYnU&0rJw15z+Ift3Jn)Q<L*N?sUqngNW-ed!B5Dy`R
z1r*GV01Nou-Lv?kD5UGNAgV8x%x7=n*Zd&rp}12qIuv51&bx+?VsG7Q{LF6#<>|9$
zO^_Hgg<BZN#sjtT!U6+z^!%eyNfnwy8V$+3w&D@rzI>sI5glVkLU|O&Wloxvf#5Lz
zZP)kZX$QfeOT)foKGZK@pJlVpIE~8c;Rd{q9{p+iME$w7P(259ATxC&_@@xu!T=;i
zt#kg)?b~+a=B3tdj}}4KB@X_+6NUk!3lPlN5DnL^U2~kUmRQu@$h<d7M30b35vm3h
z0bY#4imX7(>irSJ?u2dk1Wyp&KzYA;^CpyzSv$mt);BL2@>AEK$%dxGP8q*4nDLN~
zuA*luZ$ddY5zOHZ_{W#L0qryWO_Wq8ArT*WgqrC)G@6-=N=fMzpMe%YUN-uz8#lI^
zZpoE#Wx7T21vvvVf!ZeXY@F)%{Z1R`i9;Nhd7Qh9Oo)Bo&vFiVS#bRU7gq^HeeN({
zV|At5#zZ;jSwDXKh-OeJ431&iU!*nL9DAf_pYn6-jm&45ynu}Zr8<Qa9nB4TbM&l%
z|7JmBvBxlwD0<Iy==Q+y$S$g5ZlESBC#RB5TZHxZHo!f2<CWTKz3xF@Utjvm-``=g
zniW>K8C}10?&%Y4z%Fy#ycT;nO-G!xYN1&LK)4B5#KEx_^TuKYMi=Z;)_xbS+IvPN
zP%~?%Gj>X`5A9d1B8wg-GUgZ$rsKsLw!(*KDk#iVV0swvC8PL+hQ%28e!*k%+OwVD
zRrd#>=m>g+hipXweZ09_GkB<R7sy+X5+b6j1CoE5v3(#FYO!88TWF7j?xdqV5%ZxH
zf{>693^{*1GGPt+T%tvVqzoh1JLHPp^)jCyWT7CpJM$Aa5*G36m21cnagonf5Kvk1
zvO{|VCRqC6&g01M+_!O0&$7*W4|`VZ3ORmeC#jQbxwiUqJiFKlD6Dyjmoj@)<@MsX
z=2v&%OaD8ZS$H^dHP;NfJsma=l;#pE)Jx2Gp&8p-x+Kd}IVof@!NbOVgq(uN4(P7S
z#GjUKOU}6COkO!NGXoJ*YC77pn^P`1rYUFOcm30>%fZ_5m|hqxmH))TBT`7Eyt8o4
zibq#hckJZq=z0F~O=Q6>XW=PFO1(<vb-JZ%|E>(BHY7sbHD~Yo(~!ucUQ~t$h?GU3
zOX8CfMk?ahCr|QDPn*aB3h!~6*iE)Z9&z!Ht=po_j)kRb99GWp+n|yi!>WyIVq2Do
z{)1P!+o0kFsZsfnSFRq!p?H39<j%ym$);?K24)&=cG_wDijf9y)7oca6~cv#DxdEy
zR;#XIyeX0Ng63#lHIBDlp0p}|UH8jl-CmjUKT*Z!Ss*rz8ipYy0t(Mdn(O`eiF{{<
zn*Hc!pN*7Q<+XOLjs;cawFL4yEG{laQ~K`HA4>E%50`idMBx>@8|ZfJB4e=?-e5aj
z54FDU@u+esY`cRN=9Ikdxl7e_<JW7aw^0=F2gO@<YIbe;6!N9dq?)m1e3;>9d|y6&
zw8VLB!mvnk)I(&hg7QFs?Cfyt=_LGuWALi~%i{e}3id23)6WszpJoiPY(ZXRSqn&M
zZnAMVtXPuU$utM)EX!(=CGlYsfdP)R=El7J8kLoRPVZ+yfSdOLp%VfQLfpU6{<4jF
z&5QbsE?y`#6`b61AOf|7y}`e*H$|Am<HbwWX9sal@C<NrC&~qwF}%C+4=J<KQq3HX
z)<d1!^q&Cwy->xNy)a|!)kEWP-l2Kco$}0lgUwdMI2+ndpDjDcot>S3lXWb;XL>}p
zHg>!%UoxYHeDB)T@z>))<0KG$3JZy%M{FwHK+N6^ijRdS%*2zfjfVT~$0<3Nc#n3B
zk1j3=8fq1c;jkTI=&Rx5;lb!d+RSp?>ajc*$@|Svw)X*r$X=<DkN$<{c&F>v*$T$o
zJ646z8{M}}*k)hf?I3oR9@jQ9C=z+_=7)iSfrUkPZ9t*mvWIEhydPCrbmJ<?I7c9%
zZSjO;7a6;OjLgTj=iOAS$-aJm1(esbkM-Usl9R#0xpL1xZ`cdB7;HjUaVA3y`Tl2N
zU^pC^RM-B^Q)>>kf(bX`7|TUwMti@204xlQcUCQQc@AD}f5Xmevh!2TBqLxHkmo`}
zsar})N<<7PP$n$XY`ST35lw1o^^k2)zND5dvHJmo<0oh7EqN|Pvg|{%1P*GmRP<^s
zmbHFHgjqD(Cr(uVVNwk(N*cxlP~s5Re750xOR>Q030`LPm8JN0S`BF4OsKtz(_@er
z)B7-Yye{vx{QmuW-{J`+H9;x;XOa=V1}r5V-uYUx`ug8iHwj0)q!)V>3Na~fZ@X=h
z;Rj4wc&-(+HpxurVGtLEa=cb&7*C!SSy0M^LYBK$rd|wLGp3gg(ijS^#%>Z-xkMdN
z6eR}5OY`{DQ`z(c(@1W7+}ly-6wMgihdc<$eH5?N^wD@@NLjB*al`t(1C0kTfrdg<
zRh~~{{f@gF-75$GI}1Q@H5Mlo${Ofwe70<Lx4<{Rd34P&DlTgnh}5b_i^S*~u6P+p
zhXF`4V&eYpR5PuDUtGJv)RGYZrD?@a1(;hyX$?OOeuG}x6mYD!v(Idx!k&Vzf<CB2
z8%Z%_?2FK>Z8KF=Pb)_&IFYmpD+i<%KHrOM<4^lXQZ-~4B_qVcD46JoO^OO1$Lf*X
zy~_*c?k#OhUUpT<YFgv1pGSM&UWjV60!OPne%njk($s{a=?x79fe#vko&_NlH9}pC
zjsF0x6qv@>1}wWpNSMI!?g5>Kyu3V&Y2=-<Y0XZ^&{Un!LPbO%xEIbp>t^w297xfh
zzEqIP8Xi=%lkAo<b^^Ej3wnKiAe7k0yv{>Q6h$l){PMa7H}cXfI7lbeL<b}?F3^vF
z3i&phAPX0vRC~xshE{7R0i^9svB{z=Ps*FB-|kf=MBJr~+uZEFe(0>wiG+HNIdOnW
zs_WU+w*(C*+}o>6rX`KD&h;ihv>Ue*W`1@n`i)L;z8iQ}^e|)j5nH6mwT;-L4JQOz
zhLO%hp2>MxE8}ODP^=?vsV<6Cjk(GnNNY{?4K`QVBG*<^GqnYIhn$keRkr7!X0N6-
ztx3BT*gPZ~&Met4w+KJdn4{Z19$#)jcgw9Kl?XyoUnD>NDJMCxJgrpJ`<;3F$E)U>
zw1(QEKZB`F95iALAKp0D<*C0)AUyPUB0Y6&^=p$%=JTJ@rf05?5*vOtFX!^7Fj$$t
z(mlZb&OE&sENx{R-y6bY%A2&z!G9b~s<Z*ufda8>#_(?lll@H_{*iaf4J+BdFfW*}
zNaCy#M|vKurn9zIhBEo8th<!fY!eFy-5q%T+;nk{PR*F#zU4)AUEqw+_am0Uu&`vO
zs5r^uAFbJ(>J%O6ddq1|y&X#pwfCB$;@qz(dDaAnPTIdah$&%NLUbR*y?Znc*R&-C
z7pKLL9@F?=X60Yr6h4+Nnk|DhWj+@}s)KIj%Wq!7nAa&0ybdx+jc=xKPU=wer)dHM
z)1p@xq#Yx05Bi(>#iZWBrp(GT`u<k^tbF$pA*E)7;=j*XlRo2*4PPBFOW!+n@Y1O{
zDvQ#&aL<G*o9eT{-T0&(2bvdx4CC%KWo{n~11OzR;XI7<W2t{vt`6*Iuv?O(UrFIt
zx_StUO=N+|>?7Q6{DTjci$}WV(t(M%CL~_Qv7U~77>AF~D}6SCIrtb{H>Wl(PgF9J
z$nN0Av7w7o$M$`W`7rI^#*w50q8RPPBnxT>EG0Hg0k*c(a}DPg{8JvMaDk%IqV^oL
z#>QXoK4VogXDV?7y1?FqevjEjhAk0(I=i4KT3TZAJY!QYSvxykrj2FhWMc5C_kw$_
z&BJe{6RKO+4&{ujQMXtB%DSnDB~mY6Os>u?p0sYPsd1Q<txh^_(m#$L_;#%)rL{lp
zZ|nzdZ2^b(FfKm4bJ~%Nb4j6cL(&avvRm%M>Vg_$*$N#32B02vvXXv^U2e9rKe4iZ
z)3;RdNXW%`K%VQqeFm~#>-sW2cVmz#%fVU@iPoQ+goSth*d0G<2?b3jnLE9kB@*W_
zERdyS9~Pxx+3zGGn9=;^w-46*-1Cg~+lXg8WG|!|y17T0VhlGK`?Ad(|2r5{-6L+M
z=>cNm;F}zRCg(0ydkR*DhF7fA@&ft;`}+7KhRpu%Z0^habCkujWP$qLy0Z4##;1*%
z9KF`ZPbPoAc=y<_n~&9Uib_Yl(cgF!UrmjoWl@nech<N0eXp`E%(%md;UP$NPkk_9
zT#N;7Eq}*dsBq(mJ=!*Zb-B@C;;`vdelgYlWbNQ4wK()M5N%!0?Cd6WgEkdSHjwPv
z>7u$0JY<fO_M<SD9uX7c0Avz%M+hzzZTEL12R^h=J{bDx^ymJ!499_$rG>?}b%;Av
zLc%I%KFtq~|Aa6R5$?iV+JgrVTEZwTxe`;LI)ai<!R>Bf?SL6cOvAASkF}c7Z;of;
zU(LrSxyHZE>H7K(IZG#;KXGSEa)QfA&^LjBYVYcezSKsTFKLSPE^7p8R@`1|d2%w>
z=A6@T?RO<QR=(Y1^<QHatP6kkoenk6&WcL)PR<QBf{Q;~?`6}jcH!Ln{kjoSS#Yz$
z>({r{-|fu8<Om6imd0#1ObWt8Wbh-}S3aI9Hy#|%9ep#*`2E2Yt7;d&p|QZI*ZtZx
zC^sH&Zy%jbvpJYea*1=h&lZUxVRdk)3)<NS6+eEo>9G2~=G0|JU1GXL_u9#JBwG#I
z%UUcC&}I@Rsk(DNU7OCnxZ2w?ux9n+i=N~51B<Ggv$LQzIaHGCwa%R%u9nblKTI@~
zBhs$gczve>dajF5w;@-d0-~j%IWkW3H;&|zdsx34bDlWuan1b%=9#Vb|H3@rGzT4K
zFv;jT38P7~#Jwg+Lm8sF6dcBVP;`O$m#lb*8C9)d3KT9aZL4rx^P|4Jcv5`FVnbtW
z{m<{>M!`cj*BSWNM7DN1wQyUbmuh5CCqJ^fZ#tUmzEQpmbpqT)ID@g*4=m{YRJAF-
zbG(-x1EZU!<EuCXt+|?kVFAPH#vF5-o~?q1L(Y1?&yW)JZhIdUmL(Wn_WoFJ%{)~S
zy>)o33khjw)4}W1S9yb+U$>V6F^EMlb#OFqKZVPrm9y2%9vQPh1F&UkfFr9pl%e0`
z885_LoTEJJX1#54S#txGiH_a50}o^s&nl6r$dy+$M{tv7${TJpecn3N5EVmiAv-4B
zA5`XZM6JWs-DeC}!{kopG}ukBeh~GLFgOAMj}_7&7p}PhcZY`RYAc^MIrhgp14NFk
zPP<~kJNVrSN`}oyMo0oEf1Md*YWi06Ee&yzb~wPbUInJho+#ASv*W{TvwC+a(vGEI
zcyv?ik#Xd!^#`vzm!Ht$8APs1%gDU0#MYV(c9&ub;D>Q!7`gZLyHyUNe~(GAOW7xN
zq9f27poP81`ZW<QSnMoz!WjL0yEw;qTsX~RYU;+_^|EqDm+Gk%I~R(cmkH;;O~rv*
z;0MgZZ8R9LA|$p3*&67Qt}fDyi_IJHBI32OatZvqH*aqWs$|Xf!Hn!G=vCV$&Km87
zYqPVqo|}6v=OAng1~r#?-qVFpep6z$E%259qqrWBQ+4%(fbssdE7Yh#ZPu96j1T?B
zx7D%`Fj%dysC{FEN2=>HpXY^}y83Sr-I{!h)N`QwyIU1%6+j~iVw4Ba$E!`cvmmY<
zf=uaL_(|QM12x3ErLf-c<U5TdNKnS3xycV_Co)D?^EAqzIBsaL%_7=RMPpOb6Vecb
z0B*)2hWIl>smS();~A-(F1SPoWAP9EuiCyl9_#mSTi;YhaiJ)CWF}-}kB~@4MwFG2
zvX#9XBqUoTq>>PlWECOVsf3J#L{|1*_i=rH&p*%odOfe_pZmW4s0i2RbAHbAJ&yNr
z9JqY%(H3QWchRI|dMFK!H1>3G2nZ0{o6|vcA4CuC1#b`FmsO2Mtbws{sZArT<sixM
z$VfQ(o=2snQo_P*aYsun$|e@qfDydIy0G5PHB6ram(Qx^sz%FDnPQJ+NpDDq7-J+`
zz(P!D{ijV=M?RC`0cnQ#q*X#E^YMdwIcs<D9X)!04(D7w_BC;t{Vu~NWqw|s+^E;|
zm32LprI$QNg&4etGF`2@7W%$DHeno()v6}>4A061E6;6X*Fz?riVquYWN;5mE^j$_
zOn)d8$Y5QqJc3`+AJS~Gswp(Xy4G9^`*LMr=F=4xRz6y>mml>Q<YYaI8Jl*4w*28K
zU8?q;C>_n^<$Pd7Bw%*RlaDHNTGJozIwKO-_#v}?GEj;HJx>j%^2N;-N^V;P<n~?Z
zm#v)be)aT+mHo;bCP5e+jVul710TbF-J*2nCCk7F@2=$})Qr2@sF>~BsjdLl($)P8
zm_(kOH)-APvS>2iO49NBfTgNrZ6Rg#{0GxqdV$5H39cAwk~_>z=d=SX{p!nktZ3bI
zbqRihmQiKr7HNVdbL8X<?Iq%mI(8k_z@uX2v0XlS@}$I-&+q4B4mc_3JBTK~Dk_3;
z$xfZ)LtA$AP&`G^xjD7jqf3>IJq&c8!Xc*c27C2rGBvmDvMhydk+hHy)%B#SazgSW
zJye%ez7^{49uN8P5_GELDPH_)7cUL~W0vc_xuTu2uB8UP_MqH}<-*;C158Wbtn;Ik
zm6d}t^MU!UxLDcx@lFm#5`9}lM08uUES`L6Sfk(^-J@jiY&bT_;nKpNFRa>E3k+p3
z2OR)0HZ7R0Wpn2jhzv}e%T5k&kbukMW93;DBA`KwwP4YR#UyuEmQYN)#mTfz0E=|1
z2EU<gkh5($Xz2%zrraGFPl$a2!FaE!srfL;lCrVutbQ*Evae-a6$vRR=^ay5^~0Do
z=9|e{8l3<PimB)?A7B(ZjFBH9G}VnN>j!M4iB=+nWrbRCLxhu4`MaSzU~7Pk<dn;h
zZD=Qp*2Q(Y=LswWvn=WsbevYZd+-aU%g3@cv)bQ5HUWb9em7f8^52=W(bMxxDr4f#
z8LHB1TQm^%fi<TiWCnw+LO+`{S4C5Z0fK)xvq@hY27aI4r#^QeK1se99UewYl(|vZ
z&KnI}QFkVXe*~^iaqhW!h9|Cp!YHeqd}0`b7iAkJs_UUoSpH6%d3&#bqmEJD5nEcR
z>*x}JXBB=b)NQt2#!voOU++h&-HiF&0UDaYMtXaXNi<o|wTx&;mHwc*((`%@4E2VQ
zmNi(lF_Tq>0`KH_am@vp7Fb|#rVt4@0K?&ItUMz<bFLZz)fTpSdl|(W=AEP0O`;EL
z*humT3Ek78c`jD1*v-Gipj^B<p{Ec|E4GI-SCOoZ`)2Lc<by*BG4i%;wH=8Ap4SIl
zS#zAE(lvLZK`CCHJR}^SBt1hc!o#{tdUo_skD5%J>kJ<_0|i1{Y^<u<;hqy=$vcUy
za>t1Rr?Z=~9%0)wR}>Y*Hcd=T`yf;{7`=Tjh>dK!A5GjyO^Imcifb{;<bmTyOcGob
z+-ob}0JmAcx{HO5Za#f`<1$33_6#464!PK^qt9X{`+NV+^loz2rlnu5l^xdkweq%X
zGM<b&<Bt8;JUu;AOO%qodY!czQSy&EpZI%W!QFC@)NAg~GvRvIp|%#8?PZz1#fHYl
z*<r_Qry`Z%Y2%k4Ofh>X+5Y>psBrxA35F%E3{zZ~QtVT7;^Y;y#sT6U;|7+4RIGA*
zJoMh~x9oeVjEn6%P;v<KJUpxcSPPCePhPreCJJWHTMZP;XL4(RV!6Mw5tJGXo6IUa
zol<K%!p#eU7CVe4{FA$uz5^$zE&6TeZ~#O>i&9q(Fn*m2?bnNKY;5EpFX1$y1~Rvw
zz8O+qX^hMrI$zKv@-O;sLWIOIViddsGggRg0vltwJ=Ui^8sbr8p^kfDQrh!%%pH_S
zqFLOB;JCwfNN~W!t!gl<f<eL)OhP}IJfA8g(mt>W$C>lbeb0I5(O=PH8-nQ*^eUS(
zwbUMzMke5ripi}MhW7k5LwQD&F7?^GIC`sk28>@ed0&GwrSqe0K!NQxMnaC@xx8$e
z02S4$rtag%9>?RBRZh;L$wYzT<u=x1gUGp<Hk<p7aUUB56vRGL;*3EO_){U9<{YwO
z@*9Pr7eYWGFiLfK^(|xH<>CwS=Z{rTnSF-_MJ6;i0qL|YRo3Wx@*y_1^puo|##c{@
z*F?J?Y-4mxONTXNlk;{(objOIMbk?4U1x%9qUKfIeDLO16;)ML+(2MpWl~_8cGmo1
zQ>P=#VE0rP1!_3eCqe}VnFWHvglo+=4v!t)n~PlzOzPxDxow)U?wGf3Y;2T=<Q`4c
zHad5XXL6YW$}n<v@UpnLRFnYQc0u+4wBj8!V;;TAdD=uB8VOEVzKKfKn=FcoTjrzm
z_AJl1NU-O!O{8YWt*tO1LSh1Nis6E=fIw42!}l_`EWH%a=L8^WJ;%hnx!qIIUdoeS
zykmfu{05<u@DEu74~6gG(eRrT2H})^eBqt@vN<n!MT95V|0ut*tKdLhu~6xbgu0`T
z-j%)#%Y1BQ99+&BF?n<M;b2nH>CpL!K2z0{m%-}k+gX-LEn+&NZfVJ4yjV=^yN@5$
z*x1<SGV-{&xu>S43=Iug=!FCY4;Ne<A0MB|=kE%<9mJ?BNDou0;}Q~Ks*guTY=fhf
z*#EdX#iyjKgLDtZFJ2H&+)&arHHF-W+sIQ$NGK;Khp3mq?v@tg=FY_660hQ733+)I
zmmoX4F{mnjzV&JE>^ujrsu=ZaZf?KxxZPzoF(!lfu)1V-PfrijYbhxym~=%lym#SG
zZ@VAJT%dkfKu|FLi0~ORGZv0`Xpv}XZ?3mZOiWyD^sk6|jd$SX-BUl+6GoG8y3VBs
zqqbT6NUx(jv|-^95h2PPe;G71HC4zwGK8tB=0O6)i&y*Md$Goj%D~O?^71uw9I2J?
z{{gG%Ddp%6Z{D|<@q*yuEywzo<2?<oI`01cW#r2tFXM8i5$NQgsYy8UFgdv!Sq+UI
zmg7N6N+VtB?c29;7kFV9gr$SK*_+p|qvg+lvMtA2@bsx_|5`aheVf9%pn`(J(W7}~
zWyu>W7>-3PKeRr;z)%&qxds8XnYaDnNVVL&Jb4g-aPA@^B4+QM`sh119JWwnRVXDZ
zE32qjRb0G?OPTvCutK|pvzV5ahJA)?2yX{oYZ}rUKa~{He+~vr^f=k|-I-n8KpjY&
z?G2~gqRw4dSfI+rp5zE-WMXP=Ztm^r84mc$u$4Oh@#9C_1u*U3!^3j$b(38_;oG}s
zslES)fteZ1)yC23k%iPiL;zXn&yJ4bvNDvR(sFV{EiITRbBl{+mHuU>xgA|ylE;oU
ze)=SI<cQm~Yp1oe_EA%_$$C0s*bBN8HWEgfLi7<h=~;Ps?;sVc4W-V_%@s>-!Dy_t
zyL%PVy8F2n7j$%@K;c>4z(P=x-o#u-2(pOGOl~f&UtoF>MU@c|y`TVK4i7?AVoXfo
zrDpJ}695E+hK6G3oYH91w8&Kp`s<s*LViI(s2i;cleDwy>*`k5*OB5$m${*deGC0M
z`V%DB;^N}U%F5%%kK^Ca^hl$f!_yb)N@=_(;+mp&{=CoHf*rb&sVR9>wGck~ZyiIw
z1H}$hn8t>N9_WX`RgrY=Ll}?_-0%kcY+C3l6|4(EwY@#?&C5h~O?33Y#6<I;D<<@T
zqn}t1n{Fr(7#JiGb1>n-$;{5p1-#Cp7}!@|pY`&kZhrk7Bq(=OA0tj;>h{a6dUDe7
z^5x4LKjBGo6l&w<-}%OcL$D2nIMfvuBW`Z%Xkc@4b5V0}Oj|QOHyB4o25Gg#wv~}5
z4r;g|=rog3Qn-R&zkUtAQ?-pK6s%mFoVIOgt)ue}_V$=B;<Q2=WN&XTDk^#|-<01^
z%JiI0stmNAMn*=<ligzS^6%Ex)*w<+^qKq6)Pym&KF)xQj0}z%w(H@;A9{M$;8xdP
z;*y@2cn(^5C8g_=gWnyGE8K^(o`8VBX>Dy!H#ZEx3^10+&2>0=l1R6uW|eFBeGSrL
zqEs4@7W<LNfopATg$QnV)_ZGf>s9EJ_A!vl0PvFU-AkmnVI~fk1JA_=$?)4Z1H?RV
zNfJ+Ij{N>joFwCg`;d_E0PPt>NYE4@m0QE*U7`*pJpOX!z<~pZW6&rodwMS2JtQ_G
zMu*02XncGP;!&IcqI&{pX(!31aE0qjQ#?F8*qLoDEqei(ObG5t&p?0^Id~T!P9Nx8
zh>Yy)7crtHDy;pMoskMqK~ghIMW?5yr={f=6m0*BQaop5lrHV|vA@5+x7Q?pRByl)
z`wm<6p{!SaMuw%Dnwp;8*Z(xn598y-u@1X$W#r@_nAise1+hj!uU3rF2k0)hZ|}~~
z^<J5=?5^$Z>9M_Zsrbzs{0Vo&YOGsdpFb)`w2nYI5(4DIRs3~9?;UMPZvTCM8?rz$
z4m!Fx;@G;|5yZt{Lg37uoE(At2YC3{l~=vI?mM&w1_rKdeqh*#p}2BT>H$PP_#}!P
zKTfaMdyqpMrWyhXGPppB5G(9e0^>0&RR&tnR90Snt*x~N+W(bp0tx=8py13$V-u62
zZpUC<b#<~^)gs4^RqO1GPfbZ-(yoVn7?eMtxsY-2@@4@^cx5d3*7j8-=E1cIBOveq
z2w+n1UjZ-5Cdva?i)aLfCRA9~jd?^xDE6hU%3c0&henV-g8dGHId&r})Y^D|z=HSX
znOAc|y)VDVHw=u7s6F{|n~H^zmGxeG_2S-INnxVvV<W4!?@CExVkeg4TSo^@n3=x5
z3gH7d&x;ESK+kwt93WN~XFLiH*!$}{@CJym3G#gy?qAJ~Bz*1YNL37~M5$I#FpbuQ
zNZl_eh}s6H9-kW<96W~L4$cNRcM=j3{>uTa4<ufN`WZ118>TTuY+XE1kUK6pI^I=0
z=jZ45w!pdPwZ@Vw$Wh)Qcz*l#?K?@9{roxU?%kk>d7@@p`P2K>M=XB_ktX9Rg<9;R
z@F9_lBnoG2s811s7o=jrVgak9-Wy}=Zlnn~3WndhwKJs^q3%sdiHnm{9fLhKtastd
zmm*~~W>tYKRiCS?PiSf$VP}8e+shPWj+_oTvm9$%d%GJ-b)=A$XM5pXEGb!1T&$(8
zF6BM*XtGdduRWIzJ;=RkpB9#vmzS1oMGoquD-;(M#Rak(B8IuUzn&bVKYAf}i*fKo
zjn*u0x<9A98yI85!3%Sc<|oLTB46C9CL<$zueB#cd3JH}2P~Ta!q`}7?1A<ZKW%ER
z`R8=HVG{7w%NrR(x0a^#<^1^%(b7WM#o6@$#!yLTc*@dH*2lD?IDxxEwVSKuo0l(@
z$Oh+bd+qn3oQFcywTmu4;@PujU-yLN<k(-lcro<b6>&!Ay9DL@{QPSn^9u`yyHqmu
zuAYxoV8bnPr^ta6)<(;t{jkBd2$F?=10tMz?#1L@5;<tIPmK&xR!{XNJXu4-j@DMK
zwN`2>sziBH#MMwY#^BFj8M)qD@m<>-7HFAf++u-|Sg+|qSDoq}W~x0p4IS~QUb1Qf
z4j~R{X|*m7pUi5X{c__-rfJA|zWttS3-7VdA8-G#oXH54WrA1A%%{5qqBXP)^)eFX
zFBLoV$u`3GZ{Pk1>kzd4GwC*FhvdBBijltONtLhj@1rf#W$Q1I;lX(VdzG}xqtu9G
zdt!TQdV41LZ3t1bC=<L;#-)6x`l5}k?NIXHzkdw`4g}edaUgcWGOLVY9(4~AMnghX
zzza(Vzmzk+_w;xsxm>ygTU36(QjlEVy`w#|_F_REcS(9;FN=)9S_8tao}nR;G;ew&
zW_QSHRr&cO*@fo8kIPf}>bPAK6B8dJ1%L*9{f7=68r<>lPr*|B3!#;NmZ$E_(c9i+
zN9f{uSN+uiw7&O|><Op2t44;WrnJrp`%t=}&0g1xzjyD`%wTP!VkR{SMaV0!Y4dLD
zS?{+HOCtwD<sQ@er^3DGd<o)k*(*OkW~Ho|@98D2>53Lh#{LS5($dL|HrfxR&L%!`
zLCqC+MD`{-&}POmiXv6c?sAVKf77aG@|~TXiL?<s&)v764f)0qybwwhmX+zPqgfhr
zUjvDUnS~{@Yc=25kTH^nMZ3RdV{6B#fq>!KXP&mR4V=6#mY*6Ll%`o~FIijPiYP=8
zih2W4VIc^VUEZ=9ji?wN2md{#*<<c;V1MXzTceuP-c!-gzsbtPbi~Lrm8wzU>QFG&
zu`@ShHC<w*xwp5swsz;T@L9h7a`avG^?Nirj)PR5A3zt@Xmi2DL_gaAm4e)}>!&l(
zr_pKb+h61HfLYJd$tjYze1_R&tlebn8G&PXY)pedN__Ou46ok2dGpT=rG7qRb@he{
zZEJ|k!JA5N8=du*qQq-V*o;8R5g@%@f^A4V%+oPJAeIhqXuJHTO^tM|Hahysx~TiB
z<PR2RW*H|IOqnC;P`Yfu?IIu`V0NQKxb&xriAgVPGftff^<VuN+y4fP;%K(#lUCN&
zv!s9DNB`zN!$Vg!ICoF_Cwg4ed$UXkpkeD5U_%jk_pY&EVt&5RmT$iJHH#_T?>02d
z%r7!BVv>@+Tkch4y?1>*?y0O<Pj7D~HM7q<d8yj&Zm>dswI-?`{`%y7!|c=H=oT+C
zu1d;CHGLza`l`E@mo6<X{R8=NtomV;Fe4))2!~&jIfES?9fv6{Ll-|h9IW|Dl`}(^
zkLBDQLXDGk=r5|#ke^P6zKkuPL}3qBSo^~@wz{;mG`!#^w7dNC#Ub16sn%#O`S95`
zffhueW1^yrQm&7}!x`?4eI_BHZzTSX0E)jFZ;FfSE~k2LL(8*rPKyDqAYcFWWDpb;
z{rdg;K{j3%syft#9Km-ZBWDk7LT@m$fm7VbbngY7IHMEXnaDUiJUslVZ%_Jkc6Qo|
zCW|vpea=uJS_QO^xp)aQ{Q;!{r&UlHThYj<>;3zSfcjQQRDu@iPFqwAd~z$J5c_CV
zh<BYK-_w6#ZcRl?E7FvsQWbRtyPt2&#uQDYTCJpPVH9o>Fz?FFn$7g@h*z*GiLCYU
z^^KJr1f!e7#S;w=LT*qm-@$_iKaVYdxgN*F9#E^}i3?j%nVp$|?-Tjz7^Gp9xjHqd
z-ym-J_;Ghogqo+j3@GPgAor<|B|mt8qcP-)#e{c@<M9fWP6S#O@hb%7D@f!>bBPYd
zoVUGhQ~h|mLjNo^HTBsu+fX+eq|~><cJ}s7e(Zlxx2>;>C7<c@1<VM4B%)0;YUfif
zKW@Q*c?DJgAvz{B%BWw5<?Rkh%g7LoRN!h;H|nN7-f&4Y8A-kF1lASSJ8k%Pc216r
zv^4H-(Dqb$L!+{mmJ_M~N_xT1U%s3*G`v*QjUNRUp+{e?z_)>ep}ni?zCs`0{wA!~
zzP)>g88<?RrGbRF_=nMK)Hk>{w1Vipq0EO4L5VC9bZ=n|1bx2<FCzzs36NZ4wi9S7
zPuwE(!2IIbGaiNedObB8ZHx&hGGICL?Hkcokmv(Z=|A@Oud|hvC{_@CJ|YxS5Xv1Q
zKNG#-Tz+?3Ta<Ff%E}5+mmwyG3PMXu3)Kfj$m6^`)cYtpT(df{OHeAJKB$5*(MAWQ
zj{~{#6;SaNA@4?a3Tz2|72+cjWPZLg3U&l9v`i@}CdS4|IXUB~MqJq{3kzpqL31A7
zCkU24wfmTkw=_1|p-Dt+huXBEtxX4WgFBe}em1TOl;7&ug`O6b@ii%C2M323bxd|_
zFJ5G)4J(iSfq5<yBja;}92D#*Uc<vF5>Ee~U&X4yEC8U2JxU4(2Vx&v8Bh@yrw@@;
zT%)6t(<MwRSRD-nI+~kvaXsj}ybYS;`SlTr0yft?;M;`O1$70hj5{J8Gb}#D#OBdH
zv9hv)hth*ojY?D2-KfC|&lr_cXlg1;R6UVv4r-L1zCPO3S@}gA`vKwo=Z%e5R#%}l
z-Se}?>+02*l1ug99X*kj&?+N(z_SCTmcso9KpCpGH(eMbA(P}Ikl`NW<mGE?YiFk#
z@k8N))Lk%{dDOIY25ka8Jw0>==tAM?0?`z-5nwH8XCXe7zkEpu`gI8d3vk+{@VwEC
z91##GpK1RLmS!`0G3+pbBS#=<K+=ZGMri0MU*9#<4$ySrfE%Ee&1#1!+BR~QOJC_(
z6B9r1W6h*K8X6dM7Ff90+dqho)-^Gi7#uunWHdTCIXONq<M>?!4+(}^P?cb7q6$zu
zc`}ksaSO`?(}X@`KFq25I}|sBFyH(3?R8n%DmDQPMF=`Uh%W()oFofCjsV1fgAB8F
z4=XE9b8{4!c!&^O?cMzgHR`Lo(SI)XzxBZd;rL+WgkBiY80Z4}#4Y&yIQPFrTfKrJ
zn8=aK$)TVYc&f{X*qRIx4NB!KYp(vC)OLIST{3!#5Y*n{c(SvzmPOszLa4CMoIVYa
zlc}a=Gej9gd}X=?qZCg}_gtbfR!tm_quuGoN7y1q1O<r(5u2Nv32#KUi~b(dl!YV>
z9T5hI@KwUwUArcFzZoZqn`{U$u_9)CUguy;Hs5?-7!+iLJTQXe>VM^(FWSv!hF}L>
z2z*xR6${>>P!jt2oDflmL*XrA<L6hQ#Lg!ytccvM%l9HD$JcKakr4&$;zU<TNr@MS
zskym1{8iHOv$Huu6UD{F<KyGu(<uncD6AHK3h4=PNl6PZ>_LD;cRF#wlk@}{{>!$u
zsM|+|hw;DDKPpPf%O9}cdHC=lmKD8pwC3&5(9z*x0x1m(OXU5rn|R09uf^q3)6=Vf
zb%8AaM2v=XwvNF9$C8$mgFa&Rei8Z2J9qD*4L6H0G&9?r8B`Iv-`m-#Mo`77AesS&
z_3>H7WcAn3kdXOXpbYCHtxPxBQ3m2&57TONS;e3*F}ZMosO(KhFf=haLK}wfeadjf
z#bp~M?FXx?k~=jtH8gxyW;jj|R`%?~jt1$^7uf+R3A(HN(o$nVdc=E0zoi$5MnIco
z3u)=-LX<6mvmo?A-GzUnf7aBb+?oc34VcKp_&C{3u<dd6IN`Qk|0E<P;w$=*PY|TN
zwE^9Dcr4<m<eR@E!WOiov-opJC$9OoColgb&Lw0Fs8{iI$OCXp^M{uq#My|qjj6{G
zQSf^6W(gY$5p!pMaru+X%#-Yd0h3<<%HTU$hVBw^3(g_{S75LW;LivX=2kv`g&<^c
z<&0?dJ5b=@VZDCyW~ibHCkqf4Y{U9sxORf@0Z3}2O$B*W=zCd}u7z=V+Yp7|{B?As
zMuvtb`^9$Rml5rE!K{ENXso1Z|Fv~~IcWzz$=!87WEJfzf=NIr@e|iyjiuD|b#^8K
z&IM-X6EwFUpZ)W5&DLNEU969sxmQ}(ZhUG%GeAEHv-(?GYq1j*uS-g*Hh++Z$T!dv
zznrdV9fGs_>r3-0I1$8|8*eZ18u+f?q;Rvb8OF5kIsrkoD=R%vpji_Ld&8;YF_2*^
zJ_<D>e&L*+t|cTkzU%c9#H;$e!$0Z}{?_Ge@{20dM_&!)P`UT4RhM~Mdb%)uM8ynQ
zXg<!6rO3hXy}Z+H9iE}Ur)%2z*_neUE)VYa>7ya#*iW(UAcd+K`4T$OwbP*I3RjA~
zThLE$va7zi3ymuU<>=_Bk%dLUOIIAK;kJK{T(798sIfXBMV9q&Fj$!*uqnq1Ck%#w
zDwcx)sfK;~yd{bD0$5uL%B+@0N0|P-Ox^#i?mr>_x$6J+;r5G~GnsnX26dS)23*A%
zwJa@KVTSmeZ=u4IKvlzUDMb;Y{N&4i`uw1hixgB@EqtUW78Z~5Y3u99B_`4b-*UxQ
z5ijZW*7^Uu!L`pWcv})a8on0%b)EJ`&EEh2hcVQ}B1Q%Vx_t2%VQRFQ7TX^PQRY8-
zG{M0TTkW`{B%&-xH+k9FF<V_XcJ9GJ^SkI)QH_(@(C{1^ni9XBYV_WdCtRVm<P;S4
zc6QZb+ZfnMNlE?r^QW}DTmg;$Damur@@XtQ1Tuh1epi?(^6QIoU%Yq-m;vZCqU_ss
zpSJPYZ>6UBP;}wA78C$;aEfw%(N`YF(wQ;l-By|7EuWUEC&9m|YN1Fdk1&7heIlz}
z>&zKk$P9CssugUKb8;@|>CJ6*Vd1W=;WQ$dV7d;w3osJ^7S_zyS<S-fR9r#=NAtTT
zf5Q-_u4wU*O)<%VtxhV!PA?7ua4U8KnSVe))%KR(O?J%sku-7Wksx1#CJU;;V&}8w
zNA*QqM7!m^yPt$g8foM5R5=SvN}|HT?3|p4+LNlPAQz{#pJTJ%P=RIV=cgqltpI{N
zUu26x{u1#g$ad4DO0Z1-G&=C2zDPfZC-djZb;_b=&qh#-H2gp`Gr_nSRbgF-G$$uU
z<jTlP-Q9<#RRFi3`vzy_s=NE)uG-(%Kt{Npg{1!D$B$Weps1*M!^2Piq#r?oz{my&
z5wZXG)^>MafN>TJ3kwP}Gcz+b#lSOfxrz~t(I@^ILmu7-<uCgkU_{yv#L3$b!6ZVI
zftV&J>&Jfm)op1i)KyoP*+%IWbs0u!YC>G}5zS3a`yWKbv+7I%zC*n99s%P309EE^
z|1Bf0S6ng<GW|sb0_IVdvkbIOSIt^7jG*-ZApK9<`4Z<J7`k1xgr^ivErt}z8Ln_a
z1(?f4#wjUjq@uzlD0t>Dt%tFRii!$?ApjwwC!%_rq^Rh4ZH-`z`WY=P*l8hrgZk4y
z31MRJXS}~E=NHg>W-Z(8?4cq|7rRWA=6vOfE--AeAxO^_7Z<&4!SX@Kdt9`p>0)z=
zplWEC0JA}$m{_&`hQFvSQGf%&1){OAxQJ?LDx$@gyBL8(MWq&-8`w8;0ai9!Sd{CI
z^W{^q&NBVQxKS6IEIF;yL36lKpsxD*?Mq9ZH`(DeirT={(%H!gr-_5?ab;y7pgl~6
zxrw)WAEvM&vg#TdS6y8JnnFexllHSDL>la;#zt}-3}Uu`!;+p*J#{Khtj_9KL>)X%
zeX_DqSYAfl8IC2(Kxsc$g$LBo@CUeJc*9e%cQM$LvvYHBaJq(8<=-1~Mgc&U0i6&g
zIyepY4vDGVAm20+o=PMFw`A};qzav%`>k2`d%mhXnMI>~%zHcLx%#^pmh85a_^n|T
zmW>ejf62;Lz!nhcAMnJXg9isz4Fn_v1@Wnt=4MS_ks2sSpBvaC%%cQ^spG@w=<B0?
z-rh6bZ_%5!rplI3fAtQ9OD^=Ta6}w*#Y_(Mz>Gf%E4hrvk8z$M>%AeP8Chrb&S!qN
zaQC-wPm#s5v%?5O^Q)-9pr^yHWf;XNUFQ@F_T{Yrp|Tqu!o9r}I-;?iOx<ID{_Mp;
z2RMYH181oMZwz(i+9`YNx;0F^ux(cSw=i(R*zftu+n3I^U1KsoJq{C}U}*d^jWMIO
zwU$Q_GY7;@5U`0XitPbW{(%9WDuJ}pY70hyr|urS(N=*3sA7Jsx4YZJxDDkt%ua7+
zVQ6C~{=is}XS+Als0CxC+QH~A^YimKcBnf5a~2herFD09J_F8=f<Rlp0)Yh()vWuu
zbDdZ+@|%P(Rn+zHvz&Y#dfL*LzXE=vSFnRHZb?299F30l+_}WZ*VYQmn}npLyQ`~r
zv|)=q)yodAni<dh1_g8Fl`oufMTLbir!$utPr5A^acM}hq&|D${VqN=R-AvcB{ZP!
zF5v;{CM&BU@JZ34pZEkUx~rq3qqB2leiiNefiTr+-?GfiOArO(IbtdZJQ;18@h-)0
zn~;O)0cnYh+_%-<-=9`z#m&ph6$}Ow9UUDvtp>V26htWCvK(B!ybAL2v@|sxTwI_h
zG=6z;hcc#8fHoke(&3CjL4%ioAeY^{cryb7bRonSjQnm@<DMCz^{k2IT-5VK1BRL(
zn0tcyVYELeZS^<e9Q^o;;R$UD15tzwcESecrYNrAU1+HhQ=V>?C8KqI)|X$F+fHvS
znYvO0qXm?gK$<XhyJidDu!PgF=X?|u)dr(fC_Pb>ejjVz+}NNOGDQpJj-U(Z8L5Qd
zzIIy;JzJ=D1^^dO95{o~qhVx+hBO*}UJMMUGIV9kb^;8s{*?FP1&Rbn|7nzogHUPd
zz5M<P7CXk=KWldLhnY9sgNa93A85FUCP4IIhrq{vpURhCLPw_!i-Jwn{^7$gNI#}R
zMmx&h=B;;ooYq+CFmKY<cn}|WTbZD0;j%MY1w%8~0?$2ES`cjbyR#!oUnQDFx0HG-
zoLF@H(TN`l6o^jE&7A<~yXJeum(phW#&~@;M*|ffv->4tm35l5{MElz;Qyme`EO;*
z|Lljlo)~SBWuS|Mo^Wx+eg{d@|G6`gfdH^uj0K$?9sO5kGSIxY<-f`5+)eU_-?eV<
zbv+<9$kr+E-UR@BgT92I45d4IUi|8We^OdKuU;*~%pBecSRc>n{z!}1n3%zV0g;RE
zuLDWMgJBN_{D(pOCkWS>KcPE)YJ@HWbDxWLcG;M)W44R!zv*-bT`e*;r1N^GPSv-z
zP64wDSo%xyS(t`|<oC13kCh1@FfLGo#Up`uLI}!W;Or+Owf=bbn1n=LK|xzv8~B0%
zPEf96Uewys;^^Q&Nlu;&p2rCSbgdX~yIi^Ay|qz><^Yz&4IPQ`@fZ}saUest6b9(1
z%y1q5cV<HdU!VK2<I&HvW54GAA-TQ*-(`eC{{7*oRh8I31OI7n2gr)3kJ<*H1>*ln
zvR!-jq$DQ;*cqFcsKU%1p&2+PwxzEjCd1%#N{EO6G6B+v?+ar3hXWm+xZB8?;Lq5H
zXiY8j^sFo`GyZt(xE@G>ZL=~198Hf8zo#-%?U$>RH&3LR3K{0V^WJi3fAJC)E<m+}
z0B#GM2+XMSudl>ZR&Jr{Kgmf`CLoZ&NKX$dN{nDbgyXl5lPWj#^)c%}BAb+)48UQ*
z_Y=v6wUXuTM;6D##UG-H$A|}QZdAQ2EFzI&PzC~OCt92$_O-N}5xTD_lpY-&4G+p+
zv*@nr4j$yQjt3`2?n{5a`Ps9UHa6eplSyWDc7+_ePOaqNkb76t*Ut|J5lYoxOz$m%
zHYJq7+xc76rhp0+`^4bvSsF6ZVS5luuqFuKtNvRkSu%y6{p14Q;@GjXoH0<!9b#fK
zIe*^cs4&U<-bW%kA5B8}{vt2$l3+5(g?KimZ@mjMG6sp!E=?|s02X;N7$!hrQRkkD
zWc)ont&{Hw0Ny~rW8nP_6dqs*H#9Y&y&3wcHoLGO=Q){#-{ORTYii2;cVRcl0@(_y
z7>Sa?F>omw8_{wGyyGBQ(81aGx9DFf-;e)9DGhLBd?JqozjZO+5&xqUret&+e?L;i
kRsMVWDa8JlKipzVde!J3`DyGr-iSowq_%3l^2M9~1?GINTL1t6

diff --git a/vignettes/examples/timeseries/timeseries_classification_from_scratch.Rmd b/vignettes/examples/timeseries/timeseries_classification_from_scratch.Rmd
index a9e99330b..81356545d 100644
--- a/vignettes/examples/timeseries/timeseries_classification_from_scratch.Rmd
+++ b/vignettes/examples/timeseries/timeseries_classification_from_scratch.Rmd
@@ -22,7 +22,7 @@ CSV timeseries files on disk. We demonstrate the workflow on the FordA dataset f
 ## Setup
 
 
-```r
+``` r
 library(keras3)
 use_backend("jax")
 ```
@@ -48,7 +48,7 @@ In this file, the first column corresponds to the label.
 
 
 
-```r
+``` r
 get_data <- function(path) {
   if(path |> startsWith("https://"))
     path <- get_file(origin = path)  # cache file locally
@@ -91,7 +91,7 @@ str(keras3:::named_list(
 Here we visualize one timeseries example for each class in the dataset.
 
 
-```r
+``` r
 plot(NULL, main = "Timeseries Data",
      xlab = "Timepoints",  ylab = "Values",
      xlim = c(1, ncol(x_test)),
@@ -123,7 +123,7 @@ This will allow us to construct a model that is easily applicable to multivariat
 series.
 
 
-```r
+``` r
 dim(x_train) <- c(dim(x_train), 1)
 dim(x_test) <- c(dim(x_test), 1)
 ```
@@ -133,7 +133,7 @@ Finally, in order to use `sparse_categorical_crossentropy`, we will have to coun
 the number of classes beforehand.
 
 
-```r
+``` r
 num_classes <- length(unique(y_train))
 ```
 
@@ -142,7 +142,7 @@ Now we shuffle the training set because we will be using the `validation_split`
 later when training.
 
 
-```r
+``` r
 c(x_train, y_train) %<-% listarrays::shuffle_rows(x_train, y_train)
 # idx <- sample.int(nrow(x_train))
 # x_train %<>% .[idx,, ,drop = FALSE]
@@ -154,7 +154,7 @@ Standardize the labels to positive integers.
 The expected labels will then be 0 and 1.
 
 
-```r
+``` r
 y_train[y_train == -1L] <- 0L
 y_test[y_test == -1L] <- 0L
 ```
@@ -170,7 +170,7 @@ The following hyperparameters (kernel_size, filters, the usage of BatchNorm) wer
 via random search using [KerasTuner](https://github.com/keras-team/keras-tuner).
 
 
-```r
+``` r
 make_model <- function(input_shape) {
   inputs <- keras_input(input_shape)
 
@@ -199,7 +199,7 @@ model <- make_model(input_shape = dim(x_train)[-1])
 ```
 
 
-```r
+``` r
 model
 ```
 
@@ -241,7 +241,7 @@ model
 ##  Non-trainable params: 384 (1.50 KB)
 ```
 
-```r
+``` r
 plot(model, show_shapes = TRUE)
 ```
 
@@ -255,7 +255,7 @@ plot(model, show_shapes = TRUE)
 ## Train the model
 
 
-```r
+``` r
 epochs <- 500
 batch_size <- 32
 
@@ -292,473 +292,693 @@ history <- model |> fit(
 
 ```
 ## Epoch 1/500
-## 90/90 - 2s - 26ms/step - loss: 0.5577 - sparse_categorical_accuracy: 0.7066 - val_loss: 0.8512 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010
+## 90/90 - 2s - 24ms/step - loss: 0.5557 - sparse_categorical_accuracy: 0.7073 - val_loss: 0.8517 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010
 ## Epoch 2/500
-## 90/90 - 1s - 7ms/step - loss: 0.4871 - sparse_categorical_accuracy: 0.7649 - val_loss: 0.9151 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010
+## 90/90 - 1s - 6ms/step - loss: 0.4850 - sparse_categorical_accuracy: 0.7611 - val_loss: 0.9464 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010
 ## Epoch 3/500
-## 90/90 - 0s - 2ms/step - loss: 0.4678 - sparse_categorical_accuracy: 0.7663 - val_loss: 0.7265 - val_sparse_categorical_accuracy: 0.4910 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.4713 - sparse_categorical_accuracy: 0.7705 - val_loss: 0.7593 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010
 ## Epoch 4/500
-## 90/90 - 0s - 2ms/step - loss: 0.4174 - sparse_categorical_accuracy: 0.7941 - val_loss: 0.6591 - val_sparse_categorical_accuracy: 0.5534 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.4239 - sparse_categorical_accuracy: 0.7885 - val_loss: 0.6721 - val_sparse_categorical_accuracy: 0.5021 - learning_rate: 0.0010
 ## Epoch 5/500
-## 90/90 - 0s - 2ms/step - loss: 0.4289 - sparse_categorical_accuracy: 0.7826 - val_loss: 0.5279 - val_sparse_categorical_accuracy: 0.6741 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.4218 - sparse_categorical_accuracy: 0.7885 - val_loss: 0.5647 - val_sparse_categorical_accuracy: 0.7198 - learning_rate: 0.0010
 ## Epoch 6/500
-## 90/90 - 0s - 2ms/step - loss: 0.4058 - sparse_categorical_accuracy: 0.8035 - val_loss: 0.4469 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.4060 - sparse_categorical_accuracy: 0.8028 - val_loss: 0.4563 - val_sparse_categorical_accuracy: 0.8141 - learning_rate: 0.0010
 ## Epoch 7/500
-## 90/90 - 0s - 2ms/step - loss: 0.4005 - sparse_categorical_accuracy: 0.8042 - val_loss: 0.4278 - val_sparse_categorical_accuracy: 0.7448 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.4032 - sparse_categorical_accuracy: 0.8017 - val_loss: 0.4209 - val_sparse_categorical_accuracy: 0.7892 - learning_rate: 0.0010
 ## Epoch 8/500
-## 90/90 - 0s - 2ms/step - loss: 0.3904 - sparse_categorical_accuracy: 0.8049 - val_loss: 0.3824 - val_sparse_categorical_accuracy: 0.8128 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.3920 - sparse_categorical_accuracy: 0.8003 - val_loss: 0.3938 - val_sparse_categorical_accuracy: 0.8239 - learning_rate: 0.0010
 ## Epoch 9/500
-## 90/90 - 0s - 1ms/step - loss: 0.3830 - sparse_categorical_accuracy: 0.8188 - val_loss: 0.4006 - val_sparse_categorical_accuracy: 0.8100 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.3892 - sparse_categorical_accuracy: 0.8135 - val_loss: 0.3849 - val_sparse_categorical_accuracy: 0.8225 - learning_rate: 0.0010
 ## Epoch 10/500
-## 90/90 - 0s - 1ms/step - loss: 0.3845 - sparse_categorical_accuracy: 0.8080 - val_loss: 0.4014 - val_sparse_categorical_accuracy: 0.8114 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.3874 - sparse_categorical_accuracy: 0.8021 - val_loss: 0.4934 - val_sparse_categorical_accuracy: 0.7503 - learning_rate: 0.0010
 ## Epoch 11/500
-## 90/90 - 0s - 2ms/step - loss: 0.3804 - sparse_categorical_accuracy: 0.8167 - val_loss: 0.3738 - val_sparse_categorical_accuracy: 0.8017 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.3798 - sparse_categorical_accuracy: 0.8149 - val_loss: 0.5693 - val_sparse_categorical_accuracy: 0.6990 - learning_rate: 0.0010
 ## Epoch 12/500
-## 90/90 - 0s - 1ms/step - loss: 0.3695 - sparse_categorical_accuracy: 0.8226 - val_loss: 0.4010 - val_sparse_categorical_accuracy: 0.7989 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.3671 - sparse_categorical_accuracy: 0.8215 - val_loss: 0.3998 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010
 ## Epoch 13/500
-## 90/90 - 0s - 1ms/step - loss: 0.3598 - sparse_categorical_accuracy: 0.8313 - val_loss: 0.3742 - val_sparse_categorical_accuracy: 0.8128 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.3555 - sparse_categorical_accuracy: 0.8389 - val_loss: 0.4145 - val_sparse_categorical_accuracy: 0.8031 - learning_rate: 0.0010
 ## Epoch 14/500
-## 90/90 - 0s - 1ms/step - loss: 0.3604 - sparse_categorical_accuracy: 0.8250 - val_loss: 0.3839 - val_sparse_categorical_accuracy: 0.8225 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.3628 - sparse_categorical_accuracy: 0.8253 - val_loss: 0.3886 - val_sparse_categorical_accuracy: 0.8211 - learning_rate: 0.0010
 ## Epoch 15/500
-## 90/90 - 0s - 1ms/step - loss: 0.3436 - sparse_categorical_accuracy: 0.8417 - val_loss: 0.3815 - val_sparse_categorical_accuracy: 0.8225 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.3433 - sparse_categorical_accuracy: 0.8406 - val_loss: 0.7763 - val_sparse_categorical_accuracy: 0.6019 - learning_rate: 0.0010
 ## Epoch 16/500
-## 90/90 - 0s - 2ms/step - loss: 0.3441 - sparse_categorical_accuracy: 0.8444 - val_loss: 0.3387 - val_sparse_categorical_accuracy: 0.8433 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.3455 - sparse_categorical_accuracy: 0.8434 - val_loss: 0.3537 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010
 ## Epoch 17/500
-## 90/90 - 0s - 2ms/step - loss: 0.3327 - sparse_categorical_accuracy: 0.8427 - val_loss: 0.3360 - val_sparse_categorical_accuracy: 0.8433 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.3336 - sparse_categorical_accuracy: 0.8462 - val_loss: 0.5375 - val_sparse_categorical_accuracy: 0.7074 - learning_rate: 0.0010
 ## Epoch 18/500
-## 90/90 - 0s - 1ms/step - loss: 0.3296 - sparse_categorical_accuracy: 0.8493 - val_loss: 0.5619 - val_sparse_categorical_accuracy: 0.7074 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.3263 - sparse_categorical_accuracy: 0.8503 - val_loss: 0.4134 - val_sparse_categorical_accuracy: 0.7892 - learning_rate: 0.0010
 ## Epoch 19/500
-## 90/90 - 0s - 1ms/step - loss: 0.3222 - sparse_categorical_accuracy: 0.8608 - val_loss: 0.3951 - val_sparse_categorical_accuracy: 0.8128 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.3195 - sparse_categorical_accuracy: 0.8601 - val_loss: 0.3432 - val_sparse_categorical_accuracy: 0.8433 - learning_rate: 0.0010
 ## Epoch 20/500
-## 90/90 - 0s - 1ms/step - loss: 0.3195 - sparse_categorical_accuracy: 0.8590 - val_loss: 0.5172 - val_sparse_categorical_accuracy: 0.7226 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.3136 - sparse_categorical_accuracy: 0.8674 - val_loss: 0.9287 - val_sparse_categorical_accuracy: 0.5950 - learning_rate: 0.0010
 ## Epoch 21/500
-## 90/90 - 0s - 2ms/step - loss: 0.3003 - sparse_categorical_accuracy: 0.8698 - val_loss: 0.3359 - val_sparse_categorical_accuracy: 0.8405 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.3001 - sparse_categorical_accuracy: 0.8691 - val_loss: 0.3926 - val_sparse_categorical_accuracy: 0.7795 - learning_rate: 0.0010
 ## Epoch 22/500
-## 90/90 - 0s - 2ms/step - loss: 0.3012 - sparse_categorical_accuracy: 0.8740 - val_loss: 0.3339 - val_sparse_categorical_accuracy: 0.8447 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.2984 - sparse_categorical_accuracy: 0.8719 - val_loss: 0.3023 - val_sparse_categorical_accuracy: 0.8655 - learning_rate: 0.0010
 ## Epoch 23/500
-## 90/90 - 0s - 1ms/step - loss: 0.2901 - sparse_categorical_accuracy: 0.8799 - val_loss: 0.3859 - val_sparse_categorical_accuracy: 0.7920 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2930 - sparse_categorical_accuracy: 0.8767 - val_loss: 0.3501 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010
 ## Epoch 24/500
-## 90/90 - 0s - 2ms/step - loss: 0.2898 - sparse_categorical_accuracy: 0.8788 - val_loss: 0.2806 - val_sparse_categorical_accuracy: 0.8863 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2861 - sparse_categorical_accuracy: 0.8799 - val_loss: 0.9187 - val_sparse_categorical_accuracy: 0.5992 - learning_rate: 0.0010
 ## Epoch 25/500
-## 90/90 - 0s - 1ms/step - loss: 0.2831 - sparse_categorical_accuracy: 0.8785 - val_loss: 0.3124 - val_sparse_categorical_accuracy: 0.8585 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2843 - sparse_categorical_accuracy: 0.8816 - val_loss: 0.3731 - val_sparse_categorical_accuracy: 0.8363 - learning_rate: 0.0010
 ## Epoch 26/500
-## 90/90 - 0s - 1ms/step - loss: 0.3130 - sparse_categorical_accuracy: 0.8580 - val_loss: 0.3423 - val_sparse_categorical_accuracy: 0.8377 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.3182 - sparse_categorical_accuracy: 0.8569 - val_loss: 0.5553 - val_sparse_categorical_accuracy: 0.7115 - learning_rate: 0.0010
 ## Epoch 27/500
-## 90/90 - 0s - 1ms/step - loss: 0.2741 - sparse_categorical_accuracy: 0.8910 - val_loss: 0.2908 - val_sparse_categorical_accuracy: 0.8835 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2732 - sparse_categorical_accuracy: 0.8875 - val_loss: 0.3950 - val_sparse_categorical_accuracy: 0.7628 - learning_rate: 0.0010
 ## Epoch 28/500
-## 90/90 - 0s - 1ms/step - loss: 0.2855 - sparse_categorical_accuracy: 0.8837 - val_loss: 0.5778 - val_sparse_categorical_accuracy: 0.7323 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2943 - sparse_categorical_accuracy: 0.8705 - val_loss: 0.8138 - val_sparse_categorical_accuracy: 0.7254 - learning_rate: 0.0010
 ## Epoch 29/500
-## 90/90 - 0s - 1ms/step - loss: 0.2671 - sparse_categorical_accuracy: 0.8809 - val_loss: 0.4736 - val_sparse_categorical_accuracy: 0.7628 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2772 - sparse_categorical_accuracy: 0.8781 - val_loss: 0.3712 - val_sparse_categorical_accuracy: 0.8405 - learning_rate: 0.0010
 ## Epoch 30/500
-## 90/90 - 0s - 1ms/step - loss: 0.2720 - sparse_categorical_accuracy: 0.8833 - val_loss: 0.4564 - val_sparse_categorical_accuracy: 0.7767 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2714 - sparse_categorical_accuracy: 0.8833 - val_loss: 0.4209 - val_sparse_categorical_accuracy: 0.7975 - learning_rate: 0.0010
 ## Epoch 31/500
-## 90/90 - 0s - 1ms/step - loss: 0.2724 - sparse_categorical_accuracy: 0.8882 - val_loss: 0.2784 - val_sparse_categorical_accuracy: 0.8918 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.2720 - sparse_categorical_accuracy: 0.8854 - val_loss: 1.9298 - val_sparse_categorical_accuracy: 0.5104 - learning_rate: 0.0010
 ## Epoch 32/500
-## 90/90 - 0s - 1ms/step - loss: 0.2709 - sparse_categorical_accuracy: 0.8830 - val_loss: 0.3442 - val_sparse_categorical_accuracy: 0.8280 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.2699 - sparse_categorical_accuracy: 0.8833 - val_loss: 0.3155 - val_sparse_categorical_accuracy: 0.8516 - learning_rate: 0.0010
 ## Epoch 33/500
-## 90/90 - 0s - 1ms/step - loss: 0.2571 - sparse_categorical_accuracy: 0.8986 - val_loss: 0.3730 - val_sparse_categorical_accuracy: 0.7864 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.2586 - sparse_categorical_accuracy: 0.8951 - val_loss: 0.3628 - val_sparse_categorical_accuracy: 0.7947 - learning_rate: 0.0010
 ## Epoch 34/500
-## 90/90 - 0s - 1ms/step - loss: 0.2449 - sparse_categorical_accuracy: 0.9003 - val_loss: 0.2799 - val_sparse_categorical_accuracy: 0.8877 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2490 - sparse_categorical_accuracy: 0.9003 - val_loss: 0.5844 - val_sparse_categorical_accuracy: 0.7309 - learning_rate: 0.0010
 ## Epoch 35/500
-## 90/90 - 0s - 1ms/step - loss: 0.2698 - sparse_categorical_accuracy: 0.8861 - val_loss: 0.4860 - val_sparse_categorical_accuracy: 0.7420 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2627 - sparse_categorical_accuracy: 0.8938 - val_loss: 0.3480 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010
 ## Epoch 36/500
-## 90/90 - 0s - 1ms/step - loss: 0.2539 - sparse_categorical_accuracy: 0.8944 - val_loss: 0.2572 - val_sparse_categorical_accuracy: 0.8974 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2558 - sparse_categorical_accuracy: 0.8948 - val_loss: 0.3793 - val_sparse_categorical_accuracy: 0.8128 - learning_rate: 0.0010
 ## Epoch 37/500
-## 90/90 - 0s - 1ms/step - loss: 0.2495 - sparse_categorical_accuracy: 0.8962 - val_loss: 0.2735 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2515 - sparse_categorical_accuracy: 0.8958 - val_loss: 0.4828 - val_sparse_categorical_accuracy: 0.7503 - learning_rate: 0.0010
 ## Epoch 38/500
-## 90/90 - 0s - 1ms/step - loss: 0.2474 - sparse_categorical_accuracy: 0.9000 - val_loss: 0.3009 - val_sparse_categorical_accuracy: 0.8641 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.2541 - sparse_categorical_accuracy: 0.8986 - val_loss: 0.2950 - val_sparse_categorical_accuracy: 0.8669 - learning_rate: 0.0010
 ## Epoch 39/500
-## 90/90 - 0s - 1ms/step - loss: 0.2492 - sparse_categorical_accuracy: 0.8976 - val_loss: 1.0276 - val_sparse_categorical_accuracy: 0.6283 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2505 - sparse_categorical_accuracy: 0.8976 - val_loss: 0.7151 - val_sparse_categorical_accuracy: 0.7087 - learning_rate: 0.0010
 ## Epoch 40/500
-## 90/90 - 0s - 1ms/step - loss: 0.2477 - sparse_categorical_accuracy: 0.8896 - val_loss: 0.4444 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2490 - sparse_categorical_accuracy: 0.8979 - val_loss: 0.3170 - val_sparse_categorical_accuracy: 0.8627 - learning_rate: 0.0010
 ## Epoch 41/500
-## 90/90 - 0s - 1ms/step - loss: 0.2315 - sparse_categorical_accuracy: 0.9069 - val_loss: 0.4963 - val_sparse_categorical_accuracy: 0.7573 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.2325 - sparse_categorical_accuracy: 0.9062 - val_loss: 0.2560 - val_sparse_categorical_accuracy: 0.8904 - learning_rate: 0.0010
 ## Epoch 42/500
-## 90/90 - 0s - 1ms/step - loss: 0.2279 - sparse_categorical_accuracy: 0.9101 - val_loss: 0.2943 - val_sparse_categorical_accuracy: 0.8849 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2312 - sparse_categorical_accuracy: 0.9076 - val_loss: 0.3396 - val_sparse_categorical_accuracy: 0.8405 - learning_rate: 0.0010
 ## Epoch 43/500
-## 90/90 - 0s - 1ms/step - loss: 0.2365 - sparse_categorical_accuracy: 0.8983 - val_loss: 0.6205 - val_sparse_categorical_accuracy: 0.7226 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2402 - sparse_categorical_accuracy: 0.8976 - val_loss: 0.3650 - val_sparse_categorical_accuracy: 0.8294 - learning_rate: 0.0010
 ## Epoch 44/500
-## 90/90 - 0s - 1ms/step - loss: 0.2314 - sparse_categorical_accuracy: 0.9080 - val_loss: 0.3348 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2342 - sparse_categorical_accuracy: 0.9038 - val_loss: 1.0563 - val_sparse_categorical_accuracy: 0.6172 - learning_rate: 0.0010
 ## Epoch 45/500
-## 90/90 - 0s - 1ms/step - loss: 0.2301 - sparse_categorical_accuracy: 0.9094 - val_loss: 0.4539 - val_sparse_categorical_accuracy: 0.7753 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2320 - sparse_categorical_accuracy: 0.9080 - val_loss: 0.2506 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010
 ## Epoch 46/500
-## 90/90 - 0s - 1ms/step - loss: 0.2265 - sparse_categorical_accuracy: 0.9090 - val_loss: 0.2660 - val_sparse_categorical_accuracy: 0.8974 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2317 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.4473 - val_sparse_categorical_accuracy: 0.7920 - learning_rate: 0.0010
 ## Epoch 47/500
-## 90/90 - 0s - 1ms/step - loss: 0.2065 - sparse_categorical_accuracy: 0.9188 - val_loss: 0.2680 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2164 - sparse_categorical_accuracy: 0.9125 - val_loss: 0.6324 - val_sparse_categorical_accuracy: 0.7240 - learning_rate: 0.0010
 ## Epoch 48/500
-## 90/90 - 0s - 2ms/step - loss: 0.2107 - sparse_categorical_accuracy: 0.9139 - val_loss: 0.2204 - val_sparse_categorical_accuracy: 0.9126 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.2186 - sparse_categorical_accuracy: 0.9122 - val_loss: 0.2525 - val_sparse_categorical_accuracy: 0.9001 - learning_rate: 0.0010
 ## Epoch 49/500
-## 90/90 - 0s - 1ms/step - loss: 0.2009 - sparse_categorical_accuracy: 0.9174 - val_loss: 0.3135 - val_sparse_categorical_accuracy: 0.8336 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.2119 - sparse_categorical_accuracy: 0.9118 - val_loss: 0.2401 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010
 ## Epoch 50/500
-## 90/90 - 0s - 1ms/step - loss: 0.2138 - sparse_categorical_accuracy: 0.9160 - val_loss: 0.4732 - val_sparse_categorical_accuracy: 0.7587 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.2134 - sparse_categorical_accuracy: 0.9191 - val_loss: 0.2410 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010
 ## Epoch 51/500
-## 90/90 - 0s - 1ms/step - loss: 0.1992 - sparse_categorical_accuracy: 0.9201 - val_loss: 0.6327 - val_sparse_categorical_accuracy: 0.7365 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.2120 - sparse_categorical_accuracy: 0.9174 - val_loss: 0.6803 - val_sparse_categorical_accuracy: 0.6935 - learning_rate: 0.0010
 ## Epoch 52/500
-## 90/90 - 0s - 1ms/step - loss: 0.1888 - sparse_categorical_accuracy: 0.9267 - val_loss: 0.2356 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.2023 - sparse_categorical_accuracy: 0.9205 - val_loss: 0.2938 - val_sparse_categorical_accuracy: 0.8779 - learning_rate: 0.0010
 ## Epoch 53/500
-## 90/90 - 0s - 1ms/step - loss: 0.1837 - sparse_categorical_accuracy: 0.9278 - val_loss: 0.2637 - val_sparse_categorical_accuracy: 0.8877 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.1980 - sparse_categorical_accuracy: 0.9243 - val_loss: 0.5380 - val_sparse_categorical_accuracy: 0.7531 - learning_rate: 0.0010
 ## Epoch 54/500
-## 90/90 - 0s - 2ms/step - loss: 0.1758 - sparse_categorical_accuracy: 0.9354 - val_loss: 0.2062 - val_sparse_categorical_accuracy: 0.9126 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.1883 - sparse_categorical_accuracy: 0.9271 - val_loss: 0.2272 - val_sparse_categorical_accuracy: 0.9071 - learning_rate: 0.0010
 ## Epoch 55/500
-## 90/90 - 0s - 1ms/step - loss: 0.1668 - sparse_categorical_accuracy: 0.9438 - val_loss: 0.6011 - val_sparse_categorical_accuracy: 0.7559 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.1761 - sparse_categorical_accuracy: 0.9385 - val_loss: 0.2707 - val_sparse_categorical_accuracy: 0.8821 - learning_rate: 0.0010
 ## Epoch 56/500
-## 90/90 - 0s - 1ms/step - loss: 0.1580 - sparse_categorical_accuracy: 0.9476 - val_loss: 0.4532 - val_sparse_categorical_accuracy: 0.7587 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1683 - sparse_categorical_accuracy: 0.9417 - val_loss: 0.2584 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010
 ## Epoch 57/500
-## 90/90 - 0s - 1ms/step - loss: 0.1518 - sparse_categorical_accuracy: 0.9521 - val_loss: 1.1827 - val_sparse_categorical_accuracy: 0.5492 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1581 - sparse_categorical_accuracy: 0.9490 - val_loss: 1.4305 - val_sparse_categorical_accuracy: 0.7184 - learning_rate: 0.0010
 ## Epoch 58/500
-## 90/90 - 0s - 2ms/step - loss: 0.1449 - sparse_categorical_accuracy: 0.9528 - val_loss: 0.1922 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1518 - sparse_categorical_accuracy: 0.9497 - val_loss: 0.7991 - val_sparse_categorical_accuracy: 0.7531 - learning_rate: 0.0010
 ## Epoch 59/500
-## 90/90 - 0s - 1ms/step - loss: 0.1520 - sparse_categorical_accuracy: 0.9476 - val_loss: 2.0179 - val_sparse_categorical_accuracy: 0.6089 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1529 - sparse_categorical_accuracy: 0.9479 - val_loss: 0.3389 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 0.0010
 ## Epoch 60/500
-## 90/90 - 0s - 1ms/step - loss: 0.1457 - sparse_categorical_accuracy: 0.9503 - val_loss: 0.2761 - val_sparse_categorical_accuracy: 0.8946 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1784 - sparse_categorical_accuracy: 0.9368 - val_loss: 0.2534 - val_sparse_categorical_accuracy: 0.8974 - learning_rate: 0.0010
 ## Epoch 61/500
-## 90/90 - 0s - 1ms/step - loss: 0.1280 - sparse_categorical_accuracy: 0.9580 - val_loss: 1.6068 - val_sparse_categorical_accuracy: 0.6796 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1335 - sparse_categorical_accuracy: 0.9566 - val_loss: 0.2323 - val_sparse_categorical_accuracy: 0.9098 - learning_rate: 0.0010
 ## Epoch 62/500
-## 90/90 - 0s - 1ms/step - loss: 0.1408 - sparse_categorical_accuracy: 0.9510 - val_loss: 0.9512 - val_sparse_categorical_accuracy: 0.7129 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1401 - sparse_categorical_accuracy: 0.9531 - val_loss: 0.3259 - val_sparse_categorical_accuracy: 0.8696 - learning_rate: 0.0010
 ## Epoch 63/500
-## 90/90 - 0s - 1ms/step - loss: 0.1381 - sparse_categorical_accuracy: 0.9514 - val_loss: 1.2871 - val_sparse_categorical_accuracy: 0.6976 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1404 - sparse_categorical_accuracy: 0.9521 - val_loss: 0.2970 - val_sparse_categorical_accuracy: 0.8627 - learning_rate: 0.0010
 ## Epoch 64/500
-## 90/90 - 0s - 1ms/step - loss: 0.1280 - sparse_categorical_accuracy: 0.9569 - val_loss: 0.3003 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1375 - sparse_categorical_accuracy: 0.9559 - val_loss: 0.3416 - val_sparse_categorical_accuracy: 0.8183 - learning_rate: 0.0010
 ## Epoch 65/500
-## 90/90 - 0s - 1ms/step - loss: 0.1206 - sparse_categorical_accuracy: 0.9618 - val_loss: 0.4581 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1303 - sparse_categorical_accuracy: 0.9583 - val_loss: 0.2586 - val_sparse_categorical_accuracy: 0.8766 - learning_rate: 0.0010
 ## Epoch 66/500
-## 90/90 - 0s - 1ms/step - loss: 0.1148 - sparse_categorical_accuracy: 0.9628 - val_loss: 0.3863 - val_sparse_categorical_accuracy: 0.8266 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1199 - sparse_categorical_accuracy: 0.9608 - val_loss: 1.2218 - val_sparse_categorical_accuracy: 0.7337 - learning_rate: 0.0010
 ## Epoch 67/500
-## 90/90 - 0s - 1ms/step - loss: 0.1068 - sparse_categorical_accuracy: 0.9663 - val_loss: 1.2923 - val_sparse_categorical_accuracy: 0.6852 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.1186 - sparse_categorical_accuracy: 0.9615 - val_loss: 0.1464 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010
 ## Epoch 68/500
-## 90/90 - 0s - 1ms/step - loss: 0.1159 - sparse_categorical_accuracy: 0.9615 - val_loss: 0.2969 - val_sparse_categorical_accuracy: 0.8558 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.1176 - sparse_categorical_accuracy: 0.9632 - val_loss: 0.1451 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010
 ## Epoch 69/500
-## 90/90 - 0s - 1ms/step - loss: 0.1228 - sparse_categorical_accuracy: 0.9580 - val_loss: 0.2251 - val_sparse_categorical_accuracy: 0.9071 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.1295 - sparse_categorical_accuracy: 0.9583 - val_loss: 1.7723 - val_sparse_categorical_accuracy: 0.5395 - learning_rate: 0.0010
 ## Epoch 70/500
-## 90/90 - 0s - 2ms/step - loss: 0.1138 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.1805 - val_sparse_categorical_accuracy: 0.9251 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.1232 - sparse_categorical_accuracy: 0.9587 - val_loss: 0.1802 - val_sparse_categorical_accuracy: 0.9237 - learning_rate: 0.0010
 ## Epoch 71/500
-## 90/90 - 0s - 2ms/step - loss: 0.1101 - sparse_categorical_accuracy: 0.9618 - val_loss: 0.1439 - val_sparse_categorical_accuracy: 0.9487 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1202 - sparse_categorical_accuracy: 0.9573 - val_loss: 0.1932 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010
 ## Epoch 72/500
-## 90/90 - 0s - 2ms/step - loss: 0.1133 - sparse_categorical_accuracy: 0.9628 - val_loss: 0.1390 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1124 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.4178 - val_sparse_categorical_accuracy: 0.8003 - learning_rate: 0.0010
 ## Epoch 73/500
-## 90/90 - 0s - 1ms/step - loss: 0.1042 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.1421 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1068 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1517 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 0.0010
 ## Epoch 74/500
-## 90/90 - 0s - 1ms/step - loss: 0.1098 - sparse_categorical_accuracy: 0.9604 - val_loss: 2.0644 - val_sparse_categorical_accuracy: 0.6685 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1085 - sparse_categorical_accuracy: 0.9639 - val_loss: 0.2126 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 0.0010
 ## Epoch 75/500
-## 90/90 - 0s - 1ms/step - loss: 0.1419 - sparse_categorical_accuracy: 0.9486 - val_loss: 0.4101 - val_sparse_categorical_accuracy: 0.8336 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1263 - sparse_categorical_accuracy: 0.9580 - val_loss: 0.1689 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 0.0010
 ## Epoch 76/500
-## 90/90 - 0s - 1ms/step - loss: 0.1230 - sparse_categorical_accuracy: 0.9569 - val_loss: 0.3055 - val_sparse_categorical_accuracy: 0.8946 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1169 - sparse_categorical_accuracy: 0.9597 - val_loss: 0.2955 - val_sparse_categorical_accuracy: 0.8460 - learning_rate: 0.0010
 ## Epoch 77/500
-## 90/90 - 0s - 1ms/step - loss: 0.1028 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.1704 - val_sparse_categorical_accuracy: 0.9265 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1089 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.6333 - val_sparse_categorical_accuracy: 0.6241 - learning_rate: 0.0010
 ## Epoch 78/500
-## 90/90 - 0s - 1ms/step - loss: 0.1003 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.3304 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.1017 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.1416 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 0.0010
 ## Epoch 79/500
-## 90/90 - 0s - 1ms/step - loss: 0.1019 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.5429 - val_sparse_categorical_accuracy: 0.7822 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.1099 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.3434 - val_sparse_categorical_accuracy: 0.8544 - learning_rate: 0.0010
 ## Epoch 80/500
-## 90/90 - 0s - 1ms/step - loss: 0.1037 - sparse_categorical_accuracy: 0.9670 - val_loss: 2.0288 - val_sparse_categorical_accuracy: 0.6976 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.1046 - sparse_categorical_accuracy: 0.9653 - val_loss: 0.4430 - val_sparse_categorical_accuracy: 0.8280 - learning_rate: 0.0010
 ## Epoch 81/500
-## 90/90 - 0s - 1ms/step - loss: 0.1030 - sparse_categorical_accuracy: 0.9667 - val_loss: 3.3225 - val_sparse_categorical_accuracy: 0.5492 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1098 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.1632 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 0.0010
 ## Epoch 82/500
-## 90/90 - 0s - 2ms/step - loss: 0.0947 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.1449 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1021 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.3984 - val_sparse_categorical_accuracy: 0.7906 - learning_rate: 0.0010
 ## Epoch 83/500
-## 90/90 - 0s - 2ms/step - loss: 0.1060 - sparse_categorical_accuracy: 0.9639 - val_loss: 0.2491 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1086 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.1976 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010
 ## Epoch 84/500
-## 90/90 - 0s - 2ms/step - loss: 0.0958 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.1281 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1026 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.2123 - val_sparse_categorical_accuracy: 0.9251 - learning_rate: 0.0010
 ## Epoch 85/500
-## 90/90 - 0s - 1ms/step - loss: 0.0993 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.1956 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1032 - sparse_categorical_accuracy: 0.9632 - val_loss: 0.3491 - val_sparse_categorical_accuracy: 0.8613 - learning_rate: 0.0010
 ## Epoch 86/500
-## 90/90 - 0s - 1ms/step - loss: 0.1003 - sparse_categorical_accuracy: 0.9604 - val_loss: 0.4167 - val_sparse_categorical_accuracy: 0.8239 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1136 - sparse_categorical_accuracy: 0.9576 - val_loss: 0.1498 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 0.0010
 ## Epoch 87/500
-## 90/90 - 0s - 1ms/step - loss: 0.1099 - sparse_categorical_accuracy: 0.9604 - val_loss: 0.1557 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.1107 - sparse_categorical_accuracy: 0.9608 - val_loss: 0.1406 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 0.0010
 ## Epoch 88/500
-## 90/90 - 0s - 1ms/step - loss: 0.1002 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.1204 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0995 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.6864 - val_sparse_categorical_accuracy: 0.7656 - learning_rate: 0.0010
 ## Epoch 89/500
-## 90/90 - 0s - 2ms/step - loss: 0.0969 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1194 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1013 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.8218 - val_sparse_categorical_accuracy: 0.7476 - learning_rate: 0.0010
 ## Epoch 90/500
-## 90/90 - 0s - 1ms/step - loss: 0.0937 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.1397 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1002 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.2009 - val_sparse_categorical_accuracy: 0.9209 - learning_rate: 0.0010
 ## Epoch 91/500
-## 90/90 - 0s - 1ms/step - loss: 0.1024 - sparse_categorical_accuracy: 0.9660 - val_loss: 2.1705 - val_sparse_categorical_accuracy: 0.6671 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1010 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.2869 - val_sparse_categorical_accuracy: 0.8849 - learning_rate: 0.0010
 ## Epoch 92/500
-## 90/90 - 0s - 2ms/step - loss: 0.0928 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.7758 - val_sparse_categorical_accuracy: 0.7559 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1012 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.5128 - val_sparse_categorical_accuracy: 0.7434 - learning_rate: 0.0010
 ## Epoch 93/500
-## 90/90 - 0s - 1ms/step - loss: 0.0905 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.2143 - val_sparse_categorical_accuracy: 0.9140 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1009 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.5212 - val_sparse_categorical_accuracy: 0.7753 - learning_rate: 0.0010
 ## Epoch 94/500
-## 90/90 - 0s - 1ms/step - loss: 0.0919 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.1413 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1016 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.3146 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 0.0010
 ## Epoch 95/500
-## 90/90 - 0s - 1ms/step - loss: 0.0884 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.1715 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0975 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1750 - val_sparse_categorical_accuracy: 0.9376 - learning_rate: 0.0010
 ## Epoch 96/500
-## 90/90 - 0s - 1ms/step - loss: 0.1015 - sparse_categorical_accuracy: 0.9653 - val_loss: 0.3827 - val_sparse_categorical_accuracy: 0.8669 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1121 - sparse_categorical_accuracy: 0.9583 - val_loss: 0.2399 - val_sparse_categorical_accuracy: 0.9112 - learning_rate: 0.0010
 ## Epoch 97/500
-## 90/90 - 0s - 1ms/step - loss: 0.0945 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1286 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1000 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.5611 - val_sparse_categorical_accuracy: 0.7101 - learning_rate: 0.0010
 ## Epoch 98/500
-## 90/90 - 0s - 1ms/step - loss: 0.0924 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.2279 - val_sparse_categorical_accuracy: 0.9126 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0949 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1864 - val_sparse_categorical_accuracy: 0.9279 - learning_rate: 0.0010
 ## Epoch 99/500
-## 90/90 - 0s - 1ms/step - loss: 0.0951 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.1800 - val_sparse_categorical_accuracy: 0.9348 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.0925 - sparse_categorical_accuracy: 0.9667 - val_loss: 0.1228 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010
 ## Epoch 100/500
-## 90/90 - 0s - 1ms/step - loss: 0.0864 - sparse_categorical_accuracy: 0.9729 - val_loss: 0.2424 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0922 - sparse_categorical_accuracy: 0.9691 - val_loss: 1.9448 - val_sparse_categorical_accuracy: 0.7115 - learning_rate: 0.0010
 ## Epoch 101/500
-## 90/90 - 0s - 1ms/step - loss: 0.0830 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.1904 - val_sparse_categorical_accuracy: 0.9251 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0887 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.2127 - val_sparse_categorical_accuracy: 0.9071 - learning_rate: 0.0010
 ## Epoch 102/500
-## 90/90 - 0s - 1ms/step - loss: 0.0973 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.1916 - val_sparse_categorical_accuracy: 0.9182 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0999 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.1903 - val_sparse_categorical_accuracy: 0.9154 - learning_rate: 0.0010
 ## Epoch 103/500
-## 90/90 - 0s - 1ms/step - loss: 0.0883 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.1190 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0929 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.2647 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010
 ## Epoch 104/500
-## 90/90 - 0s - 1ms/step - loss: 0.0936 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.2695 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1008 - sparse_categorical_accuracy: 0.9642 - val_loss: 1.1019 - val_sparse_categorical_accuracy: 0.6019 - learning_rate: 0.0010
 ## Epoch 105/500
-## 90/90 - 0s - 1ms/step - loss: 0.0905 - sparse_categorical_accuracy: 0.9684 - val_loss: 0.1941 - val_sparse_categorical_accuracy: 0.9182 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0969 - sparse_categorical_accuracy: 0.9681 - val_loss: 1.1464 - val_sparse_categorical_accuracy: 0.5645 - learning_rate: 0.0010
 ## Epoch 106/500
-## 90/90 - 0s - 1ms/step - loss: 0.0885 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.5663 - val_sparse_categorical_accuracy: 0.8322 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.0924 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.2535 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010
 ## Epoch 107/500
-## 90/90 - 0s - 1ms/step - loss: 0.0891 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.6871 - val_sparse_categorical_accuracy: 0.7490 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.0930 - sparse_categorical_accuracy: 0.9677 - val_loss: 1.5288 - val_sparse_categorical_accuracy: 0.5118 - learning_rate: 0.0010
 ## Epoch 108/500
-## 90/90 - 0s - 1ms/step - loss: 0.0890 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.7344 - val_sparse_categorical_accuracy: 0.8031 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0893 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1496 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 0.0010
 ## Epoch 109/500
-## 90/90 - 0s - 1ms/step - loss: 0.0894 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.3726 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0891 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.2799 - val_sparse_categorical_accuracy: 0.8835 - learning_rate: 0.0010
 ## Epoch 110/500
-## 90/90 - 0s - 1ms/step - loss: 0.0963 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.6424 - val_sparse_categorical_accuracy: 0.7961 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0963 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.4827 - val_sparse_categorical_accuracy: 0.8155 - learning_rate: 0.0010
 ## Epoch 111/500
-## 90/90 - 0s - 1ms/step - loss: 0.0819 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.8653 - val_sparse_categorical_accuracy: 0.7642 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.0841 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1203 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 0.0010
 ## Epoch 112/500
-## 90/90 - 0s - 1ms/step - loss: 0.0852 - sparse_categorical_accuracy: 0.9691 - val_loss: 1.0738 - val_sparse_categorical_accuracy: 0.7393 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0861 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.5081 - val_sparse_categorical_accuracy: 0.7559 - learning_rate: 0.0010
 ## Epoch 113/500
-## 90/90 - 0s - 1ms/step - loss: 0.0828 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.1299 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0831 - sparse_categorical_accuracy: 0.9729 - val_loss: 1.2423 - val_sparse_categorical_accuracy: 0.5687 - learning_rate: 0.0010
 ## Epoch 114/500
-## 90/90 - 0s - 1ms/step - loss: 0.1042 - sparse_categorical_accuracy: 0.9649 - val_loss: 0.2819 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.1094 - sparse_categorical_accuracy: 0.9653 - val_loss: 0.3459 - val_sparse_categorical_accuracy: 0.8252 - learning_rate: 0.0010
 ## Epoch 115/500
-## 90/90 - 0s - 1ms/step - loss: 0.0780 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.2349 - val_sparse_categorical_accuracy: 0.9001 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0886 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.7117 - val_sparse_categorical_accuracy: 0.7226 - learning_rate: 0.0010
 ## Epoch 116/500
-## 90/90 - 0s - 1ms/step - loss: 0.0850 - sparse_categorical_accuracy: 0.9743 - val_loss: 0.9277 - val_sparse_categorical_accuracy: 0.7406 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0989 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.1669 - val_sparse_categorical_accuracy: 0.9334 - learning_rate: 0.0010
 ## Epoch 117/500
-## 90/90 - 0s - 1ms/step - loss: 0.0813 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.7052 - val_sparse_categorical_accuracy: 0.7836 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0891 - sparse_categorical_accuracy: 0.9705 - val_loss: 0.2413 - val_sparse_categorical_accuracy: 0.9154 - learning_rate: 0.0010
 ## Epoch 118/500
-## 90/90 - 0s - 1ms/step - loss: 0.0838 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1543 - val_sparse_categorical_accuracy: 0.9307 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0902 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.7796 - val_sparse_categorical_accuracy: 0.6741 - learning_rate: 0.0010
 ## Epoch 119/500
-## 90/90 - 0s - 2ms/step - loss: 0.0817 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.1127 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0877 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.1806 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 0.0010
 ## Epoch 120/500
-## 90/90 - 0s - 1ms/step - loss: 0.0846 - sparse_categorical_accuracy: 0.9715 - val_loss: 0.1209 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.0880 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.1146 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 0.0010
 ## Epoch 121/500
-## 90/90 - 0s - 1ms/step - loss: 0.0817 - sparse_categorical_accuracy: 0.9740 - val_loss: 0.1134 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0896 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.1279 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 0.0010
 ## Epoch 122/500
-## 90/90 - 0s - 1ms/step - loss: 0.0872 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.6854 - val_sparse_categorical_accuracy: 0.7892 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0883 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.3067 - val_sparse_categorical_accuracy: 0.8918 - learning_rate: 0.0010
 ## Epoch 123/500
-## 90/90 - 0s - 1ms/step - loss: 0.0795 - sparse_categorical_accuracy: 0.9743 - val_loss: 0.8494 - val_sparse_categorical_accuracy: 0.7448 - learning_rate: 0.0010
+## 90/90 - 0s - 2ms/step - loss: 0.0855 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.1060 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 0.0010
 ## Epoch 124/500
-## 90/90 - 0s - 1ms/step - loss: 0.0965 - sparse_categorical_accuracy: 0.9632 - val_loss: 0.2033 - val_sparse_categorical_accuracy: 0.9140 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0917 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.2169 - val_sparse_categorical_accuracy: 0.9112 - learning_rate: 0.0010
 ## Epoch 125/500
-## 90/90 - 0s - 1ms/step - loss: 0.0866 - sparse_categorical_accuracy: 0.9715 - val_loss: 0.3366 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0976 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.1407 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 0.0010
 ## Epoch 126/500
-## 90/90 - 0s - 1ms/step - loss: 0.0792 - sparse_categorical_accuracy: 0.9747 - val_loss: 1.1318 - val_sparse_categorical_accuracy: 0.7462 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0866 - sparse_categorical_accuracy: 0.9722 - val_loss: 1.4019 - val_sparse_categorical_accuracy: 0.7295 - learning_rate: 0.0010
 ## Epoch 127/500
-## 90/90 - 0s - 1ms/step - loss: 0.0810 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1230 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0877 - sparse_categorical_accuracy: 0.9747 - val_loss: 0.2518 - val_sparse_categorical_accuracy: 0.8918 - learning_rate: 0.0010
 ## Epoch 128/500
-## 90/90 - 0s - 1ms/step - loss: 0.0839 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.1734 - val_sparse_categorical_accuracy: 0.9279 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0942 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.1465 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 0.0010
 ## Epoch 129/500
-## 90/90 - 0s - 1ms/step - loss: 0.0845 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.2233 - val_sparse_categorical_accuracy: 0.9154 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0904 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.1546 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 0.0010
 ## Epoch 130/500
-## 90/90 - 0s - 1ms/step - loss: 0.0906 - sparse_categorical_accuracy: 0.9694 - val_loss: 1.4223 - val_sparse_categorical_accuracy: 0.6963 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0953 - sparse_categorical_accuracy: 0.9705 - val_loss: 0.1201 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 0.0010
 ## Epoch 131/500
-## 90/90 - 0s - 1ms/step - loss: 0.0774 - sparse_categorical_accuracy: 0.9743 - val_loss: 2.8725 - val_sparse_categorical_accuracy: 0.7018 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0846 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.1980 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 0.0010
 ## Epoch 132/500
-## 90/90 - 0s - 1ms/step - loss: 0.0748 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.8038 - val_sparse_categorical_accuracy: 0.7781 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0812 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.4080 - val_sparse_categorical_accuracy: 0.8599 - learning_rate: 0.0010
 ## Epoch 133/500
-## 90/90 - 0s - 1ms/step - loss: 0.0739 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.5711 - val_sparse_categorical_accuracy: 0.7809 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0785 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.3284 - val_sparse_categorical_accuracy: 0.8835 - learning_rate: 0.0010
 ## Epoch 134/500
-## 90/90 - 0s - 1ms/step - loss: 0.0780 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.2510 - val_sparse_categorical_accuracy: 0.8918 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0885 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.5050 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010
 ## Epoch 135/500
-## 90/90 - 0s - 1ms/step - loss: 0.0801 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.4907 - val_sparse_categorical_accuracy: 0.8585 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0859 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.4296 - val_sparse_categorical_accuracy: 0.8141 - learning_rate: 0.0010
 ## Epoch 136/500
-## 90/90 - 0s - 1ms/step - loss: 0.0819 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.1151 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0885 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.1295 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 0.0010
 ## Epoch 137/500
-## 90/90 - 0s - 1ms/step - loss: 0.0840 - sparse_categorical_accuracy: 0.9715 - val_loss: 1.2419 - val_sparse_categorical_accuracy: 0.7240 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0892 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.4052 - val_sparse_categorical_accuracy: 0.8031 - learning_rate: 0.0010
 ## Epoch 138/500
-## 90/90 - 0s - 1ms/step - loss: 0.0774 - sparse_categorical_accuracy: 0.9743 - val_loss: 0.6905 - val_sparse_categorical_accuracy: 0.7864 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0835 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.1160 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 0.0010
 ## Epoch 139/500
-## 90/90 - 0s - 1ms/step - loss: 0.0852 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.1253 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010
+## 90/90 - 0s - 1ms/step - loss: 0.0877 - sparse_categorical_accuracy: 0.9708 - val_loss: 3.0631 - val_sparse_categorical_accuracy: 0.7032 - learning_rate: 0.0010
 ## Epoch 140/500
-## 90/90 - 1s - 6ms/step - loss: 0.0666 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.2353 - val_sparse_categorical_accuracy: 0.9140 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0867 - sparse_categorical_accuracy: 0.9719 - val_loss: 1.3638 - val_sparse_categorical_accuracy: 0.7143 - learning_rate: 0.0010
 ## Epoch 141/500
-## 90/90 - 0s - 1ms/step - loss: 0.0657 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1275 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0857 - sparse_categorical_accuracy: 0.9715 - val_loss: 0.2599 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010
 ## Epoch 142/500
-## 90/90 - 0s - 1ms/step - loss: 0.0635 - sparse_categorical_accuracy: 0.9788 - val_loss: 0.2696 - val_sparse_categorical_accuracy: 0.9029 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0771 - sparse_categorical_accuracy: 0.9764 - val_loss: 0.2860 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010
 ## Epoch 143/500
-## 90/90 - 0s - 1ms/step - loss: 0.0688 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.1737 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0835 - sparse_categorical_accuracy: 0.9694 - val_loss: 0.1252 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 0.0010
 ## Epoch 144/500
-## 90/90 - 0s - 1ms/step - loss: 0.0615 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.2506 - val_sparse_categorical_accuracy: 0.8946 - learning_rate: 5.0000e-04
+## 90/90 - 1s - 7ms/step - loss: 0.0674 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.1234 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 5.0000e-04
 ## Epoch 145/500
-## 90/90 - 0s - 1ms/step - loss: 0.0629 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.1486 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0680 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1147 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 5.0000e-04
 ## Epoch 146/500
-## 90/90 - 0s - 1ms/step - loss: 0.0656 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1651 - val_sparse_categorical_accuracy: 0.9307 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 2ms/step - loss: 0.0702 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1042 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 5.0000e-04
 ## Epoch 147/500
-## 90/90 - 0s - 1ms/step - loss: 0.0677 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1034 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0742 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.1511 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 5.0000e-04
 ## Epoch 148/500
-## 90/90 - 0s - 1ms/step - loss: 0.0762 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.1455 - val_sparse_categorical_accuracy: 0.9417 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0801 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.1219 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 5.0000e-04
 ## Epoch 149/500
-## 90/90 - 0s - 1ms/step - loss: 0.0657 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1037 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 2ms/step - loss: 0.0725 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04
 ## Epoch 150/500
-## 90/90 - 0s - 1ms/step - loss: 0.0686 - sparse_categorical_accuracy: 0.9753 - val_loss: 0.1306 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0738 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.2021 - val_sparse_categorical_accuracy: 0.9334 - learning_rate: 5.0000e-04
 ## Epoch 151/500
-## 90/90 - 0s - 1ms/step - loss: 0.0667 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1227 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0707 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.1358 - val_sparse_categorical_accuracy: 0.9376 - learning_rate: 5.0000e-04
 ## Epoch 152/500
-## 90/90 - 0s - 1ms/step - loss: 0.0677 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.1041 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0727 - sparse_categorical_accuracy: 0.9740 - val_loss: 0.1989 - val_sparse_categorical_accuracy: 0.9098 - learning_rate: 5.0000e-04
 ## Epoch 153/500
-## 90/90 - 0s - 1ms/step - loss: 0.0638 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.1837 - val_sparse_categorical_accuracy: 0.9307 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0671 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.1250 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04
 ## Epoch 154/500
-## 90/90 - 0s - 1ms/step - loss: 0.0608 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1237 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0687 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1973 - val_sparse_categorical_accuracy: 0.9237 - learning_rate: 5.0000e-04
 ## Epoch 155/500
-## 90/90 - 0s - 1ms/step - loss: 0.0662 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1192 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0697 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1190 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 5.0000e-04
 ## Epoch 156/500
-## 90/90 - 0s - 1ms/step - loss: 0.0672 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1262 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0731 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.1409 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 5.0000e-04
 ## Epoch 157/500
-## 90/90 - 0s - 1ms/step - loss: 0.0634 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1035 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0719 - sparse_categorical_accuracy: 0.9740 - val_loss: 0.4024 - val_sparse_categorical_accuracy: 0.8655 - learning_rate: 5.0000e-04
 ## Epoch 158/500
-## 90/90 - 0s - 1ms/step - loss: 0.0657 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.2735 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0722 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.3254 - val_sparse_categorical_accuracy: 0.8821 - learning_rate: 5.0000e-04
 ## Epoch 159/500
-## 90/90 - 0s - 1ms/step - loss: 0.0685 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.2094 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0706 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.2014 - val_sparse_categorical_accuracy: 0.9265 - learning_rate: 5.0000e-04
 ## Epoch 160/500
-## 90/90 - 0s - 1ms/step - loss: 0.0712 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.1137 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0707 - sparse_categorical_accuracy: 0.9753 - val_loss: 0.1128 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 5.0000e-04
 ## Epoch 161/500
-## 90/90 - 0s - 1ms/step - loss: 0.0675 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1544 - val_sparse_categorical_accuracy: 0.9320 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0727 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.1118 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 5.0000e-04
 ## Epoch 162/500
-## 90/90 - 0s - 1ms/step - loss: 0.0610 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.1070 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0660 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.2972 - val_sparse_categorical_accuracy: 0.8960 - learning_rate: 5.0000e-04
 ## Epoch 163/500
-## 90/90 - 0s - 1ms/step - loss: 0.0572 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1382 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0619 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.1079 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 5.0000e-04
 ## Epoch 164/500
-## 90/90 - 0s - 1ms/step - loss: 0.0617 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.1737 - val_sparse_categorical_accuracy: 0.9265 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0670 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.2169 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 5.0000e-04
 ## Epoch 165/500
-## 90/90 - 0s - 1ms/step - loss: 0.0660 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.1737 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0690 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.2980 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 5.0000e-04
 ## Epoch 166/500
-## 90/90 - 0s - 1ms/step - loss: 0.0632 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1408 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0650 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.1257 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 5.0000e-04
 ## Epoch 167/500
-## 90/90 - 0s - 1ms/step - loss: 0.0620 - sparse_categorical_accuracy: 0.9788 - val_loss: 0.2021 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 5.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0656 - sparse_categorical_accuracy: 0.9788 - val_loss: 0.1451 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 5.0000e-04
 ## Epoch 168/500
-## 90/90 - 0s - 1ms/step - loss: 0.0669 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1003 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0750 - sparse_categorical_accuracy: 0.9736 - val_loss: 0.3402 - val_sparse_categorical_accuracy: 0.8350 - learning_rate: 5.0000e-04
 ## Epoch 169/500
-## 90/90 - 0s - 2ms/step - loss: 0.0578 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0664 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.1295 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 5.0000e-04
 ## Epoch 170/500
-## 90/90 - 0s - 1ms/step - loss: 0.0562 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0970 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 2ms/step - loss: 0.0635 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04
 ## Epoch 171/500
-## 90/90 - 0s - 1ms/step - loss: 0.0535 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1347 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0593 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1230 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 2.5000e-04
 ## Epoch 172/500
-## 90/90 - 0s - 1ms/step - loss: 0.0558 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.0975 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0591 - sparse_categorical_accuracy: 0.9799 - val_loss: 0.1079 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04
 ## Epoch 173/500
-## 90/90 - 0s - 1ms/step - loss: 0.0536 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1328 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0593 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1073 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04
 ## Epoch 174/500
-## 90/90 - 0s - 1ms/step - loss: 0.0550 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1243 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 2ms/step - loss: 0.0605 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 2.5000e-04
 ## Epoch 175/500
-## 90/90 - 0s - 1ms/step - loss: 0.0583 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.1018 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0616 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.2084 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 2.5000e-04
 ## Epoch 176/500
-## 90/90 - 0s - 1ms/step - loss: 0.0548 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.1016 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0584 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.0993 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04
 ## Epoch 177/500
-## 90/90 - 0s - 1ms/step - loss: 0.0541 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1646 - val_sparse_categorical_accuracy: 0.9320 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0582 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1498 - val_sparse_categorical_accuracy: 0.9487 - learning_rate: 2.5000e-04
 ## Epoch 178/500
-## 90/90 - 0s - 1ms/step - loss: 0.0522 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1151 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0566 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1165 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 2.5000e-04
 ## Epoch 179/500
-## 90/90 - 0s - 1ms/step - loss: 0.0538 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1049 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 2ms/step - loss: 0.0566 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1714 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 2.5000e-04
 ## Epoch 180/500
-## 90/90 - 0s - 1ms/step - loss: 0.0531 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1178 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 2ms/step - loss: 0.0563 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1042 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04
 ## Epoch 181/500
-## 90/90 - 0s - 1ms/step - loss: 0.0562 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1024 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 2ms/step - loss: 0.0609 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.2017 - val_sparse_categorical_accuracy: 0.9209 - learning_rate: 2.5000e-04
 ## Epoch 182/500
-## 90/90 - 0s - 1ms/step - loss: 0.0525 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1600 - val_sparse_categorical_accuracy: 0.9376 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0559 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1059 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 2.5000e-04
 ## Epoch 183/500
-## 90/90 - 0s - 1ms/step - loss: 0.0554 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1395 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0597 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.1279 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 2.5000e-04
 ## Epoch 184/500
-## 90/90 - 0s - 1ms/step - loss: 0.0514 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1340 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04
 ## Epoch 185/500
-## 90/90 - 0s - 1ms/step - loss: 0.0531 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1383 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0570 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.1022 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04
 ## Epoch 186/500
-## 90/90 - 0s - 1ms/step - loss: 0.0574 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1325 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0633 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04
 ## Epoch 187/500
-## 90/90 - 0s - 1ms/step - loss: 0.0532 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1442 - val_sparse_categorical_accuracy: 0.9487 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 2ms/step - loss: 0.0576 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0969 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04
 ## Epoch 188/500
-## 90/90 - 0s - 1ms/step - loss: 0.0533 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1016 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0567 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1043 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04
 ## Epoch 189/500
-## 90/90 - 0s - 1ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1639 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0546 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1734 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 2.5000e-04
 ## Epoch 190/500
-## 90/90 - 0s - 1ms/step - loss: 0.0583 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.3468 - val_sparse_categorical_accuracy: 0.8863 - learning_rate: 2.5000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0639 - sparse_categorical_accuracy: 0.9799 - val_loss: 0.2847 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 2.5000e-04
 ## Epoch 191/500
-## 90/90 - 0s - 1ms/step - loss: 0.0496 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1010 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0551 - sparse_categorical_accuracy: 0.9799 - val_loss: 0.4059 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 2.5000e-04
 ## Epoch 192/500
-## 90/90 - 0s - 1ms/step - loss: 0.0536 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1100 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0613 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.3821 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 2.5000e-04
 ## Epoch 193/500
-## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1388 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0559 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04
 ## Epoch 194/500
-## 90/90 - 0s - 1ms/step - loss: 0.0490 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0983 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1008 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04
 ## Epoch 195/500
-## 90/90 - 0s - 1ms/step - loss: 0.0499 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1001 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0557 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0996 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 2.5000e-04
 ## Epoch 196/500
-## 90/90 - 0s - 1ms/step - loss: 0.0517 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1001 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0581 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.1143 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 2.5000e-04
 ## Epoch 197/500
-## 90/90 - 0s - 1ms/step - loss: 0.0489 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0544 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1027 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 2.5000e-04
 ## Epoch 198/500
-## 90/90 - 0s - 1ms/step - loss: 0.0472 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1672 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0533 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1310 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04
 ## Epoch 199/500
-## 90/90 - 0s - 1ms/step - loss: 0.0508 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0990 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0578 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.1236 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 2.5000e-04
 ## Epoch 200/500
-## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1290 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0582 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 2.5000e-04
 ## Epoch 201/500
-## 90/90 - 0s - 1ms/step - loss: 0.0480 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1390 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0551 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1327 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04
 ## Epoch 202/500
-## 90/90 - 0s - 1ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1194 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0543 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.2115 - val_sparse_categorical_accuracy: 0.9140 - learning_rate: 2.5000e-04
 ## Epoch 203/500
-## 90/90 - 0s - 1ms/step - loss: 0.0508 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.2015 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 2.5000e-04
 ## Epoch 204/500
-## 90/90 - 0s - 1ms/step - loss: 0.0449 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1227 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0508 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04
 ## Epoch 205/500
-## 90/90 - 0s - 1ms/step - loss: 0.0487 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1194 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0542 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0996 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04
 ## Epoch 206/500
-## 90/90 - 0s - 1ms/step - loss: 0.0491 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1540 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0559 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1172 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 2.5000e-04
 ## Epoch 207/500
-## 90/90 - 0s - 1ms/step - loss: 0.0529 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1023 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 2ms/step - loss: 0.0570 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 2.5000e-04
 ## Epoch 208/500
-## 90/90 - 0s - 1ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1295 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 2ms/step - loss: 0.0521 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1000 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04
 ## Epoch 209/500
-## 90/90 - 0s - 1ms/step - loss: 0.0455 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1169 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1286 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04
 ## Epoch 210/500
-## 90/90 - 0s - 1ms/step - loss: 0.0489 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0541 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04
 ## Epoch 211/500
-## 90/90 - 0s - 1ms/step - loss: 0.0475 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1001 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0509 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0993 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04
 ## Epoch 212/500
-## 90/90 - 0s - 1ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.0986 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0503 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04
 ## Epoch 213/500
-## 90/90 - 0s - 1ms/step - loss: 0.0483 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0997 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04
+## 90/90 - 0s - 2ms/step - loss: 0.0534 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0968 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04
 ## Epoch 214/500
-## 90/90 - 0s - 1ms/step - loss: 0.0490 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1029 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0532 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04
 ## Epoch 215/500
-## 90/90 - 0s - 1ms/step - loss: 0.0464 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1445 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 1.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1013 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04
 ## Epoch 216/500
-## 90/90 - 0s - 1ms/step - loss: 0.0456 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1137 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04
+## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1244 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04
 ## Epoch 217/500
-## 90/90 - 0s - 1ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1345 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04
+## 90/90 - 0s - 2ms/step - loss: 0.0495 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.1141 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.2500e-04
 ## Epoch 218/500
-## 90/90 - 0s - 1ms/step - loss: 0.0501 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04
+## 90/90 - 0s - 2ms/step - loss: 0.0547 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04
 ## Epoch 219/500
-## 90/90 - 0s - 1ms/step - loss: 0.0445 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1149 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04
+## 90/90 - 0s - 2ms/step - loss: 0.0480 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1387 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.2500e-04
 ## Epoch 220/500
-## 90/90 - 0s - 1ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1003 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04
-## Epoch 220: early stopping
+## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1161 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04
+## Epoch 221/500
+## 90/90 - 0s - 1ms/step - loss: 0.0516 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.1338 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04
+## Epoch 222/500
+## 90/90 - 0s - 1ms/step - loss: 0.0525 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.0995 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04
+## Epoch 223/500
+## 90/90 - 0s - 1ms/step - loss: 0.0504 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04
+## Epoch 224/500
+## 90/90 - 0s - 1ms/step - loss: 0.0471 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0994 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04
+## Epoch 225/500
+## 90/90 - 0s - 1ms/step - loss: 0.0536 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1145 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04
+## Epoch 226/500
+## 90/90 - 0s - 1ms/step - loss: 0.0487 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1047 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.2500e-04
+## Epoch 227/500
+## 90/90 - 0s - 1ms/step - loss: 0.0529 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.1087 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04
+## Epoch 228/500
+## 90/90 - 0s - 1ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04
+## Epoch 229/500
+## 90/90 - 0s - 1ms/step - loss: 0.0499 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1064 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04
+## Epoch 230/500
+## 90/90 - 0s - 2ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0967 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04
+## Epoch 231/500
+## 90/90 - 0s - 1ms/step - loss: 0.0527 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1730 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 1.2500e-04
+## Epoch 232/500
+## 90/90 - 0s - 1ms/step - loss: 0.0496 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1109 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.2500e-04
+## Epoch 233/500
+## 90/90 - 0s - 2ms/step - loss: 0.0483 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0963 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04
+## Epoch 234/500
+## 90/90 - 0s - 1ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1158 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04
+## Epoch 235/500
+## 90/90 - 0s - 1ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1057 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04
+## Epoch 236/500
+## 90/90 - 0s - 1ms/step - loss: 0.0502 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0982 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04
+## Epoch 237/500
+## 90/90 - 0s - 1ms/step - loss: 0.0501 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04
+## Epoch 238/500
+## 90/90 - 0s - 1ms/step - loss: 0.0487 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1031 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.2500e-04
+## Epoch 239/500
+## 90/90 - 0s - 1ms/step - loss: 0.0491 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1139 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 1.2500e-04
+## Epoch 240/500
+## 90/90 - 0s - 2ms/step - loss: 0.0478 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0990 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04
+## Epoch 241/500
+## 90/90 - 0s - 2ms/step - loss: 0.0498 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0994 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04
+## Epoch 242/500
+## 90/90 - 0s - 2ms/step - loss: 0.0490 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1118 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04
+## Epoch 243/500
+## 90/90 - 0s - 2ms/step - loss: 0.0523 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1052 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04
+## Epoch 244/500
+## 90/90 - 0s - 2ms/step - loss: 0.0527 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1055 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04
+## Epoch 245/500
+## 90/90 - 0s - 2ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1045 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04
+## Epoch 246/500
+## 90/90 - 0s - 1ms/step - loss: 0.0518 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1214 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 1.2500e-04
+## Epoch 247/500
+## 90/90 - 0s - 1ms/step - loss: 0.0457 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0964 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04
+## Epoch 248/500
+## 90/90 - 0s - 1ms/step - loss: 0.0450 - sparse_categorical_accuracy: 0.9885 - val_loss: 0.1161 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04
+## Epoch 249/500
+## 90/90 - 0s - 1ms/step - loss: 0.0492 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1282 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04
+## Epoch 250/500
+## 90/90 - 0s - 2ms/step - loss: 0.0510 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0972 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04
+## Epoch 251/500
+## 90/90 - 0s - 1ms/step - loss: 0.0506 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0986 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04
+## Epoch 252/500
+## 90/90 - 0s - 2ms/step - loss: 0.0503 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.1075 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04
+## Epoch 253/500
+## 90/90 - 0s - 2ms/step - loss: 0.0507 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0999 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04
+## Epoch 254/500
+## 90/90 - 0s - 2ms/step - loss: 0.0478 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0971 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.0000e-04
+## Epoch 255/500
+## 90/90 - 0s - 2ms/step - loss: 0.0464 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0954 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04
+## Epoch 256/500
+## 90/90 - 0s - 2ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0954 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04
+## Epoch 257/500
+## 90/90 - 0s - 1ms/step - loss: 0.0472 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1047 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.0000e-04
+## Epoch 258/500
+## 90/90 - 0s - 1ms/step - loss: 0.0448 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04
+## Epoch 259/500
+## 90/90 - 0s - 1ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1245 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04
+## Epoch 260/500
+## 90/90 - 0s - 1ms/step - loss: 0.0454 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1113 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04
+## Epoch 261/500
+## 90/90 - 0s - 1ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1697 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 1.0000e-04
+## Epoch 262/500
+## 90/90 - 0s - 1ms/step - loss: 0.0450 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04
+## Epoch 263/500
+## 90/90 - 0s - 1ms/step - loss: 0.0439 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0975 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04
+## Epoch 264/500
+## 90/90 - 0s - 1ms/step - loss: 0.0498 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1111 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04
+## Epoch 265/500
+## 90/90 - 0s - 1ms/step - loss: 0.0523 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.0983 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04
+## Epoch 266/500
+## 90/90 - 0s - 1ms/step - loss: 0.0459 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0997 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04
+## Epoch 267/500
+## 90/90 - 0s - 1ms/step - loss: 0.0462 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1002 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04
+## Epoch 268/500
+## 90/90 - 0s - 1ms/step - loss: 0.0438 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.1058 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04
+## Epoch 269/500
+## 90/90 - 0s - 2ms/step - loss: 0.0491 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1289 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.0000e-04
+## Epoch 270/500
+## 90/90 - 0s - 2ms/step - loss: 0.0430 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.0000e-04
+## Epoch 271/500
+## 90/90 - 0s - 1ms/step - loss: 0.0454 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1096 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04
+## Epoch 272/500
+## 90/90 - 0s - 1ms/step - loss: 0.0455 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1262 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04
+## Epoch 273/500
+## 90/90 - 0s - 1ms/step - loss: 0.0469 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1129 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04
+## Epoch 274/500
+## 90/90 - 0s - 1ms/step - loss: 0.0429 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.1119 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04
+## Epoch 275/500
+## 90/90 - 0s - 1ms/step - loss: 0.0430 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1392 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04
+## Epoch 276/500
+## 90/90 - 0s - 1ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04
+## Epoch 277/500
+## 90/90 - 0s - 1ms/step - loss: 0.0457 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04
+## Epoch 278/500
+## 90/90 - 0s - 1ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1205 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04
+## Epoch 279/500
+## 90/90 - 0s - 1ms/step - loss: 0.0473 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0958 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04
+## Epoch 280/500
+## 90/90 - 0s - 2ms/step - loss: 0.0459 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0947 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04
+## Epoch 281/500
+## 90/90 - 0s - 1ms/step - loss: 0.0438 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1216 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04
+## Epoch 282/500
+## 90/90 - 0s - 1ms/step - loss: 0.0419 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.1008 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04
+## Epoch 283/500
+## 90/90 - 0s - 1ms/step - loss: 0.0481 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1129 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04
+## Epoch 284/500
+## 90/90 - 0s - 1ms/step - loss: 0.0480 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.1418 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04
+## Epoch 285/500
+## 90/90 - 0s - 1ms/step - loss: 0.0467 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1058 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04
+## Epoch 286/500
+## 90/90 - 0s - 1ms/step - loss: 0.0449 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1039 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 1.0000e-04
+## Epoch 287/500
+## 90/90 - 0s - 1ms/step - loss: 0.0450 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1015 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04
+## Epoch 288/500
+## 90/90 - 0s - 2ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1156 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04
+## Epoch 289/500
+## 90/90 - 0s - 1ms/step - loss: 0.0460 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1000 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04
+## Epoch 290/500
+## 90/90 - 0s - 1ms/step - loss: 0.0482 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0967 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04
+## Epoch 291/500
+## 90/90 - 0s - 1ms/step - loss: 0.0433 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.1002 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04
+## Epoch 292/500
+## 90/90 - 0s - 1ms/step - loss: 0.0506 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1102 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04
+## Epoch 293/500
+## 90/90 - 0s - 1ms/step - loss: 0.0473 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1139 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04
+## Epoch 294/500
+## 90/90 - 0s - 1ms/step - loss: 0.0459 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1005 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04
+## Epoch 295/500
+## 90/90 - 0s - 1ms/step - loss: 0.0502 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.0978 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04
+## Epoch 296/500
+## 90/90 - 0s - 1ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1006 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04
+## Epoch 297/500
+## 90/90 - 0s - 1ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0978 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04
+## Epoch 298/500
+## 90/90 - 0s - 1ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0953 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04
+## Epoch 299/500
+## 90/90 - 0s - 1ms/step - loss: 0.0410 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.1107 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04
+## Epoch 300/500
+## 90/90 - 0s - 1ms/step - loss: 0.0423 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.0000e-04
+## Epoch 301/500
+## 90/90 - 0s - 1ms/step - loss: 0.0457 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0983 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04
+## Epoch 302/500
+## 90/90 - 0s - 1ms/step - loss: 0.0422 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1233 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04
+## Epoch 303/500
+## 90/90 - 0s - 2ms/step - loss: 0.0417 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.1175 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04
+## Epoch 304/500
+## 90/90 - 0s - 2ms/step - loss: 0.0467 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1007 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 1.0000e-04
+## Epoch 305/500
+## 90/90 - 0s - 2ms/step - loss: 0.0466 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1058 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04
+## Epoch 306/500
+## 90/90 - 0s - 1ms/step - loss: 0.0402 - sparse_categorical_accuracy: 0.9899 - val_loss: 0.0999 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04
+## Epoch 307/500
+## 90/90 - 0s - 2ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1130 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 1.0000e-04
+## Epoch 308/500
+## 90/90 - 0s - 1ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04
+## Epoch 309/500
+## 90/90 - 0s - 1ms/step - loss: 0.0472 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1179 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04
+## Epoch 310/500
+## 90/90 - 0s - 1ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04
+## Epoch 311/500
+## 90/90 - 0s - 1ms/step - loss: 0.0448 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1012 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04
+## Epoch 312/500
+## 90/90 - 0s - 1ms/step - loss: 0.0442 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1172 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04
+## Epoch 313/500
+## 90/90 - 0s - 2ms/step - loss: 0.0438 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0990 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04
+## Epoch 314/500
+## 90/90 - 0s - 2ms/step - loss: 0.0432 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04
+## Epoch 315/500
+## 90/90 - 0s - 1ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0980 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04
+## Epoch 316/500
+## 90/90 - 0s - 1ms/step - loss: 0.0429 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1030 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04
+## Epoch 317/500
+## 90/90 - 0s - 3ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1018 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04
+## Epoch 318/500
+## 90/90 - 0s - 2ms/step - loss: 0.0423 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1024 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 1.0000e-04
+## Epoch 319/500
+## 90/90 - 0s - 2ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1197 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04
+## Epoch 320/500
+## 90/90 - 0s - 2ms/step - loss: 0.0405 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0994 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.0000e-04
+## Epoch 321/500
+## 90/90 - 0s - 2ms/step - loss: 0.0442 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.1499 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 1.0000e-04
+## Epoch 322/500
+## 90/90 - 0s - 2ms/step - loss: 0.0404 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1253 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04
+## Epoch 323/500
+## 90/90 - 0s - 1ms/step - loss: 0.0468 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1081 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04
+## Epoch 324/500
+## 90/90 - 0s - 1ms/step - loss: 0.0418 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1016 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04
+## Epoch 325/500
+## 90/90 - 0s - 1ms/step - loss: 0.0420 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1290 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 1.0000e-04
+## Epoch 326/500
+## 90/90 - 0s - 2ms/step - loss: 0.0436 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1270 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 1.0000e-04
+## Epoch 327/500
+## 90/90 - 0s - 1ms/step - loss: 0.0417 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1009 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04
+## Epoch 328/500
+## 90/90 - 0s - 2ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1141 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04
+## Epoch 329/500
+## 90/90 - 0s - 2ms/step - loss: 0.0422 - sparse_categorical_accuracy: 0.9885 - val_loss: 0.1198 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04
+## Epoch 330/500
+## 90/90 - 0s - 3ms/step - loss: 0.0435 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0988 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04
+## Epoch 330: early stopping
 ```
 
 
 ## Evaluate model on test data
 
 
-```r
+``` r
 model <- load_model("best_model.keras")
 
 results <- model |> evaluate(x_test, y_test)
 ```
 
 ```
-## 42/42 - 0s - 10ms/step - loss: 0.0917 - sparse_categorical_accuracy: 0.9712
+## 42/42 - 1s - 15ms/step - loss: 0.0980 - sparse_categorical_accuracy: 0.9682
 ```
 
-```r
+``` r
 str(results)
 ```
 
 ```
 ## List of 2
-##  $ loss                       : num 0.0917
-##  $ sparse_categorical_accuracy: num 0.971
+##  $ loss                       : num 0.098
+##  $ sparse_categorical_accuracy: num 0.968
 ```
 
-```r
+``` r
 cat(
   "Test accuracy: ", results$sparse_categorical_accuracy, "\n",
   "Test loss: ", results$loss, "\n",
@@ -767,15 +987,15 @@ cat(
 ```
 
 ```
-## Test accuracy: 0.9712121
-## Test loss: 0.09173314
+## Test accuracy: 0.9681818
+## Test loss: 0.09795157
 ```
 
 
 ## Plot the model's training history
 
 
-```r
+``` r
 plot(history)
 ```
 
@@ -783,7 +1003,7 @@ plot(history)
 
 Plot just the training and validation accuracy:
 
-```r
+``` r
 plot(history, metric = "sparse_categorical_accuracy") +
   # scale x axis to actual number of epochs run before early stopping
   ggplot2::xlim(0, length(history$metrics$loss))
diff --git a/vignettes/examples/timeseries/timeseries_classification_from_scratch/unnamed-chunk-12-1.png b/vignettes/examples/timeseries/timeseries_classification_from_scratch/unnamed-chunk-12-1.png
index 42a808b283089d92bfc1c96ff707dfbe77c5c4dc..3337ad475e71c6b075b0da602b91a9e9a58b3483 100644
GIT binary patch
literal 66684
zcmc%xg<F)}7dMU$-JQaaQqls_AxKL%NOyO)q|%6#l$3}xLrP0a2}nzKch}iG-}gP&
zIln*PoVhNTVVHaFd-mRItxv2yA61lOu+YiSArJ_boUEi81OopD0)e|ig$K{<(JEqr
zJ2YomT{j2>1OMp<ZfrjA4g#Tu$VrN8cxUd-d-<zR+@c(fP|VExrPh6}<_ihlqj~cT
zKC0Rr{ln8^i+;)|E=2Lyym@<A7QYv23fXE3{pcOW2K|D95U)?(yk;s^Joah33jM~n
z8TcuaPWiBLQ(u};3ReP^CXA4=t^ncb(b|v<5(-Tiu0#|C5ft2^zVOs=e)>nj^f5g}
zJ93R)nc{4sES)86Msy@^oqyQ0>a%;4m9cJ$g)VGHe@s04y@jqS;r=O7;jk7WwD%>Z
zSx#2$D!(}H-YHVDd?7<pzbI_R%?iP|#GZA4nGijLNApJu8j_5ONiKNff%Wk4aPS(2
zvY8b5!s?068n$!=@b?~X#*BU4NIZAAMDB#`K6l2vCvUh!$7%DtB{wN%2=!<81S=Kh
z<m>C}i}WE!p$xTRbJ8BI8UDEZ5Coorr%#v=1o!{^K&l<$@;RBAneP!$eCvYRf*v1q
zb#>_}B5@`9#QX@9$mkzm!K_9WW`0u8{yvO~cS{ou7|oNS&-(E60wxM18Ar#1U*Q<^
z_#flrl^-AOpGCabmMoy7XJpK`LWG1x6h3}29CHEZzJrauEO0$q>X<Bhr~HNI1s@+D
zJ9~M-Y68AiTVR0wd`z?O-n3ScQr4%qH(&~KlH;C3%k*m1iZyI&^-3mwHZ{F-YIa$b
zHZh?YIW;jc;hn+g<P>%)Qbrx9#{XDQpe~Fsa*Ek`H!#zBwStlAu{-Q!Xw>AnFB)(o
ze5eU4C+M!e(9wJ+lo4PFn;W(a5Z~v`kff~|sbw$C8WJUWsNNh#Zr7UGJI#7HG~0ub
zJY;nke~5`$^9lUd;KG%T*?x7rn)K-tl!79aRo67hVER<=J?p^izSXmV>WjwUWRI;O
zQ6rV?O*Z1_|IW};&DzO%F*CDwA&1a@+8n?>f-V%eP)Z+CR*e=yoh<J#rctaVh#ps3
z%If9i6=A~bFuS(7IrUvNzbMcysiooPPhvvC>3#Y9I=znhx>A-{Tb?hUuum{An#aCp
zWMt@-)s5JD&dq>-Y;10(f~l|EOqQOXpLcU}``^;o1&99bamg$xDN+2wJR#rflD$7y
zzu4?U5Nudc#4U{m)`MEFlCrM1d?YpnF^3^*qAd7yeiwVX|4kU~iLJa$$MWhbx7}pv
zCnfQx^+U@{@rL=oh?w}kL=7IuR^^9;K7~ei96I*X5+f91<_CAUYJ2}bc)`^tY-0rc
zk6jG=oS2=bo;P8Ytac@lHt&Q-xG~M@XCRfOB3cuZ!<IQSdv!1us8#(_6lQNqsj_ZU
zj~MUUgE+|+BDcR>H_bUb3!)Oi_+PMl-k{vGyi8Z%wUHr|g3Z{MOxnL?PM)>mOtBD%
zgU#84xPOXxFyx-Rw}Y1TSwp`3LyoZYAxm*wdBHwzo&U4Q7!qaer)Nrg-=b11|Nnom
z{_lLOz7=eLJG$*2O~S!T7CSsV9EyUI(r^A>NTbqJe07H|^Sf`UO#IL+Qb=Ff-=`oV
z()@ofh+e$7ll1{VA0IepXXgZ2y8ki-I4S~9s;YpyxfHzV!E}|`Sb-dRIDGqHI(Jf1
z5(N}$IhbaWbm5a-ufuwA9p((Rs?w1c?B=dy9pH?aw<JK!1>p;#@ny!)-x)(RkGedN
zT^W*!pPmItE^ZYv&78p$rB?ixK{463a1|9LShGZnh>%brlYdmt0b)H_sw*!3UW0jJ
z_ndboT)vQtEVldat%u<J_337Szdy6aca^ZF0dua<4&zge3LB88<}HITQf{j|<`NSV
z=_xjCghwz!jB|_-gh$9Rk-?<qWMKtsFk9$OY8`sbefnR{W|93UY062u_h07SO%DO#
z#feD-Wnx3>-5xIzh$9#Eze?hpEYTjh$pK-Vu_slm@kQa=%9T1b3rmT_WxQ$|&8MVR
zb|c5Lt<j3vkn`LAjSj=ee)An(JKha;#9lVT%hNf(OJ1ji=7KjOL-xFLn~Tcbdh@Le
zLVNaFAlta{=8szE5EX=mhR%aDM)G-;Kf=U5dC0dp>~mbmYELZr=;$Z`g97BwH^qzy
zEb<ihJZ+f69IyM~`}!UDG;Ytcqvg)UU~bjFwy*sCO#+@Lvs^B9gnm+zM5XBpOa^ZO
z8xdvMMy&`pAw><kXJbLf?EGvEojW=@vNqv@wmMTDP+mF6zz$PV+-X6$UzE5LLJJM1
z$_;SN4ylMpBG(G!%HAE{k)PB&UaiKr-JL`W(igAR!v;RTQ2%ccO;_-(WsD3rHa0rU
z)@=VyW-&B0q@kgqr|7lJ%*v99gT6@W=OI&+vjRE9bn5%kRjO>^*iVn$6nAuRz5?^l
zammthiR+u2n`>*I|9kGy!#!+OvCjK=WxH4DHT&-Nrclts9p9?CCODuFk7C)|%);DU
zK`elH+{$F-uaWn5v3ilbARb6(kL7uU(2GV>3ZE$H{P#__`BcKf6Sf{7d%($0axSWo
zdPnnR+8%D~R0-$m9Cr8ink@!WOk<@CjjXM$SrjQR4tkEw`28zY^QHe+5rd`%h@tA=
z6l~_|_7k7We3oAaDL3e9Ir8Zss)1^dNv~;myx+r^JqIiq8H@fK__elO3uUsQ;bF(I
zf_{+y1>LunHaAH~NVH3<J}82Z&u;MZe;;+NFy8quxO%U{g$zC?M{pAG7Xku8^ef^g
zxUxXU@y?l5x4OWv#rO7N-_6U*igaVjD2Q!R=P9xIBsK3PM8ROEV8+WHa|At@%eG!W
zW%X1Un8yEdwsi{n3*x81dk$V-^&GsNj#csAVq1=7aRNz`+on<~yTRSh!0CJ$oS!im
z;G8U93MK6ALHIFB`^Rp>u-&hWk5{_?%L*S1zUne}^<1p~Sl+{gbaWIK7yqB<y~A~x
z!JO|-RgfU&+Khh_7Z>j)+f8NyK&YmZGe&`7)yJ<ngni_+&Nr>D`&78sMcI;ks^+*9
zt8J<5=X7Jsy9!Er(u_@?>`f$breX+3J7<hOd;Nd)kPaVhpxQGd)`ax7dH8cJY{rV$
z@4&utJ2hHy_Qk&8#lU%nQ|nA?fFD2_{Bh*mmV@CKBn-;g`lbDbhW{6TA%}K|j(D2u
zUKB_A9Q<qSjKqly3v=I{9J*Ox!sAMhF-c>D#ge|H-Md_LT5O%M{u7LhMK--N@j{Om
zlnapCnA<VHr)e>C%9(jsnAt-aIrYpKGUlANLMql^KKE_;A41?9RL`-m2wmkbRI^F?
zCSRM%)<2NK=68SNv8(=VeB1W#cRUITgTWa?EiJ7S#aR~*4>l&I%8`&a;Pth$y<*Z*
zQXUV1&qNPC&eh*+SO(ng*V7X{Aw_epSZT8o<imv)J07wUjpCD=AcASPj^&MwZ(1e$
z+;i$#LxJG>vNumpPv3%q1s*rC@&Eh(01t&~{hTpn)h^J@ogLe!Yttqb^}a)TEfXpK
zZ=uC+)Ct!=0FSKBcW(=yh0oq$tuJvG-96#=EXjB6a($be&UawJMJ-u~{qZ~7Qf&*o
zFE?n4%4-O?y*QX}BC%sVF>P&amHiM}!QuYXlGzUm#}*P67UtnY7DI|;eA#neq@3gU
zui+S@PR!?It<i1shi1tnNCH7D&!0eu?@Wl<&fj8>-ANJ8J<?#1d+QzM>iE?YgE)=b
zgLgY4_GeFJ2#U@3t^i!`<sG!-zG_pFPLwtDNAw199C}IdI-_pdYbm3lf#yxb9XTF=
zLWC1yBv6AU&w!m2a3!zoChg~c)_T(>27Oau(gHYC0$1n4f{}Nwt=eKBupM=7>JG87
zS<2r&6wS=2@|Y^<?mb?9$6J+F)4wsrPh{ak*6rqh+4PpgHG$SU=?4}BfYYN+oPgmR
zK2l69GKIinH0Z&>f%g?|heblto1!2Bi4XVSpnRXxjYkP=2?!^qdWB(2>WnL>7C4bO
zVz$*hmM<MI_ExZ^0YH8=$_i0qzP`C}Txiy<m>O6=`7{g?-dawJYVTUVnP=^TWME(*
zsB|K8ZI`1mRNY-&)0GxKctH}a`5Ll{Rb$)%zj!HhQxo*qblA$W%?df1_gdVy{Ywbj
zq<D6-ezraSRuCHuH~wp+-V6poLEp)txioGnHxhrhgOS_I!&G-#I(mAVW+<fL=l(!H
z%Qiolb9o36)alFFnL8q~=nV}g=kw?JlB<x{VAPMt<>?sBriN*1Z9lt1!IjbnE0G0^
z|NWtfF>XaoM@YP!wIww1<KAy-dVKdBB+AC1$H3E#p?9^m=T{dO`*Tj8`{O8t)T&Io
z<3(?_j0ONp=n7QnIR2(utl`D$I9+2yFi=fZrbnOD|4?fXPtNBUV9l>r|L%TZHWqUn
zOj~c8?M(H0k{G@O>UBjf7=pFs<>TwkoW}tnfE#wrR{tDwL$+pams-WX{{ndJgfy#D
z+};d~eqVot9u}nD31orK|J6OuusG*wlKjyIu<p*z&cM}0Mn<N(XKGh&2h#Q^c9m`$
zr=9o++{Z%!8v?_!2M}N-M`r}qEf;;jH|5v&95P)!Jt^P-6W-1_6xe2-)Bp&pw}oS$
zu%AkC+~;p0&qYNS0<}h1v3*+tWwfvt9HN;uNAstiUM4uo!q4yKJMIJHv|N+9%=0_n
z`5#1^P6Z^3Hp<lAelJfdnwV9m-9=xn_XRuPFV|$K@H)i(atq=Q^jTX(f4{4mfn-CU
z74o!JnJJJW4J+DQR(sSV`Bqj}v5*mZT|i~7o5kyO$zz)RQA<0`X}iXlAJN%SRT4iH
zncwE?|IstW!pOhz;?pk|XeXy<h9aR<gbBdEuw!A6_W<IqVzmR<_@)3YL<Ml-j+=rR
ztGY*7E3=s~o-`Hzv`_be0rW`>ieETQey#3$1YEDDULG!bd3qYcEMpaCH~A01xC(h4
zoLyc%Zso_*;eS;85}+AEO^S&eE^)jT3-Hk9CQ>JW{?iV;kc*klCvbi&zGE-dUR}x*
z1(0Mi0FQPb0m}yD_%BJ6K~?YA2|b%(^Ys7l8Bf}mFM73$kh87ucV3t02lw}vi*0KZ
z05*Q2f18(|50=osCa=T0ySuHet)nAn5|UKf$kE+%<%jQT#Wi{kiSJlMxt^%G&Htzw
zSNdRi`IlQdKy5+({ci|f{C{UzfIo8<vq03_*3JS6z`;tLIT>6&GkLE|d3ian%@2+I
zRpCdN-4y%TPUw$`KL;&r#WvFU$MlE%AQ4_4EHp(1yt?r`E7gVl`WZ0oxf^#B@0Tgy
z_Du(Y;0f(Wy%7Cn;<m7`AS)}m&WwvlNZ5RbS6S<2_QwhW@&9A7Yjs~`j|9#7I`!CK
z9(aFq9{utqsN!n!PwS0~t6FCc)QsE7;Vhgi0j$`K)C%e5JG20bfR>=Q@{-25?04Hs
zOAteNh_F>oFXt113^KA|=8w`eG+Y3n0;lQc^A|S#MJ<Z~m-DG@hc!0i)5Ypu1qJ67
z`c)G(HuK*U#XG&64-`g5kco_p!37r(f;db(-<_gzJZ9gS3}p&@Y3qG?FmLn#>O<>5
zDu>a-RJ`^;a#P^F?|Qr#CMIS~T%4%qo({N06SM8PVXtA7>Qt=&%7GqkI+*qn4-fA@
zkRkg*8mad=&{2*~5FionC7Dqr60MYO|GObHcpsZ)*d%WKBZ`a*bUFA}AeUrMO4^Pi
zcISL|<-);HQDxSvcWM0wpwV>aRpK6Hal>-oyDI?Y=IR2#RX@JqdAptnN}B0}D^xIe
zT?{j*=^(l!wyUcv_f6!*-i+JsWEsb_J-}e3H%PWq;uWHAhaYX`+QDMZga$os71(6V
zdXNfvmO7B5IIs3#@pz+PB52nqu@;SQ+fQHZ&A>$FYM%f!hf%A2rzIHZX*Onl$`=4x
z{?48kL%#N6V%2fKDYeR|>y4)hTIk7_j-4H=+I>)6jo1^r3Tdoxbad?NgFy1Diq(#r
zhoUwB(6*k1Lg3*qH*M@&S5)SIdbk)<4O-GMzA}3T;6zCZ7<C~r?NFypDl*ZZ=hH5A
zwkaSph~(2GKOUc)s4*uu`6F=p^9+6~&u^1=`nz=)Dd;XZ|1$A?1qY<Y`g}7dNYv?I
zHy93q9h=PkY$>pyC_vOD#9@50+<;sLJ6BnZPSkpL!}OFuav|_`pUB2Wh{S()V}sZB
z!JoViN=_cV^9u61I1Co8Rias}!E2ub&Q6gRvic~epdi_Z3R$9e_Tpe-a&~p~wIwf9
zo6)Js>>G~AAkS0MIGkMfDQj$KuwQ5vU||{P?q2t?I{_T(`gFpsopW5s1In{SWLew%
z_;AZTJ{~`O*E29+x3@RL5!9PH1>m$&mf*~fSE__0?D~L3NI`HV5s{ZoTv=WyPap%~
ziUbsy#~SNV(-rOfrGvP?e|gpV7fY~8bYYf5*&<Dqm6d=MD1PDbamk>hq#U``l$P$<
z%&0eBpyeFwYJ`!$ven3y2)A<d0qfy(a|DnjAs(Ky<5&b_Oi}<?#KeBk3b=3macaBY
zI6M^dJ~GZ={oMpWfUbaTOisvJL4684=FC}ief?zu{aeE}3$Ow&;?d(Ci~O%oP`)XU
z3AnB)CU=VF+kn^w_2~ZAa(Q`K?EY+g_wZo3GZK{zW7k?%@*1p8D`Kb+Kuk3j<O`0$
zs`-3a6l2Xkr&EjP(ODvX7pt{xOE4PmBkGHi3VT<dX1l5KTwN9jD(YR*@`eB9{BFJ6
zf8ZfWIwvJL+2eFSB25J6l=#B1rpug*Xt5Qr?BLxg=S$v|Rp%4$EJ2U#I|rZ+W(j#I
zFpPs#{xT5%OMUxQKfwL<{pH+TT<rP{Dmpq>gNxbxF7Q<D;n6HVG#9gytavkwg5o`P
z{&qYB;7a|A4p^K!JtwCFX)5XuUR)877CQy)JF1T^v`Qv8NC|utT^sLaM@qCS3>|ng
z{Mc?6hKBZ!D?ImSCmxF?m<Yx=w}CPc-ij+vRHK)!aB*`xUu&oRI3bqrj}(*R>@x9t
zX=$m57ZnGmcVYmQBl6Cl^lU^3Zavxk;GX~^<L309tdY@T)nef74Hke7FQmRNdFugS
z^3t2>pGZRbj65faOf5~+%Bmba09*u&N_V%s_d)t`PMe_?%JV-*@6Y5E$l_h`KEyq^
z9{-xcsWj<CmK;VL$Xga-(JD=QY1X4o4o-CqbkVoHG4|^#!k!12Q;YeU0J{O0<y0~W
zxY=h~4QF9Cetv%2j%F~&F9@RyqlraY4G6oziXtH<=Cz+bTIZm<^#ZH;{$YilRB=Fz
zd>SC<O~>Bq%Hcju0U#(^QceefFi%Lp%>WwDw+RtJ7fI%T4QI4Y&Ll?V?7JI3T7Xp`
ze*~b)H|JAE0J^_)S&`)9s|OHgCdjaKy8aOWama@}e)GPB%esRt&$BMZ9Dg8*82<ar
z6ead}=LVqu#!$xPnh*Zd8;Tl5&YW@vjegZEF{<?6ESM~-v=+stXdjaTfVoP(klCoU
zSrH&(4AC20*tgYqgEX;v<s8up-%AQ1PwzUL-I;IuAagl<dXl5b!J3x=2+-5EBlCPc
z#COZ}_G}y0-%XM9&2%!;rR`BwMa3D6nF9lc=zb(l6IF@+e%D6=h^d(wc;V``VdOJz
zOJNWC8>7H$i?jK(#Yq3-vd{s*ps5zKKYa>GP=OeeUR_&QS&4CskQn*9IBzYjcfi#L
z>RPopf053A#%rHub$f}7Lz$VJd@hJnXEl;-RitR3-6DVXEp{a1!>!lM%nVs9gOHHl
zpIMJ9Ptgr_Xwc1G*}Hl}#;02Oh)SmBvj%YWeTjCQo5wqi+pKAHId6YTi(Z_KZ&t^7
zAAU?w&abSxsjoA!{0Y2WJ;6YTXrJ*JZ50U=^7Nl>gq>7a$mQqdaXK@ZyYl#3ogXYx
z<<QK0SABlAk3vPYw$3}ThJ=XNOsy76_6o4a<Evf)w+(yWCK}{`R<?s|9xhiv&MQv6
z0q}cQXFrqr?+R3Hv)(wxsPB*flSS8MRF+%oh9duPK!LuzvTMCMU{uQFciT`{|D8Nn
z?_{(_q{^Y+fJ;JhIV^NIeaGbimdgWD%uq$g@uA<4*kzgL-gIK4OPSYd55ku^zbwzs
ztSlC!gln50gVoSNJA|H>ITrp#k4t_T!`-`ca$;I;<h62rQ)?>{(^Q17+UCh7^RX*^
zbq({m)iaW~TR#|c@(*xzD7^|bf#daQFTt7&3dqXj;9y|*){&6=T^FN86e-w=;Z0f3
z*N%jNDtFa?DMN5@aAdI?K9>e9zURu@>E3P6hjS=)w?$po`e@B{KpKx}dih=BnV%O`
zhES2~`fm<5j&ub}-V8S4w|Eyi|G@39ZEb+1da(wO*nt2UDv`V7BPu4=RuKC1n3EJ!
znPR(!0AM(vVf{ah7Fhqu$HMsx=de(}Nxrj^ow34!0=ub;1EHgoNoO$*qs=w_Rtcox
z(U+A9&z=uF1Rf}Bl*5NoKC>Z`=o}Pwf>%5FD_u<Z7<4Eo{s&Iw&ZC$+omGVM%4_KH
z@k6P~vv$f@;_OyPLfVN2DbfIw&g92c4b~E4Qlf#M^7PN{k!fkL#c4jo!`!#-N(2`Z
z&9VfPx~rK}JdAZ2<2+0SCVQqdIwnfARa@cz{iC|y+rIAY!voSk5KD8|Zx`m~GECB}
zS`qy&5B95e7z?b7mreWDP&vG8Hu$9csw|<RAv32CYGKpyZ;GJu`KJ2;e4o^+s<6j9
zcWU?tzn3#c#A=m)cq5gk8tryOYwX374MuM1cf8*FD6|k3vg)xBBiuhS!Z9tL?y_^$
z--A_+KJh9NXTW-L#Xq+^wT+4&?T1#l#CyA`R@L9LH8u#;jEq(tAxJos!rm2cBDJyv
zw~@p-J|GcCs}fFtUtvm>Ywr+4sR>a-lyJ{NzSf~4tFqRh;#9*ptTx-n2_Wu*D23Ha
zAL+tFbeMEp2Ol3tj5w&guOY7`yIwBojE!4FB}Z9gc@HOAPzZp*G~gfpXqM%(0DcJ!
zr<)mgFQl*PKit$?^<DrdI#prx*mAkxXBi6Afv$_J4<94sk+Kfv{XGEsqU6|eyr=ft
zczd*4(PnyQ&T15af}_F?@6(7Lp7((xMJjj(Gdogxe`)FHQ>+ebX7gQ^32FA`J~#P5
zb)B#B3~{f^MJ6CnO(MpFz$|*wcK_-*1Jye~%kQz3FL&t=P_}J)J3Cjc^?V;~HL>aG
zlmvYk@~xbB93xtqrm}?5cEdLc@iPVJ?M{?5G&Dd40O}*if85;M>gwvcWs_3ONRSjb
zQs+sIPcm8OE8Wq+={df<q?*Z()MrO=rhi^IG3-FfPYTg`XbAeN4Un|2>R;V3e{oV#
z$A86MbDg~?GT2z9hHQDZniRdoSjc76)<~)jRa=PSEFbj5`(QdXAu7grXF&m9tdYd+
z2-S=pOi)s#RAvr&d}w-=rLL}jR+eJ)HXdc3i<Jv+?rn;dS;gPKtKWF6DlypA>I}T;
zx8@OTtyf?$)4fRNfa`T(`kO1`G#C@6F)R5iKqSC-)Ued36*}qGMbbAc>D*`FHI<nO
zEmXUXzpUP*Ap$7&Nt*{$>S;hz&ei7}VKe&)QNTi-`_CyuihL5ZWVDMBvpSFI0EBEc
z5K8?Vi5&f+@U>s<ZZCfT`aSYek4OCeJ2D<?W8RF_)z!n#EI1@nUMpsN_S2Oh;dhYp
z((;s=+_gBhUQ6eXYW&dHKGrdCbv;Svwn7vqt+OD%xscJ<pW1o{ax4OM)Jk9C^Pv9z
z&~7g3KS3LV>G5VC^0v+cWd^?>KtAF`WQyGn@h#r1$`<JoVjzk;;GrXmo8g@xy=|51
zBSDPq&Li$>VO093F7Rs<kIchbCoRR~l$E%%r0q;T#bZN<B+kuLch>}88|{!LwSUMG
z{B5FZSGe-qtsF`caB2xl%vWs{&O1EFc-aE*P{mIn2*9{$+*ZTK5-mZlW1WR!!RxpJ
zNWzP4^6ULcj4XsvB>yfCk!1Y+?|`X`fsXDA*bE?>v}-<c$H&LOD`|qcA!}FgTKxXp
zMwM3R#vc_tyv#%<7@3c{1&vz)Qqdy-8awza4McCpYiDahejO4}J?Ng6O><l2YDi?E
zL?r6Bn)?>L+~I4_MR0ciQNqISgjc%z-gI30U-Nt03Fm{oQ6D!==7~CoxeZFOkCP5Z
zyS_2c;*XjhV^IEYFMunA(?nr5ZXenU6g!9WoeAmuDw%jnEHWN%t_W&Wkog7woT^`4
zo6UdG#M<@R`m^k@kOI=VLQZQM7ZzkVCyV||*+9UK)G<so|FU@s&*d^9{bT*kN}z<9
z(_!V8S8hWa!qN02v}?Mow&E)95Z@;LARtN6)EhK(O;6R>={a?T?hc<GKsUSH9HE>2
zr<8YMn2ElvbI9XXZ`gaMN7F<|+$?k7FiL*zO&@<=J=POX^%!z1s_BdxvUKlA9z%&j
zLNb{Cj7XpiVEBrguZCj)N4ZwodF@R{G}SN0e$WBVwR%3IhBF~>?4{ly$>6DUs6rRQ
zXo$#7)jkPC9J@ImxjA3;_Y!b!@RYiU=QpX83?M~2zoh0A;=n<<G&_*cVcpyeh|bKE
zA&?x&{1rmK<fHL}3#{UI?2b&VT~lUo?bfgTp}z(?jN9)Kb1n{FCCDjf43fS=M((V0
zq9yR!uX!kJ1^ocorzEd)x5$<jJ*F-|b(D@J^8f?-it4ilvs8ys(TBA)eq@m^TFh2y
zeal8WGV!9W>*#fRh`k*H<m!wyssWT_WMmo)Tm8}}Y!cbsd3a&M(NYdo)`+7wIq=j*
z=3M8#b~c}^>b|oP?_R|z;kt)FmJD;@!bc35W35PWvQJlhsIo5;k3@wJ8JF+rKD|bx
zqDwx?LoFtB6@c*b8$TMZtx@L*L;jX|SuMQH61<rPay=d)rjZfEdUOY@;%;qcsK%yQ
zC&$(dd9KNAy@M|3HvqzFl#LOf6wFWLPUpuYkMFJ2{ZH|L)7f_ij^e{{j;wRD-MJqB
z*dR=sImTzL-`Seo#WuzcM6_A7`{SD;`w0@RAd^11o5RK<RQ0UzJ<44pC5bul-wCdK
zg*_kRR(lh&!g~})z1p`&Q1b8nj?UnNnKkqC^8rr@e7L*L#vnF*Fs<C1EX$bsTMP_|
z+aSzl_w6yzY{8uT8we0UAp)F?HAWEYgIxr+kH6PPNv^ir$mhL5SU4k|A&ujyIq#g}
zKYmOH>X}Qv!0TcKB_#>xXZlUP5=lWTT`yQ~U2Hbvr~=MLS1ANsOP^oHa6>Jg)G9w)
zy;4I$x1kha@xE6XZi6Y%XnMJ%w*5aH<|KTa3f42#$l+WA_PihRbP2~5Hh7Y*Zx;0|
zAx>zAFYUdx^q9|(uWnkMxVpA54yCgsLnEf|{XoDO?nvE7$H(K~Q@t*d3IrhkJHO-J
zV!dj~1Ykt}J|BI3=WJ|lzLu0a?nh6Bc;%q1ByLR#;c*D`*(84I7<hEyDL4@WA{Y`9
zQhbF>(DJWvA8squ{*SeGli$^0zF87wIsI2_7w~C1$*b!^UL1T5F0bhCXOCCEpx{)R
zz9Js%F1m*xa~E~k_blap*FehqP@cu<iz$gWRq<Siz>MKFE~m|1gvlE~zx_C0VHqx@
z@gqUl4bvR|V3YA|9k*utEV%%3ltURWk<%*cv-QxOuqSv<on(3?iwfTpL{aY&nv^uY
z8vBY&F);8Usl+niX1l2A7CWqjoc-=ugS&~EvGePd`?Y6_v`nNSf81|A1B@-)*!0AT
zu20t0iqB3*#40q3Kb`jg8Xm+KaC6qZ&YWjTpQfN_r3kAltwP<lbhFKUIGQLCY}Cht
zX4>FUk}2XSe$#+;-<4+fL*fL>c-YwVe+Q{FdD0isN-+3Y(uY}Pb-Dmh&(U_r#qb?d
zSmd4gw+dZYk1zJ28L)%jX_gT)Yl|zmkEr*Ou<9JG^qvdb*8T)pbM$#Hbu*OLK}UmT
zB}rM-#}$E0?FBseA^Sh~*uYh71TG8AXVZx|+5*^Mt5f!WY-?SUj#ocaFFUfEln+~o
z2>+w=57Q#RTbmL&;DuB<CSRRclp6|pm0672m82M)o+z$1^fc-+YL&IWJ9plOXV&gG
zSUmPb@b-JoiEAcJ!~3ndVuTUILKl<$?1yIkOkr#zG5h(yuG^m&I|+%2l`?qufpn0}
ztVt%~TXSxAa&x|G{RbD~xcK{k4_BK#-xLdCtQ!_qSWqCW>&!18pc|whFaL$t0T4Dh
zS=rzca)?HWA@FZbS+o?=xh&QfTQ|nX$APwn6JgTxa3R#A95!0BUy_J6S1-WN-+YC}
zLl*n&*)vg5(fC%n(%Vn6aodyz`aK`b=%oj);O^tJD*)!ZuZb+tDytrr<cv1`F>}&x
z?QhJ3Wz>eJm;o0|9mSfj`U96N1u0W;pr&rsmMfxqZ*IX5kIM$BkyU~{Nz4jguTVbO
z^q&Z)30H6@%Edi!g-AFIg}+a_qhYF)Ohob^iQ?cNxi~?XND_b6ZoMs;Z}M{LOQ5le
zgZ3>{SBZxr1N!Wem_Zwsz=uEteGFc000{$7W8&aZ>zJ4r{o310mhJRq$KfxPuC>h~
zyK5HO#hS36VQ#Rr&P<9<E_G3eN<M9{@THy)a%b7#-uIGCB@u<jhjYZ|BW9O}9|Yfv
z-s7qv<3Bhaw(|Qx10Q64K6K=){_-g`Y-H!+0_s&i>3ET{(awJ4FcqbM$I0(rs)2Ra
zprA)EJ!X%hVG%TJSRqLGl#w{O5>j;ZtwdDh#KaeaGCU@~!U9BYe@&4P^q9_%fSGLa
zYz49$Xy5{sh6N=cqMm%|qO6?C6Dc7(+ptka+5H}sWH>(%fnN~rb?px5Zm6}L@cs9V
zDGCJ}DyyXA2!u>yW9nca)$@=Gkco@SDhQw6k3C7b=W8zj`71Zd_>*_NDg?YziV7_P
z3PHH5fbEs5D{njdY!i5T|F2<A-+vvo1buZOsAP3qApM*%TH}>t-9j^I9K%cMXopdY
znZ%N42+wY@F6`S+${_noTdj}fMKYe6oGuy_L$bUsa`23O#9qZ4GW70YMe>8eFDO=<
z4xuQ<vzXq#g+sT*O`ds*@1P=TWqPj&`+-)!(X%MyEg;#euV2e`b<A}Qj3z4SH_VTx
ze-nOVY@Od=k@N1&H}ReKctn`6h8PnfTBY;2_j^saa+MSoO%BUnx_J9e*6I?PQDiyE
z5!DAh&KtkXQI->$tIIDOM<IQDwbL5=ouF^<2)`RFiEjl3q?i#q6D5m{ZdP6=oxZN=
zdS$IJdwR&;?8iO(f6Z|ToOGh3diBG=SR(lMFEKe>`Id$nH&MOpIY}q8r0^;FY4-kN
z?_MJ$A!XLRyBfR|pY@-wz$%(t1f6G~#g2`Ru1oN$+N3jLnT&10CgyXbP9v0nq0_!c
z+o+VBuMV{8Kznr~HGp!`tVjDM9oJrG>QDFjXI)fTB?ZGe_&BA$-+R?QonX=ufHp-$
zL<D41O3J{ytK;C{VBhndEPj`~f%lzP4JeSobPtP-Ga!ovJ>Fl2P`mF<R{_2X0b&N%
z*_0wGnnN0Hy=gbP@~*ATYj=0|PHc**6RU=wkfRpgna%y1QRMssr^q;;2((0bTJtgI
z^s}*xeMT_y@<0tYKao-HZm6IddNXOu5>N4rj7$$iIUE8qp~(!DDq+iPTcpz_LVMO+
z{)$;o%(K(WA#2$}RPbZ{fA0B#Jeij;unv3`pm9<KzpHlyX4BQyXuf>((e21?pwkg{
zT#ms$-TnE-{QFlw=s>^U(Zz|E`;m)^oKBs@`)}|();d$OapJi7BUCMO<rePl_Ge+o
zqs&EVSwfQ)y7{;3+TK1?JNIa_ZShxOZrO&o?@z5%Z`nZZyumu2QTZGl-0$w+wTV_X
zOU+2`i-5h?+2k<VfF`<+W4!%uriDt_duPNhx_hjn*KX?E?z;|J(B92jl5+MJq)r!o
z;7?LLZmq0@_@P0THsd4vV~do5gs3Flbm^BRJmK3JkrVWw%aHUewr9fYz;pu&nLgVs
zUIwCg;+<~LcPONNMyhBW4i)X`CMPfd<IlZWjZM6|Rpj0rAMmf@cBdXM=1sTU4K$+G
z*4C89t&Dr@Wea3Bhs*V9Z9z^17XKtLjcRIYK72qe_V9QGX$QWoN<F1(_0kS}Xzt2^
zL58n8i6q?@1+TLR>(H08=0D3=nyq-qkY0j#eMJiQRaJd=K47-ZN={*LP&P7@AS49W
z1Ad*wcsHdZ6vYepGJOG|CZsy7xKf;$?t(xL4v8JI<Q^IKO8^}|uc<sMO4JJFfi4TF
z1PKY~`tt_-Q#n>sFNm=5x6Af2Ywki6>&+w#GS4kx84{{<Q$k%c{c`MB&hAGlOy;cW
znkB{PUt_4sGvSPnHAG48Z0yw(6qIc3_sOczUb;{o^qci!ND~BJ#XY;Mo&HL5z}Vi6
z1fl!%jtmPCuH5nJ00jgjni-XWDOPaO>FSB_^XG5!N4vVa&(qo<*m1ehn~~w+wGML^
z{kE&15AV5jOvC;*FzSx}Ih7%x=jP@TZ4NdDY!aiRZ(u`J!0NRu(aP7Rf}l)Rv;fn~
z($aFVa1kMxI@p3HwR0W;qF3MD>~yST1{Jt6smKOAI^fpRe|FJ#d){KTEt@WiMY|m8
zbLj|x8*lnt-8u`d*2U;|nSH@}r$r@Y<&nO=r_MW2oU?s56d4Evy}B>o;^K#(T#DuJ
z!$AhV#%QQXGrs^UXklaML3puyetvDc(9`2>Qh!+ck~d6X*!VBH#FrWPwl<|)iaYC#
zxHVYE+t0tVa6S#l7Ivt~J^M(f!wf=|cqB{c@Nc@8L1s@Jg`J2A?~4~UAn}8$0YraL
zs+E!7%@$o&_OIgUe^VU`HDu8ih0Ou<0Fc9FeS>#F5zfwEmzA!M%2}=Qs5DNb%>4En
zpxFVqlwNRY3f~$U8k(D%0Z`*!9ud2L^Xsta+d^BA(JAM?ysxwc&ZT}R&ATG6&+;wM
zwcO$1hqyROc_|g|8Rt=2f^L;YRvzwJ4@>4>RApf%{58)Fw{f-Wr9ZLrZFDN*U+hOe
zx@?CsuwhM=W805gc8@;4vS)&5lt<}#km)yi>lKF?J~|qDN=iy<X-&4jN64q+YG|OQ
zfpa4XF<HSmAm7^gmoF3Fy9`uc!5~pgf}*byQiOzqz*7N91~Bqi>A0RsJlPLU0sF2w
zq5<VbZP-cA0IY3)2ms9^t#lA7_YP&2mzH$GE%54}lH#D!v0uE?c&fCpWRyyUY*toZ
zut5mXk<d@zmL?VdMH^HaivtNAIN;(OMrY&@7@ASMge@}^>U`FB3~6J-9!ABKh|f+9
z(z6f|TB`!A#W72mXz1P2S#e>2ub7W913i7=sCNFJAZh@}BdJU};A<QavYmYQPg>7+
zLCJ0?QdLJpT)DfB7u)jRy4W8)Au99YsnV}mz%Ap0JPLjO4KYp9vYv8j>#uo?Z?QuN
zjd5{7U{L5Px;N&{A?Gp&pjJ5r11NC%t4m9}yP!K{ViW5(t5NG6pEA|jn#tPK!0M`N
z>&@?<h-Hdf?mq$DLPkVHL_)ehovdIN5t*NuIPcF_1VuOg`%<L{Xy`Oh-8saNT3cV2
zl9pyLY+`Ub=M)mmR+bm^e9RrS9vm7vSM9r}px{TIXcn1Yi{ctq!aHDp9~ZZP&74lH
z!SqK1q5@+!b^2-#!KJnx=km|0Ms&;gfv3%$Ex*#$6%s-%(_Pcea!Dmhn<)Dw4(iC8
zfr3fuERXxrff*4lsb~2_>h33;;{nTIZ_wGO%p~03zP>&H`0`CHbQp)^3sKb6U@%xb
zR3M~$UZoZOw>H1?wHjC~K$SV-M2aS%7C<YRGT=(*48pE+>i&^q!V8*gmf605?%1xq
zY5g|cB;cS#Mns5SE86prRp>W{bVjNkI;@cxA6GZp6sJaFJ`?ffDb;RVr$<G#Z}Cjl
zV8*JroD6XbGVLLOEbaVs+Z^TqE%p73Fd!jK1?l+!HiK>;BqrKBY0YEU>VG{}_}06g
z?c|m94B7h*?-F*=E%c={!yr(;MP?<MHE|#k62Iap<)fnn1x=WBh;51wjrcD+{^WjS
zI1R|(Ztb{gH<#!65|f9TJa#jdcYoVLpiZs2H0NVX3Q<+tju9GF>>P-YFNDK?XzGr(
z(r=95vT)uw+m9uu%yBvS9En3k%SI&POVfZEGGH&_z9b}G*<brlO-=2``9bK<b6k>3
zhk*^yc@}WbOEtdj4qR9MKIgt+JrVPxQ}!#X99H515Kj6A2058fCDiLFAP7{ry11l^
z1wAY}jr8~H{&e34>Rx767Pp9SdC|8XgGODYUm}AXADrD1oipp@HOG6$-xa$j4%|~{
z24XYES+r=%JAM6RCQA+}K8C1>B8suvJ8^BIuQa}CN{UGlG_in3$4GMJQoq=flT&<Y
z|3Kun5t`aXF};}k1_cKvesFMbx8;WR6-9E1aX&4OUECpY=SbV#oKx!QnPfCz!z5s<
z%U)VqT4x7M8YfYak_1Dj26{1}P4UqY;(!FRRCtttO+QdU!~$;Id$+mqR4MD7vh{w_
z(bE39;d~M@TlA$cukl#|Uq@>lorlMdBsygD9{b;Bmd-B@ca>#`GMJ@fQ=~Y}L~)t?
zArTdPM$0!Ph`}cya1AgbKy$V1{p-8CbHK;mCD_{A%go7H1O?&yhp2`Cf`|9t^@!4+
zB@9rp)%b4mUffl!{gxtCj4u}<590W0yAFC;p|P1E%$hE1eQ&}`FK1wi>(v1H9Sz)^
z2Mi#h(s~*-{BgRZY@UL%8fh1Uv<5o6cCDt@<E;lF1~%tlzW&AeZKrlL4+{uBR6bXI
zn^{6AZ|SSsc$MVk!9=NrqZ0wr8Y*`2)s#LgHB=lYA2f)6$nyqGm7v)FW&R#Qou<H0
zrq$%U{Hvg#puPRIlhYySLDDGKzb<Y)8{laB`be$u1JihL3$H@M+1%Wmj*$^G32ge1
zgNWNO4wbh4ix~39>+grbM<Zwbwm@LP0#nqMhYa-g@MdfdWuzu2PXN>eu(xPqp;A$X
zpvTUaIWJLc4B&|Gv^k3!n3yaV<plb6_2S@A#g$!7Y8wT30D}!^YoMg7-;4LPg<2Nh
zjSP&aU3&r3ULPQA&@Btf93S81aF&pgbW6zQo6IZ&^Dzjcj7&OD-{|O^c=1cpkjyM$
zLUg|H7&F5&ZP%3k%#DpiR_%ht=Hp7!2~4WRNU^ftq@v7HWy5!l9~Wk@NQQHFWpG)y
z=1Jt_-V?Mcp`(TwwmEh=1GhV+B&esoBOY0F!6xi`lCkdLTG!b^03PlKqseT;ny>ne
zt|Go?{G6sd=B~otxWobi!vnXSU)w?Vi&Bp0{ZeEOH4o<IFf{|R$AZL~29GUf>;^)i
zzn>acYdTyg+&Dw$5XHRcugsaFqu_Yuw+;_r1mmCrV_rjg8pTltLK&&?)(g!(i>(2#
z$c8{miIQY*`-Eol)oUS7TGIns1~TpPudWsiKWTW883^FvLCTxO>{}<jCxU!G{ry2A
zgV%wK&oMJSJslXeLt}+k2Qq6LrbEiWFchkzZBQV}&jmI)*!cihWAop<S7YN#2k$Qq
z&3bv)%-@>;INi{|WT1aOJUo(tyfkzwA3J-rBrb2uJf`+_BCis(YgT%``W^9{;}Etn
zgik<_m6<sP+Dd$Gb5*7~RcoD>B_N;)R=|0w{qBv-S5+ZPt99j%T|3F%O`<=vI_ucl
zV()(Ibpg-;+=F5;l)zhBoUTOEH*h&%t#jN~Yoi=FML@wW1who@{S4UYz}Wz*6)?uL
z($jT8!7D8-9m?R#ncRMJ;);KVpu!8CDyO{jtkWwC)Spft+5lR>P}<AY6usEvB-LD6
zh`)%b*5)v9EI|j;rcW!dF;Mj96SLMm_HCuDb-$mQAG<`Xi*+nganst7ShLF)hzvvk
z8~kvn9|JHnN?v%)lI5-{Y%j+;yQu@PfdJslP-HAFQcTbx(gL<*flImp4*SXqe7iB{
z-NWr6u*y)5%>KSGFGy7_7Ja-s1$H#pMfIGQmlr5BB@@z;l1$0J!O@x(hWF7NtqJ|+
zV3Sv_3ai0SckoHQ1pR|qQol|;e=d@?O35#fuP?w^TEV+Wmn3v`<^N3Wo6HoulwbWA
zYjR$$F7x9H4d=V~79yx}bt{`9!#HSs1P!}H?XdNK`Xb3uDHb7?*)1w_QyEq|5$`iM
z^({_r$zs3B69pwRe|1f6kCY1((0x!VuD{EX`8R?5=8IOw3r+Lu8^9VZ<Sb@TE?^%o
zU-_E7A5F93WUxCSAb-WilQAC;{avncJsQ5Nb+=Ws>D6P*SzXF9Vxv23%(-<5MI12Q
zrfr^w)b3LA_8gX&Pz)p+Y^=qJ2Dor&H5!+*bE1~%<*%ZoJwuCrX3=>p$6;=6JWpnB
z*>3)ekGq{q=XmwR(*ZzIptTd#`@fnjkBH~^!rR*_qUdCp1Rw#Sd_&{vLqJ#0S6#Oc
zua$ULR{lJQX#9xTe+M{V%(IVC$&^`8-!tc|dq4IxBp&MzF_`97xu*1`x6hW>IlbV%
zDR;Mbq;V2wH39KDLAL#E0`8_cmyPd5NAkcQ;@7xK`=KnU$4bw=eV3<A*4A^ypVKYK
z7~}q!c&&oVYME2LY-4;>|F-)<jO^84TT-kg`UV#Y@eU@Y_WcZ5r4&NK53}%YI{YWD
zG|73=ikSjfC@5>2s{?pCHIPfwuT%LBe)>(wBvxyiZ`dQd;gisqi83=ys$Lg`GA$iY
zeAhU_ksQ+FuOz{t%2PF?V|<_by10wP$mAar0FJxII(39W*)r?D`vxUVa@F!T_`#5q
zwlx1KB#)zvkVGfZ?vm`B2Uo*Sfs~9+hNV|8?#X+wAnyDOmH7L_(NR~hcp5jx$B$`k
zq-_qp9rtN4$Xnvq6}@LOI-DfB>71m0zN4M_N-nN{!e_(7b9N4^pmNlIBaaRbpODhC
zd%p39Q=>Et1*e_MdJbEtu4A46Cuua7dpJ{~NU6b<$fwb}o`Q=zAsHzg%Ju9svn*Xa
zt$?67Gt$6{k9uST_D!DDsaz66%MJNw^f+43-6j?e8o8<-LppDQU^92UR6&Cod?RmC
zbIK<)-Mu&t56F@-iuf{gBaT&=H1PYWE?200uZ_1z68bA`rFujDZRyw61-=n8TC7y>
z{WY&|u<qLHd+rZ~`g4ulDM*uCM_W3HcGIB>=D&zsb)6USfEs;OeY>(cNZdgiMcZYc
zWoX4U?|jLOiS-J~|MeO7!Y^0bj|Km(MrV8jq`WTgkI~fP2KdMb798T+sX<TNn3Vq?
zqD1wV>*SSC(d`I950H7sG$0M0nt~?-mKN~CbVaro&|hIy@x&vzQkR}=x)J^MV^Amx
zBjXi{>8fCjJytQ{D?77CLmr!`cl9cpY*3MqCNEAOW<vSFOl0xknGjvKqw_u)D>Huq
zLaYVxTx_pHAg8-j9pMBs{Te4|M(pTl@W4=I<XqxrS_7*Ni`?W^tB<H}=)DpKiMF{#
z$SS874JCuwW9tLD4d{YK!*liwmkjx%!j$an`97Y5PWYdQN*Fh<yC5ntX>6sAF;}N~
zh&WDmq!LOYzU?b0v>mw4;<Qqxw9U=Nyfi}Od8jGjFpN(GCR`mu2($2nJ0?7@X(Ibk
z^U)Xx2+_AN5{)2?Ryrgxd|$q5y#Y83{v#sWVt0#m*S|NO#30?b=u~1VST;1gW)Gdg
zgoL0%h%pekC4=Gk(e(AH({d-<hY*M{LP8R7am2)&p=Z8V*ZGyW3<z{;eMHXgVdc2O
z7@+?p6i$QLS(u-=8IkK*Htf?L3-^`@Plt7|UMv@lu`a7_gV*ae5=>?hod3MS-XL@!
z3ajg($Mv51-gifn06GVDhQZ49e`Y0{ZSWI?{?0xi7FXz>+F{%9&mSEb?RZzLB2VYz
z`=%1Mx?ZEekt{KMjAmQgV|?bJa?StUpF%^p^GZN4j9+LYLjegnDM?`?UoE16Ur3%F
zJzHCzP9AM!b|;h)VhoS2U`fzvtJ|}*R?1Cwjzfix92#a(%aAl{Jm^!x{+W5a)=nwi
zhAU<K%h0h>xn&tK*5dm}#<8*Hcqk{vibyyDA_f$y(uD1ehJ7G2@5@AzE9NClMHOG8
zQR9b`%AO~uo+%VrAZdhJoNZ_eqdubw%h57zf6ZAM_-Gg)4)+=T?z%`x+j?u1yv;#o
z#<^wm`{6WNDtoSem^h&aqcYe&1oDQrK&DvaJHF*rgcK<)Vhv`}*egpxi2lzlK^Fzb
z+Ar~xKcX?XxH-*9Efqrh!_wU<&EZ0RQw!it*wOG1zQDo5g>X=r7jzC_xC(%IGEb0w
zog{8JXdc!LUq(!fL5e&xV+?^IS#rlnrwuuKbgU0pUtw!t>zqILzsDo}psw2BK```g
zB@q6v?Ps>=<>zqJJ8Kd$-AM!uhWP(y0_C@v_yI2g?q#i>gNn)@fERE6Iz7UWdXr90
zPCm;QYA_Q?xqyDsr#4T}C5R*rcIbhdth~JGs~u`a8LH$lCLL_LZ&xhlYy*f{3i4<!
zE>Q#O+UF+?8Of`;DophFwH?mSxl00rjox=KE%PTC<4GmLnGs7W7v{xPT+V0e%Y2ml
zT_xR-w?yAD9H$m{s`bKy0jBm#XuurW$yA8XSp3(x?mHbmUS5-g37(Y9HVel;n|ev=
z5$h01_<S@>%-6XckYYl7G=9UfNhSs9Xr?WCwaQZIaza<w@)AUwkB)vPOCLQnBq4p9
zlq!`Y=?(ixsw~?z8HC9QuHG@kL?J|n8r9id(pKK2rGBV*``aFCKz`44sbtW4<%}Kn
z|Lq0%l)gEnq-5FnZ)J~p&HNb=GbxrYiFS(;iWg3Fd<DHIHa51gPSPm#k=pApRG5S<
zE`&3oUnRjw>xEQAxB_<8@ZJGC$OLH}r_$*XH98MokO;G(PeDw+I!Pb#q|%0rNjWhQ
z;UHF=oWw8>!cfhUXK|s?7_M#ts*)x&5W^w_NO9P>Elm{b02eZX8L!)pzHDLX%&)YV
z(*CYW%-y|TJ(4i8SEu6I2P;UKLIbH-GOqaACA`>F5~+^-o`|f`uItXL$olit$r3a&
zdSu>|_-Oi>x#RhBkqut)2G`GxT0^Q8g}S;|7DPUyi7Q~LeR}D?rrY-QSGXIFV!A%C
zUbXjeK%W2>i1`<V`AhtO^x4|lf{l6`!&!4JewRQ)XAF9a)H{HSxD3R57Z;a@hlg6b
zDZt)=N<A_>?CI+2>ftdoG&Hg84m#$2TNX^KYV4+z@<;dnHFDF_gT0^;Cj4<`q&?<O
z{;`;tm^iNEV(b107jiUxdq0g_*5hk!(o&6rFKl^C@`K-0NUV@X7;R5NGodvMuvE2~
zT>a_4-(krjB90c8*8+kkth=nq{J#s}sm%m9lA*LUmTxPQ@=GLA3R%uHn9+wU8H*KY
zmOJ$`Kk*=`#U-BzLy$(u*+R~ZqbowWri`7F2A)OWA?CK5b76$2(T=7M%4e&e;Afj5
zsS-+|=ArJAaI^=j%F)M1@<^(FK5hnkWK&*Z<YjKs)urXi%coZLDh>Fsf8Q*6+U+%Q
ztJa~)0{~<Z*gtEwKU)hFXf_}w0cDF2T?%YY$-ZMFg3|Z>tnsn6-BB-b&|^)BiNS<0
zByIm|bhE7`dkv0hX>JC4=U$P;YA_EC4ubYUU}J|+1J$;%sR?Z8Uj?lB#ful9F|nhg
z!xAO{wo-xpP*qh`N=iyjE$3P~I%!2kR*sJ49!ilK%p#!W7QlWWNtoYAyy$(P^4ol-
z-CbLIY-Oob0rfqk4xtXg+H1+JGJX4ZTJcp6@ym~7ep8aM(n;3#RoZ(|Ex?CyFYr>T
z;64rHH#DqZFkG1k`<MBM5|}B#?KfEykUe9!UwcKUSumPt`cg_&uWVA8uCG)Ld3J6T
zb6_(;SCjd<_>nGepW!flaqoI^Iet}}gyFYw3r!Y;{k<!wyakUcq8uzHrWc`oHGxo;
z`|YW0)SCo+(RdTxD7rJtSvfk+F`oE0={7x5bj&Jt=E9WXxVcIfmv4Xn%F5#U@nb(6
z9$WnPwQzDj>xQd^zhU?Sq|(!0R0`iWT8d`LPLa5Tc}1Ntp0?H!Ba)iw)0+C)DGp;Q
zolOdnGrw>YE#!*@oUFHgZ9nU}ODzK|W`q$9l}X;J%31ak9coEW-qV+GgixarOR#UI
zADQNKYyW+XOZ^&Bn%4Uqj+hlLuP0JE81fqbt8NNJ94{=~!a8C-fleG(0z1+~H^h4~
z=WmU^*<;(`mQ=6)&~G~NKdf(psUZ;-Xz-I8r5S(Swf5dRh%OSt;Zm1gz#wwLCXf3S
z;kj&>ot3Qbt%^{CVLFIQw{tH}a-XlPtOVYAJ_gu_te$^|iDW1`H)B0ehL{a~c?B9f
z&Mq#1VligTGkIVPw%SWcNtsr81LeZWoI{v$#I)+~Q17SCWt~zy9}mo^JlRjB7|dm*
zr7?L-jrvOWc3P-Xs!ykUr>QA5UZc+*Byd<HUnm&#&@7#ghK1$f<)!@g?ds_@Sa871
z2HTh#ybcX5Ea;_F)rvoNE(7vaUCouHWW`A;`{}bR9i7}41qRrR!gvk%vIto^W@hFe
z<>lZz0Vc8ER!rH|>Xm8P&DUy`(4$JwCZdK*fUhu_+KrWdF87H?5hGlJJ+)-QHh_s%
zGvAK^4>iPM#<+?TCNGDJnDgfPcj2?-3Fs${P-9&!)XBJ!k*V$Z@H>f?(e&Qh%2J8;
zk+H2zZ+&bz6-1CnN?zxph9EW2rI%?$2+mpoA(HMHL#l;_re9wdKKo^{nwd!s76l%n
z$+u@I7CwY>FxJj$_@}{9R+UfsD3h42Ry=90RHk(!-z|-?|2)-#C7BZcV~_bpLRgP|
z%IP&Nwu}vzDxx@^xt2$5ygu@48eGEf6Pn2R=e-L*avBJwAht=x?~^)*^6*h||Hv`P
zmX)3)$m^v1zo>fec&y{^|NkOnlaMk)_DXhk60-NoC@W-U%go3g*&#c7%ifYr_9oeT
zll48lKkv`y_WNCbNmshOUeDKgKF>Li$Nh{ZvZg41@_I(}t&&rCK#P`X%zTv2zB>9@
ziuSWR66%$|=H+o=k;E=28Kp@s(cQWCtwPxDtzXMm{5@tINz=#(<B84~wG(7-uV=X-
zbQ+(&N#uXOw|9%s?nN5M_^caj<Hbr9#VaNk);o*eKOPr;wVF~lb>p#!%-}Vf&hKHm
zdUobUdrc7BF@|j5+Mvhcq3$<2FaVKTo(jJ{*UhJA`4`tKUM%M*S}k76mk1QG5!C4d
zc6^o|VQcJv_Bg35<;~8xZNg6y&@{<EpD36tuXP1|C%mi&v?HaAI7sFKYq=9-*^Tah
z4~m)k-dx5aHw=APF1fn8x<hBd=q}a6LiK#2PZB2OHQ&AkX1`^N+uYdD28W=M(&EmJ
zm9=&8kQs?TBQrBBIuPU4)=ssx`3|gVXlR%k8-pw!pfpJM=qTXQuBhOmr|;Cpo}8Q0
z%O3@2vo;HHtTFIo8X7*hy4C{O0A4Ky3wAYQCA869@1fui2E4CdA?}rxl|dDjhK2?N
ztuJL{($mumG@7cb;|)n*fpR_2iiM5{BNNkWE%l5czNb%3O-(o73&a|~($(c7lh@Nr
zNlqr08y2KcU?9vJH3vmE_&&z#QVVrP`zRfRQ}s>X<-=pjeP=$YY#UD~{HE<vNRp3D
zzfo6PdkPH6%RO<Oot@LK`8IIJ4vv^&Hi~)v@RppEJE(|Fbq_b8RUI4^2lQnfJebgB
zvi5CyVWbNt5$MU(8g&u8c+q)p1b7T!(<m{_*f~74$yFpgUe8Ir9#x0$K;xB^M=!c5
zV8NN&x?9NPm~B+fbei(G`{u1?#)Hl5cZdSpIL}I=CNVkLHT`!#trg?g&0bX%hUpL1
zT2AY~>)1WWw#@J8JTpBw4P5;1&$GNNJa=oi`QIOZJvM<7@ZV3cRQTpD*?&Gun}6u{
zFuDJG)11H!R=FzIgFe!?ov~Aw+~1z&_UZqKefRgcWY)6Sd)vfKXk)AI#6%@2nwF8^
z)0~uwr^Uv%g`eHs2mAZEj6wSw2S;eWi=kVp41^oUe`_a`KXv@(b%?hnw#Xh5u+T~F
z<6<KCBul3<TT$3QXDOZ*LEH7;ZxaUtc_F#7&`M7c-pgxD`do>p#am!a*-(pzmG#~F
zudIQaJy09;jgOy##+r?t9Ug%jtiJO9xq6oMXb$o=Aaww3K-1nzTx8_XSFu|Nkc2`z
z<t$kC|K3LD(4R$3h<S0m=>&4G)q7C6-E{t}$WZWUf<(~S29}_B8XuUClBVh)uBym2
z*nSjW)PL8y`+IHeOe1OUG=uG)eO?c*PBQD^E0v$MxUa3PZ{YiNBg;wKa+B)3$vK|F
zl|*F5#>W5l_tDQ9dr8ad5Q3$Kxy1g78z90vIyyoL%1V-BbTTo>T{yLKX|xYH#ev`d
zS(@TE)x7bNJ|j-XnC^LZ62Pah>(!GycrcaqGH!l`M@T5SbLsr-tiI%nVf;UL&}7;3
zLUj;i7_@%G&}BP4C+hC$0qa}WqK*51UL)qmpbdhEE&$#PxFY_BkU-#)3w(}?)6mt0
zjt~e<A5c(08^_My5UetwP=qx*RrO8>T&xIC-b+bKe+mmL(kP}tWDLcpr0lG$tSl@<
zfD|}B9<&0#;GH$PZ20I1_~1}b2e%7=ioSjO=Huf7j0(Uuy?FlI*u><(uAD6KyJqcs
z#_-NtqQghPCjtA{S49ToTeo)Sn<(+|Ih^+vpqaBXRhj?wt5?sme?Wle<$fpN7Sb6$
zNkm0QmmBvGXADh%BNyuRmqX02v)DRqCZ?w$$c)QPZ&(hT{K)T^nw)Hi%)TBQ;i;?v
zA{CsZ0`Jg(uHZ!6^oZx|poje~0YSkmj^*<D=H8q0a8?C<O--|RlwQHX!48HF4#!Z!
z#-$YQU9|b9;b`*oY|ztJR{p)d{+2TV60$#C(Z{DdHf^or1zOLdXj2E++1MHZP9mNL
z_3D~QOJOj0O3?;|h3Ua%1uj9y>(cUadueIu=g(2a@WaBwWF1Ws<n^VMK@>>_zC9@J
z7V9?^DKhYpg>C$Dd6BkY<H|!A1O|7hj3H=XD-$k5klNg=14nRE+3ySE<ro4jxIf+9
z-6Z~yV+odey!#~iqTKQ&yAzEZudV&$DQ$+<<pTUYT*?R7{y_HP$c?8`{5Ixo(2%8!
z)ng-_mr*<(<CD&UtFNJH@O2&Cls?gz5ZSvy)TaDfyL@vJ{^%YYVEAQ>Q(e8aM)Op(
zYA{VrLnVi2hx=MNOzjT-6cs@MIPb!##{o^Fbj`$8+TLkAw~ur{(6ghLgVtL$CYDy(
z8fpF|oh<=Imf&323kB*g<fiRhuUxN)y|x?b$a_G=v!581iSF~2K_+K7G>`IqQ$>M9
z{w~GZQ$8+Qj~k^sad*E)h^U#@8%4Rid($N7d2FL@RgiQz8^w7$P0^SO2NS3Nn{xM=
z;2WDOh-ndjC{MV@jav+v6>c6(KrqHJXXZ0Y1oYPtxaz<}`_IM=R!Hc5?QJKh(eVBH
z^-DfQkRQ}&yNwt9Mx0<C#Rl1xU(7q7CHBlP^f0j1{(B6@$G?NFoV$s*l8}rzI4du2
z<NWchSYuvz!aynE+p@5>rm3W4nEY^geO*gY(co%AoP{qbX?(2ni9&AwBL-NS6yX|2
zdq`eFAC?G_uiI6mTi3FU$Jy+qH^gn<lJc(Fqjm&bytiInF5TPc%W|Tqw#_xqs7di{
zZRNm363NOeh%)(menGoTL}*^|u0c>RGb-2x6&d$M|0)LG7bl;qft5d#SST5!+|Igs
zHA_R$oKMIp7m}2<R~V!JFpXY4^T8ZRlxb+uW9Ze$*DA52(6@d}%2MI0NV4%~Pc?Te
zUT)`g*HTco$3vvkm&{T=Yrl8x1?a<H+|i9P-&}H`X4bH-a_-KQ$$0ovFH)_rV(AbS
zXnAn5V?^;e>Ne;7G$G$Spq7^@nBr$Bp^KZE9>Ud^!H{TpM2sbduN%cRRFNU|jVG~P
zx2Q$@rL%6<(9cJVkK^7LYCdTzF~A|73L98eGOC%Q`lA>|ZCENv|Hp=f<&7apqTxAN
zcPoc`&}&VSw^-qsn$4{ogwiD}%zte1LnZITy~(~GL@VVpn82#;HbNI&ClQseI*EnK
zlp)=_#L2?^+s00n30FFZk1TYu_mMO)zT)Q=@fUngku`0Xs>atans_CSF#mt&a?O37
z7@11w5(ADVl6HM}w+VD-#H`x&LQCeK<ZLeeE-`#SiKD8f=Hl#pxdQZ<95BSquXE?$
z4j9w1J(Q>Db6o$myc8}X|MI0h$hN?~<mw9L-r#@ym_?h{uU|tS1c*FNaClbEg&+<8
zI>ljyu&t~H1r`r<s)*SB%#N<<O!NpoiYl);pW}S%C7z<Jt^Re)@#xq?Q!g?{84m-C
zKMwTbvueg%YWG9MgUpwf5|p+3Go&!$?rknQ($R-gY0~r^VPf*7#@%Q|OFpF#RzgQj
zzWNYmx`NL4xlTg7oJAVnQ82};hjDZx)4egIlUv)2;`sqX5VbVLb0jMCKt2byu?^3d
z+1&NfWLIpEMu%IO-6bo2k-R+#SL~glPD?3VCdi#<8$Uc{W3w9kDh5L5ddM$8I09y)
z`KnJhcaj#)uom~7p$*p6*Y|F|@rpU+TV7rsJcVdzXs#|U4SQgc_Sa+qz9Hdr48K-T
zgMs}yHhUd5a|iIk4qjh<>bckBaw(Xyko57sE^g=WaCFb0fhGbHB@y>0ovnk<+QfP0
zJqZm=O~1Ykois{M$hInyRyf2AHSvs$phm~);c~RE`Jxe#q5M)J-5{4%q3|S1h8f47
zXD$V?6B~=lxFv9LF~i2j9nXrHCfdK6bRS&XwiwFIX=bsJHZHVcdkjIe55H~%p>qs}
zcxUs|8m8a(H}2(Xdn64Rs9B#$6qTlE?=tb*3PR~YY~}?;rsz*EBhV6go?uJk<BE%U
zYpZ-4;|u!8qKPY(cwe%8*+hfcSTH5Qu)?JLlZlysnpRat@o8xKZ{!%dk*=0!Of@>n
z*-v!Zb*)Ss9B$Ef);-FCQczs_+&lcTgCFECB1>}XdcsdBJ_-n^Zr~tcK1B+s->VqZ
zp<A@)W9g|n-k;gu!$7Y%K5<vn)=M8g#l_`|jOlqKjTAs&TLU>xw^<0*(exE<n;Odo
zDOt4$>yoXDabU((3EHv7Ts?$ajg3%c;c#P;#OL9nP3^r~$d%cJ;j5vozuB%EUpy%z
z<r9e4T4^12zn?na-IF1aKb|P}nr&Qa*d%d3wrl6}XIOQL4?>x(=H_e;LVSJOC>t<&
zTJ~K>CR6(8_~y{yNCZkIkJV(STE}hkQqA`Q$;aL^;KT;6kxtc~2%JcZ1BT34SXm)^
zEtW1h!IY7aK|yZmDR-umjmOvivdbD>RBc-~f1C1T9TXSC(Z-Wd-2%na+~0U1mjhZa
zqb1}k_7$S)%H7N%Gnt?uWFaB#7ccbrYy}6u^%W^Uuj7B|EEt)b+-qt6=eH3g4U^PM
zht-}lN!M}B$O7el##+gfy<Xg!W>{^3H8Yl3-LI(S3%_C7D$cO87ITtF=oWpKP1IBC
zpkXYWRL&mH$IiyT?bmIjOd2ZrH=%q%$;G%x=3VD%aP>&E%%eWYOsA&VckJ{QLo#0u
zS+T*jl5cKOQ%!%IklJ1)zFJfrF>-crUS~{%rQP|ZyI+nmKVrX{jbMM+T<vqx%O+Rv
ze5*O7L|Kqsx-F*h1_c=l&YgV=#ONHj{8gSG(s}Xw(m2@K>!mGHUl}iEFceZ`EG;T_
z1IeeU2-08O{vx~_msB`pCW-o2uaCVpU-|UJQ3D-inj%|$1E=x&8umRaK=>IOFN5e{
z^G-p?z*GI)ikv>Cj9lHExV>t&*O6Yojgopnw4|eCRWiuc7C&9}!ZEhGA~QLuXEy4W
zfm=uQx4l#qidRg923kCdv?Gaq$|9Z?B<p^uJjy6tSXd&;y2wEU^Npj;U1>7by+h_M
z22dw?lxwDuqx?yifshlMhK;#TCEuQgI&FAyc9G$y)6^wR#RDkqL`ZFIZDFJN%F=C4
ze=mkZaL4QQ(fsjExtcPJu<<rJzH6lsbsI}4qaFO|T3=sZUS19*<<$cj@ISs?m2G7^
zyoH9P2jpnUf-b)%Cu{VZJm%)+KrZ-de?Q=ujF1tz3(S$jzkWe{9)^qw5N`X<g0aSc
z9Pt2}j)j>anEBZ$!(S}Ne5}h!{rA#`?|_0*Nl8hi)ZoSLjxR2)WG!FU`Q^EW)!B5H
zoT$pZ93`AKwq>FNzRx+Tzkc=jQU~g0$@H)GoV(vSb@0EfDf*5x+Urv`4l!07_5IkG
z{j*WYH-2*qv;<`=%qw=>CU@g9(UDQmqr4(LVr>$iMX_ii(6HWR-`^zh3he1p$wwu_
z4bQAFvCmasWsHfS)%rS?`T2A1moIt9o27o|^#OS8f<<%UrmE_P{Hh<zhd&j}Xex|n
ztWO@35t)`9hD3%!lZpxT`T1u;w?R6j0Gg%!vY0%~<Q7q#DOa~07gI#-`?ho1VxnN7
ztf&i_N?+e+Majs?wF_^Hc0Wnnl6yj33Z_SXftD_eCr!{&I<tDjbBXSU9DL7_wx!Hc
zLZUrI$n9VwAWey||BJd&w#N1p9`0>dp(cedrAig1*D4}D%V?vdg3f#39sro?&Rj#j
zhbLD>qWat1|3N*C&VA7}GNmb^CrbuSj807fsLx=j{v!dgzp9l`QiWW6X?Lv6sCE3w
z<D*xW=l9747CpLs##nuQeEtq32dKX*ad?94+q_7{iOE}pMDjq{T4&=fe)~}vd9Bm#
zb;mt@T}RtnZKC<80lo{a6Lq9W-ewfO`cBRA-g>(WKOe#q1b>*#z<2|Ge@$E4(&Xe>
zI61PPp)JfMBSLFo@8gmKOi>ej0Y<Hwm%JZe0JcQZ@3UKQj;!>{m(yztAwBn$l#qga
zq&_;5N4S@NS9Sj?jU82-p{BzXBtEBNIpejCG5GEAO;S-2TW7Lv#NUaW3L{v{V_Hv&
z1E5Su0dX@ty~>A%fq{XMv8%1^mMB#1Wo2a@9Zz(s3achiI;{`Dxep+SNLv2a?SZ>Z
zH`hD+ZyJ2B*acau_0{+1s%=4oF+W1J))u{rSnFM(@b<v#VPaps$M;FXSXU1m%Nub-
z$z5ILclTIWru(N4W#uWn%zbUmWsiQi;lVx?-xK=YvEm*k@-~rRl=*^DF=yfz32`4P
z^S5b!hVM;+$1M9fhL=<HvQUg=1zr+J#I#T??yE{hcN@IN$JmMaT<R5>eT&F3K}{xW
z$a!@1*26UCqrDeS7sYJ4n-ptFuL|82)gS)08>Jgw7<AR4%(Rnk?JZ#yoW0!oBa$OH
zlE`{YP9!gW2a5sal0HW5UA<s25us~$CK}cUp5leWACWFh$2;`*PbG0?&l$Fr^;-J6
z&5sk0_YQu9y_+Z{TD(K@grehlVL18uMt(6A=S};+)C?H39$V}kcKUS0AKZ^+x!uvS
zLTP$&0fbI4#OemW#VAGT3{U@t-n4}fL+pLHnFWasC`p7oE}dgXaFs$0ITtrSve`{y
zG~98-)a<pf6}vQ$O~R(6luh)RA!B*<C#xVVC<wR-V*nKD|G<A6#XFVf_XbgK3Kp-E
zrDi~&|K3rnAML=K7f|8aovqWjL-LVMu4Oq*!y0jBcy;C~Hb38R+sSDS$Jv?>LB@~n
z@7>xm|IM0&s6mAPzB^JgbpcXYxk1{)=r$3AI1(Z==MxKYaz1HLFcq;cRm)<tfmbRe
z3zuCPK0Zo`^AWx-MTlexHuVGQb2@i-a3kh1z2Cr$YTnOSI5bQfs%GSTf<SZHY3?IG
zcTt(Ha8zTUdsP#bG|qAM>hh9iYYX%@yqz(|CjDQ|hx+vmd7c@D|B$;w7DCk1yd=if
z=0;QU_7*yFst0Zj8TJydtMf_3X^H7Q*JH_3J~9QA)Nr~!n`^Q#`gAeA6v39jNPP6Y
ztHjTJB0BUtjcET42C#}nf~GLTnwy&;+tQ(Lht>+PbbuRDZ#-=tEN3H#*Tw;lzfkqE
zspr%4OCDCc&w1<v`Z!LG23jO$J>Qe3@1P9XPE$N-t2jAf88YvD@!Z{x^R2V9S(@Y~
zHSycN6=}H#4ilxto>CFN%m{7yb$tkJU5W_Oa#S~%<6L^OXvHx3iw{~!SS$Kpv;A5l
ziOoW%R><;D)mvvEAn@vc-BqL>R81Dz)tmKA?v&!^`Q_G~jD2}Z#KeKhWo59sSaV6i
zCzBr%JkE;hrM%oiKicHpa-C0h5j?TC`@ZWhg`LIu{)pp#s#D>O!O>1I>3E6#Z2_m2
z>jre})>W0X(ci(05nUV>Y>3{($<I|0s4nMBm9cGw2gkP{K*#XB{nh@ulutllYI^$i
z?b}f3=}61mlU2%<VQL$<kBIpc^?DB#y(mCZ39;8#a4Xfl0=!UnIZ<mJ+q^$4>~mP6
zj1@@sj7>D%du>ZoNcZ^hP@$842zj`4szE1(<@Ft<AqJ+_(u@)n(Lx_znzAR4cW&v{
zBMON-p0ACd8sgXp-qB?X!n{L*os2t~aAtzkDDM^h#K)(3nT7_(oZG|$C#co?!`k3|
zCo3@)I<5yioCM3p5zNhLus#WW20G`3Rz{3Ic40SA*zpHpDrq8uZUv*#-JPmxJI;sy
zczZgcYj4s>HU-_yEhGt%hL1<Vb?GqOaDeF^>2-A>s-vii&Cag+IYYzx&+0?^+eKC+
zLd2bSZ8uO*d?Ftn6SL`Rx((_2{(pnz!-tEP=ZDY?U)^_xMB^V!6(9%yJq%P?&wHHh
z8Ge%Z1%Cx}6MtliRO-#AQ}-+!;tsJgbiXhkq0u~hSv_<1x8+F`TL8bn`-4;d=&R4P
zqIWXmbQ2+=rn_S=Z7yjUEq0s04ehT*--@0X(usIuoGjDwzTRAJw~^`tS*B>l!m_D(
zc}Ib#;Z#shk?3E2URS#%FXySKEi6>kwZ4@jld<op)sfaHw4&FjWXv(hvPng_)_0V=
zfG?XEbepl-ktjOH+xYQCKI+GH3CEq0mC#Pgrxz8kZdcb}dCYMccN0c;Re-`zXj=zc
zAR@l<f{KrfB-Ce4NP=2v*`G44q%()|^%jbeF*QADXwui`%!qh?@;i}%lqiVls*sX+
z2!>EMq~wMw9nT9ZT)VQzuL)b*N|#sVlAoSzjAAuIjf>aRXt81O%<)locZtE{gOv0N
zlv`*muYa_MKa5;R7M61J5xy$15tfyd%9MKPmroj7gd+X>XIdpvBOueH3%;Sqy;)RV
zt2lF~o^q0q=h@mCtj2i2`~10|QM+Q_nyqsV9vf|1?St?gU!*^ddj~$P6x4U>KNxU|
z(*;Nc@)G6djr&rG4s#?KK5jEZiNlI{@Z2v#+K|he_6~8F4zD2(O%-Y{-4}j8^G6?%
z?|7g7`Cl!-9tt{MXBHNa&J<D**qE3LTRtXzk$*|>D5<hhnYzV>*G5T~jrkmqiLtaR
zTehe74sWG~w<1?qqT=Gc%^m2qaSG7J;pL6*qheGf^w^BeXJx@kCKe!9H7CL5*m$Tx
zMTL-gq+aNy8R)OHHi3bd^TeY_S(`z9f%j&J6ERU`45Scpf%j(_7Zv|^Os%>}gmMw|
zlYug3T&f8MmWv0&KP>AX73nqn-CGUa<>R-kbu~V&WKIT{g*!zYZSg(gr<i_yGp|zf
zMrM&OPqMN_7zziISQq8PxO&|2iEPVDCd8=tzC>2mze!392#Z8nWV>Uk#&~-4>991q
zVJ|2yXUs@lEk|8-3hN_<^8S&1Trc(;`i0G2;_~36(>D6>ZsyqcW@_o9?`lu?UhAbv
zQ(xJ16FzE-sMp!1rdh49`0hNjmWCsan`vD8-HI2A;lPrd#V87YffwP70<yes_|3co
z9`g}jY3@%Zv5X9(EykbmBYXpLKEnOyw*#wcs`~*(Ur9VKX(_Ai{^fwkjjp=-$$n`D
zAlss!Ffma@iqm!0JTd!)&Gbl>X{Pqmj%|r%$%oAW41F=`WDz?;T7nswA5qKk!l-?T
zw?uEwGM`-({NvuXblgYr4)iZkS!jO@!OZXQu`xj&6S7w?h0st1KQ3i^Zghl{q9RTL
zS^n7TZF9{GU7-fZBMAryicUAw_7~~n9}}5iaXmN+@60o1VDt~T2Mhr-G;9f3tj}>Y
zRQU4!17wuN{@K8)$oe>m!cHmZfl}!IbHMcC=-Ds+kI>WWoU}KaM9z_K`RZFo?|Rx6
zb#69_=41uSIaTgQMc3LOMsB#6P6|$t4ZneAg{7Ygy`pB|?8U={H{-Zp1jA0qBb*8Q
z`$@P6i;B)nh^H!`;ITGwe|>$|D}~c&0W3gRAE}Qv!;_~UE`Un<Nsh0{!d6HWK|L?>
z_o&hZ(ygiRJ`s?_<;S*-<JIst2nylikofez@4Y5?_mt`igF>i1P$_WJFXrVPLgWO(
z&by22sCamLejEvd`S(N@_MfFmW~|yR{=vpm0NhmsEjsdRjCKM({+nr+=$`%2f>q&i
z&64&O%RB}~$;$=P9>E5gmoIPjo|!?XNE?bjEgZ7IUKABYAfmfQpso(U_eQxk%gfu)
zUMRI&meHTO{Ard#K>WU;WI}d$7TFz@tUs$BnMUrS+n7*HeGR+OZZap7Cg9_kj^ol;
zr4Kbyz{Y(-4x5`Hse3uwc&QREKBo5ePtKGF*GaVITzkq$B~037X&+a}OfFw3?I;a7
zBBy<DtomTS(ZJ2TwE4aIOz-l`%W-OY`Sqzv=F|Z&Y`}{jL=;G8^B#NBy1FyvlHJ|V
zKDd|C6!GPY;M=}aetv!x*z;&;kp2Pq4)d%HOASs0%_`#$N4uV5!P6?J(b+p+MZ>{i
zV|{}(AZw_#iGK>!{DENFv-bqJ<X<d*;psN^j*rI~Ph#C;+hSwx2uL;PQTW;8tBxfi
zg1s@%*X+5S9ST{g#kj{mr&gJj5dJg)qPCM;#BC$7(w6OZR+f$9k8PcQs($M^Yz&Nl
zKmJ-y#IQqokc&=E(uA&2R#bAzM>@KZ&z{YG5FgySY_of#mhOKS{kx*U_N3<78en6G
zEWOzNxVq-eH2dth`zGK6!8Pl`KdG2sYQxAzyV&xf!t$2e?U9ixl9Ov|{e>@<IR|B&
zrW)}f)lT=epBh{VX!e$rclK>goIN3CM?C+%X+6k*dAL#%8Cv50SirHRqAM^6KITuU
zXE)BkCW$Am_QVTKx80!SdcgG#jo#F9Dr8q32qMj56OA|qEhXsaUNU~wdJ`qMo8#$f
zq)=8@oZw!t-|_d`q31_n-G1R-j<_o_)nJweSRU)4dI&6VyHMp4L37zADfQAfoW1Ck
zUsqrjvlxE7@clX`>Ix(?H2z7mSz`T12J4u7{LeBNlSD{K$6hbs^YQs)FRp!zCpOT+
zN1<~MN!ySAi|TrWdA1(Vd-UGxc^xjqj}zR-qcPBV3E1>|y)XWLJ&3Na#D>De{ZqtW
z%_dxBMzUo5`xvJ8N<_O~Ng3PNdTXL+E^{GijAJ77<_7xDmtOEyH(jyo%#A!Vheu#F
zw{U?3nt1>X$Hc@0&VcJ`e{$ULhpDqC{}_&uL_Pl1HM&d;WR!pE=(#gKC2f8kToA0J
zt4!zY?b?^|SWGxz0<wFWo@ZU=k&)w0!k2nAJNl0jwcFEbdED);wbj$pl=pcC_4jVF
zN;K0-RI@dktZ!1PUI7E&yTyyWiR8}ouMG#_b{`%e&%(Htt>AHnD5+AX`}6fRGFhPH
zD*OKL6&@7i+R_i&&(uIl<QuQytVh3~Kw>$ec&G;KuD@L#Q>4U5Z1)@;(XnNurJ-p~
zjZV$YeGF-KTKC3_9^$sy42O-jMv-fDs>hG-V3dpE2U<ndZJX)?Rs}`u=d#_A`|SW+
zs;WmJXh-B;zm$8EKCJZKPt3l$yo6Z~cj}xOF|ks%pYl3E9UL;->GB0qXO}lyQX_cV
z*%1SU-`Wf7s-1HDQrYu9<M64DS5v$kFMf8Y87QG58FKb|i4L3clb5IjPwN6wsW(za
zjzV!kfgoI8DbIOI4c35S>!02YAfnkioi+FSKENLucK@&5(w7-GA-5!B&i3QK-^<&|
zPPoYF;_@hjYBdzmKar&2z+D$?Fc`#33j}x0_2126*M{q@S)DlOv^XNK477Y^kGLV!
zWpj5IjW~0qOHa39z8{_4JbsVYn&-XUjG$9kbhHZKETI@s)9`!c3!lH!Uy!RqMnCTn
z{}`u+f8j7P`c?w=v`0(SGM^nuo#XH1IYsq1H*=Geo@5}&hJ_N3CjxktM_zDz+n(QN
z=CKkz)HKRuf4~b@sERuP6hl6FGO8MF9Yi(!YZL*2=$F+)4`_qWTCR$8(W9i($36d*
z9j^R3<XvAS$l@ZXBPFQ$pM3ZW4AVe)@_Sp;;d|&@=$*ND6`9fsC<fD>RZA=2w>Q7(
z<mBWu8A$nX)ftM8ok*tc33o-_wt*v;9`cb!AspTUInAo~V7GbdIXBX$XS?;6=p#>a
zDUBIL@DY7OR@5!I#5c1}fg$a~ISV%U6k$a|qQiV8c;o_N`7Pa9pO`hqX!E`sw*5%T
zs`6O^;+2bw3xM@WIE?~Dss8m`=kv5Y19{alBWb0*MVl|IzjRvTu*F?m8UnkVUy&}C
zlf?9U(A$+AnLB?bXL9;xXWf^}Lh?2&Q2l<nKFs2;$s%CfB1}fPR4EdkU->MR)3>Iq
zH!|Flo1;Weoq8^7hTlQEJeMuxvr%eAA&(O2bgHCnz=1$VS+FT=>`i=SJzIMW)hg^Z
zzRXPLQ%XdnS!UBbNFnv5ZY3rpoWo$B%S-pOGi0ywVEG)kMVSx#9rxUF21N6D1~(%U
z1^7kq>{q<02C%nozvwjW_q%n`DB|1<qjp>g3^=_90<>A9yUevMfH(VNv&(FVK%}5E
zCmOu)_fK$%gP+q9l(K9tEFc;v3a1th6<U^$PY>3>G6@1r6Juj=X-at3$<Pu&w;8XL
zH8R<6u%w6lX_{8A?@`6Km(p4E3Nwu#raL=V0dc%GbT^Kjx(ZdhqCNL6^moI;!$pS6
zaH?l`5)w>T+z)@g9_ovaEEvWc+9*?gl0u%Nh@D1)=rzgAZXLKsz}UMYKVQgKZBOq-
zD&R1^J*}wiqt~D$Qqgx+2fJ8BEbiISumVyD9N@r>Mx?>U6G+Li9nAPGuf28`e+-f2
zVYak79oYFvuVJIX?+^?ZOx>NKr1!|kK<cZgc{_Tl(&7{tH}fp)A2JUR`d-p1durmT
z{&$AQ3;(&69Q~03g6BRwB;cNEc*4O^GC!`b_3Gtr{h@qc$Hsh<h;-C#Jc`Ub=EV1#
zzo2inT5JLv=RL<wfe+gPvdJj^Z~wN{LQ`I2k{S2i1?rseM3d=U*ftb(5w1sTND!t<
zZ(3a-ca39%U7d>V-wRC?<gt=0ulz&aA8m!cz!S)iNx~!hHW^%8Yz>d29GyyEl<-c`
zg}wd45|$4cua)k9UNhkcM3!<5T!z~QU$CbXVp)pv@&rokxK~&u4shVfSAQ1~gM%RS
zE9mt$?hsG28OfFdGD)#3t|ujKz%!*0N|~9<95MWl$6{ysIOjNeYm=ZEx=-LRT-C&0
ziSyw2__*)q&xxvcNnJ({o|wpjy&j9`&yD!Cl2``7#6hw1Wh(^XcPVc>x!RYm<6KL>
zywX>1gL)F?UC<#lOx_ui>T*IRg^8h$R5D$%BiqTNP^@RZ`)%Qd*+%wlnu5z;IsvyY
z5kKGX7N!a~<P{eeS8C4z_|c73-+PfJGM0yr{tZCWxJ?KN2`}OCq?Lxa&%07SP-^fP
zu{BfGAeRrx%4mV(+F+WJPF1??-K}X~I=LYs+<OB9@9j&k-Gv80CjoZgj%7Ol3iIu^
z&|s7MmnmD$AWF`DzeH5qxjc?@20{mtZoPBq?j`CTo~Wz#0UoJ&UP>8D9kf<Kd2C-Z
z)81_!azDW5vYJ!_jFaD84o!qpML;42^-Pb^4As5!TOTj~RezUR)}Q%f^Jk5{o)dTH
zzXeZYI^z8oUJGE}j1l#x2tD0{r1E0nNIFwkpwf+344>R-yJf)%>1)}Sd>f9Ni!C$_
zHhN`-Ktjw`RCg<R`JF-|@Yz0m84Cw#t%TbqbR(*0XFwMPL6DA?7C#J;fOdeb9)B@_
zjR7}oYGhPlKH3d>qgoEwBw-N(U>RNj!n}ASRPL)857-h43O)N+5T;aArek71`7vz|
zkS_#@|N3P4YA8j+!n$ARCOeC^CIE6Z+V}rG_F>MH@1|*ZP0baXVK`38O>FjCe?xSi
zlBt2REjSH=zzzJOM+YS>OP%2$5}AX^F!%B}W8&g=r@ym5eOd=_ihl}bVdi8-hCeoZ
z195b0Y-}kao*oc=LAWdo&H#;jucl$MgDndcxdp;p9lbloClmq<O~;S<hrqbkD>Zyq
zf*RULQT27mlMp&|U^94YVzL5g1URIOLwoICsQ~WqJ5r68*GfMBR?Vu%wXH>Q5j3ax
zqYsb7w4P%roH~(y%v4if&qx|b<PN3+BHSOV`5US@X;;VfnPP_GXIh4RU*#g_Y$lrK
z9xBXZVG+ke#<i5Kc|mSh1{KAlis9gUpp}N14Lj5U;Uh2F1RQjMM9>!zu3dPtJ5HJL
z!^^PLklg)h?_3qZ=d|+FGyvw+jcGmw4zbFxiBI}h^$wOZIsn+ieS=R;hfdATz6;eL
zz{r3;+wB^m#-t{CDeC_CuE`U1mRoHGU3Q3x@2j1Xci<FJQItJuet2+jd3EJ!O|=2k
zW%#X~Y;FG`CN@8%8us$7;S}O+O#(p2#RWt@RgI=iy)fNpE>!?$-Wo#j!v5ePxo#t0
zWAy?Im&w)o$1Lq(*Y{5T-4-sKcW}^{uDm*UGxjN1>X%$rZ~X^SZ3oMZr9x&6nC)}V
z5k&PMY^0{9HcRv%jj{9utXOW0%=>k;(CpcR=b$5Uy`HYG7mT7U#BeA8q5&wZh=ITh
zxOQr4<l;*lO$bzi^t@alC|>K)FLBA;LtFET`+9tqDuJ~+4}wp<VlZT;9)G)Wn}!ZP
zcL_!q+-ry20^J(UM~^zj$DQC3tq!E>f47>|;qLjdYYUD1evi3Ty945icag7VzwUM<
z4Y&F}{p)&b?fY&Z|3hyDrO@X-D)LcucDYo`wGPbMOc|TJ&M5M|zlbd77ZDsgzH2xZ
z$^EG?uBKYV>7kL`H#H!MVB_L8l8B(-w6<{QWPfPfs+m6qAU+ixvEd>De>h#MJ9(2n
ztVQ1Y*=>QXiR=1E6tU*lNY7i0K(Lnh#5ByoMO#}tSV8!Cst2FzW!vGZX<wpRIfw~g
zYFE7nQ8kyGTL+hp!x~4nJ6di*l9%ax8uUN^9G)ZEU)xGa{bcSBKPJ-8RYh~$_;`Km
z4guyuYhe=f4nfRRJi!4<hH%xtqjAHOF3#ts*=}<UM0f8VDv2W_h^4qmip?Fgc5$87
z@9MuUEkIJta<4LWAhNTc=7=r6eT27YJ6q@QT~bCbvI_u?k0BGj=h(iqFD@<)Gb@yH
z+jXd(J|*qbLH3fclQ(r<oQ|`Y43tQZ3%wNLBg2*bVM=NCm-|3%<c^?_5a>*=HFV%8
zB@(QSfm=RV53v%eCuE{Pt^u6TRcv9|{xnE>%UI&lt%zf`WaQjr-+%;Tik@-I`fB^n
zFMImxx8WGRX)z6CqJDn;_%iNpM+B&&x9}0z6P0e`S;5~l@GeehX=x#%nZr(V`kWep
z51tEqEv@_1=?E{{{ayBAcx--lyPiBeM{8Lf6{%*Yr8OE&1NiaYIX8q{w}brq=vbYz
zPbrIZ^~&9WI#2QxspWVueKc`hrSW?F=q@wNX9Ra|<+X^zhH9lYc!Yms;ZZ^$K`vhv
zoUCbUG%@M)q-JEqD4cH9)`~EWj6S@KXzVhA6NJXww{HQ?2&zDf*za$LSKuKA%_Nw3
zxO#*+NIb_jia)FCj;}Mb-iQ#2fdKwh=w;5k{CxFL3S$MGHOWPF{o;<-NU1k0%F0Zm
zF~p<47I>xLGmpQU2vmBr_eZ*ZAo$>S6U2>&rZ2)9&-Nastgt;8yxC>ugMDXzRfV~w
zW!VlW8NZ74MK<dZq@Ea~qoV*?xiX@@+oQ|MU^FY(zcUSVuaEmtTRDdEVK1k?i+7VF
z@Pm4f#ruv)_XO*I(klq-)Xo@i)L-n>f$9GNpUqd}X`Z<@w-1tGW}|sTUVtK^Oya%G
zI=#@FD`-55k%5t~@zsrtLP%z9WZ&f$GBWZ?RY)?w3pEKEp~H3(>Q}z-IMx(B)jzwt
zH<yVpIL(ECA|)Z={@>%?)r_J{WyU-vBrih)o`&D6L7ht=w`Ogy+1C=FYx05}B9v03
zF?LmkDMp+M(b4i*+F%XuO`<}wK#bu(_B@QC@z`s*eft^B!bZ))l0xx^?^6Pkq+Wi;
zyK6Y(B)`qAuDO%-uD;ZLymdzMK9r$bN2~I33a9<pM?A8^On9g+ZlT6#f(&=nS2EIq
z*UIZVUKsiM=~A}Fw3d>R?P4n`V1i&*961=grhyfFJ^ET&&~<Qn+U>}bJaG^C<m3cm
zfcxHpH#iTtqsIRUq0Ob=y<$?%<q{C61+Of(@H)^?nz-{VFG=CdR_A$h4XViotseSq
zw?hVV?ZY1pRwC-3iEVdhud|dsXf~7+8e^by25?uLp=!UO0IYXD#EjPQ2${S0dOz?!
zw$1kwYfvP6DzL0?eF71_j)24&e(s?jMV(}*m?#w{W-D>8R+Xg|K$HPo;y>-w!ud2u
zc4siHts2&GDCsE_$YbY=kOHs}k)g^eD*E_jh=RMu`kMb%Q1F`{Um9Fd{rp5eTn>2(
zA3S(2KEgBcQwDP2B?#GBW8$=lGZp*4G7rzpc^LX5txaY%8xFoIhxWfh82qg33`;e8
zY-bcD>GV9mE^T--<<R~RgIb$5gb2&`{gf@Gd;DbDo0Z`iD>b5T_b?H4PU#i2uiGzr
z=E?ugx$TZxazjoGQeBuFT5;pBnjc^U1%Y(QF!ApceD+qqyP|>1RVmV}F%0EsIPx{+
zj_G-IE;R_=O#Zv>*EDbffTLHVL$+6_NCG&amUm_yXoY(8ABOMSiQH6tjZSq+&}*tO
zEU}rF-xR1Q&wo@OS>ti*Wd0Q?2>kc3ynJsVD&BoR)}$t8Lqa%(!;RDVV1xiEFud3R
zg=Q&-hX-n17^eeZ3mg=`M?Hq6*og;Pw$oK(jJH5vA@%Z^6>cWy7yj9;hKIl=XJTZ8
zy545bL*xOP=q`w?|84=CFQ8*<3DRd;0+_q8&YN+<5R^ESb_I%<#qCh$xFx5ZZ&%VZ
z(sr;d%&V%~)y#~HjAX^gNEssr1?gB>ak@8585v*c#!5=`_V*S}DoRAjJ#Qr`<xpn{
zBEWP;L0Xu$b~4ygw0IcvW|;mvK+t$@7~i|H=%5<>+G2nDer@k^v(dqS)zb*>DT&b7
zgS+K+e?PO6tPEf`SAc{KuHF<TZm8s;`gX}a@`F$l_#CjtdVc;~+t?xaZ8lFcI3V^j
z?$Cue)`0*MsYEa(%cHu+kZ}88`Vku>mXXOAp=ja{i}buFk664j)Gd;cE(8I&FjZsp
z*!85fj}$_Hw^}g@8WJMl7QHD*3Kbbh{H5jPuRUhsEu?dB(Ap->?E&~%qGfU$ulRmO
z=ggVMt3HXodD<sW%2&7e1I@7;?&$Y>OWGeDc0rC=!s52vO%z27ifz5ax{FEE)MKfa
z%&;eNe%Qb=q^@iMci`(%;3zGKVTPbZ5Mmw_<C3j4+3c4q5<20rifXY`bYOeqj~B~7
zUKdH^SoCv5K}LFigQ@;q(*wpjL2^5gdp)j;anlS7L1aUJ(2NVtSe+Md5MIVG$@f3j
zi0kiATbn314L?rZ*w_GpNN0Py=H0UiXWyB67nLmKjvqhtYV3@`2v9T{vxA~an8ukX
z91>=>zHZ3wo=qT&D=)jW68qPXCGNHXoV}UZ^xv}IQTW2Qe?D$O5$<F+)CwnNQ01ed
zp-DQf*DvJX-#fBW`78nslY1vt93=jKU7ws<;kjFX;}(&C$76qTwZutO>+L-P^~%SP
zVz!0#fBja}(E*m*y8&&JUlRPRi4k6kHoyjfB@Nh3nBc4VMnZ1^>MkUYF~cd-Tv*VN
zl^Jz)d%C-`WfP*-HjrXNFBSu!5I1I|kB*C5m0jv81j9ZrCr`*hi&g6`eRovz2GD%i
zbbBZWAD5Xh&bK<H3?364?oTKY;%yXH2g5y~|1_B}y=#?X=7m_xuNGft!8y<`tzeS`
z{yjFVs*q7A>~c_Y`$yFrLGmrNxV<wYc|B}BPq{zU2}OJyQco-pM|=ETzj^u_&YiF{
z8_3<R)4uT8zJ@*RS?fkkU`MFPxskY#XJ@w^7S-~;JkCz%LyQ+`_+Ea7Co$>;Z}>C@
zbcG0rM?-pU*?|6vXq1UX+i}cH@aYG;u1`w?P0YOJBZ1lLM|U!Y$;rd7O0Ij;QFYmP
z`S{Mx-DMCk0`Teene@>{=%z+Pk#6aK6aeLit2>kBokK&m?X?aF3SoC=P3rR48@}tF
z6?oc5MpVrl1_TMSvOu0CA7u9N=b9QRkJa!d>>Zb_kuX8~a}2d0@`WRfAhNOXJM3CG
zM$rk>E;KA6!Yuzj1ST2e1vkr##@<5VsqQ8wrOx+7KO-UdQ0~wWJ4VXD^aNqSB8R?*
zb1Bo-^fa890k}VY{Aip3!u4$Fn-|*JiJ(Bs$e=;g=z6#f<H6{RMH{}h@uCkd)oUWk
z+S);TlA85o{F9o8g@q5&|3%VOmt^yCIJB@|>Nbd(CF+&F_EXP4J~=7+^kB%03u-@L
zwG|T+1B|50%erfOjs#iw#Xt-TOQVWyX!DrVMAl+){aS8w0<>jZu|b#He*%WfK|{Z@
zwo0?0h!~+8E~@{nBGc!sw6wV4f439bicevU!iH{SY%J6h+Opa<Bn^oB$;r{JqKb+m
zuDI|CV#Hw1JRo3Rgpll)se!J&Bj7H)Z{X&nUW>b@@GYykuri@vB1SqfR!^CV$82ay
z1IM;K!xm{QF+GOF+AtpChaBJKW>p+&XT-y2Q9r_jB%l234Uv!6hNM=SSsg4TzEvY!
zIz4s4#*=XH0GJCS3yUSphUCs|w%WwLt+wGS<KR6RLMNA6n4Ef(P~LVMr)^u$+#NJ{
z2ajI%TWQi02fKt)Pk5#l=-4kn)bRB96c;zX>*tcEng@SeU0y!knmjy5DHym*N!eui
z+Z6g}3KZd=uB}I7;JRCme7F`JH0!&;qZBTX?z;R@<G7s&wH`=lU}$$#YU=L#I>rJ>
z1i^d@23x%fGbI?5B`3exM6O-vc(%7VD^;s(JwZ(J3$kYTc}AwDPWGywH;>iOmX2Fr
zYm`nXQPm5w%>B)X`#~_j^5-4&hk28rW<(XeICI~~gt$mcWOZkDCgBMz%#dB)<{qq$
zj%4%`mrA7O{(ju7M+1^nW#xiX-_PaR0QVFT9qRh<TY-DWg)5Z0ZYxdHoPh3uGDBEU
zknHv2DF?4O-dMKUoRU*^)_&#uhcVR5!>-$UqEz^>^<|}}i!;THNuj{`>Kd#ITBqci
z?RRBag$N-{NPfQwtOYWmDwrbW*R)gHdv8l4&t7)GuHyis^^6L&$VxGHf4h4CcjsPX
zCJzj-lh1*f3)1sQW>Z@yjY1~nE>QWYe7taac6m5}2Wr*w*rz;2^Yr+a*%rC51INX&
z@q1iWTffgp{9XIcN475_`_7b<0uaqBo-ms)iDtn1BOr=+Eyh@AY5zd!Qao)?JJPNr
zhzJUDnCR#eO`c7vOxqKsG+yAhQBzaHK=I)n)dw?abk`A2ncO%BEasE$19^|RayGr`
z^EK(GlnSAfvXPl{mKdcPgcc6^?L2RH7ENLv<Pyc`6Kha#KkkiiGM4^S@V{Ds#mxW<
z4Yaw2V+)Um@3r0eu`t~b@n?6c1>38dYsw8n9)TjJMPT?3KQ4tJ-}`mWJyc4G=WN;)
zYrN(9aP*Un=XhAEJt@ti4I^Xn+vs-Yn+7;SDp7s_sQ-Ybt7Y<uW)-HRqh`6l)_z-n
z-E;OA;3yk!`Z94r1TH9i6r~I2K&X*XR5*z@;B#5vmfxQ-Wh0=Fu85PqVA`6qegD%l
zsurf&vBf=wmR($i7VK0*)^qOF)#+silb7TfX{V?2?_dV$iAfWpr|g&fs}!|_uC8mA
z<&(p%Aq)IZ5@^0@RwhX+`_2RRr1GEI)aXXhhDt7Z>K!Wt^elsxs#c3ij)ho9?f5^2
zK~rm<&CS82vOaB*-B7OM@7Yx}WWex(aW-H#HsjiOU;EuO0-T$ABv8wJ!cE7)u?N$;
zUzeUishWE?_|c<Buys;LuF0J{q5C-JKSBNH-jZq5TVH;>Cz>ALwoS0G$r!0g0wVg*
z5cED33mrE%&B|si-?ycrDW9W|BJt4xe}MqZ@@z~n<K|c|=;Y?F2rI0D56b@1EiFZ6
z+`02f_=>_KhAO&CPnZ-AsH3ObFNcqq<v+v7VzAKbH^}3RJ;+ptM0otBH(@87qE(>m
zSEZYiNb07(=c|k0GI%T!F7OZ&mtCEZvB4HjT5HKKWv8wi4z7-?IdvDw=sXLjK;shm
z_GP=4vh6LSecY1<azgbcjOR)RB_)8iyV9e%T48Ta&swl~y#I!fsI`y$R0UcEr}J}4
z#8@LtQpQp>r$J-j$4*KR!0>T)6CS=n+q|D&UNt&tUW(W#c;|59qn+AU`*wOh4#aKH
z*O3FGk&qBD0_x$(fvF(>ri4#({ABup1hD5s+88(_&x0tbxar!QRow8E=H1@??4a+q
zHM$LYH}J({>~bhXL%r0^)wMtV@iVqKN)Zot3FkE*X5QC=vo*ADJ6BWt`YaayOmH`k
z56JHnm)x=`sCT^{PEW!pM^F_bYWSr-gk;(We(JbxfWQY451^RDa;c5n`%+G~FMixY
z>t$pRm7pa-dr%40ElrkUo-J;U2ccsd)9judtA}vj#sBWO9T|X!j~{sJBC$zCw|IQK
zY#G4IeX@<AS<EdQZKm>gSXiXBe|1>2kfOd+nmCkaW+qDAV?L)lG>lm(kx4EXE`Ml<
zFxZx~w@1^ht8m=WMGS6o-;}|zvflh>P}_&tM#~BGn}p%=FS=;w=dzmBdre<ZJgmCX
zQL%`>6sU)VTK;qX&0B_cF0I3275H*8H8Sa{J@)&ZgME<4E4H~GtA_wf`5$Acb-nYn
z#mCs2T_v5C$1qFw&mW^t5+G%VK|=UY)`>-V0Z<jFt3DGKrx5WVqolNc6(1W*42Ed<
zvXJeB=j6f!hS38D*rwsib8FUARAqG>v1ea?`-x$oC8|VA9{~|kp^j3^Buq6{+{|+r
z@xDbCc=sKVDp|E+{*ChUnQFhFTM|fG$e3l(5y|zGMF+|fArU`Eg`4nLA@aK+YuMSP
zuJW(8pyu>5cI?6c>MyX-B}n&VD0^<MyK?i2<kv@uJ=#4>Q+Uc25Mqe1=^8X@2z{e0
zgDEKO>G`?;)e0%8V-i0NJeaHDui&1Z$v*HF<$zf1S5*ZTO~{|$?aVyM-$e>{GP@S6
z^n9((KnP+D;Y+65AGz<SAa7KCEMyiGoO#_D*4lILgJfv4&bAb76g+pp(uWx?&;dAB
zp(NC6^1yN2Mj)hUORS<(QgDgYcipNh$_ILS?CtEp(xdIPupq+XDCe|FhE{&^M%$F`
zO=eNmmR)Of4+bRn_Oa|5`_HqovteQ&%**Pt-J!7MdoVTGHaYS0r&Q&898?5&qvPXA
zD#<X#@c-1MZII!Tk|x0bVWt>N1PoMdX;}p0bt1YHTigo?2`O5FH*ekmXC98*pyGpp
zm|IhoFVhq!rl$U$pO3m>rcDjQBxJcH9wC9dIxOF6I6Xci1-sBR8Fcj3y_ue#Wo39U
z*?%%vUcusRmaM}|IbHQ0^FG2{yOM&?>Ac0dMU=W_Zr+!#*6r7?mYMW{&6#iC7~BaM
zlKP6^3l<h2?`<``zkjraqma6l%wYxRa0C@0CM-z0AHIOLJQ^;n<FG<TH2?N1z7bSm
zSAc<@?*$raYisB3!dk0(GBMbR#dh7Gtg#6LGp+W{pm?^)F+}OJ;v>3X`?np&&BMes
zXZsPe!7=Z7C(7wM-UP{T*Zs0Y3&o4i{jxte$XO|0k>}%WYKvsGJS79d^WTO{LhMk%
z6p^Pk3!*?h1=BGc#~u`GWIK}K=H%sJ&Mux&n_DUrJ4!URc~1@VBAF``W8~41HC>%d
z!yXMCdowO=OUnX?vQSvzCjSx?7;Uq6ehHr?ItUXNk2E48nO=$EN9A{W0cUqvx9JB7
zy~W9eIa&7+BT~jJFjeYLp?0vFTTkH>AWu-Yf)E66OXF4{o&_l=0JxC+xJ}~c)|TaX
zf|$W9nw4IF&_(l4bi$y<Gz%Cmr^N0Lv5k_?#!Ea6Hk=#?DsmpI)nFB3Tfj*Bej6b=
z@nfWqbhGM}p8`D@JhDwbc-V|FB9@wNP-;E<2zLbD%SjUA<%M|jW~ujg)ZoXS1TtR&
z=KP>33l$76BvrN0Iv0oW17jw%8{5{F)x*Lo`nD;2gx98qlg%-Bi@yFI8o_!*Cg(*E
zmMw<y;_lav&+e*{D?_b>AId4P`iwazVQQaJaynXHicpfOszFgkpD`zL9iLTgs-@Gy
z#8%jL<$8t6`u(dy*1cIx)gbXy=Ka0csi~!_E`MC|zeWApr105XcQ=7d>7!;Ls(|M_
z`ZeNKeQKQz9Q>-Q`BYSffkaA9o(!t}@X1mv^&+%f7ORf#EfxAEv*e$he^0itqLD>k
zVs-c2W->ZXH~&~#`q;?CtB_pK?ZDRJd#$CVrH5AKM$uQ3?_XCO)vXex3f4AI5MNOQ
z{Ev;w=h{<bo$POaz@4dHnX32LB=Pq?XiI(NC!S%?^PE~qRdob*1o%IKi5scWecZ(s
zi=jdpPVyL8KTEuK@|blEjyBNjV(#fCGnxJ&tHZpv%Pazfj#!NnAe3xd?$omd?cW~>
z%fLg7a%x2;XRM>^*QYV3h>dSA&QV25p*U}Fm<U?Vp|cf5{Ozq&*nNDFZt#QUXMY&!
z<JQ5!lJs=sdQXJFtu;g#IammjCMSm5+y4w*|Ac8cc6?tNlX!s546GC&lTPK<rIwkG
zGIa$Jm0r+velPt?l-hG{?Wer%`@k?t^h}Y#m5rchW9IFX9Ob`nEslpxPEE=aXjv>W
z2cVn7_E`hXVUrV1Tq`jABuQvK_u{0`(7^0ZLNdRgKrR0Zi;v=I@qD(wLxtx!T6fom
z+J;E{wZRs7K$)`<(XFAA^$UenIMZ&i)lW@iV91mso=_%KRg%t7mg$jB_7M=c?x<M|
ze-qJ!Vm~&A-%Gqt7VOIbZ}9;%irsXB&k;Y~Fdan1{D8j$1OOgE!q|FqHdos1fr}8a
z;iw=4%usc=7bTUxISLCa4)IU$?Cci}XbaHM5(w3c-5n09!@}f;JsuqIIoI<vdT_Xs
zNwuf&7EwHP#HQRihxrkmOhyNt;z~hMpQ#D6MgoIw8WMj*i0^cm9KSAiwJ0qV;pKn!
zoDwe=a%wP7ZNuXa@g7Rl=G%cWQ-zwPuXmSnb}igK$6UYPIRa72#*UZ2Y9azBmkZ~C
z@>^uC%yf*8I#~y-K_MZQ1Is3As=~O5w;Kehc07M{hGo*y&rgwYh8Rk<av-GKUrl_N
zrSsdli9_n^?#A>?z}Vz%4&8kEyMLF2i`IE<VY1wm*Et9h^H;pwACA_PPKkQR$;kFX
z|JAlCRN)%LtuJgLzrbr7rdL#3uU<37!U~?WQ0IL({jUxS?d<KXEhxuooL-&5YF${w
z5Sl?+_>o@#!%7E=))`{DQiCKE{C?AmFWyaCt31-bkA`OOGKar3-pVreZnWXcoDUm(
z&|2u|=%}iy>eIbjtjM5i()+QaM^xifaso|3LBY-KVuh^ajvJ*2D(C%fm_Xq@VhVR@
zbnp{CidZ8N#88>6Uq64M-%K?(f30HRwu7=$$cvjJ>o@1>qNpIS^h#^%G|Dc`IQ`3!
zi=c{-889^wc{|;hzta>HrlG3pRr+|(8Q&9$#~PJ@(b>(pm|kHIGldfD!|*DoGlt3Y
z3hD)cy0sHTgm^o%sCAC`RYZRdMzWf8VKy|~k-dVH)Y<;pV-jAZ6k%!o1_3Uvij#+I
zaah(tbDoDMtO0H3XY24}!0(@{cqvQkK}XOKIaZARD@bO&)#$x3XDw&KE0Xsgx>xT8
z0+66r$b&$(PjJ2=G$f=zqxvcs00cgMkK*GIh@<V)&!01EKHmDn883XdmDgK*Qi~y8
zJx7t<hw7(ibRI@0<altzHP^^JJ3BMuDzKK4mF@j2EOgV~&G>tNvExvE>>bZsBWIB>
z&R!lZri<)Ga1S<l6GpTrME|+FkS@=d4reFbgTpk>F0(?#ZbVYjMefI?H?#zdN6(Z?
zpbaMEPlrGlamMR>{`}d!Y6)A9iH4OGv_e-v&7_T#g28rPxx?N)s8~Hevx1*(Zh^OS
zeA0hrmO0K_w(%gK(K+?=>jV}x1`ifuk#7aAOoVYCxI_mNW!8Thyf@(#c)Xr`KSX_m
z-HMp+)PT!!5|_krspSgxmi~k%t1rV9^C3HkNWIO?-TeRPdJk}{`!H<yHnLa9o+Zg9
zn-D@)WbeKA-h1y#Rzi|pBq4icMfR4J6_PC@yw~0HzVG)Q@ArN8;dl;DxX1th`(3~5
zI<NCOPm|Vge*$;v=FGQb)@d9^hV-ZyQ5QU`^xa7#8y?@XVeow=D@3b=FJ6PSV9n|=
z&HcvclHN#d;V_BE16~eAii~V1U!G8gD`bA}T{@H)+)K+tnz4FpcM<xD;F7VsczG2L
z+W?;ga`k80oR)=uP{?r04ZO}bBu8(8=EL`U?qpL_l9raa`%l~t1}oOUZ(};YFY-}n
z(O_;WjTnfRXt=w(`=kQqwp)6>HDNqYOhY4P%pjlgHMhqi<skk^MEjfM-=;dl#n}`I
zDoTELn5#%q1{{wWuvBeNr09kw$kNjJ)@?LckIZa(j<=VHV=Vw1`Swa>RtRVlwdDT$
zK8<VNpFbc%Kg}r?vDL^^wXm{^levj_D$fK=qDBzJh%#5##8sin<~9v89kwTq!gsWv
zn|~T!A+1J1<=EFxTmE#)?NEldZecV5&UEAE0AH!UmRK1$QjF|Z)l5rIwiR+=Kpu+j
zmcqiWg@qP?lkjuoQ%f{UN=l%eH##vvfQQG&$LC-%2td~A$_n%ozUwuhAS9V-8##Uf
z1dAGqM1j}mN&PD+Kn4qF`3OXw)ai%9wHiquZPf!Emgj%Z!z$kn8+d%8VX+V!9MbgO
z3Izk2ZD+;SdJcmG#iY2uZ)DqYxg6)+hTaY{OQy(XVJK5MqVm~lkZ3e@0M?cOuiD-&
zMNXdn;st4NaByPO(ZiSAx_J*MqJ}p-a2^)qK7s*TKxZ><cXt1cy3gr5+3}A5T1SY2
zx{nXrcR&2W!Q6WmPpe}0Ql5KD?8KPxW<ruYRKP~Z>4Kjl=4<BQkU(Dip{B-YVW@q$
zo`v+QwQ(W10YIu|eG6LqTcvWzZEe>_Mi?Xr+1Z!&mO<DButesRSV09%RI&Hhf0q_F
z>ltP<6+dISl;|D0`RrBol+%oZwLqC@mSw+*DhsI?$#?{u?u1RjpzGJNTpV~J5i}k=
zM~V|El}B{2!yaV{W4IZWC2p0~SdIGZrsf^oni4I6mM5LE{Zlakm7#hI24ilooU==@
z!T0hz>}EhR#ZBAMF~X5_aO4#kJ96qa;f5ys^0z?{)q}P;3~`nlHk_z%HZ^#2k;nUI
zWudlehU$z}tLOeMXGndSkwMdP#m&ZMUsHFQbKf7@KA>2MkcadU$Cw%>Ogk>aTh|A9
z1>bKQUV^(0^c5ADo-;~E1vH|@$lNU1vMi4~rxu7rLr_rF7&dIChAt%wIIc%U!5Ynz
z@=Y9U=_fHLUz;?nDSgb?P^HuKN$0*!|1q9O-7$T#5_mfC;EfuzqaXml1ah)#P79HP
z<C5#w6zSo@!opIqXFc9RyM$l9Bq%46XU*91H8?WI%1HI&J?}KTe*7EPluMwIzp~y8
z`c=jE?-%E|ELP0}|MU6t=b(9$mYG@7Til!gt%j~-0f5KW_kW1$3TK2bt4QS9Rv4Qq
zSl&%+{v4RT?6FOMCAPYB59HP49zL9F@~;KfD8SODsEqG)&~A6m2}aMn61qu*_2X|>
z>rYq2?s?bK%*aT%7HQx>6l{pfNFCZ+Ut?|$IEX~Ec3w?-iE5Yufd#K>F<1k@juU=d
z{<&wlJC)2}6*q$3$!bVGgF1d%JAO_-ep|9AWs~Wr=dHGuiNok3TmM_8qht)vUU&C_
zun)w+l7047Ok<C7P~ZYFaqvxBBr)eN!mbSDkHE*nqM>dJ&XY-cUm$H0;&=7&`T>7X
zAe@yzbqBwin3xy}5f2+Ht3vE%nEEdSO}o9Dj(Wk5D-Gzzt5Y5Zks)6B9yrbK4i{i_
zxvZ1TBiMO)-7H=2_8SK*&1b6&5AXIpK_G4|2eW)SiHU)Vw(Gv%{$-4ZNRTuXuIT;!
zp`92Am+faSUzUDdlpm#eb)Rjqw!mbrP);{?DN0#l?Z*5fuAQ|Ng=!C4b@iET)wxY&
zHQx|^c`Wy@3n@Ag!<)}+ev8G%#x|QSEl!{v|8;dxArhY@hiGRju1S&v06O?8jD7uT
zH<)uDxch%E{w$y@?k#u2Y8kfh`O*$c-Ay=4!SKf#I>Ol^u8v98U?jyz_*$)HTxHtY
zy&4CU5CAgT+iy$eg+$-+5+M6=i8*Q|W!2?kE{)iQ>_yCFG0FfV;L=89yuBS20oF+4
z4W&P{M$k^Sf_KH<2k5<CGkRF@AY8!9T|`?g6L0Fi5q|S9R%>*bppsm7WG4fO^JmwN
z;_H0^&InAo&wiZ}W<_f%9?+-KX(FS*qYyh(J&}Fk!hjW49AAo!8D;7dh(-AC5nv9d
zws)=z=mSDP!-*UzQNNS@(YqlKV|lJ6sih~5mBs5|e`e3ypeZZ|s(bPj`miif3rm=;
z69;$P&m7cHj*&%0qQG!SLTv0KB_)eW9Y7gdbHhxh0JqC78VtG7_PVInwR<Ax&rU;K
zUCCagM&AwcGT-$wU%`dPsGW!qw$v8|ZxP`}ZkMm*THn&4VV3SC9K8ZJ4iGAUObr@?
zS0g1lp`oEb-Rg(!iH`?baa3_cyLE;qD>v$1$-JT#!wO?qXy)Sla+rHFy{||)>sZ^8
zpY4yP4|#V8f!Aw|v@xuT)io$zHZ@X##eBHDzXS(wu}(GXS@3$7UPc80fdDsmv35;_
zZ2r-CF+m0lYEw|58num%Zb4aW_ejW&G)_^ht&Vm623?ES_b*`zN5i9Z<@rs%n=kql
zwLt{>m{#Q%9({3T#4;4ZI0=tK=<l>w71i~nPujD;iG$`l9B)P>mE*{ceT&w+hylaJ
zjZs~;;L*bkNG>l$RojO%AWLt_`1PB&H?-t(+s@~;#+s()`Nip<s*)<Gpp#_0W8%xS
zooEmlN>(tFGew@G2nCWflTv^-Dh;PW!zK-16*kHOWXo_I@=-Y+H=agXtp#go8vnqy
z6Yp0MEK-Z!vN`{0=WW@%LnoI%EXjaybSy3(`S}CnU(4G*W4|sUY&xg85d9rLnpjZ)
zr8yXhw}9b`SFhP8Cy@2LIrYhwB)lyHy2ef0aNq*VAyM#>iS&g_zzFQjV>wyQ;c+8V
zOS$(;sp=c<zCVULcXB*8F)qN#H>NW+b3<AD65J0c@nq)|w4nD)fQwsJS_)<X8YIk#
zP!c80S5pV+UsG7sZN}Qi-P`UW{pw=){#m_<3cFR)G$q`#6aPKp4^C*p)41L{cKi0W
zs;;hkaKZiZ-UV={n>#xbKF$r%HfCX?{bH-IryMKe203+qzY;V}z}Kx<aID_L(-ZUo
z`Md+r9}raos?Nj1!?JX;)&!lCZp%J`!|gf69?TGXk+aY=+BmNWXOB$;q56XobiD9(
zRB!J;$+>JqSW^yOw=lI?NP(2_H$?`eQs_hKFf>|sx1m_L07;;iWV^V}gjPJSv+JYK
zpKaQrB@9i{78ETHm^~Bc!T$s^&XKNHZ0ukpbx+;D;&^Ci2-loBq+mR9@>I`t{V!!f
zbh;7EmtjHz0;l%ju<-C(NBFG`!DGG4m<1!yV<;8WgE#*JOcV~*2b1ID?+FOhR91pw
zfEsHmTp}S*1(j!_f;J6+=%A9Ye{cXnoA&y3wH-#YG%9IqxsEul?mK^e72aCG?Oj<I
zsiOZb(VNQV=XT_nG9t+k^6=n+qJ5+87Nd09M4%RE5P;dJTfD-~t#qVBTPeXGU|e2~
zEr(rMAYyP9#O~k0G=4mbUch_0QM##qLvv8ZZ0L$R>j`IgNA!~kjIECn#&%J%`TVE9
z5@AKr>gb>%GW4P2LL=(M-bg7IAMg@~x&whgMO{v&m&L|}om`2AN$|F?(8|IBehn=D
zhC4e~fgub29`Jw?Y<QUG)-7`g$6%2G2j!oXJC8T-AC_=Za%>%7cSC|e{hf}QS`<!O
z&u%ogx&T<FsG+Ux?Kfebb3sR4y$ZyOLM2Vi%y?nCHgU}<L!<q(Tc!7&J;LW*KK;-z
z*vy`ZEtpDNaEWUz5NV-zECjdB&FagYDZLV^oOg$DAfK)END~vEs<%D>lfaI)-6bWC
zMYp@7#H|Z06vgltEHALdE@QuL+6RPcl%OMsCAm1XW`Ai5M-%X-eg#j+;^&Sw8lX7F
zNiU9df!Utg2YoWTwP6g`LryKNkaD^&IsJWozy*B?vpsr0WQfoiUqY&Nette`!w1k3
za8}VIfnnmkGam?ZcwnFhM{~WVDz$Hns(7wT|Ets9>3APDqNDoYtLD_yR1h<Pi1FY7
ziqZBS#AQ(7yd{_P9i;r=T+YtUo*9h4)x=?T;Txx+hC$)AGJPOiAhtK%Liw29zfpc`
z;wyz-X6wv2w}()FMm4uxE@%lhnY};_MSR9UATXkUG3QDDgf+F=_c`c?PVTfAODJN*
z`eexOKIFP%$S$mB<Xc}_Z*bhq>LrR1m{4Amo8x59dhK0*+w8%zHL^pPOUvuF(gPWG
zaZvqQ254xuO;`0!64#4*!{D;Uz2qA3c<e3J)V{}?XMA-&YL$3D$nCN2bkRps^aoF!
zsbXbH4Kpjs%5V|DX+;V$H2NMOnOo5Ltrr#CC&~+G9+8A`7f6lbTk5DNkPqM_@i|f$
z-O$z5J*g)IQ2O0F_JI7nJfsc`h)(J1X3Cxirl>!Ebi-1C>|D>jEa78Zrb6$~=03GV
z5}noPTE-wlg&bj9E5h#x<D7uI{SUn2QI5DtD<WnJ+qUF`{W)~acN2+YnOInQdaNUs
zLP8<SfCLjXx1ol)|6}Ar!?*%qX3fp}a3&6~rd}AY`dY00Vk=`w?>)M^a@XKO(UIMG
zR_DT@$5Gjl-5bdDdzr2hhPw4`$DN9x)o8PsyxwO*u?B*HrX3iN&#z5>`cPFR3e%7+
zUS5k5lrx_qr`>@W;#~bDmH|tOFDEAlCU&1G6aWwc6ktrGk18HCNnk!miTMDM8lP{L
zjr8i_pn1Pk-?@Jp(AbNg(hHp06Ip%z*>Frk7~sj2&3D~ZxPgEZ8R!Onetw{}q1IQz
zgJ+&Zb*pIfQ6Q!&J@bv-YcqUy#%#D$nM&>4cnnr2trLIf!lh7{h*zv?4`_8Ou*So#
z;nL7@EkJ8{{nhIk>Pv_YiPe?SuYFYww?$oP5-6^%uJ(xv3yd&o<*5R15!^=!DS6+{
z^o#8)@0ED#qSnzM<Pj1sho}jO0eC_l<MW+ttgKEvIwmGqh{d^`(}Q(*$K|!jPfFPD
z-sOSfQOYXBG($Gtb-bXWg3z*{l|Q6WOi{6~!po?-gIg)T#Qh!*MM4@u9tfZhiz~%5
zQ?+}ow{s`-*f+-dRxSy?9lxPZGQ$V{a?tVxAxdg0svorl0N@L`uk}MW0J;+JJ&+(H
zAw&!f4`X9t=`{IyHbsL{LqS^4xZ-%wSC~>cst0OQm)*_RKY!YQ=q8{}aCU;b4i^^}
zA|@_wbEF_1oTtHp49=>(QeEiyfQU1)%y|jV3H9)^UJDR;20>1MK#K2jSwFek)UT%<
z*QxS8!Pa8m7}XWWL^6Ny*NiX|(_0{t!vjVdfHRz{&@Lq{T|4E8&=-~ejQ$!}(I^Nl
zj=^N%(t?8VMRhVz9yL}Ze3c!d6sol|JE1dFOmcX{>~h#|W8C;EV)PH@iR0~`?k3$6
zE!Ik9?_J{hzJ1Gv`GcE^xN-5$+uwgLF1|i{W($VOvSrHn{M!K8DW<X4F+ax}vf2UF
zB>;Z+rYv|Vd9o~k@CA#@%qt!o%BAu?6y)0=vQ>&e95nQG0h%p*pDxez<<~IWCSy>%
z?hnR_Xqo~WuP9S~ettV)*zfJp4=<OoCOIZ1CO{)~Zhl^zH{UGee{lhz{pwW{YQWij
zRSl`_%<-PAtn3MSK|#T$NP|>f&%l7uuXV-SDL4H*{sgKURR@gQR#He!9v|#&1MAa4
zTO0OlN-qm(oK&6;Y`HpmjL?M>Ma+B%v!JmmF1`j9RW<K)aZy5de_?xU9|y5zLa&i3
zpyd&{;_>V!wAw$oPLFUp8Sc+*yic6#u?X<ACz&t!{d@if85wjfk)o$RemvR1f(%II
zl`y%I((6(?I7eU{O5>sj3h@$+plU%CHnuW2*nUU^pL|c2f_j(~$E>*oEyRHHj+KM)
z0r}a(-L#^JMEsU{J&~svixPY{yC+7}_+T{4gaS(SX2XarvaPi>WRH`K%nJGiR#s`S
zW}ZIf26b>qN3UT1#X-Oy7AutqKZvl}8|qOWbMphaYN!wG(<kUX0B2TMcxlc8fe5Ze
z3vqXj%LBS+>(iw_<z0jn+>6Vz98dy1(y87D+X@=;BF*5O=#RWCT)3ZX)wz~Z@1~Ob
z{4M?%=Z|XYScBqQ7e7z-SHzPw>zmU6rYHmx2*!b%YIMn%7;uJ&h$!pu9vqEd{=JS2
z=nt#vJMB<2mCE&7$AL#<{AIW&0rSWX#`<t$4}EP<r;fdLAM@f-p!42+TO_FS&WuQI
z=SP;Hi5aTvI!Ov$#^aL`uEdX!)B4q(!6AyInIo{Uus(x)TywMdB}cgdr-XqKrJ<1~
zODZY?`*ps|;Y!bSAAP<I+3fsEdQT3>t6ORvB0|HL*6_bot3xgL;IdC6y8{JAFk@u%
zLFD-5ODZYl3H8aRaqx_};AFuBiEWGDnT9CAotAp4djdTC{Mo|pR?zU12tu~=!RM-P
zc@JhAKGys~7#jBUV+Hus*7(>g8^wL`?_*Wb)QV44KnyXabZu>@tZwR$7Q;$lAhNNy
zU-lO`An3M+y6PuP_5hEmT(Bu$3zTkuq!cxJenLY-+Y)g6ukkofWfJy!bOSVWGXx(B
z)2L`_-rzAGJ?xyY0FVgy{639o1aDD5kC~l}#C-~c#I-aui0%or8#%C(;dXF)cFF>l
zB<B35Oznt34r)Wl{GNjcnV#O}$_gnRow)4?a84$k<cJ3<<PS50#p}YtRn=$!m}VlU
z>rKD{QukzFjqJATI#qBQ@glSFuD@-kXEWmQABUfxB$MWK0uA32#zR~8iDS*)4UhkZ
z9CH1@=H7AdDC>jo7JuE}nf=?-PEN-61R%*>5q-T$)dP~a(9q}L;D9Xw)WC1!OE$U?
ztENP5xx{_==)Rc`?5)yE84zC;`b=*m7M(pSn?!S3GWS-&(!sJ?TkH4F?_rRL5x-6*
zJC8`6Hq_rLF$jEbMP8<Q3xtNBKi^;bMvVYX>o6c?cZ5ixP-g*aH7$t%33zrD`$bEa
zXiSvj@Njc8{CaX?RS)!xK@~$<{JNy5?<v8CKlXfBgI?qcI_J!F#zS~+vi?>~n<}rY
ztgNK;sj3QYeXeAlke=F+obJO=85{&39(6(4{l1XV&<B%?TedJSv=5R<`V#`XBEicc
z^cVy*SkKgks;RNicYi#D)Ky<Z686N7?r0$n#p38ytipJNIplmsMn;gS@KJF<_TP4t
zyC4E&Md0+ZG9P^6OJODL413v?)jSbtIt9XETV!KMThEwXM{YM#gO=%V?G&elyeFKP
z2t!_9UO8tG?ZKE<MUEt3n%Muua&f&DQV>tBl*zlgf945M35xovH^+e$8_Zy|@J6__
zc|9^B;uVzRP@(f?Iv~B^x)MiibU1!MS0KOmsgc%wvEyLgNVDU#_uuaJfGV%8&IhbR
z;i2mD`-rD~$|Ylz1{bk3hEn+1eDbWRVe`@YTow>)VNA9Uc#SW;KEd=5ats!5Tu|ga
z<sbML6^7SIl442jKnij?X&n514%w9h_!;I`Wl}<`Ltv)&v3bXap(twWXLoYHW1z^X
zHI^pF-C)>f8tD!^51&7`?fvy}1V3B!Y{O3dYHpp(_^l*!il%U(JQEH+e&Eq&YGh<5
ze5UH^>RUH&g5`;$iOC0#jS)Y`m6x{t89a<B%;DY=J>PVH4J&qHQG2ZQXJ)2zh7Zbr
z4u%0e|K8`f?Gi|ki$*Kpa{B;0e4tXv6!ZPK7H(?_v^RV@Z<f!svwa_NehyXsZhZh_
zp!@f%LzRracjr6(KB{D|wD7k*&HQSYsY#6psdZ+!{1H3c?BpkMbc{^Zf3B9j)+nbH
z_W&!{TzqHPJ1tom9U1hDtMr!gF=b_2FaJvERAS)ZmZov2J6}!$T&!)ave4+8Vr{#&
zxjFMn`U0RUZhyKVN)y{7AtoJV6lnf3EMyGcp#hWBrwRqh^b$ak9M;Avz6PSKzHRfx
z;nssMKY30GyP9nux3{;iYL~aAZ6k<k+-DcZG#Yaq?^BvR@_95}S85kf-GCc^8vpFU
z`b1Q{QIkZo^e(6|4O!cM5T-$BMV5n=Z%kz5!{sG7h1*6_5eUPEb;FZ*+5FHVazsdp
zZa~3VpGnW^K4fh3AH72&*!o0-&(6>9Q6xO(54MdcDY<K5EqzX)swLacsf>Kv1^fGM
zbjjAXw$qFGaIOJTd*tUU@LA1JgNotxlM<+mVC=T~<Hr#hqHk}{#s<2c$uof*ub^P9
z-|T6Cct&s!20J?&8{1a)UZm=$%H8dak8Wb)yJO=<BlVi}*<ES1ApScpT=>w_({s5Z
z4BR$EMei6i-~^txh5N&i0@}V;7iOqR*b#plO`{AvFNo}Df$25TnzuMlLK=r}9w^8p
z0T@Zg2S(4WR#XHMrteLB6$qTjYxk_XXk&j@ysr*Au&VB!#sZt2f+w-lk0Aszr#$MP
zD%RMX{L5dG1bX@MOzmCBpnQtk$(;az^S@vXZct-?*4}VJol>NdZC0s++<(I*!IOW@
z!FUErj1$Qk1I~&+!$>&>dSH$uZob)ktLEsq{d=xKR8(}5lVn`Ac8Xb5n@5N$kr{lo
zpV&5SLoP)ptQ$*6k4VIb6%@G;YdB8GH0w<h@#a6XsSM`-QU4($H}Pv*8;VvrDmC@f
zK7Z@R57pI<0l@-Uw4|g=c!a?pgQ}aFE&!1EYE~(*vTi*4VFEnEZEcEY%8p_?%^1`W
zi05?eED{t~w-D_5b^U{b6;8&UjUzU<tl<FjY-kJoRvIQ#&Ic;tky4C2=C>U7<Qhj<
zOyQdXHH>gIb#>|fDJ=iWlBN-slwOB+k`l^|T}E(>IW3xee^hPH_PmdfBM1<Xzca!o
z3L=J9<C4{3fF>-iia=>#!8;8#+Qr31rcz;L<sQ_7UOV%SR#sYidVZIGeV@t8FsBUW
zO3+hMp6+!4*9q!TP`<$HfUJ2-*+m1{@{s)Z7#-MqpFeB^%MN-|DVlv<wBix#sddB~
zY2KDaY-Otow-G*emE^(l{2F-$1*u+OroG(#h-oBQP0i=t{9sfiFc<6fVdf1U67wnD
z%W$YC$=pn&;Ca(aSzl4Hv+ugR<S8!pnGg-5Y2oV<^xx`{$OzUO#)PZva%CY3M9Yy!
zTgz_@gsZi^w>kwyFDjM15OV0?)vI?!MSlPT0puQ7@#Xrp15lj<3I<cc)eUfxBU3$a
zbOt!m$z>T@ra_md>(_6|er{+84j>yZdFcYwmpkI(mvE(UBHR1=?2(m2@PKzj#}e>$
zFt6%WS@F5<imK^yXtJWbCeNzwpOQS%SO%k7$ZIDc;E8%WH7#Kyu{B6vRStjQ;&kSw
zFw=^9y|cBv2gV$ir|v-4wjiM7TdBPvng77^h^TJt`*(wFo=LzEKs(>8@-x)O#kbkR
z7_MC1%u{9}q#1~I=Sef|>`^4W9;TKgDsR(2ik!Emsy+k(GsLIrYH4X{U3M*ym{wvg
z3nb;z>Rol)nqRDF*aJ2Xa<YUQfa4NjF1hiR4PXZX{?ZSs6}=YwFcS^uKZrh#zaj~a
zSFqgQ-uX(9rmgTo0pViGlTJx}XjVn#8)v`?{I`3&yzNE$py7~+wSW_!E@in|c6tEp
zw5+NhpB})Ti=`C_(>6wY*3~#%=_RqYUN5AL>$L#wVoZclj@Wf_@*?vYyB;pwG0LqL
zFniFgvdT8KN6Q{|9?XHmArHqKE9`IjxYFM%|0#;rB+o6Q9C5BxHjKe+LCCsdapYRp
ze=i0rLgSp0lyqfjNv+~aHr!d-Q&3M|zv%5-fOnuM<txHThvqyCD?^F~dl0dhZXARh
zs0wCXDo$hbN>AJ2C?jGaMGQsu>R34}zCX$wj)PG74ZNa;i*Kx~(w_PbfF>2acmUSL
zS$GYB&DM8bZdMkSld-=`AXfrVB`h46=m5-Xx-=T31_*E8-f6jeS4%M-9F>-Lb{76F
zgUZ17de1(f=q+c~A$EaHdrtum6%}Ps;a4m){(XFX`LDnWfMFpzl}%eAo%8lHdDfsO
z{P*tZLt7Z~<-O!=&?|+=H42LgZmWgG+s)cCat=K{$bcd1KysMzZC-MHwoeRX(Tluj
z@EiBu$PK;(bY*ySG>gx1W<ldDX=L9mlLqnY=O@dqC~1#R8mHG~!0$bY4(1gfFwfEx
zL)S&p-`{`gLiF%A8Zpnx8gwB8HtS$kBk_P1S8HO@EQumVgx~V(3qMh;$aava)X~$M
z5RCz)>sJ$9*UN&?3e582s}d8HNsOAA-xd}5v+H0<2QL2J>x_-1Prt{Q(yJ;O`-EAJ
z{}E&_n{DZX#l_Io2Gpkbq6RMcKM-}mXEh#x>43&P*ls|_a|a)C0Y^Red36nC<jl?;
zfSjQL!It_Ju;v69?CdRKY6d=OsQpCyATGkh_}IR(oD-3uez%2+GGStrvFw#IHacpn
z|Aab=V8l(t&x!Z5Yev7*rNSWkeCIEtrsR$B*T&GAKp+Co@7D)Z8ny3%yn&N?VOYvN
z3#;Gz)S5eVlW@n#HMGqsWQ&>i;d4g_AGCg@5^HKtV?PsGg6?!qalR*$876mzG;5!@
z=ykmkjkFgp08%Rs)B#c_ddceV-*f;lfy)TgUTcl-i(k5+V`4Vc*8{nf7BNv_T#;AG
zfbRO|x8aHF%}=~22%Zq}sEd)WF1&Kd$G2;};^N|zl$A}_y(k1|MxLbmw$Rh6x;71R
z-wJPW``A{GZ_xA|uAX1AtejlOJ7JSa0@AoT#h_CkRpr#%Gj^_ki$ZmF=T6b!ytR4N
zw5sMc1Vehq9j%{0ujk)Ab+6R<4s5J0=eMBO#i8?IF)fXtG0;8G-Y2|Z3JSfaYY^ID
zSm&EW5^N>-O{i7?JKS>y5nyL~`@U7TEP51INL9$Wj7aUaWJNqM_M>zAS5MBXtX#y^
zZdO50?Cskx6B9M_&*OqGPpucfjxWxUXC%<y8|gm3IxIn5a$aaoOiBX7EtoA+pR2cn
zNtU7M4=A}E$hos*>1%%zC@_ozBiO({nZ-`xsr&#1)3i52`j$?u_`PF|<vvR|_0LSs
z4zp^Vq=GK<>fFVv^<j8YBi0o7=CxMeuBA-0md$^(RD>ciy%`5tNZCK(Uz!3%1L(#B
zMhYJZCPe{%#=*r!dRIgLKC$c1xQ3P%)Brg<zdWv@86^v0?yoca{I@sp`s4j8jT+Y<
zUt;U-sDuK1xwHjrUV>=#b5#!yK6YIK1I|PG69JEJMIZ4ib!bRQJ?#6@UqA8g&p6#b
zshkO?pr(e8e4#$9@{-T?&mkp{x43eg>9%JRCNDZcZ!X*{sUvNhfr!ZwKm6_}0wOw>
z!RMiq&ekOtu<g+G{-)x-dm6Avg+SQYDl(NSO~}H1(t8f)F^rN}SutN;7vtgrj}BkR
zen23`?y=B{+bPi5&{5&dKHMJafbPLwCRc#?LTBeglG{B=boWKHlRJkC{SiM43UdBX
z&NQq0XG~1RuF)k#am=E05Yyj=5a<Jhl4?R2;3A6OogD4C4zAzM)s@$}lVJBW!nv`j
zJatNHZ@<+)r4GlIBXa`ue?tAZUaD(opgYbWqEI4H&GydUOED%|olLJk?_Gh3K|YBn
z;C!yHtw~MEQgCBA-6=hhmPUS>r=_?kARDPai<s;02UYyUHWQmSO6{O;3O;aJpkoed
z2&$?f&W8sFxc?piZ_Gic9uIl~1O1U_c=hg<r_1_#<@$m(`Qy3RKysG=O{R_q;d@eZ
z3I)V0FaC5;ve(Enl{UDms=`PS`Y@)+>b|VPXjVps<Z;;_Pea~&;<GL9IRQPR4?0<5
zv!+IvI6Ln=OGn!n%qu2xb2sS_nfh~12ny9v_~&*!4&OiXc%ItrQlpg&o@t~#hRNKr
zY$^x<wp5xsE(z3v1EZrP6=cAY^94KwV&C-4OzHjCNSqzf4m}*G<^}ptLcm@TGP?#G
zILpi6c6x}=a;c0fA&pMI=Y`+#{`C~l2kw#DdD0$8$zmIBGFXs2?QL<I9&7M+2H4&*
zuu1UOJa&y2QLyPq-p~5?s=z9YTjpx^*=wJfseh(Gk(QR$@|Z%<emH=^G_N@y><G?E
z?i)AyJbUqC1q_XT^cg@y5{fa%2O$q`U2t^}fLJv%F>!2PZnnDTW8#3v%xp55RB3xr
z^m6FO#iy8QDbx;bk!+RzDM00T&d+|dsJ3@u(8sY~#}BH66#b^L14UE`Tg{ae2ezh3
zsc|HXr@%IQ*0&0)6E2?r$!=bLzPY<y2%im16vMw%efR)<Ta4U6dLT<|OM{*Q!0XjJ
z3!uf~bFhY=_3hFp)N&N|XIFQ(zMkHX+lE^T%F3QI^|XcG&=H?K_KMB@fzX2hL)fq1
zz8&rF^Kx^mD&pab`0lT~?T~A0y7T>W&}DjL1l0qWw_zgQSWfb$@&uOG207P*mP?n5
z&2sT32~R@oL4lZ4?=;zb7$!99lFd`yOuNra0C<uq1tj6WI%SJSZxdrfV*aZ$%KE8Y
zf^uq<u4igmn!pk}Rp1@FW-gY$nvjZUY9hT=fYpz1adjP*k%TK+Scrj$kTRX?G6zRC
zcCyJPebw2Rx3(=A$&?fn2^2gq-3Do{A7Lh}Mn4gY7SL=hH{9SeWp7SyLO$-(<>;B&
z48dt=da3~26zhtfnC)*4x%oyuwK|E-f0Gkwguy{kEZ}6wHUT<RlSKn6D^8U(1ab^S
zNQg^d_e(=(&}zK$Lz&-XiuuDQ0pkBYM&YqjrSVIbfOI%<ArU-+o)ILYcpV@){Bw1+
z+UgsXGP;GmeO68mxZdUF<cNdb-OM(4+&_kMhTm0IbI9gX@SEd{y>5D7lI%wf$+AY+
zp{6Qadq$+=u!uXP8NW4Jl&lvuf;?I(q{kzc$4b;8U4#Z4;3_~mN1B5v2*IHak}yzX
zCnP3<sVE4zfrcpn3@IRC>GexT`IXv3GBPsIF5z(71h~uS#8G%^O3G^OZ+5N$TRd<(
z0=i!)+@2qMcS^znOUmBFMAgX1$<9tszxM#>0C*Vr^ZJgyzQW1fit_Ts4c4P@D93dE
zm~r*1)OlBDD-NDWPNQB>8^Qx|vYKwp=OOz8Mm|`TKflPynFlJ<PGM-N%*|6LBit~C
zn8HT|BY;}r)}N`Wli2jkeK3_iNS^Mw`Bm?~0u+FM+sh5Or0@~$fxZ%eTtJqFo*TS(
zfvfK^n>y@d;<_uMuo%hMbzE+?AR_qX$T)|FM68hjaJK7aU$AnMZX?ZAFsY=*X5Y-W
zW4tpC>ayhA->a*hAU#MBeGmM@fWCKIF%2ki&=Z9Pw<P*&zG=3<|DAe~3Ve+Z4`0WL
zR4-KW1=e><OYn9MCIL-Pe}4e{Z&6WEL&G^}Bmo~DSo&}B^C2^Xfd9X%z^m!Or=^^h
z1^|daZgxj0gCSKxtv!^l8!}Db_Ayi`f%dd*l3J%*Yb(CUw>$^FV<33|OaB_MR((Nf
z0vc@4-s|b>D{IgPJsKoU;rDmpMjm}QEavVPDP{iH+KSh<0L(19rR54Bc~1`y$gvTv
zBO|<wjCl~>A=kci5G$Qu%7C;NN_W@>d(M!NCDdy(C?6be>vPva%j21(wl)(nPiB7_
z0i<6ME_5>&!M0YhG9O)*+Rp{N7>X&`%XJ&P9Ji&%9*><;@Y|6Z37A1eI5RT?>xrF=
z?bfaQdA9}d%2=AK+q)8IUI)dCz57)kKO!4Yti%SK+NnRVavuvp=0;CKl9HPnygkDo
z5O2Ua+1XHjt0$dW$YpWH7Z;R3+rzH=)w9e>!Wr<?a_;3M-{$n(b@&ukyqVyUTS`iD
zPf(DHjSZ>*18D8T47{r9)97eSqt+@;m;wTh3?UUC(gLBo+ZuM)(eb+`LPtl(i^VoH
z_%SF5`1|@oHyavY<HiH)(Hh9513g{fM9g0<A~Wip1?>wOtS9O3-*0a_E-CI;)l&V0
zRJ);|0}0CssFr9zlMQ-q<BI8*yB&mJ&7`gE4MRQ9i8($vFt5~slHT)=F1X`Cm5jXF
z4WviMK+`w}ojVv*KtWPW;cfgmJY<K2LrN4^lP!^zG!7UhjpkTE=IhW~H}>PjSJPhN
z3JeSs&%H{Gw<?YKu<E+oZVX*=4qACMETa?Pyu@WxUOzUIj%;}u^?!iyc3gkQ<&)zv
zE{fO^VfOD9Xxx4HFx1%zEx^u&1><~EMkt%G_V25CrYgV`^1qB=2woFuE})_DcK8Ft
z82FqyfLWte6mNrV--{Pe4)FyDMKg>|4JFIw2M&{FR7CYC^FCrCHZ_-?Gs_C;A*X(3
zjyA?|+;{zyJg&a86mX8{<m5qE&k&Jdo&~9yn3@&Kr(#&e30CAv%(oo2q|8(}+qe^y
zm`MZ#z@fI^m-rt+6rdS^+3-5BsI%bLtT3<<5s8T($f<K&irEB+AC0#~>|{Yw4Rk2T
zJ?$(ltAUvU*Z%VIe1{5uE53NIqDCr@Va-3}NePpciI1WafL~J)Xb3E7YSUnlkbT}$
z38g*s!AK-((vsew;6$QceHnRzLKJZJFQS=HOq3gnMoKM#oy_85>#7?+6&YMHs5MD6
zcMgEPtD~<!IW|U0PF`z22@)^TRQN#OhV8o(3&|v`N6=G+bRjiW6D+k|Ts|=8OYw0E
z3jTo_3DzA4?Z3;P``WHOPlFX$^Sc%JczCMjd(Ja}?_{U5Q@d}KZ)a3Ocb0>e0C}e_
z)IACQCsiIyP5+y_N={Ce?KtC7R8nH+<opfVZqXgXLw7IXKmqhtp2@}aJ_I(P6Y+W6
zMBdFdfMj6w?f-_`(L%sP!FyxmVan3QIy*GtS`4t?E1UlM+&zp5tp|R8aj5}9IrwPJ
z62QeoaNEHu&zBvaBPJmUCnn&3F3)ssY|(m#k3bmwMmEaB!uVX@fODh~p%FGM&0<@V
zt^d`~m%GRX^|qW#UWiB;_^k}Y%~yr>FAF+HhGGK(1Lx-E;Diw2FnkA~ZeA7gI?Hpk
z-=_J$DL2I$tgth|SMkmJ_xG&_Xl}lUg=P+PbE>MVHO;Ywc{6FmP5-_DqlwR&Et_#X
z=YPt!dg$-*@TmFMO+hgT0R&>z56c`Vq>0{=EzMH^QQR4r*6Zu!WMo?4!)Ij`<vcbz
zii?NW=)SH3R45pf(-Y_c49t?8E7X;3=i_rg0Y6Ots!s->7+~5D5M3c7y%@O#oEk!E
z0jSyD!4%<w7XNpswEM*Z(AnAV$Kk+2jaKMD?mExwzme~_4~`TB4J|DM$K6EeK_Nl_
zx&xeyY)}Bu#gF%b+g~0L@vZAQ4+89jCJALEIKjarwSmE_$+Mx`Ztt#YXd!?*wC@2r
z?dagp{N4c#VI?aIRW0`|7S|OxA%CV3V8VT8Bx|2rT0A&~#vBm-m~#ia1#!^Bpy~jB
zcZtiB4(O&sO9w$jL6MOS(|JYSa41&4qUzv=sj|V}fa!)j2);^8Ba6DrW!x;blpHuz
zN%D^HXks$5fGt$vns#lxzF>*##2p@gR#ZfksR1W>k40+=69)$ihDUV1D<~OmY*c%Y
z=E|(ZCw_PsvMXVL9$}i%+#IARxA*8v<J4}bKeP+`gLsS=$eCy$D1tcgC0wu3(b4Qz
zuMj5CrLZH6`DO*%4OCND-qDttRO<Zx{aaR67Uaf&$mP?1or;PzRpAlS^ZKurn?HYo
zW>4`5hnHXU@i~F)1AFzD7Ve)q{b0LpdZdR;_;$YEP<&lVjE5IGsBZ26O-vTjprDqD
zB(Ny5;S<ko8Jo3V&M`Ex$0R@Yr>yv2TmY_wnRC(Wb{Q~E)-KRHHa@<3aQfx%J(;Qa
zM3o|Vicra+kx;%6l_EtZVzZi%$;ruz@^a=i33O<RLU`-o{`;-?6b5DC{L&*hQdZ|*
z(gA`lP^w|(f(K9kvr8W2n!q^%yej`V#KF-KF2MHoHvG3cfl|GJ2LyOPp#hjOkmsFr
z&3rBTkA@I8X9feJ7z=gk1_r-8H!Yfy2>>GPCosfJ(|Vd->#{}W*_2OIS9FtZ7Q|7{
znC?Ky=tpzk!NNi&!sZ6fHPhKE)J?MURjhdqvi~RmubnEDhkneNwJ2u9-K_(6WjD8d
zKsn*=vlGWcJOmyf|MjN^oXIgU&{Y5i%H`!HL@LNY?^*Q&As9aPnu`!L)ZPiUJTSzL
z{;#jCP+)xa1s0mJC@~tXXxp4<t(Q}6WhFABf|0~j1x-a0M5|R5_IX>9N)dEmYdsk7
z|7Qm}m>1CAe;sJ37P>5W?~>vqciV4BQngy}cAD|<ER59|IDt{>zcjqdtdbi~UE2ZF
z5qt9Wj1x<}g<yhUTeMP#ed2pDb*54dkWRO2kr$C85kMKcp9$Xt&Awm(&I((1;9&g+
z+QG>WDrCfb-~tg3AYMvt6G=e5eSCZX27zV7Pc<Rf4gKR~%N}GPS;;D}Yp=8!eglC3
z{F9_vQ<2l+xqNX9r767@#S9e4QKz)hPCEEG8K{qda?Y18GEfjhW@*dEzT*D`q(gye
zMbg3pjaZPe`!D8?pdt&23L|6`hbhaCI`1G41PT@$aJZ*rFu4M%N)!ZtPPc0pHp@Aa
zfFDmUMl<33xz45+g1#QN6f(vYKz9BjHPzS859+=1lN0cIIRiS)zo&SwSIs&Qj1%EB
zU)23x-{BC3`b=!G5wG97mpqTz0%i0EM95VNV?{Z9wj;eMcc6m{^9T-3PUXWNo;s@q
zJiI|cg@W+#?kXEzjv}Nd@W08xMxsKOyU<bH`QslJfdsw)d!76K9Wq*If$-pog8YYU
zhg9=J^Y`owr;J#UE^9A}imbPf0lj>c#c$qrP;_4J35IOCeb-{(AoxU)iW3Yq0z^lo
z#s(Yu7&?d1$rc-fgYT>Q$QB<lBN-VPxS62T0$BgtNM2sMu`=vQ{2P~cR2nB`*s4oh
z%Br@RH&YcLtt$sfTR4BCmLD{m!b$KG0|O4%5eChzn?R2MEOl;rT3276dV!dlx~#Ie
zS;;ZE)(RLo$VrwamJ$uS6yrjWWfY}Ha!Q~Jw|NF?YS06MfBRJwX-WYFv6aKOx+&21
z&insvxcQjlm~|a*1BM1P5_k;RuqYRnHyH6zDKilVK9**M!}#iSk@58tvg6H?Jb%KI
zr#L*$am2pzkt#)?ass9FRKs`pM`RKG6eql`xTJ*9$^lqSW8!byEqKS>ik)AUrpN*X
zZmv|5?f$<9S$s9n9uuP?`HUYv>>RwKB6Z?5McQ2b8ZzXPf?HB>r!O1+{|1xbj~+e3
z&d^7QXN(j&f`^@=Tm;;gzuqN&(YHW27S3N578__H!z)Vf7=4)pxJ=wR9`3E!<8Z}2
zs5cxoV&D~?UU6!Y5Qt<GAVA&-3Ky6HfY|*q0Y?oDCz6<wvhHKOnVMIZgxWbC9-f{E
zGG$q5Q5<KyK~J9xZ6&zNc(fvrLBoKr4CJqNc@-5`kWANlkRo%bH*W1ncTs3m>FMfj
zgLE!Tq!;^A|4Z3QVG#qz8E&s(OHdQg6Aup^KpzFU3T=V=tN8fw&Tu~FW?eXZtVQ7F
z@Bj}H&Tc$P-Y<iLJ3tM9Vd4oJodwD|gJzrIn;neO_?QF=N=gnJE__>%`pugzRV_4b
zk@2m=IO%_TR|*Sitr*CCzkY3xuSld7ofsHUNl^=Q9(X0}o?(uaRT!?Z*<dl;fS#O|
zBp(i&1FPj4TGRvYA~+8k>faoHfDaY(6&o{B>Mqn70s<HzkiI}FV#~q+@4|6b0BFai
zrfT}LKqd1Segjj^vWO2RZK%qGKMm^KZZk7OH87AR_?ROc6<DGs=4c4Xg0M7@=;+eE
z2uSC~LyJZK>9KgA!>*+mjr`n1VUVkXPCbCXXMc8*O?3d?1vO2raszAxv=h{K{QBTz
z*4NbqxjuM8kRi^<%tUF0c@zvNcL7)6&-tnTL|wgG?sv3t8i=0_{5^%6;S_Z1IZ%wF
zIgL?5M;Rpsqx1jJ0vgB-y)!npw6H{u#sHo>ZpQz|djOhpZLQ#Cb#%1cwm)~|o6YMQ
z5WgZ9BD_63`r&GlBtc~h9Oal8EIBzjn6(WUgy5*9sX4rH2)50|t7Aa91R&+|6!VM_
zotdP+pQPFY@V=M9qDYAVK|y2!Tmuq?DSx)jxhahO&|z97!aDGpkHq`>%A=iytHg$L
zpxOXmX7*$D?rxCY7YD{rQ;KXAV9UugPxV;vn!?pI6OixV5`f*f5|59hI~CR)XD*Ow
zj$Q*o8Vs^R28d6|y8>AZY#12#1LP_|oW6x{(1I5z1I^Dm&A=O{1WrSM<zNT}4s7%g
z$XZ}32vW<Si?fo|F(6q%K?Z01(>)vmgFIQp!$`<DnVy%b+fy$gSs2rJOvKo^lG1$e
zoXb;(kOm$d>9fAo^Qn2z-%^DD{$30v^)oI3yp9+eYM$Uc5Jafyg-@_3J*XpN_}|&m
z1ccK6eYSv*keW0$CP-G4l%OC$&1aTs3UnE++Gl6aq5bL8&I1M~0p@T(9SrJ2vLWOo
z29`N`I9giV6O*#qP=Vb?->{IM%83IYjV^f-nGN;KnF-S*Nis0}i&48ym5}hoeCGgM
za1Usk5X|D0w;|KmdFFAW<ZQa>-MjwL(R<?J^WEJ=aF1XGj;Kn)><*-mh^GLY+1tlE
z!+enM@h?im%Id0ZanA-6Ol@6cGc&FI3|Q0XMFUopRGvSF9+0df!`jm;DhN7ow1XZf
z_<6eRE&>y%?IqjKm9@3B++2DfG@6(|(hr12pf?UfeK0jO1<gcQParF8|D*9LxaIn-
z@t_C&Sx=E+X8)U-r@SQ~3ec|CZ}j0w@9X6L1=tT2PK*-sWKt?*@!7OS2j)QQpc1*|
z1T?n1<vWO|2Tq)%H>A{-P_?bi2CV+QF=ql-YvB?V4tF6$h(du;qfcdVF+7Ji1X74&
zcEa!Uh`ipC<aVeT8qOgv<f6L}tYWyMFeS(rop1a3|H*g?kA-PuGUOJ1j5QV?Wfc|_
z@Y@VAlxWP1gu=#V=mD!kTybQcg8qOinwa3<H+;YSDW0^ked5cN%4_9TGL%ynHN%J^
z7rp7=V0U+>5E~~l>_a7r8kYcq;TKHBff{kfwcC5~i5ZnBp<Dv0#tt%<-Ix4<gekKB
zo|sYSh1Wks;f*WH5@0qDh-8*k+yA|MOjlk68uGP?63^+hO2)h@otc3tlF}Qan+$B!
z9*d92^4rJX&rD14mYctyNH*s)dH9(1Q!f#Q4$9JyT}$O5LSQQLnLIn0B1iODVIU!P
zH1wVNTr-pkRauzUa_<Rby)REMG4io>Z-*r{vIf6+^VxrNL^!5pZeAAvj7`A0YFdmg
z`9tOP1M;|Z0c~W8NO_wEbPAB~4*eA8|NV|(H~c$6sQ%nnqPl1{CC1h=jJ*+8Tu{Jd
ziXRh^p&An#k)ecxU`Rz3PkyOz4d)s{eP~0nh)vhwc1nQ>cj}CYg5mwh8UmuvrYOke
zGY8LDX(C2f_b%eFJOY}_8hTQt?^<P~{xU|{$=L|KZ{;qiNs>ywCCIHoLDh?&kQx=G
zz^bgQb_;nYtzSpvN1D0`ZzwaKDl^s<hx{!>EbocW%RMSI@jG)9&~GzC3okL@4(V+l
zy>>e?OpOJBcyhOA^|gkH84svs^YdrO$BbjiH`*m=<AWcW;luLso9pY15-hPy>r^aC
z92U%lIQQHm>DITJtl*lgv|!$*;doHOl-_uI-BNwu{ZYli0%6jacyZaCbqW@|#1#YS
zYu%D1Wn~ujmD9hdLSM%grSv^&w0l?jT;n*&Ku^j~TZ+{`I<|Ag`!;-yb@w7(j}52l
z);!a%P1V(5%j{okkwISk5|B|bCS*>3=3aGIm+P)BrOvl2YeL}N49_S{eAO79(Qq;=
zy!_+OtD9Fu2p?5(Yi2A;LrsM>K-pJap0)o@daBvD=9u+e_>#QYt6!ZqJ=6E=*RUfY
zJiCmcYI~k*hJi{DkV1lGdwbi(U+`!?zl)>pURO{()~+}F6<yxgzwW72ZvN1E4~-}q
zf|*!t-@T`<qDQzweq3|q4XT?O%Ej7^y3l9+<xTIJ^20hVzAhnG`~qbn)BgoiylzXx
z|DBSZ?PzQJ2uOj`x+Gt}evN4Vlfw7N^LdD$BbHqY1dV0d-9&}RFx`DEQPJ?!bm_F1
zy=YRrsMfJJU2hVH`VAfwHU?|i+J<9-Z&r!M4{BZZG*uQ2*3_%X`Rr&pwN-z#t88^m
z2oBStOiU<viVTui{yH+cmg+L353;XYj)}H=(meY!(0}jN@>YU3bSAA-t=!ahezva0
z#kF$(Cb;=J=2sYc0yfI?rx6Yo+f8KyzE{OeUx=hg>Pr+(0uPrqN;PsXy*vxf)76W%
zezNJ;)cxi36Lk#o@NE<r43q1AcAY>x;N6%ixoa~wNLiJala{hYZYrE^aW(E?4+Pl`
zG^6pAjf{M}tQfaTWhwzpf`aJ@6wa&KZv>M&<IK;SUHi<k()1<%OgM_r5Coo~MG#3b
zlcov&{BLFpRN`E~Gynig80)DIu!M7Ts}lqq01asY>j_98uA}EICmvx)_icMg?DyQd
z(>$Yyg2ng(fg*)9v^MC96G6lvi3lM^T%-_(AoAXB$i0v>HI#n$hMn#?Ov*jDJ3Q>S
z>}j+B#w>vWg}XYwx6$T3(&@#CY{$hJmA`EfDqu58;%sf^ZG^t0(<Uqw9C<+!@|^yQ
z)BMMRRr+@hc6H5p8xQoWUs0H5P5(*rMLSDUQTU{#tyjioPWWriz1hP4_eQwo4H_T!
zs`P3l`Mze>ci_7H4Rw4ORdwVOrru6{Ax73^9VrH<eI~J6c{z&wsHFwERAJHaT_>9W
zt8L>u$}dUx&CwVmhK_I*+tgH5fp9N`=;`SJci*9{Z~?aXxHvs75t^h|uU@6qy%U-L
zToC3el1&zG3Cfw1xu-k9SN>lkf4m4PL9~Y68xY3qs{5MxHRJ2&(n^GNkmjh+daLlH
z1b-+Z?}aJ@>g{ma$}8thN8R37;sONG&{S;c=B?Emb(;KC)|Fbmc6QDK50+oK7V?(!
z(3!mK`kZT9+BMdv&m2K~^T|!snLDgfZ#CX(7&~YpTmzplcL}c>EE`xFSiY;XEVNUC
zKLqW|Y~q`$QrOqAw@Gf?#nyNmLCm^YfMAS}eBXU8A-vF(8|<az_G9Ypm$z8whx%tq
zEd`GY`kJaf{4}<XFP#&|Q0Az8fqHg@%6PM&Rz^<Y1<s4O2vr3ZMucQ2!i7L)VKBip
z@c>~?WzK4il6Z|mopMrWndUGpG(=MG7~=-k2E3{o>l0SheV499egE738lI+J$3Rlk
zH&Rz4y<4#Msw$2MD3JY`%0%e8X^ASs1Q*x%9c$7q<*SAX1>CLf9kgK}^1e>gXB}IS
zrG<5Dcx>KVz8g2f>m87l>PU9uVI5eG0PTUUzw1l@rUgKPIyNRIgWI(I$lA%qhA0F_
znZBkK1<Pds+x|?|f_0hII!Jj`h_LwO*SfEqo;#1_e+vhzUOjNFJbU?3c9MKuksw|3
zgfA7Ynq3|$qvYp#T13LZ+SP+)_EtS{8@QFAz&`QXb%4@5f^RTL8GGsNse3kYX~l?O
z^BB81l{Kq*P?x4ME_eAJ{il9m?nPGa0U@*j;Tv~VLpN8GUP~Z|k_&@;n^*dNjLfTD
zUsiuE%goF)<w%H68xVZ<I<UZ!p8OTtJ7<kxicI&R5cjS_ZnDJ)oHO^Lh?uu^whX`-
za9Q+w^o3&5o}dX8*QK1ND|qVT%ttn%pZ@vY8cYb5H!Rtr=G8{^MzOYK_y~rGo{tVQ
zg9!I#i6^kR-W!`8ynZo4El$klv-VdNlZ4(Oj6w1z&NcVT!UZ4GyF$Ob8fLdK5Xs%<
zwX?H^)4v6@?egXlgD<v>w**VGx+M*gK?&%}t-}v7ko7gJkc6I}E_punbuKE=%d01o
zSBq)aOn--ronsW-emI2^;(9rLIzosukp1qRRVyXtK;7k|{!nT6Xql}vTlHm0t{X(k
zf_f^Se@3%9pAjA!5nZ@vpsl|`{0ygisekN;!s#iD#lfL2l{KJ1go99SQHT({68RbY
zdLy1j$Yq3v8jEp3OUqF#liIfOJOAsQrtp!oP8sEmQG2&sBTNOZ#`C}+mDSEPIfwa@
zZ{JW|$|w2G?u8UCG_R#?i5XqcJa@5mRt-hHtr0Bv=EbMOZ!RH)AHK!r&YgDd4|lY1
z-lcXMY=ZorqoO3Us0c=N?HwHWtOXk8es3Onf9QCmMyf_iJ=>s_xYRFpzo}!18*1D&
zv8lt24e3-^&GFZ`K2hX^f$R+j^sE3A5ES%=qH(+YlMZ{jvwH!V$h;5}kWwahW-hv@
zonOJ+<k4$+xiZs?Av=nE852E^FMmJx2&thX2+jSezUNK|v!NlH&Sn27IC>YGZ$0cL
zq;|L<;*h?pAH*8>rnK~Z@Ql;)dwr+r&qs!w^mWq@>>XVOj?$75G$qB<zYphbkPG))
zWj}q+$9Q|gKiOGd**vLlwD8+R^1PK`%}-Zdy#e8Z!~La7Z9Cs8e!GD|I-5Z8Ym5<3
zOFkWRhHe$v>Unw9EbKg1QZo2x@hB+h61e_p9QqS}mRXL6qUCzkn}RK;iQ{&T2cuqs
zPS>B)5g=j`6Fo(;LnU7?e}k6uLXf%eVQP@3YWdV(^KQ(j<fWxw8~ob~C=>NmeM=XA
zzwnsgAwIpBZ;nl=9t)CPN97JAlnV@O60XWTHtC>C8gLb&0dD_Vztv1Q$I5*gINTU*
zOBEqXyN1tw4hwhRI9E?=YOo2UMYv?mNq9+_tmtq)V#FRo8NJl|`jNfwW7X%LkHHm(
zbFqaV^B)^8yr9%Gd%iUNVeu#WD4m0jiw6N2O?JpNIiW>Q+_2lE<X`>8Nmw}%2o`~b
z&-!S$y1s`z$yYStshk(~KEE^XG<{Xzu^FKnFmT*bfr(*#!mPYH%p+SQjm2mo#x7Kz
zG%?xRaCw_-1qDI=1tdQ%U2CI`&=tr#-<UU6?HpSxzi2rzVDCw3?z?{AhGyP%dgpsm
zDy0<G+zPxQEH%xIMeVe3V-n?SQe%vn^N-YhG(HX2DJ6Dx`jfGfDa5VZ7mdU%NDDuI
zyW<w1ZjFI~&m%%5g~ixm(&qZm>_HA70iW{d5TD+XE+h!+4O-+&ds6CE=zo_ui_P#K
zsb9Z0GG!7NT=rsSK{QZ6r7w&~O7IT5(98n^72owcPl7lRE}d&xV)vi&pep%#QmNmP
zq2=u#4}QJ6#l^lrLyR*xCD2HfAv7)Mg^Cq6yhH@=TUE#9FVXsw0UqB$l^;h4#?b5L
z0V#zp_QmV*EtxL5$I96y1k{Yd$Cav29Htu-d(H<0t(()H+Ia9m5+VrKUV>r!L|({`
z2$6AXUerD(ZDjVR%v=On?qbm5^^Mv5eR9#ruWqwh73UwB&){Mi(qb`sq_l=DV}4r7
z|5+<{VSWdL`Fq!17567B>;#JZV_K8By=OW;vhyw4V(v4dJ4R&W&K|t*Vy4#f(@}VB
ze#{Ly0bLTdI9k-E?fK?nf3A8&*PS@~q%7;UgD;tBCCofnkiQ*$O-|pSl9D#>9M>Yj
zMPgq)Z6vE0Wy_kLZB~#ZdG@C&7Gp#^7A}DW9vSVj-{SR!FTIi_I!~<^P6YEB`~Um#
zHTW@&!bPo$m&6H6auOr{(O3Pg0+Q~80eA%#_WF($N4n%YOxrr|wOSUO2ks^@J*wa2
ztBLwoCABOB8@~7X?lC*7`jimC9YET!WrWpPFm&yKNkZlUR;}K9m|=T0t>ph9_Kp1A
zrulFVF&VWnzHhI%ir=spRZRZ-(WB?+2*&<G79Zb8CpDMviyIsGrZd7fj;76E5VpLQ
zv1_HmOP?SR|5eqB(QkkHmC1|q*s4yiBwIp(R~$9&E;Umst<z>5D341R++UA*pL^`g
z;nsCZ^(nmY7JN?RX}}mk#EZr*^fVS7gWy<f7mGz8?_>M%gx?Ro<%N%Mg+x%`5^%zf
z^B?Xos2qN2H@lYjcCY^N1Oxmb3jE=Q4R-ABPQ}mPRO~rt=BMSAd<W<t4dZVML(9Cr
z^=vU{_d4D6M_YC;9G$P{4*ryZ=Pbu1qv?68W6<co*I}R*QHryA@!sSsmj^s04l(?w
z5q`9u(`i5y*?w>0@qBdo)eWZzqIz4?wooPIt?_Q}_4|@BFS5)}&lK~Q;g2?%;C)PA
z!@%ED&Ot%!Pa4bLY2g<kkiyCvXbnU2M7gXa`J3N`8q&CY>(w$oV?_N;b|KZt#Vi4R
zXo=gD?$49`5Lm8$dR+?3bCC)sNpRk0IrdHth&{vDHnuTC#Ee|bp8@|&@rqv(3!Pl=
z86s3CZ(`KVIR~H50*M^vONpg&0*|M`AhLP49anosXY^CLkstKTk2XfF!AfDf{Fjd_
zfECaaKt~jfW~Qd{18)+b6J{nRL@P)Yf^lxed2D`pG+Uf^o7w8O7TkjMRW6A?%GtIa
zH({l<;nYMg&v`EZzr`My6^;V45XFHQve4EA<QkZI3xs(B@OA+^vl+wby&r(X0<i>&
zfz1cuNIA`h(o#E+e?Oyo_dojj>aZx^t?P%9K@<jQ97;hzN+bm7k(LgTP!I$}+Mz+p
zK~Pd9lnw<12>}IZ3{dF~rIk)8DS7w!d(U^B^L^*~`~y*Dn0cPL?|tvR_F8L8INO@W
zD6PDbCTh6zBPg6ijRu>yfO<O)Oi#h}w|o9_1%1PfO=M(b(4<0QA3cIhx^ww!G~;IV
zkC_>>!hWE;1Gow!4WPFLaJBoSde*WM>tkjnDF)<~w0sU@K~(fT*c!ZzY5G3%FLR-P
zV`N~kJ|lnV1B5&Iy`heagM)MK!;TV&F5Ra&_qWxhQ_2TIE4ACB;Nd}qNF)o2isFDG
z2d!ccNen!PBq;rGe+F<=Fl%G=svy-t<ADb2{I%e+o`xVpL>J$%A}lFTs*iT6)_T3Z
za*bjpVv*g6F|im?uhN%5&Mi^0(9_$vKiryV?5Bv+SJrFwDWSmv_Xx69IXFS$^bwSi
zMAKEZ;ko$`K}mpdrN9;eE)IySU{4>(CR=qcYUw}otF=>zn1sZxzuZ9^H$VSnmz&Ut
zgszt$Sy;ymEor8R(i1;`nSEv0jEpXIo8IDk2ztdxNRlHyol<|?;*ni1fnC0S>tK&x
z*R658!|R}IrA}YM$f#|n065$~J*&kHsP2j{HJ)iH<v{J83?2Ghib0XW`~LLtW2Lze
zq}NN0wr@-+I5WSh@WmIcdUa`W4~9&Q9UhDwF4X@av_*F(vt#6{ZhW}U@00@MisWSA
z(xoLQS3~Q~(-@4)sThdHL9hZ4`oKyxICr<Uw!(vfKn^JC96#8s=Z@tSQJK_N<=9UP
zNHSSZA3K8z;eB}IX4%kAN_G5Fa;UW`$GgOfTxBDUNjcBfFR>zqM&fo7a6=z+>1k*s
zx0Uw^&U`N?gg22U6ui?jC@kR?7s=h9`ow5~k?~%I*4vQ_1RfLYoQst$fDJ}OM8Lvj
zWoI7)q!q{;u*xL(rLU6)0dbE&S9Ea&-6I(J!Kh24yLS&hKA^qyt5wf9cZ+>?fE>5@
z`)R+q*x!vG9b1s*?*7jFda)PiZeZ^@m@w1QTBki0H2j-_Xy=yMIKFy0xU_XLaQGAx
zA<g*S*=gDO!69j>a1-nEETOY!eX994b&R@6g4VYPG$W(2g@sGIV~*?~oidHGh+(eo
zrOSGB=^b|M*Ga=Z=$V;0{QaS$#7Ryr!y!_|b2Ee4Cl}{sF`m^U6RtJj1H8+41vV&Q
zk|O9ER_!j$-X?97UXc?rgY&T^ts_z!HqPwB5Y`WCJ?VcoT7>I?kPNP#7r~hgBs1?>
zC^I=;KfOBEDqYC#ScZ{)wYgNhby@Txgsn_ZQa{#cYWe+DpgDph+5*N8Lo1=>t1GeK
zO@`o&%-~3xhIgA&+u=Qt2TQPBV4dGIIT8dME|>x$DJy#brQL*PXy==?wx=H^Cm%Ey
z`d6}Q`UsLj*!@Cg=GvMrf0*XC47$o+z|jg^i?HD0<_;BSzz*OqKi)1PGe_gsLlZvi
z{oVX|g_gRDNYTHz0F==nhuM)oT$eX6Fc>H_V+5twSN>u=;n^COHGhrCu_ADYQU3J)
zO3jIH<<1K^jYhpPDO0IGHURzFy4!vE0Qav>V`qnVRAUVGw#c>00#>Ofw@<Q?!$HHX
zNRN5Ub@yO%0Aj=qs(YQAoFApP*_>lCJjDA`*-xe(Av~xtKu`iK&VYai25&$j>Ky<z
z7`Nj~I6Iun{H9wgocu1!cwn_olFl)!@IXRFiuP}O4Kh^ddcQUC0dP8@Am;SV6Q*N-
z{)kKohHX=_*Q(M!DbHidriCN)Xw~>rxHmufFE|b3$;i)hjyd&w?6xw!a@)tnVLbDN
zWm3!PX?&*^|DelDj^21w$V@xgcdN{wHouIkqX?fn?<^aYXqjmtr+wJpx>}U)nnMT&
ze%}6m0`J2(eNo$mXUDuoC=b1x?|9dD%10e%y6K+5{<lhO-zjhPFL@?)5;6A>(!4ZF
zo(!q?4jDHdzxsijYsuYXBy$|qw_lWk1EROladn4G?uS`Yxv#nLs1VC;N9GUqifpoO
zk<Nhw^&9GJPFanZ-A3E?AuBDv5<MtAbJ}w9^QHLJkaJFi&F$MrlkXTe`GquSS<e-}
zdM#PRw!H&se<yWMq^UJU(6u@BjtNC!(c>|{fz5{O9G2Eh`y{FDWP1zU_IJ<9?2~NU
zsXf;1L@z2!Gg;%&INOD)l11zYb0SxVY(ym?FL66Zjzux^4zfiPs@Fos?Ck8G?rtbq
z81Ac~LQh0^{I(snIap<f)%GTCH2=#H90%OkQxN`y19u2YW|b%wWj<L5;XtUoeEG65
z=*(cUXUX)X*<G?DcBoLh7f+fC)9Y3OepA@yG-3$ZC~7NC1HuUi7N9{_36vU;q60?&
zmWqv?U6?oqn>kr+O||eyL56K%tL<aly<Rjn&yVu$I4?}V>?=qNsD`Ko@{m%~LWT%#
z$42x~ZXt~{GHO?4n*6h`*j>mC0+i8^cf7XLc`38nKmzPolM+iJq-J~6ZyU~ai^JUk
zXimD?UgC|r>K-{$<mdEllwrNT0`q-_UH%IpX>zBfcsuS5Gm{A%eQ5CCPTFEWTw{lR
zBTNqHw2qNw=yyehCL|1xd3_VxiAaToWfDXB=1_sKK0Dh^4H}${6w#xDUJS4byeqyD
zythCUklgn7c`5P^R+g*h#=q}&$=hqa>2iZeWJ}{n-F%N)?y8=i2;9vCaWG7w(&56t
zZ@J*dR``<PtPro6#Jj+KrV3T2REQ~6^_G;;VC4#qF;kl+dKVg$yUHwI9~5quXI>xr
zl{3?*`p&Uw2m^Ocn&NGcnzlAH#Hk$U@XkzZ%s;PhoU0fL&DZ$JzJhLxq@9Hv%j<+A
zwJMNEf`~}KKOD`@UR*p>ch_%FpOem0K%yPDLh^Mw3akEl^S~)>XPd-xoD;7*NNVP~
zKW|*W+gBVw|D!1>r*@<JvQT0HcvgYN%*o59IRY4=AavJMQ$vF2`LA%DIdgrK11l(|
z1%tRCQsdEg`?--=n|GUB&*1wl;e)qzOrob8#vc`b3Xxe!(6YN9jh{AouB-=Qsd$^A
zJUd9wMq2wQqr*VH5fivSRZv_vY<o~xG3xuG8sH8Nad8ij%)@ae#K+fF%+zm7fM>}g
zqN1XprRl_+SOp@TmXk_q#*ijGN#B1y`>_I#)<7XBLWO~FDxhAF=3T!jc1ZdBWhn>Z
zwY9RHtZpLvu!D7Qb0?jO?I(s#uXT3fcTv?eWAUGDm^OIq?j00oua<f3`0urtKny+o
z=4^J%kugwLHG<j!;Q-=sMutK$_3$f!vuEcmX%K+iOUueYX$ex+Zs&Uhsx{Q_0xh5<
zt=r-mrNV}T1iDr1o)4uSUEvi`Dn~lyNBXxN-@+UvDz`rBrLjcxI6@jka;|;z(5T6<
z>42q2(tw%rmgZEX8(54fpfJ4ECCkY!i7~Ay7cwkfIUcM`NeOGG4f32VZCnZ5C=MK{
z@xe)+r^Vhhk(h~`u$3Ex<I_n38PBN+DGVriCYt<=S^}$<S1NuOj4P}5B}|(TK%T<n
zUSJ6UAAm_S8(3xaN?}>NaITNUvkF6E4Q{zqASZ(EB2`%S$PpcIdWAuW)gyAyV%C;f
zZ=u0%#B{`#GnmHdt3M{>W{T)z9n+rGL?~|j2`p;%aL(mV;a94n^9+_r`EK$(8{(Lx
z(GYoQvtyWqn6D|l($7wWmw{_J_Oay5gx~Ef-lTQWhE>K54N^r8{8Oo9DGOaBmg`H(
z5CmtUAgMBsYd79eg^f6SRgWO!Yu<~Ve$|lKK%2gMMI79?VRDJ*$^D&<z#>B4)%G*l
z%Gw$p+z=<~H?-uL*a8Wx=VwS$?pRxA2Qvc)d$q-DY@)69<gPlc#;rHs3XM795c_;J
zh2qPkSQPjB7qkMaqjREiT7I^RpC8mduD@t;WnnWw*VS{mUXZ%S05UDyJM*NR(CoE^
zo5T0C2&h<;>|l}7KRJyMf5fLr_ls@M+RCBHZF?GzKby>flH@q{-kmiXLKP?{z(f<i
zDn~H9sqVIV?Osy(?^)!W=v#BxS5?6BDBfG0qL%xeQCWHQP*nTStAqpat?i-yz`OIx
zM`mg~<as!M2sE5A9&e^eP_KOwb7$gFpv5C8|4O0`INz(XE%y)?7^48I4SM=V%3kN#
z1FtWjUS34R4a^^aV*$K?tB?{d#5=vUFS~mEdY$vg#daTm|NR~v`8FnpgB}TL#`kPg
zxjheLF#}2~6Sr>NI(Jb`dc)<Vxr1<s`>)E)9_k?6;jCMoecAgsK_MaVt6}q#ETY(Q
zb&viqkylvwPhQKpG6B%oadm@hrX^&};C3dr^Me+5J&c5sio~Nngamo+LS-U<NQd~-
zx@?bK<lguKkPw}K<VAAIXzhlO)0p>nSgG(;<qdfP^Xn#c!-9ba+-i^I7aoowswhsn
z=NU(#`48sw!B83Amp+a?Gm-3i^7lc)rM(}*UTy_~u~NV0x1rc^w^2M!9M3hkP+Tu0
zAaD%FgxCD&wO8H{E&@4TS<FY#9hGK9RA!MN#C)LELr4bidf+fVud@ZE;*HCpu@vaZ
zR1xy;LVWTAyML~9Omdr^zdp$T#XjF)h&ALqk`E}V=FpSX4n%hcxZ~)){aK4%$b2Et
ze#89?RNIe3YV)U^Dn|N&>+_WGhbg&j>pQN4kPn_|B<q66Y#KwlS{YxM9NzM`fQ6?B
zg0i+Jee~}32t>vOsCjLH-UIyTk|ykDnM%ja?|U3Ek<T|hzP_z^z*jFB1r07n$HujZ
zF!nTX&W9Wxx>!T?fq~#0ch#En=2@Au!)2?g<_Vz>Df_A42J;Eq2cfaH433SN7UTgi
zSPOLV+B!O5BI_<`z<Kkcseg!FqZOKTO|#BphUf475jr!dm<?u*^)qRH5dT)H7K)%@
z#`8~Jd*=D3_U5Qt?q<*S3zwMlsx^Tdl7y2~H<1S36^wIFmvlP?p&cr63;(rG{KYjV
z`mds%8CVXYOpUFjR^w~@$Ri@JkmsEIdo<pMgCC8J#z_78NUH-Cr7v8(V*BKM-R-Gl
zG4WVAI?0NFnrpnxjhe6!;w*Vw_Lf`%{opb{o8(frV++D>AVA)o2xWZFDx~&*Lr&*s
zUfm(|-p9B1qNutdkTF>+vP}%A9Pf-OzU0QKq*Th27Bc(P!KvZ)n_d69lAG1eP{t_f
zJ&#LTx1?3p>k-4%WjxICuD$-ZvOJ@H0>Ph4&f?9yoxgDO?YJ7;M@Rs`IZkeGQM7mf
zW}q|xvB))y@<cm>Lx!`InWL_Z^Ipzo`PQk3m5BgXz6#Ugxlu_oN1k!V*Qxgajv{Vn
zI7tPN01#OO(?0_2<s0QvjPkhfunl(@Q)!jmXY^ay1Zs_ZNFj35d~XAf8Hgi$>*<Fw
z_TA6Z4;JliM1{ugyq;z12mxH^Bj7@;?97DxUsDiLYM8POZ}1W5<3WcFx?Uz(Uw2p~
zz1+9>YHQ0@uE_n#jl)HLYT9WYxlI66&c7^R#B7fR1}2Nc<{(QEWZ*;)-LPZp1AV4j
zOOLjfC%Z(TYWiCUh*tSFuKjqTGa7_Jd0eZbsCcw{aqfrPLi6Fxhg#Jyin|`5v7g<G
zuDfcc%N-o~p^!&;`lIx^_WW(S_Ef#mZ<9W=dV+F|v+%**6l$bmQAT+vp=G?rin+N?
zqz_arIi-98xNI13s?5%DhKQL_a-W-YPT-1LtK~08SaGorEE|J$Kd*k~?YK+rR$!D@
znX$iq&FbrdFZ_(NWL$wauAGfxd5>-KF&ABWSzSH7FzP-Udr?3VeN-gjdka*U8HNI%
z%a->CXC>iH9KGtwLFaq>FnH9&i-@zkV(e<&u#-CmZp!r%W0u-8|1D>7t{|ANK+L^(
z(RZlS26C5wPoa2t!cI$IIq5z#DVhuIv~>JD@O!|!)1-5rP?|FH36M3z9C_fofpcCZ
zKvKY_2WaYAT`33!?tOyjWNT|X>NAr{2=X8&GSJtbh6%*pYm=_emGh3b?~EP(*@l==
zP+Iy+<0Kku>Ri50i4+cyGzhCunaK%^lzI&$y#W`n^TjqNHRgVnd3`OF0k<`aKfe;e
z1c_pSRtc4PQdodY^*<9JVafkZfJ9_vudQ@Ix*yB+63B9p6oVQMtWLki@lAY=)QtcP
z2xETe8`!?5Jj6T`iE9rgvP*G@#N9F_rNSUVB<SM&6Ieb@V$L}9qZ1l=3ZL8eL%2-L
z&$%`ftJsX5YGJ2KAw!^}Y?&fD9_iK;Oj2=lk{_32^iD=ccD)sNYkZ%2Qc;=C+yZ1_
zJ#*zljz|XY!=5=uLb3|uXo(5f$08Uu_T?K%Nl5_@6fhf$YR|$C6HKtu5t%=~r+`it
z_29t}_!X&H(PH5uu-%4|fJtYcE{{P^x=KcB(`Z0IOdwNyu9Rh%VaQ<!s3`|23f&%z
z?xdt?Qk>GXmT9=E&|}!NANexUZM2teYCll>#~tlu*51R$%zKintTPX}HI##)i;N{5
z?p=L8`o-;vLrUMpUXS;4?psR^KQ_DC{7Xy(7_tq@bppvnIk9Rm?x>5e=yM8r^5`Yz
zd`FgmOKiw{L)XD2&x&uy5w@6S=ZXpf{D*i$@t&$ZgX@sZ<<+=yC1Lb4*ie9YaU*1k
zI`B96kYvR5_w|Kxc^VqVeOtO>B+gYBnvlf{zhRbcsRTrg5ief65~Gj!l$AgK(@v-;
z-=aR?#XBjOgIAQ#LLR@KbM(@bIb31`q@{K#_9>!G%m}%5P<Y%qs<u>&5{FWT`@;6l
zBZj%?y1Q(cBxjn?z*N3&CIczgb}6E!%BG>^to3#Bt!>iY72$q>jC5K1QY|58O0dzR
zyC#&_r8vylQaZLWBN(6a41h=pF){Z^#)%{6mKt+-k8&y<8de3wb%VwvR;=$u=!<bR
zaKCJ8(*k?swzg|99kZGr=4X>ffToU?bmEy^TGxL8<Ti0ai0-v(toc78MQ{mODKOy8
zI`Wy6+06A$h?nyNHZ~S@hdRViGf<#8;-G%u)cvvb*1yPHF6feNQk@7I3Mzyc!DD!9
zSF6AA{E<9;cOs))CH*jFF9rc32)EK$8d`04<&YKNI9%X09#lwhb#J*a@67ksp^d4w
zS&pSnucT*X%&A$dks{b|LO#od;ePl3PsV=F7Zgwb{5f|m?jL191K49p&VVjkNB*FE
zbg1u?A(<+ylAgI^q{N)|g5}V~wm&R+V)HIDvk_h~*2cuh$e`BdD%J9n`h5&C8!pl=
zJ)OjB!vDW09P|>q;BkN?&#VIk2{>DCe}9r_GBiJu+f+~#MiPqXR{tvt_bCY7#v6?{
zIH1E9|1<f+uOgjbT;ZWY$H=yu-ua7qX7CFkS;IiGMnoCy+VE%<8e@O-tZM;t3E@cj
zVk}XZyLmsVPXR&+1tk^Uxm;mackNN&)Rh8V6X$QgFRVER6Al92MF_~B5iY0L?MHz<
zzFg>H)t%;VzY^1@#9UCB%g5oVk&V`@Y3%YjR9{Hj1<ALE-a2P_yio}Q5YH7_CY&E!
z^?rT#iScT}2%)fDQr~us1<31(pG4hJMrFprDZzP<OeK0#z$b(|7i|<@|J`P9tiG%C
zMD``fO~Ro;61!IZn{?ok{F-)pn~h0cetG?y(cMM~nGbjF-AanXwZ8SBrOH<1ef9p%
z3q9!4oaQ=r_OjMV$C1sKPt*DD25MY9xXXcyb9wLdo$ZEu<?@5y2i+B_f~wT9O)3bw
zUj8N37qN?U*bxQ>4C)ywCUT>--hx+FIPkRrd}WmuNRwe>k{g!y#l*aS$ArD#KcL=B
zby=!8e1H-$*CWm@?X4q;Y!B7-x=%)q&B=bT;VEBkDDy;kWkk8)te`5@)o9i$Jz=}e
z+P%J9OsPfuQ|7!v<^DJ|nJ3gKBuLD|tr%wZ&zMi#J=cEGXF4B&+u520p4EVYKxo_*
zut5}vnuZI#nv^ywOg|d%;0<`{)_;3msEnzs37(#t6mrSk^uJSRJpZj?Uu{hD(7xq(
z`#au;`4GUtx=tz(oG?$T;Mdj1I6f{t)(+rLl~i#Mo_8q!aXq+eir9XR1Jz`EY4%Jo
z;mzwB8*e|EGvMQ79AV2)Lb5s}%@jUY$3-qbTLs3H$rtrY^gel~F6{27Go~I6ynBy(
zb6jDgzAxDAhyv3LMiMz!q+>`)8ic%D^Ck+?#S-xnU__-1B@QjxiFtkssQi&5ADdcF
z1#fojFGe4(w^T1zwRGOau`<z8c0817elEJRyB+n8<JKJWpDq(<&_O%5>YaPomv1TB
zc51_~ha4fza>U)8gYY=EasdE0DD9MC>@f@Lb5^KUJ)2yC60zoOYWEF17&6~DNV+y9
z|1oAZmZHoPXn2-ye|AQlhQ{mpR$9WabhlO4ibvgRw?Ors%u>pD=NK;C2l~Of!DtPN
zz5?>vNu-_m+&Ifjq+7gb{A*<DtTj=TeTw}e`3ecXL4G+|74c8n%C4EL7z@}_Sx(`9
z#HEhLb5Kj@n__j?vUBe=^lkiU@Z&0ZWNk97rt3KInE*MCX@eBjTqu!1BuIq`mT0WM
zoQ0a{X*@(=LL~JFV~f^pTlp$w46tS&p^}HI!m*fPK?(z)CBTmSY6q4Mse<RvchjTJ
zwHvjIHT6#4=4g2Om3az-wD<O>78IBTPF6ptePWlh^+hv$g_U{8&-F)Eqy3AhcG`HO
z+<b?J^DL6CQADS;W^9<qkEDpd`C^pwGO~hB#J7bsTmgjurwXm1R@i(p_bR{nG7#;b
z)YQf4W7Sk|NHTtHxi#u#HR|>JZH|4aW8abESHfa!`D&9TC_nUM#z9N2eVzf)ght7V
zBlLTJT2?c}P15rsRjSnF+Bz;ADQR^$NVDe`7R>CXQi>tPJq%-?wbSS2Y`cIctQ2(W
zc+Sc);7^1b#MxIo*Q|b_V~dq>V#DjrtS<=)jTq~DX1uC-)uoQgEM$4El`R=0mRf3G
zMbue(A59FYN_W=o{h+42KS6Qx%)Fe2V~^3pZyR$YHsj;O(yKY2XXHdnydam&`_qd;
zXYBf<OygvCov^oh!k_f}T^d`|pYm<FqSvZ1CspQ>{_bG!jH=iHD|2VL?9AoEXT~Mf
zmA%FJyF*zLcG0F`D>n>YWyMN(je-Pa>;WTPZte=Er><;bV`CzSteG1a9K^uj!1*+0
z78Dk4ddmn#>>?crU8#6<)6=@r&HAhXT0Q-n!m=*KyxE0Sv&35{FRM~%kuS-yvoYbf
zdj!f^E(}-Mz+(2SPWM}uT9}j3;~?h58jL7xcG%xl<0m;-JT@R{AYSCA)mJ&np6xcC
zf*X$$^GHGA^)-~Ryqv@@PP{nvqA)DRb({`G-l=oeKGGrOB_u)@Hn&}Ykw?}pHE=oh
z2Ws#0N4$vogAcTRyjDF89f<Y2KE`^!;;%3BJ;{3$?nIH;VK#6^EHgwoUZ+Lk*AVN-
zGV0}Y9g3X;V{%owy$FznSu9XKBvYMvx|Mny&Y4-NTtoD{1VBB~J59ZT2vR8?#lZM*
z<|uv=hxLg6aYrS+Ek-Vx8u`wa`C_AA-`7E%Pk{45gz^5!fILafbzx%?h^ZWE#Pq?)
zM4UcRcxU%z;4hN!XtMiun=}6wKPXEfW3sN<iD98SJA<U0XMkKsOYkzDI7lH};bq45
z1=m^wGSmBI=crihh?-7uwJ4}+`kF6xdaH&Ubn6~7=5VdanqLk5W&JZ=E=t@chc4*t
zu}HF@gn*3xw){x-xOw@nYT?3yf=jJ`R8iNC!=d28ZjLUmJ9txfKosOHGr5D!tc*>d
zqv$jG_>*oO_i5i8K@>YDX|LW9eq(q3vc?TJs>;QMH(<p4ZEx3JtEVQvDY-tkz?d2%
zd2d?i1P5X+EYIRkMBI6&^wP>`MjWbBEIfYxCuvX<q#klcJ>Mo$L`Hin8i^$9dpE6U
z><^#L)`^IG@C7t+lGco#88dEv?VyV?UX}msFX4WX@m<_FrY8Su(zhGtIxQx+kskLm
z6d8>R<XP$EA&}xu(*N9?f^(og5Vf}eZ3V(vU&T|z8ytL(goK;~S=+&vo-p;jE+DPb
z(h}amV`z>(D>OS$HCY<Z&Fvuj<jqH}Lc8;WOUUY=bqq{V$HB>D6d!NwA3j~GP@fqS
zVV#1*0`c`v(Db*s?t-)rQDtferxx!nea*-V4}P>&=b60v!7A_b&z+pNe{N}wyxwgx
zO=m|W$ysO_e+*3|w3!kwOaxkZ-}oHMDO`8ptf8TCZixe5L?1m-N!kn>DSBcyg+FSd
zl14I@u>k+;eCDgT>%27i1;g5=wlTW2Crtc_BO>ggy%`ug=(#b9;mukL3%9Nb78Y=O
zh;LYl7rs-gNs~x+iwNaDW6xI`+8cnqeW+qkmmzsS8>hL~e&1U1J&~UzGzHP`;Y0Mt
za#)ihXmJJpufS^Bz`oQC2<{&UY2i!5y?-YvxIJFldOF+0kS-?DY713zwlzP<YU&C<
zikMNUa=&UkNrRYZbx+agWd|kOg-!2<sWWWUPOo^{Rk{b&%D3u6+Lveq>cSE_pKx#W
zo_KuK5u<w)d6*T%i?z0{+&YHkO@Ul%Hm6Bhon13u_(bu*6=PanzMMXz4XgOkpP(U{
zEO_2$EK||FqgrwGs-{51gDH{M0us?uzWCxxWSlnzphCtbx)#vy=I;2Nsd&Z7cEKr+
z=lEv%>tL~7gP4;tY?E%Z#fjds4NRo_KYsOK;_gHod;F0s*o?l2*Zj3x0rA5qWtBUA
zToI90G&B>Hvpm?(gtvt901q*(*F^0yA?PYO?^+{B%2A=Lur~-Nyfooe0p#(7GMs?$
zFIIiX&{ITXyS0^-LqAzj7&&m-wOa!FXU^Re3b=da_s~|uCYjL4qE{A~aX4x#I|yeY
zH05Omksy5yp=xXmGf=&usKV9dSoYrofiMjMnhrHn(WR4J$NO1}#8}QoFbM8$76-~&
zeMDv7bLS=)O#yNaAd9totdK4IU0m}^0|wegQgz4IZ1$T0l|i^>HKa?i>M3~t)`5@~
za&{H+IgRUnOX7(wn~P-~kVTj^mQuncVQ&nuYQMyPjbs+ZP=$5zRXqi58QA8H&eC1$
z00nS%Ru;hPpN=t|;{dN6!hi1XtZVtPeJuw$+~WEU{BUHPkN%&#l%`K$bLb9E@X;fP
zR?l_cIqt-kK2XO&AC`r+c@etjel3C(sE|(E`Uz*p#BWR2p%flliHVK|9qcpShm}1c
z(2L*ezAUW%xEBCQm|K`4a(_?I8$b&HLw}!Sc@94K|Hi!mZw9yeP?9ZI1o@|3v$->q
z4zg@$*N9w<_Hk=pUtd4m$dxA}CaQEKyyk#i`dyeXwzI$9!E@#e7{oAonZ`no9-0!b
zU~E@ke}9@3?#Q;;D)^ipRMa0FU%eo93sMwl_go`Ta{3CMJb3~yNx^_YkpE!XSh{1@
zfBDuzDr>slo8h!|a=FR#MQ$!=&1evC1>sVEpUUtYMif8~?`>V(=+8S+7^?3siurkY
zzb7MvH@?BO8$xplD(KYITDuOmh64tPt8iVCsoKeHM_}*3H`!B96SH`s>6kvchDuzz
z@I7jYMeLTYqvIm@S8lD0rGtP_T2KuttCV#RKht7?(6C%#d9DdY+pL0n%R&Ua3Xnj~
zi;A*)j|X?iAHtvm@@=!H<4jZt^7jxXYM5Gs^_V^7&P=$5IF^KU1R-fxS5h==;`jNt
zTL0*BIfs$4u{0){5kS;buTd%QRStMDP@IC>&@=eBc2B`Wz{z3m0%5FMVq(~ypk_%J
z3BvK0oy&;a!Qr4e92wnXQ$FNUdGgyEn31K3PV<>&K8WG^Zm&2D%0xJIi5eC*Phef^
z$v!N32L7H1gGR9hSn(0?d%}ajJ6~Iy3ZbK?2MNVQ03~e=6*&_0pQWuzvq&|3x(Cx2
z>R+1{{`1+??#>s$R`Gp9P{C5ro*CDsRPpg4xY%&3!|>ML;&V}Zf@+|l1_*b$tV1rM
zK2!0mu&_miGwX~DxV_HhrrSos>yiH}CU|QAbN4TG5qjXjPNah4=l=;Fjl&?l15;@i
z_oDzu1KZ&LJk*&1mf##X-TC_NLas%P6xVJIgCX?Z_DUdZ8kwCRPj>PCu(RJoPqn+d
zJ7BwdY;N0k`Q6Nh13UDfC=l2hZ2qf_Fy&9+dd*W2+wmSnl!qW_#qiEYLBAR>lj;RF
zerMIYfnhf0J#`!bcf%QA?_j8!7l+C$0uMHUssW}-fQ}O*O<-*eDvZPyg5eCn*RTho
zm_?`CE{qYJB%#?3d=Qt~&3>>1X?rU2Vb<<GdY<KPOfLcTX;M-W^p=Pkz$XQKOypsM
zNiSDs!*4<e%P?@GOBJ+#wGf&%BeI09mRN$M8c5&ZqyRi^u)~6oAqUUFQ_$|hSZ4%a
zM)0P~daZMmvo{m~)lRKO+cH6mI`ev!%5O{4DfK(k$#D{?hVfkLMn=HCpLHMwmgdX+
z+pn)~h(pU*E?q}h7n<Ux6HHM39|iM<tMvikHdH&XkO~@EIp#jtl==DjhDIaMWQ~KF
z>6*_<Jr=GM4=i|AgZCr!DuJ4l>pWDdY}5zP7PuVG;%rahb$KkQ$*~tg;omZAA8DaC
z2@rXV-0mziatVfsfPTXbNK8!Bfl2XSc+%r+c0ff92q1Y7`@yU#z$z0dU?A@gtpd69
z6`jD{CqQW5_2Ix*olU7Zo|)Y1P+WUOU;o$M*x~3b2P!j}_+JtB2H|yIR#jazGMbv2
xilTzUiVtoKEVs;(4+dxd`(Z^m-U#9OATaa}U1)`g3J!rk>dM+mMOe#_{{g?2-V6W$

literal 57721
zcmb5W1yt2>7cF>^6cJED;!+aQpma-@(w)-X-AGGIN|&^BcXtU0NQ1<sL%QdO-}`30
znfKnTHFquKdgC9zIA@=I_U4P6jQDd@0#pbD@?26vL;(VM@)H7qyF_^cUfFy7@j3YQ
z)K)^>0RnmU^6>|5V$u5+0wIM+iU=yXrtU48cw$f8BORFxo?E(0kWyf}GkuoB5j1~~
zi6EcciTM55D+CeuL+y3i0@dx0f;3s=A-)pQC~Vc(G0~MYDf=uZJ{7lL!{GFPmt);}
z=^l|a#j=bYr0?CI4=1sNQ%I5qU<#qoNd0Jn2fxYIr^FGF$-qmYP#oyLckGpHAK&=i
ze|&(x^2E)}jnPazOO6wY?lzje!6}1@;F8~uJXA?P#0^DvK^_24V<-kr5OYUWP@sKp
z(a6U}d+k!GL;rdT#!KZcS|^&YbGfiXN(Xx@e~9JSNh<ct70cOv8!=9-&QU2IF|Jy)
zc9FCw&7IuVI%C$B&Q&N>qMbE%;DrA5<e4;O1?T@eOf^56H*V^ho@m8hJVhIt-Q>S6
zTs*b8xjD#^V#$-DQf!3Rt31mYnd1lVC=vL0^-OU8@4J!jIF?QP`t_~tO1p0z_j4#G
zGdX$Jq8$oZKy$KApk&UvC>S0SlN*@=H8Ycik9T{s(h2l-F0aAC%18-TFY~2kWW;);
zAaYsy_V$%cYv3$yY>x+>Dc2LWTn_y~zxR6@Jay><qxsi7TbTC2u`3wkLZj{=YGik3
zrffN1mgK*0`mJR|uGP1C-JI{w&nyZ)Uc}&5H0u9;9^^Y7KM?);f0?MBwQ4p0j&jFs
zg+QSf*x0O}+95%D3uz&b1K=ZkjJL{ZyYha%*6NLHx+OE7^<v}30q5fsUwMS~p@^rj
zTRC!TJ3Bk;=%i<4Wc<4$vh_n6V#MI6sHiD(?Rs0Xu@jAwX}7gxHj9agiE54gl@5RH
z%2x?SYy=Tfx%Ob%dQO^ygM*tC*ZtQxBi9U5K4ObX#w-v~kf3#$)@ZmH$6A@Jxuxsj
z%^I({>7+AJcQJFP$)wM&FU&&vSu~H8c~K)zPaOpW+SIFzt(Kavz#NPuv%zEH;T0&+
zPMOz`qA<>eQAlP}Hx{wY{Na>I@4ySvQ&*{-6_Ljl9bXju`)1y<a^I4EXt1`5%YWcR
zn8??%YM{2t4vO5aJZrj?rFcxu>Ad%Q{JUf<xC;&X1o1Mhe3`V=2T563mjRt$<~86f
z^ZSfuBQ<IXY$+oLGG8&`rFxjdEvvY|l5b5jV<iadc|G=c7RY~B;25ex@stKV?!Wsp
z!0Qb>w1oEY5u`l4p(94@o409e@w~DR6v~>}-44xrV9lnkv^@T{_yO!TvrzGl$Ek8T
z&~nKqWttz)5pNnxLypK3Px?tIoxmAY^b%~&$gME)uVn`gFue}_;q<mbrGBtZ7Kwr}
zg(OzNYA_r$uIlK5Z+-&l99(SkSzi1C)(18*4pbyBPHKY(?w->V#g3z5^`^gH!sX5D
z089l}u&1vyD)f7JG0qBpON+yXOEqeW{!KXzZ*rjcI@ot-hs(>0G~h_}kcj^g0sfCG
z@4q49|Mv$LiftKi;-xlXobiI>sj=zl`d){xajNotn6c+Kupg!Rx3~TE@NyMs{tY?r
z*idcNF)x#3429$YdJKk%N$kIPQh(ajL%EObQYO?vhT<nZ_mx5tTU51XQ={@Zh&~jO
zl_OO1un2BVvXJcle{<QuBXMt-vQIKGyMD!YzTts`iXPmxT9k(%Xsc(zNFf=+>u(3`
zJ8Ay!w8KSPvClfQ!=mH=^NM!?kFFe-iGxe1C+9PCRiib|(Z};EIluT9EB}8F#Qzd)
zTTtmDVqQDJM$<^VFFS^YicQAS!RGrHp|GV{I|b7z-@dKrh|W6k8~RZG6dno9)&&aA
z-GhSzU007J<_jIwmd~L|W)mn-%K!eop63OK8TwyP(f`s53JQj_AwyyUeqoc4ko;#w
z(1Xx5_Bu3sz>xOxyhNjBhiUG3mNOjxY)TwnnO1q+h#W*Q)Evp22U27V;=Kmj7tPH)
zJ5I%_WsDMM?aaf?ZYRdSEl~sg^u8rx#$XEd(-pd6L#blTuKsSYr;H2@S@cJfo$}q-
z6&4oq^YcsP{%z1a*Q&>Rmgu+U&F@~neSLO!bMZ>_IGDt{-t|~NM=XNdaqFl$CYv+@
z3*6S294~qyl-IRfr9?w9U*<U)8n4}-fTp$nzyZUZsbbZLw+8;p2e&6<0s<Zef|!}|
z{%ep-C0loQZr^x1Rdg<w<Dop8Qy$mLro&c`r%6xA0&ZDwqWp5C;%Pr7v%;;cu4c(m
zFA#|{vRnSP%`{{2<bHAT>cL{8$i!yYPV3NK@AnmaHq&YfTN=016Wi-kv(p{_Xt~pe
zt)E%KOq6V3Y<;r-*h%EtZwx~2FV~{LHGWLzo&Dd(O5UiMBi_T+)#L66<0^bgVTi9$
zmVEJ)dAiT^vqtx`9TgsD3JMCx+Z}0`;tmWJylxmr|E0-ANJ!|uF%0$xMAmwt+dtCn
zk9jqQ0xV^Iyr6OOn!m#dHYTy~=`j4DuU72ou#GFfcW612+v`amN7J7B@>b#K_T;QU
z%ANN$a>hB$CMY7r)|x4qndJ(Vil)p3F|{h6NydoCJ1T=XZnvQ8@3cK(wAmi@vxetj
z5nru^po@77eT?}-o%LesgNrqn)6TEG@tmAWdv2sluUbzxGGd-CW)OK#Bn|Myutnvu
z7l|l*Do6fZQ-YDv{A_B+`c)SC``TH<d61mD9@=DEIg{-UyZftN@vp8A#Bx|KY|WJE
zwq9`OlQ(mMrI|BsS~ssfk!-Q;*x20M+_sb+9bNA8a338V&2BYUpDSCeL>n&^9~*n}
z*X2%&Hhyr`w*O>*cxHx!hetDgHhp?_K_+MXh&Ll8W%q#e>Zd8IO0jY}&UcXBIzC04
zJZ|U`l_BQv;z~QHco<$*eYkCRcehrXx7VNUFOb*d<hmaFY%XtN!lI)wHSAI`g+gv|
z@>ZfLwkds*oTuoYgLN5DFDf{uz-7PQUvIZYA!)wc+LD+!GhLz?#)|uxzI7-O894J~
z(&j*N2kGCUW{ys^^oLGMqlCd@q*zy0_~}PZPM+g-vMDbwZ_{??T&R!>QnT{+3UF}D
zzqB-_9WP$II9?4Vv7W8W1qfj>@8h$lPiZF99#gklJa+YpT7|wS*e@U_z+kXG=Ha}L
zDWU)O+4-b<+@@Gz5BVQc#D=~-LV@oqTl6@OmyXTFE&UH2iAIhpJYKBC*PJS)$I~jT
zZ_&rAcBtxlO|^JjU<lkYQ&IIUs)M(pJ#QnblsY>*r^|GiLiNLN8H9v|!M)Pduk}UA
zHskpZuzX@YAmZEX_<ZMx4q^^w1I-pn-|6;bUj$*kLLo0NZ*LTdfI)Bg>j+U)Ms{}G
z2!hXgnKR^>A}T5gO<0eEB1;Rt(de|Bos(0e!Qd;)fAt-Vh+^Icn!!)sGYQzt14P5*
zTaXkKH90LoEIE)70T;%*ZKIWbKmMLC;`fl$Vxv>r;iARRVDA0>ea<6pdkl>fBw#Mw
zFAf$#@^@MGQjPge#N&LrHJ&43@U@Nd-Q#28U&f2h*Qb!AUC9G*jK}?y+IMp#rOsx#
zm0IzRuc4s<;NSd7b$VQ|VZhP^@U(bp=K%XkjvAYHEM>$PVBlb(EKCGJY|+Q3;4xqr
z^KY%?DhyQq=+U=xiT0$9mugznIjWmXlq{Pej1c>W925(cAX@PedUzNZ7>TUPKlpqC
z))v8q{z>3*Iq<yMuLIbPE?L4SrC18#aihkWbdb$|g7!(qD2dEkvTN$-=wzAX+Cty_
zpHiJ#Z6?g5c`jYo7LAzql5*Yk1xR|zAD{ZIbWUqY#89{)0n`S&%I>cP>x@j-2cGG0
z)P1o?xj0VBh^+EI_~(Bl!a$*p${+uqsc?*mQ=``M>ZLvAUrs^HAkP}yu*A=L&IgN&
zJAEX}9*2A`!T+EL>!|R0zj;mP5Z35P))Rj;B>#V_z>N%m3a$zBnnR>ZgPAB!4<y2A
zr>&nI|G-UibXx^FG8xlkC=Q**!>xn6yZenz8#+3=^S_B^3P6oi6<1N22Al5G-K)uY
z|K-b<(?8V7B`4bM&&t4;8hJGIj(C@IrC$Nq6@dANK*_fGH5!s|?!jS}qDl9nS)Gxz
zk~slCF=B!?&!08I6!r;s7}y~TVVmHCkx(wbbDJztX~%7Xcmt6;l(^@@rueL8?#F*b
zUGg39C1cJH7I}Q`Z?WFU)bl(8yQb|kxS{}^GV$q$j6t&Bc6S6KFJc1dRKZ4CAQ*|~
zf4Dutz#vjrYW>Tz?0wONAyChQ4!-6G%7&hAi`J^t*rN}1ma~=fQ3BSw%&)}OemU>^
z`S}HflfX06@o#C@TB_IeI_55W-#o|4hkJj8D!`z7I?8ic&xQGTm@K~>wZWkwLVnLn
zYhm^ec`a2YVj?28&^nN8Xa)udB6{cN3EzXjzCbj2#!B!UE$Qf3RA1=*d*A<HLTXyh
z&|Ef)={@T?w20((OPzY#Q4r(O>JsVICt5l}=K`K$*;9SE6phm;jLV?c-27KqpoXGG
z>eH~X;Y?Y;`-(aIqUZ}V!5#e!kKB5HZ6O)c=(s(RBOcXeEeC+;{(PNgi3+R+3NkbH
zD<xZOX|Ol7njG=n&jt53;Mj_rcp@$E(1LX6RP?8L%lF2ws=B(mKrBr?Oi;1{p{qg|
zQM}?8*x6oE8y)`0L#njPH5RHaeQuG`iHvFdUXOXTtmS-;g<V=g;(hMmiW{Ur`vUB2
zSvpP-KCpSfIaSYgLH=oLZBpc22FLIyy+is+RBm_j<M(ete<VJuA%GTE{~A$Hy)Wry
z65sXtxofmZ0ixyi&`EA6h!D649g8$t^mKH)0uNVXgYk4Eysj$;^rHH^(<OK;=x5~2
zj@=aoed!gs8YTQE8BF}V*i&o4CBd7+=%}df>iCcM-tb4>RwBl6tv5n640m?l*~P?;
zGG%1C&F5ht)jVh^-~EuEi;L?(X5AfxJ_!#9#7!*SXEa2k!F~gmL8Dr~Cv>jHqAlF^
zG4~_@MAD;Q0diWidl#W<nGT;$EtG%*&i8vai^*uU_2S%OmX_su|JOSH`^$QnsJQj<
z6IjFJ%EP>%26dlU&h}i*F4hbJsK!2OXe6r?69EJ!ect(4y1ONrypLnh#FhMl;S&;t
z;Zc?n94y|d`Iq;hx+#?&cc`rGe&_H|H(!P*5$1^$2_lP!SS=V6aZ%CSygVcX1Y77u
zOx2fcYRQSA1v9v}XgO2n8%g^OX(QUM=_Yt$wX>kSf;}5#4wuRQ56Fl79aixKpp9Jj
z^XJb}xe6sJP3G&uF%<IHx|^I*a&kZYYOQ)B?l?FPCUHkBrhi;jw>UsA!asqS&F6l~
zsZxXVZX)>die9U}y#KYp7d^ZLql}yHT&jpT2xL4wHvoYc4JDMQl*~!@gTjM4RJ0|h
z^Q%>@i3>Z2zIrWb+6cq{j9^liBmV{K#A?L_oHf53fcQoqzUSvReHX0Jxa)bPySLah
z)7NKmdwI+Rdr5J3eYQK9A4MUF8rW&cE?~t1PHuMya&}b9Ig&;#Qn~we+*=wN5W_`Z
zUN9-q;sCVOe_kID5OCyk)<?)tZr!!cT?6K6Eqip+mUI2e8BiefxM93id`(piuf|R)
zM~rJUHq-^pYc&2_prUnQ-=^(1a7Ip=*D#tDsnPW!1`mmfibimG;bP^J?F;tS+eJ-3
zV=^C0le*G^_N|VKCsJ4b_yBuiv($_akCf&99KzBUf&W$?0p}io5e)fM)JVXJ;F{fl
zYD6@Qql20Hh3hXe+?=y29>=Yp**42L0~df)p>2-TDRXhj4u+!lfihbrgI^mI*;&HD
ze|a%rZelSAlAg_Tm<XSZ)QbcjL69;y?(6u(M6ieW1L6cXXeB;+DR$r_n#2356fXi~
z75MCxr>7^aQo+grr;kR9`&qapxmd*F&F=ADgG2ChLiT)B8xh^^wD~ZP<_FU%uysZR
z?9pO4>vnk_D<gwXApMJitPl)f%Y#QBWB+KU$(rrSW~V>$LfY2hsvH07*PV+r>5iMj
z_~9ui38G;MGbXanP*5apukyFHwkC3=1scn(EG_qLEjY?Qz4d*{k}oPIhJ=hPuoN<4
zJTy2+E1w-eNDKj6=hfHkj{C&1Pt2sG5F&2<rPoTOT8)loE^pj%l!}y|!X6;LgfsB)
z@Nfl6MIj*};!{YzadFsj&pj@MO-;?sch(s8W~<)RPtH$?`%HiW3n1Ubgh%jc0ctck
zP!Lv})7^R_rEr{=sVLW;FfQtY8VinS2{ptLM2Y*hvoL_Ju1Ggu^Ald&kNX4MG~kjF
z5`qF12$Ftv+;=!)-8WSrzjo|#!>IcOQTk}R8@{Yg4S?QN5_oX=bs*<pC>JVhTn9M`
z++P|L=0(iaTJ0WizOz5yn^m8&{&cU8B6N6c@F#^05OQO8B?}7+mzS5I@?p?vc0FdP
zJ3T!Ozq)T4N#SsS&RC2P<SJZkG0J0SDRijcA|l{f>v(naf%)jx-txH37))dma6MY7
zudh$%^Elt0gn^+xkfC@6mV#K#4y4_V8tc59EXp&D*R4|P;v%K&j{S6h6ohG?gL6>G
z0^CdRRzIMu>~O8Fik>G^jLmY+mnFaae27uI5&K*a0A4#5K-ZB3qZ1{+a3PG%>oN@|
z;5q2Hr{<Qgl8mLodxsrgOv-Z;5ssXdl{I!3k3*;OX&PGaOTwcZ+|ro9!|k}XA89y=
zz<pEk45?I8{ztRl<z&8~3Z}+JB_YXZV`noNP2EIB@9yiXyZb8;<?IE*Jy<hGM@R3B
z2J5%vA1NFSCEMF5kgiCx$sh&FM`uF=18!#0&Vwov68)=ORp5ROx3{@$r;1>s){oW1
z*ArNl+!J3hiy94*HWvW=mapR5Z$Fo7EGILAi}wQeJ@+mBX|a=5R|rTc{NtcHlZCF%
zRhh^xKX?cCemuTwWFQY}yV^`)bd3hdAQTS=`jBHbnfHx{1UWm}$hXHGK;O|co+h5S
zo2epYiw-|T#06Ktw&C$Y9gMT}$VK)G!Nhxd-sW_Cxx92$Dnf(M;#AK8oNuF4^j7bs
zFJhI+sf>*GEkc5))m-&ctb9M%vDnF>Dr2snKR6*`a@Vl{sLeSVRi4K^?@C2fYqnh3
zdH8Kvn^@~2kDZg94T`w9uV0TZ_i1Qoz~rNCot|e30tDD4J*3DPLy_e~czT7YTK2vq
zjV^PkUmgs(<I;SilLeUKZiHL+YzN<r0YksC<*wz^<B^dOFY6m<UxzUwB<!q5HSi4x
zSoS|}&SC*hT7f`d1Tc^XyuH20u83aYgCRDy5kNp(y(50R+lqmTYQMw}GDe#AlBj7C
zNon^rquS4Evq^y8lt4Y^a=3&V_-DhAt{#jRNxA^x*=yT96n3j%sX!I0R^4}PPP@~P
zo#~P$lZ7_`Y@(v0A2fM3P=~5DqG>P;Z`N`J`O(L6dtM&3#J<VaaO&0}ZrIw;ew7Gl
zlCVs_HA%p?bhbyW6pCxO^Mo*|S?$f&9RglKPfrhUB0lG{-vLqXFbI!EfDWOZI#_HX
zVfdT&_3P)K?;vuyJBPZZ8Y%Jq){A!M7fm2fXYk*N{=o?Z%Ri1*nU<W~z@o)Hc+a^&
zseC?l#@*TcCm3R+=froe9)0JFO)j7!?H?!4U>BsIqM{-r%aYF#Yi(@>C8tZRO_Kvm
z2E>-_C*c6(XvuXP=PMVh>@T&<kB!;P#0`Lu{`dU+;A%>B>^}bY@82LZ5RbB4(5pJH
zb_Pb3k-@FBe}*Tqod1k*{rcQ~A>ipy0)vN`qEzlnv9&G|G^DY2K8#Kp;3&dP-Xc{T
z<t`Xq%v&=g8bnJwr#<-wy5$SyYV-pG1G&<P&QlTt$7_8}qe)>CWHyFI=me}=KTih=
z)Stb5+hzR0W3FuL?3$R*oi}F$?BC(=)^Mr#3dh+)T`zoG+|AWhc`z`E2vm<t0^G*W
zr52A^_L7@I^|kq+J)bfSHkX41fIw}2muHDb5p$;<n6<GvYz}vk_q{)CI6lfk)%E7m
z?+)oZ<a6A@bU*v-16eYy&|6+Ii6`_`DAc-KYBG2e8JyvRJU;HWekAS^fzLwada}u)
z$-!<mv0fXzIZ;!LQWkYuUH2qi;QF;x|H}ZV{4j2SrC|RHkywgkQ5sFmmoKp%JpNEL
zH%q64e_ZlZ%%9nP@PYavp3-;u9{*9J9NJNvjy2*{n+{uR<{S+(inqKsc~+)TgX)0$
zzVLSAx@M>C-rZtolaRw&Dfu)hKOg3h!#^@y06$7slBu5?LC7JK%31OABdD<LUeR(A
zbNk$T9v0!?XEsMAuC<T5!}AL*`;hVM%01ktaM*ay*$}ZOXI^yI(EV<)2+DW>aIVpO
ziq^-+2iCGN7~d7IhJZvyS6q|dR|QW&Ny%n4$66|#2vWhD()G@hTd>GLrTv_g^3BEO
zuR;%WBFnwQneD1C;^A%$e%}~S)cvB#rPTdC*`my5ga0O>lSyOSBODhtDHR+WOGCc2
zt2Frz|9B#xd9<$P6M3;V`#!g*T4RGUG&er<eI!rznXkjQzA>gZZ~7)DtGw^75pCFg
zVo>A}hC21XuBJZH8;BheG3y6tOtgG_cRhr&O)iJ=Q*?JubBi1{y2GO*#Ai>F>ma_l
zVT_~S&b!Z~NgiWDZ>aL|3FC0;^Ahx#QnZMMOH-Y|5o1Cg=e^$g`RAmOBm%7f3XjfX
zfbtns=63F57(wXIi`3p~Vz$mUTB%CWHn-4KeHe;N3x8iry8cB!oqF?2OoufGs$YcO
zNq4>~#@JeqT{^VbM4f2JTpi4(x4?;q`II?i#WpUg6>m20BdGJ3Bos<XL!<3Q2VRG|
zWq+d!Z2&_QA|%Kiz8Dl2x0x$7{S37zQ$a<JW{57{y)-&s#A>$E=yH?c=;VUSdswbe
zYGxugfnHr9dY14XDTd-FSAT~~qpQ2Tw`b{QJh|=ow^=4#EN9iNl{Eidizlf6%xglK
zj$Uav-rnAV3Y_<`XJS^P5u^to?pnR)EB4!bH$aiAw!nG)9e8ss4YRkZJ)tS|soo6$
zo15+#OMdR&;$3wVN7Zbz^Zwiyf&Jd*KuC1#qSsJw5}k@6s!&kZ*Hznv0oD)m)lUU;
zKd~bQ2gDU=QI43mq{N2hljPBm)=-<P^{VDhJN)1n<HB%ubx8e|M&z+st%J=|=UFB6
zDU+km?IiWWz=56Q>Y(XROoZrt4%P2Amzm$P8r7!q(RbX)N)tqptij}q2NJJxm4e9!
z^d&22V=Zg3o4W4fh?O-83yM@!XNoM$*x1;TQE4evSCq9B6%_%^<6%GSdU^S0czewL
zoxt7cB<w!nABU5ESVc@sOcg7cJ`4I!SVJ`f0SbkN>eKoa^?-Ga``6Hf%@^vFaS%lO
zr!T2c2&tzf>|!P+9>ZV1f8&aS8u{Z#n;rcMegcDLFA-nn>ecGmE}DSt>Fy1NTn?3j
z!VO!vTC?lhE@nRpBNU-VVTqdgN$BHv9oX{PqL`FwH_^O3`in#^Sa_|;GsPU<X%1py
z;K}<BfA3b})+usPxiPdjIciLz(o)!U+>2RUiGzkC<vno6lAZXd8rSkOva~6(nMOhj
zI4pmci<cM~8Y*qw69=IMPt95G_A+SJ7EJA!?A>(+K1*cMwf<Eg-xG@S>?~CfFk9H6
z;>+*|l2Ue@f2_`VROYpd_iLw|hm6Cj)FOPY45unBKSnf0st}b<1zG&sm?|<+CE?$n
z6Zo-HS+%^}Y<;#fg*ZTk;>Rogf|rl)<nLd=L<7S0L|8jl!47KjF6pr|2v(?d-B6X^
z>G}ESz`)mkOj(V_2y^(XrL&|3{ZMN^Z>?%?F*9h-*w%kfqxzWqW2x3@t|@I=lgR8j
z;qH*?09%q=-d-K*My<a%4jg4AKGlQ3tZ(}p>!0P}Bx5)BTF$pE9%lH|%BV|AkaDM!
zPU6ET?J6XaQ~#;ZZAF=~yQ1@-vRUJqmUzw0=0xG4({7(epFsMDY7TD6NYCGKx4=W7
z`NI+ydgTU-+~N3P?WE1K<`b81K^k?hUY^XC&ACb`kbBX7T)Dxd+xpl}?DJPInFgR>
zvBjefP-&pUP^ey&cj6c&K9)s_0y0a3=T#+$(15N;_o#Rox#pVgb4!V0`0aiCJE1dy
zH+u_pJf)ges`LrI-6E;~)dE<kXjaeKPnxrQ`IE#unZ%0~{j@!0ZO2^ctUB49;A6em
z{*V9{1xvX~1l!FyX)`gqmFawgqM(i==)`(RAc9V)WXyU|8_kRY=(G(DvZr*dM#F*|
zPY~lB*g+FNm^u`#3#{G5WxNxCpP(sUF9i~I)N^8`d}IDtXhhV*N;#c_eKSHw2CNt{
zaYBRM1bwefkC}DSoS%qwYp`(HI+bt!yG{!zYFW^*%0BHx5C^v2jaTA8om2UzOm@Ee
zCNQuwBoCe3_Ej0LZ*IP(Kp=VE(I8;^w>z3lIOvD^jcuf1UIz&19v26}#xlYp@VEBW
z57mW`Y7GW^drUlp;Jc4e4_vHR1O%ZYnqQOs76n!l4LsX0(p^b2JpT@CqALIVKAT9V
za^KAi@}0N7|0ALE3FjLxd?8JOWGtYpF1nF$!!}?UKDeR)Hb)}rBIU&SG3Bmky7De8
zQAt+d?uu4z#LU`vST<PK7(MNj&)F^*NAQIkEROp(%1&M-c|}qKEW-dD4wh-UthF!U
zE-snPlxo9WNTZO6P!+6Pl9ViYn=F-hv>Gc$PrckJPbk;$XUK!3u(exC)u~xiyvaEE
z+jKOgTKn^z8Jl@-JgpO(xs8KO86kOJ`#kKj?9H#PAYv5SKn_hWr=#}fxVZbfo8X`z
z9j@}n`Yu0`IEbc9`_q@q(ZhE0JLB&1NFtsmww%xr#RC$a8y-iEg{GNo(J&@;=bt8P
zm`at(I@V(~7EavSpmlYy(4e^QuQOUBj{KYeo6quhS+3ODXxir-alZ66^Qj^%i<wkD
zA4Vk40I>+o@cZ?pQ4(UNXY9)nKJ~R~8Lgm2;lsdLs@32({S|_Vhi%mF{YR&1@xB9@
z1ZwpY4_a3vn*oY(&M3!wWg(%?-xUTJDZ^H$#6UXn6zw)ckd(6P*Hpo-IWh6U+b@48
ztIkqqZ-WQyM=fP4c>esT<t(-eBO~+;GBPioa;n)mvR(`}{bdyIm<^N=*aD9g6nFYH
zd2{vcvM`oIgr4AMv>_!6%yFUOQwQ{r?Ks=@%`>k<$H;9F*KVf7vQ!7o$fS~^b2v!w
zUEQ*Gm+VbD^VUU~O^~-EsILGM^>DNNaKD|O@k3=djz+O{RT*#jS@vaF#e<Cbyh@?c
zU*0z3kz}xfQdkt0AKW9#%F1-gbbp?ufx=Fs#{4ZBJSjc>4^V$bZAt>DZ4Qbwz@P%~
zf4?j89keexqy5dN;4-~M9334&xpv<?zv?@HP=ag6;pOEumLZ^<D;3{I7X!LA-c=TF
zt})u<f0yP;^NqWH_%J;FleyXgW5v~YbGFJvv&KCBkcG?Re6I^Wn3Q>|y`|}7b3_yH
zr!48t3`6YGW<}4OM@qduB4n8*GTKR<kx-p*RdLN(vV~2U#a>kppy5GW9ki?>8f(S0
z6-$ar<bwyw?(03T{w_2)VBl2>6lr?&hU2p)OQDc~a^xwr>sNtB&Da7WA-m<})!uCL
zX|?i3apkLnsdum%Q@mp&XxTO&FK2CTo%qnaYL?OPeoDs3yL8T@$->iNmI_q|zEg?T
zD1{ES<|DD<T8_q6kCoeoABBp5EPkXAY#6n=n-1Y2L$8^sd3bmLOX6EscX70ek$6SG
zVmy37%OE7g?7VPQHj}`peGRs1tNWQbD67n8O7E!OjGcgf?&t+s9HTZtoekgZs`CQY
zn^Uoo<hMTe3prd3XQU$v1<|e~iHzyj`cpY!-yC;-nKg{#)fftbQbv3<<w9gFJw3hu
z_Tkx(ks+@Efl}m2CWdeH&+6)G82<@-N{Mcpcb{ere3|QhPShh^<UH^avKQkxtIuci
zPPbZZEM?eEMKgi)R)6@W=d%3PUgjQU#&pMm?TpntqwT}J>y<`0Q54N&=Ebe~Ow?4J
z<@Rh<{$ooc<faAHq_EzQ2ps~bTd|yR>pFqPjot`Ccx3cu1zNlTn!Wg;Kc};|brzk3
zBF$gp@7<H4d)?FSE11fjV3tf}w7TtnklIPQ?B*F#B|m@XE#U89f>Cz0)BOc&68ioZ
z@#zD~#R1#R{v`+qt)TEfPO0-V+H~{+tv~j#S4>NB(IH6=a4Ve+W?Vt3si~A)ZUw3&
z0tadjf`_$M^ZYkt^t!E{D!;tH=VgkJ+S?b=T&}FFfKmxiG56!_06-sHWH7z(!C1CI
zD0{E)Cm8tx5Y7d@V-rB7qbj0{)Q3+0irr)s3Upoyh4%LL>Ax)ju`qc+xe|dJGFQj>
z`qm$VzkJqmR6fabD9!;kS~Fkyoa7YVcVDxP&8G#lKz$a6sYBlyTU7VFw5k=;q~|E9
z?VCZ=^9`jnf-lL-k|E5JS3UXFcKanMh9Z9J7rw(QNc$pB*H~H-x6_U;M~)LG>gVZI
zYN$qV4yGwlWcGV<=$Ha1;B~#PD8Yk?DQc}AFgl)Xv(Pq^F&OS{XL1b}k`tATd2-s-
zT*>Z7hj(|Z$&GmnjUALh2#N1ok}=Q^Zx;=@=9zCVqF0t|T%@Yh#@SyAi+$?Fjw`cW
z*hIm=kS$O`lum%Zv|QQ)QjOK9uLk9gFDRb+`E~`fFEx92^&PG@WOj)k+}`g3ZYzV!
zArEvj0XT4?ilZ<ajH7vIuq_N!Su9k{S6407TU7{o-5d2e@ghFSq4a2>BWd!XK<94!
zit0|q>$6)JvmLJ*I^>sm%81Gl%<%8}K-W7KxXbHnes2$F=X21i1D!6=xaM)%VFiTj
zigwdz2G?x8*L;6}|INiA1|j?2%!H9G?EIoDNJLDGfBZ*Vn?Ot~$-S+>0N446Dlz@M
zTE5v24_EwVUU{wC$peGJ!rIXIN-4yA?f^oS`RBz}Eo6l6pCT^H<y#fVi<`36u+paM
zK}5vGnRJ?e>sM4;%#?x+gy^$^*v<R_midzB;gV>B?$hoj2lBbg6(eXrb7sGY6u5H=
zAlFqXW@Be}1~<`WLXdeO^4@p(i-909x43Ci5G6dJMO=gwlClaNiLQ#`lRxe01~{-B
z1I7U5`cvnb)P}0BUn9jn{>*s=xxKJ3H2gHoxi{~>)<$~8f9meIAGz4X>_m0yuJy&L
z#o<O!UTBE@^6YZ3zxJ^0f!|lV$@w{_&x^xWjABH8<ZN-KYBOYrg9%vy#q}Ao@1lU0
zYVmcBV5Bd)jo!Wf-g2<OXtp(HBrzAKi*t?nl<`=)7l^+xl+rQ)*ZKC;4;an?Qp(!e
z8gPuDp>(lkVUwwV^W)Bb@@6sP+x%Ov>(eodX;C$<g;0ye`!>+by|u6a&??1i3x)H1
z`8lEI`K(FQvJ<F7wkPusM}`UHa^}MESyG>+!ZtTSwzW44f;()Da;qW@VHgTzBjl#<
znA~+t-`;sZ_cm3_D;2}al4r=^cy)LYBoiDz<h^sTs+&L3S`RikQ`_x3e*RVfbpS;-
z;6g~SN{s-1p#S4HDr@I)9gu&3lm-Ymh=hd1?*<2$P4?QwvX2knye;TsQdezsB-^y8
z(0}=qgtV_QGh;@Mh7ad#AsT`sx_iS$3m1)k*sNUKNm*)sx)`t}Afv5PgvF@+gyP%Z
zpQU{IOz^%GS=$!%?ud$SI=XTc%>Q;&vsx~}<-UAQaL?+KP_<I;07EZQL59Q=2vXYF
z3eYNxrB|5g91Q$+Oy!D5<FQ?H@BJ<r^;gz<-gq-Kw?GH}BG#DAX8Nja83752QN42g
zS=&q3x;Lddb{|q|K~j4|3^aUv_Uj=k#a?%3GwKOI6ZZg`Vo}=7AwE8SFLRC2h>&zX
zA6-L!mfSqH<ET05ai8ruhm-gdu*tsL1Ms!UY4^OF5u|oD$1OR~UZ<S`OxY_Yoz#?+
zn<bA!!2HXkakCXYkX|DH;KRf@->)O_I+X``J0&Ar)R{e@w7Y`b^fw;SGJopV(UEVp
zdR<7620z!1RnB;W+mUZB9hU526E<#n%YQ%MG2=l*o*tfEzGj)X9xg6)J|4!9l^p}u
z)mSbF1RjWhuL8m!A+d10xBA^E!Ce)0YrWxC^Dh~7dpRsBmk->x+FQRNhQWRnXm4s*
z-etZo6bd}*jo2|X3V6n@8<_1Ur4F~HL56L146#q)s5fHn{-J@Fl5pH^xKwsA5G_Kl
z9dP8nb<H?^chNCBY5JG@<)7mb0*Q&9`V>1mJ5Ze&zSFx=iKG5}=|ObsN%i4dIk=k$
zv6C{Uwhs~#A*q~CSzzxA3HVGlb2dak!(c5BHo6EJ?`I4tX|UNRA(4^2{j!jD!#USe
zHUH;?VEFjbeSY%|8#`q1dwCZsVz{}b0s6brQ1Wnt?ydKeY41Sekr!T1b#w$(^%dW-
zV0t}8#&Bh|zU;;VI<K-CJ`cS=hDv2m<)QDqhp`a*03{2ALnxVmqy;TwDCqQkAtUJ;
zvl;7j3zmuxeP`Gu0D<fT=`OcmwF58_IWSpn!ZG^rnaAa44I$+6ilpEB@=SHCqNL^;
zS0jS-GTE|r2zOqz>F&osGZX*#&IV$F;3qk3Oec;P?<Em+MN4RryT$f-h_EIn`#jDs
z?W(cg0u>M@2Oz*)OkCwlHFnf;#sSIQ`JA_sUK8LJ(AS70U@Nlf3C%6-tu0lnP`N`X
zA!C^|WXD5;Sub(BpC%f-GB_|c5?^erJ07f|>&TkL$2XThQYlt~zFN;J)_$F-thO~D
zKNv-$Xl^^;E0I>D*HCUBzOGz@>T>IPy;U(0PyzbOi_07>v)7#gc9YJBT-r=eBqiCL
z53f}vjsQUL_xIPVHa))HE%R`7H8e5t@qiKIA<-pv=y7PZ?rB}YzX$zOK!HXP^A)KS
zKjL*#8k%jjJsiAucBjl@5s{7F<QMJtfWzRf697!M!WU7Pi(JvK8Y|6M1ATq3yG(_(
zi^+QL=N{f-BXhkPHOyvDXjR09RPxcGrYcmY#}-Qo)bg+IM$^JJ=sNpeq4^6V_nlpz
zEJT3rH2TS_rL=cz2fHIa8m-lO1DT=~s^g3=p~y9j<_bc-Q}*hZD{KA{yW%xw+8~>Q
z@U0zX8j@U7ae|!6=ikixt2DyFhqVdo6{ss<Fl1ktn>_X_My=mg_ni?u&LW0JqhXfw
zqU9w?yf193A-<=MEmCF%>N(U)HM2~=_r7Eq$+Zc7h|ti@%_FJNcwp8%nA>Z}?uJi?
z!pi5z(s&XR6JeGlz9Yt12&-SPbYo*;{`~m^h_ZWkp*<jvLMP_!ue3BZEtoQohrxY>
z83`_=y(Srr7kikF%Sr5YTP1dD%>>d-{fd5;lvEzT4gF8zcY=}COGP-CZF9)*@G#i)
z$p(zZ$O!lmy`b3<l3lR&yHew0*iVHB>G)GQYPu^okBz$I1mzg(jm@6;GRGldAI;qt
z0$zg=6|pbrlq;&}`R@#^VRc}i9N2Q2|0-DJRWq$n6I$9qNbg!e<n%$;WcQh<6b1N0
zI*z&m^tUqn{N$U^Yi$<!E&B;=n(Y8{51OZsT5qc}jmyiGAtp=`lI=A%y-ox{%u<~e
z?o$P{C3iwae&IGa+utoFA4X8=qS&qM{;Ep6G#)|pmF?qC{ga%!@L>C^`GK#PosqHh
z_@|!fB)V!@bxkr6I?xp0b69HuTnJQPOJ_&p!7wzNIl0}_0n^C7Q}v&2pL!-1;ctLe
z!3(fQpq?_H%p2i3Y=&R?Rj8O|Jl3&m^J#3XK!u2m>~{sccdO|H_Dosb>NmiWIqyz0
zfYYrG&w{-G!DYJKuaX7Y52Cpn_zxL@-6Hg<n&XxVNQgZN48eIL(Mg|sKc=70nW+Uy
zOTE)5k&ujBZ;)NuZM_+KpyzW$P_cM$cLJKV#FP||{kfVn9vADeVIhgF-8vg@kX29v
zU(?VOg47HeLzl;E+OL>;QW)$uPwV)W%RtLWPA>W+H4T))pp`40s{r}|d5?wS;moS7
zh^VNMiHSG>UvK5>Li*3kxko@gPVSONVGYiqer&QN%GvTp^ymWwZv)m=fl6^pOH1Ga
zR8!w`D2CECh(WdX10{OynLB%%MKLN`?Dy#fX_*Ee0m>QUx~H8Dalf#GtQPXL69Ex(
zxco3n0@E87wPrkclcj^;=WloWs(=lH&OiKgiKng(RQ!jH&rPS0q;d84T)*dcnfF+f
z8H|l3Go%b3ItrVc$9G>^n3=hjAuoB6aFiNcW3!ruWRWvpv2gCG)q|1OzgD_+AyP8G
zwR0BQ^atv79${{1<kbnRueaCT&8_ABdKWj$=r+*QlNFD{MoEkHa1AJ!CcaCy2>QQ0
zR}6U#Se_oKt)DM;@5AEa4xDy>0O|4g8t@Z5efkvizW`R^1#*4R20L78NooG`jN*;K
zhJq(6LYC268{DPc&j#Y~qiIv7tjGJ@q?LTm4*}%C79Kmr%Ebxs@ik1<^ynm=saN%0
z&jjuv5bZ|&9)(kqkWHIEzrU7B-3QIS%sG>D+bPKvUfK8eEVD{U4E|+ELDBv#_?vWr
zCP}``r#X5Er^Ajv-P>i|h9LV5ou-U>ps;yFY1gLIzX*QZNmY1-wcKfjwD5j+qVw-4
zpEd00ALn$06sxe7s8`7X;X{+N(}5o7gz*RuzGiqnQzlrrCC><$SUntCCF;hrZo?b=
z_lV-Favkz0K!Suw%Xz=v>$g@0loOBQ5E236SFif)Qh`{*swO);Q8grAJbE;zxlXkp
zaY;INxDM5>k~4s{ii?ejNxq~dBQfy+Q<10n?tQLQ8~&S;``fu!7FE10w%gek)snbj
zJ(U(r=lh?>LsB?w*SN(ZIvNk7V*~lU#*3aq&<Fzv*uJKOy~ttSR{W3@X%_p|5y6`)
z=){MV<5dKKaAD_^nyXvQ`fo73yN->3S?lZT16bcHUM1?>W_2?n^cl<<6x((Zc-{-2
z>EYFI`CFM&kY-A8u&~yedDpm}errQ%exmDn@6<F6Qnh1g{q+!2n`25H1c!E|+fKyU
zm-Kx}5H%=1t$R9F-;2Pfaz^%kJX(Qc)D9rSdQPB!$Bls0*#(ckjmM7~e7@EhIDOWM
zL2$yXj~#>o!~&S2-$uT%Va&6y&8($k5W9MK^hFW)hrJ?|k_Z=H%j0xMXmKawgKus9
zxg(cHk1eaat`vjm65HbaG2HXn#lcFV0i3Eq&v1Bn!?Ox6I_t$wO`_vTMv2$$q<3&Z
zQGVkgx=~%$!rO9MO$xO8k8Hw6s!6Vgmj&`z!5I1-uWw-j5>f$+xH0FSmYUsUc@Q@d
z2hLf1Q6oEn{0_5Vg&GwwSduD;eOQj%ivUjo#8B;Z<IKo6BAt6GU%V4XZO8N`->=Ta
z$cH4t??SQoj@4W?eo!%2seI32zFJx#VOHOnbK+xOK+zP7O)vgV5Med3`j;;aZz5rF
z^hQtYX_eRmXX@-z?FL1}UA>--r9=@4-XU#qK+G`8Kb4vcQ-%qbPervii$ZL<sJ13$
z{mzKd;HwtpXUF>XY@zjDvX^EgsSeBnM4IOsdyDostA|nTqy<x3Q^=4!&vR>yzh@tW
z9jiFTGs$YLk%khQ?Hj7|?;}U#Til&eMx3`NC?_!lgmXK$qAFau@;>&O#CBAwHJ~LL
z{LCUp7ZEHG+iwIa&KmQsnOCnEGrKhFUE##Y!P4BN5{vS$dzJUWPvw~b<1h1YXhd$9
z?eUXy;`a1K&Kn*CI2UWZQ#E?c@ayODRaEFikUnyn7bDBvfhPsJfameskSim;h0i?k
z%^>0BU4bFTU#rVDl9F;DJ0pK^i>6qR^%H^%M6I)a&4y2A)XrRQON`5cmQ+4RDv5#?
zFoM+b=G1*bR;cikQgNjDRK-CPNic@je$9;5&zAc{O96N=b5L49ET95D0kJQ`P{)>c
z=Nd${uAt{Y=mBUE9V3kWT;%OL3)QxmbTenpX62%n+$9OHyJ~Z`F-@Y#iS%@5c}!Mr
zADhD%VgjF<#(=3m`BS_Grr$~ba*p=emVZ<4(6d9KN(d~=_yQ|2uHvRt@31%SAd`$q
zUSnavja{^>b42MJR7X>vTb8*jn8G)95NZfT&w0_2D_`*vvRl^I_2*C>p^sr1;^>VG
z@#d3X-NZ=|#uOP?w>5R;Cwnt<G#jU-^Ld1DeDC?mhN*=u%1%(2<d@;oLKQ*g8{~B~
zDE<9~MYXxr&4z1wFmmT^mYpKK?h_0m1qDnM^qk)I)%IVKAgeyx%r<5)G|4h%_;G_y
z`Q&R{$OM@j?f5#|j~m>>i{7P-)qQv$A6ARV^&0>Mxrr((<tkn-Sq92?=~Z<slO!d9
zqG;Rf8#nyL!JlrXqrYM`=C09^3Y4qcPi2{I7yU`IAx-e;$n?f)R^{`b>bi_QT(eSd
zB;g~8Q$Z#OYmU5%**AgUKm&o;lMF;m(4PW+pH4ulfaVeqqCYfpuM3o{xM~4QUPi_;
z(62k~{jO+k=1r4%g@u(<u2F=1Y37=4@J&&208S&KOf1%{))uW%sdOb@W#oE)Z<j3B
zpSPOdJYFSh1@9Nik?h-KM^v322RMxqm5yRAoh+p-HUc6FBe-7a^<h#tXCA!X*P*yf
zHq2-f#OtQjAVm1SjQ;)u8H1PJ7^iPTt;CGGr5j?@iRr^prxfz3?llzw<&zQamRuo3
zC?6UIhT0D<)ZE|MloT&`eAZbePxjCud6_X=6C>FQ>aCYh08I1HM(?dBcTW&re1h7z
z)M_^x7OM}%B`2HoV_UL|_Y9(^zoHmF*tMB46-f&QQc6X}_M7imZQ+9-QaJ6hhcXyv
zy^+8`ae~zH1P3ab1xz`>{`Wyf=3u&H9<=;CPPdLPuM#dzUO}%KK?wx_KIn3fR$2mn
zf{%|c3d$Ap_3=R#s{iTJiZ2{|>%;xcZ-G&Jw46`s!PLsnj;c8Kpj~-*C-i=DgI(mk
z(YZ$Lp9^2EcyKY~2o7XIi*eBAg5o0@y*S?I9M71?M7Z4(INhM?@z^XBt*oUPzq=}$
z(m=#u<_;7ZIWaR<R2Ep~ZEA@r8(Z8<#cT=g(4V4p4eQF1ryfQ}9%4q9S2}4f)1Z`i
zIm8@;@D#Yfq#_+cvV(=5`s%g&+T9m_!UtOpu|v3UMLiO({rylIH{Bo#T5K1cy<6ay
zwGZ@(C30%&j8U+UP#Xh0^8E6u;V-(fl5J@PKTErSNkN<VeQgFD7%X(K$Us__2Qt_`
zAi)Qw&MsQzA|T9>%^BylQ)i;()JZ*w8~yq7C&2B#zK};<#6B3m7N=d6PoF*k)fC8*
zz|A^fSOh?#tLr7`Bb2D@0x1ilZtLFm_M<Fidwbx-Q|0lIKp{y#bbWoDmz%rT;P8fl
zVPt+j1?Fa@B9=O^qgCy@#wi)&n(k7{NPvmiUTLYI5Ho9ul&Mn82kaE~RfzHmboJfP
z9H1?Y96}-w&1UKg*gAWOnW?-yF!=QN*F565Vcl-L)cY8`Z~N%XKVpo$*l1<rrD7;z
z7s;d)d{x#`SM+fZBK3IVP<<lA^5ybUMrhMd6pGMP9G;e_JaG+Drr}K2!=bmRo;{__
z^)up1sCf2z=pB6*t@<=o=%{pc!Gl(DocsxNtQ-wq@=4eGj!N@%cswMGF~?PPm7Ze=
z1&rY(XOR5e$dt@jF7NLhEY|G`HbZUE|8=YZgURi@hetr*zW4iW&iLiDrVX6h|5JUO
ztx&k<+{F7p@0RYu34JtZK8lO!CThNk$PW(;RLojZO2xU}o&!Y^C}@}K?S!#KgS)=Q
z#u~g!y1ly_il@7nvnT_kkN`iwPL>>AL@z*>z#<3ae6YbH+%QT~(o6u>V<?D#0}b#-
z+0^E&$(fm?!ChpM3P7K$p+R6UtZbi38j6}QNqaRfrYIS&5Jy4T_cW$JncAALlJ3&A
z2fuR2&7;VbpK(%ddVYv11WsdwL0F}HX-&pird+3w1v<0>o)zTl3{*&Rxfuti>ETiK
z(Wuvc*D)zq8*vWSDg3~egiTMlp2ha9gf@MW#ucY8iO4SqrVK4sK6=X4JK^qyZ!`8T
z{W@dKx{S+}f|d2xhHSkh58>DeqgL2J4G>T7cj^K$aBcDMKtmOrtsZ;Lb<*U(1Ab`V
z`)Zkd2gWz%>?uy%X(zqy?e76$uTre-gh>C-R{}e$=8TLrVGhz$OJLZsbZ;SU$MOJ$
z_h41qGLI^Omop+4AeY++H^1bHQcAvjw7TRfY&Z9qvJ#*n`S?5x4}Spv1_XeTv09@<
zr>xE8Fdc}M)hmIJO;Sp#61=9P10+{^cvPT+25bQM`1lnS74$>y9v&$lsKBwdx0BM+
z(&FL_>*m3ZVF=X+kAWD<IyQ^E$?ecTNr_e_2I}XOj<ub3NU^YBZpiGTwb<c4qKTI9
zL_hXa7r)0KU=1j`fl>#EcR}4G4WgTj1LwN%qoceiH5K}3&H<+7f5!5h;vU>5L<%%e
z?(YgHjuN@l*ils&z-R>gk!`lCLf~fJ6#a91_3IvUe($tf=>JN-nCAEBrV3O7o@En<
zsnxfzaY{F0%UBA@M^iE|;6c#3K*4_^8T+rho{?HLaQmN5yDP1#QS6C<XHsY%6dfEQ
z0NbL+0N7a`Rhn$Hk8T8Dz$#dIH0A?G)}xjZh?ftPITKX|-~Cq$APok)@jDS1Dxd{#
z;El9=1=-|`G*WjfP56)9J{7k_q0o{&g||xHgbrcHGLU?JbaO6uL_Gl*oFB#Dv1T21
zfN=&ARbGwv*RzTFb!zmCvxQ<gZ&OAdz4qIVrS9KKH?+)@fMKs`0+gXOvzGt$-m4|V
z13zpjYtiJky@P{M%Exr30*74rM;~k?DtiA>bSQ-+VCB@RO~=<rZu4a`7&UA6Km|WJ
zIf;dZwTKt>Z|F#yUZ98dyu`vf>E>ty3gDpUgii{908vXT)xqiH+c&(b+2Iqw=x%6N
zS_J8(cA38GuKXdCW&3ElWm*A_U}zs#83PPh`)r-r|5>OUS~Dw0^5m%3xk6X=EdTO;
ze+?T&0?zOCtHk@i0|zI{IpdD|b7y~g!lIVJYE4^{Cy3Y_503yHGD->xpdke6!Ft0P
zv@SsA%-*Z>dj~39*?MOSGzOIY;8eYc{pUweDUScAQURt$O~Fcfy7@9Xh#^lp6Kpi-
zfr&>am6vWju`JVv%*@OT4h~L~U<3be3-4VjmqY49R&h_`3vGt;GGboW=&xVp!KUha
zLk1){N4x^Y!+1CGc!fe6Os$u%xwzaO?(h6afuR5y8Tn#={^GcCAFMjik_9$KplL5q
z$PMlI1gx7ttqH&=Xg((;B>^F6kxq-d{5selk83(IBSTk4Ci0)0tKrtXLMnHH@*|D~
zu)%>pK%iTu(*k%}F)^{$+ZW})6a~In0~{`ZdjT5H8|fvUuL59ph=_<@zElA8tZI!J
zd&=>H;+M3C2GFYmmY++HIx8|GVPWC5KY#X|A88U67Jyf!!LqWLDgp}14`tmR8(z&1
zKezg#NZNpZX?QD4Bk*;%;)9~+M0(wPV2#Zp40czt6Q~M+1UEv9!rLD@`|*U4fCeZ6
z6JN_yXOlckoGsV0e7H!9)5hskd4eGj;O22%G$kpP^JUdj-PqQ(WE##p49OZ=d3yq6
z?y7t`KyR&p*pd)RiUK+^8g1Tp1F>%&yDq>)ISq)SEq!3kbpsYp^O|eWenm!y;=MEM
z2i&lA)$9{^c!A3m|2OP;FVH#c4^hP3-5X`{c|h;|uaA$`()^8xkU|Nr4M^fzB`RwV
z;+#N^`)JGp?vGi^S^yEkiTNI_MpUr_z*5z|F_icyC%gPTR3nWLXMVq#pW&ShOsru1
zKm|<w1DA40L-ebO8dLmV@6rmtGMCAT+@&J5(lJ?i@C7w!r2UePi-Y}Zzb84?%ZkSf
zX+QSnD4%$5T~F`Dnv@wD8YY(*5C-{Rc^M5_JfG1D9k;y=xptb?35}q}cNLgXs|wJq
z9LFYBd(F)-B3^<^d07zeM`7j`7_;Vx8c6*GI%Mt&{u)EtpMgUpyPzL9;l`jp&*-B9
z%(|ulIkx|k7_h-JpbM1ykE_S$@Q)R+AOW2zRKS~es1m3_HGu;a_)^0dwczACR>c^Z
zKu2W=j7*7&1(<<UHo*@cK7a@wOZ5?$8HlYCqNAAsIXPUcdds?FXs}ce(kOmF`HKm^
zerF-~gCIR{bpb_P(g*sXoe7262LnL=W!E>d-&-Eix44)|y+9O|tKirR<<svft?2VZ
z^f>$!fm5OPW!w~-{58Qd>QHJ4#pKi614d2QjR6+Fj;KEpn%nZ#UCr=<y>We$!{&m=
zm@u$=Zy6alk&~ZTsW;@5M*8-|&2w?{@Q}<l_010(D#FmsF=M?O7C(rq(ZtSfp?q}n
zBn!(W{iX4N3Ydo{%#t>6&>Oj_JP(>GQHA-VfF;xnyN1A%%3msrxloBaqSxSO;j$F&
z-|6|`KJ-%1?6x{eNZI>DP83<@@5tHuWMb76ZD-dumEY?cK*h0iKES6egAERRG)I4W
zo`IADY!toh$4q2Ik-b#x?CkXP;bX9|Z*PUGL?!%t;XcaBUK}0Sy0|dW(3mYYvITb)
z(f~v#o632-K7b`HEp1_8!I<cAv?Ayw|8;oq{N}KYiJCg(%NMts^AZV)ckwE4EA`c$
zr}H+&Uh`%ZYEAkXXBpgUfXi%gs!=jOi#T;@y+Z~;89XePTlw~9C+$i{a;ofuIp+c?
zsoawB+*6Wz3}M_m<{n8&yn_R=g>k%8ig^lbGm}OWW?BT=*-9wiK5z$`XZ4iD&`~Ry
zzD<I1`;TN!681^U6-~VijFHKWlPES*DvIv;z{D`KR?b34i&npII$@9QuR@wYV9$jZ
zuTN*SI(hp9DPDsyaVVaah@GyuQYt$d9{Y<{>61XI&kepz33Lh~MnbqJE$OSEg?{Pj
z?Vqw}m;v{_>)0t8y-)Q$H#9=fizu?a9f;A9K|fQoP)Um7C8%v6u(1pqR61IT;2eHz
zZ0x<c8ae?1A7JPQR;@{BGZ3ACDzd8T1jyQ{3kOgM9}O*pgoHCcYzF)LIqm**H>J!F
zojR808mm36F{E|MFk@+S-fslXC=hELC#EL!E9~itPr0DT+WR3Kg^DT&9`5c3z)%OQ
zg)b{y-N2tEh)tK1TbS!+W@Pfo)UNt5Smd&Sg9NP=DyjYTvYrsXnUQ9He)xJO+~sAU
zP{02F#nx8`RsBWZUb;c0OAw`z?i3Udlo09eF6jnAL_t7Bx<wkKyQHP1LnNe2y6bJe
zzxVs&&AdB|BMyW2e$F}j?7j9{YkS6#>B-4ekyyVc#-<3SGcD;}av}-OYB%0n{a~D1
zUQuG?CY?zV)o!j#u6ouvjOULLFQ2KJD?K`D9Q>IUGgm%WIWJ$01?QopzM7hsRlInt
z&b=rN=0&;oOfM>R9m0kC9Qs(3Y$Z~I{Ud^VdNESD+;gw`OHXdSuikccnb^Ez<?5PP
zSzoP*pUm(wmsQ7mC{=W5Y_y`Hf{&N?xtyF)=#yGqb$6Sd;<iQG_o}qFKm?sV9RJ*0
zS{f}`QGl11jfrVzbU5op?m%=~gkD{JLp{Y#p3U(8XSJXo4U(^{tSk~zQg~|ivyHyf
z)vv<sbj9E}M0$e%f~6&s<SmFf!{0c``K(9Kol!)nw>+ydFpB={?98_Ur{+Uhm1m5>
zNqsSs^@pbX*M2{TNt+{6UHQ0($m1i(xc;7HhIP;fy9rMn2~U|h)$oTg8*-b;M{B14
zGUoadjyD^vb_;brCwsrLzD4)PKt7Q(IW974vizty*C`uA@?N1kXDG3Jyu2EH)C=Yj
z?3yHn?At#!=w75hQDKN8->;Uh(00bk9CdPjcsE(9iz&Z4>9crdbWc9rV8(Pk`CWVG
zm)!HicOQq$cbP0ibruvBE;)H>Yfrhm@e4&q^Tx(xDw(hz;B`h&Z_Ll%zI#_OQ-#mw
zX{xB_-cTD+N7jcO!qB@7gCO2ZSy13sFOqd3T67{J{_3q%UHw&cWP3Fot{!MIBqSsN
zx912dec0{eZ0w$r>48%1<>Z(muYWK<eTEi>_xN$8O8U^S#@ZTz@0p;>UsU-8RP%&Q
zo&bna_(St2*l<3$a<=o09335Xb*WMZR`yTdzkfe7Go#QsPn)ao;>93{e4gEv5S^-8
zDRhqy>x{Jd?f*zhD$vX<UT0&OJK8$!aT%1q&t@AAM+R9wsq0`@sG_F#86OI{*VLrw
zZ?2~EPidQta+`PLciHJm%*HmSmNEW&i;g(3QONQMkSb*Vq+Vt@a>0TXrieoOf=SCo
zTTwbXIEFT({P|DwA*K<!_g~~PN-cSFRdSW{qpkT;yVyRdzGV8y^aYotLgil7$dFgu
zxu05yTNiz1NGfBuTK5~4_b7?<CFL<Ma>grMxjZRYSc|ROErU<?5_xf6BLYm$@qZuR
zjT3Ta;JGix-c%giI73F7?W<%U%EJ}X_`0I_L2k8bY~N=lBFU!C<_2P9hJe1e^3fbo
z1UAFw{YgT|uW9e!e=I9&0XsBS4H0?0soR!US_-V6%U9vp!E1B(G;5Iq{wi?GKO4-X
zw7<em=f}m{U3Itp^ZwAr@HZ~P#ihuszK-7;A04mb3dQSL%oi{miMHAKE1eaDlB2r1
zDn<Ka3J+muR=D<+q0i|}PQliDLXc)%C)~SX3+yQ=8-xVWVnS}#X909Wr)RIC=r}mG
zL4=Bqjt+%w+(ZUzfOsYUuwTQUzNtJ_^~>e4qC4>gA38-&_YP~m5fO?TDA5H66!n;s
z?^gGpc~J|On+#hC$l!jrQcmm}@_`VJT~S-09izhxp}Cs3Hc9z~q^E}dJ<}2@7gs-d
zISA->Z?u`ea=%wmyroQdi<3O~j+MFR(!Z4Cb7@_yy`!X8JnMfy`ZaS!%70T~2w(n|
zov8KVo<{ETQy(6rqS(U2h|YmUYgcdQVT{S`C~1gM{K0|G9-q;8ej<yBYs(te;!U_0
zEAq9bb#mO{?;dtXU8QAu(T0(6H9ep6zd-57A^1@1LFQKT-2oMW{IEC<3p>8N!lL{0
zk}~s4^`eXPv^ju$h>3~syk7)5sEA^!pPhX$p3V=$dhb0lIgY-hWC9@(r4&&uTkDAN
z_wT|`!ru}%dly|^oWHvb>CeSdtN{xPO9Ux#hBGM{#XSbELk@unTQ@^PT3+%FOMky~
zOui>{UqS&roj^?-o4@q_mYtr#fxi_JL}*OY+K=BVOSdgX^6=r{cnCvk^sel0FDzss
zt&d&Mgeg^7rKDZL!^I;FPCYt2gcxf#T_O!DJM?J8U7ntaiAh*^YIYX%Zy@9cF0wh`
zbvNPh)F4IW9_C+Ym^q^#ByO#x=;_z&nhhyCo0#0)9{c^AgJ&VzGZ@aBO7Lg-%wC7z
zUW3VT25$+xv@}1fstk>Gw|wk5rDZT+j+N-+C{Sm4_HDE#`fqjp1!-`5;RoD)+`VTk
zB!^<xFNuSNe+;n|$#0tH(46wKLWm^^TXWfIYn;)Gq_0=cQ7sCUFL<M^Z{ER7pDB^~
zZT8YWE;vBqZ}x`t9l41BarkA}?VlwH7F=CiN=r+bh(fgs+1c5%R5c=9Uun<QTppHJ
zIn!nA2aUfT=$uEP4%@57l_{I?1&zWL@S%YU5-Z`)pq7VCe<%7;n7-ET_h;_76oH+Q
zB#v4@Uw9uLe)zkAbf1KlpF~3MiHHMd9GM_ly0`KRB;h{<)-2I1QC(1O@_XzU2Kk{B
zTR{b9rs(hD5Bf*&G!eG944kEVp^U&rd;Rj3FR4lA{0>qM7OG-%VWKi~dS>vnE*loI
zB&~<%-z#4zpb%g#Q6|uIE6cpv+d{n)#6x=C*%=n<ajC2^a}&(dk#Ey)tLf<2+2EYX
zcG-?tKYj4CA2|%I2Di#jS{~a+nD&afwA9c7!-@~p=V^Y4nF!)rcAwYXoBpf=|HRd+
zpUlxkK}jm7*zlK*w8q;J5KawhU6{GK0nAX0=QaaEbGS80*9y1@!NwHzJ}SUSjnbu?
z8Z3+|tlaq-*4At;;}pF<cwWD)n`CcpTKQ{Sf&F&;-l@F|dgauV1^NLXtp8-r;48sR
z9@bp-ETbTaaeIGN#Y)@C%FD`g)|_9=u24N@F{lX02Ha(3TSqj`w!{1frg0%5olx2@
z0Qk8efi0#PAyVlPM|Ma_=@=#B9rZ=hNJabjU}S8}FD9D4yjYB)-06I&=kIwH)B-`-
zihA6hpJQ`hNg3@Xsomh2x-t=gDp)wIo7cBhx>D47vei`Ai?vSuF6SoE5nkuM7G~n6
zKW~NS0%}Tyzp_50W;ugg=bl!22kjL43by<O1qKBK=)ZtfO_(8@GSw)=O;2pn`MRgI
zkkWDuzlLgok?!yGVHG*1K8Co2(?%9hycKLl_kP4X3i5THmOUQ(&d$c>bGQy6J-v{W
zzaZFx^5TMUnf*r{?$ZaDaRKITJ4_w9QMbYd9S}@J%1pmT>!67T3r;{KsI0HwSQiut
z?gRt%+xu&{&A*H~O%|N`dW|emg{d#m5hn{82AP3UuiH>L<Cxf<M5YLO;`$(c_~0mV
zhc!?#Bj}J)#y@0mc0M-0+MV?A<*R>en$L4a(^T%o3M}bPfhW`h8s%9UwhHa7&CP;O
za#1}?Q2ldpafMrvb**UDIGIU~Y}wiU5pp>zUB%$Q2@??eP^g}F_ItANA%_whi`Mbn
z`|QkM1D?c+Msc5I8jfL6bUi%N7_|vYo7<0MtWNK;SU@Sk|AUE^pZ^HbR6o&~3g3G#
z;wM*!sHug&Rxk!kFOEklrI?mzBA`>SoL`vl=@z4|rm!|A2Ht`<px<tkq1pc5o!6`x
zvgao!mR{h3P__IR5S{U#gWB5K-m)Kljg5%|#7mW&rqWzUzv_ZnZfMcORQ0BHo|cgk
z6$u-Sd~JE__h{q#empq#AtSK&O3cOZsh`LQYYRSpFl2g$Jh!r<@#4kpQ{KBpcRnSe
zIM5UrpbS(_)RJFaDd(rlEe{R~u(R94nba>S0Tk+&n=^z1y`z0TPEA2y?1u9I`QQc=
z<J37%(U#U<MH1XYMuzWLW4XN9zw34ck(FDAKgKY$5)xQ;*H`}NaU^QG@>U|eYHKuG
zl(pzdCbFgF{)+ycc&a6{r=G)#g+2%Cd_E!zKPAg8U)zk&%>@LdE25)=BMkvt;k{_h
z=TtT(CeBLI0kpKS_t&c`dDo*>>m1#y{13dXcbq1KXs_1Vq8KThzkC8$E8Yi(t~YK`
zUT~H`N(q@;M`x#h(!YJ0Z%DKImb}`-Bg1)xrgcE)T*bLf&GsCNR&aSftDkyI++)+u
zd}wN7$48}`jw2E=PGT<g@Lwv02I@{=+7pZA>7HJ{FDH40pVBi@B4XpaA_ol6Lz2H+
zjs3oPQ9c6R7bpfle^<{a$GyY#*Z76zPxCSw2|u6JU-#mlqhHh<e=AVc(MhCzmrqYu
zzK1|u&qKH4@bIwE?gCKu5Ng49%~u{28m~@vpc@w~b?=KjChEJI5d6klVV`2W4#p{;
zGn93n?!P4MMGI=oiE;>E_-a%Tq^H?}Q(f`q>6>nO9|01|@z+vo9ZLn%)g{YPZ=~*_
zYClzyUHQH`Op1bTGQ#Py{XUibaqg2`Ok||~WUXFfjz;Hk{zC<sNPS1w*6t-iZzNs{
zc7sxVLHDzRwc~ARJu}WzjAONMtVb{4{pdEmh0VDg5FA!w?>dWP;m^er6pXgF-(Gjz
zJVMmqh4K@Trt$*GRsSw;?pQtx2DON&@49Msz#tKJCJ2(4seMbVC}$p=*Y{}xP!<z2
z1~OTYcAz37MRt~zm7#zu+*&-&@?Sf2^FGh7`C<1%1{4#1xt^<kdio_fd8^dOE<ACx
zm{)7kG~7ccsv=x5TcFPhr%%DAyYg~aeMGVqzcMRV@MJ4@n*yJZFluvi6YNOB?}{b#
znHOo6(9zL-EeX$O#N*5ES=i0X&u?lN)9^Jc(d3TD{Q)fBQsa(;ZLvS8#6;rVeH%6w
z!($_Ha+zjdqP-L2E4jIIw4x=8h1SiFPPM0II$N-vXM<!~3xqhi+EbE69%QOvOD9zL
z0-ZCf_z4|YVJba)=6$kb1Xm$oK%foTKiLwAOMvQPaV1inRk6u8BrRH*iOYmLK_sY)
z%EA;}+5FIhns<(xyZLTmI<B?c8z-UZtyV}DKJ0zBeTDguO-uE*=Szktg*Z+yDe#Dk
zzbDo=+Peug`S<VsCCpj%^*#U}g2`4zNr@2AIT3%QN8&60ZF>QMsswISNmMkn;3RQ8
z4S}5#&1y1hK0i%FG<M%69A3rbC+ZBS+7p$8sw^>VEm?)ixtv@G*@&S2M`pp-U0^VD
zca*mpo}?=}!_R)HUa<#FuwUX%#fao}1;<SeR)4Z!i7R*e#v<A2VWJ9_&l(igr#YY!
zDCr9N`whKG@KAyR2d*~<8>{=Qtj4=HrUjY=6d&xxlvR0OzXcx0IIp_0vf=fm?nN;X
z!ua3T=xv{%KSq2(g}Ke9+_|dOuC9j$q?gN_@y~;$26+uMP?06o)YOK{3%l_76&_Wk
z+h!;;g@4M}HjwrGBhdH9-OVi`JRIyUoUE;_O|%cqct_&qoNBOxzgn?VfGuu94_xn?
zhO#0yb`OaqpI9sjuCIp@hxYgNp7WNCePN63lJ70}P<N;tuk%`-_|}`&qd&nuNSMeZ
zF^FIJN?q|8W}~Hm^jb~9C8xIHiSuLSu@b@KkcH+3kb*9{Y`(eh!KIGCr$$Oj5u+CK
z4w7nb#jrH@ck&6StE)>-H&RoBm9H-|+Xv|Meh+JwclllLmBqNe4Uo#>@*}1<)p^;<
zX+qx6c!w?A-7OCaTLtX>rVEME$xVv2k7`by_V(Z7<H@7CMpU7B`r*R|qW4eksgSc@
zeVEq0;G~i0-KLvUiaYmJQycxT!&6++9%@J>^r4E}e-k^m;E^j|OGHMdNT9~o3XHo~
z;#OPh6ee#8Xd2lKsf15jesLNe<nU5A%<{bodtpL{70?-^9lmK>OosU<tYqQUp5T}r
zYQXnDg(^|_!h#Q)6jwz}62RW}IfFN}8K8dB)6)Y7sFb+4ob2q*B`5Hh;&Q|kc1Low
z?aNDRX-RnS&z7H(&*rPMvx}{7V{?&npP`Ml<yTCFG5#9e8Qu&`AMvKHsWH>*bY6;Z
zsdU~ic6jv8*ACf@_?<WS6Nv8a+!=!ZVPs?kePr=Lu7sSN+`!j=y_>%;9euv$1~g^L
z#)^-TB$Q9fv#4#Y7Pwf+Rh09msn~qwCBlXlMceGGgaSjzQ&ehd(Rc4v4W;JFfnRLh
z4Lm~(R&^r-Oa{fbj}XCh7FQ6Z1A^(leU_jw=Dc-|`l2b_!T-cf7`#?MY)SGa6+Mxs
zug?tfUFgnSeF2%-fMSZ!zFV8nmspOe(%j|h;NW(RS!}TIG%<O(ZvlE9kY97Tko~)k
zXnu_N(TmIeJvZH(Zw43f{z<9hyl%13%cMjNfifY_E4B$+RaL`D0>e^Btg&%EeV*1a
z7zbHrmRWAf%zBiO#L^OD$poWK1KrfG90wC)NbR1HHvhQQE}b@%usy|a=RLN3XnJ-e
z2G>Jx7jR8dQi#5>Byc|v^g8O8zn>l-4|=+xH^Xtym#=3n;tMJ%coO^Y$pzfT0)^(B
zP;BkM+GKas{R5aMEv9C|qX*C*bTNRjKqG~enz{jGlrnr_-xQ&q^4r@!9!~s_c4U`)
z;KGNY_EsiT-7Z?Ozm4&8MqGUSjdlP~S&N{cH&RA$D`)?kRI3Y+N=GE35E6cCl(bR$
zq7l%80$Yl+g~owLS+ls*U!q30F}IxRj~Bw@WAf#k4{GH$x+Cp{X>3joyb-@{TRpu$
z;J@{*-S}=KMgZ+QkgEQxzxF1uD+JEKxL@tKvbs7$wEV`}ct=Oacdat?sO)>|_<4Db
zr*t1aywLm=Cbhb@282`)b2=krBB<isxANpOjY~A4+beF-Ke;Clh0ytNt>fg2c4<!D
zCw?hFAbnC8ar@cVya!Ec>J3q>h=4A1q*~QHHg+j%>wt16$Md70va-41yOdSNJsr$<
z3s-vMO)M?rVqz*^Ey%ooGL-%}WNHRI0B~%h!8^s1mK&~i8*iaiXBBY_B-6dN_n)-1
znAow`hu-%rjeX|>r2;S_p!|N%{TcF_i)Xp9t$?^gccIL%85sdtD-9dggHv=>b@l!v
zuAryS9jSit<TQvQ_|lwg(|2}M;(gf_0nP@K7#3F6P7uSGbrbbTetHU}ZWc6e9UQg+
zmW_Z1w_ZECV;|W^X=JkSIOEFCk;dzMzAv$TIP2{6bZvDtE-vl{$sJDlB{9`E5cG))
zaSL@Z6lx@JKdWVkQD&@{7#F^A{Itj<)2Wn|fAFa%hUic2sbm*WJLYEpJxb$y(}#<P
zcX?3@o<5e>z8Hq)??sv<HAuascIobhEkDG;O>#SNR8;IgC|fCU!V9RgQ3sQ9tF{#;
zqVCCZT-!fL2y^q`Y{k`qIF;!}ld!S7O0tm^O7c<HLh}k}+D;RCr=o`$AtGg^zcNMG
z*p$9&i@P6KiiQOlNFZ_w;w1bVWek7z_y2=k=$eq1nThrS>v<n7iT|w*L2zJ&bMZzh
zJvVo)lNg+-$v@NIyLVdkk|QLj+3@1(d>b&3ipolRkbkcS(y|vCl*vRoh!hJTZZ3}H
zFYfCzz5&+;>dA1YnLQ@7v@)8#I>5ONX*vQ-bJ$%rf&-R6t@L9mC1Yb^dRUtfl9H~t
zEiv|8y#oR~5Y#o@cI|-4t?;tHzkfLV;J8R1D==>1-w~ipKkM9q*q4+PK}k+?OmnpE
z;-MfsHSjSe23^1%G3Rt|<okOc%bbD&+(uW*4FRNTm-&mdpFauyqS83Rwe&n0Nu1^-
z<2_4yM7b>*MklN_HfkW45S2S#nbgSOtbinOOug<h=-YVFm|b8ce!w^)ZwZ0BTF&m{
zry*Wd7LYBQfh5RE{P=Pu2|ImkaImICSd(x)k)&RJ!OkZI&Y4ADGG#_#Skh)1%JuF0
z-&Aq|-M{W30R`04s|J8rq-k$?czD2elcBw38Nr*Z$8sJ0^ucHFaBcEFws(9s?xyw?
z%a&zA8<Dpd8n-!ZcltH;Z6pFp!2XqeiNmwR*jRV1tRFuI5s$I~z1E46-H>u)Pf9>}
zR5Vkv(t?7F%=Yl+#aGeY-R<J)3SG#PS3%%G6tAHzpRQb9Q9*x4PE&Kj2_3}*`4%-A
zPS}l^WNj^UY%Adujunpa|1TFH)zb&KL&n1-aq$bj_8cTZ$EkIqZN^;Ibo8~g0B@x>
zy?{XSv-^z&Wp8}sGg3}phSJhA><Zq=<3H2W#pVxvgb}DIqE0%T$C1DDl|N7k{M*de
zaCB*V^Fv}vC0|(unVodd-NI%!ODR#kE(6dP11gCtd%wHbI4fS|%loIGr%lB+A9<gd
zxdf>F-u^z`f3KdNo(+T8n;<-^2pt$KLJ$veroT|Qm)i#o_)fyok|}L<X(7ZH>%)cP
z)7uUzm$rpPS!TGYg3@dxN{~8cg+7wot)UOw(3NU7FMAP{P*wVQKf8L$@Z~}e`C75v
z+Z>S!6EeC_mXLY7TjCOt>gm*V<i{Xtb=m!aHXPey_q(!?;hu18Pco-b%O0yni5(AB
z@~_;Dn61RYDv2b$V$-$5B}w4n=I#%Hq??$_cfI}HFg&c`<IO=T)m&AekEE&G7*?aK
zj-)D4wfCTY%Py(3zZ`i8cADN`_X*AAI|6oROWpME=r=Ay>P7vh+S*Ld`jZp5rP6nr
zQ~rP=>VCivvASR>qk<>3r<41ZB$VtA`OGv`a-ZJ|iMhp-ZFFsfSREEHUq~RJ!oM4N
zQ@g-Ux3jauzz9*&I`qs@wN8#=xGDax4@bu5bMo@6RYjg6OwG+>Vq$7*YlkvFm#nWV
zsxK2^OQY#3DsGwC5J$&fiWco?{-xBiiEOO02bIddEC-E~@(>j+<K338wN&`j)XD8z
zQ}+6#xQ&L0^!3~2Pn><A@il$8fipl-3BZ$^eh8q5v*e2e8Um^s+Y_~tWn*_9xw^VO
zdi3av3C`08t%^w#a1H^A>fD;dq39cUwC!a^jE;wdV5RgCz(;&g01v7I<`9%e;8_Q^
zmF_<H|0Y4>437ur){|Kpe^CJ6v<Fg(NHHX54fl>7!t4P+0BOo^`Zq`$0%^XoRKh+!
zLv4@6ynN)9e>FE70mvCFzf?06`YP99*wo4Lx@3lAQmbq<)Joq@;JbkV$S!2F>CGF1
zqWOk~*IjVPaAklPA>sPs=IK31O@~wfHYhI*964wyZEbD+fe#>4w(gH#p)KZn<|1ib
z%AR<soWs{s;VEE)gJ`ne;BY?l=@VG<a=EVj-Z(xy+f`{Bl6cOs?fg=s=*M4LL>kYJ
zoO7*wWhM|?@~^FWczTZdnNyOHK|66{VUO+;rg#V9hxzR7XEqVC@T#db*-=K^uB#mI
zIkeVY6}O#<DCphjiEa6LLf{(tRqE%GA@`@$qWt{xSLdm8;^J3t)3G{G5hqKl^T1MT
zsw{n-NsKsH-v`&?Na~3DvJr-!oF_g9Ym|*<m)SBIp|o_Y?b-4@4r=PmSjdPV<o1mR
zmgYX8VF#=|xaVhE(=~*TBqf`H4)vWl8kdt>bIP<FOYSD-iR2WuzJi=RVE_ywD}uK0
z!sIijYMiq**(%G+3*y^y^YXwAF7XLnNmUhep`7l!UJ~3PBYUdg=j&K(@LJQhZ2{>c
z6Y*n%rf_XhWJHo45zAry@4@!x{0Sl`0lD!J5%~DjKXSO9%Hg}XUR5gu(gM|@=(ou0
z&W&PYX$W)%S+$fvxa+bLnUlaBNF8w4)^ko$%+6q-Nzm0-Q9-t$RA!neBTUEG*(4|F
z3;Lc?^=ZD99>>UXM1eWgXYSD@N2+4><9pW&1i>voejq%z@7)e+ZI6<Dh}?KyjU6r%
zq=JQrj$T??LPJBNXc(h#S`6L)S{fz&bz0(~L%*?KcK%jTAu&<uhLjgeZ?EIJ>Fi?+
zn>;dd^3l61|7{<<pW_cgA&s@ESMUP3q_nc~==gXRl)u2RF;{8^MB3NK#|zpGK<)qr
z94l+S%=#te4m8J7{m$fcbk4zg9vsa9z-DJ<E$i>OyS9b(<6UR|m!4JwdL^4BnFkFm
z$-gZpvF91XJOY7J$`DnvGlGRn_929zzM-M&{2cmu+Mbo)_b;yiav7f#?KAE4jb{@U
zKI4&O&Q+y=CU#8BOme4dPp{>lLT3eS&YMKbk5NfqqMYWATw475s;a8(e}9fsI$B0>
zr-WCh@NADl*YiLVt)Hu^uBz(8QvWF~&g1;Zj*>DF!u&W1DG&^3vIMD095^tIQN#bP
zuX{P~XWdX&z_sU3t#4Y+SEJ?L_*jxl1f`DYH;G-_4Q{)$Pgn~WxMZ{%gaY|(*-y;e
z^V<^ILPc3zi<LRmW$XBk36?%Vty$jSD5<SAcHP<P&v;4(dLJyg%hN#V48Bx@2Cq@L
z=Q>I-fI^k9^FA!>8Mx1O%>Nu181GFM5fuEEm38%u8b4Z|4^p7+m4UGnFWF8BE7Ci3
zu|2Yr+LM+x4_6mkzYbysR;TR#i!J}Kg4QlD#KRmg`Q5GqKLU6t6f}1kNW#GRTEBW{
zZqE~71`ywIli|wBb}G4cF6=7nA9d20Wu_81-0%Vlf>{|98!rD9_8+Fkvg>J|vjsH^
z`dys##BvBxMKm;s1wfJuslj%icjV_gn8EMfAL(%tAd(fYzf|3(q}-_TyDt;bQMM8V
znm3pU)5nY*Tnbo+R*}I=CZfKh7%-+u1qTBj^tzfFGu?M_pvC6c_YbHMQq2pJB@nY3
zX8&3sb&yRFff320u3?cD&{?!J{U$iLPs05!a$z&FzJf8-H+SxQ?#ODy4{5r-&i09e
zGn&kij3)*4I>Rs>&_lFxtt~8$VH(M<M?J(6_sxkH&W<2r04@&{tuh7+4pNyao}&8q
z<*0xm3ARP^u74l6lbVQgJjJJySW;KN<9J;eLPGQ7cWEefiBhMaz(65UcBp)6p>D`{
zk>h^H;?*VCn&zsmLyWy4SS>BN9<09TPQ7DiUEu}xlIgFM(pCP<l$W?E@~10Tw`BjB
z8f9&s|1dhfHC~j;kOR-K7^qY_#A+s6iNSB*S`*0vUvRKa0-^c)iRsdt>)fRmi3uV_
zRz5&5i;9W@KP%-IFSyJ%;{YhV3-)brDu5ZcI+8owISM!TJ1ZVTdnD5F)@LNaej~v$
z`5-uZ@^yMz7Zy38J>CCXmvKp&%45|%)4c;m`b>li`N4}bFT!wKZR``8RoS1WrKi~g
z)_cBsjZTx-{mDkHZ+)|dJA&jhAs5Zc&6ds>OehV=F8<zp8VRgYi3;0O8gPCe`Nm^u
z%{$qg_5QEG7dwUh#4AAb{s42_!PT`whUppIrwiBMqTtR2tEpJ|Og?f7K-k#9IQ8GO
z5F=xDg1{lvi0W!;T=BhcRU#L6J)COl(?^Wy?{q=!#G2ZXw?zfEC_prs5_#LfS@EUe
zd3SD(>4}Txg}C#p4x6_m2GThh+|K-BANWWyr{XCLc4>Yz9)nb`xq@CD6(d!6a=jz`
z%a<?GMNxwlvQHO_VELgK9XW6U>8CO_bz-F?!$zZ{QG3Jc6h9jntF^qr8XdhEuH<w|
z6ZQa5e>a;kUCi>9e=>XoSn+%|BmrPIqF>{2H|TPA^?0rD&xYr-!otExY7v?2Vc@4i
zi?!8mbFjmEJL};%aVS2u@Cf_qMp2W#oL|tiZk)~|_U-NkpF#gEXu+C1K5iWw6JXo`
z_e&suSdEr$ED<;P3*2SlV<I9WLk>_`@;cm0=4)nSAr6o!&Z``Fp{{;(EhmnPi-V1Q
z#k}?++*}PXa_ds$lyBOb<IRSgrrcWpL2Z@j8ew*&SF2Uu8u4X;j}MFp0H(si!T`2{
zBw*$#_vF#h5uh=Oii+?j9OiK<;IzDQcD`{<hc&TQp#^gb#r^bZ8O8nWL0an)mgseG
zI0E9bnVA_+g^=&%IjP*YKiLZ2JmyrwXMwbS)Iz)(ay1-;70;uPtM#<~4raD_wCn7)
z)_<>TE^S4o9u-$`KYz#J;wn3!kKpwC3s;D<lM|;OccmSANZH}kfFAe;j}?&fe!`Mv
zL>QK&(X(-Ij6Y$szJ-a3{5tJud|BO-9#tDLxA=v`^RcrvT;JU$o&Q2MU7Vd08*L{^
zefz*;_&EcPQqQ9~7X&7mKWJ(Rj@khJBbH=+Wk_Q`^GIuLGn%zAC#<CI*mzj|m*sJ3
znM}KAj*FW@)Md@jFH*Oc8#q?>Xo&(9y-!cLh(Z&xqUFu(2tK#Y<NkH_uXQ3vfBR1G
z_x(*%lZ%Py=x8{$%ZrN#a1uco=o(|Cxm{^w^?z4GGOyha?VGl($)n2+Iz+(S9Ju}p
zil;i**(C$lmx^loDSCy?I9!f4psRw42KF2n!`{1h4|;tX-*qReUuD$Pcq&NsW_7{s
z5VO2&kifeYQP<fWC%d^SIAD_f@}<g;1?032v3LR7)ytzo9Va1TZ0TQrW=ZvG%`*x|
zn_D&y@)t`RQ39!S@ZW#XYVwX8ti!c5|DrOZ39Nn!3gg+kz40M@8`2>W_S4lbhV?*z
z?0ALi<g~MT?5BM}P#-Rg<yhY7@abnyYu5r~)nimpIl^@UnuQ4%fwMmRlsZTC*PBL_
zm*Q76TH4xlj2IgNMpFkV=w~!-65=RG_1|M)z<_0A2}#VZTZ9$25V2*eSrO+@q;s)Y
z%Qxd${lx~^5r>S#NuS1MNWW>U=d0h#JV?Rj;pe{^$}b?mAz_;^8{L%p@ePZVV-~!5
zxN2$P37w8h=2|56_4l(gGs|TNW~#CPeSWsybNFZFXzbfiN3bJSRa3i6#teD-U>#s1
z{S!}RCP6}c!j~@p0$Y%Sux5!TMG}tJFHN-kEbNd1>S|?P8mBsE{Clp|c;U91a{tmR
zq5f%mUIoH&!#t;^Br0n7W<W>V=lL-hR<rrB%M|!P!SgGU*(j@x;wNATl%Qy0Vghz}
zwx*`Mz?D1e9qBeLjbc>F%FaH%;R}Hd#<zq*6CBLj^Kx?DMMi>9!{m!5_t2O*M0c7t
zf*VDF=RUjnip$b|U{H`m5v3G@d9)P6xC^D^;I*cTiU=<4CzttG7>V%*bNdHt*VjXi
zFxvwFk*SFZR8-Uv!RKrw8$p<z0s*v`tw=$|uW%6c-Xa@o3t(dFtNUC-qYB8VRx*Mf
zqAAr3l6EfxTjzhR9YAT|KlM>~&QVaoE#kS7QW5w<SplGnK^fbVsmz3D2Gcr*2M4V_
zW?hW}Okmdb?lzp#-Q#1hGX&RMS8ThF(5xUQ%&TvUb##6F_u0WN3gU4FoyPI|(dggN
zOH_8RpBL)q+Ldd_AH0jCe-j99ysv7ejW)*9wEFxA1EtcdYig1K^~o{=m=NA-AMRwh
zzOUo9{f!u2vBGM2nMX)W;%&PzpA35xOhX#wKS2?sg0Tvz&-&8o-f4`M%HCOfT(bD-
ziFU!oH6vG$)b3m{bv{eDvvWteo51OS4<&{8Q@t|+sdVL9r%89-FzFPr%U*uEoBZLL
zd7W||;kY)G3A4g7RVLv)e!4F^X8Yx&(~9nP1U%gSMDCjCPm(HfR&LG~+ey74)OE%O
z?gb|n7z<xz`Q1?;A-dY>|M;Daenb|Img3^)<L1W3#oYw88AyAMmWUrIH9hm{1}|Se
z9armL^%&D2poH&$xxNloQ_1{K{(EtfeE`gde@5}^)W{RT#;^8#l5NlOXym>{wtd#j
zEmt|bb})3o&ZH07YF%)dd-eO3kgm1w%9VKW)^Axs!~;ggBAu##=34Cq{C8Q^3)05S
zM9G24_7k&nTv7(ex7;m*WInH3UwFsL`?gg#kt;OMdqCM8*Bq2p=~NdRZPK>z@X%_(
zIL)2&YALeIs<ZaImTVQNdWWC2;9Fn@y>7YXZ9o?eFR2Q2Q38K-r+qV#JvclBX9(Cn
zV0Fp&QllbwV4Pb>sAeI-Rr3yNFqP!98NK?Q*SBo!9^B~y$Q~rZ!;o?UC^9nZtC~Lw
z6$K2DgHyyyvGo!^M3;TG%E16U@4-NXK`BMItgb*e(`zp~YPY^t`}=pk3sn~7Tw2T|
zVNd@tm(HdJ&6QLq!F7~1M<2BRhxr=yJ9Cf23<~hO9Wpre+GT6yz5{p8lIk{1%&kov
zt{tZ_0W8T@8BVC$tgtjIlIVf)48UjW?tu{{M4=#|0)DcOpINKH=PzGS2P&auZ`|6<
z1rsiK@L*)zdi7%*pV{rHQ=Wt3;(&mFGnaJre8>Td)+qyJHhg#H;1lvDc8Z&WUy?WK
zR9xNaS-3$rK9w!`t(`i}vpUVaJZ<%J3#Bj_yPhSFS2ZmK?h?9JSob>vq+Z9UD_XD5
zx(nVQa!(WRp)qdlmb-I$(bo3F!NFl@{ugbuJOPb(5+sd94J+080(F{N)fB6JIFsA-
zg>&uVZ*;%&bE@J}nWyj5aNX0;(6F5hJwN8MN7k;z*qlDBH0f*_5k3_Kdso5RZ;!U_
z#n1{(j*T7h1V=J9G*>@DkZsa9dl-mOM}QteZ^`DKA##90t72Pw`)K?E&X4^9wqA04
z1Pc1r15CON-#+I1!hgbgc9)$SYN!}VqGZw$IfjG&9uy(^zK=5I2e<8CqHNfzXR3Un
z?7jIsSK)x<&|<dy`lY<DWnhrnYoziRzzeeu%7>nZeII)7MI6cMKZr;WN%f`=%$^LV
z%PS`18&qCtC1>*Wz1+%>VSlP9{mdP)bL4rnskJ4VZtxnwH>EHVDj_mp1zT+Y=`$|T
zZ}KaIp=Q(5;wfC@#^z4Utb{S@9(xm$HTYXq!s&ySIa>X>s$+gfvB1{)Hp=m>lyr1-
zAagX{nPe=9j){l|Jwq&VqjkY!U$D{@@9x8gg(5oqA#dK?Lgk|Oh<^RL83T<su_S|-
zMZ&HS?m}xTtI=We|H_aoM9s6MZIuJstXpjvukJ7G<2aa_*1E4waBLTrR6deRXDcc8
zCoEB;%>xW*u9Fz-%HAYafp^fB&dXsg|JgXOJ{bQw4Yi&?y*55czJc^)BmilWhK7*9
ze3_dItMPoz{?NQYUvJ<DVv_jhw`J!?ft}k-b4{fcbqSZ^(hN#O&#-x?3M=L`)KxPx
zD+o30DY&M!Wb69TZ5GzowO7K2ior-}73~@AE!{}}Wg?2R*HbW83yM&}n`RwA*fwst
z&;OTNHP(a{NQ?bOxaBh)e)2yP`za}d*nZQ!+>tvB&gI_cmSU|lM0ty+Wx^ZUT3UtM
zi<IS#>`Wu0m*-QHlYD7Lpl6#mW?t^aaC~^N2&tvS&k8eJ+ly1*JU&&zC$-O0*kiMM
zh^)9hr|N@!<Xnkwp|mX$kFi@l&8{oevZsm>3E661A{$)WxeC0jO<jSkk5@~^)^<kX
zv>`nKAb&^z_+djS=Z^vspWrg(R5J}j&=?r9xbV(NX+J$xFVGx}m#op%=VILZ*A~;?
zkACKYE!}2#`oO|`s(StmPY*wF>#%Dp7luU)ervPb5$j-y#^$?+N6~Oqch3}cH%t4d
zRy&3*iczCS#cYo%_-p^@mKSe=SlDDzr|bNF0cHhHqMit$Mi5Vaxa`k%OyJNsTcCd^
z8V@D>pAGTYx2jz+Mf4P6JlOC9E*l;hNq~tt(ctZFLaCwjO~opxyw$vHqezewW{-p!
zF|wLD=}^zRv-h0~l)d^jjI<M`%5^ng=4#TZ3+#)6f&w^}fP-K59*q#d=LVV&paa`5
zVI?=$s_zwg(602kD|90OZWJQHcy69Ly$Kyozxq*<fI!+AwVoN(o|&ha?}ye=OCD5c
zXI)r6EzVUH;qOW5D_tqx(qzNH|NXjkENc(}YAcFx^$gr`4@jl=R|Xgv8Qmd0O$pPp
z<W0~nv`<J-v*oYrTDS6DGdwr{wdN9T+9hH4lp6jPEkDY8olb?H4Iu4*DMaEjMj4Xb
zf}<eb=txuSnJ^Vt?oUom0;)+V=Ea-Xrw=k3xc+V)NTcuF2oMYFV@uFm|N9{QOc4F8
z?DrJPmaj+?R)cImCm%n&bDE0x8)V6VK_v3on46oc`%{AZoO>w8n;Ic@8PUsK$8e@D
zjZJk%%)g+2f-TY6DXWmV%p46ou^8Qy3{~?U;z{gwk@&w2Dq11llIemaS`R1KFi3_!
zwL2N|^sV8pGfHv#Gwy1bJYJqZ5l*nRM&%}e8x+35{waDRtH@U6T_Z_KPKHzuROLwt
z2~%<Ed{$6$>5tgG*x*;rbvbn5=$tt;9t4cPe#AY*8xJKInY}6}=cmyklSftOcQM%x
zfQmTNQXZyu_qHV9A$}}f!W73c^S;kLOMpZk8qBBDzT=G-)%*T5s%xl^jfXh2puD$U
z>3J9;?IlwT?pu_^wV%c{#aisPF?dmuHr!UGH+bg%0Gc<`eIdUJe)HyFW1NGJZ?vuL
z+lxNyQdJF&R0)6mEEOI4Mp|9nqM5oirdajd_nurzEnY|MZ~365m?OeVf)W;FX!)-B
zv66%COQoBh+9Nh}Nw}MGp5(BC3D$goTE3CdU}mWg@c$|pwXBs1J?_U!+<x}=!BFgI
zscOxI#E|<H78<AVFHbeKwJy1jtW;q*q!=IU(hKdNHkBZ5JaLkL_6!7Mp8=X&Sl|Jx
zK;bg4C_q9!UJ$4lgqXlDhL|)qHU>#?SVY7OsFnkO$q!Tvfh<pj8&%5-T*1|K-r?Kq
zc59Ywu`%-b8WxM1E*r0VpO<d~tF7t-`Q?q=aigQXU9aBbg@J)T*zZhDx!Mv!d-fh7
zW3P5C<{7&0NVic4KCp9;DEWi>BD$XvU|~%HRHZO4mAU7Zd7>D7lA@`ScM<thG)kU}
z5%U>KX6E<r#8F_@rNbR>?jj8Tx;jd!;}#&}O?WQ2uo-{lyv)0KsnzIn>N)$JjOS{s
zh9~f4_IkY1O<VrWVp9t2e_&PtGY8@0>uVFU85|OFtt3Ph930%SU8YlEm&|gf&+fPO
z57WE~*Emo}jE>P`w(NG?7=}gIjDx!i^j?I0{`s7g%eL%u5q6+Fm6u;Hvqv%SmC~On
zi{}CE#>OU0ZMY3}R8dY2CqF+rjLhaW-l+9TaJ@s%GCHsnt<lX&6H3ol_TV{7dwAc`
zV(}lL&1KuM8EG7jpK9dR(d3b3u(sE$0nH~S$Dt{H(u(HE{NKKP0~4posVTSZnagKY
z14#qDZ_98H2S?sc%MaEZZx-*PCBW&s5)mr7sJCExV`HAR()51eAD<c*?fP$C!(|xf
zok}JMBJB2oo0rndMljuCUg-y%c|s}vTRcXM_c8n1v(^#ZEb}V+3zZiyc5`*(b=j+A
zSSmqj*c80^7HFwnt$#ykI!u>k)ZP>Ov+ui@meuZh>YYrxiL#$74a|W{5ve6^ZweZ%
z&+H6ykwKN^;Mc5OEj!W25j4RWgzVBM+dwVezLB0)So6I-yT8}J8A1Ss_Nlah^}G14
z%nvRT{H+aj*Dw2-ZZ5{I70})^nL~{gshF&q0|Sqj*&<`~3LeI*uKQA7ri%&3INQ56
zd6e|?S_%sbSGdYRl-`-Ut?l_J{gK=&F|(Ihpab@}<K3uoVSQL@hx)pk1#!f-Oq*_)
z7W3niUWj9YRQkz%Cggy=m@d<b0mv`A1z1>kc*=gK2a%AF&}cMy?9!R{zpX(N{ryj`
zbf;$;&k&r<3My;>#sT6E<MX(k;EMlFzql!6oVNVl{jxlB#w8w*|3a9&mMu5~HOm4A
zY?NB}y@?TZ`}5RBvc+Eov4wmAlxEQx-e=FOc;ga?!E&ji11ePjh8p{2tKUKr-0N}S
zG5Z%yIiI6YcQ-blx!wA(y)=|Ca|uiLp<~6PTVA|Vx9plZ?TlFQ&v}T&L`A*+{qbh~
z%cg4G^qCIeWddx_xi9gILIdrECc+tqiAb;7p#IhLj~|Ax0tVV)|DB3wx!=Fz6A)aJ
z3Q;XG1S)-1&j${sD1hAFViz5v3br#-zN$E~h}%M*tu$?N4i_+SN6=O3!JX4f4_mia
zS{u2;5ZA79$AEhPMRu59jk?Q%7ifqi1QU~zE-p@;`(#d4<9+1x)1Nt=1s<be4Ot5Z
z554>$v0->Vlskz|m@(i@iCA?x3q-@I)YM7Y9y^6jlR;h~1sU1IVQgxNCkhEi5|>E_
z>F9rHht(~ZFb&*ypw=|h*TYp0^a?I$^?^<fD2&W6v*25pMg<!I$~ixu^;G^Q`=6My
ze||3Qy(eUxQN3e1epl)4z`tMiVbNH@v@K7FhdTz8rDs|WZN#1{@Ka69nlH22&fp^=
z6mMusp}{y{WGt?&jn6**Qs*aL+^zectkJ{BgLac696v<-dLvn6;YF3HBILQZXybE_
zXm9(@9dVl89Kv*Ez*lR&ex(C7vv4xi-J6EaH3GPS3ZP7tlVg7P5Y`2<5<6f70qxG(
znnm&!^dbR%g18F<12OUNe0Tr0V`NiNQ(v#K<^(nQeZ{5ZFX&%@njZ*C4wl}~CwBWc
z%}f#oO??{@B;X1JVGxA28^d#ekIG-Y_p=kC#vd0)DI5!c_<v`OGWb5c)pn1>-(ICY
zq4|x01QSt)POYYnj{E$pj4#-~$eB3uSGqq;fm<WZL$>BXX~bW|({DsSVh57H<^Os1
z<9Y~H7plfu^E{%tj{oHX*oJZ;Ar!Y}xOg6JZSc;EtA||EeN$PxAwUrlLVpT)@kg02
zNdBw!Uw!@h6&V>h?s*m{(P0xJmD^5o^YR`5Vhhr6uj?yccqj_<14-5@7Zt5s&?Z+(
zE(IZqvI$}itlr>Kg6XO{RrY;dU0ebJ7`F%sP3-K%AfbnDFQB)=f`YsrO*9|l<FSNM
z6dx{Atl1BZgHb)BKmNoMH~v74M@*a9>PKfrNCB`>o;=jUrA&-C-Om0udoA7YZgjoa
z8ZGN;ttxj!FR$H3r*^D{qFfxN!2h})d2Yn{g+{K!pAyvE-V;MGSakO6l(D-xXmJGh
zCQy+|W~$i4oM8n^A%GkY%aaNSH}GemZ>dtZW1+~$d^I({I#x)DsL)PMNXXkwTy#1G
z6)s;VWm0)LF9!z)0u~>7PZS}oKYq|c9t|K6M12M`bt9v6sdQLs+am)un;Z$4KP;2M
z9Nj%Mq|$RA<5F#uvKJ3&v+w7bq$Isl)b6FIa<1Uf!g84f@8!|@C~Mv05HKUMvDr8q
zkW|eZ2?>=x4}11Rp7_K^wdZ5Z(C%3Ha0tG2=}&(|uvD&6QX0^A#0aQsT^XpjCzUHo
z;gSfa#smKon%{1M_ikr&rq@!$Z%YkK5A^lvLGE}-yh-t^GAn7lybIlLU1*|F3@%va
z-&v>Z7sGEyUN2|ynf<=9VqH(`H>hkHo{Cjp11Jz$uW?)^d3kwYG+YW-1mH11odSit
zR@t&rhH=iIN#4FH;kT~b_P1}}f}`N<(9j-8yMe+AbO97)Ev*{ZHR7oQrra6@n(3b3
zG0@S?O-(IrYz*o>N+fH+i~&^=sBS<9`~6jv6y;LxVKP!1DN)KZ`N{Udw<+T014scP
zRZ0_+AE-r+f=FQ0tfi$T=mlVmGO%yf4<zzb^TdQaMVw~Ch~yC@Y-6I%gt6}AZ&aZ>
zy&}w~*Mym=LQYNHXUcseowoBc<~=Sv`UHtlhMlkD3ct2@OFs4Al-|C+DF7n`IG1Nz
zIt9YNiGMuaVx@JM(nveqwaq!tQps&}kxNp^y&+9O0b*rnIJdB1VrIq%86;57mR44P
zCZ8A=2SeTfbsYz4%jl>paGz+BLf^l)y46D&!;nql4YMAhF#{bHm{}OqXKoWC1~qNu
zzkmOLAp`coMA*T=7cSDvG#WJqeoK49E3@*lsO7n}4+UW=OF+v6jeJCtVoOJ1n#wE7
z>wydz$a4IDwLp7|zvs*942pn1-q7E^WRa>PEg?xN(r7ylraQS<h~?)U$7i)B<-v!a
zxUEVPLS6IPL!W#F>@fNAP(!ydOK8Z~cLxISy6rtSwoSMgfJ(~ASq$Ota7{H`nTVK}
zoPxsdO5`~J^wbJ&#h<K83@~y@&WS%-mxN13y$q|Us#21d2MVaXygY@tPxW#dI7#^E
zU<O)lx?)*ZOz9s-e!6|ipFf<nbqR+KnsCJ;9*(Cio@v7??zYz@RY*742qrdqK=b2N
zTiut=NeK36wE_;NZgnQ4w68wJIf89PN+@|*e9S<Ff(*l^-tY4JDwE54V4OD)vx<aR
z@4SaiXJKwW1`!O7;%Sw=NjpW(7x%!JZ{D81EJ!wZ5VO`r9mLt#MBar$Ms4iiF#A*E
zF$8p}A;gHgv0MhpZxD#X0$lI35`<@y{Dz<i7gM25)%wOpVqzj#0KpxMOD%i=RuDI(
z81UdA{ie*d!E2>T^dzH1e&-e2Ai#b#wA90w4Jr+7myPkS8#hx5v@P_&*5q3j;z#0x
zzI8%GQy9J2Fi-Z<q8aO#U(XAzusCQYieh@!Z5I=F;bQ=AlKfyp0z6eeJsp|b;?S*j
z)UV0;U909S9V~IN6SQpT=2p4z?o-t8>}j%S1xS=2DwVrn1S1Qh9N|O7NK^GvPw$4s
z3MHb9tSrhzr5&YuVS9b>@e2ENV?)H|=FJ87K;dIkCdqHIUzX)lSi00yRJ`6Q&}klj
zDt0Bio!ceseqS>E0dW{yIW8rYWaSbr6U9b{x;ro!!l|p&j5Lyp!rZFF<?_rnm|L)D
zXC+h~fm>1QIh6lqPyXZt5unWU>8W_dv>HtGG3CZz2_y9NyAAH|4Pu_?xOheM4g9}L
zGMcwJ!`fjC^dtrb_`|=$ike%-H{2egNl}Ok2nzYtfpdpTzv)U)7aYdsx3^QpUSZcB
zUF`mvf2NwJ%O3Iwaq)_Pa_oOFC7HiCBgSxjk6ODX^4metd3bO@F<Rh{tvzj$qq<#v
zp+Rrecj$@~hjjuZk3$zh*h!$tazuQ*-IhW(nDXU=bzmCe$psJE2-fbv?jD*dHW4Yo
z{675HhV27xZl{AWyI*(kN5-)6@Cu!W<%0k>F|@=)xE%>K=~{i$7Y762$81Q5zyK+*
zdiS&0N&szvlVH*s#xYNPFF`klP9@v#5vt@m#E*$x0f!m*ujdNw5(jg6{OZJPN8UM@
z@c)-Z&;I`#kmAsAlBw9Fk>s-F{U=M<M@2v_#%#!mP5=2gi-e+P{A^z*+N0|{(A^0Q
z;f`O|?(iww^>BWKs!+7Va)yMzH7!~v)htXx%@;n#L@gojRP-#JNRki_uoRj+U!=DD
z`Ev=}ZYa42Bo1K<e)2jEfNgk5LgpbbkLFb7{()Q}T^TrV6(H*o#(|OMvkz2qMsI1n
z?v!=<Tv2fdoGcKX0dT!s;Zl;Hzfr}N_+Q5A09ERJK|w)w_J|v0`2WDO+t-RAC||<R
z=n2>%BQTwK`S2kMN)Sy5Zm$2KRHl=b{(SVFJfaqWs{27)Q!O<9FEes575mcFA7@_G
zzXdWgatKP_vI9@q*D5Rj?LhHP=++j}(cSrXIhJlfBO%e$v5~_M{5oifLy89Xl0P|P
zhDxrV@YJ|iJ>(6m2kg3TKXP9pw4rGNjVcFYj+<vM*ZO}|(F!NjzsAR}1rII_9(j7!
zq^72(=UD17ruHU%xK!IKE-v;OqYrqQxs<;j)~&{Z%_$>W{r7sJZofKb{g7a8;5doL
z+1!W*Rr-mlvdXlXTpt>1(RpKZO=f&njq-Yh?(klAJf)U^f?bKq^Wk2!*y;8*-q@!<
z&@@z0UmG1c_ismL=zgce=WO+9Ub$R%<!}~E+K-Pf>TT6(jPxg_i9iD=J>Um9k3E<K
z9`lSVkQrRKX~y_ItX?M2sd`j6RebgPdn!B2Mj4Bn0T>;?PD|T&dj(+==9$tju8CNV
zK${Q~A3x?;=OiooFHGt9c|EM;pfTvWHRbvPA^JM-mKXaM`&tYG1A`F8SG3+Z5lATL
z<xuRvmwe{UzyP=#6vLP9%=#Yd#ogOJBvEK_5jPS}rSbcd3?BHsb-YNle~gn|SQm<C
z5C~V#C5zE~88Pckzn!_}c}juiA;G7@lZ=O{z*G>myAM4+Q*KWW4>tY!6PU$O1C;XE
z%x8;XO#auG$Do7&*a{AeuKp>cBj6W<jfLfTII6Lh^hmkValYjZQqkir;q!kp&=tFa
zCX#$D8teezm<Zpel)_BkGj(W&*)JFHJGLjwI?c-(4##v9FUuy>o-@4snBAF5?*1Dc
zO&fLI&F_jQu|y7GU?FzGw2&f66pb6dnD0G4p1re40cn2F>!R2Fq{41zr^lBD;$|XX
zqJ4E7WAG@$Gec$$AQbo!ot;83XEI)%z5WsQ&}jFw!>mUGXz^%#cAD42a(`vhY>IH6
z4oIZBMSe$tQQ3~L_aIgPakt=QcEqKG$MHYC9CBR5+zJeLOeP`#qp!;pJ;(ap4oVzS
zcD6WKBn0%4Ax*(QR}xSqx>?#%r{F-NW4#9U*9ZSBdO}kX*R}0u|N1`MKSf7a=BYo-
z*FOwqO!z(u_Y`|lNLs?jSjvotXZ+*9M)bcvOPpS~sym!@y}UebaSo>fdfmLKv1=>8
zZ;2=j20a`!;dB>2Q3oSz@x-IXvYv2Aa~pfqf0VC%OgtZsE2(n7Fu$g1fVojqr>W7u
zYFMd+H5XN_z@RLJaNuf@42FS+goNz>i%ye;d`SQ!a&xw+31SG;MD0Zv3qjKa_Oo4t
zPw4NxymZ+<IU|~szu6z8SC`wa*C~6Ubw@$*JYmzS_V(;V<v~T-hd?<CZpv3>xyL~|
zEk?Ug3C2qx6y>!=B`2TLL3^HsZ=WzEI{F#P_rN&(w820boQe(II=8|T_GT?DEvWc=
ze*U!lmDUW?oS<_CK9)1(YA$drwkDIAV4!zQ`8EQq)Vjsa$8Tz4uw#Ur$k@cB=bkdN
z;0_(SgTr=9Y&JNJ+Jh>FCE6P1<L;qFB0icCGR$^Qq(osL@YF6=_>k8nz=|56ii2S#
ziHC04;6Acy$-Oz#Kv-XIcwUK5>Bn;MA?ta3smYKjxO64Ai+Z+)g4F!NxU!{c<Hv6t
zXi|%A-?uPm9ZZu%(JUvMDn}_JXE*o`@pxXW+kjM`8))FEQ*Hspq(_@m^KcYk8oc+i
zSQU8MpMZluF?O(GwobK!grlzQhPs)A+LOD?3vNko_H4gL?5K{IXI$WA3MJ;>Cu%Nf
z64O1TDl0AB+uO4SJ?x^pMxnMEjL+`0f5GOIo-y&ez7RaBp{8a(`IQ-(akc@zXJPFs
zzmzth1Cw5a_f~HE^(1Oftogr5u#>}ITyAh(ntB)(O>gSN*R+PDModp52XA%~@{d-|
z?fQL1IKD9Jfg5T?W;mpP(WAMe5Py6iS2PZgB}%}gZ3^H4-7mAfHHs<?n*%@h-&PTZ
zAoB-8fKWtOczAe31pCFq8$(B)_~&TPriJT~aY&F5anBnX&n|#KNhr+)#v1=B+^#N8
zfrdB&*aLikJ);l6a2{P3KrpmRnKRhVpq5D>BQ$IN&Q+J7k#@!4cfQ)&{ak9>CI?`C
zm+4oYM3vlcWepWl>4sz-0GucV&@mHXC-%u$yi^CzDo(Q1UYRY!sO>fvReq^=GA~Or
z2Bl{*odEX-tq!<Bf|3geH?1b_xxnkXF<@FB$wkm&BC{%0&j}yht#)&Fha$rB$&)u1
zB^U_BbrKzHnD=z=_lsOr^Rh|nif#fZ+_AT&f~NTxL@!1R{I_-pG+%8uVk$q*J*oGq
zAg!=Y>{1<4cLJ!_-(3Uwc`6#ePRjwa2BknuHrEzZv|LJzaDoA_fZ&dajhz4mZjx*d
z_2G2bIDl4x8=5q~?+@(1`PhPEK#<#TwMlL{g6g(y3vWtZz8kFD@V&iS`x04Ya4D}_
zBX_1K9gkW-@4ckR1V(KkV;qu?TsbM`U!YK6=EbL^KqK+8v@NSfr$D=qKmNIkxd~a8
zPlp<_BbBTjzja;vknSH9k%`4t%82fcQd>D3{CGNg^1EuGUJZW3vd-rtg(i32ILus|
zfaMyR2D!Yb0}lINrz<|2v7f`P<`x#eZ7b1mNN~xypA@Q}pP&1~hEMgs@&p@IbMs|L
zSQQuudrARk(1YPSQ8>VmIy>I}KXrWvJl6f&_GOijQ9?#2Wki&{G7=#xBU1J#$tu|+
zD-lUXc9D=|g=8g3vJx^186_(sO2m6y?*H>V|Nr|w@8@&h_;g*r>-vrFIM3rej^h-j
zB;_6_ddVXPn>5>s9c-lGI<tbc;TPbe9KSE@yD~+ZN%`wY&ZAH5pQ`8xHFARr+jFPt
z2YP<BJ4)m{e7J?2objYhdU7(ARx&B~Jzd2U-@s&Vc=<A7_zm){p|$m`;ZECz{W%qW
z3?iqCpG}hP#r7ml&2)BUDG}|Iz>7c=n5FqQ72LT8SQZj*a*}n~E2q&@I{%)n%kdRZ
z4~c<!XhX95yBc2d4}r6F%AUt`^DH-pn1}a#naY8A<dX*j<$3o$>yr4s6F9a0d^f>y
z_-s+~w`qGeGnINYdkKH=fJa!kP+?>3GRmItmHPWF>lQE@)Fu=0P>RlAnL^G@>@Nf&
zIAf*D23FSERVP3>rf+LrXtWFkWdYLygFwdRb0A*?NLh1GvYITP;Rbh9$UpnoOkDGJ
zQ<by+0aLpp4I4x<3>JObK6bSWeX8}V>U?KOKSYn*M%?#25gfV)^r6U(<7qgdX3rrr
z!FAG;{V>q@9Gvu`Wd2l&v(cff>d&xk`i+E_dJm{uLB4T11tU_lPD6n*+!kd)J$o~H
zu?@B_d_OmwcBHbV$~0qT@%wO{bLRxjZ@V-!*~m{PRi^AzoG&!ebs3+Sm>8@z930$E
zIAlLzK(~$m*)y}B3y1t=k*{qbczFCmK^TozkP7xWZph0B4=d+(O;XX$E0n`NEz-GW
ziHZ9G&e+yVW|@f{9XD52CNg}MrW#9#Hk9ymkzVBgb=KfO@Q~-&`~0-pH?6S*v(NGQ
z!H;^sXo@sWC5vDDTu{ON_-OcOZeFolJ=W%^v{JjrrWM**oj3nZY5IJ1v^8N8WYW#A
zw|VSxxtbV-wY;Qwmq(o{brcvFs7`CoorTB0IQ{ut7i9zGr`Y*-T+CrAl-yLihgeMJ
z#Yv2pJJpyaFO^YQP5jsyM$6&QL2+QAOm<;w)54b)Y)~JO>Jq7XW;t&z%2EkGHq8Hk
zEIK$SNE>Yh5=@@W2uanhey>7Z^R4}T^wp`s`;GntjYYRfEDRK=1Tzq}czFpvHZxp`
zn?812?-K%HjHND&?w4w*ClBn*6(<mA$j0k(EQtMcZPHNb4EQ~A9XT<=d?u&v^>*30
znN(>Gvs}7~j=CzF;^1_1X;?o=8>2@<mKs|$3lj&2JZvkoI&)9uX?XNsRaJcr@Gzjp
z$63*(4G*X<-OGYOIoAyUQ<(dJSeWHZ^_r-N2vI37T=O$luJafql^b&#>&{T$vPRiq
zGGjL_x?BfRCD0|)O1Er{Y=OZ>z9T+!rx3tPdwfqlQ?P}A20Lh-ift>wyD-}Dx%5QF
z`0Q*!@AMaIjT>9W-dwEs-sk8LCiwWPLQ!8AAF8(I4o?*qnTou+JPPRqo>BkA5GiQV
z35?zdqMlHQaQw0S(V3ak0{L%USvcv&qawQ{&VyqR!5Gd9DbcS369PLs?n~R%E$I_p
z=|J0rG6thKWF=}Z>QFw~7Lew}D-Iq;f1`cYa;6?9X9g6CGQ|GLYJ)3Zpvp`hdj{C=
z^j7|$GX)Rvjx_DWv@|Vy35TsIs#)A2QUYu(B|}3UQqw<J9(m^x6GucsNQp7#B5vIh
ze&+Gu!HoRN!x&Yd-!4!0>cl?40$J(CN!Xb3Uw(YkM?(Ekw!vfSM`o>JY$e%EI&#$v
zS7x4F8ZHpxlLFl1F|z6meBz>que6+8_3x<h@$ogg62&SO0t~%>&GawAWqFrxZ$kr_
zX)&4N)vFTZJ>&#RXU_}0S5G%7QQm)Bh$<bzVX%~cU=7qfcS-TaKo^)oe{p9B#Dcxs
zw4GV|$xNvcyVqee?1XRMzAt-O80yGIpZ(kN@p7Wv?(^Q>;wusZY9Kq$q*`{hwQVmc
zQDnSk$t7QNru6lGjrFBobBu+Qi4}n539f&rlEu%zvzUvXeK)oL_Y(74;q>D;5A-Ak
z*|>b>V{yf2PzNL%L>2@M2{B-gva-U8_HBBgz_CLQ_umyH|LmtQ{A=yX@>$z?H2j0u
zQ)c4Mt?1HEb)TM&oqo(W_{1dPoTTMi(XRtRXXJSvXdm{o&A5X7=>o6z%bS%JVl&AS
zLe{<7i8}M6JRn8nm0X?e=d8Ve5s^K^6;7`lA74X3oTi{RV5xX5a`M!vQ`n7hE2L5H
z=r!|cE51gqWDMI>e}8{nUETUtPM{VtuA}4xxXglFXWvR0n~|Y_{$kl}kJx2@2wh%Z
z9a)-L$=g653C=y5A>Ni2VjMS2^$OF^DJ+SBV_4an9ocBbKUu(sI56-H9~ZgU8Oq1R
zw6BTwsR1jzj1jOt(Y{8nocn4fxrT}WyzZ2~J{%!kflC5GLKO;;LsMg;w45%GMi-ZP
zL{CRVMV&uA;}%9BGl#CROhRW20HoPVeC11nF2tWjM?!pCm7m<mT3qwh_3rDY0inNg
zlzyd)IK9+S_&XlMll|<6xQq<zj;q1-1j#2A&3?LN4u;oxTq_TregZF4?|1i<5Fom`
zGr`T+)@qI+8hZa1YF*lZiOrt50H~L?)(%JBk(<j$AU0Ff)I8Y7Pr$McsGs-m?E2?=
zGB4#_1w0N?+Ki2(K3xhtH|PqV7a5=J`}`Dr-`R%zFRnCQMsa5+BL_4Kmt=h&8y9>4
zZ;?2n(61InnDS}<#4K<OGw#amdSuYQnh`I4@%urktn0W_FN=SPx}iG@%qQ;HQ};v7
zgRW$t%jkJNp;YUf2H89Vs_MdC=JgMVMAV|!<^=fw(KVo^+~crBq_HpmrNz>&k&!-@
zEGZo@=ihc(w%?2U`=d_hsN}!gA>E-}Ij=88Bvxfg1uNg@^B==TASIw70=lZ3n-jXJ
z!9B?5(e+zL|9sn7y4{TJx$A^8PM;VP$8NDUNw77sm-_*vJ6(5ytD*##g*wGmGk;U5
z6x1O~?RRk_XzEIuO$u%^l!~@y2^(+}Y<a&D_BO3yGlqq}grI0&9?>#XcYXrB31NXd
zM3mM<vEk{iqsoVPg6QNkujpodj3B}q6*@eFwW_rL?0eIs*<UZIg?-EBUc?*CPV0pG
z87Zu6c%%7|a6swz7w_s#R)59ojD5(F_WsFtWro~;o&L<Pz*}=*vCppLVW-*oe2Ji=
z^GyLcU$Az&F{xdtSN*kP77`7!rd$z4_P2aLQFDjrQWyr65|IAGy4?hHcQ>~q1azj9
z*{6##07kH}u`zxkvY}3ZMCOs}*hZ!P)6d?9YFcZCV2Lp7Q#CFysvIr8Q&r`IhTP;z
zE>l0nJ5u-Lz{0)AhwkYmI-`SUYepw0y+&SAsK-)u>{;dkfAE>xZik+*acX!qWqVRB
zyfUW^_-<Fan}u}eP;i-fL(f4!Hu0OtMJCP8r~i(e@XG2uf%SUADwcOWQ-ZB*X8^jR
z2iGfZuHg;5>#;d)zU}=!A=VH>{rK_YIvwYMkdx5BAm`GiV&vfP!}!AFTKV$__InQ>
zJb07%@WB54;uqe(QkN|thqudDi3{X&Iu0AnpQ5@RqKoKthRa7IQa766#~LXeCxZZH
zk7+>Ig}Ei!X94IL`epYsPq_}Ah=`Ew3d$!a&zFT()dNj<6h4zSASWm&h~89KuHh4M
zIO!xz2Wzg*CYLt!H}BR@>3aCPj?5pp3o@ChE6*`45ORamRAEw1`dW)XFkbrd{l2r<
z#Sg9O^{FytUT$s+DCcEnWf=+>^82TL-N!Cf=EeWH`)hWveo7)~ntqCAERUqP&j3Z-
zvd*RTjE%J>P|qNKg9$Q)sF(j@n<P@{7l9<9y{#a}wzQy{%2#bS+2^Ct=qSPLyCbFT
z>w7<ffgsWrqx_zqlIjXR%Z?ku>tuI^f^UB0A8W$$)afX3OsQp8)CW+l#4ILj<}og-
z=Y#U~xk6&5(P|yB`1kprTY@cZb`A1o_?}w6gpPYtNz-uGmH8(-vmH*ug8j^O3{3sI
zXbt80V-)u#ic_I>X7yo0o1M=@nukP+U`(Ry#1~rHr1w2u*l4z#tGZSt`>blCT>yUF
z??AQ+7_2Hi2*0@2meaz!`jeP&&%dm7n6G7!>CfpTZUl(Gm|EPJ&0u{vN_U-HVPy;<
z_XYw_6kcV}$qFbHDiDu)9AK1HSo=rAY-F)RfsTg8u{*aj#kqEJWW-X8RqZAVw*JF5
zZ(@SPdmo$JfrIDm+nuOYV47m3d1G?~V2=-wC1CCNncr+<WrEgc1s$pU4MP{DfM+lZ
z8qcG{0q%>2xeNTaJT8|hdIqqz{`LIuUH;0RmqNOQX7axm83}>hoZs!?R8=B~9ASX-
zOx<rkgI8#keQ<b~f`H@=G;m~7@>jma{QbyGK2lh>o;g9o3JAsL=S)AycWJ0}5nhzc
zsB!u|#odJ63p|To=C^@^fH7LPwh$@D`3m1pT_}xg1|$j~#(2MNOBl^=xA8X&bac0H
zgUA$-#}M~XiA&z_v9teL{EM;)E#DS#<C<Q*dP8@8J6kzm+#ParRQn|*nXCQw9Gzcy
z+tDGc86anWak%#Hb^DI?6xP*<oF*UYque0`LX&G@^}<{0i`v-xE?znKt<SOTt+2m-
z-b`f&?%#IKqrQa!;&0w2uPLL5vO28oMN7oA=G6uVD9lX-6F`^a4eJi|J!W5OiJgUF
z_*6_LTL55l(^|HB(6ykT#~H9zjAjPv>S#9+db;Y$<f(k40)pcD@Mtf)gyY?6I|vqY
zawZ~$YbgmbY3=DPoEhg_S^;w+tVL1jV_iU9wF*5sI(^!&TQn{$v<No@UVB0Rrv3YU
zi^&XrU#PAim*?~6P@*p%Y6<z4Kp>EreMIq4o&uSH?6SlqlC&z)7xtnGp1hKX+!`2e
zGbmHaA9Jw^$onrYfL8Ws9bH^RTSgOyv>Ubw1Z=l+7<Y&E<m!zfz{K-uL2!t?QXZ%y
zEFj+O^kf1v08i#NGFvzhl>h|>ZzjO8Eht43(XcxLBygKRzydlnj7w07QqJ(xumzz^
zEt-82is##EK;7V;bAIEf06PNV?%z=@SInl9C0B5T=Qx@U8z)ed?jGk?U9Z)}n6v*&
zP6+IAphyq+t(fQgc;GlCw}BI6?0{Ocva)9LBy8{;(Mka2QxJ?1FId7#L7cS>C@@ta
zkt~Oe@im5s8vh<sVqO6zWnNw$K?H&m4A$r9y#`zmljPt9e;?<_UB?fD0%&ujK8s8i
zEA*ks_Ed7`$g(bCEDbDbIO?k;TeV2Qy04Ew7g*CWeya_dmk^5IXZLcWHOd)cGFA07
zb)3}o$=Qp)W$-uuJ1^52(f$C_$C`aU+nYd@p@X+7fJop22({>TA9<Bm93zU=)DaNg
z{y)SKf6=spUE1wcOUuE%dta*8qY;9kH&RsS1A($2G3qk+nH7h!)%rC}&THkL;I`_R
z1w`O&@!>TTTRRpBh^T^*U1EjPIGT2Bn_8Qm4&{i~Q0Td@k@c?ZY5G4}w2tJpy~dwv
zvD>%>;v?D}yJ;mtmFq=l%K9mbf6=&0-`UER)_;DF<L7sD%-0=rGE09ol;K%Jc6u{1
zavHD&7%%`^Qd#p=#t&+~h4*k$o1IKyqz%h!J)N#%&vf1p-IS<-i&EqI>sRcut`?DC
z%5~e78Vg=AieZfk8<^J2<#42FkeZi7YLgt)pOImWYlP?rU*U+r6IwqAG=DcTn@fos
z-H<AG%UPTw+!Mc>8ZqE_1c?&sm;SRHBYC-?E3s)@mN>1(7i1k!8gC^VXTEMi%;-a6
zu)o~MWMN<+f~aZQ6Wzwdz8Q~BVgn@l7~t%o=7mKL_Q4^^Vc)TC<BnTcpwK*?*op#%
z1W($D{X3*1u`QsC1nCAIIdn!H84AP=$o`?XnXzC3HkS5Nk0w?Owgp&Irj?oydve&?
zI}=-Tp=h*LR#-SEn(~s{2Z|F;swdk!h3;RteE#D)8m>Ok##<?+Lu-0ni2PQBruffL
zARf^HTGYBYJf2XA7cl(%I_f&zlXLk4HIbTQ#1C(``d_LUC<KrP0^t)wypZ%mpc2)g
zTjtS8>+3ugcYh63cY~*SnwQu}BsG)#&RJr44@JDt#K-1!d#Iz2ppQv+IKw^_y|{@a
zettbhg!jmm1Uxp(@O`^DqDmc(I-}2v-t}QpP_uy~tqp!;b{f9v^S2CDAt^Z&S-8jP
zyLYh%aiN6^kAi{%AK!V9Ul)|sV{6gi5nuMas_GV-ByG-(_`j&F*VU~FL&nZ?;S;E!
zzvgZg(%=(h`XRQdR6lu=hL$#6{Gt({y(l$i12CY`CsHfpXpKp!os{|UYin!1^FOhI
zk$a}M=S5;o<Gxq)`Glq>H67j7K;TQrW4uNXT#^y6GD)&Vf#u+Cdo&sIW^1h(#n$D9
zfgbB*u|tDiyK!~7?7DDrDeP_0a_V*{Gb>DF{yH%M{)PumA3fo1@dQ{UooEOM95uAG
zmX?=?O6_lDEo(1kC+aK<LD5rFGgg7S*?YcA;J|^``}3$`pSjHKii3_mf1lj0wC!@X
zZW9hv$n;OEc?xiHauR<fZ%xT6>Fj@4ycU1&GZC*x>J8C&a%$?Q%;VGq7E!yK4P-b3
z2@^pztN1f#k!se3{*r9;>OhK!jO|O1b$^L1x{O%fotbgGJ~M#j-9qGDzYwr#wy4*<
ziHmZ?YaqCEN9vx^0E^dvDK5-FPas|*C8}Li3yY<9O?tceF&#F?LrsfvO|B~|D^F-o
zk*7}xF;S=ay@8nkK-6z**A-_i^z(Z{=vNwYXIR4SuN3yXS!4Ji*o4?(40}UJ47Nf9
zX8y3WMoM+n^B!E0fF7Viil5j+m<3O2X<b4A0B+-mK@JKF-dzzB746dVVLpDly@Q}0
z`;F#6#NM|4T)$%^VBgA=>aB*o71GITJ8(;q2#Bm@cBZ!V@JDb(?Bxz=cifeT`Qiwx
zclc&w*n)fy)CYl%`>A`7FyaqlEfevjh=|Dk{rf@Eg82c~60EfyZ{IrLCf>Okg=xpl
zix}cM8giT|pfE>4iI)N~61G$dI^s!kzP~GRDf!Ma(s)X4`%*+3|HOr!=w#6t$&8Y*
zHxg#;s8GCq|Lv=xee8gv&>;pEr`W8psTBBj^5M{s7<s@l#h_u}-`bKyC!kse-wftb
zR~!V1v)}LlRw^VZP+Tq|=@MgY8%D)*bJ1fW9xt#HxHV=-#l>DH1O-?ZC@CqQU73v4
zx5iqgoO=W?M|gNR3+i@&T8R-PB6>_`De?a~oM`i|rZ)GfQa`7QLD_%phg!~Sw*o|$
z4#;Y;33{VhSbiySX^Fpf&La=GG4eC8Ti?>b;%lt+#35qEkxqMqg0r0@n0sVcU-ati
zgHrwhgn?D4sQ~vni7xko=cEOhG|!&>g!nZXf*Yq#QqX6<h#}sD)+hVe(FGTEOQam`
zZY%<8e}4_X+q;9Hm~}01iZ)ep@EQmpJ60#mx9K1Ko#~%-MEgcL;%}Wm&AWd_ctciE
zN8^a6#}x~-4tYs^8YF5s_b@PweE)t(_{ObU9%mlKx*FV34YM>5FhqUH;;k4Z-SyU1
zmOJ+$e+;3&?pP;?m|-Lbq)+v4A2^JeUcym*{o`EAJQbKVBoEsN1kdcTVd|p-lab9O
zf-NOimYZ!%jPC2c>(_~-2gi1@b-B+XYipA$O#`+yTN9aKT~vLt?u)XO1%nkq7~}{%
zxc8qD0~BYU^7J&?^al6EYh<Il8$j9D#m)g6JsRfb!#^UKWgnSZ?7mPo@KQ%qP>?2?
zGdR6wWVxVSX0D`~R7pw6<*S(31*(9wJOIk-l8uXU9gE!(^H2L7uaujQdJUZyd!IC0
zT(3%3c1G(`g>l)LOA;xQjxnI8(QF}0>&K>w-Iabc7fHU%qb|Qt)fqvb<9e8b;z=hV
z5I2TY5V{0QUi;F-tafj($EKJ51SK3c@S4fU=8MSiU1&zc1c4$E!YKJ97MGYPiNcZd
zdZm=u;~}Va!CTh&Ip$V>J0-<*iqL-Sa{b8I7_1hKA<<PJ8xPtiEZotf6rp&z{(jda
znqy27>tvTb>-7>2&I)Yzx#a@L=MSa<@_FDqa){8tYD;CM-gV3JF5S`JFSt|OJUliS
z3kMakr`RkHF~K}}8MSXB_ph|$s4Unj!1r+gas0>M_>X`mEHpG5tWwckn^R|(k4_%&
zt}KMFXj6{~sHLS<U{sL&Z&Szk`ISld8lv?%NbO$egyJ|9Mx64i{iZHx(2l}AI0GGa
zrC8YxadExKD<-~Zkkn`DauddKu(1VpWW>if?7un-!!T4%zjWJRz|%hx1M*I+2_+;P
z(5aQMco7I>{eRAjnl^e2j^=1JN5Um5TZOn;n~I0>B|LZFj0N<b>Z?Q>Y%gI@zPLyh
z2+GFDHV)!!x58`(|MEDZ7jwTQL*)}-u3@>dZ@o)GKrS%9wB&Aa53x6N^H@v{oq75V
zC=T!of(w9C1T=IkBIq(qyg9m+#DxP${f!&9>zuHZDk+FYmwHfNh1Mi8B7*c^`)K9o
zXGr33zv1TgvZ3K)*+t&MN-#ixUZ+n0%$$xnw^KPzV}DxPH|c2oJcB3NdgPTEOlAGX
z3E7ub?UgAz77_e;6%`9=qTAWMQl(_MK7dY(7W;^h_I3`%3l`3m^sKDUv2Jgy)@@9q
z5gP(3qe8R6x_|(C^7qqAtWjGC|Js>RRt01u?)1qMo1aYD6WDDKT!3~VFuqtekZA7T
z&s`<Kig1bZC^(r|r$Oz5p_p#A#;)s*mDs;-3*WoHy`@CVekZn`;Znj2M8N&${f}+q
z7PHS<+)hDo_;{d8uJ5y6hw(VyKMV686x5K)<aC}VV*HXro(<|0*Rk=kJ!Or09rWP^
z67dCQEa~8G#!(Xu%-&FQysw_c%^o4!daZ}5ht%Q|`I~2ccE9I|3E_<CRZ0g__U5*a
z-)3@-do7kDn8A)G+gIdeI>{HWQDdg_4`7s1(Ol}TFo9Tg4Vx&iA>8}Zh`+(c24iOY
zf#Uu9_nVuW2{CvStgHgUFev<siA&&;mj}lul}IY3-$CWBd{0gI-Q+!`-)7ssIR+7P
z1VbrY80Z%u4i6$KM2e32aW29CTO-6nwHUL2c%im|L|6aRDLi)UEa>m90LVHuH5CAZ
zjCCE|Ey+Kt4dyu{gqx~w-eS6XyspJ3-=B!8;pITwKm?}}&Aj>L<a~i{IoGb(LAXi;
zm$-8bjvP6{O6$E{ik6Wo=AQQQUvyK0o-NL=g;r_yn%YVnRoe9+K85Vqe-@OjZ0V=?
zIsUB}(x8)vB}>*ue-~@bY3cqBZi=n&SdReyG=$6-0V2wvm)}TmgboBG7`w#LGO=d|
zQm9Q@)ay|hyGR8GJ3AD-BPMR{Q#NI1&T)_v=c?mOO&A$*HOcKR`ixa4B&N$SOkW^6
znazOlxH4s$XjITx`a|pHI~;l`QI>o{dB^o0NU*(==_3RV)Ner#zjxC29}vg&7DhS=
zm$MSd8^wt^Z(aTg0Qj!kG7~3UDlIiFzJgMkgNdEELb%BYN)Z>6@hkBohvI_{E!#dY
z5V$14mbUoAmE9iTYFZ|xn~%4Qy=GRWynM$ZQ)Fnnqg0;;$yU=zUD-aVXflGDtu+fc
z15&sdXKeYTP9C^(tUSvoKT369T3tK3DrON+T0cb}X;@wx`lUBo{j*&a7>UvxLsdRU
zqSv7eMwFm~EN+j_k?=5(NU$ybOpwbarakxF0x7osuV?<8cx~P^hf`kIShXGyVclK2
z?WC&Jk!w8~kD06vlM##sbzLZuH1_ky#VUjP`=B$NRZ=%^kH02kz2gCigNKvCI~B)g
zS+?KoCyymRu#+Hirr?3@qqypCv-`ZKLmYbcdw3+uUBt~2yR1R%-TU9mB0PM}#$WF3
ztpxoPfRIT-nf;fyA$PSbBt)4+@3vVKi!g~A3v%^^_n#yuaWs0#Z}8{S#>(!Mg$wFd
zf_6pyFRZxjb{5~g(M<lue<LOHU9a-4fcx6H;(})%kDhKnjEmN5;ipjBEPS{CMqm~?
za|KB`h~m7=koqwD!_|8UWHeA}{g?ag1zVWvN~)Ebb{uyFxx=*9OpDTzfM-h}aT6cr
zl2oA}D2FREQqq$W$SDc8Py7BXtBxVz3USqUORs*bmc4L_U*0P_QR7SVVtV!aI}`Gr
z*}NT#RPB+cz4JVU(<^eKvPS#!*KVDS{Bo^1)w*~*NszAfKyZ&;(SVzf9l@$-fLsqv
zOFrKp9b`;3x<7A}@9q#^s5<-5^Vz{|JT?7RJ6^Hh;qSF-K5Y7Aa*^|HYrkD7Pd(Wk
zb@dm=n)9X0A61n{=h&a)oEsQQpQ(|>e-W?ttrnVXjCPdf963U1Nl93p&n8t4<W}+D
zy<2(wdg9U+ubhSaf;bh@jn$VslnE?cgdN-=`ybrTC@fLRLb0d2sVOI<5N$B;FMN#)
z+6S47fB+Q%8%Njs_v3#)7l%US9Lfny8Jo(klbX6~Zx^;;XS!H+W_Ot0u2fRK;NQQ0
z`)rdvL$M`1mPaF2D^4ZGF_UF#*7nY;>zXQh<nLTkZ>fH1T-;Or^3FsJr_qk;DkxXR
zKY3=SDCZfvoNZoZNTH}dUzX%pE*IIex9@RgoO9{6%=5d*m0wuwVj<;{OiFlRQ%Vv@
zey^g>F|*BPr<+iMZ@y5kV{_Tv%O~yf`66O@PH<_sT~d+?3*S)`Qbc1(IYQYW5@OBr
zy)*Xu(#}BgSe|R-v3%FK)Pwk_@a9ZD4T+VDdOH+jr$xf(muYYFSnM7x>{KB)@GJH%
zQ}wUq2)cNM@`>x$_L@22HFTJ5p-AOX-$6obqq>7feJD<AN3guS$~JPnr|OxMx<$4z
zf!rasl$tDI^wHdc+=2}!!_V)~E0~&;{q?mYqYe_Wz2-V$mgm1yX<a()gypG!JT`v7
zvAaAl(OfLKv{L3c!??%Jk6p3}`c$+vX2Lpd2w$lmlK44}Skr+Sjp7wjHS3PI3o^)w
z>U>v9AfrG5^z3pg8a|%B%-%wuP=xy%IWg#!opWmPp@BDF{28;6CkEaE@n^D?^<wW^
zGm&E$J~fGwsJAFfc9aM<#1!6AZ&%c}P+(NklPURe;c<doWaIg=Gsl|+D1(0fejK<k
z{70~1UJ%`vDJ-QDZXevTJYq?<Qbf&XGMaVDOTbGnmR$YmjaV&4A>z}jB)?rDsBRIj
zLSL^;jvHYaC)jZKW>69PG2<}XZI*jD?!@4At`o78v3wB{+Z>6<r0|>}?>rC@#7Cm|
zu=DBS`<}GKCm$9TW(*6pr(XmfST|I5H)C7ZUKz6sTU&VBxLyBSZw8Ng5RW<`lW#z!
z`M?b$y{A+Q317C85yE3_7{-rjDMvaOlHAJ+h;Lm?gb|F!#_K9d?pJA}k>}89**zqa
zL76ckcF)W~Ug#To7}DP&3CFL9_WA=GU-bpzY5Q5z^w;7Qav0gq6nwA{Ap~}wM<jFn
zkf2*y&ZYZr;*ksq1>8<bGFfg#7-4>C?eLKo0UB4R6XdcU(C_DOa%>kjT@?H(7b)K4
z*l9Yx@pwhTw6-zXxooQ71L|)A8VlSzw<$92-^NpUHwG>hTm%9+4O5D*-L?1)pWnAz
zi$X-m@2X||C0Oh{p2Ek<Lyyb74wD*6hV?4j$xbiMteyiIl@`SN$!oDqZs2!T11n)+
zYc)?lPvs*p@&umx5w;O{VvcEURM{qYJu3^c8ufN^I{Q8;q-8QIMf~x|_oJD5!OT~V
zW@N}*EILDNrMV-VE`vhu)1ecJ;l=a$^&+>TUF3WjjFnlqGBW;5N6j<)|5>$au+xJj
zwD7{EOs|Lk#)TL_EOa`D4-AN}QiuMPMJ%4ol1V!xE1R;3P~z>_^h}5yDY#P}y-Pji
z5)u}6fS31rXy|@kUVLWVn^2AHg=Y8k^3c)KSHn?{ZOGBQTDY$9M8d7$LzQaRxva@2
z^;de&Kes<(o7A>@i*<Rw+ptZ$gz0$Q^Zn`qQo1I;r|n~TxI$LubX;#8N{QhiRe8ce
z$w1BYHjOo6_@VDogSBY_i?*3I<+Y$kBm_mi)XL8?@i~{O=HG@^oGl{f;Uecz60H6@
zY3d<qzqYRJSyM6Hi<Ll}v*7B?$j;uMD>_c>ibrpgh0x#dyPIUmQaQzKr8IMW{bXgy
z(SwCxD()QiE&ZKHff!+X#Kv9z{YvzR!eD|_SGR#o2_yn2^U!ZW-*d!z`i+7V0yCsd
zXwNLaJh=Dtq0ce<*a@FObqR<Ova!?HlfJ|D`lY<TvWnlcW0A3~In2asP7O=!%+foR
zA0@v^eNDWDmu=4oABDqK;x7)lmwHs5Aq~56Z{tMOnApAL%Mh!s*<Y})Cn20{C+kVb
z==1T|A1&%WcQIeTg1y8Np%uVmK`D)`)X}G21U2lQe{W)-t)e45HtkGRHbtMJ^|wW}
zu~Kvs?#x}YsK561)&pG@pY2b3wv*`@%&r@{E<erRiR%^MHFmk@w!ipsv8lPKr*#aE
z;mfPv*UbMpP>y&YM+3`7&!ABq!--h$UxlBBC(d$g#%q@K*Uny}x6b+g!Fx%Gu}8MT
zEm8m6=1Z4}FNIj1Jm=l7JavulQMr>947=QkXoq$P#a1_t1abGdedJphQVgqBc)!+K
zJ2|UTa;tC)O2r&h7qp)dSA0CWRW;bV%iHSSk7!>UW_uln9ahhl`01s!FmK~_S<KXb
zuSCk`6}ZXaxM}t8d%}(e^O!Y$dfvQIP9d;9Q-QPG6HKu+GYH(hsR3#h)!VU;<rb>)
ziHNS72tq+2$FgH>v$CXwfYIE&E7kwJ@Zjyc>bCP@GB&aT@o%k)-}R54AVLy`=;EAU
zE5sC;C2^wMnOptF3;qWqS=m$KZ|`a`9+y#j27-P{+a~Jn%HAY$lWc1WnJYUlC25*-
z?!V}n0OUbuCAe5fpi@w;mf_jdd@`xPEY?5#@fvrURVKdWl;>oA68dnvf8ffyPi+>3
z=R!F@fRwhsvMO;uTiKwMS2~c@&97)4w90E!1r!0-w|~)MFMFa>v=l)nk87&vaj}m=
zXIJF-w0ND<UITxgeR$6NxL|jlvVm@&dk=JvNER;ocs!FK_PO5rB8aT>#Qc(|`{cF|
zzV>vZRCPCZcH&JB#m$Qo5^H7ep&}TpdX!2QRuPZ5f21Bqw7h>_Q*f1%GRXU)Tyx9i
z%_Fk$m@-+oA3FIRPZMtL&3FB98L|EwMEC}G;xw!~)^~K2dN}3pDF5ekPQAqEX#e~n
z{x@Hi{^dETd?~;8J+24|<F_!e=aLrn_>GKEJgDO>l6-%T1C6+KZ(v{vY|)%vE|+mv
zq8F=sB`QsKcJq~Hj8p+iWemB9qqP=dx#h3NC>{VdC?@_p&5hH$eYVGV?(&(CsJoc5
z`J4qva>Sf>l3SvEIdE7&K=6MNcC-9JCwXZAjH7LYchgJQAW)?O{rSHj0Sa<*seKZe
z8Q-#Sv+{;Yly3W){mgf`;W#-30%6F^%l35nk0WgI{%cZAt^hKCQ9C1O27Hr#))PjK
zR(l`|tNSyRPXq#?V?&J(EO0qFxdU3sKk={66*`Z0TZl;U03m%?E?+~WLq`cUVL&Cd
zeRcbo^(|{3O^aF8%X3wGjS3EVuQJ|=8d(16c3E#Ton@q=D4U(!wfR$=bvypPu*Wfk
zo|v+1scH<m6YcUKI~(R5B#N+5$G1ZqaEBUJo{?UB8zApTXk<S!Di}Zt?>zV%T6^X_
zdxoBzll2k?=FrmKZj@&LILG~jBN1R=pNQ*ya8oKe>vFXl`$SNC%d8ob=-iR!3B$Te
z460GVIo<4ThgIw;)G5KO{%6EeY(<%i`-*>VOK?=XqZpk8!G*};K31;ZDgC9vu(0={
z7%Kr~HSe0*H=&IRaRD|!Sy1*MC&1Ya?Z<(tK+AP9r%T+ikDfqmvM@I!AEnw*^7xq5
zPGy#`_mO8Pxud<4qZ>tTUMyMKL&%@IBU@1^q@cd<wXz<`xW}D=fT;z|i7Ce8FY1k`
zXc~$HHHj_ii4`+RgY^n^&~fs~DVloC1K@iIxc5D<bnvbiOCJJy(7DVKrCMNQI{tE$
zlT^{nEWO_mzLZ-6K^gPf_uo(i(8FeGG~3o(evknlHyk2!YYwR6KbtIR2XIB^?(i=+
zCO@bx&Ym8B?LPg)yzP?tA*>vBJIU{_mCD~al_$nRvw6qzdK{w1&K~JYS3jzzI>CJ~
zePVHOaTD3=Ixn{QSWVzuC5=opxhCLU2SjD?rG_4CvpUd`ES`Ti9(^W(5|VI%5FjS@
zlz3ao13?`Cnq$1)4_<YKtOqUOeOFgL$~Dh(A_R7Ker)=5e<y)YXV1i;?9Juj{wRRA
z{{{LNvitJCLjT{3H@}4_%hpPM!!`rd(bsnkogzRNI#d3NZRfjVALd_|Z;4WjHIS`f
zAd3p%#V!=v?-Q>Nv$=Jc%m8|UVhG$9UJ!8hhbGkVbGU0Xe*9=!*gM7_zvH$}-uTzA
z&jkp@XT*km8*S{d$6$7Q3bK{u7(icw@A*e2fcJg*QYmwS;r!9;%zuMLfAjBJSqPj+
ztx=Q0nCgYD2b?%)pq_y%fmSmfC=$%@@W){h3$(3#8X0jp=by!=bEf9Jn1n<*bjMHt
z6`d)-_`En&T?uH@MxnkgS$g#N(I1p`&WXBN55FySCrM)=tBgP$^Zz1ExIV0;`!6m4
z2oy5*p~|@-f<hnN61)1Uc0W2-$GU}vM8H`R%*u)WQtD{<Ru`Dpt)u*lnnYqEWzc72
zbmJD3@#HPZGQYZgD<W#rpGe)Yxx_j$MMP58n{W{D%c$L=b|@kcFV6TIw3n?0l<{|s
z8#@L{3Kr)5*FT%xznc5)#xX|j5Wa<(NY%141==~QKbkl(e!=a53s)3MdCaZC(e(1I
zw}nC<k-Ty+oc*&aH}y0FSQ)0vL)k~r&;?aZ^h4D6eh;XVC@SN*Vl1!M3Y|bdDLWXk
z7*Hug%uF{*Knw*s3U%1)XpYr>`wR{R|20I4sOIQSQW~zc-7X*mDim$~DW=^QNUz+%
z>qAYOK0$(#`W84C(NI%sYH7LK34~~L{2A5Eblzok#I35LN`@MreI2MT&Z852Yfz|B
zMRo+qrSBcNo~D%vTvrt-@A#%`sbAgPp9ss$#A-JbYySUKy9Wmc>wb??6VF@lRp8Wz
z140DFN*~$7Nt?S$j9-nxW20&CN#UflDMxx#5oDAPQ2$DK#b>V}(4LU_VcBhR1t!#^
zv`4Mgt}!4%$tk*T@4#}Q)05~$DM?95A|cI(8m!_|d8pK@*iwL`R$!E$R*?5L3Gy4W
z!ko>DMs36kJ*<#WrqT8rgxd9<%=)K%1GIh{E$vme^!F|XH@MW1d|hE;?*<9cD3*t{
z)Rp~X<<`Zniq^cHr~ZanEwMhK{i)zm6d%MF5kl`!+I*Jd!TD}GAMGt{4ShaG?n6@Q
za2Ygx2z%62RGw8;wP=oxjin_#vT>A1$&%Pl8AMj`RWX*QbN5YKljt$NHl|az@=j+|
z{jRWDWojtu+|lw}rgP(7`5mFn8jJ`L)Pg`~;`;Nou=oFz44K)XH#xQ3(qxDy1NaC&
zz4J{>>C%@UVo*j$hr~1(o6o*{Xl_|@BtC#$ysqyxanJv@V(VPri6_X*{DfE_r&SSE
zpSE+_nWgbt%XO`&%*?N|ski3_-Y6Z)o1nYj{!;fzN}9bvfs1MTLSm?ifq|^@NscNF
z!Iq(FOo#C)F-q>AsfaKP{WEVBE;ULJUT_ZSxLw|SvgvAwdMARUAH{z@9e=m&k$hbx
zaWKi>La5TQHSDm*3^s^!+WI^h9`39m^xoh5d=lScs8cI|N`<~N9H<uW+{-=AsN6%)
zT;%)GJhoPwR-RnO6ZPSra|B<Y*mq?C%qqyl57XR<i|fX<p@P<a_iz>fU+uQ*F=QrY
z5hG6?98UUWP*uU^v)zR_M!lm!!9!xjD=xkSz5;YV;ND)od|4KK5MaNc-@rX?yrYFx
z_9|J}$ooSA>y@9Uq;R3^C@p~9LP<jtS4T@jQ|&Smgc7tsyf$@MjD9%DwS=#Fgcoyz
zN;E4^qJuHPs8Oe(VV5exmJWQ4G!W&6<;gDl#)w_OxQ{;({5J%U>VQgsBLX9=3z!Cf
z*VYIYU>3n+M8}E#{`=ymP95hoY)g(9%2u%Y(2**iO~V=_pn$p@7A|1u7XdZ}aaC`u
zuOgOOg*Ir&cd>2nY3)tQ!S{dY%@A30bFz9lT7va<m~L@lfml?vc+pTg1Nw`fTktn&
zkA?AOl!X8gvJrUH_lCULIH*8Q2(zSOHDXX6cRIPyTW0uH$KmwEp`5>rvX-Zfoa*UF
z1MBJnK&t>t^4#KMK+UmOhUkyq9(xuZPDv19fm4hlU+$LlxP%0zUArg<BO@a)7jg5O
z?|X@-Y_*5<#lII}PLFTtTy#V%G~AIUbd3k|@Y>I!*91LMWgY@47lZO1mbMUjt``J)
zE*g>q%!?Arm7^P>mQk!UON-JVJ@;N-BoG2a{&+4eeGd5TGO*M+2lum%Y#pqo0=sa#
zu+|Ntvjesl3e5<9sh{38s#zqkLV<uH@~rXNF$nkmqZ0FGJIcyUaM*K!tu$6N{`8eQ
zEMc7vWjo?$CB_fU6YD}@mXzEe9(<`DhT_O8-*bP9085xP3qUSQ7Sa%UQo374Ct`U>
z!!voV+3Y;76#F>yJOg<O*8tCC@6&^wJ^7)NzC*X14qw_}$miQZn&E!Ffquuint>N^
z5eKuZdX1(5OURhTL59=YC46wvv*MRR=uw*hq`-;=!ijG#*YV1_rf|S=ZE}uwng+Vj
zvs4{A(&JFJC74*r0CX-y47&I2+xH~{mK~LzKdf%E-Mn!F9B+78*c|EK2#LIPsn{3<
z;x`}I!T2rhpFsqt%g4yxBt0cmlmVq{l&y4_<!)ul6NDfhsvRWsN~8=tdZc=>(=?p|
z8WKuHH)0JLFU9g{#PX0Fd-L+ugJ;o)-K*u)#GH!$6fV+m|DsV1DEL}_)I7gOW=S1|
z0Erap_TRdp1N9FL1a^n<Z`9kt^%%^M$+fe3e2jLbNt?I`wx`YYy8A=cm*%0MeOz3h
zG1%+t>z5y+I0RY=I}=j^$R%|F8+)0FspKB4Yqf|Fp*e+taiXvm1~!F#KQUbVnVNwO
z1dM42{9_8F6V_J7agmVot(9sei(SAjp4gGTpPYrj6@p;tC4q5Gn8f1l^{sN0fxf?w
zt+NKwlm3c&c<a{s;lfKT+U`L*wx0(5q~#q3Z*c?#pDNo!q|)hUFt+tz)sC`cNiH=J
zWRf{}0(+AKk@-;~s7dK+AhF`PZSU2D8cfrVsA_>D`E7dopfJjQEnP-T<lKi2wObYc
zUR?!!jt49cZf?ugyN5%WkCwMv(}^BAV)W!Jzj`c~SCE4qjIbJ+J6bNQwu>FsxkTDa
z(6RiUuM=IKbb#CCIwlan+020i7+Ux%MEUfsxBb|C)_lq>EbC?Fze(ljjB?+q>$t(5
z*jba&W6G-@tDSTCs@uO5Jk+$<O8*V7LO-%Rc^kA~{IA0Ie}~8ag;`-AibesvFBZfE
zX>Oeh7WE^_PlTi*c$wKd)^(Z5XLY9Uno1r|D;!yp+@CId-4RtRQdF^mDb)D!UYd@h
z*$c1PoCVK5n{N@W^L3P#N0r9*Wl5NmU)ivK<EIXTzvYd67YXgHP<hb)nhCN7w;tFa
zx;R|+X9+obY++t(nKie6UdV(0xYxKxi{{}?d?miEbWUcnuNbr7(r|4Jk9tyQ+%c(;
zw^6z^Vf+vG5$1WLtx|RYtdPoSZMdd;+~(5zpc;?Ut_N-mS8Dz2+~#k1Zga7v+kBbA
zHe&LP&5$ij5jRQ>Z$#aWJu^m3OAXZfOcmcOqLDI{@{^{Lu=Kyjb%bLV6>DtCbiJ6j
zp*pFuyk}${``Wf`#h3Tch6sq1z3$G+_;bB%rfvGs@XnLM9&SYN<`E5UB*bjsq>AOJ
zDuwR|3J}+Ca+(@0=hMz$d}LG_Ch6+F7w?;&5s-RpbP+WjN5{vdU0f>LO#g-@$X3Lw
z-|&6uocSa|x8OMuy3uX4>lObGl1d1lmD6fTff2`E$|)hX@buUHLJ+Ev7XONifRF$T
z&V76LQVp;-H8sf!?NVm6V6jd#8*n~-xKeuRQVI=O7~!e~Lc;f0=UIqh$4EmOD+Tvd
zuJ}VUvYzSQ&zq2d>av70^vau@Q<A_&t0XzE1*lZhQrpZh2{+Zi-4d=~7+kp;zp3<y
zY#g29I&NWN#CTj*&GYby1+S7HJ~2FbKIVBQA;(G(&7pL-?AQ+8)|iy`chWh$`?Hib
zXjQ|ypo$zx`ftg{=NFEEt@TnKJtGRo5VaBu!qr}wJquvU=`4qPaI1;`1dM&)&W-j?
zol82$D@_H<u;MYTo>VIP&D8n!PSZ$*`N=)$w3UHzMEOJbpp~Y|cP3Q&A^3gtZ{mll
zMyq;~Wu3LM{~wd%(cO<%=z}iqtbaK4{Py_tW{xAU=l>IT^EHUPe&VMh>>Qhz)6aTK
zG-hvzt~&S{|Dt+eb2$ex5d{7q-h2h;vs3j{zLxyf6I2wJ7oR(9IBn|Hs9p~RS%bU|
z;U66>tr=8=RaL9Z=Ds61K<`BEsQvnB4=xqg1M05JI-3VXyc!A$riaC~m_v6i(L{Bs
zn_o(*FDZ{cGfw>Rwf&>V3A(d`%|p*eh%u0XB{J34|0fM@0f8hlp;BwB=`4MWJy%<c
zJ2A|9#DcLbfw%+PEvQ>CjYX&i#)%9#TtD}|>dsF5_T?DO%2dG*m7+=+WO9ZCl<@6J
zD=mVkO8Xz5%1dS1#yy}eP*&VaP|ve*9yXXA`o4J<B>xaqo&f#<zs025(-nCl!nir&
zRcik%u$p>LO*wKX)i7Od^8giDAA43P?4iw{gn%9S252iFbA2b?#tx=a9l+WC$=5p7
z?DO?EW8iSJjzdz=8~^HyMLJJ%HS##+3-+FfPi?wq*G!i_3WxQoh0B<4l20|{t3G?K
z&Q`Q=Z&lv4gZpc{{N1hnGie{E+7!Oh`S0vzJq^YPH!I%t4XL`iq`<#Ne#rw0<wd`*
zL&1Sv0Cpi=U6--uqkIkK9Rzd)kqhr1fb|-ys{BTsN855nUU&7c!&jy9uZdA%>&^t^
z3XG`lHbMWO!2^&)O6nPSED*Cjht<gcpiiEt<RY?hhY@2F+K>7EMz6shbS6UtNU(o!
zJQwx^;Fymfeumsb_g#Am{VCsz&dMjc1fB~G%06;B`*Hb0Q8Rk*(CnnA7lA?p>Z3SK
zmk|2QG<~Y*{}8d=p^k21B9@gGMFa&;9!($l`V~%SL@)^t>DjYqV6q}Om)<9;Oeg{u
z?&CJutZoh6Re6EKJW8=D+f>wdsHl_b(eKxb-5_J&Q77G|D3WqiOKwRi|4na$vX-4@
z{tE|pziRtS{l8P{0zfZ4`zMuNIh;!S|BdGwz?3FYJay_lv_2pcx#A@LBfKLLZA4(3
z(cNtT2MiFjr35N-a&odHzPS9W^wnDtdsZ+hHdfhDf}%xWw`#ozRf6TVh9e>SiBSTV
zBF{ET5_MuM6pK11vCP+1!s%Ffcc9AbTlnkklu#Kz;#?jJrN$y8<YtbI`pZhUM#$It
z10GKv#-Ot89+I>0<A*DLWqK7!3#0`8%_e!V$;1(7p&?j^u~M%y93_qqLJ@j-)|hQT
z#rCLY>OUbd&>E?O6e%kMHfoPq`FMFvE_tU!y}q#z3!}e(ZN5I^wFxiq*b@^ch^wa8
zkX>o~w+wH1n|wd3y+4GWpcmUJp+uU(!#Na#piXJWalIX!dzNER$K9{Km#~BIf;k|4
zNq0fGmnv3~lq*H}VOUf+znibOj}L;3_bV5}5yqX$lZrn0n=MQLH_lNaGV>e0YW~L$
zaL-8ole`Dc^Cq5#Ve@}8YAbcY=Uto!wN0=E`W3xR{7e$-5}p(A7-UTgFN01U0Lk{+
zNTh=4oIos(9vjU!|4xm#*_vNNDHlZ``GEk7I>OuRxLJo@Q4IZ&D;@P(=ghqh-*ZUl
zAN}Ppfdo@Qh|o62pH^>k#qMCE#X+5JyDVuIDW{^&750{4(I;UCp*;Ejm!{;Fi2(}a
zz?r_WM2!#}$N~Sv@ih1L{u=Zc-XFp^l$J$B3!z<&nYl=?tYPk7*+59ACoW&UFk@*H
zv5!64QtW(yaf{6$bB+5-A)WAos>wfL7iv^{iIQn48M>TK-;iTDUAFf2T}e>`^<U@r
zv5RFW3rS*-zunEmekg=O?e^W&t;tIlPt=;_8VyQWgg?-gD<?)&|IQ1lsWG5eUfwBR
zbF*fv1{H<R>O+Snrh5w>^1%OjQLJ#Iy6+H}=c8a@a_9?ZPx|n^QBDmN%KIfKS8&@G
z_s||*N7W!x%I6FsD&ls*eHzI=H*ZOg^LeLVRD}5(YDJOq%HO=g5UjP^USPO%!-0aZ
zR$miQw|<4V1-NY|eu>PY=HU;o+mvDreEgB_Z`Lu~NQkc-RX$mGrM&hXD!65r*%GgI
z_7F9^)j%3_E4f~E=T!+k*h3iv_8R|}j1A{G|KJq9MXA7z^g+h~9!AHmEXJhwx&oBD
zew?_V)PDaYzply_{tZ=&nOxC6=IGA{DIVvJ)$kpoo4F_dSw$#7J5q)wyrFK?m69)f
zhvp@I&-e(T?Q!qcE|s7G(5{Z;lx-KvtL_hX<bPKd9sc`8ePQ}&ir-UOa>BcucP@mV
zyV8Hj8|`4d-KKKbpfu`Y<zsg)LSSuh7}-6#AJqZhNe?%u)>|mgYS)$>pH?Ig{8Zu_
zKN9{_!VVPs?~j*Bh2i1*RBy5{sJ&uoVo9QCSnfZ*^L^L1noG7@X#)m_>KP8dX4x0r
zN4KYti!v!G34<yXeSSY&CneCcotGk5&BdQKQ?Gs>o09%o^tEWTb?b}6WKFI&U2h&%
zos&7lvpedS$(@R}Lj%8iDyiG2xz!VFq<PfoXtW2{&)?>0(Om!IZDuwEJ!;ueRze`l
zGd9-fkJH`1b~L$yE2)}w_`oeD(;%)_QzF*L0MjSjQ?%&GwGw#8h0pQ4wn<VhVc&Z@
zTuN20X&bx!xYxC@ppxn@ALrh#%e7zaxHmB~w&7a4zMz%3P*v`;MZoWdygV9Vwb_*}
zxq1%LN(9%xa7}e8{UKM_Q6{q6+OE_jqyLI*AGy-KKEG#yr1_2OTk;!KN%9-R42_Jg
zF1B8Xc9q-r8n;R9X0iFY;el8llfBZ{Cd|`gB?ppsP~Mo<PL=8XpmfBQz2?uG@n0Ow
z>;3P<9M+Dp|7r;}DiJ(LSI<(~??^-;MJ$6@N8CX}JmYu;|1%bydCdCGWo^rLHO6-?
zA?WV4^fkqqNP2>aa-BHqM(~)_7cz@LuC>3nt3TD}`RfVF`v3Xe(Q9K;3yG2+*;aZ6
zwFjxQpIOGFqwQZEc3RK9z5Mg(Qcg$0lfu@Hj$2;D%N1kqRBB+L5My^xZ1}+@#;&K>
zz>pRf$A~sMHrDm`CbI_DZYjKxn+te6KHT2^SGX|2#v~5=U2tr-kUF;>jYceAOUcQM
zxK{2=&9o<VdN0|Ws~Hv@pK~~#m&yzd+edX?sync-k@WmYCi3C6{h30MH!?3c&*ldH
zO78vmeQAAW`K-H-|5YEE>LIrEKv@==kXQG3yn>YW{#pMM_4DVIf>JVq%9PWy^Nd_W
z&0B(<helu3l!bkLeN)t2@SN_#=vN1fM5~)puftiI!WqU`e0)1<mA0w=s9lv*Sc?0A
zg8AOiFfv7E8{&+J{XNP1^4C(Ml5_p)M?bnR>)#dD{Fn7J%yUQyzXLFFzHRAPweQ$x
z9ptipv1{UbrG>|@2K};jat!Nd_!J$fc5|^RS&tnI`+a{DhV0wQw=O)rg}^1oPI%s6
zY%%rod)>)>d&hds_xu0ZHn|Xq-oGB?+qVYpp;*ZIox{A-`X$Bnm8^4ueFg65tYxOu
zKypvN{>RmgtXn%f7B?y#|J(~(@jb&!N(kS>N=x`l6}@LVt-_e;=30-@;`iB^1kGs`
z0zpq1hh5ARYN_z7bUbpoowlXjLux|2U?&Md<yd@SV??I0iOJ*-Pp~{eByRXV)Ao=x
ziR6kV`;qQdmAuJd*6#@|)zjyOtB;Qs(G(mbA$;0x*zS@l<4*MOuCD$xd8NF(JZ!K(
zMN9k$*|(%P$E8Dl8f*Hji>oI&yIGQ1pK6m6T<i^`8#+IIg8i$S3aUwMZEZ+ySI64X
z>o!+1e(Mz}c6iOAt(&cFBaei2tHry?w0Ba`JIR%%87*08w!FT36f>-YrK+a7>Iq-)
zY<tPz9riQpH2H(gWoNZ>Hxi<}0$&y!`{;vl^mgZ?kB&iH-!5Mhvn`suoTe>LC$Tre
zP{Oa4dP&~!sQI8%MZe>Ly?2_&uU2hd?Lr_li_hJ@XdswEdTO;J<#58?ySX0|vlmF;
zv3=P&XjOckgNMfUoTuN>`!^4l_WL&V3o?06ndE(snLlyO;-oHbvj0}LCfDb)T+-29
zvl$0f9#jWR#iVa#`#W&;XZlgu&f1)}2VcRlyvA(r!isN&LWK7WZu3H51@qU}3qM(3
zF8u6-=(N&(+9<AnW@UWo_UQuW=BcYimR^6pLE8bBq)E{fr>&-~i8)Q9Y7gFvv%@YF
z-I!In-@S`TOXIj|?<~>thK4{OOzCA48)|N#5OYvK0J0;<Y(F3dG&Jn7{+~Bl{z)CF
z-FNp?C;dZ%Oo-%0-70@3X;lPo*B9t4K2v}$V}Dv7eynF&<c~)c^RAr1*{-oKr}6PF
ziJfz{J+~u!LSLcJu4<NEM&e2R8CucuHUGMgvjAngWlpd>|Eo&I@E7`z19#LjI`}8u
zi@fCa=rJxy_kHUX?mVBi_vVKWMv;w~9TRasW%}ZFUMPP4UcbUw#c#&x?x`E`sPfeQ
ze6NXGTCdCrxqb+hU`vCpkoVkUq(niTsTlIU2><^xTW|0p%6D{hJX_(E_Caa1b-eiw
zY%@||{p=p&dFtG`*&m)jY_i>Bs;CAI;pBA(2Su`X97HPuqkO|@M%M#?4HXp3rx}0b
zJbUH|Va=JGa8Z<u*+nb@Kk$!sAgp(nrOP(`H5?GYMgLBwad3;i06yoQ%t@$m34v20
z*~%(B5mvUg*-c~3Z{F|;3H8WC51p&^^#XPv*YPWt?JY|}?=)<Ktx+>UH7eb2*(A6=
zF!Ok3X(=lAr|3htkA=zMR>kt%ce*`2H3cW$mj}g^wR09jna$wDC&Ey0w(^LO&~|ry
zvUV4G1_ZYMKJ6Sh)aq<ZoE#j^)cM!A`8?g@er$IG*x&C^e}gC=6ix_tqce|x!`>)r
z*J@mK^^(2)1h}xe6>qRXT<R|gou`U2hSUgZ3zYVcA8$k!rzaVIZ-i^FgOgKxb2E1)
zIOJcy+7*m_OB|l_kYcZxyValN#Aj^`qf~ey9AVhbA_5H<qmVKVht%*;V$0YMV3FNI
z$`~R<=x{+ag6TfW-SC=03rzUC-PD!rA%25f)!^HOwgY#XXRltwm7F}#1sja`owt5o
ze}4Zapl@*jY3UW{SBPCmq+IvnQ^v<_OcF4#S+LRWc8?6<QHPMgyxcLV<VNQ4H;xj}
zD>}i^9na8Jiaj?)!)5zchTDx+KfG&H#urz>$ok+qs(K5bzR`j5;h@tm*u20qDT0YR
zrwb6RdBEQ#kS@TtWOa{JL9=dHjX-Hd{P6QoNx!9o>f)lKqspK`kSM!~%70A40{7Hs
zotrm?S3C8y*E)3Wr`Jy?ckX=(vOWyFqQlqx6|8A+(ijmK)L3fRX`7I;>prEN?JI5=
zaiQ1K)YSC$mYI6E<J0xv@Dq%U@b88y9VsaEV7Nnba7M2}-UqqJ=9BhDVh&IKcqeOA
zPtmUsU)K5ILpiczCHmOd*f`mfSxyXSDtbmsizu$fqt-Pw{Rkw))<j!N%kO*T1dqD%
zV0T~`8HTgLb^cw%Ny34x<bVAY-VGBUCPCwS?o@<uO@wU(f}%Tq{fPu5eUs&|70?{i
ziB$|>VhqI#45t8#6&e>So5X+-;*N>c+TQLD8@Tx4Go^M>ZgW`}3iO>Q%Onb#9I<P?
z3o8xv*assc1V!Kaf;N&rq3Ezfd>ZmTM)V=L9>7Elo%qb@>1nt;Vq8Gemz<Qeg0&9b
z1nJ=r;-K~;x2FR&d6+-T%E-)I^?`s09rwUW0Z`5fl><gCx=uLw*%0VCiJ>nw-0)EZ
zF@EO4&2X;L?F$wW3}Vo1Igi*-GN7TcBV0-(QZAv>?OQE9n604a*o0dpChASqF=S?F
z7vBhKG;sE2yHbWu2>gN5Kbch`QDnz{{Z9C9+t-qkBVYE6m=&H*_>!E=8og?J%8))p
zmrq6I=3!g9j>TA>P-SkIZOypKq+L@~yr4@Ud}n+zLiol^MNK{IHNlbC^5xem1YLQ8
zs_sCBqobpB`F_F~Oe-zGB&8aUp;ui+$pHF@?!2<HGXJ{a=_R=MGof)bBcm^L9rtx}
z5wJQ{9K2{pN>CxO$0MVt8eDG`?dY3bR^*x=zoRoSyNm+3HX36AN5Hs8H*Iv-L&Kfl
zQ24_LRcWvH=nY=fBKNl}KzA()d7&a#k^f)s{GLZkt^~#Em!p>me+_UiVS|6Mu;!sV
zJ<eYwn-pjFs9`s4p#Kzx-F;m?adE?GF2XzEdn0&-&||4VfnNsK|1|>t<JT|qxGp_*
TdJegH;#<{^YpXm_wg~z^&-R60

diff --git a/vignettes/examples/timeseries/timeseries_classification_from_scratch/unnamed-chunk-13-1.png b/vignettes/examples/timeseries/timeseries_classification_from_scratch/unnamed-chunk-13-1.png
index 954bd4c1089144ab346266634a3d623ce8eb3bf0..705c81360a200177925ef221bb9c849e391cd11c 100644
GIT binary patch
literal 61201
zcmagFbyQVP*gv{y5Kusn4hf~DyF*&KrMr>tl$Mef>Fy3`L8QAIq`SNBeBbwX@4A27
zweBok#6Fy}&zYHLKJ|nte3C$aLGS{CAap57F(n9s9fcs68)R7Un*(w=bnpemUQ*K$
zf>5#k`wug|<nsVQWRR4Yu!`HygEVI$9L<#;U)F}>yyj*?MP^LYgQGthaKf0{>Qx$5
z%{Xx4Dk@A+1eOJiMa)s~z~Rs9=;l|iDvs_BYD$g-crx1cw+6Y*wg%H~hi|)unb6>f
z{&)PWK)G{ug^>PdY~Lf{5^??S&?EkAzxuz!%h~?7f8hU&^QpH_OT;%gFGJ1|tSDk7
z4XbWK&O)d#kg>uxj*q%I5lA;lVOUb=vBFLipW5EW^@(JifZwabiz^aDUb$ZXJKoM9
z=W^p)#x0HfnDgb==PHc{g#Cw*E`6r91NMh)(SiwXqTyGU=1<HLDcy!rdE@QP6f04r
z_VM^=A6;FqUFqpi;9(iLAkL!y`{v;OUpMy~0qN!cjA8Zv|9A5L{ou5U<eQT0?9S2A
z7$LIWrvypLo@ml-doDyoM43eTTaUwk|NFuZ6F0Q1M0mjoiHRiK4q}v8m)5d!a_lTD
zFOZSVt*lB)O702%pU1M^g2#FZ2!8l*obz+S#Kfe$yd2UuGgDVn%P%NEf-3adO<7W~
zUzp5R8t7HRAqT|t8Izx8d~bXt6r#i`QrTNsim*cu61mO#uKVnl^NkD{d><93>)Zc|
zs~-}|Q)*vV{D=W&pi?lQ;!HY!%zUCiex$E&w>5UibYj<OPM0H<VIesqV`X->SD)!)
zB*CT0J$UB!^@VF?1#GHVNnh6wZ8XRMt2qx8LgcYv@{Vq^+Fto;v@PMW;-N7np+bBl
z(ZBV3Im)#gK3z#@YcJqh%ZQ6_B7JT-94K$R9|@6t_v{zZ@aUH_Xj<c2=w1ruj0XEf
z>d;e^36;N4rUEs1i6@^no%rxyzuXlSF(GUmoRrj5-x`-M_Wv`UT-t(GsTmmf9k&Mk
zVBjn*Enkufl;-EZb6oBC{XgSYSm^0(jfUQ0?)cJDWU5M=or<bzb5oOjT>o`saj~VD
z*?U{1+crgIWh*<o=a80fZHVx&!kU`5U<t0gO20w<Kg+x%5cMTk{y#~7WM^kfM3Z>m
zUr2fhJkQmbot~bynpM6)MWrAoCm<kzGHYsBnV3drX09*yXI2{Rxb)!ouu#C$Yzr!A
zYD(pd)z;RwecV(kVxy%k&d)df6N;&(rlyyJQl;@3%xziO<Iz&{>Z(529{c+X1@fd2
zGAgRfFMF5x*r)nTJ+Dt`u_iKi`zq?{agvn8#Az#Z9h(lgjGwsz1Ex`e&O5tCesVkR
zPnXFgF<w#xH2W|A)w{B3a@+h9_Ie+uD6zO-`}#03Dd~8n{kdS<`CzuHw3Ob+XuV9k
zA)DG`wZlI={Dp)>4>}<SSWuAL?b#-H8#LJc8+~yZ{GNYJYs`j!ay#!$t{kX3I5?=O
zB}POHboj%y_d0j%q8nTvE%~~VLXa_Ljou4_$afgXKGe-0Qe=<PW-cZCJ{6qbJ{->;
z-P7Dq`)V?w-ACi6P-68JtCoa|mbbRyCaSLNY;9dhSo(nH33)~8<m~J@?Q+Ba@87>f
z25r-`EQRL0FOONGx0^VRcbE6F$R>)4V@1jZ;5rX4Z$d)C=EjC`!O6*5m!F@X<Mwd(
zpFfpz)?neK=PxWQ;4)}U-qN2<7AYr4*@*;S;IMPr@p*ise1E*N;|AV1uk9l-+oS{`
zAv{&yj~_qo?(Zdtcw)5wyQOJIZ%ZqynF_t<)sCl)@2`%Ij#@`b+S&vd800?vH(o+a
zXxhy#Hp5d9cAdxV3>A3HhJOlX@VhG2D|AzEHyK^)D|SuADO<hu)P^Ix-<nq0%9HG8
zW^LIPy0TFze}DGLDM1aC9aoNtwmrmSG47R6Utj#?c&Ay!Ydxq-^_~>%SK5ysP4Msw
zWpb$zFEVB(U*R!jR#%^m^JJhtrWw{$FSmJpDXM5qO5eI&aAhM(tF5bBdtQOd%F2@J
zc@-^6_3CKFic0a|@bI}q;rXoscHWq|UUB`ij(x${@mJE(F*H|#M_nmd+0x=-0uJls
ztgJP42ZEvD;g@J=@g)^+XJ<16ytNYJPB_CNBIx)%4Z;0F^zK)N3u*V&xKm~t?LMv3
zA><~smpIaGU)KK?*bxY1_?~U_EiQhRlar(8t%)3HW^>7%nZOPCh-QKs)V#l7yVAyR
z&yzGZHy37*EJi-p>}uz!b>>8O-jla!$)@GZSrRv5#=LQVef(kAo%8o1Wl51qWW@S@
z@ef$#eVc{)G#+PKTH0`JJx_zd-p=|c2`Upu8wO@H+LXb~C>QwwTEd@qn_FAU4V)i#
zcV?5~)8BtDEGiOz>7<r3G2G%QpU!6liYM5!3=9mYxQs2FqjQpqZ(D9e!;ISNtY-S}
zuFf_GMs;`(rlxE+a0%vk%XM4teg`7eGobT^G{Sa#y^j?M`x=@Js}q<&r|#k5@d6%}
zl$2Cg_mdi@SxzfXkmSzlDel~Yn_%=3`z$*X_v6##sUeMv!2TmDS(Yk}f%0D(rQY`}
z)i`EDq-^Ff!NDJou?@hHfl@W9cf)0xR}&bU-I+hjiOKp77LuW^eHGypo1^;O;^`yL
zZP_85Q*DO2S_UaAER?RUvdw*IR;7V&kVPH9^(mvzm_@JMXUNvJShwcDR&n=oQG_;D
zpV?=;T&MY|<bLnEBkiNfYQQ@08j?HCB_b}-v*=GgcNQwDpx^q|OHG&O7H$Cn0myc*
z@Al1g28WkiE}NR0?=vunKIpZfy?lx2mnV}HMZ(|I(!$5b*V5LeD<##}+uJ)gcf0yK
z(Dh)*u*C9Jo@w<4&MP7=GHo50u1atfJflAxQWPFDECh<EMnlC&UEN}A43VnZoY|k=
zu8>!BG&GO86JWW7Yz;T@*<wSVy))!9YRo&oqCgn9xJf^MS{oU$x}R<6G`pbNm8Y58
zvK-m|;U2-X();VqJ?kKz-F?LYRkAX>cm6gqH8lk_Zf0^44F#nr%tu;m%qiVIHa+oI
z`1oV?xMMm+BA;hfeTJj8>QH(XoG7X^C7#;kGA5I;A72VQM5xz3hR@Yoo%OD&epp&{
zN>1;3{vI}?ElS-HwGh6u$!FMk=^%8x+K|Jwvc$?v5eBxhcRAQE^X+X=$vv0T+0BO%
z%UO|Fg0%VGt?$Uxn8LVo+DWd=H3Zu8lvrkFW->A|h=He|sG<~y$L4Js$wl_UcSg2k
z3pZ&&`UT~|FmH5p*$0I_WaSTk7B8wS3k-zu7s4wnKg-G)5Ef-<$SX_h$*{0tOq8A=
z{1HkmED@zRI=#$*!DChAdWv9T&6psa7x7g^t}N=#gCJI6W+<PQ4h{<GilvHkQdKQJ
zG>v9w_fS*Q>@)tv!OE_r)Tnpy`;(4pgmCnOf-rgJ671RwvwPN*<0PK>aj8EMbH5mf
z$`RAr`sbc>PeYQ!_kPvCVIb0_h+thAJ`+)UYBPX?(Z5brnHgFTOjEmky&52j$jH_9
z`pH#K+?U|9{lj5sw_&U#<$VBHq-SSm8HqFu3`?z^4RlGj=lV16ILxFJH(NcgjhiX4
z!i30f%v}uptyX&R<Dksk)^noRFHaA*%K<qpEf1YRs90gfyW@E-E-uy5<$CSuDJi=f
z8?{cm<Dh6WXxI17%q*t!)u(r-V?8Bj7L^T|EEJdY$4cVp$eIwS`pXVm+3<3V+enC<
zMTVst7CBwz6ta%!(bCb83i|wfc;`|Yc6yYH9b1*s($W$etLWju)0#Eh(~T*-D(1m~
zDSjCoU0PDIj)OCa*#{4q(N>9S+8ms$6&Dwusq{B%bNY}>K6|Oz+Hf}OlG!V%ZO|4a
zONn)%Imz%geZ32gjEvN6B7kD1CbK1}lu`aXOmm<kc4;aT!_imdE&W+;%WC;d!c?!2
zE1Oq1UTDKdk|36gDJfF)H8j&0_UQO|>X+Vw|1Pt^)xn%^EA40d-VTHw*G|Dh)eCg+
zT|+@lO-)fz-{9akG4iEm*W;%wI9e6jp5Aypl}rW+yXi>)l)eQA2bbEbswT9z`&vvl
zqnN%MAhNkPw}3hZB}N$EQN~K<7RYDlzM3uiRDd#A#>UQ0M@MI4V*`%YI9qs$!DKO!
zG0wWn4;QknEiEobi=fn`rKJJzsy(6nDaz$lA6-+THj~%GSANf{#a2&s0UFz^*2}4%
z4=k?-^7He9j0<;7(Z?p{;^Jw)OBZM*Tl9_DZM;b*Wc{bG*K9<w1a<JfoBpWNEhhEl
zXAv_bmZY8<iC<8WhwrjKZ76!Ub7twz68n*i+Z-(P8wP6Oj&*K2x<pr&p?Dlj_}zc!
zn>5&A*!N~_H+gu7ewANE5X1hwjtHX;Fwh{3mY~Au6B9uViBKq{tda~9S(BmELi-^^
z&R~1YrlwT5V_eK?zDX=gK^5fjrn#bC&C)xko+wb7(!7tkI#tVD3|5(}R$X1DxWtun
zG@_zNIcJRL#tRJ&PTZX(T+NAD$GL7(GIvI`ZV0*Gw5AMuV{*6l6|qj>rBgpo^99bv
zxz@Y}`I?)ukvj|bueP;7p;Cn8g0Qzxyt4~CZ3#*Vt(elTg0~K0n99OXv9U{oqx(^?
z2i0L3JT|kkTEDq<y`u8&8)TbF6&SLr`tpQ<{em)!%uKT1l?~45!Qn54h{`3&iAxL6
zq0h06$paK|eaHdS$Ovg(ZD~zf?(bg<cPI#FH8pGN3Ae@dsOKwD;!XFj?=#^A*$^-M
zi~A%Hc}Je!*|-|xj~aFSwf#}`;_#LcAw-P)_FL+jI`L+IfB*hW1>hh3eSH8Ejtmbo
zvazwTvQ8-UDjx}+B<cCMU+$@HWWlKw_wO42O<^^gmj7{LeT5C*LB(}3)w0{KBK^s6
zzx?k@etv#s<p~7TH`d_%MMpS#lkka+)%_vs;UqLGk64G7VBT)2Dac@Uu+2N$OkNn%
zcyTEdy_}IE@`AIwO))n{r<Q%hcq^HfE`hcfkSyB_-usqq<aTwGC6V9K??V`~g!FXN
z-(!u`a#ZY_hD>sV^YXg9{32ImhaK82hAoa!+a9$Q67`8EEcg9~83yeBxITDz;r1zq
zH1aSNvRFyT9KJSgL_KTFGMSzQMm0C4IVhWh5m$WdQ#`V<gQHH`yTG@J(v8xzIXzLL
zlDVB={JD9_IPytHE=Af$QFDEzeIz|sB}=2pL|?C7(v5~!5FnS%MfW(fL27F1-jNY`
zNtrl1izappG^p0W{b@*6%GPCPqY64{$+k4Js3WMU(bjPs%@mHn{qPPeY-(x>Kuk6Q
zo4?=iA8&su{=Mr$yMQ_<4h({@!$?>cKa0~8)jx@wMCRn*wPs_X^YEMB!~{hfIc;qA
z^QMSlk7Z6Ypzk$M2)JkeVh$5Zwy_!eyD8^!k{cxCalkAaC40yHmb<)Zda=(v1<NQr
zZ@2!Y!C-T$$@m|ldeTmH|3X~PyQ%5>wr@$r#rr!u7l#Y3hx2v1qDcHH-1y=#nek{P
z>^@;(VW-zeB!s*FLE5V&PaSvea><MA>R=j-Qu}&e9nFdU%}5RoNh@=nLuMlA4Zr)-
zL{ZIsqI=nwFS2EJ*;vRhqDYi8?1Pcte7L-`bAso~|6n5>*RA(N3xX3rG(7v;->rGu
z#EAN>tD6S8pN9n+$>pAydVg@WG&H^uuv-GGiHV6x|HcaNlXvgl>1~vEAcUZOw)c7u
zaE>oAT9?b=yy_)+@CVG1r8dX;T8oUtTaTQ@si``{Nn}^J)|Ub+J>AS)4XfUcbYViX
z?0=?YHFt#^J`$+DETZ=0v2swd>1x}kB`?>n&EKm#C3z*?Kg}0yqtzVn{*7Iv%y<3d
z)VAfR>)SKqao(5h`bY5=uhPY2GcF2it&daLN`wPBZtnL>N>tzAbQAFsv3W^;G@7_v
z?5s(rfXK?5hBcLS!lYyMHEd-{ii+?{s{Dn?nR~^s?nn*!pXmN5(O`<_;Z6U+t)x<t
zrOw1a2B#UT2^AIXrkoscpsZ4^0y6dd5H6bo7lG49Q{}j(Kt~6MGi=dTS2+fFcy^`4
zkwUvGCT3Y1b6RT~?E`(~F5}I*`hhKn^0pCQW$kDvo8i4#CK>0z{J(<2B-pTv-O|2j
zK@}p|K5Q#XZt0+;zfrJ$a5VT)`<$<cc_s%2_P=FqMSa~6?Ft$6BwFfU%cD;ZgsHz0
z=Zh3u<jXuxoxkHG@IZP+a^TIQtE>C|V(JoS;~3YM+R;U>(dY7L^0fK2a&ht!UzM9;
z$8y-~_dXt&fW3p`MCw7o%l;-Ny!>cl$qN-VLNiUyyN<nEsc5M6{j^waWKTt$$WHHD
z0<@;%^TyT&5qCaa1qBS1c;~a5962;}oQ)*jIhg;BvaRELDhIs|fbD8hwMk+&X02V4
z>o8M<AU~Qld6$sjr51Nq0Ri2(F7j;274GxL$45_|*Mx+gwiAd1vF1$uM$b==$N@kt
zuvuwKQ_`F+F3y^qcXG<rHk5lz{|ReFTa-2c*a;YopUcBk=)0Msr>p=?ot~e+JD6kT
zNqgGZS-YS3`&!XyZGBsRyTh>RZzLh7-7+84J21dow^xw4CQ6bJzhZgVV!tM$q@?8K
z)k4o9p{RI@x@_9|`~S6g(;6EaTa+bS(e|QYWLt3Ye<oj8ICW)^m{e&H;o)hhsO0A6
z)>_S!ryn49cXt!L?CI;HVP>wn`j=80oL`gPMVb(w(cAmf?L8V)+7dGHUe@=<>3fQl
ztO>h3rNBD3*JBC1nH)L;;-ji+WL~iYU5HsxJ{q3d>~xk$jt$8w)_1*wrJmSOa;Z9W
z6)fSjkNc~qs=I6m+}zbFvhSbrf@YZgZ2d8W{rYgR6ye1&ObFO>vNY)v6hg(k5x5z6
z)@^jWo!l=+!(PXw^E0c&M3<B+*Vq1N);Guy4y1&vrtj_4GiM8nppHo-`Hi~^-u$AS
z%Zpnf9pd+Lmnf!xA$?bFW4|c1PckKM0#BW$jiFw1YoFZm28YdllYzuT&Pj){>6RaH
z&oT~8KVq4~vqiE-MQCYlkFlllsOWG9?zlIctJBhS0OtAZE!X>pnJCtL=zNv|?mo>u
z$#K|G%t1#~R{U}eQxs;^ORaosJRwIS`s)0A;iH7>JIZKIR<$fe&K4h|{L-D=qV(la
zzAt5ZK0a+hLEZnlBRr8RAt7gHXCWcK+(My&A$}&N%FImt^R1x?oSjs}MoDJtht^*0
zz9LfManX+-{{|wxnw*+S`k*(w>CoERiWKSwsGOtAAqlG~n!gZ`0h=8*rGab-*uQ6|
z7?3SiTHNDOQt)5DW|h+U71YI^$}urA@<BjAfQLs*PtO-1;Uq>Kmf=s@`etb=+P-m=
zt`|FFdJ-62xk|ed6K>U$TR?HmIjlx$2Y_~TR6$(4dvhQO6q(C(gm~#Z<Nz;hN5a>y
zUlS3@7Sd;|ZVUQ8Jw$)vW?(oR%aH((dLWTOSwZ2Zt6^CW7MF;{{`YB`A8ORI^U>n3
z91=p-sZMODC0!tCo;!=W$$Tu^2PiY~yZrs);^I|RRlk1y0um)at8hp!#l}`H-#0q#
ze%DeuF!W6QS3hhMc?eXV<!TcdAEkd9%0{ZRmvz{HxS0O5^>*jxgj+J&og+kSjIVXZ
zP*4dH!8AxN`VNbT7oJI<H7cta#(=F=QVa_VIQUt{nd}1z9-{2qjL=LrtfK6Q+pL_3
z++6y%vGW?%G_lmSme`V%j-?EmB5S*@+zg+OkVOMie+5k;^LSoa1DW|~xa@UK@NKI!
z*EV-{;0Wpc#gqb0_ivB+WpAQi<fpYE@<(Q-s6m})XAU|#OUuUzH#avskImPw2;*nQ
zU83FtHDzG~8R<Lg%V_)5XD$!_GG{r?lIcOqr6y;?Dz}BwU3oJ(VMu9@FvsZ|r~wZ<
zV-%(up>}%j(>3_09;9xXtu^Zo4-bJ70j@d~GNu|?xzn{5E;$!wWROCXl$4);IUtBD
z!uofN__o(qoSPfLp>^#2yPWbS+7<gJiuv{HSB0X<rY0UiLBXNKzeFhDNZc#P@KI7x
z0d$3xo&Ax&`y_R-@k<vMw@*t7GgKPtQ+Ig;=j?nb=#Y63zPq#Y@Y~KCtqY*Mjm=F#
zXD5s>F^MQ5_vfe9s;bj^n+1Vq%bppfTjBp02awaBTrrX1fh;XX{$=Ljmp0NRZ+c>P
zTxfJOLB4cedANJ)@iH7@;FsqoZ}Qki<36mp20-_&v4($IuQ>!RhFC}Ymi*4-tmd|g
z&Um0^zzH^BW@hH*=KeC0A^3$fd10DFO~bk{Q{jF0^n#Wtd);7F738;#Gez<3qUlBi
zC3sAV65%W(<?Cvwm=k$bQ|eTQVF@c9`{0p(lh)_h9bK92e+eZDJl^dq^Z068DRK;T
zgGVW9Z>|U|PuZPc$@G8vM(`K=#o>n#YbWQ}_i;yOl&Ej$F$9tKOUiyxF<LZc={o4F
zmu9$zbsM6iM*`7j;72O2>k)$E<3|q9r2%+xs|5;)sQ9ELy%x7v6-`Daw&lhz>jj!L
zo}uC49%mb2{{H?A4X&c1UFAXNOlU{4BoUdt;{_iO>9ziWX{N)+!usyD+fvqK>E*mM
zSBM8hk0O;Opjqyzk5DODFYVUAA)@W*)M(HHb7Hu^e{Z3oA}LAz_!}k*qh8x*T%YS5
z>ITL(eHXFGemJBck+q$%98a@5tcdOmk{dlmZS7`@`3-(ir0JSDb?$!)4R+57^KCGV
zt2h78{JmvUS0{a@iqTchGTCl)IwIoH4^==Gfe9506+?rCk<2SpAfx++i~vj0Uf%Lk
znB1Y;5WzVzD(WRWx&Md69hzQe)UQKCjM6z$?tJKo=v|qr<m?<Ah<?Szw8U6h5g)`b
zGvg#*U{_;z_5bn$^(96cVQKK_$QC2a+?XZO@B8y4B|KsW7ILBGUYWIvw>Mmhq`iW=
z)jxX@_BvDr5qIyv?<!)|)ymUj?s2g&DnF{LSvdKFLbDwLgB`ejo-UTBsBJ-&G*?3p
zthm5Nkx%29!oR$cv*{Ay-7h&l582s|cV@A9Cre9D|A%R?sd@dT&;YWhdauXkGCsii
zz9Q<{98}=+HsmPxEa1E-UIPgL4h~L}UJV@lU_2Xjj*fss8%R~;6cpWtRUsigKo9gh
zwlOy^EGcnxa(a-x8hH)}=dQSf1aM4zeSMLTLgZ1sVpkkAH8tJb8i76=fPjjJ_E)d;
z&?*o$iWL6;z|mCl*h<S1QUoNpnwpFtcTyvYuX91wO_*w&4>hm%jQpI>Zpl&J#Y<}a
z;)7xgV@4PE^0O>UbKMFm|9Bk1s$Z{F_0c%n*rcbYg8~h87_%HNx27g1n;RQ%93NL?
zXP>1e3}6`+7Zw6^vba$D6yIa-ufcNoJ8s`@x&J}&dtBW4`8fitKd>KG+Pp6>E{qcc
z{{H=&`Ku<NCmPX@!s3{I`etnRVj$^5T;E29?~|sy{Bej@{wmS~!+O9v5)3ZU3X0&W
zXt;D<x`1~RaA(GIrP^=vZtwxhvb8M->TyBFa7KV6rMU#*{Bm$@UG`zw*C?0T8e#!D
zCnmK0Rn$Fquh+bu;Q_D;)Oh~Llvpvnc;SYVzV3nF`#9~Eeti5mxx)r=Dd?7zl^sql
z35$(6YW`Pc*cB8C_x3)OmT4Q?#Z63}m9|(;KuM0r%Q7&pL#dpwXe#oCq*Vd<C_i4=
z_OM+G87|Lz>GGktI1&fPX>+qPLXiGTZELLh#XzMnwCZ$OUz2|a?-*C~Cg1gibd;I#
zs~<dN&CSg~HqXiF8XtGKyVwO_;Hjc~<M_+v-jq7-utEM-iF!pM)#(Et*yBSVa&US#
z)Qrx-*1dE?@X4Fpdd38>3<JZShSN9TNB?jdZ@Y@y2I*SP%2|!si0yJqn87S?X!7M!
z-w+W!Jlr~Bivt`G@{;&&$m3G=wm4w@>Tn@8HkLZPT%$@Pk^gD|SWoz1=YG0@LFn0=
zDk%u#+M<<)38T@jSgNhBf1;2G6U)|au;u4>9G{q_E-9K@GFt+#@YG7!6=VBuelE(z
zMe+yXz{EHka~NXa)2>%5d>j@AHnjet`wO}CM(wT=0)nhC^Ufsx`PskJTX7(6SAWe$
zuXv4v9$7mlI|UOMO~frih5>ArWZA7k#hm|C_lO95w7-X~Fy}U9SeAP>xE@34{P&|-
zq5yy*2gp)L0^!gFHR!QoNikdL&fKCBzVnOeU#}{+us{ZF>K{(er)8W{05%~d(reAk
z05ycU`4^Sb*(}XkG%@=W(vJCl-+)bo@51l(-QIyLWJ6XH9)BT>zpZEl0%G(DpI-X%
zd7sx$SH>{F-ElZJ>IUKPa|=Dc0a`vhtpA|tWY-E6S3qE3L#<24P1xx4p8IAeFu2+@
zCt7_6%F4<(2Gl#!AN{f`Kdf$zQ$-vJ`dtzVQ-8Hd-A@<up%ER)py1%(prjN7s`kjp
z$mr;a{LFdmsv9T_zZ|zAeXtM!A@?mG9vvOs*)a!*s#*4p^)jL%^TDjvm+N7>t**!U
z|0K{hKK|inN-qJu@2UJY{an!O&feL>?fJ@UBJeQ*=(Ba|_WJ#&pX^q^kW0#JJlT=t
z^mJyY2|jG|4m7q1cX7^6BmO>Zij^94<<Y=B>g)X@B3B6>CQ5;5ZrtFI<3QD1@{^<X
zYNfDgzqI-??jN#TRN)XH)YYkceFM@0|C<G<t2+-5Cs9!yVjfk^Qhb$f{DX!rI1-Oq
z{+-?k!%u!s7fsES=pd9}sr%cqR7->D;q|aQC2Zj%nK12<pAA)2Fwh+AFwk5V>TRM)
z`HFJ-e#xh)DJp)M?cORVR7YWVemy7)2}Q|{tH;Di=S@#f?*%TApE(?`s}v-s+D3WT
z{GKu~^E%t?d}Vc5=DdL)xb@-U^b`h4dHe?-#>UJh7H%MOMcO+!_|wV|8Tx_7(^|iG
zZqd`m!NbD?>KZC4D)3X2*{$A$U2MadYintVh>F7KFSodZ*~Y=aSzB8R4-Y@z9!V-Q
zzas^*a*(1C1Wit6xH>=Yray>-vi|!oqOm~kRa8+?(P$3>20S%36NiPl`Hf`8C1O{l
z*+_a`K>=%u>>Sn9%*?ZwBX}5qu0X2<j`(xSda)20m&e7<>A`iS1)Y<N%6~54@4)td
zQWK(PX4{2j3;Oq;gknM#T;dmB-u8Y?ii>+qOk7q_!0CB)u!6b_#EZVBc&FJ)1QENy
zDtt2J7)gCwU2SbtD4JAodUQ0I(~cxT`f;E5@9p}-Yp?@XA0();rIGZ=;w7O~Zy9ZI
z@t}+hJq-<wWBg`te%jd`XKat~=Twla8P7eg4(Dy^YER(+JY!mv4GU2R95@q7dgI6q
z%TgsKDK(|A)V)nm$JixoJKA#S{x=>q+;>gdURd~!ypTNTr>=?<6}wS*(A-$8?fpvr
zZpN=Ojjs&ZalsLdHWoB3e`X69Ib1`2y=ia_-oPOyW&b8i)*IIc#VpTmP{|XQXLBtN
zQfJjK%WC#DRK(Fnz7eqLj7y0_|FI`6Ep2FMs6n4tdJYe}2Fk*_^!2S08b(Y<f^&y+
z?S+ac(z4%~8U2Gk8cHrK6of=SP1YiWg@yCa-z?MJ2?;5}W2_AFWn=~d8X@X(G>7fb
zv$B#Q`|g_W$K%6;$izyJiLtwf$4de>>KktuJ^DUN%h(^<mxYnwzzYox1z8C@&TI%D
zAKwV}6;Vy~xs^FsFXwiNm=WC{bXx#K!+H^vadZfbUqF_Cm}!GckESvO{?+PKR+g3y
zPI(R)7@cSMpTvrq+by?w6JlciHfAw-eo4%OO%e<jjdaXnMzHgES`*`LrKY0tpz9AT
zVPJ6qA}+|%Y;9wMKBqZ4iCo4I^PSImPsQyPwyBAXVXB%;of6AirLDaD2tdl9pdk7;
z0}BhDQq~W*U;ikC2naU?dnYp3fh2;gHVO*LYx$o%w(G0sTZf=%EX>avi<Fg?%Bqop
zObGaMbaZrRb${QpWdrWp6~sgQ=N~7Lt(MmO_DEh~AvGW0=d1o_H1Ha$<FDdXg(zW7
zHs8yqUo5AlrSW@=EE}o0JPp|pa@xZC&Ck!DoScAKw%i3QHng~RaIx@4e}%n>oLm40
z$HtMO{BDriKvGvZ*SXcF()jJ-;sPWfoSmG!+U&S|F&A^s(oW==igNSwbpaR_A}iIb
z`3syt`AI*6+3pD3xbNT5(9k$+<}W<sVu*=}0bnxO+ExmKr}dvDE2xxJHmUyASJxtP
zmK<ip$Hx{5&&|mD^u&(@qy9|_9mmHL3#Y46Ew-_^EKp1+e1XSwPUFZu?#IOV_vKOR
zg7P0D|Av{FscnxzZN<{jb#}@nl%V`^wYsbF9QpnItC>bFa6ZUHlu%>$qoY0sMQ6O?
zoOB#jAha!f1=%(nHP|=Kym$LN+(&9<xsh9R^kv&%_zXcNqiv@mhh0p5Ti-I@_gSC_
zT}~VE0>Q7!mBM;Tg<e(l2sbI+onWa3x)Yi4sotzV*ILML_py=?xeb1o%3(uGL!+pw
zdU<lPlja%NZP>@c5Jk*;6)pdY&+CSXoLod+-VV^@YHM^*Q=7>#{G%BEh;1&txP5IU
z<>cf90wT$&si^P)dwZ6EK#Y_=FL$N$dk!}?2E;N$9rZ(H^5kpJsVX2k@tsO0J95ZR
zmgGr*j*>FBK{%_eZMn}1#oSzk^EFYL!7v}>S2=28_*+#}8E+2}vcDL;@9^e#AMuz4
zth*8qj)B3AM91yDiVJaPS67ChudTK9!TNemW@g9wDe$zw#0k8<I0brE#XhJ0iGo|4
zn^m`iy!=SI4y=D>c6PvT{YHB2mn24+btYLFZXit}Pj|c1@cHv+dhH6^&He=7(pgwo
zAiX4VlcDU2$%?ty+N!29>WNB780_uEYcTup?aZ$h+uSPf$ac04$uL_#k5iHogTP^a
zPSI3o<NOvJW~6BY04$Y9ry<Le1`0v>TlOEYoh~|T@LJ@W>fH>0lTe>)Z0``f&8+C@
z8qJFVU$gytV0(1~EewUpoQd*pNVzVk?*(~zm%AT0>FEcr`;>RKUsP3)H`wB{nJXN(
zw#v~C4Gp=_sr`OHuxG&I>k<0oGCKMj2p@3?-~-?@*he<q>M-pZ(I!Pa_~CNGB>6|*
z2vuwTgs+M0qCPnIFMt-)>eC&QyQ7m_CzH(H-rfX}^`=8Vz<xVWo`6Ss5BV+eT8Y?>
zn%8>YU!_@EAQ>5j`Xu6bT?!7vJ)+phg`+Lz2ArFi+u8~=4_|Ll%2%m0*>_h*RQxk0
zT><w-D&SRFRdvf&y9Qjno+x6F{Gs^xEt%_!;m403&CPe1FgXo{RxX0`iFsXm4;oqA
z_;g)d>cIfi9pqzN3h6E~1aksM^t7}}=weHq>S6sZAAd&lzo6$Kl%#$E8TBE@_<E-s
z;!XcuFPX3?x|CyIT9PCB1S99u`&DX_Gtg06>%%u<=Xd9=Lgeh~iv3nLp3UZ0cS$JS
z5oY2EsvK3^Ip5Hv=Xu{R`+oMvlK;uS((e2GxVfoJ$|p>36a12x01L}OVlWR2rJP5L
z^DI1gZ=d5!qY7(8Au6s~`R84syaUhF{Nn?|L!MQ4@k>bOFm&9(hCA3!uON^ng_Nh6
zUL~FxEe8Wx;$stxUl6eH@2_Xdb?El%M|;0gU@=SPsxjIDMG6K6rdnMQa2i2kSa<i^
z=k4gTo@5Ritz))5SpQXNMU(?U2+Axbz{ISwoT8m87c{p=K}Xkd{Xpd8tkW;p5(lfH
zE&63FA``B?NZ50xsH_ZKRaM^@@ZnIwCl@3?0D;Y^aFV$Rp?~lJ@$uL|s7r5sMJn)B
zT!!p`<K{-#ythI-4y%G3!h0dQ1a)N=ole}x^sFQJQirtqLy&YoK=##PF%}c~L&?md
zYWbCw_Q@H78k)iuQj=}1tu<Ab0!ct;=!nu0qV!L_KYShH2`>Q<z<%+Rz6m-_U!@Z_
z8ZjdD))UUf)%Cf@I0<CO0D|1Potc_a6&8k>wnM~1Squpw;h&}80%QXvwIMM4g+AV>
zMa)k$vY>hneU>$Q3_QG>WfYLb1iMqzYbGFp-HY10#cCVmxSqWq>FBa*;RB)|NQ~b<
z1I|wt@fy=W;w9hlK|m&jAKB_E-kUdw)*KpI!s6USF9UkUgvk7ICbW)mY$zl|Z4Q4S
zoSP2+oV%w7q}gDBQAlw|aP{tTfaUjTs%w0H{uaZrs&>6~k)Q__Tb=&K&i1{_CGtyP
zFBg^v{Qc;51z&2TUZ#a$#@M;LUTMq?`GrdZQY$a`$Y!_PvImFs$$E}ST*9-bNExjB
zXMqP%1THRrG1w(p+63BI$xnGELZb!{Bv%GPL)XK<qRve~WOLY80)P<_5fOLyU%>p1
zj*bSz&Ds-4xCLQZuqkMeEuse%6%|-gz5v;mr7bL7IFG2P*|oXl$!7@gUrOeU>#5PB
zW^f3n%ck4k8lKhB5yggnq+}#v#e!K4`*W=Omr<*>u&xeFKo<Cm-Rq-9!W}_c8Y?Oc
zW;NFic~lsoAZdQnOpDB$eR#wKNhdXR1PJAw&F0B3EXCKUB~k^)l5SQx<(-(}#FJBe
za%iOS@wAa?PR57xfFx)YQ#YKtq0@7~h<AodU%ey_HBjg}AhF1;5X%~@spI*Pn4qAX
zu`>UpB#QL46C~fjjt9}Iv_>m+z)Tb?f{yduNM!p$iwf&iK;)B&%hS2nJQ~DpfM>tb
z>{`*%vZyXMG&9p6nB-nunGZu1bw%XaQ2vSWy&h!(<4R@K4-sKSl?wqqmlu3hjn^B0
zfQ?a5rj;d~;}AJCT&{77{x=D%fz=tdw;gLrn}txv_Bkkl6k79sJ)ZTx$H$wKhBb3-
zZEf;|1Z%x0LNTuU(;aPkLc*A{2g(JXK$fS>b!~Qbb`B}P?^A}Ax&6T`Gtdm(PXEd3
ztw>bpLP__=$4Bz}1xiJu=er|oUh#Kn(s{5+a8PC$!ryn57W@#b3N4YqOEdZ3({g0k
zx>vfFMRV!kmi(Ug1wX%(le@jU+)q9F3gh2s^0nD|`3b~sDp{d;Rlfm#@4K6!R8G&C
zn3xzQiWA(-u}jNhzYh>M7V=GN4><Q~Dh*h7_WaYI+U5O6_74L_sbZa^a?`L-^yqCi
zDmAfal*tWb@`z~u#!7=9P~*axOSQ?%&a_^Nft<GgjmOf*iR@QPSTs;azwaFKM^9J?
z%>@pctwM!-RAZnLNC@8kf|3kbpDC_unzoGWEi#<A!9SI{@$ve+0T?(T$aDy%1A=nL
zg9W^8xDQ~9;E}~JB7%x7)f?>e>YXVR3O#6PonZw=@lUw2ZT3f2$8mF&j^*|Rv52U{
z10<1|$71lFLxpnhZ<iS95eaO5B>n}fLx2Di9N~Zd{E;DgX>2NFE=d%5w7%~6aPtK#
ztXBVbz(t)EX-ibl2{IXmmZg<Y2a?YS@jw<WUGI;DDx;pl+FBG|IP&STjO;9zy9*22
z1Rk6DT1wyNQDSYs9@K?ud|?c1abbE`v_Mp|tfoCb1!H~vQ3|#%ZS;*&o4jia$tu9-
zL$T<Kv>v{#qR`^z%NXA~IkBHB_0iGM=_uWO8~0Z-cQ*G#l3N?8NJUFqJC(<lD@e$E
z`_yZnmr=K6R$0}`%4+mrw}3zRgZmwMUfbz(E^}DjCCIwKDvWh+gmt4^M+Jpfr&RwV
zA{@w**DJTLmnkf|X5tXT{G+HGdG*Xc?`HS~y@ofUkEFQVftt#(HcXf>X?s)OleOYR
zcFU8S^_(9#&`nyn)ea+On|B9`b->#IKpCvoFJ~(ihx<-kmuV~sN*@XeyG)A}ih{$>
zj<x4)<q0BSIQGX_zt`!+*E7;<py45=K1NfIHz76wkihuvT`)cty><&N6St1V9z9^M
z-~fqU!~!Dn7`E4!$OsAucysY(R({!qKyIuy8SwadQ2zPXcklBbzpBRx$uIj8=tzg!
z9DaRHk@~mr?F{4&>$_VZhBC~uA&_@QLtF<;q1g`-4e|FoCaDF2#eM5QLw!9WJp5us
zkuzq*^3V^|SELVjt&O(mV9OY@go>w21b85M=;P(Dt)YZO!^I+BAHN(NmW?&komK-G
zqI?DF)+HRlaHY96@B7WWk7Ti?%6%Xq^bzChkSXBj_aDFX(TfIx!b%yB>_w8SEo2}(
zA9P;sv%i)k3PGC3E-rPKvc%qh02T1{!9o4;gJMDJB?;fGYuCHk)>qK#Mc9m$8ZQ?Y
zy=L{sE`eaCk}&xx7xrvivoa;tAxJ5ImL?$LgQdj!-2CG9m%b4a!fizq5ld~IiFB?G
zR$tqC&+Cs-FpPvOtjL3mVXYKo`o(1o6=lkE%fnO{V%W4^C70IiWN6rVGL&U<k$z+f
z>PTB}geXO{USQ)y9xrScXx2<hQsR&+2KnW;c=I}6lPIVK`*}kU;Un1z8Reg@R$evA
zf4Aq`z@G7J{|0iv_W<!i_B+%VQW%d1x#fx0FdUEqsUXZt5(KDMHe^)W4@*e*C0pIc
z`Z{<dr0dz^$)2ddM<hRyps$UOV#WX!S09oB?b-Xjeq~DE-o>A40P51;|6A<o+HPIX
zwwOx}m$BAyO9T*hk1S)Xq^0g(Sr$%b&PnB51(G*Ob=X@Vi#y-b6Cy;mv%5RA+-e6@
zK_Dmv3`fx`>!2ep@i5@Pgi<rKCnwxz)mZsFw}l2x0SHY*ObntVC8}(na?PSy+5d%#
zHJv#Gl^xE!_c)T1rrX{5X>0M33d;@`8|ft>Jd}#ms8LeVzewQ$5?eT7%s4O=8TIcX
zB|o2&r<WCze2)zx53R`$K8|RV+ImM3iPrf-X2x#8#d~2(K#KtUoKCR;rW~$gx=ec{
zd57#SM@_7q#li6v2oxtJeW0cF0QE*k230C&fr3U^N@}+tP-1U?KWEkpg5Y5o4nwa~
zCeLUIA~`I^b0cAJl<8#=u%ti+qi%P?_xx>;L9(@tQ<ch{>?afsJ0t@BqYY*dTTf59
z1@-L>4i44Kv<JiIo87>o($WJU+f!m$0LH}vSEE49=lkU4<MRZ-4hXOb)P(nE=!2Ad
z+&j6wogF3?mL{iNB@kKA)+T(YC4l@yBDYZlXi0&d>pKX~#O&Shg&!})FNOOv7wS9H
z4pZS(U3)@AsW8XZH`fAU)Yt(nofW@vk870QFv3`+?x*ZJNJPQI!+Yi1wLhE<?9FRc
zX)L}@`?52dMR+y5dO6V=@X(D<(@wAbF;B^z4`dnfQ)G1~dTtLkQT6qanH480^h%10
zflEmr-J{*&_QkHI7nFd4PigaWa~8I?Ao~D4|7d`tSPu?H1nz@)#2bF}MYtmhAT~HI
zFJ2EGS-m1jY^Xo!izh-d8y$z6^ZI+hy{BA&86V$BZ4j^WLcG06yEE4Ug3x%r5wFCx
z=Mh0Su=)?$>Ew_!d~Hn;ej)absed^ma!^b<$KWir|B!zt><Jzm8X_gt&E7sfAz`YO
z)kL8RY(Qmd>ieT5q&6Qi?9ShM`dy|mNg<AAFC(2Yc$_UwO!9jE4y|${($cBp<8$%w
zl()IR{){B7d-PY40<4PHcjyp<4K?jWeCS6Hx7y0NGI|bC&0nI_Xfl#efq)-Y<}XEc
z9RP~YFykLTJody&b0fpf^FWYRI}ge4P(>!gNmf6zsZ0LtyyAQl`vYS+$MXu4kJ0|N
z@0?sZx=Xs{$wTB*+qyjKfGal=AKljG<NSxB%N<pyem<k>i}z+b<GYUZ({<P=QW#I9
z&@d189kLYJe5E4t*xtp(hv&yP$YPlaFwi$Sg7M`m>nCc+3`A1B1bpe_ks&{_%$$Yg
z<);z(k-K(eMlO3F!dS^#+a(^K;bvR^lLEZ}K7M{h?9DJLjy>9jvj`a;9(>C20qzNr
zmA@IHih6o_FGK#zD+6h-OsrX3qqr~A2x2WAG!yU&ChHY*e2ws8PC?ONWuaENL|u}>
zW*Dde4>zYcbZYQ>{U53u<_MC+gvL;=$ra&q@n)?GH|uK;Nq7{<yOF8?mFB@fN@(j5
zR3mgHAjqB!yB77ep{CxtBC>HsUb{wyOg|=+IR}yROQTUI$>Yx$>*CxUr^_q9*qp1S
zHX{p*zQv}l{UaUVPo{7abulS*BZ->PL0lXTv+Tj)^gJsd?J{vP4~%Dxr`Lr=BA(4!
z2LpJ=d1BZ)ge21kdwZ@|o$~`pea2_$F$?Y2UkL=fZvO4hq|o4B(!bnEh0<W4QC}gT
z{EhJbCy9)$zwe^YX{;$AEh*yW;sW{6q^>pDR1O9Xj_K!No!J%~v41g}f~x#p9r{eL
zaBw3<g<aAap`juF=+xD`xUFOWD=;+7uB*G8tund>K{c&UC~Bqt1Gr>2FX%s`b&E&P
z4+-*Y_{b3_C&NOr88Au1T`dSFd>+OgXLE&xKsN;4FxznL$tfw90$)Y4WEEAT+#X+`
zp=D-5r8W@s`7WU)^hk#u&;?%Jg~i1dSDxx<uRCCx>u>QvkaqK3=t*?29cW)MQc@-)
zAXq#O?)xt+m4WQ@G&<(vI#v8gr$(<`U%I`N13adzEEq^@Ym1TZsQFc%P!3Gk&3HdY
zOMO;%@1*&&zglcEtHT8%RA}?{3GR96+h#jK9;=qg`T||!5AruOm|<tgN@|)2EM@`q
zYOba;vu^{9I*)ra=lqLlp^kLF#CwDPtPUhtVf9x;M8fL0y~b{jO3I?>rb8ivPRTsP
zkdSs#OkAt|Lu2~8f3*F-J1|1UVnShrg|=}29WASBmxz&ZrKaF~ZaUc-BFuSW1OpnC
zm*?c?U-H>>U`g@XctA+z!U3_JZ%%i!-??jq#HG{#m9v>m6|>)c-#ki4U<DeJs28TF
zVk>j;l*xC$OE&$%%2(-+mn6_Aji@j(=pcijs$@p&P<#gwv@U4=50(&0yYy@5ga{5|
z@??ZM;w_j=EXnlj85n9ZVlmt6KaP(wb^I|7P1tl(jx(#!3-EvYbx|RMXA}6}VEs5c
zI-Xx$k;KO->*@;J+|`Y6v45y#LJjkHk*H>We)qNPIm)w>i5_%bH8#!;4ar>*5()Y|
zOn2*M79r^9bfu)}b=hGff%d@jRM32Mdw(B|`=vEMDoKB~@lczej}HSrrOiTS^pt`q
zcE>|ls$oSM(wG@>SCS4-NKiewDx0>XHJG|=ek0o+lb}97_KQM5Q#<|l8<dcp-9%UB
z)_bmUZ+u*CBdkbCFD*5EA|jx*Ij(gEMNSmlTpzDzW~EE!>I(SkM$V00@b&vGZ5E#H
z-z9p5HEK{#xsc*7%zEuBe$6e-mC+npT6)|{HD*a^;iIL!Ht}+oNUBLh1o8BZ&3g2M
z8MfIakV;+Y&U@cClFV!bf}<0T0muQ8LuKI$yg*B_+r+)R)_G1T$toyF^0<<fl#~R*
zAF>rHc{pr`kj`Osb@lE<!E^qzsa>zJ1Gwtpai`DI>UO-$iGG<`3i~0W`Ds8-m=a6e
zwdVKqOGy805+qQ7m-VL!9Z5W)l_VXg&WNvI*qtFzy@3b~=7tVfGsNc*WI8C#<8s1@
zfRsAqbAMF^GyziCG|D#-@;|x0F!3V2lMihC$`BL;l>bU2PRMVBXXY>!acdh7AAjgf
z?|RH%#=5jTk1}u{DlF2pFcldX;BX`)m)Xb!-F%joKN1twbaeQQdt;V`sQ^kP!6VSh
z$cS6l&%h%^wLQYPtZi`Z1)0*MAi=LFajRQpzvR>55fCnA6B-&CfJz8lbPX-7CXl!L
z=7R`C@Xw|6u~M(VK1xYR+4@hroR*f6`MpSV<JE|Zm|Fk%S^Iu#)SzUoYe5oQR;J`5
zr7d>Eeh|4p@b4eKX*uanavawy>{nj+5#2z64;B{Xm!!-rK{|G1vntt^|6y!pp6Qn@
z4IhBzZ@+f3R>GInvD|k1w1nzoB_9Ic!}u8-arT#ITXh1DDhcdP&i^`(JF9UNl-R_j
zNFY$2ymjPsSDr8)(dg;l@(yI>4D}gJw!V*rkiDo8D2&|h+(uiTw~4Lo>|RMkb@q;t
zC5U`_qoU>)#mMK$V?Jc52?Me%V4knzIYE+}jja@5rQ=q_LN-YsR5(PDjhytl)2||H
zshoB@PCS#7LPl`Zw7>UpAFq#r04XIUg&crJ!lw}?Hu?V112hQB$jR0HJJlLjCK<yA
z0_jXex|3@vi^&TKS{qE6`9I$|sv5)lz76&5#v3MwOGk*!i;?;L<cpDHu}ejyW&hp>
zi(sOl;DZ3+uvc!pClBuX!u7(3kfW|Xz@O$1m296o>B)q{`}YOUZbqbhZj~cT%~QAB
zM)h_HGz|D4sl+`w|I+&&iCEBm!74T+Bm^vkzul;y62~PaWw_otXlW6IblvVmb>Hr<
z3V`^6UVqcd_;|HJSFmds%DQYM?t=EK@d+3|ZcIfp!`H^`{O{uCmY02idv}RFsH?-!
z{Ob}{0TlXdB(Co$U-_kqlxR5m7XR$;qWaDV$Pig0VD)XB^eEF0o@veRX<BQ>n1Swr
znFU!Z*wAl700RSu*Q|tE@Uv99ijv=nuk*_5-oC)R$UlQh*=A6?2}?{_9{HOHpvhRU
z{TR7nMhbZaa!HT6?});@#$RjRd9s0U4FW=4U7fp|Tkkh|(y_(GZ)BkdCF;SFls`bP
zg~4kb027W)I@dybG7vR<()afEz(QwD84N<jy&A4A6)>fTKR|FtKyG#J_+zjA@y$&+
zP$HU8yk>+?aDXC_t2A5JN_|rdA|=t$BRV6e`qr>1yZd{^zE8K?(FB$@HgzeSq>B<l
z-f0zye?cSJAHy#?I%z=vw)<EZE=K<0Lj^8Qg8c(qZy!uYVaF19eb<j30Y&)6CU+`{
z-j)YDdwYge8o*Tmf+aOQy_2J(cAM9&#1*liVEY0xW_-fpve`=Gmz;(`)VMyjRBl4V
zmtS)^@6w1tOPpq%B~F;wKYK24yZ}<_vsXjHr~|R!v$2sew{6%ahlfjBSnTR&)G>L%
zW8;w66IKKL2xKx$jE%W?dGBv-E?VB6ou7x;Ey4qEeEm3vvW_U55j!)a<$V4w`A1+I
zjMtU8$VUtYa>Z1!$+wuQ7?ek2IWM@~&8>p2Tr?_?A^kE=>%|vIBY_F%*to?>*dqXh
zfvQFt^s`Vt`J#kZMrPn&p`@?xGx*ROYHJ5U0t57|RkMIYUQtmIB+`9+d;m#{^F@J0
zLUOyC{ra(ssdRi6`{%uo$iuua9O#MyhBwHDnhhq8Y4i8?_V+j0ufc|AgVab=RFti)
z?a@-dodY;6(%9T9>jD?vVmw;U5FE2xO=nW~gBIWqx<q#S5dHMmC^ZBGgm-b=E(aR&
z^74A0`Ff)N?<|1c3LczMM`e}s6j0Ci!<bL6uG-|2khp)nJ+85~wk|Fy>G_6V10*1M
zLIyfI!kt#c;OMW}otsVbz!ArJN(HsPU!YMXz7-Ch?=J*NQW{rDO5QI0`Rs(vP99gA
zrvyj-TTN;#S(YU)#`#%;?WVmasQbs4^F6?yJ&!LhFVQKKg@wOp=njJ@bX%wm!u8t!
zwwrvQ;^MGf<dW}bQr2iG&p1z<%_R=J_ubj~mQQncduxC<&8oLFJzZ}y@aBs!qoqyH
zntksSPAuy?7$Fl}M^&l1`g)G&p48~@_bI7R$F~sqsgNen`{#9Y@_VIoR$SX?Y#rA?
zxrfKHaHd8B1$LJRRk?P3h7igb6X&=x<b59&ytUnYH}=m4WKuw$_TQ-+;DEqD@jd0S
zUZ_8mmSfPb?3`r+;a)ROZ|_IY{@D2WC|f+@zlK^vI(}?yY$7iERd@C4;J1Om(6Csm
z!-frnpf`j5bTe;hFguS^Lcao(3shGmzr@5uAS3Y?S|!_=VgV_Bm-Q3`i3ML5tpYxy
z-;;PyV>Vr`bN^8X)w1~5cK&2H9E*mACXPf9;T@1V5M}7}tN#Avbq5R}jn_4JW_pB`
z9dzkF^y2^5uey+vEiCTbA_n^5@>T|KXXocF*SB9S_#i@|VNK?Sv%K*Cd0-8{h(AWb
zLbDCc7oFTDCY#C0$w39fer;`^EFHRrmoPl;XZ)r(Q6zTa0fj$a{B<6%zp6LkY`r{6
zaCtG|(o8iz)f3&0cYt{I0>UGYHEhQprNf2+eUmY9hbujO;(GE)V&jxpQ=!j{M6X}h
zoanriO@ATs_F(o!NEB>9$JBre_wiZUXZ<;O2;!^|Ol7{DSWV-0+-g~FL@!k79iE@m
zxc8hJ^w4PYa^H|I@qdeCc)BC%uT*q#c?Ah6n%iZ(_SwGJoyZV;Xx{{0c!_ks{y27w
zw88pa{*D<7%qh4=bt}$ffCY8L2KMpRAA<K#7l0L?DAMjz=?3K3x)SDd(}8^S|MVRA
z1DIzy2#S5~S$0R7ym<QZ;C<a%wy^wyW-P|Ke{k8;SJhaoTB-lrH<m73{MZD!erdI<
zcQjH(wbwRF-afvc@2+IN$GhwLPT`MmZ7+KYY`Y7L+<R{c$qaxvw0F@sNU1+A6fgB~
zHwK-rAdsG(oxN!De07J8@%%>R#nnMb3#_h0^Pe`YO1Cf73i0Gv6}<MVK_)WBW;qrU
zmL8N6c?8V(avHJ}Zzhtv*-WdxY06%i%{9J$C)>zp#fXiKLAXwIY26);v)RnT!b1PX
zLw}7NYQ$(Nu!sYn&CI`fwE#3O;}~UH%I7NJzN5a6vP-vZ|G#MZ%BZTgaP3VfA)p|j
z(kKmzAl)Sm(jAi0(p?8c1f-;-yQRB9q)WO%Qc6;~?(6-&%Q%0|aENQKz1E!1`_x=t
z5Phf`<1}xbqyM?vxV)o|TjJr0xr%f``Y@=~bojyb^%o0G9-|{8NnoC5k6DyHek3-x
zl~+Q1#_Ha)>-Ai#vU_kaWuoC-zT{PUo@BZcFHEusaGUo-_G@`%y1w3Y%L4HipP-8#
z*^N7G_(y2Mr+)2E#KbGNso0Q<pZ`-9A)j4MDmXk|b?EBNeQ|NIu(04{PnwaLd5XVZ
zYKGzKTcl0%<43fF7Nn~Qd{TPrAjGrb)SNb){Ef-W)3aH1WxBur+w#+}<MxkC8ZXhT
z2Gi6gC{ymWt(};U*<POffhD!PR7qJGwgNEX=};ug2P%A8rLsCj3t%<;@yW#|<mVXv
z)8Rq(g;X>W@^EraBulT>NKu1y!>G;yni~{9E;bkW*GP;{z<GV7^GUXpl8J2<IM$ej
zI#@v)me!x62Pmjs)=JTRqmt-+`;y`++K&ORXM~={=Aes6@aLwv+H>s53#!enYZo5%
zrBO{0M1=IgrWZXGlhxt$5%5iA<#?PnU+D1hyn6L_=}|*F(-eqUpU7$Ui?eP05k*V&
zk`%RK)2i+iJVC$&^U1mBGXcRui;rMm`?HK%W8aS#AF+ucrE+UY6@`(GxVJ7Oznj__
zx^sQqu!nUv#vvRd@(avS;rlx1FKReLxUDZwnCHKN=c!1uV%=RT-@x>)@p3vION=x%
zI<gW*-q;XxGFF+!&icAD9S07@AhR|@B6k5d#1@*@x7<DR9|eb?yc=R5FMj*-@$K_a
zLd^E>-=Eey23kM*(MfS=>K50R`1hdwX!A^3jvD=YU!Ub^#UqNWF2Z`%dM|JV!Q+Qb
z#*1S;oBr<o#>U$q+S*=c$E|OFX4mpmlsWpM2UxczyT-?lo1Z>PkwN^rn$EB1LAt~D
zcLM`KuEa*f)=i_p5F>tYNAc?RFd^R;DaxSNl-;BZ^}V6X-CYU!6FL+xrfZG99qU<!
z>R9>Q+f%4WDV%hnJy+|-4HNvJ_1Sg~az8vg^&rHvx_$n)X==ahl9qW^=KHQ&mG63;
zG*m<?i-n_xwI$)}&T#s`YAGWV(erTT{kf*enHjC8k)KJpN4~~!)&;mRS`VO^KY5{E
zjHcJ9Q`{d3PJJ@6;+&ibFuEV2y-XcCP1s@+)+~-n!3mYVqac)SUq2Jn&x9dQAbJhm
zA>@f+l+Vyu^2P!tpL#skA1~JBoi4tp$o1L()#h74`S|uef`rf3;KxsffRx@y>1?g0
zOHZBEC!WSWfz_Z@mgtTHoG4T)l9rd}c=^)S#U-wZASODxyYr=mcN}nunVBmCAH=Mt
z?-HoGy1hLiF*8T;?eg3Cc@z^CC!IYwIGCH~C`pz1q!QMM@Q~Utj3|o8R%;tA4YTZA
zNdp6eDD%-WS8l{ykj{{)N>XgS&+ScXd)rqI6eW6^&;L0w+SWDMmjegGi1T6p+!M&h
zLaqAlzU0NZvG#Tp-%j8E$OCe}MCB}ojQ#wnalX&O#@3Kc1Ce}vdHLPU3^N%^DjFu+
zB-!`#7xIcy#=?|0KA!?)gbl2)K0Fm`(V4G1`h@dmvREtT%a@T-gLZ0}*axU>hGjW#
zQoUn)J}Php71qMy3h`Hc{YBYZ(w}2uJKOm~WB~O(bu7~>=X{{ISJdKB)gGOl)n+0D
zBfTGmj;?Mv=8rTbam6yIkB9<$2Dih<9^=9ISGk7nZ2(cAu*k$FTlJzL4AK)9$oKi&
zKJz0fs7r*zehTCHvo!eosO$W_XvMng&EMS<>(0*GxZ+FD2;px^9B+nuqfk6}JN$iz
zG0x&}Is*6M!<FrAMhF?~?9j+1sMtM~rNWa-;XBVLX9x~X!v3qOqT<QsWH614`rmN-
zaNRv$7OVuwWS^h9gRO^Gd6cr`XG`8&7+@gQ2++Zi#|zBJ=jZ1qr(}lxT%S5@6EB%;
z^<~f`Dl5fx<_3dSKr2p3Otj?jz#(GyGk4IaLiK%pPx(#qnP-3h=%|B>I}JX;CE@Dy
z>}+`Rlb}c=#M`~y7`a3ysUeXTq+0hU($X6fC}~1W7q#8lcmV;VQ?kU?e+Z>92Q<Ik
z?GNvYj*kAORka0L!`{I2^t>rrK^KsJ^vf;JX}WQ7o*18<#Nj=75aG7|!<Q!Xl$7_&
z(ylp>3hV0}GV1Rd>o%urUA#}pPg8lk*8Ah5cq)}mD!3gl)&t-CuBxgwPgAGD4?8~M
zb^mJyy)q$6DMpNpHI_A|H8$rAyfp)@ZEM4K5d;J=ikaf7-=bqv7%;h;`Z6%?;3MAK
zAFViI^YAvHw_FiL#>AKu<UCczu`bDtiil7kvmGOcQh=RmgZATl=@g%K_ZI&tc^4}o
z!NF-cH!PVyf2<bT;Ew-VS*e)p$9qV!|BI|^Q9qXBU|e$*+#VpOgQpK3v>x$ia08(8
zqJY<tOGO3g?GH&St?(QF{pIO$ftWx>1jKIujtzQWCx~aVThBOx3B}A13&BQI>4b@i
zX>75$W?MHmyk*hl7cL}okMi9mDYuT@dui$3uF-rI#~nwDxyLW!EDa14>6`oqOv!m1
z=@=NKRaL95qE;5F`s#IWGwPm*42Eg$O@as6fLYJ@-8*d!jcz^8d*fsLemEc3R-4mD
zCLf}*Iv}ndS!ZQUh1{vQ{A<&i7RS!<3Fe2f!iI!|8Gx)O@!AF%&vqA0F3%yJWj$XX
z)4t@&X*rQ8asT1NC>X8=!yElShEnv+u&~Nw^P@p+58B<k&F;sr&<u8WqqC;J^c6`z
zyS(J>>g>%S(r@<MB*DWBr>3LFNPOiY{%&?;WCRkO1^M~hyZ3%5y_ckpi*;{ed3C9?
z_1B%A+|8|)@V_QB1&vP~yF>o|5I#UOnvZXRvB|VAPNlaI7c-z~j{mLc!2wT&#xGlj
z9vPL4tSn=A55QVMfQ|~26z`s3Yk7=<QvGsEzGj6ffrJEPETCEhq8b3ii8yVNl9F~_
zbzt}hKHY2@jc5a2DocS547~*R^y}=iuNz#lv$KKC0aX1E<>c+A{U_>2aqe!%<(E^E
zJfs#|!NI}wzeNFs5VM(4$V32qAkHmz&#J^1{*R7M$bJO>XkArc9S{sZHDx*wiQm72
zJKpj9bTJV#_(8}YRweqJ&X-o7NN3*~1bq;5%tW-V^?9oi@{sa2FWu8S3?6KHbMe^0
zq3+bz<2<078MQTgm}!bh2Iq3%JmM2!5z8Q1T)TLqN5ggNfAzYy@*;OheCVNk=>8v@
zdudf+6IoHTeZ9TT)h~5DkK}pztx>37bFcg}D}zJgu5**202N<fe=q&oP-!)4TDS4Y
z!(}AM6}AlaSY~{mpD0Wm4JdS0&MCHYzEN@-^W$4Dviw~8m|G+V+}Fh90#5ud!?pgT
zWj8n9SiX*t*o@yIaaK_IJT#)}<VMhpi0Z+@#H@Eccp4zG$=kJ7Si+_AqUOH8^X84N
znZ?qN12W>t$@;C+y+xx&enjQ=1CL{S>>+yx2j1y|=O10T%HN;b(J06MZhQMuXQq0=
zeI0+MvSWbQG7!h0Vwxt3wBSh*z5d#8kv3;-|Hm9cd;1+RKk4C*STI)$tCy_*{(Z_v
zsIy6FVPu5T)${VDke%Y>oE2zL%-6P~W^B`M5vfChUKebsLoSYvJAc>ZKn$>MeKEZy
z2GJf>-TA_fZ1oVCV#1iLUK&z5wg(@c24tkBzWdHBhr@sS&BT(9(P%Pky1}_y?Tsy0
z(jmLpt?twbsgY-_hpQ_I3wwKa8Tt%!j5Jhz;o;%E9etw6Y{&PQl$UZ<2P9eJZCYg|
zrJbD}<6&{?-qZ#d`!}XAR2Wv*B4g^>3)t~va9p2H^B9nP)AnyeJTA4uB`0T=Ij;4(
zbXHMOX>k7Yt*EGr)7<l{pT5!>4IV@bvvCeVL488oyn+I6vNVL`n>P}fof{(rf%m`L
zVJ^_(Ob!=#Uo8R^tdU96Js7avXz9i+DeG6xikNbCySb`^h$_%~)ObA+l%mlwG4%Ms
z@Zj&jz3|@<MS~CF*niPoxtf`g;k+|tU}3><nbkwrA^_EL;)&I1uO5)!e*i3ps2-Av
zYGllGpl}p7nTpOU<X>QazYZglGq;gWS0Fn5qpDcej0f|4>~))I#eFh9*QM`w?)neE
zyGeo-v!mDnDJ_t6?$NI`htjE9LV@lcVO7QSnha#NvEU}Yr|zeL)f_hSNz1<?I@iUH
z&U-`@)5Yu?vi>?t(eq4se#?1tdmric%dmK|=)c!MmgZF|GlkH%mfKbTPd^#@-y6*L
z=gQFT&E8+uum_uI1a>;Jb~V$>mkKe3l*|umbweD@t({(Rk30T+fFQwnx5d-a*5Evh
zw|JbwkvV6Vi5Fe2v_7BM|L6^6q^}6>6HZ<C<DAv`*{A8BM)Qx<CmWRW$NtRLprNAf
zyEfiI@?Z!{8)LNM@BjhV&CLxqK<kmL9`)Jty_VbE#TLQeJ(swJ>Z(K+#oQw43VM2E
zE>DMh2q%kUZ<l>yqUiI#PU9lP!<(G{Xq_9ij*gCQOqESWPBw%-F@a{kqgXbJv5n69
zD&WTyR8^Dz-qt;Z7C3-sRvh5|u7?FjHKjN+(_zx553;!g`1m_6e=fG}*QhXnsu^xJ
zi;Ic5o7vUKZ>M9ruSge}G}O(LkYZPuC$?{X7$=`z8MQJ+-)oso#oOTV<MU$zWt}nh
zWB=TAq&=^zjnM7FMom~-)%O;}C5XjztcNo?KF+!<tzseM<l-b2#BcwTlzMH-u|C)}
z{mPx}-o!V}QScyvk7!<n=X2tV_*bVh3FG5g7~0y}Ms=JsPOY<Cj=PvqIf$PPZ=k!6
zOh9O_IYM&(%1t>u0CBQAzkRTeVP#cjVDxK>1<YaY+SO+ZO`*H|3Y`+NjxlX=ufb*>
z@fCsn9E&uJI;A2IN3+yi`8b!vBNfYm(Qy;g%WGrXDD*xMYy}@b?Z>`)Hw9Vg(m$AY
zkCOb`(?tUrunWvBEMSsVJ2?f5mxR@Pn6SUyi?O5gqdEshX8M}a($=tK1%)5Ul9E)9
zWCth+jfO=g%ZzjwYoDopkk0P?-oS8*8SouS4Cojc)$4CaaB}$^z~jGhF*6QQSV@YY
z|0Ju`{vY(sSY6(}_A1zP4pzDnTU;sS-8n6mYu9PWOKicRkihKP^Qe*Tc|$_dm&`n>
zC{OlV;}#L6zzA*<AN0k9c3c92D$kK0wO4++UhaH@-G3Wy&#t=LCT0@!IQN|Gv{lyH
zH{mUYaT8*^V2JUN`|_0D5O<|Vblsm9ABo&oMLm2mJ6CKGAtWpN8LlEd-C%(Diu*XO
zo$?tCO-{wO&eRme)>_zbX7i{XB{Oq)kFlE8hGJy6B`(b3yQJ5K3!t8vR38$ApO5I0
zLHk^CK#IchvdD(}^J50}^9c+H)GsUu%%xGDT@4#9Ro}j;Eo6t@LsAZ#j}`Y2I#`#J
z-<}MYt@I>a%_9G|F}fGUlWdxs_fuK1Ii!&!PW+#1Y-IG^&s=i%(>pNIlhrcQ`CMNo
zfyxBShWP8(5SN6nXlQu&;BYKnLA9fQbAJ|{n;St`@eW91`?@*kis_D)z10?s|N852
zXLuxArf{<ZNI0l_gzm^8Z^@XortdBTZofC7SCN9k7v`G;c9~dac39X7Q~HF`g(x1E
zT1WMptb=(GXaOVEcrr4w7{`$UDh>|k(HuEg7lBSW<10LM=nNzEaQgL92h%?>&`PtH
z0>$e;p_dk8bfTi74QZm}*F*BDTsE`$%RgUJgU{Vh;>{Ts&+<|%Dbe9MOU?U5@YKpL
zR^OYge!Gc@aQ*wXO5@k+s)|WG!Mq{k!GVXt2-f*|RLm-To!WBbGSbplS67ft8LfBP
zhBC=GS~7n3zj=%01ZIn5Z&FC&*K`ym!YDp{{)`30<XwNt2j~-kEd?1Rra>86-RHqy
zqHq9e=NT-GgoK2BCuq6{_K&2pr?XAMNjsm{x$W7-zlCu^-<<%+%;Jq^HBwaJaPOXG
zuTUiwRH1@`7aMcBR#%Hzd%rDGS|}i~^j{*HqvK2CGmO-=DjY6!KZqwSr2oa*zavxc
za{VQ_3%Uc|SN`#BzGkr-2~=f-$IaqPk(z?SOX!5;RpGgLf;MUjB1o4HO}}a&lo-&?
z)#fuP?@h&q9^v5?xOGmFKxo|fo!3>@-1n;g{@W1k?9#m|55oS7M#rFGH#LT-xH}S|
zAe;0-U6K5DXi!N_tvCl0CvatLP4#9E3~_V|3vq|{5~Ri6m$MF+;s%ERfCM+$nNM!e
zYjXBx)>j2dGkKDAwR0PV>Rt6i65n|pH`r`DdRyaPo$M{@A4n301Uw~+uI(O-m199{
z%v2RxF{FQFzW)y$4g@v#!areFsHPhh(*FG%fxEG#R6G)yl=Rtt_~3K@7I`Q{AG`Z~
z%UOGS-5FF#g=VLw{_f6u_y`NQ22h|Y&~^8#uon+VthMajDRmjgteMkcXRl%=WnyM-
z@jO2qs@a>Vx+fDWs)&mcxTD{a!}qJZrzaltS!}o0S-HuY<x`!Vrul&q&xxdzM7w=g
zn3sWSf=!0~)hlA?he(#692s#>)K{RxA2c5=r&QnEh-em-Pk~`!!(~E5PG(J~MNCfS
zj!-uvI*&7<rk1+WZtu{L3!tyK$Z>t$@vbfj4qe?vDq30(H#c2|U)ZDL&~tHF@SF?E
zVK-vWy)u2lpC0(wYT7R>hA|cv&+6W2bj*N5{nx0JG1BvMb0=M$PH{$nhEz8H;Jk(T
zZxRjbqm$*Q*BX_!0}1Slqhmyr*CmmQ^%tiyR6RX4$voGTsrNaEv9ZJu(!nM)&+a60
z${y(EGM?5U@c)Y~vOT&R%Z!w|_sXnQ9mOyBF)?wUVF(0Z;9XcW?xSRM_%HQ+cDvs^
z;b%>*`$P#%6jdb$qw|A<HYT1|c$lQgR+KS3OkWePZ;^FCAu(|HEzEEy+54-Ab+x5>
zU5Umupc*Z^O{dq8;~>=3E;_JB3thJ|&};I`4GEF?Tyca=h^86u;MM$9nH`EvKTKD=
z#|Y5iy05eQvUnuz+`(daM|v@2#%q<qJFZU#9S6txKr@uOZ+H$1xlK)<9DwJRH08Ms
zd{rg?DnH&i|Jj(YUGj@1{4FB?-0RvM{N!3%T7PKY%vU(e5nC61)XDz#MaleO)Lwpo
zl2U&&0lbVDeiUYo2D3OB&dKQF1SOlG6a98Sm8e)Z$S3D2R8v*DD5O>UreW99hLNC^
zo60}FSZ)HZ^f>w6>Z)U_3CE0+kmH!^<;)wUeOJC{iN_Wed}JlPzL_vC&4<g^$0mj}
z&TZ1jfsM3WU+$r0jt=AVy6Wy%mm?iRZ;%u;G(MRUB81+hNn01zKYR7+2$*w-M@(mD
z(=jHd(dqA|sqwOkEw9`O=)!chL#A~6i^x;TZ&!^bY7EsTazpbEMD|T^7Zz^&9b9br
z@xFGYJN4(!5nuO>ecl#Dd4P!mYBuAeqB08#F5HTx!pZnY8XT7nTo>^Ngq-ygz=RWn
z$sxlKqsBDX-_LZifvb!r@lER<gVVuXeYm@O>V=nN>##R?T{wajc;H(q7D(1G&#kVn
zL$GtS`6?1n6fZ>V<mJOMG7KSWR78u<;Kkg0EZ+Uk_@G(RVK$2MCRH{?@R5*(ML}Qw
zsM+XAra2<SsVM6&V|IimXNz>JV$FmnK4w7sk|g69?e)RS$Df&X+W~Hw=%bq#6qJwU
z%$H5tO-2crt*^?*Lk#fdy25k-XbP=b+oiIHl%Lnvp>VgbC-h%kE<g`}y+BienFt4g
zhp{%y-y3SXe~v(pG8J1C#v&MJ-o%15l7XW#;^!#%APbJ?p)8vHjR<B>|3}%T$~sNy
z)#DO1&|8!34!#3-8#=%4rApUC1oV&p^P6AY>6KafnnB=@h<R@gsv&T>URP%<aedYk
z-=<+_9G*i)tgf~h+jEgH#9W-~rf}IUhjBZwSfLsk5>CyG?Mq8%k3e|^pZ8U*Tp|aA
zv5E{hn&aXo>nNZ7Vna#f_+~cRJ1|g0^G;Qf_rF5Eh28!gnLB(K!icwE1&)cKvB*1K
zw~tqmE~{RzD!So}j;46<cVNhRykPwIbNIqZAyfr-x@xGGp<|{cXsbl~{F}tU6Hc2s
z_uTx)0+<+krW#drr+?{bDJhXcLj}UF*e#*SK<@>7zb<up=Q+}ruBAJr31S%V;Z(ij
z7`>P+7QP3AlI6|iu|8g?p0j(;*wPLPu73VNRa2X4B=ZkkI<LrX@`%JKJI0P@y9iVm
zXngPL>gxQq^-@GnF!Q73u0Z`ucXZ#hyvM>VZD5DVKa94Jl$W<z<9j&xg06edeSH<M
z+mpS;vSPgq!d0Yhqf)Xw^BOYlgn^q33}oNd*4BXGvd3KLH8%BS8~QPx#b4gb5gnhL
zEcAX6y~N>iGPLYEsoLO0q$@x?MomkL=WVNRf?yypKae|tLKPTIG(<_$A(nD~uAD%A
z6!+G7DhjQ9`hsE49>OVNVjrMul0C>fW(r)^hxa!(rthi0a3kh`XIR|W*x1w469kMx
zP4WZ%yCzy&(c4Q^Uz>YyWpI8!LR}-E>5HvKHnp12AlS7nR(j2D2hH2eR8${&jCr`Y
zVD7sPW4;euhkCIN3kaAM6_<QJ;Aw~35JVz~L4s3l<4x+A<~dhbY=!CltU!vWh!&W7
zA{^4N_!r&%KL)LZ1q6UDFKXH9&Qg40+<E}_H(Ui?`Dn8FxtS_0hug^Nq`9RwFQgB&
zG%b}D+V`}Npfq7{@OQd{I--7-LvU|Kn0}2P;Wsc%c;p-z6rjPhd$PZ<)E-!fZxk#1
z0II3MIodD@doNT6kIM_5X`|poN&=UIPfBWSX{p_yLf}V1BcJ3t-FRnp4cDIIW)*{)
zI{Wuf>b(Gwe3h9jsVIiu(iQ`NCc*Xug9E^lW>xb_cR+iOn$76x@Tq7j=j)0<eQ55<
zHycL&J*cNsy()84b8>QG@T}{5DJ2f+)bPy35z`E0XY*Ao%+1fwkB)7m%-`y@1!Nee
zM<XaCvT@cJW(pa!?F#P6B0-bz+V9`DVMtx-QZh2L74O3PuXD}sggjvVb;bf4?A~;R
z1SBJPcqXKF9oQp~0<qu^MDuPW7}(j5ArLJ~a|F5LWY;?EI_cSz#wk2JExGC(NvbdW
z65J&69lpQdhWPu3k0#~t(l%>eT;x9uy^HzU*ucr3QgC0KN<0+Fx0QeCVb|nOf+j-X
z-!TI+pn|ic&rpySs=xkd>DTrn`Gy#H$bJU5C@}+dMC5-H*0?XF$~XSc3vj&rsqb4b
z<L*{cy*qwgeLdOc^rLjj2+6b`9f@DVEtcb-oWzMh+PZ*`k)PiSHg`bj03ty+Ajrf7
zGF?X}Op5;PPc(ufWMpLHic4faF3r|^-2*<?JqzEX2NJTrlV<m;W{jJW{l1UG)r|VY
z_R0<J!)JZ!K>sYg;eU*@3mVidzR<0Y{u)!|wZ`+jh<T<A0I=i94T_}He*x5oN2hiD
zAgrfZsYB|Kb1}%u%TwQy`p!=?JZw4P5+pobual$S-Pic+>+9Z5*pfvsluTdk<Dwh7
z?dj=j8EU-&67?zb-ilxycYgkkg0(IcZJPuA*w{-$m9)hzcWkaLOt7u&fK>wnvoRLg
z_Zk&F;4N!pqV@n^B$zIMj*n?DG%^a8Li(+V5b;CK74Wt)o43%{^H$I^C>BHZmCD9a
zP;pdHwqfqdk9!dr5pi`Gfz(Al2wZ$)Qxm8#b#>>!xtg)fsC#vwBDd}M2M%Y_ZaDtk
zLdhSqYfJiughGdp`I_SGYrRByToI}N-oE^hT#u<;6-C_)_nX^}z@<bBr_5NskMNjH
z3|hb{&b?SYCy+FDYpQP>T(Eu8DAGWuHB1aEjAimmn&Wa}2M=VHiIB9X4%yR+(<=~0
z#^x%`qt371Xr@}v8blKRC3ajRkS!Sxrp#BlSR{HP_gp_`uBjFkR~Vs?J4%yJ$D2oQ
z8QGagMdib}`lgk95X=eJf5(W3xA(knSfRhNt!=3=7pnM$<*)++B8o;cmD1DTL#!z)
zPc0eX>ro=g%II4>-ivJBE_{Sdw26W_TqJ#L@s|Pgq&o@}MT`yi{M%lBJqYQ5+7j5i
z>|i&AP5$)S@(H50zJ6wQHbm?x%sY{iWAMf@9@_68h|?1Dd9f}G-&_`+v6z{g%Z;fB
zJ}yO(p0nZBYzQZ3eDPvpcsM`VM+|<|-J`z2*B{!y<9S`oBMUuPx0+CK8eh_RZA#kX
z6rmy|Ec_qzDJUs@`PVFyue>cy67V5wkkWzc=0QQyDY%TnI7o-W9-~uK<+;8NGH(Bf
z%}zf6<F9NDQVM@tZIGmVLQ+h1wu_7VUm3)QjHipmskKtd2vBBVk*9kBsX$X%xgTF!
zQenJ>O-15l7cGBmcj6c%RJADEt-ZaLaur`B|NUPJ(Px~;l)==5A$#);(5ou`KnyTs
zi$&dG^4R&#4d_!qMDsZBobi**iTH7;wa!LHovKlw3f;M2pqE!w-lfw=!(Cwh+Ws%6
z77_8l+WHSHq!Uv3+ClG|`d+;o<g=N@HeKhkH>+@~fGe4Fi`pV)4P0UVk7Vi02z<$~
zh4!u=?0QRD78W+0b`*x)$`&vE%g};x(wlZD$J=$WT_`t^A&Tf4&o|H9L3ro}H4sIJ
zAx1u#=UDu<?WI|xNo-$VtJmT)wF7E+PyPkSO-!gO)>dt~V9MroJERKj`Raa{rNU4|
zWBwONtA9?a8K)_Sbw@8V+mTI2DCc*ltF=J)12NAXN>u<LTIBkw1WD&4FG@FSlnb`l
zy4dJdYasi|rGHjkl2=zZzam}gW0r@Ue979vS5^XYC9?j6s`rb{sq;@s-blA$-9td+
z^ro!6#i&*IFir?qLne^V$ao5#7m0tXJ2e}NzemQX9?Q!JktM)JBBn~J*<w(ZO9B&C
zcGg#~<EQ*D?)pX-_V4j47@nn;8$**OwyIO54GQA=u9&zuXoCfYlz2f@G&JJyit6jP
zEIa#Az^8{}NYqD}p`C=(4+nNWCS9_#A=LO|f2e_qrdLML(bEHEr{Vy0N$1%OH3qYH
zN-}95`*e68)wFhWn2?+O9B#`SRt&@eOUmuH*sP!*ybecxhnO;1N(c+K8Dn+=33ihz
zX300fVi{9Y8i0dSr`CBW`S?hTx-u1w*epD6EVxpKO7!g4(Ozn3AL)#EJmwHvEh2ob
zbv4%+5ut(tD{vV)D)MU@<$!_1Kc+`CoO{si6lv`P$a4I6JM8anikc3e*X|s|GF9Dp
zp1t_!L>;Z92K&xbd1$G_{pDLHb}*<NP_AXZ8f`ecYke~`35^u}ZEchyGHBe>DMi{b
z9vU)KUm%0?v2;~IjYm~|wyP^Q;ROk=GgeB7@VnMd4%h1hq}%tB2dba~Dp%UC@usfT
z*Vl&>9-m)52cm8E^n|q03n3IljEARm?@7$w?;@ev=d^pqly7qKhQIbCr5Ce`2lU<8
zZwWz&2kxuQ6AlsS!}?|~h&TDX_1Jz4?ZQ7g+UJlBaf4l*%;>Z%0X$7Hs;{rDfrodT
zN-E^T>O0|oPX%C<6hY5!s-yLxit^`y;S6u6J<j%*Tpg4-k|#9cXDK)tVRIPy#rL}P
zorF$~L<@pKRdwZ*lxcYUw$by>od93@sN6x9JN_)WM$q5HqcR8Aj5l13;(m-&I@9-0
zt|h@iYX$mPw9=8q2H6*}$Uds9RM6w`mNlM^{!U!T$QLcDC#4$SR12HUUL4N~=Z-3>
zog9$sr8kO`%I14MEIIQd<v!2F6x@nTEYo~7rt*=IakwQm`!NZLXW^ny<u;<#tBnMt
z_eIV!JLpnAee?qB(AHnlb)@e|edAMX>;x3|c{?*oN;c#C`|{bWre6M%76U+edl#EQ
zejEhE56S$~1Es>=Pi{pO?Xp#9v}Vwpr=C~{3((=iIU~X~3qRuSi2iG*qVAE8l>Bg*
zZb~Bq#S_Rl=?Z6y&Nxt2qc$1<isZ+BZC{#B*Hh=+i-PgIW^%Id-)n0GNzr_rKU-&K
zFDLu|EFYbNr>=XT?aS+*KX+7>*gW?Z$ki^3mcI;eyG3@8&lmA9cGu+<I;-a8AAC@t
z`!JHb|7kEnI(z)9_w_P#Ct$P7HqlU0p8LSK{YG3)&bm8-GKRx>Yix5M&+eL8KQ<~#
z8`m-KiXikU?yJ2yV}ZtdeiW+&XI^)MiHSj!f-vL|DLWA#87nIZF(=a`QbkqOpK6a&
zCxl71D2CLrJt=5{<b$a|%CUkOx}|Izyp^afJD<&C+saBb1q7h}x+FKa84bj!w48yZ
zmjosH$J{;~paqcc+sGl}TsySw4^yIJk<yT_qw!5G^Qx{A$lR#K!$|5ZzVN%Bh#P)y
zlqdCGH1q%PVby~IC9DM(GHcRD8<jh<&KplG<Ovax`SEG*1pTgcgCl@y+8au#bJ~uz
zs2PO9WyZxpkXJ}~99KHAITEdnjE=^0X+k<!Sy;|fVm)KBvZy{Mq9Sqzl;4n+Ox9b-
zSy?fe)*}l!9LWfyZSl@T;Gr|WI2q{m((FVNx^tI~{>7MzWpA<ER4%mNo%}d})`5+a
z9Vp}MtG8jo!06dSdSV;ld$5DM?auZ3Da{ngf4cgLua0==)%5h5rBTGe9t}NcJoy>&
zX274GojYvIKbkD|e)mN%ysH5&Iw3(T`*ZAG*TlrhRescC0lfRW+?o>u*adj$(Oi1n
zvtN=g_2`gWGZZ=t8HSY9ty|Ap7K34O23H28Z=MiOnmv|<b9f5zYM}knha+o$l3e5u
zRXW`j>&e>D-&zgCm%G?~7xyXgxBF1C<$oQx!t4*}ABeze&l|RU&|m`Z+9~)g0b=|C
z0@f1a8w>@eMqOY$GS1><HG_%$a`2<-2Z~QH+<Bbt#wR2|b99A3tYNHFkpKCeS%<?B
zF+&8VN`ZgEp(8uNz);pW#OnFSChzMutSnvYW{Tqwb9@^lRvu)gyHZ_@Shw~e%i53o
ze>nf|GgTcOI50>@Nr}~dRT4@=8Q!F4=avtpW)KqkN)uGp5IC2<E;26ic?@IOla^`n
zP@?H9FPQ!sK(xE>98#3>Z{`%i;F(5lE$`U#Kk4IJ+m3kej*p9~*dG5VMXa8=<n258
zlzub082QA499B(L&GUObgvvRx+`rjS4lyI7J&JYRR=XpKIPwVrlP)4)1)v5Kpbv#o
z{@C!a9Z_6nzu^E`t<PjkAtWbmT~LKzrV1F(H(~~TP*7nYeyNi2{w#{<5*yG3TH&PM
zVC^-B4NRf`$|R?sUkFZFabOB|3?K`$Cu|?fK`CN8p-(BdJfSIc(~f^2b;Ii!OltIU
z3kbl&Kv>_M*OWUZS1tCeN~aC!Ku*06KJ<X9BRxuZpYKtkgb7B})^felTZrIDM#>_1
zv{>&{xV)??ZH_^^gZ0M?8BrD~rMW2lYpjV}j5<pxzocXjy3qlH#xZFs@eW09Q)j2W
z0%K^|H=W4kI0IN1ev^<qU1$<jTR&`W@`<I2R-kj7s>dd-_LZLZI(s|0zt8Z>6nL#a
z+-}0>0oh9Q2$Z|+uSK1bFl%VUEdfOG?D9ZP_*0Ob-m>bc7X@7A!+)0<3XZXFwqJjk
zc*%;nCaV9`lPUCMW@mrBdnF~Uh%20l(J+NkG@Ehv{y*Lvf}amimsVHDpb<%{l7WlM
z6-6JlWKHr7^U+^E*ZsH8F*VuEel3T3n+@Z|y^z&ts3)WzV<f@(-5vQ1>MlS(K4(E*
zfFdY(H(_9hCeXFbVhGdx9J%4*otUBnZh1tr=jrgoPUExl01-dL+wX>hYU)Kn-s;>i
zugM6wa0m!A8?=7}9O*Pc4wa*VL@<SpPC*yeLHhMjW6++<4pWG{<y7H&n6FG49uJX~
zmFWK`_H>Qm2rWF?Y`ohtv<obj&K*|)X$toT6UkjxORZQBA<h7_o;kD^K@zoDOHxGz
zzy25%L7FUc9#FLn-Rh$=RhEw)J$hH+avF!H?EL~1N@pa^=f955|K#u*4f2(82l6*%
zoa{!v=7ttG{*Lea4K8Wv|Bcdm?93>7_wxQ{t1Q2ekDT(A0-;f=>A*gI$;hBk7|I5<
zgAfyH`+X$w`7s#lDQCNvY?+vtw#qzIaY4`_C7lQ80aa61zY3&a(<8UPIesHbhtE*<
zHSBr6JPx_g<3x8450o^?A3HLXJB5!ZBR>(d(<g7Jw40#_IXbdw)dUoiIXeC<ZimMU
z?02rVXG@udPDEH3e74XORzf&ZW+d?;IZ(`RYSO=oT~LsMh2;P^x9REW@i-$B6LyQS
zJpETQXKg<^Iv^!<_aoTS;%uPk?ZqO&g9oJ|{i#P6Yst?Ojn_Tn4a>B(a;&c|jm`rM
z{mh<q30UNj4`(e2WtWH%8<tJM8)}gMmR_d$y!IfqVGW#p9|8mW1PG3M;qG?@=pGJF
z?A1))!g(mix}+EwQpuuWwpWT)bG0lh=c7I3Up}&1+w;-bB>l!ifXU-zdZluQNrCQr
zHR+w|c}vz%CHYM8&<78(gHZ7?GcrHZ%%VUwGv3@If9cl_6)*KuL9$cR?}m^xCn3Jh
zi8hp;axG5Qj4S~6%9YA>r!aPEnY`JwDkf&U#_@tVTiE~Zrx1S)EFx^?6yp0gL(}rW
z4^2;Np14Gc)jy|w|IVhFNj)>OF)1-8A=Fc@!g+aRrNnYGO@<TEJqW$G@~^o=;i#xl
z79+sLPc|zm*`P{YO}*(d2@FZ!Ex8eFECN;SEpVvOvHBzUv|fPgTlkgo9bp-V+Y86n
z<#2D_|I;PV)9r|LZ_#c!Iw0WAwJyo!cTLRHH>KQHH=-8lYQ;rGO_Saul&Nco&d;RJ
z`c7386om9%jIl>V>Auj`<A)CiAvD971{iPxI{JjNGom0~?@?nje^C^Abq`)h?p0QY
zy)A;t5b0`Jl0|sXrO`g2FPnKU&3sF;Dnk`g=CDl9nW4yjFH@(WtSl;i(d9Fu#NObN
zUwaC!=*R4Yq&&z|>uXDt561Khn4)^baPJ38OUgV?o&L8Hh5{y7OaXJJ!`=el?2JWc
zw&kDCM95~@MdNk<r39C=9^F_^{Mn{J56U6*={BQRuX9$#B4RUxqap}uY8x7U*VfG&
zmezj?*mW{Ap^ovB4v_r&P(A9#8AK|$j|-~1JVT6nu_^IS@lecKjcYCQLRHq)1U;0f
z;Vl&4|I<h~h!(}&KGLXkh#D#`I!$}YAC#qcXA)AJt_HiFc?FJ9((7L+=`niVqtjm<
z&#vt3G`^PL{-vp+;%pNySvg!Rqb9XkP?@BB``M&144J~lre}}eH!l#)&CagO&+q$b
zoC8qg7_+yp9bWNyxqEEK4(U%6W)(+^Ge$;5RalG{K>ilaM&jhG(eE|?M)O7C{hjZx
zH#Ip!WDt2H<_=@yG==nigbg&*)J1wt^?7-DKoG#RQsmb3G*Xfw##_L>ZRG$af%ill
z;-;O;97W*yY$+4f&;7EmuIshxZ6di;B6wfvOy(}^h9|H<)6HWE-g2-Qucm?3sc=sq
zi-?VfFO8P>6)Apjn&>-&+!y-HYwx1qCnDmlsi~TSle}i@0XSe_dVnaCd?JUOu5Qx=
zHpF${7)LnX18C)Dfn9Po7V++B_5=&qkMo`4o#c<hGw|{qz0`J#=R9$Rf6_<y6!h98
zSj=G}jRME}G|5y*`JRUh+HrN^$mY5>Zjc!C6asY<ZIU~|oi(!Y|99{9$rgU#rJA|9
zT@%sMLc?M`E++<vH!5C@V%&{d^!WsG<U7t%ap)(We5&Jmd1Ad7BVcRy+Gg<HyWcBa
zmYc`Y4?a{>91RSwq<`~!PCWeMF{0kt!}(0;59ZZ@&I^VU$)O7he54JEA<f*v`On8b
zIWJ+*&5(s`K4N3!7*shYJX?@QU<vZ^;U14lNN_(`ehs52VC$$T8!D<6`qlEOML2)*
z0$OaZt~?QrJoBK?OxX2~g{Tj8QEQl+(@9Z($WWS|l4_BuRJ4SeD3-SmDxKb%+9f|r
zjgQxCum`{0dxpXcO^1yQ-=VH;GeoWDLCsGOSK0?SQnK+8(HFvWBc?xHwziWjjTPu;
z5JH*5?!6BCly{Wqzn4^6&z|H~Ruh72Q-fPp4|OJ5odw()f(_gNBdb1ms-Im0N8=1@
zeRmJ~8(9uT<sMa~O-P5p9?gaASZSo^M?rOQ*F;nt_n2AZrX5Dom80|2a4DiEPnK5A
z9XHS;(KpRwo*P+whI1yBzEBeWHz>5ydCmL*eJI6hvH4o?_DcESWsn$@ENABDYnDt$
zvP+0Pn6*FTG+skT_Y+Q+RaMqZ%g@<2ad-&9H#lexCe!21(XPS4T?vk350I3QJ3}S5
zs}b_W8e6B2vh09Vj06C;Ct+*5?`Seu#x`7s>pxd4v1>J*q?B~&obOR0{t+QYsCJcd
zeZZK_<4p_i`Uw_BK+@b}xnpp@syN=;9?1^5yuRG#BXo>f>DSs(-EMQHiE^83_ZrIx
zNE>e4afjvU_mL535j+ip|5b-sv;52B1EF4Q;<NQBH~Xs2{7K0<GUCrF7qMeiLU~I$
zl`51%yXe?|gBrjOR0s-m{DUd0lOY>iSe~u_Eo9*%BT58>4JuTFzwfzovRbLWn3GCJ
zw`#zcogT}IfYvlu5TU!7ALnHL+1~D3v)?Z3SVRi%I>N$kC(=i>wFRQR7W)g}a~TP`
z*)>Yvww<bvL5t3g&3plO>*VC4J~h9-(Ei>zaEHJ7TcEqS{hPT&a&s;wO#$Cr<-Xi8
zeE~z2#^FW7OGpFDjOM0#oZtrp%#!T%kl^5bS;RjqyetI2-cwpyTH^LMva+7gWdwG5
zHPR`GJ8Rb7H<#bTo2d-**)omPZ3Z&15sd1^jW50N9FOH&Jky)(R)Pr_3k3COn4Lim
zyo*f)dK1|6CK~TSgZ=60>7yVqz-zGTmYfG60Dq;O(8sHY%EVSH@dIfDlheOacX;ez
z3)!-`5T$q^WU7)=parZtt;vL6XwBZ4%O;N79pu)+xSEexBeyfHoqIS@J#&x-tcMeS
zUcaWPu(!edH>V-PN>&f<v?s4jZKEDmNcXM3zk~qd-wnJHN*eLN1?n)3UvM1U!&H9V
zv9ZTO`5KNgHHx&e#cRLUS66qSSH7~+5e^0b@0ThejxImKY~kv9t+a%LhK|lmRgbbU
z_SuP3WZWc}0h--|KZCh9WtU8&8BcbCI+Y+i^fL&umaZ=l{QOPLhD#l#M+$1<8{7>>
zZ3GRa5PJeG1_V(x9lE96T{0d=Dk63qL06&cv3%TT;$w~V78~GaI?FlU7{OkO3k)aI
zq&4w=$QR@&Y<P&YhH2?0GUVXy+?7gZ+nwj+hXbFM8M#h+?a>;b9`M;XsI21PY&n>o
zwStaG3SjNFRnKT3;V#Yw0TB^TndR>o?5e71CJ|xWsCUQqH_RUTPa~mBV&Ki9ZG+RS
ze6a(hr39E4=jKS#DZ2+c+EBoIb7tX@!sq6f(uCAndEOJ<8}@q~y1{(c*A-NdLjuA$
zs%+0^;5)#{{Man3Wwk#X{EQL4XU3^Rq~!X)$C}q*k3amAK3!*Kg^GEn$z2edgS87U
z@gNj>Kas^cCT+FX&n?DL-3Ni&L0x?igjQ}(he+WQSEJEu-k>D>5^ew}eesU{hZS`y
zPR`S>r{yWV!J#pAh}FlAYD!PrM+(H~mVa)V7}~vKH>op1qsJ=~6Rog1a}_zb#bU*K
z`0%~?X6pzh!u_-fB2RD<Dx+!{oG$YxEiLa=*FT*+iUq;QSokY(lEsy7q2vB&Vg4vn
zZ_}c*OzYd_8~%_bQ48;4ESjiuw|nQFH}Q?gm3!!j%r4y~_i=z{)t0^=wao1Cu(0f|
zwV)rfrK;Y-4C0aD`f|NdlIq5$<o%2kFzgvM73Wwy*@F%a7%WF`u20c8KSDo1)&wkL
zCEXx`YHyl3z51!|`rKYVG4@pH)sN6bsE?I*J*mx(rSX^6U8qBO{WO-%N{7RY>LC7C
zWo#_rmjxe3TDOOmW}{0-4B+>M5;w~QDi4p#Ic(yT620{7?6tKO<)epdYirEk{w`Eo
zgN6|QvZD+F1MGq|L@=$o20XxQGhUYamY%J7UJODq<2SwJ#Kc+1aRD<uDdi*>vfYvG
zp!`bd^;3jDlshJ{neLyPjjq=`KnHY2nU79yeZ#1vrDd@=ZE9IkjBy8Qt`>;a;fOpY
zs<c#Hy(d1GQIGnp0ZPXt6>P3&pGv){|3U)w3EiuIZv8|KcIGXiqf#)J_*fPUzo!Aa
zX$rZOrTYg5>E4<?%Pv`xPj!<bov2zK_>%dTNxf-E^qbSCbC;7&yv2?fS@*#>#<K;1
z5T11y1|C0t%*Dmk%|ZC+5%_hRD=WvKLa(PMe}vw+RWS&TCxI|R@c#y3esp%)e;*7p
zxB31MGsal+0##&zI%{U8r>BRM-#r>SP7>JiA={H02MDJWTuLEscfw2u)QYq^-oEs9
ze6tnm&iei`65`D3;1UG)Dw(d`%(j|ckfqi-TKXsC&+1FXKPOZ&6cr`HGa%RcT)?9k
zP2C*GPxznf!4V62C8ZHymA=~_(!?ix1Jw3$TMih!_r6$MH+Wkn(rbVn;J9HD7Q+v4
z+B67fe~*umk_3I4!I{wHeymkvqYJ-*^SSC-dxRtvJar!A-a&>V5NW9Z^M5M&D5$?6
zrS{!GiTdgE2Isf@NT!*8W-Qk)vBy+QI|Y({*$ar`5{-+wUg<Y)N|d<aXym3HHWZ7k
z%kRv@ckX&eNzh3(eTj|bIR0h8q>u6IqSplSLDoOhkWwkG%I#}ReoG_C@p$<ctC45d
ztBiX>@^+ba=vNKB2vzXD#y%`?L;P>IgTNXWc)fxySA@b@OuReuIygEi;>vogkIwPm
z$xi-5b8~!f;8ZosvuEuPqwoz1>H#WcJ9A+%0I*~NZD%qui`kop*YIolIHak*Kp`_s
z0`E*rRC$^s)0aNxD}QAp@<o%a6X+Vm;&!{y5gt}u$uYCJ%Rn%clJThq@coefDf6R!
zfh`S<D$k>JIWLz#xk3+q0BTbQ{}2H0)cM%=|0dRzKOQ%*7lK6Np`bIue$(XRT#WxX
zE9>!emchI@l$)-s41b4YFBY3sG&nPA*3?DKoL{h2AzBMebadwK7sdgB`6yjaoC^kW
zICL2SW_kOdEQmRYiHX7KR*-#}otRLo9)=_zd<LBtoZvgaA%;`O;mP%$Mydy%PYN5H
zM16J|QW)jw)lyM3n(wlFDcs#l6yA?%<ktSIU2FyRRU!M!ed5U{u>}4i={Q6S`MSBE
z)&%f^K8)8fUf{F_Q%9`)I=1XjX*k||n3D>m(Kf>bd(YTv>vhA+njZ(h{>zl;-2w+(
z3!FLxXI(&s#0OV9LQ5!J0WJV>c5&g}N&CI}sM;YNDLWf*wP1re`}fYyyNv2krCO4?
z#PdE;vXY+#xDU4Y&pwH4L>X!Ul|<0iF27c&*W}*ThPxh1$F!jr@y^^_>Qa&6Og?tK
z;l0H70_reQ(wA?AuqD32(b>$5{@QOmZ*^}-71h*krseV^ZVI^DEb`(=!r<nBWprU|
z>@SF{RvZmLRw9612NGjsbQEqZoNGsQ6ecYx`2Zc2jYv*e`N%Tc8wK1MP(akOv*Rr$
z_9I)hugRL1Z>V>HG3U>8MQ&c6nRf|U2yd0PEB_bqbfyaHMG@tdxmg!xx&FXt0<F?`
z)Y9u72bohbGWMDO^8!$>^*zjyjkmXGeSrA77wm;bd8v3}+t=clZm0|Y6g;Os|AvWX
zuRv_#yA-g2Y;Le@jNYHty#0g<b*|@a^CgV6IyQ=mktz(N>=r7uzgZCxk{31_9zZ~*
z&Q~TJg1!slWb=IKonYzm=l(G<6WiN)KEh3j?MvWL8PCXo$5$#_$;ikE0mzWg_Lpx)
z#xh`X<I!ZnkD#PNoiHbqKn)HSuNMaP?%6k0|B1_(a5+B(^Ke~h>3*Eo>nm^Ly;i{n
z0>M=GCrd9mIKqO0SlHP^gMyy0TSQwK>F6ZE9s$Q!aJ*m8DfZIb^;NBr%KUe`OXAr8
zcwNDaoRMRy{P5oi#9r_E;XI#C*v(_kuKI$c;K0DS!wt84a@YtjB2Fufq<54<NJ2>U
zPdKKaPOh%>2cSW-g$2k21c-}=<1M6Tv%3a(sno`}4;YrgXQ#c?uHFVa>|<^sQfmZi
zkw!hAp#H(F37_{fIPq5OsUi>&YJ_a;k$?5zOP*}pQyodOGg5UK3Lm~JtdSyE(dB?E
zFrPmJInb(&d2ogLW4fjdca}cBxw(NO7vQH{SMX4a<uR7J`lQwvBVovGfp4a>%ejjL
z6X9(eQrCTAiK~wMINwZg^1#{#=f6O(dt`DlJR}5891Di9D9m(lbi5JNK<t~d{9gPB
zCp$ShCiSj;WqH!foaz+MVGVs2jc2v({ugsHs&mIo7LD=d&rGs^p8aSR$F7i;(WYYv
z&Q=PFemiWgs9ERE5xe#pIQgEdc2~ZX!a~c9pkFptvA-vo6k)j_S@jwrd(L=m!0mq=
z7#H_wXr4dg^P;w*!BS@^%xdgw?D{W{L@yYh86iG-jIuBp4_vu9>B-2-$|^`bS$~0e
z(D*LydK7ACs|t>^pr1&xS-J_8gpH`^O7$+RtU*?i?ep_Z0tJ_$@Mon=FCNcylC-9)
zm(A+DJ02T%%PufX%Q7y=;c3h-8xJ~KXX-r8;+-jAC{m5VAbLJCH)GVJgp=G^Zq?&`
zAY%0M7t6=H3kwTt%D>>%gYsao{()hf-Mk%)JfgI3ny+XG-P{EH@7^F)bbzW1O`NF0
zS1-=#4E@eHY2WKQ7b{594d?Y$ve6{OD|{Re(W!@a+N8NIA1-^5^ilzff9T?z0ml6M
zuT@pq^CM_4d27B|Id8|7)wvlZa3;I0%^1s6{5%tb11HSGaO`u#hrc|;APN<9H8y${
zwc>^L0JY4x=Zmh~z)L4n+a1gG0##xTt2_<+fz&D{!fjwHfJ6p8Y0>!NB1HMku(-4H
z2|SdqUcCZkPfKxA%#<|l>(?omO29HX*4Rqd+`1;}&Zdt@Yw)|G;kVWm=l8~b&ct+K
z{7#Pt7mHzF_qrkYg$qp7RDyWGD5rxPs>;Q2tHpsgP@Wduab$A-a6|K!CNx@;DegB5
z+`4%lr{yF8X&LV~Kc94M+8-Fib$(Wy7#o9J9$eaEek6o!Vs0|e4gWxz0$n+EFO|Lt
zs4?mv!^Kgce4H~?UD<N6xyh;lT2X^=I5s%Xu5Oy(TVOA9c=+JQ#kVAD?sG#wg)BvN
zGfR1erHRw8SfS$gu&^5KRz9jQ{8`_;p`~y6=g!f)QndKlatFK>0*-r2#fde6&;OR*
zZ<>==_ZSLMkClIAGgmuYq)a>5(_U`Y&d=bCCt*&lK4A4-j4T{24WJh~_J0~5_WRbv
zO{k8873dn3Is>JYZMhzEodMyo)*w&iBh;t%w(O;&EA&>Yhq$X~P4M4bukY}&2s<iA
zgjkK(8eU5fvu-%nL#YCoW4ixW45SqavmJXRlD;pqiR;Ki+6n>yaQ;bOzOc^EB@LJ=
zQyC7%znuv*_G|lOG2D48dLzL&7QG5s32GqgYHMG*{BbWGu2C&ido?%bZLEKz^;MQe
zTBeQh1Ky)>GPb>$I!|?d)wK}K_@86n+tI*JTaxo2-gV<7i4Ws|-bhsS{8W<Te<3b!
zw{PYyM90nxdT@G~Vq3diVX+wRCWIFmevo{{z6-c~iKXhBecWMO9$A07B23>iBYGD(
zZFCb1C;vcV_y$T3t&PoGDO5E8lnkkN`2*R-^Ao75Wo>K{Ew@i9Dx#6U%HY>Cw%cs1
z{Cp~c>+3BY++3farT=pZgci#43d(uI?G#vkNi7z>_8E%jWa<6nfJqXS0v{7j>4usr
zosQX3m0ld)_D`XAL)AE3v!=RHQ%S0YGM|@L<@ao*wEW!Pe8l;_FGe!f#F%`DnF_&9
zWL;D|@nH9KEBPwfC7$j5<qGA%><wObTv2`C$u69zPVbvjz;MaRiU4c?S)$Vj%+U~;
zPtK%GO-;AF2^3t9L^sNY)0EDyKXwBf0GekapBt<21D~PBOMsuSeaSue0Z<fx?@>jf
zvIqJvTkmuiQ|D$4<hNPnBMhW+s!Hd9fSdL{VsUMeMIrff@OzF-3;ArXylamwfdffE
z9lz~<)fun(p_BFnw>Q%_-G=acOc;Ko-v8ZPP}4=1H~V+wm=g4*-^v;7L3B5h!|Hpm
zYMNq+JF|zWjh=eJEHDeORS)&`5%arOhK9-|M{8p5p^DV}?eAVzLIgK{&z8nKx!^h0
zrH7ZUr{sQ}boL9_QOfs4tmm>`C1BrZw$uRx0lq9rof%=}b#>)!ak1;|e0t3Z?V>1e
z=No%n4?r+zc_(P+5W@3*2&%!V-!FQ@RHN@Bb^&ZhX(=M)4iCyi5p3k!1*-tiuPujJ
zU4KlaCssFlU)L80Unhb5$op!R<HpO_j{b)sm^@=*>!GW)?)rVx;jcCnP}kuauDhi{
z!I5n4)&Gm5y9hnHsah@aVe*rkGce(^vyw8TE6{lz8(O!dWDb4wqGRx8aYIHhJ`cq;
zFu2_?XC<FUq1afE7|Yll&(VkBb-)O>Q@-_xE|Ay7)5~kSF_<R{I{000u;7qF@%C$v
zSY_HtsWRBUC!a7u#ewgC{?DF0E7Vt1;>|*p$H%`gUdsykf%t~8IT}&%m;+fs{Pzn`
zz&7D-C#!2wg9Y1($BWSGVtJzstVr@HI-GHlFz~KkQc7@bA^a%dEUKaRIzHxqe~z}J
zaeNAChIPs6k$+qF?|V~@Gx19(!W7`Z6EZ}4|M|Zc+c(w-zd`;F6V_Xqsvx+wN1K}&
z1S~y6F<Q7<#?S;b?K!YTdFgV0q2ig<=5oFLeK-UF7z9*LgzG`$9D6B_n}XH^FQvJf
zg?PQ>x7WFQW0TwkxdC1GA>&B#;5Y-&wv*mw92dlWZl@ZKe7}y~BK_KnHxDb`{JQyq
zR2rRI27Qg@!vmSs1*-{C&DBrF!@bGR^rgirx3!?1Uxe}LaGK-dsEPx5>LPi??2uu+
zMRCIK<E~R)funQ2A&&&-_4y;Wlv5N&HPP^t2l$?+ySmxOAr$C`a%LYyal*(2^<o{{
zPEy&?<vYcp1Y2%M{+_0w&7?d-{8(9wMC-k~MH!VY;bfM)Gl-8Eb7=4%104fTGLDJK
zuMO0vxaenFEklwJO$EAgb(V%Kyrtf2wt1d+ubgNi{4vm})Lor2AHI_kum?FYVflXO
z8en17cyE#MrWHkKXZdw*d%F@5QKqRE?L597HzY&S%8|c)`%hH@#)1iEKZ<V7bht`j
zp-Wsn<eSg5M-post^a&|$Bgq~@2)R!c@R9C8COGUZS3YBc@pwvlKbvtiLVBe{bt)~
z^zT!0#0~(xy=zCr&dwn@3NrGIPUBf8IlY3mgLm?}P2%s}-51F$&m@@2Tz%M&%$%N+
zx7#A3u>Nu;#7{-s`0x({@Dx~ZeQmB*qNiJK*g;MeOdDR|+=a7-<_ot_U*Y?(*6{E!
zH#khoSb`F{+=nuA)rsB8;(TIakAlO(7EIm^k2XnU3T;4>NSd(!em1o`r_Y@-jj^(a
zE`L!Yw)5Iy>^N1MXx(k1ARN+aDF4y7?Q?sn@8#80T>L_~nmr!8`dkj{Sn;wzZ2>B-
zAu+P#-0Jv}$=?4zZ2bo~*ZuoHjKAzXLI@!t$;zG)LLmy-WM=P~m0e_Ig^*-M_Li9~
zdt`4Jnc4Gyy1w7v{lD+~xI2#P`h1S7@*c0}IM2uV80mZpaT@r@4`-H%U%yUPv%56>
z!oq%mNmPOYv({b&{oCq?II`bF-FLq(Ywf0%WS!gHd&CkYC+s5Zu~-?{GSfw`$ri__
zON8vnN#i$f5622W-VsbwHZ*BY;=<T=NRd#{(7^5iS6FQH!s73sy(ljJ@;rxI?*eYu
zH_=6f2Di75)JKEK^{PND%|3%YQHoi#sKNeEj<4h?8z!PnVxN51E990M!Io;871~2$
zz5wR1@+T1WP?J$rm9p8=@gVtq7#vyCG1f=}V6Sl(DGnm_c3x|#=~!+nI>NyV4lPb9
zJ;pu?@)t|BVB0%q!A*9(sf4Yd^))!Wnd)B5x8QA4VrxnF4Wj+&0i!jCh{7;KdB)Rk
z7AIWOvZ|`1?^^g74J!jz(e8MNmv!?Kqdy!XrJ6IlPDB*3@4;~TF==?$t=Bo5+R9qo
zdt`7h+IwDAOKZF^hv&!4pLq*QM$Di|^AfHQNf|=Oky2rBVP<3mZwU<9@Uv02eqa@F
zC3M3~(x<rM)3<N;ekS{;=`}1gC5-};Jp{YhI*0gO_~=lw>BwWtpz+AJ_Y}-7f>ghZ
z_co|G0;85wzYa2b7TR!x{pdHS$K^AYz!a~!x%nO|D;Pb(VgaE2rH#$SvAJNB@}ILy
zSUVrXIsVkRlSl>PyLMGxN<zn(B7k35xaXPdxHgod#2*J_pApcfg2gY`k9q8^q`ehf
zl1uZ*sh!_Z8X8G{ReVH=iM)_DB%U%0FS@0fkt1{Z+hhT*%#^M;DIX%Y<N2dcV=#F=
z>+CS&ysr4>;CVl>SwQ;INP)gjsS1|mA3_mY(V~t1g=nkW0Er&$ENHo%5sq>k6_dP|
zo_~7&^x;_0`y<bb6f7bGSw`e*#`zW00hBav&nBp&z#1|qadxQnHleGJ+QvqZ%xW;G
z21I**+f_&TT0j^8@HjC?^3d?`K63MUx5X9Qp~3bmr8Xm!#RE}xG~Tr4`Z`L3+nrMo
z_D7_ea3>g))N9N7-o#{VE;ejfa9?14r&!N*k!B_6If#be?0vc+5Z!O2(E2_!EKJQr
z)#9@rn~<Kiz>2kR?`e}ZK^VfNpmg{p**P~|_f0Mq|M(63W{H!9pL0X?qWFIFyM8i)
zS}+q^IO<$-_PhwqUE|<g7#u6W{GPJCg99`JBjIr&$_uaN`Pk&>u=EmEhI0xqft}2^
zRUM)&EcJUIEEPfPa%wLmy@^3%qqz8#h=}Obt6{9M#lsmNagT$%@bDgZOf^ocUn;|k
zt7?CbSIg&NhC^BfBaKb&EoYox**t-Hl~4IzKvp%>m-O7Im;`=*|C;lhtKGcjNB_i+
zB-~WQeqP$?=pbh6tR21SK%9>0&ySf(%TYj^#UHc*JN#TcB6_-ZgWqyaASu6~KpbFa
zMa8hx)YSQ9B`csQI@kb9*yNI69fYrFPm?e7lGg_|pm<n|wgYC0>BvrW;l&*@D}@t#
z_tmm*(qBG}?r(GbeWL5d7QIgqd{4|!Y6zE$|C5Qa+^D2>$31B^wc(?Uxk7WiLJi{b
zyGtu`HdEC(!Yt?yW6+sseyKofzqi*>HC01CG)%Pll5f0`BboC`=6ok>%zV!OWFJ%E
zgO*Agt-#~BoTC{48rZj@X8TsRgTjaoF7hg0|JJQ`iXx;G6cGVd$o9oeP_fHe)E_%u
zA2Px-HZf_8Op{>nExIr01ir!!c6O2K3%ymfN7X0t&ie!Nx^8a_PO5!dWt#V#cC6js
z)I%f_#F0Rh1b}{V9^T9pkb2xVO5o)5P?n;$7EZ_9+mgi6?tL`j%XcB@O@Zek&9+x8
z;?hCP-X)~YeDl4g*fM&Z$Km%?8qb?5B6lC+MZdMSIt|)7NakHl`ZY2<JoN#oJjC+A
zg7wVA^eBpR-qx7=c=g@KmIylZ=^AIBtzMY*JXukdfpN#1U>`pX7~yPRB$t&ruTSKW
zhDMBX{g@`l_c@$1CWvMtPcpj}m(<6dEYdom713w=Z>$wYa`AC7y<I&C0=6lyIo4Mu
zt9XphF+3k^yKl3}d&vwDqd!CCZR!_=M>^wsn=mg=Ykm5ahPP06G8XdE&;gtYVd{yB
zgm(=;6%Ab|WO7pZzU8D)o;VK<aLOtK4A;!eA5K$K_o7(wYpbiPrxjozfJ(rrO$Zha
zvi)9s@}QoMI99Bpyz!KgBwlv&<g2k<i5!>QtCEHFwSr=*ua-JEf}bi<dR%2NZ}0Dq
zD?SPXO{tRr5#@bc#D<&#n1N3dl3F|3CnwLEcIlM<hW?zh!yEmcIk=h|j{T5$2`;gJ
zksXUy3}G>k7mOs0NksHf2rW^!FtCfkvRdP`)7|qX>EQ5!!a<wy^!I29`cL{4++I@_
z4RcxJ6SN{OY_rCB6ojGS;c+%yUj!km4>;%o?w&7;J~Z<aQF2M7O?B8nXl}GICCy0)
z5vqaY&2>**uP6x?F+*8~9$xM}fv;M`6BD*|DJir=!{_G_)jr<0h9kg5*7NZ1BjT_5
zX0~)SPhJ`tLWO{f*f}}rkX4hj<=1L<kF`ahAxk5ClJ>VUAAw%c_XZ{ZH%;)CXSKCO
zKJvVJdXl&=!6)Z_32a6$@Rka3yRLXedl|GuudG5#r7N?nkt8L=^uO}RqE;CBkRk?}
zD&xuL->U>kI&AZT9G&-xe$$w<6IhGdh{g&xzZF(i&q~HO^E91p@V%hmV3giOr?pj_
zna=jR<43=S30n}{EXv&fEuYlWx>E8xfXD&T7QhMK&DuIYER29Q;*Quw^wI8(S-Z3Q
zq(4pBFDogq(Ixm0q_7ky79X_tF4KyONvfT&5c+5pLD9g2GN|1}W;oG|7H-!wiL|s}
zprf}}od408J{BH%^uoWCL<p$_=EaTYSyaklF=g8U&2!R^9s!<;4`L!P+r)j6`yjY&
zAw4~PG!~eFWK?09pPpVre`p-c9LC=a$jIoLIn$})b}rikyIBNTSzibL4e$ibKdV?Q
zB}0hucS#`EPLUHRXLbyj4tkP9DIH+Sb7$`o(kd7Q9f%$D8Uh1a#I9@ioIOROejkPc
z13CBeld{^x{A-~RLtJ7rR~zhBW@h+Zk|feHZ{9R9r{em-ewS;%<jlt~5i7`zr<*!^
z(XN#AEdi3zpRB8c^)RjpHMU>&9oSm<1U_P`n*y{*t5a#H&vEgpU|n^{?gUe$TtJWR
znAhZuswyYpA1$E|fJHTOJdzKrMf{8Vl|Y}`#W}BsyLW4Pgxz&>(tc$SVt;mUH!MTS
zmNK#u`l|4BYPp=&VqTOpH_qyzojN)H+6uGsMMpyz2n8hdw?uBQaf#{rg_!!MxE<q~
zVt|SM$0NI$O|WOnX2eH-NbKwn7epcDyz_!?qa4<RktQc*IL#fgw+n^N94!k+o<&tA
zOA-a{Jf?dKe04@61Uh@?fEs9xM145qUA?00ZcfGaJp+b%Fd7yv-Pf?48xx5X8u!TC
zcr;(lc)7KmpVCH>Ch!inzpPI4eV1$Lay&7?%(dH)Ln`XIGCz=8#DV@W@&J8<d;;H!
zUj#5O0fAAFnt^Sun<(ua^3_k`{+xH4@bSk=mgz2LZp6QeXmcq=dsFnMfKl{V-dy`p
z_Ap^IaEavQZ&m#@A`~bu=SUc)Z{R{c&)U^to|L>Nm~t+?-mi-{G&B?t7|66p!L~AO
z4NICVL7lZjGJhIAUQLSyYN>W!ki>18U!T8ys{sb)<)uJSl<ip%ytW+UCZmsnY<%0o
zv&TN_Zj6@Kei|0})wvFXcm4(p%Din~5;2*BFPEC0$LH<)Y(?K6WCk6Fw8hifGe-O6
zv3r$uoH(pXc$@KRU4=#FHcP;HE0DPF%%cDG4$<2xP9%8YSb;q0BkCKNr$vx@nYym;
z=2n+S_ev{v@gwxCtZS|_cl)X=Ep&CEnuF3!d+cSHhG%i+rZIbo79-}>qt?<F==*IJ
znL?N>1D4dJp)I@EG!Nl**uqX_Vkq*@Tn9|<dMa@~YkNB258(6tiY)*{!wO5!JnORl
zCodO<<daiVAki!+#fa`yeenYGK{TX{5MZD_v`jSI#*nr6Nb!-4HV6gL4dv?n&C>9@
zLE+zkAUr)6#LJ~?4Qy|~VjOO&P;&M!bN1t-9RAV_YeSD_`L`Da--m?(Rqv>U++}O}
zW=1~N4Cm?oiP}Y)6hN;}aCCC6w4zJ1-XD1}XvG1;SMQ?gShPEQF+6GhQhsYiSfyRw
zOh$zuXYt^95PU3YG-&vkfpGO!w}g9O;-A+}O<9-E`$H;3C;a!%7=aNA;|p?_j51ka
zqaoYLpNcF!Y7_)PRszAM0HW(mo<9A^l~xu1%AvC6p5-Rrg_w~|VaikDso&a46iigu
ze)QJK)r>U8+?wI}b9RCZQ7mm9<g2Tz5507(vrqU{y`Gp0)#Z{5j>IV>DO&V|Uk8U^
zdR?xB^ID*`?Rs^fqdu%7DE|%h6;x!?liyMbg+%1<Z4L<_2yMgYZ@bup$A`B0Vz;8?
zsLH$_dQF>TG|t&ElH}Yh&_DWwS=WU~Fh@`9)Z~>eg;+e*B_TY1dfv>X0Ldmrik6n^
z??nW}tn&g4-_>LY&F=W&c$4I!@MeVq*102KCFsLo8mH1$2Dy@lOkIcjtb_ViHq!1(
z$w4wm1DXOaw}z$ZK!POZ4exufie;E+tPgc<?yvfEt?1eu{xq}~c`CT=raD9|;YC7N
zHQEi5zg-I+4vx)nPLI%a>Ohhm;RtV}flgSmuaP!!tj^cB5ykwG6B2nS-^!)1DK4dn
z9oPi0X0pdAEmy~I;V+2UE!Nj&BewaLUDuu4hHN7bRL_SqOF786+s~RI-j`~>KQGr+
zvrL*H3f>LXDM!UZK)No+H8T|F$AUv~67)yx7QJQWbw~q+{I=kyY)hhtsivO!<+cE{
zh>SMUlCxtx=)J@Hb?q>zSf?dU?m&GQBEpsoC<z;ab1Oz-I28Gyql<LGVWQE$O@`~b
zF>dlD{r35tgf~82OR|%-gY{A3*5kZR5D^7PK|{-R<c$Ifw<bfBr`I+y+EIR;fup-x
zkpwpKeLQA^JQLpgQx-pZ>@V~ytplS#I9C4C(LA}9NTD{2C^s*KjqnMHcpVT$@GQ*F
zYmEs8832|l?s;TqWAk0qPc00{4UJ=$jlrmLRU*UVYwKgJMue_EYl!~g$%kS{;(ZxG
zx%W?-uzW*sqW#w|F>u2MLf80?#(h7qlu>TlNd>|dOowmpg#~kJk4yz{G!_cIZ<>E*
z)X+z4+EeYimBJP_G%_*`ET-)2m48{~R%6_EHX515nj6<gUYVMD?>LZ$f<fm8IB0U!
zzsSLC=|{QC{$a$$WRBuEu+V&xTS-FIV_zKAa(ebGnJ?IXQ#8Nv^k6Uye|y6<as@6;
z|Iz%JudgWWE_;F6l`BjGSIBj#3e~#zzf1w((N<OwbDOSZ*YrVc=<Rjbb0YX$iq4i#
ze_9nCJt#o!4*nXE*Y6p8zC-iC;iu0@%-zfJ$n%%WDsO)iATKQvV`7G2ysE?j^oAd&
z^Gvfsl;JmLcuZ9#B;HKwrRV^L3ctLT0%q2zN8?|pzLiF}`XziqtKMCY%FbI+U4~AH
zm<<2LNZ+?ZUuR4HZ^E?j_J$>K+Uj9hLffl$?q07j!`1gQvVY9n`QSD?evPT|$(?Y@
zBKH!rh1PFsd>04hT?`C$W8<K|G}w5}#e+4|LDAIw;IUw>6U$vxlt?*cy{GB@59-gr
zcZ0Z=hDI1QC||4Qi8X0>0SiUQNW7X(zAZZ9_+{j&qY53G^MtkHGc#T))foR+K5TS7
z;oV6d@`uD<*{|Lc+@q#;&Z~KM7YcrK^?uJLq~m_WMpCzc_veo8k7AE_NX3D{vau~e
zaI>$I&`i9|92OQ<R-QHib7nKs7XTP|4;}dKG|sY>aO8c-eaO~`*3Hd7X=w8$>v3p5
z1)IjU@y>ir{Y*e^=WLjG&i_6tc->l`OYgi44CMWWnf|Z24>;&)8V--%_tqRt2#kJ(
zKyK(4pxA=EQzIxfkV3HEyu{!{tGI~#XE;*Zpwx4?x5rj*pWI>yVNyOtjZi4w{8W!j
zo;D|q_B)z(E$rXzpr-YCWZptbNP+0EEPB0I{!z-espJVhb^x-1D=(P)mEzLD{glQ0
z?+@HpOTQC~itHhT5-;S>RV?*XwNQ77kEWE7Eglb&O|_`FL%H`0?-4^(%f}+(@|#Z1
zw;W!r1?>awjVSBuU+yl+!#4v_GUT<y{7L6$$BtlX21D<H)SPeMx=c&7`vN_2?*EO?
z9rse>L&YC#U3ihy=eN?T6{0DwFj%{>dVPljcx{)^=Yc5`ICzX&KF#r7`rL1(z^syv
z>yy*r)72@RHE3yJF|^nvcMTie4{UdG)0M?|n|3%sZFe2lN7&(X1QX8@QTSa_zy=jz
zOkAAb4>U?j1aTwm=<NK!Ory%`-0%j4JzQ2@y@D_P-k3b?W!r~cD-I%L(z!G}ulr^P
zqZ(GA=P#h1Wzh)Z<B>~wUvyo+2MkZut4@9+4s%K+zP~+9O2E~=YI9in|8N25QJBjM
z=RG1m?63k8o25OtTA279{YC^7XZLvJoST~qt*;L^&v>;j?`ETW0?<`(a8tQF?P7EK
z2j1W-zbXtj{L1H=8PA#@Vf(S%pqe`(JMMAzbiSUM|0{VmxWjW@S6+TSBHIhl=Q)Qe
zm>T^B6DCQ2)qAT}Js}@<QGJOK8I^q(?T-nh{ab1iFJHaFDsXJfA+@V&_>*463_q0c
z#PEp1a8rK1>(86!9htDUY|LNP<-i1onm}3k<yy`&hOy^*&ei3FXrC>wxoCYnw&>0s
zJum=aist$#3=&4<lW0ilbcg5Yu?JGp0v0ak68ILi)lnq+D(c&FN^+ZzwZZEY7>_ph
zHA7!l0i2-bb%Cj9{<47e8w&|GN<(iXm5q%>UIs_eEp7&4Y<PN6xr6{WD!y5^J0rM5
z^6UfWJUVJo;Psh7?cuqzdLZFWo)`cqBa|txC?}`vEQlcch8?*BA**yDq3Dt-?D@Y5
z<AU?qAN26UwE^P-z>VQtRi%U$3j520KJ2!c_KJdQxvX3v{I-5{*}qs&gADHKJXldd
zL3BL0Q$$srrR{2bf8>_dDzKm;K2mEgcq;zA-a1zX(VB(m3cqa5iorb!coY}8Z^^Do
zx!Yy|XtN7B)ah;V*_hxnII8b6Rqs>Zd1&7<XBW-ZP5<vcm*t$KUquCLupXelCM_mB
z;$-nSk6uX6yNj%wS%&tOfDkbm)vd4;m7SgP{u;38Sl_+4niax$33zz9gm7GbJs+G5
z@Nb?I^*SjnFF*K~Lk*$xdBPqRD>dph%fF7-3+h#=|MZGq)|Tgbo*Y+}@SKC?US2NO
zbE@C|GvokrwxM+MbIUoF{u>!QpHBd>a6>MdDf>&xuUX()!78MB!+NaP#NoMv9bbt@
z!z2vi{yUo@U7kwW<^We6McxM)O%Z-y>$!y@=qq7pGRW(kzTK05?!AE0AT4{^7kdw+
zy3Y)<VMD=ai536Ny~c7iRoa4_W<;T^^gt^iY~WHxtaI<Y9#(2-*Ake{d4lHi2~LSS
z7rw~$VnMy@I$wpHf`U$kO>(ZkBr&>qFE8($pu-||9-%LUm5}pNZC8bnyffQFNA=xa
z8@~4-mGgs~8^irap^^IPN?TT0YcJJS?+2es*=yE8X+WBNBCefyqoa_lWo+yih>jr<
z-<N<hfsOGW2*DOd6}Kk?uky-RQ4ktrAVrW-g>d7bqtZv+5tmCIZNAYww~T7iom_L}
zx76$YU5P>MIl;|{Pq{7|Ch9L<-!Ux6^;aXH4YFppoo<ksaB$lB(vo)TrO8H1UP{=L
z*SBGheayp=1$^LiZ<Po7M4$x_zmpDZ{fZ8qjyxWoNMHROgl2WtP3^6#+rOZrv(=Pm
zdPt-v!}k*>(LP07?;jsNeWD>I{=DWE%O2XLt)<gCi-)`{Msd!1B$8Kj-+gxxidhKg
z09T*+okWoY9H_#DjL$!L0ht=8P^6@!$;ru}j68<JH!vu}?8oZ^Pa%`qBJ#QG#FBw-
zf~WP+A6U*^P7m>p_TN}xMi8&l9M7~62e_CkD=Xi=ecPRvHx<igG(*9Cglfdz^o}r2
z)|zcxzx>vtjOP&L`K;U*HWqquxE(Qe0F+UXs`C>fH3I@Z2|IT-HX6YDK*J(bHS@%k
ze7%iT^@Z%V*%GBTTLDV~@p))^I!vBM3rq2Deu+B{KHJT!{~U^^Q!QvbfUbu14IgIM
zue#Y9GybF$ThOl|Z{q7VIpdbe&D6<YKS<LWp9{Z>h^V)lYXZ++NHGA&xB~Q3Vm=p>
zy}bqO-|sMzQGDPTKzSo>HDosWApKGZgl)+Q2^|YN7k2hSPAkfdbcYb>#RJ^p0^VVp
zPwwE6#UA%yge0^=L!0gEjX727Xr0H5AE5RbfMOvmb4>$kR^F&QoHl5%mwQuFKYnB(
zc_&CiNE@*={B0B}-|JML7-ro>JIlK0quvTTU#q_OF|9^VOe5s52=3;9a{%iF9d#}%
z)%&^ir%u&dB#o2qK9}h!*Sp<*w3q2BYQs?uOQXi<CHnBNh)kG9rCr(wG~jElv=aEy
zQ#~CadI(y2h^O2+J_`xK1s)50cC@Pr4B3sOGXUCO1qIHpYw}4x+iyri^__(Y8mO&6
z+~w$YvZ5+0pFc4QYdLp~D|SuBYvH8~ijxCo5v9#cBBh6C?4^?$7Sk)}7F|z>0_eO0
z3a!%~t3y22KsESPbKMXcw;H&LBK>#l`QH@si;eNkIcz+<uI}z3(54IydK~X&0I>w>
zCo-x822pWu#`abn)XD^A{&ORxKTAttO9F1xR0{Q;V$5zR2>QN#^4aV|W#z~LNwCBY
zkBGQKj16)@K$PDOoCNxCdTvNC(n7>%$@&MELiANf9S({_R%<vfr5Q4Qbnh5wzkJ0Q
z+41<U>uItOWbL3X?ZIdi-jt#s7d}{g#y$WTl9A-G!L7Tvb(ycfXWT{p+BqFJ|L$g8
zMco_@9V2Z+B<LIm|8}m?X6~H^wt#KM6TDTJ>EX@yGzR^mCz+Zi>$G#`<K<O-I$9-q
zG5=*irw&0fZ%|2VC9ajv+`q&jWzM^_A?Hd^e!*WKoer)(yC=*aAnF|{bY<~6(QrR!
z0HEoHCTTdJ17H;h-X>9UUm-9WD15BEY{#UbcSku$v5$gDCZCGl2&YZq65qBv@wy*$
z%}&r(B#BP%2T$E)<nn#H(_FPDcx!++_0<p&V@uFWP{f1ZB~T~tR;%D(Vtuo`otuk6
z(*055_bU_rxZHx3r)pgwdWv$JgKEQ+*5-+xI^SR;$HlVq$|8QzowlGaYpd9PW*js@
z2HYu7-a7#+F-mR`_Vd<OM01DZH7RH3G8%0je*W(T1t7stlx;uUilWUhk{-aQUK;e1
z1Q-+r4O0iqmWMJ^-?JEhvD|_$jublpI_urzGuVb54;DFyv4MUG<Fcu#sU&LPcIK>q
z*fKbmA>KA<D<_JN2G}KW;%y8JOOt+GEStwN^bgGM4$1H%_``!?<=g_cC3yPgyjl>B
zrNQ>XnO^@kF*X=Oz`cHz!v|dvfNKjilPP^>yv)&eqvTX?zTGvOtx@=|AT++@ws8Fa
z7lfLmgzu!AUUC6=1tU>EAkk!IWOUpo3}mJW_`r}HFi0O|*QP+`d^)<E0X#x_Vn8$T
zo9iwwPUT-?eokN57ybjn9^+NHBXxVhPvB-RuJc+#>PLMf9cX1ImFdJ@laZ22F+@Sd
z0W+b2$*!dK-CMB(?V4ITT1+I-c9p5!3UPfC2Th~=QFiU|nBLa`Dk1$GASKm)GP(gL
z^;Mw5^XK!+%W=Ts49fLdI1=p~W{i}RP8Rn00EwBj5n0MCNMvfN&tueq;k3Jt-+?zc
z-F?HK0@U>Y_R)DwHNJx3lk}#Jz81J{jpc0vYNXA@JD|gWKcTHwl2t5&`ci60*wkoD
zUH08SN;t@f!|qr%*+c6Ge(yk%hOn9EEHTj7!pao-GFkPm;FZDalgX$D`a)rHjD8Z^
zhvMjx?Cc5N++fAnSK9iVWnJyb3r1q7_Pv_sP#Vy4FCN@;WKEmuA$kNqeJDClKXTv{
z4}KdM9-5VQxBMHGja40_y1Ry7#}WwA*zURYU5xKxgFWH#a^{^c2XD&Dm5hEscp%#b
z5zy|OJkNf8ysfNHH`GyNek<anvdKb2O-qk6Ze@O_hD{-h`bM@e!_`GV+L36d@JX<L
zbo3B(wxF8F$AmBy$uxrwjPGf_p6jd#_DN;v-<ug&Kzh1jr-H@3%Szu|^gpofUgn_$
zOE(~p3ERy!fE)%^YyC9FJL#*G2sn&@Lw}QohHQsb>wScQc&u9M$OwvrE3Qy$Yw_)>
z4R%u-0pPvDj1&|wuMamNKF(^N244!QLK@BB5ikUAYHo(y3NSVKT2SC~v~3U=#Dqne
z>2;19-WA)Yh@LV%U<4cl0scUef;*=Xl(bY-kkgKb*xTDfK@XG)K+8<*?RSE(ZrDd>
z2CgfQ=4sxR7k3k#9v)VkDh@;SwflQ2X<lB`@O#@-Ry)Qzdjtf>arLjGxvZDR=ZgVI
z_@4&TBzm=%Els%+%2t4ZP#S>B2-V@){=XK(9jH!zTe$#(5kg2Zu)uu>>d!H&a_~=p
zDdJ*H?tx=Kd@oMmF(pLzB>0{G8(mB<$?APdxe@QQ5r0jpvkMofK>mJy4u%nTA}F=m
z7EXH;hTZoBw^MO=sqiU39<9ep;SxMEuo)8IxOcaJG<1La2zt*t%w4vUsQ7qwJ-unT
zVaW?-hZUgYCEg{S-QhCt9Ngwo3-*Tt-HJQRj`-bAc)LJxYX5B}BNLRIJO#rAXi!5#
zLo={|fD1Ea?~~OmAfhuVpj@ljn<b)TpybxE+P(b8ie66WLmq!3IKCeJ_K(AKUU7x+
z52KohbTryvo{>yJN-D6)XzZeZMk3GPwe=RBT7ba>YwjPixS@9~Q!I-f;V!$s_`Q#p
z;(g~tvL+SFl2^1fHSe~?O^8ubQ@48%AlqpW?eOitKHemHU#1u`k-^0tn%1m>%R>qc
zL&JHW<g<ef?bYYK&;dbQ`N;S<6#~^JV4fcpOo1h>g0l7g{rlEd84!ui;3f&ugv&9@
z*UF+cw6*ZUiE`UpV|*$mPFK^*^Dj1!ZZe0{g1?UmQCZn{W5?Yvg#$KyJ#rEU&#A<I
z8O)>#+xQH!;6!?2>{!9GPiI64R)h$8VxNnqw(_D@z79i_)#W+A^d<_j^7|?-?cR&L
zNTXAB35KZgCxJl#Ru;X;uRf~R(B~P1fWV5s>Y)<!rD-#ds~Y|X3RO4ze?Xxt*Url&
zM*?nxE@5@KU4W0D|4G{fge$^VlYy1|=@SURuiv=w7~+1y!ZNU~{JC^=^0Ko2%+2+8
zx7W{w*4M#)1;SnuzjgIBkKAYUyn}%@%zLB8$(rtuR9W`{tA$}Etj!Nw$uY15K)XL<
z^OAFG3))l;jPvEGZ?>QIPpr$7vt{XN{+bCi2QVLUT<i~rqeRKY#Bn{lPD3~v^x!La
z8-NyLL3<S2SE8>$iNjp?0nn606OTD6wrJ$jy?yH?N1mj^H1u?_9cV)r>x-ZQ0zD0s
z?8o4JNQT?e)rE%u;c6P-c-Sv`)lMA^4KKmvh)(~@%D{3eh&Aw%?|Vg-nV7n_)Fwv(
zRoCI~IVtmP*~W0W&&sosO*;?7mg)!yFt_E^tffuLD4RdsU5w_BOD5wN--F#~Kt$)8
zd{fjrhMF#GTpKNWAe&htR)!BHmpAtolv$N-J74x5g8d1|kwisB>ps{C3JEPixYXvP
zkbr<IbmRm65DIyc<7@C+{W;3g%23W=!X6e7@f@wKuU9|3m)eUZ<`o%^URen+Qypjm
zpay~Pq?)a|bue$cH(?ti=NH=beMA7Yfiia*<sM!j`MwrpxC7eM+12H7NtQc4M{w1K
zTAQ~nTPs4l1oz7oIHP|Da)O}52SY3#o)WkjYinzPcX)hyD*NaWp!D#b;kNZIZv62<
zRxrjKD2}5+gz!LTw|$e=CBawR-e(QJZ#Yn@pVLSQoTbVtO)&MjDhe4RIQyFB__ej!
z`1?k)?KOCt(s25)gPCC%MD>LUCm3k;E-rCfPoiBu*@5*FB=3REDFC6c@;qoo@k1ds
z&}QnreIbM;THE;JW2S>SenX?Zi6)aQL)ta$HY>K)j|mpz!DLSac~SoNA88(#TZ;8`
zbld>1M8kRWU^sz+J;v_UZCFGvPRB^nurTNRJ^3(lxx5@;OQ4x^Igmx}BcD?Vmjc3K
z17F+T?=>vK$5(#re09AtuT#W=Mc&{7B!?2htor^=sUs#PW*bG1exVD~tQ~dR`%Qq2
zOE_K?Y4pE$B_?4#&!Px*FaIWjQ&1=H9MV3(D}C^;uvEYqxM{AUnqS>^XmH!Trxplr
zTZocle305ZXGcaAJqcZk_{C9rULJl#o9o}-J5Y?ltP-a2GhT<ue=TJg7#I#FmFNuQ
zbgLXppdZzy>4pWMU8}sru8-i6!(2R`Gt(ReXQsX-isd1U4NungU%uRyeKrS!d*#Qr
zS09JTvgsNHMaAoV3rzoK@L1u{h6@kMBH6U44iH{MT0irK)&I#xmoqLV<*Q~xYd#WL
z>@Ec@ph-eGz>&Yi%GlmcIwp1f_~C86ls^+&uYBQx=J8nZX5UJ~Ttl;X@K;a|g{e8<
z|7uy-k$BD1(*3E!_<z7`X%ndHVSvuf!vjv`@JkQ!a^MtsK@tw~Xx4?YI^PR&B_g7G
zcq+Qd(aQ()?2Thpmf%syhJytuA*&li*4A9yrX^#qU%$pBJi9bN`qlX6t`C#E*(BT5
zLJ6L1HkmyPeW1CT9t4JyTQm<4f6V6nZz`F`UZ<#6>puAd-&_M%_t4&!;$vW)pZE;(
z0d<xD6(vw2`wt%SIlc-<^o&##^Nk$35|1u_UUbGaNDcjc4Tb;a=TfHta=nBxOj!oa
z6Qzu>RT)eVwqNanA{qPBPw*%>oSrCp0ag5siTK|7sL~ah=10$Z+p_6CxHWTfa*B(K
zle~+8R`bvyA>>L|B4Q)CT(&5sRFdUvZ#Xm(sF0%N@{iS5Lc?kM&HEfF!23Bl&M)Qz
z-BeTtKd+cQYMcD3%96OBc7zd85ZG+ZFQ6%D>4Enx2+9&@g4JZ9gZInU;90X{WMZ0v
zwZTZ@4tmhj(=JyM|1axyz~hF{+>jPS&^b^kUYx#XY2x9qbuoBcrEDxg|5HQTZFSvy
z$(TT=CSuLb3{^E43R_{=q&N1dAp>9?!n<@c29^)VfrI$m8T*%GS>65Y%9wkNR*!#H
z629n(IjAiG92I+Dv!i-T2Gm7AeuxD(=VWKwxwybJS}NH*VAp2x5k@hUBzdsWGX2>*
zuh@W*<=@W>o<9S_qe|fT3knMZwOS=d)t&ENL!ny6bsPn9M1i|6qP(2GqO6e-9wGYi
zr@Y%V)~>N33_8_L=Eu=l>i+67niGLRL7m^mw4&S`yq}>)2Vxy{y!Y;ICAlIA(950n
zU8v`<NWOk8tgwqlf^)Jbmg;u#Ms%Ew82enDtbPH{Y5U0k#qtBplU=jx@EQ+?`~dL*
z5&Ix(EVSux4Bq2q;~OorRChbWZha3XhW1ViBLYV;&8OcBE43K;hX+DR%GPoe&ApPW
zq1Dj^Mi+Pp;h_8F8eRRkMTwCB=qunuI{p294FT&@xGbRJA-nNW3xpYLlf_HzCuMcZ
znMvi=mL`^?>(p^%@%Bs~#om2<`D)|ySj9KwN(*qLgQ}tBKCIgE^XKPeQ=n*!qd->*
z%{(08^<TgK{Pm0d-o1PD^b%<XIYR90`PLI!$z}-BP(-!dTKoh@Lj2}NN%@P`6{6Q~
zW>t!|9m!_x?ncnU+28^!r2n`+=;rL2cfz10<pU!Cki3JZJqXa)*xA+0G!hZCw|RNf
z$}jGsP$~_}7gsz4Bwmh@g#Lqsu=We3)UKu6|0(4OVUxETb0hF^ueh%uy~f9sih9;T
z8e}j$JiPXzIXuGPAFjNM`Uuhx!CXpuGmpjWAufW<?i{_Ws3Uen-<<R#ZOeR`oV)fG
zzC2=~$o;rZIOt%6K|~`6;bhG(xu6yxql&8qL=(I|wN3Z2Zof7*{t6tH+}o(g%Xcj(
zff>NdVW;6}YH|uMO3S_doD-@Os)VR#s;Z0E>14A8)ov2PdS$>W-pueb&~R}n|MqPi
z4&2M5g$Ov#C&tF$Sj@|E8a#NR(x=A?bN6ve1LL$T?U8mp%~nyDzaMEl%oA{<+Uq|b
zF-0gf&4vHg2+!(#wV+8b>4UP7@dXF|Rw4rb=j!H%x(gi85iU1~GxW!jCu(hJ>9{wi
zOiUPTlxvBBWL67XxfJC#`$8*V@$i|igqa-ur?N7K(f$|C(y)NBzu*`5%$cRPM$lb0
zZ{0n_llMLnd$iQjSzKA$zx99&J78Hl-`ImX#`@~iNlZ_^Lj#D?;lz!E{<5r@o_1ge
z<#2r%k~vsB`$ARpJ3+E;wJ+`7cm%<fu`+Xd+sF6&V_-7&-(r2H0(ij5QoQ~Haj@8f
z>*4#En^qqDL}Ye(`QZF;rlzVYhb!ox!pM6fCctrP!VlCusP8d63vA5B0r;W*9tneY
zP`g-v*d*-e|GK-#@_@Dl$Y;zE4ly;m#0lO7+zSRf`5Hy@6*JywZ;bf5XFoVx3ku6v
z(=V;KFWthAAfLf7zPtOOJRZ_e<p?)G2pT}i&!`!qR)Xog!L}Rxl6!l4FcJLbI8J>V
zTU%QLgCetj2K;6}KR;YNJcunmIabLfyzDYg<{g&-uhEs&)w}NIOQ2MFs^x;r<h<%l
zPL|}WmFB^CV;+QAuaRJ$W=MSr(w*jjvl<^0)MP^S54B$o{%1`)+e7quJf}X;>F+0W
z!<PnM0^oE!l=ztbz6KiLi3D{EoWwE7$zJe?f#~708&gtJlB?fH;VCkARM068Iy|6L
z{-|AuqK=Z|1)XF`)1k9y-2!7hR939{>(N40TpqMYLlaYcyD_5i(u&f?AV{{Owf-i+
zjsSaiR}^Fg*+$z8LObP$NPzZELIQaw4Oc0(kOSkDRWO82aX}#tsqm$xF>MQc$!D=y
zL!A|*;lM{a8&v`Nx&whs3J~2k#@g{|S1}MC4&9fSn5@<}_B0av_Y*BX_xmbfzA<4g
z4KvMtJxoDKMa9Z<O&YNEdl05TdwKW?YZWsNy1Oe{XoUF1%GTB;5RfC3E;>~#mL?np
zA$*k|Of})Qv$V7nPJWsNVSW}G8XxD4EUyy=OP#)oc9p_?l%Va9H&fb?mq~>n&HRI5
z%2(jCA8!}P2SIWPG=c;LQwIlb1ZI^mbaDZ*67WOeIwZwLN9glCk6N;ZlxwEgU7&7v
zEDw6jm6zD{ZT)u#6c9q^{BN0VxjlcB)gw)_`6jHz7%kV~<O;_a4+8^1kV*l#oAmLc
zoTYF#oO+NO0ejrZ!66a;r!)4RiVEC37B64ULR%{C&dScNI9xD`XUuMG13`q~>IOC%
zj>`(TfyE%k`uS5@s5R~Fz%p2u0~;UO8?Z*Uq)K|uk}3CBU*jyM83v(G$2AB>N7W>=
zo>OTMR+(h{g1Z+Y!9W54%<|dQ)tt}2$)tH^OjO5fr=&7|@dd!mMz=*?17J9Rk#-)0
z&cSn!9nme5`>U=T)4Z@wy1i3Aa{lbrv;AhnqE@R(^rQ;kxRHJ9q*T|x*`;Zma99$I
z_PB<sB|eF2FZe7N-zLCF+%u-NuV!8zm7IJI!)NGwK#R7&umGNV?t3dyko*CCuIlw9
z3y{mM^pipYAu=hZSL^0Way778irklx0)GZKw@T>OLEH~m3fu^JeTCz348DDTybL%Z
zl)zr8sN$J*xjpov22tV#^Ekm)Q}8NfnF~2NIsPNT-{)~;>4ZtBslQbtY90Ok*Yu3h
z;_*;XMBO1)^2>jxJnthOmr!SD2ILsQYRA#uKCRyjmq5M)<g!2jihCj7`T55Z-WhI)
ze1Iemdw!_PNV0bI<0nQ&=_x5R<++e3!yRyj?k2kDC|=L$02VF-3*diFOoChediMVI
z;Z5yjFl#)i8>f+yYF<p0jF8@nm1A^_$JEr;{`TLsrFd%{TlW8}aCuHuq)`i8kEDbW
zw$@`E!1Al&|LkBy<Ayv5kfbKr$(0I@GogkXANWaS`h|9IQUDTV@eq&hc9dL$4LXut
zFgkK^Sws%^*UJ$UWZMm$fTQ36WFlOiZ~1^)>gEH_%Kwhue){6?&+Q!?o+v7o6&Itv
z+1+&jodg(1T`e^=Ryg~TUR~YITs(MZnJ7Vx<FQN~>R^KL{)e%nyNWp3XvoiS;{hfS
zYI#Gv^`8ZrCCo1ET!kgUuSY74g0)=g)6Gp!0S>EG&qwj_Xzx{uxX8$9ur@H5gU$TW
z#)1&zcoaMsfK=1<MGb;NGX^rvXHPInpFAS{tj0<uIkF{(->jghm=Uq+RQ-3RlY3w|
z_7CoJhynxkNmsC{loXnV*5d*cDqS}6F3JvltDYFYdEuhpkCGhn<?jr}nNlMShD8#>
zIPn(rB=58i(&66&cwm5{AMa2B$&szCK}Y@Nr9d#R0{?4}&jX9j=~q;u9GrD>a$J0T
zy5N4{xMdIdjTH+-IlX*8x)RZXn^#v>KD$;;4MJ^^?RuP~bs$^$L4eSbL$jV4th+a1
z^!n$IEJGC7!N5eEWkzr@sXw~Im?QpP7j_%m#<%Pqm|04x4GXf@0`$)oCHiMK)0^cx
zX=u%}_V~X`%ujp&4;LWM8N;*u&CI;n9fgl}LKd7y?}M>?pe6ilA|9f2!3rla98syG
zNienm<MiS);~0?58NKjCWz)yVV8W1(10N5)1IxQ)xba<&ZqsZ~2TDR~4eAcBXz%Uv
zg#dpEdQVS(Z6oLn0U^2bf6Dl)Xz`gD{WOESVSp+RlHP}VC^Km=i%pERizz}#=aQ&}
z!0<7V^^-68W%l3xNP*pn4iBFq_)*>W{*<Z61}SZC`i*?$(6wtkNM4qVQ5{v>1j-Zh
zkxSEkpdG+(0|A}?CVc0Bc8$UjoZj9L(+1f~Fi7T3IeXi@&&gsFBd1e#_g(pjjMi>~
z*D6>8FE2C4XO73gRVcM<+xG)&X!xO+yt;aP@1J_W#s&sRrgpDx4yC$eyY583&?gxd
z%dDMBqQ;J|JXuWT8PA<q{#aY@;dHXZPuE>t8`S>xw(By`kd{3=1s@F$j2?d*vDurq
zU3R`?^8L6f;<XPq0*r>TpMfwK3N#qyKbMrSxR>F3Q(2>_x$r`G{LkOK{g`5*P-cx{
z(0?<@e~kcv5Wal8Z#wDQ7SOpVH^D_SU<kk#Fwo&y>wEGuGq-kjT7U{@MwEq<`_~+-
zNI0NBa+`#}#FfEM+2<}8z6LUR;!}V9M*7M!!@roI1$;X{r=uYf^j`~watX1Zn-2p%
zie9DNL+~%2nMr+ee(+)u#HRp*K+TTOlZT4uG?c=b-phGB|Ij_kc_91$1}+wGiZRSg
z84hb&+!py<7!YBQ@><ybW?tEjpqlKE&94-()1SG+Tn_lPZgq|y3UZsT$O=q`dK_mZ
zZdst7m@m&)5q4gD*OE0V3eB|pAjb-20$cI=(eV;^2OD!y!ma?G4I{}TX=yO{gRPU4
zkN~&pUs(A-RfXRL>l;zp2nl+K>1g};)6+Y$#Sj2QMK<yUyGr<jKoT!B;gtOk9pnOl
z(8R0MoM>!PC@^C{<nAkBjWjE%H1O%t`<c?&-fl;;It6K^{{#6xiP7ieXQCl{Lx6Uv
zr`<=5d}roMW==~AL9LIp>rN8j<l!0Y18MSLR$Bxe)N<fblvGo53P2Wc`udy_^Yg7?
zwq#~z_WJb+1rgBhY+*qH{)2{zZ}zJzif@h*R4bs~hHION<Q+uz0LrB`XsI35ICtX1
zvJA{(;c*<7k?NJFA@d1INv9`YimtFP;v`L8CK|98Ny{60qECcO^Ju;Z4_7?(d5m9Q
zlxYj@k;vzQf_kGiLYOlH3>NY2K8(W=Bus`BH&9>n2#Nz}3Fqu8;U+C#UTROYvl=U2
z0^3ud@dNt>9yv@(#!AgqEQMeA+@SgH(>OEM>i$2Kd1p()kI$?331{Q2C#H22$U~Tn
z{Cz&bkRU7YX1%o5Q$;NrPXJm#9Ra|83a|M<+aFJR*Zc?~NPz(F8|Rh2Tfn^HQ>%my
z3-smD$;tWYuoS_nU=w(~r%wgH?(_d9c}Gn(0W|1`Lk)m>2Ck1l!qg3cS>T%x5)c6F
z5@VCX2b2RaB!c`zeOcK%un7RQZuJH=D<1^cL1q4oh0L6)kGt4rdU<8#2t<D7_AfZ%
zA=jlBc2sHQXm5_izjDsk4a?m}%1u#nCl$$7QhG~~(0pF86hyumRHpQRkrV(EeJA|a
z(XFR5ar&066A|m_1EI2Uc_#4c&CG25^~)NHJE)5lEoBqsN3Ml~K$?J(>m?}ZD1+b@
zFoy{t`?}J(*xa_MH%tF-cWPi3)hi|85Z>HUjR_78rW0dTsd9y9`t=7D*UJWA6&^x`
z1DGE9D{6X=rfcNn%bS}iA3mTWAWc?RRwIt>n%_MWCo5w5;EmhE5AFu`8{>Q;A~n!L
zS1g}L*T68oumlU?;wdO7P<MvFKl-;JFzfyN`SVD{?RAPZ2sQ=C4s2hp%3M_6NQfx{
zbmnAp_z7Wz__>t*4~>Cj$?%G@=Q`~D0Jyxtqv%z-w4wOr1oJuEwG0z5KgYQ*o3@Si
zNocC`y7GnwOiiGsx{}F-QLV|sc}butD+tkiJ}~Tuq3^=ZQFnLuEvX>*VPIx!4=Dk-
zVM^wdo{d9(3{YrF-qJ5Exo@>kTc{tEn275=*w9XSQgw$(SJ=-$@Kbfn;YTqK{xBJj
zBd&bcOK&sxHHsf@J53&5EDEVO37Y>d&v+OZB!{ov0*xO^@x?WOJ;6&1bUw5XJi)<1
z8*J>LW(HW8g;y}cy^TVllQWm(U2^+ZX%T88p$(K%eQ*(<eFWWF=u*Davs5dotD_V=
z5|Vh$KU8C6GJBuzt7}xEzuphZ8C0tE91ed~4X@P>+zVNE_bNDu_liJh1xYA@l2IHR
z*6X_%k~4l^aHFzsCpe8ww@Nnx!q5%d+CYp{@<o_%tvDN-LjTmQT@x!S{3R0vVYr2j
z!NLxcx~Ras3e~gobAY5-^T~m3BWltav$A3aD8{4KO#DZZ{a??%IHa|wNY@&^GRkCH
zCt6H`kcv^pPgrlVp(sAxtcgg4-3tff?^^{a#7l{=Dg9eQZ8I|^6!(vvZ~b@Bf3=P*
zBg8@`VWI|&1lST!On3l`6D)T(!7s+r;*F;Qle1uF;Fq1_Knzp^EQTF*tgFsg4`uPs
zpBn4m*lE-#%`dezMy6AfT7wrE`1ucDhw@UfGcrn7?-yczpHjEJNk|@=k(G7F==i34
zf4A$6;W@KI<(uKXIe=HVo43;Kfddg6IY?AN%RzxmPEL}M$8ryaw0P5n;GqM_`NfAH
zq9p_f3mY4H)>8x#q!Dz6Kl{O`$sWWcd`cI71P01#nm<f`0i3-!T#VV-(IEstM@q`H
z9|j&QHskufh6YHIzFHIF06P4JlMGmrT3S?u!P-?0OK|*Mr;7VB9Lh9^8#1$f#EW{X
ze`Gw4vDXR>frLMd8$`40Xq4{Ti}pZKS^RwjI7DL?^|l)qkJ7bw7^s@KYq#~4!3W++
zv3=)O^OsN6=olDfmAvc#a_lMs0SW5sTRc3Dpaz4uDCn!<Fc<{g4scN>?W-oCTGoVy
z(z)<_Q)<VNBjQOzYDI}lX5`W|MeGwVhdnv)`KOH_61|oVa^SXBAZ(#gM1NL!m7TrK
zY*7}GQ^UHmJtsxUN0n!d%+@750$||^GrFRw9dqM1e@#j(^G02Q(dnwwYtUoG#;U0>
z^}<I`VTplz%E!~x+S(c#2w-PFcu;Z$W*>Qa54M(hK1h{JE6r42`j(r!Cj3vtZk;GF
zFg%p}2j3T<c|*_keLUp#Eih@s)$*O+*!YoEd2|xa?j-EBu{p)9H~jl|c1FhV^0Z)J
zaYaS&#No@|*u<e}pBunr@W0LJnV`&~1D2o8Xt!tW$UpYMylCu%AAE?9emMdj3x%hY
z<RaHYy^(+g07odnatg#V(BQ%f_xJbLo~ht&X={^e&{-MALZ(rb?JC{0EnRO~tF;YF
zqWt%(IJF)iqGnD<2{r&Rj9Wskhv4N|!zb>|v>_`0u8H#|negHvAmRVLoip*0c-sp;
z{nv`wdzTStG(N|dr&;h225B}P)K7MGfxW6c+$i8!M*xCb9Gw1<5q=&X*jgX=0^nYQ
z+4$i~zY$>Ns;seHOFiJfQd=uZPyczf8=PpkDc=K>0*4|L)-T}#5T)&fL>1U6P#65)
zN);c+O5G758mIz8-oE`P<d~_<Vik&m6=d+{^iNC3qdR%*n5&(yyuwZ(99~$bIUrwz
z-g7j(I}5x*bHnArz(PZPs`7>N!fTHyTF^L#Ev>J6K$HY|C<dUj25^MIYe8#&r7=k*
zr=_ifZWit~Ie`Q4WIj9UhB=600{5fWHOKM#-Z<FU5JnDp^N{a^fk224OwQoIhoG?Y
zq@8x*rXt{yz~=_9b@@vn7)HWW(zMn_EopO$&U*vMdO^oa%kQ0>pE*l+HXCy{{!LB7
zFg1n4k)NNRH4NSy`f+(%GEE{a-hY%AQ$ZJ0{#R_NSNB|I*4xWx-d7zb@O?4f0HJ+t
zeI1-8rQlWvxokAfE&#aHf_C(X+~wi=D>$^&UoXR5NPv$I^GD>3Ogf)p^-+lJ9UAzL
zV19Lvjja}R4tsAk*lZjvQ6mtPNijPlP5R8?(ma<?HV3AKw+tyhCi=eFQq%bjaJZUm
zz#BP6VfawcHvXus4I-lZzGep{s_doK!^aHFH$@{;Qov|@18g4T!;F9>l9-qXUnGsV
zw<tikfG`7EHh`K!=ji05prk~{V|uj)K(KVhaZu{9VvRDiwY^;FzYV1|#Qp+n4*tIB
zFvH0Kd+halFTQ4FogN*1y<7nP<c%9QiVxEqJib{Fs*R#be5~r&?MYU>8D6})w#Loz
zDT0yo;086=KawnI*`u2$fSxm-jZJ8ImGAbPpOZ1kL*m8tx6)sL$#6C4nt5jM^X)at
zpc|Bw3MryO+}s1by-NR;O3Mq55NdRFFl}tG-^@5#Q~=dbS3D;`frv((=P{7NU?c-;
zT*UkAXt|ZsGUM6*;Vhl;6h=b|&16rVq<yT6(g>GSCo9U!u)EgzIZfXRVORhxQ9y`*
zXa4AL|Gwh4r{L%RL|z`6ZE*L`H8RH`O~oNDqDDTad9Nj?T#eJ0SQS0uQPil0@v{e3
z{_s{0to*-8NO?>otWHJWy}dM<knXG-cW+H(=jZDdzI>SY<2Kp#O&TH3Mn8;@&1dO$
zRXVtVe$8{!bzbY&O37Xy_)Sihh@*#5@jV$%<C~x~YNmEp;NnG<p-p`558Y!V-&`dO
z5rLOzX0`(3L$LOPy9}-dowMmaN}cB%C?754rVMOy187M}NCsLJ(GM?>LhoN8uyy8k
zioc^t*7^1R;nc0>t%}153&Xi(3C^8_-=x5=Rk}&#+Qfum)6Srh_>O>0ZQJsZ*2l|2
z*Z=K)1c3&|oq&)0U!0`0J(qCdu0^{)6mcP3R%~iM^|3;FceUR_9OCB-m5P|p!+zJA
zfJC!V>0yhm<^x+3h@Wwep4KlwwdQcB$7Ln>R{FwHy=UbwK8!68y&R9Mj%N_&(32`&
zu*L$FbPV)AMlfuL?gL`Yz@yZF3z99#aS6V~f~V{Ezf(pl<4L9#ue&p&<GTM@)@2tK
z+QEEQf!PsGF#SuT*D%_jKwFmRw_|D7-w-OS(tZckovW+?j`W#lQghW!lIQO0u4At-
z%WT!u)Moo|5D9s_QfDal95=JVxQ3{UQ@kH6P+j$YQpL<P@TG(K0bD1r22K_t#DRki
zfSi_j|CdLN!U23=TKmN5&dcn(-k_jq4UU5u-zFzMs_^-EZ_TEbhNsCLemuNT*LaQx
zZrcqGlPly_zrp{EdSwa=k&wkQ+XF0<zv4{eA$&P51q8lZsGsS5GIfJtidXwyEH*y=
z70(oOYfp#!uS&UuM5RkB;6@TcP{-fDY|snrum6Fx^z?o*suz}TTMYZV{-E7CMH;x{
zd;X}XXh++rs;ke8_M@#X@21OK83L~-pvOJ|^Hry=6Dl~V{=P+I@37q4ZynbwB5hfP
z=nQz<wv&|x8)o9>zn(yC&a)2x@qg)rPEew^S9AzxfUSAY^mIz|9AK5;O#qOlR+>Q>
zrmw_oh;$jy1;3J$!y#40mk+WUDfb(^{HQ?>Wr9LOQRQQRL$9Y%Y}UEDAYYvF7g{3w
zn`MuHp`lk~*aDYMS*h`_55sX9Y=DoHdB*zzA3R$~bN!0Ck&&6HrKyP`0kv#!uq^nh
z&@4o51=~J*)(_u;?@skUw;5;^N*$PgLg#ewcMPbHG7!~KeovRrZsS3ijAiI;8h|1|
z6QgwhzW?s7Txt<8i;MNYC2Vo$HurC)!2nirM4bFSFk#O<OztK>y*$aZGc0n0wFM0<
z+>wf@S-=qR+*`SZz;w5|s)}#jvAhaw{$bBOxQjYt2*|dm<MMl*M9D~anJl+3q_+_%
zbomtm9FOIv99P8oP$**{q4)m<lpV!J^Kf$mB?~C3pexnY(E$S%0tE7y;TJ-D{rd2{
zCSm(!nd_gOXTygVZQ^%$G)<XrMFwOT+<%C6d@D5HAS+RNq+N>a_U{lmt*E8v&-(Np
z(bF6HkX&n=#1)@=US)SzUibUkNf!Bs=y=%#hKzWqG9iq&<ar)5Cv;4Tx5^J&c>DJV
zymIJSKOXHi;QsgY!7cYc9zCD@%X^j_9uHln+~i}MT-Z?-L_Ye~2O5FTNkQ#y=Y(zj
zhD!~d+pI6pVzi|noUEi%+f>e_(g8|wl{0hg%BKnf7z*r;$jHfM02Z03u>iga@P<6#
zB8BlBblN+3$l9cR+fDh+BxVzn-Q#0TG|;PSU?V~H*V|?k!5{!m1AWMz?uy%Dqk+5H
zrAAj83>u*B8ogZIAJxA+F(6hFKX-y@30U8ObgqEIEtFR=^!@uipNLr~wNnVnI^GKv
z{MdQj-o+_?w%v?^KPb#b9*Pp4S<9N333W<Y)p;Gz3Mme2_j^RPoNn?AxZXFrOmOT3
zVBOMD<>J1Apvg7!(eKIA*T}es1?=!*Jheq=!mF!=uR$b`t00Yf`4zzITBV~e?!pYc
zTzNS^8(X)lW~Ict_wSvmbS~)3;!;yNBRI*gRsH*p)_Y+ylBap~Hh}M_%Mm|)S1Wed
z;z#aXETWnz?+NRQckRR86G?WRa2vs@%{+0p?zvl|!-1xxImkQ0{pi^o0JW_DWj=7Q
zuzUpcB6b51S198eM(Ko%+lgnw8tUXhP6HOv$3A^#R#tg2WL;&W9s1(XwZGCno7p_w
z_~sh&0=Q;lP$Ml*XY3k%bm_D%dU5fR`ICdHYD^IxR?VtcjE<JoRj5&KYHvr!yU<YR
zm9-5F3_u0?7V3&K5!c2Q8<<2Sc(`;JMO^G2okoc6zMZy55H2d8-sYWTTCcr+Xk6bc
zV*kaJ1qL5*_dZXPPcUDlo%|)mWBSRxf4K*{Kkx>wGqo5|TZBOq53e09A<wKR4Rd45
zAc5iE1-!-8>&1pknZKXHkaI|c5J6__t_K>y5D2;jSK(xUv<|J!L$TDUz{NFPkY-+l
z1<FGZ_ULSZw#?HX)+@Y4sEGxgRw6UafmYMi(?cEwMSE^HrVqp#nYgU71z)iJ>FM;b
zffU%%>30Zngz1CV2Gb@u$;RONsF_NMkFT&_kjW^rE7SAbX))3I*PXt#>2CP2YqSId
z`MiI8TCNe_H9R!rwB!P*%EfX=w`d64;I`g>ih?w_{&Nj90vHLhkB|SB^uGA6BV_pV
z?abM-z!M`B1i8HfzYSHo`1z_5%-tb>qZ9^TF)?HrMF{dH>Zqov2!bZzfML_Ez9PSY
zhWiR;p~6ED%6IjFw?#xm0RDyo)TQP;TIqrNP76hBD=LD>%-haER*9UV;uIW<0XU>U
z>VuTInE&&8l!A7%UWZc^P@w^z5@wf9aQv9FA;?R{3-WOALB_wqy_jDy9TFBc%xet~
z|9{`@x-Kg`NvK01c(ZP66Pge;nX8vu7{!%^8pUJlhwH&#%@zZA^Y@QEXv5(>P}|np
zgHH$Cr*-V`ER^Gbg+Lhs?;Vh=d-vqc$s}CqJhvO+k6ojq6F2E3OZt`RN`r6_`Vdi_
zANTr9gZUXe4;Ye9+f;DsR4SXZecp$0Lxkpm>Siw{yPZ2soxWZD4Kg2fLDTM!_E7F}
z)*TrF%1GyZGzZrUd?i}?dU`2myLylR-hC&gKb3T7tEdQj>~%0I;=c{g08S<-My`gG
zj=f^8;2{ibBbG3X(mEibK7L&ERt^@}x*0V>ECJuo_O{)%s*98Lh?^pDpFZiRs($6Z
z{@EWI`@8yjk!`e5ZFFAj9#OCx8kEnbGPn(z0^q<D1NI>R8F1)`A9pChlh8^Xw$LrJ
z7-s!pguJ<RtSy-0b8!qe0VqsoE-wy!F8-xfxltp?c4~cK^M*_P;k1X6*nZ5!##8V%
ziir5X7Qxl>0>A=tUvLNrra<7*M(1-jAkTSM5fkhWnrE7dprfCkH=MCQg}NU=%=rIb
zYgZl(W#5J$^inF?NEu5?B~ravBdL@`$Q0SP5NQY@=_O@Pr3KkbS+Xx#Vi0eXB}GV<
zF|<)*BC=F`*VFg@@tyOXZ#(DnM<+AK%=7%7-|t?o`?{~Y;RpyT`ajM`hr#yvP8YB}
zQCUMZ|K`45&yvlniE0qsJEYgM9Zi6rg)Ci*p`Pm`Kr@L552xN<LJ&n~u@EG^Y}|vv
zilaT!YAecB>l<coCkUc#tV9+JDTqUVxb2WwHql`Ua$PS9A9}HJT8@bRtb}GoYf&ss
zh>XnlVq28=&OY0agwqdx$}Yc!phNC+avH`tU9S_fL`)2H|GRR6FfGe)G(lWv!|7n-
z+8sYGV_a|#oc_NyXNfE1o9$t>T<8;quzabGND`0!byj#H_G@ak#L72%odXf4VGNha
zHRct_g|FmEam8=%Hc|*ez068k??iRWF{k`voZm0))@_qpo;cBNlW(Rxl)Wm(y}>Z^
zVOg0mdqsAA5%rr7-6u_7a^0mW*1mu=*LlTaCt~f3IndG-lWiM1%^SQR?eq7lrkjgN
z%&*inW>~oNx5oxbX0k>^Qu8kpUDi87s5x9KG|9g3b2WDr%0vK(uhyq{UF)!YBNgWq
zSbK?jB`nOX%#2%>vflX`<C$3_E>(Ty3M^GJg}nNWnoPXXa2Wla{%-%Lm*>?X%SASR
zW~fz?evu#omIiWh<RLbD6@SY(NzmMn{VE`UlRk)d`0&n`sm$DGYv(rEZhy<vR6O<O
z9uA}Wi#y1OTPdES-WnRipWy8#u5c0acNo&Ss^^1t^vG|NvrJY?_q`rtitSU6CMOYI
z9!@J9khI{%(wjF!ol8-gLiqdbXPywo!0zz|>0GeAo0@b;^AOKovYh-$@!}=auMG%Y
zQRs*J?__MZI~;S~!tM~{Dc#)*NpoJhV+G!X<@|lbaFyJ#R?U%9V(u_e^yM6c>zR#|
zQ|c&3o0WcPPuQ{rdQTReSVuE?UQOYx&@TACckl60&S}<<Ih9#MzFXMz$`rXb+$Unc
zl{0?Q4x1;mJ1t(gu#NOgMiDhH?O(Gz>1%Odx$?Dt`%h11wD>ifOg>Ot(bUkukdSPY
zwtea!&uJPPcOlCYUcGvx>_g(oT)H7!iST#PH&6mh^WbP^ysiUKKR{Bx!gLEZ=pa}f
z%q5g}cuqOgcBH~1`DS!9f1<ppeN$9W(4<52n@4A)9eQ|4_B`-bRqqe$^1CdjqLq_=
z)RWwgVG&;93;#jS49ZR)ITTia)T1DSX)X<Qu)150<@G!4`rZWuCS%|X*GEv3qD}W}
z<@UAuRX-kC03W(}S0%=QWAFx&s_f<b3<CoLx2=`s<5jz_`#ekE#->julFxyWd^|9v
zw3Os#NwmV24-5>zvW7lIf8S~Wfrs|ZvB-UZ;XIjqimNO&lMR2e?8|vU!SHwWzTP%A
zcPnNd9lyLX*Xa2l2t*V!C(czFg8U~;?_V(Fck%7qnaLa4fCOf2EoOqz3hcP%&S7I2
zL56`b?E1SNhk=Q(R4r@kfpm|(f5<l}+hw-D>FLSx3L{V1Il2X%n!apV4fDE07_TM}
zNw#W_LR@xnaYxC^ArBDPrKF`#A|6Dcj<VItKLiscW)MsvY(!u2A}fE8<AQ5PZ6Q_9
zFE%^QtX(L2_CQ77F4d_x^sfnuR(P^Zo>{Dqj5II;qEQtgx^sr7YJ}guM$8~KRhh!a
zV8Kbw#q9jlH_fjVQvCVb0!4;zVP)5fZPPao)m)+qMm~LdfWCI)@dtH1oRW+VzTlLc
z99^8LPq{zx^14|Fhd@kkJEYi)w9Mna>(uN+Kz;rk0F;q8_s}M<p1lr~Q7=iY*Q*@c
zCQ$Q{ne2jxVIjw|Wur6S@kkF~mV&Osr>dMQz%ro_fJ!BM1P`>a?#e`OhuysF>Ub)M
zbqeh*CztCg%gRC|v!Pm=0tg++kx$x-r0ZuAG5iV^VW|`%(}M>e733rI&>+Q`?V=Bw
zdLZJ*s34srwV(LZ%FPfmzHvX?uG8;X>l6IHO<sf`9v`3{-n3Ixk2e2HrZz$FxoBzZ
zK0>2G(-$f!aLgtEq@$x15k-N>1r=|sEUKGq)9*zAu7QIph`!8J91B$QkI4w0_36s+
zWAv-cyba${`Lv~ljbMfCf2+fxrYa0K2=q7y^E59gr<oZWKjRI~2WRf+kt2CE*4{V-
z$7G_=qhwLmjdCAYAfZofukK78cxTdv6?_+zbTC+GY2=++XxT&LQA&a=??Fo7IV|ve
zL&}bGXMDbWmP*srvP~)N9eYf|51$g|6aEU-9`$?X4&>+OcYoM9KIfJQ$1yqUWP)PB
zgzh{-U09u2icU20$0rSTeY15mVkG@5H3$%I{)2B)=H>anyxf$Ig9su5V3rpu^NE<>
z0=ZJ%k!#)IZ}BrL?3;Ka+@kvRG8w+C*htx$fPMe;BLB1gJ8pAaEuua~Fh^FrEBIn`
zargHs%rx$0NHx<)BN)RO$W_NgUg-B@^vbHiG@aW9`ubna^T$CZWLUb}?j7&h&ymPN
zO6>O#TQ_I+arpbA+T1w9cN$T?pOKN`X5#|*b++#@9i1i^pSf=(Rd_luG7sGj5L-7G
zDp^tRduwi0$N`UWHHY&HufCGotfrP&%t2%a)o=^~A&zGG-E3$an~iK5F(*CF6pv>{
zwG#S=6y)VT_su4u63YMf?MD$F7`+vILJjC)fw4`4AE_?+FRb=IxLLEj_^C#w>%1>r
za)%1LoSU1QIeedsuf$x#*SXel;DA(E<RZPhx*#pfF}9cH&=B;&RY^zQ&Yd@^hUu|`
zYs#6)WTNMSR-)qHvg$2#2Cb@s9&eG5xLaA-H#quH<lY404RqP1&I~@jL@n-e2G_4T
z5TrCr7NS!>UNzr@NZ?=Wi88CR(YyiFefyqZfg$8ZYJ_()W);w(i`>XxAyj~`=s2J(
zQ(C1Yd-O?5aF>P8kR<8R-)!RLF)0}I%~&_FY-ySDM9NjQRy^C}?7ObXl2sK7hsWkN
zNsM)jpmB8wtN6n3^dcUUv9~w$_U9ZM?aJxoMzdU|<<Enp(<%<Dxe%qHsR<ESGxiF6
z?Z3U|e{n1S<IC7qgyd>r;Yn8aN>R~2zBVfcU((OwP<QwtPLs6AErF$~s4!!Tr}n#*
zkpoEB`TXlyy>p)|=YZ@OV{xoJvs{aUP+P%~XY++$8y?=bHfZNm|3#k-zn7v`!DhGV
zjg(R~Jb%sxq92qz<Zz)n5dv2_@<=hcXPu2bXA8WC3NT7kH?1#pkt_<T!=I`iQ7y$p
zMRQIf$_z|7`l_>2`^YOJFG{3rw05Ubog!pPLyrnvFE=sF@fNSt-m@nFZnOIH>-(*%
zfp4z8!Hx>z&PXp+Dc-B2bD8|qb?c5pv+t{J(v^^q()Y0^$FbX!U5mod-*bHz%lAWq
z*Nz_Vy-rRK`c5|m3_^y@TVKgU3uhL{e>(O%F>W1YtF%d48Zu<Re%Cn&b67uU{LtxE
z+t6p!=L`*_Tx`*cjo}{{<h4$Mf7cIJ3>FHDp4B}G6vMMWS!2Kb4reGD!56e!fNC0Y
zx8^ja)*^#JGKBOW=Z^I92f);Ux+{p5M=NX=;wz&?ls4+gu8d6RF4pZSaRK2dJ08aF
z2DL!@J{avtdH)I$Y#4Q0s(y^2-4x#fd!;{mmr+0Daq#;$%ihpX5FZ_zjbIe-o(M!w
zc?Yc|Jfzt^Gdp|YR8cnzL~}9VB2Z(48-Zf}28k{7aRYFMdTB|pNCX$6^<Sqn1hgCm
z?WM&zmVjfYt|vS8OUDR8aiY@jTAoRE(2oie`;zYSRD}i0b%DYJ*FL_vcnhi(N2m%L
zBm--iuCLnLLk~d9x|aFn6h-dDHTHh6X|Q|F2c{rl0%`)yOGr-Ut%{9{%Qf3Q$nQ5*
z8|PAKOr^Sa$CYCban71N25V1f?5k5(XPPtj`}l(T!ENoi5~<Frv8h8MYc_6_qCjl`
z;?F3V;a|{T#!mGV*lDAVf;%Ho*L(NYG3vT$<;`Ae^Ni$SkYuA9S)P_%zx#}zU_{JF
zJ5(q{AyP^C!--K(D^Zk_iy77|bWC%8VB%OK1xO1Wp<Iv;eKKG=p{Oib?@9syc<|7Q
zMT;#q8p>8p&CT?8QKl&0g9Uzfcz75xgBjlD1hFK(NL=dH1#@P|uV*qn!yC**WK(Fq
zTvkD$7~#SZY0CR}H#@ug8HP$aO7HN9(VH$lZFeu=i49ot7yG2f9io=|VGD@--NM#Y
zM1{2t`7#4nLN2<OR?^J^rTQm&9N86Lb@>Fikpk7pP@ph8_f0g^ZNz=3i)tfb_cp!R
z<-@I8x5!b{Y&2!WL~?j#fZWQuzw%F~TbD=OM&fmDZ`u_aeU7aHNT}~6a!M&KLEN>(
z;)!L#(t&XBd~UPxAy22>;q_jrU`wyJYck)&lVo|OtzvqtWZ|;T!=kZk={H;=!p*-l
zZTI=WNmSpvY^;Q$oW-<fV#f1Y4nlqF_I_CbEQ|B6l=6QxVE?U?m&@(U^x|F`3F}U%
z#kCC;bvdtDv;F}Un+?dJPuhv2zaY~DDPbU{*|p3Ua);RB*}+o6dYEzm8Aq)7N}0g5
zd1%PSVRr95(>WwEj~BEEoVP{@>2|8jU$Gh-9LVMLVFoq*qPjqCrwV$%QRM-dQI{>F
z->Bgy0>pqWROoscP)LEZH>95ciu$Dao1p379N@4%spJl{m7vXhT$khB<uWocnkQhJ
zgN)lh*`{4Z-T$m=A{wog6P^QsgSauA$^{SPMJIF0>TL3TPc|&-6~b0Sb7Al6*Fqv9
zJ5P4VWVSclj7TdeaBF{X6d(>sW`+JhTwL7kg0n)W0tYcR4oLvYH{hj$-|ADp+sd2X
zhn_fxr1QlY8CtWB<frz55`^OeB@mpSX)xn;x~`!t4r^P8?Tux<etlW0^QQgMWYB?|
z7Kj8J4b$&F;R;{Ac!7$r*46sK^6%MF+dXVCgjZC-IVMB{P1iv|zuWCy4XtBf6==Oq
z&~*S~f~p{pBEZLZmaOroePMe?$1py{y0B}c;_Gv?{!Wkl@#{{}8c!0yv5aEMZpVRO
z;hMnk>Ywm&cXLCW(aqB7I&#?%LMoc5&V;~=d`;9!nd!K>>XCJpWz}r27sDLn>1KP|
zhM*%B7NUZJ-(FGmBsXlR!UFyMPa*Qe%gd_)!j-s`lone_f54P#|FO;$J@?Q`e6g2*
zdbZB4A!s}em?Y|6f{{T%+=tp?3NnOPsuY+pOiD&13&VzdwOxgeldOp8sg7a}+n*(=
zO)ny>5fUTY`1UXYq19rvYe)&Vfcu4bS_H8X5>=Nz)uq-A@2!i-_%`6$OV14K1#u1L
zKf@T+yC@(@<&=-Ks{BT9-Te!t@jtCP+2anRP6v`%B%q5T{h>tF={Lxg;Ro%X#}Ac{
z_X3*CEgyLVcfTRYw{`L`@VxN^cSlU~lQ1}XbRyjH$6cgE0K{lCTE}P`ht<0M<*GZa
zOhA>M8x))KMm!9u2e>g*mqMPZuq#OMc_8Lc4al*qQd_fz*=}Un+|sf#64bFpqN=Zl
zJVXJi0&N7A2B8EDD(S28^8@U;XHN|b7vd2`x3>rAyS5Yi)tSU18<=j8j6p!W9^yMY
z{Q(IDdTIMHEeyTH5)uIW*zggzV`98J&Aq0dzdu_q;)TLl6-p1NU(3j3fsFv1j6^uG
zb%BNKds`>$l9itRV)6#3mCy>lYVpsW-;t-DPYch<%>3#@#})A{n06|0JaHnun<cpP
zP@Au*jG#7vl5Q5?!i8T?LH!$rjLHB`;=+?{6#w0qA;yE~82nWj6fjLD%;!mi9gvmP
z#PW1`QzRu%{+vOAhPM%?0;JbLU0qwdJKr|An^oyOYIE>l7~3R)SmK)HN^nPFxDd1=
z0B%P&%Aa~u1il^E;+KWC*<j2UDs1>;GE?yOp0b=Bi-u@Q$kUyZJ=<wLO1YiiUuue(
z`~94djsDc-kQ4z_`ukVHO2@2X@zyLn{##K|vZlp4Y;mHRG`z{8(iDGd02090N=ubU
m2#bWu((8Y{`OmP~99I^1Y3-|h@oLzEgw{S?jhsDJ0e=In>AA%K

literal 55312
zcmYhj1yq&M7A<_}?h>S=r9ny>=@gKV1_|lz4rvf+>F)0C2I-Ja=?>|5+jsB%|F_3*
z=x}%r-?#VLYt1$1T!tztNTMMVAwwV#G-)X@We5a%0s?`$MT7=_b3`GJ27W`bmC|s4
zKv1y%{eqfW@qUCr$RW~VA}X$_M`<p?3db)4Lc6OaZ8zy*&dNr#!>E7BquN|TC{hN~
zCk;;#1C>LN-BhL~NHrHPbyp4_pBn|9?oIduvrlhYpR*kFcAEuv9i1|6n##w86>;HE
z|Fa@aU~fv$I{&k7PZ4m4U;SsTpwsZM{%19l$l2=uXF2kH)3fTN{meM`@hDP?3K`a4
zcNZgkU@P#$Mn!$L#kwsIF*+?KqIcaJ9hl&rEV{)*6U0!l&C9#Cv7=R<s&iL<$dyEm
zN7^Vvm?GMhe-(Ua#PQjB%a(^eesSgc@%fS2KcHvhv3Iplg+68U#Ob35Z`!lNsVIhU
zmZB|>whc$hz*eYOww{&L@61V4CMKr;e6bmarcLU+3Vr;@_BF4xOu=+YN{UC$LR#t@
zSAJ3)VqdGG|GvN#<mdW--*(TLwaf26Yw}p%ALc(RXQ>Mo0sj9kiG~0q^#5*?_5ZUo
z3fwyzbc~EMQ&ZTx<4}++Mc1=+5&ZDHqN4W}bEPM*3I6j<TP`_TYCMX#qobo}$jD$1
ztPpT;aO&#nV4$JREi6Wchno$x|NHf8vDO+J>-J6i`}-vJ?K``>OG`^IzDY?M-rnua
z&AbpQ8XEQ_IqXm)GqXD!G-1qdQdIR!<yZJ;uKY=I&$DWdXcW;QJsa+63$V~o5O*CD
zQ3!eLP#g2KDcBZQ{`N!#OYG3AlCoRUK_mN$2alu2UJ1<HmdJfP$ME$`<)T;`c^P?m
zE1w@jMpLF7=LK>!pFGd@rwfdYjZxuyy1F)Y7bj#3Y${R`!2ye4A_rcSpzRa!5SRbn
zSAqy*!luj%h@Pg+^JrIG{W^47L~mxV@>p53<eqe>Q`vLmO&NuUpa+W<O!pyhTQAc+
zf3&hX{zxpVrj{^dbltB>FVqmC^Y4U1#J<SNj#O~vT3k(O(%akHgT4FD0r%CZEb2zO
zqqNl~gtfMMvXtdb4TVMyY<cVtPmkyA)>iuYiKwaJLyU}#317Xsns5AX7<Z(mV-hzt
zH4S_xIb7@Tk(M56_j>rVz1@fYzd(PXB<~oapZmC`Y-VlUU$5d0v6RiDjgF1AFg4wC
zGP&swjkGGdqPn=aaB^}255sI#G&nfu*dWj$)Hc<I_}{2;@%#TnrC>K(<v%y4yQhat
zz&*F1ph3UOzdl4f;sve}@BfBW1SthOJNy3a225B?eG_4OdwT}AK6}#p_wP3uXFp`I
zGBO_h8HyDb7uO+z6Q7Kw{pjxIR;F23+}zw;BB7?L>f|IAG3NW9pBXXMy<z*s&=9=e
zi&ue=$F*f%Sss5Ruja>=^V44RjQ;-qvDcfP7PHlFKjp_IB$Vs6CF8iyGG89|za<5G
zlSWLz$LH~IeeAy%jHO1y5Ssn>@VCoQx7Wk9RR%wsQCI4Fhn=hD!GJYlOIbMZ!do5q
zq)`NfghLy){SkzeZ{C~-v~hB9{CX|uSet%blaKEodzmkv!pkxE>zC){PGo$%>RP*3
zO?5RiG&CKxxGPRgPcVk8j106dIJFOtpP1eb$<Uy1vSMe%#K#+(n$9=atgWxFS4vwO
z@e5{^e!08YW>>{Sp^boo&_`opVO5!oCSMy?eJ*{tIgORdoy?Jp9s0AlxF{J#N=-#2
zwQc<M_iS(k>37Jhp`kc|h$io+=JIlDH8nM7XXo=+Q(W-{Y-I^VnQ3nAjyXwxVPVC%
zamyyp=hni~nUD{wJ15W($yjAFA#Z;3nL@HwvsYI722~wcZEWtCDB;J_m6h|PTOH(#
z2|XL&WD(%t<SC#7#E{L5bXr}NO5Yb37uQ=Y%}q?`o0}gT9x^a9QQ;!r94*8qCB0>0
zIzBzEC@x-X{~9fYi;s^_MAR%C#y%#D0ijuQv)$akyk1x%<9Ed*Y_zBj-Dq2e4(=NP
zLG;3|n3$uSkr*dlWcqM>tKH`QcWWRjFfcH+wt<IodNhT5cV|a%xyAu}Xfk(36R8M1
z#nggsyxiQchbVlFzbM`**ZRY*{Qk>>!&byRmPbD)H?p!Qm<Nn+Z8hkfURH$(h(Z}~
z1uDvCm6a(PI2MZN2spW-rl#r5tT#}^-Px7h&?2jGk)eG>G3ld)d$;&HihbTw%5r!Q
ze?!7)pM!vnq5rvl8@U&`Hu7_QPnxysoDvroH#aX&Lqo&N%<RcxYn7QtMpjn+yUS?_
zT@<9+W=|M$d#D4yVv(PpztiNftJ~^o=MfT6m3lNk;o%BPiy4|gBA6T-EBf87v2%*Q
zqJ8ql@CoE3X)5gSKI{i4>!o^m%6E0?J^Lkvtdmk?5SqXh3~&6kcKG36N$)C>Ok>Yo
z>xEevxs-~EHp+wfARM-h?eUQ+#GBo_dxKIsJ{ba5by3YR%DNK$HG7h>S9M9Ax?k=F
zuy{4$BIULHe)x*25IVkPPa54tQHuLE5Zh94p;V$M)x8x+p`?Kl8|lv+pzvlrmZ5}N
zDz`y(_UoE)dZZV#?$0|4umkk-3CQs9{XRk%r2HZCZHBe!WA$Dh`N#7Dj(Zb&rg9Pz
z6586S;NB-CB?*&H%*;GGdqhh)9?e&&SLlWYvZDvrG&B$-qIvv1z=L2Sew&+9^YC~G
z#i5mvk^=eX+qZA9Xe7B(IroCHm@dkrJEq8iExV6yj(>i{le0(1qKz{U=VrCncQ~O$
zaodhDHYX?)dg(!2wY9rPSyI_DeabPN^c8DDag8-Jk`AICmUVu5w!hRQLaWUD+8+JM
z6CHiW%*I?^Um7Ptg{kzDQUo4?6_&vLE(d-ANsOXgQL1apxG+wiWV?Q3fqepMwH4yq
z4)H>Bf(VDnIZw{QLtqH^uL%?=v4uKb(`7Q1d43o6mBe%-;;hLpkMh@+eizGvyEi07
ztFES6J&!G>Y+{wRl<MX(4$D$gh3KAkWMwTwiLKVluVp^V@<%7A$=?$$mQzvG>f867
ztFyvBwI?Dit()GC3#Ur?igKiF%JlSfg75H#A{;^l{}-%0-Z&gD)@hwEixH2l2)i70
z1x!s%fefH?oLg8}*x2|=<j2pSuLubVqorn+m-+2Bdzqd7UTlxl2$m3N-K@Cp#3jU0
zMG9N8u?x6g?Gco;f*?IvX=$L~G!9A{LPL2MRf(Y9AK9Fu^~3X~z9xV1_~`NQgmRZZ
zM8nX=im_nB^+3#U=IH|8<YnO!)MUaab(0A+Bjfy)@La6Wys2vY2J2A{?%9MGN5xM=
zrDZ*DSjQAGAnCY?sbpK-1V{((%j{JSgwbZj)w~!q7~$ygFkxSc$j$X2`k0}?5<D-4
zBpB&pXz&o~rjH1Ja(iEh2A_ki_k>ZR2J#f&M@A;cV9SxR<Q2n1nVN4$PE6v4h$X(O
z=1(YZwB3Sl%P)=@VkhAftSFp+&GLgPI)UHa-mLV{e~|X@Vd-E<YSiQQb4YgRHrdBU
zR3}AsyH?knj$|3?SYuwhi%vfnS}o0(p+EV*EGtreLw@|sjuE`D4@8q65EpY&cljQt
zq9h};@NflnPz)Vy`XiqRT)d{sY5UgJR%UniFLiUxFqX30+?(7QZYS9D2{Ir3t%@9x
zaK#xI7`C^!0a^sNcl@>a58GR$hMr(kv)VJ)?PiH!46^6NrVN`R?PkaQ(;uJAu_GSu
z^Q{7icLY6;PE8A_sW)C8x0`DS<mBXd&1RE1Eui;YvBIq7T}jy8c_2sbcm4LG?zFVD
zb}*z->gwtRrKJ(%A;ERqqezBx0+DN6gIQsH?P+NZPCt5%5GKD$L`ASLS>PdSJJw>f
zDri{Q#9vi~$xvNKCvKm?jmr$j{n;*pPREX<fsPZ&iB+8Z7%F9i0?RM_s=9i1l<6qw
zo)`VMwo*DRZhJ)$S!OvjM4GImn%c<Jm>MVYFVuur=un8fPfHj^0LSc?Fd2VcTG<~V
zjuw1+dTVXd0^^Z{NDzc)1*KF$Sp|iY<t7JLe(!~9<DH!yh@zSr&D($L4gdtnmih<2
z#ba5yvGNKZJuSUTQ+%voQ7CQfH4+}%b?~{nySwk-zgt^d$Hc@aD=QycS(};yl#Yyy
z4E7oniR1X*00fK=qN48!&C~~4WMYRv=_8DNIXgSs`?JznuhNW6!{bfK+BIpY@1H}v
zUlr>0czX_#4^iX}C~j}xHW>|JuLLb-=jYE#egt_cQyj&p2v^eSU*JONpXU_Fj_XWK
zOx%7^&p`PubX3|y8?}`3^G|8koTP~LP|ke#2{Xi&;%-Gp@bP?r42MQezv}Z+z171j
zf)YWa#*Pl5fG*RuuWD*@t8E_d-o1OVuZ+o2t}rpNA^8J=7`(p=D?e#2rXOO_vri#v
zA`6S#cZ3SqFtyUT+L0HczT-tt3=yT0GQy(o_Az^tD_aTYS&kTj3NJ5F-xRN|siUKQ
zXIGg|1TCeh$~4_c_m*kJFJip3%wpsG7v7)Kot;xc_7E0a*Of^AXF3UxKFBeD**i8K
zZg>kGdl_0BLd#x;Hk|6cK9!-w+yPe~%l>2{OEfIdO*;$n^{~=(GP@3E;DL8OfrDET
zbDvfr6;tQ}V4Kx54vFLs%xaT}C@2=@=J)+QbiDtTrljQ8>!XE@jSZXe)C(@|rr6jD
z4!dD+j6P>Upp?SF!)LoK1m&!7+pIqJ<>j;ZBYiIizt#}tY_DGZE4%I{UnIrFjnjFm
zjrCuCB{w5|#5Uzv)M)Wm8xCXQe-8N?3Obaknwl1;L;Sn_oV>ZG&z{d`B?iS@_57q!
zia!TtXU@;h*|@oH&d<%HpS5yra9_PDEGTdRbqPE;z*u8H*$mx%TF!<HMQbh(cv8jH
zp0vw@#pOtEjzky!mZolVCD;h6&Am$6xZ_t>kOgOOQ}<T`_3aH7Wo8|o*)}9G{7myQ
zX7$zJ;5w%MIUIH_iz}@oQoV2|E@BhiFjkf}uGP-@d-+HrZ0sg9v`xC0$Dn@P_jnD`
zvA!P`yw*EWTL%@@cpKUc2tL_f?M+_mh>MA7FA`;n71*Q7DWvjr?Oxg}H+F5<a&T|}
z(2fZ!EiK)(buOF02;Lh8bZdLZ`ecG`jL58~2EW$$qrvqMCMU;4)Ya8B?2mx*ZEfXe
zPhxcb_?DTu-Q%V_CT1#EI(CtT6#!>@9%50^o^pH}4}eHZ)GJC$OM_4eF9I6QB|Bjy
z)w^H^Y+z}4+)=@4P+8&d2?+_&(G}+98G~=Yp;dVNVu&kFNl7W>dCO`vh-qlJH5OHE
zvnF^G)mKk1vt=FlfL6(uoJz#u_yaofz=^xMBbhx=7?EIeEM1^QzYCgAZ3`5c<1v`x
z&x~ApmB0G^M+xK_N!aukQBzW{*zS1&+AHq9Xb%dK=yU6F;xr7CVKBuGi^1+4j#`|;
zCQD9MB}8Q>rIn_MMRLXCg$ee28O5UDAr9=uMr@Rqzu-7X38U9i%_HL4brR333DPt}
zx%|?FU@R~Hy1-8j20oJtTZRfI^jpjjXoNs-cHB~2QSnhqs<G8-WV?zPl&geWvCBLL
z$?wESklESU!?`jXBjfeRhoglW5R`cVgr0&Kqb4r%5x4N*-@I*i7!g+Eg{KBkW^j1e
z`-Q2qxbJz~_x}lJDv+ZV78aiF|HdXJp8k1@hldBy<nhO>>HUuxf^9CRs{jROG@`5J
zP9{hUD4;Fd7#dDLiwS0A{QT*9yvTW3%<iGO-D9)LEaW}BxOo5CF!ZAV>#Kx>1Uoys
zNxu49zQqbJBA-<&qYq|x8@V$8`H4r$w;=FAciz?B@TN~&{`wVGy(%Q|@Y`N1{B{Z%
zM#IdQ7Y$Nvez+eKb0Ef#e&>DHiGv(SCqs=HMwBwrs2wLa$<Bd`J)~hbuKl<6hK6SR
z$b#Zy-ic2dzinT#xAQe!=*+}&tJ!ou29n)%@vxt0s&o5e3InDJ+rqntWZvQu=uQA|
zG7Eny1j#BE&HVZEr?XR-Iq~q%pIXa>>eKcFpF99ynwuA=rY;7a7}&bbg`oeJp&#kH
z-rypGjuSwdqs`5!Sr+nWsoveoP_Zw&H$OY%wY9a)%=R-y0@`;5&h{pAUTbk<Oe#k)
z(&;)q#+8*F?^-1%C##iyelt8~IXkri+89uE%i(S@X6HiaZ!0(Fk<O7lK`PF2t5ILn
zOjj7iY>^gxQcSiqX|vtvuJ>-#E^lbKQ8YLVuST}J2m%+E647aQER94$?AwR+OW%Pl
zwj@5TJHAZlH}soD=5i$5T!%X5ZX$wtc?%k{0lva+DB)sR6Ihv8?eB;`hGg^m{b0Zy
zR2+mz4G)+*Nz%SGHYWe1$PPDXl&@I3JCU_`bmN8lXKr!H7OvZDBl^xYh6KyS{mM37
zk>}{ZO5@V|H7*s&jEYRMmV_?KyMxp9jSV*MN7r*q5Sl4W`isSM;~uw-VSPK{VnI0)
zHJ=#+V*LL;S!ufR19aCAv*tXtejVE3js+SUGAJD44)BLPHt+cLpF%$Eh7M`$Y0kWy
zPmWiy=(3w@nShl5S)n<yPCRl{_w|#!ynLSl7DY6jfPjvSjEt66i)_Nta#QTZePDd`
zm$Ri>-*ENX+FH<yFV4^N7<9u52(*12Pfr7__VH2!+-j_}t0zo@laAX;R;K_tE`_JA
zh)CDk`FYsIMF0Zt3JO}CRS|bgtkuRj%WsY_v1~Z=Fj(SlJj8CIpMcDP_9bA^{mC)Q
z@XnOG#(w3>sZgvtHd{GIWQ@L=Ju)(qE^K&kFphvATo89(MwU8G6;<(5*BdEaFG>s%
zI@~bacz=Y<&GSM<;p0uTQ-}-&RFc0k9WmB@d*RQOiOw%lA%jMw{tOirUoYUO-tH$S
z;NKM^lX)BxaSaGukzz%?E-I@V8X|=}Hxe{mTKDdT3xJd%D-qeq<;_7D_3~~rdZn}p
z`TS#kO45<r-~6KZ0dnluT3Rr{JYGiCJ4SecK|%FGzeh$6+h+?96WNAND|Fk!dNves
zSK2&W`d5|;(>DOX<7I^Ng87$g|Fv8Hnop2~qb60my1rBaHY6`EkA#FI`c%ihqO5G?
zy<7Gz-ixETdsy*xsYcE4$Ox4F*?Lzm%Qla>AxJQ?S+iog&T~(u@9E)!$>Vw95zvW$
z3V75T%Zj3<P_nts?*=lpBZo1Q965H4!a;@s0v<$l_G)8FBA2Jx=H^IFPWerMXD%(n
zdgloreFejZx)Lr-g&iV>3;4L|L!uc*#zN&O3T(nWMPE6QAtUlnVpQykMaFeb+OU&s
zTz(ad=x(32bP4%nzDwvlP+1T5kG^^HrpJwu-p@9Gq1ADJ3fxBXtuIZ`S<^>NDEN%G
zUodP|Ti-h${Zyp8y1qV{E*KAz-U^UJOCh+sO&|CSLG+BrRn|7KuxPPf;RV5UO$!eP
zXO{nbY;5jH{jR0E`Q>1?#OF`?RqHw(3rkc~)bHQFU$W`l0qLWl@I5DIYq!MoL)O{(
z)_^6K$DhGyO-)U($9j<e-UAwtD!RM7ZFy38Fen|BY&p2N#z?q~!a_x;!_z9V{(a)~
zT|nO#CQ>dQ%b16e>ae!Zq1cG41}duZ$~rMf_IVr%0kh)-F+iwnY-|Sy2cY_D)S6C8
zqXlBfkE*|`xk0sq_GLN`#qN=yr|P)o;)a4`6xaz$ewUpx?cAm0C-o2W9TC6fU$-xj
z4GU*q`(zhF6)h|-oFHeij(J-y<oPCy{SKPfPq_09SrVf$Z(CJeUEsi{TqO&hRJEYo
zxHuJYZ^1~ZQlqMf(ozR|`y*;XG5!-fyHF{r4^Y2I(s1$c4i65H!@`=jjeBZ2A>>)5
z{$I=!qT|b`O^q+%n1+jCu@{1-ONH}vrF=T(oAo9R|J>o5#2B$dez{?ao>)}HNKC{e
zIJVij{H&LN2_8B5O7iN}tyf3l&W?$Y%kN8TW(cOb^7B;V>Eqqy*47prbQj3Yy>%ac
z%F%?19kkYbP*$D-#0WMvc6D2S?Aa|h8H3GG@%_Vt&#E#xCSc`2BQP{K=L{NzZxu(+
zwoS{AkDjWkvo<YXuPfI9OYo}NnWXW%-%XNi99~;%D=HdVUl>0(H#Z}rrH{4XXU(ph
z!9gY{E-tS4`1rqn{{{sGfj(m_^*t7mX6FxEcfyym4N=mlo0?zcpj9X-nVy=Wr>9p|
zQo^dBto~f;?+;Bvk~YpTe=fEs=||pD#~HkI@5gf6x%JgS>xFA3>uG6~nuh29>mW!#
zuz<qH6rYHhWm}!@1}Zclr8&F0OlqDU9}je?Cm2f`td{Z%3lSlJCe{}dcTFaO?kq)^
zglaf-@@Amo=5_%*u9S5`dC3ys7ZoxD)4cyGbEY^LKJLlTFx2EbXIDW%sNz&j|B_`g
z(xQ}=`DRO5FoZ8C$ETEhC_oY`{2`V*Y1*A&LO~jpElmBG+KgTH;Z4doi?M@iS*r=D
zO-KSfG&qx>hI$X@(-V6O6j-RWc>;QvG&Dh6IX1Sl)(6|z*r$?<C=O_Y-0umeH3XZp
z$pRIt{7~7Uq0-*of??U^<>g=>X=rGwtE=A~S@oe40!;BBp1acG0wZi`c@!!CT5`{H
zdye~v=;(IGaC@h**E{UOro@8Z>q!n&4SD$tA)ja0-}Ng2B(%%lU3rW>g&!Hzb&f$l
z{a5>>G!;8bk#03;JzT73bG1~v=>lBi>{8Uf29yofEBAF@uepEnyImlO$~z(TPwjmk
z?|iDSuNTTo7@jWp__5-KEhc_cUn-5)Nk&9u<Kg}-J$-db2jtho2k+IUyUXR}<@UMi
z7C3~c0gH;}$Hzwn3}Wa5B1}){<YeDB8~q0G2nZ~C?T=hx0pE6QDN!Mr<<5&~6M$1M
zkBWfi{M)sir&VuB=y}Fkstp4<JM%6~`}^aKsCU@H?s$ft$IF|+Az7hFGso8m;(=YO
z(~%Jo@D-XcJ3D93PjyTwc=y*MJM|`Zjd{=p=uwGl!#LhtSMR)z1#vG3f=yv?h;`s(
zt-hb@Dago#G3Zi_$^E!IkT*9ko{z19`QkFhpkB@xpB<6<=efOpb!`oRZz*Nv7&)4n
zTYkHDTpuUu(I<DHc+F?%%=TXfw<8eYq(lYEtKMwSZ<O>esyIB!;G>`;+>`q@wuCH6
zMN3QAXS4^T&ZuUUz8Vf&a{mGS!#snmIUZ%rvkgCsPL>WE5!Mwp+07Fw=|fx`)ifbl
zadGsbIfHRu$Exwz{%-`xH@ar30-ik;*4Tj#`)y?Aoeg@vlJNagNu3T+TI@J@G^Et`
zl12vaPH*j^rE2xn^>uV~<kff~0L=hG1)hCs`b2zXh{;t+Gg?uz{LS0wt19!AF3JKm
zrVegz4s?-ceh-tqZHr9;J3^1(mL@poVi^ydSjjsp{1lMIk0~h;MilAZb?02Sx;wmM
zU~x_k#!XV``%)1WinZ(J=<Hlxu6rvf23uUV>%PT|6xE`uYbE`LtH``mtAUw@X6Lj0
zFcsw!%Kqs<6q(QYX5SVsy?Oo#!PJ^wqpbl*5@{(!FXMhN2=%8+zg5(Zs?3iGybL>{
zvP6TgebN;_eaa~>|Nc^bVW^~}^bVc)TQU87Gt3p5FkNe-!w5I5<jpzY(>5%ALj|yf
zDe$>p6}om$n{t?12Q_OJRs^7>K-Ql}54*a$8mM#icP!~x-~`D+#7xbzm5UBVZMxb6
zAZJI$-8qs`zIWqVG7g`DQO(xS!m=$Y)_gNXZJBfJ2CVlC(J3bVKhZKW&>P(7lE*@c
zw_(B}3p(;de<OD{iXlzPm895@mcFOTCMA@$dcFk0pF{<$_+&^($j#{*-~0D<+uxok
zz%`~G&1hfzha$a<?Y?yR!@(q>IVCLw6u3b;w<8!$Ej799=Mj)~q@{~0D+@ETiemb>
z()T9S1neiX#bJ`o;|Z?<A8vxQo9KItw-|&r4-ReR(WGQ+T*tqp_ZK9q4m<K9-e$my
z{xD%y56L!Wl0zod8#CIMmMxNIRW33AoXpGR?EE$<X~^ai$y-+oW9lr#72KPtms{yL
zv4n(pGr%D5F_qnl4#lGE;_b&l2h~3GhW`Fwb3CKwrmekMnzfUgRnNcElo#{ilZA!5
z{0gjS+iQ&`^?@cDuTu7yc;_j_mb5h57$tf}hToHuMP+4wXXYRf8;5BRx4?ogTfKaU
zK&tpUmWuUyWcPA+rBR6}<%fn=qSl%|9fM&>G1c(~-k(iuVq$GQy^gDS>1ZjVl5DZw
z4M?Yw4kruCN9|}%Acs*=Q8kxZ-rh%A{hb0Toe;<Dh*A-)1RBXDMPPB>C^;6CaE#pM
zxvN!2^bpXy()l$Wea^!<qXzgK_Y}{DjbVwIHI%lkK3TlHoz7QCBO<WZEUSQmpw3Rs
zC#9|2X~04dFh~LUm#av3_`8Celk<40Da}tJ$0?uZm_S6yvEMHM(-HAIDKtGE1QHcx
zT;-GRl}5wLidlaO%}Yto^!N6B%iG%<!U|y3pFor?>RdmWhnep(+qOIz-d8`NU;p+i
zPyZyo&@CGLA%V%pLe$du;j<>}@jNugy%>deZwMB^)oHcghK#HiYxnKFvkK~W=E}6(
z_;rPag+Jd7H0^lJ*(}0p@<JeU^_e9I5Z|7z&Ab<~KLO+r;Xn4bV%Y2FimLkwi8aR8
zj7}6#B9kP1&cx3Tqo1viE>i#J1)%PGetyO!Nz1VP&H9W3sO~Ene}8||icNmUx&W9*
zBK5MTe!h&*P$c7ti6NJ6C0x#=^~o;dkK~xQ_xA~cKE=#kpAUG6gF7d4UB;5liT&0W
zZznHF1+Qz^Yt+<G^KdfW7FTH1|0_h~n3l_xP||K&#rX$ThENay-t2CyonNVwW1owg
zYC1&*Q3Zt2o_PLksH^_s?o`%g^bzAUX?boAFepIZ1zeG+?x(&7bYegK>p%u<gAG}Z
zxKblyniX$W6)nn~e6@6$C&L4kOX*l3DJl{5Xi9HaHr%{V?3yyN7ZIH<?^t7{A3j3(
zEXeQ{CrfdBg95R%vhMNH+p+SHRRdXE{zLce#j*F({c*|H<D#MA@$rmOZ4dKR#{0}H
z(IC|Kb;i@HSqg-lP9b(=$brhby6JIoMn*<6369}XROx&!%zS)Hfqjck8y~Ff=6t&-
z`2_^9U>7P40vgd`hnCfqPmImQdbdb%aY?EwG06&)i$)Td4l6HOK^NW^PEbPK!C+xv
zjUo6+Q<HaB@kdJgN!d}WnU;&^c_N)||37LdqSeQt`xRvV_9!sCp`p<{IX7}8c2gNJ
z5{5&ksRS%9UoiCkZ1e=T(9;`CbD3JZH<~+*<`j-)EWy||Y5$0WCPrT#=;|VWwE?|v
z)<PTcLfzlX@y>4y0-2a8_WWA}U6!4_38X(16civY;@E9duu`S2(TrjsN$Xawo6R$=
zpGypl+%aio1Db{$Q*C~MRL~8z)@fZScOWku0J)mtlJ~^l6B6cjM})wA`zMpOwmt$8
zgfI8)5@7p`QC|P7IdfO2CpYzhOM9O3;F6LKuGH|si|ecgcgX9ZuCCzxt{KsYiZ&pZ
z>M_mLHRLi3r$B#P5CY*1NKNiGO{)ESwgL34Jakfc5C3k9;-=?k^>Xd~_LMsz6yPs$
zrv^Os4T&Wbx#?c!dzVvhkLZQ*@u)g`YJYMLqOu;zL>4{gQf45<UL8G5LMkV!PXRe1
ztLik3YYdecG+L#m2J2fD<o>%xs8DH0BXJDnq8ej(iZ185qy0U)$TuG1>{5Ca<tEI(
zgJ~k)S<1*aZMRVWq8b}(m#=L|Nt4xZIn1ftaBvbi92E{^)D)_i1DzU57!c))<u5G<
zM7+_i>!)!AhCxhGZd?T!gb*?J??Mz5L;RslhZ_prTMfNJ9*?(GJXrV{<B1fpP{K(f
z7Uhft`>zukSYJ4})3C5ORb*@Yrja4oRA11-DP^<f3+XgJe+B|B9^_=LW6kq#H?*t+
z<m|hvQL~ZsG=0Y1#)i4M`2#l#kjHr|UAt6pdSc0jk9Jw^x1xl42|~z2c#v-_&XZ8#
z4IknymK4%)kpo{p^S&H28URi|D=Q1Yc%XFg5RU=Xe}4WJpw2_?`MtwKpRL~hehJRd
zj~1suI;nTr6!5qv2HKXK(&V7V+J((+YH2BBWgP+p1YVGsg#|;>Bg@jY5OmZ43L08j
zaWN5}^HD>E3TnIz2`!V`sU1gQQGir!fCx$B*ltUr0cf~mV`3bgoEYfo>2QZ<iassP
z9q{(pdn{3Mm;2@6eOrAc<l?+!2kobCYWfNrJLboa&M#F#_oWoELy62fVUdy4(NY2M
zR@OK;Ai?Ft#LzP^r0tC2pALq2<qFa=F^yizA(meF6&F8lk1QczCtkp@oQYLd!$Vlv
z+l^<K378_kLo3>a1PN#U`l2~9h$$mW92XC-nG7Xdl&S*Q$~AEm@N}|MQ`dTJsP)da
ze2$M-LTVMdPqx2!=w1-(ogon9uu1ujicgvq@W2Jt76BBKZ~_*5JiOT?Q&6fFoc?C)
znpsm)P;73GBm&AH1;A_q?#b}5u$>Zm=@_Z+2gT_wWW&mD3ya)lx&q#P9h-5@Sfa=6
zk59muQGA$~@f?%W+pNHGhLE32*Qr#0E>f+VWpob8{S^cgj7WtIS$;a))Mmgz!hT!V
zY(nhI5m+jflibpzWIhbMV&v<#bYb;TWh>RjBRiuhHD4&7h>tm_s9it2ktEN=MqF@0
z3zOI>tffMVLV)n8Zi6ITwf^e8{hwir#QwcQ?Xkh@D~yTXzxC=IzFPFRcyG~YqQ0KP
zCKK8oBcb<WJ<^>~VX7-#yjLAi2pIs-cE-n(e#o^|nB3OE!NNld0T%XK1ZkF7!PDfx
zya0czysaZVC1s9iFnZmSE9gH93+af_8%!rD)YX5FFEvEfv$C=6%hjoV`ZS%U63Rfh
z09tEijJD;Dj@!m>-+KEaU-Q|RzXsg9?I!NtzKGer6!N}>dBDI{Utgb6(F_y}yr+vI
z2ZT|#^)|ieeWB@*Zau~Md^CEvmEIpvs9`GfDwxN}Jg!2bqm`D<!BJgZIXXBv*j2^&
zY=aC|vZ>b%kwg?k^Z5nQJ<M6CsnF|KbwkhslzffHjlKOUhECn40}FU&Zqtd(dNOIM
znDFqC-84t`tU*BRcg~{ip-vd{$Hrrn70T_N<CvSv7S_^0e)2SV8S;krZPxk6ar6pY
znwtOQw9idD7Rq_`x|VZ#V!{+<RHwtcP1@B}O)dX2MjRMU-YLKbn2{Bh1bDdUpFj_~
z3yG*Uy>tHJh{q_i%?%x{d<PF$maF|~pu0RSmy!g)<8pA6dp>$*1`&M{^#HH}P$QI*
z-~U|RF{qX5x5-;8D#id054e#M{i7!q&O#lPbNvP@%}&Ro_>x7v0|UWU*GpW5J{JQA
zBNW{UOj<LuvshWaiHV=?kBaDn-?FSX2pIH!$SQ5xR#Rv%C=!_(NDhZy22cfX_Dk-o
zRffR^&dDX|V}B|i!W@Yt$<LP8ZN7eOuy1`ZGVG~6AnIe=QY+WKyMt>7#di|_#Y+?c
zkibN@^wME+ALK@(NceV+kK3fh)l~hf?$0k_FtULWq_>MRG&B_0+tAU`O-xKOZsz9a
z=clH!v$K`KPGx6LUDFv?eFk^Nkv6l=QlY5az#|j_5fl_WlqyV*P$^O5+1EL#v-sV9
zni!v)?7s4KmM!s38T7`tC9DQzWgoTgoan(0r>g>f!mQTIjpJLdI|6_?2xN0D4Gp*L
z;dns7S`92^tMCwi2Nd<YOYcr2d2?$Xr-PXrv|f{%I+JH4s3j<f@)|tH9J5aI)kCxU
z#h=jK$*uK0SQeaeCJlbi+q3$pcKkeculp;>!$<OSyqQ`52Ofy;kM*yYJKz9!A1wE<
z>B17?;-sXdozBXvbG$Yycxet*ERV7dG%H;oxPdP!u86+Op;8PiTgE1u((EP}jxiEH
zEPj^*vQnVDETBs?E8iVGuoe)(3Pfs}n$2JO!oG2~lQW@dacgmLqP}A7Tv|$zqVi^f
zv3dPDPUe4-Q+pSn>8Nqi52R&x=eQYbsvy2VU?!^BeW@F=@yl~_L%EDhtp-12?(h~#
z;-2~UDQpzc&Lzi5|Lx@FHdI4gDG2)dyCifn!B!^5Yk<Eo$OLC*X0$onuye8_6t#UB
zo_uRl_$kaZvg*r)WxwuZ)RafO7Vq7tr)~A_#c676{FTBTrBDEvi^tg#b-)b*pW_!L
zWfc`5VEozHk)Vj~J?#aca+7QAGq#ZP(R?_{?l)d{kYGt!^?m2<vdYVud3YMDswPK9
zEN;&>gmh)|>gqV(yb<~EVe|5422hQ4v9!EJB{b@4<Ew2AqoXw?%Z+xT^!Lw3hA@!N
z4;<bndITz}sy@9t=>_t(V&dYXQVM}~I1mW>@yEqKH(m;v6wy*<aSX5|09a9+q)q%b
za|k`AUtL{|X7~*q3d^5<nB@gnbIXT43xC6*7;5f;k3snouJ^F`EO3zd=C@n(lh;Hg
zDW+CdY1}rtRH4vyb$*5`CgbT(Zm+pSDXLpH(Nb`<RCPjnTz-wKsiWWy(_+MoK64sm
zL430dqoT(94m2?l6J8PS?eCXa8}wv$K_FUOcxgfp711pGq@e9^!w5B^V`AI3UzL@*
zLv_>!gbu)mF72*JsG&$=kKkB3uynuu(|m!S^$Hyu2ggR^CjmF-+OJ_eGU<vUJX}1c
z`V;OhU?edS2pLQKVf*O^i<LForytX?(NnPr?&>-gCD|%d%ia2{*QaYAz3!Gp)gK&v
zg<^*Q6ySi6d$xzy_y1JtITOEYWca!iB>GmxFTY+<OTDm+Hs=0HbEnP<P<(X&K)9nF
zF9D)G>PL&qDGezf|L8AQi@ivvCo71)i}-OogF5eRIJgFpM4SgpzZ(I4J-+6?{pU}+
zO~<;$kD)&XJwaaQlsY;q3@q3nZGW{eb85HU+nrxpvJt5DDNJxD2~LrTpO~C9v$A?R
zS@mWTqrh5>u(5jjPDS>`mS;IIp26h46qeVkIQigP-P$l6J$5LU<-9Ey6O7_y8Cv_;
z*x0d9-`08OE>tM2r!H-dm6w+nkiJ9lvXlD)sj10)7rqScCXJ@Q6OsSB-W8BmihAP(
zbavY63hD?VG|1ua@^nEj-cT{@tRFpFZC|RNcWiMqQth9GptQgF*ss~mtHHUwt-v-o
z@odC_S5VNmM5e&96%<&Hjg4hN7OV88gb=TFxWtw+BEAq-J2X)9^FL-4y^pX%@V#jl
zG;R<bGO`C!^4Kjwr4s^V8L92${7-1(GIRe&pfkPdzyeAq$X<yF38X^ap24Nx0ho&l
zN0Hb!XLz?)&V2&y`w*rIiXJCR28=JFc#ZiXlcM85_-_jmOQ20A@jnMaIe#ax0rtnt
zdkJttx*gt6v_jJcFx+C_qCO=I>~62igkx<8>8bFfN@=KO=8XTI!+Treoeu8-?p}ai
z2XO3}y;R$uuC~eczyp?ms3?beEG0=(H8we!SHIKOy7kiveBcKxkD6yh(CDFHkmz`y
zq=LfU6@Y*Q>;Ksnboj?BEr|nLP0h`Kr+xZ5{{Xir0c6+n>+9$5q`CkA6B9>d18c!<
z2o~isvtGN;0Z-*;lseAYnVI&bO5o{KZ)v`8ZS`@I|0ncnG{YY8L7>|Q7H4Mz;6r~8
zfmiYZri7p%`1xw2U%%c6mTM}^EiHL~i+g>2z3Fgyap8QrI=XEij7rGva-w}Mf`%5E
z!fj*Uq<wC(2_&Fwf(ppnB0irEE`GPEfq~r?mIFP5s?X%B(jri(s0wp)9UCY(3#s|_
z9efaBrTrZm0?yf53-y}i0T|@YomcH4AK$)(gY`Sd(f{qdX90ERR<JX+KhFe_8sXvL
zK}AD$zu$nFQL!ODlFRchZU*4k{`SnkPxzk(Y0&+R<uyD66+Ur<I(EpC>-D0iS)P$M
z>*Z?+1mH<OZ~3RXY@Zm;&1=Osuo6g!rwKfmwPm%pKLaV<lIww(jKGuc!&LwCpFoC9
z?F>&rpRP8KQ_?=cx6B%rE8`K<jZH;&5QO6`E@yLhbO<BHJiB2(s+|xnds9)JYM7h^
zCQo)5DjP=MfSVZh*u>}3ch5p;o$9~@L6RT=qyUG**Kzw-%lGi-(W&?M8^bS$3SYsd
zT15%7(S@OFwnIbL2jm)EMF-s9-#@*a-#<2?YF1%$*}zE`?QKb3U0o5AkhD0u1MD|%
z#i#$kOukNLy_Rfb?hec?Aglq+2b4L8>+|DX8lOvCd^`c;=ZRS-dsq=(97NDhBj3YA
zAg~ltvJO1NM3MbQ>?_dqo%K4x6kT(pd}vpXVd2l$C=fv*`^*09iIh!wSYVpk9Z?G?
zwRAi~S}FLrbQ&HSTlzyPcP}3wvqGQnRopikr+w^ZSY&;kVZ5>Q;6<VzasGGV&NJMh
zpqmSUi>L5ROr{X)ns2Ns48gMd8kD};FZ$562u~__wtB>3w>y@E@>N6@K`cAG2Y#va
z#%yHH+{Q-ecE0LsG=}dunUbo3v9Z(NgV~4()c5b3xH9NTqjoo2pa8Rk%*kt;SXE``
zvGxaq^ur??nx<A(^YAAT-PYWK{CqBagpCGk|5^?)fmis8Y|A8Dz)6D8fBM1E@rwO*
zcr>pj<i{C#etWwvFdtE2Gkvbq52%h{jIU-pK3=W#I2Palez}F3ufS8H$Tn-(6bT2B
ziXvZAQ@s59_vyu3oWgSf;ful?=b@k&GXlgEmV#W$jv-@uN{$x$F{2}ykYi@-4@!Ar
zngMutK%Y|)Y=1@WI^Ge0yjp)MC@(+3SPs>daCg54bvhz45)f>UktS$pXqmZ>`)T%>
z4#45;f{4zQLV}M^gNr;6`T9m2<6oQkLmgs92!Rxc6crWe;1L5G`@{e$9WDgI_W-tt
ziK&chcQ`H%Hwusilh||&9vxrAQ(j<$dw|%59yRb9h(A%Mb5XC@zO<Z5fTIet=Z4p5
z6}Z{xL15*AJ6B7a?yRn6uRO$ng~mkG{{k9nItGT<goJm*w&Y{h+!OCPIsb-#=Rsk-
zhnHN2k!5J~c8d5tLson3K!4}|Rc_7!|KWYCDkWz3H|fnSD%h%grPMtRN#4;gnY_&(
zLXTbsJtklXX9NS|<anWGV0f6GmiBAsWDiJmKuV#^5GW}vC3ZeG5AHp~Mc&@*3kM80
zEOb}j*LYWqJiQL@<0TN&e(BG&yHQZzK?=2C;Q<KP-exr<G&Gu%)7&Q!WW-}ElF=_!
z+SY6LZ@&)#hq*t@mJ66*sLyTEczmJ9*J`cM*44FM);_iTiVmr{zYdLxy1BSGu+2_L
zz*i>+c1~F0^4uozvCrrbVXE##Tr7tg#HIzr-B0!sxKRYArbuWJb~Gj>X$V2`_6WY9
zFf)rw`mn9JeN|LcY_#7F<H5^Uq=WSZx*h<Vp%&p%va&vs<iZ992EY&;A3r-TbNA?A
zP_r;JGz6$3eo_`LF6WV&-E}T>>$R1*gnG|A^-Jra;bHX6%FW^uVKwyh-ud#+bF+4L
zp3fOR&xD6Oh*G&NH6}Xko=u~7nT?H&xw*aF-5)eH7n26ObXAe;FV1)tt@k&X(I)5T
zKWk_p5t>ab!&0%gdQRFTe|>--&kza@jTP2~`h!Ptx{Pn5j0o$W^zLgvFAr2;v-9vw
zcjc1MZ6-S#Tj*-FM(cO>%XzkQ5ul(94u8w1$^7x=jrG;N6Yb4W;F}&`JY#=1XjHv@
z)WkZA5*D7BO&k~ZW+!?jLvK|H84;1S)tQM_(E0WkAs`O#us$uBiccmoj!on5IN<Yz
zKfEyWiNRLohKa+^epkqO{5lgF7%2X0dVtjuwpB`>^%dZ-=H})$=WW>56suI|>w1`_
z5rFwDlbr0}&`%+Fw`#7%+1ZqwlJcGLa2z_qx$7eVOcR?oF<@^W*?EW+med#^5u^g{
z8b(H6dUN2Je|yamoe$RlLy4|e0H*jk;zfP&;ywcuw1e}B^W~+gF>5l1)3ZIPRA?Ca
z&aQCE7<n|Td(a0_9KY|RGE`8Izz=44$VvQy0b-t7s%2&-Pkq*WQS)1{Q3D`N*zP1@
z0%62qr*lRqGt0ZZ^a}WMI`-Q|fUP`e{%fWU;*v{ly3h@!0?djCaBXCmO&j=9iV4D^
z$PP*cX9keJg`4AIae?&|wS@>PD*luHl2cL&<dUYIv!<khSn_3-(_kJEA_Z8Gr~A6w
zzAJD+EJ!8%s*o(#&Ub#9ozsCffRvO}hdv@EHg>Dl@;$08G$iF1>K`NNsn(#U1OpKe
z5O9r!_WI@5<ClPcP9qMyd8O6#5w=he(?TEMvLUEZbVQQ<w??id3snxLGrfRQY3JOv
zMxF6=t>dLkmEoDUIzx=23~}-1YX8$uL6}lYVObP4=GWHNT%i0`7(dZshpz3eT)fBg
z?ApRNL5;;5FsfPV`$+ghddzVV6RDXpP2$CUVHQByFm&QU)a7U}QTE$ya3wsEHuxK{
zkoTkHMOv*074{Z78L#6WX3FSwy3lxM=la@s*Pa5X;*&YG_4TVw4h4XT5fs!dsxMZP
zcDgxP0pc(OIH5kK@=Q)l2p3E*@k<n`97HfJtw3{aU{+rJyyyHVM?jDm?KQfgQ!QpE
zc=0j8E=~^Xfbb$x4zx^k?OQ5(1jL(7`lRS0xFiTCPnNTPTACd9v0*JitW}y8&A)rO
zx@NLCIPB0-Z54QJz2@hI0D;{9)B(<Eq55ENm~x#K_*EbOs<*iXT^FjhN31I90lB!9
z3ok@OnK?NQo4ui@q?!^E@WmxUPl~Dw^$t}#i)iyLWSFpwWn&pa9mXRGz<B2)`2xIM
z0kem~IwUmigGUSAmph~5Uhpd`D~au19>0EneHCz0bNGqE`R&`H>ma9Rmlf4tqobl9
zJ}i0z_0bwdf9Th*Z&P3>lG<4DQwwlU-Nc6<qEg_-yieW3-`T=flW6Hfm4+SMH{&!m
zKD$}-v0AarzCYcTlA?;KsIUQLX`+N1xE~>i0^UY$3JO{#Ph`Ely;M9rw*`)Y=UX7B
z=Ijb9IRexXi<F&Px{M==KscnKS~D+uePnit0S5v8e?S`?%oG92G&z~bdQq!2bfNr^
zyCVUa$jpq!#2Ax(`m8Ra{alGc$yC3an+wnUFiY=ctgfo6>6+$lSC<?@jtK1wO+Jx;
zvLSRC=Ip1C>bf_PmBto@FkJ}zEsvU&hvRS}i#kLQSNY(&lFq?b#q{c@tFvXIV7$z-
zvU>iwf%NQ~xJf<<J_O>uuGVmvee9#Esw$hv+@_~4q@ZK~3dk2iKXNIcB0+?|eAxs(
zXv2k6yr*N}nRp~Qa<SA#5S!ws49NIwQ-3L!%EM+dnSB;#WNOL7>7$#n2xb$zG9;s*
z&^sI+=kG3eUD|s9Y6DD4(C?Y%kr7bfB$bq+X+Pl19|e#88Z<F+Y7yhi$Gt#A>PJMn
zf1knGAkf-b%5p_kX|vXT?elwWN66cvL(-fs;shf5`1kd>UdC-nZSB=eYLcV`YP$Vy
z*LGFTqO!f!O7ra6T86UnH*PjQ*R#xuii+SLv8r0MAM?f<I4Xe6Ydbzn*6&e>0xOF(
z>XtcBQ5&)us7<U;nAh`;jev+K{6@ar%2{i(B12Y1WhNrF3qS~K?ZCI`ih3PV(gwgI
zo_4xX(eZrYg^h9#e|FH<g0j0*@C)P=i;P<aHnz#9DZJ0MxUkTlJMtMHbsG|(u|9qJ
zWOzTou_pw{&gpDrp`(AlYXS#MQKqJz$MN^O3YJ_cOt{EPeA3!Y_QuAyUaVX}i7YWM
z$ooe1r{hB-Bd;rs-twUOMnpK&n1GH<mzW*8)`^_uW<1dj=<OC3q1lC-QUvojutx0K
zO|Eu7VoORGQbzF+VRh&gr_ur8Rr7Tm3{C+0*ySyIQ+vB0D{EzKtz)MzRE^0fU?;>j
z3oP+LcD$d??->*B+}h}DFDOy_nUdnODhe7hFv4nRm;)wT3wCKU@W{x3p?*wXACWAK
z_|hJa){Wn!TJu}R4k@7sJew&c^{XC2N2jTRjn=j{O7#i^XehC4ERTAZ$CM}DO{rT1
z`>@Vkc|4@=qyqP!fXn|i<GH$eml}{{(7}vk7(NX|j-BiCUmolHCTUmQ4gDLW7U$z;
z{o)c+i|gvr&9jZ3WtX=&G|g>{B|pD>L4*vW^0t##l;Pwyh5md_h<nDrliWtci-+~P
zKbQZdcuz|pYe!`zD;o~_y<KtMu&^*N)kaNCU7(P5@Akfd%xI=iX&)aS3?tH%BoN@@
z{$tScWu!cIz$}R6d?i483~P03Tosj-nD;-Sz&uoD5x69OSH>5|axya&vAtQSsl|;G
zxPfiO)U>jGb>@wkOvA;UKoXbLA{bZ!VhZD2`5hLw77R?l<3tMB%;w8oqF1k$TitMK
zogk1av|-T~$`QWsK4^{im?JHMJ0LFfI7%-8>t`@LX}I1%sHt^&SIW@jQqblx-Ee++
z*}`c__3I~z!&9-R*tf()^(6}<adEi4J-^S0$w>tG_})skpv$CjUZqe}_IAAb0gzTB
zy^NX8O2LG-{4s$h@5Br=MA`Kb+%i+?(5wg+R};Bpj<@V|m_bqBcB@WKPSQF&q5W(D
zxt^O#g&jKcW(szzBXmvN_HkLo1<@avgHK$2j@7`-Y(xIzAvz)~G<xu1!~zx;7U;1E
zh+ajt;Ej=EFEqnP)uM7*rm??!7pRdG=H&?0sjc)Zra_&;qJIe&z&ZOdKi7(kgS@F)
zrh73#A?J<T!r2a<@{b!c>3|O+ql}&Xq)KYk(2ucMn7wz&=@En|K~uDIrK9bRSx<(I
zwY4)Y7gRoyEi(8X29`&_y6ST;FgZPq^Lu-0Ztl6A^!{r9vTq*Wu4}1&U<ibb!5BX)
z>$m>?tkFm?0@qmKvFr63%t8_pE=)|Yf>A3UCy~tQeI1#Oc^I6Q*zL7vJl-^BU1bVP
zM8F(JH;5Vxvby5F2QhJ-2#Tl9Q19SOLuN6uItz*f`bSw=2V2{{KXl;m0G$pht(BlO
z7^z)fM>InI@m7Da0PxM1;lvFxBVF^j5dFBr6uvBn-7%|_H(=J4L|fyTk%gd;!<G1*
z(aG$%4;GkZR}1kEqla`NCU(4IKP<CUFy75A*M2Daj^yvF2$7e6B9-VpS{lJbK=la~
zdcN%e?*KB7$=~7fetuagDNnxA`vRbIQGhN}EITD-DT)Q*y5HtV52Zfu0;JB?ybB;^
z(E9Ol^DWY<s_8)~>+5F%W1^SWQ+9L@M${|My-_X0_er1+9Qd@lz@(8iOCJvNKQBOb
zw!Qw)2V#HQ84hD3qX<XGIaG%P_lzKmLV_C_6}9b~8vLJEcbjw4W@bgRbH|nTXb|CF
zIjY1v-GL}eq}hP$oRM=qu+Bt*pyZ^Nw&CB`5iNAQA*ok^kb?#as3@Q+Uha;+M5Abc
z(ageDY&!bxmCBopZT?#k_pM%hlol|go12^a{Pf`A?(U4~06Ifp*Z}}6s?-Lyq3Z0!
zN|-Em{M|Gdw*pkw_5O75#kd3S`}b}to%pt|5zLBGBGCHm1i&2Y^`Tdn&AT(#%7u#C
z>#{MSp`Gyzp8+fWy@Rfm%78_pcP76~U&;Us^xpXeO7=U4onIBR=Se_;tD$BPQ9ut1
z12mMsPkG#gV#SZY&y>ilok*Cz4sW8`3S0(|#U3`TRuB?L(geNk9S~^OFN5=F^x8e|
zT5x8=yZ$%g<i2%dPYX8ylqvNOCFv2kkcbXEyZyhfAt8~GOlX4jz0Dbz;$Oc>hRXc{
zR)HlCL5g4aQ%l^vE<=dHFc3OFy1nT{8v`blM>W!q%!wsz1{AO-!Py^pym1*B-@JQT
zERF}Hq!<MoFK_eSl^vOf1#ai@&I$@rg?s1e#_O{?PfUWNJy?*_Gd_SU-__l>P6mPe
z!0gD7xYuBd{Q@@Jnql?&*aWbcO*|(2A$jQ)A4i`;t}_6}{Q4Xyt8WTx6Z>iGnYvuC
z+4*?!GG|#>x~b=FbaeEl<oZ;eY|9^K%}<|d3<tggqyszyK0ZEz8EbboPNZRiRt7rs
zFSp;8=q*#e{leFmf<VB`Ds$ib6%C=<9VG07@7lxqC!jBDGbf5sc-`19?p{T#hlGcx
zT=C%NyFx=!1#q?mR=T@4C3$<nAk3Xky$b7dcxYs#mASe5Q0ed3=wf3B*yfB)c}GXb
zZb7x|2@ELAunCf-28NedEMh$^`TGazH+(qXK0t)`QQPBnEQCsY<OR?npuc{cqV_4*
zfg=lhbIUk5)TQ^e6`dp~Aox)NRj=wx;At!^j*Vm`oWIZ`*NbMI9Y8xeQ5g{bQnI7v
zvcHV&Xn`M~c7$T8s{FDFZ%P>VomgL?np#4{#n;b+ruY=bfcDZ=pXS@+mop8T%ii`L
zRCt(PY4y9uH?=P=Gqz4nPwk%n-W;?S_%CEp1D8w4SzaVq$Sj|awB$!IQj#J!lWD{#
zB1eoJ-UCC=r06;U@MkF6Os#PVlg}U2U@Knjl?s9DnZT^$p3@5b`KTj-nG|U0F0UR-
zZXI{~I5_k*#5_nrFX+eu>_=fqr_0+$a|6^tCfgeka$sP-m(0w<0^@Vz%#$!C^4?5j
zO0PML-Y0Q)Ldn4qe8~YMym;5SCpa3DiTGS-V4*G|d}jiPH5Vx=I%fZT2>?bQd^abg
zS}I_W3;Yieinzyq+ik$+3Cu-eTwF|b^^3J;pB@ssfs06|$PBZQ3<7;~N|KVoFeW*5
zsYn;MvNiC3X!;Ils{jB0OZJY8jK~hzvNMytg^-A{_s*UnKFOB7M?}cpA=zZ_y~*DE
zpM8Jle^2MsIn}w>eZR)@`B+cuzjKK1-)-^-tdGMCjb&_2K-RVqPTI-JEG4%OSph<x
z<lQDd4goY#lTOWWwhWVSL`G`|e*XMy%j2c#TRS}|iZ6S-d_K!QFA;GM=R0LN8Rg|<
zb|;)WjA+cv18NzqW>7Sf@?w6%O`VdGGW8=2*dCkw{I*t88p7UvT`j@4il?TRXUU);
zO%rh^e5M8@v)Jubc~Ks)d%d;Z*VDUHH22te`0fh7H2P-;XkM2Ilr)-%UZ;zmMiSRh
zr&Tww?M>p_p>T6|ugqcT)Yks|s^ji~<4<*|^sLTu3YtGxr_&OKZ$Q=P`okK@+CX*e
z%t=<MAvaeBjxK51eYg4t1e9N%SbNkuG5q@V0K3lA>F}*y&%+N@cSmlzj0;NC3=FP|
zP|y)QDZ*oIZCN=v&+4~tn`GSG&w;@Kj~^)3KzRz{p8T(0Arhe%DjEicoc<;G6Xnzj
zc01C9#I|83p9j|!7#Z2wPD>pT2L}$%p0)RdWM0wac(&|xtio~=yj|YNT(B$o{4rFW
zFkr)paQAk+?yx#xSPU_rwl{t1L|q$xPfe4yvzCq_Tjya}N#$tbW#h`PhPbbedSgNk
z^q3ScLYKon(+zs{dBOh2xqc}*)^o4_@;@*0RpI_q9zNm2#I;M2G<-BfZr1a?>lN+w
z3G9QzIHxVBm2{jHe!Nsu8@wca?1#9xfO^&P5XHF6ZGF<Ylo9#Ya?uz53g{8=tRp}p
z@pg4BT9zK%Zb~Sjp7-Si2ykf$Lydz7(VG!r(u@7py7VN(pKX~w^Q1MVjg(f#JMCsz
zyqNnu3r9SD)(ImM)9uaWo~yN$c#vzatgEA*e!meC4a6tFK?ZVlkR4nf9E2CqVy;sl
z5Y_KR8V+qo>hui^>PifKmQI>{CB~TWXGnXBeO@1D<mG8w3s>y5e7w(75)^qyqx>+6
zVo_0XvdY%*u=BHQcE9y=ENtwp>6$}8IqMle7S*1HNYhSeJ{qDXCC%G?s9w|{#;2B{
znYx4-8LLGf{&RCVDbPra^S+-4r6Jx{mRB*z816sM0_uk<7{!zQ^mu<?m)D&8Q`e_7
z(&o1ohlM|Zf@z*b`a@AVRq1oGtxH9Shv2;26&BngfN!Q3D@oYrZwJPE(On+VHm`>d
zsb>_JIkK)CgT6NU`Ws=PKTmY!&zb0)E86Utp7tC_mjJRKP7pFbak7(6YG=I)FnxhR
z2Hbda0H6YG>s|Ds4Qx{$-k{n1^-K1c%c16KveFVU0CL&iZ1l~4&bHfl%?1gTZ?D@v
z+*TlE4Vpp!BJ6!75V_Q%ruOpkd$?y{=aK;(K}!b-B7M#m)L>&Q3uoNVpLc?s^BRo0
zjvB-1u7(2dZKP~$s7mg6%116~qqZ)Y)(bR>Ul@`1r%2#0JD}N5bY%CxLgC`%JUb8~
z)YF?1ZrTxgfAZCwXSlhb((<FW3OvkDv8aN!zP-VLZr`l+D&1YOCDIL8B4jo6dkmuu
z0D1fdHRj}G?IsqQB3tgXa(4C%YR<uJL2yCMsuWtjY0e<HJV_*428!@w;_yhZK?3@t
zrZ%AA4x;%=Fo^3NCr)(t3J9b2AL}8Br+qwvwNv0a9UC7HYgLqqsk6WT+YReBZ-v6M
zVe(t-sp@a|Y=)>-cvp>E()05mQ6964m%k1Q4Af7pBICr-ENtA;zCK?|JHEeDfF-7{
zdgLnjO|#+Nz2-1Qtf3{e4f`aGq6w)I^#X{qFoKRPf7k3A9p7}$=aiHN$2DcpC%o-R
z5UO&u5jTsMx6ZhaC%pt>EuT$M*4X7v{CxhB!n%&4Bwg9sQ>~Z0vpIKwGJ`9n!Q)Qz
zARj9!pDmv4_m)|tZ1qpwfA|u>kg@o<_#r0-ViA7_(q9@q-eskx&Mvm&+TBGT%rE-#
z#Aduyf+hB3eNYN}C9iO3csSt7lav43WMs(?0D+@dh&x%&VikC+r5DBVSas62+94T2
zwpxen-xWn1`Sc!X@y_1iu!`8o&i2bUZ{85yy_>Pc{Haz76Fu`0#MD5s3u#JV)Vfv@
zQ0|k>qh};w`Hdu=^N!{+BYwIJ2}|1IUW821wi7;~JphbL=ZF3Jq3BV6x4-Ayipd-;
zkVSnsIk)(j<^lS0e^l&}Fox^u<i~nH)=%nI`E4P=;fw7wa**-mI9IHt+f;?Q<NCnE
zl%G7nl>_VXP2Qwo{ZEb7WaXbg*&Lz%C2EefIRcE4@>eD}TV2LzTaKRNY@1_~QOQEi
zn_=<%N4waF!?t(O7L1lYQ1trHzF44<m-?7(Q#prCr!+kY&+}ImHUs4c8L;|B?hQL8
zl)n*D`CApOAT5oOQISC66Lq%Ahd`t}-1(!mx~f*xTT(j&U%EGw$}5jAcXDzLh28@R
z0ko%Up5lv2Kd6toWx4l%#VH{;Klgt9@uq$4>#K%~&Hg6;jKQ3>M4&nJ>s+iy-osO*
zrK4jY3{6X$104y)rkoexChAX`x&uBa`7UXY_U(*)OY0l!da0`m*S6xxT>Nl+L6I5d
zxA~N!d2BI#L&uz9|6>6<MWu21XSf_O;qAux`p0s4toN`U(^I}b-4&h6AaZl9VJ9RU
zErti1klX)(_W|>QIPW5Wv(Zy^crH!|-!jt0hYN&<EuyL{ezGx@do)|)#NT=;eQng3
zV_?%j4Fmq2-x2=QULvo_NbcVkJYG@2Y1`hmsEXC5jq=U7i|{Qrn4P#ia#3T6^=e&f
z{$1JpT7Rat2L3bd5Z7{D?3XW>-ut-0ezDF*Mj3rXc$MUDDRk8*$_~PaR<2yB3q2B|
z<)n@UTJ$4{BCrrn+;W*;HzxUfd~VO_o=dn3F0VvbFQliKcDuW~4-A8Vx}~1wp5?+v
z*(E<hJZU-r_+?LSMD17bQ^pHi5#r|Nk2iLTxNW!_6j+SNR+pELM%uP_cD6XHLqeW=
z9^L)Bi?h~;N5+}HX#fP(Yjf1iou9KQ|GF2#$cw)xC&30yezANQs597yl`k$fAQK5X
zCSESC`@CkVR2Qx>k7Dvz+?ZI;T%YZUWo)2WAwsCWcJ6d!6Nx!)ECYc%_RY{0x>5M8
za%L7#9iIQF>pYGN3*w)H;L(CYYX%hAGCR|#53?4!mh9O_PF*A#wct>o8#*IB^x1T*
z)BI^{fA$m8u?JU=0@yU=cd~V-QB^kBhIJ)hsVXOPGR;|HHvu5|?}6whXcqt|ESe2k
za(n%HJ|fKr)yr$V^ya1m!_v}{DmW!E@qD$HLPZ5y6TQRde1)W-OmlH^;*IXwhAR~s
zMEFiBqbWyr)q&IT0CnTqg9AZu`W#)jV+DTnud)qrc(pfKSrm*PQ}CA+xuwkEhS*O$
zw-pI-XZ@!M&e+;TnXZ*FU$Nr4Q>`>@i@gXq)5MJL;NS*7$Is*12q$@5=-!;<0@uvh
znHxHK$o%~4<7bpRN9yKw|2qc*v+kN}?sfBNdbRp3Eu)|2hhipZVv#Z04l7R<lg=h=
zD)JTvE_H)$pwj*DEu5kN%-p4uS+~>MP8EGmGFy!r`S{`f#Y}#KmV&ldVxQsors2zx
zk!rAUVl;W8zN`%XxmD%5V>MfU{v7|DcR>Jf(e3Ip4v}TYA8-*!&XG+|A|coeVh-09
zzLh*`WF<`dI-gcDAImbkXXBi%4%<t@L!b2^o>7*CC=yJ%+o$*cW<1k(8$3@SmnkA*
zUN}=uS74F7Ll+Z2#GXR8YI4VSXk2>eop0e?TX*ZL?d@%NpOP(R1cD^>DK^EEJBaEE
zL7-!@E{3j9!H+x@<ABc`!^s-{TPb#-Q>FDH!q;eibQiQKz{_533Y7ns;&%JVWe1;9
zpwwcdkR0u`i3!Ty0b<fm9@X=PG|sX<u1h86P&r1s{=!aBJx!XjX9dN6;=#wsl`S~&
zKE<QwzNLvhGI^4zPko-65qQU3KtS2sEzRMWP$ENh{!A;h)k}vthfB$d<~X1TyVYM(
zEp2pJGQ$oaxIBl$O9vg09}cWbh9xH_E2*Tm9%vy-#Wqg?tvF4Y+rJfyPx?V!)FuVk
z1SjX;&6r2;)OW>oC#W^@JTHn^wV3-4&X|6)?uOBC?w#F39{tjeHTve%sO(RKvV|q^
z4W~>>^3`^D@?r13>bwfMOOW02?-P;*E=ta3FgxZTBj~YEBcuuIyJl<NpF$z$L^Y|s
zJ=<^zRSg95Ks|j~p+~H4-u>&-25X$;A&K2-UqS%-rbyq(&XkJtCh`L+_V|0z+9e`(
z>AO;1#Q${${APWG7A*2W<+?w`C_^KT!!J(x(`S+0|GfC_Z;IhhkZK*0fsPr#cK*Y2
zC!vPTu)(@h_4tnDZkypBKf831$Plj)sW>k`J#ZxmDZV{NUgm>B(-i_K!oskgd@)Gd
zCXgT}c-c1iVf{+om1@9@mph8N&LQ+_Pb@(m72zY|#ywiehxlvyeg7Nw$?i;1uN$y3
zB&4M8tgV4fKMAt-j<pnkeuU>{)fxjgpi1!iR_JTdrV5{lnb{ev3IMDY1fD`I(SM9H
zoD2$?2KU2UgF2VWLaUQO?Dq%DT})Cw9!<wGZQa;0@}{z~vi|SU(^czkqV1M{-9-z?
zXk_K!*n>k2{ED$vnp!)VgB)Vns|PD#0YU8)hV}mVR5M>O0~ch_Bs?hf<QrIwG)orp
z`)MXaM(sSA25`{TIdtT>ekNzvd)-0oogrgC*^)3CtXbo@9G}>(K24vRe{GEx4H-+6
z$LN6(0c(jH3kz`doNY>}YiMM=<8amr9{-L-*;zCrJ}wK?uFD%A|1J$*@o_=>p>U$k
zTMeF9hiv~|n^W!JMQCKg-wv2xu;Vv%cwdEvx$XEtdJ(inZ;XwX9nPDjLJAqNARK3P
zwQ_4lBP2vKr*)xF+%5mBNGgZsyqoC{B)MC59=-$H6~nctyV-K$nCR--0?IXMlJbg(
z8cL?<?uObc-s3Ui86jMY1x~I<m6g&Xj<4r`MbTB>9|Q{U^|Bc!4?54`8`3@>(HVrD
zt7}LgE9YCb`reIi{nkAbXo%@1A8HYij37FM`13uGp5Fd#!>`3F97O|D5@R$GM~bV1
z13BN4wo+Q8e9k{7_63_Eqk2JPoYvJV^&53Yjk!g|hhm~9q(>X*Kp)f(JaN!iX@Q##
z0x+zneiW~XVEOYtfjfH>c}Z)4qn)7K5DB64GW_B^`UsalB@)gO&QR&vQnwrk6g$cK
z4v0sdO3QC@6#oYw$$QU1zrDP+W@u0e?Sa#5t#eXjX{wc#)x0}n{<~Bj9;;TFEul=I
zgTsHXVds03pE$IoW8<h<SUEJ9k$jb#k!zCGE<ASbSNuV`hR4Ak-PP)ys#(){w%2r<
zdi0LB!pXl~LH%TR@J5RVWstHW7|wGROMzeaH~w^!a)y$9kTzZ0{h8{u2ir9__lNm#
zM|=CT5WlXK14tSYGpyh2N`L&aVjUY-DM^(ovJjlguDjEM$ITLmao)ww!K^Pbt%I7c
z#<fP8M6c;WkE#}j%0(J$-^M=*J&Mk_G4m>EA=M55bE=_U1ts`0j~$E;ij!;`wWKTs
zDlm%|@*nfI`ThBzUZAL=!f(vE{U8rIPh>cuH8=Satjwu^RBjP053aPRvF*1_VZ0@z
z{5s-Hp%vA&48^F`;K)X$F(|QPMU|sEHX>yybu~XXlNO4J8JPW%3%&@bNlvn|YhWa3
z_|~C7`zThv7cGotGKdgwc}tpBO-E;3l^e%mB3UsD*^g#xzV~!pI%Bx&pIdF3Kb<2$
zk$k-M>3q+9Dn`mV{Qn{0iBQ6WixR>bHEn)~045H!-4{N~ClKsN6rDR(o?B4IPGIwh
zrGeod5&~@?zBkD#doD;fJclfbF1TN(0a<@ieDS!HDk5{9!k_nEj2(@k>r=+J4VC5;
zM05yd5`|@{V3nDBatXpsk}O}En8K9v;uRQhxWhT3$LbrLAteNCvqeRgz&O$w7M=@=
zJlF%^9<o>f+=YiUuv!2fp8KYIkbTLKCaiF_Qw4Q2ax-j`+*c}r6Nd{*F0)^C0WlkW
zD?7(SInQlanv<_NqaT47NR+N<B)4UXuO#QgvWT|(NGUipHFXJoss|d38FFsR6anz9
zv6hB8%w1VI644$1hy87iH)rd=9OK3S^q);0M1+Kk+uM{bht7ILS)I=q9DTm2jYKrv
zTx^X|R5g#`+c1JC-2*s>Bu}&?ZtttJBCiX-@{u^hyqUSW&Y>Z7%e+XBBNWLF1On%8
zQ@MTdMF)^8e~<4FVs2BhYyH(^(W3iyMjzk(O}{2nljZVgLn=qznM&DU9zn`-MiC}I
z#GsByCn(L(?=vGDwbf+)n1(^Cws^J=zVsrk5FLo<tF5Gx`6N@K)<peK=(+brg1g;+
zHTo=S%(lzR>~i%=N86gJs`F;)OfQ(9%+#EEI@#5`3hS}wUB{S**v`1Px_;{_Eh{VI
zG~yngZ22{?ylkQ*hOP9gySwi9Yl-K!c2<~o&_QF=@B*JSyd7Ly1#F(WdJlVd7IcLW
z%}rG-q?@q0&}?Q<5X>yw?de-jrkd?Vcz6zhnMo5CFVGAQ3W`v!AwBuqm9LhSt!{p;
zQmD!dTGyAFnwqy6jacX(Rxmx@HsE!4DaFg0lbuBSy>f7@D<*kc<GL%J);K?nH7!c`
zMOW14Mjv~hMs_LWt-z;EBYD*0-wX$Bng~_$FIXOzz!6J&pKOmrNlAY!{UAv`T9^|a
z^)NsY+4rf-s3~$wo}oH1*0ajFot~bad8H@g!s12`6%6dZ7XVV=Td%KL;4AK{&B1nY
z`RA$(bU>%62iO%hckcHq_u<}3K91<WMurj^t2_SIeg3~to08$DylU7O7;eX#T0@07
zY$TQola-ZTGPttr@hS<N3nCFno;OZwae$nkA0_0eq;=dUZ~H`psAi0!XJ8m9&&BvZ
z&6zU~_OI>;vH>wRwy#gKye7nu7Yh9sdCTCdM6s%Vz&|}cuq{PB2H~R;R+I=tq*NaT
zFv`8p{ArXh<OM!i5d2m!f2Tr#jg4qC*%2|Fxl|4P=^gii;%%?ncn?+r+t^zFiROp>
z#m}Us#1~GRWqFC=q{aC{{_;6V!mhi19~D2zp4<u@XJukT0$2^t+n@WW4eJ#_5seqm
zDnaBzt>OJZ_`Q%Svshi#9*7Bhsq5Z!;wA7<_w-1xTzd`IT8NpMa!-KuN`?5IpRAil
z*CIF>G#j+Rl{<MY5V5@Y2c-G^Q$pge2@qDgHC8fegL!;kA)Z&$Mrh@5%W!#fyzI@`
z`%Ce)5(MYFlcRwFRi&PNdW^s=77bh+3hQ+K-e1kzO^=sA;n7A!_@pNV6|4QElnX7E
z_}Sg%%V5~LZ?eDQyxi7Rq-`nCEcmaf-jwxt2P<eXBZyDh3YqX+Lf@*X;ytTM>TIcF
zUTOVRUPKhMAzJ9b6o`tyYe~XTEogn)|Lb_apf>dnxYywRji`@2(>}kvBt5?zThRSw
z!maAl__-^sNEFJLD2?LD)<3f}(YUG9ukN`P+#p#-XtD(TM)7a?Buk&RN(7W4QLmDZ
zVHNqW<-3$|(Bp`zutdp<?_JB&rK>_&)W%53ACy^%+U_cCj7WbyE_<iJTn4T8*GN-P
zUm_2ah)BI$Q~lk4)#%duJWUquuR8k&L(jq#rWnFW1Xt^`r_V|4^v#W3I{VZf#$D%)
zp{5|wb0d5sil3=9LZA{<pIs*0Y5lim<B-guN~%In&^(^dzXlxX-jthgNn1G?Fk4-*
zMd|NstKYu^Xm8{JL|vg58z3Q=@1?6#++qzB{!`5!H~Udwq5fqF9%2R|=*Z~vmz5SH
z%dUGd^5BwizE%Nvwwl%n=iaiE%L&Ksj%B0KW|6w|qH|DtDDqvzC$8=1Ub~8K?u37f
zxV5f_9|-B+59A73?-#l%{P@THL>TTNC+cLpi213i1iPmtBL(H|k_-9%gxYAan~UV2
zlQA*!UOZqZtqhR5d|sk^9b^Qs0&X<pop@|i)ay{PB&`z2)L}iYN&z)oG5XoRu&T*7
zXgFl0rW06XqG+#7uFWi^XbApg8G;B39BijMQ`+4~V<h427l-{~aIlYCxq#uJ(az*r
zuSLsZ^sDab{u@w&a^7ZuYT#YK$8%X)13lGy{iMi<yKTXn_N@`P)|+Dk#1f5q`4#ST
znb%kYP%c5vo38Ee%7sQ-p+efIF)=T1;hl`Cob!6+)RsJs?e(d)z^=2k-YFfPFHm^^
zAPPkzB&xtWlmFS-DEhL1@CENKWMyn^ZGi$+65Bc-H27C1J9@UZdr7>i)`U5|)K5L#
z_7@?rvM4=09on-%8EVv9M0IriK%p+3ZjqXlQdx!j6B(Uj_B|_eK|Eh1oaOIp31oum
zk7^^gSXIp(ttE4MEwOv;Jey#JxSlpY&BXGNcX+`>X=@rrRl^Xigj4I})O7rW6k#GC
ziy4iCD9P=3@8H0EPrmokmDhGwL_|d2abFu+;nm8Get?#rST{D^;#<f497u+oAo%s-
zdJlevwr@PXExx1dT}AO5yd}2NtcfC-rDvLgDk%{crMibo5if_6OcDuD%r#Y8_fymh
zH23RVUt!$bBKtcL=khPa_e?CT=KdhD8a=7Qe=$?5!5Foz>^0s!>T%gnfu!+nopYX}
z+1}H-RZFJ{6Vcpv1Lp@QS%#cdaeKzc$z~gZNFtky(pAYXPq!I2Q}i2a@_B2&O8PA!
z210w2Y)uA2<;I-#F%BZcF*{wcI_)XgFRz=BYzWME-iF)>hO|TF*?Oax@bX9DYJ-h!
zLqQ4<09loncbZV14h>Ij#JM9UPQdRc+T6Oug+fn=VJo@qcXMcKxoPJZ+dLNjKx-Jw
zq;2(>T~z-?$?o=Kl!T#;^@P%Y8fi9V82+9p#S3QA748LJjy6%f7gf;E)N>rpd#B!<
zJmBT$*Pe5RmI^e={QOmsRKniQ$sAu7267=Qv+t=7lGS!?qvr!+;^Ps%LM3C|&b%bE
zaUtC{2xb8j%3lc0;o&I{!X|}%qxD{=QNOG*f7wnq=w2sMx@f?Y{_%YDFS@rUM?!w1
zc4kB`+tl>P<pkg`GBJVd0uT+Bd_9E>{U;EVP8j;K!VDtI&i;GgPlz|Lc*)Ih&UoCx
z*KCgz?l@D*HlF+cwE)u{#n1m?J5?FvasbM}Mt~PPJ3C*^qH2|jjh}ca$b6HNDaFK^
z07Dt!8>;2u<z-`KmBM_k1g3&_Msk8@=*@>c&DGdIR@|)nN(LEQs%>NpIbcTK5vL$`
zjtq^$56PMB2_wX_mW0P8Nf+G@$%-I|26%6#)Tj+Of)UoUz7*lLot+tI5umkv_wL<P
zemE};Zgys-aet~Pd?X|!q_h_$G2JV$vmu<$K-k&%ry-4~I|jlbZ*;(R9v@e`M7#=Q
zf~-vDye4o6<cSEk(tM+P?<1y^<E?pps0Ta}$ft|d3xFH7_`a2Q7eb{2)Qk{_$CS6T
zRaF+XYWb>G94w-LAC8zQW%Y4!ZTA~x)!xU*R3m@H0K)7X8|Ib8MF<+tY2*?Un`>!7
zLV#UCjXw)5aLG+sxvyXBc3M=}QSghC=e|FD7cWG`8KeLCn2vXMzPVWU>XpYFEkAsn
zP+_SO>O*r=Y(nwUcj>HP+H-v!H&}+#Q(98ezl3h+UZNtTdkZz>JG~8h-IKs&T&6R2
zcO)n;k0~EQhTTPaPP~lDs<_Xu>LpJ<#@8wQrWcqElbNv{*?#G$Hy7Of1T<1$@S<?}
zO(M~VOP;tufPu1Eiw#hOgYtLS_n4TNii`k!e1V={YpxphLuMu|S^$^%P&OJ4qK#81
zWp<fyU6x+mS~wBhtxzv~S*_2+d?q@BhG^Erud3Fos&(CChp`3f1<-GRAkgCx!>~|=
zwwSa8jVwBjk(<-mdaIPNv9XB>edJOA8ZL-16jyfSbG`!~Gd^BDiTEH*>nCpJfIAL<
zI6pLP7gNYU_#t%I+^+Jqie60mjFJU&B`PX4mBLPXKQsEk;}q4G;@Pu*a3!obFSM_1
z*mXC#iixx{_2;7(T2Kju)6pG%$d0Hv_aTn|ZBL2NCp$uo>qR~~#z>Rnm8B;PHmbzE
zyg1&vjxR>I;_Fb~ZSsgcS!;&Bff(llISLfGOdiaZH95kDN>I#|rAJ1T>GYUzkMM)$
z@#gw;4$5>$`YODlOH4{qXTC=sk^6PpbU+-+{^+_tO7w)5m+u823tBwKO>^$S^?xY~
z-6mF83>yZiQvEl2RW>QEjA&+m?D0;(18UykeXGqJE4%pWE%gVy38&4E(a{<!0<=+P
zGl6M04;G)(-DTRFoc&>~2l5w$EX4To2~#Tedi<t;f3nTR!*j`uK=$)patpXU=Zz4j
zVUGPh+1N5ONnQh<&uXljz#r^%G9L$S%3s_T@?AIftT<$p{=LS37NpN!mD*o{$FYsZ
z1#~~K{UUc2-mVa*GJECBZcoNZ;M=|xk~PG?)2x+-8!k?hZ%AJ$v;^DI=v+R1=(X=H
z+&IF{o0@w}YQiYU5;^L8H7YiUfmZCgmXWKVh|TFDbNU4cu5E|wBPBT*Is<Y_%CC*^
zfy?1yfesM*VS6Vh%{j;y7C#mvcR~BCoTrd8#8_n(BTn<j<)Ri+4H;7K5UjP%R^_Lq
zUkXCK#0+nhQ<6JWd@JuFO7{kk#=Po$92}8+(Sx3`cgTqSu$U-IoT%NIf-^x!un$<+
zNwTIrzPavEmdB5kuK8b9*-(9biH_4T0;I_kE1l!6U`5JKaL`46aqhIQ#|xJMJJElX
zf|)l}AW-REA;;KJAj1ikrX6%*w6?K%+Fi4f+kWULel?M!^)JNZ{InQWo&+$tQ(^K;
z=g7xvI_PNE+HdYqKfy-O8-}B|l?nx3-~LC9qrnm@<UXooKgb;2buYYKiG4e%NfMM?
zB!!Rpy<v?BxolU1paz%XNkUQ*1muCU`BkH*JNVMzG!*l?a01+$JX@Ox83ED`vAs$5
zm~KPuzaD5fleNyQh<fj9@{dXg-@v6Q#p*W^Q;6ou&j*!>6J;GT2wx-WJ!JrK+;uds
zzSm3sCnLJQeg2t&>Q7bYA<EvAcTz%JY^?dZ?q9chh%5RRld(Qv1VZM?M54a_e#7#&
zN<bWf{n_%>(_>*S;L9F)Hp=G|9-c5zeTH_`VRnzf^giJu05QGBd(s5AeOz_~{+{i7
zX^(*n#v<Qs5_D;A;@Q#q@c<PyYVlM`MwUoo*9^(JTQIfT0>ZHFcn%=Dz&4~HYR?qk
zZJ@7jvc&Y@Vb^6>&So2^%Si;B%%)dyXXASN-Zkjk3rtQ-)NBd>qcGj*RVPjZ$;XE$
zCokrzowr7{I9~aMFkQKuq?_`<bvrOHP&h?WXcOMs)g=hZ`cP>sT-@uU#conKMwdR+
zkycj*4Eub0!k5HpBx&t~S@|q$5Y#K|xYU?0MoZVK+PoWqzZgD}+uC!SGR{%o00l;Q
zc{vzBw*({pxd%Wa4sLfC+`y-a$yaP0{`duRR`>7IxVW`$Vubj^*8u{_h97a03u1B<
z;&(0HqNdbIw#Moj-_*MwHi~GbRJB7I(o^??pY81{j@_xeSk~bqj1`Z6{Ag@R`Uru-
zvfy@Q;7;6LT9V38h50VQ_|&hCeSrW7eN5$jmzZM694mIyUH9C!0RQs82f`2qtfJBn
zHD|-lB>3aKc)vC@c$t_iKj&Be@}&0|awhm~Z5X28AS1sC!-{59xq)g~ETRyUjMvrs
zoZhs|_qxPx;RaL68nih37%Ad|w8V&nIHLY^HT4A8KY>U?mNmbK#|U4QTj(-*Lj#u_
z|4o$mZi_@!w>?|C!xpK%8eZuPF#kTOvC%J8*_!A|mmS~*DW3ERsTOF$MFYlhIPlIT
zMXS(_yE49qEtwK;rj!Ir8-MVdRYCJuT|Tw72h@>(7a5VRy%%B`XUYB=M%}=xH{w4U
zrFfYi?X<^$FH}I)j7h`@4l!@1c;J}%EdKl?{nq)^laPeu#m;921uXaPbwv+T&=3@#
z_jVY|uHwEu5OknNfIH}TeQ;^mY6%<eEStA)(WHX?8}7I{*Wv`iQdGfLP*MtFH)yUQ
zt}uU}6*LT0y@hv}(T~wjiAab~ZZo~T#jE&Vd2re;9CSXV<@H8e^civ(O6@hieuJIW
z_OD4^vuA%*kFmt$AIpbxzxJ_U4ieqha>y!-sP<lp_g{=_X#)+|)7mPXjOkaJlMfF~
z_edu;319W`ZjxY^qbuWuD@CxB<048SVe+y7fsng>VZa_i^I<goeq@Ikz)=c{$1f)n
zdOqq{0z0>|qUEj)1h4Uw%YY98q0i3dcCwvVtK`XGysMX$utXN{ik5_<DbYkwz-?r?
zi#p<8QYT2qp_~L^t6G7fZ2$<EF+;1;14x-;RW)_28JI!Pcc$uy`l_)MBFN4U4a$sB
zA@;yO`Z)-yn7ph@>M|hJEwa*rIIaKm<fKHuep>TUEDa%20FrM1R=GH&Ba?qmmD1y9
zeoXcHDE8p*%jUJi|5iy`e;OhX>mR|%@R3TG1feN#MMv=9QBgHLL4IADDwh>ayy&F(
zH@d8gSC;T^ergwc5WNSPBfIAy?c=AIC=#<!UuMxocddVW<yL);+#Kk_zGgd}pJd0G
zsF=^}b1UWUzEbjr?_jv|&v<!)dI2w24qBv6L*Vts8Q|WB{>Y?m?^m-i85!ue!PZms
z@cisd5w_u}V7Yj~2fQ?cog6RFq{Plrk@lzJv3MTle*$!7<mwRx8JVXC-}7OBxV>+$
zT_2}uO0gHeEc3pew3HmQdtW#B<;jS9GQgIN$y`hNM`;Z@AA?x6pS%uq2E0$=Xo1|_
z{eE``w-l}Ds}?b}S5ZtpO_D_n5kfE1D1NT2M>kfCCbSzD>s2WaBhg_TpqdSu(W%#<
zJQ~lsPD8rJF{`Mo(%9}$BIkT9zQUzmPyr=!P05ZpyY0=z3j8mSY9m0px-9o+x2*Rg
z7b2)QfSv~9wnyj+qMb(zsxBud5X}jP_N@J{@<)i{{_~tvVBp}e-5AQrJH&2Hep#Yl
z^PQ20d2&p;4vbF?ns64Z8dhVFyeLi*R=q;Ym7C@8e@lRIqkqEeRWkN2mv<BMSbdPO
z!t6-c@2!9n8wl@NSi&ST6cf4IXJ!n%mOkGJX&o(YOMO?3hFg?mjerECA<gG?G<rnb
zegwK|F;P(q+uJw-H~l5nj@FM6^Pe=`8CTU+q82-;eZIQvLEf0$3te|ZVG80%QjB)o
zSC9VpS<%t`VvlcrZmrh2^d@j+8FfByq&6nDA>;k3{oiaTp}7zqO%x$QsGJN4f3f&p
z`g8^}Aq^cJn97lumPSoRw!XHOw`KP!7m0zgHYPrPt<IC7M-PL0V$ggzd~ufJ)Y0C)
zfQ|F`F2uFJV17Koh9F?QXTp77lf_}$-T6n^aEJX9nUn;#fE0p%hHAK^J%w6*k?y?J
zuOaljKvrR-sBya=Dh+e5BL4fYs;Y{b+eG%-@^x}wFFLCwM8x53ymuXO<Xsk~@7&))
z9xT9m#il}Y#hJ1so`W;I1lp#!dw&qJr@1O=52%mmboA2Z$nU=~%fC)4l+_3%#51V%
zxqa%ifwjp>ZmrM5&HeI+RY4;wi2h1TKXtEw!c7=#;*<iaqAlGf+#1IpS6|gVQe)08
z+3nrgHd|AP;u*5{ZAW`cv6KAzGA={;ajlMS@>BPc$!NmRpHPX(<*36%Fd70IP%+tQ
z(Bn`esSIn7K7I*`64A6j<D-2()AdNHRP)RGDwm_Qzt))TuYSmS{_v-6TZ(6ywQb^l
z7=>K@^`U-b$XoUl8fTamw#dM3wt#_{r?%S~eGH@lAE+t}EGE7)PzvERdZ(*4mpi?G
z|NeLfZvHtnP2|xh1k|Th4bNU2^yZxI!ogHbE}R=3lqkU8)V^H<mCztMo=I=wY+nx<
zM&M`V8I-d9V*KZq-^~ps-Lz#*9fU(Tl4~8zpD8NhC?=qQ&!9`t;+31%Fs?7y%kJ-t
zM>W!Y2aE|)kdGhWpLY}-eoVl+aevUXE|uECK<G8m^Ux!?L=o-oafX=*^F6E&fvRdq
z`s~wk79)4OzKL6)z@k}(8B)S#>!EzzTsU7iEpuT=3@BQ=SA=(ST9Ysk2%hqfj!bV|
z%=X@I_gutt{D*VIl|6jUt-IbHuFrigu3(83mEXGG<Tun`HzmJk1LM~~EE}Ml8a+Aw
zSiicIkt-oFvE_@E*;^z+IeZx<1!pg$6lHvT{BO4czp9i%mu|7z*W7Nqc*syB4WBp?
z#LtBEW~Pf|-BtKFT@Rtm1)7s(Z=M?(8XANI@ZvHr3?xFN1GMSaKpnzdIxSnUD5_2n
zgX%5nL$?k6#&EuC^yl?rDuk~Ltv#1jP7P9!ED9n+|L);MWvwD3=&W{L^n>suPbtyi
zrd5ytt4-Ty`U|cEc^pnB5o+e4h&P*xYT=bi>fPp*qqjWf%>8Cq$tfu?peP?SU5se(
zjYTCV*RRB<y`R$EbGz<vmi+`Y5cF&GgqRUBbOacdcS3p$8&TNUa5)S<ER|OCZo+Z*
zt=^l+<Ef1*cF6Md)sFX5wnWWB&9IfQ57~91Z5?BRglGX}ppr^d%*}9l0TU06iBo9^
zQrGsO@=iUt7E)#O8$e&Vk8v;~?kwO$2ZPW4TP30Y$a%Ks*p^H+9Iil3Osw}^w}x@G
z>H6eLMuriT()RXc(b2>Iya`D+3MPBhh}UlpM9adb^oiJ?F{^SV_%O@ybEiQnp5w1i
z4{JSW(?%D}2vw=@c9^wTmd;wcB?n_2QkUEGA|6Dow;AESF8APF6uC39Fu<r8vh=?*
zG8i%5TeXMwqm+7UQfS_!r$AhcG0~HW-v=y@05L*4q`|KbQxH7f*i;H1Ka7|0dZGTK
zGe&^`!wyfIQp6&mv4zt>3>)QKGg99^s`I}U%+J!sD<LX(-w+PQw>@Zn>ukjJD}t|I
zoIu)9wKanR{FL~K`<9MgqF&_02+rmNNRb}56NHmm>%X6zR?tC1Js)%MU6eQo#ZSC_
zAuJ@Mu-cgeKa3{nuOe+j59!bSphvX!E3R6s++B03O-vD!iCr`qB6lPj-@wZ9gA7*<
z<`^X|Qt%o78#2`uU(!f~5jQ8hY4he>Z?kRSm_ke_Rgn?ht#(+2vgN07tX~GR8*g7p
zt{j%XSsG#IUKm6CdFvtZRXn%u%Fbi+KYo;P_+M7!^G5P21pR5|aANGtroixYG(_!?
z|3_GH_n#}|bT<X&cQHnTYWwrW=E#xU@YBs3EJUEI;8D|UYc^p{RH)lHb8^d97Za30
zazA-zEt-!jm?eLMR;ZtR`NY-prE9(O?a7;RRZ&$GiKhU8760z)dI&R=*2jntF4OP-
z0$2lzIk4zsA%IFMyrz}?<<)&AOdc&Co4cBsPLR%H;av+QB0#+f{<LCm+W0I-!B59O
z?>LwQmh`A7Jzv@6xuP{tQ8`ofxfY9Od|1LXKKGaQlRYuAM++BUZ-0C8uH2(Mar!o{
z1LRZSIe6wTE7IEM(iV(s#&pl$vmYLxbs&Cx877&hv(}S`r&LQOyo|9dy^nuNJQOcB
zFe(8gtP=+V(HXbqILoR14HCAVQw5Atg-UaCJ79mELy=yboDpt_{tVq@{#JUUFi7H_
zJrIlIP$Ujzb^VnPCHK@Sjw56wsOwBWs)U&J1-;@IR4Zkt*4R;WJjDb~2}>?IjG^)L
zKq&<~%KyT`F4od~UMVbx-<-!Q<xHn@;=AnK1ceY_A8zDjaurCM8#6*A=NR`C!WTdz
zi9_(6z8Ix0erC-ADrgW4{F(cbq6Y)!uox&O{qAIkGLX9ahl_aU9*KqXT$JlxPkeR3
znbeN=WyFwfUPT}(ERHufO>u+6X=JlP2=N{Z+Ajv4Elv!li0qL&#;wF%U2J_Ls;>u`
z_03Ia(D~6)2v?boxiJgggP`H9p_)QiBlbQI(B_crb_xOJ7i}H853X+N-|{!^2V_S_
zG$)erbG#$oFEUhN@KZr%X6xA4soo5i59|mS_Xmigps=vdE}8SYXl#)d_}a8@8b9MA
zHK#}%u9SKSvRzK<lbjFEo_zbc1=M9E<)-jkCok}a&?wz!xyoSxwEE?5`j@4;2+sB8
znzpV-X%94Nf&+APngzVce$wWI7BIVM7LQtM1}&YHz3BvVDR5stFsbOGD)lyo1j~n*
zgakCg@%?Kdfq@m!Fi3P<Rs0>RwqG)~q7t7&^rik5aQV~RoGziP$ts_70Fo7b_OX_X
zi;nllkFK17!n%(+mag&c!++tae>KvcdKJ#HYAo<eXTnC)NljmFqgBO1+G>4$=Ad&e
z$QIqt)M-dV5)RbKDZn@Y<a#-E$lwv*3^8h+ZT&m9Dzc$8;EL~4vYC65mHl!0LLn{b
zw*Se;typUWBG?GVF8<>#Ql6XPfK1wA{6k3}^fkuI@??}42;U+|;rV)Pn54-<1yDLM
z4|YEDZF`3Wl%2T3Hvnh9Zc%UMG~0X7;23_fWEi%D>DeNfI`0r0wuFP6!5_ZSX?psW
zYeHJmOBn{R`kb$jRy-z=AP~n48U0AkXW?a2a(n$8a5#t>yO}onnb~^#j-qWd)|!T|
z^EGFB=HfMeP7Woy-cF|-yd!JLPKzxKVq2%t#KPRXt?zri?V~;u3HSR3DtpNP`9w0o
zK1HEkRXF<av(YM!I!{fT@;O2Fh9qW491NiX!GzCB`XcScrB$1>h!yW2^BI{+u)2C|
z{&|Gc2Gg$8I5t$uCbna*y+}Bc@F|sP0#=s}E1P1_m}BK>eDVz>82R1WVqn;yq5gNA
zS{I%l-)sL4FB921j8st&gx(Qg^I^}!j*euBq{GM*WL_|1<9vm>F#eDLQ|kh4F+Ivz
zkKtu8s6`b;iSC5Q-H9|b5V=i^kB7@k3iqZrf!BQKKd~ETlJJqoRu%n_Jrpu9;NwQD
z`s%@CPtUJk`57|atkH(6*z6k=YNOVl7ToS4=Bpn@t#_JUI_elC;L!f&AwU@vffM%y
zSWW|4Xri0co$nH1-e-ywcfa4a5autlsQBVe$EVm>RBHo2aRkE70kZn|<OK@bS0DhX
zq@=IUggYxM%XX%=eZg*eW@bw;3AuCl=jU$E7SqPRq0%qi>o7x?yN%8rbyCz){b)Y8
zWCTiSfYd7Hk;bq7)O+DSTMkKKNUry;tRmZ6N?}Fj$4Ge3c}4Eou9sMW)F7mIDkF>k
zm&sq6UiXR5!Bo`dP@?6)hCROjP>QzUAt4i1U{zq|?`P?VdC9C3({fO1^9x1z4&k6k
zI6=Li<N!_c)6B54o2RDg=6UXpP#J-e3go-jgGLFOl4ACOs`~kvgN9C<M&j>y-S*$X
zIHjVkv0aFsRAf|{tc-3E$5v~41V<@V)LESB)qmnTs-~<0DZb9eoSx%7jdRlPhzc|>
z4ptPPbH)qF+R`CFDZqwrR9;tCR~WK#7!4H12dkbdkoSDy2`^iMp$S#L-*5&cdv7wA
z(WofXU)ycjY#zG)-8Oe+?}Q10z{t(K$&r9F;4NT`xP0W9dRUY`%yF}K!=nQiO``I3
zGQQS_2O8Npz2s-iq6lB;0;T<;TQorU6sKt&0nt45zl4MwWx&YcM0MAnrV{9Mr449$
zw;P&RHRW*vW`B$#I7ixc4k|5UG0}51hg7H-8odIdA30J#RY<8KlQnTWZ*^bGEr=Wy
z>rZxH{E3<J^iK9&ch=~+c-diA6fnIG{WLDyyk+?OfowEPe&biR>l<!ip8GJgYfu2B
z_Sm_-C=`01x=+9Ag%aYSn1{i|_Al53<}85P!*e3#QnDslj*)3?b?S8e^u{JO%;V-B
z*CnwseP{^I6tNib2YL{<D&%TTY}xl>9ReMEd_?Xh+FAAP_U0NSElxrJB5lAM4Lb|q
z=4uvr(!}M4G87ow7s{%i&3&(NLS9wQd(XB03a6GSx{uZ~Ku$)BFcc+05rH7YynK;s
zO|bkP<UzQQF_I+W{yik5a{!kjf8yJ==S2uEWqxD0$K%N4^LrR5p>wZuDragR2CP|!
zA*x&mT+Nd$w~V6%44I`TI<#lNF4@;4SL%lPA&_*fr?=@|QRu<Wq*|fs(@(P09~kiI
zKcgGVMJm2JeudNQC%=)Mw<bwjtyW;Y{%5c7Z3m1^y49~hxwsJDY=9DHONbIhViTEx
zl6-Ar!yQKB6;`R*=;vhTB7)^E@Kgna?li+?0HY5fuPmitv+}Hu%A>%peZh>XeJV!8
z`--ey2ss!j8kHmtH0ZU3nQG{usgNo=%8))#W%Hsx7ifbvg}W<O#<DZL62d|T3p}Ka
zy*vtd&0&|ROkKVQsg_1sDu4>3&H@<ze^BKGDk40^f|8tw5ZN;xzo)Kl34O!E!{g&g
zEd<qA!CqBrOk7<V@_Etb3JcUL2G5Te+#Q~+pma!;Rm?}P`oA?%*CX$ZRd&u9o?ho2
z7CKhAe4(qO<7s3IRbB+O$MazpKx--j6%aJGa}uK&<t1(A2mcWI>*4YB6uoNw-H*Lr
zD5gfPjf1$g*cv(Fpn||}A8J+M#$m;(c^oksr)({iRhgrq7VJm!iyx<<#a#OTkaMW4
zAG5Ks*wVjk%Iru*rHrq#pxfBUYWv&B@Q-d0@#=rZGIFQvs8J`GB`(|hJKi0{*DjM|
zlwj7B(Ks0%8om)?CJfj(L4=XPRnSIhGu>=jnBHx)_d45C+bkvuoOV4Le$JJnzA(?c
zL5+ymt`ksy2S59FS<mP6wRE2Wxg{2Wr>=liLlv=k@~~~-`F%8~Y*}$`_b#(az%GJD
z86lGef)0b?Vv6lWPnUIZ#IJ?@vELXVZ1w$Y+}fBzL+DpoZ1G2cM%z#+Mv<|5h1!S7
zW*PV?KXDkXU5l6P0@)?%1z)GasnOjS<EEFfZ&t8v4pD8hZ{2r2q%+aO7g~(nOUZT>
zgEz(|*Z*LpD96jvM=ak?Bw1#1SmCrK8IL=A8*L4o{%y_W*B9%{Eh_7=<J~rvV~J%U
z2`5g|=N`kWDGpF9$R-FiC47|l8=?fcZ?5i0)5b=XG1FkgcP&IGy6`bZPb`x}z^HHT
z#OZr&C0g=@KlhyqwtuJ|eN#=QzZc6KtF>pXoZ~iR{z@^JDPY!L{`_`1R*}(aK=Y$a
z77So#tWwB%)L42S2kjpPXv9CLVgo+?dstue$N7AWh(e`f?8KMk1d{$0-yc7Igcw?o
zsC=XrdurFBPx>b79@d+&(c+IhotP`80?`cQ2Xx9=Kq3LiDNj!b&O`v~4bUS!M}=!d
zl-T4hRlSNVICz~=_akvU(9s(1MB9v;g!|B7_?zL%3y_OLyz|dxxyWFTeuZw&ysOQP
zjq~@d$57n{O-<Mv_Y=;p%-;)hTgON)f*5!3mM%`YBLcWs-E>B~h=jI7B0(<vP>)?D
zUUrD#J8v1i(v`iv3RW;|xBA47%8KxgDf`8wr_!L4(1E)i@lQuK)<av?AWV6?mB!RR
zKYRVulwoji5OziOTEDMs3<}C4r5?mQ1byu5dqLa?0;XQrF9oNirA20aQ<jqpg}K*n
zrc5VGXMZo>H<k-lJkg{DxgAgTP$91TAFD^pE>1}lDWj3fw*Bd9O&f-=tUt(P1ZKU0
z3XPJO*qKA{zOnIwxey9|$ihPeL6fs%kcKhm|JMR+Bt}2!AD)XRs&rPR?g!#9d-*yb
zJpAaW-ii2p&E(y?evp+%b*!_H{F^>DF`+wlmk*JM<?}Gwv};*cT{l=Wi6Pch&a_qK
zg(Lx2Pn@L9vz&^I;TbbcexXTCe;SOT*Q%{*t{@9-&OGPxyLsBrbJNb*Y~MVqQjYGJ
z_(WF)NqqG>V7eU_VZuEDwl2J<UZdYLMcyokqz}vJ@q*QIaO7sP+WyUAcEM{K@2q+e
zT3TbcBx)n`M}NouRxDuCVCnw+J5|B7KWB*FA?SPUu^EZUce;q7FnQ-p^<?uual9<J
zy+;2Q0GhnOCRE_AC5#;=$VR&HkzVl}NUrz-B)AT`{Q716A8-ZOZI>92cXOYfLD3~(
zW{Uo%<6TV5(S`L?iGeUK4vrIrPo<m412PIEgk242V8#|asZ<Jnf09p=1B%Q%So1`Y
zr8i!?TsTy+d7C+g<eq~gOM8^Ob?@7WyO?Gp0`P1`(*v`1DJdy;S4l-4v6l@mIrTRY
z{qdkT>P<S!8myZ!_$8g$+9xq|db`B~*H<$?0?|CcDM53g|6?C66pcLP#DoMWlEd1K
z&DTOT`AmL&3a6k;Pvf|9?Cs3sij#V*#X|joelztQKEr3L#H-;NY%jtx!Juu~oF{qe
zFI68s&cA2Lov3MgB3Tvtk^n`bQHpsXUl=Rs`^KM%Iqyp)+D~kguFWGO#6;mz-PjnW
z3@mg+pkC0N_%#4~E}%2hNRwH_<KED8<0Mj===)K}7Fsf~y9b9hw^xTCMXos3yy2js
zN!KlZ3-T@-qYOyRoy3Po07@VFD8}%gU9|gNQOh$-u`Wj+q@T02hswZX(o^qJ<}Jju
zkpH)0ex;wTc$EG4@d{#OE~#VWJ$f)Dbus%)LXmxb7E~1e>kDo&G&`t7hXCFWjm1Cz
z1~8+PD(AScZ-dnK2rEn{ZCfzAc;xwe_}@$8a;;cCYzYvL&#K96gn1u+Qa}Ey>8fR|
zkXLwXIhZ~oau-E{KuH|)c|e9iC_|%rM6_(CM2kRP(k40N%fri%tOYm^n;YyMQSl7l
zA@vk}^#Z{HP5B&k3YQ*NhjZda&QuP?dssy_>N!R_@6j5F0(M){t8)08QGyj2Egej8
z;T@8AW9)cwEQXXo`r6ECev8oJ>8{1)wwrIZ$Hcr`?M17(TAvmS?0ESSjy0u3?iEjW
zRE!||t<f8R_>S)@1+|HmEpr<Z^plE2UHK#yK8PCkli2%}?g|b7b4{!aqm%#U1)hS7
z_s;!9nyB4Wfq#iX62Z^@Jz*RM%1VgvqbHOb*+6$#)%e|OiJ;k;l-m7M+eb%)5eUR;
zv868=9|ZDm*1PhrFXO7qNlVWVMJg6-OAGFqpzc}6u5uTf|9NWlg8IpDiZY$W>*!55
zvhY<mz|U>xIh33TP#f-3l`Kbi=#V495?ctf8~~{r`&VkvAYh3VCnO}avBo8Fl6{XW
zhB;(%8LA&p-Tz>}Lv*gNmvDb$uf<cPfEF{|5)gHk#}YJ!J8cRvzlVvhfI~cOCp>=4
zi?h{Xd}73NS(=#i>XVwPDxeVv1Q{A*E@a@s6fJVmtT>R=&!J))9ebn?6S>$p+Skk!
zXfPR|2f_X)jGSz?zZy8mDQiQ!%enn`DGx@+;OtX~-(GSkKGc3W@nQz$(wYLq3aQ5w
zuYbzAojCbhen}GhOYO&5SRvxa5wj$KAV3Il-)}n-xS@RVB|m5DIgCaKOzXH4g3Z9-
zMf}~C4WN?6MPq<@anKOH??NDI9^i3|Au`zYBqrR6Pb1`;IT58x4mXe4Gt-_1W1_Nn
zN!g1Nm^GN1LB`FC7a%+hfh5c){aY(5#7!n@F!<f8dllpzXPf)nl(%*MO?B10*KLFm
ztD5qq4_uk!t)C5^pb-HM5;N1({_|()#5Tw+EzY-JzBV=<QfgM{@y3;rvD?Tz4O^*=
z#E>9BLBL{=%u#=2v#VzQ9~JHq-Dh<?*8|4J-VV_8rN2%t*(r>LnX8F+DbQ_pUU&8d
z9&b(lurBH?uY<^?ni?d;ez^IIQ1zOo?5qEVzS9N~{*yd&#uMfCJHPw0H3rU21sHwY
z)3bSE2QF@~zj+n~HF4rh)jDs1*bu_iWt7g(r`Cf5prrfvS;}Gcfq-O=h50P&7IDpF
z?rTKY^pJ(_&CYWN@O9`^P;%l-i{gQo4cvS?oqtiknH=mqW9aO5SEocr+@a<A<15^!
zI#)3({1ULaaxs2tyAc$<FQ8xMmoA+nhi~S?2MmMfel!>d)UXVx2ty%ZQ@Y1fUhF+G
zB2xQ<$he1)EdQSvd73D3T9D<{Vl8NF5N^a}ekMy5pW*0@l20_tr~cY+{i)Fm@b7Vs
z&&~Bgh|+gkt7`qJkQg-LqXXxM^f26WmVt}3Lp4n!)Tw>y3oxiJD>Ji#S_WJlBkkDD
zU6>EDl_n4CPVSs(hRJ@<$)``{P}Tmk`72$8pKH6z_{sb%AXgar4X0x38<;_@!4!3o
za3>aO6~Q$CDmNf(vLP95>jUg)a=IA1Kbne)J&^Nlt@x3SlI;<TR)%an{r-=K5iT%B
z9z;_iFc{NZBmA2D7;L3YAMvQZmzSr!V9T=fLhX7^h}x?3Eaw*M8NwF{@cldr3Xc)n
z44F#kO+Bn89Clg-RSP^;OzLWDpPW6iumqWCQ(}UY9fpX4Hk;lL=dBk8&jbH0YbZ2i
zK&KmBZ8|*IIHScv{H*z#>H-xuN=>+SOOjTp`;-LQ(tQ0n1T`dm`C`e#0`q%PPOF>x
zmcI3}Ud3}D5Xin_@uD;113|e3>MtR~1%_e+d<_|???#=}>CA?}k4~3x7Y{N2hvlN#
zJH`e!vSv~4-Wc>i9UVe?Md^+pEzHtS&X=Yo2<6V<{{A7JLImf`)6M}o1Te>j3wkCd
zd~#mdu_%jvTtDW_QUN)7Gz4KVs{O?9M_j6WaziD_tU(Y6@_NN39YW#b_K4&Pt#u}*
z^o5xOraz7BvBgX4uC}%}Ko7w1$pe+70H60Zv-LGVJ%QZf#q1D_7o;7${GRY-FMEjU
zWph@@Yhz>%Ltk}F#NTxz=-HhKi%(_<K3o|#-aimxlA~ud8aI2>mQCU^#nxN-$(mOZ
zhOj27JUr1Y_x!!}l|dnH{a>T_Nv1HDhZ00E^$sh)qv)OkclZ?MPk`d3!|q(4C^IW-
z7+)IK(M9CJd-nH7<DWheAl65|qxKdE&Yx{89;6o0L6PyQC(VbMebnG#6Tj^f#1ETw
z0#Z^^gU%9r*Jq0(sO|hgv<MobaaH#Bez8Gx;azKo&QwW#kcJGxo=qVv_z)d<MOx7|
z5B?*iq7sE<Y#7O6Yug04A&A8IWhr3*2QGJHlAj;af1Mi4_nh6Vz_@>3`#ExOft}|-
zKo95*cS5H{BK#>n%VAmS*$>DL=n!c`LqYb#Ky#}=y7ot@Kpc}VenAHt5uF}ZqD&UJ
zs2Jnz2FxmQb<Vm621-He(iJq5U^t%E_L>u{f8sO)&NP)$dBY5Za>cVF6n9{-2;e=8
zq%OUN;}HD5V;GQRs2&;_`GD8#6PW4&#q3WJ1_cEnF)^RVky!*<9+A}(VQm@|UzivS
zBS&?s?MUQ|WEr(&O$V_uSHDNdY!xd$Wvxo@4BysC1;=vtTLyt~t18~A5f3ehod)2r
zaDyQhfXPS-{@tIUFwt3(p71V4Amk_&8`QnXYT5V+Wl2Y0(#Y3pnz3L1@>y)his#jp
z5QxtGB00GLDM=@)^pkt6F%;qQc=55_ngWQU*F2<2WHm$PwUO9xLYsS7B-w#I^i69Z
zCRkcH*x671{vC(x9WygCJG)YufMK9>Nhl~-qh#@bkW()Zpu~H0Z8Wv&viZK>q|t1u
zm=q#5O7vl1<K|4=sWwFfOr~k??EELf+MW3_kew?-FVI+5?WAY_$gltzp@Qa>QK0sY
zQEJTo;s5LIt)r@ZyROk~U;v^Rw5W*EAqdhKC`va7NJ@y5NT*3iiIhsIlr%^yAtE3k
zAkvM}f+F2;=Jq*byx;eo_pcM*A7_v8{P2AT?0w(&zOHqxHP@VT;jorhmzN}o<kE3m
z`g4QI-{kh~H4wb8+Hdi(9|g$=B({x-+FbovQt3dt%f}DwAz2CQyh}Ksb7EfB<K>@D
z5ljq)4B=$vDTTBHd5pqvL>?o<+{V!YI!eBb=wr{E?}Cfi?aH_S<)xHxR?D1b8rhQ-
z8Fe!Dk*E1pm)&lMSz(hOUr=tLrC_MxDF24*7DydEx{ry2gCi(@_m17$KFWuTuU7e&
zzGLLCP3Sq;by;|Qp+ld5&NM}-I|*hq5VEzfSyhwdR(kJIbL-waCVg=?55ZTjz&YdU
zXM?kgO;aD3W}dBoIvlc-Kz{WzoA+uc`99W1yEx^8@Y(Lk_yWl31=E2-f{&4?#LA)&
zZLSs`Nw+hq!F$$@aIe?tv$5BUmBq7XzPZVo?OY;d?Jl!2ax4f8+~+5?J!Fq94z0Sn
zPS>d{^5;5#H+R3jCuiL%%b9C3durt}wTnC9m0>6I$%p!Z<ei_B>}iST@~yA<P@?Mt
zR5@^@BYkXyuOI8T!(6L71WwK{>uWmn^>@D&9_|lot8<+C+g~(M`817(EL|bxEx9qq
zM7j@qu$X`Q{cJ0_@hq~@2PrARQis>8Vy(bMFB9@~p9-x}QY&)#93060qZ8+G>N1Ci
z&=fX%fyIEQta5GUPd>JwLxk-MLS%9Qob&?}hd~C%xbAW4tnFM;85khymg(k?xPQ1U
zkHSYL(RFjZSc+*I;f0(aT-LU7oZ_}A>$=f@+{!Kec7gI1*^^U7es7GZr>eCm1v(C@
zP5G5lPX*Gu`B4hwB>2+F2hh3s)5)F{BsbV0rR>^QHto|;KY9Cxt(h5h?0}D(A&n_x
zb!gvKtnl4NnALoJ{xH-<h&^xmJQ-DJZ%|p!`*``QAnAhWZ=56-t=ShR?cKa3)~(VN
zNlGRjytUb(?{y@2^=o~7ePiR|?5t6LVgLII1K34^lWK2k%X_N8B#8rKUDTF0^;zw9
z9yy{sr5Nr*F5hPE$9nbjjVa}sp@R~SjB;Fr;4oL-UPvmi=Z(+))%8tcDkCv${@kbX
z8Sj3dDwylaeiCCiWnvMx0`VhaUN@SXXs#2r$iULmtKZ#&o3))iQ}Izs<&9TpV^M~1
z#GOksRcEC23=_68e59`@?4=vi*AjmbB<dNSZVV@Ib1N_GKF;KA2vbh^bV1J35)!e$
zc2iPbOFa8`PA#zg#_{FUcWXAD5~nf*IoVE}xc|#TPHK28E+nMF0g5wLT2-<jeC<TX
zHyCC@kYD6HV`O2$o|eN2l6wB3*$F*{k+Bw@wcD+4@2piXP~R>;IO)LS{Ges;((e9X
z9meiA#f4^>G7T%M)23>Lw{pEYyiWX_a4_!G<yN1gAOCrC#Q2g<p7{kg$554PHNov~
z?+bh<5G(^yjD<cMouO<?RiCU2=Rhq0R-k3lyQhdCM?6+cw6*CdV|?|#muiQf-Xz^Y
zOSSIl{&my}jdEJ2b-DZ6L*Lk2F}P+jdo)Lq#U?_@!rif2bfLCdTYygUU0}rl?8hVS
zYrm`HCqZH5cVD&79O`@y>EpwS!J(l_hXVxGayc#`d_(P}ZcF&D0~nSkPE=G@K5Wy9
z0Kz5ksp`+tRKOS4`{7f=5)_M4pAWQrsoa1z8OqSCV`9$75BdRvCExXsZAmG;>r%ey
zua-Pj7ZR!AQwO;-)pAq~95OG36CeW=Bl_h&ET%AWLk3WAYJqlUWg~_`BVEgIl8csL
z8J0p)gX9gVLdrj_2hklNkp5hmv002lA<lG+M20Uba?_z|;7sySf=3P?c6N3qXB2xr
zr&$VyzOuWnR-uB~GZlA+-+d-_Oy9@n$uHF$JLLhZP)f+$2Hrc|28x{A_L7^s5kON;
zp;t$IcKbDBUQ4+q-A%DeCcJxSPJgPp<LyQw8ufDJunZSBx3#PWh5QQzDbeySp}*CE
zfH|H|DiM?zU(9F1+KG7ay1`}ms!-}{upXZ7+=Ar?>0>4sZo_E%)TyeX8K|-ln;^G9
zG0<)+jd48*)NBH-8)NyH%(Bb-_2={{?{bFUtD3swMc1I=sP2Qz%FgwZns(llL7Lz+
zcW!#i;|HH2{DP?x64S`Nz4>ZuQ=e2Z{&_1{w?9$knxo^Er?Hv7n%d`E48$+fb%>Jm
zF=Dv${CJ4pZh3Hk$jO=Cz)I-nm?*p2+oxe+P-=vOb~Ze$mYO&EnfI%&N%IZIW8Y}z
z^(Y)4i+QSi;2z`FU7kpegFafhq$j6jJKv(k@W>w|$*{j0^>`2|?jo`=+?w}if9N4~
z3$txrkIfPX);(@Nsqa3V^Vb@J2kR})@Wra{n;x&1h8{k6P^7SvFcOwhsD>^R{aN#G
z(6sz?BSquFrbie;5uw0BHFhq~r{c$t8z6NCJVEUz1%*v@AP!Ft+y_Rc6KkO@=@Rdn
zAz>k*lBJYC5I!u;rYA@i1v`taRh;Zz)AaC&n~|r>>MajuU>Fac+pn2^eQo~M&qsS9
zVosxNDP!o-y<2^(?knTQaklG;Om8HP=xVNVu)pJvq^{&8uk7qSIZ9p$8Dx~(pEyGr
zkRP9Iwqpn{CO4&bOp5Yc|5E#Ar>SKpm9b@~hJbz?o3mJ~lu_ya!t#d#?xb97E%XHq
z4KbAmRarJg5Z*a-AS7HUhyk^x2WFmV(=tf7YcYj9di3akGFrso0f1T!e~QWR@lHq?
zm|Xu3-p7=Lxs~Y~F8NZ(Mp4|`Z>{|u>-ig-_{8b+Q;jq$%G6u^tAT6g`Q5<fh&~8o
zZcB+G3%<ToU*%UFIp`k>C#X#LEIFG^+Rw#GYzc~o`m&Sm+*+7jZuP73JC8sU*Tlog
zd2{-(CCJ~}8SSHbBp^6rqKgD?flMNC5+XjBv%SYK!rRL5px+}ynkauRbW<(z8`Qrm
zC@7$S6Qu5$_qJLm|LVv%X=brs+RwBX3Sn3>r76c>fx>;};G#%O<MPE@yfjb72n$O~
z=R6G;*A9!s{ORKl<g>_k_Ts#iFJg09IX-EAF{ZMz5)y-^IHAi9zmyG6n3%{gKU-2T
z)+Ti=-Q2N%?XE)qd}|(gOwfs6(^Eo)DwlUl1P8vY+PNJm^dGiPq*InTgU8?O54fSE
z#EX2NUtkB@IkS(8zY)QCjSC7_{(4)vxR&-)(HAJOwp!#1E;Y(a70}=g8ph_jD2xvw
z_#r_sUS|>&CFX7NFVC(Fhbs0L!0q-gq9W=B&=N{ZN`4Lvfvp8+y8#GrHW2#kirtZL
zn10c-v&bjTgvJauSfSl$BPmHIe<-Ine@oGI;CLOyQ4u-V?Txb?>Jb)}(|z}RPTEp0
zGNnb{^Vy=TH6taP)Ti*+@R!CHT$~$l1U8+uxES-{Ky%P3vSe{D>;6U*oP}rd2c2C4
zpycPFJ7>Kf6oWAkPy_mxXJ}*)wW_}6KR!K9B)uhw7-StA(c6+X8z=euK>MEK-fXK0
zA1+ugZ4NNM<imgjHq}lAEfMLt*&4}fZkcVfzTO{%6|=N1Sd7ehZ<k(n-`sK-ov>dX
zBq2~4TU5;OL0v=d5hEk+F`JvM==Y_jIo4NAUi;T!b)joe@JDi9KX~kzexgh`n)&r3
zv)_-hC?>~6EgDIi7To`$!>Tno(oBAo{x8n={M#RHcgzIX*VWQOPYCPo4nM-H65jkl
z*4T1?UlhbG>DT!kXS3@MD2d0v{i4op;_LGD?~RvvgY4HZuEo2qzjZBlC~_;|l&nn0
z(%nQQVUbe{ueC2p{ZI(1XV?6^lm4$Y>=M!Dg3iU()|NHa5>l+~_-=7jJSh(f2(awV
zV-t1h1V<@7YQTyjMCPu8>xVDO(?{P|#6pRt5^T%#=1So7F;U&``kM6QWG#A@GELtP
zsUIZOaM7?Z)~u5_$ZE6f?g_EhS5K`(m6dv)a`-VRN^Z0^HYW6h5tK5YYumcd*0KQR
zWImXp#vbLi>z$UB`s>eCAv9OMIUOJRRc-hTVR&e)QORFpeyr-5=eGdO6d#u4evMS$
zw?6S3eEXfu?#uW8c))ilz8U=0ziLBC%m=1miiI09xpm)!z)Pko`4e7I^>Eh9&gR9m
z7uSVf10G99({egL%6grUSP`X6p*z>YwqCrCA7U#WJ+a%lycTNTE5V<6vFYE#pWD;#
zWv`QBH#jHg;^sbdqwD&|ruuq}4Vp?MaV~_4A$oQ(ks{`;{D<884rqy3=BSR1jit3&
z_(OLj)6mKogw&EKa|9K=vGEV6GT1S=!UJiwU;UdWn0GIo(GF~^RPCG3P>r}AAjO1i
zS;5cqlxA{V*y_|+v=~z=PpIW=YWjh_`P#pqpD|uEHC454^gi(rX<mbZ-aV%~wzjVV
z0=&v=zpz%>zfHst5Ey*E|Gjxhg^KG>KNlb0_w6hs_f+`v2jMqc)iep1t*ck6>Zhvo
z3eR`)gMROB({v4<6dWTXl};8za=Jp!-`!xMeOdEF<$-3N^7VHeHNn;PEDsG^6Ye00
z78ch&e@*^(Y3Z1L#;>}!*O?DUY0RKpjU`b)y1rty%X^fI`3>z|(|ITboEvr$vM(l4
zF`#RTG;J>#8QO6;#^4%hED+(cmywy&M)wqxdQYS4Zdvz5zLPN(+3POVsnoQtOgtgB
z@sZNmQ(J$6%uG#HPVHx6@;+fUhQ#GCgMlNU)n1H;lwzWl^enlf5Yqu#x3QXIfnHW&
z=ZDQL0Z*pX@z|J{zy~`%zI>r=%L(x+BuGf-TP*THmz(S<<UzaAx3)f%VHFc><zrS~
zzPw)@h|ZbU^h&K#{_1dTDA(Mrbo=jl-9Ks-w^&)3-&hAN>Z?0Ct#9@59~H5GF2Ve4
zr@QNF-iOkoNt0W)sO3s+e_X`ob(<frs5GvY!VP-}y34nis(ALtp3xT*pN~5KceG<4
zSyp-5o6BU{g;2q=NoH9oL|lBGJ>0eJRe}ufj3(ADHkh@$Ocm+aq9^~u3$Esps%i;N
zj>?Y<&RL7v4f~D<^Y8xje)MhYk4sF8x*r;?stb#il!Q1}ZoSmtFna!0=7@K3nc-y(
zsKm<)EH_cP_N}|S%F(QW1;B*&X7u=Z*5UUR>Mdue?h~$s7Abr#h54}Z<bh{buQHw3
zVh7??C3`%F{p86ZJO)seZQS+@Kw|V~=>rxpi<85zd<TxyO9T$h>NpijeUTbPSM$NS
z@EdOQkG6tDF&=3D%mNdWbk5rca@tBtA8%Ek<2{h!64E@UEb~%hlI!5+ud%smi^?x3
zLr^=wVWQ~8><^?3a+T80vL38%jv)Z**5r^PF-nr9zLu5zz}@vPtR3sOv&btbWHuKL
zybVk4TbzmlI^N@$ru6y~>LM5&1t;5XOV_{dDf4)1-f#S;Fq2B5bynKAUI`4TMx&NC
z)mj=xJT)AH3g5K0JjrENO{RabK7Z458>NF?w$9M?+S*;x#oer6O}j4NOYL|SVE=+V
z^z$KNsd4*Kq2dzfOAadk8t&^iUu+w~BA#D4kg5~xTNZofluVjOj|T%G`-sU3+mX!{
zmYclhX=<uo4D+*#kcgT|b1m};7C=mS+w|)gdq4dRHZ_WCp0jH4M`oTE|2cB<2A8WU
z13<bkKI`5eQ_+Ha6JM+Ri-U~0T$lb><*;PJ0p`2QC}&;mYv0#WQoDAfs0qGzK13)<
z=afu*lR0ozv-!rfc8DJRor845SKYN#CtsIXiHSS!v~B)Uks6hVPu4T@Y@LO012$fw
zhmOswFMo}b9OzofICuS3D{>GR!ZkWPvEvdEc}c~?QW{$w_?q6Uxg|Mm_rAOr7mNvi
z9ZlBRj>JUrbWqB@;iE}PNb-maza(|S@twS0xPDv)CZ>#tPM&*n;-qK52M)cw@$#(=
zp0ton9OcBZ;5#1xVZ?;vn6|sy4vVV4W%hgI%WC1-xhXDN)?@m^N1t%-6rzM{X&H`a
z^;ESJO!?Zx>B2C?&!J6cxYlOAn<ZI~gVV^j?CqdP{2Puf`M5C*tycW8b%H5%r8(5&
z!}UPobf1D2v+u3632jrWc2kw5ZrKdfTGijW^Nkj#<F)VS-LCoQH>7Mu2&2V^0s4@H
z{56zC5vis<WO_(l-_fP57ilS-WnQTz%TQ<>@=(3pthe6IB9F{7Xk(GXVM;Dpu{~AY
zzfBDX=PT2O+O}}tw|X1k?|Sc}f9HCI5Klrz<z8Ylu;EtNqZn6LomxQnc;r8&GISPA
zPz~lbNrYZyo_1E+Cs%kz{I+K|Sw_Wy<5V7#Ln}G{Euyi_8$bPd9_f2|tfVD^u6s<}
zgfy)J__6sM)oLF7#KK{xngK7SwB#MErw{Cbe6Cnw8)4MUqT&RVzu#LtJ^*um0ReJX
zuNv^HcO?tboR)A|G<M{sba2epAG=-~3%o=5(ZwIs`tF7y_E7l?F6`U7V|X~Y#-B#K
zuC23e8-bvtjpu_HJqcSo{1>x&Fe4k&0%tY{?iV`0Zrdj#Q>^m#rf<^w5AtsZEgoO`
ziwB8nM=?_s`~q4-i;5ih0s_5eXP^884cc+SHK_|~ho3<A5-oh!6p*D9aLawS^?W~z
z^r@$=N-A7yH#ZUMcWsin!SK5n#Sh`5h$<O{x8bk$3|T3n?^V~k#5+T<rL%lHsFc=5
zUTG`3wUkrx;0IfZi-?EGYl9ELW;arXH_CSq2nK6)^>&yfqg<&iaIEvJGa&NloxI$^
z-?#3_#3~FZofg--eBT1USQpHvN1P5BNXtLk^4{TQ$7XL&ZhggH>yEOX)%BITWHm~(
z<dvbS)Cq}`eyhU5Q%iH1f4fWc2vm!RH&~k^V`F!Z?j-b^biFgd5$NgRcHu$=T*tZM
zl3VMXD{`PQJi}D^sCof)d!WvGGy}9ZoG(tOCQr`rPT*{psQj9xb|)~>=k4&^S<LA$
zH8abl++UXr3YFkX;SXaop`PNdST=W$?k32{rKtVs&i9`@d`hXBEC}b{*?C)=AHP>r
zb=PxzuB{DC>xT;O+__VW^W&xbq`8L)9FJvagO@U#MnbF;uM<qLz%d_-M2aHr?5$@N
z2E1-pS$&eK|0L4pra0--N!a0N)UDZeZ%aC@Er^!?%4?;w*1gNn^h@33rNJNqBsF6g
z7irna`>=u+?%|lV03Cn%=E=;V^6RubLUc%J?hY0BHr<{c`@kf=q-<M{yU@0bndEp}
zr?7NvKr^L}<bnQriKs}JspDVpaEg-~zx^CJEU+aSOW?12*rhd@c93A%_kICK4DiYF
z_1SvRoPEb}4aC%8?_QirXg}k08OSz#h#9tstomD&9Ww({8#bjjIuRlm3kMeSXrr+y
z%HTjhKU%xr+Dac32KLh$<)}RLr5GL#RB%b<qp^_rJh0~M>L;^eFQ=eDQCz&SCV$zf
zS8H3Y79*pe$;|dtjX>`8Gqyb%nL~t+Tg0JEhz(Dqn4Fx5T<1f|*c#}0Z9&8jmunUQ
z0reC$s_FHhW7or%%D2I(<)@Y8EXugsG;@dc?4176bZ|#=H#IF;%lCa!8NY{hN;pJI
zY^a)@lvoe{p0f*L6qa&aoL~D9ajEM@K?a!ke3(hBj6p-FPnI%S-H`5M==So;%0A3o
z7P`Y_%ZeEu%*@Qf!Yf0yhjRFi-OK+m<1E%+Zo>fgZz+5HKB6t0LWpjnkYjtgj5)1;
zNiEq5%mxZ%)t!fDIRj6347<h4*7mum6Zcw|<+Eyo<=NU?l3NqI-dx~TkX&~4yqbSO
z3e$jz-TP9p2>+F>_4#;O>ZY?a<jEM81M6+r1<TA#x|Vt(k?_~;(`-%ebMJiwUq7cQ
zcSM9(`g0yFB@6TGZy;}s5afFvdhs>%{3NOS2pp{lEN~DuKl>6r7=Q)`no$)YpJt$?
zMUUO4x9GJ-n%~vrmeSwS8hg^JUaH>LlN4fGz-b+9u8>F?->3W4PAf;N7uM;y48Kgk
z68CMUTv`2o@4n_auc66Foj3Z5GBP4V+948|ic+mBq1dkrn2a6h2Gc)cOL5ngr;*ey
zYf~=;UD|w-3$J5&fp3eHgL`v%JK^sWXxS$_vN~W*cJVQ3^$7WHjJ$^P9tsGOojdVw
z15~px@N%@Z#c%pPdEz=E-7eg{AD1ehq|qTcQ5*h=i8A=QYOx9r%kIoWrT!XefyMOF
z{t=Xv#YLOzb4>_?9L<z~n9kQ;OXxThEIl(*wr@c9pcSG?cSyXN(s{p`;gQRaNlv_*
z6&>n=CRyY2#wBiUtCm>no&tF$w(`jBg#HW14`2e#kMKbB)Ll&K(Uu-qJjwB>&8Xqz
zwfMbWVRrh75p&Kx?qZjwZtAkQ?w5#DlsQF9{+Tx{=Ef6Ghhp6_lh4^FLaPtONv1f|
z19<6JIzvO=vN)AE9-h$rE$^EBa^ZnuOSDu*m&q&@t*!q&;pi~mWMUHW93KQ9CtHk>
z5Oz$9tDW5XFx#5A!nKZHKu{#vODtq94WyVWc_(*C2fR2FE+*DJY`WC*F7fd038Z(>
z;3}~KJ9itR*!A%KvO5#g>!-Uee|6u?OW0sF@zY2<6*4|x^VulyN+n=Ag8%BPHlzE#
zjl<MZ%*mq@^Q0lRg`aw|@`B5?^&nM%sdm_l7ZU(zzdLVd3_e$E;y<3BcJ+reOR$gE
z5*NK$&Jn<44@=m+sOR<-zyDsG8?&g@yh9pzvawv;GlW*`?r<4(Ev@lizbyJ0AbvL$
zd+F~lYi*sWktQ&7X24w0@lT{;>@6amIH7P%F>=4oiObxPm=E=wBgzC!N~d{Sn!XYj
zegT1e)Q%Js?R=S+nL^&SS(NJ8tf*qdi!f;0NJA58KK}Ui>t%R&)wQYphW`6>PKRRi
zM(LPe0c|o|CN0K%)(SH*5Ctr`3nunOHI0diMw-5y;Hz$J$_MrjUrY{8PB~fGG_-P@
z20UskIHL}Ph6sp^q*NP>j~f(f{@A$Rc7;|0TNE)sTQR>P<4Pc1zEw{<U*%<06%|a|
zhGIpWESiik*eCb<$DTcVggjRB;08m9bUOW7=xGQu&Kgt9DZH8(9K?uD1NZYUPNjX<
z`|7qD?fWU_Xvc~(scacz&m<;6wKX1C5;V$zj~I?GyQ2v=LF>K}pHrN<9dIC~4IVPa
zoflU|vyxiLc4Vecw=W?5F&6Fb=`^a0xXMgkdHBeYJNPra72I6#XDlqqum+0qw{oq-
z48`Tomy?2jpl*fK=&a}O(b3cFjBBQ+-OGeO?56QHw0X8;NAuUPR)<>?QfJd2OxoEf
z#?n67$Hc@KY}P+%unkNtA|7Y;_3QI^xjfz$W1F6Wz@!Fd7Bh<;&#mtyJ9d#9i1rY#
zMX=1f)5uaH&gSlPuhdQd9C@1)iOwl(eY`sge>(7{KT9Ez#xlEj@gndM7->Sw4VSL3
zQBY{Vm@$WpA-*~LjQ1roUb(CCYZ4qjzGdlyB0YWb@q#*b_s<&;uZ6sF&*O_a0S#?5
zLHiBO%)tJX3&4c8l()Xo(bv)hXvi5h>1oNmVI;l&caZW{t7KDbTZzlv%xBLVmp9Gs
z?u)7!>N48zQ(9WeRV}!2q-T^}GXe<8|614@8~>Ee1keJ%2()Huy1HC|q)$8l)U@Iy
zS9EdZWeGl^J*a%Ah-25b`wMj9uJb`5@oycEu8#F*`yJxE`yJj8P^@4aFpS7xlcd8Q
zey|zi4!0f_KJX4a9hg+p>c<nbY5jtiV4&WwZ%d4WNd3_WW$U-Yv_W)Fes|_PY%qu*
zTC}K(ooMiVlTcH4J-495<jNEG(oM<WI9*oO!+c*wvSh@M-nm4Pt{u*<{)}Qu@#@vT
z3kw3=n>V+SQE_%Q7qPXadi?xJvhw5zzgZ*m8~9(%&ap5ZB)xXJ!~E>U*9~Hr$ul~;
z`5@kFYZK;r81yQ<W>^G58`rP4NZ&6tOObihHoYu5$&+i#wfUcz*n+!8hq|g2tebsL
z-GT2Ln2sweD-OR}YpH5j@6ldM{EjH1H5&kSlv<h~rSj0=hk7cybDn^x{7)+;R$Q8B
zuG9->f74B1jq8_u_YSwp6f%y~$ZAuP>``t2>bGw;<<ImK3$4A^T+7QYz1Qd%GMvRH
z3kMh0*VMmE3xq#g-b;zQnemgaB)L%+1y2eY%*Bei*rClr&C{>zTb_x$*?yCOvN`0^
zg$oy|17T_+N++;ZU!KJk;-{Ix-o&xAO-s$xbQILLfyEZQ2PS@g2@4!G`emF0gnac^
zTGnAw5?NuP=;e9?Uf7Eu;jV6KvVpO6R{GYaZrVP#u!G5J!m}%qtFF6h6HLFZJlP+2
z>=xpClurH$MINR%w?g$9)~m#7`y0dj%F_?{9P;wm6LESIR;DSrxpR}M57$xEZJe`y
zv+IKW%%|5b%0xN)^lO+zX=LiU;K{;?4!I${C_nE^WR<~B9$6!YpIPV1ScnI2e<=dq
z0z_khO<iTPVRe3oSit<nlUHN<K4Bjnd_%zD+grGf5KW#0EzNCgY<dsU)7NXW`y0`m
zMo{GAn_W1T_*Ovt*eOZ1+r2y2#Yv~NHOj9(0Z1zDb|qFXK_hbP^7RTSsKC)1tdTnC
zy6Il;YY3`{$4WA+aFUDXt{qP(_+7qY8p=lEgFZ^3|J=6cF}xB%n}rkr3I}oxJhLp+
z!$kU=LV1Ge&t*C<?tZA1uCC^?(Sc3>^6As3Kt$l#)n)<bPZ3xTVP@)|KbsBb2GaG*
z`{6t&F23>c{tlVMXH8?rglr8^e`MKwt*za=e}Bh>J$WUkUP%$ytBGrOfwv-+!%N1A
zf*z;syz}$ugl4tFdlp$sEVMn4ec=`~QKxRM8OnIgj6`=KMZaALtj3t@Qi==N_emc)
zx~-pE)C}a6Py5eHC3NJT%h^XL2^@|TuOjhzWNw;!r8;Z!P;-Sx)rW~bc8in!O15YK
zE(4lFZ^CP1K@%J=EF$96b7(`*A}V)JtLo@*0*Zx<O2}(-o&Fpa=q%7%c$~YnB*{w-
zHvwNaZ%b`Ml1X$w>zKqCReXW-C(QCam%>uBrP*$2XA~-BH#JEco|Juu_R~5VFYwU7
zE~Rqw0mTDWM9BU$u<^k<(t8sA=RODeBnfY2q&^RYNUh5xPd+_y$uo{W6g_86o?zYg
zk8;BZBjJsG@kN8ItgK($UjDjeBQ3T6ot;>k#*san9=kh4OUFC|@66J}^%lT?{mpmF
z%gd2I43M`y^2IP7eZ5bsBY|KLH=&hA<ITqSTwMe;F20(c4^B2{Q~=BeF9JXdA8L&K
z!Z`x|swr*~G725P)-S*D@YIVf5PZ8CZpdos9~7i#GA)%m8dKz`UTFIR{Yx$v$K?Zn
zM)fzVB3n748GDauPsVFn&v-w2^tmlF=IK+VyWgG3&ao4YzxlN7l7G3WuLh6S-=^3d
zbTMUf)W*B_(Ip7}*i#*Hrn<JG1HeAYD~J?8OdA^U=ocy&8VLmX6$QuA%uF%*{XSnm
znV`Z(je`oi?j|48OWY;gFLX7?f<$H1n|x|th}>}SbB_Cz_U2=&teW)(5Wur{=qQyR
zzN(R?usgb2=J@gBgINs&45E%-Mf23rLew9qXlQ7tW0Bp(DrwpjoAE-hVV<oGZ5+6|
zW5gEO5Fs724Y*N=V|Eqa3rjq>{(c*0_PM~*3elaw6cGa;EitB55;^63jw`nHHF?t?
zTb-0&3%tUr$z%uo7qu_sqo%Q+8<p4BACL)WzY{fl^jAlg*Z5a9(p#6jZY(S;jEGQ#
z1r!!85xd?1HBPfNpA|$ty9Jja|8gr_2G&h~!5m0Yes2+dvuwlwE2FbRFJOV8jn~oI
zIq0*gpH%i5DNZ?(Ff=>`X~=<-V`hndisG$&Yc_Sm;Ai{}?=6g)n>_oYHR&-@d|~(g
z*Sju+J<Tptoc{aQ)_Sg_0ifqyv!=2S=9w6S5^z1YC<ti5Xe$JD;239>ty%!+u2M{m
zc#tw@d!QMfPKq%im!(keqOQ1`qPTK}p|R4?KKE+Z+8voB4NY05{;{#&pQ`5Svc%fN
zVSwe}@Jsc)!1u{pzY?f@ymH+WmcZ|b`21ar!Lm|}f$E`)5NGY|oNY;D_y9p*;nF(D
z1C2v7PgrP5?I%>upTCD#ce{e~muU#!)E$`!4Qf`(>5;eH&M^wfCyjZ?DwVoQ14#&k
zk^5i>ycD<%v8i^srw3!O6HXHVMB*;9H&L9$c`VYVt#A%%8!9gkh3UqW1*yHO>8S4#
zN!`bUYW`a_5(AUlPd*$v%2-WgeBAqv7Q&a>+G|&@@+w$uqW6a-2q~HP*|V+fWI^<o
z>m-8XhJVd3Bs|l492wfCMlYp?Ue}{O$s0%kWy=4utM~J!kl~Fo(8wa%3`Isp&fMnV
z$wJkKwKEi7S}+-*(zd276QrQ2$=((J*Dfy!C~<9V3MLHbCKCvRWhHD{{2!R;n}4U^
zDauB48Ezw3fd^n|YTR)nk$uY0u0VnK$ba7sY}-XBk;mSFFb#SawYs0KPY4EA@D?x#
z60;lrf(D|4NFTm!$&L9IbkX<I@;z#^ow}x$;wC9m+d5(K<Z^_|>C0(tq(z>M?S~7V
z9jeR48Ezjd?qlID<uo0wJu~p}<N3@(+di&^8x#iulo=i#=F~0v20Dl@V8!_p{6Tld
z%VhdC3}^3V6r^h9`D|DaG4&(e`Ag$%YY3~H&7tq97Kef*HOvQ^<K3E&ecg>A8NAV+
zc|&%Ny5*bUS+hU*5{XOjGy?B)w1+G$r_fZQ@qPi*F*UWr=w;2F&-k;+DlDIt!9?}D
z@XM5*M+7J8w><NS{o$oJk?R*HWLVCfTk^jl`Asv;{MLN;*JwNg+n7Cr8TKMa{i>v-
zR3_@{<+t4v;9vL+VqPEE^}n0redJDyg4Kwf1r(;nf=^IP<F>1qoM%$=s=w6Qk^VnZ
zn)K}9Po|D;#wc9+u{3{+w8xAyCLn-(c7ENdV83KJ3S>LthXAW_vi~7w(H+nj%eXv3
zjdLvfz{3gy-PWnf-AdNod9@7<T>SiyQ{+0IZkjGzQ(y{#o5I+LIOtdCwi9Oq&&DPi
z{}RoVNo3z-eyx(ie6ZFhI{Z)yofRTp3SCCl)#cncr&F&_U15hlj?tQ1GEV49zeL_$
zx0)^P!jIQvD>^#PG4pS<R*?-4o-aRYnR8bZZPor%%(te#-MYLsUH(ub<MXVzk^(>p
zWU`EQ_DP>Uy*{Fg#_jBn8bDU=(-K6kGavw*6_V@rNo5nj%90R^U^%AmvCvisflC#&
z;9?5XrR2#CR^e;$DV&F==H@ajLo6!Iu}<a7wj{VVtqaFZU^tfD=^~5SW?<D%^%m#y
z3ZE1kje2r~gZRL1q-tcWp76iAt#jv6&(?bRWAf8YyqjL+%3ptbJvrC$)VE1py<~F|
zjx-2w4tMTMU}PNPBRYSmA<=*@Um2wzq5yueG&`HBn%s!<6g&l_XizS%1F8j{MKxUv
z`V!a`&?n|w_q|8J1V#v(K`2si8^1vy0V)2K8avwO`1O8KRc}zQ5bld$D%T{jCKun_
zsap{e-||>e#!oZl#>%?Z<3h!28a->aMmfl9j9^fM_Ho{@i)>v*g&`U};33b>%={2R
z3~+x7p%nE0LM$vkzBqe{$}9#Uh{P8VS0bIj`)Wq_9dX=7_xW#Z37%<Tu%XaLrJY>E
zk$}lFf5Cr8LV?i*%uF~@;IrllF9eKb^s-@8l^n;BP8=NshC#KBB+h-C@{NL>Ugw-h
z7gn1XC5x{o*bmPcZ<M6@S-HLA1D%b9TXT+QQ#(V0=+dszW}&9kd0l;ZcIeTkLe7FV
zAaNLha6lPO55Ir^hMJxe9z{b#`Yw925Z>ZQJjpb)Rp!=2G;A$0Gcp1T!Weh!`=za|
zE$m0nG>;G!w{8*gHo&IDhC?Bo&QTyQZN3puq9czJ_p~d{{u0~dukPFy;rkE&HaMu?
zEW0to^rLKH5_9f(!eua{>+AFEH_3&CPFJo}<6|*!WE4E<|6~hvEPvgvqfrGu?*PCy
zVw9R}x5AZWO<gZ}nuT)=1rqUy#A?R`xtlj{{*~p+`n+b70wpAlv$-4Z?S;3>VLj&z
zLR(8y#gY8}10nOn!4EF(t(=;lS=X{~+i{~l88?8uZxJhN`N~NGVb9}F$cHgONi}s}
zJctVrVdB1sse*o)7+{Lip;rARD<d9ABhDb1gpIo~PZfuotBcD9axnmrQh=F@0PyF*
zga|tw<K4J!c_tOvxtuH2i+lPM%WuL<)DgF-`8~AslB?@`+Tc-G=0-(*SnZlCZqoCm
zF-;CO^DXTSs4?@?<>p~!9Td4-i{s?A((}kjvua~p>h$u{AH#Zn1=a37g5au1oEfSJ
z1mFNR&*Z}NK-<fr_wP+jOuBR}bu2siUIf$({loPWZYHRtzy@wcz0b9%xEPkmow}AS
z^n!6sPL7V;%@wjsvE!qo8czn^(Wutgk{^>G?kh||xYhc$8GRc}d-`I*nM?EM*Ltu@
zTWv-E>C}C^-zOJp$o$_v+fAu_bv%&1ql;Att&UG}9x@|HdN5{$7GVJ)0AB-KJnQiH
z2YA2I@dPVSIO^`ce{gWd`S<VPuxE0v;!vk$8(zZ6L%2u#yu0#$ZvLF@Z@gdFJYkQV
z8zXgecjsKg;pTMx{aLWti4}TJ;g|>of$$s&f;37R7#Wt~zc3#Js!Y>ilWPj`13Eg&
zDQZBvIVKu%_s_$~#qou>5Bqzcx8YccQeDa_sDiMfM?{cnJP-Zv$%yahCU;EOzu&fF
z*ShqlG}gluH$MF8X&DQcd!bbg^5(4>yJuGw`bu+=)k4UlHPwpV(=M%*zfIq9)EUqu
zA=08JXjqA9C~fa%6@TMkN{fnIxc<;oDok1Jru(N(BQwbm{j1garo9{{dP28z=C%_E
zT3o8T1JNt_Z&)7^Pf@~#mUzXP$u>4P!Mka=E4je>n$EeFh1+?nX~_R%W9f+9rx)Fo
zXYhTXs{>UkUVUXA9{`P^EHx77@_DOUY!~>~v((XwdTxcAOB>C=&Sz%mtf*{+vTR`_
zW4k|<_R1<7q5UK#8v#Fo6)d9vp*@_1B}@b@@02ZddUEfdN2L0Cd;3dSqZN{MitS7;
zT(}<`tSBY*=*|#!5FlbjraF}Fh@3Ny5drk)xSjqQ01vI@oI+TunrtL|bQ{46+&d|M
zRPuF6FE=WgzV~hO#fP_jJPl|F=O+ukQ0zkHgH_elj+5=bkBaKQw}&w9(c{OLrKPc%
zF@%;9V`1>3p#(W96-C7{#*I=a$w(Tv6OR4Cd$eZm$e%thwOi98JUTi+-G5sh4f>hL
z#j%X0k$r1tICf?mA}Krd1`DG#O>=r726mf)PG`37*g^aUiI1|odx;bin`i1GLOER^
zIEmMoP4&ic_Jx}()878b8_-|^v`>((=Xvzx@l(}fb5`61Y+RrF`(y3wHaAY6>u78Y
z>bX+I8^9%KX>1x5bu!x5@=U=5*P|MKhoYNZ8V~3bfn8$%jn(TQ3N2JWI0>3$=ZE9r
zGWyp822qV;*>i0=0_A|j%GS=V8_%ltLHVId-UxS!_JDvx>0MObhG>hfEpePE)q`K|
z{P<n3`Pq4`Jac6%zOQcXu;>ViBg%W>sX<;zPRlnOZrXIh+Ysk!k%BVav{fh2v}Mb!
z&FOM&g~yNY>f{4i&6dk{GN2jlTMEi(`9NRL_AdO$7k$H@90dx=#-e`m1|~Nn5VSZs
znc<trRJ^{`ex|m*UNuFHN3rtAp+m;&4+5)t^0YH(gKupsD5^d}?_Ws!ySJEMJ0t#7
z{gP?InCDCtR+mln!*aKo0;HewdbtsAsGe5XI`>aSwWVIKm2)z&n-R>{F2yE7(B8d!
z_g2^Ihj`s#<B>h>ew4~4ml?qH*ROI>{tdr=TA3W=*bqZD4ULr4VQT87S(b2Y^0o|}
z$Uy#!U5BWtr7r~f`s(%tjcCt1KqtMX_JLw%h53MzK@12OI<r$F4XB8z&pfy^bh@eb
z>k3NG`eAoz!g<ak3~+2llLZtTYinz`KcLBn&eF*@*kffgROBBg!O^*}w)9Nqve&@4
za`CfMiU4h_-n+l}uCKI8_TE*Pm-pK<Gaj3tKYzZJ7d=Rta@%R>p(R1-bdH_u<EF7-
zG`yC3fS=LYd{lgS)-SN-!i!dvD1j?0KJ8g>*w*LPx2{zc6%~zpqPK|a;wR_-S>6mB
zBtp#1MKwj`)an?cde()Gtm~EE_7M#Bns#iPR9|P!AA$Qn-G`BTR>%U{uUy_(?rYyv
zUuWUb`iQc~Kz1)_5Y8_+Lfyjgi^m4sPU@`P@YVJQ+s+pc{`~olDs9J(upLQ3KmMEC
zoyfD!eX$RvP2C>sDy**QuH2fQ9)M&=;f{vNJiQtb?cTf3tq%Y;-#Yv9XI6v68I0~D
zgc+icf$jFhx7U^6GL!@QLz3<_Q$>wGan3>^0)*4V)YJ|gYC?Zg>xKA3EDGz(L%|Sw
zWi*>3y)$JvOZqWf-*d&}OJPrnp15|w`sY~_Afi7+C<%lT{}gcsEGc541jSKftT;U#
z-FvqsYYpRVB|ZBk*Uu}@rI^{gl(-Er0Qo%O-V*9MG{P_dat^Q_w1v1GL`EEg_m=oG
zOE$v#o)8Gsu0$??!EG{s+=3!*0RufyIZNP);V5F)%nWEdbx#M#{Hx;JTT8)tHz`LS
zZX;xS;`y1Q&-Lm8Ob^9`u|H7)xw^UK(vj6A0|x=H*i%(?iizoGpFaoGzboLJE=>>Y
zj}~gYLS3+nz^aK>hZel3K~qJ+e&fasTJMz~KF72INk+bHtS$9?jV?{O=6}li545n(
z)0@DiF!zHNok~KKfy#a#I8FHZy%4(dW~K*CoE+M{bt!<Qm6jNX(~j#`)ehAlaZ-ML
zktFjH9)!#D)=CUQ=Fu(s3w2>c<@w^#$w}F*-jht<Ehf5hod#4uvgk4TMMD9&3P9&s
z%g%3*u@{{Z6pYAMQ({C1X|$e<x?L{z{LSS%zi!?q$kOg0gdI&POOlo%zOw16o*~DK
zj1%tZZ#Kk$JyiBXA{6Vli7~La5v#+J*WsHv@Dg_QRICKNm_uvfM_XmVsc}@;?&ivz
zz4q!ryuO0@ITQAdJH+)Tq3R|Ce`rs<1(94}(K?Qz4HfoH0jf0Qx)VdYq@?Ub?d;iE
zSz#Kg>w{&<T91cH9A6nD>O|}@f#C$wEnwbvbpGmq6_fQBRrR>5`m9)n<Gb-022m*_
zK3bfo-++dDqu4GHe}fdzGRa62sk#@P;91MHcI84Fs9U^&j0?k24f_f8<>Yao89|`K
z12z0_WEYlX`-m@OB3i9rp}28fzp`zcJ<p%jb}k7C2`Y+L9g{9%s<Q5cduXCXmV=Ls
z(P39#E1_>%(cjPLUw9uPl*z1HKMv9fK0B?blW!4zN-;wtO*(+)M}NH8<He&qdL`zy
zsyhf;d-0I+w12~a3<*7&dobe1jhJ9dJddd`IR5&n?^SM1_V5TXB8y?EJzd_oI#j03
zVYHQ6ngYR*KxqHi`oI{;FxDnOs&XY(c#yB*Bt2)-|J8zzF!!rFe{#meKSkMC#MAQ2
zdNJM!Vdt%a2NP4y^G8e%D{xqz0vcu8*!o}yGl{S&FXB_-W~7>-Eg-16?KviLZGbl8
z1|H$yzDW>=z~Zh_fJ{BP&7Z1e>=NsVvbZ=NENoUE;Lq^gH#JTA@IjQ)#KZ*5E58Oc
zLi+M}e6Ymo_{sXX!V3>~l{dX~S^jGXHes25z&m$*kBwkd?j#}cm~WQGrJK==!Z-q7
z_c?`&;*L{V@TU?yv^|UjNid3Ki4HabL5N6?p1qrZK*{;%P&KIz-sG=dQUn=J8uYEN
zgWtmv=Wd5O1OOY7Fb&L^A)J3<_)CldJRYnr30V2bo&Mmq#69{0e@ke=6`+Pe)v$_(
zfGjl);xTOF>qOv-VB&|w7A#L7tMjNR9RBpW0GY!M`>$YXjM50_5lrYD9UM$(MtzQX
z8VJCDkPvn%iGt3--kw3&E@5VpxYr|g<NsZEJpIOA{7k=uAszqqBR9eYTK5#%;A-9g
zElxph0kcaxx8HZkdI)cYi|9he=j_|HqvL{%98(CY2h<na?|-~PFIdSd*m2SKDtmbM
zzmlT;_Stt44=$ysy__+J>j!+|?M3(*Ted?mfwwY&N3%`<*5iV!cn6!zmD8a_wzR*M
z|Gp?g8v4KtHFm2%F*@9lJw}!-I(y=pB5QA2j^%i^tJr2Yw_0YdJlkg~xT~P5^NQ|n
zwlh6}4GxdKXBT&xcT$-end+>!bly7ybPp--;r~y5jDYtbFDpAlEEB>+S%vCCP>#36
z&#~MOO@^3b4H@CT!^3VTiL~%Li#B%clISEk$v&HpU<nixf!C2eq(OGW&U)LfF-u7u
z;Q&dB$cG}-Q66R(`I7EvW!`e-z3U4Nsilb)pGnkHN<n*yC<qb!V9~gmHTHgR${Km|
z7)Zo0vE~UoiA#tC3W}ue$>d@D_MgEBQrv$w1qtzc{=ff2HEKsBQWFU5fM=k~00gIw
zh?^RaxeDFm2SCKdBG=oWQ(y3HQT|GBZBx@2@s!ObA=zy^3V=81bi<c;J{K3&jaR@!
zfy@yIoev)D5E`1ER&=rs)l+m8@B5IZ#7dL~5iBccC<X(vvZhz{#IGpE&=}ctNdCO*
z<m5yw2njk$wuDG91Mwo_=!Eo2t|PcLgQFbc3?ebsBX!|ujv+Ea@mU?X>!BeMTEGTG
z+i8AN@=9LIXjDLi^aJ>9G`NsBVGDu~0ppD-dW_*fDX)wWlb3f^un|ZOq1`W>F$j%D
zbV3g)W6C_x4>){L;_Pqg@At$uJLWO33?MkbEmr6b6AOzpCsm<ETV2358gEKyS?>o1
zDpBs)3F!=&Z6^<tuDF+RRh!aO8}Qm5P52BtK|@tRl{eRcJv81qRh=-L9@XAM*@dM}
zFX%cSsO2Jy853CXQq18p{$_V^I5}^wyGTiWhC1`c>x++ZVjOHnkV5yKJ+dD8)a`z9
z?7`8OJi#^4R_i9S(xU4hpJSDSdh-hFVQfep%M)UZP0snn6e@gz-PwL$MkS6tvl6tl
z&Y890jI4)ApC7!@G0>0xT;0ug(B?tamIFx}j<V_p#xS(4j!Q~TjyeOcGg%B5|HmKU
zI{%L=VjJ^@NOkzfd>InCO2jGhwb4N%eiTy(D{7WeqaH9LYH#8SMor7ZWfEl)zmJkq
z<Y-05ft&zdLZ`cgJ||UfOwDQzpZ7^La=5vJe6j8j=%1WB&CGnD8LG@DHeg*6&bxe+
zsi0(Fh(_=H=uxmIM&bE!>{vN!eeiq``Wl~~If-E^K>jSFsfpitXnyZ+{xz=f-d^2a
zl^g>8$E>N@k)F=^FWk@<s&Nu=+LI?MM~x!KBc9~u^7pu;A3ME?T!7I2dJzBp|KpFn
zMHB|gfJ8u^lev1;yd(1laEO1QNl7m(>wz(Zm!jJ(l*;daC?BX8ntAW|yUu|LNxR)^
z_db;_%DAgs+99Eb7B-rY75{4`{rj`{+%*1DX9cPGr3Dl%caA1U!=|wQ%a=is$0=v&
z<Si^x^|Ho9;Du_%p<k#5r0=%x6Orf_0&lM@d?B*KRT-^1vrqh5GG>lka&Q1NDiCbe
zc4`^~HW7um3$c1!cJIKr23*-dycnG-8uDG4{0E>=Jp#(gK|iSsjh^0QmDfsdo6S+b
z@?ii_%}`34g{mWTDwz!ZK2)41m{#v}G#%}68vk~`&)w$4fj#yjeQxw#FdTW^Y<%r>
z@^C^$a&VSTVI$v6|9|L994j{M_SiJE#Lb#?*7CM0BVB<SyNN)6e0Bt55HzX$jPjgR
z)JKlo%GV|yal>}S#MAO0U}tVL1uQzmf3kd_NF5C#T1~GkxH4*IoK6V<l#2d9=$se4
zidD3<%<qQh0SkB`S3NWXo>XAjU0Al^-LXzV9GY8U6?$6|_=8~W;^yYmo;oqeW50NI
zWAV1RIctYse>`ZAd5%XmT>Rcd&QErLg+U+)e${prdKKM?N(aWcM19}Jgfz@7GKWfP
zKx=`m2oYOx-z-5d1|3wwM{g{5e>_Iu4#Ogs_wGbxy?giFnC*m*qd)%NIZ*x|o=5+?
z{#@Z>^#yd%0OygBlDfNt$;T8xk46qG4Yc4;%|s{Z>5e0(OGp6GLF<GDX%HZ2w!=>_
z9b|lb9HEJahlj}(GYpZiV#sY}H7$;;k1tWwpMu2#FYi;|{}V@|2ip__yE`&;oZ!JI
zs6afmHT-e-V4md*xhqn@Y7sz-luw7YA=yQc1F(SP4z3x3lo=q)fnN#@B2;~*<Vd3B
zPV>`{n~EioAiH90s=m_pcf}nEn~hDA!_#L!=2n<OzTl*(<i#j32sr=cYz3X!k3l@j
zKfna5n<0^leqBbDqf{IuKK}!RqYEVPt@6nsYCv>oI|Szp?ja|iUtGj&NxH!5fNfGt
zAsT6IlMBR8{_89@r?=Z5JLR9SG!lG4B6qG6pF`_yNK-v(6pCXfN6V7PN9!c7^sO=~
znA4KP6TElt9wY~7Fk<k}V;>(lg>>hqDa43i^J7Ba9kht=7$aH*=qLO;h~!SIrnTWS
z0&4OI5^-5-?8N}A3TSi?0(7t8SX5^Z|98qZ41tOm+fZdna|HtddPNH{u?1|iQp35G
z^`A}sBeWcS7yeri=QfBT=t`pa#7MK3f>w3aF2pZU<cx-j+RH+pN>t|O&UK8s3Ju%2
zT)41}n8h-MpmzX<VCKUxsNQm_7%BdTlja{OvgGFp^MZ@Wk&QI(M@8vex#HbW_3v8T
zG0cznAjqBZA%ynu$OpuhDzbm5>;FOmUj*n9d0jT2c`IE4<^|0)oU_R9#=*tS{U^CK
z`7HmF)>Qpu1-JD%i6PYwcnbqslU$;#040MceS2hMT;jze>a)z3j~w*B#+ekn9hI7m
zJ1g;o<czoMS1E>_<8>ST)Ehp}p1s{V5ASO38_XHrq-3mrg38M!JGSAn>LTBDRVt%2
zbX|Tr?`r3mlu$FA_UiqDnB=rRaE>!%qeCv{ctbLdC=#EjsGd>Z3n8I3p&B~I6QA6I
z6yl1CF@O>Yol|B!-YrZT7kK;)whRDBVvr#aeCq%o;Ab{Fv4=po=kUMn$M(z}>;6q+
zQ8VVC%BiHZmek~tN7d`9sfD4gr1B@`GUS!mP2Pq@_$SgvyW-way1iWnl}Odj(U3ER
z_yXjGC>+FJ{3A3v8d;T#SkD^4(x|eaN-}U?K!bq1DtPQJ$R1IxkBD6DDny?XFb}$D
zPbZI$Ttv$1wK~B9sTS6c2dZ$C7*XH>cU-8GcvwNy%cIREA|zxRL(=*99WhPSGUF;#
z`<e!Tn>B)~a$sO!^iOhUCKM%L&}6EHk)iQ&Uh!Gg{8U%V=pVfcY+(&L?D#VSbz50t
zNvWmTZk+;NB_@kg9X<?l5l|T0qsf*sii&YUcL01G@iPaD@6Vq<MWdW9P^M>P@x(QG
zB^t)Z<i1|Eh<}`$oSkh8m<B7cx%m!s3fu1!_I+3%gndxtB>AX40^t4gL}dk-eQT?$
zM$EhEG3<3}aF7=}8ejrmLoah7+yJDHeDe*k(e3vK!~NaI8jtGU<#!z+e#RJ%Dn>>|
zu*SgQ#e8{1S=mdK+>&+OT;a&jK~-QPB`4ag4~r;tg+)X}ynOi{aT4xmhcbml!(@m9
zf$T+MW_mQa`7(=LO6~HSKXs|bUkOB#doTpSK#5hVl3TM*yCMYHC_^>6l5=x&NJRu|
zZ@wc^z<_yxorCrLNx<x(8vF0Iw8$LIIKA7QMWdq&b!}J3%oOnm@J6tfM#!%M0QA~0
zlEcJ`{C|p}jSkW8QXr16=7B#t+9}6$?~*x>Fya15xuVr^DuB2NyZmP|iRPUnGS?@G
z9A@b0PrgA=ymya~ITR)jrZiH2DoSzB_0G!59UZ;f^@_g~jiz5DGiy5LwO%ncR})BY
zHdTB6Klk&$WXV;;3qY0tl^@WT#@gCjkV9t{la$zn+-bP}n5r=rOAc&r*cL#q+bHI>
zDMsg5{J^jL-%d2i@Il#%3W}7tK@E=nUw$l0!na=<&17PEX7XRZcJG}r783gJSd0Kt
zncm<Mpjk);U8=0Cj5veQ{Hzpa?v}v!W7p1|L<!%r4ju$wV{smI3ruxT7<Xh1NFVcP
zO;$Wss@L=*ZU@2S+V!YzhF87#ep+G2;rzQ>;I$GB6;**rJrzj&>&t*wz>tG)FwLPu
z@k*?on=513X$O^|3x`AjhdNk(9ypxQ_6F#K_QM?54=8p>AHM;O5m=#RPXSkl7cC)-
z1Tm=2_dl?+w~x;uWjniBu<<Y`u@f5&cR|^Vp-12roWtx_&<W|y!v8eK*Wk2<ZSFs2
zbqMAzfMgBmDC+F3pJQXvQb0$)ASqr9-hPj72g&a9v?oGqv1|kkKkZ~<=j4PI1?BSa
z%x0+-`(e_2Z7#|)<i@?9#^x4WOEuDt_(@fBp>|~tuhV*kHYIhnEg_5&%;Y{8NGK=-
zsidG>i@!se4*Ecu^UTGxHq0<81;~hv(4T7-tdhdX$=M5G+tq(BOUrbPG>^rp>q<&b
z?_?+t2)UQ`<BBL@_y-jG@L@EJC2;72|B9Z80KO-z5|F6L_U@%UWAsRiv&?<P4p^1n
zxa8J~BKY~_yDD*9A^Qh}w7E90_5S^PyA3D8`L0J*J%qY+eEW$B37lHl_lN*!Q6e$y
zPZ2-yMR-h1Mw<niheZ_I3HKiCAPjnS=Nc2scEW`Xov2UyRf_hma~>fWum}<E#S9V%
dj`*EU5|^XAD<R!;#B+ym?XrSY=0$@C{|CAd!9xH5

diff --git a/vignettes/examples/vision/autoencoder.Rmd b/vignettes/examples/vision/autoencoder.Rmd
index f3ef670f2..6c6c634d8 100644
--- a/vignettes/examples/vision/autoencoder.Rmd
+++ b/vignettes/examples/vision/autoencoder.Rmd
@@ -23,7 +23,7 @@ by [François Chollet](https://twitter.com/fchollet).
 ## Setup
 
 
-```r
+``` r
 library(keras3)
 
 # Normalizes the supplied array and reshapes it.
@@ -55,7 +55,7 @@ display <- function(array1, array2) {
 ## Prepare the data
 
 
-```r
+``` r
 # Since we only need images from the dataset to encode and decode, we
 # won't use the labels.
 c(c(train_data, .), c(test_data, .)) %<-% dataset_mnist()
@@ -79,7 +79,7 @@ display(train_data, noisy_train_data)
 We are going to use the Functional API to build our convolutional autoencoder.
 
 
-```r
+``` r
 input <- keras_input(shape = c(28, 28, 1))
 
 # Encoder
@@ -139,7 +139,7 @@ and target. Notice we are setting up the validation data using the same
 format.
 
 
-```r
+``` r
 autoencoder |> fit(
   x = train_data,
   y = train_data,
@@ -152,7 +152,7 @@ autoencoder |> fit(
 
 ```
 ## Epoch 1/50
-## 469/469 - 4s - 9ms/step - loss: 0.1371 - val_loss: 0.0727
+## 469/469 - 5s - 10ms/step - loss: 0.1371 - val_loss: 0.0727
 ## Epoch 2/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0712 - val_loss: 0.0692
 ## Epoch 3/50
@@ -168,17 +168,17 @@ autoencoder |> fit(
 ## Epoch 8/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0659 - val_loss: 0.0653
 ## Epoch 9/50
-## 469/469 - 1s - 3ms/step - loss: 0.0656 - val_loss: 0.0650
+## 469/469 - 2s - 3ms/step - loss: 0.0656 - val_loss: 0.0650
 ## Epoch 10/50
-## 469/469 - 1s - 3ms/step - loss: 0.0653 - val_loss: 0.0648
+## 469/469 - 2s - 3ms/step - loss: 0.0653 - val_loss: 0.0648
 ## Epoch 11/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0651 - val_loss: 0.0646
 ## Epoch 12/50
-## 469/469 - 1s - 3ms/step - loss: 0.0649 - val_loss: 0.0644
+## 469/469 - 1s - 3ms/step - loss: 0.0650 - val_loss: 0.0644
 ## Epoch 13/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0648 - val_loss: 0.0643
 ## Epoch 14/50
-## 469/469 - 1s - 3ms/step - loss: 0.0646 - val_loss: 0.0641
+## 469/469 - 2s - 3ms/step - loss: 0.0646 - val_loss: 0.0642
 ## Epoch 15/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0645 - val_loss: 0.0640
 ## Epoch 16/50
@@ -188,35 +188,35 @@ autoencoder |> fit(
 ## Epoch 18/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0642 - val_loss: 0.0637
 ## Epoch 19/50
-## 469/469 - 1s - 3ms/step - loss: 0.0641 - val_loss: 0.0636
+## 469/469 - 1s - 3ms/step - loss: 0.0641 - val_loss: 0.0637
 ## Epoch 20/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0640 - val_loss: 0.0636
 ## Epoch 21/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0635
 ## Epoch 22/50
-## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0635
+## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0634
 ## Epoch 23/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0638 - val_loss: 0.0634
 ## Epoch 24/50
-## 469/469 - 1s - 3ms/step - loss: 0.0637 - val_loss: 0.0634
+## 469/469 - 1s - 3ms/step - loss: 0.0637 - val_loss: 0.0633
 ## Epoch 25/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0637 - val_loss: 0.0633
 ## Epoch 26/50
-## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0633
+## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0632
 ## Epoch 27/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0632
 ## Epoch 28/50
-## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0632
+## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631
 ## Epoch 29/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631
 ## Epoch 30/50
-## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0631
+## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631
 ## Epoch 31/50
-## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0631
+## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0630
 ## Epoch 32/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0630
 ## Epoch 33/50
-## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0630
+## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0629
 ## Epoch 34/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0629
 ## Epoch 35/50
@@ -230,27 +230,27 @@ autoencoder |> fit(
 ## Epoch 39/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0628
 ## Epoch 40/50
-## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0628
+## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627
 ## Epoch 41/50
-## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0628
+## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627
 ## Epoch 42/50
-## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627
+## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627
 ## Epoch 43/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627
 ## Epoch 44/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627
 ## Epoch 45/50
-## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627
+## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0626
 ## Epoch 46/50
-## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0627
+## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0626
 ## Epoch 47/50
-## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0627
+## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626
 ## Epoch 48/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626
 ## Epoch 49/50
 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626
 ## Epoch 50/50
-## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626
+## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0625
 ```
 
 Let's predict on our test dataset and display the original image together with
@@ -260,15 +260,15 @@ Notice how the predictions are pretty close to the original images, although
 not quite the same.
 
 
-```r
+``` r
 predictions <- autoencoder |> predict(test_data)
 ```
 
 ```
-## 313/313 - 0s - 2ms/step
+## 313/313 - 1s - 2ms/step
 ```
 
-```r
+``` r
 display(test_data, predictions)
 ```
 
@@ -279,7 +279,7 @@ data as our input and the clean data as our target. We want our autoencoder to
 learn how to denoise the images.
 
 
-```r
+``` r
 autoencoder |> fit(
   x = noisy_train_data,
   y = train_data,
@@ -292,205 +292,205 @@ autoencoder |> fit(
 
 ```
 ## Epoch 1/100
-## 469/469 - 1s - 3ms/step - loss: 0.1004 - val_loss: 0.0939
+## 469/469 - 1s - 3ms/step - loss: 0.1006 - val_loss: 0.0941
 ## Epoch 2/100
-## 469/469 - 1s - 3ms/step - loss: 0.0936 - val_loss: 0.0919
+## 469/469 - 1s - 3ms/step - loss: 0.0938 - val_loss: 0.0922
 ## Epoch 3/100
-## 469/469 - 1s - 3ms/step - loss: 0.0921 - val_loss: 0.0908
+## 469/469 - 1s - 3ms/step - loss: 0.0922 - val_loss: 0.0910
 ## Epoch 4/100
-## 469/469 - 1s - 3ms/step - loss: 0.0911 - val_loss: 0.0901
+## 469/469 - 1s - 3ms/step - loss: 0.0912 - val_loss: 0.0901
 ## Epoch 5/100
-## 469/469 - 1s - 3ms/step - loss: 0.0904 - val_loss: 0.0896
+## 469/469 - 1s - 3ms/step - loss: 0.0905 - val_loss: 0.0895
 ## Epoch 6/100
 ## 469/469 - 1s - 3ms/step - loss: 0.0899 - val_loss: 0.0891
 ## Epoch 7/100
-## 469/469 - 1s - 3ms/step - loss: 0.0894 - val_loss: 0.0887
+## 469/469 - 1s - 3ms/step - loss: 0.0895 - val_loss: 0.0887
 ## Epoch 8/100
 ## 469/469 - 1s - 3ms/step - loss: 0.0891 - val_loss: 0.0883
 ## Epoch 9/100
-## 469/469 - 1s - 3ms/step - loss: 0.0887 - val_loss: 0.0880
+## 469/469 - 1s - 3ms/step - loss: 0.0888 - val_loss: 0.0881
 ## Epoch 10/100
-## 469/469 - 1s - 3ms/step - loss: 0.0884 - val_loss: 0.0877
+## 469/469 - 1s - 3ms/step - loss: 0.0885 - val_loss: 0.0878
 ## Epoch 11/100
-## 469/469 - 1s - 3ms/step - loss: 0.0881 - val_loss: 0.0875
+## 469/469 - 1s - 3ms/step - loss: 0.0882 - val_loss: 0.0876
 ## Epoch 12/100
-## 469/469 - 1s - 3ms/step - loss: 0.0879 - val_loss: 0.0873
+## 469/469 - 1s - 3ms/step - loss: 0.0880 - val_loss: 0.0874
 ## Epoch 13/100
-## 469/469 - 1s - 3ms/step - loss: 0.0877 - val_loss: 0.0871
+## 469/469 - 1s - 3ms/step - loss: 0.0878 - val_loss: 0.0873
 ## Epoch 14/100
-## 469/469 - 1s - 3ms/step - loss: 0.0875 - val_loss: 0.0869
+## 469/469 - 1s - 3ms/step - loss: 0.0877 - val_loss: 0.0871
 ## Epoch 15/100
-## 469/469 - 1s - 3ms/step - loss: 0.0873 - val_loss: 0.0867
+## 469/469 - 1s - 3ms/step - loss: 0.0875 - val_loss: 0.0869
 ## Epoch 16/100
-## 469/469 - 1s - 3ms/step - loss: 0.0871 - val_loss: 0.0866
+## 469/469 - 1s - 3ms/step - loss: 0.0874 - val_loss: 0.0868
 ## Epoch 17/100
-## 469/469 - 1s - 3ms/step - loss: 0.0870 - val_loss: 0.0865
+## 469/469 - 1s - 3ms/step - loss: 0.0872 - val_loss: 0.0867
 ## Epoch 18/100
-## 469/469 - 1s - 3ms/step - loss: 0.0869 - val_loss: 0.0864
+## 469/469 - 1s - 3ms/step - loss: 0.0871 - val_loss: 0.0866
 ## Epoch 19/100
-## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0863
+## 469/469 - 1s - 3ms/step - loss: 0.0870 - val_loss: 0.0865
 ## Epoch 20/100
-## 469/469 - 1s - 3ms/step - loss: 0.0866 - val_loss: 0.0862
+## 469/469 - 1s - 3ms/step - loss: 0.0869 - val_loss: 0.0864
 ## Epoch 21/100
-## 469/469 - 1s - 3ms/step - loss: 0.0865 - val_loss: 0.0861
+## 469/469 - 1s - 3ms/step - loss: 0.0868 - val_loss: 0.0863
 ## Epoch 22/100
-## 469/469 - 1s - 3ms/step - loss: 0.0864 - val_loss: 0.0860
+## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0862
 ## Epoch 23/100
-## 469/469 - 1s - 3ms/step - loss: 0.0864 - val_loss: 0.0859
+## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0861
 ## Epoch 24/100
-## 469/469 - 1s - 3ms/step - loss: 0.0863 - val_loss: 0.0858
+## 469/469 - 1s - 3ms/step - loss: 0.0866 - val_loss: 0.0861
 ## Epoch 25/100
-## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0858
+## 469/469 - 2s - 4ms/step - loss: 0.0865 - val_loss: 0.0860
 ## Epoch 26/100
-## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0857
+## 469/469 - 2s - 4ms/step - loss: 0.0865 - val_loss: 0.0860
 ## Epoch 27/100
-## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0856
+## 469/469 - 2s - 4ms/step - loss: 0.0864 - val_loss: 0.0859
 ## Epoch 28/100
-## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0856
+## 469/469 - 2s - 4ms/step - loss: 0.0863 - val_loss: 0.0858
 ## Epoch 29/100
-## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0855
+## 469/469 - 1s - 3ms/step - loss: 0.0863 - val_loss: 0.0858
 ## Epoch 30/100
-## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0855
+## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0858
 ## Epoch 31/100
-## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0854
+## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0857
 ## Epoch 32/100
-## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854
+## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0857
 ## Epoch 33/100
-## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854
+## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0856
 ## Epoch 34/100
-## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0853
+## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0856
 ## Epoch 35/100
-## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853
+## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0855
 ## Epoch 36/100
-## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853
+## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0855
 ## Epoch 37/100
-## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0852
+## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0855
 ## Epoch 38/100
-## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852
+## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0855
 ## Epoch 39/100
-## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852
+## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0854
 ## Epoch 40/100
-## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852
+## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854
 ## Epoch 41/100
-## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851
+## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854
 ## Epoch 42/100
-## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851
+## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854
 ## Epoch 43/100
-## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851
+## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0853
 ## Epoch 44/100
-## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851
+## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853
 ## Epoch 45/100
-## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850
+## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853
 ## Epoch 46/100
-## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850
+## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853
 ## Epoch 47/100
-## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850
+## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852
 ## Epoch 48/100
-## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850
+## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852
 ## Epoch 49/100
-## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0850
+## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852
 ## Epoch 50/100
-## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849
+## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852
 ## Epoch 51/100
-## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849
+## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851
 ## Epoch 52/100
-## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849
+## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851
 ## Epoch 53/100
-## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849
+## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851
 ## Epoch 54/100
-## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849
+## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851
 ## Epoch 55/100
-## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849
+## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0851
 ## Epoch 56/100
-## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848
+## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850
 ## Epoch 57/100
-## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848
+## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850
 ## Epoch 58/100
-## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848
+## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850
 ## Epoch 59/100
-## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848
+## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850
 ## Epoch 60/100
-## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848
+## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0850
 ## Epoch 61/100
-## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848
+## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0850
 ## Epoch 62/100
-## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848
+## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849
 ## Epoch 63/100
-## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848
+## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849
 ## Epoch 64/100
-## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847
+## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849
 ## Epoch 65/100
-## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847
+## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849
 ## Epoch 66/100
-## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847
+## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849
 ## Epoch 67/100
-## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847
+## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849
 ## Epoch 68/100
-## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847
+## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849
 ## Epoch 69/100
-## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847
+## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849
 ## Epoch 70/100
-## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847
+## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848
 ## Epoch 71/100
-## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846
+## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848
 ## Epoch 72/100
-## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846
+## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848
 ## Epoch 73/100
-## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846
+## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848
 ## Epoch 74/100
-## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
+## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848
 ## Epoch 75/100
-## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
+## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848
 ## Epoch 76/100
-## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
+## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848
 ## Epoch 77/100
-## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
+## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848
 ## Epoch 78/100
-## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
+## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847
 ## Epoch 79/100
-## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
+## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847
 ## Epoch 80/100
-## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
+## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847
 ## Epoch 81/100
-## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
+## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847
 ## Epoch 82/100
-## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
+## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847
 ## Epoch 83/100
-## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
+## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847
 ## Epoch 84/100
-## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0846
+## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847
 ## Epoch 85/100
-## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845
+## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847
 ## Epoch 86/100
-## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845
+## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847
 ## Epoch 87/100
-## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845
+## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847
 ## Epoch 88/100
-## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845
+## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846
 ## Epoch 89/100
-## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845
+## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
 ## Epoch 90/100
-## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845
+## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
 ## Epoch 91/100
-## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845
+## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
 ## Epoch 92/100
-## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845
+## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
 ## Epoch 93/100
-## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845
+## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
 ## Epoch 94/100
-## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845
+## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
 ## Epoch 95/100
-## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845
+## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
 ## Epoch 96/100
-## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0845
+## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
 ## Epoch 97/100
-## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0845
+## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
 ## Epoch 98/100
-## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0845
+## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
 ## Epoch 99/100
-## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0845
+## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
 ## Epoch 100/100
-## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0845
+## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846
 ```
 
 Let's now predict on the noisy data and display the results of our autoencoder.
@@ -499,15 +499,15 @@ Notice how the autoencoder does an amazing job at removing the noise from the
 input images.
 
 
-```r
+``` r
 predictions <- autoencoder |> predict(noisy_test_data)
 ```
 
 ```
-## 313/313 - 0s - 689us/step
+## 313/313 - 0s - 673us/step
 ```
 
-```r
+``` r
 display(noisy_test_data, predictions)
 ```
 
diff --git a/vignettes/examples/vision/autoencoder/unnamed-chunk-5-1.png b/vignettes/examples/vision/autoencoder/unnamed-chunk-5-1.png
index ef0ecb7d5f34e5cfe46b39cb00999cd795b79dbf..7c791b98ddfa211cd9ae74c8d8f77faad568c6c5 100644
GIT binary patch
delta 53795
zcma%?Q*>X?+r`_Yu^TnEZ6}S@*lyI=jd7yJwr$(CZQJ<9PTu@3-uwSr=W^CMbI#0~
z+0SS1=ePj8wFtcG7dW7vu-d@i7H*vF{a4CaBG}(QOA(Bi@P{{Hi0?n&qBtok$}DIw
zFlyo~LOS2Lnf3K`I)jSK=W(vg2T!~6eO;4Eh#xP8uZ|Or>5eXe4;}}&c9HI{r+U&2
zM*q_T{Ey@Q=V2co^~R$K40_cD`y=xf!R%3%q=h-z+4=eTxw*N8h4}?Jgd}9Toz~|m
z^PT0|)quxx<a;rk_0(N_=UapMc*|X1p3z?194qUPrzSTxHSBdb>BnjZ_@Y|5Zf!zR
zSV%;0c&MI)x`>Ff<tWBgc~0t6^KvqlDb?3!5ev0S?e|>ZRA`lX>-mK3G-x4Kaf*?#
z*5S$u<NNo3i_j1l7?5oZGt<=T<nw#wK26jm4iH3jtkDF~C_zJ6T6C_uIJK6WUtW<{
zC8He_3I5y}?XrG?F7Vt)Uq`3cY>v=!V9HT_0~;)Dqo$~;s-WWcUP?J46=HSprg!JL
zTx0yj`7FZZH|7#?X<o67rGeYrSyOX=@$4V5XZN^9=@5`TBa3`2N~O+IE^r~V8lqwm
z3<Pl~%1_sTpe2WBEKacS*x9)Z+-{SW?RLRePI&f7{av}#-=RFAni#Z#)1c&dQnT`M
z4!+K^G~6SqRdBWHI(NFB&m<cL-cyfSEsymMNVMC%l@PqSV=g6>YYTtF=hPJYqsxRv
zR_<3&$e`O$U+dgvf0t5w&gEeE#wS&y1+d@L*yNDEY+mE-beuJ^fu@vZnXawZvyF}H
zruzpUbbrea<qpab`&qYY^IPQ<LL%|wfRkOF5WM+e&ShO%a9HN$toDtN5trszc%0<a
zKHw4P34D0J2omvg;Fgbv|DE3>6MbCz81x9NV$geE)kI#ApS3YSPgrE-wE}Lu0OeoW
z2KMHf2d8O!g%5m{X#ypcC4^*Sy+Xn*2jU+)|5%USsEHL}TVyQPz-C5T#A)$tAjSAY
z-SBZTqvF!Ozkx0sS6Rew=g{&*Tr^BS3O&S|81(8RlEPB({cpZgL&<Wx@P$ndGf<Zo
z72BAf7kQu(xD5`ED2vMo8n8ft15YaiZ=skogrL&JSOu0EJGc<43%2Suw-^1j)3deL
zo}HQNkrQp(6B{ngl#>h9D6}0TLcNDv;1UcQ^7Z5{FK%nj;cC>3pmpzwvN)?gw-qq8
zv_E(|%pUOFW6ZQj5aTYf*K$dw4EDNZ(ETYqxrS{3%Z)+*Re_4lZ#Kdiz^B+}V{dG5
zzQ09)4{t~?9`JYJ%HV^ycDK_#(A#wOXolRgwb1`AE+jcPHq20?BBpSdG=BFojkT}S
z4&G}RYhxndg=Ix217I9^JvDW2=*;+9&Yc3ck(rG|O^LZ@o^;dC2xzxuZ9U(wrLecc
zThXt1bIZze;d=O)*{30Zjwf5dc{MiBG0xb3$;``f6J{Sfu3JHImbvO0+A4m%S?i@M
zIMmu}d)T$UeXC9G*Ld5<qj6dLj^kPH>LP9A#U>p?k7Vn%_{Xy=cFT5GvG_0#*2s=O
zS!1c`IS1FNEeq&)w9WjB`^OgOXsJ)x##7<>CXl~^{Q%M(t<e^kMg2G;5RlV?T+?lL
zT6at8nr3FKwzuv5<`nt9y=!v5e)*5gD4rbwJ~BeC!}H;NHt}lWB#qSej3JY|&AE_s
zvhsrWbwR<$=^RqgK5C$<3NC1r>XOfPZ||Q)h7XElr8Le9BxIoYzyL)-!At6Bt)2*^
zD~|@jtXlMr3F#9s-RYG9X`xa?k5Kai0}=o0HeHRB`+cgq`f}?!OvJA-+LH4x5puGV
zV_uK9#yPDGglx~<coY^ap7VN}PsZvxUUw-7I10+W6r}aSI+J^O^|>$WP;#iXA?B-r
zU5otuOk8hHe~Q`*82R2MvkG^Ud)@c^lO%!L%FIH#534(X+jd+*k)_HOWQ`FUr?6CR
zVrjPxYs<&X&(@mPP;cX5e{w!{_7a)d@#~&F!HC=+YQvt>Dvzr1NzK`7*?x^iTuKsY
z2V4KjO3EE!Y=UPE<cf@v_*~FbP>-3z5Ahq|;8|H$&xpl-+3R@8-*w~ZUmEiI@8#v{
z^-g>+SU}UhM)NljQX_N~RwUf|y4H)EEe(4&Sp{QZtxFKv?aNzWWM=2DI}WwIJYJ+b
zhid+kT*97~tIy?rTDCt+CRzC@J2*y0NnWp`F*wvSFigY9do_RRd)=~-q5p*&gx~bx
zmD5??z{$f#|9Vkh3t93VcTeDXadK9*OgAtj4xmjs#BPC#)Y553{}G*}qN=jEw!y;4
z{e;;4el>f@Fbi(Q_Agm-5`V3xcB#_>?uEhYq}plj8G%$emGfJJ<!W|;=i?liT4vyM
z@l(Fa9bNhxN$lmHuLqX`UmgkrT2f0xYxT(dU6zu}r5u!AkMusbS~Fzc`4?xzuS{m5
z^FaFx5<%zI9^Uu2lB^1-fLc@~8<XtSOn%R2cyEFeOUh9h>7ZN38dl}_t=E1zK8#Io
z%YcY@xxcHjcb;N}^m-4TxS1L&Ya8=CoXkvop}gV~lVJ#d*;Nwv{)lWivMxsN=0E?p
z#~SR<A(_n~{~+q3P>Hgg3l~t7r(f`f0prvbcmrIo@t&?cGqYfFo*q@L<0#>J?{lcn
zQDJG6WjvO4!{ZFJRIN4O0c@8)IbWWf(>$N9z0C7EYnvN#D4CYV?K^Y6+(o|MeZ-@b
zuo4F8)SKU#muU<B5^6#D=EztUy-`tCY~pfOSD$`!9#s_gnlf?4@C`i4j|FtK3k(kX
zM7TZn3*0$0uqwV7ces=h2e0YX!_F5k2rmv(nH^yzFLSy+_8U2w9TD)`P29KGm|BV{
zcuvl-LR;{A+hXWPyaGHci_`KNX|ecrgWLZ14A=drC9J1GK?~qr!6>;Q5kA5H!cA(E
z;$G?5==i{u;6_6u=s7%vGTL2~fQ2QVD|FD8J0yaQ&P=>hvkK}V3Ag*H(f7TtWLJ8-
z;S@%v&{qhgc}8e+b+@TW8eC0J?4d!@BSU_JZ(As#Hx07*UfGjM_B2}0g;(3OzIbU*
zNS73l+}I;GEU|`Q>t$i{`AuHItB9&*zpW569GLB4rKqXZ%eM|t(R{w)00`;ef#b62
zoqJnn7w4F0*l%E@RaW3idf-U7nVuXsPDQyDd*h>rk#Fmk5y#Xa@7M}_?{m3}X=%P8
z0U5#Zet~)te@pAqa$-^n8aA>?C?-}oTQ+W+2{2+~bTl`){I}m>z&^0>o|m0-*Ja|L
zj)N!h-Bz@4<)1~tw9xv01J+8jQ!`AAJ@{c$7?K(gWC-bg<P;OyRSqp)rxWd2;@b*W
z#Wo=&DXrog6JL?+psM8L?i!W%3;X$+FCF*Kw)w6*#ZU~fq*e$M;XjHUK+qwU@d=yR
zIUQ=3QCA8#EUJl>O&e{y1m7{@lM<8^S66so?FPR_^4!k)5oQld071|l4(DmZ8|OW=
zoqgGQ?>JrQXd4%|VZX-8EOFH_#YB}3y<s@d5a~HCHvkYPF*Q9E*_)%FhLywO)?;g>
zcVLs8oJ^QAM_h-j8@B!6{6XJWHZvcYPN99oQb|8GylCmISVVk|=5%xSdpu@#9@mM1
z_Z3Guf{(4Kprgk<pzf>(HmV&-rbGOR<_o?D=x9n#=JTZMkPU0P+&8*|i?>wUDxKa^
zINaJf*vDL}kr5P?o&+69(tv87z6J1y1JN-F$?0n9nhL9P8!VjcY%gq}T9(;ZR6`Ox
zHV8AOY_AJM6PdqGk|5?+*m^gxz$mb_E6cPtu%7SsRS=HA(i!XPjLp+GUT6R#HtLm{
zrZ6k7rlL95+4Pmp`;A2Ecc;huqZbDLbM-{ZYn(~<hU~&1lDQ@3Zq5rCwla+>YV?;i
zj_MkRD-et&1_pZKdVU3*?;{*yMTQp<N;N|*Y^XF%iTP=&raUAM|Jy|`E{I^W(;ZHm
z0N=8FNHzk1zxJK5pnfHuKn<#mVnA?5QCwB2yA6-bY<KA&zqxpuDzqC;wy}o`87(!(
z(OhYLffT3MnAl;_Sj&c*h^$L6=@+=R>-g<tJ1HyqbwMwetlb_VC#KrY#|AHpo`|ki
z8r7oD1&Y@#Au2&jb8%yOes)!W)1RB!X0!bRbZh}^nG_IRc!rZDUuAbJTFGIZmb_B)
znJtB>BXu}ayFE6AN)1sKnGtZitsIPm^Y}y(?<&?@kDlVBu5!0{Kak{B;oV)liF<5=
zeTfvf4Hqqzd$ZBzuGVuXtb@iR>1XKf(r8XdN(+dN4+Uz2mQ<;*xc;T5yvWGu*|#_{
zuu=n5B%L+Y7A}w&6#Bd@Y8A=gUias7k3+u!IUPZB<*3E^n}QcMG_psHA}<Jx5qlAQ
zvceo=1M$iiL>0W=-2|tK4Ilk!$>U~YM=Q^&(`StR^N$10?J4lXQ+rh5c04(p*Y9aO
zwEuLTaHhq~#W)EGNh+J9MLi?$JKn@)HF*H-PQ1s*r_ivrn&<m>JK8W&yR*!*-m$r*
zPgx``t)Z$-*>oQ90al0~U*>)40!hRHZPG|ZS$a9o!OG)sRWi7~MNvJB<(gklw=P`t
z79en^$AkE;HujJakr1OM_oh=W<M=5}pSXS$dzmND(Z^~?i-?VmA(*k9XmUS8g#*7L
zk~3=>oSXMCzvJI9vsBhWe!D_d$9;K9?dk-0_}4b)7t~V|aj|TB@AG^HWU-j|Ra?RZ
zn3iLH782+FG1|vKi~p9{QgG4TD^5qtpwoPT2V!KVXQQdCYbnmP`TI=AZ}V|d)B6@#
zJ8Ss)Kj`jMY<BQnmNR+t$jPX?hJjzw9or@c^M+r~-3$~pF?3PP`q#N32gK#lYU(<S
zfV-ANJzI5Up_9|snUjHtj%X_F_iLFglR`157EPRzQDy^t7rkoMY1KFk)Gvh@dnSB$
zOT5TpjM>HqU|eB2B?%cm!&kFazxM40KLj6d^6JifYfc94GqSI+f?nJ`Xn>Q*duxNH
z#`fxhvMuAL%+mPch8$=COgM0Ks%aa6mEq2+eHyh&(KKpu{OM1PgNV!JdEr~0dvllC
z=y@ps8!0O>JVqC=j6?6!eVu6YRe)=4bwOSwB@x$aa(v*Io`#Trq+jrTVLJI!FE27&
zB%`GO9xI!Gp_P08-NCtgHxNl;7^B-hK~r94(epe}^$>C@`0j3E{-><77KMF318TFb
zma~ZbjV3HopA=3C8<IZSN@^v5q6|0PChK`E)}&gJ3Tyd^481<QJiT4mQ0WqszG(CH
z!y4C-^oBVj(1kmVSw3<5(7-s4j6(OpA;V214T1>I9xvlbM-%Fo9H95twqtuA=aG?@
z`$E+GkF36#il-8egpS~S0QUv53)rm#d$@%Jx&KD9(}q!{FEktp9y%7&v>YER%#1VV
z$=DDvVY5SJc5^tY?W^F@>{8y`*jZc7%t#94lJkIFVRh{L=W2<)telKov&8m#>Xb#A
z(uBOvO-zRW`?<*F4>VAn|1;TmYtWN7-Mi_VD?5A5sVP|*NQk)L-+O6Aa#><}0S77h
z;oaqPRql`|P!|#pOG}G(L?@Y!+X&nw-Hx!G5`{*^OIx-c5INu+@*ri3^ph2KQc;#y
zSeOdMg8B94a*~tr=e<FKY{Wn17_44lU%*<8fa_1unA8vhfModDu@sOxwBu9f#1Mkh
zK8#%|Ha6AYJ;Qr_G&>7r{V_WSwyD3u&+7YthxEiTm3d}UPCq0O1}{h|?W(^JPKpz>
zQ1NcnKY7Hn0XlB=t_yi*vN+_4{BpAVZ@+##{S92Wmn7GalX#neIoak*B#Mo0zxZRb
zI^J~5W?Qf+u)Z>1>h;2<LX@Dt=EsIJB)>1DuDiPx99rQlZ0K_dvCl>r5@=ka5Z8+8
z>l$Va+TP^%40;!lGh9yHD}v&mSQxyGBW*HUtoT~-u@~~A6D3-asmk^$l+qL{Q#AHB
zgfZKAI=G?D2W}to^`gxe0k$bsrMNp{*v){C;wQ`>zy-}hjQmWs?RBUG<vGuvb9)Y2
zrq-gIisn+_FScIb2e)D1)UMA5_+#ck%L(kP0l~|9lcjzZQERd*Nf@D5UF63fmlT8w
zc9~#)r|5EK*pZiClR8(h5mI!trC*s|L3?WH9Js><PWO10ZL)GQGP2onU)jh7vJyYw
zEC&zrfqp)iHoIxdO+<x-%qsNo%Ec}(cq%LibOgn^r3M|zx2<5f3I0ffnNWT30vi{D
zKNnNG+~-x>yj)ED?A&0Iw^7pXh;R>tpxt#x(PKOPcYLQp+pZ5&wGX3jpVOkBgu=N9
zsXDf_?RicykvE!2y-pcuxbT@4_`AWsvl4*{80Pj0gTHzDCucV@w*OlKlFuyYC%v%V
zKozy~C|3V6tI@*=0B{sMgNSSBt`G#v_LQ^^c-LC0X5@zq!7ZMPs{pkZT3ZhT+(Tz)
z6LBpQjv%)I@&c4>?DUtbi)VvZ%CBj;k61o~2*Ot0;F-P#Gpq5=7RUIE-1e_?Ts&)^
z`~gqm_jBOjX2%Dt%bY#L1k(M3lXG1?>%3nPk|_VynconUlf`caZW^YY_ema+8V|Av
zN}11Myqf|(v=)2iB|Jh?UAM_?_EO<?lq3I+4v!C<gwQ$FeQL8k9hU&><nsK))W*uX
ztg^h~+cMof@K48;U>j%ii$^5<b&EZ)pe~J%`<#j7(!+?xhhu<rg2&G1H8b-Z)62$j
z={;41sqs&R?%a0E)%*a7ZZY{dczWBX<04|A;5lWa#z4DdHj>|cspOe?T1PJ~qTL~I
z&iGrh7xUIt3tyKbtQ)(ozN7>$8>c<){i+VHhj5SKwun5=FYQ5}%S;%wo<>w4S9r?C
z+f_x>%uZidR$s#ryaiQZ3u?C`SQmM%-37y!Vk7F!ziT%zTOEQFjrE>@%X9g?WHel>
z(G%P^Q-mUaKTes$c{k?1()$C1xWx~>W@A=VOiMTBnf^{*S=3gLU5`rIY%Ae(zngef
zvEmz76tyZq@$_x}ShezBxtckk$^K4PGxFb2_^H8<&bH4nZ;#Bhr7+S$d{{bnhwyq<
z`MJ24;NIvN=td@8(~}dfi4jQo9Lv}JOf?ZVoH8k1Qo2|$Au_p?IC*bT!Q_4f?4YR4
zv3i@e+WegAtqLmop`2P?28HNT=6OQ*8g%F#c=6y^&o-}`fM#TpM-KFNxWvb+DQYav
zEYAwCa(~6b+D)HtW*`Q$S;=IYUar5RAlX3v>pnzjcEoXI9j&x(;xTiu;yBs*6)8ic
zzxNx4e;r=Czi&UT^Iwmm#@ynH8d3%x8oqr|AxmYWGwG!an@_FO75)7SsHvD~5l)<_
z^VZ}WJ_me1Eoq^??FcYI0$RRl>f2Mps&s&p>e`)OnCIZ-c$Z*Yay>mYw<@Dz7#aR0
z85ZpC81Xmh#e1;=_|Xn$h!?*Mqu*gIuxrX$uv!Z%QWqCxKth_^GI?9p-b&Cxh@@k4
zeujtpPeN!>-~-WH{C2oFx?|#C<+|tWb`JB3X|hL{U=1nMD;m&{U!LRh_0@8b`YEQA
z{6Ni4yVZg)WZJjnCA0<8E3Xy*kh;1K8$3QiLrGsMvcm@Q#Oe1{3FDLcZ-Um~O_&{$
z?kF#}c8q5$WF2lw%FEu70Ucot@g{<N{tg1!_+S-3rA2%dN*gX_n7Ew#SvaYQ$Vdu{
zavHEyC<N2^ftz=}0gj)28ezQLbL*OFx}XCU154$;-Kd|PJQ?rV)5A7WC)@$hi@mo$
zEgZevJUFc-64GBOzTpeNljRzlJKP8g>9T;~c3A2q9=?YgG4pWEEKYx$-~)X;#Ug*K
za##zqFO;4c+)f~c%XJ3AQesUDa-3(w4mD(k+5KBQ2bLv7C;KKyC`<KSo<lfjRpM1S
z8Xgd3kRka2iXR3Dy}T8jx!E-!8S|c6khtJGf5rv9n)L=;xNE%Ha)>)B5hzGC{RfuE
z>;mpY@Vo8S6=|rWDZ77KOb8^xf7;n{k-gpt!6iX8V0uX2G$L2Uvm3o=BNxru2)ggu
zDtsO=-m(Ick?-~dy(eWBX%f?|p94ht^8X~N&Nn*U_2vO}^|U11j|p#*xbqj{z?I%`
z<5paNf}N_irdMFYu!gvtdWkPtXhwIn8JHY)>ObH2&QyGHkDP#!oU^*T!)>ICCl}Wy
zF0Pf8GzZ%UxNsSoBEc&JOPx2w4i{GGFQI^%Deb#BIFi9*J)^T93IZGtW~DbW8rxES
zL2)jyHZcDnsVIveTMrfSv3=P**>vn(T;07fk??$SowHV^#;}vq75&hPAm7+-yU;VT
z{hYoP^jOyxQxd@bIPP8ALA&c(RoF`v>seCMgDdb_qKBAzE}@QtYO^hjE0w>l`2rx~
zH+Gz0p*<0|;zF{$^`h$Cz}pJ0w+3f#+z*^6+743iMY{a3JuDbi2^?iVoXp_0o15u=
z(^dEQ!$eP;^aSGZ*mY2_HF<OR=1ilO%bDO4oUZjH&9ND8<V3UO1wm$vc4BEA<o*e1
zW`Zia^pSXWRP+<q_8v|3pbRkE!hp~aXavNj)BSC?`S+ct*#q%#B)r->hnxS{pHN<z
zkKH$qgBxVI!C3)t5T&c|E>RsQ0u@bEm<fC38It@D1)YBJiJ79RosNUM+NK(>U!^z+
zWNS)EXpJ5jFC5%e=kE57%kk^7(!rmCfdHtxhzy4|tRK<01>bS#le|7&0JJhqasl@*
zH!kPrJpw;mlDf)B$i*95qaN8UUlD3b=(U}i4%~PsiHT~f3Os*^#}6Ph311Rv{OICm
zozjI`wUQ(7^&Y_c?KS*-HoRL9h;)2vUVNIGQQzwIp#OK|6U05=cT^`c7+UQoGa<EA
z{L~TWLXqX(Ay_I@4}ggj0*3Kb*Laa_H->Kej&Cr}E1L!1k46_o=gUDg?;$4Mh;Ot8
zQjty^s(bnU;sI-SZQDjR4i|gXy#ayur^jdW%4+ihPVXfg6~$j+%xXJxVN&vj2#ahR
z#dVJcRWG6-cOjWmWX^a~8;8!8${mh^`kzK4WUcXa`5!I>g2JRd0VT!RmB8Tpwp5Z>
zqMX~kCD2bjHP7KwOgZ6;re)nap(vcQ#r4u=E#t(Twy=>75(yCXCmL;P$v4?MPpo!A
zo)-XhxGg9Hjct^eVDC^*ku@AYMm`nTHH_rYx#A+<-@dhL-*pu=B%94HC={m8Qq8!Y
zj*qd$2RF1CpA;I{ukI?yC*~kK*<L?&`tp|r6}-Lu#GG1+2OWLSxUu+xqt@xl6)iD7
zQbB(6>lTg_^NxP(!5)&$%x~;4QsuBS58@bmYImfRoGWkl2VtRUMr6U(gUB-l<*a``
z<3DXoEJU3YMZ5!hHBfW?p1CzoO1{`FTg7NK20_C}l^!Jn8Bh$y%8G8fu7-<@-#Uol
zC~OK5y$dNc?sKCC4IbG@C8YFzNh!!2PEF5Z;;vcmK=Kof=9NJP+*ED9J6W@Web4VU
z=3r-DTGUaC!p1vu9Eat#a9hEq=Gn_9BWtimK}E;-9=Gf{^?Hr)0j)k*?AN${8OwJk
z`bkDQtTD#{q>BymA(7`@??EYy)1f1ESJ6B)Wk;k`Exjv{=OZmU(-V50a=gkshaVKI
zJv2=7DZ$BK!ZHiWU%~KAUdMf3Z^NZSLSbP*SN0BeRn^QaFy0P8&%m9v`j~Xm45BQF
zPH&UAbJ_$RPgb=~SZapR(=~w~Z-h>*H3<T0B4HD7%6c14N6)zdf`JWp$pA6)wPwZ1
zS0wjbv`p%OM2_VyO$r9YA>CT1?)LXx4yp=P=pi(U>3YD{*0j@b&(18%XnU^R!zQ@h
z{`D2K(TkAucFJWrTyL=}y2KuA-WvKqZJ;VJ+QUjx7ajk|Y}vD?*jpY=^NqPaX(?!s
zr40m9Gm^>rY8~A034W(n)YfM|JN~K~>^I~N5dv$|#x465_IFM>E)H$a2uD&fnSInC
zBqJII3u4|@9v|t|wbl<CR;vl?3uqL;1<|M?XXC*#^UOb_W;BNhrNo8df@nD!9D79|
z=XE>qQGYf8zI@5u8x;)&J$n@VY7;+-B*B0&S59?~X$6I^hdwW8XlDjIe<O5l??Qvz
z<G~O23W3a{l40P9b!he~?U>(Kl3y%_0O1wV9E0D^l}#sCsbTRggS3s_;`pdr5@Axs
zlyK#PU;_F2vTzVm;UwdEui?!O&((_>mN^DN#6q=PTdXRamKc`1V1fQYD6?=+nH6}D
z@x&6ZndhHS9Yivtw%*n!e6#B!aT<rDCbvd8PRDjDNVw;?;ERxf(%=#M{xPfZ@#gID
z{tgo%)>R;~o|^Qt(ILQGOC$McI{_tUp5_XhskEq>Oa(EcuJvxq>^pSiDr5mL_>A`T
zvoHv+*U#K}0gqe<H7(F^k)cKwg#p(a!haIf$Fr*%VhPz^p@>n$i2Gc~V^M|qtPoVl
zTs4YOoGa7$@~FFmout05b><+(Fj^(7Iy|1muXabv&(%6#!*SlZGB8bFcv35Z{W97j
zAQ1ZSOxFvr;Zd^O>8T4DE3e#Wd*CEzYXtkghcdi#Ljg9q18&~FgG)frs{#~+-slc~
zYu4la^fkU!aKVN#!pWfuWyTnePI&%dw|+nRuyEMiOSIc_k2*v@3x!g9dl1E!E7b&l
zAQ5D8)jv*8iK6p5PXLZ?=F+YU@O|ePvtPbhL1O!uO)Gw?nP(M)TU0$08YPMEP)#Hg
zM`{~Umfxck1dQ}gw+S#36#(r`mMe&Foc+gkRRqAyPbJx;_)q8jGMM+mmm;d4>l+zb
zmx}XiT6w8+*KQITaxiP7^8-?(+@wL!?wQ(T>z6NtUnE5Yl|f=*CBaPUS!d@LmNq3+
zG-I-I5s@vI`WALSRYH)qWCNoAO@FvU^FgE*`LZjQhzE9Lo9wVyN&>>@YKL%tUgq;4
ze%d~GM(YL0eA)STD@ukwe-}nqCioi_7e6&dM{$*(g6;K*WK8|*((W#5QaIw>!{3HR
z)`j?ellHK14>VgGsVi#fD<<MKwenYN7XvYVDizOKIC^J%kwT0)r*NP<<VTd5xudf4
zAckzorkCFkz_Dc)q5?>`#`(#ahNgwwXB(O@@T_0==82bTL5Dm)vLG>vleTn!Ryz|7
z2?Nh5B?ju1Zugu^m(TmL0V%7ZT+owi*ZVF<!|MKftO&9zZe37$MHLojvz)|Q0lpv5
zG4dTe%Q5ol<oWtL{#Ue|R}>S=hP54LTx4u?>%HHK^}$j@egkjo=X3~c==uM?+y_BD
z@ao{vMxI<Ipl@frK`Nvq`KSp=J_PN@YdvI=AF4P<5D7{T(IIshJ6~s&kRpwjsQkFz
z9CjXin(vE)w`}Vnbl!C6x}EDU#qS`r0HQOr6xZj1<K_!DJs-oAtiBdS!Xw;2-2HWI
zAktM(@RD_#xdA{??i(BxnwgAKD@5IsA`yi{xmhs!ozap%H4*l%C2r^{TMTbW_STI1
z1YnwsBRQREjW#*rMeSRwNQ7ZFIr`UI0zSX1**lb;8_S8{RI>%@(Cz#iyNVve#8#b&
znZE2FbB{gnPOXWCL)jlEMMJ??j0zrpWF0;c?x8se*+BpPsh586a{RfW-Uj0CH@eH_
zG>+-G?Lw_LzT$m<VI(eNEhA^MlhibS;!(oWPuTWv!ubv<Jgi&k<BG++X@{UeRwBQ&
z-$j&4a#~-|nJ>+9QtQX%YB?d%kgRx(J|FKrO`_o{Y*sY$wdC0h6Tji*HbF*`&NTb0
zU0G{3DL`{<20AZ2-1>q}v-~h_Wanml8f>=AA$`8)07}X)uRUxRSCGVl$X5yC=Vqs1
zgCBJ`vWHsmDJyPhqog1qBYL*;ZY*7IMipymb(RS2aTF^3cs;Ku+((O66R;f{IiAQ}
zD!Xo&W?;!}FJ~j;GJ`>~S;k*fSQ@K>B>rgU2V9%pP|W5Om~KLb1vUI`?yVdBdJL5W
zVbxS3n`$}9q@a1bJmC%H8`wA&=9S|yyum++>8XkP9tviSM|Z_uSe-^Jw@pm42#GD>
z`d94@+%D)~Qvzj|njU#W#%Zmvo*5Sqpr~^Qo?6uXOl0H2Utbm&L9qOJVQo7h?(ky_
zK-a^$88K~nH%e7^^i?sI(BV^WYq1^cC{h^~HnhdHULm<ypa96R)BbD5<N1sMnD(za
zBauz$*@f9ExZ$EB>`bR7blh}>6>eXGlrb6L#QpsCU57Ze<CJyy8hy#Q(I-JzT});1
z(<3#JL!Rn6QB<;k`d*$)qmYwwJM>!*kTcP80MC=h*#A>N-p`8w2Ll2Ve{agJzy|3=
zrT)u^`N1U!0wY3t4+u(2*@&)<$PiR8>z4t)9CTx>zwW}pj66MG>5;wi^ckGtj0xYP
z6{=}PO4j>(<B5#*LETu^ff~H<3x%7BG4zd*g^Br#txZv7WhJ<(mR3S$ut5tCVC|GJ
zh-i8dg|??}l2f?W<aNifMwnxO9<@^Jc+{7V^Hoj{odYSC$&8gG1KFc@MBWjO<yBeL
zlVfW;>D$n*#1B_8Ctr)DiFM4hr?H+B_BqFa61GQnw7rN3!V6PF9Y<G5ef*$ZeG%u&
ziSbfQ*&dS4_{`Ms;EWK<-W3}>z|EXp@L1>5J7>^&h?Jlzy8O?CEE$uQ3xaPpHnIEZ
zN<*>#P_F+w_JYv!aH$6FAo+oKN8ha2(Dr09_f>C0E)uD`#?sMIMMcHf*jQ9lP}TmI
zOVuUap>$!`L-A0E?GE#j$|kn6j`;9cql$jx1`3VICMd3_KwteH>*O3Tt*DfOd+6{G
z5%xvUjoGqz((TH9O*bMTMa33k)^}N>i86YK*rmv}HO4L@=VN2{SI5}0Vi<9ceWJc{
zeH32ubmTN~lCUVuBgA1N!=i#eQ1bxi_q{w!_xMbfEZk2-2%#^IVNPc^;>i&x#&7v{
z0Z@B~{@Uz{J@5(Qie|v(^yeW>K=HGM)iQm&y3g)2pd{auUEET(C8N1gs#d9}q@<*{
zlNqq_(0puZV`b-(nbO8d0Ec_0(|NzTTX9O6?9w<?dV=53c7U3oJ;li?J~S5AX6US}
z6oatm9g4(DUnJZH{+U8@qDQvJPH%8{KnlFgYH_kXf(gVUvjZ3dagGCL@b*QUD_L~O
zC<a6Y5P%;pi?in?<#)-sQ1x*Q!L~NU-)=S^Q$>_%U%!W7zeH-bKJvRpt1WWyEG&Lr
z#Hb>V+#1;D2|ES8jeR*LHBG-WOy9)JE&crA$Jo-W1za)`l(grUrUlq<lCwCtnfOEU
z8O!g`qXaSENC8^fv#Z^ysVVV?OE=w4x8;+#<gc3OL&MPNAC10v-3tnqJ1DU#)9kzs
z`xCpEKR~D?gd~;~qi!dLfMq1wi(8H(Bdw<LR$_M{-i2^()_PyS-x1~RRX8kWyQHHN
z&~0JmAA<t<d`Jb0>l#JWOo32ncQpIW&4&q@Mho>1M?gRuesod{%I-t1169Fra_uVA
z`k_bfVDVu*;-PUde&3i>6sk!h)zo@7|B1ezrpFdua0nv8%@6m+n0`<64qm41<(K@H
zA3S$d7b}~0DqjAnZbn9<^?KX!_V%{@e$8DnYC(W0W*ssI939nF0Kl+>0{>-_jygBb
z@hC3W88B7!>P0>|&d%DFiL1;_PGB8`gnzVoKZHAYu}O-6qj%kG)><8U?U~p5s$XqZ
zbq<gCrsa`*f%mR&V^u;uAqg(dzce|xI=kCaI*T4Ct!hFSk^JLqak*VdOxxQ5l?~?p
zw3BNKapQ4(=0A_R9R)dAacW9{hiPhBUEz@%AfxZw-BF;^@X|y>qUQ#=AZTLXqfk-m
z`j>|WOHI)CMhbY5LsxHuEnk0kVS?DaYjV2E2_OFlx3Hk9YC;=<g>z(KQ9?ZuWOMKb
z0);N2+Y7b1elM(uux+zv<5b^3w|8r8N>sLPGk(|oD+svCWQgm2Lhh^FK}JqgS;+-}
z$8SFH>YCvKw~yR-Msf{o^D6QC`4nRRAYJsF>smdgpWF72V-E&@!6TimmS&fbM$VQB
zEzSdvSN>rG?+0%tl_7*6Kb`TYynEs~xV)Ncu)n3YLku~mQ%{aKQIIr9kKZ18nDaw1
z%VZDZP$VuXBSbaaEl6zJC<S;}j;RuW20VUCBO_DO-?m4{dzwXA2errqP%M08RMUF?
z`xe7kod5FB=I>;eHr%43v;;UYnf3=B_4u;e!*6i@Z9s68(RFu?(Nks@Ma)}zTm<sT
zO;GzcyI*cC-O`rLj^{{jZg)f+w2Z`f6cyud9a~zM{V}&+0!@8=^W@gVHXq>u8b+n0
zgM5|$pwdAM>*bT43XVX}a4AVkjLXqj<GKiN&CO~;4IggvM_|05am)gVC1m#a6B5ui
zN()>bGXul3AJZ&J?Hqsah29xx6^WIfTUNDBzMeH}m@%OCT)Ok<`G={aqZ=EO<?FOM
zQ4ftuSfQgXk4=tGjQtcAY<aH)u0i!ix45Zf4W-8^I@i#_lRjO&JzbxLvzC{37n=hf
zmh7W^nTF98N^w;5zgU~noPn>kHxU<JMBDNa)pZ!9V{)H?v@o2%_&EQhg@1FVzMgfj
zpAs7LHHmvRmM{+##w4I&qHfT3jl$G$n%jSGyWITi1x{B;Nv*4M;|1ac+Q02xDKW;D
zQxA`N95{bop6NYdTb;VE-yvm3{bS|q6YsZYZSBO+a2<=RFMM<Hm8yhZurOV-v8oo$
zqNwp~PeagmMuI{@pyA+PtT52FK6%e?Lw$q1!fevO<}p1-jW3`xu$^ma_sEqdsQ&#q
z1TP}e!GBji6Byhz+UhRA?K!f3wk7(s*slp;*|)AMxQRvr5sHG2i9k{bu<1P(SshoS
zqSpKdS^*lecMXu$VJJp`yR+0M`5y;+ZQ~76uM3VwOdQnj0C1WS{%RZOsa&K$ZOVlk
zq?+0rfa}m9Q3qQ-)7R4!84Dpf4-8pgXVf_$1xZYNQ_vwnGwcK6OZYtl)<W=u+!d_<
zNO`bI=vi~C<4>!^?3frC{mv@P&CSowF6dceBLzi>T|MgU<yf%Kbf+}UWRNT@UJm|Q
zMA)KAY1iow7Nn<eG;Y?9cdz!*YxhaRu;xfmUidV$PH+;E5KNM^TA#(qVOYle9}C*?
ztq@?4uAFMvS1SQ>lD?6#x?mI+xfiI->Os$bzzILW(%BpOIVSaTx2dcqfe6eYpOI-V
zAU-?f$E<U)W`(AlSZ`l91#Na2@S5YkRjbw?Z;FQ<blidMg>!sqSzJmxF)R@g4iAx>
zx~pMmCCVgwGG7Yvf$n}!6=?ZGLbo!IuWz}J@#Chz=?lPMu(qfZgLdX-o{7r=V`TFh
znD;vyo_&`vRatlRF)h}{#a_@*i=sg4LFG9@O)Vftdw4SQ+Y={IFfl%NPQ8VK@Yi?n
zvabA%kN!?DCh*CH^ey{(ht3DmZ5h|osh7rGVb7w$JLcPhaeX$s15vPyss-$DaVrrS
z^{4?nPHSKn*4_9swk9tDV6$Z?41>wO`?T@#;K}krKDoB?@TBq8$Z1G<C*yLxJ)j~b
zO$1x6)*p6Bx>t%zx+19Ke+2$T)WHqC89+_jlz>zE1QYG#32N;GJ=O}CCz8M0KZ6J;
z$S!qV?>Ix-Iry8MyI$iXXI<7B`s^iapgBWlZUFJ-<Mc8I5|3&jp?%99DsRkH<(!0|
zU=XrPfYC*4*LQ6KPZ2{WjRC)L0lQQtHb#N5>L%36ug&!)^s~6@&qGJK<^6dd(Sq{E
zNB1c8>Z#}5vP)JvOYUa=35elwKD>&*S}0Fd!So;!8WD;X<Y@A_9JO>6Ge~;<ogHv_
z08~|9{i>l0GqMCj;aCw<GCFs#ZfVIn3*d%Pd|mEQ3-rM}Y;3?P8F0b;z->1cD|PvS
zGw*^NP6AY}i-s4Rrd+_4S#_wE&kCT~lJsy>&^bIBG~@iM3~|jhmj%|nPP`qVJg|tI
zy_fEVin%k5jmu+rdT>MdY%B{AVSeod+|SaE=&Wi@NzvL4ueTpNjlb_SD-Fto|5e^^
zysn)={y<&xc_F=BGgxK&ZRGw!w2<J*ff%!ljm#(d#FNQaL^@JODT94hDXf9Si6Svr
zt)Zv@o@d7{t1B4Iuo1_0w~eU>wrR&9Z)|6MF#1?j73JQzo_@}d#%Cv@XBapK{6Tb6
zTqBcf+WDC544)sMTJCo`!moF_az{&lEhwutIorLpm9wxi(-pHXN?eE0fe?HasJq1&
zam4L}EtRdU>cvsJ@jj-)q1-~fyOxpU8`+7<8XW7hWf;>5-OTF?fO=}utLF+N-@J8X
zjW_Ct;QB$i@S%csL(m>bi{?cIpi@R21e<JSd3l&wyX}z3X>Gm!8R&1iupMieuc8y|
z?dl_^Y<8F=s&zo*fW~Rx{zAF)qaEDUonma2sPsUxDl>6aWfjh3Tn6F6qSQJOZO0&o
zcwpoH1H&12^srH7PWv;1O?PG;AVQvZs8)X~<B@`WVqQW!<c5X%w|77pkWR=%96i8T
zaD2Ui%)d+C<|Qmk62@9+HMMTeYbeho6y)ZcS={*IY82dLAI_BKJ0riIPe?&DHaH<6
z+H!Bx5DoH>g=LRE*9@HGbb$<@FiK#({q@sWI`0sB`_yAX-4KW1JSWZVY%-UO+P2c0
zR5pZ_lJ|c9bmt^Nx2QK5kdbV|I~QNHIS4%(e6ZPUGH^KXFP;|EDPNa@{VccAez6*f
z0C4xsfSYEA)k&ZrEtb!n4`e}Bf|U-8Ch@tESswkk@C_14;m2vE;pvzl;vfVRn95(o
z%(kO5M*(7XE|$u&ih5@w_(rkax4z#dhQ`~cbB`20y7Y;%1k-^)K3DT64i;g*v1goB
z>ehYPG=$&cU{<sXp*be#{JZ-e+oa|iPxlt9SMa<Y#;N?T$+APwl?HR(fHShya*?Q?
zd1G7)?f!?lwtf2x^toc!Wpr1J`Xjn3db?{pGK$CI`t(YVh@O^`jHIBdl(vX>YJ7Bn
zWpB#Iv)$l3=X)%Wiy61+ew*VikDHcfc7mVr&dcUqXPFuBvm-m?8|FMQt674+xs_=;
zSD|;(oD*4kZy|kicm0BC=9jrVk(aUTscv-}X~*f)L?*dc8{KMtFVjgxm@iz|q56Na
z@<~a^HEXYJoWhwtu~5!-ysCiZ@n6hTavu`qqzSx&y9Q^V6&k0XxQ_e^!8EGN+E>bv
zxfLm`%I*e1+Qn}H<`z-rXJz!+7F68`9>1NB?nI(&^3nMQJqa0oOLAslY<zG=itpdI
zmCg548@x;I`(dl^s%s|Owmk5##`BGL;T><9uSZKA=~|qWn7z)g#PZZ)h+a%)5Gr)J
zv5ER*M77_5a{EA3a2I!unR)q;22VFmb^LjycoYNie_Wr`wCv{oykYlapYIHqg&DF)
z)#<onq~dGLD?pbWHCVew<`ihqQ_=J`+a+d{u^Y5zV}jtL$(gy}F+V@UNr+PtMy4WM
za*<PVl{NpR-Zf66YKxM>$$-g2g9?sED-Cw|;q?#jnR@?&p~CI5uq<G1eM&65h7uu(
zV_}7<Pp0<`A2LE6iX#{VElu?q!SlzZu|1&akkS{}#*P*~pNFXY41;X`<+R9YmG(_U
zS+sgR<idFGFhtvE{s^0GFXH%i*co_&{=O6~&thk_L0y=q|M&jnd@#7V-ZMxL?hpbR
zRVWuoum6l~$bngNWw6XEMPnF|Q}o|0slBqOL+50b?U;)#To47zv%`!J0#9z5sybp(
zuDitY7xD}dF`w~QoIiFefe{%sj;$}0?B!L6GgQ+qXkz5J_-CgBNKLEhTs3kkVo82~
z-gxu-61PX8&c3%I>DeMlCMofX3CG$xO`HK)15VST%PdW}qxw{4NzMOA8~*DXXlUC(
zf8}k@bCvamMQG|>foSu{#ouVxkA%GMZ^Dv*_S^viVD$sxD$jITcrGc5(W_1o(3!TN
zRrvM&rC?=8>3KnfRX&!e);{Ie)S6H-ayqtup`MJSWL;Twt4~QfnDw-x;VQU4w17qW
z3}#5VH4OegQvNTzLRM*v<PdC`X${G5=M=>bA%mNO7iS!_@%o1-3Ci;Sq}$6Y+#vWo
zleOmgeG%Y2ygP&;)wPtY6#fYHdD@`Om{UqYS4|_MN_c<3qdg8sE6UGWeP3?=h>P8<
z9a|YtrG(>rduM4o|K9srp;yB=w*nAN^j7^?`BIwo%J^_VN9el9(NJY?dxO~bXKodW
z34!DbuMszgD*9no`+)WRVFb=ek#!>SUKT~~?z<d-w(wF|ut{2T+GS=tYcfuSAGwn!
zKOsaF*FW8*5iVMn11bSG*;wgDH(&0$V#~`9b7N-N^;@TNL>J{^$R$|6X#xzatn*s6
z6r_SIQ1_oJe_Vg;Rqm33agog<X{|#?j9QuZ-$C#Ljz`gyeFjB7WkjOCa3I)pK9iRX
z^i>ZTAnMk<g8V?jy2qz;TQx&hUALWDs1AHJhBVgc#U(PDj^x<j6r+x2wr*D{M?=sU
z+1?}-zHl>3o?E>{s%W<W89+Nuk`8q)3@UNBxd(95Ut5|QtbTtNPl3`{vvp<}+EEnL
zbzPmmKb!V*kJ}?7jJDiuVSDV30uK1!E6~>}9M%?2?d%nJWUn6I3ZoNZSXuv=<sq=W
zn{v>Cfue5wTS&yWk<Z!#Cu3$d&=PvDDANiq#MA1iIj_7pDs^}b0XKPR4X08@f<bcZ
zn!B6pS3NzRO81_NiP*UsuKwNWg_+x;7mo9&n^a6(mq$5Ck>NfP^3pcU&bvD%?GFvj
z&pfp+S|17s)ss%;DQf4!p?CV^TiGw^5LjELSq0?EWC}V+vgormAuSce^yY&H)^@OA
z{hTQKqGCp}waI>Lz+FYXc##3DJ|!qcMczCyp&Pfk?3<MsxFDpy;x{AJCn^P^4cWwr
z6s#^SFO~)<tKOBT1@tL=MfQwwEoH=X$Lq+6l4K>`fWmi;te1UMNOxyRxEeYTvGFT(
zXjNFq#da(vZsn#}OpW{>q=5uVS8*JYs^9#yUqsjoWwsNP0DXRXRWWSX7{}6sVQ+1j
zUy_rMwu!UM?Y1PG?5!9yQ9_4MIM=a4HhNs1rj<2iKaf!O4ZcJk5q!c+tF4;NLxx&u
z9B7_)lPjE`SCi%W$wVGo{>`5adBsDL3za%|9B?;4jmlQMD9S(v3Ht@o_nXv%0!HyE
zgZWd;NV)+?z^aTus%MBIJI~M_@}dzDq6(9kDANZ;=ovAKz?=?9M?P@w#Yl6tLT{$?
zpiMn0qLSb!(|SwX3;F0-h{_>VR5k1T*Z#1J2{CTV4TH*uUo;ww)a?4i`M8+y*{YRC
zsQJx{j)|nRZsmQKy~5d-_&YPK#mbbXg(Ws}r8@g({wwPBablh&fV&kve_GWL?87QU
z+>Wgb2PVjBHQVIEmWQ{#%t7|bC9j#2Z71XOpQr|LINScvr|0nSBi*iP|AUg6sy3^p
z8R}xlyPdNQ_Vi`>E9LSYu=^eMgp}QZF>2cw@BL-xtkH@N3s3VR%gkeA`O-{N1F9_2
z;8gM%F#mL)T0Z&CS<G_Jq?p{NW6KGhN81CtQlLuT!FiQDEI?l;#;*Qy(A*iC?{nhx
zNi+YXKxqF#5SG@J*kR{B{0@3`Jnvc~z%!$lf_ZRk@a>;2_ZSQP$_o+Lbo~|1eG+Oi
zh8eab#eEQZN3?UdUc$uPVxtejU7NoGs*)Q(9z~^7rZZmVh83gNFUC80rm(aL=rdlt
z9an*IFJ;bn(&cfGm$WWL=6HnCYlSX1J$mhqMbt&g)ipx*adA4iNo+<T%KOQ~w4wM&
zJU4saRN<QkO~3=b4@O%%uqY*Irt#Eg6nDRMd@KBUntIlA!65OT%lSGxra~_k{96O?
zBQD_;ZX`+x_z%x8lR?ylbrtP^fvg`!;V({DhSpD?bQ0U_Y>7O8zvBr!a#s&|R_G%5
zz{e0%`>jV0gB3oj5F&n{byQDu&5m(#HNpysLSv}@>Tv*#?UEe<m8Q|V2s0T1T}G#O
zmUF9{?8EC1EXibIAz%BUky(?Lls`a#05#@b&d{ML`D?rficJiD%(CJ|JcmfbcgN1e
zRV=PE^$vy3n9J^~lUohM8h%72>MY!l#b>)8%J+v883h%k#i>20o{H5-iNdnV(so)t
zYS6Q2ttU^T70RAL4!I7`We!&di%3JTnzq7CR`PuPDv|-SRB9loH;BcCM+(s8zi;!f
zykx%`=PkQMgKJ^Ockr4wcT1A=+1fG2_#TayF4&d(wc1%}={fFXvAP>SaqKU;=p*O!
z@4mLhX9KCJJfpZ@5{_L%xK?={2*DiR`gad{_NrDR3K6M$>s$pl6OC;j@zQvR$)nSs
zDJmcR3p=RFifm5tlF5szCd_~v<3WL2Cr+y@MBBrFA7#k`7fS-K*wpMq%j+*+FLVXv
zV)g5h1+rQA<~5UUr`~+4xyMqHO@}#X2|-JldbLjL<S6-m30t1y^d)6Bq9j6kwxbLJ
zZT*x$ziTjKJ>wGH_~^v1$e5<5_q$4V+1|e+pFLtcKbY6J2M^IAPiuf(p*05l*#y0+
z2M8Pg0MgQ?l5W?<t|ZH}icxq$_a7YTdOE66@YiNEN_e7CZMDquHrk!$9Z0<9Bi&-0
zcn=HdEd_@aZcw76e-h@L!#zbvxaFk_xfu9|?$Uf}pkH&9ae^^y9k{a*CR<e2_hJrr
z<Lul1+-nP%+ka9ONM@ij<FQ2@&xhN%=(xlB{UuGUjGUOLu)sv9*Ej0k4ktlsG^-6y
z@T!RzuZ_V3VQ#9p@W1`#k{=nKrkEm5?;1d!m~{^Q*>iTlImcqSL>{bG6j!Au3d?ib
zX91+zr2R9qLN2G+ApH!O{T-M--Jb0W^EDr8>S2u|9}vT-0Wx3oc=9VLyT$_!D*u}(
zJ5pOllE1_1#;)3HPHpOE!-wtaHvcZ9(BR9expd7o^XX}#>=BLh^;A!Ak|SKzSW4BD
z(P;!9BnGCA^{yyh;Q?A?Xbge-YWh+kv?J>g#+>V`uy8j?p+nEr#q8K;my&gp-qu^%
zRPU{=vyw5Ozc8AF%rIChmU1n$lue<@ce!fK1#gqp1z&oExW1dXb~#9Dg09T!gz$M?
zB!-0}F8<-WapcS<861xoHvQZM{u-wEuI((=M8n4IO9|3*J_C1~;;l?%2``_HKm2^Q
z+kNIgn2+SQ2E>v<yK9)ybN=5>-N#NtH%4`VW{<#m+K!#Ic2#SAbpR{HDY@oI`03)<
zi(cAqe;rq_kmo|3i^0Rjo)C1~_g&1kXHa3WPs2*i;_!52^w<Wy9X)oD92DYPn@#Kf
zBU@YAcXP-DS+D40u6`8+ytiB%fnD@n$dXwSYd!uFDhQ}cN(qb^=d-z9G7>NzgtB(x
z8f<_ZG<wm2?6ix}xQLe5ZJytYwc8$Ikj8p*-j5Q-SXL|jY5%HJZWYy8vU>8rTho_}
zgtZ+0eeR-H(9S+f1*85L`;C*~LIS~-UKQgLFK#O3N-#$lz<krW8})H(meW{_8<Tm_
ztix+3Z<X3b{{2^!;d>x{8;gW`P>8tL=@DQ&DbBF9vpvBNn(}z(gu5x)?(PVgW0b=&
ztfx!;a)E<6_t31wQ0CTmp{uR%aLDzf^NJnsb5OGgBlnu!)iyyN{7?HSFcBf{v!UA9
z`R2WLZU=VD<6~&<WwW}uI^d)xj?t#o(B0UTYFHw;oA0JkQs-m&r)jfKTv|$m!Y^Qa
zZzjF45@%q>gf#yD!vt5xJ6u=W)fFu{bxVT}YgTffq!_MNp-hvoWw!=>%a0;}<<a+Z
zZPLIE>{E#5VQSi-l??gvhmqDN@m9!0q(x;z{@P&nSdBsB5H}VPI6wz6TnwO}RprBO
zUHbVgD!!AW>{vJ3;@RP*1Ia;Y5&xjJD$1PaMusU^=}W=j=nDUIwh4zWQ-{<T`c-HV
zs3z__2kcz&h9hfAU2y#$O>Y?$N82@RLkLcAcY?b+1Pj643BlcUa0Zt^aCdiicXxMp
zAKc}e>;9gXwfNuAGu`K|U3HWxh#+HwhqVd&id_CQudI=U@h}GD(l$59Cms7HYuq(a
z#})-bVbJ?$Q?|Dqvts0Ny_lEZ+bFNYjV<wFZWE~RrgrPS_FOW|9s5ST0L%uS3)qU~
z=P!np`GrvmW?dtkWw8|Wf*sa3+)_Iq#1Cds#~13$qfMv_j$Q|W@xduFaNC_j$MG4S
z`uk%7!(G1naVt}H68|ME5Nnk)pL7Jh!BNO}g1<wz96$4l%nXkb7g)P?N#fQ7o?%md
zapeB~9gTf^Rk?PcGT2%Qto^QYmw)@QeU0(;OtJ)5vjtkb06>rl&FrPEh100!a`;{Z
zj%H=5)7%k}?AjXT(oX!Nh{L{{o+=?3K2CQAhZ#?ve@bqdQT=*pTgA7k5&sV1`lt9$
ze~<7F&?=svhIG#`87WgyehG?r0`Z_}eD}SaBD?*=+`OWhk+_H{@aC4(Rh8XP*NqJ6
z3%szyC!QJZn%vH#D==93x8&AWAJC7=B>7&L|8{<y*LukApWK~$qy1&u0!&@S_5P<l
zaSxd&dz-3{4|^#YbL8fZbQo%t$i8AxNu%15+t=_5fOqyvGG=MdAKrsz4)$MK@kV2M
z6FQM0Tq#>B(hM4aj++ha41O>zUkSs4`G@{ZEw{Z&v=5i~^Ya?`a=tp`b~O~z&_6%#
z@oO-mSdAjE`G{S+Wj+w73h5tUqpET700k*(s%>6~I-%c@CqqRT)kE9H#1Z=hn`c;Z
z-SzI8FTbLHK}}Lw6!kREEX3qOLf+xEjf1){_oeEc8d!A(f&d1G;<%{6MVZKzL+kFx
z>;EK}$2^2~^3P1Km2>e%Vo+UCm!z0f;e!-Q27Z1%5^*&-wA`|Abu{OYD2NKPRj$xZ
zk!-d3T=)B!ND;RNk)%NJLKpgE8yN0g)3h{0F%J9`O_<B`zWA0P&U?a&;;myXPdK~G
zMB6*m6G}e=oI0XLO>|afyR}{E^vq(_xj{>OD$K<qQ1v&`HWKW9Owy!t5ElO%Ww3+t
zzFwliTKnLZu8izAG<?gRC&<V-uu_FS4S>TyUA=OsX6epdc(cN3j?vLp_i=EiLGqfs
zRwsShreDjxU0$t?iCh)^sTabV?~e<+z>OJoWKqTiRGhB62Iu@2xa#f^WEjspLW<VB
zlKV~jm3N6ZT%g_4gWLa0W0lWWX4=`olzz`UDCZ#Tt+n(pf99Z45Mm9pxlDa$nN?DW
zejyBuPg20YTwxj&>40;}4V2Y>9~PgYuA+PSfo9wu%y%~CsJz!3KvwY^XL}N^4neXh
z1w;ZI2oGa*gtuEOaJf288MAdI8OX<3c$Afs+Z(J8)~0ug&4O{)t7BWMYP4ZTeD00<
zS3#At+D}_;vWD!9D3KLf|JN4D)qKypVj*Zd2maQj&Da5MdouE$M8&gBr;x8=iOIJ3
z0;0r}Z}qaqvswjI^4`@fo+Yg((I@WPN!%~sY8%CK$_S2uWMX&eBA@9QQH=V^;|Ydt
zPF+_+DFib8W~Zr(s)E*ElE^WJahZUNX9zONuBV3$k7djkm8+g6WCI&>b11ojl$xAv
zPqBDM-CwgoIJUu3&{5U<04yj}bqN#o`_7@6^3AD80B3xMJ06h-ijOmdE0+$I!3R7L
zOBW08VvUV&Z~K{)jf}A>x5P7^B?o5ZM@bAMH&YscCH)wj(`*bw?=T_N!*{v{@2r-*
zskw;K4}~oIcEtKkHrCzFq__oEZ>-Q)|968gDcWvnv$}m4>Ke8$Ap>TZ+;_65rN7v{
z@1bcGpajpc!`0rI+oxm4ndC66n!1yLOPuc{_8es^D?1OTZyMmZETUc$+5GTIKj0)~
zZ4)U>!Tpcs%2%1Yz^={TaYQIB)UXfdt5l;MVX@|DxEhMOvi9`xIBlUL;okmq1(E}Z
zn+-7{x=ZCajsSQy=0(&1t6&*nRmYIv548GOxXVAK)+bpnL`58I^IMvCbbQCa#67J&
zZ_BlT=VV%(c!wZ7`5!KzoVm!&D6mBn_J>b@Gf5oAf6=0v93SrUsbmJl4H#ygS^$)?
zyz$;@X}%fY`lTZ@zWl8g3>?^@)nw^=_|1V$5whjNXICo$zn95CdJ?LitlHT6fkuxb
zOZA4K&P;d;DxvqnBHI1tumoI(JgGpuQi!70EidZ6IG^LU_Swv2sheZvd4C-OpSt}U
zWABn-H#MfxPD^PenIx~br-E5WAMv;AuhW8m?6#2yR7(;8rZz4kz*=NFSY~WL;|e5m
z<ME4nKm+}-fAc@vn0{J%F0c=|I_A)wuIeb*U#ROQNQykeC<Y)N135Mq=N$jqY6$7W
zX!!MuOr$7$r5w3gvY*<5u6xJl&kt(B9XSGCUKx`s^8rNQp`%BWvzlBw4`8vlH!($s
z?(j5oNqkqg3h-*8F1u~i=n&=4DR6Zaul&OmX!749y!tV<VLfJAn;=kL+3f?6s7~0o
zTEF>pAf}3L6&4qI1D`Neb=^KntIP4zV&1L*U#w&j{>LCm=bDe+pzH5z%wCV<B{$tq
zQVv4@tq2KO`T8OojYHpU<NJq4p7CQ@h2>V}eA(%^xuIjMqOC_EH;!$ZtH|%#wTpa1
zm&OGX_6vX79xPx?-?=Wxm}uu;vY&#oI4C&*WBnywZ;luEaPmIBPt$C-nkF-w)%OgE
zVZ+1OJ0e-*)9FIcJ2%-~gO~39Cl5nC^8aG~Rs;@4{wn0&L~@)a(AxoRd(I!Blkzna
z0@e}ph$Dh2?8?ovx5W)-f^PsZYYJ>S0+WGWh`s1k4nnM4$}JY4C1vKJcpq(Hwu$rT
z;(H_TGvMk-%3{Syt|et=Gtshmg5)o{xeIUl)wO7>!d38=<;g!P3Ib26A3gW@S9xV4
zQb=uQ#wMi!O7s?7lWn$jU!?u|m}rdW{g(z#|Gxg=sfrK3xW~iK@Ir((Wr;}`a{XgH
zE-XATHqt9BCf|<R`57KzR93p9MXeXDvY*HabVil~{xVFMaKRd&Me~_Fiumu(Dn{kK
zemfxvL>GkmY}|k;I=2f76d&jcBp8PapzrD;IxVJmzh#3lseu9eAg_Ce5Qjy&*M6zj
zfG0erEu>%RMT;M_+pjcU=B3hexnDMR$lKIHag2B;n{37Oac-<Pa$IoL&qRCC4hn%e
zb!~PK(j^n9UrncV?#S#9wQx_0ho!K6FK)l(Z70sK)!ie@c&lixOd}`Bx2NHr`pu87
zKfyLxPdH-*fsM;?akiSit>1PsjIdW~P)1^ljE!;P)wt_-)U`T3pI*TW9up>2?-rlu
z_!;6LMjZ}B@!OZW7zfE>ybPz7M~46M-??aPqht-}7hQ6_<^{_3Ht3vqPltzT!xjC;
z`?%Yd1Oamo+yN|V`>ThOemOBps%04i@Z*7-Z3c*58tJ#*kk2?@^L90s?M|@Md5ToR
zt7go8v>jQyONyuebIzI1h3~+l@ig!z=lRi<p9_Nl*==Y0no%va_%OfI!4K?jIl++{
zLdS=GY!n<~@)Y!WZq3f^?69*w9OK6{h(9#>1*_X-|5HZZ*Dp0T!kFxDLa{UC8faJg
z<q*&$x?77VUDhq+zID~VPr;x8b~X6nmAmP?4r-beePt%U{(kB_2S-b=_VV+f9&nGW
zFUrkcjFy>o&o+O1!^((9JPL$<tqZi7uZHxQ5Ta^o$SVTkk^^>;7$@Zs9$iD4L6}Ec
zm;(guN;fu3{1=3%X(1T*sqZ(oNK#5>9Y}2#_R7E_xZB{MRkxs@-1<1Grc-DHQPF|z
zhmDh&koy8$T(`(Zd;@(HvBEE!{ogn^MCk?_UGr|IZNjnhJBflV{{mA+rl52xB3xx0
zl*Y3pB1+s1><BUFVV|aDnLUE3a9o`qcN-O_3=#hg{#v7V(hY=l<4NbuEuw4VdBOQg
zz7E))JHyA#@c4wL6H?tbqY%7xt>apV#WB_qQg~LiHihc1%UAr+6;>6sjk<O}@{B2=
zv}tbg##cuk3=V;L@c^ENv+at$+b%<H#WW+Icpce)j+zIm$)0;<9W@!<p+LOd>3iC+
z?k9Ngr~8lN+_=|9fWcNl7O&IY-%S0#?<&`N%9iUu<+9Ovc)Y^aG<ZKj|BF7riqzD7
zIYksAc(waQS-rRI{wP6v;4+a+_n36j)0o;D(LWsh`=LmfuMa5EWb0$Yde=-QZtkH@
zOa{M_(dH7F-UZ<2_>eQv;I{X{1#Ay(8_GAYQZnB^i;-}zu~T)sN{Mi3$6|#yJO2Hp
zxzNAKU6>73C47w+q*1~OCWSB&&Z%LVZ17~UQBovT^bY@FP-In>whbvQj4VmG{nM;j
z0o1oQH+M63MgmHG=U&oC!ZGU)d2ZuR=P&KjSIhgfp7(D<Vp{iz)t#H)+z?`e)J-qx
zEUe{b&V<?O1Z=8;xkoq?UK|<IIUxeN(#3Ec?q%t0I+kCT{s<`0mxC^#Hp#a_qBB(;
zm{c9wzj0!)UO-Zne*07uy69qqcti8-TJB}?FI@SlAO@5&6?Mxl>a!r1Ynd3&|9nE(
za}Haw_9c*SegcD+T#GGF@KRIt0t-gLPK+J0b_3_6b=l>hP6IP{XQ6fe@geCp2Q&o0
z*SyVgW9Je68y6!_oOCkx$DOgC+~q1l&3leJPa-a0zriTyr_LIz3uSi>b%=-d`uACW
z4ls+R4G79Ro?jnb>=Cg)4%tq<-)wvt?LDujK%Lxbx3uxm)G%qwhhwHE!kaOyObG1e
z7uM#KN7yEx=cknnLuk1$c9B&O{|n0EKDa6u&HblHt@=~cHmUWJOeOp_smdKjAZ&`J
z^bDVX2#*+_@E8Qet7?LFB?yBM_9vJx2T_Rj5oq>G*9kdAc=bNUgQ?<mer9Q8o86$|
z9~JhNW%a7xw2}JFm?Hwr9?PvARYY6e3AU3o_R<EYbsmq0hgo=@0-&Hs)4^`hzaE6g
z7lu~uciD?fCyeGY1S6>f>!h;uxu&PbBzo%s^xwVut;$+NY#Q8YrFJcX2uRBmWwn*$
zfUuU*yv^><A-#BSsf|ACg}s@!!)4>(?Ktu75hQogN$F@fe6Qt!e<b|JNGIPNP48PE
zhW1T$LpT1;UH&Ie<)FP@4fa<TYXQ;SHP!9!mPu<pQ@aKO`87@uziRvJxy|gHOu(_p
zpVd<?uvUnB-dFBN1C8|++CSG>x1rFbfgUvn(jXxpVjKFgz#Bs|*@vFymn`ny>(+h>
z4u*EaH9&6K<j2cJcXjeUn32)ixT&<h?ycb|<6^KFUaSaY##yTyLcUOoMLW<c_FL~_
z(T>@WsDhzN_|}bzIHD2Bfzyc;pv{~-`i=8?6o{|B_Y)-)oH7ztrEZcLdaAzwe(S1!
zDqr6neOavZt7!^+Pj?YNHgRdSAHB=Q*7-Tw3csex1uC7<!*l+S7B;jXK?eoW1@lbc
zo#ovwr1v29$FkZZ^lZcwnlbTu!UPWFA7X3FH#px%$pe#9e*Qj7J(Jm@kPy6cajj?L
zUYuJdgYM<yD-EK>`3RZ`vmkT@BKT-=`k$J1-!u1pYq{>Z<SJAQgmkd{WW9jh@+~yp
z|7k#^BT0yxlCH59x&>kjuwgs34yX_5{;e@dBA8!MND4`bF(NfHdoXbK9#up<qJhkP
zNG*8tAK>7B43s^gB;p>(u0;PZ1s$cm?inX=n&wA@{76Z1+1fTB61b!hAbW^5YTT*Y
z*;c)W0px0BOg*fOnmsMLa<tWhv8v1Us>+<*g495^&ZW{~-+Bs84i2&lFRxt`LHzyL
zU92%MLGDIbHwD@<EK@O{X8FnlT1DiamVDXqWJt=xQWF9&9*Yl5<H}b&-YSae?use!
zJ4MS%!#jLj1R`kE0ySY_eMe}D&CO4EgG}NZ{(<8%9#22K9=?|~H|CWU*+Ik%Uyr{d
z(V(1po!p@b$`QJMdRk-kLkWcq{zSXt)S9}~t(jKowwipK5Q@?R`4?V}(3V(eL7q!%
z!$zjJM>rI({gNPy!kx7kaww6NznOc@l!Pas+o)z&Mq?gC07eSoL2M%Zaa|Va_f>F%
zXju4ISs!<_hKu)4O*Ftj<flf-0wWJ$nh%-zJz)dCv(^-5W@c_~c6NUF2sj8avF%sB
zVxg{rO#$#Du+^U*MC!kQ&~N+#S1AhKgySv;l!5Ld^4G3@)Xq8Eh6JWuOu8cWV`9q{
z=Ns>$MzaW(K>i^(7@Ty>qx>#k`0@@)%(8_32V}b^rnG<^{-Zb222*QvAr>B0luh2K
z@S%FO{U(GD?;0{|^cY(VdTNxe=wo%-lncw&kckSS4bYF!P^rmr<|;eb7;6Pr4KoRX
z)#`XmB5dcSz14!cAtdMd#sJk|oS@rc)5hi@V#rSxV1+Gu7yr$eu*={a|HDv&$-vG@
z9>qsmH}lZK3C&FC-{L@|c_6o-i4Ev9tdTW|`yrwa6Z@e0!lj_43O(~jJFHr_+}%7@
zBf_l{_&&s-GxlC42oYo?AHpw%RGn6f%%H$MrSsI^!3i{ev33UHxb+I-mru~ql~|nO
zrfRJMTRotra`<ea_py-gz}y2a?s<caqgPj3p4D_#cG0EwPzY{<(+^-s2=Xlv?3*Xy
zRm^8p$0KqX-AJb}P|zkZJ&u$&V4s1MR3z3HPFh{npY(eV-1lPA(7#{4e2M)EN%=RH
z;wWODFh2V$d|~6(qx?e4qhS+3C<9sNaN~XnIR03|{#wbW)&{ufSe$T@_=ZUl=%(p6
zIv@2uwKqPr;ub`SrCNXY_?y_};i?s#5IZhCMQ`!Sa2qRjRDs;Jxh5tj@+FqK<WUTY
z*@KUS6*l9CXuBdwqVcVezQ8RfF2&tNRX-NM;KC62Y*L$d?y#@MO(4kW?%=}(b3;M_
z^npU7d<WQyuxg2`$^9W$2^SmEnS9X=iH$kn6b|ySMt4?uaP2ZLsbcca?Z%N`SxLT_
zQ-c*Ju$^~tKkG6`d;7iXhu#0vYA1le%;x?;(c5sBg#3d#;+Y4L-`R5A%+_=LA|2Ea
zJ<PNJfK0sg`m0DWYY?pQUIxde?+<SSWiA0+ut<-^!{LaI{+)=~wKpivGr(_wZXspY
z1A;Qe{xs5Wd7)#K>U`i=aTK;3ckC>i6Qb1RdPRq8*E1Qv8TYh2Jhqp+5j~-QW~%$^
z3+3<$SgmD``|i)|&ZiBUn^WDOWu)uwAX;XMi+gL?yW0v%*g)nddM|AtrF{b48T`R3
zZ(l}vHh&L12TXgVD-ezxBd_wg>)v&tVSh^<8@1B!8tEsP9_Hhk-<q4z_S4z*T@1{L
zY^_aiMnxf*h7Qj8j7!ia#HD=Xd!dfM>v;9WDu-?@aDCTd_!uLWVJRSmD-@Pvup#=R
z;HlS1j1Lttf4ce_;)~2*9Af}X$glN1X+DdA;*)k%&J|t{MO(##d=CKL7$6`Dx085`
z=UBWSX)r>EEeYB<=L4=BAUGdLNQu`UxlwZt_qOqk3_yq8jCZ=Cme_C0eiR&DPlJ-g
z97{O);?NoVwv}b1<V@ozX=nBt%fMR05a_)8o@*<q0w|In29%vtdhx*gpyy1MIpIq+
z0ihq5--<8+mbl3z0B6B7410C~F3MXii$A|XxB&b+BKxb;V&<tS*+{rMN8Wk`F?HXN
z02czmZ_p=;jLCiPTX4i;Z{aP_vf5FoSy`xGf0_llO+PG>ko%CcVp*pqsk2u@;Oowy
z)Ma98EH=5Ibf)_%R7(P3{%!82TG7|;*}4&NuaS>UK)O_qgw^DHLdltKLFC2fsOURB
zPyaQ({`q=4uSik^)fR&Ui_PkDPSSt6xt1lchk^Q~f2$_-lfJcoTB3@D{+YP^cX6nb
zzhLCC-9pndv6uK8Gjm$jfSyy`JSJ3K4adHauz*f+nxsXe$gKlV&?#hYctEf$zZDHh
zrrqb2c*&HVA0A70G+fv$ttAoj*NIV|(5%h_wQ$qRQODRa`3JkeUX|An-shac^2_|o
z{Ta9I7aUDPbY&PO)!VJPS8025XG0Hpvg2GL+_X#y3D%<{)1G6`=Q)XM*DBAp)l_po
zrR6TY(>;D)9g_>-n_*^0^1#dF+f8W$%gJC0Nbb_7_r)&nCE0jiQk|ZmMMZ9H`h_R`
zbE3+&|860tPH9`jGP@4Cr9)^$625EK)~Xqi-s7sN^p6)xb;^abKl_4O44(JJ-RF=X
zF$Tw=rO%l<g|#$3A2_V93r}ei*GE06+u}pb#zeU%r6dkyj2#oJk!8Vv)un`4v&uTk
z?_Wp0auh@$dQJX>EQtdB^)3kZ^Qe-o$N8*ud>k6z<B(HaVd48_syZaK)qhib2CbMe
zNpEEh^~_+;knU$Gx<S+(&&5nm`QvobB+95z_zq%D?!^uRh2B+3LA%iGNI&naXlial
zt>w{92ciM^B9sU}<a1W6LJUTuaT4`$sUzF|5V26}2$1;G@kKOmH<sjJKS`Anocp{N
zwIZX)BDw^Def*WfQ<7h+rn+Y(<kXV`{Psck-5+GayLnlS9U>bn(XZ=vU!P+(Y;Q>@
z@$H-Xm^_v1mqbxzbwGmp0fde|OMQFU8A=)`vKGLiP1y+KyL#>=wM2n2X}NYBK;PET
z#j*&IxRN~I_{;}0=#GA>7I!BjY8sdH`k^3h@%4d5>gK&CnQrkJp9nEGHwPL^+K&j#
z$vq8De7MYB*fscN`{A_j1e{sf;Ai3B`#=yFa{iCF(%U=G5KbJqNYy&LyK9gm;@Sq}
z2f97+!G;deM0fA1nPR$S1Q?ad#{d4<HuT#e@{S|T$u-;AULzwbDz|_bs{>!R;j}~d
z=7(R-&Wr;^1Jt4kSS@LZBJSVpaG7{)=eIZ9q#WtWINhRA^M>lOX#85Ck+pTVfRn#|
z|0rM@?DbC+xD6Tb=dxIaRFK@B4l_X=xLi<a<2d|0p748yw&i)iB-^gD3_P9L|2Lgk
zgJGN<HLaMuTe08mba~Gl=dG&>Ys+bv>7fQrzkc5HLm!&VLS6y{a0&j}{Y07KRSn11
zdK{i%q9Civ4SM0w?Z89(WVy9)P!sb64G$k5(|DyG17{SKlH(Fl-T9AipizKFMvGT?
zv048zkzOfonK4jaCv6MkAF_YgjgkDA^8FQIUXLcWgM!a$zeb35=q#q*p5OkraI-T&
zLP7Tmr6cR3KDV&5rJ~ME-_6U~+S%FI*bT0=5=YQI-AjmJdT4G2M(g?pz1Q60Zp$s|
z1woI<sY~{8e!pj9(k-eMQ-~hOIKv$_w9kE36J<ZHBdR>SVvFF7kG49Og_)?%UGD-u
zNc%R{vR<*wEQF@pb-S$}m6z=q)OokIVa+Vj<lZMETyzn<g81cdgdi}slzB+J&$BCQ
zpPKGb?d__LHBtF->h%{l%mBli?}m!tSg?VMr<uYQ>GU~N!-)e$-Wd>NQ^w3Z?14+-
zes&72!H+05kztr^4z=I>nQ(fi0nz{ZQ_v#w3y$>@#XJ7Sh5TB|#RenwxQN6XY=57z
z4^->c%yeBIy1c}n$ros`vffp2s-L66JpkS(8!X?CSowPkDlBWIT`sIrA`(+3_vH0=
zDQCaqbNH9S>OdLZssoKrdl+Oc1Fn3X<kf>MmD{&eD+fx}<Dgt%!7yMN1q(}?Bga+H
zula!Vqx+0%8$zTYM*j$GRa$-mdW70oBhdh+48RXBVFfh+Z1JjMT%7gFRTU;}*Ozng
z&~cm0ejeC78?&ffRzP8{4id$db$5?(BSDXkg!ou@KY1fYGmxQK4+#nS8*IY`RI`XA
zjZ}F4t)Q^lp>6bI3HdgoWl=l}H#<2|v=HpOS{W)|Iu$Fg120_}41e*PyX~F$J$wDw
z14@CN{U&m}K0~O6qN=jg+&1Z*MSFmb5nbx5E~B9hf>4)Uatc1l=|$kLe~;T~5d|@c
zxjeBv9~Z6tLcp&ctQX`s<W8yT<4!e+2Rs;Cc4$!@Gy3^eO*T03D>nKEZmXchj{Wl7
z)J8w}bn(3+S6uqt$kHN|JpXnbWV{Qd7%(zp4(On|#|(uEdV{_ODb{&Rzr&W6D3+(f
zG3MJsxGu^aR}lR~D$Id6a{Nnoy86pBgvf6avT(%I9*7|A$_Q07_*_!+5m|UT`!>FR
z%<q>p*-Y5LBAO=f;qzygq*uen>^K+J`<@tJ>H$u^IR9?iuEz2W4Gj&9C%j-VG(Fhg
z!9kCSR-fT)aN%`@esVfY>eF%})oJ|d<J(eF<5#&DTpD|x-{z+;4_;2|ibf-<_V&E)
z(WPH#0)=<qA+DKO`uC_Tx=m4F)lbZeMJ)cABQhu`eNW>4g$=OvF<_<T6<7Oa^X^@E
zxCo#YVA4TuCBjQeRZ(4-Wq-l{+8L^EV1O{0cTfmMr0BFc4=(IXLqhFGKt1kv=_Ngd
zKoH20*AnPLa!!bEPp}AW&S`$3vS4Z<;#^Qh1)3J*XXoeV=jP@X78d6G2g=WbDq;AK
z!o$+roW9G%DQ6=%CpIL=q}zrO7n`7`4y%)Fuxz(AIW+<Kc(FSBc0QjRU3MhDoO;?F
z!o!jEX90y+%!`787&BqZTQ`~TKdCsD7<hz$s;(Ndz4Ko-DZ3z-G-#m3hN1l29OxO=
z`k=c9h0P^#p7BR;Qtze<(u&KydgSn6{N@I|P?&#lLle2M6Y8DGt=TupHDlxNY_6B^
z`hndN<p<ex_+`y~XPR2nHMMZ4UUK*NB($D@J8q|3k?jBT5?H%NM99jnC8ua<Hrkn)
zm>8KIZEx@U9UxGu{S)+n0lvyklp_;4Jls8WVc}&ZA_`EohUiY{QwtXr1-GTr85lZU
z<ieWQDaHJX<(nTr1}!nXk?Ds{l@FqIJ$cj@6}&vhIU5h%)^pFTQIdTh9vT?zm4rp%
z0Lyg;t`?*k+-G@_)bj3`w+|ioZuAVN9|85_)X*l?Bc_(bn1@Gxz^YMIAbWH%Qs@4m
zHE_ANA3{J#CVQ9Z)A#3D6a>5M7c+!xOAdfG*Q<f?w!4{(w+U;YxjfT<1REb8ND&dT
z9U}gp6bJEOIB|q*43=F;Ajsj+pQLtfKxo`UUHRKlMbTkmjohsfu4i}x7XjF9xoG1Y
zpa1*gUvPj>A1D-<1$=ae9}nf$Nb+T+({uH7@=nNLL+Ah63~n{(ZZ0FO9T+LQWEIdi
za_yZ%%n^73xT#uuIcoo!oa>E6u#TJfayRGKloDCm9^VFOMl>N8aaRwZr<V+ezco;G
z(BSTQp1?$-@WK1{{;X0=4___`v65@Jiyo^y%U0`nbAtfnS9|P!?!ve<W74_1MQKMv
z2QdFQaIh7za54IAwl&RDtp%a%ZU55L@SXX7Q%(;BHZ34{wRHG;a%2?gK#uD@j(mCx
zitt(vcNgdmTcDU~r9`Ra%J1aO$SJPnVDJMO8$b++CZ)Bnvt?yz1oD{VK9>LKth3v9
zH@<EbG&Ke+wIoPgW0B*!l_ANV7iu2kz=E83Hlk{Cr5%+D4~M&UsHtP=v0C4%l6ez*
znt;P%fqS|a{Dn~Poq8v7tEBIBo`NDj-{b%jKt~HwR#IE5sLUxb(YJI#k}`kux7~!K
z{}4INJ1+_Xv%x2rJ-WNQF>wW<u~CGpE5R&xB3>Yu4hq4Ug|8(Rvw2<47aJ~uj}fV5
zP!CT3V|ITR5K$MibroO6F*dIf>hRxvgDO`3&nF(YpaGNZ^tu67&GJyFEcc$<o70!W
z#fQks%uYY75~y^r=;mUpbG2eLNZ0G{C7^R&PO1k{_kEkViOz+9urnPK20<Xp2O3#&
zPYmdtQ9+5<tuZT#qga_+Sea8$kyqBW)IB}y)^$*WQRMw4nq83h-T|O1!dkmc@VlEI
z(=DQB9mUs>F#VNj>~EXP%z#H#ZF+tv1ZIpV$@iGFaUou`XCeqkQu=-c@_wi-=4tH3
zKc51xB42ykJ0?WPqocyE5M+jFt&Z`DAO?VctqdG2+)dP6<g(*zaofeR<1U?+hivSF
zf>b~S76wU#a;kyH<`%^?w8JBkp*u4BFylI_r_d-3kfJ1ND0`uwf#r)!aeL%Y-*Bl{
zHcnGLo$ns-FW2lGx3g1%3KAxA_AWO+nm~fX5O!!Ta{A!}y_jX)>0dnp^wKjt;1_`k
zVm{KzS!E5I@{qRKo8ofv5u4!}Ib5HfUUCAHUrNe8+>vqZuLT^&N59H9cPGJNjFBI&
zAH~%c>K@C7o}X=95e_Zb-0VSZjJqPvRoG^4LA(gx7bE-?72Z{XqM~3uD;beN0Dp>R
z=eSu`?L4M?M-Q)J@&FH!Lm3L`9bQ288xeH_PVEHf58h;A?D;<UWlt}i^b|tqLN+xm
z)+GUy6Z)&!a@&$q0%2(1?`);`YdH|pZ0N7Ey@Wg&uiB*u?6JAFy4N>9Z4HJss)D7g
z1Sa)5T$p(YG(~&=pu-iv`8(A1&|Ztq{>CpI`x|TLxk5mDGgA&I)`JvKboT+0MO<Ak
zO=T%^uBG@D?hXmB&f=53CHFa);b?HXM$MT`W^`>|kf0~T$LMV?uW*N=QGK;MA`z*g
zyGfeyB389IrTd#@IslX9%CQi=pRco_yuj^?YIp7oujYe~o0a){kimn31Progn3{4!
z^*(x0%c`@T9I_rSkK$={OTPuSpyT4l#j0t7DS$WV^x6Kb9PXdCkQ?-#(cZ{p?^=_4
zAqGCwjE}?iN|oSi_EVRf7-wYp(ba$`SvkSz{e*R2{i`yE{33f0-|IR)E#xT038&rL
z>zzsO8l@TAS&)YT8%bim0ZK&rF0^}9+e_1bVy+KL@Zy`=R2G;x;R}?Imk}Hw0%ma^
zZNXUjQ10y|^^znkH37Z}V^}!Pke>m{t2#Q{N%=WHbloJU)v|6gsW+xPUSIT3;KQXQ
z$7sKjUoz*A&_=6tKWjrsC0#xgrrsC|dw>~&$0ppr$+48UZWfU5>8Ig}lUsGdCQG2k
z6-c=i1Kmg$yP6^D?SZ3?D!F!$jZjsvTSzV5)C_O7KaJ-F>*YEt8(^QP<oaHP3gd1z
z*kr@o*3!;K5glja414Xq9I%OT(*B^DU0?(c8uMP#uY}&cvty^#mphuwdn;iE+4qlc
z7U~|3qOw1aCrI~a5iZZ+M}!m5q!3<yX=HLkfU4EGIaeQZYk;LZ%;cOWKB~(V*=551
z+K2)V|9v)gxWkE$*CErsHhbatR$EHxdI)Uu@g4f;h#JlY?8k?cSe%&i<I&7#^S;gU
zn=?TmB!sh9MQ4@Qv~md1bN)C5WN*2<@*_flH!lFb-2c1EzpsSM!G#IAizmKrNI|z%
zdjIaofdl|G3#E^^@}ZIu_?l}elsWd=EP+0R82R>|`^xRav9XVb*4TE5uxRHj#3BXW
zoKgI{Pv3!ly{}jxd9gOcCSuGl7w0h}Ev{J(m8I;=u;9*~aL2hh5;J2{lOur<&zCzb
z9}xosB?tF!bW&lb^?oqO-TdVW3jZL`cFnX<fS|386VSI<Ln9k3EdwU1(FLFISA4kZ
zU&x!T#EJ9W%x(a8lexj-q$Y0ec{PoALFVx(Pjy8dWnC?>^ZF*Y4T#GTwaYi_0Nc-U
zQ?f4*(Q*Mga~Ygi*<3u`)^&kq%@i_U)lq|xQdyf5d2|XdOXcgn{W3n9bF}$(_oVL%
zBydzticd_?P*qn`Tbf^7Tv+1f;A#Yr)<ns*!{BJ#-M|1DuNbattG;O;h_APwUhII0
za&U}`taH(oj^L*PME@ET=<ak7r-Pj4x)?<0Q3eI&^WL$M!MyrAjUes!s;;y1{hcA7
z#L=c`9v6>gSPM9fl$-EOkhK1XFlrT;e8D|EqZk{^h4`6ozXF7nX2K(~YATC~%1m5r
ze2DDO?cEs44&kjm<zHJ})pK@s2DrF9-rYSs+}}UudFO5O+QA#CJfa;tc0z;D$usT?
zzz1H}O2655JerAc1p6A%nfw3Ue*Lz(M+7ORQfqTqbx0XKopIkun%YPQV5;$+VmwO4
z+<2Z#DvJF+G9PMQQn$vR255^%{|*J>S{BaP1y!~9cka8g4}ORdfK!CAr^?dR-S<U#
zymP&71$WC`%6YrwZu8j=pZAxEv=m+rK=M$472#7(H`dktys*_lU41g@w4|j$egOsL
zaF4&b4OclJ<I?KwFr6S1n5^~ki}ujdm;Tk4?%*2;L)|0V+2F_Ig)F$zhvGNaN1pPJ
zPpsh;N<bQ4I!Yw;sTDH}HO`MJ&0-@NR+1fPA|h&WU@G$mk|pcsiQofgWl<;1p3ks}
zg6EjGXFR1}9=z=76>Z9ppTCOsSnmM0b3qscH_azC$EUv(RClm1uoSa*uy*nxac~zL
z|9oyH=7O2B-@U@S1l7{Mt%)>xhs^3)C53Z?Utn~Bj4C`>`BTl_c0m9n7MSwp4SRGX
z*l+*I=1r>memHUpurld1CqhLn9~vTqbhTs0)oFU>=Y8j*G)qxdyzekun!f*tS~(#t
zLi{>2D<c~ZUs8div%CK7A&>sv@<gl)>d`T%X!{4o8xJVSo1bzJnH>19Hh;W*SBykl
z*1AV8Fb{}ekq%IsZwS+2#Ek5UE9jVMM}dSS#LhDlo3p4Ki`RAR%C_&}yGx&>l8x76
zmZ^jeZC_QjGZVN0QJty%=4l_0*Q8I6GIlv_FZy;%;fp}3z?<45Xm?&b3)(P;9AW=u
zRS7N8pP^A{MCZ4~mITCV^fjcpN9by^tb)6zE{qJsEJC3b1KuyGF6+p?p4PDZgxQ2)
z?-sU)dGoOF_6+BtDSpAR5y=qL?2IdP+=Op<>g_gFn~_pu^q+ICI7k;He!O7%CQCY~
zib;ZJ$_JSKLi&S??d{=TH&`0tdi68I0S6@Kn=}t1M}>OS@zo)ZTaFwzNpN2m+aV^t
zZm%4;##dcMp5JKZmCCbxQ`dgN)B=sbKY}(ooX%Recr`rB-diniVsYsS-VVG6Y-T3k
zXa60CxMOAWKn|>RNeNKB46I0LCRYq#o05y!{Q;s}wYh!Nr|soHDJI2v^Rr_FKP=#e
zz7Y#2$yt~%idhxaX^8Ey|2@_Ab$q{DNB5|$ibggZ`tgg@%E!982}%{bcww6(UXMaR
zf$szy%m%Sv%2!+IDtW*P;jD4-(alzT{{8Q3tzSL{H7F2MclljWxyt6UyMEUz&8_)#
z3)tzyyS$-|a@t}cRfE=4r3dZMMkoYWcI@g5u8n{rA%G%c1Y@h7R<9q@`&y(3G>L@H
zND@O&agm@Z)XJI0accTTC;6vY1%E8hZEC8hKkYk6d#N7(gAf8a5mRTJTUW?_YghnC
zhhI5aOB@JtRX7%nWEs4xJ8GM<JQp^CfYoOO(aq!F_7dd!z{hX9LOCvs+X%~Z>L~Ni
z+baD9PPUWC66F)JNG=Vk`c=s6M7(&kSF<7tyzTo$gPs4t#T%jn-J>UO4Wawg0}0<Q
zgi}ui3k9yzmRY&+G^6c`Jw6z24ZJW3{RG_KaErB%ElTJ{eM_O)&8xmsetA$T1Fq}c
zQw!h2;b|=osQ$Cws0ySMVyu8nl(p-0$q88_>gCrSt)v;2{}<kjwe576FQ}cTvO-Ia
zyec{KOS*o^c!O>t*c;Zqa1riyk!f=YRB(W;#sxs`Ozmq>-?lx^a<Id#8hYW!rw_j+
z77B&$xOCmTXs$SbB+p)j{u$A{0!+p;9}WvsM9Is#uCTm?w(nD16IXjA;>W;!f5I!)
zi|o{Wqq;sO0zBWFD>#`L0Hm$Wqd{6pMu~%br)`+|;(nZDo13d$gTU@50(dZ5uBf?`
ztafXoH7oJA&c-S~SHPsud#!T$^SuTIKy}Q-vAM0K^hwIYaGw!q_qZ1VL^<EcE2JT(
z3O!ATL9<(FM%EkoKySVtvhgo|-4i7Jt)jHG$<G&zZN3Guv!M0W>FNdnR&VWW3{3Mk
z>hovTvv;lK+$zc|glmO&U(WHtXl|(bw!6~}#w(Upw<n&=sQlo*926X9DmOpJB^=a-
zCWEM%G86-ySyV8NO?lt~aqzx0^6^`#cz-jV2=`mjh>+ia5g0e>g5kSrcOO&tuyvcy
zKx}rwx%aH7u(Px{J3c)jAtc1dCx&7FQ0y-#sl<<bB0nOio#7>FVB07w_|D8og``G1
zGiO6ioM_cKsIZLoXw!~9+WOx%*q)My;iYGfdceHM&2?)V?4SVpgEbHEHY}1$v4&=1
zN?xWN2jA;{_aqMaIJ>PpAFdaCh?&1!y+Jt}|NPS}MF=KIjFb{+^SS9<eQSm{{mNE*
zdjoD*vBFRNt^Ip#Rj>W{*xCl-A7=|*2|UXUJNO}+C`r}!TT0z41cEskD;xTv@5$Zj
z6TjIgGV_n3wm5L8|M02ADJeRL<aHbEI1T2{-i3{gO+^JGBV)CG%qPX!Oy84d4)>e)
zWJ`c9hmoKh7POQS*$*cB#yvfwjV#D90ceb-&^Oz6s77S!^(ozULN|5}HZIl!C3Ri0
zL#b9UX0)A#nVpmM&{lHW1jJrB6s1X~^)E3sw!9zNhZrc&h3z)j;|lL($@FTZFB{n;
zOaQ<C!_IzsaB}5>F#St=Ry+cy7aiT`*x0DFj0{3%H^1j{MfVuaRHkDizp<@<ew}?1
zES7ZiT6Atr72d8A9yz);wk@e$_0p5?fdPL}Ty_Z?&AzSdZeK+CdHQ;L(bNs1(`;OB
zjKaoiJw4FTk5%Hv4IBt2{e|%BtdVur&xSaUJu0fHsmgclV%R)|7rNhgCdH2&nQ0kq
zI)137g_n0lODiP>4+SH)&rbLDmd|^1<g%KwxxIvzewdV>2nCj&9mB(~4=$-9Xt|36
zUM%+rCuXz7swuH@=L0M>Juund@b3C}87ezUTLM?}hNSE}-}5sTM2w6Z%D#uB6|)p_
z$iS+aOYK-;ZmNT&)I~-A(DX!j+&!rgdeCOw(o=Rq#U3rq5dJMGEiEe<_-0)C{DeEs
z;WscdvNeSJ1#!^Bfh(k+5tEsSPZX^sQ)86x<@4cA-aN-v=WvS%rg9I{m!2<j_{Q4&
z078<viuRG`UJk5BwS_x`%_=JW+=U6xu;H&=H3*B0PlhA%`Ll7dMe;>{%^%K#zS{8y
zK1>>Haj-l-ujAR>-*LJB<>scTd7J^>QY1SLKtfe)v;jtYjWRN@Rt}>TGW-b{)ha5M
z$yku9f9F6kklz9TOkgW3Q7zr-D??NZF#Wp2i<9)~1x(Zzt8&k%W#4N1jcn|AC0XQq
z&#TG`HQ2Gro-Kr+nuxXMD2Gg^Wv$~S`W;L`69%mB84CM;F3=~vdwDZwfE6`0UL^yH
zERi2h+H}%74(Pv2d$;(&pHOP`Ff_*e%}MH<;)bc&%lM%aFjUbS^<#>64c7`d3}07K
z3z!gs)n|o-UT^-n4*&_e?f0+LnaO*4G`D3qHbDL1RQ5kVHu1h)Z7v6ru#7{k{Ahkl
ziB@%8%JjyTxFE_%SIBg{<+*Sk{`p(VKg!)^<F^llklk{9LLk!0!}8OBpT+Xd-VhPd
z`Ner85j0~R9b0x;FuF?crye<wc)<_`T?pJy_`SBR9tk-)AVp*iJf1HKkBNl_pnbyl
ze^9+4G1N;cPKZj0y9i>?)5{<_vDXE_%6_W{jD+2Q4?G56lRiVR1?8jh;cn5<;uP6y
zZo@`tO3qF}&`vvru*mq97qKfneWGontDK*N)kT8Pn4}tt8%Fxou2sO!Oz=r;(snz!
zZ$vgc%}(#)vkFMAo<HnYh=+D1atK9!EsQ8CrecM{5wvGu0@j55vb*4gi<~i{_X_`4
zzZ0jVs-mK%w$dP@r6nbA2ulKsD0l#-NqyD{i>J(#4i-)bcc8ZHz=camN=uClkB*MW
zgu&U{m}OJ_F~V5{f`$~1eVP(N2{nhV;r>d4e<VZ0!^6Yi{f<nh)#~`@_C8ry3Nf-v
zYYg_V(H&`2$0h>Qymt?N8V&vLQs{TbjfWQ^Q~t#zC5429q@|^WBR>4|uT1@%+17ab
zwQa<S2j-nZk&~0pab-UQd)g0fMdo(Fo!4&FW91DY^LIl)l&Wqs)%m=ahRS^sJe^RN
zGd;pIU!Y;cMMp<}CnfJu-@UdaJ+|w!yCp>Bg@<Q_m{C9PKT9p2jYFFgjw6|41OH+I
zKR+QC<1UX}&in`t3MYOyT2p?fBBRX9&M(X<Fw=j-N4l%$nE#KX3>LeK!`BuWr{mmg
zR_hF9H4V;xi|p@X#;Z!6JKrHCAEzX<a0T7nIoC^`t4dyxdY~Wc!ZJwv37)0iRQ9zh
zexVnY<N+6yfUD&8xUm1*mO_H3OJGSA8514D@X*9aV@6PlJHbsJ3(K^!QZ%~FX0vU@
z3brWyVAhh&GTcVvh#w*~2KZ|zZ>tNQOwm~3;iVu@*)65$si?j)P;?LVO^hJtZozay
zT1vB_f7ci5Ec8OGDI+q-NXCqo9}4|CIWWddK}TCwoZBfeb!ByYp`@V{4~)a!B4ePS
z>7E)I>zkPDm6Q>wHR)SLAl?&g@!319=$Aa_xLa$i1B2!NhRTf4(Ug>0ogAN?fyDKF
z^{YC;<3MF(WMb<olQ^^%Exa*2+|BKGdI;_LSnG|aTWv>+0r$)A^>HdQ)2!BOlRbUR
z3{;eDD&i_j!}B-Q8SH^zFk3ahwR^Xuf{dJ$l8CPSFKua2on#z*N4sA}dgMJujcr-n
z)>PjMgw@};xR`vvnjiGRhIT8=AS{D*<T5`G4<8>fti5w}Qyo9xZkSAlJ<i+HrD0wz
z0qek|=Wu9hp!Hv5tnt{3%h@9Gt&m8U*1kEph&edjX0W%7g`N-#)KH(B<>cZw2JN1>
z@Uqa*&|EcLfnN=?J&V1@RhgBIiG{7Hr%b#KWS19rn7@}QC=Y9f)~UWLA9G9$Fn_1)
zk`Wu4?%$oR*{jSv@~+hB@L1RqA|>6{{=X+2^K-h;1)z85m@FJ>F!*IPwM`B4fYSBK
zfdx!iu+c;TdRs0@o-g12OGikxzJ&{wrahmX=HzJLIX(a1Bih<gTKWcrO5oGC^;#dE
z(NOR#GWY(9^mtlAFAeVm9_Gs{)S@z-&YGeXXW49ywy`k5nXzehE(z@Rp=X(oo*X5I
z>+5TH_^_}rsW5nlM<b(Lka*B0QoGl6c^jf=Comx;ET*$DxVAhQVu%ZW_Fu8sHGbIp
z(Y>!LIvX9|e|se3xFj{@C00It@A;A2-G;T95pXa)0Sonjei&>jf&X?Rc(&`c4#u^J
zEhd@<r%K0uIlnU|U>UCh=xjk-`-uIN?`zJ8Q_vI@QCk~6uih)EOZs9PJoLjY{<o$o
zQ0MK<T}sNrLdrqOpr@|Ly(LOz7ZkJ!*X+D`8(-IlM&P7EZiDq-YY)udiL^!3+fS{K
zd>1$nCPxIra4b$t{6;~`QvYdj-K+a&^l|+9kqc2Ds+e9bxc*S;^nJQJT=z)cGu_bv
zP+!L}yG}b;_wP$^=P$z{DjtF`aD_6E=h;<qwCv=vsg~b;k(-8t??h?y_Vlpw^8eg=
zADx+(laUXNi-)QI?W_Vlb9z2Hb<Nh(FKj9zTvSb5?d|fuEVVZL6iA(!Wv{!13zMQh
zpJn558@`_B?aB_#FW-HstZA8o=z4>3+S~X?*+M(RU8Q&1b-w5Q5fK9H2<oxL6Hza1
zXxK1d`>kwA#kENki$K>(#T7}6Q?(HWN$|&L>1rO_=R13I)8+~iYX7b6#n1g}>S38*
z)X=!EWjgvE%HQ)b><XNn@#uPBl7dv%)*RD}0`(Z7r^$gu!8=K=F=_^;Iu&uTq3-3~
z;cJy6ud0o|lH)YsIT@I&^9DqHL%6~V2^#TxNZmb;KOSz&A&Y)zm**NxMd0T#FQFX{
zZL-z=ytN#9r0za+CTK#9Q<^1R(vbH7gVrrQkz~s+{!INrM(+A#Y0GZ^5dVBa+J1Wv
zK_L<HdAag&x7fLOJ0BnGsZ7+~FH?|dCBXu)U;UlpP0BO%TmbF^?AfN3W8$ng>LIg?
zh8O|pbTI0+WDZ*3r@x>WhSM4N0GEmL_Qh`FpVa6uv)ws<itfuj{iYDJe3Ub{8n$VX
zfMi6J_%ceH_kDYKMOb4j&x2?eF_7JJ{Tp_^bNO(r#BAyYUvQziMD)K&?kj+*sJ6U;
z`yZ>RtA0XJe&Fb9Q50xP<nV|};*~$mY78;UxR7#4LSnrI&246*XH-%}DLo`0C??q7
z?e=9M@=5{1&LJ(jJo0atn4;m4?Vr5>^^hncp%b6MG3M=G=JW5c7Fxr(cCg_e8u5?J
zVS1_VykKIeR(x8<GmjwFGznnhb$%w`rBpfhi_Y3f2~d*jJ+O(hJxlmFn?q(|c#xcd
z7M!JP0%hN`7O9xESLt+NV12T%S2fpmHdxtvy)b4I?GT}ULb7x<Up?HHf!lEsUq)kl
zi+J%$P<D;J+dXAC{4ma~L)1Cb>~64aAb-Q~H72HVK_V2Ly)t)l;CJddx@p*szVdvG
zQ)YL}0%B=!)jvkcPIkQh_fPE-TlC%vFFcJs5{G_P$Rd5C7XPYuV_(gotPzX7V^UDG
z?B7Kxz)?X)S!BG6>T9(*yVx`E-afT()<kZIp~8Oqcho9%wGDQV$_$(I4DKR}Wt@C|
zO1_Km1Y~PhSw<((JT>7FkMFeh**lVF`cjGjj1^sHs5eei=kFDQlst^trY&wn94*Gv
z<a2v^f!%0#dfa^PkG~sFFAAbOSomK}E`}ZUCUD~Q=n>J53f$(mihp)ySyo0jHYV*v
zG8m}C>)N~c)2^u<7X-lH&ALu}&Wf3q8U`1_u0R*Ko}4!U?1he;C}}7J_<kHH0f4#&
z7AD{h6xYwIr~R~N6T*uwua~E5DAD(}gQmJU3?s4k4I-Nfuoi(;FaV7g=34dYd*KsR
zThD@nae(a=fgkW$zV(%jz><#%KA}T>@~BGu=Mbf!SA4>2d2xPwX=6^wy|~8KG%k4a
zGF0XM`DS48F|q#yE@{*-8$#TV1kTOsTr&tVVXj<8r4SU4k-H<)eqdJu!CPa^%H6p2
z0?(`BvZaPJ@KFI}7Hq*wP@kXYZgPD<j)+i@of>EE{0#ikGVE(dob%E5S`gI2J%_PJ
zF7`hiS38a1EbEA3tL-Etzv<NlAVsz!h~~rO2B(?%3tKSG*FUnQP6KE!3v-hq#9UQr
zWoZKf56J|$_;{Y2_Z|-yv#$zzw;#`YZ3Rtrc7;@fL%$>>qWM3z^*vn-ObvflR~6+I
z^w?VXnErJ+yGn4kj}vh-Gr&=r=hZ*j!SScyNKrtLDZ|&+a|Tz0BS&?lC;PDTcU9kA
z_9eM$I`>J$wig6FuK>BLil}t|MMeHcnCD75Di|O=xd`i{TK)T<@5C)_vs+C;R77s7
z{*S6n*T(bq>xvrO0dwnX@Gm_%S$VlGA9i+r?yl<Qx68gFz0TbCTeJOc&!z|pJ|`!q
zx;l0TocOx`x6x;3x7&<xUQ8lgd%q|%Bb>O$6kjd@UNr&QeAroOG-|*w7m(1YJLNef
zX-8*6o!t{9`s*1t%T0mF{w286nYoKzAL3&t5#_f1xn(5<eBH2XrHI4>L4Vq|9nG_X
zk%ZXsh%rz!1c~MV$QEEN+=_K&!Mo3>%BQIX5+bqd@V!iLZ?5P$se=(~ikLc3?_R((
zmkBr^*G62iy|aT-?Sn|mpaD;Jy4I|VEHh$fekLjj-h=rc@p7GSi6a5Swjkb#qUH2W
z>+33M*spE3-Kk}~2Z+ispWO0#IM%GG3jY6A%}pbRU1f7=kvSHRVzRWgq%6+XTAa-f
z;;V27lCRiVI8-5gr(>Bvq?<21Ppfl)e{l(TX|ES22$rh)@-hi!&wmeFr0_nAn_%8o
z$h&R*9iy`nQ*KF8sY3**??N6udvb(_Tu)6+kt;DX_=^&o(hGBoUWFX#fp^S@Lh|hd
z_Jbe3g-FbT#Ml=G*M{%G|MEG&{w#C^%hf}9-JZv~(N(m~t?`aO9*+(|Pf;LXQjY-_
zr{DI19V@RO)W_8*+LQbBy}6y0&B)vkYme?Eb}N5SBkQzcESlY59vgP?lHazJxmL5Q
zB)!#T1E3StnVGYz(Gw#7$|)S=aKuxn9dNm?5{FTx8yVI06IsL_;d@NL+~?frW(dQ}
zWN@un6b9%igU`BJCNLo)&>Rt1dkh>T*_~N>l>6PN%<2?ATylEe#OFXpLsyaANRZEN
zhm*7g6nTTaJ7il&nSLZditJ4H2*^al-Ayvp{nn#=*ca-{4k%2mZ6{m<Ytf<HRlol~
zp3Z_Tj;;;T5Hz^E6WrY)fe?aQa1ZY8(zr{I;7+jM?(V_e-QC@`-~F!LAJ8*BbItTQ
z=c&7@PLr!Tu}^3l$g4^YQ%D>x5n9vIwdVYHF5<tFw7fvttZcvo9jy;8Hn2nMfNpdY
z3M%r={hibO&D||35(;umuGaPyuKW7daaDVLbM?!}!Um!oT%D1-IhaiqR&S*pBho@#
zZzle)vq?;Mb)x$$LEi3%l&<@5C#MT}1=_ohfs2J#QAJG|cJ3}BcruBWBb(RO=Du)u
zx3RFwE~#s$z%BJ$_O{sSZarBB%!V4aWbuy+-%G_JeuJgP)km;6cQ(TFlyTJ&$NUm{
zkZZWHt0=2mvBOW@FC)i8NqId`-a<OWr-K<~IaS5^b+6(QxQ(0gt(jWAnTUg^b%FwH
zE7Pia8VNX?V2wwkB^(D76E^-Oj^NVigoL`1!@pCoN%h$(eH<>Zzd#tEvpgY5%K%y$
zZ_Zu^SV93F5pX2D3Ra;$VuMYy&k{7II3hn&Bvgk+b`}(DC8Q<yUej=ON|6(c`yTk}
z72~b6|NFX`Jg9}~xWpI=Yu+02l&d2wm*J?p?vPzu^eWv)U($-z^2EHs=QvZ;Etkq}
z{<!o%dioN}W5Um}fbGug@uRT}1$_Ij23uX^d*|92A=&2BZEzPbI@BDMnHp(^xpw06
zexG$q2zmaaR(p#cmYaF43)}@i?nY&Z#oMYev#R^9wzSmMmu6>^y}QzufffZk;PsA@
z8};qQ;P4dbN(=dJ7PmkgCKDvP^XOUn!N9C`_d3J=4?longMG+j6mgx1?fnmwn54VE
zXX<aG^3sT_4@BOt4xv1n$7R}x@DC2ntV;vxnhNEs9j`L6v4mKdDxk4PC&N<uP|XgK
z9p<1xp>3EW4+?CbNdOs~`pE$5*4b$M5O8&>#l9HpWT`MbJMrH3QS~vmmY$y|Nuj#f
zd%4^$1X#-12PbEjRkfZNIVdT&FKr<`u$n_?_C@qlB-p#a6(!|xC@Dk5eaJvf=s$65
z=j&(JZi|6P7{<wfJ$pGxXUu1g`P?bT2zo-@o0v9T^`_c@C+NW=-3|i$0~_1FCD5h&
zMg<~Xzv5CeV}GaJmsXb6=R4|y8yT+}D3N)fB5RGMEdz!|%+cT~BSH>twh!8UA=&KW
zAo;l?i%0+OZwmVUfiazqES;bggRX)$-*zFNrz?~e)*$K<$uNkIdK9mclGdK@!x}5=
zqSE}*5w`mhn#0xGqQ3+(ip#@mAs9yIz%K%^av41yOf|e3I@V~>3H$CD4ZY5Bb9D<W
zqw=Bve$ITK(z1vMPi$<#;zEzawA%v%C2gmjwgs@^Fa-Yb(YUTms3+dB@q9<kD9bIx
zDB!xLO=Xb{@+{;A%%*K>;84KWM!zI%_>WIwt&u#da=PtE(pVJWe6p}G@i}~ziVmlI
zJ1u#e`|BV1XAmCCRqfZsfw{Z#J#dhQ+PIE%Kfiv%gCi1-awlwW_D&*>F6XiJzq^k0
zU6uQ8+!1hpnvgqJ?xRqz@lvT2_a#&>IEuR>*i(;FH3?_Oq=1H|_giof^m;2Xjy(&v
zFdY>`N9$W(=P-VwWaqPB<7AK0*zjC#0vb!mYx0@&%aHAx?JD(82Lb@r#Kp$_wI>r4
znJwYI>B;3{yC=_fvhR$fnJa{l9GjOj4tm+K^sc`joufOj;7dm6A{j_8k*Y~)a|Dc1
zfm`Qhr>oH7=r}xFs5Layk{Nv?QPG~0-rbhNd}41g!@*gjL#Tz#^&SMyxTz>))Ek#J
zv%gF%KB>RoHs0D=a)5?%IPJS8yUQu#cguO(;J|(|b48f%5xRyJ_#InPe3++H<_7IF
zBQwAMxL#Nl_T=3!I;Tm}9@bacBhum%3wUyDn)jeRVbwgGL3w#V@XSx<yk-6;i#AUc
z#>D4}eqi>XxOial@;{?C!unCyt;T2v^~Q9WqF*86qM)Xc4iEyNdJSQr=>&@>jbIn|
z%7-&!RkFoL>5G?#w<OeaoPE;*ntllbVKprc&4i@inMAHEf;%3azopmq<yx|wT<o3i
z1s&Ipb8QvWPkPdGj}q6uRBE*AyoI_Ce-6z$q4gwkpB?=sA*-976$J@kEWc}Y1VyIC
z{+9myb+mW#7`O}YFHpQ8M`#)*)8H$bLn7m;4o<z=dRZAsc;4hG_wdZ*b-~&i#N~Bg
zdmnqfDa2+XZVSlP+gwfRZAdWbTW>nCF)e67tO%9B5n`NVz$X@R6k=|G6_l6h+7EL@
ztRagy_nFjIyA%}R*){;_lK*%O@cb$$*Tl<5R8t33)e0MV&Wv>(_6|AvbZrzdlt@G)
z)apU1^`sq@4u+!$de4AKlbXr#VIP3d_0M0wfDN(@VoJj=z4?_hTXUBmFNk33Eq7o!
zgjeQg*M5LX-luH?QKtu4AgcycF>s!vI{c%i%{#OvQL_@}xhY-Kiko&>LP;&?hUMVf
zj(|h`Dm8KckXR$|arhI|_Xw!DNxoFA7DVfkOUfU^ZL;xnJe+M~#El?>EdGzAoqe)p
z&)(DHHy_+_zxKzx5NpO!)RT47Y+L<C+m0bf4n*l^Wn6t2h$f8M+en#_t)K6^Nrz{D
z)1P^mc~g&06LEJ<`m+c}->u0CjP!TWfw*7uHGIxgSBmD(CB=t49FEy;96Veon2YQa
zTqMo{gXjJs5w8Z<ub%fs#Xd0J;BGT(V{m6B7hy+~^UdT^dP{5AF|HeGjHrQKA%3Ti
zbc+W~amscd11o;E%IOjrP+t(d!cZ=JyKwO+eHS;Jy)MM3vqx_zf6R0guwNMi=9XT3
ztNhx*K~r#Qcy;a-Gj`o7L8|7`vo(<rvkqzuke~)O4GQqTil0<?T3lh+>hYU5$xBQ6
z+X`^4t(d*pQhwyZgzG4Zte!+4w`5Df&WJx4eqIjh<H^=k)>BMR%0zP?USIARnwy&W
zF0brd7}>a>rZB}V<Z$#;0h$(g=LC}+m`b5V1XfgIpEu6x9LMH@Tk_pfE`r_A1OzZz
z;<^k_--{%uto0kjq+Mz%U<C;15o(k{Jg)U<_dlp)W<fz-3p29s-;k8FRA|^48ag&+
z(yxTLM3rd~x0zf2_l$*^m3@#C$F%JrYp-Ay;d==WIfWEb!3WLgfFMO?LSl;6cZYo&
z%;)Padp;GR3R4pk^sW8%_6(9!>k0;zK{+TW_A`{Z{-q;OqWFG-wqauWjL?ms`D`uk
z0V7UrvZGdi)0X;<eEC~Gjxyl0ntwAVDG9o~)NnTwlA#DLR+`Er8{4Y~Y}t<v{8RJt
z&$g^<XVD?QG6+yaf&W;|>;Fms9V%<8s;jGN9PJ$J9V6(@*o1ey_ShX}b`v>WV#do>
z{9;S2(2KPA)d|c85gjMc%Uniyttj#gcd>u3)(xyYZk7FMT@v1LgiFrK%ECLbVq_VV
z3TyMeC29=-p;kvntMza4vVOiV4f^N_v#i1=Th6aM)7${<CFQA#I|!wN&<gk5ug&#u
z%}Z>CRbi2tzvX4*RF*%430+D$k8>`Z=v#JynF6u~CQ3WV>l|Ytvd${Tfe-FYyy_as
zc>ke}elIXrPznkPN=k}~$~6f8&bVuxE^gYeGoEVydb^qAKl!bgWr{?%xuZwo(S|#0
z$|G;T1KhD3m5AWPdcMK_7EzYY3=cYCT<lEmu1*!#xUhGn$zf?ylvHvUZ)1CF4_RSn
z%av@kpC!-0Yq0Ox!{}f9zxvG-mM}o^m6PkKV7!>}nalVx^kEb$l1NlZDit<l_4Bf<
z_d8PchJkC})K@)a4LJv+%9xh6piLVMau-c{0O6vb-5lzG2)Zpmo$Ng*AxupaM$%#B
zRdkCTyk55gO+;WuGx4|Get1Y*ldvvTy<N*TdZN+~9=<*ITbqA1OS-<GvG(k2?{D`>
z!a^k(Ncu{n2@)dHUtEsj5d4I-xzzD6aUt<(*k+ebg<JEIeaTmrkl{3jxd?GHYFGxw
z7$|C_k9{-tXD@bI|BjX0jC?ne?yHdieVMJ=;E~*dHuTae?-SIk82tJBm$cq5l-Mql
znA46Yzve%;Ki14sknGLh%5BoGc6`{1B^<@x`4(W>YfDz}(`bL0yiR#wWV04{2+-#J
zprfm+s(Qyr$wz(Dyxyejf)kwj{bB@2NalKH+6p`1b)hvot8w8c1<gGR6Zam-Xfcvj
z#=mbzhy^QF!iH&sx5tf*cXz>)<5%eCOg;n8iO<T4O1>F>>49#?iL!+A@2%Ye(e4xk
zsy=ka)`rLNNun5;KR`NG+)|-7i`a%~T&}mLgt2jW%HLD4T1|sDz1{2iEe{xoA;~W+
zEX>dEmsmq-b()*qyzpxy5TNQTdd|8*waz!d{+~agh3IL++<N?mfQ)2@Rh{=y{68@H
zbma?Xf56U8LP7|ta;%Z?!`1%<Sw63Sfj<yHgIr^iQe}vWzYwv_S?Xt^DEV8nerBcb
zWU?5M#4zrphfxu(2J~+KRb#jCj4gTS%WiS)>FMbf6+2&P@>v|d4$K&Z>=B~B+WL?k
z<PWdH`iyg_Rg~=Bi#2nf%ICfsI$U;?stMH_u%`)&gCcC8p&~ThGgjzbvEFFEy*H}W
zASzl;&ak%D?^HppIvE5{pIr7%d=4S<TAPyY7p14ID?9LS7GM!9Yjl1QN_0oMG*F9W
zG_UykNf;efcYlM??^kVjbl9kUOoT9QTU%QzbGpd6PxslcMPZ>R9S4l$0=iO<Wu7-;
zt6U?qV|=vVife35u%QDofi(`@Naq{m5<IT1sHpPe(}nU`PP|q&rG+_+49p_~U7}$0
zL;cWC;fM8C0RZ$fCiB7c?f$r1)@!^a>oC6&y7R-q$co3?1C}N+#zRA*@UUi_ko3Q*
z8lL-!MMtCD-MI`z6AS(Hw#&*a2nU<V$xz`Cp^*6|v+CdtF{q509C6nxYvb<1xIPQw
zkx)SOh*kQ-{IOwVWMV2SEiEe>jReo?u2K;Qo#eJsz*HF_&&#EHC+trJNG!D9lXaKy
zk)BE-NYn?Da7&Au^;hYiT0fC6c!`exs}a*w)EQrJ%ZiQ7=~`=h@^oPsmJEltt6}Gu
zQValtQjg!<C_bn`u9yPkpPGtpXTje}KT5FCVG1AUhBo>=<5E&mVrgb(Veyx)Yhv-s
z0oI>hK#M!~T5lbNgpa*wGd9O%qj%SHkt-nE8}77r00ZcsocfHN^>3Desb4?SAMSvh
zmhNn&n2d#nfq|T&vj;`kS!rbR&2+jv`K#nt&HZ2dw}CHvNtV8DcmillR5{rO`X<|3
zm>3v1IBQj6U%!HU7^2USQL>TGDsIz@ny=V*U}NwXPvtcVpuO0^{?iYG^bFYJZAXuR
zQh3N+?*9k#gU(%FF)k=9Iy};B`sqCNwTnv1!i474@S0#?9^w*Bp%d%%s@TlT-2Cw1
z;Nb9ZQfao|d7Xc%<AWTEqE*aj%CDKLHLC2S4|49Gy<l{O70Mtu1O~!c$Hw}bGlF1h
z0snZwmEj>t&ufC<am&6l!xbwb8(vx*xz4P?>wE`ssl9)&o4VzGA*fP954e12oZa?K
zOB6Q_#m5I^8cmrpFL_@~i6_d%lW8cS&QY+iumGjMPXv{q5WC3u`<}D|D@Efqjf2GG
z*3B)W2eLsA@}q+XKfh)wY3ftgZxJR?f9al{|E3xPMgJbaiuySmV)GSlPhc__1+<nU
zBhHtwH93pXo@e=V?f)Z2N!B%_*VH7qc=;+WDfvcOLee(CVCiT2>X~A>O%l^1FOpaD
zL#qJ;5)Bg*^EGrq*ve!R2P0`(#EYNy&AbPmJ_6?aGFemTO=xPP(qW7|jTj%eAcS~4
z2|g#jSP|_TVQ5$Gndn}8Hu17ixz{XO+4OB$g0O<@OgwBquIQ08{PNLV1rx5~*G~6d
z6)MATfg9&D_F%V{Lmq8NxfZ-qS7<E*>!+50b*UbD+PuHOMbW_Tiyc$vHp?9F1O8$T
z?nhGBmr_~Bf2MYLD6h!1cAo<O|EKQD(kC(>$=dT78Q!?xxKhhEB2+p)>TmZWi?IKb
z|2jZkt!(+8gQ?p!c_<$BYPsob1EX#%4Sh`h_UFef7q>FIJ%TjDIUB#n(^6YxHEu<_
zSE4oDXk^FfzRL+&JI1^51)2!8lK4m`Wlr^c_vXm#L?9SS(^Ypt@=gV?atks%K%T}i
z%`k2xbFeyIHRC5K&vG^8;zNZE45avXAb*m1VgC=OWLa&zN2@0-W7~R1sFa@ysgj>+
z%&EcNiXoX$>@TFt?Le^m{JDhM!$y)TPmFnuhl12dBt*!yagmQ#!7u4l0zh8qu}9=$
zaGT*4art*Y=RM(ZYXXQP9ytSI1{R;p>N$IBOolt)H<kjlVQ7o_zW%-~OCN*#MrDli
zqt2gsS+5;Qn2G+NHE&ip+vHUQQ}B-D>ktK5nT7u5#-=9&^{K{fMC{@?-e!e-RsmR}
z6L<EIQO)Kj74pDA_o7y1v#naYMpK&YJ+T=H@D^0~IBasL-T_FkS#AUKvmPDw)mZO$
z?<pJh70s=2^iMd^g)AYxe}w6*kMOewLNmQz9t?78C<M44{I?-rSFdsfA2=ni)qMYm
zT$sbrdQ-x^pvMfmVmSDAJFhPs_x)B8S=5Nd#&nt+Q<o5Tft%<4_a3zI<){Ii_41u;
zqbbjEIr4h%$^l*;y5>3qWZE}b3huQ@r};4yuR>P$U?0Lqd)BSVlNCJ(c+d!Ma)ZLR
zp~FHhp>6KG9kCWWw75I2z9!Oc!6J!zFJN9=C#{+g^P42*ThO~!_Nf|SeVm`En=U!b
zDU?fLMIf~aZtk|Bbe6qEQr@UI>iGk2n&rl7@(qBB2fR=#rCMKDe_Nk=?7ls?>F(#|
zWqZ0KZeI0!Ju{XzxGF<*dB(~Ye^^Ml`2KRx0aMU~gEw!rzk?gA#3Q`5=SoA~LPYyH
zN_LdIDo;~mSh@#!9K$xfX1VYa<-hUE4m5$gt(G8vaJ`d}Gamo)%<+a!H*Vpf;C!Bc
zpH<Zg0C<)j1f&NxZE)v1V{g*gweR~PX+vi9_6VW>Xiw{0nnEXgh#>t5&P5FCs&QZ~
z9nbX~8zy5hHurm72mH^WH7|UdLqR4;e{@}Lq|EISqa@@JzwlT{DGT#-->Nzy$x#4o
zX#4mEPxR*c@KNjd3yu&&R#9p7-j@)^FKjW=fTp5zYyLec<YkenD&TCAupSm*(diQJ
z7mgM4qvFjn8{BJEBYdYjXR8<9^rqyl`D2VHm+8k0ZRLNx*<&aBt#Mw_+uY<^GwH!e
zr`CprmsWAT{m1{nIwF@22cEhj^M_LkIR-?2Y^L^%^Rn-#*Y!M0Lw)@+^0LwL{I2T&
zlr}zTtJVj3Yj!8yU#Y1k0$C4ovSQbUG;I|ULSCxVeC?si15irzjvN&<dUb*ks17wV
zXhC_d4>m(Lp88P-z7^azig1e4z9>u{tX6-k2SABC?fewlsD@DQEAd}ppO+gF_~KRq
z!n2B@u%<nee5SsCBcGu>oacX>FtF4EKREw$;EgP6EKWS?ljyRF*-y+p-sEo-?W9*1
ztc6i6*up-cC03>SQWfV2qddEQ{Gez5vvtbx6-ECi9J2dO<Vfj>wqUE*%gYCUQBnO3
z1=o&UbQ;%nt*V&tBTwqYHON*8h_6C$p1Pd#dsHi}`W<>IMubpInICxp)QLTSh`@sy
zkGEq3MUjmRo{W;Auu^ONX2Krz+;<tYkM=o(&I?~$Qj3v#UJkaY`AKc9O#AWm)o=3A
zAqWSFfU7B7Cd70yH1uYp8;tm;KfBm2^#3!oO|$X44ZiJ|pEEQVPG_ptqsWM@C80Cs
zDAi;4$!8M;mwyluLZ-3;6!es<DrC|x5J1rAA1!Th(W&bE%+%bZxS6Zdb+ZSenV)cl
z`7t37pmTjLG9v#0u7B#Kbm4fPSZ0Q5VMw9PLon{_?fpa1RJKqN<B6bur;_JA$!lBi
zx97XGWD-^ZBiC*yf-3{Aw>7A$Z7Q;T5w4Y0ZI$#i5@30LbnZJK>mTtX4;yufYo`yP
zeiAM{%m60LoUAsGhAnshd-_!tAJXt%HW+v#XeP_e&-G3cl2WyX!)k`SbHwEAioj;b
zKQoVVnM{vtp-WDkSF5$$IWu)vl+m_rzmG{HpK;kvgy+CRO~=$wR9$Ro_S2^?%c!*h
z{{ibOv>rImm`ngLjWj$XNc5r5Verr~#E2#C(=<rItmuQlz_T-V^r^Q$jRjT6mcw_O
zLh6s_YqF?y^H?UI){w9G4GjO#56O1GylWG@VJ*-j6C={f(ifQ%QfRdd|3}I(>t2C`
z-bR1M_l@p{S`hlF^E}v13yPU=!aDeZ(f}o&D)6>12N11wm=@CF3Qek-v9y}}l&Fe@
z{z2cn>tcRHKwQbcwXtQtw*q5J+QINPZzDlfAmXmOIyEgK$bISIVt}u53n#Jq)z(l_
zRaCu`&os@Oz(KV12jN^?8Ry+nJ$?f9<JUi~e~;5XWNEBtZ=^Mdfy832A*d5OTxY4x
zadjNv19%y3*DhelMrp<uz3uCf#^ofTQ^rV6>^`>?x++VbGU%8OdDwZm)@ByYGlw&a
z5o#4MtBuYyj1=^HU4}bWwuks#f5p9h-y-yfwLBIzGVaM%-Xq)cLA@Rx_adW5t*6WO
zLtD8mxkS0Oz@OhU$Q!#<w2a*3bbMT|_Nrs?fPY;0Qo$XwzB22piI2f!56eU|%;u{K
zE{4Z}aZFx6|6hk(LB`ro$8+izwA{+9_6Wl#Q){L+#|IvFN}Z!Eifs3oB!V)Y>7c`T
zeolbD&6lINTcsFRLQ9%t9Z_&!|8HTi>5|VnoRxW8nDTciMX~jhgQ06Rv0P<`%st%S
zK!VEL3>WCz(R(U6T!h)!hdAcW%D&&2>2=dO1gk#_V+czAi`e<kwo}!Be=u8P^9t+m
zjTznD8LB2v&SoENsV5gR9`cY+J`+2^p!QUa#9*D}3;IHASHYG9+dK=e#?fE8P{fVj
zk;kqObw`V>#tdADF-3xnwO~|ED-J;L%v#u3OJvZN(^S`1f;wB?r^+n+fqceXy}4*G
zy5(@!DhKKZw)&-1*^4IxqZYS6Bqo3MsiD#C!Qrm!smVgyLjqFZP2ogFPCX2zet5|U
zOYXlj*}BL7Y&hD#TIIfohDM~gSeJRsm?TH~hX;XaVwPFIp&ZQH?0J~E3IX`awg3L^
zv(qBbNXdIRiB)O~s9*|E-m8u^vWk41C`$_@AqjaV*8_79!-Bzy#n>a<dRVBWSLh$k
zpgt;NY?awMkP7hbsn89OP?y>7YH1qU%$#e!@=phS^SU%B_?G2EDlv!U1`GWRIe)A@
z9(tP?c)DW)_ayCA3=wMq?bB0I=)@9Rll9zNe)Z;*G@?W6|L}iKDvFKLfRt!|GE@PM
z*Hq6mKmNDuhEOfh+0La$QT?x0ADq;fi%ZLkOG2n+bc)zWpU^())ak{z@TlP!<@bUd
z!m*WMX`S*{--!R3%Z^hKNVICUFqCJwg^rGhvFzWw^3wgQQvs9D9_yVG7lRAKl968s
zTYJG%+K1_?J5cv4O&H8Zq$x%ILDjU7j9)YqHovfpl$=FXSYY@QU3I3;t{D_RnK<q>
z6wSc{za?Sqs;G}2`Ax#Jucx1G`hjKfpmYQ{e`;m8h*d?~{pMe&VcC_p{g&?4jo>L0
z)BL;^DGB;mRJEf&U9JZBIv#Mr&#Z3B!m2f<;JGQP#esuE^n+c81$8mGUqK(u!mKn5
zZ8?%v#`~%AZGO7mY5wqFiyN8aX>fMR`{qfRm#3~A5L5I_8|qV<M=f%@hD@4`U64n?
z(A`G8PGcu!cGO>A=q0wro~bs|&FQnV+nYo(%$hX-d=&=UkXL_XCgI?^j2{0fJ0uZT
zWai%}Yr2!VQNIq)c;YVXjNs^YSF?Se7<?%Ccu>ao>d~iz=|bXr5Rxj*o?Tp2b!G$e
z5fX*^U)=+D0O9?B^E`xr{Q(^xVxUjX*&+KA#OudxHO^}jc$^basIe9>ae!X>itT+N
zU8r3jcvWTRy<<Xfej(lA0VxG9u8%V(eqk3MSZ8#q3Ia0V$e!AlT6?wD<V-$W(<CPS
zKbU}A?pH)mwAb*StmR)NL_h4yDZvA`HxDanyQMyx(}U~nXH*7eoko8hK-t{oC-`tI
z6r<*+uZ(&NftA06$^JV2;YX{J)e02w)6oKU?I&@b2w+V3hJU*(s_O0fxIH*b!&KWF
z^f_cz^OU{kkZ{@KLE;0|vnyV4z&d`2SWmG$X(DdUyaSIzNEAZzhLv$?86(x8oIC;?
z+Prob3XNr+^=UAgp1{Q&!|@QderOg<uf#(pZ&5pSygLzV?pn*Om7Gp{n(7L`ULX0p
z{2BHdl;I3Zu}iWFR*-p*%}la$XuHOxq^|}r%pZ`_bw)`_l;7AExn6DU>xOL6-%0pS
za(V?wSK0!y90e%3771Fqe?n%_d<DlC>W$jX@k$e%t-eANlN*4<%ha;QhVotR)lk3$
zL#9iCM+<hZTf;P3h3uAJ`z65bqHAE!ggDPIsWIz)t!H(+{)t3T(j`eGym7}#KkELs
zDAzslloSD6jOV(PhpGq!yW5CQh+O-7rkg(Pl4@qW-SYTpXjSyzQg~EE(U&Yqs4XTt
zYFUCk8kYYO%tx7gxu-;^eLjl4tV+uj{I#qx3xfH!`t^%*$7|EZ0}a6CUDKHlcRiW+
z6PMxP0cBciQ7IMMb$H+wjg@e@t4~I@_FLG1=2S7M;3jU0;-Wyvn$~-u+s?1^FQNB*
zHn|^O;twzWi9Aa9wvEx@lkkDrQ96sS9yt7tOolh5K0Sh>FDryQ0U_L<x^!tuZ)UNn
zf_PJk>dPh93<j{bUF3jvVhgx*Z%Puin-L1vyIm0q5^a+%a?~)_m5Pz^zJGye9q!f`
z9e&B-eo>%$uKWJ0<-INPYK$&YcJeAbcNVvoWuz?=kCeoWQ7Q(79~F7J4`u<MZl3w_
zhaIiL?9AcRs>JRp$F+XL^TlOMn8ck&;KM~kbib6Prw#11H8nsF1sz2(CB38vN!ZVq
zyyjy-97*o;c*<Bkjd}|P5PgG9x95c)5L?8ft$+EIi?9Z<Z%}F3ktgrwfiEfu{}L0X
z*N=W)DIXh>0WxywZc!L9vQ|`_qv_t=Bsg`sFhmo^Hz*8hPgIw7f!Pgr8vj;HG6!U4
zu=8CnYzk@Cs{v~FfkdmOhu=}etvyr!n%6p?PM$@`-k^wZ(vFMWuuy`78|Qbx=s-P<
zv*^y;UB;E`wT|~wn)m<DQ8}*#uwI~4(6f;Dj*3dOyMuSYrnMI*e%Hd_I-LnVo}BOR
zP?4e{+tZUhqF&&K&+VH~#+<yexoPfFL!y9k(DOI%;vuzF$A=f`SqRn&I9#^HC#Wq?
zEFh6=wRq;}-Hj|$X&(&YcCZQvv4L4~3OBS*%}E^8P0QcvzSVfp{zD2}5m$*sY0>*A
z?W3viLt9BzS3_rdVQ!H}P?)R6w`H-%RO5n|Z{^1?hyA#SJ`#{-^|2c(TNRg&VFWy~
zAM6llg^l+2ghp8yeRMTfCzd`^L9kBebaWmQ6|E!HCGLI-(iPg)H_84;@9MNbGCUi-
z2j_Pzx|uY~S}(NgNcrUBCHkKy<tcBa`>MJAEJv*-dGsA~Pl#mkbYN+?`lqcMUm9We
zhI!chQ7%nRcFuY~{injJ^IyY3mOyq6XwUx7)ACK%UPXDJlh>jtipVS5w+4d2HG>Q0
zkd4P%C_07ExBuri_Jf}_d&%$PPqJE}L$t*P!>hu_+l8;?4{*@BsO|U;0#9H1Yp(jo
z$QM+Qvy6eRI~94Sd7MArK$j-zQbpOP{?YIC)PxcCnronZMi7kSQrPMRFhA-v5uo8=
zdGRnaHT<dfhPcyVqNsX*8V+%d$Tks}T>g>zOm@{Av&_cftwQI6^A8zsPfZ}56l$#6
zm@o-!imR{6&*-r)#H7(#6%_Qg{Ytf;!08ktF9V4mcu%Uf<5K{q$&e}7>bf1btT-)n
zC0d$bS)KnPa_k#P$VY_?Al{>-=y*nsZ0sb&*9;FSula3$OcAdm?n%xgAt!le7P!eR
z0CB4`=PYFj**e!V{lafdk#hu>pXc1BK|KD`%yKqqUhtZ(7^NZRbif|Y!1z-L)owx^
zQh-kn--O>aKEnkU$I`0$(<iPwlp>81NxP}iq0_U%EVenA+L3I)x3E!5LVmr|jx=;_
ztcT?j^R>I-`o%);%}IAlOvl#I>~`BI$cs^}w4Lq#G%TX;5^vy!Gqdx6H}fCv5EGU9
zrs`9eTOEERtaU%UIW%tZWt_PBBuT{UEfQVe>Bi+}WnPY;$aNR-db}$CmDi1>LrV{>
zi_M~x6pU@HKra>W<pLpGss+hPPPaKDS!;0;bdf)r_~4V1<at%|a?LC%+}$XOLYHn0
zUa?u}8gBdJ=^I7O+pdTPB0yOgE{^qg4UG;LW&?vl8*AIua-SZ;=D|diiD5yrA$1Ua
zU0BpsequbFgMl-|Ht3JCv@}A2$~KA)M42MmSo=I4*b9vP8|wn2U<(bMudY^Sa^D-;
zSWx#JR=d|sQKP;ZWhxaRrLgCJACw^%6A%54k0AN|I|7{~XEJ@DXt2!3lzsQieR6UN
z<9nU;=T^+;nX}=ha@%{u_cP$0Pp9nc{$9{h$g=DiHh%Z>k=fZWE@w{)F{xPv!>Fu$
zC{p83LnlDNfo8h-T%D5{l}t*$v&(SRLy6r1<uZU8>|;v#uEHbyYQIUfzx|)c>1imt
zILvx}J%H`PR-v0nDuiMbf5hWd73>6spOzdx8AQ0rA`RlNo*`?pqkiN>JDq7Cu4j$i
zQzQ-A<qB(!9yI1C9ZiS1-H--tkg<eIe)+dY0sB3H;5M)z`fYM(5oT+@U_b({c{$ZW
zPyCUOpd_rypx=6!*Uily2c1z@zMH~rBQ#&@^G(T1^-^NyW|&6cgUIHzO=U@O^Nwu)
zGz8DFAwsv+dX0guTqn=J<VWM$@y3<t@8eA6qAvS5q3^7G+%xmDa|zEU<t0r8z0tdT
zfcX<{XRqN0g-C(>;;hz<TUKVK!aoId=cgee9s0PdBM)}ATHas@^h$uGOYq;VWxu2G
zr@CQ&7d*EcB*D0l$Z@@vn3MkdXgL<Nn07sv2HbWxyICnUll|%$!6%j%xVEb=CpYO^
zQXYv(Ho<)bMoBYJyP8PXHSu|1$-DD!pl@iiJ(`B=?h-V;#Ko6u**M;=L-Y$0@7vJ2
zC8ceeU7X{hqsHVR-QFjNCAF5d`;$=;gT~?^cCDfZ1jKqSP9jnEkSOaF8*o^-qgs0-
z&wNwLT37OKq(~r1{&?`BB60!vf?N3hV2I5nfYlmy`dh%Die>#rJ(3Kw#k4m7UDIzX
z5p2OuY0HOK5^k-L2rBH5Wp;1s;zJ1MDvM2zLA?dobGMN!Xd?FpTc--Prj|Wvjn8W1
zM|21^?30ShR_b?EEDv)E)mM@b#b=g~F<a~rgdPL`-Sx6uWDM6{|5}@ql-N&=voY_e
zcl|0`qa|`iP&^*F3m4gv04^VoEz5cNM)q4{k8E5*-8aTtMhxF!P_xp*q;Gsd6H>gI
zf{}x+>^ucrs(Y!(vTE|hM)<A+X}cB#z46mBEI;?!YBgL;W?<Bul%_<LBB#}gi&13-
zJLS~)&<`;OxMdSqg797bs4G2)cZfeXL?7a$mS{>@){)%bo>)u_feS5Ivw5ea{6L+C
z$t1nCG;9F!B^lBw4W<{&))~q0>WRA|3=zqTR@UpxdkvwhRG$8I%{Xxz8GUm_!u1C0
zdmaaW(z0XEgYm%3L_yM{@BA(BJP4D%+bi8_gh8x|5Vl&ifw1vF_oGLm4$s5KtXjAM
zVnN?I)dz})qeVjo;4n|3GBM6Pt&>Efj<PVrOH6NP7S(rB+35rid@+;2ik^=uvDn`P
z8Q=PAEMscN@bzgmSKraI8v0dAmLuXs1;-DE5bbOYjkP6v^Gb{z;G(Q5VEar|ZoN89
z(bNPlOTP24SuJ+aP~;dKxp7dm<+|d4*E$~wgvX1PFCG94@E2Cg-QU~nchOL}BkM22
zzYiW}Hi6?+%_ZS5>!~-yP>G~aaCtvz>(NVe_U;_8xrD48i`!B7+)6g?lKk)+L!jLj
z!u3#8cZY|vWLqE0v=1|82-bJB+LBzdk63vD<q}sLt)K);%E~&10pV<=-T1Ls7qos~
zlqy^}NC1qh9}H`bS&6gd8M55GRzGVFOuhPu8W8@JsXCw$5t!DwV;>nlYuvg@5O_xM
z(UX??(o>mY9TNB?DbW!cDa9JVg`zcC>a{ih%jQpk>0-02==6)j#&v7rN2?BaDw3b=
zg(t?1Nf~|^xhPfsq3_nS{7UL*(%&x|%hc4R=-7b(E-sFStFVE>G4WZM^t0X_&bBmY
zh9CtGsL&ALiq!L;GO2qO&kqdI$OcuJ6U3U(6{RH{wKEzlr%oudbCDOq(1CPu7;I#g
zm2i($R@jP}(XQUSbG0^X^BAu&LVBpJ6g7;?#-&45wz~Kv@a5~B^j#boG9UCd6*<FY
zIROFVTPs_^p;AJaFeX9$cbE)5@zT4KsDvO@^^@<OI5Jd~;Y9=jj}rU;1&Ja@$;!Ng
z?`UMxlpm9R?USI(AqZ<5&qVR2tY^$GD#(KMT&?2!1bY#Ngk!La8QqX&!qcUk^p3NI
z9;PQpM|^y2V6GC7H>f&9vGuG*f603djDSlh?rW|tPrb_>%9}uFh{PG>_421WMj<g>
zRU}`Ssl$|}arjH$N0=?wQr@lDFeGZ)9{Wb@lpwRacj?e~Z|qj9u6DKS$bp_6cG@y;
zIBZVR4%wG~QfI%yBG<v17RAq98~(PvB~N?NnSPU(0L<k=@8R6)(calTrnCp3APqkD
zMefz|MVhK8!Ujz24<2`KPrq0!udE@Xi`<@*kqp_Yi=(DC!BSzl`cX9xnFqQBx(7aB
ztB<|<GZ9Q&5>E)6U-RbGe_Shl;6aOFMO7T9DD{}4Vl78&-uc!~T{x*WToJ0Lh{_93
zb_OP9#Vq=z7F3lX!c)C4gh~Jvd<8oH`o|#0ua-TZLe}MEn9xMt-Sv$zQT<XS_KKp^
z+%#pyEO9Rg(_eAXVlrHAK7<EHzp>e755h?dtdBFIiHIot{xZ=^c^$BE{0g;ZuJa^%
zUQJDs^;@ET0q`0^q{`G=>)f21KIwPanRPg;=x`(T6zjH{$`;F0*ysfCNbWkdpMLIx
zm;Bk^SapZ`xaaX4@Pw@HLs@8xs>12aL@cy{yj{^Na>s`nO4C};*bdcZLi`a-on5dK
zv$9S4q+8_`7Lo+0^?(?3=T|7*^XD&zC<$(BP#y|C@v1O=(G^JJhC{?(Tm3K~QGV(_
zub0HSnp$Y;+o2HZumE9?#?peJSNF4@w?k5+W1~H$`;m|5%bye{adyK0tkQYT?0J|;
zX;>@6EhOp6cu~o;Qb0!Q8c6$E`t3m?6I1?2H^bl$EvZOj7HgLG_RPFiB|2?WYxa+7
z@dU`yRNq?q{3wT`)YY7SI_l}&`un8X>t&06X&%#diiGvi4p_kO3|8-eG!tN}sjgz5
zR91#4ETv-WkO`9*JDNQn;)b;4lXDx6@OL_m`FBg!JAH97U*GD$BHYnh--L<z@w46;
z{WOXDGO-$2>!roZ<Hy&%27B@~w@;Rd1w##jVG`F%Tz{}QKexq^YPgZ>z4tf>v0iho
zC~2B%pZD)!sR3LNS+llis5tcS(QaQi{37Fm?Kzgg@JOdV>(i;3p+vUJj@SCc$Z$xR
z$Z&-2As(0^)ZyFCUS-GAX-?OsYtEAF*77=2H?O^lUe860ttUKc3(@M>R7l!*#86-5
zk52;S&xVpBkGoQW8?Oz{gyt!WO)!0Tr@{Z2RfA;-eE{`51l`=cy#xgw$E%3%5{z|V
z)(I{g>mE7T))!GkD(4%08THRj=RSPce6(I9q0gks=bhmnzI}lSK;)s+oo&!x<r|SB
zvX&*FKi{INl!#o{?~_BICopl@_(EQoTTD+=r}Zl>00v&&_vG#VT$Ou}tI2|kGAlAV
z-&5bpMG-LeCiIvY&%@-!F5aBCzS(gDQvwc(;f>8PzSda?@tp{FG1PjlL+*+#4Mawh
zsQ!+}!rb_2Xz*wC>)pHhVZSYcs-g;}>3eee-rd<-PL*V`Td&-Up{=_`Bkp%Qc)FS(
zL%opj`L6DuREemO$Y=}tVX4rIQ~l+U?}!n+Wca`t&!WWB<?;9L8bR1G%etsOit38<
zQ(V4Aw`MnOyDUBB83NQTj&OvCIab3IVWXNLAo-B+pP^RB#^I^7?Ut^G66|m41B!0G
z29in+aoa4H7@QGcC9R{Zm9V)Qe4VLp;iS4*;uGQ%?&dq2SQsiwSpexBDim5vA~wz%
zfdAac)cvZlKJ`VI?Tg)>Vr)e~)Q9(APotY~E6Uw^Avjs)HLrA4-T9SCfykN5Yr6+W
zA!E9nOw*Xb&taGx#8h9<V)nQ`r6u#aiQm%^`KyZ<ZreQ`A)}hMaP&)XCH(1gwXfk&
zjQ3VEeN+2!^D9_Fa~>n1x>JQ9rDVW59}tL$_pi_-4eIddz94^=K2btAl0cGSI!#|`
zdns?>PZu&S^860Tf7w*eZbKo?kK5rrt+B&1qmz+n+A(K7?|U#`H+m6&8p7w*{A4I^
z?l7prpkvOAqDuXNZSRYo#n8cm%UPo0G?%cU<K<OdnrlFoV59fy?Ap{$a&T_KARy-g
z8(z2mtX#S1K>fwtTkHHHY4_cjp)ET!2a)0D1bQF^ERKK>s0#Xt4anrIH@DoMW={<M
zYT*$~x027|_4ux<l<dhM^mwFVx4xG)l6Q85#OQzJVd^)hCvAGZaqxgO!QIDg-YgyG
zFU08l2Yw(K?zrFDXb&rq&+Q~31E>iZ&j^Vz#F)m+^?$9<69)DI>;L(nGpiomu0I=Y
zSYr^tCYI&?r6IBrH&P8irg!Uii{+CQ{wdKnG$w`DWmu(LX%{hMPN2!(tk;2!NASo<
zN6E&|I5^+4cvstHAU>ockjzu2gIp96@l+wDwNZ|;CcM!5BkIIf&_ko13^0sho*Ef*
z`Ta@QsbOMCg12AL?4*R-8uCbd54U^`2`MUE9;`^FACw98+Sotz8vCl|o&^C%e0cwc
z=LR7Cg=&yCOV(+|jMqUOX;KieA91KY9Ifdb-b)SKmI~~X>!WVe11B1NxcvJ+<JrL!
zT|_;qzCf5K4_-|IbGnmFU|9-^(`+*LjbZOyy}>UoG4^+WveNX(;=8%(=*9sazit$|
z$h5}GmfvzpPaF}n;Yfhc<g^uL>@-`89TTk}KO1F5X*sd#iq9MEL2z1BSYj(4F6QF>
zUyTi!Z*WI|IWZ#7h74@WWtS>;*1n0sz)%A+9~<PD$&n{tc07p>fblZm|3au=HGQ^F
z_Q%wD@mz5vg0l$A((Kr}nAz($9x=hjwv}-aIb~}`CiG7q<o*``JqG8iwpX<Tnp%C;
zM<+2a1KH*p+RgT3^U=1?x8dPrKPn~)mu8)`J^d6N6l5G!4OAS*2WMW40BLa>DVag*
z2FGtlN-w2^9nyj)z*wC(&9B-voVXt|(E>y7VQIzl#zJkUZdcj7w6XA_SBMcA2K#S4
zdt(ime?&WOgYViW?;W>a%VuFxSaSVsd9@BoPIe7G5I@yLB<sBlFkWfaUn5A<9j3o|
zh_vUizOC&l6L`W?yLi2Zh<)7*?aSBNl&rWK&dGXSK770v15WG2HNxZ#O1>J~GY8)`
zF7TsRRVF9IMF(YQf#p}K`!`e_gEv>qk@w@*jvUIEo%#(mpQuKb$39ECf%(qZE3c1_
zRg{FRC%IBj`-1*I^>7pC>z$CJWk+RETxNdn{1FDW&TSk;y*oN_5U!B_eD=Z1kbc6(
zt_fPV=KkDA1~hEg&~1FjMSJ~Q4cD;HFC+zP$xdmb%c_J<p0KbPiFLPt{%el2s>4SF
z=Q}HpD{9*_s|eTB>W|p06g*<5Q~#7ZNCT83@$HYa*%Z+Upu!ZK>AXCqv-P|?TPwrj
zCInVcoWZnG_5$f7q{JtOX2s*~IMCXljsC<4n(X)^usq^>CUEW}t1Piz`6=|iAi90?
z@=*Rf1kCV7$c}h>a}H7FzoJ++<5skSMoqp@z)@NtALl-n<bg;tuX+t9kYHk7MUoyV
zIfm2y0e{))fWeDekM&8=mtKPpQZ$UXDXoj6EO5!*lIMM32!`z8EjBFW5!LYZ^mg~L
zLaEafuyy(#4sbB`N;TVX!4p9y4%;r%)k<hy{u;ZC-xvBop$m4!leI!2XD<#l1gu&k
z&*rm`t~;^MLW{OzL6h%9EFXY2b}hZ8XU>}3@+M;XP8w5T*(lJjp0HEU;d@&1<IaI&
zN{Lc_{<{ubql8{5ZLxYi;z_K4TkpcvR8$DSmO?cuhPWl+sC*vmDUfP0JWcVEKFXO7
z{$vh(a{ny1MoHK;Y)wu_3wMpz(07(hPb@rZkTtDNYw9T`W0D?u4sGE%E#%Q1E{WKc
zQs(8F8tm_^!c3NzTk4w_pKp?qWU0)tb4F;P<^pnxW7p4_F0jy_R*};rZ1y&`l7s=P
zqYccs3)man&yd2;?jEHbc{$ar0!&BcA;eB|*S03Pg*hK`mn}D%QS3Sp;6Fg5p=?O!
zyRWQ_&E9uSC$t_#Px7fBAP;7Y%0=GPe|ZmH96$41A}B(WJE_#1brD9F<Re!09M3MA
z$)!Gt^7gqCvP(4kOE(_bb`C>)=g<v!X=fKU#zG?y!yHn3%$>GQROm>)hHe(upd?kS
zegrag)tC96&~cm7g5{(AOA`ism~omNM72JOmjc*yR5uMFWAwAzrmIl7Q_^1!@-EJ%
zLoRd95<&g(WWP%|y3lDSkWalT>=SL1M5CUVD6CFgB%_x5{U9lKwL>c`bUNV3kow%e
zS!u9kn!Ru=wQkf3f9uRXFI;JfnA7_<tcd{YqN_+pB}>-wO3T*JOc**ZBR4uSY6uH8
zD$YU)mEEJOgK#!C8DK&MOXko|(=7j%9qpIG_4T>TGiH10iazamjl4Dh@GFkZ-22yE
zy0hRpe<C}R|Bx&R-<t^)YX)E@@@6|8<;s8evaw!AJ0ju{`M;b8Xyo|y@=#xbov`HK
zU}a#Cy;>XVw5l2Ek?i@4$S>k&NN8zr&Mnosry>jc)jz_P;C0GcsI5YYX2M}#!q|>y
zJ~6JL44FWTXO`n4ZbyW3<tg?`mrG=yx4)&vc)YM5>i#~;laYH10D=o@Y0nf$!YBFn
z@TWWaIaGQPP)}Aa{^Q@y|Kzs~*J*t*09|g63KwGWtjDaE=ONihd;(P5FNqQP!Z-5Y
z<!Qgkd`^f3rIEAj*wNp<wAnpRD6^~@yr=BB0fN@@Md#bQJHCZggL4%DMIlWz<c0p#
zdDbgl&oehn>E0y0fC-tk8!gK>IJ9=J6auS~G5Huzq62f=0!o_Zj6{W>??HiyQMMJ`
z24aXb=brM)h{w-shRzFuKOx?I99r|XR8HECab@a$7~P+3t^ND_5he!IxRuSarVOAQ
zwP;X5X}6Gt-65icKme}4z+lWk2BlVYH(v;Fx<7JXaE%TJ6kb&3t$(gDSE`*&OmfKa
zh;BLl^(?5Icz=O^KB(oH-R%!~(B_;@T&;_bhf*ZcL(PR-iSH({68u1M#a~F&O4{45
zxso7eolo<eDpiA#3GKm`IjI)Evo0!0TlFsbuTn?-+iPPGB{few*_ZGL)R=m#!fz{n
zD4y0T8eo?e!1&`6ukRx5gB8(A>p8Rp)t?T!3UjMtKRB7o_h&a-L%K?>8~f@Bwci`Y
z<nN1YhU)U8|0t>RTz3Annd<-WkQ|0bBmK4|fxIE06`?15*<s}_qbOsyk8(GPG=ud=
zVdmK2Dwd52Qt>0%wg(G$Nwp`$dUQYQnoe34hV%IH5HR&wOw|Z(J2;D&0|kR0<qO?!
z%7Xf{hMuh&h9wCRrk5XVV9JKcKnUo$PG_GHqNSnh9`=JWx5#k33Y2>+{AkYChlr_`
z+;ed9UCY?!=ht6J@xOEWAw>~Ln)e#N-Z5AbF6w>^{mx`T@^*u7{5D*uad80l0cl+F
zqxf_x{RWa^v2byG7fb^xvAc2Hlq5BE#kT{aTHfP#UOaxF8k})VB|yo$h%x1Ns@=!^
z*FXR|Wr&Byqw(Q<X2g<y$e5T9Ur2nS(~@~{bJa1>v<l5?$tw#AYfnsVoe@StAQ+I-
z6li>gh878v4UBQuhJ-opmcQk~DiCTrKQ%IN1Jn&zNC}y`pi%XH#D%90l<C-t$kAC5
zq&`Rfv+jX~s*I7-`C@V7oh3c@&7(O4wU~BL>U0%KOk3M43#>LMC~uEFd}Nr2fla8)
z=HZCKqwksDnK6==J&UjtnRg7<{HN5doz}6w`65x3>4E69(shR5N~w%U2xq#%f5I{%
z58O5U*lUkOZgcFJzRfKQD&Cx=#ETrUCR+7A6ZFD}zqnG-l~jSM#3S~Dvqkfqy~jM~
zXUNfuI;WEMMuq`5m+D3E)NXbIi2ed=TT`PKe_n#S_*zk(1$j8Q9&Mc5AZi)Fvaekm
zTSMn1(8!#8w%HTHg??~=pUK%!UFGTz9P$1M^ycy7`DAJU_)pHo-4yzPoyBIfQejus
z97;Z}1)HQWahY@^+`Pv}xS0uR^Y4(VgzvS<5BEJf$i_|P_UJkHP&OX!joP(6n+Gnh
zs`}r1YkF%-dBiVXqY*W?+VG2rA}?G8EVDfz{si9;ex!b=OOPRY*<%08zZcB|fOET9
zRG(g~yN=IP$h|~2N#}oAM*R@tT7>5XwOMp(8|vyi?dQh27H7A?tTR-Kt?BC$&8O^C
zeFXFGKK>|?VPKKhTk}ew0;z=KDij7q>$1Ap;QY%;vOPoqq>sG@7p+^?#>0tt%IOE;
zNaW6O_2Mvlw%E;#iCjAf2()xS^6Qr$UH2be>{h(dNFEv4ZfDcFr~YX^B*X1)U{#`r
zyd7q5<ZM@S)0}nHUH6A{o^1VhZf+OozLoy~^su&p6MNwX8wgK}AaGt+GvO*Qp{SRc
zl!Eold$ZZr^44JE=Du}t*G6o2Zm?kJ=8@Cf<mu*ML0~;TGWt1))d^5^O3aslY1BBR
z3q!E0shL2Z`pSnTNEkCH4C8DlFrK}jhOHAnt;dF{ea){0hIU^&>X{Z$b%aLrp=Twe
zro@9j3Z40dskK=Fh>j0r1H@+kqN0%QoYf$B3>>Y^b^Y<{#Qf$iuR`P?3VTnM-ZXY%
z@hM1*Xgl>bUd;egWkA;a?RaLV_rY{hQNCLYvKxX#;+sf>yo}_qm$J6T`W(j%%B{%*
z^pg^%?Pf#8ORoZ{C#JJx5suNPUy1@)mV@RRd@wH342N1xPZIe_&Jb(*0e;{>%_o_N
z@C?ixF)ytsD`#Y(qwiGIRt>XT1Jl$wKh&Uj^V$)Tc-`g&0Ny&M;$h_A$sapN9nZX&
z8$(nAqbD`WNvje1e(*2ef^Vjk^ZJNHy4zO|Hy2nzU6*?9nS>-9?48}#F&X)wUx|r{
znVD}#&Nd*%pdrqM_CNs50PT3dal5~Ge;)kYvP586^Yg4(gNTo;6YMROl#_yx<s&Kq
zkKuf7fp`A^vowKKuXLrACzO=*6s@m<K3b@#$S61Ww|7pP2ZyI2@TA0^Ji=@r9Z}ld
zcl2zl@=yNT&^K&8--!sAAccngN>0eltn>UlQu0@qw<^eZ&mLLx;TAMmbrhE5mE{-b
zlo$N{`?nd)b1bN@Z?6I&ubLKIbTc@QU|tjIbris<fom=yU--(IH>W-=KtJ=qp3H~7
zwc8UFf7vsL8i%u^FP#b}W#~n|leB|9YlXMM)i>$RG~@~UqTz`WC+RiIgc(VJQLxji
zYyL|o<sNiGVN7IXWOTc?05`0bU1t-~8DEe$OO{9^>ivi98~=xA5>@^w0rR8+HZV$u
zln_wjHQ6P&;LOV<oHR>I(W1W`%*EoC0;d&I>$94ADydn-n0Ajho8H<ppb@MBLinjH
z{Ux`O_^OAOwgpP*k8*@I7f!9&<dTyY(%#Ezi|aPp`Cxu1QN#v-qH@l0;kK#Ivob#R
z9p&1dm9H@={^>l?&WB_p`pLvauKhKY24Eo*I+Xhw?iAKLo|gut@EVaGrGG=g!ob9K
zx28e}Wy}^W`@c8#z;R*rK@Z9^WquTrVm7k~2}&w75!rsN6S3dh)V6gXQV<q~gw(dS
zBWSgw*?P2+ws!xbY9V@)r^SpTf&F!X2c75~F_8jn3{|yprBI6S^BwL4*T2Plj2IKK
zbH5}DHLyd&SW)iVR88o_|7q+@!=dWmIBpxe#@dW!5HV$~h!{(#hKjLop=8f8NcLvz
zMApZ?jk1#^WM5N}B8m_VW-w$OdxPeGdR{)S{^!NHuHWl(UFY2AzVFZHEBa*@4!*<Y
zHGR~mbILj>%7}pA(kU#0$sI<OhW_}k)5=#g)DKyn_8&(){id4<hB@*4!-=QAYp;;8
zJNW(&3uZnxZGKrb?jXy@=!F)h)`g=lUkcPmPLk6eRX}s4s{m#9{=AjB7B<N9&f(}$
zY$;drmYx<eG{D_KKgh`7M?y9deDbt5UwOiem*gyYJpBR#Iw^*jjH*8wTf_JTdtJ_M
zfaTa!tQjto$VX)l?xMs^-d!W(t_#yFzxt8)=u<+239(q49v$4CukHiQxeHgsr-thk
zJ$>7Sy!mBl$$To|xpL_nPwZ24dDdOE9aRp#?%_8}(bKE)FZ;KCw)_8%m|*dg7{wiJ
zYJ5P^?u|uSu}Ye2=$War!EsEN^>Tuo_BT!)roy&yx^H7nex>qk(D$;_*!7CKr;cLt
zqe!pfeZTS)#(*exU5>F$aOTSg(d0+XwpVf`{|0-ISJ6WP?J;xm*wnx&9lmz@*!Dd4
z+jvJw&5%&)!4pV*NV!~#g*Ve~*^|Fde_PtppCOlKCfe!h^H=geY0!0OpyoxMWhC~<
z7$z@XYP^<W|Mko65jHzW8L*-kQd!?^1`IECMw4Ibi2Bn#Ldnp~Z8#I~#eVBe#gkoe
zQO_fOHTQii<pw<RdWp&VEfX2b+PF~Hu;jc*z8f7U8b;k4d2TE#IRf-b7O1uyq>bIo
z(W?a1D6!R#Q*9@m?B-*C2+v&Vi&bU!EAtkvancBd0%R-DrUCX))`*xfN=aea%XBF9
zjJfJtH<S#WBtdUDX){?p?sl;4_i^RdiUcyoG{EF0)-E`7v45s;M5%o5DxX9ES4Hre
zSV54rKZZpDMr<6xhoiXdvXl;9eDJ~96R2SJ{F5M=oa5(D>d0%@*5cZ0(qy-K>QzX`
zH%4ozuMg>Fg=B!3m?+3Gozf-W_>b;6Q>}Q`Bt6+zYnT>CLb(>oxwW`?#Z3F%X`Y3U
z3~B}2tM95k4g1Y#xGH!h#uJ!MTHneX!5_Y~Tb}gSnK;?|jI2L7z;@GN4&>h@O$``y
z#pbn+^<jh|HTp@y>3k%TXtsV^50gebk@a-h#8>)W>@9p!qPYB_2>F(5Znv1o(ecNR
z&&cEe2M06~8A}Q2=yky88<`vX4Z`X^`D4oy{Y;(wcYS*AL8IysI~+xCnFU>2cH(YV
zuRyI3Apl;yWZD#t`3B__9zZJmdAI1XJr6I+#jJ14i~$(jAN=OXL38HqOV-d8qBGiu
zFnCUd<(%9l91C0J$@=vkdJnPw@ed%?ZGu7qKr13RJ9JBaGEd67IwL@uKfxT>zvdS$
zt`;TbiAmG+vr8mrr_MkD);aGoinY`gLPY*Onk~WaJiVKP%3(t@sfL%)5Y8;4F~IcS
zjyW_6b@sXG$<tu6J2`$T-mnV(>DnLZy$%ySi23m=z5x)t%#M=)MZ=!As*AtPJ$(c1
zKsb{^-@bD<qw8n3xKCB9xmZg?FeUZ|+y#<u+N;FFMn8F!!-%pC01{OrYeCGansDh;
zr-wEk<3y%EcrQTSdu3dur2f85N?sz%%B{Y%R!ziPG_5@b20A?}r{&`&vB8rGsl%U1
zG6>o)E2(;2jP~<oT{9;3_$_+YHrDW~;)|y4#IxcZUCK(sYGo_13qTuHaJ5SM<2v7u
zw!{qK6<VGZWU@Vn3QW2@thK2D;EQ*?G{P6YaDTHSWtl5C_uKkEXadL)q37%ez{hyP
zEF+6|dCAeKM6xGP7)G1yI=kKKh<18ZR9$<a(IN`RwlW<)GA=-8DL|2&2Zo*}%*+KB
z-T3pH1NK=$qp%ix#O*>&zq$T8-qGKGya1DAbqyeges^Y>$2g7PP+S<L&d$2su^CLx
zZyV#{kl@!dz4|OgGniL5F9`+xT$E#}(mK~FD8QeUW@czN7qe<1yXW%n3Ws$3Zi51#
zA}Uk|<<GS(J)Iby?gJqF?F%wI@F+b8x`ku29v-YAaz~!#bDiXiu&iT{MA1A75N7po
z?OjlkC~xYf{|pt~xV@Ml5`y>&9<WQpol0>~@D5@ku^O!*#~=2kvkKO0hEaf>{Nok`
z!kAkjvu8ROA$#r#;K9b%aLv^DS<dms<KdzVcC3$j8C}j1H1BG4g2DbRN~Rf$JqCB;
zu?f$ZvCu{Aw;B5Q<8}T<o?svdW9RrVj@cXS;sD|v75sjR!Z1;VL<v;Oj_?S^mDcU8
zO!M!mUjAoQwjK}V)sxfQnl2*L6i1`q6&4DK2>!mXsNpwg`?zQEfkV*H(Gi!v8ECcZ
zC7l{#szr1Xk-{q)6<xVS^E1$yQ304gOIcIkHF9=sLGof6;ytW>a=jI5mQ}f-Op0fI
z0FfMz71D6XXifIc4dUu@=t6#U<=AD<Cr)jr+z+)wZAQ@rv^<?i#Ykp^ziC-v0aBL`
zmV4l1f@sMDI~eb}tA<dRVv&9Mpxlvz{iX?>*DjI+>Q8xv{01PDQp69d96{__B-qaS
zVGE~-VS__aaropzpL5S}u!R7c(B5!sK|UVA!&>z#LjuTQ5BXyTb-h5)_+%;uR^~Yd
zq)X)Ioe91~t6bNCn8B%g`r26Nn0Zl!?KL)G>#w}IBBw%LQRp++DAVMp;GVYzsU?nr
zZpvsPUuufkn4$(LSXxo88p|%_5zI6n6m(QVA9*b?!<)KF?(b;nRI@4S82-F6Wq%cP
z?O<d5`Wh_0z)!tFsO|hI3CCc{;yS@7bt_PHA*?Jlqw%n7a8dbzO?N=xLubrpf5^2u
z_i9jGgV75ht|1HNF_OKqCS;tR${GDwMceA+KDS|4$K0gJXoELR5cZ2s3~6>#P4A*B
z4KeD0q7xnabfjZ<Y5O%e$7Yx9<Ht>rmP|u{^lnF*$7_|`*l#6pX+u6xm3DCY`ujva
z%d>%*=?AKH4B2viO-f?Z9?m<Baq~2D)pC%L0u?tJThE<itux^C+gVz|#~ekUBN<OK
zJ%Kjy&JW4IKl#M_Skf{iGc4TLuq6}N0AE~kKL0@@2s*#^>||=c34HOBwu+kafHOs3
zG)sJnvV;yFL{ZP6;Pt-VyOA|lbl5^F8rU$r{KsMv;s~n#LG@mi9*fUwR0KU5c!f1I
zMkokey~(e);!7mZv_~`8cAm<Pw#>W{yx^7-7TQwMu&Ia5`=q>ye6N;pM;Fk^eF3mA
zy<je?nvy_1*>IgyGpx^^^autlXmyFjZ82o_%YT}}Qelp*f0g9hRpEt|ZzA)`5A~@<
zvG%B!;y5){j&2d&d2IhTj(DN=>10=!ao;41Rd^btFY%v0oTKp0iSvtjE7>jH-Yj=6
zy5s8ou&utk2L)lhA>as|ew|1WJh)wOev9;Yf-mXkd_{9iE`heP>tRPASG096f)-MX
zW!tMB*neK6ND8aUEPVFfYKARI&dZ{1Zo7MPbz$=tVLedT)Qi%d%apO?bO6|p38aO^
zsrU=GdUGFqx*XVhQuU-nuyO^e+>ekH7`XciD`(^9>)*D%yy-JRjL)n7Bmo~t@c-^|
zH*xO?Sj5yk<){4XdpA;&-4RuGErr*|<uaM~s$-G9Va;q%o%9qVwTjY^^;SRx))xJy
z^!=)dGdvLm{RJB{wlGWoCv#R@5>OlWw<U69#?hu(GT6PL%5p-gu5#`c)jANlr({d9
z-iF7m;bSZS6x3+4T>kkkDi<oqFR-#Ie9K`@wjluvpY%_Xm8lSd_6^#~aK&YC8-F)4
z9J}P@Oq#uTp&fbK+T1|PYQLzxEQWIKsu`ct&1m|s#@qzJ{L`ssIO}oGGJF0lUpGF>
zZC!V-=f&IIIB4cz;i)fceEjVpsj5d<rU}y*gBiShi(uWYNCy&e;m7O5r)$p6w}J%}
zOC?Sq2|e^eYBqq+IINUv*<6n%5f+w+kXZ=;K#~%ZX>!jhqS2IR7I+w{puj_~E(_F9
zAeCvxy^`bm>~1Xd)V|OlU9Mr|0c`ePLNB5tDk>^^eh_p785q$;A$71_&wRyyGk)Dq
z7&^eg2B^#)=Us-aS46^uD3nS!R{$#Khw|rSL;3lk0A0QcSUxJ1jEp_b!^LN=vzpp-
zM$c(>SN`gHzKy$Y6u@uVo)U8=KEJRqJe-zQ=!OBRg%hC?SHKW=t|o5knP>#FSloYZ
iM*lhg-$w#f`Hy^+E20m?Sj9^NTzWSRZq#Z&i1;5ix)SyP

delta 53633
zcma%?RZtyWw5^fg9w4|o!QBZEG`NP~?(W*Sy9Iam0KwgZySvNAZR3~!R-NZ_s`|03
zR(G#nQ@$}~TPgH$F?6jgG@w1dd=L9lPf*y_)+YXm5(W}d<ohQPQ5bPLDr7~~?-($U
z2qH2{zeJTW;?B>{vCxe+Cfgn!ye4{DcC#LIAzjW#pX`Kg(s<3TK=lt1W`d{hZ+7Dg
z*8kTF_`l!!zuyUZ?`pJNf4U>!vR-R8S&h*FfBIgyw}*v=m6VjEs)}V?*dNq(<!Zax
z=rURyO|Ve&JP+u(4A>Cede0V*Yc*Z;ugXb%7nMLo0V{~aG<RCT+56nQS6&cs+icXi
z5D=5mQ4wJhaCiCT=El{Ag_WkFlW_C13v$9BfH6T!jV8NKdapVfD+@MD%^stTXZ)@h
z=!q(`?DY;;S2s6E-`<14Ez6p?=Csn`TRoetkHF-^JD}BNFq=}<?I!S<9@bSCgO1lE
z8QSs$j+5hW!+i4Ieb}|rpDCrpMD#72&AM$3U@^JzF4Za~H6SJ}QBPGyU0PF9T<y*;
zJ-xLqv!R)kn}C87rrY<has1t=vqq~4bt`7$YHmz`+$)*mR!%-OtD#oFWo2b>r@6DZ
zt}=*}20#+z#3S2ijP80mt!}VcYPX>gB4fp)q6qZ^3z?vF><&^>)5wm?r)%M!T9u9b
zK)9)8n^KGeZM570qi%lBt<f1xXWfAc{FpUC?IHflnr0eJUl#>z*V=s!R|e`2LPC>F
zzJqyE&4=M#ZQtfM-GXh_2dnrm!=>mf@S1}j0D2-m&zZsFg!;&e)%~v;H97)j289*W
zG(BkYg4qAg&oX@Pe(OjFHrK2+U~i$K3Zj%O`Bv?gWK|1adSPJ^;=ggwbAjLPesIy;
zq7H{4*|RAnZrMD}>$!`gJfT7nu+j5TyOQC}_t8``dxF72?ne)_q(=^yJ_<OiiISYy
z0A-N-&ttkU*H>mDPx$AjyNt%OH91)c@lo1Zg}&jzyNTUqaDP(6SmoMFK!aKAg>H+z
zvYwi}vR|OT_rrqTYqZ8p?n8zfh&r#(=wNHLAm7aOt<FsmbkqF%DqNbb%wkg1TYylW
zXf|k35DA6u?R_Rs(q6C2gWvOHxnB(#m}p-2N>Ei)Rn}EoT$<TXd!?r9U0k1#J@m`u
zAwlAtZv6jCkX~xJVkFG6IbDa-9;Ie?xe5!gzT1cxI7n-HnA~L~(E<u3Dj}B75AY7G
z%IGi5k1<ePCNw;E4W!YD(_CHd+yZ}_J1X4ljSW4|cYntp>@*C+En3H(mI5PT_A~Jy
z@cgDb42s7=V}NQ%e3pW7Z0q#$*G>gJY|f@eH`j-IG}PF}Bx50@OXmijN^0hwNg26@
zR}El$AHPu>+L0%<duKaQai-o8DMEVmG?jnD`-Qxut^_c5Hhk*qEveZ(+h3jxwAHH<
zaFtSu_#JM2So3y0%KhmA1qgfKcio9sWOqQ28KJkr&v9uZz|T>co}QnDGa$)0svJ@9
zbbMcdQSou%WwnK!0iN=PlC9#9V}har(&Gd5tZM?&8~u|LO91Cz20rgo!{E8GwIKz)
zya$ACua6y`yGN)~20hJh`ldCa3VuWeyY)X-+++yDJ3KH{`)K9>31@u==oPH#X}}od
zV{Yv1ZCGAX%_7Kk6tk*1+gI)d^YiVov*2%e7K?vDZn2TE!66(%WpU@9ecr{@Sn|Z1
z5v>r3A(|55G2rFJrLL`!l#_4kp2})h3^AWj)xGO!m3b0<sYeH{Pm>&XqV78UOl6u&
zz+(5v_rgKe($@pH>E2=q@^Q2*?keM;*)Dr$JbXvle7Ad*kk!FAtzjS(fco$3#%A)^
z9G)B#e9lPFQr_g`V&xU&Z>j}9o~qjUI2=T>=(%|z`KU?T;=ky<p8Ym6H}JhlVdO{n
z#xU7GK|x!T>(D>9auxAgZ|P>PJ>h90ewr2j`F1QcuMsE@xNWWt&6Zb}oWH-Edh7Ff
z@A^0c)|<g-)x4IlU*v@h5G;5(1rujcHEe~EBjrZCwbVC-SP<5-wZP!xo5ka|gSxf7
zBT_r=j9q*-@7p%uu1PPZDzJvI`MjGwR6%s0dys;XsUY9T*u?GezK<sC!o}tl!rAW$
z6xz}<4OEBBw9g$Hpb3Ix|L%Xnh4;OC&@0Xpm$4sZue*YexJcHRX5q0ob-WQ-x@iCf
zB%Bn-yeUCEzw{0bDCl)gAUCRG8@ESYcG&{TlJap^Ii~`jXR9{-5G)Yrj+WiU?}tfv
ztPRhc*J*gD_wO<ul2Oil3fTW!{^h0jyPg#wrzC%JL-U}mKtLPLg862NlVU;8=+}^y
z6cFMct$`a<59bp5YenY{HA4DdMX81P8R+Bx%$C%DzkM8CT+WMWeQ4QQ@@mTz$_G!n
z_!3#3_6VW{KI%|%FbL#(VWXX-WxI49<GfwFIC#hzX+3lw%?a518f@<l#YWDG>Bs@e
z;=DDwpFCI6N~P;C-Ysc9mTwAZAljPfrC^|{@jMFqQxw>ya1^;XfZ{_M2|4`|8Y~|6
zbU3L8aY?Z*hka3(VD!3X6RkR+8SZ=6^v>G16*KskG`x?-Ea<PtI7Kl_ZZs66jh30h
z`{mLeNWh-x?<C3K6F)wE<PAHyk2b)7cD7IYIIqhv%VoPNE+rv18F0WFVrm9j_sOB$
zQ)`IzeT&L{jrMtESy)updb)Q}9=4I34O9lbkEwK_!U=$cq1O;fH6N+4^@T^~zmh(w
ziOAt_RX$#08|ih9DMa78aLgxRQ-AVUI{N33_-e`hM$xBc!DX}3j@QpF)dCpe5Wm?%
zq0jwsL<*DnM{jk6m$tgp=Dvm&JxW`Cn*H+R^6!*+3)#;It-4JD90?p;7<j#O3-9B^
zexXz{9sjZ?C#g+CNlsQ$m}g{cx{I|^Q0lPPkM}&P3mGrnVj}mtWs(!R4Z^8>$PpTk
zx%<nlinU)P+;M1~NA)@h9sqXBIab|3O9<_QVz_EabI!=Jgx<Xg&sb>Y$3*P>HmA;y
zPg!tzlX@Lx=I1$2u<l#not>drDL<7cSROOvr0?%|b^_Fzn^DBB_6zZB*o=bTGc~^b
z)V8e2EWcNpWp}M!ynvOUZf7dDAQ83K`|dZy)5XHOG_9x$f%-}jdjkxiNxiV63VM(0
z+n~PnBZP-ZOG(pN;HECOxWkb39OTCM{|UYej<h9i@_7lQ&~1l8upXKkYw8A|vL`2f
zaE~o%y%OK9E&NIfTB_TpgvrbG=;eAXVOZ(#7*zf^+TS2VNJ)*-ml6v&FLn2yWX_sQ
zw!M+DhSwm`r?*NP-~)kBXx@@pBxe@ow8OGtzTVIIMcDx?v%))56l(ZC6JUH3RTPzH
zyNQvz!uoZ2ivsy16@F&9*gJF`pFPv^2=Fn{-N;GHSzRoJQf-l)L)<T?^)#mESN4|<
zJyd71FRTr{#}4lrkP&Bk7IKAeoK<GF2jnMs?SFjV6JlOlOTfbCjlBIBsxM!!bA*n;
z?g;lIm&8ECW_t8LEF#4Fzn9_<KFL8mtY5m8<mVNb85@eUvC!l5!O~Pt@V4r7-CQ~>
z&89Z@&qaY=b|Rh@vbX;|+WCbxPfp0n-E$=$mIxu;b!?lP*nd08QR)z-3*tA3kI0Nj
z@lT76)cdY%3UrN#oKZ-as+*l<)vPr;y(`&&9~<4f%?y}18I0+iwt(R8CW3sM-RXS^
zT|O?`7@W!MABJFVa-wlg>gO$c$Dmq8$!LOH``7JYrf+Cqbhvkby-ZP(Uu5Qd9hFtz
zwiuWjW+q+3R_D0+rIWd)-0m1eke#Xt{sIo>-FoW(325jzsCx&!+Z?s;2=_l0rE|cb
z2(t5Y=fnj?K}A`}Dr7l1yL9=yD@pCu|CYAU^nkgT2Kg9YLyzsmCBgFEl9(3L47FJz
z&KL%qH)pZW@{>}4(JW@Hm+GKNw&am^@KBkEg&@hMJ0eb6NyFUi+Z+~!IjG45FMKRK
z)Z~1OfKg@GwoflKN6&NpNA_Oxs|`zAUg!5IlAL=%%e3zZ6JqDI-C1El0kFE)qK3+j
z^y!{M<5kQ&dTsld=wFE5>G`{In;Yti^Y9FaqZELj-6nOZV#2&aE9%*pB%T*1kC>j8
zb%xrv?@^7Q!KyZRDY6!*mj%d^9pxDoAOC1vAgBBC@So<z2m#;p;0^pt{3FY|!_`ew
z_vE<f8eICQV<P#BEuU7#QMuF&d*VCh(^c&uq5}<Fb3s!({^!1rK;eN)_(;x4z_U5G
zf;#cMb-C1!Wi9FdLZsF54OOt;r?iYma1bq}T+QU27gP|vdKu=oYfYMj{UtFeC0=C;
z;4w6HnqGfa`mSv$ujJx{7G*U3$+S$n{?kiuXxhI&+dZ8jJ7djTUK-4)M7*@jZ)ju`
zx#RGco$R%a2w~^(YO1rGbv$lY>5F}FM?tqQ2`i1A2-*ZElfJvCg55s3=Qf5*M29L*
z5nketOGm?(fK}&qtBd)vDI6po3L$zNut8H(nqOV!0}eaJ^J>a?2+Z!~Y0UrQWbX)x
zIpO`fd#Y#cU~6jVT-#JpMb6B+;~yHhkAVV1C&Golvzh7}WPFjqWw%jh#Ldc!0*%zX
zM|FSx5$+r!>ti%*pFRnAq$I_^ceb{8xqF(r+7d@Tf4&?RW}J{yh(f+~ZA>u+`oV2E
z+|zMi_7C+7$v5u!6B!K<BaLf=ux>$d>IKzB1;8yB;YMZQzIw6aRjgol)(L6!^c%YH
z9mZLoHx4+Kgjy2Li!c2YY1R<hjyH$bRZ-+uYGnL1H#H&D;xsHWF$NnJ1^HhECArhu
zZ3LvacAXB7NnuJhREwS-Qr|DYtnZ7HKmOm~<?!v!iG`4pb};(IkCGVL$ZIFI*#ykD
z1$LMtMdUDRT3jBOqP=<Nn6%7>y2ig<M-dK!yzE`2?Tsx~`qr>LubU8EEbKyavm2^f
z@t7nBP6PKr_jdBEgLB*QKeJO2e^80laku^WUE}o#8zeDE<-b=|Se{n}n3?bN>2306
zj(?;oH;%;=oQ??3BZrOeRwT@D>_k+oZ+3!;kTgB$^4LV7Q^Ztq&?*@>s1v^hZ8usj
z9(V+v#2qEh+mur~?g|i)1D@UdbBNC_%**MRWg~s}j?MFG7?_3!L?y4<vOODiTVd3h
zx@RZ)8QUdAbRL%e%{HO}Op5{?KG34sgPpSfhCQA;p^WVW18W~z{XCA}EJ>(g+tRIM
zkm~dV?j0NX^2sL#rG7}u)SfmG=k*S9&RGX|B{h&W_3wUB&WZHQjzbh+rmrZsv%NSc
zNlQ%|a@FmEdwJPYbo*hWX)rN5)n>aeG*{HJjB7y~cIi8*5}Sq!EHxqtoU1xVt&y|Q
zuY?>NGU;~ut`5CBxQ|5^YNT~P%#5@aFHCIoO#jm?+>9wcH)ltj4G-qxUs?U7a!t)n
z_wuHu^|QLLfSiVG>COu+D&UqVhHsYTQi_(4jc@<RcaoWw;aXGF()4__H{sgyh&P5m
z+O&ET#@E(6w;(Scpyg)7-(xRcxfZq8PDQsHvO5+i#%VHv6UuZp-9}GQnr9U{v%f=q
zDHD!V7#|CSCZBhSW+YnoA+^d$&(hP@{{DRaBX_quhxo*rmU&n<%46@?xU7U>LS90&
z*=zB>D#Q1X^*CCKEHW-7Uhb!Q-(^xk=tW{_bAJPfdtm=BfFj3umbLB!-4`bk$wK$1
zY|PE?ods6@_4y?#NYQSQ9hUwxsIdPNn7_&HkuhuNR=-I!9TNI0%_gx4k<sZ7Zf~)I
zY?{uIUhu}k@;W~Y2h{a_8rluXd(<8ILL88eZ(cz+A|HbwVs-m+`@tU0KQO^;$fhPP
zWJc$WRM`=bvFZg5fu7s!tf{m+3(>y%bA^y3$rBrGg`U&}Eg;kGk%hGjtWT&~k&v9l
zoUJxmZ`^v7i|UuctzL%H9cwm&#UHGQgm=AX-|3JKaxZkC#rh~pFMjk0oX1F#Nmlz}
zc@+v@op_Yiw5|W^i!cOXX*GRY1Qa&^G+GSVDq8}uT(%;-jEPPf7sAqG%+;k<XRrpK
z!;2{^mV2Ttvk<-$nQPqvgVoC}r?2=wczcqW=3;*Fu6tu;{ZjmeA_l%%F5-LP4oP4X
zGEkI65%Fzr>jb-a2W^f=mY~cRc^o6?AAu{fUU2%JkPk(-RFSWZaIg|nRD`Tu-$pv(
zn#X_#Y!}iHlB~Dln~;4xhOxfEQ_$GhEwUq0_a+qnk*K4MmYjjQq3RSPO=RD5FfP!i
z&N1;|V^1BDniLQd{6mq@)hH&XW$*ZbgS7<tM~*h%ZK^u|j6-&IdM56W<I!Op56L66
z>@7$8q>TI15f_6VXdk-!-BRP-kY^CGm<gDKAvnLLKMB2!(OlMRBOuIpDz2+;DR47?
zQOM$MSVSev!6hNW!zVl<@T2?z29Nt^pc}rsc;^1Swt0%A>0&N=A=tz?poNQ#>lYGt
zIinaFpKM#djtrY*_|)un*H=wJtq|CC0aJLz#mdDez%jMCIy*nTj&wLcU4pS0pAIa1
zvL2!qhuLuW%Nk$<-o$VpW0_+zlo?J=Nl;Uto#Ffz2FsLMmKu_R8rLN63kIWHE}hR_
z>H~Hfl3G8EkY;Le@iKyv$6%JK($4LeCVzx`ct2cg8p)+?#sgQ(E_}4QPKra_{k?<d
zGk0G83cKtdh2KisbI4$^kazzj9MCg~*NwW$4DBPBbyhVVefO7i(jpLVqaV3C6>bAu
zr@i&uF$(_>w9WmAc+owC6UkyA&~wl<#mxGQxmD-p79lFkEBT=l))OY-zD4-2Hmh9N
zM)iHlVCzZ5Oho$AK_k<a(93e*@Wt%nNAZ+k&BN`A;Bk25%f7|xtr_%79QfBg!OT?P
zuDEy^w6E#)+lu?5H{DxZ#6t{j#x^@m?<JrB-8VHGmk4xvZf;{)3KP@C>>as3%M8<@
z;xiuS{TZqbj2UVS<NIUlPoH9xz>XtaZ0d@(V~6K!%7af=cU&n>nI??M->Qz(!dHSV
zoI}X-K8X9q;?|Svhl}g_O#mxJb-{mgs2ja-ev;AqiEmj&FB1dDb8~${TW`OKAWh80
zi{+*o8Qw^Jb1$hXF(Y*G)o*GTr<PK0erY<^=@1Ejer!@MHWu00t$|}sHRVf}SK<wy
z;2r0PFg_Hs;GPzf?-Senrt%{fBkIi<0^8>|-|pRzJf83SoH0EO>i{zgSlU=tTR=IZ
z%GUZKSd%}Y*%w;C9&pbsDJbe}tpwZ1n_Bi(EP4c+kU|Y6b8p3g+B8^2+_WdNr<Aas
z$HTbc#fTZFpf;C36;0#sNAq|38%#4Nusdxgt8)5L+G3sk4g8HXM3nWZxruW@y&bO9
ztWzciASOb~NWMFJ1$Glz2K~DO!LV8QZ3e*S>mPvR7Hgm4?F$=RtY(`#j3AJfnsCoY
z^RDBd>Eh!gIoW+Q1E!eJKeWj$>1e60D<tKnpc1-2t`E{!w(B9O=%_&JbluWPJu3Ij
zAUr-ihNsg1z64Yt7-ZU_saE70Y{je2h1{jXi6|KAk_&GE__#~RQL<a5*ML4av7X&1
zP|yZt-Xz#V^Oz3e?lP`B?2v9*Ty0#ix$euDc{vi<zw2U(Ve_CgU$HHRK1IV3N%<D{
zQ0jI@2BxY~iGC~3xJcVCax+uBkWoDoT6S>Vudf-E8VHlwA^P|!RP%B+I_4a=F6{+u
zXeVTu?<jWwdWy3b={ZBMW4Z(vQxmk&j!OO3ni0Y05_t%$Ki+{gfS)WZRc!Bc>dA~7
z@9{roBZ|I&7jKr?X&0hCb2&69;W=Hcq^uw#HO$wLZlln=!oOpL7eQ1wjpTlTpgo2C
z7QkSUK{gh}`R^R`nqEnJ6e3D9a4KYM)ZwcY(S8F^B-WGULCH)+Zt+)}MY$wByqHFP
zjj~0n9GhjSb(AT`&vd-^B>Ww`rs+eWlA8Foe@epBhfZ4(x1eGnBLC~9z9i2d?R>-8
zt>f-eT3bRy?G#<s5`D`ol>cPou&_U5%|Qs}@H@bJ`O(W!qV`#kDmxl?>qu$;QePc=
zlh^`oDkGSXqpe{VZ6!uHrHr9fn*U&+kA74_>V8dvg1k)h43|Aw1R6{&n^#ED(qrC8
zP;2+uBB{5rO&WXc@K>&afq5&hCwc-QI)U?cWYpp;aGe+N^BjD;-u$Eo^Gz;%vWr(}
zAdb0S5GDM92!|H5l0sOtI|$f(E^b>DZv*O?xJiUO5FFnoHvA3j#R8=4T}ulZdHCt5
zmrfIs-!33tJd*?>s3M)NFU6Fg2cYr!o>F4CU-FfaPM+*-;GG4w*n2-q<}`(G`{FLD
zrnHZGz0$KX@d+%w3w>K(-d?ldZ_4N-W0lqnHsO>bdZkdg*($FY_P}#o9DnM+AOqg(
zd?`Zop>kY2c@U^*8_jY8PDE@3*|nNhA(f<CNcn;x*XP!Dl-uyM$}&DmJL~ESTHnNE
zd=i4UrHc)@*so0TI1|~8Jdkk@Ug>c<GfRj$Ef(mTeig)c<o;?uZp+^XhV6nFH9k99
z@|n64Ln_gwy^fsxYL$$V|CuRt>IjU!)7f|6Y59whxbAiK{pp8mO}85DK25>J!Nm9s
zzRX^XuBevBv6j!`VC86Xx$;K;K!Blfbs^LkTTXf*8y(g+{19mMu7nOLkYp3*$r|dy
z&fwB3>8}})#18@aiFJ`y8Twha-loKhwz}!zcxN_Mj6NC30c#E$*%~^aNnkazk%g1%
zgH`Qho}T#~C3zS2^9n#_^Wv^`j7CWo529Y+K=rKGd?${uvmK%kwRQ8I7CZtI7_0GW
zJGQy*`r6Ano<FxvC?Tuivv0-J_AAw04l9~LTQ>=M?Ob?0aCN`rzqYWam=1<Kwf;JJ
zGCkaiejumlYOed^q;GhB3VfF|vGrc+_-ZD%%>en8YJ=ICn4&deuj;mPt(L9A^(ZX9
zY#&bx?(pjeM+p)RZQuU#vj#6m6!$u{6qaB{?zTxC$Ao4mM6lg$boY7yi7Taea0vO<
zxdD|}QOMfV>(Tb|##u@#P%b)HINZ<A`Fs(otFULdhladEPi|=H7Pt-m@q1mc<6O}F
zmWP3h^+rxp=NUpd_**{T+Mo9z)rt^UPyEqqJwc?qJ7?EAF;ZdI?+<Yy=@I!aLKew=
zVM5=Gl@C^DKDxd?XnHh!BB~#q$6aPx=ZX*+*C(*vo?t?UKW;XB(4Vm%P&)U&nY`9o
zpGhGY)shz)ohk5b>VaDMP|mTR!%I?<as~&Z_FY;`F^5%*a?vqI=A~uyUq?nqK=G;z
z{2o28Z;9YcH<PV*NV_(?LIsS6y}V}_S;)ghvRF%&ox~XyCj^<{QbImU(_Q!dl#7C)
zJdEkd-NfGwR&Qg(;)o45q_ub_7q&H?8Tgp+hutwG^NZ8U8URe##X1EUHTm%5*P;*Y
zV*6x$t9cI8D-$;(x>a$_a_xEo;ZFbJ(lz2$c~1>V$JxG5wLgzWprvEqa=yQdg9VFV
zQVUj{y_GAoL;w#2x8N1>tg9y6v4J|<I$Hj*`VgV0!7K|VbUPV{_zGnU!XZ~Oc|J+@
zY)k(fN_*xL1g4a)VDEQpvV<L^?teqYrZ6;Ol3RK^mbVU;fNI<y1wFFk67KPp%jqrg
zwfLVyhDv@LwhAvEfBU(3D@4I{c_983dXJlljJ|)eZ{Xj=U~j*a!ce{BU0CvNbZGq5
zvE2xMYls<!PG7&h-owoSESyR3a@pl#%{%A0k-6IHC7@3dp{3~k-a}z}$fWb&K}byW
zJ2fR)@8>VIP&#4#AJyjdL?Jg)4xYxPT_p!{%FUD5T9|U2R33QJN+P<bu3YM0OsOSc
zJ%!Q&CAe%i$e3pxBP+DF@<LI{U*?wdbG{zRHhfekte4%-MEx9<FP|OZoMkJSbstcC
zvUL@;)d9`bshR2M1N(X>q#xnPT>h`E@Tbn}XL5fZugG-e5BRQM1Tw7|qMSLBj0-6#
z`@aW$YIB>tI)mA0^9&uNKEzt=74cxW#9@dbPQo-Of$gq&cj>lUs?G9?N(t7Ia?E+c
zxVia#@##Btu~`?L=!^obFe2v9wbUCD+&y97jR%0!Yw0|XsT;%XvO0{Kqk=fdtc+?<
zH7=a;FeE1@rzZ2TVdswxRYJhC+s?}o!P<zMC|uPn<d+1G+VUe`whiMpe}5pF60|+@
zo)xC&?Sdk7j+(nv4u;Kteb+}8dD{i`vm~^dNvym5om(D~laUSUvKm?#CvfOxIfcjl
zv=2Csfog;!+!3tspxO0kFqTi?h+R`c@UO`U=h^_G%P^>8RC4!K>0$a)vQLSJc%)yT
z7oVs48C;tmGaOOUy8=ng3|uXd+Z~A*gQdxuKvDDkX>n(K?);6`Yhhvi5ELeuDk0dp
zajAQt{Pg>t;~EtoITLf?jTI@e0}Y7S4d60$;VT9LQD|JlHJKPvT+@>zrEy2i#h^5b
zk^A>e7TD?Reh`Ox7Y#G}!S-LLkn)|%AV8-oP+^Fsz{2}{<TdUm?a4A)W+-3vB(axX
zBPV|)`9Cp9_%|^6Q_krNpY>{5YI>R$Zn#AiA!*Ovd<J5i&mD4%+~lr%9el8*0kEy*
zIgU;kRz?Tt#bF(sS?7Ia{`Ny0)nT(0fmbfBt3EzeQ%V=^W$!h+zvzQOALl$l`AIgz
z*OJuyMbo|$eoXFDj%+;tzO*B24W+DD{BQ6$-R)cd=vcr=icsbpOZD?78&v!li)mPx
zHr<l;u<M&zc795l;Y*?kS|P^R2B3A@!i?EI_#lbvUd|`$+=^jbKIj$9KEz@C2{)YE
z_N8pt$oEJ~|1(^$hKKkAz2y;66VEBKvc@H8htJK5Ap`^|g!Fe2)fy{X$tM=1)R3<u
z<QdtbN2(NJfv-M-I$5<ON^yzt*KGZ(^DCErSU6O82+ilDn(l(IPbeRV6L{u`4Rm8-
zp+^93utPpyVM{?dp9w^rq=or9de@^;Y}F^mMTLZCX-J9c>P+r6yzXnEiRN#6scyDV
zaz<f0q-JJjirRP^*VeSvSK%qy$b_Mw1+i9j)=}c11Uw%^Z$iaN_ED9U+ilygbA!Z9
z9egG!(`O0?g8fI|3eSBp0QDKU4AN`i(N~vL6MKHw#9WFRHktG%YV||r7O!VvkCf|@
zN+K5@SaEN6bNk#bxIFCnMe_C3!o&DH$fnGySgR3)`fl-$RA>#oNMs4&4#lU7zNE#s
z2GMDT^?@n?iKnY$=HBg~hI3ys_?JoV1N+Rn^*!gxc2YOE*tU7VW062eBp!Y!zq-l|
zi-t4GNM+l)wyD0Vth)R~&`*#WG`&t>+emXae~(zF=p9NMEjdnCU1(+LHSl~@-_W~q
zj_`e|4ws9*U1x$&EqRM{vmH<T&5W<h1F4*v`HzD1P1rxWxIT~PP!Z|SopNgQjc=~w
zYm&0hCQvu*7v~@VqND@WWYM^&GYZvb&*?P1ugvJz^FYgSIQ%#FKy5dvxlcCSD8HK%
zCc^7&b^WKy>?-&rgyrs=RI!Z*C1KZ`8>Upy4XEFiyN$5CrOcPV-Io_z8M|!kw<>JS
z;#eFV>0MkZ*ANXA;~x$E)X<{vUUOtmH0*on5X_v^!aFVnhT)y2FaR^V9VQu93=hVF
z+26KLlEv*+=b7Lq11t*j$D^lUO#GR(BrV^AI`OqUnnJ^akS^ECLb3jvy{NU`>y?yh
zNLku0_=y*|B_DhcTyB9t>qy-Ld>b{@`SC8C?)e0ce8y4PzwjG2rLBt<gqYXj=+i%l
zC8vwKN`H`mQ47Mq&X<=y32|B*Ty;Flo4<5Yld(`TI3$z4Yf5pwsP3_Pn($xIran?)
zbHt{v@_DWKU=jaLSJBp;omznrx3X96s}%^<KJRWtw<@b)fSlC2jx6yP;?|zfB3e{i
zWv2SHh+y-0YUf4+Q?VuF0`0`_ws{%UJ`!_P<Fxw<oVl35X#P`Z&ti1h1D66PijZtq
z$cw@WGfv^%0tZQ1r_o+<;;CAfo%vq`E&^+@gXuLHCd3fnq)4r8poH&6t`kIutED|3
z8R)@zIIUu`IwHS<GSbp<*Ic!xp`+?`uo$4c)STtEP>T<1ex!$zUUG`_(oOcBw`TK}
zGYmcfv;LAz5a-3)Sjme5t|O2Fb_SEm`W97H6>bnMhac}Y<xzM6Uy1$ehmN?CujxqS
z5lh#q2BCZgp?YHS=L3U+0@AgsEEXG@4;IVeyfMzdCob>tjO%KCt#i-bWyK|l1!Do6
z_^J_lr3Q%G*PLu)a!u5dqv->Bvr|xI^#wrU4|Z0PKeBa$Pw69d3Hbrs%W*oG*u;eP
zi@OO`1FSY`cnD;~T%Iz9?Tqi&hLAjN39Q#rphXrIx8`k<EZsRCutQx})xFPo1rEjo
z^Qs?5STxqF*DJ%;GzV&qUzk5olfF$yqtI2mRJeFSkKcwdosOqX155oU(uvd{_!T%9
z92|tzzq`X0Z;_HcJUuqFvd#UY2ZidP5|0jAyUxOOfuo~{EeuG>n;+2fpVa-`7(;F}
zBSpcEN7*e=PJU}&c6pEkC(yc!L(<+A(MmJX;z35H;~-}xZ2M$`!A~5>_K)uuDQGKV
za(qNe#QY1|#d@c;+X${^e$YC0%QjGhYYS&l7Zw#UaaFJ3-K&Cl@{B{>ZK9EolZ}42
zwZdBfodANr6lTs7p;;Gsnjp`2U+SH{QIefG4Y(ex-cFkY(hlCA99$hgrI=V*Sy@_^
zS5#D#N8HTp6o|dF3o`xs*s0~O!!ptmvJh-b7fe#?@}iSe)Fs7ch%?JaQNROZK`n@V
zW^;b!XCn5Q)4$tX9?XM;7mMx!eg!<R2}ewz7cFiAOu5!GjgaAA!YMWshH0PgpBR3f
zR80LAWTe{GG&awzX*%Tm?L>ouIuh<MUGV4Sft%cpBEWNy)Tpj~4?8nXZ?%bqbxK$7
z{zE>l>>Bbga-l=5%Tej;J^^Y!x(ZVNBsNrpqCOg;YGLnm<l~uIm&X3jLnbv_hs(-H
z9<Z7<dR-G^V`IZoo?ouOJw#|4&$EM}XdujeLsBa8?$wsF=iTb9Ij0Da$~<Rd$2}=q
zRkMe2cw8F6>8Yc#SOCJFUj>@L!h_9`r5QEX^lNC;WUD940Ck_(Ccw+lQE2QJzoA{O
zC^D=k5E%oH<h$oJbc4T#pGrKf7`JCu)H`s!`8a0to#O+yV{yi2J9H4F+%{GN29CA$
zfAU!l=OalKyi}qEGxrF9L7mvd85&YCZYe1e9z%l*Uty4mx`>6G)8bbD8cycM(Hy0$
z4jkOm3PA;F;JLUeU~IfMTY`asu^mgY(cy8Iko9+f)mV-6E6SMh+d32ItiOS<*GsKo
zdfbZkQpJdr!rEro>%MzlTP4BcFy>6Fsgmuo`8zD4H3hq0d7R!ZHhOaAU1LMzUpHr1
z?)fMaJcvz$V0w05CN_av2fv()4*n*AY99XVraycZm2Qd#KrA{9G;cRi^PA6B6J}q9
zuG%5E<?F+Ag|B02MOitIFa_&g<F0g7hxVRuk$W&fOi$&6B8+AV#T>Q*0Oz6f0VQtq
z;wp*cbl^NiP-me!Y0|4V+q@Q6pC^cznQKlpn3i+?vayT0irmz*D}n}}A4-TJCYdO!
z3Qf#?mTAa=zoizRj*rPi`Z9RBM!)=0W@uB~(r}S@QRs}6xe(6vc}eP<Z}V4@UcT7M
zBf)+5!z$QrKO_t@HFz#6`t$ia`_**!1AmjPyS9r{%aQ%YK~nJXRe|RU+};CxpnkUT
z;%4Tr+ccHM(CGAGjo=T3Or>v~k+TR!lTM+XS<`$Juvf8$8m<w#{E6}l7F9%=RyU|R
zGW>^^mOl~j5>SpNip|G^^eHgCDJ`6pjMUA+(b3u2zP72Up5F`qKKn1H>VDCOu!mPB
zb*T^A_V_&9bBg*UC^plz<AJiM*23N5D1R&X9eY#^0^IdDsj=WD`q>dyo{TQ|1EQwj
zkm9ibT)b`Yd-hvX3KIrDW^oY>RBTKvd?iFVF4tcZ3f3Q;7D^b$$=VKT8;d`B&Zr<#
zZX`zD?U=4`Sp)sAdASzm$9s>od>y#o?`A1FY+Uw(TaM#}s@3`G+LMHfEL#k2T*2K+
zJuy);WMVf?NAUU*A}}$xZ!k9O&`YZs1eWxGOvJ`Pv5?p-m(lL-P;^ub5j#haHqu8l
z>_{Mg(H{N`>T)8++4{-r6-w1*MJVBXE|EL2-UjxoP+#%-*`;!%!@1ehMo|zDhidyM
z6ceQ|$=oy1F+oAew---vF1Fme{O?a*x0|GuiV=kv^lRVBFZ`2o8AM06nS`AH0gx=f
zvG?5__#JspY9va<q1_&kZ4w~xwmW972j|YWX*wX#?0rxxKr(MSrr?XMBWXq%wjC7&
z@xo<BxK37tY<eNL{;KGVtsIH23hT+giVq38xUQQ?EFgJy=Mj;pjEJbKOGwDi4_Fhj
zkY+Uy5wdkP^|W*}j2n58BkqijiwFi3wD0|@2gGZp3ZdWy7?8VTOqC3`{;KfJR9@yS
zwT^TbywX~FnnK34XJk2b?Bhg?L@YMBQJ@^5nftqbnc#hJ%hx{cAI|k6#t|WOyY!2w
z+wpDj@p_Poh(tuXzHy|;a#JRMA=)g{+_t9SjqKilCQWH}<lYl^wHUn|N@E3re*a2+
zUVBfEe)IJ`8Na0II_WO>MO~@=IbvmyYl8cgd}pgv%*G0ha3@>DQZ0430%ihg-%O<3
zIrjmZKj<q3B_%Cs_vGZzXx~83NB8?pQcPT4^ix7w72u}VGeaADB$O;hLu3a>^MwPM
zA~0Es7v!THH)N63VGZDrROJE}L3@e=5~9+Jx4rXTWDay`9^(5hjO&3?_c340S)(Pv
zs9hdegb%mzizfU?V&ku$^T|b_UfOAE!qUS1vr|G62xp_7Gmfl3s+%}aPeIB(ai_wL
z)2No@7qcO_Hm%dIn_bap?Y<M=l>81ZCdMQ1=GgKA_>Jx@aI^daMJ#|8((*w6afV{m
zJG_h*F4f=dqvKMZ7_XE^F&Q5<Z~`Sa3JNDDHWd{NFE1Y5NOe!B8^`D67deT#WWR)P
zg7XZHzg>?!+ctgZDf!ErD(RoSU!%@-U4QQD+H7&#Y|4p>PmWNLMjkZ>(R9#BND2%V
zJ81U~z>Ed)kNsuGH0lG0*Bu4TJb#L(lYEnt6q6J8Z;oM$yo@VF6|#Tdcq|eM-kdFb
z8M%)iK*Q|oIFsYt7nknIK150WM0Q!B*Ra-}7?=4WLw(cM+?+Xfow`{pnvb?w4>Tez
zel`W$Sl6~y<>B*VUtgZ@J5jG(_f{H9Nq<HW`E#G+eWVVBD8U5e2_nMDfdm3~Hs{a`
z#G~LmN0r5Ff0Cpa426QC)zp0hIzL>Wh+$>8nGSd15K7eY%Mey^5}|P+UKwAJ>Jz?w
z_gWno{O;9-3&+W`y*fQaSgc17S3c?q(%abtj+p3CS;dJG>uufi-KxutoUi)!q=XsZ
zlM6%E8`+gH@^yeBSnL(*-fC3xZ|Sw9(d@O(?T}t3Qrggm%bi<%lN22+c0dLS4e>`a
zXY1AM&tAjuPfVe~Vi#>7hb1de9QpF06DRxEQqaK_AafWOX{5FRraE*>oUXh6wi%o4
zdXMNEud{(lBTQp<@WlW&k|Fxrd@z9s%R4M3Tb{4m8Z)r`p%j3&&B&oQ(J+T9-(j!1
zB#B;rc>f|UgO=<jd4>cHExJbgJh89_Y2VrD!n$X&D}Jka!iFO*m~KSSyolPyJ@Y3l
zP?2Bs>D#TB!T-}EFavx~Xy&q}diz82?e$umKLgVf?jGTf3g*j05p08)_(VSvzu%Xb
zY{)-pQdNMjsdp{hr&tq<Z8t)>xqjR~XoE6dXa+;Fk{HUpR%TtT=YQBInurJ5=}XTB
zWnY+l2dUU<SG8ZbXrLDz1);wtl4m;iuQu8C2n+%tubaL`4U9;my>b3d+_dy`AEq?3
zomtRNK$3z1QT(wrONark;3*mHyq11faUGKKLKSGRhL62Zebh8lu>UlRRG#}C0ofc!
zh$0b)(^Jw+%D88v2a_qM3fS3LxN1wf<lu5s@NUFzp=J)17~-S|n(N!ToxNc;F!BrY
zuFd|^P)fpqlYdpBB5R#DpV2R!Exn=KQyptJtb=ooVz6FsC(aw)3Q??n**S!X8zS}a
z@&J_HPUpZ&VftNd*&3M>Ib8h3DBgz##pFv*S#IYSzUR2EIEBkh4(I95)WX}8a$+5s
zV`%^ERjMWULiaU#%6Jf)Ns!l?@nKCDVl*zmp+iMobXMQ%nx$!vSh+b@Hs=wu@44|1
zrhCwA^;`sEWjl{g&1uiD_f7JsHy$^?Bm=Aym{Sfv%D?$HwEcudorz@zerJ95U0jyY
zh>11jT&crVC(G|eY#O?h`L^()$%BlOoT{UZWVXOcSil?C)YO;YZR@#Hf0+yCR^7&%
zC2VACo$I8C0iKZg5fK}r{~qxCJ`@_*C5TXwI)f2pl98mNI?Kg!=V6Mw{s;FIcm)Ff
z^?Vd=!eGC*s?()Il-Rha2x6Jjz6jpB1^hHEriu&Pe(AW07?kd{JEA@KTpd>)7#Bq~
zC3y*LzsR%@=ZD7k1XjO&r_byNBbIpoxtFoBkbTX4R1}x%{<LqoC`aWCd29*b4A8eb
zEv=&Rp(VJX5+lG=8L8iEbU>5G0SjJMn_$XTMSTp-r=NOC@R9X-z8i}=po!EB+!G>Y
zjg-x=)ZM|Vhf}$>2oLWy3X-rj+&0vQajNWmZg(Q8qJtSTPZN2^le2+me}{=pr1}*x
z%qVn%e+k;wK#XlKZ3VDk6Ya9g6F(3SQ5NohQRirQ2ow_zw)B)mapTtL0wyPABM@8N
zUh|{Ngc!#Ef#T6I_I8c)O2~sd?%!ijBlbBG>LTcmwRGF>c<8TO3|v;XZWq%;c<7PU
z$a0mta0!mfGd*t=x&k5O^f5XS!mN9D;tZM>vg0k9rwF8Xw~A*Sn~!h9ySR-tKFo^c
zbQizlKVw35vMVNHoUb%G0DU3vA@qTd<2HGF8%T9OCv$(-I7XJIw@I1`72xw+Ddg!s
z8^B<ENl-hQ?)IHasroQ$lIIti?PH`Qsm-u9b{;)${X=rjfgs%MuVmNznR;Yb!T3)h
zHWsnjX<%bU(W#5aj}R}iCknxO9jtecs<g&!M}3&2%-!e$z&A1V0x9nd5{FD&;EiM_
z>JJ?`Elk5dsup#?XY@ma_v9ibLr4pR-`unK%!VAAbl~8aNQ=hA$_MT&8TE)X3Qa1a
z+D6BvA;1(wp`x2U&sya&5^H_m>D#u8N!L&xnt8MZSAg91UCwGxUr}m^ud>XsOEsFs
za|<~b)_Rp4%YX6$6Kib7$<LSIF{p)uA<y)Yc$*yE|9FpT(PLVCFJU1vMOM>AllD9G
zMU&z+XtuF=@0F7CSqE=NfZQ$Rk^J@pvkzdX(Gl|nsHIZ(yq#U;-H0aCjl^x8;iUb7
zpjqFh+#-}$^K@u0$>X5m<vZHo9AY2jqn`<=tfiMm$OLG<9g3mvfB>iE9<<O$_A0-C
z&b#DL*5&5v2V=Xw=f$gSG&>$mBDLQK1vJ_)BSayVP~746@KPg0Q$mQ7-p1D%<sYFm
ziif0^*RL=#eqv>35kUd^!;8qDS3x@=9r*5cHu%2eby-q^p~&O7@T#;%ur~59C)YFh
zc^pHr8;~!oj93W2IY%Q`K_Y-fKO^U1_RB!WKP_c)$9@K%tMNw`z?p2bkXYl`4iugV
zLm<UWN;6klyT3^&3gn}PI(N{tS$%lVNW9vz*yq(!w({3I5O1)ggb^v(oIxy+vma*r
zGteW80NJSzbfF6Qu9Du=$?@ekX4mfZ{mNqlK+OX3%XQXh(r3L;MiQ3D3OxQbR5@K`
z0f`XmZ4luLKT_35w#fIr0Ke3z0F1Wbw_eViT5#s|0!HRFZZ+gO6w_vDH~(+uRFZks
zxGfXP)$p>Js`s7>c<!`8>LP;^=R;{rI{IjC!FQAV`X{+-mD{Ncoc&zgXpAel)vIti
zz&vfqqyN6loQSjDwb^@B_(fbo#N=x)K~7d6BOXHuh=}shOV6o}Yg#1{dpn;-#9mLL
zG#X=&l6p5`y3xLaB)O%~{RMMx;v0uTkd!Ne%s-MTUe}qGo^_f%kHNO|3D(Sots%`M
zNOiqTm^(Ec8wGrG`g0895~_`uQDy)c!1^iZHx!egp}u%hXLoDWTn6_f^zKpFLC!5S
zc393xk0=t{gxGBta@_~=CJ&=NwBYAf$RTx>=y1Yqli5Zx=IdMM;r+)-)|p*>$QZqK
zAg23;UX<Zn_^D{>jT;ZUDmULBrTO?K1uJ%lRhjlM7i)yxe5*1Lio|{|OA`JCSaCsC
zSmDvW|9-#poBM7jfhV=|p`k}YbLB>Hli;_t?47;URIN!XNxw04v1d1FzP0<gXLqD`
zCGAe{;q*dX<)Mh~L`jU5SQ&SOP6Z2ZlM8omLp*=3m9a%++)&<zwx*62NdSNQd8aq-
znMF0jn7jw^!J*6i_85ZC&$=xmpopSR&(=12UeY*OmHQ7Ix6gFK4r>y>m}-r>Sv~EG
zN^cX<v<Jj*f##DBJEf3MT8iGUP1d0M9sMud>@%}aY)eu7!`*F*gz|y4^6JWA8EGL{
zXj;{eO0ry{Wp4isAJIi)qUz@T3$Dr%#O<h$PkF$be&YshJIWytOVrH+5X3*OICv03
z^+^OhVld1=L=yeh=z{W;Rk8raIdGUht65+0Yh)>5U}Ai<@RI=vzWZtmSi(e|fsQz~
z>9^UCVCSwQezs?4GAuFO^aW+!E_#Z(s9v*Hr-*`HcT_nsHMZ0}q6;d^tSl?pgI)7d
z_NU3VLKbFzOccDHnDGpN#Uz>J-iI53%H0gSkCx+j7d=9IArW$=ugbZmnuPE0KC*1I
zLeEB?d0WV@;jiBo7!|LftaO?>pD?5C0$n%jB|QdftS*k{kSBDGGpR`^cE9RaH)ZtW
z%jif|ykOyno1UCL<ZumIcW<8Y<B|XNoSxrPRTzb06}hJkt78G~V^jDrNL>we6IZkM
zm%-4rhhGej3t7CxzXV6QEmDk<w^uLaaPfM7Q;!Ob4o5xr>r1loCHyRll&U9thkGf=
z)MQ#F%D1E#hjnsh?ef#7s}O>K@hV0Q!WV}sqc-#4k4lXF^2O+F)4p7D)&|lNO77C(
zmz0XRJt~-`;}6wED*V#TcC}r*@!>${yLnKa#fdvVLs6-T1tKQI`;%t391Ud{?Bco%
zRP6CM=mV2V{*)MYSGAO9<+&#62&}~j*TQ6<QIG%q2?`X+WeWR?w0zg^>lQp~C-c|Q
zm4Hx9sWOuTcYcmz9n5OcRvyI%>bEO7ICeQIJSAdN`usDX;#+-x!L~$3PiGIS{YsaS
zyEP#@@f||NPp%c^%=@{A6HN*tP7gW}d1NL0<7nm^T3de?3gT_S=R5h9_1KBZqmNic
zY{fVp<>B0Cz?mXdBc}B!$X<4Ecx7NznBjvi)Rwp4n6gQ|z2Q~ZL2`RYK6~-~!nLe8
zho)8hoQnn2?mu^8=R|#x&-r@vvHRh&%x9@-BBy;wB+pikyTl$#_+`T~-NM{GjcTMh
zAXD_XS+4rF^H7YOw71{U3sWJdo9)QWwqCMo>Y{#u+1?K$jP)UTRAYJbF*#ktV74w_
z^JB50Uq9ILUj;z&eq%~ek-owHlD`7_8S<sH7<d5c<GZ$zgALX-_#=%<tYcBY#`k4c
zE9PJrX-Gz_-KfZtj-|nP?{mURjqgkQP(gF%iO?E#d906`<{vbsoQ2R+XHAQLmrd37
z6vP{D-5&b|W^3ny;a<raP&><M%z6$x-jQKYXLH7@&l(tRACP^Y@>5G{ne=5!*8+}8
z{w7dI07mWoD#wuJYb<@`p4&-^=UNhDP7A{Lbg_nLi#FuT$6J!L;;?E}%b234ytp9j
zuVeLQB7F#{?Hd13vqZ>GN<%mNPfEJk=ds%;W}S)6Q+~UHv>_-_<sZx)beQi%@5{@J
zwws5<o}d|$jRmVOD)l9t-oL+BOQtM%huJ_N4M+fbS9kx5#^2kh2}8ek<f2wn#R+MX
zg?h>JMhVM=fVIk7tnZ_Gr;$XWgA_(v0a`y}HF-9W!^A_39U_L-lX)E=v+s+4jSTZk
zHao8NP0O4u*sPX$JP`36yPsZ--nQqD6j$fbWul%gDj?Br79)8_=4PPMxa^619uft9
zG<vPpMIJ15VnME1=T+J`pX^lmEk!-ZiIK2AFA0sgrgS1cIC2Qm#=P}tINJ!Qp9CVB
zDpw0B%-*15EMK)SmwqDvZ;ww#p*^_$<(xmD33AtIm`cXkGZcrVNa<K`6EsyLKp>)7
z>-BuWs*7hM`D|w&F;sQYqi|Mtr?e0F?7V=!K6P&4C)@@gK7DMSu-qtPd8yqgQQBMB
zDiZjr4{J?#6RJAPo`l<~4?1D#@gBRI$Ob*m&sj$L8EjEPfJX88hJHmNWnua+4o&Dr
zyM7^kkZH`<mTv@ZCl)ZtF)INNcQy!(Vt$AX{?GS){fc&8MlKcxGN0N(bA<p;+HqMN
zrEN>j+xVb`1-8!aY>ZS{Lus{_qx4ntwvFMD4UN;8&)54-eUzr{Yy@NF!y34Lva!c%
z5s;7+yg;2y-rvr0N{ahw6n=IaUi%}wQYn%9&NVpSb=cdbv+px>1+((CFfsNF4o~)p
zj2E8+1SarlNv(D@c7ETF4;cXpf(5Bh;gN1Rrm71C|DK16e6dUezS1y`T(oA<&C!Xa
zpMR|#!;c8Vh*z3sz4{mp5?dL1jWa8I#8%uuEpgXQM%no7G=$py3xnuUraD?LRwpw2
zET(-3RG;%4qy^8*%{mutUfG4*x$BL1&JnWA&vToRLSR-kbX#<3;c*X?;{W7bzeEG#
z--`2F+@P|?1AgFUY_z*O?<buLp_=|=>RD|}=r+P8`T0Ot*S_<<WAnr80>YG^FyeVA
zHZhRs$lbSlJW~B><TD|bJ-I1WistT@^suuJF=^%a>9y9Ztc5h0FkMqwfJxfxWY^H;
zCo%6`Klp1i7qM%I>kcM>p6GBk`=|AVl&cM&8z=NH^T%}Bisg^ZPWhP2ju316l2C%R
zo;<yKn^1yb6RafuAlkISl0LSpt+|reBlv5uxz9#D9Mi!s<)!6%6X3{ADBCD!LvwPf
zA;Py1C)72U_4VOu<6tCUEU0uymuW%b#D3sdhOvGSrE<6w?<Wt?yon}$=jA0S{E<+*
zejvBl*f}ajB*&)&1nE;{>BZVaUY!XWItkKl6L}u}>8+IVgFcSghpE|yb(4(jlQFb5
zPNg~+?K=B#pHh&L8(?biTAm$XW(U-^drF$6gw$Ks7x-k#mxj+M6_nU4le8L3xx@U9
zx}+tn@4`u3<Ol|^4sZ-mBiEz3S2Q&;lX3Bm&RiGv;@VlhK010i@bsaa(w?NR^dkIa
zKsV%YL6M(erXxNa6m)E2Rm93KqNIR*7;}SB^&Qg>@@K%sQ0A(;K~(s95!@zcMtTDQ
z_i_uNZIOwE={+>?l-kO{%=jh~?WE786PMs5ZI#Zq=1B;|f_`53uQqPmFMJFDUFxeH
zsMvW?nDlgE#y*Pl*@R&!a=LgV{hWC=k@m$YkmnPg`xT!<hz$XA;DR-v;kZviPB2VI
z+KIL>elFmfjlc_UFLSy}Qo7@yVk0h57#sPR*O5v2F?9cy<vA9MSNAnGC?G^vcKtTN
z&f2yQw=xdsdt;Y(#$P^%*-CTzA2R_W9w4M)tzbTiz?x{SOV+u|r>#FqQ{(nD(zgU!
zkx=Ee-5%W$kYKJMKl{h11?`cY{NQ6DRv6aWqNT5F!Cb5#s#6ZZoY^?SO^5EbntU2&
zv^2*bMvKP-aX#WnbGAm}XIzYk!f5uTa{}nQsthSlXhP`WK8v2GTJ9`xZ<p;m`+vN>
zRahNexU7o?cX#*T?(XivAvgqgZ5#pw3&9-{G(d27f=h6BcXv7cuXXm-zTY=oP6qSo
z(Twp`)my{gr)r#UWBjK1;xH<n+me+$d6Rd#M$V4Hzx$SHwB1N$ru>!&#T}h<f+SNx
zNN44U93^g-z+C5P==ss}z4o9DF*(F$5IFPdSFmOs7=G@kqtK8TIkHnz&|AmT(kEkd
zpi&5LH#+?RIB66@w;4{PAAt(B%WtWA;>_MN+G1--9)IFS&t?+rOyekL^t@{mkytc$
zE-l=BODW+dJ2x%8<VV5ifswjz@ZWNRf4Hr0PU}J&<8o06UL|;;@@ArtHiyB#FFWq5
zlB%cTSQW3epO6W8hM0n>BWBLjpao+T^JV+Me~0Hl>e7YVcPcge{5E?2(UE^rMj@8F
z0VW!v33L40q*?lN_XMdO6UyIE?%cWN3uB}p&RW!3qrx+67V9~)Dlpkmkm5^XlOja0
zwSAKkG$Y*Ju1Dp(+_YUBmqs?0<;J1{cf;9u90usXbo}OyCL7=JCEuTF55)Ok<De&6
zBZqH5y>sv=wCa8>-ObEypHJH0<(uc=v*;y>L3J<HPs?2_=y5X9V^7|X6pb~YBwhF_
zA~?4m$A6>y1vxp4K-WwKJ5_DyD7PKssjTh%8t)4(99^D4J~4ygA(Bsy0>@|nFYXp2
zRoR=2bk%~8UU8XO9@4nCv+nmOrhM?4p{E`IjgKvB?vrsn_uRsW;x$SCwk2T%Ocztd
zXZKAj8wD4^nG&gIPPgy9He-xbETi{N{hWW7($+@DElqCH6b^MH>~KX8eCm6nqLylJ
z3Z6tx0%Q6Q^Hun26R$pYO?RIpd?Y&^|B-qZI)kSmV1;T7+k4&EGtKlQYF>1VTdh$5
z@Z0`D_d=hSuh2q()h!J#7<>lZb-Au>XV26Wt#t^!;ty<{eE*gWFL}0ir)7DwE8^%C
zoxus-;L2XM6!uMpkVF!kGD&-n$q&!*0&}dGm|QEF=cS_&B)91t*ypBY0|GnKCv%&x
zf&3<rG@4GU4cW=Tvi_ZW>eooAn|v=o=CICafe6Hll!@Z3V1g*zYKQw#Ng4G%;{&Ry
zFatN0V0^-`i(geS!4GurWFb-BwRs&lMZ7`JC@cX5OvAx2^tCId`eSygX6)FNf@dv{
z*pCz7m<3Ao;Rtn5Tx>q&o5yVDgi5T0n_S6x#}y-arWb~0cb0_Aa?hi&g^UltdlFpe
zpIO>H{vn}*P86xbYizAYEFc#hO^^zOfI@(<Aq!2KZgx`PcSS`0kI;ekfqz5IG$^Z+
z9$I)_$Y#${Ax*=7KOsHDl_^!6G?BA>*WvYOA-Zj`+u(HU@00T~mlg-ZbAzi)`(y5q
zXI$6^VUt((cs5YV;=Q!hvg*gc!BB0g5Lv{4bt>20*y!lzzjZQ8<wzUnry1G(iO_xz
z_)$86#?DA043t2%$@`7vdRoQsnvWqB@#9##N-S!G8y3hIKxCWFudh`ed4)Po)HsYY
zN_Z%6?VH+cF<Gch&bqHZ4V}E@-}y~oTjl#bq;HzeJ++YI(ksGT+ddr-eD1T*M)Q|(
zd1ph4#EIFg-g>q7q`OU~Y2IuaMVw`+4*Tq=uE*Vu1u9<Wu%ghS4|f<}=zIeMOMVLR
zQfzIa6i>_OKTGr5dRD#Ypr&bc)dw?1SXVuYMk2$1oUnsj;iXx0roI`5bqml2g+)xQ
zP|rv0quXi>l%HJ$FgpM$MRa=tjN0;?Xk4b60lpo}P&Cq=XDA~Uhx6<jW9(=*CL(8Q
zLea^lW-6J`NGZX~3qfzIm}gFRu&|e)TL4bfb94Y(oDy@j>|sLUkJTT7VC<Uzr*eR1
zQ`x^2F>{x{wdAa++Ml&0C2u^#eqyv|ccnghK*!{Yn9ad|BUc14Lfg8Hii;`xB~_a=
z=Enq9R*+-ThX2*-l1W+a;py_Y-^2qCR|L<O(6{FrlxHTwaN4wLuBV9J&#WJI`@Q?~
z+S0HXS??Y>Yp`8DBRAWdC-OSNTBo85T<ZwA2Z?`v7Z1?1`1x=8Vbp8ZA(RvF|Eu#m
zL(#Ys{;tCxl|vtpZa$FLNpcBiukdHoXJGw8{B(n`8>plzE^v7bratpsGl^Gx8eE#G
zTGmS_zpHhj`~*QQ#fEK?TkMQ>azAl33!DOTF^+-WZsMH@%$Eu-DJD6A9s(WF*_kr|
zMyxji?Q}wd6MOIXC+QNjJ|lC{qQ*)vt)|(wAg?qwN(E5<&a1TcKMG-Tv?{juzKd7A
zR%8DIMn66hjiVZ|g^pLrU2arGunptXAv06l($p4N?qHp%uIs0Kzv!DMd8jNU9~w{!
zZd@6`#Ts=oNg`7dL}cJ)x0T)<)S3E<2ES+x21#n`kqvLaF$oG<Ub0Sz_5}&ftR`=D
zP`MapKCm}xot0!^DYA0CZ|btM5y$}9@Pp55L9;V6DP7l4<+OUJ6lt7v?M=;PTNz<S
zJU5i)DIdIUEL~-VIh!>7R<P{<n$2f`iR}*&;6w(VIAQb+5(szC4=pV7O6i&V#ARDf
zZb%wEa$-@x(xH)OB!bM0D@MD_r4~3DI?<1TKpG)Q(3Zep+lpnA7WPz|johVfx7T1<
zik9%_O=l8D@-7b(6X&bX{b?xGOarC;I(A<Vo5d0BZEVkdSDz_Xx?`7fUj}-xWsc}E
zU_9eeKG;na_!Gx!^)V^%_TrOrr^1|I8eSv#<QRXN7ly<Oj*IVFH)MDjYtG{T)d9q_
z0lZi^|L%4pQmQS&Kl%k_Xk6K~3^0pZT+?%~w>EV&w-f1^L}0Kn!*xXV8retwKtCHo
z)I6<jAga6(vIVv={4FtbxH&M9etf&aByXMt9gY_1!SN;{^_Ys+l-4*Y(*mF~_Bl}O
z)6K|CMWdo}bMs}Ooc~~0lO#6;LF~mPfOy5~9I<M5h=rM%p}V%idrMF`AJo3l(l~_C
zp*v1se};v(VG+^UY`GP8+^<dndCES5<ZNs5#nsaVCCh*y#O;8rI3llWG;_2lDsrC0
z40EJ+TuSjz!OC|UDhmuvZh`$Xyv(fI%7Riqi#sGroy8?J?F@|Vb1>p+&Wc6^P!+Vc
zox~6PI4EttFCnS*yhZ&MtEdo3G`jKODF4r4bHAuatUGsdyL@~kCzExY?I7ZBs7Dxk
znAje~C!N%@u)beA*u7GO4<e{!85vg<`@y^<8*!qJJTi71A=x8^Iw9}g+d5Lpu^BnC
z66(u+Pf5ila_;`VZ_gPi#-py80Kb&!o^XW*^tvh$=?dMTl&rK&SSMr0I~jXxT}pVZ
zs>NRB{j74fTA+ELT@hMJ>H20vo<<VJ=~_LM2hj>4r*HRbB!=EqY*u6c#<%0Hikhc~
z!+4NEaO6e6O7-Go9704Oy)dX7y?3h%ejlO`k@z{qd)V6Ck}~{;{$Zd9F8}qS8QJ4k
z!mK#Vh{Zzp$Y(6fF)(@qAKZFYah#pEFHhh@U+ne8A1eDV>9OO-5!DzZ*??3$v*I8{
zL(eZpBFp=NGQ-SXeRG`Dmh#28289y=Z-c#aYSPKJ_X?%b+rFG?cRg=9avJJ-I5lY=
z*n;J^yJ#!4de$H#=2;q8EY*=6z-0e?C!DdDdM2zq!CGl|LY$WNED$e+5863cLmZcK
zCs#&LW@A%cizB<7*5Xv9RK~h`7<@JkY@O%(dp)i<0*LUlbGlQQA_eUbIgP`fSKS-f
z=XAtitaMZ>)3Zz#`yRy&A!qEQ!yWe$WW3dlefw4w_w=WTshNPgKTS)p&&JuB^H5vs
z^<K9qaRq%Uv9IilM$arD%7&-UNIcn`8{5fQriZ57dK)86EehjS|3N^MePIzkAinL~
zQ{Sl*Vs#ux?Vps^$A%MX^6;zR7gvEjyz?+x9IkHE8~9}Ho>A4&F_ebnU0Ruk;Puu&
z4Fxc~B%ABDS**bP;G?!u7Fh?ql%qk(EaEJhewi7S&_zV{*9>Z+%PXJ0k|0tAE|4Ar
zDayrJ+=^lQ)94~VvO$e?)GX!FQwOgt8ui9i(7vp=#S7<#wS#-bL3es#j+0B|`jScI
zkzymFy)*@x^)}A3p}%_b1eFhAjE0=Ho$PCoIj4fY;3R;1gZ#^tjcaoLQ&Kki+r_Wi
z`XMuI0tz1DLvxQHJT?B!t^1miS|?{1jKfzB!4*A|jNH4e()dhniw<;V*0SKt`9^Ub
z1_u5W)4xpgjLo{&VXNP|nzG6|-nz~37nCL><adHQ&QJ%hqGdiAKOHfN5ItAP4A;IN
zgfDLr{BQ=8kKl=E(n##8n8#J3gLkot$XWsjx+K?m$-RgnF%VEHVn#}Bjtmz3grekz
zt4>bNPGvJEv79B!+kZkPiR1`1xeWb%s3uf+`ZUJuwgXj0XZX4m(^`#)ZIW=o$oU3>
zK|(<=2s%}_>jG_pQ3C;uSMdEeRgiqqSr&oXLkR%d3v7H`LL3~3p&$V#RxW6tTKa=n
z!S}al?Qd`GiI<F|ZWnRGmG*~H5u14IpQEGlHV_<qFm!6j=G`R%*6VQGCjMN<jFgcI
z*x3bK{tczO)a$=(4|s*{3fK@Xpwl+ex!IeoO?W0%nWGh`))<jdta0P!9fI0WHhRTc
zy9YM7#(sg;PuK5-p=T|N#PbQ8h3l`qWU=WVR$Gu9`OLO)(bro|v=t`?Pq4Ev6^F}h
zh)BBiMQeN?j2!<rFVjF5-__o07>f;4u3pzeNRT^q86)!NaV%RnjUcHsr=`ICQ(>oR
zB_Ng&pJ;I$p9I17{ocTHgR0ztFDwtBtYgp$3&g&})KU5X2NxL;5fK@gzXN#4i>vs4
z#@3qW_b@cD&255!gNEI_n_VGC4)zMIvm^dV%qVZw7ry4z$T_c>xbt-RYX!4BAu9z;
zBpb@88_iFifWe~qjW-^tgn<9<AfS01Vn=P3#;I0g+nn^-u+t(yLe*iH4%mIOTmQ>@
zk^T(BeT!~ek5tV|H7u|A(95xjzu&kiZcBz*0*@jqQROY{d-ahY>C6o(G7!H>gbzaE
zFuGu+?%`k(j@M`+lj=x7=nJwIwfD$x!q?yG@T6?5#0gqUa2^l&l#h5N)ajUq=RTKp
ziE}l{)PGv~TIDu|p-af&xPhN5Lg(W02W?3$apI%DdOC~;%SS_9C*O>n-O?+MM9hp)
zwe)OgvS0C@_a+PNPD}=t6gbVvd<3$%Kn&cDdH%|4p$oU?19AS8>s3rQ?BZOi<3q$>
zn$8HE@0H#1&IjUB^I+uJ5VmK?T9wqNRdA6!9|cWNfU?;K$3-?64Nzh^j<oZPir(~=
zm1arC6ZG3+i!BG*FyelG-}=t!jCiij2fur5=(L6w9jiLRP!YszGrAI>N>}lzJvs)y
z$!qm7t9g@x#+D-P2jSo=Q6ajPpbs~F{bzWloqLbEX8PSBqf||{9`v0&s@2=VN9+cY
z`k1t6jHK3Ncct-`6(B-5y=Uquq9Lc-h5kD~$HMyZs`|YM9Su+4{V${$6s<&z&Cfxf
z2Opx)Zy3G5u^+tr=t{nVlxo9LNi^lMK2c@Sb^B1Rs3bQwjOHaPvHIQ`oOqdju28PF
zj<^4dJ=17-y{KRKGIXRt`36-YoK6bfmu5H|Fi-A*V_ub<vIqYDN`TDhpD>9eT5CgY
zbF?pFj2SdWKYjRjfmmK2tSS>pKwRz8OoE?zSJ{DZ_K2UI)y=|pXj2-bo*f@KosRWH
zh^PT|QrE}SfA=W(JW~1&r4nHGAUw~x(eNG9qVUn>5Ci#QIqI-UY8Q<#OX%G9c=J0R
z0j##72hr&Gr4w*)EX(!dF4~z>{cUw__Xmdp#y3sgo?1d{XJ5xq?nQQy@jZRNFrP%D
z64968&uGGizi1f?{SOXot(ykQi!8C<vN1diM}1T0{@z!aM67ywRdWSZh6Z`wtLm}H
zQWQA`GCyd~!h2r-O-=kIanquin)(9y1Q+L0jRf#gurmPsusG#Gbu~OuUn+gUwAUMB
zRb5AUu@i)Yl4<%g{}AX7QzX3QV%vGm{iBVJ^m5bKCHrhBP>Mj5gq+;>&U|su5jvLn
zkNuN|s{)};*s6u8@7|R1```}f*<w#<lMp!NGW-M6^H?Jhw&ppuc9iz(N~F)DEw@G%
z8^S5r&<lXie5FG4cU{O<OcCYd4J8sN&iiF)YPGwu^&=tj*oejF0Y;|pXAi?cM@Pro
zTQFq8glW|C*FX7aPLi8q!rbBQpI_wOU+F4h-$*a?{@t^r+scWJrN0w;#}4+58$C!_
zB-bf{&?7Z$Fz9s>n|;jOdGRpc?_soUZf;)KB?HL#Zhok0i!A~XXim-9jsF4iKx+wB
zO9x`sQYWGRzEBUxG3~F!Y^tuu#J^th|7F6K7@zyR-~IkjXzhd~r6m3c%`}ly($K$y
zFdX&+32W794|OXA!+%v0I&N?8+`T;N=2P+ZiaMf@0}nh-xA?Ek1>h`xf>6dpXVOw2
z{r6cwB*Z&)`6kgud2PX&@qeR!OR<-IG@%9+IPNC-QgzQCSxFIVAW{a^k+%&xr$dRg
z#O5d)0^#GJN$~sFY`>4~r8)(glZi(iJM8@nsd}J_)xndh9;PZog_+0p&~XF<%)K<s
zLrC9-L7bKTM*pT+!HZMyK<QM^QU%a91&PwsH}V((=0&_q;tXad*LVeH)|Y43mS%z~
zc)$9e_#M}5>&0vja=+((F*0V540z|fs46@Y$X3IGMxRWA-gw_86Oy2m4#SljEVcQ^
zjYk+6pPt?9e7N*JvStveUGL!3b-!j#NwG>!K{7=_gQ9>Kl?)Xf0#8qmhzQUZ1d&H$
zVnR(tb0tlYOs$e<W8S^yIE~fct$D}gTptQ|@7<r?cU}v@k;0uDx{b5%;h!J7iSEd2
z_y+1pK6k;W-9qX<1}@}qP*6?|oL)Un|MJZ<g?(ZXI{>VIdQy2~IAjW{#<cwN{F5uH
z>XlL)?q^ansgWe1)eM~B^#E@2Vo-)U>dKnuP3gOfNR^GIKPR`M_46b|MNnE8;Cu!2
z3GIw6X@z|bw)ZR?%nGVXp`v+C<gUAOfF3Cv$(Lc>zJW)<OfuZfZ41byQPCCPQrm^_
zi|5N{*Rsi>!11Bm2rn^bi<8=$wxqJZcOcQKDccoHOSKcYlfXQ-0Q@=pK8E+z1$FeI
zUm8#(-UXTJiMnHKa<Ai<&y0F)p2Yi#BUU|yN`vh#Uxo@8lDpZeufGn{ll|kjva@vn
z5b88#Xe_a{xateS7xrmQw@3eTDOW>GFg5Hw>Dxtf{O9t0KCq=9+T05jWr%#Gdhl$7
z?t3;8DP@TzO?v{mJ4F?+yzspJU_+yv=?<F}&qVG+sd8wGcd>hN`d+)fdj}%&m^oH3
zcw@Xy9uGHT2}J(b?SdQ_hKSjg$bY{rq*{mkT@v?O)zJ366*T3t<{3oq)VyoP(^QA<
zg>&GO^kNk|kW~4ubl?%g`X3)H!$ruhlizlS4{e^MEY$!ZN~Qv5HkL&cUTR9{QAGcG
zJZ18pH*y!wATEvJFYVzkvBn<%c(H;H+m@MiK_U6ycXegXWI-nSNshC90diM4kSNnz
zFNl`gC#neOY3WSQ%*_h&LA6dpb<T{ZJe4Kg6r;7XKm#=N?kr8yk=Qs-qGE#N#`iC)
zVKv=oY`|fz*_NWFgd=}iiG&Z%zyz9fuO=c#S<8mETl?WNIhO!GRcA%VZzp05%MXEQ
zir+)CQE*@h<jn%l@IKyZ)A;ax${%FNyN$bhd>|6y>wt`YK=uAes{AtDs$x|JCDx6B
zTl@THJ*;eW`}Ai~BvRecfQpgxJ(A)m3w>!ts5T%%`zyh0;m70G%^aPCVd}6Imj(_s
z+|`Luxk#IpyjP>QwG#C_96`JNR4w*43O%7&<13+uU)hN@b?{^#v!2~L(SQ3nx&7Py
zb>4fym%R6$**uG4wAFY~>t&@u%?2i*(AUs>M=sv=zon9O-A7IM9Ua@0xdvewF~8@V
zgara|J#fP;aD5iZHy>HOSAeU664I0xxH}FAdx&eN4}zIx#_dYqn*44*>R*`6uV;7p
zBUzNQ9vTb`-t}0&WRvl2zrQ-UcP%$cJ~AErp&+L&FvEhN{L!E*bOr+>`V~ktf@!gV
z1E+h5>Z71lpX~|t1&eI`+F8h<1XadNJRdl#n)QB!G}TyaJkY{jIeP8>O-kH+v3mw`
z57$cyNe=0$C~1Gfvgkz4)S>()7Jljd?#iey;QJv6`kpd@DJwHQ$Dea<eN9b0?FS~_
znb+9dO%yho@QPN`cJ9+8yV&}dR5jk^2@|UpCeECd_NVv6fkJbch?n~liWQ{PvI790
zPkJX#3@+<9+fRH`_4~9*M_EPa*;Vx?+O)5k8Tdp5c2!J6DsjF$$7kN)pi>AaK&s0W
zd->kR^XVF%#aOukmqxhBQ$|-mEH^YA7k=*TP1RgSSie0QV-gkl`Qrq?U(Z^TM|OLj
zJ?RaPc<SSy{I{HOolh<swx9AQxLbkmEr^sSGikl2xk$til`GP1U(#Vk434`@9U6)S
z==M5@N@m|Xl=uYfKKW$?*{~+JPfc?Esd2lEW&-VwF7Ef?m9_Gvi?E>WdF*sGpt^qY
zWaeQ|%t@ChO(-6%^?dipnUFxakm9BMLp31cS!DOs?kYU4*;<_U3kuGA=MAvA8BgM}
zz6RSCW`nQC=Zz{y?kSVH(ncmjYlW-bc!Pr4OQOT+rfDA9emZum4HAXq|K;ERmmbXK
zE29KQUze&{A3LB&Yd;QaTvB~OMt>i?E@*x;<5#a-C(x8`y`U^hv%pSvfBv-Kg9;y`
zSV}Bx=0;GbFQhmDshc@Z0vHLay{J7z9X}nnFgAI;oS|47xJfG8xVW%skPob^Pf5PY
z{*mlk55s*$K6rxWu+CLiLAHt%m$`<EL#JPBq*5;GOTHNenUR=kjOG>yh83h?!T~w2
zi1c~!aluD7J2PAfj^)g(zUe9WIGE1Mr!}j#<|LL3ke<Ft+3$myz$ov7b}fxf`xD3d
zx?0-H!v3%x)`9)bGYJ~L`$Ci=IXCm9Jr;IqiZ1_WaOUMocOmbpBJRR}ka<puPfSQi
z3JC=NH;>DxDM&p;*hD*PGUhWcDD;%mNcjFW!op2QxPB6&oM@B3s(2qyo%Q>4p55WG
zzk)8#DdhDknbbJIKl2Hl0t0qM-bYURxG9`DxfKo!*<0X1d>zk6z+NNgZOOvBOcZ}>
ztmMzB<(h*%<VfbmIDL>+xL$`i6ZA_koQrvA`wcwQKoGL$kblFU>YR=W);k*%!iUC6
z?u$}jD6u^x%56T_!y>+>tg30qCm1pw7#R2~BP$!BZmR_7YbZsnD_=ZSP_V*g|5V!e
z_<GE|2|%oi$eSC}ZtAFRJl(YCJ|C&Tb&5|_J0pPt_(5iNyZ0$i+T%D=Wss+UDHbB$
zUiS#h!O}SBWW8(MJ@OvkYKqIq3PVRDqQV&x)l7fKUH8mjL2gX$zW!(ZjT;RCFhPU7
zzKb3W{R317vtU^&2Vf~?j_|%nZcoS9Ewy&_e^`>;v+B0={4d*v(lTKs5`%(v<LOq%
zBUnaD1ryC<%INGCvTh@hF1c1DI&nAC7K_xj=?t}dedpKLyGytbM$j_yozoRwfx(`n
zvF<+tEb;fN8tgCbuck1#XK9gzPE`Rv((x}hfQDmfqL}TavYOBSc>_@)_`tlVM&2tf
zc3ZRzh@WgG;<z<Up`l}+xhG89#@K$TbY+&=&A~sT2tVV_8`)`Uz6~!ozNf;hRU~Xt
zGuoak4s#?vw=4Sy6SKB{UG~aA#VkDpU2$PSNU;_-_BEjJ6!|CXy=_Aks%Kk`K%9*@
z;BOKN<54T!cQ!;{^67yZyFOM$uxTwY$n}B{HYIptJj}87(9b9bI(a(FVHEPTpzS%>
zW!V_2EqnPbQDC((ZzGY{y@@1IFNRd>t%hq({d>Io;#=hn%HJ=fcU!wVIM_clr@2KO
z_D?pku(Oj47FR@@qXVy=t^zI1_Qsq|09||TlEp434p9HI49}0*HmMqW=3KDhf*ngK
zN56D?{U$vTtad9etZln6aw{krF>aI40gY&<k7~w`LRB8)=q0}IuC4e^Ekk~dCG#b(
z@<Mv%HFGjoOOfD=ZK=q9>;uH*oe9P}Dgh%zz-?XzVvP1SRrn0aa`#=F#i`gXK%yiG
zIgU2WSVH`dliw1_9aT-4M-qfP;#Cke3UQ*s2#g?dB;~+7`m`AGng5F9@_9j7Y7-4z
zZ%6^e1Y)-({S7BFkIPAStn<5;zrmTIeRz0KyHbdC$7L6jC8p!e4MKOG=Jz6S9}RWr
zaP$74Bu4ougMjSFHng&CQGOE=;LXaL*3|nzgVxgAM%CvBxh1I9XHIL|Wrc=Tp`o4-
zIz=d=X1%Pagh=C57yI#K<wfE%oF$HSyv;EgFL$G-vuEq+J3aBZxxVkfp4dV;x{i*O
zj9*5%$u7wL5p6p@gj%)v=03YiBi?euYjgdEJlPjtP+=(QZ%lYx!K4%b5bkW-SyvV#
zWADky4-F17(9^5u6%-WY!-ZX6Ai>ATD~vGEmy^tingv`6G|lFJ!QI=aUT0xt?LJ%f
z*ac&B|G-cc-b9)WlR__v%uZP5_dW**3K3%3KyN7YP7YykV1^jK-@1?y%<iDGLl7JW
ze@JC+0fhzSeD=%S)WQldvot?H@L$CW3{g@T3(x#Qi>oH0pcsR+Ev$^w%$gJN;M2@+
z0>OGtI9#(8V%Q~n$~sT;#^5g~4bw`yFaECM<gGx|vN`#Wla1}n{IivK*DxYWx8IuJ
z|M3!V^=xUlMwdl$d)&kkr03MZ7_7D@loZ{xX)JWxL#6`|u-~qGC&3MWSP+C#o4xD8
z{4<pD#ncp{!R+ms==cqSev8wNt*>hQ+igf?9-exzvF;b8x*cg4@8*Rx0^8EOQ<C~6
z>7P5=`7>iLvA;v7f7p{Yw!K10QhvtN<S@of=~d?89I+A2UQ7?RB3CEa@EHO_is|{4
z5yKSjHvrJia;XX_?LP+pg&xrkpQ)Nu*4WO!x)G^j;13{5BuIzH%St910u6_nxAz{G
zsh1_<H~zNu6GZHH6Ipx)k)^Op-S)T4W%eDdrNm86>+PFg&;&-XsbRer(m^ZI*NeAt
zj03W6rzn%adrOqjDBk(x5cV>IR0pMc91ChGRN$hzmTfY~Mb{V-!-agw!ouv-xu2v!
zXhYpb%KeLmg0H8sx-Cu1kQFTaD{J?VlC_89mAQ$ofu^J|=%tgK+LCxNq}N^<6FvwO
zWP7?CS$fF~j{7@YaKiWL>ZqEF>>W~^0)rhvo`I_f2RuP3F*^{mv>JB0j66GvnW}j!
z0b4N@ZCk^>eO6{~r-J0OaQCU)slE1ZHQ$>5{iBKo#he4wLZi&l=MR@v>U}^UoMJxL
zJcU9{>Tl)x*&h}(;g8Lq#D(?6u=Ov>1O}MD5Lc~Up&|NRalI|Z#Adw|BwZb~{N3Zx
z`aX8G1h*i9Z1dU~2+OrULC82VLW+%t3v6tEHa9B@oo-SV*-t35w7dOqdA<>2<L2OF
z^>H$LN*5O^H90BqyRMdww50Mf`dRMH6>ve%CB(?HxfnCL((@l5vw<afuKVa&+q8&F
zU||_{R~DlhhRZ5fGG~pQbS4w~sVeGRItqCA1aC5-3}Y(MO>k+vlRA-S)KG>%Mb=9X
z=7os0=Uqs-uGoR*MLJ%J)(jWd|Igiblq2!?Z;VblE+6tC-0+<f3(ifA3H=t1u!1L6
zi!iv&<F(k^Cn_;K%XazxTHONAF)^N!baU{0c1KsYgUUc(T4rx^c#xm(WM_Hq-%H0P
z#B=DQuj450;)C<)!l)e@=-tM04*hrZ6ses&0Ii0K<Xp=+ot%g+(lz!$swnDT`Y7%0
zxeWw=0?L*(#a8e+Ua~Lh4alOQi3sjCLNXV2Cd1m;!Q=Oq)7G3gpiXWRlebrJ^znGL
zpOohf$+`W@wdlW+hRvXea9PK#wNY45L{3E$3`N%^B<|#RW2%n}P<GfkBl{FT+^nk}
z%r4z9()7&tKk<<p>5>_4s<H7ly0~7W#>&aTIbqyZ<xluKTOf0j*TmNFJi^aSs5!~H
z89DY8W};fjqPo^_uun>?3qY<}?VsEDD+*EC3J9~Wt*RxVd@!u!%M!MQS8a-tV<<Vs
z&#4vwW{<@|qVg_K^{gi2K-RI%A6L4J-S=d~p}kkUv?JFEYp2xW>ih*tT4+hg7gA~L
z1GIti&YGj8gCFU)@<ym0Opa)~rIu&$`2MLQzdFdv2(Q_ro*5f}&fTy_MM0MO?)wWg
zmYFD=6a@Xp_Lfo-TnPR5m&?~BL^k2f1E*3J1=T2T=YdxMF*#h^q<zZl(_B~gV~dxm
zg$8bQ7)<&kCA!*UtZNPAoOX2yl9I2Yx$<Lqk?a|dZ;c^#Cdm<R-i|Jh8>$@rS;+B`
z)4b>#o=a}X+iNfLPXcyOf>Rae^=d_mORX}7ZbEh57xy;Q50uu_6<x}?fWFB2umgC*
zt?%!H2~D6?|7bYIb&B%UumS};JnfBRx4IJ<{qlonPSn-(M&{yAk3J9Nm4SR>>`xy>
z5L)&Frf-v8^^maKn3j>o=N;EBk>V7_KbHBfatZ|Fs)-Y1v;lA{nHzq}Oyt6%f~R^>
z^Zi(nGz)UPf-_DR^&iGF)B9)9&@ai%NPbyLbw2{Z`<B+J_Gi3~t9Q-EkzMPe2lAf@
z4@ghRNF{&!a;;T$Gj<h*R)5xa^u3zCXsv9Zl`Zl<M0gMWTAd5c-<Z|yGch?69Wi$%
zz92=oc$z2VPHc<jM@mHSG`aq~cguvw+UmP<)qKxJP02#j3Lo2~+7(zNrUJQb{|7!g
z`LqhKXNrbAy!?mJ-N>3fr-^PsyLO`1**#x+0*(-u@86{jk16)(o->$Sb7h5Zz$F5r
z7wXP7d}1(SD{dDHYfGFQao8_&W?s+>CT0YNG5?{EnGkC=w<tr;Aw58)1f&za0H%~D
z`(ooX#ZG>lJJLSFOTxq_o?X+PE_N_L#Y$4!D4x8Fx>+~cOTf!nXqGSw0}WEivDZz!
zvbk{$<hk>2RDus-bI;ddX;BGcfD^g+Z&Xr09`O4FKeF6woa(`cp=Gbm>^qy_)a<8}
zOhP<dQUV+-qSS<&lSQ{6u=+-c4I4V_^4G?%pY>i{%?M8z^be2n2dZ;gnh}7I$g^*Y
zKn^GuV(*(#zH=Vw0cd#}w+>D)#mTeWUJ_DJTk6;>6T$pzbAOL&bYWRJr*r7~r9+Fj
zg^w*o^rPJENIA`8^jNT<Pj7!VmtL|R@~&12EK&>;&H$nPeYr6D<J?uq4mL4Y>Vb_N
zZ0rlLjiW`stSu}%M@Lx0)Bu=G9Be--HOOo+_a(9Kw8Q7Gr&l*}PWDE6fq!e$CZE^N
z#yxece2*ggy!C9qdcFS7rm~oY6K47y-nr3#`%RhXHg92V<&%k~p0S2jLZ-<lLw$8^
zgY|`wsU_pT?nUOPQHkRbQ(xGg{6<!HliJ4)zyFtik|6G!u4^s@$SqT6>e^18sSB$M
z@&h);UY78%ugQW!9@JEc>ehdxfJ8m*`MKG7UNM_6h<?<{Yq1ENz`)m_23I`^sVFy%
z&%NYJthu6=$fSi~O?n4|2%i7D@5S1_Js0k5W&DckP!IF&^Fkduq8$)FS5noL^|N==
zbW=2twf1r%1B8%a=WR_hdD!f}8Y1LdK$MQI-?#a9-^GRxu^XXi^It-P9?j{l+`cNQ
zSU*{7-G7G*1M$Zfu|&x+|3Mx+J??Mr?rv{y@9)u&gBH3u&p1wgz^W357(ia+hvNl|
z*Ori*pjo$S{uL;zDS&Ib3&ky5%`^gU7#3L^vJNW%8EtzR)HuCnjJ*ks)x|4rprr)Y
zP(q-3kxFgsjIBtX>n9~lzC|P5G)#gGmw!_yO*y|ny#J19zR7K8aG18%>_gjg=)#PU
zX)H%=$fc&tBfsPbX&*8yN*KPi_5R)-&u2J2;;g^HqS%y*0+Sl)aI5f6t$d%-L0A#a
zrqu^vcwOqDwb98l`^*4h>-Q=YRt$Q4RCxV)^5;O9v>H_dpS|g4QdGKn)2YYiyYs?A
zTDnJj<@0w+bWS7&QbE62ULp3{l3xy{N2jS(A7@0VM9QmQHzMN?zG~#v#y$d*yM%Se
zcNVGv>iX_^skQY4)$v<a1rJ0O;_r-N%h@f!Vz@!~P&(``$U@zA!%qwn)rZVgpNFWX
zAZHB6GS4foZ2wuxhr;5IA7ssE_CRn}Rm1QyBHlMhS7>SFwfgR2s4wnu+=XqWLdt*K
z{q-JF@|iBocLv9X<mBw4{Q71mQ=R_qERN?O!4DVV=}0Ux5yrV-^%^hA<Xst}!^;;a
zX7PcQ6&+y=*?mep3_;87kbJS`>iUc7<cPIh*c<xVzMAbYhElI>39|JjD4`t%^hiW0
znJFl{Mw)XG8j4|{&ogICZsExDcCj_*YMmj*TuWg=qf_+`5wYNsI+12_FT}1^xKJY+
zn#4SrP5u15j(Fqx%S<svAgIV%$pZO`+2leA!5(_VAHr@{lI$XMQiaZi<CMp$Y>o(^
zEfGE~-*`pA{p&RGYlX1Wk_=cS`CUaMwh-RDc*rnxiC^V`0V%@biCaAp`saM?xa0+T
zL-Q0bf#3fmxz{5%uh=%6`?MDy!HHVw0cPFwm8uPMPB$fgp&NC6dhw~H;}=kHqy0AW
zk^U}hVbZVB@I+P87Wv~KV#Sy0l>4fX`#|g;Nd}jZ=PYTR0|M2$WCRC?HjX8wL3|Sm
zemVF=B&1IV9qZu8CdOQLr{9}z@snvljPe<{3Wy2eh3%QQ3{|x+zUoGJPm8<L`>LH#
zN0n&vMY`h3z7`;3s~+?r07V;?f01mOMG??^+}GEqcMEP2pusRF#!cNRE$`ebq?%()
z3+-I0@HvPAGgw5-`Dlf!y9T6s&wOZV;X}c*I-U$)yx8dS>oDf6G-<c6w=IIU$38R9
zll<2~0G%ib6+5ywbX~dD?LyEGf&rk#DlJ~;vkG|x`V2`*N1(ES%d(`9lbM|bI@4yq
z^{Xb&+o{KCs}C8&HJ@J$zbLtD6@__ca+2Ii27U7Y`K_fSf~Vfw;xjQlEqf|jOyl=o
z-=j*M>Ue^D9Tg<p7YhB>avWJwS+hj_j{4XgW+gG93`e=Nl6U0hA?J*p*&L@@=uRtF
z1G3VL;*weMbVUOJW&RAG8}q)K2U9Ntb7ej8yBN?Y9~@{*I$z1Rm@8;*YgOX*N&SO+
z1$p{tPWD1-hk4MPF0S^J=<WeR8<w4k7bQ*GIr=-okV_Dgsgr@Ev^pxb?*X*QgnjDC
zgkE|kKIyEMZjhe`eo53lCI)8S59R74FKSO*l|YRC!aY>r?4B}bH+7cB3#B97K%B-Y
z7r+r@OcbB<w{&qNO!K;H-gL#N@S=kLk0WX8B2^n3lAH!l=CpsO>{#_gMeZ90-ijrn
zeT!Mw5#)!VyU~<K0oHt3#zi@X*4jTcY{!q&)ne<JEO-tHgxMjnVQg(<-QSWgkUvZr
zg=d!sE(0w7pIyQ9x~Dpgu;M@_TEX<Sg1y0i-hB+0gbBH+4soHZuJPDAur~&Gx9G35
zP@CU&S#LVlz-=8W(~$gE1WG+*e)hJF%@-1;?TBE4h$Xs`5@4?XTwXs=&&Ym#Vdco{
zt8BKLn|XWGpl|g%^E{GuKGL`7Hn6O%nznBVsCYGixYdTD#B^2gWVa0EOm%`ADBk@s
z>xHbslCNdP6V3hpdTy<j1Q#ANlwbJ^yjku4iHB~h3@#=2kk*rT&NPP!NR}e^tgw7$
zGW|<-=!0=TAV+SXxd`fLJaO1I`*`FZB|e2Xz;1NU-$h)7#1`4iM0J*SL?oSvt3#3k
zHmQ;sa_^+^ZJmVgiw$n~q&@A_J0GEH2x)o7jnVQqOj2gJ?qg%y#fP+*nS&?s@NoAM
zB$Q3O6q(iPqkLW<+B&m&_<cW-+TyRv!da;V@owQ#kRl76|6P_GV<~`O;qM5J$LO4I
ziI``0$X;!SmXz%6rJ^b<G*6qs(Z3J_g67tbpJ-Y+>A*5FCOPpxy=BSR7A4aTwb0Ly
zc>x!4_)Y!Tb;`e4SA#<0<;R0~{ORxRDs$etG2naABO;!_Fk5Ecq~bMa?tnDRYU}pq
ziSyhFx{YAh55|qK=&AH|#F#+It)P%}RgK+)uWsXxjs9d|Qh#ZSL)SKI%9j2Ddnq~>
za<l&yVL9fvUb1fXT@t$?*LV$y9aMAq0&k*fvWhMsNe}#?RWbWa|CyW5y)g$}(^Tk6
z*XEjN$S)t%wby2*u2bbL!gzfP#r+J=%*;GCHip8z+Yg?UOsGY_!H0bI@(4+318=hm
z-~=O2qhC%|S|!%zx6{`J-ZVNO-yAPNBcecb-+A%&uR|wX_vXLLq0;nbY0&(R^Fw=<
zyS|H&VcW-lW;-&lQP_}p!XTS#YFa}#={q)m*BGSx)5g)q<W4hf!NYTCw^4OASJ3vs
zS77aXv;rIT+=?bal}jt6Ixz&2{pnd*nV6Ws1^TBaLfvBrFJzyL>jP={5bPSxEmS`k
z527SJv>b7^FbJq5lD|g81^@f1Ip>KduKDx5cR>AeVv4i2#?AHm3Mo3T?-t>v)X&gE
z2$-9~ZW$=2AEb=V8jZcF(=yGJJsdSRzq>=(;6|HS0=XrN4`Sy{4Z(jN)34)n^Nh{|
zong{qhll?M=cTF&K>x^q^J-tI5?IvBmB`65qtIcoDJ(3EVA7_y9cIROo~JcSMt*LF
z?X%qZG0PoK%E?h%-3*~B64~GPmyVi7R8?MCF=&68RX>-DCn$;cfP;x~FrINt0}@An
zQYfx}_s}Vb2c3dEzrcEPLz*_oBR)ONAlu8e{m@O6kAsB;VBxttoK8!#vB|u4b@Qxl
z2$F^PfeP+xvJL59-C{qZqtFkFS2+GSecIkUrzwtb)praO$3S2JneyC5R7g^(T=aZg
zAHnX7y+iY4#pJS(iex_b3b$&Gb8|7MNVx%7otHc1?6+42@(;Gl4d;K?k?aUYzkpM`
zEse<J8EzC{wBjVFQ%p^5vQQR{?866<i{KF)8Cf)@kr6Q@(x)lCH2efxHW0bfB+?x4
zoh}z3rH5M(u1-X(xasO1gHsWH#j<8?T4^~wJ@!K5B3SqEs^OVc7lC(ly+~Ann%_t7
zM^3x$oc?fs4x!M5{Ci$DoL&Szx+lq_A$8?!rTG-t^*B!1AIsDgZ<f%MC+zsk5$Z#a
zX;J^)piyu6Sr(c~=j#(y!ud94qV~L~+3Ak|>}|_{a@4#19qxxRxW{x}z1F?+-iB;%
z7(aQ2lEVd5IYv8@MxpZ)|HmsDI=X=vyoroMG7;+WMl#$V1MBVP%PCY3UctC-O9)!e
zcbMOSsXv!7BV=>Dg68J-*1*5Rq-$+K#a=2`N<-wLPtQ|NbeBkNr-|xnoFE^U$(|r%
zdEt{muCUwRVZ^3L`k*t!sQY#?XCK8KX3-DHaBTWjiD?;MHN`ZHjpheuPKx?Vr>!d-
zM&rT4lj}cIm`;_RW9yM7YPk9r<=J3trK1!m{rdIKpTvKc+X6>daN$jQhntamzcqe7
zvYWz`ibsU1ipi-A^w7{#6c?2KcGH^JSa6}*E<C!zgyX|!_}@PsP}}k-;(A`le^D`3
zHZV|B)YO!e+>Qe4HPI&mWf)IRnI6~wP)MV4ja`Vx^}@<*fiv-B?b#W$-e1ndfD%{H
zLuR?r^*yAP`$lM7R?1ft{3r(c!I2R%0ctw9GOZ1g2xRTj1xoD<G8oF?SgkA1(+@}C
zO3%#?;Zjolebm%6XbN8uAjm{q3z&aYyd%H8B590-=Gq4nH%3KOS68FMBs(E58fy*Q
zy#HB6wG{Y+aydjz9T9G~$j`^u3^X<Scz3&TlHd@WphW!sZ2_RBr6L_Ts>=6v^$mA-
zqXwfuA!T()+&{h-#su~{SuBsQPUgresK`qyDk{pN-K?=am48~%xY-Y?X^T|#%`=f#
zRD=#Ctkmc(eoiT$dSQF}cBe0Xc!7nEj){pWzMvRY*=N2kHE9BS(;~a-2Eem*t%s6&
ztR-m^5<7Q6!u>z~0sLRTbMswP%x|i@!M`gN6|Jcj1M`%If~GjH=)+!ECoBp#2%=&@
zxHJaY$Py9~P*5Sl-qm!Mb9L6QyaK)pG0C7r%8tN5fGvo_jl$?FARwUMSDKI!jTxPd
z4AY4hoee=ohDgG9|60@Ec09?Fd;pXn+$SX@YHCbRadP~pt6m@>#snaNuxw+NyVucY
zd;<J~{7wWE6dHnyqm^cd@<rSpo!Dvw1~w`f&+VBy<mXR!wg2;0hW{EB1r?MgEsde8
z4;d&z%7V(HWA!UQ?PO9SkDO{z5ngDaF~x~u*wq`ZPoQpwVyaYZ<4YE>;t%$*)gUrP
z*2e`R{qAK%tbywfN4vPbymEGP0Re|>QyNL=Tc->9-GwU+ji(FsM4!HWSDR#GJKf*G
zLB|!Pcau|>Hj*?jFp!m%)z!T(sf<J260NK(pk`&Ffn<JzF2U&nfyOFIP1gG<uD1Q&
zXCYmn)MRsT|6e;D67W@in7+jJh(y$7c!@M(b-r4}qeo6v(MVoJNB<}<tSK#uO_Yv_
ziko56k@}7qQ^>CM8CL(c`urm|G!k4@UfK+WrH8T+v^ggdQ>NSg7YB!lToBIZWbZfr
z_vvJjGlfnG`L>RGCV6MK2n{q0l5Xr;tjfyDD?~MtTw1OX0^RRKcbVZ0{1Vk8|JXTd
zU9W;Ce}M00rH>9QhAy3_`{(E9_x9e+Chgv5b?NzWDSH?mAdXD>F|^13<hext?#r^m
zXkmQwe=ooxdfNm1Mei``o#c38VMi;Ux$(>4+f1*v_5go>|A6zg+uO?yw`K5stK3+w
zg1TQZs*D2Mx^vBHWA7Bvggx@<9hAz!ds3eD8BpJuG(fIhB1D0oNsxB$b^#9-tCrhI
z{K@^}<$2#hMwp$Pm32cT6}fhQ+2q^3^b%Hjd1?M6>j9#>`yUqZ>EYG?jv7#VCr{rv
zMtL#n%UJP<VA%Fkfz-i$7Cw{vn|3ndOD-NB@c*BP2n7X&2+N%<c-gR!8Vqd(PZ=zW
z2Dv=E!j)Cf0Z$yCp$tE|V@uiaUNK|5bOMX0-gIG9)@G4BKJxrHD8f|NoSwRlD*-pu
z429J-WtcMkA3(*dAmr7l*|=KN$!%_U>LqMQots~s6!)mzU^s~=pmR}DN<kip#6^e_
z09&U9n8*2zTRnI$T-^UtG+bQd<!x>C^nCQ>FUxyEURF{Mhg7LT&V1IL+x<6R^b|R%
z0{-V}lcmH(?QJj=Nox27;+z`0F1BK_UIr}ERUWw5A$T5{4<HqhA7>~%Ck|$R;TAYn
zG!cnMxNF@EJT0r8hV?Tz2CL~f014t7YUcw3YR?;3YdR5jSQIM0?ILaW*x$s4j;lyt
z6_iGpOKs224o{Dd&&lwKi4TrX&yH-I97>s)2K{k~-UiRdzuAxDefd55ExwElbKkm<
zPSS9wQSJi$a<Uz@Wh^uXB{!j|uC1l=&(Gh}+^p}_VWV3SXO}-&A##E6p8&70@=yLD
zRnnE#W4GsD10HzRM4U#J2*$KE*#<WM%pDuTinNeFX8B9}2L#l?TlFWErm@D=={O@z
z*8|xmCcypMYh?U6c&|@M#oe=#^)s+nbe+b`5%UvqpLY=g{@#>iRKW9M(VWP31`wsd
z#kVY+^sFnYC{K+3?PvW6P!#5s<=dNDd8{FP@i4nQ3<rk?)8+4IVee4>-KLhl)$I*@
zOpo#p+Z&N=&)`KL4A{VZVi=OY!#r86e@z{WJC6^F4xl3y!9Ymvm<pv~g{(>2PT9|P
zdV(DgnqVpWa)O`rDM<eP;&Quqf=sl{zyx+i71aaOq4q4P2Sfm$my4Xn{adDe=6HJs
z1ya+{8UeM@h2Ygcg?NGlC|H8I)kRrJEpzIUnr`K8c$G~)kFX+f3UdEh5J|I*p3iyP
z0KIIIQy(Mk<R4l1WJD-`$gT#TpTegjWOu^;A-sBi>{u)`rwbZy3^0wvOVtUgtc|cr
z`S0{21RtY31Td6+u>n~F#uC@4phV*cA~a8@Mi<2Pk27jQPU@^L|7~^$zpc%a{2t4T
zH%@aCO`c0cKU?cqgnWbC_~?k|UQShLzKfHnKF7r-;&XKJ7?WzW(%$93L(5K6`@a*#
zo+kift&=v;!R&K&ZRwG=ijK@u%zn^YLMy3?H4s~m(+`%T-l>B;tS8>Nxr?{gT!&@x
zA4x&UR%C)z)jJe8C`OW+BenY^Ro-2C$nGt=+zq$L?dbtX|4^^r4zKB=9zD8VR(9k7
zYd0fH7jc!pF*{8MQEI>hlP7KjA>wDKm1lxdc?3^K&oBVOIX#lqgR8=S3o#=Cw*ij@
zXl`0<3*g-_v$0U>xxUGI+4|2UsBZJ`{hny8v&|rb^!VJ{NK+qb)&1I7;)N~@)>|M~
z5X6Jgp{_cE7>(-2FgQAnJY(sxQ5tWx&8VEOuys56MXSs2>fWFyRYl!r^*C-VAAGK~
z#RNwMm0q5`&~IaEv8Y~+9F)PS7Epy~Z1wXem^qMmUM0XHu_Ry*^pq)UZ{-#25rt?P
zJ%^kv3uwbs0Kv-q>sOZm`VAikt;nOa%zwJ1w6dh)JlLrFGW_i1^A1`H0`2}IE|%tZ
zRQ_ryD`@+;uQ$^x2(o#I>_7#|<*bnW0oFeKeWD?|^%4Or=e~1607lA=)Aa<6J+5z>
z?+-4&H|@-vyd)_^-Q@%)@=hZ;&0{Q4tce04X_21KUdK*&)?nAjbL+A}2BBT~uwwL;
zrn%&+lfZJuF^IY6gK)fGz~@GMU+h4NH)Unu$H|Ne8JB-P_Ug?MQua>CNdNv`R#(tc
zdFf#<>-B!|aPx8nEDij(bTbE^z^Ac6F;y>J>;KupZkBH`#od#hz?_z@^ifwJVS!0?
zgELA$>QQns$RXwcZG8NO*B$p4m){q5W-4BEEww$sa-#srLZ@F=cqzK;fW*HzHnb9;
z^Ul*Y(8|@{y`-}dMC9*!n1h9*8atuOU`y8k*ni&Vlg@DY$E~~f!E(USpHc(SPJq-F
zMbqy-sKot%leM&@6j)taTU=ch1<PJV_&FONJWpSX|6B9r>e6!mV|pnsXk{)`ixcIV
zG1g4RAj5S&jy^>IpG3zGNe-W$EiOk;g^E;$s7mdemo8<*EWTh$`MO9m*R8brf=(j<
z?4{VpOVyoQ3wRKSgtdoQf?TVr>M+vJ%FWLCZ$A?do}335C1zrh>}-2p-ngLfQ+Vo2
zRJ5;&b)WZ9QBxHETBa*;AARx_Q_|s<@{#?Yp5vLDQP3&Tvo&=!eDu_cmMa2CKBuzg
zChY8;PEAd1ZT0kkR2CauNrpOLRrJd95BF>mjwwa&+R`bj;HClzRZK!#W%%}3S8=*e
z)ejzMtp8&sAfH<H`!GNU=JWS($;b0=UP+SMfTtH_1Za_y_3rt$-JkuSAibEL-;mK$
zx1c5(A5$DxDu2ax(*Jo&{7pqhx-nkcu=izZwWK2BE%9>b+Zce--X7f4=|A%9^o~0@
zP%ZRtUL!ph7w;%ww<13pUy$ZbMB8CmT1-j2x2uOG-|_Q427apM^vs7uk@o74;gy7H
z0PAx8J-GR{Yq@Kn|4${@R@!&rclICO1tv|dn!Qk*d-;Txve@$TJ`T3%m%R;wEW-z1
z82*{OvD4!TLO^ec$q7Juh=Cx3AvDqX6)8wGe66t!Qc(pww8W#*)1n}_=)Uy+$|2sl
z4Zw@a4psehRei~?`#y-*KWXIX{Kd}E=atH8%)HXozI?J6rQ`JH&s|IUi*M~4ooTA3
zJ~f^7Eo@X2FO|pq<{t7a8LJDrb&>XXAjOZmz!XAN9Z--}I$eSO%rUY|v9_POR@wgC
zBrfEh@%v-wcjBI%Dt6&kh`0T3R2L&hpu}TuWNWOi71Y|E{tKB*7zOsw80rh7IfawU
zasyVv^!a}H=Xo~%{VlAFhnliNFK749RxZ1%bX*BpBwIvF!`8owY1d-o<GyvKr@`!R
z5Mw_bJpqxbM1t$XqjI`nQe5Wq#py`#Ef20RE_VS%)p_g1T>^VG60z0{pXXHfm?-7X
z>Pon+0F3DUSKU4UB7Fq3IzouOSFv&Nuye*|A<=Km<I+a98XOHH=7c}Cd?M5Rz5|{%
z73C$W{g$l;Vj){hWtCcURV6l~y5$9R&o`Q4fuz4{`TxG!YVmD6-EH<pdfA(fN#Zr}
zPQE(iRTY;PVztd9BcleFudhrEKk43g)Iah<pQ7M|>#34?FJzMP4=Tg{_URFBZ7|2U
z%rl1bt+2TxOVHrAvpRL>B&YoUXgUkFsM@v*(@2-njWh^IcS(1Hba!_S-3`)>gmmXf
zNC?v1-ObR=x1aYozF)weVb2}cTI*cAtwrXTet$LzpRGe4?ipj)9f8Gb`MYtGU)cD1
zVg0YiJ&^mT{pQHi^e8a@wG}s4^h->ivG%7n4p7APk=+*L=i;~EfVyyEq=(0;ZA)%}
zWX7C(ikr+J+b%~G!dZ~+W9K$d$e<)Qy1O@VanCQz&T(^bwzjyt-J!)Pj8m?_9uqco
z@dz$2tZVDPQ2x0;Ch(Ui^uL+i{T%AWUvJqe4k9%MDzEP$vK5EAi-z}T@d0lQ;9r)0
z&W~ru%boXR-IQ-@SEt#9c`bv-6^_aq)10hazw4rF9Q?vHhyUC>I2l^XSxb(F5aErw
zW5^&oH%_PEm?*qr&$x(<P!tuITKL{78hsO!RB@MkhvRA2>|u-GTV5#q?ci`cHe}?e
z1@d(@)pwVWQ3=?p1VEP+SsmeN<6T=(OC_A@5*Sauzd`)M|6xp*6Z7%?PW~Xpg%AV%
zI3Kl}S6(goy^?~HwYiMp^<z*-p_P)qA_JY;;bj{Exjp1iot{dz`rgLI#=VZpn$I1s
zmt(6d2^ojNRloH!GKq<f9qDAFE;~Iy>+0evvH&(N3*YuQ9mtdCJF!sZH--PIuDsY;
zOwHNON?Whz9d(Epo=e*=9qhHZ2Sk&ipRC;cg4CQ;jI^~-Cf%jKetVj^1i=4*F8HUj
z3A(5mjqN&cnHPaT-M{%a?fL?h$;}8KtD!D9R#gd0??~@WzoPutK^8%aHxF8eUKS2`
zV)&txOLSpd$v840t)!&%%qfx}HZgSbwKN%R4GuV3;#!o^c2u~gh=FUhfz6cUqfr@c
z-^yvmlt^<9W=cae9g()-Et=9WeT9|P1yObo3a;`^ez)ISsH~atik7&7w{w7^kF2Vl
zy1d?YBc638Z#&S%NAfy}0s1YK5s}=fbWxaM?BHWkwKvx|sf#xnABhsYK#1*E#k<cH
z4S<pnENj<dhASNGP7t#1ke{%Q8Mn18!aE5^u4k~fp|`sRW7139&tP$4GV;Y_`8Fv9
zmcQrS-cY2@1U*q>;0)xA#S@^pMdjt}6zt2C@f*`q&kf<MUh!YvfRLr2BN4$Y6AFQ}
zZBPG_gMkq+5*rppkZ9qSlF4>*URf7YO~4u_M|<RxGWqsaez2I~Y3-Qz@r8?|k|r_7
zmhlu@1jF|!R@N9&p{+xlOcg+p<l=3t1K$Pg#79%So$W%U=9SWhMo@TxgW6`n(F=m6
z*JP8_3318eFCuCn=i?M848#%B2y^&5H5@ySQ7fZ7S}zQOMK1x_fVp{gRz4ty{`6M)
z6|G~<#KNwivZSklhnt<6f=1x5t2X_9EwE>^Zpg}NDt)%)E^eY&N%ChJ?7>^g8TEv?
zz?lMY9Yl9xRXtm4UScy1r2j5so!_xdmZHY}AsMHQ5`zrn;6&nL9N=MLb2aAW=&E5X
zX|gL$Ms~5WZEU8h>8$^kwd3cj^NwRH>#SZDF3zF%6!OQKC9Aq{FSFKSU~y4pYq|G%
zDFRo5y!>n&tZxOc27=EC56cP3pt&FC!z-qPPt_;QujzUY(mF><7;DbCjqmz)R9#1o
z`4I}`p=3dUh1t-m(+DvE6~#=Pr|~<(RMEf~uHYFB7pprvF*Q0bGgnISqUt214LoSP
zOONn$-<Nr^Iy&3GcnRf}X>VP@G9o7z>2>J4gA8|IxKsMR({}UccwY1}y7|H5wXZg6
z0?PMQ$8=2m&*`GLK&dRV^X^Za#WRs53!eb%TM`gYF&==&iABE?4dR~H6h-v)`0k>o
zuC3g&w!&Q-BKgEV-*Sh?qVL>(zV!?+0gCIcIA2`&yTNzH#gT$*I!UBkfSp=bLNhfz
zEDg@~OUwImd}}%|#fy^?8J+}irzTq`^+?L?ItKbuCS+s;p*0+F&7TT>f99pR>1zG^
z2^7(2X{7BR7PH_VY+|shA@&RHP)pnUo)f}so>?#2%A%WXHEj9`(upKfeweY?X6~kB
z_!}=pA0?000t!O5khTx>Z+EO1E@=F~-LohW3{kwDjJWsvEl{HmOm75zkAFr1DSnxl
z8B066vl^eLh<<?y`d_x^r$m`KzRwQDpiu4^jZA{Q0~gKrIxyUO0H5k9MF64@(kEEP
z1!xs_HN=U;gMn(`P`RIYM)+@VJ@!VtcL8Y0mIvY9f4&KtP|%WEnX&kb+7%SGrcv8N
zJ7J(X%PEfZIy(KN)sNAdYB>P!YshO^T1@^&RIY7_tr(i+$=_}jCi<^k4dUPdh!ZtP
zqq`2=@b-4L+OLe!)QNXs8MH>Sz54eA;bD?ud~7T>;poV!oNVg9V2#J>;?3xQZ7bQg
z&p+|fimwXePNw9Q+&$z2v(KEFsp&;M5Kf1!-kL)KQ^TV|l)lN1{5eweve9|o9X$nn
zc694ZV_<?~YsU&^mLVTfJjf7eV`S_xc#M9=6v!2Q><(&)x-H3u-jd<1Wgy~5SRbto
zQ1G*r@DMAn9eGUYkS)pqULx(A*#88H0tpa1Py6BRAzafjUQdx>4)L*%zW&QsPBxm;
zq<0bwyhxXmnSBdM1gU0D^vYm*UKx6Vc5!4`hKl1STKg~FtH5TL2YRh|a-SF)T><=c
zzP1K~g5a&dCWEiZrV5hRwYi}{QPgTMcEn9p&1&4g^3yCygY}J?s__sT1z5KPe=I!G
z!<{WV&!>K8L1s<~0wm)?M{>{IwjVQS@2tvi)~AGdI!ojM&OfWa(n26>ws-qdKL&V?
z>LR>gzJ9kDhb@|32H^q>hi*FPP7*&}ef@m06KK4ppR|AkMykcKsGWs>d}0GVXkLO*
zv~S}5i1FbAQcQFdko?H?a0(N8>K`pSo_~ufiIX~A>goW8lC#jpm=a!@tZJwJd$Xm*
zqK4(qFOO{~9J>uHTbldrih>doa~ZO8s%_}&FJY{SjGx#J@*7{>jEn}yh7LiKdf@Im
z_OMh3oZd+M?|Kwmk@hycXNYBZ`;~rm<1DzOE1C!I4zoKYF*rcDwk|ceR|l`V9H`Er
zA9X-%RsH!l0)DkC;OK=O=QkBlF@Z?^Ee!6Vt(|A_zVRzN+gDcBuWXHV=a!~;sVX!4
z)+c_~2HGI8{mQ4Bsj#@Wu=cm7;9cblM$N%xS?mHt+eXttqXh#78bwi=$G*}#ZWtVw
zlc&N75+iSKZyTT-rHGZC_bKU~lLrxR`@q67ET4!R^PMmjh}j{OeQDcxo@+uAd$IC$
zKW~hL0xklm>eOvVQU9H%3Ks93k)?@M*?BJlK*C&Q9hL}<D++g9KP(1??_9h8^AJ>1
zGLNDSuHUmwPAM18u|(d%WTL8mb#b9FaQ+YHuQ=JD^zWAyK~~?5X+R#C6_XF?x#!tx
zRB?ssOO?9zHvxdlB|u=cb@(YqZ;=O%&M>?^|Io48`^OLBPi_Yl)tOvw%*BNOJ<b(G
zQ8zT;DDrKBxzNVQyLZRH>uRK;B`K^n>Y{wu_ieXVfwt1rCVBCj0D}kfbxdn8_Vw}e
z0p8Twr=46*@8PWg2lTSIrXQk(nW-6(m}Iz~LHvmC*N(Azqbw_T)6O5rrg;Jg!ERtW
zo5Sp00d2oLvTxB^casrRAqoSGb#v?>mrGz@$Itt{{mR4JU4v23+S*!H*4{zK$jjIN
zp2pr}@Pqm>qc{62Cimld@mFZH&N^^fRCD7(aT!6$SjhDT_J63Ea+=`~!T!E_vOp7I
zkHJR|>Ftr@n)CC#JW{#`q08S94+frf0yQP5{^#=Q-{2m6Jua5({a0#z`msP)Nafm(
z7z`&FIqlmd$r)NRf?@QAV*TA;WMTrGU{OB}BR3dhK{kW?{<w?COZM%qYUEG$6MMmR
z6cvHN0>Xmy3=eq=oV2XypPBo6p}hXCH1Blw@2J(2CTh-bw|1VBC8^FX3b(>XN}1qc
zrq8jqcy!@>%!W=7PX8fV-|Yv&y~UJ&Cz(-C!`fR@Qc?`WfQ$1WKPxiC!C*^J>g{J{
z@YH?}?TbTHYGmZ6WmVI}gp8FOu?6MDYf3I^G`}p|3HGOwC*Y|}a`AiK-W6xeHqFlB
zu^9aPDS#N#omUfopa`_A{fi7#@$_n3c^kPW{ClU&3W;R2A{F*O@^Au~80cS|YLb)c
z_bCS^PmSb3W62B;4^KelNg=M?wwbxA8tRM6!eQO@^fit7uW=z=JY<|x+v0T(H8r$A
zJTH`8f8-w|Rg^4H+f{!5L#?ZQC?O_;C_CWzARGY1ld!Rgi9fnsv^;-8v_&Z{j`;L5
z)LR4v-et%?-Amvr0y+iA`V$Eb|NXO)YelDJ3lwAp5}ByugybfM`(cYB5Qv-oK{3!v
zOwWx<%lR<*#(eSN+Us-EW=%nr`MDea&12<${jKNWfuP&mG{H(m+T{r`fffA$j@Be=
z6VHX;&ORCjhNoxi$pQ<%wr<v;?XQ1iA4NG83b{HjfYU{2+7Bg=yyDs65n~tD(Wl%Q
z-9Rt->O7^>#E!gQKl(KAIKC`v;~$liB!Ar9GBW;2K|2^>y>UmvNp{#>cp8|LYbY=I
z*jDXpWFk5<w9MC$4oykQUzsLNg@6PM#_^Lh=GYqGR2A#eQhv18cco<oH)W8sATm;+
z!Lz-QHGe2pqjH}Cp|pyr%C3|FAN}$Y6A7M^gt2u}G^|f5j){qx8594+wqi&I8B)i_
z4rGSU2R45DKVM+LoPGXWX1*1AF4Li@^=fK3kJ0;E?h=Q&{JX_K#6w$-aA9Sng2o8i
z7LE-~JnVJ@!*>LPCm)}>@D<}daYTv#vH{8!_MM4~-NGRJz+X_uD{X@EK68BSz*{;N
zg`F~e3cIb@ARCcD%!?`xC^4#``6%BnQqAhB`q1UE?{haoIv6iw%JZGB0+KcO>!xfH
zQY*<eep^@us3T=pq!Cqk9A?7s^6C%(`G}C@WJ;JzA*rBYqj#(2Px|+`3w2-@mYM=@
z6BNvZb5KJVIzLdb<8cm4S*J=(Cw=WIzQKnEk3=E8J$d}I8;}2~rl-=#>VP24U=A@M
zy&%%Da#RNS+SRpgQf;ubVh^EZCn&<3hO0n!xOcoI|3_F-e6$_QTg{m;$1`kjnQ>^^
z@8n^L{KM5!Zr#8KP$7;pD-+5`Wqka@qobohSrGGK2<?9jVy&L-7clLNlk8{0<-YUG
zJSE_J=BJc*Fx0=f;jR1C`f9!3(6b5=NG3U9nbu@fRIqfjSevh#n<EQ`;CI}|ZDS}4
z(F>A8o|4ki(qIu8BsVXlOAmix*H?rUl}xMz@Rs*PT~ozdZOc<q%fECP<2MI%Z8-r8
z(0AM<iqaBzusGr~26p$S2`~IQGqx%tS$RoOQBm}uSX7}!(c$fgfPr}6hknD9vl;6d
zI`J^q9LG)ymJff_Z;fd2VLSay+O;kAOp-_*fh$A}PX~m3UwVI$%~l&2tepmH9mZ5U
zS4u%eO3@c40(D61`H>&)UJT-{bWcM;`JRswCGLvitxu~@6xx_*G6w}CBrl)i+|&6{
zd(7aK#R`AIjpW6~rlvmJqcm;64DS@WEgt_{8;Y$4qO6dM_rNGZRvqZsJL6vejC-<N
z=hZNuWlgaI$RW)5!NeiWM=d*Bu_Y*)T38g)H<W_l_xfQ2;FqsxOfXk$aYqWm*u)!W
z{`S(HxFWbt>IVmOo6(>MGs(AQXM|@;X@8fQS%r829L5oR$|wX2#!2rNjTIce=%PjS
zo?a++X$0t~x3XQHdtga}AsAvG_Y_OHI(cppt0sk&SBx=Q_=5SEkkGi3G~Gy&s6Gkx
zN}P0Le&)#P$PwtpRK03^sR6p|gz_k|oe{p<c~Gx{0p_YqB&)D_V6Qojx=#iNtRl9W
z7b3>bclp=`>*C`>)RdJq5#VuP)=KGWW`qsTk!3LmzvkS+HaDoc&8B*0^^zm~2^vkn
z*=Kx!pXW;>*xZT{{1+4kDn><~e4sby@{_d?l3}&*-&2$}F|ePw0Sz85ZL@m)^bYl}
z-UAVQIBKI-PssJS0h2v4)por4lJG3bzvRO}-v)TKHhOmBJ#K55zMs;LZW54_^5>hs
ze`Z~VnLRC%^-04MIFF#U_|YTinSpEiV2wS}+W8JoXaAhrIh6TWGMkXwvAK_zmZ3Jq
zC(u1*Ah?iFE$9it1}S5=5y`?!59`2VSm!r&p5rAYs%Wnvci3?U1Lq20M$zryt4da`
z^PZ3NCVU9-L2y9&<S&QF-TfCrP<z}th>Ha;P*C`FO)Z7!{V=Wgv7RcTzwO>w#rHP9
zza`f<(TA-22FtX7IP|zO-#`=#V?cODH=Bs;2E_7UxM{2l!ggWcGET*%oBl3T52o<p
zT~bNH+5?^C6w;(w!@kgqERa1|N5n_Pq3~^1=Qtt`n8Svhyg}OMSgh8C6+#}_fB+#Z
z8vHP<-ObN;?qDev9|k_j06;h@C`(G!(O!ZBqXxzgN@)|>gPVEwAojE<3g*^Wq_~8q
zDzK02P#U1~T)6TU&ci^@8`M}CmP+g6L!w**z4ze#*Bvw8qZsAd7~0uAYWVhy@_H+v
zm2PWe#S_-sR}vT9`G=`fh`oc%*hx)c)MGOXKJ<^I(%^m(&y}6%X-YqUPx|d0&m@AZ
z+!IS>vd@uynf7$;T+mx(c6J`^W6egFcqnuaFE?mPHTC16MRkII1ttha`IP@IFmvQp
z?|%R-0kVNT^}<_@Nf-u3N5m5slb4#DX#@j1z%l~2NELP^412^hNL6SY{+t$TujltC
z`P$Z3g?;;?yTJRM^^K2Z{-WX0xGIJJ8Arr}U)9EIUv=zowb^%yVDIdANO4M|T;&B8
zhy*S2zU1vVHgW||ULDSBF~f@f?!1>2Qv6OJjRL!>!}QCkzvno4q4*)^Up!J1DNWV`
z2AL#LL%!D%(Z6DUzB;L^6A+?JYq2T3{Ucu*aGNLr+mK2$eG@q)+vtV`v)rV1e}L3Y
zrSAbdXbJfsO?%@zK{*AsEDdSBcm&{oA`v*3vP^A>HR_1(DyVHCleC!;Uz@Os0XDqX
zZ<dIfutNJDIBI9GAv^``ccOj<ubb1zm<VvuiTP8i`?=IIL0{4ci%^?{XXb*Ldr~2g
zJ939@-zb?xXHlag@?wwKn^TDdU#y8M+s^eWlPsmc)PK`|R&UJY0;=9ia4JB-mxpT@
zo*qU>ld9dF9(`TQb=f?;@bJ{spXP)-7Q?UTnx>v5N`JJ({)ywh6o2XbLH!NA{wwq7
z$hx$Qk=$Sx#6(U~6!4(^t;66X9UbpQvY?AqS}}w-rCZvoeJ-7!pG=qnbR(c79MP9w
zE+272z*9aC@5u6LY8;&jY8ylwPuGQ=qN+SKePOze0Y5e{);BWpmxZAkK#;$L&OO?C
z_9b80@NEPRX1mk9cwVfJE5R6#3?5fmbyt?To3{g=X;#+P15|k?JK|L^BHaH>LDZiy
zWEpK?O}Q{Oq*R8N25w3E*qt^nU2lMh5*@2$g>=GAyIrAsVhAeeruoIm@^2|@`)>zv
zB#`P1``N)JFF8|y@^?wi^TgmHY$rk??|pD_Oaw^V#AE5?jb7xLu;=)tU)fU*m~YO{
zJf9!L&Gj#}XrIy*2z<!AbG^C*WOHdCHcDSv{)=pCGSsT|_wG3fK52fIuIkGCyTu#H
zD1Ru%$V~C?YIl%1eCE`BO+LFimhn<d^7$a;*qtzx4YChn9Uj8|zf%F2BE%rP{CL3f
z$tF%^swQXqELX#Q{vhm^oP*}4br6^#$h6-rAh<fGu9NYIIy#(XONMQ<{Jp-oo+KeM
z2jO$ofB8>Y8$!OPzwtzw8Mz#-0{;sm<CES~;<eC#U>oxAnPTso14V7gwLoHO;R!*&
zDRh;xw|L#UH>)hj>Hzm+x(Z*Ft(C`9Y;#<7Zl6S%Tn%aKD{1IAA%%ZKERKixIeOn7
zQ<#{)#NKjf>~?t(1|OAe{PzT~`Jse)oIxZ~|1%v*m~{K?A;>?EY%im+Bw%13ZFc69
zfE;R>WfMTB7wGV{bpI58msI_jT0`oL8Y&O!tgB1?V+@!KPf)b<YCAXD+*elcC<S32
zAitTIYHiO6{n%Ttpnvp@O`tbx?g=OOcs(C`yZX<CyfCHeGI*dGRpXOxg<RQW`AE`M
zkLx>mwD0db&z!_htVv-(SPar;n{Lo!L9t3>%r#ZTvuhjGwMx+LL2Z~DfVWTi(of!u
zza_Pr;~yBtO)(_7DK&Y&K4l(H-|-P1P6*kX81k8h>qug7<i7yq!kHj!af<86NxpK_
z=%~75UuDx_k8XPPfA)2_#Utq!^;7)QWx26_YBy!*uCYbl_VvpyAW6ka_Zh?l(=uOU
ze?%g&<SHV=v<z~#FRG*gp!I(T_Cxb3nxKd-bNe*=dM}{-E-^1e_=Au}#Tl$cTNfF*
zUxSNxuv65gzc#v}L<hl}N==Y&OC#!`v69-6{8)k{IPm7q^E0tH2j&J5gX9L{dmXSl
zUSF`Uq;3?HR`@!6o`k%Jp+UAD;t;&lh1T1+u^OYB-^v%Nlg8Zh-?MO!JkN;lte;;$
zDC^_$e+be5IM)q>5H<7~PnbeqJp*HB9drA>lX*oqZ$gc>hnKyb&N*{!q~>-W>eZFU
zCerOOk@~qj+R4hij5GxiS&4<+*tHT{%rgq5?G&h^C6;Tp@9{(lb)bud#Ki~$yfVo(
z*GB%rU!R#r`@jPRpRxYp`oT~#Sk;EKKDwq0_=OZDH<Hg+o)BgOkNzB?{IB%_>j<aK
zjqTN<t-G_DNsw=|qk7mFiNdHHJhyIvnXtF!8wid|tyq^?VLUhREVQ+=bK(2uLwY)@
zvJi~TI-tI<g;(}B0_3x7xbW%cH-t9Bz;kz9hy*sLftcd(C~W15Hork92`1q+Q)PKY
zOX07Q>P8|+eyDpFn|5YC@eyoEOjVmyD=~8d12Uw6eMSY-vA&f+z8BqG%XN&FoQ{d%
zO~*%3RK?la+nlq`^ypa1%s1(efLjNPLc&eUyw*nxT7f%PPf#6)@=7vGa)ykUgNnCR
zdaEa-1MlUt(5<=ir+yzPEv1c~$)Shc8=&a>n2#Y&$I#F)C)kwbpUekHmyNxfc63B;
zW?oh*W(=`R0Nmxxr2!yddtgcEyZUl>*Bt_Ce${HxbVb(kQ+VkDbM@I8ELBhbnc%7q
zRQBCyuSr2BXjlc~qHS3hzkI}Q7*9rfFdqPFiSH(cPZ3DncNVGuPT#s~MNdcJ{-;Hr
zqARqfM>e6*MquS?rf#=JD(qS~RXn7NtP=&N;Oty1e@}2Rn#1X<mT1Vb@Njrbq9@>u
z>~yi!<=^g@$5_z;>p#~`xqhA7P|eK7ejR%5pt=MlrQw<(BE$B>=`wo7W_2?4KbucH
zE9F`4cQMr3+guP3lahpxSW+yf2P!Qfs&6``r_&%T+r%{H55PT(@K)tTe`cYY#HQ$>
zS*Rz2u23Brr71PBI`iS)mT=~E>)~v5zq^6W{+%>c&(`#-SE}Ft<81XHzb#6h)Oz=k
zmFpEKBRu#g6hF{kmPR1|2IaAtqIzL^@F`iH-(t^=k(CB*cr7rcH+<kqH?^9<%MTHO
z1I4sQiuZ9?+)>MS#Ps-U@UyW$eAN}f3zpF+qgfMk4$uSehP(~#bTsn18QYzm<@oq^
zN<3J4f)YvUkwWV5MIk~#0&NpPT$69Q`auX3^h@XHyB-(|KSgDJdMfTJ5wW5yvY5#S
z_le3x01of;Do+Z2z01bZ`7|<zl@@lGOj^u@rR8EtPW12SIO7LCG}2&*Vch_<zoii+
zDu5e<yrS66!yW&4b#I3Rk$TYB0Ipu|CH!BJ5F_eIX3b`p<#9ccm8IR6Ti_sMZV%c?
zvBJ>V{dC-?MWY|+)$^=w2r#Q>q&#bub7qD8q*Jq_TrjX-=A%izOtCjWKFC^W>)pkT
z*N}X3wBM*#j)(*y`Y7V=`!Y=baWv#m!`p`?@mSn#x?>Og`#ON}i>%q@4oQjGp2ScJ
z4P1=+QBGg_|Gfl#-Gk{$7odfpzV+E}S_%gLi`C=kbLs%aKsH6f(@1>=MrQW$8HT};
zwM2f;aksk&%Ka=b+kFna_>Pb}F><>xMutk{<s;G%U<jG&w3dFsnoy5thbYw1nma)}
zqn?bty(%U1@VxA`h#ET!VE+__RCJnQ>lRKguoL(WjkeF?e$;~|)UPe;VluKab+u(>
zu6GB3G+dZMUpJTDD<{OM!~<}Sz{w9H`0(XU18XULPp`CS9kqAFZCp@kLS0AZPA+XG
z=j(`}{p|)dDSq><`p<ChWdeGLYs1&+L@axaZIw5)V_<elO0zqM!PKQM%Hr#VdAHDC
z%QU7C!r(6R9po1H7v7abgO@FU%jc?EPQa~wXY-RP=Wj25%Hj?Xl84@_|Mr4%zwliX
zf6dTH?;nxh=23s8mxz`+))M@6R=B%A=<g-HZ7Fq|zwJD&3;=b*#zw&M<oOED3{P(Q
zz`{sEAY?Nc&{+H^V-$_gFo<rjb)?}>bk^4`CEzx64-)I&5O6Gqs`#O<Tk}0?P>~WD
zfy)4ke!z6T=4~odV~xG13c;9E;<Yx?Fatrb+5K_ikDs1hEI`x<Ys<8jxqr#&foXwq
z(+k2Xgq4-NYyC6L;ER{U`R>0xrGc8^wPG6422`AqZ+kkbY-2464U0=v*E~p;rhCc<
zo1uTS0&z1Hvx#Je-@9uZ(1Zte=0;aNKr7PNV1t?E1eEFbcGUzN4jy@$i!7l%77+HA
zfv0X-5buvzuj5QyS$1Wov;4|NU@sTTPLMNLYYd+2rXhV$MZa@XxA-gA0l#);<O!?%
zXE7P<=!HK-*7%#?sfd`8^q0arDdYryd&2$}R7jydtf{RE+c6&@#0VVR*%Q7j_$#~|
zZ=Yr-z17S&#AfsmO|B^_iX~bK!|tuGy+2Yg>)F9pn2ikj4{hd1Ny~cQ3+Uzhzw`GO
z55oB}0$>Qc8?&Aj)JIL<>|Ue3(CL1ls?S7pXA<9t6p-*5_)sAiTDYZuBYgO?<fcJI
zFsrAd93>JMP7RYuIA_br;1DT7lgaO+G~T=Y1N;eUvdes(zE21}*Rya}Qrc1|kN$7L
zY$Rk=Bk^4)c7;(6LQ5w4QglXNTCO_M;#-5a4K)`yyTYGYo+2bS$Y1d?Y}X^^N_5OB
zCuBafyACW%>%SFXa-+1Z+7D~;Z+-`f$wITSloxm&U*#tL1pm<*DB1g@!PyLWsa~lZ
zEpRniGX-CmP?rBm8Q{?%dK<9vS3$u~$cqQJa^k%@q(*lBtmw6-oj{!WMWDmm{#XEA
zQu?df7qG<`@2nuE^B-yoPpTQF62A6aPZNkUo6}R&FzdF%rl|Q+>>ni^I<A92*#9N9
zQ(s;r0PCxSzwX54E~U-&d#FzW;5dWaU5)<7awwIWGm~h`q(f@ig-4_kWub!w6rpE0
zVf2L9=%_d)dBq7vz@$o;rQJj9mq9R`hFqX<v1MgmRm|ib7?<YtR|$#Q1mz7j<~cFB
zkJ<BS|De`q4Fb9C#JdD-ZWYr;9X)<h(C@qSM+`^<>XR$;j(;)wk#1c$IC=xCE++rk
zI&=HlYXfWoP=XkpmZYXF3Ajg>r^a?@1SGb|E_v<-MjD>t3$5i(3p6nZ<^S@X7?}5m
zJ$duwu}x`y+*y~pb3HCtglRiKAB~!Bgvikmv+)@FpM!z3H0ZC~xR%3Og7m*H_NPH#
z1Vj+FUZrk0UY_q8L#ltU<V&nacz|~zN**$#TkR$GRbujjqyNiyY+b7CsA*c-kLR{_
zJ53Guy*v9^IQuW)uZA|<ky5_FdZp!NXC6ELVvvsU^}E<FVV5LqfT>FDY~6)I6QBh9
zVn9>y3hIq0gXX4rD=dw?Ru^Ya!IetVVjkWjz<C4JiuLUQe&FRVhLJ<n>E87rd99o#
zf-L1T{bY@PHp#e>abIzivv5K|Xw@{9g4@l!mV<Eg2$MZ291Ol3W8@qB%V$CE<u&!B
zbV59j_rU?Rr++5I99~x9*<<ksWr0)TB81Lu7?F`OAZgj*AyQT4Sz-T24=dFGlEe0F
zeXq{9tr5J`#i_DZHSydyfcF<A+WIALHgUUXJ^6sRv^gguhwz_m+hf?b{(kC`pXPg;
zwgFCJ4hk4Xq;rlRHf%S(giHd~2*71oo6jvsIUtwNO-;l$84|LRgyjstlY9pKN2{Kf
zc-w!1nweRiB};#J-eM#GH-TDzk!0`5<oXs?w%Iv7-L!nnLyvKQIpT4UMobhEA=Ba*
z@j5I$L|3>-aK~0(*HG_DJj>yEh3>1eydEM=B3fU}r?7Caf$GMgiEA#1aqZE<$H3Af
zeTlf9qYE3U#Uo$<^Gbc!Kr$=je<mtMkvs=tuIbt+-vt|H<`a?}+Sq#$_}^b4aT<r`
z>OwMBpq82a_?53C#sw)gkrd>B;u>&i{5l0I*$Zjoioaf_FZ<$SM=6K!))pE+l?Il@
zpZu@BJqF}N<R67nBC~+qDe|V`KQ@H+5*n;>cMks1g#)9k-=>eOT<|AO!Y=d7cMzT|
zF|5$t<=y55jw)NjJ!*ia-r8ckG_Og9p>77@k^MVp<$1ikBH8}re8FA}OWoz;Fgb6G
zS!(QuNb<;J2?=H8-q656m|$cE(PWt9Z!VHWyE(4t*q<X!j^74Xn(vp0o{nyhNp?Dk
zZ$`WA*|kTPHJWXzIV}M)gh^t=D~Qb<sik?z?vc7cjZEhO6(%}DiGL$zoW90R#Nr?=
zN=jBkkYpeVLr?Ko*7Xgpx39k#M%m5JY4}`4)k8%??f&wHaz6OsXbms`8%n8-jm)w-
zO0hH3E4t7<jAe6G?JXJ`P0d^l5p#4y<+@$ky<E<2e}w?U`pn!ce1cPEkP;H4j#GJ8
zKf6Qtz5k-ylyEP*y#216q`7^ch&2DLsG-Es#JK(5%{suxudb}Rp5|mYmr=8$K|mez
zx~JQL`7b)GIVt*EY@?x>n540W?R#a|r~cyNKh2XA7e-Z--=q|v^}HA6MF#K!g}pJK
z5s)>F;Yns17lOvMjO`DGK&xLNPR$ktoz-l@=UpS#{t^M-BfXP3$(|iYJoS+^vFJ)%
z+psW`5_MEJIJvru<nKxEv!+t<T>lR1slmuApOG(t2L~g&puY6!v<CcDG?4kxw{My2
z7}0N$#)$!b3td)ufk(dH%&=vVLB6YFSH(5R?g%%8u0U-Ji8A~?13FBd8GB9uIw@Ad
zEwwP;p=pm74RS6wvzp^?vT|~tvAc`wQve-vxZ)&x<xh)T^Y59Fe$&5t=HJ`W`<wx(
z-^WNEypQ+lsQvKsW_A17U13}gf(%R&?FqHFM6L;{C+<>dBUX7D=d&-;ApBqIsY+Mj
zE89qg501%x=uGi~qOiJGjSSOC_72Pfj4(zN`!l@bSbX3%F+}A1Xy}SP7DvzdnvM-$
z`s`3^<^x^Ej1(mMnSD+7ngw+r7(EFD-}tR`yn3s`9>a7gCgi@;3YnlShzz<LI{=5s
zcJlf6V<fBA5c|c27l+o^v5)ed10OliMkhLjBZq5_zZ<iF0HG+3dtdUIFUvae%C(+V
zxq42gf+gt282Kz|2KH|FQNeViKg7GJH73%E(@G$9=lcvvnPG1!$<1l7?>z~W*x43+
zNwq0in&6#sEcd>|Ghe24^s8BaE$zB!U;j8G<u7yRVQz-9fCs^OM>IZ+t)t+YuRrG=
z%BV<V)6Q@~Nt)AN-7i1L(^=vF8Rh~#eTtN#9hp`5;w5D<kGfsT1O@3SfKs>ZFciXY
zX?{eCLJXlf^)s24MJTIXiyN;vANU3n5U{;<<V<t77Az|@;T>M0amxbJ?9jGF*tHbQ
zN8(nG=J8ArWvC=C*%%X|^s#0-tR&Sv7>R1q^_>wDr1VV;!7~@=zHG-z)?0_g!0JO=
zD3C|=ox0Kd87k60;#&W;dTT4V7OM5wJ2W*u$JswadGX4|#sZg4H{j(lD#AU<3O|$g
zr@N8?=%K2~7M+Cskw^{ZI3hAabtymLL6{rMvFzvC@E`hWd+$dFHwVGPUa?c{0H5ux
zeN)-*Ajy5f?`c3;WendYbn*S9#Xa}ni-`GgaelH_8y?mNlc;h4z#lzSF6mRZ4t~Bw
z!Nj;XR-OJDS)R?e=KoaWPKYHa9)&oHp;;2v(br$Q$J<c533`J|YgqA6K277dbEV>W
zG~d`9>YqLRvtk(NrTvECrTKx5Apbt@pKabZ&`q{*075_2D27H^1k6r#e!N!|b)3$2
z;9hg!!*-3(ti$;ME$R20?ABxl+Kp<b-5f15#hw0}6W+s=h$tx^MPbwhvrzAsoc?!c
zcBTH$rPcuh!ZqcM0LaM1!U2*_PPz~{@;~s6K%)a{Yzp%ie{Gt#9VdIqwus!IKg*!J
zPw&We@u+}7vvK6!1ciUIX2dY~VZ->|X}kL8=~tkFyYPs!ug4T?Xzm@2JpnVH;N1Hz
za(hCx;5))Ufsn|oBkIHOne{bcfwxM`1R`#uwMyTA%%7WWFlkL*j~q`^$bOB*wt3Gx
zZ7k1+JQ7aEqxw*2;FR&R!2@pjzDQ8@@r06qNB5HJ<TCZ8w}f!&6gbcD1rg$|gT*~T
zaCV?SgRE@Wcfo@L%P0C{2S74D_7s}h+1xFVliGP~22cg`Oh5KEHjo|~pxArJjr4<p
zBNM>pAoZw(m>)^uFP4;~X9I;;%3NJu{0y}vjh>j4Z(}SX0v@Am(KZ=|Xdr32qhLi|
zx|@;&o^6VKsgp)BUtWC1Yu85T`tJNfJZRYI1hKL1_e1!6+V(M$OS1tMF@b4MpaR>%
z=g~k<MT#X6S=%cD^BqqXeE(?5AgqG(PgZ1A4RXfLck#+o_UpGL)CcMp<o@DL5@w$L
z7Zc>~ouUl<-SRI7O2ufrN+9qAnbs%0l=;sau*jqm;=Z**r=?tUPg=DbOs~lOJ3e*g
z4+U%JeEV6wm}@tHg%xBvnjqT$tjDT?v%j|}q10~A-Zc<^gAwd+yY}=hZ{@9klaQUP
z-TD@R=(8nEEcTMJS{iR~Xu^Mg*LQ{29qeTRrXpS7h+b9Zc3eUY2R(qi+ME?|FyIs9
z#Y|>KH#*Oaw|`M=PdxYZV+6?frA|n^d?w8f3O}2Z?hPf{_IwfF{$!i(<@JCk(3@np
z5)^muZ+SVS7=`)HgUI6?7vz0Zi!~AD*k8Ot{2|wkp8)>TK)k(fYv5xSnMuGHHbxN~
zwd5wVR7hN#f0+e}BuK!o-ae3}r7`fKd(sV^!azR*<2~{(8ngpU7GmnG8|q=kLPC^!
zwZ^vb@jSE!i!31m4eQZ2WSZPA_6PHA5-?RGQNiLnJcRNC<l!U#6LP)xi3dOkMxenp
zTaK{mOE@~OQQ1y<9`m7W`w<=nOXT&asKK{NgdpapDX1(B1?qpl#n^V-p@wLCqec2m
zotar2ScFhZcME&L*`|1{{`Tlgv;QcPmyLIl_%yz$vVBj$>m^}PojTY<|Czi$`%gdn
zCJ`jE2exVd$fLpO6t_nMllwJ;Z;6oREkT&ep1Rn=Y-<|_nu~#>M{<uJz9TKF^*(wC
z?n~%<vKO5P802CmCZW*MBbk*04mJtyT<Sfcoa!KZ+Ff9P1l|h+ytKm%-baah6E%a4
z%n8$eqUDX~p{lS*vNt}+1GmuK0YaN;BEn4sO0?WCBS85+$J!5SLwx#_&Hc*1hs{A>
zfCDp;HuYGCNTru6e%3D>KYt+5JCc1yR_8_GmThSS+QP<6(Nv#PRZGRif!d&1@-RYe
ze@k#Y_tItwt~+*?+9+R@%z>FjTor#UOCXc#(R~fu@w#(CJpXyZo$}`#$B)w2?7osr
zz|PFZOIl4vQY<k$46EZX@g;lM;Pj^v_CizJF`3~k(0j_q#*TrLuo%@%Rx=iT!v~Uw
zuU)SMZ7ueGgTp)r5W7odHPw!G&hqaCS;47vWVCI2&{&AO!O!k@AgO%+3~F?3y>`6k
zqUKHz{+wT*gs>jsz&SvKvM+Y?QvE5~@=M`o>P=N=*k`jJ9B!!bIyrp4Q{&6KkzA82
z`&)tDPy3Vu>q3hqZi*dnG(T?9W1&jK`}OfbSj{9=;~%=c&&#n%f6ydx*x?|-CmyFJ
zF4fkUI;~zc{Z4tcMlt;j`_IL<2M-xb;)Zry;?Q>1RM3-nx?}ze29BHvBaCWpUwD5I
z`ly00oyQk2#Her0wk2_@5fU|0K^&?bLD_xoyz+YdII|zlXM>#lr&H&MUb^cS`@1j?
zXi8j`6h4HVG(nXysFGt^D`83YUaK_BXOZB%FaOKORr3J$kNn-C;EHi997duDxi5LG
zg1ICU-?K@lRJ0Zu!P#N)Irv}nULfwr^(ip@=cLK1>oGRKdr9Sg%So`6XSuZqDY-?y
zSCtbR`_m}}2@HC)ewJHsd=mT+hurQ78rj<>K%x3?0A!%Ke3spHtL7BFKYJtgZ%~;b
zTjv>MSCamT4b?~j)dp<E)4z5kZKeHq&tOfYyQ1HAyzhQse3&8yZM2qO&oQq7%wUZR
zDc0%&NPLE0gS}amq1XLIPA5x*iY?j?&>bofkI#$>{`Ad5X~6b|e9>(pY>fj2$*HT1
z3_ZPQc;*L6DQ&9-!BoCipWrEGlguaL?N}5ozEqQi#9>9*X7R2?joZTEsQ1)w?;yJp
zx=9in4n`_tW!uMgQm0~ME*t#ba=$m$Ru_}?aIyQgnG4XR&qi-KcO}~Khq)+yb_~tm
zCgPk<Q9JuNM~PpHT!<3yjy*wSP>}pLHZkf5VgFmJ`wvTB3Jlz)r}{38A7&18!akv~
zTqm1~o%<LFS{fI8VH}cF0G<aH13j3V#X1)U=xjpKEi){3{g$z3CMC1ocS7q9`TOI;
z@V7TTsX@x01*SgZdk#ikZeliyTXNa~ch0O$drP{6<#H%>%GmxPkM5xC#6lSldi%;s
zXlIu1lbr95`Qj-KWw&O$nDnW02_(KnWoCG4)52R`?5tnEaJDv;mUuRG7BU?y?eqW<
zGF+d3*sC|6H~v{{sTe1|GajzA!B{r%E*2nbYd~>g_B^}ks=T~b(l(VW7UYr!V87JD
zKe7aV@sS-DJpOyC_gW0XnQ@6`0Ky1}{6q`}W}n5%a<fCi@{)OI(aKe*u)&5x7RIL2
zR}o5`1^aqKlb_=sT4Vh8hDE=Wxs@zs6f8o-UOuFUI27!s@j=ZpZO@?mBjSso*#LZo
zXBCX&kr<r^KI7`Tih<TE+))wsmb_j;KhMVMnxYLxW!_lSJN-coCFFPO=5ow3V3mMB
z%ulx*;47c9oQr|oG<B|~+j#kLma+KT7WGf&)Dnnox$JYvTP3szgBS0?kGFN#4EzZN
zS5vNeg}*-iHXQ%U0(1(JdaZr;Wm)e>FQJqsNK=sCiyUj_PqKbAZ=O65`u*T-T-4fL
zM9#^DI{!U=w{#5@yToWDQeqe7+4J<eJ^g&6q~A%DqQ)P11w=a|JbR2i?b^B-sQx^^
zICD<ry3Ou7-CF)I{i~|1>D+9Jp4Tfg@Z+{xKg4tf<>G+mY30NK_a`mFhD#IQPr4>s
zqQu;FxUhG`w1vVoJ|av;F%^0Vjyvvh`l*flAdLZAx~Fi^DxI*Dnme0c6iG)ami-#K
z{$M8DCpYFV>&S{jfF0tTILGVt?mBz8H@>3*Jlocj9Z!pXfoER4SRWUP@1WEJMeW@0
z1f3m~tEH`K@EDl)qgYE?)p&<>lZ4{!b(`sO``CtA78R~$8iw4%FY)#pI~JeNqd0BN
zgW!01mE#fUTLt5FinzYQ9H6Hp1uWRbmq*qo+0%xVZ@|GPjGVk3c&^rM;+nYlXp*WN
zy=M8)=73450k{H7(fn2UupNujtVb*7DoZ3wJxjjQG-40^{pVM6dsnHA!`;=+2}fs!
zX9ATshr3e5Jvfg)luiBLh(r!QOm+(J*A5xL&xL@V{xY$&Z6u8QEpdfkqE~UNP;4BJ
z(gt`@@5a#Su(CFa)4u53M?fCYs*PSOea_DizgYqSjQgVyqJwzr)4d`}#7a`P&!W~O
ze*~}WLU?W>M=Hs_0!ffZR+>t%|LK~q`s;`J;|puYszH7%(;U~*Nm7kIwRC^6{7-@Z
zrY}MLqkpCkJR57nJ9JO}#pFdi8#J;7bNT*MmIGUpAOKh>LP^apLdKCE_8~j8b}Svw
zQbH`{M_Jl`st}!p%Zd(l>JA=vvXY56?jPRY$ug{xiq@m#TKm*wS30(2BEvk%@C*|$
zJep@UKGF(axo|9`?S;(?yx2O1p5O7D2Wf%e=`Smcwo;S@ocdSikM=`o>BHOwYtw`Z
z92(_Zfv3u{{fE2_yLOgfecGG7m~KCHTm8icL1?VvP-J6Kn1vb2GAh1w860ZFV%4aH
zy?1~x1GUm3l8J`iZ^j`{>^HekQ|jN}|5P<|n+=^b;qov0xoq83%roY49MV9(GYrzV
zB!Z>JvY4De;J&VQIW1SJn}tqs<t826pT9wiQ<U!&cXT|Uk$UFWJ!SwJ6xuyhLHU!4
ze1rupU13(R!~*1GD&hr&8|}J!DktT)-svl%Q^SuM)gkN`Y*F2<GgNB|n`!vRuEXCQ
z^C!RfMZLBvtXS(j_V3NQjh*RV2!a%u9f;w!Ij@Zd@i_4htA=9RG3@V_U7VWZ!|fPT
zmo}lkjXw;S|7gBR$gc~r>c#;u2c1n_uDhIK{2$)Oc9kvty+wV*CdXoMmt9^^A54ZC
zlTvMH5g*PHaynf21=`!Z;pWeD^qww}n4Y$}#^=Ld+7KODBr8mq_>U;|TS4|~U@UY}
zr|zhXj}Mb)COBy`98FzbI}QNFQdB~1Vv%_92vZP<qCDwV#(c%0y3lLouc42VviSCG
zm%sg+`P@zX3h#5c&`9l(4`<x5m!IBuI2iB_)z|<%)};l;XdNJZNpl+qrlGNDb4f8z
z=(%-y)3Q>2;jFD{@i#Id1zC{9gMe0<k_sTDc@=+6@Lx)k(68PgB)}K<uvKf0P51&r
z&Q(_7iwuc=xy0&799Kxdxc6*L7XZ19^uqDwXLwGFa}ysLv^Z16`&OAtsA_B+2Y0^j
z?B<wkDzn`pSdh%$=;5E#unfyaUUoaLt2%b$^MC5<e_O$2zzqP={}89)(gKH=H2%|-
z!krX2WjBH9W))u1O3%lHE8+AZeEu2g32UoI`EfT^kFvogB0k@(ZTtKgAQ@A)wqdu~
zk23}Qk$8W+e$n<6x4vuqiy`o(2l)RynAFOC8yuo%DlW3MJ2}P6Q(fg~3MAM8d~|_B
zaa!gAcfZYkQ8j|Ha|!<S6r!MGX<x8D>K0^g0p9we0(+nFv3D6884Xk4i%Z5sgV^D;
z3A9#?eW4iCOQ$BT*~I<6QCuzln~eaf(mhO#Ss@Eof*(YNY2*X_5oH|=jYY5pV{di)
zLNd_kl2^?5flGAST#7a8(1l7VuChS;sc-mT8D3i2`#orB>+U!>^^iQDk!vt(+AsEJ
zB?)`<Sn4P^S$27lmc<)>U{BLsAwa~_Y7^_p!U53Zn%IQ7dLC1|D22KC*{PWK(6POy
z`a0L<NoG+oFaBWLNOCQl3n?<%o&-tlYaR>!CbeRciR-vH^13Qd3;uw3Ha{0n(1&Zo
zMZp_Kr3pfJ004Y=^S6rrdT;6~qc?MSLJ4grHjeft1wg(0;NS2dLzj0^s!CTafkJ&D
zF5b>^m-ZcGcYOV<(_bu#ap*T4CbmV5hOsH=j=>x9^)XT=|0(D|c6u@A-?$b&1XN5~
zxMN>Jza^Ajjd78ll8lM9xo2X!9u^#zZTL+Uq^Tk?v$$U|QQQx<NyL%=V-b_@tnpA1
zHU2NBjl6b*HsOfwY|pfGxnY^jw5R2%QiuZLJ*QurNOUCJmQP|wKcV=c^TcDR7vV@L
zQKAs&iL^f0-FFq{V4(Q4zRw2#u?hLz;%Bu0KiA@Oblfn-C;9-~7bH^Y>KMQQU*EO~
zf{vx+H=sD8C4XbrGxQP^T++xTB0F_IO5HLh0SM>*F4MkFiK(e7>hqKH>;e`6^Gmcm
zT#Ze?KwhVxiu^+~iXyV2Q~F*pY3+>_C9fPcdEP4nwSE_+74At)AZ|j8Ur2HyUPB%q
zr)r+IM?)_ISMt836WdkEqb3xv3R;6eOJlT~D(ZeN76H~8;zV`$xuHakM(JbRD6X~7
zCx)NH9u4?SV;y_N@pK*7yl?-PLn-<?!cVhath2Zg$$R>8Di2b#9-JZ1x3#pdVg`ba
z1jN(f1+WE`j&7?jp<_$eisyBOE((8HYv8wF6&rjUJW7FLm%E_Okj%7(SP&h?NcWNT
zLoG~u-Y-(VfcZTKv+vd;hq~JKFFUf^cm`-qa~~~fB_a?v?yp@uyWB|o%olV$Ny8;*
z0nR|Gdd~qrjaAf~MS&aZ^2Y6{^VsXAjdUZLW3XZT+U<HuZ2H*_W2BVrFcnGjz>#wy
zAQk4;^5_HbO<wP--Ct^DAjNzkd((qWucf82nGpa1WAa~AccXt6RWBL=39G?SzK3?8
z1Sg`<FES!-YXk7RXhEYQz<s_anH2V3qP_7p-kBu2QSFXxWjFP=zugPd`6UBkODYt-
zzcBH_#3o3|$ogRj(7-lO9j|MRoV)MI-k&1b_wen%$Rf9U_r8D%n(&2b*_oF#!AN<L
zu|aCu-*pbktFLpV4;{GT1)5;Cw?dGI;<^c~YMIAm0tj(mSU2-(Czvs~TZy<=*8~Lw
z1b_QqAi#x(3dRSdB?02&GCv<Xie<=!Z~%<^ugu}7slq348)6Nx<WqVrXrQ_1kh1A~
zvcJC03qk^lutC=`hOFHj!kqIPbE;Z8nlF?rK(dz}y6?KF3S89iZ+S574p=u1W3vZK
z{^Y-j1p^tvDp-d<v>1$&4#~GrP#FJYzKN<|ZYflfpkqRaftjo=L0;D}UefnvzLwCg
zrZ3#eU{bO}t15<Z1-XDUOrqD%lpLRTRjjPEy}Z8df>JWt`VW2`>9kAiHIL=K7OP#O
zh%^(voUOBrcjpn4ksemdCxFkaoRy<P%xRhTJp2y3Me37Mqod@U5`wept7G10a<YVa
zh>}N5ya2*ILTX0GGGOpMv8`k<Fe$J4CB;KVNRXWosuO{Wt`{DmwgpKFT92Ns)4dz4
zpo}F4VGKq@e?~TIRhtd<=v)Dkxy+1_??h+u@ZmxJxy-j3djR<Mv0aD2+WLqfGgAl9
ztR7jS<wh87j|snvGV&%m!j}?XezHD7`}pJp@{}5GB==mQdWOH8uyle&f`=tah)mty
zO3`}BGgJ5ImgKV(ssfDD)rJbO332hL7-%3Pd_vN&5BXW%g8bYVuILUQq|I$gN>1Hv
zN$5T7)&rVNCF`yENf^6BgFDudXfLEDVzQ;ug$mLrGvo>CFkruA{*Tbhtbo=Gpc>t8
zf!O;PVE51$^l$rwG47Fwv2}_5q$-Kfv66k2zy8~#ka{>8L^Ca27_6jzT<|CXs=b^|
z&z&i6>_=wLeE)D^jB~Uep9U^p!HYjXDE@w?+zs_&#z<3HVd@O|uS58Hs39E{p-GT%
zb>70V)F2L^87P6Xa<cM*CO9UI;NJ_K-U+#dAs*-tmaW`#7NHvr+^c`(!h}PMdt%+x
zW9{_M|L)JDQNlCO5xj~PC5eLQIf8QYbBRd~9qh}$FvfO+US}zE9HQsYZ&3nG<JFuJ
zz!gcr4JKjc-*ONfbGe$42g69i4o{<eq&M-|B<?+eHG|%pIK^7nWOuNbBW&S;<i>9w
zF{yBrcawLtn(_;?z3udcX15z8xi8jXbC3JD8}H<hL({oV?#q~kLrjp>Z#w27MTHMn
zb;31YdL>(y3j>1CbccH{0X058^MAo72L9*pf=0sVSDdHicpFTR2>2)P*`ZHHXjh)!
zeF=gu+{kYl@OZ5!OfEWeIW=+q_ARC}OMIS-v)6|$YGeq?>07*cnNGldN6JhIjv<2m
zom+kTNuu34YHBM8l!wQJLI2@w`M*C5-G&qvCnYU*8`+$q<>cq!PDN=B!F3(&<8o}%
zz`a490EcTwSV=i>;6w7A_+NMyw}2zN%S?(1nXu1hz`CB6sMEq7@Xg7QkEwB>c7}e$
z(BbtC`-}^&vOg!x$?$(R?KgE*ox~OXux4Y!7~P<@O$Lg1cwTKYGB03g&kizc7Jr2~
ziIg6;&Rq0B8L}qfcOB#AV5@DasQ-=eZ)BY+A^p-IA;(zSZ4wJ13EltH5f^WxW|O22
z{R|mp#`Kj$fzzC&L3O)WjBFj!;k+(OCTto(gf0_!H+=q^aFc?HlDhS0N4uq&4+NOq
zcSZf0dIOE`FJJx1No^Y<XN@(n_0U%M9(cQQjb90~iur*(Vo^wc#&zLU42K=rVE+FC
zObfI0we^j?Mw;f3jQ%gAPKlKsAkG2l@0Obiv2xVxcz=6+`HRe?rx8K^-tN~f+L<3#
zllgbED60dky_!W>QP<+U`~B$WIVCl11Fsf-kob=umf+Xze~H1)rmB*>^u#B@_ij0Z
z**k3|DIrb<GNRQE^$UOwW7t3m*uPskxcY_0rsS5@cMMIv#rmTW!Lq;5JKoaLJ3#po
zNZ6N)8t8wT9`0&*nV%XL5qQ_j^|FJFrLnGxj0hJVl_!>go9*60Ouu`tn$fAtUO~~x
zxi1@g#^-;Oe|Y$9dv<2Dr=_|$Cpk9!zK^SuEkwJrtT;c%PAbxks~zeWz%_@HQL+fi
zs2{O{m=BIl&Z}tZop^&yL||zhHs+6Exr~Y14c<=nw^Zh*JbiTcmdmAcC(R6X)fFX$
zxOY+GPkCYmen+N=Hc+q%$!nX~Is1k@O?y$(F*5gKf5c;%%wR`dY0mSQNB2Ch+FKjz
zs>%rCoz4>q*ja*TGcgG*2WXqKa=3mwI3~5QrgL;2dA<BcMvn<ttpIYF$4-IquDas%
zry+i>5bdTwTVRh65BpA<9mMz-`!crDaY-oaS=hVW360MxZy9|3!^AIwn#`-d=8F8}
z=wQDae@<u2^ws4=x$$Op<V&E#Fx%)kMdUOLEY3RL4vEWp*)lYT&i(!U|GogW`!C0i
z6m;G!jI>qc#6R-Ae$Lv&;GnXs7#};*kG}c?Q{MpAisJ%(^BKkU-4m13NV#dnEe5^5
zA`5(BetvFt_Vvun^wiYE=<on|%x|hFNQ(;#fAG3~{*<Y{rs5ucHh2mHub!zfpd;cW
zG^~7*`?QW&oOAXGdYWEb-!m{g2CB<T$YzdM!LPjDL&49@A)!xBPK=EV5BB%<bk!E6
z#RT~{+gpNe6<G-ZE@lR}TWbS86OVReSlK&!Ka6^wSJBeZ-G|skVoy|Pt`7HCOhW;W
ze+~}~^!N63cXqTk)m2xN6&K{D#D+ical2$^Wvr(Gop4;c0PkBj<H796mq6#^w^Hup
z5|-MpWng*kn%Bc8Q1Erl9lfyfINF_nJ>BBU7BJZZeZAdX9qlbm4Rtk@<z*$HS;g~;
zorQt6`aT(PL2gzckhu*AWUh9k-vB68e+f$NJ78d8ciGcFEH1t9WmO$)GqK!ojqPc0
z*Y#i@Qn!GEE-J{$OifC7`Xt2P+x5~}YZLv0D)N#-yIJVTA>Lt0+|`coE5IpYY$2m#
z;SrJAr(s}jd)f0|Xlz<uab<lwn486#j<GL&77Tzwv3In!G}hNvSG+7OD#**ue@IP!
z79a7z+vUP(lfxQHa(hK#sXU|=e;qy(zmb%hiAzvYLH)4F=?ktt4<i#(vtN`|gN9*H
zWuISM`iQlqfOpCM?v9p*+NyFW?A)yMlxK0#aEr!mD0{WFI&Pq?swgEUzzw<&P$B5m
zj_@nMY2XlTqhes=5#A%Gu4i)Ue}aqm{fM}foZ{-1o)Kux&%;G_MAc&f+TJI}hWfkP
zTN-LAUzQZ+=VYcOKZ}cb5)m5oz~9To5u~pwvXY{LJRB^HG?)(bY6toatVf4^l@1zO
z*gCo0eh?m$lu_`qwzUV<uUf9pjt%y7G}l#@7UpH8Cnv@N&I9gvd$?Y8e{wiuWn!qS
zsU){Y1eM0z0uPzuQAKhE5au@$?*Nr5J`rhUDEKpuE?)P;<I-MKHur%Z15k2(_kIaE
z-=>gqYi&j8i`>k#=g<}x@hC9h&h1<7F4w?a-VoLa<Kt#$f!pwrIm<urnRu*PBO$ML
z$k@v6lAF){u%{`xrL}Enf8#7f{M$EB@UO-PyW1M7%3frrCB;WaJ__{r^LD>}<>J}X
zC(MrO>u4y;?-di=&B4M%PYqO%|0BPH{}z77>D<9XF`S?y1)d6W^9zYdE39cBhTBU(
zBOO+j?eA)-uLRYnXK_&xArJ1|zIEgJ6-Rs9Q&wh1hhT*?9yaEkf3Ws9DX7A4z=wgU
zZvZ#(%&?1FXs@D%p0TCvr5k=h(a&?s8gP=lOJJx9p7I~=fk%kbpFNFy^zfdKr|VV6
zb2e6{Mu)Z4mE@%*MEJR(Z=Dtuyhmg~@iul?rXErCq-W(3-m9ptYiM@%nom${MrmXJ
z&<L)J5La{@9_(rXe-F}Wi7^oCcfCDaE?)o(-q_%fmfC(<NfFpsj*@~5IpJ^`8h9K0
zZ<r<W4sv>y-5`1$!xIkIeFCGB^D7%#arHV>+hU-jp|Us!)L`!W-EzBn@$4y!V+J~!
zO7hT^W;Z7*69aU(kw9BK95_xvVzuM@7LZYQVv=oCjL=aJfAk%Wow@20nwVGJ)i;Q{
z;=sI*4EFc5)|J1=N{)>T4)DF{dilJqwVAQLj;4x&w1hCU!!t7gZKExya0=c`Onn2W
zAk0>BI%X~bahd(PrnXnTgJRN4K)XCV687Qk0(`IQX$G&dPs1PjdR~V`AoUNbDoTsP
zcR73+{Qn`Ke|a4cG2X?&E4&9xMcs)^Ep5fMkj^e3jib<I9#mnTMTg$M?cwZr_N3WS
zy#p!=QsRQZPY&-I>%I%91k{%8REV67+F>)>D>nlo64SC@lvY8XJrs6(OCz|+=cOk?
zq5DFipF3@N{D`imvOKuM1Eo?tPTsx&9P@~5IVZ2Me}cA&oy(o@q=MqI@~YZ~rq+(u
z<_7RmQBs(l68AXR-v`QGppDL0Ut4{@9Jtbei|m?Q^qA-yTBHUuCMGr)q0BDyaZEx=
zMs8slE`3Q#0LA88Zdagv&ivRB-2<wMGJ8aTY62Z4J|}PA04|}&iS=>vNGczK&Ux>^
zn7D+bf8?}`?A-j^?98;3=ZW#Lks$%To-UUi&YS>yyoNH^<H4ndfg0j{>*h7P=%H1=
zosy1;l?#?jIBI3*<m&0|=O6eeA}TH>>T!5zaNvFa+n%mhF4#dQ`w_i^8p?_?P~OT5
zo$Wx$3tto;ehjEX1uE$yB&~AD<g}xk?_K}<e}Tb|!Xh3=ggpuld=PN&4wS%La)4L-
zp@Zs55bYuYJW%Yw$7O9UdUVxe!Sjg$C8whnHfO=CkgLb7+upZ4-Q8TAuc8-OT`hHG
zMcKU|bS^e#Mp`PeZNRZ@1HNYM-vG{&2A091p$9LxN(T+i&s@B8`Kq&v>kW^a9yg%K
zf3ID+<m6y`%F^VhzU~1vB?VbYabf5>%eIRVR<pvp9_sG{Zq>srITX}%jI7*<XuqC;
z(J>QqtCKc%XKYScpRhDHGdX%hPg_$}NkL|>gs2cd4;MQN6G&WvBo<z0?cV?_T=yrC
z;NA`;c1)Z=BIUqg<Kw1g=9X3`PM$hxe{E%HVP<M_%t&ANpoYqRc^OG@5kWqPb&#}E
z!>KF2$ejEr5b<^-t=mpcNe5hcgv9sCDeO~HQP<Sc*45EIctBG_T~%ejf}AuQHxD-l
z8_O<6dRl7Ae`Ky}E_Nh(%y1Am)9{P#l~YnyQB~K_JfL+@3q-D_s-nD4URG+4f0!^7
zI~NBVGZP~n4HX5dg15GV2Q4|9w`?T=(tnJ*SUI?P`1l2cghj=~#l=KLgoT9!1t7vX
z*&)VvGSJgPag*=ZPD+A2&HiVGwD^G)LY<%^3&`8>3kV1b2@8veiXxE<3h?uS*jbsG
zb~4b>QZL6&gv;CU`?cm<KwEM)T0(1oE9tf!<P?-t)YLSzbaeFebab?|G&D5SRFo9t
zWIMKRBPAi(O1xz=c&^xpD7yccJ0ZT|cU<r|#|bhD5IGGEEiEk?IrWOzw}RM-h!A<>
nb(n|6Y6n4+A$d(0c#8i8h|||n(8%lB00000NkvXXu0mjfnnd8o

diff --git a/vignettes/examples/vision/autoencoder/unnamed-chunk-7-1.png b/vignettes/examples/vision/autoencoder/unnamed-chunk-7-1.png
index 76fc3242c989d2ce8166ca8e37e6d682794edeb0..f45a20d01404d48d8c2115d3e7958ff024d4c7e7 100644
GIT binary patch
delta 99727
zcmV*oKu5o)jRuR12Cz*7f4y~FTv-+^+!5Ths&FVM+$r4M-QC?afdC<d7zxB73UPOL
zcXv19uH8vmzq8MkDjIrTr}KX4yl-axG1Ez1Al!Xq%i3%IpbpSBFylJ7`2>dhx!Ra$
zE6a-fAf~8kZ08vgpPiAC5EBs+;N#(7WvrvBC?)hmUkHfFD5_}be;Hev=xM1kWW)sd
zJ{vG_&|m>UAt50_fx&|Y4&eJlL{3%L!qGc4Aw9orXw#U99o=p91@S&yZP|W)L|;fK
z=~}smB<2mtPYU(m>M2Y9t*`nYDE!ax|E2h!;r~nVKg0i*;(vz!FNHEdL&w0BYwzOe
z=WcJVugQ`T86c*he_?3t78sqDk{A~q9uny1?QCPJr_PiX9xwn~QbbftR0LTQj42>0
zCC^f0>lqv9YHO$}DauJnN|2wTM+Jq1MMOnKgoOlqTV53X2pjjH*o@qwvg*3wBgeKi
zl%#~Zm}*Glj|U(Og#YRN?;S93umJjoV&V)fBlOiV=|yQ#e|}D8+DviYGXn<;3X6(M
zO36w~ii-*h4jzcV-N1o^Fa{zF7|{1S|CfOYP|?&eFtN08bhWoMf^98~46m+l<?I`t
zm=G5e6&@NK=<jZCVSv}fAoNxxq-5n8^0HDAqCx@#e;6z*A)~0I&eqfA=xDRGG&R&!
zl;BfwQ6X$}e=%_h32|5mg9q`wY%vDg)X_IGwXnFfyrQaRXkC3tT7<Waz6$JJ!cK$1
zOuY>>P(WBrQbtaZrKxXW@9rO-kP#c?Zfm5eC`LarV6d>bv^-NqU0XwiDK9N9JeWFo
z(8(hxC?GI+FtG%_)jR$!ArD}xXlUyhnOWId7;!X|f8-^F2a3t7=~_5=1;@rjM@5E*
zg@y!qIawL8mE}YS3W%WB%1~C*Qd4HgNr?#!{$Y@in3Np+jkXR)Pv6kU*wj#$t&Tqx
z6Bn0|l$4T|mXZ(?5fb1xyf}+v=@y)jTUA+6URGLMR9KJ_;p=FM_b!-yuz&y<2~5xT
z?**eOf3Q@wI7a3+j_$re5pf}2juyIV@}k7&u#<%(WSMH(2By|#2HI*&S#d$IJ8?>h
z!O;WP4&a-<=<f|O0C^^|nvR~4sez7$vZ9Q*5Hh?f$JEx%KLQ~fAuP-nUH>|&@?wL8
z(3@9M*VZ@D*H%}Oml75DVUQqN3uqIn!tyb-f3$IMwzD$T*H&Z6OG+V|%gD;g$;lwg
zlcz2T31xj7@5uDhhT7`#;{5FN<oHl;M+-e{Leeu3COrc_%L|A}BY#5I*wWh0(Z$`{
zFEGU0iEG4Gk`cil;|oGWhNYozX6xeXYHOyafv%??1|Ykm(_aGJLE_l<f`D)Qs=qHt
ze*mQA6qw4Y8f+acH6=w^Nm0Q;Vsgsb##|@w;E>R;u<(e8$f!UMdvjei1@S>5QVPmi
zdL~x(R>r!TN^%l{==c>96_=8cS5#%|n_4@1_=WhnBVUlC!2*-Z%PS}-D$2{i$uGcX
zcu7?whk*FJA!C|qD+)4`qJw>0z>Q#lf1Qxs!8H);>lp}1DyV21TG+X{xx2f2czAev
zI&zIUYUp}`Pr>X12Z}4Ad&<ctEFsLt$r7(5xb?*(q-5k|Wk~-6@U7qVcLh;^xTLhK
zydttax|(Gq#YK?eWtFrH&?V{@7!(p39vKxAi*}Z!z6L{Lu$U}MQ_sxC*~{75e^ghU
zA%*vokO;h2B`rfsN3Wphl$a177i$w_4@E@=gUMvESn!R-ga!ESRVj5-m(bMG;gd$!
zSL7u}`nz)Blwc4iGk9>KV#q|oLIS-!17R7KmZ6oSS8$-8kC%s=v!lJGv91OS)*of}
zL6S-wb7#M(G@joFOB4Ae3JgVge{}Xpii?Q=zWK}ko`9D=c*&AdGP3f>4<_znWL*tC
zQ)_20U;m(x@W`0B#Be_sYeOxjq=1Bi8oC8seZ%P3kG??_HH~aMke^<hl^B5@M<*~#
zNl8grSw%&Kr6?y2D~k7%r8O-)qOxno%$zX1B0Dy~#e(OH5hpWRN)q6efA{hX=pZmU
zEXdcx*^X-l*OP)Yx^W1X5cz~MYDV_H(HUipWf@UEwg$*sgv$uoovF-XD#~MLAMnjz
z_V)zr-r`7G7)NOM{u3B1Cc{+Y7+Ko6din$eheyRFrbGw2+ZwZ3Qi769EkkP;zX)Vo
zXDfYm1yTIM2MtEwL{7ulf59(0qqLzCZDC($OFdOpRW$^4b#)b{f{di75TD^?*j%6Z
z!iI?prZiV%MtE4VdC?brgb*$vzh@w(sAX*D6`GKn8W$em;b>*7qp8YLAY6AU7La3G
zxQC~gHBD+NOAB)~)09Dm7nP7vK#snuGK--AKM3&6U-oy10mLbZe?whxc*SHGaA9*D
zTs{4Q!lUC;GU7r!?M-!*r3IyxIp&T&;Yqnk;oc4=YzE)^L#MZMNJ??Tgb~&G$zfi0
z#+n+M2wDhq2tY$t$2K6PyrpyHj8T<oLH2qIJRgp}lKZ-Z5DT~l#phHNq{Rez*qL&a
znF=Uy#AgX<_rZ#~e|G*!#f_5|OlmAj^tINJqv8&Awx$}2G?C@8Yx6B$>~{;i^iiBG
zi5+lswt_<?Fjz!No~4c=BCeg2o0nfuXk=`tuZxWdM^yox^txvD?g5cWk$x^*19b&n
z0R4lMnu$|zQqi#1;gz|GA@0@&YUGwzQ&(on%SaGAegI)0e>rU{ub3Qk)Q=sS8|!VY
zqkxy3fB;VUK<qCi4R#mpyW{P>!V>dRV}iXLEe$l8U`A140bZ77u)MaFXH-^I%k)uI
zS&<$VS~5S7k-dfvTT4S-RRx{+fN%Y-zboK3z+x$(dstOX9bd)BK7)lNWEGV)bo5Ou
zxOR@N9zOm-e?D&D#%n0aDQTk1-^nw8m*1r!rDp6Hn2<koOnq4vy0tAi_>RZdJm0<?
zi0>9b1-7|MaAN+D(L)Q8LR>Ah713=&q<h3+J4(V*?0d(H%B$&FB9A>WGSJh>%1B2Q
z4sLWG@TccwHB6m?67q+P8JeFE=xD4aMMeLb9335Pe{F3xTMOfxzwGY`+5jDnuAaVu
zp^-5P%^{8#rjJHFLlbi=8+#{LcV~O9xsfhgO$l<dmbQ*=-gNXu14U@|{&BfAEw#mI
zQNE6*Y=)vDcI+9vG#?S7BMU2P8ryn>CgxP;CZSuwL`xBNxg^@=vMA`1k(QE@Bm%EL
zd-W)Ef3R}$3h{M=h*6!@J1>j9J84w|8_%%hf}w>eVV>5I#z%&iQBYFT($&>Nzo>zs
zp%LJlzwGY`<^T&zE3S>LoxLN9sMsosQX)i3hM}aUsm;+hG%>T}+Hx(;j1BcTYz;O?
z-^kR0YwN%@(bH6tm*C5TNU9jx`Nd>aj;P5`e-8JwHBy(8g-9Mi-_`>lL>Cbimtbh<
zTcEHkCM`Auo!KZ-#737_P-L>`s7q9sEC<j)alERwk)^%6qm8+tj)sx~+y?ZiKv-Uq
zrDNvk8<E0G1=2}neM4gtGjmI>HQ-yn>+cHA02eoRPcI)|{{RmMOG7OsS<!)mAti(2
ze_kaOH4QCoj-G*$k)eTszP_FwJ{!>CU}9>Zt&ZM1j-yCOsu<Y%M4>pUGAAL}%}P&2
z3Ln@2`p~95cBYz+p@p4`caXQMou!eEI#WhY9vNL3{X|uCGsvM69Lz4zJ7)`(Kvl>e
znHlP8sewN)DuTZ8VDcI4q>3x3>s$6pf8Y!jk&tC5YiR2mp##j?&e7Qo@Qq*f_XU4I
zU{FX{L}YYqd?;jT)D^@BQr3}IU@(=GRn;^!+1fg~dgwYZF*Y*L*VE-_LpEMsN=%r~
zQI}BGxAuxiEgn{y857`SuFVvqL6`E7$1Wl!DJ=_Eu8zL3IoICW!bBejWegU%f6mw_
z`U79E-(0-}acGluKyeY_UKL-kyofB$BzXpTI-+&Vk`W;ck9<1=6LaLQLW~OdrZ4(?
zLo^^ZJ~25pJu@3eXr^qY1Ww5a!+}g(y66CgOuR07aaQIghI%@hYRdfXGzkeMJ+4Ps
za^cXTv`Alj6HR$xAsUo*1_}s^f8rc5T2Ja4T5KI%9c}DtD>0Q+)iv30y6AzcAuAy=
zxNmumsLshVl$eTeeZVm?xcA~1FaXlJYy&eJXIpavwu*wJ5Hh@^925C=hGtfF&K^F2
zp;3Tu_m;myqyw^Y@(PMe%d2WAQo{f$B_jx)Hn`ei(#Xq4FV5P*8u@sde<}<a2>f_w
zECxl6fGFP6#z0MmsDDCYfVLo&L{m{#Vlk2Pr+^lfB7+HWg&LS0JPKnywklIrLU>@`
zf;{aah!ckh7=7TM9ug3fRsfEHnIV<bBMh&lYh+>T<lz?_8J`OH2Cw(~MG2s+vU+G;
z!|>*on%wvR2NeJG!%+~De^OM{0k6sd1yN8G>E{`77RSOhC@#AqCqCHCQddc$pJ(VK
znhHxn7G2|m2Mk7g7*>M1mJT+%HM;USRQmm&0L3JdvWn<FP=m`rOlS~~Dq`i}<{J`~
zn2`_ohu{AHAE?Htsi_%)Ftj)`KGe&>jH7~c+A`phiIA5lS(M{af6lxRpHV5AgRF|K
zxg(0Pvy&qIU9FASENSrWP*5&T=7rGlK<s!$7B72GxZG-NUF0<)pAp3v{ZY7x@ShJr
ztAOK3xDTs~E)zq3Ru1AlDQaPvavk0M!191^_L9Fx)C1~<)>KuLl@t|ZrN&1F`+7Lp
zpx8}cM++`;IWkZde<Q2#mL~daRfeoMpW*3hH$y|u%+Ad(JU%kW$JNdPx$0`FDk{q8
zP)AnA)wzMx9j}P)XldeaU^^<Q>lm0Kn3$Lt8>5q6jV0eJULXs#qNJxND#{D``=)hi
zmJSBaP${c`gQ~^WH?wwh_YICr0DQyO{QaRB&{$Vf0bGcrf5e3NIN;nwK;_F8*;pHg
zR*-a)kdbF%rUkN!lDxE-An*Dm>hZGjM0y`yY=Do4tD`N~9EAXeI0oR*r4$q?5?(#B
zyEx9c35v@y)pZQbZLF;<%uI|7^f>5@R*;e4j~D15P?`b+e-H*hDH%DL{B=M05#nHZ
z93~)#QCpj>f2F0Vp`oq@v{FYmpCBNZM10#f{heVPpt-)LG%q75Iy53WE-5uLr=U0|
zH9jK1)5!)!%Vf1w3S4PbbX;npFiJ*JRFLO*;cFYScsK?o7S{F<CA+$~I6FHzIojLV
z+E{ZfEsS-wR1{^z1o%uyW!wZM<dxY5=C&@5c3cY+fB1o_aQ*ix<zZU_*8@WVfx%LS
zAV5c#t&aXZC<5Sr7?Uj_WK>aSa}118AfnIF)<kXvu0Pnjdi^h)n*<osSY4c%7~${c
z7Z#VEUs2cG+FV<X%OFlR7UaPTNi}MR5vMloP16ZcJp&>!W@TgV=;98w{6K#{A8$_&
zH+1kDe`@2%ig3%t#3gZ50O?pEDTcZpFr_@4?W};%)ema}dj{z6RsmpUu&M^SvJH$)
zEg;5FlosQ^7toKu6azWr(4ou87RvU9x>~9%?BSumaPk0r>v#QKp#w0!sk$IF%HPGx
zH83u_a`=RqOXf@&(@>t99O38gNKh6b?MjlUe~7Ax056UrE@^cCqmvzuc7612?OeP9
z!lE%rH!?gln3qezjvy5OX<C)Aj1t?3>+BQi=i$gT0Xi2Epz~kbxOf1LIy$luG&FGL
z7$N{it|{Sc@*bv{Tj&bmn4r7IA9*ciI2kR^f}qaa+5zyb-}QHexqwN{)p-fQu9h5Y
zf8V(L`nIlh`?sv<n%pw9C@m_;+XbgV6y@dRWM!dtB0;6l>ChZ5X}CSDEzRJhMn|f)
zv9*hDSbTaxer|SVT1rwvT!=Ttg(WitLPX|e04CLN%<VmcV?%wQ@~fsmGOqX?bsmEo
zfrFr<Z)|2|<KW`%O*6oFpniljQ_}!4e|XX8=;9h_E6a&W$}tEoIpABr>+cE+0n<iR
z=S2J1=rN5w;z~x%UVr4)xdWS)bdIYpNsA4|Tvbh$qJkVjh#|P?0#r^0SqCm@EqyZ^
zf?NyKTV-7f=YW`u;@Y9rmE|Z}$jwd&^|UkA!mJIFAVr)QKR{=Nx&hbKKPojkf6&9u
zL|a(~C_saHx82ZzID!#6Rtyb+&FkXf6A<d-%r#~!$@XH<kv~F`$u_oyu8Y`UPdig>
zC20W>964%n3<2NtMSpKt0hlqSIy21GL`6*7Ij*98`N7+tpWisSbH(iO!-_KE!+c$=
zjdfJ$g8Bf8=t5`X&?T*=Yhvx}f8}Chrl-M@6&)m_Vd@;5R9HK9OiT0d`nn-i71?n?
z=tNQKkM%;bsS<OO!o2NG*v$SYQ8cUC(!#>R+}zyEjBDrY4!Ku8Qg$DRGquf}{G!uK
zGNS#R&2?B3=;R>l&4B;Quk!yb;2l4?sWc_X(NIZ5-6kYwc<0(9cP<>-f3j@m_=eK-
zxG+CAJLs!oG87f$<>X|EFbZmDDwOXH-kGkcwX-)iJX1zwpp=S%4ODCDYLU%LiwpB|
z5<|W0P4JHI(<}ikH4Z)zDTQg#0j|L55fKp<hPDSmoSc<Jk(&Z;`G8$+ZDna;W@=&#
z3~S$znB=TNno3J|SJBBLf4WO6F4)7)n9Y=g2p^L!am(Pp{qFx&%m%d96~z15=`n?s
z&Aj4ETV}2~xNTMU)X}xY>CwSH*dbR{B&u}kn%I{{UKL+G9`E>GhL>Q$R*Ot6EXd2r
zN>5Er<n7D8)M;X0Mx~czB@j=c)g>(ry$_IaW2qoN&cMjn)ZCJ5e`DuhYi(t2YK&ri
zQ#+5Kn6&)LhEj@3i}N;+v7?hkB62DmGkf%0Fj?`3gaZWlOdJ^e+wcBg#Z<t^%B(0a
zE=N&_q3;lsT0V06(ypoF8Y}ZsBK?p*Y;LHd&O$D=s-}(}a-@l?H6%Pb$iv=@qbes#
zAN81%1BNGC@BAW?e-h(kW1=F$LeQJHGSq^8m%c<wai)&BOF(R9c`i_S?aVm1ego$V
zrhgflaqV4PAT0oJu(dKbf#ytfJA@?{)HF{Vj;XX7(mZ|@irW-qAXy|V&D790w|5Uf
z;exS_8bb;)2q?anQvgyB<KKSw|0>1<>Wfpt+$`Agg7Rz&f48vIl7<N_!z%LApos#f
z@6?qPq!pAk(G`oLH2;vuxa5r7qVlY`KvxS6x;SaWJ7dF3i$F!3ZDi%-6&w`c@9X2`
z>F#C^#|FglbX^q%>r%+_gNP<NAkG$MCWQOB+M05-*_s+^K#9a$4Oj1=NZhyS>+R|8
zWXCl(($m&Ze>Sl5iO#MXIeGrX#-apYYi&8g4}gCpoh+ifP8T~%V?BuWd5K=Yzy0q2
zRp69uRZd)hqlt#B0CLAIT?1n?O7hYZB5@=KFBxa<<W+Tyt(|?)wVIh<T3z2fw!N`5
zHPi)dwE?tqN48RnKt&wCaVm9Z#jCrJr88Acx^#w%f4QMa`9n%`Q{zH?-Ry|Gt+tki
z8pqTIg@tkH1<+a-9UkoO<?3KfR9kgz{S%6ZPg<~Pep_vJn3KK&?yh3PLZOpI0-~^9
zIY#Ej1k*!?AaRnUAgO=*-T$ko0~Du4c-tDP$qa&CNi#e5fXEPkFBf|&Qv+>+ae@4K
zIW;{?e{^^!=a$wqj+!uS_JZY|E#;|!b{zR$hS$YC^!zCuoUGTS)9-XdPLVBskWkjM
z_6$!cs;ex@PKgQibGHYApNXNquC|G-dtgjjLDlfu%HrI#gs2cd4@YY=s1iwQ+Xkjq
zkDI&x$oko%O5;3?8R%eVX=36HeXS6zYIH@hf6)QL0>USza6+*V@Nd8Se-#w~D4$#D
zDN79$mXuf0;22rh;-onn?RB!_NLWx#-N?=-BDJWtxpm5{g{wC0I<$6XOG&J|5d#~Z
zV`A+@cGF1<qiroJiHq*)YN}A5Q&OU{u=JXsDSJfQY+6afkg|fT<d`sjPd9Xx1G&?{
zf7&f5E~~75%(MwD^;Jb#Nl}4laY0r=R!q}2IIDi@ihbAiFP~hS=4Z|#yQ{oHc#b6P
z?lBn<lgNYDC`C{>{_C423jisRzK&+vEQtYwg+wK!p%_CqnGsFJ$orPnG;;|_E*jQ4
zt!wGJ9fwX|xp!=BM?+?SrBXkJ$DBQgf5fEm9+Tt~Mi8KybkK1^v`Au#W^`tG)3EB&
z{LGZNh#-F-Pgf^<8%r~%fY_|crncG3yQhyIF$CBGzQ}>(s3}ScsM&_)x6Ivg_W8N3
zb4TWeaFr?fmV}%H6guK$5gkEDQVcRO2_^Wy{&>tt=p`yKfDe2R%4k`7#pKquf6iXI
ze%F!nw;sR!_1>Z7ZKY9ms>tw){D$Ytp7x_SA}9eZ875rFv1{QRyQHe2y<aSH)EaB6
zN(-{n5@RAl1AV>Rob0W=A~VWIOzK{<XTy@&6Ps%aQzQJ{Y)y4kP$;2d6HzjL>A}0d
z-8)SDGid%#WlnsMTkmAiAWQ;~e^p?RJi-5NF$|1YK(LpCxvnY>vdIbxtz;odDOuq5
zsN|yh@zcAPZP<1A%(b55>pB~=0@04AoM#jQ7(=N{2KTl1=EKt<vOP8~K5n49qQ;OB
zQ9v8jJ3KkRro1>WD;1eNIKT&;_VzYbX3l}}xiu|Q=B-^cvwh@{!nA0Ae;|OfmF2|;
ztJy^sw=Uj$<I~N3i(88$Y{Bq47EooX=xcbI1VB_Apycr1?%V|fyqxGDn|wivOsK%B
zX+ZH^RucQ(*ab^1sv9|e>YPQZH}5{YZdOZath+H|0G!@Lk;W7S0q`YqgbYG|8%so1
zS=-pg)h|2%d_jFJWjXLCe?0=DQ}VLXlH#Jmg8V$)TpaDJ;f}`*F+*A=cTH<;sxC;2
z4s^q5BV`3CVNHj~;<590UV3|Z=lszHVb&_x@Hihp$C)(f3O(-EP*zd<U*--MrX8)Y
zsDu#DDI=OFOw4%IZZQVi!~t#0ys{yUquU6tzJl=TX@3nU_rSQvf41*Uxu+p2uZCrn
zlA=R=@upV=feTlkpvZ*SNMv<ycjVbwTbi2~A+O%d5&4q^H6z+vhLz`~L<PFrTAGk@
zE!s{|MWeg6o_%(9%be!iV06brA_z)DWtq@zufuP6V1HoC<2)23e*xd@C4Y~w1JJD2
z!6K5fxNd1|X2G?Ef0jAm)M9x%@R`j)4k;z8psc1>9M9L8rVpKIM$lsiq&dNU&U&id
zH76}TBGA(bc?F=l1U<RI0lwIuhny^m<#gDZYWkMWzLCj!6%ExznTg?Wzhh26u&s2R
zqY7JQZ#aIRx^alquRoMzVgiVg3=P`LlVz&1F<Sue?cVZte+Ux*En`F;+Rz=1TV%r`
z1KjPvnG)e8<O4z>bC*+CmK7W5f=^VMdZnqYODq`+VkHUvB?UV^yP_yNIR-Ypg(cV8
z*3QAv*~JlE>lUU)2D(Hyv$7IX)6mMvCp10-BDo+hC$6y$&gm&J6%`CzqYIm7tUh@2
zz=|n#IGiJMfBtF3>9`ihU*)5H7m#r=w{`Ui0eri+{2c<<C!u3W3X<EDw2-TpUr<<7
zTvA$Ad<d?ii_u}2m93)-S`)!xQAA!{PLxuZkw<3*#{l=LU_l~=v`9ZHg0$GxkkY*L
z`0xNvM+bX58*8qWCEn;9Z7rgK9U3RG5RZdLKxAT6f3S}$CWI&}D=R4}u~;l)_vnJA
z>C5+A+O@d7CfUb~Nek!oBY8;6Xvru;QYauQ1@H}D^Y;hLK&H!C!oXRvuy+p#je>l9
zVOe!vVu-tyF7lA+&S{RGA==eeHg=ZA<OM;}t0kor$@@Ck8*NT3ir3fjL=SyI?bs1@
zm4%r}e^J43soP+;P)kFNpu*!s3yS{5sb>5@FBiCv)LGEX#lUQ7GtZd(#>tDfo!&BU
zTxEin36n1G;<SY6pA3&_`GW;yRP-!e17gx~AIZ0T&)*%0R#vLhSVUGu$JEX}Fe)J}
zyP&LkSktH>dGP_DEiiy~jO7$?iI$(&Oz$U>e>@jQH(&NOytIayTX;rA^VA8W8fr>%
zQes0fBhG-Ws;EG#kwHZs3i9Hzq#_GW&?_lO5mZ~2ikhaSS4@6=`@9WDsNez_UJKmM
zr2OOvALsz=P3RyxsHN2m?R=urOB(^-?k#_Zz$q-6OGB`r0-{oLiYkUSwzN*_7+swe
ze+6nHl>G+uM(=M1vRAeq!Eu>zL71?a68(tL%CN1xWAp1K%<Y^wwrOZtemX(%(N>lf
z?%NY106lN$0x{%hsxsvyp$m?zz3N!|#OBqFpR;=Jis?;-QLcuHJe8Eb;yg64N>6fe
zLmS}#=6n2q4ug2gzQ{`A3@u|jPh`3Lf8z3*VZ%p`ozPH{5(2`)d|k)`NTnoVmt(Cg
z(qv)^>Ux$=-l6f(m`?5Z0Rt)7VEi4x@NxOW+PXU1M>kd>XFVdo-QGf9gDK6UU(z5e
ztwuarK8_$*YFZqwM|f&!<Am8OI!6&vCC#_Vj0^T46Opt+rVuG%E2ClJ6qt}Ve`GY^
z+rRDa2!nb-lBI3t0E$+H#buQ>we`cBE3;$$?2S}$8zrAI7Etcs=$Dd_Q(!3Ie2uW+
zU~zdB9jMEP_&CEJhC7~kp08V$H2j3Fj<%6?Ww|L)0iaEZLPp6x4bQ;em1Ak>8e2J7
znUE@)bfcY>YhY|vS^c<<QPrfvf03xXy1tc*Ut~(8KdH(@hwsD%f|jS8rip`JOlH~t
zj(WTu2a>(a0@6T_^^Zy|EG{W6tEjB18B&l;tHG1fLX<R>AZ~#JW|W31*#Id)Ib%*-
z6R6gRP(BqO!;UsQlwFEIMGeLBddRBc1aY_5)gUCPsH%hP?m_C73K51^e^%Gl1C_)0
z%;F&<Dzjt!><v|=i0Yg}@9LZoPSG=gB8;=obb1)@Z@u&H0+ivgbp&PA^to>6uq-J>
zPH#U>(A59{AOJ~3K~ymUUucBwPCQ2RsY(!Mu7QaG2SiC^#Dyi~i2R5xIQ+PymYP$*
zlOv`LKMlxT$q~M|V*^MUfBncHK#3g!x<F3{+>aqliJIu><7hl4IUAyOCmgkl$SUiY
z+PVcq(YXgA5sB6&R=0!79J<P|6Y@W=p181y{sUoQc?}~Q&(MUNit^IpLTH9aj|p_M
zG1MULOM)FsG$;3#N-*SL1p-M*;6#tg+)<7IPmY*y?vUZ9j;Sxpe~b-s!(4opyrc+^
zX-@-384z~|dXPB-I=5(QW#{ba7aSfN><JYn^brwMAQyvX+Ed4aK8Fp25_Bw~C#+rl
zA`-I!|JFPIE<hQc@Y58vOza^NuBj|7%F9YmPKXKev^U3$N_xl$VRdl{33(;Be(hbc
z0v_7oauS%p3Z0c|e@YA@S3i&tu;Am=(uSWhy0$1a!p9!165#ZS3-y(@AQSt35y`Ze
zBB^b`F*Gr^w6=3_cH<QqXlf-nQ$d)ZUZKTlPR`C?$f;-p6)-3|8SpQ?^6v;_LQ{;&
zb{E%HmlZ-`JPgiQ1ANBvRtKIqAt@!zP~#YL9X$gg0<n9Be_lSNmqV!9LD_pC;a>5{
z5!2z^<dz}%iNS7`BpXgdu>bgw=2j4mIJBsVv9U2^n@vp(bVw!-#RuUvRSAmAveY?7
z7Is{7d><(@)Iik3%{K%~|NJ{I`+EZ2UaG{gbP0^hDr*>0mY)grGk1GSBW>hy3-Oog
zB_y$lmDq;Ff9{C}+B#gp(j=u#jFupS;z(~fV%qSNnyYeR{2Yv-<N|7^{gv|i)?Mgc
zDx!>|t_sq$Qex2JLrdAyj9&V0oNPfJ4V^3+(1-8j?u)C@-~2s)cc62wD*9aaki^`o
zrrPqnG~C44ce)3ygr%knt&TBi`Dro1o|t0KCo)N*e=m!kHvGh<@{9;iE~lRv1~dpY
zrKEgbaKWSOK~6_rN{kmT@N;{J{#iKu^~`Kt{XAW;W|J1&5vEWJ^agyh_xn8p3%84j
z5dyL(j5oz$d__@KGK`F{<*C8by~l*%(TNT!g1+HNIb}JC;XaP$92J?qgJ%W_ieM5S
zkX*3_e}*Va&(<e4r+U=1F+&TIf?UkC_(U%UVzE=c9w<_)Ivd=79|toXCCtcIg&t{K
zwpCGLDv+!iX=!q_jycG4sxf551*y(^S6>eod#;%wM++p|3{5Sq?QwI)KX_dOsE$t_
z2GR+t|A>LT@z+n#LSbZ1_0K=*FS4dz3J{l2e<0|mk`m&I8c+cbOUO!z3lH>kwlPO`
zS7FFXiVWsy(85duY<LYrYuCV-w8EOgv?zZk3yw0_UAXtCDVj_Tf)uLAv6iPYDB~N;
zGoyX&jn%2;g5`0<B#0g;QXU5#_2$n0(HW&VNs$3=wx%3VTGr6i)M9IE>mYD&$26!C
ze`{)h1|tV+meN&blIG>?<L+c<g`5FR6|!<{`OhRxDFr4$KKVYPb9iv?W$>r9(aV(m
z7?k$}De44)lQ0JPLjdIXEZX#6XLt>Onleif`(tv-+D2Sw@8Fp5Kp!^;E;{FVQzPie
z3=4`Q!$V#^Bq6)JzAP&aD)UMb^iHKg8|PfjfKdbdc6}=s|7f&YMpWk~g}Phot4Pwz
zlQAe7f3@J+;F7L|xjD2_V%=2~B`6c(C<F_X>*VF;XoCfPlqm_tf55=$75>nm%1Ky=
zYjFP{MY?+7&(HH&T$Bi!fc}pg0Ps)2eWZe9Qv3gg;c=rlX3)z^ONdBgx7Wel&(jqy
zMHG%JW6z7f1P5$oEIoumKW_;AnrreBLtHI6f6RVn2MMB}PZLDO?1^qiLp^hQkHDzp
zoYJ9W=7q%*N`zGcjW2wBFYMKaCgh|i#S(%{gi4O48ouYD^zZBA?d|31;qK<@XlHF<
zYJ?d&in6#zhfMrA5?4HgjX<~4)Y8@geFfcrVt4Wd&_JFnG1r6Km^?8yKH%{$y}{ea
zfAk}ylqBUl%gBJz6X7o777$T#dI+`Wzs~UZ%mcp%v?_&&GAMeX=oNw&tiq>T%D3eS
z!?VpC{bDjo>&Fc%P7QOn;xI(|&Jo48EO2VQJltFyp+yG8{lQTQ>3JntalsxoMw$vd
zp*mUxiIyT2B?ex`q^Bgt$3%t&2l!&ue`-hzB6r==!6P6%J`T^uiwF-3fTBH2#-h9O
zK<XNcGSVVAESQj^s`}+*OX3d3@;%UY3BZ%vh_+radQ<iWWo8jDsVJYaFL6`|^F2c;
z^^^CgB8Yy1`$DmTiQWW)z1)xD=NALZI6GM3NlxMdA`-H2BkLOKbJ!ZHN=yYle>He2
zzelyKD{7m&1jgr9jhHy1JR{PRtD`8?x5|P!wIGWU8WiB??dk67<nA315*e42o{c<0
zCv#m@Szb|&KS4_*59kCA7GeVv2SZD)t~yhS#U}f!^7C?Yva>SM)8ayXU94d?9_=mC
z;hdwbCBEXB41RpE-6_E?IRzTDe|yAtBx6JKf%Km7@lAj{!ug-Ugmx*?>Y(tdtfa(Z
z;uawML*S=~lNvGoq`JNsfdEfOD<f?cdGWzmb&}|$VUW#Jv|ufjvc>rrD9Y(rx`iYc
z){dDzx+W*ui>sp`(03vzHBcfUJ~k>M3_4!@14F_hqGRKeB0&67izUTNf6-$x90$+f
z*bpxV3(WH;8J;2W35iL`DQW2$nem}MP8NFV3Ze{j;2^iJWLRD8(3<M1%8IgV=%6vx
zX7Rdq=p)1d6tdA3Pl-ExF|VF<=E>=*q;w)^C&G6mOivugy+H17diDzqN#5hsGjz8P
zq28yaLA0MK6OzARdtyYGf3ToHKk~g_ju-$=cdIFg4Wtx;c@r6Vo0xKGsYTD=Sb2n}
zK}mdkU4ER8wT}Ftzf8TwXJ@9TCMCp0N5a_3a8MTn8YhbV6~uYJ1;yjw5&h^~eQaV%
zdRA^>NqKgBFwDA<6(Bmgi|Z$hZyh^kbj!%*rpoNNKqoUT1tD5Oe}@+5f$17>#nWTw
z@V@*)!=v9`h};+|swyOiPJUULUfr13*5cH-A8O!_3?GQQo9`J~C7jT1*JR@<y_y>8
zYHBK!?djogeTy1j4y;}s6X<SZ*lRBDU;pkr!|PgmMP*esPnnO0J=y5U^~b3tx$d~;
zla-N{oERS)9mzkXe^|Kp!@WBqpzdPKb@mC1PDsrzD6Os=4l$q|6k%o9R$j4rwd3c`
znKg4p$MmUFCN-6(hr5|;%J9A+<ytz~TA3Q+5~3`Y)9g1{^q)Y<{s@VYi39@3Prz;b
zQ1z7|9WlNGopJ!-0GFXgbr7KMZTcC~>`X$kUq=^OjHwyTe}wJn{?$Q!Pu4F7*+S)L
zp|7qe@t@8RYK|y_7KXKO3r#6$Y@4%k&V*rw@&0!DO!_*)BSz5LRpwYY`9`MXR}F6%
zHngU)yreK6q*0RK;1Be6wYM_S(^96PZDQjV5S5%uNMoR=U73v?xQK+z+``hzAwUVL
zD@+b`wa``&e^k)6bO#NGwzdfq#*c4p9XGD7Ffqu<L|vLktrV43fFvz$@zK}OLcS`-
z7vJ$@W=WK6x;8Meu(Y(Yvf^^N)>fA0W+uka3J!)Mh8<}WNd_v<z=1(HM85$A9Cd<W
zIph=MOdu0eGYe3Bv3GQKb#ZnA0<xVgPw5o~HA9%&f6tWdFGdi+%LzF}YW;FxXwcI=
z$YDOl#*P6A`L$y^muy}-vvpWuQm~5|dI5SIAjOlSlU!`byz*hACbqSXX&KR2Ut3dI
zR$P#a3x5$I0X`ls4mJc(aN*?P9}2n)&@CMs7U1b@<Lng}5ucV@QZsZ|<A|0q<0ecT
zQIQqne`9B$BEce#l9KxR`i6$a#^J-8&?m%UKqVPPC1n+r!Bxc#8bIQu2CW!>SsXu*
zVjD3*7}Ltm#na2%$Jfu_KOiu`k5K#|C0v~x`GEfDgeSkay22=24^J8%?!;FRpF6v_
zxVXBZ&(U}F@e2qH4haj7BqEj2&=3^Lczd`)e|wP`QT-Mpt@L$<cLz9Ho9b(!$hjX0
za2hxhu4`iD;NW17)}fuPtxt4T<;W@X*X`Z7c*gkp()1`FPd67QtZD${drzN$kch<0
z!m5T*Z5>^6W_C=SIAPrAkxlir)#W7xx#-dd;lr3HYG8GEINanJ+4)6f#rZjz=vat}
ze~2U7cE+_%Xqz}`%GBu{u;qhY&Dqd#hbsjIg~cUh<pfzMjL7E?pqjK{R<fD~o5LGy
z57W{?DDBH~^;l39&Ug)Y0q%Yw(Xnyy$lXj$OHWHh5eE(_qobmtA|oRsXrMo!zlx5I
zj){$pi;G7NDvgAA_!1O`!pG6zW}$D0e?CVZY-)N&R(5V)ejyRAWM{#fh>i#e^aE)C
zeJy2q34#BxKmRuHZUdJWXS@2Q-e}-yzMwY|7~t>c3po?;?Aqt7+;M!*h9$Gwn`;VE
z5>Tv-o7=toL!;tTGV@ES>qoXunK^Iinw85I&F`AkIc@TUu`NyYL#xV)ii&{Ze}kzo
znCnCQMM+613HYFLXhZXu36neKAb<pCR~KygC~sSRdiq^_VsctWc5XpYX+}(d3$EZ1
z>ea~X45C3&gBUXhtt0wo=!^WrV!ogrkM2IKjt5<L!29tHjZMwXhvimLRWo!53Mk6U
zN=x9MUx-k^kHSLyOQBz>tgO7Ef1;8`MFo6KNip?1!xQj8Rdvmfp|!*68ycHNG!1WT
zsINys3rtzZ{iJ~&4!}y4>St8Xmjhop`2NEga)^X_Rat0=Drgv3y9Pv4`!p;hsjzO`
z%%xinUpl#O%c^-D6GjZFC@ILnVl}ZTS%not8%K?w+&Oo_vNapG@7cL+f7ANa%a<(Z
zp4BnAZS2V5bv49H40L>>D;nL==#oaqLF2HY)fJ@`)kEutH;)=OVbZi2bLK5twrbtl
zHLF*ySiWrOl1{Yc6Va`L2N_x0JG(<uPb4_~Jn>9=gfI;iRdr&Vl9{cWe?($d@!#j%
zL8KypE)BTm^eh}agJM$if6CE6eMHOXapT)2wzr|MV%(U~qehKt!5GOyOUtNHqeqV(
zGj{B_ajoOWPngg~0lboNC>X&XqtD3)+S=MDPMSPr>a^({oik^2c1)i(b;{()6UH<*
z46Q85&q@gMb~M*jk?n_${N(^iU`_0s62sp9Tvw%md)@-$vdf7(e>x=*1`AGIxMAPf
zJ6BF0+PPu*yv~W^T87tEmlfuu=a&s_8arvmyrrwwZQ8zj|KSs7P8>bBcjwkk>sBpY
zFn7k(cJ$=PF%u?DpVhT++0vy;mn>Pbc=6)ynN!<FH`P^_R1F(3rhVG%`O8+WS-)xP
zj@|nX?%%U(`_@exf7Y$;nlz#!Bg)%GS08KNSy*xHvBG6!TvCLe3k*3H!&Ez2MV2c1
zy`TY)p@0H!IP#I>!9`0qy=X{tJK*oyeSna-3<$3onA!vNym-in2@|JGpD}yxyoE~^
zFIu=@UU%1AIFshgrZ5X*HXKu3UETBM&7Z$u;lf3WX)Hote>ESztP6dNKA$&#0X(p1
z@scG=mn~nha@FcJXsNG)wZ3fW{5c(yT1O49tIA6X^RhM4kQe&v3;FLuu@}Jde-Z5v
zQzUb$TShe0)l`%e=4MtjP3&5|>*V#P_itQ0d1%+>)l25hnl^Fl$c7>1bt5NC?_Rck
z$HAk=Po2GRfBD+Y+c&OUICK2){@vR*ty#Ws?u;pIW80zCdhM3ohYlV*aNxlH{rmQ9
zU9))3lyOZ%N`^GGPMr&aD@Tr<ICbXSg-cg1oIQE;;J#hkHlZzFo1YNm3<Vi|e0S?(
zZ_7V~$FU@vF0^Q-9kg_+ATQ6r!~w>OH?(#x0sKw7e-9ieEFs5&Q3}@X!Esq-!^TYM
zTCjN8%C#FdZ{M|Z$M$Vow`|_Dk;VoB>(_7CuyNC-&0Dr?-L`G}_8mKS?%K@*^>tg&
z$HeFG0D5B2-o5+wA2@LE(BVS|5A5H!ckiCvTh^^uG`Dk7>&Tisbk<pDGkDC^uK-Rr
zkX083e@n5CP&^H3O$+zPtg2BRQ`*OkYN{VnSyDfK*0OC!E<bqx>PgSd%V&=t+_QDv
z@`ZDzPaNAkzGL2sE&EPfzT5NQ;iD%{pTBtZ?9shj*DfOS?c1?w?Xm@E5zm~zV&ksE
zXRh44d-u+r+qZAsx_S2C)|FjTM%NYBjh@=Qe|r0o3-@~NqpyGb<mt1AcW+!ick;;o
zU8}n$HkGAEcv@izGbqP^Ni6JKym*OTikFA=@bD08F|-hXwu2(j)RN)tb64ZH|Mv`!
zLR=9kMLf^UDIg}Zw4t?Q;o42xcI`cQ^yJwK7tWtMd*<}1lPCCboWO~bCr_O|edg@h
ze{<*0U$}Vj(q#%)E?>GxeBtRc@G*Q24_rczT)BGn+O_L9Zrr?a{o2(l@Z_n(d$z7!
zHh<RCma6O+A6q@vfAYb<445t<$3GybFJyIW{gcZ^PG7dDd*(EBR5#X+=~%S!z`47x
ze*666+ZRvn-?@JI^pU;rPn<br?($9he@<O{@aEUwD183#=EdWl+t)6gMyB704u_eG
z)@(m??#83Hzy12lFF*hM`SYhwPp%!`v10awhT{4OvsdmqdGiJR)xUjw`|QE3E9Xue
z+_bo34D|DXG6wQ+C4~nF1Jw?5?<v+L&9OA%;3}Ws4}#({3^i!N^NG!`YwKQje*nM#
zzioI?8K#CV_>vK6#lu=>EZMmK$jP%8uid)$@Y&NRj~_jJ_~3pIf%^#X|Mw7jVA;aD
zMGN=Y^A|5(z9R7Y_3KwJUp&WO`sguQ*6{Ij^uUXkFJHpPZ{ECp`}W=2H|Q~VuIKuN
z69;x|Sh--zh_cjRCnM$mD~8AOe_dqd6^V=yp7M^X?-b=gkfa$5l;_y^CBjhld2?rV
zPMw6}@~Pb`w;noo>*<g0-@JVO_+ihTo0rZU-M4+?s>R*QHtaln?)rmQA3y*6^Dn>t
z`rGfn|MKzu>t~O8ZePCux5ApG-7^+~;cq^E|Hp5?eE#_3`?s%O-n)2of9IM79pma-
zI~Q-*f9BRR`08KK;`#lLPw$>TybXrmxNura74qwOG#fh3q_uTvLMF^W#{6CCk~VV*
zNkKclYt3H37Z{$Up=V~}jN7{>%v!eP(D5@DuH3l$;K>VQ7_cBRA>c6v@!t<gqk_p_
zQszVk1<*!)_MG?_Rs(wCe+}`#`}ZFZ-lNChxkq=do;$W5S$^i&A=#1c=IZ_E`F}ZZ
z>Jd{~V5Bq(>KL+;q5?F*j!4<V3RC3R_{8Pcw$7S83pt$Nads_Rzw_|f>-W(w@bt-}
zhxdE#UcY$e*nwT>PTH`2|MBzJd!D_0|Km@{_{j3VeEjg{<<mz!e|N5&IkIQVnkC(x
z3s!GChz$Skk6%9h`1aMa#}Dp;lem6acgKWIv?vb4qCj8$39X#p{`mCvIok1Ok001D
ze==J4p{~8e9EfSNCVFf_Jd^6L(`CchdUS*d;N}m=x!HO}q?I;|pRpA1_YE)0($qJz
zb-{CU+vlv<1{?k=f7<X*k@c|YpFTl&$_M;M!mQLLM)sruriXnGHh)E#950IZ*y=wb
ze0Wco{z=b`OQ(<Q+rD95dqZKI510L)g0H^~Gk_^M`HPgT6%*nWAZ4NT2&x(kU2AU;
zkDWTbbH=RMbGzovU%qbZ-lJzO-+D;m!Gk;3FP%Mcc;C(~e>?XdJ$>=|orlj~y?OWH
zr%yls`a81jyVozCKDvMX{PBHT*DmRrK5y0517~j_!~gsv+yb|*UO07h|ISTokOQ!I
z&DOogFWh+W>J3^sA3y&>8vgRxWBb>2Pi!no46x_XY^eRs8(K!FrXoWFvk19%qLg}{
z1|h0bIAkOqf5r1<hKCS78a&#$E4Lp#b^h}8+dYq-A+urIKZnEmIdNdWp#PJ1a#OpQ
z-=<)D>Rcx*PCSD?{|P`n_Wa?UYZp!-%P*ZivMf2kPPd=YEnf~-fHO$W!mub4bbd1B
zB!mZ11I3i+wlkKlwI|*!D7K#6y>RKuwVSr@Ie6^!e}$_zZ{I;)7+hu#?%uq5;mnC6
z2M!-Uef~1sflr>leDnUN&%gfu>*tRj-o1YL?8)s*Cl72}yJYUP?iE`OoVoGn9XiHe
zJh}_N+QEIhcWm9fVeP67+xH$heeuRU`097a^1mX(qdWfcnPdCb&Ye(~7vpWkquKN&
zY|&Fpf0d}h1|CVpKrtQFayJz41%_ABG%&Yw^Mw)Vle$*##3%io`;VW4>9Fw$yy^vV
zCzHFFH0MuzMtw&f?C*I?f_Um906&Eud->%4tt;ozu2?gBY)wX}lfi%DCI4-N0fGbl
zAl>R{4+Iuv1w21q0_Ml70z;C8-=4A_7nFu;e@3=W?wq@5#kwuK_Z>KdqO~*UE?&8I
z^G?sh$Io6qdwl=y&1;v>pE-B_;^iyXZrr-v^YH15Hy=L!@;hAQAKtxr^`ht6nM2#x
zE}4TIz|H$lUq^=j`1;A+t7nhx;{`SQ@xnn{3N4)1?|%IB3mE=Bb|F^J9ycU2+|`6f
zf3u<gt+Y@BJ$DXDMwlfbK@l8)dkH;ve|NSlZFnsZ3h|50scN1wZ_Tcg=Pq5liI(+?
zHw0djS2TH#li)wNNn?Ke_z6H*^+(#=)Oz55hI;D5o9B<<JUnxJ^MZ->xlwK=N`0qv
zeL2zqNmv=$$HT?W!VokEMF&&5C%QUXe=u?o1^BA^TvseCJF2~-d)d0}`+3m|y6<k@
zMfcs?*DszSC-3g98`rO2zlLy?`1^N1{frF%^QVtLet7rx(am#5cC1@Gd(xa`oAzPD
z|Mc>~jdMr$Y+ku`<CYzJ;Iu%Y`^C#w(ZWIQ)AQHwKT?KAQNya4qpMPb9rSoKe;eA&
z^gbX)+kv1eCaqye<9fI(Q;i%p3HTzz8(P}C`^V*0kDNMx-5%<sf7#2*?|8TPhh8=%
zZTXo8Y}Svotv~VO&kwyrv2f3=YnRULT0VV5QJl90h{;Jy^dEfvZIl5Db25_SK(QJt
z1S^poEGWgn$Z#!n6|}}MJvJ(}f1s)f1?j7{?mu<)^2PIKP8~mb_yD@kuibh0><!rU
z^~>kao;-TcbLZBLYv=*w`aXRA`orhnQPB4D=T9Gh`tjKv<h!q1JZs{tB^&piy7utx
zA0M9GxpaK*rWM^wR&Cs||Jd2fw{P9NfudLx#)9Mg^6gJQW5eILc<RWWf0dmrWr==V
zEgsDVw*etHlqcvylOjoZPCnuTMZU+b&EGb>m>gLkj?b$ZHErSgeP=FS#V7r%cl?I_
zk$?04#J73Dia*o%1;SSdV0rkte-Ot1fLz~4_wU@idT{ORG3Ckr*1VxGUx6k-T~$e5
zMp8^zfQJKcuoNW7eiZ{#f0!1p3u2WF4FgP}$}Ao>cKU*KyH8wwcn_UFr;i=nyJH`6
z@oqnS@$NHN_9t|Ep*0GhUOap9+~u1+PhP(N^!x9>{q`#k*kAQrKE8YX;+btT7q8!Q
z@+va?`^PuV9@w&?d&-<8>vkMEbL~F3`1fw#xO(Z_WwdUdp>^}ef5$gZdywG|??#Kh
zIM&llg-5fYM;Z&#!7Yz0glS^3z>opnjSw|~l7X31Uu1YAD@V`3g#00+r!U&LAD#4Y
z8ohY^zW2W7GxVQKjl$AD;TL}N{u2%S)gO`NpFeqY|L%#+^C#4#2iqaPmLdNa`jLMd
z*u5RsJgllHJ0&*Ef7jL4R99V5Qjn6EcCfKB1IDr{y5uY!JcFW=v#Uo<TeRum#ryAG
zpvW2m_YK<*oValF;maR?r|}WFqPMS}KY8fH`Kz}dK7ad(_tCqD*G}$Pzj(%k&PA|%
z9=!eI?Sm`FcdnT~b<~vZRojnV>UsU!C*;udAcyAUxhuDNf1bR0PZ%Cj0K1n>Z7hfc
z#hbqaiaY|hfCMeE`X#L>y$tUalvpryOy}ZF2hmA}PI`2?eIRdd(%64t<=_53{=7WU
ziunBTM{tgwK0d!|>6BqP;ZFU`-u-eAvt{cli?Tp&5Ly=4Q1Qjw6%N5&A+#i+HP*>9
zFd`ujRE5{<e>!pP;_0LNcR=_BaorIV*FAXr^!dxz5P`jU_TcuFbH@+vK6LWJ^`571
zKJs?_%X^oO?OeOCb9~2wH9L-8y!-sq%bqJI_ikF=HD%7y^}CLqzxDXt%g6U_Ts(d3
zz|Mmw&R@It<jqI${9z@XIJ9QQ$l@4xqyE@4bdwiNfA1$d_t1`)mJk#9C-Dz2bEju&
z?TiO&jh()5-EJJWz_#xpvdWNFevU%+*Kfdy{{XAu)1R*Ozc|bBY5oZ=K{zVk;c%Z^
zPM<#G3>KN*dj9m{p5@aU3!>c&Ihra=IY|+L|MS29uYr&H+KPfq&=7R7wJ_3AW6FpL
zkg>6$e;IU^!$~hs^dq|ag(T)yHo-Z)d;6w!E0@fl(>ZlU_u`f7x9vG}f(~+?-oJ^0
z?gP8F>^^+@^6f{je&XHnPj8(+ynXe8j#dcd4?;Zm^e$1}>t4EM%O13VZa=zv9bM;p
zw{KjvdC$>v*YCgh@sH0?5P&maQ`gw?cu$l5e+*B1d2%u&v99j`<9}dyDAG{5dfY-j
zYVzFWYu0bxzI)%HW2cZKdLDVASFT>aaSKJY4<0>6D*!D3WWPVV?7i-ODyqW)8#4Ht
z*RLQC_83YV_#3==3+w08=bwN1{OKng4?VlQZ&l}r;y5o;I(hXK7y>BEPfv&l^l~QD
zf8^w)L}+a(<l6zk9>os~6)h|R>luQ=;M$RGGZxI7Gjsan_SP{Y$F@!BoV#G@s`XoU
zA2@dQ(v5p)!=lh@|IW>8x9&T3{>B4z3xHSu(}(wO-!^DHZvX%w07*naR6MwL`oNZz
z^QN~>>t4QT?}^KI;l?<744oaT)^FLlfA8>#vzM^hx2<2fXztQ9or7OyU$lp_ZM&%^
z+qS2g$;RX+yH2(>*_fK_Cfl}c+jZaHy`TFZob$ft?6cQe&-dBLt+D#e?{|V!9@)YW
zCB}#O@bkkP#bds{*#8LR>FqG}x{STopFf9gAcO2(Cc4H}7|Hk=s{fU`yo|Mg$`OMG
zyV*uiReUMwaBc)=Gg;8}FmT=~_(-}_5F4@cEc!2i#{;%hCo{<hwAdq_p>MVWKA2TG
zuS0K>`XV=oGa5P9k>ZHEC;psu+9L_x9<wPO5UFPg891aUl>Zd2a`EsTjCP2>mRgE7
zWw5DtP~9Djl^Yr>8oN4-U)2=oq=#}N{jDe^(B7P5bL}ZC&03dV6l1$Yh_?)|Hhhl}
zFR6ME_`G_p8oGGu`sZ~F#kbfBl!$J-yjM~l+6nw~yG7s$_iDUc2~>lmQJHJB?Tr9c
z+f9?&wf<Lc$KQno!wQ+*={y;K;NbKZ)JOJrQnVn4;9`k$lit~YM^n<Zn##=naj;D+
zfs{2JuEqYlP`TNRUk4MD%@bWm&~&*0JQZpXkd$~FMG2rkclO|f8jmLegRvKZ++NQ;
zEwkp#y8j%B@k!S+nDs%0!YmCDY^Stz&!~0i!Z^NK8bKIr&-TWB6&OySY&i10Q(Y6s
zT!;v`_C5?koLX!58Ipi-I^Mz9G&jr)sAcQkSI%-|JUq_)m9dIIRY%x!rd-mxk(kHj
zc`aPrtI<}o!E_@+(JK}(_;-ti|8c8)xk+|-w+-4lxK<eOxt%oA)lJvED(n7px_z0K
z$DPO)ayUDR?+vxb_T*=KJ+rheAQ+g`{r5Z->{Ugc^NG^&?{H})Mk+OQX0i&RE;`Q&
z1!Lrdfj7S-=ja`@>U~ycLL(7bQ898D!bnp8Ovv11-K9}VTVMs8To>DdBY#(g%mSZA
z34Zn^p-eMq?l8F#_B>UQYHzWmqL!B=@sE5CcT*jK(lNa!Exw3pAItwO`2pM4TF);7
z@zLuQoN0L|x^i#l^l?yOQg+05f9WtQ9K`~sUEt|-pkcD^s6zT{zRxu{M>E=FuK7Up
z_Q(nuIV<RTQvi1@rYQ^2*)z~-ntKbjdYz6Ak7JV7ZbU-B>G10&(7k=M3+u+5!&mLY
zh{p6~Qn%W1_JY#9Qm@_fek7UxH}mf1+tvoC?^K~L&{SaSY@#S@^13$frL^gLKUgJf
zviR^k%bsD*eF&v<&rOZdBtvk$XbwM$cqQ%m2^T0T2q5mY7-{i(K?n7JDNcy`MfTa)
zsF=y-qyE@q_IKK>0r&isJ+7+5E0$?gh&<Bu2ngF5S8E&1%Z{}1JzN+kTx8A>j`sMP
z(R98NiuhjGbeGNY7HjydGswkk@pm+Q?;gh&Iv-5bo##(6hLQ1~*DX^)iMF^ugXk>l
z(*MO-0my<?_=l#9(BHAKRaI3vIXRtxjjQCW1g(DFsn7b2q#pYe+fv)-*uC_nz1G9?
z?yH+!rf{R%XjyRG7f5zQ48OrigEsH`7scjfJ3S$<x2{Uic6~g96@E|YR&_MJglWa|
zY#>s&I?^w5mHW5r7-1HvRR!oF2{_JW*`HbkKthEOf5Li+RgIhYYZ*d|ELHy*CR|Tv
zNO4N|)89!N$ixdv0%a~Ucgwda<+`xL(z<P}9;Xk)Hr`0Oe>!77uFxu=Z5(OWHd~!u
z7otx!s&xOk!$j!laF>h14yF)WTxasRJ-=kV#<f#q8SmX}3e~tw7ls6Ab|k+~`g<J$
zZHD@OpkQlK<(0`f0h)At;awS-G(;_2;d7dt#qH@4etN2oXeeL(ADz@zh)y%(#dN_{
zZn5|Zr<*7I$c4)ghVe_`&%1~HL|8XFT_G2X!vSp2E|y?*gVpW&M6nfR=Qq@H9Cz$5
zP(vPAU7>a=R>b2i&nFWUVyOGMmgxe(RtdZYh9OK5lF>}vnY;g+jsZG2ia4%+wEwJ6
z4D*pT{#R4*m&WovDl=f`=ER6{pk6V9^VjfKmNn3KiP^6}8SitWy|s%?tl10q3186A
z@s7KxW}eyLpZx_xgr3{4)oRaWu`{YfB_>nB%{Li55(eOLE~ZE=BLY(-4+aL{_mxG}
zB)yVKWGM{txC~k7?D9U{D4|OJM8ir>>RnS43x?x`e0lI6E}|@ETB-G&Pk62?QMR&8
zL*h*vecXIQvEw~ZCSGl_U7xv_2sgQMPju0FBodOP@(}v?xR}15edT7(;kP`yO5Z8(
z8v+CIr`u}3FI%4WQ29P*o!kKrFZ{906U2@MepDTkkC^Hz7#FiGe?gJ;@8-MR1Tl8?
z)qs(t?QH79vD1`)r_t&Kq_lEYr~C2Ey!Fu2e+^9+P(zy#j(b|JIr<EMP5sGTY0&8k
zt@S>3oH*zJ7c?A}<%Gka*Nz^r>!e|~0lb_*)@*g(2%5VxUgBwG0~lM|SBDpM%%P-U
zFu~Sc2>D*$o~TKy4<HR9lB|e}4i@9YDe({-ZRBIa#zx}Ra3?a@O;)H3r(!R4n|~h0
zBf4$5c6yx8w9F1Z{xaw!in&2A(56w!m~XduUQy(KAm24;arrzceb>>LxWwE1uA`Nd
zNDvA!*1Us0t8vl(1Yo(oRf$Z!IKOt)K167m+X_9PcUtf{n+{4tx$_ypf7f4z!IYmW
z4mJq0Gx@~m#T49eU&VaP!WryZ%CrvU63VV7%1Kwm7~%g}U|=;KQqk&qx!gpmnJ67g
zMkK}K{KWE7_1_GIU+$#__ThZF0rZxPXBgJG89g|h7EZJPkz|aHPkX`EZls-5$f)qy
z*>$bz&h_65VLUJ>OehRYp1y%GLd!N$3}Mb(?>L)qJ4~DS5dKpkar;dMDF2WQ@Apel
zaWR4IY|bn@ki}fp9*iO3HRd@#+)a+gdB}t0=PK7^v6i!T(i2xhbnl-vckTYN{LlI`
zUXM^?taoMrkZegSg=a<Y{*X0eWLLlJe7lR?^nHJ3e_7w`Y;AP2^fJp21>g4AN<d03
zGE${*1@FiPL7=)$B%Fa0>tar%cAT)@{@bN{oh)f@=CqW`u*A<eLw8IU`o8|;D%Yi2
zQwz&;5b+!3mC*8V*>@{0r=UU_R<qTW@om!+?4=h-2Tif6v}?SNcO(%HTqd)4iMd9u
zjKm3~8x%9@?E6E`Ut_Y{9bdO2AKyYY+iRV^n-M4=v-ha=c0gzz$CALs?7BgJ(4zdi
zcc7$s6P19oR*Nrz$cy!}^Qu+T)K=A6MBpeJAtWRuI0;4m`?rD^eBw0%nV(E%waIPr
zLXRK#Z}wq+@)Y0u^{;iv!aO$@r#ONk0hNTIXcVD}PIivegG~@jiAHo<!vomAU2^2B
z?e&b`lgt;|Yx-F`))U|3D2w=puIC~cIGx|pbyi!iwC602O$78IA=r)t`aeP4QGAjn
zBMim8qsK1|O>m^JHkTmi5<Md+Mem!omKw0QBtW1AjLcbrMrc1LPWl@Ly}j->+07@q
zpc+BALqk(E2-_%Yaaf8gyV(hd_;;{ST6c}Kn27sJ$o+$a)cL8r6R>+6!7ren6>rPx
z_DX*L+PiDd)U$}MjSg3&DKm%Wp)JWvCk6@rCzas|bI#9_Q)2B6#P+3Mc1>~A0<Nf0
zvV%K|`%~x~<sN5!F}#Q=z5h0=jdlkVGt?Qg`F+IX#a&o0aswXnZcg&<6)6Yn8)Ged
zoPteydwPbrUe3rXoEAy{MIfPC`u(Pj6Rz_ydX2jyBm6e@WyYyq#l;ypN@2XB{5-Sk
z5+OWH7Hrx3Z2#I69r=#O<|rG=8|b#q_w1u95}vaAp*}AnthPDS2a4@kU!Gl@lM<0+
z#U*4XYiL5Wiy%k$f+n)DHu&?1-#ZnZoNnPP63a+odQg)0sU@MCV+Bf_kHW5J516!C
zZ8vM2{%iK`a=&H&V3*P`3&JS7GBAHq0IN7d<wX<Vo9e;4>5gE-n4>}ixEKM+N!q+%
zLt0|IDM8lSkZG!LI?FWPXG!XPP8SflZJV*iYHj!U2HGSs8Nic!@{`B~_>Rt*);EAK
zB31U<TZD+32rFkzzox&TzpH?67Vh6|KB^l%2vH5ZK49%R4Fr6aoL-*}p3fVWw<lis
z2u6^!NDh?}O=RlH2?>F~2`fWWF)2|=SMNA_65eo^OWuK$2QFPib`x<^?=D<|?0SK`
zCn@$s&gG$}kSPM2NUjcUzPss8!cRHg7LRA>U`YutGU$ByxU<^P$w|uZx?#BJ#MtOc
zGBvR|8|}us6QFJPn$HVO?<A307Cfb(Lfp1w=fKsoUlJUtv;fc-j1^tu)~mf^#lV~3
zS5#pwf&`0>zI6*}@s7*z5vcnGZwqCFlFSM6P+y4m!Vv1;_$p}H#arUFoxrp=g&L}I
z9qu53`lq#oo6Si^6ACHq4g`MfraMe~oP*fnL`?zjn}BiYs?Mj~L(t`TN4)jYhjNWs
zSB*wq=kwfp0}MKdRu^qGj6<6#i?M&{$@mW}*V)~yLa4XoH!yh%{F5#R;+ew0x|~VS
z5Dl#{bx56)_0|P_-XH?SdwVD;3i3_tOBlkNo0pbAQGJUwZN&A4DR^QcEWEA5!_(c}
z^ZkAJbkJMHh-uFBWhPyD<3r5g=5g{;Pte*%QN1|__!|vRQvHhEg+$~TKtwGJwJSV+
z4@$F(T<BqQ9FJSVvw3oHi~Fk%=Ot}yiS)CXK2&b_HxJ_Dc+(NRG)SzTmfNv$C*qLe
z?_8dr7}^?{2Iy>++n(F_J%HEB|NGL6&c#&G=CQI<G5D+PDKG}+vGUxiidg}vy2RY@
zdRRXd@ZSh$3+%m*8lwG`z7(Au^;olzvQK9X_3Y)<?6(X1#MY~pLE=VuMI~fb+O0{J
zCp~e<0qXz>D@|jbe@@(sxOKd}eF2kojaB{N&qyT(mMap{T7NRAl+|QBn_YlwCwRdA
z2Fdc)OupPUJYoR8#+9BU@wGo<sMLP!OfVoH5ck%#Q83hPA|m->Zq=V51y7{D<{G#k
zsM0KP%z;7_ChR{)vaFPu5U5mJK8EC+x{sr=z!ja%Vm7enKzTB9%brVcWNdBsr(!_l
zmm++zc4<NxMQvf3x4W_H{n13xs`8rm^PqX|&$Tx1i>H_$POF6SvpwtA&Z_onN9$+C
zfVoj&!9)VK8vA1u-JhIh!_k0;f0-;aKG52MbRhW%2iohKAG4QQ{TZ!#9#+@OM-m>n
za&uVvu>2hz8XInIjjdIL<yj#NCGtO~oD-X=CRbG1p}z#vTwd3pJmBQI`(4Q9)$76-
z2{%V*66y<IFE^OTT-Sn#WWrIfD`J=u5D0Fp4wJ6X7L2UW@=)oD6SBf1tl;%g=+!?6
zna8fnLV<B!^@OyS8+aybVgFrK-Dto1U}3moy!R_2J0@kg;dfc}deR>n4qxE3LukZ5
z2O_dc?AodLEJ2ej2m8_5<?~+pT9fzn^YMF5@<$O<E8=$hxIiDhkQ#IE=cUSVz?<?I
zt0C&VO$^hKU3I6RH^5u)b2)5UXr|8FxTv0knp!nX140Nnl>tY4MeYqkJ&7WQVV`XJ
z`5Po*m3^+4^O!-yeOKBk>gJo6-p$gLleV=}0fYnAm(L#|q%gFykynk=u)U*yPo4DM
zC=*RY!q-G1p`%>3Ozb^&p-YK30o27rb#nD@i%s@-DB+MTgkq^IP>}@cAY`JvjE9CA
z=Ziwm8bNW?k%+)5ahs_-Gf!Y{zWFi8M&j@LKG9Ezn%((GaQWmlmDXato<Fe=a#Oza
zE#S*Q-1lz_bDWL!PTRwC^mxru*wIItF81e{H9tW4SH2kqMIV8hgj988AO>oSUSEfF
z-pEQF^%x>-=EZF31p#3yQUOmd<mZQ+EGC=f7CCwZVcCiH&k1mcmLKDottqx$&V@~>
z-2rv?kCW$m<4wE!lAXoL#W>%RFfPn0su+e7iz1=!p&=QmWyUS5U62*sA-lqw@e4y<
zt6PuJNU6q&+r*6XqJ_Q^0mabbsTrB^I^XqEvj|A>k>py6Uz+#fn|Q1oqsDBTQ+|HY
zEQc{w0BMp4Kts~X)nE^g9O1SB;yQCwTOCedWhvz5Z12d>)Cv<NjbB<clam~a;x4c)
z+r+}v_BFs(o?q&Rxm2@`AvhH*_hpwop5o<+#b5)Ncfa*(-tR3AU~_?n2&}^a1w?}a
ziC<TPr07Z`s6}ZhBj9?8OQXb8eF&?<>JwfT@Z}e6h3#){vPv#}i~e#5n#XGhr(DK-
z67-tDpj42E&+!Tbn)JSjL@UV4D*3~wArK2ry&XKBOm|DLXL{n3zzQt=5ZU|^J;fu|
zR0d1C)7M9nm|w31G-`gwgO1t}Q&m6)q1SGoVc+(4TX|PkHThnpi~K#i%ll7VN&L$$
zNWblN>%R~CWN0`m6E4D;*2v&R`xoVpF)~yP1yeD>MYn^OVi)`@KS>v;<>MBb>K_ZR
z{!hnouzkLLVjAls)3u2AF_}VB<=b=jrrnnSRZ2ShQWOq!m+lBK3aD<WrWy+55&t|O
z!plJ{mm*`%<N0{@Vi8cuYW4cLID&6d=Ar+-h}S_Nf*MCH9R(A|?mZ0?FhKcZD&mB2
z@KM~fAa#Omgs*r=I!abv%AEe8IFp<QRixX4g^h_e3S(qg<Q1%4`#*nWf_G{N2KrfO
zK#h&PO)H>ov*xmQ#<{h&&GRwb>(#_Wv|4BB@pyNwC_Z{SOHO}j_{(~{frFb9d<U5|
zS~X<+K8F6!ubfR*UkODcZRgy-^fIcve0*J|o;*KzG!xJMB7OGeRC;yzD6c&vo%cU;
zT*~muiybC?4c^-E4`L9Pl*c9Z(nmioipKoOxC#Tj&99;w_&(kLo=gqDzBtdC<=1eI
zsSM!R7?60SM~-c9ciahFXMpcboT<y@{0VbSgz_V9a@;zrgx$?&{`lB9q&}fBoEVSf
z{1OyC*N`UE;rB|#x`=S&$m1(1>+8cHlJi{9Z~k=Wkh~f+re?>jSe2}^2_IVrO}9af
zgnxkf><Bdb${-T#ER6)zlRM9<(|A-aCr%_8(ub&a?;0aTf$?5_qe`?mOBKV?=IZ+L
z#!3LvU8jK4Bf#cUMVRw&7X&YAmF?|daBf^?2vsf=6CYgRgm!SjtlRl{lqS#Zncen*
zPDt`twkFhI;xYJVB<dR}u5tD*=+7kTDgmfu9r3+upa^t=zsdObaXD1q9-h2LB+c^L
z%6?g0S`cL6<7E9JFf>R^eD<UoytUj1ch0TIiG8-UgM_AlKT%FFUt{x8PPeP`U#gIv
zU_j{{L=111-fH|>A8hl?{0lpxKY0(ri0ri5-6!rEr+fXW2~ZhSGz(3o@wL<YfUc9b
z6$eJmmI5+<L2|Ys!drC?P8qFC=F$02YM487?~l?b0E2<1Z{&4kadzjg1!5BNubfuL
zL5Dws!k!-wB?hvM`xzQdN+P{B&=d27b;)L(BUh<5X&^b4ab6%M&mNvbIUl@RF-66s
z!sV|rKIuPp_aED)>EZ!-mQXt(Kq<UR9=JZ!gL-is_2T^W!c34h38r(3#nBRUv3hh`
z`vV%5{m(oe;AqHoY#GFWkX*6nx`C8cc?O@BllQlvs&}*6yW6HMZ}djBAW%T$O$w&D
zUd;YJj`BpFAWXphhopy{BUOsR!8@OUde$rdQ{wY8k&kcOE}bVTtH>!JA}0C+_7Id&
zbYs|X-|9)ua3dA+y8obLdy}U9*6!<Z`_xE1n?9;m+I9aIDVCo4c=}_)U6Yct51Kfq
zF{QaIyr^1)@Au@`oo<Tnu>ud8Mo<g&hYH=0e`s&-n8@#M-(NpdUq`BWYR@jiLq){w
zLAG52i~n`-u2cI%MFQwz7&{ji7k%q`G;Fo(ubWL16=kcYhCkhKmLI)W%+1-3D`#CU
z<8!VYr=5JjIw8W7Gc?kUf2-EHpQa6RGm~sKnf*}qGLgRgM=U+D=`B{v?)!f37Sttb
ziyTK2|8)!}$#bVd8RfB&TmAQeIed^k+^}U}nmUMg^)*tF)dD>JOUu(I{xRk`ntk|n
zvHPix^bY<u-L%&l5vTCU;0q6}NLgHf+jb?-eIe}jxIGIhyxyM97dK_4`b$tRFEW?c
zH}%ud4~%Wg5Gm0b<Ecm%ps$V;;$vyk1HerNdiq2NF_g1&fQ;1ev!QX~_FjlKT^mM2
z7&1l_deTNZfB|lf%2T>>8%=Aj<@hcU@b&cow&p$<|KfB{S#52MPJX`81h*QI+Z!g_
zUOjYq@ZCF|B47k&D#iyf5?km(cHj)-^9GyEBQxH94Lu@^HF$#tv9K<=`GzTeNwrM9
z6M|&^&D*bT+08IlC$Tx5A8)antjPxt$654;KMF$ujEcsIFwEGno$9#7INW`sjDC6s
za?}YMDT$V#AeHJL-QT%ty<n-B8gjB^=C7LFy02Wc?bcSurwJxdpojQTXIQ&fEl;A-
zbp0%*oMIR(bbO9pVE?S4Txc%Bex)Oey~QtiHQ@36xI4K%_pbxte4Ev^CDwL6l17fH
zCDHys?UGH3E#wWRIo~U}$6V&yPHkDHCLn#)_u<>X6EMi7Eo$o(?uhq7M%d){XDp&c
zp6iU?>307V)PF`lTo?AG(Mqf`5FqAhD=jHy>E48iE`u_UrM=!jW0LSF%%i0astzK<
zFD<F4RQNg+r%KDdbmS{enD-p){!=MK1e^^3DQ&<-ag1_YU!CjC(h2;o;oW0CjK)Hj
z`35TETgKa0l{Y(mxBI|%(hURvx}r1|g&n~2^Nb{{)X!U0N8HRvT{X2SFs(6Q$R?$j
z{Q>k#MuaOXD2@8PGhm*Z$9sq5{=u3_A#qrdBEkHlcgaEGK&^*Jlb-`_x3M6OdjXIU
zP$a}Z-xCfec6hl?o>Zon%b?P#nMppwm{5KCk_Cg&v+qqvrgMO2i|R?L;Pioel%_#C
z(9dEyaBwBy?wZlyhu5`u)YJ=wDO^+H(CAO4W3&^pWx%kZIAZnsS3oifP}GH|`!a)Q
z%n7Y}D?;OMsEJR%evxhp0M3sC`#>am6b&!m(yTx2s%en9a{nKZRN;U<_$BS2kYH&E
zspnQr0>dtF3ca^?>hV-45|8+g15mJJ-d29$LdbVtDBev@_~<t?=_W;Tmx<2rhLmC`
zesB{BjCL=|r;PZH#v1=hu4x_mA!BatIs0w}R~*|?a)!S~rsp_lSqR$QE5J=pW}=}p
zVnof((6qdwc;p$Rh?^CHVjavRE&kTw)mZUAQ6juMQCAlEh0DzrGd<w4CuU(_Vn5?C
zr~UbEmL>f2=H#O*ygftRd9s3jR6ZK{-XRY~h)LZ+`V#Lwu8;#xj-L&AZXc{xj6_l@
zB?gUBX>|zlaR8$Bzx`wlpoPtdsBR}@f3Q;UNGjwLGV(y$2|^9mM?oW9l+4X0J0o%Z
z@>ouSoZcu$s_?-w4KmN7$}*$_`x!XS$jqZ7K}>E`E(8WeM5m^ygah&mi?|2qqHp$B
zkXvctObuWAe{Ozfon()Kr{;=s5X1Vu6S}b}?eBERsMiULz90Gp$Sz#0(w+MLL&n+B
zx3Uola;73Gl9-CEIJeI!@X$Xv5aig6OoYTUjOW*_ZV1o;TZ{e>c1X+?$y+jtFpCbk
z)AKoXBW`tnJ(eEguQmfdk1?q)nth(zBI;Q*U~Xo`n9f0e#PnH05UR!L$*DydMc%id
z8WAEjgdZc!xrT<oA#RSrrlKx;+BVadh&Ov_%6SnNTOUf8*_DlOXo$WkYcSiJZg;ys
zWPE0jmP+4i<7Jnr8=P>RCt%n6R?2R8bIINBuS0<uGeP=mVWD<ktH+gorx8arlYr?d
z?X-H?UE}Zqg+q)K^?9=;3h&sK@H~<(7YVe8pjx?bsi#B$y~K8p)!!8y;|HrNT-fB_
z^CP|1_pM`pFJ|AjsP7+P`VXt1*@zu#Ccm%YI|c3e8Ir~4IUS{}z|9HSh9b3#EhMJB
zp=9M+oh3U(IzJUvzTGyq+SFRz{?b7(=P~#KL66!}7=QYjSDMZ;FL-HRB8+YAmewC0
z(;U{Q3nnEXR{5K$x3-cBv?bkleeHn88X1P<2?6?RQc^wMe+|EUU0jWWI{!9=Hzq(S
z?8qo`X+Ojgh->jKYl&UwQ%q7QO@c1m@hWm$s&?nUCo52jWeh`u+X=ehH%Xi?OgVA&
zE&${Cc4@FFDD83?lYBleVL3QQ6=5gDBqA0?bqx$bhf9am>sL2%kUKCA&7f(eQ4I6=
zlB>Y$4OX8OZxEZ4W^b(Nk(`z>3%S5J#ExRLld=qVG1Bsp&&^Ii89i3>K(iT1)aA$m
zg{<2dJS-e+U&yHi`B9}6UvC)y!<U5>c7z3*?G4xS2YQ~qk<t|Vr<b!dJniiKWQ;zl
z1E8W~zAgKoTbs*UYI#bUFFK1waab3iEh#_>X(-s)E?VVc>bF0ht#iv^>^5z7w)r@|
z-W-d!axq!b$)Y%NsN+zzyOqQ!tYARsMO0__7u>!!pyCLzU(1#0w!N>-Zj$8aE~f8K
z#$}ImQ!)e(j4Da2P#Pz)J7S8jG29z21GhO)cK>=PyS_2O<f(!e{EpWGe`}3=lttgY
z#QglYNL!~r)F_-TLFD&zhP#irSE{c2Q=0v+EeLG-a_6#_(R>RpU4Om?#<(!n77L<I
z)btL7Aj=)x*{yrsjU|JC6Ca6MKAF+STTpyL1go~Qj*DhsRa+zEBOLURun=bfA`Ns&
z>~y%nY(~9dmj;>|{B?Af1I9~8-*2v-(s}WHi&Z6d@_H%LUAQm7f@+3}s=a8h0Ymzo
z9IhLAsE!u~aQGPu90e^jEKY{6Oz?jOLCeXjDB3o35v=Un9UW_&2D4q}S_1+v5rq=N
zn+*6RpNtc6rlk3eF{Ov4DOD_R$fZi~lc`%a_<kLYQf~POs`!bPfIsvnSpD5qY2@Oi
zV2eX2&;P>&KDU(e4;Q_4s~`wvx!G9MM8z>IGA)Fv-+&~okj`Tca}vHUP0wZu&7FlC
zcGdfcM-scCO4#>tG^_Vr(Nbt2L5;^<(rjLYu4|T=mVpl>s0A7p2Qw*a8?vt;qvtRa
zC6sQi936Y#^UbaH_x8@tc6Lrq4h{$*=6Xj$7J})yE`&Tk2E@^lMS3QMJ3w?OtHqlh
zPn(OGhL4IIPXkX1deP&6@zAIH0@BVwhMe}ERdp`@+?tds$b`MZ$nvEEt%g7<y+H+J
zM4cZK%y&G+1uBK%h-N}3H+{d%Ba6}>jtBlNc|b?DakBJGimk|tSYkjKv3H~b*wghe
z-Z|Fh7kX2{dXyc%(Dqs`1@AXj@;Tr3M!);q59>=|s4aQ)?{Oe8oC$jSGs;m3(FnoO
zWD_VT6s*N+5!_gf+*nYB@j+lK3Yv^$jE=0V%nP&e0#*5?1#a^L#D!bfTwmQm$j|(3
zZe?w;xxSu?c710Zv7n3C!B;~rsFPo0KafYL+j_b(y4QF)*yF@Er-yrm8NR^V7D)f^
zEeRcgux%_%n)hobGUTV5ciaf($NSOWxJ}GN8w;JeMGW*HHMy>|J|10bmy|c?mdZ{<
zUjePvI3vqnMt@3apNX&2Rgrdge$*h0*=TpTZH;$b6mf==!0aS0d-P*-M2FS!f${8w
z)8Q}Aq1YUtYCaG>E>QgtA50w<`RGWX2rJ3^BX$??@&W?`6A}_4)u_KsC+TuN%4NIR
z{y7v5;@Lqw`r(BSRlN7*3TTi#&C`@805DVYffR}9mOld&`q6dq&pu3L#_vlpOE8)+
zRhi;Jn!j)2b88{8_^U9%ZzbA|Cd5gR5lNYOCiYg6UY{3rzpFMq!X$|4ID<%q1zTLL
zCPAw|J=<=;x^3M3SKhw_Y5MIvLG=L<Fce-H$A1$=eh}Gn_udbc8Xjb*F4pb#0Ck9~
zxIYG3PQsOb9*QTbG=c5CrYEJx2geIw$(bK8FtA62er4jAz`$<->GtU$-;f=0@R-Qy
zikv*Th=m-_o`%)TC$c+5O361{U{?u6bbmm!GR;k{`-f-Z$+88+a5W3o+Q47Qpr^pf
zETlRYH9i{iX8zw27xoEVrV1c64W04r6m~`hv246QMJIf`-)+AK%7->FWN0K~Vg_N6
z_PI;-=RqLbV}Sq$wEIyBjcB6wZIMf+seaQaLCmkXHoWggV+R+tc;<TXvS;2b&~=Yg
zHzdTr<1*7HcFMMmlWF&5?4edt!E2eIj*l(rMrGuC@fl@#sk<yI%mLCQ?d2NJ&7w%$
zi7Y{<m$~p=8d1p8o8-TXlSELgV<T%pN7W*vZZtabYA|>lJ&Z;m&0>@dkLaVaAE9NX
z8U#!#X)Ne?2Va%r@aepd?1{z$%3zIHb%nHUc&0xi=_x`E(2Vj;M4h6Rdmlmdl^|O7
zV?Bf4{IODR>Ra5_CGc~UOpYaQnS;w%B3zn^Ey2{P$FSy$rs(DZ#tRc`5wV1eo41db
zLsCKGm4s_hqx}sstaYCR#dTT@_=Lnx{Ql;5uKKR-Ju4J)4`RgMUCrftM`X;>#ql^j
z9sW@;;k>X*EoDW3)p<T+2}yw2e2dy3$~h!Bhs;M2<*K?XCxBHH)Y8hgys8QDePL-X
z$`ZPqU;5^gMWT*oN6x%}11(GqVRW^74W{qlFT#v5#Ja6QeO$pQTp|~N|8D|U|6#LR
z^$R|)OLRt8$?G12&Nc_**PDgfIW!u2doSGG4B1#osXOiht@g&4N+An;RvFaM%l#zU
za=)-fad*+v6F{6BrOJ{X&TR@KT}D1uVsIc222@SBQ@Nrh2j!**f3G*B<Ca(G$J^WZ
z8_BlY+m@9Yax!u=mp~HA(92zs_iJ+=6}0<pasAlK6Vz$ET&ug>+BTt4y3}vaB^Tb<
z?2f}&o1R|a<`R#y1-7i0v@lCyLyc8Fmf1DSalAo($g9z_lZk&I$uzDLBffqZx?uA(
zyuz*v{Al@wzSq`FB5su->gOzTr$5tHs2ix#A>^qyQt`2?FTo$?*-dBtYB!Tq#H^x?
zk57m>d7%tF2Q_CK;FSLRvc2|~q`X~NMaUKu*gw2gcc@vLt-Cu50HIYn92*YKWDZ7<
z?bM<Y7*a%dbl?c(i2o}<L;<63$!M)4@Uhs`q->`rQ1k2b1eC){j0wz0;lTDoN7cjo
z1Lig`Fx<;b#TV5`!v$62BWm|RCqAB1+M-B9M{Ytj$zB`EMH_O{`LRGc_-Z|w+BT3q
zK4{mLKj#PZH}4GMK2%yU%Re+t2ecxE!C`&^4+iWC)f?_0ojS)h*8ZWXNuCzVcYTT8
z8SYXW-_MO7xAK_n<3FBg9O1xvh8bzOK!O)W%q!bc^bJ?3vIGGJTMi+Tk#BxFs)5@?
zsc{-=3CU;|kYBbLMAZoZDx<Tkg*9Mr10-f&>qO98!KT-!&}#MVEoRhtT^X4b*TVzK
zdh+2|a|_EVO$<lsgO}q`{Yomv6p@VNX?mpFuH6NqXa;-aq#i!5JsR{}p0B|lq~v<Q
z%t_-b!H7TgIvl#R!O7LTwygVO)S5QYJVH6&s$@t}>IHrgt<KCQ;qF?3yW@~=ET5@K
zUp>}9tY2FZX!E^<^i{}qaOei=6F35qU95>}VzWJhY;{&2H_zCX<S`*PZHZ*pM}J^z
zn$~&}B2L=+47tBJ44cf5k<t~3JG-<cl!b7Ski+xR$xMuBA}-@mnn5B-#j)NiTI)J&
zeUV>q$9Fu`?J$;<R<tXxs_+j0`%^v~a2JfC+EMKU<%=`DjDKXM5{1CEAR7jlMHkIS
z+qQ%4$K_xhSc<=KNh5o5_Vi8q1w*rF8sDD$Tf7i-yCiOpc|R{v{$&8h`bH#TJu99S
zooBEg>g&RBqojUcm=wv{+oP$WD2fr1i8sy`-$<P>&*p>>HXP*R&2zTfLNksPr_NdL
zd>t7jKQSaZf;-f+uBjLhshtVfJ5a8m8M;GJCFeoHMUZ>4suC05lG<O={JQwPP;GPB
z)mq{PgXZpgZ9EkLa!{O<(_H-2XwHcLofJ?@AS-H~%3d+#tFMM!NEn7203t&KXa{Ns
zavLsBaB-C9a62YaqzG;b#Qyl<$?k*;OJWZRZ6EJD{k9WwKHi3nYnK8}m#Y5EwYZ;L
zeVGAdW7EGW=CBcXl)SimXqFLXOiSHlmMg^vNgxCOiQ&;YBBpFedlmP|{XdmM`Uw59
zopj~YMR|Foxp6RExYeX%3L%`vf2MW&ndF4{w^T&NW&~o38>nf8NH$6aIyd7drF~f5
z%e(2jJAA%?KEREY=Q_aDJZq<|oxGZfkBU|E@94aUC!3uD88VELn952J^e2jso|PGr
zUYk$UketJs+(6b<J3!WBb$LRJhU4eSYdf#fyB1A$eO2-kVZ<tZ#VeQ^hs*i?Qo#3q
zxMcE9=wWpAzonkX!Jfv(gi?TkLzDejWr#m}t(2^t_N4sq0O-Qxh6=~R`K{hHk0q;!
zmdR<Bl3QQa5nSMAVI!yH;BxyW>2uE6BC&5<E~fG&%oTz78LVGIDAse}K)w1_nwT5R
z`&(kP$IQuszokoLQ#OoRPar=Cs+eK@rKIMgXr|@hD;E?C2|2bsj9QTIpTrSA<A>lE
zB4QPLmrAJ(2V}xVRkKU23G%}pL5m_u67rr$_v{ji?w~iOt4XL#jtKIQ;}(+rSL|nU
z@jl&?3U}l)wFO59hl9rqj8NG*1d&Ma3~32kiW~bsu}kPp@*@^#u~1>~`Q*DaN`0rw
zKiu`}`+Jne?;b$x<<Z+FNx$$oMkg=b%piA?nN@iW1W8ypun_y%%J<M>Uk=p(sg1VZ
zw8wj*2kj`I<-gV2mco`im)A?=qgGF;rEF!064HVxW9;jubBMw+z_e6k_Q^q}a5kE1
z$CXiT_FD*ae)3)KJvcTtm(_p-XX^cHk|0vD-T4u_B`PTTtOS>(YOTZ7(pvIoVM%f&
zLyH?glDAHTLJ$Vt*rq_x(lseoXDCzHL2{o!-owF6^ZH#$M&L-luMqqD`0<5KrxUgc
z=iu`)e%z1^(+=5S{-OmR4I?8ZrQ|2=RbIf|hzPyvCOJ>VKkM~wf2YAXB7JFjA2V6s
zpN_hw)wsw-(9O6Mvy@4A4(IRzmx9KU@%(>*;Xndj1!z$3iwy}pvH)Ztjo$J{E4Y69
zSnX5OWw)bK1obuM5)Pp0xi0yA&bM_^3Wh^VnwZFx+!<48Gs<M`Rb}zB`VylAg;cbD
z5M#<q-<1DGGX^c<8K}+0a()jKE@R<2ByiN=&LhHn6@OgyC)0^To9-G@N-*?(69@ua
zup(b@hl=CCl!7|NHHBpO=;)9m5ei|L3k>(L64d8wtR_8nj92Rs>~DYcw^&}siwaeH
zYb0Xt@23V`>5I@8sSB<#j59jM@w(>*n?(x}`+I}J+<_kqYg+%^hE&p%Y@)lVONEb#
zleOs6-ckRmvT(J4O_zxWsXGI12@v9?eM|=z-<mdUk2>hzSLv)!o@uM1r|jJ8J}1|c
zdN*z}jx1sUh2!@9H5jAH#A<hkFj;?Qslj-(8-#K}{MuaAhLtzXD~ymC!We9&Rq4Y_
zUYrUI8aKkPG_u6LP5CAL^wJ@D&e%}YQd*Oj+ZTkrGOY5u427+q>Z0%%0652Ue@6P(
z_}eX5R0KnV#pNDa2>kmLx(?$REHMnze(FGOMagM@0P7o%JI0HDYC>fqU9GV)lGl=|
z&nXE1hsAvQ1HU1c)~R^mKx%84fobS=oe;HvnmwVN)%pc$&DANs6Nl!qI_Ru+Ag0K=
z>CtT-_X`X!`yA}+t0P6MFhFPZL#q8aP*^ZJsrDvHhB>3v^u5E01_dL{V0w05TLbhx
zh1n&s84!J!ssLxtkNDFQD6L5KJuP7kR5J4C6xNb($ag3y_+ZNlxu(2f_t6S<cM@`Z
zlN4E=<D~f8=ym=8NyEJ1`Sx=AOY{1mzoPDyo5A+)NDQiL-HZm#As~e}gdS_vi8vCV
zhnkuo#|CF6FAp^vT;q>3zL9&2(vcx&*nEPHmz<;rIwWS9z3PiFUEjJ~4rgZf5F;yw
zxu7*(CMVI*&?z@ltMSARCx5J7akL`Ncm)g_gUTePj~^T<5qVgQ<+jY;OqylxSL&{A
z&sB>txS_QKO$k@+835KggW?%EcV0AV2lK5}RBb#MRGFF}rvJ8Eb~{^}Z&em;!5Jp=
z1_cSO=Xd$lT^Devf;^Ym>-S)t`lJ|(t0xa|lhwbALr@#ej?I)6KZX#ve~WSv^a$&-
z#@AYEeTGR4P`@ww&jZ!?CEZbz8(KWFdS>w(IQCLnI{&jYV2-sou2cK-HLs4lGLzrK
zl=RB#Uxu7W{OC6=6t2p;FawOxxHc|0YV$$p7VVR-VPgamokhyHGg_gxBSH9W#$bCc
zKTA345WCPODXGAXv2wOHk=`D{thAr0lgCdto$ki=Gf)luWS*I^=w3d80||eR(J3O{
zyHfb>NPG&s03M{}dQo$yJg|?~<?`BS*LpSC^r&SoW47_CfT@kK)t7HNV5c#CwsFsN
zFqjBa{;O_8b$#CzGa)c<PFHa%ma1>3()?RNA~HYv`Izz1m9HxF;?d8b%f4qmBzNT)
zO0La@+OXZ-JVGK-!4S0hrBwRi2sdJt#*6U-zRyAk=;_2w<)zLu!A;U{H|#~H^1gZ5
z<)*l<4gnqy<}1`%)m;|$`p6vEp&dTb5{KgVEPPQIz@pCrTX=Y36fdeX$&Y6Qql<Rs
zlU^gzUy!V&aW(!sho=*)T#h0@u%o608TF_G0X{O!h>@5`7o;Bqm+f~-so1E$1=CbZ
zs2%}k2T?B#T63AWM|#Eduzo!^7rK=D>>?82LYDTaE4xl*BiAbyVRPQ$!=y`44+&>P
zJNkbaLCY_usR?2a#&%|055Pq81~^E!HxXxWqocFL<u>UUnk<hW*fpQ_G{_`7Ayw}{
zK7E~tM2Pb=DeaM&p0%@bmAgOCDc&WGmNr13`MpT^{<gH;_bX+YO584@C(x5~l;%g>
zl$FIudzGL-BpctYAB!a!=6r{iC=p~iae2ogtM!l9XX}T(@76*4;7~Bh8iSJUsV!5O
zt2u4mKO_<l%HMF`Y7A!L2=}e>d1QuQ{9Gc^r4+PFqkU$O?8s%INCz0<{}hsu^M(P_
z-a!C!wO>+sytT86^=HSZVdgtQkg_2mq6Yxlr>tD0pUJpA5syZ6U%JoT3N1xAeIY1U
zAZg3${S1TL-liLNRr-!Y)^2QFjBg>9I4~Qg8(re~#0E*oUvCgEYIS52h%s69lQ?yL
zs`YfH%jL@2>zbhB5fr$x({HT>egHZSQ5iKvyqW&#kl*PO>HjT|t}f}_!zOB#&mY*B
zl^4;9W=69&L-ps~L(UX3P$+%?VtG9bkREE2Pd5|y(!bV$HzI)uz^G<>@=SMHJsrys
zF@eJvVvSwe9Qkt)Rm@pTCfSXkpKTC;&*IhQ4Z*QH$V4tKK(OKq`MI$71n6laMSXRb
z$)h?ulet`pCl^%t`e#Q8H8ieK%~|Vl!3h-c_q=^r4~@+c0=!Whmq9UL{+hfxhJ|^N
zgn1|gi=~i*@nd28+o_X<02Z%t(Ue5Zu*J##5g9$HFsB=&ok93s3!FL#s2V>N|F$IQ
zXY!Be++QM0l1`hZQ=i~nz&t1~>-%#5Xdp6mx!l_dBGr#A$D$p%C3~m}8l&I6NHNbb
zEsi^mllWn|!$-JS%;Mv&ZOmyAusBgv`#Oc^aIqnORrgFQ=bk)#oS$mox-pbRIpNzd
z(-!VxXtcN=?z=&@dX-_W-NC$5=G)Jj@;pftN<Q=I9DR*&h@U3`WLo{6ga{z*<t$QL
zL1)TM2IAeqK9-0EE_i%rfMh3lebDs{&NQXN58|HecDbMWS6MbEqGQYI_{_l;vDb-+
z)GA`5_2(*1iIk9R&(gNz*d~dOoiq;Td&AvP{{)9O<JA<zR|X=M_A}8y^|*0AotQgk
z)dpndvnA&PIy26D;IyjK`!z~LTA3t!ZAk^9OKE|I>LNJzthhrE5_Zv5o$6J`8bLhA
z&sE(ymgM+SuI;+Ek8qMJa%QsR9<`e@^q2rEM-QGg1odVUD#drv0ckR071;U;_Nybe
z@AK|>T&*u;^Iv@n%fhHw*4HL!OS;Dr(y+A-`LJ-fn}WaTKo-u{fVcS_J6OEB9fzUD
z^ppw>6S-(}P?%U8rZyP7!^rOlPPiTz3Owl^E+;M-QrS^<Z&fQ#SAF-18@$&DS~b**
zHJ)ZGJKxmxty?0E0BhWaBy_=!hU}1p@@BBMar;5Y?%u^<3%Y4yVV^HeVCB0;@g=m(
z^wi{El(E1kfDWVUPm?dm2P&!VeaZDzg5H79ZO=Cs%kQVSN+fG7#=d5rf9k?9mcalT
z9Kjd=-_>6uWJvwpxQ3;%aDojWPy<RS)0Czxs0HEouvioQj}XjnDQ+F8R|4ax?lATR
zTf*bx{BM-;T%7CNgZj*p=3GGMJs=)dt_WWx$OyPB0F<RCUJ~4$bxo{@@DsaR2@4I^
z^0YQ5$57lOptyjW!d;&;zPt%BHa>cOSxF&blE{U)qN3st>~f<D;&q?E&%s3gvwfre
z9PI|f-Ro62_uF&x`Vd&Vvkk_-t$qYne`&b?tu6_Ni?;IE+fpLfgYUJFGtx6tl7H<R
zR5V402kHaFP8FOrWo0OPzuH+$56$B{l+2_Et8RKo#*2IHbOxd@8(Lu~HbpzRtFQMc
zf?5gP;U#CZOgVxbmZJBha&8#K#2IRODj)$_MsjvK)`8itGH?o?r(Qw}m3P!@af$S)
zq}Oo&JWzp(VM*sD8nvJJ#yy#)?MlcKY|ecL*zmi=!fd?VoDQ11F%fUJ!Z+8@<y9V}
ztLjn-8k(*x0k|?nVzEYuH_lQL#9ycpdgc-2zMiD-d5!mX&zNQTGn7FE_oIao3^;W8
z1)J8|+dC%A{Yl=ppph#rfXiRc{%aud^M{wPBT7hLK^@5%Ehwbo<ZSTSmH@@fzAris
z@I@a>;TQx{@6Q)@<)!n`ME-_v72UbY;c_@ZQX<(J0)_2P2vSpZR`~dqdi%OptmYoB
zKlQ#9D@);QTuVA>obf$gv5&)k-oI0N5@z6R+nW4gkct?NSs?gO4siZse}!`YXkYvD
z$eLDec!<7~>@K>J&*3-1%~<}PNQsgp04A%{_Z_00DD}lKyFC)p{R8jVa`&Bq$$Y3(
zkQucpHGr#kgPudE)oP=595haP6QRy_-xuyA$F^>I;MkXJO`}E07wS^Rtj4zcKo8df
z%?4XvwMSJ0&hE`TG<7W)(AlBWW&Es4t?*Mpadvldc>;PTOi1Fe8t(*v_^txjL(5cO
zuKD@dKWG31)I&ZTjQivwyI^|6-Z+_B48tdcC8MiSed@&#$Nw_5(oH3OLbs%)B`2#Z
z<qhuLgCzP=lqklU{Bt57+NpRPCjrJyW0DN`g-Y=V0{5!drIC=&^r)#oC-j0vEX|w@
zxNd+BvH7nhe!=gwdrXrF@}ErV@pSw;O`kZ4Vxj5neuEw31?4~#m~&R^w!2OqY9)R-
znfVHs_AS-J-zkGnI};1M_3re%3lgOctb7F)-RzPAi{m_{4i`WI4EHVk5xxkGrpXJP
zrsftZ>Y2&3(h`Pl`Em6DuIr==Cr?W|$M3nJ?P(;1>v*!-=&*kO1b`z8<3FQ)vPr~{
zD0euk9x#KFp|FW}IAcA1jxr(%m}qENSV@a@H*m^~mSSS_Lm@k#M`M7e!{^Cy;OgT2
z1f48G{cIv}kl)~`km9M|+{p#9^e<xlb#*kmDryPju5cXm*+H5_emGXTov3OT)G#?1
zjM&7M%33H#2!c_XXJApZ2f}6@GEx5U!H(Z|cb{1TGcpOy*VEy>@1es38{bGsDJ3NL
znjPjTG7tO~nH$ZZjBX?>n6a@j8$Gaoq<D_4Idu1EW>?Tq(Jw>6)zeY;yM<qDEBi)F
zwWqhfT(8*t94}8%q1kY;Ug#v!n$N=pq0-y0ob1gOXPd{64uA>Grv}7FD0ypYP70P*
zUUpUdazm-Gkh$i-L!nk4-S0uzTo(S?kjuqPK9rV6J{;XwICw<<0qbuW%G;3j*EOf*
z{eW~yIbomDs<Mg+)}oFzb$Zsnj-t45&>1;m^y{d6IEvV)f-R-f6$(-;@&lDG41%#h
zuS4CjKXEM2HE@{qp0A2&LeZ7qwUO=&x0At{Me-Vl(jVu64LS_>?xwNh{595~+4r+Q
zH0R@9dBt~szI}AitwM({XGkqK5gyAfH7a!95t@^jB4TAR$KBo0#9TIMc8eSp#qk>h
z9U>Jq&1GEbT-%;KsWdyWq{<!!`<F@3w~8P@DU+I^3P2Xpu{5*S0{Mn<(luE|KdyJ*
zL2uAuatX|`4jyR63hy+D*W0GXqk-;ViA}6#jk`J3T>oSbxH%76<FOI9gNbB;`gYcZ
zUx)dZWJmx))d;t*priMy;~89~2@x@U7_E3Fhh@_tr+B<X$<5RjtH(}aet5-Zws@aT
zkH$&>JC^<z(i+MaT8p#l$$^qCLx1&D^fUt1756n7?v(#()!&abQoG=Tt6M?zdNU)j
zu(CBd++88Z1XY=#8X#WF3>+AJn0-_Itn2@d6#oHRdh~ep{;M8x@HAtvi3vL3PjGxp
zS!Q$BS+~G|3aZVdmFZ8jLx^-2@aze$Z}ak$K((=gl!Km;YFI{a3esl1QDr7PRf5Hc
zjgc0ziS;7bPU@p;18g0n;*V-1Y|JmLQ!MaOCcZou$%^X~jwd*k!rnJM4Xw)hl)Gs;
z;mn^GoVnv%OtuwcG325NZ)Xgg5~>1;k>{`^(3p#V+KRK!+sB_-FN!02@uR<GwRhmp
z0xJ{o78^eCe;{6X#iGW0-~Q%ms0-wFU?;^HtajM+Zp<!{0wCNgM<nBIKC<LDYUOF=
z1d@}~LiQ($BHs*|9^XAam+J#c%mh=V-4=~;VL>r1W0|SpE`GfF+7j}eGry0=M`<sa
zpGBf=x(+4=SOiV+OZr#2h?^~G3OaH*faA5gNIJtiSM7$Lb*<orz^gHnN7()pd`RH^
zlsk34!{hmGI+;@E_hi+G)D|X-riqDYu?(^#IEsjA9A#Tdejk{QVA?iqT>W-PdwXDD
zU_n6vW~q&}-A^kdRw?CM<^V41oy6IOo0yR@n^UdU-JxKeNul?L)22z%jSlYz;OJvn
zkA6ii;f-$5lqts2q$SB@7d>$iB8SI-*$o$TFb+M->yJPmvd*@SECVso-hc)~t2#<O
z#bl^rq|mqgt{(R1Zljq2>^UihxNrXTQBmdPnQd(WNY&=eyvrqt2#*p6$#^8J!A>u(
z*{zPZ8;KgAj2S}8H0g$4`SS5P@OS7R3O6*Uq>Ltp+;OzN-6_=}WzmcQzxHr>hWFnC
zGf%w?o{!SYBC;_m?_;Sm)YifjwhycE%mhd<X@#vsB`HI5_jXc9suLw9x;wq!K~d1z
zP&YfG)i$Wc8YF5c8JeaVlLwEX$6<D;^3FBl3OpElF?q^_?5Ga$Z`x+S;7R-6!{5};
zEI*)WP<3qLtU}J(jBf}+B)m?Le5t@jvC)4gNkKdnBJ&<bmChcG(X$Dd$gjLS>a{pL
zq9=HWrA&hu#8zH=Q#EBL(Y7jN>(Iz8t?+)vcBf+))KPR3`m;QL&e*QKsRW}x6(M*5
z4}G>=+RZPWxX!PX<yHqkh{Q|Nxv<0a!Y;Rg@8|Pv=%U0%m4Q%oaotzOB~jFaEx4aN
zDL!io=#oCxlhsK*pkvsXVVwlAw|L=m&;1IsP2BBnYqJhrAGm(Pu7bcRf{!E7`4c2N
zBYbQ<HEXuZuKG}Hto$9TQZ#NH(iSeS<Ns#~4w2@pC?x?K$q)^1t-+OE(8)U94=3-j
zn;+L8)s46NJ(PNa*UKusA(1p;y26LQM*KFq#@fFG&OS*ys7=w5P?&vcqOr2$F8jfO
zWquw4W2Bs;E7AQd+mO_dB4x4bwT^RGB{bskTQ>vX^LfyXnPk1i_xNmLzoP$INWmrV
za{~G35*jFJ=IiC8Dr#<FU|+#SU*iR-46s-Q504UJ?guZkmZn6V75RDqR<>-B{0*eB
zbF>9`xfw02ZTM@}J`rmZj4Ti65I1A9#{tYo5<#!~2TJ#-_nYVSTi7O;iL52DOjmTK
zfg7wK>zh7O&}2;9QAhov)3-WDdk*7~yG&kFALtPetS`8U9gadJmxE;VKvy#2Rm}em
z7xBJXZYKNOdCa~fYj6Ab(p$o;S{Y!0jOtZ79!a>>N{?i|$9n<}TM8Jf$fN!bRcF~2
z$JRhu+}+(ZNN@=5?h@QXaCa@--QC@S2MF$(;4Z--KnU(Ib>DgB!}K5M?&?$3NA_Mz
zh|#=zZlJRskz3*3;MK{0yQx#s(|@*?-@752a%3OH!UPrT=e1nDZrAzQJif{IoAZqA
zx7+KAWfkDBfe^6!n}{n+Ysd06Bg}mh%f|QK@ZIlP)Yv~cqK5%_wLe*t5R0p&iA!j1
z9q+Le3>@*aTt`iSM;fh1_XTi5`umAIPVr-oC+qHSf9jqk{jVR7wt*)EI)$Tna-Dg#
zEFzN{7GZO)p|dwbC;7@8NXzCjW|pZ<0|}Qq7{CsJ)qWpOf34?kY?|8k2w#f-BpOYB
zLoF!M9$X_gEYL<{V?y?cVc54&4k9VJh`I#n9sbItwcO28o$Wr^4~a-%IGLlrcQouq
z2z=5aO&j8__&7P>OkuDUTz6z-u+q~&h1aLkg4oJ~$6EGEtV{hx*cvrlL)6X#4Bi%k
zezvw+2x9V2`mqu}tIZ!}BT!MClDR!%SgBa+CwU`%|8gfiZ1;5e`*{97OA^wL{6qIM
zrizmP!-w#9)<|B70xwQ=Q*0|+2uC<+t(wdZnFAi?H7~Eh;A}V1z{c0%BT9HW$wM(~
zb*U_?gNCta#AFGKor*HGuP$Q*9i!<UwSa)n(On&g4lyKccR3kY)fi16+32;j1Z0$y
zQ!~nVZ1d37J7Mi@<2Qub90JrrL((y9^-H2!93!XZ(RvmNBN5bS{h1PG_JcbIaCTz|
ztK?i_CaQ=z^138;DMqf6(1<fF3QhhHYOnPi{%&2rC-3!wb_o~h@o{?HNZKUU27Z2<
zLhxkQ!X!)R!q)1J4Ufvx*1_^1lk{cjgFDgl39U$LW7hjoDEx6GrHWD*JH;*>ZrWE%
zO#|N2QOI9idU|MRVL>*54+dgU!o~vQJ|i_qL36fi5#G_D+wE;CswCp>e5=*=DE_~S
z_<Cn?a}OK)%3`nuV=%=^T=x%F;QYjP0<CQpqg2j+E%Gj2Q17d&vvY`o0C~D%>g*Rj
zEO)^AKe}@vvWAMBrZi!@$WJ3N4D%<&2;|!5O1r-)I<u{B$J5UV`Q3icm)9x?Kv2v0
zsi~1!Ap_q=sM`YcJl>{#6%yf=A&qEX!!-(I%J$=om_T;5Tw(T}uRbL};)RN|<Sva;
zURTmx*IE|#X>dr^SX)6N?S`&+gNgPgm+A^c<q0^wEzG_Oe6PK@IM?rK0&dsbiFxka
zdD1LK;5zko^&tCCn11jyuzlj1#YXw5dT708i>bzrkU3sWpwkfna&1~a<5RHU+JCZ0
zlxuEqb_yeyx8yfa?Mwi=pls6Ls<@GP3%Z^o>z%eldIG+_AEqJe$<GZ4W%4)q?KP!R
z(;G-~$1m12O5t33z(^039paRY!nEuCsgb=>A8;Sk%HdiQN135j^yS^gXh?uBaP&7+
zC0bI|*6R1zE?&DU`lQq?0IAbn1z0a{be4Bq3*g`0+<r4$;;sNX+48Jd`uCKTQ*+|N
z8V%ULGyF^`8XA2HrGvv+#^ySu1?SEU5!qclTu&#%x1@0vx|gCy3w>s~|My4evYBf^
zoT$854Y&iN>EE_MBGu#iFUP+rb`MumRwD4&?sEC2Ux;Kx8n&YF82u$@XIhnNb2kLL
zUZKSD`B&C~@d+ITIdzPhRJ7}12E$Lnau%fF-Ih*vK4zK?2{n0PNo{RBP~FGDLxTSr
zEER(s3v|DKIn9y~SiX0cD0huvIz4Lncy?~Qwz;ONmYGIE`rF1LG8U@i03~mL!?ttz
zW9?-4*9d%m1olsfmxY`k#r6%*s!VIBSnRmn-OY&;fW)EP=yX`u8szz6=#APq6ftaE
zNj1umn=CYZf^f-~!{(ff^Ui-av>d4~FSixio<B)f8d~Z&1mgLQZ&HWaN2o7qiAhBj
zBDZ40+v%3--vWAsw~WaCI@I5NMkys7-~EV&TB}1}Tsi87tUOl5JQWG<eK&C}%!D#+
z&matO0%>AjEJwbGxih`D{DCD_Ia_V0Eh(a*9uW}~E-5TF-5Thj-Ml{H(#*hZg8A3q
z42#Khluie~b~5SG{N$y;iLH(yW27brYm#<+Ush95QSr;|E<qkXEB$WdYNHfE1fRyc
ztfZ<Q%owzxyJz+tyYHq?=KOHkPRjt<M(_VlfYFb}z1{83Hrjx;A(=PZIMOuHB)1@1
z^a}gfFL-L$fe=5T+&)UYs_`O$>~c|b>>Fl1QKM}zP*PD2>MxmC$Kt%~)tnt|91wdF
zhwKN2d6JA_b)51}6WQENT`C10rs)q;xm!LOb``n?*eZZ0vJQTrU=ytNDTK9yjs##G
ztB`QNVQglgAFuYnt#u(yORP{sdwV9ck}k?+F0~&5vLiS8u>Js9&o+h=d%$le=a@PT
zHnP#ba&V~B`YbYz!s3|7P<EkpIjRMCff@Vr?N>e7W|Nz;oP0wBsf4y%aDh2<hRgQw
zy&0?5p_JMdE^VR%F0*07albJP3lY%eqXwR3g=WMBC0CcTR^KH?V64?#eaP}2$(TA7
zX$Ww3RWmCLNh)TYrv)|3U9kJi_->w_?u{6HecrBU^2}Duibx)idV)*mkOmd7J_#q!
z>&aaFjg5`&=+R0a>uvSBe5@@M+)btZGQZW-W%YD+@rnF-7};wKz;A!xz62tf&JBQ@
zyHRrB<?<S2;r94>`<?W4f;oHyUgB`pOrD<vr=`>fYRqTK0-GxNEakP1sJPBHD9J<`
zpvNEyau1)8QBZ`Ot}=@zr?5CE4=^yYw=`#zy=QCrf~(1u=t0Ml!s}Yy5YA0<OC0ZL
zEY@<)F5EWe1n!~`3mpCeZ1d*l7F2(U|DMA6ywH4ypunn#c;7Osjg>V#cTV{O4q9kI
zh$ttMV~Qm+eTk>d11&zEVou#)b7NLrBRK^Ndw0*yR&33jA}*XgphW`uTB{a%t{UH7
z*!g}QBosc~oa~JzkpIowY;CGzrk&Z^fLL5EDQ<Yth8zzSB0<Lj<jNAc=F6|;s&ZDI
zCCl+FXr&WB;3HkRG;vrpE0f(0(vqPwL`qMrQFF0>tNzb-O&A(cdJvgG7MK%`Q2*-}
z>}f$m?nv4Cuk-C0&hN}Z1M_p^Onm&b-&^Wi%NpxDi(4uK!}<vMt^3Y%qd>(Hrlx)A
z4Zrh5j;@a0`Wndp{^0{3K1yS;pQed>R5}v&=*tI2^YP`F#ba-qNwHGDWV9tId~u*A
zsKEAqf7p5b@!gT_UpS%>rpY7UQUUDZnhgc%uW5GFNxzV}*bW6yJ?0%i<a2<!U<yXS
zSQy1@X3$i&O2=G-8(=N@y(31jD{2xr=T!~S81;iY3t53F=ihC=lR2Fe7#&Wxd7b=@
zs$peemw9PEQ0B>MnE$6WR+g5BLJkU9tZs;54A(kIcBN`x!R#79==;rchv1BjfklR!
zlPNBctV0kK5tSaE{Q>Ko?9@_#rX0H>H8;CHD$UqT?sQ`Fh&<r!<pjDR*jVs~i`6R>
z+d21Akt9$)^3<#&p&B1MPp_C8TY6}TNiWpZB=^Wodz|*2eQ_41$i!NXeZm)_)GeZv
z?7B3ywJ&gPzCij&0RbVH2o7OdB8UM|Q4qJ>QQhJ`yaZg<Z8UY2)Kd_;xy?0QMNisJ
zAi2(An-4(gB9OGtVp3oT?9FxxefVvRmVIiX`~@&W`PS1&I6u<Ay1D)fv?;JL@X^q4
z^{r21&OUIrwzM2Cxx66a+C`$8+3aEE>u$DsyqAp{Cq9nFfc)G1?Rk-2ki7a?CM}c#
z<p`I(ItT-iRWhQUVniUsVwq7X_eQq8p@~8W4^`c*pB-)SrZ0p>{@t`%c?cF}kheIL
z7hs3x>Onmx7DHN-m+iNwWTa%JqpP;SPhM?sdtMz~Rz6=}5$J7$FtG3YTUg30!pR6H
zCe8*0f4WYwz$!~(vPmYH$N_r8%z{}komWqZI5K(S9gG52Tqcr^A4Rh?Ku3*~l7`H$
znM_FbrC)amkRJ>RTTcEI=qz-7xlRhI0E|U|(tQzShg19Ugi9`MslYVdK~`cJ?wK~K
zmBA4mL+L<skYt9l4VxPhyzBbmXesRry;R_PQc*o!RT$idPgZJzT-RP0lAU=N@D26h
zO@cE=5N2{$4pNd*pmaVGt-p`<LvPx#1*Af^@8+C$D=Z%eHZ`;mDT|U00?89SP!^xj
zpz|F*z{*71R3_CM1zW<Ol*J#roaO2V>fODI1V@|C8WF-R;)D^=5c=R8npthZ`MsDR
z3tF)p4@B5|ZS~`%XKwr3%X$XLI1=)8yI8uVCdOZ~2>(Pp@k*~WZzU-#sa_e;nqNFt
zi&&b-)r-M@GMJ3~XDw%jNhP-fD2H2s!m-z(xRl^bApJ}*0>_pk^NVP_qH(%#0VnV)
zVnt6a)F4w4%7U@5&=3Ee<M>vg>r<QRiT@1YuFWZ+&@*Wf?=Qu_V+k1RIhCBDT_}Xe
zlAPhSs4I#E@lHWjBS=$|=-&|OHjzXL?b!okOAB-Tr2l;h&!B~quzeu$tBi~Dd~vJq
zuMMBadGJdZ3HAV|Yh|xB0w0p0?OKK#W8Vp6pkX#iR4C|C#LXm?E`B51*8N@ToFf?v
zA>D*`c2>_m98^p{E{g8P9TZiNQdTD@#l%>3<v2m+_U9+wM;D~is)L%dNw*WY{&(b=
z;Fv4YWp3~2YwPOot_!FceYSBc{81NG-o=0%Br(XV)3ny&fWB@gwomAAiWDx%HUy1V
zomZpc^u?^bAf~mkJeZ)$0To5W0aKUT-&NoqBC;`{Z5x&*08hOFU2dwz{sIi%4{R=r
z0FVEkRwb~UxxX_k%^OGsM%+yh@9Ch->R?=0mM{$pK{9a%$^Zu94qCP<GOk*tJ_eQO
z3@bs2Y$CJQDb=NeQ#kDS7^_LZk$fC<%no1Z0oS(`-^YXAo*r+vy$ulHlm78<?z*aO
zZcaMJuC*<c_AlTDl`VB?v3~?As>y~Zd%fc^Y`9$Ni9b8&2jr=7xj*_SscXbnbDg{%
z_whADdN{J4XW;e}UKOex%Bnd$*U3_1b#*1jHMWGb_yf~KOcm`gH>8G5WcjK9Rav2j
zk%oooS8>U}2%&jNbh*u$Ifj?tj1dcGbUT^jA0g4`>cK{IA^h<DjhLiLv?LUhuN#ZR
zE{K|X*?kx8ZFjvKi|1BY%Ra3EA4r_)5R{hHjPzW#0M%y=lIp;i5ZSI=xI!jDNFoQ@
zX|C^0u5PzCs3>tao{e0h0%Q{~Au6ikWDCipLL-lH6+Hq~$_45hE3JO>J@d<C-OmTt
zV9d?YfZzX%<NHhWf~bHYW4hE3fefdlvwUSu797c~Q(Ew0L$vTbEd_sfWqlU~H9H3a
z4U_dUfTi4yOk;toW5Yx6Z-|z;<g3ZfG4Adj3MV;Z|LV#qX2K<-JC?hw-~5$OM*jQt
z-d+v8XPqurMQuCVM|w^n+9+AUvAbv&B}%33sBj4Of(T>8CQ530Cd-9_hbL7rPo~mP
zmL<iRFeCJ?nh|$$6<e+A@e{pgNlWct#+@Fcz|J+e@_0X(uaWuAn#bSbO6~k-#9QA+
zekxb2gC+LXQd}PR<op&tQf-!tje$sb7mlP?4r)vr38g-jss#$UKuJr-=lW5pj}?u`
z!pp5g$D*aGeQgMtY)G$)%Mn@FSEN>wl1q1Cfy=~->&a~CiVlxA=SzCn#4mV#`@0ne
zK%Zu`q?Xgr)6>$@(F~17c&+9@QhecuuPWmj!}qcJEoElnq$8=RXdtd`;O2$URMN~v
z7vL}6T+zJmOmcR9Zf9RxS4T1Ow7P3@$$&x%l@gte|81@5V(0*DPQJffK6VGqM|jj5
z3xA^$+DI#+i{3yOA#stCWQWlaBxFznR0aN7V<)RE@_PP5idRwy{uEsUt!aysfy{i>
z633F)m0Q<;s+pP*BHihXq=r(>M>ML;pQ<Xw$LJ`%K~v~jS%n;RvF?9);`q%mZ)0}(
zb2<?Y77-p9Auef5-)rXtxCz``gaNZMrRpw7LLiKThYR}4@{HBNwF(aD*`67I+}X)7
z#*S-v-nR$)w=JcP7TQN?5WJ(^OuSW0al>uBq_v(Wlzt@ScKcjBMigv|bhz&v*Q5Gx
zreYV*v<KQ!Hc>Pnb9o$N%W&!bDy(b}HO7*FMuW4epOhkjmmP#5fk9aSi<Pdyg9N<1
zycRH67->*#SO{vUaU#o#yGno*nK?<W!I+=4k(^SD;!8cU%G>2G2wE~+JfXZ(cF4CH
zBa-=Kh)9{mZ=ocVva=C?Qdr)R->(*R;$6nAbd5JvHKl+Co#u<OI*8TwX<RL+FonCm
zRL*ZL%9^d)3^_a9Zq5IAp$yCb1w5Rb57x+DmS=fA3NpLz!;~k4ya2G#G?8qM@_a}Y
zGCF!$e1zwlN##+fDW<jG5YGuIEZb={<Ieo4=A$t`D3ezyKnx@m7>mlo^4Jw!W{uBM
z9>>iSa5+-mPQr#3WoW1^FS&|GiM;PGSK!~*T8$^T&GJgQX8cB=x>Qz#cK?LQDu(z;
zi<G*UX*6Z62(nYCa|pP^A<8gPU0vuY1vDJ>Ek2iKwC0nXh|0d4&G3ux|0r)Rq7j;i
zcxd{q&&qv>o`J-?NLgjShh9GWLoWPZO@c^wkLSs;M`(NWr6-Lr-i6bbeRf2ehwvI>
z=K9HxbjTqD#FjEP%uw^hnY79UL)3NCH?dQdR&_s>5M0=olVJg8Y%wuPO4H&_o~Ceo
zVG(Nf4=d}iTvOI8YAwA%T`>w1V@x&Hg+sio>)vmd9}|As8ER(jPbb25n8JJZs&cVG
z#d#PNT4w5e^1<@&uDUDuSK44HrEX-Qa1Y`=O`j066K$B&guqD7QRG=?SdPzODh)h8
z5hE4G-%JsM4|KrHhy1xGG|H^jLS<m*&w0eJEx)I`tGf<pV&C;@7o9iiJ;)9!sGV5$
zSCn_2*<&q<U!ij6ovmDf6#cw8{%ewCpP(ZWTcRJC1WvDZU(Zm}B2!aBbew5PO&CTj
z)Aq~gCE<L2U{rLMH^m~5%(i@pYEP{N1LU3Q@xoaTt_fh@+{x_q<%Lb3n@sJKJ9MIH
zWLQ1K9}dW3D$X{SJH#ktm+$PKQ^?wyN9D^+q8573|AqO|^LS{}cx8_@wxm94W(HXQ
zw|lbH*1}FdC1;GZnx61DSQbpi@iU#%C2q>msBLfR!1nuvi1*`PY>Il1Z-44P%A!b-
z*0LS#%k2Sy?ZKk*fY(;=Bu4RS_;dywY`m5R>@vL5S>6@&H>^R<o_6~Kqctf}l<*6z
zWc9f@oA7kjH?^uqE!qxi#3Zq;dXLOcVe~LAvfL{A__RHqqpJ}jo8S~0Git?Tz7j*-
zSw>p&Gx%?{$}7kYA94{67l#!?xmJ3xo?z(zbPL?|AE1ttg=jg$w33v_^Yi8f_;@II
z>c%$zbn0X@$KQvQzbT7G`PDx6Xp3gl?SAne0ATC=;dXV=T;ErJQe7gbMn1K9FQVNg
z6v6^nxKo{~A|y$rDoxL49gbEL-(4-_{KbMi5{P^KjQ_K04APQ7enkDVytwXmvaJ4B
zYe2%?+DO{Wt&)5a8zyHG^R}y~Dy+5JRYzKD^p8J2SwN4wiMKz;%#IUMktjqGi>O9+
zKL#2_<OskqSu%Y9wd+#7<^z%me4od%y~B3;+WH|o8BbclAok{#Mg}Hdm&3%$Fb$Db
z9T?G2U+msFp8j!94%q<iXGh?p*BY+?iR4Gno;h6vAD`Q4$>apHkE@f{RnBtzSRk^+
zY}TH}CN#8k)r^STSS*Xh<>voK+^Vap<|Ec}_DHW>O~F!IXm&#lmvhum(iQtGZy=c{
z;2+iS;?D@;%BG^1wx%OK3O#uF8#}Vhu<Kqgn2>9?)25c<EV##IdDL$K!$8=GM>56Q
zLYK)wdWbFPj_+cDb7l43k29olCB_4@P%kBByL>}mu*@0h%8m%KwR*oyj)Tq$;ee+X
z5CU-VH-B|gq?bczYIsc(o@@AP?5|HMFi>?A*G`F)r4A*!&5TAr;vo-qK75Q>4wFSj
z*DIk?SK!I79L|*+>&``+2Uxf|8hUJvT)}d^gO3>s+{iqMOronBIzocVuW#0;y5FCN
z(%_j7yw6ajO)s@}DrB&X?J!}SSE}=y>Np!+KxZ}NxA}wX35^g^lzq69mFe`kwyXH@
zI(^jw{8QUH&f0*rm0_GV-BN{+Dqpv^+@3s3a#gNb?Qf3x6dz`b0T>ZtB;-LJhp#Ye
z7&>05J#i)zR3(Mtxf8$nf>k+pTQ&t)WU$fuqH?<i%&^4l3#rBx7M}m^SX;QJGg}o%
zkle$r-eiY{v{pw%IT%Qrx+7=KQh5$52;gahrMhU7n5Z!da^4nVRn5=y3$wf&?8N(F
zY&2|9@ySOY1}CML1bZc#OD2Qf9ZRWra>20zle?j@YHGq<QEPgTp~K$`Tnvw@M0rUM
zT?Y*k<1Jdqbh1Z&WI}B&cjhcoP3g!Z22{V8eKnPm(Dww(s0d9^Aqo^6HP!x77rXV|
zEVo)^u;WE!OHa{M&9Zi-#hqjv3cu=dn@tX$>?zzl_TH|q*8?9FaSXngAMRsgh8i5A
zwrFZDE^r41$SEg;!_(|2gj&I!M}1|3)5W0@r9*4-aYBwoE$)^crI+n{YRM852gX`v
z*=b5TeK(7Ojy~p#_}<-&r;H|iJTqK0%Ez**KrWGkWR0)d`zjnvN0=F>*!cADJ1j#R
z{HuZ&4eR8%A`nL_(x6W&<yM7<Pw1N~2c;hASP^LnY3W0X?C!{_mWB-uw^wDz0w(L}
z*rykPZ4fO+oGA=YSAER>7e#v&z%Szt<rZ#B9?vu>f!Qct&l@!K8!?PehVEAMscES=
zy4Zb1>`^^pQF@x&sn$nl>~4&LI8D5)V(hr(u2g=}L0K|xMuW-!b)<Z5uO6G}LEBI}
z@YQlVSA1n~vZ&np3{#sAhVow_bo0|tU!bVQC<NBG^4Ot2kKVF}IAc-(QY03AMwNmN
zzdFhG?0P2lpF*aebhTf_h`u`Lt#2<&l=hU-a4$@wq~Zl$mWnPMw+XYaYF$6Kk#+dp
zuHEK*|GUxs4)L5Y!`aS8BQwgyT{tdamRfkw(2jFvEgN8kN(yZ!8MBA}6iH%^W+5q7
z?!PO^!zxCDY_?aR!{PY|#A|qNLH1pZgON_hK{8>csd3S8Hw$9lAzZZ5^G~*Pw1Q&t
zFd?sjC~9lgCq&r<e{7PwJZfp9vy!UQA+2wP)_(~Je|I;P%FXF{z=6bd{r0fCg!{wO
zVN<p=;H0>IZG(Wka6qx|N8az$xeovJcw^u)nD2~C$j>RE%so&Fs1}9Azk~MCtBx*J
zIybhf8kgfEK5-lz{jcD>FrSz#jA!=hH%eLAqLRxOqAw|p%T=Tb|Kt881Wa=F_}o48
zZ|X0jf4iubzx=+0@fc%#w*u2d(uYhbXq*!6DWktVwvf0_9*HKA40~UBxHwAw`nJEn
z_K8qf+6?<4b-Ei!nGjea<8SahYEwF0u06A5QG#Rl{);%@5o)1%J9V~Ea!r(B_bK3d
zTIBt5{^<T&fk>y!xQZ>(jyrN<&1`re!x`ik;SJ=c`wQ{Z@XMBe#n<tgzq#U=^SaEh
ziSg|D+#bjA_dB2ivY)l5nWAplw0<9!Zg*cIp&`UN_80#KcGa7ZDoewQjpW2UFto7-
zw?(@veP)+96OI*ppya;R|JD0Z4tQTAhZ&yRr|Giyw82G-pZzPLzxsQa0G@*8cVEMO
zA#pS&Z};->Rq_8?*qROTfp`(3L#m27JBtEfs{TbYx_P__k3)u%VY(X#lGlucAFuO7
zp~>mq`#qn5UN@ijZ{|>8KeD(qz?tFFVv)5)UAI2iyhlPaPxx!l>*w(T4q=ABBo-)9
zdF^4tG>L4Kinc{PM4CoPKYxoCC$BB_AP&3we9vF$s-&CVR@X9t>V`SBD&YNI*4Wcl
zO+`;RG`Oa!m8`A#8TCL&u(h(s{IUGi(!qeZ-R%vS8aI#7Rfg6HTKKs%GLdX;%7Y-K
zLLxQRf`s=H*%ww$HFhegC>gn+O=#yBg&?*aLNxN}LZ{t>BeFJY%^PO)VA1{VLeTkj
z6&wX*+ua^J3#PH{yCfgM@!L{9@VP;!R0`s;Z(@S!17pZcU^YMUOuSMKq4FF1*kyMO
z9VhtyviX)!HW~gUoRgFC7>fMZ{amxfN=Br8b#EXb9VAr5e0ErKJv%Ofe!e?f>q&Eo
z()w-Dfrx1uKY;NsuVJrW6IJSkz4A~;gLFh8br@4OEYX}Ha6bSDh<Drq3a^AMkkmsw
z5hKSL3Yx!cZgKJ!0RHbUZ*j=<Xi&Q!0KgBtx}lT}mgJMl5L$A(4EG;wh}Jrvy!z#X
zk6PnE&m$CdO0$B^p-B=~A&+=TerhTPIX*Us2qil{Hyfo<w!5K{*qXndw9#L~zpY#C
zvL1YDwf%hiO=n9wXTk*2{@*xc0p0&g4a7h#F)2X9XL5np7eyVT5)OENWQ)CZw?h2v
zG8DQ)^8g>`%AQY_*J5_5NhZhbWGcsEb8OIaF!Q+nvA4(9=V+oTL}2N*6}0$vaA-<d
z|Gs+g7e<T}yTAXEzQP<i5hE*0Ev0S_Q!kDDnPGmRFLiTNgbzgxtK>CrsuglL*52R0
zt6NY=R3@aoMh+LrTmb-uC}W|q!O39!|8VJ!m7*U!9dP$((YBNcX(7a))DEm<x<v^J
z8J^L|z=vO)D2n4FMojvCAIdR<bLm$)JoZXySQTrFD#9hpa$hxHemQQH^#iW|q1f`g
zeh;kCK0V#d#<@m#)}Ef%PRCt0b86z4nd36-;WS}*p{dkc0F1ndp~dBmDi9TvX%gf8
z)y<BQUOHAzs)4=V(93y>2O+k^sUky&;_W!$2$#UaQ#H1en%e66`l`CR8j$!-O)V01
zg+Ay;xUzFX$j+?mlSyDK)o8H_n9##N_asri85PDk4rTw{NkWqvdL*YK`^1er7UHfh
z{kS3Zs()7i^gR8(U0z0qQ~tZUvW&Rp@>h{m_w8hl(&5g~Po(KJXgIMVR&r+v_L2#o
z81A-u){KyFywMbrInSoDyD@>GP6Wmu(#PH2&|kbbr8)WoF@{=oCB;VpdV1cEAA1W6
z*=VQciL6OyaN%5_mUv8bKC#7_+)L1q8B%!|nU3#l0eAa+c(xuiqv7!0gOM3su#xgk
zS8iym!cL^mG2YvJFmSvGAh7_R24}(Z<95UI@gs4<mY=Wt{ce)|D{}TEN3kEN;sT9#
zdBApHtc0k?0-Ua|2FutN2KnFceT)oMrS@hAXM1)QrHmgZIHB>#r0;szb#cANF?-?<
zJLTEj0ICzCV;5Q^7?@%4N|MZU!kl%!j^~Ks@_ig_?l$gaExaQvk!*y}3ME-ZaU?f}
z3^#7!5CsIajoRE=#TL1b)OyB05TJuk64d@9gOigXi~;`cSBk|I_cjtNlcSu+UB58Y
zEZReZUVlE3!mAi|T;FVd&j2%?+cWceNw5uol#tZyD$Yl15)o3y*ndaAEo`z6qcDCQ
z$A)ReT7LO@AUM@a_*C(a3l(vN6V&QZVM9Qz3j#Y`;n6Q#5ZWD`zCO=T1Y7H~1}cPa
zlYBGLdF``?mvKKC>qndrm01Ti&|lcsa%vP@)XgL?eIpDu=a}nypN#e}61|3?vF1$x
zpOJlTX1(4JO-A7Td^j%U$J0(Qylhry?C&Y(Jm#XA;6fu*k7Nw!!$z;n2v27nTc@_v
z^fFI3WhF~ep?K0K<2+Ho+6a-BloS42V*A2ba8q`9+t1-V*Hb3sW(~8!!DE74FTss;
z%>_0#b6h;P-JEC*af4}-1AYa~;jw<;BOS4c(IuuB#6Qlt&ta580`%(X$r?mABl8A$
zwQeZvBMBZ<+-}*lRrco@2-pe!GQSLZZvPEGgXe^#+e^!zlQ4Joh8DDwun0uJM=rQ5
zXHZ!1G$D@SGHul1r|a82#S=yCaehQaW?ytU25^lX3-M5uAy;9`(%h%*Ns$3xJdYNh
z#m}kxmSq)m^drNPQYi9*LRTh7SQr@^l=8?Mqv%S48<%(>><Quq@y^+`;VZ#O63?Y3
z^#|$-i25-D)gLBb1YqDv&0nnO)mQXb3H_f77os0u1FL&{j%H)-jq_1RRyXF=b7786
zNu>~b-o<vgDKyh$U-=!*;35GnluInUBZ%0pS8Sx5j24^}R?!f6^BW-DqgY~K=dDrq
zKoXtTGvH`|wp%EsQ{Zsg9)#sGvy929#3vBtdR0U2{>H+9O~teQ#lv+;ql}zn9yy2R
z22Lp#A*tz*W4>CeFUV#7IlJ_O3eJfK&-lFPs2ul6(zSk=ycS^}u>#xe_YVg@ekFV)
z-Dqf_{J2E?Q;o=mcZB>1y$DTAd2curwx7WW$_S5<y}F;+=3#`k#1cPKlfzTjmhqDp
zrO4$yv>I0zg|e2gE~)xkr@N8Na`3QF_vCZ>E$cbx(VZQ5WNAHs%^GK~9oln+stv@1
zlGR0H+Z@tWq7$}O-32a5_YbXXqqaQw7ZUxuTT1zCl_E|^ph~+L3IDsIN|fV_V~$L{
zRhE-ct{L^40sn3eevp$Jt$Z(U=I11dCeR{Gat>YF5!Dh(PR~e6ra_mb#pT9>hO^%`
ztqex(@36Q%57C%Bs<Znhi|ovmxK|`Qvwhw}r)th5?Cme`4ul4efN1WU8`e|(9y5#g
z>P1`7`_ka7@t%BlI1wEC0M-TAdT#P%G0_~{J~v|(DAML<ZxC`$i}s_iL0f%?7+Lm_
zt&gxe@Ul*VtgTn9)>B9X$1L8-1O%qq;($PXsrzufYsR-tDC%_2g2X%;g~fQ=mqHJ|
z838DL0x1ljN#a_UrT9LaLk{7mj-Y1$yil#)s+LmRF;K{>7SaxpN=euooP=#xq+Lg6
zhC8%|3c5lzn%^;0VSi*#xMIQ6=8rrC^M2~AWamOiW$9HB4BOHKdsD|<$%$&3Qmh(Z
zrTJ-+5x?0pX!eGpv==4)S(1#sEE%p*Mzww4kB$ep;X&7?|HV$q(YG!CF8HyWVlQ$9
zYJ1#1UKR59p&Heo(CxsrQd^Kv8$T5~lSqQ96<G2URZn|9(goe)+m4gPtoR)EKe(&s
zslj*HQ3FLW*Ne9QId|JlUpqld^|`Rq&B7xS@6WUCg=Hh{OhLZO8zn`2;!Hdh+Mb3g
zAW~KkO5JP`57Al8RO~<fyBMOkmZ5ohH*u3i8s4GlXg(6cB08ToE$yy;bs}_mBb}0R
zzBH&DIS-z*#dqL`b9$oxWz-Y!yz_J1UV-?!ctde^%Uh5=oo_6(1U5H3oX#58(OgE2
zxeqp*tok=D{Xpdr7xPw2=)-Y6T{ItXnL_s(^d|A*sQN+q!-bCL!^~SZs9{|1a^74L
zickr<ojVmcx^vXoP|qHb7c95C`j@zfkMF=5PDl1GFY8WBH9k~^{`ZzFi0tmr=fj+M
zt_B+<Cd<93LTcp~>pB7ZbMT%^5j)3}q`4s(Y$DEZ5RoW-J-N>KkER2hJ^+Wqtyx9!
zPk-b&8lJLw{(95Pv{Agox(&w>;!)u5^sD2M5D6mDIm|vPDST(KWsG#@)!NMoM87NY
z6A9q1>6;4Q$^+3a`~hC?`K0<he+Au$P`|xhymmK(;maBhIQsmx>dPx9?2~;H%cS85
zx&W(&YlSL8-5;<6nnTCF#Q>>cad3J^4<wcdt(jZFeqtd_4DFX#gOaPG`W4@k)&Eqq
z-2tzM=j%B#yl&d%FghiA(T+UN4?(uhnc|rQ#6%S6h=1a~TTaVIz}2+Zltv3A+l-Ic
zB<fB#g{HR}t6Q%}8~*UT5O2KhyL5#H%j|ydw<mq3E~Ka~cgHf}z(*+zItgilhL55j
ztbg~MktTgs)VwWnILuBqz&>V@g)kprs4c%|D-o67*ouYo%FGr8hi~2hPYRa-7p23G
zr8LRi0uexLq%Vm3yevvsQELKlop_)pL+tsquCGr~0MD&tF9dBK-cc5XCRI&Dw6I^_
z*38PgG%7EXZ3|ToK+ED6_6wy1_r!U+eYViQm{>gJaoBA1eJ|RxWJTNPx_=nXS+DS{
zw_;L3b#8$vaTAlQ&4nx^VwZEqocWdeDP+h887W4tnn<SucGx0Ke0P(jrPP~FiQY%J
z@5zQiqsw0%Wprf%mg{Fr;teX=OG1~BL7v!=pI|CWMIj3(pyt9MXcn^;IRqWCd!o@d
zZ@kjn)XD~`{1E;0B8^UfM6c3k+}+c!31c5-CLmK5|NT;n*WvHUFN=@(f(aWcH&>h5
zf}$meU1SQCn^W5~()PEEiPvu3cmJjX)G}?g```EPDZocvvGJ~@aOal{FD1Hs5Wj53
z*5+*2vku+^oHgZ*yfvI{G6gjB!yS>}zeRGXKAaa)CkJj%lh=;G@BQhwU04~R<`d>;
z9$Ow9-XhxZ9eJ2l&I;lib(uIcavrvS_5E0!3wj@azje>A`{qQg{nR027E|rz{~MIb
zqEP}ZWT)hwN`tX0on>|M&z6fz_T&K!Jf^bA^XJP0;F(e|72iN*5-koA6?=UTCHuH;
z*)1pw+Uk7YKL`q{S*|zib>2ISI`^-ml?%En%wdRc`0G`paB8iz^Ba#!-kp>h**76D
za%gIxx*SWLBNQOYXsFGmoF9+iTVc6LrHu;7b898BtGd~=Y`a2G`Vb1~Sopo>)hiQo
z)`=a^DpwI77o@sW!HC%1%3J4+TB|4feba=%&4mV4E>QwoEXc%)q050B6&bA{D>vCR
zWgQOpBQUlzD$ml&EEAu$Q2gYA$<``qgmYvJ9|o#)IM)Ew3xr@@+Z^88gN}{6^Bi7K
zH|6rU8|ELli0o<3Fe#^2Lb;J$qi&KtP6GIi<*#kci*V5~a`v{AchIm5%*(5YCB~`9
zZxX<qHIgw+W+0JM^2uIr?C&1hP&~l-3Q*7f1s^J+S=i}?jIK42Lm~kWzqj)AdFxS0
z43@R>@V<Owdc@y-Hz%2-So^?1EyU7l;Z^7_opV!^3q#W4=7)h8zLO_5kJpM4*AJ|K
zD6^%ltu4rb*;&Y&;WImXdq1$hoM9*Eb}(Mz3woYAyzUm*O^k-0^-de4CAb>rAaqr(
zU7D1#85!zqtWM7G33F1F|9Z>MQ+L(g;)bn;;h@_26>~7@O>$C!$Pik~6o7=mMk0&L
zV2+<B;4(}}VU3vSO1&6_F;R{H;83zS+Qo+rW<%I+`L^bn<h6aV+2d{h`zoya^?EYp
zrLJ}8efLv>vt2|W=khOUeJssM(Sc}U#Ok-G4w7zWOH&uNzq1n~Kl{grr{~w#bFc_u
zBy(ZCp!6OqG~K?5xa^&SeH&1V?z=mauSKdwSoCuwnj++9khDamPL&Ff`&R6iYlm%#
z$aJj32S4=Xa34P{ReO<#D@3FrE~x2P(HRvvq4F&I=VIOvFfy*19})VF<w#&VkN|D_
zoB6__3Z(s9w?z=_$q1x{oKd=;L1|0~e3sY+X`|lf6T_|*o;TAh@z-Cg6GVdGMy(_5
zg-~_-J=V<T$J_;p*NuTsYiL4h<BN5e^Zl!fgOkf53^am*g29nvvZccD(H=_A|AM_w
z<2PTg-iAYn@l{CITD|44=Ui|si?*#fZ5A{l2PslC6o<#QKGM}RRd<%x_Y^fE_Q?x-
zh`OaOxgx;*J=t;3!yOl<2oa5Tz|i8#|F;JbtzxG8oIQwe0fPi+V1UyscFqk-zE&vO
zw;gadtKNV@`NuTdiwmy?<`7ik9vM1?vfVL5yxRiaTS7L{)9_s1N4zCgy1!R5k;m<!
z`>Cm^3kwS^EiDWp^sY42D1J5R-1KDc*X!#bVEI2B`hn^rR40G)=pteG>Quq<(i*>~
zvVwvx+=0_aN8k^k@m9y%abAh-5iZpq((xM%91HeNXN1aim?N%_+rGD;^<lG;G>G8b
z@-YI!gNtv5fIqBM+oOKf^KuqM#lW%V3whuC?)<w}%Z6_b+tb@{PO{ey)#&Mc{ce6y
z1Kl*y_Wj%53W6+1j{e^(BsCPPI5IjK(d@gKXAxk=>j59VnX+x<Ti|M>`nP*8mi_*D
z`pH(BcB}cPk?(MGK0WV(vY`nEYFM9>0N)d}$gpp-I$O4IVq#L}N~gL>bpwLS2740G
zaHQDDOo6Y@`&vi65a_<K3##*BfDb|=Zq`60Lp=BBTgy7GoQM*`50U`mcc6O2Pkyg7
z3UKG%tN8#<<irJQK`>AU&a{6DlV#m`*H%Yo2lJAWO6(yc-dB4!PPn<2DqK!&(v>bV
zBMsdB5EJ9a*<IC-Vd6ri_}E8vtG<W(jf3zW7b|m+6tSfczJ`Pj<@B%9=M*d1>D6wk
ze@G>jhS>Y?QZ$>ta>laj?NlSB`XEi?0H?=iyMGQrs*zGN!gI3^BUHAs8trrq`sSJN
zYKQCK)lyap(-oG4kR3`YxE6S9nE0ov-er9YyA@fJ(*ZWVp`1#Zx=O*H9`nhCUw&6`
z0^F6|J90Sg+-x=Vut@&iI3@eF`rdj3#{O_6I0~7k&dNR?p*SV;_<>18kB<_d;DNhv
z_&U50MhbuTccaak^bC_%>x+~(lu?pY?|Yp+la8;sb8to$)dj^@UQ9Ph7>KmXs(Koh
zjh}1WqgYNho#kz99v*jhcQj=Y3Ja^N*<^P!{daE5TcFQ)Dn}SoGi1;&m-+S*kH^6r
zBBYAB;gtPHGRdyZR|nH7TGUoxaZet}g+qOJ&~L|3Ju#3{^y_)>^yo#wN>?GBP@gzv
zZw1>N_M0Y5ngX2bwURzZueZ9pv$BgD#)>xh4E7&HM<tOM4Gay=CRE_D*MUyObp5If
zji@A(IP+!j1KRfBWldiuBvT3Z1U&;Y9pnsL`PkRkaor~tt?@)5q%J8yOy0|GYV|;?
z?O%w=4?t!#I*h<3Ckw^TN|~p;&^!3x*GM0vn(E>ge@i%LZnE~&<UOiD(5|MUWF#)S
z$;t(9R4QZH_MLY>6rAVneq~p+3z%?5;}bKc5V6VT>#3z+qUAw31247ot=Q-Ur(j)a
zkHtyxQU}X?g<>^v0xLj_&SVH%H2WSD@y@jF*C01M9&|jiLnO&*hA_wB-1%n={^ixi
zEZj~OOh%D>cR%4k8S1@Z=gf!8_otHZhA7ga;_QyP=B?G^^5p+pLNg*A2K)T%)wyOG
zZN?rT-T_0bk3xvONmgIS@RL?Y51t8I{m_<Qa-WxV)&1>cSO}QRMk>j54-E?og@J{I
zg?aE6kxR?E2!WC@MWcA_>$xf^lSZe)svbe>Vb3bVw&doetk6!4oBj{U4!_mo*vm1x
zqREyn4jc9zGbLF=9X$MDBMjup9;2<cX_lSK{E4_Dt&wP6WRQtQ!9AbTY4sL6&V2w&
z7?p&HxwB(x3KSL1$PA8g{vOBANY+wS*GMfgnLR81F!D19@ez9hi35uYN)Sxjp|UH5
zheo!!YR)bkJhOIpxtM6VT6aYhm9}SAMm7{eh_o`T@_sDBl^?|m#u3+(SBLs<d`vZ{
ziH#0$GqBdw_LNt5wNub@a<fmZ&noJx#Xhfpn_`y+?7zY8oVSX_niU2|m)93%rIE(m
zB(op-omMjiYL2tfZO+fB(hACXvC$)pNE!;x$4rxMZ|LZ=^MLwgoU-?#9W~YGp#9XS
zwqh7*eL1M(g#P=+m=DCC;D(Mq<6F{$ZmabiQzctiU28;k!{wFTLoN7k-QCV#Ue6|Q
zj9q7dei$}1a`f_y<BSg}+(reFsR4Is<SY_Dsmp9k_Ap5@wA52G($aD8ami3(3qH&R
z=MMHfPLP<J_Y;&E!Rb}O$vb%bB=h<~>Zr^vZQppB^~Y9~coL-tRz(}ei%(`x_r>Z9
zN*o*Kd`KF-PN&2+ajG$=%jnmke&MK2W>LRkfaXaF$;1|lh%YVdq_LkML|I`&{`kje
z#<ZU2{*nq9x6@NiA^ZYuX8CXK(#2noI?10x=At38GP6CjH1$TY;oFN9A(B4kCscER
zgu<1Z)X>aG43psR+>2UixCf-K)9uwFtz2whedCe@me{$zJ70n2(|;ja=urWMaC!#u
zKnHB)vQ)aex(K(A@MqBBf{%H8U}lSULlC7ag^NAe6ojd*f+%6qXz(edu`~*C6q0Vf
zUsFL@hd`$(h<z2T#G5IPHv0C82sZnxNf?Euq-SIKT$toPGZvsI`TqIOLWY@=n4Jax
z-&IwD{W(HkUubQ&_lI08=~<^A6!^t5kRL@trgrtDiP>^<^ldIlYKX;V#L-aCUC9%5
zO5?R1s}?h_!FA`kn7s%Z<n9MGIq!+J!_o=%eHW~TdSLfNa#cSO^7Y`meR#Pp@v=O}
zBS?wwh|9B|`q#(9%)?#5hn>nA$2}8;k0l<Diz%kv+JN!J+7C<KNm&BB2@4o}HGrs;
zcq22yB&`^G8ISSE^>#De{*(S^N>xU+Hw`pP<L+7}QV>i-;-gBm9iHvrlMrUS#F41f
zT{t06Ru-BqFD|cd4)-t3O+{iIn3KQN@lC+9ZHz+@z*#9?A|sf-Y7>3}vq$F02)6{S
zCZ!cpe7{U&f6_8kAq04OiUV6AVgw%eaHeeZX+`+hbRBIFXB$MT?GNF@>@8^&XeYP?
zWblI=z8dzlhzz5$4x+OS3{_Ty+3WniaG}GhgoP-65-&nw3w5s=AF{R2gaH%((=VE7
ze8;;u&^nhh^rDQ#(L`U~`x-?;4OB!cg9b7ID@Ao_;s5T=n*fAGAd#!pRELTy5GLOd
zWxWA%fxkCx=aNxX-l~RaQjT3(hbrl4)6g&%^oQ6brHDNkW+tSF3$#D`cC1O*mqC56
zr$K<#uqpPJ!w5>lGMsEGgo@7ZtN^S6@e@l5)eXTrZeVM8Qe+DM&~L1w&<To&zAF<~
z!f<p3N9Fn7=Q?=ctQSNp#6<SR!1RmG4zX}}iceMxovxLCJ{WphJ>4Vypi`Wr{dpPA
z#%;+*Xwhw;X4yzADT<H)xN0q6Irj#4(dBgTzjJZ2)6CTrQcO;>yByzNKleH{4-bur
zs=#NM-N>Udh(kNJq{QTa<h5^rXyW(gR>X#!>*$H<;!NP=FFFMszAuvo^ygR$B&+hJ
z<>lr1VKaCfxo-HSYn}x)jiGh)kud>>JA&J=KVCCj12y*I>L3p&Jw89^tw$1dt)Gai
zFX&8C5&?<Fh|Iy7RKGVgcnwrmi$(HptfPf;{d>x#VzA|Fe}t0(bpU^u6#m7TU2Z~k
zcK}}EaKIAl+uE=6RwUWOD<6J6(Zx_BC1=wBGUYZ*iJpS(@iyienA1Ne!~}n=tC*O^
zWTTOyW&@mE?h#R9lw~b8o8|&-U9XW75)je<OFQoj=p+nGv7K;*1cR8LwTZcnsf7(F
z45GVu<u<(Ic8SW613q_?W%ilR8luM2VUrMv06U0|QsZrsf>r(M!YtKURG}m<rPd_~
z`3P72FE1GDYLr){mO(ZK&Fy<xr;HxapC>%Dd@(h-)nV%-oEWHPxe%>I@jZ^{zMi}0
zGSB9qRuV3vR9sxuX~d_IP*#_fgzaA!q0lmr!_q?38VW0)hS}gAgo-?EU?@o|t0!#b
zfu_LR4N=%gxw=^k(uC-g71`X1wOI|^HZ>ew;^E+ZC>q%XdsCHe7`i7tn?Bu<m@6?W
z^ba*DLcE%&CadDAtT>7DjW?pWqDpc+C>G2&Gt?;=VV2>@?kL@O5Ok@1?Ptzmx^lCL
zA=w0bKPi#e50!;?<UU!n&R4q>ch_;z0;3IlJ{F5{c7+WCT4&lo`$BDs-2y`YTmr*{
z-s7W}mGjcYp-1!(!S_wYI#R}2duPa(aB*yS@~+zCHS6n~YUE_`4KNM3AS#Iyx#P+?
zpS)*IHE7~0WvnHgnbZ0%YO5qEBNZMI22+%H4vScqmc>3nn_Bp1?<>3!OW`~=fDf5(
zFotSXHfIWj6dn&Q;;@gnKNqgA_sdFYrs{d(K=TW*>V)lMY0dC>Ud72q!9P)J!`li{
z2hu{C`xZnl6eDUJadILot;JOq$A>A1D)KMszC5X9oF(lPpr%N;NjS&t4E>-IwdH*f
zP&6kTtiv;6k8T)RM_fS|eq4zH0%DuO<6AAgY@A<fX(3D)6j59_q=UA$s2TXVztxsj
zQt&bf(IRS@7Z%b?joZku!N?JwRE<YnHqNS127tU|JZb)8KWy%HQ=IR3@bm&oYEuNg
z$yJsHJD}mFFO1lvf_gXzzW#wFD2vHTdk$&@vd4(5UirAJsSoa0H=zMYZFFx=x8rYb
zD?`$7xTCa5ROj`$iIM*Z`Hc4q-w_j?&VaAsnA>IO-_GD~KfelPdjNW+gOY#!vZaz~
z3o<CYJ3CC<?Qf{=q$kOChYOG0m3~R})PHF5?>Ku@@`cZ@RgySdMkENsv@aoNQ#b8w
zBD@>l%qkb(LN2Rx;J*S(Q>T0&3di1~AuhWQ;+P1oVwl6$mRKJYLBX#~<~O?>Mdd^+
zab-vg*xt_O7Sqxz^!F_7y=*rEB=5k&oAb-N+Vkk;yUaQH=(~BUKY#G^0tD14S-ugE
zwSh;iNOi}M32sR3E?Xt9jf{b~^fFjIbM(93RjR?CcBp2Y29!)&7p0NMPyg4mqz`Y`
ze<dbW8)0Dhj{R?q@OKAWh$`GIHmwy$duIpq?)=BT=@A``p6#_Wkvrov@GZ$(|Nqa?
zOB^(YVaB%MOq+<&wMbP{Y*C0R;bjz`lsQVGcQWHd68%^T@*L0EyiFx^zqTX*bv;zS
z*(KRo<qa~|1|d)>e@`T;^17qRPmzn%+21Yj7jnm%u&g#9j@e{?dX%vof@#;WnnZR*
zflw)gcsFnOOpjOE7PiCs`!cIr;_J7;{}%D<AKCcMG&o3z(85IScmo!IkAvA<TpaEM
zUN|Mf5oU*upKz6G-v$NBRcuLt=9qrvyu#!teaX)UH?+-v6y^qn>fHCi*8y&zf-+hM
z+58w*8HfqSloH}w=)g^}8RMHCT^!NT*F{RVW19~qWn+2Z%eEdzc71zbgh@&&&@OI8
zS40(ylhn(l6_>Lm%+g;M`0)%ADmen@UreJAck=LW^z9J+`Qpo!Nh;vR{LJuGD0|m2
zNh3L<Kk!4mn%x<RmyBFsLIym-v2+5~e8Zg*iGX!~_%NqUOtQo#GglQ05(^8r)QtQ=
zQBQP4hOwv~YUY?3XSB;aR&lWZCW(%?MBzh>#a@V@T^#qmWcx9pbb&FpdFR2RTWnlh
zTr3JIDk_Rw>jI=U@&kZQrcC9dP$Du_`yUUAYFG5<KxuC$8{RhzoRk6b=Nyr<-jtI-
zYZF?P09Fr_hzktgf&UjK2#<P7#Ke)wp>ZM}gvnhi$A&)RFQQIaaz&Z0&F?U~e+ru)
zJ)(#CltX0ROEF(o8pOvo4TaB(j>p^pMpNTrkS_0Cfc{LD?C((XsgQSsG3#SdPUsDJ
zzc7d)^)THk7PPcdaV}%?A)jWkZuP?PM7yw4a!K)zZHicyd95Lem@pY(wO$%~U+3S5
zv$!*>AZvS~k(|`z?M1M5#B}U2%ZrpTz75k_>_bRG5nu^}5RrtH;&EhN^7Ym>cN+`k
z++J!YAb({=M_*s<*$Jvo718N~vT^`B2tqZtrB{*Wl8?uWqcZryQ~&lCJY0a_wS3kL
zMU{aSJqZVEZf2EuIsk1MxlDtrSgQIGPow)Sy2{v>s!g_~Z`=3h1$WzHY`;j=u9EO}
zO7@9>p&t-B0r9HKmzUSbJGXQe+^AIa3A=$nbs8XlFs1d}nebPK+tKBd{J$<y=c&cv
z)u=<vks0V*ToX7SEf#2vVii<|4^O2WK3|oWM)>(&65hT<;BWx82{SYl$Kv?G<$|l-
ziRo$PRhVqJq1i>PwxXKyTkU5ABJd`^O#I2=OwjNC_nE}|UpCnEb4xMgk|NNHq<}H$
zdHf%R7!8q3A!NB^od&p~E28t1%oRQElTknL(H#x9I2vhK1iAPaX10Hgu1^vkY!oVm
z%}d7)neY9U9?wc;7Dc6HgupF>8$(c9&nEnRJ`Tx4MX*-gR7(KJm~+cHO!+dz8A&Ok
z$&F73p;RSi7T1@7e-k4sh+%T64FI7zm#YTC%6DK5pjLp2L4vtS%_f<(j)3xG1Fho$
zfEcDt+}sRK*}RWl12b*W5fP`yTC{&g{`MzNr}<q%?l3t-XLdErvDct25SR~FD!i&i
z{t4weX%7SHm|C3xrJ}Mdi(_g)R-5#Wmh!J(3pBU1-Y6?uRlBK}IhfV}YNDdqY-5L1
zPxqdqLSizErRS$Uflbxiq`oiy<k=@|m}l!E=e<d4FcJOvcVGGClQsVjO=lGqN7uDm
z+})i3!9BP(5+FDP2oT)eT^kE7NpN>}cXti$?(VLq-tQmhrtf-;>aN|@d$0AZIl~1b
zD-Qj=h%bu|%SNX3UE;JcZ5)g)c1QR~2hZQHPxp^}0Qg(!L@z%FV)fJ?4OA4@6S`rL
zC7#p?-$UgTAvZOZ&~FK`xt+q33U}}*f2XhSAe6oWx7uz$ENz}6lO!IT25p6DKq}l!
zsgt31RWP-Yb%5h?f^9eAhGFdfh0U-&uL)EdHC<gmG%{nmAzq<LJl8F-J6Q7`B0~w^
zX_>(EK(*0|-e`6=9z2GXpL>$Nsy^%s$;Q6e+4uV*$&Bkk&-4_seTl9Sq!0eQVlK&@
z+}*A0*!}a-1!{<oC!0~KJ42J5h^pKQKS}#bGG?Bc=-!^FB@#jwp@*tDQI_|oPNE^(
zvO64!MZ#^w0!^6v_RKdoE;&@p-GjY-V>1)eywc*5&>7<bbR<&rwq0gm2HyPW5EFy!
z@F>XA-h>d-g>fV3@4&Cr=u_~|&Zc}woZdhv=u6y~?{37%-_{^)(NoXG{jF0_Z-+Gn
zspUA(h`h3Xpa)R8ui&-7G)ZyBEm0QmDbvIkgtYD)j;roYt5$}3MoCsj;r#}-k~c{5
zWAf1`_O<NOzs;(*BexSN$0@5W-cCD94W4Py)4~N7SF-6U%Hb}}61>1e{QwR>yU51y
zfD9v<1ozOut|XU<K1=@)C++(ei}#fNTH--`lRmg?JLrwU;i>M4<({!cB}J`RO)TPY
z0WlvQOTuF#2gJqsSoJK|6OZP_C4(s}@$W|g85Ftl*^Hc$*<vV-AyTM-dP;*ZUR_Rf
zv~Plnj)n=M(#(<}djlNs*^l#_*og;xVfLYe8r!nGEWq2`1jI@p1xcHA|EQ60{7O&C
zLF-zKz4=c0md2a~%aU(!PV`rm*T7SJZgDJ#O%LCSaSRnjmDiNUy;W3ETpD2MdEH);
z2SXoefjX26kQrIwM_s;5Ph$l^L<BBXn5`iz4H9d#!uP|@=zuj6j5$rq$hiVAWl8*H
zxT$T7tjx$2e~$l5rP(P6;%M)Lg`e11aqf;BD<{=%e+}pj1P<`5X3Bs#;191+z@4wN
z1H<7@0Mf8)nDE6)h+0{}z{7^a?4zhI2N0`bXqZgRbt7Ta!3F*K!(L_j4U+Fje=<UC
za+1dk@hf`w5;(anJOQq%P>G97({|8tAj^BG2vM}WGRMjkT@|b3OOH0y^N8jq+%>}P
z&6Yc<5rIGqTfu2njpPL<hC?+0HsQAC|J_=8Ka0au%;tlp&`;#*ZI24FjvNftVo09c
z!pejusC&s%9j?#eJ-L8lX1D|V8=Xlwr&3u-wFg|cXPI=fKGcU2S74%SAneFS{JcOE
z{9}S?3Ol$tf$2CL@mj1Bp4^dXY?v=NHH5@O&S^C8y}E@(_Djm{rS)l5lGuI()lfx`
zQVNSUF5XSqY1(Srt9E%5=jCS%o`_A@XE>M;uwC!TewFe*8A1Aw-|-|7Sl+Z&*u2F_
z8^PeI$lb(+M);P7<oaJ>qG-Y40aUz2f7(f{$He%e8h0Il9j%(U)W+f%mL)%|v}lDg
zs0R64DN$A|9`2@PeoQk9VGR^ARni;M)uc*iHt0K2l#j8L?`KQ&FHHSTGSp#3!G^@r
zDwos8ihsj7^?ZM~M){ZEJZIzJ=gu{5*1Uneh?xp&kaOA4?iAH5d?hJyw&<*lzAJAE
z=5T+w@1drN&5WetDUc>&?px<|fr9o?ekcdgGsz4r5$b+z)3dWSI59RU0|GH)z4ugw
zp#j|1<tBYFys1`@t<1RXXM&$F0-f1}4#;1jdli-&VIIH1MlDI0)SWNUy{m=7iU@;y
z*(JZ~&h!D0LJ9$vp*bu?EBo4SY74tsmvbNJ1op6AlBUsCWu*kj_zf`;QPmB9RYB~O
zwtV6XL#ruEr3bV7m~-sGtMF=Tx%SB@Q~2nczw$o?Yw<O$h>I&ly4_;1IH*y<cOFK5
z*U#iu!ZMIrC@*SDs4f@4oX6cc;@|?)jzc?}{(^n3p7vdS^#|oiq#D%m)+y$?VNi75
zM+`%S{-&~Bp5gEs>v){YIQWRHj-bni$4M26&cSr9h1bAFnxfMD%xsibXY-1UbaEia
zARBX8HT&1Fm<T}`xy;#MB7~UnXICmL^6Yo9)yKXQjTX28sI92>>R>>V-Pr_AOUWoM
zk3hE<DIN;F)K7w+BHD!xmdO@g9*|Ryo*O8%fcMiyh{3s3(uc8l!61OYJ@PYTp%q>n
z|4uBTqnfDnp=Cb(icV0wnv#Nn8$P%<(4~t2i*b~NVmL$*VL9rECEot~!q~)weh8JJ
z^VegxxY*iQK+iT%-3q!DQPBj$nS@bYk=mI9MnBT+6HBv#!U_ne@BEU0l*V=vvF~$%
z#+#O;F6K8X1T#{n!PpX=Z;|Ja=%z=197XUDJ~w!`&Dvw`gl3)HSYU=$2L<`e^=x~A
z?l0^n=O=5OuVPS;dx+%mXgrCER`GTX%tR^m*^qS3S8w}@^jE`yN(WecFEu(kx}~O#
zvw%Bml?BPkO9I#%+308w-c-eOX|~g_F|a{<FzY}YsUTYkz!Rh7epmkeNB#T%#v$t2
z{2hd!6UxfSoakhrOd13W#>jf&oZ`-#LqqAP)CVKMhteQqm#~8?hL5owdgwz2xxV&Q
z9g;IP_Sga>&&z-|a9UZ&x4X92TuQM(XukspexEJ}1OFq7UBA;L_*PQRViV&qO5BWQ
z`x?|h{*2E{yAfqf!z==VU}Ps1Bj_ygUrU+gR3#m&TP31%5T()N0~53ir6opUX6e1p
z2w$;n1?fqs2o`omGrpMt6-$lZmk-KQ>;sRn7jKK5o$R+WfIu$zxrP7d;p;F~9t_8N
z)*J#rcVQ#^Zf|a{??=8*BL<sXL1dp~l;b2#PsoQvM#UUu@lPs-;JMe{m5hRE!7p6U
zN<hktN73EIx}14j<FDGJ1QJ4&!nY_i`&w?LSUh8wb8}nV9})_^EQiEW8DzINH3cmw
zXBFHXO_J#O;95@=E#L_XT#Z3pN>Ex&aBxdY3d2x%AX_8wBt<_N6YZ1`(^VTBnqKc`
z{KP~<FT_PpftUV~1+huCFryULQ$%3i$~ZDQ{0+N@$dUwPe)tWojJ_~0+stt9@SK>0
z5Nq>n-`vQc%uF9<S*-z0fy_61N}AB4Ki(s(+ZGSJTmxk+iEtHsIN%8bjIS;d75m#V
zaikkX+OJ|&;6ODMyho8Rn{fxt2|mIxsD3~PBQ_Xp`a>1qB!u;?%8AVMMjn|LlYbBB
z2P6w|QOrZ*a<bvHPbnRMljk$q6<rU<4PH3B)nyjB1qL~s&U0|T8O>IuIJ?eZ7mS3v
zIxCn-j<+GlcBE_>P`$!K!$RT>vg)=#9O9z<tR5jjO`VktuFtOiEhvK|p;-^%LW&S3
zcFsri9`vXE>3^3qfFnzQ$Q)E*p$`F{L|s7LQwZOv<kur5(~DyA0J~EtpB!q@R6DcN
z`?hvE=B#G2SGAxv$EC`K6>Y_d%=!Wi*0Dim6p1FtRMGv7C%Z{b2d5#>^788|wOLGg
z3a`&O<tl?ckT^2KHAUGHEVq*B;SCodu(g4(R_9N#WC(YBj0!v^H+zrU92jiufZca_
zU5|OQGYS-;cEv#vh9T^AWbX~RwaB&~Jr?*?pf0Di%I!8^C+#w~XH<(t^{Qh7maofJ
zt}H++^KkDsL0J(wUR13uYO8Kx!ySs{GEo&79Aaf+Y1h|&tMibZw-KoF{Gmo<zI*$`
z@H;7Vw9=xOIrO1%Dq%mM?JLf~K>`~JCl?nxH^dh~6Ie}p_Agmx<(sv>QQ)5q(8Ri&
z{-I2vM?qV=$jGApWBtS3-J<)u;2xP^G)Tm!okFALU(-2lOi5?%z1AW71Tf!V_bYZD
zaafVpIHf)oPaR-`!nBbkbU6gfiwgH~)aSXzEp=L4S!~fpZmK$v+HImKj&=$R&GoWk
ztC0@%Ee^%h7pKVbL-9;$4nt0|+f-G=(?z$~<;hINp>^?>I;Ht|4zXV#!I5~7z3GR4
za)+L=Y^|<lv^%@5hJ=FU>N-$th?dyIqVb{rXd*J9L{<1mO9Mv@2Zq;CbApwZAld`<
zXhu@4-?7OFk>A@cF|@b-{8Unx)xpJf>-hrb)BJ@zUgsNHgp?_b6ug&!7V!z)4lKgP
z@58*?)ts8+mf;!A!IUQkGBB)B<6+x!^T~3mRm2Qs+gaAC)SQ1rYhr6@Y&+^?A5qU|
z0@Lou1k9OTgLM$iKlqo?m=i_f(Qy#Uxe@4n=#~<(gOH~n$oz+?ni`IxvIuO0DeRCb
zTNqOo_Qw}4hW4M5YCAQKb%J%lG%^qI@k%W$r6tI64VZ|WzjR>WxWGY>aspkWbKN7e
zBC=}n6Lr%3^0UIjW~Ae+`8nv0)svKUwQTchgYrIc`?Q}6P4;mjnMu{t@zFz4x^jlq
zujo^~XcM@Z*Q;%nNAtn~OsXBZo*wAKudC}5h}F#upfniy0CFe}WJW73FQ+*3?LQ#k
zlT4<H(}mI~z<2tA;Xfj$bPENe7L>yvd#NxXceb~7;D4DR+BXA1W)_CP@-iF0!~Ur-
zZ*3cwGUV=m{lboidZyi`MIeV8RRvK^H64}IS*?9j5tp)3`2yQ6dtxH2NKx00chPLj
zDTg7ut1ZZ?gPdO#o~ZIbNM4+3|5BNZL@)`}PDPWK(hU=Q?rI~>K`iB#nRU6?^cd8a
z7dKl6c(!Oc$`psa<NZ=x^d((Jm&FAH5?IY`i<smTWu+D5LL;KWkWeoj++c#W#j%}2
zA<5Pi65x(&D7C3H<jE+687|rCmi6_9{$!ds%aO}roc1ko=F;j*cby4(L-pir2Q}-Y
zoA>dydclKX;C=-K1E$_V{VtTPfCNl&c&kS}JVAXD^cv`EnUdq5Ex`qN+-MW$NneR<
zRXC7k8M`d`_^EpNuu3|`bFiiz!$(AmmPmD0!WoX2p9h9eeW-1UuFmQ7?W7u+6ow(P
z@?*mJ-2aH>Qd0HwUXIS1Ri-~P7|4fIwFTeXm_>unH>Rp<e9mu!mo3xu@~HcRp(8h@
zH3lcgme=QI&{esZLQCLgcK_hy|28h`!Cgvp+&3dQsS{T+YbL(+W{+o;t{$4lVuB+k
z$~D^B2SoO<Ng2uUvFb{k4S7KGn~w&N`Az-Bn2aXmdaPtfV~fU*jiVqQrkTBx#1!J~
zS^%%~&Jq5lAIe8TiHek$8y_2=Izo+>9%U>|{?b9mJo0Z?4%rz^>DBcPEoebRH`46$
zp6IQG)OR2Jv7~bJ4VX%<K+|vEvNCyOaIC9~s_1S_k}I*M9Kz_6JFdIx&p8J9AFNlJ
zOqY6^ISOXg6LEVuF5&ZGZN!}f@Na_*RVY=x>GE;$U{WRjcEk6*!Bnvx`~z9~yb8k@
ziC6cA4Js1>0!0M^DcNC$o?due3okRpkE*2IvDfKi#QHnb*g`q0j3o^fT@l<VPs=PD
zjxNKe148Inx&;#OR(gI|MgdaG$CZ3msofbKPNL3_g;j1JF$2f{V7ZYsc$4GsU;9Gb
zm;F14h(9lW?k)J-ygB~;$ILWW)=sV7r+CUkD}y3Ip7?~=O01cOZ)It<SABqDpi-CL
z?fLOfpukjjrcixH4ILw*)emC-fnq6rW3d4Ud9v@iP=6!A#~StlKB6)2?71rZmoG|k
zcCv!P;eN79OG|ugtu2uONHmYMw3JL}a{_92Dq8y!c2;^d)_cb;vitIt<nTWKVM=vO
z=poSp^q!ET&GM%E*aO+0m(b2H0|9K@s4SAQzI=2H;=N<tq|^l=dh|C80y)4cf<z-{
zw=pSP&H$zz8I_Thksh3#z)p&e6%*=$&vh`7pxAJj7~z9cT&Y8mD?eik{ADMCkq@St
zIBx!;M{Teh`(vTwX1f&sUxwhhhyBGwIWnLe*InYy&CX$?BE#sABnqBq>DWR{HY!b8
z{cUkw^&QnS3i=ee<u*oBuM@pj(GW+AJkuy2P8f_4_<e3VB%fLx7=yVyj7!@-mL9)J
zEL*FxIJwvHLMJ(1UaR(gpIx3l4N_u-SCRMl!=9)8hWy1ea);Wu8Y=l$c`ErSvHGl{
z)n}iUG!!Mcg}>N_Qd5w&O1II6%kZD$<!p7p`r;?M+h4)N%!q&%Jkjs4uIKnQNW|<C
zvKGwSYiLU&&*|gjC1s}01j5=hwJOU8z)B^G!7||ZI13Tio$v{*&bNC;lYbZ5r_0R^
zK9V{fx-xNqt~k3TJIYJnT2lEg&LR0v1|AWX5%64@U%S$YiXZ2o^}FV~MDXK==i%x4
zI#)O4VQ+tTjUE;$J3?2M#_Q8vb0@$b111shxbMj?<vG_DZ1L*dPLSRKq`iXHw?N1#
z#J9tD?a=qw{<a5)?svBJ3Q@&TC;%1+s5y4Xmv~yrN`tfXpzA<zoRrQA@D~>mQ`Q;1
zwi#81B|EUnWzH`%GQ9|6d4Dx_++KQd{?(k#%tZH9L-thL=aWiQcyyE^1Otu}*anu}
zHgcn<XOf@;@eF2hCbjkIv3-Nk>+W(HV4u%3r77zrkdp73<`)*(Aj#rbJmEsrL9rBo
zQF|A)xpX2)`>{MW`Y(N8t9@YnLar`Th>Uvl|D-S*A`w!X;FxVSx4QRe!l&2e)F4LH
z{!Fx6^xb?cuElSu)a`V6=vKQ0534thCuw8=^<wK1igBLT8(c<qLKM(iBB@;Ew;Zo2
z2z5<JimfRJ>EdKQ&B@}huY?(ewE$rbAT1Cilm>JJgsF&l>#C`oq|DRfE-9~b^Vcve
z5ByNjJ?ufftKk@M6D-yJbn=})OCDyY2`C?w?eF8_L^BVZO7SC6l2n160=Ls?5@fsS
z^W1>R`x+fH@*0wl{j2f_U`1=Lt!t~qVIA8RGY1l*Kw=_l0;2T<ksMC*Me|9}57j}W
zW~kNN5LATam*e0AoJpx3+0fssN@_J>geDL$(B1*@xE=G*VpY7lwg$sZ$IHP-PAlrJ
z)*Im4)2H*%1>4n*2k_=p)Qyf8H;W!ydj&6Z&*)Ee-QQdTuf4&;`gwgp?t_tJ?^Lo7
zny?(Q+5F6$C~DElQ&a59TXaYFSI)U{Gj&{9F?ki)VKq)7HYV}&|I9O$1-O|>uo!Wh
zTC;fz{o<%JOV}_5CmGN70he9+?*tu<pK3zBV)8m~eZjz518)avkJTGLNtncO-pZG!
zi$tu$KUn;oQmeU|%pYVL?DTQGI9|$qX(+dn$QJ796@&iVA!nV|9;me0_(drmOaH@w
zV1+=Jq#YuDoF~}k(QWd97-spjmyIqS|CfWu?>9E_VSY|hpG)14PV%HqFhpH$Ko6x6
z_V+I}H-iakk>E<3q%Su6X$>Y;JZFOTx13FH^XV_><ag`M&NWVh&-pinY6t!ByaWu0
zG0$=0yPbqIfd+p8mIDrrsYVVn!<RHM?WR_KfyE^ae<j{|2nuf=k*_dHEp<I3(zQeb
zdo(buS|=(2eMLCc-19WgS4^aIMvTBPW9Z}UX~4F_4)}ydKAo@B<UXzWW}nsEres8%
z-ajm*NpS36TR_g=?9@O?F;@i>m6PT6LR&!-zW248jFrTHWCBf260-8Tb%o6g10H?~
z>4~}d{`?|d$%#?AWhC#3Cu{$>5!i3P5mgQHOX9h*==%C&e8X1Z`0{0XW~O@C?5C%v
zq@=0o4;Jt(ODWAPMdm;A<dx+P<ELf$l?O6@*MgVl@fEjop+k??-K96HgP3e}-=o)I
zlcOyFIc~_*H$u)WSgwyy)Fo8qAHKG;MUI#H7lV{+_hie^PJWUPhzPXg$)9hY?wZ?~
zLEm5k!SA8G(`q%;cvy=mNc&a$9kl*0Mmk)Q?qGKm(2w*+`&+cIz@U7G<;bO*6+Bk0
zde8Bc`jdIP#mQ>(=f_uGuorN+R}twoi=Um{{>IkqFtTbw{E*S){Xso?MXoF4<Nk8o
zKO@D%nkL9SF)A0foH4~CNiD>H3e&uJ2}JhA#o}7V-yPdunuC#>D7t1LHQCzjNix&W
zz)~%aVm>}PIyN@)<3WhnmX?-QRwu{D#6+nDz<0vYiObN)b;8eWw^ILcSv`Z#veD#q
z0m*-+m>~pyI=tWG&;gdFo^UA~cDSsgHwyE|1@OZ;m2*+c`!x^=VWX)_fv8E13hgAw
zCOfPUh1`3F$t>@}g=<0$>%o_!wYv|-#@ReMJK8@rH`_TS#E7;WuWqUdAf<=6{-mM?
zJM<Y7#%vKi4UiWo?+ck7h0mQR`P3LKtoyv&Ux4f{_00|nBxXu$a@&~Z<Vk#Trz5Ni
zS*`Wz+=r)4M%Bw!n@%Q+N8LO(sz^R&RuUCG&)3?P+wDx8JOUbv1*H{?l`X*#R!@h8
z(L`eUA47MqI25D5JAdAL7yRM$J)|4@2_UoTuLd*?O_e>=<i%BPPM^+z0$Y6piv}Bb
z{q+eMkICG|#>z^s&G~6$&T7HUdb!5t;Jh84A3az5eLEf?ciah{`&{2X0W%J|pk{FH
zi}S+!nMN(`q^^4KjmmY>2iP<TWngAKZnaNt5U__Y34G?~^RoUSMsNr&{)WjcZ9pMP
zZ(@Tjz;pW&NapGn6H-dmQYoIhqw?iu8k-VIC|gtU=vZ3+S+ETgauQ!eg!i%EIDu<S
zx8bNOyH{sWA&uYX9=K3^7TvbRHJ_u&f%}60w81_C8;51#^f&6@i{*B+3*p=E&Sd<)
z_tQ}??W`NMBp;(8jg=W7Ah3L19pwZZvE+@1xEnu>WSB$Yr3{2%$5!6zHGR-X#@qE8
z2tybdcd_&r;Qx?M;kH@_{!8cW)~k(P?*BJl)n+lXKd#kaz0~5ea-ZJnnv3pJg)Nk^
zLp!z89E!vn2OfU76&6>J(gg$hsfa<hO1Dg5Py2;%&7Im_9h5ydjLCO9U_~XZ67xbi
zh;Y04U8bw4CIb@JFc}GUrqo}w*8yLMG{W0I(F*agU`c+~?SSmwQm#fEzMaTt@yov|
z;Oe(#50$WiMHVPOxle2Hcmb~B;T+_?I5TsDu(SgnjA=!;)nKb;n^;+;`ciASoFgB?
zqSN6BP$P(W?AAISF0W<)itWdk<7;K->+RNB>H@2t_Ue*u;=a-6;T)@+VIFBI9G*eR
zF9PwI$*=F!xdZG}Y?C+=GJcO{iSt~I%sTA`C+C4Wg%g<|xBIap6<S3Ski+)wuu7pq
zx-i&h|CG-ehs8DbpYIv}^X}&^kk9@0Tq=*<Mu*qKdv8dlF38LI;W;0W71~+b&_!Oc
zseSIt%iMF9gX^t=S1D()p`=ez=B=pU<r*88(=i-Fm!s~ZD6l*wiPK=~$l(QX@NrWP
zE#T*`byLs*s-Oo~C#YFLrfy`hr@mK`HZU8kHEeNylnt>!EBV{&3GKX-??p+n8X?8K
zXz`xBYy)X4q}{^{I_T{c`Tb<$cQwfeu}z~uqT<9Y7tYB`-O)M8)*5Ng<)v7=!0t-F
zrzHx{U+iNXc^{4gKcE^BYs$LTWVG=o7QbTM_wn`!V3v522{(Z+*56*x$pqaO!3P9c
zO%rKXjf)$gx5!g`<BR{pJI<ZgwkF84Dqhzs5&Cf*b`lC-8R*^*98W`8Gfi7c{mVLx
zTb)LX9p$jckBq%55?Sj7DvG&zYy!-Hb7^A+n6U_vGo=a)Ye{1XMMAoAj3yef76eX(
zR)kst9<cdw+zqbIx~ES^;DbzIZ}*S$Gp&K<PEbb^Sbj9<)V}a{-8Fp#H+gg9Ixqsa
z(dHE+e5H5@+>sYJA(&%w4%dT8B>OhHc8~xRshwkoruQglT4-94kE1i=PAxV;eTJjn
z0r9KaMPG8n{2BkG%k^u#)`OQ`oBR9eQLOInE7|E8Fv~)#ulNT{QQ7MgJ;BGnzxfcV
zgYx_ThXyh&IzOHPc>X%Po2_<pmEGLf$Sj1F3nb9k35d@HSwBWUII_(s;~bDmv&S4^
z?yZgGj67SY@v*Oqko0@1&Wio?cJ(xrBQO{Ixg_BAxGk*W{S^ZvozQT~A@tVypCpmm
zPgNP9Z3(t`e||f-pvvs@z1!yV+{+a7dU`&4$@CSC235H#$lK=0n*UtdoxgkeCp!Lo
z%lCTr-^ofC92_lmp@MU>Ne(`C$5Vr9nUU%x6<>Yd(YG8%TZ3^&ej(-~3$3|=1JxCv
zic;}$*XA`eyUfi8N-3sGX?p&fQD@Z?q^+P81`ndad%dSMy>TI4jCs&Te_Zav%2?a!
zv^wjbon8@kyQVPdUy*aC&8?)0{`kigppRJ}StcUR351>s9Dc})wq-^&;NYJqPZ`gI
zRToV<xz({#?M#LO6W(!)N}$L(n%`288SHI!s>R9F8OJZMG*LE5jjk8Tt%shH(ZG!d
zvz)w3Ccd^byLe358dy-H-uwWx!f*Sew~2}wo!&RcPc!Qo@NRY+O->(EvG|o6Z-Rb1
z$(pSMZzOb$=<ohC)+ByRcPC5wMGX2Os@kG67#rzXb1nM)1?__hnSiNdB5?4ZUZ-Tm
zU}47fqtmH_F{|FalfbnA_F?xGD`4C6v_IIN6F)haGY#Q8uKIfjvTll|mCGUNB^bbr
z5s6zeD0Avv%mY}DN5awM!;BSqR$IHoWTyxIJN&!HkI3OQSX9ufXOmiA^KSz_Eu-bE
z-&dRiv8E=(L=9Dt+1<HHqwaRx>--(#n!4TkiO2Qznd0W--OVwwp-}Hk_zBpIb!8e6
z8}U~6R@m$5^s3jZ(N6nF7>p=XwT^G+OVoAcgD~0m3vxxim9%lBdVrQI{t?cw!{?bB
z5Yq$$B=SE%OwVOzpZQ|ZW!L&4p4g2{P^|KW*j|XlYh;VHTh|{L%Ph!T(S#@CBg5rv
z7kaSUI9y)j$CUWyBdtsk4ko`xCq7>-?tPyjXvlU~K^#c+-MTu0-LWjJtCOdfh1KOd
z7q%c;GIhMavu~jKyLl|hZR<Z({N(=i)L%y2Z(lkWN(tqnc!}2wUP?kDC<JoHLPX3!
zXLKZ;wgPn0-+la=jBVChxgn~2(R<OJ$S<(ZgVXONRS;F4KGos-01lfun6mPZ9+;7O
zumgl3TVsfE2zt$To5Q6K(7YKr%nO{8P!r-9>I8DD@4Z>wae|OmRHux@#y#$y!$8e9
z-xA_^q!>0Hl>_fnC%LfC9vqqzoug=Up!Y}k755^s^;?_5s>190XeFrK>usB!<kybY
zGA;f<6Y=mXrgGyuVCKC;L1&Y{BO;0YtM!E}xNC-yqbChz_n9~22ZTdIE$38-X;>)K
z5AzASif(K}FKl@}PnT#6yl#DA0U>HkQ^EHuSgzUxJCJ96<H7kjBmM*l$T92Ww*SNp
zurPdhUF?YyYtiD;K1=4~$|p7G@6AtCV4JaYZMl1Y+}EFjYiXlNR@XdUSL0WN{%xRc
zy4PS7+gRJ=l+v)Wu$g{#<!6!d&1y#9I{s)wTgd#P4F%at+!+d>NYI?ir;$pDweYqT
zW~mA4faT-wnD}r}eX*f5A-N9c6;f^&`td|H*eUw3n;Y_=V57In$-O=rNBQ@aWDxro
z5<eHgFk?12&ur@y|4)*dsxrvsUhEJ+3=;sAYBJn<0}EpdF{fDBqGVpUzHG7ll26n<
z#7Hm}gqoUd{FOHQo1_^@DnGQ;fHZt~I7DaDD<VgNt&n5!!TLDkfg-6;li5lKRbi_g
z;Cn&id%rAb7??Ibd{yU~(5&%Y=nx-N=yrL$ya(4um_@ssuXQ*abnhHg0!9&6h{2tD
z1siN)3~tpwc>82e(7jpVr09FFFJ^8{?~#~!<7^G=-aS|~GL{4P>zKvA=2Z5XHETT{
zwp0>r?{0-fUHFXahUyoe>e>rWdqWh{CLT%o-rrW#Su^g0JwuP1E%L)U@MKWDHpv?l
z55c>ts5YyCJJUC#G@KLKRnE)`4StV7Pe`k~kOTb8EfkDJ`ugM;nLNIpHV&R~G40hU
zpUD;(faV$Dl2=F}jw;uLO$KKNCl52r7%O+9=kvCFI{x`ahv(MppEm)X%9WPNlT*O&
zyZ-vVJMw27d1@_y=Z46%u41vy2i=+PKd^#Z?#tLzU~5B})>L)v&2Iim%m&J)yE(2o
zQLfhD+I$cMf6@kFZG%z}5f!B8CH2~C@t^3m%c)gky2$v1l7cbdZ9d?I<k3dY4DsaP
z2o2T303ZER+T~}@6oMOY=ld%p=@b6pnBHQm<3Lj!gcWW9*E=n$$>}i-n(!8Qn6KYG
z!e(w@J~)T>6RN4u*I;=R75pr6Zu7{vXl7ICWI+~zdGy;Nw$HK>Ix2%BLY&R^J+}?@
zvik}^A5R+TA!@idrJ-o2tGRW7HRF7_(`d(XF`0ke=j6)D{xv~DWiUUauV626K|Xz^
zqs8dNR`prC$@2wiY>T^Gvqc}=Kd>e>v2p0Z<+npPKO(|o1ua+Buc6nH@z2`koscS;
zyxYIo&pDFx&8T)jKBLv}Btx8bLa+Vi5LPFn+WYya`h734qrE(a^yB@^>hd)o+UjS?
zbl_=G$M69!S*Clim!zDo4tf3|P`x;M%HN@t&s>Rq#n`u|`}acS|A7uB{9+8F?>spN
z`x80{@|T{%;0PlnF#SX_WLkEMBSEOzQS9`o+ngzR1E_1HrIW<#e&=y=7z6}UTNA=n
zUMmCjS`V)26twThQ#P_@k^iD(CYaON@=dlFytfrIPC8s)M)-CgqkvMOR)fL9meT^f
zvmoD9!?}M|(l06W+Z-@0D>^z4kGaWfn8AP<@eJ>7mKq<FA%`sO#LqSZ^hgrho+1CQ
zR~>~O5Q85**Zk2aWA%P%?<BuN-0U(J8{93Wb1HES9N){ZV?53;qoW0p#znM@Y%aF9
zu(9Hkl$E%Bw`WUMHT1M}G=;lf(cv`?>6jR)nHWU5i$%jk<S@ZCt%tWcV@()}0@(5%
z%E}IHAynu>9p>&U>MR+c+Tx0u&#di5Ee;dYsI4s%nL;jYrA{9i=_~hSd~-fG-L;LK
zpmtyHgYYF62}Dh!QrdBA@ep7rK9?<}#o>U#@ijWC6mMLo#rI&Vc7Ysi*ZrqYPZS~d
z_e&_vwt7??d1$b?p0|UMM%`1NzEfW}2OCRio$Wvm*9tl*XvPN>pDmA+BWnYMcKK+R
zJsj4WrUcO@q8qY(f4mm~KR;gjFG3PCxn&2`dljzFkR@nfZHj0()&+6Ad3YY7otdGo
zot%nsYFR<w%M<W}oT_xTYMm-v4knq+FXMC#niBZu76PA+->RBOAgC&Qm-#t5Erjwb
zAi*R8Z_L)Vj2u+a<cU>c!F|f>zWrF8s}q9mLbkf$a=guzHV0}2rVEZeVn-)Y^+2Tf
z!cvN9!V5lo&*iP^|HwfIsW?t}l`9U108NTiaVlTr2k^ocU|i)y$;{9NJGWX@x!Ogs
zfHX(|w#|a->e5r~RJ7A+_Sm?p1-1!*Z1|NczTu6y6YSXd@%5opZ%g-|(czj1h4=O0
z4^@}^5Po7q!^wZ6$Nrvp9N_KrU<h(W9zddotYQVvLymDd5{`af)gPp69b_Drn0acA
z)Fs9&VO*S-4KGAg3BhVc%pn&=fx-&YM!*0bm=Klz#l?Zw$eOe=E$DLHw>&%!RO!m3
z7S^_UrCM5|$pnI9y2Xojsy9VfW=qnhQ)uF5E)E4z*=kF9Y}cCIPz8MZdlDq98r|NB
z2RW{dilXrc<N70+s6Y9~QA~ZnF|TybPQWR7<U~?2aCI6Qa>%GYIH$KdpUt;t(6WNp
zZ*6)}cio;ZhQNQ5;ap#4baA#WHrhSvUsj8aLeNh&h1|9mzk&sHL<p|zy?>C#4D0Kw
z(+JYAqdsW+?<!#9{mzCa-?|9?ZY+W5TSh(@qR>J|CD+eNN6CVE`XhzkGnhui#>fI{
zwV(#8r|2sV&R0A5&bEwlJ8Rpyx?jj(E&Q=mfmW0K{|Y~e6+b&IUe0~s(OpPE9{aET
zrT^*5uD8z=J3*Z;_gw@1999t}G>vtYORkr@d!@6+*Y9`8gXp#!Rc$tSuTMv%=x~mL
zGtEZdwS&`zR~Lr0rGNoGtub=~Ni}MnPxqsAl1DNxCGuX;Y0Zv1_iq`r|G;|fCz-Vm
zcMpTGB<~#C_lZMiF<Jdl3NOtnQUVX>t2G8&vm>Y96lLWdFhz9_SABwa+Pk58jbb^F
zWlE1n9I<-aP9wRDap&rW6jB4JZ=L!dt5<$~?faZhZ5~XK^R1{N>WGf*jxZk*2=jgY
zJlZty&GM&b7mTl{m6q0um@$|@zs7VeSYFHZR3eNRrn42D4CDjZ{*3Q7LB3q$J+bSE
zbEuv*wCHniu4Ad|0Zr2B>v1yntF$p-M-Oe&Jseq4_3u_sE_|4YX7#%Q&GBDBu*cOV
zIzf9CZMnhmE`F@?_XR=l(K0h2gY58rS?`r%b9djUL|<4q@kwzm1A)cq1vk+SE&A(6
z@&A#4+Q^%I=B734C5A;OXi7A~AY=Vr4I&^S6i1nhOYAHyY)Zz$?&@dMF<`PV6z&k>
zlZzl^-OxA`y1x0vDt<+B+a8akmuAP43&~7`S5c1mZ~U(yfpbhp#3+1zs=<Ty_H^<L
zZ5%}~-OJ&@aN&Ng96VD}C~{M9@;bi04k%1Op0hJI7<}(t_jjXsoQ>tnj+?Xbkfi7-
zNT1UC1}7UKUV;HW;9ecWZMC(tv3n=%uI&(lE0o4u<6ZbS){4|-^Sc4k|7ogDDBg?g
z)_op1(`r}O7w&dH){WYRId&gj4sAQVuU3z8)4dv8%5=fY+-BeTe%z^j>Z8u&O7DgE
zC#c#op44ePzAquaSM2Y)jCGnf%v2auygP-7w~38`7u)5pBmLuh5IHx-lKa=Zct{aD
z=o*8X?EocAtPW8U%q+wG*qKIjIi?b;<WBqH>$wvio0Vptll`cZg$mtf@3#gY9ka?+
zk1?X^&Nesj(c<}Cgz4NrbFKFBo7;M_*Bp>n082wEz9PwDhwahp5%Le)C9tFZAP35c
z#v&9He_~mOiiYIgtqDg7&`6HNasN0bdLpl;$mR7gK$RKC`R1kMIa6)4Vc@58E9iA~
zJ(t>eA^bW!cm@5G?kKdj>>*`&zb-X&q3)pIX1@SVxMN&C7*G424By<Cfa0;B8Oa6i
z3hz#m^ODu_u?<ac*+dUK8pe`Y{&n?>Ir7)Rq)){_$|1sN384nRhmnbra>gH#Y-la$
z*MQesWL~%?E5jR_v9yH_Yx%*3HTYKkhqKk?YS;e14e@kq{y$Kl^Pk@40vTx^Y<>!(
z8(GGH^;#ab8})oWAK>Qq$kxQ7HyUh*+bnLTNbRpj-uy{Eim&KWJZKXXbXu&I>aLB+
zkxe>S_t!r3#g_&yBV-thjusyQ%D%kS#o`7~X4p%8AOq2=;LrQNKls8gaF7aJBaneS
zLu2_h2-fO7&T1N#W{66VRe%HqiW+s3#ZPbx=-sLyaO>Urq{c-HpC|tFvqllZ>$e;l
z8F>ovor8m&o!*Q@@%YRy83^{(F}pZj0x0uzCi9e=9_GheiRph{;^!OgqbDlTTI`pO
zQMFvp1)NcQFWLnfoXe<p!Ufvxe!FeNCiJC`nnj;3wV7V6<`Wkgz8$z#W(fKE315Pf
zfmUsWc{+QsqvPndOKWsm>yoT!!udMiGJ7Gz(%@HDzkDbbP3Xd^v66jUj=_Vk&8ZB?
zXoT1JjWQp(fAHQAfJp>CRkfcUl6i>=DXorP3&j9{Ys2Gl*F;Z50u&N!%e+=t`164`
zJMbAb<nctk<=>83qtnSi{piZUggLmZlwnvg`qJgg%nywGML|#@6(M56?>~C{9R|y%
z4F%V?Y-UsvkgNXs2fI*V%DQA^=HC7_RKGdtpdKjjh$IO)Hy7nK7{gGA1zYY>ojK>?
zaBr=%{bzWA2gzVYpp-&dv;F<&67l~*1Jn-?1#O?AAoA_DeyqRiXbH?Sfwf`+6d%q8
zE5RR^&ldmEPu9$0E;d?RuTJT?@|W^m=#~LG=C<R*FDEkv;70B$4F&|~Xs|lrCX_%L
zrO`FsMo$!5l4WLM9GVC5C=_XJG7|J}bq!E4lopuTUXUexSLHC83T~&(j%@unTw=6b
zy$_qwG$ImA^@3LMOzQ`MJyrv!AGH=(x<#HR-`7^S%k}E5l~2+LRgMP}@%Qp*gJ1Zy
zj!R{bvAU+r#y{ErK*#lRHPCeNk@f@__R%CC2uO)2$Y~n5?kK^9J9wEmGFzWQpha*k
z-pr@IgXCsr|E)kHpp9kAr|Ur490LtyGCtpD?T0y8{D};H-#jPq2e6+W^1Ew%_)jp3
zCZ6=>^>Dgejk*UhhH(BCA>~$)Ca~V@wQ~(Sj?IWPt#8ZYalh>%fJVOBYW0#3b@HP!
zX+|jpbIT$>Dt`2Nq2`oQ2JXINDtX5dOH4#cDpF$hX)g+a_LVnC{6B1~q*yF+Nh)61
zvTG>AMrP+Z2yB|asvvJ#-MP`0{fCe>LtF)qkCgiAK#^PkMM19-kR?7bkE;+_GRPu3
zO~)Ei1+=Fzq0a5~KOVBLn0d2|orN*pFXlyR`{@uB%ih#R1(e&gbw*fB=*K4(DI%zx
zlz!<l{l-qkjt#{PNr<W<)mc=x3$v+odE@$@MJ4SE*zIW_IwU6NZ~M!Li*|Gx$my8Z
zU-h=b`(uT;4Ym=y@{9_Up_dwrfVM)&Fw+jH=I@i`km#2*dhGC8te&=BtQuLX!g0M0
z2U5=m{IO&{7e`#W%;ps$RJJ0K&X|_4KsR?8oZE}~_|Gra>tKgm^{3{qxOL+sK0UG5
zr9=l{ks8OR=W;J}a*)?k1Nz_KS&Jr2+5M|UhzQXv4aLk&h?p4Z1<|<PO3iiQN|Wcf
zFl>h3GaP?+a(e^3g>=GhBQPH5WQxu>D3gVgs+;QrBSI8KLSt&{{}cqkfDT`}n(&B~
zy$y(?mgXNJ1j0=6xwvLUdUZs}4x>HTUjuc(09}!uO}Mfp$JyG~RZ>RGTGC;>?nhDA
zgiez)toWfl{PswG!~v^LCu&hGdS{2H``hVOI&s2;jaH4NkPHkOxfvT@CHb1*M!nfg
zIPCb{&b12Vn~uMh5H)zUkI=@+jFU@_K#HQxXO*Pv`S5VEf&kG*1p>KQ;AiTg1)Vgw
zR^xX^_|BDeu=@A$p>=q;%c$B!Ymuj+D1iqrw$=SynKS4$zYw%nMwAxM;efRq)$?U)
z@A8ZF8sEJf502EVt@O<J6as9#(+g<KxoMQ!p3gxZe3IV2W=Idm?4!|P&GF@c9GZVV
zvqu2&>oVerCo1{G?Qu0)R7nBZ6tomrnnZRGQ<dp;8`)CJRE}B@sY-gO!`17+V)u5%
z?E-Y_5m|y|&cEIP8<YF+RN4r73m<vNP1??~08V!Dzpt|=9}nq);D%r)BI&~;2CJ8f
zJhnj0W>aL&@xb!Pb{mi&#?T{YRdnOQH<L;jAlM5P4Gea=#Qb5aLZ#oX8VAl*qX*^o
z-X;5zvws))u`&%EJSESjaVY5Wv@Y(qB2t>8_gyMS(BVb4YsFx5&B<OsLCa3J8R0Qc
zXhq=nN-FI_9R7YD^*|R*E)c|PkwBf23iwKlw#L>*d8?UDZ(dT{G@fKlk`R5_4k^jI
zNJluRTxFG8R%{37taThdtpR>bCUSHdC2W@5n;+Axb+qt0NlTs~B2K|)x>)qtJ*|fq
ziXnRt2AWvZ(yCkOG+jsNppR#qpSNeg(43lFMth#oziQjaGM~HqUe-=Pz_WGP5Pwss
zikBye{ls9R<0M6nb0K>@y-nq(joMj-Hes6~Jdv~)=>@an7W_p^6HK@`K1p2B6{_Kb
zI;g^L6Ar{I>5k=7JDu0=18G^-bA;(<8V#P_NUsEh5A^*iBo+6!0=*86wcG8VH4fHE
zQ|Fm=*lHcbG<`j_KQpl}YNi|>LHx=KNQ=aYqJ75}@ZWtO;NZas@Lxt9;b}7tz0(QB
z3}7J`ME^xdfB|k*23taDmodWvCXLS75zSKcte@%HJ)`q>Ap7m@ak6<+2&ZE92%L8H
zmP-O*{??pgm$&s9o+IXPJ#e9ONhz@X_9l_9-<22;Iq6!S15xtvBQ&|s?jeZNvoSyM
zYaP-<juP&;%TQxae`l9o^)akY_w^W5@l#Od;%E$wbWm9a|8UOQ-tD=AcV~o2QGJ8A
zo$ZS%S^C{gBf*;7ljY}rwtbWfl)u-iEU#8A?u<!st4`sTOOU=}ZRb#mK{W{=T`Hdz
zfad-f52=bi<AdmWbObkbc>ijtD$EQGjBkw3YhV|RX@C?(dMR@3M{3#=S#qwSWhSCY
z;iG|W;KeXFRS*0!Yo6Af``>}Od^F}NbfPr9-Sy*FNZGIA!iDIwuGX$k47oJSUEJoM
zV+b$`TOt3XGz9Rnu6(jgW)-CF8SI9sO}l&fwisOP06DD{bu1i3^lY~FLY_g9tw=h~
zl5Mh+s>JEAp2F;fmfq(6_Kxk|d&T%Zl^0J`r9uJ5IA#AbcIv$IJU40<<8r=UWBb&@
zC(mWlERE^Q@`J{+DrH_OE9S2i@vB)VGktM~LEfFHEW!=y@3(1fo!lk^2s_Kc9s#z2
zQtPe6pX^R^@6ay~XKSry4;4D_G3o7*Rt+1VYCZ5%awNHJ`YIx6g84(}n1lQoDf~Z>
z`{gEBxW?<@T<37L&6#Erk0UDu6PPDnX##L4$(Fsdmr|~(!be&yj}p&(T3EW``wJ#y
z@N1gi2j5IZr~}@(6v4)kybWpo*ptkx%W@dq=$Y;L&n|C&aA$+wsGZF}wQO{{9RT8A
z0PS|vVSj!*Y@`!%Z+oKAZY(-F8$6OT76i5jsS{HvJwlqIYe4qf7B0YPL_~pCAG@kj
zL(mE3IB*<%>U@SL?Zv9hcJThf>~U+U2yuC^`$h|w8EDtQO>Ak-aj2JRb~@ZNq<h_y
z!%&8J#VC*87w=?~BEPkStc7qhXZ%Pe-4k^aR7mIdxb6$v8H^@ft8cJ*ehk=Eng`1c
z)FFwO;JqVEu802|08(p_?(`>a1?2F(x1uz1bk*=LQ0JzE;;-e_$Ntx83GuD`TfG|+
zYV`F__n0{a|3XO<`4_Th{rMjzAy;!C&<6JhT1uu@XBU;o@rki*Tynz4*|F|@hX)=q
z?_WCey8Jdt=ZwVdo1h*&Iwj%92f*LH@rvRRaTmL2TH{AL$e3$*N|>lCq_4FYs|_}^
zha~k_1BI{GbD$HK0Z`^LSKAxv9kfm3%*@3U2+1a`*J?aw2BM=;PuL6bIrRvXP-|Ue
z5fs0AEH%MU@6P&l2P%_L&i}jWUE<Y04N|kp>JL##W!<z*P^JtMGbEbt03-It{^a@8
zsn_QD0_kk|BF+e=fq%~>d@(7huJ;<miz8#!Yr~i;#r|(MRxl_9<H@H<d{RU=JzJ(4
zSGf9K`1;PO#`euazwr*<bSDp;J4&KQx`}}AG68}n<2TOM4#^y?G7)oGme~pJA8uD~
zqtTu@;Ed4EXjpZ{>9+$0d)~A?be~xzPPo`=a(fwCNXDt#qnw_|TWBuA6-DvYnvJKa
zY9`^4ZkqN&&2^20;FWp1i;s#bfB%g=e}m&s7(GQ?P#&|xPLI@yK}dL$U50SVD$rW_
z#+ydDI^~3*P@2?XymD2GUny32e+TjV=NBVdUn#fe@e}n{Aid}bHs4?=p%-wQza4nK
z+B0(E_-jN3)vj~Kgi^1zmseGX{qijVk8$Vz-@g+!ht}wlwmdf0mrHVj{dQp?n-r_$
zg56$mflp_D-;KQj=PM&d=!!Gz^Nco%0ToZS;oDtnCcOP?h`J)#YK!Ch?jKUC<rJ%d
z0*UPj2R!HK6Uza>NBbi#F=6+!6s*T$ZqkIGWswefC5E25mI^5@p>nXOyf~qbU#3{(
zl@TGzkq|9DIvDg~mGM3_WJAf4tAC1@tRmobrweC(huISiH@Eph`ieF_2IOhcFIJjO
z-b(V34-5bh6przXNo#rYpXipqD9RyFy8JYSF9wsD&H}LDb|`8J#BO#f_?ge8J_t8S
zqomyblJ7~`ZV|gpmh8l1G2ams=#yc*CLZiZk<@m9p43sVoyM2KYCu!^cWZWZd?K&u
zQrsIDo4Dh`(aa*+b(a7b*Q_X*fqKODnK|28zFe2@1P+4Tb52TXr5FP+;jh$EVC%6K
z|9nO7sa<eGR7mA&&^YiB=Fl|};ooun7!kDZr+?KBQY35D=eclSdvXaMcX4l@MSIX(
zp0O}=eG|WXc+O1aHK1^TSH&0&zpk31L==CHWIbAfE>`0mv{xDE#VRuNanFG-&M_fR
zQ=Y3a1-o6`Boa7WpFl)|Uv7cE_8|H>OhL!<HF$ORo18g54hf($!`i(#K@~jWHVsy4
z46hR5qVnj&br~aV?*&>r9Vn0}5Qct<FFQx8o$nH~1*%;=)=pgmAA1&~A%@SNJk*$5
zvu3Z4WZlx*qKNAOMfAmi>%i6L>XT+L{L9<!R=d_h?z9bmUG~kFzlJ?f-hNhNt3X)`
zEH6#AoZDvfZ7MqBQ7`l|64MYy3P|)yzfXbCOu4MhX+%;$DO)?ckp4tH)SRce?`-0F
z#d|nvoHl#@rIi*mv5+Jm1rHN@DxU-=`JJvGw@>>JKzeqp=sW<MA6|<QS%nH($0k@N
z#O2O!$7MX-y9c^G%_d9e-=XX~@u4%gGu-$PAq9`*^dG1m(wa`j^Qxjk*8To*$?9I`
z5Yq6=*yAPUH!>1*a|VJ|VZZ3tUmUaslJ#va^Kr}9J*Tt0-sM`!ghy7z^i+j`Sww8Q
zO0h|ur%u*B)n8!#f0h7IziPXLg0W9jbo$vx(ejWpt^f^sJ_@|B@`MjgM+zr=HkJG7
zdb!Wa<UUS2fTGX#<|!!_)Y;ftEA-st$u^Wo9T1|LKK%TC>nc1%zLZRyMHRD-LV)?!
zYD!WX0sRpI=|K5cPc++*KtTU-?oJMXDA6?73Ob&_wv+Qz?dV~0;;z3-daPAM<Jvqz
z<n@WM)qa`xUxH#0Zsr9aX}`!)!imbz%nlBV>8bq;by`$f)@a}8pv{05{C7x>=}H_5
zscpnt?I}Q=+BZOBNBEb=Wo~PQsU+ncRlpsB)yr0!x5e%xMZ&32zN{||>1);O@PFgS
zvl`@VtxVFyVlaZnbU>h^q$598F{!f<`&69qn?CjIM;GE`XBN1SSBu^4GSMR-PWe{!
z*;Lm-0@Wt9rRyq>ew?Y3y?9NLcN!Y<1z0^SnINZ}Qm4US6XX=yA9$vpzruM4tRx+G
zkFtgtl#>{jcE$)kC$h1!DPf4lPbFdYISsj#RY`ALp+sR4R0O-Tw?>r(eEH^Oc>XQ!
z;ZH(mf|K0vm<w3P4dg1=jtGYuc|gF?9vI{~v;jSdN{&N9f`vu2i=NjU?K)4xUsBWL
zJ(zCqKKIzS%Y_061C^J}><kdx1UZObdFEN6DKSnfDdfd$Vu+Kx2Jz*8hLU`w319h;
zD#{zP&!faU1`$J<R<10trt_5BYWp&fxWmheGfS3XlY(oMXUm_dlik@dpw+?{YPO-Y
zt*vv1`wIU3p!X3q6sAx331AQG`wS^yWmTX_QI)=8Q9jkj)kTIW%HUQ-KDY^bKsm1*
zAH4z4PqO7&le6t+m6MVGu&DvPlt$aJ-#Kck3pAa-nk#MGNm0%OF4dN$2?kE$+LZte
z=Bo`vaOU%RXr16x;=kh%r^_(IWr)n6>d2id^L?tp-p?7@SM4~ySP6y=<vL#)iwih;
z`1ltGVbR)1n5SiG=!fh(=%8!alQL*<Rp}d%vQ&bQCEZh0NkKtsgExj5o`lpcI#kF4
zW{Mz1GXa8KMnfS3>fZ$Q=;Os&O?R_zzR`tZU_s~dERQL#3(tq*iZ=nsuS-ghP776s
zeRXL)0H+jj4%eh#;)yT08j@xIr(-}a{WwlsqBKL%iJ`B_-&xs+D$f0n_G&VGGRdql
zGLbwey<<W{=s}|v*NwjI;l(Rfkc(hcSC(I`5W14WMwD{s;YjKlZwD_U1to-2AcOQB
zc=)L3_&Fk=*WU?Ght@bXj%Er!wu_O^m}4h*g4FZfTg>pgqYOlqOAk}S)nTHG*imRn
zN-zMm0{E%L>fR1B(|~E~-bHZ7t;B80>A~{>=os4SwYs|+xhQ+e=>yiPt)`ZOga%kA
zEmH+MOY0)yR4kb|V|GlgS8b2*Z<*jjf?Q}tbefMv>}>qKt6z8d85G=$;Rz~p?A7;D
z(RLfFE@w-A(X_k*&*AVjEfJHoE1f1TUdWPvem%D=b^F6>T6?couOu74N4#}R>wmpU
zjloYh5!LWZpjb&%<;YVMf-#Sr(tgcRs_dck(O3O{G@WByrd_m$vu#@wCTp@M+jf)f
zx^t6_$*##Y*>+8~n{2zz^PclNeSSW)AMAbawf^h6R2G-V>Q<x^z-wtM9H0^bfa@VP
z|Cc(u=B2>a+}8C|Oz8t;&Vyi!-}`K9l76-iH|S^QCh8<<YvigUvR|Z)2x?Ew*V#{w
znai%~63VIuct;>U4nBp{=|tt!LBly4C&=KVlO#U9%|L5O`rJf4p49CvxXRl>B+3vZ
z?9~VcpGSPeTB702V}Hh%xSp&7&0gwjazu~Q#1_b~2Hyj4oM`QynI{_*o0+eZV};Ai
z_fuYugaeUH6a?fBTc8F-ism4S7q7Jk7m_UHt4T8PNl(tL?4?6OJAC(|Xr{ItYhRs6
zhgG$SM{SNmaDXGbinWYWnLoP_d-dal|ApZ$=dTOHQ7^XsfXY`7L%RSJGrNR=bv1oO
zQFWDrnT7f3(e93saalxyMs6rqVW5+t|2OU|OsKK@30ue{*&oA@XC>WxU;a&IcEt`m
zKFc%cW}c4UCQ?qrvq|O4WyMjflW_ezb*wVwp;k{#D2BK4vDtCg7V7CE^Cl5Wl>IIP
z|8LNjGVzw4z~ZakM(ifAuwLZo086VWXGMhbOP$y5I+aq>8?+@&*%);8<o-Gf%;xn%
z3UQ-JwH$_}!I_`Xs&Bo8$cwBoVZ6{-21S~2jb=6o3Ho)a3)y9z&u?=Ousc{X&Y}cI
zcCsLL5l&tX9zX$BRh+;*!#CLBIcptPm2Qu{Na#5mSr^ddRUfdEGb+xEO42a<*cmst
z@QeAWh@LG1Z62RCKcw_L14*u4R_r2GbCn!)Qu55uM6_IQ;;)K5t>^O!9VRY5OkI);
z>aMh&4m8rf6UU9WG}o{iV)n?fEI*tbLuRJ^&DKlE47iv2_N|>1U4vE&MPP=tonlj6
z3XcvY1mx$tmjfp2yeor~5UmWq!^b|?4?i&DRmokVW$7$J#RA^~_I{KR?|E9x`R&zv
zsoKcY@3N{Hh>2w7X1Dft?begJ^J_GPFns|{V(ZkV`hc16SYYho{z^3D{cVmeGex6`
zT_XA19)Al`rz>gk**wRZYUaMl?7QCaM?&Z%?w76wa09LDe&9ncb2M1RVU}ug&?mc_
zTS8#Wk1^wiiuOM${`S)59H=gFgB=@P3DRGWhUBn@4W#4PnO$r7L*7u1T^|jUCSnT3
z?`hNKpxnzjH<#N<`te3)9t{xMKs5Y3S@l)Vp#ekhX(1Shf>*@1QXY|!mF{sLOaF*k
zQY-D7o&`X{Nb+_wQBzq}Wcq#(1pO?+#O%ALtAg8YVwFDK{HNs9^XuaT-xQjwP>aXo
zR8{6ujXrN(v*~ChsO84mx@XC_;(jM{d3l*5(BiZsepoGUmlV0nFF#7GSj5iJ=qj8!
zRu&gVW``8PO6uqihhXBygtD$*;=V|&%>ZV&4t&U#eWvVtCa&+qp7*zhtp7Qkj&*x_
zD#{<WvP7sL-5R>?m#mN5!A2vGyv_@gY%)_*gAFL2oyezK^?{L~J;c&XJ18CwFTBCr
zzg4JC@8kE=Q+++?PxF!Ph7pEkKHBK6z%%~LJ7nEHO}KQ};$LfZ+wb_I{u~kN#zd!}
z2l(8)o|0(QX?43Q_$iwNRK;@OBJxR8WY|=U?J=5t|5OZYcUDmeF;WgRJ~LZZ0I#C!
z8GOL*Nu_F?<Nkvv_k}w4GHu~=mA2xuhhi^mNAK}@j<(I+L#a-1U=4jd%%5`D^ea4D
zC5S#9A0Hovr6{#EqGl*|P5E2Uf?DG*7BJ97fe9)s_dy1B5mBx?btuwHHs9yV#sTm5
zmew_&@V-L~8;1(H?Viu8RiNgm@+-y2^M;g1mF}{hZkEQk9~b5NprD(1@3G4+1W}Bd
zIImWar<n(0XDXimV%%XwzG$xlj1>;#8L5LS`;scD+bOdxr1|~!Q|>EI&NaV0u(J7j
zwRwJerKTWU=@5488j>4T+;!6I!XJn{lZ?Y+G(XK<=j`~0ec>5SW;{gRJmYp`cy3_^
zr{rfCnId$MbshBm?ILN%)6tUQv54B=jZDoQu2X*R8zSSpA@TF=cbn1&2OTa;XJ}c|
zv*X4+K8=Pqp@U5b`f<B8QbGGiV9^euAP8c1<Ny8;euj|2AQV8*(?=p;L<WKv(2>iO
z{fvQI-e~K0n7ki|x)yr8pxnHiV12-|L=qTzojTOE8Da*h!vt)R7mA*iE!a8~>+*2u
ztY@Z?z8x*;V7Kd?##TUH;TVq3Z;to=N3`|XjCSY_CPmKjjDPU};D`O*s%kJ4J8wc-
zMtwy4A~CK=jvS;c1>_tH-u0`H)QX;PmAuD=IIqROwt7N8*SQm~@oS*?!$TvB$x0nf
zVM}yBmEe8+fuKmt6R(ltN%}H2-iMx#c-W~ZYT)I!mLdhbKOaNpbD}m9&^=`FY^=E8
zBPw;%Xy^aEL~#uQz+?=pH^cN}>~Jsnhx_g`HL0Gp!P_EHN%&;S(Wtn8GWf15{X!BQ
z&=S$#njRULU+)_u2xv)!AaW^rx1njzt4wWm0W*c+-w{D|%<3;2=zgZTc0KrF#o6{L
z$@-Z6?R2jWyt$z#=o2&FF4?mdYnu{Rv~uFl@)K3^J!AGeK;cI$&Fuc|2;mn8ByLiC
zGFfoqIQzrT(G@ksq;{cvKc9!Eold?o$xCaSAJ#?pNMEe4GGCXN8@M!5s>ig{n+~s5
zWc&XL3AK0@Sj$J|LjNv}J@1BJ5FQImd*%zV)Y+aMn%bOMS(xk}?@jjYwseWC$oK|J
z{f`Q!J$<L$6zB`aTcNi;QjGwv)pzlKSq9i%YqCRJzCr6P)##)`-J*d>O?(`Z1bu>1
zxqdi^Na7Mu@Pn_%s@h&u3vxYgOsh-1-?gJ%);*I*{3ppoYzJI(;|$SGAcFjkiE^}6
zTyAcHyFWldKWzR*4+n$e^KX8;O7ENR!>d!M>s_BgK>pgR+0pJwNk-La{gT~=M}v#e
zzrjNvT$EHx7~L<iF6-&y1rTH`5x2U!<Ot4}2N%cZr=)m91Q+K<1?0$zOzbt4IeuU#
zoT;=h-;Tzw>*U@%p7`c`MSJHXvKEqTv?X`(qX^h;>F$|FCbrHTgt`FDmDTk@Wq$<=
zo$bYU07&#iNE20)D+{ySO`UDHiI&s-MY3;RMLjwB_73D~xqUvo%FUjmM*YjX%`upL
zyWW``FWoK{#{0LRp?+p(d(q-~`XMV?j@{G<rQ75z=06(yf_VrdWf)XBxynQ9b^$b{
zrnBa1ib`yaVXKhfW2VI?6I__v1awvo|0Wv*yfZ7{<j?$JVC%9~m6l!sT7M#fU?ZhY
zYVjYJF`L8`e_yciK~r})pxY*q5a0FFd0@;M1lX6kchorwX=KF5t3ifTaZjCI|LD51
z=X*kYd|x9lFM^OU<<jCjvz@ehi>2$2*MEd@)=Qa?{|L9H=T~Pu+>Qj{fX@Dj5nK;|
zIai>g)YY7zMWx|k-Sg?=VI`x$j2ZIUDU%x^@Rm>lHU|o4Quu=mBKza4!{o-OFeL2-
zG~N+4XZn^cw?OTs1!U3|nVki#4o*V<CZB2%%DT$S(<I>f=)n~f2$*#38eE^D<e$hd
zC?WKydB_piRBCMXl+bDD{`0$}5^N1<uKfd#Z1Iz~HU+YpgE>?wXObGEbs<R1$^^|F
z<_BVYBF40SYG5bZUlkRNvfM;7FN42&-oygJ>(`b#v0Pl7&4D+)OnJjX!zAK=!@YG+
z84%bD!J`2v{Tt@$#&ZT~)OzLpVq<j<+<V;$9||QzgiK0(Oa`f|?bx5v-{k>V4{KRF
zUXh1m#1QQJbHBnlO;y<C0U+Vd6EfX}+9y`OCD?nhl-ZzA@#%eFZ(>gb#q8st*rhaf
z{(M!h!`G>(1{4X!`Z$wg_aCZ+hn1va)EeoHz*ZO`Io8xe{>1}JV<y5`^fdw_u7aUu
z@f!bb5Dk}$Zu?tN9vDhP;x1rP@NH^loFVou`aSf1>}UDMP|C+GajCA(+LXKd<DEXl
zcmizRsm$*b+NVK_0?ChoR-0u{7QWRDEu0B+7VSga#Onze)3zTZROoDMYzXujZITum
zAl7ylh$0aYm3B}P;<JKJ7sX@(^N1z8)UzB7LhVteFw3MyARyHoU;~NqoQ??E^%lB&
z+VR$%pHI)HkA`D<tQO6lR*IGMAWN)EV{_rYqyEBTBk$-=P7@40p7h@_4!<$Q)xXn}
zge0C0N8n7<gbf^D!T3;Fl9HitG}LYZJ8K?uoKsN)<1f&Rvt(arC`L*xa8nzfw?pfk
z{N9$Ym^$KwXF>~c*8qghm#gQ|#@OKHNE#OM_LiHT<BxVfS{v?~?<mej%X)J?c750c
z!~qR#oao=aee;Hqq_My$EAhBSfQyor86T4xE=l?w#_`E-uk3|vs@oi3RhDH24M_4m
z65<$dW8y5;U(1d#RQ<RZG+|aW=qT2s9Wu|UOQ-e3RwM})^Z<Ou6J7-NZBnUW+I5d1
z3VqODv$R!#SqqfC=+RbwTv~kzNW`h0DOzXg<eK2R$R`RM7Ull*;?OPcHTi6TxOm!W
z5DY#A4SoASsTOI1PGDL<M&ZH$)4()@#PaWR^#gmUjzU9ryQdvqr4@-jufApm;pvZE
zjKoAx@Wg8A9=O1CJ35NHZRYN$z%4^B9j<HmyLicn_)LYx8B=NJ2*x}xG_<-pHa0ff
zyTQys!%OLb&809cu}|Ec4)QxjH2!(~OSl~n2)XS6og%+MnOa@JzwE!WyUpZU*7?zM
zMTDQ`1raG0m9Rw$(%Xqjow6R;u<t?WPc$Z*4?gi80_^gy(tO&ya6jOqj|T$OP3YNd
z@@s=~Vl1_sTbi@;>+x{MYp7%rZ20M;ki02>*ZgLr{W|c+u{yc#G7%YBpK_p*L~uj)
zEQ7-tw!^V~DvRH{whR6)ODebH{q4$me6!%rh|kR>Zse}d4!QKJ8&SoYay^AWsxh1B
zd|cGiFhFPl{-<xgca3oT`sL-t+pFbUU>&df@=xi}3B$}Jj2%16kPBIIC;aI>u0O1c
zD#-(jk(eaieak+Z5DFyPe&Kn^K{tL(mhG%kL{_t;W%Gd3GC=X_WpzXvx6$g4PeY8H
zLWnq9@J<xtrWSzt&GKcygz>niNILHrZU>RX2be1u2#zbOF4kosN286z5J~}C^K<A8
zH57!tyZMFoH0<?9;*LL&wVI^KW#EwVQ5iM7=02Ct?M&E>n*7Jh(c~fQ`!KyGjVFQh
zkF}@4L;H1?hF4eB_v)L1w_<p5$AiE%)<HQ_#u&OEjYmgEcvy9Hb?CkRIbR<%N{Yyx
zHGtliw}x~uOBXvYl{iIxRb0sW!x%#WIko9@7$<p#%jL@5?C@Ip0TwvQ4q6wXSOIAU
zL-aO0ha4e-etTQbpRN3yeVM&Bue`sO!l%^|(lA805&mUsY4%5}uC4Qu(M&I>Xrfbp
zaN}JtfyGmQ{noV{JB<?pMiNLoq9!Px_5?I?p7#UjO6}JeXsF>|bNjtR*B&_=jfqcu
z1j3sS#Fr~>#|XcLmsC|R12DGXO=;LQWAX@2oT_PB1LBKu6e>s#;yQ^$5*8K*_2%O^
z!{M+-sl^f5Qc4rMXA7Cr+HoX$kl0%6GWjox8W+0OkK)i)GDg)2WG6)Zsg<jGh`?&L
zrhS~i;9}sO-oGJLYb*33P=Iy|mS~YW-Mng20m<nP=b{SQo^dAUwId(V0UC^BJ}3yq
zI)}}7Ey(*K`NErdIuhkr{}!fc=xBjq`|o;-*iQ9(2Ik-Nf!`*io!*x#JD1<L0=Rgi
zZ8WC$GU_fMGiTrB%?}VNbUX+5ZGm+}`r%i1O?xJWQF$asFj-T{_p<L*JHJ|r8!9^s
z*cP#HaH{p&-8b6Y0%RWDn%I=sYuyolYA3cg5q)nFj4jcrXZx=i+b0Skq<j&5#BDic
zvYR^72z|e+e=nuO$7}%Wb#Yytu;q=m!~VH(imO=1=(Tk$<}(JCy=`rm2hdR02K*3>
zP>p5@onBH(#M#+PAJ#(38yL6#l^@K}GbkzrjuH1&n|}(ow0dRBs)0~MpR4iFwP9K_
z1^pm{FIaJsi=*O9c<E|ngf1O9dc)@mE>jYC+j|~axTPiZNW)E4C$>5!A@ced)>52%
zz;+@DOU%Hz*pbypJr{7d0zSJo_?nWdne}jRx2=jO##n>#iSIz;6>QP+3=1A+U&qJB
zrbhL1qzXi$IrceYZ1o^1K-tq;Ev%Q~`R@XL(d3`u2koD0dGk{iPLUPA+QO)qnOEA{
z^N;6SWw|9UHvr*?PGud#ts}f{0J8A>wXh#jfAZC%;17ZvpRsN~Ab5NDv_l|vRQ~0=
z39n8DuLRNkSMWL!`=c`49kY*=<@JI;Ii$<hd8pERx<6nl{xsHc3HKdFSua18R#x&j
zHbLIohg?s9=AMQ~1bp6ijyEcBSxT_h=utQQUX0I7WGBPUtWZsTNQJNMsstfkz;wB=
ztELPg-tnUAYNdl}fwBQy4}Ncs?03`MUCbmEm9!#rD#@qzozSd;)PxlF@=KLH%3!|@
z;wrhj1~qK+9-H(t7(iiH8iXtrMQAGnv1^#tTkQS@n%Zro!PacFgx(X2T%%+mbX~#I
zV0`W{1+o!!Zu%Tp>F<|tYD<G=D9c#(GsBs;0V?T_Zg@r@IzYQLYFQia;iRl3X|B!{
zITF>qc~KQhB7zogsH`n`@Z49$IEIG=4yJ;aMT-fEb7rTPp%6LvLw$eY)%x^=@PX8d
z-}B8?b88{rM{$dvq-!-8UQ8WXYRwxaoV1&mkjJOCo`+$In^Dlt%lJ4pL+suMq&<g1
zU;#DQr*HsodX*5%FX^d4${flIstwxRVCj=m!$X*}9`v&zscqR64j^m|?oN$a-aF@C
z|GwaZ?Z1afD$t}vXMJ6TZP!n~VqFCA0Eo!yfnF)?K6WH{tFrfVaJ99?b9%$HOpFUV
zn=*>P^JQg1&fX==uNNG(Zu4(WiDfP2&CaOONcTWUc}|iSTTUJrS<(%K37n1!0$mr0
z0@rdi#J}MzRsj<`<!ev`OLm;=vfSp|f`H@e$9T9$^+OXk>1wkJX-lX<=j|1?up<Gz
z><zBvLDko<8ybz5JzeE?#qp2-*M+8Y5aGnVu<9|KFs69*?5khi99`J>&!9EGw2%s*
z31cM2tIlw7G`qRGJ3G6&);7cD5c9_&;xC+rz}7X0|Auv=nYM<HR2bD$R@2aSsXZ1y
z{WW##2eRfB=@sqF4ii}2`NVnAunZ8kI$f{-E_{8oXMkyCCKQnHZ44@1AJFjphDH|D
z4+)_GB|z}*D@l3@dir1GO<zYBwYml1Az=9U!~%nE9`0ZVaa<?`<gu9`*>x>z0&2B*
z!f;EAnf066KvU}n)qy*uk73mhZUz3;2R$>Y*`B)W?t3Pd<05K@Y4)3KpoI2`PM^Q=
z^KA0+l`xna{%PP#CzVwS>s1mJlePq??lCbt)Vne{K1y)k#a8wxVV4VqW)C36Ofpv5
z5l;sjJCwlgPMn40d@4F+WQjAy{#y2u*k3?qlxBD+s3-QZMkV5PD(@sLCebw&qP%!{
zBps4>vr^B3?FY3rU{NE9Pt!_6+H|dhY0(aSQ-5Ld_ss=?$^Mc!bFpCKa`=N2Nk_2k
zEm0)-vJH+h$2`D>RFMW*;S7+&ios{BhQmC0xALxN|H3~7kq<IJPrnc(eH)0xLn7p(
zt%GZixgCysGe>rPq))<_IgUs%0^q~jE|wW_9P@=`6%`d#RjX}Ic7h$3BK`(s{`U5A
z72jfW7cg6Np|O$eAWdCtTqmuJL&$7@dO=H2n_;Onf>0l4`kikZO28y7?EBE&7LcI~
zJ~*E~I@SvUBNo`1#hgSh&sPQ&%tmG}AcKa;F8H^E9Er_IpiB(9y&hvH-qMz%X&=Ku
zH(EZA2ebaz-K+sJTvb=;d`XLovn|?GzEbc&1#kcPO@+^tlFhk3bFcxWzyVCn<vV=5
z9wapen3I^hJa<F{Ag|Q2WGD;-tRM&>CY~<;zA(nH4EG^wsXIP{vplQcE&!q8!41Pa
zN8tc_Vog6DrD?$wi+Fo`{ov~1;^f@G%{{FY54MZcSv(}Bm!51~!LdDOyyL92Nx5L-
z)k816wZ6F_)CZbGfb~86o^qILrX*72PcXM0$}hTGFSAMmaDDCC9yX|T-&@o-s)v(o
zT?&^ZEx@?MCeIZ9>uQxSm!?P>jk1B)u{@{JZKrQhnJZAM9EwxNs+g%IiY#`4B%m6d
zD;FzO0J;wn$B<tqm4)(z4jhCllN#IbnhNF?mFLGuIV$CK*EFYvXI5lG)8m;d_x{vp
z1j{l$z)$J~>e^ZzLbhXF8?tT1mNvoC-Sg}nkU0G)DVwYB+_DA1b7lw;(@JS2q0rdi
za|{eNW0iK{9c#A<%-CAvXwAH?e3+~9%H<<>Ci@yW=_>#3h&NY7bZUNwpjW;(M!HY(
zHerK{T}eJR5*nIhW(<xofA3Q|(X3?H=qk<sy^mx7$Zv>M5&!r;h<Biz#6LfcDQJqD
zVbfeG6LpoR$7U&a1_dgzv7i#haebQbmD0yzd@@J!@tVWTtwZbiitdW~{)0nBB3_8L
z6%Z2>6AcOuf`Bv{s-_<u7L3v=2j@}jooDFE+nsJD7sfF-6WChVbnAMYO|jt)m%I=l
zGkg04yx)(ew!Iv4!8;F<kH{2#XmLG2M%|uxN3a5E0Q|kM`}|9?Fe}Lkt044?IjDL=
zPq_LjULx_lO86z}Biin)x%;mf=*2%F&zN21&)44qd}=Mk1}|RvC+(K4FVUy?VUBzS
zW}j~{C33o*E|xcAyre}?4!l9g;;4hktYu&TQ(d5#M|5}s8U*jg`5IW4zo~Fl507!h
zJ+CGQ@lG}ok4r|SWAQhAJk*bjh+jZJfPZ~sW8Kj!Mjz5jE+@K66%J<2qanD@>2x$T
zsj75ss=>o`chBK$CaM|e@OFLl1~hF{-!BmE#w@8Mpu<8WB!{V~N(&}-2*aQG9s#0I
zn4YPhL`u*zr4TtneRyMAD{r)RrrpT9R3<s>xR+3Fa+TQ~!37xX&YEeq)$6(dTz@od
zO(6tuCwLFmLFjP+6bL_x`IYuqakEZuENY1<q(`M3j4F=Q{D8h@@+3LoY*XT9pI6pQ
zL>J2zJgxkV6hh4WNb&XP{J=%T4X84=vbU5vmy}1Y4g3T4NP$7t&s1f%kKNheXrm8l
zeTvG#MFLf|Is^%ea<&CDH2%RYe+6ZwW7mm}j;#c|^DGWi6FI$pm62<;G4gRc43^)~
zIS>+S3oc$}EpQ}gC}7yytsKN#J9_p6yvoty!O%tZr(S-N`R5N{t>D5(0^`4}33Zm+
zLDn1E5_8;2Ch!D9wZ$BJA&-30i&pg6t{}P_<5*v|w*L<k6ose#$}{)(085f_PV56?
zr)Ox0pYF<_iwq@%V&W8oJ<~T+yJsTGCL$&}Q<k)2k<3nVd!f?7bw_6*>|xaVE+>P+
zr&7qm!NI~Zy|l0hD5+{B4^uj$lZ*HZ8c1`Rs<9PSEi37Cxvj<Ow?8hPZFE*R`wSk`
z%P)Ih-JHYL<O+aQ)a6WZsqJV6C+v<bgxW-bFga>XUi*nG+?d$LO_zAXGyDk9@q@t|
zJ46R7>?~?fjOfMcu9MvuO&_g+FQg_`sDrTO*Bq|$jPQ@Z=D)kjjs=O#CYG0W@cFHc
zK?R9<mbAbQlJ-r=YDT=MeK0LX(L3{k62(9?`$d=|8In&?IdhY}Fzog^XzK)lj`v{j
z^2#riwMIBy{TA$h3ZgO*eI_GiWyVKF8R&nPl~+_0$|$R=t4pY>%ggJFiK$|i{L^Yw
z5S1RDnBn691O;rodNT@;&2>H0k{uYo>itUVbloq>MxH#ZUibS*zf<Agaw5<@(6Fys
z?lQGpxaeGX9lJ?_ux+H~E%9dvW`Snl{5}Hy23x5vOd6LG5ZP0Tma4J%v08mlIz*)c
z?@a@yB+@6QEr$E*>jfJkF<fe9c37p;{Nr@$<XtKQSZka10p1@reBvCl<IbgiGaa0U
zH#6tjKnLbw%NF?BA}5PqA?<A)`@9$a41G#96!pub_qKQ-2~3cdy;bR7uOphIA}cnL
z@F!!9{60coUQWjVs(_D<eGkq-DKjp_A;_wVo?w(mo@OFv_yUm@*i@HN7!GK;?k1YE
z6fhV7Nx2|x8zu=}Zew*p*M48juu*EX`PvWa!=d2tI1XX~{{pYeuu3{jO5TghWhVJw
zqiq3FS}G#?64E3BW(A^1%4=V==JZ5KigG^<T|eRDr03^JD4nM@sZa`Gh#@f%lYN;#
z<9{v>436I{*;kg8OW^tsKl^tK2CR|%vH4*Ml+zY+_juWT=FA2)d{<<!!*}%SyBddY
z{WnM6WiAgkOVS=vfd?8I`Z~(IQfL38DExOl*(sGta_|eamF7%gzIk2+yb_9&ar|8G
z%VP%x%UAA3ryJKMPVQ-l*mAnwCN{2Sk6XkT`N;{xb`$)Qd6D&TcI)F+;uuSq3rHV#
z!1dq770ccIyTC@a<r?3&Z^ol8`M;e(mGok^Ee~Ju>mi&E5l(DsVIP^uxu4(}l3yl<
z8w1c}-kw<_2sTYT41(-Jj$uxY*f{>wd$>P7#C@0htN*9YL=m#%9?$nF-Z8^pN+%kG
zlDT*eZpVvpb9Fe5Og6GKHAhMbY&;5?2Ogf$j_jJI<Mjpxp1c}JvXW!70Turr^0%A!
zvUSXtR2kC!^#PjVIQQj?7BCsLe+>r|5Kpdtj@jU?w7f_J{7lMEO~g9NGoJ3uf+5@L
z$DB>GqzZdA4n`NffAqW@p;I~}<@Xi;{<ziU^?m|kBvo&<INdH*J%r6xviKn91KL}&
zNO1GT4T-5?Z3N$Zu>Rp&Arb_|cw?jj7BwG`tPxm{&`NAdF$b7IDj+22>`e<6Tchg(
zvh2u+p=Oth;a;6aZ3aoNk4-#($+@|qfCTm5#_>*l`F8Gq1J61(#x-@Va_l8?BSfWm
z6nvXo2AZj9q1StG`3`qFh~;ZA05j8XUQSQz%eFzml6pj%8B$v=zM7VT)QGBf0b^I*
zIAf*njmomSVt!dn$n$0rM!kKI`T=>-Z3$IUj(XnwsnZe*<<a>*wjZfO3d?$Z!Jm<A
zAZXap_1>^z4!_sS-LdfPl)96>cY$08$@EghFn2_1Y6*-?7lGI?bQr%7pbfXzE@CZ_
z;G;9`4V$UJ#sAQE=aNU=hhj7L(1|QxTc3yH;lL_`2-&#V6;xpPw0IvYmSQv4mxU}6
z9iL=Z5cYtLRx#J77lQ?T;CndiM7I)hcy2}F>sD3lED@5;M!R>p9he>1=mSF9=Myyj
zEcpESOIK!Ymfb|Ux4y^`AcbYOgI0FE4my5IHneboV>r?VSqeftwNAf8I<m>@klCEH
zx0Joy$!i($1NRqmxFqW0(K$BDwaZId7;7}ZaZXe-=QjO!SV%3Y8Nff%e*TlDYj%pp
zYADLnc={MWqs4Y1EOG-`O3}lx%`1F;jg_m(GafQ?wYMp3((4)!#2Gsp5=Xs*LOw?%
zuLl7ztL~G<dAmauf(!A?_PurGuQ3eYhEzo;tA%Zq;0Itv{Wi?_uQ?kYV`QR+yR_+5
zf?RtgraU%SiM1kg?PyvSc%}A}IbB^J4LX8sK)DkkKLiWg<)4?&QKy!t<MovKcUEtx
zlxeQeQm|b3kl%rC$2-@j17n{bdwGq!wQA#fP0*m^55bUo&e8I?zJ7gi`GuqGYimwa
zu%{cW$zi&dqRWwkEAfhYGXA~@oWWPKY*z9s*&h0@@-Q=wHuHDB6<BPFa%<paHC3-%
z782i%3;1$AD3^cA(p%%NX=Q+-di@7#dT{;MB`jn<fjI!^S;)G+07;4KoW{(i@=IJe
zY-OLd#)mYtE((z#Oj1lvlqSr~zCW@4d9%Y72?0Ty%i)yc)@U1(7A~}@WXp0o{=UF@
ztS~IyD>FrMrhnMmr4Wh?wIka;Fft{zo&cT@@q?EkYOLw<w~%Y)y{*Sq3Soa?ayPgg
znb7>2N;6>YrW4X$6_HxN!~A*%&)Z!<4nn*Ovn@x680DG92GswyysN)gr(R(-u%H*M
zj0RBK2ZR5!R3y~ZMMS`Z2W$1P+0EX0g~Wx>{vMe4%OeYi(;k*jE%4QB2NE<rkODf^
z)R#zjTpOO%*6@35^G&)+OPiZ(YPvsn|HB#r*i4EsP@kO0yee3cWjFrCMtQCQg94}#
zm=uyEpWO%93C~+2lAa`KWJBS+UeJ;Q#f<u0<z=pG7puX;iiO`$J4$<9)9Ukm?{6;l
z|46wP=6nU+HslB)4ZHWNK5nnVuI^Fc5a1Exq8~gyGK`^&G#F||?$q&>Hy2lD`S+fH
z*_J?<l7iH(I$lrZ*C5|0p3ApiNhOooBdFhf7|wQ)#*<oIzE5C&$tW(7ohyX1u-7^P
zQRrl3nr*ilo16SU1B1hrKR4HBo$DL#=a1*?h6!!x$0HHyRSMs-nOw03aad0{r=5O{
zPmNLv!&=jOiOfNZlfbP+=a!&7btB3Ego#C@Y=3H95dRC6{Q^<VgTYmuy%)!JZ^kl)
z_UtzNw%0*08re#M!0f?GeZDPkqoopc{5tR{3j&o6m(uC+pQLd772&NijQn25bnQBZ
zW@(c8Fl{<VzP$hP&pnbGV(j$%KwMMt=jDF>fMZ+;63Rj(GAXe5TGpF8!Uw<}JpqbJ
zDBm3%k(?NDDevhYwd|i;4ICT?#x=QEH&!!L&@TP@pmAQd-0_==nG*!aR1|?jd^36b
zi!1Ywrv)bg<#GC{0MTM>KosvIIM13NVYVl-i(B~zG^ZV;YS2IYRIoOul*Vwyrrb-?
z4$hmaXI-|<4lf?p){5tzi%H<LeixDmIVEJ}v(@s+1A@|VV%jb%quu^C2f4@|Y|m1`
zbRR2GnZ4E(RQgQAJ}@EX{+lZY?obTk{rMK+Ff&hy*yZOCZDkpjOc>>a|E}CMqW@z=
zf*5?DI9d%ib75Z89S?~VR_OGQc37j=+0<gD`h38o%g|)ErQGVWbO8wXRM<Q$ENEhV
zjd^I8Pd0nPM7wdHsJuNgIwV8FqLTP7)cKh^ha%!dfv$GDqGc^39+Y#;>lseX$VB(M
zOwhI9Tu(@{xXTOD3wXTPn42NQ<pTvFn+vV9qnnGvpoW2MoKHKW6=x=!%k;{Eu_5+*
zb6({sZw%YLDHmt{8-RF&ee7qdyw>0VwXKtgm;4Wdpqk>0kq$%u_iVC6Cl@9vep|fm
zQ_T*|mV0<6Hhp#mK3Z-NtGD*3WgwXvf86;7L8Hn*70z=q@#b9p6@`Pd*^PKpyMd!`
zLCcuPEyww`IQR<aC-VgtPJV~#n+GtG;QpW@S>(Tb12<+h0m>{7{)HQ47gR08fPhu)
zu6q}SOCaJI`A<A%lFd&4y7u#d-t*8O#tJX>QC+5x_kT^%LptE|s8DhUPb#<4(T%=#
zJFdgUsfg}LzT?f|Ci9wj^4Yz}{bvzt+26mnJ8F$^pGh;zmD`7yuwj{snej`Yp=8Hr
z=dsR@E!7bR9{eE1C<^wHaG0h4g03(Ov>wlGH~RO(Q+ImA$GmMe=exp$F-}-r(o`Hj
zQCLKvS-Ft=d~PgR_CNa5#oGnZB$<z@dhu9Aj3oqY;#j6(VD|12HN!~27~`~-JG~wp
z|1BDQK0mzc-e7i2V0jI%iF^zP8ls~M#1$3zfjSQWQtn1O&5%fn4QLVHay}-n`@Vus
zyrPzp^P!d1L|4GYD|XaSN9Y|@L^bbspx`gRZecYC$-#jkTB>43=HL8|<(Ch0-52*<
zc7NmC=1T4IyKf<#+R1;H8RzBGQQsAxUjy<={Q@d>L7^bq!8#L%*RwYo{7HxBElJ)D
zoFb6<SYycF8w*ovEt3kfRt`T=WG>=Qc4ezSHZ4$X<fHm`IW%}*TO<{M$dHCalM2a~
zKD3MSz1;uRF25mASI%C$f8~9D+*I$rc$s$YOKA+aMU18$p@Sb-t}j;;-_8IATkRUS
zRW+al_Z=@^Oh|y8uh~=gysnyol38%;QUIv8^J@6hghu*%7i<&WD4)Xxhxcm^xSs$>
zu^dF8bq)t!Vk`QR3y=DQQ-*zEi-IoUr(1J{rS{`oaC+A5n5))4!23F3F|N(2#9mb+
zy|d)XBf0p~Yuhe^;-PoLr6XJ=>ciSf$gX3O#SV@$>ZQE3$n^{&Ni+G9;jiM|9~D5#
zgTMbiqF`Z8%Qno6f9IViYR6%Kfu_dZYX2WGXea<3ADbAHFfBQwi_v4Syz#Vof#}C+
zr&C<fV%S>6Eyg}1B4%;x5Q;_8*|Kw>qXI}mtC>hHDYG?m{>%*b_Ow?6Zl0qr43jb<
zh(5||GwwlZdrndW!%KJVU?}M03jnT98p9v;S#Y#=GnW%ppI%1BqCDm*nzpNcE%lDl
zEM*dLif>CkD643ZwdAJ8#ycgkzx9+7lBCoH-klSol*yfp5q|M*j-dEov{(~4s-%AX
zP>#Z7R*;vMmR3+ONc^R%)Y9c@V}72Xx}kZ&?+uCJnVA;4Over<aJ!5XFahM`)yBt@
z&GZ#U8px^WXn<RNhTJ|hw+VbAvsO>#vc~Dkfs3gp{#mlG7Mxx5KpaTQ>GZt~Ohh2U
zKe1k~=O2?$gt)8yp#3cVWKAZ&tJH%uox6!*SS$)}#|P@FdTSARB7Yncj-f@exJq0u
z`x;1N``I+-EjL&R!A1{SbO1Vc@vts#ELcG=wPp3@#@WMNXr*XuJmI>clgpz*G^Xmk
zksa%2=CX6FCWT1f)rVKxm^X-gUM_BzpNHzBg%Q0(8{mVIGUt~l>&Oz3xH>r8D+hG4
z4No85JO-$A<ZMyG1E^YrTEb}#vFs`YHl^h*K&ZjbVc0yS;8WMfd>|6L?gB;yvkz=O
znB|Y#J$!`1lvWPWiAD9tpb5!rG&ppF41yip;P`m|WTzE6ZfO9Nh~AY;9n?NXlatN2
za&`95Kmq1}Ts$yrq%BcmWYVnF_)H1V8@V}yZ$r<OmWoxS{VOf$mtl+Xc(q?y|M5wu
zv&-Gg_+##H+oOZrEx?{oz*yA^zAMVJaCBm2V_gPvveA8^AL(Z&`|_1uZsgX#-S>qV
zeyk^j1kh~>FJ|1Ty%n}0wG!xux<nnTHf<>V@jB+IXsR7%GKm^O9m3>(g@O`AA(21S
zy96H^_l$@Ki8+QQBMwDc4e@qFmPS)F1gGqn`eW}<NKdr>1c1@_x)^ri+^6SKx`zS2
z<a6{xX9QY<jsk*T&oi{2y2^$$tIhh<|E!>^tCq~;KCDVTk6E}UDX07S$?Hx}OuD=|
z5Km-#;0R}$S1dcAF(tJ1S43TYU>6GXJtmF6(B|dQ!&@5E{@yd8WHdz1=oq`nX!*@a
zp+3jS+a8|^81UzEd1^s=v*ZTzhNxYbM-@CBKA(Bm1<VBe0Eu>@bEmx!Ie(ggn*1vb
zeWU;~l?X)+2P$6kI;Hyq*M$fPR}mPm{-NrF+fDkdV4V4Z8VbJH(oxo|PIP_MdcBjr
z$aQM@OQGFijS+);%cWqP`vNwt*Aap;fhw5A{5*RTi2wcI=;7w>T-yxo$PItD7d|TR
zB>Bm28cB*=Usoz@vdh-i)K#Y~!G~h>eQ^W!@8+Ax8m7TF<wmZ-zoO{6P#<L-!K5K+
zgznCZOB&!2Rm=^6c#uSl6hU2l1jE(eH_M;I_xs&9w?<G{juuH4y|spZ<_YH4+mrlu
z$}b^<o&ekqj+1c5GCWJMG&oTM7^L;p2PJJqJxUH&M_lUvJfGfvKkRaYug>uGR0gxo
zKqyZ~KR+fVz`)u*+zY*Numl&J!w6d6ZlIuJq@e$(vi}Rn$0HIk3iV9&PSQGT<0aP@
zRAvxtbXl;J!75yQ(IGM_W+R8yq7u(bOG-&-ss}iZ_gOzC$<EWB@#J_xyG)njb({HA
z<?pQ(zo`QZO?EeUnF$318nbAcnesy;T{`eu7fvsDaFEHBG5!zS1fv~4@cGd5JSTNp
z(S)U`Mk=`B;&(<}RSfwu6j~Q+9aU?{mv4fwt=~O;^lzXgz3eATvOg;91oxiq_e=)A
z3SjNl5GTp%oQfP<^|5TiEhRkjFNk~T^6$DrNYDgdJlQLKy9joz>{>n~F^)NeP=fW~
zfU0M0u2en~W_DW@zKl+7Dyj^KW(!eKQC3}=pPgAK6^FI>cwiC*yFIHC5KRhrFZnw8
zm&;{nLc7e((qu>#=ZA!wzG+9E{=gJlHlRB*LF;uNOT*~xljr^zJE{Jv=&ILozs=e2
zMnZglnk3*FDk7Dbgj<ftNlb~zXn`-O@(3YtHhJF@6JI}x1)WZzN7Jo1Bu8W-`?|&X
zH{atvXspCuSyp*hcgmo}*X8c|)B#h7977=%ZL#DfBb`#hZFoBW4+}dx>y9L060pTX
zw~}`~;7Tl}Dyu6cD~1eRJCX@vG~-@)a*FB3p$RLMK?a#@>dm**v9Kq!!K_NAY6Za_
zwTX@$8HfM%y%HAN=sFQl=M#~HY`%0w!_c;13#nthbGj*OV6SU%xxTx3xW2x>MHP;K
z6^=k#T7N#%k~Rbp66lmIl#D7z0Um!2ZA|<<%(%^`f@~?BRc54h0zXmO4}2Nl`<J^s
zco}|;PPS<nb}R4;=rZm@ur=JT3ODHKiauU56!i_{$w)2eE3KZeUdx#$bTU^=Mj7`p
znK0lDYz^*iJ<k%&yaG;{<I*=vOd=1$l5&m-AsG>-!g>cMlb}an&5QC?K(+HyNmgp>
zK7rN&Kcaid#Y)yaJ)7QG6J<)gOil^9rz?i|OF!C?y=6IM4i%W4x#38lj^JuOF0pyH
zX(InX9E}Y{a2NRTK(6g2ImRbE2I``OrcT9Dsl3`QCwVOmR#uoJkMr#fqA2NrA^|27
z6)HH6GmI#qs4+A_bHE4{P|24^H#8Yvk+<iel#r%6$Ii_<zaT1~8Vzz<Us5cWDOm2K
z#jDM-HMuuRnYZAOQGrK-6qG56+1+iPbBkl0Zm^13yh4sPXDfV?yO)o|q0})s>2&ss
zN;NQuT5{{GVH>d(rOYxN@H^XsawsmmI0cFUC<LxkJxyqbB!h7PO;jw<ko4j|9f7M9
zG=O_){cUB&Wq)zGWkBfH^uW0A_sB#()G~(W(7_^M|Ez7kUF&Q=5{7=rFfK}rR9Gto
zIURp*xqztF)U0U_7{nr${DN<j6-mS86P14e%N?|Ml^J%AJ9n3cMwZ1nwa}OKiJ{Sc
zZXS21hm)AK2QDrk_ZTLB5BCy30=7M)Pi=l~L>f{#Hr$3B7soI@RaH&w)5AuJ+hH5q
zW-QA2Z>~uCLH$&y*VTHf=TNjW>hIyxHPYwJp~0e>U7;6pLmGeW>*~TP;?DmvEnc^@
z2~UTZS)NEL!MC`DyIRX&HIXuQoY;;ji3y2dgXAbygvWq78R&FnE;T*J{-vSRX8ohG
zyRa@}z~e>sNRgFaR#HJ(TV6~TS3QWW;7Bl+GxV33lFsDxD%13E{~)-TeV)=lQ{D83
zdDVuB-#=Rqp;YjXp6_pkfr}aUvi;c&3&{aS0c{*DBRk0uI4L>5WByo@)dJv5;<fl!
zIB>J5QF(xu%VBMgvkk;XQTNm<km^GZ+Si_r%m>w_6x|+BHoG2^72f6|<oR>F{`3+e
z%i@(hiw~Mxfgx~#v|cx}CP5uUWty3i^LBLC$>j5l_W1hkJw+YzKHN1^KOdTAQuYk*
zCYi+((sAJ9(byRSf9mJ`!a*1dQj*rH{cpfA7>EgtNC+h1VpC;tMUauKI=e>1t=S~-
zx;nd;lH4UlZCXfUihJbhx6ZnP%4d$k{+G^^XU0<NcqJUct(J!V&#2e^v0E|tE=hvE
zNu&Rj<|pXmI7;V;2vYgDq5MsY5m(MfaeZ3RQ)T1$^E*FpFFsFevu7WMVq#7Srf}4+
zTc8E17<5Ok66t~~1?Ld2`-)MhUg+A%Q*@WrmxiNBT=B)Qm1=%LEyehF^hu}837Ibr
zF0bMYOreHsj2$>&{)3A!`XiI?by&P${?)VP+ln^caao`_V|YWCtoUMBMiOKQ=gyj9
z^gwV$Z_|RTC=uhGa#GLu6o07B@L!8PY(O=j98zn`+Gqtf<+X+dmHH7Gk!O3AVgR4q
z|DR8x5Lxy^jWY;SImHZR9upBtijmvs&rK&Py1VsJGyvdpto07gYz|?lo4uSMBnIgj
zmK9C28meqDeeK?ze#TrV_JaRtTFndS+?J)(`DRhi`lL~w$kJY{^Y$-fnGWgf5vb>0
z7$O)E-s+9dS?f1N1vB}8rMy;j$l=pbPsBXjv#Ktn8e-pc4sg#eP`^0NbIgBJ^NS0V
z!n}_0`}N4|{2AvD`q$0^BYu7N9ku6gZFl)x(Ovy#YJwT}O7LQyBN!(oC%v>jx-^ch
zu15p&?Icn~OT>1p+X`&Z49P+S0DP){hRGY9oScJzmSc+W?jT7r^&7$=OYiIO0F)cH
zI;KL<fGHEdXh@*lP`5?xwX97#&y#pqI81-#LN->(Km8tx%6+^9RTP2|Q!3yIdq5V;
z!&}#ST%!nzuaA(qub+!hmTh+{M}hxYal&l98|n#Dy;@IcyZ;C3HRTo|5G%cRmGrNd
z9kX9`8vR=~zj%bh>6Re$6E~HVIg!dYcknOJnWu^#4=C$IUQcfuhRN&4{jy9f^^F?l
zBR(I(N(X)e>!GtXAW67u9s4jXbEtpm;}Mxm;LaQ48-irD@edpNNS7p$AY~0O0%3(G
zYvbL1<kWw7D=5hqzijCk0J3ti?$~4!3$S8~L!+40Z`jjW>P=U&2C7~SmPnbgg)MO(
zlpHf(b2hu}<Ry#3Tf4#vl37ss5Jl3%w_s^W{yluhIAlE0&*J~ZbE<q0vFJ*I^;9Wj
zD=0-?Qo2izVIwqU3HcM^nz=T<OTPQVXV7bR0xW^Uwm0AU0KKu&3OL-U&KX|{zYy?$
zCZQ!U@U*f59n-q>iX)XdEqnL{3n5glu4OfuoxEknGo_C_khMQTBDv1x)6jAM*%y~m
z4axu9y}Cw6!{dcE*Gaif`=up6+PAW_k~mLZP>t=OiEBwK^`BQxR+Z9^7+Ia#%e1+y
zGKQ0uIdJ%GV<&$N;C9gK_CG&a=<Q{Iv_w7E%OyUv44<OFk8jHw2Fp{<6wWg(59VXo
z^yy_%GznAk8V<8mxji%;_#5ZzU^a9lgR8oG6`~bTZ$V=z*{x_yB(wGT<;wO0)P6S@
zIpz~KZm+@jVHWazL7>gi8hIq$QcvHt%pbUlehD-R%$qU=hO7i4Gkj(dC#C0=&~x^F
zFAqXsRWs=Y?~1~o+I)l(;XzQ-lT(+L7371)0}=Z5;PDPbcL#Sc0=vtprh+lzc+)^B
zil(N#B=a9XjO{Hzb`qo;X@z`Q4F?C?ZiLMuG42{<y|eRH#~t6t*6Ql15`oN{q~Por
zczBc^UsHd8=uI$risE2n9PX=GflT`sGy8NWMX5H7!l#7G7;jZpIUJJmXBH)}B=x=k
zc`djXBiGBxoa@(lK+21PdHQ&QjD)7J=*K>Bwsu1yC<k|1R_ph+I~m?x%gpY*V@%pj
zv$CXze!s~Pa4m%Lb~HCaHFil!Nj|aGt0rrZJd**K_95^uaa_P!%|4|xHnU8}r=e{_
z!4Sel)Bm<P4c?8apzYYA$MCiNqqOTakm5Q))ImJyishs%`2`an)7Z?+%mfCa#T`YV
z_serJC+*jD|LdpCD|uT@*gnuOdn_rRj=n}M)x@M8F`wnNvx^g(#c0D+L0V@-NhKOA
z`#})^xtU{i>?kD!%YenW9q_-4RtG45@Y`nbdxn%1{EoFGb7q^}ExO=vTKN>hQ0$En
z0oRmMkYAeT#47tT-0on!)TVhUD7tHC+<%=@m{Nh+DZh_@q9BBZ*CYve`~mIWwY~p#
z=?tYXk$?DeeVc@uN6}B>cnc1xu`yxw1m;5~O*4kUAINLJ{&91Dghz^#M!E)LQdTG#
zrMotX?8z-A&iS&(!^Ct(P+B3oeZ0~69-Ne#cE;~|a5HlHUn1+?Blm}(?~&2o2S|~+
znlV|yUeHn5X#Tyh<J!PWeD|FHnw^1mVpOz<*y9E*po0eG4Vp*~P3Wd`CSn)^FuKJJ
zFo<s`C{OJI-5a~Axrz)8WYo~+@4rMvTv8O%!A+e#uT#`pdmn)OlPiOKglk{X6|%^l
zo{t40nmQcKUdN!AF<aq9wvA|z-*x8JmnT}lXie3xB_<lCxZwwPo-iW+7$pSI9UFy`
zobw_+WBRX+{(qK0e19Ieo4`v2qWT3Y8;ZORwg(ujK(Vj*F3vgp$YhzZG5T`r6M~Ev
z3$7^x@SVCZ<4c!TJYF9!!H<70H%NGZ198*)%?<T-dJo&0Wu0{VQ_IpCGB`?4g|GGo
zf?-40)|f@}G3On9enf!^6P&Q6#Pui=Tm~G9)Sb62J;n)#Qe6P|iG2xPCYf#}h)HF#
z|6ga+7-0q0l^f1pRp)q1kw1dh!k~v4KGGLdPypdr`8NHC_fEDTp8n1GoXvi7e|+1q
zgN8bY^;QPRSgJ|PbB+*yd6~JMzwP~5I;4T-CH#W;=Tl)^0MphC#oqrVYG|Do%`RIg
ztr8=Bxh^L=7_c<*)U&qO%t*XRi4p^O%b*Sm23(?od||{;10@YGB?ltM=-bGua8j~b
zP5NWo_Oc=S6+b<_E)ND{4~9B*o0HS*R+Uur8n_q)dq%%Yxb7f{%KTDPQVW1p#Btg!
zUWhsGr1VwZ#a7xKWhZIDnGcCfH46LhYo6Fj#?jCUctQ<`tk3mLEfC%pE1)zj;#j<7
zgbZR{@5?<4-iC_nDr5)K>ocjsUe?aPA-AJ6>J!npu?oIRQ;}rf^+Sy<DrM|oT|tr~
zi4@vsJ^e$@5>r7Ot9uE7_~gmrJZDt|X0~ANr$Psr3?$*W9KL)<shz<xeZ{6V=pR{4
z(U{2skWl;r+V`aH^LVzm8z-}TDOFt%@SmcV9v$w2I!tF)IV&_E4!$u6POo5zr{Hha
zH^?Fj;N^*r0iW65Uv0yLz~Sk3v4j3w5X5+XZusak1>4xFq=rIHAi8lcxc?>z-7}ay
zxh?C_?lYfIm{nrw)P8~ztN6XNiv9J&3l8AA>PLp9@cDM?ko<(JNV-AT>T<EY_=d*~
z`K2i$Koiq}e^}GO*GWM%K;X$gCQXEFBT+i4Z3T&oxU&W`S!1CY)QeV>`>U<4*H!5K
zIU_*mjOlCFK*jrAnqSps_0);&+?kS$BK8&Rt|^S#vgS)gxx=>&zvsnwT+-ZN?!Q3Y
zi@td#mpz?!oPj6L@%fCj5HRM7VE1K2Eb)kLXP*b{izd#Y1#|&~^2DD2O77nUV2Xse
z?(^@U1-E6wH+3gN@T3RzNW3K`gQg$LuBOQV)M*%%RCMxEoidbO7PM$+vT@XPG%%|G
zStt^p?vdmyH{FW3tn=>obYqQ&9bhtwHt(6&v(4R9qa~?&AY>ZqideDGCLKrmhY2Bh
zpS@3qD1?5<sWn>A5RyGIjA<`>z8z9E()NY)?v5hyvT{h9y^o(4G&Q!{f8tQaqyc1c
z$qZ&>aVKqRPy=W_f|^mc@yFpu$zm)?N20IqqmH4^ZEptgTC>BNMxEO!kjhjsM=8L>
zLDecO0Y_%s5zUuVuPPmJ)x@omvTaO_n_ZUisv}{%gNo}h-)HjR`&C35+SdOcCQ(WY
z6=Jrm_OIh8RgAj#{9^K#j<Sx!z%i@E%3tD*B9+L2uh0sJdB9J7ThZOw+sLL10nhr^
zv5ivFAC@v#Uwa}whRYX=0ob)45%vC2K2L{rXeN5<sLLbU16f6Anl8~XMV}NJ--VeQ
zl91avuzE{ktC<2}C_q|2>&p{jsEn_z3?g&IC_EpuCr#Mqv@q=%klXr-n!1R3R8Ku0
zVKbzONK%wa(u&N>O4`7#>u_k4rbC>dum9)N!bi2L6snr4vT73rKs_+3Bw~b8m$0Wd
zK*#08Y<)?XjedG*R{nLc|Ap`%KX?pFOd_znkKCW*nq`;@kEM*~8Z}CKYDBx$elBd+
zEwln*y0HPOcypt-PgqsLfZ)}|WC9;1SV>h7#R>?-F|s`+!OPC*^zLsu`JVt^J)pva
zEgctcj6G*K5x;r<aOBq2ix+=78Y@;N`&p?APl#+E(Z{ne`gkQQQx@&_wa6(o5DoNp
zu`$KSSo&3?WZ|RYQ_?bXSFA4Du%)tke^blZ^B3EX)ox#(w<Oxn!`aT#SWi=h!ruQz
zZ=gYtR9acf(9*#-a`E!@+iH(?boY?DXQQ{T_IIB>*@Sh@4;?;oq-lS5Nlu)%m4-M)
zpc?<wa*C^(ItL}7r`F9vQ<|5R*4gHvRTM}@*qk4o+|i?2ApGfBIQvB=EMAtkdVSfp
zs+~KwZ7na|xW1?$YjJ#-pPRk8p^loOw3r|d#rB&+wv^m_0%9`C(9Af2XVtQT;?2A2
zj-2f1yE^>vCAd`(@#=rcy&>%5Q?n^I&dW?$06V|^C>3!0<c{zX*1=xFl$$Rd^iVH4
z*xT6(o!Ee+A*Q+X1yNYEenV;bmTf!s)E@!~r-2*&9Vhlzm9JZ#2ID;}aY?UkDe+7u
zHXhW$>YF=wh9&2$-CEOh_TuF$SFhi^edq4zog0G}FLa$bdHjDw^T}2$hIMeq+VoIo
zLpg3P5*0X?7AdDj$OTIxpws7Lr7v{Bujh}me{@Mh@`0w~x&r+9GV@jyfhKhWMv7uh
z_VVJD*<h^WW^ZYPjlMwOpA!=Wp`pZLEsC0k77iW(;qhrXtJZJXRePuzJ2c&U`WpL`
zd<1n<P{g=&{!D-Kp~`g`5w1oGe6zpBHvaRN1wo^0sKOX^M5dZDUs!>Vvz?^ebnNJ8
zs-sMc;>{5R%bBc_J$unvT(fWAzS_p4C(-MUPU{;NyG|Xc+qJnE43g~(Rm8bzlTVs?
zKaX2f4s;R&<1$y5?KyP1_ZA4;fx_L;&1+XKfsIMq*^Ym%a~FF1E)JZkE6<Pju~Y?2
z7#1{Rh@3KUOKV$uM=xEQ8S87OE62zB3oK*VIe7)e=mL<~IG~#E2lw&xjGj|W)fk;J
z+3PlKsoYa{@aV}iZKqB)A3u8JP-FFuvUMvmQ(}UBT<!2q3G2GzH5S0d#3W_pm4GH0
z5DDIdn<{^*>JB%b0Ws_0dyikdW%vz0yLbBvP)use3sQm|^klhbO@uJQxa}OZ;3Ai&
zDqkwtGxNym+WRNutY4L#8WZGcZ^lp#ohPp26q&WDuC48K>&ccA$B#C(kebX-?%f{j
z>qM6{x|>r&osE=)<}oO~l!VEgMWPBi=IEwalD~hUaz6}iU^jDT=+@2aR|f|!UF_?*
z(0u{r{Oa{vLw9?QRTZU$IOvEAQ;a)W5Qh2<OL7wf9rfk;Stn5%OblaE`fi5!p~U|-
zg|raXvGI+|D%!EPt`R<V&!zspp7ZBA+s~Xnwtp|UDrF=_26{R{l_w=32CfafJd3dS
z2l#)cnK^ieCM;c1Ot@6FU$}&BxUq*%U%Yt_UKNDL_>++v7du<g>$5&P&evL7ifckl
z)PDV9A&*r`VZLTh&DZFdBcNpB9=Wt=XZgCk^tcc&2SUye2(aw3rhaK9b?rm9u7i!(
zx%Rfso=ew;Mjt(Y`{L2q(6viFou}){mdAg2m?-ef7$@9Aw!+MuqKf*~9wABD>nipg
z?;0G%K1_H!Gca%&#idK&T6717m@jU$@87&U*27p<LYTa1XtfI%LO2DdtSB!?4RJyB
zAM3xS;ATr$Ql2g>#>w0M5WJdRS@A0xyGCTJ-Er{P$ulVHS8v_DbNkkfYl8!qFM@x&
zPSuu@RoRPULw%gV|3yv)=U#wMNK9H@8T78u;hF+c2yjGp_YdB@`+$%yfB)%E+Ijua
z@L*5dv4-87u_u+f_{6AQeHoOOhr_pC9<0LZ)&VK2wjHe5R#LDuA=Jwro%T`!9H=Ab
zP$o^oKfZbXXmkiQ9+$77n*7<DkAHvv@&5Jm$M;9>-27Gwmk!zqes%fYBkh;(zW7Z1
zoa+O9{TD9{Tp7G}{U*s3|MdCcrRLph7YAB@M|~9~!N@f{y>Q3cj0krVC4o5%*OG7l
z@(%_LiWO3`@=IR1^*}peCivhf0N@`#d~k1c<a&SS$-}if%ZsqTxr>#dItYL2O9RzV
zTt*Q9h?aIfk&E-NpE};cK6v_yHm3TE_8@$4`%+g+<KBw(If+3|c$_-nc_$h(7@eBy
zfwn2@bmGy5kH{mVYwH)EU5q}+ilvF+ey-L=nu>%b=|V-D@chb_Yj0jY9=mh>a&Om}
zbA5xiMjyZY@b|~pPw(HkKG1*LbqEz@A&&YoTpS!63$dmGus0UMl1xNF7uA#TOIMff
zI?!_d>e$QApWeQFa)0FZb?SY;2<6_j8@GmrNBWwpH|4~7nJa&rOSt20@EsrHW2+;>
z!~Cz`rZdmu;1!aPl@KBo>?bNnS&%t^LJCM-z5P&E@1?6ZhsPd0dxd{9{|H@Iw+7Ci
z0Yy&qg)K`#C9xCsVbq77RtJ*q<nA31la^P!ZC_Ix%KNRm_t3MAUFLBE54anEU);OV
z-+rQ@3S5T6Jgl^31?DrHU?(hyjvOVP5Vd8U&M<V8R6{*7dOnL+<)kHq`@5rO1GC}L
zQNK{ZDkNuX)5Rwb?t*_i=9!kJ){fr6q5IF@{Y_UUyGvJPK#!=RtfZu<sGuM(FR!4W
zsHmi*jLoj1QgaH+cQqVu$CKJm?_NE7bnou%>sPN_zS!4$p}VWA6GaD#BfIfF-sC%p
zCr%F3$H(}ANh0b+X_;HvB5$ICR#g<$vI|;LxU&frWq5)999w^fJ$>@%Au)qEcA#eG
z=Jl(WXCy_3U@EV(lY^ZtY^(!ABV!XY3rZ?#4z->GM=Er;zkKt7YT`b=eFe%lcWzxH
ze8pF1Cx?665re7U$dYX0f*!@tiN`MukDxfVr1B1lU7Ves6cg&_?qFrCtqe0bl=ua5
z<^fAL?e9jX{H1>j?XAZeYY(67#MGz1-#of=rTf&8+8w3q@|Oa<!vP$vtgWmpEiA39
ztZi)V?Cd>*qEoY1m4L$A*`BL+AHVsGg`OVW8yUKBb)diJTt{0g)XAt%s;$`y55AYJ
zfif{Y9G`f)CGnwJl3(#8eSAANQSl2N4zbK-6W4YK$tZuSI@Wvn>diZlIznsw*^|eQ
zMs5!DcAPraxNlcQY4NJ%OH<=xqoX3j!$N|CLL*}mQqq=Y=dCTR+=srbeyHglJc0E)
zZsD*)#j}T~oxO7L!s#RXwr^OOnFu%oyl$o-Jrnk67>T!|5<iL599D%LyR)ZXNMw9e
zh`*PMy`_JNzB;(Q0sxl+#oiq3JJ$vlq1aGtVCdnSzhB+E-qU(;&*s9F`8mtd7bnG|
zj~)>o77Dc1kkGJjP`656nzv>nYG#hKwD(-Oeec=3&wqX-WMA+--`#%d#Nh*Vdv|Ty
zylH)5ZW<JpX1Yq>CZ5J}(F;J@nuMP2O;r5y+wXsbmW!mWV_4?;z0LiDH-_%sM<+cg
zHjZBUd+6G`&~fVcq5A4w+saDT7Ubq+FUwq-k)8&!o%t)*6qRh+wx^+~6+2X5k*hcF
zKaxG%ySL9Dg8%aQ_SQqy73=fU<ANz>7}HNsZ^Ifq&|E^{h7(Ue3J#9Wu5KP)-oBo$
z;23{xqzAleeykhGCS~ZEw0h_9OJJR|zh>u_4cpPR)pz6mtG{0iUpjNBYSW4(X&ISW
zIe9Bqu3lBJB7b>qPS&!l?A+z~D+*Q-p7E%xZHLZ(?D4A)pZ^53(aUE~@JrKq>e#`3
zRTZU0D|0haV#5QxoMCAEoy23yv@guY$n<}V6Y!a+pcf5N`pywq8*5rGU%!Rr#^1cB
zb><&Jo!irO2JE!ZbGv;@DPA?LFD_cQeq-6@Z98`DsX5RD1CMJ%_i#1;=G_OPhkJ{D
z#FMexgYe-ScW+*|d~uY&i-m!Pf}{ZF+*v=taDzy^wuvpB_!;wg#Fcd|oG}#E)!Bd1
z-WFg6z_nlqS<RQwca6>4dhp!Ymc#YCx1khm+}Y6FJvjRE?`O9zoT!HRdK5_V7ng0{
zv%6|104g?@ZQeq<%GH7U9vCfOh5F{j+mE0B0&4J^S1+GKfpGT3!F^PL<mG5*Wo~Ss
zqpA9x#M4q8^dHf+_k?@W&<jZ!L)U+(oYK0sLDX)7GyGdBZ?9fHc`yPe*7=Szr&^9T
z9Xe3Iuco?s@18xocUSGHscmRH)O4&F?8gRg4&Q^c6QUoV2y2R0FCPwH>+3vuq`s=W
zFeeo-23n}Df%X3MpJ22?10Zp$28o|VASwgnB18PZOw33}TT4?z4ZP;)?{|Nmn2sZQ
z>}yY-I8<A;b;FuGx-t9v$+h-_+g2|QcJzrz%`K{^IYJJ$nhqa4eB|iy=98z|&USY9
z^bcIUaT~j`yn6fLkI#SoNjPmng>e3KQ{B$8!ra9%0dCfYs;Vj~%F0k6f<Q6b7m5Fs
z+Ktmo1bR+K!-yv;XylEok$ZnkUU|dW>qB>8%<%dhHFkM}m#IVGoqd5A=(M)9G&eV&
zIC0`Q{@m@I=gt#52`CM)-5>~<QWm4n@7=m|t`)U|WoxpMv8ggC06**ZpJ2E_Cf?Y_
zL>qc^lz1lC*Et6!WG#*j@dB$(Os|!dm6c<-e9aZrv<pdFw+kJ~sDgimsz1AEd&8-|
zp=W;q$FU?c+)2;QKXG|!&552Na1}$fdhdnaeqtm)e0TKT{fCdAJbM9-dmljP=I_5y
z>S1R2?CxOq$%A{gtj$Rdhl)-dkGREvf+;P^zle=xeB!_JepBkJ6J7>G^?<<y@+O{f
zE4DUv-l8AdKd4db>*s$@9**5bsUN&@W#ICqi~armePmp`Gyv0y+js7c-3M=3@RI*P
z^zYbI80G!x$km><rdm|aFH1z#tdW`&$zGi{<tG?!K;nV+4o<Q};#qiM4vPBsyv30|
zFl+~+grJa!m;?iIPnybX-Cw<<436ZOkdzgr)yL1@eEQe@zN3FTR>Zj*Nb0-A=5KH6
zz4zCjfE@(&uE$RSQ}*f4&;KS~jt-tXQNN=kFD2B?9Gvk;q@IYBA~p?YnZTLMKZ?l|
zhD*`RJ7MLv!`+}m`4qkIAF&lYUa(N|bazpVjEszqj?x5?2M-@Tc?!0oq(Jo_f6{(i
z6k+Pgoy(mk8g_qfT9ug);$ee<CG;|F;&ohTe<3>Yuz9oe0M<l&u)7ud_jy2C1h7(4
z;N1+a-M`Ei)3y&uU$^V%!F{{nq|Z#vE7{rD)_><YdgD*++mIFMYTz7^SyFTI@`Jyi
zM}Igre2a0mQy={^E#8T~A?!T(_|E07mi<*_V3Xxx4eNixnRD2%y@iSv#<R_5p7!r-
zN60<Uw29ZUrTYa1Q>^&jbsM8%apM1g#G}l=C*<kwQ~AD+;sL}%^vRw)dyex?eR|?)
zK_>+PtQ@|5qYt(3sEu11AL5D5d1&J>cLerPKPmA_;}budQ%uF!(LZ)+Vz`f!nU0b;
zFJR>0g_nQER}Sm!X}>Oz(szl<-c)<K`EbMD%FP=JSKuim6n9UqcOI@>w=~in=Glij
zu08&XnB`sW={(<eX%LTMu}bp?^y+_jg<7C%s5j{9KHt*^)4HCs$M^5vQoI5_y`7P|
zj3DOPgX)d3j;gE}AIIG9DK61Lk6FIND*dp01(knzHV%B$=WjXCadUVS*LEMqC7zV0
zBhvj4r5oZg1bW?{V_kBbcSt;4)IEDNHjD~`-fn2)icuSf7h*~h{0o?9iT|IKcvRfc
zcl@k{!XQ)^oDkvfYGb6SAkN1xAS41WJXTLxFqe78R8CoA&x92f2hX*)LesjlqI75d
zv9o{uHy^zI`}t5`Ys0p6%i=QER@9&Dz5VR3_s{PS4fb}lHqn(lUDLgNGIG7IqvcRd
z-TubIM^BtQeYW{f?JiU`WG{&g@x}#)keD<EteD$SP1{uB0{`w8L605Blg$|P)*twy
zXuzPnu}5s)X0o`$8jODsiT?zYH+*K3>3)CmgfPtnKfUMhy1&K_6y)6wTxhIj_u%f$
z!T#>{)?*F3%GaWjHqg!1R98h>c+q^wJLU=g1jQ~!;)w;!^hq-@X4%dw3=e75<;D5o
zpcj*nl9mO_7Y?9Aax0qqrLL_yaj_SMElrK})r}|4_FlXD`0d}X#;$fZH&kw5oB@At
zAK$&&bLRN|J^KzEX+GU~;nG!TfS<j3_vtfCdrrZ$r+8x-c#+i|sM%GqY2C`4jO6GL
zA6HvbJvBLHb!`I^3tLAwcs8-2{l8gRP}a!f1r8^XJHa1Qy~2rSWn0K4hpPJJW%X_7
zQ;ZUnZbsrEl`o$^Ba=;=C!%=yihO_FNn6Q3h@1V{lSdDRuV22<ezIwQ^|lQwmnB8`
zxmX%$E6a%TE@YiS-&j8}@upPbr_bXSQ-rm#tDS`*_(BVi2fd6Oy5xDGahk!WY7@M4
zL+zOx*9I>3oNI483Eb{$!w;Unhmq|7YD)HPuQ_xQW_hpwetrKYy1i<5Y}kLiV^7_o
z<EJ`%Fgx(sn~$HL-|0Kmuzg)tY|@g<oPxsQO<PLW7p`2sEG;EIGT6_<37Sdl+~eZm
z9e@G)Vm$NzU8_b*@{McyCuS|nXy**wqH;FXp1MMdP1A{|8giWK7r^m;iP_$-UNih=
zK2VTKk{>rVa{VHBhwt6KsW5+cNnEg}gBhkQ15b(6>ze#CJoLU8;NX?(v^kuD(kgo9
z4mM^09g`6gBoBI7d1<U0IDgJ;K~0B<+${&skKDx@`TpMXc(eTU^~b-7gT1r0=_Fp;
zJbU*yD%87<*KI3az6!=U^@p1oLN}mxv3*TisH3}Ya727cMovLq&a!`uB`Jw<(cvJP
z;o@LxY2)DR?(G*88tLz1WuPi0u#kb7|2D>Z+zDWhJK7N$;s?GcJivj(%NV*wW&^Vq
zwk$BQ{X{38TB{PNhGf4Xt);+Qip)BJr~E{dCT?HuKGjsWtGp<GX(DmE%1eonRrFMv
zcl&>kcv5#<4`#IB5-)#-@{SLBIRzOB5n@&%qT>>~vhw()Cyyxu@H?Xq@cR63a<e{e
zL;w6v|LFsj>vNORa#j^@#wL5@@$ea#|Lf9%Y*clOtsFf3LSvGX65``xq9Y^1LxThT
zePMR!;o~0^8W9tp1Vkw<c@eIEXV>sM^8#KmIdy#t15h|!!18}PgR=C4a%YUmoq<cl
zvJyP2zcY$1`FA*t<ZBOAIyMf(sm9sHLD)r8Imhu1a0{b%ZbI`;?Bb$>z@AlAhTsH)
zZ~Sk+{ts09|2)2!)#JoZr4%SN)l?Maq$S1Rf`@}%UO^UX)}dEk+`uzw?cUQj-n^v@
zz%hvA<EKCW{_}t1JLpdy-g`j!Z2bB6vs>qn@7<J_6d0M9vAl58w!H_AoyH~vZ$IC=
zaA<Q@sErC3aB5%>VnASEU=WJn;NT!4LL;JL6OtFF<L=f#MS|zw*)<TRdHR;)RJke2
z(htg=(TSJVcMi{7zvp-_X%P9Ak$9rE|3C$`ZY4AQ2P1!Q;$J>}2zkHMalB#Y#+8`~
zu)oKOp@fz{`iP_+>(9tLV>?GDo*I~dQZ>Q+7Nf{+n4u0#0VxyTB^w&LM*jMDvlxbY
zQF~*>s?=ZykHDyutd;Ax?%99*OwZNf$8Si1VpT43j-+X1W9Q)H;^yJygDp@aqGI9_
zlE9oOD>r|yAU!6)*-TSb;NS6yF!)*5!r9-&Qcsys#hD<?9b-wO=M<VzRCN>=srMeg
zc=PeAtd5UAsi4O-e^TAwTTBTbywua$SXH_*BR0SZkj#>x4>p4q{g{A0^xMEZ{~2?+
zgk)6Fjc%c@CM&#X-pt8UXR^$Px>!b95)==G1yO%9hly@VDsa|=4$mAhUDw!x9Y^~g
zK7ltKb>x53Y5w~!qC<sqjxfv|1yKvYLq;a1FE89!v1dOls4m~Q`|!nJ%dS;%t~$aq
z=W+^2C}<d1I5;{vySTc!dw@=@uYW*bP;h8?WHh9IaZHfAEy&q1$P%;?(PYxDeG2nJ
zL1}+A11lGQ!a9RY{6xI*MB>pUFQIK0n7XRsz*$_^+<)?FT){?P_TS6>pTwUNb-<g~
zc!}H7dHO_s<;Im6aY1e*{fa8)p?v*0X~(thtohs`a_WXw&ghhp2hZl;rp;oRzmS_>
z7`^HuLV^N(yciEIEvKkLcmXV0IFDIK-8O$Xqj>Mh%K$oq(~gmNJb?u19@Nx4dGh%2
zqer*;&m5_S;d^@Ss`Z;Is}CG&Y3u0j1LzUZfa5%jWESuUODSsTo7uXea3KT!x0@Th
z{(b?Fe;NaXE*nBfpJ8b>l~ARbIiCj@x;AbB3>~Zqy5lLPFuRzBwQpixX-x}YC9!`e
z?7R0=lQu5C^ZGMdKGeNG8NGF-rvtjUt;PB2u|e)O2J|wH##a7}ypvxC6FaXsOl)0I
zm#iegzmOS^q%prsNRXcogTlG_g~X)flvFiUDEcI?qNzt*ZfR|6XYZApBadFbXC$7!
z<HyEENABJozH{fw`L?6=yEYf)uPJ{i-?6v;@QJpw9k8eB>%Y`mTb38^W2q)6E~lae
z?gL&P?ryHmPL2TEv9-a9DOfQBkO59M=7w6J$iRm^YN!21)7c>Dg!l!gAilTU0~u)X
z399NT1kQXBHFM9HWyL!Ww}AXAFd@kP{EG|y>(qbxL=t64*OB{UH!pX$0Um!Fm367n
z0d6)%G`s!_@vMoy-l?09Wg+%Lvd02Acp;C;XFvep!-`f5*s*Z6l$^4<4k%vZMG2R*
zF5x?eK}3MKq7(ne`!_FOM|Jzw%^TORT^+nK(A#<X_`$l}+sZ0-RW}?yeyaU^clWuj
z&W`r>vxjz-fGx4UoU(?Vv88{VvnLiob9S(^v9vHVH8BGAou0m-k%_69g{7&Hp0>KO
zybKAmo-u`C*-KK>*cb4jk{(?co<X$6+C=s7^e|XR*~B$GZS|JABQ0=Yp~s$@#C~1P
zeWFi(nk4h#{TpIAfx5VJtw;7%mKH8gix2a~2d)$_|HpN4KOyZ5XDEMGE+J_Qt^;*>
zSrP7e*faslz61FOM66*;A}lGVqN#_S;W_6sPh%HRF?I<_&f8d72b0?SuNaAE;AJoM
z`9-!p001BWNkl<Z_x1K%IDf9=%&F#MhZ`Ge_a8cT@=WK2{wtSyyF1UcHXl8-r@SyL
zA=u3l&*q%my!?X$d;ov%V{H!LRFGIzLN%q5GM3N4m;i7`kr3wNL{HyTdihQ=k2x3d
z0BPOW+R2NdsyI=TJKB6tK*7KvC~5hIs`?|usDc{CzGsy3pT3NbBsB^ER<E8velR+G
z>-rTK&BG}Vlq)+^T{StfW12l3kEACUVn+q-3^fm3@fwC!F1~*No|YHmWoKey0bVNG
z0?0ZR{+`9UkVi;DURB$WcCeYtC!=ZM7Muu#h@+hYBQF?<|A-xNuU@)<-s+C_Gi|3^
zTU$>;w6vT)+jXJu^3@xF(ClbEcCZ$2V-muAoGq;FNn_z)e=j#j8*^hlZBRjzkrWdW
z2J0hn2}vn1Kf-^HfvmV7&jQxjzfYpAdcn;O7>QJ+;1f(+A^mHDX7%*$pHEiT#y4hZ
z;nqD3N1-hpeM)DZ?$^hso;U<ZSl!#V1oQpsrQWt9)mzu)rbhU<*qIxEOfL@?MF;%}
zNGmF6XDD`34mN<$`sZKB!ZK$LD;9p9%gVw$8}<auY>R&cB;?g}O>|Y|i2WEdr?9+^
zxwC&X`uue#du~5t^mQLzJs!IaRL{0kt(aVO_~`MLQ)fEPVNTSITX#n8J-9R2bM{1I
z&5lj$3bNA@qk?^0u(fPxcvNh7ppTn_m5IKlsv=Ac&~*+*3S3-^7J)m3gfuEOh;wHi
z)3kBQV+(%>gKkL$H5diC`-XS`kb$2_{J$omE25b+JkpvL9$~5Z8@E?Co@j>!#Zy?g
z5(^ld`tj%eNBV7tw4-AF*`qN`4DCE#yR9fEDb&LX^arG2!VJRF04SOKvxYYG$3PD-
zOzL&a>^+0L9Dq_G#5EV&h=P$Qk@cxlew{fN>qLL+nGuFRbEZ#52L+*T6A8W|o!1^S
z5>HS)yU#Qq0~+D}`r3W_k2Dj+I*=>H3P1O5^qpy{-M*n<c~-{a#MsEtz~E4#X-|v}
z!G1Z~%JO*o&o*Zk*u*oT0MNIPxU7<fp1Ojh5IEWpa_TtmoZOg`NEuZ5g~CpkPW*&@
zWAJ~<gRO>hVEnSevdY>cr@IG+9zK5Z^#5z`I>V#9t~A)7hI$|MHlxvKMw+6LMl<TY
zH&8{rOQ;KVfha-(geW$q$P^neO%$7MQ(Q3aCXQn}vE#(aCY$!GH)Z$PeX_eh_MUUU
z?|vgg*dG=aUVMJ~v4)v@ZaL*W@5_7sKa77o|M$6<Ji7hg{`U3n{|OYr-+ukVv1cX*
znoBdI!c}sLaIu0e1dPZ%l-HdaySO~?i7%>xS+8V+I>?)jbHgw{o6t0~6Zr*eqLNUF
z#t$ZGm`q|>$A&#;Ui<Wqg5zPJ;?ob`ym5*69A}50$IczpYiI!UU;q6-Uw`%E+n0Zi
z@7p>uJTy4a-?zHAw|8}4|G?nT@JKJo+6q9&kioo|@eCblaXM9yubcg{C;32|l1zkZ
zNc>5a3+h`d3sMcC{;u=scus5CBPcwvpsH<X@~ML-F5G<k<1b-16VKBSZ;t2xKG*NW
z?SK92pEwBhE2n3+4g&)?iRe5;P9%RzF?pC*JTZLic)vS(k&~NNP~O<pR9=vV;B=nD
zf#dKnGc_eCpG-pn0jMa#*04fW#sHZrB09f*aO$~>Z+-qXwj%$9B<}zG%b$Mq?i)8Q
zpF4Zz%<SyxQ>RXzyY$Ly2&De?ZvnIN*MI*{NbRqX)PCc-HADTqU2QFR%ddYP7#bZL
z=xnSk%1(|B3kl*Vby=+xxFj^XsCcY%_=xN+o><ChVtV^g%`r7<)d10AB_r3HFJqG0
zBC!fNA%-_?-**J0k3aqC=Q!FA@G1mc{Dteu0SohA{`}{Eg?h%H{`iOA{|*w|pJSGD
z`TWWKQ==V~dGVS6Byq5@e5ij<I~XRk^wuKbyRSSvKD)TKeXzZ@C=)2d;IN~P3-SX3
zH?y^iPY?uv#nr{xiIJgVcRMq#xTnH#+NWQ*_THDj`0~r2BmeAA?!I&T=CzAwPo6w=
zdiKoObLU^YaQWKxo3Guz1M&P9U;XwEU;pj*KmX{?%jb^notjuT($|00+E7(W;`z4j
zzTvTrBfYJ)r2r0K15=D|CsH59i=TxAdc#%E@c{eL01)Co4KG#0D$w&~BoCs*2uRTj
zOE$0$bTLVU_ZFarKlzNf|0{|k{S&i4qPKtjD`e*-x$P%E`2bq)v&RnZ-GYkDdW8>G
zBCRY;sox)-<FR&$ZzF%CZT_M9<bsOk)nfp#iw~1}z+nfso10O7W)k9iNmcqJ;`dgd
zjMoA!EocmTgIZ`@e%<PceWzae;Dh(y!`%GN>$hIH^3u7}$B!I4erom{@%q)PSFT*X
zeCguF3ot?Plh1$kyRU!q#Yb=5xN!0i;B))h8Y&92mzPyHb*z6LS-*K~paXhTiF%D9
zkc945Cc=<ceqK&i2FdLc@r`$}HGjfnnXRK6K(iEbd2n#BOor40`slbsnD&z5v=`^L
zZy((8R49&~fl7T+Hd&6Ik@4w0hflnC>Du*MZ@l&H2Y?-;gPv#rx%|~vzaS3(>{FPb
z|M2~H-+AlJn^%9%pL~Ai>1~^aI%<o7meB**3oXq}mOQ+U3>uPsS|ZaY$$cei;I8f%
zo)~Vc%1_iv-K@AP-jov)cM(AtkdR$e-P#MYTu~~qn+?xD6&R6L(u_>3*I#-0+SMzU
zaoX+V@uSZlI&k>tiP`fPuU>!k`qfJ>o;i8!xdX?}UcP_%7Rh}7@T;Hw=<Qoq&O$YB
z?dp~)vYpcNOKV$uN5;2}kMxjmBR2)|8<Id+nKHhe&en$Nl_b+kp>l=9@rz9@?P)(r
zt%O-*sU!gSlvSE=oiXeFqItpGv1*YWZTw~~FRyP~y=K$)J%_j-`r4aszx%<DKK|sV
zUwru`+<$*?1L6$m-S^*p`wl-{aPGu&`*v;FFhYNKxZKYb35hJsmOf;^Qz{fDD;T6r
z$SH5`+pv9oPhDYhxKw081t(2RmQvt>r;ooP0!XM^R|D)eHvwS3j+B7P(;EP}ZKG2&
zr%#<c@d6Ia96a#s3{DQsJa>Hdr7JgHhas!eN1lJ(y=~Ku{m0I|eEaUte)HvLI4N}I
zh5g&s^){Af$7z83tEqc*0tPMHA+;1lZ+X6*>G9FtRqzbN>eRuYP-?c=)Y=ggufhyE
zEl5d|<j==1fRZQ5K(?5d&S0Vv-)7yY=S^7GIwhyDg1CNU<JO(e>_2q$<k<^XUm<xb
z4vT+$%FTLz{G$)wd*{v@uid<U?FvM@r%xU~v}ecU*kD(4O$ibJ26#A9EUn3sCmv=r
zPYhBco_ou~D_94dS0kJEOb#@aq|zCDj+BqQ<RAkqK`P?Pmfkg!Bb{|c=}}65PL`0E
zI4mKrx?^-||Ngzt?0#zJ_UWmqZQG`&w{L&nv18A{V?g%t_FdpB+`nUdxN~Ik>BDDU
zzVqW>!hqTBTQ6TccX;Rep4t`3;iy$p-!-~r&-BLO?xxDZtOPco$@A@;I=E|MxE*BA
zBb2x&%ody3ID3la8a*hPDPb~N<VJR*7bp{YySq4IBWKY(6>wO0W0@V`y+bqr9w~n;
zuWRidTsyvH$8IP{pTBVB6>{mncNaL<p)mcPFids&#IYlX56tW(ZXW<PsoXSjbKwSt
zERGVLJXF6g!YrMt@{u)C7=SNq{f>jvqirkGb%D+%lqiMVJ3N!VDk?Rf9>?*4){5K&
zt<)WOoFMD7@s!bxvt{$d_=a_BM~8n0hey|rt>3t5a?7rnBeR!o-nskPyVuVi-UZZm
zjeQ$;J$LTLd!K&>K)p9eFaRVkRXNdeH)(iMK~3kHshM3sCQ)0Gn+y@07tkW|d^>lp
zoY=QzsC8u)66fLUCV+*cN?lwUkjRo?P$aO^GlD}#KKyMZM){Jzxs|=MJ5YZ#Y9eD(
zvX+-tH@5W-jZFXr*3lDZUcCJBtv8?zap%o9Z@=*d@%p8g&YnJT^tppGdw1{JKDBur
z3<N^!JwmO3jMdJ#`94oBzP};i8<b?pP3_~WjLBZPYH0JGW4kwWRc1y8ItdcIrl{U5
zSL>2;O6xm^H|?C6Ue{A!n5KW1Bl{GRhC2ExB7sGCaG<ZZtD~)@siApQXHP%L%QjB$
zeSY@JYwvyX%MWkN&TJlNT#;B%+q2>6qZe;~`te5}zE9$u*Kf`48}BYp*ZNs`Dx$M1
zJJxPL{LHouLmi+T$p&C~zMWs+y>WWqcu#c>@`$-Pkmui=gKxm;L0*3*e8#Le2@Vqu
zkU2lKdiFUy_6`_V!lM(?a#pMaW$U%$Q%~(Z@ci*JFI~C*+N-bLx_R>k34E?xI6r&h
z=;3FH)2FvilJs@ByBXPWwDLe+Vdx>S9^BVZszC(D-J4vAnuPr7uCeV0&&*7&uF2L1
zIvP=$@`Lig<~KCHW9EN_y<3LcfSbdgcEo`(o@zv_Y-*^ju2@-8v^*cmCoLVl1EZUF
zAD+E>`|cON`}pRm-D_J5<K@x0HN6x2PT#=Z4K(Q9xo}{jt29yJ0&F&^Wo>JB9-G-Y
zv8D$h^*s9q&$mMXdLs>ak!rbw1pFk^<G44oigS_y|1Yw;FYteQe!6q?lR%n;%Y{Lz
zQ6g7`8{*RPimRGB`qyll+O_AvkrQVxUV}K7Br+E+oW&vkr*}*dr;iTyb+@;Yw;hKS
zM7;jPLk{T#^FV{@HweloK-RZ=Y}?GXHJ#OY2^z68n?WH7v<SO1#IcP%YltJ^!Y)ZS
zC<N*{#A3%~6jpx`uT%Xp)TyX#Z0#H-Tjlsm*Wdc_t7rC4_EeKi6`op*DZtGekOIKM
z9GV)a&oxLLk<Etez;!?l+0#<P$y0GBKBI2Up7Zbg=A&D)`#1H{6HlkL5n8D+Bq6gH
z$?$9ht{BEnW{s)=CQIf;O`6lB!7hi5qli3&!OAd(6zzW>T|Y6k^O>2$$BrF6a^(5v
z4j(==vj?3%Hj2RE=7t))<>AZn%<oISE59#7#(p1fZ!a&9kOsNf;yMy{t?6y4T9FZ}
zg)u2Qim-H<tuyG4BCC4az=rAl+tzl~E>DgKa;IG+AhmVyl&B0zIpnP8<z%KMCkRTv
ztNO;a>^^^V^1_WX&u$%RDoWD$DPtizhZKO0%pW{3HQZbbT<MGWxvh0Wz3olVrXh*A
zQtAzkcM6U#?iimr|Mu0B`?n6`8&6X;(?yI>nCjV+?2S=|2-Fx@7Nfw$d2u}J1=HG*
zECe{ZfuppxxxKq@c<si?shvA^Y~Mb;ZQIn;BxQdE>1uCjs;{Yp!;zUpsm(E%H^*T9
z;l^~##9#pwTO&f9fy~0H*43RYbrr?=St$aGAtMrsNXRa20Jf%y!PbhLIE`3<JF<BU
z;`Nk-xM+O@mQOOW^BB<$ao5PIm6=frPgz8AK~2Z#mVJ950ob;6^Ta@NS*8I-Vli1y
zFRXv*XlbY}FJ7LL0cV(UUfYXx8CBimduI<nJvq`rqI%G(r76CNB}5?#jh-*?3X~x{
zxjloZf9t3ojmnQv@CPQlz5&Q&20elmrIo;M1Y9>GBg4Z(LxY0@tGhc<T9H13z%UVS
z;KqKLjKz24_eDWqfnq-&PYBUG#5f&aQd56jRZ$MQU?h`@R7xoQ6)04A_yi~osRdQ7
z{p+XK^){5GM}_!0+VS(~90)%?Jv9kM!&S6=5)+r0l2Z)KNgJk~o)}tHnHwJ_5d|ob
zsc3N1#`SAQhX(q3y6THFW7T4j73EosCfNgMJ&^TDNs7`c$bCrCIr0j`<W_a8-7$Z$
zrmw9Q2B}zz&v=@s*s!&^Q0->=H+06<#(aJpZ&b*!u)+=kknby0T0IEb=j0cbtgNi5
zM{IaQLqmOiO;rVGc;;ix5fxl{Wn^n!xaPa^`=SuAU}=D_CyCIo*`n6Prr^LzWo<)C
zTlET#L5d9xNyB0@OUQFD0R!_%5i);ILAM42K<L%S8gziZmU7w?8f`>uYEDsQV@Kbb
zo<^?h158E4w~crT;N~mMO*W_lJsmA5n+-|KK#eRVIVmwA0m^eY3S;RiR>hDkzq_rm
zh7MH;cM?^f@B>M743LFlH@S-Zg2PC_p8{197zUlM1?GW%rX<3%0|bZ%h>(AUsI+9i
z$0sJIWn||9U=R_5x!FK;hkDJ1$Ovs1r&NVs)<Qs3&d1mKzfA)MazdUWM_WrfXL4j@
zp;~=H22@SEht~5_Ad4BwjHGNy`4ugz*C9$uBN16LYE`^!1Ic9@5e8@(e;*GoZ>XM0
zq;T4jvq`E0A$okc!e4}>)rNnhtir1D(!zpVa;ya%TYlz^6Df*`6}-!jbdhtDn~mqj
zpy6se{MKa96(LZvaP$regTm7anDq8>wwyo5FXoZG=A33V$PQA)MHR?1=%ZqyiA51J
z=p!S-!$6rVL>?@Y21)|_8P0|u`u&dFzGzVdm|7tv_l><3DzJ)R&uV`_<D+}+);*K`
z4TXtnKfA@O0}Tazpd23Vs4Yx0g!noMf<+hr0(Wev93W#P>I2Po$QGfxr8UHpG#51L
z5WivP>J3f&q^$Jhgct*C6-Keav)PCPvCl#d50vNJ!SO7)&>LVw#3z6pCVd64y9`|i
zrUApe%Yp_d^{L=BnKysq>Ou6v2i*x<sD}X?J(LW^&?-ce5(|`w1N{9sAxdCfcV#+G
z4^*Rk7zR5rDjWk0TZ<(enY+ECSesJZGDMOsA))8VrX9S4BTz-8r93Y&T;^#f^o^OY
z6g(XeKXa{ID{E^IDwBbPe56J$@piReMqXrRB)5pfi9D>TqHuo;Xx#A{1}?<;vJg##
z(4=AW$w&mOi;5G9dfa<!&VcB`uVmsCU9~HK^wMTNmvHf7u9!oWW_aBZTMoA7#G4~A
z1T@(J#sS;iOdabxOW&ErgRzU086&bfw|`?talG7F_)ZTvJ)By%Hz&bjzEp@&p|<G)
z=m|z0&jg_!Ks$fQy&Yi&kd?<|of$>ogvSU3n-4J1)61<SA>mkObzUsc+&=ae38qm<
zXt$f0gCsJmx_5Hs?6aHuYUh^lcopcnG7^j_#qLH7n~V|&(`9^RxfuserP9j}*+pSb
zEH<I|4P+R0p9`zvlO$e}(11-Ea-W;=BTy!$6zxkQQ5JvI-)l^d!ZS!4pHtS@GuB<l
z(cC`vrUo;jbLY&~SDmn;p?`Ak=7C12wixM<0duIzkerLim=KAl6UWdY=#YsRiiE3}
zS##}vc=(tiuDOk~hkuYVJeoW-^crVI14%mvXLqO=>0%Pov1CgE13|r+N>7ky-~p86
zOq&E+=GlLRm5rTs#hLT`#(!Te80=nhXDaN|uzEvZV@ZZy?r9`P1*wsoq((Vu2ypcg
z3A}QYLX;eBZm@UB;w88h99=kua(5385!QRmO*t7|lJv83EHmV;fqIA}SQ&1JPsxNm
z7Ap31v;+}f5ECMw2r`op`~{1B+?{MK?l%y{s*QgUQRGAN3d;aA6poU{kG%zeF)B!A
z{6pSOA97=SDyJnJl@!O9ADH4LMK}|;*kgsC#MKy|!>dmxu-rs002fE7fwc=KqswvQ
z8v)|DATdy`LO%bT<t4c(IK*kiu$CAz2SCuw5%KU}-Z)X?inxZv<djqxyUNK+jUlPz
ze1d=PzPE-w8ly>L&X~%aKq<f*mr$u;v?W9)75fQ#&J3S#&8w+W;bG!JSzrKFT(q;b
zL8(_$J9lvi{0lWqQQr8Upvn>?XeVwjURhm~m7s&<pC)=_BazIAON~5@7_Ghmr^BQ_
z5|e(8f%gFc!DD7~b?2BFv&Ybq88cS*Mw)*F$W1{@?;G5)v;~GeJr^&C&nQP0iRZ02
z^rpF^w+u9hYTEBryuy<z*BBC0GjoaCtLmC73R0t05)Zqj7S<%kBaeG1*-D%el`%8*
z+$ou5&QNX2Eo;Pi92oYK{o{AxF*9qhdv(m%WlJl`#w*3Rg2fW7PRNYN#2DWM-wA&=
zwdRWCI!<EV+?-Q$w{Z3g(Wfr2S~a+;5^$aFMv*o@vyqcuSXy3P-@K}`z9c(dEAw)I
zQ~<?BF!{#`X(pPth{5T2&+}S&10D51Vjm`(LwNUi8^Wva<p^Z+-}e%YFcuI5)&h=I
z$jA6X1}j6Eaq})IaXOBO5-VI#;{bnc!<_P#{_(yhh#$R-iYhd5NXjWK2ZF1%uHJ#x
z%7T<gg|8#vfV@$knC>KJ+c`XUJf4noytSv?kXhC;xOt?bW<^S5uor!UzNJRv<7%vS
z%j#+mNT`fikrg=7U?kP2OD(GJUB7F6cMa7JV#dqgH8i!hclGuUjg0j)lw^NKEB&2G
z4(LgqAYCkW=zT<X7IXTTw1mbyPm<Ea!p4EkAV@|#{XZCQrqoQRv6Arvni%vUw^#;e
zPmJ2&2z(IPFI)1Y&=2E2!khviV^bMu&bX+Okh`*}cYSX|NroZB$6hFRJCj>N7nhb#
zPJB&04pcQ#P74>lq@#~FXqA7m0B?~a-_U%5F`)=;cq%&DI<$49y)rLe9pGfbe&@Gj
z#Q6T3H8l>ExQy3re1d^2@9{rQ55e3R=HdGIbP9~}71@~!Zw8ND#}g`3k(-$hr+G>N
z2QMktmTf4@NemD6b~MAzCWxsc^FmBwdQSfG!eV5-DkX_nSg@ZfOY?uAt0Tw3X$qkw
zuMzE%k<G*Iet6>(^D0`0Fo1-@jU}~ZXbZC7kBSKm1P2E|NK<BPib-SMpYs<CdjJ$O
zz<F6w2pm~bZ)YnO%rgwLxi!fE#B=0A_~-%N{<yTf5|HAHQj<RyKrA6jO?V{svs2SE
zvvZ02fzTDM6+5WzgFb(!R)h00GAV<n(iV~#B`K$#=fR)NmE<c%DoI-EK*zpOO4)9K
zmi*B$3bynB6+n^5mDgs5>ypFM-`h3fJx|W823T%eYxwa=8!mkp2&l}h?Va6y0;EFA
zl@W!}7%eF?KZABwU2GArC|7Frdh8%X>U8?3=-9Y~q-0<V0Oo%SH_FE&36h6~f=-57
ztp=_bZ}@1Q_sCM1=w`Uvy!1qh%>(6eS65dY|8QBLC4V#wD|c84@%8cX_Hd&GRepSC
z$&>dP)#dd*9K7Yalme2Eb<~k$D_ri&L2{Qq!NC*A{{uZ#XYds(4l*PU*$#4Lm@X>L
z5U!^C!p%n<td4((PE1cp1i2xNQXYa7n%W3mm`Wi-WDPXBef<I16M~)k=s0em(o$%M
zGJR>j$e0|@QEK=iuoRJ{#1>}79}&Y^eXuMjNE#R@5&MBcn>_$R*yz-~p68FVgKubb
zR#|iZ_)te}QF@G8;>O^%bZ<~V60#>j%?{D66z~ciJnDZwd5AJ3SSs=N@p5<dmMFpu
z3F&zy1z9PSvze>P29X;bRhT_+C<@E9cs0Z%re#Az9>LTAc|@8s);m~=qQX)|m|CU8
zaufND5-9lv_=7Ffnm-bT&?~Yo%FqynoMR=nvtpx?_tm!Ja0d>4>V$&Yp7p@lQ<X1h
z!<#;7gh7AgFbRS)XwTlw%g+yP8DbRN4e0Iwd02FEc2Q+xB}NTFKF%&M>_MCj$p3Gc
zzYNe^nFSE`0VSUuCA_k=cOcId$8O8;0f_zrE>=21Et4TAW?@$R5iva&h>3yTt6G6L
z2?raV{lerQsaZnEXCI(VDQz9uc4*he{^qi*7#M%4!7yuyFg^|j#X3Rx8Vi3Aq51og
z1MK4D05<~M;9fpKnyBR5lG;^$EtLhS`cQw@WtJR%hmo!^&=PcbA`>!~mp4F7PA&Cv
zG^Ydnj!puSXED;FkU!=^LM3S0s&SixEyQ|1GL`@qk79BLePmcD2RvuQCmgBb{r=Qm
ztjm9_>e;k!_RtPG<tin~m6M$n7VWu|&q5el<7|nUm7TM@cL2FufXs-4;vm8<RG*Yv
zR^K_aslTZ-!!W0o$DiE*UvE!$Z1K5ER0f!)?gcJ0WH01-4SB4kQC-%=$^j>+$#2wy
za$Pvu!&=kth0zmFkGuxz-@XX9MZ@HtM}>cJQNB@_&)3EFO^@|76lX*${k`1pIq`J+
z@C71b$dO8tHldx$y6>{E=#>1jy7qxpRV&h>lmQ~7UO|>C3gjRsp4<|Ud)V2C{6ln!
zIVE)+4du%-<8{hFPq+%;E)Y&UGZVh>gGXMaLh(H1Laq5DVf2kBw_j)odE}*H$_amL
z$-T@D$Z2Se%-Lpq#K*t-Pzlc{Z625y>8dX!VZ99Sl5~iUz47LZJBK%n=z*n<<ln$S
zyF|cGN<u&YtE9T6nja+ue69zu+Hp~U2XQ+&?!dF?;v1}u%UDqfBR?QE8=+J{>lfh4
z_cGWUbriWr9&F)O{Sh!Z7=-mw2<v}2oVSf7<-{RRFF8R@aKI1}y3E4pv0h{e>Z94E
zjlHedE)NMXx??%dn^HbuvV(vn5sgInG$Hf%cB77GT_0a*XgH$pi}Es(q9fFDi0CPe
zkpi2rp5(MUL7B<gav6|t0yA|(O+^{9o+e`72Ix(V`*qZo@?-2QtOr}TReyg3jLy#S
zi9EcY2%q73K}iY){|zozWV^GXt#%xrptG@=h1I2bfQ$<Cau)jfe6CNcvrYiL;?P$(
zV*yfD)bS)BU<f-7e4mQ*mN77lqtS-zA`MZ|v3jkFs+~D-q<c`CK~BEvs>+J;m1QNx
zML8+45s=t@v&&3grX^;`(Byx~B@u9c5L>`yUwFgRh?VYKkh#>x3gJy~AqPSJjPVqs
zI<(}Ml^;vaN{NkB2aCO2jgA`64$hrrFUZ^c(ER-a0>seD_xECs=V<m^P!U0^k7b5M
z+l;K7ymX4Qb)lSl5O&y#{N*s8xw4|Fy0#w3Fsq9)<HLh}odMoMw<doYBp|T0$WMa(
zM-IYb=>Px%AxT6*R7-bg3%l|QY`|aRY*ehLbcU7?LL)z9NHF-`2k>&PF7%$|y-TN4
z<(fz>uXD_5rF@l#CDMT~NFvOX1QHWc0O<3QC&7lBG*+m>aLP6%op`*Uu()((MRhUH
zrcGZP;#lO{GbCjrK&OALt8ZvccYSe2l+xdsO`+irWb_4CN^;<<^<WFT@(XO%)^q!F
zoY^`dW1K2H3<QXz5`S)vnmqX6c`U^s9}>9AAJ`Zj06J?T(0I^3jVb&u7*&Np=B%WJ
zA{-ne7q^q0CCmMY>#c1${$W`t-1ylA$Y$Ko+}ci}$D9Q0;IV(QlBJWUBvcodUQk-y
z*xom?Vav|-J#{M*!^F-)__>I06LK(p%eg;tK^K0Z%~UWFvYYaOILtZ(D%26t<fYXp
z6~R&o%+Pznv1gbZ#@P-*Hs1f=sP6)E4#nJsglV-}k~v32L})cCWr&>YB|lHj@r*hQ
zahi~ddj~3Mai)J6xlM;h*Y(sFryCSL_I%^$iNzN7ZvOJHsAS+4>|HalbN|t&)^%27
zaJ)UNvBdWYsYs1cTKVmyN&deXE4;w8YOqQdlf*H_(;FXMe{Xr@0mfHO3{g>0(b3T{
zF)`%h!hnX(cyfyI(x7up%^bXg!xMAM8hiVPM%Qhe*g8vnACwLor7o;#ADVdT(CKSO
zcZ@XUM+Z60{Y7&zIX~F_?FYv{ruKv5A5;6m@sFwf;P}VI{ukbGoCGA;NSFWs002ov
JPDHLkV1hC(f$IPO

delta 99907
zcmV*BKyJT_j0UHT2Cz*7e|=?mlv%cQ19A6M#kJy|ic7`a-Cc-#AT9(85In(yy9Br3
z?(XjH-O&D>eO{?bCp}-&%tJGGX8pLghxpk?wyeGOcj^EgLvyyHn@?c4AIH{IM_ESr
zJ5dEq6MN5)`0R|7gqVns03Q!WYZF~n1xdm0`h#CoT0uoi-^9vPe_u;gQCd`h=VOBg
z58>k%5EK*?;O84Mco5G=gk@FrES<bV6Vmfahc}O&*x5ayK0n@vts^tQkLV9^B|U5R
zki?<G@{&S5*!s#6f9b3K2MYf){C_F_XZZh8{Lk?JrTCxW|4X3^(9ktBV>`Hd`nfw;
z7-%x3g$IesYZ%$Me+5RTr6k5hhld3EdArz}>8mrOga!@5mJ}8d6%j_(1Y`1xNXjwQ
zwDnC4^>j2;l@w$pCB(^(qE8hN6cQE@5f%~@=xccq^doHDgJLss3rnkNn?{V8&`_Kb
z>T0GTg+Dw9VKDrkzQ6CF!F>Ga8;Xi4Y8j)ij!7>}i}G_ef7fA%aX)7;pMa2vn1rN^
zl!TawkO1Fc{Otx09)d9#VbGxd-}%1`41kKJuA!-wtrN$=${3Ed5Hh^FfwhZocw$0a
zOjLMiaG<}tgQX$f6GPCdic8AKDay%6ii-&H5B`o%NL*S$NnKlCkEN@lt);1<uA&4V
z6%!G}Mi&(me-{^njlegAXR$>UwauJ-BU1~CO3KPBtB2Rt7pFyd+Zw3A*(L162WIMP
zpuzk?q7u@w3QSD{O9yxV@Pv%mAa^@sO$AZ<QwH$~iAl*ZRMd4eR2XtnVnTe>#e;4h
z0Ret~K0aa#e62P9CLsr4sA%Zu8=G6(SsJr6l;k9Ye+G-nsp(lddIiVEL`Ox2hlPd&
zc{y7fYb(o&4CWU`%c`iXrlqE=C@U!{$oJh4K~YIr_!k{rmcD_Jv5A?Hp0+yvsHm8j
zxP*kHl$4~nsIVYEui?d*EGxI*gxt!C^0LyBqQZjwln7rZGkkWz?0o$EU?ea-&+iLH
zm1nAIf3b`$Y@OVFgCgQWyqqlc)Z|2nkHbk86qjMB=@^>Xm>cS-F=WI9!0yB?B??y$
zJUf7|y3pSmqycgaWHnuVV>3ft4P^ysF+pT_RhF5Zn|}mCI6_#MFM9rURpms72%?o&
zQr9ssHPBI4l9LqS|89r?+6(9qs>1d$v$Az`f3deVGtg0E%1KBfn@h{c$jVA1%agY*
z32|iuTkpv9l7^b9vZB1~^yK(ZZzoHAY(mmA5F$MT9?SEKN+Ewj&&0~c-pSS7+b=N0
z+nH^wtt2gsKa3v;VQHp@fw`TlFUQVYUjsc)-wi@`N4LK?dV|EU?F9f|d8xlGhy$c#
zf8`m<sv6q5T53uPG7=&JLquhjbxhdK-oYWEVPWAB5s^`W9u5|IYVu-3geB#bwe(G`
z9js0CG?iq<1<>^?C?X~)EvKNWZD3~O<lz_M=Z<_qmIf0{E+;21ub?0&4L3hOkKrX$
zjU5BxhYlOvTvMK(nG_xD;|gwsBG?Jpe;r%{(f*!+poF}Nj*+Fko1444yN8E|r>7I!
zgr$a_C-^9seehs01@uli`-CNg`8ZqQodmDGsJNuGtc*12e*nJrT7Odz0f<RR$;c@n
z+oPvhT0%@18D2(7%Md-Het|(Dq2ZBHF|p`mSs7?3it~xeFg5kfZC$)vY|Ql3e-$P1
zc@h+cbyd<bvU2hYicX0M@o}{=MfOloP*h|vm`o;oV^JY~o_AGJ-OM#KwWMj%sQU7u
z$&vmXHrx`5#LWyIoQNngk&qyNAJ0HYnyF=E?c^04=;!0*;pXDxU}d7G!G!%s*?owF
z63fEHFDi}e_rcageu=!If*iVge<Z|2g#llEvA-qYtq)$Zgru~L9P)#Sw-{MhL*LBC
z#mm<}C?q^GCN44D&(+3Aiy^@;F0Y1O0gi7N9sAKQsDh@ktq1bci?R|U(5Istn5m?s
zq^zu>qQX>=m4Y3`{mD|AmL5^r)uU&QZz|7@4RE#O`eMY*jJA?Ec;$UOe*-!Qj1CL(
z^>DFgo5S-YFNIzl!X-pLp|qN@gKu<3X=7<dl#iVu@)qGSLUv~;GZ_kU*x3hs^~L^{
zfZbahX$#>94ZnZ<e4^3}HI}iJJ;&20AUHfKE-@uK(A~~Nn<*(E!O$|YarKKpwso;K
zP?s0M3qFJoeG^#?6Gy-3e~glb3Uq{hU99v~RaMmx)Ya8h81m8*B7!`Im)2(c#1}M7
zTsWn*JTt<>N}C&f(N_rJ67qTmq6%6j_Fkb0xv6pC0Ul1)Cc2ucOnJg}r(ywFZA<s?
z^wQ=@&82B!9CJ-+WOxyAX?f)6t12@U<>3bbzWQQ+gBV2Ik~q`_e}`98S`i*>wj;;W
zFDN`ZJ|!bA#M8k{S6NCxN||Nh<P)Bhn-uQtXsWHqvwrCIb_q!-Y8c;Am6sgmWpARX
zp^2b{K!*S{WOVHUQp(!8R?Qe$krw2jFVFSi=qI_qO9-(5Cn!FrGCwUQ$iv=@rOc2=
zfg`?4NW1eX=-K-xe-$-OS~#h(FwxgWSC)!9)U`F$P^5`0k6oLuv9RAP@YY9hwgh&-
z(cKCT6+fS_q#RQnMMP|SXE!gupwP(JP+wPDQ<kbcy6N@I9oz#VlOp|G*@o)!+yMGJ
zNi|dF;H1L3_NI#5#1MBILpAcqtEnq9<fO%k6F-PBkgSfie^*Qny6VRa&yDrA(Ur$r
zj-MZ=d?5Chlmfep^gr<qUSWwtQ)7a?oU9Br8DK^cA%1R_hEGn%+A}JvvTgdv%B)Ba
zOD*Z|$jDwpS6fR%T~!6$_<*my*54HH3NV>U=p9y7Q^!v+vJan-xQv3bhOU9BCEMPK
z<Kg2U<l_czf4qj0tdb^r{GB}mxcOZgl4>SSfeCrTN7t8Tp;z0Ah2MDm%=4Vf!T4<v
zkk__w4NlA(Hfnf&QV7RVM*+PyM7l=|j-v!@#r|)+h@6_f74p~<BLh91t&Me6;o?T`
z0dIOvM#IcGC?Rjy=;3(@flelBl2r7s$<o!;(b3V?f7ZhI>Wlp?K?k7A($hCEG%_|p
zp*h6yLiE+BZ)9p=ZR_C7ad&ZGTNvwUt0_Tl*2>Pw&6|$CXrKtq!9Omyx~--tEy~x)
zOj}Vw0Xz1J+%z8%q9Y3{Xqwo0g(l`y<R+n4!Bk5DPPqg+<}xVgl9rN`lpq4HKYH~j
zbg*{ze+uz+gNRX`**7nXy*nvYLtD?V<ow|UDPf*Akj6)bmzGyj)6&z^M}MfHk&!Xr
zt1tGq1Pg$rl{MSe&fdWZMO4}<3X;M^N=8vhO;d+uU}S1;#kONxnVT5tv$Qp|Sq8>t
zmTWsmwyD0RlAJhC9z;UL$lfm|tD>bkFFD-Pf6iE4Rt6$@0R37If)HI;L`+;!!@v@S
zWie^7A?VIVks>y_oV)^qNk?5GLS#9B28!cVb&RbX+?{MKjC3`W<l!}-Zw12g5=>ol
zC*Oz^ZYq#YDjOJ?n3`Kyv26ffd#%4IxBy(;+&#T~eEkDF9IcGBlw?E(^Fc}m#l1=@
ze`*?9IxKxdV`C#jLjwbSeS9~d%fZylP)8lDJC37BNT?Xv`9z^Osv;*L*v(pBMG{}w
z0Q%CVJ$8ngu92m^t9Ot$$KJ|VSDhg(D~F7(jDDgjdKqNV4Gw1K@0+uQN}wv_kIaqq
zwA8?#7ZFC^n2-D%?4*jxs~cGLN#O7ae~Zf~Dr@K%7^4f!#@@-r4e*th`rCp(ATTH-
zEFv;GHa--xH0ttVgDLCC$tyCHlvUL<G_`ef_4LtmU}|D)XrQmh(t&KeoTR7_kE1TG
zY+&ORky=z&k{J`=Y@x#tr9qeSkjE}8Dj_8UPp+<ki3Qui#?sUP2W5&(a-V6Vf9MZ<
z!2xsi;>4v*)&a$Yh5A%{!Scc~IFsZV<mrU=F;iNYFg)_@3{5SNy9zNX;Hxh5w}xmy
zY<yyJYI<fij?m1s8R9r4BLo*R@#vxp7&7sCXyL3aOpWw)HPw`P-DwiyO8RV%u;hZ_
zg=vw#4yKxNLV`3X>kQ@>62UoQf3%;}HMF#K^>lTxtF6RPQdQT~hTBCSTn!m<VZQ$5
zIiflzr>MkGfae3Q5x%~gW6&T->uMXC+q&3U7;3A?O9&#vOUN>iZ)ap~ZSUgY6Brr=
z_<F1S4I&+ol`}NIsHCj2nj$p}qEa#f;Aw-aEh>e)e6(;jjyA~0(^OHEe}=%1d&go>
z<Oqo3J#7uuq>1_`BnD^;Qb{xwWhEv9Ie+qKQz<AiAg)jYvx7%rqOYyWkP#Oe+`k}C
zy9i>$B?3kt{D)8C7nPC+j)A!mmDD2)ucc>fY3J<W7aSR%3it}k`|Y9_P+CznytbjK
zwXHfgKEM&hKLc<S1SJ(ze|5pDazsHC6h#L395E)#k`ok{U7iyk>}I8>BtF3B&`C5E
zro0S##`y;Ep)(9SL0wB1o81OI`7A2^{&#?45(yav^d6|eV<0Lxgi95%c69R%iAv1K
z1N_Zz|9=Bj7}eF)!w`lSWyXhkIhwOna86qqTry$u5haUqY|5Dzf8;SLMRSl*(X((u
zF?M!Rq(8^TSeq#Y-W>|c#mKx6x*muVufXJH?+KS%O<NCnjmT$2F~&d?E+YKr0njSo
zIuh!~>Y~fUke`)>cu$hrSY~V|cR#Q^;Hxe2w}^T`?eOZ#^3vkM{H)aY$Y5U&XIm7z
z8R%-kLoQ1O%A#Zyf8NT}KwDK&MvTYsbhTSiL*Lxq%`ZGYGRTKxZ;4!WHB}WAWpt?{
zE92_iVCs!mKyS1Z@h@;3mDF_&%@9mYO-)SDO|Qn3>k}`Kg<28PQxp;52L1ihx-?4%
z181m|Rlq^j(l#)+adP(!j!Xc2#by5X&<bd*tu6;HL{efxe|#KpZX%%aWru96gF`Dw
zx`|86F)-5tSw%@sN>qUR{1Wwe895@o4+|UM<H2#VV_TpQzzD|xEV`6}B1OWhM|Ky(
z88-nj8HT#9k%g^|wWYbKv7tT--O=*W;=J(!9Rx~IfZz|p04OCRCzH4C2R}j#ERVwk
z<S^>!XlrR{e`;u`s{yUl$;~GS2qqC<ccs5Ej0LpTSC<UUNQw@Ph>lB2&CJO!%1Mon
z2=H{aMbR=@EtLdUS`}TFnkbBtmJku(I$rqM#w;F|p{b>f14PLjS63GoXJ;n|dplbj
zww0xco|cM&v?xE138{>mfViBpwxNZctCKz3(iDE6e=0oxeM))QmcaGEkVjBtDnk&U
ztEa7wejgM8@P0BTTSCaFqOQ#{G(mxg0ZT^{xfQtn;K1?vUpO}jFuJj-C^Ip_-_0*9
zE<LZjwza*rrVN)soNX=1ix-k=)CnVQZQ7fr6QcTtL}JX^*1^fu9cuZ3{(e5*o*r)K
z;y2R4e~}g8mWzr>;HUu7v4WC{>iWQx@^G=Y213^WtPSiLpvzkYfSJLn8tBP3G%>S;
z7(+oyl=odgKLS$>WRXLM9w$2}+Z*X=sWP#LhyKCY1Ms!i`kO*0U|e%merlAzt2HMu
zF1w;>{LH0uri^YV%T12(b9W*ri;#9DNmN8te}tbKM-h)SdjHYQ4p+MYT3dTpuYj;<
zOwx@E4-Mw#Qm`Wk#ebSsB_yq+ZOnG@3H9@EVw(b;iwMwppKV+`07o5N*$5gMICBgU
zfD_w{a5lN0Of$F86T&h@Z;wCnTFh}WT8;@porR4f;A^k-H-))?Nv%~w6M{KbEF0gr
zf4uq$^LqAeSvhZV+wj7)s331woCZ;llarN^f!c{Ul|rXOb9kiT^|Y}vhnpH*sX8V$
zuD)UM>G^rN*_ml6NeOWw-V_&>%n%3?nU_JBRKv1x@C=R(^?}N-nmoz4;&s%y3~mG#
zg06vyxwWmMtGhSN0ONxC5mF3IL&)Gof1|65ZLFg#D<UDQNN~vkUwf^;DJ%j^8(EbT
z?PIIYF!6{h9yxpc;hSgoZ(7<lw!SzmHWYJJHJJ+XvIHT9;HL9aIT>Ufc%-!q%xwvB
zEl_Wj^(<WiVls+qhF4XTp=cpDJ0aB5-b4$tHb{aLabtW3ofYbaY>t0aYILB7f4!-W
zvNTYDhV*T_p#yOQV|1+;83CKu)x#$s)W?NwqOBy;he1dF2nmL^i5+xZ#0GoXo9QS?
z@eAX~QHx~+_^J#2tzji##^|cdFpjB;sE$iqdB=(aw|;zn{lw0dv&YpHX2gg2y4slN
zs?Y`XK@`!2&c>lfT20T?#>LCkf7V=IgDE32L|VhlB{->|X3Xfe)~5Q}VU^|CaY5)t
zQ5uN#Lb9n6bCbfn9Za<u15u)AR<)I-rKN?1g}FJ~-o+hquRNseeh_2mm^=GLrx#~L
z`@2}^GR4u&LDriA|K_Fq&jLR2lbcIYf}D($gw<_Ba+<o<9lm}3;Fjexf5$bHq{oH%
zx!FTs6+=-$UQSk4h6tmehNeRK-r$|-nc28_W5Y9~g$GNj7}`R$rnUyzyrigLXij3N
zmxC!j@%@@5pryvqCnBXFEjoY$oE~9eAt7jc5WvY<2^6`><CYIN<u=w<mgZ)rCcv=v
z4T(w4Dxj&fbaxe<ETX%#f8v5Y>`k;85)k2I(j{&g{HNFcAH{4ydu@KapS?aqNZH&g
zzNBsD+5_8GcTXKvQ<NSZ?1LS0RRy9-r>=>8Y2;P$)Z_7q?_+pzCLFcM)Pnq>Ia%qc
z$%))^*`GR1oXe>6;;aPXv(WC6l7ij`$ha|8kRNAgY+`0%#kRF~f3&l)wlFh6vA&tT
zM^H>!UPVI*MWw}g8_3wv$s%D{6_&XJ`n+JW;tvT22=Ey=F!-m}{vX9uz=(>hC@(fk
zK~T}aF(|cc#PntJrjBi_7@894hx}m+BVBbSa;a4{b@h=WO+2k3;n6`J4(2RXSrPiG
z$DAB6JlT5Z7m<`0e;*qY6%iJKR^Hl33;JF96Dh?Qx)!bhv6*GLK;^YJXW{w{+%K5^
zWn|8FaCL>W0Kn1C+QJl?Gtui1mYiSRI<W~;X*Hy{{3;Z;$xB1BNJxsIVPN6l9)Q9H
z6J0e$Nys3e_+C~XNI{DK^xFTU7zd~?N(pnb)Rq&F)3$UAe@iWH7~fV`o|gtq6gYjS
zt|TududIolSQMrCheXCDXXF-^WyJ+@ELrH`qz&(a4KF1O6>)82YiF<Epa6egA1_aL
zHwU;jAdaW&swh~OM3x^yG|>TZwjeVh+>c{t#?sN&)KCLTB<5;xyn`Zf-=?p(r@ON~
z+rn61M?=}rf8HlLyK=<j1rr+!6MSuSWC=e2en~o6M0uUA_EskP5bbjly?}pu?f+5W
zlx<~BT!53Qh73P)$E`Slu^Gif(-R_bBnOL(Gk0>Tx+XR*KImD^%qyv?ZynRoSdtp*
zijLYK+PNcJsfD2;j#r#Y-C1+%E@bIURg*5A;bLxRe^TDClHAm|P+vEDB5$jsrJ=?$
zvqfQHTzWpV)<uU0`+IR5ZHQ{Co}GU}QPZS_n-)x{$qsWikjLFs+OSdRWD&mz>{pht
zg$cp*kS0i+Bq>PppI-Za6t#e&v<PoIBQ@zE&?{+f?;a2t;_v0^U~OioLohCoKQF7M
zZ-p-Jf8^Yf>c)}dr_EltqN}YeHPD_V*T?XBxQCuMrGu08dUX1oj>svp#dqS$`Zk{7
zDTTEah1n@Fp?>ZTK=3m)GSJg8wQ~=QNz1Qns;MZ-O-qOh@$+!9F^4LVl#X3sYSq}e
z>kqG=J+dUu!$c8X>`YBeyrG{Jf>n*4C~b6se=vdYi7A{=ECl@1YyXd;9028WYkg(O
z!9o&pN*XL<OFNu2*G6ZZ>^Kq<kX1Lf_lZa?tZ8kZGHcQ5O}z)#&1@@<bvIVThG&`D
zIFsFUQbOohi%8(2ySkbx)aR6x=qxO~CuqtZ(Kee_+%T*(KPx#V%-_=uJ>@{|G_-LG
ze~Qa0tsgyYd|Q2GVOCO9Alh7zRge+Yv<uFvpSp7I)qN`_*QEJbFv;#JuMn;yNxORt
zMTkk{z-yEwC>;OwO_TY6lt^DEa~-DmAU;76aVaRq&`oAUQ!(<sWi-uQLy`;Y+NaH1
z*0bZ_smphduIp^b46srf!0?!}2a%W*e?DUpJi-Y4RFe+6PKXvsOwo+aENiZ-D#^=C
ziHivG_wnR7JJ?#8I|syORWwhSy`p>ixRznS7Vt$5Buh;}f?v%pEU#_umNU=KZk;<K
zH-xQB$+slrB%sg{CyVF^LV{wDkx3|l|MkaXMnWG^i9tNz`9x_gE3cT`n)cbtf7bUN
zK6mr++h6V;Trr^}%3c*2UV+!}JlWF$6h{Qap(VqV4LNo#oMV?zHFEHaMUGlyO=U@b
zc3NUgL};L|mz%SLjaOtwS<9sEwYxVgojtL&x*#>e-_6cUR|SO<Dz*{D<CYz`^XuJ1
z#IHf~e=2g~gWUQii-uqlfQ-B%f5{X4?-s+rhy?_DIa=tc;vk!>pwLPd5|ok!ZjVYX
ztRFYMd-;amL#MCaJJ!?Hm=%alJmoy25WoaVWzx8>y)Pf029fQtb@g!r-4!)OX<>PE
zP`$&G^Qy~=hGwNAvj+$GpxfTT*4o@9Fg~}sZOZ(0i)VI>7*>!L?GFTSe{E$sF+Mf>
z$fEWod#-=Fv3E&(QG^{BUe^+;Oy&IzPm=(MiUX7!{@b0qV1So19b}UWq`-g*teOTC
z-(@7Q?~Prs)WX^k<EGA8yk_&RLp`(FN@Cqj6bHfWO%!R&P!IrLB1_0140N!BWt4SH
zY&m}60pJT7XerBrKj{$|f1NTkD=jH5DlEv))6Lb%-Ui-y+z>OYZSuTn?afvBY0-gh
zIBldXFDazy7+ExC!On|sFYR0~DnHCd1sfjc1L!!D23?`Y{Tj+DYX8gJ;li|&H5Qc+
z<T_<U6NRZcx7sbLsBP+q4(8C(VU43E5MF&b;nmar8c^<magS}^f0uGkLqtvu%PJ*B
zhxp<{uL=Se9G{@bgxE-Ab#Hg%+1XfGm>MIm-rNcKllj#x9c^`GLsOyx-R-PQNx2ps
z=cvL_^R}LOc4o_**4$w9#zP_qN<*cY&~2~FYj|LPV9Vn?6eNEEUu}`UMc4yqRx6*d
zgbc1*nwVR%?Vx22e>k;R-VS_b3y?!f$;vOS?i0uJbfy_VXPPne*a2xyV1T=xYIn^^
zi;oEObVgnQs4hWIZg7Av_U9odi()xlZA~=;D;M9$<e}vaRfU;};qbm=PCu}%^jx9}
z+GcM!cAt82h}5q?lw@K8h>{Eq+RKw+sA^-j0O0Gb@;3-me*i6GL|)p^9gSOL!y*IR
z?ZKH6<|gC=LLqaPQ&5@}8|aE}RGNCFsiQ}18B1a(3H~VsJ3hO-FgrO04!xxn+s4k`
z(aFWt2|eqUX2ynkL^rdt5<}C-+Sw;GJ_91TATMXOi7w9RDKQk}4LQ*TtuxjfxUqlb
zlv*6lkvadgf1-3;i{q{G(Y_1FxLDY6d_n+UZ<W76;QAzVEJ;Fgo01mdc=-i|Ma3nh
zWyOc!I=UzwhFRM=xuQK092Q07)n!E}g&8??SFj9muL>3<Qj`)NKt+%iyBbz9G(A2%
zz|+am!QR$}ZEb}QI!i~3XkdrNNi4+U=n)W^7!~Zpf5C(hWo2b0B_$@4Y2qH8-#mTA
z?u)%kI;xX>%o((B-T;z^w2YRtG9-lpqEY}~ahbn8U<NW>&JqI7ilu{lKxh=?;|ofw
zh9-u%Tk9bYneLos=^LR_ZEb6BWkNm>B)wWfQh|K0gT2w=#G-ipEl>2&=huvBsjVo;
zOo|GIe@ERGyM<aBY6KM?Ct6VSFGe-v2YR`}d!){UW-dj{mNxf{$!navWZS7N^T$>s
zc$qTj@-9wGnEk=<n3m7SFRh|)#R-T>$9*JUbDh6A5Us3Kr?Ie%imsWxdtg*TT6TVE
zRbBJQVMF5sKwDrC?HJ3-;}R_|ubDniBzZ25e{R0)Z+IyUbGPt}^46*2M>bR!=cL4j
zVn&>ywyJ_WtwshFc__$>$&iXHI6<!@FG)~snJQ|UR$eiA^&Rs!9HxQ`WOyxbKa=v3
zBYdC(urHy5=%AKTGqU%IN-t>ye7#lv27yypG?#{8K?Ovm<`k9>Z)|Iy)H$juD+<&^
ze<=G6>WjYL3}mnDJc8pg;ejyKW{3|UMk}pt?H!v}JAQ80#4*joOY_nRijR)6j8Olc
z7=Gw^Ll1}%OH-90D*;__WbIYg#wT`Y?YKE>_N<)VToA=EQsAnj^cUx$fmM2viy9^X
z{+;XiABQ1aWnW|^F-0vCdrxG!yrQz|f4Zg-W5zcWr-Xp8Fi#iqAW|ubIOSL?i!_<2
zyt=-Xvv+7bG^SH0e$Ze_HW+^gFnnBI-Gq5v9itj6k+U8V;O<~)puv#h(l2R{l~!Xe
zEgwe^Of@YQ+ao-+q;dT0m0cr=sFLQ}WX1)1kcmiIAybf)u$9&@bq-7zI&2i+f9tRI
zH-sU5Ai>lzcLYVNf}+xj>YDnd){5*{KL=w~+(yZxj0KcCxcVifW#tv+alS@KfKN<L
zMHlMwAwDi}hT)ATKA)#smNfkMd7TqR)RyL^L<N90B?=iO`ZYWQe^-{Np=V<4Xl+WW
zXwr>#)||lDtkU|iog=GAg(DF;e{}<ESHH-VNPkk5i4Nb12Lx?TSxr+%znIL@|6TR?
zI1VOznfaxF9_t^KT2NG6Qd(Y7Sv@R2nO1`*rG+SIDgoRA2h1o9Rk8t6oN~sT*``pf
z5vF`99)=xlcqqFRf{Gf7<@J$O#R%eVpQk}kLP1p*+1-QGEfpjTudJ@4e-A2$@tH-#
zS}L+*{2Yu_C5h^sW8dnWAWqRUfFg{u&vbei@K3GzHv!7<*g66-Y6fgKbXgXcAg8wo
zfhXQstl$6uAOJ~3K~yxtb|*fK=u;IZ?p#AtLl%gVNQ()H%M$q!J8<}MM=dp{fGbB#
z8-5y)yOJY(amNObGzO4Ce}ED@1ayI(j<_E~h!Qo?HNeq$Oma3v?anxA7nV`hHM4UI
zh@x{3L?ROHO{{JQl{xg3VJGB&UOn+(6a5E5LUJ0$ww|F0Ipt*~MFr3dkscH1W^1HD
zyq5$!mS|4yE0tix!U_Zul)#BTmARuF0j?Y|;oKp^PaR!fni(79e}=jEOgRZ*F4LX{
zjxr$b4)h>%26S%G%-Y_?(=RwYHrNv?Oz1;IP=Q<wnrTm64+bo4Ae5kM34MkQ$1fr=
z8}J2-_QmV|kApHi;ioBRnL0ouTwPI8I5aCgIUy#<)4>8WD(N93gw@5w#pRUX`E}r6
z1w3@ZWyLXp6*?=`f0Pu7T>W4|z=DTUOB;U5sG7pm2p<QuOMufSCfHxvf=ulDMI_T=
zilnv$%gEHi%EsQ&#f@8NpsAJQOa&o=dWANp1vxuIQC39<sDMGy$$&3dvHuIEM<5fL
zqExoKsHUp401D$_aK{?rJC?gU@WcrTNhw7&mI>R*Gaw=mf4gUB@hQC=Le&n+-h&DE
zibsx^4(BGf4a-XmcC#YcaKZuu$A>hxf@s8{MNLdhOd#8AW@e~MGI=OI2)C(9Kum_I
z&N8;NXItR+NLf)0L_OSmL$LJE-&v|Zi)8;ZLARGGv8-GJ<FZN{hLz@JLjBC$!OB<%
zdEA1$rFwA*e{5nUZ6o6JL<4Odo?t1G(k4nvkU?>zuN*OL_(`pmIWc~YCQxz#wbOx0
zdHw4ybT1WA#!*)VX<A89Xz`(?>}f_X{V&dTAdiM_77ggbcXs#1)#$&oQvcTsPv%@z
z4A|}=iMf@{HDyE7a1&$y=^nHamYN>4I>w~srNsn$e`1O~kH{p6{w#Xh@DrQMG9o<L
ztN~^i&>+;5lJdC01(&i1IUPAkQEt4z%k3fhXW{bKH@D;XdAefFCM|d)%%B$N4fq=i
z_2<c}|1@CXc2QA6Ko*7ZW;l#5FU(4Ykr8%WHF&!Bm@quL(LqJfH#{k)G$%3K$H{`F
zBHe%Rf6Nd8VNBu!k}KB05Mk=u`NZZ_jhr@mcz#lltA!Si=;dH6cFNNOMQT-NgZuB}
zXs)Y-8TqQvBaO?pDoPA_l2s!mMUK|70C`R|MQJess`H-X>*4CaHaB8vfn=MJnU#$L
zZqE3;l^Vot8ssONMv1%^m7f~?Cm2MB%>B^+f0JpMFfRIh>c4I0fw+V`K|hrc7gNxH
z3V2vTR!UrWpr?zi1+u$}qKt$vA6J7GW*T6_YZ%#Z0%Ot&steMh{GBaX%3yb)zN4mS
zGBpTNs3ON&j>@2nYb?u*_H{5(r?v~0#}O4LdZ0*o9CX!NxcEnBl;k8u2DsUou|R2A
ze?wDKOIt@r7lDO4ra_fhQv);@Sy;1_t}>G}FBczoXM1bp3}~v5m1C>VmNX^JPDVde
zErFk($2fh>MSqBooB8Mm{(c~1Wg$F519}MI2MG$04a$6EZ~DK%@EQO$Wu^l5$7Gdt
zjM*;U!7<^1K5mX|bkB3AM$nNN78FN@E{D8)NJ4g5eQ8!4ROXe$>61!>&bgWcqXzix
z2G*|r(P+1{ROKawy4x72NYLAp9w-`rvt--ilCGtN1+-FP-BlDNC==o+1Phey?B(WU
ziv@j@DG9~TW#I58so}K~Lw<~iFl7|Ng7`lK`+)curOze8g)om{Nfk)qzlACNZbB{$
z{U9+Ad!lz5A0_PfrH03i;+R1%CnYW{h2369cRx=KJc=kBSH_+fZwU_A%2;}T2!(#$
z5c;)N4^0f=Sg{xb%nlMjL7ygwj5!e9jz;<x4jzF~$vGv%$;=CrE0hSk1R7s>_+Hqn
z4^7BPPl_c3nFy5}O*Q<^L+Rhw$J^V>)5G13<797RX=aQWItntlM~6)OIT24hgpEMA
z)6~+@1$_m*&ocaALT#O#;zCq^jj=_9DccFr2NggV7k`WnfO$a737|E+$Ol)H44ANl
zw+zrip?y>mJIwUtG<sy>7a1PkdEobeR;3_O21N^smJqyP6&~GEo+D2fUfbNsFD9d;
zer#P)YM8q<OHsK098vtr0;kr?!_CzRT4Ye%9~_mCKD0P1F4)7?SW}*VD^y3TAkk8!
zBE-PUnDmsy_?XDB-~eB&S`BGI<gQyedIW^W$Kly{5#eD0P_&21Saeq&NL^!5Mp^`i
z2@`TuRX>+(Nxklb3oI?meH@7cDI!dqXY4F-L0OdwglK?lmn6}0Kpt#bqMwS08PEiR
zb)JZyMNCc;;<fx286L}jI6GS7Nls$?!s0UUBI_9$u(UN)l^F6oYVcHkk7`+0(6Mk0
zjL)rXnb=a65$Va+RS@i7Wx<?UkVOd%3h?vxbmus`dk2I>#wDd^BahJ8LQhqOTa@EX
z&=Sc5I)Q_Q*uccW(2}dC&X8nkll@hBLvwSovog}t;zE60ZD2Nk9_=mC;hdA56@KCw
zioEzN!{e+r$f&`<E+#{fvOTHah%HXG=8@fbw6-ID8^N4lMG~^Ivc!p&Ciegwb7>gS
zCr1dpVu3(qAR7_N1oYrE5TW@u7(Ni->11uJqar89hgBzuP8vnBd5RXSrBb#y9|J`>
zT`RYc<bs;f(??Z*=R|w4b>;c{PXwg~N+iU`Mn!}{$E$x}NLWO4Y<yA#h+k?kCAlej
zEQaIg85|qp<!Fg{{v^XQBt9WADLExAJtH$d)W_LUUtL~A5nVXQ?JKUUtr=cjRasG9
znhhN^W;#r6*A9J!ID$epdg3W@htK8JQ+^#dpIFSDYUCz=uVM+}z=HMqo@@LuY<7Hz
zNhs2Xnce|d*B{iS2!4^ehL$E4=7SbAOhf~vNt^}~<mZaed;wwrINhx#FFKe~2<A>?
z<Q`&5RFp`_Aj7k)J;KwVBtEV-FV4qCS8m9krrzSSGt*O(65^sGVQghMs0#v(6UF}W
zV%*(J@i@4DL_a!LADftxo|RirT$UXl46|-z_=%40qWbaU+Q*C@)i$EFxgt9*(Aiu|
zUXYg1p~ZP%x&}P)^w>G<NPUjsiBVnT5HnR|5o``(8%Yyq7Hm$<Wgrf<4Dm5+c4$-}
zmD(uUAh`pG4FG~-FryEP#A|7zfJGOFD=1!3Q&mxaVk(j%L~#EOFmv~_fz_*H0^Mzm
z`po4W=#MA;8`|)CHeOL#6|GYi;9*a;y0QatYDum;uK8qTq$MZD$3{o;PAL}Z`^kMf
zBB1VK!glcqi%v+*&M&E|ZGsrk9*VHi+SXpNLu<y(oil6ZjLzv(r%Y-tOAmLm(3IwW
zL&~*(bhfiLGr}cA87!wcV6y06fvQvB&VFDM1KAfh7%EU{1Zin<s;0aG1uDfriN~lD
zfZ<H4s)W`nWq)EWBMk3>!;ZsJtQ)5XbDFg@)v@WR&cA<);mH;%Crbl$1@V9Nc2ILf
z6=`8uOSjOJ;>HPcR?Qh-R}k-SZ@^F>JP~PsV#J6ddZ}2J&c2Z;d6i8Kb;GMG%8Co}
zKpG_pF8)Aoj)S$SzLqi#9aCGkfT-kLLK*`_?aJENfs07U%q=LX7zUJ}+JfXzj-`&g
zfV_^CJ7_pem@t0)xN+_6W5?DOBnCN~s!MU7WDzNONYdgK9|K)2<f~FQ?dQGm)Br?(
z30W9(p>JZrwuT521&~<T51YsUS_-IXHyB3bOb?i|4BE`rYzo%s%R$Bot2mis1z%G$
zGjj_IOOk8u?83p?pw5_r3{B(4hWel_3^YgK0T?Tvk05}TGjfX52IRodpr?C~!+cCk
zoB|T^YQ}Ue-MnmOdtE_Nu&cR=k)b|+3rO*#=p-8(a%frI$cYo$N4K>!*4I>5losXZ
z;=*4<NPv%rtD`Le6kIra_=ke-0(48qh6Q-K*t&QHM#QJ(7FQ3iYiwy7J$C%Wmh!9^
zAA3U;aVBw<6xY|+H#9UhHZ?V)j}Vsum1Go=kWo+uR~0vC0Ew3xv|{pkar|I^eo`D4
zm-=iRiHxroz|+&i!`<D@jl<z!KN&{ey2C#JIE1jPE63dfPkx~QQ$hUv{Cs_VeSCb#
z@8s>{<Ll?=AAo1NMnpzaW4wc*ci7DZddf@<bTyP<=5v3J+UEoRnoc%m23ja`9zX(|
z2F`@*nOZwKIyyMm+uPgO`9x=bRg9Rjpl8p<B{Rm=m!wCbU(nSVs~Q0L-qR-_BqA}h
zpt51)gwA<$W_C`UIDYJ?5zX~ARb|Ebx#-ac;lr3HYG8GEIK1Q;*?EPfMR_@y=vs)0
zh$Gu}#<q{2Fmck9sna{*$Om&Qw4vh;R|@hAii%6i2(nNZk<TAQHEF|ttYkF}Z5DU1
zJxognp|sD-)#Jo7=%8yGniJbIHa0FUK0YBKF(Dxyg&T3PyoezX9UUDN6%`p75fvR9
zmynd4nnnSpBml7=YQsqgi3rGfO-W5lPtVNC&dD8`S5Sz;m7MI%40J!nMWZv~>xF_Z
z+&3ukFRb*}flnK_yg1u`)j#z{15fh>y@|j8e?MQynSf{4F=y3|W4kvjoz>A=ou86`
zVr|^q?&TjE6`zusS6o#;qJ7HD`ODU>TE2L}yjfk-CXXM})?7clvb3<U5GX#F3WK>m
z#6J`lmym!DR19xu9X)<>=Ntr(;G8!Pj(n82odG@lE<Q0iEh9UBH@~nXBPPHVSMUh+
zYGigrqCrxF7&8a0BL>KDp8jDmpI48k<`wD~Svk1-hsCDm49&|gEGjN3D=#Z6D=jT0
zep})XA^w!Y0)+hh{Jgxpq4eIQfM*0$0xAHYO;J%%Syer3*zlU#y84F3rskH`=EjCP
z2v{mg3UV`3;DUsI+KIjfQ~F<>&%X@(8sYa3XUHKE>QiN*AtJ9~Xu}DJrp{?tNK!%V
z*qO_=9J+X7@0Qi`JIA*SD=*H^!D2PBDOm;O!y8AAo7^>b;qtW`x9{G$ZPWTSE0!+o
zp4B;d!k7_FwbjH-40L^?CmOxc=#fU(L1W$Us`8TZs^Rs2O|2uxj-NDb#+><!m#^+w
zw|32{l`EDnTiS(=d?I>P@E{`_2N!o}>WKuWpDUh8j}WE-@ev-UWNzo?ACZ_<^m&{+
zei4%W0eVg`skx=Y>l&I`MvNRirXBy{W5*zj9zAN*sF5Q_wjqodF#@!OS`eC>n_EV-
zjT#M?7B9ws6BtKf{Dh8)lO|7|GIiRt>789&GiG*8pE`MB$N2U!BN}U~N(*w*lW;#9
z3x&D(cz&VbK@wOK2Ped^w?EcZY2co>z_{!(;*Cy8gu#MS7j4*k=Jw@N2X}5*F~4i#
z*tRC(cg!mt-aKZ~jQPt}_iWm}Yu};cr;i^wuxICg)=fRDmo1z-V`>Nb%n_r<Pnte!
z-lFBpmMvSlbm@{MOS)%HoiM7owyL<Yu4Qz`wAl-muUfl))7Blk_8!=`yLbE6O&faF
z%$w9wo)P73t7m|<?~upjfE6wy<B}r$Tw%zuD5l!Upo<y(UC@Asf-VInT=~fHV56;@
zUO23OwFB^%7fC==8tZBT!80ziu(}C38&jrr&6?A_05BiW-92ya+&Ob*&z?1F*36kR
zXUv$<h0)pB*)?<4oVnfe7cTk(7V==xqQy&=E?d50<;qp7R<BvJcI~<~E0-@_ym0=!
z*`1TejcTr|D#dN$HV}Q$BfY)=3Vi^U|C4BchnOOnQ`OeeP+MJIjIN*Z=85yx^q#o(
z^#1h=Cl2;*UbA%mtZ5U+jA$5ERy$(+^zP;BcN{o!?BtpAm#*Hpb^Y@B)5i|&+qHeu
z+7*lD&X_V`Ob4`DuiLWg;DH1C_wV1gZ|~l%YnRNKGPZeG@v!FhsdGVa<?zwtCr_V$
zJ%91?`7<Yu9N62tZ4)~3HF*g^E>Ms$z;8EXm|Z>nL%1AEvgtyLX4*kZrwa1&3{4$j
zym&)<*HXY=UL-+rS*8Zd#0oMJStYe?6KBm^uz2~ZbsIKs+rABB>(;GXHgDRral?l7
zJv}|^)~#K;b`4-PV9mOo4I4La*|z<EUtq`1-d(%*?A^Byt;K-@2M!)QxPR}io!hr;
zTE7mhSJ$NWw#MO@`ASwd{_I8m%fOWZvg)G9ROlxZPeV%6(mgV(a%AU}j<F+~>xWeo
z*N>aEeB0qm58l6ea_`2aGsh0>-rBQb(VXcM$Fz>?oWF9*-s6|<+<Wlw(UYft&tJTH
z_UP`-s~3>@_U_oUZu!DFos(uRSh=zH(CN!}@7%e4`_`?SH*cIduyxhEDWhtOYDZ1&
zUbFr1`MdY-qpyGb<mt1AcdlPLd*blE-Zk?kHkYPHcv@o#GbqO})$!m$FK(ij;^kpI
zJUql&6m0~c?Vt!WwYaHc?wVbHfWK~d39>6NC^oaCe)QDtRqHlx-MRPR(UWJ+o;`a8
z<MgSMCypOKcJ#>M!-ozbyW#Cj;J~57M~|I2dHT$NIE!)a{Dq5`E?>EN?b@~L1g>5>
zfA-YzqlXUkZeF*1!K`T$Mh!1W3HPuuP~oN~zZ8_$${i5YA2Paj{>i0(Bc?B3+&yy|
zx~dy%Mt3gWxc}^(SHJ%F{o5B$?%%$4>D1vpThT8$bIRNmoA#c(`ryqkzf$<|!<!e6
z@7=n3@f0%sMszvMT)cMs!L!#Nz5Vr<pMU=8ryqa(^y$gfV>?#P9^X(@KYsSA-V--o
z&|m%Q$G6WO+`N4D_<>D-OFBnGKOZP#XerA{2n`YfsvYLuQ>;sxV`<F7RX&051jM8j
z)u0K_CpNEkLU+&pOMt(;J`|J3Cn~+DcFgpJ>vrxteEiJCYq#z{q4D_9!~6H{-nk7s
z_{R0?*REZqaqY&<+jr0&e$*F_DZrOMefIpt%U5sSzC(D6@#^`1lSlXO+`M-A%+bBu
z*RNbOd)mnAq4EBXCTbGAQ&s;i@O&2;IRyqD@`$Ir<LWz^pC!2lLxv2NW7+y8!cg}4
zb7yr;orL0YwDGqdJbUx$_wV1leE#_1z1ue~o<6d7`^MEvx|eU*dFbr52d_T<_|s26
z|MJVPzy0>}$M>&)pFO&F>)LsE71l26p0NlFf8+7{-+%r2$B*B?fBX97-3v!{u3gwU
zw!Xb<$%cKWZ$5*s{uyna-+uq}?)k%8VEB!TrnOZfzn)98q2o+iTbCwe!VF~0-xU;-
zR=|_!TtiaOiJ!N258!hR?}`jxTsO9JQO~XeM^2u*eB<ta!)MPaJbMbZfB4}3y?b}b
zON_#8baTPQ_3V$JzxV|*`fITI`wukUzIyTW@dIjAH>_GRx1*^f4HQUa`cvFKADD=N
zDJ?Kk8U=NVG7=*EG{KHY*+clCz_Rs;%d2UhHG9@f<hOKApEhs#`kjZ)T)Y4B#q+06
zV87qFcH#7Y(fz$!dRA`OzVFz%YxkbLegFLr$oR<eKY#r2=H=5z_ikT4eR%hlwM)CZ
z7OvTL02%(>?>~R~{_U%0k00CtCvpAq?#}UDXj2@5O@Y4p6WTey{{HFhb9Ca*9NWKP
z!Q__G)KE?zF$ZGWtf{^>A)ZO~*Xe1)*m`t@@#E%y4^>TFLp!gCw33E#GnN59$M9IN
z$vZL)op@~cqo>YYxq0^yG8x88v<T0z?ZNmD9^6Ovr*R*@ch9KLrN4y2D}>i?klo*Z
z_@0Y*Z@}^o(W;(5d3euubW3(ZHO$9W54zg_PU5)_%*}DqkeK{M%GQbs(z9iuk_81D
zVIf6-JsWQjkDWTbYsReEbLY)pu%c({o+GC(-F!&m!Gqh^E}l7lXz$J~JNF$qb>Z6W
zhtFTVdH3OmPe1+g8?x@Z*Dszvx_|B5vAtW@EuA-g{_3s!&s;}_|LJ>p1#VtBfAYw_
zotxGo2VlwCt$U80zy1JSeeb{j_~Xx{;V+$kIl8Z>dtzf@Vt@meW<#BC?$9zqH5C~e
zm_^976QR`mGzd|hf?*@@D4x$SJfX{umDtBjCk%i7%FTO^Ur>g7^$Ki{&izxk_8&j)
zbEiLf`t&*6<XpVw;tj@IF#Ct^f8gQ+SpM0QM-OhHl{vh3=jL_uIvR=+109V9nA!7x
z`QQLtKyns_MVX@elOZcEG=v%`rbM@$G4*Ub@o7P^_3Z9N%T}%1w0$@J`)|;9*@HVb
zuAD!8{P6xm$4;HQboJ)lhfkireDnT?AAkAnmmfcVc=!6{vnRJMp4h)_-O{<!x>s)5
zfBO2Pcjy{_@#xM~^v508yKBeR%^TK#t=_PG&*4)SuHS{Peupgo3o<-<<1d{)x_8~&
z@wG!^ysf!3oBo6?dWxwMCCrIOQYoUCj%v9Z4*2Va$BccpGgkeg41eJ&dg5P_MntB2
z{Tdtp1-|zYp3;El8(-xE;T?hZaN2+P;UgE{zekpT`TQw)Cid^zwsd-1MS2*2#|)&U
zAZ7nWh7SV-2l_#})yV+}EXwkDe!Mu$k5>hTBonWmvOXJ>hO0-kPwtw#cxBI)U3>Q*
zMA6#mvllL3y>a{A!^h8FK6`xs&W)>=&YeDc?!u+ZSFhi^b?@QR7jHg%{P{O{$nhS&
zclGqa?dz7#K@Q;NeW$J=!+(5#{p8LS<iVhDXUC46J9~Rk;13(;8v5!d&tJd${?pH3
z`1|;=TQhs?u*`6dDVJtL|E{!913h;RN=BF`L$U-F@Qn>Ucb_*~mhd0hPN2A31BTyy
z@Yrc|qdj=~3fmmp5B(o(e%JuS4M1PzufgbG+&_T(pnp$T{Syy9e*fWr-CHm{_{0~^
z96!8o-8|UzUe;U<x-S6E9VcOBXde$(drKqG92DWBbWikjwP55RdR0^n*c>b@JF=s*
zdwI|HeaC5>xp3viorh0fzJ2}T89Yiikxz@A+AGArzx&}QWcZ(mKjqPlvxj%|ESWuN
z&hkxrvEhGs`QZB5BfB?$qZnz+j@@uupwRuor7LLT+<Aa3|NbLocoa3Po;j*AHP}&~
zOS7TPO!M=J(RLuHO0ioghJ*}5jT|-!_zc4<sNtsFFibol41W<lWzSx}>1$H*{J#6c
zp~g1mdB1tD^dHD`j%@uSFFt+z0h|8y^T+pZLmK4x)+JM0iW2>Q?YIosFF+}vASWX^
z4iu}gLa-9a!Gcm8j11ROS3!pf(_^Dj^DCQCkiL5BzLQrjT{w68<gp`%_M`XwDq4g$
z@O!>~`5fM*d$(_*K=i`7%h&HbeE#~wkH4Xy?I--VJiCp2_nsxQCeB*AanH%C58wX&
z;o0qr$M$Sm*}Zgs^~N3hj-I)6>n6lQXy2f(e(>bw+aG?yhQEH{<l)_`y4p$;{n%RE
zSQfVdAvcsO=t9pUCFMD#rO6&2F)@mKk6oM3F}x}!p@t?G3?~eaqB;1}2($M!>Hr{&
z`)OcMmj0<PNYj6K|N6y~hj(vXyL^7{npvYO(nFlM!rxzifM!5#W%1CAq?oV(4@cl&
z$%~WyDu!k-EnW}ADit*hF@-9#s160PJ-d!yd3YDSJ*SQy*t26Va`A3GeDMzds_1up
z|L)Z@_~?Z*C(d5Faqr2?_n&_I?bly_!2$cLdzX&wTEArGgqcg$?>=z_8UFp_>u2_F
zS=l{h&eEQL9S2Wey$>$_-CNhMTs(UTE$%b4Z+`#y=IK3T_(Qu^Om8WQ^)y%E(roCF
z#sYNZf=d>{G%*>Nj|{vUL23e}BG!ERT*EuKg(T$@hCg)t%%vN5AH8^s5A*ljL)+h;
z1DlnLpZ^R*a^vI24-nErw0v~)f{vP;NDj0FF=Qowg!%vfzxkg7ySHOo>naPgQ)0t>
zId*1x>IxD9l*F{7t+hEYmQ|Is46PhJgQAkNt42;+yy?J&`|n<$$Qp;3+xH(of8*iH
z?|-B5@hu9wubew^@c6kaw;n!!`-%IZcMq?g*u8$qjPYHIVf#FI`}^AmmyhjSyI|_b
zDc!4ow;#E9@Aa>r-oJcu@8;!mC(d5JdGE=q_k`gg1+Z)R)W-ZsP`vpopvWWe3W(DZ
ztDoPAB1GwV@faSq^y8PLEB+DNnX=+9pMhWhfIc52%>U_!_b8@EkLcOn<<pu9V!g}^
zK?q7p<ckbX%$BXKD9i%6L1<afR+bYdxGOAwg1bU!NrHFd>=_u5FcegU*Y+O2dg0WO
zeLEohg1GK5it8Rce)|07Yly(!JbQ5K^4Vhtb{#x%{@T5#Z$5HQ{L8x+kM3NzsB2v3
z!nHe&T)6Z6)6098Pwd&WV&0TF%hvZEId}8%yO)peUcYea=>DAtj-R`F_sN@&;Q7OU
zPC9;Y?TiscG493#v1jNeFPh#@cJ84QFC{K2{P!~+g5t8!5^jYB`)fx{oxghHw%rHd
znZLu$DnBQ3F!<j8f%|0t=}G^?#ZKPqJpMhonSTC6Ms|<S?^)H=TAbiBz*N=G2fpfS
z%JVZpL(tXE(pXoGAuY;J#>PhG&{+<DH@zIukLd0fl9*f34EOY|?VEa5EnP6DYwC>d
zC9BqNL+{?{^OvvRzW@07>!<f`prCty@0MMMPF=e7=+zJ0H~#6(bBDICS=iYQf&2l8
z=bqjn%6r|*)^6F2Hqfm{cdlK)Ig8bscON->?f#4JfBz8*0&oXxnm48_-qUn{Aj8vM
z9-ZIlKfw6!0bhVx`X*M;1XMJ9#Kc((mabaAwRiuKlc&#|LyqX>E7xw^x^wsbL$vi@
z@NR%U0oorPdFo$@sQvxh*T}d}VGH5!0NEULll=G-mDha!^y1z%Guuj&{J5#AFTgNB
zX<m9lM4*=op(ZCMDNJiiA>R&v2=*v;QdH5xBCwtzC=9L{F=58S`EzDYpWM+tdc>Fs
zQ@Z9ZT()}s)?NFLBB$prI<P48+P8D_x~+SUp1b}4y?;lj@&EuJ07*naR081D!(aF2
zG@XNY9sRq-W3#bs+l|$@VPmtgIZ<P)vDMhN?WB!u+sQreZ>@X&f|)gE=KEmp&+c7R
zeQmBt7w+`cevDA?jxlJ1WEn~;IJ(g4^c$b*3?+p{G2d+RTij_K2e{Apoj#W4ALI5s
zKj1PWvLc5ysk}4TV4hTutQm>Um38^-+&?xU7c*>so?@eAr5G&p7MrY0TnsY&wcVI)
z_nz*8gheI!MZY)5K#S7)36@ZQ^n@Xs_7Ov}J+k{b5KoAX+#wp`d$)CA>+~t;`LKi4
zCLsKFbuDe`;j?G(73l5&&d*f#>A<;N5N~n767`(sclwPae$zeR6b%N4ajAWu547Vk
ztMHLMnoQH_A4nfK%Reb*D_GIaz(27rV+aLpkfZU0t*kX4>s;d|+I<M-*u1y#w7NkO
zO@_y8;OQLaO)byY`*6q6rP^I7sPFV;qu-)hxXpW)oj1H4XnwpH`V)XbYrWcJw4cv6
zFJR8JF5>awQkFw3F-Nk7*_lF*5xJwXGHCoBy6A_UdykO=$sxe=DYNCc&>TaI+V1;2
zwrJzc{d1+xcKE_KG$vcn88ipWXa;rI{&EQtV4pFFc2A1G{9LY3F9;qiR0zG_56-=x
zmV+B;*xyP3l9_nvly0vYi;L8;2GTeL4T~|qwC!44`ENf<b25UG<5A>egEd-v-}V;l
zSlh|7NfeNr&GB*_meyG?r3A4>Fc4j8_YSa9lGIVk|B!H5E$uX}KIC+`JUsQTuX0y_
zn&JIop<(6?zX+tyUDV4>e|N9<v+a%h@dHI70W)skWzBb(WuwdSd~ER6LXk+=Yws#A
z2p08hGxSZ8R!RYPnbq!~ZW4BS(--;q&HZ1qP+L_!`avGjRY}iJ6IVS7#EZ7L3QMDW
zhP_J;97`^DA+$BPh;I`U3{|C8)&v5M+J7gOZx2|s>wfO9AWP&uto@SHtPVOSNyOq|
zr;!T)qRuzb9;g$1S+WbX-j^EX4nddYayM{Xe{|B~ij(#MJ*EF1tS{5TV4x@5k=NeI
zPm#p%@c<20MIvKh7!GoTt1HH#3jL0&OV%t*%pR!WEso$vQ~8;-?hDc1M?p?bK~a#O
zZ?dz4kvC0MU+A`w<QW$(X}7*~U!LMJt=j?E4d*%Y<aB(VA1aZAeVZssd~L3V>>J!m
zL)5fgaeca5WYDbA?eck^E0Q~8RVr~`E#{Ita8txx)Y4n~$L2nHq1vqf{xPmZl4pJW
zc6>Zpuj3q837h^UT!%8+^RzyA|JjqGv-=m>o-##4b(G_=upl>`P;-rsmhb7v>^TNN
zt&sNBWa*8HA)N9deEyJGzts&x6@6>&@Xt_3;ryM1IV$4^QpqD3i}&_IZIHLy*@`>b
zCgETEP4BxqDpMgKkODQO+tXz}@VdXgFDrnce9gk?<&XjaEjTpDnjW1RW{ya4Nqhh?
z+=~!=-WT(mb$T0X+xpT*@93}HgYA6)v_v=stq~3{Niy^{1RbZwY(r;nH|mYAJMb(C
zer~wGtQ5GnFh;5>AI4lP*LlrWjP^_fLT$0RNK}aozFfR#{GI|8xeY<j9Ml18$1@9a
zOMfDpi90`b+|S5+ez`jizo~fUannP-(WRmwRPfqcG}Uq=t>sd!z%4y(ati{LyNEAq
ztiIX1SZMgayanO#M&eEU^;#?k+Y|&Ihy|VAFu&H1>;nG!rzO*#qAB}ui9wSj7=yZ)
zb!f@B3EPg?>`So@D+zZ`o89fMKI8c@D{C@#s>e8Bk5it#9bm2@4R8vZ11nrJc=V#j
z>Y_1Frsy)a97!|Ve;0Y!8*6|N6jw`?=7ue>ZnK?^#`b3WU~-+mdyX8~)tk^i(l`BH
zw$I0DQ0)x5U2e9fq8V@|GC7M2oG%i%GQ3bTd+;`4mGnD2eNK16eOH&5vN&wt!rE@$
z?w)EUPHLUKP;~ts1kkZ^u}EoVkW4+=W@FsDIAaS>HVr7hmx%|Dm;`|=qsg6-p#}~T
zy6kpm5Kc#7wTb+P%u=%om)Vfg%0ABFg$9B?6I3>W0TIYa+oL8ZlDef^8=cOFC(jt6
zZWN(zqX>8AcXNWVzc2ls(i+|#NiH(inoPg@>NgND$=^Y5#ZapxVeV8RR|0Hcd5L2H
zTv%KdRWJg&6ATg|L?R%kUCg{u=XXB`U0<SPZJ&X}mnQeP`37Uhcc4VH-d<_(!~O5T
ztVf6J^wN5$BARS=Y<H)(%lSgrB*TFs{=AxAQZg>y7U%@!bRE1vRPrmi6@EmiJfi)T
zruyDH?{#Qo(YK2JFqqqJ7M@O($or&dw*j7q&IV6+4B8kay8<GV1~nZP^G*71OS{JX
zwhnhMb#^@;N~L{5qARVgw?m+++07n2LjBRWh3A1#(C=XGULZERyEjBSMY_1#-FkSA
zE*iR4H*1>3p9Bo%mkn!SaElPFZvz2HzQQm0LX?Lh>Mgi811IVE!<Hd7Y$5_(6=yPs
z!(@fha3=Ot8)$Gl+`etzcI|e&-&s~2ey}y@hN!wpi9g3Ei3bk!P2a|OE)Dov?N7^~
zvv$*M=n<U1?<)J_X)dt)E@mStZ4Z1x@J%Q+Kd@ZV#=6dK=cU|66pKr{`AGPeaOmUP
zEyG~Q#Q`jVu8cCs5!3a8sl@9~1HWJ?@FptGv(`J`fxx))-NZSkNcvOlVa2n|cEiz|
zMMGcA10smJBRYNS^<`)O+e809?k|`#cm750M>C>(7?rw$%{ET2g;RWy8h1aBg&fX6
zRrg~~*T)A=)wa^5?$^dv+FsZYp513Z1Ak>OCTQ7a^0@bZg1(zidBae`Z7Rggb7Vm5
z(NSU|5U;`Gco#YT=VunDqu6Vz3WavMC1^kgIt-L@c-{84K8Of3m@~PGIvbnvT5a!I
z!c~i>Gn*}+@qIe)?R5D_<Op#7b7KuiK)uo=H%)5QU(U^g=6Cxvm{WeAAM1-k<NWkr
zgplB;A%B~eG9m|ksYSfH-{W1bWolVK;&%ralp44(BA{|?J<_uSPd`J7Q@By42vO}n
zd*ZH2gBvpYBGQMN9#HORCeykt@u0j;19aOVO=zMwHa4;Z+<wAZU!+WCaNB92>Oqcg
znGp(?6?M6u6;@poUBM6wc<)aY{3h{00*c)S@0Jow0e2T=t~3L4kBU2KRH)(Wc0s@%
z6IL6g<qye6XbtI7F}Xdo7zudiLyJ0LDgT()Qm%^n(!YN*ATR`o3^5Ne^*V+uKq|A^
zZt+;Pkwu`%l+NM07S<L~cCy`gA3a$a2a(E+L4^4a%|yo<<P;vsj-J`N8=I|PI+wN=
zECl~5lCmpynGjQkX@D5g!AQ3#8UvJQ7?xR`=Dq&yT}*`WrP{g1M~e{yI6i#AbId%%
zAYluY4bk{2(BUlODKa{I-o(jwRoO$aHRCEtF-lzWW$dRqo)NK`^dYpyBB>&AShQOl
zdf%RJ52kw6xR2PaY|1K`8Okc`350zwb`~?dQL{T7c6%cq3%3EV{!4B60-!rkP*elW
zyBZ+j&5XQq4bXrRqr?8fC6TQms*x}w0!B}b@qttG)sjm>b`77AW_n+M)*64c1$L;=
z<odMkCSt6tZHWy-;E++bw#wrD1(|@`Zmq%UKB0AfxS2~0Ya4~$GLK%oO64|e_ME67
z<63w7Urh~bUG!IPt=F}CSfHA;UmSmn3Sm#l-e7@^QD_7kvp)Qe&{j=PNZf=2(T2d=
zBBsRN5YEd-l$*rcQ}>62xNi!#ixR|Fi!8YDL~`Dp7Nt<-8TR^`x?1t;=ujon31`{q
zQHEf}+_s?<jeMZkWJiKFSfLo~(`07=#q!3MM8NzGv=P+SNzXV50n}5l56mnI_fnAT
zt#bG+Mpu};V=`O4F7deEXwKHxh1<Osqg0ad@^TTXD;aMkL#Cl-#y;X8dkY|$6#V^4
zNSPEBAZCjrKV5!OP>4r*#)%=YkMR$2v!+wBQ88guia7powVe-R!=QGbN?+|XoJ}xe
z&=3vV2nbC8X-JBwfr0`ef}EuEcp=Vixbjk|P-qWl!49AIAhG@e<J%LEy~*R~<aRTl
z{CVfN-@7?R)QVkTZBRs0{D(@&c<a1rJkc06wMa62l>EpD%XAFt&Q^S))?>Dr@xI}B
z6DJRw*buDGlGy$18`dClTJB1D{~QM^I~zx{#OvfF;)9M0pbG&q*-x?rZ?uh-8TH#(
zTN|3Y6G6NHN5CQyiH06DJ+WC4)!;8^kVTg-?U+vXnoVV!%n=V-UBRSe>c>*O?`ODN
zb52JA@+UZ+-(&?_e_gAyMmLzJe-a!Cjo`p9%xFi4e(fD=`5y`XznZw&2qjW;hEccZ
z!Qxj8SbK<oBx#WYYHm6vfmlNKnLz#9n9-x-Q~d@PVyB}FLp2|A&H$Jf&sJ|Q3XafV
z$(}e_79I=lCO(4ozka>jY~hj$i8yv&oz4d@h2O6(_Eq;t^uM+k`N3Yr(oPAYjxQyo
z3)maO-ZY!to7mir#&~K!PGqGU!Gk26JIT-3)I%BoJ=zPFmK5ST_t6Hdy4l-du(Z_h
z&@T4!-NZyiMa9_}p@<vz%jLvG6o`6}gsJ=x9vK}T5e0{ci~>@hT|c<EBZetXR$E)=
zaDxhMJ?W`_Bm=@JqyC)LdN()kN#z@>lvnb86BraB(U=61!0Q}r7;X9+(#I-<fO2R(
zghIf=IY?N#bT7^rcoG|AH$@@56j|HV6}d~&P+LN?t*&nyU6a@LF%9eH8J-<Vz(lU8
zt#5{2fgNEMHDBN$6=bfga9v5L9Ionq%8G$`tjPGQVi1SBB{JQ!6W_@S+lgT&iM`4}
zUQpp_%=0qgK|(-;S5+q!*WDb-!0YEftp_k@uOxi;O)SJL6RD61%AB(LpOe)kB-_KD
zMEJ8OHx6x2xc?LjotPLKAOFi2Liq;5S8_S;7H8e8EW-11xX!N(auy)X{o6?jJV)N;
z*2I0N{YV`(x1fW<>CLWKn8YB?V0nuzCrh|m1XhZ=QJR1Ulw@dbTrcL3>-hJ008uFV
z3vWfsIm|e0fkXKBbwWX00P4Fpt5gpUCpWaMV?!^;49qmh_hd!P<u?{J3i`5+->~Q6
zA6Z)%6&4hihH;6g!6lP0-iViuh0>U7MxSUmy+1|&?*4>Zh$8qsMZ8+;WhZSStKA}y
zt%wjq;G3>@&J^vsn8u!Ojh)Q~Jl|vB*!}pYl7VsXlT^uum1;;T1jaar<ccRHGJ8sl
zUN<6c%mpef(Q+IHnxN%VVbVx^b!AzAmzSTPk58bTSAdU?pZ^~P14e!n=o}mxIh~nb
z%v)(`yguq^=u3~6;F(K?tp@Ticps-PPmeDw8NVUX_|&nm`#^$8AfF)t>Xc#nmb5~B
zU~D$x3NT;$Ld<`{#&j0NqbOp`WeXqcU$b@}vC~F{NgOhKh2%RT_5OlSn4F}ptnzo|
zYinm?RX!;PTY`&%nFP#zC@v%vQ-5L}V6s1z$Yby&i&JxoSh&XQ`7EaR=1dE4hsB%|
zxd<3p;P>pcG&LbiqEGh#hJHt<HK$onW(B5%cWPRS*-3r#anXvaKI}Fcpq&mTW;CJ~
zv3icg;8>Blr$3rnGONA2yStZ_B@oLORY*>nBL8>Vz}b+Jh9T3%9d)X`Fz<af*QuGb
zElk>fxycHAceeVb1~xn-BW{$-rUT%Cx@N5Zk?{Pt_ZN`=RRJIIqinencBO+*L;P24
z|JJSp7s5&W*A>dq#z9yae$UW>fHh<?dE7urWQJ4i6Z%LH4$r8Ma-z~mCK73=e%%ZU
zw+1EHCo`fe*+xWOO65GYii!X=v;7r6=c#=rZF#l6rMxqbk%2oYH=OR<2!jm--%v7x
zDvSL{IQ`FKUBK6HJ(Df@PrySP2qFL}SeuZlfgA(1HIAf&?3ruva+TPGvt8Gkru9vg
z=#joRe2JWDqURvkk7%}rTns9Fi}R><T|D77G&+;te&75^xKQaW^zm`({5*{(FE8%v
zwLwA%3`u=4E7jWgkjRff200wOqisv8Fk?y2kcg0`Y#^S_9Sj$pCuqT;+Yg0*09Mvn
zx;jqFvLF^A)T-C77rk~Aul1K1^zxtU_}<(k8*_h8%9cU&@3~rgu#t=YQC7xwo+bik
zUK8W5*ZnqYY~BZ3O^b-d(N?tMiZLis#oT7-{dtvfs<tMIOBH)}Sz`4z{MdFnOMG?o
z9DVCw5CKxYHc!})eoL<y5Jcn4l|7qarNX3iI<XEhk-^1nbp?cRy7kGwy<FE{(J@qy
zaEDrIsTCh|<yNWrkU8oboJJ3G*`TfFh6mIX(T~DbZ}Z3W<?}|*+Ft#bqiERvPv3FS
zx37{|e1T6e$fW;GBwAh$9+rue4lI4MLtiN30(f<In7u}E!<uQ9rQnR2DL(;^;AOiW
zEu^iiC&I=W`u-K<^YcXn0TRQM_jcP`dH-p*K1mk^c=c9L2??Nzt3SGzUK>__3~Wp?
zw3kHNLe*(eMh&rYvvG;X{z>L)jURx@t>qzY^x2DAyrqsU9g9YF;c7Nr<>zfB4_yt~
z1<w3^WqN+&XP0ST5qq((71-M;SDUcn%I^))#`7mfG)Sd$V!;M&8)c*NoBWKO+3Q=G
zoyVzOZhL%^B%IoY1eLg}0wrFWlZ1L%3g4i{!u`T?nNm#hU`G-tq##G|x{km^chPAt
zj_+c3VM*&$<Snes^f>k?h6<?)+OfP<0cNyJ*xW5aTs$01DfoTUTwj0vN^j+#QjWy(
z;^A-);=NjKR`0kj%eH4iJ_qy;7Y>A<ptVHCVeG|5!+-YSgKaa<<G5sZmzsJbogbt;
z;iN>7R{%}r<U`DO^`+JfUsX{X2&NF%4}MK|RdM$7s<Lkm+^2TC6`El~T!|O(#Vj(A
z!Y;@8a^tQw9*6n0u-Fq#?@xY$6B5>pyW>slWR+zaulxOh-jRJbB77!E9@hT*Do?ZG
zh1M)z9XZ7Utdw)vNQ7T_2$#m@XgDg!=7Oa3Z8~r%m2bd#&$g(~Hmiig{eAJ+>R5@6
zib&O)Rgiy^>_eZB=E~OSJ1;DdV=3mUs2(0b#hY|z+hN#o_oRZ^OA?tpp}RIOSN}77
zV!eJCwJJxT&U?P>7YDx-Prxw-IfRd(F5$m}p&9&0NwlY~#JL)W$Pro+Hru|DA){1H
z?6b@BiycD~S(q1q!uhe}$mWGicRmqC*ir?9zoY?|i^_kTL53ND>75nO8K+<@s_F6_
znwxA#$?ovD-Ht@<Mq4X)w>Q`n?6O^6NSVXDL(;(`<I&iY<E$756a1Dy`MVcKF4wAD
z`AhW8-r!V5`6bJD#d1M8)&_;MHOK;043oYfpS9V+7!q~3Y=!7q&v`Xfvil#iQ=xSI
zX~`4<zgbeb%yf0cT^;QJ&;Hau>szat?M}B}4;FWIg_IvI^aQ+Jrc*$3CixuJGI*{0
zYY;<Bp-kc~F27P1sbZ=eZ#5)Y;HDmgD+uaf^94uzE=G0D;e7x31u`{Tqm<^pqE?O1
z)=uSHT{f|JkFBW)Y?sqO(zA^_B***3uI8pmzsbpBw22Omk{vxD57w3xK#hU-fyfv;
znv%u_UX{|u8z3HoK3tY~WlleXuzvSke4{zQnc5}%o6$Us!5V^si=K;&p0j6sv3F%6
zI7+jYp9az;#qwwcwpb%3yYqq+)4@EC7kCOonONrczGc4R$aRk?oyrsbJnpnPj5%NJ
zG8*h#eiHeOq7V(B+<;vZSZ&QA^v#RMl(N{B4ts%cC1$Oo@mlO@uZKS7t{0!NT}_7t
zAqsE82VLxf5sa_ixS1kr>+6-f|G{)Gss_jcD52!U9NgXA5z%x5p+do7a97<o`J2n^
zLS;j;e$1VH`m9*M%9JSn?UeRo_Z<7p{_rn#ONkn5z7>#VQJ0IoYDhSME_crs1ojU)
zX;}PT?qK2Tsk-i)mCm*J+d1j{m(hI1cV>$z$EHV-(zxcRnFp?|a)X-EWe^ad)Iy8W
zR)#-+<<eUZJoh#ajPhjCZf#%9sA-U5EqXW#1w|3(g6*g`-6>V-HSFJQ-6IZzWNpjq
z8~<k8-GJ9^=f5NgXHf>gCnTA}%mn^OqULlCSSyi?(DhP@#1k*>AGuqO#m!>GQLCUd
z4L3!7Q56t?QBvJbtR{m>We`a~_jCE{hyCF1BI5Z9)0X=Y*57F?9}_Ob=vHR7;)t+-
z$M!2XNt?`E%_a~s07xW7=+HDXS%ITq$(Id41H2`Eh;?u-Pg}ETq$Dbw(2zq>zu3eH
zz9?nqe5OW3pN&jr`Sd|{>YXzh!jLkdFy#EfNF#9(?4<bX5Ji8n=5ey!y&>GW3OH>Z
zBulV$KT3RNm0W(_vmm(lOKkN}9)3)IIJ=x!{KcY%ZcinJ*WsUOf$?Gb&h8vJW`nPN
z0H{UJ@R<LIVvohgrubxZmGUR4Pwz_0($TV(o$upnS5k3k$N`>%jMX|kU3Z^SvWMPq
zQBSZBQeT)-*(EST|8$3R&`ztl=^Md&I+$}%j!{uY1y&+ZD^Rt&i|l-VT_*X1PyCx5
zw@Yz6`n5%1xo0^K%=2*jDR556Z>|AP20+xFrYftkUF@~7&vg>0@wFu>E%X<h^3J>f
zcAwtn>w7caEn+U#oBTW<JC^U0CF!>Hn~==d6c1WM-v*fU-r@zV?L=igUF~%JF+7Zc
zR_(XyL;<&e==5di!lc<63|wnD9uh&|u(zOuWuxQsi9uz<=gr?#rHWX)JC~aQSo@V%
zgaE?OsCMl~KL$8ZjZq*==Id_JR2ntjH7q8}LgRVWaeBglwD%2xW!&O&l8Rzs7DIcE
zn52oss_<|KCgCV6=kudebHU(4G+TTxC2S<-r?{<>`&^H90<MoM&%3YTHh!~f#NRlK
zk_G^;_a~IFKmI=7b;Q+G+?-l~*n+gCKsXxJ34+&fQ0qfYSyX7z=eP9b+=9;WJ6%mp
z0zt-_uj*DKYDrCIac!2g!%xNiieENv#O-fevop7vG75W|=`7xkn<rCa4Ti8pt~-p_
zd)e0t;nE1;OXee4k(^e6$OUZ?`HW$ZH~D%3);yk;$c|bt{dfMRlui)90~%)i7Ry-f
zZRXMab>LF)(R$o6XPjeP0WQi<BFk;OWbuVN@1xyz=cT@69rPeC&^`F@dAzU1>k*_d
zi?L=WrJ}J&=l@JZKgbML319`FMlos{;ULYnj=(e-U|<MI=LUh+LcG(S6XP}GZ(bUt
zf+|bA_H;<c;T<5&%mr{bEwLlq37bDjnp?gKTJ=|`lo{ULGYw|J2cwH?W*k*wnbu|y
zShpBb%RQGnIKYit<F5QS{Rq&gY3g8wHO7mR!Ne<ss*-#F7dgm~ai`hBqk?=RBvvdV
z7zhw0k-*6L!RCG4<}1KmtvB1AThNG}v^S*-+|AR5e_oy00iV}G1ixJ;s~D!0A`x$?
z6dAq{Ofg;@5mCU&P&`}ijE^b*h8#ig#KhAMaA=wTw|9{NQT^mewHQSxqJWIQtS8N~
z0a_0)X5@s;Jv0mZJwLCX9xB0HZV1*kHW!gDjc0l!4g~j@-s-$~RCqz>Yo3&i8KTfc
zE7y;N$7BmR0k?()J#}{usEABf#5*zOhf+>-BfZlz18ZYizqON%>+Q=@5Ag<rzYq-Z
z21Q;^G!5El+^KA>@d=%}QRKgk3?$yY?B8aTMTRL(fiNLSqIDsysR-4qA>S>uc)h&a
zAUi;`j)b-Cz)Gw&h%LLj&34&XD`nTYz0tS273`61prq5{zA4C}Y8`lfVJgCX7!Pe`
z2_@d>^u(ewmT1Ax`<9mByJE>b0SL^TeE|pQMqTe+Io0EpY5TNDW9X>pdRcONY95tn
zOE0y#s<|2Z7PaC@E-VZ2OJfYBL6gJ_kd$E07*o6&ya7H2zhI>LJ9pf{!~Z;eMI08>
zmp~?BTR2BKgE^CK=j~H+FVAQGxR?3Prcj%k)8BN-`mx(N<LxAiTw|z7zgpfgOlBhx
zfk0It_>zr>dj_&422LzqS%Z!Z4q&JL^V*a<X;%yvn6Aq>JL+03b`l_Sa&AHS<>~qT
z)FSp`z0J+)Gu?CXm@MW+CiKhRYnPcuEYQJkC{s~2$qECju{Sm;n}OatmIV3MB(cEM
zVS<>^&O@@^kjqUmBPH&!qqY-6{iuJi^e{#I75uKvVTIKLn;xasVC8B$e14YzWbDh<
z(UsLkP)|zEw`KeySHv+b|Ms8S;pcINPGTY^-j=xuh-nR;$m>+DU{j@PLTh6Ic!P{k
zYI|t|R&FX-;?kcjnfPI~BWpY``Zn-%1bm^zDU&DQ$A`E6jTFz<-R~d6CGOQBF!rdr
zU)yb-GjI05bZjMbEONL#`<j0q#<3B{#!*@ZYV&E~CAOr{CE1^Nd}og_kWmx+OD9<J
z$Ne?!wS?PMeW3L?QYl?(ZMRMWxU?`o--56cWeYdbe;9&m4;`d4)E1lalj1es&fukG
zqz9Gq6tJgHhku(un!u;I&c6keACsN8uYZ|;GJb=*b!{>&9Yx@v+4iuK$f2@y2_8fF
zX)sO);tLQZs;<IcpL@O@^!Vtbb2-Nzt+iEg={yn%xSwAn{w?f6pzZSmAbjHq-Vc?2
z>amyePOXX|V;M+D3vFi*Rn8W{wd3*fn@M^Wvwey(`k0?(={8!fE=zZ#W^1jc6B*JA
zS@Fl#V0w5j^J7@WM#+s%$RW5k&jp!HZ^Ey+NJ<3e5uT5Lo1-N6bhkVF#$@|ozpl2=
z*=Y2!GvmjIV$E&>AMm09{{2{xP}0*E^LM7d1$P{0%j{z>6VMR%*;OTo?1@BWQ?ThS
zoLeedMFq?Jn2mHV3`3wj@$q?m+s?AVQ6gDxDJ5eWTbBrL*#AHj5AP<h)SsAQVMcAP
ze3j|p?yAJQxZc0z$@01ye>W+&Fnh_6K@Sf_iM3n;tw+&^$Yc9}i@br@*M!P2Ut<wJ
zglo}+&$|B?6D%%6Q_2+l5Ecdi)9Ww9L^(+2s>w`<8vdkxXjC*e&$ldETh3b`EZxX9
znxpZyT!f%-TKef0BYzMM!KfSsg{NS1{2Z$@vMh6%G%>_xQe7>4$4Sy$BdTKciB=~n
z9Oq+B#|zMtH0=4gGkg9~50bj-&1t9L@Abs(hOA;>(`?=(juvElC=Qg{99rAtztTj&
zG(FEa#xUC%xSu_Q>V#@=7QN!jDrQbG(3clmSf8HklMoUorlyaso$h`l{j3~*7ooTx
zpo~ux?8vN1CQUFoKResmIXO8vxVXT<&q&ZyTLfm>43|Sdb35cU;5s2Cne;Wy=Jzzo
zN}Z3sucI|oBX0_N(c_Tu$S3Z)PohkuE;2P|5O;F?JS#+Xz#aJ&>Yw+eifB>8kKdUA
zavqB7eaIjgUWc3a)pABo{2b9y*87dy(M2*xsGFVEl71q_u2~V&q%5;imqI80By+eQ
zK5${3p&}0!;2QFii|efaR$G&}<HOw0z`!@6Id4FMTc3>&IpkMRiGiUdrK6n10X5X_
zfT|?oet<yCAF}FV##pJqQVQkS1rCT!W(Ecu8v+7QrI7SCR*aGx6CbFl_5I|!GedJK
zpl$5rg3opEm<^oy;cfvwtsLx|I)v_WF0hm}Ff_`5)PXmVCC&lKcPGH_4HG;}U0{3H
zY;KDLQD(@UYf6{qA%VmBXeZ>k{Xf}j$NLijlk?;8BI#Dl64P-7gA9@zx6#QWpt_@?
zj=D0v0#mGkf^Fzb$(tE1O&8*LU8V(V#>KQg|CtYHD%mE`i2FbgRJQ~clQ{w!uptr>
z!!$+a2g(99c*2Iu&TBe}YRE(sa;kfm40M4W_!0CHETz@chO9`S-#IN?ZL|#G*2FJ{
zzxEq#zBsa!o2_0K|Gsig*_MtdMZ@1fv2dH1(km{%(2-KYxHJnZlH1V~u97oR4-7+>
zl3ClC$l|TSgnn=^=`2A@r2}Q!Pn1bbOq;$jn1g<g@Tye8U%S1ZpQ>Ct&s{;qUiQ*U
z1bI^7bTb~7|7KB3Ltq35x*c9rnbyS*uQmv7fqpZk!FnrKdAi~Eas6zISiZA<CqYwl
zoi|$QO?u<2pg2A;B$MsP*@-qDwUZaY5M3`Jb#a66Z43+$U!zgg1S~Dvc!>F<)&*s_
z_H^70y&=QO=?(w>diLhk)=s6EwDm8A6w}j-wvzLgj+l8M{b4efywaO-?0|4x`GX)x
zgoiE;lhYRBdpZAK!lQ2R2Uz<JV94?XRmm=Mx!XwgRldQWx{W$umX!6AcLq#aR8miF
z8Z3HaVPuv7<?aF^1dtyZVW_qKI8a4Zv_GK-tuV3hriWlvSz%eoFWIic>8$YNtMI$p
zc>mEkK&_;L*D+%rA6wA{=`o`xB&kAVE_tE!TB#G2Kj?Qj5YG<9k_dp~YyD@&aC-p@
zGt$_n8VZJe_TpXr0fJbkrDgOp85x-yo90%RSIXX<=0mIjGR{OqIUVwTg6XTg&(ao@
zgTc@qt^d+u;XAyLApTCZq2#5xBJvP}V5XPVw8sSg*&V>+)mSYN!@(jBb;v7k`YCPr
ztvnvH#?>Yad3-#js);JKO(5Q#EmPhlzRr@Un)~ol<8KK8E5y%{kQwYLKlIsJM|fF@
zo%$UJy|4j*V!T^cbru3PvAhIv^i-<_X3(N!0Lok72fj7g8NrC9A&x${dbxi?8X`{)
zWn;P$u}}*R)C}n}4ywK0*Xlf8uC@Nj<=!QP+&U?0OohqtH({iiv+$O=lJEF%19ZRk
z&kgN%w@5Nn^-_rm_Ka*Qs8Fw?WeFm2mE6Z+nB!Xkh0ayQO<$QkqJgT9r|6KC^==PC
z$bXM8-u$US*Zha69^?5xI-6puzFOj?v7>Rm%Q<3MJJaT$<Es2?I{_JC7Hxl_$7qWQ
z&&^S=IbN57PlKTlhp&rMk(HX0VKHfq)kglo<%1bGT*9=V3V!9)!7pVPSC#W3tSk0A
zM)vgvsyjUSJx=Lm`hIRSnLKVlhQ<&JH`*+|g#-qJNke{fke1fj96=Iog7mL8vKKET
zCXA4L<||QYDH%<Qf)ynF;+Lj6|3fpC@Wi@`3X*%XWAkonh<e5je+(TX_Fw+ezBjX=
z*Gu4t0OLOE&dF4o9s~N&+z=FHv1dtaI3Q>S9LptXgh)wA8K5g;C^sXI+UmC*-fWmL
zdFHv{2kwF$8JnKsCB&slBv5b|L(rZPQ)!TrMyU|ojZ_r0va|5@{T(HoyQ9%lL=SQI
z8b8?$`uuph*^WjWezg11X#P$;=jgDzQ{2Z(!obwVstkS+RasvG&>nZeKMcT-HnEHV
zliZGl<J?@$$GRNV)C_CO%Grk**~_>v8|FWrsFU3*_&l)OoGf{Qn9?n`qG?#A!XA=M
z#+n)T9auL#ASp~Zw<+0#o;Hi~ZpiU9{6_bkHP3$VH8YM5>;C#2K8Emja6*+*yJGBV
zE|PrtM^N_dhP824X8e{ek=jAdI<*Z9=qThAVC%z?@2kx~lcJEpeJ<pw=JkPft@1C@
zYx3DY;W`!hURYg8PBF2lrmlra29u<==!#37;>Id)1AW!d*jfwz^H}03jEA@z&BKYY
z!t@9&E>754Ur$slHz+TG72>O(^GcUc4-RrPA)z>j4UV0l*E`j5|LwRvW^U#HU@%cW
z1?Rd{LdvSzWkweYxqhv+6yRgk64J>@iR$!%gaWUC7+YF#(kgeTQ5?#TH*y;jI#5vX
zm5fS=p}Mr8j#`**U_GH_k1akpHffHmjbC3@9a87N211R}0<_@_UVOOD7aJO2>2zHF
zIF<jgOC^_bcOUR+E^VuynL`AKi-M^wnFd5v1tqbZW%=hwy0{_fF=-EFg+mP14b7cM
zxovEZK662jg8J?O_uEf|I|K@ij$MRs=8u6&kPNDm=C@Nf*e@tuznK20ud~xI>ec?;
zxRD)+>Oypy7NF39!t!ELcyUEt?hhF`pHb^HKG-4i-mhOF9<JARE#Lr|a(u1U7wN$g
z(*v@7MAL1%Lh4>gCdHN(Sr~AES0r#*vm>xTlW_egHR<6u*tOTujMow##^w3Xli5l?
zsBD7<%WVj>u(ZEFl-4Hg9C#>L=%CHGB7WJ~St&f+rdyM)lMcu=IPYHP;*R47Ep=r8
z9uO)9Pp$qOm;cb|yU2hrRK8?683Y)l7&0p?(7%V@`4BnY-E{fkwZy%U=kG6Hn>KTE
z>9&mc6-+`)<j-^A%(LD7_`pOwTgvHPiTb!W8PO|y_9j=<sBZxQ9noENxQ9?Of+Kv=
z1107-8vf2MCh#imIwvD!cl<qnYw?hN#}$&}Hn+aC320;dPObxx5`Vr@RFW6}#EaA4
ztlxc`S^sZDw_aCl|IrcCYFn?9j(-|CPu^jRtTw<G9!nk|lgZ!K=JhfFZ6@R5gl|0-
z;k2OPw?cUk2nM=Axbh%SaFqw?$$o?SCyU|5)?n5U1vP3WS5C{@%k$}jLCYtG2E(UJ
zM0XBfliBE59k53#9m4qUXS~cl+tcygo+9+Iq>6>mql=(p!MGx{u0s3*l;}hFS>RmR
z3%24{Bgfo}zk=>Y2Hv*BDxL&?l#}4|v&5voim=5`7i7n`a-F@Y3?qY)EST;dC$}jm
zd#&d)mGmTC^~4~L<G4?xEvgylsOx_hc|D#Y6^@F4`3}r2EX*its)Em;$(+X%J4+x2
z#EM1LaEPr5c}pKbiy}!9@SR8Z?!JbI{5v^*`%Vc?R*>K5J|6!OdMVo<X#l*R_m|T=
z!Z2s^BV}%`S+d9}1P2704Wzz#IB6)=xig{xA*H9D%WUb;;^cYrG};yf5|ZRzUB_`A
zSVGRyEI{0Km1SzZ!*bijk9|D2-;%Of^|M^rp!}-BD8V&aez4V);A-ZLc#}_7+xD%y
z6=;H(4T+oIxt6QnQ5A2Ok7XrD4D(7DdwHU{mBF$p$67(-78ETcCo<t|PiW&GCJb_v
zyvC+al2N0OZ}vfJ`gMq8r~@?veIZrJ0!m1N&p_Hk@zeSHZEwbJMWU6)S_=&;>zev_
zcG`~k)8Se;{4nsQjucL(fShpi-Z&Xwp%W_cU@QL@N0A+H1mX;REur#q`}LS)iABOo
z`)Shn<$*t(@R9lj$An>!GP4E<KBaNuNIQ~C?ntuLY0VEJpUVX@DAff;U1bfV3{+p~
z2Y`%_IfymH_+#{M^65`}Ti&r+DqxrCP*==8UySK<6SIo`fEzj{sc|)+3!)jz6K?@{
zn8|_SYAVxXOuS!9+A1yjKk9jM*c22*Rvyo`^$30krByXIPW{sw+Yxzv!t=?m+p5}^
z$iDv~Hjx7!Cu8NW1NK)9evMF;$a%*WIuD@qSA-rY#F@^V@AP`jo9wK_vXb~)Bo#Xe
z9ybbIM@Q+8v3-e_f|{Tho6`g1Z=j_s)jPw-+1X~Yf8jigdiJm7i^J_AmFF?MN^e(<
zk7m&Fphxgyg63kerqG)FBvZm3mrw35v*^xDv|hC&*mu2_deWgi@Q>eYF=s}t1L^>B
ze39>XVDf5=e%ouHmHz$1b)_8W@Y1fW`9n#yFesrO)yEVQ;LVFd=~iU&^V~S}#4Dd*
zs6gY(P@%oK#S~Z`y4IS?InK9^nDEMS0~aZ}I<2qm?vrmX9a^AeKV$r_1bT8ZIl{|)
z2<7+~cAuVunqs@{<7DR)JMpKNu3x}86UX>}nFb=^@QAL75w2zYBbXSqvR!j)%JqY}
zEu{Y?-B^KhGlKa;2@PL>f86HMa;|+(Kd{TA8$`ZMv0DQvM7M0loMOWMqQXuQueDh_
z>X>OX#5PtDex}M>cOylxTNs<zl}2je3mSA>&se6T#}W@csiv|6x_ixoz%q>>TS-T4
zfyHDK*6m4TLZF`fV(9Cu6KSk4owd4D=W)NVP;^q=L&OAaPN&EE251|EmS(UdRNYtw
zIyec3gjm}}m>Cz`7hqT^%Gk-1+`c?B{_&l=v%1UZ9isVDVX=KiuzmRRPc`}I@K92B
zAZ|a+lTuKyx>Du<ONqrUK#-xzfm7ROFX;D5P-*_OeS97%-Bhi=pq)iPvS(aIHW<ko
zx0%}}{7o7A%&R9!EkTr4N1HsAISSn%(o}KwCT6W~?%RqKXz>B!#qr4mxGLI;I+cbt
zes$&zYF09)oJ#k_%;X{*89C+oTH>^nzbFu+pC7G&JyjK;N2xLmVEFpOkr0xF#mFZ0
zqWdwYAgR*@98ch64@GT}TM`m59S1ck#J|DJoRAJ(!GFyAv(O1jl2K8V#3d!943>jT
zzsrEdQOTxX!5+&7lD<%@x2?O5f*tv+Fh|m0D|Hm1Bw!<Ny1R!1?ahQnD{AOlp{}5I
z@6$)RC=wf;T9*w7O6)WuF%ho4i^+(Vzto*Qp(%kv==dr!0^@c13P&)eX|WK_<R`Nr
zjoIZ+4j5s$dwW3MLX=BreFaTWmWUG`?Rf7{a@G^I0#3qTn7qBrCqo_vJ!td?qdT1!
z7qTqZFh6(vYFsc+y;S<ipAf7Aw@3=X|2+g$$nnz1G9b-cVt>J{YOMn{plp+Ea>y=6
zxTV$J&el(?^mk`?aVG2VzeC>^H@5H9ySN*}94uC%1MWq)C{cF#CmhYr_jkxh(Ghar
z#v!Z`4cFsakY^oZEKYtQ&IGKw6P_8D4NZxLL*LsX{;u3~ymI+GKYc&!O}2h5rm_`r
zXj&>7@&N{T!e9EI!-$ZbiY9180qJYY+hHrgd`ho>;1VWa68w6~8sih;rQb-0ZqPsK
z{f(%|Grw7{K2c#e-;u_qtj6m5J-urxe|X&Q9PirDxK*84(|$v^Y2cYxPr*!O!xm^2
zhQ{`ndg#?9oDWsFsL3Qdo)f}5%*vvD#NxVFumv)8*9AK|eBU8!=|St>r=bJx!i$2M
zFx;iN-d?H#8=O>KcAL?%Xn_X`a5SiLLD#dHF0R|tn|vXr1<C7_SUK(CA(p%vbWT0U
zvpdt!^JS4$HEmGvNK&4xTHsrzDiFL1$0ih>o;~no3Qc3C(Fs*n;_AKDC-{@GX0s7#
z7U(lqbFYF4+l~hLz&?@ObnHc|o<9bI2)wnwsfy2XYe)r$p6eiMNWn8cE85N%qvR|?
zQl*5Fw5s$1d22t4>z%LA!~f23z{uHodkYBY=ny1?Q_!)moyNadJCpOrq*9X0JNx8?
z%8t!Vo<LRyI=}zDL?XxWa;WERZES931%~?7(w5DxMhM!Z0wdmcLOLvYOa_g_xWOt^
z7dRUVlUM!?DeYn>NL(4MCHQ}fsB|~=q8mv4+7Do2u4P2R^6orGuj?p+s0O<myEwwW
zFIj~$Upw4S@2ZlsxPHVFmsVg0B_F3UT6BTB(M*mRL$kiCbHWd$d61wffHnXNKs5uO
zV`!@7gjBGc^T0>OzE(r|9q7*}{)&ZH81i)uBEw*knzlX9{c!NZhUe1%c&YqQJeGK^
z)Ajvi-<M$(golpa8`(x$Ak5WH`_5;-K8vik2(YIOYTGg|NohxXdE)?G{};^ArP$+*
zTOe5M7oGW-`NnmTv&LB`ufr+uv3m$=*^6ltK+`XQw$_p&+TLlY$hOuYbDrCh+{g*K
zM7k8d54YL0f@G687m7dnkYe!G{4nO+drx}WNKy1Ml~Tp^6rDEGA$Jmgaw7zU2*p;W
z*!J~wrV^NK4a>hRa8HL2BX4v&`}kUTW)g@eEJGn!{`<8*tzG;1(yI<s{@O-K?`Nc`
zMXT;EZ_I0pLK_aY8u*P7GAnNm4QC8q0O3g&`)u;I8bE-?Rqnr#{!9?N-T}+(eEId`
zX7~`*K3lj;={uzjyI2;kB|-%K{v~l?F)GuZ<Tp1bC&$FnpCRyx!BG|hiPE@lc|)QR
zPq$k5Sg<e~hQBDjINjX<&-I9_I2wcPD9t#SbB@u~+6}vN<sW+MT;Ug9{T~=AEmrCL
zFH*(Q|K(87M>xhW63et3a%iByBRj{2MIr2A_1?ff{tiI@ru~q&tRUy6=kbPwBpHDG
z`TSm964VUKoCSS;&dXA7^p_A3vY#1E2b*57VpP6NQXy?Z?Q9Q#oC_>VA@2N_7F7*R
ztS$2E7)z4Ho0cmSPc^Ox<E6jM4PBCmKuI{iqS)^}xzs)`b-VwShTA0xq9*YfKHQs(
zx8#q#`)2@GRx-%Kl-@T(L+VN0CLN35K(XQRV<$RiO!4?|{v~uP-Q*ZMGfow{cZ}xw
zJ~vIMPU?b9(v|^u&?EM`K0*He^s>8~VG+~}@^(8qtiPo#Z!=3F2%7Nce@5muUw$zr
z4}3c+GcS<*!M2I#i!4$fPk{VSol_)1Qa;Wb2BN`P1d)_X@%cL&tmr=xD0Y9XcU23A
zz`OW{jz9L+wvRElN~y#qg-`>XI*_m$Q_OJ9B!1bu-y9}^z7(hY;pW+S<peAo|7N@k
z4^oe()-Oy{ZM!8CTiE`tf{Ztq`({OyGdHV)EP~s>L}9OhlFesQ#8hJ_+Cof{+w0ZM
z>^Vai9Zx$W)r`K98>Rw0wjU{$WMOR2xa<x3FUjqUo*=^$nOgKad|4$3KFJAF(l#+g
zebcX165eRQO#vCc%0^-aE<7IXXRwO&@Dv3lb3tL1iHy&&MfKX>&m<W^*CmQSCjt^Y
z-385T1n^V4TM0{z_ezY`Cl^pWV}+$6#T=-`<63yfr6Qjk*$H;a5Ur?OTwLnvSWNkp
zjyG3`&m>A0)YDt&32~~k+z#;5RXPpF|L%X=TaGM(kgbofQ$*|ao@R#=Yq%^7OtL8Q
z-;9iqRtE!Y2vq$0>50g6%qS&(J+*{0CWuAP7n5B<wIEUS)7x3jjx6FimdvFHe>d=u
zj2HLb*%YmZFtq-n&|E1e;kF(aEe)%`HK6K-`JY9GxBEb;NC195X1wwb2mn7ZJ|!cE
z^5_Pz&Vt2%ecQ$>{Um$w*ohno)g6_2#DKBFg3%t^K&$XcFo4fDOz#610ncB*QlPav
zUiCX!I?dVW{PfS|vCmoSa`Cx5z(UM0qO&NY=H4X`n)%cqO-2w>6^JY0pgduvV_k_;
zSN^W@mvYml>R8o5UL0FJu+JDy0(qC$utyB^sp943leJzLb7{aRT2O(SwVR$4W-o8^
zg?2&-87QbHKBI+U&H{lv1~ca*?DM;f{KW9*v*2Q?B#W@P3VZU>d0`WO!?%m>T;;s&
z7FrPs_Qb1ypI<P;&Cpo}Nh3k@#i19VB{uY%Cq3VfI?<Sl@`ud!$9eDEVDTLTR1^S2
zl`-4zTgLh^+tq|5Y1C9pD*s+WN<>fS+Xo?-Wh;Nrg;`{}m#l{NBBhztA_wjHFYS8;
zSlph7Je6HUOQh#RRm|42Noh&+WBtF;kj$0a8;3|b%M5;xP^vw(=^u5+S4FS(r2`n%
zZ?8Co#^)&)#c#Oopc7^R_Q@KesSgk$Wv6R*IXJ88TDn;<g!TPFOpSwYGEXgr0k6!Y
z&J+@!98}IzS6Iu<V}2x3b0hm<mWv%8ttk)Q0RFavNPtr%>|u2!qygdKd3kf4WrN+U
zen<_~o^CUafK(p$1IJW)4<{L$<O0=C0w3WU+{jO3#ckGSuu$3KLS^|PyfdJ|aLQDX
z%*w(29s%X*n$pSM%p7UecVMPxLtQLTrYt8%Nqc>S_^12H%I}~5yuG$&m3Xg@vIT&j
zAP`p;SK9UzO#d^gmdxak0DXb6+vCf7If#nyGm-p!9TzX=tAH<zf-@~L?|O#>7yDo4
zRs-jp#`9s9vk7Kl1RZ>%Gw^^sUHbEM)$8uWg#0*%^fE`d-^pCn;C13{kCjD0TwQjf
zmUQfWIM*eIW9;U8evly1#2x9ml+0WNFWAC96%=2Iap`z$60ZQqjLy$XtS=-O=wfp^
ztcV!XW&V^jV8zHcUQ5~NRz(UjrTELrZ^Hby!pQ5LPA5H}fUWv(i9k?TpBpX&arG-`
zial@LwnWnEXsNDa6daU-<}@JMWLSz$^_A?;ZXXLHwPYr`8?oX3Z^G@<pH=0Iy^y_g
z($6?-gw}`9%I(kPZaHn&cO0zSWM73k9lah;pcMv1c-ZGP(Dw0v;c`BJ-y(yOOmBWP
z4yHNQ3@$A}LP|<&Dgg%Hn1Z|;5<`hE!EGScOr6-j?-cYy_zLM>xdkup&MPvNQ8SbM
zDPoG`xe#<|`+V5dByrpUK95H$Bc<IdFEe;Jc$S8>b-XyQjf{k1_u9JNdA@G&w^WcV
zMzZ05%T5{ph1De|;-bAh@it(HqlzqRM@cxDANsUNiZM-=Ukb>HogduU33_}?jT=~D
z1uH345wEWN)ks4}u{6h`3&7j`QIpx56EYP6LS%x{b?e4&RZY<NR25cd2s<DtKsn1{
z)d7LH)0R9aLN&YSG#_1&fQ$C`pOjjdy#L}*M19U0bYNkRXJOMc*&LC_OD#`Vn=b!>
zv|yndJ}-~mhJX*`QKE%AGtp{mlLd2p38{q%?b=l-;`ly-HNO;gbG3lq=RnBRoaN!R
zI5FW~uGV*KQ`anCF_ZZl)h(ep6-?+##wnE#n8+YvLua{CJQd}3_0?Ftu1^H#{LKx>
z-L#=TC~N#)o~&P<C8KRS5=B<VyC*o<IoY77w}1-|WB^i*#vV+WCiL?@S$y<PVdo1K
zB6q^zg{h5EMYSaIVLTb^7n!20)+B|szItnZjSD`XmpM?ay7?u%L<J7DDiiEV)|DB1
zk*wNH(p3*xn=9F1k)E{EZ98RN2j{ODjCMJE)Qz{1e|TPNWnzhe4Cb3Q%9RZnF}w}&
z!QVhBUEq4u@<&T(G3KKCgYBC4$;wv7ChNz}&?wRPp@x|h2&{Zewu`4hO}tu<pZ;UD
zJ<wb)_je?d+Wj0{1#ZTV@0v-3Cw8D*6&_E_5t5=2{&-a_jsB4#ra0aM-!;OXGZ9rR
zM)AR5StL&Rz}Zxi7;iI-$C0%*=u?y|Uf}+rA2=NGIN<O{%SK)2t+y9fvQbxLgiqMB
zFexKT*h$k>1-4XJ-6E&#Pd1iTb<s|Z@_Ex|;TD%qGk&c66F{GAC;6f-VjvB-BsftG
z?Ms6|d$jrjz7WIx`}G`WjOlfDU!0&lBQg=<d;%{$B0+4^T>LsG(B*i6r?Jr;j6;pb
z9k{|^TToF=!d5r_=OtPEo6`+kXkFhR8<?YQgmDctvQw}mrOi$~vMHmb8w(-{FAS!g
zJ?l>)w!H~Z9IZAzLC@O!*ZKU^`a$rR-Qn{&-aX6wGB_`d%1p9R=VIro$oTX#!1hLs
z+B9N888a0n49O|@FNBh;k~t>=rESah7O-`6bhNj(Cr`hco{@$tT619JM>W!c(VPwP
z^Kj5RSeC9|cKi4|!uZ__SUgswYbFxykB)H(#{3a9zR}*oI6OSxJ~=bAK{oHUHdO0j
zskXmFZ9GhW=5WqmlTU)8j?pmrZn%y2w#9073S&)yEmJC@EhVLaO~`HLFVsKT3rOLy
zeM!?~zmH-{Uu@nz1Ht8pzk(9tG;5;#ZXrx370<P0mt0%uK(8*B6GGH<Mp}-@`ip<B
z6N3CmHE8ci4$E8crmr>mZ*qASW$0V6Id;kt_W%xIprq1mb&%6eCbypadLIY=+wZ0e
z0l)2+phFOWK)!Mt)}LPbjvd7dNFS>!WC26OQ0h|B&og}A-!`=!^T*J}haVn25(vE7
zC=)6_NTGSvE<;eny`LgmL|XP3+~Y!GBqX~mM63;KDdhapGpsf}>T-ak(7E$%71UM;
zI=uEK;@ATpSU;ptkvCd5fK=Y|u$!Tj)_%#Wio%4Hjb)dh(EYGltcMYxCtcdm$<oX^
zw=x7b5M{vjK^SC2^Pt?BNL*;TR-#Yqgu0Wf-tc@o>Ax)eehtFJ^{TS+42_E+-$6d{
zE>qXCUP`=HmD=xOWoswlk(``cR+T^q(trmyCJ6OEApgIXV8J<J#74hv_bg^(C=*RU
zA@8~XK;ukJ9Ripb3-WD@#Se&5AAH1&zlTMX7m<U?qhU7W#uXWKo!JF}ybhDeywm1O
z5JBx_eR%WZ{WO$k8l({Ndp^6sI!|VeDL_`Be(Nc>Ji#-VYs=&tkhFs`5Gj#{*{34x
zt2pj)7+x9##h?bN$KJAlP8D(G_K9>)n+KEFTBS>U07a<971X<DfnN9GU%Ys%hp~)5
zxA_JZ71qU6<<iINjo;9Evx2lslrZO=!|Beo+|Q=2?};S-7E-(=H(v36xgTO}me!)V
zk~J=YQ<@;K5&S=z&MGXfCCb9MJHaKmySo$I-QC@t#vOuNaCav-!QCB#dmy+6n{#Kr
z;i=!K?&{N3d+)XWmFokomS0j;hdkQvesZLDjr)4^4s?he@ACzFeO$~8ORAFuT9b0F
z4^Sb_JPScSjFV3W{0$+xOBhewRdShz1LUDC#RyT;5K&Ey>D_RV4LMXQbWo%3PAg6A
z4&Q(wxmkzz!%Apj#+A_f%^zwKNh5&(cQr3JmG1i1bX-Z17thlDdMLsYaG7Dot^1O=
zm*y~!Eetz9Pa_{UhpXgH3e<r4`T(RF(<oM6ay*dsVA!3%nk|llAmlbZ$=&_yFX7Mo
zx4l~WlZ3C@@nsEkZ&M&0?Nvb+%d2g>tNN>}YNf{|91Al3%w#(9kO<9m3yGIC(tLMM
z6$?neozp4VC|18f5Vv)!G%+Rnev6$^!0yC!7iwgb(>pWB-~aBuKYkJrRO$%=Vy(?(
zX%KpcNTV<6ENP+RF>$))hH4)1rAZy>I4=r8h#W!ZXj*GMumAgXcE_;FOlMIswwF4I
z>5q_X6b6qp^WX&<5*UY;;7ZQy!=OWm)~Wn{Cab?`+Uu=1Lp`skG=Ee}KEZ<ir4eb6
zj4@9Rxsd2@zcE@x&N%5Q<G~s;Y=r3&Ac8WIi9sn5(}^!7obtJl2d-7Fm?2}~uwQxg
z;-qG(82pQhVbd*D8n5N*a@sDh`z~I;=k<mhTn<i3wK3%jb=#?V$;q(I3&qE)-hdGH
zr{}1*<64sMrN8pSbmQS_)S4cn=dLz54;DV$*|CTGZ5b{Lw9t}3k&l`RT_}lRn+Hvp
zBw?9RM3M|B$rVp!GMSyCYppasjsd{MI-@S%(*JehDiZVi8}D2a(ev5575CJ(WS#pb
zE->2b1w22_x4OlW6SanA{>E1*2@p5WdlL)JrN1a4uPSl6z)M%v)=HIqGmWrK-YjxG
zgGrweD!c)Tijra&HHZOxGlqOn(CX63&bDBe*Ts~HGF{zF0x^5({^u8a2sZqYM_}}}
z=7V<8+7~OoI>AoXQ)980{qpeAXqb+_&(1P#X+GDjg~jRN{J646ZLt&=uON@~5G4+#
zELfrmGe0NCs$U#?Qp3mMe6$bPLBA7jcG;cG5dCg*`rYX<*);R7s00nfjaXI+7h=r)
zc-h_FRZmMjq$!?A^<YE3!OwOgx-u;=)|r(opE}RsL3Z}h1lX9KNwnkyDOK<+c0by^
zVZ|G6lM?W!kW&ntzX78j>MJCnz<>LknQ=V<{&)NHTLkHzoL?v-<FfwS9eKU`hGIH%
zZ?7b<;L08?n#{MEa1hGIxP$_P=;z1&S-+ifLmfLuYm1v}Lvv#j3pXd%((3YpGG$LR
z&66y=;K^8s&gA2LKEIFoPQvuL=KUk1L9gj@cI!F!y$4sC)i892&YmuK|DWpPS}Rjm
z>mq90oWiNakyWxHQ#6(o4HIp@FsdVswv{hrj93o57Kt*=4ImHqP~#(VBF=`#$OLh1
zPqW!a_9vN?M%4U3A3^Vn`(A~~<2DgN(@AA~g8%mAFf6uWq|ul2XB63XLc!Q`%jSOE
zng}<20G5oQY`bHN6IuLEv=ohbt`>LaCq(F1@9US9Rp?R^WDgB2`zOY#3$*m#OI-(V
z(UAOHb*EX}psqTb$IY4WtpNHJhAi&tGMZ3p#{L6E#Z*5!OtTJqPnYhDN{p{}nE328
zb=1}?a<Vd$#;BY=4%YYVvS<AGlg*exGnK)^O(fbc(o)Ds;tO60V|IUjdwk5#HRF^7
zU&eN2Bsdzi7L-EN^h~#yLyt;SJ_|#y@XwQ#ey73j1pRDmZSE@n4niAKi+twMBLGrh
z5lqt-l0<Qs{gWs7%1{gex2eoLX|LlWcErZU2HbrSBE}s{W+s=4_97Qgwc}0tBCJ=<
z`FV)^NgBW!vNf9PDk=v$Sxq$jlvH!m8(?_Qu9J8?kygu|wR*}K;vBvR-E*Q~_S0)I
zGu=?2BsboYh}xKzhUKN@czZ$d6jyq^<Jme|+3)b~ZLAz%3hHQ*;s3is)<?QFkZq>}
zfTn>NSr8;TU?(it<>C1mk@xc~=NS!^01_ycSXJmgl!Beyaq&qOVDR?=r!@5Q>&riH
zKi~E)UhN-b7t&N97jxrbK6FVzxu?V-&}LQ9)HBOhD|$#|h4x!yBvAo1JPFS-g&+j#
z9?Or20|0RN$C_q34mnDCjG@Lpv$?>@Wpz^rne#|e9^9V^PcT`V7wEi<85*VkSRd8P
z!b=I^xs8#f1FM$UVKxA$vjK7$L8nNGHa1Q*V{!@iUXsaLeA#DoG5`Gx7=b`DBg8Js
z+xk8qx121ILT_&_*1Ly_iD)6wp_S<&G&qPrh!@PV5z5_NKg2M4e_-CsE?Vr;;T(~I
zT7guw%i)SkQ&4wru93c@gP)m>r+)+g%|_Aw_RuDRKWX&uFuSww5=sX@RM$JO;ni?T
z!+e5v{8b%&#QkQAUUJ>^^s+%`c6S&Mxp5l9yqR=EtsSgFnqbo;k&-aj37*D`<<+Hi
zH3$&7o&R9INpQ-frcjlYp=|FBA?^q`{drr-qST4r3%veuN}dJ{moBt84`DW?(1RHr
zaXbfxHUeh!dN=#Mo^XAWzl(u$XcC;NJmjxgk@}n0zFfJTz+Dl|RSX75x(y+nN&9}6
zUNjb_UUyA35ATn{7y7E~&Wa6jVlnIbiE8uXG-W=k;SZ14?AVkLLS!rtMuX6OH#%LB
z8P`Cq)oveoC=};y{g4y{g6Qm3EbANXHdyCe>{G$AD<}Ge)1j4>8FHTgT3QbCRe3Y%
z*+o-f&cfXM>;WD=E`gw>o5r`S58lk=VF`kr*1ff90HEFD_xbT2iTV|W`O@e&Cp+~7
zWbIz?m+1*SL3C-KFNj@>c~6);!>JDr1!Rh`lB#C5O>Asz#&2Yl;U_1>P^I@Q8p=r~
zU(xb~o0}UM*hhB8)#WrrB_VCB7TA2)so?F_`c!^>to^hgIXwc|MIxO<YxOpoUFNLl
zFe)HF$V1H_nMeu}%wDG!Ex|GC1yG`UmN}xXbF$nkKUtgGo7y^>*jNCWtyiaq&}591
zoYO>%{}&YA%jAh&A_%i!ZM<hjBuJIi4C`Sq%2MH|xc%{Gt1Cay_x17O2e+5vmA+89
z0O#8LfQXz7NH)q2pFYDCEm3}=$#p^kUxwWWAwRXVG1N(2*TqwnqSCdHgqfN?l<WLZ
zMOb&g5eZ^mdfhvM@<<D(OcJ&|=sK~5@MZzhc@eDt;ibd)9W(FuTJy!<-iNK`$^TBQ
zoE~vg)w_u^T|IzsO*a{Xm<5-UlmfFkgGWGshbIPF{w7<GS>Bx$o)(`QniBg%Nm;_)
z)iTh=L&j0p-#e%rlcd@sX^0F8@$31i=$8^QHj$U>7<&6Zzg>=*lAhX}41f9Gysh`m
zPYWu9-}F|5Vo4eL)CeA{)Yif*+-xpVn`JpEXR~@C$4u7INK4fVh!erMG%X}!l)x}D
zpnm%RnSJq5MEki5oXjThLx0ZBO>GL8B2yVf4U}<?d7Sl}(XkZmWq!x>ypMX;<a}3m
zeitjoWRK$DoT;IspO}_cFeh_v(8cm(9d4jAa8-oD;SbTmg}~ih0Vj2Y5GOA>NLICx
z!ax+iW|?15R+B8+s`0NYhB3(CfhyOSGUzw~y}duB{@6Z<%y_KNHTprcXDQFBT;A}{
zw=KAU5U)J$75yUCssWQ)u)Ap-y0!E)t&MYj9;(pTMuv3)h~zU<n@Uk#fq;~)2gA>0
zCMAKh#U<fDg)X9Cxlw0$Pp<y2pW}1ccU(&35l&lBoRXJoq^hE4nEE5>3jy&EU=RYu
zCx8X7;eB7ZPyzpRozsVPBQqoSeu$r9a3C@e@PC7Z2n`7l7ZV3sGMl+*@kVNLa<Pt#
zzt{^QWUyn&jH9&V4d359V408uy+6}SpmzizsRid7PQf9udcqi(&e6#F(@y8GhP=yJ
zFZfEMQ;6YlG=T{1LeUr|o{_?AVsB95*qZy0RcL$`8g4H-Wp^W9!Ht8>4xH+orWk@F
zB9KVV%BsTnI#&~s=U@d>pSKB&f-a}ToLj-WLKkjBY7Vub;QQeSYn(!ryGUGuJp9l^
zC;HYlH!$p@qb3bk`v4x&_}1^QdWyX0f?sLe=qBj_C;Qnc9&VOc!{hh|tqxjn#7`$#
z`X5_eM`LND!oNVTx9bP}U2QN44wsHhQ4c@CRFt)O<1I1R%7TAlk>>sUcE$#=I2xZX
zaRY$uXn085Y&{KZ6kW}Ox^gRn>!>QjVd!|H>IK<SsuBr7!F%`ybDjQI7JaH$y1&`2
zEJ+h;N-F+)pv<TsB0pp+4=5Q{E`piy*EH5JE%GWGgL@OwdmBrMZo81-uRW#Me)?|U
z!#%)H7~&7%3{DV-EYEs+7w9@f6vTBVG0xkeSzN{BQo}?D^@8q9;cNtnNNoR2Ipc^!
z<AeIoP}M<TNrcBX#pN^uLJ5RacZh{4LIR}?bv^Jsw?kQW0|nRx)`JkyU$cDvwi(+F
z1+5GCu1E_xoN>r1fLo_KpG}G}>uq;jj>YE_?+Ad*U$ZYeJX)8r{tKt`;T=~ACiW~F
z%1Ypeli$P<&Av|=Wfw(;*gv&wbSV>nYfWVOUJddx=E)@E2`R-d8X>PMV`<`Sb-fQV
zegLe+6mBsF+Qiw1nQ<{Xv8xySZg=a{x=&$EaBYi8UtRzrwR|ynh-1Pi`U$nop906f
zr?7Ulbt|1s`eUJjTd;-Q#Q}R`FAz+IH41pTbgsz*-PYAeosuQYOR2PKq@Z2aN?W2#
zLr|CZ`JdN(BV?2u{+0j*CqFy400rPz-&0pUF*3KYteXZiI8;<lTl2@tMb-pul)&J{
zW?v#)gg4zr1ooq2FR6+Q`bQntHvl79(#gs4)-Gwjd_R352tlg-ZXXFPSwLz?qZic9
z>CW`q^?4u{CttWb5FzZkd9>}G182lH3sjA8lT`G|Q2xRYV$C3b-L{;z_l-e3XnZ_a
zO5fDb!_G^}%|qpze<m}{T5uwZ@cf+`-2^$iQn7ANBGC&@-4ag&Zi1P&<6V<rM&8Hk
z2@WdE4DohH;Gd$Ibz}`d^%7lZ=sDv2@K(D6CT_Du=)jmrQ(YF^(bEO;*7r@#2c_V(
z6R>=;0KB^%Rxk7Y4vHoD97xPt+vEG1-yZuD?^?eCy#?=gEk8RBU{^htEyH;U##}(C
z?}$;JQD=Xxo#Ys4>s&Hb#W|*hk}AX5PdGGy-TwyT%cqThUid9E{o5W+-!2Iu`!i)<
z|Il=P1^j)%a~@lC^acs^#^~S#h}d8<@wv2%8TtL!lXx2wA%O9A(Q^Y%UKK-2I?8?8
zp$~FRHNwiE*ih;29GC+7ZZLdDD>EBMhpWTmBb+bTSi82vBK*V%;QiDzq^P#zig`Ov
z@#R@+-E14_aN7;W!^iJdb;!LRM_aLxB>qiKdPa6SvO&3Z94Ak6zk;km5i~N$ZtROe
z#l&^0bA+1nZSKf%<1}^EG*ducD$Ka-Mr#=4C4`I)bXhhsjDQRLWCbfjPPce~f$=dp
z<ydqN@eOXiDz!@F5I5siz|HutZ4^V^Mz@Cnog+%gmku4g%5jN(BKg*+lt40@e~KJX
zQ7;1jWS!~0R|q8citfpQptouhF@9>Zi{khSUf2Drd-e8Z83CGPCtvfqo12~9XUI>S
z06plWlF}6_$!Bg|XScQ!JtKS>H(V?v?fcOPEDv5~j7!`gaT|gzm1iEQOV)5liAoTp
zE5x^N355P+s$+#C%CIuOA>&X|RmvKJCmYhEYyH5M4$~umexp3-NSEQa9LGP?g<Cp&
zK1FnieJ@}>J6g?u@>H+4shXahoSc)Bnwgo1c6MP56O>51D8pE4(mO{E$&3*yHb7o%
zc7U03U`8gEM3kF`esh7jpEHm;>f-L)(AM1A*cbsk$-|=T<zcDuIsM!nR?{o*)XY19
zZ^w5(sN0zH*M5OKJZ5Xy4zEeL%<wIm`_E?8A;7VfkSGysVU0yd@iYS5q^=U%@69ud
z+j+%W%jlTl$te{@X_e`{9!JtMI2Pi?qfC{D7kNAv3d86IWPI4yq%cc!%a|y7omRu8
zehL3_G4(Uj^U-y-*7lZAcb6xGcsi$(yKQz6BZX_oN;(61^RW2S2=EE<l9v~>02K)@
z)zIoPio1tJq8VN1M(`-&rh^qfz1k@!GVnnEDS$0nlvtih)O@G{C`5TZo?fRHC#$IS
z;wHZr4S(RAI5`zi$ge6fDEV_ic?OTLD7#3cJRl^Xpx%<Ra&+EHN5}G@Bu2pVz_Bcu
zhw<|EnSkPc{P@8J0UxIbtp^`g5iyc)2Xj-Fd{#@SKj#0<P(~qE{%z(C6-3bK>P!^H
z!v{pE6_VNs9Jy15nV+R{KEz46@t1{WANz(&P+J^kEOSj~30KnzQxd9QvYMmHtpV|@
zu)&HeUX%(8G02OQEj0l$wP%Cn%sc+qe=kllo<Q|En~>g6Eg36Gm<5eBz{1%i+EFP9
z!xzaJYUOj{P>7`$hgKGrq>V5)*hOZX<tp&`p1+l5kf`Sfsb+wMkqiF{N=2|=8^CB%
zH5HpDC}*|b7CXy8gcD~jFRH6OiNgS0?$v*1`fIqNigr}x6mrZ&nk2EG%ZPX8N~Rxz
z0dlS4i;&Ptpl&6HboX<QKA>d~DsHH%47iDj?{UyrSJ~$BbuED;1wiNLZ@Bvgb~Mvb
zkCcHr*Nhpa56z{J2Wlnm7oiJuzqKxTUC;Jg0rK4E{h#4nKq6L`xuc(sesN-5!}Jgk
zrbLpsOHda$uMUMk7s#rEJG9oxqhf{*%G?eLK1sb1SlCE6RLM%K<@AZ}uRGRlB5@?6
zp`oCVS6_|bx*otWrvmc2+e0FFYbj$Mxf8uyWbk|}IFVDIM=1GckUwsB@6*FI#)Zn7
z3O<ic_r6BJe2d6N*zXpZW%=oOOZLwf^WxX8jLB@w;agRDodv;D&f3Or)h{Owpu!RL
z^@AjC_voogy@p0VO&i(7ltMCyy{5N4Rnh6}HC_wkoI$s<|3u-(`<%(5fcxY@&_RP2
zOlw{X`y`L!@q@e=b^y=pA6|NrXt5TupqZTQYHcYv6ugk)&vD_~F;+tHMfww_tC~rr
zYcCdMGF3$s)8RDtpu)^VgY0}z5-!%^0Ro|u1s*z2k&On13$2b1rG^J1Q2^VG)mQ&s
zFaNF05NCc)VijHwm@$?iQ7tf!EHITMJe{uRh!I%7HT+7M?;Go8lxhtUx{VKWFZ`q2
z?iy6Sc$2JH`_I4?bI}gRp0BmFoRxc1#|U9Pz4^a47M<$mN{{O|^aV7n+j+NXfmZ!r
z>$tq|oZ74QZxyq=`G)e_m}@`57wJE!vw95^JoEO0$8w<%qg9n5o?JYSif_SM<4g2~
zB@Qpd8<HYO5my+=stXIYFq(DX^ES}SrNF_b(l6xrn8_PX4dp7$sicQP+54ZnG2ij4
z?sk-feS?iUO=UYdh@7n2(fDW|Cn+_y--|r-TXgVD-+}{B2K;+D4lYHe%^TEkfojbu
zlH=#j^L4j$)AFf_ow4qutwAKa8A6;LpRTsQOwpcie4K05^Md^<?Dul~K1!|i{XeH%
z9yW}=Mo~`gjO{Km3N@Vaqm%<Gm9tt*qONb}7kZetaHvGkFSIrWTu99sgGOUb3nrdH
zWw!JtRg8&$ni3mL6M%U8mOaIE;4LeUWKBPz;sjZq!JbfmfW4Yu^Fk2x`5yl+cdizM
z5~&6U4H0Dzp1pt+;T#mt^)FV3*ntTLH(zgSrORX|>qn&4ENu+fPh^xih4IV|>&YyK
zEsreG;`1_415>5|!f%rO&2{hlk=$3*ufhR0XBS<tIH)y7bINE}uwFm9ahWI4wRjST
ziW}?bI^Ihr694?&gebr(DXaZnBcPFnbyO<4>Iy@b%E2f=&QAY3yGqVg8OzEkJX??!
z-B|%CSjI`s!N<hY*5AAVHXxeiJ%E;Ty`pAfWm_Q<#I)__=iqcDUtq>|QVH~soaJa_
zXe-X~bvC+Q{NoYhFDOJ?DuLd|G^}Qu7R9SJ(uw}<7(vIkBTj~m&8=sv;h`%u%rfN)
z+Xy!aMYE@s6t7EEA%B7Uzr)q5UNe6U*9~*=E+gKg!aB+WaQrEuAZkhOoD)fhEZ~Oi
zR!x*a#C5k#xMtBU*9Dw(RITQiQMyy%VVjeGv^yh9zbw!GSk%|j$;n9i5*vKOF^bO6
zEC@l4!G>V_yL0uDFYtv}&4<`8R7Klg<M}LU2!~(I0HcVPjpjF>nO$4Zsroga0be;8
z<4_VQxBO^=n(XflASA{yFd#4|KYpg-pG{3HD0*?t2TFqSD0RAfKgN=I%jZ$3K_W6w
z$PXBCMmxw^rcprmftQ2Ph3wE@30sRB@2PYm1z(lNg&?@lg&x3(O-C(yUbPS^K!8!Z
z-o?sFLve~rCTCPU36d>LS~2GB2*vVTBD$S&0^_GDVyaFc{F;-|_?zG9)Uw|!VABMt
zV_lWehcHqVg#>KPJBq0Mm&Uy_w`QM-GZ58B!6%znO~_1;1&Xkg(gOS3QI@pl*!qGE
zoir|Eum7gZYO@YilG;|k;dirlF#0_ojg`aw=sH63p<|xdvVedt<c;vx;)!aANStN+
zuf`7w`%^S<6rE|Bq8`4l+#!nx4%#4RbyZCj3voRyGuBfEOeK7#;EKOzj}|?#$I~{I
z^-T2g@OLfqT3<}48<S$re5rywW~;L~K`))PB<@_!Pvaf6eg0GkOiiqAkMT&~nC_4m
zu{1b6kPD2G7s-`*3GkjVNV7#@i}2zkvBTjKqeE+mH$(SBZ0hH)u$(PIrFlWgk$~K&
zD)-mrTv}ndxKf@#;Qi^|VC<`=mu49>|7j(C;mumfRoc_nISR<;q$Di_mB>Cs=6c|4
zLk&XkusCE5gTkkZr<Sn;!-D?a*Hl(#@xxCy1~Z_xmBKQrQjdgZeM3Xe58J)(CnGmg
zAG!m$^fZ)%xLgt3SbgqdS0HG%-weob>Giz399;ZyoHoj{7|rc<8x40WiN<T-VNIQy
zmii?NvoA^16fSJIBuZC(XJ}!2*zHi&o5K8x9~xpiNSgPiiy+f)7@Nb>Nr>zE?WMVp
z`a3bo_Af8XlZBOisIPLI&^F13pIaEraKW%d+9Ik*NN^k;xQX7J78D@O*;#S6auFts
zEp4j0Jv?a_e?KQP$IRloXLS=!#QOGu<Gg8t)tb|DD%Bm;Z0uu47+J(VZ&eaocmAEV
zoU@JqJrtu(*N2S<f;PvUp2&BpQ#^g1<l<%ylet48R@pgM^=<4&4hrFx2oy+y`Tge<
zp?&vs@{J0TU~aVz>2MHkSKndNN-4kMmhZ#a%ObGy%`Ug|q4dj<O=&Ihj$h(xc#9$%
zd4KP3^X&!t0!1QdE?R&qI*_0!W5~aPF+3%$QQdd{@6y2vcGgix8Sko&Sx7}>c$2@d
z#ACgVNRJDx@=Axt$<nZfS=nS{Z%a|ycKvTJfa60(77V<<2KB%Dd1dOtYL_6WJDUg>
zl$TaXZ!85`=*V2YmnDC2`M-q>6p;%eS4PN2y*#`NiS9F(-9(L5U<5?kNnp?ShwuS>
z<=H%eZqUhb-ySeRS_lP3NK%28*sx86ePlS^y;R6o2|s%aqru08(TKt+5HD4xs}lsD
z5B8!to7n<D$ipKyv_D=cwA^cW+d;DlwFIlRhA7t%XelSe+~IQC-!x{Yw#lh}x!fF^
zts1?Zw<%p234?BTQ7DCLX~R|-Me4p0Kom8=QXkA4&Vd3cirTLHJjSQ;Whesq@_AIt
zpW>OV^?@;dY2??$lbNj_kngWr(~NT0Tw2Gk2roGxPF5m_1HJ1CQGHoW@U$i6C3@O1
zlml7QXMzfxWrj`Hf8YT!-^s_PIPX?!3>#a#ZbE!6fO}bEpeK?fDVHO9s@D@(NqYOq
zR4rh37jE~v)BOx*|J`}s9Adu&rMzANGsY-uF&}4+qZ~lhu`GEdRB&zF9;eR-E-!U<
zbkGCBc1V9b+I&TH-F<??_a0=TsT~P|H@CF-om|VJS91S0lMf-fV(oQsWybj+sl3TM
zfr~+~2Old#Vj`BgA{M||(@mD!A1;yf)p5LLn{kwzu|@&4K@|&Ein*U}<7CRwrY2v<
zAnOP2S@h|4QIjpr6}rvv1Cbo}CBL9^>*qeGW05+6*pv_17+TejU(b$S4zbyU#&@4a
ztW2X}SBhi_UU`wU9+MTFRMp6$y@!$vDxi|_y5~+&7F#JZswH=tFn~Ncmf)m*!^3rV
zeAX#n7(!4O!xrakDDlWNMEaLn0VmeakYP0o@Iy#?z>ZgfBnD}FCNILz<-p~E5+w;r
zxFVT5vZtA4M`@h|?xnz43P+V2Zk%mBPgReyyAl)?_IIK4XZR%drI0Vc`t9lRa{MjU
zF+_LGwiOn|B4q;hthjb#Topm~fomUBMkW-&7nea3OWuUgpWgG=2m<Gq*R~C(SK<w9
zbVPSHZx6v^eh=&Y-H&oq$44O8%L(+VieXDV931fbmlmA3@vKl;E?8CakjkY7pzZbm
zcrR&Cvvvf!JiaiJrH832GXrENfm=WRUo>DN;t=*tke8R0REUd;kBw|>ba^|Or*S?O
z$!C^^CVzdQx54S5EdG48R;R~xxT(~kr4k8y9_AN~aSt1(bC67^Jg9oE08)h09b|1{
zTT(AB*N{w3g+$$NWNpdR`#aPEqOR<A(mJaHY6P8md_d!Of`0e=-;-6POGADy`<<U2
zh-Y^P-He>WzX<jbBB6#FUi~1{?6D<|Bq>Ug$STnxw2GnWQ*CUGCoT!lFcN5@S;1$H
zH-bz@e)jeXZ5!ql6oW)JmrD@BlhRVOl+{fwfxfvzfScW`07<{s^pDl`d4LE2xnQX;
zZv<Xcx5N&Hl3tnD+09d-nP5Luo{Z)L%S;2QfHo_o8@Ks&A#LKRZtwHUL12l!U)Y!X
ze$nz8i;pG+KPAJCr?-RZ|Hjb*4Gs=QK&n!Eb#xWn?<Z$M&_6PBQfJI)kJfc*10UkJ
z00<uBN<vu_O@lWw@_|9I5J<>3f7VuXBA9Dz3{k=*XY(If$tDN;M1;sGy;Wz~*&A8x
z=Vt&QevC%C<ZY0}0LIhXH=;~aUQj-+zG$t<UbrDCG~ido9GKJG2)DW7)3tOY+5X#i
z3?)tcq2ezZAXZldS-P71Huv|L!4nu5qd<R7@AtQ)-17|2WgH49K3p1vKn1}>&YPpL
zM_4NHJT{DQJ)EI>eFud~t$6IoG5KgjRP!@+@r(EX37dD)Q^Yxd1#wJ(pOS2#zZ?{$
zIvoDO+tK0k@&X+mE?K!}$R&LqRL7Et*}yfpv@yQ`Lh}@s*q^5~y&}rP*gFtOFl)4w
zlGT@JK#lVr*9)qEqL$B^8HTMhGs=1jhzxpx(DjJ1%k-15)SUjK_g{j<B!II2_k8=Y
zy!gshuPt&0aXNPM-SV?<Bj|4ha`R)EH7aJGGej>KVMwy<#)t&!nW;ksE%O9BGB$Ap
zN?;>sJ?(`yDNymaQ+6Mav*xhxN=RnVtB{b;V2d5~wM>m|uB?*-5LeE&lFr83j=Cy}
z@6QVoWC9MRs%)wS;vGZQ>P(~(F3p}lxmKup;;aKMOp)NqguiLHIO>W?ppkr<^g~-=
z2=-_Z*ywx+LUQM*@^SUThbzRcT=6*X5A=b03c2$QH+%g3Pc|-jNr+GXGc?_C7HY3R
z(Z*e`?poMpABUsOp2mf1#A&H(E)!1u!h31E=0JvD)8q4nmZF4!`ss5S!i$^+{Rgbh
z_3rxi29l-wdrC(h+uJ11Tug5J{ML2+3~8O^8Gf>AT*udEvli~v)Z?;|aMDMVj!sai
zj;_&bPp|k~wIY*F$;(c!>(hT(V_QDYZx`h!ZOnulP4<T3F1BH9MB+MecYaFgfzP6d
zA-<ez(qc%Nqx-s$SNjbsB;~2;X-e{r4pu~h2}CdTMY`bI28QsIMC?*mbI$-H{C1=4
z=Zs`-^LcfQtLg#Sz#rVaXOlq(WKaZNb3*~<j(RfwnI#Op*8eeX)(C@qC?#MZ`(~B(
zup;j535}WIe@h+{C?&X4DGtewqSXX6NkmGJrN4ZMHz0SloH6p+eO?EGrG$Jx9#0Ah
ztRCz11iHxRx%!61q(sBsU#Sf-LkK4@9*EYyktt{kNDn}1`)@&1^fQBc3rxVvbQ5mn
z7ITp*Btz9i6iswA4BYfaLoT6-Y8BXl1EGn1K{a{bte8BKGLP5;U=u`5R-%<cUmi$N
z5np;E7Rf*qC`HwG6~|W=65C)ZaTc+;BJ9RYLAN3v--yVUhnAvx63WPh#Aj4>XvvGJ
zJ(U5VF~c68tNA<buOMP0{$9vnjik-8xZSv`d~akp4NBEt2?Sl>rdZ<LVZ*#&<X1sM
zMaRR8lqHCB*ZsI3t(EK07$~SwlXWD%?dxv*I1HB;hHJ~LR-SHyQ&mj^n{laOr+d-V
z)CMW#ZtzhMvauaj1X<n~<>6zj5;m}W)En7nLFg&rlFb~nXCMlBk4JAI@O}kY`h5Bu
z2CBX&%(8ghl|X=?5%62d;Ph+zJJ?)nXa=g&WtKtS2lPmC+E**{3h~56Z&njgmY9s7
z3`yBI+v{)my)NUY>#&p=GlmPg-%t!k=70TZm@*6H)FD#+==4w=?S6^v^vif;{B!Zg
zu-kesq=S*29s$&>YMK>2dIF^lP7amWL}=U@-JC7&f7>v0z(2M%K7-J6;a)-d>T4t8
za-5B_$O|Ryr^crJq8}~A>N4Sup<Q!=mLscw<2Un!y4lVDe7Ihne`&P#3&q&Dm*k&}
z8H3J?%Qu*+Gr(mLmrCIyLRVSAipOI8iXx6>4>!YDeFZ{G)?~Hv@w4!)z!Xo2BohTS
zzT!(wSH2uRTtH#~>Tw3$&8@Byr$0?c^qO_LJ(gPlKwXQ$(W#DZN**`Q8{t6iD*DUb
z?l&t=_8VID{mSv&q*6SZOmIuE3+%pUUDWXhv|7ToCGrU>O)V;oTFNnC^cqgh>Ou#B
zEjLNf2@119*Lo8ryYjje_P_mm^n>5Zvo=#Ni+Eo`zhLgC{Ch8!Ae66^F=8>Cn5VT+
zwdDP_G8KG3UTh?tcW0yq{mHU2`M<BH?*}ZUP!ox9M*VYm&P()K=#3br)BhCoy#8)`
zU60K?1l(oQxu!%==8ZguaPxLnuyVkqGX9bm2nTJei`t%^b|)vQs7o@bSw>Xk;TYFB
zSU(u!Ig67o*GB)h+#&KO-@ck`pOHgu4<{kd_s6Z*P&fm>^TVH%jFLA(>3#5D4o-|a
z+SkXF3nActfw#(Jm{^-8Ooh!Q5+Z8^70)2+YNbTGqI%Y&uMcr&VkZ+zodmpPWJP`=
zg6yFIh?ZZQ<5rW;V#LGwO5JvY$La?;^{fFgPmhzCk)MIe#P!f3H>-iYv93Vcb5WTo
z*$td@w1ytVkYqSC^s{$66y>3YM?3@3CJ{rFn7VFeB(fhJiMnuuds=GLj%qj|OJg!e
zG=@@b5=V_cW{h6OB~QS~M`+~tJS0Is(8uk+`S@5%%Se96VR<pMtjWgf!8m;Lfq4i;
zaq*Ovn6}n(8th#N1yqge7)+BTHv()2{msm;7nU_qn8F{kO<hBuFs1wEB!G6R&1&_r
z_6JwZR@3X;IO3|Kuz%n=Fy@Zcomc?A;;9P6#7+P92d0O|h7O$ID;>otvfA%K{XN_4
zW0>wirQtkO`!2Y;moj5?=)a0#YCXzpr#s7ICUVCYY2}E!`j%oI)BKo`gnZu)$LAKU
zX7h~rTYff^4e!K6Yy=3$QKE1(LGM|W&^wyjg1e&U$y@X%^%ooeRdlBo)XQl3GUA&_
za}z%Z`#N@m`77H)SyTKf0Z5Pl5%ae=Jv~knoK_xvLbCNEkYFOo3AAb)9PLppL<`_o
zxD1R9eMjO7q~#PRcp@R$zzdXVDV9^8V_UT%xltn<d6Yr<?!wWo8FDI%H)E1T(f)`>
z^!}>(6Kl8~Z=#?#l&0txXkUROE*=SYOgzN*fGD$hA*r9?vZ`V~p+r{^WWhByWvL@J
zIU?Lq<FGyh#4i`EHGZzGfBZnr>-~69`7A_^#Yfyv$`SmRZ1RQ*mqN;b@fR^sHajyW
zI9JKs7hYvKvg3!HtZRI6JqdUmd~rjzP;ndtt&1N!{;r8BQh#T;Y>3^uk7P+y;Mf`?
zo8R%k14k9U@$!k+W9y+LsPj~gbdK+L#lHpy6pH6#fJqzdPrSs@`hfeb>{xSz?@Ohv
zxpGQZRxY3g>0RMl&_8e{66pj(zRLP??+{?xRK4Mw0i=DKOg<XCq7d^36n_58?JvaP
z2Dqdvnn~Da=8~IZsv%L@(K-A>6vL8GNoP3G57=L@Q`9ZAyI@B_RvDuEf($JsJ|jw`
zzCwNfY-!ZG15}Yl$2PDyW?B;Okx}0A{ROu4MNV>tC(?nHtAFBJ=FR&&#Bve#vW2lM
zqh9WG`PtfeiE9EyJCrAu`Igm7tVpL(Vc;c7R1HKeA&U<32mVo1>A`u{nRm-53fVNE
z5K|szN5>!O>i~^2IoY3|>GTGk=M9$67`D4x>;mCep*W~$M<#}GIBo(`v9+W}>S6m<
z<aL(LtqV}22d7JKF=fdu&Zu$@c)ih!z(*TA;DqW4u&H}qQkI{c^##4-@z;GLD2Bpd
zUwu0H)=2zO&-3VH-_#YsU3d$8o`<kFn6|y2CsL@tgJ8Y__5SPeLovbeY8mXhO?1HU
zW$18lD&wMQh`@!sdnyIm{<kdaKi75~Y|?+88&U_BoMrj)WHVd3L&$grDv~I_Ads;-
z1{OBbu3J2~Vb^aoT@96fe9&vv8xr8Q2m0$7(KjiqC?}&!X?t}qmp-&mDV|`H$$Ah0
z<ki_e&{#<gkXE+P0;kk6dWey(!QSBhNQvT!C}<(GZ%l2#7^z1WaFbOlIG7x3+(&SD
zaNK9gPDXbVGG>9fIrGDhi>TukYAMEPhp>GE7-mUXH2rFgfD>jr{AWbb3tdFUF9fH<
zh8P_cBPT60$u@2C4dzEsTxVks!2N3HrYsNxbp+7aStpIKe;dPrf+!iz+3NO(3`x1Q
zJ9%{AbG|&DK8Mt**Y9=Tdo%1ilWhM%Hz}i1OuCs}t!kVNzxa|U*cz%OqHXW0DXQb(
zX5(9u(V7>st1d+AL5|ii>RTgkMCTv2OET@ICYx;+v`esm5F*i~^Q1mL<OVMjyp(PR
zQkMNwvpp@b|5dQv@BCluN9>6wqa9=XQ=>ALM3wQ5=%}kXL$G!ARLpJDLi%3tBc8*;
zuaK+q(t}VL7NjeV?4K1D#!w@MD_ou^C@2YNii!yZ%$818R_7VYYwg~H{gbF)H#>YT
zZh`E}?+|x%fOR&K7r!Vv4ar<^dXm)x^oAsAqX9{e^I6e;h~zq}mX7|%p}3xAimGI+
z;URve%D#3C2zHoDJ*4>LQ=cc#;?Q5<`}qMcsSx{{fc{RcI8@*-iT0Da%6_>EV)0Rv
zoVBjcc`~C^JqGjJ1%VcGzOb9CosyeLTx2Dia%QgtbYR;W47gR@uiBp!73slYAS`aw
zDZWpLV+H2PtW3f<zm}h^UhbYAPq#NWg%U8}=vLr)K`Y>uX$I#gsNMd2-hR6aSqHFQ
z^?xg7kla{R56MA3`&K5Vr9v+MVsBmK-;)fqMMnoHS-9xi3ffzWd@aS<|BRM1@QLdW
z0w#l!LB43v$ht^gWZ0+d7%e+B=#8fVW@e!-y{fy!`712kil7GDjX)3rNAnXHYjCMv
z^bmRO*<JbfuiFg`LfjA$MlBdT=Ry-k!?bWkBU31itJpu}>ki5lbN^xe-5JA7)8E3d
z%8va}-ex4?Y%L)nL4mHKA{{`8b&-vN9`3e6-1ENoFxL}|NR4{A+0tIg0@@yxRm+JE
zET67WQ6fbNl}BO45{La<`wP^T8=e0mM#_$Fr>u?qNciN=gQ5LaH^l9LX{f&_Oz@+(
zsp7K>*f4cvcfbRwawgqu46=wANsV8X`o5&7-SRj;RhRE25V=<;6zF{NFAWiR6B}*W
z&p*ZJr{W_gb_6d)35|9@Di}v4jBjn(2>*r``k1j-n5Zsq8^$u9hf<f8mRed`?muU@
z^<d4j94-bA*1}`l0fh8_f8@xr6%}x%W~8R3^q%yd)Sg~mQ&U+^^&X>;<R{%{1v4*!
zR`1;8vye}QPuqUOt#$N*f*U*Qgf?L&+f|k}zo&(_g?fGV)<E0S)!2cGMtu@iDE96n
z1NKy_N>$a>x!P)K=`!@2lUd%BaXCNzXG;Yq&k$Yi6SD8o(Dvlrn@8#`36l9u`qSB8
zb#Z3<+@Wf^R+G(6pMwF@OZEs?oro!)hl<_r_1_}Of$a{fE69jcX{AsbpCZQ_vln01
z(xEY3#V?kieL*bBaACjuSH52J&XZ9?nVmw?vSDEAY2nLbVT(F=QK%N<v04-TYo5I{
z5cKn2jle?0H?AUH5G@!iGgpTd=^$$Gq~fb`HC2|xC98A1L{DOMRoxnbKNv}<WB8ht
zg0^o`ThB04Ykm1cpVjT*!|qi}kv`UA5!1{&CY%>@s|FOz;b8j`hxH0}stJEXvgW0)
zd6qER$jblJkXm$>CCAm0%PZR5Tk7F4IY67XKA(quwyzKALh{#&rx)2?L)?Oo=7FvG
zD_nzQvaO;MfS9M(*8oR%2+%Imma@Xp;zm_sc8ZW(%U6>_pL36)%ldiS7ypO}B>3rZ
zfxbDsM1h8s0a>mEP)hJs7AAAoCiD@wc(4ODKvghD^*9E=g)0-*nUj#r^S#MJWaE_g
z9A$^E+fh?8xvg;u>}U0pk2!+H@!A`FJb~`9(hsnr6lwQ2qKq`f6EG1=#}k4y6~00f
zne**jy_I_1pU#GZOY=VP!3p0c$b<tELRzPdLDL??@P?K#-GXcD5_9P2VCo|$oyl>|
z{8lT3l)Fpr3JwkclJerBGLe``#mdUAY<-aMXF1B%cDL`%W+kQwC(qRAxa!yP29*Rz
zTzQLNgYUU%gk*J=$Jpt*s%i$DRTRS1<di~FINOxa@YWFKn%s^8o>$K!BaMH+v16X|
zKw@%A&v#XDZAh+n3OWK#8$(t5Iftht4Obm?+D%8LbY3&yjXD;XmQ1>Bt~PhwS!%YU
z8b_4j?RwUe@JJHBK9vOo&FK5qVCbtkC#Y!{sKDo7xW+BxEt>yus8fnhtL%P(P8%q0
z^vlp%A3#O<-Sc~;jX|Ws!IJH|kas;Z1au5Pk&jH?7#bH+MRoE+XK2}aS5-D477W5;
z7d{zHxeakT80XLYpW10o-ol@@-P#N=G1IfWGvL&xh=)=SnBOvvtV)L>f&+HaF8Z0i
zIQLm(#OH5wWO(;<Hq``eYUTYrL^v!xp$UvQI0;r@P(*22P20QdXEB9iV{_>|8v?<;
z<+_@t+e-u3Y{JP-d3>0Cqkm&@<-t7wnFXc5I;|paJIv=?lOcy43*ZPgs+jbD2<6pz
zN~<u8D5)^@4`IuV45da^PsX~@GSr5q!KWSD@ktymGj6+Fu7?}Qog)_Ic!Y(Ahe1I@
zLqk3L;MKXWtX+}9N{OP7y!Z9qffkxV^TxI^R5*N9r!#MpyK9!hPQkbd$dPsSOaf^C
z?B?%28BvA0e4(@Mj?YZZNv<lZ3zorYnGb*gPy1fQ6-g0ao1?sGjo?a{il_%(;^&)!
zGxNic-_tzso2j{kgtxc0wl&Ste9)l+uJ`S5sX12a(iaPBZH|nJm_|2aG0=8q(_36v
z6qu5foSJyacp}=oX98VLxBcv}R>o6prY8QC2@Ne16&)D^O`p=NtY@4YZumkGM536q
zoQy2k>x|swS$742ZWpvf>2W+AmErgA2oF+RAV9*#l->Aj#gmt3h&b@4mAJ;VAS9-&
zt}rW&DDpm;(-U-FMIWRt12W>;TwG8t^-gYJrG^{H>s(umohA~j1$0J$I-e^w?6YuB
zCGj=*Fg5yT5mXVtCcYote{GjYizkHKcdL$RP5}^+7t_j%x}uu8V)Cn;KfO0%epX-A
zebwc!CL3NQbY}sN8ypQ?cPX!e;~Y81Oj;Q4y<1>QHY5YwE}BfBUXz<F+8Y%haDwTv
zA`Ti8tS9gp4B1Z7xJH#3u~r?DlAGTOHOFyuoOb1u)>BX|0NK|xYq-tWOESpn)cEf(
zD%HpWT(h7cu1`oI|Ls=6TXvFODmv_!;j<>N8?2PmCEB1$c$!`HFD0WegXe{P(wnlU
zU85P(`fjJk<@}={E{=q0q$co2cJDvl9l}-_1;@dJJ%fNBWdA=!0ee5NScp2PaTo~;
z8`mH-VI>?Aj{!?4;iug|VQJcKdO1k5^3_w%*rM!&!jAZqNGIp8e0FIFF9=SSeb>K?
zBEOiLa&rH5u)a5j{9S5~5@E*Cb1N8?o}y#OS58NcYX|wt!UaoqxMG`)qhTgM$d>`4
z{+tGu26ITWrX(%Zm%k5ES069C_bcI?<YYNOz}1)^?EB_ycn<D{nwQh(Zd7L2(iFP!
zj*rbcZ_}QbD#h<sXJ@abR+g3U7`E_V&%gVA5Z45}TNSq8<Lx@y6;+aq@JEs1B&BOD
z%yBj~P=bCWOU!eXbpl7oo4}@cB{UHSA;BKDw0BUOz&ONn5`Xws<y4L!&R^fn&o@=v
zgP{`gw#MdoJTCh|Q#H33cF{iz+M$2FNlEv$(a~Ks5D`UFN2m*-UO6~O=~*{tp@aCA
zehtzW9el~s>T>@5N9yFM#=c?#$ph*^&&LK{g%lKs4KHTb{{9b9?&tB}Jsd*3jJIM!
zrRgMOOtj*A?3yWMUo{MRWMNoPKfWd#SOhq}+8cye#U~wkc}=C2J-C!$5jJo?q?uv2
zPIeP-PI}(jiuOjuH#ud3GHKvXE5*SqGj#yZ?BHx7#W5h1(}UknGz{KznPX_?Ue-(w
zatFo5AY0>OQ(`fSyXjfY1~uUFL8JyV4_$pxZF^Lr&0lG{+w5(<vy;)>FNh_DY1mJq
zCLt|~!I${#Yux$KnHQuSN@(^`$O_1T{&y2z5=pxUFeo6{L<00A*ND|m@0xK5<{APJ
zQZY8pCR|l(R2Y(m?k?+?W)e_5e-4d<NZy#HxKqFJx4-&zY=}FSLbTOUz(FtC77@vy
z1*awJPc{`m$ZBpa3Ul-!j;Tmj)z#VAn_TT5eLY4D5ZYH$<^hk#G&CPpUS;JNcKoWi
z?A!O&6;ad@1=lU@j7yW<E_s|_qupqyuy{C;qb=+gkW&~!itl6L;ZX4HByM#PBuP!!
z^N0{5KPB=u{$!{(a@T;Ze6X^psud9?k?1J>5yvH9r)|&5$~L#yuO5e22`&jxHa%Tk
z?ym12A5h_LfyOtur<<XQj)tpc=PP7XG*m?yOb&axLczMek&%%S&M=B~vM=h=9T#9?
z9{K}owok<EClQb&K+F;E+=2|clpvRrniT<D{>U5s_#Az~e<dW~Ft`l!_JDhSApFZR
z^5jV{nn!yZ5uDBLa}F7e9Z&lUjMPh)_8Gn0HS*#07DP`6$Xg7?76)|ITB<{3oEd5^
z+Y}=|v#9a>GKdVWDuP*44dfF__oO5jiJ8V3<!l1M!$p{?00cCV1GLfc5<WT#6smq5
zNe2Vk-$$kzW(SK3##BrkENmPtZ5_ekko}~hobEY9t{@@~mVymNMf4Pc{j&)c!4g>y
zFj7^d=8vWhwX@yDm43)#NxFDL>?0@d@k3J{#^-Sr3J5du&26RE5!={Qoa^y;!$?wE
z;FL=)Fd+P;K(49^2aQ;K2?@ArwXke9@kf-*bQVHy<6p4_edA8&43nTsY*v{aTf(qZ
zL>~du#Zczw3?V0~sQQ+`I1b_J3W-HR)f!R5AxKHeg&{)&<Z2;Iu1hwPbb!HQ1n8|T
zG6zTZrsF%O-z1j?-4-<Qah`l@0#9XxIuZltC6Yr;29IML2St>{h3Bz#umV|!6_t2j
z*uK($$<qY8tQR9RB^QyxaI%g}kn);N0TCl9t`k+|>c<j(dN#5s2hR5sRRrtaP{U<r
z*sndS=~@3qY`}?==ggA8J(Bp*uiw86>LR9Z$=4Fu%saS%I||v(p6`!L(pBcM)%Vf?
zNYZIlDMe%nkVG)95H~v`uMia*5|!4yhDBJU+woW2+UV1YvFnCJ5?6D-jxHcm3b0LS
zLoDEZ?CJMaL%aL#lLfIxtOcpiUC<X8pW^1|3z#?>@OJbN#i>h3{&A6FEoeT7P?2_2
zfp&1C-ib=}E)aJ0Xc!>|M}o)w{VGv{oscE){X*0kG~y_M<-wp2VY;AVb<@SnBxo$b
z9*lC%L3JxWbo3EwQW>rlN5ydC3Be`iBE%#s{7pxEbc0=>)%$H~xV9qg%b~eEim%`I
zg3~2*3(9an@e3p{QV>8wQ|=Y^tc!h-OH575%g(_CNPk%SCx7hiOv2WYs`?*FJ6a=<
zj!bDlF**nTdE4nT=f-3&Q2Hb2dnw(ih7fRJpM8DW`I0gwAdu&piA`uF4=kCs=YDn;
zV$gE)qj5@C67c_%EX@cC_*^c96Y^!-7;9LxG3m(HXgIeA<L9RE<Ih9)xDfWntta{N
zcbTEP$Zd2=|L4Wp<56?f-M)W~jOOv#*Q^|nUQBxwMnxcq2^~5d2R3FOBanojU65&N
zc6vo!>ml~J<3+s&l6`c>0Aw$2=Hb~8pDluOijQ4B%nG2)_4N4tU^6H4di-B+2w*i8
z=;J+C&I)*kr4`dd0_u<6svl0Rh0ihqlip`<c9uU*o<Dg%Knb7u2cPhd!lNLq*Ao!m
zhudp6^iu~o)QX@$kNtiH5QCTO7Tr$+!x=MJv<(&)gdAv~x3h*`T+X=B;JR1yud%d)
zHm7OBnAC@e9`NUN521NggNn%~@R5%^Aq@E;{~|VTpcDiaK}o`BaeImQ|ATH{$F-aA
z!vFD}|Na&Dzn{XkpKfoT#s9qo{om`I_>eK>A!B*kMp)Ad=wS<u7NnL!_*s5)u-i;=
zTB4YQfJCJjDa}=saYVZbw&DQ$2Q2XKq%rL@E+)PxkQ`wJ?39I$XN0Ml+`h7lHRA_C
zD=Z=G=IqiUnu}B8!hGZrruRg*=O<Hj9J?Ug1^7aeRz?ml@b9yOp!<;Z4is1`EmWsp
zeY*{_<IXgldEk&Yji77Q&t@b}8(ec+qidSo0MMV%sU26k?23;;{u*%`qV?f|GlmND
zeHaYiSgQnP8Punim86uElngB}CSKls-;$)#R9omUi7yYvYA5MRgmSHm{`wAat!bsk
zV=!6Re}ioLN9X0`K!)Dd^|SruHE-P*#R0~)aJCc+`-JGS-zLX4H@4Tes7WZ;xK7zA
zbvhMBBfq~7;=JaY-x{~LGpveu3gD;KLr0yY9a^9%bDHmx)RFQOQ}m5aLPEKR!p7KW
zzfxf<*z31h`+AtTR{RL35|k_q2P03eU(Da%JHkoSgrxE>0oiW;znGwDEpA}5C9J~O
zh#NLx4vprc$lgCOvbHfI2hIz}3?}MNy4E5q7Z(>B8wUpm%iOXmtUlweuy=%NQ5({~
z!Ksb;Ep8ezltTu2zG}o`@9=`mFG7!~Mt?!ce}ZfZlBtEcaRv$ti&1vLUAbfB<RCo3
z_;&9(K)JZatF(M<HiDybQ&o73{Hshsw0$I2YG(I{tOmOH)^H09&ZsA%{Sfq4`Exw@
zI*tQ-wIbPUeL}_U@i}AHOl5aErsO!VdUA8Qs#+Q<szbzri1%g`X8^J>$eWsqm!D~F
zSxm3aYz|CJi%b=v>Um8#u=P{a64}xgthl}i1hLtos>dwrjTQkOhF5`^v$~%Qj1OJG
zDA1|eLPMgZ>`g8JOqEwno^S^l^~?@BkCMH8O>OO|4-dF;1r*P+GD(o?GAkf5HkT|u
z#GR*!iJcl*-r3sLSJKc-^_n|jFY%@WljWY;8ALDCHO@^-^?f+mk5H4<`8~9<EGz&b
zO)2&q)Ghbj`EGOpN+g{nDm^4;9FUsid=zt4NzE^=IDdYA33u+6(NPFZPN6;ts!9XN
z4##2nZG5-aV{P-KfWPh*ahX~kUXR{GkKm3VD26aE&{}3=DZK~o3d4WZHD{;BH?(2D
zfA$B-pK)-aB>ifYvc^DjPeK$`%gYA!B&MewRG>HDg=H7E+KH%7M5(N^2BP%k^}i5f
z8>0d=pVzPEg~ox7W7yQRL$eJm^JM0sS+NGnaN0v@qNs}B_3IEw><j*&rLzZqlpT06
z5pH(Zn25>fdRsdy7yQcqAD+%BJg=^e)<KgrY1G(gtj4x&H%a59v5gh9v2CNV+1R$(
z*k;4MzJFhPpS-89=bYo2W85RUDVa62ML0dI9}m)nSIK=83PB?dR%yCTuzae%A{BsC
zP;_Pf+&!a_XxjhCi&rL%EV&_SaZNiS?YO%y6r?64r@S~rK|-1TLrxft!Fm9%cMSvT
z|Gp||17&}|ES30t=M_@Oi(wNxjLy!SkNgrAKV*6=_!)4L;-;J;Y0_qD54OWs%~v_-
z(*znxXh=aZ7tky?wff6s*>4UgHk*gL6&G;i6B+u<6d{B7G(9(Cjb{czv5{}$dIgI?
zcVUGwc8Z$rtd?dH5iEjF1_zpKMfAfgs_l1QTzS@muIAI&@(+{;UN}X)V%5Z*EAJ%v
z@W_GuXXD`P{0Oy$5sqN)(^ZL3)1=?Lw7s}RIKCH^=Gb@OcH&~vE(2JpbL~zj{rsCX
z9u=Q3)Y5=SM^g4}y1ltlM~yx`E|zHMOYG^@je}-J?F7D{oevokRXXjpQ4f4BL2&FZ
zn1#@?RD)>)OLc_^6icmI4k;yt)fon^ElIhVmpTSqL{Z8v?En$FYnr+(OkOUAvhrMW
z%d^9M(edsWC*4JEBQZdJw#fKDmqBA~O$RL_jX=`rBU9}TNam61`wenNe#HU11aFw)
zA7K%VE?xL}5Yv=YcfURINQ*-H<<VWNruPO4$bDP<%G6SjgjsMPw6`a0X?fTsK;WT9
zjThriQiU*NTYiTjPR+&oqSC1K;l6J1tr$;5^uvE`$;;ZQ0Pc}xDXme;*n)8GwtvKo
zip;m5q$ncDE&nV9ayZST&yY}WXe56(Yh0B@k*I^<U||1ZDvG$_fBLu%X{&fu^8n?5
z`bgB6Hj1^AQw?W%M?xJi+o(j*g{MsH>HC76gl=kN;RzPoZdglvm0t`yT>SAjryGg|
zgr<Mb4FxOofFU80NyC2JAx3e`@Gjr`m3KAKCn^-ge!|nUA)3kx*h}*$uixSE!TKV~
zt9=t73yqt0s&_+8Qd>o+dy0mu&}6$!J=|<Q^6OX#(ieT`&7Ivt19KZgGwa{Q74Y%s
z-o3DQuMH>~KY0}9YUc*G(U>Zzs=6jp?H#&v_PYF`0odxeGJ0Gag!tSV1WY3>BBCV}
z^(rOYM9`@)+BZQ)O-T<i$jWZ05Q_kTyT<9r`F`5)UJ_JL2{yhfe62js`VOH;RjIj4
zK4@)3RrDL0>3;V!=%<;$Qi4jJVRA!~ro~}xD5|AqB^_JrUqNW`_eU5U`%sL3%a&l$
z4-<?6oIhA~cK5<i@LWaKs%v?0<5b4SCMD&3tPZN4z02@|ymp7SddO)uC_)&GjgT04
z@_*H?>3btLRh{8zbiKbanJaI=WwHInHTw)_|M#U=9vigmtaQ(hGIH^Kw6kx^5QhnN
zLh1^EzZDtkFUhgmBZ`X&r#D^jl*tqQwbw8XWW46jAk@X@iy9<|Yr<iY;2?b@BQrJ_
zkAZTW$;!bl%gMEy_#AQ`Ii1=TmIPZ}JjG4Kz82*;kmEgsCMZ%_m1kw9Sc}51j%#@S
zMz}{`?YEZw*hU;>d|UVd$~m&4e3%zWOqXClEUB63)V%6dksXE!$%j?uock;2mcxVt
zHgTnGU0d~~geM26X!Fa>Ow8)5GCT4v4Zf!}$9luU5)YeWU?j#g>LPJpM1m^K;#(5=
zKC~zD7r_d{AA4h;`3+O8N`&>8g}?v4`%4*ok1LmTsicEzVe2u3Qg-4<o?n9Fi@K=v
z8^us+suK__bs365?l#Cu%#2S@&Ms>JVqsmOYP3EuPPtKXy&T|2rT2=ZdKox>^VM*q
zhri4$mp3T>(uq`tI}B#hh{4_hR|ywkqnN<lJcD;q=g|#hN;A^T0U8hbVwzIWM}VKM
zEuv6NLNml)jh*aQUkbTwp$u86@igQz9EYieBYdG55`6S@cQl_o@sGQfjL8i^Vo@RJ
zzafg?<F$guLD=Ys3Ay|fE=|kg{ZIIu1GZFUH+e=kLSr*^^uDvpfGbfpg<vP<T`g(f
zlw1T&A<Bjv6J~<U^EO&@ql<>k#^g0%IG?2_BNF4$i#4O0Hf-t_MK>4rXn4rY-2hgj
zg2uqg$V?AkpeGWh@>EbsLqrMyVeZ6W&{U{s$BV)hBFvL=bo20%Xi9aK0`(#e6PYP)
z+Hejx{Zht%XxKp7E4Irnf7&jBUb_>Z%TS{e*#AXcFaBao^L^8(3-c5=mAlkd*W<J9
zlE7DvffX5*%)XGIQQTIE{iw_aN7uWLGA<FZ!$E1MLUQq2xBakf)fgGTmIm_qy%H+M
z4&hq5j_&+WHzQ9z#!@j-f>`sA3mt8hsr3ob&jJY}KEl?0wJX0<Z9{*B@_N1Pcw7#J
z%1$(>hn%6RSEE{pFS{Y)UPb27r{SA6{O?u~vSOk1O4j$(X%bW8L<aJ^Aw*rdrg)Tv
zHapo$OuqMhwYMKp!0aXvRqBt*-!($M@2#aZ4Yfg!laqvo7sdO1aE6@;>!aOiB>V2q
z;Op?wlB3j>*2iYhJ5)Lpa!CThaqxDbgJg1%R|c%rqf`gXF+xITY04qk{p1pTsBz%t
zj_7p*w;i_w*XbJ|=fpMP?>g%!$AR#g-ii`<p-xG#HrW`ej|I4#_BQ=BZ^{kkRQud&
zYirqdg9<i9UX)04?aYJ?!5&SDuv;Nj?I5vG=*uX$tj<GJeN9lHxWJ&s%rLVwIXDN(
zki@0rJvBwNeF=e5&2Tv-R5U#kF_J@Srjm?y{wU@zWHdS`%ZdG2@`GviWV}!xn#ySz
z7M7sbGSDHc4Tw(8^b4}ke`HUim%C+?YGoMe?e`ZGGR7!fYxyrjq!qTfI7@bv9u*bU
zTHD5^f<I-8{i~xruQn~H@$3E-pYAJj>EZ_om`2j7LMsDnOGBFjgFq%)Oc-1b3U+xy
zA2QWX=synPt9HfihH|Ol6PB-~QrFYv4}PpWeg3Sh0aQlWyfbC`A|InYU(vqLzdoL4
z3_>2#I`_4&=@Fde{LL*c5yAQ%<3?@%`6w+JP!s!M4{=E#REWa$l|Cj*0(mIPW9lqN
znt@D~M#XT_^f0F*W>8g_<BpV0)-s83VY6p#ZVBR0DfhwrM5>n2t=UqFqicBQ+3_lL
zr1Ut22~alQM24Ei;LO6v`dof;Ra;!Zib&5*FZIYz%QFxncvLk19J?@Hr$ztD=HQ>(
zQ5l7`4Af$%1lZUyS$?o3`YsiOL4Zeq^EY@+jGS~<9U9q*oF_RUE;fIQd1gk+OQ0FZ
z)_-=m`X~*7%P)OhenybF&bKvR)6mGaBc&mX3DA(R@sP6fbd9eF$$yJcZ82_E=fdTN
z{$pTE;88R^IeK(lTmNUp-2v&mSM08+(O|vCS#ZHqN$yjpYx2S83QE%A^4#3Y$|8i?
zZIUqta82!9c2cs7jnSNEt_9ud2p_|xWfbLQLMWaEsUeyN@7OR3Q8A%S-NZe(aV^5C
zfgsyKC$<LmTe)(Ab)P^oQK&XBBuyW2hZrIBXJny!xR;TQhsIdUp+BV0fb)}$wm&3&
zCT}7B$!7xMZ9}U9*wz|rw<z|oouqA<T!_Wn$suM(vv%(Oz>6}GyUd8I=PEsjsm@`g
zRdhU}-L3?YR4E#1hi2A)TsAxs$H6k)02sU-9Syq%Bgk>vCGPV2dcrDb(e92t?}>|L
z=F#5~Lr{d1yAIQTW|Nt>M)|{YZ|SuwrBwg+UMonQhY9sZhDCt#Sw1H~)`Pf3z^nMp
zGT24g5Sq)uhA<qTqm&T;X=>3whPp2+aO=IgmUC0{*YPvX&VAPUz6Xc42zfm!&{E_N
zNnns9CHwOaoGi7_FkPXcMBekuv?`@Mjt+M%mmn{{z#7;aDW|Eff!tnMTA3Rl#dY3I
z&~{oS*T_%~!EEF`G4xWeh43ak0bwQS)*17A_cU(=UiE;!PsRUF37Q}kvoZVuuAb^K
z4iyz@PX&_SnT({Wytp#*CKrY^a5h}@n7#glHFWa$pm${)dF7=zaI^l<&z~zGf#x8*
zp!79}?3a}9s=c;P@zm`#O?cY=SO$v&xKieo_k9F3b<l07=gLZ;t%#zU5%K2|>3Q_%
z@w7G7V-R+S(^zP8jCZlIlCuOYUlLlv7p<3@9L-Gee(X9wVo1sL?M2!EzbB#}nx>QX
zgPO%KcAR8?z^0|8;ow8yX4M9iC*aKI97OzedW(2Jj$_z!|5n-VWl`lY^+CZzxBxvU
zlac-dJj=rrsc4XrkE0=lXt3YUwrSN9swjND@87t-eDdVom|8XDf`wG^Uvm)^7#tO0
zAD<TF@KO`#7poLk_|D2p4g4<N$Iy%@%jO1#kGZu75)QV!!`f`3NRRR<$?6%WhP7av
ztJ$1$X-|JBiimw;MZX@slhQ3M$(_i%T>65-n+sVLembdmfCJn1efJ`-f)hvlG3Z(!
z8dim2gj6)xP&phs{9Aw&L4+e>h<?$+!v#8p3c5t!l?Vw7oqx~NF0hiEkdT0i4f36E
zk&L~vFL&F*lncQEsZ@($^1e?f-H6d6FfI*q>WQD_=_Xoqj}FeJ17)R<`^i}2^8?#6
zD<WMI93Xl)$$4n_o2tt#b<fl$8qEazLFXA3f1c+=+P=knzs2Vb*O>G8)2{yY(e?S^
zjUaNkpr9%Epzlm&fN39=$I?S|<W2Y6y`xXKG9(@n_Av^-_(<0*G-PmB664kk45QGZ
ztZ*zr(JtGs9kp~5J<H}Uv^=E=yt%4}&8X|tX%-&*V121!QL7(u_WIhPKTm2)NeW?|
zli7aheF(tofhZ#UyLW5=5)=zdN5odtxa3QGRH)_+d}%O!fSB-KXt|lcYASiK?xa()
zV=|Ib<Mq{eN-f3stSu3+<bA>vK|E1Sz3+4*`zL(Lq-ZJRhx@C$yBm~`h=|uWj&6-j
z9GnZBY#hvW)wPWcoK*gljW5ezP{KD(<we<;2~Lp(eG8`-+42@0Fk-`*Q@DF_$p*H5
z&*zqxR+d!A0z{E~sYp|8Ts+*|@knMikkAOu|4i&VJAJO}=CZZ;5>>l4W%qRU&4gUa
z%j8|w{kAyR$Z>>SJKltIKTkXK*<EcxG9BcJZzv$WcttLa56<u*_gK(=lHC3Z&LmjL
zd~8Hn@g?1|b1s*fAAj~0B+S*{{s5?0=ZZ}PDRRsRffVKW>Zh*zyHUL2dGL~~xR9KR
zvaf$Y>=*1~GcS~AWo{^U1|-pjLK5t8?U%JzZgC<~fv3NQ77g94!>Rg)x3f%2P}}=k
z5z2%JIo8GfYwC0CF1JYXLNWNr9GiZBW2UMv!MN0qH0dP1`q;*>jOMOtfByH%KKK*&
z7{VA$Kt+9VhMldU+QHsc63wDnew?1Z0-|j1P!7?UJPrsJqbS(Ji29Ig=H_Svf?2Fi
z@X<M)6Y6s>8vIG9q;0hkhaqwb72~+wV@2}+3rt*^9y2W}05V#$q}F2cH%YUS_qIBn
zZ5|VIAW<uVaVE~F7jnSPqTKk0na#!V5gbs8hm}wYtFs%6Q5b8I(U+*@)^Zab>FUKO
zQTLO0$6TgcG9-(c>ZDTY9top<yuvzkwEZ<egv=OCWv0%_@#gwdSeWbxf)i`p&KJ=a
zenJK$w%z{6=GGL(Hy&d`MoJn{dGhqB)%1+RZpe-eqRI(WIbnKJga1*Q%xo200XuuS
z6_6ttFM(b3zkeMhCogxWUOV&_dkpptu8<&Gs3Gi>3%1GFLNH@@joB0;JpAnqrGEfJ
z7(%#+UeaqqNR+JH@Z8>WWn;ELE3lZfpe~C&mMUQ3t*$Ms;VAc8kecBKe%fk_J6zCU
zk=Wyu3?0EojzIE2xIRxPEl!~Ib~M5RkLM>ADy-{w<*?t_WVF=2TMC>kh8h(A!y-_1
zx~r+*J;I|~QOzr+BA^J=lJGK+&TP)FD9KCwpx(xLi}EJ?RD=~Dr>ZtT$IR5|XnzAA
z6`?pjAtfIc9g2t)u%B5NnpK4GfnW~FBoefM7J=s?)fj?pvs{XNpd7~e2J-V2X_l*m
z<;+VUNmtPqYlJT}h;~V}cpg=$H_MDM$e(W;P-z7-kVi`tLyx@3HO6fD-~LWS(qT^q
zU_7|iUYW}i=_{us{!E*f$Z<ISjFTEOEgl;z!w4xYp-CSU;F2%fIhIm<B#K!F{+vqw
z;*OXtt&?0O1aV+C0X!`p&%t5?l!XVo$qCV^OcpEU14S^Ouo<-*z57ck<m-y&BEHfM
zMSOCsTTn$Kp6Jd6@|ma956futoBaa+09v}1whsM6aZrxcl!JpGytuy|mJNqp=g1Kd
zAW|y$!~Zib;;6F9Qb|Pz8T}N61;&;K@N$FFxkP^hy9pM2fiwfdL#ES43*3}%tE7Ry
z?1WG&0J4damfGw%rfX^Z#kSX;ljO;40g#?_ewKa^eZJh>X5lICAuPd1;vLC`SfS_L
zi_Q{GySx;`aBVMwM41D7B9ZKy%B(n>eEL;@q#CY^Eb3U*@DC3+>|Nw|`6+=Ozc*B`
zG1{yXf(%_5U{%b<_SIUu-EcO^hS&9MdC}Eiv1nfQU^0WvR!zY>Cp<NX4YX-hJ-wL@
z@O&4+!W^QvUVkyzX!d?d;3-u|j)xri`^`~Ach}huKcS7R^&V21qTy!oT!*u>k!yNW
zLRLh2v4?_$ry_@zk#q{zy9;^Z)|BQ?$(OW_miGKQfNmuEL-R786-~$$@@Y&uQ+6B+
z(om(<GH|2+{uJ1V4bFx(I$AxRnq$dS^8OB~a@ISbM2u6Hr?Jtp&diF48sQWV^|tW(
zmNF{_exjnKDf=yRAUHBoy8DePxEK-|g`(zbJ;TVLLI#V~+cxG&)9DIu+E>DQ@Kv|X
z`)uGBfc`w}-(;{*aD9rSufy*FNgZ?l)>`LlPvyd8W`KY(Bvo&NwCF4brXzi|=YP-i
zw4L52{7f-Wgmcm}rRgS>L;Y>=7d1wStI}{e$&?&A`lewzNo9e#?tvT|_|DR>)54?n
zD_B%JrH^Iko6*5BIvO64@R#|=4>=;tiyEl-K-41xMv%lKrLHeiF=z4J(z>9w*X!To
zl17FyEIDHRBtqg{v%=!vHVC2^79M*L^-y1n^q;<PXv$;P<Wt8!s3N{14I=Uhde9qQ
zw%{50Rg@nJf1ovT1?A=~{KwFcP(_7F|2&EHxu5}Y1}hHRO{Ys7&)4I-^rmxNZZGFe
zV8MmqlUXFA46*}<2G*AIWam;0Vo-L;=)H*q`EdIWKWz;mDR*aG6-zS(EH%q7(m(&Z
z#44&Pp(hJ+R3|?pk@OLI2^o3%SGFIo$uzPmA*mt0x->EzuG{<{c*uRw=^DmTR|c*=
zG>0%bB{nrNo)@Y|-9&Ik8C4h_8y5{Q&e25(irIjtxRZ6u@aS@QSsmnLBv`MrFukd-
z`9+g^p0vWEW$+PXD~2ICH+(=VB=Ug^EU(Jrq1{D5Bwl+shzWz*LMyW4i*Wz?NMOtf
z0D^-0`ozR`#DYIuf`3kzWrDFO=Efr5r_+Do-!z(Rms>8Tu2|2KD|1#a);ob|-{Yxs
z1}hz`xny&3&y=!or>8}IYxiEMQ1~lk{Ix1<Nn~}H)IzB&iQ=efX5}sN!}}{|r`$R_
zBy})o?t&!m$N*_cSLXb3yMHt<Zn1@`RnoPNG|Y`uK+vW9OIeAmDxo1T-Azc#S>zi>
zOK+(}G%m00%g?mX-4O&w=0zZG4?9tLW{R`H6^g-=xO|p8zO3M`tACJSu+z))VI-Bz
ztFb&)G3x#!$|ocT?%D>MjP}5xZadQ)T8geat4-lv_p4hdHIB_{VW4yBN`9dZr3kH!
zfcnd0#_7q=goNGQg1)|=ABqBEV-v$E%4>G078JADt2~CU*(-EhPq_j6-QDGI&8p32
zm*;a3$xFQ6;dtKXua~~qC=`jVuta(rbEL_K7uQ`a%t*(KK**Z#BRmYT_%rr-<pi=T
zhXx5o6dno2CKBpr$b_#IIY!%KY053|-RBEW^&~Zs?H~h)qmX!V+MlJ-@fI;6L~)Xm
zX5`QTooX^=naS!tqXoFZJ6P0=A|dq;O>5@6oooxpi*sOLsjGFOJwB+~@VS0sjoR7l
zbay|QNzJ!e)xjHx3b|*~6wiP=ob!l1)ga7iKOG%F)VP0M{S{Tkd7XFnC73YeGE}r;
zX(p>cdaLgMw*`hGUm}v8j*fxm>z8HFC^MwIhnZ+RApg4}n+GsnSo`-Wxl*gnceW99
z@Dp%5?DrH}9p1r36A5~FzV8>-4I-be)Y|&$S;*uBW}R{leKTwdpvZxG==wn0I@l{n
z&p|=T!#A|p+rN<}a&UO`<4ENwC=i?|wr3*UN}lpVeTlCPr?sNJp3Q?c_+z-GFxfSv
zr5TAyL^Kd>bPQlcZGiYx7)<ozq8QCqi|H9mI?W0&XLQ!;a)PZunE4-5h@{FC;m2cn
zT+O^Y+T4PCT3x-%!<FpAcL##^tGhb5rb=Bd7n7ZUl{_v+Rtir~iOrOPTXJ^PU80{j
z5K;o<Vn?f|cGx@}D}QZ|`2E81QFyNfc->(oD9xg%0eS{v)(+R}h@;seg;bUnUzPQO
zr>9}nB87B;e8Y*hqWyl^<SO0SR*RD;ejB)G0zsF`*XxFKFs|hO@jGz;(*Ga-G>hBS
zezLJ}wyd^CF9L(>ZyzHEd0-9eym~Aa-U4yGsmCvJlSDSeu^PjqxJ*3kpuNuw@-FNk
zC=3Sh;Ff6`8mFS}T3nq43GEXD%dG#!EsH<0(xt>gk)SZ6)j>I;;G5Dv``78K(T28}
zGg@f3n5_qmuezRY)R|vw_}JJyKiFw;HL&oGjDqIZX-?YqX3yh*{a@i&jM}Z`rXD9z
z&{T};zSl`Rk04N--^cx7An~4mv^Y}k65vQI4nRkv3B<yCS@3VCSrT;M7nC2mZu&&1
zZ-t{4gCDGjqtqW`FF|gGsTo<8$>-y0d=rN`s*}W^SL;6AV|HC|`CB&mb99x}dNcS$
zdR`Cc#o2sL9k18(_)X^<zy|@~ZF{e5u_~Lt&FOV}c_;Y3B9&J_a9~z0HTsu4;9WzC
z8&TX&<APp?iSi$_Gv*M~&*eXvrruRz^Xkce{rXUt&>(-h_CkiXlh+dn?ail4IP61+
zN0=SQ{e_?O%M^Opo!rC6qZ|7#P&tf`1JC^?N%LdhS`9b-!Hg4;pfpQ@)4`;Evwd#+
z>$d;G{!{H7DwPlK=3)7@^o<5^zSLs%Ky<BgDrkQ?)_($a1U!L@@CET^yYJQQ^eeBV
z&Vd?*SNf3B>>5}~6Nd=-Npy!5V_1_1W$hg&pNaLg`B2($YEOplcezbUlfm%yd_{WG
z#d7<90S28W>y3Jgn|%hIM%(qE)TjSKR|LEcPp2MelX`*n=iT{Kz+_gZ_Z_$&aoTNm
zkX5||@-8xF^13+B_DEB}cO+Rdy(<nk0N`hYi0=h0NkTNVk8JO4j;XOr@ylc7kS^Q-
z1)nG3c<9r}j~omyUj<J`UGjRV9Dh_I%=DPCG2LS*Ej+4<<($C6#sL4!e^#!UZ4M_3
zXT0_n{N9h3pKAtzXyT@(3abny)B~II&#Br1rdvVK*o=A||3g}E+O7LsJ}#GmT}(S%
zZ_%5AKHl&5<7LU%jhxZ4H_UOnQ|!#U=}Swn%)A5*#pbl{-*b29A}t5*h<m_cK4P(+
zketMDvxPj}@O=B9v&*Yl?1^lCr>D8loxvyqe$U&b8sI-{3YSM(fz27%$=x25y9Rzn
zp_9V`zzvwPd$<q;U*^@$U=w3W@A+wE(h+8fe`6X!{W3N_xFnx;JD13&$6<_7<FHq9
zv5$?*=^5d`Epdy<%`b6NGXu^Z4&Tijt;J%h7~%hrqKAIGdT@1un&U(4{v`6$_eMmJ
z0tGY%%+0hGExevZ`#$fmdb!&^gyAOQlHLoKkey)LaeERH-S-YEcD_cvzc3mR48Dig
z=sYLSn?2!$x#(IK@km~s-<x^3cjiuAHAOh`@qRh~|DNl>L50jt=hypJs%T<?X1510
z8@E`s3Gv4x{?ra}tz<OA+M%`^*bHnEe3dwL8xiR6xjUVHUFEDK(BduTp_@|<7QOh_
zAH*v#HoBpu_ssw`xPE1tm70g5$yx4f2(df>X^$TBx7X4?G1CO0bQ0RPBb5FB=OyUd
zob4Z<4L%-|fK@$qY$c(k?&EbfAa>o>*7~MBy0kJ(M_pKN`}Xt-U=#U1oq_0c;*CzP
zL7s=nLG1fVOPqMZbBs6+$C;kU;IG|R_xoqhD7(3WvDrTF=M36#Pr=%Md~#q27)Qn?
z@2OVPS2hVc_P(>}4fsuRvvy`P1CtQ$?taPtB;vbFJ#W$ogQXLEQy*Wiz`Pxipr?EG
z;~Vmlo{>GQ%69+=rQ$uiS1e!bz5-V!;N|c6xK;4NBz`jbc|mx=^heegqh8*P(QY|O
z<qS87alRr+nhP^5jfiYeQ0yS}DQ6Vs!Od*ffrkDbp`GcGjpbfWkY-29F7V}J$ONLY
zGl7C<kr|mh3j+_i+}BhtyR!|ahmgt5Hs62S%d>-u*+8c!NS-O)4BdIC$l~YVoS|T=
zD0h8ZX*a*J^@4nVdOJJOWXuxuk$ZdIJ88P?&F0M#O%yu6?Y19Ovb~s(C#ogm?2ewh
zHwl(0vB0cNWC<Hou+Z(W*px;q((m-S*qX(j%;qoD1ug_upg9}?cwb=j>6I}Gczqe}
z^=hgFh;3~MvhladsYpkp?!0RZ)$HtGmHFKOgz(>{CNOw;C1fK53*!Xo#24oI4E^KU
zv_aN9%t_>)=TRJHYP@V;zOsWh<vEnt1&m!~KXV*LGIdL{P(7c{u7bR@mNK{O4$(^U
z?IE~_bNh2U$Kk3}R$H-p9j$ND<7<gv3(g<FA^l}%7Q1Sr!}scRxcAx3Hg?bR_I5A*
zq0?o{y>apDfUZaVs@K~HDu=6Z($i~%NM#(h{x|i_o@FX}(mIpV-ImdBZiL*x(J|8Y
z1J$H{yNRx%Bv15@CgaNq?)~nIOhU!&6|W{cg5@bS)-N;jLUDvN;CITGWrRjJlWYnQ
zTzw>uB{a&k3X6j}b1vlN@`b2sj;s6?Z|7c}4VQI-TDAj+P=`hay>>RFBNlucK(d0I
zw{c&2PL%as>!&O|fuPskL_DXyp_K2<%HBO4$O-X&I$o!;fB6=d-RBupj2WHX;5_?w
zgI%RdbpfLd0*P$(I@f&9x7i(8JG_DCl23zfBtq!eu-2SAt{DGL!%eKzG`Fp2bvJF>
zpOD0O6s0lwWz9Q>cGqHeiY9bgeFwW|cT3LKfRDk8R?hra{41(PlPT*_tL?I#b@1ck
z`RQ14W6`dxCMlh7`Hh<9y(5`CV}eNdb8u9NIqgT-MA=z_rJgFHvjw$ke-i=x4N|%M
zqSOo@{){tfX?qtk{HCtMxi)2p#0QcRQlXmyX(;L-Crgfrjc#S-&s&CF?szduOqGWJ
z?spc4v|S$d=fe*ahy`bT&+dE8s|4Tv?QXx_-B=>*w3E$-4e2$zQYCz?TqAMYs*?o!
z8~-@J-Cp&A;o=UD_oEXOJuV=th+&a!3Ti?WRr7FIXv%Bm`a?!&^@Sbc-5>g8N~Jau
zi?y+$q&r^Zj5ev->)_oWvgCzT&MN)S;Gs0?9tyFRgKw7?RI=bdXtT{*LSYotFPw{5
zea-m8-l4yB%zMA_k3x?rwZje;>?TEIaoKEamtf)=D!@7t5=>J20bHxvX@{aUVq%b(
zX~QrYX_=_UrgnKWgAo(TKgFj~CKVK=e-cFl-fv5(qm4u@*SlMp`2WoVTz_o-Urxiz
zb#J!fSHd5ef;C*=(Wk;5u?3Oyf(m!iBO>5@y>-QR|1q~Q^Eo=R-TP!K{6UW^3om4j
zqmg4;Jsuky2jIw|;<dh|>-G7l31nB5S^)X6`YYzBM#ySx|J`2AB9u~&Yu7vWPC!$w
z+Qm%m(O@>kEhRl>ab-6wI#((b+t#o0{(F6hz5w4nvgT$e!zU`t0nfCq9I}f=?kttx
z2DM#~ZD=x{yRQ-dx!#EdJr)9zQuFJ`^bq$vxMTrwMJ+t-G^Vv(G)u}TNYHmmsj*)y
z<}MmQ!aznBo`17q2IAyHxlQL2O<|TxK(|r&C_byt@V+Rv^cT;6Yr|wVe;QA@ox{re
zZyx%t;5gal6Df@o^KI`z7UVqvm*@3bp;Z&{TC3+4c$)ds)M#tR>v|3@Zx^lhd{Hiz
zK>Ctjb#7Sw<t1ciZD8S{yA)%yK)&RgKoHEIZYHYIQkx$Km{}@Jt-jw!TPrTh7cT^R
zLkOWchtxxEc$at=j`X}T9r@s6h`{)y<H`0@o;#yzn((a^!8>kdMrm%Om%Xm%_vMsU
z+(=ms<7${bs#B`!A!rwBh9VNW-v$Q60IHzT?cDM9Ej6*1F}t+&5-n|6DekgYaPh9@
z!gwl&gR_P0H{BF{YuoubaS-0q>2@;mFFyF*cC*>+Cac%Ha?RuEezG_j#YR`?94lk2
zELEV>w0A+zX4f9vd(ZFgXC+r$t-sCI`aZv*W83ljuKwD|Rm-uEvn6!XK}VPZy5Gie
z3}%1gK|^>>Wh$JHbX~m${e?F%Il_wL%B%I=xCX0X2zb3h*VOEO6C!0%oWF*ggWmV(
zgh@fcZSE+r9j#yx<sx&X!^BZKpb7U-?vj*hjuS6*X<nWH^b9+NIfU7WpS+~?aFHPG
z(|;MI44Q)xg}pszmXjI8(pCX1B~1sP{E09{Y9ZKv>=C8uGMQP<9``#@Y9{ZK|9T$S
zRa4Mz_T5|tjd>Y7ZEiXzGAXG%3%)m3`1UVFzv3kegZqm1A_+J>TyIA1(%l*fxjo)S
zDn_S*SF0_hjb%?JL=P$jz1M&3XfKQiaauu>FBnyj=}l_7)^rSCW*h-12@q*PNyto`
zVAa;YB<R6O-8R>QtsBlU1+z0%IO8{cO`-!XqL~cIhsjedo<g?i!GK=uC-7L3MYw?)
zuenG?>M0~9;`#mPFjVz`4PEdjH~z@Y{jqf9%kB9(F%!=hG?pB8A?BP*5Ge>Ir4u8F
z;Z0aIJj3$nX*cJ%V}PC?^jVx0MYUYK+_dc`YZ2lnZrROHZucFTI^Q(yHhdo08g{^j
zgj$oQ!?oYLYctI8cv$6Kguu4-^Sn;-B-$XT{@C1(S_$DMzn{!p%|$i2p#1Enx8`RG
z%xdG~>kSUn7?_$&z5KKJyT=gR4Tr}AaaDBhhv1RI7cVFcfzx@Nl=zo>Ku1!Nm2pt=
z$;>Yz#Dtr(lIROQtKdNIE`F5fZVS5U00#t~k<G>S7CKsDN~}JIXV31)nwGwfp0)ta
z@$n)GtdEk0jFwtJS^+Et6tj59dEV?N)*TTr!OgZOE?&}2?D6|b+&r>0v(9_E4fk$`
zRz{)#TVsA4_+D!}xaDLH7U;AA7nNr^uEbxiCSDIN-n?BwvvQL54gz{@SSjVik1y$7
zjE#BH?ry)he05I3emNzTulrt(HNfd{XDMgf8>dIIwFzMrH~Qz5d^!ua<xCP!*H=j`
zXeS3Iak&jWZ;mVACJgqUB1|gv_uMbJzkgI}>3cx|oL1@14*n}VaB?<Fsq5wxy9+a*
z&EYe&HiI`B>w-A`dw4FZn=O#n{DqTrX<k<~s7f$jKq6A*6GAu%iSEx7CcmuHb?7hP
z2F-sueye6GPW?NhAfY~~&<lqf0WayRbq0Hd?aQHZzdCv`=U1JkinGU>d`V<tzUoe!
zi>-J7yV6MQ{pBe%lkQ+4DHh-NWp2Qfe23rhWlsB4T%X&m9Ep;~jPYCDS=UjS9#2-0
ztp9KD!4PH`(Z#Pu)DOS5rDL<jPgIYN?U`|SQ}u?<(u}QQt^NV$>NyimF{{(}9me<l
z>;W0OTe9{0D*|1nN!@0fjN>Z0By#=N6g41?;766{#CIix=QKW|-{!IZ(5Igv1+r`v
z?m4iE7LaWPNd6{b5}z7||5Og7j#K{bYsB?L`qAlD%A}+q7mgjT3WCubpS55BH3%E(
zJ0hm>K~6zH%`7%M=Y&NHM~kb7%UM15I0IPB%wvD~H@6oQfc&1P&d>9!C?wI6-GM;S
za3Xi1@rkrtr_DP!mR(`gYw-o8PubyP5)9HOO}q7XJA%oP=vcn*1T^8=+A=~=G@?20
z&@zqusSfbkafAQFi?Jh9*~&cni`IiDL9EWfTfRAnH-6+91oFP&+5PzOubsE4QjZTV
zALdU}16#y+_+8`7t`gLL!W(f682LF!USv3qDRs_f2!Ts``T5P3TudY3ujDJ2K<%u!
zwjr&r@Mi}LgQl6e=|EgYtGFfTrV>z|OMiA?3(@~RxxmTM<Tw%lbAx{{kvh)=?QcT2
zV7t`nq`2ze3r?K{+#v6}Xa8#g`?tNH>QfPJriQJtz`<6vGUK{ElUN2U+8T5oulzH%
z(OG1$N`rrYpG$lOf6*0dHHRoknF4Ez!@82n){oJL(^D!BJ=?Sr_X5+n6g%c)`Ut6`
zN3u=l{O&KO{U>hvdyk5T4^2ypU8Q%CWlC>c&29%tNLF|!5=Fr+X{ElRIt%#pOne-m
zK&f}s)mAV{NzSZ+%>jUtR1P@V-v?n=`K!Og`p63@aDqr>VwLppkEmmwA{HPp(6RE!
zN1KDCEx&mR!F4$Js;bUH8I6Z0(d%?rj<+H$#R>>l^4B5}33|adJS8_x5-!zSyPioo
zPBmGtIhDV1u$5N>4uk}quFEgDpEPV?2Rn~@uGp}%sEh5#*#NZBTBFxYxj71trx%D1
z$aF!4HhZ6wH28e?x7G0*FL*uoXXLH9+rjN?Cz+S1=Ow!DV%HLgYG?(+{d!zp+xDwp
z(J-6n(>N#@yEnUql>MNLyd!tWy0&K>WoZW&ImfE1hC=TeW;IbUAnOHxdSWD|{)Uir
zL&l~0Yk)gK1$_CH=+0{VO(g<)cI#kInAY+$CVKMJTwEG=r2OG^V%Z{7qXX~U>ChQ+
zgY4-<dz;hN<wD|3v6_6q_V|_)XBMBw%i~qPBnq(rNz`VO%h}8TDEB$ecs*KV2Lp?s
zq^KP1rMfz{Fca083*-2bBBKy5qW#utzGO;(ZVs4vfHlc_t>$71zMM@oF!PuN$laN~
zAl{g+kZ#p;IDYJIU*)XO>-h0j4qtc_o29{1%LS)E^pB1p8<r!Wz;^b4;d_IF;p@<x
z5U&k90u*wODm9B_C2GoLd{>~Jw1&!0VoFrFs}LzVXul0ulU~qriMtIed@SY{k*cV$
zDZo?%*z5MT!GtGQ=>CT=0XxkaEA`qP|J63=+!F{m{ku0-2!EYl2a8lI^|+i~Zi^Ci
z?z3B;P<vAk6tnq!-FJGfd~_DUTB-(ze?{$7t*(5d)lofI_Bl?Y$GpEx)#UVe+F9s&
z`lbAv(ueZi^6zjzVi<v3<D@L*<wR9%TQ0yJLkSx!bSx}$A?{qdI&|y96;^t<{CZ0_
z5tCy3pkZlQU&+TQFg2{E=g2j#e1{pu2y0AsCNIC^Bzigr1KLMo)Ivi(e%krP5Ygkb
zE1<dK?EKSo9+5t?O&M1$!5l*A5HUcafP_L#Rw2u<OAnpbXe^>-TvoGUv$pQ+@(tks
z?=Zh!+idYKNG~`SqgTY@&D-bwI+PzjX<S_HtR~z||Lx>K@nTa%?&grv;U$S3)ElXi
z=YHtP7cP5`laZ3sW4txa*Kvfh#SwD%HA?#@92akJ##>g%{=*b5H(+rE@NcE!4@!2M
z`?v^gN6!^;_m#7jTl%qT!`VhH;M>1FkQz^=wil=p-VsMVB_(=wM~9$#WT2J(3)Mu~
zM>;S1SC|mv@f>+Bjo6Qu7jJLxLl5q_e>BDoe(6?SN{|_b)s&=lSTI?^d0D84Ox2E^
zGb;-l)61Cg*z|fWwzK#AyL*E_oM)frKW4PMKM(6{ucKUS2*F?97tRK|0yer0KPKW_
zoNM1!pBE>IH#?nnMvek`b#(R>v$d30tAC6~iJIF9p*;TwO1)SUsP%O<t&I*n?V9vD
zTdRdtFicIH{bm+15$edPj&(Y&a^s6)?56>>eQnuV#PQg}xbFDKvi{9?wdUwrGoOPq
z$<D%frilvWVjYBnjEJuRCtj{GF}D5N|LTesG&B=-DSE}4i!xAq*IqHBqCdzE4bc(m
zv6^bA^24KLMq<EJLqSF$we`8X=Ul>!L~X0|-F5kdi1d;Nz!Lm{Hz$uZ4WwG^@Q*4=
zs}0Cvz_35w{|t87&AT2xT3jE1h!YTX#DMR5Dj0Q^1Sks~rE38rtAC7dp44R*<9dr<
z7j`%SU+<gKtuw1L^Qg1+HqYsD@DN!iI~Y6byR)&AyaEn0e72WjMd21W7eJ;`YMH$n
zk`}&<k?h4`#)w5e*px*WnedalN$3Gz^aoptCL)a>Up|ZE0(<3#N5JU=Ty&NQ+h*bB
zkqkIK1|%D-fsHl~XgI>qeOr~qr!DAbSDV#(TlLYrq88TTIStJutla{Npl2Rvs5IbG
zsC4xjGivZR5zGB)KyB$2#Zm0{1s=zjR}0;1CfjeA?l?WOHGNd#B3vO`5K7{SzV$dI
z#En0W56&Pz!0iuo*{Of$EjGQ0TDJ*~%H&x^wmTm-27>P|)?NN`%^SQf1}8^fd=I1?
z+gShOYP~9A1knidu(g-JnmkFg+F9qKy;Bdr_s=3kA*<oXcw?Z=A2;juxmGjN(bKe<
zh>wbNHfmq-XG%Q#De_DVF7PwCX{ks*I6L=oiDj<a-6A??=Zdg^eD<#$ew3*#CdR9{
zx6e14cEE;eMRH|()GzMCX`}f2y-{q&A%#w#R|E*GOHXUPIwK3GoztUus;DwN^^D}q
zBpme5a!5#_*E7ZIi)!SttLUuvO9c_SetLK%vNtGrA6b-ibw#JB9f@RoL{heFAvi?S
za6?5j;@Ui7{33!%^7YleIYQY}KYIKLCXf1^!UmWx5BR`E$0W38uDLEs3pk$N4uj3x
z+rwoj@1EhR_FhfpdW)?N;N|$L+qr=I%YRd&?;XXkyT})$t3LMy%>CN%h8u?P%Z2l*
z`wW3DT1#AB1G_6SN&-$z?AtH6<*}WT>wVq4d=v=B4=XZpgp?P5)^V&a>>VidWI^Z+
zFqc=OnyzudH526vNca3x1g>~boCnVAa=ys+EFVl}3HUg^-@C+B6AR8YTAd6u&?T`=
zTBUH5m|2@@f4=PO6DLKAB9!Ws`sfIV8p;rlfz3wU{k>EqeS^y@QmVoM#J4^<df^f@
z9f){*Ce)5B@S!p}<@H5Dct4uyhMbfD2;Dqd2?hTvveFUR@atMu6*MEr?_9(z)pZAk
z)z9~O5X<Y$p8vLgIbm0=x4u92r$+;AzV>Hh-Df9BV;0}qjLs+tKAJIc+YzmExq*O^
zqydW^$-P3cr$10lo-!N1;X$gkTvZh=YnX)lb2GP=%ha@?F0(;%2tmNFHNpx&9!vrh
zfPT}>{)>-?yU7bo4%gudk_LPG9B~<Zxi4?IW$P1bCtCR)>yUvDe;4NOXTkfp<qn6l
z>wa*Yn$Ff?sU{81{Z({Ls8t63%Zd7bPx*;DiQ1Rwo4He?fnvfLM7gigh$X>u{9EuB
zD||vd`myKT$>74Va<GKDDiA#bO(jeqIrv$P{$=~748KUR$Q{EVuGZsj;k7tq){ta(
zYo}O|$Z-COGDl9j;Q4%(nA}G&00Zu>E-l&4FXrNS{7-aFa`(W$JCi?y!S2iHmT4f-
zeb5v`V_sboBU(x^0^#cJ>e|`O$-#B^N5phiR}@$Oh>hik^5ihbO~6n7*>O<yvQk(C
z!E@pJXAALpReYUqZp7`Ni>ByeH|S#9*1Yw~`W<FXLA*kf5!AqT&jakEX$KR(B^$Hr
z`<6ls07gzpUc|g1GExh~sh-&tMExyy>QblX@ueRGK~bn#kP@+9XIP@3Nwt+1<~Zw^
zIX_erg$d5epKm!z0F}xu#^zPuIto1APH|O_EsNN{>U7>rrIvAh@k56?m6eAq7JR!O
zWTl?wR3zljYja)LzWWo^NLG7)_b0mZ{onQdzd)U&$#k{YY&`j4O`4N5-lv$G4im9&
z-XCJhwmzNwJ>5UriJ&*{eXjF0r>RR!^k0u?+d?Q0QGejg`U0pDj8T+YZWAEB!vA0-
z;&Iv|iW2uu>Tqg&etuA+dBglVMut;Eg1;m2T=L(%Sii%5qQ3R`Ixl0c`O-iswi+B+
z&o2~ElgAnoST<}jp-aV|2u~t$v68w4-XaAfDsL82jc1P3?>q~kAcl0V|6qr-ok)PH
zsx8#F>>o>f1`Z8O40mQ4bRGyh?RQ8cKOY!GZrB%@Wa$b-*gPJdUO$0N?Afi|a>_4P
zR28avsqr`x*H{c5v4%=K)vkBZQc=iIUy0C`NknOw7=(Y8eV{il-?2!P`zYH_Uh~vv
z$b05H_)s*ZM;?C}23tQo63L5_)#`k5W#w(}J{N^_4=Ai=UhyuYpq!&OZ8rO2_7G94
zOqQ1ssi!1j5T5QB+1Z%=&KI7sfOk;-O!^ngTn)Fw1tCS9AL2+~mpbfE^XMP@E_-Ee
zc`|%XfK$g<K8I!Jg$EKTI3z$?)=j_7&n4(AebH{!?e=v3kK*RrRn=r<YKEt?;bt25
z&b1L=9w4bU`K|ro1$v@ut`0;LGo*8Kpop8!6~MsW;dGI~_8r*@fjti-6TH9S_{XsS
zWAqvX(3@uxg=T5)!4@>(>G|L-9i+cL+eO0s8v^;CMGzEI3;e-~dTsyya?jcDpolp;
z^F_Aw=Fuytb+fzQT^HV?+pI1y*kpWHh3qi^x=zHAwM=Em-IzXJi0}=5Z1jOHVP=?L
zOs4f=LhJF?6YE6OdFtAl^5bmS`Aev^quzKa%aC1TbkWi7<$AHv;4~Q3m~Z~oaN_>J
z{C@DF)2ENNe;j=^w+HQvD3-gl!^3B_1-wVwBy)W@U%z?g{F(|`-%tO$NU+UyfWsCN
zICm{tnPNZfrywcJvpd?xK02_LzQU^ozUt(d(#jrd*}+DjEp6VpP#GV%V?^lou+SBK
z-DrFM8+xND?I_Ys$sJXU>^X44*7Ni)UJ~Ve16-L9;Jqd@cpOeIyEE}M*d|M>h*Fak
z)-5E`PQ7RRAekUmf?1iak>rTI4G#AK-*zt+MDaDgA>Q#M2n*8>1meg1Kbm96shgJ5
zj!TBauTYM<@CF#H{t{%Vv*(-6uPj9BG+wN%y52fJH~fi~p15z>$tC!7*9vzA2PO<v
zVhKU{q{8L;)Z_98Y%_4!e!Mc4j3!#HX$4E|UUroxWCxNEgiNvCuyfim8zKPEKc;=9
zGO;;U4m)})?2;m7fU-dNW)@ipslnWvZ`J=b<*Wm;ZrwSv5j`TzffsXzAGkk!%LLVz
zaOBR=e}}@erACjtIFDJY(lDnuW0BXGF6C4)YH|Q?fZZ?axp4i>rWIzW67Iu=sQ-r+
zb4D5AX4TxljbjXUef^kUK#)rP*Wj~&hFml}uz&|VwbBGtuEQ<$m46z1zkeGKr<!u-
z0{gUHT9s3kvvT3NO_y?(+bnj17Vbc*xZWI-gW6XNeJv#<UYF6iudoiEWQIy01L4ZJ
z#I&gfAMn{eqz02KSBHFHGFacXAJAe)M55b#%nJpN<>sF$2_+4I!=b32IVBs{wv~(Z
zR*>l=O>HTnXL(kNx$By(#?K;1M#|Uq>EN?EQ&M1uSFnZB%Gu_Ww+c*P44jz`U`nOH
zV=#;3>Fs$Q^I_x#gDNqst>EM4ls_R<VRsLLXJgDrM5UEhwd2k?nO~@+vmtiW`|W*y
z{PV8E<oRa3^TJPHCW5-^FHXv0`doDmyb^w$`FI9%O&$pYn1~31F7=N^VZix!859?o
zSMXN}XHr#dmgDas66-?ul~h;{qgX@4KX;FuFT?9d=leJc=T`_}GCx*fiXt-Fe%wsY
zVpp!UJv{cWPQJW__N%n}bMWrlaaQSa`6QT}r7u)zFV{8$9&c+wPgm}L;Ci)6Lx~Wx
z!h3i>5grKWHNRRx^OD;xDkmmm?C%{v(2eph=N64KU9c1+EY(9xs2okkWJdVvsYwh!
zrh=nHZV${=6}yf1g<m^@9loG(X3vQV*+yrc&Bj{!8@WbQU5+Ck*wI%Sb{$(t0agPF
z6W#C1nF|B#x8>2AAys7+B~f7!)T22?x%70TYb}lsqm1<rULx)&<=vU57f-G6HIJWs
z4ywndS(7iIBH-CEhEl@}&|0ETnH9gZN-})`?p~zFR&=l!e`N3*9~Xq~NFgMnp&y@^
znM;pe`j->QAI@yqzBlX)-(v<%(J7(Dp%Q>sAL@Wj(Z>&m0T5m-58^>Ct=?4B<TL$D
z0(38*GgMeU?`K>Hc)y%dNG|{vJy+I*zU$k|E{Xq2V(E*&+qzPY{~{L2TGiDTjfE~H
zjnbTBcDfV^!FgHsd&a<oyDO`VElSuaiShcF*?>SwskHmQcVQ$yaIQ=?FsZlddbJ{7
z3gC|3YA-iOiL167!GEh#)qV~WzcG`~*5&n;6W3ODMWFWL{f2vF5lfcJw{~eL_s}pq
z{ua6BLtSt#gl43zoF$CmN~F%RbWcICBFB6}wh_0bdGJxKM&d-c^v5!sxf)0L%Iez(
zQYvELIsX&rDRaG8X)y+kRLSfLgn}%b05=-^S%6<6Kl#w+BEI{K%@|^XI%a_wS56uv
z-*y<!e$+-aWmg0vS%4p;5Ms>hW%BtNW2Zd^>19`db2#?W<Dcxt43QV<5t?+bLF{>U
zJ08bZWMq-f*Grw^t|-esW<$cRL#7l#&}zu>w={##VGXQGHuKB*Q%p7nNjoYF^z!l}
z9_rakzO6VE16KL5UnK0=7S)q>iPy@giVb>q;ShIN)H6wVVH=dFzvgxTB`9^biQg2I
zf2k&2{J9P?WI5`&vddoUa7OzHw?FxtP%k4BXY1hPY;SA-`1H`g%GA)(B&Td#<;cF0
z$iA(zC0@}zC%FzO;XvDG7=X?M&J$v9k5Dw*t5Yzjzl`|q#JSkeV|A}!vB3xbKQn<~
zi}mr$S@GX`(Vu#nN4(V{LR{~=(>^#6&1=Y~^$hZ?Cj$wyF#k{mt(jt)#R;so1K|w#
zJ2Vvs_JErUd5FNznn>EYq|UF!vu-Tw5<xtt?_GDwxj8Ahb_0Dtp$(_+L6?;PYa=9U
zzAe32)#ta3^ND*6GYQYBq2iHPxvb%TzGghcyV0j(NJB|fcChs!Z+gs?lEpjKZopSt
zUmU%2*I!{1#p@&O*g0bF`-I%*;MEpV9Nf#UUYBBHHgB!hP8bZURaw{%Bq&Dm*nod7
zo}Zs?PruLMNDco4p18CR%Va(w70TRbWtm%b61u&hUh+-ar-<RP<p}oMLYqT8ROg03
z=Jv<KBE>Z!;xc>mck2+8rcJ=`YW>tismd0tHNPqS<y0j1TSx&$rh0A|5)8E5LDr4N
z)^`gAS}*5zF&ZB2icSbqalv7dE5Y9h#t1tr99m3ha97&`x&!(#-JdN;2zqNJ!0~O>
zD?FmeJO1G8W!gcPt^LW$UiiTz!CH;&(HhOXY0Hr`vk;B6GCa+Q0o~7$ia&Rgul=78
zF~p?I*doY+`nWk#d9w<cp?a-Du$OH>)*7Zs$prG)rZNS<aF7y*YlXb|uWW~=Sk>e7
zQ=gn&ev~H=vI#weM2tZ|fQE**i&D@M<+=a@6>C#Ej4wWYXBS}hKoXUuvO~LH*1_o^
zoV<s9k9288p!s<J5JPD=$gTUuabQAw78dj~z<N3YE_@qTY?Wc9tGZlXmhTJmpYSc|
zKK<v-fc9A<v{5S6#-esVa`fgj{)if4X!yJ03mx#a3u#*u<2;B}eMC2Ah^k-N3L66|
zN(*xij-B0a!N0y46;oeUdNk#+Kn-ft-F(m(>CG+e?D(wCYmqSnV&YZYi87iFKNbxW
zl>iGDJv$*SHZsj@`g5)@;UF>1jGf73t>ZP~7tOYk%Cxo*y8MCfWKH^m__486$8W!h
z>fHc}y?JMn-&H|59ae*?>skv}h$H)b8}f8%MUfys5{D4$@ND0}#>_9#!||_j>*0D7
z9t-U3Yypb;N(Gb&Hj;4)8txXk0?}kR-yMt0ajOKD>qHjVLGC&=q$wtiNfCq7fHIET
zWN<U=l}VsTe3gCa1H2v&uUnn{Hv>W@0Y=gs5B~&-Qx)xDB&bK>tEzw3&`n8>$Ho6^
zR7pX_m9iiQR$3MWw0^y&Qx$HnA4;BTcKj-7EfF(B1moB0Li#-8<XM!({BH+;@vQUl
zRy;ZC{fse+it6=O9aZ$XtsX~{ud}kJ95q^K>ai7M6d`TB8vW*3U{)8mtPzc68sJP#
z+ZA}9@-bN8gfWp<Z5|cOF+llMpw}+_ADYg=Ewk@^+u3fiF>$i3sU~A`O}6dYX|ijw
zZB4eDY}>Z|KA-RJc>javIQFyGUTfXgb)JKgQ0}~%c&C0N0nu5VxwWP0-VX2a=Vrv9
z4ehC0q`4kH;C_fNEbD6btHWIFm#Tp$Qq3!NB)<CQ)EsxQ!Qk9WYnp>NZEj-xHw>k|
z%Rj|oceF%Cb=#$D4>Hu*sl@oY8tbh<Y{i#01#g4#w|iK|cfO%d3FM(EHl=PzsFY(O
zD;$ow$PqatBX%Hh-=rH+BlVN`)|WIyj2<-qjPLjc2RX&+sBCyur8E$W@{E?l0^@WW
z5r4lJAo9{(0^%Bn=baV&14~Jm=Ykj(BJctcPDb$P-Lk!wmam`ouEsP<+5zBnW#BJ0
z$|d#XOZ%`<IGZ74{_G7s)r3xs<8AQ0l^TOyYcrlki@#?WLEATPVS(zhLNjZF-6M%G
z5UJyeG;<mUwYzR5VO_WUG6c?u2-B*oe<w_?685uW_gLH=IRb1)`rNpWWUbM%r~V{<
zECoy<R(W<_F>fGlxJ3qQwiQ^tmVL%(bN&*frKVdNi1I}P)pb$KEm|8bY|-G3Pq4hX
z3_Ct{>`Bp1(n&^I<KHM(r+sg`i_LiqA9q&t2@R6Kmx>b2&E1ekJI!Tw8yyQZLNvI!
zF|C<aSY8#I|2C(sr7cz5J)^R5$rJG=f94uM=4#>S@xGp3Ma(!gJ%9st82$0=;zFk@
zmqyXn6emSTYq5QMYyb1;3AJLk8g$+a{?>c2AC=%B7@!Q&RQMl%4Nq<6`eWjDs`om|
z1Xo-z%W7#3F|whh!FUR#any6S3u})kMeE<5V)y+f^d=27^WdB;p`DaNcyzUQ6c^pC
z`BxE68^KA``nFdc4TOkYoP!D;If8QIpP7OV&WEky$e0qQL$Fi_F`1q2yC1iY7i;al
z(6!C!26aPfzx>49?97pjTB0XxT}?eL>+;$IyD+>&NpwT0P8WU`jLs)T_gH|Ud$#;R
z(ZDOf^8re5B~N7Yy4r6J7XGyo$?cm*ndP=%@wFtb(z|2?Ea}%x6Q8~mvX$f@-NdiJ
z{UKSyQvOZM>vo5rFkD@hy;x_lS(M~)|3}#A{2T`t1059OS5{^-dyd{!&vtd?FLJot
z04aRBQ%O(sHwR6eEJvL%6#?V-j|WL0)(CtfI^;2?I$M?avWx=h3#_Oz!ii_mK?(8c
z)*i$Pj&cCd*Xp+VfqWX1p*^_3Xn?n$fMdfeOZa)R$@_u1|5QBZ<974UU*HsHGD8t!
zPAPFy{Fzow4eq3Kw}yn5IGzwUL{&v?c1lgXXrEt@I@pMh#W#-D0}GP?JASL8v5u5u
zBwJY+IU-z+rX;JydUcv!;WNjG*Ik7!>llmz+ISh*mpMPo72q<FHL&E?WXWOly*V7>
znj`@ydky<<$Y4Lj25IpELrR3SK}{<-pKBKNm8Hly6&Cs6O5+3UhC&CM^chK>A^m&w
zi_EVArwSj#e;y>H%bV9?HU!BNWwig<N~2*5anS`4&R#O%TZLVaR^|;ckQDIDmHtV~
zNCBaMd_#9ob<#xVHvIBeqZ9m-p?G$F-l@JU<wzv>n2FM~M)z9-$GOjll|vFi=0L#h
z*#8k0jIQ11cCeuwbYmO5*zt{?%I)rMKs#Co^y^<i=H6_fPGBgwDD1aGR?!@hf|QxL
z<*^^<A0=31kwJ|sOcEi6hKWfwYdE!J;5e&vj|&alxAdN%mx&7XZSq5kvRND0+A$JF
z@ABDf*iEhZm=?g87qf3_F_Az2SAjcny+rx>;?P{pmDVp3q#T(X_b0#DSVg7HuaI>t
zcdz~tDO`Snwu+7@j+lga>-fix2eo<uvjU0gW_w)wok|A`LZZE6g4O%VK7D8n(DfOc
zciQRnx)8-iS(nOJ%F0x$uLH?LT_yN_J1&M>tI=ls@YLH6rcGpFi{cut#IdOMXNac2
z?XbAJj1a-Kw*Yl?X}B&EB1^>2Ysw8w8={;jltAmHA4k?a=-&gL&do2r53VBDN8AGw
z#ta?@<JA$5gxGHr5L4(FqWqi80LtUNgp-sah>0%+4-anvckO%^C40~D<&%7Vs4@3W
zR^*JH6})WJKNJ%@h%f6=YGALCis_vMc$2`P_p^R}*?;)^A2$EHLl3_RjP7d8nTD8S
z>Q4Pkn3LKWx9>R~tb8^m&X#!rId2d}sN}e9++uuh`oraxdlCZlCejq}ZIBe6s&)Ca
zG_ci$i+~3%*Dv4f1TR5dz-_z}@osHyNC7kr&TVmj`DA&-QdBjy`rPG?t|Wyajru<n
zwiJCX=?@wzY&u+;#>4>sa6eLIh`azA1R6tqH`%AnN#EV>Ew0T>@Xt-3g;uNe22+Vi
zp2^pX_kJcgk<I8v-~<F?&Z|B(t_Z}4DGl9wxP={Uj1~NqSO87nDNyid&LxTgnPcx0
zqOdSVjz#+RA94Gb3y10-mF_Hru?a{FuyA`TxUX3SZbIV7^!&@}S}S%K2y@kXM&V0;
zbG;T%CUVj^qqbQdZgfFA@r{*!Qa1ufRjE#BHr$P34MoH+K!XIL%WQQ6f@{kHo*L{H
zLuXM~W^qSpQUJ|k`*A)HSUb3v`psISd#=}O_~ch%L#BdM>3elo%)<^TRFvX@UN3zN
z?{oCZO(0LIgUAFG_w|uVuTwh7_p`!XoRPCc605~rc|)=;?gtgW3mp;Tex`w|>|d1M
zz@_h6gV<670Kz3btD|m@m@cnhi&G>ek!1A;xoT|-j{C|cHb$#*--P9AMfC)Ztf9aY
z=fgljaJuTYjZXx`#Y<hxLo4_rIzjg{no`l&HPWgp12L16;vw?E8%s}^;6W<WgCsRD
zP@%Nmeqt`$v<Bgyc^c0%n`!qhaXoQBsid=n?R8-~pke+A{CK-GK9Fj*n5;}sRv>|4
z<)YM;hhPsW`uIe%6b*xw0T(3WXM~^}g%AWy4G^g}L7@1Vh)k4LQL6E7@d0=voiB+x
zS#e$}gA;}+YjFsx3MBXW_#LHHWqBD3Y;Ql5dPp`cDrto>+KA7zCgwJqKTlQ@PXB~(
zQ9Y#r37#iQAL)w78Ncz`Wo2bI$@pg>LKYfb|BvqTK(+TO5FsWz&`)bGH8=y}qItVT
zdcB1sw+Ho`ApfG6as8sU>5jA`2qe4z<A1Rs76PSsCMsb_(I|tOQw%YMtDyJvPeQ1D
zso0Rd|Awwn-l(uh&~&F%iRyV{m>Jqo1f&v;mfRDFedVY{toxm_WAwmIAOd`jza*vo
z)bP0-Ch79|xWaBTUpu$7E34%H<$1RE+*+R55u*p1lzVL6zl8U8q|b6)GVm@g@m#&E
z<}dtgQx5!w6%1l2kSc+rrS$WxsD%cswY7S<KR-c3{SNY2CMcKb`8nE8pKo;-19(QM
z%0Qmo8$YKEeR4q^ryIa!l%2+y6a2ZP%vw>3m&bg(U=O6Yty5J08JnsB#fn*Q`tStI
z7+fR%mLa2~Qt6vKNSdp;gHzB-Cfv3tq#YQ)dq)+nXr-1*6={7ph$D2~n3YM}6zl>Z
z>1kk6t~5ZA_R~k=uIG2Ra2oL%zy#HLa)g$q(z^5h3i;oPb-07d?uQv#{_(BHz@PhX
zzexADl~`nUF#7my?rM|onXwAYnb=HvuGc5Gx((GcKz%8#hn`{W7!~EI@=K~LLQ|i?
z)>~3jmrF~N=?j<9zO^y?9iD^nm+2Afr*r=(0s_Cp;}0{=<e=6T?WH;nU=+toqt`dG
zw&d9)F%k)yc|df(WHw%%{ZVeo{YZ4k=q5^1@7$L9c`&=A#G-j&{qYHl*c^iG<~}g}
z-Yoi{3NoaqF0C=EINS3Sv8mtdBm#N0T?sBjYOdF?w_ghCIS|hI<?l`Sh<_A@?ujH*
zm|ha${-JpuRZkzQMOIE85XWYT<5n$)<l6hb-3%Mbhe@Mt^%;cxs=&40XUb={0gx~M
z%-8k!$4}>{tN(<h;<lR8+PA<M!R^}&M~&G}xHJN;o|Y_1Gr99coy`S4dQuk7flXa9
z>@#(6E#^bmIgpxGJUCbKYz1OzXqFNGDY{F?rK)|$kcft4jUa~^C?Pe_{+X7Yi4U*;
z_V*|r@M)`}q2`&=`kfrCs&saHsj-gfucc_cY-N4yXOotnL`k)~+>}%ScFN941}(ss
z{cjI>4xIBth%%~No%1&QcO7wmRX5Ku<Uq!&%#Oo>5Jog)3=FT*XDh7n=DKxM!<YW2
zB{-5w60Yu^P$<p^;5VVhcF<XzZ_j*e?a%Ib_LAT^O2r)q7wAP^7HPr_J!N|~4-fmC
zKWLa()2`t<e}G1tE7S^g36*<+UqtEzmgSZwC-9?lS)KmWOx<dsD)f3g1exLZ&m#w`
zOYY19GvhyC#!Q$s56$AQ$3%@<Er==6XJ%$@M(}xqi;^1UfeS%@10|W{LSw2Rj{tmJ
z9L(lojb^Ct)uA^HTq?v$b&=|CqNFg~9{W)`eY1|yy5?>0k>R<?c2bRJp<|d~pChrX
zV$c_~DqBT^FkVY|R{z56-$_e|i0=Lc!)_3G3d;<`6!t$E9fDKE$9InfEEE2uM$IOh
zEup}g%0$D$2DW}SAj_@#l)2bzkO;Yd66^BkoA-)@3Ut1|oj;!k|6B^E(wBN!fHY|!
zLakE80WG^E=94ubYRPI4J^KrfxrrrteM5uGJ#v)XxRTuE*)a(|Rx(IN_a$t?(}niK
zV-3U^m*A}Wi0cG}0$3r0@-iV!giZZyM~`@5`rb}}k@@H<;<4ObV#7o|sz2`Jw>ety
z+O4@81jsnjsAn44txyD=dGTL-&Hb?7bH)h=@e-Uo<WX9~VX!kgE8U)M;7GhEOjsBX
z!V-$mQocS+d$uH)jZ^Hwv#Vkm_xDhVZIJ7W+iH%AIXXT@U*S{e|F%(2uhQvMQPo`Z
zCi*)BbRoOUC+(R^c;WKFQXB6R^DR2V!lW#hviQ0dY3xC8?A&o<yAjzxDHxa@VyaIu
z*Vhe#x1(8E(bLPw$RK)gZN`>7VWpndIs~B5T|SBT(DsfDpAI40A6*9rB}M7!sOc)H
z`mniM@%xILYNB`@(t;7^s)s8J*|(iLiVyn(Yf<#c!OhyowBd$#be`}49xT%PqbRnh
zG@;D16SHiXSsmi=sg_sJV2H;uK24Y->!)bm7oL#(x_^HDG_K(bvx@Opn0p4<^cH)6
zy<fwM!1IiLOF^sIKu*9lc2x<wmDT4J9eYmp9g=hkbZEL*%Q(hXrloqhJ3e$`Cfl+D
z8BDbQ!1)nuSViYzbl&=q|N3bf7+&uG-go!$26>Nu5%w$*@>zp=`=&M{V8(&DWt7!9
zDeOlM=~@I9bU*a11{`9tOS#0pBiosxW|oq_f$TeCTayLkFq!Ujx43>!2U2#2^D<b4
zA+B~hcmzk*Wn{=2+<vGE_^1#4ZV(9o6{v+M4t)(h840cEJ1CgPi(MrWB%A>!{bmed
z{n!DG<NLffJ!&SqMRGWfvEULj)a<!5fv!^f2qkiL{Ju&wo;;CEBqaPJ4W;Arp<wsT
z-$jNW>1#uUj}e<2p}?YYgs=ZE?K<TosC*pkHlm*r+>wRoUn{Zk@bG?<^YVHE4zblC
ze=`f=EkZLIUA5Je6T1>plL$+Pii>gs!Iy7?tBD8c^|`o-2DJjD2N?~d2p$qMD$DG^
z$4}JlLI?Oc>WeWH2p{`N5BqLIa!y@4&onk(u;MlvUl0Nce2=O>gCXBZf{25H>oN+M
zg>dNopZ#XwO*6Di<v*0b8L@c*1e)sv{pKMUQklqSqFSO{1~`U05!vV{3)bvlr?aA7
z`o~Yw9M>*3S^rIe^S0&4dxS?E)j0#A6G<Dh2h(qjKX?DI*&|4;!~!?0ODN})tF3};
zj7tM5av4^fnnDONtnKI)QjT9rOH1k1t8}ml4q`Nu1{*K@l}2Z{&c2%g0-pBwe+<TH
z!cK(nmZ#@HBMi@+Vd`QtoEqo(2Z}<o^6Gk*dz5%=r$`%QitDafa(KHe!@k0@zml$|
zUxRb(7^waY4Ra=2yzBwFuGiT?ctM>-@sUU56VS5G1M=+(D1zIa@}?_}y@X>4H1edw
z62u>?)11Exc&_i@2}fLkID+W;&ClpNWb=f+W>aK=Dm-i2)zmXHasl-p18ZzlMnf!$
zJdC`K`gXeV2g-m~4ZG=F48k0~FK1Ih(I2z^@TleG<#bM)m39xOh44ZHLHVSYvKO_g
z52aByu5$>Cs;0oOqR9G!=9JLbtvnK1H_o_v?%kxB`nz!1;RzwY6T>>m6Q+idZ_E}j
zGXAJuZQT@X_vE$5DVf=aZ7h;}T{_aA$Ii+9%;`@T2IEmkvD@YkhNPBmk1V3){qdeT
z-mp)0eH1^i>&<eKmiaw@9qyzqEHfmqB`drh?QrebyNr{}!ox)F5f%o{?MyPL>~M&w
zs{{Y2Q|p(nvAQd;v0Yb|qzzf^OJH5T)I~M8E-c!&#$f&2+HM4j_<iG5uAz3*ei*?8
zV8u-0e$S>(iA+*cGLA3REBi>EjDY++kz=amQtY9*!5P814H1C5_sL3qe+j9=U9O6h
zfKMcu*@v0n>8Hh%4#|^FG<dB=y5l1Wp9gUUt)~GpG6Z0oEpeibs_+gvbQ9k{I{&gN
z{kG9Y%dxUZT)#Dtt4kiwdV$=54%Go!l=`z@>GF((=qNne(*UJNFLa!N_w?Dn66PiL
z?j_W;26R_vK`V{mIF6&#^dO^=tf#i==gHOUUQ}L8!qIRk*yTR8?s+Ub{c-+^Fsak(
zdD;^(83-(vBQzCDwh}CRK!)30Iw)T8*O-BVvznv1B(t%a^<e3gAc{gv^X_#1@g5Eq
zW*a*rnS;kncwxl5&b6q#$n4Y%{JdLO?Y4-ZElSd0YZ@H_Da=1P*Fd2k%NTrWM#(NR
zO<RQ)-DurqZ#G=E_kPIe=S<Hg1!WABAG51C?m*x&IK8FL7W$en88K(w4gTQ`F(QTj
z0xb?bTc0kGkGG}`VsiHqDHr`OX!Jz)0E5UfK8-aDUiZ0cVP=hSzaC559Dfv4T?+=>
zyXsAs_LAm;yZ+(2o1ZwA=~XU|0-o0&%kLMBqlXHF%T7=IEv=2(kJ&!BXB9#&`c_(z
z3V^xw3?1`^z%NgvIl{$i=_u{9Q{$@gZ&L0_0T#5l;+U!e;_3JIA<N#z&fmy<ioc1Q
zm#(%xL4ok(7$4FvTEPBtjF)WAxALBf+3rCBZcbXC^@U&1Lkd~tooj0w9>qXxqPzEJ
zoGY4%@yG3}Iuw3!;mh3gdO9mcA0&T{DG-lC?T*EkJ3=tYMuOvZvczloVLd1lS?=j>
zE5@HwcBtRGAw356ub@2?Hy|kyB0Q!opQEuH?3smHHfE{b<h1de&+*okgs!xCZf5!Q
zX?)gqD<GSZR|Cp6cl}*q69Ve6-#??mq_mvNdVJljxuoLgs#E8mm!WfL(_fB%oPeJt
z<Jz=eR|;GD*)II3A)h2j-AB^ZjYfh)APOd>sH3Ji+C4TnJUl(!)3Y&w;_iCIU&mIq
z=z!{J0naDuqH+Zl#3WL>8Q=7I6vl?b)a2~^Xn<-F+5Z~xlYQjv5Y<i>MSvL0<fH5-
z>3jz(8S4}zD(iP>ckYA9R2~y=2Vh|Z?aNq1ErRjgbr$DKNoL?M5m^vtHmQD19qShw
z8aoA{u}hA*c|}lir@`Uo4mB#+$X!#}abyI!B}87=`lS7HM@jFoL1JzaD=D*WF0_dw
zP+%{4e7(c%vxj0RLV=KeslleN45Wmu0RwZvI}Uj1fHMEXa+Tosyaysr01FFjAo^0H
zo8!H6J?o5G6884*Mg>ARE|~xHhnx>bJvD6e&<>^x%aj4vAxHDGFbVB>jwdhJ(ZnAS
zM#+_9D?xI4WdtjB_x>47N+;;7*?PDE8hi3a_FEJ82QpaH(E)V^qCs5|f51(L^VN)3
z9_J80e9T4t3XyP5Gw>pT@l{z@185(D$$_x#4R=4bS2IJr;Q3L1)#1+ATqwD7v3DQ-
zUqm(xa#0|DgzVV23TrUb?Sn8B(6z?XSmmpNTGIoPz}eR~|4}-{bT6U~n8_X%+#o<c
zF^`6pmX=Yl-EFAooBJlDkATSA+F0nEk6|{Sx0=G1?f#$nhPx<WtYV5IKMXyfhU@?f
z4LvzYeR__qw%+Ob&Pkjj``aDCtS@0!7zRcP4~{@=9o>qATJ(jl!8AN=IcLwk>b(8=
zL`NrVU`*}^bl_7A30@&FSd|dQ&sSIhuGOl!40`&t<iDI>IhXndW8c)+Xm}#8pPNt6
zjtZ!=OS6ZCQn48UNYY?d$}F%-h4g6fG2sTooQ)r-mW#rGr>ChQXmD9sD!()hUKy@Z
zdDbg&1){B~9+v6LxV5?Y^%EOB2Aqt9&zgKCh5pNEqbXZb-IN@XUj8>fFcWXhRX8Iz
zs#?l}1%=r94~#eOHyxA_uL4g7RE@94smm?T3=a<MvoM=L3s80Oo(<d3G<c!Yi{VIf
z&&-b!t&`TuNZobi?80V{1`jQ$;t>?Z-O$=cmkrh&jDh+#V1Jl^jdNlFS+^#-hB)XU
zHW|y8&S!;SB=J33k~{%?7d0$fn4zDRl2a%$>A;}n@A2)kqJYsk@fX~!!`=PkixWeu
zXaos76=p*K2NBFH#q)dNCAaVO3YZ9nbxC=ZRl8^9?L)^a=`Y^Uv`+BYH_Nh0sL3Ou
z<?{Gs#s_L+41Rur4#J#%?fTeMf5|-Yz-uq2uhn_t;oIXuEBLQgW7}x)^URI6NV);i
zDgwA-a&K`DMwG?|H%Mz<2;svy1e0S6Aj||WUco6axzMnA)C;$3Rx+56yw~MThM|yq
z1r~$EXv9S@fwgZp5sK;}iABF~MX2{Ji51F#n3K>((2%c~JQ(8>b0G80<>jo!I^!q;
z{w9uspV?W3qy8EW9v%e+0SW0U1g|X|isV$*uoPyC-ve`Uso}Xe{ngW}m9M%<&!%<8
z8S5QDYr1?u<|n^K0NM#ox?Hf<%pR<@lY^}YB+a-$P5{3xW+pz2?;J0OAAc}t2E_yh
zn}kVRu|q=R{WIO%m4jk3bL0QTEDc7#eY=7Lha^$&$%%jvHu2gF-Pc@2O;qD-aOqAE
ze7buM9RuD+(1a+Z3WXWlof^uVH<Kc{D`o*E`u<^751r}c@2U#4(Nipea>8n(Ydoa9
z{iEo*oFMu&%qaO3YA=$9u<1^xJ0tajf6e37mA#vrlatH9K8GuE4vWXkUR_=8HUbUd
zqKdle;@rZo&Y7vDHQ!tL&8?|Z)UFO9Py6Eh(xSY(AA+9y>iyzSP$9vRgWtjwzNZ3x
zTqv_{oY?hGNZb^8x^^(T<K4HCxMCmx;d_qF**`x8SXJgu6cPKMx~ijKK7LpIW1zfc
zlDbYwRmaTQ%CtxMtNdg~=WEFlr1ygMOal~-CuP*z0Glv5@#bG13KTI|6R0J;#WnY!
zul?p%{Bu^$xwe)$l|?iR;k-T|)E|Hc$ERz{C=Z%)I*GbeTIicv$(&2b-PQWvlG`$3
zkoGYYnjGV$#&<OBL0O)Tj*Ke!tJ!#HYAz2a99rV9=C*#`y&QM1Pc*`mSfgAFFE{gI
zaX``1&$6jkYi1K}n^ZFN4T+Yd6#f4!!A<7hG^&{qX}`+z0G<K_Qh=CwOK=uF7+l)0
zX8*NEq|`jXg#f%G@u#vRNjIOf#Ymcs7a|G~J+G4Q!P~&e%BVWNw$>^i@8zAKJ1En~
zm-jbhY<4gnwN<NZH0ELhiC%(F3=6(j^$M8&c$)0P3P?)Jg?wO~q4PXIiAH&NaHYv#
zz|EiNVYPv%L>z@417MgDalZUX9EhHyK|;E_zXyE=K(NOzdsm@48j60|G3ui8aDR}b
ztQrq5R@$n!rs9l#gV+_kH9~;fhP<_a``^dmwq%8FOC%)r$t*Cj=<!odW4o0)o7ai<
z_0yz~R(yIyhNox;!twlASWW&Vq6u&)NMZ4A3?*sJp);mlU_pqP^g4k`Ar)mc?Kx2z
z&71@Jdw;QU<>W?uLK|P#*3@)wd_wU=MtMb*UCRQr^w8z-!v*~?qxj{UzJjEf$?|lE
zw))o~53OT5ICLtAU6T?_ux-o2n5w;PrvANU#Abs5TjOXgV>hNQQf@o;0-W0MAZmQ;
zy6J)OunyA!fU=;#WFJJ>Rt4!M6$?x9^71P3^2$J#x2jz5pN<t;)Kw)mCtFw;=^2fd
z=YtHZw-4A;JDI1h#QYAmEvG%@W<i4AVX<!+m;^ob?LIyN{)afxO-#l*w9Efmd&T#-
zR;^JgqJzC9q+c0u4+dWX=Z#24`?a!bW&Z6SsTuH4ay=mYEhm%S0$+=}`88x<@GDhe
zfr)A5YBP`7VU@T0r^2ritCs0o{UhY41yZZ_CcAF6hR(Wn`_F~3Cw&|?WGUP3;W7Mb
z<e%*Q=DzUvR&=Mmv-rfxb-XNd$fMJ;-4SMfUEpC#8afgp;+j%QBJyeyS_pT}!pH;y
z2U~!(iMgdgUW_d~9=t&_Qhg*yqx~*Q0b-HBQ?ynR{GEy4$?JtMoaH=BR%>|R@1t7o
z5NNVf$_h8x2R)QZZnxZ<o!WB?bjIMiwaxA1H{QVCQIbDIiPt=)#Hh&sp*6f?S*3yK
z(f_30Vw^u6P#vB5G8GBIMj2V~z9Relv;rjEnDjn#rUptZrDUbY|7mqutje2T5mY}S
zDd(?McTEQrPr`b&ZEf(ut4;boz=Q{3&y1BO4DQD(1mljUffWceeplnHx$i}>O#j)t
zE|&<U5o!W;C*!nyZLjiEPZBbR&`^}LIPm_`<F|p1t>O96wy{xBH9VmzdVr0Iu_<tP
zLV%H(k~UDI1*%Cct0?-m+_Kb|k)ZH510*jm(Tz!iFZ9UBQ8jxOmE1KfJ&U4YmwbYG
z?na0}Zdo09jsR#`=?9a`|FW}dOc8_ucr(LMyXSO|M2|1*|KV^(mK$TJR4U#%*~Y}h
zZ=oc!`1)j;tR86hAR{dlI8*m|1$Z7ATvkRW(a#q?FVpu5d}Ht5(jLncIA3%~-7pc!
zX<F9#6=21=JDGpKMcH8UzEkIj$V9vf((8CoPw7RbMyYE^Xo7%iDteF)&pH+LIVdrq
z8Lgm$*yRowDy$5ooVe7KNMDYZri*ZA2!oVvgP}zV6DKZ<GqS=sk7uqAutlbLn9Waw
z-+SQP;dbflw%O@ZTzq)Z;iWFWSM+<O6w$Z1?yF+5a(LQ_W!ceLT(VIPgE<`MiPT5$
z&8LG1ji`L@;6l&}7i|S2Ek$t&h-zAxZ>Dc)@}Hs%13kT!?DF67%0nAeABOg5f@^;E
zMh7AOS(n$Tr#5IFl=cJ&!}?gG)quG$?A8^nBE%7&niW&@jgCF-(rEO21LsK3NzCY#
zX<{T=Z690x9&7`_lJ~NoEcI8zW8e#Wl+D_itcyxPQ1bx`5<)4brH6HBQ^N1gvPkA{
z8tyrWXt8Q+AY5R$RP+EwD{H{n42<oxrq>9;tAiV)mDf!E+42<d7i3Zw0wJ0PA|@hW
z7l)cp!oixoMt<0HQ4()brlsMZ&^(tR5#i9(`_`ZhJ{qRv!!}w|i2E($U~t;y?ZktO
zJTFCRC{-JA^qgmO{F`&{aBa2Q{XcJ#g{ymCLdLN|nkpmjm{mn#slBUdcjKSDLy@I&
z8G{ib4!jRzv`YYUqQ=&(oyw-Ba+C^9>+=4dC%3C^`OOiADG(7GOZ)ruXK0wVa$m2}
z^-Tl)Zg2a!T*ID-J(27o@b0l7O6KtkbiVxBb5Wv#b&w+9FE`k4ww$!v;Pwgw>SNx6
z7#ZlqsvwfBV69JTOdQn0BOxtXKobWkvm`8}hGdk|Cs4?@xMWn|$|-T=r%Df-U6Tl5
zKc*DXR+>|BG1xaGHtBE3GnN=jyzji2yk8v!LM1E4WA7h=0(~RR-AsSUW<VY93TG^F
zZV9pC9wnqY$KopcQ%91w!Ji@&Rl?Sa=>0ImKI`B4uQ_UN!+wM#-&4T|JNJlBxUC1Q
z*&t2t0(JT@&GS6s#(SCO<`!KW&^`>;d0p<;7z6Z#)t7V02ZbSgr!!9^5{$<P(J0oe
zgS=)3#=%Onlb>83)By|O6birrPlX>z(Bj4f7q82$pvsCawHYXh15@d{JgehitnFE!
z{M#QUw!B+SpRm!{PgOPycG{kP?k-XdA(greG=G^}Ov@v(*pB8siZ1>{x9<N6`eRiv
z$Rgj@>pRg=g>Jidv66a$6F-=?ma&o}DRQ1u85<Rlg@nL<6D>>fb+CF&%;eEi^(t&R
z_grLfZrK!3_p-CI1G(=2;Pc&T{;O9FBf?;$J)^#1;$yuoHxbA}_ofr%IP0ve@;L<1
z2EW6KltRU$5w+`W?F?a`QOSR>loGfR@%{8TmX?9dNz^xsDP%XFo!)3Hix(h>@9R+$
zR*H;qc__H%?j|S04uJ|VuAvC*79PqFm!=|)qi-UbkczosSH(6qcyn1|Al~yu0xe*X
zkx^0bxKISQbMUhV=z<u;@(Y8!balW{P~Id)z(AF)zDNvW>NA*boAvreK;KZrsC;&t
zd+7zMexoAG=EA~;@41%b(EW>(_-kW@G}5DE{giq#bQg~&zofR<6gc)HKczg}Z0v{K
zt)M6t_9^&oDpyx5NSNA~nm~ffbeXTycCmI!t@*nk_vv);(GJwNcRx(qHvl$JinJVi
zGn{b8rdfh;f?~1(eS32a_k!$MJv<38(}p`F1>G+4-cf0pa5G#ze6Fc(mAN@KR!AmG
z5=kebl*ow`W)~DbA<}b(+6cC93I`KpED8nYNo6Q<G2|&NxnWkE))T|)e?O;ccV-ey
ziYIbD-VIe%UF60KS5{gP_krKAH;ol?GLctKtRNoezTuoz&ThbRxN}ZXJP!LW;XVJ@
z@h6t4yH9Q!n}6G$-PZ2SkVXtChgIwGZJ-l>_z6t+;kFiR9m+LKU%U*WV_lN?6E|+T
z)Xg{nP9+k5c}NTBK1iR^wvdOsjs1kRdqBr)FE)gn=Ld}?o@U!q5HR>H=BhE!qx&oy
z-aWD&!;i2B+X<XPA)&@Gs0NDk8NLBg1I{A74Qg<ZNkK}lgVF0Wx_y0514rxu?5|ti
z(b9In+d7|<8a3lX{mdU6s9SD`Pd3poLRe~?V|xV$3|}<7eIZndsF02<;XgKvNno(h
zsPxpAu_^m_1dWiSZ9rq~pLzzbKnT{)i|h617pJe#d<$R2-B(X&;cBBJq!A>xQu0=D
zFJl@LTEQcQ9O)24a_Y-Ee<u`nrbVJrkJfm6d`EDnFEq8j#H_WRUqG6}J{h?ge@=}L
ziG^_cUeP1_xS4nsPI|5@@q)PAr_YUH2B<EyBBz6FpKA?XlfcHC&nqGdA;0t9QSI(;
z@vorIAZ;8mHa-Rt03o^&9cDsIRDlQ&2@P*Bqj34jeU7Gb0+8`~>#XOa#HefF!?Zr=
zM`gujdgVM>&zs1$c-`F0TP1pRpCH}4H7uwl5{_xN@sEwG$VD3I*nih()R`Tjr?VOL
zzZos`z(bdB1$GdFGY|LN_xM`P64B2g$V`k5r%bHeunwwpcZ_oB_%9Fjie!hba6znQ
z(eUK!*pJ_j2u*^e5LT8Jr$;Bp1i1Kkf7uP5!~fuwFIH{Vun=6O@w7dVFw&JbYb-h+
zY$YWQFiPC?mE0x*TtrUTeieu?+e{GOmr+S&-_vt808a^zN%oK&*iuXI$gfl;HWQuX
z&huQlZS#CjZ_q9EpHGHY7vo8-w<bfbRnfOi^Kl1RA`wuLWX+^|k>Fn)%37*xore$r
zHl7Cq3Vvyj2m9XWucYFEIVDd_d#%@4*{?#Eqn0q|g#`V~ANvqvR&bd<t!RZwxRH2-
z4T?jEfFSv+Ab3v%h0$&jgMw~c9V-Kc)BM4&5a~%h+f!n5J~#8(IxwqygD;)%l<c7>
z%p%Y%oG!gyH<HZzA2za2qxNQ0Uq50{$M?&d&V_=XxtcCf!AWskxw3VIeC!`hPyeKa
z_i6v6(23f6#_fJ!f$|j(a#`PP{PL^ibM^hj2q4HmQ{@hcx+)3Zv%A1j14UT(wGmG}
zPa>S+-^cc_<q0j`@4BGm{EJ{-j30a;6+7_Iv549z84Xk?{Oe>EJ@wwMOF9ezwfb^m
z8^rfaA+#m}41|-H6NLBx3Y(bE^Z*H)?y{eXx{C<1f1IYoWA<~iVr|xkCTpr-aty^h
zfR9#p5=1Qj%SfhAkFDj_0)OqXm3J(gz{+CRLTIgjOoXZKNOi_*^j}`0@5<BI!+uF`
za?r6|et#eSRi3AFN2y6YLt7jLZi)i@2+<~a<4t`!L0sb5lJ03E@;i8eh6xNp=e<k#
zIm^~`K`*#D#{tSH>^}T`RTJm609_f-0VrFUVA#@Zrt!Nw$FnRe=tMdC4HR%Gb(cpE
zZ^=;LgvM!XIFQ)hy~(+M&-I5}R?2_9Lz3p+zN||sI{QK`{H;8ykwK`RM>D{)xqq63
zw441BxlUf=30sK$o}BwWx6RqdE@e%1|I<m@auue(dReue-m0{^5WjU7uS(%dC6F~}
z$VRyfe>s$j1W@QI@o+%ONLGWe?E~BBaZ1xH4q?OeY>O3}^)`Cj%dHPtl9UacWAahS
zG$bizKhvx#^f`ya8E``Te#%S9DW&1kzMGTf0wnB1Nk27oN5`hQ_{ku>oaY)gI#**_
zTa)dCyq~W?MJfNztg2I-nEirafcNCbw{Cf7qdiCi15p>Y#n^rhzL*T2*baWUxx5hS
zk7_TJ6}0dgG9yz^o+e0$)OWs7RxvI?OjY3BF)l($goO+tm1pfA%Ey|)Joi??w~Tl4
zBnIuIl$2OB5|S={4kw$3#<h-;m5#ZYd7(RE$}87uq((JEG|vMDeaKV+0Gv!j1U&Dz
zBvwaBP$I}7V{p{Rn`}{UI|)d=@yC6)skxS+Ifjs+x7`oUgq4|GbaOVKzF55M@^*i9
zg!96l$38Uu-i-h68=-*a=V2~XuGKLHT4rJc2VmrwQa&AOgFO65LggcdCyE|ibwrdt
zNuOI`wvUgS2_^c98>H|FJl@5^I=L`^2`Iyd)5oXhlP5E_M`>y<i_6!W3%|EoteBA1
zOO202C-4w1<JDUPHHo$&Vm4u_lMXB4n3E~Kr;~CwOZf<3Ad>g!_sJLxNmtB2tFLE$
z#*<?4NeK!3V)J^8ErdEPX?Z_5?+j7U>j>c>g3ErmXHPuW#5}bC^18)!9gU>h8G}lr
zBHZka-@;X|$*N2(DX?#2P3=+43RRWO#b|VOiEV8e8RetJcc2`47MCFsWw4R7XQzXS
zm}?H9uQ21>oc~SsUokLlh<n>xirF?(`fGL?8hu=>WiSvdR!;mFAKB<UR8)F?UMYI0
zYC{d#@P6T3a&(jg;;U;ZO)SR8A-AZ`RPysH3v;WA%JM2oNSR<K{en_L(a0wGh^zmh
z!mnJoqY$849)rY+YXM0WYt5%ev{P>#kZE0dwFu<qoq7V=HDnu$qQC9<H9oM>IG1|(
zXxIb3{KkjTqh|Lx_{#!Al}MIY!z`$SbDbZk6CR6C@x3P%C`QTtv_}{zIo9UY#w!^{
zaG1ypmVwf_WKl|C-xlZ3aR1#$TWExn*z9{L<+r+TJF1)BV(_}rS7>I1fBj+zC-A;W
z%$fa9>TU+-D_llSAw5;pG((YE(t&Bwpajf|=gOBkh0rJO_RZ<@XQnXtIYOK(Sy2o}
zIxTG59vL=H;1DO*wX>t#?P^&&)*l8Zdnj;`k#M7qe@en9NOGwQ*$j@F4vdYA+mRoZ
z6kC77zI%<pmv03Pp+kAcFpr#kX;v75a!2ac&tO!*(9G@}`^Z)fA$Hb9aK2~6V1n<3
zJOM|Vp!vJ$9=1{9xxWA(C#c1JXn)+oOXDCyclCY<Y<3D*(=Fp4R#9+c`OxJ-CaQwO
z;H*t9j-bA>V|~+KaC<KJyWNmsfhUPiZlmy87>@D$!A;hcq>J1z&<$gf9+=#3STQ>(
zsd}8r5MC?LFfEAJ{PP$6D@h1bP%*u$BgiG4guYKwirkO8wY3ZV?#dqh%fDR}<R1z-
zZ<hpyQJ9YwF4S5z219cMbL(u0OC3Z!YnbKI|1lb!{fYH?GNf*fPb8e!^haD9wP}K-
zF**?HL}tgf(cQ`G2?1!qsz%H8CF()`5Br?1>L%)uu1cOK*$71Yz}axW*qYEKbI{9>
zMvCK>k~%op)5lb96jjxR+=i&|tHQ2r{u9_6S04#@+CK;F2aeGhUvkv@IcGTcvL(5{
z`xWOG<|f5|Io@Y^8z((ae#VvM0WC$H3fHYB6Jx`h%RUo3Uw(2ppy;F#<9z>zE0;rn
zTf^`rSxE)-iX~Zngvl-NlX5h@o$PK*ls6noqh2nj0dKN4tC>Aq^+_m41ZI+aC4eLN
zU_1?h3t0Op0R~<dI~ARw1C4Z7TSw~utigOXRqR?#hh6kwQ3`5p5C|_1;4-X@#NPz9
zXT5JXb{B)pajg%%(&f*`!>^f5;e4aXyh(Lv!&$g|t<3Td%uZBdVX~3-;H?z~hQzVH
z)RTABm8At2HAWME<z?RG0|Ore{x&(HhCx20r+xVhz?(H2`(F8KO+|$d1mV={RE*6w
zW65^xa(z+JpI)uop@0TqOe(BFufi~2@F_p<Nz*{vDV$vTI0TKp%C8bjf;1zRQN{RH
zoGn|kB1GGuL{PcTl4`p)lf{JGbO%j)<+I8IB_j|wz3B(>S6>U$iR~F6d?jgOgW8Tp
zy#DcWh~pXsO^wPEKZQcvWoRmwjg4hID~iBI)>r$-FbpzvkrhD-2(X}mTQVAP&6L4r
zbEkoG;jW4X0}k~YpOofcTs(?^ZPr?O5p2FzBLMcOH9(rm7M(U7HG@ahHLu|4X}S+q
zL;|wu(isgyvuGx$mgNp`a4e~3Ypij3xVpP}0Eu00CBtBa!$_$IIgR`%NR;wqi@P;2
z9U?h2ck!HepuZpyLhPzvb|<V>31)!y>z{s@UA%*Rp@cF~4A&a>(t3zvIXDh~b2q%n
zmOAYnV`I9#S4JbS;|h^;3womdEiBVdtxtT{79oU6mSjMM%lQNN|A`QoU}_yd4qMeh
ziSM!nuY;Y*gV5P28$|i<9O!yEz6oqiZRZa~)MRvb-=aqL3t94YPz%hd{jQ;hH6vG{
zDgo2qTuDkafU}1*tnJVH4Q6F*q8F+q$cVz(-klvCT}PWjiORBI9A$<i)1GT&-<!79
z`}2?ekdTiI!gpXM%#DlOuBvwZ8Xrvq6G2{#o3O>>89E1bhdL^3X;{oJHN$@@SW&}&
z5M+x*IYprfb(|y;5X*v~yQwQ{H64WEO)D9WPeMcA+7F02VNd!ULHUIvszT>X5w8j!
zPRehm<yn%lz|01O#Jco1`dUoKq<7xV^>jl)aUD~g83=rhsw#oAT~MrsLDZ04V+mM~
zjtD_5d>fa>EoKyMF_YW`BVs?w@V6nG5D2fOvT}YqOfT(%p|}p0@Dp2A^)4#Q1rdgG
zn<$+=C@4F<FHg+f#1sb%NUU}W$tb`7%Y=<Zefs_VtB7yL7T=C#rVBA*A7rrFDONnB
zxx9+9nj?_d^*6Psf>aD48AUloQ6%bMKKBBdo!v~%K)3mavgYDg&**&5;Mxy$G3XN~
zQv7EqaVD9~8l%nHn}}p>;iIQ}YKfhmDk{F|MI8|}giuHRjI#_4ZxLxVaNJ?gy^t=r
zt>5mK?JS{8=5do$#M`YFr|U?(ePZR9#U`WY^-cf>^J7}s(HKdg-AQ96xPzL`GL2V}
z@A1M-3oG$QJApL+U6)bC3Cj=U3BAa0BV90eRTax}y1??p`T}kml-$$Iux&J90@3SH
zOKp!}>dNt7*%;@C`UP~+3`dgD7zS!FuRkp`p}*?K<wz@PL9HLM0VY~Bq=lCH`}kYu
z7uEnOE0-dT;i^UjMw7+l_1}92qV<lz$qjE)O5(I-8(M>Ct+H^|9d~dr9G(p2?z|a}
zTGtymum`4`-^?`*R2vW?F9zuBb{B-!E4&}JGZZ%;lWYpq#0B;^)OKsI5FVrv&(WNW
zo|_>UU34qsTYQYMpZr&21Q`+6@oo>^c{>2>6f87>stGiY;(|`7XG3Xy1dj<EM}oH2
z5GSea{e)2)S4HSu-9p^{AwGw6!4FxTi0_K;m4$WzU(sq2@2p+H@zChtkwGFRr#g^`
ziF0moK*IC}Cffhn>Lyyxyki5IaK}v#Hw<tsuVS}Ttw^R-+YO?I>zMmSI#iD)4FmW%
zdATLJLvmR7{~lYqATIf~#|ih#Sids(Q4S6CfFy>XM(keoOtRwKO)#6T66K(ar#aj}
z$mGLIpx7gk=Gse5e7irUBZgW^A*f@WiS57v;RhY)Z13-lE)QARFkn7UBUbV|zBSr_
zRvR^&X|2K5BQzomp>HPP+DdU3A5bGU5LRD?$k5ddGW&?kHmgM(u>^1BCM?`oT^~TN
zn1we|r4=|FuvkSb=YzNEMb*O#gH;doAxMH<O+U@0@=yMAtZqppWCdPb!mmQq{i60~
z&n%q-y)UDiHDigZh9Sr*0?jN4cl45zuB3i;eMjhZGUV&VoB*rKsEDGl1aLnd@rdL>
zNPb=Qg0aiejfcm++Oym<u>zrL>}h#}0g*wWqI{IXlzWJu@CSIY+`nF%mwB_U|FF$t
zHv$i|D<lX1t6eq_qlZD{@Bg0VN~NU%iFd?@J2UGATn*nYg1~o!Ki(qR#t%^rjr-mC
zQY`4hZ;+Y@bAFYW|M41}1f+LFpG#keA!7zF-Co1Mb3c+(i!qrB3=h!%G@dh$o!H%j
zGA#bC)NjmHVM9L1@2m>xLlk8tMh8c`#{qPVMm{zi&{7qo52p(3eum^Qe{%q@s+v|8
z;ho@jv*@!iX*fra*M#g1;czVY(e42>T`ozphoB_3+D0cwLM-|O*nPme5?gCK+}t&O
zpp9v*|1gl&<uoOe3mL#`6NXGBPl`pm<+6KOEf2q4poHS-CSV%-tB)=A9wpEJtUji%
z(+2f~salnxxYcKY%96Ah6}_#IM{EBi8<^yPU}SjJMfko4DMmBMxVMwQW{;%RXzL~=
zCw4lX1*@Rz^AzX`ur+(_f7kVFPMByOJE6T5G`tW!R)>D|;9gDJPDmE*n)Px<E9_no
z9ecY8X3Ui$xNR~W3~AOhAO02q6AS69mGWdC_?1VZ5i%JVgh<3fo6psglt!J+R{s09
zNz^8j)qH|3^>xsW-@eKQEj>ElJ@P}+JnvVvK#SdOxV;-2AiACWCzW)p@31EfwnZSm
zpC`c+np&Q6!ZXn$k0TQnT5z;EWl~PxFRZ2?pN-`?P5dz`!8eur+KxtN&M7c_3z|7B
zaiQ@#Ols5h${CW*-V-)N7bx&a)myKk-$=!`tfu`=F*}6TPc(^ENky4UA?B@fJ_c%J
z391I;`N@3+bfPWnTCrICZIfEV^VWiBP)JC?{p0?qE5{|EiNX$6&<ag<TS_0u3(8{`
zprE@{Hn)ICsk}?eD;nA<xF;6n<uoIBag3Q;ET3DxdY9{2d~UXGZkw(1^P0fRR^uH=
z>f?+p6*#xgZ$0~4Rk~D;bc$uZ0K0a)SHrY<Uuq0Mqr1f3N}YRq#=T|(JwACt+Fy|^
zu^QVEqA=tBo_L>i2_I&SjB-UGJlshWHCJ#7D>cQ<7znGdF?#LqLGKyNc8#aX2J)a(
zE3ep%5mFD5Naj%Fv?mnDV*yTDLC%)7&WFMvBX*9x%>@Q7<__{2ExfNR7!H_&gC?|z
z3j-h^hKYL+-p7oC!32{G_f{Tm$WL>f;#68<Vr>M;tBH*bI!F{z4N8z^@8Q;Y_kG;Z
zuW!k%xIMZa-aj-hER-{h%gIGyNxGR1!1CasV-Wg15593f@h47@AFRtD)=U;jx9Q>G
zLpxch8-4+;XC*mUb}6OcEG&-@D(JRE{=kl;g>tm-=`p0y0|X;DyyChmvXn;0?$z-q
zSKH+}z<zhKekf$#c{g5`ubl*|QF;~SI`eVpPvV20`u*9bLIdM}xN7QN^Kljf=uYCw
zH)M8;w~>|9)AB$jD&{3&eHA4+naTfAsUSD(OdLSfAEg}+KN{1^ef_dZKOlltI`v;D
zoS2xH(NDDQh6Xnz>3<^(71rlI?;smM+fo%u&^4o)E;n%DzOk~O3p$xD@kAu2Cj_#{
z6uDpvoJVpPL*cL*EFf&1hb5u7-8q4^26_sG*1p-Q?366}|3*{7`hqQ%jZ`k&-D^cG
zPDN&$Kb<TP)!pMH%V1=c3~V(=GQ7%^SIxNo1{8zWV)z&dN93<K)Qy|_qsW-8qdv|}
z9m2~E1EplAekSke2^XjOt>jw82eZUoxgdXoiAk&R_<P6z^@^sEyv*I|0UIRFfb0s(
zpcK7~sk9@G<tHS=$NzKCAuVl*LtZkZd$tX<xf-Y(;R(%p_IG0<2JY7{%v-##$BuvQ
zQ!6AUeZjfds3Gs2m6l0kA0fE-BT2cj04emhPLy!$`-%fKDtPV;^Y#A^6a2H<={D2;
z2hRfHOw=2w^6`&Dv|#$4m3^Z?hJ!Zf5%<;r5Z~)&&6Rsg#%8&oUp$yQxXmrJ9<qAF
z>A7gs5fh&jfXkkg=j*=4@PK=n4q4D&OxFl*tRPp<Dvcl=fA<p9TN@nse(If|nDLt;
zm)jcj_5A@`&hxiu+~5#1PIaEcMB{@2(Wz-JaVIqpld0Om`c(d9LDq;ynU0PY<l-tT
zwK_`G;`4Y35vQ(LQM1o`Imx8X))hE8Z})7edwZ${C_+$)XqK|OxcESsPc;QyFo`R|
zjJ0;}NJP{I_;^c+#w;Y>>+1f|Xl21D4R!^pj$7^?gE%u;sn0Ou@3K^|OFG^V<QO&V
zib~3{$$ygiqir+v$v831d@)>>V*c@qNTdlGVXu-~Je`rr%y3@tI=(2cW>S;JU0>`T
zR>wX8T>7hSUHf;P4J;SItU+7riM6SUhB^Wo8QWn5m@)Oym3r1MjbqKi)M8)B7HI3&
z*m@*%|BWkrPNnd{cGlk>#;f*Pt5ukpU8195C#1A`8ckHWjEd?+et|y7>vxLq^M(;a
z^_S2EIX4lc6Vdcf($1m3;^6aLc0HYs=eI2a1dK@}t8HfL8x!X|AYY1f#JwB43bsME
z{14iEJuh_BtZzI0GNpLS9t;m6VabwV)RXR8T8ef=3_VCJgYBQ+Il?t6lC%A8td5Vn
z@08H@b#;R5O@=58E9N;``-)|tP|%@is-|AE+?p#|L3xGI#UP7iJHY$-=>n_U+Di!_
z=;EDG)pOMG<_sb=cSdH*&glGUzq9g7!$&j=z@12YNGS%6rbRlOa#=>zs+N%=F9oc2
z-zz!3<Gcl6R@<8Ii<X<`QHOP}U*~%&BH3SQ6-5hfhv6+Fy$!St37j{WMPIJhS?s>J
z__fGKAltKoE13j0eG_Odf`v@{!BY>+&tA*=3pV`_Mz1FA*YIu1{zukRP|B9`d2j$Z
zMXig98Ge2=0Lv!yO^z#l2cDIgR7KI9(v(=r<!5qE;c>bf32obw^IyB8T}jd7n@1t$
znvZK?lKN}Bk|><l3GBOU*y};2w^4#F4x_Lxq9<SEWD(Ltu|Gc|S~Zb4i8`tQ%vj~=
zzxFrq+G!cJSv5vHtpZMAF%3oiqRJSP{oxOu89&<AzF}bZ`G9$9i|x*G9T;4}=R|k5
zd%73D-#pKVdGNVePc8@Rx^o|&Pe}=p=<(y)&~}T-v(aqt<D${3W9*vtum(1#NOpax
z%mY&(pmv=jGFowHf*ZodB^v<;#+JtZvv!OROm8fV^p0sL$Y3>Oh<a!Ar{HFWMdpM@
zYnEu4h6Ez|IN~&Kx{>gJ?@hr@!rx-Va(w(p67uil%Uq<3@gpk^zG@D2-{#AbSR6Zu
zpX=rn4O)M&{|(g&x2Qx76@>=~tOtR%r99{tS(L;d%9=gcb1tjL!@w{-UAcw%@sU9)
zr7bDzjJJiYuYm)exGICsTo{99i|=7i0u~4I*4*RUJIFLu{j0^jtVaLy!{_w|#M6S*
zVkSN~!N?$G0E=bT9VAj%E=9}h+Ts63*^H{{1gI+MlQ=m@a%`~)|5B1x65E7Y)qo9>
z2o8aPeR+0oY2(%K2jJt|6GKUbvdrtc>(N>dd7KVvat1MYWnAOw%B=lb<C^czeo4w_
ziJ`u)_atYV59K{FW&ghbbUus04&1xdakai|@7g#Y3l%{oy8RwQutN;1>H!-Wv@|0w
z*u@NEBk6w;D<q&7+R@rnPeTE0>ge)*>YRm(B$c#H>|DG87sn)|WUkwPlDqFn$?3|v
z=E{<NThf;XxtIaqT~|j-6I>+t!2X8OACe45Lr_UiUJJtRwRmjx@ae0^{oNf+m(G<P
zr=l=#O+v7vf&8L5#72&Ti&1RiUewMfaz(VCqlua%4-1gLfb%nq6o8*L*w;XeDe;s8
z4J)sRteT#=y{ngBaCl6Ad{XL)q-F6-W1}J#2YR~#?nq4mMh;Nukzgh^c6LBHadC0;
z5Ecsn4|n#90BMRNWfy9jI{<_5nTjuOpFO<S)!tZp{%B5Gq??H%KVxP!h9LTQW=0>c
zh-Jzmeg7(QN)1E<yqv8~Ffx{Y)hJo`h}eXr)XX&-wrt;X;P}aZimLjn*RPx}-oI`2
z@<?BICtC|6T@7Uldq0Wh3*uKsNz=drlSQ}fD?ZoI(nji@J?!o7XsNHRz&hur&zwD5
zaU>@r#>-M&j3Q7Ce`-0zR7{)#m!PNC)m%f0mxb2Z=Al&-Xb0FaPVN|<Q(VHbAo+%R
z*CmN5=~)|4IfANxk&Lv|74cD_e(nxd#=06R3Sgzg%gM<_PBsBDCIoy086|9G;Od2S
z&GV0ylSVzy-hLtT{^`@JC;i>+4K)>|d)F@ucF>pQ85iOD;gdVUOIRCw2~%#qbkIY+
z$RIB#OLSrbl7^V((icSbmTlW}ckS6%aJ2OFh1$mUPEg)|K6!Z8=2b~B-op}?^y-%O
z<XpfbETgJtX73RipRs9gVMYD*n_b;^?)LUSc-Y@_>w0U`<?0KS7prTq7}lwRP01ln
z2D03oBr0$&EmBU6kk>9>44pn73w@yr{B`~~YphEeED8anH|Qjc?YzR`lhanO-@JX-
zzC#E0@7<k$w<Blk<~3={V;B2-I9iz)Xsav9N{B$Zg+*xk3F>4pYSG0zD16C^tj&1`
zOU_<uzSar04X@sR!MdiOK7IW7;$hGA#_BW0*qhHvOOk8GXhGk99y1|mbPZJ)qmIZ#
zLwbZ22szt{%S^_Ojwaelv?$&j0kE7&%Q<=soyCQJMMXu$<>#u=>yA$Ap6gAQ&Xye7
zy>(UMVlP_*Wif8r<dbIJ&*K&$UYeNH4S7dT*S6mSfjdyR>$`iq>jv1E)YUgMHMh23
zyWV-JWLH+Kw}lE|!mywjLnUA~x0IHZS7gej)My`D9a%n>pJ5rx%E2ojN*92{#sSrQ
zW8BAo(`b~1!isvfUg68KHg4Uyr{HLD+1b-&CrUt!zaVeR+H|bb>|kwXpbdqdxP+t>
zZe10XRaDghn{Hy`>>UC{(dOKO<K>l?Td{M&(95?UzY@5cj~_k^^><ypbhh}w&NYdl
zuBNJ@3&(}^J#0HiEx5?#sq#n#dnO(k9Xr2&B^le+rzb{%6&^!5be@>DV|d!mlDfLu
zn(C^`3+F1T>YC8&e*f|FN4>YMHKI!z-OY(1PKJtts4k#v7AOgmIb0(0+GdV~bIVB>
z+`w+8zwh4NJKeWBZ(P6D*4omFa^4O0U2W$NZ%GQa*G9J{$+#njBNOOPn3K6Qz(G%c
zj-O=$rNJ}|t3;jA5MxR_>Bhz_sbTIOwrovq{=s7<r@&gP;c{)w#S7=ooC1NiHK_@a
zU~>upb|o2EoN_gFO)VW=eL$I^V=gc<VddJbyABqXpU1a57B_wS;WG`mdiUhs^@a;4
z4(4u1iSo76A1~FbUq&qCu}Ue-*X*c&`5GN_7AYFLg{N#exNGz3<d|SjdqU0-2(YX&
zCca5IC0F|H-2of1<|}oLZ8vWBJsf)V;q}m9-|ZW1jkP6tt76=Z<$0#ld7?WaCJqq=
zJuCO%xb)5WMHiZGJp@`Xv7PDcyout*4R9^O!hf%OuAJPxD%#yhMqG%zX=t^73m8H;
z1|_W7wKg%>8P$I*KmCfO)r2JE=)z){yzQ9a)%1)W{hjuK@maaYPMkhpT@MF!ch}91
z_SWXcE0yKP_wU%4nH0M?(96Zv%s><89Ds%7R>IES(K|FYIST}S&Ro3Q3}%#se)*@*
zUw_A56|jbXbf@*wsiV8I)0PH*xteRr@Xw!2C>)L_A%ybsupjN^!6Kw)<)5&A-zn^b
zw<N^V4xRRrix#4ee4!F)8V;%*5BqN4Y`=LM)#NYVfBEybPw&7pwg2vDDO@^eC-@D!
zj-9=7^TF$H#GivY*N*EqI=gP&zC-96zx(v{+tV8tk8D~NVD%mKRc3yF1w)sx<m`e?
zD;B#MD=wPDa4i}AFOM-ObqE$gWiyZHjP1uR;7;V;gC{QtzlD1}x7+J4lpWc#ZEb2o
zH0bhM7;0(j>Kg)>l(b^>@bXz4pSEEq5CogAb)ggT#ar5l4)7PBz@FyKKv(01;`}X{
z@u8kJa9Og9uK^tn+?c_C=+sOXv`ty25sx+$-#pSfHomdxTMzEqvL<C|n6HbKp@ssX
zNy@HZ9hP;V>h}9L&j$PN+-z?`U*p!jhtJ-8{`1Sb7mxezbhbC0Mnze$gPt_!!i5Xj
zv8DpBH`rlGCM>Um>dDxY4Y`NPs;+hqzWMg`!<*-i2YT;N@B4LsDEDrIZ_~iFi^q3n
zM0=Vkjm{<9@z(f`kMg$BmgZslY0@;Nc?)?3#es9gF;>peh#;lj<}DD_u=0(|+ErY0
zrR7>D=5mvn@4b7Y`BDXxH*3<8;v$26QGsu5Ywzgn>W;lP2`A*`nb|uJ6rZWVH640a
z-hCmRroaC7`NP|P*Dr=14?gH@yil?a6-lszAeK+#FX=;c<S6lks4dGhhM}W`D(aEL
zm#yBqJ|k&Kn4cSZHZU6=9rf(;mcbc&E3Q9(^5AaQwdTuJ6*b_b`uNqyKR>>F)Y}E*
z*CV;>S3r-btfZ)@pdc?VCnqN_ub`l)sD#a~A`&yQcO5!^ap4M{)PDU)sDHq*dHq^@
zYfDp8BZ>wTXOG~0yzzGuPn;a6kB{;NlSI^u(lWQSMc#M?J*<?}u?t+bA-}A?1&+A~
zkDtHBnI9au-**dr?^8twcWqt2dgbz^QK9~X*=p$Gh^Uw)aq)@ElT%i%-LmUo@tNv|
zYn{Y@{NW3K9q(U1e*$gI4b&2&ye|vKI^YugY_rGLYZM1PilGyaUm6|(F>FcY6&$@R
zJvlBa#MjN<(nw1QW^gF+Y_ewlDLYTLpi}-v>y?`G<>;$4V(QbMAj{Z-O45Sd%~>e`
z@303)D=SM&3v&xgODk&|TU$F+-z285&&fYlcA>t1t^2{V_uqc|j13nD`g*!MJKCBX
z>Y!RWd#db2apB(0nF;8~(^n#<hr<(3w<O+FO9GSmxvaz6x$z2GHbB<EIdsM5gJ+xC
zZ*<?iKlt?JTTEqt@@R0N=SEB2h4P|<ySHy%zd9{BAr>b*VOi3O)U=FMS!>p9$lknj
z-%&7s9B%8Pi+S9}K_B<#`J?+io#;KPD9VSt``DZ5p>~*i{;VG$zfUFJmP-5tQgc`Z
zcI-|bzQN(K5y5_*&UO~YdTQYI3IJRR6npp9wdOjo2*rkCoqbQ=|A{J-wi>8R)@5a^
zOkNfj8yOM4I4m>-Xsy8^p<$qI6`!(t<Bt4)!zE{{uC#UaK6?4_+wa((Vdycw=Uc8^
zsytIxa_rE)-8;8sXC^^mX{w_*I`K4?i(UZI)+F?FZ@hw*4Nx?3k4)cw^a2(ie)#11
z>-X>8yn6Zk+0!SF`no%sYEkBi558_y8j<j|>o;x&Pqo}#d-C@kDLDlLkFL8|`r-9|
z`%gp{2bkKIkNa<5Z@GN&^zpr*+l?A*9dyKkyYLUNU4X<(D5Ate;f51WKMMA!96{HU
zmyd@FI7S=l0<W4M>qah+H1LSqaPY#7RxnK}Jh*53{-TPyYdw$O{t24fr$KWYKjy2^
zQ@(!fnygis8EGri(lgOxzji(08IL-D|0~e>4?cVQ`P=W5{KvrE&i2Mj=T8+K&d=Sl
zE^|dfbeO-V6AX>NlXz^IHo|P|=SfqhjTG=1ufV%XkRDgH@Qz)bS6qYM_dYC7MwrH-
z)DO{KxrIj$?%$KUeM|P{tvNe#L7@2{R%k9caq=uNp1ub@t1n)^Bf2<T%)c9ddT_g=
zvHEQ3;k@khgfMS=6WEdPbAp1z`1>vpiPti=p%XuSK987^j=2+t!n!y)*x6W`6I=_1
zkkx!~J(uX!drvjjSDh(6vJa(b$H5a9TW&pk^XJQZt(C`Nz8<kODP!H%y!}Uy96ktu
zirsm;_mHk~C1qzTFV#1<-$X@!@uO$xn)~(#_$>mi6bgj;%2P!LP(`^Cb;O<yww7i_
z`q~;Q-$^_z)j|IwT7+TTJ!$CbLDR-Rem%P4yYG^6Eu_J~tCuex-zO`c(`BW_$B!J`
zzjxmOlyH>qvQuZyp1V+4RgJzjIx6n=Kcw6VzR+0eH;?b#XsSM4xPQlg`jtyVF`5i0
zV+-cYobm&F7ia(^Zq*?1GYLdxKumbBFPMoLYHMj~sH=k49R2&w6V-NDoVKI5w(@lG
z;l0~8uBIEaKcC;ea%$g(WkC+!ixV@q<QJa3Tz9D&UDy?8@OQsheW|X#v8An}v%9DF
z0Z2K*=;9mZ5Wp`8Ln|zQ+?<`cEXv>2%0NX$Sy@R53PcbnUN9o@#Es6u!&oBFb2=JE
zJYGQy0qHt~ByBoyrs*yXl_I<EH*aXbU^SlSl$}5sKYjLGWmOG$>Nd4pZENqic?*B<
z=dUpA;uAI;#7>j1Uk!A()}ATM-<Fje11|FFG6KA$3i}W6T_6*GZ)9z(1wA@S{A}3Q
zIRz|9TNWMc309q$UMnLbBg=64nk%AV8=SQH5IT|%?%oDffBKgFCoWy<d-(@&9CK2`
z9CdB|mafVztZeH8S20wpx3{8ijYslet^4@tGd#I?k0B@D{`}()AP#-~^76s0mg-YS
z_iW0D4}*$M4C|49ivk5xN`#+l0rT+0f9L(C)K|yD(qTkQnohdJgLm3Cbj9Bvd@@9z
z)dZdW)w4$r?sea6Z)s}0QeRhlsk*ABw!WeHYFh^$PW0UE?E}UL_)onf97e%c8YLdQ
zWnaGNztI5Y{OZI=e^+aLRVe}X1@lNn%klSJfW!ms9h_u;iNrJW!W<O!@2gQi0mF76
zN(g|576Womn#$}wdAuMGj^rpH_8-4+_3n#59$!0GuqMV$Uqa6{I%|JL`=dX8|N03U
z)Mw~$!#2vlfBTyNcZSN+f}GU}A+BcNj7K8%ge4WQX*lyZ&Sd^k(6AIU&*;qDqM94v
zABN8A&tHFkCmvapeWD+`d%N@IjT_fHI<B?1ciiae?zz{GpY);UFJ8T7IE(&H1)%6b
z<m%bowwkg7Ija*xy&TMR6vg>iXOTkS6Mul;2JJ6ICmuF$7BGxn5gX)Ysi!2y1JWXZ
zm68PSW^nEPdA_KYU2yW|L+4Hv9fFfSHF0&$!ScF)j{aBZjlWd1JuTct-)V7bPGR-U
zCx1YX{&evEJ;vEie)B)R!OQ|n`8^yQ#Lk1y`foN>orLNpF2vmm)`c_XEWj_CvL;v@
zqDp7#-#z{`czN8lY)Pc3q`a|PWZI777dsxJ7ycD~kQs@81BfY<`R?vpU0t0h|HKK0
z-ts4ZL(i~>Jmj7D`oB@-Jkied-@DUVU3O^un&lB-+@}U@9Om`Ikp2e<_z*|^@WjvJ
z5LGsE@QY4a8s_b2s;wx-3m7?g;ia(W2+ORgzpzQ_IY*@LEUvwH=ESiBySHbr$tftU
zY=+|Q`JKiy2R5gKyDeU^so-?O?Pq@wv%H&sZH-s2p|S>7!k}e_UX^d3-#&lTe;f4%
zaQ|I{X<b|Wg_B42Y+aKA?u3SF(gK)k52`mt+A1=rx0(Ar#U(mu^NNv@rvF;KfD!ae
z(7<JJ3OefBA3S=BUitSQzx@6ik@zpvQ`~>=?%f`s0^KHe&5t1MU_$^#V;@Lp9W|eS
zg#kAR7PmEDJb7@(+T<udM@!Onj0B8M{5NFW|1Id^j=tk(vI~JwUC@%nelFIA8uDU%
z{EGyI;f2TQDQt6@rcdUOG4fclCjV6Pm6{8uOOGGS&&4Xdcb~lb^HtxqniKmruZ&6E
zlwVri-uv>8Pp=;L-2z+zUCGlm-G}FY19z@9RGls?Iav-K+qLx<PZu9TRYUsn=wL5g
zV301YMrPJj)An~83wrE0T-lPrY8}H5ML|6!bsa;Jw;Vdx+E3PYpGl`kl=+XSBYpPt
z(Zl=q`}_O)`g(is^-=W<<vxMmcYL=~2mI5Ay|-cLU46c!VEfu+umUpB08>AIHs%rV
z{4sp??*$|A!~$m8gc%sKZ0i|{hqP*PV*GH>i;7E1$$;g{LZC!)E13BvZaQ3fy&Z-v
z73HPJ%PZ^KZ$EhU;m@~&-7ObS9N5k{13o-^(A{?V!pWmWWoIweHn!gAzJ~{1AHRNs
zX%F5*ZQYRvUSuU@g@^KYZeEvvu_8V)*xSX%L|0W-Nli=N*xbg!6`oCOX#Y1mO3E5}
zxWFMPl|M$+E6T1|Ov^54S@wamExn{^*k{Ti^vf5hWv~<}W_zM|@Nn?a6G;0j>^6g6
zcgXt}Jeh|x{>dN=y_@PP(b2hg>*}Pa09Q+0byUyram<@Nh44`Sv57Z-p%OoB9=E81
zmZ`m$i><i<_(Crt4|-`?bjkBV<20R5#X2Zud-3I-+nwl$x>8qNS%-0BuRg)ZwzIkB
zOws<r)73D`d-vzN$9Jz?K380@eRsjplG7J1HMZTn-8%rQyl=1iuU$H^e{))N-15|n
zwb@&D?#YFT*UF@X*zh2KUw21nCQ;VG>CDR?1N23C=Ko!*MoaPyYx>7$ElY#V)vf&#
zHtav$h}$`I)_)@M4w?UiM-eZc4LwEi1O=+-@XP+@Ey_CiwtptKGN0aqlk9`Oo|{+e
ztIm`h*|&2;dO`%2g_e~N#{K(n4&E3(`gdc*00*yJr_SLJkW$uvHM6%i#V8U{0rH@i
zk(0u@fvB?-(6C>ex#!f?fd_aa-_d@x<JR2=PhPzH@+WbyH`Y{C<F(DpkAI><y$Nrd
z*26fb^vp$u&<&_v?BAFa;^5{Jv^X|lMaJ6I87o&TPgoig83v*m&h|DI*7i<rUcP}L
z;eO7R`YMu(*cq6A`Oz`l<8Bf5xT77BA;$1SQTTiT6$_uG>-LsixzmSkd34r)`bx)V
zvLS!@@&ysp36I}(@@1!%aj=#`CGYLdtCug7AK#z5Icvod(w-IGuLUf#r{O#8``pVh
z$LPe9y5qVqqXn0EQIvOl(96n8iwhI85@BuU=yeA!+<5+fj4}Z4fA|Ei&;KMh>%%tm
zulhS`%MNVIj7!Q`zjgP4!tx8$@$l{GjjE%YlLD<(w2dt7-F-u%;^UUY#zaMiFAfU{
z3h?uR*`d3)Utq}MsMt8Xq?8lp{Ep2q9aEUtctrsNs}BmNY|K*_HVI>tJELaxd`f0s
zv01xMTt*jv*25>yU%mUtNIa=dM;Zr$tOMlyJ(=^*RLY6?@cQ{<)b-y$<!sr}z1!BU
zj0eIpP6LLDf{gIrGwsI|BWCqD@slY9N)1(I1z9NxQMlmYpqG=!$WeIZ#q>SmHXW<&
zc~7~alH<p(zyJCBmyhppiGvEbH}6o*`{&DhS1*8nkyb$X(iN++ckVk@cD@#y5PbOd
zsP**jv=D1$P9O{$nA^Mi`v(LB1fmEE3JN45WN}3FlK5rGsY`<0t@V|~dFC>(T%*4Z
zHqFzs7^cci@sh_VcQ%pYV#yo3N3F^$sqN|^NA<&!Ny8<n_(Z<t#KN7njfQyt3YB%;
zH(DEiFP0tMvt@O1Opq5U=Rq-)ofS(nPWhFg#yW-%{yzl0lA#k%4NO3(8icAvMMNpG
z8)m4ZSAL$PvCs1DCz=NS_`6vQL%rxqdH(vuAba<Kh=jCt+x8wkdEs(f_x)$@Nr7S&
zPI8W<VMqfP{1|&|FeggOT#XtXe<xE7nMDhK{)R|44ZEF~JNY?V=qeGaIOBx5qwOtG
zKNp_9qp0R4HiHIQGoAO}Mg={kq3zLOVuMqUn%w($ZnidDI(=-<mdwNmUqV(|h?|v(
za2uoAxN%QBW^zrR%PEK*GR*Z<WrVoq&6qfO2J?I@(IF)Piibi1sF}k=H$`PQ>p{(b
ze2%D&OZ3`;a~)5C-wDI3Kj}38NlEv71iUCZ_})JRQ47FBhA&NCmAxbX=*f!e`l~m4
z9z1=0tLo7D7#D4!8FM)niOZ|&o7+1$Iyt+zy19c+t&g970D)V<TkWVoHyB_GF~|}O
zS2xnG{Z}S-0V!2|OJ_gAI)hC7c)am{Gz~~n$00a*)4{VX9bFhg^Y+tcM#VPz5%`@k
zp~FE>_J<zevhM7OLwVU5i4o`%8&V?jGs%J74@x_(b!X1!7M4{ruyjJFj2w72Pnrtr
zM{a&0^r{OB3M}H|#paz-vI@$C7XTOgJSIUko1hh2k5%8i(>L%0)X5l$r*%AkP=WLO
z`Lkz3L-#r^pFIx4_vFm=+jbu~UUt5!uA$``dSf13J%ee$(ri3Jk_zg2rZz4poXLRy
z@9N6HEzuYtLLLHEGPDd4b|jlVW5#?QVCY)A`ZIK}#_5hH7rygY#Wbw_;@9pjt)=m$
zABWX#U%vbHQ*SwL`SALF=t+Nn_chqT9?9Rfc4hoxUl%KVO)R<3#zGD3e*PhOCx4FF
zth{0{v2{URvZ6RYI}@d!zeq?>fS->Sw2iod5G<>xqM=ODCwUc2++#9xi)#Q;FfjDy
z6C?5T9gmvXfd>!n_xE>QtvgqGX!n+^jXAptj+LINtgCNmY`)rdt>Z?2O>y4pSZ@ne
z0Wn!+O>iIZba!)gadLD3*p7`gR!qT)8GsCMv^F!)1VsiutOPW560N^S=@89f;Sj+0
zmRkS=Ej~_FJuwqxW|=RnYVH}Go^!aO=E~I@xBDMGfBT-O*+2g!_ahH|ssI4H8Nk)`
zb+ul;P<CwJj*S`1V?w-t;l)!V)XAA?cKr_+*wHs1Gy5V5Kwe=19K4Xn<TFrv;KPbm
zY^+$gT2fX?O&b)i@uGxNN{8^BJ5$}*fe}}9;(tT^Ddu+HyW4Z;cK5BW&i2OI3#U+@
znwNj*_=z(YE?v3W($d`2*l^`a{pmwFU`wngtE8@LWMS*%x{vvPhX4Q|07*naRDng%
zoa}9_EzC_#j17T(rwia@6H{{w6GL4sH6=M|5@tR9SDMv?6=^`YnvD;Y^ytFy2&6UE
z#;cE~J=iGe+0wIB^{x2AdP6knpGKYe!}I=ykf;5CUW&&L@1rKT6}=7z^ERzYUb5KV
z-42WvB(bU)W+l^q1H19cHx;xq6bq-I6b9Eh5{rR(SaT1n`hcDfh*-mxL`XtbSwj~)
z!*k4Mn#wAyY~&mqzk0`k5}4FJe#=Na1221{<63)L>(%Cl%a<;mKU039_~hyH)t4Jv
zJGySPw=`a^xp?mM(OubTOM+Z2@NCY})zdG?-y85gR%S+j26{Rmv8o6x6eTR5fiVH#
zjv_9^$AO-{$@KDlCM`q+zD`C~j-Cuv#qpZlF>v*=x_16cGII`<oFS28)G+oFo%*k%
z;tK_{Auy#~*IR4P78Pt?m%236*UiBaU+;p1+0=|FKLAEge+|kRY96}c)eS72eE>Wy
zC(6q@dp0wF@KP7BLDsSG_e>Uc9zk(A6)gkW!DcR>w1&AWu=?|h&NX%pyk;c+3+DKu
zHU+)a4OcGL)z;M1R6|r%)z&w)Uc1@d0|?EAn)9cM@it~jsJD~3l^tm;9OUQe>R@eV
zq^kugXwnj*f|zPZa*;6m4B)mrY%H^;5abr%#etiD9Va*6BC1mG4x+7)ej2A)J;|h*
z3ED9hpdGXOaLL)4rt9duqt-8E_x9I9kI~{oO-DBG&GqMs_vd86Xr2^y6(+SJQQ7(f
zVDyZyK{`XRl5(*Agw{VlJCM0qu<-L-7G|bdz|&+}z_mzRPF2TPM@5#{k1=rw$!VK8
z`9-FGWbZAhZtH!?=<7beeKy$JeZ8f=?otgVSDiU`q3Y7*hGxu(>bciH@aReZt+x8g
z^1_0ho7bi%EsY5BcE;ASAz=~GVFBK*_LjzaB;OQW=U}A32^3>K0by||RB90C&b--E
zfBxmyVFFaVi-bfa<W*r5<mMCXZeyq_$xkGI{-^QiifGk84lxyD=fI^ITlavXL2Fn4
z(_sr&dQvko^~C#*M;=cH(67_lPz#`gHOoP9N>>9k9#QQ?iYZL~8<o5<2Q4)WlX`7a
zJC8t5d!SSZa?ZszqF^LSWF55?Gv;EQXkAmn@Mq4niRhpp^lieySETXwGe+XU1N+W@
zjh4$7&!0Pcy8L8manb*4?@FVhywbE91yT0BfMP4ED4+^zp`f7H_eEAg7TNb51OeFu
z6mi2nagCZ7O;lXkn7AZr%$OLnFP(Hcd*>wGJ#(hheWuUMnIAJh=DzQ}->o9r_K#pN
zW8PnV`g9|F@AuvNF3)n|@^wnA1G!RvK%e~mPhWiS+Lg0U@7*%FZtY<2s`lpk#%9{I
z_jWd+Urube7PtQ{_EumMZ$kn=-?AWeSX6=`R4D^T8!D%cdG~+<RH+gSs%n~Hr^^#R
zD^E;de2V;aiTPC>!<(M~j`N-0e)VUzh56B3mZhHkXX-2Ux4-`Ir$2m75$Pv?_HP;P
zsLD?c#}x?LqSEKR=z&e^=9z$&Jn)KNQHgb>K@;FbB^trmfir3s+lhRGv{6auMB@ug
z8q_@t4vS7M?O3<>%*{{!&6s!~RDAO8Yu8>NIR^^qGf(5@4(2ugMK_ZF^S?iQ^Wm)*
zp54D?XmDUne_!wFo}Ql7y?y<EYX$~~dPvun4?cz*=EXx$=tzsxs{?%8?3bbmt_|vB
zkR}A&Ck=7bxuCA4B0tp-B5|EXy=tM9be5>1GmGoH$ENljJA3uD_dow51K0fhuFRVR
zl_>q|U;h2OFF$$j_A6H|oIW(QwzD!XK`Zs9N?A+H&5Vke4<hS$*$H`ncl0D*Zd!g>
zLsw&2ej0+)sWPjTB~=UqabD8$1!_ql024)cHLMmcv2_;v1**fN^Xk@Yed^-v&wjwQ
z#9v6`{>QIB1&HyB=gyuvGd+F!)Txu_UU=yy0;#|K9u}GZ@t^+<t^Fm^+OHoQ8R+Zj
zYHP+*es%xA@MwQ$L&b`JtmNp>-~cA6%PA}%rKr_M#izuCtK{Ay`y~q&F;7e{U+Oug
zL9ZGRTCCvWdb1Wn;vKw#!jp1VHukLDyyx(#i`Vab^vS0J%!V%G{`xnQz=h3UDB<GU
zZ@&8S<9A8ge{<^Cp>1Q`^+oAX!Jv=^vq*~>d4B+j$A*@&<4E~`?xP5c&nl{EU(;T*
zA`>LT>;yetl<Khrbx0TQ04M;9s*17_BSNI^c8l3?PmOfiJtwZb^`!thA$7~0Ti35#
zJbUuwsngSE&YnB}o9AD=^78eYw_b;O{_}6X|JNV>{^u_~c>U_RXZCHK90R6NePs!$
z=i65G4vwxL>S?KeDFJW*Croj@9Y!z*^Rr-oFF5MWmN559f9ic19HJGhYWM_t)<PH>
z*m=m*QK@-l4XXwxw(kcf$*tROzVkj7FkgT3hi||8p2{$z3iBVo|L!}OWqkA17oUCl
z$!|Y=|6Q2XojP`K&(s(o+Y-V8#0*loSjhVW`rzl`>=N&PMrhk4A(6@X<xQ(c;n#={
zm3zR)4r#Yo3^or~;(N-~kx3-)Eg%`M16f+|81@3U(73$X)sy>Az4XpIZ@-1T`RlLV
zc<IuGbEls@cKq2>(+DrVeCg7QFTQZ`;`6|ufRV=!-+lhU?Q5`-nwl8yZL2RY%vxSr
z)!4CmXnfOuXn#jTIZ%hRN`F$iTbUV2UU|9M%Q8rBpNMz7i><{1v!%9<ZUD_vDilFM
zL4kpwGZ_{c9hV4fFFCTkSk`{}49qz4@}(oM+P1#YO~8b^@Z!~LH($H+_WK`x^zkR3
z{{HhXzWR!M2M}zM@i9>T-+mK#^fxY@KlwCC`@~3pcVk&za%6~~NT}&dc5x5ndPm(Y
zOH}$Ky{}9I+SMI{lY?!Qd5Jo?n-x3a&6%0FiwMeqge)L-gJx`$TIyya=ui2FrxiC1
zZaFml^2;w>y>j`|i^z66`7DTo9DU}w>GKyazx>L}mtXkJnUlw#dh+<$7q8!b|I=?!
z;pN7ErL!>2Tf4fslDtmoc_lS1Jwp>ybc~gg0yZS1fv_^?dOMvh^;IA>kwWbXN#f_5
zTiVlol13FmMMXhRS*;Dzo2u^5n-$C*Q@vWa$jS+JiP6bfD=Hd0*Nku8edwv<C(mBE
z^wKM!;z6;4zeU3DM<0Fo!TayN^EUY)UcYsJ^9DfeFPuLK^plB^zRo(qL5WHHn?3em
z^G>BuxUFD-E+M<DsdwG>@$TBf<S@Bt2{oJ~y(GCnNxdxgmMFtPgt}!lz;1IAKu^w*
z3Q&o?0Fc`@y!GJeQzxH0frOdEPaZmW07U8zKK1PMg-h371(Mb2V}~A}+OXrm@pD&y
zZ@u^X@4oyU(4;P&IdOm-cnu|4ail}YFKb*iJPAb0c4#e)rniFL&bEo+o>sU9V)dFJ
za420o-`v^}9IrwRdL39vljhIc*N=)P&aiEoFLd%h>A|tgFSR2#k3tgxN^g~ozyqAx
zz3<7VpF4Bm66K5BzVp^wNcMf>4M5j_-@E~I5`kNDbl=X+>xNdhH&l{JCQ2J5akFR3
zxP=dJ2a5r%5e_(AKszi*51Lm)oAz$(Z!As)NmFoWTLkwFxyiu>ScX!>70o>(8;3e;
zSENU&B+QnOgfuiEm$Wup4;<L{#N)enZr`?b>(tb?ZQHl+*s=HU@#*KU-hAVKJ<uyW
zuw!Ddb7<q9qi3$Z{^8f3ef-{=w{Bd$c<$)V@$Q;}<S_KAsp}fvym#CB!BveFh078+
zK~vD%Idyo~<X}74o`<XOomf17v5m7>s?bJ)lbH&b(IPkUHhO|Hp_jXhBQA30&C&pe
zn*lGT0*6XXWPDm~QDsB>>cO#p$*JA@j~qXJ_WbiN0CDD3m|5MpasB!=p{JcbdE(g7
zClBo1vvbq<U{70pMNw{Ad?Zztq2T#N_Y%IH)xa*Dy7G}{q%?qD*!Yga+lJd#rtAHk
z&8SccIdv9WF&khgW$<&H=x-^{NzlpNMXpSkUmQqZoXwjiLGNpL5IDhqqvPv0Y}~x-
z;4$<^{@t4|pFO$@r0*Jf*YA4j+_kqp11$0Fe<8)diO1J>S7t{m+~i?N`PH2xTMzEq
zw6?dkrZ^`VDmYJ&MHKXQUcdAlhykr!hQfKs-2||ZT&0go1G$+XSZr~1dM<D%<b4(_
zM(GdY6{S*3r$sf*oqfZ96I*ueJM#4L=T4nD&&!$@E`o6?w6sqhIkf-rUE2XzHrU+?
zx>#x8!Wn>`+B1}{XC=k=4itQYkt{i>y?s<MSu0z?z5MZYT@{%T{!T{uRdaN2R%rA|
z*(G(IgBx}pBppayVOpdD)u&K2)X_&70V=|4sKQ@!V|`O=XLlce>C4t{+xPVJrJHYk
z{PnxnrVnoFZzxF2ujyX5=b4MQKKbZ_ci+Bq`?XhZOz)pqRhF*vwGu0%vno2)Za@0O
z)VhHVaE{~zSV3>++xM=W-apY@m5si%ZVu%7w_xy%9O3K1Ys`vSa0vQfvoouoH;<JH
zf=Z(Zc{S0A={ZGz74_}C!|S*1e&WEBM~|I2IeiX$+0*3ygKFo{fxVdY^<%>Wy_ojw
z)VK(Z5_I64Y?qopzya-yK&u83eC}T4NYp0eRdtPSKYZrk#?{qXk^YV*lBNPtei>=^
z1}1hKJh5-{U|U&stXjei6PQQD%EtPds`8b^E0*WM_@udiqo;p()8j{{FW-9a^FMub
z{nX=YTMFY9(K*#UllxC!!`%%m=<YoK<YZS#qS6J_Y*I_x*6zf0RX0NF1@#R<Z-)Z(
zhU#-8Gzu9h_(`Y7v^SR(WhVpvUu1Vr;Pvcm=Xfk&g7Zu5oZYddrPg=ty(1eoPwm{Z
z@8IF5j~{=31|(UJ9w8t46T5dzZQeLBHoOK$lr<oYgFfEW)p3d0102)M3=a&LeuJTm
z5^Q}}jZPh$8tJUcP0&i6IR}L_&?4N<ki<50kB~&dfnA(#P#UG{5Q`m`QCLZ`PW{W!
zr=q5zrE`$HD$ibc`SypeoH?+uyNbM2VW~yf0>IUOcjYqa@SZ=iwZAUMAa_JH8}bH@
z?cTC(u)DdM*;BDEKBIPI@A*65eQ;y?z=j_B#Z$I6LMyd~q^#tauwc^R*|4u?zR6C7
z^Ur3>O?tkt3$}4YoV^O{Me<9ln>zajN5<AqZl2n)d-txLJ9li~zHMsD#trL6M+VpQ
zc6YRYHq}*ElosV>B*l<>$qzR&Na6pP@;(n0`@OxqJUzie8th_=YDwKS($id7kP)i`
zniQoXAV1W{U!jRfFQ^2urUO%JyK0svhX=S*QBe@Jbr8$chNNurt><QErX?pBoqk(E
zvh&Ev=dYbPv}K@iMUvK66${llv;dScfB4CNt%FTPpp`yP;BAc!^t3m^nuavuD!CUV
z-YF=)sAJ;b`8O_~Jg{XD?|9m(nUgb#6nDzds6^^#Pr5gLY8s_60I&)`tyxRFfC8{$
zwQsP_5TBM)xU#0HwWF)2Z_Uu!v9Zz7wId_L!$SjotGm0<Zmqh4d>Z*V%TN+JNb3E6
zAepfD4k?5m#ny<>XCSk%vSoE=b8UH1-m(;<iXoQ*2v5i=sqY%zxMy-rOL=yjR%(Ph
zay|yhdP+iEbYwV=PcpJ{xzG(s*HG)q%qXQeFg!WGx?_0r{=LuuOl{dT+22%}X#h$r
zHtXqy)g8_CRb@rXvoqiurkdCGQhi2$<*JE&(?|Dg9O@udJ$Tj97T?Sgst{6S2?JiE
zIuvI-1qwvZpIp_m;BG=oVO%Ws@k2&uQhH87Nm)g8ZGB@)TSrHGds|y;YfDR00~qX;
zmBJ-R^(CV9+7MhnaZjb6Dev<FApE7i-eM@xJfz5uFRretEH49JFw#jysAN=s{t6r_
zJiO6Dqq3!MeA`%0eKAaz$@k9q=nRA(PyQq{4Oi3gNlaW~N_G(_C#~BGvRxH9@u4!2
zp9+<V)@)cmzIJ$^zqh-qt|&8BBNbUuoyBO<J(R2{fG3oa6s1$jJfWQ<w?IrzWyjhb
zlOw%tH9(}|Ek4(2qGrR^7RG&lCilOg99tWU*-Jdjys$#>^pR0P>J*ePEG#N6Eh}5O
za%E|02|Uh%{M?+Z%#5@YYGe@(tQ2TAjn0feQ{LwVLj=iDRoelVEgF4n3KCW-YU-QY
zstTBb6bBT`Lt`_G$#nn%T=_}ifnuX?4Hkf~tB*D40evlJ-V<6~cx-Ba_KJ#zj^2^(
z1~&Eqr6L@1ceXV*z}Z)llWfrViyfCxH5<~Hfg4#$a#CVK0*vR73S;RiRmYGne^pyU
zH6>LU&m_uj@nz5zQf+dr7;+T(28EJ>KP5(|L^9~CJzxRhX}$z7cuuZv%$x?~`Qrd6
zmYSZPmX?M-Tgi!_+z#M>A@r>YQNozX3jr6ngQbYdS^2g8X=ow9PDm_rw6!FqDM+a6
zA`>!TYPxD*T(AO}&nd^GWlPE{Z(coyC@HN>WXUED^XXyhPY&DgP(aH_ygfX<V0tE#
z!`GIaMY`N3r1^td8;Dku61cFktORbm^c16yt-yI>CPgu^#_S4zl&-?%c{y1bU_foq
zve}OCYKjFVb7P^j#L+7NOr9zVfa&e+Y&naGXKeyQG}z<#+E?ly$OVf*xdp`ibO01n
zt3rY)P+jIH@$~_#TRi2BhM7N8ghL=L&(+=v9au#k-V##efo;IrEqgci)fXmeeC_5l
zl#UgcK*xaO@L)%OO<|fL*vH9eSVRRvNajPphzB|p@N9=}5zGjn>d7tuk2=I}*tvSa
z5<h8KdU8UH0bUg@u_370NYT#=E(;jXxkKW4a}jBP7a~3Z>@evqfZJu*I+z}t^g;5X
z0Y-glxJ_m+1Ta6AcuOt0!5MkyeMpBa@q;1?LLwoV_JKlwTFf-p0nLU6j?|R!v7a&T
z^P(Wa5H4%GWFZrCw|A84Qi_@fNV8>Z=moK9+(3hiNOM_kVpyQq&WH@+#!_%~AmNLy
za;>bb!Kf?{EaW4!3YnLy{ZeuxQx%yAWaQygl@+!|4+9sHd@y%1E@{xo#=*sn{40SW
zT76WUaj3_C3Tz8L{4)NP+`OWzrT|1QZD#o?^3R@J_6X?*c(<+KLgK(+OaNrEv$eIg
zv4LH;6}4}tzpSu%pXJ}{J`oSW4^kdn$m-mI^&Lg=3TNZ5bf2$>%l-ke3b5|R8g-$C
zmAwm~C%AMxH-x&6>?FH8LJeRmkIgy{iogkrF$!#d-X{PPnNvYZ!qLvE+*pvgedHqo
zLaUU~Z8r}GS;R6_?mM)pw`S%DPjG>*EhWX6O6qQB(k=^BLJ!LCT(m&oI53w=Quv@D
z7x+t?QT&F2@&;zWss!yC!IIE`OB!;XFBaM-GjodeC6y?z>hBKjM<EW-#b=i`bdRp8
zWiq#ak9=q#Oyt~I*!pM^3hMhd?%UMg0Mix|A2Lu5RU49X5E&CJ6FV85G8it3n*Wef
zdm-2Jr|W)qu@{4sS(rU$VdLx}2~dSalZ%EP<2*P|#T_O_`k2IY9NCh>fICAl>j`oV
z+((j}dy@cHI;*gvp|iFqbGGmJUuXnD+%4{ZOpSf&SFh`BD9(sfh)wLMpfysE)+jp-
z0j}O6qplofl#x%Hn(3M?oWBrX1xFXAQ10#lRV+Zt%okcZxcdfbNYl^9u{=<_2F4+>
zAXS(lJ|z?0u@I@)(Q?5eY9>T}BCDNY?&a=eyX0PkC_Zfrk0L)Lx3CmILt$uX{KyA?
z)|+?xkqZ}jJH5zF@u|#9I4UX55QZw7kY>8{qzGq{7JHoVle!w~bGY>>1Itb1;^bgQ
z?X#>&uda?v$Sh#m_$GijL19Lr)<wo9rDrcM&PhQMrxnLqdcm3<-F=u4kMPTzHfn5$
zYrt+gV_8;qZfXo^C1(|U_e%}j(U>rQjahJ)IisY25Uion0JSAJkWL_&pEJkjTO)di
zoc`2!n4}Oac5xG7XKRC2ujY2{(qQ-(s=1@Q=|4f8B}mau(q6QZOSRKRkGx3!GSVbT
ze0_bGGmW53Jp)yRO@9P7{Y-)PJ_W%e7BhY4c$lik(2*GrKKDkM1Ubn7;{RuVYR57O
zMo6KHC)8(DBa77YRt&vq;pi0z9z@mccROC;m#WYj5>qpCNZKoFo67T3qt!AGyG2W^
zNsmV^_Ym?bF()chW$KwMnWfG!ZOSQaKt2wT{R02-z3_;IHG~yKd6{&P{II2CZ__8m
zwSuKGW}{{l6XSXl!Y16@nhnW+^~_?P8>=pH_6?3qU0&I`rnLfao$e-;w!qoQ&MPb_
ztEy{i?W`-#iq{2tIzTIc!h+cRBSV^-=FQ{76e071S6+VyVi`jNX9(Us+JW%uyXlw<
z1X{}7LL*!Z0)e%FV>R|;!W@HpXDnuXmy|d?6QaZc7tA=o+c3MVxo@I>w-M?`Pm`ev
zZ5)!aOUgjts;#T1zojBSB|_=r2sj`w^e3iolC$j$ojU<f#}aQXRv0o%o7Zd_>ZmSA
zi3swfchFBX=X_Ly&u;l#?LG~asVcG(DGj_KgPr?|x}Nb}<EyHvcMuQ3{;s~UrM;`C
zZ(wM&yS_LxS|xENJ)oF>TtWI++|YyE<BUBfouKi|lcqGWu%Ukw7?ROV|F0p0ISn%!
zi@1W`KVD*RqA2<Hr05Nfzz322QceMw$1ht83V@7F<)As!p-KX9kH&lIi!%(t-u4TH
zX}7gAIVJRQX?f&}udYKvRRh(ua1lm2k?{tdD$vhM<VZ0!xYuHTdotr1o|=xf3~U){
zugHzp_&J&Jzw;+5V*K*Mng&B9a@maq9AtS{{<*Cx_TiE7=@b~{BeJtF-WdXR9ly{(
zH947ua+*7~dCJ*Zw!SnwF)YZ-aWO77on76;sJsx9n4S#_ZB)G~A&ppQkgqFm^I)r^
zK;kro(30DT>fexmmxrJIaK|U+mbajzOEe5_ENLvon3D(ou!Iv0M@L6kCD0ZdQqPDS
z_do&}jg&p4!Jv6rUbu|fBU$lco@1CTtkKB9)GkD*9^meeOUo^;E(Z1`$Z{iNgVYT{
z!76Q7gdrwAF*!Az;u*3)`->bab|@zaj_X?7X9vnDjsdoR@3=x*Ohw9R<^}L)Qw8}I
zBUGd<W&0AeQns6;NB(dG1zUQ63!q5kDtI%)amn!XcQ1`NQPfke1(n;DYJom!!`gSF
z0F{Ncy|cTwpWL`|rL`eRF|E;(GV?O%X4S>kQ3PMJN*5W48-xhGJ~9f>3`xlVO#|f%
zH>$@Y3s8iAgn&<mMx)V!aV-33LHEeg$mr&{+uZa-ip>M(aaUJYq<^@~(IbC2!jG<s
zePL7V?d2hGbQyhy-r9F1o-Sw|ycGJBeA17BDqDP*!iPa}7cCF~NsDlY>I}K!!$FSZ
zA+Li%6{?SlGlXgAd*S9S4blKMJtYzBhO{b0FiL2D>caJ*Y9$qg5xI-OBEvrbj|IIU
zI*t)4ErqmO8~vq)A!Bm9VBwCyQbd-Ln42g5kOa=^g8~Br<o^CLsV_LR0nFE&Q>X6E
zyzn_Y_=H3+D{bnV03F8k7>&%0!)@uiVP<a5=+~}-p37rKt3H}OMX)M3NG_9jd%C-N
z$&{gghJ^IoVy*+jrm_L#M3+i@ygfaT6ounjJQ`vW)3RV8k6>!hyF!^WzB^cirowV%
zs79^AaTEC)WiavsP=rJ>SI_*R2!vjd=YohJrb=vQ#i^3_v@vC{0|#GCLViv6_^u5D
zZIyXOZ+P>^jCBCRB-j~~L3j3Up1!_t%8;Oc;BG*r_$fk>cG^&ZRYQQcvkQ<tNU{O>
z|D%uu0-7r`AId(E<g=sj7h8J=4p=qf0}%ZMTC9{p9T<oh)46%#4@pEqfSDNhy=s(*
zlW?#R)Gz*_;u4g6_I|pQl9r*VBfHl3HKD<^ryG`83u)pZ?+`GoGg_YG;14P^i4XaI
zfL)v%;6y;El6QbMDmkaPrnR@ZB0n`UMB=*ClHqr_=$f~eCxb0{$<+}Fnaj)SVJ4@M
zdpcTB0>7h^5y>+j<x$B0nU#bp@U+$7YYs6N&-<Z?1c-PvlQTp{goZHSITt>;4;0K^
zs?V(K-mriA$PUVKm6PVm$<7Lg_N?W9TVf=wvEUN_ekf+dpg4%I3yDn1DXr@q*wEKl
zl3|!J%M-p`KOZl#JFfWLWoiSkse3@n4Alz-T|)tDX)>2JvvNQNHTfGO!MQFB<3T;s
zUqsL^o__LLn1A~q+!h0yduD+nRQV=lzAzWtziqU;z9=JFCGm8>>x-wak1!yAB7qvI
zKs?~<RK9%=42@37E3IwsZ>=myi&FWCP<jPbt|*X$eDUOzfZoH-MkEQ=CuSGdf~<RH
zyk6yx2u6f3@-Lo;nK1alPhPD?^E}mDJ@bbm=pB!!UvkOIrBoBxlHJS>sA*`8=-<Uc
z#V6eQFbU5nY3iRG>Z&UtWqlxj;3X-Ej^FVXTsubytmr|dj`ZK4LA%h1pOgiI0akHU
zb5(H;`W}e@pX&jtcB~5UAZaI`JLoLB_yp<VG72hy@&jhG;VLDpegRv3w}QRNMv-0e
z5Oep`AA(RoKv^$`vR>lNM3<;04!L^C7vuyVnDq=$C?5Wn;69pF($K?yEHwN~&RCXt
zbE+q72T)T-8l=Lf4VlEtjV7LNeSG90VTit8k(-ee9j;M8MNegnl(>Wylds(g#!S|h
zOF@Jal&R~h%S%!9G!geUAa82AucNWZ!pff9FA_N~>mlausXqikxpP7z5BDd+X9QhP
z(n7(12Zt-F-C1$LhWU(t8=F~JRgw$HD1T39<6d9r_33oh37}UDeT5tgu(G0wCj|jV
z*fH>ZD)KF3fQ+Nnh3O*<QPHuHIyH4Wb6}!-Fq=V5zN*TK@)(G|xBvhGy-7qtRI-(&
z#YL#>4~^ZAx6Hs|UyKS(V%9{!`9We1AN$+~wnlt(hX{Xw$N;>52@d1{=%2Bk;!=l}
z!ebR^$;(n=BQ!x$Pgj#qO;87C-?At4ZN3;1iJzYoR{0W7o_Hp+=Yoz1y2w}_IJC`J
zmYthUakeg0a}UZ6TaiQo{LGc*l~px$Ack4BA~QZL$j6!LyYq^a%NN+=`$tE27<2pN
z=Qu#W#@S?8Pvs1MEun-)eaPS-$h|k<<y>9pIV)tBvQrh>2%QQ|C+&^1Qen!&8)<(z
z1a{BlQXwa<zrV2sfITm{5^SiZNT5<3iY(idbdvG>!lIIu<$}$olO?@vNMccM&ybXb
z0G+n3-hq)-bwwFbDv2}K62U(Z_e`)6#8HwdyW!DZ(pp=8&)m<kuysPkICWSk7!b*2
z62_w@7d~VjM=_{}1gVmMPm%*bXU&YR&2&#=4*wIZs)C_&R?$Hb>{evt;C8aJ<h>tB
zy|pdV9}WzG6F(~-)r{+#TG~nVn4N$dJU&*kbP~%#^l|C=B~=aWy+i9Z?;P*0El3QN
zIvdN+dBU21kiqmVXa13M`rzj}%#A@xc5|T+2d;y^QWG9cZd$EM86=khhh7YyJ;&s5
zv3@AB@%;bMd>52+DCRCWRHxIC&N)0hT&Gp5f)(Ul@)fhhvr5p6;xwTZ_wrZK;Y<^1
zn+^_-b=MW88<gJm!s2Mb{3Z5o5=CfKa#m4QOV7xE<jw=n>>leZ&tSSee6b|#38_hq
zNn82TtV#aA1s}WsTQx|nk4a)m@$|&U)ZaZGxlag_6GK!~RCIK7OiT><aiJhXXUd%7
zx-^&^6o?H=%qeZ?=^GdxTR*vF+dWV^oJw6--99k6`^f1l&+Hg#%!>|in)w&a#N_-M
v_l{pE6aR?DuZe#|<JZJLqVa3uAC>q&pQy4m^R*lr00000NkvXXu0mjfBM7u~

diff --git a/vignettes/examples/vision/mnist_convnet.Rmd b/vignettes/examples/vision/mnist_convnet.Rmd
index 8508fd3c7..fc69676b3 100644
--- a/vignettes/examples/vision/mnist_convnet.Rmd
+++ b/vignettes/examples/vision/mnist_convnet.Rmd
@@ -15,14 +15,14 @@ vignette: >
 ## Setup
 
 
-```r
+``` r
 library(keras3)
 ```
 
 ## Prepare the data
 
 
-```r
+``` r
 # Model / data parameters
 num_classes <- 10
 input_shape <- c(28, 28, 1)
@@ -45,7 +45,7 @@ dim(x_train)
 ## [1] 60000    28    28     1
 ```
 
-```r
+``` r
 dim(x_test)
 ```
 
@@ -53,7 +53,7 @@ dim(x_test)
 ## [1] 10000    28    28     1
 ```
 
-```r
+``` r
 # convert class vectors to binary class matrices
 y_train <- to_categorical(y_train, num_classes)
 y_test <- to_categorical(y_test, num_classes)
@@ -62,7 +62,7 @@ y_test <- to_categorical(y_test, num_classes)
 ## Build the model
 
 
-```r
+``` r
 model <- keras_model_sequential(input_shape = input_shape)
 model |>
   layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = "relu") |>
@@ -103,7 +103,7 @@ summary(model)
 ## Train the model
 
 
-```r
+``` r
 batch_size <- 128
 epochs <- 15
 
@@ -123,49 +123,49 @@ model |> fit(
 
 ```
 ## Epoch 1/15
-## 422/422 - 4s - 10ms/step - accuracy: 0.8895 - loss: 0.3636 - val_accuracy: 0.9787 - val_loss: 0.0793
+## 422/422 - 5s - 11ms/step - accuracy: 0.8895 - loss: 0.3636 - val_accuracy: 0.9787 - val_loss: 0.0792
 ## Epoch 2/15
-## 422/422 - 1s - 2ms/step - accuracy: 0.9666 - loss: 0.1112 - val_accuracy: 0.9852 - val_loss: 0.0550
+## 422/422 - 1s - 2ms/step - accuracy: 0.9664 - loss: 0.1111 - val_accuracy: 0.9850 - val_loss: 0.0550
 ## Epoch 3/15
-## 422/422 - 1s - 2ms/step - accuracy: 0.9743 - loss: 0.0824 - val_accuracy: 0.9882 - val_loss: 0.0439
+## 422/422 - 1s - 2ms/step - accuracy: 0.9743 - loss: 0.0824 - val_accuracy: 0.9882 - val_loss: 0.0441
 ## Epoch 4/15
-## 422/422 - 1s - 2ms/step - accuracy: 0.9786 - loss: 0.0695 - val_accuracy: 0.9897 - val_loss: 0.0399
+## 422/422 - 1s - 2ms/step - accuracy: 0.9786 - loss: 0.0695 - val_accuracy: 0.9895 - val_loss: 0.0400
 ## Epoch 5/15
-## 422/422 - 1s - 2ms/step - accuracy: 0.9803 - loss: 0.0626 - val_accuracy: 0.9900 - val_loss: 0.0354
+## 422/422 - 1s - 2ms/step - accuracy: 0.9804 - loss: 0.0626 - val_accuracy: 0.9900 - val_loss: 0.0355
 ## Epoch 6/15
-## 422/422 - 1s - 2ms/step - accuracy: 0.9823 - loss: 0.0557 - val_accuracy: 0.9912 - val_loss: 0.0332
+## 422/422 - 1s - 2ms/step - accuracy: 0.9824 - loss: 0.0558 - val_accuracy: 0.9912 - val_loss: 0.0333
 ## Epoch 7/15
-## 422/422 - 1s - 2ms/step - accuracy: 0.9837 - loss: 0.0499 - val_accuracy: 0.9918 - val_loss: 0.0310
+## 422/422 - 1s - 2ms/step - accuracy: 0.9835 - loss: 0.0501 - val_accuracy: 0.9918 - val_loss: 0.0310
 ## Epoch 8/15
-## 422/422 - 1s - 2ms/step - accuracy: 0.9849 - loss: 0.0481 - val_accuracy: 0.9920 - val_loss: 0.0310
+## 422/422 - 1s - 2ms/step - accuracy: 0.9851 - loss: 0.0479 - val_accuracy: 0.9922 - val_loss: 0.0310
 ## Epoch 9/15
-## 422/422 - 1s - 2ms/step - accuracy: 0.9861 - loss: 0.0444 - val_accuracy: 0.9917 - val_loss: 0.0302
+## 422/422 - 1s - 2ms/step - accuracy: 0.9862 - loss: 0.0444 - val_accuracy: 0.9920 - val_loss: 0.0300
 ## Epoch 10/15
-## 422/422 - 1s - 2ms/step - accuracy: 0.9863 - loss: 0.0439 - val_accuracy: 0.9913 - val_loss: 0.0297
+## 422/422 - 1s - 2ms/step - accuracy: 0.9863 - loss: 0.0438 - val_accuracy: 0.9912 - val_loss: 0.0294
 ## Epoch 11/15
-## 422/422 - 1s - 2ms/step - accuracy: 0.9872 - loss: 0.0393 - val_accuracy: 0.9917 - val_loss: 0.0306
+## 422/422 - 1s - 2ms/step - accuracy: 0.9873 - loss: 0.0394 - val_accuracy: 0.9913 - val_loss: 0.0304
 ## Epoch 12/15
-## 422/422 - 1s - 2ms/step - accuracy: 0.9874 - loss: 0.0372 - val_accuracy: 0.9922 - val_loss: 0.0289
+## 422/422 - 1s - 2ms/step - accuracy: 0.9875 - loss: 0.0371 - val_accuracy: 0.9927 - val_loss: 0.0287
 ## Epoch 13/15
-## 422/422 - 1s - 2ms/step - accuracy: 0.9888 - loss: 0.0344 - val_accuracy: 0.9918 - val_loss: 0.0293
+## 422/422 - 1s - 2ms/step - accuracy: 0.9891 - loss: 0.0346 - val_accuracy: 0.9920 - val_loss: 0.0292
 ## Epoch 14/15
-## 422/422 - 1s - 2ms/step - accuracy: 0.9890 - loss: 0.0343 - val_accuracy: 0.9918 - val_loss: 0.0282
+## 422/422 - 1s - 2ms/step - accuracy: 0.9891 - loss: 0.0343 - val_accuracy: 0.9922 - val_loss: 0.0284
 ## Epoch 15/15
-## 422/422 - 1s - 2ms/step - accuracy: 0.9894 - loss: 0.0322 - val_accuracy: 0.9915 - val_loss: 0.0284
+## 422/422 - 1s - 2ms/step - accuracy: 0.9895 - loss: 0.0320 - val_accuracy: 0.9920 - val_loss: 0.0282
 ```
 
 ## Evaluate the trained model
 
 
-```r
+``` r
 score <- model |> evaluate(x_test, y_test, verbose = 0)
 score
 ```
 
 ```
 ## $accuracy
-## [1] 0.9912
+## [1] 0.9914
 ##
 ## $loss
-## [1] 0.02448307
+## [1] 0.02402576
 ```
diff --git a/vignettes/examples/vision/mnist_siamese_graph.Rmd b/vignettes/examples/vision/mnist_siamese_graph.Rmd
index ace2217db..e70466795 100644
--- a/vignettes/examples/vision/mnist_siamese_graph.Rmd
+++ b/vignettes/examples/vision/mnist_siamese_graph.Rmd
@@ -33,13 +33,13 @@ Gets to 98.11% test accuracy after 20 epochs. 3 seconds per epoch
 on a AMD Ryzen 7 PRO 4750U (CPU)
 
 
-```r
+``` r
 library(keras3)
 ```
 
 
 
-```r
+``` r
 contrastive_loss <- function(y_true, y_pred) {
     # Contrastive loss from Hadsell-et-al.'06
     # https://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
@@ -61,7 +61,7 @@ of digits (until digit `9`). Once we have paired digit `0` with other digits,
 we can repeat this process for the remaining classes for the rest of the digits
 (from `1` until `9`).
 
-```r
+``` r
 create_pairs <- function(x, y) {
     # Positive and negative pair creation.
     # Alternates between positive and negative pairs.
@@ -82,7 +82,7 @@ compute_accuracy <- function(predictions, labels) {
 
 
 
-```r
+``` r
 # the data, shuffled and split between train and test sets
 mnist   <- dataset_mnist()
 x_train <- mnist$train$x
@@ -108,7 +108,7 @@ names(tr)
 ## Network definition
 
 
-```r
+``` r
 # input layers
 input_dim = 784
 input_1 <- layer_input(shape = c(input_dim))
@@ -140,7 +140,7 @@ model <- keras_model(list(input_1, input_2), out)
 ## Train
 
 
-```r
+``` r
 model %>% compile(
     optimizer = "rmsprop",
     #loss = "binary_crossentropy",
@@ -161,48 +161,48 @@ history <- model %>% fit(
 
 ```
 ## Epoch 1/20
-## 469/469 - 22s - 47ms/step - binary_accuracy: 0.7565 - loss: 0.1644 - val_binary_accuracy: 0.8782 - val_loss: 0.0989
+## 469/469 - 17s - 36ms/step - binary_accuracy: 0.7566 - loss: 0.1644 - val_binary_accuracy: 0.8753 - val_loss: 0.1001
 ## Epoch 2/20
-## 469/469 - 1s - 2ms/step - binary_accuracy: 0.8886 - loss: 0.0873 - val_binary_accuracy: 0.9220 - val_loss: 0.0622
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.8896 - loss: 0.0867 - val_binary_accuracy: 0.9248 - val_loss: 0.0614
 ## Epoch 3/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9246 - loss: 0.0590 - val_binary_accuracy: 0.9380 - val_loss: 0.0485
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9261 - loss: 0.0579 - val_binary_accuracy: 0.9409 - val_loss: 0.0461
 ## Epoch 4/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9456 - loss: 0.0436 - val_binary_accuracy: 0.9493 - val_loss: 0.0398
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9474 - loss: 0.0419 - val_binary_accuracy: 0.9529 - val_loss: 0.0382
 ## Epoch 5/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9572 - loss: 0.0343 - val_binary_accuracy: 0.9582 - val_loss: 0.0331
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9589 - loss: 0.0328 - val_binary_accuracy: 0.9603 - val_loss: 0.0316
 ## Epoch 6/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9644 - loss: 0.0283 - val_binary_accuracy: 0.9661 - val_loss: 0.0273
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9660 - loss: 0.0272 - val_binary_accuracy: 0.9617 - val_loss: 0.0297
 ## Epoch 7/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9707 - loss: 0.0236 - val_binary_accuracy: 0.9658 - val_loss: 0.0271
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9708 - loss: 0.0234 - val_binary_accuracy: 0.9665 - val_loss: 0.0270
 ## Epoch 8/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9747 - loss: 0.0206 - val_binary_accuracy: 0.9692 - val_loss: 0.0247
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9747 - loss: 0.0201 - val_binary_accuracy: 0.9674 - val_loss: 0.0259
 ## Epoch 9/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9771 - loss: 0.0184 - val_binary_accuracy: 0.9695 - val_loss: 0.0238
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9787 - loss: 0.0175 - val_binary_accuracy: 0.9697 - val_loss: 0.0247
 ## Epoch 10/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9795 - loss: 0.0167 - val_binary_accuracy: 0.9697 - val_loss: 0.0244
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9807 - loss: 0.0157 - val_binary_accuracy: 0.9684 - val_loss: 0.0251
 ## Epoch 11/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9814 - loss: 0.0152 - val_binary_accuracy: 0.9726 - val_loss: 0.0218
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9810 - loss: 0.0152 - val_binary_accuracy: 0.9710 - val_loss: 0.0230
 ## Epoch 12/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9831 - loss: 0.0139 - val_binary_accuracy: 0.9734 - val_loss: 0.0210
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9831 - loss: 0.0138 - val_binary_accuracy: 0.9726 - val_loss: 0.0224
 ## Epoch 13/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9841 - loss: 0.0128 - val_binary_accuracy: 0.9748 - val_loss: 0.0202
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9854 - loss: 0.0121 - val_binary_accuracy: 0.9724 - val_loss: 0.0224
 ## Epoch 14/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9852 - loss: 0.0121 - val_binary_accuracy: 0.9755 - val_loss: 0.0198
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9861 - loss: 0.0114 - val_binary_accuracy: 0.9738 - val_loss: 0.0217
 ## Epoch 15/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9860 - loss: 0.0112 - val_binary_accuracy: 0.9730 - val_loss: 0.0217
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9864 - loss: 0.0110 - val_binary_accuracy: 0.9738 - val_loss: 0.0213
 ## Epoch 16/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9868 - loss: 0.0106 - val_binary_accuracy: 0.9758 - val_loss: 0.0204
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9881 - loss: 0.0101 - val_binary_accuracy: 0.9754 - val_loss: 0.0206
 ## Epoch 17/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9879 - loss: 0.0100 - val_binary_accuracy: 0.9754 - val_loss: 0.0202
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9883 - loss: 0.0097 - val_binary_accuracy: 0.9733 - val_loss: 0.0214
 ## Epoch 18/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9884 - loss: 0.0095 - val_binary_accuracy: 0.9773 - val_loss: 0.0185
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9881 - loss: 0.0097 - val_binary_accuracy: 0.9723 - val_loss: 0.0216
 ## Epoch 19/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9883 - loss: 0.0096 - val_binary_accuracy: 0.9747 - val_loss: 0.0207
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9891 - loss: 0.0090 - val_binary_accuracy: 0.9749 - val_loss: 0.0202
 ## Epoch 20/20
-## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9896 - loss: 0.0087 - val_binary_accuracy: 0.9764 - val_loss: 0.0199
+## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9898 - loss: 0.0085 - val_binary_accuracy: 0.9752 - val_loss: 0.0205
 ```
 
-```r
+``` r
 plot(history)
 ```
 
@@ -211,17 +211,17 @@ plot(history)
 ## Evaluate
 
 
-```r
+``` r
 # compute final accuracy on training and test sets
 
 tr_pred <- predict(model, list(tr$pair1, tr$pair2))[,1]
 ```
 
 ```
-## 1875/1875 - 2s - 856us/step
+## 1875/1875 - 1s - 793us/step
 ```
 
-```r
+``` r
 tr_acc  <- compute_accuracy(tr_pred, tr$y)
 te_pred <- predict(model, list(te$pair1, te$pair2))[,1]
 ```
@@ -230,35 +230,35 @@ te_pred <- predict(model, list(te$pair1, te$pair2))[,1]
 ## 313/313 - 0s - 1ms/step
 ```
 
-```r
+``` r
 te_acc  <- compute_accuracy(te_pred, te$y)
 
 sprintf('* Accuracy on training set: %0.2f%%', (100 * tr_acc))
 ```
 
 ```
-## [1] "* Accuracy on training set: 99.71%"
+## [1] "* Accuracy on training set: 99.63%"
 ```
 
-```r
+``` r
 sprintf('* Accuracy on test set: %0.2f%%', (100 * te_acc))
 ```
 
 ```
-## [1] "* Accuracy on test set: 98.54%"
+## [1] "* Accuracy on test set: 97.93%"
 ```
 
 ## Plots
 
 
-```r
+``` r
 par(mfrow=c(1,1))
 vioplot::vioplot( te_pred ~ te$y )
 ```
 
 ![plot of chunk unnamed-chunk-7](mnist_siamese_graph/unnamed-chunk-7-1.png)
 
-```r
+``` r
 i=3
 visualizePair <- function(i) {
     image(rbind(matrix( te$pair1[i,],28,28)[,28:1], matrix( te$pair2[i,],28,28)[,28:1]))
diff --git a/vignettes/examples/vision/mnist_siamese_graph/unnamed-chunk-5-1.png b/vignettes/examples/vision/mnist_siamese_graph/unnamed-chunk-5-1.png
index 58138957cf44a33e1c7e6c6de0145d80162a880c..915daf865aa65624244e9f7cc9e47887378c0cce 100644
GIT binary patch
literal 31048
zcmcG$byQaE+bz0ilvELrMnRF5Zlt?Gx|Htjl2lq!x&)-VTTn{6ySuyL+~D`yXMcO|
zvBx-n9LMl=yw6zAdhT_{b<Jzed9AOq(jsV&@g74EgeE2`C=Wp}=l6e+VZkSF#f>Py
zKaZ?LRc#>%<>~!jn86v3YX~BN#02>iof5Za9NiR^CXx2nra8zX#mN0g5s={@Nr{sn
z%PV5a$>u)iYp(q8VN^&}7DwYNZTf(wa&bK^<x9%nfyc9?Ft5ZYNJ+zIv-)WEh;cq*
z!%9Zf)I=^^cwBFzvpO0ivqxUr*sM<5exdI6!sMs0l2gM0|B7nj9wU=t&EuXBT`qS<
z9vmDnNm1Ql30WU3{$@#_p{2Fh9L|ZoR%l?r<X`8=77x>kSxHPxWSw&xS~?geoNjP+
z<mdlY?|jtN)%6$!h3+C7PoKmW6E@oASUQ`Lib?|O_a)NN;i0^O0t^W`IXN929WQUw
zEiVnW#7(3*O9Gys^Vf6=)tHK#@A100Vi9CwgT;vPS#-|N)QP%Fy-AF&w2WraeKGxY
zHysn0(wsSEv4rHJNRUHngS3j2i++sqN2iW$oh<zc;dDE<y7BptH=;vNp<SU?00zx&
zxbVvl{Ndx{L&2t&&7h~#?Wbb8Ye9r!^rNc;TJe}Hk;vB{k>ZU)9{uQ8LQnW9a-S?=
zvgOI9E#lsg2McG+SRg?Gt-4Hj;K6Kqi*;M`KTx$>OJ|q;eq5Kx>zl7tgagkvB~8$G
zL>nv73JZDZ;>LDo?zsLN=umkjFV;NLPID=BD}9Iw5<hag^=9<2TDeC58**}eL&G<3
z-WVDh$|SOVTdKaiQ|#TCYihE|$j{IJ{n#{|A~56<Wnj%I`OJxH@>b(5X`U_&go%Z<
z>`+&+BWf~>&Lp*U;?gsR(lZ4EQ3RSOqS;6C7249gYhVEPef28C^X}HLH&IMRChW_X
zVioGx>oSC{_sD5m&kp+DQJcFSbM=|dW?-PBJJe`QI;4W9txO4od++4rq@)yG$?eiJ
z^*PJ9Y(hg$E=?Eh{wa>Q`i_{HnP=zxvNrnrWlzLSX!gC|`eHt|`nQMFc3V|^F-;g$
zW2(%j3@X$V6cn~rH%P&xzw*5$L56MpiH{ONrmv^BJyDh|5P<G^dz9ki<I_+xmakg2
zC_@y6_Sj|JZ!Pw=_L0aeK7tQX8dUDEGr7_o%ZPe(z7&Q*z}BBohlPbzzyAxR_ByiA
z7ky)6!}G7-GoeLmDpm?p8VIurC5j4Pqc<Z?)pV?I<Oze`e(G$4Yj?URt=2Ukm&MO3
zEY1D!psFwec81FRV+n4~l(I>i+l#RBa;BHahby+kdT}<|--Ahzf0gBr+v`w5-|eq4
zc(1MQZq8T>^b2LclU7jVjdUGxb(hZ0tqkWVto0^Ih>L^0UpL6!wi6@t*`|&UGw@I*
zMeVd@6UJRo%;l<p^^%cp(<!uCLolXg2UhH*1&$~~wWg<U&QMNY-(8_0!_*ZbUxa3N
z><d)HMVnuDy<K%~Z9j<LH-+v%P;at#S$4fMyE_aC11$8>JNO-BwLH(f#}8*0f~;&Y
zHBD5Hm|J4yq7P@~lF5IxVK?7CM5V8))#Tx>tGiH8H9J|T$!SR>g6V6R>@3{HrMO@f
znsD=R#@-uxuOYqf^ktru7RPD2?G>23X^X`JJ)&_>kGW)c+x-bO)42o-hOHAptmX}}
zbchJUy6!!uXtx$0jV*yJ6I2Z)Dn*lW5JFKCgo-LoqE6Lr2aezW79alJiblR_-}%%Y
zTCx^YE9@IPzT?V$XkImgi27<M)}G_hH)qY6CKj`CS(o+qy5>7XJ{L=2ysOT$3G&u1
znXo>MFu2E$QC)<5cJ{}bZkcJ<RK)cJ|J=3~x!>w%OD92I)f)2-PhumhXWCZ4WU?i-
z?Z8$ARnJ`1uq)o?ppzOv9R)u(!@s}hBHDMNMGZcGEEUA0psDcu*A|&7kCM`}xeTNp
z`ls!1q|Ha~DdN^XW5zAPydT|wft);+vZPk(9H;fZ-kco2I4YhJ4kMzm<&uq(lxr@9
zbm#-dZ_n@tO)7Z6*2;Daoi}pXtWD9rGq5)^+h#^NcH%<8p}oF2n|t-@)!k`TlaQdG
z(?yrKgajda!0(R-9yR!=E(nP3aP;fX$7K3rqoT}aYMFU>8ZGA<Jqe=87Qs_>zn2Tm
z6<@UR?iSLJ?pldR3N_dEw3px~fX2Cqa9aKGj@z6!-itiz;NUI}mMmlFgKO_Zt=pP!
zN#K|C4HwV04HC6zI--r!<;#vVR;z(wdS#pVxAgr$=`KHxQ+p_3cMQ!Z)zgjal-<dS
zyx6{5F!MxQPQ@}QJg<a<tFQCWJb3l=^&75s>(VKtQ+V8)y<mErcgB2iB$;3Q8#xU2
zaoZANcBBn^%La+`n`(_HFx1f8#YH&Yn{j4_Y4XRH{l(+^R*N>DyGNEoD<y}TxAa0|
zor#=g{5n?8@nTJTVzp7r+hQgOT_0Vom-WH6!?kvC(b;kp2mfWFw`u~X-ctL{)x_-h
zQo-=H<fA?qcET8{RQAOg9HM)qZa*vJqm_1nQQ2caMNMrsl+`oh`Qa;tSC8;Sew)QJ
zQ`4}WDT(dMUhJT6FWYqA(7v%|`!WP0b!*;oz|hxOkM5MzX3bCpm&8+*YPFYzMlF@X
zjM(S2X*_EdW6!ZdaM6Wg{2Uv*P_^te7uWS}-QMZ#v)`qqrAKRhZ^+2t5fGRU$j`l3
z=P+=OrTqEkAXFE>O5X9Bni|g}E^E<xeeHn@*R=+ht9d>}DdwDlOp`n@b2R<3Z<BZ*
ztDc(D7)w%hqQY@B{5?t?k|3blvQurh{gv0X=;NcWDm`brpChyZ7BxH4S!>lfZtR7R
zH=Y?%Y`2oC>I*``ojRNudW|jN&wO)wYz<E<;IoHnEM_?PFu^u;&$M{^giELDnJJBa
z)f3a53Cku;`@VIO&}e<k($i9rGtHZ~dP!RS8`3Ep(O7t}5c~7!%mYG;dSlG>lTzsm
z86=LGl`_s>LzD)+i4)ygk?N}UYbkdx?ber9f)<?{5#))|R$Eg=HeY&^jE+(#o?IId
znl67=QfHM;;`;g=zH0?OJCcoX-bP*;X`E2-I)yiF7qyn&GZa(jGvt*&)S0EzsTU*l
zy3;7aN76<%+zd4$`1OPi6b}+mABMO(r!FdV?Z&`+n5ABueOzter>m<=^&!8brza#d
z6#da7ximD)L0m-cL$r@oax0mN`Up8WX{Nbi=3tIVKja;LTV15xi5|LyYfdIqb|z3Q
ziVfi_CH?v9mDy}{iFvWL^1?-1UHc4+b;Mt-PqGPX7n~_qM}&8h7z@t3f4~X!sEhqQ
z#FUhnxV_SeT*1L6JO>eEC~KHYeu&A*eVn^aZQJ;Tl1D@->Pf&7C5HZyQ!q6Om_wyi
z{96CtZQ>k+`yBd$LLY>cj}cX~16)${Vq#*5cs=Sh>l^^CnvLB#a7vWU?$kTHrB%+p
z7`J=&XPt_QO1Vg@U})n{@QbCqnG$Bpfcko_gr3n%37kFN_CN1j5Kh;>Rf(F@Xlg98
zyO-*m^*X@{?+(^@mClm*iFB;WzGG8iiPKjoHe(QT@yTbcZ!{X%7%^pwXF$h24nrb7
zuo>#>=;&BpUj8Fpq^DXbhk{yOoK=7~`qM(h+}u1{EOKGjT2}68rxnBV_F~PWB120_
zX@s$l>qtO<M3(SwGq1?yqH36`s6IkY1^%14MQ`Wra->XolGpI&xp7!yeZ5Aj;ToHC
z#E-3d5t(h4hMvv091_vdvS59TAC;(XrI5fSMlN?GH)M`iepK<rB7$*gRU(q@+}hvY
z7mj{{jr~DWvtTE>1ks0YqN6d6Oe~(;_4G}<ZS%(Ea$Gjp)jK^6p+wvqp10RML<gty
zslmahWfN{ab@^INx8DTftgZ+lD8=J?W%#X_n3%n`y=cT%Y04#~T$H{uNX_i*>`5H<
zApXAcg}fwU2yq+@nl4x8Kgb;(N~u*@%>3z~C@zPq<JO;AH6F@>fh4|seyLm=G_>Ky
zu;l)o?FBSHrp@qiQZkuqB4I7v7?0NxrU}i({ElA_S2%)fWAUu&wH-$i2{O%5w}17F
ztd=-sS6V)NSGSnZi>TNq$^tXH{lz=khnA0P=qVXiMSF9V@)Qetbm-ZVq?P7S_%IE6
z;w!dQPR(2RJg=taTu!&0J0B6W*UZFfaVe>&oKKn-MaRZsG%ST)5xaD}7Pk<yfg2Zk
z0_6(HA!6!HP>MnG-}Le*3=9ltd$?iTFTJT-&bG7?d1j<&1rwC6mm=3G`D8sP5Fl`k
zzJ7FtIT9)wx=7U2b0Wf>>T^c*S6>!?PjxYB;jrZ2RgtfLMk3l@)!_fqVpVb5ZvRWh
zhp2sBX?sB8sivH;c<)eV=8xriS;e1@+6$_vi0`QR1C3t!i-<n?_E8UO9SuP2^|)B>
z5{bdXPfcrC)91fX=*}B|x!}3#=eI7XlzbEdJ8Q;q&OP&+dlmH*dCrH3qzJv(A4<G8
z$3>fJ>q8icy}eDhTpQmuh{DYy%y5KQ-xpB8N-0-!i<?k!YXp!fs}&F>Bia0x{y8_O
z;QJ=F@cE~#oQR00A{~AUX6{P*2`uK&dl|Xw-(#Y3!{klY<YHYM*0Aa6pD{alnNz#b
zW`mPsqoWC_Xg{-<r&hiW4R31`{COsEARZVPD9#PLJz;<JxnUM#W*)cU9)o<wITplR
z_jcPfgRw0P=WskdqzX=qXcKN1*Ike|`i_N78{jXIrt81|rTebjrfz1h@oG0sAmH+N
z<EZVKjMGKQcZxvuDvJ{*E&+d3<6*oq<wuuza=A}_tBIA0mnub=ke7&K-W|=|5y@;L
z^9i$kWBs_V6F0`Lmm?gNAF&)xR!27WCEdl7&<g7v=3___D-AjA^LL}i5H7zrMc0DW
z_RWpsmq3_U#@c!_tGnCUAfM;LfyFqjtlw+H^QqH<u`U(@h*`coRheulOp=-7`e_-K
zd>M4cAjjl>fD>{NmVha!!1P{93az_TxPkQhsmY__>|~eCZq7emnS{$JjeD`gG1RIy
zosVKYab4H@)23oPA7RMAK`K?NcP>AscV{HCrNETW)K*Bu(t--?==R8GdRilIWU|6k
z`I^46gnk_#lb%mTNqOLnZQQdOyB1^lyOsWO+<`1nOq5Upv9dO(`KZalef<W5!){&n
zZnrUBpHytH+<2(5stu+cjWgzC!Z2mzj;}&2LL?^o$?PPGHMNpNgk}VlM&ZYVnI7v8
zUM6C>*qL~8`Pg7q3lD$)<d*pIEgd&3JHPg^WQ$If$cW-uLmG<2m#<m&zUvf}U($~W
zOfiMxF`r3UzdyzrP#DgmYpP%Cts{L2%cFfeJ6~S%O2kI%c-Pj}w&`Lu0YoVPg!;Nk
z5yT=s6luAGYWVi%Dmyci`ASH8pvHDh%W1(cCC^mEA3i%fTib(Xd}R*f`Sa&V*^+)^
z*sww^zs4NsoOU8CN#}l1eM&$D+p2(j{cCsUwMQ_g%Xdl+bw}Az{5FS!kNHBu6`}+0
z7<$YmO3AO1JRAF)SRt<4p+CIt*Phna)+fYk;(Xfj@;2M!B~ejP9UY=CQZ+EC1WWj_
zsNmD&qjCiYD8(w$^&)@Z$=Ud5s42LnR>P;KLy+01RfCou`-{{`9NnI0AJcP40vU$v
zFh3Jso$`%YK+r@}NLa{5-1NkIIfHlx`KZuvaKS)Ss4QY2*uagrJz$M5Z?E>IDoj~F
z{R!?qs%mkdprCNNIT-`#D*&C4!*=!WYEyXL{u()^2S_4eJeQ&|D+f#MW7@jfX#X&h
z*C_t+;zs13Pa@wFw^42!`P+Wtl9puj^N0n0ZP0tKiuL=gLRNKY*w@l&-SYxa*2QwE
z&0thgbnB<9EI;XW=29;W^R$~MA$#pjR`E3`?}t&?UZbBkg}BI*S3(F1ik)$czq54u
zxA-ESI+~|2SR}?}^)L}ie#4AM&)U$5XLu5LHJ5{z_o2XL+XsWMC?#T8cOYG8mQKUt
zd3+!7*;=Zl*!@6{*5TT6dhpxq=S~AnGOiUE0+8QwPiwk%tWx#TluelN;>0$8lJObh
z+nfHdZj*xZTb_?=?I0@fV~Gew>_UFG5;bz>iGicn0KAXo2a6bpf~wdS>ruvrY~bTh
zxQtR%gli;$kF$_P<PiBs^EGNROCLjs62EL^xb;8cLda>x5WOsxM6;;e_T4R!i%F-t
zwsbwlu?xwMn=CXL9*))o2V7Q*X%ItckYRUO0%Qrd2Ut_>Pu4enF<zfa+3__V_Q}^R
zsgFACX=|HU@r(LVBs(}uyG|R<{Mvo?#-n6{loAhmA5+BsxlMAYgj(Trs$}pSKJ|rF
zox+&g)o76CS6x+0o?TA+&8VrJ-RYVm=03&knYy!3<vaAM5HGl^p@7-z{Z<TDXOWbl
z`Zqz`_wXbE)%Cal*P6{P!BaO>-<w*ZRI<bG=d5$#xfVa!3+bAf32EizsiV5?=q0<P
zQzw%x8TLny{Zr?I!h|4_sI&}R;S0Kmw54EwOtE0yaZ&$J{TFaTFwK>=2xs-j%Ih;o
zr+e<syWL6lTIMs4pe|~Ty_2o6z2>VIs;a7wS_7P#M4s}m+l*&ezNcCr=w`5`R;aqb
zS+s&klb55&<JzaMPJPK<y9pgEMD9{ygmu`|nxRQaqG#ne#e^(XtHkis>b4dpYW$?|
zJpH5PnS4oq*OYWgbuteyLs?l_9VkV{yEPJ;Ezg3@#l|y~b;24ui$&%xF-;4=nOJAc
zhTcC$>we+Uz8YSflJT?>t?~J0;b|o&<V8xB{HbpJJd_~e@6lI+*ya*l$p+(YjA_HN
z36&W0;V%w)_kVt1Xg}c>Rl+^KH3A!QBcXB{&%|RhC!2P{Dvq{etms0ma_Y>tN_D1T
zFKo5gECJtajn5KlfYF1(@@#cmhCdr~AJ<|vNIMpMbIcg{xhlbjY>eSEyJt3C{jQ-J
zYw)gSc17)OF(t_JcH<WTpXw_5b|;UWj<Z{K(d26^s-UvEY4~|WLQk{SB6=%KOs~z&
z#sm-d<wDMK_vgr@cc1z8938gDVP<OKpnb0LuerIofa=Jz;%J&ITSL*hoAOeR8gk-C
zM0g<_?`^?|@Vof0CyO{;!wvTFHUh85-Mnd$$ptJXzXzhvEM0*r+QB1P`KNWZQ`TYo
zN-Yybc_xVq1JHXo)JF``$d4Mi**awROj6?*4NrHb&dv{(;O=gUzzP^EbMSZU>GWSE
zm6w<2N^NzT(kY*9*KCBa?ok%NLnMl&1*<Ln;UzoIrZjY?0<{`kPBusKgiM8yD-dyy
zJq*wH7qnA&j!Xshlmf`0RK1JJ(j%E<?j)9N?6;%o_V&nWqewnl_Mn1W>r2_2sjFhm
zdu~xSc_$}d$;yldsd!yU^!9e~M34oYJ~|7Md$)-bg!%YA?(wF}*^Hz1j3e~)*}}+J
z=<Qh<g+4;NKY=wHPE?e6ZUw-T9dNp&Wvm`4DvBidK4#B|BVJCG>x>|$d8Lc@2c=g#
z9ehma!N$R1wck`+NgXk9Kk8xKad4HF9}IrM2sl-M4>Pwv9!=Z!{r>YOSMF=2o~p-5
zdy4LWIs_4Nx*z<;>EpTDUdc|famB`tEi5dIhB4xQ?F-+8X&|k7RiOcXHTcvuTQn_K
z+ba<Bu}&`h&F)Oyv!_r0gb}ACCu^yw7?r3%Iy;jU#>U1~GY(Vumi6CKQY@>cL_+YG
zjR(n?AMZ;r)ND^?Qp&lN{kYBuq}0!x`_{roSMgPJm3=0@Xysa=x%9N;*}Ih|{5?O!
z``1=hGPAN^-;0ZjYiT7vJ-(mOu_9u~Zgco8D{HLo_w@Ai;NW18!bk=Df6Do<B9j&c
z6EW`*qmYzvZ_hf<t#xlTE`M8*q0AkU=%z$?0?DIYHVKKGGOojT&7CtQM51Al2PaWf
z{Qeq&oVF@YzFq)ozm^QGqAIn5u9i?jPK)v479%E$xkmT?RKAjul9@Wky@8DO2y%&E
zt*v$TTZqt5whXWHp`NH{2f*XQ6!|MXNt{(@7`z1BF2@TOyp&6d(7uAuitQU2;wnxk
z)ko_rtnJ+T8&2)c;%La?XKtuDIH}`gH~ZrBgd<6M3Kp4^lvG(mzUlY50B3pgQ=75j
z%}KtgmHq1gN<K>_yd4Klh>ak$Hd9eJ1h2wmB=<Sh2eLPBkPr~wHn>8#fCmG~GCEbe
z+A6JmDUAZ0G4YUJ^=;OP6oEV!*v?x8Re?>~Gvr)mi&uNMTkYD@3k!75pWn%9qCf`m
z$<Lp^d&L#ibndb-J$FGo26-9I_Y8-)Cppwj?O1rANo9>~0rJTo0eUYgDk>!vGThIQ
zNF#jYkZL@s$6$OV00X#hf_7^H<uS)&L?JnYGS!9%H54Upp0{t`a&mHl3W}4n&H}#^
z(EecS0J8dEr)q9@IA`c^r5nubY=L^!_4W0SA7m?uwLC?Y-B=QU#g{+w(Te!to^t2d
z<68%ju)p3sn7KN25Vh%IK(NyOd9kwIVHJ<4`XTuXEzQDcKCJ4g-Nq^b{r5=yl;M7Y
z!{3wTemh>w6hIEB_y5p^#A%{c_`lHEU|cU=ya0uDo^p`~z#>3V(9oFPUuab>)L?hq
zo!;CuQ97#}!z2}qB8SGG&KmqVf2%&_XxC*!*Rb1udJr@v958eegW@o5KUY9PRE7I|
z36K3UK7pImH~h?M=^NrX_zyX;XQ@V%bY>O|DJj;4y5#T;sljFhE{;%Z*WDn(g-v+p
zX|vsgo_Cf7`mTDsB@;~b0WviI=M~PvpI1`;IFdQ*37her7}YtFm@qmTR{h=*E(bUp
z9;zZZ%iYvlxQ;~;hsdPM0=UN^;c;>I9O!E@vS57H(3)Re9|Pt265R`(H0^1iHd`|n
z$zfTA)+<*lq_Yx{Fo?8J?$d9Uf!yH`%-Y5r24UiGbOJVdx7lk}PP@yY&g+RXL+zEn
zd%32TUECFgwK1=kzkVq;Cwx{Eb$C1h>v2Q}&EwIJ59I_=+Q0HGbj3ZM1=TPi7sIm%
zOE1oLw;0#-mOd6rgoAI4LX3|CDFsX5`U<3iUCBA*JvYLy5!wt!iZ1X3<s{eViReXF
z4xjLk(Gk=he6X`)O6&~q6||~rS!#6S_A&o(6=~R)bcBr6{CB@UHvLsmrFI_0=9rsQ
zeuaGT1ka^K>OLzizbFkDKs<wf*X)}Vjj~(A;)xG^Dav;e7GfR^Kelg5A3tjTRkBwR
zBF(6^6#rpn^~7%BMVxNpr#IeOM<_Z^_&=li%&K!FSxl7bTT~?gEu~$ic4nv#OP^g6
zvZ|VhBEr|VFfx&H-`;B<_i=@3X8*`k$Pk7pkB{iSq!DqLmuH$n#i30l{yN@;ySC>?
zzp1vFl#<eZQ$8Hj7jol^Uz?{y!H=xz%Qq)zp_=U4c${%44y~t+(U{`k_mzw_CQxiP
z-NjUEeAL4q3s+YxdP?d`zn-uf+|GC+DVNpy2pVb?(Eb{_T(4g4HCLZZ$w5_?cvSd^
zAtWRW)8x^2=8(z>6W8*64L_22qs^Vl0Y?N^R~jl?hFH#W3@EW`hSblvX_*=baDeth
zW@vWRf9^>~`%J$kDD;l0&^(>Bl^KHGo8;Q_x*?TrZ+K)_39u?{2_mOm(yuqHM69R^
z{idSKgVFJQyRIFB`)ucA*->@W^Nz-v?MDYo;leAWd@x{IAHKclmtp(wJ#=G+gkY|j
zDHr29_Js>x{oR=o!%6;T{O8>z(M4|1GKp_^pYFQGly1OL3!`D5tFKPrW!VtN2P;;}
zm7LP27FXDi)f|^bpizeWH)K*f@-fT)x*3buZvEQP(&jCbd>JfkcCrb6g?D8sNA&w9
zszQ^0+lk%w`7D$DzNA~sunJ<eoeQ3Y+_=fH@gp<$+T$JH-Rya$sNW*MBS?i>l<9Ro
z-Fz(3Q?IGkSX?cMxNg~hrhYc_{WRutjLM7oQ1YM#R2Pda#M*Mf(G7xEd%xT>F!|&6
zhf)nyEe*?<J_BlJ_lS=4CI#AG>7v`4EY=<VDR&~1YODr(F0oJ()<p@;&lZQ1?@Tss
z*jO{6LGWH#Us#{ZBV^aWxkB%8opGKK4g7rhHZa{-ZNy<e3(*`!mLAnMbFPg>00dQv
zK+K}Xm0?<eDLh<%V>3>ptE45~Yo>K2B?SezCpe#2IkM{OuOpsqr!%dzZ4l?0AU8|A
zlk*G>h4;GJYr4zf6p<3$wX3eS>mS}2bHiSAC^P-=nL0?bE{HAVDd`VFUNo1m_KaDd
z0Du3`tj=2Erht9qbBB@szT2YPA*IKQ0)A@_>gK4VK_4@SP?@Q;LqbAsjFw^m2PyS6
zY;Q2Lo0)3Z{QRq;awp&G_TnctOWL7iV(ApQljvh0;Z!0}!1MJx^r<|Ef2uLeo^(JX
zUFn+^9(k{A>esU6n}XOd$#XvMW7&9-m6f$xcz4A_NtwIr^jYM)RqecQH=&>$aYayr
zo*_lq1eeVZ<+Aw=Z@1HZ56B9=Npp0=U7ZUoY8O~kf_AB7u8Rnt_N2l`7k+D8{7l`2
zM#PSAQ*D1py+zK3)WY>;VlDWymkj{e;_tCZLcCc)=S+;0#`*B&BWH~2RE+?a0E%~i
zdB9?&d3r9TUc)T&Hp?9LS4@5>FJdt`XuEh~k6lxiG%(5j`fO)u>S(%R&lW*(Ky`Os
z3$mJV2{1EqlCc7UrPwT@yRmUjuD#KEHr`g-kY7dm@K~yL>^N;!&MOuOO8<H1j3ycp
z9<{yU=Cfx4&Q;bC%lg&vhHW4B8IUf8kHT)<O{JuE%DrD!QDZQ_Xp%1WW!htO@tC8{
z%~u@VNURF2J)J-%9e!Dlb{Y9bhUaoUgG#w@8x&YaeY~E_9Z!_HO<p+OUeNx>3o!R#
zj7QC?_f>aQd~nL|3TCFg$;a}VzjtR5p+de^y-(p`_rq8{WSKOICwlj>&2_JV)%A2+
zzdHu)1;Y=JlsO%?Wh>Z}Xo~VZ@>4~_SL(c_QvOt|BsB1RwrG`UTY8f5iF?NKSj-Qk
zpJ%g(H)|=LJOJkdRQC7p-wM;QnAli4&D!I%03!Jcwi7pbxs&OMqo>Wj;q!OpLAnV^
zhi1|&wl9$=x|lyQ%`1%|lX@=hAcf(xTDUEL6Zuf6@fO*8#v+>{7qHt!O}DkVzjKy{
z0&^#Xels-Rq^)}X5mxBYG6}Apx&D&^(>#+`H2!)LiTtH+_@2|$837=J{>CwC(kcVm
z18}&2A5?(8vstIdSL7@nb(xT_>>bH<!9i8-&vFXBh4AfuX1$tUz~t}92Ge_Sw6-_b
z#0v-tQqqO>fefJ8D3?)IxM`^t1dmVSKdm1Zc`FkeL!s_b=N}g3oP<I2gw)rqYbrHX
zzeXwnrS26_?5s3~=-v)?;V3cNzrAyq!cF<X1?}T|!iqh8$}!*O>>KWR=n(8N_(Kec
z*o3?uT<z9iH3=9Ef^+pRrddaD<UMSDUYHY~tiX72-{QNkGW9cAT!dgAs)LF4MnGu<
zyb>s3QM~B^anh{}e`LrzJj|o&q!qQ|`nN^GE%s0ltGZ|FVP3GXcN36=kV&<{jw2V_
z1k4)vNvL_p0Rw1uQ<df>*sF}{`IT`9Yw)U|e4=PwEQ>;dEcT>xv(!fMr{NH<w)vj$
zcZ`9V5g~TB+Zrt}>`UGmE1CmDrCK-YrMttLfMK9Ushe&1L90VaPYRuO!0>3Ps{`a^
zx=*hfkkazf_d2$UugPMj*4x{g-F9^vFt{Mq0(L-|9ndzvgK(zIDyyrja}=^EkqSO+
zC0I5ZS4~AmL>T=2J}}ST7`<U7-9=X{F$iVcGHPpFsS!7wC@&s-F;C}u%k6$%3^t(c
zY;Wy?GZ9qluu}_!P#{)JPfzD1*1MkV`uh4hAFW15M*}^M5^1bZGa)9X5>(27u?3(L
zvF{-58SUAR>tAYeo0(%YVP)&?Y<h6-lW26x!eG5j&qeFeu}PQL>URdhUN9N}<_ti_
z43V&2ASr$QTGQ3(?ChMAlLLiEL`1~K8iGn_{`%tR=JEv2OFD^TWpy<fh!Kajpp@Yv
z!Vr?HT}AUxJVJhUgwhQU0Fh@LF3w;_bimud2>lDnI;qV{3rH0^qK@CqHM;T#nJ)#W
zUhdKU?C3m(x`n*j9py+pp;7#3J>tF(NE5jw%tp68OK<9>nycnKI2jok!F~n0@m#5X
zchl`jQC^~1g&G<n9JGGok~fk|0LH%LkA@#CyaY;PBs9F?d9W6L+5bls6Mr3%3%}6#
zs=c?G8$0=`!ce?E)bdb(S(GFGEmks2{OFu>7%5p>6^2#-<}do-K|wtE+lV1dQZhC+
zl_|Vt-bqry3QLx~idu($2X*JW1*Lvhu4KnqXT#BaRfyhcf4;@1=v`-<5!2+317yWU
zAiU(K8S}rEGt5rCgP9W1`1<<#QZDQ|u|KS{QNqRvb(wCIG^R)rDy9w0w|(}x=mI~s
zsy`BsILKzk@~SgTQ6rJnnl0xjddtQ(zG73OK^sjphcBElU{GeP0ztk30m@2B##c+Z
z&Yb;Bz%xWeNy(HCbDc`)l)UELn3eVCs;-}j5Dza`ORT_8*>kLzy3Tg37u0yJ7pXuK
znwXe?!jqDcqN08f{O#=Am~FTM`qrni2@d;BFpE889f!x>={F7{m_qJxI$|b&LcNrK
z84#v-iR*{jo;7u@VT5Axo9sKR|IuSO0TmF~p|2yw*h_6zx~P(-n>_D8HG6z~{PQP8
z(C>)I$fIt$CdmYr9l+AvoK9JSgRr=`I9X|~ewVn~^z_AipV03uQ*#0#p3tD3%(I&)
zk;Q|3&PvGZ&B^O&3~bV2HB8l*26juJ(#I0iTo^o8HZl9v-@_Z&JxfXR2HGnRNhUC{
z=q=MWUiY7?iJmG^MQ51Ex)U>)SmpZ7ywD#48(;wiSkV4TOL%=$DWZMmoa1?!UwT<E
zXajx6e?IXys_E%saoLq4jF;&=W`zO~h<+VYsdtxKMbY?5N=i+)ms?&iaJ$t@=p(r%
z1Oa4CO7iFczw##sqAF~tRwt={=_dCRagulWd5ncj`UBC@=iWh3Sf{C`mMi)B?3qho
zzLZ&2e)^`R+&8URKn1Lp_3`B8=El?O@&oTip)*fTjzJg!JT#W7>*R;U|I^-B+`um&
zz|>>s5Ba3^_Vv2xUXe!<yRAW3K3pVaUtitbEQma&$$fj1!h&+afloLbEIy=xY`O@J
z^_X_(>UhZ~I3#q%?Bp0(PyrSmPwzAYz%^$y)S6G0Z)}>D%Q25^m%M`JiI`B`YIV^0
zkt-leACK$v1N$n&zu&*b#AHcx(~vST9ZT95kac5`s%EZe7BSaE&NaROzgL0$l~65<
z-xu?c6g+Au=dKk;5>V(gXjcM9M}wBCk=B}AuIg(z{CDXOLv>sIHCOa<OA|YLVfF36
z+^UgUwy($1X(xaGZd5fD6cohixO)w}7rAONlR(_OIr%JbH&b>Ot*ljZ88>~}Ln8Lk
zEgd(u|79j9L&Kc$k3s&LYx4Azm6ZiPyMYW5K&KQLDBXHf(&&cW0W`q$ya-(YnN$f}
zT1s7IJS1F0_~c28b)&ZMAB+P|79VPquY^JpiK7)HzOv7pIfZ$R2Y-OrA4<%dyi^@c
zr5yT#QFx^E8b(v??=m40|1>MwKrJ=^MI2y?TD%eF+>UxKPqq;FzJqWB41!8D-NeP5
zRo>JnJCSYcDg~x|01cV8jY?#ISitF!&#>hWe`#lvTOZtWLkL;1fAp0#bxi85t$FRz
zZV;~jF3o4WGU;J3W%Kz7h~Zt2g(t3DA&0dm`~}HX0<0dKTfz}P0FD8&(uqI5h12Cm
z_Lc*=8M;~==Nzq8c^m890B|j;7kG^Jyh(9mcms&8hV!AuhUZ6XK!9OAf)SHqwm+ta
zsu49lnQDLsd46p-8X~>6BkTnL(&ra4P;0YSxy%R8tHLqI`ll|fG9-aIUq5j=4p_N9
ziO*7v895cOjL&NDd^Oy9R8J;`hn@OhIz%40Zs@Nk#PtXKj2tukb@>xIFy#?s_JBGA
zFaVl&s6)aasyClvIq5od;?hc<022Oq1BySYpTe6rZL0N6pL~FfpWuuMalr*J9i_#K
zP4vp%7YPHEkYD@K0LwbFC6d09)q|n$GbaC#U4}`j)fqCh&$CYKemnqZ7Lbi9wVONu
z0;s*LZR}aQAxe|sIuJalcb3q0)8$XpzrDWbnbwG!tAm5*3k-ZR>4-%)#aKz+SxPMy
zQR$l5Rhn-O^Vke2pN~{5VwJgE9^jE$`>n^1AFuw6vz>E4+pwzzk`~yTXKbOafK$;v
z`z-)$Ry|zs(BONG2}DvXw)&eo|21c5eX?S!(fta<J0O&e<cs4^r`@!zMExa|w^6>|
z6r?E!l_I<V0+f7eH_+1y1`0kaE9>jmV6UkWJwZ6Vhu~)t85EEA`viPtQ}-)H?m)1l
zQz<6-mj+qmB=0WAnxJ_==$MR-rhm_GZ8L~Q6DG~fzTZ9Jbk0*5Z+wr}2jC#YdnmN;
z_h_{{mTqr?k>sOXM74VzO!E+@k^x@HEe1dG1#hA&Jk-3Zr=awlp}ofSbo73UAHG9%
zfgp!HX=wj=oo4G;ZOj{!pU0mM<9H*|?tnej*l|zs;Zf#VQ;{t0EFjF<A01gb|2_h6
zOhQ6JsX%~AQHb645+16)Iv%^ftGr(o-(0p#itBETVNX2s2~NDz^+EecPVS1;m4nu}
z4jF^^mKxZ>&%xYxM!vaUS#Sy+cBd~c)>3$FmXV=y%Q>x{NktI?ht(=-FiR~1&c3qE
zhb-&H|2L>#VI6S#gfINpFF#-OhbJ*PU|HwAyFLO7koDt-o`FH3W?f}{z1zlMrb&@*
z4&}?h?FBe!ewP!n8Zhydoj*LK><__b<+)gnZ1qP|i1~x)1$gueqY+Va`X>FS2}+~*
zfCw?wMBzuy>Bvh3rc}*ZyPA_xbvkw)>qWum;wGfqfEk_N9YGr7dXJ32Qsi%;5;Ft#
z)5cJ?5^dHBeYQ-0COLzYtIO#_h>?%2N>YvN90nXB*h%q{uU9&{qN$4PN+|+^aOf&0
z%mDgV$d*<@pWHdc;5lzLDEr;k1~O{_cnAPWKyZigT1$4NWnrmwru$(%4(AH<etl4i
z1+ilJ=%uf0H&`N&Z`5!m#>)QzNkFoK5P^e@twxOvjf0E~A}Ywz4NeD(K(>~{`p0<x
zc#S=as;GouONjw-vE@A_wIkeTSqF$KDj{LZjspf@@B3+0o*>`?W@cvQ=a9U-{NdqY
zRBBjo@HDt906zd$0&dx|>E?r?V$`>9-y$Ly^g1wCLV;q19^j*ktB+$7O&@^aMJ=(j
zn4==$BcRMA^^CuKhBrB#;voiA{$mv~cCh>6tKfv>d@=up1>Ns|D&Z{93uX0ft@1`p
z=y9R<MPup(A69gLV$e?PS#LD(GIZ(JuV1K$0%yB3uE*;odYutK#0KykrWr@uO-@!;
zhaqm(c`a$Rp^(b9t}VY6YMy5DWyZtj68h={!e4F+3X>GD`dTOYGgCB~F;PPhGAVH9
zOr7x`Imp%${-JWKtHkv*LJCaB8$pjC%Tbo0O7B{o2YAh*3eKx_;is$ehkTWH`dv{x
z?w9#$6(&ckJyp{-K{jsV#k%cb#LC_tz?R+B0t|Io4aue-F?NR72}?T_2|s<#%|+~6
zaMr>S;=@$TZNYD8X`zknUa>J&@fk2GpD@G2hM<Lo1>iwW&*J2Ka6l*YtmZxp2oM%)
z&dF=0CMNYwC{ja{r!m*QlnFlneAaO99UL6w<s)o>dwiuTkGk8Ngj&0ii;4;bIy^d3
zw?R_d?SKJCZ5u`HOz8>#PyL^z>`r&LH;fDngF{0Z>FLzi64KJWbWPU`&WFnYue*Y%
zFPw1zj%?A-pH9FW+;q8__vOnMU?bM9k1@@VNlA@Tjh=C>_{#{2+SM<y87ZaIh6V<w
z>jR%a4W?dc=Dgp61Z0?DDv8WA{2NwnhBr2#9!jd3vyUFKS{F{cNZEuiiHLw3%TIvS
zpsXf6oxc^Y8DlcdSb#MxE$!Y$?!;;Ls@ulccq8yRRc{jK7^uKNx$;vj22a#}#7{N4
zVgKb@-+ueHsk8*Z4^)8~O}U6yn2RY6c8*{=%^g4u;5o1u2r9czs307DhC{Nku~FOm
zq4*Mcd!QAXZwb)_#O@1AA5IpQQ&5rT#-vlOo@KNxgr6XizE!=CV?TvJAZAVIA>?!*
zpx1dXDCldmJZ0IWR6sfR31bHdaty=QPgMGfS=;M`e@xeSz8FN;_|xR=W|~Cc#*xtb
zFZ~%+4`@K5&Y_<d&`LqrG?!;hckZ1BU^KZ35ql*_XRuztCh(Z$A@o#~n<Cx7VQ^s*
z5ZnvVdD_90U+&EmSdi6*vv2>&#v~u_$2MfBdLt`g*7NQLWX)1)*KV3B5ICf6Cf$6v
zW5l?K*vF~K${)h@pjXwW<1p@16+XTeaNFk6?d%i}m*aK0=Q!Vw-=22x-R}yt0tXAg
z|1yibVnOQb?pFBVoy=q`59TTl*`;=#N$CSD)6Rx}&=iSpp~9?#4NxNV^)qeia&sv`
zaa9rFCKji(v0>+9*6&1P>5DnoRpEMERDbvwB76NBh@1Ay9p8MzQ#>xgkS*k!Dpy}R
z;3qf|KwR9G!}l3w@Nb0=4+n?Z#T=gx;#(25?l$q$^(UA@Z>jms@U!h{00@r*ladj}
zGYM<|bK%z_3%9FJR$UX=c$KgYFCS}$%S6Y*NV!_9XFXh;>4oOPazp|-aoB%5L3~JO
zG|rq2rGCt|t*(o95z|Z1`!fmCUjhS6G~K0@SM5VgJx5%vMi+=B)Kh<1f4w<&X^6c_
z%@I6u0G%HEOb--ce3mgNZ3)$0@uFQC?5g}BobGm=j$m)Yet{6;N#Qvz9-d`9EshBR
zvs~*!a{)Yi=+Fb6iYkD|SAvroa%s>4s#8!iL9H$W28>rWk_Rea{XXL!4^P0>mg7=%
zMGgu)<V{y#4Nn|Q+8bA8K{UQkM#gmg-pSP(e-A!EBzEn2V8<ep?0YA(djvaim9oR*
zkP9P|3Z)`&$J*mUz{xS+4j6|Y$UjrFDZ6sg`AGzu!@yV8)4d+MteB|0<B?+sFuK3(
zMG*AZMeL(zH!Lu@15WV-LAsyujqTSS$|H{d+WQ@0@Y4NdssSMFM7(6of50WgkHS;m
zIN(1dj~N$}hdaeWk1kOX#DvNL=~hvQK<axfkqUoP^2%$*8Q@b<5_oPUMbYSJSRv=y
zAOC)vkXJLTQSQf2-1hOWib5|6Kl)?x$D;VABE|oC8-35If2Ozd0S!5niW>v}n}|GP
zaX0USVaB2^Dft&zsmR1?uL;09@qp$X50DccJ$eKJGH~ty=d>ca#Z)B?KwK-a+J!ke
zojpC~`~SI?=f7n)YHSopP*@mTUI5}Y@TiiJ=>aw-Hl++G)LmWTNz$w?$41PLkDj*D
zN061|$0(0eUhyeJ#e6bFZ9Q-O(*<<W2r@Jj0HG-YU-}x|-naFjwlX=CS$cOz!+DAG
zS^hV=LsDG)6105a(5SUpYpAHG_^e^!;Katn;Gu%<pb?6{5?y`Km2)qBF$2@f&5`)O
z0OC3dbo9X9v(+o64|nf!^j_WR$IE$o{6A6?Jzhvx3lLu=;}|l5-yPV2ChMJTeb)Yd
ze<x5Q_u)g9u&&nPzx>0LPx>D=0?ibBk0n9szQ1S9xo(CwFx{US44+A=j9Pt+crbAF
zUvCWP@QKf|=SXrPufWa?MkRY_@%eA52g6D=27%A$KXcrFwl{YN;wrE^J$dp3JBa+D
zZ|Bo=wYBMJejKNxg_hPV=sBWg7YPCFSI98XH+|JDJUlM}RyMl~WDJa=+}vMj`2SOj
zPAclm@bIvcCr*5pfPlaVRZc}SN)~ALYQAs5k@W)JZR9j4fG-S_9|qkARHv{6h}`_h
z2yCb>|8Ex${W@ra`v3lgiw$%I^Z<cY!)#-<7=8T!^#Ut`H-YJuoKz*$TL(_}Fan(Y
z?v=sQI>r0IvCp+<$HDwS;~0V90azv0O)U@wGF{Z32yemSWnF~o$6Jz%4l!AfBSRcX
zy$e?JAG`_LjsLCf2k($v(4K_q(wv45#_sg51L=PoyMM#MAAE@z>YD579)qi^xx3lJ
z6Q&pU56v7pw4u@HRx_%^1bhGgH+-1Byf+&%;CKK=FfJ|*BI!N^%tgZY@3_yNG1{(5
zs>Y-^Eq-M*?BxOwIo4Bb>kxl6`v}aI=(Bqi4{CE;8yiY0D&Q~}Ez)iRJUU=80kBk!
zQ2?$z8=L!d1U@{Qb=~Xu_A1sb2ar^1P2Pw)U~T$|S<EMo`vowp32>f2x1Tbvf_WcC
z#0@TO=mTgIFdRZcLML=`ja{+VkI`pHz!xY2(|@A07_lV4=l~uVkea|n5443{z<b?9
z79El|dEc+ICoVvqhAHl=2TovUfQ?6k5?C#Xm`#R(zvS5EW}!78`nN-rK0DrZ1o7dJ
zP!I5&Fu6H+s%*VxW5XS*h4BL55hOB=tv_Zg%8H6cDsbm~oD`=2^FG5rq4p->x;Y*K
zBeeyVCN(uRH@EBW?>-T^ZkOEmOpucU<PKPUBM^AN<j&OEgE7pKYu~eCue3i%Ap54$
zQBiR^?dt%xe`#sS{cs!f|MYkk!}t(E^y_pcaj6)h1s?0GVBS1yZz@Y=c-(GoJHhcC
ztOeofkh6eiJ(d=fBf|w@r<<pqjv*Q~HkqR<piZ1KKH-xYDi?`>-XC_qqX5}dBQHhd
z4uqDzYRkiYUx&KBWLKwKQ?*zSPd!AUQ*r)#>tk07kS|>NlDQ$B3bm_~EyuMa$KH76
zJYal<_W!wZSaxbS0IAa%V}9SI(w`;(Kv0nuEI3!FO5Xn!ahKY|WMpLMsHw|Us3H1$
z?rEtVt{DI&Zky$f!NEbf3}LBwrko2I94xG;`1r}|cEGE6{d3d2?M!%}r?cT3UcPxF
zl09@knV_uz5RRb92n3mqa8i>fz!-##lm|IoyRq>_rvfVsq7MuNGWCc_!CmG^o-zn`
zgwX%7DhMF<P*GC@wFFqMclQxoYN(W2BI1WG8fq&hze>zVrGIKF2^xwlE-IpV(J6@F
z$fW<NM?luk0zC_(qS2|Wcp(mTgb)FGz<^Ocr<S_<mQ79677iefvIz+OV{r)!3|wTB
z&K@#${ftT~7(r(4qje#bVGelR0>Df>)Hr;qE<#85wFV%putt93A(L*73<dstdi;;d
z?d<FXgzmF9()pYo^Ua@$xLqi!0KW#B{LVm)v$D1ZcH3K|z1l6>6-3(4&87KMJENyv
zDuL->V88?8)uIFVyZE*I%ANN^Onpc3O*o*y>O1lc2MXs<IYX^gl7c>wJHY;ZiR_Wd
z$wW_{=$5E})62lX0O^2_0m4@7eZk{)4r*1fTh%o+lK_Dz`~u7g3H@m80lVk=z>*s4
zV~s6DtAd0eHG=~D6gOOTIXO9Xb=S<1Djon^E6t}u@t8N`k=%a+i#@3?(|}GtIQH%*
zBg%+Ax`)f1pzw)Ju@dXI1QiqbP0x#NZRkCgP<)nB?x;k;Z5L>>JY;8igHP+=4HTEL
zq6Usa1*B{WkIP@P;H+0#7(R_#13W<N+&wDn>gl1OqRPw9$AVuVzB_Lhz~KHdwgrkB
z(0+MW55f_4P-cF<8F0)=XGdmjfW6{*z7P;goL^X&3`94IK;Ljb6dYhLDK@4<x*slW
zruvb+X8#tXh3BLjGamvK?<Uj^{rJN2YZ%enW3t@%>TD0=(Ia*s8UQr_C>z?YI~5==
zJ$m+fd2MYh37eSs4m4Hu0-N8}=?)qZ$ahOaGQ8YCe>Ct2|9T&`VxMMw-1MF`C>u$p
zGBJG&>lGP2V$SKH_Z_A4t)IpcoL7QH?ITF?_0TUr6f{HuX=&*jj5|bB+=u#*C@j0h
z2Naqq?6$7~Hon68cpqUiw=txP>ZG8U`qRmSB4t-W<6(t900e#~put6l@v?`)c>%d&
z?dzLlTtFuQUO@9M5wU%Di&#=pk_))g0ksTx)7LV|(``7<pl+;JzTuOry-8OGr<y5y
z6Ri4OSeCt2D%5ixSLPNLg@=9j1Q2j^f|JjRi%RTqo_5y93DSDitcz*emnR%ah3q)+
z=NtL>iCSNWsGfsfE-RN6p#me9%p$Yd`kMOsdcgEC8vIoOk*rhCWO7L-3{3sCA_%V5
z)(7gA4JV*AhlPCwrX6IU4oho-GthIubZ1>G@_)_lYVQ3&t^>^JVassiePb&h-{=1k
zDDQeX_qgB-_x{XX$VjvSC9HxL3H+vX9>yg&aK~XGFkVi!#aBjI|M>=v<cv)>jV1Nn
za?UKbCwp`X+oD53Hy<(riE0d3cd#-uh+|TxEl9+If`W>Qio<g<n_#6!HtZr_OXYfI
zl}e3(ntJd$DI1JSCYj6mdG{^gexp7MXnUONKHLc}tew&Q@|gH<nogBW7^){=B@^zx
zgNdnLWd}rP?o?K8oZ=%FIV>DHEwi!-ptWQH(&tX%jZAWISlB%nSpj#9j*9v&nRTDv
zeZyb#&VUW~Cj@_NI~O2-@1Gg%nU{ASx}k@65->4D+**!os_m9r->oiY?wM9#*DEeA
z1_7EzrFfps^Fm_gRKDv`*J}k}EA#|nx3`ZEA$mIi+Px{f+Mcf{^%HVI&ck@YKt@Be
z0}?m@ggaAJcc5vC=k|Q*5?}6Z5UI8w<)>r7dr6CmerL7R0<@5>Zkmnke_HDmRMpCZ
zrT^mv_~;2xJ1`gmxvvJG8K5DIP{4tp@eZ69V$bW6+FCrwDBfeIVhl7WXo7ory1s=}
zigj8*n|0IGE~kD=Daap}hSI>t4~`nB)Zx9zC1SV$+?cAe1pVJy!1)EWwYT3LcGCgp
zY;F_hNc7La3{dGxNJ$}h0Z#zH`$4~9p@)wCJd1{jInTQpJ%)y(UQX6&D*-#ZavORo
zs_q3Vdeosb0k}Hb3f%atN2EO$uQDzGOfinvS2Z{7uY?d<>Fw3+Oh9bEx5=bm+_%Yt
zuZ%kw9Xj^TO7Co0)XnSyCxVA#yEG7RZ6>rqNUWtRQ5OArf0|CWCm?#=U%pi4bO_KJ
z=|tJe&q}vvn}+uxwJQ#AZh$6fnaL>>e8_)Yco!}WvnCakNGC2gV8PLedGfD^49dV(
zkWAt@nY+7n1Xu-tENC8Z62LDY>jCpq?t5(1mobM-kN7O-<fc_^y<B;Z2_qmvV)COR
z`c&@ut?s1Sae`KPe`YVwazM-h%r6#E=q@j?SmV+p&32k_#!zWFS2(r>n%^5XU@Jiv
zI<WiH?KP^`+HHUng+HzJ7~*i)u6Dlye0P&OFsXu`iW;|9xwU)OZEbBJz1VG!TZ0t`
zq5=&ao#n7=ns`5ch;ZEuI)omuB)%8eq9ZCHVG4R)fg}sa_kX_8L#1gacvd1gAeN2|
z4+94xm<wPd{V&Q0KkWQRRa@yc04MnNu^@QZ14Ls`c9HD5>%0`g3c?<tzH1Kn`C72a
z9+ZI0?CgL5prVl*0wLVKH<29<5f!{c;NENuR3Kh3<AAxC0W>0rBw`tXa^&kk=qxQO
zi;Ro}u6eLHPMNkgwzih_2Y&!4b=VvhyDwoxsp#tWy=^`LU#<@#TJ4}Qu-E6_^-1(j
zml?{;>;U?CbjAamsVIC<Rvu>ID*5w(T;d7F1O(PEUyvUJ*2r()tS{IBwHwc9C=Fn-
znp*t7cTP+*R`P+usQ+5VzM1<I40IOUu+A~fII666^As86=fZzAk4Bz0EX=*?*@vVX
zkLddF`iuXqaQOeNO8Vf9nxU_Ym3gzr%a06r%1Ss=!6{g*d;XV+`u+XM5I?%-`S>oR
zjTF&t_sh01%J1G;U`h?)@zfpKW`cJ>fOj~6iyXj#@>}IAa^|Ek&b)A4zyLebtG+2{
zmAAtKbneXp+Em?(A`rYnLj~$PA_^0ka?rsQK4N`ILD8|}5S0E?7Y!hv2j=AgZb1+r
zbd`Kon+R{<Z#4lL{;v{I!wa5UQ|;GvOr||(2v`ogwhL~QTQ5WK@8gY89UV=J{Co5O
zo^Q!9U5){x$P$1V7QlX+@IZ&mJp!Z!|FmWu9F$-$x+M=xI(zVf{cy-LE-<8aa->RH
z51iDpHZ0kCrcT*7EfF;SNu>NANdkTts#b7;1z*KwYN$p8sYq%bzG3_VwAa1@9LuHe
ze^D-psj)AF*WKWtViD_OaM>*;8y^Oah~@YCC16y2(0XuSo*&ES3k80G7yDQi_K%kd
z0-O)DPeI&C#GP+X3Z9J)mUg|)0Tl7#5<klj9#j+$(td9(7F<7c@X~?SJUu`zL9le|
zKwP>JhIBl5h0ABjz+SrMgAg0S`)_~Y88$76l!6zkeBs0J)i@Ug1Y!Z`$ROQ8_}C9u
zU3UT>m>$$O@O$my37N3J^U)=E1E2;q8!UparEMiqa!P*vZ(RYym)ab_<l*MVB{k$w
z=iV7XZc#O5#=;A7Nqv!{|KV}<H}<%0Jz{bJ%jg%QRrg+nf-jWZmmoWVUiH5TtVQ5;
zBE`kB>=fNLfWijuoQ(q~NC$Xaz!fPV`GBkp@Ti-X4<K358Gzb%OIVcz%&udKk1hcU
ziHgF4M8(890quzns_^`L>VrkRUjT)Jeu4Y9k+hXk9#_BZN*aWNswJ;Qu}SST74+Bt
zm{b6!2I}8B`LN}8@*5PI4-vSrn2isBW|(^*4UT?1r(>aw6?i1@86#dunPJ~4A0ZrK
zOEMwwC`Zm9$_>oUg=HD5#*m90@}Wunrbi|9U1BpgO{ECo{Y~nN8zKxGiurv1cu2|D
zksv4&mxC>hC1gB{UhwUI^4_Co3^-*D#n5V+RH$itT<AAw6%l}X!g#EZu<xjAU)mNS
z?t5G4vR+}iV>a8N^xj_fkNQ<Uy1;Z2WW3S$a#5DEUkv8iq%q;m?_am_Ih`U#|DQEd
zHRgrCu_Br;W{T(S*ge<uzf>t^`-vdo_A}jVm-UVEvVnKhtO%>dSyp=J*PaZf{3RDb
zuO76BY1kz^;wp0PupwAGaMFm^nwA(UAKDl$h9JN^fj7$x+l|pbI5Lx@vmdhNK9`J~
zBG8A8FDc!7wgM<UznuJ^sHCL`Tfo*a00i!`kr}JyVhr%MNfZpk-!5JH_Ive=Q#3FY
z*9w&r-qb(UJ^}p|Qzd?LHA2C`rM%ZaV;v6yh}^BLin~wlfSbh)%l?*k|ICnwkyAZl
z##f4;p96D)TQuq=3CT*a_m^L!E-Z?&T=UY3fsV<60gj2<o|F{W?~9v3tU~#MJm~7{
z8^P}~F>A=v#EZ@5i^-zTD0X^t?Sm9H6W8PVCUVT<lriPPZMv&+9U!I|3*~<g51cj=
z*bR&ApXEV;3K)#%`7rU#)|f(aSVBR+pPti34TA3W`RHMd^1t|5h08k=NEH81TVEa3
zRn%>JkQNC60TBrW6bb2W1q6`}MM^?KI;2B7BqWtq8Uz&S7DWW4yIZ=u@A~=Oci+2X
zJcoZM<8Wf1efC~!t~uw*G-fDS^yxuWi7#n+c5&&vFVdkzZ~_mQQD`hv+B~)h;#_rr
zcJZ@GAscm=k|ADjTD;0g>pSV09o#$NcTvajlttpW(*A_v2K?&&gXo&Ww?vZPNhl=<
zr<O*1l~cIXdun#%P$lEAdul$^+*-MY<cQMEk!%Mhpr+(VGA^PCdsty1=YPyPEL2cy
zkhw4&>8{EOf8~=)zl&|=y#HN#7WZQsR7r~J5LzmMNA8z93H{-Wj3qu3gCDu@@W8qv
z)lqT@RcLwVr_qI;nX+Dsi1Q!j_hiC--^Tr#hP}Bjk1c0rhFcq1<N&pRJ_<>;#Sd(M
z5(lm#e!gz8sHK$h!Ea1>X1B>JNaGTVK|zyyHXJDLZ_xF4D_BLICH&h%9RDCCD2H$E
zFJU8jKTd)_<%gm!fYY6J;JDoC@Z#svoFbJv>M<@C65+c_=a!oYB02jTzm8b0O0qr=
z!h-X=W^$!0++#l6G5L2@pPXUrT6$Ti<w6>Xs`~&|g+BSttfa1E91(ZaSM16I;>tYF
zNtzzSMq=6%to94{*GB9oYFtW3279w<h$K6cKN@FB+#{s(BW-5Z5#MtV)bV~!E$Dzl
zod}CdZF8$Sa~xL9ZN!`H+DN8EhuK=U?{@Nmj^}%oR*GojO0xU?U3W7--QBK<s;8A8
zx^y9h5h0!aB1Bh4`IH|3Rl1N1Hc~Ny7!|qPndXlI=AR@$_k=!H45IB<<<`FM(x=O#
z2{Rb6>(Xf^zZEeqa0}TryyNg*YCI(j`E*w{O0oN0e2hx@=-TV*Yo8^ud;9mDdHGAV
z)*J*GWL3UkHASFjs%5G$omVTJ@&`d8ZBSsI7GutxfSRw|CxwZ3Qk-W5$_)=+;bpB3
zm+F7_8ChR{Y{o1jMIx&*-L8uBkt{E`Y!UqqPUBdm{Ysq}U5e;gpO_U9!P>#tSSr(q
zI?TTBU8~P`_ZC)qe3u%7ess`?ef~EKHrW^7C@-h}l`lOYSK7VA0%#OEmWcPoUnb@D
z4^Jfbt^ZA3{3`&kc?X+Ym!}}KtHZhfezjqlc(;|noPb!-^;c7D_I3x;zwaSK+}9I;
zMp&d@z73-m8PR8V2lbf*YyQ?(o7y5nO+@MJIP~9~esGz4aiKjC3IdBzelsr}fe7%t
z{^H=mM=S2u*x4~U-RTimMupP9Y5g68au@W{zSxB4pjKb6S}6o1Gzl{I9AhR)Ug;y1
zQ6Ty*m}H`eMpz`KT?cEUPOULakV#($u@*$sBqSuhT5&4EXI7Yq>ox_4`hRZsE!-~b
zEWq~!QZhGX7>E|cE`;HvQgV;tW=JIBFfICS`)P%U`%dVy3-9%*0z)3t8`#5uA7OsG
z$Kb+=bqNu1jeKg(QH_F&pYbpMHLojMo3C(=r0@6Cj>MqrBlcU&Nrdec#jKtA84R(0
z?f$dM3rqNxMa5Ukr0?UyxA*jJc-1@OqfA+ni~CG}>qUf4PgA{a3fRpp;|n^(r5gs#
zsop$Pe|H(m&S<5P%6xZp*6YdQFr0r_!B(PWWK^Q@!+uQU<_Ha-63eVOTjQKKs1G~1
zcetVSmY>qBIc~aDo_o*xZ!T^3o@~Tob2AnL04<=NrBeNax$TwW{O<br$%ti!g3kP-
z+gq;wzHT?FbYV48E2VIZPDrRSXbJ(bkcp8IcdjzuJ7Eves%uD%mP*;A?JO35vu#n~
z(>)7`BauMGabClhkS^to|L!dkLvje#8A1vd0--WSYT5<F9eqg8KHUxQ_iqPW2MYW!
zGs;qeISFtFU9P+3rTxA)?yPVG$pq{3vQ=QcLuOiCR327t&-XHjmMwdkb{}3x0tfMw
zS`4JiVy?S68u^&uz19u#E?|LR7JUYeh1SMQG}c1W{jH;QPQUw)EmTueJzZ>VrhlXD
zWo^9ADHA(qQRQ1E{^43nO^%I?q<5Qgez38%@%^s!>{+fpeiuf#ua~XO|K2$I3=#|^
zf2&`ZseE!iR*+=D{xV^4Hl`M;`VniQP{jBFhHbSU`j9FJbqX)(;JE?o8z7t&0C2Ax
z>H!+@GnnMn(9@_4AO(zCmBZwDk}Oy{f(?)W$T?s+nVp%TcA0yJb+k!AmF31bQjzi5
zK;eb0V(E}9>%hS^roAyvrL!O9dFXC6wbm~aq%C0>lzeeYq{P7SWKh?w-H7u;```Mr
zHETc>ieAh@f|!w!fr{%OI7>@Q8^H#x+lQ#A?ViW?pRvTPuC5-}hF%;!e*3jZV=ZPl
z{m6em;3+aNni)9O`e@AR#l`w@JtfTNx*O$-fRTCi-$tRBb?!&;dY&$|I)HQnVvCKy
z*U;Y5BKzo3#s2rj#l>VHCk-Hns;L2v)3@?OTY}c;nZapf($26|r#*Q{{6Nt+SV(5a
z&ag~*3pH;Z_K#-M^e$6=l1FWOd<4e;I2D)OH~%(#8gI!%8P@(XfB!qMLx6;F+Ritr
zmaA5vRl04>Wjpf|9sqW9SUm>!19izTH~qCdn!BSfc%I9Y?Td6XmUY~B>`tRgIde<<
z{L|(XN9?%cxK67<?VdXL)}^0Sn42YtTdS*9ydNM9e*om09tQ6E*|b&T)2!KFV6n1R
zKDaLh@x_m61j+8r?On?AumP5qYL-0M<e@mQ<<^s;7DMdF(=rS2qVbCap83dm7xNy*
z`>W&Twq{MctKaxqRGh-9G^+Ke9qo^<(xulQu4zr)7h)NA<yA<hmKi?;g=eb;?`+PE
zT3tQ$%uPz%5E^V{QuTYW{pR@Y<Z@={uPM0o9G_901_1I8@fku3#aLF>0#HE6Mj2m6
zC=BCot7rEv?HzA!+GtpROAkA9)FL#DnA2fDa^G|3Q4D0WQ{#qDS<f@i%kr+xITyyX
z?PmTDTO8@1JVFw<$3RGi$srX=2H7zKJ$(|4bh6V#<??s<<~AS$a5<Wfhgwwt!Of8=
z9wkZ9M{h%?k9MdxN_-3|Q|hfG$Ib!~@8#cU9Tw)1sCCH}!}W*S?ff73s{JDiHsc<m
zowvUYb0~i^c#F2~XgL^``g&fb<V(X~Wwreu$Bk^uyF-q%fqe346YP$-6YT0H5@y|e
zH$6(1T?*3CrV4tt+Xs$Q($_}=(2!Kew{c{A;<gZu+|_dKLT{M;^&9&ZK4(LT&!-(~
zYY$yHxkuZjmV|F}Jx2zV=IQ*DzLcJ(VSDq<7zpM|MbnEUIemXtKjGSG`NNy&T$`X>
zXz<@rO8yVKUJ0zvW;ml~-qBt+xlh`EXDKs>LT|wep<N%k{A7C@3vwI=dLL>;MZM7t
z*ES|{@8wE;>$0PAn}|M6dJGx)_HaFqnhWG|!XJru5q1`B;4fZAbni2rd{;S8uQ#s!
zKFS5SS!&s(38z3J^KHqyi)d5Biy9=icR3rPF{PdrRz+y<jtSy$YZd0HIGXCA$H8j(
zok&Z95V8K%yx*6zRxHf0AsJf~zGj;>P+@%?2gw^3n_iyA`N+sN=cD`N_Z`_S4q>m3
zS&x#muIGbzc`3Rc^EnhVcEQzUe5P+;&>A4vLrV*5@QUh_rZ>uN=g9w(5nj}2yDsqn
zO`PA(J1(Br$ODc`ljK-Lqt(fG;o}HLK7Z!C$S(A?abL6A&MZNHMpk}4^aVAx=$E@;
zY=!ow?X_gk8uA&8SN@c~KirjD|LBA?ch}gT4r4v1$AdjhV-e!LKW}*_XkO?eTx_H_
zUso+}UM}Wn9j)a<f<}3-Qke%6nMX+qF5)(fZWVBqP-g$3Zmo@6C}@-CUq|!(p!Gvt
zJ!gOV6Xd*EcQYUeQS)<;BArxFi#>KH=^$4d@N8h{H62ICLihf1awjQ|nywGS-?a-}
zoH@nwT@=xe9RaJ(pEZ{PSqAs7)rj<FRyfb>1b;~MQKpl`>Bokf6OW>GPoXNBt?(rn
zG(3{38zCsX^Haovl&I0<B0$sls^k-VtTU-`p5=(B5gb2@;*pmN<O8prn99P+-R^^p
z<hgi8etZ^$h?xGST{NQZ&Jc5uFv<fOKR=Hh7_dav+)V6kjM8D|?TIL@zbWSmi{nzy
z^&Ia{Yl2<5)(>LqC~o(G)s57II?MIbg))m#kP6IaQwMc*THeG&tYc$pHI>!#<Bci9
z+`86K;a8J7Q%%hsn3iNA5_(!%mvMuM$grre%L(`a>5c8|2;vnbzk!bwk;WE&<{Vs(
ztsQrjvm}Q2;i3X9o<RbK=jum_U5L1-O$&WyZrN1pw`F;r9qW{S_c^R!`t=BFQ}RU%
zU);zT_v4^feL8Qqxi4yLR&5@Tg%L?QN<EFXa2E6xKG=ZWd%}4ZKfnLDS?t^vw2_)3
zkD2ybwljK72GkQChM#a2tbM@|K@Q#Ppw!&l)FdS>J+L`rbV63_!9~))x<oEsRP@~c
zUaTC*z`Lt0F(oz!mKck%GBonXt;&l{Ak=<CbSvajn|gd=`He_aYA4489!aGmkJGy`
za#LPr1Y>!)xK#Rx>5xjvMp033WSruC(5Oy`RByr65iV?|cKaOB7N_%!=db@T!1&ec
z=TR9a4osT5%eA@DD}`H*`4UBgpg1kBUe8bOO9ivm(Y4FK90V4K5hv5ho9si|v>3`O
z0j+ZW_fPM7QayxRvfJP*Mjc*e$={dHf^eD&6Omm0Z8MeA2KIu`uYuc_$vk(~LT!I}
zU0J_q5;3fEJ}XVw&**b$emccJ?Lg*5IT$O2!vx+wjOLOWj10SjX33l<pEM`!TP)GM
zZ$F%>NT|ncv78YRIYGQ#SEN7f@>%&Q1^q~?N5hxVpl?3tH?N5i<uaO^%v=3dwzewZ
zd5JFL?@bYOYm)#RgKv4|(-wO;bPO-LCZAF?7bqw!W-326Z+}iGoV0tu2P9+1AGPo0
z=BY%naHbWhroVkf$EM4mxN_{h$aJ7CXP{T`tI4(~2RH?2I#E$7qb_c{Xvki~ji3k9
z`3X25BmE_4?uyLU$;nbsWbG;HUidbV{nlOo{$u{P#?K|Ciwe4-YcQcp*%x0)wAnI8
z&0q(ZiM$m_&^z=%4pcZKY{Kep@P~H_Fc{sep5BM0W(tm!X|v6bA_)js5`HTZrAPXk
z3Rfi&LowA8p|u)*#8R553Ygh4p>N$lpPzdim6!$%Bgg)!|BP3>`j`!QX9Ha^M@WS>
zWfD9{-y-|silo%<o}avg<stfr7c<kvs~5rAhOS(#)xXBjV|sQ>)J_h#H9p|bVR%dE
znZ!g&S!!Jf{K7#X99Go3yWKaW7O&NO6d!lZ$!hlUeQpI1C1X(R?s4B?T;8joAedr~
zHXb`V&7&M7JJ_vNmovkT$~dyRnLGbe9*0iN1diOx;n+^k;H3;-EfbPSYHzpI6Z?*9
z8(x`c=9rYLGPeKV_COHPWiyIheEqkg?jGhUS2|M<oMlqmMlI{tsoLc8?Zc?>{BW8H
z@amII|J=GNNr&fKN7mZbhBBA}m|y2a?S9yC;Omx~eFm|LlwP7<rAt*@xyK{pb8G$N
z$j>K}wv5`gM0BJM6uWN(-S#cAv1wenPga8tL=P9lE?m~If-ha@n9oeHCntV2!3+6I
z7JFQ1?=m3b{Ju{xyh3OB!c*40j+Q)}=&~c6L95o4N0<YDodUB{^x(Mck9j$VQk9^9
zr)$YoL^LO->cHL8#WCLG`h2oem<c65EA!QIiz`;+FjWF!ssKyt9FxHKdSU(4Rff6w
z0p*L}h-<jRmQLZqiEe+?+wUp=A)2OiBqzE>B}7lv+z(5wgr5XW4%j<g(LTXsIYIum
zpViA`4b*I*zYDNdi8jC#9h|-C$mk^H$i;97&n(-CAhx{+B_;fKrEdEXAw>>o?E&4U
z+E5YzCb^-ZVVLwRe)&d$y0vBesk3J{TlMS_UXvSDhm>sr&)7yBvn?T&93I}Hzxy7a
zo)Dv0>qEz`Rv8?BHjC~-A6gusiQv-DpBXrxpV>U=jBbAOl}G)rP)AZ{oq3E%X2;jr
z_ZrQxIq`k|VP*r{wYLNfT)D&Dq_eF5<aH2%TihtuYJwFN8TERN$t9)M)OL+C?K2+8
zsPZDxFaYm>%IpA$Jmc=`>F)RfYh=P%<KQK%g={C~yA=aYnlYb;4;{6Q{U5}o)+?Pp
zn!xP67H^-)i(5!{8xcQsjJH|Nu-m)kr<Da9K@fZZx%_W?qNc8{E=YiW_yREr%+~Ow
z@t>e&_Pu+rV(x@Ad`)j&(2Q+=%c~+;S2`4s)Ka^RrDa`IH?dy(hQoH2ucSt;G73-{
zB_+0Bf9K|QR|W}DQp*IW)kH=rt@(m2H^_wntBrRWt8&aAwjh(*DjT)JlYW6V<C3D%
zx`S_HGXXlFY;v~UB>s_wH-U^3USf1c&iO%N$!*M=&Ksif2M!jy%$5fIRM-gnUn`Ag
z3YTGfIcq#v<d(~&#qo>0app%0%(?=N^W!gfLyB3LpL)Z}Kg%x}G*gcUKuPBg%#}PW
z;|rgJa2Uq+m%ZktNLsfk&=^YzTZ=(LL4&Ff18g1dB<y+WtgQS_A^Qfe#Z&gnctn{o
z90Rn|#E|P>y{bH%`$|&MM=tY{H(kN41OGrVH@1zHkk$**cuU^r@uiAS2z%eqHvDAT
zoT!(D=zLyoJ3E^xvo$4)su|ZOO&?WWnRZEGa{ZvSH9IASPkNyPd&J$$kijl_Oa>G-
z`Tyh&oB2g`lpF>Rot+P3g*x7I{J2PkELESBj8>TxFl(Nla~Qo@Pe!?yUIZx~uGc!<
zzI7{rkcI%c)Zk{@eY<2vFGSAEz6y)x!ctl-|3kO*HJL>b67EzldAm-R`lr*C_DRpX
zhu{7W9;%e9_PSc9>inj8nE2MLI1>-Wl@PslmHz`Sg#+9~+Jpj^Fft=VSy_SgfrmhT
z0a-?#N7uBC1+Yl~5&@RdXvJ4!1$0{39>&C*#L?cfQ{nGGB2l|N?1q1wI+A$>Z}5oX
z#N=7inu+~^KoVe<gF`29)X<A666|SOKT2^WA5q{QL3t7w4nS=I(i})$Jb@ck=(-6o
zjwd7-@B@@J3>3~W)2}os%UKWRe9WMr@V)r~YRpyUzE9ui#U_2Br(^T!nbckvOKNxf
zn)5ShWJ1K|F4C1edKfJedzXr??-P-+n^;kHlf(Gj-@kSHBd;{_9ie=v2s)KLUgoW%
za}~9!?-rQe4`VmAEdHEfd%ie1V3$jtrG~w3Sx@%n-E;Mu2o!8vj2a7Xpn!nf&5Qv^
z$h-!_n(W2}8j7Tt!oX5gSGFB&nRzH-^D_1wK92v?L!3&8{CY?+A^3%PZx@#lr{jb=
zhB*30C57oFuOV-U;VE|G^}7bIPh21MJN-^n$ILpfY)H>AClp<&s8#N@yS@=;)UKIt
zI^mL;{{V+a6YS+e-}DU58i*2`)%34M4*Lw?>I8)AP{-4M)_Jni$*glzGUr)inzbF&
zO~qXFPw(W%D-IvmwleR10TTj>0aTBG7xlD3y;WHURmO+q^{4XN_OXQobbY7#U3>yS
zsavNSUrw!GFZVoZiX84WS#)#_pIdCqa#(VEtbEkm95H&W_6aFn-zw^GsQxA&mfa9L
zbXZ%tI9AR~ym{l>M%L@jhm<Aq9RmUFmp(QJ_u_@f?bPUIGE3p`puCR56|#NNe7$FB
zZ(TCbdR{P~A)HK3<5pof;+|a@)@H^ymmHF}t7_7m(tc9X<KS}Z4`iVL<|N898n4LU
z@bJJX6rkY!Vw7z2D6PaOve(lkIr`BXud0o9nVf^@bhl&mST1i7f(1N(1|3hKo#q<6
zPRofAlk>(cAy0*BuSQTf#CYIAdW6~Bt@$9=b~nBNSTevT1XTzSGv%K=sa<~Ny8ia%
zj}fYHz2x`BoTWcpB)tww`Z?_m3J+2f3+I<lm<kUo%o=QW0k2rR|J`=iF+f5OzX@^>
zV9TMP-0^<V$1E?~BPJoVxK39+;-o#vHJiRuxu3Jxb_2(6w=2hbz})p!-#E8Ho}7B1
z8T+-1A6S5$H>A~L_||pU*lxZIUcwKzCCnvQQ(YV4q!r4)s7$KH72{p`Y}i1nl-pSC
z@Rf6D!)Dz{TYHo~$xi3t*L1BKS<>EzA8X}e(})oip%cVx<QOl&OB6*o+XYi1A57o6
z;afj@PhRTDJ#YVIUD4=sKE?XP(&O*XiRk(H!0pb9=?^BaM{i;$t*5hTmW>+5&cRSp
zH(v~t5aR|f{C*aF?fg`8)1_Xg(%eHuPRQfwmCng8YkC?QP@c~PWDc`VK~VCM#E_$E
zSk|QDj?mk!JK=(Z1y-2@h<8K_P0aH<^>sLGd)-3DuN&tD*X>g6+=|CjsLB@__X1z$
z@e@Bj;=PobMjTa=VCs#L5}}D}i81kr2|?LhOz=(ki7tJyo-8k_0YOwZyLE*i(6sXM
zTA^D*^_O=uI%Fu!K!*?YGQwVx?IOq*{q?aH>c{1acf*Mg5S&=M20&`Ym{T}D#MrhV
zGI2)E_UCw`oZTIR9V?R|;1I(;tlYp%fbAKB8kLxC!U)-2%yPG`gxI!=EkcpnE%iy5
zjKBnPshxmXG~v+u<V{UF&~ta2sRK(Ar&d1{opZ{3?`OD12yAGvE60LA(u$<*uYlRg
z4tS8p&KW7Lb+NUp$c|Yd`sQIp_I%b0Rw~-=rEX`yB~A6%zn;1K!6apAw7f_s4o`SO
zvZ&~3)Meobm0xzr9g?Xr8(V^f7uSgtDhGtEGq|;;(wX{I^A>{MsEA~0h3DX2t(mCS
zJ>(j562lR$0UuPti3rPnIgfO8Vz6tdb>C!vB!?AYGW2Os6r^Vep_H$xFvQts5F1}P
zS$vKYE4R0p9&vnO?Nse|8(SUwfH+@@zd{rKT3wr8=@}pW_>F3KxwTfCEq-tX6NzX>
zM+C!H(#V8z?z7dto~J33joKET5H7mEQ$8sw;!35W^5%BlA_XCxhHRN!N>ai~qQJ_G
z7ujj8wXy)I&M9p~THl4e%cU1o%_PJUxpB9(!i9SH&<zX_1mk1LgJzxAG8may;h|Gz
zLwhpeoWD@p0hpSB-<!7XFXE3xfvm^o+|6VjJoK{s_6cIPF=FxP@d0<B{-$@5=x*7Y
zariUIeqqAXs8z-=z>WUs+;P(5i{**><3rsdOie8<SK!YA*;7hJCjQvnUsvygVqRnh
zaZ$Ns6ibxFJNnQ+f1>7${xGzWrK-(4cn|5Ht;P_c&o>IN`KXA@?^5BXYx6hz+Zr_=
zOwvRdDdkU3y@@dC4B?~T_A?b=OR^ITCn?<Fxg{jz1|t06;9%fZ|1CTs&E4mB7EV#m
z*MM>bc;nq0LXZ0*wE@raZ>r$o9zPE1Wiq-+UL0;KMLyM2l!%*W5L>k<udp#hfxNG`
z?+mK1N2z$3oh%x}Rso|IIsdMLY%l-BrgmPG;4@_VfadLWj+MxALh((m7(+jfK9NPp
z=&!zAT}zTR?_;fGnF3zfDA}l2ixQVH80eE`9jLNzrDdx<2>NaN^W~`LArU>Jgp><m
z>P82hFJ|Ks|EFpGcekgh`g5haSFYi7+a@L4zC|}p#Wy=_GFbA&w`^@HI)b;;jiKRl
zXKSlg_3MIe(<IO}f!e~`xUG0~&-KQOcdx3)Q{v;}lafC7n-_WPyV5uV_p7#f7Th%C
zztl^<fDI6~O|7S0deT&{&n<XGqT5r&9tfwjMQ~x=X=7H|ppYU~%{au!s-_SRDW953
zxRE8y$<<c;Eb7mB5kav<9DOLuDE;ugHpYmB&~dl4S9}PP&m+fr+((HWEn-KcVE3$!
zzp!P2M=%+egX7%#ieUIGS_zf1ZPFw0AIb!@NucY`%hTFDGN)k2k&hKAR``>`@qiu!
zX%vvdB|^|Mkp?tBRRX}5OaU@V5i!*Jfc@3BT)+a%nyuc}3o#?`HibKHTnRwR#T*v9
zf&{$$JCdj8T3ACTz&)l}{f4%i=%XlZP|dT7xcbUb9q#OdUKfgZh?n+F)c%ipeDki0
z2>R|BNl~#u*VCZFPx5Cc9hbBC4h=?s+gkedy$%o37z`gB25=cDq@XB-VC!bgcA?U!
zkRk9z*9Q03vD3E*ROcabTC~Q5_uo?dBueM)u<<Zso7Jz=$s1$Er?cRFl8eZ;G*GOL
z+b?WhrPf#YAYe57<Goark>nrImUbf(c2V|AcOJWDqhyO0Xb5s{4Q7|qL6>om907&)
ztH|MAyq<!(#vA&*P-`y!+q2tY-P19;FkWr8_ts}fsC*A5b!$xiB&T)z{P1i$n5+{t
z>4+86mI_kNwiis+6n&qX?9thr+7KgyAYd5&MGvJ&Lqo7L{(gN5i3ubTi(m@~Opn5x
z97g!TLH!n^(y{x+b3G^(ioZs?a{QQmF~57;_gpUO;p0#<Wjf8L;&?j(I~#ZE=lEMF
zSex%0&&<sKG#V!Q3J#vWk0-0kADrATRv>iVuS8iK<@EQzxa`;1C>h$+^YH<;UHTgl
zPawqsXZKLTKuNdk?|pj!wO#wMG|P<6maJokQ_fDZSkH(W8?l<oQjX_TITbCVRxciI
zalDNGK-j?a*b0l-P(v2WdxwERrkyM(3=_HgyFOP(h|dtwk2SjPjrRUp@{)waExH8C
z?3V^_KUng1nZ7~XPrJztr3rxI8L9az?cf#EjZ3wzoh}J?a{Y=)){{!_K6Dqgo>dgI
zE{CPy(F?RnrG{(QynoovNB<%EdgeZ1d3BnKPWM@g&Gs1hl=^BZJbn89$8@A`{Xj^I
z(Pccm*(38`AA>(R*D7kuMqRRqyo4B9faIBmN`$~_a9QA+Oa!&g*(=VMvBro^Te0d(
zSf`$=*q{kIzDX837u}l%skiI(A!A%5iPOG~6XB=W`OS$`P}sMu0w;6@FEq^qY=8$^
z-sx1E?ve}HutX^agr<7EmfhAq-ib#GDHu=*z1C-LBVf3T6|w9zIVb$Kf9H~{?~&ud
zb72?A#n6Z2h=>nMBgsAoW7QT%=i&L#wiVBin7P#Ic3u-%3R7eaS#QMCet}jSsd#z2
zVq2wR#^T-k({JsLZNAa-8KhZ+CATZefKl0RZt0g+^hT=y#4GrF=5N0u_2%09KoL$B
zapQ#+M$kd#jRjQEfKXUrzqvML7shXA)1Rk~=1Dm}HwTiE9PqjVEpGMx@T&-4)t6R`
zpi%i<Om*|DUSN+Re`yr*oyT!^7#}ou=zqQP?3*35wK}(!<Qe3J&PcWBSiYiE=Q~9n
zkI%<Jf-XLBwb|CSRx}byl#G<SLyo=9I(&mp{Y`u`4%<fytTF@w5!7-$ffsD~+*(Xo
zrKBXxQcb@L-m(;5>pis^`UTYc{q}ltRY@#)mpKRqaBHxVYKfP*YkCa6x<l#7ywnw3
zeD109ILQo3Jh+Js25G?k+_;w?VBQB^V^Hi&S~qnK4K}^HF@vulE)ju!sj2V=L?h<l
zeXFxU_&h43^|a<`woXN);!daiwRxNNXFt{M3EYbEzwxwUqID`?F-~ma+YRK;Xyrmj
zeZ6h(Mziez;!ABqb;iKIoYeuJA2|AwPtow&R#y}gYiTvclGz()Cgp}yB=KxC-o-4?
z(0C=D9$PBdwftV1dBs7V{8wY+yiKiF&;mHCf(RfoA;EP}$4SSHMm1gee}a%~aq+#T
zhov!*EDZE|o__qvNtGd7>Q@tEpR)cjZ<(ksD$vUO6k|t)MjM5p!Cb0^mNwF1D4m;}
zz7`s4-EXdFlybh`*WyO)@mA^~r$kQWdlX3v-rb!1eu0<!Aw*!7zYl_yzWGDspdUI>
zg!bGfGcyyor@@0OXR{fy!OGGzHbn8g7xx#w`2J9!BIvNXtswOEY0ycPrsxNa=3BQI
z9?<vRnA^0;;$!#lqayoTPd~MD#JKxdPi$QQzw^TJq;OQ%Jrg`e_wMU%#j%tvo_&$h
zgNREejE<4<<-q6Aodgaft6)jjWoC;g)TQ5TgTP)(<@Ob7*?W6(TY~uS=x4r=1ST`H
zRG5=XGxhqr^d;~GT<O~sMudkdXOCuODq`rn{5g^fN0t)K+(jpW;?Vl~Y)--I1gJ5(
zZZdZz+i#Y^+hKJTGk$IZwN?Pv7wi>=iKEEbw1;vHWemRA{k><vsT>;o^iNX191EiX
zM>OK8`gS|5|F=1-X-TmLo_W}34PWWDz68r^CwuIJaEn9(Tel16(_NGNLG99_8P;bO
z-6t<X>;8KhWFOM-A|UEXY}eSyxiRBFTpj!ESunKJxhMSfK(l*gsf)3LcJrsz9kzv1
zUmx>^yO^Z`x&88LTw!E==9FaX^DbEft>!$Y+{HO)VOc{4E^BLEP$GUy!C?RzmOamO
zp6_V~5Jq$TXZ|vs2jb&%)~Lh<x5-lNhiYObDa<$!#YaQ;rg?gWz;AhSd7ryN4Op!W
zFJXxzt*Z)bjK7EmQCBReJk*gWctIKVS;ben{UHxYzc>@Z0ze+<$>5^`dhi=Uto!%w
z-D72aA}fpQ9#E{Wbn4#lzpfm{6E4cdWe){|T-+^Hn)f*j;$v*6CQD6~I#YIe^r=Rr
zZ`!_BYTGQ4eA1XCM!a!-0&V{0<xwOL+-VadC*7xUyQBI_oT2sJHPv9+5P4DwH7;eN
zdso$N&AK2@fy_vF;m&nibp3>D;iAsm(Yu^nBH8MD!H1az=vb8eqDvFqcS3PX%)+#h
zPhLdSrDvvW%l1W8{~bk!NzqSE%>+|+cF;*FDQOh*ya>;V+_uvE<lUxNjE@`(?+2}X
zH9z)1O+mT`or%`_Uc&4W`f%ZU&lr5<FnvOTZ>i>blfS2B(JEQ&iqH4pydgAu)ZiA!
zYc;l*8t(vN6EtdUXcOdl=2|r91{e+22_E+U+!J_(7mdtDCMKRhuaRd318C@2D36VR
z@!(NRHu44AQ~?)*(F<0WVkg4rwzCJZjx)6f#N{N~Y%{hb<%!NBKi9A#76Qr>A>iCO
zlA%fFdN$&)*zk(GSPmlvTSDLG^4+}yMh=N4=afxhlBO`R)2B6tE1P|Dj@fnw(vt$~
z9|OIKUN=?g4^m^Nb{>FmS)ph1mf~c7Nx{>vw;D=iP)6XeAAnVt8PuZQc&+{4*ZQLC
zglEcj2&5O_;ta0R;5PH_{d?!yb~eVk<|o4IspVE@Yl)6JpVIB}<PGmAyzN<mhqB_^
zat<0qNA)rb8pP8j#O)z<j-N@%>E|>&@5F7~ktYJoQ9?r_-t;z-#8TAtdUY(zQi#^?
z>ZN;E0Wx^M+|&4UIW+Y;SSVc_?Zg_6W>T{-eREvrHEWFty<rn`=BY*fy6KY^xB<Cu
z)E-bpCE2bND2kmqc$XS>o&0Aaz@RH`%_>2p4XPqw--2^QLOHz^-(k()?d?ID#ACH0
zYU-Bq@2k%1A55H)89QRxOR4b{<Ft>bUX#e)61~6W`k_;1Y&~SVSR*DKf24rsidW0&
zQ5m`3bueL|y#W`R##hDRafIvzm>l><&I}|{L{hbEtGR??I;G~ZS_LVmGe!e>>a=Z?
zG4l(GM?0ydE(=L5mKT+#%90MG?6lN;>>nz|ku)qDf#jbnbj5$@X||3L{e>)=HU%ox
z9d~EDR_Ny@J!YlP`J2;O7!iMeTEB5UUL*S+^S!q*4DS|<f3b+R*tp2LKD#{kxFvG#
zXLS7;8E9R_EV?z(pYM8xlpCQv-$lAAW*0@MJNL49>LzTzXca`!k8h6tG6u&#7_$<(
z3wQsvy=|FPhmDCd*XOz}l?H%S;6-UQ2P~x=aqLuP(!k7MicIx~b@RWM8EP98I>)*j
zz!do7W52m|%{T+_ARyi^Y%;hG?GqZ_preoB`A!g4TM<cK&QWhg7l*e1w1{V;%6z{S
zzY5d=%bSM)rHMA5MhOFQ{Qn83$^OX)wEl^u&8HI<H|6B=bvUcu5lQlgtCj_T2wsk<
zllc@+WuF!i2U(RyzIx3SW4uV?$}eBo8;%-%<c4Rb>?$pI6SWHJwzA@FsLG7d5?1k5
zO#$UW*=O;X$MpE$wSZ15U<P3ZAU6CDEYlAZy^edK1`x~k@C6{=U|cgg(-a!ZmT~kw
zLg+vDn3w}iNl?N^9slj~%|J13e>Oa0@KgiSL+Pb^G0<iXK$njW*V>6+gLYU6FOc=$
z!zBQHO-Vyn_Z&L)fcOKNv2=<}W<9x)$*l4VIAKs9V9Jc5uLT>s*VQ6SOlC0ja)bRj
zY&3q6BFt0-&Gi>&))CEy>=e*7hACy?!M;%rAv9`o&wM3bADx((Kez&{=%cH*rTz4D
zqv>MUe}O;*WLm<&f=*e0z^#4f6h7WEi{U89F9%m**}iaSI}%Cme?ElP3Mk?f=!Zch
z4IPl+3H+v7l)NA&n}dfTB_pc?@e#<EG^@yrE3^wCPSi>96}@vO8%TIa7Z{i(B~eK|
zekH7V6Lqg3xN!y-^-JOqXVc<*bm8oyfi`mK{pO&-I&VNHgeH7@ps|*VPe;O}QASut
z)l9(2|8}&%HfYKX(*N!r%1(7`I-|}IXm<iW2<za<1{xh`gKBS@a%C^jWA{l15Sdml
zIw6QtOcv}fc+O|Mq73c6?D?sXDF?v^uXBv=re1?U9lD6zuicH3PX8YL(dvuT<Q((?
z>6tWY153)#P}M4`c?fKbjg8U7H8eCp+R`(B_cmM>zG7uLFqIPUdO)ohQ1u;PVypVI
zeNfMBgLeaOV*B|-3sF2+4aLWogFR+WPEKSp9_oH!WNwPMI)Gg#jN};@)@&K&+^BhR
z8La8}ExLhghWF18fc2jfK<U3uo_&oky3)x^!QKJHi<(uJ{Su%T<9{Zaa`ZQFOM)2)
zj09UC>LQ!)rWD_^e}wx3O#q<sAT7o8#>6877b_@1SOO97yjB<3=g_zT{y^${$-BiU
zf#22uXlHli-$UNuV~tJMG);<=W7hKLk2Vws!1)D30@dgQ(qS9~ymAC=XZ*TNsem&H
z=9o|r0|Ol=uxLSmD7H9Qlsb;G`acOKxdYmDNUk+MKYa(uCpbPvMfCvZ!g90>{|2x8
zl2bsW0sEqIZ`6kmAd+(d(;jqjPtS93bOcsBbdTK}8hVPn7k1eOgVXqg1VN}WU5IQQ
zjM*!`e3><DLCt5CB$pWy5z*PvAqq$VYx!7ILc(PP#EH2%IZFFa=rUNYxb~HpmX1It
zvGybZ63{|p8(K7lT%&UO8lg;tk=luk_z_*)j!}6Q=OLOZm$@ybqN}_9y84Xf3JzU^
zsB2%}75Ko9w&!1d8=kidqY~&5^X<=ik_gWQ5hEDW2>$co#P$NAFc>3E+qlC&ptsa@
z(D(y=4>sVC(9quA-ZK&^aBck}6_F4Jx+x$`u!x9^gKZV4w!mrvn}CXfUB5xcX<S`f
zTiED#B#`q|NSi=F4emtf2(|0tM=08G`~tz;7<kx~kDd)Yx4s@GojHju`2<J7JRqU!
zVA88TEp4h_Q%}8$$S;9;wF=m|WCYK5b8@*p$cv^zEGD-k35?1AUOo%Kqv8LzmR4L4
YCQ?zcguHJ919JqCl~Q<ED5>xBUwKIkLI3~&

literal 31043
zcmcG$byQW|+cmstR2o4#q#LC>RFIJFMnbx~B}Gb+Zs|rqIt~JY(nxoS#G$+S7T)*!
zd!Kha-}C<Qjqx!C;~eGez1QArt?QcCobx*2N($1L_et(U5QHi7^0^8GA)MX)q9cO;
zVUo3A1TXg-UTQl-&;z`?UxZ(Cepe7g1<5>@Q1eLJn)5WmH@gwr8{|@?|5Wm7R|VZx
z;693u9V6A3fXDCA(9t~}I5EywQA8Xd9kZ0Hy_jl$pXI??|GtnB9>VBE^F#g1uY?jd
zB`?YbF0R4dn78Z?P=3X3<SrdvApQDDd;VMIa^Uw*uEA$SCIL8-j4qBkkHG6oeWD|D
z>W9lNn{&q>X{2LgV;S>$Vq$68w7#@0IndJ6E2MC5G*gbTAW;XGI~9`+b9Fg6I~)36
zyJik*);V6DZ2$cE)A>&y6AKFqGqd2XG*6E>IyEvg`Eb+R&eoQVOzgm1Qf6l6$Bzh*
zrKRQh`FVE?rP!%-YQNjg09M$usP?N)M=}%nnTjG;!F1N%DpqSPq`af%N0l4<!W{3j
zYb!$jY>T50Z@8>c@LEb9x-IfYukb5BBz3{h87?%C=?`I+u;(V}jwWw%-<@~no8ET&
z^fOn_x^8ChQh91^znfCbZ7XTaO25^ofh6QS90w7a4wd6<o^l{7*cQTslw{#vw_gLd
zvxb_<(W!g9Gj92YuR<Q+F`o=)cH8g@r7J)V4SMF)=gpMyz7+mfjxpWOn99n^Y^&PY
zb0^?*GQUn1zuWL$`iNFclo(@5<qWSnS!zL$R+&av$LH<Ll0VyRj(S>JNwxkDagevO
zCg7eK_Cwvi(JLMqniaW!vNr-n>FDSf7#MhXco-PoT%GNEwcN~ti%r+smgq6Euvp(+
z$>+K45JP8xNu;Gg5Nq!=Vb;*qE6qc<thlFL-2QBVpUGVW^@FU2u1u6ZSiE0~Z)j-H
z*4DnhIA&pF{L$AJ86I9aWwUlYiEKs{JTw-Lyp~)!O)>l+FzbBS#lxe%du884m}K0D
zH?`(kgAB^f$tfu==H8VWsU>I3(<rIQ&X(*Tp+cXQz(JP4v9hwdg5AsGe)?3UB{t=O
zu<?EBU`aAm5lQrr`uxkMgw(+#QBP_uN9dd8GCqD(=2O5vzZbKPE2&A#$9KB;EeP|X
zc5vH`QrP25W8?H~YG0aw^Gx%j-UUOa-9>9=78X*2KkArw2S5;eqV>(yxqyHGSULXJ
za6?SgAhEg&xZ$l;&pbX8x*WcZy}9`&5fZd~9N@GWG=mw50IeM}K5B!ru`p@3{7#R1
zX<gnscNt1LQ)7*0fGuz-DluypP`yhmO8F8GI`e5QTHNq{g$ik5w%0BAc~9uOqnw?G
zag({KVw1V%4Ee;NEEc9#)5=&4eb~FbS`%Z@C>`2x;WF=ApQxbRKNrXQwn80=8$)VJ
zEKKpZw{y)bHdn9drfGM2Lc=vOy04;`89QQqDhJI&7;5trh9W&$f}i|3d^`96dVb!2
z<n{7XX77ozTeL@S(u_T9E;HS6Gzu}Om4fK#y%Bmn&V!4ps;XVst*D@&SLZuivNg@_
zjq>doLXqR=vZki$E?W-<Me!|poei)L&)==n*zvs+eJtC#@;Lj)^N)uf;zyoaC!5*r
zgoIq2doQy;Q#+#~Bagt}=DcBN#D7mTpunH=2y1n%X2Q`+mbmRtPm165#ajdHH@ZRh
zq^REPzFQ-9|Fdn|o503!vdvalK5tjWZrG}agY3`zElbmAXaXt7;H^YOVI4V<34FW^
zY<?0^9}n=3!xy`A4W**)YV>DcSia)=Y-P_j(^siit#I~b#~{2Z3O<jP&NUiQj`}(9
zl8xPLuq-fgj4R$}L>2t$*efe;-%FBd1Wvq!^?UVuQ~Eu^!~M;aj{Glgk<gw<KoC8;
zIDgk6>ZhM<@8!FZAfhOUN_B3*d~iokT?*Z1G5Gs#Yi}z1Ezbsca5r7gMt0U`_t#cl
zv`{3uozA&mYI|B*7WJ-HP1*RhN+Y6PYZDzEw`FPSiAeaL?Jc>&$LZ+l=N&pQABFxn
z-=>o3TruFZH*i`S9$o)&Ed5}|WBoIs2FreZ_zb22HhAeq0xupf7u!=1s^$8~2)-jD
zxCpNwR~b+V54s4_6D3CnM!yM!)HF3z5BK58iYMDkA9G>XRmXV*ESSc|;!GnC9QNP2
z2&NzY=@;@kvS@PKzB#Fb>GN-xw7{^dSna2(UOfB`Zv8^6q%Rzw!S8arZgg~1Azko%
z0nwvfEywupMBrXXaP)Sr{1p0dy>#f`#89ScY-?-QJMJDy{7jgA&nrF&wIVF>`OhUP
z3NY$#aO$X=?^K;M?-BE$54y@z`QF&CIRg|1Ju-+f+Tg^4Tn5;vNFy6W6=)JhUe~dw
zVtckzLlx@nLQSU&ho^lKL8#}4N~C;tKD#ZaM_$(^F(L^W8S_5^ZxjE(u!)YMLM05b
zF}y<l_)iY)Wi2!tcfb-_N%d7`;i04qvp)D>;<st-z(Crz|1?uLG!2qDdJ8|dxx(0e
zYC=X0DXpHU>9*_C+M=N%;bTyt%X4zJv>kcnKWej3V-GUeHRklaII3^Y`R>*|ghttV
zdvkg1Q;JE-OD5!2s+cLN-{@lL@=a6WZ~+bDLaHOH0u8lA@=$DRdV0FOz5VR$Y<+#5
zE0u@_OQ@)5Da}nq_gNa&fPq|^!oVtWSUNvh=R{I?`itZZ{`P=Xb+tP9)S-<$JlOzh
z$*Pm`fRYT10--=EI0_ENLaFX)#iLt8_}&-%{jvmh{mok@;R1#9DlJxHW8<5|Fy}c&
z&UI4E?%^Dx=fXQHQ&TK!t(^5W+rfhq)rt$_#^&I%TQ!Rd!!5cGtuea_6L_bLprDV$
zk@7f6RE*to@|*$SYT(wJ7+?ACeM`z-c~NtMX4orVztecQ^PVr&_Yv}JKi`6|&)Hs2
zPY)rR4)=Ny9W5=lg@1548O87fQp`HRTamz$(o)mN7cZXU)J4?mb#6bldsMHNHiN>>
zu;zl7_=%sqbBXFCMea0+E9j#(YoCpA0_T*Cv3Z0I_uHR+tO*;6qQ@S3&(o%kMipVA
zxBERO+cTHi1}|Q`@bK_xDS2e+M@!l#eH2-#-{f}g(;69xQN!ThO#>c1jn_k?=MQCE
z2{ct-Srv!3BsdGcYdJABC|`65zSk_Xw%dmB@d={%J>xcw3|7+1wue)AUF{v(_3`>w
z3I=+6<FRyd%*@Q@ta`uwdyvG{)Z%W1Xi!XSx*~})N&BeQrCAM{ea@YZ6PypcS~7i4
zv~6v--{I0YphSeWXGv=Y*U#z6Jkd0e?Uijfcv{{Z#}5}cdqoSGkNErN#}P;NS(paC
zC4L27q`0_}lZHFx%?SzFQ~NKmDbHatr*kR4+*FssmlSWt636>9y<VG`7<w#)h>JA!
zIyKGtDG+3xttJ}YT<`izzjz^Xv7W=GTdP*_g-$M^d2fsa(h~HYULVf6ZU|@XEzu~O
zZgRJ;FlgzDCNC*X^RWc^Ig<+!TOK8V@}}kWOs#DY8g^@Y4~O~mSIgUtX{6V$Gg5`k
zb`MT<YOL~pA%)OxH5Q?{%`t9X7y4~>hL;N5Rs^*atIqHSP1A0alxz~yDiqn=Gg-oA
zW!2+NRv4dl_ir(m?{<Usj87;lPZfw=B*Z*gz{A8M7c{M&V)vGcE#9;ZGxR$*yK2^?
zX0=gM`<?wF;z~gIu<b3<Gs6opk4;NP0g>M;t6=brodXKmCr?qKvDiMxwBgqR%G&oJ
zgRZnH3`i7JfgUNKFYC|NDWP50Y?YphN|osfJ<-u4BPi7`UGP8(=ZNA2=FWb5`UbUB
z$~+__M0%FuU3BO{m7A1_d~d?et*;rJ5bf3;Q*`0yrWJY}A~d<ZPoE=Z?;TlraNrci
zNGyOyK;Sy{z4cx|a%E6R$k1a1$OxVwFS53lM7PzbjBGiisjhB$wk|x_TUB~Km6XDP
zWyeW0YsbeaRm@yJ^8NRyj92jw=c6zC&7bV#%>{T3qQlK99i3cQBi@-Nd~sIn!+qhn
z+!>C4^jvY{lBeAN_HyA-Mx^r-fP89eYb7FrShmNrl5HNb>(zT6{wXXhT+8&oY3GaQ
z+TMdbwr{?7vrCXAyft~@k!1F}a9BZG#c7<5D~45w_^_uc;t2;*BC976*^i%7FFchl
z-bIsd4}S*1)hANGlo;BIWqnHj)O^B)?#O)ca8R^o-M5#m?yQ=l+i_`W$x8F#RUr3z
zjsBiN!3dMJv$a;xu*Jg{!)w-8;=59@W-973J+d!7i^{811YHB;5=MpQ(-6E%_Ks*O
zS_U4o`*;3!YG}Q=u+ALh1Gy#Q`?9|PVZ`?hmkY|H+lvX9dGo1gaLC+Pv6+U3#@s|u
zP3)GLQyFcO*;Kz)8EtqZ)5@X$we4D%bc{qMMq!!lW%pZa`-T^$rBbn`sOs52&_{pB
z$E}jc96b5+!IZB5+W-FBZ$$NTghAhO*ItpKL0*z^50I&rw<Idx;vjW5%<b&f?gqHo
zO2pzF9EM_3+Kq2utTi8}@Mnt^ya}bDO<U0@*K5deCVBqinYK3UnZJA^oE_;@tc6c_
zkOlTZDjI*F;T+T`&y}}^J_ltgu14|APkLNOW%c=0Z_V*kw0KRWs>%EsFVjuDs>f1D
zs~2LYXJP3b)W$)l#=P)-pe{jjH0e6Rt<QI|xmjl2`ktZmO?Fg8aW28Co|&h}$|C`V
z=y!3;_`icSGHvG!uHY9lf?sccFH(aUd6oS1#n}Bp5o1|!_X#C*4PXP&7HPz8M~=x>
z=_hPg>9NW1h%O|~)IQS@gv4E4OQ&dwG3?Ie#e#H91tM241g)0&k-CY|m4A)@z!kxv
zSw)saMwE(HF`5!`yK-Ty!~e20p+8adid00JF(70GYHN2ClynJZ6ieZcW&rhLj(w0O
z7Xrl0ymNN-DdfSU3sp5cyB#!2A0uuON{>YZF&G9TJG<Y>)EgLEMLiR}7XZ$Vj*e^F
zIEBN~V4tsk-JSThy<$8uK+Jv_!O^<Dh8bVCjY|I3c;YYQ{7iOKCEqJspN;6%Nc3J&
zQs#;O(ns9xpG&w-E*Q2S7qMcDz?P9`&3O||Ol)H4v;Tb{UFh-{&Sy2s=-*m}1SPWC
zUtYT>GP-TfBQa{S{Dc-im{FR@8dhtX%T|0<O(-$@6K57iOI}gP>c;;(N12<Xgl-D=
zU9$#Oa6efAjZvPObeq1xzO**+B;GemMd396kuRTxm)iNGkq8x-&~WT|&xPkXqxXW7
z1QZ(3T3B!QGHVD>Q26E@o(0j{SQZNH)UOo4M9~5f4xA{bs}J)M4ODh3+1Ua*<YCt*
zGf|{`ZkwYU146q;{#jU96*k-$7hb_SLtIjF?_H%2*25)to>Lc9Ech>M{F;PeL~VXx
ze)0W>@~}9Q-f!&7v;7rCvFjragMFQ#o4oVG)%r91Y^^`|a&$${AND>k(nzqT1xv!p
ztU)88#c5F}orV!}<t0&`G1(&p0j};4Rgx}8^XN<Su{Q$4l}1m$g&HP^o_oA@iF`|A
z9$l@ZfM(S4Lwxsi-rFA3Y?YOj{+BZg?7|4LGmb|fIW)NrL<|*=8CsP*V$*p@Nm-#z
zmy)R%AhBQpz$^O4tVG(m%8tg4hQ<!h&YOi@VWK>zhy)eMl4m?8wwZoU%cKP%`HR7r
z!LqBZ=D{;p|H;QD3@`SRy1o10-tOD}Sgs0aK~R-Xco7cATTb(_4M<#|R0Hc%yuT|1
zMyE2+=}0<poisv6j*J8yT}+@=45nM>ai`HddSD$g0l!|3-~eTtbQB3e;eJF4S{iGw
zUEPcY_r>|S3|&ec5~^>`692`JvYwvL)@0?;(Gh22(BOP8<*c+`IoE_TbBzMwr`SA=
za4_ab73k$_o281>y4yGDx({b!NOC%tEtOgK-gmdmBWCWJ%ADgC9M*Tct33SPh!mL7
zGN{LY8E+(K@Tg$q`?|}%AW2S+F&Soo9)I2v*}av*44S;|=qP`8V)+b2Xs-er)_D8}
z0SQ&_WTS6DD9+?w7#8_A;}|Apd9JgXp<siGOn%4LLDOt!`@)p?_&Mjvm4)Z+S=hMN
z<E6<bOVV`(?#Y#IuA#?$!P<Qmanr6iGp;zFW}dX%G^aj{z=O}5n0e54cDZd2AEq|v
zbo+=V>fJI3BMt>l(7`=-D@|aOLRjJ9U(60?v9{mHr#YMl^^Hcmzspc0eDl`8-`Vwr
zlvJqL;Cf{@h0xJn2W<UFN#kPTyyDy$KUk^d3p}5qzXBdwS-PQW2hT{@EJT4&sTgYZ
z^G<Pua>sqcb++)GXEVfvGsNaH#6;$WV@_W$*h+0!JRW2?@bQi)LJ^P6Q2c%VT*Q!%
zYB!A1Z?eVPJyX=T{BXCK;jX{{Q58hapbN@4Wfx2mX-bC_cWvZDJ-%1>n(Rn0K~dVz
zvVQ)|f4$CTv8NYmQ*hmts++u?!@j*bEr`$l+PgXrciMGrIBl3MfcpyCVKeGK7IN>~
zp7B<isr~8Y%P%r=Sbk2)i@dKA`0CzW>9}A4f^noH&sckmwW~BXQoslUJ9~v67j@C{
zAWfO{ydeMWWTxGq=k8LOM{7>jt__X~im!AKj%D3x>!n@N2~Pc7&_sE%+bQ{G|H}}Z
zUpT0~hqS#H3+`ZuRHB)R1v8Ux7P-P6=pn^2=a{_xAPG5mLsV}kj=4#iNHOOkL+wzh
zWlv!%>XD%f^XF^t30RON>TlECIahJ-s}mjdV{57#pLx}&IbB>P5thujXpxR&zkjms
zYVP6s+_wK!t;RLmpBqBe<<+XDSE;<O1SV)CU*Z!TeS7yLAv4pSH<cqTIf7zfuEupl
zdOv)@W!tb!zIPxgWCc~XuY)z;*f?QaxHTXB^X=QuI4;5zgSj%F#*;g;vX|p=I|^Q<
z=G%yEANsc1FTiMH7fiCM=mLL{w)f?fE(P_|ixjLDaif1W3YDOAxl5Vl()RWH^pM9*
z18m-X!8gpbwm#S9Rg^w=FhW4v<jWE>Nwwcb)!`FIYBIq})dAorP)72tJDzNfl#6sx
za7-@|AVBCcC1tHMg)la@>#GWNT*>ui&h;RL)15gn!(*l-`434L?(;PrEKZSgT3(ys
z**Puc<m@%SoXY5em7IgYDPy~%AIRD0&IsZ{E>%AEp8NO$-m0|2JUa?Z2opb_qw^4<
z`r=HjIKi~^8ow_EkB1AtbtG$Uw*FN1H7S{GApG=a<r)X_TO7$7CAxJw8J#aiZ#$M=
z%FCy5TgFxm+ttBtF5n6UTy%71o1;Z$W@cP(2GlevJr8~|Ao1kbaJi1cy;of}CdO(o
z#HD|1kAHcDzpTUIAEuU2?NH);HRojdb)4sMa0-ihpx-~F<w{pnZ(koPGc(`<pgmAV
zVGwaHUcf8-w`(S%DaCeN1f`?N1dQ8}dG4b#6A%ypmX3*u>1%8FD9dxE>fEiIVylWi
zXfc29%cErz75>-fTPB69ZxT4yIxkJFpMTPf!nsdgjO^>@*ZP#eR!2t%yumyA{{6e}
z<q1jvz~R4FS2tW5KQSnpo0;Y1<zW&(ectf7W9b8JI8OF9;)CC%EU*#+77gK>sWlM%
zKg2GB^e|{uGp@gCV&mzlZf>ca$A};vI>pT(T3a!NUS*?IegFOaB!gWF_eT-$MvK|F
z{iy?UBaIldb_{KME!?)VJabU)sRz#6)uqbQL!2a0ZcX}AldZlWi%+EX|LITV!zSS_
zH0w<Oh1~Xd*$kkh$jQmmGcvFs)qMG%y}c2Uk$QD@=_DaMPr4>oDE*KtU?{;hla<@O
z95BG*<vWd?62vQ#i>9o&Ac%YC82dH-o_RgsGVso@EcW0AS@!r;{)x-Wu~IJx;v~YD
zCWIhd+-d);d!cUcqQ_e=%cp9BY7a9|vB4Ps!@RmO?8lG^I2~_}W%^xMT3Ho;f2|z7
zgJ3lI#TZ0ymB5=%ilWNAp<UeHt5}gL&{mV$ZJ2}{n@dYehZsctH;>$T+#vLV*-w?j
zBiWCS+u{mXZDw*8YVV=Ho#_(7)A8iCMe8rfLrKdgf*ewotHH07bze$mVmx5NKYc=i
z0Qm@Jy{Bt-cD7u<N%ZDy1uHD)Sr`^z0)Gq)pa!gT$1u>+7AlAS;g4?0v3<&_jUncT
zz1!jqpR9Z!%ToSR=s-Uz9jTt-S0hc{Hq8@d*Xl=%T)jvEZ-$h9l_e(R(F9ArW0qK`
zFoXpL2KM#!B_$E1_9vvKrpCv|$Hjd$?@!S*^c^#=2E3Gk_jU~^$5KSR-CSL3#8}?(
zsutAQ-RM;Ldl;=<&y7fFj`j)ZZ^ml7=PX^i$=KIg;W`i8EB|_yySJ^`mc-E##%Gch
z$^)*5QO>CzBknr)GWMvg?M99v(v!F55~f4PCHdIZMMJNz<s<IeD+wtwx5Mt<m$p%(
z#WmLLEs?)M+2{(f`o)To+~y$2Z7wZDpD!wuLB8mnikH>Vc6voaZI1<NfN{3FW5K}i
zBaABAY>TS{RTW*DFkSte?&=FE<Hx~DfU+uC{mM8X?C5p+stz$AulA4Q&&$p>>&>Z{
zCreE{N2B-X(aS5{IY-X%hiHO_2->qWzdst|8BAv`dPql3J(5k637`Gu@$r25Sg7v6
zp8`4Bc|lkz`kM?Ej^x`gi3Lvf#9j+-gV$kCmAx)Z_oqpOAX@l2tFC0|M)u{%igd^;
z7!>ek5vS5twyop^I<=DJ=&P4b4SJvGU&%JZGbZ8EHna7W^S<L_f~c#M`no1-5JhzO
zblR^!cua~`#Ze2TsM>@x?nftO6`E+&!H5Vk>(|HM&?rm$>rqj$5Hsz!7Bbk$h0kWK
z=OX*r*nMHmu?Sy>54CH9TOY-EKvc1&de!gi(?CS>{>|8iUxO=I{4MKL)6$aZXdMY^
zJIq=kHgO%XxY{_8=MX#&n?ZtBXi5)tyOJAvebn&$E)obcO`GR=F5KuX{B;B*8G`|Y
zhuO6pYiQLXW0`k6%2eD;(@8w&WhpKf^0~sUV^EP^P_(J}F}_^IG)OtwySsoC>>(B1
z&S8WrX$43cf7jjl>j}*@-`nfcpO)!TQ6wmko3`KP^ZmrJ8pt7P>g!~}P%O;H+sQ1i
zU2y>+pia@>FigQJF$zbL=m>MD!FdhT0xXHx4I--V6|Of<Z~~-Fa^8G@p(=JU6;4Ys
z?kC|R%Jez@&7x8~96WCb<D%=_UA=#V;r(3X_w{w5+F+R0l}NA5*@m}{R|+x4j&3(X
z20{dm6&^VKiiwSz<A(QYJjB^%qw`wMVoBOPDp2eQ4d%~>vrOSwLT1-;g$V>L9Kf|@
z>BZ(hOq<<uKx}JK#`RZxw{lHE_)}dSUCajwn$PfkQI?1=fv&I4xqe+Uv(XCM>txEb
z`28c5O*b+E-M%f#Z0gI2-17d*{d@B;y#k#mbZRv~WWs#Z^)D@knfOe~Jm;ikYPo}k
zvQpu7w%G*=oEh#5PYD?cKz@6IanWl8CDWNzB*t+CSoh_-<1a6hBprb5_r-(@@(7Lk
zK$A1dgijsJeqS-E@gau^_M`M3R;D))eg~U!#m$0q2Wkv_?2b(Ln@8F|$FPPW%izs?
zJ<K~*s?iC?Dc1ZzF>Kv5*{o2I6Q_YNo8j(~*dcSzvq2rP?xOnn6wja9RK0BelS=7O
z^0SW2rffKDg}L{esd-K}DZ9Y->N#I-+?r?h1{r^ximf4f2o?IB))sK5PmxhSKb!#C
z8qhY#x_JswZEphur9Ysy46dmj-^5UeT>MG3A0Hi^bsiJ}63I<nPVfie0&AO_VH%2x
zkd*}b8WLOz-nGtfcuo$~RycA`a8oga`xXcG<`Lv!*Q}0*G4z2UkM&KVn~+|LLcvHa
z6f(Fr!n@96k0#4hcq{Fw4ax-SU=`TS(WXOUYRqqD++2x@O_I-%l?)jkuSbGn#7#%}
zlKmGZvtJY_+;S0o8_?-K>yVc7ech~3_q0e|Ge;se7cc{@XOlF8nCsQ2E;4-q5{YFW
z>h-&JTT#1w^u0Z1^2L!daZThC%xouQ%_1#z0}y8rtd)LLm3m_KkB*(WY3k`U|0KD`
zru%jneL^2*(jDzwab275W@_hGq$tmWzEJmS%S!lApc3eeF|2-The;Z(A9D;rOzstZ
zzm!NB-oYNGE7BoVc)*P#`9n;?>v=_SeLX)TGjah^B>kDKcjAUBy!@gtdox*;4$lOM
z?GLsNgVwD(qbUGcK)l=;e6}9_2adIcBsn{=(C6VBkO@hm^K_089mV~kG`PGBM0o&k
z;w!$n(E>$EvrFAPf<uMGrva79xDht@zE)nhcdk6SI-`X4@=iZYe|?}ehfY1psA)2U
zBtDoSA_g*7BPDTL#P__29%jhM$>n#dOj$B(coe3WafhIaYCo%S2277M$q7Ke`)4!K
zhB`XMR^%jj#NN4uS~BMaE7^u}?^eo<o)fb1cfHv~fKK;5_6-H>$Dvc_;`s-)f~R8A
z6?qN-6c6PJftEoR1*7n>wmJ{m-f3|Co8{N#zvthrbNSrzaMHVNFxT!96+Q-6l073m
z0x&CC)c2xTy+qJ$>ugk2aeJmtajYsORL=aAOl;rw6`SG@F<k~>9CFVP_rit7@RIV^
z^wr*s(O)IdQ6BgQWvB?g?tJ$mf>7kJj|(Ug^B<_6c^z-onDr8>N2tcDChW2^UQF!_
zIsewzZ6~sRW}Z`ut*Mc{P=g80J`1Z3O+${rkt{yoTh}bt6Yx4(|N8ao_2sGmVL#vM
z>MGE`ngH-pXHyVSkL5nm8kTAr`SH=8ZrZLkRDVO{skzpz$jjJKL7A;MigzTql8l^J
z<gSN@gJNQPZAc(l<GldVwG-Jyj&uyj<LYC}YAQ^!b(9>_YfifSb}v*)5X?v77a06C
zedCP&mlwc@l8qlVt}$!@nZE^9k}>Jb`?;9Q`jF*FzNc`;NP(jJ*5rN%wit52{z`X=
zb}BRT3(ML4Y+rVF{#6aS8N}FwMW_3)8)PUW@~zVmfp0pvJ%ly;p}}+^_la_S!U#G6
zfpb9fg0QV#&0M1<_v>us#ijm^LyvEQDcttHyketqhhpby98I>*b@{y?<REE7=cIt)
z2g`ZYNnSqEVBtyuUJ;w?*!}(_<JGwVf~==k_6M<gY-o03?#H^i7a|E5_Bp9MKD|7V
zpv!u_;|h?8zyyQbl4qrjph+iV5>05{B!U}PE?eu$hiai?+ZBAgfMn7do$gqR6s+U`
zro+&4HJ+pO?DbruYc``oDz7czXp0jFv}$67mpnek9%M4iG@>xCBAjit933&`k9a!#
zv=3JL0**A(-uaa$2T&~320zB45cU|!e`Q5T$jP2Or|<B(ZiHN_LGcq}@<At@fcF*=
zYK?r`M}k|}^aA~J8#uZ^F|*Zir3;@y;b?ot9qgTcGvt=k%BK78YX#%>56Zk!5F2n(
z{NNRx#&4E1>=$kjnstM5?z8#_`E5_v0EWBTyua~qKnN4HV{59KqY~)H4+vQu_{qY<
z!vV%n;ho+-nK1O9+i=Ox&6Q6VteN*k?CqUw>5kx?N=KE{phvNwUDei8M}b}PL3<d)
z11>($E-?Tay$i~GfmD!qBD=oZ&TMi@N=jm)dw6=C{X#}sn(&)}G$0peXFoPu;al+G
zGHMGrKU~vPQzH}eYX(;U#ERF_Ps1$*5o$`hDO6PMRRnRcc0bY<sau0=p143t9sG4=
z7x%hQIY%mnB0WCd73_eu)m0!U>AhaNy1D|&4)jt^u6J;-4pdA^dU~^U_J+{<(C23c
z2J=9hWS?aa5}H|c+HetE?G2DJfw&$hl$KShO3uHk)u%MJ9qMPw3;rNiN;ybAFh>3p
zE&7vPeZ1zIT&8ZvIF*cvgKETX$2(`khRZbjeyOLB1DQ1X@Kp^kW&m@;vl)a(Xqq5e
z*tZ;y-&|jQ`t<1$2jFNcU^j=Ex6PdcZcTHhPZ0u!Exyid_jCb`3Lt7wM%d3cdk$qu
z9Pb?dWXOC-NEjLt!lqaMWq<JA;#3X_WxOU{fPbyMv!;*DGyC%A!X~9RC1%>wU$CH;
z6|G&hR+vS(arXnWw$JbPq(+6JKoE7O=~Mlq8VkIXx*(@XW8PGrQiQ-9a{g#*C7~Zt
z`l5`TX9P#0nf6QVA)rD<4)_L?9SY&IUze%<<^?17fTf{iN1a%}>G$SX31AdQMn}~m
zzN_qt0{vH&JuW_;XLAFBOcUZ@+A64!vu@`+{hqqII%W%+1P-kX-iyk4>+>y<(2?(v
z6El|>Mwl>Xv5>GZZ()j<?wnuOmmQ#PBE!Ob00jV%GbM#YCYHNY;?5?bQ#LMjMUR&7
zXPXK=kP01mO>P!~Ri?=XcQE*2q7!?<Fd%({ly~k~0LmRu^gtiL6ON6|w(0Bd2df&s
z5+k<bzz_Da(yplHPSE?}b2>9<&qePjGO18}&X)Y~`}XlDXmNWaGnowm<`4D*zkib*
z5L`hd9s@h-%NH)Li*lWs&DTPw@Cgp!7UJUK5*3{{uU`H02jr$0Pzc|2i{0!4Q4MSh
zU_7d|8rz)pMch+^#>Xc!h13xtfcz^Q$_gWfcc&Gsij#kC!GoSL<4X1;?N^^3f*mDd
z-)net@Q7ejAO}~UPnUdPzx%y#WK?D7?MQ+H9lX_`6dqcy_b2a`ilWZ@?4{x9e3U;V
zW;7A?SWvLBxcGjPU8#okix(lGJmNTCpRF|7J*KH}S*^!e+{N{sJI(vX)V<PDv6vfH
zDM2_QX2VuayUHN}JJwuw?EN_0dPGqy4DxkR+d|gu)!|_L12Ao1f>m#CuI(E)=*Nn!
zi1<wKV+b^4RIqcFRysAjT*t2$i%ZUYVW1e?tI0andKipzkO`B3`J5j#_5w<v2j`5+
znCf>;@R?_UoF?s5UomFtnEgfa%h=iCnxtRKd11h6#lvlWbF{v0(I2LnxfbfsyT{3O
ze5rM_x71<ZydT=Tn!;(^j>$LcFm>JV3-4wy2wJ4@VJm<54#&#i3iHCbAV<JsZ;@vL
zc}_+4rl6U%cQBf=&gQpkW&Y&Gp@AKUy&&4K`{EnJQH|tnMii>(3@kqbVDS|k41(mC
zeukjyW&i@xXi))Eny7E1ujzC69w(GE)UX1I0uFM(_cH0vQ-e50^oyI|-%P?c=kjT0
zdmMgD5hE>-Q*-`4BBB?F)Gpqofqu3(AMcG&G#uSrfHKYGwQTWhmHLR<JCMSD?`kH3
zvfE-LKWbz{*!RK(n7+iW_MWNtsu1M43>?JdlZiU^?xgKt%kNHAujRRkYUc{2U<Z3t
zDDO8T1mmd0z@(2r(wnQXPW=A5Qi~N0i)?)XcI$gOS72pUya`r0&{Gx%Gurbr5!xmy
zs~?h9*u4Vq2FpKYo!A7ZDLt!6Nl7K98B|_Zj37mmt&s<W9io5!)3ZQxaSxv!gIv&M
zw%*aJ#c!-wJx3}^rKXh1(OhP7V8iJvGuIkbuoC{>|CrV=)+=d_OmUUo-<{FC41qyD
z3h*9B>%%>TsdA;Qqeo6oPPH~uzd*IshIR3<))slyVV(rH?JjH4k8erO*#Jsp@4@Y^
zeK6(m{(HF!?=dR9Z%=g8AhNZ7^hVVV9LYinFlmj-e88~>ZaqoJy*x#or>!8<@4mk;
z>eGvF9Wna+iB38Dy1Y=pB-5+tNUTxc$H5=yQ?3hB-@QXuqOWilMB+ih7Et~{E`%8D
zalyNSAAl?Qau{4)7%1QkF`|u6gb@|eJeK1Vk(@GJR=l7+*DcoWPL$tR;B(Jz$~jRW
z*BrZ0;MQ<ley$FSac$7M+3h0|a@|zZ(@RsBNczb?_)H<4YWD^SntZ1CrJhjJFDxqO
zuhGuUj4or%S2PoZ;ec;Odmh3!w+Y1{x={tA2QIob-38(-;8%-NshF6_(j|_Pir=)9
zx_Tw0ozUd6@vB^42-@>%0pWQ5tK}yzGai@kPfNx~3PVmK#e6GF<Yl*)xx?PNJt$C?
zb!3vbBdFLDA0*<9!jDs6gdo^g#Tf-zWyQT73c8gL0|pczC}r?FX4@~cdY^1H{Dp{e
zEMJel?!2VHd5lF=>dFRel|(mw!~lj&gR2fCJy`)H<7}<%%+F5@>TGf>SJ7o{Nweia
zWSLq2ei*fd3LP5ClS>3hEk8g1&+2NCc06zWCiBgZ1oXinTQ*n;eZLNFJ_>TQbj(0c
zPviNY)Wd@VJUq$008N-P>5r<}Dgx_t<P^92G<TrQ`}ys-u6@fv4-1&{h({b)4<4uv
zNPudiBBh3hY1w7H>J$nnSfDDimO%e3esKCO1Ls$nE+0fB41&V9H?4(wm9Jlb@??y;
z%yJ*LOO?$i-Tls1HPCv=IT)w$=?%qj81M4oy-PQwfP!5f9Qnf<>foXHjx022@pK0<
z@l!}hMEb9<<tPBWU<6T!#~wBLBSH3=H#k{SPM~D>Fd9P2bPgC*3mX0n_bO6v$CWHh
z1_(?}PENh-8CT@KP#r=*fwz6vp^jz{Aa?+W1%vVKOL_tfsK*V}BqB#c&o<S@T)^J>
zS{kgp2_M#l>i|<L!42v)s0Z@_lCv5sjwTlpJswpBv@cQsc>KlMx~fCF(^$~!wemij
z;YLCb8bV+aicI8BUUvc)M26>|<U1+u#|P>bujA<|vxwsFMNup)!{lNBD;f~%Oa$Uc
zTJ8Uj^@8<|v(~1&Em~J6?{==1o)I$j!>TJ0t`p!I@H<-y3&SMUsM9k9Wwn9tvHIA3
zRdPo`7k@GcvfuUldurwbmUUlIL*OI=BDvrE$sNj9Qo&zx-=&RZ?Cz40>Ay)u1&<#Y
z)@bLEvC!xWuQcgaW6$5fkr~~+B!`+?E?3gf&_56Tv7dOQrKJUYCl^N>_=JS{Hrh2-
zW8d8pXf3}5qF{y5JbB{yD_5pRs$itS^e0|e$HxyJI6V)39~>McbG_;M{vDwWu&Yy5
zX1y%lk1=zEzGU)~_t|_Cz{ZiJ9$d)E<hE4NH#HmwSPNi<?kLj4<Ya9%HQ+CSi~!&U
zE3R(NZ3a}W;C!?)BmnaS-@_--K8rV!Pd;p$$l>d_JJaWZg)B;L3nc@Cj4G^_3ApcO
zQ2~1;zlWcnpOh3Tl$)2gy0*q5K}ky51PUNva{_03_wL<YJ^TCTC*X1g-q2^E7|M3_
zz`wY;X(fSk2LUEL9!noIRN)P6yj(y)4O(gk7?M0st>CcEaa5dX)ic9lx-55OW79U1
zo*q4rwU>l2>;D9t<lT;##S$~Hti{hg%OJY^S8y2@7q{A*IG8DBSTO<2Kgsg>fEsp#
zk8f|+Eks90D<~?W1t6her3t$B*zg*<&$()zG_!A<l0%T?+}XPyUKItvw)++HktBMa
z+KizpumYDAgir;qy9jnAh4NU;WA#+L{qxuN%rw><YTmPuW!U6qYizt!s$IR)e5erg
zjT6Kixh)&s)N};~s9uk~daBgs>S#o9w`sR^zQr5NxK~FgrmM0jND6?gW|FuC2#bZZ
zx2K2X)Q3Hyq=<Lz4-~-F5~ilByDJ(<^)i<BeYPym8I7>;?41{w0@Io^wIA5CI}jo9
ziV1)zWk%x3@A#UcF{`qc9~IW0hMUQO85sGq#I`t-`gl`oPf-=Whpeouf&v!w`}gnM
z++0RdIvJJdkHjX~jOALDkA;LtA)u?w$FU3@z;+?+1f7}$9qx?@7|5^$>V#;!6BUNQ
zhhlDS?&H(kZ{FMAe+}H*3<_yyAcXmXS_GJ|iwZJ|3k$WHJ@(&Z`V!*f-&Hx{!il{!
z*)ez3r;o@bZ$Ax4S%2PTwq!<Df->G6K5h$?o;&qO<aG8^T3V@rHnZ#8p0_*kxNk43
z@rh-O{_7}Q;3KlQQ6~S_`uqjnWmTi0re>jqge)P%sL%H=UM3;dzSVTpBml^k$OHg1
zAW3W#sTKfK{Kcdj3*eA@ca+yz<7roXE{>!oHuxNHB}9Xg{S<h?>>_xdEmnxnjULY1
z)yF=RTxVXk9|G>hUEl^In}|%L7~P$^2dP+*a3$B=h0K&_*ta>b`2qtG)#a24ii*a`
ze}d>aDEc=_yaJ%Zt7L2%=r^Do>hA1h)NQ>wkQ*z$FYLFlOVz6)$wH<cj?*{|YNkPO
z>wvo}uwr74rM<op$@0MjoPcI>FWNQu3zz@RmHWKsTJ!dVo^zBS9STKeD)2;_bG|D|
z|7Rf6t3fwAO}kjw*uYbJImRKyb~UdLEz0eFbK#dmm0T}&KF^|j@){Y!Amew~U;YlP
zp}#$E8sX)s!k$$ZH7KZ>8nTKH5vfK0j)?huM*4O!2%C_Qa2{YU;X!{86#*d8s(1KY
z9FOweZ<pOj-fBc4I&%CjHvbYwB19V;9Mq&oPv<n21orc4bg$*Z&6`PVXx^^nZ+7{*
zw9D_6+OMIlU8GkIkUTcFW?8QIFY;MMVG3aCtA+o~u(?sal*6#I<p^jf|5Ylxe&Z}K
z`UZahMq$6jwzr!TgwUJT(CumbltY0QbZQqmcFH3*ov%ta<lGh?s`45zN`EU54*$Z-
z8l9=>UOkmU|BT~$o&_{?FZ|KZsMoD?5LGR_jn(+?^rYOI!opm3gkzB-=PjUu-ig=L
zq(_ZnRpEo(?Paf3NbfQ(HQTvRb5-~l8`HeS?>12=z`(cP7;~rYGYAiH1a>vqtX8EX
zmgk83!v3oqo$*{GQGVq?`FQ;^KqdXRGQNX5{WG5h?*GbywG;GO?8~U`%PjIscXN;L
zHQf)*LuSgWkE4Q!Fi;bFL*(vl9((m0KIos3(3bro!J{M_C6(hv$Mfj(lxqizVMpyN
zV@$C$X>ecoff9;7^VNnO%Z0L_l>44FaqYO+H}%DK+FJjCB)TOBa!|$sj1+3#5ZSZQ
z3dRW>df=twltP$^9DSJQK5;i~_wRouJZjtm4~4$QY)RuU!pOqWk>HT=89;z=_N~ET
zf-Jd#8H1Of>h+AcmvTD++s^Pc@TScG+H>3JVI2E=8p6-`N4d436k_+G*E)QEPeX+#
zC{Zyuk5t84W5M*j>+g_IJ}TYw>T*FlMS`M>pQC?141PH@5@@2QPB*gAIm-R_xJA6j
z5`%AcQJ|t{Z_GI@?(PI7Q3)(2M>votA&!$7&>evi=puT7O3lOLq{h8ppEoY^cRD2)
z9tIjjM>)^)Vd^PRJ--7PjO&GXNB?J|jLZ~>;fbu;%MSc_czE=R8LYhrA(wi%IZTi{
zzzo#V@uC-i+yu=YgUMV4K%*=yd^uGQ(i#Yuj|@CM=jYRNa8%6ug6Ty`W0nAIGnokq
zsQ>oO^mI*5P6CbaKPk}b6fB{K4#h5WlXp!o$Pj2gv8!VtRaH?*;4l>Rzwv1?pd=tr
z-xZZhWM^Yz<4Kms7Cp<l>w;0~A^K#d-9ZA9B6aYZr>*rDEmn{MIg{lhpI|=t+X@qm
z^LM4ZRgncrh&+YnzlyTv)!I{M8wHtnZi5!@Q;=<r0i@rYV-OWJsF)zdK$VFdW$aT7
z#?elw!Sq^9mzn_-gq3c9<X@Fxksk{JO)K2?Xp-o6S@!KB1p088*1@<zjiaKf>g?py
z6+>BJ6Gtm^?ratro~^YjS~u@i;`F(+R30=8(V_>^%49QIX(0_Bg6pm9gE-yo$et(%
zvQ?Yu(}p>_erJulO9JNR-1@Vp5PG0Ex}_3koJ!u>B6EQ(r7z6y`u_d<Pr0~EUca{C
z)wQ#;)6f{71Qs2TCZ?+2tgWqKlk#pg`;IaKT>=NfvGo??K#f`N=;#okh^du6GOwOm
za<BkxKF_74@64y(&nrBIlw`~PH+>Lv$Fl&Z{+U0%tfi@G?%!?3iGnDJ{#mXq>u!&Q
zNyLC9ffYpYpY|Dk3@@;69RF#^;shNqjGe|n0abd}@&E0BN4awMtXc`VOysqD^jN%F
z3}R_*6Ngqj=hqJJT9iOg0IborebHTm6)y6(Z8y+*1@fJeEK{CAMV&<5Q<5m=v60C2
zuk2ZVDIBkg&QmJmRP?wAAq>=@-qm&<lVh)zzc?kUlz1kKgfWlkC_tYNtjUA_VerR5
zESZ=G=933?LS)gmm#|yw3ol?U0*`>CYul|tt5OoqOak5g`$q2JcWDYwT0lk7(3k)c
z1!^PmUJhpf2I)@#rP@w&cTGkTbAef;0HFFo#jnPz3zQ9h@Bn{xUhcyDK|lwRJ=iON
zfB-3SX8|V0arYDRBmw-SrZ#!(F9UeMq4U*Zq1AUyf2`6(x-3&d<Zk5aFyNO=^xkdJ
zteEKT?!K!vprJC|Iv_EW=`|dH9wr9G4B^v<t8#h-YG2?Wy5B^s@J9>bM{1N1K)klo
zE&#1~3ugdM^kir5ayFgAQC;cV-5lPMJT?8%P3gaNcn&Oc6Q$Zn5Gk(>5rD>{MXFI!
zD*(I2|8$G96xeKGMn~Dm1=bKV5>YSCK39ASifHFKNZiH6MO~d>z}2Ki2ejO9|E+oB
z8tndeQ&yEk%;WdVpe5r3NCcf-U06h%R7^~kpkxG_-w2Qx0HaAtwr7ZV^V&>)`1tW-
zMusS$jt|ShlT}5^)KEna$pZA;46ug*eg#l~hzILLNB8@0zaRurK%DD?Rw-H7GX1S^
zbMV|^W<7kT(BuqTtd{|wS;G$G@IZxX><eHbVIvjr@rpT$v1>5~Y=}-GsNW<CrqD$4
zMj3h`Dl{|7haiuXPsQZ;uYV59pBLDl{yjdUnAYgO)BDedrQ?CI@&7DTx(3B=eZZ(7
zqq-ST&3OZ3E@&U)VrR#KGJFnxCa~%9?o{51NZ%?_?`VJTCbuU~p1k$C57y*UwR``@
z-WXCob}p{!!onZs)d+1so9K!nMT72mWx$gu>F5YL{r&;i9KG`nZPtXy$Ve6#CD@iS
z|9E*~oB!!zlpSILeUO-_&%m?%{47i%LpT{2HWCteO@H9rwLpFjwVIU?mY7e^k{z1)
zFX1`Udn|>=I$u5+5u*Q8WIa(17&dRv)RU8wGcq!QU<BxD&{;tku^O*wpq#@g=1A5}
zG;)H0l&&Dn#6yHZGA_<Ew!wWv7>0uf3cBsP=!PT7AFZm;J@MfmDK}SQ8z*h;9*>B+
z0dMuxmWv=l+p1G?|DVR~nwpwB!p;9PAT4^jj5|UBU-k0d1$Z;H?bc#Yu{|p;n68ju
zrk%i3`pts-efIj{IpB|RkZlJi5utR2wSAATzISmudWA7BMKLMQQ)o}PVq}A46e}19
z9RE)@X69e#%qO5ueEP)YcXigd4M3xJo!wkqFJSN1r)#Xeye>dZ3&MR|6tKIhvReqD
zAVL;?=A~5Vz_&L8W1%zUVTB-8!iWj@*h!xj06Z#I+$21Ia|X1?Uw#K&##r)+mCeZh
zUqd+|v^8B*;(fZ)BRT<0GeCGUAI#9#*B3ErgZ}AH{rqI&Rojvt-~IkIUW7C_D&$a}
zTAgBEo*LjotgEc7tgGu?IaLRGdZxj_fJrcLGsyyt1~h;T+r+#8T=kvPn*Ng|kfeZ5
z|Iv<v!4S|J{D8bRD0;~Xy^PKE#8#;m;{x4~h|z=nZ>!P1xiwuMCK{5=d`{Pm74^BO
z1JZW3of#fI`fI1A2lh9h4gg{cSVQU>8t=2uPGPs#^WM8WBvJeO`@5}|JAjw~nMzxG
z3M?Pc+mPqdpTYz3dW(aJtLriFC)3G14(?0k+fA`9x11gM8*L7WTW^}#*D;|%Lud0i
zBSlE-^2MB;lYAO~QASp#u;&3NTOU7u?6y1KVl!0*q9N?+pqH4K7_>rOf(e4V$;rxI
z08ai1Fu($L*hJI*9fh$~*$wm|-w;F7*LU`u^W3xG#w>S!vYG;g+WXZ|+sB?wjqRC<
zbVVv@y+02EfVC7+TuW=R+4E4IUKM!O8w_ubcNZw{O07Zim%ck)pg;u%$|xMC5ctD?
zPMLNpU(_TI&4V!fEVE)?=HQ1kB@8@-D{W4t^9xnt?=Xj@c497b44|yk&e#dLXL?=k
z49-mgR4?Lv0<?o2ki@~%MEN|W=6v-8u=2JiTT{J!_PPxwYoO#<8@%C7y>qSg_4caz
zk%4~BtMfxZV7z&n{I`v3Wgt!95Ofz1J$W7c%}6Pq)FO?F+%>3!#nB@VF-#K?t3tSh
z7_$N+4)B<9iHWB`CIqHLp!VK>@W6WZdJz{i!=e-cr!io}FTi;`Hu4fl(hmZB?^><D
z0x1+^uJS9ty?6iD+|P4)_gJw3#VtbYy8|{+d%I*=FCgl&Mz(_dfWUMbxK`p=*K8S)
z45-}kRlqL~S)TmuWAtOghp2v%?1+3Zgx?Wu>)e<4`Q=SJjT0I^dlnTtViY3t=ABV@
zf%d+{-#R%}KEDyAoYeMbEFB1$!0xzK&TzaGMoEGZ3SwbeQ$dydSd7?>$0~~=7#A=~
zFI#2!0~vFqocN;s`qWw<OOkOEG}JPw=KEMjAs0By+8mWsw0f<c3b<~L_U=@_8O&%j
zD=z=Q3@U(MfD|Ibl79Vq8Ms~tS~h=tq>+)4iBsvCyi2+&Dj>9-{Gb8wh~(M<h*IQ2
zZd(MvPbvAPlaik{^YU<{I_@?RfmO(%(9XG7VscdUygd~g1T!EtfxSy39qj|cXNK>|
zRO4!19C_S}{6@F!i#8<e;IObJy>dXmK6>;hvk51@N?zU3aUa+PP%(%cy2;(^=XL-;
z=(qO`tv8WF%$=Iq?CEbgrM#Gb9i#tgb64Sg`oFs1&E8)FL@)jPd0z@o3Ajffb!p^a
z+@TOq@xv~6ngAsQWyOKgycg`EqS8`fcTXnB<8FL_W~^wvkWKzlnw$H3R^$#-xNS`W
zHjUd%BjbO00g&2&4bvC68tmqq1%!otfEEXRh>JS{dfdtS@XG915xZ18&BcF{X8~nZ
zU0n_TlX?xj2p#S1gzS2gKso?Df{di#5Blth3I!nhWQ;YWVC2Q&IMGo)Ehqac#IDtp
zNz5QV6ihF-3s-mr*eJ4YT*-g%db+~IkekJk{eV6B^tk?sSBs*^@#x73dD{}%hGXl|
zFF9Au{YNt4PfWUmffWOC0C~;O2HY;c&)JtMjM9VMywhetAjFFt9v%X$+)|>d9|_v`
z>t^Bw4nQe7XQ#<a64eFROkjx7JRe?i08E=a`2K-(_xbRsDBwvYg+Q8T01|r-X#l8W
zWWqgc-)t5DobNEDXj-f?Z^I4p20AtUCz+W$Ka&!cP@j1<kn<eAf&u~43QaqK1E%U5
zd$iV^zJAU@Xl6U#JY~UcZfbhwj6Mm7RKP8QDX{{SC(wz??q&+0!)suT1zK7cAmyV;
zcsAQZ3OD@@XTDligQW>(3nWutAc6z02&NaC+C8uT<XrtN^(V>lNT?X^A|gnIJsbeT
ziFb6T^Ok=Pk#<<ZeF3oCt5=xp!mn#|?)vTjVmXq!y_e$A2_6}gf-V+8c27ZTCs_Vk
zW#izXHlIveKX}BhmXK%#_|-Yle|-i(NCoHtVX;|+;Z+JZSY?0@nz5<9vrd5+2y%q&
zT;nlY-RuGIC~_APssFbx9C$Rt0Lo6Vc!3jV1GH9=fNu%-ppgOmwyW(~pxGCb#HDf$
zYz()Zn6>Q&aMz8!Vz*b$bE{|+9^wt0&VS_n-z(?At3d)pK|x7QP6iQ#H<gW#kB@}~
z-tHSsAtDGy_5S^P5D^QNQ6<r-+jvaiz&v{Y&P!TO`~a#q-_q7xN~wJNYpXyUh?*{z
z{}e$f*ALM_$P|~Dzc<3~@%;bqg6Y4OWO^Ui>0UC8?F|b=gKu5X1$E#Gf4}umEril-
z>BFCWSMKpt+$l~#`nSMLxjjG*r^58gSmW_=S>2VLF~p@?t+542=_{M~w?m+-{$-dM
zVekJ@lMceBvPjUWH$iy?U`5lM9VVqd4zf(_bLG)5s4WJ&hvz`j4+Su%#+Mpuv*85_
z|9`Z65)9OTNE{sG=oJz*q`XlZ-V#j}=eL>GAk_a`GCl~&TC*y~Tqt|ud+>G~u**uE
z_kRIm!#H^5_6MqVK3zdxy~p2dvWRzI`iPZPUfv1`YTOd}58XilbskYbZEL3ZGQFRl
zf>{Wbq+R#j7J8Z!Ua!~K{Os=sztKGpq+mP-9Yhe|ZOY_8c3Q+^mcRzwAOdtWiL0WY
z_gC9`F3GUII>}%3Le5Cu-vKsW0|tb1FC7R)hF<PUUGtpze?P`5$-Mqu!BW24nP=?l
zV$$D_eL*kEdJ`zIIe8EAAX5TM;7y3y5K@5VM$6A@d_9GKi6tuuj;Yw+KP(=HUx-!x
zWi10G3sFRHmf_Rn&{+#?MY9TgEeSQz5!8B=5vfw19ht%U>o2`%fQ~A{Me_ZG96=oB
zUw3!O4=0kG>p%gnL4$_Gu<`2T{%T4lpNWrDRCEO%zkm1mwj1H@y3|o2=0ix-*-r<Y
z7It+K0mVb2Y8JvI6HCadwFo#Me0;DYU3S#LzLJrZ&5jdH9{|{D27V0e7-1A%FL8p~
z#us1hmM>heu(2#D5dLJdecJ+*H7hAKfou>~yq%m5fUzD_vG3mxVE7tG0<Q$<=Y)%9
zVnd)=XQIY>LcsY?XLq;#^jD@u9?t3)Hi?L5@vMN6Esz}oEJi_I9)kEky$4mp+iq{(
zypfB|y-G?+5d=sW0;G@dV2N^Qg*68>bnW@UW7cX`3|I~y{5MJol%{|kIo%Pd=6nhA
zF@g~Qx}Y%)psm^}$Bkcr3eThev;pD^hB&#U{!)RxjC!8HF3wR?*X@k5EU`%yD_T})
z*RKtiV4V1Q#OsQ0uPZII0@_WVK6y3hJ978UEB|-8>HKvQXs_-AT_Ahy52SLGr|>`*
z+yoqyUvqD736{N%SI2K5p*q<Xt^gb{`xodR&9rB-H0Lc5$FQQFF^vJk>0h1#I9(x&
z8y%3-f^q(Co&C!S(XT~#NB?+Uii(N=-2fc@74Dhfa!Rt99?Rj*eDhxLvYrs&tqM?+
z6m;dW*HY|}ZSnM%E&Fuc98ZNFOB<e@0FOKr-aYmL>`*9=1KFCBrO)HQpQfNuJY}u4
z7APbH$P$0KFC5U}^6QQ&k=<=qR{(S%5UmIvJ~TBq@9ONFw5bK01)v2FPks&z%)|U|
zLGHdYlK>NwcGOdG0lC^|3_w}kQrVyeJN*al9W6z#Bz**lD?UuoU`LCtR`gHPZog`f
z;UpUl`|As`jalzEukKhH`T{}kqN4#h{_EtO!M~QE_qPl4>D_$FfBd0aQLdlz(exYo
zUWqwcS+l21YFGjfm4SzGE=2v|zLutc)1s3H+j=o0MX5BdG9fa-3xVGcKa`+55m>x6
z7-F&g#63ale078VB@g-KOfG)P?&WH)rVRZj`wRt9&x_y5Z;DII{w6prP+yL}t~|TJ
zNy(>q{1{R5SATX$2B&)1ha}d{T0~{jJu6!D&mTq)0NXID?+pbU#4JdR?;ZhFmyjCw
z$ftlEuQygFV!6+<0qnrfe+cm;IGBiAO&&o;4jZJwO6&K+K~!<C%YeimrRB*#_0I{x
zk?dzx3p98aO%SUh9vcUv{LlmlYAcP;ct_CU&M?~l)74i-MfFEr4<Vq^3?U*VNQX*E
zNViCXAR!2XfONM=BZ5dtcZbp;p@4`eozmT1($5+HZ>;sq2N$lHxpU)p&pl_Kz4t*x
z%MuXjEU^PS<^J{TH0AEdeXLn~md8}@|9$Qu9dchxb+beS&G$hKjo&WMU4R4pRj#=s
zw1-oOi_<I$mi@}@QBC3B3>AI`YK6!69}&$|-%3r0B+}_C{NOl5PSJg5t*ABC8C`%D
zFhC{2xg0PeKzH{BgS;Dsatc`I_(xuMuNla1cZ&sRkSI!gYLiSpmx(I(hX^a`D@faP
zihZ&EP3r~(>;%D*4!ytbmq=XLh3b?jHF-^KmafH)e}J}DFjhmB+TO<rgm1?v+rRHV
zp`(NYT^%LINe{cW9D48Hi9=kQNF%>Ze_XYJCUk(49?R(nCtR=L1a@@ZPHoJ$6Ay~J
z-`N3}BExw%b|mZ*dRWmgUq48iJBos}C&xU`*(_A4yD=9lleq(Zx+!y)NrkUX7YC(4
z`{7^zctYT}rNu%(lNA&m8U1#NFF=AWG(#=lYNX^WH0NcY5wk*<vT=aK%qbVWtp;lh
z^P1*_sSS2$1`b`>C8voEc7TL<zvCMVT7S%vltek;qW7=t+jU;KPG<GRlHSB|MAh;m
zzs=<CLg&rHN6?ixz%stO_uzDV!o$P*IfV>TI%Vl*6<d=r1;C2@n_Q$m>C8Wm45fU{
zc_`n_VTwn7#u^=ao%1r*)bvvS$0R;`C$R)PNtOC3!|&KKPOyXMo-wuiIV%@eP&5*O
zNKRRDo1$?glCG^gF!q_q5dPFYn2oKsDy|qcFs*+a;~4UmS4`5I2(4KB@Sce%g-L0;
zHRJ9>k2e-Lfil;z_|GZM^<5^eY;pOiGtG2zxkE7;EK*Z{5dDPIv_Lhb>%hYehmN71
zcBA*y8W=yK{y~?(qfW*mpb&Dlx_wCdnbjq3^Ve&UpAGfn)sC1{1CltE?EGH9>;_=;
z^k{1;TBOGP@VWPtvUY5LfLTCA)dedqKyhD{L>=eIqaH79_S`^>oHzYjA;&kc&ZSEl
zF<ukaZBseTd>KbQ@kDv=EI*wmA2l`^$R;H^{DE3Z#~(_?QM5pELr>MHEXY48a04|9
zwXRgK>(rd!p(9Ku<<-LDK1kzKQZ-_o!*dFZIm&@_9WLVld1u7K6XrD24vAo8B;0k#
zm4^CxR8#Zp`*2!mK>;Q7Ord1}Fn?-fr0wFbkG`>t<l0lO5n{I<NEKE7yPGa%EdS9x
zenzvfX_ug?^zm8;U>acP#_IhY+VGDTn4Sprs$clG*_PonAE=T}4%Vt6q~_%@OZ)&u
zC%SH|6^`8z4K8AT3p;q?jtegi-8^<<3b!dY1&~8FkKH!MYdKt%1YAz>t*of>na%7E
z?Ig?QQ9xz_tCZP9jb6#+#Zi-DEISJaN3jnL-8Wx4MB15`sGWcbGd>&+>O+q}{#oh$
zs`@r|3N?RDQPBnO^R{>TSwGBYomqUQxD?^y&dYL`4TTRbS*mMjX~~eMdI*%aY*$3D
zh}2n4eyP16LoRIc6C&c@oCc4F`2yrmQt7Y5@5jJV#C_N;q&+j1Edd<ZkAs`XwLMlL
zp`p8F-M_3eCdCAp3`_~>9Lp`QmRnn8RQxnyaoD@Bpdd}7Wu}s^@jS)N8km9vCHg)!
zn!CUm`eG&3pYq_~XOc+k#-U3GL?Ta0?zJ4a#!&B)OWOg1j3p&Z@Cvqwl2<|!_n}@d
zU!m9M&lWGBIqfb2`0+H<>q(Du4N9Aa_&k?3`zDjKs<e(zy_J_y!Q#g-^*O*i2A(_8
zv8GUS8n~~3R(=V)q9|}RN9%WJIF>IzYr)}|R|0DbK-y~52!@2k6MS^UVKX)TTIEw<
z|F8r{D5_Rj4sQY^kkYvhOlc?uQw5z^ZB1{4e-C^pg&wcw=-tqkkf&k&Dv>fj?dJJ9
zd*?O3<~?B?zr%6@=JF0+bBbXf+f0`c9QTHlIRQ5={E|V7S9~A4DZ@fT?|po<Ye1!U
z=$!WQ&ogcfZSBr~EssxNJXh3aymL;+qXe})W=U3}G)62-qi2+cZ3>4pVp7RGCQT#B
zr9QPmWid4-`sK-NR4n!(AyZerEY3u<=dFJzHlPUHFZ&qeqlSuf_5g0sGkY#h2#xhy
zAn1#V+8ucw;nn~hIxd~E))(b%?d_K<%09cDL9$tX22n}eGkQDjVFwt`58f{S$l2<j
z$Y(3J;fL_BxbA>Q`DY)+!oR;)DJ$x-IgVnFo7@9T7bFEvu!4t2T>--bU>XpLyOiOe
zR5h@Hd&Nvw_YW}8Imi?Z3=H}W+AjC|^De1xUO|+`+&mgN>&4D}S#n7TT98BHpa}vY
z=$bA%%~7AjHC~;tht009uiG>pFX|F>ba!u!RdXRAO<7!8Vq#>}_j>;5(JzSfmY~Qi
zDEQXWlDJP_vhVn4U<-efNpT}JtlP#ijx5)F^-9n2yW}>n`h1Jq4$lbq=lPnDA@HDF
z{7(0`l|-4xF;}Z_aIdmhx425T*uVz%*38-Y@LRz38-XEjzX*Ka{dK<^i9|x=ft?9_
z_)_0++0hH1Mrh5pi@A7wq!weM*^aJTKy9KlKbt5sAja@s5PqJ`P3dorO8LKKk<u*4
zYsz6A0U6AJMkfEDf#FoDL0s_5V-AfvSCoc_0Q6d4st5%?Oy!;`n-EyGsW1NO1?GKV
zAc)osJ-$laQhtzF+d~k7E~hS(+)(ld5zycxx`p>}+wFLihKKl8>S%bH$yq{fKTe&n
zuKM*;NQmuWl{kSJg$$uT+Y9#!>-O)hYB8kaIjQ5fzw>4D(Y&nGtsdL3uT7Y#Es-wP
z5}G#{`u&W1#EQ>ts-&zMLs}Kkmk^uKoDU7RC<0YkWAGA=B~_!<@^SU#`NMMXEnQ<$
zQc^%83hkT3M8M3B+#7cGSNc<2N*UzOva_#4FK6yJov5E}twV*?ASB{^EMT{~ldV2K
zUbT*Uibk=2zilPP36C=8!a93a1%EiR)O>sC$p`Hy2Dkilw~!2G8#L+zQOCsO^$oFp
z$BF6?Jve2^Ytq1gT=}kCZY@vA-O%R(;63!9+@aY8_ycr`9;ZHRfVP@D|0Ix20JdyF
z>4Kq$9YY#vIz<zX+r&Rki!Q9A<{oHiF&uB>5(c7jzvlJv)wo?Cr@H-h|E6)@(mi7G
z&4(;IvoL(2q>U#p)DtysZS-T0T|SPlBX&xcI(6bhjL@X8wzi#Sf3y5bk|VtB&(chi
z!wG*<a?pyLOn)VgN=_n_?>|nm{C=F1BF%V*VdvG@8-~wg-aE=(kV?672rr}2hNyZ+
zQ7Mz`mS-QH|17w(8D8%spn}xb6C=l*2t9*{t=UQ7j-VjUIhVCUBEA@?R{s8z^&zG8
z2eV6V38LA03RnEWDx{VN{!%>{&HZ;(T~?j^7?ju=Bd033Uevy>R=9RWU#CRaZQO8y
z&R=EPU!jM@mmq4cK40^NxNFVwm)fg_oAQDU^|FSDXD-%^_T8?MIO#X9#X$B-N;leZ
zwfCA}s)G^-=RzX)_Ux+YlfFQr@{o?vwf1Y!7)D{bsAJ$%lsCp1wXXclRcf{G%@8<)
zHF*sIX+p5(;vOk<GEXGfmeIhttdg4!AMx<skf;80>wWL><ofG(KeukE%^a&k!A@o`
zPJkegp|~Ih9{?^r=Bd$Tc>!db=>$F^bWV1;Z_k^Z03LO0NXQTut3zw`X%=I%SlX5x
zlEZ~hHRR;y`tOTHX^ipBXea^%eXTbw_v*0#%R}|Hp=+~}0ZZYYx0z;aTVxb?l#;o$
zQUqtF;0S&gO7JYBi7BeGseK@h4yCX5ur~^kw0ab$$Kzk`v5$suZhtZBd|EF9ocK3A
zc<u=EYz=lr`2m#yx*&(e<31T_k(v{N_Z3F1#xD-fR=3b$lLQiiaIl5C2X<=0N<WSk
z|2!}y4Ig>aTGo|*8<h<!Y95>QD!~RbRZy3<nBXY|&2<Eg_MoRp(aToqzweR*qK9a4
z3Y6BipXz{;=qp?bSB86Mqw#Wxcy+DMKL_GF4!YhyXGq9V42@=rIIb6jGJ+WQB<zBS
zd{3;#0Nr&{;<!rJQj&&KMyl5100ZH?c4yWRD@_bOmqqxmafA{w?ap@q_IfZbfw)K>
zv3L^yol$`xmE~{C<a8KqJr*tzWV6+Gn>MyxfQB^S3YmWfGrqU`;!(ueN*uYFl78h0
z>hU-h<8PA7;H+NA?kBLKrz)v^KNhKAJ;IE5hJP!qlk;zrHT-I{3hoKmXvrxA2Fs~1
zKQkW!K?>JYc6MIOOZgiXn%Rj>xoI^c6Q4ALj}LEnjvdm5t_RYH4|OKd`}df_FGIuF
zwh{9YFD=gY+99=w`#=MKR8Eu4uwaZ4XU(xk7pW$XZJXQDkI%$~8El=S7#?T3yZ|An
zF=L5Ht2S}EA9bxO%dopTb@=Z9N3ytD96eL7pN~;SJk_Cgi;KC|GbU<wb`}M?w!d4$
z`>JRpXbtiKm5Zu3_xQXSP2ggY(_dNmViT14bt>S5NU0wPY(|yg!ghbyaT;1GL?`<;
zJttS1CcK78MCzzGOKlHto~;&^8tm;vNtycKN?(agV!e1NNZ1!lNfAf{nZMJSY`#X_
zg2AwOwwfIk)0Qf$2Xs)M#5(;HNmNAT1qnGX;|h%oQ}PE_5rYmSSNqcVDHnv>qD^(g
z30+gVKMUO<jea8iIYMUQG?6rdLb2}qcSQ5wK~eUpIEQ2rl|jvfY{lSaA=$TfE^C9q
zl>6=??RHedKVo>#C4jDH#RqVwFXX2mIvBRFQ&91>;_I`#l9JfZSU7&p&vc+X2BWT1
zN%Mqvz_~}odljm>9eu8FcTHV;3h9k%A#VzvfxU?$T6t^nFBceX5RfjvBAPGi0-jL%
zxRJm$7sNfexm>Kup{%5#o;5=FaHC4-#roe@-&Sd8QiLu83Fc3535{oQ+VlBm>i}MT
zVAL};B38WE87&nXnK1M!;>*EB{nIKSxNm+Z$b4!2OCO#AUvDhTtcC9ZY$s;ab2>l7
zy-_wGMM`(-w$uHcve5NS`b`2lzOIEE&7+MU3*Ni=k^<?Od)X_7G)MnqY*u!j2w%Iu
zLmO|Sest)XklZ6VhA&SWygB|J?tlmJMS%De0;3{<G+*8b=`=F)rTSBAZc#3C`|?hd
zgm^O73;S&(tA8Xe2)uKev>I4B<dxNfK;3ht&Gcc>ds-QpdMq<EM7gDq=4iZSBJ862
zvmX8@HsS6{y7pOqT~7ATyU0=wX7=&4yBj6!S&1+Qm8Xc)jVxK=qa;Q>{4)HN7W!+^
z3^F?Fi|r{Kg0nTfEMfivG7)hataW96qZf+h10X;lhmWcWcAmw@CUxDDF{mj_bB+7$
zeZIjcDZsyq4|m-H^R6<lVmRQ8;LU`Te`~Lw7`<tsSxh^rty1>K@y-46i4CC|As1|U
zwO4;%Ke0^AmGdY3mS*D%(D-1XHjRMo^e)GHo^$=R`6BLO#i$f^7O&i0tPcydW7g!i
zGecU^4URavsz7TFqI5%`MZ35!zh7x1|8Bzd^wYDi3uBeY11F2w)C_tC-{DTSR8AHr
zLyxZ*^7)RFcGXh-j&^i!t`FVzPq>2BPzt^=SNl`_h%%=;ZrGgHm2c|v&|RN>Zq_bM
z;(M9EPJm3|*Y~KDrmKpv{P7CWy!l5a4teE^wZF=bmb9Z7nM}0q<~nBghDX!)@2V<B
zkvx%be)1}_3%Q_w8dYAB;swEkHgvUeiZGGa77Ql{pY@ztLkcC?Xox|>5jc8zv{R99
zQ2p^)kJqpuqgJ}Wn*D@#m94`DFWJ_e)(5MPdkG(}^BqJ8Jc$5dB^)exBY}9#VyRzw
zf#Y;G#bdK}=tTo>f#IJVXHAHIe^q*qkJt(u@JAF`V;sJD!LlJI=dQ2DF0%M{hH(;d
z?%%ryX=-Vcj-T4+5~ZILptX?S>txF%)8DT9lTAp{ZW@k`aGtVG(t)3(!aea-A=<UG
zKU=Rczc!7n_-e6B#KyB>cJ()4QoFb6U0)2T=OiqalF%WqaW7*8AcRRs2G2X!&2wAG
zNYA}xa%-D5>)h`{Tr_^fZYQ8IRm`v%sDD5~=kNJioJP!#w&KKTZL%TpG`?UQ_f*{E
zmtr>Qcue^^V|Jh25k{1*(oHrEQLhN{TT(d3x3Ihry*wKh6J7ecC!LeU6Fy^C5vkgP
zZ-v!ePkw%k%b1*|a3N5Mrr%<yA66`-fzp+pR-6mx8z?_`0Xl@td2I>@QCZ31HW&v8
z$}e6AmnEwDh&zT^3{uR?B#$?%&ipPl^zYJhWmV1yTMlhamIyfL<`@%6aHxy<dH=7{
zVz|~0px;!_U3ppy9Afs)RY$>WwHwsb4`oWn`tDR#%Pd4^>130thj9%w$ix(OBV2;I
zXypr18*|^r+#7mJXkmnWJ@OLh{Qz*I(L(b|R8*9OrDH7|I%v@7?O8jBqj5c4*LgPw
zAuv1~Ps-?F#>+p3_3kqP<U`8wm`7EU+h0AP#RS}c(cEgc%%a%|`#D{Q4e$ErcJ{`x
z4}X<^=C(%HSC@{_@A0fh&k<EDXAPjIZK9=7MKVHS%cDdhVZts3v7R}Ukp6T_;?Q}U
z>MHOMGi)6xX~o2CDs_1&@ep}~7KMYW2QsQiP{5sRw-A)-*B|a;fCZa|ipn!?idDQV
zp8Lv0a!MBl8if7_FRo`*V<RYB%pO1P0y+lTvvdQ|bj?Cp<)u_R0j3m=Bubhitu-Wv
z5{B>o$lR>#mR~+rtWU{FC$3hm<!abV^wFsi1Q?xLRPGKDGHj%MIC$nuYmN=7sWI`^
z{w_4wvC=_{4QC;Q0M)zBtv+}D65w6V|9F4kZ5Bqxv3mwafEcRr5l>=CF<&p8+x~7c
zVlOYQqoluWv}Ln_wO^QDE5MVA_KAh#n8uWp&GJ`jbgWHbiB9xLL4*t*VnF}sr51ZY
z6uouZy|Iz2w1#F-nEkODe$tj+D<v(h!%jByC#;nh;~#AH=Bw@T^!u>&jpi~aoXI-z
zIL}nbmHLT@h+ea;yda8fvh3^ccW`h(qlF%zh{(hmSPgjr(FqEb$=*y^Ao|k;-}C!w
zS=zwIc`B+?dbYRqxz}afPIjVxpQlpbv!=6xF;Z#Cp+u;Q>^EnzZEzF?$=a7W>ojK8
zBZ_*<M7@~X{<^q*`)HwsSN%S@?ij~)l9H1j0c8LP7w*s-1;%};N2XaP1XfULpfL1<
z!yaSz{l>ic0CKMA;_@eGwBf3p3Rb*gg}tc)^ym8`2!WsoXP2!B;h2Hj9t8M^jEsy&
zcSsJGp~v8KfX8V`Vh)Ab;qpxmTJb4II?{b=(I*y$bt=~LSJ1g1eB0Kl=Q}yDT*2`0
zC0yF>1)MgaoPq!@sV}6~6#H@TK%(?!yv4pbu;!dw-8hyOhiELg>mp#gJk{|^f5PJp
zLq1=rHnpJboIap&{1p4*3{g384DDPdBz|;2)$B#h2nY{X8`MZdT&&odu{s5L5MaV{
zWHe1U>9)&job*IuEU%us=Vd$jE|beOJ7x-mp&7<lQcOy>>`&da!q46PRpy@cU?&4R
z%cp;p<QzMcvF+E^1NvtBVmgQAnz%6+ARm0Qo(R^?g8U5!Hl~`d!F`C|bCZ_$ccq=A
zb98!`?gH)nZ@yc7K@&S=$%Pj?LbKnlgcC_Lf=QkrWOo5mFx!9%KaZJ)g#1)7aUddU
z_UU~Ew&Ewz&WcNXw^GkHs`Yf3?$G&L)58IpLDRf^tST_>LeQf3sc6#$rL-#Yb;!(#
z$V#!O(&J?rbxsM6h<7w<TD*&YJ4!8n@lkbvcMEw}=8;6K+y24wfjdkBcvpW?l5A>9
zll-7snud-T9RV3cuDDB_xTsvA)`?RC=ID|VMpq^_B1zWgYFDNW&++gbDLmd9%|g1~
zUekJK&sSxh@1va!vXAS&pE-q(ioKsqa<EsM-OiKaWn8k40Je>vxQ}`iGPEYWx<SQu
zk>;lNnKbF4lIPEeV&Y$E<3%R?aW_BO&-rq1eB%Dit^Wk8^V$4u<gD%T_b^(Ty8JK!
zs`VkfpX1<cee?EngZ}oEM~`N{edDwt%un+u9Q5KTXw+5Gu2gVZ80i9{Sy_3xChH)H
zvhe$a1YMh@vdesY0Y?mkfTP1z{0SaPtOjy^)gF(YrLeuJQ<u19IfQ^R%VhEA)fd9m
zgPT9>1daup46o3wye%t~nb0i$sqy*!i~i>P{^{eU*DYvU4NrIHbYo4|Hnk4<`9OS!
zz&dCA=hwZ}rbNDvAarT}8<gU1e6f;QEWm`2v#2b5%H?o0`GB~xdvnC5n#NIp<D2H5
zL(*fFd@z98@HA7oe1D_5b*Q$?f8EBa8R{k66GceV@Y<K32Db}3{aqjUXj6~>cQ24;
zu~pif3g5ZAD;~o`X0q?4`BI65MFGxyQRhJr_*Qn@vJ+6Y)#K&Td*p_o;Q9LiA>byX
ztp4go4qj)4QS>b*J$kuU6xlOBE!oJBNR-t#5F4Ov_u5;3*L$3P;NG){$*{!FvfXy$
zD3Th`v7nD8)c#I$k@`9Qne#FITn1fI_xkE)B#VrczPH?!?xv%;I>y>u;;5+o<a^<|
z#qW<M?cQniz%SnFta$$M&s_WD1t{@)(3~&o>=)k_6|vK>@g)AvU_O~_C@3jOfzIo9
z-LYZ?l+!9uH1>C1doy_`lX>h@kH9j+b?37Fo^~V=wfwF8=W|)4TkU+GB~fO^vGa{h
z=jz~{t71REgz>NCVQv)WQMbF>H~*`5#06SvyXwZx1cr6u3Vq3Ule_*DVtaCKh;BZb
z%<?|(qcHuV_C8PJI}5V++Sah9A>@2dPY>TnJg9uN1|+kVxZS1SiPZwPNd!&(+JsN$
z+JJ>?sloR5^*UOR_!i1>QNXd^o`^oHlC<v+@sfF4{@hQf@2@jz#Jtb{)3!dw__9JX
zFgO~c-ly(}Q#I-ir|UUrufpHHUC&oN8psn7+WzvH&GiCz>EWFHf<s9^Rdsz$D>e1Z
zsE=FyVOY3!7J;N(wBNh56}g0WY5BGZLuyFFr>FTth&@AWUuAw93$Nt}{FN2=1Y>3e
z$>30WcDfnIbLHT^J~awne1w?I6T?^6JU!?JjE&%8lJ#XaUHhy<YWA3I_i=B)Qm}1X
zp^%#Ozyu$CmA2ts!NuK$9}S1o_*4SiFD}@6szu{1a~|#o-xjQEUf`TN-s7w;f7wnf
z8622_%afhUETSazHl}GQ_4T|7Gc$#BR?ue6Z8h<ren$qSp#JDt$|ZyYRW-P+!3(&L
zfN}C>r*xWZbx^}ntEs60KG21`bogx#p=S||z1+(U=K112H|*+Hq9lepNPrvkM}}f`
zk}-ck!ZRa2vXzM;|4+`?7bTbc;^N=k-O9?!1cmpFXb_M5R2CO4F+b~0!#|-*92q90
zZMrE_jW&j+kEocDMtqiz7Ocv_nuHJJ9fQM#&nl3lkNe$2{n^Y#q}R+9WK{(dCTmU-
z)j#i~skR)xx^z3q&ssL;m0n?HeVao;EBNPh_OVQ+9kk~`ehFO2{aw8>tZ@*_9r_5l
zNFGstdf5_qy!S<GMEGnBA8eJ8csEs>)BY`2UC$oFSRgY_Xd70Vk<JOa+>0;F-@TWY
zxQ=9$YR8dhF*d%I8c)CCh){X@^z<iNDUy`5(s}~b%mO+?9Fx6ZGJqNjZWrLOJFw#D
zv}GS0rH<stSVl_MGw5J`We+72ufXw>C$v&n^!shlRu5+&f&5X%w8gsFo1nl93JJJQ
zv<z=Eo=Eu49nxHY33}R}5nlH7p%f7o#4Tyzo6Ib%YQ@A4;sWW}rSq?<5@lrMaVH4Y
z`1mN<*$sU%f|O8+W#t6Ll;kL1M5lXJ<hgx12(-}>t_VRe_y$WDo!E1*h=@u`TAy>!
z1kdxuMR@4-uCeWE_Y4mVphl6rX%mcA1s-kpQR(Pi2xM<2!+NfsxZ`Z)C~z@%k*kv{
zEMISHoyqekrJ!cXs?@1ktH#eMK$G7|ydLTust_;g<1MCG=T6<AiL`Y6$`JZOoO{Pl
z8cxF#w&2Q)XZE$)>woa{>(CMLkM0`j%4gdLt34zCi<G`4eV6B!jCx8hQ-bfijH|vW
z83@|9IY@tQ1Z6_k@82RG`+1?N__yw5Dd|xmKKbXpyoMO?-!0Ng&tUGp`YWa3H!xPK
z+s%B{&;qVRte<X7?JZ4w$^bswFWF1X?uP_a*kM!kCmI5!;2h(2<j0?`ZS!Na>5#^U
zhWnXV1Kt_w{@zlzPm`DIk9AzKy$za%OEBU%+06sDN=Vu~5+5x-$h1kl`#dnBJ3xF3
z{J9J@H5up1@8jMMHHKtCgfi%NjE}CW+M{HyjDm(UnTwBx`+e9niVCLF7Qt`dLf~vn
z<*qbt3(bx5Ml-6z{y>asjD*$+XsNRWCozml^|InvBg$!O7(4ESK2|+hi8J&bx<Siw
zgW+nGjmLaSy|k~c_`cP2loS(TF71ePFN*?vOS+d7bBi*T@E}{eolwo?hERtEhrPg8
za8k;<H%WYBboFObhVTH=sN?c`+2Pid;hz$b9wd&0r?&yf_Eujh_g4%X`j2hIGK3h#
zTgI${tMJqmEDEhwCO<PjEjzp6!xbm~5>hF&i|Wss(U|h+2=!MSdAwqXhq`Cy^QYLr
zj4RG9)OLUwI$TeytiWJ=*Lx{bg;`1!X{x`ZpREQnjJAUCTDVI(<I2i(@*T{6)ZSl|
zd@$oQ86?)PCJ_}K$<W3GAp=Q@7aeD}4Va7f*-pEEByd!flLUH?D0wKs?82&N>)VEN
zy&fk`do&`gpuhr3>9IMI^iSI#+5rG>#yH@s$+*RN_2%7c2B+kN=sr~L#H}GmQ#K^h
zam=HP@8hy`4G7WL{Sb)C3}(6J5aX`T&rfl#^xj1va+UGc`vQ#OJL$~HF}ocEFnd`-
z5!2`$-o>@W=~h#dK5QK*>zB?df5LDXlI2i8!$>R<{0U6Sh{?6oRjPIlkE2(E0a0}F
zSCa?W?MBun*;t~DO|mG67-ghFiB#2GYqIWZ)Q;b857&8M^wuwf`rK|V%+NbIHg@qj
z-tTfu^zJ=3H#a~Tp_QRMwN`mct62A7V7HW(&hAO&4#>~%Qs8Qfeo6s7_1oj=+pe2e
zxCe0!_P2N~hX|)iXqrhf23A1)BJ5+~YF#v@<ShS;{Bl^T(yzw!v}(cQ5M56B9pzxA
zta5oZmZL&cc^8wpxcFB{cE!~0MI)6ue%75-EES4w|9Y|Kag*;`4?Dv5+=FIq^7q;o
zT3+dL?Z%!j|6VXQgigexPn$EpIMck~v9g40FAo|5#p}kOe29-bvwir@&@g!_T@t(W
zl_mlH1UMUbycXZGo4czcpAjCTAy21opb!(+rzuk-LqDcx&5?=e`^Dmet71^audWOk
z49emxJd#05`OJe8mu4WM!cz^UQH5o|hVekm(QDWOyL?nnRkgS3)zhC(cMOW5_5%R`
zjEV{%H;_|SZrZ^>q}@=CF93qE_n9k*s2v^m2agOU@L}8*%}IwD_kkV4cWbC<dCCmt
z+@KW{S5)kSK&Ys=I2l|lBIfvf#eJ?qc1f{Mjd#8qj)QJm1jLs71dD>Y(0iIBVIN0|
zY+IhcC6q5F;?-q&#I&*>HA%aSH%4%|bDE$@VCFpHS+e|O?5*3L^%<jy?~jK*!sgv?
z;c1W_ME$3)kc<1*^u|+=B~tg;5hpqIgLAn7oT9%>)0nSEilk*Dtn^R4DC8tPjvfyL
z5!EN#k9^ftT<4p0p0cHBO6J;dI+E%oqzjaln<2`0ymi2ZKnQ?p8H8S7w2UH7gIz@q
z*}qE5^;OW?qof0&f3shX(BDjEx}m?@wl=OLPK+6`YPCqWuncLF8rHR1QgpNpfAb-O
zA=zMHg;BC5t>5rWi1rbadCMJ<laZ&dJ`g+p$>p>1x+u>EO@vY@;hRaxLub^(Qg02b
z<B=M3_BIYq>7{jrZt;CXSGqGjZ_j$?h_|;$q#1r{NPbjm)xDk}_v_9dE5E1=ciHWe
zMt$Ka+eT6Qc)NbVstUP2a|WgOEG<Qg-))x~ta*DJdCZ|GmT?D7qqgI(BwkA`Fb+Y#
zNFcH>7|A?BhqSWnXu5f<=q5c*s}(&NiKZnT+5K01Bu_a$4@K!-->I!-sMJ-<7cVB$
z(KM3I-WVU{{M=i~Ic%2wy*<@U3*N~l(;*wMknhkdyaiqLry3eO?9FE1(GQoozEXRE
zD=TWj(w*MCNh#0}RWl*$Gj_fa*GXT<dTL^dx7rJCS6^Rb!zFPfhABC>h7-sgm3Z)2
zEAzAzixku>8DjqI(e1Rv7JE+SQxv-ohV*xPouy{&9}b`VNh<*#DBvo}$jU-c+YvOQ
z(APxA5<y^jk^Fa?pnesdJ0iult214h3g33*y1jDr#`!B-HNVk?f=Id3Xp>n--xm45
zH+>ZKhO?W9c`_8Pu!iRuGsa+Pu&QQb#VDxdD_UAyJU9<o5fLO$uxQF0f#GvBM=*~>
z4cy6cly17+S3I0Ud-E@MYfd|gJP*hF?euK`rFDS-2W~4U&ByKobt%H;6<YkD1cX@u
zTF@JN!RD1T)E+swY%UYu#6V;)s5T}aX=oRBX1@zzw7|>Lpf5y^jd?=)i8Vws1lM6D
zwJ1x?r^%=#hhTE+L(gjaS@~Ym*`IcjfOY3nxT7fo!Tifal04)K*=WRn1By_KR1)ej
z%$>Hc+7SKrglsCW@>BIodmi=%VP!{+6t%7}JQlJO2<gYi7h0HaewizPPlZ407>qA8
z;a{CdD=#A+`d9%yM#1FCH*Vq<g_O(kqAU}!4mn&5)|fX-sa`wTcX3S@^=`(jg15|x
zy+Rx<4oI<nbabrr=XReTP5B&1p1BGKXFSo;BD-~K<Mb~n;1y{9MFR*35PUv#%s;pv
z+qk;>#NevmcHdriZ~FxyL(N{ddilu_Qxb^)(~Lm3Z(m!gK9!xF|D5opmGMpnLkO;7
z;UIBwBn1`)7V_GAqYw!?79?>Cx{}95)A4Y!qqlX_U#+Lt6aZ5lcboD;(B=;yXg{+f
z%;__&oIM)OQO+faJ`Im?#t-9G0M^;d&g$#y1L03awMJ_GUD2k$EkO1;{;P{BIp0iP
z$8`r_oZzzRP0Nf5YfK|SuGQbov0F6kn{k9k9?}aR$_*ZehG4zBorIysIcrZ8cU_OF
z&8~GLN{`B}HaP5qz`JWBQJ%z|=;>nlk<7XXW>#h^5^Gw_$6A`ztMT~<?4%u^#E594
zmq;Y&F)7FpzJwu~DAU|GSIjav-_9Mog#HB4iH>}Jbe<wjtiP{<Q``MM9_EkWRq`B2
zvNCb?%9j@)nTU=UH16^EZ|S4%IS(NX7;Y3311#E=19!u!IEIzckN0oy*)#h7{VJK7
zbYeCwK)>C(fF5@pGsTu_TX^S;7H4`Er#tnb-4|Zz0fj3}IF(`<6}piO-yUF`uTrjF
zR&CTB&|L0&eKf{(hweDxHE)oSK^SXPE@kj%cfUNJoiz?Lg3g=gS)>MDiRXNFoIi;y
zX>uLAUQJ|pXq{_&cFnL#bju8F5(&fAaqDTf>i*-eQ-+>ta~!Mz_6HsP1P^&7JIfGa
zdQ@_Kmk*hwh=e;06o?mQAKmE>_|o4Ym^`?k<}cgY+DbTy=X9cZfL-LupYO2sX<(j&
zN%^F)<OVvZopo8KN@<sgNJ#dMmr^}y-w<}oiI`W?JG2?{A)Eb=L@&1Yx*xprSg#ED
z>AbPZHMWI^(F}%(j>9D&3W9{L=_2ov7Zf5djdVSu9<GOFGNmLR``Hjkkk9mllM6V`
z(Va$KneM?uOkY(;*uMN|9M*OZjHjmJ3R|FIwcdN%-x?iG42UYMmWyuA&#YY`0Y1n?
z7D@%27w}JZP4-Wrt@}@#Z87uT8`t|*Vjh+dlhXPF$TnY`URMWALmv+AG4owMbOeZ2
zfMz(_8Ci``MRMuYqzzh_7JZeRbhZ^t)+*F=T2K%eUg|7MPN$Vqj+X$JKq?V%9qr9r
z;FNj+m^Kvcpu3BzCwxFkMh1i44j>bLUr+cE1jyFl4Q$hNUNs&t?zue&^fTD46Ix!|
z>E`HYWNl&O6o^^<jDAQM2qs&@tPOi@x+ZvIio8_TX{HBH0;ke;BokD~WSnwl6aM!y
zd;bnMUGVbpks(kG6@P!sTDGWmVBn~$tG`Sj!<+%x4UCNhkOjoV@#9Xsg`MGqWwHSi
z)`k`4?>iUnQQ!j3s?Cg~^Wf&WBI2^CS8FMz&CJdYo>?fz5$I9FNYC+wA7W8ikVHYG
z^=J(yx)xvzmYUZBX2Fkf{EM3d5QrkQb?Qb3E&U@!SiLWfmmt>yO9W8cbeZ!&K^ipu
zwZ~Dwk;L(5XD6IZfO}^_an-3GCY<zm3D9J&(tkz?A3t72fb;|gjF!a?d9LzG{GcX<
z1rPEYgzu+M+|Z&(z?SSc>cPGhF4GTT5G4|aQ!N`o9aB$rBX4jOPC@(qM`#GRM47#=
z*44Vj|9?Z(<MxVDQc?izgKipSri9wo424$@1ki}3Ab^_Ga~}f}OJ~yEB=`TJ^pVLH
zH-5^tfx((Zr7p;8inR)No|#jv_hKhQ!~18yDH}Wc+lYv`daC2>+}x$bMRYN6B5-%F
zgL8J<Bq9`iy83y}*UP)z1$SM!dGG;~t=*^^14>pRtcg)jg~%{T>VcO5z}%Cbd%wZ+
zF;f3J9efkq!KmRe13WY!EP>*doEkU$4bvmwp<E9V0ge3sUC94yg%6-_fC3#Xq3pF^
zt9<%rQ-E6jSCZk{p@v+*`2>a;nlB7F8wcx$S7TL5N=ja!$_GIz0*YqnkT|cbtUx7s
zBy0h=0Q?OvL$3o;D0g!}EtCf0A(+CL${c#Hk;>mBNRc*ex(qgh;#h)DCJ=$ab@dF6
z81W2*Qjoevgof6^^d6K`CfM?#bg;0h5GbuJi2DCnRl!Q20e@2<=WCZ50Eux5@?#*o
z0r3qCZb5S_==s;S*6@Zz83<y(nnK@daBvXHSuO;UoE%I7bHTOvw#-}e!2(i*Fc6kd
zhK+EKfofy^0h2dia`27&iN9f|OepREzQDz`1$L)ExwV<7<3;QPU9F%%VUj!`@(cH}
zs%@`1kMhIgc_8f*AYisaR2K9e)Cbz`%4Dl`><J?XC4{SccsN4oyZ3NgT&;nqN7e53
zkb+Rc_xV2a2rkV-VX#j{4L;~X4FfIr8-#QLTmsxu48ivgeir5()VUv6<o3U;9)qJ5
zK^gwF1fragguCNnVnF=ZS>8-1@dLwSpSfm#b<h%6V8B_M71->RrP&xP90uwKRIEIp
z$%gX<UlrMDC1LLbYlJ2MZV=mG$pca?-KeaVAOg^{hRoG~=>`zI6gG!ns}O#b{;l%q
zX_*NKH9Oc!ztFqT;Y<|SE`#}juE07>I0WsiYf8kscd_+^{?=M3`PRU<{}Xin`_Dfs
hi|aus*9(sEOAKc-x!YXBVYCQ%$xA<$Dv@~R_dkAK&wT&@

diff --git a/vignettes/examples/vision/mnist_siamese_graph/unnamed-chunk-7-1.png b/vignettes/examples/vision/mnist_siamese_graph/unnamed-chunk-7-1.png
index 33c831433424ccda8783c897be5d770638d20f39..458fcc97646e3031af5f2138bfca808a643ff2e7 100644
GIT binary patch
delta 7991
zcmch6c|4Twzc<-h2xBRfv5Y+-OJOp0NkmBYEo6yQnz7!N!dS9ab|PD3tc_(XX@+D=
z$-Z^dDBGBPF)_`Y>35#zcb-3<f6n>i-2dI5{d(Q^^|{`k>%DH6ZI}*Y{wG7qm_=Av
zxWONDgf)N8X+BGo<rE;=XKJWtgDTi84A-@_?ds1R3isKXYuC`^`(PjrEh&8-En6a6
z1vj+XR!mAf-j&H3)6`t|<&yd{YYXEEHT4io=SE?wuSNCG6b*9{aqs+Qxm%U&ku6|L
zlY<R@*fX8BNPE+=e-KII(bC@EM;_qaNiCBHZ^WLYXNoPb@c<EG<V<}~>IvTLIQj)Y
zL`e?ja^ni_RXWO4{C`6M{I`>+h|?1KkH&<81-2k|xb+3k3tp(BbW{_$+w9iJ5r~q^
ze=Z<85rZJD8bOrQDhL+?65HWbjw=Voi!3mgJJ*txa+<Mks~-UiM}Yq$A&o8l`8+qc
z&xAfD`~~#HhIem>g5lBl6REyr4t<Yb$9Fh5pnI2f!6$uG+sK`fHzNLz)N5R1$oecH
zP*?2xeE~)$OK?9V=y315gwnB{6g#ize$sqg-s-nDiPO^MI%}7qX2Tp>%d^b>Shtlr
z8lMtd8#@iC3yxBkJE1=M{)pK1%W<OHMM1uwm~!}@{x(>eQ50!(&hVj<gvDwl_!uVH
z7ub6XQaU<U9W$&rAN1y8S!?Gt!AT3<;hl5`wq<m~E~)K0mSZg-Krggs#ylDE>l<4H
zw_7sinhC>V<A^n3Ykn`hHA~<P=dBK3a(>jr8=&&kBFhw5^VxC-e%q#}?TFV_dvr@a
zy=qZtlO7U4{_1J910Ql4n7t>nGS6H8Xa~&qJ_OD>;{4V8iW16`Lq)b8Z*Ux$*eXcY
zwmZ%c%~p~%=8mB;Y=29$zrc{#Wuh2HRgAD%D87irH0(DE%gd5>2H-FcHwr*-e0V(&
zV<toPmUY3;Hn?8EvY#)+=YN|X!<{`WCuQ`MXQ5OJPA+dwL7$llUzH(O<j8@A)K}W-
zHWr{cd1e=<cqmS7jsIwh(=@LA{>GjFYC0T8CQ)_@@Ij`loA!i~ZeO`MInc59cOv4H
z(o9-}@7s#rLWwT9*ff+EU?v>&MTZk!RUKHFi0C(?wq0rv{irlDb`xujzqm()xr9o@
zVAsulyvaNM9m+5nG{<GKsTn{X4?GjDktSFCwhIat{hHlhIB~1R8ejbLy*aM&d>odf
z5f86yxl@@Kv?SXlSDuaCJ~1Re?xcq1Sc<!+qO&mw0^?3i)_9=!>58mNn?YiKQyop~
zIk%P(Rjv*kT^%9boi}$_n*L`q9mTT-+L0$lj(_=Teq1X7|NdSTQr(B-^=@%pE(gzI
z_5SxjL<s3m`&vj6CCjRSpis`&mIQrN+;!g+&Obc;Kr`&R(ii*lXpS<Cn8iyYLjjjh
zJrcbFKU2((A+7+OvOixfc$mV)0Pbbgvp!zn#dd!wayoqWMgP=hsyJy~=@~w*QA%);
zI<wuAT>3Q~bt_ObX0uak%~bM9Cd%z*Dq(j^IHLQdmML6$)yL;$7*w!zKGpRKTx~ei
znuj2>7Ai5xTba13b92$cbK;>INp3LZrYZPMvrvm;ARGwG=)PN`_Klw$;j=`!CsEBr
z2q$g`zE{sgDO8!AH{34Dn!SxPgajR1l;OAEigf6+=M&H}f=}ANjk+Wp6oN^iqP=lh
zs2YlVAi8|PCsotywGJa&TV@}^l~bwV(_z8bF7cxz`8gT0B=9`6_(f^t&&NQ4MA35T
zfJG<*n0l=_dBXT>p5llc`1?&7DHa<SRF6L_r39Hz68Xt<S{@Gv5a)7JS}tu>{r=4G
zbL1|ln}#}^-0FmFDMWuPkm$;_o~+T`;7SP0Xu7b%m)OfNvg>Gy#?4hi#t+wANHOEz
z`N=rpGwj5MuJoF<kNPxbYe6)&fP=l)9O$V%8qQKBFGlVkR~i@_ym<oY%<;Wbeod;X
z^jb2#ZmC@zfp-zyjUh2Z7LC0iDags_yKd^ag2lOqhD6)$YS-d5Mw`M~!<q5n0bEX|
z6$Co<@h`7^<L~3ox;U;xXe)lHIFbkr;d5iCOmr&YXa0%#<^A|?xENK>A>jJ^LD|^a
z56zTK`ozxK@Fc%e%QT}4lCIn>o$lWWwf?8>@fKvqZnytr!3`H5rN1rj#rBf{4viC8
zBD(7_?P-|VZ->Xx-P!M7dQVtlgxzT|b5gqK#mUaiS(zK1{|I~H<XMg?xrEXjmRU3f
zIv3dM`s5^$8a$Y}=Xb_C00lpphbm&=`kyzeVr2Q_sdcZ-T2J5vzknhdhR&kniuWVS
z7`0+bgE#aI79J4aO?o&)ynEHzV%~DNBS;YyBl|rS&dX?0w%xV%68!>(H!pap)x15|
zFG#woH>}#n%+g_Bdu*cKq(1Oka@zdXpT_V+Mm$oQx#Vsba`zCZ5qOd#2#CR?G0MQb
zZTCf%AD~GYQON62kp)=}>u@9Ccg+E@(DCBL#tm+n(&YZL7nQF@#&!+t?cC>7iXlN(
z=lbnxL^+cOz8$^XO`Lp8bu$+l;dNC%sXlrM`*ciP=RhF&^}Te8+Bkf<pt$N!pia%)
zYxx4nt_x??M_Ek(gXWP|muI)%osCd92|<Xek8%Q^r)ZxALO(PL7f42JvW~;8ujOJ)
zBjU%T9|<!8p{qhGE`2r|lt{KkuhuHO(*zYRSndo3+r4Hdv+UX5iiyMfloE5xq`X3P
zNlm&kb_d`Y^7ls&%$coIAHr?7gB4CNq6Cb?o6d1I%K^S?ozIEdCe%FBs!;FWb#{}I
zSTC~l%uT-@J@A3$&b0?--Y>v}YMI(NMwp8h#>1+^xvh15q^obbOoG!Xa#f+k>CA~I
zlo$?03*=KuqtiHir$A&OG`v9SIo);RG<m?w+G>XhRlj3Z@B?r-yLE<KQ~8p}{Hfs6
zbkJ`vV1hn#KOHp(KN__-{p63O0*Ak&IQfjG07!R<Qtv%?VKuQ^9IDR!_$vL-Rtn)2
z*&K(zZZgdBZBQ0is9CN(+q}c5tH!&8_)d9x(VM+ZgeUVXEK1r>loK%Bmh9rb$IaS(
z$%OjRzA99?AkF$Z!9L~!>rXLqlhsaiRbDj!DgLEqT>k)%$%)J3z;~BMc6R8WEAX8>
ziC(>y6lC`5iJ9enM!@raqWoCmK_Zkaf3@xbUI|;`dh|#!*ETQo$V`8|^%Zzfrc&e$
zYur(p2|e(H<xY=V(|KORfo8!i$Y_4tdnI0Il(5W%Cir5QQGA)ytFnEO2Eo`26tFe4
z(OTubwt1!cBm}e8?8CP;xF!}g<#?r<@fSHH=5h4PuxKv~wTP8xqG%6M`dHz+wuy0g
zCBtDB7~qvpf&1sp{Eorztd8ti&GLpf=$pcUlYK4dwCH)@)rl)`Kv?p881h@l(xayo
zh?&ThTOX^En`+Ir&s&<}nE0~+7aoLB3p(E`9mPELFc|vLu-RW5qI5(%ODQq!wb3Aq
zeB|B=c$@R929&WdqY}HC{g%YY98=s;Sc}WVsTZJ8CW<$wrB@+prVy0@b77rm3A|8r
z0C$0*PsNyAz<~)-(Wj0{cD;4^BaEILS(q>%2rEk+XGx?-5MLDc(}9)7Y)k}qI%+dQ
zU|9f~blTS;0#_qfv+=`f7p4cn6h2obN#?=uSH6Il0GnQmb~Dm9M!#F{Q4no0D)S{T
zLv6j`)v`)lf3aCe`bJxZl1qHf9S7>9ih&F}mrz8@Yc?75q+VdIBXwv|OggXoHG=Lp
z+^48Lz8Q8$A|5z-HF+Lvf*M|X{C`{7<h$XbyC?c^k8_SnMY-<34Z?Em-dEp}?$#)B
z*{qQ6SJc@u#~ZGu%uPR%N=>NjZzJ?q_358uHH_%&F03=fox~X+d3|{J%#(wrctS!g
zu^)C{n_K#hy*jCLIthNy$K~Js%LWkyz+5;M_pyZ((Kp<JdA3dQRVuM3^LppOEM-Q3
ze>bePn6d=RKBqDj76z*0fn?d`ATCkrNz8u8iUj4pK*M>&@V{Ycb@)T8ID^YDmH5Mq
z?*)gb@V&;)E0ABR!ZK*A=l+_;XL)-)Desa8_}NC+NI;~`tCf4T0JaeCnANNb2<3-e
z{Fg5Hdfua}g3CJ3LjJ-x{jV0kk7&?>hjbMgE%v@`PMzu(i>$ELPz|$4URBFv3ZG1%
z5O)8=13uDNi1$C{Pwrx|0VaX@Bbj?{?0$z;+?RjS%EX-}bA3gEFH^-|NKv^|E%Pb<
z3jmT%c2BHRElVy=$>SJ;Hcf|+7#@5;`1QS|cGtCaOtNAZTtuBa=PsXpz@&Wi7|NEu
z_F1l63t)3k#(ZFG1L99L!5ww)l8M>2gR-#E6vZgTo0UkjDV6wL?RcRob1HdguynsX
zqHcX?3RL*Q*!R;}_)-fGMixW}u$%Ft;$uVmNQz>Y-x)tq<wHJu){n+<r^cjdYu-{0
zn&IVjs-DRh3nNA%OZZl{b}GoxwI$e%>HaH6mX{EjcSd%vE%sE|H=}`FME~wBR7+QC
zLp?zi$KE5jJ;ozWP(aLeZxt!+7UtsDzkPWpk%Z8;={&6=26N#CT>Fpy){feILlZO&
z;X+ToNB%LQqU|z)FfaO#A9tL6`UDowJnax=&YS7_KwtLbqu*3u6syqHfbAp{r$BDt
z4na>+zZPOMm{9z*dEewuu8w{Z{mzGHabcVXmXo=`1bX5hK9-b2yJv}@$=vuWwV*4X
zLcE>G{fE<z@B@HQtwBcY&yA?pI828etD^*Qh`9<FNLEl@UT>$P92QxEoXA^;ZV(K|
zU9ghX8L@j%(rn+f@F|KBqS!qxLXTmJa5e0g40R$yxmr3<D+$*Q1a0O@mWNuw#m<o}
zFaxTv&gWYDulJOu4vIX!Fp+;X5s=I<I*kG{VJF@FWlD&eMpW0VTPwapx_5fz83$T{
z1Wl>TesMV`=F{V6J}>|3o0e}iZwW0)@V;el+0XFABQ#>TX#A=?!W%6BBjt~2(Gzx5
z1N(rfLxvMf60;ZbF*Yf0;GD}>zflVqrmwJJC7VqENo>eK<zpO~9+_}go&%~VyXQ2*
zWW2@H2L7HRnqGQFyYT0GoD9L#=1f6jiH9xkAnIRPMha%|7sF23RP9OpfH;wpuTHHT
zDzm(Nw?(Ki$00p-r``U3E`-0-U^mO(s<vhw@YjC`jt{?w{Z$k@j}Ye`3mxU!Gd?jB
z1*Sd}dMAJYW(daz89{ihalH2kGAeIvMxz_=xsj@u#mIYG9x0uXdDi%Z3-RzQ=F=O6
z7YZd7nwUG@*x9&|QrCINT#@xyQmk|v{yDf`<Ek$}x8>Z6kNgCCaZZ1j1wvQdHLC&_
zg|~Py-QjVUrYxwxwnSswk^7gU<)iEx9V5om5&`rlhWuwKZxC>}*$o`NAu-#K(^8Si
zQ0g%c3AML;7}-%n#OR+gT3vM@S@L1+2H(@$D`c6(Me^b+B~Y;|tjLktTFJ}zvY`z1
zojM<-acCgWeW%(n!h-9sKR>A=&}0_d&mEeZ;g6G_zx}L(FlwKcb2QbNK=SW?4J_KP
zNU=ZQb<3R9c1#KnWleS_)ad=G+E1Bkysu5NayKLI(u^&X2^Wn}_}Q?biAVnihMj);
zq7{@;_E16sC>86eO=ip5o6z{WHEMm?ypO-T|4_TgXkyt&pP{)djC0Kk|1H|XPq%z-
zN3BPaC0;hne{kM-1dv|L8!%?%B%cSJV82fCsbi^|c<52|fWi>;OszvhqFfa5Dux1&
zbd)%5bl;TMujt=+(C=pFQ#qZ1?*iQ@ITx7P9^ax+4j3NV*>qKeq-$)A;k@@yw+2<w
z-rLNrHvO3i19Rqqh8|Jm6NUsucidM>yk1Y@3kCA*f}(pfPk;~JmU!tKDEM;T`&L@i
zAeB6J;iWkF!1s9fQem61r&p{&vh?oGM93*Zlk)i4Tyawuom1+g$3W~#Ga4E=``kxD
z!&sTUh!UHrLC(79`9xpRTY+D434Q%x3)(e)R_`c}N<LJG*__(oZ?7$xuK&)|<)=?Y
z;}hVLE|qUtD=}?i5@~w|zwy9_RgGM3bQbRePs~1U{)?-X>+%_~uJ;&J-_>BHjHN4G
zdU)wR;~oh8E7L><Pk!Aq!KUyQ-g^9Eek7W*9uOv@<lP-6>4q+DT!WCmTwKEI^WBVZ
zTNz>oMS7LUBl35(oS^bQCSV785W3A;IRM(zl`QYSJSGsPDsfp1UF-}>M*S+3ZTq|H
z%ZSs?)Lcs_sWyVs`1~bzfmSn}&GWS2#p_{x>{E>bPp7oa*O*PHE6?P~zb|^S8c6Of
zG=}N&0=3wW+BH|}mrq;V^3w7v8YV10FB2RE_rzcf6Ah4IQd~cgMk#L{1=`$>)=XgE
z6x(koMqMv}l2VMQZjowg?&s$^c*Bz!!xi7+$81+eD3|%^`;#G#$@FH7E2&oN)}n>4
z1Osu@2h;)NfQ{mFqw(>0|4NN!K2pH&mD%~R4Af?8#Utq=shPlHdk7{MYT#6u)E%5X
zAGKnN^A61WT#f95xYPre&I&^?&pe%}J*scRZYQ^pd(;xLP^q<f3r~=JnW7CEqB#$E
zVJ;%YCJ%oW+Tsga)Sx*{ou)Vq?_rVyTqLNb0?8G51t-17#ko{yn{cM3k;%=%I0L2F
z{HpditZ4Y%ru6Nne3O|d#Q-6`PuiX=Cmn7vE4M_N;@G>6ReuJ=qP~BSUcbsjOrXJB
zSVGx^aUB?vhJ)P^<wY(x_o|q;{PbRB^MzamkbbU)Ij3w4rXv;-{Cx)BMDTqi)P1NG
zkLS3(cP({6NW}Anm%1yb1UUwE(B9KKi`cl^=r~XrXBxrO#kF^_>8<M3tj)1s+sn5g
zi|UGeCqhs@=bC`I%nK#I@5rJ_d8uoJ`_6njr_9!vh=1Jo_*_^2ShpBOtAq53j+)AC
znrt^wMBz9yv2cA;nzl(ppivH1Bd=-ZW_W0t<C`zqo+tWhkXIp3fs@#L%Hu{p%-&ow
ztV`-aQ6MR$9P<Rjns;$|Lz=cKBk(;pYV{tzDL6*`5ODvLwO!7%v(G9RuZ0+Iro<Ha
zSK`WLLDvn;{;3EBe}l)+_RGq#MkP8oE-2C%XRLPJxHE!Zk7^<=5RdC15lyH^ojH{c
zjT;xL*1LvWzcMgDCI9!iW&a3Qj#G3`k7_+q%J2ve^$4lOs`fgIx7c`O#Qe!cyAlHS
z_NWT5QP|LYe9EsRR6W<I5YPD-(v1n9#gZ1*r36}&za$-##;FP(DhFuxY+QApoN#A%
zP`<zKQbSRx2T3A<jK6%VNu*iS%|&$hK(wEemDiO-GG^T?i!!lk(InTx|B?I<8yzb5
zY}aIS+1(-HeSFnmT)R=7opWYv5}+{tgAIt(9)z%NrT?Pd5=_-<4*0L(+U7y7I04{2
zgVG3bBc|za*+G;?Up3@N=EPJG_V08a^S!yb#3f5J^iYXL_Eq?t(B9mueyFi+Hl+Js
z_oqx2)La$sz!j}bD^^=RwY2MQaftVvYKk=7E7x4yX-jTjfj%BLF#mxzDdOdejP;nL
zSnpXw!DOwv0NU%W(A9}l^^S2CPfKuA=^Dq*h?9ZFI~%rP$=m?hHRlA1>b(i+_o2vq
z==SMr_C4@yLVRbMkSva(9Wqq?Ro{!`EEdAN<EGI;J3FL1MnIhL+%OGT%}&^gljY1g
zd@!{Qc(5M_&eNuK9B!JdmDNs$Gd*LrR?Q`JqJ?DNQs&*mh@bquygpPC%~*&<PS3ZI
zL%J^sP|mOIS1h1X<X-i`DfqKg6pO0mZ8C{<(G894OEM8vbv&b-U(t|JH7R-|nNG<Q
z5DfJR(7W0>uom4WS)3YnR@Ksp?Ak9>>?@FC@9P5$J>wW6RW!lI&)Pl~pc_ynPIRh1
zzo=JLwAWquF4M{<DN&x*Z#C0icv{Bi^_3l^pj?763D3!IL;Yz}#c{td%LQhcuud-;
z?yDl!IG}UDQLpcgTZIk$U$gH;&A}7OOEu;MZ$u3K6TnJJk)rldW?8)Foiz4Bhje)f
z#jZehJXqKkAD}{`xdjh<E?XkM@>oT%{fL`7KfJ%_P34NcVcYW=>&bpxmKK=mydhSV
zWY}4{R*=LN0AI^<A>$Dcu0LXL6+VV<UZe>!+gpE-F0tU{wV;}1Y;${bc!bpRte^WR
z*odYl3(4kD444#T{%JTz(_RjOq=+Ql>I2GhVq($sp+?41tg}<vuYE-*oN5?Cc(Nx+
z(NMiN9x^u12e5m@BUEKD?5L`xEZL{u;j%8N7;To)Z7%j)pebOSEBFbdTR9;a*#|}i
zQ6qaT8*{wqjHv`h#-@NPg>45qKGf9M`rF6yA0oU<bV01hhA*3*(b3ghECDdHAGtZC
z8;l1&4JHY-IM|C~)&`P3mO?bz*i|i0lVwcKE&nKN(A0D)9#J~yWxYk<<Ts-J#tAuf
zs%nJAk@x+I^s%UMlP9H#^s_U3Z6o(~m)ojF6m|Bw84F89oOws;-IDOvzqI!uxBJ>v
zawS-sua7B=y%yC?Jn_7#8pu~+A@FpIrC}c`m01GB^X?2hHJ=lsp9rWt$Nswc+Mw-C
zML~zYK6&^3<o3;|7=kZOXJa0$oPQ!D8%rU0M0^TA{X{EZhB*y2&cS{p5%6RRn%}e`
zw>R!5nrsf@U8%oqc<X*BT}b%CH>3Fr4LqJz)$BHW`Bu!&K!@xDfb}xdA;XX*jYHw!
z<WYlprrZw7FmVqWEwdL*-VZqQGYZV-lMl_`d-*WwU+;LtD`d^B^`E25SlmO<#No^2
zM^2-i)NbGRA0BBVQAdM@qka(Fi#h`&k>xv~j(Y}LYw*>Zu<$EiP7l#laCwbt%cL1u
zw!v2rx}QW4Fc9@KuAQae)PS^y&CvJFDJ4%bve;!w5a9L`vm=+?dtvBb-a18@)kt*8
zQd|$;xw!$gD<xy^rk6r;E9{^tt8ZHQdPjwU#lG!xRju5r>Fa`Jzz}%1l`sA!_k!Zv
zd5FBKW6FXo=!{Ds{}`@+e%?Jxw1;!0o$zDsJB|xL_S%f=sd1Ml+waBY#5iG37e+`1
zy)RTg5vlp?kCsQyw1=AQP={upU^YkX+0%!+Jt|FvI~SIsH#hf#9&1mR3NZU~^u;@4
zt_7%QF$Wq^#rJ=@Flo=i?Uwerk}F5gt_esn8h+tx^(uDq7x70=_J3vXN94b>ctO{G
zLlQ{>ez65m8AHy0Qb&(`GG65T6AkzdM86cHu6;~kP(2}DmxhkLADgtban@GDZ<(j~
zbBDJ5#xm1~Jrmqe-@YL`SHm~I&FlIEC~tR}TdkfnnuqnbXLQ`Hs);&Qc<bVQ69#A1
zp24`(QJYAe%Rs$s2wZu};I%>|kqj;js1<3GB<p+5bDm)s{J-3vM<L~G+zqSWKY{6d
zg?sb|&f$1@@)T_6d=Jdy>veF9rzQ%L7Jq9u3#EpCU5qaizxf9y%GI!j&7jyUu;m!m
z9^8d>6(O0Wd26CX&@y8Ge>xGEGN!&0zf3Gs3TE@No>Y}gZ%C#P(<_A=+X9(_GArz&
z_QXlTS{|p!+MfJgLHs27Ys%W~BIt`f6L6#I&H|)B#Sc@EtG?mWA73HZ#DkNC=_twZ
zl@0*xAkQ2ZW`zVjw(}710$>bBJuvlC#PAjow~MP$W<y`ZznpRbcC0##V+_D!%>Q#9
z6uESHflW-JlXC3tVkCVw5mAj?2$uhxOnOAlZ@;Gj^Eo+s!!pA;!rSuUa*^KU%CmMw
zc~b?!9?duIB><&1b)fU}-4Ev%{_p`CNt5|@KJr1VK;}0FQ0bSy`j5+ktvxr|ZVOpM
zJ7^3mUqeJS&~k3jp_MvY)4d(?>*C~(?V@9WPvx)P-I%Ll_u5d!3hbOe&bTt4YqGS;
z+miz>e=2y%2`>q7$ZpL-S{+TNG`Z5i)V|mJ<=zKvKuX_sP<9$-Z4BQb%lhgRLavqE
z7G?J;s;<D|^qKpcTKfu-tH0`wvHT=pzx+z0IE+r>eNc9>%;=!X@9z6h^B|Ytc%=C0
sbQJV0{g=*y6r;cXcgSSnzclkN`hrZ%P?M)Lm5K3~8d(_D>AS`L2L}lGyZ`_I

delta 8077
zcmch6XH-;8vo44P$r;HgN#c-^43bfTA~}gjM(`!hkT$3YLy#azG9pQm<TT_V2}2MB
zK^SBZkj!>OhBTb<`|eryoVCunYn?xL|Jz-=s-LRrU0vNzy3USHiU2W~DyUcQTX*GZ
zYufA8H#bPw0A4;r9j%AZyuEywriCkd`0e;{{(8;4kiLSn8AG}t+&Gw?l=<ORW;S|;
zK~e_Yg@SO-VIQZV%JGJ>vND;cZ`}g9YTRc3Da&yaA1fQwn;9ofO4pgf&W9b4G8}Y0
zoBXBHgU!R9s{<6vZzhjJa8Ssm>ZT1s;En+zQ8G#jAY+8w5^yv#Koq4wwQhjV?_fw{
zkW`d#Hs=2?0^ol=I}OIP+u+&wMnWhYa9(t|+JlTJ%f%gSUrH9M1Q;N)|Lw+=o89XT
zxon22^B$}#9YQ}t>msA8zU9%SKx?PPy9Gi*0>ZH9=SmX*xxwF0G>X1}r33RPMDR@x
z6lS1RxqCPMnYt#XU9S-rp<Ybt8sm&UP~znS?Mp5`bh``>1#t!kw;@b0?PS~P_;H0z
zD}otK_{vPr*|(?bwC~(2>WoPB(P~LK?(Shq)F(EkDvRDDqGVoR(ePr>&vx&VC9P3r
z7QhNngAL3TZ7fud$4<*l?TF}yB_Q;A<+6XjA6`7#X{Zr3y9}|Aa^B{yfCa~YV5iVV
zSSl~x#m2{aemvw!Zno14H(B+XRaId>k}9}<HVF8jA}GFah}B@NJKaEiy5QAtBAf8w
z={<saT2Al7i}NqB0M*>}+}2r_y_`V}98eNT!hM@plT{;<LmL(FWUjCMv3%51l<zO=
z&Ogbg+&A`}^jS4(zhX!4n72`zu?pyhgjG9q;{G`LeoGm2pYYH`HOkg~_891r@_aG8
zC=u|FVsP0e6^F;Zq}<{&h(PyNeehVEX<-+o{N%)+CnYq;{U6PFyjv4-O=%efP(U}2
zbJMEhA=bV#2$?naZbRwtRcGOgCpHC}pC$O!T_-4KRmoYfiNU*>dBbX1zU<(r>)3rP
z7b5jZlS?bL+ID*&V_l4;QweuxiKL<4)*ORI2)mb~aKHw~(*+uv<dMoHQ4G^q68&Xl
z_H^d*cWh1KCxYEkdho$7$`bibz^uW{WR;cCDJf@ukP5CB7VP{}*q>4uTx?S%`&G$t
zwY0Q>vh_8TeX{82dy+?qv5NE$Dw`lXgK!ovM@7%OiIx#Gx_5tM7-O0nG)6q;)he;R
znN|w$`FkkllBdUfe_Ca#guomnUUToJeP2=kAC^zKK^7UHy{}Pi)?>vAs5n*=X!T&E
z)3hj<9_nn{vssXT10|9~LS$^)Upyw&@Mom!BfcanDg9RQI(5cyg%nP{s#ucPGGXn~
z+es-Z1WG+zzrL5R>-*q=6g;+A&E<6;I)GJ7G#g0*8aLHDv^L6|Ilf+G--%PKCMi!y
z4o}=2u1w5Wum~e%q#LpWv=cj<oc1p&`@IZRxlCWN%a3<Y0EM3)O9-H|H$k-%Se`VR
z^F_<8+6<(2gWNL3fw8mcEePSo-!rt97~_Xk<wUbE(tR3)lm0|AS)f>(UqfBU?38CR
zWoZ`9tZhmv9r(cByvgjC?TG%}yC1fjQlQ#G58M0|>X~QG<bZjLm#)s-V1Tz7Hgxlz
zKYPxx$jK*Vuev*!RV9X4`3xD2&F70ZCHT=Po}as(sj|BFy*$76q03G`oKhcCT2jwK
zfi8Cg>bi9Z4n7u1cU0oBtL-@kYdA!k5^)dtSv9uv>w*;PP=w`Gp7l)7@l@2UyB1B=
z1Zz!!lCa(ecHh(u|MZNsZBaOwV<?oS0t{!mmENs|McSS!C4EVSUfgDE)D%95S(EEt
zkq}T1a8vUt;G!MgZbSr4w_$U<8(%|7G9`{jRz1|v*g?+~PY6l|Gt8p+Zew&!Lpd8_
zLVD=S4cg4Wm?04iG-`$ZWXQ8+BMi;gH+r;%w9kQjkNe7ct3;=c9}}Vc!NqBV!Y@7$
zbxp#_7tLm5FiBrSOn5GTMcYx<sp199EHW81>c3x_VVMtKLuP+`AeDB~W+4?oqj+w8
zC768tP%@0H?h5G05=K!igu?WQusejwoOcu@=hN{eOy)oDsr=}Jhf*Qk-pi}=_GP@c
z2?r+9QA&!-3k}Ms6QTy3p=v^wBlVnmP3_!qL~p!Y!sV#V*0W26t)-+1@{fb2wh1}q
zDf;})p>=dn&IUddhQukW06eum)sMmwKLOuh_jOR_M&|Dn4vhP9wRWb(BX<BA1ni<H
zEq6}kZ<UD&03X0wgg}PmzDz<X#RIe50iT_|24~Hu4w~k-(aM`jb2ByFt2K&_{6i7M
z3%KQC#YQkxLKSai<3~eWX6ZU6AZ^t4H0e0Ci_V_qyp3K(-3SZOTjTBI@}{K2h^!C4
z9dpW@`I^JTX95%@a>KyS8W-CSVRi8!!tmn*O=N4^8CO4RVGyrZfvDsCAV9_G;F368
zBxkU-_F4zJ%IW*I!Y0WGraZZaSKgGn$~+^?j}m4aYb2Kd8C_mpLL*uqz5eU!;2Mu6
z%6xde@)Kn5P0&`_tazm6CUlb4>94l?6ZN>9^0a_|jD}suKfUNK3MsM)$5{?%IWSC+
zrwmSVozZ(0*wWHkwgA+a23%vuY_ZL`dg0XbhJLAo<2*vL3Nt_{uL7?E-Wp_>OVk)0
zE7_ja5x0s$?YECJL0Ys=K*22`21tgf1)n-kTX_rT*wS#;`*u}{@9Eg-U*xM9_vWf7
z$KoKy7c5={WN50CFh!>wnw5Z=7*}MLpvLo@@|eyawc#8cfOac~IDe$?=aLGTZCkpK
zU!-wrM>CbH^9?Z6fo!W+3sB<y(_Alcn(>GoT!(X-VVmj|%wOT|OD1;^*C*oQugb#x
zOe_BUjBtY&uvqJ#XD3y_I8MUigb!%cSAN<=xgsZ%t?F%iqSH-|`P7Y9zL}53InldI
zg2Le(XTKeR{38FL$8W9m!d;i%RKRlD6{}3aRh6Tp*4MbN#)JXS1e=A}<4=al!*+V-
z{n_ON09gCSyqH*?DWzo@44bY-QcijPn2Y7_?Ff@f^j0QA53a?jWP|w7|4to-<gzBD
z%@qlOhmy(DMU8=7UF&1+J3NGK#G=Gl*fCTZsAK~N0)YWL{qxV$lkYaPo{218gkGb<
z9!<8q#@PWca>)Bhn3m#uf?Dy@nG8<cCtO}U6cXzDJd?Escj8(SajtI65DQc=f28=(
zm-ry0zG`v~`M@y$a@_HBw?ebKIDfa$d20ns^L+<;IzxPjf}pbQT*lnvnU{e3cmZ7R
zBOt!HOFFB{XG^9MR%fbtjWF?j2mwm!0iqhFK5^4T0R6}@xImgWoI-G32IFlkiVxE$
zHw{h9g^n6w^t)f?kPp50w$c&Rv3;9?<V2az)Amtb(hJYYV60+t9-faD6`*q;rw~dy
zPjHQuDr2Z*>izrC>0aF64G2)O(O2(pNe6COUxphZR(H8(w8QzP9JP4}$yJ_-zf><8
zuEXh6KirAqttXYIOG&R<d-f^oQA`?i&F221ggR3V+b0>!4oVE4Q6*OLlB<tW5F82!
z)M<tCHkmO_5_el6=1nxm1VFBo2v^}xZ>c2+WsUPWO00YTQ_2`MfC%`Qf~%>3rKl=)
z-wn^k%X-vmVw4_T);M4yjvPo!gfkgdZZ9D)-#W<9@~hOX0h$<Ck#~1s5QC!td^5Uw
z;^~mPG{S&!lEh-27F>v9iZoQEQ%ygh=j~yGe3rqW!Yv?Oa|?F7{?w2(B(@iSD;BEZ
z@F&+lIR=^Ax(S$gZE-z&8+ZL0fA<2ae|y%djtJ;H9tuS25b9@MByKF5*FJ0a7vEc?
z7~iyH<QDZ`flJP!u((FBk8UQGn6WM@@Qi*(sW}dwGr00q=mZcp%p$u@=oKlmeGBz1
zqS{qz-N3biH)UPoh5$u?x~9uMv)R*wzR`aBL4BYRS9gcM5plEM7oYntV=8;O_k_0)
z4^HGgdOq}=Z@dah_Vo^d<qv%k`#uU+0Po-OE(#<jl*S>~VSyaOlBjtZAB;X<@KW@@
z+fS4yZMgZYRq2(0gcT+~Cf;Yn($g|(AshJ;-kXo!cMY@3GU$XSbgH{O0mY0Z0DSJF
z8MvPVQ(%`>Pw$tf6XYJc)TB+Md?8=F*__fkJKMWpetle<Bu(2HjOW+AjfSZIR=TRS
zyRmgxV%zU#*A&1i`_Qf<okrZ@#PIjdn|X9a5Kk{J5Z<OOL5w`WUX!>bMvaY}JRy>J
zTzxec7qgxny;!%c66|B&>Rb-&+#~V<%j{baxj%#yLfGAkT=&<E+qDNR8Z3)xYg<yF
z=KF>L8I|%riw@b*gdfng-=QMZtl{N)-7qy_hZnqG_RX_Fm};|e$@s6_uU|qh?15~S
zYcV?0S`Trez|#F{XhD3<Vq8-RLmue=rsrK|eJg|FH#|7s(2`vW_zY90^W%3Hn%O&(
zy9<uu-esl^LgK<0<>|7|C~f)+rAgK!X3rwr&q{(m{*_JgHZl-oN9NXfV;^<ZQiUU@
z^jkD&MeSbF*qkF?4&TQ{!EPom|ACBa!Y~J%BLy87?#Cit>^A31?}yC@LWNk}$VB#A
z*bjKrr_lre{a{X}0jDJLNmA?f+8`*tOEs?9-WnhguOD?Y0b;DXhkL$}Q7`^m?5{o3
zO?$Qm;gh?%68s5;`FJTW9;qHvmp)-Bl6ClGof5}q$2uQYNCWPz3$G4}#7$p@Gn7@<
ztN@p*@}7RHKPlc;k5waL>7oMGi^w0jA{|V30O%FqQh|E-3R883>l09S;kamnxKXiy
z)E-HlE{sQAj$W5K-k4`;P89EcbaQ*n#{&&_#m9+#AL;&}ae~}S=Cb^YUj*j;*Kv&;
zkuc_WEH)U3PKU{>BZ>wbLc}1qVS9l5zG{5d_P!-v`lWc}oj$^e<q@r(zli!45UWTQ
z`|{;2LV&{O<X+{FkC!vp$6vY&|HWFUR6<OMHKf0d*-y;p*qu^iFLkgyqnhB`#-V}a
zAi(&~S_N?lt>|BEBO4k*IP|sKnR$<ay$8cN)_<d9n$yZmFtNtp#(c?iwr3wZ_8>Uo
zxkM!XCIN!U)c`<5{d0HsxR_GB?GUSz5*^{&nE@L{uOe0OP1n0$C5Rzbwo8l1v9B1m
zPI1&Dp$Y1>2j`7eW`_nmcHv*{5scVAZ({~X1({&=gAi>8E25l$X#0dZvniPt+`2g-
z;htSF$f^N9LA1%Z@-t_O{dZP`(?%hj;OQU{V;w+yDYPW`Fna6yJ>H}3Lw$T7aR3}y
zwbn2?SaLaFp-_BhRDjnW{K=+2btqDBd5vX-wloBBvs$M3%EcC|TzLE@aA)wwE`#|0
z5%Vs`cP^>i{?|C^>C3AgIOdg&Y_rt{Hm$<%@%n{fWHf~sZjvepRLqOrO7#?wbMb&O
zYjhV{X{fXlV!+Y^<N6iPICXf-F)f{@WBaPr`J48(v!m#Na;W_Gc)&w6VI_f`f(C9i
zobxzyyJ4a9?cwlp!U$ojNv{XX(CK7n^$ixtEXb2L3XjO{tgWQS=eV<gq+{08%h}OW
zIK`X?h}<1fd+_a1^?O=bdVu3n&vml5>52eezZS9@Z~DZiFMM3n<DPJ&A)w-TaYpDG
z*?Oo$s@bH<n1F6o-%D`qGUB`qOKcBDCdz-u|A>;#hZ=^;{|I>n%sMq44CO^#r+5u$
zlX>wV4{&v|o9q&JYl=VkovG~mh`^Jq!CJ_>_)DvF2gF<6kuyagQ5W^1BUvM$x##!q
zPTDf(3D<80kC`-f<sRRZb7Se*+_t#LZKm}dR82KzdWz8pqOvKHYz8}@pWeSr*iryT
z0iAm<hte^2OBepSEIn=gLXq#@wsOhk<L2bG=EIJt?Du5&=q$p0YmCnmRp^<DrSoxV
zOUtM|R2Y@LWsw%ZQuqiv!J5f0bjL**B(ddoQV!3jD~dmEOsM0GCCs&jjP(l&Utl2|
z<jv1RqJzP*{SMju|9UCW%Y5*&$9epc(NRFIj0qC7`?bw?V1hPlUV6}veTSa+!!stD
z0;xhAx|bOUN!1R=m>>*13=kYfY)VK|Kml}nBj8A5m_!R=?;&1G!>wM_4X9KNM>JfE
z7vUZ2AiaN{nqmTum&*33ra)N`sdIwrUtNi^J*8HssvI?$51<Oe;}_wtkyVepg0gV$
zf;`?5vRqcYVRI+kJ}bmhmR*90Bj-+Op5H4chDppvnZ6s~Czx8ZUHGANwV4Ot(NgX2
zb+=<MIf6lqnIng{F7L;wFSuT|*IB|M%{;>~wU;k1cr)g?HiiW|{IUvTGiCVxAp!p5
z%#1(ug(s>HMCAk=rR$T_kX93VMlr9DV(V3_)xv0@d`*2!#;+>)d>rpZNS-x8<-ri7
zNAEl_^j|M`Cdlm9^5ub{669aozjnsG2OIB`oB0>t04Ya51T$)M5eu?$FxDk9wcdug
zCrOtfYK6g1Q0TixD68uyi)urJ45MXxT-~h!3bI#N3B8P{oA`VKwu8izh=des>djnA
zF=E|tSUV@UkbFacWQIo*!!otIIizCe+HZH#aznt8ct@xmV48e~QiT0Fvok=1FZy{u
z6N`j5Tr@zxhx5U+Cb1&EU9zm<X2(8{x~Xo04eeY`-x6^CGin)^!ME<}(8x97B(8Nq
z-Ng^hy9S~?dGrpT?e-@2=qlXjcI8;@0I%EdtT95@8>+Zshu4vUXBq16#;YT(n749i
zQ6R%OuRJh?z$m^yUd^v(IN0W-8?UElgd_)`k0Roo(ks1H<@B`;5vtol$6NXJ*;zp-
zazfo@gd|LoE5hh|M7I$Iq(lK8%<wE-v??8%J@=#S<dvus)jZ_b>X&JruEw%Hp^nnC
zkgM=4t-BBX@|)Evxx5ReL&*6J5!9@#BZm{#K)=w15z$7k2jjup<-P_)BN^{Zb>c5A
zX87?)do~9QV*|^yQQy6Ual(%cbi3~zBql9XSTXb>*t9Wf^-t4%4yyYOsPaD$ZRA^T
zB%f|QQ3r>+*6pXAoVAQP)m@1iA+KqIm)EuR?+N4meJkCU)kYjk@3K$rvJbp!^I0wj
zgo!YdxACew=M5&9gL_!<GmbmP^&i1Prmqq;+0i+J2XuI?Ua_>mzV*vl{quc3(}>>_
zv!T3SO6VV<?>*L9aVGO(<?TG*%_RwOfotq9pFOKWR~TqrP+JTarY)Fd(HW~vi{ph`
zfbu%po;dz)HRA;c&g55SuhkXQpN9&-xtv>(8_2)cbB9XwA0%YVl8}nH&F^=KH;z4h
zkcxjHT;gNd%S&bNr(VYt!p+w6znpdMz8K}KbNxs^i+3fQ3-t>$gbmr8J_JQ_JD!IT
z@4MfFx!EuuutDu)7e@HAqs@*%_ZFp|E0Xx^<R!^atBq1eqFS<S)Y{hy03uCNj6B#{
zx!8+MRx%j7@06i3AsEUNIHU5$?bk)yMvOQsx|Q-#;LmoIDG_xR%k$FFUj==~dY|`-
z)-|!YT&yS8-1y>ftRV+N-;+;8H;;CiKg(@<PK8FD99<3G(f{kfe;%#9vP>AQK{|a;
z?1c3|N-H1L6*s_KGGQ^9y}~BkaDRW}&qfz%KptIG{ECddr)iB?vpO%4<<;>unD}YL
zxF(9wz^U8*e6}#h^59(kQW=G2uX?T5<!gy7d1FJp{T}jvZ*GZh;d}Yk)qd|=)6Y{|
zGD-D}yZ>`0MIxWDvdLs?W_f*M@3SJAF#g$ZfRX+n5oM$u@w+lAwyZ(AQ<|jN=}XAJ
zYN*NlH2))v>5xZzbWdjgQi7%hae&{AyWyO&DVGelH_h*=Qe_xBf`Zu%QSIIwcNb&W
zN4juSYh__`l~Ho)h10bXZXy-hF;KP@br@wu$Pk8I$6;zo`b8<8ST-d9%o>|jiv|u}
zl-V5+-=fa>=3f7su$ufOyKT3ojFdJ8c*!fMvxgWJ{;-T|b`Q=cM;}jEuYJsF$|Aex
zAi~+oE}P;F9((T>g_vqj?c{iqxaqQUCU=l3Ph#eiKclq{3Oa67w3^6NNV+dne8c#Q
z<16XA5VDp#v5OkpZv|O=`en1D&w=<A#){yV`COT2hw}Iaf8tymvm0M4rgi1>v2w6&
zCAjY8(jRY>8LM2HYXx?aX{*NVB?wNdqKXPS<Bau|jbhEgvvbSs-Te9(X|AMpNr&;W
z>S5|1=I4=qNBouRvKpstr;O2WEe6Zf6WK>fai-)JH=yp-;sv?U?1Oi0fIgD!Xw`TP
zuCD8YDlW30VeHxWn-W=;6WB+Jp(D|31>fBs4b5^W>`9Jm;*kTih{LN85?P_TM>7XV
z>K`Vmzk8`n-(#hv_**kk)-Tvca&dQK3g(Jqg@S8N175oH8!0daH9tiotz7xS+>M=c
zXA1?c+XVTr4p9HNckUql6+p9lQ{Umn{wNp^wfr2%K9Yso)IJL1n^&niHxq`m3zkG@
z%n`C8h(Gv{g_s&@_-4jd@=V{0DYubPHboeWHB<IluIJmzJ7prh(GJh3>81W*aK6wM
zLk4rN_DJh%#Tq7}YWk>u=%1IuWoZZtU+=}KY)r3EB>KU)W+W1rPm-TfadC!JXjl;K
zVydJ_+8KI+-WQ{7x_XMoguWuJ#D}PV=$>n7MCjYX%0ydqFGjtFgd(*B-S~ttkVNVI
z{(Z_BsxO7rZo3$^M`ZptBb^JT7oQ@19sn)2_3*lC0wC@4xo3n6WY5D(>x(Tfq)_Cl
z>bRyPvhZ*&3^1rsWfd&(rJ7;t#QFanWgAzpg|WHJS86yt{fdN$5+bE_E~bo1dT3(a
z8@p|4&7=S(IAl`JGSMOn*MGb~PxJ@Am{V>^fDBUq&<t;G`2u$~l=6_JA|3=n6Q&!&
z9<X_j2Po&_1O>CBhY|{<Ry-EwrLF~W-BSz(8v$t&8nzy*b#>X$=^r6R7fMz7s^CIG
zqU9d)p*H$gKm0QGc}?*@9>3H|@b4}7^Kkt}CAytbqj8R0)5t>1c3StFSGP&_*L6ET
z^n|2M=Pc7FbtDD~zk??$hT>vWSjQg%b6yP9B%4h8l6zdZmMV>>85DC?mqdT44PnT4
z`vA+T=Ew2?8?nC(!&{t7%^qg+F&T$w_rRpb;J&AUC9o*U;LSHQoEJ17yp|BD?LHwL
z;?;UR!f>snxI_9^N;Q@Y8D8Sk>?8dtF;DIbn`a%Yn)^C7scODq@TdJ6`Anxf<M#Hu
z;Bmz_dSk35=1DoMdvMR6%SRizD&?*Tz%;eXoYo=rK888i2QyYh2*>hcIfkh(5vJ8I
zys3{-?!xV(f!H;9AL*QH^X7gl@DrMkb4F8P&mQDj+eo<a%|@KReL7J$LSIPNXxNk_
z@%qo4w!9+uMF_%^O=IydAH}^+dq-9cU)qpjKu@;(wLSmtQ4+Bf4cWQTK1>@(W#Lhp
z-5tx9@7eiIJL&zaAe`-$V!>7(5q8r(n7A40I0*B`R7fxfjbuftg+PjY+ca<K^c1Kt
zPRgGiEu|9Zw7SjL$bck*P@us3_f9)Mfg^L8<R%bl7kb|bblZR96fcl|oEfh^(Ou3j
zEk&KJaV!GImWv!nmp4xWi8)rQA{mp(oQ~c=<!~PgktKar7Xmyq-P^*T$Du_J99hop
z5T%Fvu%d%WS~8-z(!H&i3cbSkKhl)(p2$my$C{p1)4Cp*zHWUzJ{MMyTiwo9xK_G@
zlBVf$OP@TcwpiE=VCdn0m=;XV?hr%f_VPDBDEif!&k!}w=@27CmIh_17Hf>K4F!+#
zv<VFx4JYN(16(B89u*Y{+-jh(U(nO9XxhT#IkKwQWvce|^DlZ<#n%|?;zI@^?mA?y
zO0x(Qd?)%J0sbe!3jDvQ*Kj)8>kdRW`xAIqE|%e)R|<lcVZ$FNAl)k&eiPJg3^3Uw
zaZ;A)!A)pPQ<uEg_t}}xnbog%8$}+BUZg#AdKzeR%HIL~rh=cP0k$eeQ}Npuu$H|Z
zCzGuADa(3Rr5XZ`Kxlv2Y1NMH`&Yb|fkz^h>%*JsoY2vqAAZLo@3zuW)c<oYEoZ=;
zwApB@4sGrnc%LE#x|Q0VHruTq8s?<mBIjTD`#S>1t;ZHa1n+r<D+iu>8)PalJB<5s
zo;UwqFeX_{lB6VF9o!6U?NUE~)dVTU);p_f`?Yq-_+(DY2Rid1aD)pajj{#2Jl>*2
ztS<`bwEkJPMPu>upMhgNw&%p~kjFcdJc=Dxng91BHJ;Z_)o0q{p`0=i;W5-T(W$-j
H==pyDu?zUp

diff --git a/vignettes/examples/vision/mnist_siamese_graph/unnamed-chunk-7-2.png b/vignettes/examples/vision/mnist_siamese_graph/unnamed-chunk-7-2.png
index ae74b9af74248351673e248cda0a82299ce1a887..3f11e794022dd7756931883bcb5c8ac8315d45d0 100644
GIT binary patch
delta 9027
zcmZ{pcRZWjAFx}qs`e^s@0n5~_H30Bv-apvF>96}+^s!|5~C$HMHOvrB3go~)~ZsQ
zShdBB{ic2TywC5C_s!>%KXTtEXMFE-pL2b$^BOW~_^#|C;_XFip_-bSy1Kgh`uc{3
zhUVsG6bjYS(t<{#TU%Q{e*F09)2Ft!wszhN2vMRY5+AKo4#Y1aK49qPX_x#589Lfy
zY?AODX;Nx>V+u&9aKJS+6^vH6UUgd&(G{>Bk7?t)q(~jFK#h3fr3Zz#jZ6oAJsoD)
z{`n#@z2zoz!=tn3n@}hJ#|JyF4&kadnyZfwkvpL(F!=t<tjWNQ<HN?RzP^@AVOL+>
z?!WNj9uby`Dn?$0d#WFSr(;8Gaa;yOvCZ;1%ykFiS7nyfhUa3JD-BPf^9I2V+qap8
z_>w;pMOcsIpF_^HzqwsjeuH$Rz1e$E;$RG<h`UPkem?iAmV^ZRnOpFRV$YeEUD=QJ
z7Tlr>TatU{_QE&T3f3z|Hn>_#uX?Qi_#Q2()c{(4;xsj5btPd>Aj$Fl(oQgNz!?-c
zro*M#Q#pFWC5$LZRESkOc}I!QfBMAW1NdFQX7*l@6-&vrCn7)6V&d=v$=)8x9p`z@
zcW!ZJsxnd8JJt4iYKuLt$-ikma&vzUU1YJk0OYvkj~=Dye83;d<~HH>v|3TKJT-?F
zzib{d!ps!9x#N&eXbN;(KNIBx9u{bw%qQMoj5AE^VoT334uj#sW9!7x4hmOKu}tM5
zV-I%cYvpQ^a!+R-s<nscRy79XS1@N*fCBmBSu#*6k1uO7yhCI}*NJYG`Ye(gW8pB2
zgHC9sI4xUknC$9oYN2Ft6TLALkS4qJ#M!XOcdR{ReTQw}OP0HHMve*)yi7Kcv6~qv
zKh_({`W`)>EFPq)-lL|K?S2*ioG4LR2sD+uGYO*RFxF!Jm3C2Q=UXOtq4Z8!rM4Mn
zGZvK)J+6H37<+qZo4sknDnf2H0m((PdKH=*$-BO)_JQj_pe3DVBBUzP{8#Y3Ia0zy
zH7N8?$T2{=C!B26)-wWxcm(3kkT&TXoGlUJ{c*;JPIgG2p)Wtu*daIJecv2Dwr3-y
zi=+kv3mzMVuw+{Wu~jt`_C(3Kue`W>iBNr_{x!=kC!WE237;tCl#Wj}l382S-rG!W
z)rUc(oOdDiC^hKPE>ATinKV-gH2pT-G|{@T$)|mJA_<g7#WR9`bM)N-ygr~`VS3VC
zwb=U8E4tI-NA6jhOOlf-*awQBqY=Kr-@)Zx<wTu+;{Vh%-PGjd221&?L69>s#0mo7
zggSo=4Z)?ojAn*<L=G7M!<oE~Wf=%#dPabI$_PS?DS3;T`({ob>!*l9rcuU_<3ez>
zfTUSov5z4Ax0|uId=*Gv*h*$Yth?2e5M|d&d}kIH&Gh1=vJyJ3+lr^_H=z_raUgvw
zH5YiZa_Q7(mZ97m*RXe2SzqFhCH2>}4|>8^65txA3hg}hwK>mXTMvpe6QYlw!2pL|
z5{4Z8!9e>D$cDWACs8Q94lsvq$>EQ$wPH+$h})Ou#bVU9ca@Ul(4sVN<hM!p7Oup?
zegu&T<`gA}8&-L^xUh;?NvIf4Snp&1xg!=zZk03nA#i8WMxeY{D30{W(pRVM`7YOI
zVyXIiJdV->&iP0jO#Idtw+tXZ0YN$S_C*DAzJX;v1_wUyAjNUqa2o#h(;5$00zZzu
zqHdvFRfrj;y*Try1Ots)l0l_3P-jf}@Qv%Z>bd}~aoK578ANPXh4%-mV!Oh&>QQ7w
zfO>@8JmpGvY?tU@j$W*Pw{6O=aqvA)#9Qs+xPw_g5}LHzx4oH#GJwLBMyP1arhUsh
zm%H_p&M}x^pQzg?ni9T?aRGvFu__8${8Q22li$UGGEW~Q&iwd9{7uz&kcHOoyJdgD
zT|}j)2^RP4hIk7eNj=s6CI#je;<5j>P)WYhb7=GmRs-&!j%s+yZGR!NyU1i%t?At8
zx^C<CPrC;~N)O({u6Y6iGuiy8<4+zUW(9eEQ7n~EEYaBXrB}1r(H)`!s?5Hee5urx
z!Z9G~n0j(dJQ4O(9f!=|`Q%G4S+Uw-0K>M4sNT^W2eTeg>XEk{JE=xKe9ta8mX(#=
zT$M(bB!69x*<nBdEzE5X65?RPxQPkJ1l+>PEqB+}*0%F^Vt|glms-n$PeWSry1I(g
zd*+I=SW<+Xk=<^di{>t;NHDC`*vEz>ATfm>fxpC8;Vm1Ajc0fVGwh{}J*a@HF&OIX
zftABrJ=7lEmai%2&au0@c`T@%F9sDCgt-xVU}4gI<Fb2NO>|bTI$Y!;Zt5a#!FO=?
z2F{Op@26`gfQ?Uj$|(8h?K_zNOiK~s^K%pD0($KGJIkz7{!>M-D)Wd)x$RZBr$F3j
zTS#W8U=BnsCtBy{6L}mMEfUM|GedR8NKuHRA;PZFHqj*@@L`%_N!~lzu&T2X95*S>
zRqF1Ew}_Eks2BDW+|FxXnR79IdSqZg>b(|ODp}ta5JuS)-<~MO<Wy)T3S6=yt1*Kp
zM$lvlNBUA$vXd(+SZ!67%=aQy{FW>mdWB<vjiWA`F6KR&av0F-6_Zf~{fISQB0gqj
zJe7Zo{qugIb2Q?0mdqv7h>Ab$lww5s+~r<jMjT&%uX$P1FmYNg>RZAEjlH3)dZ<ww
z4EPOp>xKi0Uz(1h)B@JpmVvC@SJSsACRvd%wcd9z8Rbwg=KFY1aE^zkCzqI%)4e}a
z(!L<)S-eGa1&bj_;A_EJ!&Ui07t|@(s_jZ{&p`VNH2LxFv|vTUjV?8-hD3`sx#bzm
zu}_pX%3Xz+rOMyN(+eA-pv7xnmje*0X{^YA3<*qbYjxatmi~EkO?tgiD5KD~xF_vZ
zN5YXOqLVavg7cG7J7jyiLy5s0heq<yfou$EtIG4Zry@<mVL=6UmxnonoOIqD6xg6X
zbE^`E&y_M9|0Fx3TJRA;mES#~I>m-aI*3u{3{d7CJ)H{@k%|Fbjsa1o0-wV0(h57v
zI@s>@SH*V2MK#C9ywGYD%bFcxF%R;kpYn6wa6c(TKGy@=-BhfSreA!C8pU9)vkUnH
zp)~#~GBO;uJx8j??Q}o3WG1WapFXijoKq4%J-*7~ry$W%M|>Svjm5SecG7G;t9hqR
z5o?$y0<W0mPXGzWjCAS(MA$thMohC;`jae~meN~C;UCxU*T1Di7w_!PnW?^&Hmh5e
z)17uG^op2Bvet!MTX6jl*rF~1e>etUwFIUjZ}?$VRw%Ht%#0Wdb=0PZsXY!KPM3Bh
zBT_bl_|VON%%=^jmwffrv53-|TIdXX{iZeCfFW!0$%z!Nf)OoHmBD~<lPon+M{x-^
zi?w15?TX(G<}EPRm@M}pxzW#ep%7Y!TSqsbtk`9Rbb7=7e$y+G$C9Uwd8d&Kn2hAJ
zuU4H@O!sNec@B?Z7roNGwMbh?inN*G{e62MI8Nn7%>++hRX=AJZR56I@1&U|)cR<`
zh>i^B3iKHS904~<&OHW~&Kf+%>p-N`mytmVr=Ezj>6P^K^s$c`&rCrj^X8{Jh82g&
z+-kJ4s)8#`i&D!=Re>vk%hp8DGGvO)-mh*J(iTa5MwNq4@}vo#AqGRW`^Y?8y<yBN
z?TZi_MzBErN;o+aIoLzQ0p%+u12gh|`aONS!<XWk86f?!yI2{gG({`3U&-*~^htdl
z?x&#f$gsV}To#bxNKXoGj8S;hp{4405f?><VP~WDbXG^@6Mo#f1wUAeqOS9k;jGMU
zP27y@_dRat{7%U(kF!e>cMi&Ls11<7Mx{`BiF$pBacDU`xk4zbx*gzW0~(1_tlFGm
z1C9VCN2NR3Ho2k8ZSw`40nicN$HJo*N{C9H<0B`X1#J}B4EH$8=9+VYXK>V&-ngSp
z&iUrE;IBha_i$P6|6BI;!_q_vj)Y$wtak8`v}0?fhR#F7Yv`=aqhFnX0al$utD6kW
z1b&368vCIXPACcBJ4rQ0iYfIwA<7%>CsUk$$AOh@bNBtS)-U)H|LwJ&$HX_!v8F;Q
zP-7=(S{6z@&BQa#rpKN%EdO?{8QrvXCO3YMDu!K8?qk_Uie_H~j?Emw%`QavUIjq8
zrr9bQe81x3@EZvwcIfYVstTtVzmbx&8!6{K4Jwpm21iMI__JNH6~n=fnx0NhZinpW
z+PsvEus1!iMs|)uhi`_l&yfKk3G{QfzTGw}6)506^GvR+%Xe@<`-8{WmzC|+e)UR@
zoo&}7sYb*N4BM-O{F071PO*nBQUN&~{nk~JYN5${_-wjn&oe<Q_<5(i)1(%9Z0E2g
zm_}sFEQ8@R6i#So<>ORKz_8m)YM~Rq=n%azotmO_rNjq37o$h_XRYrg1)qA}?h_FC
zlX3s;)uzv(1RQm3IqCD2kQodJ<+JuK+_OC34l0Tg50zfL{k^{1LalWXFqKDFel)e~
z&B@xo>7P<XJLbO-%tioH*4WFn+GYS2w5-9nzW=Pf>e};A+~vbw5*-b-(EaeF(<=mI
zT%3xmvvw}ptm*jG?N-qht#k5ZzN8!btN#w!nxI2V!~D9p>F+o-!uQ^PhHNuw7MeSM
zOfCAS`+rCp_>0mvr@fhX2dkl=$)|+C+}OM|J_;%fOmJQ>7@li(xTaVTCBA5&(md}w
zU!FtLzuhNH2+Iz$+k;jlJkp-k%Z&Inzbv0|emWtuP)V-Gd1aBiJ6v0~4b>V^iSK_s
z@;LxWnov`9{wFBbs$K^z%xufYbN4xbIcAQZu=_oOlZ6>CI*`fD(9m@F4;j~yU!#|m
ze=NYOR`)(LZ!=j-=@A!;%kmIf`2Wx}K8!j`(UGZ*kXZ0BkPpANDlNgqDK(ApLKRDB
zUOkBur#5U@d)@k?ih9atG$P>7?0K<QE}Ii78<0b$XXUC{peZ4b>^;R3jd>+hy-_=j
z{LcApNU<aL#PuwmD!>_j_jDPA+;@H*#TS5(BB1mwlao*Tu8a4&J;}k36a+fdeBWof
zNHpes6zN$8onaa>Gcygz{w!4j0Ctn&8Y6fQp~`s`pukcskl><xf@0K!NHcHC$ag(x
zv@O=?S$M0h+4ZWY*d+ah-&pSVn<tc}!-j0rlEiFpVS&AR{C>!V_}0y~A&6h=73X)@
z1qHF>e(!Y9*;IzB9B2B4Zwr~aCL%X6`hu>-jS6fA{L;OiUs%kdZe%6g$_`((GT!qU
zqhNB&1irmCX~+RjB;|+U`3zfkoaf5V@-*TzX0L?9MuV6y5z`wE-bybSsjI1Z?#Bvp
z#z`aZ(}HaJi0;3@2E14pmB^C!ME`h%vedVNOJ>|Q!W5-xw*|#I4szA_L2T)QDLKtE
zGmLc#b)kj!z1V&t-S4wh6{uaysAB~ax@oD0fYapjipcZkQ`gJZs4LK`n4V#Tz;zte
z65Wc&HaXS*Ui*v9Re3ltJ1F^f@dqoB&t`FY(8-E#Bqdi5!@hWRKIk1WEO{s;GOU@>
zy|~y=Z483)z@oZj(;V-VltnlXEi4&ozz<d;4a%EZG}^iCqiz6Ku#do$6h^2U_nAQL
ztYn1oMMl`dtN8{>BaYmM8+Fz9FRk|tSsB@CFUgVEAHa33w#+YO`+V>9ysM`?=|itS
zjD05|I6}>>%0)|qJtfAji+tTQQw-VF_`0xV>yqnJ!6C4uSNJ~9YRL=S@J_Wz{AEV5
z9(K7eN`@zURt2aD$D^OZXg67CH;W#=!M%K?FLy_5UYTL-()v6SWzuWMl0a$zuIKXN
z?}<y28mxbL`<~Cn6H&e#W|7`?<)#SwAWlV6+|AnU`J#(B?kR1Ykl@JhFtu+w|CdF7
zxyG65tEwb5&rdFjY=G~&)N@gSD?${`u5u_`T5>QC!aoP{JLbYloabsHH@@m}l1r@J
z?Rsl=^4accM?AP)*dF6@r=dfBr#11H27b|Lq}=9L;)UrP-?Ot3bE8j@(SD?RDqu~E
zK|i+5=o$HlDn_j0T}>2LPDLz;hv<;|j5h{)5r=&W;}NFYL}jXG6jme3VL_!DTlTme
zQQj$^wQInYzAaUuN4^TExDT~#tsL%!de~7bH^Vl~4~_?-25r=G-g$rPfvcm$gz5T5
z;^P8Zd`SAT+B_AZb_S#!sS{q6wBq<UU}^CVQ^)rN%E;9--$qSnkYvO-6A8|qR-F5+
zLCLuk)9e$akJ3@$fM1OJho;+^QUMeSq3#+0vGhGj7{nZVbt0=3CTaG-O)*PfE%p7n
zAy(KBaC}r3{3O<K`Nevv^hSEfe>Bb2+aGf<E6%2R4_CVLEQB)djNKfkip;0mY`2nu
z_hSbd)9y@abRS+7l2*}WNO%(uf+il!*9hvv4e1QdLM1fr194xXj32xwp|DHBNhzJw
zNG}nx6tu9tabNVwu7btd;A4d94*S{0@62WXO7icU5n9SE74=J_`}_Ov_bo1Gzyf-`
zVgr`e?#y6A9<MH7s>5&Pj0`u&g68-WXrPiK0qi19J1zXFlPB@b*y`~zGwJRe!9W&Q
zU1UhU8-d&#@wUBa>Lt(Dq7MUlm+Gc|59vw*Y;?Scm}S$~#?zS)7D76^<}vsz{-Cj9
zgh!PVDzJwtq6nbP=9*2syG&sCtVh1etfI3bst8^FEd^NOb>r8t#_^)%-q&r}sP~rM
zpGNJfO#=4P{oks}5CRH{x`|Q~qK+A{1dM2_?35*)W><o8M;W5+gBHZa0_&NYE2rp!
z{EUOBAAQQYewP;HEuXscjZv}MFb9D;<HGciV3vrnVr{IUxRcuKZ}gwB?4q9oDUQdC
z6mWj^-%Aj9m%9uggUD6gB|NsTmhoCT362{LdNifkL7&h$M{kkP-!!gvse{NLc-&=v
zWYGdDiLtq^_G=O2o8Km<G9Cxg#^a)X&pQwarU!EyhaC^9?#QJp*s`kg)8S?_D4FxV
z65FTcGm>Stxc?9-y$G|zs(^82M8!$=;%CZi;*$$d!&P#=2ng!s_hj0bgBcQBCZ+YI
z7^uh`G4&1Bpb|RO7lnQtU;u3yZSSX5GVvzFi(6&8=ww9Z*Xw8Vk4+Si=#dj8#JA2l
z{IJNtAt@Jx7Q{g7aE;cp6fYf~Bs<%hA_$(aAdI(NQvnZdxVQZSux18Mf+^stgoOYN
z;<gv$Mk|&f@hRa+DR-BNsrSe+8Jwn=^0VxkFX3cgtzP4|epCZrTa_$Sek&fU&gtxO
zNnx**1@XDf-n%S>4a(<eS}<fIX}r$k*yNA=`F<|9d|vU`#ifku_Xg&p_xOj}`A{Yo
z#Wu0=+{1!OH%>(8S!PL2Mx}sP`WKPLyGc24<Nnn)?EzmdxM@FrctqRIt&f5YIF5L!
z%Q#bW^BG|3Q?B|iTPxLT{)^no86PzSh>^Bm7+UA3G#+j$8jm;xP_iX?&_D55XOcl5
z3h4?OKSjj9G&NVA*JA#^eEk=;9WeoYkAK~}qcb;qbhwJIyrfc}<fh#6&YNL+?{nB|
zmN=W>f1nxoL(M<N`}%$t<^y!<_k1c!caEqlaV4!<b=BH6d&?P;`jW2j0RR8t@}CJ#
zABqccW}C^1=+#DYP6C#*RGn{VpjTVF^g}x3EdQn9FEO5qQ7b<O2V(4z104Z&ja9Yb
zZpd@YZ1Z7d(&Lj6XMXA-sZ{r;rq4`ooxHJYxj`t3kM^{s^|Ncbw}zSP?c7|~sLx$$
zlH<HpKT3UF{$G3_>YO}zU7<9#mEv}?ZRfkVWk>g+BEJUb9MVqcQaR<OXEoLE(}sLU
zV&rAyJ0&P$fX$zP*jxY?8o9ED+brTm2hX`=Y}TY0Le!)t_sY(n&!0;_?hDgEoIO-Q
ztDVLjAsh#e*UN9M1p;G_9@3lyYzw*Co6Z<7I6f@*B3NK<<b3&9OxjLm72P!``stJ8
zY?Aaqx?49-rg`l=v|7>db*~!v!9Y}Kf3{uC>4FPa$R(!mN}wg^sQ=M#!`b_lT{-?)
zoqaQK!n=z5tETaXQ2$z$^HcWemrwdWxb5p_=6x*u(%or3%b(<^0q(XS-`{AdR(+1F
zdD_awHL82>70Z1yOj6==w|Mb0a#B0#?f%^|=Pl{-(|(YrS4>h(W&BzffdYqn&Xq@q
z1VWYo`2r?+>!g1~H5ScY$$FNN`&?mgcl}$d<}_R3|F*XCZM+f=dX6l5sv^H0zsCRW
zYy9;$YPgJD?#n^PA7?G7Z?(cA;m|Oxlditdt@pXJ=%SH{=6-X^)ZOg5t15vNd-$!G
z*2EJTtFNe|h33a9in}Sm#pa4>4lSDwEwPRSL_$TIE@yX`*J74VMgP;XTm+-n;mX6m
zk-U3ym$~ylB9Cj`T^qe4KSOBbYx98rez}W1pLHN&55K_@omk2fe+<pfwUzf=85PLY
zVka9r_cM?6p8NZ(@{mP`1k0_}tqWX+#n)c+QF!)4Fw}6~Lm9t#ahyd*F=M*_Ngr=b
z?BnMO9XewpfTP;RogfLWhGToeU`}!G620T8-BIrzEpH6m`HUSsU%l#}2A$t{l{?9H
zIP2yqLa_8H@0k?3`K)LmNAzO1T=L#t5E7XQwyV;qqqX$F20uO{;yjs8<pC0sdl{aD
zuQdNsZja}Zd-7TIckZPlNd5MquZiItQ-ELX&0aqUm-H&56@2UCj1>7ls1EP2wy3fY
z&$%qbYLkpmP9dEw0!a~A{mW&qMM~$>H$v~mFvrCg6m93qS7^L=Td;<qvgaL9N;2p-
zXBULR_yRirSsD-r)I7ld3)V$W+mvQf7j{{I2et+V--L+CNg*xB6gAud$scUjK;<KU
zdv97#VWAlLR0a(Dmw(wk45p=^g2|<1Dh)(hrNpx!y7kG_n*8qwW>shxb~`Fcd|T!{
zixgQEO7NrsG}OKV$Y(MCuseXKJi5PlMf;=$^ut|wjUuzB<3YEhWETyhyMcSrr`u7=
zR+1%YjwP8$BF)s)r%_)Jp)W{3;H+%#(yLFF^nF4<WKbugvW#(6ogtn=QRH9iy+X=H
zO*nqYG<gk^?Y+=}s7YQ}u9c9G;3G}D6b=Ibh=GVHiVfQTAH$~wODXH|exeCe&@Lx_
zR^+ud9!qix=>1lUa;fil&$<^(%EBU1abLW#xd~49BPQp((GTOqO5*T|tT$Gj+$BzO
z&wnufZq&W>UPq4)nIw8vS+m8!K5v$Gv#&eqvN=i|I)jCJY^v4)ZfNpS*@AmM=$Q9*
z`y!WLuEoB$93|MjilXmBS(ItuQFq0Jk@N@CvD&<81<jUeGezxV5-A$GB-sS8(?RWN
zuZmf=GhqU%?0?q087G$EC;S%_rG>g8#36SN*Bfyc1zPGLh|bxf_9z>;AfGsYr{vn2
z*ZX*(D&s(0B(QQRzDp?WLm%?JrE;a0_FFRu9G+-STlE^o<U1+hq+23#{~5KX>@ye}
zf$25i#(H$QJyyh9wV$1&KCE7GpfwCwy1cM3l{Xc}Y{R&=`ZJv7HnaZ});mMRbR9X|
zNy3yszI(0ag+3YAK}{9c%S@$w;(x3<!3h(meg*iI$#Z;^myDZywU@Z%)F=)v%QDM!
zy>?Nwlo7pa7W8}|pirH?BvNQhL6YZpVzJpqfxUSVXAaS?tf=5HKT)()uK#{($%KZ#
z&f(@XkCFx-T#o?vzDV$^w#-{4kIN9UyBDk<VRn!We#Z4ushW*qM+{u5IAtLSo^TO3
z)kD>OVo!o@>k2M=dgtoJhW@zYAZ0=%#2W1(Q9XGniCDiQe@C+b^y>M&#QwHevHRcD
zP0T_+t=7%dsw>BAQpT{5V<<1{B@KDuCLcIu4ddPXWg(%7T_e)WITglm)H}td4ef@s
ze%6gw*5Gbt#Kf5JO}kGI_qgqagks16+@q2<thcly7k?&y^2G6dEbiit=KTr-62(Ic
zZ8)0+QI&b*sZN;|cvL)V+}lJX>=!)R8oVQmYtYq`+l{8cAfCclAT+qHmdk9sxEv%8
z{bn1hB<@&IQBlwz#yG?bH-cbeEN3&>XFKMKV-A+YQL3t4dR32;%XnE#%z<NFs01?7
zAjVNKp{luiVa-N4WwxpdH}6MxKCzcnpD3}yWlosMgqiz^Q0;$c|M!wK?$^YZ;v7;5
z>KwHY5EVuQ4Y5mtIY8<bJci&GE|8#fwKdG@1}4Exj|yyuPR9KzWvsz=8?V};sRUkb
zS*HMc@3l(6WoEsHXL`*BM_)aaH#WXn@T=oOk@x8~zp!fJcI&2cpyT7?T|pN7mVX8a
zTD+1$VDcbuXP07soy%RTB%U>?gq#;^*Vu+@f}!TERM#g`1~9Qn5pmR!M|Ny+0Sb0+
zmRluEwmk+TNkj;_?iGTCZ|Aq)uLJCn+#Nk$Q5q;A=u1q`)NnjvW>Iyd|5T*6(YJU2
zIQHh*-K{qiLP_qpNh_#Yi9p~_`x`0d12;MYA67&MWp}N_(-*;*Aer|t$G%~(Z=_Gi
zFyEbNlk7y15AJ0&4_+#HuNPM$_NH$wR-Oc3pDG@bm@(&=u~XeWZi-qx0dme=;(|6g
z!(qniD18kLlw@zHfW2$4U(`UOJDZuga`rWqq^a0mg<_Kl3eHKS`<Q>I<qPhPTtF+g
zZOTY8<K9XOSOfKv26N2B=ovfcBYf>5bW@xbw`=icsmeHLTpb5j52c)Q3l#0KIyoLk
zyu)8oL(_gCOYi<!)kJ~=4hb1I;c!alaXr^TWZZ9M5Xm=l#ij(t1qp`lYyV7UgmZ{M
zq;*9_e4DD3At$;UyQ;#utLKzh28vIHwvrj&%wgq!GMROPIv`Bx5nCguI*J)rId^=Y
zT#$2<D)4RTiNOO+hO}7>SHY^y9p!|Ff@PGs>V%^;<rsj46muNzRE=xEC(#E+d9d9M
zPj@g1-r_74l0|!w2ejonu6|H3qJnT-#1UTmM`2<3FFq@`BOyKJ<YsD68BtjcvR<oD
zb(93RJ#8Ah=o{iUxz(B2b_+u)c{4Ne-(>FEkYAwq`@Gh)or>wl#ot@mS-o!V@m!t9
zI2vFC|K|MNR)Zs;+{W74_3vJ1v)89qyQmX(qCSLwS-P6pRg~MhShiH`Mu9B;&H9Bx
z4vf1^*Nt~4$fp@)(0j7RBV)c{oztp3P6XxcVLX#M@rH)e?AlCCF&x)h(LbZnK~U*m
zSo#FKjJhufWo%<MVOsq^n*qqs-pFKF=ff;*JDXsFeLvGpA*2KIyo;$!G>0!p|5up*
z-nChAlhy2s1Tuy-gq|s)#WefY=hzHE?h}{*l@EE#k=Ws>+P^|sgfDb&f9DTHf1V!j
zXt5PrL7~IlPL?-ctc%lmgd7!9BH{tI>1@0BzicdxW$`b3<-Y8!{3Ez1aWiP+Sr<Ws
zcdWJszt}w5lrqhI{~t$7W3v+T6$wJVfCcJ@v$NQ6Tc2TDQuD??Mz0pKpUcJ)Ke-~n
zK4UhMO4sUYvH4rwpUtwfkl1BNURA<1RO5n!``4EmH7XOL2R!PCzy7PiMQXj}TU}NY
z<xul%t?&_~0^B0P-A&j-*?Rh2;^xsx+wu5+=3Z#6mlbi5<^l5|FmcJ4E8fOZ_yXah
NdlRBvsd+E#{{R+X2|)k=

delta 8830
zcmai(cT`hb*RMrH6a=IndZZTtrS~c#Efi6tMg@!t(o3jY0U>m0A`rTWihzLhP=tgc
zN|#;}AOxgDS_th1&w0=Hz4!icGe*W(d#$<WSaa^R)_&%1Pn*62fBry0nUs{2@`1ue
zu%@P_wzjseuCB4M5s5@LH8nLiH@CF3eEs^ht*xz{{|pL+N)bQXaBj46<9y2USpgvR
zPE)5y)n?@LG4%Gs*G`6?U<B%OC1I7CS`m77a-ZcSK1re?ib`JL(@gw^9<C%O+jLxJ
z^e((*-WYQ;R;g^veu+Pvt}6V}=k|Tsb%giQuPrPB7toub+^5kO*qqB=4jrq4E+KIT
zp?-Z^-U=^3H-#DH&j@Bdhv`yc=;%@+D20Le7~-)>XT%z%QO9?Z{vx5vuCMr5lF(2k
z9UvI{)je19iK31@SO3ZMrZio-_~v`BWNjAZ+1wzvEpr$1kc%Xhz{08qCUeFU+zrSc
zvAo<yld&mvS$#@viKH<x<2yg&@_Kw=1ai&)@aV#xP*Z|5w`y*FMe{<^)zIDX>}jA6
zsf5Gs<sE2N1&v&y;=Kcwf%D`1h1VJ|StDV{b3r^!1J?G@@8P-0Z$`539b3iJJp66z
z4K1wLvzlP9&8L?3&J$`-5ZuKyzqJ}IKK5{-`Z18MmW*CY4}FB;n<b%(_VOf8UN-v{
zolO)M;kc1hg?ICTgja*4zdGo<`v7;X_&;hApbVfGzv<;@T)dtpP(P>vRSw+Np`H*i
z4JJnCl3kCMD(086%IQOX-(uU6f)U=8FlN4R*~bv2$W!iJ0U09pz@(vt*iyY6p+^zh
z%;hEIoUj6+&U3N->o+zKK~WRzHTgf&Rr_ydD_vg^n@Zo#^p~CJ4Fx#<<YN6zVrVd9
z>Ei9eT(eg5r3Ey#cXQBBq5<6qzZ0S1qXZ)>p^@1gGhc(K4hbmm|5+3g`#S#z%<-2#
zv&9gi%B%p2?L1Av=*NLZw~{>TkiBOrCcNF4ZotHT$oOf!Jb|ygIlr~=TX!tOQn24*
znV7^UJvgTN4q)Y`#7dyF3T9;R-|p~y7Cj?i;+T1`{9UHsZ+%;XdLm*RI|3q4S4GL&
zhdJxzt=V;yCpigxGs?T)Lf*di7h;MwVu4vI2N1p@n!G!IoV40Rh&tH%E=CU3^SS4n
z3==<+WG$4k2aHQ^9}bk?J=q%ei7)@CDe!WL<HxWE^+Ip|>2geYK`9`6D`s|97BLRl
znxp_*gAE9#2#((2(0Lw@Z(iqPHDyZk^XG?>!p{~vfV*>vdVg9e`AjT87))20?s9Iq
zU<&@dV3s5cria5pOAXE!lSoXdI9r+~o3-{?DC256tJb$gohw!bJLBkwFvR)`Rh8cX
zVjUJ@*&QBuA1NGRi3O$UkL|D?-R=rsi4WEeSH@m(sLn}>ZaLci<=_*g_I{>|UG9x6
zFXqlFM+R}y;qjQy{c~`DVd1;Xd_8wIPN*lEcEt|bRT9yGH_UG>f0SeY0hZ}{7X;Bh
z{MtDikhjJfVAhl*{TKu_MwN`n$`AynodR4#pirDs_fvc{W~7qu?-3h<YS(X6m2Hh{
zy-lS}V8J~z_g;%=<IqoRbvpG37M+SW#x{%Jod%y+I1~eyo+0cuAH8&~zVNJu$q>=)
zF?;uN?$eCX;mV&Rm9mx<N+m~uKXJILzwE!?_;(o3WflGoV}J61Va@Irh0WDRGHITM
zLPhohtK+WVHdRoY)NiVtMU><C7u;5Xy-cE0lmh+Oq@aX(DTY$X{;RHYl?7rqwFj_N
z-!rBn9J!>|l;ii##3#WZy?;eB%=rlE5Y|z5kkZgN6tzbKlNF(6TzZYhfhV{hAuAF>
zp{*ECRb86t6OzJ|$bfi&-}P33F}S=zS&fX+&#DA^V@bl(KR~=F1}ao6?F9@~9{QmM
zWM6-e?`q06pwVdb%Sm4)yL+w_IEPG$@Yk%9e0hz=5DJ?Q6qd@{ihsp16LRnBRRaFY
z!C(149(vkHb<hmQWoa5X!&$VqL(;iEgSsy{?27Zzw4I@^BWgTlw%JqZBldorrRl`T
z8GrHjsq=@lGbSotd&<VQ!tG8AAMG)SI!~COp}|Zi)tQhYJ32ZpGC5)}0O@<a4|8k1
zLqFEhB5%iY@CR)-v)sC6wZBKnmryV_a#plnGE7y(z)LweB54Gs6pC4kzV~-pM!X}U
ziwjBIykBSia%A3eO|*k@?C<aqg>U3bKvg7RF7xnCi4O}LR5gYw?IuuA^XuoZljlT|
z@{^lyU@EaBS9wPQuqyx>DT=!i3#FcMQHlO8`ck;<a-zLq-)6#=Fpm!vtLPcR3I#!%
zg0SN?7<Y}}Q%O))Yb$H)cG@&C_|@9HO%XMrjBl0y^)0Q(xUrLqb`=rUHgchvl-m)-
zf+|5<5~7N<#)$*b44AlBSbgBltD&aiX_*<x6h&WUk#k{yThN%Nkc+}MTuD^FvzE)^
zTitV?qaSA5ZcZuncXx;Rs|)!rA@Wr_+k9_o9}cCfP09Zp(JaIK*<Q^#jIsJn{_EBc
z4`lxBGLO|4u_{kJTl977JxZ+4LM**~)QOGe%bPECQ%5D@-q6d?cv&@#eeLQ4pTTax
zARJIxX&^;v`v3aM5uCM6fEY|ob7T`iy>;;P5~vz#XflvG=NBHIA%6Xl6DCC2QP80v
zCn?sdv6S6ZTK;>%ue!e!3!fm5l&sn>L8OM-U%j9{*q#+Gt&{E4w605t^2_q^!B8qY
z@;lW0eVd{R;RWJ4qqJ>b7rF$P8Zi=C$#mP##}sL~Ed1Jhvx~efHB>){{bn3gcs%*m
zQ(LfBf3x><7Bcj7Lj<4h{+<zL)T+2Bd~a?Tl#}x~J<K;UXPog@(=wa147{&(u5JaT
zQS$=OUG(DJcskya4+(BvblIKbm?jNVbN3lVYa=B=%~EFPX)&w7!ly^)qD5+<BYxH?
zgX{Y72%g^i1M3W*e7F&gv)`RI2hb0bLg5u_V!Nt3KC#7`7YB16<Ig=5NfRtTf1Utx
z;7NUUYs7~$iPie|m{&X^gFe@(idb%?Bz$%c9h*?6$!=E*J8ZO>*D<P5!I<V%9GA}v
znt(*%qa6lkF*~dP3o6KC#5YT>>HSSocx(29+E292MO(YrVEIonmNhtq?tVz2$IGci
zPGOM*FZpq#n>o$wo84UEz39EUxNKN%U>Gc(ii&Ur&VYROO<DuE#{bDQC7DJ;EUTwN
zQzycI)vNxYc|+cg2PTp7_FMF*T*bJ3C!atvB^4o1SsMu~g^5$4s9#hz{&qPWzC$BH
zXJe|}{_WP%IfAc~pm!KiMJ8E!^|EPS#V{-^GUwsum_J816^|GK)SM9K)T`q59v)6i
z?3(S2eO{HoCB7^KlCPW#56wwmTW&GGg7}XKteQ*H2P)`2;;F00jVd`(SDfbPMtrR`
z0CEqfYo1Lub1RE_tSnw?HX-G@sH&=R2~bmkn-IyiJAG<3>rr8cTEoOw?^he5-sZNf
zS3uvoTtrlR8M^%})X!z#R#CsbJ=F+dmun`-n~!#+gmpkP`5!S+De;)krA~cJ<hA6U
z^La~NKBfK3+Gq-}{tA1ake6Ba8Ig1nfZBkW^efeA5AUQOg>~p=>Df1)r;0B>aY!~`
znOoprN@Zo(hf|@rI4`?D)<Wh}5F*c$FE|KCwO`(i4gQ4X1rW|ROX!$x%yTA6KAZgR
zuU_}|NI7VAE)Yd0#5HF*m&(}UckqF&#z%f;kKJbtG#V2qZQvQNxliYX5Gnvl6I(n;
z=WVf#wQ~G!qT^?Z&lblKi;f=i+BR@%{-rjC@>dVQwTqQ`?ssgg;~JYo7JrFwhgbiP
z?XK?oXi59{<^9(h&8sWw_GD9n0JYmvqsC|aMwOD;s97Zim<r56aPgzB`CL>&sGU`k
zTyF5{9?poGj@O@^j4x9~`#w{BUgpbv-%W4oZ}SiFjp#L7e;=&SC^0RI?dn`3|H3`p
zC)l7LrCh|a@~cgSlh^M_U?P+0Jj6ZP))6UM42CHl44nV`IRI9|QC31gaB*y~7b&Se
z$MndyfRye@{W>mVC-EEhz`O66h9SFmZ$`t$QC6=D-nqQ+%9R3M6G0{;xd#*2m*1~h
zz7i_nKkkceUMD~x)uH@>Vgo^S0pE8*z8s;I`C6#|Uo!T!uiMLXPV3w-p6~_Girtul
z^d<#hpjK*j>t*BE>f>AR_l+%^gGTO8+ETS2{UzQ-MsH1_>!qp{hvh8K&<nLoy*C{<
z%D=Si#2VTEH1>IR>lZr2puEiVXnur-6W>@zrw+4TtT<(0O6x=7xA3*uQ1991*prl|
zgvbausjBaA()pzD;_dSp4}kX|qZ>OS$G<3U43u%H6Oa|lIO&v)?;dxyQbaTkQKp`d
z!PSwh{!_=Qr^MX$4cnA*EVuu#66St54qelE|6o{ZSA~;_e`QNz2H{sIY|P;lnICqQ
z@eToJb-ww+8hE?6esq_|Uta&ev6w^BRks9?E?4`Tf}JWncB5HyfdhM=(L0&ws0fwK
zD>ggUCU0cAF6mNP^NVBrMP^X#&y-ybe&5*mXmr3Pshb56n)S4j9jGEW29YP~@^jg~
z_D=t;Sw53nCbl*1LwKmwdS+$VRW{=p`3-M>6Ve8LNIr&vvHHkJwRoz4oB<%?{(Ddl
zT`_;?T#PhkK4C_wOmZOT?Gs~fzHDpaQ}7#gH@Y@*a>Hfy6)UVC=dDfQL#Cs#e<=9*
zRr>ErxZj$)KmL+myc3Vny)k{Vf%^{AXMT4{=Bt_ZYc4P@6!}uY99t5Z%oy>lfWx|&
zSO_US#(ltZLgZ89K$dJYKe{~re|YTk)6Q%zb@37#;AEq#+g|+7nWIUF<v%@rQnH!;
z`h&;X$b5tb#?<h|qbeRAyiYy1!s8~y13^E;qL&7Q(L(f`cOV>C?})D|!zWS1-$klH
z|BB{;9%0-y)f?HTuTXN9!UB4)F{$>F(6hTb?e8d=@)o_Q4K&K(_pS;{;FoU+SB)1O
zWc{W}o4=ZtY_K;Mc-OKC0oFlhz6K2U)yhQZ_Z;7Yj_0r&YW=&0*B%dJoRzR3^6QlC
z!3~T-L-+47Qg3f>H!oIC(jiio9^<_Fgz=_WD7AGC%&+IQq*YK{I#?oBI=#v|=1qxU
zRw?p^6#FF`Rgp4y6%~WTkDm7_-H1kW>0<RST7y0?-4bzi`-RX1OH{GV5+GmgTHU-$
z`pHra<LFg_r<aNtIbm!~ugHz@UgG@UeK_|G%`e`bX4<kG3JwZced&f4ulCi_dK;`1
zn5P~t(2BQbfEyt#X19uq?v*Zh@$hi?7dn6?V3D2jX-*O>?<1TBu{cxh;FF1>H5by6
zHaS3YEC4yw(i@^P*>y4L^Ks!Y4zR>^_Stzu8L|9kOD}h(SL!-ASzk65_JJn47o$yD
z7HmjXo#Lsm1KS7D#kSzEZX50z^ujm=%8C-B9qgfnlpQ$)7Y%iuL3wfXrS*!{Ru(Qe
zJdtDjvcOAE&93k~m1?W<QVNVzb;aZs6vnp(fI>yd6irQqYQ(>Js`jYjp7;Pwec!o8
z9o2e~a%9k`o}+fv`WiKNcO}L#Zd>wE)f+@mwg+m6-?=VZKhaEQR<{^5b5{Za=Df^+
zF<}7~{*do_vf%hsuP}Qso51tuuah)EXrAJaYrBpVP-jSj=1qP2HmdYAUJb{`Kt|l0
z*n|>KzN&X}r*>)6ijPn}Uwv#Lj1kI>j#c7`t|ihRk20!Xm>rl8@THw*oSW=fpFNqN
z5wF{PRhIXHT7TKlMs)7z@?qp=nvPxZHI#(gP*s5~V;406s-;E?XQxKdYDdHNer_+y
zWM`|Phji(}_F{6`V~8=1d-q{LB!iNetrYf7YUxkn+I_Be!JO>sU7;@){Eb#rOn#rE
z$r$m+4~`O}PWIF6V2Q+~3BYDpLWk5>pNfmVTZr6>xc}Aauu%~bX|6V1XI8h~VppRw
z<b7qT3<Tc`zfVDkYSMNTnn{s;gb7)}V`9w?b1cQ>Wfw0RI?MnjplVf-Ps+zPpOMR^
zq4jaZdK8J<9_9T=MY6%ESc*V_sF}ac2k={~IH`V2XmrkPUYIZYP;>0b%Gb)l^=DXs
zEirIostUF1Y=@@y=(rhtruZ50J-RTZ#40d6dF#5HQ(&@ih9x_sk9(uXI^!BaT~F@k
zLG`(p6=`%p;oBi=rUqWDqaX2!Z!e)4p=*vT$YI#WdzX(@PYZ_}S{+VjUuW9r;Se|-
zjE;f@+HSO_sH>~r&Ms$>Cd?oROdCIxQ@haAQzJdmQ0n;L^3pvfNT)&OnO?7eWd)1x
zY%l}S#(j2wG_-bW-!4Jj<hRstrc#v7r~=*_AS_mT^OVgP-|1YpYkk&K!T2Yv%31?`
zDUG?BnK3xSTwFYCeDbXTJD-PbxlyoiyWvWZ0rNUYMYD~kWH8{s^8;!LCSy5b6<qP=
z2gZAo`#}wu;8kldlx^}-HDKsT(ARPMDhvOe5x-9w3gjgBq4a{UiO|DG@0ndL2kh0P
z-ue25wEl|Lm2UD%PeV@jmzDUx<h4)D(Y4+b{(cRfUdQ$A=+UR%Unw}j@)K%;3<crm
zbE26NRywDprb_p$b+9g#vh=(iR~xgbzbd6lbgqIcrs#u*I-cA8#LnpJ0|WBTh_b**
zT3+pT5*G0Md7-p%E8I-Hb$xB@GXVUg_KK;a+6s3AtFeo_4po)HB-#=c&yiOhlJ&AQ
zvj95QeN}Q?Q{cvYl`B3$OU7E%N#-x6+^B^z#f!LIM}*1}%pMb>MEG#FDAC~yz)Saf
zolsT06VCWh#Nj*M@Dl34BMkO~s`@O(dWHDB{bAC~lL&?dQG4nH>5+VPV7gz-e@sK^
z6>osWu?wfqkd~L?2j1RRm*Z)%G#QTI)zsxfoiifv^o#IXY801vbMKs^39<a`Mf$y$
zA~|mNM5+0x*^Qe?rahQMp!|h2=<kxe=NyW`w2c|!y=h+gfb?AVh=Nev$wK%Dh)4$a
z!<AQA_*w!-_Wf~@00WECj@Jl*++_dn*7;fCkl+53HO~&b7V=s@q{|W&@IcwxuV1cl
z+^3dgIM&=6Yv7~#O=*tREht;%O%6S7=*&qf6Y?1OCRTqZkvGJle|6h<z<VRqzMt4J
zdc&T}l!g;Hg1;e?ug{Gax4lX<fIu_P=R-*(bYrbfAvG(t63+*ST6&kZ5GBHHg}DVQ
zuZ^8MZ$1jyrR8LJ#^$@m$s*T$9AqGD{yZi2&F#Bt3j%Ea#m(dkC)D2TVCF+3E9{FU
zG?^tYs5eEqsMUOVCOf+`m%NMnRT{p^sF>>d{Pv66H-W>6wPKr7&Yp$zSBKk4tTYLt
z<&PH>yF6dRYc!7A@LaC(8#9COx_l=}1Pww4lfGLR;EI2Gcz^Rg^X$$-HbZe;$0Aot
zW9|lNH?1ny<I+gXzqXSM`NCu_FTmN7h-D>vK1FpM=tS<ij15xjyOutTmc6cZ_jx5&
zC1+Imxbj%r>#~m7Q0$ky)o((#p}c;Iq3((e>nx+yYiAlpJfGLIdg}hME>c)$t973q
zh)=h}TDjSLe!da&%8{-penY<2ZLYDoay!AT^L}Z{Dt#{C{@4<==dQ9u$d>xujn`ia
z)Yx&4UV(J*+>?1UqA8xZ(WhR!FKR6(EUqkvLwq>sN~cl#h;Vgf1(Rh&Hj({VvA$9G
zo2m&5bMiZxl>mUZeG^YeA9t7sjG|}Se>j$H6s}44?i1C!=o^m1>aiR8b<%j>;Q6ur
zOJ7#ont;)(C2FD2!7_<v1GWQ)Ks;$_1F}1DsJCErswOMc(1-*G(#ED7O4lHqv{Q*A
zxAhKFQD4?kzgsN&SyN^+Rk`AuS_Y3tq@{eLP5+yhMgK2e27OsWo*WU<s3%+Ok1E$;
zo|P4M0Hton9oi0qV|H6`CCD&%^#h3+*gaE_HyWP<o4L2WmEP_Ds161EMn&Zk#nz1i
zrvsTMY$$|6a98yc9qt7n{ReY}A;xSMLh#z#GsLRCi97#s{Xd?2pBm{!yQ|+p=R&^L
z{Y%ZYL~{`^5En>8*43A$<7%oPCbX0ND?Jj48xHcJ7L?X`>3x9=l8BIpXdFI$r!zJs
zzO>DN7tgMXSO6*(Kh;?NosM->7Hm@z`V5k@Z14MIw!r!ER>^{@8zDL;o^LYtAlaeh
z=0-#5SwqtCW1XiHl_6PR%|@@$R92@KyoXGH4z2J;=AJUi`G|lKbd>N))+eE^fPc4)
zdEL#m|NEK#k(WdNx4f*@9-DhDXo@p}?425#;P%jaP;0V&=%9hQGYFTEbwXjCXUop-
zbH775{Uj;W8bB&_kHsJsk~BQwf1cz|sMYD*$q3C&r9x)t6$TIfCQM>zH7U3FolEeY
zBMR_2{q?w%SCqT}Xy%W?jG>EvOFU9<&p48A;UOb)D$5BfW8T>62Nf0Ay7xXo|DC*?
zT(F9l+1zlC*gkWM=%3a_4Bg^6LZsKJ#C*!opcympWp_~#xMC_Zit8I*;MnPt)(`B}
zB!qHG4{I!OpvqQi)Z9QOqlcAd5W$PdZ3H~ApIiblkp4s&sNg?5&3F<PnyM_o2u#w_
zWziGEUiXmoXDg|#c9vLP_@b8-G)f{}mgA7$BCp!(sbRg{RW|Q>SA~r4PHjt9*$7HJ
zJi{U(+sm1KRrGTBFL^0p#PGzHEv-q3&^*FrzV9Ezd}FMzab{`;iq^cg!0h<Avb9;X
z5}aXaI8_B*;(1f@f4JIPI#R#;(Z2SzsK>sbS1bu3s#!%4+^LA?q<c|<3E`!jTYJ^=
zPDE!kpr8`L=-1~&Ob94Lh+}R`Ze`lk*g&>gGBH;)-lWj6mcp!iyU?{cI*&h3Gsy9h
z<-KvUftYk)G0yJraP26{PimK<G*0vlqVK+IVh9awiu(2JGF!43CiL>`&XTqe_=BbB
zTs3-KHS5RHmJhqcC9uk;A%2e38~ycU*us>G*g(E=mVugqwZUEaBaOW4jC@@I!n|a<
z#(!pv^%p54D|CG*UF0PZ9)%f9tlonw*K4Y)+5xm!4)P`uQ@U~?wo@e6zZZe`5iaz&
z@#!u&I5^?%<??qh)~zYYM`A3Z`Fc^cnObUeaB2c*=Cutfy(f)=Xp?IA*SL3NEox|5
zxdb`IHEe6vsQ0xwL6-RZZS~gcZrHDkr?^k9#%2RBty%3-mRMq4vG-By@YF?~Qz$qK
zV6rI)8hK8_^U9m4Sk=VAYylPOihYARtM?Uo&I%?&Uy}_!6bw4Xz2KsnP?CTZWMetc
zVoE3oya<p3%VkJ+5*H^Im&h1|Iu_?Qv23>zj@m6*R&^429AWx_%-adf#g9v$_^iBE
zvDRhCdnu5ELlqeX#^<wq<5@8cK12aq2qgk@pLc>NA&qXv_}x7dDSaT8+R|{l#DNr(
z#HrM+<m!^Kc9Y0`zcQ+qc08XYHcVNER;28P^ZDcg8XAXMs&Xof_Mw>;QVHZ>W)}E1
z(rAuNcJfQd3DgQ}iGu}87VfylV`N8Gmg|}4@96l~L7&bG+8TSg6teijJAoB#Bp6Ii
z#VWW9;~C?`Czv6SG<EO*L!j_3`J85QPGvuezxlbu3o8ki6Z4hJU^46ZA%b&24YT=d
zy-!Vo-I9~(jvkyP2!dw9hx890w1jos`XinPd2|^4!`ZjZ^f$GYqXiz0$SbzRcDgX~
zeSgxVq0wnr?gk`%;AgkEi!*>qB1VL7l$o`TPN(+tO7eDZj$(;k<g%BP9M$-4g4B5@
z_!?O&J|KHlTWRv{W137S6Zm%y27(6SI&&hp%mHs}7PNWD0eb>UI8yOdC$G2hiJSh|
z<=4?(<|Xb!O@oi;x9gxZd<v=e+guDZ`slMU3tNEm-a*Nl0v_A)yS}l?Cg}p2+OtCs
zDFVHO{#TY>fh0XfA9#`DdL!$Aaduxt=NF|-@Khv>UYvO>Nv^3;jbwmoFn+gW-8?sw
zJb;Q$jEJFsdETBg#$Q?eBL`B_V(Z!9-2xWrb`EMX!+SDG2iZ!4Q%{R*^7)r83;yG@
z+siu_x_r!nn`)YU`~CWvGv}xbZfIK$F1H+w(WoEs^j|FRIm|RpFpWe<GhA*^d=cC?
zUFZI3$Gm*&UX!GGjMe<x=$mA)hL75r`D>}gpC^nJwTYegtCxuP;H15+W^YdU$P{^{
zwWY{z%^S%r(ul}4nE{$H>bSiX)7yi<-e0KLvlwUc2NYjV@^ZBBey1AO>o^3mBlvi2
zs|Sjx;8Od%MPNSOl}{#*9%zS+X`V!Cyll$%QtlZ(@C<`p=Yundi_S0=Et)Bv?VYg}
z#Nf*<XCZ~#H53QITA{R9mw>j#FDJ+3;*M{=lU+x-HAW8y+(`#DpH{AD3PRWFE;iqc
z@KQ&m)Z-lD0hv)-@y4w=7gUqO$_3Efi?@WF!Tuwi(VE@aGt{geM=e4cS*(Ysw4Nf1
zF58{c!Ub;}LH>TAja&+Apu|+e_qa7LsLBvv?p;v5MRz9V%nMP>0Nm}qBtb4A<E>Qi
zS%NM=K~~sVF7sWo5qFlsXc{hX)jR>w*#O$vUxQMchly=GQx}2c($1w;508}#@LDR=
z=tUY#`{HoYD{v1z!~4))CyS?Hafqtp=P-ROBuHG*^3rMV{3&2Rt;>H1(|U1Rx;T<c
zJd*kpn*Z&BZay0n0cwF~lQq)%^fZV-<#6|uW84H;XX)tDKD}q*?vZR_=|Mh7^Rm2#
z;7Y^#6+Esy=obgx<}9@IRJdLqz)>}U`&iWX)<>!0-Gfbrs|j!O2&0Mcj2mPPnng*d
zcI7`vjSP~I`wwY*M{`@sw00M15L6uiW;uGd%f3g>EqR3gN9Lnt+S=IFmUJhwwp|Zb
zc1G)JJp4zH{+F_E-V8pGEEOYTE$?Fo<cp1%{-L0{4o9O&g)MsqsV6}1j7lBvKcq_L
z+Ud}bFUjih-Vv3(YFBl^jFE*M)-*Jn8yfw16)O!On!k<^*CpfOT!sp*A6mr(LZ4`6
zc9KDu;XH!^!QOOc-y_Dwu9?tyuwUj5Q9igxCfk2)n~yF(C3C(&U~^I8Mnj;f71{K#
z>qXXn8G|e7;41yF{6Ca`n3)|ec?#`oieTD?GCLXHr3e1lJ-<SgRj{Z1Bz>L>5A_?{
z{}csJv2LCRBRtcvZu!u~cT}|G5<+>zY=Y?Ey?VZRRl;av*^dnQDF>Xc>`NyUZPg*?
UNQ5UTXUIPTJ@Ab(o%><`2cNdj2LJ#7

diff --git a/vignettes/examples/vision/oxford_pets_image_segmentation.Rmd b/vignettes/examples/vision/oxford_pets_image_segmentation.Rmd
index ff72f8f48..fccc5b71b 100644
--- a/vignettes/examples/vision/oxford_pets_image_segmentation.Rmd
+++ b/vignettes/examples/vision/oxford_pets_image_segmentation.Rmd
@@ -17,7 +17,7 @@ vignette: >
 
 
 
-```r
+``` r
 options(timeout = 5000)
 download.file(
   "https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz",
@@ -36,7 +36,7 @@ untar("datasets/annotations.tar.gz", exdir = "datasets")
 ## Prepare paths of input images and target segmentation masks
 
 
-```r
+``` r
 library(keras3)
 input_dir <- "datasets/images/"
 target_dir <- "datasets/annotations/trimaps/"
@@ -54,7 +54,7 @@ cat("Number of samples:", length(input_img_paths), "\n")
 ## Number of samples: 7390
 ```
 
-```r
+``` r
 for (i in 1:10) {
   cat(input_img_paths[i], "|", target_img_paths[i], "\n")
 }
@@ -76,7 +76,7 @@ for (i in 1:10) {
 ## What does one input image and corresponding segmentation mask look like?
 
 
-```r
+``` r
 # Display input image #10
 input_img_paths[10] |>
   jpeg::readJPEG() |>
@@ -86,7 +86,7 @@ input_img_paths[10] |>
 
 ![plot of chunk unnamed-chunk-4](oxford_pets_image_segmentation/unnamed-chunk-4-1.png)
 
-```r
+``` r
 target_img_paths[10] |>
   png::readPNG() |>
   magrittr::multiply_by(255)|>
@@ -99,7 +99,7 @@ target_img_paths[10] |>
 ## Prepare dataset to load & vectorize batches of data
 
 
-```r
+``` r
 library(tensorflow, exclude = c("shape", "set_random_seed"))
 library(tfdatasets, exclude = "shape")
 
@@ -144,7 +144,7 @@ get_dataset <- function(batch_size, img_size, input_img_paths, target_img_paths,
 ## Prepare U-Net Xception-style model
 
 
-```r
+``` r
 get_model <- function(img_size, num_classes) {
 
   inputs <- keras_input(shape = c(img_size, 3))
@@ -443,7 +443,7 @@ summary(model)
 ## Set aside a validation split
 
 
-```r
+``` r
 # Split our img paths into a training and a validation set
 val_samples <- 1000
 val_samples <- sample.int(length(input_img_paths), val_samples)
@@ -472,7 +472,7 @@ valid_dataset <- get_dataset(
 ## Train the model
 
 
-```r
+``` r
 # Configure the model for training.
 # We use the "sparse" version of categorical_crossentropy
 # because our target data is integers.
@@ -500,111 +500,111 @@ model |> fit(
 
 ```
 ## Epoch 1/50
-## 32/32 - 27s - 844ms/step - loss: 1.4284 - val_loss: 1.5502
+## 32/32 - 29s - 914ms/step - loss: 1.4283 - val_loss: 1.5499
 ## Epoch 2/50
-## 32/32 - 2s - 60ms/step - loss: 0.9221 - val_loss: 1.9881
+## 32/32 - 2s - 60ms/step - loss: 0.9221 - val_loss: 1.9887
 ## Epoch 3/50
-## 32/32 - 2s - 60ms/step - loss: 0.7764 - val_loss: 2.5123
+## 32/32 - 2s - 60ms/step - loss: 0.7764 - val_loss: 2.5144
 ## Epoch 4/50
-## 32/32 - 2s - 60ms/step - loss: 0.7200 - val_loss: 3.0148
+## 32/32 - 2s - 61ms/step - loss: 0.7200 - val_loss: 3.0185
 ## Epoch 5/50
-## 32/32 - 2s - 60ms/step - loss: 0.6848 - val_loss: 3.2898
+## 32/32 - 2s - 61ms/step - loss: 0.6847 - val_loss: 3.2947
 ## Epoch 6/50
-## 32/32 - 2s - 60ms/step - loss: 0.6556 - val_loss: 3.4525
+## 32/32 - 2s - 68ms/step - loss: 0.6556 - val_loss: 3.4559
 ## Epoch 7/50
-## 32/32 - 2s - 60ms/step - loss: 0.6302 - val_loss: 3.5625
+## 32/32 - 2s - 76ms/step - loss: 0.6303 - val_loss: 3.5671
 ## Epoch 8/50
-## 32/32 - 2s - 60ms/step - loss: 0.6082 - val_loss: 3.6537
+## 32/32 - 2s - 62ms/step - loss: 0.6083 - val_loss: 3.6620
 ## Epoch 9/50
-## 32/32 - 2s - 60ms/step - loss: 0.5894 - val_loss: 3.7334
+## 32/32 - 2s - 63ms/step - loss: 0.5895 - val_loss: 3.7374
 ## Epoch 10/50
-## 32/32 - 2s - 60ms/step - loss: 0.5726 - val_loss: 3.7954
+## 32/32 - 2s - 62ms/step - loss: 0.5726 - val_loss: 3.8015
 ## Epoch 11/50
-## 32/32 - 2s - 60ms/step - loss: 0.5566 - val_loss: 3.8266
+## 32/32 - 2s - 63ms/step - loss: 0.5567 - val_loss: 3.8311
 ## Epoch 12/50
-## 32/32 - 2s - 60ms/step - loss: 0.5407 - val_loss: 3.8028
+## 32/32 - 2s - 63ms/step - loss: 0.5407 - val_loss: 3.8089
 ## Epoch 13/50
-## 32/32 - 2s - 60ms/step - loss: 0.5241 - val_loss: 3.7225
+## 32/32 - 2s - 62ms/step - loss: 0.5242 - val_loss: 3.7293
 ## Epoch 14/50
-## 32/32 - 2s - 60ms/step - loss: 0.5063 - val_loss: 3.5891
+## 32/32 - 2s - 61ms/step - loss: 0.5062 - val_loss: 3.5953
 ## Epoch 15/50
-## 32/32 - 2s - 60ms/step - loss: 0.4862 - val_loss: 3.4399
+## 32/32 - 2s - 61ms/step - loss: 0.4860 - val_loss: 3.4506
 ## Epoch 16/50
-## 32/32 - 2s - 60ms/step - loss: 0.4640 - val_loss: 3.2488
+## 32/32 - 2s - 60ms/step - loss: 0.4637 - val_loss: 3.2588
 ## Epoch 17/50
-## 32/32 - 2s - 60ms/step - loss: 0.4398 - val_loss: 2.9895
+## 32/32 - 2s - 61ms/step - loss: 0.4396 - val_loss: 3.0063
 ## Epoch 18/50
-## 32/32 - 2s - 60ms/step - loss: 0.4141 - val_loss: 2.7040
+## 32/32 - 2s - 61ms/step - loss: 0.4140 - val_loss: 2.7031
 ## Epoch 19/50
-## 32/32 - 2s - 60ms/step - loss: 0.3877 - val_loss: 2.3552
+## 32/32 - 2s - 61ms/step - loss: 0.3877 - val_loss: 2.3504
 ## Epoch 20/50
-## 32/32 - 2s - 60ms/step - loss: 0.3622 - val_loss: 1.9751
+## 32/32 - 2s - 61ms/step - loss: 0.3623 - val_loss: 1.9645
 ## Epoch 21/50
-## 32/32 - 2s - 60ms/step - loss: 0.3391 - val_loss: 1.6415
+## 32/32 - 2s - 62ms/step - loss: 0.3392 - val_loss: 1.6364
 ## Epoch 22/50
-## 32/32 - 2s - 65ms/step - loss: 0.3201 - val_loss: 1.3455
+## 32/32 - 2s - 66ms/step - loss: 0.3203 - val_loss: 1.3376
 ## Epoch 23/50
-## 32/32 - 2s - 65ms/step - loss: 0.3076 - val_loss: 1.1034
+## 32/32 - 2s - 67ms/step - loss: 0.3082 - val_loss: 1.0917
 ## Epoch 24/50
-## 32/32 - 2s - 65ms/step - loss: 0.3076 - val_loss: 1.0204
+## 32/32 - 2s - 66ms/step - loss: 0.3084 - val_loss: 1.0189
 ## Epoch 25/50
-## 32/32 - 2s - 65ms/step - loss: 0.3429 - val_loss: 0.9294
+## 32/32 - 2s - 66ms/step - loss: 0.3461 - val_loss: 0.9181
 ## Epoch 26/50
-## 32/32 - 2s - 60ms/step - loss: 0.3633 - val_loss: 1.0385
+## 32/32 - 2s - 61ms/step - loss: 0.3649 - val_loss: 1.0231
 ## Epoch 27/50
-## 32/32 - 2s - 65ms/step - loss: 0.3294 - val_loss: 0.8552
+## 32/32 - 2s - 66ms/step - loss: 0.3294 - val_loss: 0.8574
 ## Epoch 28/50
-## 32/32 - 2s - 60ms/step - loss: 0.2888 - val_loss: 0.9904
+## 32/32 - 2s - 61ms/step - loss: 0.2891 - val_loss: 1.0065
 ## Epoch 29/50
-## 32/32 - 2s - 60ms/step - loss: 0.2722 - val_loss: 1.1511
+## 32/32 - 2s - 61ms/step - loss: 0.2731 - val_loss: 1.1668
 ## Epoch 30/50
-## 32/32 - 2s - 60ms/step - loss: 0.2675 - val_loss: 1.1777
+## 32/32 - 2s - 61ms/step - loss: 0.2681 - val_loss: 1.1974
 ## Epoch 31/50
-## 32/32 - 2s - 60ms/step - loss: 0.2710 - val_loss: 1.1230
+## 32/32 - 2s - 61ms/step - loss: 0.2723 - val_loss: 1.1259
 ## Epoch 32/50
-## 32/32 - 2s - 60ms/step - loss: 0.2818 - val_loss: 1.1107
+## 32/32 - 2s - 61ms/step - loss: 0.2872 - val_loss: 1.1112
 ## Epoch 33/50
-## 32/32 - 2s - 60ms/step - loss: 0.3111 - val_loss: 1.2046
+## 32/32 - 2s - 61ms/step - loss: 0.3146 - val_loss: 1.2479
 ## Epoch 34/50
-## 32/32 - 2s - 60ms/step - loss: 0.3025 - val_loss: 1.2817
+## 32/32 - 2s - 61ms/step - loss: 0.3034 - val_loss: 1.1946
 ## Epoch 35/50
-## 32/32 - 2s - 60ms/step - loss: 0.2902 - val_loss: 1.0870
+## 32/32 - 2s - 61ms/step - loss: 0.2880 - val_loss: 1.0699
 ## Epoch 36/50
-## 32/32 - 2s - 60ms/step - loss: 0.2812 - val_loss: 0.9639
+## 32/32 - 2s - 61ms/step - loss: 0.2770 - val_loss: 1.0195
 ## Epoch 37/50
-## 32/32 - 2s - 60ms/step - loss: 0.2769 - val_loss: 1.3830
+## 32/32 - 2s - 61ms/step - loss: 0.2669 - val_loss: 1.2986
 ## Epoch 38/50
-## 32/32 - 2s - 60ms/step - loss: 0.2629 - val_loss: 1.0656
+## 32/32 - 2s - 61ms/step - loss: 0.2544 - val_loss: 1.0411
 ## Epoch 39/50
-## 32/32 - 2s - 60ms/step - loss: 0.2439 - val_loss: 1.2142
+## 32/32 - 2s - 61ms/step - loss: 0.2383 - val_loss: 1.3622
 ## Epoch 40/50
-## 32/32 - 2s - 60ms/step - loss: 0.2417 - val_loss: 1.1791
+## 32/32 - 2s - 61ms/step - loss: 0.2378 - val_loss: 1.3429
 ## Epoch 41/50
-## 32/32 - 2s - 60ms/step - loss: 0.2437 - val_loss: 1.3024
+## 32/32 - 2s - 61ms/step - loss: 0.2439 - val_loss: 1.2881
 ## Epoch 42/50
-## 32/32 - 2s - 60ms/step - loss: 0.2340 - val_loss: 1.5025
+## 32/32 - 2s - 61ms/step - loss: 0.2406 - val_loss: 1.5361
 ## Epoch 43/50
-## 32/32 - 2s - 60ms/step - loss: 0.2330 - val_loss: 1.2029
+## 32/32 - 2s - 61ms/step - loss: 0.2417 - val_loss: 1.2444
 ## Epoch 44/50
-## 32/32 - 2s - 60ms/step - loss: 0.2228 - val_loss: 1.1434
+## 32/32 - 2s - 61ms/step - loss: 0.2275 - val_loss: 1.1713
 ## Epoch 45/50
-## 32/32 - 2s - 60ms/step - loss: 0.2169 - val_loss: 1.1099
+## 32/32 - 2s - 61ms/step - loss: 0.2168 - val_loss: 1.0482
 ## Epoch 46/50
-## 32/32 - 2s - 60ms/step - loss: 0.2116 - val_loss: 1.1009
+## 32/32 - 2s - 62ms/step - loss: 0.2096 - val_loss: 1.1095
 ## Epoch 47/50
-## 32/32 - 2s - 60ms/step - loss: 0.2059 - val_loss: 1.0853
+## 32/32 - 2s - 61ms/step - loss: 0.2026 - val_loss: 1.1017
 ## Epoch 48/50
-## 32/32 - 2s - 60ms/step - loss: 0.2004 - val_loss: 1.1272
+## 32/32 - 2s - 61ms/step - loss: 0.1986 - val_loss: 1.1087
 ## Epoch 49/50
-## 32/32 - 2s - 60ms/step - loss: 0.1924 - val_loss: 1.0651
+## 32/32 - 2s - 61ms/step - loss: 0.1924 - val_loss: 1.1055
 ## Epoch 50/50
-## 32/32 - 2s - 60ms/step - loss: 0.1844 - val_loss: 1.1039
+## 32/32 - 2s - 61ms/step - loss: 0.1846 - val_loss: 1.0708
 ```
 
 ## Visualize predictions
 
 
-```r
+``` r
 model <- load_model("models/oxford_segmentation.keras")
 # Generate predictions for all images in the validation set
 val_dataset <- get_dataset(
@@ -614,10 +614,10 @@ val_preds <- predict(model, val_dataset)
 ```
 
 ```
-## 32/32 - 3s - 83ms/step
+## 32/32 - 3s - 86ms/step
 ```
 
-```r
+``` r
 display_mask <- function(i) {
   # Quick utility to display a model's prediction.
   mask <- val_preds[i,,,] %>%
diff --git a/vignettes/examples/vision/oxford_pets_image_segmentation/unnamed-chunk-9-1.png b/vignettes/examples/vision/oxford_pets_image_segmentation/unnamed-chunk-9-1.png
index e10829279310f5614ba7ce8ffc58f43747d3696e..1f21754f9724b18a53c00b87029e64c3f93edc6f 100644
GIT binary patch
literal 36912
zcmeF2<x`vA7wv;fp`|z!io3gOad!z;+}+*X3IvL~YjJmXheB|QyA$;Cy)*ay{R4iP
zc_uSY^6Jb!>#XzHYey<6NTMR+BLe^cRB0(O6#xM0@xKQV`r{3aj0N?_7m|aNwle_m
z3H`qZYHr={1ppuhNQ;T6d1Rihn}Y}rJhz??P?P!G6|+TfO@0BSnW>lq)Ud@zp>33#
zmUtyoY<Whnzta({@8$}0-2;K+vS4?HaourF3WKkt;+{vsk)VcRTsSmh5mGeVkWsev
z`PQ9p=i#mHpBaT!$i;t-?A$yr=*_FX8trdwa;V#@f34ym4ndP)YFmR5MkH~uGd%n%
zO1;N?i%0@;!u9(2g$G4I6tRdP>^n(_%I6>PXreUXd6I~Ep#B-M5T&vI{r!I{^S>ha
z-}d<bQi9>%-T=fr_Icp+#032V^;~XucMjV1Y8TFjtnh||RO0!sN1WnGQ3Wppzc3H!
zgCBOPzx}5`Yx$ChofQ9^S~;wjGQ0jAmE`DG_hy=Z$dl3&%TV3Q>1w03qQT>7{WLO3
zinY`M#)nve{J1uKob!~pjkmowpEq#yJw&Wu*PN#r#!II%Oc6VOJ$0Y@Wq&2*-VKQo
z8>#*xlHVr=Cpw(YLMiROi2t65wVvlwa44QDVx7yWK2azkc<bAJLUi%3LXSFaG(GS!
z@@V!bMH<go&KlJa1qriVPiN{;y27&JA2N$06kY)@3BY##b$N_~!ChEdJPk@Lc!@SL
zshSCAb!&JG5#i=8QMsNlm=#$JLv)86>MK+ucc7FkLqjjMu^g0Pj8%dKbcj<CIe=L-
zn3^3`G=hYKSxLC;7cu~ph{9eZSDp$`jVWhNfJF(mPGg?`Bc~}vA)YVx)8TUn3|n$U
z<6ID{OfR_{-AZh5{M#52>sv9TR72)$rQW<%@=8LIrt@}A*5J58QQ!^}pzC#11lpNx
z)HEE7!h-m`o$h7c$Y*fU6lHs#w?1^gzdo$HZ$gN`kXn?qU%+>;v!C^GfT8DF1vNvU
z(oDJ%47vO&ls%PlK@$JWtjIDo!^<(BV%J%sn_1~Htma6B^JlXRv<+hq+XPP^`ltBd
zf2k%4#U|h|OK=`1{-oI&&jxCz*Dw32xyQqoWveVY=sOngO++C%(#tV~3;cEIP-z~R
zA;f@0X!ft`cI<3)<~q_PmOwUe7?mc<AvaQ?95<Vd1RUBJUUK2LP>^yoTbW2OJOu|y
zv8{DkB6*V@6S7ZngrS2_WC(2d53Z;(L;SU0qG>TQGme-n!4!_a5qoW;aR-R2Z8Jor
z(jx2@Tv5hQ=%jBSALU3*6nm%kWkD=uE`59cPmdS1T+^E-z!MBAg^%jpm~lH_gIqId
zy+GN4o6dY<D6y6*wd-xcO?(#}H)C`)y63g@C8i^=vtH`2!aR!4{Sb~MZ-m5ZYuYN)
z%Bu95<5e52)&qnmeJz7??jQLDZ_hA4-d}YDX}+al#i=@7`>x1nu#>4|v60zg(DgSW
zud)@32Lzi(slybiohw6pX3ndZ2zhLlnGSh;M&7RUA0kd6dlFY(PrLF}{Y@6gtG@@n
zDhsnKt0)bX?zRNKCRmqQGT^@?1iRRlbas(Cc?P4;*-W?UkJizbMT}Fkd6a-D@ae&G
zDudGGuS!bH+z3U~MKNe&t>PjGj-@>}^i#U>(sHndA=IY0VP{%%?7P$e8TxZ(R23Oy
zF}4&(y&_>c$nJr{xqwzQhVS71b*MESGi7Llz`@J=`Aq8#23@><aFtH;+rToKs9pOR
z=g?o<=Jorn;4fi@9^c{JvKf9HqB3+Xyz*H{i7?F9iaBRvNYaQJYYA{wgP38Lfiisa
zw02ha_O?~h-}OdbHa`s>M&KR(F@vX)jZ>b$ZKWqGPRVVt^)BeaY>sv$I2GcbWLrJK
zJ3pB`U|m(d^I;d#Dvyb8dnU?)*dOnw{dsV^*D{#PW~|`~YC?p@l9U;gruMV|7%n(E
z(`i2LXX1X*=Poh>zO2S>3(vYIq7(oCG7-$zgvVNH!?n;w4CAaQOVvzhW0`W<&df-Q
zF;uJxk-u{5?S}qI<QF9-DoMj76ElnxgH$LmFEdYQ#`r{V&@I!~m>IB+$xpuvYI|R=
zl&c5>%rbUV#-pg!=ctZ3vUvkH0yf_6n{zrZ>vLXDYmJAyAd~jl?^hiwv2k(PT?jTe
z`;)Tq-(rX67{xXp;=c=cJ%tCJv{j!l3Lc)E=;%J)&MO)?PjbM(zyO#zIh$I<Xk5?V
z32Xk-yrs6<i$Ye0ewI>92{cOK0I7W3B4Jbv5Fl4Atmuht@Ci!(R=oHDN#G^;eu$|S
z6>74q{1V5fo%%FSge^ytI3kQ(n*7R=^CYc8tYlqq6_IlHYNJm3c(hhVd-5S=5o|}e
z_Aqg~>F>WKwd*Cs+LIfmOld*_FKR5>KpvxPhBmchrN>3Vh8(h*<#y0(o~~Ir_X_!?
zYG@^nm|83kCAv+OH<M}3x<WnIBR}a$O8QlLX5h;Y(TLtO`AADv5;63_YICYUnL<UH
zo7Wyz0o16(nLlH~TDXu*yzE`(a%(+Lm;9@NK%S~V6(wZ)A5bhdW<Wgfoo>%Ui8$kL
zKI749z4jDS@*{^K$GFka(W2oT$b>mIHnzh1v2nbB+dcl%^K(u2+tc81mBz`3{%L2{
z&Y!s%yl-cFkJpC(o(a9v#3y$Vs~0fX<D^TC)0+s)pV+Bef|eEC!bq@C`uR6RDFHT&
z*Q>z!5q`Z}(E#H8udnAYA(mW=8|#iMvlsn@hNVorc~CwYSgcHwAwq(Toxj3UpkudR
z#d#ZgX0njcemb`i2<3H%mbJNSp5*B5Lcw%z1;o-T53OnUGh)L|ufrY{WA01MGOVrY
z^*#>Ook|94NH3#@4cd~)HW;OdBhzA|4JfyRk@`m5kN~Laq%RT$8JM3;`zOc9(M4fJ
zevuoZhlA=XGb|-t0itRXsU~Y2JYRPH?yUW6SVv7Hw`wYYNQknx#BF=J;lk-Gr?6>*
z6+tH^NW&8)^8*6|C4zTld*25${)*%1xeO(ue&h7n!N@7qs9<{Xk~zZZejJ?k*=Nh(
zvKREaJ!E>nW`YT@1@Ad|Ucre*tgNieKU^y+C@9FwcSgqXd~A5*pWRj$4L2P&Ws5L3
zFm>&um9ay{KYAU*fgL3V<j}$uZHjF-<ewwWP)N(DV<S0Bm;w>b<B7cUy=H~37N`F`
zFWMC7eVNFc(Ov1M){HHuzsJRsHK#EaXDlofYc`hWj@a3@^g=K8W?iwau8Y5C?6|ya
zNVPU^s%(_;9Vn#&Mv|*J$Cf7GE*PaSkP#J{foR#LwTo9~H~E;m7ne0-+_0jBmB(HB
zdX|Zkw7i;r&=`X_bz&<|+6JM{L*R|8B)~sc8|0Q7kxbBw8Zc`$Vt9X21Bm%|SE}Kq
z68~<20I@&1S0YG7gNWy1D!-MRd@81_(`7^+L|#E*u}O07k$>8o0fW8!QMo!MKApE7
zWNS9|vE5JQ=tG!%wm;QasoR@YpXmGEEk=KtdQ@rEeOo9IbbY+suJJvyHuo=3IsRnu
zD4IzAe18TLz~p-*+kL0gtxk=ZvP4#-8}&vy>==T^=XRQ!ojqk%Z-{%o+5MjRQlU>Z
zUMpTh!i$cW->)yo@n?<(Gk0^V{3g!C2_cPfIBAAAEhA$`SrWOb5LqsXd@Vr+10aOC
zU@qa(%I3;(2Rz={49$oKikG_YQgeoTrx}m;P^l=77lufsF*7%&s99G<8k+RA_}e%c
z7UyfA;$>{8etWJh74qr<{~05o^?s%EHH*eqcFTuC+y<$j53?Sr<Sw}B8nYj3O@Fsl
zYloaHPkCs_KZP*Q@4$26LG`;<*`byHB@V%`!!3p{Vpk5ypfq;O&#JU!qr2qG2dhP<
zfVf*Lp*zu@s<%jyq(xhd#gU<laCqOus!qJ_kFiYW*^SVmRDSAO7^%pU3{B*i$JBoD
z*@6+eGL7E$+X_VR*?gzjbnFj9723n!;Kel_jw5V;xgMW>n>!GEX#M@J|Ms3!%+Jbt
zJwg`0<~U3!E9>RurQk18rUrabqDhtkFmrI+rHA|a(f>H4nNLnIv#nEH2(4ZsHD1!;
z5=M}DH?sF5*81d`!XSA;$lq1}WXs9NgfC*}qQ$^IaJ36cU9E$RL9wvG8H&2wOo!~$
zq{qMQPAsi2N#N1E{{``BnI-d2w$Mt0h|pz<Vnk@fd6<#WsT*4iA9t&Q6`J^e>N;f!
zv8<$fSkdxuOo!9SSvlyb$}%KJ1s76;-ZsX7Vo>^v;A>?I3OV?=v^%GXd>_PD#<>pQ
zy`J&NCN(52_kdH6rwh8a`eH29a3L*eC~Psg>z2_Y%=8K5%D5`TQ5kE*DTbcevLebt
z3i;G=q5Bd9QFi1s!u1=;kp$G9nHWW`_Di)4wyI`80^IrZ<D2(!Z)<OgWP|H-_ipd2
z-U`|`&F-hgqG5ygr)2{OI1lG;K~=Fc@yj?{H8Lc*#%HhZ6O&Klh3l+1&e>-7rtA73
z8LY9faS72kE&Xwpldv-X)*G6|IwCLjhmuX;I&j4GLEimKRzn!AnWD&GovHTbWYy!$
z%pXSc$19%MQ&Ei61Z7&*>ue{6rYq69ccqtZA}VW1k%XN2cbQK`_V6$%b4JBkE-=HK
zt>Zl|NeW^d0ftE36?~C$(8*Ftu&K!nuc4o7u$%>a@58?FB}uMk73@wlL`)(X^Ts4{
z!^=~%1SXLmOPopfPvF}PR#yhFcHE%9r&Qr^f1mcW<GNf%!P@7OpI^{Vs`-9s&+)B<
z@}SgIiUzMhVTg2<M2%8}I%4N1^AK(kwmGQ+N^YnPrIQ}tB`IDbFi+Zp!AfDOxjuW=
zzzs1kMMPk_IFsFXb1CXtYaEfi_>JE)6nxEORr^@u;6D}oMX=>@D~|7++nDWU@%*dT
zCth#oE{zHb>1&K`M?1Uo+Y-q*0v@M-Bb^T$?G67havlc^UKb6Zdd8CJ>|6Iz{2#;p
zABX*4hKp)GfBu{@zIS)BTw~DfXSqEwF)_)t`EuF+rMt<0{KQ&Yp_!ydfJ~=-&z9|?
z?LI>2!0)a$+5dSOXZ)yzPr&vLXOKCk^2pcm4cb>tQU2>tM1rz_@4!P5D~hFrWW4jT
za_;D(@)D9f_?el{Ve`rMpT#1p^bAse@a_g&1tOJLupG2#Yr-fc`A<v$^e-Sre-mB)
zdeCPz>A;3ba)s2ds>R|J70Hw_D*5b<rTtQ*rP~puSZ?LgYr6i@M*=#mzpLmlNV$zC
z6-p9aLzT3_al~_xv@P({p7qWlU)QxuwegsC_mX80rGcb#35Arn#Z`@;MK>o++Em(9
z*os3x^Pa&5I83!FiS~@W>GJ%>kWncvR0}(^SU(Rd1l{oZA)7yU8FpIi#S`+~L#F-p
zWm+E_N=gp?JLe3nQ2E@?eXd8T6tnp~hpq<&0Oswl_h3c;sAPZ7j}^T*I}>m}F?a%e
z$H(XOxXeY{88>)i1^8Si<@l7@YqvU?RxI+m|L{^zF(v9;YScCz1l11|?DM{!d@CpN
zpWY>c*jp=R0gLcu*iZJ$cMg!be8|$=G~t-fqRl3cD-IJf?IJE`ZZf~laZM1wq_f<%
z7@bD&-p>qi2zfv5V9nNQtB&Lzab<fjb{mqhfKYxgMqbzPA3%9XfJ&)TXrW2clD=jE
z9h`tuA?zCIXt`l&)8xvzJ0Z+dHbeA3BE{hKB;>$Oc56#5F1F9d3>AHLzwsA1QK1V@
zhEqh$_N~R-SQ&F{m%y<SVJD#qstw1EdSR^u&K+G&6ivy=ljSXRBza~-#PD$ETKtI6
z%SM0+RwO0DKxov#%#|b#Bz1^NUMdT<()=IzCrKlD>RJA{-`nmFDK7VAa!kArj(&cA
z%hh^BZ<pbl4_yzPhhEh!J5hAiEk;*Y|JC&)5(_`S&pqA8-D_$-r_1pk&|#=fn@lDd
zN3Y;2^rK4S`Doey_HcF(Aztw5a3{F8w|BkWo#}jd`rzh^;N#Yjx3}Q)%Jf?w(@Wpc
zyc5Cjs0l0Ja-OKw6E(#&TAyuYVF9{7AD_A&PFgCy<2nAb&@I#U@9!Nn5vx`TqOz|)
z=jEHRLaurLL5$Hs<-@|<^>v~yj}s_(`b96eHg<@M6V+fG>FHt{7hwgBMVh{XQg4Te
z5v&H&(AFk-ju8|?xwN%EDCmw#Xx3071oE**62jMb^HUWHdSa1g@a}BFMH-~{Kj?A{
zA@l=%rZ@KOs|3|8KnbK}X-r^~A^t=?o1_8(hH}MxWW|lDA7*P<b~-K-<^&l=6~Q6~
z0*=Q0-(F!obZRv(hUnBa4G_i@ScETWGJ@I)-jGhM_=pu1-q;Y=oTi9?`cEcmoTWo@
zNWQi9p3Kl0Y~T_1CD5KLSie<M3qs+(>H8lL=zM(qw)r?DyH2yS=MKw|RbFpF!+2=g
zv7lBu?f-Ujuyf{iv4INcemm*@49I!AEg2pf!X{$UUR$g;*&9plzLo91?I(Ks1FXt+
z{5pB_Q+t0F!xr4O8@w<uDxF8&9AO1Dx_Mn48oeZVyWQQTPeYn`c#+e%(OfMS-Ni0{
zDAnXgotDYg6xTNqRNbgnao6|MZ2HXKX#D=JhU2MhpE!`|#H#CdW9MPSD7eVnnzkMK
zBuC}YE<wSv#!sf(j?G3WT2$L(waY<efL%xv9Ku?*j-oCDA^ZtKlc!G36{|Phk6IXy
z2jK^=q?WdjsTFL{;?((Nn(*XgXJzp6e3hJZYhSclq-_<gKl!45P1rr?^Qy$*d4TNg
zr4f~;om6Mum$o8iV!MxZI?e?CspHnXa!0ng!&w9zr&dt=d-mjqCN+s`k^XU&Zq?_q
z7|Z40!ZG)qXI-xpXSq38#dBr=Y#sQ&9T-5G{SSW-K?i91S)J^RrkHoUi7Ot6l(3=f
ziq~8(eJpd^N%akLfDj}}$P<F|pEqd)o()7|fSc`!@bI=XKau#o>=GHcE~<M!oD&ie
z2`!W9X8Yc<3(?<vl|~1kiXL(U+Jz%9+f-N3{4TzH{|hP6WS}<zvp&Qf)Vv<tDPq_|
zWBXvynxS*(nf~s46&g4uR`O43ht<u%+3XyBQ>cEGDsXK~RQn{)7FTcgD`jo!Zz02t
z?tXt7?EnXFUu4>%w5YBqG`_rogBk5_G}1IG;owo*{S?o?G5ysFu<AW>va)zf*1qSr
z)v|pt){~2T)n}kF9=mjb*0qEJrqhpWn7?0>77Z)jK0le=k{k_@z``NNohlp49y!#C
zj#>ErpiFpc91A(B=*p;>`}9N3ckNBSx;mi~?)3=fL^RO`G_fdZbj(z1<#1M2aYs!!
z<&mDz7PKMO*#taE{L7!Gt**PVT-k58Z_gL;2Cv`i_1fJ!`<C9<reD`qK2cFp>Ywp-
zEoM6M!vMOjV(kGv?p-%od$?24+%AVRBO@a_pmI4%Hh%uo=QoDtHKz|^mXiZTKVAKe
zp$$`*4LLn1PDGk;Dma-U2IG&E#OBR&{93d3<+T4F@%p}Y#f@9f4gahg2JI|!Yj0w5
zB>`#;JBZA*cnC%LcS%%ZqITZ}zfAAVES;9@0ZM?<RA+LqThWY<;oK7CgOgu79%07z
zH-W?hw4?i*;et`xJA(dy`26!fS*3R*z~r{)W`X#;ajpwcMgb~g6c;P24ymaXq1gwG
z?d!c=*><>Zmy+>rz#D!$hiLVK5^9U+3YcM_Ze(Dk-Lr1u)<F~K2SXa1HHNuEfLM=M
zZe$*tYrTY&LZ~;9f3KtGw1Z;nLIJhdx?PpVau7EE7Ch74KUAs$T$V&`jn&3$(P3q@
zko@Q@e>|>r7u6yasDRMW(D3hGha8pLrzeDrXGxxoDfjm=adB@~MDJJ8UHjq>(=Rtd
z?>E-o!_6%@khC06b${<H>yOISYs*kCndfmiL<9WiWw`uyxw1bFTh$VUtP?2u;YB(q
z(611*{4{tdptU^WHg9Aa2q->>5}0E5zM|R$ewx2nn;<xmJxyO9boF0@G*!pd2a=57
zay5MKE@?1}<B{mqh$QY&fL;EK`B_i9!(H+070uMNPz~2I_<*y96R0P!yq#rL4--3_
z&8c~(Vpk5gIl?_)BrFdPTS%Us;l_B6BR7<4DMeyYjA)3+{+&8JL-Ue=OxT6*7Kv-M
z>fV`&;PYzizy&*aJ1p=mE$t7mra9>GpU>&A5vQ6jPpfYzm&P5_#*B`2LnbmE%H7oB
zx0vCOnM&KefZ?EO`XnG4!rfD(T?Sh)@+^|k)W}$#(a^Y)olBoEk|Dkur!hqoJ1^?j
zp}o3rs-do!!7&)&GPTuK=L751#yX?HsE;F&5hn-l+c$5>U8PR*hbJuSNZdKGxQb%;
zzCY#hxZK)Fyy?K|_CPeZvO1O&dYT>Ys8pg}^Vp5O@Hyanf4g}1bEJ(s@YK|G9IT%f
zJmH=SL;VJifbao`FLK%)89&P1s1-<&OJRX57OWwRlFzOL$alpAWTAJwFS%!1HGFxQ
z9%j-&9+KL-KV@25^!@qs1xfF8wh_a<`j6ns^SyRSC8`YlPnN7`PJ?DQ`V9Z;XDYkx
zbV_6kQV^7*!G67Uy#E1@3d`6udr+&{j2%0YIq0zE=vzu2)@%Z78KOh54!Hs*NeD|W
z$b5|)xw*#TInSO)V(O-?i<8FlHj*m8bkShqIL31B<=?FsDSB{(*{3ju?CCm;Kz#wJ
zC42nqQ=by>gqDkG+26A;oEW=QJ5E{1%W0oYJI2)H6dkjyZ|8|dO))XsiVa^BrzM8~
z51v?}Lp72yXh3AyO?OBwa{v-!Y*L?X{+0e!R;$r!5l>d%_GFrS@3kZAsm1sC{^myF
zh?$R1r%K~ntVE~&X1n_Zgs3N5z*~P`f<=}#d9447kk8>v-qF!f>^CmKs{xE670Zv)
zQg-+lhbhYQoW9@JiX9NhAso)mgrN_xV<sjxmQPbjSCUAT?%6U5V}AZYwDy2-;U{L5
z*T`IS#p3DgYz8O9Q^bdQ-RHl?<2>E;Q${kEg1XoRMR8iUwf53skdTTp4?Y1?jnpNd
z5l;sH`Td+PQfT8Ep}u-Pi3|wMv1X$$jhxOr51Ryoz<kPWNmrh!lE4k$xX}1*2MeaK
zM(vN@@*jraRPTq2W-S#LPu#FicSCEpAoC2#FN%ajHwLLFW7#}yW9uz*9Y1f7k#8SQ
zZE$jbF-Sz7<Kc1=x|?{k)YOzTn_En-qJo<Zn*GiWO@HgPnU04ACetx?I9PY3VmbcZ
z1nZ2MUdJWf*hnNFv{5h(<L)lx0^NMO=k0a`pEUQ>^)6rcGwcOlkH+PGON?7AmVdbD
z$*HOD{2qV6XI%?L6pGz)S0~msHm4t0&l55U5qBi;&Te))Ta_tF95q2CktzGfeqa$2
zopKOnf^3D!s`i_sMfm0?PJkmc07OIKijJOqC^lF4BeiR1sekkN%-d;$fTR|AQS#`l
zPfOCdX~ZKE{5tU{<om}{&7B8H#NDzvV?BL6F@dURDF!MJ1vKTb=t#F*P1L$|$pxJs
zHwB5L<WHIvrBv?2Er?+YhI*0~`hQsf=W%wM@bpZ)>Qc^NiC`T#yA`ggm@sEK7^R_S
zT7g|vjO{)(^e{O#THosAnD#G1ZVvfnz`QkX6YQ~7#@a3s?|e=nPJ?cz`F*2mCF&0s
zj0}feN-nhnQTUB7QK+_-d?KNFr{M)XNVB^r(GFs|d-zNt?4&J8?pL4;H^{zMMIIBR
zbRtGB-L4)_7MSJVI3;KmzR^+LdA~Z&*iN4@85bK{rQi8vs_!{8eDCzpR+|1O;tyTV
zQz%ibS)S%5e8>cb&0qlJSA|31kwTyy$?4Fk6kEgtl#QVCfYzd=Y6-+q0Z<kF>jsa#
z7pIR<=3x^aL~?e!Hf-+lOUQ!*wOLf>uPgh3Apj{}jx(R5Ek3H?aVT~r36!DMS!42q
zeY|$(U8|-%2(@W#fpqoO*Q6Eg<q5Nh-oUS-X{^+^@Q$n#uK5t)Tq=b$`go{Th$C*w
zb@B?(_G#b-#}3$SXHt|wW1*2=(TJT+h<}->lD)uM+*o~naprtO(Kji}v{q;DGvTT`
zCRL=B2vV03a3s)VO|#)-Q>)e*%-qHf?2%G5og67&X)Uv%tidCse$e!KN|pk~y0Pru
z-+@s2Y-%PvG%!l$xKvJQcKM$)$C+ce9%D1#?(N_2Z%fF?$i_Jmxw?ufWIvXYA#3I~
z|80lqZKuSi=D!x_+wJRx|9e&QMw|1&luVJ+^I5Zj_gQ0&k9zboR`lQwk3jMR%6Q#L
zSkM@NJOph9g<UC8lNrzfZ6@OU8+wrxN(R?LN>m=xqFmcyy#2H9X<Ilbf_KbE^A&p?
zFM*BiSBQz-b<^(Vqs^V;jzxN@F_p7u8gZivG6+4B)A$CW?b?4Y+g@+Xx6B#7WB#f^
zb_;`yCaFgOyN$vQC+>oo9%k(IdE9Hwbr&Mx?KKwB&wZ5gDO8~XvP+dsaDI_dh9Hb&
zKJd^^D3qPHBsPvkk<FB9m$T7O%|3hi$<5B?4!OS$*>1||G~w;7dJ~Rhb=^%-(O$MR
z==bP9U6@-XMf+B@(nj9JmB?`}`^(^;rS3{!du`Zs&aY66{i@3w;zEBGTLR%J3fGUw
zyP($!{pIz~?$<Y8*Eh2=0$)rt^;{R7I`1+@dU|?V1>uhV)*1u;_`N<6et^)wwZSZA
z>68FnmsxRH)kpV_7wm9brt}J}Jku%LKW#{*?^?iItBdT44S7AwGsWbegd@z-oh+q)
zMPjo!(}__6DMQfnc_@~`1)XecnO<+3-3Ai7-3_0+KeyM}M_Gu*#p<!UKj1JLz7O}9
z))kbM1%6c$<{=B27@Qe4HSIUUdM&8hHCS`lk}YWp%OejNw9RS5uBCQ%X1_`bp^zSE
zF->inqM<Nd(E7{8={H&|gIOjJo=tcgqd|>~w{aNuiJ{hi(~#~8fg0tDNgSt`V&ooc
zw(3+BK2ShEqcN+}IyF1%tmc*y6Fxk=xELp&yxfGf@>?Tcpxy}nfD?BTE3@g$=Qim?
zCR~>;7Cvt7zgCS1Pw>_u(@biz%9-PPyjjH&7+~U*ZJ_6uFGF3N<^&e6ktq*tY8Sp2
z-j0cTD+=%96A!OA0*1#$m0Go=8I##vCVqbU)&fF8-3=y3x~*pQmfA~v8&`2`8g-8k
z13RWs2nYx`!_M>aTnv792ttp&M|9I>_1&-FZoMiE1bFzj%P;R}fRDa{B7^?h+ly9;
z4pN`w`>^->DcKi2hd}fJVqRD&b_-DyVt8TUQprp}z7z$a5l&#hQjA40(sH7{-^u8E
zlJA9(|Lar8Rr935Yt|$}tOL=sSK9`zz*AM;7@Vqt1}uwv3#>Q*1lpo{6-Qg0{Abbb
z<#4z3O=$A{HD2K@#w6wnH)0{3Tp8SCGu|kM9?4A_#B4Sz8utx#xo}bgbc5d%ksd`O
zQsr|saJx48Lvgs#ZIrD7ADWRsR@d)rDAdc{#pB=Q=;&faShf(a{)*Oj{XJyy4?2{k
zTRaCoc^2uMrNyRO+b3UYRmbWZm{=&wRw>)!mj1gvK}y+-dhUFb&I}`ixM<1IQOOzE
zz?zLML607naZ~emrG&oog*=nZY$oS`C(b-(sp;$na&C_-`x^y#YgGf+V7*+;w)E%2
zC6h~^w_kJ>QRp<=2{>#CGydMqbmz^|<m;PPY;1qn@ctku&*uL3<|j*4I>1FpB|rUq
zuNG;<vyT5wlmDiwT6u*bCOZOpFg!rMhjf6H|5FMQ&^kE)z6xnZ%vv64R!rB=Rxe&f
zssXm^<}tYu!}|6oyo5xHHLpqlLz;*PZMg_d5ysxfB~#E3G#u-Sqd$PjBHnIK-p%;L
zSOn53Ucx8ME1;H3UE1>C+^r^!7W%fDR%1OIYux$1r994QoB$gnH#vzzKd4sc*>jaF
zC8a{mZOwU4Si{NA#g}(J!fnh)C;4PV157v$5#ii*09!!(SdvVe^r-*o*$H~TnD8M7
z)Z0)di8)s3El1|6ft=I43P`YS>R$VU>7K?sr(&dzxj{1JDMYg3(A}}^cKWUL<~0{<
zt@F$=IKEeSY=_g`Y!c7#Le&m$S-zH0nnmC<Mt#>@jf&XVSVAE`0)RpdKL8|CboSv@
ze69wCCciNNJF1lhb2eKXcR!3v(}!Z;ynoV*o3UniWrn{qct5K1_soy)>!AD{urK_!
z*<OzE2g?N0z08#P7qKxkkWTyqXTlsFEeRVg(przyvL{6OEO<WzLNaiFQ7v`t!f{mq
z0x)PA;pNE}lSt?Xw8`kwS}caoD5I^aJjY%9Aghc99tspd<SbadU+H3g1f$&f6I&C1
zr~^w`H2JPMf|KmHaB26Q(q2Y-gf}?p_tn6CbTy^o+dWE6HJf}JQD?f2e@guKq7pPR
zZ3y($Q4^guk;^bk$bU1~uDbN@&nizhHTD9u2!<!)>|2}U#4z0&=kzHxkIHbx!u-N5
zfKF#VLs{9pqm5`$A$fJUlfUbMG*c0lwNgg=23ebstW6%{(X-}FzoET;K*baGAOH8a
z!RO&7@L5_~+RBPH;QvxQ-pRRWF1t04dwB~3nCTn8r{oT^@vmMIPB;(LW%){*o}22^
zLKo<%aDS0kp=SVUFce#s0+po|rKGuyQHY3D7)oL=@8|z$ndPi)#le~70kn7C$B76p
z8Hy&znO4Y{)Mzun;7Z80TCn(J_giyGzS)3GnoZfvQ3JLDA++hsY7~N|m=JTSNEu1L
zlpApMB4j<lq8y2gyjtL<yROxUpuWS_(ayz)5aLL+ASPC16wjn}@l!zsSx(=hmvX#`
z#y#TbE;(w!ISNv@U<5Ayr1|CZ!KXnDTt#)5np6Z)IdEo1#sMX_-Bj+3UGul~bI}TI
z8thP5(b7aD<6p47N(NsVGaJ8gTF9gw@uWx+JIPfi4QyjR%yxqYPkOTreSBU{Tk$@S
zjzJsNLbR8jLWD1B%8BphqeiuES>t)O*@xl%*z>-7)*iRBlK&y0*4Ebl4#d~%-`y|&
zain#-W-WRJMaADTMZ-k?K39sJ#E3z}9|S`wPbnh5^gokq<4!U#XN#miw!`Btg4QwN
z^X_cU!x$+1b60-KV>#2Ki!Avr!`~Sq^tv%$lwYIH6v`X$<rjd3yA}!_lwF_}yaa?v
zsx3}LzUV4L4Wcyy-WMCWR8~V9q~9A<Os-($wO1{oI`J-rSgfdBiOB}7G7I}1jp>Uw
zBI;zKV^<8VHCQzX)I3(d2_VD?+#E(tm*pn0ujcFTx-p^{%e=O}Dq_QXYu?9V(sk|O
ze>#Jw5_4ip$=^~3W(_%vrW%x1;*HY42X}Tx8fuJ%x)Iw%t5c7g5tbd*8xJ$B)_E4M
zvZT_c-S>%xr=bl;s#Z7j|8f|1bLO|hTk+X)2gVjtrmN^rc_w{Y56Z|mYf8V9D9UU^
zp+bhSV+J#z_vuW+!H{P&->GoguHNR43q7<>XZd>nn!ljvble@9@4l_*eyTVy@;EGU
zy#6=JcE0KKWV&gf_R%1$yx*Chot+iUb6D1Mw_T~*TQSc`RyqH$%~y=dY;0`cCR-hp
ztG+&Hz(;<_%+GJ5>CCNNkMZ}X(7u(E`1}Z!WA3T(7{l=t27pqP22M@1`;gD1vn`Ju
z1>*51)i2b~Y47>XFT`5Yt(_;Iz~^tLs$^Jf@EPb5E;`mff=LJRW=f>7h>b)6i()qi
zW?Y9J5u$}WFw?F~x0NeazM_cv_(@^uY3-iF6M-s4e7h9GIG`Q)gT2bM8Z|X^v@-f3
zhU}1Z36Wr2w><xaGyzV2*Z0V=Nez3J>?%r;bv6AGuLj3su;%*p>{*2(J~6)PVqjjj
zMm^YIWP-`XETnp{q1oNVn&rg0%aPgLDzB@2XYu%~H~U(5zS;K2*M3DKA?2j{Pr!1O
zT9<s_2_f#`lOWqpWAa==B+W7#Qb8>9&k(arIZQG564%X(DDK}3F^bbEH{yD<L$wno
znUP1*!2u^cpJK~<g#-J}Aj?0m?upHbGT;JUpC6ds>~*i(*6Md$9X~qkFPGs$y64Ir
zBCr49jFNe6{w}W}1JVC%VY|CMWztHQ?{jna>)BxF>+5T0XJ?q9MU|waBn}RKnB!2E
zad=pmg8vii{D#4(UE1K$&A6-4K;3D9=4>qy#hGeO`}Io2WD;*&*&NYxy|q3!7T_po
z4!}8sOT5GjtCWi@Oh_zaU5<Vb|9Ksed{#{PBluk%w}kqS(*wUnLuFXsz`4llJcQp7
z*JunEVavOGXjYpBNC<s9Vq=~6&EezbBO<m(@4H#0<9CuZ>r2FQ+e6hp5Q935g0DLN
z+%4#}4W4|2g8}-X?XyWtIj}aVS51v@U*T#iPgPR`-Ao-ETFiMlHBfF>70ed5g!?9a
zmgw%ybRtV{Z{y-n<QK}Ti1`II>CD(-sH54Yb44Q*NW<$^fK?kh$FYgIOXb1Vjc{r*
z6w(paJb2ScF6iQ-LD_nxK=>RyAs!BveeJm4cl6W!7{&VaIO{CeZKqKJl73_T@kjSB
zH`Co}!o$PEI8^cs`e&XrC(#JT1=MZtAGo#q{cZTWw_M57=ZSHr-LF<P+IZh<Fcu-f
z4^wYf?%U73UikF}km!@Q6G@_W4`Wp*l2!PKKwNwy08*4JGV$K>XT5cc<N#_JJXuh&
zn6*qJF&P}6YQ&ex8HJZVpAy$P18Vt*2`%;mX>_ZI@|51=;o(5aRv&%Z!=saojEbdw
z1-*xA_rsKOSE>5@mvCgoH`TKrcjjFOJQ-}E+YG^qsw+({m3S456Q(wPE_vtaE5sHk
zIL>!ksQYY9wk_JULDjIwrgeEtHp^}{in9@v%Je~>!ZatFeUWpqCKwv{m-E5|){$7_
z4ovGMn%6&*PCJo`zEWS;c}iO@O>%+Psj&yMjS7vc3J2vS0vw(H)P$$Ik@j~vegBaD
zQnt9_x7C{0RcoE;VZr;_Wn=CdJ=`!^XF4Rm(ww~|9s4`q_YYL3T&tzq7P+O_b)6jV
z|FRoX`(Xi}!JBVQXht7CPf-x^?|88Sr@5@z77YKsIXz!j`Ni)tIC?YnJm>OS_k<D;
z&j=^A%}Yl9;{EnXQt;PlD6`?%>v}um>%*`Bal#U8gg(LdK(Qo1gX_<yAp2O`W#I;L
z85(I}D2uX)VwONTIM(IQmyK9B=%exgV#-Zruj45&rI7d8HtM;S@ykM<ueX{VGrM=P
z@~;W~cDMDV$!|NvJIN~3BUTl4I|4%QQ-?ivMd;(&%@hxRk6BFtl<vXRn>OktB|`@#
zif7($0SLV=RP3P7!NTZswu=}t&aFQ3hg};gC{G@?`pC1$M$u;byl;m;iA?2ai?GzJ
z8(V>>0qH5b;fABG-cj1fTRhoH!0|t|%oqcDW#Wo?f6s<edKu<jSktu1QjhJ{zD7=S
zV;P3b(@neGXQs_h2l1>c@~^g@y1TuNND9_<z<2~de}ov?JveGV0CrK?Ez$eqFw}o7
zcP;+VD75SBhYs^A*$<kx@$sCmxBspdr!A^nJ_-5dqWL|A>NZ3x6pD4Nq=tYru*UUZ
z_xGuGPKGKiDDq~{+wSGQA10)sVILsOjt0mt@Uq@|`0R&<I3KB@lAsl+M*lTeSQb!b
zixFUz&GlJRhZKndOGV;)jU&fW)pu^5gLj8_WrMc4V_U@COAp^okDF%%D)?r*vHOQB
zpERgZOoZT+mtNoIgQpt(`lrxOnx1i{CaEqhx4~}q6!US%te4L0GDe$ofoHgTK@6Bj
zXLL|pMLz}Hl*<MjCxSAvEM2gJ#8|(FZI1%y<H`D@hdBC~8F6F<AVCK$#nv?P6_X07
zQuFL`61&{@a#ZF(5mHi;LQ1(L;=|Rj6&=O{xp-9>l=-c=nWHzI8};_Lr<3XGmz{~$
z-*q2XmjBu;hlI2abK@B!M>@CBU2k_aOag+wz%x81<Cwi0h_gTkSCDDz6=LAro=<}(
zd&(yBGNO^9RXvMV;<Hsp3N@6!>-*vf!rQ}sto#h6aDK%5<JFx3e;r*MgADs@Zjl-g
zBN@$%^<#1c+tk${k~5I(lb-a`5olq%9iDQttF`{4H=uaNr$PJh+xeA~ivSuyLE+0C
zikP$-qsglh%kZBwpVHu%G(7FbqnS4%J<HA4-i&5h@Yt2b^k22X`bZUL3=FvFn8lMs
zXXGp4SuAYAV<H}Y4vGey2`>gXIMN{E^h(G{7#$wCLfojZ8;x9KgVm)^!pmD>a%sFM
z(=b)FaW!nQ;#+KrhRfuoL!;Ru_CA9`GJ4Dvu{3+pTGOjn$$Hv2&-Vif^e1KX11*w_
zQEDuV<fp1%npQu5ZhhE1K5xk5<Kv3+!AI$?L)Bi}Gv0amengY5{vO>e8*ExN0;`rv
zgkFzi8CyeGnp`Xk2G0WKo8b{jm|P$3LE0(b;sZ!sDBVv=8h^<ti#E)Ke0rX9s;kMP
zY*UFccqjVpyZb<dN8OMeFXQj{b9r<Xpx5!uCZC-|jIIi#01HqDsZs<h{FaI*{411d
z*?lqguZ`hF==-B4e)E^__uRP4x^<1&h*&g2aaeiU;dp5;Q9j#x7g`2<g6CCJRE`5@
z$ct+ub-mfiIWN=hJ{Q{;UV{_03bwC@m$Syh^}6YXzvylWaKA8uL6#ippa~OiuWTN-
zKZy)tGI)JNv~6jXJc65!c)!TgY~k<^b*n~86K+m-7gry}1eO9VSD{lQcvCDr^v$Z3
zE1o*iN_DIMS}YOHBV&}&(3m?r$5xxC?advp9Cw)3Gb=gb%1>n%7RMy__QBiD8u&r>
z%r}iqX6@hh?WZOtI{NN11-~HBy%)QE0Q!&czy}DmX+^jms-s_SQ)YhnOX69i7WD0-
z^-&A2AWa{`kfw#6YB{dub9lpbt525H%b`MBZcGuNROgWfD^r#t!&apR3;a41X{1R}
z;<pzUptUYH^RjAiF}0#W9?{F2y_ae@B5hVT|F(6uteBnNVKlBZz4c2yf3a5E(vjKu
z157j11kz@G(*|1-kTtGGLaYpU-UrAO!^Hlf0BQELjGxAo-A^p?Y{@C8I43RKtPJlK
zVa|%tM78x537#;eNDQbkorg=Xm*PGzt#<vWS_#F*GU`%4h;gJfF6v?L6Kg7%aqifV
z<Fp22%-^hyu?(yE=~yV+FCw|Z1+G{nLq(Fd&RC(#EXP#Bl<)6bLte(^SAFT2QFe=H
z-v^oeu97wVzTUsepIvVE7pWY5%xnnm`kh<MUQe{;C6DK01Oe1#tgB)P-e<%)FCw~E
zc6D^F)vQ@%3qfo`F>E^RHc65Z<RMmNy(aW?GE%uAR;m?JG=qrSR8(BC{Fd5rU&Y^-
zHM`M;uXfIKXyX_`Ug&1rVXh8V`|H1T9WQH#C6;5z2kDz95AM&k`8sANJb{#B_J3@I
z@)$`Nk*Izo9I;!`A{7wmHTosnEcTPXZO5B!`tJ6O{JEMiu^ucR8*N>ggk&GJMiRX;
z69DUbnb%e8F&w$h&qme1Wfh22)HGYLAcH=wGMIO0?SE*ownf9x!ME=vV=lVjv{W7H
zJ?GluI}MA~rWmp*Pn48iL^eVLCp76H=hZ5O&UJkH`-!IXfx2&g0F~93D#4?D@e9(O
z=eG|A%#{65n`??4mbc5jtm`~!-NJLJ(_1&uHf!_(Y2M?jK+dh_<r)xj=6M!&dlUF^
z%vDn#-8s-fEbC1uinwcbIiXh9T@6P83&4abYlR=8F<fY9%xcVvNwrrGxac?&<q^1<
z^zO!3v9@nJPh1?NcjvFM-%2yTPZvLF%cxGvNgo9vc;p#Ox`^2dDrR2wOTJps*)_|L
z&rRevk)cDa9Wgw7#8FLuymnT{Je=||gRO9om%=zsA+;-!b|-bRoM=<<Phr{Ij}uL?
z)-Ye=?^6&XMj@WT<7HNK5&d}^8(w@$>85&Y*uX$L_+GK9SvmBtgCXcj(w*y!sqo};
zbrHq2wqS_piYB=MqnlBxB|?*JduydN&!oa|ZA;Do>k)g#WN*jsaUE5RBoFp`ly<i>
z8P&ayXkiWG1KI{b)Zoq>588#-a{CVV`PqM;!2&JW6ys?9h{Ftndb{?g@xb}jKkGD}
zoD}J|%2IX=wat1A7dhkx^4BtOMx=Sg+eEgwpAuTD1`rq1t@0zpDJ+P4Y(L-RQ9&vv
zh&)d>{az;K%`ErMz%n|UH}%_}6dT&5J9+)*!5fT-zApyvwy$A}VMN*doGX^}x}J+z
zRcUE*iYtWY(HK){Whja;wX#@cP7C%mf1R#A4G$f}6Kyuw_4~L=1Q4QSGqw39uPyi#
z)ReFaa=GuNOd_R{{df($eC57flS4#7N#Uvt)iD2)np3*O5d6>9zUjN2J`Z&uolM4E
z&rklju`vhHvwcy9ve4?}MOfY}V)KjyP}3gfwK$v@dEMgKKtgw2$-y>NHa@M=P-4?2
ztpmnbQS&wB>(Mep0O;IfbkAvAAcQMim$BI;9%}kV@MZf8lVH~eIrQJ5u-N#+Y4g|N
zBYeDUZTtSk+x~m;vg@*E_SyME=SA0|K83pF=AQgU#HbN^W=IGM8nFN@C0aUsUkJQt
z@lm!pZeJoo*bywEbO}-x(r(Bv)NpPSN1O2|Q`sfP<igd84d3=>rxeJKd9Rld5*9*m
zjq&0^jrX+oap&eiYk7^r>(s@IUq7=K>}(gbLr{ye#0hKJC48I-02hD=1qJXLkGS2V
z`2#h^V&S1_Tl&@TdB>vgysQT^5Fi5kCq|g~Z}|a36VFTtVq)Q0?gG=>zIPIN+?3!`
zV~yY7YMb>#_1RZcqe{i;7!uM*H8-yY0<Ye^is-@P5Jkw<^!tsi{p;1=FL{x9J#qjA
z<3t(~Vdu6&O(%gG!M2m8dPM;sE`x>e$dQW74E0u9wd=D!OU00u65<eD=)glL+MZKy
ztZ==ybx?NknDPjPG(J;uO=Ttab!#97akdE?9)aWe4X|Eas>FB`idiAU3+-oU&#x<L
zg{Z+7U^^8>!|Yic&Rj_iec-@&SxOO2P+g;$EXU<!rsO;NZ)7}PCR6`eBJOOR*L|#>
zPlwk(wGfJh$4vB9XV4n4b1h(qVYi@P+;@XQ4rBiP@%dx*9d9t`rfZt)Fh>gth7v6t
zjTi|U@5)cqBogf^yoRspfY!eKU7vK~4M*^W$nX6JM*9T;oqVRi)x=TB)W0YiA>;))
zPHCm~`6P1Re_QL&Me?HVlM2GxX39i}!otuZ<g1zy`54kix3*#E*&)T4v6FU5xGFAt
zbIEWFS;>`Zlo_r*@27oxUAA2Kv3GmpZFYs%X<=t9xb`_KDf5kW3155_$crwx7sn7&
zu(Z|709k&-#>CRx)<6)Y2tfs`VIeV+xSEvKnCX1>g}7k{P-k9*8eqe@RM(mh%2DF^
z5^Q2)qpL3GfKhX3Un+P%wi`A^+X%GPlHv4(V<sq|B0w8~gSYXd@!UxwO~>I62|)aq
z&PMtfKunBFuA9w%3#O{7MMgtGS`lic#7_{$B~LX#MEez5`PHW=XqnReY&<S*HQxW!
z|9xRJ2U6soAyK~kQ8_j>`_ZHdnEkiB!TSEOO*Yz!Bs)6{?IS7Ax4z*b-of?@0NATl
zo*q$~xSJ^>Z_ghRP6bDg>sw{^gnS<0GdUJ68+Hx>)iI#5e~&T#oJl2jqDa)b9UF;W
zr<ZsnddWelsNU%UU!=utdrFx)vL}@c+5vGSMGSKvaonWZDpJrd6?Ju7G)64jVQHiC
zQvCR&RA(l+9csiMn<}%$@r3P8uJW>Yx+?8%if8~VBx6W%tgyaN(;M=BnBUUP%}u{b
z-6&lEN!1~i{74jUI|67(yN1a3mFRF;kk)H6jPz@OaiCiX+{uq^qjNxE5;+X)KT*VV
z@{Kb1&b0@ods6QnbRvw)_YTO*Lf5OVilHS~=w*KP{c*#8_hAhZEnAoW_e_H_>&SND
z_cYUGgi{`b-7u0Ur#&Fk8AWCFlW1d1v~%A{#4*aRxsNWD%hmCdS985a14wkJ5-1$9
zA!P~@{<9Gw1lcPvqtukfsVmz#y3uSQd4@J&XD~Kpv}OOKO;<<~p?&%s2|$@CYK&!8
z@R3L;gcC^}GYct3i^$OXEmc=s&H~4yM}u9*GoJ4lL;($el@>iKCioI3;Ob>q)N1~q
zTdSoOId<oQ8A)yut8ucw7VQfLZ@BeSg6Wuq-*Nn}aQajJjM7DIgM@`OzG6#SaQ?7+
zHH1YmO3n>B(i9p5Zle7{8bK82-yz*5-U)zCTpn%0TqX&Mib(UW9m-$~5gXVhB6w6W
z^I-&;LPhkXHNTxs>*;r#qFS}(N#jo1W*rZ5>-@hgfVQ?)5EOM0MSO7UYkdJ<agxM0
zHLARm>p!8a7*3{K)0)4^iAjFd2V!@l77XpI64zub&eoZZ?sdeGfk#p@?i%~+$(#$4
zCY!pKn(12Cs?X}j$5Ex_IZj1}zpKf^KzX_O@kEODiZD62=qaA0B+Gn_@p_HJ2~C~t
zx_~6)u&S$vK@*EQfaAnOL#2*86)1ksao{<YpFGXZ|6W>t0yj-hOif_cr&RtLQ8dv5
zUaSmS?U_Zd5r<^!{M-77A^!M!v%?}N==bCKdJ@zt-=!GVkRL8(5-o);#kfo8z1zsK
zjao`HQ$hr_-GTP9fDEGx7=uLvh?LSLnjO}|scoYf>&e4V6~1k~suHcZkGH6-k~#`U
zN&XU!7}C_h=d)W`wpbP>U0wq3VqlcRKd-kC(}8N$XifQ)TN=odhoOwgi%8MF3OH8#
z@c})fyh||1e)xhx(D|g|i1ekDc%61a*5yYoZ*+<VrP%e}&C{#jFFwPgE_Xg`1?nZ~
zVCEx4mU=2z?V71Ld87SPDa%h*Xt^X~ih-7&RhySOYO5?yNpoC7bHaAsh7F{oNw0Qm
zgYn+7M)T~-@CY{2KawyqaWb<S*P<*KK|XYAkDE7F=+?G3b8fZCW6pHv;@@~$Y8G_A
zgduwDk*)bBz86gNvQ=-NqmyTtB>&ckQetl7<C@ld95fU|mFtCu6V)&)f*IN9+T;@k
z7kB2^%X2^gMI91=E37JBN6sz17HH1po{U5ER#vm&2Id}iqCb%G{LEs%3(k2v`6AFF
zi}8B5l;L-dBf~b>w-z#!J1=uxuxmSgp<9MTdLU8n<vF0E^MiFOanKv*a7Q(a79;uc
z9+^?`{pM;s9ULZo$W1aJ^*dqR-af5V!FJ{9a5kE1DpN~K7sRY{Ho&TqUDiyniL~3I
zi&y3j1O(~QX0S?aHf2;{7y5M0H>m|v(YthZ7`mnt(x;YNqJK;|KfPv?C;a_{XYROz
z5lM|3z-=Z90AOmDo7|0Fu?*i>$jPg#%grm@-Zh3oWTUy%5BFQGA=FX&o+26Fc=G`O
z!FR~?7x7Wb8uGAT`f0+~y>m<*ahz^*!uxdq+|zCY7{wvFMpQ%nPVTo>in2|F07p5*
z0|IDRBPe1iuUZQyX_fqexB6xd-07d{a8>v!g<6v@>+cXgD(kFB(8mBk^UMCn#2a7M
zN5q`Mbk0)K9;{abo=%Pe^45F3skl3K0w+o&$N$<5Pc2L5jHXb;pjdOY+DM_uv&Rq9
zo^tj%F%}&i0WRI@8JYjVc0jrq3_NaSdPaQR*8{&A{Y4M)WT8)%L^u~tnlxK(j5PR8
zBgOp{-I6z0rur~k5EJFcoZB<m;<ZGH>V(OvU8=mc79AYB3xCcal&El$FgijQjX1Pi
z%s!fw(X_EJJTxV{ev2AW8a|hk25lsZ#9{GDtgqc(nZe7T!RejX$5vFS!)sXi2WLp#
zXxB4}E7V^x0jGj91})|o70A`+DCbL?x~2CSPb|G}H$*jz7?U-SL~e;pd@dOC@i?9z
z+6QmktEG#)r0RUv;6s<*6EwR+1zqR#bV1}|QEc87y_;_<;Tu<P2JUN!vR1qXYAC@_
zj4&dY;%Hc*(!T$Prn3x+t82FK;2s9|V1v86y9al-;O?%$-8HyF@Zbb@ch}$$+~Lmq
zRo$O+s^-tGv+H!PUh8SMq6SmlB<^qcySs=dOjWJj54)%zH!q9P6N;s+@;(JMqP3)h
zb6V%axf(X(69exbJLfuFKK@?IW@slegbAXx$0(u@-*Mp}cOnxbnH4ed+WdUv+^Vm5
z_V<hOh)ob+poY^q_~13IFJ?OrUm=cCj8asX#Z(#>AuHA750k_%?*t?`D2L17Y6Xs4
z9xntuALb6{)Ufd@El~@Z$Byg^=h+>+#&z?+M!nXaToqn=7N)OU^haE5cAQ=XxI;lS
zU;64^pCaUuBVXtdVTLj&0*5h;;URjVov1x03IJO0X&|x!TfyLnZ_;jC52iR$t45}R
zk^4)BSbR6u7cJb+gI!@`^@DjU9Jf0JIFU9V(>)(HcN-_REFFT!tKIYNFRY?J9_*0#
zUl&d=_LFsu`;OxCCemc9{n)Q{<8!&kh2FzH#%>-m>x0ynVx$#t3jX<oh@fA~$=Rsx
zlZQhOm6l~alvod)uN1KT8!z41vpo~PUl3hiI84cFZ))P_<JmfWhzcLH3WYfJfP?G<
zDXL9|!p9x3H3z|wm<66y4&cJ2SZB%dG&ZT>Qn+Wp#}RRbvKwrc(*Qa7Y8dv(5qg#P
z=rm-%n|b+NlrjYUi*kKFaz0(CFqV4?D$rvLc-(^G;_0VRrFTLXB<$k@jvB+m`~Vom
z{Q@8Z0KXxBqlN;5RYYho`+B&A>q#2W2V=<3Y__{ozGkkN+-YGFpyM;Cn+HF>{oAy4
zK01iYeEgh(dyFk>?b`K+ghj58Hhd}57kXFC!bq2-%(hfos5pA$dv#bZGhz*J8?<kI
z|1I>ZAL9Ndb74$WHMJgYWQlCbVrV`}%b-pb!@Z(>b<0(w^*s>j-x_iI`>v^_{bp-`
zO|59M0pgF_Xe1i-ZS5L8ZJUfr^rvomI=ul|T7t-*_NZf-gnhWCJ5*@<qLLYi1=0=j
zj?)g~Km_b@_!HJU7@^kRlBTCT{<wD&u0s=tuOT9>^NS#_nbncf8a*dxU(q%zGA=Ii
z|1xhs7<S<z{d<IL5twYkXFZ`I2LlO5{#ArdiLgp=mm~;_K$t^<PfT2z8KOY^7l_AF
z7g}~^03HKH0#MrI>t=O*TH*h`{dr)M30<N`Iz0PxcS-S6>|CNQU_N_ji0(`di3Uc@
z;g{R=)SqOkw2sL)K7TegE~M|=ta)rhjU<IWB8PZAf<ggbq%|Ti!&c|#iwKU2HZica
z3GVPYN>Yq-V30!_{x+Y{eXZ4Cy?-#8e#lLHG&7JoV$@$@P0~Sa_v89nrRZu~vU_K)
za73LtpuEMppW%#9oGI(I;I!$?$@dE`Onz%*3<xQaxW`O=)R605b_ScKh`Fg($<Z38
zk&{smLJ00O@?T3iNTAKa0!f#ZeEU7=5B2MbQh&Nfw2jn${=_xB<Nz*_Ix3uD*L;#$
z;|`L91O#aUc=DCcUEwbNrVD^X!+=MUEclj44>9!ZNgh=UZv@_09<M(%1MzNX`>i&B
zkMvn{?-E^K$57ACVY8=8(?xg$7gM6b!GZhtx1aR@CwUcKJOT##(e01pd`N{YUX_8I
z<vMztp=fsZGFps5z1_DP$wg_2FVc<vR*%2qDyplT85wX3b373!D$f1juDAp4&*L&P
zhx2$mtgTRv$KLkvg`Kw12K5r><1W@8yffg{QYQU7utJqN%_Z0s&$5VqC`+qrqhQEJ
z&;zeSnA2JNXOa=V2r$8SHAzNg+6E_<YRRCFI@3o)iT`ez%cfP|>s<s%C=1i7_jS&k
zKJ<ld4wTq)u9_tWz=%oLVFDasnXyAjf_`h2e3+7^u)q<HK~hU6kx&A@Cm2Rb#DF8-
zk?^D>p*eE~9nX_x`N94IdR=`}hH;QR^$m>j^R$2GG`ubBDf6pJ&F(kYsJeP>ByF27
z{*hG5$HYv>OsO@T%+k&usIY9>tvdCt+Xpz+ptT#g*i~dDMLm=KNs@<G5M6gCg|((^
z=7zuWSrmv0{W4+yh~8~;_OR}8F+pGj0TI87p=9{G;dc_<fzN|*A0nz1V{)Tw8?Q>W
znXAK&r$O-;BF4l$e`bO;3)ScfyPJe+7|;pR)q9iKkzeQ>_9sV#$<VU(nbtSyF89Xl
zQH$5OesrdX>|DjNdgTu96V^tbWYZ$9>7a^f|A)r@w10cf{mvi%C6a!n*xp2$!Aqi&
zj$}j_g)&n?i^YV53rqPWrTvnJhSd8zBkYt+=|?Oq3`{f6MEF92nFRgI_w+#4rlPKt
z&6%@f4fC=4Z*Rc+ZO^{xJXEV3buMqGOM}Wvqlc2enXmi$QX9EPCb=mT-$ZJ3g=kyr
zEVTvwPK2$Z6Gx}HJ(EcJg?Cb6t3W(j+qxS%M2OZ1s}=85a{AKNlc~ehAsf(1Q`~R;
zD1m|A_B)Lji~K-+43;XkWt~ATqozx^HNM%5oq2=C+Kk=wyjt!&w+U$|cX~ZKw&lEq
zB~Th3Ca-@V2=&)>%?5`FMT60>v-7vIL$l5Js$Q)nDhZ0-^8CH6-;P#^@&L4vF;D)K
zl02lgkoO(#Z!1ny2WrkL>akxbow)p59^ISv|ErqR!s`ohdX;cN$fnTT$!gWqBr!(+
zIFzX6>$y3Be**Pm6AHyhaZU9cH<<%>guji>V07<m3cV@WBZr!w|Mq)RCI0-8|Jk+O
znD6iVsPrytI-?u#q833xm;3|zS2aRKEbIPZn-xz@A_x@RCIVf&vVE*sS#<A@bh0#6
zqYH>9&*$yd{L1iR%>M6Pb^P9(FDG|hXG|;dN+ycmpd7PdaQ%VED2tYyriOamvaEF2
znTRW6EVYo@+T918L{AdjvrZ~@>`U5X3Jbd}Ca5o>245%E*J7kMh6jHme1X3S8&ALc
zN$O-MMeodKyn~JaC!#VDSJ~d}a@FG6-D@?#PxTXQW~4TLP6@{B^f@B@t?gm@`JYD@
zYu@O88Iy1Z<77?2ewrYlIDaGwIzUX`6p;lJJ~(2&ZeW)Oz;NlmJ_rdlgeFdZ_kBmI
zU>GC+RdMF>ICYkaIRCR>Y04*ZP1oUL;Pb)9=wj|zU+8bn`%Q~x7v*PBPmW*{U88B0
zbh%0%Mk*9gCbBq5Ezfc;JcjqoO)*HkDV0rAB_bs{EFntQzN3hplW|m%ZjVaSniHm>
zR>S3_i_d~=IT0c=C-)%GyMU`lXBpG{YJ-#O3$J_6(eIkPGjjj8Jqnq5`tcoQH6lC&
zUsUp?Q}+E(3FY4JdbHT4*b+g+KSXi{5TchgHKl&#b6|;qMH|56F>1S&K5eN}QfOu7
zsYi!FPv-c+Pg`EJP1Z(+5`u05Ggj@%k*$eXDFg5~P3UPSq|X1o>IcKY>*2$?L%@R_
zZg}>OVot9=cyY7y(1QpuVv=S63TQNFJ@^P}oYBLXcU{!GpFl|T2#~f>$}ijjnaXeM
zRYleX$gzdiQ~JN#-Fa6HI`1za2~5s5(P&ECdss@v?`%kOAuFJUx;2^nFRDEEZCy{+
z3tI?lYpWKO%|C0xX5aeubdHAyzL|m`Na6^$Iy&{T@vCstny+RUUfr+gl<Y?1r;4MB
z{HOoY8#pW#W|Xmdk>}o{Yt&b}_i!CQa#`DCHW|>GscE<EFX?X1VC4LW>2N9rf9yv#
zCPGbgq|-n$OB4&%uX$JFTEe(eP<&2&t4%>@Jk7hV<dh=d?0^0521Mv@D1;JCtSm%B
zPW5V_tX*$gt6h~2q4Gy>1EBd$TR~@s@io!Kl|ERTrmNV_1MWo|zU}=|x4l6$<Mz5M
zz<{8}fN>rJiW9>3!Wadsl9CLE!h->VkRm~(L6p+yL)tq}TFNO+5@zF_x4BfrA4-rk
zQcNDI&{vs_IHySDs|}?Jj#Y)Mkm8UMZZ8UK*MkAy866H9wVRWomX|~4725T&TojD+
zFj{pt{iNcm_4Kz`wcX!HG^bWWg0iMCsFCS5%iVMq)-z?`7=~>;%q3jj%l9Lkm7}?1
zs0rxemvk8r`gMwOqrG^O$tr^?iqJ(#qxKywn%3O9s3V}p_+x^Z%QH$%#$sbFU<X)A
zlB(C5Ehr5of$4PSTtE3d&dmYP-G*ajuq~>LIMxINVZKh|6B@948dU}~sf5G&y4o0A
z+G~^lO)HNyI_v4r{=gCYKI3$?-7o2a30{x>k39e3S`6@6vNvpz2am&y_+<#EK!vm^
z%JB6OFo6Ixc+%Y%C<GD&s6Yq{APq7`MySjV3n^w`MAROL%lk=F6I+8kq}{FzXG&vU
zGJ79Vv%jRO?b-?gC`AbB=J&cPcjDF4H-7&b!heZs1Q}IAIC4M-v$BTE@Avk!B(Scb
zv`p0+vvfAOf+AT}B&-P@g~wt>g{_9sA=P5EV2DQe`+khoj$X$Ua=eD*q$!?_;YSAv
zKc`@c7Yt4gF5dgCw>~3;C8R|UdRJA9hVKXmd3JHkfPS8Zf^yn(zo_|<Ncabum2BI&
z&xgVllvKu9*41*FgYY?imfoZ$s<f!t@zoZ{<eh0-AkJz9@hh%OVz<kb&412GF}c(Q
zT)Ll8W_PB54~8^}83G_o9SQ)5P~$>Fo08)G0E3Ilfh5u(0O{XQmvE4}R1z(&X{2;;
zAPG;8&2qRFk(|c5@>zitvCnY!wzctoSI;v6p9?<h=+pVs$w|E(Ukj@O+@LmFE~>uN
ztzDs|qwJ6<DCFP9$@2<Q%hh^^>)yhOC&w{d&{<qeTr*AjIdg<*aH1(y>gmAhchj!L
zZwapJ8)lp~&;~{%yTgh_#N>fR8L%>}I<$1wvhMxhFcf_{GTU}bzcZ)mnQP<f9tr$+
zbW5bnh~VL={P$BXVoi=)oXgeRDQrEG=?QB@7gqrkc<CH!O?feho3JF??{Q~daoNo(
zkT;@jm81*DZlTBs2VCn>AI)G+ipTR^a{k-FeeDbrF=ANX523f+BQ_T{_MXw`VPMQx
zauT^XQp;C_9TrR=YXVT7`v6`T5M*W)8VbS~f(h+a6Xy!&M*{$+06RGnwSgHF;LP|J
z=Z1$;pNB;-qa|19^YSn(tSUWT=b>R<8yzv_77l89`DY0FSR%6;&mGNC;pYTJwe~7Q
zvZ-m24DjWB>-=1;W_1x0PbLbdZ^otG*lf-lvmul+W<Le=AfMju7tZWqr^%&qp{O6k
zQfS<kV_3B)IgT|Z!u?CCN}U=2;?C!=wp(`(NgRtHrhMNLPBxNM)zqnB-xYe#>Tq1r
zyLhz$`yA35pBGLu&2!Gy1*BA6O{_E6rf)TD2xxkjPA?K=txT+0BW6lc$JrJu6B+31
zmJMG#9-SqVioxC77k;JmZT^+E(daS9O3llRNyFUMcgc7PmQXE~81XcU$sK4Yh`m4&
zKz{@+tC|~dV1yMfI$|FH4ODp1YQ)5fO`GfaX#DZ<k4fp;wYIi14;!`XWNC4#k0uDp
z%@#8WS!H|xSg%S<pRbPf6R*L<oThJSCB;lOM1%aCeJr)UB(}Y@^4Ye1-33HuEZ*ov
zBpzw36)uq!QOk)RRal;+vT2ZliDBYpY+RBsIk!@k5)J7bDa}yk;4`b{MjrvGzQjr+
zM_HV}$+e~rt88fmSIgJsHlgI%RbMG7IHjeMbif*(DE>8CfgJVm>jFwFNhBThH5q-T
z0WOvtmLx>Y<5X_X2=f52fcshu=T(QdrZ=0T>jp0D3a_3A>=iscJ(VN3NY2hA#TCHW
zSqKZINK*N#4BBha$T%YT`3q!>n5d4T0?oXq_D#fAX$GXs9_xrmlMbIS#uE`(XiR$B
z^D#%!ti$U@vDCOKz&EnHDvZ)TR!dH;EmAp3LE>3mLIo1{S{P=AhlOg@m`6jsh)^y_
zm!ydwuEbMMvZfm^I;2gOz#RgTG0HjNT89y`)>s2aE5Ab7E6N-A-8?Pq>b6sDuDDDT
z9u5~6XRu++e^t1l(U+|N7W#4`v1nCsv6Gu0httvphSVo^mR8gRbvdlJK(ho>d~HkF
z4_mCQ7~!KF!dg!Q!4w9Gek0)#=$Nl-#U)m-Z1C2}&DnCme_kA<%L$CNU_x48C`wKL
z*hER}|AC6C+t(9!)c$7SXptNcDd6*v!{Y)A_}2@CN)p<OwCQL8R-K@Uen&<?3}KQL
zoBx6kCY0!o6-^a|hVLa5BZ~}V!N283AS9r{zt0udFs^oiM=yMv5t!5Dm5?yzq__`1
z2kswjKhK{J1lZ*UnAtu6iqJs9GazHP0y9i<xsa^l_%ep*c80+jGbVACkffhEbBcAm
z9Dm^iIp<;F*wal#BAD-V83sq%x-By;81oIi|FUC^jXL<6I@N!TmJX6$RGYr^Vwo<X
z?_a<tP*`1X6;psN!bA&gICSke<gQJgK8|%aTy|Q%21Likl;GlFOZc=*3<a}|`Q&mY
zItu2>6l!szh^38(FQSUz4w|MaSC<Ep3xtUYVXLVj-(HtZZ@rL<dHVF5fDJk;=pR_N
z?QfLZxtd+4>eGy_K`i0GEihf7veKZ-`yM7>gE{7_=uesmNDvS#uK}R}c!*T22ke)%
zauyKBv?%)cL+moR@mMlP@<2r^%zkc&Z<`+9boJskOL#%RF@Ff}!yN)8{<{JD&D`G?
z4DVZ&QEKIBKP~#;5sQ_-f)wHUDVr*B$7Ecz6?dhT>jBIUK?RgKNh+`qp>h(HMvxd2
z6yFv)B|S_Y<F@8>$Rn{J%|eHzT8IUlXyA9>!p2oybQ$QD^>iRa>1*pW#!V}$Lqi;k
z`9qkQ<^8(se<+uiJJ-xl6F?8+A}+bM7m<7EtMfXg00A@}{JP3&>WP@zx>X$R_S<R9
z^}iZc&TKOf-Jz*d$AtUZ>D6V)?<Fmb&Ao=roZzqQr6;DQ{HJ-0al4^q^H8VVp22V?
z6tLtg1m+c6H?Jit37&9IA$=V7j}}Q~RSM^dF*1h(f06tRHZ63d%~a(pTvJ~=qhvRO
zNL9xh*G~MMpc;<yg$&n4GFU3!;Eyt*aFiNNKmIQddJa6Sl$vmfcBFWG-K)J2IskL<
zd}Pi~5;JPDlH3R>GG_$Yl@V?veH!plK^i6`(hf(|hd$a_ly?fvuu{xc?ZTH14Rw`J
zu;n^$N;AZ|@^yu!E|w_@a&V~+ov?=QjLnHU7_lYFRPmN25>pJ$QEMEk$X@W|S;>;d
z!nVANwGvfwaps6n>4U+bye?K&pAXuhfws8(5n|XUQBf=lDC)`TyY)+JQMGbK`^nco
zb6FduHGr|>7^dt%@)GueIir~_uLk>vtL;yLqt1PzD^G+JUnMYUv4ueLtN+vPA0uW=
zmh4VnZgAQ=5`*gDax;7UFccUjS^zeQS#tn-S5`8tvcSltG;{ODRU4}T8yg#@o%tW8
zcUq!^P+$>&`jDFe3Q<@ZO*Xh03t(1^F0Lk_nP@g3f;38tl`{M_)^=A)b-u#i)W}DM
zHzWr)-~Vr2ne&itz}}?ADCA(&Xva_Yuew&32F8C0Nm=I+BzcbzuIfa!EjNNI=$A0C
z7ZU9u1jL{ST)hEQB#zI$Ly1z#=*AS<y5trM4RAUWWPv5zQ84X}=IY^E^9p#R$ctT#
zd`xw=%#uEFxqs$HBEr1-ARYd+sHRoW@*er~WIIC<NYDKJR{yG%^tY7qtVV~%@Mn$z
zzl*4ncDuO+Qi*pJ)#x7BTD&T#ec%44<%DM#6y5x=6{&1=d}~=(o#WN;%e;O5%BMLt
zySRL{%HY1zSkAM1BLJLv2Z!SG1@_u6OUE4`0nEryfS+gWrB=({{a|)g0NC3I2(;xy
z0)V-AfWYhPYnxlc6|HT@n6FRFutAlh2r%L>38skTkw&d;Xp&G8tqH|wxDc@W<zg{8
zwp!@GV8obW+<#+DCnE%vt#dt3-&Q(gwLZIv{VoziJgW8lP0ZYd?#wJoffGpW@Lm(#
zR3ir)BBIPTbO)r83}2sP;BZyXx$rKG%_5pe^>8$)25+oQN+pngEkk8d%CqL-?>Eqz
zkt2#i^_vdIYtsnik3xQlENnXeBhX4L_%Ko7-H7mW(@C$o%yoMDFCs2!^~k*idC|X*
z-ZR(J{pT2HyjlA7+t!Km@@BqpNU+!Vhc5(mS>n|I0tcqw@}++1#617!#0dr?)sHVk
zE8m+HvSLZU3OU-;tm5V4qehW)$tX997BJf)Oh<>np>FNm00Nl;p2x+|8b`-tk#HEb
z(#*Bftg@CRYYjW0k%y11FcEq~Mna5%jko|(k{B3ZPIefAFwl&JOdGjl0tq94+_nw?
zkw`CB$-<H*=usJ|UFY$^7P=PrEC1C)=&{kkzxM)eI!C|zqLv(aNPL_X?T>-x2uigY
zv<I{b#DbGr2${Ummv91=F-WwC60I3w2$Bb~V_qOUw@xC=fg*8<%7)8#c%vz628Kh2
zwrN9c{)Y)GRf#rN2`YSY6X&Ll(+YxE3!PSRLKQ_;+Qf*^$SB|=+184dWg<DjkT_57
z`1jIG#dCk)OTtg8{I`eNO&`d*BXWL6UnWNq%N*9xIje(*3xisI3LKYLhAP!6o{N{Z
z{cXm$cpSvc)&BM0fw8qAJC3qgb~i-N7uz2f1d^OwMqF)E;8@j1jf3BrkD?Da^;5NY
z3QqMIu9Trtok<RS-GQV=XP3t#ood@b2E>tI(_;t4M^N@*r@GRZBS7=S@&LshGXqn-
zf6^#l?Fq?^l8EdQ`|bqz-6yVpKC2y#@o+uV6!%b9g|^c%BS^c;iSm7`=a%0Gg@XQ)
znCJuza8Ak3d6F21u^=WVn8Jt>|Bcp46h8)OS|c3M$APrNX(rD+8dY6f-5{iz=5^Ta
zVge_|z?@y^acHZ-N(-Opq*4u5yoh>9vvoJ_Q1rCZ=8|>Piy74u?=E|q%5^JG<8L$?
zF5+{zjc{G^B`a2L5|}<AB*`p#U)S8@(Iv)X85eB!D^fPkQYD8IKnb-X49okSULOrv
zljf>JlNdz-g8y+TaZNHl+RT%=^!<|wz!?vwZtS`b#W%2u{(f*^)@4w)d<Ld<`5F@o
z6wjax2{UIvv+pI5xupH$-itx6hLlOZCv#Z}OxcA-0-44{SRf9C?WBZY+Q5dm&+&Ln
zpMB~1-2QN;G!=MrJ$n3%4{PmVcs2YA>)^F`x>BnRq5)LtC6O`({}RhkFo%L=X&7UU
zgvLyH1;VnIlOm`<6!#8Bk)4CZrJYb_Qy|ri-!{U<%|UVX_Yj?VSsaZe0-IQGL^v*U
z?Em)ykZ32tgd<2q$&!iMG2!0%7LGNDqL0A^m5sIoeM?qpi`_uUm%U=Zg*3*qtS@d}
zB?fSir8mzpQ8(Ib5Bj>w?UT(lhdgWu_{}J6i0Fmo-*`R`lEE|@8p}D_62)zq45;6{
zEIb0zg>pwqL|!3)&pn~+>@Vu-Kc4QEdp_=`yEk2*74j5HR1(d-oSd%1o%EL<ueSfA
zYL%3f><`BaJ*=263-N+Ub4b)i+>+Cz0QB&raHt<)%A%(5Fo`G-K|j&%$w(~C*v2X1
zfU@Y}@S#8>m_H;@p7pN-`6*>CSD%X>B@8!I`G$NiPk>Avs*zYby!G$&^5+>MB!2-U
z34jS0S(dQCUy>!k=d}>@5VpO=az)rc7DZ^~gMpUBVzFTD;es<1G8*ad%uy%)_EZsA
z0YkaWNrS7?o1`zEocU)`A}qN?0}HS+6uHJZH7(ib`=yza8F<wr2FDMY>#4Kow_-Vg
ztFtCm{S+FDq?%)?%>S754N@{N(p}P(xXh6^aS7KN`P65SOCLLdB8jYvHGCfV<R<mq
zW7x}0CWGV+$jUlUpLhSn)~UvxWuO1T&wkS3YFITA+|V%Tx8Kd#tQ;H;#S!r_f`5Q3
z<B^8Wrlu4yX<<_mUY`$j(Yo1H9}b|`?Yny0d3b0(>}2rRzT72k5}qu@R7JTj908!A
z1SQ!TRXgH~Rs#pf0q{~8(m5ibXnoKay^$bi{2SiW%;_#vn!D+MPmlSukHoq#!)Gaq
zFBpS_xejEq4qnUoIsDxnG&pqdXU2Q7&iFrZ$q+`1CbZE2CXM3nSi7yDK-1HI-O;$@
zZB75k(Nxqq_{A`yisR&&A={Q=Yscr!|8)N8eO{a0hjUkmRg<Dk@o}2KxUBDOtPn2-
z{Trh7q7~<Yr$X}5PP#}qQq^&@C|0$pu6|JXtIK*N6(QAKx|2B?={wfhIV)dQtz*}v
zLll{7!1tvh&;V@pNTWVpW}LcMU0?t1$!<?y=h8u;UInadCFC6;^02dd|0~`8{HZyN
z%KxvodHt%}nG3o9wliIHDd4D0`?gFe-=7A?_U7iGFANDNI(=)VotKpb2{_;Ec=Uca
z=-7V0e7)>of`{CG)L-5N!*Ql^`3(74#8|uorDoLd`PAI~NCAs#(A59TLB`6k-YAp}
z$ZdeE;KB%NUd9Y+c;hm>M$mJ}_WL4yOWAN$J|B(Xtpu$VAvG3Sum(dhZR1ef+jhR^
zaqISN3-<|Hk@PRJgoy-bG_fVzXdM#!z!&}!7HK@^;ZkT}atWwEIw)EU%G*O$c#<2J
z)p%{e0HAgFPhV#EuLAyiR9~Nr#pX2D-P99(9)8X@k{Xp(x9~#InU|+AW_xqyBG3@k
zWnh4kK|vr4<MIT`nus%6I-SFNZ-rt75y!^mLML?hH1!faS}dGpYs!?|kD(v|J_5U8
zH(ndM@i3VLS@SVT-pC!<?+yylyu%F3TbzPXNSQWrA=#mp9tXGt1ReWveBJv^{{@Qr
zYcw~&bGfHG1p`5&7z&y8G+wV*8~1=%#j@M;r!7@5Mt-Ta`+g9E4Z27QU|X{n7yC{<
zaga){JlC$}K&3mb!YDCS8=73XU>r!LHD6JI9sv-~a4I14d|oK&k-J#sWaFL3B!1^x
zXOARi2_#8s14hxKO|ES2%h4S%rn)Q1I3)-1gLnZE#**d3WckB>U#TJ-6L)Zhe|{$<
z1!VH`p+G@nYMR|P&8lNf++!-5eWlu5jI9&6zToqiR>6Dq^I-nh)G&K15JY0`Tde2z
z^2UWCp-?u*L*A&PyKSJ(Huex$e|kJJCQDwtK+>tkC>L&D!nQIdnd`Rhj#QEq$cQta
z8kxymU_T=M3r)h_w5tG~`s4(bN-Ho_Qf7O-+q`=5%$sapr@h=5SFA#hdZS5Va%pL)
z_C0CC=c)@3s8YdBPXYE4WKaGjkLPEl2Up?MBS*JwdS3vJe<$!{k|-%D|Dz91O-|Zv
zcl%jc$t>S9=(dO}vkM8;Ri3V_tVGjV;7<O5_y(bvU6?XhX#^k{Ink25loX9Z0|Bjs
z`(>7(FXFzh+ICUs8^q)o;IbKX9g-yPs#+F3`j?AQU>cWNB+#1{8~-%54b@AV=7sy%
zX39IL!A@=nWWnY{yXYI)?F%6bb%Gw7?;DxpHd3Zf0!jp-Q(k6P8T;g}`k&vYtol(W
z|4@<qEpW~kpU>anbG8&n5&%T(MSw^=!kLtxVg(Fs`<_~<F5V211tt;9>wa-qcRQZh
zE1^(b=G4GP+c6zfWB#>IBI)FOo*f@Fqh0038dg{>J6O#q(u+}B^|CZ8>*l;Lk#@71
zqwsUajlr-YCKgWSfcHA}Fid~F)Tf@U)pA+?u~SDg*8dp(kKxPmW5Zse)K{;AL@E1~
zXW#k&%i7zGpOfH{+&R~xkw-`f74X~l`KGT=6gS-PsSk%XHR`NAol$>|-44u4UZ&;l
zf~IRR28%dUdG5aH9(o!I`!SMS?M~@KK2l+DMP1~e2u~f=PEh98T^hV96*U?>_MM~d
zr|p(QkPlE<{&K^P8X96`UkgAF2quiS9SQ`l{{Ch8$e@|WukZ7w3}lCi{VfDgHWY{G
zMFc{^*!3mGU^1ji07(CkEvGf&m9R3ZL{V1s+6co}EdRCmJ6p!I<uYDlHLey43B(td
z`7>79m<eV<f&S1%)8XmQGgM_vr^%5QrONZ9hBt6{{QFs(loHt*pT4A_QR2#LVY?Hy
z?tTSd$epY}A+KY{gVb!#X@}jgM&i13Mo=YJRnf%W(~jSf7(qP;F0+sPe$a+PQYcVz
zlgp(g=@{^%W|GT6Z<rC4=211!_dqK>Hyge-gG)`Y|D|o}{QP_{8mA8LePcrp%+j1o
z?}i3E{g-%-_d7SmVKP7t?se0bP<}XHZ3O#g5)%{8e4b(@_hFXVV!ZG85#j>&-umJu
zYRN5=zOP5H3#84$=x#(f!z{+~h$J|<PrKtOm&^AaD)s@S_1yM)mGT|hr6f~SIsI<6
zPVY(}6i5;iK~SVd=yTl!W8pF1b2K+1F&EgJTt7fmS^PW@8SxlHKS2%Vvo@nN92Q>?
zO=)Uarbsoy1mJ1FleJ7<P&*kw{utx)tw!v}7cTtCZXBCXKroi5nb=bE!s%(H{7QWH
zr4)9$hV`U{xYx3iyDiaps>^+BELhqn51V*8KVMtR%3@TOm9n4=+t2IZHT=ao`!m4%
zLzcym_g4UFY6?~SS0U$PBQx1BUvsN2QjwBx)K3LJl0CQo_~qPYgp>@X%4ybJW@h?$
zo7(LUtde_d$9^Za{X2^qa;0Vd0i)!*bpV$o^im|W2F@TK92|hD(>ws-j?G$)DhKcR
z>{_La%uLcyVR%am3-G$8zO&gkoPFvius=dH$LCb(i1?%VI8n&{E8pkB_0!Wc^`G*D
zByWEe4>B&G7t&AYO(8K!EM|)xBM#3!)BFs<a@&f^qv^Iz$$-DxpHu<8c;@uRDUkX>
zZJqSvpqswU3QNp66}1tL7-!a5vU=8dNa_e=nh@m(vk)g?6&k%UsZEIZ%%pJ$MAz_o
z`#oD2k$yXO{39IHEpH#fq1uZughzq=sL+uN3HCVZBy$N4BFmjHWx-?)OsoL`AxL{%
z<v^*@(gI@`k(_Cr^@poS7GT5NJ(P^1NaBFFN@O0Xqp>*_IV&$)w~8S|`2Ag~`MR4a
zFu%tak2T1%!S!BtIy#s{Hv467PO|?JUxr?@v_RhdY7L1?X{ftNUQy9sDqYBFZy=RU
z)4*Fm=qG<ib1xXv2u$t#rbf%f`2)-Y8g8Ck0m&qRjAI}G2bQze%|KCbGAfNihAdR?
zQ|_*Vu=0i20yn!}YwLC2&`#j?q>)NcGXEnR>PJ9@a_9T*OAUGB9D{B#_7MK=58AyJ
zTJ5{z3RoiKUYjeL9KDa#DovVO0C3G!J<^6z7&?*rT4awZ@dpcZC9Jwxk2RP-w}pw`
z+YU*XA0~(@+JOIS3^Q6_?yTPzPguH%9RoSt68a}X$l9eCz$(WJSF6cw>S{eRaqQ>%
z$>W8JU>yZ1j{M_h0tS;=%lb0Q4f9Y&jD~EBon_;TCuvQ#j-4A3i$R42TU+O*O|2_r
z%?z8_s>5;~|BEXrm0iz0Jl4H@38qJ91(6FNDzHjI;Z#b5C%wbj>H1+4jx);+B%{G%
zN-ODN&T@`I=?>m)V(0GWG#Ll`!=1Q+DgXU#w#@%U=nYyVo2s;%YkBkeRet1oU+821
ziu=O^6BuBAdwy#^X_2hLE6%b_1r{{9j(ovLLGYo2>x6~o-sr2>B#?f$FCM@VOv)5`
zEUUek?>0j|>cs?k9*p#4{=V95rV{i&JwZjgyXG@|ysE7wwDtHB6JI2TVHS{>gZHbF
zQhnZ4iS)kh2N*eJ8-^=~)^w&sFCMm6Bb3bU2g2~fWR=Fn5f>F%{y@9^Y-oy6o`g(b
zjsT{S9QEA_v>w`4H#v4|QNz~A5#(+W)pfz0NMy)@IqA4Y2}balXG8bn+{x21e}m(w
zxHGacw^qtyHLHvX+YDs6ojQ6*<<QNmGZYp(pr$g+E2J)YQp9B%mojmQlvTVv{F>M8
zUm&(QImko<yqz1?tBEqQ`Th46B<?p_^j#RX4Ud~Qhn3EI%pbL;eBbI#J+^$L9A@HS
z4i}cOo>%Ir8dn$oFkl?kBTS!T3{ggBx`FJJr8x2Bbj%BrkkHm*W&<3y!0==J9{+!T
z;Fgz{w{P<vk(H+@0fAtuCOqI@OKX?iifFf?bKAz-+Z$L7sB0$l{P3G$SMXtR+Z_4k
zJ#;hR@DDUjO0#iWySI6@^0a0s__6pIb1<>>XF88=tFa|uef99*p0Am=j3d2B-uJv*
ze<i<T18FJm`Q{F_%jzWh)BOB0A0s6M_*8|k6r`PC7UzHZK-HqvNCuC-oQe!1S?{Q?
zVFMC5?#NS8b7A*$^J-3*jI(SU?)i9<CKYP%q3VTq$*N`&A-$bRq{9NfF8ozKHvQvQ
z#zeZa1~p{dwb<zDSC}K0X;HL^$L)?T5h8L`BlHot>|ssNoRlir;CpyCIGTx2`5oUd
zI#`^=1sY>HUJdKA=P)4{p4FAv5ocN;q)=pS4v{U{$6bCppU=)bs+G8n0$wyMgzt(w
z2BmA(x?j@Ndfd~}uW0FHYBJXUP<PmVy?)bFk|uX;Bj!EtKZTELp@Eo)$PawbpLa^1
zV18ttXP%++Fp-Dt$M!!+=Z`lpKri@^9p0B5AOK$ox&N6!(-~-iJBn6tv>8laiHwZ2
zj3)>WViEMX-1v`tPvCC`zDr#4{+BtHw^_@bf5TK>^}>zrfHQ04LY(dIkA3z1S-2wY
zoX%HFu^aDGF8N;DY{MTpJE+8jZ#Qj;#66u;{&yNMgY}UMZz~#)G10q+3?4n1FKWt^
zq!s`XQUs3ZHn@}C%m&4+`dBeLvc?t?cr)m}xXBKv?dTAaUt$(hign@qOTApvIfDGk
zcomALh$p!MGHp2n3p0s%)1Vj$o3oQQ>6P{f^p#)wKaa&#-ZLbO*_CSnBM>v$7isiW
z;?BN*E_t0Dw%t+iPTSrVZ*fW=F18z7vX53lfodZs^$gc*^HFNwj8gJ5qF!Rd+QoSN
z@IdqHl+u+y)9Bi3-SJ&P>6zQ*@qOrrX5$<3bz3HZC9MAi)5on0^2%Yq>2b65m<}tF
z=XNyw_J3Yj)UVsxh=JMRLFxx5@6VQ}YY2Q}A3Ki(TwGiNZn*+(1$y3!h~LY>@c*(t
zh1w7n#S)bYwMsokLt<Z+8~JXf%kJ09vt<z=08pDs@EVugv1#A4-DCV~(OMt1qfDjZ
z2eChQ-2Pd<Y?3P&Yn{*QMk|B9-jc6mlY8}p6t_!!#Pai|_46{?@cB;2-}XH2bEHu>
z%z~z&<(2AF{Fmc(0cwu_`84&@$iw>f{m$5DlTG^lNNa8AyZrkBG1ldWVZ#0xPf^n+
zW3dS|gaSY=F!iv#>xE9J)8J|VTdMsJ37~jYzo%)TQJ*YByaJWv=%!p!3U$Nns!Ci-
zm&e1qNUNe8-fXU&`R3Ft2t)dXsCYyzDdmQg0)c)T1|prksBL4XvUDt1tYp>R{q<d=
zN<TMaql-g{8O4V)5Iv#z`u+9wf+T;50yM?{d^{q)sGE<YJZFEGsnVOn3$fk&$qd+C
z?(sdX^@%4bWSn5Kh&oYW>8Y<TI4*^Z1dXE#JjFkDesnK}!61NSq={elOg*;SEQ?d9
zR;!7^z?t6W$0ew$%D%Q0<XIsP;&a|@r~Nx#2hV<jt$*QMzUk%fQi3;$@|3rJk?RM1
ze|UOsr})n9Uz#ZirfU!GZ+2&;h`anRUc2qqr@wj$($mxP`%F4PrI~}n|KNe5N5>{u
zRr-ua^7BU~cZCvn*m>()-*$Z5ecpcQt)ouOUntuexxs+Mpq@F`73}SMoDA6Ym=^dL
z-m6C~*?RKV>?(5FRefIf-=LN!+4%R_^Ki+}J$2K2`*izt(_!1AwVk4w@hr_3+#47%
z{|E{N#*wP4l0q4CtxjlIQ?43&+`<iNSYTK*@Qp%>ske|MPm1>TLK9MVf`&L(0_$gu
z%~snNRkk-(wugN#i7P#uZy^4_?hf}%8_v-^Uo5AcRY^_19((P3<#vZtAP!@6tbUu6
zX*byoQvcK$@rh2gPM#kjz+=daEJIXI^jNiHQ=P9a#p1b*OM~r5G_KC7tSoJ*MbTJi
zo96epSRyECD5Ze)(?PA*fv(eKu#~B<tS~oV26lDnDOMHIW<W>6M|N`a@!`cO)P1+>
zu`;cj`pZ;YX4jpi!umY>>PH!5*yaVrq^aFyK=;>%Alui#RGS_by6n#hFgeqVpWys7
z>*SS0V+Q<ZOkDNKgHFb(Aaz=6<BKJRumNe7stS5yg{g^Qi{-~^=|NR=QWM{sr)v=j
zX8--|4?AALX6K7`6Z%BDgW9$27=7F8k!bq%O>g@{w#<pPUBOo?uw(;966<)b+qZuH
z2c=5E2|T<Fz%H8g+;m$`l!#aPukua*x9vEY#R10eOioV53w=B}8;+a*qF3b_IJ(Y&
zF?H}*R{GrU!cbKe`b_VsUVp-^Z*h9SugE&|GCeXBRL>+rA@*|KK^0og$k(>EXW?CE
zk3(aN(aPHI6jVRt)>gn7gMfN|IJMNbNG^;}O;pP*<^m&=>7{N|lt~o~vPbP1$4aY*
zbp0%N(&wVEG?;#66)v&!|6AVv$nN<3dwKQY<})4VO1XQzh_!zUE1-11I*VJMz?3>x
z(omRqD(@?pnU}n9KGjUnAuzD;tw^o-c){l~(DG>}2rLI|UH<F^c5MoJc%O%*8(i2r
z+O5<}%xXz8($G_3kF%*_=rhE^8N7<o@czB~?e_=eP=y68l4iAd^0&@miM*#)5=_w~
zKc9fx?c}n2Y@AlLMabe_O+-+3&&%6*&zW_3{?}*jtW1aMDg>%I?0$8jjGlj+oi20%
zi4FiHut(33!FO#i;EUGz@odqq<CCgCtDZJ?9>*tmZKD5QHg*mU27aOhpmQRDsNqE4
zhB@ABvOHsYPP8t}lx?%!@x}Y3QoEtR-J8Q>I{wmjm5wG`8kkaW5d_;WAT*vQ$Qf|@
z)AG=7Q8X%f+(I!$yRjr(jZIvYqa*iX<ee*d(WIUaovm>_=f#y)xdzN$3&%jVG{KXx
zMbY#{pY}bn$v!a47>TktVAobhk<6Q~ve?I%{#+&&d^#qEvh&z_#rmOT9OJ&EV<;U;
z=Q2OP`}Ab1HmAmrfA`Xvy;N<%WAcqIX1gYBobpHV{R-8ze4W-}GA|q1@6F0tSF_LE
zwu*g!s56h%(r{RPlvtP9yC~TG12tPgbi7?xn^xNvb={;Sllx^F!n4Db<m+Mv>=uvv
zaH^r?GO9IBJA@}2=+^$qa{8j;Qc?E~Qr7hD`*1s|_^x^1;jdmsV3>|-s$P?e{<OMa
zp-}4$5=W2p;V4zf8wTAc1)-0DHul3`ecN3eaqxtuL2ueCRhB|w9x+UmHS^8Zk~w?Q
zpziV9p3u9?{~G#Tn|?g+@yUEx;N`f;uy_0|fZfgcI1uclvJkaMUY$6hEG@&g_Itdr
zKPrCQ_P>z9u$j@y9m#+W8T_W}fMPJNqd-6I6s1<J_SSr9)LV@hCAPG2*dI_|VumiC
zx?o#lAU9nm1%CM{b-h}WCJ%3(>m4`FYW;Rv+@cgUuu|)@@3QimGdZ*^v<q*<8$9h-
zo4vNTwl6>5J2+}`Ap&A0BS=;*-1K4dczD)&T=bV8YHSGF+gF_1R2Wkzrph(t4x_fr
zMsX?)(({gBOn?2cZ@rD8j6PKZ?IOT-K^XegY%jNMp8S(cB6ZN8JiX7Z=Ipnnw`E48
z6!^T^@)Ic%ZIU&TnC|@{g`Wi#RJ!y*J}f)S(j`-VKo{qozuKpS`h!=a4i9O%cH_4q
zhqhLm@Og7*-lNwh7>e9Q&^>0rlYEy|ih_!2Slg&ye)Avus82Ox+WY0CES>+K@zqur
z9O~E3i<Wi4_wlahzuA6%en{gIY5JO)%UDfLCrNc3(?`+bWQo$D=Br^pO1r_p8qRFH
z-SDFZBV3F3m-}l^z1R7nZMZLlo@X_!jONvw8yg#|t6mSA!X!Y%Ftz(nJ4JKW%&hEe
zFos1}cm4SIyMx0Ol_1ZNO84b>V_f&t)uFavDh&bF(jIdQG=W+i*RKgE!?xXzv)1zf
zhn*uPe*NX^*d&|QsU8i7c^dS(3QC!IZk0r}TQw~#GYZ3)kDYK98H?59-x=dEjV~J6
zC3}sFTFCWn8=mfhX<&TUkzZ@gB6ngk_mYXlgoQ#iwKq~xWhDq>u^h)%KKAVD^Msh#
z8_YrTy1SZWeBF=#9I)ro;#k$$_WRuY{F|Hn<MkCTH^+Frr6$CI$lC4<Gm8uNU}D7Q
z3Yw4irXqCt3e&u~__P7M4#oEP(&zHqK`_k>1r?<j)s~gsY&8u(ol~3Iz_<B58o7eZ
z6W*TIszw9LhT{uH8G0SxAQSFt%%*zR`-fV^qG>wp%GuF`D$beC(%biL)K=T|<jCAh
z`*J2a6OL5-@AiA_<|ei`mHPT#U~C=(PXlveyYr!J-Es$b^@u~K(@t-Y%XES~B~CYs
ztGl}(c=UwhO&YV!)M3l{-!?UR!3HpudfC9ouC3NmL{3gFAm9_MqaLn}{pY^9KV1w>
zC@3gUA|%dv&wG5<4oB;%q#aFuy}{qsI%9K+Sv=%Cj;uzDULKT98a%=Bd$4P@ySPv{
zVe64W{W_;6MEAsQKvG%CmVqU~tZp9kU1;OUR(l@HrOiU5AtN^VHq*$DIkBd6{zRp_
z7~vr<map4u-eRlI3SZgHJKT~(v95tvBQ5ez)v+J4F^5U=A-w~uy_Lh;ej+(k;<;|X
zhT%~i%1icyh2)W=+v|F}X7%&tF4vd5uWgpg_B7eE3!&v2uFe$r@ho4`C#`pz&>dQ`
zCb9E}l_RCJB(sG$Cg9VAEiGBHfAL+EL|NZ*eew0|ySg8_#Zz=(J%#=;&BlW5oT2Al
zMz_-DOv_2Gi9|$1q%DJnRZ7|0L9letr(8DtefCUEpsu2%V%5yAmozoXT;kl?q4{}P
zPo#rHuNKYE)O3IK)e}ig{T)nu5rOyGi=;~H+>7_WD~isEGR`Q=^FHk)o@2i#NjGRe
zh<?}8Ya@Rd8XBVDnnobSz`z*q=u9%sxV*gF-Q9KN^(%l$$qyRd$0H_IqD;nlF$S0M
z1D{~mQ|r3lHHN|1vLBx4G|<lfQoe$0d4ZFeS1>b1aZ23A+9n4)6r&)fw5G<iVS{&{
zGCFv^C(1$hH@<t)CEPA_j*6Qf5N9<^spAq<NsYFMo~KI}kkpr{PBCXpqd8P%8#Jl^
zMvW05XBXSiJyp$`Bd?XK{7o%(79obLqOz203B6tzGgW!cno_}ewP;_JYW&1Pk_KyJ
z!m($Hxs2MPOu|q`-uZG!p~}6@v$ohAI{A-<$8F9~b_{3L*~~}Cg}%N@b9sdce`3>W
zE=HN)gO^PPeqEn4{xn{W$kZ_b(Xoe?t3}zO+J1Is?lp|>>T~tHJ&h*qwYXZ^+<YTT
z+3MOF@L!W_dDeH#81Y?gNyK3jY$z@Qxko>4+;c6WJ1^1sg4J)GQ@XcX^%(j#4sD2K
z_V(>3rGr=Dq6OgQCL_ne`yJc^b&PoZ$7lafEZ^7%%e&y+c7{pXS-0)ZV)>I69UUF%
z8@jx{PyhCKVwtu?kHJ$PV#6Wbv#iq9iwE#MK~gtJJG{#WioW@O9^t<nw&4*>M^Wa@
zuQc>qGL`Pr>EMr-++<&8Y+h(c_CMMiP+7^c^Y+XhwLC+4*l)97M0R`b5C;TkS?WLA
z8L(`u-ijAipMd5D%H0?{EXolN<!P~b_$&M$+c^!4${G%5y}HXbOTLG;YN^XCu=f$j
zTNe~pCee;(0X1+r1NO)zsLaf?%@rCm&lz+%o3B~~U#oDG6w>lP?>{y>C|_RNyBm%O
z7H7Hi|Jc!SZB(Y1M$Z3eh_WyblNQw0#QA+4r{6hPa&vlDQP9$?tCqLmevgZ<L*8Fx
zU*=(JRBXD>n;Unw_1k!7;QBI;v#Zi}?f(Ah$w|pCT6D~!gPa{pDgB1PqfVnTm}kcC
z<sebG_YAqom*2B`>a@b{`{~u0rDsD)D+4jgp{p&WYPoDll-ggfTXlan_doxU_indD
z1g;>x!i^0)cf*i!5ivLn1HpXIhlhu-Rt^QZ5|wcu0ZTVm*MIy0cpjugxVXRd%GzDW
z*`ykKVRQfh)CL)G5w&e2E;MsN;;d1`;KeJd7;fILDwve(9u~`&Hj#tYZ1BlN7~p|y
z{CI8*4t=oW{#t5>_d#;er%3~~?(&6n+TVcffKyJ7$@6o(_#ig4(%CatDaw3aRt?>K
zq=lN7)3M+r@M@^I-Po-4iWFLLW%$HZ*-RRBEY+wZS7jMYxxzZqe`5tjY=?c@7thho
zl((A!pZ}6S_eZ;TZ?YW}^?G!D9(zB(xa--}n6Apkad_HjZtBE@l@7-Ru&+?n8Xh$;
zOAu9H9`5eq)s0^NssC7Vvvb$JeZFXOH8C@az?~4t=EOQGWgr~?E?%KwiXr8dH=S|>
zlUvLAZQmx5UZ)~K8JSbA4RanLZ8h^dJAO89=H<$7J|3QxwT8Y^c8xu&j!97ckWtnC
zy9es@fJ&s<6Ycq1(#a;$d;7fC(IEn9`C9`4K0Z41pQ}eETaTj3)0;0x2&7SkzPH)i
zDWDVdx`9JyeK2+Z=zV5q#|VV3t<~VQrS#Fs6{H1*JL~ZB5tk$3678+n{@v-;^tkAL
zX#R-*+~mmis$(LE*BJ}TT*&Fa#EhZr8q^xK=9fB!+%ogMTsq@a8ZN@FDiE*6A1-Z>
zV48kEH{AZ1e%wB-&$z0yi%(gwfSa|sO_e3Z99{gqsS!IrPHWnV9<I!hswUp|-MLM6
zAc}8AFH%Bs|3tf_y@E3VA3;63?XqT>|6zal<MfXdd#u;<qvd7Sdb@>~{Z;po!)AR5
zb5xC$O(@ZqEk~c)N!<~34Nk83IC(<v{fpT37I>5(f>WEZ+e{Nm3pZ}8DemcOSu=T>
z796&QMDj-R{?4hr^2dgll>^kchk)Xhd*o3qSMS_m9A2LDFR*IOdp5Sr$M32kyUQQX
zJ%*3X>r#rj8sZl7Yo(R6LsUaC_S0hz*b-<R-fJAT?k*5YLhpBNa7+!Z=&7H5BMrV_
z0RLfkR0(8IFL>&)s?H?`A3QTZe+w1{a$gI=!^0=DxunR@x(osm{SI_>b;0uwY~l)y
zDloJdxOe-E9vM2kukHE$>BvmmeyGx+=Y6AMSGBPE(I;23@z9mldif0MOH~i)&adk*
z(LcQiWJMB1%BjXNQS2-Nbd|ma^e2%A*H0YUIT2-ri->&bPu%DoehPBzOdr!?<N|gK
z!Ev+dM{TlW7OlF-?VglJsGg}NdD2)E$;u7d3M$%lMP$}uMYCz`lCwr%X>nrf7Z?b<
zL)`Kn<L<3g>btw_rl*?!d*i>>`2O3q<@b_w<lA@tJMpjloEAAQcV(u1kjeE|$(s+k
zSS&>An3Y8uTBDZ+l_Pz9)?LW(3*=WhITurQUK$L2KPk59^7`GNrNyo!>h`mC!DGIM
z>&R}ez4iLWvl=n*miK;}$IH_k<S_QsMn2p5{Z)o_pRc}m0d7zCHeC0x`prMKF6q+5
zdY&#Ex%JkZ_Otr0l!m30?0<iJRN2M(J3BXkLG|Yyj~g!2TU|c??Lcm<YV5hMaS#AM
zI{FaFq768lnq|@gZz&{8<bJ(W;M#2JeC20DR@K$@dAU153Nuhn`OfaWg?h2kmVD$@
zGz-3zOg9o>$akbTS#-Q~5@xJq(QNtr3BS``=%RIrU0j}drVUr^a?03>I6GT@`E7?G
zkLS(bq0$vwwUlSxc(LSrv3*PVYV126v+zaS+HO+ZxWNfnIWbj76K{?E(EZVasiAQF
zE&*cXdlY#o0=@8jLC+#RG&sdcSYPj=s=QQn30f^~MgkPJ>TbeVZ97VJ^LxeN504k$
zW`*Q>>D8YE*thpsc8|3T!D;FDYm=?#NwV5v&D>15#Lr6GUJoUjS(@%P)^42G&1njM
z8JGWM7WmY)sZUu|<}E-kFTu96p6q99)cD<dysq08jy9(*WBlppw&A#4`sDe2blIUR
zXY=|4c2Rs}$JycO#gF`o*ux2A!wfy)yawvIS>w~-WaCMGUftfheldEgb=deg3wTj*
ziQ~|s*B*U34tSbc+1ZVYTl(fnNska>dPCG|I9)s}wRPd(&=MMpf}h7MF%r$x{oC^~
z`1a-Fv8U%?1T3Ine-fe?f<%D9e~c)TM~`m*SLMp_8!IVIQ{;ib%NoG#=~)LdY=0Jq
zjbaXWJ$Nm~?T6F<wRh#;Y-dqC)h#u&lqu3^$4<l^TN=lxCADR0B8i~LR1&39du(lu
zIy$wr3`r@8rIe^xg4jx1QY4ldR703f?WG*cOb0FVo%u87^!@hEx##|H-@E6&b3gZe
zKCFY32ZktOSpV%-K)>DqMK7-cXtS^U^@I=Ah<9ARO|K=^>f_TLNM-eNjlP{+KH76U
z5$5MZcx1FNHR&^`UDi!WA8_ELNyIb797$AA!R(luiS2h30^(?bdq?qv_astk{}|Kv
z(f~8}`Xue046?m1><<SdOaFuvfiX5BCYlYO&I%L?lrS0R*eJmxxaGPe+DdhEUdUgS
z#I&uqa1m&DMg7knxS<A2*UT!k$Te3LGe|Zyw^VUU4ro9$HD<aypx>88CyO4_O*5Qg
z@nK^j5#azYI;KeP+x*IFH&m@}J=S|`Y9U##_tH|Gl^xSu{IM{At0*n|QMSAxXoGk(
zoe#^dy?L|u2lmVX{nYQ1wybJ;&PH}d*C?M9IU&Ta72P>s6BJ1|-}PkdE&6xmyOa|U
z$JbjE+Qm5b{9M`Ie%(rH$mhYSeEqn<PvW_VRxQzgf|ThQLW47+e`PBLX!Y#A9RHHG
z2H$0k-O0(xAcQx->CI{fn54D#y{}`jqd6O!RAVHvt>sS|KLMCiICsC7!mIJBPxjsH
zu5)s7VxauVG@4<)o8iuIqC8>d7nXvn_SI+V^2}cA2ucS`Z);?y0>b8UF~%qSUJ-Tk
z^mlwMSp59x>`PHgjozd<pG<KO4;TB4$gtdi*dtz)4lkOWXDwMOlnA(6&CM>6kvnb_
zUC*FxNMgr}*)V~S<H)W8Yun{i<En~rR_P>^%12wXH8aSCP|fqFqPbm^z_7}upRP@H
zEOd2jUzm2YkauouMwp6s3|KSR!#dOjX_ix$Ia9HzLleS|TO7ZDp9YRxV1jIhomiu3
zDTzc|PB%j1BVaA1y!TgTLy)X$Mo+Lf0HKnBFAI3+N*R>0R)F}ggj<Ea%346yc;B2W
zvBBPJ1fXJQ{<0s?QG5fb&Nrn#@T?e8KyCI;@Wj8-4?cu&9@1!`;LQ8SRtR$nZG-&j
zLL&xuNd5~j%rH`h=F_Hk^=uFFsVw5>H2@!G23z-xJJ(K7d1BrTe_>o9hKj{vt)QK`
zZ_di33PRpWXnf3*NGm9y-jkyV{7>A71<KFx;@*H!IJcrgn<>H>GP{!QJ}_1)VC?$h
z(zc+z3aLSmY)xlvhXCs=GFVgxuf7K9uH$gzaAak5eHh6r6X-doajM|eu(ap(@NQ!`
zT3H>h#ZW`pgNIF0D$`u{xB2hA@Z;bS&r}lFUehrz(2)#Q1Jr+@qHu?KD+b9Lhr>DQ
z^}}js6+INU<5{x^9M;^Op_NA>rW!-h%I;Wjw_LsM<f;XQlvb#gBIka_+)1OL0w!(r
z(9>BnrIPcqPDafkH?WkGY<F2PZ(<`&(=4SActruu$wa^l30yl$5R&hV!11Dnr^=bB
z!L0);QSe@48IM4B4@4GKB}*e^Daw^EyOY!Y_Yo1B+|SVaCOvyNq<?IY+>tsTu@+<E
zPf087*c9rg-}kD)+sO?XKHZK*v0M5YfYH&;&RfC3!7(v!)<5x^ngp}qJTdg-T|k>B
zTQom&Ywx401VPydJq!d`R}p%$_oZFdtb|5~<+bJZ#sC=37WYHFcMaQgn+|l6seXk#
zRLSwA>Ymn;gUD$>4iiWrpNQAXQKv5+u>(+OG_t7kb+UJXugemXs%isz{v>K<F#`VC
z>sOcZtaCQIg6p6-NBgBi?_6uz+xH{}K|?e`5?U$|LKt|K0BS*AksUQ<TV?<Tkv+y4
zqIvJN>K0`jPDe+FxvJC^$jdak<0_t#w!C@>q<gpOEC?i2+XjJh0gu4`^<mKElO@MN
z#n6kVK;K)ZoC76AfNemadAaznhF`<;^-}yVQ{jvMs=jep0A}tU2w2XJ7zggffaHGw
DJfU}R

literal 36929
zcmeEtWm8;TwDjOka0%`n+}#2U?(V_e9YSz-8{7i~2<{Rz=-}=I4{pJRcb;2yzrR1=
zo~kpuYGyv2y>_qeUVUQJROB#FKcWHv01O3r84Umc=HGueGA#5PCZH`NbU|^I*Y^Mb
z&~W~{Vdgf2UjYD0fP##qmT%tQO}}h1o1o*T?XCqA8}Z7=rMWdL#eqgP%Wg_bM*>R@
z_!t56?BwKxt3O;{aB!r^og+nZL~z?VvS@Lgkqu!5%qICT6@RWeInuDMHd^UxIN059
zRf0{<x+W<^b(5%98xLfivO)^`PPGaT7H<Moy%r6%WgIY&slprMK0N}kq>x4XIPQ?C
z(%p#s!?DH%&_$(?tw<xuslvH1rIWCvh@*<-kc;C7XK2FJM$C_~r5N}B@9+QjWd5%R
z{_lAFe<8vCgeV|fEz_>n#@aeF08gKTgF^z@&$@mX3Md6~z^X#XUFC#mEUWK?;no0A
z0Q3iA{{PUZT{a2JU*Y%Bt;5m}!Jw(I4`S^`VIBQY>?!_h8Q*irTQ6vNF!({7HC!+A
z+Q<wFUTP0Lr`q*G0S8n3+kwY3RJHdsNh0pMQJz$6(#H`CAzm+~@mRlz8MMlz9$=9*
z@ynU<gl6M875r<K;In=lV-SMq*by-d;99CI=y_4qvEX}lY}<a$gf-^W5i4EflIJ?-
zrgM%oFJaLoOI0m59VjKY8nyy`qSFXd+^RKUXE--qbWLZykutQ>i6wY!Yv*Xs5!@T#
zBunCLzt1p|c&&3XmP+tY(JE+SajM1PMz<_Tk;HS2;9-q?C#a&AVAKV|0%&;RY33=z
zi8SEg5Ykbr01`}N5}M;>KxB^L@39&R{T%I?Oxzd~o)KCaWx#O6BMAWx%@`RLHpWss
zrGe(u9@@a8%QuSnKHJ{v42^(?zxo&I-81Sy_MW>rhL;n#4<Z0SmWe-8xv%HB$cE2$
z5dVhj;Od#+Q7)g822k|oH;qg1ZEoK~OTF>G24kX$WR$9VA99p6%@>S`eG_dvl<;p$
z;U6jO)=>Ny5^^{1rt2^RKKorin)<wsC<;Mb3sLHlIgdyuF{!$(eaUv;14STXFzzy3
zqwMRg=4;+%3Dz|ko=FeX_1FXkO+11k?;ZX=a^JVCF3Ap%6wpl$`YT7h)y*}gzjDGB
zl>wAx>IuP<<E!3lPB}}H4=H2#VL}ShOq$c^x#}?C?w{#ShOw;{_LS^riX;`3qwUrW
zs(F+hm*WP%GFcFb$XinV+MS8Rwa-T6FH@s0#qq=$oiwwXqmgo5r$*$Ac37{#(x@mQ
zd?XFy8U$*Ja&o4j>a(nMNab+RXQ*_%nuZ)n{~f*+&RKe1X|RNrC}Fd4mv{aPiegCU
zdYelhR_Rzcufvk#cJEt3z{RWq`=egwJlB_RwmsDieHuk@v#+2rQ_Kp{Wi5jV8N+)C
z-saWjn<UH!3Io#<p}~?G$V>j<`&$i66%hn=lu0-OV#Lc}z^R|jZRvzNabysYPdrcE
z|BHLLT2iBKL8}<c+rP$3H*qB)&K`-PDdbx5(M|tl)B5D&bDZ*dSgS3{S|L5Bx&)89
zhT6#Y{dQ@%28Sv;HqdJ&((#(JO_25<G(@_x#T<{G|40IMmb&$obG;moUTb+da83^~
zrO3<`6Q~X(rb_pS`-Yh&A9|#p*8xfmM;<^{ALl?{Zz!{)cSBCs+c5uFmW~%~J?Udy
zF2V8?CztqsBO$7ApE#8fx6EaxglZQs9r4~K4KA$wAtGd2AM$p_HUay!p`k&e>h6mw
zO?dZpg3Fh9Cnzu3jiTuN3fJwLNp%-VKye;K0MbyK@MN6@9Sy0+&cN${l^N;Kf3;-J
zJ*<~2*M`=f^PY&tLHgJ-_eQ7G<h-~=OuXY$UH*8)XB&LmYdueZ(cJh%r_g@6!}Hjp
zOLB6_@+av0`)8^G3T9n~k&U3o$yfgS>YVSF1l}$oYdLj4_y-b^r89<rbau3UUjXI{
zZnrGD@P~QX$RG06xSfI*M{IxM2~>d@0bg;bW8tEN2o@DeB<v;{(Zjqx#)U&`+7*+&
z7<8>w!G(|GIeo9}TxP(`gp#49hBDWPF2$rR1q1FXDCs77pv<Tk!<g4dQ7PT}wSWIY
zVWNhL-0!GpWUtRz4n&j@9n@r)L7Q0}MS<A?L;ivjNd<2X*h8KMl8%NiNVh>gn_kOR
z2b9Q|6k^?8XG@w6;|E-T`1p9D$cE;_tKGqT6`RdYFT*zH+U2ua9W(%W;FV<g!Y%Lf
z(J!uW6Nk->0Hd7DT_P=1ns^OL30n1djiN{tAV7_|)P28L!?KUwKlh}^FzNMY({-t%
zwEV5TBz#<`G0wwPvOXgtmW(v0A{Cw|>vmGRWc`HHIWlcb#&no<`R0d#`fU)t*FJ`H
z00jv0`T>!`#RPW<k!XQ~Ih>VV!d5CeGn{iAeL%l)W`=Dnp1Rn(ZT@Xbx|XcwPJ(Jr
zE874Yw!%*Ht313NEzePJVVot##zI~tH9-Ts@;656aH71`^lW-usWP`cfA5dgT8Qb5
zl*nxJ^F@p?=ryq6vTpr3yMmadom**9s(hF0;+4E=sK8JwM#{)3rHM9UFoIH$&Y#;S
z?xnw5JpuZkQ~e5G|1f~{JT7}43mOhbNL*O)P%Ft>*UvA5AG;V-3zSl6mFJIwqQPMY
zD}##1d(SNBID#dMIi@>&ETBZ=@vxG~y~Zs>MJXbz6nizZm2kC`$Vj2?dBJ}`(Vx1e
z*HrFqwhLdlF$h(M;FN8?Pug42w;Z>0*r`wORO55Aj_g{92;1B-*M3_Jg?vv-^z+Cv
zUgH;#rwE9jzC>|92^Vcm&3fiB#uBZTEeepd=O)N$B%jdY7|1?v8d};Bb!9B{Ie5^X
zYY8`ZTmOw+n{Aa+0)!7&*f{YTWNbBtm7vLpEn59sUT!h$>cN!|fn`oqVV6#ieZYG3
zoZk*35e+0ti|?X8GEddZBZ>F+HwTT>s)%eW#UL(oihvXMT+h~G3!B2UI#d!}Cj<HN
zMf`4pE(5mRllEjC*IS)zeylcFj&^OoT|`7gFa(N~-_cM!{Vs%@6gqEpKRut-BYlNH
zf`iw1kOnzAH!n^+t}fg66K7{<31>Mgt&oFY|301@dxz?>w)ndSt3Bw*+&6p)6c9O8
zi(5vNVFonY!<X@^rbgS2P-e!?h1+^O<FwzBjP*RSId&z~2Y=@%vd+!_Y>O#(IR3HJ
zh?nq7mG!^OY4s7Hk-S381ue)Gt2}J}PC#k-b%Vvknc~7w#PhK^`|O|LlEFc+1&>Cp
z%*Uz@>kTCgd(MeWH-H8=FhhT5(#7lk@e%BTahKRoV8O{~pQD0@`0)!!9_-C%6+frb
zmE&RD#`8P>x7m-)zeGy8nJ1lmFyh)k#T47&?{JZ@fPo;eH?IVU9<dVM76+l$niLl&
zvcdYxrYo;54{u5aP=Qfr@@eR&@-z(pNtU#!={Ae$)3}Go?thD4&o1!`u}b}Gl=$oJ
zt$$4x*RofA_sw{D1b47jmDbi{?<1Lr@0Ca4%f>3Z7BJ}FAce}j=Owh3A1@HT_Wg_N
zBO)gkyANl`+xM)#(KT|D#glz`I1fmKkb>h5R!Ww!#K}=hO2x!@+vk`F%=>q$U)DTt
z{JD%3=!=6y#QBH!>+lQ|@Imwn9W)vA07+Wg{`i0HHmAVTmc<DOyouKevt()3P>kT*
zD5+s2OiHW)J)O*WdT3QknSp~c`e0IO==IoePPGBAqococ&?{ky8#vfyklCYs590@V
zHruJpY84`7xF%2`^>?#esK&ZmLPNWQXkdHC42w~d_ej$z7iD-30e|}#GEtqw{eV0j
zB2}L2AO|mFc?|Ui53!=g`5&cRee)Uoo=jj%+0fV#-tjP_D*~aNjK%;Niw+iZ&kBah
zQb3(ey@@)fG7U^IBf>}oJ^dmc&g3h?2vGo(PTr)e8lpbt`)m=z|7v<2!XV)jneg#F
zF^hMb25+Gt0x16VBTc`p&9`l%MM#B&yw6QPGkA=IgjH2l-4dU0=|#?HR@9W%K7Z-6
zMeb2cO3-)Nq$Fc~@9KG)c4foUtHaqA@$_6CSYG|xZ5#R2fZei!CzqsQVLx%mf;2pd
zC&SbOF%ZXjpXod@wQ1dQc1mwRO;qZT#@As}+t#+L{uZsqYy}Hs8V^-ZmZ%!WpvktS
zaCOg++)u3uOG`UX`=C_LaQGyjp@326U|&_Pp^)8Lo*bIlklJA0zCh{i7vF4iL{HdD
z5Wm^X=JRy;YPJoqEL*%_t~Hv-$<7DWfa3&U%D-YQKjxT+=R3;{MViO(B$P(Os&D*3
zY(Ss5HA0O=RQNcmK+VISDt~980Ar9)pmboix}V2b`{JJHWS7e|zt^9=bVny@>L@6Z
zKVIlQuguTJmZy6yeqW`UZvfdN?E7~ojtSGh>cL3iyAxV@YvuQ>O_i1D(skZy`nG5)
zTCcj@>a^Y)^kTGZEQD(j7#Qg8e$&0R`_x3*<edHds47yl_F(+@=l!9kW<Bm{YwKjo
za<q20TA!s_rj_kX{D#GujC>ZqkRl(qmc02Zaj!;$`c=9^Ljqx1P!gRuL{MKCG4{0@
z!pD}`M~Z0xjLT73LnB-N^9gQVxr=iV5J~r~;G%*HLz`I=+e~K-g`lh3WPJJg97DJ(
zEy<@diXqz$I~RAv6R%PM7NlkY%V4(=C!7|U!@W^$Yz%|%yGi$}rp@G!ugGOb4~HJN
zQQl>%l4bx~ufJ>2rhP$nUTG#(`ImU1UfE{p?@EL6Si`@};yHnA(=w?aCE^6dy^V$M
zEQs6IQxtqfot3A3+zU9>+>nbiB}Ks%dClMYmtt>ZCWuVdG{e}-Dm&QT!!u*wc^;1f
zp~!1!@UO-6g%?r?juhS&m11XtqA&>sh4>rtX{vt@#1pe{dTJ`)z?m=i(hnQ}D5|f&
zyy&`|K5&P!e!oX3Qh#{A`T+RpeZJNQIqM64S>J}#zdzSknsD*(+^sG5@?}lbn)X2!
z#-)mXnh!>HUG~1{LuLzbC)_(RU@nL|o@-q4{NA*akUTCbFN~kS+x|IuD*p5HOA-q4
zS7V)9xp<%EqV5;)%;SlAtMAh&lgByAh$+Ozjpah=aijG4xtuPSS0VMa+bWpsdh{q=
z>Yio;UAFkU;kL<k@{IhiMnI|0FqH6Byqu0*%tB7b1F%e~i8=I4+&JUSwvWwo;p)Kj
z3IZ=81q5DF?6%SiDNys0sYdW1!F!_@z7)L8kHvExk2)-7n|011A#K^^9X}WEM2?cK
zIIoGv!1^LBp!^y^Ln0mTFAk?#FCv7x%f~X$a%&|tGH+ofz;2#LaroB^xF?_(dwQ9Y
zLKXV@Z0CHOyx^r*n5`h>+@z%7=Z#z<>C+C9IAghyzh2EE1akQf8QU^nt}}kz4tc-c
z9|i06H5)!Twr#jioP21wIU<PMT^%(mEDw1d4VgS}zrDS6T_bdb!v3f@#Eoj`zfXWK
zbZ5~3`}_Nk8y;ZjmORXxlFO>Mtp_<r-Nmy_i22u^HH`xqZK~v^yq-l#2!f9=#6w0y
z3<LItm>0E57CCe?#MA;0vgYi3w2|jSZ5L}H)#OuW7187Y>D*G!a@m#=ETJHl5ei+o
zi?#McCnVEWYB4ScRN~fehe1hre2%n{sp>;?>L!Utq5yETCO(74FeR-=;B0NwXyt*D
z3~o6JjD0qOc!G3BS-6`uw>2@ajO#C_#ad>QN5Jh|OFm26k63?Ed$n~Q<Ybd%J;a=x
zdfe4}W3hF6Pmprq*|4)>Gl_!q=O+=5ZU~UKAtPg=s*HiEyut-54AzI0JhIq<6JVVK
z|2<JMG*V;h$AOZRcCiub6C#5B$EBTq+Y%Hy5W^VP>v|7uz9`cR%8}SK7!2)tFUuis
z%iDJq{Gz)rf1z@0G?7ek!e_&6bdG=M_wV1U;MXArF}LSI!fEoagg@+Cp8xG9eZa&l
zcq@gTmH6$}`4d()Qn5f^yIrVO(>Eac__6ctVLjxQKSUFG)|RLDY072GcRvAer5kdu
zOYypcw0&Ff=(yHwcQBp-tpoiw=gm1q_YR}VdGq+y*0=bmyGhmwf*$L}@+yQ#Sza}3
zwA574>wqam_t|!7Qt)Lb$HjL+6sP+^<>j6R#`I;nB=VFZEgG1CoDAHO2^;I*znSrA
z%{-*P>o{+2>G|_nS%$g0OXgFGBnBXjBm}A(>ZD(;XocOOSdRaRv`Wqn`zC{ioaSq9
z#4oLJrQB82iGf`SBHp+U1=h;o$msmCi6EsNu)5_pu}jo4X4_9c<4I&)=fF5zh3t<e
zMz?RC)1%#LN|JU(76=Kdvx1Te9gnKS{LI{}rZAC6QY9MJZ1jizw3g(#ZlMSVPZPRr
zpq6@<Da~7bL?0<(98(7hs+swbkk0QgC&+*snYFjiBa2T2u9RH(!wPyU^`A6*uOiq3
zchOAs3?Oq^y?0-HVg()(iy#olJVik)notx&F6#ZS55ZSY|Jv7G`p5ucj~!rR|C4gX
zqwP=USAs3BJN=<>NSJ{BV(F;NB~#B?*(8yh=(A3^|GA*<gh7}0Idt0z2nh?5unU~^
z4`*?m);meQE)0z+7BjX*+ru$+Ki|i#FKykQ6?T7SqMrMA`@zt~K)0eC%FsON48`+Q
zril$l&8*dz*Q+<hnjs@beU)|FkHjrgaW8W`yXk2uv4*atmL6j<6=TYUge2UXGv~yM
z3$AGKuIC1mp2!&^)HSLL`BJjUndneU#yhAw9ksKckqV^7EE%J-KAH7hmxzr&${j^r
zLHE0LsWH>xH`*yTF;QWDuh%a)Z9@6pUB>l?akVBKMr@v8d}w#ZXeVbnEAvenlaruy
zQM{?i3ThkfdRx=rRmAnifLaqQB!V`kMS~HA>Jk%n<N-DN5sCQ)4oC34zqw-Mc}E8=
zVRq)pA!}?-NbjP33-?mq_1RO$rvF(j;Ob7*$yjLhCjO`}Na%wQTu7?o%e{Y(YRL0E
z_)PBo4}<vAm}+n%-g9NMXTl^@Q?i|3?dIW19UdKVfAptp=AcI7RL}%qNFCF{cgaWN
zbsBDB1--LhZrr!An;K1eeSx$jncUajf55q<WkqDxn54D$4>f)O6-F<be}^!IptSR`
zZ#IuXtb1?gtzUq2RRo~t#3twH%*~5`Wo&3h1SL5L@~58ZxZIWV>Fwd+JOv_SIaNkd
z(O(Jx81!v9hP&!4G5$jMS}@#fY*q{3AG_we*kb~hK@OhA>rA>g8$1qAulrFS&V&B!
zngzFNv2hX~h|<_CDl=XB{aCd*MY6^jS3qs7A0W>qWFb2g8u`hCY&B}wBP#Ce>KU|s
zJRX|2e`hfu4@91o!eZLfQ!}fRB^y`5XAs37!G;_4p#pxXw&afC-TAe0v^<?JtQzt!
z!6lGJ@Lklp%jYr}R2wp2M-J7DBHOBgRUHp&4yDK9WR1_z%7+JRzaH|lPXe}}%eMEr
z(~<AM!})s1(}d|Kw#)Ul%lykg27r;*qBcJiZ>oBv!8He~UZt{0u(qEiHD^#5t4jTT
zx`(^L5?tTwLe5nCykBPC-v-%0_%1@hn@CUni_^LX10sfoVbT2qFb(b#@86whShW*q
zoj(SwvROWWJ02-K;+B(#-~r<uZSKG82n?g>ot)g+DLf6XQ;2(KX%H|QE=2r!3XF;h
zEVGTmY(6Sg*v<XTE*sn=Rg-3jN$iq9R|9tZ!7$9l#PzuUecP>4&%a=LCOexH?5)}+
z3QnVMTN!x8LvV6yd6&;+;mn@T|Eh=sIn4x?(KVSlouXr;5@jRvayD;soQ40<zMCkd
za5pj`snQ<jm8H74KfRA6Xg-GF_wCT(AT{N*C{rkDcEAr;+2e?p`5+vExoqrX++u+6
zb$EDqp55i<&e&}j-{yy4i|S!u{@OY>AKwE%6tz|V<uPoC-`2VWo#h1WDUPptY;8ZZ
zw&e?<y2Ipk-2hyxZ){eY?KCUr?f7!RgGG?EgLZ$quQ;h#QatmN5)QQ}R7RC3X&N<p
zAnchNHWy*@uJgXP!<uZ%p!A--hq=zalVD?ZrJujduM%`yLm(M9Ai+Z-S;O*g0AT_X
zUcJD-v2$brr!J8K43_03dL$x!el4>XCmo+IKQ(vGbU#XZXj2?i7@(tXQxU+~b5T;#
z&2BW`D*UwN8c~u)m3+gI!Jhf@6UWKq*KIstHlLAl<G5KkiByQl8)P@*<W+(Z2FJ}k
zD^)s+nYMqckQ}`#e}8n)!qR;mv13K@d^**tX*Imt+wnQ=IvfksR4Z~NGn0X@@geQ!
ztiaNltF;<cr}+S}zM#=f59!foe$))C3iG2t7VWZCr&z`+EfEpU2hvRD4}b%AKPa66
zn=OsU;<KoP{L3>6v<?h>-MI8P?cYr0=F^%bMnpuc>o^3720XByW0*FEyhE<9ua92C
ztgWq8M2(<rac5_z3hK#fYKp(Cf~T)<EE?K9j;3i9=;`S*mqcE#25D$$)bUnZd!Qt<
zxVZSg-U>d?R&5jMa*jT*&-j*E#Ds-m0K*wQCjk?^<c91Cyb2{T@hMqdea|WEQ(_`w
zSTuBhpQsG*!Bp9(ROB@le~7_f3V1Q$KGSNJj6q;6_P9Q*Sx!7ceSF_psp+Vwf(4;k
zNz7foudbDD2Zn1Uj7v|s*5P}D*anE{gzOL^t!!mIStXfaH+BdVG-Q#@(HReVb4O-d
zScD|;^b%7ZyG@FeffpT@y#mf3(%skil?}4<#ByR6C`%S2vEvBc|LT3${g85>8!6f~
zbKVjdSI3&=-*a5jfH7aI)1j7aoOkY7+Og_~VBOK@vU$3QS5`RTwdb3rGoO}b&Wdsb
z;`pq9^WZpYr9@|^aYaUxbt@8Qc2YQ0tz9#%=h0hRuf<#`-fdPXW)g7TLU<U~XzuOp
zZEjw?>d5yoLDO*Rs3qXs$t7UD?`^$L^k6=dUcbQodYG_qS)gX|bkk>h)45I6RFIqd
z778^sc29!u)<T0^8<n5ou=L<O^=?sjWp@zaV7X?~naWYD!gJu!KUpI;K@ZYaU-wC+
z23l<dQ-aT!Zi}_f)xKuuI&D<&O-xR`<A+L5bc1geK!&TGNqmwxAKgt93mkha1<7)$
z%G1={=8nszZKbdnZ;o`2e1ig8lm%B01Ue?$0VP}|Ow#_JY0ahI-N)(nh!!+-o#b`U
zQz7oN-o}Hi=oS$7ZLzp=x-2>M<`vs%WmUgczkfRmv)4N_U-Ygg8?-M*G7e>Pb=8XG
zY-|Dm!$wRhUQVt<o*T0%DG-Q(sgv*28pg6o!2NTJWtOq`NhTW!qHbOLzrETB4Hep1
z2~$(SOF~kH%hh;hu-Al10q01SWXoR!$+r1{pu3}k?oa=MNAiy{6AOw7(|31woz`2~
z-cAW*XEWIh{d-L6mxW)@t2A%Tryo3_=f&m{;dvDt%qJ!m48^I0q$Ke4wAg_#t%63C
zmgBlfmp2<gi#emWxA%@m)rJVXa<h@FB$@wB)k`)Etr_5rO~e3@$kEWK#8$6Tp;PV(
zKY{e;lK*cOfQ+B?74rU&(akR>UqXGa;vdJcG~3M<$nF&oBILGiXc*;{l!gqQp7?jv
zcCht|d}^^qN46?z25T?ZnzRJpm4un1#t4ZJmVsQim!&G)#)(Sfxx+jrcpRfZdBk;k
zwh>eGZLu4T^@hYre{v9u;vaP#a5Wi9hpTvEv#qrdjaYK-E_xsP$1?5Z-1mR#|FG?v
ze2)5?cK>w!G=iVYmvu92(P2=l!}L?F+#EFN6R^NxQJz|0#NS$7Z-9nh8PComd?4jl
z<RXBO3Kpcs08JsNDQRx0lnTT*tH^@9*Ai$6R-SIXM)c*6Wu0yhrxXJ^GwJE`L8p+w
zl}4)xVh7UEle4oXr!J_J{mboMsMceWRAOtFm+*Q2Zr-iXWdh3|wnzafdWJGojWY2I
z-5wrt8!`t_iBii!899TQKn9z`%**p7D&(Ks@_RD3^z)YCJimKuP8Yp{+}@uQ<IxLW
zLx&I`fHoiGE#|-Dm9~2T_{d0uxWDn8*zQM%i;qv8HHpEaTBZQ#PaHXCPQJ%fo+>(K
zJl1%+<S&lseeUI$qcb~?%rZM_LR@S*b*)cYHfQ%ImPsbvc5j`t8Fi)$KSXq0*jqb!
z{`i%PR9Q)#iHgoFUlNRm=VqC7TU`#dpS7o_*jKSK)nqy-q-q6;XWd4zXxF6kZ{T1N
z)oL%t*MahL$Da_Uj?Ju!3`o?do%n_6w$JM`Fa{j!2Ygj<E7!ygs<mCo-=4V8_Ys0=
zDnlSgA!2X4$H&L+34dg1(WL>tjWt-}0Ubup`y&au#$Jnc?=c>|mZO;4kM*6W%-fHJ
zzfjbGOjJX+oNElttX<BGj1-h~;a{zbLQ}telibnfhy!)ueJ5a28<3FxXjdYkIW?f$
z2Wjvz4$8uD)6J=O={E3@JD5Z-!_LVK+@EECTO9z$l-gA%l7i8!zAXTm)PSyQz)3lq
zMV9)WW`5yb2PX#sKLK0*Qpz%b!iJ_4*0hACn#^d5oj}FW&%9A%{u~$YipqFe;G9Xh
z-rrWkzV=H<N+=}fGjh7?=Wj*J7^yjS%FRaVhY=+w4`<3};|#fyv(rhnCgyrNvr~qN
zqCy-qX#SKkmCIF&&udcnqTApu#N%B9({iI9ZCz+h4D8$8RLFz7y;QRBO;bkYhr}bV
z8{g)Hu)pP0*b%2EtF+}JqPnI*hy^X%CN-b`+K9irqW`Jtc|Bk^txNiBBk*!MYSRvR
zK2ueO@dZXm?VgA~tr|v#HB`}}$!$U!3MvpvFc3l`G3f*WP<D~7sQ68(MLye*j%$&N
zE*)!%slhpDG^Hiz?#;AEEamlZ*r88!Zydr@f+EP(>vVxe(Hs6Yykpf^Q&km;ttKHz
z6Fx~d1Ge%Sw8nq0sZHBn`T1VT(E2%rHk>a1YbQYiqsJ`|#$0;2-DJB}cIz|?oz;fX
zhOn61ScN@ljcAgn$RFZ!dAy*lzp`;o4KA0GEEtGP@#xP{xg|Q*e%l_*Puhbu45v-4
zX&xB!O&)NIz3BrpzpobVEb>OQw{3db=P}MLrYSQAbJB~M231xR=cQ(gZl_kEz1zEs
zdqrG}Au!mjn3Uluuy@@lhPDO6g1Aom>dNddx5r56Qe|`Za(U;|VXj;10WovNxL=TA
zq1z{^Vl}<K;J1S;)80EZhqrx-cVB3AibJlG{>SnrleEc`f&mO$T#s#q*xK5j@DHEW
zbvfH9r_xq}v!)K*0cPtDU0e5ceJ@>oa9_or(!}*UAKoGwNDc2kZapROQ*4-W(<1$h
zX^l4N?EMZi1jm7L|9xL=Tzd^+9>Lju0GUI%J@xK}B=NnltM7wGWxq>aAJ;vf1Nm*9
z4efcupedP(^aT;~0^Z8pVO=WDH_sw?sjlDxZvxB(`<iX>2jehx3sAfVrG`WP2$=<u
z4wVam``U~Iie;w$WB~63uU!pUo4>N*P^~;rlC6?wQ!PpS#!TbVH?@Tqt;3bo?MeI0
zesN&s_i-_9T38FDBd7C3fTkugyktJ@{^UugrM_OPnh)dz24^7Ileg&nFp$m8&Jp}o
z#VVxo(M!-0s?2{0X4RoEF;Z!@ZQ{8t(+=E<g26eh!)_iBJK=!O`yD<zd7NPoSg@Oj
zWiuh9laW2}=p}A?xeszbPhkR8)Bkx=-M1l~7oBHfcf}uwBO5%9R3;iTbqf4%=1zVb
zP4G-<UV;Tpg#hc@+wV`{kl+&(!p57QN=TM5lougSl_Y&#m1SD0gG9ENv@jR|&{x@L
zSzk<6+~y@_1PlY*qN*hjPDxDrYjq@fX|g>^;PNcX>FCli6~$+{4hJEPuWUJzlFZeT
zOy!#Rr1=6uZrKb|UW6`|vDqc_;s$zz??~n8h80U6EsFOAfH_M$zI^+2)QTbe&BEI=
zC0Vro+o6M*PW03m2sxl!c7xa|&)Xtg#+1}%J<lb_zv7)U3mbnwce3Dad+Q`VFlaPe
zE+!1*UAnH@B1w*cIoCfCpnc+}5cBWGjq2B*ew-Jm^7$x)pB@X`a^cC<;AH3L;A$@E
z)7Y7eE?1$hDzit5(V|5cYCk(X2^d%^?1Z@^p3asG+0Ex%BmCzUVMQpbUcdZOD*jwr
z`JOsr2iSR7cS+j+H&)Q$DSc)0_q3)S@hj9-g_=~$-G{uwJ{KaR>`36W1LU41u4o*!
z_hqN7x0j$dyhGj&cpjvi_igR`ueJTU8|9h5B)%yj+t%V~gE%mYD%Zn?wQGKukIu<3
z->6(xyxlHyQa;uOom25w0>0uhwIC|fE~a6#(hJfs<g9qVvC+q!o%>CA20vDln+D31
z8=&S#iY9}GBylEaD<*d)e=>%)3+jfvdLT0Nm<Sk+AH5A6@0k7KV?AW`4A<3HPb&Sj
zYxMrrr#XM+X->__>>zg5JJp#~zXhuz1A?!?q@p0EyVZPBGG%F5;N%bn^OIJ=V6)%d
zDJNFo)UI4q+_Y93gSxaxk%z6z`<G8;WugaLIJJ5i>u0o#Je^o>3j@~7?5A4~Z)E3=
zgG}yE6o`6@gvC6{`vQ<-Iq~N^)46l+uFZ?2;Jebw4YTcbxBaF*O(KAA0TKW@*s58y
z|1Yc36f$_*NLp+|``!pjWlp?f=zX}6qwqqRgmpx4g0TfuhvHl{ldCUiYhlkA48w;n
zF)@~ezK{N4=1_8Q(q7N2PG-KR@*2zkRg?H6tHO(biU)n%l1pK>)nj{041Ko}8?pMU
z;EcFU=YZ=W%c6L3z3U$Q>kk;#YWBYwGdEi?JJs1~K~O|dIQ{U}*Ad91p!cg)fWI>)
zF|3Tlj(X3%iU4_uKRqiQHjU7<d92ZCoXo=i?EvRKPotQ+Wu^CIZ&8{i!lH_dP7|$I
zml;4+;RLnuJ#b0+UDS>}g{Dkg8aAslfl{zkT4U*)@`JAJeV^HE&Lc5E8rJ@a2iVbM
zX=glM0>eRfsw0nYO}Z*dN_nDimun97qFyZD8Se7(uwKwvmydd{YK=NH9C~=HdQIvd
z{YBSXqaq{49vYHFr+lLK^^o15{FOI%YB1`<Hq<ccf%;3|7&gAw>(nUfii^ozL}01<
zbwreVTrAa__VL_Q3N9RtyBooV_K%$9#T5A6%+_fwQ9$Mo+JcUMxiigFCrQCk0^~!9
z@>T$0K(KnXq(g1!n2e@E#A8bp#y4gQgNF)@z_Q{23D5(>;^9Wa@6D}?2)}$=sb<Hj
zKVJ;Lt#QZ)zr>2kwnVSx;beY4+-SD<5~;s?4i$Y#67dAbe)#?~mruAv{m5G^76%yC
z5%fqH^{n%dlvy2m@G&l*o<YW)JEi1Udwugzel^?lM;-AL3!-#TP?fn3%diTgW1KeA
z!~tp5$>6?Ro71XD(+qDmbMC{QMOIESZIxz~cS*GCsQ0ai9Z822k>~nUCOt^QWGo;p
zuBRlo@Vvz^!MY{237sKP%9&%G4SQg{Ru+!7faCrPpVQi1_?h@s=!d+ZfaG}_O~;M)
z>pP0K|3(G3oFaFQq~gJ^3vY%Mm6b?HNJ(VE-WNH458MUO2g~)d0W-EdD&nd@V22I3
z0NP!pid_@W)czal1dZIM3M4X>C+_E2qAPM=6A}<)s%#iHSx*TTyyh83viZX;L{(nA
zHorYN+!xdSWwyG2Ts^ElMJi|@@FkPNz%>`Qz`8QtIShm{(&H9^Y$3;g5%bYC0Xyg-
z%RRnjjRS~O;VPiCk-I*y@n;I32@JNmyW>2Kp`GqjZnVW}pft5G!9D?D?7V;@Dn0?y
z2&p)6&U2*2+9`{=n%&Bbc~`(|!(mK)y+IQs!>}JPn+K}<hl%ap{}`dhEyYf|a6z)(
zX^lU2?_|`~%JijnhmB9DC66Rh16Uo6jZ9o-@9wQmtN!cZEdM8;fUO{cb-P2272=LR
zy?HZnMDarJ{;2sRqZ87vm<T^GcjZ|V-9%lcqJmW5c1~fgVyAK1sfxodGazTg`YpJB
z?p$(OuY*U>|LytSCXeve@k0N4!@n`Ewr_~d$>IIM<^ADh`yB$6cAB~<Z<97+w>jXz
zk;We(Pgx;MSrfgNeY+SgfUo(&K9KSH*KxWmqsBuXsMp+i2!aL=^*t`05><8l09PWH
z8yVvFH_${w`3KQH@uQ|z)|X@Lz&|#2M?*+;zW;!??J79ccU@u+vC<4ul%i@M`w>9^
zS_3%%suuQlI2k)-?bFwLOX1Km5F;u8zJKw$^Vch)D%>*gbm+{bRx61#Z4^^Vig+Io
z2>#~gs}t*;1qcdk?O8z+genxuJz!$*NhI6*f!Fo&*7ZvM<vBJtCn>k1it^>l-pX)2
zXPPu(a~KEnzt3B)f$Ii(*(nOAoQ)-^hU$%fPiJQ2orE}b&>vP4`|NEw4y>t;GM?Sf
zwX__9EK3I&&d|;|goJe24+1}@59ipZQOWRU40agxY#p;a4%_Lu@b|X}!B!tM(hjwd
zZhJ`Ok2E9~*$@)4hJlqx3xxzP^b@mgS;2$26$K3p8=N<J+i@3yo(~z<vrM%*XYmfR
zuS+g(OQTTmX>nNI3VPi9FJhRb^QqkX<o9_5#h)94#2H-sdVLf!%KHn`Es>Y;y;&_1
zyMf@#%evry>QNG^(o)UJU$HFgr9y4hj5O2?f*%@Ka?cxKWtl8dk*N&0OAv_J*4B|?
z9Msr8O!=(u!;Lv2l-*$nX@yHFN_U$PyX_yt&+wSK(n-|znbJxIT?|#njV#rh%%3!#
zjFR`X{Nt6k+AqEgn6~Z{XtZK1f{M{Zo$G<UJmvYZ;m8REB0iQ1XIidHKfgX6wHlOj
zU{OV(<)qtYp5+l*uj;%2_O2{V4e5GYM=|~+TWbWO{mT62jE@L=7PnCi?nuFb>pIHb
z%Gd!Nw}*4_;>mE#)myjb@2&zHxA|7mE;ZthA3fatD7*VKMxdyE9A8=$a)lIfHMw<U
zeCfaJ!I@`sac7qg-?vcsTU*x{0Z^`SvY-tA--JEax$`8>^mVWB{i)DpwXuBt92#dT
z7Zxz_UbR}ZU-a56*FR@xn((O2_4Ld({&E_DYwy4PviyF>cEU~t%yEDJGoKawctoD8
z=Kw3ij*%o$t$@6?CLT1aJ6gXe(M$_uQjmbLt%|Ndf$2kqXom$lEk<a$;{u|`>e<Dw
zV)#`ctJ+BX?fX+PxJl=mr8m+ktCjI3Umn(4&RU(flEXRcGaX86YENVNyWhO7A?q1H
zHk-}N*Pa@dbeQZ7i~z}+9u84nPVpd%%hDH7<jSs;Vo$ut)MS&c6b%rV@KoyYF#~r-
z?(9}0+B;isq3{gRQu-~OvnpNZr&>LD@eHugoJScuUT4U1!1(pZSIUm9^l(op`>u?j
z#iD9iku}V);^bd&iI$WX`$;U1SzavT$VrBcaKQ8=@w$Jd$ieJ1cO9?qZqGj1rQQ}y
z_Z6ElsV_91u=6WS(V_PyqkhBhC_jBp(=>s9({ujqar*-xJUQ#{<3Xe2EN+c}{O9L;
z{6Sju<_pL)UBJ@OQF`o`)4*32;lAKUN-C#XK>!v8{MY05S@PGJ&^B0ep+z8bDr0hN
zIV_$n3T2BEtlAXX$ghZ2c^ZX?^oG$hql8C-2Z}<p;O0wLN@Kfw-Uv6>k_MlW`!G0$
zs8;83jJsQ@2$*X{X>&u!l|$Pfq&lOf=b7Oekx{Ndda_SpKj(Gth25khB@0IEaP)+l
z$x1bvBdCMktad8!epqLv1Ib2NJ3Df$eK?iq_SNyo>f652(&zoFBbHSt*Q&#hE!YRu
zmGc<edX;K{D$Kb+v@te9e)FiB*l1L|=AdwMFyCY^kj}+FPs1V`(I<TAqi_hw0qPC^
z9WQ)o{05zi{FhC_-6F$c=;@AzmvptSe&Jm<@)%L=d+fP1zQXR=VVy{jLr!NuvaB79
zc}?y3j1{1fJ5}7cVR&?}h4L@T#i%3rYBZO>RU#KqMg0q$RQLXSzbkF1L<Seb09eke
z&SVbF12S5vFCnKNWBCR)FpL56Q-%EBcK$f<`8<jS?uGWqjc0@cgZmF7rOn>2Pj=VL
z@<uW&K1Jhsps#<D5xP?j@m#DJeR%te`hw;XwAeD&dLy#MEv9|%ShBD&mL?k)&;K#i
z19i-744;6EoJdrFm#*1x+|LO?mU@7r;0My#=jz_`MN3X0HT3zPX2e2Pr9myYO$k6j
z7otP0o?i3&Q|qQ$d}D5|MzyxnxLblsAj*hub_Gbod0nz}{bc%pEXji=;hTW+&5!Kc
zpmLlNlSV@Zn~$WVMnVh+=(xDJsz<b-CU(=rj1adfE#|?&LB)yf=cP91-ls$8+%yPE
zgcWn8>_7!UO+T|hRvp*Kj8Zl?{N1K#V|a8jEXwUa*=x%Ja!<dg0<Q}~y8kM^-&ckW
zKa{#XT|QijnNXA|`h+QG(1+WWXUtUrielZXzpR{NpESI^M7~bv*2&jz4TQKKwb{)H
zy3XvIb?>FtCTM-vLr^F|o)VnALSsQ4UheY_dOHqp>rAHSY^%}opc##{XW4$~dtZCJ
zgnX#~LvgaSoRmN+=FT%(P8{)M;q)%!yGq};>8Q??R<lSh<X!|#Cjg15i9U4%Pv>P{
zcB0FJrdy>kJnObB7Jdgj%tky9jeRbWo^M9r!1iIAzY+<l(>F!-Wzl{LtXj1fsK77R
z(9{?^I0A`+xmXSso6v4Er%Kf5)I|gX@<3ebh;g8H*^sA$Eb&#NXz|xUagn!7i6if4
zC*y`2cKiPfg#Rj4ItufAwaKPq^ai#Oy?EK+*G5m$7)u$>2Ia?QAdf2$$K3H6SHY};
z2G{Kj$#nG*H3zdcMMFKG5hgsfe1cXD*rElpE4P@5<xs&3@blk$3}&O%0e>(oeED<k
zw;qeYskhuj${j0k&Cr$Kw&gzcCuWSyFw#MKP$%b8JQx@kmV?xV9Ik+(b@B>LXWt@M
zEhkN}-|3=0zTH8hqq`+*4n!POOgRso$ymJOoBz^Q5QA0N>U!OO&f`ciPSnt;ON8Q~
z4O=jUc-p(2uc-Cl3M_z(9W|*A*D<DvD!J5Am1uxg42dG6XSW`L>tceU;93z6C4A3t
zf&}j{HC?Q1V?8s8J-tt@hT(I(rDtEOJD&WDukhwOxWF9o3^lKEVmB$3rcZP$kB+{G
zkMV&_dWmfQQg-)^|FS71GUf~%XuvL;*0**4Dn&zE$|YLpn`D53x1~edYnHhpQnXZB
ziWu~k4LVY=bXvIv!$79b$l_uhc6Y%O?(qVU5V3o4DaM|^Yi!zbgcB|4#EIr)*`SV-
zR5GNC9v{akTZ3`8^}s(_0ZWOFJhugzRj43Veu?jj(TXKw^8Yrijmy>ZDr=VNQ_nxA
z(~Cc^{#6!6l9P*%qF-mx(M|$SIe%KHI8uT9m7Bc+VMLx3WQ*q9rS=*G7q5zvw!XXo
z2}&h8ih5!%>A3ow?~g5-L^-$b1hgym*QUohhTW+OZP#q&Dx4;05Aztzh~qitQD~7;
zo(tyfd`R*i)T;Q_ov|mJ5vk<uZnK`tav8eg<gwuUu1WgbevN*cL0fB_?3}gOn<YWp
z)RvtQ%%?aE@fQqGbcSFIo_?rrRSX*HW|Vs&l|tUP{x_r5SznAm2M@r5sp>!+VKQH6
zZpm*VGo5U!>G$cmwh$D(JrM3CUU#VPdQM#|C-o7j54kFi3bxDqQBxl<{JIctO8jRS
zhIbzS{t4EN9+MoCU`?UY(je2CpoJ_2bw|at{BoRyorL_@!>ZBd60+)Dsn^W+!#pVw
zT=2Jrz3U6yuH(;;f4y)YoVMHf@W%8^bG~Nh74o;|8Q4ZC@N6tEABO{*@*L=;SY~QD
zMD$%+=d^}Uis7}^jy+>5V5*u~OYAJxy^Hlw@R+oKoAA~`#ZK$aVn+rlHS1oi<Yh5R
zU9~z6-**nsmKMeoL_P*YZg%<DRNWLlFH~j*AhTosM`dp6jJsN0wzro5yQ1V8RP>>x
zMzzNtGVQ;gLGdB~A<`kg(MGdGK|JCvlB??)_>`b<Q`KCg#i&xT!1HZT*^-m5EsMC=
zKvNm7;X6DAeEXES*bQxAM#Sjl+cZ15{|sw=w^e}O8Oas_S!eycA_>~<^5(yflE>h_
zxBPn&9TG{;*WX{-I(&yGe41^nUk!%IPE<9fbE?o);Tk5it8RFXf2`azO3Tq7CXsJ8
zI}g4N!~=wByVcxZYM!c|tV3#rIQe~sGpCrcO9$bKk-yR27>UCoBC-%IS#$|&G3OcC
z>LD_%Y&p`bb-T;M>ZZ4TEdJ&DBjdnB>U_yUv{tSzV;;V9lfxyqA-(kg4~-5%rmS)E
zjNa{MqfXaLFH&S&dP6gAbKFLF)`Q41_XTXq7#aIhU~os&Rf*^6!o2WB`#ux9<n6Z>
z=!8|%<8xup0kSBary;(Oxkku}e){KodRb)viUgA_;5n0Of4v4G{;(x7U3XUfxG|pt
z&OhqpRn!LA7-nXy(5vjxs^pC++)^el!wX^gen2MMLh`)}dlXZ~g$*n^p}#^26@fjO
zQ6a*S{^pc7mL?+K_(dTTSPnXy-p`2=y`!+d<Mfu#i#k8kyMH^&3-Z}~s}a(!Su%!>
zG9@t5sb@Uv_0(tvZiY&WJL}PL;bGyi`?+9y&7gMg#;yeJK+=9<0*?4o97WtdqsZe-
z*oTd*jb59}f_$obnjhfFL>lTC8S4rIRHOHsi~}jFxzg7$_P2RupFZ{*SX&E54&1Nv
z(PeSSGe16^95`+}X+3UuUQ|WhEOsM?=P}d6V+7Tm8My2aaM_0=M3|N2C5fsct1}<u
zV>KJx6C68KlOh_Wmc>}uxjBgzPGCAwTnnVB>?eC9EgPN(GUNO4eN`V{2%=ztM~shB
zH)8z5Cltg~0kv$X3x&GcVJ-M;=+i*djO(^7+Hj^|zq|TZs+-vx-~-`XZ|U}>c-zFN
zLs-_#c$hj_HY`e72b(0^g2}fMxUX1W(XPOeXgDR@slzwBv3}jL2uE;9Q8U6Vh>=Q^
zm{bxt@LYRg*}rk9_Mp8)2b{V6zO)|Afn*B#6Y`WGp11MRdA;|iLsPXd=rqTXYB!=6
zG(E(5>iLpdmPFPoTa>PFT+pGwIKfH^fI*I?LYB2jVH8S9zv#DMJajVy?*7}rjG>a%
z{lxakt|~rVsf?R={dStom|Rb%X?4S>=f~M+>MUo2Phqput7Q7dEW{oC`!X7ct;S4A
z=K3kx8K^U4|F{rS!?~?f=T?%aGbrQnXp56kQ508|#_u;cqL<D{gplFC)*gPQH%xX<
zV?GL{NJ>)u9*a_EnFR4$95o5RiyJ=LS~|N|XIkCk?YBi2-Tf-@iNmah<=V0kclX;4
z47`j43?Lc?4j$$g<?c;-F-0ygF)P$e{`b}i#w<F*0OiQ5J_U^a?~lLYthzZ{jwC7!
zTF9T~-yfdl#cwnzI1^<07PE6U^~dmK@K_>GyrbTJAvzg07Dx>JT7O7(YCA)62IPON
zUxb059r5djbyT^!aJOlpr|DR2>?wF(Xj38WJt5MZ1VvQXSjXjj5g@vWM&u!9k1tQL
z@Dfsz*R5k^Hg|k<(*8cN`I(<!N|N2q{vdD~({e~Kng)d?o+Fx?JEbv5yhuwMA2D27
zcC>Xrnz?$URA6rdV~_DJtY5MEcsELw)ns)B2Qh1*V381BLUur}*O?|68W(-}%(iQF
zWIx#9z{<)R4;&+?hxz<xmIaiI@8wLvsPYp+`MS0}ha}GSoFt8n*54ozp!p4>$d#iF
zW=<gw5!RgwKEd))Db0l3m++;S_e(<*%D<4UAoJ&b$xl^3+xK1GuV+o)SNa~Grw4}d
z_0@l<2<HPI9TFDN6h+F6Y{?V!|H>W}%0BoZGM&GK8Vp4E?Rv{v5y&%TbJ$k9T6r`r
zZ!*$m1g`y{4MpB)aL<tVxf(C_xF|Q}*_t<QgA>t2NYFtQ09(e(mak-5-Ex!jMFI}1
z;rFct3{epaNegAH=;xHOr{Lt@Wf2uv(T1W?@{JYM*a-T@On<mnzodTV%tQuY4RN*@
zxbuiyFRYfgEcN!>Mo3B0F)wSHGwyxmT|YnbV1IbK40*rhTQv!Qd4~G(;vZYW*-SL!
zE8t;)1ZL*()^mT3Nw7KO8<FvqDE^5!gcwWEQ_|pFC&y)nCBvc0t1t-T7)2p7Q(H_A
z3%Lkl;b0^5`5j-*<KBj{qDjE3Y`>L?d;2{#3ja8Zq&Mg33-WfdLwGLAPBp?j{4lNU
zGc`1Y`CUnJbXSt3oZ;Zcq=1D+TT+U$D>9QJ%G|@xGJAds*pjX^UY?gIJ@Gr;AR&R|
zaOAK?vo^7f^XK^L--3LPno~Clxc|)p)F>+`^VFIzf7DTegYkDZ=f#BkCAr<^WvqHK
z_JvxK<MczPGm+V6q=K^ulL0MlV$`q5zsOkdIPs~S@45~grIx=8Rn?s(74$TBaIQ3X
zcsqNyCM%4iqV(u3l~46*e^0M(`wdO^MaW{R<O}iQaI#ZCwFlV2v^RsKcepPF+wTr5
zMhQj%c8spuA*8v1YJ=)N(ElcvQV;7}p}yr;G>IGlEG4p+>>PT~r=)RGiyt*iW=IPp
zkkh+L%ngBJyfy{W<N-HYtdiM11CQkdBv8Ik^ue>WjfK0$;!Y9afmH-oraUdHFuIOT
z*&MuCJgpYIRLST=#^_*6WqSZ0-`qlz(ij7?MtJ`>`_$P>46X_g@ZduD$+0|nwa;VP
z-Ook9QE1u;ngGmMtH9b!lHJ5OGiJz77M2#zmB&l1%0^@N44YZTQ|3(wubZd_@yxg4
zN=#FKa@<~~m7QO7cCp0}#h_mHu<+6Q&D5h7uRNvVLZVuUuMg8>0&Yc`Byav$tHhW4
zl-SZ_ng)G!aFd<%^&d3AJ<j5%?`NXeO{inLVi9+lF?$!EQ1|1;TY4t?nn(+CKQNT9
zE1J5YVN9pcyw|D2$aPu}Y5-DEBF<=~u%f6~*c=<wik2U|gdB(FI)irf692F=P0<`K
zk-rZVzV3<!@Nlj;_MMFemk1dC2CJfCtep$!<{!74_%wWq&a1V)o6GKM2>sGuPJ1b?
zm{rK7mGeE^XUWy>u&|)}haeddQY)E6yeF}K*mUdI_+6{<2T}j)1@6ks%q+UerLL6t
z^hy$^^`<aJ=8Vygn|xm>bAQvKbN&3G(}lsb{ntCS`j;hvfb+Dxx1lj>cC{T&iY+=~
zSTAkLZww+E^}pCLj6`1<Eu;VdJbh-HyQ&zsbuT*^Wo>Pxd9}N#gz^Z*iDm8M!-(EM
zqR9H5qNx@j`tm<$B$5Y*&agX^NYdX5ee9ATWRI=6Blnpr3!#5!$B_3*rdg*R<#v2H
zrq3`15e`5!8tytxB|(V;_|{GlLy$#F7hk&_5|Vb!qoX}jM<W}%aA*2P(HGdcm9Lb2
ztM9hY9t^qd+t>_QKP&fSJ~$?;IeI``wUAUSUuHKtWpmL%?a*oouT}XHl7OC_r-Vu~
zCB*iL1V=ipmVpNU0VK(kJ9C%*!MAb|i^v^zE9j+_UA+02+x_LtWdxZs4==m}n>Djq
z+UEP1b<L695KF=V9VuE&^Ivm8!z<VisF74B0$~lns-<&+xsycPFE|Q6@Pj_C)QwV8
zP!%aqCI$|sOTp0B=)>>*_99LW{mPk;`H6R=f+60K3tNte#&h%Uh(&xM|4kZnr2Q5g
zLTh27(E5$fu$wl%YzaevY`4-cLQvvVQvWLlkmE{4Ahb#j0k?aUDokF}7tdoHD_n}6
z<LkBcclpjjz=!Vh-2SwNA4s_@?#!zJe|lTWr`mc>dhV*`r*9f*sWh@TO|Reg9^bFR
z`krrFaV->-4^U&xIr^<Aqf3xWN+_LwERT124>e$vbCUl^`Bm+Im-rI$Sjk^+^hHOc
z$!P?G72h3$Ng+YjWWlMeZAztgk)&Y#*y_`diyUT3(Zs-1Fu{g7B>;ok6d)qVM=aAo
zO=Dx`R1^QfD8QoR)F0;m(R7Z%m37?~K1oL%+v(W0jgD>Gwr$(!xMOu}+qP{xx##_=
z?$7gMSDjiLYpyZJcxGxm|36-hh7bL!t+J!U++!!%pJG43nMoSgZ2m}wh_J;TUiGLb
z8(A8fo~ZZh9`}6pAJgg3$(sGiZ(Cx3?bCkZ;*z=lFBaF@{NJ%&ni4N1JuU9I^<FCj
zxMIrDdp)R-y~{k_+GL?3yyy_1MiYiT#E}fa0R2D?RGvL~01a4_5t339qL2th5!bbQ
zLQsFSo>q9_UmuT2=uOX3V%hC>em%ar4Gb^SYmZ|lhl%gkW53v3{wzvkwlJUZ2JR;T
zv6GPiye_L8PA^MCOYZ2`zQR)GL85cu$>uUlE@xlw<xbzm<@n--L1QjjUIy*Y&0D_#
z?wu4h&OC}yDA<r$iPloF<l2R10Pllj$;OfOyYb<K;_$*@aBj=LdM<X>*{eqwvBY`A
zA1o(u;K2^46`4PgaL0?!g)sz10Lv;7jPMkTEGdqu3&?_)T7onjO*w!$^G;PY0&AD`
zb+REt|Na%F#Gm;Ii<2?uO<;Xib7S4{%w|*3ntji3#foLi=ai9q98-TAJFu~2G7KY<
zcD?{x(c<XB6dYgxr(ke>0H6o0hnFB2F&SDiA8~7tFcK^wg~1S)6yN(p(bC#6Yu3!b
zSlqloJ>lop^N`lc!d010L-*Twx0gOq<Nl=`ss0FJ`^)op%kdBXqE!V4>vESX<)F5$
zKVCN_KcLBA)Rrn`hoc~bsWBDV(_|}Y8<nxz{jKSDq&<m>!xtwZSPkML+YYCUoyYH6
z0d5~Xb?<UKP7gi3m#a)M?>}*AXy{3mecXyWT4q^P2A_E}R9nK6MaWQn`>|8!G=dq2
zdZL7fLZTVagc6K1cDj}m2$5+Lk>*TJe){@)1c@y)cJ^FnmHo?@gbVH0Z7!9#tJNAW
zN?h$*7$|bw6tS)T-30_6!~hoyC8*b5(Q~tnZ#zYS<yU|JBLO6nH&w}fyflg|C5gPe
zK!yOO5C{^ov;YMXjl@$cTL3Z(W1omHq5n@>H4Tj}N7I=Xx$i4@GT`@R+L6VY>#@NP
zr*$_6sOP_kcsQ9*=IfupH(##<CCdfNnw0k)`jX>g0NfB+{g4Pmuroge|0OFr`qU3f
zECNx3r3vXqSHcZ$ji(KRZVk0ai=m8Z0Rk)A(z>HPNzTm^*yqa)yf(Q5XXmfEhV>!j
zEML1Fk^PiWpqP6ZP<e_=x<@fVI!RHW4N6&}_{$ZqG<g)Z%78D2nTaV_K%t1@23V*!
z&tG!-Dw0RqRxfC+SD0`c(!~1klc%RKk#hctOnopanl~iKUi$M>{okY1r{<oALzZnb
zHx=o2T(ALDbyl3`_mAklLJR;1Arc};4+fFt?;)fRO;E(}bOCh+=#WU`pA4aarSF(y
z0%A}`W0+(E%73_TuMoZ`iVkSSI?>wdTRXN^eeKP<ZG-$7DWK<i%2zq^N)rGIBFJ1=
zHAiX(!~CCK0!htrXbi_OgNq*`0b*0m9`*5)dg@gw(IVzSs5q$4vM1?dZB%ATfrMib
zI7*2l8@?|`FltkmnK?c$r}t*6Z2r|WG<E<~IBSIwbdF2SToW6JIsx!iT<0}kL{sF^
zcri+yh5`cEzf={1(mxZxiE{CcQA`nAn0f=w-vYAc#0hgE80V6Wal`6ye-;oSRaQQh
zMjej0u}CPO#*3pePB(jf5|}PKlx5L2t?YT}gPo>;0;T{%fruoC80QOK9R5s4{=ymn
za0z6fJ0I?DP+SB`0y#At5mab|^3qLno8uMr0lW8&sNJ6Pq4Q|1gkjQ{FiWz;^w`X4
zRZ>WGn%`&baZ`0<iJR@Y|L$OAz}lrLTSA3eIw>kdxyDA6@0ug%FvfJJr79*xQi$pR
z8cd3bkOiJ*JOK0W>A)RB%O_kj6*3n~^L)%}?j?8J<!TPIA}Hvux?x$q_Lh#+$>X;r
z`95d_bG96rGe?t3dD-);8P`qu6g0B*{Wd1T6d~2bI*YT&5@^DLnd>L70K0bGnR`Jm
zOtEOY1@^i>@ohP(^f{xj)>(tL2aRo;whgM?j;BlwZ%X@%=f^z@+aHtLz%SAG`2EJ~
ze^`h@#ri?3L?ld4D8V%mFydfNLQKO8c5u%HV#=T6A_P4q@NlS&<~%n5Ffap+)QlZ~
z5TOKM+)TEnQ4PY9iq?$joYrAI3*qyb|Am^5kJ;HXc<vO(RW9ZJ{por<MrPu2h0pKf
z@o42`2D-jjh)C~jIWxo=d4(uR1sBdPYg-QSjH9~f1aH|y<m&N4U4S3CURty1bD%iJ
zt?PJmRU4b=D%@V*1Gm1oI5U+2wO<5xT%FOx;c6@;Ly^?#Z;4=Nrz=;IG*;VTTV}Zv
zb_k6>qS^dtx@bo7D&8BiP?yy`MUVgL=G4%jRDa_*20i^U_NA;3YtrOX6=6}=eoI@f
z1?;79U1U0dhT>4Mb(E6eJdV#3RGS@X^cXEm1=U1ZQWst~oqPM#S**Y~YMH9=<^=r4
z5aMS)Cdd>C8Z|VO&<XzubO@zHESv~~zM4?ci9FO0CWbaUECC0iGSfr)_1mi4S0M^W
z0*fnN*2{<4*VC}y<#J_G+{I`9_YCe6yIk(i+GOct0uUM_A<O&+5er{2Sql>QaQ#?(
zP|7>YAjjf0%zp{F8=XElI|?TX>rPUOff&KU)7%Yf>WzV|ua|?)Oi$Vp3FLQ&-X-?)
z0m9NsCM3nECdV5O6YfptFY~L0n4wj=dZ5AM=0F4c;-z!W%ugSvzt!0F<(^&qjBHyC
zg^EM+h>@TOf?dw9iC65TSEu12vg|i_L<ZY&Fd60L=k3qe;)i$Hp%aZhV?5I^MyXO1
z;=NvO(?RDh>`(Mxd-S@@aALl@7`_Gz$W88-%$dkgz9ajHL?8?UECq<d0R&K-o%GOC
zKhh7T_TkYn8Da?x1EhTDVYPpGZ8Ut!s?n?Exf|~`^EK}bS^70R27RyB1jI-!E_V+D
z53zW5?oL;D{5f3p<egUS;bn@M7>B}$5_Lt1BC(}Pq0wzwaIyg+{~FEEQNoj<U;QI-
z?Als6_y|YExDQH~&(Oi^t5xgEKHE)&hf_g-&b2-Nwt)89tr=94?+<wLyY{6&cHEa;
z`-EQygHnklq)8A6{bMWX*R7a!uFYtJG_XEmg@*Zvbqo)k6gPz&ksAM6yZAyoqsuVc
zgiyiFhxAvZP8A75_BrGy2mUNOK~`CF!I;d$pEj-H{oK64<<FTi7d!H_<#9X)X5gKs
zdF?#sv&3i*w#R@$x?C?Du-+xYGFn|nF{2ZU=mJbJ5Mm~O<UlA;Ixvyss29hbj}VwQ
zc_@&dB8fB%ks4qH90~O4Ddm~;f`xxF@27QFIdHCNa6b(K;Oah8<55w(wy_k5ARhf?
zg9Zrqd$LmTy#dQpA!)xb9vAg}l&X|df_~pGZp__@?T8M@6O(`t#BSI6xis?e>arz{
z{`hUnQ~NAn;i*;4l|=z;r44HQTP+Vmk}Gl&quL*;%$9bA_}tZ(udpjp@?^|YH90&g
z{GHQ-mJJ!rt?*a8UV7x?qR5aefkq;+QtI#Kt@d<Moj(I~nCgWyGvyCr#}d<LAu>S*
zUf$M@CU`J;INZ>GJe(*Z3i;{;nY%64CZ~#K4Au|t)%Yb&s|h^q3b!_s+hIU5!?I#G
zFX2Gv^A@)ynpOt{mdr07{V>3oNI*6d7!o9?JxKbFGSmnoAt*q=UqD`8{Ljz5bcdt$
zU#h62DI|__-kV{v+uwTNioZHHq!2sowlpniVoW46*+!F0QjiD?DX5sQGh^t!Y^)kn
z$Bh>piPkm{UFokLl_yw3T3r_`p5bx%yIf6N)|E$}KCCIN;KD4ae*a-eEHmv~FS)vM
z9<y7wEhVSU+z-*wN`}@PF%VyMFIzKjCWSqplx5p1a}N9qQPLy{6+#gixN2E^+>(#~
zn%s){8lcA}Gnky!sERFuRGe6;`KZdJCWM$aMmk*D?sjPgc*#|pAPr5`p*lQo)Z|lJ
zVQbF`_`7MF93{H$%C^Kd;F9Wk|6kxU1kEmS^nmwy<Nl1^)9K-4HqPh5Xxn27$ORZw
z6PrL^#vx{W5MuZ>%w~qHfHp=EtS$m7;13lXZ$A_O79R}K17HqE4gDD%wBnY52rVEY
zs*qi^b<<!ab)GMn6T69UL`7NH?~q&3JC<@W5Df??hBuVUZL6}(hFe-*`7=!r2O*3Z
zB*X|a*XsDI$*Mm|j<3~cJNB_5rp#thtPqXUw^0$i4I50sfktm+nwFN1({(1|C*fNX
zOq5p%*<9ojHf)0?RWUUV4Jha#vIIYD;)b^FHczcn7={v*MVY+$Kdnq_D0tLx_H|(y
z&^B4|iL&`ip~WAHC$HkF5S^;@qAmC&DvPdFkB_O*A`S2hBGo*q6R6a9>x?X;E*nJ|
z0xXIIafYs)A-0NT3*Aq16&p+4_U$fFw7L>vh9`uH3?KjjieLahkOC77%!m*(3?Lc)
z7m+}m1VDHc>=a6(@;i|V)2J@oCy#)$*-TERGHw`Xn)*x$`}MWcel2UbZ`Az))82p$
zaXYYam636a*WSb|0~x}}oC1TZbx%`J>L4^EG|<2Eu^KW01bMA=JDyFvhz#PtbnQp0
z409(-sqXwn>K$p?b2PcrR$sSfM<dx};ZGUG>^jF0cTd>F6@l7eDDcHqiy{@38Ihf_
zW`Qm)FgW5;l^-iQ-_#{j!hGY4DU{~JQ@pteaYFFkM^a(-+gs`^limWkyT~qK&{|gV
z_g{f;2th?9NTVMMHRZ0h;?6J@63C@jVF}Lstd1uP`0%_x`HvQJeRFmU%mpKO@BFAq
zPsfnp%llta)zH*7<fz&G6*C}z7-}TZW+X!p)*TEy7<K>*1)dWWQGfsolaO#U*a3u)
z0c|#lAPyYh&kW!&%O@r<t-H@~>aVQqJYV_|W7hqVkN-V#HaC|t9!FNC(%w#lN^uPT
zUR^4PGnh<l@YiuiVQO&aR7zdSv`Ho=PR)q_2I8}|_0+SXl#(eCmEAw!T)$tu^npR2
zF$Qb+r^H1T<r4&~!IgTfEeY3VC@8T=kRoNXLXDb~$(SI+zw#7lQ~+}JIOb!l3Fnu(
z#{|rtpx<8!hN?2FOV`L7gpSuo4x?&>j+o+HlJN{}!_N$rY&>-zEhT3|%XH>tm*w+D
zFnydDpu3HOllL&}WNz(nRdR6TK#d)z=A*%jl~`i#FR3?<Zc$3NXRQ<Pks=dQ>U3Pz
zz8VWWWPPWJB^XFF9H^(UfFQ?&LVSe<#SB(X$S?oFFC>vv7r(+L0GM_6Z!9V(uw;C^
z=l48l{(6CxGr<|l`DcHi^Lx2n$_(sj@s^6jq9`?-kf@zeo41rofZ3QdBOi)<xRg{0
zyD3YsS%WIBW&u01{D`SccRMva7-{mr8F45yfhCay{?xOfj3SbXG9?FGf{`#aEp_QG
z12$&Ev5bHqEqtK~G8IQwZRm4G&4e~bd@T4t$+T+T2+<`h^_qiueYi87J@ef1WY<|S
za^HH5VQ<DH!Qx=&tG_KDiNpbX&^DZO9<EQQZi3=Z%-1Un239C05$d4sj=r2!F{*oK
ziJEWsjSevzxooz&o}Qn{fVBfJGpO)1ILE#9grNjw3?;I|n$<)jlDs-DbI3?J1SKe@
z1Jz%GRBCfv>P#_?kYp(^@nRYgf!W&l*SwEU(ndv|2F%lpi7uUkP)@x^ud&>H3SHsy
zQBnf;qGBKz$AhD1<~dBbRCgH(p)y_}AEP4q_kPMhDv8I&Ma!~2V<a%7;P7!pR4`4x
zs0rgi1QV4w06TeUHP7wA104I4EO&xcR!9*Bc&NdY>{JWO6sa?1zEHSh2JC=-;p{?m
za6V3XB>;YC*fF!DDQqbKkK1qnD%Jf&J~nO1tAyw}Gh3cUDiJk26k4#$__^xp&#Y-j
zrli?JG8e1U42$FWcvQB(W<?&zS(D{!ik~a-K`4$bW@GSm42I@w)y@R^H}-$%prZ^k
z000OZ!w`b%Sb#GVQ#==&$w!HT(w8O_(!|CAh!~t`P{>n?_yZ;wNfK|c3X$Rrx0~V%
z6sBWM(8RdH2*}0|HmEk2X(^x^C@fe^vM~9-@)2s4zB_$(v-X|BFyRP60*4r_ph&AQ
zvCfuBeTY(|6-%oyGcO=04CRrNmDVoRN``s2`%TAo{7oAs5D-Ik+p91&hMkg1GA0jI
zbNQq~9N*6M^mJ`Sh?EHwSr;4MYzG=5iVQ3#HKo{CH|33bDMZ2Yr70IJ)|-ru=R6yj
zXIwTKbb5%0bCjc5VETR5kBEA)4nJ+f#5vL=hYd)wLJ7x7i8-MJ5XJTnsZ^DF5^{S2
zFn&^vg}#oAI{n^e3oK=)0!CedEf_ET@iDvG94s6hpcQ@DmT?HN59r9zQCXx`tL``$
zkLjBchGGF%@COS7Lh}Lv1j4%_q1j@cGNThebczFngTjXx_Y;Utvw!wc!VzN*0?Hxb
zt;Q=78s_}LgjUCen-Bax>uPwK)ZEDIJg@(BR}-O?i~h}E2nMxCey`gctOZX(gFS3%
zq$0Qf`&S)+>Cq!^BrCxf1LzwBRbdEVJVK^0w{2i=byM>*Da85>4Z_riV#JNl>oSV4
z)7OwzdfZH&w7N8gjcDlM)TxNAW<rVyVN|RU9AI#k1xF3_Lp0@lQ*eraqffpzb+aP&
z&{^eF%s?7YwBbrCs-PmOXlzrswbAMHdtzX2&9PgH8O<6-o+i~l*hPa1V;Fn#YHsbU
zx~vZ>cQZUaE9<@5DUiS&Jh=l?*Zfc}n}hK`3E1-V(Q_IHNoe8xkv!MEujw+)yLI+p
zmRLsR&lDjBrZ7}GzC$mg0!PZksgA)X^yRZ5HbdT6dGuj)INk_lKLjLUGzamQa2(wp
z0|<d=d9ta@3V%X6^!=DCv4p1-0@k|TbXXLCAyBCfmzZClf@E+rJdDU0DspuN3<j6-
z&OaYiF1`+WIJ_<$fyVT&H^ai5=xohCBH0l6MhY5pihZ^`ZR}&)XK~|WsVqM$zfNBn
zawrgxQO5r-UA$B|XMP4|U#Kopr8Qmty~!$5QJ_EH<g-WSH}+&E??nmKzP#A9wkBtb
z6FeaS4#abze@I(_A*_q=qOnR#WoKQ<xuT096}Bs~Da0srfr5o&_M2g4dZ0Qsyw|ea
z(qwNCWcPVCkdF~m<DYmvCd0n@(2?+d-R;ML8!Iw_UL!Pj%3@><^d^41-mCpa6e_WZ
zjf*2s8kHt<VvTV(GTJBAc4i6vB^7Q&P|XHfj9Y3f00Ojz)pgfF_(v-ThL@jEB}R|f
z5o;6&-i;LJlXlp9k};aY)3VDJC*bWi$bFAGFSWcwZrh+I{gG$$Kv<yPF^=Spn#d*>
zG3DwNKjt|Ka-lenCO04odQ5+XD<TibE$ISp^YJ&@;r2G)ohhb(uS2%8Mr5|u5~ejI
z>`_D&1LbXRp%bn=Er~`9J^O0iOk1{$p6#+Ju47?K9pEwTYB$!1C@Q1kI@YC3viF7&
zpV^bIgHTUADlNsn^khsc&DQlkO3oEOlVDDwXIt};K-cqn$l@-gvYp;$WVXvPZrxht
z*8mUgvbS}(%pkv8H*E_F(5lfP7oFU?31tTg4N-^x;Q_*cai`;0j@Sm1!{(MD;K4_F
zzQa?eOu1T@AQ)JaEv`pIL{tRP+f>>C*Uy~+w5xoS5nKd3-UwPaLcdf6rZ2yMJfR2)
z6BMGbRw}yEnuL@93=LBPY3C4YKGmwcovr01<&R%TZSN(pgn8M`TBhS?V{=1nV`4^u
zIs(%P<uS}gF?O^iCRBIiJ^&H3`Q;h?j;VCfh<Ryb9G*;w3#nEzaOP=UA_7*s4w*?N
z&6I$7Tu)_8{P5#PKZ;zgIN5_X*AEVo$hoG_=3ni*1M_CsRp|bz%dWM#GdlE1lSaX{
zMC_WA|N0<>o7Zim59W}KMWi&Vn{8axI_FtY-N$M9cYX?R!truPmgS##JzX)H<nFbW
zPUkvua5^5yS8Xy{kOjK$&CGgMa5M7mVhmL$6_^6uB@4*9s9^~BzPTxo0Pn{NJ-mo?
zTHPFyq|s~ptLsA}meAbLe82{R4(1C|6aW#lPaM$Onvn+vCWua$*jqciLGWZLXa)xr
zZmgjgGkUx5%1k@)_6eg$vH%+Vx#IUxhwt;OGu7kwws9s?!#|}j@(&h;@ZvdmV?a{F
zUW))t058miA`sKmKt2gs0x?iL66_HTK*Sy#3m4$OSWiRH3`vo&Q!VoY&@lQmCV4fq
z-r0DFBA3Uik}Q+rQou-5QeNz;SH-GD$xB(_@0h3>AwnXp9NdC)XKIra(qxU9RIJQa
zm0?QN1mU|Q7?|t6cgySb{A#-`>~8K=Z6`d%VFHY4=QG#7t<L?^9{D9r*8bSGX_4)B
zciT50!k3rZZUBi<yV`(+D^pfL+eO>`Ak__gW2@j+XsaE7d?Jiim4=p<7NB1<&Gm8C
zcr$ziTpJ)77)nVzz=@F!(t|V*DBAaoI4d$!l1N9I2r4K`3=f8+O{QP9YbCkT{UWEx
z6RmKQh5xO$?fkLApL0r=>qbP6g^+5JB5wva#5ZakWr0B@0k6aXPY}TYm61xG5HhdC
zf-R|T5Z%YIqaH95)C#Ifg0ymL$a|D6noly$klz}lYwDR>O}`uOx{ec?iylup55Wl4
z%Ot3oU!ZYSUsQJJjBQwnwCZRLpC4Q-=5x4`pyNrjXwH%tdFGsDyORc!LX*HURB6Dk
zL+8BCizIe6BUcI+2AR58Dl%KY2)u$Qq4w?5&;?fl^b~uc-e*b+gRUaOn#@3(pYaW&
zADF9MMG8IDQ)lz|Zzt8hd;N%E-+I9#gCczU`~pT5U8Y`7=S%h07Js9*^<qLn80Cd(
zD;iH3{tC%f2vi}ksMpnhf{35X)(<d_Mw*rGZ9&X-jMu02TZw2tw(}W>>waVA&ygj+
z=3o7c`Sv*t+s=D!c<=SvuI1}8gO`Brp-M9zfgw0SHdR>l_YF=Y#S8<59^-}bIa^Ku
zD+456>x&{S`R{_&{zzvF$MeiJgH*M!lvMp^(3v8AxXhM?Gbsj2ppgh3$|xEsDI`R@
zjQ_-2Fjg;QI2P7bG1`XcU9~t>>P(WfH6x0+phkC|3Gd=jH46YGb8P(=>4?RAhbyJv
zSrv0x?7{q$^_YqE#;BnDSKECrB4}8Tyi|#*_^Bs01FnUOla)C-B(%2S|GfbHQW#UX
ze(eBv7K9s>@bYqE?awLhYwxSe)RgVL*5q*$jZ_Q_3=<8q13mT4*4EbXH2Rv#$_2~D
z$;nBp{oy!#zn|dILo-fL{=xZ02HU2BnDbCVg2uuE1m@zKh$9xO6ZR3r!F%b1poC!R
zf-?xhx+lC(+{GCeHD4EDN)mV6dTZ_v34~0I)p>Z^Y`Ru-GFO?R1bYAi#2*vjk_;jK
ze`Je0F027GAl5xCzx&w`kz&CW4p!>pi-ZF;mP@i^h$zKFPs@I4Z%z>eRhN=ZsCN6A
zxQIgXB#pxhb;pmv5?cneWJ@wnEpgLEILuEJO|qyOv)e<Q=QuWwbO46+t2NEtS_i1O
z4kXKPU$%<J$B}`u;nUq}YIHtI0P*`1*<EJuLl+JK1#(H7&-2#32o3wv>RqTRMY~8U
zp`^tQ{`L3R)Tm;$b{n72!}gCtDx0Sc?=h(~y3RZ7CiliVtL19o4<KY$M*-Ew)^^C@
z!b7)vn;rHCV8#|`xj4=SE~kjXzqYovuCA}t=8i)XWadmBPYqTqNTC;TM#2Q+A4Vbb
z0K*W1U!VdUeIk3XMA3+3=>S=hgvI!EmAuz>JwfQtpzW9GvZAjt$e{Nx!H7V0#tIl(
zk<%Fbubg?gE1S^}ran(Z?!;_Ria>E8$COnoPyuW7vSzZ5CNO&u8$NqPBa$p+T?)|h
z@=z;tp@fAx2{8gU?j8sCa*}jz+%*Kgw}X7%d83p%rbAoQ+w}GcH(Dwr%BRg+$5`mu
z`=By!*;Hfdoz`I047~HDEUWV$Ngfgz2totGa!NNB3&wN?y48(}5yqWe8fDR<uH7i$
z$^y%AwRBRbuxA*@&zINBnRGFk6<HgWQYp~EXEDFwoz_>?S3CF;ZNL1kU#i=$+C1NL
zc-{ILw3mHIjX#;`Co=hgBtmLR{U|l^X}))w;2D?AUj6v-LuHlCZohA!RO9j3zx|)l
z7+6McI%F%A%C0u~0Aq=lK-$|O3Nx}W!VPAotU=aTEIHVp%t!!uZZrvvq(Du-dMY6X
z5Fvo6C^PJV@(Oa%M1(%v{hH^;7Mzn2F}9t9uGf(!i47^K0rp1+;qO@D{_uCrwC`aW
z-`f|(6-uE%Em8bLJQ$Mjr~`VF-~w0-xFm5r!$Gy^Pk*t%x4_1%TE(K=5G5wq*2PVR
zdJhnS1z5pX`xE|$gEAo9Ehe<6$_=}m=()<E*-o%WTEoUIY&1BZ%w=_fxy)JMRAe=q
z<doxyGy_UFgvB;YTTO#dW6gR&bPN@&`OOA<P|!_9MRL&4z@yMsX!X|u{N?)Q#Y*hv
zrn=UoT!09X4wIq-)H0}*mm~{^E%#Dv1l3e&&99U$S$Estu|V=pmR;BDy6ihJ$Hu(o
zIk;)-7F=O>dj?`eBcVu^G-}MG_WKu*!`ax__}p>Ki`Zw|{J!zBrT6`)xBZ`qB`+P=
zZUO6gg1*vRfeCV!9YrK9j+&J1=p->gIoQx-D3;WLaF1rq$)pN^5(vh^pbAWz)_$uP
z=)!AQTups>(9<Z9=!gdaOPC0eH;ynL+TzpG;PxwPanp>{ggqdb1rdN{7SciY<iWn7
z5DSjN{0#sV-G>4hIcUW?A^yWblvFsGTdrSCZclShaaFba%lqPX`7)=F=?P-W{=|~B
z@VXULaO_3B%<-;|9adC^R8fPtQ+InqLt%bQHm^?7#oDF{OWqPumnMZoSgvG_Bjaa#
zR_ftFlqd)uo#|LogMxI9|IcppP+6yjGAN4EGBR>?|Mbw?p3PR9j?L><qD|e_zXq6s
zWy#Ze&7zZRY;4tE2^&C27r@`Jj)RJH+UZ!UYKgu3by6W>bx%c<E^XYj0pM?z>mim{
zG&(9FGw*t_;&3#P5r*%pzWfMe#e^8N)YkqfKYMw38K*KuU8Dw)2a(O_n_=961z_FT
z^U$*UC@2E^e-;AwMj8p{IspuEMrZ>8f-U-g9QO^#=1ZqSwqH_VNJaHaN|4|P(g*-h
z{r*aa**+)VozD&YN=)i5zZi$iVaBmb{b4X70V0GHli`2HwdI155(!I`_v~Bh35#8R
ziYtq)ZGf0A2PC6^%193qmrh@9c5RYV{DJ`33kjTff-~dQ)dFD<JT7y}jn;e!e-1<*
zhHzY^Q}3fnr|h=7E1H&ILGbBGEUC{kEh!S6_izxdH|l^VzMO3AG?lXWS+CFVVQsa?
z%SEOhhtkbylX_Mw{gbv->a<~Uw#=*vv#71PTs-EiEGuC1E&0x<v5OVTnvkQT%gv}p
zWAWHE7aJen&q5CE%c_IH{C}0W7?4%u%mq{(+Pz=v%gcf2hjG0xCV>9&R8BpORc6!w
z=mhBK=(-?rHeh+5;25Q{Yq|aH@D)Y&nuU$J3x%^^<@N?Ge$g+4au$&|!^}oJghndR
z<Dx-tA0M55t;EzC{qcr(ZJnV)i2gj0U_GD&BXuK1a8L;$z`wZF@-ndcMvMT`O=>>U
zNeBf=^Z=sjk2rFT7;9W)9svsq^%I>dHCuxE@MnTNiv+kWijepwJo`LCPKWz>%x@(L
zB_wD8bIhSvW3m*9>4}K}MqMpksq@TSX>`X7MTbgF*)(KI3i4wF{3Vq{hTEx#y`i<X
zWJ%FzxJ~`xEgfIXV&;f+iE|sTvaRu=32!kRh$qv+rmp&2aS537^8}R0c%n`Br@cTk
zr9t^WMWbM)CX{6)s%i8BrsK#U%Nk*27!fq%H9b`P&rbZ$z_L?1i?*7Po!#wrZ?Iw+
z$Q^z3{Oo$aYWIAPkn=Wlaanx!8FoMwU2V482D0c_fmO3J7v7Xd!>ae_Bd~9OT=#uo
zMo)7odCmDHjeCWeWI8n8ra#`ukfoLrjsk{d)_j$W6f#DleK`c{zoI~n;Ikt%&dFDj
zU3bzxjABL)X8{`#O@ajlvhNR{Ex=|hQkcCbP4*`@whyM1bM~N2qK5~U8G<x{GQw9&
z_AgR{0wPd>bAYd-sx%NNy9k<DAe==5(U-=5FSWztbQEq_#coLugCbBT6k#*gk*chz
z84vE<r?u14)u?FJBs}C@-)i*X(Hdj3Y%CruuJiUZw6!%d5|%-uEH-LT>T{ja(-<M2
z-?g`QHK>cH{>9Z2Cpm@^$LH}evOAM}*LpW*suU>6m-h^cF+3a456N+i1W7$^l~%32
z%FgXq-=x6pa`i&nOuz-u@^~oRSy#>Q8%4>}>&NACJ~@kn5&DmAW|rskG?Bp!36G1}
zSe=$A=e>slq&ysC`+e-==VfJ`J97cyfE`bkjhc9{5N*}MP-bRkH8EyPSWvs;4yfL!
zzd8|*^WHi`-u>u4j_jIcwcXD~Pex%3q80cEA3IwN!eZ$iCHxH^mNXPqdRhWLUB2u1
zKQAT>OteHh?YDMa-iW_XyFG=cD2x~jQBR;Jj*K<P@^LO?c&g%?3WTZ+l2ankBOAdF
zgawVov0#6Y;6(pO6yGtVyMXf;NVZ0o|HB;Zf?_rXg$)d9bkuskoYYZ-<Br1_UbX&0
zV@zO#u|QQM8IQDFdzyjk)8;awpbCG}g~|r<d*WoK1&B}I73h*S9PIUi1L9M1kEI+z
zBRPl?B_CJ<q^!;{6V5IMG>zaeN0Xu|Qujp&+U#GMj6rYR5629*;e7a|Gv4kbt1KR)
zITFX2<;lFBFT%9Qjer|VNl8~#3ct(Q!rtB<b<OcRYBVE2|NHCHZQG~0!~!bcYCWX}
zi=tSqds3QAn9?NrztkoG03^WTxovrvQKbYT5(N_k-|_$WYE{2or1!SK8Z@OzeeS*v
z05Wzt+|Gc`S$=-!emPc{;VU~_ysRYK4YEeYB`AJHbas`IdZ}raQ+ueZ7FIvc$KN&D
z`4iv*=FDiM<=g-yk%E;Ubr@lELx_K|l@lIA>l%_cmBaz((6zki?i&arZPR1(G>#H}
z04Gua;jE%GgLWpG{}8ODR~(?o;m9z)=<<H6M=~xYtn*4=AI*uA7QRKl?HDY(1*9Q6
zAT}Y2ai~a>PBS%S^aSNK+1B#$QD(rsHBrLMNwvFi>9Ns!JKm&5$!~8q>9z9T{fPq=
z_NgP|S=)U35eO=u8cZe&)Tbg_ySu~d%9e1WDx`#BeLNdlVPg~SLs(L~+r<iFb61B!
zNI$VxoO;@{&*^L_-;JJc{GW3!v2UVQ)12>^pPc`p<UVbN^<qKyYE)qRG4b)~(WBI4
zv|6>rpk!oaV>Oq*Ie?hssO$IOcocG3p2r2HWl(wyI_(zkS4Z4oHJK0KcpfOg%TjaK
zV^eeIWr|!O=41e+_|3|#AHT=@@3T0cqlr;|k`v|{uW`SQde@eDUKYlHSn0$d@Vkk4
z6_&EZg7>?K?)Nbb)-*~KBhp~i{kmcXJ&reGhjC%1*@}3<z7_)HJFlhniErkz;uuK?
z|8&9NS};zG>SHdO_NJwwR-vw)X8Ogp&#dbfv{UeO@qiXuO#OI$sEmD)m~oCADYXXQ
zWi(vr>8S<F#|TN%aG_xtDDE>@%X$)pA?OT|LiU4b(xkInt+)Y$jdC~g3L{COYn$q4
zF52m1?vi1Vok?k#N6km!dnLJCE++-vZlh`M_50K}yyFBVNV^U0TvmLKmw|KJ_Evse
zkwC|@(@5{V0hA2sy_=@dxtAg<cqk*KkO#8FrZS_(qe#_U8nYk4O@<HKIeM=tdabTL
z)4b2twzfd_2|iwa?l~@0K8b`J&n-}D*am7=i}S*tzTfZ5Wqv35VRx_IrSl-ur$JBu
z4G954{*IkWJAp%09(d?u_bbSa?7Ban)D#cIJ%{dAtnN7a2}ifzcKc}YD|ugr<9KfT
zh&em`UJ8$4nr(469Bf&;oSK(*{$1H8$I9|p9wM01KYzxQc5~hTxYlfao*uG$WfbIu
z@^AiSgqQ9iadZ+E%iBcDN$$<>iaRQ=kNI=h;L-E~GI!rogtelKy5js*+LS2C<IXBB
z`q0V9%k4)rL+R}iG%6E<b*nZ+!f)DN5Wmnj8X%EDV_A)H+15Ubs^wDaf6iOGwh6CC
z_S<t@Y=3sOZv^PnEJlr@CLRtpV3eAYmlare6F(RYP9RI;(krXD-wY8$6OGLD&S>y`
zvp|QH6{Y6otTkJi?tZKqx7lC3#B||P;C5Uc;ne1{uj8#)%i3j~*e;!IZhd$^cG=~$
zSS-t?VGSx9S}{Mh-~IQ{dNf`D3zGl(*k0YwUU9rnD^cD_z*^hm+^=6M<k$m&=f)p#
z?{*mfyMh0m8PEgd)y!Jqq-?u&oXXgN0)Tsz-`9Pf-<3P6sFjt~^k>l>P<y0?0+qC2
z%J^uqdprUbm51}3puz|sRetxpi#grs(c6C_7>lfonm-p-Xz{te2lYJO2JjI-M*I8o
z`1qf?#<MQ)exsp^k|{dvujiQI!qVHdA8sxbMlyjj{(yp(_=`26BOdnTeD^TP&9Oi>
zk0TW*AVzSZ*}?v>G4kv~t$(yJ;n_nB=e?iNz%HFSOr_K&^mZ((L^q7Y>Rfa@$7r-W
zWJ<-3{*S%@lZ_S<m<Up)H#9($O&tR`0v=RLX~(G&tmSH#@w;vGIYVI|t*^#H%t{e(
zH=4Y{&NT<Zlt;>LZDxlgM8~KZCS+v8f{qmX$u<LE_cHL3ND_cOP#v{)8Lm_r`UPwi
z^`dNRAV`IM4YpQ9)9-1Ox)_ySM%9}l*T)pdQ{NP$0{YK9UMEs`?bg<!!FTkIR6zl$
zsj1aE-L<wiDtvFVaX<?A_bZTd;O2AxAI124P45VOo4QQ-Y_0dmYU;YLFR=Q&5asc|
zQ1A5X{jnX8Fm!)Bt#=6l)UR|oF6qDT=s^ITx^@hhJlDmp?LM?16HPYb_0_uF_GIo3
zRW4t&(LFO#!5@?O-*bAKUfUR7yK=*JUoN?;bpMKoth~NAuc6Rs{0{fc@V|VZ`5Nz6
z!mc&m^PVmweDAfQdI>f;J|jM8zhUa}IIU0-x_*^m3JMtY7{*W7Z)$!c*lk!l7n1yL
z>m>jbEv|R`n`_1;N*5`EBsg|dDO?n;>Ar*``dgF3uSK0aP6c|JOc$Nam2wQ0D2PGH
zltNPMEE4fqbPULo%AD1-aa2(<>CK-z`{x-X*uGgNEql3{OPmhj10(QLd~TpOC@3~h
z>J%Ayn&)<A#dvU28_{6H0zN%)ID3t6+vx@Z5Ln~$IB{;r9UI>EuQr>mC^AI5N>#~O
zDL^Fgux8^Pp?S;W{cSE0Y>(rQUhDI&mTt3MWjb+2G#Z4NT<)sh!%SPbJzbN~!{J@`
z8p-FnZMH7Z`0MxIc!8&D#K!N1Qoa<~(r=l)rk6A*LS+?R&xaEyNPORyU4_$Ecx{{S
zhZ$i69UPCV$0!2}i_=!VVe{Vq1{8cj(~p94JMC?;cU;}f`i#>1?YgDQMQ97v(*gYV
zzOAmOg7#?;c;fi4XRp`Yn%@#Nn~g`?XsofeW8s=^?N>;8`*l*Vnc`O^o9eSNN!wR-
zzDDXBlVF)Ma*P8^OqEcWeiG^%f|4cw92jC_(>F4fXmZh`eJs9G-Cp*(!Y_C6@Cng3
z*bUo0raxb-P^+O`Zr0<yo}ZTLV83*9;mywc1sm7Fu>2i3xhEs>n9%UPLG7kaz0T3^
z&6sfv21))mThL8YRCRGc=xO#U&j~kg8V!3QTHh$$-IFdG8l9zG6iW2S(ZoWR)!A-C
zUgL1@0@r{wsHS9%^y>YR&(Eb{AoBGjJt3Vn15;a8hc2PMJWY~^ytcAVo9?77z?^D#
zG9r+NiI0<o@1p9?ZsT8hQ?W3Mcy1{6kKtTSZW^?DxN&UtPHP8_v0Es+rZ;akiB!>E
z+ei1YVne!=^2-w_1aA($ln1qT9{4CSG>_QkRguPY%T|lVWko|5S=3hq+1$Tnch0nS
z2hIi9>3!N1n31rM1<hOctO~#y*!86KLz!Y@)d|vTKUsJAvA4HZl>Z{f?aMDtoFD&6
ziaJlSm`;cMxa<4at@}NPW*i0w4}O^5_cltd1^4JNk8gW+*5OB$ufy)N&*Rx59v&XX
zr97}Rf3?wi?yH*@-oJZtea-KDf=Eb$9!0yCuxa~zwb_1tcBW0IMuA#hUhdewsrH>{
z_1a25Is<p>!Gk{gub=^#UAzB80g{KmUY9MG_#~{WvI`aIN<Fp~z8cM>8VY-jrXM=*
zjdETS8t>qhL@TBk6(bX%@3WUnEpSWCBeZAjNVe5qnbQU<%jc9Sbcta~{wgXb%d1XR
z6wO+(bJKVXW-obUO&1vNd3Fsz6IE1bntT#(e+}sYAu7+l=j-F`O(cp=zw~7IGFhN`
z6e2~;inN>LDbOR!WyhFjjcFL^txmN(ms~x=Te}o#M|aDfH+`pfc7teXX{ou+MM9zA
zhd*Xkq-r{rbgwlVObW{zq7~&eWXCYfO31h}ap1eYWC?jxcOBl(mQZ0{TjfP$hA&D|
zu<1(!gX)PgFH%=e%hv6-8Cbat8#SWYDw;`N?Y_sNx?gR6`&>SZWY|-!$}3@IOR2}O
zOYiY>W5BItyICC8+=15aydLN2tz4dULu#MA-0ovkQ^WWz+}zmHpr4<fwwik>Da{`o
z93*DXEly5N#YRUX3h@m}DFEAyuVLW2hoLi%8R_jTQUpaz87tlkWRB=rK9iAX-5&<W
zR$+P{k6IybS=Y<f)L2$<(|7QGowr^1#bL4})1Bw1zJIc#oFr%aB%rP8R1+N|PcpCU
zYi%<87}hIQ!<=kQU7bSW!>3cm9jS8mME31TNTq0d6XlLF?6w+YBfujH&d6ldSSHQ)
zI0YNhbA4|iw|ySo{cf6lAHV3ii;;~SYn#?Kv};-TdGVrSr$bG2<{KDmrrT{7B~yvT
zss@x>8x3bKD)o6|M|V$6*j)z0ev33~F=xbex)}SO6;csv9oEa`T)Hi?PlTb2pUEUC
zB^Xn%^kI3jRX3+)8P!`>lq+y9jo1B{P3J{+cCLhyjUEmq(_FTBP*aJtEtOeR7mJYt
z$AvdC=W{rYo%}%_%eLv0HkWqseWOi@Vyn0L7@~7-+FP8W_xDwWO72U2?A!eC_G#H_
zc4^6VRaA#BOxPHQ&DtGk*IZj)N5JK{q3qf)taI<&y7$u%L?n@Db7oI9p>Ow@rR|-8
zn{6CzfRw@IW<;q?r$wJ)VB}TNyY$jOc9i|vnK_?M*C?FACK;l^t{OVi)Y?>CGBwqR
zRXX%8Q&zdGzSm+=aG`2zcR%Qj0dFv;8#Jo`=S`4~o){w0sk^nN%o#OqFzWrgtn)ci
zy;v?ds~qEy4z8nriLRrdo@&mMP?gKwRI}avylmHbt7{_F-d(OpE`o{|n+Z=m4I7=?
zW#TfN#`d`2=3F~ySe8ybmMt*@J(#H9Wa?y6Beml(tE_fy&*843Mq)$7a$Ht%|K`)U
zsM7UBufoxpuim4j)OvU1!q)D<hRl*F($Mp5di}+fB>9l&r=hkzYV;`M>t{56YD5mb
z%h*ekT2E?LHEnoU3i-o^S}g|LtbMt9X+l+X6zMlB{S9A@m$#+iU}lZCL6~i|@+(lw
zGc_36@&K0G8=`7az+qZIUDVC(a_w=ndlNW;!qdx(pPU$${c0wLMB>(68!;H>y6frb
zNvv=d2sRfX7sm`OtFE3hZ8&bu1~SosA^>on+!nd_%XV>QW+w2iP5X^FY=}NPPMSI?
zDJ2m=RaI31HLXJXlTbuOrNcuFzFewI<)t$g*5ySjTsz~8-(4O1`8GJ)p552O^t@{G
zPxify^YV+tD{$o#=b0KyF0skRlT2s}BBGN={c+crNoc8XaZ(uz9Mu0-sz~!X=PaWq
z%!cMi#Z{>_sD7<17CIr5J5?_BsnfJ6D}qxOT4YutPLhPXmdrh}f4@A(@xE>1`+nQe
z&<!ieea`GMtT_*=uD{N7ejlCmB+Qq-Q*-PeO=i7q^KHzn(XmxAQk5k<$1OEBn(RAm
zl|xI+LT3`@#L7)VTy<XCs*GlQ;*D(>199D<CNgbf4NPefVbLj*xdpiC8P#QXuK$*?
zCsccP6N@_*kEbDnEM@VflHJ_?WqIpTs=>=aXfJ-1s9fSd<ZiUQRx2C-ZRl$HD|zT%
z&{dW&zZg+BN`G-N23%zAe}`qIxkSOvZf;(kTQJStK)fs@!mo}8s5*7)-H`mw&d*Wu
zypB2czQ^}H%_i&BVzmceUS6rM>kg0mBVZ2}4grCFNfCX1eje!6S#a^u*WaN+D41oD
z>86WIQIE{3%f!Z6$y<C4wam*fvZiZRAc&N$)Wx74UxKbVaI)TNv>1eDWx5|1`8Lq0
zFci(3iV~TJ6N@U3JgO%`{%p7MQ(b_2l`fG;u0o$TW>QhpVo#$ZM~)C``V$q~)Ts5f
zH@$SPD!UwEiinxJ$SG(9S6P5qsD|bZYv5w2mds+uW~04z6J|90CUn~TeWNO);-aHc
zIM|1oT|HI4G-XoZ>vb^c7AAJJ-lZg4>TDxBCkauE8Y_lbI&RzG=o!$wLW5p0y-y=5
zOjAfc4aZclxV@DtH_b_f8<J>RV|l{uQ9gFAkVq-ZkOf;Xf1H5{!!b$sJEWqz(~F9!
zFxkn*#>PS;MY&{dy8T?uByblxZj=g>C1AO+Z^629={gzawSdZn#;#7Ssx3fOP`#4+
zEi^O~*bReeHqU%Y%KHdLynC3J(|f?n12!`&a3c-U|I^3n^X6PWQ`B@@kCHy6H(Jyl
zTwGkzaw^*XbyG3I&abX=a&V-JlhAR8-Ob;+^Ar{q!U%t)sZgZ=-Bx7e<W8*{W<H|b
zVBb3GsjTi+tT~NdRud^-E8f=TROSwARMJC&I$wXYbD)Q<Oj;#sZBH-U4=R>qA0r0x
zYoz8oB^k#kRvnF*kYyf5zJ1LtOK8e;b<n{JE2&%yA19nic=4r8m`AN;-PF`pVgh3p
zoy7=o$uX5v5OWMrHB^*y8yyv6DX54iPe#MpFs`D8;n){#bxqmJFYI!^Pp`M%O1t9T
z$M0vI&E*-@yFN$!zVFU1nO8YCE!ft1$bQ&8sx7j!<$d&fl}nG2YD`Us5j6x3vY|lV
zSDIT}B#H`Gr?aclVQf_Oo~}AF&CO`jzVJ{MWMwZEkT7SOGW|pem^JqP7zn%8;<!zH
z<lQnE&p`u@>&mBSGUQ68DAJI@!@}qOcrPs9Ik&xr3IQ_wlF$v^uU{l&fVA&8yHOn$
zPI^Qro|m1V1IPYz;MuiDCH8*BC=NITSSrm{4b=pQ>dIRmn?GJu)6+RqB8c+Us4BN)
z%D~0L>zsohG<0xxcek{(MAFl&HngxE5}QxVICgG2dEmT@IH^AhHOzM>z;<ZaJTt%f
zdaN+ffeTBr^0D<w`L57gV?u%M_P)8^<}j>v@e5Lm(Yy5Ytp1B*JU3#_N*88J6E&#J
zgUrQL=5xNJs%u<2H#keO-MCSF7TmnXqA};vhcl^J{l}udgrW^o8H>&9%xICI1Xa{%
zvZ3q&4`-Qn+v0uoo<1*&rt77~?s9|Ap{;p;+>KZJ7w=B_`bOuGY$Ij{4p+9S3l4Q?
zJ@B>uwsEsNuA+-sE}71;xm^8wThvxKuD*?ojq9T3KKv%Owp7iA*)r8=^(O1Hu)0WP
ze%5yB_Y3cPL}|S;v0!i)R$3G#<YFy{da>U8)(svG_x`;T?dyp7liboe_v~$GzOxi7
z*IIiv6AqFIf-PkX2qlrAyX!cF@nv@H-H+q<UFH6pmP0VjFk!Oar9TRZ_!k2TjPIP>
z+M6pVD5$)g?uzmK?G63ef7#Gt=;R#Fw1BK3Ambm!T6sAXaZD_va*D*WtHS&)0D#ah
zAtI;@Z@1l=qHg0_$id0Qh?95j*lIMJt%7QIe!8_)3>*EmVXeYmwS`Pgvd!URo7V2=
z>bhcdFcEBVquL^zH2n*wqWa<0%JxmtO*UuzTr751rmbCwHaWZ;xuCX~NQt82h+`V7
zubu>rM6c6tdv~9CaKo?dyw{NGvC8jRZTh3e%o8#S5?<PRy{y~qCZw&YstG%l)<nIq
z`K^TeKB4Df<v0HGvm|?st42mgK=R$#&QA35#mCLSY3gDXyy&wECsRagLRZ6p`Ke|z
z9Y4R#p}H8kWh-t5$JD)dc{(awsHI<VjYiX1h_)o_TGgP?0D-2NS>LX`v7vFB&E?dJ
zv7gjElU7I>stm`4X$H^$%E9k`Jt9rQhXLaY!rcudF2nG5%&q6pans2`2_B{`g-C&B
zb2^#s2C69}7jqK}K`E#_Lh`=^l$U3^%5)R8YtsoLvRSX#&pv&=KeMy21PkRWmO1ZE
z0jKHme(##q86JkD5<Rr#ij#k``X(u1XUTN7e>YxoH+T8IO>}S8=03m8&DJ)iXGKhN
z<DnId(x}T6|C@3u8oG`VJ-+U6`E+KnZp4BaC~>mp$$7rph|}eL(8F(=H)`Zj?&MLP
z$;=j+Nh(&PLmX3X+hof@L5*=(f$BGJ69Xd1oQu`T6i0QIl_P`23L`WcJF3_4@qZ@Y
zXH3c9<bL4GeH|8V&$}G4+re)8)z~a%PHcVBZ?&4h7$|$~mmqShHygQ?EM|=-w`FCD
z{U(F&_AtLZiZSf;`)y;wm@!sL#AX&UmXU~C6FRi;apx_`=wR$>9M7(v)2N%8fL(DW
zCg)el1r}OV3DLWA3l0IX>EbIx{fd1SBebaOck}rD$#2(b_B_%`sA9PE$dbxR$;D{u
z=+&0SUQxa<+o(Nk$gXw+9CD}Ac1xv1uJC$pWWd$BeBOC8`8y^i1~@N9%2hVFmIjDg
zT?5`W9Sgv3)WPcN>Nx(dmpQpAjfRr4GD=kFpMmzpE(gySt4Xq@zyb2u*w{dpE(Tlb
z_@MxS0MzZPx0938uejk|dzv}D6Q<My(@NKqnVKoM+$7ZeF)f37^{_pIFoKtyrgG2j
z`Ke<^ASzoj`WJi{Ybayc-M~X-(XB=q0pjX@*&Lb$5jH}a)Wyd}hP?4}`H{Wq+v#pX
z^^)mq59XLeHEJ_Au4*A+=9uzprzM}%L1Lz4+37zztIB1K#D|8$-J?v1QFBhMi7cX9
z?xo7V6}KIJ1!`&cjpJI^ZN<j}zwzV3r0{dIV}?RS-9(io;1FV|s<ppY%2;V}DhAk^
z;(h!Mtb?|z9NI!mikwqf!*QjoP4mA+YPOARY%QU}$k7b{+F0#j(K4+p*E@#LNL*#c
zB6!JVb1r6)bkR0Dclfk?1LOK{pX=-7KR|T$ab@2h+&%Zx+J1e0(XW*U=ekgzX)lH$
zJRU^_<b8NDVA5E(+AJm@loS^JuxjFc5Y5u%<m6mkTy**B2X+qafIGx`lMOe&&h@z-
zaB8IQV}(?Ae972$3%e19v#V>hW}~D+a)^qaZRg|CJ%W)Y6<d~#x%r7?zM2XvF1+58
zQWm=%S#Y{^Nl3_Jjq^uFMut2^vTW(JU4jWGE__)3Ze(QSe~u4pwzU$qdYT58%gHuP
z>@29#L?>32E1zrljjb8X3^*xhy?uwXNPAcBm5DPKzS6#0uKvZPWpHAos9DWzb#_MH
z3a7o_bt|-RkLzmAKJHD;c3j%mOkbCU&6K2N*Bh1F<muXtl?7#vQ)3RN_VETGh8%_s
zS*$T=RM~cTmyas!vSLD_g$D9tOQ{bz>+F%9sz?v(*DG`wb3=}Nl$l-6dR?y#YEP^#
zZfBYvY{*S$ld15_J2$KE>>Fjq&N2ikF1(9hvC4aeF6@sTPwUN^mx-&55g6eoovlWs
z_fOZ`o#$I#TF|_u(@$1TAcf<dN7MM2uRFvTzvsjXw+l!n#=4f{)>ak{q|bhT?_O;`
zLO%0!yz716rpaI}nKzc0%s+qcvk$+;{cCIl3uG45xWHGs+Uksvh>q)U^Yln!H7{Sj
z4W<;*^?g13{mY|Bo<iFs57-jm#;{ohx@<zafA_oe-**feVqs#oT&(HlP4$5qN4fk1
z7c0E|P~HrgO$XkEP4D~3|7-6|pV`2}IG%2I-P&l2ES9Bp)O|#hl9USL2-S+jeI>}c
zBSa6hL|l!o4N(-W-VvcuENQE%ip151MhHD3;;5yLrMq3-bpMQ<KJT8H=fyM6{GNH9
zdFDI6Pvy+nNb$QybSCRMf0^Q{r)ZKWm)jym!{zr?X=-Y!e;l%imuD)15%nNV>N>m*
zRl#zwwKofT{$l=I$#li#ku@3St#W=-FmIeSBx)LZ9z7V+^`HkOYAzjy$DFhgM9pK&
zAF^6;XY<kK`jDnz;`E=jP1=9E{%obAFk!Fk7;nKD!Vt2p%TVYnX!w$*j)m&Rrzj|`
z_6tnb_mNY|QixGYR{w~z*($dpBPkGO<xpN-%ZZf~_-yyz<lzDz91}Vt>9Z|iOo9fR
zO-GqZ65E4v(i{(Wr+lWMG1m@Z-6jaL5zT_98Y|nd&k^qY7YPx<rBvl7U%I$UhdhoR
zygKfh)VEY|6k$fkWF$lh=#b$Un;?5)VldKcJLmlSx|~_!y2T$e4vu#T<7ntkRx+Q)
zq;xPQJNvTMd#T*&$<Um3pTkp%&Dt6u(3+r$xEeOyy0WjWYki=m0F@45*^LM}aKa*c
zP(8rn_XCX+T{zwy#f}hgkOH522gfpvwXT9+a9_|f&+f!>Y5B0P<n3Ov^2*AJL}!b5
zo0EgD@}B=}7LG(VONNW|tHK6TPWL2O-?vVkG<p9ruJe{b@1R?KGM3m8r}nt6_`(e|
zCnon2j@|cbQU2&YWkXNg`P+S%j>?dvl9|?0zVc;*p!@_#`uccLPh1hoHrcC$CpPvJ
z@S<&G%62}ubx-$JYX$V2IxtLUlymVu7O4TEOw$YJGKGfKQ!Dcc;IZrt^1Y=fuh*})
z8h00m#@{?nTl-kQyCF-Zep(n(xY;&lXK4ert*@)+K0o4Q;`ORqD=RUM;mgB5vrGt7
znW=S<3>bS*I~=U+ksXZ3bD`q+gu!yfPXE&;TsP|Q5e-T7qPHnT3sjjVR`5%Xr>k(p
zt4<mG@emiYeA$s}twADaO@#5?Vr(A6%C@entn9&=)%u;+;j<f)dCRNpn`V~Kpimrf
zfxN{|8l6`RA2Rt%AP`7JkvZ%CsH-`_k};S$DNVE_>-3rjPRhc5C508MVFi1gfyO2#
ziIC|wonPLCW=dDJivKL2u>#m&<Y}rFoOy3RC=}Xemk=DXcC(x5bcy;)_P(pj8&4P|
z<?9}$BKebq+5r{=sK?Ukw<clHw2AcENHpS1(H$!#KYxlP!$+<kh~(MQCj60eXj&wQ
zc37#5W6cHD>5?QM7clZT%#0Qth%j{JgUGr{Wk^JTKSh^hY0AZ$_N!1tXtEfL;Q6;*
zk9&0KjK0UA@9RVa1fLM<hoM9Uk}b8|L7^!5Uf@v+!A}`6k%iZENx;xDf}3F%^S-e~
zwQZmtDXyB+qNMBkn`#5D!OtH_v0I}vEJ;Si9F1eNNL4sCkQ^zO4D{@4c!??Fz~O!f
zgXm85oU#lp8brQV%%nT?w<`6kxG0fFXN+f2#H~Fv*dSazwaE3o=lL-3W@n+K#UEPG
z*b$yjr+*dtq?8QD1fzc>yrX^i)Nd0CPfOc)#-1zHcB12^{@PmREk5jvl9$N~Zcp$y
zasX8wyJc8aQ<KJ?QTMqh9U4{Mcr8f=Tl8_K&9VKs_LXpOqjc&QHtY;uX_5Y)@9ShA
zi*wlz2nSY@cYV{PH=Wft-Cr6gs;a70-Y=H7%%c`y5~lv9m(3Mzz}tfkXV>R$Yf5i4
zkQ#&mRjQHghAN(z9eYwp+yK0lVM%uqFFTn$XSg3njJE`Z2;Fl}$={H@INCNk5%is%
zk<tv{X>4I6sFIs*<~|_C^D{|}Ja8f&Zw-_S$!y(h=z~D_GqH&#!plDZu4|E#005(c
zvAuv(`)<feeQBO2>`B^yi+DLF06+{p2LNz%Hvxe1lM8#k1%4~fw@2}RY=!NdJ$AcZ
Tt+**G7C>qqa35EuOGNI!tWARD

diff --git a/vignettes/functional_api.Rmd b/vignettes/functional_api.Rmd
index 7cd091135..129b30262 100644
--- a/vignettes/functional_api.Rmd
+++ b/vignettes/functional_api.Rmd
@@ -13,7 +13,7 @@ vignette: >
 ## Setup
 
 
-```r
+``` r
 library(keras3)
 ```
 
@@ -47,7 +47,7 @@ This is a basic graph with three layers.
 To build this model using the functional API, start by creating an input node:
 
 
-```r
+``` r
 inputs <- keras_input(shape = c(784))
 ```
 
@@ -58,7 +58,7 @@ If, for example, you have an image input with a shape of `(32, 32, 3)`,
 you would use:
 
 
-```r
+``` r
 # Just for demonstration purposes.
 img_inputs <- keras_input(shape = c(32, 32, 3))
 ```
@@ -68,7 +68,7 @@ of the input data that you feed to your model.
 Here's the shape:
 
 
-```r
+``` r
 shape(inputs)
 ```
 
@@ -79,7 +79,7 @@ shape(inputs)
 Here's the dtype:
 
 
-```r
+``` r
 inputs$dtype
 ```
 
@@ -91,7 +91,7 @@ You create a new node in the graph of layers by calling a layer on this `inputs`
 object:
 
 
-```r
+``` r
 dense <- layer_dense(units = 64, activation="relu")
 x <- dense(inputs)
 ```
@@ -103,7 +103,7 @@ You're "passing" the inputs to the `dense` layer, and you get `x` as the output.
 Let's add a few more layers to the graph of layers:
 
 
-```r
+``` r
 outputs <- x |>
   layer_dense(units = 64, activation = "relu") |>
   layer_dense(units = 10)
@@ -113,14 +113,14 @@ At this point, you can create a `Model` by specifying its inputs and outputs
 in the graph of layers:
 
 
-```r
+``` r
 model <- keras_model(inputs = inputs, outputs = outputs, name = "mnist_model")
 ```
 
 Let's check out what the model summary looks like:
 
 
-```r
+``` r
 summary(model)
 ```
 
@@ -145,7 +145,7 @@ summary(model)
 You can also plot the model as a graph:
 
 
-```r
+``` r
 plot(model)
 ```
 
@@ -155,7 +155,7 @@ And, optionally, display the input and output shapes of each layer
 in the plotted graph:
 
 
-```r
+``` r
 plot(model, show_shapes = TRUE)
 ```
 
@@ -189,7 +189,7 @@ fit the model on the data (while monitoring performance on a validation split),
 then evaluate the model on the test data:
 
 
-```r
+``` r
 c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()
 
 x_train <- array_reshape(x_train, c(60000, 784)) / 255
@@ -208,29 +208,29 @@ history <- model |> fit(
 
 ```
 ## Epoch 1/2
-## 750/750 - 2s - 3ms/step - accuracy: 0.8979 - loss: 0.3540 - val_accuracy: 0.9448 - val_loss: 0.1903
+## 750/750 - 2s - 2ms/step - accuracy: 0.8979 - loss: 0.3540 - val_accuracy: 0.9448 - val_loss: 0.1903
 ## Epoch 2/2
-## 750/750 - 1s - 920us/step - accuracy: 0.9509 - loss: 0.1635 - val_accuracy: 0.9597 - val_loss: 0.1397
+## 750/750 - 1s - 773us/step - accuracy: 0.9511 - loss: 0.1634 - val_accuracy: 0.9605 - val_loss: 0.1386
 ```
 
-```r
+``` r
 test_scores <- model |> evaluate(x_test, y_test, verbose=2)
 ```
 
 ```
-## 313/313 - 1s - 2ms/step - accuracy: 0.9595 - loss: 0.1328
+## 313/313 - 0s - 980us/step - accuracy: 0.9593 - loss: 0.1323
 ```
 
 
 
-```r
+``` r
 cat("Test loss:", test_scores$loss, "\n")
 cat("Test accuracy:", test_scores$accuracy, "\n")
 ```
 
 ```
-## Test loss: 0.132778
-## Test accuracy: 0.9595
+## Test loss: 0.1323339
+## Test accuracy: 0.9593
 ```
 
 For further reading, see the [training and evaluation](training_with_built_in_methods.html) guide.
@@ -250,7 +250,7 @@ This saved file includes the:
 - optimizer and its state, if any (to restart training where you left off)
 
 
-```r
+``` r
 model |> save_model("my_model.keras")
 rm(model)
 # Recreate the exact same model purely from the file:
@@ -273,7 +273,7 @@ an `encoder` model that turns image inputs into 16-dimensional vectors,
 and an end-to-end `autoencoder` model for training.
 
 
-```r
+``` r
 encoder_input <- keras_input(shape = c(28, 28, 1), name="img")
 encoder_output <- encoder_input |>
   layer_conv_2d(16, 3, activation = "relu") |>
@@ -312,7 +312,7 @@ summary(encoder)
 ##  Non-trainable params: 0 (0.00 B)
 ```
 
-```r
+``` r
 decoder_output <- encoder_output |>
   layer_reshape(c(4, 4, 1)) |>
   layer_conv_2d_transpose(16, 3, activation = "relu") |>
@@ -384,7 +384,7 @@ creates an encoder model, a decoder model, and chains them in two calls
 to obtain the autoencoder model:
 
 
-```r
+``` r
 encoder_input <- keras_input(shape = c(28, 28, 1), name="img")
 encoder_output <- encoder_input |>
   layer_conv_2d(16, 3, activation = "relu") |>
@@ -423,7 +423,7 @@ summary(encoder)
 ##  Non-trainable params: 0 (0.00 B)
 ```
 
-```r
+``` r
 decoder_input <- keras_input(shape = c(16), name = "encoded_img")
 decoder_output <- decoder_input |>
   layer_reshape(c(4, 4, 1)) |>
@@ -465,7 +465,7 @@ summary(decoder)
 ##  Non-trainable params: 0 (0.00 B)
 ```
 
-```r
+``` r
 autoencoder_input <- keras_input(shape = c(28, 28, 1), name = "img")
 encoded_img <- encoder(autoencoder_input)
 decoded_img <- decoder(encoded_img)
@@ -497,7 +497,7 @@ For example, here's how to ensemble a set of models into a single model
 that averages their predictions:
 
 
-```r
+``` r
 get_model <- function() {
   inputs <- keras_input(shape = 128)
   outputs <- inputs |> layer_dense(1)
@@ -540,7 +540,7 @@ over the set of departments).
 You can build this model in a few lines with the functional API:
 
 
-```r
+``` r
 num_tags <- 12  # Number of unique issue tags
 num_words <- 10000  # Size of vocabulary obtained when preprocessing text data
 num_departments <- 4  # Number of departments for predictions
@@ -583,7 +583,7 @@ model <- keras_model(
 Now plot the model:
 
 
-```r
+``` r
 plot(model, show_shapes = TRUE)
 ```
 
@@ -594,7 +594,7 @@ You can even assign different weights to each loss -- to modulate
 their contribution to the total training loss.
 
 
-```r
+``` r
 model |> compile(
   optimizer = optimizer_rmsprop(1e-3),
   loss = list(
@@ -609,7 +609,7 @@ Since the output layers have different names, you could also specify
 the losses and loss weights with the corresponding layer names:
 
 
-```r
+``` r
 model |> compile(
   optimizer = optimizer_rmsprop(1e-3),
   loss = list(
@@ -623,7 +623,7 @@ model |> compile(
 Train the model by passing lists of NumPy arrays of inputs and targets:
 
 
-```r
+``` r
 # Dummy input data
 title_data <- random_integer(c(1280, 10), 0, num_words)
 body_data <- random_integer(c(1280, 100), 0, num_words)
@@ -643,9 +643,9 @@ model |> fit(
 
 ```
 ## Epoch 1/2
-## 40/40 - 3s - 69ms/step - loss: 0.3948
+## 40/40 - 2s - 57ms/step - loss: 0.3948
 ## Epoch 2/2
-## 40/40 - 0s - 6ms/step - loss: 0.1971
+## 40/40 - 0s - 5ms/step - loss: 0.1971
 ```
 
 When calling fit with a `Dataset` object, it should yield either a
@@ -666,7 +666,7 @@ A common use case for this is residual connections.
 Let's build a toy ResNet model for CIFAR10 to demonstrate this:
 
 
-```r
+``` r
 inputs <- keras_input(shape = c(32, 32, 3), name = "img")
 block_1_output <- inputs |>
   layer_conv_2d(32, kernel_size = 3, activation = "relu") |>
@@ -743,7 +743,7 @@ summary(model)
 Plot the model:
 
 
-```r
+``` r
 plot(model, show_shapes = TRUE)
 ```
 
@@ -752,7 +752,7 @@ plot(model, show_shapes = TRUE)
 Now train the model:
 
 
-```r
+``` r
 c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_cifar10()
 
 x_train <- x_train / 255.0
@@ -776,7 +776,7 @@ model |> fit(
 ```
 
 ```
-## 13/13 - 6s - 478ms/step - acc: 0.1250 - loss: 2.2998 - val_acc: 0.1250 - val_loss: 2.2939
+## 13/13 - 5s - 373ms/step - acc: 0.1238 - loss: 2.2995 - val_acc: 0.1300 - val_loss: 2.2957
 ```
 
 ## Shared layers
@@ -796,7 +796,7 @@ To share a layer in the functional API, call the same layer instance multiple ti
 For instance, here's an `Embedding` layer shared across two different text inputs:
 
 
-```r
+``` r
 # Embedding for 1000 unique words mapped to 128-dimensional vectors
 shared_embedding <- layer_embedding(input_dim = 1000, output_dim = 128)
 
@@ -825,7 +825,7 @@ Let's look at an example. This is a VGG19 model with weights pretrained on Image
 
 
 
-```r
+``` r
 vgg19 <- application_vgg19()
 ```
 
@@ -833,7 +833,7 @@ And these are the intermediate activations of the model,
 obtained by querying the graph data structure:
 
 
-```r
+``` r
 features_list <- lapply(vgg19$layers, function(x) x$output)
 ```
 
@@ -841,7 +841,7 @@ Use these features to create a new feature-extraction model that returns
 the values of the intermediate layer activations:
 
 
-```r
+``` r
 feat_extraction_model <- keras_model(inputs = vgg19$input,
                                      outputs = features_list)
 
@@ -875,7 +875,7 @@ To learn more about creating layers from scratch, read
 The following is a basic implementation of `layer_dense()`:
 
 
-```r
+``` r
 custom_dense <- Layer(
   classname = "CustomDense",
   initialize = function(units = 32) {
@@ -909,7 +909,7 @@ For serialization support in your custom layer, define a `get_config()`
 method that returns the constructor arguments of the layer instance:
 
 
-```r
+``` r
 custom_dense <- Layer(
   classname = "CustomDense",
 
@@ -955,7 +955,7 @@ when recreating a layer instance given its config dictionary.
 The default implementation of `from_config` is:
 
 
-```r
+``` r
 from_config <- function(cls, config) {
   do.call(cls, config)
 }
@@ -990,7 +990,7 @@ There is no `super$initialize(...)`, no `call = function(...)`, no  `self$...`,
 Compare:
 
 
-```r
+``` r
 inputs <- keras_input(shape = shape(32))
 outputs <- inputs |>
   layer_dense(64, activation = "relu") |>
@@ -1001,7 +1001,7 @@ mlp <- keras_model(inputs, outputs)
 With the subclassed version:
 
 
-```r
+``` r
 MLP <- Model(
   classname = "MLP",
   initialize = function(...) {
@@ -1042,7 +1042,7 @@ in this graph. For example, to extract and reuse the activations of intermediate
 layers (as seen in a previous example):
 
 
-```r
+``` r
 features_list <- lapply(vgg19$layers, function(x) x$output)
 feat_extraction_model <- keras_model(inputs = vgg19$input,
                                      outputs = features_list)
@@ -1081,7 +1081,7 @@ You can always use a functional model or `Sequential` model
 as part of a subclassed model or layer:
 
 
-```r
+``` r
 units <- 32
 timesteps <- 10
 input_dim <- 5
@@ -1143,7 +1143,7 @@ Here's a quick example of a custom RNN, written from scratch,
 being used in a functional model:
 
 
-```r
+``` r
 units <- 32
 timesteps <- 10
 input_dim <- 5
diff --git a/vignettes/getting_started.Rmd b/vignettes/getting_started.Rmd
index 71e8872fb..91162507f 100644
--- a/vignettes/getting_started.Rmd
+++ b/vignettes/getting_started.Rmd
@@ -39,21 +39,21 @@ This website provides documentation for the R interface to Keras. See the main K
 First, install the keras R package:
 
 
-```r
+``` r
 install.packages("keras3")
 ```
 
 or install the development version with:
 
 
-```r
+``` r
 remotes::install_github("rstudio/keras")
 ```
 
 The Keras R interface requires that a backend engine be installed. This is [TensorFlow](https://www.tensorflow.org/) by default.
 
 
-```r
+``` r
 keras3::install_keras(backend = "tensorflow")
 ```
 
@@ -72,7 +72,7 @@ The dataset also includes labels for each image, telling us which digit it is. F
 The MNIST dataset is included with Keras and can be accessed using the `dataset_mnist()` function. Here we load the dataset then create variables for our test and training data:
 
 
-```r
+``` r
 library(keras3)
 mnist <- dataset_mnist()
 x_train <- mnist$train$x
@@ -84,7 +84,7 @@ y_test <- mnist$test$y
 The `x` data is a 3-d array `(images, width, height)` of grayscale values. To prepare the data for training we convert the 3-d arrays into matrices by reshaping width and height into a single dimension (28x28 images are flattened into length 784 vectors). Then, we convert the grayscale values from integers ranging between 0 to 255 into floating point values ranging between 0 and 1:
 
 
-```r
+``` r
 # reshape
 x_train <- array_reshape(x_train, c(nrow(x_train), 784))
 x_test <- array_reshape(x_test, c(nrow(x_test), 784))
@@ -98,7 +98,7 @@ Note that we use the `array_reshape()` function rather than the `dim<-()` functi
 The `y` data is an integer vector with values ranging from 0 to 9. To prepare this data for training we one-hot encode the vectors into binary class matrices using the Keras `to_categorical()` function:
 
 
-```r
+``` r
 y_train <- to_categorical(y_train, 10)
 y_test <- to_categorical(y_test, 10)
 ```
@@ -110,7 +110,7 @@ The core data structure of Keras is a model, a way to organize layers. The simpl
 We begin by creating a sequential model and then adding layers using the pipe (`|>`) operator:
 
 
-```r
+``` r
 model <- keras_model_sequential(input_shape = c(784))
 model |>
   layer_dense(units = 256, activation = 'relu') |>
@@ -125,7 +125,7 @@ The `input_shape` argument to the first layer specifies the shape of the input d
 Use the `summary()` function to print the details of the model:
 
 
-```r
+``` r
 summary(model)
 ```
 
@@ -150,7 +150,7 @@ summary(model)
 ```
 
 
-```r
+``` r
 plot(model)
 ```
 
@@ -158,7 +158,7 @@ plot(model)
 Next, compile the model with appropriate loss function, optimizer, and metrics:
 
 
-```r
+``` r
 model |> compile(
   loss = 'categorical_crossentropy',
   optimizer = optimizer_rmsprop(),
@@ -171,7 +171,7 @@ model |> compile(
 Use the `fit()` function to train the model for 30 epochs using batches of 128 images:
 
 
-```r
+``` r
 history <- model |> fit(
   x_train, y_train,
   epochs = 30, batch_size = 128,
@@ -182,7 +182,7 @@ history <- model |> fit(
 The `history` object returned by `fit()` includes loss and accuracy metrics which we can plot:
 
 
-```r
+``` r
 plot(history)
 ```
 
@@ -191,26 +191,26 @@ plot(history)
 Evaluate the model's performance on the test data:
 
 
-```r
+``` r
 model |> evaluate(x_test, y_test)
 ```
 
 ```
-## 313/313 - 1s - 2ms/step - accuracy: 0.9808 - loss: 0.0869
+## 313/313 - 1s - 2ms/step - accuracy: 0.9814 - loss: 0.0844
 ```
 
 ```
 ## $accuracy
-## [1] 0.9808
+## [1] 0.9814
 ##
 ## $loss
-## [1] 0.08689082
+## [1] 0.08441389
 ```
 
 Generate predictions on new data:
 
 
-```r
+``` r
 probs <- model |> predict(x_test)
 ```
 
@@ -218,7 +218,7 @@ probs <- model |> predict(x_test)
 ## 313/313 - 0s - 1ms/step
 ```
 
-```r
+``` r
 max.col(probs) - 1L
 ```
 
@@ -233,7 +233,7 @@ Keras provides a vocabulary for building deep learning models that is simple, el
 
 ### Deep Learning with R Book
 
-If you want a more comprehensive introduction to both Keras and the concepts and practice of deep learning, we recommend the [Deep Learning with R, 2nd Edition](https://www.manning.com/books/deep-learning-with-r-second-edition) book from Manning. This book is a collaboration between François Chollet, the creator of (Python) Keras, J.J. Allaire, who wrote the original R interface to Keras, and Tomasz Kalinowski, the maintainer of the R interface to Keras.
+If you want a more comprehensive introduction to both Keras and the concepts and practice of deep learning, we recommend the [Deep Learning with R, 2nd Edition](https://rstd.io/dlwr-2e) book from Manning. This book is a collaboration between François Chollet, the creator of (Python) Keras, J.J. Allaire, who wrote the original R interface to Keras, and Tomasz Kalinowski, the maintainer of the R interface to Keras.
 
 The book presumes no significant knowledge of machine learning and deep learning, and goes all the way from basic theory to advanced practical applications, all using the R interface to Keras.
 
diff --git a/vignettes/getting_started/unnamed-chunk-12-1.png b/vignettes/getting_started/unnamed-chunk-12-1.png
index b0702848127cc58b38d4783e1df4dd9fbd6a5d78..4e9a631d0c13649596ae1e9b371807d25b84e35e 100644
GIT binary patch
literal 30397
zcmd3Oby$_(x93q5Q9!y8^himEbcqtuAl)6(A$2GL=@O7m>F%y0Al=;{BHf(_X5)9}
zH}}rHcjmeO4bLOU3Eutgz4qGc6YB)Z$%vspA$|gZK+whC3x9w>?w#NLL%9zgc_m>=
z1AaWVd9P*<fuLgF{ku0Z?{N)*Jco!23n@A$ZO=Qo;X2$1?w?gA&u?e@lf9*ReD7^k
zSioo8z=w+YWHe85RR=E-F3=<tnq1s_vO?`0fuEgFOzv%<U|~|c7JrNJ;_KJ)rvARY
z@OHv?pQYovsoJ(KYW-^lJWeb25VbBVQG){>e)71!5Xc+&lpFyV&&=B)B*;3cHyPy3
z8s;$s!qe$r|2c@8G;Cy+w37vG`Rr=k{Yi`wo`^i6!c++J+5^5hZ%0Zk9$D{RPTyNj
zkBpC0u<T`|Hetw`ry**o_|9ix#V&e*BVLMcFqS|4NyhN{3Cu`Mxn{Psl-a-S`=!R5
z1&2AyefnOr;iADc1@{M1RHNturRg-62oDD2iH%SBH|FBOUf_bb?Gg^Xwxr%ErJs->
zX|5<R2qgL4|1nzsv`3JkI${`%67q)I|LL>x*_|Sd8V*9V@5Na$LPJPz#>FFF8+3%c
z^M5KLDk_u0$1NbxY=+pLZ#c_I^)MSwZy);pPVKZj8d*Hyq~o>To>j1r^Sh;3MlG{4
z)#93ClE>a;zdE8LA|g^!7HVymXRYDMdT~!?aq;nIEUKxb6BM2IMzSO|$oQEH6gJD!
zBV(y$Z~jEd_SAe`DfD?1L$6NZ%IEZybZBoikqDl8;L^SIA~pQrqHBxW>9VrY{;I-Z
z>)Z9^(M2B;192yvhv4!7vxlHK_OfkMx)iEGo(d<_%;tQ3rpjC_lsIei{oP8vNqzc^
z=lFEi+O=uHb+^uBs#GtQbE?dslbT83Oe!{P*7JI?a=t;wb*HL#mR_^g=C*urxvec2
z2YWF^K<g{zp)+A>EEvMFUSo=JPS}b0cw|6=h*<Nwoo~^(_ncM7o(UzqeFd5t8<Ck9
zfX{9%Ed0$rB!;}<S`9l8JCOoY-r|==VVz0IVLf5~IRUojLog<P3UQd_HqtE_E7u2H
zYKDZxMwg?oXS}Rd1TIY>lQ`#V8+#M<#A8eyhTFrrEu3N%p<5G?lbz>{A%l`uoXaPX
zlShi;WiF;OSu(LVTZh#3ymN%LEtn4Gs;M$$Oif#de5c*zi3EKS@<A<ckmZ-S+n-0a
z?9aBma|mLh5*2key6|CJ>y%CSx!-7kDUKPih9|G6YBe!eH}GtBQ=@N7TotyKyonKC
z%));3MB(AXhgmg9uJcnC91f{(Gn%aQ4Ge;VgR7<@v$jR8^O=c5y`P)1l&Wt#;NajW
zeE4AJgSOorMZMM+Z&0P)8A3E#q67cYQtmp>ti88c0$+%2W_$HQ@8`TzJRvkZ@xZxj
zOOKDNW6M^fsP4+ck_bmy*s6Q*33A&Be4I!WE7Xvg#ybrKnUxr?B@N~6Y9Nl7Y%+_O
zh(p5)zJZ})nwRNgC!AM}&+X@GD`yGN>ltP!WG?lVr?`)JKds9Z8zlCh{=|_D=wuC^
zONqTb(F4&+>vprfz1`zM*lVl#>C>@8lbP0=p_l7e$1k3#sy!8ETXuY?uYOP~s9w8o
zjsCmOTW>bLV#jrX)QFXpjC1QKqEp(+Nw>egXXO>C7_yH(Cc^v0(I0VmoBZ}Eg~Oh9
zj_v)&$nCo4Y(LRk`)U?Hs@n*37+!NN|8iX{2@ec>e5ls*bbAkedo#ehH??z$^-^@s
zevn@U7BIWRK^&^G!B(0VKW>J8(+*un!aPQSJwRl6G0@XruV;i?aLne)XQ<a$Y3aEp
z_B;Oi;gv0&$YDB^)`}c5sbMEDXkF8;MqfDj&WdyEfSK$pi>RivzjpqD+r%*Er6y)8
zI&#Ne!$7Rf!cu_wgtxFfOMsUrZF(-@&&J5es;vn;o+SRO)2-p0a44~nj~5!$R&&}P
z)%$txVKlOG%*x5uq(;%iJtSmd5s`;F(fE6+58R{mLdib0npMI0Qg|FTHS=1Q+Wc#*
z<`a^VH0udJ#H7=N3tX<>7LaRE({JCB5nrw5-@dk*<FX-GK8u*`uhpvS4EH?f*qSGv
zkagmkorjqxW=nUk=+DVaEK|iM+VT>G`x{;)tRdK+Lx<l8D^jiC$!ES1MfF=e-8ENX
zlF^+;r<Ud1a@A<d?#Gq?)7BTM`}KhkR(qBXLrI1CTEK*OPEj>HwOfbhq^LKQ($94J
zhh*=T%EbvG8JS1)x}lomP@2#rOh|6@#kvs2g6AzlE7cv_=ykoq483aU=c}#E=o_xK
zKY=nkXO^007bOkk^|u}!YesxzeqFjXEZQYS`@jGAm2q}*Qly9P5#|=E8$V?$(`Is+
zJMK(%7Z<*5%(}`V3qH)FxuvD#b{9yU#@5a$wqT}7j}Xq#e@&ZS>vmG}uI&qoJVm<6
zq;-7rAqnMBTHkc&#5ex7)q#@>w~rq`YWatrVq;;^1&xrKA7^yRI`16EdzPrT)mU9!
zZj}_U1*GYy*0+pE2gF**Byq7=FYpzNU;gsJSb%L6shBxtWs!8ORiz3FTK_(ecekcW
zFUqn1&Eq6#HuiOnv13KQDOOps(J|54_voijrcF}+8YgYJ*D`K;=)?9JG-6?@D0*VN
zjKB~oHQeC2T_-Ajj-1;f;mYgx*_K0EFU7*>CkG=bC0jX*NETn5bhWqUJv^n9jCr8;
zS(Jb*Ffg!8y+~ctIrI3L>-G7;bcIQSW-*BP%&aUegqMm}Rw8X%2-#cl$V>wN`KyxI
ztM-YHTnEvyB#a2YSFbh>MMLJPG=eW0?KQ<&3>frwI6Cx9NF6(@Os)k`<)5zv<~qIQ
z)8djO=t&LG;#iq4r}q=1@fN}oVWjUHv<rIvmFC9da4ZI$#8O*!xq8Q+fB&-uCf@LS
zQ4sltuW1{<M#o?DtVg_yOx)??p1caoX*i{WXJd&g#*J+j9rK^Gw#GcWK`e8lGI@3{
zcZ9mSO%|)^6cuaK?EY>KE_uy*cZjMNCG$GrKUk7@=d?Rl2R1=Kx^tF{*?kfD6wjNB
z9a(;{;o;jvc6)>64$Il<qPK#sr`z$oPK;P5yYsHUe+HVx=Gmjbh9-Pb2~M}CqrQDp
z6!OMd`SYhTEchcap13d$Z*~7{bzN0w%S>TNTgy~o$b1L*->6U!6iaL68&4vIxBxt}
zE8e$#=kgQ>3Pn<|Xg8%8&JF0$kG&!<fs?rioSEt!*rVb*?0I~A*kQrZK>8H=0e2-U
z;w5L+{5yI1<CZH<Y3IUEq0NeRc88JZi5wPF`)>YE`x7}!N+Zt}T&>pzk~tE0DzuyW
zjE`2(JuWwLZk^qZI}&rwZmUg)(}~!OH5XRLRdf)~;a~L)t_I>OK5dMYmX_L~A08c%
zdtM(fL=1m(b!_itI@uUq>oLfvd&cW%F;`orR$=^7H00OCVT9dMtM5+4uwo4(*03eQ
zX=g^sH!YUp<NmFyZ#G;+Pfw6BDYf1}Y%qmi>D3LgSK1@=XIwVFLTxdgN{Xw0jheN;
z&dAs!?3=O(srrH{)m=pS%5kfJ(+U0c?qnv57N6Lnr-q&i!S#o*(Us%N`lqD1s;x%$
zT=D%uly9G+DoE*hsNL)~>`6#S?7D5raP!RAPq(!Nu6o{x2nh*+Lt=&QT=-jJV#6!G
zNFJvb(E6=iu$c28U#!d|4Gwy4U#!)Tw5=a82|8b7$t3e&MX1Op8R0|bgIYiBVz(W+
zCdriZ2E149%D$e~-58dFT|X}WqOkm>y;oq*s_Fu-oe?qpW8;&DZeXUoL4fJ#cb}5&
z_wbGE#DIV`7qu9q;Lt@xtw--q>Ri2dk(swOwup5OJmH+lEyPaEA|IKonVFeyFM$XA
zw)6Jd#JZ(jS@a-`cQ<#RFFh$KDb?e$d+*Fi)^fB!rKFdHl+W2_;MOCv*?r^Z0XaS+
z>`#Q){mu*{kpt&|_tjr;+P=2QWHa+PmoCITl5t^je_*Lk^e!G=>Figg+1!hcg{9T#
z96|g#^9NJofEEV1KuB5%ZsD*&s5Zjugg{&I31g{<ZzB96yQa%eOYh)2DLEDaUQrx_
zo%0v{YMLdi^67M8;V?o$z1f%HF!ibz;SB>#PR|4!E+zQi2kA!{g{RjVwKMj5h`frR
z5Py*fZD?G`>V+>T=u3y8z^iT{3ZE)fVv>X2lB#SUV9Q4=!Yyw-BqXSywDg4pcpqOA
zTSn63h-XewL@!d?NbAvST$=y*!8~z|y1S2Z2>W=NsBy!+BC!UK@nOG&^sQUVS=G2+
z9Ii0Nn(zN=IoTX9uoKLYNw!<jF@1*9PoVkh2_y5v3yUR@76LBWFIF|z4i|AoNZgG6
zVl*)}v6OQk%la;L59-IB6128i3f`QbkSt3N52w6Qr3;db<PWlQ`>~Tk6(}s6&f?ia
zvOM?)hS&az&Qa~Ot6Vx}zFhwdmW-C6iCCV{xWbaBlF!!3z7EEFjgrZS)&4Vy8fq<P
zQcI#r6;k&aLkTx4vo-(<4GgfDfEiZ|oBj!Symph6+f(kg+83{%%9Lb8!eL?hniw4o
z=X5fD8<e<qjRM$TOmi|oiQpwDq#DDJ9IIM1;ocdpkX&zAk%gjZ_i@E@-<eO)*ifz)
z7n<(veM;(jQEYg1wkL>3F6-?TK*PH=(c4_QdEh)!5ibAB%hC0v-|yeQd#boSZ`@LZ
zC^8~&+Z8`(a+V6e`$K7mIH@r(^3>rmnpQhq%m88hYqGaP7DnIo&HqE(@L~WPOhlf{
z`Ipy;maCj01;f?vE_kfg4U*>Wy^v06ZKf?dRz_;><ie^iL5yVlc3<?=Qo9u`m~oH%
zu!U$=A3hqK<!&)%jgIScVVqR+={7E_#r;5f%5$<cYpAB&Mb#VGVcqz{jXB9k)i3aO
zZPI8CqiE*`-G>)7O$JvdID{R2<y1bp`&WT9LCXPMedYIi_l1pPD50pmhiGlAQV)8u
zhi}}k$l}>zcN>GBk~-JP^r12}%I%7SlryV$hzr{lfu`@wF0cm!h?J*}@W&u$WMte0
zfM&zLY-OOH+i`npqDUhuGLlwP{=k{<;N9MLwLR>%q1e>ht7-wn)fv;I@qE1l#<eT=
zyrADx!;s$Icy@E;I^=Tt$p-$=n>}(|Jdp}xE7(c&*w~nzWs=OAf-L`uWlOsN@)Ag{
zmW$2qznq!(RAURBapME7J#LRTM&Vt3yBa0Cqq2g}&OiOkqgfF2;A?uqZ6uEQWr2mo
z!ymCZALC2p4)MP6dkhoTf99B-8TaZ{bt-q0{uRe}OV<?K=G@u{Q@Qkv`6qSntL?nQ
z^(^U^l?e%Yr@1C_zD5(wk*@vXrn4@!Z}eF%Y;19y?zEchU1PJ?tTn|X5;Y{m=|#Tz
zXsj07zuE)GG$^Yyo#RR}67gfH2#Gfmi9t6ZG8bxT3l>Zt#=ykdxps+}e!Y7$ymF~s
zVcg$l4$ru{6}>tYh;%yb=UhZ1E!qA-#t-{+wA$a@-R*vL+H|cgA}<<3SmrYIJ}5Bj
z!rlqa$;84^X69pC*OQ`s0p7%SS%T3`6J|XM>j)vj+kJZtrUIm91{RhBuSaCLHc0UX
zoncK!1H3Xh_J6t}d>&ymkJxvYYg*x4NT6@~lY5+FGz23r$bdNUJZ@P__0-m5OVU;|
zLb;qFINsRU*lRjV8C+`sr(j7X-C4bKV)J4HQ?tksReyr^C$0frt&2#-E|MU$x8HSX
zuz$W4sxm)j77m)l+p^30*(qkHnfaVh{IQ7|YA+f8*RsvCjIlbpXM<$loUB^U#ah+E
z3>f>rf!QkW%g3X;-HVd#!aaR2ao88n2CjCgRQ=|wk<0o1k_Nf+SLn-+3k?lxDx@Xm
zlg0D3wg$a14CJu&?;{G>O1Jwj1qE-H!v(9=i`x7#;BIHdTk^g~>v$pu7j7zx_X~*!
zGT_+++RjU(ynb@Y^}@opXZklYL4se!hE#~Wn`0g}&F)yaoyjE};1i)8pK2s(!(L=b
zCn{(!%7;_;F83c^hPm=hE4#9EY8kX#VicweGfQ!05C(!}_I<BQ<E<BNV$|c}LO__E
zQYEYxH=H4w=q%9l{0oZ(Vec1v(ydR&@y8TRJiY<zi#m)u(>vgBO6zTAJ^>2|$^aHh
zN+VDms?3y2L{h*PJx`8WN}4D8>+IKVPD(u0iYDNQ9c!;BJ#WQeftBJWow)jJcJm3o
zYXlE${{@e;?wB>a87vSU$Gg9G)A10YqT^oWur=}R+qXa*>KH16jqrYu5bNP={#5)T
z0HJ5AS@dyNv{S0=lSin0yCid}Y>Ux-TEIlS+}dV9A4B66zIsj*hA`e3NwU7h$?7`o
z2)sjVD>;H^Ta$Y~ru_5l3212xkJev;G|+tg3ZF^)x@VIe7oVfvV;+nf`Np}VDKR1M
zp^$7e`IJ(Da*+<aStJ-god0wxnh_@;t%NnnL7~F!WL!lSQSRRnlA&|<M<!Xl{@ZrU
z%BLK<`K7t`z~iB|SoKa$0U6=)RyHXdINV{_H)bL-=Bc?s4!>A-Q7F!20(8cs(s{!q
zHQF_%wYBv~hhB^Ob!foq-i2kGA9}2{rV_x45;9cKps;T1iS$-8YN<G8<37TgS*<*G
z^m~j>i|%Lh;M@1&w>6l@?7ge~i5uS^8<xqXdXP)sbm}%3<g`__ZOpA5)=J)<qp-1+
zlWKomWM}<-`lUlFjBLBqBxn5yJA#yGWoKkr)1paixB1HAZB%VxUl|?(Z`-aeT{`wg
zIe?v*F3E*|{|6F@fcD#$qQeV1<nWCk^M28cgH6`}q&HnJ*2h1A-3hilyX8!!SU8!A
znS!e7`hjzCWunVb48z7x7PHxuM(xwo5)9sW1(?c%PDhsb`UGjMHYv{d5IqaJ-9IeL
zoI0fw*wD!3P3QWBZR=<W`m65kX;jy_*STu?R{k-AZWk*nYvb%@W9$0*!7dGF?eS8n
z&-e}xTvtdihqavMPqJvNW-6P(a%G4KYD6zVJ|}$lD;|yoVYGlR5@Dnk+ncqkK!}D6
zUo81ei~!kwF9pFdZ_A0OcU5l~%SnN49&+q89t~6$&Ay_yF#NKDI9p`mf4LLaSut$3
zXj)-i#Z77?5wvnawpyyav?{<dEKc8sIJlr)c03S7{CGAc7W6j8M(4KWL&4aHyA?Hn
zb0r>%l>i)mr<-)EI;-iWi=g;8nk_ApQ=tR*P*KM&A|8Ll@f-?mwW=vtC>l2X)G)t!
z`jMH?ImL-5dD2R~+p3CiYJcA*XEg6}#1oxJHO8o|t!<nkgQGP>n;oQ(j5Y1_T$;@Z
z_pK05ZL(tmCheqZOJ3<@QQ=<r<>7893A$E8QN_$;^puQCQ&vAcy{mm~T}}H4@7!F6
zsUcB1{AQ{&M;7H)t7Xbkn4)Uu*IsINoRm?j>5tF%tUYSi_q2Gd_#CKgf9>EY1HMIF
zJpte40fb!|9UYdsH)x%D#5c$+HT^RG7uUerQ686IP(VOh@I!DCG7=z8C?DVw5Ged1
zCs#iuVP>QnaNQou(kIMr65lr9I78=j;vl*ZTwRS^B#1L5LPJg+6bsTnUomMLOcakv
zMjlS(_#!Ux0(!Y+kN%A3n_ir!#mrc}NoqF+|EH|)%UoebY7EHA!BOvUMTXO7C6Nkf
zLy=>^W1^qI*$Uq@nFpj6eJ`?yg^%ZZUy`Z$&t|4WlcL~69@~@UD`STS4XpBAbyV7&
zr5s*1v#X_xh0uS%)hEo?n3TkAvq#Xc-UBOaY>(y{>O`?@OX8{8W<vHNy|4B6j)`52
z^(<l|CsnzYVE5~bcYbI@=@XJjpViTYN)j$lwn{YXMwSl<(fmf*&v3LCH?g;f3;5es
zsGw$qvce~PyWbst`~7TRFvPC=)jaX@&uUz(=fF2yI{Jpg{o$oo;@vFrQXStzNjMiA
zCbbfps31c~58O+`NV!>^b~Q^Tc|oD_<c#|T)K8C?-Aqwa)2(CY!aC4FDRAdQGY*Sz
za;wzGrI;4182sPk&5^}4Jr_r71_lO_P8Gi~$aN=*C{twv1NwHx_dFK`1o$We12jG0
zYA5I>kT*`4I6?=orlwSvm>U*f`qiq@oGfYzMQ5)U1r)bya~HDqM~*YkysA9?a_!>|
zcDn)^p!A{MQ!)HZOnZUUDW=MrHswDrTU$LHwnjwe>RU=(sj<XEakSp~iN#6TB9Nsn
zZ4D~r`A8M4yDe+-u0PxZS3DNtcG$$Fq*Sn=$C6KNc0Jo=AUQ67tFaxnLOF38JvHZ<
zRGeUjyB8c(OU=p`=f+f}+v2SzP2!k4xv+G^f4Fch@NVQhsx#pI#z>osq~LEZXacKI
zPauxLj^kBtUY^R#eKbfKuD1XUn9%HPdOM!?uRmN4jCJ3lkPaSPHq$ezDJw>9HGCR*
zi<7~{$Et5yTbEVl%~0<quoy0QV_GviIx+%<LK&^)K77C?H+ccAc;06f-<8UjZIfj3
zCgHm)6YUXV9PoJgx6pfUa=;C-5Qi={vFtRRt-_iXB3_D7Wo2Funq4)P8jg}nzSg66
zH4#3~%?)V4A=Z$jT^N&+|MnP%3T=NXL%PGZV)5E}Ii{<t%g$0XjP&BCBop7{PBw=v
zOK501wz1<p+Xpkkl9`lNh|RLl(4zv$SjJTMQ#CK0%f{SHh1G+RS8ifawo+1K{1HoD
zTf`MA+7jIMt5R;e8=c6<FQ6q9AUl6tANsyBa}1!;MWQv;?u@Y<E;nrl`?d!+k7$O<
z0|+Myh_=(ZIdX0K%j%!2O1RJ`Yg0?|5eDfm73kE{^S-w0zp~Mu>00t`8&6NqBGFsi
z{WCu+E}wb+U~(v8rTOp)MD8I93<_0?5j_9pvol*$V>Z4yZIa5+a28xR<fJ?cXT(Pw
ziizeBw)-2TiwcXx-NJS%&4U61#fdoVj)O{|!o%aV4sNLeE5rf3)r1}kZ-tw`aimm!
zf6_em2x9hT2nWmku$#JMMJp{<N9$~BH~p2ug3H=7s9kqYtVlb#COyx_2^N;*Yc8mp
zn}s5iy=rvo`rQCv`$Dn3<E46S@VW0azq1|ubUa@`t=D!_1dvd2Mj@XiFpq_Wd7XAM
z#ZoC^vKZ$DG^*^%Dcm(xul0n6MZ}>{w%)L?Am`VNRKqr*IF$DG_MDb8V?ZjwXP|BV
z@CZvpOe_}uCzPIK5+Tb~8XnL%Z*_Xyk#_(|qk6!PC2wZ7x%o_R)Dp2V*8N95gO`lo
zD%J>^-+6>%kkQd0Eb_1{sN899VLDufaGb}bB`>KMha6NbS3WQ+an>TOc9R<!wB=pS
zE+>V_EjI=TWJS>`x-Mo0?s*G_aEHu5HT=C5If_b}c&l<a%u+MsyzeRJv{7B2kaLb+
zQxiiXzZNw|+&AiUSwX}nTlHIbR<(N4#O<s#U2}-LOQhkYBW3CZZ&@LoCCUdfNM+hB
zQB&vg#_jLk<H3)HuZt2{EX9si#l^*qNRm!2R`GD%9nP9EX|UzZGtM=Wr+8n|3t4e0
z_vkWDH3>iuyY1w3RM9itPUU}dO(vgRL@Cgoe1brPMm&d7c^rZwqh9fO2jr^kEqYuP
zy<Q)@LhP-scq%Jma~YH;cb~f@$F*QBDP0kM8p#xwMQd1#ITv+390*T6m|$0bxpFV9
zeqC@q@%6{I@$rTgk=??A)?3W(#8`L|BdtmH!AyS|41EJ;5<{3KL$}DSnp4=c5;!a`
z+fS3%22(vLI8KRGnBMguy<rD~2~Mx|m`DVe<p|b<!S}ja?`U=JkB+9QoN1IH)XWlB
zr<g=zMa_+yDk@JQN2a|U&)Ul-ke=lBj69PMqDX>x#E<2$KC~1iD{#=$V<OV7#TveL
zMG2K)4FVT$pqsOcmPQ@Rq3JkTd@P26(_6pSNbm{+GXBdC1?FoSI?lnsB)C7G<9Gj7
zf4<g#b26IFzF#$U@T?6(kc%{|#pjVuGm+ZYpG73TAx%G-ckel!M`|b);ecQr_8i)C
ztk>ynDDB*Urkkd?@-P~^SyV>oIr|+T&j+T{EO&;(=hs4B2Vv;wEq9w^kJgYlBBX><
zG_I~@916GF?m_ZJ2ZcO)wysPTeA$(Gv4{x4A2?pz`uV`+gBy2_Y^qFGZ~WA)GW75~
z3$TeQrz5j(JG9$QrG@`&*ap`~hSQEgAad-NXs`zQ!G5M|6`}89@`ULxB9@}ICdG66
zqEmV<dOM<IOzcERWVIi9LT77lct7CkbTTez<LOxAL*BfEp~BR@gH+FXc*t~)5FX?1
zI~GOoB)1kjRV+?TO~ssr)_SfS+W550*YAAMutvWrv$%P<oM8Pv2GJiik3iNkp@cx9
zvkKA{*X(W=>yBKLt)ON!`%zotbxgExi|ozrlgQT3?;@iUKe(moutL3BT@2}D7`I+;
z-V(_FGtdEnOv+LVdCEsFboJ^eD?)LIh*rCKSY+GVzvLz7uQ(+);&*JZ7io5=O8)7h
znRL&H-cseg56NXsegKH&HI8Xg;cxg@0!1wj44nm{F`)%2HWzE%xU0lBpRtXfItW2*
zd`85FZZoXrh)S&05%q{RbBUB74bFY)@K>a3^(L95&qy}yAip}YqSCCbS#RdjKUR5k
zp5&jm5&6yY26l~lmX77x=JwwEOp+kuof2Rs(^IiF7$|rHvJ0ofNmawEu@%UV(5KX1
zxl(1bKcjmI;j(69Q==wtz<VHXh6*GjGnoX8EtI*TBMyT!`6D$p_aI}wX(~~*4nA&&
zwNR8@SFu;wTZkBZ$Y<K)3Q^DGr=(n^0CX!CsRN3;!Hj>ulNB9)^HzAl%SpmvGf({)
z<juIAsH|`$sAPS^Ww+zXOs+IW3FuYje97V>^_R~I#N}&?`7^7ZDVAVD(t7Tas*We(
z#MS3=A!f?iI5n~6mCC)A|K$a+UeuoUXNxpcZ}PUS%dD-R(>gPge*t+Dy+N^#ii+w3
z|I)RRo{?c+=m;%$Y0_K~6vE>yS{_JMc;MsO#f*)n#E1lWgIeVgDgermZtB!IXadb`
zYq<45<3@IVQq9dVU=%L?L`i!fb`%45Zry7$KZLKag`-?K;zXG2&)Z39;-!FFQb>5v
zL?Ld!+Goo}`uOo<AQzyLR?hBN!x7b^8&VPy3CwzLg@js;1_gBhCWBsHE#5AGlO&&i
z5~#n`Aw**R8eiM)IB1W9b^@jKOb^5*ol0?|&C**73yUn{<B)!1^)o@}p~L#N`E>mg
zS#(GmyR^3eKR-VUi!G3~D@+Cly1N6z!>Q8&I6mH<wg>W{b<@cN$T~?fChF=lT@f!!
zj_QJff^xOpq0kDE$jq$`+I@{2Y~1$qWnw$-l_@S+9XHM=5HlL-iS>$#3Izp)KpJ*-
zc3xgy8)hb^5|^V@;ctLYj*fP?xxSd2n|p|gZ?VvL0ZTFe8Gv;>KtEFxE8q+_8BCef
za-}5nK==wqNRzI~kmT>P>%yfVpMy+UGiqjc^z`(E4O!#29bJFv2IDgTxj~mBvBrAQ
zqnDxPwZ&AaR)Zrr^;0>mwZW~=R$B&|0&a(!n;Spa=<;|YtEr5>m$@&RvBQ0k|16X9
zW4TdpOtk^|`XbzOU!<I#E=Ye|=Z`O+%PbXC|8mjl<8=Npa~t#0(o#^mV$)iX4Qb~Q
z?QLiM<qvJyno0Ijm0f%!SAG*x&Btxb_`xD2NClT$`B|?Y!z!k?Kk)>1t=Pr)$DX3g
zNJ?r{nT;Q=^a72W9$#{@SPQB1>SW95czyWI5TFGHt!hZnkO8uuNo>*8Rjb~Pumegh
zVEoLX^%9A_kG+4LT@{=Q>|gn=q_TOWMt)u28B6ZrV;Vdn3l}>fe=m>APZnP9{>1x-
zt!5T7Lzfk$_y_j~W|Kb-`AROqH>Te5&6i-J9QaDn+6d9Ouw)X`WF;hc?Ebu$kmvz*
zA|T4JxZ>jDr;D{3fX3FB%*)BmebLJVw*X@W(<qzF6aDQQuIXr#>)DS`Z*T9B1=Sez
z>#qeG6m87fB{gPy*0!TOt8OX!uep&i<lUOfoz&>@OD0{DQ}H!rGO%<BMT4z?hWU3C
z*85(RP=JHme_Xl=fS*=1>S**>W%L?(Rs}ZIm5d+DMHj(!Mss8Vf65D(QUL)0Ha7bf
z?}yR}uhp7hCxGKxUS7t*#<ptMsm7yImP+BP%goG7n4BoVaXk&u+hc?t9+MXe=U%UI
zLq2cDCDi#TIf`1d#yh9fOj~RU_zs&EwuYocWR?yJ7@{76Y_4?{R^KQDLdMp%41bgn
z7-+0gMKk0vAKbw_B6le%DRJ@cP(EOvF=NR9N<5Gz^9R?f2vy~AMdT~hi&_M~EZ6pE
zZ>q(9I_#e^Gni@)Way$+h;toE{$BhL@|*_cQbN~$koYtcC!%2SdAr492X0W0fI2!j
zf?_n>{O^&1D4O-FCxv|0eX;ICxjog2uPGNob@qeztrjZe4dwH7AMgmK(;fv>cF`+{
zk`E+bh~j;30hA#9>y1Z|6nVBdI*E?oi;*E|c9_SLRHWyjy!IcKxWHcs(0i-(6!K=}
z`Fe?$ZQaizL9B=pS0)Qi3`iB0_YH9fiKn3^BR5aS?9Qs(<b8-79?E6G(m>MhaMI_{
zv(LP^#S=QAs1PeDluNoOiskyMOqv^xoh&NpcUtI>H+T<8jxR1Q5+>x4h&-fCP?=~&
zqexW3?|+`5pGw=Ct2=5sTet#R)8$P)owa2|YN{tt9e&NSJ#Bd5&Wq(@)vUzg`%#$y
z@_E%8{Swp-f|u(VbjpRM)8#9`+)(3r>ml9yVX{!IB71@JQ%fxiAi`9?Cf)`nPO_1O
zld4Qkey^BVxd$<&ol0BdvI3$!DgmS0Mpi6v4`c|qUw+yg+W_3`L?xHfpS87A^@hue
z24n`}vAt~XMh1;)&7&8ZR$(@Oo@3QrEy~afbU;4OFihcUR9j2~%MwUqY^<ykg=+Q(
zzuSQ}9rBD<dGxp9(kC7Rsg55zs!<0su7-3$wM&mcX(2(9XPf?aC=62y$edPl=Up$u
z3suWn_s)2eWnzo~dgcO%NIF<S<n7zHGHqWT!gs^*+nKgg1}~56V6J+%C7RmBt*9vr
zHA<3Rz%%y5QFIaHHo!vykHpd1V2#D}AvjSe(?_;iCJfQq=(QVbcYT)Kofgm933R^r
z>sU&%cI>cPElLSDlhx2f$d4;RA)jNslZV0nbpV?QKe%1L42XE8fF{EbI8V!t&I?$(
z0EOP8(UDSnlDZ^2#mX8<jG_WiWstQD01`M)qsHo&qo!t3i|$Zi=2tz*AYb=}z%EyF
z&xZuzk04Dbj-rBgf4Z98ubz@{^yxB#+tu&-`XV6)58JPQi%2|@g#DB<>q|X81Ty}B
z<QVMf+tozti4vW?v#UFf%502Ax;Nirdz$U-#2%<I1&~S!SRm`-r9w=-c8MI837pn4
z!XUu_Yy#5IXnE;o7EMS0mbkoI(ToK8@?8LaP5^NuM(+M;YoZ98A3HNuTVOb5?L@~d
zI39vsE2}i5dv#gXx#ssEa%!dI9U#S>f~5se1qKLApuEQ?CKh~D8xM@Q{#^~q%dL@T
zIg0l`-+<w!1AqYr;kdVu92prI9i3&QG%j&|LXe%Q`k2kN;r9p>lJ>5JaUP9`)et~i
zRDAkcAQx1WmlMA>O#^?@&$a=*!4Weer;{Ow=5B98PQYfdUmNJ}>Uu9FrCDh@9K)ai
zyd&;l5;**{1lbs4NwGGp7hjE4K_I5k+_bX`H?Z))4X_3b3wsM7El$+to3(Onx0W;n
zbj>!9H@|oYf#Ho2H$6wWi!YbM%#L<NPEaR@EK;|RpJZkF-OYW`gN9EyWMm8AKxfpd
z|7P{1TEG2Ce}4W`DZP81zc`_XryJqc@9%vM1H%L52MMQDQg)Uio8u@flH!#>%VMR-
z-SiE~l?qwc%(53{B-pytZAbVB|Ea6;{QHb@sSw<5rRPT|(D;C8oCgBTY0lQ)Sn1<H
zsXNZAAh?L6g);<FPhc_R3Gj1P&C$_OgTq#~H2=6)Z~u}k=}W(Z`4OhSw_QxD(i|2V
zx?*<J9q_w4RG6aXBlTnv!KOL{@>(Sj!$8uSoafW}YNGj;)xEiCwz-X{pXGMeQfv2q
z`C(cNVUTbX9-n@Bi~~E}&XeX(K$ThpiIuj@p=%5MF6kzqa;)#wE<dfAwPYsbBMTqh
z7^|e(v7rvLHIc#Uh1H*pELCCNgV17wkzTCCGBxhMRaRC8Qa*4pECQr2&GgiYBT>>C
zu#e>y(=U}evaMf2v))lFxZNKFkI@C$A?9NYSmPD<lrbMDiKfxxJ*w8cJK@@xInRFp
zPbZ4EC=y+7dHuW@ODSomEoly{hVwpE*O~N(4<mp?Ct)`e7ItxQ;fEb_+5Z0GXoQE0
z`|Tg2=d4Bb)^?O&%*V@By5C%#FJ-$T<V7=vw@&hJZoq^wFfztah|gQ~8&|X~$<@ea
z4AY>ro?vXR%!6}wT<kd&Rd#0PA5k)JXdyh{_(<ke>fq<!air4(rJGi%`*@;s+71s7
zZ^p=zJanB1O{W1$2>M)Da4;L8*=&!0`agt-BWHJQJql#*y|-395x1QoP#68tNsO5Z
zl?zo_47)HvzJBHjP2}~IS>lqdnSnK9g0lU;-2ESt_0~9`hc!F7U&QVb#b+}-iy<6&
zP<4V`iexg+TxHfEcJqgYk<*`RAb5ihGUx2Am2rF6%LKOO<|m%PRKZ11#{phJakP94
zFK*y!rZV;=^xfU5=>QfRX!7+YgJi+&k#TX(K&c0Nz)USVp;mz!D7`<+7mBM+B((t+
zdv|LxAHZ9K9Z}+-!OcT;7m8m#4-D2X+1cOM@jY?e!n=#Wx9`@#%LJ{dpdjCKQ*r!#
zndpC&u9)#k>rCpRIs^3@=dB+*0OI5Yj>XX32?z2jzw7B;d3Clk%jvkS3MS3>yw?$W
zNSW1DX8X5lF!V++JA!-$yg~`DKaMo)H8+6VF*m0v-COUm-G2W@l?v;^W*5xJls7s|
z9yc%-==BXwdorL{>FF6Mr^O3^TTqKcU?)nRklf{g&vH*3H;h4k20mS_Qr(thr-k_F
z=<zfmB=8qyfc@nbfr+JOLIP?K9}i$jA|fIJJ}gjY&wvTO+Jl#TRGSo^tMZ``Hye5{
z@gD8(4dGi1v7wDNySqX+=fTAnnyTnGU@S6^Jax&jTpE_=Tp*v*CfCie+DhQe9&l2U
zHR}}8zcVRoEi~xN*J|w=&^3BuoUf!|aUWtTY%RoOxNS6NMakN;NXvdVy<;esPhjG#
z+w&sA3j_QC%Wv<hj?lD%w6$<j!$QiS?my<U0)JnKTmJ;smhuUD=(t{pQe{kF1Tx<S
z_4)<QwC0VA@3PyohQ9$uEG#HwJyqosaGke<S3&vrt<QNL!*;K^caO#O39WN>@8&<v
zcvyT0)oa5oIA$t>5Ryj9|A60|0gE*p6N~W{9b!|bdsd&<jXS))G49{n8%^Bde>cU%
zV&p<hzh@R95S4WQED{K$_Q?Z&$mf9<>-Qj^o(f?=(tu~{zp_|!|9$Dl@`v94FFfdL
z3C!gV34QCMqocq)n<EwfK~C;#R1^+Zt#N-s3cu?>0((Je>Hb)r0$%k!aQc44KAxVQ
z&dbXa{+5uCkevLCD=sz`07!eNgpABUPfva+Is}p{6O~2MFi(b$FACgy-BvY^o&uMa
zJ`e<rD_pKlcWUPBBz-MK!^x7Pqw$_SYXV5q&OInSS0qn8AS`SmT_dVHQ>2Da70p6X
z@f-fU+3QkU+vwWbuN30yV{%)j*vG~d(;J_#BYIa@6Z^e<fI|`Jo9fMOE)o(!?fQA8
z{lE`M0ajoYnB=mWh(bkE)+9ZNa@$Q`!<w|!V#aqk{w6WulbYCR3r8-}&C|U3I}`!3
z8xPNnS=B_07v6&w<BGi5BAOr%xK6NF)wBipIS;ffkoM4#-(?}kEOUi=gpM;NJa7jY
z7l5A+A3oeE9Qr>4ezf%U^?@B1O30!Q!XG#ZsG>FwIkEzE<mBXvWkux27(QJ1)X&G9
zYSLX&pcNXsPsCW|DWlUX9X=~)w;2~rC>#bm00G<;08@js85|xy*w?37sw)J_4doJT
zegN{%&(FVn`C?~TNrMD|1O(*8!w-4})}^U%29NS@Ulb0HNdLtl2ceCvY2cR~b({hb
zNlq3P7UhDfYWn$re<f;S_ezC^U#JS8o8YfM|DWLbZ^YUEr!UQ*k;paA{oK1k+th!=
z*ITY#qB5WJ%SAhqVqB+r*!0+$FW-p{#T#@r)X1XIkLP=>aDnO?G&O9U^pqF=MU&uI
z?NoBqm8OcSHyRUl%SFwNYg?jevb=+UOs>Ls#7*!}(Tv`e$p3j>;=g>3;*I~$Ir#>+
zoy1L7ME>{+L5yCwxTjL10Z<^odAM}r44t-<ILCk*i5WI^Ta7pAdFI4L3f9yGsPP4J
z_UvsVAT_Pj&hIk8uUW^G06Zgu{E`LcIzSEq5V;L>Ur|wHtXDyY+ZBTxKoJr0LB^q$
zQN%R@R5KtwJ$?sLGs5Y{R#Xc880WzO^s|%P(+wP2ATHC>)2l6KL3FaeeEE{@?n_es
z8?gI;r+_`c$k6a_55f&%W!G%F+$dK^=;2+Xi6>B9MnTsC2z`K@k)M*j7X}20POZ%n
zNa>)#0XP%492+D_bXJOqA4+n9H|VRy7%@FGFf^pV0&bWcU^NFbo*^2#Iag;t-Jw!f
zAPO2jAlG@asRF5<H+6$+bhNbUO)z#W%Y{a61_sO4FOR{lsaf<uO<E-4LgckJm^}w+
z?MEu8D<{E4fg}zPM>rvicL3mKq9P)OzrXVbuK#c%w$-||lwiOz1q2WR__X5WTYnw2
zp%>bmmZl5N6@b<(z%+q+^YcvT+6B1jXx?+cRe$3x2X#N)yb{v*B@yBVag|ESSE{@5
zJ02rMy~<4Kj*QH;HFF<gQY$wUpZP4tTaDP>omDAR#RBXn#4Jgj{Ft=!!Z1GIPr{|r
zL&)c${1fwIAi$k}xe}X?nc_lh*ZF75pxzaa>@r0dm!M>4oG;%2)k#|-XLxY%zn+0X
z%T?(CYlG^52VVM(?5^8~0rWs9IV+;;$mE`ocie~k82`_9U2fq&samDp2>?yttk7b9
zG~PNkWv+7%q6mL7Sl`{>j{uJCw$@hBP+~kREL#^JJCk`pc<BLHcNc6};*mdXlSIdn
zALppthkT<pB>OH%)#BLTzEU~4Cuy}ClPRk+cLJg=Mmt^@OT<(EsF(capp)Fh=&r!T
z#lyQm+;A?s*>6vk0kuK92?him;HyOiL<{PZCm?n0I5u#HX@D34shLSo^}`2{Fj2%K
z3H{<(3_5@rN3$x{2XqP144&`wjHGd5BbHZ-y=FPFhG(oL61#035X-bEm%udT3<fMO
zFAvmKEwCJ-q8%VU>m9Z@+|KvQC3yhD;{tqB+Ab^6AWT)9Z2%epmEAFzY+!zN;b(dT
zPEffN#S)>9L%gI<XY1xfm++xH+KMB&BB&|wZvep_k6&$!7Z70i`uh*YvAm?Gr)Ogu
z-8+k2n9UHwfChzz0&6@T86WI;{bwK!kzd`w7A_Q=%{}&EL^`m<7YRUD8;&k;k4!op
zV*?;rmze+!&FvZ(BLYkTbqVq~D09yE&*l`WrG3rMj4;49{sbyuK-_`@5>)SscsghU
z_4gpP*&wJr%iY(?bsX>1$d=0R@bFuJ<)XmZc|Pg7*W?6<1#q+irdgxW*>->M>iTF9
z4gjPNGCC`9D4;r(UL^;ZZL4t_37-G$^qRfvqiOx$ham??b!{z|&5|x&e82I(C9Nia
zAwu9jI5Q|IDLD^+1f{Q^U4av@U|ijiUkFbftq|1vH0nhYzsOrbBg-+nvPw^$yzBfO
zBMX)nkf_PbdaXftbggY|1rxHsa1q<91WR0Vi7`@wH}VV~0k-Mp5M<EKW%(uzA<UlQ
z-+A|uB&*DHxx=E2)PlXn^*b`!0oduZ)iinpde)Tk-9w+iN+uf&yCXrq0;MDzSkaVr
zbM8Dc$*2#EkJY6bDxEz^LPf!qf~iu}KNYlxX#dT`><Pn<kgM_g*Z-#9X!J|sc3ozv
zgx4FFS1rW<Wo4yVgaPw!I{*zlAsZ7b-~GvW0QK^(Q~)r8C%wxJ?{=}%@^ZFJGV#AM
zL1);rt%cHiSnPoz`(%A6|7?16M62Td<=NitM66`$=9u1mpn~)x^rzs-w0A-2hK7b;
ze0{-8YniB!yX^~UYHEVD0~vE<WaR!M5WET*!(4zQDQ5sHZOJhUqP?fOFae|;@EUbW
z6ejEGNSOVi3OS4haC)B?&fS_1mODKOh@@rK&Uwn%Oa>Zn1c3X>rk?{u7+sUpxI_Ml
zkgV`u`_<Vz!6q%_jo{`pzRTEod2Kh!{aeX^7*alU7*{)um3f5oMH_fmgV}g~TwGjb
zW#z_b4*dGCH!C*w$!io%!+fyV;e3bhg@wQLuE5Th{ErR~d$a>+ZQ}DA8yf+Cb|(h_
zp4zIW0XXh~=#RK-y8|yMB|20ZyJUv}{>2z@AB%v*5Ep-pWlIY7Pvv-l3IQHobc6mm
zD1~%j8(FtEXK*jeS4^6<a@8v!XS;zMS7kcv9RLt7KtEGpvx^A}7iu-c1OTX04!Cd7
z)fUslGakLJCkkrjVy*6eiMvj`wG>yzCJ|Qdq>p54TP1L9SnLmGJb~*4*FAT@`30B)
z%cDwtBQ6%`5}>ysn))GFvTz7i|4C)vz(80?Na6mbqKY>WKje*zG}_yZ+erFVYkbD@
z`d(>sWDHdvd(r6z;wN~6(SQMQ1DW`4*mqqFzYafuUIbg<JndmOA5It16##Y-tIOrL
zibfhSyAtythv|4+_y&xOjv}2YJ3Hjl-JOw|KF?8rFKuU`iPzru)%6N88etlZgrVUd
zB}S%`9=ejoo69Z4<q2wEPj3~yYka;5FjQoO3v2?GOiynKxXP5KFU{9|#cX`@?97IJ
zzuNe|($otAzY!y|{ihJ(2;{LF#Wq3ZBI3R~xKYewd>l#9AJ5jgWJ5tg5kkUQJlDjL
z$gqE+1Az>^b@&hHLE*F*K*;DTWPliQ3&6VpWB>=+%XCm@fW|jay8bu-e3bQcIVBXD
zUTFk_)z4dT5~6|GzOE2XF;N3`l0(67my{m2HdPU1AEA>yJ-~3ZqnN%1%0sTeeYCc*
zbg%JUc<koOH^Sb}6*sSrTZZ%>JP^v==H)nND&N#_c+fO&g@KCt5*pQQSTl?C$Vg4Q
z{?kSrivc)<v}O)LYbdCqrJSAb4iZTW&>hPh(rx&mhBn3sNv(v@PcK9?V-qe2+-hpk
zx7Q<9x}e3?{Q+~SSKo;K1GC;AtY~fJ7FmGSUkt922Nwf4f+tz{k6m4#vHh=KJ<)WJ
zo|39sIe38D#EYZ~hWemj9QM*vL8pNM(`w&q+zRzUVt@8B17>lZ%2j9@&%`>gZ3Fo5
zR#+^I^i>mP?C6Fj(4ej_jxb2M_JN5{<sA_O!kN7pcZUqzfrtT+L9t4Swws%qvGFR%
z1u1Ta;?R1VrB(tRHx_5+;*%$kwCGI~SlDuk%WC|Hr?kx7*VDPX-l4m0KXi5oWUh4I
z_tpO$ME+-G#=mVq{%=DDfw6~*QkY-<X;@Y`4WQ);!+9VSW8~042~Am3Q(jq21Kq|f
z_lX?}Xo5Ch{$TC{{l<KQI1!sKG1ueu2pz7*OdrsaJXA+hvHT(r;;)XFCj;=CphFbi
z(Mu-(u+vr`RW_KYX>l(Wl;r~G$UgWlf0P#j7|C)!D1Gi1QZM)ggpCJ$aqp~PHU}R(
zVDYPy<J{kdG{*|P3fK6qvHHJ#U;kZn{%<DMjhzoP<o<i%mY$Y2fz2fBISS}`BnOuI
zHs*1;$B-ZBxnMT|Q#jyL6tbmW(a>~tcRwc81S(&ekPnq?il(CCCs2EBdE$5M!Ea<B
zkmDR4Zvh~mva?4Ad{9sTnkv#G5Smk2lChDI*#2!;BqXl<2sX<0>~S4`1_VKO`ID9V
zkabB1`gzbA3cA3dtnpHg4g5CD02wbI^ri48u3h|S625EGPD&yTZf6C#eZ;>4>>nwR
zlJHW0Jnzbp`PzvS@9H;GiCPZ!lZbrb3|;*bKRr+efL&Ch5w`5Cy>hEMNj8=5SkV5X
zoIcnqnmw+E8O@+f^_ZtLA6~4U&*Qyd!2$FRs9l|=aRs+cX<FKI4vy1M&V}>Mf)Wov
zsR9-oM;`8h*mi7a(Y0xq&%yv1guXY1{{vZ@-43s9gX1~p?H|Ar87g@{KSy|sFFxWM
zbmb!(_k0denKR$!=_{1`owh0@U=(!Xk()2&8jxewT*e=PZ)!10<o7D33qh|uVTUer
z?p?3^GojRP-|jrZ0C|9}!w^CisEy5dz7oGH^}zjr|K$ZZ1#)rG><%M>q8ikuUL<$D
z%NQnV9Q*G)qH}b49LugRQC)WnZ&hP(Q!%BK(Au1vw8s}0fuyo^A`zKsRWnKIMXO8b
zr$@+84=T+3@Xym0%`J~&?i```NLxo2*JhqSv)X@PWA&CtZvqlx+&nil-yrnPcL<Ol
zU~GWQqWMo&@P3F2jGcg9E}ztPWe^nntBYUYN(g5%)!|_gIqt}17(yV3+%O{kPTN7g
zBdHBI74_X0&M?qMmE7=kY7%`HF%CK_ro7#_i^GEWksxDcmlv(y6mwF>xIpX8zB%lU
zx6c(i#yJ-LvojMFaQ==cq>b#3m@|8#^{@&4(LPUxUo`SdQ^Q~VQTls98t5uo+Z8sP
zx}{q_`b#Gg3t0>8Sx4s!i?T4rmU{lxSaPwB@sEGnF38LWbd{P==ec*VK;BqpHl$G>
zR>8$td|5aWZMa_krA<?w`e-jbT$I)d==$3HY&Pj0WSx0EuxqxgK%cp^T?8mXe`(O4
z$3(|>B%c!w*5i#?l-bF9KSHHbK>_ra4Xj070|e_|wwGpuV?BD`MrX@Wm}2*m`|mX(
zt$b2|84iZsYnLNySI7Miv3g8;Z1-t9t0~yY0{t(EnXz_vci{^^nXdya7@0W#zSnQS
zM?0)wS=LNk*e`kZ?=+aM^WBYbSy`VDu&+)xns>8^au4ySNwFpcu7>?8=xa0o-#5Qe
z^489?)Db#daHC@h`Fg^uV)Nqe3B?+~tpZfT)N~w~P6HS)@Sz95qEeM@lqZ=#`9{LR
zPn;hz7Y8)0R|?sn+0}BY^aVZr_Wn{EXd`F_uNoN5;KTxct_oj*zjs~rgkzmayKxEx
z5x{JEfZatW;ox)JjsvfiF&k5rFqJ3|vERJ+OSJ7l-LD(a-HSa8ptvW{WPvLK{Q0G|
zRQ5UbA9tT8vFegyq*iiP&xG!LNpW>>^^>`0d63p&>xhpDY_wEP!cfuh&J0)$*q3es
z4p)knx=dT(lPT^>ed^wF5a!@RD5^M*C-eajSmr7&M>1!P9|Ml@-#!+co;TOPZP9f8
zJGiW@46W@NxL77C>HXW}VY7y;2YfoG@A29v9!pvbB=fB<?q^)>>ivtQ8EdYMp0-fd
zdvMcZ`ud4?nn(>eDNe93MGlb3|4}nUk+#4`XzVEe`%kp+#<&E2RzRWf*|=_w<)O8i
zDo6V9)f}VqI%szB{q7Z%4Nd%VC%JFhyaP=Y68Vcmi<R83g7i9L{_5b;R^KYDBwSoi
zDE*cy!dRz9upw4xoIPrsS(z~O>;5Ql`Kcn3e}o1Uj`fC58;4+a8FiZZ01X1J2Xr)W
zhtlBvFZ}c>Jb-}oH`wkJDBuOW^ElisDQuvA_-<b^z48tCQbckWW}jDSZ6~CGSs#A>
z8&xd^l#q#EzXCx~{b*rCwo9NVnGZ}7BPGXQ=7AW<#CNdrC@WSQTVz<jtPzy=CGea2
zY@?6TVS&-J4ir$^S<!q;*GfPpNR+Z&4hr7#0S<|tJ|qD2_4tBHyZLm+Tpd4~dt>5W
zUkd`L&|gnne+Bsf!A>#IGDs3mp(F`sP|_Cu0yiLlvRcf)H}e$4hor3n`S-R4j=0lZ
zLGzFbt`SJ98DilRm4%y2J07U1Ww8wPG^BHT=94UUb_G-DR2nc~nt@Xfpk`rV>&637
zVC#=jQi?5Q)>$L-OXUm}v_7qoQF4AU_7J3}y%`}vKn&Q40jc-^aA-hYew!GXA6+Bq
zq_=3*VTE(LbKtn~m&X}%+DY@=F6+$y9wsOMz^6vc+P3Y??|5fn?9}iHs)GmwLfhat
zwCFf%IW8xL;xfBPv5oL$pv85vIyxr7y~BTs%F)3-Tj-rGu8X3=;9~+nGwPk2;*HUr
zqgcm|?v;QSh4{~G6i6B`%B3WqJ2Gb5e!LhEk?wNJ8)@)ZCUU47g9c7StqVT~xFEmK
z&}}!6%6*sX>`g#E_Pvv0$Dgm?Qius(@;Ff5EsN-mIVBS0OAk8MF|}-p+mbIqiu3P4
zAf)ItW!1Pcwc~bt!v)q!P5vriabTDNb?1MmI6m<t|6?HqnFP>QwKPG*{0);^?zU6>
zd+6&Vcw`6b3Q^haO`Eid8+7TzqYbGComH(&f>$4SyRagN0p%rUnQtJPnv#+d7sqAP
z^9}fWl9D82|7gQK+(9jy4YY+4?Iu75emvU-gWQ&@k@+B{n9MvHpv(Z;tsPEIo2#E?
zA%_ye7fW559F;(rC4zd_#KgpDYa;Z2Rxf}^3Z$W=%mUC(*sx6X!P1D8J^`cV{)HQT
zFL7wcKa~htr>&CfDfqk>(k>>OE6Rv9j@#SQU0&uOGjqs!or0sWW0bTk$`{4A>zF8%
zZ+;AYc@*#o%SEE>Iqk2gocm8`2mQYBMaU+|Vqs#+VcmNbNYixo<_YCv%1q0}`1x0h
z<iRZ2WsegLad!7GrY($jllZL$1{Uh<t(8xIDi@JTV?9_ht=nci_3-I(Fkdf-T`45_
zAgrhq{vNaV_S`KFP4UkQ91Zab_K15B8<*VAI9DdUjNuc~*c$JM3Y^R`h7zTyI)f-l
zJLAF6D8RlzY!3f)gOUUE_3(yKgH7TN*jsDsJ@DB%F&6h;C|>KyDJ?84SQl%uS#@Kp
z#fV8IrhNS>cTE?zOgkeM6dEOys_QxXuI7qUaV69jbHuD_9JR`heYoL-Ij~6BH_#mg
z<%O)QY%C}032hns_XWy{+1c5P9ZGRK7qk<3OI%o}D5%UJKg_DkUMKus1E>Qq;Hv}4
z8kz)mjGpt?)Kr~DqE1^}5u(rzvqzXDBw)GKJD0t^?{^}_7+G>8YPLkaq-l;_9dYSu
zGM8E~-<q1fFaU0!jZZwW@TS+qp#VNvCertgyheZ_<*GHz?;fI^UtVsWT#9jnEFX}r
ziWA6Ah?ZX9A3rXvPXx*%P?mAQQN<&HtE(72Lm)qNj~z(PK$wDj{U3e3WmJ^y7dJYD
z(xIdvC4<xuqBKY-EyyU{NQb0!iUJA>(n^Pf(j^@dQX)u7mvl-uXY>5eT4$a2!^;Op
zT?#Yzeck)o`&U<dJPjS)y8AYueMp60AMZ3fmTcOQm;V^bXGP=?p9ST`Rukhi^X47H
zwT7j|#mQ*{80Go-ITI5T?4;mHs)0EQOec^a1F%UVST={GsnyL^2bA~s9@NGtf7dj-
zLzvrUov|8(>;NQ$>f#SbC;@{QZgOOxRM^G}$`Q=Z<W6B7D~pn5UJ+p=Y%W@9jQh;V
z%uiHWVT%>gv@!B_OP!9ed4*MPi9vGduIh|XdXA*ase^u7fHAcOU1hI?f0LuMGy@_v
z{8WXUa==9hdP4Oeg0oj6V=mD&!Hf#e4mJx`wRj1ezY<b;vN9sY?qkbG)<!7k*Q*Zy
zh7MAlh<jHh;uPz75}=52r<^5~hXSPVMrZ;F>Pl_J8KTax8m*1}CeXvhm;V}rd-vB9
z`M%>rRYqKnIS!vaC;i}8#?%Way`@}9yPMBK*9j3nCk-8Nh=Yvxu5j<Fo2(AX?eEKI
za7LZl{P(UG$l;&Lrdj$<lS;l(sgpO;%U^YX_eW1ib<7bJA2cS~f`K?$B_4nI%J}1n
z4P#(E1T>_7nzsq)tw~v%3q_f`4!f#G>xjbit;IiZBAt?uAPV^EM@>Tl8lfe)7v;{X
zfmugs%rB8&Y?gPcp2D9muk4qD!26KZQSq;*X>79*hk;wCEN3W;^U9U6Y8C_{sw9r>
zr{DBGTtAo0Nh)ynNL!MFqvl`E{2o)_A@&z5>g(%kVGM}J&)yk-toeEn=nFtq(cim5
zev}%8Z^FY1T}5F%dqSf#oUf}H*n0QcEDq6AH4eT5V&^5va+3FO!!lNU(l*B`Hj^vP
z&+NX}bvFlH?uCOkrXV+Ji=^y+DDZ6Y@uj(8N3_#mQhG`Xkt*W*raD5qF2jU6XfVgq
za0%mbz5$oD@}oz2t^`1VC&%@2b+)ax{`t1taH^N-v;KTEG=J=6dXcEFq^wD~Nh2>|
zE)!8&uS9KcZ?AKe2f|_4G)t%a%gToL#rKjFi4vWGv4*y!J?fw;T0OvV3?O2vr+c=D
zYmC*fP8inp*NuYSGM6Q`!1T9c!hMta{MVqt&;mGvPV$IUn#6lyhGYhTFfcGM^w}G1
zF3ZBcig$LkxTZo0XCHlz;bdAC+H|U<Y?^vIK2XA0H$2|O;}!okYa;&3GNS(c+ie3)
zpV#CSwZ;9m=Qh?D@mY8Qq}bofgHx?^$YqA>y|>#oe;@TXeraXzZ)_V5<jPg!Na$W@
zx|;UmI#5!mF75`jcD}}r*ygsm&8=c`nF!=tGF`H_nQS489*b^LxL*dXhl9`zRm(T(
z2Ccb{m}StJ$sX<@=@1h&T;G6BX%lzeWUF%P{ZT&jFN!?gRlN&aYT)5j%Xn807*|Kv
zUE`+u8y#=<>~PzEyL}zf(V{&_B*cP@AENW#w%`Y-2BKSkeR<o$NBD~L>yuKX!mKn&
zW%y({d3;u~5B-2OA5YxG5p(iYs3dOw1a+ZIh7vPx(UPQUI$u}*r2SW6VBym+2Fl^#
z`5^p-KT4Zr4PHLR;wv=rX^$yv>+agw3Q|veE^Haftf@1jRVCk#V|a@xHV@^^<;zR*
z<Hwuxh6{lm+7&p4UB?rDDMMf4Vg)NoeV3oE%BS5rM#92LyZU5F!j}9nv9KaUk`*-+
zA?TD`o9y$)*6|2!s{L(jUBxa>walgEX~<A$?`mcK6jQca3ooH_KK#E|R@TOIsm#Bx
zJw9@ez@_8U4btzScNIVRu-XZ5;%dsub-^zd2T<39t3qdItZv*<G1l7Nc)q`wuNrD&
zWK11I0nhLxQ*@SueczI5>DHY|1Eh3tcPwk6g@mIxXX{~+IICx!?`FwoeUp?^#{t##
zPh?<Lc6E{yX8gb)ave9I^N|Vh8c5(Ti&&Zzg=a9E?4FE#M)U6(6Q5eP*I^&?P3#tS
zduoqDzq4r`=sXAc)a$sRe?mWo7eUwmYm-y<;XfZ7O#hX?=)vW=!FLEK>eVzxX>M=S
zzQpody$}+4RvR7{B59F->a1PiWik0D=kA}c2Ag0&<+1Xq$Tk%H=(D;?5a+eGqMkXi
zPB&|-UyX%`I^}dZ@>UNIGp63>rLl$Yruthc;hqRR-M`utLAg}U+AZr}NtL@cUE2gi
zPbfTp;st1xnk#$i9YnXsDvY@uo}6g%Ur*CC^cvGrCAS-P<=`s+S_6-{dP2f4mpL6}
z%j4$h)M2bsP4FM-f37j1nyN{t5GphZC_FGV+*DmO|NL)v_p*ok%786>>i?Z_>xB--
zNf(KR3Ku_7uCEPLG8LO?i(TRbL3?8AE%z?t=ILB@i+DNT0U@^NtoM<%VueLYqkC^h
zD#AbNi=8|<!j-1x)59l{OK(3bdi%5PDJ@Pb?^?w07D1gsVaooxr&cs}3QEWOWTQ2T
zHT%$$!G^wAfFmz7D2m)Hc=E7iFR$et?G)uQ7rz+h6DIc6t?bXT?;{#>8w?*W%nvQi
z!2O#>v+aW1thbC^Xo%s>yUvad3<Q4K%>Bul^rnM{f$w*R`f&CC1`gX#Ja^k0O%y%o
zyF%5Ip&yz4L|g8C92Vwx>luOk)^hshqWGaL0-uKuTTDEv9JYn@st9Y`AeN}~uf2)z
ze%FWTyLTckh<EKDKJ`@4(N-N0KRfQV(N1vjtmoJv99!Q9LjWP2$P0j70|NtNY`-`p
znypF9`%9XZJ0;Y!>};y<l*7suPMe_uO`>Ljp0K8&&|R**mJl8+EUYd&vWMuWmd~^&
zlVp-tuu1qag{+HJx~j$&6c=>eHj;`(%G0|6+ttn19~~9hW+4U7UQ(x^hETcLPi$dE
zQf;pBALW%pCe#|v^l#CE(3$`SUugOYU~hwd3+W2?O80ZWsN22*Mvcd}nt$9n6G!Vk
zRV~X(%@NL^naihJUmyHfz-s~%o|o`#e1wPq{yp2HqnBuQ>-X>j_Oi+!`qEc8=DX~=
zv$Ivw6cWR7cOtrYH+chu&i)nV%^$OkCUHQ&8mjhX5^r0Amh8yIa;{BW-12oF_E^C9
z>7U$Bj}3YZbaOb#yR?7h0}V4}Ab-XG)kwwZte23po?L$18P<ovrsv18wK40nRM2Jx
z5!Ag(XG3IahGt)*$=lPT8v5(~XuvN3YpmoQp+mdv(eV1Xfacw{iJv1mccb$Tf!O2-
z+2r5uE6<;lx|~Z(gNNL;eu%RQ5=&wBJU5Wj?|Ctj+f<~3R#PhjDEsHnV9!-^GqcMS
z03gkr)z!2V{sQd1DiY+Rh4SkbQRu%XMao~CUW-%+Nk>RpS}qN5{<>0%C*ULK)^zuh
zo2*bHd&tUvkXLxXo1aahun_X(B~rh1#O=l-LrQYGa;@%?n~%`R<Rj271C@HP^!q{P
z6Ll7H9ov!kTCW5)RBEAthWDA-h5wU;95s$b3whW5B668XQ)Q$^tXb>e>b%HM5o{Sk
z#bGU-gLGPnkS%FE`#C?_dT*gy>b42U>TdREvKPZFzIkVEPd{yjc7G$Izu-q(TU^Zz
z(0j;$@xt33Tgu_);^{sSh11bV7D^#97Nz}6AmPD-2cTcp+SUXGAJBTt3MB*FM@N47
zoh^<xA$TO?cR9KkWm7s5GBpM385TxpSQs%Lp2u|kV%}-E=UmU#Ipwjl3O-EBwqh(X
z{)`9l4;9i;G5r--2=J=F=nsM`fGod%_gjsCr~iCc+=Yt{H(+z8Qv929$CULwn$nTh
z+_)l?QvVBO<d9v35`}5%sJD;Lo@ZTGLcZKvGiq7btj(t(Wd`0TE*?N<6+F_J4Fe)3
zAZ_BDC1XV0B)>B0&1}C&+_s~xxCX>ZUA3Ep!|8I}p6p~;UtA?&q^-K?+jM4eG54nw
z*TKyN1oqCDpjx@(o&BZX8DNXKBrq!-j8#;^G9o!bB9oKrA-$_Llq!yEx4dU~7*tEY
zJKm2u+j@m14YN((;#upZb)!-KQtR-mGo^H!V3Pv-$$$xc{6msG6M7oImBP5&UE9FQ
zvwHE}NA0Y9;OE?51EDbIdA>Ayh_$V##y9;bPsxevHP58p8V0phdy?ivN9g&|QoQk*
zWt%7e-e?O0wkw<F{RbpoIt=P;s^7IUKC{Vc3n-MRqfsS!amd_sEwffe1#|5gih0{O
zchy`BglX$y;Fnj0X4+;Wj9up1-#SC8S}NZAPUR*4X}Ivm!1{V^B_mG&-$zzf7Vb7i
zDo|vgj0}_R**S&iH9g+|4d=^32Ndhnqu488#+(i15lOaf=6kNSkk5F5s__Z`UE5|U
zV*729thHO@%sh!6qs;shHi^sQ_aX-Q-0i=t(>SSb3=<%nlw+}QhykIVwQplo@ZEA4
zX$u$jmUNlLO&k9ADwuPC`3@g`t`GF4uQanBs4~A<uiVYoo{#k?9jWy_1C888cmy++
z^aHMv^f+WGDq;SFB_U}Gb-IOxi}|rzf3aTl&T1@L<59^^&Jcyj-ALOAvoWk1qV$V3
z6pJd_bih$5@M+|lmvvKpAOi~yP6f>T)rBW$HCmY#2%a6ZMnKkrDW7oiUm-hKoG{RG
zRm}$x(c<Id3x&~2_?{XvJt3DOg>k+d4G~=E=qPc`+fi1hk;ucPr>QwrN>qCrKAB);
zWf8Q*>+Ku!67iSs_uAe*SRp*w`$3r8&`v<*F>|pUD*t-4nB#2j!Syq-n$>MSm+B4W
zgxHgGl@q}}=h6GM5sFHCr0z#w{4le#tVZW=I0UzdkT8M%1y+7ki$RqW3s}BDDQG`c
zPw_m1Erc!P#oHSP@{ymds)ev1IRunUfRz9|$6XN!vMR6;E$tmcQ0*w1QaREzf}#|;
zjO;*lEC{UoFJd4b-dSOICBZSU{ICPrVXj@$UZN8Hvm`MluFY*3gM9BxE3qa=<+zCD
zo4F|ygb}ZLER90nljw?&p%2K=4<s^<*iXn_vnp8Qn%<?x5*NoDuqI~WSMZE@9fTaj
zo{=JzMjN+%vTyw9htb^VF9p|h9aLgwI8EmT0{C^Z8IF2-<vRJ?7a3pazzY)eM&2P&
zui|-1Y^=stpqP7h07Lts8CUoY-|F<Y90Qlt)JhQJXX-~dyFak5niNd<{@{S`nt>sS
z_7*wlzzeO7(Ci&~&>vvdE>UB-548LK)P?7rZ|k=+tRCi44|cD%boRtJ_DDtH7z#Iz
z%dreIK~CHF=7a~YEw=5r*ukG-NMekeFoz07c2Ps9wUD{LPRXUCHtuB?V^)g1@sJ&g
zD|)kzWPP-mWGowbf2cr-s*Ww*to4@CZ3O}ONSVm+WKrEe<*dCg`X;;|uih{j>1hx1
zGr&pD`DqZ&Rj)s3dy_snGkNQl0!d`=htENbwd_xnNy8NLSrqsca(<t&mUG0blk<d>
zP*M=NyWOu9qCial`F$mMJevJ!qT(|=Csk6@2}Bg$JsKg;%89Zs>d$AcY0?JX7ZDNR
z=Pw7<7(fk|Ib5b)mv{VM>RRic_{wJ*+s|z00r<MUF?r0)Om~mI=wLX^kei3L2Lv$<
z`1V)HZa?qnxMtCPDJerVQuB+-c1t@nsv8K|7H?F3avA5?(s>>+w7TkbWH{A@gCGfT
zcyU~@_%X%xw68`XBgTTeZ1=#+W@<Auh(uN<u*mb1kW-@Y-}AB`P)fkp4&@#a9YI7y
z5(NVnr-H4k>pldc>VicC+$<dYy4%mzKsE=lfd>FrT&m|7ze*&=8B^yDuAEbu0HS)_
z7oy(C`*gJIc&nMD%V6ddqKl1>X-TOO`qkWrn~~fH+sY-jk!BglWtI_SmuHQSvH=rH
z5fj^RVe!K0P{<(>t?{k|6@wb;H*`C#p+OW_#E>a!wVdIUX@21-WWghn0-a@@ytFbY
zO+YW{Ye9vtc%Nlo2T2+rJKDEj3p-C|T1k=?xpZlYR}-%f_jHJze$r~qLO&R#_+Z9}
z+x`>xW^aWj-c-$2KX8~3>kM(QW%g5(9kTL~Eg5#HL$VVn-ETk5OM!Vi+ex{2q@*+k
zTsg=jFcFu@IEli}5JXq5v>smVkqYv8AD|M=OOGzY6)LtaX@#-d4Y<}sh(_JgZC6{K
za*UoG<zR6<24BoFeAf+XFeVU{9|x!x#T^?ORbVMd+ZFT_&?PMVdV99V+LMTqY=>d)
zLvI^*$XvCHws20NAjH=>Pp!Go-eP9X{kJ_M_3FW8u(jp6SFayqqWBp)+<md9Ns;mx
zbo}oOYlvmT1N*ZLSY{v;iL0g#)y2-vE}Kzo*x*Y~Pe1?dQ)DDafFj<!dGk9%?&-XK
z8L5op!NvqsNATJL2B;E*@5i(I>C>lBDxPmzDDc5;YD&pOnH~K~5=~5Dkm~nT_{JHc
z9ot=kY7aq2>;<gvUFc#o%ORvccvjiFf4rR&p%BYeVjFAr3PsDJZ4WlCe=ip$L(`Ka
zB|jkW$19Jt5{7gFRLSI(Bk8ec6iCZZ-G=9Wyw>*^F+r|Ssk>OOU-K33FWUx7!uH<Y
zC1f`jV0{DBOa0M2=Y^3y`61Dd*1$V;US-OjqxMwv$7OETAnha;g0L9~U|#CPsVm{t
zjydn84T<cumKZES+1CF!<yG<A^xuCSf`vY(|JpJIfR1@%SW9l;OMzhYn4)@*3H?4Z
zTc-!7-$sv=sAgX6_JITzHYxY0F2zDQ-*N^&{nsDL;XjjO2E>UG^$n2)47{d5lMfP9
zZc9tRQ@r;$4D$k^8eM<!sljpXhm)X^vNC@{H!w`C53=FjK$@4!+#icZv`5#;7FHXM
zsU)doozYbCyG=na5098pBe(}-^<&s3ZQnoj^ZZ)?!5;$g!Y_GrjJ%bD)4u*_;rw>m
z^eTbh%|HA9<aF}Cxnmiiv1Kq2uSUH2Imf1^rn=f-r$#VhZ6VFTQUo_(-rj9JfSB&3
zhSca_r3M#2&3l7Lazqp=G~`)?nN;NabbTiMeesV^$$2Q&9WQ>y?;`Qq=FTK8yv2p6
zsfm4+433+2l+Vs;<@<uov}KGbWR$LhZeSw24im3#rY|t_L2z_!)?P&|mo`4OaGl&K
zY-XGxY1`79MipaLi$ja-8f0@Cj%kGgdQ18mVBymSsvXu`0>90EpAt{ZS^Tq#F<KIO
z{L)Fx$Bz&HO0-e&fZ&#`ygOUrLVt-0m+skj+kO6}=gQKL=4F=bi(gZRz7w>+{bHGN
z__O1ikbPzazmV+=`L^uWY$32C5fc)kXl3tY{I)C|Q6&C{vVvA97rM(^*B^RqewI<v
zD2SxEf>3m2L>bn5>!uNliB*r+h(ZWt+w*6)WOQ3k0)3m^rk>l3lQ%WJtSCF@x|Bi-
zAVQHC{stNd#$n6x7i&Ugn+Sx%rhyWPvZRr}K_o^asw-?$#EnSyrSZ7)XZE)rSfz6C
zK1;WUBDG)haqFYDdNF01=9Qw3_iWPl)W}}r!1gPx#7J^kw{WDi@VA*y&p5v(L#GX^
z^y({P@Sx+AAAqHTQt9%H0O)MPSe4#q@NjTIDz6%Rjkuv>gq|+tVo(A337C;4&A*+U
z{^b`GE-V)2<|5U@tjh5N8FIoqGq?e~fY8|&WRVcyyMvUKM$jtKgsN~$Ityi3n~;Fk
z`tZw&P%Us#11W8Qopv>ojkS#sar<e-)|RA%1i}bUIxy8ea~y<voPmLoxtP@IfNJN5
z*gkE%-9;&Lje@tUL^%>gLH8qR#Fi!`-D)(nc1r5|b?neyn3A@|#iTMZuXq<)?5>d#
zAh0nFgRaS-14%R33~fg+#l0|;zrUO^NAB8=mVJpu;$hPSq#_2ek6Qa!8g{)2y?rnc
zlU|FH2MJxihabBym3aw|vOt_oIcyo1^OiOXL3J#L#l2guvy|KY&*If1S?jSM(skU*
z`DX<Re{PrNKj^>hdf_kX>Q&Ds?p{{H=^lxVhl?=P7SPI{KKx-+k&D;lC|k&cM>FAP
z$b{gDR3pE_^jGFOF6n#8_qd3yTX?TO;7BFjcbv6!75HFkgZ8#tqYi-W|2kseP3koS
zg5?jT-?yy;X?@L*kHXJ&K#g~^Ms(Jg^!qh13!81STwK*z#|<(7JIRRd)}-&r`e5CO
zwEWW#^DS3)1nejE_=>~YfF)N^RGjtj1)(4Wa;c3B`@+8K=Us#dA_^|GSa>M&^W&Y2
zaq__oQ4@^+ecrt<y;zM&x)r_(I`^1+ao9WQB$t`470)F*+L}cF^-9l)`uzC*g;=JW
z;no(yf61reKEJORVk_hpN64Hs7-!{2OR27Vf*i2^@Lh_%cXk*`s@H!2JDqf!5mzo7
zTRN2mK0Fe)<k4aWxSZ=3o=?xmd~}^pu&;L{w9olLj$2h#WnNmfo!d#0h$)rbuZ@Zg
zxf(OIIj}KF#;vb+-+TauF}fbja(XZU+!S)SH2MwT3$V-p?Acp$LvLwSI}OJl*p4Za
zk$K;ERcwTj{fO+6s66|fAQU^h)%SB#>TT>qkrB>|NoYO01THzil=ypBc<lew-F5Cd
zfU@WLGD+C^hvhw(jj7xOYu5DyMUs9w2BV=33{CNFS**qYI)vzO|M-{{0DTzYsP>K}
ze|H*)DP<dcF0dE{{fyh%EZYIFG&l)?i5EVPIsK->ojYlbiAb+s@3UWY4MGNP<tTFh
zxvvWLm-2&rGuRY?Aum{E<IN+UmNw*Z*%-fpSVK6ix~^@c;OR3t9|C-M%_~?)ckvMP
z3_DfYQKJ>yW!t^AG9we9S>KL2*q|0CMAVD<_=0-?xFGJD5pYm|-ACt}t+2<oX^^q?
zu?%nk54M_^_Ywx~{S>p}kl4Z-X`>pk{W=%SZ^)g*K<9cr$HEURJ%PJmX{cE<aIHx?
za@J7}%j!HEtp`Cvu8oHGN;aN^hx=r$2mc8lVEYP(@9NK)6}-lX_lMS(%G8eA8BEW`
z_ZP}<D8Gu{`u@Z{+VqZmMag{XbY!WKfb~H8WLEPDPWW+dRm$2@!uAH{rQ~mAs3Xp-
z?VIg+5}DF8IHJBeq}hX?b27bLJd46{QNU;KdQxl%p(n@P_nnWh5R3{P5~}}5K9m`R
z#8s><m#-yNQT6qAN7mLW#W~fO!ITsBS^wrZ>Q<X&x=uO!!N-vFo8q1WwgRk3<a9k9
zp|h_0fbtow%N)53)5dr@Uw@8h#haQ6G)rzF(`Q~3f-Ou@nKUE>1EC#TYCplZ^5S}S
znDwCI`tp||lmiXG{R1>34{{HW>-cOmOf|9^;!l2G1&O!IBjuHE8Frn4aud^N!Ej7(
z*(pgTq#$uIKXEaJe>&+EkCqA1*9C9xEqy3>!%Nd>E8BM#_>$EA3Zl&~Gy)O9fsc8H
zfhe($_WE#f)x+vR4dk4Sx%Jpx6fd^_R*a$SYURz-n3lf4m6YEni9W+p67POO&ZV>v
z;B8v^GF@mWRWkL|<(Q|-sPrZBRt=rPbuF%TYnD7p-UQrN^mw(gRfa<~0(RKKpSGqR
zo90ttW4|n9m|5pMIqsOO_Ym{j9{94fw3L&RqvPF89dvdD3IeqZMt6r3|H;OOkuu-q
z>pi;z^7i)m6`BNw_R^%db;P?On5pg}^s=!&w)=T!qNU8~=_0O?;^iT6akI;r?(S+<
zH0`tH9k5I|^p=XPpWLH=+~ctb3mBS3h1DVR07gR3TRfU(y__CZIgBZ6C`rn@%A}L1
zxo$qAp=O2-Kbx0xrtcTUr79iHy<f&0SPo`QZCwiq&R+YXJ`g{<%mHW=XpzmPbhQsq
zLlv3@+%?|-4~%IJr1xTDVYQ4dHz*kj<#4>?Iyq*q%-YJ!lb_c<wn;&xvZN4C*lr|g
zPR52`qN9+@Pt7RQRO}v0=lkbH16QQl0FCx4i<h;%UUe;s1DXDmGgLpZ*0iG&)a*fy
zpa2I|i5@x{>^GVwnIeQXe;m@<tb0p)8n#ZCuK6^@Cs#&^TK}p!{cw^i{|d=e`j+<w
zL4l#@QNv)fNj!#R@`$2`RUO`ITY(Qq(|aFevG95mu(yUV{o}LhZKm|JkBevLGu<sI
zNJu)GpZ<3LqrF$S*)taZ=M_`OjRe8OwO%G}?gCE?P@O~QYlyjQbCD-LNuh0OGiQ?O
zgA9ZcQDK=D3Qq9ACm|uh#bv93AQ%W5N+MgMO<g!o^4^MiE)n$lHDe9Y4Z?QQhq72!
zy;I6HUu$069~};s##X*RZD5Cv%`a0VBS)56tLB=;VySRBC(7s6k5>NsQEc+9-MsMZ
zq<dO|YL)HNAD;Td*`LSuKEfJQgWeDkK3|}{MA-JCTOc7n^7*6NPi0nbYhaTUwls6*
z5>a{TE!lkQl~9F*pLl*7IHm^@)u2FWZwNpi0nyM}?(T<;26gfo^f!pwQ{~LbtoUw~
z;=Pk?Ue~?<^=qDs9BE?_U-PYU9gRDD&3_b}dcq~{kBH$TeCt3gu{JE67T4s2-^EUd
z<yTN@T`^XuUo!*|z>gpIIMG7I+Brz^{hXB}TX*6O4Z7K|92W<Nlb%yMzwYFzQ>Mx2
zEsJUCZ~~w5naf}4fUj2fZB~QXlq$t947HFQOteZm@vbO7Vi2E_;Jfa3YbJLa6&;<Y
z@yM?=^l42+_+9u&m*r6x#cAhvSvkpdw?O%zO)R8ht~D(bsIF`3;q<I<JtLUO|8aQo
zyX0e?5t$znr(4b6-R$>mM_Wuw!|Og6{C-wz_TP^$pSkau$7DHLw%nHDyW)<*h|Ul6
z5`11=H<RG!wtDKdpP!Vs=(q6e%A_JLW^Q|cRJc1m-s$%`uLowoqtf<jNpH}rj6Q#}
z_`O&ywvqYeB4V~y979YL`%2@4ZDoS~3L8CQiiBb9koVJgefgcen-+py9J0pRMp}W!
z{>E5@)s*M;i0QO+agIu|ZUv3p7|vp1DQ$rS@Y}1GhvV9~Z`}~TpJ9({vHR-~X78w|
zD3fQ;E}cxXqndohkx1|Xj9K1;h^t;uY5e;<cNg(bqe!P-x71vSsQxAaf?cqL?0A<6
zd(x5N#&!3cNw2qz<Uwe)cTK#h&&Vk<a?~glL>y-#rAT#Je+0F)_=iNOzw7LOiiEU#
zsm#8AO<+Q(T)iPvyEPP|K5yV{0GI%ef>JGY%vX~|n7FS$Jc<*fcmH1w&cVS!VP991
z#jaheGJ#J=1+H>GwiIs6&!Hz=_mo4^m$u^yHF7<Y-HsN66*Iq{&KmF!s6Jy4i8LGe
z4{b{ZW5_Fulux}`^9&;u@$VCO!SoT?AQO<P^|Q8s&1_g@#581+dbRMkf8(n;6G>A4
zihxFDDN^OzeRjF#Jbft-wbO8soQhfMmP1+Zn~L5Tlj~gJpeRZtN~2)#5vp@WA#4?u
z38?R6X*Qg{nJ14AYA`e{5G#gQtMP+YKOe23p|9&-)pI-RB)Ph_;S`P2voj?XTTPB)
zu^wF%rD0q3%HSn{j~43@Z6J`A&)1OnDMfo#kZTvfh1PKG*i5$0c|qBs-Wxtd-F2B7
zxm=W?He-2dEkmlBC4h2&H_F**S@`?+rpOC6=%&CfPw{;2uO^FW#Y;xU1b<tBX%FO<
z5!nYu>YbgQ)7=LbZ&VSL7RB<D^)Dw4a@@Oud;Co%MPd}wETzzd0q-@ObuJJ_)PbML
z<F8U=sFLSU)3S9f_ITZ`zl*S~iu^_F%U5hDVMLv}G^<?*t&4&8zt)`e(^ZkPfgVcM
z+qXrMc|QFqw;A3z7wZT%`zx>>$EB;`y6nn&hcM&Gm#@2Y(a4Tj9*b~3_<)tGS>)p`
zJ4-(etSZLZTvoT|a?)<fbSs|E{W~J4c3sE!^9|?}z_8tq3MSsr-5$C7T-Sh|t?acR
z?HyCx*?jm=6TTNa4@moX^a*c~%AsAgw^eO$J6hv(DT1W1wJnPsBHPi?m<~fm)?>v{
z(vhjp*eIT}T1hfK*<Ijia4&ccO0B(7qVU%(e>TJs2rWv|Czcc+kUSyI3*C1Uh4TsU
zk|3Q6Y~P*q8;Aeq0Qy4oLZA0!DwE@7jonSyH3I2j$WqFilQoR~G-=Zp4S@|J$*4W`
zW$+Tg0R#46QUifd*Sa5sIROqdxcuVr{UcKc2VOS(>+VHk5tecSw75;9{JC<BcT_X3
z`rC7V4olb;d*g~6l_LGXcI$VTt@QpL?fq0<a;qrQ$Ff(-pKy~0U3u+~q?@88k9|tc
z!o25HR!H);SC<{ftH-wjFxg-CKzxHm@AAsnE*r8<Ae<w|I{}1~U|lMXBvRuTgra?Z
ze1j=)-hVzsX^%goN?VOQ2R&xy!OZi1X)xQCSqFXUvG@!6FbM7SPhn{1E5S3Oia;^F
z#8FUeaCbZ@=?Rt%wxGO`^D!3d1Q~#2N|1aSZF$chlW{9sC7Do$?}6%-T7FcuFxMys
zVgy?iG4i^EsyM=-f$IF+jIfRvp>sH~IrW&(#`CC?f|Wpg!Jj}pNId;g;0YJdwKmdM
zxWTA1AiQ%TC_RT2M23Yac+$qgMrnurxA-bMdfUwbumT|#+zamdQ$?$LrzwUp@gy|g
zd`?`GR0JW`LswOG#Ab@!YyYO!X@^acNzeQO!vRtJ+3_P)a#ES1F%17Ayi_&?CCYG;
z-ND`gK`QPZ_jMww`DrxVSchSvjp{E9Z}Z>gj;FIxQc_x%>%^Mr|3RsV7lY3pMDWjt
z@<t%GMQxtT5K=CZ=DXr(34_oXvMflX&<JXy+Og4MmwJCGo$V`NxcrV5w(4*q42*M5
z$6+B?aB%$KXc=`nxo}}IqYyTLr1I*a<AIl#|F;+Ce#c!I8JX5_I+I`}m3NERIbsya
zW#Ym!1V25pF0W2!H_m-;Ho~Diy7<mBPAhEgt2l#(jP)%j=5L`!w)EZOQi$v#vk^!T
z4$I})1SxXvy}v1pxI8>OAaLPfx3dR52FS~DSJ@I{z<w7Kqdtrlx@7k*O+fIM{6>xb
z;D@XZYhnjZ2Ot^^eYu&u%jC^E&+>u{ht5l>F8E8T!_p0!^aVjbK%^&LdG{Mw@hG@j
z)`BkMpMP7fh~Q_#!CEn1g;ie1Z4&2cgVw8;fA#Poo81VrPQgnooT0d1??G$?;>$r7
z^1aA}{of#KFfItFu>%PJjgZ@BHKc=gM6!Tm<KDd&FcG`ZshGhDJQt9nf92{`%tjD#
zfhSIH_?r6VDS6;;L@Er0?o!IF{<;BhB|OEj4|a9gfa;lCrpo!5l8ntgnhAkoxVTEO
zs5Gl;xRKy{i7*>UB3u2|4idd#7s41}r@y!|Xs|DD1I7&@^y5`=`v5!W<hw#d7}ixn
zhp|xnko3>l*`RN)^t(8RZ|&i!Bm+MaM4J{44qFh3=m(eh^z;-`QQTB)zgQ|nz8Gt<
zo2sva?hc$0KcOE5PworQiMXlcjX50@B)og~4#dX@Nl1_3<KtTqN$mW)urLYvC4eC`
zobBuCw>@D>Uc7WFie*<NWLKz_Z6wYB#qnZuk`5szCI&L3otD^;p(HX<xO)K4g6JLc
z{uUP(7v8$)ZWt`l9WY=Z+8n_~UC{jHold?T9J&C<ag@UcfM_xh1kTU>N1ui9EKlz*
zb5Z1rS_s{~4U_fa-;OA7kuCRSh9<CnyZpIY3=9l1Xi9FqH>6K;2knxzD&{wQc7b1e
zR*di;w07As4%M%I;V8D$pWOqVOISm?75{`X=rgIt?>EDdJ>VLKXK7yO*BO~EpAK9g
zU*Gzalqv9+2-uCgul>1m@Ho%Q>EP}$OgRGsY6y8{<@aG>1*;Amr9daPjr0Nr^y<~C
zRgs(70~xykUKWS{pER?6dExEln`d<e;R1xeV<VCmuSx&!JyMxKKJI0pFu3TMd3k$}
z0&lH8G%}h7hj~<D;-kpFa26e@v<n?N5T<d@y%ia>TAjST<K>lx%^VP%BM>sntEiY$
z-@bo;GKox=V-$?e70R~q^6iiw4J?Qonj8)YgbcvhUvSkAgV;e&c=gxBf9ts@_%+l%
zV=b)U|MO)C0TL5SV2oYpt^`#P=q=s3T>bD;QR@D07Qkt^&p#6&7w<C32x>b_ejs&>
zGw_5c6s$(DY+vTk!L>S=@|!9i2IMv4zpvp!LlT5YlHGWvW4=x~ji_s0*RXbL(Q)fU
zwqzqcKNMDYcEliTT)F>}F)uYILac%25U6=yUtb`6_3AubpvxzbICVHb^*}5E3IjxQ
zxA_i4fV#qR04Hu`WhG=WWkSqhc6M-ZFo+?Tg@vntU2r+;<jwm9O?uVkVFU0GfgK+q
z2^~5ln}|TV8ALQgWB^7Zl-5i0)&B?xQM)OI``+%5x?)hsz;7L`m}w2DtEcBN$Yy8f
z=9q6muo=*_&@8!}r1R$4LHk`r1#wXjy3_!sRfJKo7P*Xeg&m#L_@>F{OIaX-tzJ0=
zsh}MzgGL!#2uMg6l47^vU<DcbpDf*RyFAG8;%w8;vo3k@2+EO&dC_R8#bX15WVR4M
z34B1ePw%x>3<(cgV)n&a5WWXtP4IOH2FlQMcL3?(_P1dNg8*pyuIJCXmeyABi_;xP
zN5=q0v4e4E_zlE7t&Ef|LN4mYWL*OMR{*>35IV!*Ai#2BHA1U_LqzkLtrO@=Xd1Y1
zo4{?mu(+r>T!_^?j+;C{=<Eh!9B}0wfrn#7gp@G15caJ9R#6Xpg6#g~hv;4CnpY)Z
uYias_x0e5v<eS{PmzDYd|1y4o#jrKy6LRIK8P0!2D9EThERZ$|_<sOn(*<4t

literal 31998
zcmc$`1yoh<*Dkt{+DIx$r-Fc_fOH6mlz?=1NO!lSfP^S5Al)6C4naVW2I+2)?!GVn
z{^#8Ledqg+^W8JX9e0nxF!o~awchp4IiLAF&s-sIWyLY?6W@m*2vbtxl>!7IoZbDv
z?t%ZImolXTf1ue)s5?Lq`op^)grOPlD+r>5Bwq<DxqkgK<EEvwaslnF{ds3X=q!f$
zKndrO3-T+P-Vrt#MN!Sd%F1%Fg3F2|u99kZMVr+j=iwo1H}<w^YiA}V|NC&I5s4Nu
z<45scd_B97idxK+ShiTKZHL!ag3Wvl4yywF{FvA!yI&8R0vLOIaYVX&J~5F&A}}f|
z{WNhHl~8aXIaR<FDZYCE&VhrYH0oQfq+XTd_;flyN+jX8r+Czo-Q|TBE`u0tFu!l^
za6GY)^jzyyYRPEokdPnk7j_1c(IXp<n?Va<8fIxO+@#8pPP}9?!=su?t%nU$+p*M=
zGuFrMjlCv{*H5fj2`jrG$!HD7t$kOeeqY%fi=~hiZdn{8riT{rDdpRWOfdtVh$Ze~
z;TZ+Iz(8`_O%!l)DNmoc%j2mQX&6^bq^GBw^d@F;XcYa9VNjjiJ{cb$=W$paIg<}#
z#SwwMfBod5u&{7_eVs-+_Df96BRo91@OtNMDM?B7CQmmvxAPxi1Qu7hVuUcNfR`B$
zyu7`=6%-yq)YR0=Yir`KU*CHvFE4Lk@EB_U@ndCWW%R5}I*u7Cby({Q4ZWYTM*AlI
z?I=kbtA=Mrx?{nQ@K*l*0>NXs;mX%U1L>Nf=}P^OFiumnT4HupJaM*JVzwF6!yY@*
zHr7q&DXE9jI01Ff&*)_CH;$T*ZXHA4hBpdT3WmlTJx;wYPlzyre*OCO;K^%nF+ABd
zH8qu$N+n~8c*>(BlF>-QN;dL=Lpk_?I%sM$f-ky#U(c9-LfA`gH$MuYk$hO0r_~*3
zwRy538e)acL4Sne-)ap<^le*UCB%?gI@@0w9v+5UPCTTbpuoXlH6O{{6nfUFB=b8i
zCPw)<TgyrD7*&*sSlg-1?(2;8*y3W;X580&zcsfY{Yr^KeASs!#;Fe6f<a|2DTAt-
z&DY26&s1kP=*sZK_W4V!37aH7^IU*C@%1Yy)b;acNO-t1v4exd<;gam&y8nNFFpZ*
z>XJCq_m{A9O!ZiHHnwV;x#Ekt$m*A$MN+?h_1qZBdc>d-5F8Bd!0PI1u_nA2EIXg`
zAGK;rbt5AqB_*YBiYgc)l~S{=_xZ|7obGJ3m8Rm283XKl0{c5gd}+nU6Q%lXUZ*ps
zi(xD(N=hScExI?ma-DI_8aE4<*qK=Mv(MMvKm4wJW4E1`^wj&K`LOBcQ%1(Z^75fz
z6T-WTROCuaOMCh`kBL_0I4|kr$B%NlYTQ0uzNy7{I-3)xS#dh~UcV+P-l-OAZP;>W
z%cXi>Zo|g}zvbqd&NjFqCjb6gesHPlF2)mNTYt>ImYWL+QzG?;v<16Z&5<kV5c64{
zooROBq(kJ`uKWSi53RW*7FVu3*Rs$B+C+M-U25OF5)*qMiwfZ|6M!FDB?iTfcL&nq
z;q|*smkn-_cw&J80UXRiH<>ZBL}57Q>5Getr?Va<<>d#n9=l|0MiSeI&O6iRaQQYq
zRAO8{r}(QM%x~{E(mf>ST5$HCLMHEjC)cZ)%un|r?9Ave+rD+;IX~%_cgWZ^GXYaF
zDZO)oHSqWPM$p!Nq3>J-uAlhE++Jr$&^?Up2begR(dZ{T#blDI5DXpJmogUfp6}v!
z_u07KZGH9XK{K-?+gFiK+(`%F9qn~m%)xCBNJ+Iu6%1e5uq8xL2$AFBDxUtz89rYx
zRA+Itp3^DuxQ+Xh(0`pLsur)%iWc|TwjM3=@>iZ>^4DsdlsKc>ktdbd7}WgMZ@Jp=
zXcQ`cSYoa6K&-V|CWdc2yZYGLa4?=Ie)(1UQeIpx<?&%ZwbSNX0~b#P>jW;Fl=giH
z;>Sj!av;T7zNGMSbUuxEo5Dv<crqwXVMn?)b8hzv+c%ILT>$?AU$Ss&yZD`2o!wH0
z&8#}XF9)OM_XTsFpC2N9Us*PiELAtIN1QG`C{S&>UhDLl3{KK2W)|=e6RV`1v9_&+
z!O^Fgzi(Jx)JM@vWhX3Jj7^dpQMV+7cJI}kecQ90=T6XnpKTnA7{o-!Jb4j$8tl?U
zUw@S7y}noJu^U6AVnXOYX!^+Kc122B+JlF!!M}Cy7aSGe>&k2tnOOOiyysFlmuw0@
z5h-bd>;9r_GVdsnf|*%f+ro#BAC-zZj#2R4k@4LbkJQ#lqSPuTJP($C#>dBRJ$h0j
zZ%~w#C9QkrFc2fn)*{-^KUJkjJGA{+OV*Yb4UVqGJYik$y69?Y3WH$LuROFzigTtH
zGTXjmQc<zBb(7{OnJ29H8XL)W5*|GkLq|g6O@719)&})-Gon(XK@$@bHcjW*!y8{Y
z%!UL`rxoAbs+Q>Vc6EIS4=3Zc)l)V+Zjb%)rQ!DG5~R_wq*n*g_HJ1dmM1qfYU_{G
z*2@MA({2UI{3t!IkH=zSVwO51u`w}=y!JqVrOG977F)zU3H)J*&(CEw$&r=3SJS#c
znJjBNE*bav0aEw2eUePTFoG=YhP_{#FgIC1kMfeo50p=C<Z7}8Jo7c|1T3@E?ta{|
zDCbLl(HqzT)D@(*Uw@Ui?6Y<DkjA;K4{XBc(pbLAG`Ot#A)^<MS(%#Vf|E<z0w>0G
z&zmT0V0X1InQv0x+Ge0%z3RZ<<|LTtD4<7-J9*v_N0xTMUTeJC>M(Pv&9MRFVB=34
ziZszeDXy$|^5NvHr?%@)%Ub%ToL`4*orvHIb4J!r1t-U+mN4`Z!fEAhjoY#Zu*i&4
zN(9Asd0cjO>T{+CnN|lz(=}7PJJ`QP=pU3OiDT8f8R0tkT_HxS+r8SwesUoij8&x5
zIJva{<}0_|>aT<<Y+IgWmgYUbt#+1<@fx=Z6Q}!_2q2Pb)zlBv7z27XIFryLu7{p5
zB0<yX?L$hU!Lw{V_e%IwmGGn<QcIFIC~%uz1^Dhcv-KmEeC8&1)me2)5RbHWTgjqD
zIqk3U%=+eX3+m{|PWQI+?s!?7C8yk1?lMrSqzuDZ40}xuLvU%+Gcx#GcBa2^TbKqt
zxUO|r`;ui|;u4-6XZvc-lsjFVNe)j;9&fAJL{?oX1FQVcb7k+Fwc9DSRg;wLYHv7P
ztKa$leWc)I$MMz;YEtL#D{OUNEqDW8<_tm*(VWWeOCAQqZ%S^E9({R!cMk?SZ*sd`
z%T{lCyxTk~R6A}G*=X#z*!#pd8Jv8%EUg3o+mX=%)iFWWxw^6psD=K6=CeX}+G*t{
zvtKkjS%q75cjhR#JdX+`KW}=<G4{-Yl5xC>aSsu(06)aM*GS!ar*@4k)g8r8^rmsq
z=JLst%cG6qA>k?>wRL&)sRp;h6E4+FqPn(TIHP8D;mM}PqeqYW1&@{Dh8`yZXp%Jc
zf+L_b9xW-RR70X5uypEW>|C=~rM<02zZ$no_6>XN_#ge}&%3oatyo>@Gz`?|opiV7
zeDvl7!#b_jhmJ>Md}uLI5>~!VKGf*MqG-CT*k-kR_?7#FAk0wjl>CJ|7CJhgs{TXc
zgvTG2e$XJ6IP{!GQHdHD82I`7Z`Hal|Cy)&HB7hJJ6xx2>tLnFVyw8Ru<$Mp6%`dp
z#zMHt%gax8=jMtu;dzQVLyo=C3JMC*M0oeMpFof`nZw!MLdPTbq~4Q-U<wV!SiYT_
z43E<t8SHwQR*}fW<;5RiDg{*_)WbDz2F$Cxt1fy4hG`L75}$PXoNvxa3M`=|1y-uT
z!1c$YRfIgFze(36eMt(v><i(&w3ARs=zH6~Rs0CAUHr9kXgFfBj>762S8~@GA2zm%
z+gh{PZ%uQ$&&CNR^?&$BK4ak^#E?zGgs3Im-%W&mMD_`a!cp6Y2S0me*furF-4n~C
zIiIlh%?l|L!(+SOC;u9gw$ttjMGEdU+?cK9+k?FRX-|jkw>>>Q6dr%dcDc#$2)5Pg
zsA*`jPM()E9CRl-5KwX(9UZKYa9I~Rs2695NA66)S(ur3`uKJWuTe!~SI!UD-tk}2
z1q6B39)y29*<M;&+Ipjndza!S>v!8edMq5};rd)}ZIv|ZEcrTbPqxO~D~)f(ff9(k
zQe7MDXcIwn%r0=X$!P0%ZEj@jHV1EIbHMrrmq@*qa4$CtN{ah>gTKvBJu9&qFBMMk
z$a^l{{H{L*5fuv{&>5fGYf@nx6I^}HPn-;9&mV9mRb<xiuPMc~w2-9qJn4BFTrqAE
zCQU|<N@#5Otqn(y3rWqXE-Yp>goN|E7drwZYa*1r7W|PI4RV%rmVOASQA>VV(e=50
zu{M+yRl4VWeNl_{9TS2$Wa8?zqA{?7my(8?{mPqkjEzafod1l=M;TjG$w}zz>!Xz*
zrl3YS0lZkTHmc3Y=r@2vDZ#4jjB!6-kV5Ppl7iCRK_HVLs&nE$0xR(d@}rCPox4@I
z@3*&9(kwD&4ME1nh_2NRIZ4n<^%dRoKqdx;OL`%S$9(=jN0NPY8r?@>Z5xg|0&e%+
zk06My{&!pefbzV!J>`Xzu;Z)8TJ_E{I01Wmd%U++pOTZk(nM+Jc~*k?g0cOD9%{bW
z)ivTwt#_4=&IDiYv_6p5SD{w)`{2MnS(bpDT*qTze&5xb_cMC8X4i!o53;i6*eLee
zKh=nftqjyD0rDhNA1^dW@3J}^@nY7llS~Ru9xU3V@>(VK_QKF)L5}FtYr`5@0&|Xa
zv~)jxZR!n5diJ&&wC?W%veEW$zl0m`BPrSQVs|DvxpVbQaQAhTTPF(0+pb9YwvrKI
zq<1MP5k7vbRlXdhv9yN&BDi1h+Nix0U=KG3Mo`IRXTEJcL`o|yWqtfOy{|;likT}Q
z2mxY{luv%*K$G`JFZQQ3+?5|+GCz|Lw@t17$JFA5n%U$lcIQz0Xv|dWScrG#=+ZFs
zARt)v_p$gF#3*fM5gb3KhKMzPy8DnLQtKybh>*Vg%zR;?AaJ0TU5g>iWnA%dv}-gH
z6r}Ge|MsaMvv%DVr+L)xOFN?lV*pb_zEqNk=gk??^7g7KNT{e|wKSipCC$Qa?w$4_
zHIi3fZS9`U6<8*pTvo8D8PRiborZ7oQ9Vav)~;cvvmMHmg!J%f@-FJMH#UUaUa_tl
zmf%fa&->%`-19AO{KCe;V>{o1{a$#0{vrR)4iV~RLrmVcZ<Orpim@v@GxfT=oHRMZ
z>N+~0cl7h~GZ)&j<yNtt!-9WAfV%qY*<a3;=r$8~*39fA^SP{!CsWo?G;LSsUv{a*
zI@QPcyb#_CZ!!U;dMZ)?_hpFw6oHS=LC9pM(Ytpxlec77v4;}y_%g%J-*Z&?2;xUp
zj;~k{YY01pqr^-qM5CFa*Q1Q$7?WNNGW9r1KC4o5zI|sS{Fb%mEB*Ba$#)SlKfMR9
z3g{+`eloT>G5hVSK$7LuX>luliw|Mgs1O|kNmqqD*9@M7Jccl#L^Lfd!uOC?&fA_B
z^&u{m&~5clx{$yHopiLj-#pBZ7EM67d^x<C-1TB3=QhTf1t}PUuFh|zTy~WvmF50`
zKIeC#x?#n{(9lpvB!%wUa~>X^ZpYtD@M-%-?+9`MbIv411%-Tkr#$aFq~*OBLfcf$
z(_((^KC$JGf@%IL7vvQgZ%=Xz5`^Xy9Rah&#?x8NB`EC2&~a<<{K^;t)HHeReHu@%
zm_0HPLkWA)6lC?*K($b<j6WS0gA{{#EtgB@^6IdEi~r*icW8RC$F;Jy?W>X&k=@k5
zS(=`>nUo%DxiU&TbdeBB9S832>7Dz+xlt?k6G}PHJKT|JdRoclN;|Fa1Qr-c+F4uO
zlRqtC@>592U*0xe3L&)O5THIioR<@Hw>jQ?8NDp2a4K%b%)+v}e}-G?N)NRZlTkc9
z0@%l3yTWp=Nr0WbLKALX@vi^G{zU=ttmk2`aR&}z;=M;JaG$-MP70sGO89r4vaP(v
zH)X>cmfa_kEKO&Nmp|L6cu|aqQf?JDRGkVLYCX>n9!=y>GFrYdD?61A5k|zdU`rtP
zx!Sj(ChG2SX}nUfx3^CYUT``J4^rf6%K7$f9;{o%geCjCK2mPmZ`7ji=XsVKZ&w{x
z>a^a?>_}Uuoz<N-N4<oUNDvOAa|r?|g$F|Ju}BJ&b1_IF__atsW|4f|+9sJUzqkHM
zk~Ty*z4N7ldm@&Rx}onrs3SYO)r!>n3LUNR)2kfqdgr}n-HyI^&g0s(l*`48V?#tl
z9rukaX3O4<h=G9tTK5xIk5MTY2C<^2)1QjNEev<i1AtYr?tTc16uNP}ap<?2srC9e
zSXxsYIOZjf%k}dKZ1HpoxmajYv&MEoAdW><Mdf5?X6iDlo%k6Yo$29fFMb#eB_#wu
z{CzKCU32Ec4ImH_5)$_J_u;ed7RrXBXA(F8m6eqrCmg7#2_wkl8k~-7*y4Zi&LT7Q
zaPe#>e~o~_D>8-$`<BdUMd^A_o?|=-(Iw@1hDE&i6s^;yd4|A<l*fLAf+gd<KBRYk
zu;LLGs(;oSp({7{#l-{^cD9nww=OT=!KVOeVNj-)Et913f@kUjpW)%b0ciJcZ?3oK
zX^M>G*=^3cJD#ldr^<c3q{ARX;b|^T;xxWE+Au%HsQE3!;Q!>}_X=pBr9IXJ;`xzJ
zuGRwo{CSt^RlS+VA9wj}W?#lmvLjUoT5)gxl!1lz5W<0yiJqoJxyRxiKdZqptZ=p)
zx3-^@=Dz9dQQc|qs3RQH4qd0a-A~T%Ss{j-GffK;so@LC`5gUpGjJRu{`A$MP8E*L
zlTusa;<|niYEMr`Xg;`gtfRA{(mc8KJKY2a7uUgB^8x^iDY(y(yQ;wBTi?JY_3N{x
z$gST+pN-uX+e4go0%KEBu+UTdzfrD_`AnqV+8lhAGtt-<nj{sPB>Ryv$F=i5zo4)G
z>O}3d7}v1=lz|C@=ZM{Dp>Rlxx$X6*GyC=;@%#6Kyp!CfqS9XUeYcyAFCD00&wfiS
z95Cj^(7O$vyXk*n(?`KSUmq4{*zv;K>)}WuQFzCvC*tO1zcI~~O9!|pg4>>-uc*!+
zbNT9@7I|ja%r#Z*B7;6HaOffI{yq75g2wnS9!;C{p_C6@nI%imxm`}?FR;BCpSf=r
z|BMVL?bsa`d8Bm@qJ*V+V2?$mu@q{M9auPzpCmi@f8^=ly_wO_(jt{tE4A1`g!bf5
zHb76gdWXI2eg&Z6az|)Z`S|qP;o|GQqp}Z(l4}u}^DNe?y*E?mUoJj5PX!X7a-hza
zl?<#kIR4iAOLFRIaw3Rsl8ErtJ?ix{eUzCZ{WL_0A&E~AL^jn{nnoEqot7Fevx}P!
z!=q`Zt$C=Jx#tW~_8crBUOJ=Q#{-K$sB0lE5}mo%8HejfeK?jtM8R(IEB?8mQa_?d
zZbH?Nhz+Zf6(a_R%x7x#MzKd6GQ%82i!OtlNmi|X$f>KTN#lZ7;as!1UY%|wI*pV<
z6va9S84pf&>(8%UkW+5uvNq-(W!uQC-d~7hnSiCE_e>L$I!eEt?|NwnlR<#mSX1)z
zNT5Y?gg@g_cmxR0E0>t3vNUbqoqYH&M-=4*c(;rB4C`-P&o_Sw-DWv&VxWkQ=V}so
zdQfV0QeeT($1){l8oV=jM{x@%VIn!hd9zG+bY*4bS-S$>57v*bLzJ)c0pBVu5Bjov
zmnr;tWv+a$rZ^VerpJu;qNCGlOc!&K1h4kmCOg0Kxma&cR?#34IqiDA`#yDT=>YRt
z))jx)hIuiF5H#4~yRH#Ob#qFLqHcnH({+6&AQAm*zc@+U;{q!{Lp3udE{;OTo1c@j
zI^{=qf2z=~Bk$7UB5|XjL9(YyFn!fow=&oLY5jaA(<~6C8z9N36ujW!sXfssgltd#
zsMol0KrdefYttAzk<Q!ia-t-=+qa?xE22OJWDsw~FK)w@aY$6kH>G~zQ9D-sPQmt3
zttW(2*TLH<8x`Kci;V#&g|%X7vHlc+s$6kc+HVx}MtF+bYJwaR(Y<@t>h&i}kwV2@
zVe{*Q8E~%yIf*2!U~GZOW*g;8<H$YZtPSbUD_@3{N5@q==N=HU_7M&wot~(O=C40%
zK13f<qQ`C5iK`UYio2{k&2Kz`iNHpE17{gPcXYk1fz0TM0G;ff+0Qk3RmDA*l$Opg
z<Fy!zo2hs4S^wFrgZqO;Ce|uu^@(#Z8=vSsjG;qZL_}I7B0hG^9)7#Cch_U}FY#y$
z=h=lQyjb~7srs4XFZaq>nk*M(?O{~S!QhK-8`Vi6qjPr`Gr0sd<JC57n_4Z_`clW{
z(be-M8pPu(ZQ=Ky?}d}uD9vdzkAc=L%=;{2mroDSt;xJj_vDwGuS2^8Q6P~w@HuyE
zBbO@=JrPD|I(6W1E!C$(BoZf|0^hyF@z41FLK3gF8l3nZVke#!apPVs*c<kBf<>%Z
zx6ajzk-JrO4wf3W179c&w_oTKug$p3ktynM7;m<NChLcEY@u$22HcRr>HINH0N?W;
zFBW5+u{ukP$M~b$qn7)T16`cAvL};X!OrO!k7h5X=59_}N}4AvtKTD{sx-g(@k~-z
zDN!hoHQAOsCYCj^iJ^;|sf$~pq+a&j*zuH2vj7<xGE|n<`l+)VGwDWH?H5~H%QA<m
zBNsHVb&TD7)3GepuZYC+Cv75JGZmj|BwH`d#+R6+dWBn?gyBu9YW$a(RSDlu2tl3c
zj}_^oqqmyO4+qjjLFatz%CDFs+jzNCZ$ZK8dA0{1hVv(J?<tv0SinD0GMdoSjk}Fu
z2=b!b)Tm+Z8j8SP!0<0naE;s|f1E^?A28O7nuQo8T>AO%prJK{=-;R{mPZiqCigu$
zB%g5~BXbW*xI9UmZS)wM-8^i%+?k?=CCQQd<QgKOQNDi7^HeU*3G*fnK?J7UDl!K>
zmA}gTUYXW#P(Fp|gR$@~5GC?CF&zv3ATu4^`dCx)Cu4;9YU@E-xs*cfbu{1FvgcYG
zG|*oAvEz(goYvTjOBmI%0OMnw+CL9w=^IZ<q$*^hA<C%7yO>z++>DtgOohiELPL4w
z72HS7F&Ar&<_>NeOl`vvVe-q)Hiq^NP)jn$^a}%}ZB3mABDra$1BlT0_KDE#dH*f1
zi?>MqUYxFX?FDjJ&evim;}yO)_{K$kgs};@`0jENdN12tru5ZMM&*Pw&wgvlzS}@7
zVct4vCisOz4&Uiv8u@YfGP>{il7JT~=d~AA0FGXoYM~~4N7oCrWt!HRKqpJb-R%0}
z7_#l!IrI2gb^Uv;`5sx}m;e#Eef^+XzwAgkBLO}>8-_z8`nJc>`u<M+?r&?_uUOaB
z{ap-k#-aAI@sHb&l@_$YXG7q_mseI=zdi?Lfp44cncIN5PsNc&b}tr$)Kc`iO>seA
z6qD=hb!Beu`cK-_kIXEMdTc_svqC`_M2jmEXi3WOW8Ct}S0-wm%k@7@8#u_RGM_Sn
zjy2<T_+*nY`<o;zx7r6j7tQnp%!m*gD}C45o6Efv(k&TUu&K1khs$&obPJ{&`ty~O
zxNJ^Vl>_n=npX@#LfLYRFgpz)<9)G_Od0T&$tr6=x50JL8s6F2ImVM`-!E|b6&*Hh
z`63RBR0-oHZhPf?%lSguOe4eo{m(z~CT3v}0^`!kW@qL^=A|XB8RyTAmmZVX3Z^xu
zIifHsmZ}huX2*>oIE&Uje|^81i%6d<fBl;^g#_I{ePhzySdQVyP$K0H7TXQ|WByRH
zmKBarfbwW{)Xr?118i8di0wKvNyz1tl+RlzFON_&^|W4)P-UXsvK)ztiP@~Jwx$1o
zN<Fq>cbjjUHmirU;x4PwqkWPX*&s0-MZb?e<`lnUtXk-r>PALJoVSE{-H+CpPF-Xq
zCG&3ezFQ9YREKaV8^nmi%zAIp8#|ZY=X)JE^2YTbL#hJbyVHA;N|Zm8zk%MsFkGq9
zwY5Z_ns1H(q`S}-3=r(^W=tIYd=AU=__z62A5pWu%|A^cpbFSrxD9Lq^4&1^YM30S
zu<!orlKAJ8#8Y>>4Ue&Bq|q2q9>N8|lS#~hylTTu%m>a%-dnV4D-T3^J8sbhpoYUf
zzEcyEM62rWwGIj_1hHt3p(Cz!0`Y(xPNVlfhjZknwoix)R5@V_j_ZQA0flsNm<T5u
z>}B0g0uqIj=#G$tVTdS949|EJ?J*&1gLttA`lsTrU@0h;SWUJIBBCcORJ`6X%(hD%
z;g)SV2WYJsQzt~q8jKDNhLep7wx*&UFJWn~2Qml3(re5|p8+lH(W4g@zZtUz&8q+w
z8UlNxqoXf@T|9OLlZ5ljmoJ3j9ki*Q2k!7_&KdQ%%M!6H4<;3eRbhE}AuTd6_y9@1
zAG5gX5kCI?;0l$9=10)%HH`<TQSdo$kG4e|G4D&2&2Hla6H>+92U9Ml<2@kQ%ZMBF
z!tR(u!1l&5sw=3dsJwmqjbmrBih+j47{GV%B4E+^)!ls`3rnZcbg<<;Vpv#M6|C*C
zln>K0qE9~hB|oI@O;bE{WUb!vbJq84yUpz?e{-|{ER=FN-wL_WTcG(Js+XW{HeIK-
z*`iF%IfY2>xhY$Ee}BK6l+-=nfPjFjtE;Xq35f35GpRRkkbHqa+R)I@6URal-T@Zm
zXmhkc6g4Io&erl<YPd(t_=g_^iDK4`@xDG<sD14+o~KBgcl%9j?w0VE11jNl_Fx9d
zkv;Pg-_td`s7#0Ncv=e()yOX(ovIWlmy96-ak@gINXqbZXXbQ!>K!W~8#}xBM?(FI
z2`MQlAVD7k&8-SVGhkYwQupJ{@83oITT5*hzUR-<g+7LTH|215JF!giYCV@0QU`Db
zx2It+>#%Z<*QAX;)Ik0BW!X6@g{SS8c9h!x;RQHamoFH-_*1HXbUgNf7WQ3_^-2!j
zOzP*K06}n>5lpG01IHHw95S4qjlV7ce2dOJ4LNR_LZSIB?L@7D*%YHD84ZL?of4a#
z|Aq+sZEOqB1(R@_)WpQZxVRj=3$VsO^Kc+t+jq@(k&sskkL6XT*%)OI+l~8zR{krx
z{qy#ez#P4ZT=bYn5hV=s!V|0v0c50JOiLJRj4Ygc5fRtbpXHK`==+6Vi}VYUPD2>)
zr!gM_nM^TwI-UD16FknGT7^mQi<){Yjih-`NQ`kp_6CZRG!9bdi<{H~Z_&>p6eJ``
zpFiVLP^5nT{5du@n*-><C)-n8OibHF@Y&F?uqAH53E0fHykF}}2762~g6O*Qi-fwm
zy0T68X(hO8H=lRdG;bBuwALR$dP=_D>YX2~Nztuc1XvvENo+^M<1kSCdsY;z<I|-=
z9XFDMqXTMEA{H{{U)!XS<B83%B8F#(!O0_YZZtcHg-ga%X;j3(8AmK32@gCjvxaNp
zAUV~KJ}x8e{6szR&6++mpV*?>IMI_R>Axpr+uI+2-j9GuLrF)+yA2u9+Ehn);g7WH
zeE|{Fpjn*Jjl=c6nMBVd3)}~s1=dsV0;ZUpLLF!A2zDL?;+WH8Hv49(D^Uz?Ir1iE
zGvP}MbowahyYEO1UZ=H6Kba(=FS&6Cn*5IsH>bU=GuGF!k$DVpSs+~_Iv6E4h0G9R
zpd|HXO9d#qro1D}zG>$y)?|8p^jG<oJht|tD<ckH7ZnxlHL2Kq;{MmX&}nB<F>x99
z7VptRKXl3S9!$m4KqSn;eSt(<?eMxYFOOVMU=y8XWi8x6>Q2iZFxt#Me*hKbbPtPT
zv(b7Z_}R_~N`71)A6lRcnB*0rKPn1#Tp+E#6@uoa$ag|^XYSs>!FN}FfM0Rek4YIS
z0_Q$01vV8d5f}of)t~7$owIs43-E}8;wC5qG_Zxv*{VaRB`@avvAxe0f&p*bn<^wk
zN%`$TvMtdixZd|B)1QZo8pIK)M$G3wkVASPyN<v;n*I=Q<<CzKWl56^B6@Mg2}C19
zBg^$!Fs+1#R3o8!X{gZIMo#ML(XQx&_L^etP<AYiqm_V(%&7`oqlw`hp%>heVK9&n
z2k*cS@QnQ7aRC&2oHjZkca1&^zSvAe(R^vx!nQV?)Akc^)C{Ev;kie4eojg%V9fZ<
z4TzPVKjX$j1blO)_gNZ`6#R91jQ`|>7I!mleRYJtVGoZ$pbFqvJk;&bX>hgM`&v67
zfYp~eQ@tJtY#|)!xUt5%UdPI=$D=__QFV?w35hPVygK(WE!y)Qh)7rn>aLF!X`=RD
zq4A2p?>X-4HV$Vv3K!*0ZkFrqJjQcBwW>aR9Prlv>}n)@#yYr#5;&GP>vJhidz;ZJ
zBso#)I%G}z{&mC$Z=f)yHs26`Y$S^yHQx)Gk!ktL+j`|p4Ez4vJGyZA#gy9}3iNCm
zprxgy(5c{MU?>=?K>tGaEfK*ifB9q^0dg@2C44l@Pb&sX>)1aR!G;F^zIt5*)mKmd
zP)=JvPtQOns1$}(xeE%3&s~ihis$wa0IW7Eh(?#6)g}x}BLOWxYvx4zO(`}sP0uC0
zJqdT@hEX{h0=-)#_WbtGQ-13*FVxehG$t}k5tth?x)&lVZG-|r-#xL~o%oNRIbl?~
z?*nHw1M;Hz_L!iW;0T;HyCD>+Wp<4XWv*96+I;44j%&R8ERkbgmyZ^l!?vGHOSWzK
zDErqPr{Le2dIs1KfuV;<WHeWEFRz`mQj=*mj>SAHFlFsLzZ0V^>Msp|(T0Pxu+m$(
zykU8uB^X8hMs^7^=mL9{TQ-{dg~1gI@B?-E;)i6JC8pDjvcJR$D4~wcHk*@T>?!9c
zvZo)obJD!X)nq&hRnb@{!xBHHFiR{R9C9pYte>&A@wS4+^_04BK{co}wSFSsW+jf?
z8;3VQ17GzL0rI7mWMXS!{kS=Ez`y%1U)5nu`?DB{V{4uWTqNPJJ4c2v&d9b(Si3!`
zIL<5ox-2@WlCc7kAeQeu7bHvG<Kk+J_Igns<GoB|=%9-s#J?c<7Fx_U(G2-I99glo
zRo|`ph@RT|7LDYzkF!5Dn%%z0Df!<;e}RI3aZxIctw1Bj*28O4Ta;x8?%eC|B0kyy
zKfd3%LeO-Z_|J}(SS(bk0OFrhBDxwH`DUa=I4<*=-<R;G#A;A6%r?|;L~{2Vn2!8W
zu~XQLyFnomAdk#OFl#qr1NHznAAYA5w!<lU$TqwQQ{u`6Tcj)4P4hcA>~uXN(t4(@
zN<5OhU-0q|kfb><rL}DukAGLdBfXp^wdI7aD^*XEy4c{iLX<rzDQE_3%#)}ea72g?
zss|MDURY1p++m-E?|~$omV`X^%io0ky|4DgW&9O?fC}$@Y0$IgGE=q?@qAyE8So<5
zT%KqSi*(A4%GpG!o9iYYa6rtiSf$MYad`RXXWWMm%PT5efB*%kljE&1p}iKwM3kKS
zVLu-3ccvqgVWLz_Xx;7yw6z+0qe44rPmGMG?11^sc$WEnsn6|AFom}(AOig;$G4rM
zsZlWqOD!kZz`2OUF>Ci#?ba-`=TSynE<5%GTiDpTFvOjcE!ax5Ql(J+?8T)Lfthtr
zJ-E)5&j9v?E&EzfFF`j{W;Xm3l8}}jX5)0iK`PU4LowMD$ja!j8b9@V`_{|fF~9It
zE%tAx9_|G5EOd~9-uk<0_QW!4yDxWr^8Nb)Z7?Po?@=o%#Rv!6qcTR1F>Mz&2kO&n
zE=oy}H(Y6&Uw_Mk-#grMe#O#be|gfpTm68XyszB5W)s~@FqPK_Ut-AVFg`W{nJPeO
zY5RjWplCt2l*nmC+*1x{{A3QZ=xqHeNK%0_>fng%L%sKtfU6LA;7!$-&J>VuxaK^c
zz`jq{I=uT8pJt}k8_DDLk&)?>`tO+N$)PgJfT%>UccQ6}fn)?e4VrhZ2XZ$snpI*H
zsy`s-%3-wl%%R1JRC%|LGoRX^UuQ%LUbJBgY1P`(Jb%7FUS=q0hunA?U-;&`Nlf_r
zlvc^Vk&yRmip9`nch(D>uG`^i<N0cm(CyVBa2UM6EZ})yg6lzz4t>pocX15g6|typ
za2BMU2rqYM8?h+_Ndbxh$jVr$H}rDfLj<QCD>)#=OM@3b07oGeuE_QIsZ1ij+d-Yv
z*3&ofQE_o&WrpH1ZRVUtZbXM$l8|jy1j;lkIn{C`_*Af=Rh*rj9UYJOb{iZ3{BfD{
zf~WY4$!k@a$Ml+ng@z`+u!tQpt<nJgG&A^=p`qd0uLMPT`7+Qo1nKiSuJ3g{iNs-2
zGO9(qd>P%oZfy`3kB<gX1yrt%j)ZqExgGX$T^?_tLV0<4VFWA@5fQ~}Xa8F-C4k3#
zAb^0YQdLRmtAI!KPn*o=x!)O%CbTGFYoLff1YXk@h$Z(0+E|6IkA?%$2^#)z2nh*M
zQ6WGNA3i)dJdEi`Pk+3!`RgA*55zbuA~q296PDGbrKKJ1vqry8=X@DG7Tu+it<66{
zAWsmv@;MoD5Tjxea~O5uWDMq3TF%rRyQ69rsWj?fLx5m0uX+{^h_*R^Zd>)be*GeS
zZtzM%;zQ4hMwtP!%y4%rbhjFS-(=?I<`xiWbPq-n&TU%vJDM_E7(%9$ybkz@a(pv{
zg9L1Bg(^0q08zQ8YEY{@iB}89L6X0f@(ItDHz46)208ec(DMORPdQg7Fa>;j-P6mZ
z>(V$EPQvw+mKL<_yAu_`Yv=MAuMvFdLN_)x0s{lx8wH;}%?6<k1al-|&y$IFfQ^%q
zkwNy2j(!-~DH?5b90}_`#1jKc(9zJm^~rc9+XqKXHSorN1}>viwlqJlS26KtR8R#o
zZFqweE4a=Jr1jy-?hxd8?!Kk{+?4k~K`j`~zt}CprinFH7si>iH&6$EsW$(!jtMH#
z1^cRI1KA)KU2R!e8DP<Yuye<yOB1dAC$KFoCnqN<xiOp*39PCYPpi#mDmO3drJ`R&
zXVy`?db&m}f0qT<1|F18QrJmGE8SLoGFCqpq>&uT)nsCWpmaI|gDMG2lH2xb<XQX+
zAi#;Zy;WB~2l4|JNGB1%UNYPI^XE^H@HEQ|#9qC61>Bz2);oX`17c+(knvfXuh=*_
zIiEc<2U2o@YKaM9hbc$jS02Sc_@aQMfe$|feV?c1X&Eofc@0Z5S=%0wamvcdl9ZGL
z{lV~KO0nYyjj}lzW|IbGxuZM|o7~Osa(>xGh!cDu%~64ef-TnFEj5ZthT<=LoCl?>
z5e3OVFH@dz=O-U!#~~&*XG;KU_pdUV*lY4X%V?+FG^Pt;QAB!fR1}U=+n(6$ERm+R
zni?Ts)q{uLy!v##feJ-M2leo3ON;yCh$J35Z)R^c(L&rHmjGG4GDO_rR~~L=p2Dgd
z4}I3(nz=e}m6pnBr7^k$EX|pjXC^UtVw?<!5WlDbOb6B4MH)5W?t`ia=72LnHs`ak
zdR&h&eCkEGB;T))jLZon-bN3-^lpbkP^&fE_{47knC)n92P_@1ijoVsTTWI=4wqKG
z{BX}@T^zEd$?g)gXTPiW$?!mLKnjaTkfOn0A0i?Ioi;}RO0N1Sh5M`QNx+ZCTSqAf
z{+f4*%Up04-(5YnV8`S>Huh=ERDA)CUkmvK8Z<2`-t{pOITA-iW?`H38n~v!!--!2
z_g<_q+<J!BY=~N>O@Ry)PW_5*cmJHZZ+~k+V$8qI@tcodeG<6Bg=%mWjV7nyA8|x(
z(h!twuFm#9)5@t7*O}#ci4X_(-2|4Zz05g1>RjL#$?Y{mRGs;d&c?S(9R0E45SGT-
zbz4%WFPlo)`&2#W5JST9FVH|fyHw_i&cs*~@Q>b`bPISpwY;V}!`hre&CqQe;j&~)
zPBo%(ehiLjH!;5F3l_Dowb%skr?~&4%4kl^Ol>>oWM`Wg8JQBr-;bXrEniIhRh}aJ
zPvNK{{I6;ZHvgV)wtk+Uyvf=n=69#H2O?GpaNQ2D-S1pxpgr&aN!aJ+d<|@Lb7Ga@
z!L`G@MUjN`Y5&jvEWRXI{D>v!8z@P^*c1_wk^MbA!)(tU(N!FZ?0VNQFpA`cgN<ea
zVg)Rc4p?}gqa7R^@H+p=HB*y$rPFw)$3;8RUaX?fgGxva&QjB+F(x^gjEw9WC~rW~
zo(J^(C`aMb#V=NwAn8&8XbtvA6{`)pfG`f=U=e)*dp9zo#3XtT(j&~?d@d$`=T6U~
zkk=4N?LmZiXsS|F<?pm6YDpQ4D$c}g$@1s*#bf@|lDq8lG@<Dnn<}x!cPRK3i5#QM
zF!aqG<x7gPZlhEN21hQQFXzUAn0Gw#MGnQw*eq*1g<#o#PM|@h&ZMMsDbKy|il}5(
z0eeX4=h{~!=-uMmvRTlbuI#&35)PvKQHG}1d6hG68KX4hVDMM1tAt^6l}~rzN=Ikn
z6G6b955^kcazFr!&OET#x|XI3Rp3IQSh0EHzS`AKDQS=+@w{AGy3-Fwq{vVAk^A=r
zJ=qipav3W9yI(cXo1Y#1*?$ty$rKoXHnaG*n42?Q<qY7acaid8Ks)Ve^NJ*TQ~^=h
z;6I0RWa*}S&>JtR9T`DThMq~M#TlQN;?t3QoIja^Q7MI|;<@wQVz?E(0bsKE{XbXA
z?5(iwGsjZ83k_`ibKkYE(gqZ>evFLXqm;xhmH@F(b=KxsJ<j~ydCszKFBA`N`VIxX
zEm*=1B!@C(KkdkZ{uvqEw%0eTP9Xi{lB=yh5b5gwFNQw;H*c7gBl*#V7N``JPgsK4
zhzgTl1>z!=t?n3xZD$Wl%RgU1QsoC@8c~}ZM1${)jm5;pS=7rj0dn~E?WNgtm4&L5
zR5vgpf)|TUgWB42FnOVr^Yeos00H#$X#WBMPo6wM08V<k=g%KNAX$Me3&>4{%lLm)
zMC$%S<;G9ck~m0W@|!4<(V0UPbu-Td3TAV_;01Hp=1JaL0i~Aqo570B!RqLC^Doa+
zGbGdC8b$7n<d7q8@~F8oalOlKlg*r<e`{1i0*^ua2i{0^z*GXqDsWDM>4*Xs2^^$0
z*8I-A;SHtT&y*ipT3(LUBSR5OO1s1GhZY>f%DecU&&o9RYTHpzx%6Jx%i}3Wwi1{r
ztYf11&sd{E!EO4igkL)LmpXHDa%?8bX#@oY0V&aE_`5{6gr5#H$eS`b!-E`FWojds
z(Q9t-DJIG2x|yKBKxGw`{F{9?LJZC%nHfPhz|6c3YyWSQps#-o)qe*AazQgUv^@9X
zpMnlW0S=9Xq~Krdnaj=9IcE|RGjnmDA()cMll||HT^Lq^u?wJ?;FL4M9Wk6*@{))>
zO@k0}tif!K?PhjNYf0w+*}nRJzZ3uMZwxY#y_|96)gCnr(wB%XoK3%<rKuh}x{VLH
zro@2(Btf&p-*kxiF4DsspgO<vrG)897Dgj5)n%D!+_l7=tRL%Uz_tO_J8KNV{1|+9
zU^%-MPt3Nc%>BOYTySHNF}9`697h+r>HIg103wjI6AjEmp@6*wTKP3MU}UHJ59Htr
z4$#tX9J}8^gQCiTF>=!a!K2vQZ`N>S0gJ&Re6kA_6i=EmjeyY9@z<UPRj#^Cu(kig
z5dF6c^KahBwYF*4?*N237`n2oR@L>YI$tv5U_1gDid8n}a9B>rYJ-j#LKhH$UB^+v
z<OI)+9335jPuSU+6Y45&&};PrLk1CKe4zbSja>nak#?QqMhI<6?zNJ1ur6Gs?j0jY
zJs+@zuJ%Uq6l-g1fk_&`9?3Uve)aYB-KBz_&&E7dR8+t%nCvq9v#;-NmIj#1<(KjR
zPN_WN%SF7KNV_Njqc1z}QOWhxIUheZ_%mJx4FJF2UAqp7WsY2GllyT#K&HGkDLERC
zz+BaTPE^~@mzTTEHWd?~0CIbr{5d+hBN*=aj+)E7c~i)XK^35Z14h}-FTXm!eTxGD
z@X3F3wsZ*mM9&R7(nN#vDYW_(i$b3|907tEuvgNtjPwi)40LpFi9vG-)DT_IeX)v(
zuW@l;PN5RaFafh%I$$E;GtNo)`XC{ThLiFfq~2cTfWnoV3))On$n*C46pTBhvcHRv
z&EfODs+rnGf#mT@PL&uk|I4%pJYYmbL|_Slc8HFJr4sS0w|B*@$*?n`I})6hN<obA
z1Og(;71yi8r;^c2+)n<PX2ekC*+GD@I+)Je{+v$7#P&_z0L|~M03wp`*;|9E%iSz@
z6v$VfFf@H+TQLlS6=Qfnj!_r~Q=q3%y#m}>d<u%WV(t3MN)9L<fKkIpf#1zOw_wBz
z*bo8BQZeUq>!qhR4-Sq6deBlH7?%o4DI+pfST9m3&?!NMl4ajjX~cAZ-vEuaDUgU5
zdxC<3o`r*<F4yHx;`ur?paBAoPQWgJbyj&1eV5$(56AelB_)3XMbG1S^ARrYl2;V9
zWd6+p3gk!09ie!=iz+4`e%AGSHz>r)5e}NMhXrCWr@QT(i(Z2qclc$*^y*@(#G(?o
z%a+~cszeg;-2Gei;8O@K3$}z9C|P#ETZS!oHb38bm-Cx}Egrz1QG>D?t>UPtsC)uu
zjY<i(w?N+-%^lt_O{`X9bV}K355esrPjGr?372Dj^wDiq4zMA|Y!WGJGuH6UW**SS
z;fINxze^4U$^sa4gv;Hv5w$u7Nkc`QOY#DUfL*uZ|0iRJ8G!Yngl#$({4?p;XUXYt
z`!F<JEmoNeLpSp$sh6dtrK~I#w7k6B-qG<aJe}^DvU05PilWoe`XI1tzu4E>8Y|Hi
z^8S^9c;V@}W(DXDD+3#VO?`lN1zIUU6M-gI%+=NPpH>R63)!;CbwC0IP5Ro}+MIn4
z6J!gBotx`Ru6->$F*h*Illj}d;2sEB#&oCI?v?A2UT~Xl>~}v74klL=<IPqaJ=$fN
zAGF@g1{xq!KdUL1li}aSs4A)Mc~Y-8zmuYueNFGdIY~B<bj$W%c7T>~cjq?~KYznt
z9#^Gv4Tz~oNJw<54vzzH@_Q+O+yTqWx@agn)i`&&SF96)BjSy9mvPQ{?h?`RTIs-(
zfT+|q;6f33#=yeT491_1Q-E<8yXj(+!G?HrNXjN&1eS&`zoyJ}=ePmB)MNMSk3{TX
zmcxSL?NWXc7*rtx+u(dH6&1u+mD*~F@ZNzG`1X*Wz3+ukT2%^;!&G%rh{t;ALz8ZH
z2f!pyzVp*hJhC5lR4!L2ywCJ?baX&~iHQYL!h9~L=Ez;=5rCEa!GnDuoG*bMHsa$)
zMh1qdGQ;5EjZ8Z8&ImFJ&pqMo*PnlWiH&_4`dApJr1|CobbG|7&Ue@IR_3aYCI7wv
z_FkNRNcYlvAeL*6>MOW<=&O(gdY}B6xa+{Mux`MBBft!)$;sE<3?*P*VFpa8jOI9k
zBwG$zVzDrMV-u6#6L%4mlj?ov_oSz%mvQ5}Lvp!KpQjrtr@<#IW4{-05g|hJT7etc
zJ2Np>VbII_O|2rlZNb2soH2L?;$-XiVtRUdv*$iNiA_LYpc{V*FG>Gb9(I=<Ek8;W
zZ1U+ZaR0mcVDs|+tKA?8q?S!p9iGD<Sdnz$KIS+6*n;c5LGQcAQoGI1EPfY)(K{V;
z^G$7+8RhGE-nE?qF3^0E@;Ng;d$tKOAMnwcE+@4XSmTJS2jR2mT*<HYCt;I&@3o>f
z@(8lA9rUm?|LW;c^!^I?UjP_$WRkMSwuH{+5qJA9rycsQuXlYYxU5P054)?q!iS|w
z>h`@87GJj+huS3=0BU}b?MH9L8@mFHtyPfoo1M2c3=9?lI<2;zG3OvA8IxmRI>HLh
z*eoljE#I6f80KIKP2aRhU2}`;$#REdv?-<jP(*%u2l4R40KybAs3P1q{iGI}P8Vu!
zYT(4H?H^&I95}n}3@VTyC|p1Q21UfuQK-l<qUl2ICDEY8^FKq2hrNdlZ3}y8?{n~H
z%%7*PaTkZvs$@K9j8>x{9^}deL&C%w>za>Zjn}}i(2=)o#Q$Ud`hSJuU;7E}hH=_)
z<$k>(J{S<i)^75w{huLq4LCn?XzaucckUyGZk?l<bTGjBMG^f37r+<s)MXO}Y5U!q
z$tl<5B?Ya!lg0+7;%xo#Z_T+g$0bGoi(LVq=O)wJcS>6q_RsRK$;m}yTYx!C8XpBq
zNC9RhfJQki!xc^;q+>Q+X@=W>2pkV3-bQY2wLc6d>RoJ(pOApiz6-k{qSbRm)Q3Ug
z?)z!M8sPh)n)hA#Bl!=yRE4SZ;6g*pmJ{X1GP@3dyy4<s1sg(nO6kQXaD7lbFW?*U
z6g7G82SnX|;C5Q|Q4v3H7VEw(Mpg6Ke535Z-*4;~S$ur}Xtb=HoZz4!Fj9KQ413(Q
z%GGfSSZ_HKHmBgbfY2o+BwV-UHtA2%21CPMPo5bk<%5}V;A+jz%mkZA00_iDwFd(~
zS3oz&w|SMt7^H=rnwoLv4m1Ig1VF;`gg4Mj`4NVahH_A2LqkJ}`GT5HCN0njn5Ub(
z>dP680lRek1Q1jL;Jg9jjMxIO6ou>jdoaJ<($WHmN5q#P4FgX#pI(daX1$_zq|iqY
zn-bc=i*+gj&h;}usjyTz<u0A*tp5WTF8fhM4B?TY7&P##fPbC~aDD(>_>5XLIM5D2
zKwDNU$PoBG<?8p!H$K<mf=2_Q-Jj0S&xM7BopxlM1i+vjEG@CF^v&q)4-~@c#>SmP
zDnyqVL6fEOu>zsdkUWn<z@4vwE4KnxkM1w@-6sln2@W6%3)0_>9xlfG-e-IIXNFJm
zH^o(oq{x6td2e1A>gw)RNGWn~$=?FgUpOLMEv&zQwh;aKbI2CPf&~e?$w$T>a1O_o
zELEn1>A(PX9;<0B|9a&`L}8Q6C`hQBmgB7U%Uz(K1Hud^$BjzeK5U0o*&o{~Ab{`9
z0+ymL-<vA7yVL(qk0SUd6oBg<Ms=iHbY(Hx(fZ)7I@GQb4*o|gOd)Z<1|$B;i7p_=
zG)yBNc!BB-X!2+p>2Bahxqm-j==hG&sPP9}cT`=X;j2{e`tk({LPJ9XOZe&2Cn*m*
zyM16byqf}y04A1ljUvV#EJD`#W~w0$$!OBfJd#H;!*92-41w}daN(tfCua5uA^dKh
zf0Kw3_C5Gv!Hf^fC_4mUv~jF^yW5xoGoe25Mac2ZYn9vIq=B>MRh!4|pqqCD<`T0n
z)Mh}B2IB+MfbSpuba}eVLPrOK!QTGfk_T|-4hC($2JVhc3Txn;_FLi}z4d;JmvteI
zkBi<EtHzYy;6q#OG<!aGm6$O=f`L*$g9Tv)x9PFcg#$SH14Nd^uRa!yqs#Pu!A2y%
z9|mSUE}Ir`BoPw1LT718wIUTEoqG#G7FBTM-sj@~7%l*90|;OeyNT@+uzgF^D^`J&
z1!BQ02s~CH*UB6$D=;nqECCSi1MnguN=Qlq9rY~m8a;%7B}VXk`SX-bXCv?iN-*@w
zuhWMn3i)`CM%w*-buR%AhY-Mxy*P*fW~jTi;r*AD{@+4?{}0}%%R<a0>9kXhGy|}?
z+&%1>Y2s6uNHnz*X!>J~m3L$BVDr3a2+iLcECwn>6m{MY+Sq@A=6XO@Y5Gx)gtv+V
zS2#<k!Kk5RG|Vp-bf1aYV)7%4W3R)k0TKQfv7E7BmPt%r!{}wKF;nz6u~e{qzNj&!
zBU?oefQLdvbDJRyn4>pR!jvOpgqFaQGlYjLDPhEq+((5L%NZ^1c3OcL3iJ{O3AC*M
z4!cOpf4QTw?)V2F=_=r5T}^VX(`<IkXYBQp{r|tNM154Di}}h0mYka4K@6Zp_|gHA
zsB(eIbBtkt^~K5E0w_P2d_bZKnEVE!`G4h%z`m-nTLKV4OJ3d%prY*5KwIv_Su}8I
zsjGBHAZS78LIAHvrKXl}fNsUPD~jsr=bt7OKwWu}<x^fRa$6D}B(v*Jmyg2?ek*P@
zzOL*)kU^p%B3jI2p^q3$6ID`>eF3Bbg>}M|qgcHndvk?a^5LEG<2i%7eyni*V!|4|
z!7+uDb-X86B6``a$!<5797z}n1qD1B#Trg6$%c{Pp2{U*X;>d)tR99@x!?Y5I7!2(
z#gyaX73jJsCCi0FVgL_8=&9uJpZbxq!a_zy#y_K~y4Qay`mxCQ;}a9_L#NXYk!=g#
z%qC4LlpDV;aYs{=9ryhjxJDfmx~3!_U;7Y_x|@Be9{UJM__$wKPw(4|wQ8eg%388)
z&K>tDJzRN^`g+D13|^1kj6He^wk*(Q<O&B(9l1$?=l^cgQTLSJJ+uX65a1tN1<gih
z=i2geW>yw`I8dnoo&^269dEMoT>A-N^6y%rlQ|r-L`qm>r(!>S0D>q6s?THTu$3!z
z5l1eY^mMxJrG3+VR9zgTQ6DR{*digs@R#y<|M#<A7<)hqL@f!PSpoqD7I>&GM;?QJ
zKTBoa)fJKd0-lSKyBeP%Co9_o465M1d~a(5oFI6J2(YwpnGI$3CUM;!m-sL-F#)@%
z*&@ABqwxADDm30ERPcZe+~P>ElYp4-b90qX0fr2~3(^C4<5|j&<oNGjd1bY^#UBxb
zJ|I*6LqrwenRq+RN)s4_rfrDe%ky>gFL%nYC<M6EY0;p2ki#m6e*QmFLDgP?2#fR1
zW2aCgpf>T0{5{lSLG|uX|IY}%KW~t2zKFr70-GPX%UL$yJ(Q}M`Lo~l50QKSMrHjy
z^^B+j<SXF*VXsD$ml(h>H*eK-gn$5Vd*277&QCAZ{*uj1{n+S@Z{)k*2zZ>m(^>`0
z#1$|Z`R3f}4Q!x=CH&<1=Oob&`$bsTKNj5TL3-FGev>^1ps3mG_XBs$j&KNDTlC7<
zdc)j5thA)J=DILYM#v!OPb-BcFCO@A$s5h}n4}!K&}Vg0$*TGVBEWedf1|a&(n2X$
z(gd|!JAV5sMd2^?|6L4mpvbGD16$5NA4%k`v!3-$FYO%#H1FMQdMBpb&dnyHzEZBj
zgnarMT~_<Pm)=i*9O}Dhy;GlVx@w)5m$h-3sGeY)#Z(*<Yqk<SYGGDws-8%*5`E8n
zv56&+@>y7Vd4JQ4NB?Oj9tG8+FZ(aw{@oJ08iyVB$hNiwfZ0&pUNf;AL3-P>=!8MN
zbse)^CwFxEGD21Jfb`A#lk8rEm)^_Ra(ARX`P&!~Ig3dqiE8V{_c%L~k4{!G|GqKW
zJ0SwfJ_nfEliC37|Mk_?oy!9rw4$Mqtmol~0d+;rO9+UpzhAOU(*=WZ;22z7T;*q@
zU|jlm;$3R(3&yw#KHg(hmI}Cy`<)h+uhNHq0XU2%ugg2dvi-vY;P+^@&kCCHqYfH*
z^f>8W)`2K@4ms749+fOsjnMIkA{6!Iiz7&BARE#=eM-)|+>wR+D~t2qGIxYN;s4du
zTSryZMg78CR6sxhX(W{n2?=RL1?lc?>F$(LL_kRq>4ygCPDMmoq#H%LTl&u9`;PH_
z_nyBH_c-iYYt8wqmAv8aflpgk^WBp5g^JZG4yMg_r;kjp@3+jPD=|S*P<L{Crx{d_
zRd)3(WgdJH;ciku>&DY{QifNJ+s4q<Rcm|D0nM9W&YG;1t+Fz~X??e^b6x|wW2k_7
zQ$>WBm|o%So*&IN@UmqMXzgm{t2t9>o+PoT*bokIl~)onJ0*JU6WE{lvJzrdjI-5y
zQ~FlvYy{z)B}-(y?KInbK4M?fAbeH0Uu-UX!OH;e8b7@T-nCJ_wWzPV!u@-`*&occ
ziLpf<8ZN`_P4zp}?wE(E6<=bCIOfi{_x?Nm=CAq<Ukv(#@^Vs9a-f2?t6Xb2A!>|Y
zXNs=Rt@2}LU~~p^!*7Z$4UI_o_Q*RMSN}0~g2GG{_A<||Jd)VcKw38^*Z$|X4|SJ?
z+*bF&7<CT99Dw#fe5~9Sc5|9ML07xs2tAB;JjoW_eHCA#Dy<^a|6p|(z_e-LBo2D&
zome}?e$15LM84mmn~N&P%+M*hU15Ls?p;3nDJE$Zz+pT+YjxY4`E8=L#<Ru7bp^`1
z6Ded<LFQt&U>kh_a?}qGEN}Ji0nA_0bT|P{l#P+nsYq)cm6!Jsn#+4P^hDyJxodn~
zA#&o-a(;MryvGucg&6o948NrEehz-IO`M*MuT*}WNd$7rFV=qxbnyKKPKZ_AB<Xid
z@_*LL(<<iqY7)7dNGtV(;x&@l!bm)cnYMAN^H@dEaY&5<kWxt|0Q|zhIW+JVtr~Al
zMGE<SB;7B6z-X6J?}h66oUtvj&oiG!puhv5KM*Yll7UxynoONHgS)O+*Fn@N@$7kd
zlbcXMD=!xiOFx>h&%yB4CF}wMCeV~a+tw~ZtIn!Bc~~ARMQT^J{uN)h#Q)#()7zQ}
zMM-&v@m_Y67F9_ZF7z4xVT>N1bWd-FBY^7BTb~JS2A~3tz_`J#6OkKLR^z}DbCiq`
zy^Rv%{4r4<r)aFE{Yox`Ahj)h6*bPnC@EhZDX}+#lo^|LNCuzz>#g9>s!bYYTqaj-
zApiyo#mXuk{F?)0w3aU!uA`4nR$?F*;%K$rHGD9o(Xj1b_D^KtE)dBXzgVAQ{x<{M
z4-UP!RL<@i$;LpoU*h6Lj1QtTJ2t|2p%>5{spiNfaT<RC)RRT@=S*3|pC!c}i{p&*
z;oIbJIw=*hG*h?`Lsq0^%tPMR)|M-m^xN~N8F#TxRhLBt)FdKM-U|0OtgLGky6pro
z2JrvJuae_~-UIqiK#I5AIn4!ZW?yt|h;{X+-kxy93w}IBNCf7d<kjT?YCydq1^`;j
zp8oz8|MU9!PI$i6R)ZFxOLJQr5%JtkKkZ9RN;+1Wek&dtWI3KOAm5iNqPw#rF-S^#
z_cmSlY{S)~G_atd4G(02Y-8(ZHa1cT;vW$a5jHj)1R_EtBqWg5jt(h?Sb?ZUK+cD)
z&9z5%?qP%(W71M+wksv`SUE4c+h??ACUp8Y__`gQMMdS1hW&b|z~V59@6?KCf6llb
zX)7wCZaY&@ZfT}HvcM`Jl<8)d9Q9CthOLcr^E-k#QfS`0-Oc$X`J#m>d-EA69Pc{}
z%8HiZ?1{fom{oN{6+m-iWl!?eK#JTE+xvm{Y9MgcR07Usz*0pqLe5+KyLZ2qmTHXK
z!eSB$AgYc$GRN9-^f^y=wQX{lYyPlx%E?SQG!%(EaD`K%4U+fV*68WCmX0=zC*6>`
zspCO>i6LvZVTs~EA%DBg2W3P&*dDU$enzC<c9@;LD&A|sq!wk-L|e9S_=ie-Sb2Ha
zx~kI*4FP3dJtm}&^%-&YB=algpZWOB!Avbc1>tas6gg@v0#So&2kN(IfQ*2_5!C^<
zmnaOZ&@UDh(;F`0g0AXm0yid1=2I!Fsr*vJG?kRzD&!LAix3zVYut&H9Ieh*VghB;
zIo+Xs@5F|Cq9qL{=k^svw*I{!np$Q^L#q8ZgD-idF1ZP&>~R=t^U=_>Rv8aH8Qy7P
z5EK-or@xK>4<mCwgi6r$mHBw0fp;h6bmghkg0sz*KSCqs{(gYi2Q};q>Q|ZeVY2J(
z>e7MaJ5U&X`SJy#10X}d1zstjIQk(mVRNbuAmd>?N%L{t>iE@{_{wufo7<>?rRFxw
z<=R%h9>LM9wc8K=Yd#=2$oqU+Fw2C6)ed?|rm9%Whg#Nqbvy+@y<LFSNT-iqrtoSb
zLjh@J++-k*$VCZw3tA-taH|c<VoIpF$UPf1iD}gy7-zI#bafV;Q=uc{F!S}fDWXuT
z_^{B6nUG~t+5$4Ag0a0jbCr64mj;K<Feh9D=gIXgE_5kU?)^J8$l^(n@H@8^IYkQ;
z_M%o*n_F>&F7<Zl525anjEJ#xTKsDZpFAmAA7KvDz_F6A%zrp`IrT)^9w!l`Mgdv<
zF*LOK_J6H3x<{g@nL3>L{{Fwoh>&eR-gIq9zGyTM)4hGu(5cEhL@5&d*)gQwxSx}n
z!_1@Ve)wN*7F?Q7n}ng2N(!D2d`Ih+q7jx8I>2a?!(pgqP(dMT+P<XvfsT+zA~_FT
zkp9jjpX0&F987pRyv;vmYn@ENRcyD!o+oBD2D5kJN)e7J75D31VV~2Sv+Wnwg3K^8
zNJnz)Q$rNN=S#WZM9a3AY*x)rMb&#U{ufcaj(<&Gj;+737UF;dHQ+#<V&Wk1yu`^Q
zZuw|)`4qYzzE2R(ckbZ$YVxU16~Od8Yu^l%AD7J#t0StlgB5_j!YKs+Nl`eB!;D_O
zh(NJZaK7Z@eL+!Uk9b8`Z0yzNvHehD#^wey=2tBWUe*(pR_ZopknAVD#gSw`s2ebj
zN}m(BlqgX9fDwsEm@$)>Eh%bltizRR441{HH?)66eWnz#;dMJROxtXR5tttMPPeA5
z(W|dT!*=THNcPLK=iF(fx>&`RM-Oj<jysZkHp?R%#97#N$H-e7p0ZwDjl+czD!+C~
z{3nEjlQn3Y522Fb)>^)TSoD3Yldz;b7%YiZ{nVQxRP(dMl63EfRQ|+E=KF-zcJ7Y@
zQLnRsplh4cB{gXqXUN;1{k7M+%PBe_R}lgDY8Kc>Ki;J^%R5|Qvu9PV7jGK<-=AkF
zd<x&Cw(klQ`e07_ZJ^vH86^hLn<+|0sjY=BT98)CaJR~J9fRF)bJF#0G*7z>j`L3F
z@Dc-vDedZaM{}g(#ws{YrEVvdMvT~q-{Eo9?!Hu0B;mtELhwB3M&v_N@qR}c>Y#p1
z(+V@5;(1X^%s4_BKw@*3gB^@WRe#E|T+~j;5@|xlZ23B?bYHoyZhAbBs`m|_Ebn~i
z#BwlDTVTv@9tdA!0dkCNi+sVy#Tt{ge2M(J!MCx2HLJZ>=H8(Hzcm^f_{-z1M<@+7
ztnZ!~%&XwE$$P#=zlI-dQ00gH*&GIu0fOCB$gDmxK)}@hKwy-p<#Ki9IgG7pLhmd(
zKh6J$8ciATnDe3*-r<X59xZttTTQv*(f%>6`tL_TjN#wkk-AMkAN+Rnc3?V`aD7vy
z%Yn=to%dPj9l-F)z|di5-fw0rG){6Y+-T7jc>KwN>VcPkI2Q~<3AG1r>CDeG7&t~z
zt1^`ye4`>R*296y&cR0)&S4)pwZja0$<w^f8^`1M^DQhbTR@BpY7{}2#W$)PG+(*b
zx$^z5npEZaPdwQdBteHMrLT}4nwTK1!)b3fT&btRi(B0Y-EyRpCii7>e5^&)billO
z!+AT2;YS8gygO3MrzNDMZ1fZ|btFlODx9B%8<d<KW0-h6qJtMmyDImG5Km0^1&M}3
zfa_NpLuxF%lEEW|e$T^`-u+Y7Bj9K^*uzFXyOEz*f2*-|2Rj#|;j>}U9H|b~(4Zzi
zRs0qrJs~@4n0=Hcv6>#c7+lzS22aa>@j1<VA_D;i-Z_@S9_Nek1qr7o2Wy5s-8JEN
z5jx9R5ahXER6&!nx6RdBJ~iz6^ozoFR6a~W7*ZQ-`?o07y3)w`-$Y|><!gk;*)xwE
zJnQ#lIJJ14{gZz^e|R;2i~Ww1p!xUc79Ov&a3GM>R~JmOFuPkb#9ysXSzXLEccoI&
zhHSn}y#HcOVtS}6?H)P9+Rbif?5Owbla=*pRrON7ye{M+)d}jqH#*u;A9Vp7pTg>|
z7V`uioze8F*B#cTvbyA<G|nDcr1A>~+RJ<BJ(rZh2LbM#qZj1V$XNd1k$j!-Uzx=%
z@5fb<|BX#}0GD+GIir@Yr6iKbn;5!~FjQ09U#&Kr3y$BODL3S<NNssn$6r36BzE(v
zW6yJM?mbCc?{6a}DNQ<IeRfSJ<CAXJWQcq`x>PO;!XALLWrJfM03fB7i`h#|>=wUM
zcin80$89H!I_+{e`-ud*x$P(G2gxZvKNa_}GTokP#z~^z#P^YteM{FKl~z|t=#h2{
zxUPVV=vS{^v40N^#+cie9&5@1lrw56ndNW1@qq%r&8~^#Tk7ta(#hGnlv%!wuJw<T
z_mjK>G()t|qX{!VykKGpm<+1|)wVr!rw?%UfXi!UG+iVi`FMF!m>%FTqn|jxI9%-V
zNjo<CK;o`fopz=n5%E!@uia49x@StW%Dez^1S;Z`ielId2nis2OO}-qcyX!q3q}kJ
zCUn8bYY8_C7lFP5i0Bjn7mFtEgDw&Ar<eqnvU^9w(V}jRi7Ho+PRYT53g?MjeLpe&
z%lh8U9&Hpz`z!2=p(L&@`DzOw_C6FFc?a+09}&j4oPW*E)!1YQIAM_Ll|+EzEClL&
ztlXbX4R9)KCqF;VeVKJH^#<0q>A0@@ui&YmTUMIX{k0c`eMYe?t8=;8*#}mH=!pE;
zc@at9^l1^B4H=i@^V>Hw^WMy|$kr_v)WCquHU^8;GX;e(sSc$7akPQqMbho|Zjw>#
zx*u%LPlJCPvV>lHYsPuOxF(rA#OmU(nA*^-V+A~}zSmjhi1noRinEqjuEmsULe(3J
zHPSZa^}pKgn{l$U#1g!cKs`=W*+9!KLiubX1O`400|PoryZ61%yYlmFn~+SHFxlLZ
zPpo9X6DeWJKh&*d*E9cmJyM>E?@3)DE8N2>zZCiJ0jSx3I1yMbtPD=L8Pqyv#OCI(
z*-&wPn<=1#x}+jl%(pdr3nzno4padmOO1Tdnvs)DmYp&5skd^8@7hmjUWhJ0wJ5B?
z5>L?q0-(`Ui43JU+gtWkI>7iH4QE$Mt-S=9LEcn`FKc##TNrhOn>2csDJv`559Mr%
zm0YsT!W-vH43p{U>40L%Y|Y8#-*u{4@411Lx~*7k5Mf`=G#Q}k$!{!80}U!ngxpCz
zun=`D&b@f?0&<ZlbxlX(pdH`Z?fne-aYv6|-U@yB62vv{mb^%+_42<qU!1J1or>Wc
ztX@SLFRW)A7PdZj?m_!5s86qPX7R7)Jf62HT-8E*MA#0Z7O)gU<{A)EaTfz%eYnGG
z{bwg-!YkFV8N!oZN7I^xq*;WdF&N_2Sh?xGcC+kFAYj<L5$ko)J9XcB!N{-|&rn_|
zG!oL=?4aD2%yNR)8cNgiOKLomaGxi?y)K@YpJ{QM4kcsUx~`aeeb^ol;MBkK8lEP1
z^%~y_I?OcrU!7utL?UOG3<JN?aT9eJ#Zcu4^^PCC16fi)<~4%x7W~06UN7T*f@2*N
z9<BBs)(R0vd2MBbL1>4Pe1bEToyl&&$$QQ>=3Wg5Us~9o4zW@yrWv}kZZ(cOFOkqU
zOpE&LXSsOR*Za3lXg>gbPZx#J+HgO)5FX~Q`>CpSr>R={bxv3k=Ihf9Bn<Ll^%*|`
zS~Zy$`cJu&<WQoFGL!ZcYE6uWX}G~97`S)1ymtQp<)+RaV>Ou5Zhv`WCV<2s;2@O5
zfXT}sy;6%1`zhC;MT<p68gEi@?GKN5HK}M(a3i21r+)x|4r(Qcf<sER*huc{IUe&n
z)pULT&Pg80u2V@9f{2Ic@v%O1)NnNVFkG!_IhHY)_CQx*?Pl&lSauDq+PC)OJdEc<
zO+(f<yAg2<3yaCOg34De5YB3^L69Hu!+YMtY)jc7)9nA6QmleZQ++nT+}l<7;Gpo3
z|1}jRh_{2{2$zX)+S{+5^DI#9?GGw$t&Cu=jszSftyqqfxSZ`K9Kt;H2{#{CQDOXD
zqNU$flX;r2b#9D79O(@#dC7_=cH%cP>68kH=<)LmGVf-4OP9;WC}+B-Glc8Vcb?7c
zK5v-*#`%DWH~YUyX5IwN!P9^yf9Feoh%0mU69m$X34*L2Wvg5XRLO@d$SB5mnFWv{
zxdVXqQ|gmW)#_qzvUQa=Fv|3b0DEH&Jny=s(ID$CcZMcVmJp?KdtK?ey+dZtXAjzU
zhS>PCykK;}p&K7?-`Z7e43SkYEikSf)U1E`c6a03U3RHwB-CynTO9hsevx2B^aiER
z)%qokrmg(QnBuRhQSxb*xNLLTZPZKKY6-d#o8eE|;e<2)yTp*Kn;U0-=c`~iUZzH&
z{!Ebv1D}Wp3UlL4k^_?uxv;0BbVUqZycrEWJw3?Y7#J*C1Fv}4bXZq?kjrVfsnW9B
z#j2SW;6L~&<$P_#>v;Oe+1$C9<~=iSM2rSu&+R6Cy~bQ0j%5ODgmWo!C26!x%YYS&
zFPxmNlkT;KjEkV#)g%gp=HWx*Mb-_~618Lft4Qm#kAECmcKAFa!+k?<9>Mx@*-){$
zf~+5f!`88#*AR;e?P3j0M>CN2lJnVq-}6n{p-3>F=K69EGblE69vm60_W|7Q!wCFa
zEC%>y!<1-yU6aozdd6t0k@dAQ(@V||9yV?_1kdG@&)?2CK65~j;S+~s18LmvJ){DA
z3-5{9H+@yXTO!>(<C9@JSzNP+&g(#&H~8hG|EqB{_l8iss8$T!BfLM(Uc<9zVpH$5
zlnLoI1KJb17oc!$)J9dro>~hQ;1%a}y4`rl@b~nx+;-)i_km88^>rjp1-MzVhoCqL
z42>}QleIlRqTLZCcC^bD@g(Acc~DTAM}~)4z^nUsFMTI+XK#)CPGKTl1zi$-0nyV~
zx}>MltgjcX`h)4{(dbrt9Tz181|Aj&BSSYu&>f4?OV_XLmM1!rA%+PH+LF?*VvGrK
zvMfl+Xq(yEkewy1jw=*bphgn+)spPvWC{CzBg^E~8$8}N$B^e*30Jlkw9sD(h;dji
z%5t8%z3nz`ZNtE&0w^u}K_^{E?crUQo9dLIqW@ZY0D>7HMgi1~L=@vK0RbJsKPyBC
zB(o5N;tgt#0DFpgI^u{mj3QZ!>8V(Ql9w^j5fUIZ-h9I-Dtghf)AHHWbnZ`h3o*M+
zsnKU~bIuU-T&<$hn>l{yZTMN^Pd*cprI3&{;mi#_AQ8$Z)E_GN#A5D~t<s5y{DtwW
zakN5}K4%()mUeP*#F49L?qF6Sc1g3iq$7CbDAzt-X*GCAM9uP$Pk8eeNXTX_Z?h->
z4XM7oU2i%PDU%Is`F#gKojrUj(|6OfV(C_%NOMSQ+D`{jHKp4#A+29Ku}jSPluya&
zJCP@>V>dI+68^)9K(-n&aIU7*7a>|spBm$p?u0xJc0_8gIWk{Hq3KfvB&c(SFn&!l
zCkkOA{pPs0m_MYJu3{6v`)f>#o|Zfzo*>hh>^iM5JPsck+FwJTgWuh3%aKpj8|`>W
zf9*nKjX_K-05BS;Re@|@`;ie`L6&hF3DieJ6tZ4?ypui}taI~atgc-0su}oMifdht
z1M{L*;xn0m0{muCF<h+c0WJzFD=T2ZlekRLk)AZM0KiMILNj}03%tCuAEX9c98(XI
z|M+_-;Bk`d(CmaG8P5l_SsH`bO;J$<De3P!WpD1e!Y4D&VR-y!`(3izV>jcQ8`%s>
z&LLm7ccWO$6->U2b(3_Q7QB9wlUp+N*P;#m>6iN*zYiqLRJ-3Q>l7Y@zBsQD;ln{@
zq(w)54Ud8>QhfnOjT2j`a2TdN7t6Bh61~F_T?f!#d^xtTS>Epg<sjr?&{-cuNZkrp
zOtS0}F(@1cKY1D8i{O}JItnCUR4CG^k}-Qv1u?T^r?m=%5l@vlWV~hEGV=)Ni<wrR
zaLt;Ol@=*v>L|Mo9i197O-7@h1KID6@{cqhWn_b2F(3%P%SnaY=twaUjxzr5(|+S9
zYXg3oz>4?gF<&ozBpq(Z<O2e-KHBj!90=O=W~mN|6v6GCu6xw}ctk|%p&lsk3AKlV
zZ8ggB3in!}87o&)a8B6U5?dsDfBRP(1Q{!)i7xmrL*__%xiVd)4<;7&OW(S~!jfhd
zmq7S>F*7FAwD7leH#?0(|HHykz?7MQJvYf^gFk)+P$L3)(qG%~BXyVw*;V>%V*D|r
zW6**>xvpP~e;lW7KR^%c4aiP<`G!Ko+f{w$vI*-PR3jREQ9;!k;<<i*2Zy1waBUu!
zAvyu@450AkgA=%k0BB5Ya8~Jnj=w-}nO+*?mhAxH+~Il73D*=;wIQ?muGhkb)0MQb
zhQgCd)S%8~HQ+<FgG0v9UFBjX9VT8`mDR|Pb%p^q<xSITpF1{G<8$>M#QaC{H0#OD
z7_+$lOq6@rSkSAm($6xz6e&<M4t5YTe<D}%ooj#jadAgWi*Vbnb(ymu^noAD!w0r^
zb{BHVh(8%#F1C}LxUOW@&qs`ce#4{E_UemA+ie~sE$2DlkzPpU;^b7y9Rwk8Rp3?*
zT^T5|*)3bpD-1D^ht8g480L#wLbyu<nq<V@iIK;vuJzg#7VWcrYg<{~ODS+|;=)j!
zOiHo-sv|*=nA@T^Zo1=`DF=3IOj^YrSB{qpv7YA0+`PQVj(_l<KwD*V>OgsIdUogE
z(;WhgqYW`?Dtxbn-IABLFfcF@?<hWc^rEFD;QA4hn%*+ldUx7Fr03r6sm_qZVSNCg
zz@own)_E~A3$fdCHPEYoSvF4Ky$UF~$IjcNUX)OuZ4Ev!ENXHwkOKJ*Hb-OWq|tis
zTL{-k^dZ`W<;$k!Z)U3rnTWSsRsuV7pavm5lEU;FL6@FraJaN6rkd~wJZrQd8~u%b
z^QUag(@eBB!f`vkaAF-cX0KyBQcQ$KdM=Z-@&W*hS7GLUWZx3a{j?W%lO8PbkmnJg
zx%(9UO(SXCeH{i5Xsv_08E1|3GwWyyzudy&B04&{v)=L5QoiI3$Wj|wI{;FuK{p=b
zh{XF5-s(*%w5<S4ZnC0N-?hA7FlFTHBx@}f;$SyLaBQkOS{8w3=b;H5n-}#TXRT(}
z%QctH#`OK32mQ9E{X9yi2=ejaOpeEvtj1bOL_YJ=&t2sz)~f`{@20=p;rqStnN3&m
zn?SuAY<B{IQB(c~H|lCU?rTp$-na8(d3pINonojUnQBzlfwcVXk!-xL_0Z3n0UI!3
z5ALhk*yk`FYlk>6>)nIDoYhjd7@iDZN$p%mikVabZR)%#YA&t?zeGdy<f%WY@L5gk
zpqa$c31#!i$Y%m|pUq=vxIGZa^mlOq?3{5uO%GnhU>aq|)O|Tv+>GVu-VVuw>3mJ&
z#RP^YRbW`E*rnsFs6Shw#Vg#lJTl2PZgPp46#lLCQsuea7xApIBt3QjStpGccrC;`
zghhqS;RSY`Uw7+BDVsy}z#yKY-n-y78W-^M20dOwmz_yQ2{+sRFi`As-{0K>St|ZD
z<DoodSOgmO0)a_sdgp>oMR}E^FIvpm5ePwdnL)e5?%N_+!`biI^Cy0<P3cR?s^ft1
zF=w)7&yz~c8ZQ)mssCnV#5`Hp^;Ok^#CoXdV}me7KS;JeeH?~^PxeiY8F^3F$)GI7
zQ41cXQKPr4@#FykZDQfhLnF5{nN(ybRgGj*;{sQo;LeWy?k^7s<Y+4u=V4QgOH4H9
ze*6q``2c^k;$(mM7nH7O{=yt2*HVpn*i2d2CWu<}nOMlVJRK9O?RasrjGnE~zJt&s
zj{tx4{pIT<97Sa3%MfDdzvT7c!GqBv?X1BSFq13Uj~+m#VOzreVCCq~#}yT8=!vP)
zdSxGUm-QhdBi=ZpWCVKr7&_Qd-~<F?py&b$^F!L~tcPs;#oe=C5f4nLtrH8?vh#-%
zS9yC1RFCTQzmq&2V7+ARc9bi=irjomD#3$e`eTK=ygcyd^T-^p!5mSxHvG)HPx?5W
z(PSjRzqPBsOZ$A3s=R_K;|rXPHqt@OP963O^U9-F$%i<N7YwBoMVDtaPNtOn%kd1-
zKcVG8Sr3Sb55L`Um=ciO`VSgl(8<BRD7>~~+x8t(F2%_ZaRsXu53mssz0{XRj@re8
zrA=Z>KlH+0=5hj7KVQ)PW^qr1ErH+}r(9TgXoN=CkkI8immywD^z4O;B(du{W?h}J
z_j&redv<ZJ<5n+M%yo-LX8Tiv7kjTD<=m&}=ue=@U9M-j@s7ag#=t`s=5EIjPC^R)
zBME7{>rD6WQ#IKNoOzHVtuM~8vyvY!UVdM2mL~MO2=_IV_!2I5>xq<<qStR}v8P0q
zJb<}RmA(B2k%HYRn@MqT`+s+F1C1BOgdHJcrLX2Jki!~XYnd8HYuUyigX8nLn?X_~
zMh^pmcZa{seS?|L76(CPkjkv*IxJP=+|qH`ovP_jm>M?%^F2q64X(fci^*y#QQz90
zTM>QVdwcB!)`&b!sgTyb*%96daxRl=Lw{D{KLv$SmHzM{(~E9KC(x<nI$P5A*&7kZ
zOm<SQI=(ZU!%@d-l>vKyBzTn#KBin7&K@x5Ku2qZeFEy4YfY_VjCu@z*6D)?Fr{P6
zwi@ynTuc%bKn>gi_Ma3C$c~c3y7{7CEVy^}K*Xss=*|wlM5;Qa$G5vj87x)i%S^g8
z24`iD^}4R}E!8_uE*_x~-e4I1{b#<DLae2cUCE3)=_%f1*tM&z?Tu*WUKREupY$j9
zG)CsLUS<;!vul;k0TWfb_Z~b&j$iUsy_=d(gX->UM_-}+a|>?VRluaZwlQ<0sE>Er
zrVN+JBFv56&bFqe<f||nG|_!<H96;Gt`t4bCC0dZJ@ghSkDrIcTa_J6qiMGVw{H-H
zl^Xf{8X|x7Dx36z>oFyZla!Fe_s6B++lonRF>f{c=|JPdn<6X!>(hU?E0P*~!y9~W
zfPW@0TJq;#l}lt`bZdham!#u>SC)rP_#+{aQrPu63WTXcfKPa3w0JFXN37{b#&mPf
z+Gmk2Lvvaop;K^K@si>|cUV7Z>lgC2<5&9_xi43>$rHk{bveEzbeBCrpFMQ0<U)6A
z{J?FjFHUOt|6$Rq=jY4F^EL+sw8};#Hfo8{J?j)}v7G&~<qw!8(nuOXVtNJP_~uQv
zJ}{~iw-EB}oy7dJ4ly!2E@l?9eVxqV@_G|aGVSEZv7#Yc^hd}e8j0iX)pEqBDv;>a
zfYOPVK<kp-rNIY_gTUsR!}U3n`Ty!OS`(WlXDljyVTwvng@j%_qCGwiP77F>Nza<M
z`s>jVi-{Je<|}>I{d6|q>a`bIJzs6_O5Wol3s;cc-J=mpG$lozr|(#$vxaPlp&^dk
ze4I)+m@#N*k&U~-94&11AKlq$JTHQ-!;sF;@rb}<xsEMzLek}=WPMwqecl@bm*THi
z+lgd*==uDKhb`&1#c7Mz5#pr}>S~gvCXv>wjd#ZJ?%q*JulZe*r_!eqnYO$p%iBC8
z^_aIgX5wXmpBzJMyU7h#HYN?KM|i-V`uh3B%S;K7XUKrNq~McgnrPF7c3*qh&1?AA
zLX|ldX2Q<&RJ7i@9m<8gF~_78U)|yU4lIzU%iX|X+p=W#d@S?i)FycJy8~zEiLIm2
z&QlQg03&o@z`F6hCa2p<dCas`7wo(R3=WouKdsst81X(m5r`ToX1q3n-%g4=?6*l^
z^IvOMz)fjNVQsG@7c=7=u+dJ5TCVzPL?g(Kzs@F3nVlI0D6Zp)=Qw&iOV#Jv;>WUv
z2KS%+++|P3;EB%g_gBjfQ|Zq00}=eo=Cdz}OIB;{(99PYIa!2IiEAyty93S7@!!8^
zwE+Qg(f;>d;?~TT$@Lb$u@P(h`=v28<;9oKMqfUbdaLRBH_D3pf+jpcbodqdw6cNm
zSCI}*hbMd?@@rQRt%QQ1p;1MZQ>_Kx;CmPp4fb{ytQ*^XluguLA<pGL88hSjciKrX
zZc)IdM7|60nlc&@kCz_>4zk*8?05M%53;p(DU|rG=I4gJPN0eZ9jm(3JeItFdG8?R
z$s0+@l3rnt!!eILK72(E)z}%ZB)@a|>b#Yo85jEk2%|W){7e>gYmJ0M6`7+_Z>!L!
zLf70+inQZ2m9kZcNX^))PM0B7q*BKYB==_A+SemB*&unth!a)~b>oI<ths%ej|ST2
zs6B^#YJY&?@$X;HCIc~ub8Uz$wY0QMgA@>9;VCYMmt%HMa_^yw46Ko6rIJQ=#NN)*
z%1r2fp>or(F8`w%!`09j7Q#{cnQ~$hRT&OhPNOkLjjSu#F)V6o8IDtrjc9wIA;XHI
zGWk%&<k7~4QreW+8%n&A@#^ZhgqG#^u`}_yoT8$b=e7~}DWP0Ri6;2p<tsXLrY&?v
znrN020v!TQH~3Q&_-)5Z#RCFN__!Dd-pjw~#IztV<uVj@`m!|=`5g(Updpm5OiiOM
z(@BrHJSA+s|JpgBO7<sN@NvebNS@5oc$oq<Wdmt-nI?VT)0Io#T}Lrw%<)2Tvux*{
zR219bcM#-@`v0o4+R&lyY0h|chX+z68T+GPUo2F8&>>-X2kTtqM$k4yse*R`yjyHq
zr8GP|gvhJj9Ftesd3bCVrUn-r)c1Yr^;wbHIK%H!Pxy)mzG|@>@exFXYq2SH&JtGW
zmlh0vn8Pbj3X-7R!75NfO2!)fPVu6Kl$nvaB>iye%_(`Wrz_aqD9bX?dF1eDjK73H
zw<3_rrn+1Mnd88}z%C#Fp-%S~sj}_kqM^3Cq6UtFKbt@(;KJAW%u&B;6*p-W!t*tl
z2(zQ<EL?s@&2rcHi{-2D4FzS&MP6=Sae6b319y-wxc#eEk5Tp12KxMK<x~uVM_#F2
zf8sRh(uBt)v*!d3M=J|OzA5Gn(%=wcDHIb6Br(ORrp}h}|N3J|-M=!i^W)$n<%e`I
zHRerOL{o`?{S?w4)ju1;S>d7qYrCj|UEzqb6!%)VY%o8Ja#{CSov<Ln3LUm~^Qr)>
z-o8ZUdOpHHy!mWBE#LywEWnHFZHBENrFN6m;Hwk?bPyy}3z~|b{SUw6Cs)_%SL8I(
zDaVGxd6xyYKVc)b=EL@$jx5wX$K0mK`d#4PnW(ZlmU|>q##cW5Sgr4B2~vj>$MSZM
zCedN4{Hc?OEPX7!nOW2Mfux%ejWScd9Qvid4bhS3y89`(PgL~uguxFFa%$)3I>;mi
zr4Z=0VOJrU>Rb0?wVDhZ_>#9qf?~A*<f$%C(`xLz)mZ(*G`blF7MYx<y>!y`UV9df
zj-`4G_cXA}bi&(lG+4`YTCKA!_7b8sMF+UUOy9fIc^Px{o)cl2)ZiBq*=nd*M=23;
z;l#r^sw!A8el;R7V~PLSkL!7T;o;j(rv9spSs!sc{d+AhZH&3BJ;!mGtz1SvVdLCF
zPkPh!XS+WIGz^rdz9T(kloOPlb=rTGmY@(3NaaWw0}<xsPWvTVyo{-956U1{BbIq&
z1_|way4gA3OrmG5z@URY(+m>F{s$|0%bMsTf4l`s(<RSxvp1<)?C!zF!{z4Ruq}ro
zljX?xKiLP0m5smZ#o|kzFA06ltuN$P;FThdlw#gxOmzBmk*6AphN)`A`4^wXR10~S
zm^@o9rP#$uvm{X0)8{PyD#biNr@>uiZ4r6v$SymO<?+&fnyC%#w7+W%D|^}=8Z0UP
z7+g;I<Q72(f9O=Ugr}iv^j|MLptT+@F8u}#hnsVfFmS#9vvXPrS$Oe!QSuSIEr*+w
z8KMOhA6fyP*cMh~vnZVmH)7<wqg6axWXO+Zd*JKK)LKJwLC$|*1^-18I4p7P#j7AQ
z?gc#sn?Fm~h=%f?!X1CpE~nN`B(Q5i9P@f@!aIDTIb39|&mb<EO3ZqQ$#UYe#8x2G
za%w^#bhH^KU?3zSYMLmBpC4gIjco0o6%BH=dRf(}(a}#<j)<+)ww#IaCz_2P|GX4E
z(A<^)`NK%jFQnD}YI3BK44VbZ>zvF?Ma>ed5u&8^FLR6xl3~1v^j*HfH}kNkOkhyE
zGZ?}+rT++2x7vn3pHM?5y!PVR?XeAzdwpiQi%EltC^@@)%<8%-UaI5OW>mXJpNBS<
z)j=h4xNIY7qAwbK0Sr1v6zN2R@!0re-95uJA$Y@`aUUefSya^h9{vr%XnuZvva>hz
z1VMHMjc`MHx)JOcv`~$T9<AQ27}pp2_`P$QSruAkOk2LY;RvFE{fv<5e0Nas!}9C(
zxK9FJhkGJ7zyap8DDwZkRJ`}fHfxQ$Ov<Keo$hgR{#Y0;Ee}86>7a&OxV!i6!HD;Q
zKOltrmJT%b(j}uu;FJv_D(;3&R01FKV~W%gVm$>?cZw^zx%?Z-+OR%`b<2p<0vrzU
zDN@lCdUeYnkfG%Of)lvve_mO&Zj{eIL+77;cG&r!3p8Qxrxz-enLe7*<z@GD{*pg5
zD39>;M5AYnIP=t4MmK3O5D>08UlwYg>qg2JspPvJf12HVR>brguy^4yptJ~|CNhpZ
z=V6KO`mh^LbS<N2cYw&06#WPT9Z5HE7|f9ewc@@jVc+Y>NKl#Wx)LfJ!=_=d5sSQ{
z)UP7&QyJ7bw+|{mG=~5;up=JAq2#bjrL}cIZkA&gNCX67#Zi<}zW3_~2tJ3MdFT4Z
zpnZ;%oL^!3$m_bS1S&oF={6bJ${(GQ8KdpF&&|!vsZn2;p<Nwfm3q?6`}{}x2ku%(
znwtSlFY2qjR!G4mMyvTb;|_?@78?DQCI4><(^K%{Yf$r{2$v{*F3a8z>Mj0$ic+qc
z;32jf%ERw2=e^rRKj7^WCeJVk4JpN)X_T;&qQlF@r29$X=votB8QfQJ`hy-CjNQP%
zg8)03V6AEll+Ip)VXwh@9x`9Ml<ISzmNo+%i!;J9$w^5oYimL<2H>uEZ#=y&v$D{g
zD0Z@(1-^Ypilv1Wc7kGlOHYWbg7U`)1idBt4GC#!0jGy{4hptO&$SZ{Ccb{_=z!($
z0ussP<m6y$1AuZ47QcG4J5bk4D=In+7ZClg-gA2Jml`B+PJ*Qi{v=QeExD$3tP$<b
zApDEr?QIfnGS7rf?>fGIb={hwM0|aHq5Dc?td^^s`8Wn==)O86T{ui0`3+*kI24OU
zcMm1DYA-QCT9*B};RshXUkyriGJbnK(2harB}}Ps9QTiyTl^by%OGur8+H4Vd2NXC
z@En2ag>u7nb85VsK$k%Wu0~uO$##W6fDk-j^A^GnbY?^)OEe1Q83>T~y%rp4)nkzI
z*AWa}_w1Y;Fz-%O>^#gu&n9UUZG)Jw2c$R<{h-PcKa)206aJ3L-rintP!O1fAbxQX
z{F+_?CQY$5kP4ZV1#KOIV%!7;C*Y2l!5bSjqXbMS$R&g+qg1VxygxTQ@Lm2v3%RP^
zEA&zUga3Pu>yR1gy7T)fih2#f`=477Q3l}o7!cE+gE7E81dIN{iX#gu!tP+ykddCg
zj7`XU|GC7g+<Wzfpjdz;!sPi{@^>>a&zl6=P|`Q3PEh`yxVW!-z7WjRC{SjexiMCj
zHOj!p#s&d)ZXon~NrMTOa1$GQ5A|Rhjj>Q;9G>qZ$UIH^=OAbQw_)@DZ{Sw}uJH2P
z8!{L)c)P^^Gx(veY3!4t_8Wp2C`jsnXN1-Y+6a`C@cZ}gl9A-{X`+x_aJ3b}4<J{9
z2(q6JCmn<gcpYXMq11v8=smB91FQ;ra_Ck*-+|3(8hq2#L#&A%4AHKLJcpO725vXJ
z{GDdyK*570nhKTVa{(@V&@DU)MhD#B6JX`2v_wE;ucX5g1|cC_<dJ~bUGDXZ!9G5K
z<buuX8%o{*re^p_#C!jlj0{Z8d;3C<znjAKZh>UI8L)Rq1w<2fcRvIlFL;G{z*YO>
zhZ6Gsq0?`eB0;i`y8c?f0KoY`nK)=&1v~ixi3ks@78DAQWdO!SVto872&JGa6BE<+
zda}g#3e?wUs^q8oY}SEsl?;(%Ajo!w#TRGqh%^iSaqS8sV?SuAt%0H_We!_<`}-kK
zC`t?mleU%1`;Q%9Q{Zk*AGT&E?uIL-VAa|fuVCchP{a#CZH)>i4p#<`wXod;9K{gR
z5c%&jU2Yyhg9$GHc;d56Upx+Zd-ZVnoyW>p**ri3(_jfL92NxO09;tApj!dBe(y=X
zsRiRDWGz8Rm{+Qp|CuMus8{A7a0L~m!ITZh*a5RRY>kRX06&WGxhUT5-P$~|U3a+c
zO)!!mGje`K9IqhXaT(OF)&lDT_Z&3!^w=<glW`NK#FXGALglR#Ed1~PqaqmopJn9|
ZLy*i{!a<R|6imv9^izc=pCpWe{tsY4l=J`q

diff --git a/vignettes/intro_to_keras_for_engineers.Rmd b/vignettes/intro_to_keras_for_engineers.Rmd
index 2c81a21e6..59e42bf0a 100644
--- a/vignettes/intro_to_keras_for_engineers.Rmd
+++ b/vignettes/intro_to_keras_for_engineers.Rmd
@@ -30,7 +30,7 @@ edit the string below to `"jax"` or `"torch"` and hit
 This entire guide is backend-agnostic.
 
 
-```r
+``` r
 library(tensorflow, exclude = c("shape", "set_random_seed"))
 library(keras3)
 
@@ -49,7 +49,7 @@ to classify MNIST digits.
 Here's the data:
 
 
-```r
+``` r
 # Load the data and split it between train and test sets
 c(c(x_train, y_train), c(x_test, y_test)) %<-% keras3::dataset_mnist()
 
@@ -67,7 +67,7 @@ dim(x_train)
 ## [1] 60000    28    28     1
 ```
 
-```r
+``` r
 dim(x_test)
 ```
 
@@ -84,7 +84,7 @@ Different model-building options that Keras offers include:
 - [Writing your own models yourself via subclassing](making_new_layers_and_models_via_subclassing.html) (for advanced use cases)
 
 
-```r
+``` r
 # Model parameters
 num_classes <- 10
 input_shape <- c(28, 28, 1)
@@ -104,7 +104,7 @@ model |>
 Here's our model summary:
 
 
-```r
+``` r
 summary(model)
 ```
 
@@ -140,7 +140,7 @@ and the metrics to monitor. Note that with the JAX and TensorFlow backends,
 XLA compilation is turned on by default.
 
 
-```r
+``` r
 model |> compile(
   optimizer = "adam",
   loss = "sparse_categorical_crossentropy",
@@ -154,7 +154,7 @@ Let's train and evaluate the model. We'll set aside a validation split of 15%
 of the data during training to monitor generalization on unseen data.
 
 
-```r
+``` r
 batch_size <- 128
 epochs <- 10
 
@@ -174,26 +174,26 @@ model |> fit(
 
 ```
 ## Epoch 1/10
-## 399/399 - 7s - 18ms/step - acc: 0.7445 - loss: 0.7534 - val_acc: 0.9630 - val_loss: 0.1260
+## 399/399 - 6s - 16ms/step - acc: 0.7450 - loss: 0.7521 - val_acc: 0.9642 - val_loss: 0.1234
 ## Epoch 2/10
-## 399/399 - 2s - 5ms/step - acc: 0.9362 - loss: 0.2103 - val_acc: 0.9774 - val_loss: 0.0762
+## 399/399 - 2s - 5ms/step - acc: 0.9387 - loss: 0.2054 - val_acc: 0.9769 - val_loss: 0.0751
 ## Epoch 3/10
-## 399/399 - 2s - 5ms/step - acc: 0.9560 - loss: 0.1492 - val_acc: 0.9830 - val_loss: 0.0607
+## 399/399 - 2s - 5ms/step - acc: 0.9575 - loss: 0.1455 - val_acc: 0.9824 - val_loss: 0.0609
 ## Epoch 4/10
-## 399/399 - 2s - 5ms/step - acc: 0.9650 - loss: 0.1187 - val_acc: 0.9859 - val_loss: 0.0494
+## 399/399 - 2s - 5ms/step - acc: 0.9662 - loss: 0.1148 - val_acc: 0.9863 - val_loss: 0.0483
 ## Epoch 5/10
-## 399/399 - 2s - 5ms/step - acc: 0.9716 - loss: 0.1003 - val_acc: 0.9866 - val_loss: 0.0485
+## 399/399 - 2s - 5ms/step - acc: 0.9726 - loss: 0.0972 - val_acc: 0.9869 - val_loss: 0.0479
 ## Epoch 6/10
-## 399/399 - 2s - 5ms/step - acc: 0.9744 - loss: 0.0878 - val_acc: 0.9888 - val_loss: 0.0381
+## 399/399 - 2s - 5ms/step - acc: 0.9750 - loss: 0.0857 - val_acc: 0.9892 - val_loss: 0.0379
 ## Epoch 7/10
-## 399/399 - 2s - 5ms/step - acc: 0.9763 - loss: 0.0799 - val_acc: 0.9896 - val_loss: 0.0378
+## 399/399 - 2s - 5ms/step - acc: 0.9768 - loss: 0.0768 - val_acc: 0.9906 - val_loss: 0.0358
 ## Epoch 8/10
-## 399/399 - 2s - 5ms/step - acc: 0.9791 - loss: 0.0686 - val_acc: 0.9862 - val_loss: 0.0454
+## 399/399 - 2s - 5ms/step - acc: 0.9794 - loss: 0.0671 - val_acc: 0.9876 - val_loss: 0.0422
 ## Epoch 9/10
-## 399/399 - 2s - 5ms/step - acc: 0.9800 - loss: 0.0665 - val_acc: 0.9889 - val_loss: 0.0412
+## 399/399 - 2s - 5ms/step - acc: 0.9808 - loss: 0.0639 - val_acc: 0.9887 - val_loss: 0.0387
 ```
 
-```r
+``` r
 score <- model |> evaluate(x_test, y_test, verbose = 0)
 ```
 
@@ -201,14 +201,14 @@ During training, we were saving a model at the end of each epoch. You
 can also save the model in its latest state like this:
 
 
-```r
+``` r
 save_model(model, "final_model.keras", overwrite=TRUE)
 ```
 
 And reload it like this:
 
 
-```r
+``` r
 model <- load_model("final_model.keras")
 ```
 
@@ -216,15 +216,15 @@ model <- load_model("final_model.keras")
 Next, you can query predictions of class probabilities with `predict()`:
 
 
-```r
+``` r
 predictions <- model |> predict(x_test)
 ```
 
 ```
-## 313/313 - 1s - 2ms/step
+## 313/313 - 0s - 1ms/step
 ```
 
-```r
+``` r
 dim(predictions)
 ```
 
@@ -249,7 +249,7 @@ or `op_binary_crossentropy`.
 Let's make a custom `Dense` layer that works with all backends:
 
 
-```r
+``` r
 layer_my_dense <- Layer(
   classname = "MyDense",
   initialize = function(units, activation = NULL, name = NULL, ...) {
@@ -286,7 +286,7 @@ Next, let's make a custom `Dropout` layer that relies on the `random_*`
 namespace:
 
 
-```r
+``` r
 layer_my_dropout <- Layer(
   "MyDropout",
   initialize = function(rate, name = NULL, seed = NULL, ...) {
@@ -307,7 +307,7 @@ layer_my_dropout <- Layer(
 Next, let's write a custom subclassed model that uses our two custom layers:
 
 
-```r
+``` r
 MyModel <- Model(
   "MyModel",
   initialize = function(num_classes, ...) {
@@ -337,7 +337,7 @@ MyModel <- Model(
 Let's compile it and fit it:
 
 
-```r
+``` r
 model <- MyModel(num_classes = 10)
 model |> compile(
   loss = loss_sparse_categorical_crossentropy(),
@@ -356,7 +356,7 @@ model |> fit(
 ```
 
 ```
-## 399/399 - 8s - 19ms/step - acc: 0.7347 - loss: 0.7746 - val_acc: 0.9247 - val_loss: 0.2487
+## 399/399 - 6s - 16ms/step - acc: 0.7356 - loss: 0.7726 - val_acc: 0.9301 - val_loss: 0.2358
 ```
 
 ## Training models on arbitrary data sources
@@ -407,7 +407,7 @@ They all work whether you're using TensorFlow, JAX, or PyTorch as your Keras bac
 Let's try this out with `tf_dataset`:
 
 
-```r
+``` r
 library(tfdatasets, exclude = "shape")
 
 train_dataset <- list(x_train, y_train) |>
@@ -433,7 +433,7 @@ model |> fit(train_dataset, epochs = 1, validation_data = test_dataset)
 ```
 
 ```
-## 469/469 - 9s - 20ms/step - acc: 0.7534 - loss: 0.7366 - val_acc: 0.8981 - val_loss: 0.3329
+## 469/469 - 7s - 16ms/step - acc: 0.7542 - loss: 0.7342 - val_acc: 0.9026 - val_loss: 0.3192
 ```
 
 ## Further reading
diff --git a/vignettes/making_new_layers_and_models_via_subclassing.Rmd b/vignettes/making_new_layers_and_models_via_subclassing.Rmd
index 135a461c4..52d6b1243 100644
--- a/vignettes/making_new_layers_and_models_via_subclassing.Rmd
+++ b/vignettes/making_new_layers_and_models_via_subclassing.Rmd
@@ -30,7 +30,7 @@ Let's dive in.
 ## Setup
 
 
-```r
+``` r
 library(keras3)
 library(tensorflow, exclude = c("set_random_seed", "shape"))
 library(tfdatasets, exclude = "shape")
@@ -46,7 +46,7 @@ Here's a densely-connected layer. It has two state variables:
 the variables `w` and `b`.
 
 
-```r
+``` r
 layer_linear <- Layer("Linear",
 
   initialize = function(units = 32, input_dim = 32, ...) {
@@ -73,7 +73,7 @@ You would use a layer by calling it on some tensor input(s), much like an R
 function.
 
 
-```r
+``` r
 x <- op_ones(c(2, 2))
 linear_layer <- layer_linear(units = 4, input_dim = 2)
 y <- linear_layer(x)
@@ -90,7 +90,7 @@ Note that the weights `w` and `b` are automatically tracked by the layer upon
 being set as layer attributes:
 
 
-```r
+``` r
 linear_layer$weights
 ```
 
@@ -111,7 +111,7 @@ backpropagation, when you are training the layer.
 Here's how to add and use a non-trainable weight:
 
 
-```r
+``` r
 layer_compute_sum <- Layer(
   "ComputeSum",
   initialize = function(input_dim) {
@@ -138,7 +138,7 @@ print(as.array(y))
 ## [1] 2 2
 ```
 
-```r
+``` r
 y <- my_sum(x)
 print(as.array(y))
 ```
@@ -150,7 +150,7 @@ print(as.array(y))
 It's part of `layer$weights`, but it gets categorized as a non-trainable weight:
 
 
-```r
+``` r
 cat("weights:", length(my_sum$weights))
 ```
 
@@ -158,7 +158,7 @@ cat("weights:", length(my_sum$weights))
 ## weights: 1
 ```
 
-```r
+``` r
 cat("non-trainable weights:", length(my_sum$non_trainable_weights))
 ```
 
@@ -166,7 +166,7 @@ cat("non-trainable weights:", length(my_sum$non_trainable_weights))
 ## non-trainable weights: 1
 ```
 
-```r
+``` r
 # It's not included in the trainable weights:
 cat("trainable_weights:", length(my_sum$trainable_weights))
 ```
@@ -181,7 +181,7 @@ Our `Linear` layer above took an `input_dim` argument that was used to compute
 the shape of the weights `w` and `b` in `initialize()`:
 
 
-```r
+``` r
 layer_linear <- Layer("Linear",
 
   initialize = function(units = 32, input_dim = 32, ...) {
@@ -212,7 +212,7 @@ In the Keras API, we recommend creating layer weights in the
 `build(self, inputs_shape)` method of your layer. Like this:
 
 
-```r
+``` r
 layer_linear <- Layer(
   "Linear",
   initialize = function(units = 32, ...) {
@@ -241,7 +241,7 @@ The `call()` method of your layer will automatically run build the first time
 it is called. You now have a layer that's lazy and thus easier to use:
 
 
-```r
+``` r
 # At instantiation, we don't know on what inputs this is going to get called
 linear_layer <- layer_linear(units = 32)
 
@@ -261,7 +261,7 @@ We recommend creating such sublayers in the `initialize()` method and leave it t
 the first `call()` to trigger building their weights.
 
 
-```r
+``` r
 MLPBlock <- Layer(
   "MLPBlock",
   initialize = function() {
@@ -291,7 +291,7 @@ cat("weights:", length(mlp$weights), "\n")
 ## weights: 6
 ```
 
-```r
+``` r
 cat("trainable weights:", length(mlp$trainable_weights), "\n")
 ```
 
@@ -318,7 +318,7 @@ but if you do this, then your layer will only be usable with the backend in ques
 For instance, you could write the following JAX-specific layer using `jax$numpy`:
 
 
-```r
+``` r
 # keras3::install_keras(backend = c("jax"))
 jax <- reticulate::import("jax")
 
@@ -333,7 +333,7 @@ Linear <- new_layer_class(
 This would be the equivalent TensorFlow-specific layer:
 
 
-```r
+``` r
 library(tensorflow)
 
 Linear <- new_layer_class(
@@ -347,7 +347,7 @@ Linear <- new_layer_class(
 And this would be the equivalent PyTorch-specific layer:
 
 
-```r
+``` r
 torch <- reticulate::import("torch")
 
 Linear <- new_layer_class(
@@ -369,7 +369,7 @@ you will want to use later, when writing your training loop. This is doable by
 calling `self$add_loss(value)`:
 
 
-```r
+``` r
 # A layer that creates an activity regularization loss
 layer_activity_regularization <- Layer(
   "ActivityRegularizationLayer",
@@ -390,7 +390,7 @@ the top-level layer, so that `layer$losses` always contains the loss values
 created during the last forward pass.
 
 
-```r
+``` r
 layer_outer <- Layer(
   "OuterLayer",
   initialize = function() {
@@ -412,7 +412,7 @@ cat("losses:", length(layer$losses), "\n")
 ## losses: 0
 ```
 
-```r
+``` r
 x <- layer(op_zeros(c(1, 1)))
 # We created one loss value
 cat("losses:", length(layer$losses), "\n")
@@ -422,7 +422,7 @@ cat("losses:", length(layer$losses), "\n")
 ## losses: 1
 ```
 
-```r
+``` r
 # `layer$losses` gets reset at the start of each call
 x <- layer(op_zeros(c(1, 1)))
 # This is the loss created during the call above
@@ -437,7 +437,7 @@ In addition, the `loss` property also contains regularization losses created
 for the weights of any inner layer:
 
 
-```r
+``` r
 layer_outer_with_kernel_regularizer <- Layer(
   "OuterLayerWithKernelRegularizer",
   initialize = function() {
@@ -468,7 +468,7 @@ These losses are meant to be taken into account when writing custom training loo
 They also work seamlessly with `fit()` (they get automatically summed and added to the main loss, if any):
 
 
-```r
+``` r
 inputs <- keras_input(shape = 3)
 outputs <- inputs |> layer_activity_regularization()
 model <- keras_model(inputs, outputs)
@@ -480,10 +480,10 @@ model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1)
 ```
 
 ```
-## 1/1 - 0s - 136ms/step - loss: 1.8971
+## 1/1 - 0s - 118ms/step - loss: 1.8971
 ```
 
-```r
+``` r
 # It's also possible not to pass any loss in `compile`,
 # since the model already has a loss to minimize, via the `add_loss`
 # call during the forward pass!
@@ -492,7 +492,7 @@ model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1)
 ```
 
 ```
-## 1/1 - 0s - 84ms/step - loss: -3.3344e-03
+## 1/1 - 0s - 73ms/step - loss: -3.3344e-03
 ```
 
 ## You can optionally enable serialization on your layers
@@ -502,7 +502,7 @@ If you need your custom layers to be serializable as part of a
 method:
 
 
-```r
+``` r
 layer_linear <- Layer(
   "Linear",
   initialize = function(units = 32) {
@@ -541,7 +541,7 @@ str(config)
 ##  - attr(*, "__class__")=<class '<r-globalenv>.Linear'>
 ```
 
-```r
+``` r
 new_layer <- from_config(config)
 ```
 
@@ -551,7 +551,7 @@ these arguments to the parent class in `initialize()` and to include them in the
 layer config:
 
 
-```r
+``` r
 Linear <- new_layer_class(
   "Linear",
   initialize = function(units = 32, ...) {
@@ -589,7 +589,7 @@ str(config)
 ##  - attr(*, "__class__")=<class '<r-globalenv>.Linear'>
 ```
 
-```r
+``` r
 new_layer <- from_config(config)
 ```
 
@@ -598,7 +598,7 @@ can also override the `from_config()` class method. This is the base
 implementation of `from_config()`:
 
 
-```r
+``` r
 Layer(
   ...,
   from_config = function(config) {
@@ -623,7 +623,7 @@ evaluation loops (e.g. `fit()`) to correctly use the layer in training and
 inference.
 
 
-```r
+``` r
 layer_custom_dropout <- Layer(
   "CustomDropout",
   initialize = function(rate, ...) {
@@ -689,7 +689,7 @@ a `Model` that we could train with `fit()`, and that we could save with
 `save_weights()`:
 
 
-```r
+``` r
 ResNet <- Model(
   "ResNet",
   initialize = function(num_classes = 1000, ...) {
@@ -738,7 +738,7 @@ Our VAE will be a subclass of `Model`, built as a nested composition of layers
 that subclass `Layer`. It will feature a regularization loss (KL divergence).
 
 
-```r
+``` r
 layer_sampling <- Layer(
   "Sampling",
   initialize = function(...) {
@@ -819,7 +819,7 @@ VariationalAutoEncoder <- Model(
 Let's train it on MNIST using the `fit()` API:
 
 
-```r
+``` r
 c(c(x_train, .), .) %<-% dataset_mnist()
 x_train <- x_train |>
   op_reshape(c(60000, 784)) |>
@@ -841,7 +841,7 @@ vae |> fit(x_train, x_train, epochs = 2, batch_size = 64)
 
 ```
 ## Epoch 1/2
-## 938/938 - 5s - 5ms/step - loss: 0.0748
+## 938/938 - 4s - 5ms/step - loss: 0.0748
 ## Epoch 2/2
-## 938/938 - 1s - 959us/step - loss: 0.0676
+## 938/938 - 1s - 815us/step - loss: 0.0676
 ```
diff --git a/vignettes/sequential_model.Rmd b/vignettes/sequential_model.Rmd
index 0b229a45d..b90beb63c 100644
--- a/vignettes/sequential_model.Rmd
+++ b/vignettes/sequential_model.Rmd
@@ -13,7 +13,7 @@ vignette: >
 ## Setup
 
 
-```r
+``` r
 library(keras3)
 ```
 
@@ -25,7 +25,7 @@ where each layer has **exactly one input tensor and one output tensor**.
 Schematically, the following `Sequential` model:
 
 
-```r
+``` r
 model <- keras_model_sequential() |>
   layer_dense(units = 2, activation = "relu", name = "layer1") |>
   layer_dense(units = 3, activation = "relu", name = "layer2") |>
@@ -39,7 +39,7 @@ y <- model(x)
 is equivalent to this function:
 
 
-```r
+``` r
 # Create 3 layers
 layer1 <- layer_dense(units = 2, activation="relu", name="layer1")
 layer2 <- layer_dense(units = 3, activation="relu", name="layer2")
@@ -64,7 +64,7 @@ You can create a Sequential model by piping layers into the `keras_model_sequent
 object:
 
 
-```r
+``` r
 model <- keras_model_sequential() |>
   layer_dense(units = 2, activation = "relu") |>
   layer_dense(units = 3, activation = "relu") |>
@@ -74,7 +74,7 @@ model <- keras_model_sequential() |>
 or by passing a list of layers to `keras_model_sequential()`:
 
 
-```r
+``` r
 model <- keras_model_sequential(layers = list(
   layer_dense(units = 2, activation = "relu"),
   layer_dense(units = 3, activation = "relu"),
@@ -85,7 +85,7 @@ model <- keras_model_sequential(layers = list(
 Its layers are accessible via the `layers` attribute:
 
 
-```r
+``` r
 model$layers
 ```
 
@@ -106,7 +106,7 @@ model$layers
 You can also create a Sequential model incrementally:
 
 
-```r
+``` r
 model <- keras_model_sequential()
 model |> layer_dense(units = 2, activation="relu")
 model |> layer_dense(units = 3, activation="relu")
@@ -117,7 +117,7 @@ Note that there's also a corresponding `pop_layer()` method to remove layers:
 a Sequential model behaves very much like a stack of layers.
 
 
-```r
+``` r
 model |> pop_layer()
 length(model$layers)  # 2
 ```
@@ -131,7 +131,7 @@ any layer or model in Keras. This is useful to annotate TensorBoard graphs
 with semantically meaningful names.
 
 
-```r
+``` r
 model <- keras_model_sequential(name = "my_sequential")
 model |> layer_dense(units = 2, activation="relu", name = "layer1")
 model |> layer_dense(units = 3, activation="relu", name = "layer2")
@@ -145,7 +145,7 @@ in order to be able to create their weights. So when you create a layer like
 this, initially, it has no weights:
 
 
-```r
+``` r
 layer <- layer_dense(units = 3)
 layer$weights  # Empty
 ```
@@ -158,7 +158,7 @@ It creates its weights the first time it is called on an input, since the shape
 of the weights depends on the shape of the inputs:
 
 
-```r
+``` r
 # Call layer on a test input
 x <- op_ones(c(1, 4))
 y <- layer(x)
@@ -180,7 +180,7 @@ Sequential model without an input shape, it isn't "built": it has no weights
 when the model first sees some input data:
 
 
-```r
+``` r
 model <- keras_model_sequential() |>
   layer_dense(units = 2, activation = "relu") |>
   layer_dense(units = 3, activation = "relu") |>
@@ -205,7 +205,7 @@ Once a model is "built", you can call its `summary()` method to display its
 contents:
 
 
-```r
+``` r
 summary(model)
 ```
 
@@ -231,7 +231,7 @@ output shape. In this case, you should start your model by passing an `input_sha
 argument to your model, so that it knows its input shape from the start:
 
 
-```r
+``` r
 model <- keras_model_sequential(input_shape = 4) |>
   layer_dense(units = 2, activation = "relu")
 summary(model)
@@ -250,7 +250,7 @@ summary(model)
 ```
 
 
-```r
+``` r
 model$layers
 ```
 
@@ -274,7 +274,7 @@ enables you to monitor how a stack of `Conv2D` and `MaxPooling2D` layers is
 downsampling image feature maps:
 
 
-```r
+``` r
 model <- keras_model_sequential(input_shape = c(250, 250, 3)) |>
   layer_conv_2d(filters = 32, kernel_size = 5, strides = 2, activation = "relu") |>
   layer_conv_2d(filters = 32, kernel_size = 3, activation = "relu") |>
@@ -301,7 +301,7 @@ summary(model)
 ##  Non-trainable params: 0 (0.00 B)
 ```
 
-```r
+``` r
 # The answer was: (40, 40, 32), so we can keep downsampling...
 
 model |>
@@ -344,7 +344,7 @@ summary(model)
 ##  Non-trainable params: 0 (0.00 B)
 ```
 
-```r
+``` r
 # Now that we have 4x4 feature maps, time to apply global max pooling.
 model |>
   layer_global_max_pooling_2d()
@@ -379,7 +379,7 @@ quickly creating a model that extracts the outputs of all intermediate layers in
 Sequential model:
 
 
-```r
+``` r
 initial_model <- keras_model_sequential(input_shape = c(250, 250, 3)) |>
   layer_conv_2d(filters = 32, kernel_size = 5, strides = 2, activation = "relu") |>
   layer_conv_2d(filters = 32, kernel_size = 3, activation = "relu") |>
@@ -399,7 +399,7 @@ features <- feature_extractor(x)
 Here's a similar example that only extract features from one layer:
 
 
-```r
+``` r
 initial_model <-
   keras_model_sequential(input_shape = c(250, 250, 3)) |>
   layer_conv_2d(filters = 32, kernel_size = 5, strides = 2,
@@ -433,7 +433,7 @@ can iterate over
 last one. Like this:
 
 
-```r
+``` r
 model <- keras_model_sequential(input_shape = 784) |>
   layer_dense(units = 32, activation = "relu") |>
   layer_dense(units = 32, activation = "relu") |>
@@ -441,12 +441,12 @@ model <- keras_model_sequential(input_shape = 784) |>
   layer_dense(units = 10)
 ```
 
-```r
+``` r
 # Presumably you would want to first load pre-trained weights.
 model |> load_model_weights(...)
 ```
 
-```r
+``` r
 # Freeze all layers except the last one.
 model |> freeze_weights(from = 1, to = -2)
 model # note the "Trainable" column now visible in the summary table
@@ -470,7 +470,7 @@ model # note the "Trainable" column now visible in the summary table
 ##  Non-trainable params: 27,232 (106.38 KB)
 ```
 
-```r
+``` r
 # Another way to freeze all layers except the last one.
 for (layer in model$layers[-length(model$layers)]) {
   layer$trainable <- FALSE
@@ -485,7 +485,7 @@ Another common blueprint is to use a Sequential model to stack a pre-trained
 model and some freshly initialized classification layers. Like this:
 
 
-```r
+``` r
 # Load a convolutional base with pre-trained weights
 base_model <- application_xception(weights = 'imagenet',
                                    include_top = FALSE,
@@ -501,7 +501,7 @@ model <- keras_model_sequential() |>
 ```
 
 
-```r
+``` r
 # Compile & train
 model |> compile(...)
 model |> fit(...)
diff --git a/vignettes/serialization_and_saving.Rmd b/vignettes/serialization_and_saving.Rmd
index bf27b026a..1d9fc4f2c 100644
--- a/vignettes/serialization_and_saving.Rmd
+++ b/vignettes/serialization_and_saving.Rmd
@@ -58,7 +58,7 @@ Now, let's look at the details.
 ## Setup
 
 
-```r
+``` r
 library(keras3)
 ```
 
@@ -83,7 +83,7 @@ which uses the `.keras` extension.
 **Example:**
 
 
-```r
+``` r
 get_model <- function() {
   # Create a simple model.
   inputs <- keras_input(shape(32))
@@ -128,7 +128,7 @@ class method.
 Like this:
 
 
-```r
+``` r
 layer_custom <- Layer(
   "CustomLayer",
   initialize = function(sublayer, ...) {
@@ -179,7 +179,7 @@ function to demonstrate this.
 **Example:**
 
 
-```r
+``` r
 # Clear all previously registered custom objects
 set_custom_objects(clear = TRUE)
 ```
@@ -188,7 +188,7 @@ set_custom_objects(clear = TRUE)
 ## named list()
 ```
 
-```r
+``` r
 layer_custom <- Layer(
   "CustomLayer",
   initialize = function(self, factor) {
@@ -253,7 +253,7 @@ stopifnot(all.equal(
 #### Passing custom objects to `load_model()`
 
 
-```r
+``` r
 model <- get_model() |> train_model()
 
 # Calling `save_model('my_model.keras')` creates a zip archive `my_model.keras`.
@@ -283,7 +283,7 @@ the loading of our custom objects.
 **Example:**
 
 
-```r
+``` r
 model <- get_model() |> train_model()
 model |> save_model("custom_model.keras", overwrite = TRUE)
 
@@ -330,7 +330,7 @@ This is equivalent to getting the config then recreating the model from its conf
 **Example:**
 
 
-```r
+``` r
 new_model <- clone_model(model)
 ```
 
@@ -345,7 +345,7 @@ reconstruct the model or layer.
 **Layer example:**
 
 
-```r
+``` r
 layer <- layer_dense(, 3, activation="relu")
 layer_config <- get_config(layer)
 str(layer_config)
@@ -380,14 +380,14 @@ str(layer_config)
 Now let's reconstruct the layer using the `from_config()` method:
 
 
-```r
+``` r
 new_layer <- from_config(layer_config)
 ```
 
 **Sequential model example:**
 
 
-```r
+``` r
 model <- keras_model_sequential(input_shape = c(32)) |>
   layer_dense(1)
 config <- get_config(model)
@@ -397,7 +397,7 @@ new_model <- from_config(config)
 **Functional model example:**
 
 
-```r
+``` r
 inputs <- keras_input(c(32))
 outputs <- inputs |> layer_dense(1)
 model <- keras_model(inputs, outputs)
@@ -416,14 +416,14 @@ It is also specific to models, it isn't meant for layers.
 **Example:**
 
 
-```r
+``` r
 model <- keras_model_sequential(input_shape = c(32)) |>
   layer_dense(1)
 save_model_config(model, "model_config.json")
 new_model <- load_model_config("model_config.json")
 ```
 
-```r
+``` r
 unlink("model_config.json")
 ```
 
@@ -437,7 +437,7 @@ behind all `serialize()`/`deserialize()` calls in keras.
 **Example**:
 
 
-```r
+``` r
 my_reg <- regularizer_l1(0.005)
 config <- serialize_keras_object(my_reg)
 str(config)
@@ -464,7 +464,7 @@ comes from
 Now we can reconstruct the regularizer.
 
 
-```r
+``` r
 new_reg <- deserialize_keras_object(config)
 new_reg
 ```
@@ -498,7 +498,7 @@ Examples:
 ***Transferring weights from one layer to another, in memory***
 
 
-```r
+``` r
 create_layer <- function() {
   layer <- layer_dense(, 64, activation = "relu", name = "dense_2")
   layer$build(shape(NA, 784))
@@ -515,7 +515,7 @@ layer_2 |> set_weights(get_weights(layer_1))
 ***Transferring weights from one model to another model with a compatible architecture, in memory***
 
 
-```r
+``` r
 # Create a simple functional model
 inputs <- keras_input(shape=c(784), name="digits")
 outputs <- inputs |>
@@ -573,7 +573,7 @@ models can have compatible architectures even if there are extra/missing
 stateless layers.
 
 
-```r
+``` r
 input <- keras_input(shape = c(784), name = "digits")
 output <- input |>
   layer_dense(64, activation = "relu", name = "dense_1") |>
@@ -605,7 +605,7 @@ The filename should end in `.weights.h5`.
 **Example:**
 
 
-```r
+``` r
 sequential_model = keras_model_sequential(input_shape = c(784),
                                           input_name = "digits") |>
   layer_dense(64, activation = "relu", name = "dense_1") |>
@@ -629,7 +629,7 @@ the desired weights/layers into a new model.
 **Example:**
 
 
-```r
+``` r
 create_functional_model <- function() {
   inputs <- keras_input(shape = c(784), name = "digits")
   outputs <- inputs |>
@@ -691,7 +691,7 @@ method.
 **Example:**
 
 
-```r
+``` r
 layer_my_dense <- register_keras_serializable(
   package = "MyLayers", name = "KernelMult",
   object = Layer(
@@ -766,7 +766,7 @@ str(config)
 ##  $ registered_name: chr "MyLayers>KernelMult"
 ```
 
-```r
+``` r
 new_layer <- deserialize_keras_object(config)
 new_layer
 ```
@@ -789,7 +789,7 @@ of a model where a `from_config` override is necessary.
 <!-- <a name="registration_example"></a> -->
 
 
-```r
+``` r
 `%||%` <- \(x, y) if(is.null(x)) y else x
 layer_custom_model <- register_keras_serializable(
   package = "ComplexModels",
@@ -848,7 +848,7 @@ above.
 **Example**:
 
 
-```r
+``` r
 layer <- layer_my_dense(
   units = 16,
   kernel_regularizer = regularizer_l1_l2(l1 = 1e-5, l2 = 1e-4),
diff --git a/vignettes/training_with_built_in_methods.Rmd b/vignettes/training_with_built_in_methods.Rmd
index 17bee87b9..9de9d9169 100644
--- a/vignettes/training_with_built_in_methods.Rmd
+++ b/vignettes/training_with_built_in_methods.Rmd
@@ -13,7 +13,7 @@ vignette: >
 ## Setup
 
 
-```r
+``` r
 library(keras3)
 ```
 
@@ -64,7 +64,7 @@ Let's consider the following model (here, we build in with the Functional API, b
 could be a Sequential model or a subclassed model as well):
 
 
-```r
+``` r
 inputs <- keras_input(shape = 784, name="digits")
 outputs <- inputs |>
   layer_dense(units = 64, activation = "relu", name = "dense_1") |>
@@ -101,7 +101,7 @@ Here's what the typical end-to-end workflow looks like, consisting of:
 We'll use MNIST data for this example.
 
 
-```r
+``` r
 c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()
 
 # Preprocess the data (these are NumPy arrays)
@@ -118,7 +118,7 @@ y_train <- y_train[-c(1:10000)]
 We specify the training configuration (optimizer, loss, metrics):
 
 
-```r
+``` r
 model |> compile(
   # Optimizer
   optimizer = optimizer_rmsprop(),
@@ -134,7 +134,7 @@ We call `fit()`, which will train the model by slicing the data into "batches" o
 `epochs`.
 
 
-```r
+``` r
 history <- model |> fit(
   x_train, y_train,
   batch_size = 64,
@@ -148,15 +148,15 @@ history <- model |> fit(
 
 ```
 ## Epoch 1/2
-## 782/782 - 2s - 3ms/step - loss: 0.3410 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.1869 - val_sparse_categorical_accuracy: 0.9455
+## 782/782 - 3s - 3ms/step - loss: 0.3410 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.1869 - val_sparse_categorical_accuracy: 0.9455
 ## Epoch 2/2
-## 782/782 - 1s - 973us/step - loss: 0.1588 - sparse_categorical_accuracy: 0.9532 - val_loss: 0.1303 - val_sparse_categorical_accuracy: 0.9626
+## 782/782 - 1s - 1ms/step - loss: 0.1588 - sparse_categorical_accuracy: 0.9532 - val_loss: 0.1303 - val_sparse_categorical_accuracy: 0.9626
 ```
 The returned `history` object holds a record of the loss values and metric values
 during training:
 
 
-```r
+``` r
 history
 ```
 
@@ -172,7 +172,7 @@ history
 We evaluate the model on the test data via `evaluate()`:
 
 
-```r
+``` r
 # Evaluate the model on the test data using `evaluate`
 results <- model |> evaluate(x_test, y_test, batch_size=128)
 ```
@@ -181,29 +181,27 @@ results <- model |> evaluate(x_test, y_test, batch_size=128)
 ## 79/79 - 0s - 3ms/step - loss: 0.1258 - sparse_categorical_accuracy: 0.9625
 ```
 
-```r
-results
+``` r
+str(results)
 ```
 
 ```
-## $loss
-## [1] 0.1257554
-##
-## $sparse_categorical_accuracy
-## [1] 0.9625
+## List of 2
+##  $ loss                       : num 0.126
+##  $ sparse_categorical_accuracy: num 0.962
 ```
 
-```r
+``` r
 # Generate predictions (probabilities -- the output of the last layer)
 # on new data using `predict`
 predictions <- model |> predict(x_test[1:2,])
 ```
 
 ```
-## 1/1 - 0s - 152ms/step
+## 1/1 - 0s - 136ms/step
 ```
 
-```r
+``` r
 dim(predictions)
 ```
 
@@ -221,7 +219,7 @@ optionally, some metrics to monitor.
 You pass these to the model as arguments to the `compile()` method:
 
 
-```r
+``` r
 model |> compile(
   optimizer = optimizer_rmsprop(learning_rate = 1e-3),
   loss = loss_sparse_categorical_crossentropy(),
@@ -240,7 +238,7 @@ Note that if you're satisfied with the default settings, in many cases the optim
 loss, and metrics can be specified via string identifiers as a shortcut:
 
 
-```r
+``` r
 model |> compile(
   optimizer = "rmsprop",
   loss = "sparse_categorical_crossentropy",
@@ -252,7 +250,7 @@ For later reuse, let's put our model definition and compile step in functions; w
 call them several times across different examples in this guide.
 
 
-```r
+``` r
 get_uncompiled_model <- function() {
   inputs <- keras_input(shape = 784, name = "digits")
   outputs <- inputs |>
@@ -308,7 +306,7 @@ The first method involves creating a function that accepts inputs `y_true` and
 error between the real data and the predictions:
 
 
-```r
+``` r
 custom_mean_squared_error <- function(y_true, y_pred) {
   op_mean(op_square(y_true - y_pred), axis = -1)
 }
@@ -326,7 +324,7 @@ model |> fit(x_train, y_train_one_hot, batch_size = 64, epochs = 2)
 ## Epoch 1/2
 ## 782/782 - 2s - 2ms/step - loss: 0.0161
 ## Epoch 2/2
-## 782/782 - 1s - 677us/step - loss: 0.0078
+## 782/782 - 0s - 623us/step - loss: 0.0078
 ```
 
 If you need a loss function that takes in parameters beside `y_true` and `y_pred`, you
@@ -345,7 +343,7 @@ reduce overfitting (we won't know if it works until we try!).
 Here's how you would do it:
 
 
-```r
+``` r
 loss_custom_mse <- Loss(
   classname = "CustomMSE",
   initialize = function(regularization_factor = 0.1, name = "custom_mse") {
@@ -390,7 +388,7 @@ Here's a simple example showing how to implement a `CategoricalTruePositives` me
 that counts how many samples were correctly classified as belonging to a given class:
 
 
-```r
+``` r
 metric_categorical_true_positives <- Metric(
   "CategoricalTruePositives",
 
@@ -434,9 +432,9 @@ history <- model |> fit(x_train, y_train, batch_size = 64, epochs = 3)
 ## Epoch 1/3
 ## 782/782 - 1s - 2ms/step - categorical_true_positives: 360502.0000 - loss: 0.3444
 ## Epoch 2/3
-## 782/782 - 1s - 862us/step - categorical_true_positives: 362616.0000 - loss: 0.1656
+## 782/782 - 1s - 964us/step - categorical_true_positives: 362616.0000 - loss: 0.1656
 ## Epoch 3/3
-## 782/782 - 1s - 660us/step - categorical_true_positives: 363187.0000 - loss: 0.1203
+## 782/782 - 1s - 1ms/step - categorical_true_positives: 363187.0000 - loss: 0.1203
 ```
 
 ### Handling losses and metrics that don't fit the standard signature
@@ -453,7 +451,7 @@ regularization (note that activity regularization is built-in in all Keras layer
 this layer is just for the sake of providing a concrete example):
 
 
-```r
+``` r
 layer_custom_activity_regularizer <- Layer(
   "ActivityRegularization",
   call = function(inputs) {
@@ -489,7 +487,7 @@ Consider the following `LogisticEndpoint` layer: it takes as inputs
 targets & logits, and it tracks a crossentropy loss via `add_loss()`.
 
 
-```r
+``` r
 layer_logistic_endpoint <- Layer(
   "LogisticEndpoint",
   initialize = function(name = NULL) {
@@ -512,7 +510,7 @@ You can use it in a model with two inputs (input data & targets), compiled witho
 `loss` argument, like this:
 
 
-```r
+``` r
 inputs <- keras_input(shape = 3, name = "inputs")
 targets <- keras_input(shape = 10, name = "targets")
 
@@ -531,7 +529,7 @@ model |> fit(data, epochs = 1)
 ```
 
 ```
-## 1/1 - 1s - 512ms/step - loss: 1.0566
+## 1/1 - 1s - 508ms/step - loss: 1.0566
 ```
 
 For more information about training multi-input models, see the section **Passing data
@@ -556,7 +554,7 @@ received by the `fit()` call, before any shuffling.
 Note that you can only use `validation_split` when training with NumPy data.
 
 
-```r
+``` r
 model <- get_compiled_model()
 model |> fit(x_train, y_train,
              batch_size = 64,
@@ -587,7 +585,7 @@ You can pass a `Dataset` instance directly to the methods `fit()`, `evaluate()`,
 `predict()`:
 
 
-```r
+``` r
 library(tfdatasets, exclude = "shape")
 model <- get_compiled_model()
 
@@ -614,27 +612,27 @@ model |> fit(train_dataset, epochs = 3)
 ## Epoch 1/3
 ## 782/782 - 2s - 2ms/step - loss: 0.3365 - sparse_categorical_accuracy: 0.9041
 ## Epoch 2/3
-## 782/782 - 1s - 821us/step - loss: 0.1605 - sparse_categorical_accuracy: 0.9524
+## 782/782 - 1s - 681us/step - loss: 0.1605 - sparse_categorical_accuracy: 0.9524
 ## Epoch 3/3
-## 782/782 - 1s - 811us/step - loss: 0.1185 - sparse_categorical_accuracy: 0.9647
+## 782/782 - 1s - 1ms/step - loss: 0.1185 - sparse_categorical_accuracy: 0.9647
 ```
 
-```r
+``` r
 # You can also evaluate or predict on a dataset.
 result <- model |> evaluate(test_dataset)
 ```
 
 ```
-## 157/157 - 1s - 5ms/step - loss: 0.1152 - sparse_categorical_accuracy: 0.9627
+## 157/157 - 1s - 4ms/step - loss: 0.1152 - sparse_categorical_accuracy: 0.9627
 ```
 
-```r
+``` r
 result
 ```
 
 ```
 ## $loss
-## [1] 0.1151983
+## [1] 0.1151979
 ##
 ## $sparse_categorical_accuracy
 ## [1] 0.9627
@@ -648,7 +646,7 @@ can pass the `steps_per_epoch` argument, which specifies how many training steps
 model should run using this Dataset before moving on to the next epoch.
 
 
-```r
+``` r
 model <- get_compiled_model()
 
 # Prepare the training dataset
@@ -663,17 +661,17 @@ model |> fit(train_dataset, epochs = 3, steps_per_epoch = 100)
 
 ```
 ## Epoch 1/3
-## 100/100 - 1s - 8ms/step - loss: 0.8017 - sparse_categorical_accuracy: 0.7806
+## 100/100 - 1s - 7ms/step - loss: 0.8017 - sparse_categorical_accuracy: 0.7806
 ## Epoch 2/3
-## 100/100 - 0s - 905us/step - loss: 0.3661 - sparse_categorical_accuracy: 0.9006
+## 100/100 - 0s - 682us/step - loss: 0.3661 - sparse_categorical_accuracy: 0.9006
 ## Epoch 3/3
-## 100/100 - 0s - 703us/step - loss: 0.3009 - sparse_categorical_accuracy: 0.9106
+## 100/100 - 0s - 740us/step - loss: 0.3009 - sparse_categorical_accuracy: 0.9106
 ```
 
 You can also pass a `Dataset` instance as the `validation_data` argument in `fit()`:
 
 
-```r
+``` r
 model <- get_compiled_model()
 
 # Prepare the training dataset
@@ -702,7 +700,7 @@ steps the model should run with the validation dataset before interrupting valid
 and moving on to the next epoch:
 
 
-```r
+``` r
 model  <- get_compiled_model()
 
 # Prepare the training dataset
@@ -904,7 +902,7 @@ give more importance to the correct classification of class #5 (which
 is the digit "5" in the MNIST dataset).
 
 
-```r
+``` r
 class_weight <- c(
     "0" = 1.0,
     "1" = 1.0,
@@ -927,7 +925,7 @@ model |> fit(x_train, y_train,
 ```
 
 ```
-## 782/782 - 2s - 2ms/step - loss: 0.3713 - sparse_categorical_accuracy: 0.9018
+## 782/782 - 1s - 2ms/step - loss: 0.3713 - sparse_categorical_accuracy: 0.9018
 ```
 
 ### Sample weights
@@ -950,7 +948,7 @@ the loss function (entirely discarding the contribution of certain samples to
 the total loss).
 
 
-```r
+``` r
 sample_weight <- rep(1.0, length(y_train))
 sample_weight[y_train == 5] <- 2.0
 
@@ -963,13 +961,13 @@ model |> fit(
 ```
 
 ```
-## 782/782 - 1s - 2ms/step - loss: 0.3740 - sparse_categorical_accuracy: 0.9015
+## 782/782 - 2s - 2ms/step - loss: 0.3740 - sparse_categorical_accuracy: 0.9015
 ```
 
 Here's a matching `Dataset` example:
 
 
-```r
+``` r
 sample_weight <- rep(1.0, length(y_train))
 sample_weight[y_train == 5] <- 2.0
 
@@ -1005,7 +1003,7 @@ combination of these inputs: a "score" (of shape `(1)`) and a probability
 distribution over five classes (of shape `(5)`).
 
 
-```r
+``` r
 image_input <- keras_input(c(32, 32, 3), name = "img_input")
 timeseries_input <- keras_input(c(NA, 10), name = "ts_input")
 
@@ -1032,7 +1030,7 @@ Let's plot this model, so you can clearly see what we're doing here (note that t
 shapes shown in the plot are batch shapes, rather than per-sample shapes).
 
 
-```r
+``` r
 plot(model, show_shapes = TRUE)
 ```
 
@@ -1045,7 +1043,7 @@ At compilation time, we can specify different losses to different outputs, by pa
 the loss functions as a list:
 
 
-```r
+``` r
 model |> compile(
   optimizer = optimizer_rmsprop(1e-3),
   loss = list(
@@ -1061,7 +1059,7 @@ applied to every output (which is not appropriate here).
 Likewise for metrics:
 
 
-```r
+``` r
 model |> compile(
   optimizer = optimizer_rmsprop(1e-3),
   loss = list(
@@ -1082,7 +1080,7 @@ Since we gave names to our output layers, we could also specify per-output losse
 metrics via a named list:
 
 
-```r
+``` r
 model |> compile(
   optimizer = optimizer_rmsprop(1e-3),
   loss = list(
@@ -1106,7 +1104,7 @@ instance, one might wish to privilege the "score" loss in our example, by giving
 the importance of the class loss), using the `loss_weights` argument:
 
 
-```r
+``` r
 model |> compile(
   optimizer = optimizer_rmsprop(1e-3),
   loss = list(
@@ -1128,7 +1126,7 @@ You could also choose not to compute a loss for certain outputs, if these output
 meant for prediction but not for training:
 
 
-```r
+``` r
 # loss list, positional version
 model |> compile(
   optimizer = optimizer_rmsprop(1e-3),
@@ -1148,7 +1146,7 @@ specifying a loss function in compile: you can pass **lists of arrays** (with
 names to arrays**.
 
 
-```r
+``` r
 model |> compile(
   optimizer = optimizer_rmsprop(1e-3),
   loss = list(
@@ -1173,10 +1171,10 @@ model |> fit(
 ```
 
 ```
-## 4/4 - 2s - 497ms/step - loss: 1.3788
+## 4/4 - 2s - 458ms/step - loss: 1.3788
 ```
 
-```r
+``` r
 # Alternatively, fit on named lists (names matching)
 model |> fit(
   list(img_input = img_data, ts_input = ts_data),
@@ -1187,14 +1185,14 @@ model |> fit(
 ```
 
 ```
-## 4/4 - 1s - 250ms/step - loss: 0.2857
+## 4/4 - 1s - 230ms/step - loss: 0.2857
 ```
 
 Here's the `Dataset` use case: similarly as what we did for R arrays, the `Dataset`
 should return a tuple of named lists (dicts).
 
 
-```r
+``` r
 train_dataset <- tensor_slices_dataset(list(
   list(img_input = img_data, ts_input = ts_data),
   list(score_output = score_targets, class_output = class_targets)
@@ -1207,7 +1205,7 @@ model |> fit(train_dataset, epochs = 1)
 ```
 
 ```
-## 2/2 - 1s - 665ms/step - loss: 0.5600
+## 2/2 - 1s - 629ms/step - loss: 0.5600
 ```
 
 ## Using callbacks
@@ -1229,7 +1227,7 @@ performance threshold is exceeded
 Callbacks can be passed as a list to your call to `fit()`:
 
 
-```r
+``` r
 model <- get_compiled_model()
 
 callbacks <- list(
@@ -1255,21 +1253,21 @@ model |> fit(
 
 ```
 ## Epoch 1/20
-## 625/625 - 2s - 3ms/step - loss: 0.3695 - sparse_categorical_accuracy: 0.8961 - val_loss: 0.1873 - val_sparse_categorical_accuracy: 0.9469
+## 625/625 - 1s - 2ms/step - loss: 0.3695 - sparse_categorical_accuracy: 0.8961 - val_loss: 0.1873 - val_sparse_categorical_accuracy: 0.9469
 ## Epoch 2/20
-## 625/625 - 1s - 1ms/step - loss: 0.1751 - sparse_categorical_accuracy: 0.9489 - val_loss: 0.1403 - val_sparse_categorical_accuracy: 0.9579
+## 625/625 - 1s - 974us/step - loss: 0.1751 - sparse_categorical_accuracy: 0.9489 - val_loss: 0.1403 - val_sparse_categorical_accuracy: 0.9579
 ## Epoch 3/20
-## 625/625 - 1s - 2ms/step - loss: 0.1277 - sparse_categorical_accuracy: 0.9625 - val_loss: 0.1218 - val_sparse_categorical_accuracy: 0.9651
+## 625/625 - 1s - 903us/step - loss: 0.1277 - sparse_categorical_accuracy: 0.9625 - val_loss: 0.1218 - val_sparse_categorical_accuracy: 0.9651
 ## Epoch 4/20
-## 625/625 - 1s - 2ms/step - loss: 0.1007 - sparse_categorical_accuracy: 0.9700 - val_loss: 0.1153 - val_sparse_categorical_accuracy: 0.9661
+## 625/625 - 1s - 889us/step - loss: 0.1007 - sparse_categorical_accuracy: 0.9700 - val_loss: 0.1153 - val_sparse_categorical_accuracy: 0.9661
 ## Epoch 5/20
 ## 625/625 - 1s - 1ms/step - loss: 0.0822 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1104 - val_sparse_categorical_accuracy: 0.9670
 ## Epoch 6/20
-## 625/625 - 1s - 975us/step - loss: 0.0683 - sparse_categorical_accuracy: 0.9801 - val_loss: 0.1098 - val_sparse_categorical_accuracy: 0.9689
+## 625/625 - 1s - 899us/step - loss: 0.0683 - sparse_categorical_accuracy: 0.9801 - val_loss: 0.1098 - val_sparse_categorical_accuracy: 0.9689
 ## Epoch 7/20
 ## 625/625 - 1s - 1ms/step - loss: 0.0571 - sparse_categorical_accuracy: 0.9838 - val_loss: 0.1116 - val_sparse_categorical_accuracy: 0.9698
 ## Epoch 8/20
-## 625/625 - 1s - 1ms/step - loss: 0.0485 - sparse_categorical_accuracy: 0.9864 - val_loss: 0.1126 - val_sparse_categorical_accuracy: 0.9702
+## 625/625 - 1s - 976us/step - loss: 0.0485 - sparse_categorical_accuracy: 0.9864 - val_loss: 0.1126 - val_sparse_categorical_accuracy: 0.9702
 ## Epoch 8: early stopping
 ```
 
@@ -1300,7 +1298,7 @@ Make sure to read the
 Here's a simple example saving a list of per-batch loss values during training:
 
 
-```r
+``` r
 callback_loss_history <- Callback(
   classname = "LossHistory",
   initialize = function(file = "per_training_batch_losses.txt", ...) {
@@ -1328,7 +1326,7 @@ checkpoints of your model at frequent intervals.
 The easiest way to achieve this is with [`callback_model_checkpoint()`]:
 
 
-```r
+``` r
 model <- get_compiled_model()
 
 callbacks <- list(
@@ -1357,11 +1355,11 @@ model |> fit(
 ## Epoch 1/2
 ##
 ## Epoch 1: val_loss improved from inf to 0.19344, saving model to mymodel_1.keras
-## 625/625 - 2s - 3ms/step - loss: 0.3787 - sparse_categorical_accuracy: 0.8940 - val_loss: 0.1934 - val_sparse_categorical_accuracy: 0.9441
+## 625/625 - 1s - 2ms/step - loss: 0.3787 - sparse_categorical_accuracy: 0.8940 - val_loss: 0.1934 - val_sparse_categorical_accuracy: 0.9441
 ## Epoch 2/2
 ##
 ## Epoch 2: val_loss improved from 0.19344 to 0.14251, saving model to mymodel_2.keras
-## 625/625 - 1s - 1ms/step - loss: 0.1768 - sparse_categorical_accuracy: 0.9478 - val_loss: 0.1425 - val_sparse_categorical_accuracy: 0.9600
+## 625/625 - 1s - 842us/step - loss: 0.1768 - sparse_categorical_accuracy: 0.9478 - val_loss: 0.1425 - val_sparse_categorical_accuracy: 0.9600
 ```
 
 The `ModelCheckpoint` callback can be used to implement fault-tolerance:
@@ -1369,7 +1367,7 @@ the ability to restart training from the last saved state of the model in case t
 gets randomly interrupted. Here's a basic example:
 
 
-```r
+``` r
 # Prepare a directory to store all the checkpoints.
 checkpoint_dir <- "./ckpt"
 fs::dir_create(checkpoint_dir)
@@ -1426,7 +1424,7 @@ You can easily use a static learning rate decay schedule by passing a schedule o
 as the `learning_rate` argument in your optimizer:
 
 
-```r
+``` r
 initial_learning_rate <- 0.1
 lr_schedule <- learning_rate_schedule_exponential_decay(
     initial_learning_rate, decay_steps=100000, decay_rate=0.96,
@@ -1469,7 +1467,7 @@ tensorboard --logdir=/full_path_to_your_logs
 or from R using:
 
 
-```r
+``` r
 tensorflow::tensorboard(logdir = "/full_path_to_your_logs")
 ```
 
@@ -1483,7 +1481,7 @@ In the simplest case, just specify where you want the callback to write logs, an
 you're good to go:
 
 
-```r
+``` r
 tb_callback <- callback_tensorboard(
   log_dir = "/full_path_to_your_logs",
   histogram_freq = 0, # How often to log histogram visualizations
diff --git a/vignettes/transfer_learning.Rmd b/vignettes/transfer_learning.Rmd
index a39d9312c..9a5ecf02f 100644
--- a/vignettes/transfer_learning.Rmd
+++ b/vignettes/transfer_learning.Rmd
@@ -13,7 +13,7 @@ vignette: >
 ## Setup
 
 
-```r
+``` r
 library(keras3)
 ```
 
@@ -67,7 +67,7 @@ Layers & models have three weight attributes:
 **Example: the `Dense` layer has 2 trainable weights (kernel & bias)**
 
 
-```r
+``` r
 layer <- layer_dense(units = 3)
 layer$build(shape(NULL, 4))  # Create the weights
 
@@ -78,7 +78,7 @@ length(layer$weights)
 ## [1] 2
 ```
 
-```r
+``` r
 length(layer$trainable_weights)
 ```
 
@@ -86,7 +86,7 @@ length(layer$trainable_weights)
 ## [1] 2
 ```
 
-```r
+``` r
 length(layer$non_trainable_weights)
 ```
 
@@ -104,7 +104,7 @@ To learn how to use non-trainable weights in your own custom layers, see the
  weights**
 
 
-```r
+``` r
 layer <- layer_batch_normalization()
 layer$build(shape(NA, 4))  # Create the weights
 
@@ -115,7 +115,7 @@ length(layer$weights)
 ## [1] 4
 ```
 
-```r
+``` r
 length(layer$trainable_weights)
 ```
 
@@ -123,7 +123,7 @@ length(layer$trainable_weights)
 ## [1] 2
 ```
 
-```r
+``` r
 length(layer$non_trainable_weights)
 ```
 
@@ -140,7 +140,7 @@ be updated during training (either when training with `fit()` or when training w
 **Example: setting `trainable` to `False`**
 
 
-```r
+``` r
 layer <- layer_dense(units = 3)
 layer$build(shape(NULL, 4))  # Create the weights
 layer$trainable <- FALSE  # Freeze the layer
@@ -152,7 +152,7 @@ length(layer$weights)
 ## [1] 2
 ```
 
-```r
+``` r
 length(layer$trainable_weights)
 ```
 
@@ -160,7 +160,7 @@ length(layer$trainable_weights)
 ## [1] 0
 ```
 
-```r
+``` r
 length(layer$non_trainable_weights)
 ```
 
@@ -172,7 +172,7 @@ When a trainable weight becomes non-trainable, its value is no longer updated du
 training.
 
 
-```r
+``` r
 # Make a model with 2 layers
 layer1 <- layer_dense(units = 3, activation = "relu")
 layer2 <- layer_dense(units = 3, activation = "sigmoid")
@@ -194,10 +194,10 @@ model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1)
 ```
 
 ```
-## 1/1 - 1s - 567ms/step - loss: 2.1868
+## 1/1 - 1s - 544ms/step - loss: 2.1868
 ```
 
-```r
+``` r
 # Check that the weights of layer1 have not changed during training
 final_layer1_weights_values <- get_weights(layer1)
 
@@ -223,7 +223,7 @@ all children layers become non-trainable as well.
 **Example:**
 
 
-```r
+``` r
 inner_model <- keras_model_sequential(input_shape = 3) |>
   layer_dense(units = 3, activation = "relu") |>
   layer_dense(units = 3, activation = "relu")
@@ -241,7 +241,7 @@ inner_model$trainable  # All layers in `model` are now frozen
 ## [1] FALSE
 ```
 
-```r
+``` r
 inner_model$layers[[1]]$trainable  # `trainable` is propagated recursively
 ```
 
@@ -281,7 +281,7 @@ Here's what the first workflow looks like in Keras:
 First, instantiate a base model with pre-trained weights.
 
 
-```r
+``` r
 base_model <- application_xception(
   weights = 'imagenet', # Load weights pre-trained on ImageNet.
   input_shape = c(150, 150, 3),
@@ -292,14 +292,14 @@ base_model <- application_xception(
 Then, freeze the base model.
 
 
-```r
+``` r
 base_model$trainable <- FALSE
 ```
 
 Create a new model on top.
 
 
-```r
+``` r
 inputs <- keras_input(shape = c(150, 150, 3))
 # We make sure that the base_model is running in inference mode here,
 # by passing `training <- FALSE`. This is important for fine-tuning, as you will
@@ -317,7 +317,7 @@ model <- keras_model(inputs, outputs)
 Train the model on new data.
 
 
-```r
+``` r
 model |> compile(
   optimizer = optimizer_adam(),
   loss = loss_binary_crossentropy(from_logits = TRUE),
@@ -350,7 +350,7 @@ As a result, you are at risk of overfitting very quickly if you apply large weig
 This is how to implement fine-tuning of the whole base model:
 
 
-```r
+``` r
 # Unfreeze the base model
 base_model$trainable <- TRUE
 
@@ -417,7 +417,7 @@ dataset small, we will use 40% of the original training data (25,000 images) for
  training, 10% for validation, and 10% for testing.
 
 
-```r
+``` r
 # reticulate::py_install("tensorflow-datasets")
 tfds <- reticulate::import("tensorflow_datasets")
 
@@ -439,7 +439,7 @@ These are the first 9 images in the training dataset -- as you can see, they're
 different sizes.
 
 
-```r
+``` r
 library(tfdatasets, exclude = "shape")
 
 par(mfrow = c(3, 3), mar = c(1,0,1.5,0))
@@ -483,7 +483,7 @@ of the model, when we create it.
 Let's resize images to 150x150:
 
 
-```r
+``` r
 resize_fn <- layer_resizing(width = 150, height = 150)
 resize_pair <- function(x, y) list(resize_fn(x), y)
 
@@ -501,7 +501,7 @@ helps expose the model to different aspects of the training data while slowing d
 overfitting.
 
 
-```r
+``` r
 data_augmentation <- keras_model_sequential() |>
   layer_random_flip("horizontal") |>
   layer_random_rotation(.1)
@@ -513,7 +513,7 @@ train_ds <- train_ds %>%
 Let's batch the data and use prefetching to optimize loading speed.
 
 
-```r
+``` r
 library(tensorflow, exclude = c("shape", "set_random_seed"))
 batch_size <- 64
 
@@ -534,7 +534,7 @@ Let's visualize what the first image of the first batch looks like after various
  transformations:
 
 
-```r
+``` r
 batch <- train_ds |>
   dataset_take(1) |>
   as_iterator() |>
@@ -577,7 +577,7 @@ it runs in inference mode, so that batchnorm statistics don't get updated
 even after we unfreeze the base model for fine-tuning.
 
 
-```r
+``` r
 base_model <- application_xception(
   weights = "imagenet", # Load weights pre-trained on ImageNet.
   input_shape = c(150, 150, 3),
@@ -636,7 +636,7 @@ summary(model, show_trainable = TRUE)
 ## Train the top layer
 
 
-```r
+``` r
 model |> compile(
   optimizer = optimizer_adam(),
   loss = loss_binary_crossentropy(from_logits = TRUE),
@@ -648,7 +648,7 @@ model |> fit(train_ds, epochs = epochs, validation_data = validation_ds)
 ```
 
 ```
-## 146/146 - 45s - 307ms/step - binary_accuracy: 0.9183 - loss: 0.1887 - val_binary_accuracy: 0.9669 - val_loss: 0.0926
+## 146/146 - 45s - 306ms/step - binary_accuracy: 0.9183 - loss: 0.1887 - val_binary_accuracy: 0.9669 - val_loss: 0.0926
 ```
 
 ## Do a round of fine-tuning of the entire model
@@ -663,7 +663,7 @@ statistics. If they did, they would wreck havoc on the representations learned b
  model so far.
 
 
-```r
+``` r
 # Unfreeze the base_model. Note that it keeps running in inference mode
 # since we passed `training=FALSE` when calling it. This means that
 # the batchnorm layers will not update their batch statistics.
@@ -697,7 +697,7 @@ summary(model, show_trainable = TRUE)
 ##  Optimizer params: 4,100 (16.02 KB)
 ```
 
-```r
+``` r
 model |> compile(
   optimizer = optimizer_adam(1e-5), # Low learning rate
   loss = loss_binary_crossentropy(from_logits = TRUE),
@@ -709,19 +709,19 @@ model |> fit(train_ds, epochs = epochs, validation_data = validation_ds)
 ```
 
 ```
-## 146/146 - 77s - 524ms/step - binary_accuracy: 0.8660 - loss: 0.3213 - val_binary_accuracy: 0.9652 - val_loss: 0.1022
+## 146/146 - 82s - 564ms/step - binary_accuracy: 0.8660 - loss: 0.3213 - val_binary_accuracy: 0.9652 - val_loss: 0.1022
 ```
 
 After 10 epochs, fine-tuning gains us a nice improvement here.
 Let's evaluate the model on the test dataset:
 
 
-```r
+``` r
 model |> evaluate(test_ds)
 ```
 
 ```
-## 37/37 - 2s - 41ms/step - binary_accuracy: 0.9540 - loss: 0.1103
+## 37/37 - 2s - 42ms/step - binary_accuracy: 0.9540 - loss: 0.1103
 ```
 
 ```
@@ -729,5 +729,5 @@ model |> evaluate(test_ds)
 ## [1] 0.9539983
 ##
 ## $loss
-## [1] 0.1102595
+## [1] 0.1102556
 ```
diff --git a/vignettes/understanding_masking_and_padding.Rmd b/vignettes/understanding_masking_and_padding.Rmd
index a999ecdfe..7f3ab47ed 100644
--- a/vignettes/understanding_masking_and_padding.Rmd
+++ b/vignettes/understanding_masking_and_padding.Rmd
@@ -14,7 +14,7 @@ vignette: >
 ## Setup
 
 
-```r
+``` r
 library(keras3)
 ```
 
@@ -36,7 +36,7 @@ When processing sequence data, it is very common for individual samples to have
 different lengths. Consider the following example (text tokenized as words):
 
 
-```r
+``` r
 data <- list(
   c("Hello", "world", "!"),
   c("How", "are", "you", "doing", "today"),
@@ -47,7 +47,7 @@ data <- list(
 After vocabulary lookup, the data might be vectorized as integers, e.g.:
 
 
-```r
+``` r
 data <- list(
   c(71, 1331, 4231),
   c(73, 8, 3215, 55, 927),
@@ -65,7 +65,7 @@ Keras provides a utility function to truncate and pad Python lists to a common l
 `pad_sequences`.
 
 
-```r
+``` r
 raw_inputs <- list(
   c(711, 632, 71),
   c(73, 8, 3215, 55, 927),
@@ -109,7 +109,7 @@ sequence_length)`), and attach it to the tensor output returned by the `Masking`
 `Embedding` layer.
 
 
-```r
+``` r
 embedding <- layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE)
 masked_output <- embedding(padded_inputs)
 
@@ -123,7 +123,7 @@ masked_output$`_keras_mask`
 ##  [ True  True  True  True  True  True]], shape=(3, 6), dtype=bool)
 ```
 
-```r
+``` r
 masking_layer <- layer_masking()
 # Simulate the embedding lookup by expanding the 2D input to 3D,
 # with embedding dimension of 10.
@@ -158,7 +158,7 @@ For instance, in the following Sequential model, the `LSTM` layer will automatic
 receive a mask, which means it will ignore padded values:
 
 
-```r
+``` r
 model <- keras_model_sequential() %>%
   layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%
   layer_lstm(units=32)
@@ -167,7 +167,7 @@ model <- keras_model_sequential() %>%
 This is also the case for the following Functional API model:
 
 
-```r
+``` r
 inputs <- keras_input(shape = shape(NULL), dtype="int32")
 outputs <- inputs %>%
   layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%
@@ -188,7 +188,7 @@ Thus, you can pass the output of the `compute_mask()` method of a mask-producing
 to the `call` method of a mask-consuming layer, like this:
 
 
-```r
+``` r
 MyLayer <- new_layer_class(
   "MyLayer",
   initialize = function(...) {
@@ -246,7 +246,7 @@ produces a new mask given the input and the current mask.
 Here is an example of a `TemporalSplit` layer that needs to modify the current mask.
 
 
-```r
+``` r
 TemporalSplit <- new_layer_class(
   "TemporalSplit",
   call = function(inputs) {
@@ -275,7 +275,7 @@ first_half$`_keras_mask`
 ##  [ True  True  True]], shape=(3, 3), dtype=bool)
 ```
 
-```r
+``` r
 second_half$`_keras_mask`
 ```
 
@@ -290,7 +290,7 @@ Here is another example of a `CustomEmbedding` layer that is capable of generati
 mask from input values:
 
 
-```r
+``` r
 CustomEmbedding <- new_layer_class(
   "CustomEmbedding",
   initialize = function(input_dim, output_dim, mask_zero=FALSE, ...) {
@@ -354,7 +354,7 @@ to be able to propagate the current input mask, you should set `self.supports_ma
 Here's an example of a layer that is whitelisted for mask propagation:
 
 
-```r
+``` r
 MyActivation <- new_layer_class(
   "MyActivation",
   initialize = function(...) {
@@ -372,7 +372,7 @@ and a mask-consuming layer (like `LSTM`), and it will pass the mask along so tha
 reaches the mask-consuming layer.
 
 
-```r
+``` r
 inputs <- keras_input(shape = shape(NULL), dtype="int32")
 outputs <- inputs %>%
   layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%
@@ -396,7 +396,7 @@ Here's a simple example below: a layer that computes a softmax over the time dim
 (axis 1) of an input sequence, while discarding masked timesteps.
 
 
-```r
+``` r
 TemporalSoftmax <- new_layer_class(
   "TemporalSoftmax",
   initialize = function(...) {
diff --git a/vignettes/writing_a_custom_training_loop_in_tensorflow.Rmd b/vignettes/writing_a_custom_training_loop_in_tensorflow.Rmd
index 5569b495e..3470d9b8a 100644
--- a/vignettes/writing_a_custom_training_loop_in_tensorflow.Rmd
+++ b/vignettes/writing_a_custom_training_loop_in_tensorflow.Rmd
@@ -13,7 +13,7 @@ vignette: >
 ## Setup
 
 
-```r
+``` r
 library(tensorflow, exclude = c("shape", "set_random_seed"))
 library(tfdatasets, exclude = "shape")
 library(keras3)
@@ -48,7 +48,7 @@ retrieve using `model$trainable_weights`).
 Let's consider a simple MNIST model:
 
 
-```r
+``` r
 get_model <- function() {
   inputs <- keras_input(shape = 784, name = "digits")
   outputs <- inputs |>
@@ -66,7 +66,7 @@ Let's train it using mini-batch gradient with a custom training loop.
 First, we're going to need an optimizer, a loss function, and a dataset:
 
 
-```r
+``` r
 # Instantiate an optimizer.
 optimizer <- optimizer_adam(learning_rate = 1e-3)
 # Instantiate a loss function.
@@ -109,7 +109,7 @@ of the model with regard to the loss
 gradients
 
 
-```r
+``` r
 epochs <- 3
 for (epoch in seq_len(epochs)) {
   cat("Start of epoch ", epoch, "\n")
@@ -191,7 +191,7 @@ Let's use this knowledge to compute `SparseCategoricalAccuracy` on validation da
 the end of each epoch:
 
 
-```r
+``` r
 # Get a fresh model
 model <- get_model()
 
@@ -208,7 +208,7 @@ val_acc_metric <- metric_sparse_categorical_accuracy()
 Here's our training & evaluation loop:
 
 
-```r
+``` r
 epochs <- 2
 time <- Sys.time()
 for (epoch in seq_len(epochs)) {
@@ -276,12 +276,12 @@ for (epoch in seq_len(epochs)) {
 ## Validation acc: 0.9031
 ```
 
-```r
+``` r
 Sys.time() - time
 ```
 
 ```
-## Time difference of 40.01661 secs
+## Time difference of 44.04362 secs
 ```
 
 ## Speeding-up your training step with `tf_function()`
@@ -300,7 +300,7 @@ You can compile into a static graph any function that takes tensors as input.
 Just add a `@tf.function` decorator on it, like this:
 
 
-```r
+``` r
 train_step <- tf_function(function(x, y) {
   with(tf$GradientTape() %as% tape, {
     logits <- model(x, training = TRUE)
@@ -316,7 +316,7 @@ train_step <- tf_function(function(x, y) {
 Let's do the same with the evaluation step:
 
 
-```r
+``` r
 test_step <- tf_function(function(x, y) {
   val_logits <- model(x, training=FALSE)
   val_acc_metric$update_state(y, val_logits)
@@ -327,7 +327,7 @@ test_step <- tf_function(function(x, y) {
 Now, let's re-run our training loop with this compiled training step:
 
 
-```r
+``` r
 epochs <- 2
 time <- Sys.time()
 for (epoch in seq_len(epochs)) {
@@ -375,12 +375,12 @@ for (epoch in seq_len(epochs)) {
 ## Validation acc: 0.9031
 ```
 
-```r
+``` r
 Sys.time() - time
 ```
 
 ```
-## Time difference of 0.3914127 secs
+## Time difference of 0.4195933 secs
 ```
 
 Much faster, isn't it?
@@ -398,7 +398,7 @@ and add them to the main loss in your training step.
 Consider this layer, that creates an activity regularization loss:
 
 
-```r
+``` r
 layer_activity_regularization <- Layer(
   "ActivityRegularizationLayer",
   call = function(inputs) {
@@ -411,7 +411,7 @@ layer_activity_regularization <- Layer(
 Let's build a really simple model that uses it:
 
 
-```r
+``` r
 inputs <- keras_input(shape = 784, name="digits")
 outputs <- inputs |>
   layer_dense(units = 64, activation = "relu") |>
@@ -424,7 +424,7 @@ model <- keras_model(inputs = inputs, outputs = outputs)
 Here's what our training step should look like now:
 
 
-```r
+``` r
 train_step <- tf_function(function(x, y) {
   with(tf$GradientTape() %as% tape, {
     logits <- model(x, training = TRUE)
@@ -479,7 +479,7 @@ Let's implement this training loop. First, create the discriminator meant to cla
 fake vs real digits:
 
 
-```r
+``` r
 # Create the discriminator
 discriminator <-
   keras_model_sequential(name = "discriminator",
@@ -524,7 +524,7 @@ that turns latent vectors into outputs of shape `(28, 28, 1)` (representing
 MNIST digits):
 
 
-```r
+``` r
 latent_dim <- 128L
 
 generator <-
@@ -577,7 +577,7 @@ Here's the key bit: the training loop. As you can see it is quite straightforwar
 training step function only takes 17 lines.
 
 
-```r
+``` r
 # Instantiate one optimizer for the discriminator and another for the generator.
 d_optimizer <- optimizer_adam(learning_rate = 0.0003)
 g_optimizer <- optimizer_adam(learning_rate = 0.0004)
@@ -641,7 +641,7 @@ Since our discriminator and generator are convnets, you're going to want to
 run this code on a GPU.
 
 
-```r
+``` r
 # Prepare the dataset. We use both the training & test MNIST digits.
 batch_size <- 64
 c(c(x_train, .), c(x_test, .)) %<-% dataset_mnist()
diff --git a/vignettes/writing_your_own_callbacks.Rmd b/vignettes/writing_your_own_callbacks.Rmd
index a431a1c5e..1ad0a87d3 100644
--- a/vignettes/writing_your_own_callbacks.Rmd
+++ b/vignettes/writing_your_own_callbacks.Rmd
@@ -25,7 +25,7 @@ started.
 ## Setup
 
 
-```r
+``` r
 library(keras3)
 ```
 
@@ -82,7 +82,7 @@ Let's take a look at a concrete example. To get started, let's import tensorflow
 define a simple Sequential Keras model:
 
 
-```r
+``` r
 # Define the Keras model to add callbacks to
 get_model <- function() {
   model <- keras_model_sequential()
@@ -99,7 +99,7 @@ get_model <- function() {
 Then, load the MNIST data for training and testing from Keras datasets API:
 
 
-```r
+``` r
 # Load example MNIST data and pre-process it
 mnist <- dataset_mnist()
 
@@ -129,7 +129,7 @@ Now, define a simple custom callback that logs:
 - When each inference (prediction) batch starts & ends
 
 
-```r
+``` r
 show <- function(msg, logs) {
   cat(glue::glue(msg, .envir = parent.frame()),
       "got logs: ", sep = "; ")
@@ -160,7 +160,7 @@ callback_custom <- Callback(
 
 Let's try it out:
 
-```r
+``` r
 model <- get_model()
 model |> fit(
   mnist$train$x, mnist$train$y,
@@ -306,7 +306,7 @@ model |> fit(
 ##  $ val_mean_absolute_error: num 1.82
 ```
 
-```r
+``` r
 res <- model |> evaluate(
   mnist$test$x, mnist$test$y,
   batch_size = 128, verbose = 0,
@@ -370,7 +370,7 @@ res <- model |> evaluate(
 ##  $ mean_absolute_error: num 1.75
 ```
 
-```r
+``` r
 res <- model |> predict(
   mnist$test$x,
   batch_size = 128, verbose = 0,
@@ -428,7 +428,7 @@ res <- model |> predict(
 The `logs` named list contains the loss value, and all the metrics at the end of a batch or
 epoch. Example includes the loss and mean absolute error.
 
-```r
+``` r
 callback_print_loss_and_mae <- Callback(
   "LossAndErrorPrintingCallback",
 
@@ -477,7 +477,7 @@ model |> fit(
 ## The average loss for epoch  2 is      5.51 and mean absolute error is    1.90.
 ```
 
-```r
+``` r
 res = model |> evaluate(
   mnist$test$x, mnist$test$y,
   verbose = 0, batch_size = 128,
@@ -530,7 +530,7 @@ epochs we should wait before stopping after having reached a local minimum.
 `callback_early_stopping()` provides a more complete and general implementation.
 
 
-```r
+``` r
 callback_early_stopping_at_min_loss <- Callback(
   "EarlyStoppingAtMinLoss",
   `__doc__` =
@@ -659,7 +659,7 @@ learning rate of the optimizer during the course of training.
 See `keras$callbacks$LearningRateScheduler` for a more general implementations (in RStudio, press F1 while the cursor is over `LearningRateScheduler` and a browser will open to [this page](https://www.tensorflow.org/versions/r2.5/api_docs/python/tf/keras/callbacks/LearningRateScheduler)).
 
 
-```r
+``` r
 callback_custom_learning_rate_scheduler <- Callback(
   "CustomLearningRateScheduler",
   `__doc__` =

From 683f0482f184af4343139455248f17e3461119c7 Mon Sep 17 00:00:00 2001
From: Tomasz Kalinowski <kalinowskit@gmail.com>
Date: Tue, 21 May 2024 11:15:27 -0400
Subject: [PATCH 06/11] Columbo FTW!

---
 .../nlp/text_classification_from_scratch.Rmd  |  3 +-
 .../nlp/text_classification_from_scratch.Rmd  | 77 +++++++++++--------
 2 files changed, 45 insertions(+), 35 deletions(-)

diff --git a/vignettes-src/examples/nlp/text_classification_from_scratch.Rmd b/vignettes-src/examples/nlp/text_classification_from_scratch.Rmd
index 6fa2b423e..cf8f8cce0 100644
--- a/vignettes-src/examples/nlp/text_classification_from_scratch.Rmd
+++ b/vignettes-src/examples/nlp/text_classification_from_scratch.Rmd
@@ -22,7 +22,6 @@ word splitting & indexing.
 ## Setup
 
 ```{r}
-options(conflicts.policy = "strict")
 library(tensorflow, exclude = c("shape", "set_random_seed"))
 library(tfdatasets, exclude = "shape")
 library(keras3)
@@ -57,7 +56,7 @@ The `aclImdb/train/pos` and `aclImdb/train/neg` folders contain text files, each
  which represents one review (either positive or negative):
 
 ```{r, warning=FALSE}
-writeLines(strwrap(readLines("datasets/aclImdb/train/pos/6248_7.txt")))
+writeLines(strwrap(readLines("datasets/aclImdb/train/pos/4229_10.txt")))
 ```
 
 We are only interested in the `pos` and `neg` subfolders, so let's delete the other subfolder that has text files in it:
diff --git a/vignettes/examples/nlp/text_classification_from_scratch.Rmd b/vignettes/examples/nlp/text_classification_from_scratch.Rmd
index a9ae88bb1..318628485 100644
--- a/vignettes/examples/nlp/text_classification_from_scratch.Rmd
+++ b/vignettes/examples/nlp/text_classification_from_scratch.Rmd
@@ -23,8 +23,7 @@ word splitting & indexing.
 ## Setup
 
 
-```r
-options(conflicts.policy = "strict")
+``` r
 library(tensorflow, exclude = c("shape", "set_random_seed"))
 library(tfdatasets, exclude = "shape")
 library(keras3)
@@ -36,7 +35,7 @@ use_virtualenv("r-keras")
 Let's download the data and inspect its structure.
 
 
-```r
+``` r
 if (!dir.exists("datasets/aclImdb")) {
   dir.create("datasets")
   download.file(
@@ -52,7 +51,7 @@ if (!dir.exists("datasets/aclImdb")) {
 The `aclImdb` folder contains a `train` and `test` subfolder:
 
 
-```r
+``` r
 head(list.files("datasets/aclImdb/test"))
 ```
 
@@ -61,7 +60,7 @@ head(list.files("datasets/aclImdb/test"))
 ## [5] "urls_pos.txt"
 ```
 
-```r
+``` r
 head(list.files("datasets/aclImdb/train"))
 ```
 
@@ -74,18 +73,30 @@ The `aclImdb/train/pos` and `aclImdb/train/neg` folders contain text files, each
  which represents one review (either positive or negative):
 
 
-```r
-cat(readLines("datasets/aclImdb/train/pos/6248_7.txt"))
+``` r
+writeLines(strwrap(readLines("datasets/aclImdb/train/pos/4229_10.txt")))
 ```
 
 ```
-## Being an Austrian myself this has been a straight knock in my face. Fortunately I don't live nowhere near the place where this movie takes place but unfortunately it portrays everything that the rest of Austria hates about Viennese people (or people close to that region). And it is very easy to read that this is exactly the directors intention: to let your head sink into your hands and say "Oh my god, how can THAT be possible!". No, not with me, the (in my opinion) totally exaggerated uncensored swinger club scene is not necessary, I watch porn, sure, but in this context I was rather disgusted than put in the right context.<br /><br />This movie tells a story about how misled people who suffer from lack of education or bad company try to survive and live in a world of redundancy and boring horizons. A girl who is treated like a whore by her super-jealous boyfriend (and still keeps coming back), a female teacher who discovers her masochism by putting the life of her super-cruel "lover" on the line, an old couple who has an almost mathematical daily cycle (she is the "official replacement" of his ex wife), a couple that has just divorced and has the ex husband suffer under the acts of his former wife obviously having a relationship with her masseuse and finally a crazy hitchhiker who asks her drivers the most unusual questions and stretches their nerves by just being super-annoying.<br /><br />After having seen it you feel almost nothing. You're not even shocked, sad, depressed or feel like doing anything... Maybe that's why I gave it 7 points, it made me react in a way I never reacted before. If that's good or bad is up to you!
+## Don't waste time reading my review. Go out and see this
+## astonishingly good episode, which may very well be the best Columbo
+## ever written! Ruth Gordon is perfectly cast as the scheming yet
+## charming mystery writer who murders her son-in-law to avenge his
+## murder of her daughter. Columbo is his usual rumpled, befuddled and
+## far-cleverer-than-he-seems self, and this particular installment
+## features fantastic chemistry between Gordon and Falk. Ironically,
+## this was not written by heralded creators Levinson or Link yet is
+## possibly the densest, most thoroughly original and twist-laden
+## Columbo plot ever. Utterly satisfying in nearly every department
+## and overflowing with droll and witty dialogue and thinking. Truly
+## unexpected and inventive climax tops all. 10/10...seek this one out
+## on Netflix!
 ```
 
 We are only interested in the `pos` and `neg` subfolders, so let's delete the other subfolder that has text files in it:
 
 
-```r
+``` r
 unlink("datasets/aclImdb/train/unsup", recursive = TRUE)
 ```
 
@@ -109,7 +120,7 @@ random seed, or to pass `shuffle=FALSE`, so that the validation & training split
 get have no overlap.
 
 
-```r
+``` r
 batch_size <- 32
 
 raw_train_ds <- text_dataset_from_directory(
@@ -126,7 +137,7 @@ raw_train_ds <- text_dataset_from_directory(
 ## Using 20000 files for training.
 ```
 
-```r
+``` r
 raw_val_ds <- text_dataset_from_directory(
   "datasets/aclImdb/train",
   batch_size = batch_size,
@@ -141,7 +152,7 @@ raw_val_ds <- text_dataset_from_directory(
 ## Using 5000 files for validation.
 ```
 
-```r
+``` r
 raw_test_ds <- text_dataset_from_directory(
   "datasets/aclImdb/test",
   batch_size = batch_size
@@ -152,7 +163,7 @@ raw_test_ds <- text_dataset_from_directory(
 ## Found 25000 files belonging to 2 classes.
 ```
 
-```r
+``` r
 cat("Number of batches in raw_train_ds:", length(raw_train_ds), "\n")
 ```
 
@@ -160,7 +171,7 @@ cat("Number of batches in raw_train_ds:", length(raw_train_ds), "\n")
 ## Number of batches in raw_train_ds: 625
 ```
 
-```r
+``` r
 cat("Number of batches in raw_val_ds:", length(raw_val_ds), "\n")
 ```
 
@@ -168,7 +179,7 @@ cat("Number of batches in raw_val_ds:", length(raw_val_ds), "\n")
 ## Number of batches in raw_val_ds: 157
 ```
 
-```r
+``` r
 cat("Number of batches in raw_test_ds:", length(raw_test_ds), "\n")
 ```
 
@@ -179,7 +190,7 @@ cat("Number of batches in raw_test_ds:", length(raw_test_ds), "\n")
 Let's preview a few samples:
 
 
-```r
+``` r
 # It's important to take a look at your raw data to ensure your normalization
 # and tokenization will work as expected. We can do that by taking a few
 # examples from the training set and looking at them.
@@ -196,7 +207,7 @@ str(batch)
 ##  $ :<tf.Tensor: shape=(32), dtype=int32, numpy=…>
 ```
 
-```r
+``` r
 c(text_batch, label_batch) %<-% batch
 for (i in 1:3) {
   print(text_batch[i])
@@ -218,7 +229,7 @@ for (i in 1:3) {
 In particular, we remove `<br />` tags.
 
 
-```r
+``` r
 # Having looked at our data above, we see that the raw text contains HTML break
 # tags of the form '<br />'. These tags will not be removed by the default
 # standardizer (which doesn't strip HTML). Because of this, we will need to
@@ -269,7 +280,7 @@ There are 2 ways we can use our text vectorization layer:
  strings, like this:
 
 
-```r
+``` r
 text_input <- keras_input(shape = c(1L), dtype = "string", name = 'text')
 x <- text_input |>
   vectorize_layer() |>
@@ -289,7 +300,7 @@ strings as input, like in the code snippet for option 1 above. This can be done
  training. We do this in the last section.
 
 
-```r
+``` r
 vectorize_text <- function(text, label) {
   text <- text |>
     op_expand_dims(-1) |>
@@ -319,7 +330,7 @@ test_ds <- test_ds |>
 We choose a simple 1D convnet starting with an `Embedding` layer.
 
 
-```r
+``` r
 # A integer input for vocab indices.
 inputs <- keras_input(shape = c(NA), dtype = "int64")
 
@@ -372,7 +383,7 @@ summary(model)
 ##  Non-trainable params: 0 (0.00 B)
 ```
 
-```r
+``` r
 # Compile the model with binary crossentropy loss and an adam optimizer.
 model |> compile(loss = "binary_crossentropy",
                  optimizer = "adam",
@@ -382,7 +393,7 @@ model |> compile(loss = "binary_crossentropy",
 ## Train the model
 
 
-```r
+``` r
 epochs <- 3
 
 # Fit the model using the train and test datasets.
@@ -391,30 +402,30 @@ model |> fit(train_ds, validation_data = val_ds, epochs = epochs)
 
 ```
 ## Epoch 1/3
-## 625/625 - 5s - 8ms/step - accuracy: 0.6944 - loss: 0.5248 - val_accuracy: 0.8624 - val_loss: 0.3150
+## 625/625 - 6s - 10ms/step - accuracy: 0.6909 - loss: 0.5300 - val_accuracy: 0.8658 - val_loss: 0.3229
 ## Epoch 2/3
-## 625/625 - 2s - 2ms/step - accuracy: 0.9046 - loss: 0.2403 - val_accuracy: 0.8730 - val_loss: 0.3135
+## 625/625 - 2s - 3ms/step - accuracy: 0.9047 - loss: 0.2412 - val_accuracy: 0.8742 - val_loss: 0.3202
 ## Epoch 3/3
-## 625/625 - 2s - 2ms/step - accuracy: 0.9524 - loss: 0.1275 - val_accuracy: 0.8716 - val_loss: 0.3424
+## 625/625 - 2s - 3ms/step - accuracy: 0.9573 - loss: 0.1237 - val_accuracy: 0.8704 - val_loss: 0.3551
 ```
 
 ## Evaluate the model on the test set
 
 
-```r
+``` r
 model |> evaluate(test_ds)
 ```
 
 ```
-## 782/782 - 1s - 2ms/step - accuracy: 0.8608 - loss: 0.3672
+## 782/782 - 1s - 2ms/step - accuracy: 0.8594 - loss: 0.3818
 ```
 
 ```
 ## $accuracy
-## [1] 0.86084
+## [1] 0.85936
 ##
 ## $loss
-## [1] 0.3671538
+## [1] 0.381799
 ```
 
 ## Make an end-to-end model
@@ -423,7 +434,7 @@ If you want to obtain a model capable of processing raw strings, you can simply
 create a new model (using the weights we just trained):
 
 
-```r
+``` r
 # A string input
 inputs <- keras_input(shape = c(1), dtype = "string")
 # Turn strings into vocab indices
@@ -444,12 +455,12 @@ end_to_end_model |> evaluate(raw_test_ds)
 ```
 
 ```
-## 782/782 - 3s - 4ms/step - accuracy: 0.8608 - loss: 0.0000e+00
+## 782/782 - 3s - 4ms/step - accuracy: 0.8594 - loss: 0.0000e+00
 ```
 
 ```
 ## $accuracy
-## [1] 0.86084
+## [1] 0.85936
 ##
 ## $loss
 ## [1] 0

From 6927586e7b30382a0c1fc08004243782601309c7 Mon Sep 17 00:00:00 2001
From: Tomasz Kalinowski <kalinowskit@gmail.com>
Date: Tue, 21 May 2024 11:31:30 -0400
Subject: [PATCH 07/11] rebuild site

---
 docs/404.html                                 |   2 +-
 docs/LICENSE-text.html                        |   2 +-
 .../custom_train_step_in_tensorflow.html      |  26 +-
 .../distributed_training_with_tensorflow.html |   8 +-
 docs/articles/distribution.html               |  14 +-
 docs/articles/examples/index.html             |   4 +-
 ..._machine_translation_with_transformer.html |   4 +-
 .../nlp/text_classification_from_scratch.html |  37 +-
 .../imbalanced_classification.html            |  74 +-
 ...ata_classification_with_feature_space.html |  72 +-
 .../timeseries_anomaly_detection.html         | 122 ++--
 .../unnamed-chunk-10-1.png                    | Bin 17877 -> 17853 bytes
 .../unnamed-chunk-11-1.png                    | Bin 7077 -> 7046 bytes
 .../unnamed-chunk-12-1.png                    | Bin 31116 -> 31147 bytes
 .../unnamed-chunk-13-2.png                    | Bin 5800 -> 5677 bytes
 .../unnamed-chunk-14-1.png                    | Bin 30523 -> 30517 bytes
 ...imeseries_classification_from_scratch.html | 674 ++++++++++++------
 .../unnamed-chunk-12-1.png                    | Bin 57721 -> 66684 bytes
 .../unnamed-chunk-13-1.png                    | Bin 55312 -> 61201 bytes
 .../articles/examples/vision/autoencoder.html | 242 +++----
 .../vision/autoencoder/unnamed-chunk-5-1.png  | Bin 54121 -> 54331 bytes
 .../vision/autoencoder/unnamed-chunk-7-1.png  | Bin 100007 -> 99851 bytes
 .../examples/vision/mnist_convnet.html        |  36 +-
 .../examples/vision/mnist_siamese_graph.html  |  48 +-
 .../mnist_siamese_graph/unnamed-chunk-5-1.png | Bin 31043 -> 31048 bytes
 .../mnist_siamese_graph/unnamed-chunk-7-1.png | Bin 8811 -> 8724 bytes
 .../mnist_siamese_graph/unnamed-chunk-7-2.png | Bin 10022 -> 10205 bytes
 .../oxford_pets_image_segmentation.html       | 104 +--
 .../unnamed-chunk-9-1.png                     | Bin 36929 -> 36912 bytes
 docs/articles/functional_api.html             |  18 +-
 docs/articles/getting_started.html            |   8 +-
 .../getting_started/unnamed-chunk-12-1.png    | Bin 31998 -> 30397 bytes
 docs/articles/index.html                      |   2 +-
 .../intro_to_keras_for_engineers.html         |  26 +-
 .../intro_to_keras_for_researchers.html       |   4 +-
 ...new_layers_and_models_via_subclassing.html |  10 +-
 docs/articles/sequential_model.html           |   2 +-
 docs/articles/serialization_and_saving.html   |   2 +-
 .../training_with_built_in_methods.html       |  66 +-
 docs/articles/transfer_learning.html          |  12 +-
 .../understanding_masking_and_padding.html    |   2 +-
 ..._a_custom_training_loop_in_tensorflow.html |   6 +-
 docs/articles/writing_your_own_callbacks.html |   2 +-
 docs/authors.html                             |   6 +-
 docs/index.html                               |   2 +-
 docs/news/index.html                          |   4 +-
 docs/pkgdown.yml                              |   2 +-
 docs/reference/Callback.html                  |   2 +-
 docs/reference/Constraint.html                |   2 +-
 docs/reference/Layer.html                     |   2 +-
 docs/reference/LearningRateSchedule.html      |   2 +-
 docs/reference/Loss.html                      |   2 +-
 docs/reference/Metric.html                    |   2 +-
 docs/reference/Model.html                     |   2 +-
 docs/reference/activation_elu.html            |   2 +-
 docs/reference/activation_exponential.html    |   2 +-
 docs/reference/activation_gelu.html           |   2 +-
 docs/reference/activation_hard_sigmoid.html   |   2 +-
 docs/reference/activation_hard_silu.html      |   2 +-
 docs/reference/activation_leaky_relu.html     |   2 +-
 docs/reference/activation_linear.html         |   2 +-
 docs/reference/activation_log_softmax.html    |   2 +-
 docs/reference/activation_mish.html           |   2 +-
 docs/reference/activation_relu.html           |   2 +-
 docs/reference/activation_relu6.html          |   2 +-
 docs/reference/activation_selu.html           |   2 +-
 docs/reference/activation_sigmoid.html        |   2 +-
 docs/reference/activation_silu.html           |   2 +-
 docs/reference/activation_softmax.html        |   2 +-
 docs/reference/activation_softplus.html       |   2 +-
 docs/reference/activation_softsign.html       |   2 +-
 docs/reference/activation_tanh.html           |   2 +-
 docs/reference/active_property.html           |   2 +-
 docs/reference/adapt.html                     |   2 +-
 docs/reference/application_convnext_base.html |   2 +-
 .../reference/application_convnext_large.html |   2 +-
 .../reference/application_convnext_small.html |   2 +-
 docs/reference/application_convnext_tiny.html |   2 +-
 .../application_convnext_xlarge.html          |   2 +-
 docs/reference/application_densenet121.html   |   2 +-
 docs/reference/application_densenet169.html   |   2 +-
 docs/reference/application_densenet201.html   |   2 +-
 .../application_efficientnet_b0.html          |   2 +-
 .../application_efficientnet_b1.html          |   2 +-
 .../application_efficientnet_b2.html          |   2 +-
 .../application_efficientnet_b3.html          |   2 +-
 .../application_efficientnet_b4.html          |   2 +-
 .../application_efficientnet_b5.html          |   2 +-
 .../application_efficientnet_b6.html          |   2 +-
 .../application_efficientnet_b7.html          |   2 +-
 .../application_efficientnet_v2b0.html        |   2 +-
 .../application_efficientnet_v2b1.html        |   2 +-
 .../application_efficientnet_v2b2.html        |   2 +-
 .../application_efficientnet_v2b3.html        |   2 +-
 .../application_efficientnet_v2l.html         |   2 +-
 .../application_efficientnet_v2m.html         |   2 +-
 .../application_efficientnet_v2s.html         |   2 +-
 .../application_inception_resnet_v2.html      |   2 +-
 docs/reference/application_inception_v3.html  |   2 +-
 docs/reference/application_mobilenet.html     |   2 +-
 docs/reference/application_mobilenet_v2.html  |   2 +-
 .../application_mobilenet_v3_large.html       |   4 +-
 .../application_mobilenet_v3_small.html       |   4 +-
 docs/reference/application_nasnetlarge.html   |   2 +-
 docs/reference/application_nasnetmobile.html  |   2 +-
 docs/reference/application_resnet101.html     |   2 +-
 docs/reference/application_resnet101_v2.html  |   2 +-
 docs/reference/application_resnet152.html     |   2 +-
 docs/reference/application_resnet152_v2.html  |   2 +-
 docs/reference/application_resnet50.html      |   2 +-
 docs/reference/application_resnet50_v2.html   |   2 +-
 docs/reference/application_vgg16.html         |   2 +-
 docs/reference/application_vgg19.html         |   2 +-
 docs/reference/application_xception.html      |   2 +-
 .../audio_dataset_from_directory.html         |   4 +-
 docs/reference/bidirectional.html             |   2 +-
 .../callback_backup_and_restore.html          |   2 +-
 docs/reference/callback_csv_logger.html       |   2 +-
 docs/reference/callback_early_stopping.html   |   2 +-
 docs/reference/callback_lambda.html           |   2 +-
 .../callback_learning_rate_scheduler.html     |   2 +-
 docs/reference/callback_model_checkpoint.html |   2 +-
 .../callback_reduce_lr_on_plateau.html        |   2 +-
 docs/reference/callback_remote_monitor.html   |   2 +-
 docs/reference/callback_swap_ema_weights.html |   2 +-
 docs/reference/callback_tensorboard.html      |   2 +-
 docs/reference/callback_terminate_on_nan.html |   2 +-
 docs/reference/clear_session.html             |   4 +-
 docs/reference/clone_model.html               |   2 +-
 .../compile.keras.src.models.model.Model.html |   2 +-
 docs/reference/config_backend.html            |   2 +-
 .../config_disable_interactive_logging.html   |   4 +-
 .../config_disable_traceback_filtering.html   |   4 +-
 docs/reference/config_dtype_policy.html       |   2 +-
 .../config_enable_interactive_logging.html    |   4 +-
 .../config_enable_traceback_filtering.html    |   4 +-
 .../config_enable_unsafe_deserialization.html |   2 +-
 docs/reference/config_epsilon.html            |   2 +-
 docs/reference/config_floatx.html             |   2 +-
 docs/reference/config_image_data_format.html  |   2 +-
 ...config_is_interactive_logging_enabled.html |   4 +-
 ...config_is_traceback_filtering_enabled.html |   4 +-
 docs/reference/config_set_backend.html        |   2 +-
 docs/reference/config_set_dtype_policy.html   |   2 +-
 docs/reference/config_set_epsilon.html        |   2 +-
 docs/reference/config_set_floatx.html         |   2 +-
 .../config_set_image_data_format.html         |   2 +-
 docs/reference/constraint_maxnorm.html        |   2 +-
 docs/reference/constraint_minmaxnorm.html     |   2 +-
 docs/reference/constraint_nonneg.html         |   2 +-
 docs/reference/constraint_unitnorm.html       |   2 +-
 docs/reference/count_params.html              |   2 +-
 docs/reference/custom_metric.html             |   2 +-
 docs/reference/dataset_boston_housing.html    |   2 +-
 docs/reference/dataset_cifar10.html           |   2 +-
 docs/reference/dataset_cifar100.html          |   2 +-
 docs/reference/dataset_fashion_mnist.html     |   2 +-
 docs/reference/dataset_imdb.html              |   2 +-
 docs/reference/dataset_mnist.html             |   2 +-
 docs/reference/dataset_reuters.html           |   2 +-
 docs/reference/deserialize_keras_object.html  |   2 +-
 ...evaluate.keras.src.models.model.Model.html |   2 +-
 ...vedmodel.keras.src.models.model.Model.html |   2 +-
 .../fit.keras.src.models.model.Model.html     |   2 +-
 docs/reference/freeze_weights.html            |   2 +-
 docs/reference/get_config.html                |   2 +-
 docs/reference/get_custom_objects.html        |   2 +-
 docs/reference/get_file.html                  |   4 +-
 docs/reference/get_layer.html                 |   2 +-
 docs/reference/get_registered_name.html       |   2 +-
 docs/reference/get_registered_object.html     |   2 +-
 docs/reference/get_source_inputs.html         |   4 +-
 docs/reference/get_weights.html               |   2 +-
 docs/reference/grapes-py_class-grapes.html    |   2 +-
 docs/reference/grapes-set-active-grapes.html  |   2 +-
 docs/reference/image_array_save.html          |   4 +-
 .../image_dataset_from_directory.html         |   4 +-
 docs/reference/image_from_array.html          |   4 +-
 docs/reference/image_load.html                |   4 +-
 docs/reference/image_smart_resize.html        |   4 +-
 docs/reference/image_to_array.html            |   4 +-
 .../imagenet_decode_predictions.html          |   2 +-
 docs/reference/imagenet_preprocess_input.html |   2 +-
 docs/reference/index.html                     |  12 +-
 docs/reference/initializer_constant.html      |   2 +-
 docs/reference/initializer_glorot_normal.html |   2 +-
 .../reference/initializer_glorot_uniform.html |   2 +-
 docs/reference/initializer_he_normal.html     |   2 +-
 docs/reference/initializer_he_uniform.html    |   2 +-
 docs/reference/initializer_identity.html      |   2 +-
 docs/reference/initializer_lecun_normal.html  |   2 +-
 docs/reference/initializer_lecun_uniform.html |   2 +-
 docs/reference/initializer_ones.html          |   2 +-
 docs/reference/initializer_orthogonal.html    |   2 +-
 docs/reference/initializer_random_normal.html |   2 +-
 .../reference/initializer_random_uniform.html |   2 +-
 .../initializer_truncated_normal.html         |   2 +-
 .../initializer_variance_scaling.html         |   2 +-
 docs/reference/initializer_zeros.html         |   2 +-
 docs/reference/install_keras.html             |   2 +-
 docs/reference/keras.html                     |   2 +-
 docs/reference/keras3-package.html            |   2 +-
 docs/reference/keras_input.html               |   2 +-
 docs/reference/keras_model.html               |   2 +-
 docs/reference/keras_model_sequential.html    |   2 +-
 docs/reference/layer_activation.html          |   2 +-
 docs/reference/layer_activation_elu.html      |   2 +-
 .../layer_activation_leaky_relu.html          |   2 +-
 .../layer_activation_parametric_relu.html     |   2 +-
 docs/reference/layer_activation_relu.html     |   2 +-
 docs/reference/layer_activation_softmax.html  |   2 +-
 .../layer_activity_regularization.html        |   2 +-
 docs/reference/layer_add.html                 |   2 +-
 docs/reference/layer_additive_attention.html  |   2 +-
 docs/reference/layer_alpha_dropout.html       |   2 +-
 docs/reference/layer_attention.html           |   2 +-
 docs/reference/layer_average.html             |   2 +-
 docs/reference/layer_average_pooling_1d.html  |   2 +-
 docs/reference/layer_average_pooling_2d.html  |   2 +-
 docs/reference/layer_average_pooling_3d.html  |   2 +-
 docs/reference/layer_batch_normalization.html |   2 +-
 docs/reference/layer_bidirectional.html       |   2 +-
 docs/reference/layer_category_encoding.html   |   2 +-
 docs/reference/layer_center_crop.html         |   2 +-
 docs/reference/layer_concatenate.html         |   2 +-
 docs/reference/layer_conv_1d.html             |   2 +-
 docs/reference/layer_conv_1d_transpose.html   |   2 +-
 docs/reference/layer_conv_2d.html             |   2 +-
 docs/reference/layer_conv_2d_transpose.html   |   2 +-
 docs/reference/layer_conv_3d.html             |   2 +-
 docs/reference/layer_conv_3d_transpose.html   |   2 +-
 docs/reference/layer_conv_lstm_1d.html        |   2 +-
 docs/reference/layer_conv_lstm_2d.html        |   2 +-
 docs/reference/layer_conv_lstm_3d.html        |   2 +-
 docs/reference/layer_cropping_1d.html         |   2 +-
 docs/reference/layer_cropping_2d.html         |   2 +-
 docs/reference/layer_cropping_3d.html         |   2 +-
 docs/reference/layer_dense.html               |   2 +-
 docs/reference/layer_depthwise_conv_1d.html   |   2 +-
 docs/reference/layer_depthwise_conv_2d.html   |   2 +-
 docs/reference/layer_discretization.html      |   2 +-
 docs/reference/layer_dot.html                 |   2 +-
 docs/reference/layer_dropout.html             |   2 +-
 docs/reference/layer_einsum_dense.html        |   2 +-
 docs/reference/layer_embedding.html           |   2 +-
 docs/reference/layer_feature_space.html       |   4 +-
 docs/reference/layer_flatten.html             |   2 +-
 docs/reference/layer_flax_module_wrapper.html |   2 +-
 docs/reference/layer_gaussian_dropout.html    |   2 +-
 docs/reference/layer_gaussian_noise.html      |   2 +-
 .../layer_global_average_pooling_1d.html      |   2 +-
 .../layer_global_average_pooling_2d.html      |   2 +-
 .../layer_global_average_pooling_3d.html      |   2 +-
 .../layer_global_max_pooling_1d.html          |   2 +-
 .../layer_global_max_pooling_2d.html          |   2 +-
 .../layer_global_max_pooling_3d.html          |   2 +-
 docs/reference/layer_group_normalization.html |   2 +-
 .../layer_group_query_attention.html          |   2 +-
 docs/reference/layer_gru.html                 |   2 +-
 docs/reference/layer_hashed_crossing.html     |   2 +-
 docs/reference/layer_hashing.html             |   2 +-
 docs/reference/layer_identity.html            |   2 +-
 docs/reference/layer_input.html               |   2 +-
 docs/reference/layer_integer_lookup.html      |   2 +-
 docs/reference/layer_jax_model_wrapper.html   |   2 +-
 docs/reference/layer_lambda.html              |   2 +-
 docs/reference/layer_layer_normalization.html |   2 +-
 docs/reference/layer_lstm.html                |   2 +-
 docs/reference/layer_masking.html             |   2 +-
 docs/reference/layer_max_pooling_1d.html      |   2 +-
 docs/reference/layer_max_pooling_2d.html      |   2 +-
 docs/reference/layer_max_pooling_3d.html      |   2 +-
 docs/reference/layer_maximum.html             |   2 +-
 docs/reference/layer_mel_spectrogram.html     |   2 +-
 docs/reference/layer_minimum.html             |   2 +-
 .../reference/layer_multi_head_attention.html |   2 +-
 docs/reference/layer_multiply.html            |   2 +-
 docs/reference/layer_normalization.html       |   2 +-
 docs/reference/layer_permute.html             |   2 +-
 docs/reference/layer_random_brightness.html   |   2 +-
 docs/reference/layer_random_contrast.html     |   2 +-
 docs/reference/layer_random_crop.html         |   2 +-
 docs/reference/layer_random_flip.html         |   2 +-
 docs/reference/layer_random_rotation.html     |   2 +-
 docs/reference/layer_random_translation.html  |   2 +-
 docs/reference/layer_random_zoom.html         |   2 +-
 docs/reference/layer_repeat_vector.html       |   2 +-
 docs/reference/layer_rescaling.html           |   2 +-
 docs/reference/layer_reshape.html             |   2 +-
 docs/reference/layer_resizing.html            |   2 +-
 docs/reference/layer_rnn.html                 |   2 +-
 docs/reference/layer_separable_conv_1d.html   |   2 +-
 docs/reference/layer_separable_conv_2d.html   |   2 +-
 docs/reference/layer_simple_rnn.html          |   2 +-
 docs/reference/layer_spatial_dropout_1d.html  |   2 +-
 docs/reference/layer_spatial_dropout_2d.html  |   2 +-
 docs/reference/layer_spatial_dropout_3d.html  |   2 +-
 .../layer_spectral_normalization.html         |   2 +-
 docs/reference/layer_string_lookup.html       |   2 +-
 docs/reference/layer_subtract.html            |   2 +-
 docs/reference/layer_text_vectorization.html  |   2 +-
 docs/reference/layer_tfsm.html                |   6 +-
 docs/reference/layer_time_distributed.html    |   2 +-
 .../reference/layer_torch_module_wrapper.html |   2 +-
 docs/reference/layer_unit_normalization.html  |   2 +-
 docs/reference/layer_upsampling_1d.html       |   2 +-
 docs/reference/layer_upsampling_2d.html       |   2 +-
 docs/reference/layer_upsampling_3d.html       |   2 +-
 docs/reference/layer_zero_padding_1d.html     |   2 +-
 docs/reference/layer_zero_padding_2d.html     |   2 +-
 docs/reference/layer_zero_padding_3d.html     |   2 +-
 .../learning_rate_schedule_cosine_decay.html  |   4 +-
 ...g_rate_schedule_cosine_decay_restarts.html |   2 +-
 ...rning_rate_schedule_exponential_decay.html |   2 +-
 ...ning_rate_schedule_inverse_time_decay.html |   2 +-
 ...ate_schedule_piecewise_constant_decay.html |   2 +-
 ...arning_rate_schedule_polynomial_decay.html |   2 +-
 docs/reference/load_model.html                |   2 +-
 docs/reference/load_model_weights.html        |   2 +-
 docs/reference/loss_binary_crossentropy.html  |   2 +-
 .../loss_binary_focal_crossentropy.html       |  10 +-
 .../loss_categorical_crossentropy.html        |   2 +-
 .../loss_categorical_focal_crossentropy.html  |   4 +-
 docs/reference/loss_categorical_hinge.html    |   2 +-
 docs/reference/loss_cosine_similarity.html    |   2 +-
 docs/reference/loss_ctc.html                  |   2 +-
 docs/reference/loss_dice.html                 |   2 +-
 docs/reference/loss_hinge.html                |   2 +-
 docs/reference/loss_huber.html                |   2 +-
 docs/reference/loss_kl_divergence.html        |   2 +-
 docs/reference/loss_log_cosh.html             |   2 +-
 docs/reference/loss_mean_absolute_error.html  |   2 +-
 .../loss_mean_absolute_percentage_error.html  |   2 +-
 docs/reference/loss_mean_squared_error.html   |   2 +-
 .../loss_mean_squared_logarithmic_error.html  |   2 +-
 docs/reference/loss_poisson.html              |   2 +-
 .../loss_sparse_categorical_crossentropy.html |   2 +-
 docs/reference/loss_squared_hinge.html        |   2 +-
 docs/reference/loss_tversky.html              |   2 +-
 docs/reference/mark_active.html               |   2 +-
 docs/reference/metric_auc.html                |   2 +-
 docs/reference/metric_binary_accuracy.html    |   2 +-
 .../reference/metric_binary_crossentropy.html |   2 +-
 .../metric_binary_focal_crossentropy.html     |   4 +-
 docs/reference/metric_binary_iou.html         |   2 +-
 .../metric_categorical_accuracy.html          |   2 +-
 .../metric_categorical_crossentropy.html      |   2 +-
 ...metric_categorical_focal_crossentropy.html |   2 +-
 docs/reference/metric_categorical_hinge.html  |   2 +-
 docs/reference/metric_cosine_similarity.html  |   2 +-
 docs/reference/metric_f1_score.html           |   2 +-
 docs/reference/metric_false_negatives.html    |   2 +-
 docs/reference/metric_false_positives.html    |   2 +-
 docs/reference/metric_fbeta_score.html        |   2 +-
 docs/reference/metric_hinge.html              |   2 +-
 docs/reference/metric_huber.html              |   2 +-
 docs/reference/metric_iou.html                |   2 +-
 docs/reference/metric_kl_divergence.html      |   2 +-
 docs/reference/metric_log_cosh.html           |   2 +-
 docs/reference/metric_log_cosh_error.html     |   2 +-
 docs/reference/metric_mean.html               |   2 +-
 .../reference/metric_mean_absolute_error.html |   2 +-
 ...metric_mean_absolute_percentage_error.html |   2 +-
 docs/reference/metric_mean_iou.html           |   2 +-
 docs/reference/metric_mean_squared_error.html |   2 +-
 ...metric_mean_squared_logarithmic_error.html |   2 +-
 docs/reference/metric_mean_wrapper.html       |   2 +-
 docs/reference/metric_one_hot_iou.html        |   2 +-
 docs/reference/metric_one_hot_mean_iou.html   |   2 +-
 docs/reference/metric_poisson.html            |   2 +-
 docs/reference/metric_precision.html          |   2 +-
 .../reference/metric_precision_at_recall.html |   2 +-
 docs/reference/metric_r2_score.html           |   2 +-
 docs/reference/metric_recall.html             |   2 +-
 .../reference/metric_recall_at_precision.html |   2 +-
 .../metric_root_mean_squared_error.html       |   2 +-
 .../metric_sensitivity_at_specificity.html    |   2 +-
 .../metric_sparse_categorical_accuracy.html   |   2 +-
 ...etric_sparse_categorical_crossentropy.html |   2 +-
 ...ric_sparse_top_k_categorical_accuracy.html |   2 +-
 .../metric_specificity_at_sensitivity.html    |   2 +-
 docs/reference/metric_squared_hinge.html      |   2 +-
 docs/reference/metric_sum.html                |   2 +-
 .../metric_top_k_categorical_accuracy.html    |   2 +-
 docs/reference/metric_true_negatives.html     |   2 +-
 docs/reference/metric_true_positives.html     |   2 +-
 docs/reference/multi-assign.html              |   2 +-
 docs/reference/new_callback_class.html        |   2 +-
 docs/reference/new_layer_class.html           |   2 +-
 .../new_learning_rate_schedule_class.html     |   2 +-
 docs/reference/new_loss_class.html            |   2 +-
 docs/reference/new_metric_class.html          |   2 +-
 docs/reference/new_model_class.html           |   2 +-
 docs/reference/normalize.html                 |   4 +-
 docs/reference/op_abs.html                    |   2 +-
 docs/reference/op_add.html                    |   2 +-
 docs/reference/op_all.html                    |   2 +-
 docs/reference/op_any.html                    |   2 +-
 docs/reference/op_append.html                 |   2 +-
 docs/reference/op_arange.html                 |   2 +-
 docs/reference/op_arccos.html                 |   2 +-
 docs/reference/op_arccosh.html                |   2 +-
 docs/reference/op_arcsin.html                 |   2 +-
 docs/reference/op_arcsinh.html                |   2 +-
 docs/reference/op_arctan.html                 |   2 +-
 docs/reference/op_arctan2.html                |   2 +-
 docs/reference/op_arctanh.html                |   2 +-
 docs/reference/op_argmax.html                 |   2 +-
 docs/reference/op_argmin.html                 |   2 +-
 docs/reference/op_argsort.html                |   2 +-
 docs/reference/op_array.html                  |   2 +-
 docs/reference/op_average.html                |   2 +-
 docs/reference/op_average_pool.html           |   2 +-
 docs/reference/op_batch_normalization.html    |   2 +-
 docs/reference/op_binary_crossentropy.html    |   2 +-
 docs/reference/op_bincount.html               |   2 +-
 docs/reference/op_broadcast_to.html           |   2 +-
 docs/reference/op_cast.html                   |   2 +-
 .../op_categorical_crossentropy.html          |   2 +-
 docs/reference/op_ceil.html                   |   2 +-
 docs/reference/op_cholesky.html               |   2 +-
 docs/reference/op_clip.html                   |   2 +-
 docs/reference/op_concatenate.html            |   2 +-
 docs/reference/op_cond.html                   |   2 +-
 docs/reference/op_conj.html                   |   2 +-
 docs/reference/op_conv.html                   |   2 +-
 docs/reference/op_conv_transpose.html         |   2 +-
 docs/reference/op_convert_to_numpy.html       |   2 +-
 docs/reference/op_convert_to_tensor.html      |   2 +-
 docs/reference/op_copy.html                   |   2 +-
 docs/reference/op_correlate.html              |   2 +-
 docs/reference/op_cos.html                    |   2 +-
 docs/reference/op_cosh.html                   |   2 +-
 docs/reference/op_count_nonzero.html          |   2 +-
 docs/reference/op_cross.html                  |   2 +-
 docs/reference/op_ctc_decode.html             |   2 +-
 docs/reference/op_ctc_loss.html               |   2 +-
 docs/reference/op_cumprod.html                |   2 +-
 docs/reference/op_cumsum.html                 |   2 +-
 docs/reference/op_custom_gradient.html        |   2 +-
 docs/reference/op_depthwise_conv.html         |   2 +-
 docs/reference/op_det.html                    |   2 +-
 docs/reference/op_diag.html                   |   2 +-
 docs/reference/op_diagonal.html               |   2 +-
 docs/reference/op_diff.html                   |   2 +-
 docs/reference/op_digitize.html               |   2 +-
 docs/reference/op_divide.html                 |   2 +-
 docs/reference/op_divide_no_nan.html          |   2 +-
 docs/reference/op_dot.html                    |   2 +-
 docs/reference/op_eig.html                    |   2 +-
 docs/reference/op_eigh.html                   |   2 +-
 docs/reference/op_einsum.html                 |   2 +-
 docs/reference/op_elu.html                    |   2 +-
 docs/reference/op_empty.html                  |   2 +-
 docs/reference/op_equal.html                  |   2 +-
 docs/reference/op_erf.html                    |   2 +-
 docs/reference/op_erfinv.html                 |   2 +-
 docs/reference/op_exp.html                    |   2 +-
 docs/reference/op_expand_dims.html            |   2 +-
 docs/reference/op_expm1.html                  |   2 +-
 docs/reference/op_extract_sequences.html      |   2 +-
 docs/reference/op_eye.html                    |   2 +-
 docs/reference/op_fft.html                    |   2 +-
 docs/reference/op_fft2.html                   |   2 +-
 docs/reference/op_flip.html                   |   2 +-
 docs/reference/op_floor.html                  |   2 +-
 docs/reference/op_floor_divide.html           |   2 +-
 docs/reference/op_fori_loop.html              |   2 +-
 docs/reference/op_full.html                   |   2 +-
 docs/reference/op_full_like.html              |   2 +-
 docs/reference/op_gelu.html                   |   2 +-
 docs/reference/op_get_item.html               |   2 +-
 docs/reference/op_greater.html                |   2 +-
 docs/reference/op_greater_equal.html          |   2 +-
 docs/reference/op_hard_sigmoid.html           |   2 +-
 docs/reference/op_hard_silu.html              |   2 +-
 docs/reference/op_hstack.html                 |   2 +-
 docs/reference/op_identity.html               |   2 +-
 docs/reference/op_imag.html                   |   2 +-
 docs/reference/op_image_affine_transform.html |   2 +-
 docs/reference/op_image_crop.html             |   2 +-
 docs/reference/op_image_extract_patches.html  |   2 +-
 docs/reference/op_image_map_coordinates.html  |   2 +-
 docs/reference/op_image_pad.html              |   2 +-
 docs/reference/op_image_resize.html           |   2 +-
 docs/reference/op_image_rgb_to_grayscale.html |   2 +-
 docs/reference/op_in_top_k.html               |   2 +-
 docs/reference/op_inv.html                    |   2 +-
 docs/reference/op_irfft.html                  |   2 +-
 docs/reference/op_is_tensor.html              |   2 +-
 docs/reference/op_isclose.html                |   2 +-
 docs/reference/op_isfinite.html               |   2 +-
 docs/reference/op_isinf.html                  |   2 +-
 docs/reference/op_isnan.html                  |   2 +-
 docs/reference/op_istft.html                  |   2 +-
 docs/reference/op_leaky_relu.html             |   2 +-
 docs/reference/op_less.html                   |   2 +-
 docs/reference/op_less_equal.html             |   2 +-
 docs/reference/op_linspace.html               |   2 +-
 docs/reference/op_log.html                    |   2 +-
 docs/reference/op_log10.html                  |   2 +-
 docs/reference/op_log1p.html                  |   2 +-
 docs/reference/op_log2.html                   |   2 +-
 docs/reference/op_log_sigmoid.html            |   2 +-
 docs/reference/op_log_softmax.html            |   2 +-
 docs/reference/op_logaddexp.html              |   2 +-
 docs/reference/op_logical_and.html            |   2 +-
 docs/reference/op_logical_not.html            |   2 +-
 docs/reference/op_logical_or.html             |   2 +-
 docs/reference/op_logical_xor.html            |   2 +-
 docs/reference/op_logspace.html               |   2 +-
 docs/reference/op_logsumexp.html              |   2 +-
 docs/reference/op_lu_factor.html              |   2 +-
 docs/reference/op_matmul.html                 |   2 +-
 docs/reference/op_max.html                    |   2 +-
 docs/reference/op_max_pool.html               |   2 +-
 docs/reference/op_maximum.html                |   2 +-
 docs/reference/op_mean.html                   |   2 +-
 docs/reference/op_median.html                 |   2 +-
 docs/reference/op_meshgrid.html               |   2 +-
 docs/reference/op_min.html                    |   2 +-
 docs/reference/op_minimum.html                |   2 +-
 docs/reference/op_mod.html                    |   2 +-
 docs/reference/op_moments.html                |   2 +-
 docs/reference/op_moveaxis.html               |   2 +-
 docs/reference/op_multi_hot.html              |   2 +-
 docs/reference/op_multiply.html               |   2 +-
 docs/reference/op_nan_to_num.html             |   2 +-
 docs/reference/op_ndim.html                   |   2 +-
 docs/reference/op_negative.html               |   2 +-
 docs/reference/op_nonzero.html                |   2 +-
 docs/reference/op_norm.html                   |   2 +-
 docs/reference/op_normalize.html              |   2 +-
 docs/reference/op_not_equal.html              |   2 +-
 docs/reference/op_one_hot.html                |   2 +-
 docs/reference/op_ones.html                   |   2 +-
 docs/reference/op_ones_like.html              |   2 +-
 docs/reference/op_outer.html                  |   2 +-
 docs/reference/op_pad.html                    |   2 +-
 docs/reference/op_power.html                  |   2 +-
 docs/reference/op_prod.html                   |   2 +-
 docs/reference/op_psnr.html                   |   2 +-
 docs/reference/op_qr.html                     |   2 +-
 docs/reference/op_quantile.html               |   2 +-
 docs/reference/op_ravel.html                  |   2 +-
 docs/reference/op_real.html                   |   2 +-
 docs/reference/op_reciprocal.html             |   2 +-
 docs/reference/op_relu.html                   |   2 +-
 docs/reference/op_relu6.html                  |   2 +-
 docs/reference/op_repeat.html                 |   2 +-
 docs/reference/op_reshape.html                |   2 +-
 docs/reference/op_rfft.html                   |   2 +-
 docs/reference/op_roll.html                   |   2 +-
 docs/reference/op_round.html                  |   2 +-
 docs/reference/op_rsqrt.html                  |   2 +-
 docs/reference/op_scatter.html                |   2 +-
 docs/reference/op_scatter_update.html         |   2 +-
 docs/reference/op_segment_max.html            |   2 +-
 docs/reference/op_segment_sum.html            |   2 +-
 docs/reference/op_select.html                 |   2 +-
 docs/reference/op_selu.html                   |   2 +-
 docs/reference/op_separable_conv.html         |   2 +-
 docs/reference/op_shape.html                  |   2 +-
 docs/reference/op_sigmoid.html                |   2 +-
 docs/reference/op_sign.html                   |   2 +-
 docs/reference/op_silu.html                   |   2 +-
 docs/reference/op_sin.html                    |   2 +-
 docs/reference/op_sinh.html                   |   2 +-
 docs/reference/op_size.html                   |   2 +-
 docs/reference/op_slice.html                  |   2 +-
 docs/reference/op_slice_update.html           |   2 +-
 docs/reference/op_slogdet.html                |   2 +-
 docs/reference/op_softmax.html                |   2 +-
 docs/reference/op_softplus.html               |   2 +-
 docs/reference/op_softsign.html               |   2 +-
 docs/reference/op_solve.html                  |   2 +-
 docs/reference/op_solve_triangular.html       |   2 +-
 docs/reference/op_sort.html                   |   2 +-
 .../op_sparse_categorical_crossentropy.html   |   2 +-
 docs/reference/op_split.html                  |   2 +-
 docs/reference/op_sqrt.html                   |   2 +-
 docs/reference/op_square.html                 |   2 +-
 docs/reference/op_squeeze.html                |   2 +-
 docs/reference/op_stack.html                  |   2 +-
 docs/reference/op_std.html                    |   2 +-
 docs/reference/op_stft.html                   |   2 +-
 docs/reference/op_stop_gradient.html          |   2 +-
 docs/reference/op_subtract.html               |   2 +-
 docs/reference/op_sum.html                    |   2 +-
 docs/reference/op_svd.html                    |   2 +-
 docs/reference/op_swapaxes.html               |   2 +-
 docs/reference/op_take.html                   |   2 +-
 docs/reference/op_take_along_axis.html        |   2 +-
 docs/reference/op_tan.html                    |   2 +-
 docs/reference/op_tanh.html                   |   2 +-
 docs/reference/op_tensordot.html              |   2 +-
 docs/reference/op_tile.html                   |   2 +-
 docs/reference/op_top_k.html                  |   2 +-
 docs/reference/op_trace.html                  |   2 +-
 docs/reference/op_transpose.html              |   2 +-
 docs/reference/op_tri.html                    |   2 +-
 docs/reference/op_tril.html                   |   2 +-
 docs/reference/op_triu.html                   |   2 +-
 docs/reference/op_unstack.html                |   2 +-
 docs/reference/op_var.html                    |   2 +-
 docs/reference/op_vdot.html                   |   2 +-
 docs/reference/op_vectorize.html              |   2 +-
 docs/reference/op_vectorized_map.html         |   2 +-
 docs/reference/op_vstack.html                 |   2 +-
 docs/reference/op_where.html                  |   2 +-
 docs/reference/op_while_loop.html             |   2 +-
 docs/reference/op_zeros.html                  |   2 +-
 docs/reference/op_zeros_like.html             |   2 +-
 docs/reference/optimizer_adadelta.html        |   2 +-
 docs/reference/optimizer_adafactor.html       |   2 +-
 docs/reference/optimizer_adagrad.html         |   2 +-
 docs/reference/optimizer_adam.html            |   2 +-
 docs/reference/optimizer_adam_w.html          |   2 +-
 docs/reference/optimizer_adamax.html          |   2 +-
 docs/reference/optimizer_ftrl.html            |   2 +-
 docs/reference/optimizer_lion.html            |   2 +-
 docs/reference/optimizer_loss_scale.html      |   2 +-
 docs/reference/optimizer_nadam.html           |   2 +-
 docs/reference/optimizer_rmsprop.html         |   2 +-
 docs/reference/optimizer_sgd.html             |   2 +-
 docs/reference/pad_sequences.html             |   4 +-
 docs/reference/pipe.html                      |   2 +-
 .../plot.keras.src.models.model.Model.html    |   2 +-
 .../plot.keras_training_history.html          |   2 +-
 docs/reference/pop_layer.html                 |   2 +-
 .../predict.keras.src.models.model.Model.html |   2 +-
 docs/reference/predict_on_batch.html          |   2 +-
 docs/reference/process_utils.html             |   2 +-
 docs/reference/quantize_weights.html          |   2 +-
 docs/reference/random_beta.html               |   2 +-
 docs/reference/random_binomial.html           |   2 +-
 docs/reference/random_categorical.html        |   2 +-
 docs/reference/random_dropout.html            |   2 +-
 docs/reference/random_gamma.html              |   2 +-
 docs/reference/random_integer.html            |   2 +-
 docs/reference/random_normal.html             |   2 +-
 docs/reference/random_seed_generator.html     |   2 +-
 docs/reference/random_shuffle.html            |   2 +-
 docs/reference/random_truncated_normal.html   |   2 +-
 docs/reference/random_uniform.html            |   2 +-
 docs/reference/reexports.html                 |   2 +-
 .../register_keras_serializable.html          |   2 +-
 docs/reference/regularizer_l1.html            |   2 +-
 docs/reference/regularizer_l1_l2.html         |   2 +-
 docs/reference/regularizer_l2.html            |   2 +-
 docs/reference/regularizer_orthogonal.html    |   2 +-
 docs/reference/reset_state.html               |   2 +-
 docs/reference/rnn_cell_gru.html              |   2 +-
 docs/reference/rnn_cell_lstm.html             |   2 +-
 docs/reference/rnn_cell_simple.html           |   2 +-
 docs/reference/rnn_cells_stack.html           |   2 +-
 docs/reference/save_model.html                |   2 +-
 docs/reference/save_model_config.html         |   2 +-
 docs/reference/save_model_weights.html        |   2 +-
 docs/reference/serialize_keras_object.html    |   2 +-
 docs/reference/set_random_seed.html           |   4 +-
 docs/reference/shape.html                     |  23 +-
 docs/reference/split_dataset.html             |   4 +-
 .../summary.keras.src.models.model.Model.html |   2 +-
 docs/reference/test_on_batch.html             |   2 +-
 .../text_dataset_from_directory.html          |   4 +-
 docs/reference/time_distributed.html          |   2 +-
 .../timeseries_dataset_from_array.html        |   4 +-
 docs/reference/to_categorical.html            |   4 +-
 docs/reference/train_on_batch.html            |   2 +-
 docs/reference/use_backend.html               |   2 +-
 docs/reference/with_custom_object_scope.html  |   2 +-
 docs/reference/zip_lists.html                 |   5 +-
 docs/search.json                              |   2 +-
 674 files changed, 1613 insertions(+), 1388 deletions(-)

diff --git a/docs/404.html b/docs/404.html
index e10bb8028..1c8c9e7f6 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -31,7 +31,7 @@
     
     <a class="navbar-brand me-2" href="https://keras.posit.co/index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
index 5f81cf838..7c17bf844 100644
--- a/docs/LICENSE-text.html
+++ b/docs/LICENSE-text.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/custom_train_step_in_tensorflow.html b/docs/articles/custom_train_step_in_tensorflow.html
index 0346bbf5c..c67ccfcf0 100644
--- a/docs/articles/custom_train_step_in_tensorflow.html
+++ b/docs/articles/custom_train_step_in_tensorflow.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -177,7 +177,7 @@ <h2 id="a-first-simple-example">A first simple example<a class="anchor" aria-lab
 <code class="sourceCode R"><span><span class="va">CustomModel</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/new_model_class.html">new_model_class</a></span><span class="op">(</span></span>
 <span>  <span class="st">"CustomModel"</span>,</span>
 <span>  train_step <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">data</span><span class="op">)</span> <span class="op">{</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, <span class="va">sample_weight</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="fu"><a href="../reference/unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a></span><span class="op">(</span><span class="va">data</span><span class="op">)</span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">x</span>, y <span class="op">=</span> <span class="cn">NULL</span>, sample_weight <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="va">data</span></span>
 <span></span>
 <span>    <span class="fu"><a href="https://rdrr.io/r/base/with.html" class="external-link">with</a></span><span class="op">(</span><span class="va">tf</span><span class="op">$</span><span class="fu">GradientTape</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://rstudio.github.io/reticulate/reference/with-as-operator.html" class="external-link">%as%</a></span> <span class="va">tape</span>, <span class="op">{</span></span>
 <span>      <span class="va">y_pred</span> <span class="op">&lt;-</span> <span class="fu">self</span><span class="op">(</span><span class="va">x</span>, training <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
@@ -219,7 +219,7 @@ <h2 id="a-first-simple-example">A first simple example<a class="anchor" aria-lab
 <span><span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/random_normal.html">random_normal</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1000</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, epochs <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/3</span></span>
-<span><span class="co">## 32/32 - 1s - 23ms/step - loss: 2.9118 - mae: 1.3597</span></span>
+<span><span class="co">## 32/32 - 1s - 19ms/step - loss: 2.9118 - mae: 1.3597</span></span>
 <span><span class="co">## Epoch 2/3</span></span>
 <span><span class="co">## 32/32 - 0s - 1ms/step - loss: 2.6026 - mae: 1.2856</span></span>
 <span><span class="co">## Epoch 3/3</span></span>
@@ -260,7 +260,7 @@ <h2 id="going-lower-level">Going lower-level<a class="anchor" aria-label="anchor
 <span>    <span class="va">self</span><span class="op">$</span><span class="va">loss_fn</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/loss_mean_squared_error.html">loss_mean_squared_error</a></span><span class="op">(</span><span class="op">)</span></span>
 <span>  <span class="op">}</span>,</span>
 <span>  train_step <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">data</span><span class="op">)</span> <span class="op">{</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, <span class="va">sample_weight</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="fu"><a href="../reference/unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a></span><span class="op">(</span><span class="va">data</span><span class="op">)</span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">x</span>, y <span class="op">=</span> <span class="cn">NULL</span>, sample_weight <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="va">data</span></span>
 <span></span>
 <span>    <span class="fu"><a href="https://rdrr.io/r/base/with.html" class="external-link">with</a></span><span class="op">(</span><span class="va">tf</span><span class="op">$</span><span class="fu">GradientTape</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://rstudio.github.io/reticulate/reference/with-as-operator.html" class="external-link">%as%</a></span> <span class="va">tape</span>, <span class="op">{</span></span>
 <span>      <span class="va">y_pred</span> <span class="op">&lt;-</span> <span class="fu">self</span><span class="op">(</span><span class="va">x</span>, training <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
@@ -306,11 +306,11 @@ <h2 id="going-lower-level">Going lower-level<a class="anchor" aria-label="anchor
 <span><span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/random_normal.html">random_normal</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1000</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, epochs <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/3</span></span>
-<span><span class="co">## 32/32 - 1s - 19ms/step - loss: 2.6540 - mae: 1.2901</span></span>
+<span><span class="co">## 32/32 - 1s - 16ms/step - loss: 2.6540 - mae: 1.2901</span></span>
 <span><span class="co">## Epoch 2/3</span></span>
-<span><span class="co">## 32/32 - 0s - 2ms/step - loss: 2.4139 - mae: 1.2303</span></span>
+<span><span class="co">## 32/32 - 0s - 1ms/step - loss: 2.4139 - mae: 1.2303</span></span>
 <span><span class="co">## Epoch 3/3</span></span>
-<span><span class="co">## 32/32 - 0s - 2ms/step - loss: 2.2080 - mae: 1.1761</span></span></code></pre>
+<span><span class="co">## 32/32 - 0s - 1ms/step - loss: 2.2080 - mae: 1.1761</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="supporting-sample_weight-class_weight">Supporting <code>sample_weight</code> &amp;
@@ -332,7 +332,7 @@ <h2 id="supporting-sample_weight-class_weight">Supporting <code>sample_weight</c
 <code class="sourceCode R"><span><span class="va">CustomModel</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/new_model_class.html">new_model_class</a></span><span class="op">(</span></span>
 <span>  <span class="st">"CustomModel"</span>,</span>
 <span>  train_step <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">data</span><span class="op">)</span> <span class="op">{</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, <span class="va">sample_weight</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="fu"><a href="../reference/unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a></span><span class="op">(</span><span class="va">data</span><span class="op">)</span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">x</span>, y <span class="op">=</span> <span class="cn">NULL</span>, sample_weight <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="va">data</span></span>
 <span></span>
 <span>    <span class="fu"><a href="https://rdrr.io/r/base/with.html" class="external-link">with</a></span><span class="op">(</span><span class="va">tf</span><span class="op">$</span><span class="fu">GradientTape</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://rstudio.github.io/reticulate/reference/with-as-operator.html" class="external-link">%as%</a></span> <span class="va">tape</span>, <span class="op">{</span></span>
 <span>      <span class="va">y_pred</span> <span class="op">&lt;-</span> <span class="fu">self</span><span class="op">(</span><span class="va">x</span>, training <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
@@ -376,11 +376,11 @@ <h2 id="supporting-sample_weight-class_weight">Supporting <code>sample_weight</c
 <span><span class="va">sw</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/random_normal.html">random_normal</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1000</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, sample_weight <span class="op">=</span> <span class="va">sw</span>, epochs <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/3</span></span>
-<span><span class="co">## 32/32 - 1s - 21ms/step - loss: 0.1607 - mae: 1.3018</span></span>
+<span><span class="co">## 32/32 - 1s - 18ms/step - loss: 0.1607 - mae: 1.3018</span></span>
 <span><span class="co">## Epoch 2/3</span></span>
 <span><span class="co">## 32/32 - 0s - 1ms/step - loss: 0.1452 - mae: 1.2999</span></span>
 <span><span class="co">## Epoch 3/3</span></span>
-<span><span class="co">## 32/32 - 0s - 2ms/step - loss: 0.1335 - mae: 1.2986</span></span></code></pre>
+<span><span class="co">## 32/32 - 0s - 1ms/step - loss: 0.1335 - mae: 1.2986</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="providing-your-own-evaluation-step">Providing your own evaluation step<a class="anchor" aria-label="anchor" href="#providing-your-own-evaluation-step"></a>
@@ -394,7 +394,7 @@ <h2 id="providing-your-own-evaluation-step">Providing your own evaluation step<a
 <span>  <span class="st">"CustomModel"</span>,</span>
 <span>  test_step <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">data</span><span class="op">)</span> <span class="op">{</span></span>
 <span>    <span class="co"># Unpack the data</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, <span class="va">sw</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="fu"><a href="../reference/unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a></span><span class="op">(</span><span class="va">data</span><span class="op">)</span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">x</span>, y <span class="op">=</span> <span class="cn">NULL</span>, sw <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="va">data</span></span>
 <span>    <span class="co"># Compute predictions</span></span>
 <span>    <span class="va">y_pred</span> <span class="op">=</span> <span class="fu">self</span><span class="op">(</span><span class="va">x</span>, training <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
 <span>    <span class="co"># Updates the metrics tracking the loss</span></span>
@@ -423,7 +423,7 @@ <h2 id="providing-your-own-evaluation-step">Providing your own evaluation step<a
 <span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/random_normal.html">random_normal</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1000</span>, <span class="fl">32</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/random_normal.html">random_normal</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1000</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/tensorflow/man/evaluate.html" class="external-link">evaluate</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 32/32 - 0s - 9ms/step - loss: 0.0000e+00 - mae: 1.3947</span></span></code></pre>
+<pre><code><span><span class="co">## 32/32 - 0s - 8ms/step - loss: 0.0000e+00 - mae: 1.3947</span></span></code></pre>
 <pre><code><span><span class="co">## $loss</span></span>
 <span><span class="co">## [1] 0</span></span>
 <span><span class="co">##</span></span>
@@ -584,7 +584,7 @@ <h2 id="wrapping-up-an-end-to-end-gan-example">Wrapping up: an end-to-end GAN ex
 <span>  <span class="fu">tfdatasets</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/tfdatasets/man/dataset_take.html" class="external-link">dataset_take</a></span><span class="op">(</span><span class="va">dataset</span>, <span class="fl">100</span><span class="op">)</span>,</span>
 <span>  epochs <span class="op">=</span> <span class="fl">1</span></span>
 <span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 100/100 - 5s - 53ms/step - d_loss: 0.0000e+00 - g_loss: 0.0000e+00</span></span></code></pre>
+<pre><code><span><span class="co">## 100/100 - 5s - 47ms/step - d_loss: 0.0000e+00 - g_loss: 0.0000e+00</span></span></code></pre>
 <p>The ideas behind deep learning are simple, so why should their
 implementation be painful?</p>
 </div>
diff --git a/docs/articles/distributed_training_with_tensorflow.html b/docs/articles/distributed_training_with_tensorflow.html
index 2a399b8b2..6adb4fc77 100644
--- a/docs/articles/distributed_training_with_tensorflow.html
+++ b/docs/articles/distributed_training_with_tensorflow.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -276,10 +276,10 @@ <h2 id="single-host-multi-device-synchronous-training">Single-host, multi-device
 <span></span>
 <span><span class="op">}</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/2</span></span>
-<span><span class="co">## 782/782 - 6s - 7ms/step - loss: 3.0622 - sparse_categorical_accuracy: 0.8615 - val_loss: 1.1367 - val_sparse_categorical_accuracy: 0.9006</span></span>
+<span><span class="co">## 782/782 - 5s - 7ms/step - loss: 3.0622 - sparse_categorical_accuracy: 0.8615 - val_loss: 1.1367 - val_sparse_categorical_accuracy: 0.9006</span></span>
 <span><span class="co">## Epoch 2/2</span></span>
 <span><span class="co">## 782/782 - 3s - 4ms/step - loss: 0.5774 - sparse_categorical_accuracy: 0.9259 - val_loss: 0.6612 - val_sparse_categorical_accuracy: 0.9210</span></span>
-<span><span class="co">## 157/157 - 0s - 3ms/step - loss: 0.6729 - sparse_categorical_accuracy: 0.9150</span></span></code></pre>
+<span><span class="co">## 157/157 - 0s - 2ms/step - loss: 0.6729 - sparse_categorical_accuracy: 0.9150</span></span></code></pre>
 <pre><code><span><span class="co">## $loss</span></span>
 <span><span class="co">## [1] 0.6728871</span></span>
 <span><span class="co">##</span></span>
@@ -349,7 +349,7 @@ <h2 id="using-callbacks-to-ensure-fault-tolerance">Using callbacks to ensure fau
 <span></span>
 <span><span class="co"># Running the first time creates the model</span></span>
 <span><span class="fu">run_training</span><span class="op">(</span>epochs <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 782/782 - 6s - 7ms/step - loss: 2.9519 - sparse_categorical_accuracy: 0.8655 - val_loss: 1.3110 - val_sparse_categorical_accuracy: 0.8836</span></span></code></pre>
+<pre><code><span><span class="co">## 782/782 - 4s - 6ms/step - loss: 2.9519 - sparse_categorical_accuracy: 0.8655 - val_loss: 1.3110 - val_sparse_categorical_accuracy: 0.8836</span></span></code></pre>
 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Calling the same function again will resume from where we left off</span></span>
 <span><span class="fu">run_training</span><span class="op">(</span>epochs <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
diff --git a/docs/articles/distribution.html b/docs/articles/distribution.html
index 336002a4c..b374e07fe 100644
--- a/docs/articles/distribution.html
+++ b/docs/articles/distribution.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -269,11 +269,11 @@ <h2 id="dataparallel">DataParallel<a class="anchor" aria-label="anchor" href="#d
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/compile.html" class="external-link">compile</a></span><span class="op">(</span>loss <span class="op">=</span> <span class="st">"mse"</span><span class="op">)</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="va">dataset</span>, epochs <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/3</span></span>
-<span><span class="co">## 8/8 - 0s - 47ms/step - loss: 1.0629</span></span>
+<span><span class="co">## 8/8 - 0s - 37ms/step - loss: 1.0629</span></span>
 <span><span class="co">## Epoch 2/3</span></span>
-<span><span class="co">## 8/8 - 0s - 7ms/step - loss: 0.9712</span></span>
+<span><span class="co">## 8/8 - 0s - 5ms/step - loss: 0.9712</span></span>
 <span><span class="co">## Epoch 3/3</span></span>
-<span><span class="co">## 8/8 - 0s - 7ms/step - loss: 0.9322</span></span></code></pre>
+<span><span class="co">## 8/8 - 0s - 5ms/step - loss: 0.9322</span></span></code></pre>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/tensorflow/man/evaluate.html" class="external-link">evaluate</a></span><span class="op">(</span><span class="va">dataset</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## 8/8 - 0s - 7ms/step - loss: 0.8859</span></span></code></pre>
@@ -354,12 +354,12 @@ <h2 id="modelparallel-and-layoutmap">
 <pre><code><span><span class="co">## Epoch 1/3</span></span>
 <span><span class="co">## 8/8 - 0s - 29ms/step - loss: 1.0714</span></span>
 <span><span class="co">## Epoch 2/3</span></span>
-<span><span class="co">## 8/8 - 0s - 4ms/step - loss: 0.9744</span></span>
+<span><span class="co">## 8/8 - 0s - 3ms/step - loss: 0.9744</span></span>
 <span><span class="co">## Epoch 3/3</span></span>
-<span><span class="co">## 8/8 - 0s - 5ms/step - loss: 0.9280</span></span></code></pre>
+<span><span class="co">## 8/8 - 0s - 4ms/step - loss: 0.9280</span></span></code></pre>
 <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/tensorflow/man/evaluate.html" class="external-link">evaluate</a></span><span class="op">(</span><span class="va">dataset</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 8/8 - 0s - 9ms/step - loss: 0.8802</span></span></code></pre>
+<pre><code><span><span class="co">## 8/8 - 0s - 7ms/step - loss: 0.8802</span></span></code></pre>
 <pre><code><span><span class="co">## $loss</span></span>
 <span><span class="co">## [1] 0.8802156</span></span></code></pre>
 <p>It is also easy to change the mesh structure to tune the computation
diff --git a/docs/articles/examples/index.html b/docs/articles/examples/index.html
index 8b59dac6d..dc44f9c52 100644
--- a/docs/articles/examples/index.html
+++ b/docs/articles/examples/index.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -100,7 +100,7 @@ <h6 class="dropdown-header" data-toc-skip>Other topics</h6>
       <img src="" class="logo" alt=""><h1>Keras examples</h1>
             
             <h4 data-toc-skip class="date">Last Modified: 2023-11-30;
-Last Rendered: 2024-05-16</h4>
+Last Rendered: 2024-05-21</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/rstudio/keras/blob/HEAD/vignettes-src/examples/index.Rmd" class="external-link"><code>vignettes-src/examples/index.Rmd</code></a></small>
       <div class="d-none name"><code>index.Rmd</code></div>
diff --git a/docs/articles/examples/nlp/neural_machine_translation_with_transformer.html b/docs/articles/examples/nlp/neural_machine_translation_with_transformer.html
index 7fcaba392..b39a8b395 100644
--- a/docs/articles/examples/nlp/neural_machine_translation_with_transformer.html
+++ b/docs/articles/examples/nlp/neural_machine_translation_with_transformer.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../../../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -608,7 +608,7 @@ <h2 id="training-our-model">Training our model<a class="anchor" aria-label="anch
 <span><span class="op">)</span></span>
 <span><span class="va">transformer</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="va">train_ds</span>, epochs <span class="op">=</span> <span class="va">epochs</span>,</span>
 <span>                   validation_data <span class="op">=</span> <span class="va">val_ds</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 1297/1297 - 56s - 43ms/step - accuracy: 0.7708 - loss: 1.5755 - val_accuracy: 0.7784 - val_loss: 1.3806</span></span></code></pre>
+<pre><code><span><span class="co">## 1297/1297 - 58s - 44ms/step - accuracy: 0.7709 - loss: 1.5752 - val_accuracy: 0.7731 - val_loss: 1.4209</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="decoding-test-sentences">Decoding test sentences<a class="anchor" aria-label="anchor" href="#decoding-test-sentences"></a>
diff --git a/docs/articles/examples/nlp/text_classification_from_scratch.html b/docs/articles/examples/nlp/text_classification_from_scratch.html
index 4723c4d62..1ad3b0d2d 100644
--- a/docs/articles/examples/nlp/text_classification_from_scratch.html
+++ b/docs/articles/examples/nlp/text_classification_from_scratch.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../../../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -119,8 +119,7 @@ <h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#i
 <h2 id="setup">Setup<a class="anchor" aria-label="anchor" href="#setup"></a>
 </h2>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">options</a></span><span class="op">(</span>conflicts.policy <span class="op">=</span> <span class="st">"strict"</span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/rstudio/tensorflow" class="external-link">tensorflow</a></span>, exclude <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"shape"</span>, <span class="st">"set_random_seed"</span><span class="op">)</span><span class="op">)</span></span>
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/rstudio/tensorflow" class="external-link">tensorflow</a></span>, exclude <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"shape"</span>, <span class="st">"set_random_seed"</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/rstudio/tfdatasets" class="external-link">tfdatasets</a></span>, exclude <span class="op">=</span> <span class="st">"shape"</span><span class="op">)</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://keras.posit.co/">keras3</a></span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rstudio.github.io/reticulate/reference/use_python.html" class="external-link">use_virtualenv</a></span><span class="op">(</span><span class="st">"r-keras"</span><span class="op">)</span></span></code></pre></div>
@@ -153,8 +152,20 @@ <h2 id="load-the-data-imdb-movie-review-sentiment-classification">Load the data:
 folders contain text files, each of which represents one review (either
 positive or negative):</p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/readLines.html" class="external-link">readLines</a></span><span class="op">(</span><span class="st">"datasets/aclImdb/train/pos/6248_7.txt"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Being an Austrian myself this has been a straight knock in my face. Fortunately I don't live nowhere near the place where this movie takes place but unfortunately it portrays everything that the rest of Austria hates about Viennese people (or people close to that region). And it is very easy to read that this is exactly the directors intention: to let your head sink into your hands and say "Oh my god, how can THAT be possible!". No, not with me, the (in my opinion) totally exaggerated uncensored swinger club scene is not necessary, I watch porn, sure, but in this context I was rather disgusted than put in the right context.&lt;br /&gt;&lt;br /&gt;This movie tells a story about how misled people who suffer from lack of education or bad company try to survive and live in a world of redundancy and boring horizons. A girl who is treated like a whore by her super-jealous boyfriend (and still keeps coming back), a female teacher who discovers her masochism by putting the life of her super-cruel "lover" on the line, an old couple who has an almost mathematical daily cycle (she is the "official replacement" of his ex wife), a couple that has just divorced and has the ex husband suffer under the acts of his former wife obviously having a relationship with her masseuse and finally a crazy hitchhiker who asks her drivers the most unusual questions and stretches their nerves by just being super-annoying.&lt;br /&gt;&lt;br /&gt;After having seen it you feel almost nothing. You're not even shocked, sad, depressed or feel like doing anything... Maybe that's why I gave it 7 points, it made me react in a way I never reacted before. If that's good or bad is up to you!</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/writeLines.html" class="external-link">writeLines</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/strwrap.html" class="external-link">strwrap</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/readLines.html" class="external-link">readLines</a></span><span class="op">(</span><span class="st">"datasets/aclImdb/train/pos/4229_10.txt"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Don't waste time reading my review. Go out and see this</span></span>
+<span><span class="co">## astonishingly good episode, which may very well be the best Columbo</span></span>
+<span><span class="co">## ever written! Ruth Gordon is perfectly cast as the scheming yet</span></span>
+<span><span class="co">## charming mystery writer who murders her son-in-law to avenge his</span></span>
+<span><span class="co">## murder of her daughter. Columbo is his usual rumpled, befuddled and</span></span>
+<span><span class="co">## far-cleverer-than-he-seems self, and this particular installment</span></span>
+<span><span class="co">## features fantastic chemistry between Gordon and Falk. Ironically,</span></span>
+<span><span class="co">## this was not written by heralded creators Levinson or Link yet is</span></span>
+<span><span class="co">## possibly the densest, most thoroughly original and twist-laden</span></span>
+<span><span class="co">## Columbo plot ever. Utterly satisfying in nearly every department</span></span>
+<span><span class="co">## and overflowing with droll and witty dialogue and thinking. Truly</span></span>
+<span><span class="co">## unexpected and inventive climax tops all. 10/10...seek this one out</span></span>
+<span><span class="co">## on Netflix!</span></span></code></pre>
 <p>We are only interested in the <code>pos</code> and <code>neg</code>
 subfolders, so let’s delete the other subfolder that has text files in
 it:</p>
@@ -401,23 +412,23 @@ <h2 id="train-the-model">Train the model<a class="anchor" aria-label="anchor" hr
 <span><span class="co"># Fit the model using the train and test datasets.</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="va">train_ds</span>, validation_data <span class="op">=</span> <span class="va">val_ds</span>, epochs <span class="op">=</span> <span class="va">epochs</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/3</span></span>
-<span><span class="co">## 625/625 - 5s - 8ms/step - accuracy: 0.6944 - loss: 0.5248 - val_accuracy: 0.8624 - val_loss: 0.3150</span></span>
+<span><span class="co">## 625/625 - 6s - 10ms/step - accuracy: 0.6909 - loss: 0.5300 - val_accuracy: 0.8658 - val_loss: 0.3229</span></span>
 <span><span class="co">## Epoch 2/3</span></span>
-<span><span class="co">## 625/625 - 2s - 2ms/step - accuracy: 0.9046 - loss: 0.2403 - val_accuracy: 0.8730 - val_loss: 0.3135</span></span>
+<span><span class="co">## 625/625 - 2s - 3ms/step - accuracy: 0.9047 - loss: 0.2412 - val_accuracy: 0.8742 - val_loss: 0.3202</span></span>
 <span><span class="co">## Epoch 3/3</span></span>
-<span><span class="co">## 625/625 - 2s - 2ms/step - accuracy: 0.9524 - loss: 0.1275 - val_accuracy: 0.8716 - val_loss: 0.3424</span></span></code></pre>
+<span><span class="co">## 625/625 - 2s - 3ms/step - accuracy: 0.9573 - loss: 0.1237 - val_accuracy: 0.8704 - val_loss: 0.3551</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="evaluate-the-model-on-the-test-set">Evaluate the model on the test set<a class="anchor" aria-label="anchor" href="#evaluate-the-model-on-the-test-set"></a>
 </h2>
 <div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/tensorflow/man/evaluate.html" class="external-link">evaluate</a></span><span class="op">(</span><span class="va">test_ds</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 782/782 - 1s - 2ms/step - accuracy: 0.8608 - loss: 0.3672</span></span></code></pre>
+<pre><code><span><span class="co">## 782/782 - 1s - 2ms/step - accuracy: 0.8594 - loss: 0.3818</span></span></code></pre>
 <pre><code><span><span class="co">## $accuracy</span></span>
-<span><span class="co">## [1] 0.86084</span></span>
+<span><span class="co">## [1] 0.85936</span></span>
 <span><span class="co">##</span></span>
 <span><span class="co">## $loss</span></span>
-<span><span class="co">## [1] 0.3671538</span></span></code></pre>
+<span><span class="co">## [1] 0.381799</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="make-an-end-to-end-model">Make an end-to-end model<a class="anchor" aria-label="anchor" href="#make-an-end-to-end-model"></a>
@@ -442,9 +453,9 @@ <h2 id="make-an-end-to-end-model">Make an end-to-end model<a class="anchor" aria
 <span></span>
 <span><span class="co"># Test it with `raw_test_ds`, which yields raw strings</span></span>
 <span><span class="va">end_to_end_model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/tensorflow/man/evaluate.html" class="external-link">evaluate</a></span><span class="op">(</span><span class="va">raw_test_ds</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 782/782 - 3s - 4ms/step - accuracy: 0.8608 - loss: 0.0000e+00</span></span></code></pre>
+<pre><code><span><span class="co">## 782/782 - 3s - 4ms/step - accuracy: 0.8594 - loss: 0.0000e+00</span></span></code></pre>
 <pre><code><span><span class="co">## $accuracy</span></span>
-<span><span class="co">## [1] 0.86084</span></span>
+<span><span class="co">## [1] 0.85936</span></span>
 <span><span class="co">##</span></span>
 <span><span class="co">## $loss</span></span>
 <span><span class="co">## [1] 0</span></span></code></pre>
diff --git a/docs/articles/examples/structured_data/imbalanced_classification.html b/docs/articles/examples/structured_data/imbalanced_classification.html
index 2a1070415..b85886cd2 100644
--- a/docs/articles/examples/structured_data/imbalanced_classification.html
+++ b/docs/articles/examples/structured_data/imbalanced_classification.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../../../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -190,11 +190,11 @@ <h2 id="analyze-class-imbalance-in-the-targets">Analyze class imbalance in the t
 <span><span class="va">counts</span></span></code></pre></div>
 <pre><code><span><span class="co">##</span></span>
 <span><span class="co">##      0      1</span></span>
-<span><span class="co">## 227463    383</span></span></code></pre>
+<span><span class="co">## 227450    396</span></span></code></pre>
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/sprintf.html" class="external-link">sprintf</a></span><span class="op">(</span><span class="st">"Number of positive samples in training data: %i (%.2f%% of total)"</span>,</span>
 <span>            <span class="va">counts</span><span class="op">[</span><span class="st">"1"</span><span class="op">]</span>, <span class="fl">100</span> <span class="op">*</span> <span class="va">counts</span><span class="op">[</span><span class="st">"1"</span><span class="op">]</span> <span class="op">/</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">counts</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Number of positive samples in training data: 383 (0.17% of total)</span></span></code></pre>
+<pre><code><span><span class="co">## Number of positive samples in training data: 396 (0.17% of total)</span></span></code></pre>
 <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">weight_for_0</span> <span class="op">=</span> <span class="fl">1</span> <span class="op">/</span> <span class="va">counts</span><span class="op">[</span><span class="st">"0"</span><span class="op">]</span></span>
 <span><span class="va">weight_for_1</span> <span class="op">=</span> <span class="fl">1</span> <span class="op">/</span> <span class="va">counts</span><span class="op">[</span><span class="st">"1"</span><span class="op">]</span></span></code></pre></div>
@@ -283,74 +283,74 @@ <h2 id="train-the-model-with-class_weight-argument">Train the model with <code>c
 <span>  verbose <span class="op">=</span> <span class="fl">2</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/30</span></span>
-<span><span class="co">## 112/112 - 2s - 22ms/step - fn: 42.0000 - fp: 22983.0000 - loss: 2.3302e-06 - precision: 0.0146 - recall: 0.8903 - tn: 204480.0000 - tp: 341.0000 - val_fn: 12.0000 - val_fp: 536.0000 - val_loss: 0.0540 - val_precision: 0.1532 - val_recall: 0.8899 - val_tn: 56316.0000 - val_tp: 97.0000</span></span>
+<span><span class="co">## 112/112 - 3s - 27ms/step - fn: 40.0000 - fp: 23705.0000 - loss: 2.2728e-06 - precision: 0.0148 - recall: 0.8990 - tn: 203745.0000 - tp: 356.0000 - val_fn: 8.0000 - val_fp: 1502.0000 - val_loss: 0.1089 - val_precision: 0.0553 - val_recall: 0.9167 - val_tn: 55363.0000 - val_tp: 88.0000</span></span>
 <span><span class="co">## Epoch 2/30</span></span>
-<span><span class="co">## 112/112 - 0s - 4ms/step - fn: 36.0000 - fp: 6311.0000 - loss: 1.4468e-06 - precision: 0.0521 - recall: 0.9060 - tn: 221152.0000 - tp: 347.0000 - val_fn: 9.0000 - val_fp: 1081.0000 - val_loss: 0.0956 - val_precision: 0.0847 - val_recall: 0.9174 - val_tn: 55771.0000 - val_tp: 100.0000</span></span>
+<span><span class="co">## 112/112 - 1s - 6ms/step - fn: 33.0000 - fp: 8009.0000 - loss: 1.4949e-06 - precision: 0.0434 - recall: 0.9167 - tn: 219441.0000 - tp: 363.0000 - val_fn: 10.0000 - val_fp: 1176.0000 - val_loss: 0.0912 - val_precision: 0.0681 - val_recall: 0.8958 - val_tn: 55689.0000 - val_tp: 86.0000</span></span>
 <span><span class="co">## Epoch 3/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 27.0000 - fp: 7704.0000 - loss: 1.2139e-06 - precision: 0.0442 - recall: 0.9295 - tn: 219759.0000 - tp: 356.0000 - val_fn: 11.0000 - val_fp: 631.0000 - val_loss: 0.0443 - val_precision: 0.1344 - val_recall: 0.8991 - val_tn: 56221.0000 - val_tp: 98.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 32.0000 - fp: 7704.0000 - loss: 1.3369e-06 - precision: 0.0451 - recall: 0.9192 - tn: 219746.0000 - tp: 364.0000 - val_fn: 9.0000 - val_fp: 1202.0000 - val_loss: 0.0870 - val_precision: 0.0675 - val_recall: 0.9062 - val_tn: 55663.0000 - val_tp: 87.0000</span></span>
 <span><span class="co">## Epoch 4/30</span></span>
-<span><span class="co">## 112/112 - 0s - 1ms/step - fn: 26.0000 - fp: 8933.0000 - loss: 1.1396e-06 - precision: 0.0384 - recall: 0.9321 - tn: 218530.0000 - tp: 357.0000 - val_fn: 4.0000 - val_fp: 3813.0000 - val_loss: 0.1850 - val_precision: 0.0268 - val_recall: 0.9633 - val_tn: 53039.0000 - val_tp: 105.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 28.0000 - fp: 8366.0000 - loss: 1.2269e-06 - precision: 0.0421 - recall: 0.9293 - tn: 219084.0000 - tp: 368.0000 - val_fn: 10.0000 - val_fp: 1560.0000 - val_loss: 0.0967 - val_precision: 0.0522 - val_recall: 0.8958 - val_tn: 55305.0000 - val_tp: 86.0000</span></span>
 <span><span class="co">## Epoch 5/30</span></span>
-<span><span class="co">## 112/112 - 0s - 1ms/step - fn: 21.0000 - fp: 6451.0000 - loss: 9.1104e-07 - precision: 0.0531 - recall: 0.9452 - tn: 221012.0000 - tp: 362.0000 - val_fn: 9.0000 - val_fp: 1398.0000 - val_loss: 0.0724 - val_precision: 0.0668 - val_recall: 0.9174 - val_tn: 55454.0000 - val_tp: 100.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 19.0000 - fp: 6217.0000 - loss: 8.5492e-07 - precision: 0.0572 - recall: 0.9520 - tn: 221233.0000 - tp: 377.0000 - val_fn: 9.0000 - val_fp: 2626.0000 - val_loss: 0.1236 - val_precision: 0.0321 - val_recall: 0.9062 - val_tn: 54239.0000 - val_tp: 87.0000</span></span>
 <span><span class="co">## Epoch 6/30</span></span>
-<span><span class="co">## 112/112 - 0s - 1ms/step - fn: 16.0000 - fp: 5997.0000 - loss: 7.4602e-07 - precision: 0.0577 - recall: 0.9582 - tn: 221466.0000 - tp: 367.0000 - val_fn: 8.0000 - val_fp: 2379.0000 - val_loss: 0.1421 - val_precision: 0.0407 - val_recall: 0.9266 - val_tn: 54473.0000 - val_tp: 101.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 18.0000 - fp: 6566.0000 - loss: 7.4300e-07 - precision: 0.0544 - recall: 0.9545 - tn: 220884.0000 - tp: 378.0000 - val_fn: 8.0000 - val_fp: 2302.0000 - val_loss: 0.1081 - val_precision: 0.0368 - val_recall: 0.9167 - val_tn: 54563.0000 - val_tp: 88.0000</span></span>
 <span><span class="co">## Epoch 7/30</span></span>
-<span><span class="co">## 112/112 - 0s - 1ms/step - fn: 14.0000 - fp: 7075.0000 - loss: 7.6051e-07 - precision: 0.0496 - recall: 0.9634 - tn: 220388.0000 - tp: 369.0000 - val_fn: 10.0000 - val_fp: 888.0000 - val_loss: 0.0525 - val_precision: 0.1003 - val_recall: 0.9083 - val_tn: 55964.0000 - val_tp: 99.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 14.0000 - fp: 6819.0000 - loss: 7.1838e-07 - precision: 0.0530 - recall: 0.9646 - tn: 220631.0000 - tp: 382.0000 - val_fn: 7.0000 - val_fp: 1376.0000 - val_loss: 0.0800 - val_precision: 0.0608 - val_recall: 0.9271 - val_tn: 55489.0000 - val_tp: 89.0000</span></span>
 <span><span class="co">## Epoch 8/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 20.0000 - fp: 6333.0000 - loss: 8.2217e-07 - precision: 0.0542 - recall: 0.9478 - tn: 221130.0000 - tp: 363.0000 - val_fn: 6.0000 - val_fp: 3549.0000 - val_loss: 0.1391 - val_precision: 0.0282 - val_recall: 0.9450 - val_tn: 53303.0000 - val_tp: 103.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 19.0000 - fp: 7345.0000 - loss: 8.3921e-07 - precision: 0.0488 - recall: 0.9520 - tn: 220105.0000 - tp: 377.0000 - val_fn: 10.0000 - val_fp: 416.0000 - val_loss: 0.0406 - val_precision: 0.1713 - val_recall: 0.8958 - val_tn: 56449.0000 - val_tp: 86.0000</span></span>
 <span><span class="co">## Epoch 9/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 11.0000 - fp: 7070.0000 - loss: 8.2005e-07 - precision: 0.0500 - recall: 0.9713 - tn: 220393.0000 - tp: 372.0000 - val_fn: 11.0000 - val_fp: 1307.0000 - val_loss: 0.0807 - val_precision: 0.0698 - val_recall: 0.8991 - val_tn: 55545.0000 - val_tp: 98.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 13.0000 - fp: 6282.0000 - loss: 8.3488e-07 - precision: 0.0575 - recall: 0.9672 - tn: 221168.0000 - tp: 383.0000 - val_fn: 10.0000 - val_fp: 933.0000 - val_loss: 0.0450 - val_precision: 0.0844 - val_recall: 0.8958 - val_tn: 55932.0000 - val_tp: 86.0000</span></span>
 <span><span class="co">## Epoch 10/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 13.0000 - fp: 6641.0000 - loss: 7.9420e-07 - precision: 0.0528 - recall: 0.9661 - tn: 220822.0000 - tp: 370.0000 - val_fn: 8.0000 - val_fp: 1721.0000 - val_loss: 0.0840 - val_precision: 0.0554 - val_recall: 0.9266 - val_tn: 55131.0000 - val_tp: 101.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 15.0000 - fp: 7597.0000 - loss: 1.1098e-06 - precision: 0.0478 - recall: 0.9621 - tn: 219853.0000 - tp: 381.0000 - val_fn: 10.0000 - val_fp: 2417.0000 - val_loss: 0.4261 - val_precision: 0.0344 - val_recall: 0.8958 - val_tn: 54448.0000 - val_tp: 86.0000</span></span>
 <span><span class="co">## Epoch 11/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 13.0000 - fp: 7348.0000 - loss: 7.5710e-07 - precision: 0.0479 - recall: 0.9661 - tn: 220115.0000 - tp: 370.0000 - val_fn: 9.0000 - val_fp: 1006.0000 - val_loss: 0.0516 - val_precision: 0.0904 - val_recall: 0.9174 - val_tn: 55846.0000 - val_tp: 100.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 24.0000 - fp: 10269.0000 - loss: 1.9062e-06 - precision: 0.0350 - recall: 0.9394 - tn: 217181.0000 - tp: 372.0000 - val_fn: 9.0000 - val_fp: 1434.0000 - val_loss: 0.1261 - val_precision: 0.0572 - val_recall: 0.9062 - val_tn: 55431.0000 - val_tp: 87.0000</span></span>
 <span><span class="co">## Epoch 12/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 11.0000 - fp: 5095.0000 - loss: 5.2767e-07 - precision: 0.0680 - recall: 0.9713 - tn: 222368.0000 - tp: 372.0000 - val_fn: 10.0000 - val_fp: 932.0000 - val_loss: 0.0462 - val_precision: 0.0960 - val_recall: 0.9083 - val_tn: 55920.0000 - val_tp: 99.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 17.0000 - fp: 6634.0000 - loss: 1.0711e-06 - precision: 0.0540 - recall: 0.9571 - tn: 220816.0000 - tp: 379.0000 - val_fn: 8.0000 - val_fp: 2263.0000 - val_loss: 0.1654 - val_precision: 0.0374 - val_recall: 0.9167 - val_tn: 54602.0000 - val_tp: 88.0000</span></span>
 <span><span class="co">## Epoch 13/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 4173.0000 - loss: 4.2331e-07 - precision: 0.0833 - recall: 0.9896 - tn: 223290.0000 - tp: 379.0000 - val_fn: 11.0000 - val_fp: 442.0000 - val_loss: 0.0245 - val_precision: 0.1815 - val_recall: 0.8991 - val_tn: 56410.0000 - val_tp: 98.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 10.0000 - fp: 5620.0000 - loss: 5.3600e-07 - precision: 0.0643 - recall: 0.9747 - tn: 221830.0000 - tp: 386.0000 - val_fn: 12.0000 - val_fp: 914.0000 - val_loss: 0.0414 - val_precision: 0.0842 - val_recall: 0.8750 - val_tn: 55951.0000 - val_tp: 84.0000</span></span>
 <span><span class="co">## Epoch 14/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 9.0000 - fp: 5914.0000 - loss: 6.4355e-07 - precision: 0.0595 - recall: 0.9765 - tn: 221549.0000 - tp: 374.0000 - val_fn: 8.0000 - val_fp: 1851.0000 - val_loss: 0.0791 - val_precision: 0.0517 - val_recall: 0.9266 - val_tn: 55001.0000 - val_tp: 101.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 8.0000 - fp: 5103.0000 - loss: 5.1168e-07 - precision: 0.0707 - recall: 0.9798 - tn: 222347.0000 - tp: 388.0000 - val_fn: 11.0000 - val_fp: 1382.0000 - val_loss: 0.0660 - val_precision: 0.0579 - val_recall: 0.8854 - val_tn: 55483.0000 - val_tp: 85.0000</span></span>
 <span><span class="co">## Epoch 15/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 6205.0000 - loss: 5.7546e-07 - precision: 0.0571 - recall: 0.9817 - tn: 221258.0000 - tp: 376.0000 - val_fn: 8.0000 - val_fp: 1595.0000 - val_loss: 0.0678 - val_precision: 0.0596 - val_recall: 0.9266 - val_tn: 55257.0000 - val_tp: 101.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 4497.0000 - loss: 4.4632e-07 - precision: 0.0796 - recall: 0.9823 - tn: 222953.0000 - tp: 389.0000 - val_fn: 10.0000 - val_fp: 1805.0000 - val_loss: 0.0786 - val_precision: 0.0455 - val_recall: 0.8958 - val_tn: 55060.0000 - val_tp: 86.0000</span></span>
 <span><span class="co">## Epoch 16/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 8.0000 - fp: 6424.0000 - loss: 7.3225e-07 - precision: 0.0552 - recall: 0.9791 - tn: 221039.0000 - tp: 375.0000 - val_fn: 11.0000 - val_fp: 904.0000 - val_loss: 0.0412 - val_precision: 0.0978 - val_recall: 0.8991 - val_tn: 55948.0000 - val_tp: 98.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 5.0000 - fp: 5056.0000 - loss: 6.1038e-07 - precision: 0.0718 - recall: 0.9874 - tn: 222394.0000 - tp: 391.0000 - val_fn: 13.0000 - val_fp: 889.0000 - val_loss: 0.0676 - val_precision: 0.0854 - val_recall: 0.8646 - val_tn: 55976.0000 - val_tp: 83.0000</span></span>
 <span><span class="co">## Epoch 17/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 4041.0000 - loss: 3.7349e-07 - precision: 0.0857 - recall: 0.9896 - tn: 223422.0000 - tp: 379.0000 - val_fn: 9.0000 - val_fp: 1348.0000 - val_loss: 0.0906 - val_precision: 0.0691 - val_recall: 0.9174 - val_tn: 55504.0000 - val_tp: 100.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 4639.0000 - loss: 4.5763e-07 - precision: 0.0781 - recall: 0.9924 - tn: 222811.0000 - tp: 393.0000 - val_fn: 11.0000 - val_fp: 1185.0000 - val_loss: 0.0669 - val_precision: 0.0669 - val_recall: 0.8854 - val_tn: 55680.0000 - val_tp: 85.0000</span></span>
 <span><span class="co">## Epoch 18/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 6.0000 - fp: 5602.0000 - loss: 6.0889e-07 - precision: 0.0631 - recall: 0.9843 - tn: 221861.0000 - tp: 377.0000 - val_fn: 8.0000 - val_fp: 935.0000 - val_loss: 0.0487 - val_precision: 0.0975 - val_recall: 0.9266 - val_tn: 55917.0000 - val_tp: 101.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 4355.0000 - loss: 4.5299e-07 - precision: 0.0826 - recall: 0.9899 - tn: 223095.0000 - tp: 392.0000 - val_fn: 9.0000 - val_fp: 1405.0000 - val_loss: 0.0772 - val_precision: 0.0583 - val_recall: 0.9062 - val_tn: 55460.0000 - val_tp: 87.0000</span></span>
 <span><span class="co">## Epoch 19/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 5741.0000 - loss: 5.3650e-07 - precision: 0.0615 - recall: 0.9817 - tn: 221722.0000 - tp: 376.0000 - val_fn: 8.0000 - val_fp: 1279.0000 - val_loss: 0.0567 - val_precision: 0.0732 - val_recall: 0.9266 - val_tn: 55573.0000 - val_tp: 101.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 6.0000 - fp: 5779.0000 - loss: 4.8324e-07 - precision: 0.0632 - recall: 0.9848 - tn: 221671.0000 - tp: 390.0000 - val_fn: 10.0000 - val_fp: 1135.0000 - val_loss: 0.0546 - val_precision: 0.0704 - val_recall: 0.8958 - val_tn: 55730.0000 - val_tp: 86.0000</span></span>
 <span><span class="co">## Epoch 20/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 1.0000 - fp: 3224.0000 - loss: 2.7791e-07 - precision: 0.1059 - recall: 0.9974 - tn: 224239.0000 - tp: 382.0000 - val_fn: 11.0000 - val_fp: 705.0000 - val_loss: 0.0347 - val_precision: 0.1220 - val_recall: 0.8991 - val_tn: 56147.0000 - val_tp: 98.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 4748.0000 - loss: 3.7424e-07 - precision: 0.0764 - recall: 0.9924 - tn: 222702.0000 - tp: 393.0000 - val_fn: 9.0000 - val_fp: 895.0000 - val_loss: 0.0396 - val_precision: 0.0886 - val_recall: 0.9062 - val_tn: 55970.0000 - val_tp: 87.0000</span></span>
 <span><span class="co">## Epoch 21/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 2.0000 - fp: 3403.0000 - loss: 2.7057e-07 - precision: 0.1007 - recall: 0.9948 - tn: 224060.0000 - tp: 381.0000 - val_fn: 11.0000 - val_fp: 330.0000 - val_loss: 0.0178 - val_precision: 0.2290 - val_recall: 0.8991 - val_tn: 56522.0000 - val_tp: 98.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 3798.0000 - loss: 3.1417e-07 - precision: 0.0938 - recall: 0.9924 - tn: 223652.0000 - tp: 393.0000 - val_fn: 10.0000 - val_fp: 652.0000 - val_loss: 0.0309 - val_precision: 0.1165 - val_recall: 0.8958 - val_tn: 56213.0000 - val_tp: 86.0000</span></span>
 <span><span class="co">## Epoch 22/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 1904.0000 - loss: 2.0272e-07 - precision: 0.1664 - recall: 0.9922 - tn: 225559.0000 - tp: 380.0000 - val_fn: 10.0000 - val_fp: 1666.0000 - val_loss: 0.1536 - val_precision: 0.0561 - val_recall: 0.9083 - val_tn: 55186.0000 - val_tp: 99.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 0.0000e+00 - fp: 2188.0000 - loss: 1.8112e-07 - precision: 0.1533 - recall: 1.0000 - tn: 225262.0000 - tp: 396.0000 - val_fn: 10.0000 - val_fp: 427.0000 - val_loss: 0.0250 - val_precision: 0.1676 - val_recall: 0.8958 - val_tn: 56438.0000 - val_tp: 86.0000</span></span>
 <span><span class="co">## Epoch 23/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 11.0000 - fp: 6652.0000 - loss: 7.3955e-07 - precision: 0.0530 - recall: 0.9713 - tn: 220811.0000 - tp: 372.0000 - val_fn: 9.0000 - val_fp: 1633.0000 - val_loss: 0.0681 - val_precision: 0.0577 - val_recall: 0.9174 - val_tn: 55219.0000 - val_tp: 100.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 3066.0000 - loss: 3.4932e-07 - precision: 0.1134 - recall: 0.9899 - tn: 224384.0000 - tp: 392.0000 - val_fn: 11.0000 - val_fp: 1053.0000 - val_loss: 0.0494 - val_precision: 0.0747 - val_recall: 0.8854 - val_tn: 55812.0000 - val_tp: 85.0000</span></span>
 <span><span class="co">## Epoch 24/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 3458.0000 - loss: 2.8492e-07 - precision: 0.0990 - recall: 0.9922 - tn: 224005.0000 - tp: 380.0000 - val_fn: 12.0000 - val_fp: 635.0000 - val_loss: 0.0299 - val_precision: 0.1325 - val_recall: 0.8899 - val_tn: 56217.0000 - val_tp: 97.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 3175.0000 - loss: 5.1994e-07 - precision: 0.1099 - recall: 0.9899 - tn: 224275.0000 - tp: 392.0000 - val_fn: 11.0000 - val_fp: 1012.0000 - val_loss: 0.0997 - val_precision: 0.0775 - val_recall: 0.8854 - val_tn: 55853.0000 - val_tp: 85.0000</span></span>
 <span><span class="co">## Epoch 25/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 5581.0000 - loss: 6.4007e-07 - precision: 0.0631 - recall: 0.9817 - tn: 221882.0000 - tp: 376.0000 - val_fn: 9.0000 - val_fp: 1782.0000 - val_loss: 0.0677 - val_precision: 0.0531 - val_recall: 0.9174 - val_tn: 55070.0000 - val_tp: 100.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 5874.0000 - loss: 6.5862e-07 - precision: 0.0621 - recall: 0.9823 - tn: 221576.0000 - tp: 389.0000 - val_fn: 9.0000 - val_fp: 3090.0000 - val_loss: 0.1296 - val_precision: 0.0274 - val_recall: 0.9062 - val_tn: 53775.0000 - val_tp: 87.0000</span></span>
 <span><span class="co">## Epoch 26/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 1.0000 - fp: 3655.0000 - loss: 2.9078e-07 - precision: 0.0946 - recall: 0.9974 - tn: 223808.0000 - tp: 382.0000 - val_fn: 13.0000 - val_fp: 677.0000 - val_loss: 0.0310 - val_precision: 0.1242 - val_recall: 0.8807 - val_tn: 56175.0000 - val_tp: 96.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 6.0000 - fp: 5395.0000 - loss: 5.9253e-07 - precision: 0.0674 - recall: 0.9848 - tn: 222055.0000 - tp: 390.0000 - val_fn: 9.0000 - val_fp: 1320.0000 - val_loss: 0.0928 - val_precision: 0.0618 - val_recall: 0.9062 - val_tn: 55545.0000 - val_tp: 87.0000</span></span>
 <span><span class="co">## Epoch 27/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 2.0000 - fp: 3187.0000 - loss: 2.7425e-07 - precision: 0.1068 - recall: 0.9948 - tn: 224276.0000 - tp: 381.0000 - val_fn: 12.0000 - val_fp: 701.0000 - val_loss: 0.0271 - val_precision: 0.1216 - val_recall: 0.8899 - val_tn: 56151.0000 - val_tp: 97.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 3887.0000 - loss: 7.1278e-07 - precision: 0.0916 - recall: 0.9899 - tn: 223563.0000 - tp: 392.0000 - val_fn: 12.0000 - val_fp: 572.0000 - val_loss: 0.0352 - val_precision: 0.1280 - val_recall: 0.8750 - val_tn: 56293.0000 - val_tp: 84.0000</span></span>
 <span><span class="co">## Epoch 28/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 10.0000 - fp: 4659.0000 - loss: 6.8191e-07 - precision: 0.0741 - recall: 0.9739 - tn: 222804.0000 - tp: 373.0000 - val_fn: 11.0000 - val_fp: 1022.0000 - val_loss: 0.0797 - val_precision: 0.0875 - val_recall: 0.8991 - val_tn: 55830.0000 - val_tp: 98.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 2209.0000 - loss: 5.8153e-07 - precision: 0.1510 - recall: 0.9924 - tn: 225241.0000 - tp: 393.0000 - val_fn: 11.0000 - val_fp: 1373.0000 - val_loss: 0.0808 - val_precision: 0.0583 - val_recall: 0.8854 - val_tn: 55492.0000 - val_tp: 85.0000</span></span>
 <span><span class="co">## Epoch 29/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 4183.0000 - loss: 3.9729e-07 - precision: 0.0831 - recall: 0.9896 - tn: 223280.0000 - tp: 379.0000 - val_fn: 12.0000 - val_fp: 406.0000 - val_loss: 0.0232 - val_precision: 0.1928 - val_recall: 0.8899 - val_tn: 56446.0000 - val_tp: 97.0000</span></span>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 1.0000 - fp: 2506.0000 - loss: 2.9031e-07 - precision: 0.1362 - recall: 0.9975 - tn: 224944.0000 - tp: 395.0000 - val_fn: 11.0000 - val_fp: 690.0000 - val_loss: 0.0401 - val_precision: 0.1097 - val_recall: 0.8854 - val_tn: 56175.0000 - val_tp: 85.0000</span></span>
 <span><span class="co">## Epoch 30/30</span></span>
-<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 2.0000 - fp: 2432.0000 - loss: 2.3592e-07 - precision: 0.1354 - recall: 0.9948 - tn: 225031.0000 - tp: 381.0000 - val_fn: 13.0000 - val_fp: 371.0000 - val_loss: 0.0200 - val_precision: 0.2056 - val_recall: 0.8807 - val_tn: 56481.0000 - val_tp: 96.0000</span></span></code></pre>
+<span><span class="co">## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 2719.0000 - loss: 6.1097e-07 - precision: 0.1260 - recall: 0.9899 - tn: 224731.0000 - tp: 392.0000 - val_fn: 13.0000 - val_fp: 861.0000 - val_loss: 0.0362 - val_precision: 0.0879 - val_recall: 0.8646 - val_tn: 56004.0000 - val_tp: 83.0000</span></span></code></pre>
 <div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">val_pred</span> <span class="op">&lt;-</span> <span class="va">model</span> <span class="op"><a href="../../../reference/pipe.html">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">val_features</span><span class="op">)</span> <span class="op"><a href="../../../reference/pipe.html">%&gt;%</a></span></span>
 <span>  <span class="op">{</span> <span class="fu"><a href="https://rdrr.io/r/base/integer.html" class="external-link">as.integer</a></span><span class="op">(</span><span class="va">.</span> <span class="op">&gt;</span> <span class="fl">0.5</span><span class="op">)</span> <span class="op">}</span></span></code></pre></div>
-<pre><code><span><span class="co">## 1781/1781 - 1s - 286us/step</span></span></code></pre>
+<pre><code><span><span class="co">## 1781/1781 - 1s - 311us/step</span></span></code></pre>
 <div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pred_correct</span> <span class="op">&lt;-</span> <span class="va">val_df</span><span class="op">$</span><span class="va">Class</span> <span class="op">==</span> <span class="va">val_pred</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/sprintf.html" class="external-link">sprintf</a></span><span class="op">(</span><span class="st">"Validation accuracy: %.2f"</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html" class="external-link">mean</a></span><span class="op">(</span><span class="va">pred_correct</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Validation accuracy: 0.99</span></span></code></pre>
+<pre><code><span><span class="co">## Validation accuracy: 0.98</span></span></code></pre>
 <div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">fraudulent</span> <span class="op">&lt;-</span> <span class="va">val_df</span><span class="op">$</span><span class="va">Class</span> <span class="op">==</span> <span class="fl">1</span></span>
 <span></span>
@@ -364,9 +364,9 @@ <h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#con
 <p>At the end of training, out of 56,961 validation transactions, we
 are:</p>
 <ul>
-<li>Correctly identifying 96 of them as fraudulent</li>
+<li>Correctly identifying 83 of them as fraudulent</li>
 <li>Missing 13 fraudulent transactions</li>
-<li>At the cost of incorrectly flagging 371 legitimate transactions</li>
+<li>At the cost of incorrectly flagging 861 legitimate transactions</li>
 </ul>
 <p>In the real world, one would put an even higher weight on class 1, so
 as to reflect that False Negatives are more costly than False
diff --git a/docs/articles/examples/structured_data/structured_data_classification_with_feature_space.html b/docs/articles/examples/structured_data/structured_data_classification_with_feature_space.html
index f1e6198ad..e67ed5d34 100644
--- a/docs/articles/examples/structured_data/structured_data_classification_with_feature_space.html
+++ b/docs/articles/examples/structured_data/structured_data_classification_with_feature_space.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../../../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -327,20 +327,20 @@ <h2 id="preparing-the-data">Preparing the data<a class="anchor" aria-label="anch
 <span><span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"Input: "</span><span class="op">)</span>; <span class="fu"><a href="https://rdrr.io/r/utils/str.html" class="external-link">str</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"Target: "</span><span class="op">)</span>; <span class="fu"><a href="https://rdrr.io/r/utils/str.html" class="external-link">str</a></span><span class="op">(</span><span class="va">y</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Input: List of 13</span></span>
-<span><span class="co">##  $ age     :&lt;tf.Tensor: shape=(), dtype=int32, numpy=41&gt;</span></span>
+<span><span class="co">##  $ age     :&lt;tf.Tensor: shape=(), dtype=int32, numpy=59&gt;</span></span>
 <span><span class="co">##  $ sex     :&lt;tf.Tensor: shape=(), dtype=int32, numpy=1&gt;</span></span>
-<span><span class="co">##  $ cp      :&lt;tf.Tensor: shape=(), dtype=int32, numpy=2&gt;</span></span>
-<span><span class="co">##  $ trestbps:&lt;tf.Tensor: shape=(), dtype=int32, numpy=120&gt;</span></span>
-<span><span class="co">##  $ chol    :&lt;tf.Tensor: shape=(), dtype=int32, numpy=157&gt;</span></span>
-<span><span class="co">##  $ fbs     :&lt;tf.Tensor: shape=(), dtype=int32, numpy=0&gt;</span></span>
-<span><span class="co">##  $ restecg :&lt;tf.Tensor: shape=(), dtype=int32, numpy=0&gt;</span></span>
-<span><span class="co">##  $ thalach :&lt;tf.Tensor: shape=(), dtype=int32, numpy=182&gt;</span></span>
+<span><span class="co">##  $ cp      :&lt;tf.Tensor: shape=(), dtype=int32, numpy=4&gt;</span></span>
+<span><span class="co">##  $ trestbps:&lt;tf.Tensor: shape=(), dtype=int32, numpy=164&gt;</span></span>
+<span><span class="co">##  $ chol    :&lt;tf.Tensor: shape=(), dtype=int32, numpy=176&gt;</span></span>
+<span><span class="co">##  $ fbs     :&lt;tf.Tensor: shape=(), dtype=int32, numpy=1&gt;</span></span>
+<span><span class="co">##  $ restecg :&lt;tf.Tensor: shape=(), dtype=int32, numpy=2&gt;</span></span>
+<span><span class="co">##  $ thalach :&lt;tf.Tensor: shape=(), dtype=int32, numpy=90&gt;</span></span>
 <span><span class="co">##  $ exang   :&lt;tf.Tensor: shape=(), dtype=int32, numpy=0&gt;</span></span>
-<span><span class="co">##  $ oldpeak :&lt;tf.Tensor: shape=(), dtype=float32, numpy=0.0&gt;</span></span>
-<span><span class="co">##  $ slope   :&lt;tf.Tensor: shape=(), dtype=int32, numpy=1&gt;</span></span>
-<span><span class="co">##  $ ca      :&lt;tf.Tensor: shape=(), dtype=int32, numpy=0&gt;</span></span>
-<span><span class="co">##  $ thal    :&lt;tf.Tensor: shape=(), dtype=string, numpy=b'normal'&gt;</span></span>
-<span><span class="co">## Target: &lt;tf.Tensor: shape=(), dtype=int32, numpy=0&gt;</span></span></code></pre>
+<span><span class="co">##  $ oldpeak :&lt;tf.Tensor: shape=(), dtype=float32, numpy=1.0&gt;</span></span>
+<span><span class="co">##  $ slope   :&lt;tf.Tensor: shape=(), dtype=int32, numpy=2&gt;</span></span>
+<span><span class="co">##  $ ca      :&lt;tf.Tensor: shape=(), dtype=int32, numpy=2&gt;</span></span>
+<span><span class="co">##  $ thal    :&lt;tf.Tensor: shape=(), dtype=string, numpy=b'fixed'&gt;</span></span>
+<span><span class="co">## Target: &lt;tf.Tensor: shape=(), dtype=int32, numpy=1&gt;</span></span></code></pre>
 <p>Let’s batch the datasets:</p>
 <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">train_ds</span> <span class="op">&lt;-</span> <span class="va">train_ds</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/tfdatasets/man/dataset_batch.html" class="external-link">dataset_batch</a></span><span class="op">(</span><span class="fl">32</span><span class="op">)</span></span>
@@ -350,7 +350,7 @@ <h2 id="preparing-the-data">Preparing the data<a class="anchor" aria-label="anch
 <h2 id="configuring-a-featurespace">Configuring a <code>FeatureSpace</code><a class="anchor" aria-label="anchor" href="#configuring-a-featurespace"></a>
 </h2>
 <p>To configure how each feature should be preprocessed, we instantiate
-a <code>layer_feature_space</code>, and we pass to it a dictionary
+a <code><a href="../../../reference/layer_feature_space.html">layer_feature_space()</a></code>, and we pass to it a dictionary
 (named list with unique names) that maps the name of our features to a
 string that describes the feature type.</p>
 <p>We have a few “integer categorical” features such as
@@ -480,7 +480,7 @@ <h2 id="adapt-the-featurespace-to-the-training-data">Adapt the <code>FeatureSpac
 <span><span class="va">preprocessed_x</span> <span class="op">&lt;-</span> <span class="fu">feature_space</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
 <span><span class="va">preprocessed_x</span></span></code></pre></div>
 <pre><code><span><span class="co">## tf.Tensor(</span></span>
-<span><span class="co">## [[0. 0. 0. ... 0. 1. 0.]</span></span>
+<span><span class="co">## [[0. 0. 1. ... 0. 0. 0.]</span></span>
 <span><span class="co">##  [0. 0. 0. ... 0. 0. 0.]</span></span>
 <span><span class="co">##  [0. 0. 0. ... 0. 0. 0.]</span></span>
 <span><span class="co">##  ...</span></span>
@@ -575,45 +575,45 @@ <h2 id="train-the-model">Train the model<a class="anchor" aria-label="anchor" hr
 <span>  verbose <span class="op">=</span> <span class="fl">2</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/20</span></span>
-<span><span class="co">## 8/8 - 3s - 320ms/step - accuracy: 0.4564 - loss: 0.7517 - val_accuracy: 0.4833 - val_loss: 0.7192</span></span>
+<span><span class="co">## 8/8 - 3s - 326ms/step - accuracy: 0.4315 - loss: 0.7427 - val_accuracy: 0.5333 - val_loss: 0.7048</span></span>
 <span><span class="co">## Epoch 2/20</span></span>
-<span><span class="co">## 8/8 - 0s - 13ms/step - accuracy: 0.5436 - loss: 0.6978 - val_accuracy: 0.6167 - val_loss: 0.6730</span></span>
+<span><span class="co">## 8/8 - 0s - 13ms/step - accuracy: 0.5311 - loss: 0.6984 - val_accuracy: 0.6500 - val_loss: 0.6438</span></span>
 <span><span class="co">## Epoch 3/20</span></span>
-<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.6473 - loss: 0.6429 - val_accuracy: 0.6500 - val_loss: 0.6319</span></span>
+<span><span class="co">## 8/8 - 0s - 13ms/step - accuracy: 0.6473 - loss: 0.6316 - val_accuracy: 0.6833 - val_loss: 0.5937</span></span>
 <span><span class="co">## Epoch 4/20</span></span>
-<span><span class="co">## 8/8 - 0s - 13ms/step - accuracy: 0.6639 - loss: 0.6126 - val_accuracy: 0.6833 - val_loss: 0.5965</span></span>
+<span><span class="co">## 8/8 - 0s - 13ms/step - accuracy: 0.6639 - loss: 0.6134 - val_accuracy: 0.7500 - val_loss: 0.5524</span></span>
 <span><span class="co">## Epoch 5/20</span></span>
-<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.7137 - loss: 0.5854 - val_accuracy: 0.7167 - val_loss: 0.5653</span></span>
+<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.7178 - loss: 0.5820 - val_accuracy: 0.7667 - val_loss: 0.5176</span></span>
 <span><span class="co">## Epoch 6/20</span></span>
-<span><span class="co">## 8/8 - 0s - 13ms/step - accuracy: 0.7386 - loss: 0.5565 - val_accuracy: 0.7667 - val_loss: 0.5353</span></span>
+<span><span class="co">## 8/8 - 0s - 13ms/step - accuracy: 0.7718 - loss: 0.5573 - val_accuracy: 0.7500 - val_loss: 0.4897</span></span>
 <span><span class="co">## Epoch 7/20</span></span>
-<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.7718 - loss: 0.5237 - val_accuracy: 0.7667 - val_loss: 0.5086</span></span>
+<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.7718 - loss: 0.5200 - val_accuracy: 0.8167 - val_loss: 0.4640</span></span>
 <span><span class="co">## Epoch 8/20</span></span>
-<span><span class="co">## 8/8 - 0s - 13ms/step - accuracy: 0.7718 - loss: 0.5098 - val_accuracy: 0.8167 - val_loss: 0.4835</span></span>
+<span><span class="co">## 8/8 - 0s - 14ms/step - accuracy: 0.7759 - loss: 0.5068 - val_accuracy: 0.8167 - val_loss: 0.4388</span></span>
 <span><span class="co">## Epoch 9/20</span></span>
-<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.8091 - loss: 0.4921 - val_accuracy: 0.8500 - val_loss: 0.4621</span></span>
+<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.8174 - loss: 0.4724 - val_accuracy: 0.8333 - val_loss: 0.4162</span></span>
 <span><span class="co">## Epoch 10/20</span></span>
-<span><span class="co">## 8/8 - 0s - 13ms/step - accuracy: 0.8050 - loss: 0.4562 - val_accuracy: 0.8333 - val_loss: 0.4435</span></span>
+<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.8050 - loss: 0.4545 - val_accuracy: 0.8167 - val_loss: 0.3960</span></span>
 <span><span class="co">## Epoch 11/20</span></span>
-<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.8091 - loss: 0.4398 - val_accuracy: 0.8333 - val_loss: 0.4261</span></span>
+<span><span class="co">## 8/8 - 0s - 13ms/step - accuracy: 0.8091 - loss: 0.4514 - val_accuracy: 0.8500 - val_loss: 0.3786</span></span>
 <span><span class="co">## Epoch 12/20</span></span>
-<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.8257 - loss: 0.4272 - val_accuracy: 0.8167 - val_loss: 0.4095</span></span>
+<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.8423 - loss: 0.4291 - val_accuracy: 0.8500 - val_loss: 0.3647</span></span>
 <span><span class="co">## Epoch 13/20</span></span>
-<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.8050 - loss: 0.4381 - val_accuracy: 0.8333 - val_loss: 0.3949</span></span>
+<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.8465 - loss: 0.4028 - val_accuracy: 0.8667 - val_loss: 0.3499</span></span>
 <span><span class="co">## Epoch 14/20</span></span>
-<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.8091 - loss: 0.4118 - val_accuracy: 0.8333 - val_loss: 0.3828</span></span>
+<span><span class="co">## 8/8 - 0s - 13ms/step - accuracy: 0.8340 - loss: 0.4037 - val_accuracy: 0.8667 - val_loss: 0.3369</span></span>
 <span><span class="co">## Epoch 15/20</span></span>
-<span><span class="co">## 8/8 - 0s - 13ms/step - accuracy: 0.8382 - loss: 0.3797 - val_accuracy: 0.8500 - val_loss: 0.3712</span></span>
+<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.8548 - loss: 0.3928 - val_accuracy: 0.8667 - val_loss: 0.3289</span></span>
 <span><span class="co">## Epoch 16/20</span></span>
-<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.8423 - loss: 0.3884 - val_accuracy: 0.8500 - val_loss: 0.3610</span></span>
+<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.8589 - loss: 0.3745 - val_accuracy: 0.8667 - val_loss: 0.3206</span></span>
 <span><span class="co">## Epoch 17/20</span></span>
-<span><span class="co">## 8/8 - 0s - 13ms/step - accuracy: 0.8548 - loss: 0.3654 - val_accuracy: 0.8500 - val_loss: 0.3511</span></span>
+<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.8257 - loss: 0.3820 - val_accuracy: 0.8667 - val_loss: 0.3129</span></span>
 <span><span class="co">## Epoch 18/20</span></span>
-<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.8299 - loss: 0.3726 - val_accuracy: 0.8667 - val_loss: 0.3434</span></span>
+<span><span class="co">## 8/8 - 0s - 14ms/step - accuracy: 0.8631 - loss: 0.3650 - val_accuracy: 0.8667 - val_loss: 0.3079</span></span>
 <span><span class="co">## Epoch 19/20</span></span>
-<span><span class="co">## 8/8 - 0s - 13ms/step - accuracy: 0.8672 - loss: 0.3472 - val_accuracy: 0.8667 - val_loss: 0.3365</span></span>
+<span><span class="co">## 8/8 - 0s - 12ms/step - accuracy: 0.8382 - loss: 0.3635 - val_accuracy: 0.8667 - val_loss: 0.3024</span></span>
 <span><span class="co">## Epoch 20/20</span></span>
-<span><span class="co">## 8/8 - 0s - 13ms/step - accuracy: 0.8714 - loss: 0.3380 - val_accuracy: 0.8667 - val_loss: 0.3301</span></span></code></pre>
+<span><span class="co">## 8/8 - 0s - 13ms/step - accuracy: 0.8631 - loss: 0.3524 - val_accuracy: 0.8833 - val_loss: 0.2970</span></span></code></pre>
 <p>We quickly get to 80% validation accuracy.</p>
 </div>
 <div class="section level2">
@@ -647,7 +647,7 @@ <h2 id="inference-on-new-data-with-the-end-to-end-model">Inference on new data w
 <span><span class="st">  This particular patient had a {(100 * predictions) |&gt; signif(3)}% probability</span></span>
 <span><span class="st">  of having a heart disease, as evaluated by our model.</span></span>
 <span><span class="st">)---"</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## This particular patient had a 48.9% probability</span></span>
+<pre><code><span><span class="co">## This particular patient had a 49.7% probability</span></span>
 <span><span class="co">## of having a heart disease, as evaluated by our model.</span></span></code></pre>
 </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/docs/articles/examples/timeseries/timeseries_anomaly_detection.html b/docs/articles/examples/timeseries/timeseries_anomaly_detection.html
index 745f13b2e..262617c79 100644
--- a/docs/articles/examples/timeseries/timeseries_anomaly_detection.html
+++ b/docs/articles/examples/timeseries/timeseries_anomaly_detection.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../../../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -325,13 +325,13 @@ <h2 id="train-the-model">Train the model<a class="anchor" aria-label="anchor" hr
 <span>  <span class="op">)</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/50</span></span>
-<span><span class="co">## 106/106 - 5s - 51ms/step - loss: 0.1863 - val_loss: 0.0316</span></span>
+<span><span class="co">## 106/106 - 7s - 62ms/step - loss: 0.1863 - val_loss: 0.0316</span></span>
 <span><span class="co">## Epoch 2/50</span></span>
 <span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0333 - val_loss: 0.0233</span></span>
 <span><span class="co">## Epoch 3/50</span></span>
 <span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0248 - val_loss: 0.0201</span></span>
 <span><span class="co">## Epoch 4/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0209 - val_loss: 0.0187</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0209 - val_loss: 0.0186</span></span>
 <span><span class="co">## Epoch 5/50</span></span>
 <span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0179 - val_loss: 0.0146</span></span>
 <span><span class="co">## Epoch 6/50</span></span>
@@ -341,29 +341,29 @@ <h2 id="train-the-model">Train the model<a class="anchor" aria-label="anchor" hr
 <span><span class="co">## Epoch 8/50</span></span>
 <span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0109 - val_loss: 0.0088</span></span>
 <span><span class="co">## Epoch 9/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0096 - val_loss: 0.0084</span></span>
+<span><span class="co">## 106/106 - 0s - 3ms/step - loss: 0.0096 - val_loss: 0.0084</span></span>
 <span><span class="co">## Epoch 10/50</span></span>
 <span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0086 - val_loss: 0.0069</span></span>
 <span><span class="co">## Epoch 11/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0078 - val_loss: 0.0061</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0078 - val_loss: 0.0062</span></span>
 <span><span class="co">## Epoch 12/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0073 - val_loss: 0.0057</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0073 - val_loss: 0.0058</span></span>
 <span><span class="co">## Epoch 13/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0068 - val_loss: 0.0053</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0067 - val_loss: 0.0054</span></span>
 <span><span class="co">## Epoch 14/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0064 - val_loss: 0.0050</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0063 - val_loss: 0.0051</span></span>
 <span><span class="co">## Epoch 15/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0061 - val_loss: 0.0045</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0060 - val_loss: 0.0045</span></span>
 <span><span class="co">## Epoch 16/50</span></span>
 <span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0058 - val_loss: 0.0039</span></span>
 <span><span class="co">## Epoch 17/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0056 - val_loss: 0.0040</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0055 - val_loss: 0.0041</span></span>
 <span><span class="co">## Epoch 18/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0053 - val_loss: 0.0035</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0052 - val_loss: 0.0036</span></span>
 <span><span class="co">## Epoch 19/50</span></span>
 <span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0050 - val_loss: 0.0037</span></span>
 <span><span class="co">## Epoch 20/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0048 - val_loss: 0.0032</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0048 - val_loss: 0.0033</span></span>
 <span><span class="co">## Epoch 21/50</span></span>
 <span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0046 - val_loss: 0.0031</span></span>
 <span><span class="co">## Epoch 22/50</span></span>
@@ -371,33 +371,33 @@ <h2 id="train-the-model">Train the model<a class="anchor" aria-label="anchor" hr
 <span><span class="co">## Epoch 23/50</span></span>
 <span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0042 - val_loss: 0.0033</span></span>
 <span><span class="co">## Epoch 24/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0041 - val_loss: 0.0031</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0040 - val_loss: 0.0031</span></span>
 <span><span class="co">## Epoch 25/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0039 - val_loss: 0.0033</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0038 - val_loss: 0.0032</span></span>
 <span><span class="co">## Epoch 26/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0037 - val_loss: 0.0031</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0036 - val_loss: 0.0031</span></span>
 <span><span class="co">## Epoch 27/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0035 - val_loss: 0.0032</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0035 - val_loss: 0.0031</span></span>
 <span><span class="co">## Epoch 28/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0034 - val_loss: 0.0025</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0033 - val_loss: 0.0025</span></span>
 <span><span class="co">## Epoch 29/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0033 - val_loss: 0.0026</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0032 - val_loss: 0.0026</span></span>
 <span><span class="co">## Epoch 30/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0032 - val_loss: 0.0028</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0031 - val_loss: 0.0029</span></span>
 <span><span class="co">## Epoch 31/50</span></span>
 <span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0030 - val_loss: 0.0024</span></span>
 <span><span class="co">## Epoch 32/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0029 - val_loss: 0.0026</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0029 - val_loss: 0.0025</span></span>
 <span><span class="co">## Epoch 33/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0028 - val_loss: 0.0026</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0028 - val_loss: 0.0025</span></span>
 <span><span class="co">## Epoch 34/50</span></span>
 <span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0027 - val_loss: 0.0022</span></span>
 <span><span class="co">## Epoch 35/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0027 - val_loss: 0.0024</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0026 - val_loss: 0.0023</span></span>
 <span><span class="co">## Epoch 36/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0026 - val_loss: 0.0026</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0026 - val_loss: 0.0025</span></span>
 <span><span class="co">## Epoch 37/50</span></span>
-<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0025 - val_loss: 0.0026</span></span>
+<span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0025 - val_loss: 0.0025</span></span>
 <span><span class="co">## Epoch 38/50</span></span>
 <span><span class="co">## 106/106 - 0s - 2ms/step - loss: 0.0025 - val_loss: 0.0023</span></span>
 <span><span class="co">## Epoch 39/50</span></span>
@@ -440,7 +440,7 @@ <h2 id="detecting-anomalies">Detecting anomalies<a class="anchor" aria-label="an
 <code class="sourceCode R"><span><span class="co"># Get reconstruction loss threshold.</span></span>
 <span><span class="va">threshold</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">train_mae_loss</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"Reconstruction error threshold: "</span>, <span class="va">threshold</span>, <span class="st">"\n"</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Reconstruction error threshold:  0.039214</span></span></code></pre>
+<pre><code><span><span class="co">## Reconstruction error threshold:  0.03930207</span></span></code></pre>
 <div class="section level3">
 <h3 id="compare-recontruction">Compare recontruction<a class="anchor" aria-label="anchor" href="#compare-recontruction"></a>
 </h3>
@@ -501,43 +501,41 @@ <h3 id="prepare-test-data">Prepare test data<a class="anchor" aria-label="anchor
 <span><span class="co">## <span style="color: #BCBCBC;">4</span> 2014-04-01 <span style="color: #949494;">00:15:00</span> -<span style="color: #BB0000;">0.746</span></span></span>
 <span><span class="co">## <span style="color: #BCBCBC;">5</span> 2014-04-01 <span style="color: #949494;">00:20:00</span> -<span style="color: #BB0000;">0.792</span></span></span>
 <span><span class="co">## <span style="color: #BCBCBC;">6</span> 2014-04-01 <span style="color: #949494;">00:25:00</span> -<span style="color: #BB0000;">0.802</span></span></span>
-<span><span class="co">## 118/118 - 0s - 755us/step</span></span>
-<span><span class="co">## Number of anomaly samples: 509</span></span>
-<span><span class="co">## Indices of anomaly samples: 216 218 219 220 221 396 398 507 793 794 795 797</span></span>
-<span><span class="co">## 798 799 861 974 975 1658 1659 1944 1945 1946 1947 1950 2004 2008 2012 2016</span></span>
-<span><span class="co">## 2020 2022 2024 2026 2028 2036 2038 2040 2042 2044 2045 2046 2048 2050 2052</span></span>
-<span><span class="co">## 2053 2054 2056 2057 2058 2060 2061 2062 2064 2065 2066 2068 2069 2070 2072</span></span>
-<span><span class="co">## 2073 2074 2076 2077 2078 2080 2081 2082 2083 2084 2085 2086 2088 2089 2090</span></span>
-<span><span class="co">## 2092 2093 2094 2096 2097 2098 2100 2101 2102 2104 2105 2106 2108 2109 2110</span></span>
-<span><span class="co">## 2112 2113 2114 2116 2117 2118 2120 2121 2122 2124 2126 2129 2141 2521 2522</span></span>
-<span><span class="co">## 2523 2525 2546 2698 2700 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711</span></span>
-<span><span class="co">## 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726</span></span>
-<span><span class="co">## 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741</span></span>
-<span><span class="co">## 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756</span></span>
-<span><span class="co">## 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771</span></span>
-<span><span class="co">## 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786</span></span>
-<span><span class="co">## 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801</span></span>
-<span><span class="co">## 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816</span></span>
-<span><span class="co">## 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831</span></span>
-<span><span class="co">## 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846</span></span>
-<span><span class="co">## 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861</span></span>
-<span><span class="co">## 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876</span></span>
-<span><span class="co">## 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891</span></span>
-<span><span class="co">## 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906</span></span>
-<span><span class="co">## 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921</span></span>
-<span><span class="co">## 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936</span></span>
-<span><span class="co">## 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951</span></span>
-<span><span class="co">## 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966</span></span>
-<span><span class="co">## 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981</span></span>
-<span><span class="co">## 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996</span></span>
-<span><span class="co">## 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011</span></span>
-<span><span class="co">## 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026</span></span>
-<span><span class="co">## 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041</span></span>
-<span><span class="co">## 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056</span></span>
-<span><span class="co">## 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071</span></span>
-<span><span class="co">## 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086</span></span>
-<span><span class="co">## 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101</span></span>
-<span><span class="co">## 3102</span></span></code></pre>
+<span><span class="co">## 118/118 - 0s - 763us/step</span></span>
+<span><span class="co">## Number of anomaly samples: 486</span></span>
+<span><span class="co">## Indices of anomaly samples: 216 218 220 396 507 793 795 1659 1945 1946 1947</span></span>
+<span><span class="co">## 1949 2013 2016 2017 2021 2025 2029 2033 2037 2041 2045 2046 2049 2052 2053</span></span>
+<span><span class="co">## 2054 2056 2057 2058 2060 2061 2062 2064 2065 2066 2068 2069 2070 2072 2073</span></span>
+<span><span class="co">## 2074 2076 2077 2078 2080 2081 2082 2084 2085 2086 2088 2089 2090 2092 2093</span></span>
+<span><span class="co">## 2094 2096 2097 2098 2100 2101 2102 2105 2108 2109 2112 2113 2117 2121 2124</span></span>
+<span><span class="co">## 2126 2129 2141 2145 2153 2521 2522 2523 2525 2538 2542 2546 2550 2698 2700</span></span>
+<span><span class="co">## 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716</span></span>
+<span><span class="co">## 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731</span></span>
+<span><span class="co">## 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746</span></span>
+<span><span class="co">## 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761</span></span>
+<span><span class="co">## 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776</span></span>
+<span><span class="co">## 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791</span></span>
+<span><span class="co">## 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806</span></span>
+<span><span class="co">## 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821</span></span>
+<span><span class="co">## 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836</span></span>
+<span><span class="co">## 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851</span></span>
+<span><span class="co">## 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866</span></span>
+<span><span class="co">## 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881</span></span>
+<span><span class="co">## 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896</span></span>
+<span><span class="co">## 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911</span></span>
+<span><span class="co">## 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926</span></span>
+<span><span class="co">## 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941</span></span>
+<span><span class="co">## 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956</span></span>
+<span><span class="co">## 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971</span></span>
+<span><span class="co">## 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986</span></span>
+<span><span class="co">## 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001</span></span>
+<span><span class="co">## 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016</span></span>
+<span><span class="co">## 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031</span></span>
+<span><span class="co">## 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046</span></span>
+<span><span class="co">## 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061</span></span>
+<span><span class="co">## 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076</span></span>
+<span><span class="co">## 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091</span></span>
+<span><span class="co">## 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101</span></span></code></pre>
 </div>
 </div>
 <div class="section level2">
diff --git a/docs/articles/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-10-1.png b/docs/articles/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-10-1.png
index b09a4d2f12c42c3888c575afaec4bd4698e7b55b..2e6ffed7e75307a5c427fee4fc996879a8c98fa8 100644
GIT binary patch
literal 17853
zcmdtK1yq!8-!6)Rgh+#QBc0MM-5|n%G)i}OjDkvoNH+pHG*TihDqSKuq=a<C&~fg;
z|NHIt+xx7w&-%{Zd!2Py3&Dq(=eeKf{^j+%u8DZ8sf2Tv;w}mb3XY1h{1X(E+u<lE
zw?1Ls21h=uqkaNEuw0c5JWx=u36bBoe$515prFv9sL0Fc`ets;_!*P@xx(1p>C4Q5
zz~u>CWpK202#nP<`fV$xdyL?<Ri#rEh|epKYJ`2-8*i>^2v<r^6(>h+-^jE+q1!DL
znO&;*>YOycU?|l?S@{u<RE(1*vu6LczD)i(Kg(tQUVi@D7SUiCFYxll?tq_P&G7rc
z4=Nd6R1_3fw6=TTQ2ZHnA9sox1X5N~m|I+&$7-mr{gQ9lRivhTfv1Y|V7ULWHgVll
znHLFk&%fC)WtTK(uoEI_OtR?RXk{?g5ZQVXC{?C*5;eTGWaD5wH@ai)-8ge`!B$w@
zGpO22=235KsJ};w=7IBNvgs^PYIc37;Z>_|vuU|^OT9f+-Bkaem?`pI=qM<*=KtgR
z2+;V!l9m3ef3Qn!YqB3Lrk8tj&o1k5zn90s1RLD-9orXen+SiimX&g=ES(k#!P2Fu
z57L*131cL;P*6tnQ12}-EG#54$D^w-<jry|9Pi9kcqxE|)Q+Nriqg~1yLwjlcSarb
zYPQxn?0kKRT3e2Zf|C20!kia7WOqJ{Hkc$w!bI@Grj5mE>hov=1K5PgUv?&s9{=d<
zY&}~qG1Ldwac!gcO#p81(dBF8?f=FMAqTHx#Iyz%ils{huMi3OU;T&huVljwZ;Ht*
zG-1_3I{p$g&89q&#+#;KyT}rlR5oP8!^te1>!uuq988q&C}_)T6Qju>j}j}yTfuDw
z?7`8TLD=|4ZFyb@XKyrL@cMkQTa#%8lgdTuBGO<)z`Jb)Oc%tsBN?QPxNz2LxN|8&
z=w7{gHDaw=G43Fg;U&hMESqelpe+KQE-7C3ZghZ0dwt#Vro4x)>HQv@3)m9OjS3e-
zx?Lnovx7_;;W{dEMUt7|mC3j4Ue9?yYV5V{uCIi3by`Am@>y&Y&I9noPU!#lJhA^1
zyYj#A8sXezX2q;M(d7J8xJu8TFMM%ZWRDZ`Tz&lH$vY;M4<A0nNgh3QbmSzE-0x!S
z&k)8_L_R!(+T++%Vdv@8%uETpUj@H@{rdJz_WDuBQ)lPx!?occDO!T0#6(LgD~${x
zr^y;SWh?Ne)?73XQ&UqzLka?L-ami-oMJT0Y|k|Ma6~R#*M9kOGEr#(hr=Dl%W)KO
zkQdQ@tZ6m1<sH>7D{oy5cdk3fNbg@JU{u_ksQi9i(*AsHy51v&+r)YxlbnQPv`7cW
zPiqq`N)<cVrwoC>hS?&GjoI`eNqo!Fw7H@kWCU4uf&FZe3kRoYGCv#e85k6_i52Ey
zZk7hi#bgU>JYjVm^ET8h3<k*%$Y?|4-Dp18uwTT+7uLwDP1~gq!ajO>dQDhDxG}fB
zva)jRu%)Ht36xC4_4n1~IR^$mfyz7G405axIu$~eHcrft;^Nv;iHVE0J{3OsAHZ26
z63m&wB~R?A$|Lu7%=UHg^kWz?Ls%m-N4}FS!pNkxiFw={`UaV0jbyNv6B83NGc$3i
z#g>+qqG==>ChhKXKK)tmv7(tJ_Ge`UR#2cz^8hmh?pWR*J?&gKWIM7JQbkOviu99c
z#r(0?_?Ld%@00tVVt~K4ng6yV?b$%|RP|{s0@u~zi{|^sPo#1N&3Rp`V);9axk;sC
z7TxRBnThnp?;`Xc3nfYh9C5O;rV2S3dU|@w%E~%BJ9~S3C)t%ZtQS8}OIFVmsVprm
z_1T$iV+AQ9cb4E{MPh?3qyPT>`)?SRtBZ>5YYp@B^J}>aZo4S;X~Os)K76tv%Pv9{
z>tV826aXG3Ux${YvEabkFf2xqk!9n{b$D71N0nZQAOpi~rM_Ej?QcLbM7j8{#`Awq
zUH>mr!~b_q^^)(S7Y>B4BU97H>EofVgnzRsas5Q|V_@mbD(}F~6YzC_O@}*<HN3qW
znHQeNiW!1D$`;AcTLlaS9F@w^0-@DJ;f_Hvxy8lEcgP}0u`2IQ3_7h0mSJu;2G#V#
z%a~Xp__HAeG7|}+$zWn(74;{YR*X9eZFm}UzUJ(W%yX@(*N2R5d5>3-Z@ie=5+b{o
zxhd<{YziCh)>;&y!VQje6U+K1>R)h(uxyA}^!;%+;JjakUG#g;x6hiNC4dV-O-iW>
zws}|X-zts&pKKRSTS-NWn8$Kob(lC+EUBsQ`|XW-TV&3*?V5Hz4!MI3DyQ4X?48^4
z&g%3f292_&=0IQHbgcs^*hiY+lX<-|!}iY3Igdff=*sh**4EtI-)7fm<M8|%?pA-X
zGEktPd{I7F8Jw%O(Q<Y!@3-C<Dbi6?>?k*B#H9k^>FceIr-p`xAT(!+dU$V53I-f)
zW{Lagk&m_n1(c=aQXp4TSTR;=!0)~JG_(z$lvLQV3!h>A*N5Iscr_`v@fYCY7M-#6
zGu!+`<Ghg^B1s@VBa&Dn^PUn|5)cr`*Er8KY?L<+EbdLa_j0YRttGLPKO@#hy6s~#
z#LnDxew@_)X`oPs)9L;n`5LeY5I6P~J2U#1sl~m4?J+zd{$rdk!sHr&XcTf3GJ5(n
zRkO1l%g-R0M1hYY#*kE2zM!~x)IKIQ7HQR>W-#Z?gjbFxk8gO1WyQzG8<IY0n|B3O
zgDtRXV0|E>(v(xIfMleYAOD{!flr2IV9D>8M)v}(41#ytFj~*I8&gwLXPX099z4LI
z6mtCb3QIvjfsKuAZ*Om^&c&?J+sWG68m5`WtdXv3HPYa<vHs=v<Re~;dm;7cn9CHd
zNs{1Sx>Sc;2(PfRu_G%e#lG^ecBV(>^`-KCpLb<qW{#y4F5fSd3&VZz-~mV!-vz*~
z#pwM{vglm>M{p{Td)$9yrlh2#rK!l<+1dH|`C(yU$uD^~f^FH_q3|iUyZBq6@$Z{i
z%`f$QC@zlXGf1xGeihy|=k;~o)fQA?2FX?i_y1L!X+x57I`&QjFNhg?A6i7`_<ymd
zQqY#5!2=FTa2hBTEQWPj*OBriHY(3nC<7Jp<dP&&xq52riW}=cW#S(l9b>$}79KJB
z@G130SEl<!fi!Uu*2JB%VJyk+kgACP%JAs>p?|Y=HF<N@;K$<PsjX$0I7e7>)hS4X
zVl?^s-Z*~)gG+OjBuyOLQL$q4Md8*Pcf|Urmc$;K1t$jw2Lr?Ny@hr`^~`tg-X$c+
z$%~4Lg6$!dL|)oLXUaTV!0w5j-dLRUm2KtIqphjqF(a2zUbn$)N$k6KKWb#$r>4$&
z`}S={Mn+ba$$<c=ffRV-_a{>r(FGqrZco(#3%&9IrG#@cVhkkQkdP2_QXf;_uU+-O
zZ!rpfMOCzap#%T>6C)b!v6FD3r2pZU#aMBv%fn${%H0Vpb&l|dg;rp@1T<@LmJO<{
z5u0$2KRrp>+S(y>oQ5^5|N5gO78L=3+g#A`DKuNc?;(qD0cZh@8A?VCO--{C6Lq&H
zd}jS%3Q_kB3=CGN$yjC9klvP?^zGZX6tiHz<-Rm4E34YH;Pa!coWT8v>lJ>jHZhBf
zlRX;@*IHE651*C#5`dwKd9A0J!t3UO&sT@?Lv@M34Q~}S`uxnHPV4RMoeSQN$e65k
z<LK!_NJ}5|@k71D1=D_br(|93ogS>vin!+1)QnhHcUN8Xa8yK7i;p=9b+)%RUmi_W
z+YD+dE3<NOnY0AfPi-YL*#V#4X*w8yqxSanD8|v0u}0Ei@-!bl$&m`;c6PI;>|@c&
z-kfW_ZoR(TdG`H{pv&yV@3v4<VQJ95eBI~r-BIVw2+!V1n7^*4jIUY^<q!KRYuq$H
zWFK-mAigtQWu<0mx%Am-YPmn74pfJr&;#xmOCiWEEq&c*siLCd(sH`YKPYj~%hReW
z?g|VCPwM1%jPUFP@DMeF%t`d>R{f*((UP3tGrPkc&^rZVav5f0SvSrE7w_6eeR|9w
zX33$p{&T*Mt5$ZzZ<}sws*Z=?_3PKohriVBM?DZ0uG`Na3P7wY$b;>?(&x*PbiKa1
zJf8N5qr(KzE~QH3@1AoRf7#vZq*bbL1pQg${Pk7P^2$f~>!9@_YBi}nw)It5PV@0>
zfWraKm6AW%J#>b2L5H!+<2h(MiT<?butpNB=!ko(2i2(7zBcIm=*N#AU?p=Pq>|Qg
zddf6cf#V2p{*q!b^RF+!U!$@?HU4O~X}^b~rAS9XyYvsL79k;Fw%>AE@Z}j|btp9a
z4Ib^)dP&7Vx?u0~O0Y}fwbF7I6BcnX6oU2xnWD@IYfrK-pG>hpHPRkh!K<u<XFNe;
zr<TG^E$Y4my*kK=%67>8-EFoK!U7#d(Yh+Wm3;w0l{hUy_uVHYC^Kn1TIx<9CnsO*
zj5DjURC)SzqCH5<DYPCmvW?zb`_}{_Spy&wZG-(^uqJ<mx$GgsJC4$3j0$>u{64`)
zyhQQi8(G3GrXW8_U+u+3M@L6pWn+ie@Z*uR4J&{!S<Fjz4?Lpl4^VL4)6Y}R8#9f^
zh1&UK7QM+_l>F9x9|kFrFDrE7)ykF-<>&Y4PZyLw{D#egwiCgEjI;35P`{BvZO9uc
zslXGk0Y+e<uByQ8Y?}sX`-qs;3q!@g>t5Is_34{<L8wQ78>8r+*QtS@kEkF0dsV`n
zopuKW1)nv#65QbHSFb!*f6f!O+HX(S4-F0JmDB>iwb>@R`W=d=&OF>NJZJ2udgIwG
z09NSHF{MJ{69QCHIWC8*LuRRRU2B24R#C!pfd{`@J%A6dWLX2hHu~)7x8Kde6XcB}
zoEl{6wZr}1>K#5wz84#~wEVA0g#Dj6+G1a9OCjx}NrVl$&%MgcwIRf293C03IoLzc
zb@li3`0urof(W5f4W}X{JwBRpIcG_g4Dbd4dSxJM2juJBUDuReuazvH`y?bW)Z#~d
ziJ*#*cK!Vb7&$QSfa7gR>prr&sTq**kQ;z}a{uP@S1D>^xoxHvM_j&hb*plJ>TnE6
zk)f<{uY*#*+}J=*Pf`9IlSY=97YGnS8C=rRt^1ZdEhh^^Ag;HQnw^+YdM*5j=+T+=
zTghTnjJBQn%tg=-Y824lwO;J2`TCwjl5m8xva#8X7DG%Ly)XZym<<?dg-SlZKip6#
z8Zbg_WVecVRp$&IsWHaG#++05;+}dc@BGRSOLq|$%YBE6W^e1nc`BYSlwjk*Wo6{^
z6=UvVV+X8#Na4J%2C5~_-Xs=r-(7PMtHQgkuhU@{E7I3%IS+Ywco-P+^ear|iAhNQ
z9Gng`ir^fD8>nu``lE_l>EO|hG~7o)!OR=89!|UFj*32+&&O+RF1{YDi@tF5_Wq-d
z)v-QE50b(@a1XP<z4qPt?>7DEUe}k$gE`WzUK?W&W{0WIW!Jtw2h08Po=1r`ruoyw
zkP@(W1VbG$l$RDftFKdWgi^aL33Z0Um1kNMHUMgvyS<7y10`jHS!-}DRAmnYQgRDW
z&wLNVyWW9bi@Pr=N44*Ru&JB9oMw~lcXhggflIY2y<YavN>=vUN)9v_6od@^C-Y$-
z1<iog)u?Q2qoVanc7t?)DKN;g8V(YTXT!QySBYpq;7+RsbG6$7cEK=4BN?(7n_qgc
zUR-QgHnzHIi>%?j8*$J>ePa)lz4|r~Vp#)SUCPMAt|Gw9Y5?Xlyk;4_hir8{&JOMJ
zhq}7O29O}%{U`;{2tF+v3l9&+7UO>9G5=hSoauRE@2j`??u{dJ$972I?NOP)k~y;q
zp|}qt!yTJyP$OsC{%lic^44Hs5VXPb5UVK%&iMO+!k?F=mN3skS$jlMy!M}Cz96zp
zd=P){A_O2hA{^<|56+U|%94R^;iZP-z)oFhz_Hh>Wc2j$WI8^MqsaYyabSDWrY`?v
z+>_zSXP2=C{F_6>ZJegFSxgZmq?O-pmpOo-P^glRX-zMn?&w2Kz-9y;zIkuDKqTn!
zivrLI*u+2ooSVA^F7h?0gRt+pJqtb4t4vLaY><V0$DW3qR&;te&lDit8D(X}Hso}$
z8os_0kk)LxVF(TtkB5gC*rqZ4=!h>a7O(sJC=kWagv!rIu&pr}3B+BCmpI6+PD4)S
zjpmkPW}V4X9-AXZr0-m0@Pu3oP63b>Jm9XX(qj89Gea8P$UM;ZLl#{j(bo#Cpjduc
z(xFY<zJb`Ujy(tU9z|#COv?|Sa_kWEhiSJ#z1MC5ph<I~3<v%Xpsk~b5i@a039&ld
zD>r>Wb^YD9hxvza>nn;9`rfKM*32e(6C~hb&}&_adQ+MkFQ`(onpWs1TLn`nK_J1A
z&j8?XQWRE#EIQFcUnY1>w;uFsEq*cPPUvPrHmw3^pl;rX#wVcvY^NmOBQ*UJz|9i&
z#$Y${TCpcS_y6cTWd*y@Lt(<JW|!4%`TWRT@Yv_?ZWIsQ;a|gf0xOp<aGcG(d8`n@
z7IS98fI}vvp*Y)uoIfkjW3Flc;D;cif_9zD(9M>$+C?L`+h2>}iO^-S7d?3R&|&3J
zgUzu}ridisk~^n2rm~x&NYN*2o9b(z#PDS34p^D*FXhm*fz1Bd%A_Wmzl-?k8*1Q~
z!~RO??Rfs?<a;kJa554K7v2O`J&8Mxy^3YXY*P3$JTy(YXH_F4mqw~fHS)$-Ulhh;
zRR0uERd?2q?@89Ux#XjfuF$mbA2iLt^Uo;RBFAdM=>*Qqlf`@<RKHHj(epU6y>XFz
zJnLT@ADq{~ts7R{+&ZQ;Ly?U%AsXUqu%2NaaveV>=u=g~V~7$A2`qdGm_Xjc1-H#~
zv?+Jh>Csb#JolsgH^6w`iFuL8%BFpPzYQRK<Qog=QzOEaVML7xItDAoh8MR`UL@o9
zkrebvF=4L1M=d>kb@S%M*XHGPKYm1(P2yYhd^EecD+(U812gbK`2uISe3cf`vUB5Q
zd9M}JHyB>MdI*z)H|zZCpnl$P#9A77<x?2)7Je$zr(P?rkH*Ps_e=NP1uZgga|%YZ
zgE>r%^vErp2xa(g)Xjx0UYVDxuFu4UCsG@w{&k<+AsJr%2OmB0pD!pUk8)-FeKR%M
zfdQJ#`e;gcBF=^~q_!Mb1O=Eg2o@Y>TRyDR5ked{-#qvj?2vjf#r%<43<9>+g%+@y
zLLJQIb(Q7{I+@3K7Xy)TH!}eN25=Co((U;M@wfAj;MSb!QwNI{T8hdty0(5Hjmwr3
zzi)-<qc>r?z=Jp?c$4<c%~n3*&B<U@4Jv11u)5Ux;sORj>*yi%%uf_u9;T)B7jL)S
zoQ)fFla>OilKzD87sGLeDIk=@+zvUFD3H>B$6EF6w&3-roBRJBW-f*IUPY(U!iM2V
zutCYqrtN~IXsrvs2nW$H4N-9OLR1)bM+$r7LyL}kVc+9ZQ$?Jozu03WCnrlCO;}V`
zR>~iCF~$kF%u0|Yp}k5SQB_yh!;WQby((?oW-jXm+x~`=RjH3s*jZgu6WMKnKJ?kM
zXSJwms;X60RiMQJIIlpq4tx;$v-IMKgGLCfScW8#ak;6fsjp9+K~+r+v>j^7$|3=D
z1fb{V6c-og;wpn)UnNNokRA9+)TR+wfE3bGMYXF?SnL!)Uxk%b<tKFU%^y+XO$Y&B
zR?&BwQw?`h=DiyMq8Ku`ViAoMlIuk;FJHx30zl2huBtu=-;ZH4T>Y5*;^J!_gTLbm
zW5HNxBUfaK=O-4-kRjW9x+(zN3;}qu5$An|hZ+6ejm?uU!6N{e$TomH`qKFG^YTFZ
zq<cykC}rkky>vZXn$h1plAm&TDO~d`tHJ^AU4ebnUP<WlfnGC3u8c}t*2r&KyywR`
z0NlM^Hr-%n&vmm0<FT71u!CuYyyu#cg*GukA8~;>E*2KR#N?OU>p_YvrcRBIpQn`q
zD9z>hu|BS~3_eJ|_WX<){nnLFJsWa46(IWe4zJFLIK-{G&rMW!{EmG8=HUue2n3Xt
z{}yv+J#zt+@(=VD%U=?)oT<t3x4X|AhG|j8C~A(mb)@$G*{9vw{c2zP=G80ZQR<qn
zukZG}c?fU`7M8GZcX#(EPo5N0e=%<h0nj{=c5rZTU|=9`4<>J@%;qR?v|4F=;YI-L
z{QQdcAQeDK29F=}0*rrkbrqm5)6>(No15&Y75^hb%=p_g=q#bFOI5Qlz~?5X38>$e
z9#`WI>9@wl#xBX%fy>J?K@n;Dy@vGkL%Y!Ha<Rx(9XP0~k@@Po4*W07^RlG6RwT&;
zHM6JuB>;rN!@m}7zWDuI&LB=%{~nXd^VfIQ+l-MR`2{*=Ti~--;mSo0Vgzgs!9Q=j
z@&$e$srtHfIGT*)t~dwh--EWugW(ZGI^qQJP4_YSL78oixg7PLk~j6DqE<w^^v17p
z=~8*#{CudQZl@L9Egk&V*^md{35Z06k7P|f9df(5@Eg>&!1x~pdL$VA4lUz1mlXk5
z*cl96E)xN{=*1%$Ub7}ujQ*0p!3h)X;6YoGHK->!!EXc!h7oMkuE+%VGQ_+^_t$R+
zkOb;uBBO3zy)KD%@IlPuXX!#NnE!E;hlrwFVDvi<YMw0?-njbZ8*^L~zM`awN`5}Y
zxw4x(ES$sdd-=X`8J$o;%w4(v(&Q!>(a7;)yul~p@Z6G--xuzU;Jfkkv%76nyx(%)
z7YKh^gckl&e~jp3F25l7#>iTYN6<Z2Mh9lOR3J2_;P>4cbwM%U?DKzex}}fIaWDL_
z+Nd5|SC?PYfsk|&D?w9M0*;<xv{9Kq+2ME{u58WIDJOOd<u?OeDyl|BN_qXu@WdSb
z>R*I6FZ4%mqw@XrXQo}xNm+X-srM$CtfwiG_C@ANTwfdjSOj!z0Dr*gkwBC2xpXB&
z^BUxqa6tnPLpdLNS=3`$zSQW_5B9lE+$m1#%=YavLyaw)*B}#vU%O2Kt4unmECnQD
zxKp9$TXjQ4Tj8vdQstJH4Zr)98<zbarWv@!SJ?&aE`tb$qyGJ#b;kt2mA-hcJ!zaV
zwbHe=UWT2!N7x?hEu>0ahUkalQ+xzw621hP;|*i<<-ZL=Wx+G_129NK=Q{Ho>F9rU
zSuO&i$sg_G8K0cX9vR`S0VDiho*mKTGF)=&Q)c0j^eH;0R~fkjU?@y+FKg@$+H3?7
zi<F3|yp|Rdh)}H;x|<(<{fi5*-&<+XnN7~2@(Uos`br;GJsUQh4}*4NtM*8X0)TxM
z#V&(8MChSPJO_o_k~`#L!8Ntt8YhZCM?9h^E1YkxN@4X5$bT!y=`;hw!f9LR>T5QE
zm3~UR6@Y)LPx*U(SA5sj6-%o#c3t4~0F<$<=)UY3;mn$uzAIVK#FUgempM~>G^Z#6
zhK$uWmfajuDf&T-AH4|D743}h0FqyNi-zyK)^;clAfD;4s+^G()%OZ&`+k3Tl5ffu
z*RnQrelvM(=?iN@1+Qx<uVY-~=A#7Dr*4G5eanhiSCZdP>FwIOF|*&qA@$p<<t>`w
z;g+!8r3&dApF$qRC68&Cufvb8^2=|4=8TPkA}Nl-op(^&^rBxSV+?W;GeHkoA4(ug
zNa1FH>1?=z5>gyARR=TKVF$QojRA9rMvuGGG-bV~QQHmkvX(^N4eD3+X-G#x@u6_!
zRaz}*T(D5cBVj2k%BX8Kd|b$@mw~suvT}VDz-yN0_;5{ePBkRgaVnZ-5`f(bp*YLR
zS<co|V;c?L+f)<_j}u|Ie~u>_4#unY)1?t3I$DxGGlCw@wEXrXtOm)Ux)~E6vl8K9
z;Y)+0{O5iElz5@2U|uU8TdEe#XR}q_`p?W=aLcqSx}TfdZ|!NNoHknCR9=Qu&QA$N
zvwp_G$jHd-#`($P3QAdd7suJ`{jZGrW<g$Cy?Qqzp(jqv_XADWp#0s|5FPO%#%t~K
z`Vm5lzAbNu^UU2IVg&+)_=J^a*8k~v$i6-ngVI7BS=4ue@q0{%nsKLs_L5D>2YMRX
zZZ=F=Fct+9DJ0ly023d4B0)qUCiK2N-!CjBF)>)z{_>ZecU1Moyq*g6WNnE=8k5Si
zH<7LTuAwja^m-p&SQ_XNe;CU+$44wr1$xX8A8!e5PCY=ZLe9xY-xL>v`ESTN9(&!C
z6EB(l?CWBFFo|08+#eRf4GYbAo~gO=`sxn`iGIwYS(_;QgGF?`F?UpZo)<ld1QJzV
zwygNjvIw#S24VD@cIqkU+y@H|6;7SRdc|HV@gs@TZdejbMN^Y|=dDoC@R*PHrU5fH
zsppqKf**QLA8v(4C||jgi)&Mj_}xNz6g2cS1n%-Up-U!8>f`>fynLnGwNo;${mzaO
zI|6R{$y_GRhFP%n_+>@Q8M}ahzyx9y-6o>Dta}5&m8vCW_Q?hV3^g5)%?-MCt_}>4
zC>T4ElQY#Xou)j(4z?m?<?~o^$sbyt?9O|6X2}tIS!~lqy(XbfX}!whUIt+z(806q
z;-?(mQ1sg29>(FcPDSF>!*6mX#b?ihpY?onI*275-%l<^Aq4)Z6iv@y^<B(s<+E6}
zIPlGnLw7t>RU?CD23w;W^H-~_v}Ro|JDg2sci#T`X3M(o-?=lm@!4zT__NN++2%rf
z)HR|4ibQ^2HYccp;vKQ6Cf@?AHAC>l9LP}!&3tTYhPzl;V2G<_Y)nOfCGR5;)!)A=
z^+1$Q{+&0RTF!f$dUxtGwYO4|R`d1Md4_2K;@Rk$L4z%Te_nE__{?a?RxdBPpGjqF
zJ>(WbMG-KK;Y%H~j{kAZRM*$PI#zZf!uNS>ZT+*~xzqWP{x<37=k1XZLHtDDzi+Qi
z+Krc++$w)mF+L|0tIn+Vln&Y=^3@C%tPCUMl&7IeXKMK>4((-lDjlbY_T+q>Q`{1@
z)I5Sfdt<DujwFjNFCW$$I`qsaJYsk*P#i0y$||AinaS5yT2Pwb0X@v7x4445?nRZG
zg*bdgdsO&tTrIo?QUCq)+JC>hzRDB9k|P~hvy`N7ilun#xjigWJfrnYT{4q2+5iNR
z75qLZz^_I%Zs!lC4B?g7?tPnk*ub?hK`;2@xqc$)0e0hN|I9Bb1S~tKRm#*8fPi$P
zxs>i@6ZA>M3RfqRdV2Q@>cNTXz=;UZo`E??Z~5lAOrwyM!?^sbFub^k$*ft#_ueOy
zsTx+r{<gpA8g_2^Tb+%p4VMIZ11MB@)^`zr;U{pL9Fyk+XGR@&wTJplxp`8xWCG$r
z8ra2ZyAT%3at6i*hI}}9c*l4IcN!$Qg;?^9&ITQnt>oqXFW2{uTa6z-EoZ&50}KSj
zzRC4Pp~=F#8X@c)zkL8grRctb=e1rsKO`Hu_}=mWJEFBN8VOB<K1P8DlLygLP?WIx
zlb)UDIK6+6sz+<~i&am2zFrP;t_5#OQ>Yk@TFBLlNSU=U3U?`Q^`eT%Yp{sA_}=q^
z6Xf9$Y|Q0J3JKrc>`em~KWb?ur2)Hf<{`_Pv!!mOyYGaYPgw<?m(4&HJd^yI%&0{Y
zv$KQOs)@usN33ud8UA$cB$l<BoB?$5QS;YD2ip2kkV0dr#R;{g^BU9JgLDHIMnj}=
zzZh3XYLZ!B?esJ^PC3t=ZC>CRes&~(3s<>B=iN~GGoJIuDhkw-l}?QZQ1IZ^MXC7A
zm_k{nNoZ3Nh7buOM<%jzP0`e<8t3z4;va=0PS<otXGcscgoTC2x4g|(jXlTlj(vU|
z1xe&2{T_V_>I>SC(|5`tt{#quM~A&BpEaCUSoI@X#O$_OEwtjw#7T)$d?AYR01|F&
zFv$?U<`XNCYXmGDvx$51+ShSzvVqv{qCp4CpL9Owa)K4P%9$hThbwLh>ftp3Df*p*
z)AHt{$(?fqj9H_5W5{P`-Xc0Yo%|kAy71KH!pIu>11W)}#7=(t>mNvL@crCiPfwsr
zzqwEeK2b@|?pu5c@7cJ*e~27B)_x&>iC5Z7&y{}uKLPw~1t^en$ngKh#QN9#)y_m&
z?rZy-s+K7*r8%zL8`)7+-H=y`YuOS(SU#84X)f>D$O<g@ePHAqkazsTEJHqw{GQZY
zds8l;m;H=`KElMddB75QV4cuQzN-KnL=%*gbYf!ZkynBDCn}|%<ma2&WqH=v#MMpJ
zX<%0tzN;xvD0wMaVejTQm)f`^%Edv-hfU9J2tB^Eto*dt8MxE`phEa9a-Kd7hP#gd
zTw3#9Zne8}6%M1(vQLYTmyy4(LPbkk*Qhi;ngTO%5>SrLvd`WPw$zQn$=I3Ashjt#
zzpe&7Q9T$bBN2AcQjSTGzBll=l-_yJEYmN&X~dh87=`5vP)n+EK8)z$HIOb>8r%(*
z5CfoQ8Q5D0kv4mfM)AB}@ZrMLyl~$}0(ZnhGY9m<v-ha5urPfkZ+ZD-7Mek<(8Q?$
zoyzizwdxdHrua?af#VuekN%~@7}eMUm(C8r&W7WzdpAsuB})$;%n&A(l}R<sqyi2G
zMjA(g9CDiFC!?X<K9%Ej4bxYhqXuE=?MiD1Z6OXd=V8##AM`z7S9BaRo~cgAVzus!
zRn6jtt>4e7OVUfWQhE0u45~n>oNmisH<S`UBtj0~p~9Hu>+>+OxX%3HH4#xlIzXsG
z_&1M1bY1I>nG+%s%G`p&6z?@8uxQN&9`wuZNHHqKa*BC#auj#l*ipwGt@D*>xJSxV
zDF2F_xNp&LbTufa6+}#Ot_EN0vEhxSmA=}ln*$>Y$dxRBJuWBq3WiHscMgJfaZPl?
z9)c%J6e79`CZILGU(Nm3T6^Eb_@1r7E>Rcnok8my<EO+IM~BCLlx5&>r;HQsyVd8<
z9nEx8)=9YScHuTIA{iDXW;;Q{W;EllaWJ1_hr&P&DSgEKYi)FzYA&qH8dUhGZB(9j
z?%WwEmn6{c{Q+)8kNo@x&iIP-k?ZNGeo(wOJUo>(f%X0u$>G?mA+O%pd2gRimhAQM
z>z+6lva{t37roQm2`LElJgZ>1sJ``l_MFz_#G)OPr*fFfxs(#Vyj&;Yn#sRx)cE=N
zOQOB%jic^!8CKcOn@8E%b>g-+^MiE#u*Qxki*6!AAZwPJD}QG+E!25pym2SlJ>XCE
zG(a<&j%$UNv%UGVG$nn%o*!lOnVP2b0w}HRg(>_NBNDo`Ii9eL4g$jfkPCaey1oDs
z2m7(%;c4Kw8T8V}q#>T}-t6a7B!%T*k1%Iv6hXPX)6dq(3AwD6R#3}#d+Iq%nV<3T
ztjU5ups&UMMQPP$nYfLybfU55d%MJ|?eo`#WU>{T6z{>9|0hQy`YLGL4|{Owz&9BL
zLT+KR14wMHr+-rBT)*Q0yrJUBI+*h2%tBu%&Z;}<;F4UqK`iCn-K(Iw;Y?|9_nQ#w
zC7XwAJ@AyYu}XP-$EpjX`pfSN@9J4J({-LWvhz<mYQplnQ>PXkA8aOS%+$)M+wBZd
zn)<JO0JsDo=|)H2qnX5Lu5$Pa>9jcPDF>Ny9HqGmH9V#DQwN)bM5ZIOCFPqnHXdGu
z#1UfL493Ruwp|uCAS$f_dc3yaQaZ+?TE?L@*+XI#(8)a|?oHouPxO2mtGiSurRL5~
zRl<kadY2Z5<Bv<1SGM*gryQ+;CfYR6i#_&({@z~xG<87T@TyJ)twO*=<Iod(`}KS$
z!=b3b;a;8V3Ip{z=Na}mt7wRXv^>lrT4A+-uaYn4<7b|=1il#J$!h>l(D~|!w>+^|
zlXhh)8XWaOP@Sk)swb;Z47HTWyzB7ULQ-*h=8B5f%j#y&=Cp++1wD7c<jc)l1u!l_
zXxj6YXfv`0t^2LmRKcT3hYCq;V=yED7*CQm=&PSfS%CsigK}(6*lIs*UQv6~>E}?z
zMo|z3L!Q~V%~7j+V=3|Z(jATG_XQ#d$Tn<wHhd4m?Z%HjXEo|x4b(b}e-`&&Euu#a
zx4jVNxJ%<p$H%wVH$e+{Orv`~Ua>K=hMv!7;~XYGI~jc_$XALp<V-%0$_8~LH${j+
zfSa4!C@!s-r){wPI!MqBlKbXK!vlbaNR^P)Y^y26wr`@ruq=7-N={oZhy8rae?4HM
z2$g$0`%{->QHsv`ABLegBk#i)%pGc)F&2jDHBUu~81D)#o``tn{Nd9&Qb>O)pGEhs
zL%*0w_SQ&^-SF~2R)BS49xEsa1UKPslZ#tY-qV0gpz~o8B-)jwrNf=MR`*}1C|_U8
z@_KOb@l{S{5-GUNe|xR9JzX@sCg#1VEKmQ=Ge~!MBR`#R=&Mo3iQ?S!^x|2Oonz%t
zP4D)El~3R4q6mRtOtkl`$nvv{q`R@79SFsnO&WX5dda3tjjQUGEMcjv<8u}6fdC}1
z>)fZM_9r2@T>O5lx>FBxPuT%AoW&f*&XE&*VY{2i?84J7jz*FldKAUek}3@i2BT8s
zsNNfz!$@$6Bp}36t&Npsz#Us&eY}lgONZZwcnat(#ndz(y43Eh-OaZ<7@W%#q7YV8
zbg`WYmh+qrjvHs^7~$)eHsID?ZHQsJ?@`=7-S%9CFC%bE>>elE2ad2`JtQvCRNANF
z0LX<vf{tK_XXtX_rY?0;92f^Z1}=f3>*x&YqYPKIK66_`&EnN537pu>2jw%3dg{U3
zi!%DjA|{J0>LA$AvL8=ffDw6et3a)XaUcJwlC<HmLsIiwFg*_fgKsd;1R)g|vR6_&
zAfSRTWi7207*r7D>xb__eYZTy&r>%{0Z$GUPvVoIRlY3ePIOCmjSDHCB6_~XPr;o;
z(QI}J%_8cEaNXKTQggt4RE?6p8-`ei|FTu)r7)tvIm%b6Xxx!%Skz>fraoU+>x`p4
zUI9!a3zo=P-=np$;!>B%nlj~~`T6&Gk47&|sO!Y@bi~v1ELa89KfQXayqc&)>(`)#
z7ynA~0ZVJF(`gQ=S%LIUsBvrXW#jRjxP$Vw#h)W6^#wAaR9rv+1MpNdVA*O&Lw|+#
z(sFT`U4ON@x*E*M$q5Mfta<?7H?apvX-MIbc3<l1if0sEOBom~Yxoievii-pD1a_+
zeb)kG&EjGZP?P}sAwro;gaLZRCVqrLz?XHEkPqclU;8o%yEpmSYVw=Hx#TPrjK6bU
zY}MOIT6=*%YbCHs8^WNbY5D4P&Ec1PP5ASyBat|c$&H93|L4uyU20JKI@WhXOk{^v
z-eIfm28UuVUj?yfd9T8aTq^P7&3WH31vlHy{~YU<*L`YgN=7n%Tr5LEE1Yq>3D2lr
zhys+N+J<R(ry8u~@ZlOF#8k^4b<HzQ`q;Q{Cty-uv2XUX2XKCl)`8v)#B9J$b7!z&
zKCf3#XVlr@nofPW4otK8bU6cH(H0%iYojIlNyV2qG(<!m1YtYIR(OBucA(6|1PEo*
zorAgTix0&;71A6VW1n<L>NFZX{dHj6XZN{Yf3cQz<Od@Jf-<w#mK_}}oz5Cfv6<b*
z9qxpJ+bJ7`;{kmz;sm<!g*`e33wme$l8+kCV>(3g`4sUb6gs7hn>(iK%#2UhtaG@H
zkA9=lb7R1_`qt>enoqh25S|9%rnL5j8!Jp*>>g{L^V-^$ymy(2)^IT5Xdu|wlL8Tk
zMe8g9$8iA*Di4j6-=T%Ryw?$tz+yxG>g3Z~{ot94uFU!rlVH&ZLh=}S%UZ+EXz~Q#
zg^76`t2oP6udUi~z3d^tnaPG;`Neetpr{bED|&2;fI<Lf_9noQ2XF%z*m-!4eI>k3
z(S-lSq`|-s5jFpUYc1U|B8M^Jl#w(eUf7e><Bxg?k1l47l;ZJghMvn3YLdlt#w_0s
zUGw#Q)oisN@oAT^`p>HZae9uhQlYT6N3{W;QWOaDJ<pi^O^C9#f5%e4CWt2{ml5}w
z8Dp>1H&lu$Hq>p;V?F=ud@0oWXw}u=)#hl$)4d)^pVvj~1mw-m)p>!Ji19EdA-V8&
z-_5vZ!Z>jv9+~UzB&4KUl9{x#0`Hn;G2<GY+#j9>swa1E&@0q`IVz1{s)r~_<tW8J
zkdbiqscCJ(CfB%<gDM^(R&o$7X>{S=2Jb*UTjo2GBe=RP>0hEHi0=GutFLGZH1Hf5
z8vavORo=Qd8=!)ZmJop!ul^BP!i0&s7#=o!$__%39eT!>tqigsTJ^lap#-9YgI{Vq
zkouSX_O$~U!h>7hb8el~fJ_Ei2i!h@IT(op&~~f0_2A$jNNNu5(_?q1Esz^?8T}qG
zJ_KFr;I7|jP{7Ey+j@bSph6aMKqN%u;ZrEi6UeHUl9BTK=)me<c+ISBW0sa|b^0Ao
zdfI!}{I~?Yc5AkIWQAk@Otq<nFvkU|(tV79`=mEnKKch;X-$<Qb0ikd5Y}x1@;mRF
zYHHr1?Yn&1$Gx{T#HbEq%u~){!u%H3+scZI#h>2)F|_^|mx_#%oV>V84DO(pm~iJ#
zYneg5%jAy*1k)-)uEG03Tn2Ibk{6}qc77OM*qc`r5rX<lUwIU~bUS*8ArF?_>N9@)
zc$$|Nq0F16ObnIwUF~WM{h*QFKy`GV`Jk3j5qP~25mvFLu)|U2E$e0(!jy~P-}B=(
zit>tjnSCsiO#y?MELSrXR#7?Fcf+K`-6mEuIIbOL!Si;IHe!GK)9dH&_9#+)?hfl}
z$-|#=EMwPHGbKBeE=}5hv>cANXR59H3DM@?htcfMF}tE$I0F6>;6DK{X%LiOW>(}T
z+~Z&;odhT<>X*~2^_Rf75hx=Dvc&Z%r2)$UHaz-=pN#;?B)hTybsb*)`8{3-wxZ%|
z;pGD?f63QMnD3v}N??}5eddCMeYU3o*|#&tpS?gKim;+$geB*lRR(A4-gaXE_m1NG
zXNk~)0MGyt5t$UPKY@hhi8RgT!?Y5H<U?EsH0=$EA{dy4{&;qaQDfyjQs4C_l;!+E
z#&`XF>M4B9y}FH#PtjDXkTqSsLmAG(`@DGFQ{>y7cgjEcAS~zUSEpL$Ppqn+?0FP!
z4p&Cze77<<J6L(FFm_qLpH9+VpqF{?XA!6F)rM-T0wg+Vfan!_Pnrv3o-3RMh*-Yp
z6MraZnkKog$TU$lwsdj=_*-q3%-VE$TcWK2s+uRn3fd4pp>06eEEVB+w3M85-OQIS
zQe}5AER@lEa56#)q)@vn{Vv#~JXiC*f0T&c5ymd#Q3rH#U7skmH^ng5jRy=$9@nr{
z*OS!qO3eeC1u`xsYVv#XHU5b8P8VvTX9CH760{Yj0dT7ni1X=txiDw5x>$nq&XWKn
z%G8q&$m(8Ttde6pBUHaS0eDO$5-jGR8O0{$9@#)t04(C?dpV9cb}lY31m0{5#R$Wp
zJOcd0>`Pmpn>cpsl{s#zKUH6wPkEghP`V&7b3k|nwLk#iGL@*iuKs)vv?l@%V~&7a
zT`(-J`#O9Y?9>RUS)g0Xuts;8Z9iTVi&m_P>N|2qhPWQp9<A1^vyI05=UvXUGxa*H
zFHxxhz$#=wpQlgCl_zx>9<Hh?ma2ipWt7Yj*_WyYiOZ3&8(vd}9qX&d>~NQY{V9RA
ze^!C0Cj~#q!_2Ack$$hRN1oW~Bq#rzU=(qyw24LApnX8bOO}{cCoQj-A%4h~jzI>-
z@)AJL-xnWL!*f~Ji*&f2KQE9Lz8%6!BNF)3c`TYK!q_>gS%Qr%f=Pw&gX_nqPM<Gd
zza}P9tI8ACn~-GGDIKW?bXlC(kJ?6k@5NI%gNkiB0L9h#1I>RtDKhZe?W;+pj)|Tm
zjyNjOcS%WJ%SpyICxI>C`2zJ|PLSM)5^a!x_mL4XEwpU#U=5a|*AiS0sN*%z*nEQg
zRr6Un`epvkXWS64l651OX2fRCPNBgCiRL*V|6fwdCl#tEZ+V|r4@xzfYIj@r2Cudq
zL7~g12Rg8FHRXa`*E2*)y_V!?fl7i-)q>d$m_{&j87p^}6@9UluXA>~ZsMCME9Qm(
z0NDjQT`{3s{q-GTW4G5swwyYaNdyF05`OOIdy69lY@$^C{U5u&h#co&t?&ECiJM%R
z+ASTdd5O`|@*s+N{Nbv?CnLBC+1=gUMGx=ZP)JeeQ&G{uZ{@R{#)gLefH?@z$*iFA
zePpf-JkS8+Y`_bLTg1^yJJ%ZWG;UVelP&>99vCItxTFS3&B1bl?W^I6(PTqSeh2Ip
zPC`>!(w8GZXH}h(yWrhO%4Mi)Y8rgdFPw8ZJq9?4Wv0yy9xDT=7`UYm8y(8)mpdun
zB(%$3XP6u!B71T!&G#0d+@`9b6(2PyO}{i9I2})y=uY0|JYUYJTNylVU9+6{^z7;q
z^}5jSU^&SC?t9HFHlE{2oiXw4FW;r7-xtC*kCbM8L(3^-f2@Y>m_ZK-Ts~~HaOjnu
zC_J#Ad4o&6Nl9RO%3K3D>IwnJ+MNQ_7Qb4Wg}rv})6i%}#~0%d)y)OjY>g-oJ{a1H
z6xH5@X5#ATGFbD}xrqC|B_tx(c}?`d&dCtl=1PG~JaF~x6rn6LQBsa`@h~8G6X-<n
zQN|Ci)%Z1$T;ge_2*$<UXXgr5QHdZVc%}=V4>~V5IsXo5nZ-4tH`N%*T~%I=|DIN&
zC*BX5r5wPdLBAQ&C@LxfA?WKDJ`&vQ?EV{Nb;jJ|Gawir@jr$FXa+QmA7L>4`m){~
z<2to^<8qY*<}G+Ni=d!aPa+$Hc>+uuGhABOFu#T3Nlk#5)01y$kS**o3t*~pz<owm
zM^}`OiLF3DP-BwxYd)42E(0t~hDjiBscYQ+m^u;6-TCb`c1h;N(+1BqAdl&{_5eyY
ze}DfZg)e{;4a!%K8L#nXz@tu5;_Fb1r8<RIGVJ@$k_DX0;YsTHZ361OsN+8IT^uKU
z;~KZc4x|*s4(+cW5+|CQg0dgzSwP<<3b+t_79A(!h3u%HbQsKDmIZX2J8j0H=(K$)
zd9gbd*3nLLEi<+PdTYHw)|*55N~l+c#6ToFyfz@kxUk~gIEY9@`ZcKbi4&DUv(?+z
z$COu7Q`6KW0bSK2PtD1J9ti|T^X<&J<VliVC#v!~Yc{(Ah97i?Eua4S>b`UZB-tu0
ze4v)C)Z!m*3}C20_@XNtN;_%0SJEwjc9A!-=ovO6=C}IgLL^fq4FjneIX&&&bBW<`
zTQR~%14^L6ck30oXw@^$$2*;A{eUlTy7ko_Ci)!+zN{6mr0-}79V3;5ECE<o=H*uW
zW4EsF$0R`6kjT!3K&&)2QuPa6A&k-Cf<S<BSQ_33S;UX;T&xG$vUf~q{CC(=<$zv<
z=M3OmHUkk5@~2w@!69_=^713rihwdKAV3VO8eEo6GC|T@sIq&2IE~_<X!Bs9*v*x_
z5giBxIXUmas!$nNDM1TGe4T|3q#-u5TRK+ewZj0&6>tYy%52Gi`^3cR8A4oQVq!qV
z3E+5f@y4F7?5*he0C;%ek8#Y0aW4#3HN??qh(xFWwd9uOKe*6;K`m@7k-nH0k=NB7
zKyY!wrihPLzJ0g?vkF(nN7JP7TIhuE8DFXq*u;FFLI;{tAaH+^A1r+eaOd2YZBk%v
z^?#>dk3XB_Fw#yW=eOpwR-Q#cxucK#TmYb?1S$Yvj&>%vN<b_Mnn;jK(hDELh5-#i
zy<(j5uheACQ%qoh9LTQ#>A=6a1*lVi_tg~_+h$fLrKE(te*LVrxU{C`u(FdTDkkPZ
zo)V286E`<*3=uv85$?Cx5d%Ul5S4(e&Be(%(m=({+u`ov0n^IaJ~+kHIP?MJ@E|}c
zXVFL}CM5+JmY9I1Rf%3%1CXw>XlB}0!*OXO!aGE#0GtZQ-qhdh5k8>D156Gi(hTIK
zfC4=v^74sUs3_oFb8*Fl14Z%v{(cR27`AgA;Gxe%=AA$lh#+IY`GHQ0NZZ!7(6mBn
zQCG;a>n)9>e_~P+(ESz`vI=ODw70h-rTN!3U=84RFz4wV7%*-0=6dgT8`gpjjJfV9
zr@W<5#+G;E*dNz6N1-?}zHd;&h@bM%@s^--;MvF;6eM1tYOu}hNf)sDMg|s{PKS9v
z>Q0PKT5<7C>opRC-o7RQ|Gm*6A8^%8?=QwlZ_hSU#t^Y05ELHD%HhLnObjW#B!q-{
zUZN7RL=Zl}uWOvSn)Vn3&IFbRWYB<W&(pN~jU^|r%5In$978HE9$G2`0o=7d_%B$)
z6)ie0)feb>uz+Un$T;I)X)qN-Pj@%yR)!j=Xx>M#8?^>EGpXQ7Uwneb(ZuM0_4gia
z!sX-v$qgS3oJJGo(H=<xO6|$!fMZZ942dLhJ^f@3o~(RgP7W<CZCnRd;@O$9rh;}=
zb#=3;i?Q)EkYm?!hcObdXa*j^Z8VsP(hIAAf(LqjymJiO7aTVpF45-!W(Blz{5Aui
zx&wqjz_(!~c<)v$;<oUklNNfhnjiNZm{wt-Ezp`oR|1k425sPD2t+{h(a1RR!wPKQ
zu5T1M0w+Yry7_>K|6cYvLb)+sK`UsVI0iv^<oX|){X#}SS@#E^wYcXhuiY;uhI9aT
z1G&XCu&0880xTlt6EjKB=_LZ$(8q!TByZ?SOA-infNTtWVc+MZ%L8CT3^_lupdbPX
z$3r0B+i$&~E2@ZFLMt=_V#tGTmK?{K21vy?sH^rtx_XcYP|RmLv&~@70Idb7S#fvY
z2UD{ahjDI(b$GRnwe`pE^G|{71=uJcM*bQemcBY!1QH)0!=w`xt=GztD$%c?5wLwb
zriPAk>y0^jA-2O<X{BKq079Abz(39tpWZ9Q(S%~)Iggc+6U+pkZ-Fk>3;2>^48<R)
z1YARSrpCHI9W+7lj7k*u-hiP1IJr5P5H|ZGfX0M`z-wpr5>z;o+{7qos3GLJ+)p`^
z0R}a9{T1{|-DKw{i*X8-3x@AZD^fs?K$F?Ui)T`SKn#)rksqpTXIIp@xQ8K%B4GZd
z<KAOZbR3F90MsNUB?0v(@V>JgL@T(=yLW^J$<i0Q-+_PteA)$c?bioD{9g0qHfiwu
z`r;NU`n{)BmJyu2z&3zB>Pr6@$nD-RTEA#`o1HBcd~tGk=o!~}FbKWkmy`?w+X%!?
z)IyF=wY7)9o0-*9%>^is>@09P{2>5j`}Yfu^LBV|P7GCAC;><$=<E<Udszx9N(e4b
zCCbkZ@JRrGLkJ|AND}u~(oCet>o;a>>a7~L^$|9M<Qo+_8p_E(ACRGG_1`|y<Ud*5
aDb(-d<MGrzI~fp$QB)K(<;!KwL;f%P^r3YC

literal 17877
zcmeIaby!wk*Di{RfFhF8p@@`pN{J{bB_$owlF|(dD&0tT^H9<aA_7t(Qqm35(hX-k
z{N8V$cYohL=Z}4TXa9A$E(F)J)?91NIma0HxW_&4e<3G<b(8ca3JMCAl%%L43d&VK
z6qGBUF|NXqtd(n@;m3_PlInISD42N2?<>D1JWo+j?xIMEiYPlLu1~l)4;qf5{iW*|
z=}Kc}W{&!GJ@;-@LzMQ2sMyg99-DVpBnCtz#3(3iu84}hztUX$86!6}<O_YkHJ_XG
zrJLr<UlsR%1+$pQZ+m(rYCLDi`oh;UwtudzqhlFCE3YGGOUGx8_l{j0p5qI;!YcBy
zra4eK;fDlHH~dR3!gdV><xQq5dRfcN3l)`B#(+Eof<9nKg}@+a#Db?Vzgi~R$g`G&
zF<{o7zoKZCqiU?s;YN&$AZ5|Y41zePpP{RCNJVRL%ekCohNEucNAF~HZT@8Qnmzx~
z5q&PAvsb)}*zOUzhN=yTcg<C;bsZr}Z0kK!?XN~wRSEe%)<vZ4%l6x4!KzM!0<l+6
zP|S$`>*Jv|--74I{onn-%$TiMv#QAJ;`}T@R6c=6c2xsj-RcR}E#s;&q4kcYA`AS3
zxz8f%Q^kktRa50f7$_*IrDl3;CdS5X*U?F6{QYXV&0-@W(jCO$!cQvKeE2vxwna^S
zl9o6*w=oH+9*yV7Qx!gh>CG)5CGPgV>%P@WB+L^Omq$9Ou`gR@-1DzN?KOy|iLl6c
z5+8fi4ynLk<DW^L+u-e9c-%w2{UdKZG*}`GZxR%gxA!rO;f#>G|J@%@v$d5Mr7^X|
zPFNLpN3q067Y_$g6ZsM$5FBkZQom|(sHxjx`<J;AdNha>WfTY$2(F9#L>?7WFjVIu
z4ThuLb6bD*TnqE*X}dTKf>K<o&B&|9n5FQ9$EumSPPszhYSQ4U>fpfi@4Y(G&-e>5
z>3#2FT)A@PQ{1n0$Ali^q>#XhFe7yZ0y)P61cE>5$8#QoJ8$@t(0txSKBKQ1v#c6B
zdQG0t(<aaESeL}!CNHGL9pZFt$&kz>h>!esU~9U=y;d!jm=TM``P{79Iem#6FjqSN
zAI;eRiB<V;yhr*@yUZQ2I3c&n9^=4(08&n4nPr9cP^!?7kZ2kP0fCdO7@eD#nD^7S
z=GrAhMGrH;H@#hHWi{y@tF#RC_m6vIqwnarAIE7@e~rW-FE5YN<j1bFplH;mPuzN6
z-jZ+{cgM0{%Y$t@y%3`(Codlw8cLUbczC$GYrpv<r{Cr8w1}8OeUq`Vak79D3j@RJ
zxwhc*T-e{K{M%hwYw79KfvrzPjmv6sbH5AxwBW%e;V_(P^qACTCX`O#u^g?m6fpUb
z7#0>bRHzwq%4g7RT&7|yf<TO}J1Wf2CfKT|kQhv^UWqpd!c~x(GnZ2!2yMTQ;XU*L
z2bUoro&Kh9@ck|h)m9w&6!J+@-xSM4KSTAGm)?!NZO!q7*=jDG-nA?vdPkZK9ToNW
zx+AsiBXZcC3v!XMv476e<Wn9qFfe5K#QD)kA-cU$MZ#$OBlQSTMN|f#zud5%)LYCX
zrq&OViufkQh990zf68@RwbeP4^(fCbzYP~vgg{Jg<yFv@Qcw(iKvgyGq6twNa}%na
zmX?;St!*G7b3|mMFE*9V23J=!vw45E?CMY<CkMyu?CkSUJk(|j9#vxzxtxAet`&Pv
z^+2)*U8U*t;u#97PyNp8qhE9w7hHu;_1NCnVk|h;8L2Pk*eI(k<Pf(J|H~9Z+YQ)=
zAC0t;7~J;tNolirL{1y&pMUMcN*Aj@CyRD%BEN%KTM$WFT3UE`I4rM}l$25S@>td8
z_CnW``%cg4>8aI3?NJ6S@LMy9zdAbw+1uOO1uq}|h%L@Wm|0m_nVRMfeIS@y4;rzm
zLcf0fwYDUCLXTsiwL2nt!pa2R99uP5>BS2@E>gacwg!{#Q}_D`2?=Dx#D1K{DbxSI
zvHlmw?)QbbzxHD(v4LY<1BX(r7E6N;Yk(HH4n$DmNE#_vdOu<VSak~?bC_!}1yKb8
zR1xG+0<mzT5mPQRI2ziXF}zw)lpf2}6~yid;n`-~s)|PH)MBobX{Z0u<&K)1<zWe+
z7ItICS}tcvCHKCIii)ai{2UxEPl5wK%h1zkBQb?XBNO?E6?^`NBS9)(mhDMzp~>A&
zgRv&8mU3(?c*ulX!DQNqh4<bJKAOZzjI>N-Uwre3i7*iMgk6o6aamksM)XQZAjJdl
z$<F`h0^5J13;ll`>TSH<@$RzSVt0*eb&s*zQj!y*F*)HQgzmTT-OKhYnw2uEj8-Vf
zAdYyb9-ZQ`KN1@o`y)w!>d~9Gu#T+tf1g@dShRe^N^#$oYWrPnGbfkCzt_##t0nY&
zb-OF4zZNF$Z9=(eU-~oO+gaJ!iwg@CRulZ}>|0;nU3-1`6;uKacVJ*3RQUW54pMpS
z_MM-dbOo7B*4GS~zqpM=OUkNbJQl><W>L+07}(gGQ%%CS6_#WB8KIAlSMrN*Q$2Ry
z80X9BSLd*~o>A>BJpwpH+drK?pjCvBm{?L2TygEi`H42mupjPyJUqPT@><RGMMyl7
zTDvlsKOCd$xxD2JuHj{&hL|WBuN66)-k0Ac20g|*jUFfCR=nViZ3`91@v6`E>o_i5
zNWNTq>vb3P=sRNUNFec4H1ps5i5)Wjr_FzPscBU;_HT}b5C<pK;ZEzCqrg87F45cj
zYDVAp4xS%KXh|3-w)!++VQ~=+4efb^a%<NoqobOo<ddn3bJZr#Gw@_$5)#;?Tz{5x
zQX1dPJOwWdGk>r#QD-yP28&W<HQ_q@&EH7ea<n`z0JG>8YFY2y*oSZRicnNXeBfWe
zts2+ug1@zfK%7LWKea8&qwicBDYN9!R8}5{WjA;p%I|sVY;RvqBXvgx@bnAwqlF+B
zh^6f8?D%wY-9LUjwp$Exx@*A1&F!$*9Y-0uq|pyU4cL0}89?~l9IxTTZEbJQ$;pur
z7sm_lUlw%!n_6W)UXi_}A2hPQ-^uj-gc*}=N(+H>2S(~h!>`vyx8O+ti-Z418;RhQ
zv%L3{R3K1M0o)YY-XhOl%EZ}rhzv*y1g#6UHl@M%;q7xXIgw#b*6x90)${?zWh1H&
zRCGQ4%ON0J$Do#tXeletQ<(B3_}(JHBP4v;%moiwG2WBN&e_KJnCT`4#^2;^e)16W
zz1#vlQV|@+5$yg;G`HK?7>_{6<wT}z6p-(CLO`lsaSqZMx%1a*I?-aTey`IJ=@v*D
zN<Gp`1v>Xuhnu!L9|o0NxrT;{hUR&)UUf%?oQNnUF;Paeqoc#d#)el23kBtgrP7FA
z@*|tsw^y!tp6#gyw%X2r50`&@l<DnXXTNN_GLQ#Y<o(Bw;^N}j+1a!Y9;gWR$VOws
z3Y01DU%Ah$St$rtbg?##S6YrqP<e3C(k>3<DV?)y)c$=NZr*%NobfGskTOb4>BGyP
z*u-0Dog4H54lBDcO{WX6gAU-<w6gI7)iwrA(br*M0TrIur5{OSS<*4w=0oq_zsK<Y
zyEm+RQo#fkJDOKY^zPcVYZv=fO)g%^T(9xycrzg@7*8J`AOHIG>z_Y=Y};AXKBp>V
zh+jjyRlV^<cT2k10%o}|k<ZR}wEQ(Vm5$!t;_h9ygN?Jr1nYX?7{TlL-@vGfZ%^Bz
zA3rzhjir-Id}P!a6`?b7Um}G3v3qrj88Y(_+?#%UTz22?)vK9PRb7|AmDzG)ab-sb
zH}Gk-HXO}3{HrEB_fl9j%IP7|6c7+#WtER!IGA*^9>`TNF5?pvbU!~m0ym;yXlMxN
z522EuD3?Pcl@G@KS+9BU+Rn}nfR-ZqfV&u*jVD%MK-N|cjAY$>e0&YAd#*<dG3Z#N
z1h}|1YrjhpG$~;^tX-m+S#B8P_pMIgHwZi3Rct)3pC?emur_rrN!0T^-D-DQAH73B
zP;Szj`~j1Q^3fZ;&M1Z?0Vhfx3pH5N4#U2_J}S?{nfEvF&kyH9ZPAN#o6f<u-KO86
z*E*~^KFo&=vcKP{19om+-0)EcJlGfPG=;ecJr+jBTKh%&`{oqX{K+v?6X{|d%oR_+
zJ8axt@5?~PRWBTJn(`RdFUic#j`rko9WK)Lr>CL$cEXsQkDi^GDR?v=>3x@mhGx8T
zMfq~YPX<+W&*rEuo*>t2I$&m#HRe<+^v{{sx!u&ZwO|1MX~l4g>q=f{ck_uPwomgD
zQM;ud6uegB6i(w?(>~bI$=Ty|<<i(e!NC)CPS)T#CL7!&f{1PGR-f_?JQU#Jaa{hD
zg+T0YOb9SCu4EHILNhJA*>I3-(g`Wp!=o>e-@ku{#oPtgqm&~j%H{lbdbvNRH%S0q
zg9$d(wyK}5>vGnsmX97VwX~7TFO$y{N9ep{lLcKCIzM4QK1`*Ndib({nBV@7rgeiu
zzx3W6!J}1;zw^a6r#p1-e2Hvgd`su0f+BxTogPfayEs2jLrWV%A>eqlz3?)BZ*dW3
z+iIv_<lQy&daA2+6O-WOqoSe`G&8a9#TH+0*1CdfcO9qu&J#9Olx7)Gt17pduO9$k
zh6-*=>g&(S_Aa4O7tW_qJJ&*n{e%R~=hb+%P1WO+PP83^P$9tjRq*WWZ7++oj{de_
zPaT&cGln4eG*c>)-)W=%RSO!mD@5DVgKzjqYg;>R7PR>Nnd2E4M2Yj(>_S(}Ne&J2
zlhxzq)7^e~RS^oXttX#uEW(q%{MUcH>ImSV@|Az56J}=P)Zt*`SK_fU6Zt1ko&ZR<
zvU$0)*n^3M)zi~s2{zK<@ioQkyr#p@^-r4bbNRT01Wh%yY6vgClqSiMgAI^VhMFAy
zota9w_T9>>(k262`1J445})JR?^U@nr_D+2I>!bGyj6|W@nqel1(ZTeqM|qVo|dzK
zV>y)^tyX6~JK1TqekViye6+g8@J&8SSDM^<@%awylwz%#Qk@1@@EWoF4iLN>&3F=8
zT3VKRQ#O;PA)p5Y1zGfG_vl_64})cZlWwQ-I{o?qGebO>G0wIxT?{}25sTKJ!F*K+
zc$A)puX6fFU|t_cM>FZvI}ajM<Z_fEGF9?ly6Cc~zau@NC^K26?u|)d5U4WKhV^>0
z{tlf|t;|@~)AKQ|R0J1zAUJtpVZq4IFe=#sd~tF8cIUyA*QST%Kx-h8wEPj6EM#_>
z>Se~FT#(O!(>vR0r*c>w(r$3&#)YViq-8u#_Pt=cI;`fEm~`vs51DP>bJ%2A@uHe3
zBtt>rR@GPvc_3V;Rg{zBd3=ydTslIfx7~EUR~(`6(Y$`z>hE+j-1jCfkSg6~6^$nw
zb%cQrxVcrL7k)*;&Mg2xDms7W)v6RiDKsiz<m&3Gr>FNH);_nT<>X+tMp8CVE3~}F
z#HuRfqJ<=fzJ$id%09T5pd>i#iqS1tA6q@ZeRi><pt!6M-nLzQ53KqT9o^E&W)l@e
z>hT)8_F%Fq0BaB_qT&kReJ6J&z>VSc4Jt*~i}QhLpX{%TmRpYTZ2l&5y=NSZWy}%x
zjj+&9a-vRp6==iMl|OsOb(@WK;0Zr@No{F6O|%kY-nvB&!^2&K?f<+8Y%SGm_Wso`
z6U#OOEY(JTPUt~FRd*Dma8n)!+8P?9xD9{5VglGyFV<<WwX;LNP41krhxy=!E(NTT
zdg05YqEpQ6m6>bFtb`FG`tEZSwQXFFX^_cfV!f!g${@t3pFdv|Vlph+^JAg;I0vsv
zs&ubAj;-&frrw3afETV_y;=?l#(&N6kf~;ieu=5JWC;xO<;xc`e%ANqGx`$CbozlW
zy&t?KcjR918>&EB-gDX8#t}3n4g%DQZ@6KN5&onU-J<#@sMLy)*pV%14$N@Yuxh^#
zU$*}X*7t^?wx%hUS8C~EVH@M2mS{^nodPGEOKaxJhA&0Ij~nhPn55m=UrPwKznXj0
z%rt;aM9NLuHgHx~Q3S>{&2Q!jiTc3fcYaYq0u?N%mxsjI0@v{+2}VBbtgXE+GzVvs
zD_MwPoED-|Uw9ptq0Y%v8xy>Tn3)*z+7eEe6XS;g|2&A2<qhK!Bf&W4ZmX6#zAGqi
zO)4J5O6A0imeWxi9XJ+B!(i3f8u;*9?V`xn;!OC5b?j74@aE%xnM)-XkWTqK<MLG(
zc1|<{tgjk$TUDvrKKE@=II21g*s^TJS``$i<R7!I{&2M`2f=6nOl(T;5v{XsndRK#
z=-YJqQYO{&tOd_3`Tzt1NH-+9BUX<lF*qG)QJJF;n6<4rgzWo54N2J5@iw+vY7vN)
zjjZKIAXq7V)N$TFZzP@ghm9*LHE*une{-=P_+}$0>+=)SpMMF>n0%SK0@B}X<2k6P
zB+1bUTgN(G0`zU)7oI>otUC$_2OE@)95S?7Rs9QUZe8NN$-CuhOvsxq*v9o3OGT8k
zWYR*?uWt1I%U$+L5?ltiEdn-J0)_RP#*E8XD^2L~5SQ~BrLyX+ZZ;8eOZ;V6ady@}
z^RHgP65F9i-<aky%yT)h^!fYOIm3VFVdCcbVzX(6|Fa}%FVXB|B4^3s4>o>XZiI&e
zZF;oS3~8LW|KY;E+%bF27I-5`D&jY+c{ju5npYP@qm{jAXXLS#677_;zXJF@WZ)NE
znWscVt+>BV8>)H97Y7VZ>Je&|;}tk`EWfzDF?@Ls{R-Y#DT3D9{-T-bn8e^DGYHW+
z!=rwGKot?P30g3Gjod*f3e4EX_qb$&LLRh5X^&m*$hW!C=$yFzNws+4wdMkouVKFk
z=B_t0RlJs`<$U;EH4^*s(kF%3#vH_%`!_^0Ev5<_7+^3AW9mF<Cu&8d(9&^o<Qu#l
zz1=MH)W>g@MX%Z#N)hEB;o<QFoam?>mUt8iE>F?GYL=N_ziM3a`Rmoa?;`)>n5d|0
z!$2cGF2h)rsWx0YV1}`%uNXr~gJI@+v#U+%3+#VRIP_HQQSDo})gn%JjQ`|GwxiVp
z^&#X8<YvJUslu@v(J01cupF05HlT>HsA%t6VnB_{py@eW3-K0to@!X_K8?r=c}f!#
zd2{5%42WYaKE$|}roS`hFFCUBi(JSjmhbggq*q+gVV5|u*Iimo>W~Oq%mbb#Z49jY
zp%N1n+Q<b#9@G`XsozXctUQ%NARM>>o!8xs?Yj5C{+`dfg#`qIL^;y<&4^I#T)G!$
zB}}X*GLchwc@~{RlMkwhtWakui`nI~P-84^-<ZK_{hj@dcq8E|%3H*Hy++zRb9rh-
zM@zaG>C1aoK4H_1B%XSSag#RZ+9vWWMjyRK9)+md2@%mwDl(fXcvodCR6DWE9}^vh
z`Ytuj|8eAl3~io)$#yaqY_LNr?<4+9$_7b-!N~SOB@&Hc0Px(%gYRAM>32C(E1v&O
zMf!#pI{>WYSNanC27FF_X9->|9I4z=XdGhzhugkILJ#4cJKR9W+M*kqm^AJFl798-
zl_;0z>7kz=hL`dzzP^PLJEMlsr!<RmS*NLM8ZmHp?l|2au~n91g!uTJHnZQ>*4BXE
zjS^sc_|W{z+bfskt^rza-r56)O)=5V5axONe`d(=f0eE32!;7U5*-s2ML<P0m9JX-
z%L}q{m5o=LLF`Zx=ql}1O)ID-e5!}DplfS2A*$^bE#QxUFOIfT9hnEtU2kmdh-bE(
zi4DB(nrdU<hBI{`5ZnEaN}23bZf)&$0E?xpvga950qLzDdz;Ff^TAlUvoxKX_g&&(
zaotbK9!C#if#hZ)#Kev3Sxiq)w?+T>;X}pi{%p&!N=T<}+_>?2qSj%V&si`TnMd~(
z#7{H~<mK;nk&f1O^f%&5Db}<)tB9}<FED3B^r&4Y5SyWkZD^MVkLu)947|h7M|WPR
z44z00)rRS7ao@(BIG$7^5JTlWVu-zV^(wFuxP0UxZ{NPPMW>~ub@e@1?x%b3Aj_T&
z(_61X<6&89eZ3&mEBcR&m^*7TLd|!qpPoPHDpAfem>rxmQh!PCAyWPm0#Q>_^U0!(
zaT*zWTbL<jPXfl)E63L1DSC__j>vGCRE_=1vl9>yz^ltu+O);C%=}GW|9h90m-p`7
zCybk!*R7&-9it$BhAdh%Dlrj~YSoP|MqSYqg3e)n92^{^JQjaKIo!KVqiXl>QX9?n
ze(Sl1gsyC!Hx~aNm|?X2^)V;N0IVC2j6FR)0X?27eC(AHa*J&K!eWf*@cqZiXy2u5
zN?SeE(McXOD@>Oj5f!B=)=0BB(l#Zxbl%S@%{Z)!q97$jK;7n1eG~-&K_>iGU8@l&
zJ#yzu*4oYaO9E$Bu~Pm`Nk)Nqroahcgnp8hE&G2qJw~&;!TVIbJX!Q@&;DBwJ<Kr{
z$!PbM<-Jq0Y0AoSF5RjbDe4Xu{@1UqNNjb!u6lWCJX*6mXXd0+_(28X2+=?15uon$
z;A9xIX4_;UI*TtZp+l|$PB*U<AWvL|6UQ>9M6lGizyw)G5cg*uX1WN;#a}`BVgl!2
z)Ahjp?JyP-d?SI${mX~C!`v(d(|AbeeH1;N3A~>>X0v<#bGdj1fqxvw=rgt{M)qv~
z-@81DDtD0Azv;s1etdV2>WS#u7ae}DmzVy<^%>jATjGAvdZA?EjFgM~OCQj1FV^E3
zuO5y+byg1IHN&N|7$o){;eG%4O`5g@b;R(wWYBBv+ep3-!#D-^80&6BauQz_<#^8J
z_1bW~IL58aZ&t7*NAC#4R&Bw3YCL5-8KCg^Zt<#yuk%NtMhv*1K|Jq~oNmI2BoBW{
z*se7b8mizg2C=<I+A}GgZaQ33fAy|@%j9xaSJ0c+%w2u#d!JA%dTRFC+LaeU=@LPL
zTDA$0%wM68#%=zh=rR=N8k=de<s<k#j&}jfUK}l@^mcXaba9}H9DI3$hYGv}_j;}_
zA;ds#<wDFwmz_obRZoo(M28RGQd=n1W=<AHFsbx9J}l{$fTD^pGt#RkYY=s7R$90)
zHD1ScupD5s>$g7F46~oQX2?;}y#sFRToW14*tj`kNeotpi!XY;&H=4-20fRQJbb)B
zjJw_xd*@e0-Gv#>ikgGd{(b5rul%?M3s^jq1w>r)DP%7P+Q)lg%wwy&^>e?nq_ZT1
zcDgxD_#H`X4-efL^o9!5J(-`?W>f)4U2|NBsqoasfjbgIwexR7K|#TTr62v~)_Aya
zGC<MJeGk7c-L_(1o2QiXQh|ZLVWTn3I<@#ev;gQrx-80-RuTNPH-kx4`)q+?9Ywq|
z>4thJ`Hxv$^4pOenXlOO-O0B0ofTDjJFS0Wf(;bqqoLX9=}l)gHY`st>`MJgHOTWp
z!}?W8IdQG!yPlzm{6mPpl|Wlg>tfYM$%+CS>3+Jq?CtFx&8&ITJJpi?>5I=rQxn@>
zUSY#$G?i9#sA#V>fk69PfR@be#%(oT4M|>nPHD_QStt&!6idu$%T46+qZAGP)>{mu
zU-1pPtDuZc@gkIbpq3QwL*!i(fDx{sxU_+^e-W0Lp#_-G9Gq?y38&t(rxQ;c8L(Na
z0H>Rau&LgQR6m%0L#<e8-PQ%sE>(-0FV-aK@qvnQG>8gBEJid|!X$$_x?;jxiX8YL
zFH0gN>y}s#8J0_Q=i;llIB!{g&`UP+*6JtDJ^I*%rLO#@OI$WB7mT%N8o5=GE9paj
zn&_0b_oE?FF4<OPqgfL&ELnN^0|O)4GOIuLX)rpAELYO5d1}GZDL>=piS0>$#6WAN
z+U_`UwNOqa$$i^?O#8&MqfU8Cw_&S|jP+nsz~FEi_r5p)?3~eW)r*QbMWQrb7tA9z
zB9P3m;1e5Y48I^W=w2=uXf^R|dBsI4R+8R=`KfQVV2xKksUeeIl#7Puup#%mINI5u
z5)?_#?Y>N;VAm)38L$8D-~QmW3rzjCQ9d$C%Bj})=GAD;(v>YMQkqYMV7XLpOXtxb
zYxmed#~vkGhcI<T(AaF(6NxE6iDtwC8Kr<e*JCr)7K4&d3-MeI<)z31_FkveoM#*H
zj<X;{l?f}}IGH<}!Nt-zsFqV!Uv98Ger1H={h}i&`?7jJJRz4HY$aI#%11Rc<@EGx
z>nA=J^PO#UqJ<^`ior^iu3wStZC7Q*H+!3DGnl$~%QvHu_56%JwY<C@V~0b7<m*yj
zn>~)l=CVTkKk2?O%0-w#QNM#Sr~fq@v6W56-kqv3kBp0I&nyI^a4Q>BFE8^JXP?id
zW7R1xy?KYP$zl39;*gqzu=uxu6h?P2IR6k;`Hc-U?pa2dDh;wS4>>X{v%2p&EWg&*
z*WZq>h;1<FO+WLEsayUi7h~=Hn@g@XgB7=9%B!oC<E)kmfGCTZG@F|Pc7a=aX>Z==
zFuz=#?}>i7@6aQqk1a4Sx!kIw_V;1!DaSLuR0S{dn^yZK?3#!vq^+t<mi4!tPa7T~
z%?VN$YL4Z-sDx)w!nkUXZ)ZE_v~jky6?9eo9<@{i+AU%>_rt%8$Br)v972O=13zd#
zqANa1V~hqW#XaAlSr94)$J4syhPVyqy<Q`jwdg|*c`Lh0CbN!f$xa*IiVNB^5bn;V
zaRK@tQEVbj20Wf@JXeY3xxrGISC`IN%i#%{h1rzr$a<AnToBPja*k4H#aP9rSIuyJ
zmKlWX-p40v-q$e=)%RaX>90B}>htQ$=$zlFi&|>oyN2?lLTS}I&X0*u`svfBl9Cv>
zH$+3<4-3cter0A+t`c#3WbJ_b(YV(|nuk~=hgXGRM?@j#l$#ujEz`wHVb$cerBv+&
z8Pj`SL&t=A3)~~FJ#bpCdBM%@+-3A~ejzgJ<7p<%5A@fT>Q8SA9E8Zo^}NyX6rO|1
z(bP&GAOGhKrFBi%=~L}@{$L$Tw1+k4CU=IZG9PSp!$8M?w2jM0cw|$=y`PB6;6xi6
zb5E5lWf{<w3Q|%B0Ey~0Lyeonz_cwf%yzH-o#!`6JXE9hmqsUqDZNw>h_0jV7^$nP
z6ZGXC#v}>EyNt7_Bsm;w^;_1_B@6Bycs260(;ggdPswK`y8_F4s_^zVBG}$UmpVd~
zSyi6$pq(0K9AbP|Jh!={)2qjplcFaFeZ$fhrekI<`Gm|&i%-Q|PUe?+d?Qr3%}dM=
zzw1sU1DSP{hc+Q}t#y%EN?|@LP)(#UB^n~@_)|7r+JM=rDbEuTiXCOm^E{O)U*8W{
zg;ch=N++$%v+1&J?&9_rPh6)(;tzkE_BPS)R6+fX6bPWOd(V(uxhCUN9Q*s?H=Fm=
zUcEaV#M>SVg!;@|dB~v#3N;TPs;02LdJ0O5hG#S)#1nU!xJwLXD|_ZWY%*!A+|9<<
zIG5I*Z@JL`k3U7Z_j@)wxX}!^;c#KAJ#^}qifJ=mDiwG1uIt`6c9+)SZ&RA$;BUQf
zx<BdTd~i9~&ACzhvr$M{?N2FceXM7C$G-Mv3cqG9J<y8Uq};g%Kgk~Cc}4!w+$>`j
z<@zPe6@e-;Qugd<&+D-|MC)8a7-+CayDstKeAhhTB?-mYkGjIw@B6}*_v>9x4d;iF
zQq)&0n97&<lXyaxa(3~7yQ|-8^)DP--k^{a-DWih;>OzJBcw#k0Lh*}#Q{pN_;>pL
zcKD$!CkfEDa{Kd;U>Yv;2lBZs{;1=u-17?*V|A~+%gESC7Z|y*M(40D71Z&tQ86^@
z*f1I14N#jbN8l`HFln~KMs>BAmR;b;4>gUt+hpzMmNueu`6;B?^qnArv6>Z0hMi75
z=_tSuP;bxP)%Yi46p~?Gj780KqW67RUqkFyK9{Ro;K%g0{4VV-*st54sJQsMlBo3w
z7Z<9x`<qi<+h5#HxPu#JP6|X7F8&awiJ+E^31SKx9}T;Q@B(VMbv6G-lIIRmW|0q-
z`pR4C(Lmr;S9)Ci@Zp2TRa<NGjrF35Zi{2L48KDBsYLx3q{9`9{7JpZ9qKi<3&B59
zv^#g)x3APi(Y^n$n&K&v`^~OhSp~RjEG(>st%@zoOTQ}`9(YaUQeNfb?G3z4Y>#n%
z`f$)o@Bc*U1&e1O)imsF<hOlW_4^>EKz{P>-Ma`H(0a&zX$>i@6#)M>>Y6riL1{L8
z@8tVTDES9@F|N;&`n4@b?#Ekqu!T4{OP2ch9ejesW4av9MzqB+ZVm|_73dXqu^77&
z(uB9n?5F@Zkh>e}are=q*wV59ua4T^JoJD2$KsPWe!X^$jHQoM;2~aW<*^>#nar+o
z@FV3OEu8FraPwYf<9YEMfY+s7S3<AnR#mL3FjDn9z5RE&xw#BNZa>M2`{sEsDDlQC
z%vGroIl~QjgMZYPLLO>VNDla4?K?8A^1vHna~&%h4l&6TNStgEf~;AZwE>wtmzS3Z
z#87hu{k$+@TE;SKyMax46Ak6Xvl#*7l#Z@0&^Ta`a?v|k1War#*I=gTR>#tHiPGRq
zkPsZIy_qID+ejSA2&B2mdQrP9d~#dsAW(kq4{k3Jrli8R=)%2FA-Um0ar7y6Rh0kv
z`y`C(^)H%2^J<q%t%hxB-SFz3c*d57#Emr9God~2l)#|s9*ZBJ7L??MrE*q%rH|sA
zrdDraS{Dsp*XmR<NZ^JV_G}hL+Ft8tG+=tyX0JPL6b#f-e5yLGw=6GZl8&JH51G{L
zma`0PB-D=q4xssnfbtVWoz$}kO3whB8ZG{$kVwue)DcGN#lz+RBrXH-qY*MyCsWa;
zuC6XJKHJQnS*xH6*k;usAH2I(<yaVrFL0V+lkq~zp0p>Bly|*9YjaFq=t4<St>b}J
zv2+XzB;?G(7XiaENVAp@65&sZcmKX~<Q^fUQuh>sssx5{Q-Yw&_OBlk%MEcCjVF!6
zI&M7zpKDrAJ^N$rglg-<93EQ?J1Rs6uK4yc&<Bh)*6k#p+5Jcq*mSg>tanj6sk}IA
z$9y(gF<EJ;x$F|atg$Td7<r$FwIla?E@6;&aie;VY(h~>oLLhWx0R>Hc`G2tY5DrA
z{m*G}tWB{K`6umF-0Os<cl;a)2pYmX)3=7Su_=42Pd+RquMFCaq`E;VL*2MiKQMZ7
zGj?#muy<)^Ios+RIQhxDv1dh4=P5s(QdX19_xkfQ?O9w&DN`{NepYK8<Oi@v#v6M|
zSSWJZ#M{x;2--?XB`z{7WBxq$p<L;^Q0qFxRGp;$6cNF3-hVx%&R>e3o4Hl!;+gS7
z#MeO>zX@uODdB*!qVnA=KsD)ge%h13OGiT!M8;cWZ-OPJfQC(Z`YeMU@BIl!h}Xe{
z(*pzS(X~yhkxG8LoLrIjPJWUD+@CkDrR`DP!n#p9_R-1WqS({PnP9K$S;yv-Tg<fW
z&3kFbO(FPCpWbLFefS<q_)m)0Zl<a_UjO-U%on~*lvRv^v^}EkVgP9PI^FC8g?Pn9
zr8=my#2ip%wx2&Oob3;xYI?{;!_Pk|z&M^7+R=6NC#6_H<Z+S^85vPb`=9czJ-f-Q
z8k1hH1Q+&4f=qt&-LC{x)d;q~|M@)5<j5c|^jS5?e1@IWq$hq4R2xu;v|sxj?%VkD
z&nN9GMf{W*m(Npo_0OmEXZ7x1&FC`vB!39ky%I<=3i*~Jn;;%Jbua(Z_6~l;W3{sR
zoy}6a#;zYD>M!gSapWEA?1XUnlCEf04TQcPt@iM(m40kg!?$r{M>O*3=d+Wgi6O(8
z?c&91t-1D4*0S-PrCzdIA3=-As4fj2=E|emK1B%3ZD@m8*ymz)CZR*pN&F6sLPK*~
zVIwl4h9%V#8;gWH!Wq<6PNr_P<~d%Hq9$_qkOfvwdL-9`4Nm_l5jZ)W8rau%B9#e^
zSnWF`eCab&Be%|L@%x2qa!?7fx>dZpd7^y4vY^8R#+W0Qr1)2j9R($LI!N6B9>|N2
zTSaA%kXa*&gxhuZ4^>X1tk6cPY?ILWEarn2G7_u<PNTNAz1`osetse3A%3C4Ii;37
zCzWUlN~EIKT?C!xbu3(&R7T5kAlJ@XI)bcRB9q+H;E6CbY1wQ7r@SC835hz#K4^=a
zS4$+U@`<7dwY;`wTNM-(jGjN2md=L)G>B+CMj_>YL*lLORyE<MS<`}!P3b<rKL%C(
zPlvz#IF3f4q$^TpTzI@~7qQ^0wO^d<N!_!o5t&fM!@GGPeLZIPnCdH;g7f{AApyx~
zdCJG5LZ7V;zSOR-qRuZZ`JxjX5Z<U#yLt2G^Ik^?_gz%=#UUaoiID=pB!Gq~*D#8y
z3t3!#bKG||X0<ZyKCOJRR`w{!Hku?tDc6UKbz!C4f6{}B<&JlpITC$A>CAKQO_E%d
z?Lwz)0?)F2EeL@yy?>7DTUoI;;gHNI4hp0k)!BVPNkVMoletUfXoi0(qkib?#}q+4
z_1VVtps^pDiYxgMq1ort)y{r<6_vdR=i1cIjjgr}OHd#*A1+#tvYV-?bH(j5nDR{8
zTPp*-3oD5Lxb~wZZXr^M`n~1$IU;vQsANZ<j~(qS6|0qe)i0?(KVCIr8OHB}@_GoB
zFw}exesQ9r<W}Hx4}PMN0>o46TCG4pW}>TgFTWvnX}WDNZ|I9<bDAc#fm0l_qu9i&
zvVMvN+s!{>qU{7(3n4;<g$`C#Y8q&2@o};Wg&Nn-Ym-SOKAhJ(ZhY;1-aBMfHOx(1
znIuG|_Y<8oIOO5fjfS(X-X<#kFh7m=0|~YE%SjV;CF#_PD%7JjUKimXQVLcEGrp`-
zKHhEKYv+9KB%31Swx8B3ygC564yM<I;d-x8(URA82c7WQZe)jHJlAVQcX#(VZdqAb
zA-hExNYTJltkUjXK{@n3iS=02IAB+8$dCwq47|#!k%QXA+Yc?2T$FrIf`G8kXxLPb
zvOb|{3L}j<+pZ=MrwQvBI=64rMW3Xy<PcAg!NPLoX5HM6{WFdms^}>)-s}Nd15weS
z5^0w;{l$8Q$T2^rC&kzmu?1JIWShI^vW%`b%gzPuwY=wlS&E7g{dT$dVkhgz4N-fh
z&0k(CLtX}*1RQZvIDV#Q$9oqp3$1Y;o8K!1#Luf8TRQr)O!k`5sn*UdJQF_u99(XX
zv7CH)oM21+{P}YYsEPv%Gv5(8H#=LTCe#L!8Herpj<jdK(MJyp-#Fy6Y#0=w{PVqk
z5A<b!p)#0>SgEmFs&CFPP7G^0;T7--@r#wa=&9*0c0JIwzV(98Yk$xrg(7mHh37<}
z?*7CKQ>~+kg;03A6@Jf^ccpbAD}Buh;o+6u$3&c~V}UDmiLY2pD8%;F_aa2mGJ4h{
zv{)kLBt+Y{w+%+C07w{eMbP8+^$HiPkl~Ao#_^o^r-s4a#}7~C4U>zBh={1S>hb7_
zpBK>HVVy^A#>pnUZezUnyi-XpSr;TDQ)}%U*R>B<+kO@A*w#(R3+)ZmO!3gmB-QVl
zZwg8qnwWU~ZAJ%;f%95Pdr{-d8OOR%sJZ7VWF%DI2+m&vz6S*<5h(+)h{wk&y~f!N
zT?7K^5+miwXGiG>_fP&knrOV%_ulgoeEs?%RgXIUxP#7`T<|Fc|F1lyYpxT^B|}0B
zjz-P|OBIAl0XiyJ2!zu7=fr}dt-@7JCaj>3Yz>bu)Q-udAM@TO8M@0_)t7!G+;|f5
zBCL0fr*tfK!f|U}YOEFW9a@HZG4FE~kDX*`99f5zFdcd0z|`TCj_xQ<R?)$VPQ<at
zN{Mfq(0yXr%>M96s5ERgT$~FTYdgPP@S$*{O5te`8*`)y@a~h0*l?Ho0s4#44h3f#
zfu%l%%@AVt^j$?Bi{F3GPd2+GLU$=o20*~nIq(_q4~Q~K!0mwVtUI2$1<4bj`}NU^
z{L&?!e`yZAM|^=8ZgF69W6#dcLAW{6``!*a>5S3&gO%CemtvkqbX}p*9W61RSBova
zH=Zd@+~8wh;wv^6Qtrnl945=VBSA(k^PQd_wTftm__2BxUHP@c<cJDlc#3p9?q)vz
z;M@OEcWP~@&^qTmiWpBmLMpufV9nEMz4lAv*{w?ImQL#?_f=+U>X`nM!M$ShVakcJ
zIyRbt_(-9Z5{U*~PR^<Xf(6hZb-3-nX!$sA`{ALjd;ZIL+i(P1j*qgqoJ1@4B>5-y
zP{d8qn%gEVziAEGFd{_7@8iFp@;s~1%=qa2q^{RQ%zhJ>g5rK`4-HNnlSD+MmsTk~
zqKLbFhRa^k`M73DD(buGuJ}0_K3%eI740_*ehp!LU^KFXHRN)3AlH>wKT+~25;E?I
zzk`dbP#X(ks|ODr@Fo2yF=*GBaQO?yDB!qe3L?z7qH~@05Q<2nnsJ4AU==XEYtVpZ
zm*K`(#Ivf3cb62h3ouGHA>vfmu{w&Big=L|f<w&o{v#9v2Ywz4aLSQ={fb|-re4VA
zcz14KmD9MOgSEQKqwc)r;}WA;bh};iddIf291&rXE&noM3gWw3m>eC3H~ouLus+b|
zI1Lm^r;En0h$YAdw|tfHN(Ra=QSgt#cQ+}Qlgbk8TfYL+RU-VpGfKhys08#_Vcg=P
z5Z?cxD=x0KN~)s1X^T#8+vacJb$UX^SMW|@t31ZuX=*U0M%y(sCuFQO@y9hc$Ke~d
zqwV?g+YPu$9n9_s`LwpgcHJkt#)M|Bjfct+=TCpOHnIkDbR8|fGX05Et=kurU&~U(
zbRsYIK8ghr?p4MIM{KO#mq>{hd_e!`_lc?@+uCH6b;nrb==(w8uul`4A7UuUIWi5C
zFRP0a7wRk#Kg)G5K(-g{`5syVHg<Z2Q!0#<Bx5$9=*F<qH`?TN0a6$mDN<oicW7{s
z4%H7T)T*(CrGThG4qCss3P_~PhYCOme0Fls<eH+RtQ-{)fk#E9yJ5UwTeIuxQ8SDe
z5D@SqS!k^MwbIlE`K7q*1jl>C1fj$hGh<saZm}6hkBe(SBc<HVI8YM7nrzhok^O_N
z=j)r;Ub`brn$)C5Zi0NJiA&+E9otUA7q;2z<RrJ0!>EioND5viq>ri$swm#_H5{SF
znDjC&qmM<sM@QFg#o-?z8svX;#N6`LFE0p}{S&c3f=i!X1!*kfZ;N}b+DUIwd=P2`
zpOhc4@~U#h6(A5Mh(EU1m-ieDu^DdSlBl$vE)DOyei~E=(5+#{8~nuD<T=LZX76S7
zHT}86LRazKe$EQHye>*t{VUsd&QQD!LtmKfnDA@A>nS3=bzM~d)vGf!tnTJLzwd4<
zUb}x3I(u2Son<1FO$*1?Lj?Q+Sj!)ghg_%fa(|pliL1_P9FlTc*{Xl<9cpBpUn8i@
z_Aay|`yMf#&TqX773A07{WH6;yvw5E0nyoDyL7$Oy~L(F;(@fN0*y~Nbhr%_>t1yK
zeI+inox9?cP!&}((F7UW7)iLqxpcxIsKlZu_ZDJwL*h^Fweq|^Y~KWBIlbM!Fd#8t
z8M`fbkj@_<3yTmV9i0it<zSu3xJ-ZUF83Fo$4DT3|JpOCPUW>VuXzo-)+uXfjDm{o
zc;cvgNx&%{)UV54oz!ASz1YI;XDOTBE5lR=-JH>%*{Bu$w~gZbvgML$OT+}lM1K~q
zPS#sLDT^*l^fEsBaC+*W;vp^@4*;Ob?<4H2UA78I1^?VH_q}#jiwClqal;Gg6>(PH
zf2<|3%SXs6P~7&7Oi;b5ER=!JC|YZVd}ikp5t_YBMW>P5k$=$Op7S3@V-<Go41qLt
zm7w9r`y9E?-=^b)%g|s_=LLL^Z8oLJJDh{y7GOAHS&5@CF~0V9S}#dX!s~o2>5<LT
z8`jgf4@Txn<`=a#rs!@SeGp?X>3t#Tp=fw^WVbFri;*tr<+<XxG5#9!Mi1Md)X~B4
zg~gaFXn&1vKKmcj1i|vlQn5#OZ*L07jQ#QHKHjD{_PRLmGHN3vBs||AvkoTZHiMq2
zI0fh*O8DDO7~r2)sL3&0bORIfAxQ;fp||Ifvm_)GT}Mq@h0OOYsJC;w$J9NVy{~Vc
zouhLHti`7QjJDsr-Is^Bt-{R67!SHamZW4`+w2e$3a5pqQ(hNm+Oo6X&9VwLv(MI-
zUH4XMNXlSugJRcxXT7T3=NY}Qm+66(f~<Z6I*#bPa@2+8DKlp}?(&}ftaIpl)Xmk;
z%KAs8ifU@Ht{dcoSTrN}`XwOv#tZLMYb>A-P@J~Mqe^L6$u-y6f2nS{HS~Udex4)a
z|2A^?ZF<F{8`26vn^{p1`4&y)QGj~Ym&()6-`@pvm_?JV&`Zhx@L@g#*M$v_ABlYU
zgoKWJ0yozkPl$Bg!y_YiKugN|`d4PRntC)g`6C=6B9jV@uAt8DZkgxL85tR&J+6BY
zn;$Uh1UMP+RQUM#(%4Rq$fGHRmV^BbIr;~xLcNY$EG(FCU5FnlMu7_SC=EL@S5<ek
z=B(k}0Z0$)=drbeWaD|($NEhqbLxaYZ+!E|*AC)47c4#l?XRcVWCS&-!hWj&Hih$I
zoCynS-|}%+`2l?iCa!t8Wfy3d7d?p5l#Fhxs6&1IxL$f7lOc<|L727{@)*(i7b-cO
zP`q#Ci;yXt#bU%iSf?CpaC`Fxhao^hB~aM`^bJQn)-gfijNe1Me>|6wG4x6<Ai8tM
za<od9ibN{lX&e#1v%LF-pn$xr6hjnEhkp1?%$9<ozkh{88;BPc<_&8Tm3hPb=vVc%
z3=O0RmV5KBl8}%*H}y!+NwJ-tKGWnqgL0_RDEU7C4kQ=<slTx1T36Dd_`g)Z|IRP=
z3Qz*af6;I-X;(J^<n-!Tm33T+?`;a$@Jiq#x@c-u&CC!?T_DzH;uHi83TT2K%B4`?
zk%UJ@4Wx?r{4!wGsjoKdz!SlNrZv0094ip33OH@7^<}gR9gHivR;SY=mA1B0@>U0t
zX?u%3397DZU^3#NlxKBa9=fLAp;3i|oLtKlP><%6^IS0Oboav<U!%!N=xWyW*c${z
zHa6AcQ;?QF6bfMHc{BYbh2>6I%lYKrSKy<}mP<g`;yLS2$6=sv-PJWwV&HqgFZvo|
z@g`80-&vdMA&rK#92@o~JR&^I=L2>kBQ#?}LoK16V4(g1b6n+q9BVChXR5uMc&yH;
zxX;uAUZV#8FvA99_BzE){uq}4dF>hWW9;tkf~E#v0g@+I*IHitu3O#TzgN4)<0Wq&
zM`#7JL)R^x>f9YM1(k}3PbUcogz_>p1qP&lFJ(GR4?QT{@id0wndf63WP8mW-jIWR
zggJ8`Rq%bO2n1p!_~6teh#}x>hcKZ*P`><a+>TOr{6dZRyBLXyrmwqLWnVxWh)@V;
zLXVi3*urflCsNinBv0J!UEdy*xVF*Ij&4<TQmgdxiZwe@vhrn+@>`wT0>UQBT=-T+
zfQwUs)*$qsO!`=ZMg_7*l}1El{mXi{In6|W8Yxw$%G?j9c-_!$37x%u<hLa{jB+_(
z`?N`eB2mZqTh^NSmQLs5k{L6Lk!mWBZf7G)5h*&fXoMvsm{(}nFpSZimSzM62JTG0
z4oghb+O|qTUJgt|Dzc%|Y5CUz^mu?#V_;zg2L;K1VS}A>e(o+lrw27d>S(hE;Om(z
zi``BZBDlq6_XmhjfLx=R{oiO!Bnn-uew|KlyN!m)>KTBG`YvDK?C3QKH6H4_NQoq8
z8<ktC7mDB1=l)i<%oN&`v!pHNPRgGHotOLSO%OcLWn;Ttpf}Vuu7d_qMEO5A=hyCc
zz9cZ(UmM{`05@$0y|)@ME#bgUNk~XYN4%$H?6IoqjpJ-zcSJ`+J72f<>R+}uO~i9?
zaVazD9a`N3uG$&)uCj{QoL1rI&p*v7^vf8b1LWV^fPH;^Gi=<`Ym?cnfuyCSHHMC;
zk&zLgI{RwLS=ibXm6YIj==Q>pwa-Gz;30_tF~Kct?5XnC_@p`Ipz5CRIG8|+gmiQ!
z0fhg2q&oKbRST3KF%kwpm6nzwOQHr~9-PLaqN1rb?pd{Hb|A-gcNhFu?~}RqZK6cE
zrDh-WJ7Qbs0kn*TfK0))-A@O5g?4i;xz~UbH(_x-JsW$BF>l-unTraKi#r;#hHe}2
zxv1#4IG2s_n#{~h=w84hZFfSLvw;^>XCkELxZKPzZ`aF41P#CAY#Kdyim#mqcH7+C
zT<5qxTI*o0uTKpxGCi%=_8XoORphf8lWF2FWfkpG!wyhyG&H5#JTFsY0*al4UH@Bg
z)4413Idg>+D-Un}PeL%1!mb(I_}n1C#-PqaiUy$dDOgFv(a}*-^1Hxh{Z0?>l*Lpd
zMX=Inz@3N32b<Ov8keI>%gF4W|ICq30X5y_z?_TK)z$O!hSbmH<U#@i`)kQ5)BK;0
z)w=+$hAu+y13yX_r1R55a6O`;py0<rg;{c}^VkEbdg+G{w1`0Y^5>&0@UCk>){25$
zyzld?XP1LZ*fIu0L`BUz)<GkIL@;TV10T5yJe#i9nL}g;?5p*Dd0|;8sjcj7aNlE4
zN8yt5$0TCuj^mUl9@Z=zLdI9WpO1{R>YZ(%cZId_keVm|7mHT)Uy%88wmp6JEX8eu
zPerBg(PIT|9}w20iX)0*kX;>SH2>ax@y{QjfWPwa!qNg*{NJBnX{cU7Isc#sHjVsS
z2&n<x@!a`Rk+jSj<*|=!sBwYy-c0kQG=msoZx4MQ!s{M;-y!2m;5H{DB02zc<aV(V
z%sc59i2MST5DBg}&{<H>w;Cy3c!n*Mi9meJzlP$es7=*z<3$5Bk<27mH;y#8Rp}H{
z0l+W;OexxQy4^L?;`{9B(+vm=u$pb(zPSUb4t2S^=^&P~-xwb;F0&l3-ugS!0;Oh9
z8UkdVfa%rH(2$XlfetBPAg79Tg$svop}60I_qzkM0Jt$IoxPz(%Ap`0L9?0tu(lJo
z>Cd-*;vwWm@HVY&ZMUJF;l#NVoyz?Ua0N$4N6-h#O?(SLAF!_wKdhmJ!N9=4Ki8l=
z1RAhLiwIB-ufa?)sxd`FTcZn=^J}DD5T55w>+EL4u4@AmRH!h#osb>vh|W?AHsbnc
zrDbK>EHS*_ucV^2ICZ0_r0F(!1`@IU=<Nle_rbVb=z;nL;GwS0&IqHUK-NZN^9b}7
zyev?gvF8VT1)J9u*+=QL37t1xrS8z}^Bz!Nz8gxaaSI9O6BS8GNn!y<i>FV$yB0`=
zT<xJ^0H?d{{$V%$nF>2;cVprN_<i`B6944t%(z0we=?vajnx3`!=q56!nQ^W#HqJ#
z+~{)^Liw`!7Y`G~vKv+(_MlR>OkXVy$}}d65qQxzs3zSgqt*7y5lmgs*seiz6~+Bu
k`|5JX{jdJA#;Nd=y;rOAG(=}mJ4BHZlM^j^s^|TG0qWMCZvX%Q

diff --git a/docs/articles/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-11-1.png b/docs/articles/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-11-1.png
index a08eb6338088d6b66c2d210a06744ede43246489..4bfcae3ae3f21fb1eb83f91660bf4d610b1d0ee0 100644
GIT binary patch
delta 5965
zcmai22{e@L+n+I($TCv)Wk!o764H=uWFmW%P@%>eV=vjCq2jTW7?M3i*|V0NOhT4O
z_9bJ@ntkV+(fj`2_k8C&|L>fc^PJ~;uKQYk*L_{T=eg5Sfv6X!pc@6Jb)hgvM@NDi
z^d*6j%9Mbk;vs}kB>;f=NFoFRp?AJ&;s${nJx2LMJBR#sArMX(4UEbSkA(RlPoLvf
zNne(z(h$^)aZf-E3<7x<&np~skD5N`4i&>0(U!(}$q+FqS+ho2vBJl6Gw*o$>)+L<
z)c$1Ph6Yi8O0P%ed<sE$M8H*#JfTifF0Dv<8>gEtZ)nQznKj!pG}1n6C7L<rDf+{S
ze(1rY<AHGh<8SpK$VY^GToQzQI(~qab}1*7*InGvaMDOvM<P$jGqoI<=C}x73ru#g
z(_y>T9nRP!$d0vYc*bF5BP`D?HDlV``qZx>Q$`03#mYWn#peECdg75?sM#!ALPd1e
z-AF5&Mv0Rg2WFSkgwL8xgmq0XES8rh`TfjF3XRgAQ$OvlwXV5-zn$G^(hsO(7Ik=Q
z<}x23Zql*Gyi!*T8w^q&GfR1XeMflXLY_=^o|EL9P`gPNzvf(1H6K6EL4{Pi?hL(G
zn?^#W4VU{Be&V^IibqaO$o<a3rA9x^c<I@E!%5zUzqGkKwLKFHG&Hf3>7vBxQ&tnB
zUnN@SGw)1wrd_Bo(?2)cd$|lCuw?MPua1{8Eje__ESaUD7A4i;#$FQGxh|&n@qy)Y
zsp-m60)u}1W%=t4T;pHap9r!D>OI{o>)gM+KIg06C6E~({l>hH`}A`@yRMulCPSoX
z_};ZDxr5U;y)s>wUw2L)7(bT^JRy94#yAOf=g!TQ(V?*}r4ZE{A-#Y>diIo<9aH-J
zR=_gcqAI$iUw7f8;&oe_8M(3dXO|J>Kpyi1{Os8A^zSF><X#&^({X0o<?$Y%xoUd{
zM2Tlk<RyO4nP*EA7Gf4{3Y$3VR0z|gdnncp^#X_0UBFz<0g30XM&7~&Q&qbMFQ$qU
z2i3hYzO{9^HQY18s9FN2ofsXnF7%~Qd0p}RR+9eA)iP7+Nx{a}2&<{Rl6}Hzb6)l!
ze4u=-a?F2P4$acBBfRtoGbz_>nwwIrC?v|VU;l%H60(GDN@8XyV}&3$sr*ZKK?t<j
z+!P<uR}(dU9Y=6IF_2j-Y?G!4e=Dk2c5@3qEmzBfEm#n?0Konv2~prKZZyr@PQEOB
zRhw*PDy%*a4BRX@rs%pl5wmcwKDw@=4>D2C+B)>XMIhZYT#X-;5EZABv46vx4O=2X
z`N0JjI{_<3{ehL9j}04x8O8bE+zk#IyZ@D`R)smM@7Tflbx-%zOYYms*HepP%1d$k
zuIk#8U-?0s1;>tGPD4>;50PHXztIoE$@|ZO9NgTjO75+L^;b4RYt)poWQo`39eHe<
z5&2`5)7#9G_-h5sffVQ}qm1|gWQt45_bFBrD0ZlHurtdlzocP@reAYjrR4|HkO(7;
zuumrq2%<5kr05EE16tqu3o2TG(QXFu!uldb*#XWMg|X6svW}R0_aFXxSxK)V%X7Sd
zOEoO_28T?Odon}Fco@=pd1HUzmDNV(>#J>?SQmqs%HS=oVEM;~YhCioGVV52Ug5Eu
zQ|35cH#*9L5mfMbeDz6?6FfLa!KX;<wm7h6GSJ3w<qng_rnL!YwdpC>ELsI07<ko;
zETM%*RJJJixEbsjXP7gQWexC0j~u%kOLlaCH6B~loc?RI@-wTk2x%_WImI;G^~?7f
zRR%j9D*NAQ=qA%k5hRH(-*Vpm{_D;=6|uNt_4$)%#^5ld`(D=?a#011akpj!mc}Mw
zTD?s?MNrnhSHa$`>LdAg=LSP!x7YCC_z%3%5>zmf10i?}Dk#YMUoQH+s=HJ0CF;I(
zY}uDz(yG-A-eBnWx0yP)_L%S6vcA@hMx!iC%++GkUt+wsFH!FC;8kjnV9g~;9()`?
z1f8Ud6iZEK<c$crg4s)E&%fJ%m7xkC>ZkgJ&k)v#BXHJW@<rGc=|0<}<+iOIQStt<
zhwT}sFjh=^=Pzlsg-^_+nUQnX<EcTU;igJLov$Jjh&+LyWmpQm)4_1NP1yiOL?i9k
z#C!)|u7r0%Sm7GfAk$M1g=fl)UBKAAUn5$lXvl)iXRq$gI{&%rORrpp2thEJv5$@Y
z>b^9e_vci6!C}tVON|$n`PXJy<@rR7Jz6klI>nO%P@$vZB#AC38MLysK8)*pbAn)G
z&{2_+#9qyZ`h=Vj2xx~FK5YTUW_NvV5Zj|g-+*BN5iV6R-I+*~r1D9Zw|+207Bn?V
zd(b{l#8}FyBQWmLEL5Kfo^HA9LW~S4pM@OWFuF{0x&Nk}h*VLYK|xZv5t>b$sRni1
zdV33-Vfslj6G2^Oeobv`Mcjx$>kLH0jf(vY&P9`=q#DD^3)=E}Ep~X$xLCXgXvGI$
zaX^S4%<n=r*>4zf|BU1%>+=J=2ZesBuT<u?hh0lpW|WkEx>0hA^=`FUce1!`^uvVD
z8g7yFD!qHc+l>v9+V%Uwc^0Cnj~tPO$THx5)1C=*rv|N&=aRmD(4)f0O9gt&;jh*p
zwUNyILP;XoJe+WcR!&mD^gHUiGJApNpT6>t22$rrD(qPp!BT6Ix|>S*;@UlirMUJ@
z*SWlY7%Vk67H_QBz2QZ{Ok}i;0TFow4voOzkASDBFz8dP2yPXh6gEIBLKa;tjlK{d
zYlA%S;zaaw5t!jT_iay*?5j4N*y9*MDJo1hJej}J*nu0-d;~78+NA>lnHVU>?!gM)
z;88qq)x!7wAIH1z+o7niSJ^MZ*V;R&h~ExX{`fP2qZ*clzOBzK&OJTS0num2v{U;g
zR>6_n6mP&`OaOfG)>k0E;{lpc4L{9-of_1)%Y{mELQ|E&#Fr~p5)}Dg(MRBAbAS8P
z22lwjnzhhAQi`M10W-DCG#GSL%#`{HYQ+r;e3;aR94dT;6<}MVJ{BjuIz7p9sDj@l
z;n`vAyjYwO&q@##aA<{hL#3=d$IM~>8k!p8Ne|jgaul3skKg565d0$`crRGUyEf20
z5Jpg?R#Fo;lkW)l1v5?mr9VGZ^K9$x4*pLR_)W)8t2ELBl_$QXkfUDi<X0a=*^}5R
zW`>G%DS}S~en<p{iWHde0C)uDV*v5O63~J#MLWzN8U8lu!Z9<HJ=kOY$NwX7*$Joc
zX1PqAsCQ9)5BK|M;81-tDw0mK72o&HPM>+n|4iU*CW?z=@Qr1kdDh#R=j<ZacoLo(
zuO>{(9&p1|<=g8~6JPe`BFZ#PaV?#IlQTQ1`0eK60M{1%;d5w#rSaHms$Y=rynaI0
zX(z0o8cBDLHVDZe!~(+_pF%wMTQ&AtV}toD7+imEIh@KQ^ROFs2qVt4zO%3I=Ki|i
z=5HoMpb-_er}Z3oCDC_YWp$JX&6rP3)G=ZVOYHK>%~u4K$P;`Nh<4B4ia{lN?t$%!
zGfF-yi%;zc)&st-Ygah1LVlj?w7-`HEJ=N+hXU1GUS<8oa;1sU!PA%Lek<e$5n_bK
zkHOtMs@H4lQP9g>v<>0Y=wtBE(9jRnLsNdq6+g*di<K7zT5WF8{wZB0l3#=I55}H*
zd<7f8gtX7R3#H+phlpZRDu$FcjNyFIuQSLqh+G&pi6qhAD+cU;3ZYR@xtaFxQ6~TF
z&R?|$cnx;z*HhfSHn+tpABWE-BdpFiRTE-FEIaox3iI37JWbI-o%qci7R?{*FCO<k
zRd#z+(Ec_OVaiSP%>!8Is61kH)o8G~>*$h;;|Q;px@H+KG|)aL?{&+!-%&)pR=tnL
zy=Oz8ig3>_+l<iP$Y+^5bj?-c(d<O2wUpmd)ChS}Pgs#YU*9KpcvMUohu#(=C7}k1
zq-7q;o((fsm_Si-_|GyDyxp1(*o@a0f>Nrt8@#z`J_EefHJ5`eJ-uTlg$OjH!j4Sl
zUJ7dPwhfP%r-o~sLijZu{exa6Jl}P1!;##F*t_t>Ume%y``L8wpk^}RX+*%E2J~0q
z6+XkcT*cP&%!?f4;oym2SUT3T7p3KFLj&j2e#)7B;+x80hcJ|xGth&hTnH*bGJjDh
z2Y;2)dB>os^KinMl}Kno5~RZ&6z^WTMTJ#_rtZNXl1FJiMVD~xs5k)ur&psmA--0f
zyZE5^)_%C|9p$X2jKq3*kH@O~BpIzOHY@c7hRL-wvqto8xc8Urk|Vw#HlP4MZP<wB
z<-`f$IS<*6Y>pd<kOL>G{UlBt0*AcWrs0r^NpH#PB$E^w-2X*ehmyZo3(gBJDt?(g
z0^UoMB(@mNU@czysX8Mj#U`(id&h>cKsHlrn7A88Ox$Lw^Y85up}74QgN2^${w?GV
ztow9Te=p}msHvS1P#4Smy`7qMV?NgiCwI~m&&kE5%Ig)#SE*8Fc`Z?>bWB|u(fsva
zl=f!;NYO}U^sk{ldyuJ<T=hQ$_J_azWc4rhf>9(bgZ4^A0M9^qRvTbPSrP$oQzH1g
zc&z^v=ebHt<8)?siY#YOgcp9A6XOsNq)gxN@D2apIv94+$=$vWX#=w4ZS(k;321Uf
z;Y%U}drk0YL>L>IQ4>GCNsXUohVvyu;6u>VF>*EK(GPqAloB^<-UG$A?@z6ftwqM(
z?H-x0OzMJRq{tedA^Ho{cw0$ylspu1ixEDd3jx_-n6m%p(VNRT%^Z49_6Wu*ZS;tK
z?h~Mu94YucD)hv70wgsz!vEb+nD1u>S6xU)HzcBk8V~&2#qZV2`vcsXzI(K7?^)nY
z+UE;lKmhK`>;31dq7jc@$;PU;X?#25ks9-nT0pC^di*5i&5YkfXkG7othxV`K#YfP
zlRC4YuTWjwT@gf_76UQa&Gm;Pvz!q5?dUDikF8J_qHl-4>Q05ez=h~1XBG!A!sinq
za2;B3ot)tr7{;6Yc13`uR*@5Jf^-biLpm(+wu@vaq|n8W0;Y%qKOQEFk6t2><J0@y
zaf-et*5|p(aXqRb)@8DBO<k42VaM0EHeNiU0u5Xlu=>5SP>cvvaEreC`Cy%VCw^8(
z3H?S{WyYhV;3T(m70|?i13!}?iX9z0Wz2@-%d2>qwwd0-hugdO=K6bN9`kLXx}fRU
z#-DME=EB><pP1NlAd;46-ZQ$iMNR!syY5G$j0kL@&Oqw6kq%A~ijx6Fq2t4vRB%;m
zDi)&hwAprUkgS^ad3TP3TaP_P#7VUd-A)e%3VE=fk;6tiz?SR1-b)1nB(#fozIns<
zGmY@s1M!z9chS7--hP8&962M?rB*0nnXg)=srN~9cZS<SrF!pEm};J+t+HW4*K`(s
zex2M$gwUHYo02<unB$!p=oA{n-haJ}>!dT)Nic}$5Vdm)Z8<aCSmrIx{4-jT_&2#4
zZZiQ&$OW@D9`0q7#kAgeAO0Hgg=Lujj{4IdWhAwM6NseyB>8WC9g8g3gb8&RFBt3p
z&j-*plM?cI>_x5lnHM!&^>+hAc1vw-ATBQKBTgCnj%E5@V5s`C|FSuwGs2n{i%Tzm
zr?H-6@-nyEB`Lfrxow;$f1w44(Gc<JTfbC2_o5}{zQbmr7L~a*amlK*6es?2sefCn
zp!)0?+n>~Y11l42O(jB&$IWd*_lmv~3)76<;a}HDYL4;eUM3RV3siefxT9O{q@TNJ
zO`{}x*;+c#_-{^cLnJVT^!_t@xOduVt~fJ!@UkKS&;Rp7M&I8!BJS*m;l$G{uVTeL
zYm4JgvGkVKj6;haAHg#a3(*I4L+=Fp7Sl^SGRL8BX|^pn(rCLq18%@(?JGlzAsslp
ztv%lMBDz=xeSwGEh=@2QxC0Zsl@=@|6X4d>CYYnI!7|=Sx=P5i_LsRPzw<Y_CfKAV
ze}45lwB<zPmx^mh&)TBGgG#xH+4Kk|jrG$}%6Anb{6Rr^0`v$c;0h;|=JYN_4=4K$
z=1%B%bKXw$`s|>r!S%aj{8fswSAL9ilB}L5Ov>FWD|%^{*#mbidpO97*>>xqWTG3~
zK2j-%SLw@aQCh3AoJ^Xuyvvrd-g9mti`uH(noFKL)cI?!4Qx{)PG}=S{4LJ*MLb7&
z<?XdD#bb2EKJl;G0XdMyl=H0i+V<R@e^|84C6k2OUd=RAGfL)X+^;b4#n}?GHV=}*
z1XE*gT)dddD(Ytjt|2RTH%SzL-Ky4Unc^j$_6&=@xU`*<`lLW-zBjWQP`-rA2!omm
zTwL?6x35ZAB6>FSyx4nDJ#Y*Iz|L`dZ9J^6A;`X-7$EupZ7K<<j<uRx1GCv88TxFl
zo$SsF3r`ZDD9hr$UliZ{o|-(rKjjMS+0v(uEE=2WE2YH{q2eQFCf%4xsXjdij`|#x
zN@u_7owdZd#ph>DT5EEB`=M-5SraKmK~89M=D0OeY7KP6*|d7>ndSr&1-fK6xU!qC
z#cjZ#Qc1pN47$NKV?FfM*wf<Z!OjmZ=l#nK)fI2HNnUbe!7EXypRcB;tu<HKJz;t6
z&MG+EsK%Xr+C%*6yjy#4IZg4c#vK|<eIav2dA-{oC3M?Mokvm^@B$BpS7udx6=toT
z3AJ4x#^>ei1y4KGy5;sHC->=*@GqTI*;eE>Wl7Il#+MAkWK1QLytQ478q6Fxz5_L3
z(7wubBW9&J6A62Td4-9@*M^`Uomd;lhZ{}Y+hd{R!MATkLs)|GMN%^#{uqG{+~cYm
z;RXKQI&<qrg=l9+cq{wF{_Coc)gA|bK1NUtn%Wd7@@rs6DWa9V*r8(#GSHDYU-fHY
zGl{39)mwVUyl>}wlEZ#zuww7j(6I%vj*At@ysOWu_Bd`zpT4**vczgivH7_gw?8t)
zj2d!VuND!-Or#$LnmbAt0K=Px>zZPHLvzKwW0;o7IQ5|W@uqN=(tb0UEyq&9qSb+y
z2Va#hE&IaBZ8RCsJEM@j9UQF?m4CT%?)X4Vp2JYQv;UrpsD(z*XMQK<yK~;{1AZS7
zZOOI0dOGKDc02hiVN2oDSaMf#5Zl5G_4LY91cG`a3Sb<_49Rt1JekYh+jwqC8&{q^
zhAlcuN?UlOc;AckDS4p8>s<)&V3H?=3lY~!)A4zsDu->(i5DB^1Y*N`gvz?u@5c`T
zg1VM-M60Rr-P&~<u_=MXEj3$O&^q+eY~W_I_*Ap2V?Il2@7UGd?kpiirzhh9n`_bL
zH%0)Vov(O{^dVqm^zS7+5g&&>=ZSrI*j7O=&<)d{tLt_TX<DmHDc><$DeU?fp<J{)
zaI0a&yjA(q%Cm2bl#`Hxd-FAV4&^1SjcotrROY{&*zg=4=j<s5Um5Bw6cEiJpN&H_
MuIOU&F5eCKAHtCHE&u=k

delta 5689
zcmZ`-c|25W_@5zUKPW^PTgsa3I~hrZ%34B=?8`KiouhO)2$#80Ox&m_N@GfvaMeLf
zXp(&kjj>CUWvsuMy7%|_{qy_dobx&FdEWQ=KFjxc-t#8uJ=XhE0l7_7v_s;<kvZ%S
zA3ntMvAMH1H8tV=*(BM?WHSCX8v*p_PR1b+2<~eqFN7fwyuz#t`FSLM3xN=~vq4)p
zMiLfAcrQ76@3byk7r%XIfy_pFoI{@tIi#3#sOsroIileMmo)sn^X~h5mxVA~P6c*^
z<SkUa-qGtmzx&!dYtZcG%f@ga+o9`PZnh5AmS~}GQQKVvnt{C5NByuQDc^fzA7jlb
zat}^jk}fcyJwKXw^o~3lHMPWzy2_8YIG~O<Js=GphEWAqf(omuv((BjcXU;l#=okS
zEsh^hoOcqN?2i+7hyt&gi&g?E-7gFq4by{zKa@tioJ+lQ$7}7%84jpvI0_0clia&}
zqUuDOemD|yl=l5;&H7+3MQ`cT*3qgw7rP~ig4f4~qGxZCNx8(VEg(&~?NWzXTS(l6
z#lBcjaPBDe^J?1ZWAC`;MG_Bixe69~xj)SG0{>ch?-$tn(=V?3%8=@Oa(2uQnPjWx
zJEm8LYzqGlwhMhy<^5@YG~ht0tQ_16Sr2O0dm{oPqT^z8mwat~w?5^}@9$jl#?6S$
z0q-XVX5-Y<J3D-&bWF;<1zXf-dh<rqIkf~_!Lc;k?%?Ukei=nnlFn&fugPH2OUd?r
zlJV+<TWJ5+uogQ*^rWS@mQGH3?4ZQMPl<;=%f`1D?tUn&#WwC7xuKP|n_l5XiN)e`
zL<>qRuWEr$M~!E@hC?-t8j^JwdR<o{=*M#&WqKQ#PgXkfVWnDk4kNSn)8Dw-GtEi@
zkFCaL*iLOptTqtwD<_ANkNO1U<5-40FLa^rf!qCBYRz6*BciiR{)uu@3IFb{3(*^A
zfqW-FBkcXp8n=kWF|KU1;{Lf<y}2T$mVbmWBgK2*lbsQ5#pMHnifdbm>ynlK^tVMn
zT40*-x<h}O#~<42FSE=e`FI<Ge=7!MS3Q;*+7*>-XPCi#&|sc@ev1ZUHdF-`a5lmA
zqXq@8-F96&%+Qg0Bjw!@a@ebDIq37yMNdhi3z^2dvUl!FkpEVBBpa_ym-o<w>K`GM
zsfDE5_*juT0@mv}nGviDIrQTPm6d>Ct}lPM&R+W~25KqjB3T=FZhQdsTI_n|gJs^`
zdm7wg;d0V)>(4*NLfz8R$m>USuNuj~h5}gc`j&0i5>wkCpwKs%^G(n@fiH=XI|xbi
zoc^kHO|suS!|i%^U5L*El{*i9f|colS3Z8xon&G9@upIpzj3yZ0OlOn5i(o3hKZbV
z8|;;rm;3Db(!w~-ckc2cb`?XPvwS6WInj?5n>8Tagwe=pT~$v@2v&-_RmED(mD638
zlY-B9z75f>=4HVy`B!Xk^!8w?$<C#qZn@v@8+R|s3{1_gjV$?A^|avc1xnUB0G#+H
zb{qUxc6oVOj^(oB6v3T>z0X`uTFmP@V}D)-?f3MkJVLFicMtJEKiTltxb&3R(CDJG
zNs-s5_$cAKcD&1<lB$EJPt|kfG9oTQ$@*{%yugt#y^GHRj)30YnIrX%*M}Rsd1nyB
za@E~ctLPf5od+H##ZrOOBH1ew{7@Ji%7)sgID6ZbAfLmLa8k*94_X*0hsr)&D!UkO
z{p!RhC%TqH2lf~7nY&@27{V?$a!YdJlx(>SD!WSyqP{jQNWumjgvQ!65T?%h^iO0H
zxIM}}pZF36Yn;-LN;c~Vz(&%do(KhHLew->dg9)=RZhTZo1N+t_tGaMxF=5FBC&sH
z3Dyr=-g)*#Oo!XFAVrW3{-@0ZzP|jewS~{q-YPCZT)!sSRiZG@EZ*_ra20DNLi5^>
zujj&S$A8@NmJEnB6Qdpgw9EjF%%P)DPhq>ufC-3clm?u>jnko4<ya&Bv8(29=bN5J
z^Cwo5a>c!;(^lt@=d2cGryfkjM3d4uWdd#w9PZ>?-c`8EX)%mT?hmF2Z>}Vi%B=wx
zIArvxC5heEgS}g|PFQ-pr`;JVCZ?!8nnf2kPieMPO@YlCVUoJ!ToDH$6sMiE`7l`v
zTn%j~8`TqVZuo{jaD)$FS6Txq(s?t*-|4l!{aP(MjT)ubuIEik(uxlNV#dnhL&}Xa
z+>r8*Z|CSU?r!U1fwRvA450&2UMqucgbW|^gK@9>)w~j21tH~te?WF#Ludw@>b<C0
zQape%RnHNOSMFsmN<|0(Hs7QtZjOM*TKN+WvxbH(FCCN?Eu&Q<f#zs)$1JYGJYEMv
z1s9sgau5=p9W(M2gfJ$kXL|*mXD!c2n^<&>8(AkzlMzHy_+EtR=hGw^ejx4u%J8P^
zyrU>;<Jhk>DT;xpqV>Y&Kd&TAi(-O5@<-1kOut7^OyDxOL(JtMDnndx$J6H+O8M|%
z*#eQm(`xuQ9%2`M!YRBMdCtOudl~Cqx$-WK7gyt2#g69SP=x!YcqL2_nwQzo<_bcn
z+NAzdvA4Q7;S+lScgr9((J?lRJYGmh0$%}$NEU@cPbZANTp(>n@vy0zI|`hO|4Wel
z`{UXzal8c|3S_OIoY-IdX|XwyAEJid7`NFi*f)VJItGn9EO^}MOIWusBi4?YZO5F4
zxS)1a`w00Us=>^5IDVb|mn4%1djVt?I$<xQz@rTLQs??Q+290~QdG`QVU5c}902=^
zzi+w5R#Z>vv19(>fD%s1{YRj^_+nr`s32>nX6U<dmN|OE1|pruEUA?1MjS@571ah6
z=7FyFM0BD@{6&)4<WSGZoJ7nBJpG6OQ43^vpoCI&?q!C>b1t<}GQ(xT*lZtwJm)Sv
zaW8_r;=OXQB<60P;RWvBVn5$u8UoJUC7CGGZUncy6Q=bJ?S;f{uY(~P%WP}%ND5v7
z<{mI&^b&}jtKwR3Kb#Lf(pIrXpGb(bfGPw5jcaDdj}tbktSW}bd{cacF(0oibYva&
zjX^ujrseHnCQ{@T=uIj$P8hSRu&k2qwKKWYjXuUrq=zhY6m)j3p7bo%(Dl&|L7*Lg
z&3e_G_qz)$0gxMKHUFwvYjC@HK>G3WQ^utA<L2P7VFU~IWEA{>)M~bTm+l_GlPjh=
zbk+fD!t#vqroCX~1}6|lxLqx))SO2$`Tmn1rF&c$Q&<wd*c)2KRnUNhPn<k{Cw#TW
z#c_8gUyb+Y;#RSS(v)q2c(n}_p&HHzRG2f114*7+6Kxkb8Xn{^m=yyLY*wDGRa8_o
z7-F{{Ltx|;WETO0kw132^>oO&vnD(cGJjzv|3{*a;A3`5cI`>*9u#Mg@B}eG@5w&F
ze&5-%#{sU?z%6TYzJ3LGNZR1f^V=}WH|Ai!+P^k&6NCBf$9?4<ED-gLRh_{u96QS*
z1-Kwz2WoD!i#T$9gI|o94R>vzeD!+S+0m*cqmjoOYn_4|XmiKh<x<9nxiF6b?BcJK
z;b^Wnh=l*KKmJkZzTxlqWBmPR#K47!#;2A@A)v=;&4E6n`$T{>+r=H)oIE4NN}U&m
zp7I{u7_j!Yl%lNYg`tq&YmU!Ink!`87MD>>Wem`NEutuEs{86^t_FRoP#@RJ9HXc}
zCYw{UBNI!c6DpIQu)|<}dj?BM1?o_FZXIK@oYJ09?GQs6g0VB-nDkJ_1D@#*wp$1)
zQ~AGvwiHi&P6khmHjWNIWD3mkWXKdSxg%(1%mFEc?!I?WXJ;9_DN_)7;8)Ps$B(!$
zJI^z6B6Daz65?8$jpKljo|kvOJJPbEEfJbuMS!lWd9mK-1NWfX19vo0oUuPV;nu<%
z+xdp>O!6@aLj=n{Tor4y9P(!A%(ej<|8txnOnz;qa^^R&q%ilkywq8wWc9jV95;m1
zUbP+(8*k!4ZD>ki{>u98x&8fSeSDCG2(zL{icM=kLIXujJEkexY-rFzJA7$r34c-^
zp9kpR%b9HFI0QfVL(ZHpk4<D^MbNf~9sawG*(5vTd+N+&;{zi)kec7a1c{*@eLkwH
zxRAS(oGb`6$@)H#S&s+LXPb5YPM80BVTL5Ln)gtpF&h$bM9ufOL>ZLysPZg3)RYqg
z{U17g<$?r94V}S?11L^6Y2$zwM=p8146^KC?4JwiCKpWA`PcY;mcT6!A}}whzZ&A~
zuznzAlPp~Lar!tC^Jz*NGwR<^qF3klUAC1!e*Fv6<op<@{FWf$M|abVyp$e-6&ZO*
zsAte6Rr6n5NVwkEOM_M?{O@OF6F6}4oqW>w4owYKDPV?IDKpQ0Y4Ba6s{>Yt6=k-$
z#+yuiocJ6-0o+Lv*YL>JC>|9o`NR1p64%N`>9N>*Zn44CeYEv?dbqRAesRE)u%0)1
zw}}_$ErSy)G41j5z3Yf%!(~-pOtIj@mAW=?LOy|epk!)E*_8zGc}i0Cm3!;4cbPvQ
z(l`hY_b{YToH48d{=22H$)zB&#9>;p+J43D2|DStvbh2$ZuYBH^!u?D#(?5})cHjH
z3#z_sMJ320SGJ;5=2lAeOddeSe&``1;Ik=*;6zKfCvp#9smKi8uf5V<Aca{x??hUC
z4W`A0=h$#SXjuV?!yZD_o9cxRyw4-$&OtR@<hD2(iF8JpnJr47(9Otpxo3bLlfowU
zitt<r;GLGwL|ITGi~*yZzRlfaCT6rWDpir$lvynZMuq1+)Z+%EWE*5VKi+8p6+2r}
z#NjV@qq`7A^6<{Y$MX5I#Ygy5adecEGdsov3SGM#7v*ct7X&4L^PO*vKO3RBC5+<K
zg}baFu&ZChxwK9^zO4&TdYsmWZG?B8A&U4hZ#-6gOG(xDZ{+IVEVSQO=F5&rlvsP%
zUB(9KLhii=BXRV-I5v~pks%Lbo5qE4n_N+0O_U^0OC2QUq`C2c!`spClike=jp?Fq
zxN^0(B%W~t>>?Abxk_`*wF&w~b^W7m!^TnIgZ_m9aiZZ5`-#;^^L@yoT)<3RAt$pg
zVZAOGQy&}TWgZ)S$2DUQdXvcjs{#wviW`-cvOsPL#08EglyXjdSe&?h;z^1(FSn+p
z=?7h|<x4R**Nvbh4Px7{WjUem^#R_~1UG7R(-9cMkgI6%{M4A1uhFrO{6|=G=r>Rp
zCufK#l7ON(FavRST~7p&hPJiz05g1UaINdxY&G3Ry@8sPj@3F1ca5j2_&1Gf<^`6E
zvKa?Fu0E~$>sw~n@Yj74Blf8-Q45E#N~!sRtD<B{I$YT*jI&2(al%*Z)8;-OR~4<k
zdC#mhWX-BCwP(yXE#Ip!Ot4FH4FHRkSFCJr1$VTcsSn|>Tix-h?H5-Pz3-B072rmk
z7uLw(c88DYI;j>@7d0^y2lcwPD}xc)b!#uj+%xi+8efZ$1*DoQ-7AMpMncxE*TnqT
zj~O7=69V-NQnc$vw2I*(>vGrdQ-4Mf%Z8tqj0~r@8H`dRqjHCy3@$BR19L^F?sn6v
zIWCvh)g$mq)rrQIF5(de?`Pa~JI}TX{3!lFnOx}l6PN2iy>FA_d+1!JiD}1<=?AUP
z4t7sy%AV$Del`4iG6!YtnH5djR0rHDeN`RI-0|<LjQU<$^W<7oQzgGnLm@BExWsp3
zx#P2JXtbO;nn^?E10Tc14W8#%rkK|7%#B^3`qfBrJv*pYmfKDV9a51}J7P`sGgv!3
zF>dMv6z-rY=FwbBXALA|LQhfs%5q8%rk8D7P?H;+4F<!-c4wNnkVP{HirD{me|k$F
zOf0(jQ()wAsCGfbMXKLYeR=3n?SiuFuaX;`3<fifC|lQEeugSIzqz}g%$*tTd?c_L
zZ!|McQ>%Zm`ly|vF5W=8h5ybj0ghS@XA9|6RW8QJtOj(yxkG19l&=^l38{YVu2Ijp
z_TD<iKvr()?8wTgBz%~owgj!@?Ydo38O70^%YYEk*F=At<SMf3L46r@V|25or&Xy$
zi9Yyczmt95uE2w`@r<hV!b$m7?Z{VB!Hq`obJd1(clnfPrGDDaxjXK5*n&Mb{(K)5
zA$8`P4*brsGp)8aYDq@#T6|I4vh96>%aV)kC4W^~S&Uxfs~g?=U<vr(e}T+x<RwoB
zXgl>;2+&4u9L?$ja%o{9{?4@eR4)Q{7S5%P4B-0h+mnn2R+r4iw2PaH{js|Ik*_<W
z72mfRC`+X8W><nE#%Pz~r?mp1`b#eoQ<%ma_1KhbZC@C56RXP+d+9{&u5i{p2&U*{
z>TY9Db|;4Z><w{afzX;N^?5ZYX{LWwx`<r&Y~B#6o9+1xBmMKvi`zX+OW@K+a-E*G
zw5`h`Y0t)W-)z1Jh>w16-5qU2TvXf70s%@aG^Y&nLN87@giFEKM%}qYqWG5`f}Isp
zZj2fY(Qc9kpRf22a{C2r$Vj=o@7Q6dQEfYRoXYhE-#sSmUO%_O_}3wWBje+07pez5
z=}Z5|DQlT|6~nH0vt0Vb<d1r%`@XN%2dd_}I>i4Ds$QXZ7W(G}Zj4*iQ$}soH-kgX
zhN7*0xyF{91`4^SKbbZBF#fhq;LwkqDERpdw^x}yw!x!84}Za^>2!Bd(pGs$wV06=
z-*C@YN~8sioHS2*?xIc5w6?<2d5XxCeePUdEWgsmja2X`ZVTqo00&s0`)D!!vuYR@
zh<_5Q+$4^QzIbmXeVJ6;7_1p_6jjR$n9Lu_`Qg2*bc`_}@O*QA%8j(1(Gbyc6|1eg
zbW$dAK&SBPn@tV%wXcOMsrB3Uhyy%0r@m6R%<p+A=g!%;znP9rnD)Q2mG9g|80mXE
z|1nPF1~lLG8f@3BTD%MfDpzC}Rn=3E)U;Bd4xP;5t3kb}#gwhefOijKoocDAx?^c?
zc539c+0pjhs1pT~PRR^nTk&-$m+;NV;m&g-Od!L{K|ga$X)Q7ipzRX3+XeY2-r)!`
zA<lJA-_vONVps|TLFv|p+6XT8X-g*N=JPF#MLK}jFI>?EX;Q0gCR6Cgeo}ui_t2TH
zV2=&G__cjRD&dmi=mst06CYq=n^;QRT)I=dWsB5oGeFn0s;mVBq@{QCf<mof@jtV}
zHo@__&!a-v?J#RUPN+aMyoCqXw&H95Z8%yM3eDWs^mOc~+!j{gpJ-IytliidVvrIA
zTq{Qbto6TBbd48Jr;nrzR~e{a#_AC08ZNLg2K{0o_o<tVPQ|=L9J%7{)|9U1n#+IQ
uVAi=H*Sf{-{HYP2tGxf7sq%~N$P7rvF%la2-Z0O95jIwK=oiN?-})a^!P5}{

diff --git a/docs/articles/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-12-1.png b/docs/articles/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-12-1.png
index 70851fd9e29014c9279b21ffa59a78e048da7944..2a620f180e166b6676ed85f43f5d03a433396ba3 100644
GIT binary patch
literal 31147
zcmeFZWmMH~)CGtLh=6oASGv1H;L;`C-Q8UZe;Vmd0qK&K7Lf*#mX>aimY(PO&b!up
znOXB?z71<#Sr_lGo;YWpefGXlZ<S?Up%A0M!NI+flZC3m!NEtu!9Dwo3=jTNy#M?&
z`1R67R@WU44iy{rdN#QnbPoqd2`2{?*YL?XT=ovozibuzvx9czIF!_4J|IU*M-}P-
z{}NGN*Vg8vt!+W4Ekic^a%ajN<25`>H~JSxwjvhX-7tJvcdEC<A3yz<kRXoq%$h%H
zgN99RwsBiv=X#(HZ$6E5vEDK)U?q5ncqbrNE$}{o?zJ5DGdQ?6K_+zV;5VELN(d3S
zk$B1s@a_!x|9|uU=@^L3zak_{A8E22NgHxPgoDeO(MdDx^lOqR$cT)|H|@FqA#~Dl
zz2ZNT#*%#)p7Si|JbN|Z$llmo*CQGZF1EosKhSHX!&kjjIrrb^6`|)l<K4$?2ej>Y
z*6&L5gKo1|?`g;LeV=fi!)>-2wtFcp^{Fa@solgv-~!W5F3$e^|J@svk4!aR%rs8K
zTz-GOzd5HX<A9UP&CN|-{*QZMVS%>&C7kcG9^v;Z0q0{wPVhN1ZSCz(_d8-3ev=!g
zRYskDY71N)i%#v;?d|^c`p?*JFh|W#SDzkMH8nM*i(L06i)G?S6Z7v!j2}527a9u-
z3$vZO1hAvPp@NCBqnTXRU<`KhgS+nYa)rEUDM~w$zw+DJ+xs6>w;+kM2HsuWk9NE8
zlSV<oqvMp7mhSxS2~A!`;eSq<AyZvmzV|}xZ%>I*Hn^>w@%*O)<EN}hbTUDYIYV!7
zxxJ!zQ9<xvCqdWk+QxyYJ2#;(Fc?#Zv5XK*0>XMtyH~|_hmICo?gF~xY{&n6TLV|h
zJJeGA*y(?be)JWN&<<Of|BsI_W2$`Vl+yP;K8Ig_{%*uuyN9`ti(Ffa-OaN*)3dTV
z+1b@E9+Ah(6ucem8;D`+`kPuCP~6;{7Ww#hK+LamW$h&C3IeymM2h|b{=K-$@j_E&
zb2Be_yvolgPjmbzsM}i(>+=}n;D<|a+NBKVqw71#f}a^;=+^ND2<MvGhL@8HY~?fi
z8w2sdHzUR?o&J0SuK5p#)?g*rM)Y!YUF)@b9W6B34_s%3erCS=(~HslcRfZ*O3JU(
z=n+p(Uw>yb^L2MYO%3amHH{qcHn<sk8}<P+4sp!k;bCENm97Wv^z7)HyVa-1t#sp{
zA)55qwFdv|<FW18pziLL7QSqzueuNd!};3It1L=nsK=BNAB%BUM|pX9ak1sq;e5+#
zkU#pd=&_OCUNMV)^WkEP^IVPL)0OiRt=OIme~xnL{oh^;v4EScbagREbm9H)KWn|c
z24Eguz1jxD&8qYH<HwI*kG$efIs-&kCyHbmS|ywqw`XT(Uy<;}9yN&Ovk`xYyO}M^
zF#XR>u>XE7g5q(Vpu4QB49q~#Zb3w|({k2GdptqQq9bqi2kD~m9sjc-I`cY0tiUx?
zW1r*Joj3}yW3Tp;wu74OZL6EH37csf?&HOl+Tvmw>?q-fi>d#)A2RcmX2(U}om}fC
zJrVNpzbgkb+QwsUxD0&B-3F|4G8BO+_xipX@bBO7vY;WrgBzlD!yP?q8`|IBw=m=3
z=FU(oSy~DVFa-0-Jd&24ZtT02+Op&}a&@%G=d|?2q$lL*e*CFpGf4rg^irj4T5lu+
z@DUy!o~crm?uV<zbT(tBmPPBaES^y*+RM2Rl>%96&N8!=uE2KE(G4Sx?x)AQqPCKO
zjhlYHuEbkKYV09#?PmJuk#x4zuE0ARF?u;QcEVBG3{{R^<Snz<n-El1omRKvxq~g=
z$2g&5pNGr27RSYAux9*EI{99BQ+U%RGkCrn_M9`M6VvGqdbqtjP+!d)U5KCv`U^&y
zd`Jp*^H`4I4eI=)=t!YS)EsE9#WuX!$<SrQ#e2SJkKSFMw9Gra4h}XqH$NNq(7nQQ
zXU11(M>b2StnIpW4!&EA#$%H9Jbg<~9c`A@6B|t=4I#igZ$If;oSd9oUhXs>h<o}Q
z|3nGN+sXG?j}kim^7@7M3x0dBPvbViwQPr?1qjjN6kgHo1hNy7-pFKSWg#sKjzr%K
zCcfrd4LJXEvbVQarvpZu<?eSVhO~cR>&Y`buFW^c{TVZMo{o-3>wPg%HXtNbYE>_E
z_`3f%Mt89i@cCn+qN0*U@G_z|EG%ru)JiT%Cyl1iqVK*`>|w7q_`yESo2u}7HTbFH
z{0GU53=*O{H*u#tuDR}}R$$=6&nDB*#+DWW0)hr+`<6wSyYcKmcCgC>^b3)0Y_VrK
zM)nU5=BjkLMkxkuwW;&@UDsvAZYL1<UYVRdT!2e#^L5{}jt8=4$Nbl8+P<6@%TzmO
z&zsFqYzD##BP;8VuBH<(-#cPYH{DMUC$FDm-QC^o*duz^oH-Qj@yQNBfE94;VR^xY
zrIg9JwK<fGA7i;Whk*dUIxd)T>G$w2jL4yGZ;hm5JUtaQG9{gK-5p0c;Jz`!L|Du9
zUb*>3(3P;|2_8&aTieu>3X3LPAy?Sf&7k>qzQKC@;d*u4)c}(C`vxnplADLewrOEA
zK2i4j;Ca+kYDX`jCcCzl7B3IahyBrvH<T{V6YXW$#jcm!35)|yWgok+q*RaV;0Vbd
znKBVX^Hm;$mRj9+3Z-H=J01UvP^YOy$|r93KH()qn^(z63l*48T?Rh{qe5PTWUWd{
zO7qU+dtU8$C}E8F*~J=h9l^ggPH}N@Kk?3dbv4892{_vvf<id%{d1J9TZf=Y)Y_v(
zZ9$Zn^d|Ga5?GYMvKU*v-{!LwJF9jMtSm2QxXj#S7Jg?+nK3Y5c14jP;Jyt@4vMww
zE)@;4`p%tAPRnhlDBBi&O9Z3-S1w|Ybsak@-rn9<J@-E)F_yg+9WUa8tGomaSqZRU
zgG7xQrmoJ29koj+J#02vPl;Umq4VnM%H239lh;8O39J<nkKLcLH5AAQvCvbho~6ab
zy9N8&VwPEbJ=l+0zG~%6{&A?FiNPi$BUgDR5zWoz+V#5o>8^Xuz<umn7^)KO)Xh%*
z>d&8VpfAu#zx?@zdeV7q<cl&2_KuMAO2_*8y63?x&0MkY+2HG~tu2sxhw9G5!^1&d
zRtn15bE(i_=KWULQ`!#>xC>(opt>{R_4GDwao_oE60}F-)*e*`B79RGc{Yf)4|mtb
zo->+_-nFT+PUnW+%T4xkHKw9q20R1}?c~93WJ%=D@?9=(Yim37x|0o=|1~Aa)<NlQ
zi8BFWMDXKze*7lBc)ki#w4~6YzrVlpm$mT5QuQy($Ko=^_Dy<<qv2D-QtaLLXCtTt
ztT`h7R~1y?k)uAF_J&hD?8>mFA0#QT-kha(GmEwQUpplaHJT5gw=8yAjb*JqUbR$L
zSL5N~89&|5MeV}=9S4^j%jTmH_W1)AGm%G+DS9ai0(?;?an8hjkl27L`1JgX22R4p
z{*C7$^HxRID@6->MoC%O+1BHExk`OYOG^PD?+)5i+CBxTLlSe;n*ne$75z0mt)Z)1
zKDC#`fENYjPJ)^nvU+X~CK7R3<y<)$8X6Ak#NG#?CN3RxIj@Sg4pR&tTy}xzJw#E&
zN+6j+c;*cPbL(D7_9Rln$kzVuZUV(fg!Qh6AhRVHfxgXJ00J9oYL1WFPk!|2emdz=
zaPCN)!7EWNCUGWxWQieQOQS1!00<&zGf`$!l%rVfCif&D!g5~Q$k%eBQ0nXo(R<lb
z8)SJ(%wJ&il=zmZ^POE+1Fx%f8)z>>dP$Xe!Yg}-9Ph4<0K^LF@rmW&>g*H@mm@Q;
z@o2Sq4A|deuyYpk*nO*{G<Lc%FyM6xcEHS$7i<9p%EZ116;w@G9AzJcC5ch^=<V%o
zhT>u#nfP!Cs>&I8=$eZTOEmZFPeHd$SrA_DPP+4NH=kn&|LLW8OxXCc$rZS~Y(J;=
z<TVfOVC3Q?01O_;o)_yeY-z!YUzVZNXytn}a-4){%p>Tzi(6jp$vvdV2qkVyOEs(-
zqb_;^S*e|NGZm^z??~Hq8|X(6Us|2puBz$kGKl^5?E+x3x>~7rZsg!~R2e`aAoZ0;
zC?SUS#Kpyhp%M&_YiMe|YWvL|q5fgb&PCeZ$*HEILQU+?o1?ErKRMpgVcUtzR%a-z
z1m}1CmGaw4WiUDc$V$6xW*~xENtitNYTnw|XDtjCA9}0i1bzPe2IDYyEosk`ntEzf
zyLfbU_h4yh8Ppw|5%>jUqy5H({UVuU20ZkL)F=e5+2|Vpaf~~z^p?L48LNmrT(Gn(
zB?<P8Cdei%E6*7z%o$p9W(J5FtE;P*NZ&p@PUrr`Cda^vA~KsXtMlnx2?+_gy}gB{
zwwm_iXz3#S7#gBca5ZTNM`h^?JgywAxRA`98&E9p5_s3o+i>5($0@lniHl<b<Q^Rz
z9nxcR7H=8R>v}n(Eq1e+3{8=x1{q(|0QQrUgD80M=ZkoRIu9?ek^2~rc^B9(f`Wpe
zG!b6-GLdyTK})QvadlOK)KJru`a>*V^zX;R?@>zr*DE@z9QLxShd*+KR)g-2>}xxl
z3ks;z*;@eOO-;?c^<5R!`~1drGje1rslA+sc<gw&o$E+&a%rjKo6{T(_VR-O0cse(
zsztvb$395jo>AXh;we;)#fW42l=Fn=r>2t8w%<^`?Q_-8mD6I1TED2TmyAHS<<3&X
z;e7}S*4Nj!wk{12E!n-q?zJFBOivFdz{Sl0nH2y`YDoGBkN?%-CSk+s37ds(Cw-w=
z_|&(m!@6G}?*J&m7!CWFfts3{p<(XrKAyJ0=`d4mz@MHMbvlP2AD4H~`~^rF20@Bs
z;-w*pba_5zKbccOR3Yx2`5il(j_~0_nvd{^f-S;(45Vbk`3aj8OWk#TMn*=f3{bG>
zhKesI&`3KvIuaPT^u!Wzh3_e(tCR~~o_7Uu2X{%7Uwkp3Q_0KWv>Xm2FybF&cLxhV
zj=AK+09ZQbeo>~s>W~`7ccv(WN|HZW4CnE?N?`sstx8oorCT}iRtgboB9_R#abeEL
z-|<I|fbjUpK^2IK-885Mkh8l7fFQ**x$gp0b6M5<9GdjD>h_P1kFT!0Aa3o;Gcz+o
zRQF(L-+aPDfNe13?Yb`)lM2C4cdKAID`oROT-FAYrNn?N5`4cwF;{DR;=(Wb_xJOg
zvVl}4ZLkwoJ9i6XtbXM9_Tg!xEzFo^^p=yN57^WqSvP`hS6B!U^sRq`Suc#C!Gdf+
znNXFXD+lrYI<rQPE7h`C?b#X&4Lx4>5L8f5aOBnBucBAQ4Zf^2rU?#sdboZ;3SZ|;
zF_u5R=#cnBxz)_5&a4k0aZaA9&d%k-b6c}I3wG4^ZxkyvQNH~OQctto`}(Y!nkJov
zOvG>6fwvUN9i$t)kt@XWM%yVOtBw8-=i6fn$Dz$2vrLOLJ_J2gXjUpCHmv4<){BS>
zVRLUUZ(p=(7$Vp^K2Gxo*j_uA@=sDqN0u%oH$!j?oJEKUdCa$O-@v%E8q|O~afGXR
z`$ulJVABy;GF7#;0CD<)LN)36B)~f=;F-+LH^%r@mwx?{h3@3}AA^Ozr40r#c>Q}6
zn1#|jKY8+tg)gq=3JNi5<)mvfzo&YRy!3U`4zD0-V`pgeA1E<Zmkwwka!FND2m+)~
zC_vZ3Myjek+<f{vUO&GdHa9l|v@9+oi3I;104$E14Olsnb4|&YRi(AEmT=o%{|c#>
z{m!Lt!?mPUSA#x@A@$FlIZ~*dVh`yDX-!7Fz_X|<dfzDk52Rx7i=BKXS5}M|k*S}=
zY!({NWIuIv1wG#146RP@d6d-Hf(XZ?U89JO$KdBL)gJh>!&NSc_4v5t?nK4t`<C0#
z@*K$44T~+>;k`-JL)u(4*jYZLATr^H5K(r^CVkHlmQzUkbxS7@pQ(pg)~JDnf|WSF
zQ>j5;_UXUK!8LX%NNQ>-ZcJt*5@N&D9y0@j+|}4H2#NIyOFQR#m$zN>KUfK>ja8Vm
zFtN-Vh|D+yGNK|ab<4Yr6t~WA6lrmqnl2ZP)*kJQkdbh#8U!u%vE`CDk2Yzd&Gr=n
zvm?WLnMX>rMnNi7uFen=5_0V99EzTwohq8JvC0_Bw{=bro~o&-5$_QOY{4dF9dBX+
zX`HzJM<@iVlYa8?Ve8bVo~ixw=g%!Ib6akiAw3KzVfO>?S5M{!%!YN)deBP4Ovsz?
zn6#+95uasX(kcH8K`b#1y#WQI5{Z8~cgUOK;#+7)B%miMQ--@tnio9_ejXM=vIJ+S
z<w8SR8ycpZT9_z;1LXYu&*ZvS^8*&+$b`~Z^rfMVuCEO}@eIAmME+7@sfxd6NZdU>
zW(_zIach5tB)PDWhfX1Cps%kGlE6;9@i5PZ6dKZ3DF1T1`+kef_-0sp^3e#?>I>T4
zw=<jZ<nh)OYI0j!qysdeH{YXhQ1O{T>Jt(aL|bE@KY~KlOUf1&ojXpYgAQf|#>U)A
z@Bzdo#{xVAD5Qmbvm-r1-bhJ>O80;b<_W!sF&<t1Of{bx_(NcaKUX<@r1<)#LW9wF
zNe~m+rq`6ia*y{7E%LT%WmT0yo5!9-3;Yj<x{{LLS4WchpfEuSeNRkcyRU&bjxm+`
z@bx)HV=2PU+X)*D8K|Y@kO}L~mo5`6dNjMsK;t{&nHgIt*SG93g@bj>4_7Tx3{;!f
zcgc9?1(FflqnVNz!f%{A#GN~(9+(1$?09If3+XxI#gtd?^7_o=+C9BMiFUZ4x9-cC
z`QM}QTa++p3{4Z$U3JUKnO`Ste10+kU<wGdP5`UV;zdM6ni7%6vIpuN>dC>Qqeqy(
z?W@wDm(9Z}eEU}TaknUbGLEcYN6~1@k~f=1PX4hK=Xj4{_c)KwIZt@BN`X3K-ONgs
z{hV1rA#GQLJbpw9`u>f{?Zw{F(NW&9Q`o9;Kf4?I-9r!}R*?oQGfA*NR}2YN5otPE
z@sAK)<vXveORT8y$)hMSvl0{x&)3I9kGO=E<GCl&tJ&JF4iJ@4VtyQ!kK33$9B%bw
z(q%45v0Ve)2rkNyki#_@#m#m16pjkoFW#?qP3IlbMFl#s*im5xZ|zuV0WG$h69Ui3
z^g3DYP{oM>H>qlNddN5egCgj(kATAG_xl3N7QmW-R2U2by{D%qR^q@$`PrTA@sfqM
z7d`Ie!R73ulSO7}r(xoPUT0p2xS-1$rA@ciP4xS-v$M_P+1cjikdr`Xke4We?>Vq+
zabDJZ`j6yGb45#wwh`S>T58|oLZmE1^wf-9=@gN(BVLJ$H#N4K^_SI?9pAjIFGe6K
z2_jp)lcW4kB8AmMJv4J_uOsuC@WxYL|4xSDm3xdVbzv$sBNNv}zP9NH={rU%h#LLw
zFOG_;O<&O_vnwBA{Ft!EKQC^3FdULnm{0D5x~b*dqyYy9iI1@Q@k#vPqDo6>Kr=NR
zXMBn+*movR#Fl9rpbI7K^0N9Qp6ues%`^$B=FjW`{YyW8vO6wtCl7i2%XQ;1kpZIl
zy=yvv$QAYm^k+(&0k^jkhz|t?1tldVw`uy{7@h_N<0BN7Ka`e)qmxpmMr$)}nHYH=
z{zBBUcgx7cKx$oXx2~KSh$B;=#`aU)92x#a)x#X$#8g?CYG?@ch#U9rnXOhXO?Gs)
zPG@C_geKu}4Y<bkB*<P5puAy3;QC6P@XeHUnRW(nv;g?yq9;pBhK1G5&NfnD00N_s
zko@XnqzivlA~hpJ9lN5lv$MB%OKZ!u2K(YsqxJYt6EDGB9$u2BmMKI6Tuw!|$Mo?X
zb$Z-0p-!XPhhB`m`Vpkn`j21x*>^I=G*rdI!lK_J+9v@t(rTcgJA{YJ{?I3ggWllP
zu7|sow+Sou2n;>2u;v|_!P;3p;mcG+kC0@Q1}ndyC7l#KeCKrmCKjN?Ml*Pod^eFj
zJ!!-=aX(y*-6tt!OOK1G#NadFqO>d?c?g#Neb|XM<IH#!g<$BVI~GR<1_>1@R2m|s
zOC;vv>RWGS#mHE-=0c5Kjww|+GPAPcgpSmz+t3(7`NIAC&PJnNzXf)K=nquUOUzNY
z<Tof0ntN9Xpz5NpUyQPhiezJuvmlfLS(w#IH|W<nO79x`0J}w<jsPnrGKgV3SmiT;
zYy@}{HHP3p8E_v{Htp^0vsIuw`6`HrR&w%J#di;nlap$#Q;P?e#9qh3JdHUPO?uR}
z;c9X-Gtlog-0zQ%3#un=xINCOHMJNWc$>SkvtCBrguo;A4(=#*p8pVXW5>{oVNTxh
zqzj#2oa$vtg;H0{aMY$p#*vHo#Tls-B(#bBl~QdSFiUCEpKr9iNbmkxZ`omVa&qV)
zm|G(IiIGqdsX;XHqvt`&Nf!wqAK$-AvLq^U{TWgdImcA40R;gxY{CZc6?nZiiwl5t
zWg#w}R4Z1SZ?TP99GgF?npd1B+d;H`4wjNdB$RuP_L--LhlWdzFm@FqQ!=NAWxKw9
zkzTccqzDUY1jwFAeP&dkng&!b*=MW|z2f8;U=c{0b6e_a0343k`_@;Wt}1CK84m`x
z+1S!U1=cRzpcb63x{7D?%<FQD_2e}*q12gUYmXnSl5gYQ+u0Q|sN?dItUk@XB>2Fn
zNSkK)Ix&(yTDi0#Wn*_EJ~_Ei{T0f#i@%V46L#XKnc<jbkhqxwL>U=5uTOH<z1l}`
zqq`X9>usmXN+Z%(He#arYqcMH`zB|-4u5gncm@h9^OCUW^Ri*??(MDd$2IEBe049`
zaPpRT)MjC7kSPNEl4TTvz8<lIBTH-_Kqci<twMs4vRKG2VPPG8Ats<YpD>!=7~FEp
z<##o!*LOuCwDNzE5s(-ux?E#KMn*=H7)k1|AB`f_R$~G=qY#=kGBjM8<{l;cw)Q!7
zW6*(DZW7?u?U`9lN5@hlFaFIS!&$SaQUri;42a^@eFd->)U5Jv9v<DttOPQ4e1#Pk
z!W89c*rKNw<h70Dpo-$+0fQ9y5$I%D0`9bqOR`&AjP1E6`7isVA#O10&xD=dau~Pm
z#>&o-<#r9D1-(4OvAP<uWx1rH3az;jGx68T4?F@KFnlQQ;f98mCnX4sZ1z^kKNS@f
z{kL&@rJbm$HQBn8G$XH7dHTn+bgKO4L8c4;ikOQNCIV-+?M%g%eoGLb&lULNxU<CZ
z+`nyLZM_7=E={4&{om8RIzsoGtu($Bup2eB`qR?v;z_9>=EBNaOyl*Ww81@lIlevj
z)9NawMU{y<pH0N~-+Lz*L$DessZ|VQVSDm$_hXwN#EcZ(J)A|rpM<2Z5K>dlKpw-u
zNg6k;&5=EZB>s0Q|Iy>A`+=nM?zlXi4BZ!ldBqM-4Za-^K$N&iT-n;?OAmn)rY(0e
zcUOVG&#_c}o!N?=?sx{HaLy?3JC^eZCsPA=e-?}fY;#?FL;a+|h>EKD;9ib<%J0lQ
zj0bA0yQ%?6PtS{|N8_V5&k5giB*RaW2bObH6HU#6ab&L{&F%UE-mF+0g1J9e=W9?A
zcB7d_g}jF;_6`p{WGCO5Z1XSFg+>nA@$_x5<mRGu<|&rc=jFYn|IQw(n>1)f)!<9q
z+ws|X)lDGdh1TfHco|N@$jIpE!@@6@f7bAXRk*CvI`7>QsZ~|6Qd4v=NRcCYOQ0cD
zT5+-hvkza_4qdkq&qEMV^FS>M`iO6|GHg=l&f1qt-bWN5!atjN|NJ714HIkrXd7)u
z2dc04Yuzk=Y_==Vzpp~mc6Ewils1_)kst6D^~1yJT-}jng&|g>ln_hPf{P)#q0(IC
zQY!33CM!mI<jY#Jq$MYF2$l9|I5dPgEw)=<<^>)eR^sFuCJ8Z8r;f34@D9)1pmV|^
ztynF-)TG^3bnC!g$L?=&zZ=U*JDhYwEpCf(M|ELkPfrIash;gI=qHS@zmsYZvkSjg
zGjs&_XrG+zA5Bq(!WGLC6C7OR6hBjypu#Fpiw0e&Dghd&r>BN`!=F$L5VLwr*l`l)
z6|LOmsLv+yL}KNo<crOIm#~_tU=V$PLP<;4Z#MD+zU_H%oA)z^Nzg=pdi&OP&$CkN
zVUtdbA$p+v<2&q^j^Rh6a7pa_XzVXDY#L*BL>_uGpL6pL<I&9@rTGMWy)qKb7ar4b
zaMn&sz0}gardENHLx|Cm)Z|dYl1yv9tfNS2&%HCr<B18nWP+@tqWn$yq1nAUE>rYr
z1{-U~<@?=>sLVWaNACUWJ4A7I6p4NlEk*|X<aAYAd3hUI?Et`09Q~wf`wd_CtfvYa
zVRAx<_3nSQ05+S^tt|yT#;Q~;;+jT3UhNI1wTG2APUC;TPNcz4Rxs77C3yY}vX1jo
zt#Zhz1!st>7X-l>G|4k)wRqJmc5(d)WG6?R4q3T$>f|~bn#hc=R_=i!wekLWVb2?A
z=#^eF;75Y1a@FbKUtq{@ZGn;ER@I=#g+hyqceJ(nczB35c}YIB)sas>v&Mko3KKJq
zTCD>Tetn6gPtP*7b#zKJoK)s175y$XPfrJ;BKMAGwKVH?a&X22h`l>XWu|Q+2Tj<Q
z00Fe=fsVXQoh+A6#fVEo6ZL40$+PyE6F2GlsUFi3&8jlBO6#VeqD)r6MYsvzz{;7)
z*=mo+n^31}Fe-LLaiH;mRhoQsBKsB}uRI)WIO{fa9`qMr)xNuQQNXRblr!7RY|^sP
z&)_q2FshuA`C{`9=y~}SbIHBp?%NuOp>FJzgyAXj5H)?~;YZeko)=ZO4^{KbVz?+M
zRRi(v>ZRalViY+y8bdV+N5}4mBj=se^buGSN*T51-wNTj;Y3vTvZ95=4>V*%;<N1E
z>DK0J83X{Toi=7WzS%*b!xjhY)TyPer2lh3>)GyvVhiSdkX-3&NT*S^&#9T6qY{Wc
zZ{+6}rl!obEAMWtq8jn=rp4GsGh{#4>3rWf_3I1_t+mC&ed~sU5hEq`ndvcEMKqHK
z^l*fK@j<J#nC>DRwrU(y%VTc?>aM&*1qdCQvXE$XneiJUA@dFzdV0)qAo(CZlv<ri
zzYddtYLKB!9L|5zvL`BG%UE2*8!9#n)v4w!WFUSeK&oNrcz(BKvVGH)C>nHokt+Rq
zisGx;Fgq6sn%lz@u7uoBp^b)!J1tf@D$2BaDK=JWTGABg)TT{Oe>4lng4HGPtmN@{
z_|j^nayY)idF~J_y$=4HSoz|%-yx`Z!H>Q-15oSI`tqnqkDiY~`_q#&3L27D+B7dk
zDTbJQ3B#$Qu1-ql8>TwgXfpVmR{~}_d;_14LUVcxPVyI>WvL@K5Yj74OJ#cN_wN3S
z8njC#;K0Gc!zFxSwGT**#?Nh8qgK8JyjV0(+X<n%YLz%a!G)Cyng%%?k)|eXpl9FJ
zjW%sZHb5pC@QYRM>^Fxqr>b);eY83RD~l&4(kCyyy}p-)1;y9IpFVqx8M#<k1^q=`
zm0Iz>jq~+Qz>rCa?G)?Jy-hePUEZwv8v+tE##?{nU(wO1gwlNW_*j|g%nX9p1`t4M
zCHa#HlQYs&7OBPnbO6K^itxPnMM+6M!7_Esn8?!?7daJxnb?K(&%vZJh*|mhDH^e1
zWp23kJF&5{qW5j2m~J-{B40RKRL`l)yr^Tg<+fe1=G>D~T|v0}z_;?{a|tccsD@6)
z66+#nd`xk1;i3p-U3iwzwZ<I_e(zV4M<6^P7%)2{N(_8LXC{g1i<G5`{-hOS=ee}=
zx<x<RA#}!$6nhjUF1g6CT!S2p3ALOBFHYh&3p1F|!TBKGE^en&L<IWj6;f-X$jx+1
z@~%dwQC?DidfII~1;*b82RBtor7>aEl;=9+h$=xTCr3(14fSn)ZPXlY5rD`H%+CBH
zJoJQY#FnXO!M!iVk!Pw*@eYt6>@UCg<kVK-BF(l3b+2Z~z7_8&FD+4-b6n0cQ0w#U
z94n;%Vvs|fDAB{N*r3HUJ6GBnhKmg9;Ow!B+XIgk-@R-zPCS>K)e{1#(_eMw#f3hM
z8IHWZdOpJBd@&Gp_1=pMz=yAe_#Q2&^VRDp7G@Be=Lek0S-QKgj<VhGt$zVsWx1q#
z5_w`J2&PG=QJ~w&Xb5}*eZSh{-#=igJ>OLs1>gVfvG7m9M<s~@CxeIF<g0RK`YkR-
zm6`0O98!NMc~isy%C$$And$QB1!C7$<=k-&Knt1t<ZvX$;*bo{T@KbYbaae0y`{}r
z^<%0wT)Zy%CL!pPKpmx%hKBSS3q2!bjVj|A?K6V-gHja=k2tBr&7qZP4cbEEhiLME
z&-G9K21Lj?^vn)^caF_s6_T2oxz63WkK_fIO!4^o%rMHw$Mm)(Q;`;uKi&emQ-GpV
zRZy_&GoXxKbNO|0txptQUOoYZbmfsN=Q#)H`E};edrFRhg75ENbG^^Mr~Q4p=1$_F
zkU$}2`z8s>k1^1cp&Ex`wXyngg45!AOG-R~cClO8iB<Bjq3<$9Mzt-@5<H0-I)u*l
zI0#e%=((!ieDe;_?^+r<pwhK2ofa!o)YMd@hQxCyph$6VH8NRJ7cw%ZP>>w#`^ENm
zg^DQB${#me{gk}OJxN%y1-)x2EE&$;Kd>519G8X5>d$k&>{x+ZiQsM<iqQ9f6wSk-
zyS1y*i51z&>F<8ZJ<2z!GOYhDWRdBdZ<3H4I`2?ICS)^|bm7dE$vC#H@6@`rSDN2-
zSU*x$yfrO{!K+V|II;zpZhX1Ia`jhHP-ht#H?Lo|#d+ov6BDY3E^i^?3=Ku*#7dtU
zxQIpp0`B=JZgjR7zjHHfa(uLldCL`FTV)YXj(4Y^FI}rmgAKaUJ@wyRq2KZFqR7ZV
zbubjdUj!|5)^*p-6d}f2a^jswN{t>K*;>_m%V=)oEGgLwbVc&e3L9=+j&n;(snw^T
zEGCs)d4=>VPa7_@aaSM-@%hJM$R%|ZN^rR>PD8b|v*v#QnvGvMt_2EF1^I8i*>U30
zhPm@XZF?hrY9(avXlpBL+@ZvMe2m{vp_Yw<IQVudpIq3-Wu`)Nde+PCtXbOp_f0aL
z%58vX5#?YH>A^0clXv2KHLgn_iXy-zm};~wcaF4gny9Ji>U<kXn5&G2r1bpm{6%gs
zo~K~gBa)4sT}7u+UAGlYwH8ZsdG1kw+{>xuPC$<fc=`9&I6%}G83c~?wX~#hZJf5%
zu0ao5X@$f9JhiAXcxpgRM#?73ejXC(6^IME-?*52TEp18THK0Y9syj|zqS9KK03g3
z?PIHI$Veah60#y~`Ak<F`i(jdK!_7dIm}hox!!rdPpMaL4uB@)_wn+Xd2>s8I#;h5
z!0EK+>L6{{p04*THan`9Yv9JH%BwU<7YVp+My{S{*E7!_^+O>O%VRX5e>{?s3!`>H
zu+=Smi?%yJE5GYecYJKoH@fe^0cKUM{Ct`)bvVC1+q<Lab*qM+n0Y@ugjN^jzyq}X
zURilcg-y>~0#${tlj?*EUc-!E#ZGHgRgS42F-dB<c$Zso2a3Vlax+n5KzZ8RGYuJk
zJVfr2YSOzsOW%qhWwhca`^Uym%au=jetzx!DUV##a9fHw%|!BfpP6g8E3?j9+GsVm
zZ;8q~)`a`D@;lB;yZ)zAHyi{}^jpJj%Ytp^Kd^Y15<kB8_Xb^YfeeAT*D-zc$fq@|
zeeA_MAC1*3UtQ&!Pb@7Nthv2Hv6?W6Y&Te20eye&%DFosA-v}UpK+wkoreYV%(Mv>
zpK8BrYq47Bit4U|CsalA@aB4Gt*Nf^n3)R(%EFDM<2BIx7P_=^tuw@wsIIP<-qL(}
zJaNP5{-iY!*!A^vqe;?>gpBH6R|rPHMHhk&g@$mQQ7z}P-r|=rd?8;@B6LfLqfHge
zoH6K!c?gg$>o*U^#~88(&9UBaP=Mz`H?2-n!w4YlpREoJFXZ9X-=NFCU`edkK_R3)
z^r+xuK_(wIlgqpRH9Kns)UNJ7<N^&jiE*5~r+090Q6THD!ryvde>6!$K`CSykl`f9
z$ai>SYa2yhkrcYFSFMM+o|`+A=h9qe<K%h@BuWgeKFlnYmm2Kn9a%d~bs3C?2_xtI
zsrdnJALf|Qg@2lTn%-YYQAU4P@Q)Hoq7IWNcuQ}<klUEETC4m+S~<|&)wQrVY{^M%
z97pwInQU$>1mbkO6xXr>fyh%!gqVm183Y+&#V{nDM@P_|ype!DT?czy&Xm{wk&>!f
zBs<(Q)nxSDO8V_q>}YX|s$X6A*tAe{>{LvNrl&BLF=nQsGJ1eFV+UDB3(r^_Lthd~
zuG7OYG%O9e@KGL{Z`Lzsu2gWcEu1KWa!kzo+CjfMb^G;2$7Z|I7Hf?{<x__XG&n=8
z2Cr8okP*P3Au=zfPF5tUFCNKU9TN4vSUY@gf|P*}_|T_UEo7;!Y{_0G*J4kruP3i;
z-O<4HNef5ysJdmg#wa(S$z-PDZ=MKrbGfOqvWy-deMCR54AJxpF-L08oA+>X*hee5
zZ6}0Ss-B*f5G)7V#8E52WkL5{_yf&~d*>a7k}1eKJ4?L|4hJ&ElIBRRZ#GS|2tO?3
zk#h*-%6COBvwnhp{I;V$;_r=#)%ynzZ(-AtD4YtrtzG44*R~;X3C`*d>)dl@-K4go
zm)0%gzxn0wtUpM<MhMl5ml<Hsy}KR=?{*MOi;jN%a1IAI`452lI%L+(XI-DDh?b*A
zcn%H96_izNwzWx&jlE2cik!Y)>YbgTG0tta`f&XHCFJb>z<4byt5=WZXjuVDEKX>P
z(L4kdr8t_GM8!u#|J>H*&Xd)TA!bpf1zGo9`EmX_KegI}AKH=~302L(LU~1OLJOI7
z+7Ze7Z*>(`J)8g5V}PjP<EhH8)-n=K7kdzX#=|S`V;!lli;Gi#4KJ+1h_~#|_6qH;
z2cM;IYeZ$auC0x=_oZH}GF`Yt*qY1v;r!ZI_UqE>zKFLA4ux|}ud<Bb65xB#65LOH
zLQEqERkd5J&$l<HZgpbG8sjn}Be`d07~|<CXK6oGuAz{{X{8k>(-lz!$+~Y(XODrD
z0sVy4m#8(0ivRPvOD0}Sw2UG<(grj_pksH}Be}h#19)e`AHJAr5fG?9mB+Z#L~W0e
zTp%4t5!j-WMc+LnNlQXelFMTwN%&h84fV48&Wc1m@30`Zy%h9Y6dyMDhmN#LkGpOb
z>@|$?+LnLW+LkC)#tlqqjmBy*g-L|fWjObt^u>|YDZVgA>$S--((qtDipqzB{s&u!
ziLyDCQG*8hBm!Q{0ui!Qa<<weBmfBdbk<#6HO7=jmUUGDlvA|+$Mm7z@|cLLP`^20
zDOKcK^v!YmUQIhac0nrBi*D{fKV9eAO)sX~mb~V=!$Br1L)}c$Q5F`KDf7C5QX56%
z#z@q#4gkfJWuWrO<%+C{>NX?8YTjlXwAkfx!ZxPdBlY_$E6-5dbkdyKqPos=v6?d@
zGtS)dKzoVs7Q1rpx^3AmO|wR4jo*(u&+G1VO_v#!k<sgTNnju2o9=Vi1SQHU8r|P!
zjvExHf!43{-<E7LbXt6zwzdqk9@6eV-+uOgP*hRb%Mn0qu<FasmP_K7E8OHjD{b;%
zcsO~Q{MpH?$w)S?AGx`aN8Q=QpC~K#Hw)doDGPy$Jieu=DHHMi<mHPp>v7TBvk^ck
zBu+Z8(x_pO+RUY-maA{qxF<m~`3|gfXZb&I1xybQ4_e3zF^}zCzW<>drl$MFrGNvI
zP=OstOV|6Q5xCI)-SUu$(H9pd=BxPNd-K07AghRDQnv_A^@L4nRm*epT~lMLmnfE3
zRM=Hj?B6_`L<rfLaclJTd7^|Z2{31l0ab%ikhNJ|%E6^fJo%BG!zQQ0rmpK4PeWV#
zR&-{g9#0}`t1cfF0K@1R#7MoaUU3R{1YhY>(yebdLZ=RVOSznQ3DQ#nM%lk1-%+d0
zOlN3RC<DFMC<wm->1bAIJfqp(7ngHU^!QO|4G|i#mUy|j6=*LAe&mY<ck3;G)Ut0{
zsHmvuo|};(_Z!T!ocR@>$#WndBb9gGx8Sf;woRqa+-G;$6Z&G~1$tytlkIAuLEXAp
zSV+3Leo+Axp8Ni3;;By#QS-Axc}?jQ$36o!2_i4PjSte<i!3QAe}s)|HKJdW_EAK(
zZl#TTdwj}MLrg#c6^s+$juQ#Lj9B?i*i?yu25x2g)y01j1#c7-sLvH4Eo!!Q&U2Q?
z+x7MJ-FDpisHjn86&3plQ#Zk+)?~h7SAT#cf(&J7V>oU>PttOWUQ}OaH*!Gi@vph1
zWT@ZTw~G^j9nw(hnPHsfCcQ$cx?$I$6Ndb$%)!PzFrF{%nqDUo-q8E_p&&l2J{1S|
z*)U^7WJaSv7W9o)k~zf!a7C|MX$lgUEa!0FX#i+>N)^4H-gQxoHP);I62-;*xDh6L
zzOZk7OEpVctMPQTn(IUvgZqkyAZZs7S7cdZ;|0)hKK)*DpC@r5JgQAFG9(L=aMN+)
z)n8DO<Wi&Oo+@n@{rh!t)=E(~o_kk>J2=Q#yID^OExGwMQ0ga8lj(OG5jRfyQL0wY
z#;nsL&2bHV4yJSF*1{>bvx_V<-&Q=i*fVeCAMqmkFx{=o@RbsFo2NjX2N)`FXJ;4A
zO0Tv<;6=t;c)9;uTv*>Lc{U;;U6j8wmT7Is0$AeV6b9&bSJILCxq!>ci(ZPjV}ij?
z&1r-swb4yF()zKC-DjzY1M%8Op-VXeM5~P!a>mu8?4Kskjv{$86}!8yR|fZIUJ#S)
z0D7l)S*4UDLgR;`E$1mTT(=>ztg1?piO>InIt|aP&ef4Pz%O?E0u#>XcCQph2LVDX
z_bOM7pH$^#{9r6mPp(iAZ+3<+PHfrCK2Y~tqHim(b#k{JU4hOqP`IbZl^pd>?v*uP
zY0#b9_(zFdw{ETe4d=e+wYXkU82ZnhKU|qL+wF${6W~dt^PaY+RNQ;FYd%dt2e^-g
zB;-NzLfQU7QwX#i4k5avr?I^Ji@=)GFAk*0+S+r0=i4Hy-I|x|*gYQ;l0=s7ZyJMx
zJ8GOVMsW_W;(R)2-PvmubwdKKm%Zb^ti3+p&JM(gj<MA+AB6%mM-~SiMyIm<ls2f4
zUgFz3!ZyBsW6$fTC*V<h`6kwgB74w8OP5&{;#+$L^iX;{hwrXkbhL<vJP`PqwgfYt
zzkg>`OA%y5^Oa}^CfjcFJgCU?rOh){eOm!#r!PThaP31`4u2K0{KLX%Gr6qnSDvf^
zXSim=2BcMW_x^@E_|Zn}sP$s<?+c39i{){#{B617B$`{`8VO!dNuQgopV+n0;}c|T
zXf-S-Gtq@)i8ep8A{r<#3s-dG=qE*wpa5ztAhIl*u`8M=(_k!B$qxeRpaJ&p@cH;a
zRt0qO6>;d63M00w%>PN44U>;Fqq2VX^ptbrOE-POgs7#YKaKOV1P^z%odmDmu0G{v
z`)5YS$nZG_q~Q7m9(-;XmzFit#lqvVNQg1%I%;0@I$3!D-3q`?4s4tPJ&1LlZ%J3z
zBhUkn^(;Sgqa;Mz=O(tZXHuRoV#J}sO;T%nqg$;-g*-h~G{}yV5ebCuJwzm<eSLi~
z_$<Q{V;*e|1RVK4&Kwu(yCeUraTsI<&WUVZM>{8{*^BFMy3A=nV9H|D@kP)#n(-?U
zD=tcAuW3ak)5$)N`Gtm))5z(yI4x5={gnv?Y_6GeE&m^lLh`1zPt{}jrsixAMtXYo
zxR1%CgBHV$lMLM-nMgQCr1}hujJgE2kjds}tMwf-g^uBVFatx%?&;48O?B#WOIhbD
zP%Yiu9@P5Zj<e$W`4_(*I$~vEaduMnCu|rst3zwhLQ&Rj2zTo8tta7t&q#fhxm9Qu
zbXYe~1B4QH$L+N>H9t()5kh-<BQV+xYJd!t1}O9g-<z<PdAHm4qjgHSR+X2Rl+fM)
zCNbdgW(RE8T7*#X8zK(e!uQz$QY9+d$IIEa=4^t!?e*@_rnDYp=U>+N<uGa6u@Xw%
zutpWqm~9;#mg+4P)YX$0kD7Iu`Br>3hEtg$jN_4AkY0vK?Eg^K>(D8m;-L_dCNNA1
z*fU<eTX}ALzkX0%9n@_cBpnS!(j2`HLG0?j@5^YV1+&=&I<ItPr|d8J(Me+8&<g0W
z_PH+g5g6y(#?Boq1681pQz#s}@3%Gf)miD(R7K6yr=oZ@V@6PU*Q`DrPZpOlZm$?w
z{r*-O(&~<Z#WExuU8Pl-4qA*@ED>^4b<AMpqR~2rCIYo4kNxa_y=#ijFg@-DfM>fy
zKtx#Lg$=)%xNTKY_I5lgmC&b<;>7D9l2jp$Iy4iKC{D+ut(;jeTY33mO8kQh($`J4
zL5ImM<TN&8QAI|a2_QxUP8y-zA)wxF@9YeOEF!XNb#{K5A>J9+Mi<T){IxjpcssWv
z`gGs(m>l`Z#pfrXyOdNIxqJH$M&=j}t3iYa9k^IJuZ70-xAd1pMoBhD3(=Ah5~9Yd
z>WRogpPBTlwTx3ZnP27mp^X=TT?n%E##hq#<N)&Z>2ewdLvfH&O$pb({7GR@1dLll
zA~KhS`xs1jD&rUy@YVH~2?FPkly>vf9_UEh8fS5sOQRFRu}?%7PIOI?DbjXQB8O12
z3GUiBZh=xLX?PfyjO;8s(<X)Npd9*#;k`-tD42UTpa|~<s&^SkVu4V!$yY*AL}18j
zrTm$~a28KG4m2|{eG<@`Tfcq>T1ZK~+U4Wj842i!&Fs;Z*CkMZPEY0*B;y3QG=MT4
zrgR6Sh`<-JH*uio<CH5rbGwm=+X*zPB^?!!iPJgsf~=P)PSAQ!%TfP*pWOYsDm0pi
zb7X?>9B2f$;~yLO-*HK>6E;ShtrBu2ew_H5t9;{na=Kw)%YX$E9@>L7wnAYU=@)I@
z97jR~E{n7xlWkvecm3Z}j9@oQarl<0O4OblVQlLt#I)5XMK(4yZ9it>Cs9*njJtth
z23xkL%Hf>@VyJi#WkMh1i9S<t?Qsal%)h;$Bs_8&5pno&ivz-F^`;Lk6OfAaK!ATy
zr^A^s<PhiOHH;yUJ|b+f_dYV32y--n2*~a9draOAu}|kW8HkH7Z6#5V`1$i_YMyOi
z4kWIs0AmCLZnT^&<L1loojPq^pln1M)S}0g7h(L+GLO)rALJ0#mz4aiO@q<SmMWIx
zIq2bj2FqX`1u3+=r>b$WIFB3>-Dg&w>cN@8(T|pKkJ$T${dw%rj5b|6#Q*s-+AR}$
zC0CA(bF9RvJ;hfhEy{G<yh)UiP~`0fvH>hct3fz`vS=xS72CarFr-@a<poIO)iu5&
z!9%B4CchOsP-8RFN9Oa3C(5Q+VvSl0XpFL>0fHq%7Q6E_{Qj*+yNc2fEoVwPlLQrH
zU%*eJUN3n(!m$}@{u&XUBfMv`wt=+woY4H=;x#vGnesom6uI)rcwCl>jBJ^21TL^k
zQqfU-0=oS8#iOOf*B;BBC$LV3=!mHnFAP1i<HL_94?%$n=lgAd!~QcCD<HI>EOaq1
zs(7+Mg-m9Sy$mmSx~&cL5(>FtjxY%a9+GGM>tEkLpQK6uneB{?b#l%?8mC^%fZIWh
zg~h{naIl}Ga&UQz5p<)V*9CM*u$gD>&!FN0&jsrT{Xj&uIS2YBgwTq@!f8NRK_Hge
z>8`U?o5@I_RD@_r9kD1q0cjij(ztZ6_z331{KA!<x>jNK;)+qdKPf75>x&EjVI=g|
zqhoiZZ0+0&_-CMO#;Sa4az+4~VnS58B;baGLaC$MbYkT)13|}i0JL(x>BP#*C#evl
zAt7)L62|osa{c>s5zs-YN+C8i5@JV9Z>d44?YIX3SzPq??Qs9=ArpKAD$i1Oe$+Y?
z!~(E<Ve=oRRxa#yNH~@uE)z$H^HN@yRSNpD^YQQT@^H$s6uo6E5nMyPDIq|DbtbyJ
z*ik<(_B=KtwJ>FCRRT5H2?K<@Q62`g2Z727E`%r?a2bTJlZQ6hSW8~ZL`D)?eYLmZ
z(l%BBHTO6TUGTHdD2P^;lC<D#96%SbZ7QBXOq?NJuJ~P#S*uiCQ$6{Xn$b%3o1hgT
zQV5YrEHKv{kdTDEc}K>X9MdvyXU?&4IA5+}7E6>wm?s0a6>I`wwKGt_DbuHh$n)&d
zNSUrFLez(kApl5KzD`8GbQXiR=F%$o`hO0Qh+I-6u=%Y3C%{`<TWBfCMO#~$tYk`C
zyX^3ejm;cijw1*}|Mo^6Wh9z8XJ*&w2WR0Hdu@l`DGrcHT!IJzr=P@(5Nw2JTb!6!
zF}m}x-D`3IRW&1o=yeB)_#d}+dfk1TW6{*Cn>J^;1)A-^`_q9|8>&q{t#T;1?LVc#
zsBmbIyj$hYjW7aiNnR1Hg`;5s_wb-&U=v;L@I6Q1L;nHyD+wq3eKa^Vg7_f?ofV;U
z!CSPT&}C)7D*qfe3^?s`?tb9C+Cb1M0QvhF5f}_<C3))aQ_8=IeK0?b+Hm)8oN|k!
zTN9xh{?9#5|L0>sd@#C?%GQA?qws9xoc}vKC+LO6W)a3K91=ctyU$yugHSb6Yyz8c
zRUDis4sF0xF#7=iN`H%pOTRdKFFRPpMQKWXqwI>dwtIh@U;N-l{-5~*r_bpp9)<gF
z$e5iupcO85A=1k7xkdk(aGHWRY|>S6%<4G2EuUky)tl=4b$dq0BZv7j_0{|vZLooj
zS>cMpqlxOjGo&)N)l)r(ogj-E+1!W~@eQEkb^aZ{;rcCTE&(xogZ&2ef9^a()+Cmw
z{@ce9M9!@L*a+eWa8SbBf{ovuCXFGzhJ))J=}R90{#)SxiQN5rx&eg5&3e_e$$Mub
zY$u0@qW~TDlQCMsW$?HDb@M%HnnMt$EX;Olv%NXn0_HWqSO5bZ%oF79;{DE*jt09d
z<C#gwpVq~G*N^|J1?ZTEnJhuc9=hWT@dHLyd3kv^U+;IPA<;x|w90W^2Af6)V9mii
ztC`{9NXbJf+3n9`*)&Gs*j?RDx7>OV391ZQCr#L476{<(q$~v1&T4~JwIDQ?ek`1q
zNF&8?CT5$3On1xPt2BkO)NW^+Kx?c56&@Em{f=WlTQ&55c@7eDt;(6ErlzxvVuf^|
zn*`P>V^Hx3*u#DG!E!x(4X2n93v=lJcZsX(DZ=s>pcn^6RbZW-A~6DvSAd<ZekKDq
zXf0sVKhi#nO`t~pb%P2=E1P8k<e4zX7R=}jax^ejtb>gd2uiR45p>{r{Rekoo(T8t
zQE`>Wx!?x^Ub^>kbRa^5t8yE&!n`oRPyuXnkv5=70PL>ar~M=Y2SM-Oydj=PuhXk;
z_Crn{`Y-p;WU{~v>0MNEiP=h<r@g*D$pGl@oZqVioh361HTJIk0mjY)7|aKjK;jQ)
z!p54Kmov3Nml0z31#0D^+rx0l@aC`qo(yQ^Ck2fUFY4fXV5@FG+Xk*W$Lr%|6_HB~
z&>9dXKiS{++s?EGHc#iy%UK6jRCe9B9M8Xgms95uyWK4UD0wT1Mgcf$x-P4oJ8$<Z
zga7tY)VB@**E+DE@eqHwFX}VXZFZOk_SDMC%7#|vuNFOoTyTmx;E91%RS-}UpaydS
z#$ZHL{KJ*bJ77K--6jVfjch(=@{3Iv+~?KjO-IaNhApr4PsXg44fC_L#$ra-KL1bX
z-Lk{!S%Sj<9P@Jfd|WJeHyP+1Rx(cNEQXT$`}={B8yG%Ymz`qz+%`V&)fluUNsc|U
zrj^4a6fJ%674GZ&>#_6^wxCngoA=amFd^}Nd8rjJ)onTQ$F&0odP4{Dn>>JE;Be@a
z*x7XJsc(FM|JL|+5@<sQU?%9z&zf+)ul|j+@{@Kj5UWnujEbwn{QwaJE`z<4$Q)nF
z6&Snz*~_yL5>BW|?~vheXqDKX4z0x$9wuzKe`IpeX&*h~e-4sfEJ%2t2iVb4w@0=B
z5F7~<g8M;PmO<r@-5VK&Q2JKUOOo;g4SD<Ke8<`kW|gMx;()V8R;3Qemq^pmiX6Zi
zWuJa$@(847+;U@Z{K>LW2t_l>UQEA7X*pvtW3V12L#U7t&Vh$@s>>D_Ofrq&`AMQ6
zAge22kD%230#f%g)hxF3*D`VU``X>0O?ehyr{e(pZ{Yg}Je^3861qS4AX})I=`ti{
z9BRE0l|SEd>wU%NLTcSGFDN7g1HqtVF@8KtZ*?Ym^N1~lFBzJU+g0Q6j%re4^z!mj
zzs2blQYi4~-&}%?Gy{SYFzbP@J1{&Zd67_x036R2F_vLI0re!$Zs^C-V`l6`ybfUo
zXu({k7X8vGAjaM7vV`+RJ%A0%NHnsQEFG9c@Vx^KE^FU_jv07K1FstLd9ud<t~Y@b
z|0-xTh)o+TPWwFM1{l#`l_{C`Qk~zWUBmo+1Nat#;&db6ErRW8izW<83b0!(UL|)^
zCO<P~?J;q=+@H~_zKQ`A8bSBpKG4t(1m9qgSC#f`lg-0Yaux*eOf*9CM_?KJIWcj1
z*f0*#j(tsQNn6`3FiK-x-NO?q;_3o~{Surrv?sZnKTf~ZZR>Ts@Z|I~@Gh~rl3w48
z2Lp-AUh*XWJK->M3MzXr<;k%MqQG`>eK_9$?4$7mp+m-S&w;X~iVFN+k1>$8ScCZk
zQNS2HP@CJ<Fp1L_Q}7q?trcp-U?f2rpWonDd<<zs3TQP5y4iw#?F9j=fzPt%+yl5^
z^WstR4D-7VSkeJ6PNF|s_24W6<o;Fl^=|cg&s|{9ecl|#GkoIuJ+cM+rV5Lu=f4#}
zT(2JhJz^@B!)sJ_J2YOx_f@8$Sc3=vg!;i2D|o^5xw|jn;{yM40QU=fSH@8oDalN!
z&&>4ln*|^I8P-4403$I_!RsA>PCVddUEjTuru=uXDS&_&4ImT#J9c+Cyoy6O-Xy#c
zmVYC_9nhNYu>Aw&cCbsLjpG1l1(V*Znp3}oo%DYX1;y<l2jBw!0R#;5(FBpWUZmFv
zz~N&uts^G^9ud6${ih190GOs1V484X*Myy)e3nC!wiO+?`6%Vo;+#>~yZ#-b`wi5%
z)C4{M_gs%z9qAI8$&zU+ZtQdLSZJU=YyswLV5rLX+b;(OYaij2nVHK`uJP%)xmCY|
zYJo38U%w<%M@h0vdcjJ;&Giw>sS>5Ju`%Kg(vK5nb<F=qaqk_Eb@=`bf9x%pWp6@8
zME2f$L^6|P3n5!ZlAV(5oxLM_ldXtsLN>|D%6;g2fB(4e*YkQk&+o6tUoJk^d7ale
zkK=v3kN06zZ4Y+_m{b3K^bwyVS_QCu)!-7<0qn$c@29jTtc7n@mCN(n2+8xsPnVJ>
z6PLz9k9_CZ_lMCY-di65bTfn0!B%r@%pf@O+-r?X8-A-0ZWHouYw^h*UW=0L#J2%Y
z^c|Ps*YDpy0n!7A2#8XY-*ar(?Sd@2%%1`LZ<v+;lu}f5{1Stm1$nM5ZT}}E0edPj
z2@{FndGP==Se{s2n&`J|_*FOli3yMphVnNqz-{ger!vT-I7|r^eiLa`M?(^xU|W5*
z{2J&921tAMt*dY|tCo&m@v5k;6{d{Tu&hTA7nD{Qp8w)}%0lS_R#YMmQwvW(#z0eT
z1Re;L=at30ytB=TdBrp{?@Sa2_yngkBM2)Bm{ug4XBY@C&b<%_W7)tYBLT_*Fo<P)
z6bP^g?-0TgA$R|aiX@;H$+XfZFr+9?9dBIJc+8=wC-0!Oq{Xyi$KLb~X8DalhCxP+
zja{8dhS9uT>{dN#+br+kz?tJ-@O`XqSjFVi6Ha~k;$9;8n5!`a6&^A&pFhpfu^ls@
z%3u%=>>Y2o9R1wx>2n@x+-dX(C6ejI$MVmJnh*MORYr;M7bX8XHmg-I(QCBLUIm)g
zjtC+dm-N6Z*Q9A${{0?s(`xjpT0b@eGgd-^wjwVdQ*aFR{5;Qp|HvUg8A88AgP|jr
zkZ{>x87Gw+ItjlFb`Q$iR%l`}B<y;I<7*WhT=`k)^cvdg|1>hHt|OM1N&DZ{-QwgJ
zJIVRI9^Uzb_wVg9!SJ~x|84C5eihWLr3r0|1VY=}u>wL5!>>0AlZlvMl=5XAnp-I3
zd1qVjRVRE&YHu~mV}g#Y)8;G5<;_bH%H-9|?_$84UUde9U1u1z`}HSK>qtqSv7syC
z<U8c_<Kg1Mw^{$so9y|rXd!(sC@9FxXcIhGkDb}Apl30REsJ<EGfXy)U3UF>AF{Kv
z<rEd0tMAFk$oTpB?SuxAqSBfXt<%bIb#+8|ftA{8cbW&@<UOvzzP^p5n3(B>e_ePf
z_pF|D{RaGik!Q?Z6L%|`PLdECo1KemvobHn=fUL{DeH{z(z3G3jgHn_Q5IrgXl-wo
z<sv^a)KhBu*Y(S(2*M70BFMWc#wY3XXLw)$j^;4AObI;vo~!@*p$47A4r$v>OzdlG
zD~Nk7azkH7#{zyHJb2zXp@JICgY9-j;u!CMG>|@5U!UCXcUbpq<FK^dr`%ixGc!8m
zyS*S`zdrFZAL%@AY{&@c&r{^ZX_I3gz1MVBlEHBHTebcXt)$=Pj~}Rry&x5q6(^2Z
z6aA?8cMApa5SF)J$+-mt1tq#HT0`(!R%&i)>!K+}YxML7a~c$>tMl>w@28(dUOymh
z8`X1N(;*{!YZZo0&J_@Zb>VNQOJYBX4q#0n>cE5c`M<vJ=KQ-mikSa>eIg4!t^D6n
zsEv5vJw012QISXmlA-s|zKkU^J{{rg?ED%I8dh7eQK-P}gLhodhqVq+&=go$dqpir
zDPS>=ilf7d)a1W?+t<{D0FPYCf^oWzrNPThAcb)6CyJD>Z)1EsIU=R>`DFpe=bl*C
zqP(O3%-#<)1uf0Wswyrnu3$3G`44GQx_hJfy2{GR7#J8j`FirzmHByc6B9VqPVelp
z^LYL(yH;}kb!U(VC{9bbWZZ*d4>Kz(IT_jeq@=!|(R0<s?6-)~1FbT6c_d6}V>6qi
z;ye)S#z)zgV<W)HS@<VjE-4|w8q#MsHoT92KLV5=vP%s4c${|nWJ4tgu(ecGzGjU~
z45jtmFdeO5dh!FZ=10DN|9&(jpQmUZe*NGRgX|0DYW&Sf5EB6e4>H_ezkXd&S!u)u
zcI1=I@25xG1Q+|dcQdpuKYy5_`&~~Q8$^9jq%<@JAy5Bcf4{1#id7@|o~h|9mXTuK
z;|I{{9W=C{-d+_K5hkXGpJN!*SmU}EAq!$+V&WsW^2qpjULG$tp?<8=($yR5c)qa-
zJSKI_u}X+JoW8WQv@l?hqotvt0WQ{BP%+bJJ2NpX+U|+(-<Ot_EFV9H?-=L0xw)B{
zl{HnBfD+Kq{%Upcj~3nfh+Tg)t$3aL7F=Fv0pQNPPe|Bb9=p1JRVNm^zCt_N)x#q?
zGLj4%eRg(si5fyi;F&^RMU@e)8O~qAjjTny2|F0Xo8F?m;DMS>Cx;Fjg|xsdVL%3y
zv$HcFA0OB{Oe>%2>Z(VK6;#rBi1&bb3HgtPT>6fV+d#lYN5IS3+uvvYaMi2+aywEb
zr=;lW=->jUx~S;0FgG_hm7x9VrYQR1<L0Cja4$g+02v+~eUo-6R)Qb`lnx-MfP#YJ
zTjmz*MGouHDIHkqB8_e&?_15$F)-ZpOw%(_eIz^^uQxtC*`}-n5rv>2G+}X<rO$A5
ztgf!&kg)C>BG?DmGF-WE{x!C=w?AwNKr!K(mirKHYrC3|FemTg-J;~><s~H*AVnWz
zfMB1C!v1WugaFGZ_s;zRTid`_xL$=Tff*hzrim*<L$RzL?F+WSP_RH(HbOer*9X3R
zYox$`oz<k#4>{lvd64lu*ShHF=m7t!Nra@^h3Occ*Fh&HAb8NnuRJW8%R^i#MGxcE
zz0e*CCu^__L!`pP$_dw%K>|WTE8c4~zN!$(bI)pwsI06^O*Q4|4%hks+Y*ER(XH~%
zvy%1jjs~xTb<k-5K{9X+EADY|zw29g0?Fq3#rNIjTb@TXby`(U*t;jaN0$a2ivyPi
zY&XpMidaT^f!p%8Z^O}W@Ty6vpMu8}HmaV10h=UD|EK!;D|8ZP-!S=PoKW)L$h41F
zFbFf<<5D(BX~>dl%;ZUpGk0{P1(KuH&zX7;@Fq^pH?R-5d|I$|&^bViMy-D*uj$V2
z1%xhgHPf0N^^sR_CUq}5-${n3JZR7zwZa-S43-PLX~pcJ=itC7YBZ~zZu$kb(^V#g
zrRC+$jt)@gf&Of)u7btYKQdCe;jzh(EyYZ1&&O1&`{gStx52-+D6;+4<8XsyqNV+O
z-4B+fn$oMqK{u_ku~)skNNF`MpJ50kxq#gJgL8$e>wLsGQN0Q--NPfgtsQ4kZM4F{
zbvTNTm@v`{N;Q}Fmf>zAb5ZJVZEf{!)(-Z<Wgstv1@k*MH!JJwG}jKhR%wi^g=vMQ
z@|+WC1})RuA92kw(|qsM^5qyW|JhPvA6%!*2<O;qQINv=_YoZtT1WG$TrrI1x8B3S
zciE%omltz}QVz$Ar#PKVBZ^V~4lfuHZdabys4`$kWqk=o=t+A;iAMMSox!BkXCb6P
z0SX&q?aje54?xdJ*cbd?IGJX{@EUJizB5kmRZw#bqjk0pWRb3i(oycrY^WSV80oX)
zs|kQE;F@((2>mO5dGV<0+;D%yB;WEu=Xu}q_T=3^OFhG1vP5|r{4e~(-3|~32g1;-
zl9IpsYa^iQ))YqKMgb<!<=1L8Q@Z-gMu452{*RA}#Sc5Yow;U7$1jZAZ#hLZl2^w|
z=Rh$51T0SImG*he4FqK_EtBkPfAlz#C47|)-u<nsc6#(E2L!Tu%O9Ti573#`y1*F+
zDk~P@h7T?;^dz{@NrRNs3BW_jD>dsj#}0q>`8{zMv$%Ll^3T0vbZJBi1XzTEP-I_d
z<!Yq#TG-gw5NK$yy0|Wri>#Agb`-~`Ux`miKtOJC(ubeFBuI9)CvK^~(4h3>^fbKF
z0{MQ@ipBRq1Ld+IGk3uvV;n!fk$Xi4oCcIW7me<wsbX1*H0}SU4NV~?Fvi-JTtwK=
z$cVo?vas1XDA`kh(W2|+RS@4$Q&l&4cI3Dif``PsODX0d3dmx*b7xgwFG5B0Lj?6i
zX|;aILKfk|JP-6L?QJBfY)Rz6`@iQCR#syVyY*j$qDjfxtR;$iOdlMe;o_=Xj|j1M
zx3c9xp6Kf25fbt={anTNK59$zrv!Dk?s;uC7#*!6Eo{`e?g-N71@Zb8?t)1Jg03!^
zyU)t(-kJAbn~k{6;eGT%-{3Uo!-kxKt++oS5>doOAXGzPhKC;<Es5#v-JZ+psSqua
zn;m9IPjnglU)qP*;LoA7X3sq<+Gw_S=56imr6%=boo#F|tKZB|Qa82bd{+i$EmT{v
zBIrBQGcsZm6Kx$F2oU8g1qab*yh?Z7ES0b$XdQ%XULRo5<>6n?<Rn(k(b;=}xt=b~
z({%Xvw9Ep-+RpB3Fr2O{S5|$GkB_Md6=$`dCcS3;d3JX8`LmMC$@;fK{>Os?e0;}0
zlbpOPmjUkxkBr>1Scr*<Sp>e2iE2MUiXis{@nO%PRWCINYSsWQN?@RK@80Upj;038
z?}K-AXWplKLuN+c{MW&OGtjRndEw*#=a(bKaiA2Uo7?ieTt6p=lyz^e2qlBI)AE+9
ziwoDyn<(ag-*OgiPahwwY6IkoXq_%o{G4q>q{fHm#e8i?M$t7i9GW0i%KKMTJ|wD-
zH*LM@UcAH|O^g=+b=#S)9hu=J=P@-lF;Um(?&@j+eLbuzw42*1OB%`~uF}%dj(x?b
zwY9YmyCMu5z1$QOx<F^eh(grw>={MceKsO&$JqvDjX99)9HZVb_VPNZ?C7vV=PL{9
zNUpUZdEAaU(cLgTO(Q_*sS+C(r>mtk{I<Bk^e)-cUjc!=`}_NZldGK%9_$OvE?z)<
zetq3GRtX)6jg2)C6DpX0lI(x7_EGWqw?YHWM1lNIpFSP*Z=zud;NjKNSU+S!M{miL
ze{!@P|DHd`%WK9IO-4Mj$CC2ewY>cNSh|l@RWI<b|F*auf+|3XyeulRU}Ee?TKbCy
z##z*?)P=CD(u7@e!StDtuS7)~N)_ikRU(oD4ZoyY1|`NgSFY^*nBst87Id0bqoqdR
z9RE1QnqOFGcfJePJv@&V05b)=f+fJjz%DYGNS4w5+L4@=#zP#|+t-HzYwmfc1xgD%
zJzi|YHgyG=AM1(?GuaT4@cey=UG}V1RNNvDf18*{Nk`}O@S$uqYGFZv<#mhM(R?2X
zI^?&mZnLU-j_+By$;O_3m|WEp24O=Ani(PzCL^81gijUayK5P;pqTWZNY3_&X1o$J
z0Rh1l&r}^*H6*|*|85KUNFxw_JMK09?B>%X`j-G4^BnEo;NU<FC;%6pPGb74|73&f
z%2OQ1(~|>@-j2@BFKheYHEE_vn7CucB_(ZW8TDBoux%HdpEB?gp`s#E9B*}Y-EU+g
z32LlK1KSPwU>|0xzPm<AS;}pD>(-B)oJN6-1=-cAn$p8?9^z;kZ#TdD_a}vhQdw$5
zBBq<3IqzjFJG@E}cI_G;P<KyqTS@*jeJm|4>b^-Y`4<*Jso=A1piqe8h2PN4jk%cz
za4hV$<Ya0@NmKLa;DCnLJfH~WoE!$*I4Qn*ZkSg{$bjQ*L0&^1x{=Y8VWrg;A%1Y0
zKV|@|<f0W98~=-$w`8=!F2QQvV1;>_&I)3M&d<*S+y{5;7MS=55j8c}hPI*ivWlM$
zG4g9?)`40OKQOQ=N5`*kB8QhRmiQ`)j|5^(N@`tY`^%Y5l6&u0g06NsNOUR3v9d5T
zKRsugyW{P?@}rYSqN6L&ep=Dl`FLw8XLomNRL3TtFEneyCGhNMCC$~y>qUsV`hA%P
zjdfnSSRaN=u2NBn4z&G9GOGF9F>&$toCtS04N}auC^mLx)#0|f!=WO`#=}$UCUi%d
z2$TgoJvPQglM;eMLx&P?ZvdW)lsS!a<LT=9HTyj>Y1#xcTth*jttXDxDb+s8K22IF
zN<cv1GhTnnFyw3RPVg&x9~)U%jh$^?7dA06@-`pm-p-W&TH3sA>EKvft8P%zAaVie
z2_^VlpMF?OP!-y?wXbw^{rqSfyLarf*WdqZe;;<@;6qG=`(1rep0%OH57?lf&6t#w
z6b%|K@83_<MJO>B%zoN!L_upxyTixDg^zq483E;n9#$89{rJX4aYPHW7jAIYIL-O(
zVs8Ajy7L6#Hbs{om{9TZx$e6A!&_bbQCJuY3yY%~Ys~pK78X^f8OrxbMxUJMNGZ%F
z%iG&VtNi=+o{F?CJQ2$q-8wjcXD@vY;;`knoaC68r|w%Py`%d;g@DsbGgYjow-*%w
zMUts$W6x7TgviFmw*3VPGBhe6DUxVxv^G)MiLULx$NI9<0;DuaQKe(Tv#Fz)>04%p
zwTS7OS@>P8H3)0leL+&JB2Ftq>yz_h7XZH>)njh6X++qr*$=%J`Ix;-?ER(ptt9`o
z<H*QgDtkxUiMKOtM%ZuiJQO-VKiW_8XZ?_-pzyo6Xnk`NS0l|&ean9KY(4QZg7CL2
zU23@5U*6>wvPw=NT9}_A`6Rvl?)fg(lRJo%rsjv@dkl_lwilLPv$2{lxKE$$uYQST
zQcMgDZrNXp5)=gKulE8ruS-jLc7Cd-9L?HQS94qEZ;jd7!UVRbeWWf>^7Qm%Gbo~;
zo{=^Y7Zw&~VZi{qL%Qr<Q3<n^KayA!UQb3QNQH}wr03_`bVXcm&&(V&G!*wa#J_cG
z(&Vm<hK4_;?CIM>?zAC;T3;jIi3(}8pS!Z5ruIS-vo8iD${mu5zRyu``{)>{eyka?
z+i5P1Y-nik!^iCGUH|@T;WDNJ9?sV*62qLFb|ycEkLKzRHxxx(t<3dEJ)f$ps-8|x
zpLtrbR;ly@9>3&y{0#%IqK7t+(_ns9{PW<8l_4{#Fk|*d7EQiMoTL(FjrRfNo-Qi7
zed7iZB`yhDTmSnqt4KSS-$0b*IX)2)^T~rg8M;<(s`ON1UC}r_ZmerU!y+syETU#7
zmGC_g;OEy3Z4DsfbWchWb($&gJlVIze7HSb8!I6Z6-|pGC2gEc8N%qZkBtcY-aF`y
z!U)LCkfplM&5sAlhYl7xE<e>wjE!qc9N#A<ZuWQ<goK=IZ)0X;U<J$c{@L}ja0Vy%
z(WB*JqnfcILlR<QVoFMgmx+Qjb3{bM@bK`Mbb*+cz{??uU&k#g8+!WjDIyrifv4xP
z$BtuIn6kD3o2f*kgQKHhv**b1J2TsFqa7h3!CHA^(?u@pq_tH)_HAxULbUD6bbL2u
zRW$v&`zHGp9M4^CuK1g=DmjxLJBNF}<~_Z`iAhjewCJ^_DnicZTQ59no5g$*DagB`
zLB~C%PULh*a@@gf(DMB|HtX6|Ep6>`$icf=Qetk8Pv?itX+TIRz|O$10LDwRdKzJB
zYC#az_(zXHzlF9C5XcC5@gj=yHiv*fRc-Bc<PP<qJ-3i=|1|`gfWV~gDP)D?Rwu+V
z{@UA{8n(q(IaO2326nXfPO1(KZ-BlrJ_6*oV-Vo%{A@j&qv}63@z!TozTS@Gj&EJd
zz<?Y*QO$`WM63>zg<Z)v)>_}(5ZU|rO+k(b{Y%I4I&O%_RP~x!vxaFk8uI7RXtJA*
z-=O%G=YC$6xvGlFkHtllbmkk%nWa~ZYA8Q^7!u+?_3Dey@z@z;zY%L<+dnYyXnW4-
zcl#Cd2PpdbS<J7&0^1+eyTH0)ANLxe=j7~@Hlbl;^s%V|xFQ#iLgEHj5gX4%iUdKD
zP&->%fPHpdpmy(KUxw^yQSnEsAwgADT~Jo`*|&Zf5j<1Rz=|m*mZ!J=HI7a3i?#0P
zsCgUui|WIXKhF>%Z1f4;4}{T?>Jt7<2fdV3RCjqyzFwC!wS5pvMPcwJ24Q98^_MVi
zCd8v?t97M|e-{l&6SbbM)($rjy}9~#Hjm_o`q7cB6R+HGJcQTRckK$@YfinMA)))t
zv_F2RaGMYd3*UX8Fgo3&&VB2r%iWY@^Rs8Tp=jY0Q$%t;K4;*?Wa8qLUhMb}4h}-_
zjV+okZLV}~$8`nd47X<L-<?6syu#7?nWJ5j=eDtp-Y5YHN!MaG5>xvS%j4`BdH6Tp
zC*}j+3XA;>y240^@$d>#O;hr1<ki$VBdH7p1*-};tnJZ}$PT$A`S6GjYTD`=tU|+n
zcOP3$>+2ToH(*g9$aUe%8Udm%=yI9A=lnQn1=XsV7t;Lo$Hv=#4p44y7f?r~XFQk5
zk~W{`y88`u(=ls5iO~o~JaIX>Fg7(w(KRsvV^rO<7B@)x%dnQNc7AC-O2EnFeS*7p
z7mK(m=3&wL|NVj2r70<`gx#Nvb9l(>%qV^{QYEJ?2#eSDO=|+*>%$V4M=7F5cFH|z
z7#J4`?`>vk;}T*J#LfR=K#;#aCbo5DkCT&=$E>N}oL6A7-Xc_sE^B11pL?1<ec@-9
zi-*6PL|>0`R>f}+4wk-gqdi3C<x3<WC-jQJ%%?BLrhjccTO+9oW@jzL#WniO*G?Y~
zT9)1?I~fpyoVjaa31DP$;Vy$k{vL}^PgF#QRb$^kJM7gfR3x+Zy~#c6Pk!RdjYndi
zrro>iiO{d|Ia{0*+(m}3(eb!FMJFTsvr%ejW3x+3TeZ9Bkgek~T%yE$;xd?wFfpya
zxq%>5g8eHli;Ii$&d&3Ti+4>45i3v6`*vdiQuA-$=bw*gW)377h={D4G+@gxkdg<c
zq$n}#e6R0(!^ekrw6$nwn`mTs-fm&o)q(x)CK^&!fogcc9Fj>wU3AcjC6xub`DtcN
z_=vYv|FzY5sf)d#if@HRr8j+sQa@Vo*-vJRcvbzHZOr}itgetVFHg#t8XYsRNy7C@
zCS64(14pk8V}4$qwTcR{dSbSTz5)@tEC<Hz+eq23y@#*2ntOY1g<1AiJo@_kckcIl
z1LY4{E{-+Z&rcEhSS0}#;^DD)hx?HOgArk#E=$;bogs(kM=0v*B-=CmxVV8!mc6CT
zFK|LSy$`cf;b_&Ql^h!xCOZBznk;JIw@zwz@{}R%iVTBsgLSzr8M4}Zl=|!M`B-z|
z+Pw6a{WXN%`HA(*$l|CdauLxL5fSpgyNZAyH%YZTeNsjxLA}0CFWEftRcF$)!0wYz
z?f1c`hfWukvkevMNgOV&VJ9aP;nnt62U7{^>bxW*PKMGXo81a$KVv7To%1~`868Wl
zs2SSZ6J;e2vJW;;>o-v!_;GTw`RfTo5&<G(w^7Ca7zuD)#-01Blbvt)`;W7a{U#UT
zV{lx9g9D-n5veD<o6|U=?g1az@(KcTbMtG2HokFlN%;RPHcEW(fQTfEm{P=4HtRx>
zPTIY?JB*|X4GY2qB~XLF`G*`pykH?;yLRoV-8)&Os62y`fQ*I0y#q{qtwong$y58X
zw`9SgXeoq<f`Wn@m7rK<rT6z+>l_QKU$Ua2kQNp7+i-NqJ38jv%qq3>*dZJqaW$?Z
zH8w#(`Uf0ns9y^@5GRY|(2}C#zo<(x+6F^g%jfLbRnL@qGf8!at?c+d+hP;LCNU-Y
zYp>(bkT;`xw_Q>uC*K5NMH+JbnY++4EZQ8^gKGrmxIE*z=PHpFJAzbKE8us<P1U4?
zzgi8a5GpJXWM<>J$q`7x2H^ASt*P*kHdQ~r)V}z;xFKcULVu2dHIIRb>28;xk;XX7
z0lsB3IJs0M%wU25B!w>h$NQO&h>Z?l!)zg{Aa4aG%7EQ?>cNiYn|CDWyQ7m{Bl~OM
z3B!%(U~79a(W}znD=JO&bz}UB;eFb&N)iS6#wy&{m!EqXu^Gb${GKc-M8xS1PE_8w
ze?JHOC;|Ssw{@k3s(N>Cr-)s5bkZb}dwTIV2he7PuC<wZ*B68=*qQWfY%`zp4qkPN
zdN@{9OV~e@HEvSV(<i19BpNTd%J?O`v&Qk8|M4TtIK#Af_h%!y#!fpwVD|1A<|HOs
zv9Oq&4Eeu`rdHk_kLF5x^X83%g^Ut2PZMXX<@%}~XuhF4KRP|E($3ORjQI3-qjdam
zi?gCkX>5F@$W)!Y!OHr(_2U}DyB{q(U%A^3O`VP(Z1jzMwbj%6s*xhHwGdnHKqXBi
zXFAJt^Jc7;61jI~&Vlv$$;R{N4oywHLz*C*xAgRUb0UdNy$%*sY5uY0fo&kY4v6m>
zOCi!k6ck)arWH%u4W1WV_p9b6YJo!`!?CsGq(UTD5g#vi_z)*BMujRc(A~?c*UIwI
zqpke|mi~cv^>wf0GC`mD^z?LPY3XUx{oq+{YI06#Ma7oo>i1?Tv9U#kNzT8SrHT0T
z^=-KH^`AXcY4$^xUIJZIhhh4PdzsbMWd*NZMzdXUMwcc+m3~$1z#TcJWu!_JEWohv
zI)g!)D8H$RLeP;pJiFBR`ohY%Rrqa#5@uVw@bwKks_SnbTPYab`$a2mtIH}%Kfl^d
zM}l77*Q;_eqFGF_+ur6^R<1lY!h{~^`}glRcXx_Vd<KfUdV1)Xn1iX}qLPvqx@>GR
zckjMQT;JL8hhh;zdBPSwuUP;taH@Er!TvD3Ha$I{fyJ!fVy3hRP^7i&vW1SYNnzo9
zPW@~CCpY$Yt{|Hay94=e9fJ1lk3YM@aco1F=_RHF2&qsFidgC+6B^mWRZ-$dOm}x=
zcxhhCb%VUHYSeQ2lb72;#bUQ7IeCT7yZ(W&^z@S;EZD1vmV=Yo%&6sU^MK>B2U^-F
zNKSdVx`INnff|j_pgR386oig$csw-CFA#@B>QIY=r_|4=Mi4yG{fO%XLQb})Cy90S
zl?8<%VyRp6txuhtOrJc-ZxlBi>*)!TB^ti98vAD@4Pc1Izvn0|*8A294;Zr2Tc*3a
zAO7q&<KQ5cJh5(ZYK~<|!>4PKlJDH?rVATG6WN{%Wl}_>UdOc`{$Af%&C@m8xkj4Z
zY>Bh;oe!}#uI92GWv2|1Ca2KOe-n)ERT$;?x;<Ojt+rN67Y%9akc&H-r7CwTq>6t0
z`|l}YF6zO3TQHs;dpAq*g`ZzMr-5yn*qXLsJ{9@l53#z3PYEJ@Cg@8e@Sp88;v-{c
z#}%lcFZ(z|hMh~zz~I9dYX~mo<m8mnSO*PHEeF<lb^O`2O-yw7xgm0=EQ)5a)KK^D
zuMDe)Ov`7U-=ZbCzl~uc;h@QYi-(eai$%UDw`+JLXraAFmKq62Ox%v7@(#W7*v1YG
z=^PkHrtj6&8GRk+u+q;E{@wq0dG*a3Nl1yh|Kg*JO}=^Ot4un-^n;CV8bPj|dFl2S
zm_O%R9Wr(3#1nu2;bRk@NgMjmaF$MdaJ+Y}s$wl7lAb%Zv}Iyoc45D+=Mww;xip07
z9z4hZS@74Xv_Mvh%*=!cp_;>u^Z>ZdS5{Vx3=L6`q=BEi&)#TU`N2_L?T*^2CX!O?
zCoUW+Y=O=xVOpBYT9L=jC28idp-)c|QC%$&X~j$)ly6gCXz{6Dl};i%ywg@h#8_4J
z?Z}7LFWS{rR!g6QA=fMdKj>%<3yH)b`AUG#PWlRxHcVXke;pG<(_XD;Ok3LABuE#v
zCdw;tC%s6Iq*5Fmvvs~B($s`#mE_#Dx0K7!vi~_$!^u6Cd^?;=A?u4H6_qMDtco^c
z=Z8kyFRv~!CZV8pL`EJA3{<<jbCGj`an}i{_+yf+cm5<*K1O>y@I#4ND(V?2_I%~g
z{4m4IR~*PSwZ9%Vwx}&HCn___hyY>2i57bHg>gcKgMgrzR$`P!v|9a_goFfWfD@5p
zM$=IccPM!r`9(z$1YuD<^F|9;AI^w+da|YZ4qrtD|NM+=dd3bX(0Lwt`rvcb_pi4?
zH<_L6?Nh~*u<@pH3f7mGSvaLKw65VRJb3T`@EjC+cJ@_&(3!R~2#3WK{QkXJpKpLe
z8Jk)juYf(ZWRuN6;&W5e4>Z&TVq%Dtya|nfz*j<=-l_5+Kv@gT1U^=sHGOj{&+Lgy
zddIVZ+^M48225;obQGt&{PD^l24Za;Ane*SJm%-$ii)fXIZ!&Eix71bUVO#`2Rtq=
z?&NeQGnOf108w*rXexIO3dKUowzj&5t@Q^7D?*6F<bWlL6crV_>X$SC6HcJh?|JAg
zRx88si7tSBYF5xfEEaxzy{MT>KUj>tEik`Px-cs~lBIaJ#}^MlI&yRI$qTb?uW2Tv
zU+mvV)4)Pr#l`u$S1S}&9{~67z)f%T8fJTEI~syHl#<l!NP_^XE;%+F5XUCJwq{%G
zej7o^$ci&$zZnh6PAO)+A4roVL6TEY=4;;>j4bsa=8NlIYN~z9?N=UQ;P8}w=!<VX
zJLYw_H#B_j*|%m1MO#vJ^WRfa3Q!UtV&dXPrL*nr?I4NL(lXBxE+1249uh29Ibl{l
z@#~AC^V2%R!W9Cj-t&m^`ZxRUWkdZ%2WuR2;QrK36bzK2UAyzdT+WrB2;Jv&%jWJk
z=^#v$fU!8X^b!s7;getO+4JT%A*H8LRMOvZ>UY4<;B`+=aZz`CBv{nL@2<X}R}V2^
z=!n(hCoV1`z9;)YF#!O0&=J5C6GMI(r&yo?9)I-sfYSqVKljMq#wH1(v}8Z*Ha4Q|
ze@sbp)3kn!o}<@mYjWJN&HfvQg01bVkWN)~dU0`g+k)LlsvM(gDJiM1-aBi@$B@p9
z8gTA^VfQ*-Uz3o?^SbZ66p2LhTnJuQn(yJd&epFd^-fJF2o+V*>(|7m@!L`eAf+B|
zZWxH7VmAQa+1bn#6z|3rxHTI`<!4pBmMAajop^Y7IDwZ$Wbm8JE<y~rTb^bM-1)TH
ze6jVUT<^Yej=~j6Iie29)1z>d-~6}k^H79$LM0Z@18=|7)UV|a<02#bO3a+<xtM~i
zYjvhF8pJI2tz9ZWBENevc4YdUzX9-Pf^kSbbC$w~^z`+`-8^aUUGLV;5}Etl&1oQL
zc#kMGb^3-bAV&?2jp0Md$2*^#0WgO8Om~?Sam}KR`^U}9bnZHMe7tO+r)OdB7`E>g
zS4m;t*qBCT&5l^L`{-D%t<ATYX0tA<o%5|4h&KafDJaj#$oPVQYHZsKoq>hnb)=!^
zL}Zqt|JQF@y_C$vVbSqpnhXr3+`WUJU*5WX&L%KAG{gf4k>G_BO$w)}=~l^BdDCBA
zhpoXHb|UQ6r(O^-w;u!~AH*eb0zdg2yO^8Lug5doa>_I)<>Opbb%7w}=Z#+IU65IA
z#Fwbc!R)X;u(iJKHcg1jlGgL)`xIJ0&-d~*(pQUpCL<6R*6<!M<EKi>8A{zM$}{(R
zR%OEK-EGTOR5U+!(?zHLTe{=GJO6hOTn7>MXvx2Mg@sT~Mdk1?jyB@@-egJ07gJOJ
z?^6&9b05Q^+nlbg%+9tPJerGpoL{Vdx)c}i(QZmCJ!pAhx3SW-UfqO_E)AmF5VOYj
zUd9zb0^;MNK0K~0cU~#~C~K6ocf1n+_pkrjM(MpWDP3JxIo0vT{^1N+I&rV1;kT3u
zt2FW(^YXS5m5;9^-6`j^1K>!Tj^C=IPuzi-3M;he{FKh+4#XUsW?GgbC?9>54zC{U
zRVnx|e&|Nc1LFTllKxG$g?K)|>17dKSY5qFM&@>~Y$Qvj&BJnL`jzSCPbmC#q{Mq+
zHNws9e{seyO~e@c8CU|`euc0)GBX3INoEU~iIugqw3L*TlE}AUGMJc{L@yjYJ!|Xh
z>uYL&%2D4V?C9ut_3BlV2}gJLLr4>uG}(ueZxCLvpZ-+de)UUjZAUXD*TAxiue$np
ziSdLsO8LEh%UiW3+dR_P9}BaymvWNmP1yi5m6U9rk-y=8VUm#gx0ESXi5YOpw(-MT
z?$V{z31(t)`wq@fm<IwwcMJ7<L}UEdAV3IS7VFdo*b`kBnfYv2EXTDJR|aoE)EfvY
z@gIv}9Msj+p1Qf!SKyT2JDWT>`UGTX@ZN<}s=}^N-zj^#_O%Hghhel3qDll)^@a8I
z@!TeL&Wj4LvxUsQ6<$*Kh-79)3d0Pq12F)8$HluO>;d`UKmWr0$KU(^`f}-}cUVY#
z{1}idDagnUcA8R?lex&lMg37Kf6wn7Q&Lj?j*`*FMn^q}uTka@&eww?KzgEnJvBO4
z!{iXD4<pMoFJ*{uLjd_KiZXL76oZN!UOO*$<HcX^5kOLf6G$8t&k+^%!wiBmx&-O?
zF-puNtZD$Mzi2?y#s<=ygxCHm3<Beh6loi19@q#>!hBt}dk;9vP(!J1Uq@me6WD|d
z!9+UrmqbNu1`Hq%{y)9!eVYI#xt`ve4K|NJ1@At`_ziJVTnsx|%nSAVS-@}LxI3%O
zjyo-M^1@|?yK~uPmr(3_QRG;epe1&owY~i)5PLl6ai!Twb=XNEKMneX8r=;EPJh^6
z&gL5b=R==-zww+%2KxrlC9^UZgibEGmjACWA1U|a;NY~sAmHcay;orw29aiuJHHp!
z)|74dJbzod<PwkdmzdPUDPl=CRgAKnA20YZBU!Wu;w_;Nu!fQ}Fiudc$0hgc_O-3C
z7cSe^Tx8i{@!C5&`W<c>N~B3X>U}djtmUc5OlM_f1q)b=HoAKe2OU-B*j9m=c;=I{
zEF5C(3o)*f#V9QK@dkE5TD>XRd3i5;EEN<LU2CU@Bu@Lke2LZiL6bEzN&m*BJBBGo
zso($Nyr!~}U)XnNRveN+j=PvBpe&rOZsma5qr&$<D29)M6a_;L+ZxO_+4oJ036`go
zFU!!atSD-XfBUA*{$_IWe$EWv<mG?(mXvPPRREuenueyw25zUoK-9}7A>=MV2_&Vv
z@Tc){At51(H>Fl^hNnvS7C@jQX~dC$UHN0feX8EtIJH!<V@On5da9#CoHPJ%vi{^{
zkF^GAWTW0N@<D~KzFEx{i*leWz3Y4Hq~u$gkpbLGrdzivGBex0d~t>-YQXIgX##eK
zkL)WNZI&R+>V`58WvD8x1}2+)McH?wzBoFtuS78@s_Yhsf*>UnKGblnHKvTr4R#v5
zF5&YBDvkB59c+xlkvs-a??9Cinu^g)mHIf~YM0(C{{hT9$myrOZA;326t!#Xz`hm5
zz@zfT0-TS*{(j~U!qkLG1Nv>_@sLmtG^Oa|+>td{o_Ft(+2zhqYE9Z|52na56RRii
zN`sUs3rk49Y<h>NLjC2UctL$b-hx)h(HNp&YHAU3j5v6BNunMl4NR|tK)j=+MT#(#
z>8*;#_Vn7uMxW)aax0keoMLjA7Z4%gArFUG9QRw5e?n2{EVKWe80Fclp%Wzwi_Gxw
z@Q{#BfGFXKhW6O#vNB$Xw*Eao#lpk{xHy*oZJ8CsH15yQkHV~IeyqwTV3}493<{c}
zCW9<4>_;<vA_ed#z9l#v<jhjg=;5GRz5OQh-zN4l0@4!f9MXT>Wf`UC|3AK5omu$t
z!v%_gjFT6ZmJ0Fmt_D3LlDR;=WL5vCXR9W0@$rJN_aGS)0^g8r(cRGzvj<~q<LEeO
zRS8ikCQhG~!4&Yku2E49CGchC=Kh3>)6~=yp?|9qN<iffWY6O>%-}cIpxIgJqn$ZA
z37=<%h7(YvqQ-s#6@lsa3k3*)A(<xO>$Nd<v*I2sNGU0Bp3n7-jqM*iC<kL5ApBRI
zKj4<!nWzjMumYf@@4$mTJHq-mA;K`acMZP_Qm`Q184MFxd?@o!cognqkEc)310cr<
z9UUEMfe`Y{^t3`WEdjDIR$N(DHoyDK|7<t3xmhwqrq=(W$;Tf<rWXfplpT7(Bq%+K
zgXnR*P1e=d|GB=-VWgr#N=633S7A|65Xz#V@f1r1p95mOO-)S@MohzsB5Lc17_NUQ
zlTr2lp}jp2Bs4W7{mv4Sc?ATLMcmdQw1Y@-k+U%~!%)pNde^|U-o5}NY<x<AyenA>
z*eLV2t%Bdk!B>DPRh1PLK$bk*o>8@O^!L92<E6W^^R|$X|K5NQM91Y_UH3pW0eIbD
z_00j$d4E=$DLVr0!E93S4WN!G0O0&j=B51o{aNF15TNcZEGz(rR8T-5B0O9aO4fl@
zrDW=63gX=m)0UG9Za`C1TGPS~jPbyS1xn-v+_(S15&u8B5B?_)$6TP{8=L8$@5e)a
P0)i;ZYsi(zm<RqZIX*s0

literal 31116
zcmeFZbx@UG^gfEBfJk?PM;e4fA38jwAl=<amvjh7mvn=4gS3Evh={bbbazSTfxG$q
zzQ4J1XYSlT?#%t~o*9vN&%5`#_u6YcYpv%w;Yx3%G11A;k&uuuWo6(hNJx*vkdPjI
zL3<2-Q@Dft1^oBSQAXPZ3F$fB!@ozP^M1ETNHj>YaB(${%)NO}Bl5E>-hYP_Ee^fO
zK8DR%ALpJulm4M!g@YC>;h=;5bmsA_`|QD+?PU8y`{%9uwXV89M(mz*|C1QqFCtKp
zz>+|FgfS$$@-i8la?*Er;HF0_!uCy!-E_P}_ORioOgd??T5NkqLL6;4n9FCOFNN)o
zz&Eg`oft?+N>Vrj;0N<3|NjsFU#`JN_A^ZRsM-C6c3<z(Ltq+1)~x+ih8+gEO7cCu
z!(WX2&L%Vs{Sjvq<(etiQbS1hcZc_P)5Ctr<|B-ck)Yk#UOT1Tk;KEP9Hs(q{X(8{
z;l9$YU7OT(cy*?q`H0tU=CqsLFK%=A+kN;Wq`tWfhjxBn25J7M|1Wk}D7vcsdgoc&
z{k;34eyv$A_dJG}l)t}!sl$uV?Cfln#m8dWVo=5Wj=Qt!!uF@I@@BoNMZ|&oODwOp
zg|pqMlr_I!ZWr_Jt$I}|i%-S2U!*duF5c}gre|bCORY9<W_w@$Grn4)zQ63zuCq{A
zSD*LX%&O6{MO>eis#!1p2%c?n=vWGTzIL%U-^zCa+muBrQc_eL?q}~1T3I@nZ|&#0
z&l|B~C1N?#m(TC)>}+go1OsJ0!WqDyoSwdlcJMLnPkzOgV$&N>*Kyg+9=5gA8FaDW
zeVF`87kq7VU-Z4u?6{tI!`}Au#79>*L#34BVGs>&GR!BBUt%3Bw9Ot~JUj+bv{e3x
zRq3G#SsUVfHlG}dB*oH+g=$c(S*Wt@+OiG1+8E5ZKM}pJ`^3en+qfq!dObqoP*PQO
zu(ww~yAQ6*Rr=lCQ#UMf)2ry`d@$DxKb>8?o7_B!_D#xUc$BI$V#Q5@5d*v2$Z#0S
z668yhe>)cQK7lB*J3@(r_&C)6dMwB9db^mxcUz1G2P>2T;RN$0c$r$=c0L0Z@@=Jt
z|K)OM)0}gEyYCGwruFu?pU-Y)%@I~~u0zgeKljyXbv)bUNwT^BaYByJ&0ZZ?Zm%|j
zJN&oVLd}lL5?7d&m6cZ_EV82{53|V+;*XO-3sln7+&uH@-|uKyQ5(3wjJ@}mP}kIV
zn>J6l+?#di(EnYC93V!E$Fzpwn9L}pdOlgH`&67JPw?QY>0mlfR#w*h{QPqw)~{fO
z5sqg}7jrJdc8l$Z?*YgT?N=L{qW7LxpSzYF1+tD;5jPk2+lu~ucVUbX$3d?{v7(*u
zK9uqZq%Qj1>=(XC(W%sk?cr*eI=(?j$54O~yGv6Q!Q4GhHhO(04=;LFk7NzW2)UwE
zm2Xln4j1q1au5}{xw$PZ*P)`f%Q2LK5uRtID@Ps!7Ih<5r#FZPtHlq0f;;cng{dft
zI1Q!t+dA?%XuDXbs$#nuJo1>c;--Q*er|0QB#N|~sec!hfFxfz;yWPdejs%C?ZlQh
zV`?{R#-{Cb^v%<A2%+V_l-Yg1!?p{~jwA+J5)2~e&`u*9Fd^x<K<#Y4>Cw@6*@}GW
z*mU`#6+K^TU$CJT+Kuh)cj-1&SH~$G-@Gplhz&f9w?_*XZx$}tTK8(3T{ef(dEVG#
zeBDU5u^7ta_dehAK4`WCYwOnPdv|+fnpsfbPvrBY+oT4U@C2NQb))R#2+yf1eSCr9
zsa?bm;`UFzv_z4FxlFnQ-8?pvdkbyedDMxtOa$nJcmpM=#+eZ#JwD69#8vvOz!;B&
z?vtv0)`$J?P9BWVe(v%+GB&~Tj_QyZr@5kd^Vy{CI%?Cr+pJyFd@^E4e)4XOLG%h(
zG-q}K1<cV%;DB`KEwE$)blLcy&?ujut4A;jtnvwgb;1+D!*8m}${cqWIWQ;4375Wx
zM|Y+St@GPJlI82&YX7^d&0*2|+h<uq6j+gYT1L=(laM1usqWhuo9g%O4?aZL``l+!
zaPb8sdz0FGuLjJMMe?h_cWoB3XlDw(7P)@4fm|gkIcpgI;p^N#m!hN7)BG!=Pc;TY
zv8mfnh6H>3hL@H;cbjOpd7Y6veB&57>9@l!NzFxznChv}DBm2)YIR<hg9rdCovPGr
zvYTzp)2Cb-7@eHtg~7CzWPz(OH#aZJ(2l1~HV<c1<q$pki9alKA|IgnB2T~7Ez5nu
z6aV^mm{Cm6>eI>m{r&y?&5-Cnv$MMT`h&F<^Y%Ggq7KWQ6c{M|b`fNW#drgz$qh|S
z4u8KGM^XRgx1^;O^}E$wyq@G<Boo{Ay=YxTtPpox*2MZ&d`f`lE4R*PhqL4iTk>Ww
zm74&+QUUDr*4}-8=KcKpDa7#o?J#)eiHRi20bt*J82F;|kC9AaK9~RAJ-6pi4KFDz
zEzQsGGO3BWzg5K>@M`lLr4Fr=?hW(==45)`!MGW<c5~3`AmD)hnH4SY-%fe;0yr8^
zvn}CJYzz!F4GlbHixxF$>7GQ^vl9?-girsxrsyeDValeSYLc_GEY>I=6pHRPc_nl&
zUK7|{Q&Uq{=OW?#>q$^&2C0QB6r8cK6a%kq55L8*JjNz1q$te$q{I)4hbbM@7r=H+
zx+8oX$Tv6C(&{>;e*rg!f%1A8xwu<wNg_{PwXuJl41L2@(9_b=a=G8dONc61B3Grf
zLSy%GHnh#4dcqnEE|rfGgHJ#DMp!~xTADeD>_+%)vCF<q^~fE<xmzgr1U0A=mEYbK
zdVf~!kGSx^zsRJ%8ck^*pPo+hrR74$Ho`#LVB*a^Lj~UaqTCR{yjcD>bEo3$xSxH|
z=VUM#wXS!)<#w%CzFUZ3z@fd@ci-kXU6d`Q{GZX?GPb`52zCkm4e~g1_I$YSU0tIr
z^Bmpex=lEQ`{kZSMdLkLuCJMzowYeUEbh3a!yh>ExCH@emERvqQjbR`_1;t(5(g}A
zgLSg?zgJys$;3|VxfCUoE&_MpZSjdCmE_m!#2bJJ4uJ(bT^+B}Cu|2kXM=g2GUB#@
z-6Pg_9YMwxqQMUE+;5m7VmHv!*Y`ac<g?r;6uSM_b#`_(I5?Qreggc^g73xr@v2XT
zDji<9Q#Pgur-Ao=1G)Xz+<!(EKa5>kToO4uZ-3$wpy4Rf;?>OV=eswjeoA0ad^k!|
z2HvmQZjWcOsjjkt$6MfA^l^z&4DK?{dYh7^GGe7R3|%o}*$4=D)H*McZ8RCuRc{(P
zm#6@5bw9j2iuD)De)A+qJfF5u3gIFNi5vFW;No{L{^ud|vax;Ax`8AWvsgXK=x!|M
zuHNVB_~~<^3i3iU%^a)@K8L%>ruKEWR(9YXb?uuaMZP*LiX`;bTMkpj_S6}7g&5u)
zr!XjH+ge#&&TWcF!~O4X7X5EmsU2n;L4?}};EK}e2M2F4I!X|}FnQ+4;{c={CrnJt
zU{o&haFQo41XC&L@hM?qT$ax9;}a7NMxB8Y%o|lzRX2wnhS|uns932_UvL%K-kk57
z`h2OYtE;I|E*>*iVJ3=vM;lHrsK4`Xxyxl^fPa4$1qFow{pqtn$<jpKd29+8jpkMU
zl}~#IV&5UH*to;+C0eO6p$jWf>uQXkGfLg;{=x2U0(F0e<(8{}ym`nN^RK1qYQM8`
zFmGU3h8*V*-+i9zenWJDt|(I1n|;#`^Yy-@<L~wU*Tw!`ApEcMauljuVc-A5X6<jk
zT#01SsQ8q^{uLk|ax${O&Lv>ghhJB*JpCh4seK%~LNKQpC`?_-WHT{`tiaB+1FThK
z#&1JUHbI{-?K{A==$~W%HQ@ZPnv4YI@o#A$jY||5QE%35RqMgAhY+xW{Xayjc6Pti
z<k~Gmj12&6<)Bw`egA&Y6(-VXqY0yEoVJA%L`zNN$s}M_c7<XOX9>C=4rNWFcHAQl
z{ZBF$*I)<ZxoQ#0rCZ-ltfttm05m{7MhH@i_*}+GQ#FIwx?cP&HuMFal=?tCEhz>{
zvbl8ZJ{UaB2n!_$CFs$A0anVqTDQqw0-1)v;m(KIQ6g~KXQqjpm$whwBiq5sYHNFY
zWcTd&RtB<ja3JEiBzA70nyXY&UF`r)&~e<x#s=>Hxe)rmTMrIeH?(|0;F;~H$&rR*
zl&4}-6)<FfPGHCg>}*=}^VU|M(9NHGr%0cmD10_fefKXE_>*1JfhlVn8*jwfl%jAf
zd^~XJ$m4N<7`Pt|4v8sc)zHw8MpD`I?qGo$H<@~&$^bYa0O$BxOqBVPv`79bQpjk(
zU1zt%<|vn;Eon|;YyQ}1&yyza*U?c|$F-<{@;G2G=~4x_mS>(ta=NTUAQm*}%+Ai<
zUr%*Bz*zuUOlr&m`Ek$&jHV|S4Acf3btKU(vNv-;&S0Wfnioi4%eJ&;mW)U6B)7P@
zxU7ts0Nvb=RgfZPx4PqQs^g9aw@{@N9Ok-~hnD~sy6fqN(@WlB0jHIp(R>TA?Yd6V
z%Ia!>eunY~Gi;t4z;s@sML5C)Q`JWp)io2kl&7Yf&-R--pe`Gs`8(Zh-WPZK4*oZn
zJ)(Z6TO&T5KF4dl;LfVU-d-h*8o21q^Y+bYI{71evrX)aWiP>6*<hmBF;C=RZFq3^
zJ%7wK9iR-tcXqQ!jUo=`ZY(`scuY(Tfco-K;LGV_@>>LS8Y0>e`$dwGSX4qKBUZ-<
zB=jebg@uI?2*mMS>dF!g?mBrky&`6lPGwIl^<sy=C~ouofue>wrR@E^09Y$I+Ktl-
zfHuZ{l<f8X1?K>y1GEt<ihjol>FMdo$;o?~)gTWdHuO0HYam05_mJbP({(<;qraxH
zl>E*BVF{oAHGL^n8dbwTK|6$ES)WV$_nW6w=i(BX^4gDOqZVR*s#t7<k1MQ9`yALf
z;qn*zae{o1MLlQ#<_M6TX?<aTt6G-V-H>3tM~y@-@P(9=lnM$84<4OlegI%;^_o=r
zw1EP(ADb^_O&G`r{5lM0r%RIx<ol>Z{q%4??isu&9^-#gS#<5C!Yo^uuB=-3es?nX
zIq|U&`*58X09ag4*>Ime4w$a9%wbMP_=THGV=Law)R@-Wqzqal+?46FX*xKt<9Z5Z
zD}L1rIbEtYf?B;CefF5y0`}_3iJ6{LH{t!6;k|j&Y?JNu@c`Fw)bH-=>+8k4^Ckca
zGrE>NR-^gWDyabeym^SQ@o(7D_#761&lNUN4+T!w8AkD+(*<uTD=XVt68C0SU-J8G
zE&l2BTFuM8Mu$N5PO*Ergg`R3C*P%20sgXT#Z|z@#)gHNxmH>-Jn!-(=U(W<6%5eD
z8}KPeyYjq{W#eR6)PM6I)uL@%TU$}5_<IfL^G+kVQnhS*h_eW5|7h-OGTA<PT><B{
zuxZ;Ntc`_*1*w?)YcEKicAZXA-vj7ATIo)E|A*IMA+s&SjRr@n$zI<L%D|dD(zdh#
z54<}w1QS?G_^QX&eAd73rn~Coa=pllgQg{^oFbPCOh*E$C}nQG4)V%5`v)wuj;k9<
z^s0|)#%+*is=lcS8w;x)q-1GW+aQc|w6@;-495p>aWfpBA<SyJ)*M8W1jDxd#@QHZ
z(F5mZD*s!j=CR#iVuP)N0}bY+RUvX8ZVw@<jBW{rx8qKF0p4Qoo&;Fi&^2Rf#M_2H
zQCG*s<)@f5tIf>~*xspy4ul0vgCo$;(D1S`1Op`qq@A`K+rUMnkY0#!6Ab8?7k<LB
z2<n9X642{4i{IXhBE?8cl)FMa%U7mDOSt4(J^CtFm{-LXv&u;<>~*>oLC8`|EVRA9
zU-`CB#y%j2r<^~@+QGrF)=YMT{k=fev`ad2!1tL38!mmx2pRa7S?MQrw6Z$lpB=x-
z609eDowxUlNSxj0mCR38lZ&UlvMo}ROOVg|Z{?5^iDlzcW3U7^DWtDk{YJS1zmHEa
z<tsx#s)s3OPM@l9<Y8+1^6BFZ<<31|4c6A2c6FQ`i#eq8`xiH7UWhLvLkPs0Bb1OZ
z1TKMP0Ws0m7%ITM_3P+0gXFNk<CPvmESfMTa%QM&_s7e`CpyrvD-+Zi9(%R%mM4Xg
zBo9`lYF6o`>2q9MT+Bk^aw9y?_jZhw@<%|x>oKnR?Z5r~`}Z)TY#tj83rH<n8UbYt
zZYM=#H#ht=_Wmm{MtM~?kQvb8IkoD|h<y&(T&G425c{4gQ-c5O30Edjpy!VvklGBC
z(IAbjEQz;Vu0)tQM;ZBT7p6G>GT?2XQL?KPNrFI9`X@@%;=dKt%~xTEZjMi?*i|YM
zNI!A$_wSgu*JMuWHRE)F;-Hx=G+GX4gZj`|>*46j*guGiG4qs>+bcMnH-jV1wOKET
zk}^?-1S2GKR+v)2RmR>D?|qBeH8w$DXX13l5c-BFfM7YdYYZ-T`XFi|#*0OgJa^8#
zEE$v340H53KpNr$lF(^F7em7wkvJMp*1r|nGuhc7;m;CvTng#9*B??VpGa?$bfv+8
zl0_dc*+E7NRaDd`!kkv)WImc|x2(nYVT%rMo9DO;@xLwR8lBG?-L)Y=Z(bB2!Pqh3
zpi-EIVxet3v$3@m0@>CZUt&j#Nbk0Qd}(Rw6wB7p&Y*hRv}CKOqP;@^-;QbWzomrr
zypK<$9B826Q{yO~KnY&)HoS!l=2TM>4&*U9Q%H}LKv?KOF`~#s1R#|k!OypM4U0oi
zTFx|PYJ;hK6T^O6a22ZLF>)><*bp0hM3HoxqthToizFxSHHnM*rM7{M5h_{E)5>(S
zo?>`@<pWlR(ibJR=L8gF9<cqV94suXA$j_D3qr}}TxV87RPHc}m&_ZgT>0tgCHboW
zt>n(P4u*de>hHJZeG{K})r^n!M4oJLr8{!n-Xe<z3JzmPu_OeydHsVE>_=N0oB#s_
zY~=jxY{uTf+kvcIBNF~p<bV(KL<txu53w7~;sc(Q_gWmX`vU*<<)7cx=Wf<^>z;m5
z5!36=(ZY>1WYN}i;X6C7+eU6Oqe@VG4h&OFl+x1h&98Ii&VF|%DPQ%{Ed4q}DX{uk
zY;Sx-%5@aru86aGAO7Ch@ZT2YDvY4e_4M)S=@=nO5c`z9GAcHeP>)qX5&bVSys_R_
z!Cmo#MFZ#$C`A~Qj27?jE~HcD{W|VZMK3!akSPH85O9p5M;HgQ`inAZyOzHgR6F=y
zE{jcyelBj(j3cG*J)vzv-J36u?U}c4n)_e~Y)X5+Wo4~5qT1IE)c6iN?nQ97Rhb$a
zlgUMznDN4YBhO#`k`nNY4~r@tkN23{c2#j;&vJ?3Ns1@$LJmM0u4Ns5h}IHN<irmo
z;m~G{KnnB?e1?+J@G&bNWaO(M#~GuTZWHE^+&;P2ULEMV5b?D6XZ$g5Ge7^5%HFJF
zv8bN1snOc<zYAUPonn(sJ7&W{lZmIbEEwJ_-{1N?;*w~`i#p85!;9B5!XWB-6pqi&
zvJr%eJ+uU~wl*2G;K-U&feol>Sftm+QV9##OtL2Qx`MLpp<MN7((=U(C05V%+3tEx
zf<blD%x1pliB@*3B;>fqawzlN_03{;MPXS(P>|m1uF|?x`LzpGP2y^$-<`LmL8wzY
ztZDWZ=Fs2whxbF3rTlJtTFgnDVGu*Z;TUv_W)qFZdPaRVNIErI#owmkZP#PA6t7-9
zU*@FJRQ?!K!U&Q;(zmFc7)p;-xXXSw$h-%|+N3V=csPZ$x_bDsH@-PMe}uZ*WWI~I
zg$zNJY~E|jiw`U^rQbY4ii?m-UG2{uV*C#jlA86_vaU;f@rDX9+oA5HXK|adc&N!p
za4hI6lCwYWzMU5-WiV<{ZZ<s`f$j|3iZs;o`e!s`bbf=F33y~}y~0W)N4zV~-8Zvu
zt(>SEZ|J$6^akMBFlJ_Esg!x(9L+fSDX^MJ&b-<*s#7e62qtzu4okzOW2yA@^aoq!
z<uyheVtYcR{CX{hi}YK6+ou#83T6$>Sh+HFnFVU+$g7yH?qQ3-zydK6aLUE!<pxlT
zG_aDPeUcD|+xoV>0)<nPvp^%4W-o7Vg*dduKcD3<I6?!(V>WxO2!6G3(PuV*eg_?m
zWd4pGaz4N#>4|b>r7s{=Rd08;d#{{Q`rjw1Fhw_S%_>1N<cX3oQG(tUtFlBC6IHy2
zZ4L63*2JkZKGs$q85^t9!`V9Se>YhnC)-zFi268SW=5xZWOnmpduGP!ROWg4gcP@C
zVcv#?O1XBDwQG@7zH+qGYf8%T@t?Ytwq9*mykzV9ZiI2)7RSr0+nG^M!kT|`dg{5;
zr=p^wCcz8TBFLgC20+o1Kh#*Jc+5=7H!@E+?(@uys%08aWVT=Wkg#T4)kBn2rFCXb
zRb{#!O&Moz1KuU5^K*zXNrr;3k6{1Dsf<ypurO;ImR0a@6YzrV-QD*Bl@Hi~DM^k$
zRn>sqvgV5g7hz)-aVF;ftWAsFn9mJZ8wjqo9UVkn6<^S2>^lZ97h*}5s%2Bsw1{b1
z*-oQ>`%Q}=Z7()(gKb?pm6e&u7r{01y%ZG{^J$geJfGg5RAW}<04sBS?ZuxO+$sLH
z_+^sy8l7FEr`wI>;NbW;99}RMTuU?>k7<WIF(R&@&=(P*3SumiJbjG0X-z6)#M|P!
z#<ncK^SXi0Tei5>Qv*(EM(54*-=to{<E0XgZ^BDDIy!#s7M=|A6{9A53uOxg2U5g<
zB@)gs@QKr?Tp6(FJ#-hyA`|~|enu(iChs@@Np2*QpH`MvoP>hHj6bG+2A8W6pDLE|
zLU(FHogNRAb=Z<jh}hq{x#_Grl~rkwMK3HEKvYm3XU=EO%OHJHjGOGsmauQSxjb4e
zY>(vnPSP(Zw%u(~q*v|*DlO_gCeAE1MTL@C#nWSBv*c6T7VBg>Nx0Ecb`%V#4+U1@
z#zxL*a#?-~HQhI?{nNI+6-I6XS;XC|U_o>$lNno0lADTX@`8hc<cE0bUlB<%!Wk4t
zM+-ru*ELatQVV%FE`0MmJ!m!KWMW~tat&o6j%69J@I2LEB(S!&&g-GdU+rOOzekJ^
zo_xa=t<UxsL0tAYL!Y;`Rm{Pw$9iw*ZV-N}-;wlvYOba_hHkqik8i&Kpc5G{tJ9uo
z%bVZ7?keD!A9r+<upNAhQA(iAT5sGn0m|i_sMHlJuu*GiwSm%3aapX5nHg*G%YR+i
zpdm8wlvwNjA8H28r}-fPiXGFQu-}Pt%96o>o73(Kq^tfsNCNmlS4Tb6JrbI$HLlyE
z#&5*Lw8Ogxv<2DBPS4KLu5Y$87HJ_xMZeN5{e%D1m@cGom19#UyI&1nWn0nyaHL3+
z_Y(J531_&w8WIehu*O2=;l{yrs0+`^O6vl^SEIv_&2ydC;L5|p@n3-N&4O-6b{JWn
za-YFDOVFweL1}&vC~Sm%?duya>9he+@7w9OlQlnWbID*%1S)fa?1Gh<HZM`+72-~+
zEql)1Yb!T-Gy5c(dGM@;<YeDLH^*;=`H+(z`dP0rDom@*KptCB)mzB*Vt&IAA$KPO
z5SxB{$Ws@6%6Ug*;HFP^O_7n&qeBwqGH5}SEX0@<ZdUV$e{;YxbrYLHE1}$k$VWu>
zfD%fAf#I_CC-;O9J)N)m1)lPekQ2r9{x58iFsb|^vv^zHWMj1ieQb&$DoX<>yMD`Q
zO%E05ejyQj4DacJA7xXSo>M8nJB>aOf6S#EwYIkP4CJ325uca{9P>OzTx6WsmZbl?
zdXf|Y3RB9uy0e~K7MWyMl5nih%yzj}eXoo80psGa(vz#T_bgWxMnv=W-rUTZ1J`Ep
ztjXW}buAe{P>lPrjI-$N@G}y?r;AE0@&067T+-;YSKMlWVmg(29N_Fd_x9St&-rXq
z#c_MtlYbG(MV~M}H()!bb#{`2A16YvVr>&1HnO{}KraCTx|5o|{)-6bYBxi}#Y{72
zEhra;@CgQP+@J-nqylV!n@?3?aCGQx@z^^kNl0LGS(yl)r^CIUR4Hw+1c#3@ap$IK
zl++bSnzyWMz<23_?jk*y2&1x!+wt`j|C`yxM*0LbW%1qux7yu+u4!an;P}7)T=hlo
zUMex3h|!7~?ax(x-|#Aqr19I-P}b>>=WyE7(|_;Z-=%S(8v5}s(y55#6indZ4xeZm
z`ZoO77va&22uo%w^uM!&tl05NPCm&NqxsH-;Y5L3W8ng83wg!`D|tivIN$>fnv^&R
zOKQHdr$t>$Q3z>+m>jT;^?%E`0j5%>^o47fylWBD`)=%CBnv<MZ!Un4^G9BLTF6FO
zd6^WZ0v~;XS)H=JzPZp__~a?s67adW;Yu!C>KYny@j{&6IywwlWD{Lli8CrSDtWtC
z&6@gWb3v>WqcI|o<jm+b;Rs&|cVEE83>l$LdK@4Rts_&&t5mSIEtsy;HLb~|w{Jz{
zJ$)`9D2N&UWZKq@dxyS(3YMw<5CXo_;{^%m8(msGla1d9m8iF#_~X*HP^7ZI&~`IU
zJ~LZ5Wyk>8*;VD^dG;m+UAy%~^rz!1cx7@J21=Lk#oTLO-!5j0U=yo@PrU`h5$Fa4
zOl?9$hN=m24h$91Qc1a=zTKrH86Ts#Sd8<<l@&?x`y~97q0O7H-j3$u?97xc&5cM~
zHL1}m$bSu<#E1qWsSxco{x<TLT7)fcS!JbMAzt`Ly0}kojEAfW98<&6yNq?de2}LP
zqV3fm?@C6Hby~RmyT!;?#@a~MlE<>(8TrGG5fpIdMVIjIRJu^?16Rg+Pj6q}&T7w9
z8kcN72SY+>pByBwPmYY_a-H43xuPZxR36BmAsveYxQT=x8(KLjqp`_`>)EzY*t=G0
zL~m_n<*^ii$|-qrQ{CZrTkTH=37)OY;d#?!pVinVyh!)cNQn&xWT|~gEYc`0A=4F#
z(%nl-MlKenaEQ8oRw5OVy$pS`75CoBm5)dr?K-AK#{RgTarwljh8bR5-+!H`C!52?
z?d`cU!Yrv14@d0h$&nFsWd7B}IEHUg-Vg3u+Hm*}Ry5~sy*J}iQ<iV&*F_bKIeRnY
z>*0`kqer+*9)66eH`fGLLCx{z8TC5+spV}3{HdOPCB?BhT$_;mI9b#$sXTX)&w@ES
zStrLUWN5ihHgMzqj^^d?M27vgFAjJV+ErsQL{U5a+idh9YyWRGQsn6=W8f`RNnkOw
zm!Yo=GWqjHciF+lKV%BWW8>dHT(8MOMo}KOkaF!Lv#lSq7rnYfygn;`E4>V*6!j~M
z6UyjKe6|5vZTkEUR6Kmx^JecG*xHU$IFcYCxN`@co#L;$J_&icb7#CdajMmc?kewk
z=-<!H==8akp9p1O@i#EWYp|3}$aaWMPZj^xK{JO#K}<u&CB-mn(4pP_llvMA(rmJ!
zK+j7?C|LuEBiW%>bT}Z`JJ6WfzdD#^mOW!8RM&`~C{v^bP!ZwhPoMhkU@p>CV94YJ
zB-wmboATZkR`JQbn`VWKY=~iWFh>!dM69RO<in2}H~;n+!T{2m#7b#s*EUV+Be@R>
zv?c8ZBqAA%+^~|Ruf``qhs~Sh!onSIhp&u%Ma*d>@~jkSQ+FO-;>rbjEMd5D1n3;j
z(2PNDn9Tk@w}U~<McN>Waw2i(UBBU7ZBdbh6%i+{x`xhZu9bWtyj9<^HI#1Big&g&
zR=4uka@zIX%^~|6`Z`T|V{P6z85~@8*#adQGKC+9#Hc80X-Q(XjU%0z&5sIIJV9hZ
zSEg;v3L~J$$B#yN{HWZA0iY**3KANKKI2ad_ZsYuP3uDEBG9TS__KS{(i#+il#w<L
zwHz8Xi*K4<Babo1EVp<WCl4*x(J2cjxpMDaPf4H{NJ^r`R=Du$wWiBgW{>Dy_Q(B>
z_K>|U#mkz%iA-!TqQ#{XxrJKQVR|oyV166%(7v|IT)fYweWtH}OrRTL9JA}1FD=qv
zpcIzWLzgCBZ>Q__{0d5+kY)^=C}TuV45bS^zc_xvJ|&1hO+Ni;06@NH;Jhqqb^5GE
zyRIj_j<w^hW3=e`3pyKr{?5KB*UY(gU;63@%Y0X!!cF#q#H`Ig-$Hpe0zHz3wsynk
z25~naBxX=4a@&b2mrR$<04ePL(QP7<GYpYEK99*8Bsx5xhwO&En4Y%f6&&W<b#;ca
z=<57DI7prKE$i?Z$|UVHngBG8pN@L0Hr|XPWa|CCz3>)(7YSCDdz#O&zP4M`b4E1H
zv3fXRo9p%tw$P`huOEC!iagoI;A%)UHQw$nzH}_~dU}|*fOuo=b*_DAzW0q=vT>Mk
z7^9GB$ZnFRvq9jqP=5Z@L5rFC@Tv}xi%Re<q+{_Ov|lT=Y9tI2Y_{lH^|S2>(4N?0
zbPZ&dhIjh}1<jFk3Rz;Aw}eJZ;VYjX(~MXp-Y*61HF|mG|I%WmKOBLRMdM&t=POGV
zweX}#WzsI4IBl2Cs;DRJ0{Q}1bCLyDj?ATx2u~LVkI>(J35RMLv_>^%2Wa^OL1bJ%
z9V*w@UoG5l+UuJ_*Ug$a82bfBablmx<RGI7hx&C>qSJzq;l|79Cjp$VccZ0(XpMuI
zNXP?8KNG-5CTGgX+67GWIz)RuGdHAr7*y31n5i^ajfAHdZj33L0cOQg$zY0^JZzws
z!+~=I6sIi|4g@*UcA|f=`(O2E@Y=N;_p$VD3nf*!wPqR5*}K!=e38K$%zP(P2th`k
zeqG)3qc*~on7ubLou4969?Pa&lMxc|z0_#ZTD9}DZ1%C>sU<Y+kzQ@s7*m162OC>?
zN|Awri!8HKFLmam`N_el@h}7efPaI+KJtOuPVsothV}*A*yr|q8SmPbVCnn`^7Ifn
zrle;%hg&20u|mgaxJ;`@^4%4H?lW7@)RQ(<U2IcL*=Lch$%(;i@!yj_p_S#FoGJ&q
z4*5DS-_pgIQKE9?<sHtLBsWp1&+_d^iaAR@I>m5kO`9Vl9PaV3<vJbN$FGo!m0%T<
zhkh5Wc6N5Vla()(X?YP1O3KETsWCBF9g7ZlPtrMF=g9xMxprPKd+Vc7)Y#CVucfbW
zthHWWAcenwNUbxO5jL`2;OJqM?o8I>G~I9B<L%vjI!t^PMfxfzNJ4;2wpcaN#Lcud
zuPEewD=651`(t116VzcAPs=Bv^!hcj@|W_+euKYe2YURePm`FpFQ|pRT!*uT;RUjV
zf8}4IcKx|N;~%>9zr#lKcl@br>KoQdmh8s(&)&i5%`cvaFklz8PGW&XkVbc6izbTl
z?Dnycx3u7G&k0y=kIFz(-!aX9b6U-{n_cVC<1YJzytfj$-G1|{Jtv7p6R@RJ39(eY
znlT`dLX~PYDiI$T4jQ9&GBXQisaoIqiu3%#lXruoOkJVE7?M2l7T>kyV?U^hXlJo4
zpJ;R1<ly4smaARYq2w^!TyxggrSdtDj+Clsm8(AwTwGlqm257miF0ZO%e?riw3M{m
zM1z@$>%+I2nmBV*#Y`oOj{A(o#L%H$!8hmU=h;G_IF%TmXv!5kLd0Gqc<_nZnIbfT
z^jC-x3gaW$_(VB6u3MVb6DDW%_p;?Y&MQyo@MJ?ub-w<25uqZ$<tm_$J*+AndRy-_
zRrUS;#{bxqjYzexQ^-c+;OKAu{$Fuoy-&Vk<r96K15Om$mGt5=rx%;wdguHtCQ(#D
ziwx7=J;unb)in3h&lC91owK`k5#fH7Nhz(sUV>ho+c&Pwe_g`|czCN;U?N=iau1vP
zt%+|;z1qf}O2~N)5TjT(-C_v`G<HkRuR=n0%s5BK^85VmK`-=Gxb=Hx92sCj7oDi9
zx6&k_zX}T2q@SOxMQE>G$!NTMMT}PBT=@v1rbgS+`pyyh`vg8-Jm#c*gWhUmE#!aS
zdiz%dRswSr^Q#E!QGfZ%BWDuuFJ>C1QMm=?mxEA4z3g5Qv2?++hBEom63l9Tj=D5>
zdwH?W&(~XxeBmR(V5;U5fiae-!GDrs7^m3$Y@`V6=Cu9M)YE0@M>;;q`#g}D>!WVI
z@-vCr4pCK=I;@`CzP?%e*63Tmz6RGRoQ!?7*G(GbmfHG|xRc?;p6C<l7}iMm&t*Y{
zX#9M&kY95|7)TE_vUtFPiul?IO`Ui>fEtuu@pI6qtn5i~f>8DcP()fTV^+6V;*N8k
z=DaAc#>2hxk=E9jn&5F2N>hue<I*ap^Gx{Dv_Pgja_rW+cJ#5$K&p~HvYS;lu)na6
z?}f){mgWBKmRoaTJh!tsUeJs6GkRYU@k)Bgf>D^4(90rI4mWRRn;(3Fu#y1`MHiR~
zYk3yOO9WXz-ZNxmJ$m!~bCzT_${ijfsd^=|n(5lzcS#b;g=(Dq{*zYR63`NuXKj)>
zs?>fK57kaz-%E!!o^b3R)5ze#IGO6E{r9vsN+A@J8+}6UjT$?LidYzMHD-u)+x=a$
zwo49k_18e(-B%Fwy}9kuN=ybk=e{!4(m8M5C=1TGVLonF&!1`~>FGMM=<nalTGV}m
zg$NLt{`%W#@-u68UqYa*41(KtE&RfCz+z<c&81H~#eh^k4E7rn+hNF_xQ^cEB;bPp
z{s2bT#KgPM91X`-LhW_-vkO&Pi7xkGvR;e+gNY4h7E4Ep^~{@siHO`!ox2nNzH#mP
zw;plv@lwsSy5muQtV0E6XGU;#hJ5%f!Luwz>MSj|sIFvZm%mAHZvGWXG`s*%J*N3x
zIIG#Tn%K78RD}*e@BQaob7+H(fa8ZS?mXCnksHkF{%(YF01iLdtRCGKSn$gzE%{{0
zrha^@rTZrO61lvLHa0X2uAGUF70cKY9@e1s`;a(LTsi**B)E4?hW6+3rsm{0lxn!N
zxO1s=T{`UV_4OXlckN_C6o9VQ=<`si3oywUrxuio>l;V;H#1xINx3$jOAHxVxC(F(
zIK%UWD=XV61>^A-kM*>bUtA$Z|B4bU?LGL47C5B47Ov*L8hr=&KrUKL5w??`i!l^b
z6Pz8PJFXGUd3m+9$9B;nZu@d=2;Nq!5#t2exmHaP@<N8o>|gHfuSF3|h~t!c`}yA~
zL;RL6lrLHjos*K_Kb_`225PJ7_7}M8>(NH!2ThB(6kYrbSEHf9u2e~v$AqWV=yR&u
z*`3R=wrI(x&EMSg8>&p+@W%k_Hx_<8r7xXxfBP<^L*I>5|NTViY__nsRJL%jcAEiu
zXhR^5;AZA{ws80nMlV&TRi?iBds~$W-b^}>GD;Fh(vgIOSv!2IDXKfEHw}y8vM6~R
z05>!|Fi^}3!o(aQHe8FPZp!pNSgO#jp%kQd6D4|Xw;(UIy0ujB^f_6Oc<-w6?;B=k
zy{A3VqN(rC&MH7*81P74R*v?{)HK`&c_!xP+q0?oQwix+h2u;-yxXg4l+zmU`u}9d
zl4|dU;o|n!rduVFQ|-I<zd9Yui8;v*4|`|3<{@I~ja)v}^UWZ8b0U&>cV<>~^>#a9
z&`f7;yq>sn`V%!UxGj^Kk8@ATCde>D+!Ze<?Mf>W<9o-yi?tg~>iIa`CTZf0Sgl5p
z76m^~NGSQm%xrbC5l1<|UH`ElT88Rhw-vXWIEg=$3vl$3<meMXDGEiFj8|4iLzS!F
zdcNWPu2Q!x?-V{$9eG|j_u1J@MR*6gY@QD}46la5QjY%J+?&s9lYc%PI+$Ui6qU0W
zbEgjrH&r$9B{`&2a<Ce<a}&LWc9A@+b4pcec$jY824eBfZLi6uK}Q@1-lQ96Nq>+>
z$TAl{-dhXE1AxeqH8{8|c(}%}fJjRyE3>YW$gQfRgY?=~$<Q7jT*1-@HHs&w_?>@x
zUqYixOt>7HmqX}$eJkcrD@N3C-i5l?b0sDHs!(A4MbDVjfbkkXi-A|-&S)cA%Er)e
zeEAEzj#)(aM?HP)Vy6Yq^Ow>yGc)KduP<c))FseoO_pKz*1qpmjF@f0SN^dZy}n2x
zVWd5@Kg1W{0XW%$^)s)n8#K!8?Clr6nf2--5HXoS5ezZf^;-f7vTT#2846svy{&|V
z4MK~Plk{yTG2CU8x<!C6`Lh4mnWJ(7FHWG&C``V%UynOYp3`)|3xcOs)^a+jlAx(B
z`6hYICzy}S2H^*~;+t3a=)tg|<@eJB=tb@AY)O3!?$2oeC^hQao33L>(w=t0NGr01
zOmq{oqdobt>{!w&nB8$prpAczw%84BQaCX&A)KosY^W&>2dm2PHuG|5>9bcE*&AZk
zR7$kp%A2Wzntb1~uDGM55>z}-_nY2J$6D9Ac~e+iIal_MGankT-alWfc1Iv1kN#Vi
zmcrp7OJIx{nG|MYebaI8JETb<W!gaVkIND-Zp<oE9*RH0Go=?N-xA_*{p?eK5lSjq
z9;4qy2zd;ph|hJC1&?`xfrC=1KrB@>EUWi+@i<7Xuyd1Z&G5Nu`IMlwby!JbqZ^<}
z@bK_lKhALBB4R6e=#5WWte(`%Z03L4rte~@Rujl7dd@z8z)CJGuQFL9<lrGQA?a%%
zYPyZwbifvNwdRl0gEyp8))laQ`e9}gR$o~O=iYM6v>?(Dp}zSGDOSb#$kfh%S@OH1
zni)y{CrkCWV{EG7@8k{xS)%vc4ufT>;f#8Q)H8L`r7DHT{{5k6*CkUnLyH1!+`^yJ
zS7FKK4$bSy;qwTRje!>pUL{U{O-S#5v!|Wc<>+6H<?K8Spnj>;anY9RWX$@8d1A|G
zQnyl9<o+sBvpO5GSNF>F{&Zy9Bw_1<lCN-D9=_2X8NL-XQEnOSf_sc>%WFW;tz>0=
z2)K<(9LVy~3K_hwN=nea-QT@iWRlF6Js-b6``PSbdUMfpb6s%ToKdfZ68;NqgQe>0
zO5o`+jXU7g5^Cyk#CcQW507n%igFvxT4z~7L9H;}9Dgs>kf))cSWXd$8R%^KPllgM
z$BNu!9e%vOl(ye{#a%yhxt5;NBW+&uHH_lbE4W!Ue?o?B9bByVWs|p@1Ez?;!=wTL
zmkD%Hbva4<>ja$})fj2b-Y1r&yjsonq<;sEit{uvUl(I;VDyJp56<|kw2#>x!;J5b
zs9$5#8*tz&_tct$+EvfZfnG3Aoz)0ttGmN=?RwxCV#(<AdVEV(TUFYex5;IWIXWb)
zs^iXUf|iKn1q2dqUU-Bd{h>~!hGoMD8rHD2l)rhknREYBfsZLrT<-7%M+k1Gt?<2%
zuVwMp(HGMwIyabJ;ODfT71N~+CQs*GhT)<FZQDij#D+o(?j5(zLtU)>24KC45w5B>
zHWTk!@z6g{S|7^YyQT_zyP2A<(klLRf&`KtGfF|FK@akF2^DZ8l|gGPU%6CmC|md@
zH<U^)%G(|ymbSaSou;YXJCIf0t24n*{ept1E(B9>h+mo<L(brkWVNhdV?%#z`n`aG
z&MQD&9>Lc4T2GqtUw*#o>u!u(roh8fw6P&E1mz8usJWZeeJ&dNv(|)D`_gmiWhqIg
z51>*W&Ndx__QT=+z5;y$GBUEF$d#G5_l>^$y!yv-WCC%}Qk2ZE_wiic>Miv?SCW=K
zfAoT4$e=sjeEJD)N*lu{WBSYtvCBT1rxeKf3~Edy<JgME2wPfyZ$B{9nQm#V`a(De
z@?$-L`~(2@G!0l}cdggi4oGGi>+1ob9!U~tI53x?JUpdzOn@1->*cdecYNdE-1<<j
zCc{91;Jw6k^3dNJ-%W9Ir9G*jmVj%NPr$PDi>WE6^j=%S>H3^XQeWhls11Nt(`=(1
zxsxNPk`OjqusqHT|FQgqjUaEw8f2IA^TK;Pu?+v_Tm&2KLA&+%5eHse2sYXA#H0o$
zhShQPym5`zFOqo7;w{(a3uQJwF!kFAWe(Bnt^5aN4z<s}&dEuQpiUh0U@S_3HMds4
zgX@OqQGP~~$o(|AnLl0EYqITmk4WRPv@nRs61wuvKU$uyd%f*hZUSS)Tufr*@oogN
zmg`%>I-pY^`Z_?Hh#ziF#|nMy2?Q*fl{)WNeunSQHo3dG0akZpxAV<@Q?-6;az>uy
z`6D?t3M>|15ocJzwbvowyM$n-rCeiCTHkFeZUXkTuwRD{JWA@FM|Xu@?azzTF{uQm
z#VcJMo%ZXA>bGNqub(+)ei*6#BptWG;X>gK7&jKwM^`>TYLV4vj)Nq}0O%|Be4qtt
zIS@u6kcd!$5ZM@fg=#6s?d%os*o4VMNTn^~=P(nq{(bFgerEt`ifsamI%AIXD=>_^
zKp(lr?=rx0LNLjJI-s4;cH!HVD3Ae3Q-C~6#_i<`G1Bebm!VxSf4`mW;yP@Gm7}8c
z^sb~N39pDcg6PqID_?7BoWeE8hdNpbe8xXBksq^66L+Tb*!+?)hs*%c&aY^4X^L-$
z?Khw}S$xutM9-203I5Sy6?beYD)?S0UL~(&YB#}6@az<WGOF9kgW3h;>%ZKx^p!YX
z`)RR|8!^`?C=gC}z>ou=J-Bgd`h#a@nBdC4%jLm<Tn?J@1s?9t=t8$u9^j7=IwRc4
zN=OqlLu`~3%Q{3$)bMasHH&}mbqF%2hHsS?XEP5~N@$C9V_XsDGtF+hf*x}w`(PSZ
zF&ZBdbiSgOwzr?&g@Qpgkmh#+mXLG|ngti`QFOGASHoL+D$5W)^Db>bP`Dy|nR<U(
zEQ(kJ>91+c-z`GPKgJl;k4CKO)5AvlgZ>Foqdj3IdX9S)Luty)5-u|_ULdcSVzT07
zFVIB)kqfEc1poQ(Vv^3=iE6)whK95Dn}Y|I;E#2r2v!QLsV}Vj6t9N~BR<T&LUs9)
z|5{mH5-yOyRA&=O8xhfKPmD&5OCe2?gy~688tXaV^8QUSE7MQ|crO7uKW+Hvt{dIy
zwUL*F+TNP1ov$#cC8Ka4Bw1zSYrhC5F3I=&6GHJ97x|A`%hC8$qzoK41<=~yOElt^
z8so~3M<7CeNkKSCFdl{EihTgd1ss~Q|ECAz%2<t8EEgOb{-nH99Q01x82+r+)y?zC
z({<r8l^WRpg!r>QMv$U)Vwi#gI{`BVRz`d;XxQ-T)^-^B4P5y&PS>sX*eA&um_8xo
zf^M0H+yW>Vj2<iz;sLs~S4_IfFA9oTcTG6#>?&{n7OM4if=Nr{GWJ$Vq#1coy>XTh
z0cBYU(1J{&RrWGO5AJ$VSj_4Y58qyasdD5qk^f1=G!=Uo8W4jIjmyfaEPO;Da<q7?
z-aGduX%VCf)8GFI0XGh&CokvKfCE7f?V5g6QChi<{0QO<cz}=grnY}3FfdUma$*Et
z*@lW({KoFYfHI{R$Zbb3Y+P8f*OZo5Yd#5rkGrot$!PdNY}yckjJ)Q?pheW_lWgii
z1*`)U(&@Voi_gz<d#GZ~8Y~PrczH@+;zG!T+*ohEb{d7T8pylPIgQ)*ELGQ05Rn9M
zJiLQ0DX(cEo|@C1FTp_nV;BKSkOWTHZ|^m<90RPVuv0IfcW@sA3bl>F*WF+w&un<j
zxceh>bTB>AJlc_V-(%CEP18}A!rEX#!HMl=6}TBU*}C0id@pU_6)V_u<A20!4@fNP
zLWoMhL&>k=-xa}jWnt|R!`m%D_7s}fJ#&UK;5~bCqK$QuYK#Gvx;Ly?#Yz0LM`l=J
z=VxaRXe68#t*BjOwJ#|Rkk?-jpyLLThJ~$?aGSrT0+-9~xWl&EF2ZE+u~^E>>kK1G
z)N0z>uVp@FaWS|6Et%PuRA5Cfz{9?dF9`B#n;W#1p-t`U+tXYX$h!aeESfRiAcx-y
z3Ni$mE>|WJWN@V8&uJ5!q2vj&WFnU==1|zhe!^&^Yai(Jph=v|89Nf05TZZfTI))=
zMnM{qkt=kL?U$?}!A+}cOpbp>uX;M6S-8AEn<ip`gfdo0BdRull+amg%T1L3&?Hlm
z!!xhEm4ur$?99%na9Nm90~Hq1L-c#t5kOd!OnH?*Wk`*{(^Vc>a&<o0w4@%uxO*(7
z${Y0vEWGT=#-N~v{;Pw(oty(<DITr!nesa+?872q5u}`2**&gIzk$1e$^s#rFpccW
z!F;Lmb3>N$vOu`{8{o>})LdqtV@ZE~0J~BElz<J?{PCmK`8xYyDdRjm(xkr*95Dn-
zG<r-pAn~-Nz|on9FMFaWOn(Bk1*3Q!U0tBGeSgot`Lm9mrN92wt5{7nGlx;ugqLZg
z6Q`J#<fk0}C0qCmvUqX5X4gPY_iul`^`_3P>=P*q^Fo@i_vz*naj|9$T2!v|+85XZ
zrYNZ}aU9Z+F#Fbhphj)%vN#Ew+IIsHZBgXf1qe9+-OZ2V0H==}6owymX9cb-$0vP%
zyD6#|t#hhXsNxH-FD0TGU{Dhf)yMq2mCnCVb45K;m`1{9$YgdS2~mzBOUfE9Ofk4_
z>Of%bIz28|GHM}M3j%8ZWlF+h;Q7#R#Z8~kdxXmGqxRx|+2J$%cv&)@6|YRTmMw_w
ztEXuHYxEyK&77SHxp4IXKMT!bDNzm>!hiSSNQ$Q?A9$0(2Dp5o#pixYQC&u|Z~r^W
z#wMIh3XMz|#;G&PC)vxkUB*;gYoTE9FA~?OkI;Mvp!7b?Nb;$PatbXbC^Qg_T%#Hk
zinRcC;tyaJy|Nb^$T%qeovFZc;fF+!qJJ#1B|&c2L!!s<9Z;fC8-Subyq$#Ef)S@!
z>A}Yd{ab$wH3mXGF^pkTa0G)M?!;n7#f5~6+w%foAUvODkG2IznBMK~V|hYZ91!}%
zNicRpzeOCaI|CWiFR=aB57d+@y?1rf^7Las!#c6JXb7;LJU=j3s4uv%_WiDg=grLj
zy>#{c2PTFhm6Fs)M(O{$zx};K+}s28Sjwg+<Qh;zwNzDA2f)1uqxw)jY^xTq6E{z^
z6c!eim%kBlKHXAI=vB)Ee}2Vnj{N<U=~qfW;B|j>+t&%|biBPhdeAY#>w$qc*i7}s
zJ{p3>b3C27_}^Rr8YqS4PAk%f*dh~d^N#Zw8#qB^w}5~EkS*Z?wI<LX*!}&Ii>8U1
z>TLDj=-)&v8gzu+5A?2%fWmtB<f_qb)~%aRlVBeOsR$j#|Izo;Uh;uXBPap7x~8V$
zSTGQf@Y(*wiIzHPTYeCJCMb&VuPgzz=GfoAFomWk(6@Mjq)0^yF-GTJZ8<k{n|Hui
zn;IKMfZ_pL=-+oBTPYI4nyS#|l48amktEjV1`PyU)cbYAyQh%}V!h#|1i7)`2GTKx
zhzH7hCcjHdYiqa_kS^IwRx|@Sb@7f@r;!rbqJ*nXWurMkLhr|?E}z#xb_AxX%yIa)
zCcn5ibQK0!@w@|?X}18(86a>3%E)do)#>SJ(%2r)YzKS$J3wSU8+7nfEmb3Ow)hbL
z=?N%aBgrRFbX)^QCa7R>Dgs@wp%E0QzUKkq-uw0lG(x-+de;EG`{7m#3w~4j?u5=d
zA5_G<F_6X>q`Pl_zl_y$8RRL~sNC7zy#ZQ5ph#i|1mX^+H8R~s+wGm5Q7i6a)&C2;
z|L+FWVCWn$G?0?I@BOU-I{d3_?*r`?*KMF|tZLp!m#9)UH0%JaZ(LMxfiigge)mWY
z<#`m+_xfI8{j$8gmBv~7j4~Uc|3@TV+VVt<BV5bq$$wk)AmNgt_5l(k2H$A)a*ZsQ
zlL>fKpdJD8IFp(=pry8%s#;fa`A&-dus9hMowuMLAn!*b3uF?ZKzDEi)OG|Oq%o^(
zK-m0P@v#y;04@(}0j<_)U_QA75^ls#29ae)*xjn=U4EGRQ>BH6t*Ynu6;113=Ol}k
z{7U$N>*>bB_VFuF*D*$*Vp0630QY!<wEk8?mG^%Qn@^2kyeS>A();RYTyucBgh5lN
z0s_Ym+4zq<A;$F-oj$-=2=7IZhG-ss&4KYU0zzN>-Ks0l6O@jw7TlM<2W)qce&G5@
z^95b9)Zy`vEnXKIh}EQ0<U|9CLPzj$d*mf~$g{_#6NT;1VRf>sEVOu+*v}sR{2<P_
zDgw%n7mII@EMLl}1O|p<yrjhg7cxroU#DVIi`+g)O>oeDE8>U$1d0wIkh)%3AQ8Kg
zpyncYYog7R#C)bN#^1*gd&QM=h0)_gNJt1IN1C8q3e2hFZnuNzeh8^Ah31RT!TXIz
zzuuKFksBpa7_tG@l%U%l_(YF~O92Glwt3L-gD*f_9qI3awEm0`3_x|OzSH`_IWgY^
zNa#RfS2t}72vDm>TdplYyZPU(iC#tk@Aw!z79{mW3I*W_;$pD_$R&Z!?7`ZAW|c2v
z5Euqc1}z#!dTlNgETd0|Z}S|`fdGxyiDvt868MycjKK|R#Fq51@I_|N>J<Rx`eirB
zj1TDeY1b0_Z%Zv`s6;Nf&Zc1|!@gy^D8pW-#-#gI)0|3~ATpya9)QA~z~hQ^U8g<>
z`T&0qNa9p`&5nPOJJ`N^cLnsC!8{#EQbusxPK@9c*eS>Bw=YrsohX{z_Gf_T{%c*`
z3Gh^_u!EM}stiyqkhRD9ECi4;SY;Ai5UbxTAX4?&rgR<J8i2~PdMdKh5UK04Nk|~L
zW{vB(zkBzr{ojx0K#4s7RQ{7ddz1zQrNGAmZ3100{|C-s<!L3@1H$TR*Q;vTDbqCY
zGzY@JrUkEE&{!30oi`|(eavVX#Rvg0<{_yzHe>^*#P@VWnw*^c&3is1OB9f;2EXpc
z@Wgn&;o!4MUf6>Df$Q11X~+*JiVg5Uo~Zth#0)@;G6L)NAAXI(l~fy0VpJRW4{QAR
za!f4$2t4`&%6ryTVw?IH&Ehxc)<B)W4xxuAu+mUJ;rjj&cuMphfM|{Z2$!b-F_5C?
z;VC~p#C8Bi2teZH`GBn667Yiu3c&fFyDL5X3HrZoZc6)LKxjc7|8qx^5T@@=@id9o
z^q(mnkRuidv(S)DWiac9xtP*DT#o)fnCJ(I@>*iw!-r@r|IfWwzI;a`h+6gpX1}Jg
z_aM5ArzO0~LsE@QqGj!U_Vh=I?L#6)5iRBaFNhi_W~3`M$fKnSRUSU9Ae73BF;3>8
z-l2*OM#0LjeGzN&<OF=4NSniAdk?k$x27iWQ5RSk7(!QT3E*Ua5|Sov6&7?|Yrr%l
z@c$_8tD>rGyLKNWq(MNsOOS4mMnEJ4K}A49x<NW5L_)e7q+3M+=@bMBL0Uqj8$?P#
zVo%=p`^Vk~d;H@+*kkN<wDMW^bH~hUUNZ;$VfN1Qb1uDVC@QrQq&zP9w|u`R5wIA#
zVUCx2`7TlKvwzO_Q!s+GBEssLn|;A@0`?A29J#r<m%0(3I)DQEWf;XSI}i%2=0*EL
zL9ibntMv5r@Gx$(k=$}wjREq8-=E3C|LdB@il`*uWWg1U3_e<qLZMc*!_S$CiM8Jz
zoTR;A&ozSvAE9;(j$_8UIFNfm9tGP&?7EdHd)G6la<@9XmNii0{%Zydxm;23jX#nH
zvmbD^rbf<r5`BgVPRNT>JGOLi`1$_nZ9&Sfe}XToX)h5DuU-PdMAd}t@N*jk8HUx0
zQU6bkem?)TuUXQD<>S$!-$zHUM|WKY1dy;757|IOi(MB_0lc?CIi0$Ik%GeK)u&VV
zA$c`5?q2qINVdo234MHwgJh$TzqWv{*BLGGApS%~o|jJHy?;Le=kNGGZ(qlT?e(9x
z|MyQXpRwq{Rc39$a@%c_1NtJCR(6udal}qgj)5c~Wg2iWGr+V%Sp3lyFLxj!gJpDS
zv>9O_Y$*Slm+%Bm0(heT4B&Na{^fKq{qxn!2nhI=aHXOLAk}XC`=)X5^Gj=yImd*s
z5V>jok4qq%%f_dADfK<z#9p2qG^~JEM*j+hOB0s<ni;+-3f#q|$BQNR8CMJZjekz2
zqSEQb;kI~o=$;R96yJ@B@+rZN92*lG^A%<;W3GSL!;RKb*XM7I%k1n%vy}BXXnbwT
zKI%G#doyz9-+MqpsBk})qEIyzD=_9+i?rr$fqw+4k}VJ{B{zt;s~bdlZ~Oe3X)ssq
zTg_q`wlv-BQKV_>h|m~Nl4qn}kj6hL+MyKiqLOH)9}ytRA=K-`%xQiZN|E!rjZ{Fc
zSbgp{`ae(C{jjjG(9zL%qplnftM*|Y;8v&H=b3k^0}RMU#dokFF3D*Ke$y-Xcaa5p
zDNawT8W}4#F>`ToadW@xHSX^2K0ZG7p~Kh2^mr~?Y{ATUO)8G~AgQXViu-RdRCp>e
z_w@E|WW~fxKQ&f_&*d244;=2Qs0<Gd&N<a(CV1{H-gR_zeERfhi$c&MPBagUfq`^E
z$b3YPUqHae$46Yf9{vSNSQ%rBf9A^V2K_S-P7-4nM-V-xIB|SG@g>o~^z?M`gHjE)
z&6?I2>VNK!L7ow&rAdySFv5&K*<q^F=jeUBF$)VzXJ;oAV}DP7%e-?GpfND($UdTn
z8Q{@Ul|W1?yxWR0>v)S35D;&RimoEI!jNWoMD)+y?JV<*=v85U@>d<pko@a@;|AEo
z?ffO|?A!SGNUM3}?@n75*+h2)v&n?nBa-5PNGLS$u=~cOqGGq28a<(mtI+D|X@R`+
zUaRZIllAr1kpw#n_m5OeuLoNYT0_Eh3OUYBQi1jB8Ta#2oR?#|O=FeNoR?cei7)gH
zSFce^;|~`~^78kHdy!z{guIrH<}zn_aXA|pdVR$;5e+_i^*j*f9;~u*T*gIYd?Y!a
zU^ckl)~5kZ2@34bJu|e`wojLr`70}zsQK2({<)?1pCi07LqoeaPP~jN8F&c;0s@B%
zJ<TlIcb8_@b+xsN3JTDfiGa_AU!2NOu#%{C0Y^^qC71cg1}7&cHV)3yDdF!8WZN^6
zpO*>?3rR^xpm4du)<QtQ`>BzJ-{G4lObTZx|1K)gHIlH#JeiA|=H}+G<+AfoJ3A4_
zo!K@ZjLFZ>C%Jw-H8r(6>_hvf*KhT45J-QCiHSMkkR9;-voWJg6r1e97^~pxVP|K5
z^yrbVuP;1-?~IL&RT$ImEvLpQQE8VS{{DSwlq?Z*tb03&Hb2nT77-NOzi_*@emzi0
z{u9ja!+ZDc!7qo_zI^#|o#T<HxOh=vVfEcU#OQ<9N0p~@A56M-*iLxH=t5z0!pf;3
zG3n_W+uQHT%E}4~t`ib!7#PeD=-nxHpHtz~C?6glFMsnU;a-c^K~HygDAcKVG`@Mm
zNkx@D#?tM)(wv-ZK|x7*G9M(tsBo4e9mSK(V+eIa$a~ufexR8O2nfi{WxRSd1fuqb
z?PJ&0^#dQ{euvHdwWsH%h=|CI8(l*~nzy<-J6Yk#sfAPE;?9m6&b6X2Ng3kb&-EQF
zS3max2M_$Z;s-a8=ND&xV1J+0xdwU#$+LBIM0ox963YZk2VYk9x+1A=WG<|)d#w$z
zkcN*|7uSd&-!OBtnB>n2v%D7bo5r9Hy`m=1)WX8X%IaVMMx%f>a~gEg3YQ=zCI)RP
zY2HV^My;(qUj5Z9pZa9sh~I19l8%lJA^~%bQs+k|NUh&Rb98jH3uTTwj+7DF0XlHG
zfINWv1kwfN6`1aFol3=bvJA(o(eE~ojyw=cYwMnYf%5Y5s|c{EP*6}%Q%_&cZ*u5o
z4{hOfR}$<X#k`O9jKgdD6awa7_?Ve_&fFt)9zGm}l)<g7xjCpYpduq%`St6k0}A~J
z2S_0U6Gfl4;bCXkUO%|h7_rc=q=hLPDh4jzwlDWlRab{f>E(sUpw-OFy)5$S(<inc
zGamzQ-i*9{UH9#4oZxezMYoc^;bG!RelN18;S>kgC?gfO54Ui>mX-!2<qWJE7Pjd1
zJ>k~Aefy<6qpJ=-3(FGlUTj&+H%%)w?{pg~UBet5NrQ538uWGIv)Gj5=jS|-!8+QR
z3kU$Z2AKlQ3ERU)&pn6~-YFXXd3c$?nUL|O07e;o{U1_MWI6ZlGQ&1-x?#@y{`0BB
zC|#(SpkP&D;WE_q13FBVHNJPr+|(4DK9kR3*DoE-shaP=7WLI$AfEme!0Yy(HDbDA
zbn!7M(3|rHK74p7<3rDt`jWZzZ*p=z(9(+cHU03I=}wf3ySu|rdc{>5deZn}rv@7$
z26ljWJ=E?M#~!(Hve5Cfua6V9-q$`LEm&V$JNRdldhC&DVy30dr%`%deQ_f1mvM9f
z2w?$>eq0=!csH{>kH(0I2pC02N5}r<56qxIKr!B(P-!Ce<HykYTb_3=f5p6fjY)BF
zY3WC^PeXQgc4=uC5c}xYk92hv{bf2N)TBmpGh;%RfvkL3*uOLB@;pa_ZJ{eV`CgWo
zv*O_iN#Km+k^i-PgpHHbvF`F7vBajIoB-*L-&xE3Krfu;XbSc#Y45lTa%5-jXG^`v
zlOkMa_>dBzKcO_U>gAP^a`ARCCI$TG@e3-2;&G)*$=#h9_5$VOhx?a5U<m1G^!Po0
z{*z6v37w8NfaFD56uu69j5|rO`YfnX+Bl^zqEE29sb03l0NCMj0GIMA=5Yj<IkP*E
z3xOSXIgyvDh5&XFI|32ewvJPtpi6bk;7Bc?i9EQONRLU6>3KE4i;mSi&?`8w+($m_
zg?f&Rx}qu;S^yc3fl?8VVe=DB%^{IzIeys00&<efv9qG)`IwY!B*o<ey}b)=zpfc^
zXunIq3!7;4bk^0?HB<~am%1DaAxz@uZ!!gPK0cjS=_DW^!Hx6lM?-SADUex+GBM?4
zXU`t*tt2KUo)I#iQ0;Wk<<NdgZ;3IEsZI%z^iE<VZBOFLB%6WtZ-4b_ceLzJh6K<&
z9Ro!E;e!YJ>KVmFynIX}w_&@6<-{rn1qMF9rOzQ!vlj=K1WlVz38``?`%rm7A;6}L
z{b=;8^qZHL4AP%~u~nzU8e2Yk$bwHB!N)gv4Ldz8EnncX@iQ;4dY5`YM$%&<vyP%5
zmk8+y1S~LCH$XD!P*60ry|Y!goI+X<>6*BVuC9#I(m#8`a?AeC&q%KIegFQwwN(Zb
zC<4rIufoGo5&V#adqpo_qN--pe(P8qR+knsthskF%HgRPJ%fKap|=?^Z7)yD;5U!0
zO<Q|AJZ|nFKE9#UuDo2i>FIk}QfIqMy&2wrhP1R${M+XM_04O75kV_S&(W)=5}p>5
z{ZNZ(d}mN^P6(DziL<jKf<!ha=(B>IopAbF2b;ex<J?GbSs9MUuIW1^!H>mkW1r@$
z(<bMrM2yb7@Iy>8GVx5ht|VfUVB3C24E^kOdU<xw(Th!BT8l$K3HK;s%c}D<S0#Kk
z-}w8Nii8Zcb!*4bfi=F-?_&JmfH+HXnDW*GNge`ZW^uWcoZR2#^z8cwnH(97Z^(1V
z1!)d-^sVf*{M{B`+Xrp2JdoA$v>k&s`bM(R;jf>5TazE{Nu@k@O|vkDFHEBuy|iO?
zeEO95V8|^VzdsEP5vi(it?ieG5Y)_k40m0|JvzBnU)#saOTYD<?|qIeV{FRkSX?mv
z*`AF`Qfq&vk-4x8+v47_(;2!Cx3si06VvUP`}-+ztjnjTr&>k#?iQJtm_TK^ovrQZ
zSCOClJw1OmC-!#c+U-ft52qZWsl}XtqH&w#T}g=!1aDkj52Rnc8<+4o*$4OU!jUB?
zDCotD7eiLDO3bT=?579i&FdAapc8<-X|nJ6{*;6J6Mb(IQ@x`_o_un0Tt$2PcR@ku
z$m1y5Cf^Rs`SpitHa1B19r@xXwFoIGDf3X7b~y$P+U)Al%J}|%Yai~ai|fsgWLbQg
zpzfD!*gPd>EV6vYzFz0ntzwgsYA$-psasWCTB>~O3<iBSo|Oh@{E9eY#aYz)z`miz
zzjN#BoMU5C)9u^04S@{q^muRV^Sunw!LLPQkQ|=KZiSKdq%CBsKn1J1S|pWFnCBn+
z5Oi8Psf<SV@=X(8w?mWA#r^L1OZ!7lL*rZK+f(ADf`Sq(EE;aFW|KQwBCkb8>Og`&
zc=!-2NDdKoU4I}l5P6edAjIddUGkk5-@bhVsX9MDKPFJ=_G`@gc=(U%U966-+qQ2=
zqeI41Vi}Ei$^T_ZL6ENKvdx*T)deO!3{KJ*rFBY5Z619}WyUiezMAZzwZx?*$;o-(
z3RG565q~0R(is5-L%mW&XoKIj#b&h24Suxv`H2`ZWu~V$z_wazLqP)No`%@P0aj~_
zZed{oNUTt1zov^^5=;VJjFXd7eWh4XZhokkw6q}`Nm6n$s*I11WPCT8Kcwl)4VrYT
z=@6OyV7$<kepid2BK0h&LalVEfAc1y+qBu^HzPYc)Kn=?Vn5$oj^w=EmmvluS=SKk
zEU6z!X{EWP>-Qh*x42>j<=&0Yk9YO{BTOsBsi*lm!IUF5L0<lqd@c~w(Aj@1Rx$oi
z+uYo|V0M+@)5!r1cO4m#=GpHq8t$Q5pQ-b#t>0*}PwJeOm<)e{orZkr?UV33^>9D8
z+RM#5z+x;Kwh!*;fJ%pI@WKI{(9>@uN*(2+hL<;*RolR=A23llx2&e5q9P*1vVY*+
zh!N-on!>1ZA~TsmM^9SH**seYsjjvo71~%GF*83mOOipj7IMV*yAHSh%+F5__(2RQ
zH!d!{!M%5R8U#DQ)a}B6-^xDf<xATXSF+f6C5Y;u6)fR@gm<eQc20q?P5Dk&iB^%a
zo(3Ks9yn-qb#+M0>(|>WD<U2{Mmne`G%)QtDH$2Xg`IPb%tTmqO9g+xj0Ug_1W45L
z>7eG9@R6bVmL-qvW%a>O)s0xCWirxW@mueJvEpET6zJGqSGBl9QoDlo`r+5~j7?j_
zveB=lza3TBj-MDazl0J&i>8fTnD_5OMa<oV1$fk}uNzofKQ)?-=;;Bo8x0+u<;D&7
ziyK`4^EX}Y!zU%V-ZW8v;991opSr&5^!HA5mF_)C2(6e7^pIhq3A?)9H08gr>u#pH
zMCJ#5JzpN<vYHpH_jFDV>uE}wOUW#bj`p*%JgOfR(=sqH*symDpcl5ECO;q5j(U0Z
z7jQBG15&wR3x@xfpT1O<MjGWMI%8YcGK@bQ?XL|JUPr$Z)w=xky_?-;3<I+j6*@zm
zrnYuXRn=x=L~ujH>YA48Et*N!J<P~Rbgt04!a`Q3Kik%ZhQG-ST`H<mqv@Q*Z#~)!
zjmYR-h`h-j+w|e)@uZNKlaoPNyEZ_RV#s)^&CFR@S>29@YoBbBp`+7TS%anSB$@##
z5-b{v^8-OSHj?FcPbgSO9Sj;hARhbAs;#cF*)dv}nc-0iVj@b+ar^8X?%UJXT9_Q(
zKN{-TyiQh>S8%|}kdvR^@%{VJK%JSH3L_)wbc>(Mfi+UQyEpI%bZJnK{yQ!vrgj}Q
zyO&B2G&MCJJXpL*QjwR(<?8z9^u#75^9PvcqSXDvMe3Cm6^Q@93Qu_nd!0K0!o0S&
z&drU`kBkr4-Bl_W9Hb5?Ztx^V9;BsNzO8@x>J2AANW7w=yxe~UCnioTt*E%y0+p4r
z8_`Wd%rTg7*q_3(W3MGQrk$4lHfQR~syJ6Vbw==Vz1=OnX(=WUQ&(dgWV!wzG3k)S
zVWHETtB&`e=XzYu92G}dW2HqyaVERgESJs>h}qtoH_>k0x~`se#ozvEj^XY0?&9K_
zch<e6N^0mx;=YTrW${rP>O>q>rTGs@7p24BU>?)b(k@G%A}UGWr>5@6%frTlBQZK%
z+qS!BQ(bX$bM<*yeZAMk`B{+mJDIZn`o_k_NDghHj~|Vm{sz=$Rh3|<yj)vDMeG$L
z{#1Qdb1wCP5<$wkykMcwuSttbC^A}F!*6sdC8FtC3JR0FmvI}HY#Z{6zXZ{z47~oB
z`t$quFD>5MW<4Z+{Kw!&4W+JYm(%8J`Z<gSH$R*EWK65RId`9gYHCTnx3{;_&Ysn<
z`FUTj4OsP;L>1D{Yr6~^R&%RuQ{{@{d7hAyUT2Z4Z+?F4)RVwIL|Vzom!6WMG|&qO
zCzh?H;D9USBKVKq25f))@i$A8b$MePM=4*VH|Ybne$UshG{)B0611=6dHtm1<!kRK
zc)k|@qhK`qZP65j$5m{!3Jw-@*og$x-+g_+rO?wh*w)e71I^Xfr%D=$kuH38GgkDS
ze(!X{cyt?7x6I5DNbx>Dr$%;POVeR~i%CF3O3~513=EKOvzt%cm%?1^0*;QHSy^G%
z2mzarmDT95rM!xEg!TQ{=AxqC>e6-;5-E$zq|7962*9bIsdu%3lpV0~+Ie`?W@Tjo
zxi9GDPwnk`SMU0rjFi@;4lJO^em6I}PIC(lneOPg<LN1F&~hHoMjscao9tk3UD4?4
z^QAxY38mU&gL{M>i$g;vlHU0$y-~C*$Hy;fL~2+zqeA5qYwRC7Ok*Og3$kSvdw@Oh
zHD*-Nt{MAjm=JdbS(c#nS<^hbN-#6FG~>8KFMSgmsoGe#Z8_B}8vVSGv$lrEMbQNM
z03NpcmX`Jy`ud_yrcn6^u`0mBP7xDbk&?RE-|tHEBa_ViwDz`#ho`0G<ZPRCP>_&_
z2&#;tVz8l|-QcTh2q7(PvuSr6Y%?7m962Y!r=J2LBI0u2@=HvK@rRRF*S3<s+1Y+k
zP(a>;AMzFzL)6QApw?G%W+<-GN-M->Uj7alK9#waA&<|Slo8MYa&mG>R8;Mo6ZO^A
zp%(pwXoDjoiv~I+a|=wbA`=hL<J#NV4WG9?sj_U1zu|yWouALl&W?p(8#d)yT7UXr
z()syw*~5qK4&Mng5*GFKxf({k%sIYs`$Y>Pa(mmzi1%GxU3K;0Qg1TH&`_GQGYY*3
zlZleAloB(j5QxWs&RJVq^Ak4mJw_jU7le)@bEhbLGM>0VqIq+|`TM5;A?L!uGVRYi
z2As`~D24aX-Q829XrAbI_xC5c|FZuw@W+Dx>8VFyu%fQ@O=PETdUC0k<M?#HWoBXg
z+qc$6m%Cl#$r45~ZpzAS3tc3)Z>LC5sHz%G35)u8nR(Wsh6|bgn)#5Ji0mx)XO7p`
zFYdb_yl=`(M8#12Cj+rYlD27_WMBNY$hsC9`k9PdaB@l;mt4bjBgrL}A-ag)6cd+5
zIV$u8fiyKI=?xYZeQ^IUqyOCTsLjjED=XVbEiOJTH`p?+?CtGgWMV2Xgz$v<6Yj1V
zsg2Evd7E*ks*hlyA+4dW{O`u6sG29fwR_kb|1!UVi3wu8?3|AxL+^fmt5=}@dSrEj
zz*?-$l$8Y!V1<q+F0N}A->2*u?y5B8l<Io?qD@Hrz5KOIw?e*}nUC)oSM=3%;Sz7J
zQghQCDoRUJeop=RC;C49Z*=IUrs&QF{LtS1vghDXaj-0waDP{rFQ1z$r+FXPYh7V3
zG_>`~R0`6nI;yQlOS6RS^ZoYlPEQ3+cThKf;B;RV!h=o%6qJ`e0=fk$Bjs{ho4jY2
z%w`vuJdZ1U-f!AXsfyS+^$yfXyIOzBxg8xJhtM-InFPIP6XU<+;DrJb$d*9OnZAD0
zo<44luT0(`*xY16s%ojg5kkXa9^BNVtDxZKR&8=OlsNk8+#JHY)DhbG@@l$x{ZL<Y
zB#T-~c8_VHv=?(GvEP>NexVv9zMs7-4;jqs9xmh>8mt2fkG!KJ6kH5QJa;{?c5x9z
z2#r4|+#mE$<hbkWODkaza^okO>>C0su|K2QNUQgehN;C>670>XmGVM2ZT(EAo$PC=
zoK03hF{G%Ld28fn*8Cg_vU4zwQ(77q7uS;|!S{Tht+<vPndTP|!ly>)ve+jk95)|#
zyZ<~J{}!{ih&3|OI$SjSi^s)f(Z<TPrTT9?KdX7$U|u98r8(-=!s4>OK6`3Qcj(%_
zraLZRaOxdeU<vE}UCH`5{tlfelw6SQ*Q5|qD`-V@joQ|CUVilA%y8qQ!r94&Q!FMd
zN;LHZEW0#c?E3r_1h6Eto4DrYs?sfzIy#Q4F^L+F@82Ixh|?5*mh1M6PnPf|5ms>t
ziocbNf8oBds+n2pW9mkilKjRD2*K6YxL0QC+dY>+UVS!x7>Z`f{z6{UtWv<?=RGmE
zJ|)AfdN;fGk2kYYt;mV4;N#~92h%)!c=t{egkljHU8jn)LRQbdlu4Cj<tSRa48E85
zwNTMy>ph2|&;T()N5kBnwmNJ05`rH}#*K!=zO~brlT%Pt4T_J~FqGcdU8<kd%?rjj
zc-n5O7~wyu`zo4@fRfqswASb0cvt76@82a{T;EcN@h=bNC7gKWE=8@*|7sa`aem2g
z+r`J{z_5k8**V0<60@^Yv9M5keccxiyMl*<!^H(nNB#V*-k*_Cfuqxat*PU&G31-5
zrA8-sNjo=%lPv7-X=!<%oqB2-&%fzYjpoub>LvX)jC%dLs5j}SPqRf?^b*t_5=I|X
zJEO<#{Vdjw_sY?)#1lxPA?x{f1N@P<JS}JeM*8)ptIMS6DddouB_zx)FTa>=L-lDq
z#%baJ1MDU07Yx3dPn5!{dG#lguO%<oc|zJlUZ`Rqw{gOk>zoB}@hX#et_C3fi^Mmg
z@F}h!x&yCM`!lCD=QT&1P|8k{4V!s5sdxYW;&r6|W=Tl0$U_8#^-at-l>vJASdYNy
z!)yI!Tm+&I*9!{~M3q%b@9$j|le~{d6O}cNqVZaqh<`?sqPzQ5BwIu2{{8hE>H(Dk
z3VP}BNf?MUP<`hDTX4Y~3I>LCQj#Yh-%MRyzM=0k;8f+{NPl90Z!sXJird~@Qd%p@
zBmEONQ@gznFHK)t9WZ6U7WQ^Yzvrjf+41D@V@rGcdj*;URFt&Ul_pJqlc?Gn-uV-9
zyocg1=H=3U_Sc<T->JLbZL;O3%e1H(t8+Ve=NA1DQBkCrXu;xctEc<K#buSOs^5Bg
zG`ssoD8vtW%5-b+-L}!RwMz?GZ*WDX#pX!4mPW~FBxQ10iA@Rd@|PwnWxu;eNSBpY
z)@#bIvg#6lC%Uij-ow3}nEb*kGIxEY6x7}xgr&;L&QI0cAt5b&e#)02O>p<Fkr9&2
zEvtn2{7lGdFjv2`v(%((tjRai`H+*(Hl=%WqBNRrn24y4jI)@Zm#yC8e&5ohhX>*x
zA5G9wL1gMK57`1TGJ<p=9OS`|X=W>{dG9~PGc$1$l{VG(YR-<?eOUw$trNPVpZT}@
zMtE8}hwnv`&qEP|mU~dJTkUr-xNiM-bybrrdHxk9;y+%#HNOyfxP^+OrKY-RY56>t
zF!UN<ooU+XqRW4)r_XKB_|#7SVxxTM;WM5m#f~nvuSM_MtlYa!K!9ja2^21auqZxV
zAP7s>%Zpa;cW5c_-H(lK5T9<ssb~p!f%2po*V=k^5iOYq6@jHw<2>)(`pM1?lCvX{
zf+;=c?07}&3X1A$+q3n=!`};Nvedxkbh5vuzpanSp5eSA<90)hgEBbS<%Lnl`B?<)
z;qHX5?xC-E@2U^fdI?>$T%aT8CkMB~!^Ilh%Y=p3Cnqad)sw1ld8$>FbEQ15xVgP|
zVMmZ~g_^2UdB)f$wMLD$6bSF5@@k(Q1i5+dHM8zH%jbp8rH@ZY;!C0<7H67l5@K_1
zvnH>s*eoxvpPZc6MJS?*h#)bz6q??DxDni_u%YOPz(-B!eFqzA{aJtBmA#yu?PIuI
z`>UzzY2}M<G#Z*81kw_1M${POr)PgIgoJ`t27=n!1kC#`iJ^Q<^Lj}l_*s`B%)S<L
zdW&=;L&j)&keefKWu@y&kJowk4e(Ioq`vi^?9B0qWfV_NhVAS)^6+$_pcW4oq9EO0
zzb17Fl$Ye-(VZth<W1#$#)#}}ZQ0mQf4j-fM=NchT0m;hJWVedgogM(CMKRg`(qPD
zeGQ)u9~l)Ej*N^Xy&X&*NvXijiRM2Ph}Il?f8)AVXQZ=@?LHG`3NOn3zR|{`6v@AS
z7UT<EWtmcHI@O%4Y{h(120ZkwBR}+;3WGJZOoW`6BPg`?CM?W^g@-DD6LJ70+w7b0
zv;9>e8X8ct2=C6WX7|AOmmYp>T$iIwfy}_b`n&NMHH*E0d?u$@#*2Oz99BNqjb=EU
zj}c`4^tlRM7K)-G-T0_jLqj^Z&JYPPDuRbYnsKn3Yi+3%E^@ylA%vi0XvLSDT&DNA
z@k}G|G0JdaAZ^1%%jf4ab91a}YOJ!d2(L#sk)ko9EMZ&%396r8<x;Ou6dj$GZqH;L
z@#GWKLQMuik}IfYHW+x3S9BZ>qiA+UwO3a956Gnr4)JVkHtbYcAwDeN!TV6n4*G=v
zfKh+O@?{L;$;Puhp$eH88>^D}<uUW-ccG#BE>*K#bR70SG_9<24Vsko8>(M@zJd&=
z3gC%SBgIkS92Or|>+1<Au`6(EEC!BbUn<5c;lkOWM@RGc?NPwOa+82AZ6J$>%ecw+
zEM;V5HIx`@;v)nXX8!yXIXhx{sP=a3b5Nz<h5w0HaMi|#>#pkR9w&l(pTilD{6`mW
zk!(J<H)6?pPwntrymVyz594nf$KOyP15!KfM~@(AsQdWwqNR1%!n}QFm$;Z1lzweF
zjF??$barH9WdVz+3<8lJ3EF5#5oxAK*rw2ANrxzp9u4^AJCnbDts6~OMk9TmW!6g?
zCSO=lWQa#@q{^BeCcl#u8zjiXqjaw@T)vc(bf6>ucSq<L6Hc$BB;8f?5~=6Kbc6`9
z?&~|jNg66oDd^#NTZ<So=pdRdUUzpji<-I<Wwo}}KdfhJz6R*}!4?jln5ZYdaYd$}
z(EP&-lu-GK$M0KX+Xs^lNfC{b5ouL57LG?U&-n5Suo#(C8XNP&-)wHm<KY4INzcsd
zZsWx1`s*X>sW*NF1#gRcI-R`HDest<c6j#ul$=%y4I3SwnhlLy*f!<O`|**I=^o{y
zt(vYYFBWnYDvqzCV!Wr5H2!G)UO6&Vlu!0HDn3m>J~Mvm=G<J4nY}@^W6s>_Ad+ou
zRnlerL)vFDJHNwmUcPcL&x-LH7h~?(`<TPPM~{w&y0ViJ1T&@8pVYe?tPOv7TYn33
z&6)RjsD+ffz7ZnkeJQ_zQ`N0bH*kha8lU<LF3yHyY-D&Zlb)WnjLdMF&|#k9+4knC
z*QrO0n`lY#qTTdkDq4dnC*+upU&myCHhy|qT6)M5uRYjoYldsi$u3`i7)f}6I)RR^
zD>tViB0`U8X&-FX+ib>P)R%^iMCeqqT}s|6s=wLNzK!MvGrjr^fn0Zr#@-#DJnBzH
zMObf#)K@$%?C-Bo0D37%QX-sOTzk8_Aoq_Td2;Zk@FW@RN1gDAo}SX)x#R4@WlPKA
z8e58QYksNa<?6ciBm6=S-?~-^@@i;lht!yQowdsBSqxY|e1OQ9+StS=6^>btA43lf
zVg07{cW<`@tw|73?#Dapl>>;3o}Tz^1$6sq!e+06$>j-2=D6iAvDREGIW&30YEow|
z5|ONu`fPiL%U97+kg48{*Yzj76K1YRaLu2+>m2lKn4doN54_$Qiz^Tm5>hE1QN@7j
z&nq(N{d5qo21u)q&zZEK;Hydth8tI*w5_~s1LGFWr%!Pu4~8E#T><9pwa(7OpFTC?
zUy2XbtQWFn9prOg;N#mF*Hl)9=W8;spjX?8my`l3N(14Q_Ru(_EBgxyi%Nta>x!Td
zN^4`)NU66)TW4F_!#h!k3{V+ntt@+adRv&9R!u;Tm>LTDY%DCUBErJLmF8d9`_ry@
zg+yTD9qj$_o6;<A4{^7$@_cK6htO?Li4>Z>s%(3CH94C0?U%2v_O9Y9hUAnztfR?1
zH8XYbdF2<$tc00Thn<mN2&csmVq$t&SrqX-%MA;|MsU3@eA@T-Me6If#+9k~J5su|
zn!HA<@``BHj(^|D0$ZpRa`lyORBt^}?&<661Jo3<xwQphoYb_m_38ERWv^bj_dq}v
zpoj+q+WdfA{`5&MfF8IRYrq?O*8&lS``Y7~LdkD+4{nLp#$WmTU|oTXEPy`F_aqcy
zQ@v}|^njU&M$)V5tcKOwTSYNSRY;h-!L#xQYcFPOark==3FD1NYo=;yc+p+)_nhmd
z#|nmqhEC7UK1W8P$p!`nLXNeYbnAW9X@76oUgVpTRi-w7l#VK^auefDfHOkprZ}cM
za*EORTALmHnR`u5=vQPnQj6N#)yqoVYA0g=z%xA!#yD{2;gjN^9jB!Y*xHFlzeP2E
z78Z4HD^uLe)~(~8I^fXIP`faB1qD1hsmAseOgUwUX}XDCfOiPtogX?cA9MOD7QB8^
zReYAaxw$F%=#jhciPfh-EC7F09Ixyj@Tg{dpPL+VaW%BIa&1<S%2Ll81cH21XP`}|
zCL_Cn=M4r7bX@uo+UOik#?uc7q?L`e7=Ed6!$Xa0FupOGw+<g2pNfpz*1E7)dE3a@
zP^6_1d3z(@=)|ijTfcXg9B5((5fKp$o$H+l3-?x3_?8xbi;2QJ>Se{7N_Gfu^H0*!
zxAKch2;L0ffW~#VZk-$-+x_@(`_7%Bswx+wvTIZ9d5W$RA9<~(Yn3?460`i8ZF>?R
zX_1_e(04p{bsS2f&4YsbpENgqMZHz^V_F!GYHZM?yLEhAgq1b;voW57^O8Z>J#8HP
z!pg^)U1KQxVh&#^r)FoJImaN_CL}1RSVg4{d7Is%-zHOT1pM+!o*F-Qo0I(g5rB+r
zzj>j`ddI`_Yo<u~86)V_%}ntuQ&Z>&_Qn-e#C0fN*1>8}n4BtJP)Ph|ZfWT{T=Su>
z{_NBUKmXLsj1&O-tEtpQV=JYUk9_jAb^D;sd^OSNJeb$-B0L2D8X8m#iBM3v@98Zq
zEdk*8hwaA;V&e}Uoe>n5fw^?jEr2?xXWkk;c87@NC1ot^pC1kGDl4^hbqWd!t`-dt
z27;1K*^qF97GFu%^$|e!t}HLRoi43g!+^x~tX}pmaSQVv8Lti1Tqg{b$HmA~Yq=s<
zT%@e5++qCePp`E1A*Zx-OS^(jY%Gh2{pprchw+Ed(7WjiQ$L4`xR#~q7*F1bLNXAN
zXpi1JX80v@UI;l~Xw7nWY6FZ4MTZtW%^anM;2F1VPGVYGDX3M@nL2@<b`W#F^~1Y5
zN{M;h>8#0qxKNFOfg!Uh^baiHqMi02q1><R-Cd{lZ5Kf|Gr)jvjF;C`RGh4oSrb5(
z7?WW{z$Yyw@%KC1KtegCrvPiiWC$d|gtR@pd;#dV|M|;C6P^#>AUwI<@GCbj?_y>=
zOVS%!Pu%iDsob@+wFT%>vO7sw0PsbZlyr+@2hjuVxm-@ql;Q5%2Z3@7$hZ36aSmqm
zgqR=;Q&bBZQDa*mUD7OW+-MTLcp4&gH2V??ftnAf0um=64i4xYu3u~<0gpKk|NLor
zwh-KidLI*-n*smxcPQUjq<JML>NtxeDrZ7%>p)`X`e@l@BNqFn1cEcv?}?Fwa+vId
zfG1!GjJX0>!~fgMCFMR-{#4aR@3N+`ZXVE3agMu#k{%1jJx{Mv1mMOc0!)LX)?uGu
ziIV&ClG8J^*SKSfHjk<J(PRY$1uZQtzlakC!5#gh+LPVsv0rp8@R8#_Cy_VQdiU>t
zif$&z{Lkn3KMV4zf_ZsyadECaWMgB4$DlD~V5L`_`grH}Z-$R45}p+<g+yb00KV7N
z)MWho{V5)s`p2pgyrt>8#wI4285w|If~)}KWEe6_s>BsTKIUYI2Tv0!LsvSGTba^+
z%}`*wK2kC_H@6$5B&DjR=Ire335H+S!rpP<?r4Mt8}LpA2L~%c+}Gt^h)_23=uopT
zX2JB`u&^))ls$Puo!omiAI<}M1B+#b@75bUId*qkG0m`!=VvFbfC^gMhu(Ep@hF*p
zdMv`0u(7nPN-eP!+5<K!QBhHik_S3B*WY6#<m*n32Rm&u!;wzmeH!2bG2kS5#?g@x
zwymhar*>>BQ4DvK2NU%*G>o7LM`m?R4G$YzjDv(zY*v<ffhM>XI-E&aS#%-oC`)A!
z>MULv$bwS&?q!$9sLGmLTQ;}F+~d0AZ$NuBxm%fOX+7qZP#K%(*0uUxIKjCqhN(cQ
z51Rf}{2}H|RRsp_C8s4S&5N?~@<03g5|E!(PzY*jI)k(dip?^YP<GX5ZiYD4%eHTW
zgDC!&;yhJgWhKiG<KHP*n&w+nVmEMwni3C**Dh(jOPVDQSe`paL2C<u%jFMe`JLAV
z1u3|?3XQ44q{7vz#P0%r>JFXHKEyF9Ee4_}g|HL6yDSZ<lp9RGglkr%0CrVCK%ih4
zaAvl`dRT^fNhv8F7w0}}g3#DYgAKUD9}Cco`42q`hiR0{`35CickbNa;!;ZskqLE=
zz`vY<e-=gpOioxmXdfB=c^<f)`_e_!XRdY2>gY_se+%&QGZS?mZcUw?o|5BY{MYz0
z#P6!AMkq0#01*PT#DXr1m}uD@|Fxwgy8muh`a&#neiINV_RW@qj4x#9e>pCiKr#@b
z>(J{7@^cUfi|>E%DUE{HNM-{?4hFmXKqY-r<5Q+7BxLQP!eB)LE4IlU%J5iV24Be&
z%KK%a(u061#(}lJJjhKPfY^aIu+jdJY56k$A7AeOn%mr@CMF&>`vBd5z^e3pb(LXX
z<o^(*=XM6o&(}Z~5fK?%T8cU?et}6>Spk9eqpvUGt2t!#zF>ECUAkbfs=(F&3B42O
zc?PqFj)cndLw7nrMtxZkq0jGt8{Owgh>4lq-ZlUqj*yU$loSJjgz6<#^uql7YiU2<
z4ah8i00VY*b`})g+uGXi)6>tPivSK8H%X{G9IaQcUV&o=@Dryq8ccSk(neGn%mb-g
znE*>LCJGe<wFJwA+6rh1=+$EG;6Q)?z!^9=5JF<&1;}(QEukZj))E#GS^4eJa&hi$
zWJDh-udJjL^Q&A&Neq<As^P1z=5WXnBuFl{A9%N&U0oBo67K;_wxF;OG`O<#<kZxj
zhXk;<Ly!_EfQX6XeF>-;0s#}{$3zr5VjdM6d*9c$+2sU|>k!uM+wE4vg<BJqR}p!}
z`%X?3v9W`ov!UY*KR-Vhi^2FblhOB?0uca?%PAX!krf*otD~a>_*Zx`)JH>3P7Z&O
zk^*yaiPy26uKiF~=LzTpfQ8Len5ls0(wL{HL;-_WVm~@Mf|O|I`1p8#KQ}LLOq&tn
zpPD*xe)gA)OV`xWG6l{YU^Af&opSjtp2zRA>*~aA-c+7>!IbZ!geLn%90N8d<{1_M
m*Z&)J^WQ6@|979pTwG1+T1Crs{T+qC<F3Lz`FC<g0sjlAfHEHd

diff --git a/docs/articles/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-13-2.png b/docs/articles/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-13-2.png
index 040d25975418d605b5914b1100bf43295b4a95e8..d47de13cd4705025c7a2e036c242e8e7574b2945 100644
GIT binary patch
delta 4489
zcmZu!cU)6hx19mRU`ehZHViQcGD=mEA|RR|STG7m6KRnmD#YkWAV98Ykn5nJMx{s@
zR7MFMMQTDZj37;VNuenUp$H}fA%PdrdEb2R_wvuZci(mP*>|mV_BnZ}ldYX6`SpQ}
zA<abQ45Fo_rM0!St*x!Sy}hHOgO(&ik&~IG9HgnrZUhwvk6Io&9h@}I4fkCCDY<fL
zB}^}JpPYg^US`vZDlGasG;3%3RqharekwMlb0hEpduSKaaq_k1;n2bP;~km5=_!wW
zw|m`v<E4Yvikv@JuE;O{O<|iQPce9*w5P72X~MUw2i=ULcvYlYJZNk(Yra$+r#`Qq
zV7mo4xKAJ}^UG73EwGt(YNZxP<*X)F*r{YYZOm|Spg$ssn*JD_Ynb~?n@+)5ob0bt
zQayvK{RD_+HurP6*5P)kE?^d=SX*Kip{dDwmbwvN%@wYhQWN%y8n6)aiizdC$Gqff
z?8xQ13tGzQ!+b_tnf2V_-@S9(=QFaY4srYd`sI*{?{gyASd*YyKaiX}x<5T2&N@zX
zcMZ?kG{JP+elhb4vhdrbO%B~tqi#oOriz+G-<Z3XyAe7w=ac;Lxv^Fzn+G!_^P=Y5
zwZu?LTgy#{7vY3!wg`$(hYOi_QTVXa=;edy;UMU~-9B&#k%-v*5|_8dE_?B4#s*E)
zvz)Bt2IN;q!oEgb7MS8kB7SS}%k+v;)Fi3f3^t9$TqYG@f7c|vAbLbe)~!B_yX9OD
zpEAETC@(=71r}70bsf_CMi0|(y7xU7!p*DUwvF9q7{8B!Bgn>%rfc-NK4H&=B=;<G
z_thGC=N9d(<OBPgw~zW}ugvID+CkFzImaIdUUz?-01fg-Vx-@L7w|`+UO*IdivC%L
ze?gXf_C)L4L6t(Eh%Izs?l|D;=wb>Qd`gl@-_@DXbSBoJnO``n9-7mAodx7(=t$D2
zb#Aw5#(T@QbCRDTbPP*OPh2W}W}6fBQY%%JcdYQNPtkOI;-!F*=Z1{ux}9X)xCrVr
zpELk<!pHQ+pP_^98VWGltmSl0G>`nS$ND8&UW%mQZkV+oEreFqRxIVcM0N1VUS7Kc
zwnNEL3pHwy(G)6B>~II^F+JJ2P^9r;?m6xwHEEVvz@ojv&Z2EDf$gYnGjS}QmE40I
zm{=cvKEICZk8eZXy<*NXI_)h0l`YOQkHy-(bs9|@r5<NnE~Yoz`uG{#j-1tcWRZ1E
zsjvhG@vb{VEg{VD4+0NeTTMp|+{BTe3BAFBAgNNfPmm1W3jyJA3EN6u9NRf5j4scp
zc+#uWI;nAyKZ8<!*c=mCJV`%CW_G(u{J6k5Sv<P;TiEWbQ*JF4fw)A@ef`^}S1(cD
zQkDzkbXh;+tlq8kXmxg=s$-FXV5k;Njel<x(4HK--pH-<L+th_0Dr8$nGgpK^B3lQ
z0-0rDC3#F!Wb)62rAPZ9s{qz&v$#>O#QOL6Vk~FgQ}lROE?cq^6KqiHvVdL?wECFR
zN!qBZZm?K+lX?qQHDe|-v8jjhTdmIm`f-=L-3TK5mE_dDdNEd<CurZ@6+3P}?cZOL
zx#8V+@ex(z6)XAX#ETm&jK1}dF5YNnc=i{je7hNI4X+!XZWNn3f;Hktz*KlaNf;KY
zbvBG6z}9W9AD&)|Ry)^omWj6X)_ziNM1}4k>!fh>8wz4}jk=|GmTJ9G?ws2W8gM_K
ztlbBwHDtV#j;T)ie;)`-fI30$ES=9g4C%kKU<Gs~iyB^V6O&(g><*DdS8rVe^q7|>
z`f<5oA>W{nYpJlSVyt#|Mx3B!jy+SS0$Vpc8|7ST<;2VszNbAflIEL2Ox=F!-&yZR
zZnEJWg!DCU`ZZ>5bkXzGd3n`hsgfk>ZNLjo%a`2)zDU0bOL`1fyL@IpROvsmP3FG>
z(`zj~zssxTg~_gSW=6oY`wq%ICTzo)>#hnJ%|0NjASL8$SLLqEQQkX0%lIMGAQVpK
zj?jDZ0)7_uk}g<b&*bcpOfOfyXI1{6y>qzybJOw|s>x<*G3YLG%M~UD{)7hy9)0dv
z?A}WZS_UMTUE~!FZ<kbEyf(4JGM;u6G98aAdoU@zm(KOxKJlSWI~hziS6Je}`21Xs
zMom3zoy#W_enB;RqtNUKRJ&slI62P{;v2j%mWJP>X04vA))F@TDv`n-R=pYqa)(d*
zhhOH6*NK$W8V1yDXhg(D8Ue8dtmB|%G_OX%k{Xa|m-mpM%6|f9qY?j?5Se--AmWn>
zR4L`l_`xR?-Bk)h|L|qRY8FP6=$(Gid-IbDwNhbfA}riKb%AC#UNyZI(@*WDyiE(b
zsKq~f-QbTUz*^#GRJ~vre6y3}^<BQ_Gu1e)oUiu=j(kK$<<vxFUBzT26n*^qm&e4>
z!j)ZvSQtKJocF*NA$<oJhd9Js>g7wSRP4vA3TFWs&0MWOHYal~CoN;9|4bMxg63p<
zcOqR>bo|HIq;j0=g~-{3ojXYbXToX^XtFc{^t8*<z6imCSShQ;9+!(Oz&`sYH`Z!c
z#=n*N;|3Csa{4n(>cTO=saR$ytTf*$7Wwe)lA8uXrS2nQ#N~uCUL>PSe#<ebr4^>K
zz}(J#2>2?4hk#d%5}qQ+mAkM?c4_DkwVMzj#1GUi&t{BXII^}Fe|_#VaR@w68}^&j
z{hF-BpmgzxymqP2i|dX00`B}wGpKhaE~rrM=M=?{s-hH^<~Dn_XZDoTTR4aiXA@GX
zc>YI{y(_uIA`PzlS%+U-hN<0w9=+zqI!Gz~!3))|E6CC>laj@{;=p<P-~s}7JPH7f
z(K$Jfe%z~z8{ZS8jQ#)>4!w&(hPn{nyGb(?G%FA*Sx@9Ef@cV=<2BQ33mO!57gXS{
z^Fo5?=FZGRuq2=?t5ZXHW%NIS&z{fBeIYalkNkMg@oQ9ppRN!)C%;m%&JVi@%zF4m
zeo_Icm5ckewEMQnEp)l$J2=)bst%*Qk`}KyT8*{waZZi50*>=4mlhqEtn84k7eD>!
z@kpE|VcU<07E6ZjVsj!nm%Y?4PPS)s@|pZX_cooMEu~rK_WCtl$4#4XOAX>3oLH7K
z%M72%{(c1fdM3F{VI4T!jehJnlU1-{nPK=8@6CzrZ+^(sw>{=)#k=9cnB_H%Kj{c}
z+Io!f?%DlvyzBD1#X)`IVxP<nP88aexEh8XVp(cqa&=cJe$ZzpDP!U2=#qsvsr#%K
zfqu`ZpYjLIcax{$CL(Srs)151lh_lb^SKr?9Y4I-oVZBn40y$a8A^QwchZc6QDN&~
z>(pi!y)|@}+KJd(1NpD3*n5TASDG?ZdD3y4jD<@CENeSP&Xk4DSx(oD)_rZ>{nyE5
zFXKHDMZBJgfzlT#^9Pm+1He9QmBO8pWy3hC3_jkSzPfF^%3%c{W6IL(J$V9rurg=Y
zb)Xwf_q{LhBlpu~U=hecLnpd<G5r@zf5o4TQtXX2l!9u975BmuBv9-p74HNPT2>TY
zu%vTT?NS3ksnr|7e*M`%F00oYon5c^cYc`Je|6mdZ7`^AtQ>|4TR>m2JwmOtu+&|3
z8}R;`o)C>uo8JSwzjO&MkfEIdwn&esJ3*(E8?dC(O=_1%@la((G+O3`?ydg>{?*-M
zyuS~3P~H^yAJ{H!>gWE|^EWSSarSZG&M)sX#`sisQt1W5OFwj8K+|`HS3T4#vZ*jz
zGFH+ZjP}bnO#89;@(92SC$Edn=?d?<$oT6Yc#5my&P_RSK}pHWfE~?=qW@TKn064@
zWn~jJ+f|a{Plap};6zTgMXZT*C*3)OW)9Vmq+75356uBJ%WamMcBh4THq@PzN1cT9
z2NR1J;8;(r+|t7hTEDC^cn|{8@~Op7|4jWnCb6cRu3e9V5<J%Laf4Em2e|J0f}Osu
zT-8&6!skU}6!9_dDNvQ}ssvlef>XSGxN-HZ;2pX%Hpt2EBb<}?$8N|XuaDT2(9h(X
zAjyw&YcmnF{`Nsal+7vO?$r&f<VKe#Y~Uyt4dfIjs8V{$BrNc*zi;A!ckGcm(?LUy
zl2KcGah3h&(8-agB38gABq?<89IxNx)u3;pVo}@2I0k7@IE(Guw5ad?Ehy1qXQS!V
ztBs89W~W9a4;m|(#3Ht@IqUVtNV5>%KNyobBBru7f2%0m%ZoD}qxrCAE9nLa3<;fF
zD41o}B$u<@hCrkBe{*&B^UAmy6l_#68+n|;PF6BxA-;@pb&qxf;1^xq_)zqqp}H9}
zhc}L^@ejIyS{yKLDhtQ#K*hYC^tZiDD!VGiO--H>M?EumhOj;=t~kdTe`?QQzi6zt
zg8cPXDa6~yMq^}#+lQXsyKW+Mww7x^TS8LiBY*(33>vfKR;grLJRo){!e8I*%Jc_w
z$EdH(y|Wljn8RD-e0h?Y;6x_I7>1TV1i4_LLjb-Lzys9yebh~IXy3KV;WjCHf6hRR
zxw<AoLReI(J|s;@Hj09GrRc+3VOUlRw5<4tc#QZZVW2QHnH?J#_$Ria#dI-CpYrk1
z;D?hzx|{N|z2MnGl6^sFmeqRvi4lS*hWvr)BEfkFyxlsNX-Jq%pd7oom3*zGFUZ~T
zbJC@>t-X7g4nc@c2y-@!-dSJDHUe9<^x6Riwz=dK-|a+@AuDa;i8gXze8oH<0)Op_
zZc?-Cx_a(4ucbBQneh_P$>9RydDbC#0K<LH?8q$>L8rTxLd(8yWd6m1E0kGOAfyoG
ze!4s_0xk(m#d)RB8chWQ(UhXQfLn7+;lj;f6Tfe8BUUkXatr&Mi!8Et`dE>?iC{>{
z0R4BleU9*o2&Kl6&Vw>Lcd@lfKXxnWi%au)iO;LiTgHOoBhL%}&~6^C+F}dsd$*Y8
z+aNhpfeNPsfe<h4vye^jzlYT`_UR}xN^RQ~^@i8K_o?*1Yg3o%htdw${bNhtdCJzy
zUAZbW*Dob_?CV{$s>y&X4ysj<p3pU<2c?Z5%k$O?@;vH7n4m*lS<?2GQuKwaxX8w7
z?Uo`Zfr4nAp&fOTjXS+)u|*uL#eAFW>O2sBwpHlH&#6|PP^HGN!>sXsK6<U6!K-m{
z98FY+j1+YfT+vpyT&Hx(@+wymNAIu+AL`IeeD~m<AipEy$JNm+&*|R}SG6XxtwzC+
zPo{<+!HF=dJ7V(F&Y}YsBZG@S60~|zHmitkWfrJS;G?~)&5)(EiWCavrGtAL_ju{T
z$i1Kn1M8rNTU&iRT+s`}>z(Gj%S`J_D%|st#{B)+6@ekeLC{{{Hf(piS-6LnUj5^@
zh=+w`-><7YWqx7AHn%xKO0LQq-hjpXNA?Z|h+K1B9opeUd9`(DdD@HA0_KV=iU*;W
z-X%y-Sv^%!w^y|YlQog&$ba;GUJ09YjnS#B1Mm1}KoMyF4gu$wJ1<`OGK`dX<ncO#
zAmwzfkt>?%lj>PmU(Et4jAver-gerLO^P^T300jvbERLHvO*tgILeLtNJ?@>hO^<e
z2;}o@AU8pkJ2ISKns-?1xIoE6;PNe#YkFtl@6Cg2EY)b9h~K2bDQys2)ZWLxeQ!3Q
R`it~;^f1mc2jh9;e*h7R$8rDw

delta 4589
zcmYjUdpy(o|Nn-v6eh<#mrd!SNRB&G+fWE4_gm2+k%ct3?UNGg6sjfHC?&@|O753x
zQgUbHE@I4ew%Nw;`;2qW@9d8~_IkZvuh;u^dA^>X&kB$Ib^Pu64*rac4C49@ZT`~I
zQlciG^X{&$uI}#co}M0JIv-v@n7MYHsK74;h`XIdpS~EHK0h3GNrGJ4K;w_jz9D=X
zNl1PxaJob(|LSnY)ur88kUuY3xa1Q=kC~V}T`g#LgXZ3`x5+QV30=K6Pz5vjKILq(
zXZ6&MJ#dxV(*65%f!a!<(}JW=z)9?~#R7|NcCu2F(o)EI_ABFnra3>~U#ob&Q@_EG
z0z{OkB=MA}4ADktAMgkVQ?R-Aj<yh~RgEkQQXFx!cS>>^)l>7qx6)r5g$>z$7wX?X
zUWdOs9J`jwqMn?cq#E0k4Xp==;+_+f7-`lGd6c0??UE54nw8@((#iENM$G;tB8BsR
zoz)W@KFY{=d>S~pg4d~B&rQg+V2UKxdyfULmf0rgS26g3+VdgL2R?j9`Rzi-_!nig
zC_v@$5sWBm7(y|{V~K9Go9HrrsKoo0sbfpdl%brTECuKFG!eEU^ZQksfDnQQMO$7A
z9Wt<KE)~))W%|5P>b-r!HC)+I>gdp3jSB@CgWS7Q**SRurl>roXyUa%?s;k{_1oNz
zVuoFJ3$g_WTanaO42hAwrHc=;7&@@hvI1FC(nLoDMaZt<erz@(A1A`>HBphMkWJT7
zriV*wafYfLO(#5;`a(y`x7H%z1nQz@U4g6K!s*)l%7NPRS{%#FmtSW8pb7-5`P>$3
zeL|giMQ=H8`yO$wR$8a>c)-30?}%ue4I@{NIM@y7c59hB$#^nmsRSyN6=IXu7b2I>
zAMSXrclDB7*?O4P#o0rxP#1e?!FnTRi(z)Nc3zuvg01zmFjW`4;8Y~R;bIa(Ga$%?
zd!Uz>{EKPZV3qQmz&CymOO4QO2q09IysZ2#F^uZ$WqqiO=^GN&+kO#9xN5i@-aQV4
zZTc$;m-xMZnUfBEzJms{vgt$kv6aAhyk@jPEEu_!V4gn~<zD%V(k5lpXa9Uj6?>If
z&M>}6!7zo$?)Am-1Nlw5Fe|%y4Fz+tEKMbCQaJ-dKIg1nLHk_s?Rp^bhvuO2+}$ke
z)|8=)FDXSD%_~rk3y;rx;X44Xve5-$kDS=N0IBl(_&4nr59a(Hh~ySF%lB5@A9(g#
zmO;aA4gGc<`P7YHaB?mDKU1PB`tio(Q}yNWAgae&^qQOuSEq1PS~|=Lz@ieqHsr+B
zdU!nP1B%4QHxw1gv!XaM?i);>KONc{hLG<qnrQ!EdC)T8ei|vGU*@t#W~yeTQQUf+
zMspo>{ji7UP5SMsqPk2oNf_zktq<qRTaNfw<!#GPd(*qsSZ3<sij>jdmF3ANRSG@2
zV#LdWzZ0!Rg}}wy1#BRPA`X1E=I?JaAmLaH|J<PPp}-T-K6eWy<;T_4+{^NV`cg|A
z?k)L0Dyu=YjQZ5&Q`2X1?)Xp1du~#{Pj2E)y~z|RO~Dc{5-b^m%dYiVEL5oWS7(H~
zmc#Lg$X>7S`6oABA2mAGML~{@o{F|q8})aZ3css9&qk!U0(B`jW)kDw;}~sjkTWVt
z7!~fka?@lS-I)zB2Stcj9od^RHF)lg^{t4#2heQ$w8=CFl~i+|(1&nPik(cm=gu16
z{waq<-=MyYL=hlX^3OomkGY?hg<){r5cWLph`S--@y9YMnlKg`TQQOMPO4_~m1loS
znm85;zcV2}zVnro@C;+_*%9S^+ZxZ2fZD9g-qH8gL#GLlcr2lDHiQ#Y*e9P<aThAK
ze-WxUquUx<{i3gcBo9VqhmyIp3lpt#6r#c*v_OuC)MYqYAeMMu@OLGod;kWFA<}0S
z4RKWzgWtfR2@Om!7~kItA|NCU*rZL$t-X1q6UHlair&}Itm=t)8$dK*olZC%4H>OL
z?Jx1b7&>XmeNYY*^ho8UV0RCJr9PYQRzJ6uV=<I|G;Z;?J=+y1lg@=Vs1)|G&iq|~
zkA%r`%xvP$;O&|zcJvJ<X0}ZIfRQBuawR?!icQ0?4j@1QtajGQ|3WExmjA5TzODZF
zcC!i&yP6Qb9$th7*OSA2i@{!6E{o7!_dV3{Dn5w5@&rpzFVib_T~si91buuOgaAOZ
zyw@8mIhxMa1qb08y_?v{e}sq;6asEI>>xC_?Ns8!O5BDF+5HdxuR`ZuG!H2P&oD}S
z-*uo-lX8THWnuIs&MtTlh=mTd?nTO0i+EVb_Oi^eqDz|)mg4?n_coGWYz}JNDJW%R
zmqLX+!$A2|NiM-m^)=zt^kpc1Z5NQ@bMMCn{qwx`(?i2@HL^PQ&|mst3;1_vj9(fR
z5+HOR>KNwMT|ZmvAtDck*jCsbcMrTWyFU6x|4tebE@0&zQQmh$0Q7|v)HJL64@58@
zwdTf;y?F$QO6*IPD`ysQ7hfI`{rTq6S3^}&yj@i}qC>6lB?uP45ti}33lhk@?<;%|
zEzl$sXfjA&E?!`JI-_=reJt^~6S+>BPY5`NIm~O^U*hm&*vh+Xt*0RHG)bb@x4k%B
z-2S|{T(ui?BM|N-NbZ$W)pZ=4&g(5ZRZ{n2L603P{aLRKi2nj;@GIb;O=2cb0zgp^
zfvIr{y1acMvIZ@zpy{|yVblE9MYmF-C46{Vzx$ain83<%D>m&_m#jI642kjD!Uxr|
z{|FkhU5eZ4Ba<_KJCd_<v2xe+{~UBt`dk17ZVnV<v|j7qnN@}h*c0Yg`a%VOJ~Oas
z&~~Q_6U8IoHbgfFmDC?K{P>6pDM}zf9btIa<76>#RbYg*v_#_^!)VZ%UI5jx+^-Wb
zH2o<x-jlOS#`~UbXqS2{_vb_7Z2dcva$ppVpcnpcM{uTj%8qrX!5gY03m!C!gjj|M
zeAj_QNsOZk>r%DH(t)mvW^W_!LVpye8XG%u>gi94`V<<Mg^^>p%*0=lX*w9~xSB?<
z0Aw@<vG8u6ox2$QV;jqo)w+_L2K|}F!r7b7^~qV=x5mt$8Ty#yEzq=vP|ZVT8iagb
znMqPvPy%NPYD@`XUvv`e&ThK$b_ka#QFd1J$7|-Q0&w2|oEWXnhT>%MxaMd0A0LOd
zNu<(q?ms&jFEid(RUpZOga+!E+M?ZMb3Yy+yb0xH*JU-b^*sl7S&WPDrn@NC)Eh}c
zJx)j3ZYk2GRLz=2`fhwJ9m!P$`3Qs<W%9U7S%tq(GwLV+q=xeX*%MwBam<s>$v)r0
z$XuqD*(Q9_CiZ;{n6THq9}{j9t;P{={>GEZd{LFxr_MdyTR>RP<7|45>aKM&c*dWH
z&%*e?_cmlS1c5KTAP(jf$-R=T0)l;sC_!kjwpZdac8Y><uk3^`5dl!64@aLs9V`0>
z)Cb>03@|LV%4<uS!j->|P~u2RN-i9|z4sj|vhvEW-X1fMB%)vFljgl1B=y`Bz)GBg
zv_KVo0aUA0*jN!CBuXahnq)4=Tofb}*a_6oWs8t4qsU`dgKf`T$*9jP0EfPpF7jA|
z2NnY~j~N!BP9RchP**s+U`ry8!Gqf%m4IZNM#%%hO4^wUufWGp2{B4YN@Y|F#Vc?G
z7G!&d=g*@BHoL3k9PDuz0{M%#=k5ikh(GydK$24dlt7k$o}<K<q!t5lt&)b{nDHbu
zWIurYtKbpUKTpPiW1}^i-3;QOk?B5oWPa;%tIq?=zIKN6+5acEC63>vNr`U>Vq1(%
zSP!50me>DQej7|8=k2D0a{~Gi>s{MeG8WP(?qXY&C<O|FqedWyUJLqnh2s*oPD5iu
z?av>;RJc2x(Ahf2yK60aY<_TGLw0@U4b35v_>upR)EyC;1HW|$8X|_@DL>Bc8)jFD
z!MSjvf~Y+3SDC}w-kS`L#qbmy-7{c|t;`%-102dLA!22w1ZWf#hD7ZRY#6)HG?R7>
zxB%&bqUJbvs&S3gWyVJY?h6NgqAg(1EWG|Y&qGpfob56^Um4t$sXARv+~+B|TZhD0
zuren&9T<XkR~zJJLV*vFNfUuAEz6mddJkZ#0_c%$I{08D_*nX_cN3|z-ueNx!BJkl
z=N2ZgtyqeyZohoQFe%v!KOg^Ghm7NnqzyGujK|yP1AAG?Yi)s<lw{fhZBNF{OnO8;
zL2s%w*=wpISgpGIdGJ&yD{Lgvq=lwK+OPQ&fn9S*_`*#9$j^h@Y8N#6+Cvgj0K)+s
z;<Hax!sz;o>UHDtO0OIeH@3QB_I^Cq24z`iF!PD3NZoI&+Spmn!~#@XX}b6eFZ^@>
z_NZFmZ|P$Q_lv&}BQ!_sTC$bKU=zt-s%r4Le$`;p7k?><*CT2RV*%Ls{)H!t0ob_V
z0Py=kznY%Dx4{Z6S<gbKYsfraUXH$hd<rvqoOui1mmF3_^2b`$x<TpRrX4>YdU0Y(
z+{SSn|EVe@q}-yaV(`%MHZ<WI_Rvl~huCT&ANYPWd^T#V=d!#Qgh+#k105a6$V-=e
zYo)tt@og>x*%@tvhd6a#Wyqj@E`b?5^4yC`sac&1*I#T&`edXCTSXCMObHuM&ls^R
z<LKne(bJ+ZZ|N;{FX%I#vZ{ya|FNo9tX7yqAKAQCLGil^Q#i9KSM+GEwUoZFha+Vp
z=S;Zz8X^XV!igqAVt}v;sa)`vM`il}{;ErDIy<|rGA9<h)0<UOKbx-OX=aq}FuV9B
z=GH*WK2JMZCM&L{X{aF5sPrYmiffYQQ2!k7$)>DVOw`{>u&R=DnQHe-{&BRRTRRz=
z#Ow^Wvks=`)CME>b{f$%DZaHi-xfOBt8b?EK0vMw$0l!f0O2z;(aF=a66eKhzBn6c
zPr|}|)#fty^ZPc!;#jINEyb~Pe1C*IOeIz8BRjRAVDx_wO;Uxp`f=9_iWIkzV%w{I
zp26)Azqs`aN+fhboaRPCs+U`{K?~H&s>Ww%cRqEZCdMZs8W@Q!?u<_U#oTZ%E-{w7
z95bE4?l?m979}@@`KWh<TfA%TXg5IY^DTt?A@wn+-6hl?^$u~hs9AES)E=IBD6N4%
zW~qIRdtVW!IoYz*?iblmPxv-6JIZOk>s%gg5-XP#F)nlTn)Nh={H{vTC^FbFCvHvj
z9O0`rQEa#P-+!*50Ew~Jnn|lClcuY`9bG+SHEdv7+>|+btO>f#{*3>L_`K^+Xg85a
zod9%<dkKqYD3Ye3i;^L30ftXEU*ZH{t4M-xr419oHXZ%+y*Y%e#o^Rz7;7UR?)4~Q
z=FyiQLNUpKbh5q%snLjT23!};#Xk^;PR`ix_N=^=`UTj-9N(x$w6Bv^r8Jg*9VjdY
zS|GGDo%INR;4q97H$S4MOKa+d`YDZzd4>lu#8Gj}sS9qMOvTu;^=!S;{OPx9oMs5E
z^`mp<(i5hU=is!JGO6Feciry0G8t76MKB}GeCsTyu(bV+gRp0-j|k2~nH)?_#0PC;
zde2e7xW2Jne8Z%MCNhD`FQvkIPmz>Kj9=4cahM&d4zq<GROY&*9t?q>e;6AFRW<PM
z2zOhEVncnbJE{sAfBx&R|A+J2m9at;V;xmKFUxpJE73+BQ(dTRh0rAE&iQW7NU`jh
zPQ{B>RV}if!e~RJWqYHEpvTF%M29M;#oEK=moEIl@X|yZF@C=u9oop4K5hGDhAUaP
ww-aDz6{kwA%$>NFqeYS>g#KrIqLOUDkR1BR{IC$_f8gh=i50pCdFjso0Z8`%u>b%7

diff --git a/docs/articles/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-14-1.png b/docs/articles/examples/timeseries/timeseries_anomaly_detection/unnamed-chunk-14-1.png
index c8572f66c853a17a005406c9229427b07e24b0cd..61609bdd6248bbc13613fcc48c860cf03c73fc43 100644
GIT binary patch
literal 30517
zcmeGEWmJ`K)IE%DK)OLdX{9@*Lq$OvL{hq?r8`7QQluM^k_Hiwln^9DM7kTKrTbm@
zd;ZV+jx)}Ob3UAJXOE#<Vc++C#aeUCHP^a!n2M4NE*3c!0)fDlee&oj0)ZTgKp>T4
zAj3~`|Dcq^8>Yh(Z6^c*o9Oxv(yuAsa|D7GA^YgTGxxNODL2ELL#Ki}+cxq7jSu{P
z7ST3hzw-BgLi`AobBVP;t2`^zvqQh^u0H!su2;C&7;m1TKKKxr9ZZT55JT|rq349i
zHqA<BgV*4wX@5rYlvrkSLLYB`a(eP!n$vQxwrqkK!9$EYk0_k2$o#MWbw$Hqe*FU}
zW*%aACw~~03_l(qkn4R-@Gvx3#h4M^`Ck+9-FZ#${!UyHCcNWjw<}=Wd2NaD|G)JA
z$IBG@U=#V7vWCX+;pW5;C+eNoF*=I*gOSwY<CgrMCJPb?Nj0_;pItY{C+zAKH($Zd
z6*e|qp058$7p<`zXr-Vys%Xc_vY%<fmpm^Y&!v4$@YIV+$f3oHBH!!(%MA{7$XA-X
zINJS+F66d#H7)FhM<?^%PVdp<Y$uEJ{dXUi=&b%Wu7G{C*OX`nay7N|COZf@TwP#b
zK9I#EA|@mbq!J|{3t>?!cjsws<#sL@H)CMUq6WJPz2_&PQT8p}?Y)zQ5M@_)t5bir
z4F1iVx&v#!u&}u8sf|r`H*2iO;tB$<%@E_+v)7>*H_+@RYkAg&G<VPULL)vI)CboJ
zO*;0xIonDVu+@?9k0rWE`cY5gJsx>CSa86rz_;%LOP)_$ts7Lx$_n|AGbuMEyxY@w
zg5l`+US4j)wSKpL+mPFE_I`Db-0uhC;1h&hbN+q7e&=mkPoJvPa4My>pwmnyCWR(&
zyiF3iJU^jv+r&EF_EXKrGw)TYFMdRI?HrG?gq^YmzZxQF<zuTp-E><1#Kzvf+;>al
zQ;Q}4_sWaq)R((=E31Y$*;LW5y@zmV%5ArkCW#N7RNnOI)5VVPli1hq--k02lk>MM
z^*DWT)f+F*cG<UVIKFG%_q4wDe;$Jof<hrMn|ak9Ma7+ZM1d2Ooh|9IIvWvTV^eK>
zxUn&quhzFrY+kjrs=B`!{)xX@Ug7tHcg^7Fp}h+HR<9ofZisvRp)ZV|qK-`!J@|Qt
z;%t4wK7@<}_lqM#R@UsRF-qL~3F;i2s<;Q&(+W+xl_nIhHW+=dWs8sTWN^*7GEJps
z>&<XUWMzcE|K0Sh%e@tvC(6p36J+6}*%r$G9(au<4clFWIFuOL-^QwN`_tdqe3r)R
zRL!#07+&PFsARx=@SqtT^>(^aDm||IrN@W7`98rRPX7NEXO~y|TW_*fHP&F>TpPZc
z&Sx>xs@!#F4y4AmxAwe^w*1{RzO~(!lPYD?4PRXP@hO69W`e(eaIFgyxkj_l#M}>A
zX7ay_+<5Q4H(ej5Y73b#^RRr>PLxGUd!MVvhZ653CpuwyaU<-_A1&Rqneyy$21cv*
z-wj?9%$wc0W8>2|^!>XsmFVGdSu=Us4>oOW)cc_wu_nBcE<(;TK4k1Y@^~~q2G-p^
zTjjcjg{2g90xas2QsIW6qzcY_ak8*GJ9G`qMvR*Aqah_qJ$$&~Dn5&W-~V$hEtVe1
zXU1J>lZg19>NUh}KmUxgK724%Uo$sejTdMX|J4x174Z@kbt9X(FpE|z!D;{Wem>vn
z{)>(2632ir&wmp<dMy3kLOF1Xb8J((=-@$WNqXNfML9}<tCxf!5^ax>tCv4}+gdwa
zbb+4nd-8k4PA^~X4k}BQ7$bop5QLzVH9RG{rS-IIdW6rM68#T~^z27=`TMsF9Yq5D
zJ^ZQ4gI>?JqHc`kxMqim{8s3#GVjx=G!1)$3OMrMshX-*5;37s&yS450ht3NOpF_^
z0-iNaW3HNM<M$#tw;a07p!ys5sn`E}MD>wj*}(7OpgoM(>M_%6g6ucpZ!`Vs^cz+6
z>VL#6-OsvJi}e9nM0*;o>pqJLq8I&(_oZ-(pv;TH@gT-ODoi{Eb&gB9iTXO9EZ?#Y
zfH{6DYidn*a}{+ob<i2tZox|OpKB4lAFDruU5>0|h_8a2mc);l$YpZwUX88zJG<4X
zw~}3Qrv+uZ@koE_i%Ze*=%1k?GA@1H<vypCAJs1gmn%A{_so$Xy4A30?tEuD<KxW`
zcQC{=M8)!d++uDR{WZqE>ViTG=MyqHlC6Os6`#d?!t>u?sUcWl9&@chiYffJd5j-P
zOG}f`PzVAxy<F&S5VjsJ-rsk&Ply6P$a1qUeqJ+oIAJgDkae$|rVBNqrOLnble46Q
zAUE17c2dCGtCRlF5bCG(?T_xU;+Snu)Yx~tA<53p?n@Qq;^4r=#ht2mD?2uZz$}C>
z5!!r7UgLW~I?GIeQH&Tyo<Uh*SVR6vGK!9adWe*(s%k}+SRfljy(c*1-KKA9YRYdr
z{wXW#zV~g|eC9x;8DENt58oNHupMw~$uLle{0X!#(aT945ElMr0h1=3Ds~Mqzo|d9
zK%~d0u;@=V?-M|Dv~U_y(xRyP6V@Uk&>j8r{Woni4z0@y^>qK)myL~$tn7aV^0HL-
zJ2q}Q#s`F&zc9jo`F;UA4f-(Bfq!$r(tX#Sgj#3Jv!fzi!}mm|E-V0q#)Ekk&p#t#
z0+0=f5ciSa1Z4Z;9wR5=h~es559aNz4vbczH+mL6H+_%w57M_&?_paH3+;P~(xPPh
zqcEeg``^a6(c8jLi*XAxjfVHeix;7h|BUaq$v+3HK=$-8f7JfAxH#cHb_=4*AEg}W
zu|FmTiV~7yfSr6oi+Nixt`%8IL7*pu#(5tNwue$2jA~?AG%+Y~$gV9ItN!x-IP}Q3
z?;{A3<6d`COKBxtn`hQF8JgD2$Fupoz@gKQh(;pxzs-zFPfKixX@mCAzv(`bgV9e~
zO7V|?5mx{C?Zr7?Wu-K@GP-`9{$5I1w0Ww(FM^(hED5)La|T~$zMM4Y-)(Cg8S)gq
zW8wEBu#iN5Qq|%hFwo`vwOVL<68#nZ*=1{;WtWhN5B|}#?THGMp2TKRGdW3G!We&#
z7C(Qf7XABZ0!SLNyeRViCRfKH;~flE@3qIS0ZxMr83jowz^P@%A1MaGM}X~&W;%;E
zD;${p+q|A?wZik>_$O6x-h2PnkBcvvw&#gryxHv**DTI*ggZJn3ehZr6NzTr2EW`O
zot5)jwe@I|-lik5%YUOum82nN?A5B-x?6s$7R?*K>;5ej93gsHWI6gm%ot=FuM3y+
zV>*d*mCc9$=Mw7~>S)QWxDO01JXshR2&{mE`{;%$hK9r~NaqM=*uTG+ckik^`*4j%
z{}z(KdiZ>-!o+@7X_mUh^@6&JuvR`H-Jj|SQWKKg`<&gGtM{_R|99-e2+y^(`m^3b
z+``;~&<gm{LQUIl%z~`6df>EqsOjI5MtOcVGg_wxu<W7|4Z-Vm?Lt(-ix&nc(I_HW
z1V2#}k=O{p4h#)H(k~&4)Kg)TW<5ap-%$5DNvLA3+FB8T?Z8)#%Y4H$>xNMN$O^JS
zO5Y0+J)Eo>9Ukvm*nyA#`13=tlf}Z?T6$XiCx~FAla{-N_%S(yw#XSAoR`t;7AW<Z
zKBOWa)2z8f4qp@8ZBOP)o|~INap_&AYSE`fPQqFZK>hlaE#RKHlc=^Y*6P7p{u6mM
zAPD#PPr_m37`V2MYrh`T3J4w7#S-OeI@A0t%9<tr`QgK2NlQysY%CI;jKJlJWQ7?v
zy=)&cas2;Gc)fHoCg!_84;L3UGoliKZHTlL9kxa<`yH(<H8)ofrLHd}Tb7ufsR%*}
z-ZlNb+x|{j`sU3=q?o11mf$Zg(Fi5lo5yaxzFF)tU5;7oGidv|pD>V+%C&xT^863$
zc^LZ!895IT^Wk7%;H8GOav;+EI}X;eVZ@=XLO+reQ2fzC@E5uWPpQIn!ndwNIl<W2
zV@5S=#K3_4sLet6n^$Q+h+1&EV&ifLWn*u$_t>MH{(9P*@|d{FnnPsg+I`tHjVEh2
zVq#X1v}4B?7axt;NaqjYP{z`y4{CR=DZB|gv8XcdM^?%q%#pdC3;Fe2=FP^&v}y^S
ze=gn{KVZE7YfVY!@zSlxo-Z!-Sp0))rn0<Uj%R6U20t<-5vJ94k^TsXFVCd@@tlnv
zBOGcV+s5;W{*&)fP3yW0lRS^haSIll6H*jhX@6Vcx(OMN`LVF5R<%4*zh;Z=J%4^b
zkVe~T9I>gkyRleMV19BkXJm9@WVGhrX4;L3)jhNxu9&xAWMmzX)vFLej+(FbzYfM#
ze`3Bd9ogPK+R$+8ezT94xk`*Mqcm5K9-@)Hqq~xY|M$<jy*d#Was=7g;bRBx`Gp})
z!T(`al|`CwQ?aY+Ubr4s0q(RK^Q1{oC1l$3-ufWngEKd0<mqEL^<@DkxM6j5)_UY;
zDHW&Q)B4H(*k^l>#L10*@sO=4vMI+NJlR+p@A57~hu{!<DzRF6PP}?Ix_cyJV}J&4
z!m+8vMxUwpzhfGxE*f9@AGFj@h<p8V!8F9=XP)8+y{w#UScTRbLEDe1$+u(DBLh$d
zvXC--$-jQBbz~3_){ZsXUk}Ms{C^Doa1+vyA5JSe<C`XCx5+qf;E><^Rh9qp2j#?Q
z#powY4jscw?+>|il2@{gC(CriOvZ;c|Mx}}Yx8+uxV*T!tU}b&-{B#IEwBDKm7kfi
z@}pzB`D*4*s;4*_zQkaah0(>SRbo;>*mc_BKGq%o<Oy?F*b96yLZVkc(%~<Q%*Pj}
zKwH_hUmeY8)`vclvMjNAh_PEaZ~W-7+04t%v-^!%wENr96ZSG!m$eQ^+1a5{t{27h
zvX$&b>QCRJ{$~W9hwW1M_k?`CMIA~DFrI%tpW!e~nSRXrQ0gC0fBiaI)k#EkA;?|(
z=_VubL#ZK7;sMV8%=LNA{O`ia1db3S3lwCBja#Ig&*~K()&Cb|sKm)V7oTX(Cg&;g
z7~c@EZ4E#n@r?n0`tpyc4i^wkd$Mt}Sz{B##3BbGU7g?y*gfjcuu6=uB)n$*?L8Up
z$f*Lt5#MgiF4qdJ9z2l`3^;xK!T7&nyKc0cpCFph_RkKD4s9AP1inQSZY;#oW3p2Y
zP+C<IJ`5H7&g*-It64^lg*}22;5Hc$OXR%v>Tr|oipDv*PD3rfv%zxUJ)>d@@TMs8
zd#rSHQt`aNZZN{h==g7H6y0Orutx|`h`6+xL>KC@zt>}>UnXW!YJdzkRnT5ZNl74C
zfva&p_W1fDTFHyo5jZQhHSq}v=Ez@O-?(ujOF6S!LTO?2QBZqYTpYj0-^I_i6Vi_!
zDI{^<VPTQ|$An^vG<kf_*O)RXpFZQqcZj-$6q57j>Vec)bs8|c{ndfJ<-VTp-%s}X
zCCNB+_>Ed`?3!KkUvfcd<GcQ9FJ7$qJI}ouN4=|)KfYMj7J@H%wo#>7roTCmBNIfU
z)#!OJ-x1!^kerk>T4^S~od{G=$YY5_A?3I8NU8jNW?D1zyJ8LS#2k2{RhY-mtOp_Z
zG?$PRGASg*BqeEGqAYxi1WKu!ENIb}5}%R5`N97+L90}mKFnhFow6@dYXHl|866Ae
z*<rTZcK0PZuGxqq@JGMnxsXi1OJOgc9*&wcNxx~kns6>OU~|6;2dmGvXKpm0%ud+v
zE~g$}dR6y4`XnFJK3S|?g^Eo|Pfx$NveKU_Sf-pQfu@5LLNMR?=8a871w}yW7_*@z
zGZxnGYa1ltou6`>@!6WJ<1zWRxVX5#HFcJ1-$ZDD281r(?`r34TM%(^dVu1eEB~^S
z$|^C;GEqctNEno;WImbrpAbBBY>LJ2-bHor{fp^)K_c#D43r(l5<YyLw%fC^x0f3Q
z)lBjtKe3%6S7Orl)7#`FRQ!({?GI_XuV%lxylC@x()(h#Z&|Fb*U`DA&GN9S6yr{N
z>W_4(sV|X|7yANPJ)doVuJc%`3|$3MCw^S|m#9tuF2B=s+)7Q%hY{M<{ZOBpRi`e1
z0QEX6-_3(0Kf^9M!W*U5c~B8_j8zp9n*3~=f9hC+Y?;hp-ebKl`>2lO0+8Kz^_R^r
z@JA{B^E$z5tDs3;3)98a`<~&X?tf)}ml<?)GRVH4)a@Bq<hs83aLGV{>TxixAF?8F
zc$05g>P4&)oISje<|B^($d-L9J-eQjoMI#LDkYw7t-s)O!QX|rcx%!BoPKj6^J+ev
zDS#td>{VHF@R;sF=L_cggnz4YR~MBsAiyf|)iL7Vhh2(^721Bc{}U(LdlERTUtV4K
zA-j2>^F&dr*YiJG|8LMG2GQxF{akt?h|!CFKjUSE#pREBjPGCk^Ym1WlGgRI9yApH
z*aGGVs_=BtqGKUkH{yQ}P!yn0$h*2yeut@H4a;i|w)fvf!T6m_ndOPnMI=R<3&YQc
zI0y%X{=4EGlT?}gZVQSeLTaV2O`hwHfX4sss{Y|Oij}Lg8SU6lCrgPu?hZqlxgb^j
zL*R6qnwq!|BC@j1fyt8u|2C@9q*JFii3i!zdzAnUw>6p1iY(NU{J&pWWYishMK2?d
zlwnk85u7m`E@qXG2T1*QFSVCE$jCM>e{Mxerz?e+_Ze1wwJV$V`)TRQA@eVsvv3nU
zevANY;<uX=LX_(maTn;azBl@BZq6e68`>6Qm9zc`Vt-A8j?J2)|6Q$~X%88JWb^aC
z*wRstE>4p~H8+vLWan<e*7FA5ar*V4Y~wIm;`GVpdjgvK*Gkj>sIQ^HKNp_<8=WP#
zIH!8gKVhKkomuji)^^3~=l;J}Ug5G5%YTOXDd>M!f8A0;;N|}Z(k^bcu<?HjqE~Hh
zHn{M#I!z?ZV=zxKUDOQ^AHVrv_&G5V(K8m5Yx(y9!0+$hzr7%fzBrmumX}{2%<n(m
zUFb**xYmprNr?7J_3BR#H$|OSf5ybbB<g#v{Vr6itE-z2e{d}r$7EMZ-h>w^>2R4r
zrdOA0jx;WnNL-_Y_pt+r3wQ6{t(AcXJ@i&Z4HFg>J-Y;yDmRj<cil{2*XDKGnq2Bh
z6nydPwPx4s^~(^^43Qw>HXkWu@^hUUW%_!T%3)z)9M3;5suNwWt2OJB-e|GEPe2od
z&v<uIx$SR^RRO5si|_x0SpS2^N?(7PE?UfSvFmN7?`a`Or6j)Q*V+5GNVdIYfWu#d
zgTe_grW+1^8Uo^+kKvA>WN7N>r0AES8*<;$_dPLdLKzoG-Ha05S{*K#s4(e_kf!V8
z0M<&A)tPHmhsEgE@$K8zRK4bjg}1lZOnZatrcV8ogx{ri{@{xWEp}4u&d6)=U83X_
z_1-H~up7#;BF!>3%~G-Rown;a{>qgX4vvVve&OTbJe&UB*N2ObzjEtZ@!eG=*-t{p
zx#a*byt~lZpC(*oJyME}PfaQ8NZ%Q=-E>xZ-ptH){Q!lIX!(xzc7;>{MF&;b5Q3)<
zjJHP03{C`LZPzYZA&UBa9M&28kWfU+1MnZ+7-M#_zczS{8#7IfqfZGRKH+$LlKLzr
z$?M=xZUfBe=rty>v55&8r!I5(vuk;I0BmS7a-{o5Z!ZouHn9qr{him7v(#0CYhvEV
zI{^Hsq+$PrYFJ6Iq>OdRw$@e==T+K|VzA6lN;DoAm)h)FU0>!taYet(srTjCVI2fk
zuV!vw=$WfB?4f6uOgw&o53D4C1P{BevbD#mC*h+?=_2aK*%0&UDkTZ8Z;*qv#gi%Q
zWDasIyf!j5id_0L#7#P*NS<@U?>`89oE2U!RLlQ!aGI2q6cG_I*A`*`A-fG<@}>dr
z^(RO_W_5c1UHlHaP*h&-aJ)08mZxz0p4IOH)tt-SXunV{%IkHUc=ppIDk@6CZA;hL
zc^}NQOs}4llG6KdLwlEDFzfp}CbqJwswxiB@FU};r6mvhh4>1cl2&5|CMGVOPoa(a
zP2L_F8VUYc20jNv#L8k2mF=-Y4@M09d@fH`x+TkW>ngpD?6^8k=R#;M&-av<L=_Yi
z_NC*FkB>pYiuZ5=nlc@ln~<O3`3J5Nci*A)N2l?27^^fBb=^4LteJtFg{v4RmfqvV
zU--bq!O@H#iWcZ<O+IIcbMR;2EC<rXe~UGWx^mNP1t#LzG-G39Uq4<fc-4O1%h&I`
zJ$(VH*+OSD4eVCAQJd8sGF@R|A-MNO^?HY}-{tvjK6B8S-04<N_EyH+wj6|h{rW}i
zwNargdGVVfz*PH3Pft&Ozoh3rGX>SIbR3o~NcixgqcedGYQ8v36(j;PVP|G`@UZ|*
zZGEIn(qXn`s7Q0hbDd+0(8<w}h=>TG#h&mW5G^+^kJV2En8ZIf9)?Z#SpAt@WznA@
z;d5HmFOe$hww3iD5DOc7Z*Pyr_t+>Z;3jaDYkN&oVme#-Ap6d|FXiAHg}pmn0&nNz
zq_=c3c^^K|Gcq#L({GGdub-@B&eXeYXG?|r{rgwnYqPr1>j;Yzp9|K>2TTviW~S)^
zSem5MvI5Ae;I>-L>w)+ZLcZs<g7X>TUamtQ)w?CJv9a-~#cE#+GTOI03Q=}+bik5*
z|Nec0N;^R&in0#Ii%TKkPWQe>JtisB`|o$Pe5J>a9yJ_Id%;|&Veeq^=$lN*D;$@)
z%bHF<mX{Ow4_T>yY$z@+hLxcab?v2iVBo#m0oMEB!v_GdwRlba6Ob9@<>V0i>m%EL
zmwG5sz|g?*&p?4?(<pwTpzth(cDk)J&1HSKFXdj(nlq=)C+0hM?l3WR&N(<lyX?$<
zwVV1<hlg^Lgk;R-^TGOv{lw=!3Qd@_&y4SxEdn^Is}N<W=#sO@iEE(i*5t_|P|@8E
zG63t!F&mgClTw-%{3lxSay61`F94mo7GTfYyW!W@?(g5jqNAI=PyW6kVVm~5@@bmO
zfMLhQ#bI52@;cfAi!9Zxi?wf;3c-W&R^OU)hNRzRCyie(yn1>>VTA|R_P-smD5$Cq
ztPSQnIXQuWl>GkvEPpULIT_ZJx_-(RtN~1TeACsw{o-u9`Dl9vpH@`V3+#$XIpchP
zP}xj7L(Ic5dAV{0vVubxzTL!U+wM4)OrOK#!8HXXB~E7M7_%`M8Zkga<uswUOB8`$
zxnItM6k`z_5`xW@q#;BZ)h}^ovpHVfFSZvkWOcC+E$Qg!h|nq-Zf$+Y6?vgw=h$IY
z-E_89Pa19pzPN$&{FCLt*Ff}>Lv{#5n+`(YZ|pq`|6YJBMt)ylS)!;!g}xboi>A5i
zW-C+3l$bKF5>EdCd!^UpebO&^_3YG$Qky*i3?wf<pKZIVvKKG8ZElu&7kq(}gTpNK
z0*n_)DE&tmF;fPxZSekO>aCr*Hd$F&ZEfvJb_;M*GqZ}D#??C7ss8tt-A}gax8J;Z
zgBZ0DIE?Br7WO%HHZn4Dbo>h#(!xWEsGri;(ZQk>78Vv4zgQi}>Av~f=j>3g&M|1a
zF~e=zgNco;$F4>+{fByy#^y|O^QY_AZ>wsFqe-U?<bCefI?bKzJ%1sQS(u+6?Y%Qx
zccX>#el4YneyM(=i0g({ct`8!%=GjN7%FTT=>>#oaF02X9^(q{y*@$LdLLLl((t9(
zuYt-EC#H6Gc5-r9qAAk_?KcW&c_4h9KqgXR(0mEus>W{W&tS>peqksp^?inMn)M`%
zZ65X{av3zZ^W3>JxA^_tM%g4?hNxRqH8T}nh+|(@Wdpz~48n>{9|BIhWBia+^~5Ha
zalTUe>3ldBf>PYm`Ooh{N)sLk^^nqmp&@+FHf!V)IRVfsbmt?r5Z|JxdcLd$1qHFN
zuxw!OOB?&1?{;pwiWYqx`!@LNmoFq_5fM1UYI77n>}nF@;>ttegNdAa#`cw5d*CR!
zy;S-Crx#$>>r9HJdCCs(rB_W^JpSD~H60zR3a!KKnSP(`G^G{bD1Z0%pwuv$ubc@c
z3AmSX5f)jtvx!M1?)lriDqX~dm4ziN^8GfT*?6@zOlIXrx(SMT>rUTHB|F)_*Tkl4
z{Ztcp%&>9Qn9YVLyQF;!ac*ud?CA<*un@Dx9)p!0ZcSNNjRBa}*o-;Owjf2#!LuO(
zlL=6EkUv$*=MxuCzxUKkd%51N71+U8Up(aL*XdwRw1m}k!wH16I{;#H&LW$xXC2{W
zdw>6G5%;b+Z%x!hr|ZLth2T-mQ@lLgU)y+n3BhO#S6%}aYGne~_~g5B1waE>^Zfig
zFdN&cFC+-BJ|0qkKqW9_=D-*L(Z1f^XS@tETD2KT5WBz@Y%8<?w_(KEIU#2)y#8rv
zl&-<Ep5!Ka3WrmGCmHsSLfTYeNoywjE>A2*iVOFXPJDB_hc{fkpx_Q@a~<|$V~L7}
zJpV4rHiezE24SP)Q5qh3rYo%kVUxLTPp8Mkv~<(<@E%nK#rpv=b}PyWRJMM@>*!T*
z?$~|C;$-<FO}NcfQMPw?celY9$w7!xCYoANNy$9S<6Fe-;y}Ba32ZE^IJzz#&~Tmu
zDUM!Gr0~DII1{DB8#X@}h%(D<?xZ9k83H0YT=E<RB!uvmyDzXsTjWT#okalA>#avP
zUF{g?>DdjM((E5Cy{hv*sdS`ymS83z6KEfuyu?&Al#-mR{p{H@6&1sp2`ao!Ad{LU
zIvu6V`s}1VaM!`X!JZA|vwT76G%eIcXA!lhPkH(H^tQE2b*kT(a01ul;N<+#*H^;L
zlbDc@5Epm8B6&4Of_LB8*jQEdZHIBim<@ov%5=@dCa^42R8;Th)Mbr_DV5c6aW_A^
z*~A5<b9)y_3KZgUwv|+}Z_hOQfiokHr}up}t4BEX8@dj+fERY3m7+9&=}CANIbK~}
zjE#+zZbPikk%?~2&6t}g=<ez=l*#|_p?%IFP3WaDFm`sz8B)Z8gAf)L7SMZ~L?wAu
zRYFlFA3+|?9m+k3dxAEj<>34Rw&M|0qOL%ZAe@jG@J1R|BdmF=tlS{c0m4OcwhqLZ
z;+|DI+b~?@b+}RZOd2s7!-ddJh#!y96hh#IEF>EE3Og=7dG_pP>O)Fkc1zvyim3u-
z78X5~X$c9WWMrKT8qK~J52d7fn)`|PQkhx_DXLMxIL_p_^cxD!4<J!eRaI>;_PID1
zIruXK-qsz+Y3A7rWT5*AI~l}&%=%=<{Z-YG@1m~t7eHaEwAj6gzXN8*kKDd}8y7kP
z4t(+J?+a;cLAa1pZaL72G4uHP3Kjn6Z9M_VvP4?BPN16Xy_^HNkPpCSeN&sx90Gt{
zm*Q<h`5+M@)&Aw5-z<lUo-f{vfepuv$W3)w$tNT=zN}I8$MWn2f;80<fT9eMakeFZ
z%)q-sz`{(IrrSK7NFe>I3-17gRH0#i8_wuov8Y)J?=<qr{-zgx9Zv;T=tfmAS?9C@
z@u~OQzR+|w7*5HK(a%qx_z@sqdG8O%cyu7|d_|{$BFojc18wBev#x0|uS51Sji|Oc
zLlieyjN;*Ah|FUO=1mLlHWY-MmLt}E$2MJE`-7&AXFFUr7VEd0)L0F^-ShF>0>D(s
zxZ<o@x|ReXQDx`ZJLMgVS2sFAK}N29t)hCOr?zYg234^gb4Epa_#PsX3v);H{QRwf
z0~V5{x1I^{KAz0=uc)1DscSj8goU+VtXKT`eo!Gf?7Kn|jw?w@N}Av&b_%w+adUCh
z<j0R6zH7OOaX&Xu>wQhN%`fDm@?FeOGT4ocjPCUOY+y!NF76bwpKd^>c5kWXwvmqb
zZu(a&fxQQKg~`~fbbP!Jsr<o;2N?FnY^~-4RU@T7gaQX&?P{197+&_Wm4<mh1=1{o
zUoC}BmpK^)k?OW~Is!Z*fAIS*t6^bLYmpEutK5sAv(@5_zipQ`o;UqLtSf(zbA0Tc
zKUg@Z?)kh*C(Z%rje?@0I;WKVeiD%C=OY5|dB;*+GPUqKWr+DQ1Lw;0y>}mvtek79
zuYQ>&OC4!r(D3#?2QH?C-gwTgBCtYwPUNJd^aE@mUss@1+<YYZWkXt;l7*QyP6ZYl
z#FT}=-*SrG<Ts#%?yR>JNOezNaKlX7T<x8%S8qT-fUbbP_M;^+XK`uBGswK&0!&tX
z*E-O)s2VHQSWmfUUEA5+)5EqmD`9=%;7u^>p?4y=+pW&Z+H76AU0!wyW<=RJ7neeR
z@A{=KEMF%Xhf2%BvIbkueAP6#<_&RmEJE+<b~huIfQ44!arey95^PXc*ArP;kCk*c
zR<U<+DWRIiKl=OKs5Xlo+A$lvdgD2a$#37roQ3dz9kof6PRO-?E-spmWaE7{XdqQL
zL+R-j`wGg3NV-zhnC`NPeu~xeWokaJxpAyr-5~}GlVG`t0jW}pW5}P>mmO8MEcU3n
zOC7Ayq)<`bfc&ln1c(>gtB%op>CbXfV;o<(!uGX7s;<8$;_6!8SZO3sKV`>#3)-_}
z#LXq!l`44~rANlo7MvAbZ9x@brLY-zAM{t#jQy=9+2^dR7MCHZrEIRU!z8w|ca{0m
z82Qe1y4K!JVabsRRX=n+Jx|Y1_Sh79DeE#^N*8*&;~1Kvr#19mveih|wma&r)}L?U
zZ{cTE-Hsx$Q!5*(_l+?5NinZg6z7pPbx_K8)b71Ge$Ol;>+cRxj@Zd}LW+-t8)cE?
z_vDn7^V>H<SiS%Js8%kVEZ<H0MWrb)+T`Q4RMzdSs=uP0TrH=&(!^??qL9a{naK3<
z_<da13}x9}2HoTU(UJQN!6Tzpvl~Sw;X)#?ByMFVuFb4xyzyK=tAkeRC=Qc6P+PC2
z4GJ7$lI?}^2Zhahlm1!@tsi)fv~T2e`+4ba@?0mG6ZXw!WsN+pdT3r&&6UmMIKlJt
z#xzvj8WzRhG$+fk48ye@!#b2`N0PZp^*ER6SoBwtr|g>NELRi-r`mV#WU|JV#(rve
zn?zE257n!WEsedW)UC5bV;xfd?%u|E-k#oj#zQo-LgH52(~as^TG%twAJ&HtH^$U1
zU3u$Sh4IWZ-4$=+p!q5GcDE6BpY*TyI6I|3i;@~sPg{}BdAUAZqM!a}PW-n?X*Vu$
z)q0L1HS3It0C{A*Pp{LKv*@)DbjUdIEGRx*kjUu-Ys#f7(WS?|#C_ITMDNT4iWt*1
z_6ErclmrAV6%wq?%+Hf4-cg>h3F=w|SJMic%Pmz`a;LF1=~Z;UvE?T}!80r3WvlnD
zFw@phqBi?ZrJq>muU}b2!MF?yL*9KT2+66q%b#aMnRCa8`d=m*r3`*@y&6UFeJ|{%
zzEpoVHCE4fs_a3>Q(qePbz`!ws#nx3qqC#}%X*Vn+&l`Oi%P`V60#YyX`7MK^mR#L
zC3$C&bdroWZ}JpV9WmP{%SR_eD%rf76^sv6myVQT>6cjLWo3;<Xghn-8Vv`98zW0Z
zR!*bV@0f6y$n{<>0~;?5r_u<vj|M&O`P!fOLR=GLV_E?o85x;9ti+6rA9kA1!YIJY
z8ym@0VesU&8&Q>L>b%!RH+Ql^sW|;-9a1uqEuG}{emBSaAVcNk<n%Pml<J9+#%=T0
zP*C3#AX<;a`@-f&)tx{u>#;XlVY2aSVcNtzUfs-{s{U^dOQ{Q^mtxr1Os&V^PRV5W
zn>TS)B?`vod>`D?ElR`cDp&5M^1o<hORFRgR?4>?EjN1d1j9cVm!eXzW70&!!C;`U
z)rppyyBxB$ZproEg$tXmh{Q<ETb$h7F;u+493>wI*R*=*n3*xA`{Uj~vhfo0nM*C`
z#s&A-QnweLnoYBCaHuagPS^-=F_`l-do7B{=YeE{Kt@4fkTo7bE|Rnrpg79(BlRb8
z%4M~&`Lf5OKiB=N+PumG6us$H9yX5(puMG3bdCD`Y`RaM5_x40t^tT1uH|R`Iq)=F
zv-ujP9kxECaGQYOXOSjXE@8{d>Ni+z@iex})ow3dypY2)_dNK6dLLp>I?<#FDPJ5F
zC1tU3bn;npVcH%9NE=of(JWXuiIbu~UNjA@bsA*F@ie5Qd4p@CnBV^>AF}1@wuU_l
zt9!cCpTTQEK@okRzogHYMlXADc620dtbvS*j$Z#6O=#LggOqPz{Xirgmgib5`$)eG
zY}V=DDz?}NTuUJ!`mz_jEM8)pyf-o}Yycr7j{f^So~mitKs4MqYQ9dTjK5};sC4U~
zZX9&F2eAeAy;iY~B-D>nGi&ij+6Z!_o_6XNuk~Hq{n8uHS*%}1R{pE@>Bi`&*05GY
zXsAkQ^+yVHav2!y#v663wDR(DV0Ue~d0Mp&Mi7aE7|e$s6xyiw{BcwakfYVOa$Wry
zApX#n`<6SnSxx)ltG;5@KNxS1lITE>K6=f1p783`D=oGUK;3Qp8dKzTKHGdZTUdhZ
z()Bl^Qd-xv5epyK@4c51(m4Z`i%o7DpY{(II7>TRBRlZ4AsOxVEB+SnOq(sW(a?oV
zxAdMX$qwL&E4{20<P6EA<wxZ00J?uhE9m?okCTgqNPw1TrMNTCt*c^OdQ&t_4xd*@
zD5;t!euz6l+5~89qhK7sf!<2JT<nLzHC?}nH=06AI`Q-@V5lI1ZuBk-#g*8ExX|FD
z1uEsM%~z6{S0!%`YD?!-cvn;3x>fLuz}rbSIXG_2#{8t$fhtj}-~&0w%=m1rTXDy5
z?MstGB}+?YYu9pxoRM|l6TJ+ix^McLc<hh7bekDQcszzp4hQlLNo=-ts;!2;xL6C^
z&rM^Re$tB<64>|HaB7%0eOdM$V})C-n>h{|N>KrYfK37TiPIa;-pjCb@zNuVu|hG2
z+53k-0I3&;sGT4`Iog_fc`<KP-5}UncbF6y7`P2&Z)a!cj#RMo`f%g|5#>fuY>}3y
zxqT(sby>AO<<rv&R`Ga%COoJjP<5UjtUE=Z(u#>q9w;Ux^_f@cm#v&_M5cx%lW8aD
zMU#hD+f9iGP$OClxgi}$FcCU@CGO~SH07pw!Go{veg|O*l3h3{2c%|gM@HOVN2jNt
z)}i;s*+|misd>^HFJqA6%U(8qe&%@(2?+^=v*(~E%<S5Z2vCN1D5<(8unrV<ZfJJX
zn=b_uQ}7}E1o3bs(=WpdHCqh%76pZ<^)S1xWLsPU(u@Nl80U7YetG*O)C;=0x@P>Y
z8ujX30bR9i?O<!&LVPN`)v)DzOP<%DIRGuM5R{UV%6b1@D~nf>b{q4v8$h*#&pU=?
z5cs3gh0y{7whN!;VNW-B?A335De4{UZD_4{8TIq0%Hd!#THqaktAnI?k+)|m0u)()
z2J@9q2o0n9GwMhu4%iwm4ksYBwd5xUd_6l@U&*Pe)NV_mViXX6lYS0vX-wz0;koE;
zUb+3Vy0ds>{d#9$wfEA)5s7jNWg|zc0}FkEGsT)^)D+q`4w5d^&BT`>>w;<v@rbD3
zB)ZpF8&fPj$mY!a{Bct)Z3o#l_Rcp)Z?%Aq>?|?`4BJIS&%uEkVZV{Cl%ER9MZItL
z<kq~)K!%9R$AW@ThUHxOL;<VcU8cMskIs7fd`*#PXzB%_`3kB;2sC`^;)cS(HAaOb
zSaftquDEZ3Tu_T|)(qMkl+QJq_^ZYy!BqY#Q+P$;npK4bfUwF>v0z4JnXY8Po7}+9
zmg-}|_^$9=(``^ua2Ko7*DdX3N%xop6bDvUSHHNd|HWEi)K3&XOD1)V1@n@P9hHc>
z&6fubhk)OH+vRl=qO{THKl?N4KNCtwM=xuvD562sf5a&v0b^{|w40#73I6%>CnQPI
zGBQ=3zf5^mjSr<j#xu9*hhdM2LR>IA$pN9SX2w^vR5^a$5!2`D;>f`Fgu$!tl!ni|
zcNJPdK`Vjz+VCa=i2&ZxxZj04QARE9`6QPA#V`7i_FgmQ3G=Fo8E$-B+@bY@Ky>_4
zP?e*p#rHig#E&NVjN37CJA0RUbiX*SaW$Vm)zyW20p0ouH4T4%gsWei-X{}i=6GJi
zS>}3QU!VH<r(Z7B5PoLBR##UoKPz=W(aoJ<`C1?f0#nM=2bB!ebe2BybfwO3D^LwO
zo(;rz+-Uvd`~~#KRp&3@p^s!_1Z~ITh8i}KRW%m&uvWf2d2DJ*FW(DFBe)Y-uwvt)
z(E03Xj5W+KF0f!^9UYQhD7Apl6Q2301H7Sl8UqgxN=p%uksSwB46LkHQ2mw8c{|em
zM=8JRaX2ZY*xiRny(#zPW9f%Y7u5sJ%*{c`9<mXTygXhwp1ExsOP}1goRyUofP!Ax
ziEha};kN;63QP`hdbHjD=M}&Wa}NK;Lo=>get~Ej$tJ%m-$M~*W@a%lUEx3gvRhEW
zBp}%NA-c_PGaBO+T?aaVoWAMV5U@%MXxcmX>;;__g!mVRhP2ihC>Xf7=}-;;$O3(k
zf{JRY!bGM~-@u%?_8a6>a&mI+?m`sB*m!u*jdyjeydsJ-I~_60MD7X-CSHub|Mes~
zGEyB)XOvcef_m0*0~Ek!!>`D?H079>n71Xog-SK6K_o%DmZm|wbf*q~Yv$1va|aT!
zjbI;8dI4C+Mp2HaoqT;Ith2%NW*mDspr9t0tN=rAgi=G`t5<jF=$I5!o`Hn%ku2dO
zscZ1Hq>k`}Ys(BImn1x)wO3a+4dUCc4T3ak&>x{*GHUf$uI;XfNJ{$Q#`Ghwfl+Ve
z5hE03Axb=fS5r{AU83e&*DN1tND%_rXS4zZVeP;cmb{rbW`l=^w>4I^0`-V%-G$-3
zVO$d1vV#zaYPi6*R9>vF9e5t@Zvv=H0z;0fs!&Q3niGi=cV1P|m_R;0-|L5};k5Uj
zqJ!S|2P}sm8&d<mTK=SE;7L_rTynjc*nSQoILa+vQw*;HO^;?Uf~(_B$!Yh6NEmra
z-!jzXoKASb_kDeRJEN$y?_%QOHm>ER0{26^ANv8!LjtO=Hg*Du4p37r#-0Gd6_yJB
zex`&E(7X1_ZqMFQwV&dq6<S)Ev1apGS+s{Y-znwS10X@&1IvHGc^+ysX=!O_xa1Mk
z;-{c|+$aaGE1+9AvR?1JX3U%1+ScZwUfW&PItfWDC{#EY7~JHMP=^5?c`vn3+;blT
z0|Tb;#0+0ha|AMUD5xQD$oWI5=mZMXmg}7Ky_hOj(m^+ez*N<~;W<zt@Sa{4WCRK^
zcLy-P&P>ghLX;xurBKjDLC0$Vpr5F=W|Dmh)(GWxEw%WCaeNzYdk|h9Ja_;VE(oq7
z!pzK;yYn3!I-f8wG3Q2uiDmmbSFYX1YpX6~K@6{Hl!#I;S|;{`zrR0#-<tCm2nSx!
zxI`IJskdj~;II?Nq9z@2E4zW1oV@!>IVB6>b%6>7wY;>H4l?ywU@*>eDb=zOIHdGy
z3e{oggDf!u^6uq$c{u1qMnO@u*a|h&kf0zEQc^r}{w}L(G-&6#al;05LkPPdnPr5i
zHGOYsX+c6kTRk`hPRU~7^6c5~=k|@&AZF{UO~}d1YadwflT+|p#>B@n$o>LVx&Pyx
z1oM4QA7D`NGkAa7A-`-i?!X$3Hmz^@_7L&_UgP#qxG5lVCI9z|&m9N4M39+%y92<9
z&DF5ePORJut@w0wZ8ic<m~K$CgbFDXYd%{I{i$2Ypn&8FR<TUK5n}tV_m8ocO!U8S
zHpnHI6%FZVX<^)dv4jFuDU|0ml~5+RIvr_7^k#^sYN`lS1op*x%mtAF9su;E2-;u%
zlIN<ZH&GTp_?;%~RNHQxkCoa}h@%qp`t_s7kH!CX(E%nV(yzg>1+b)8E=UaDC4saK
zgMp@tT+yvssd(gCDcN2^LPD?x=qAwD)up7c!-ejF@eSQ5k%=y-u=qj!0zfN4oBg4O
z@oP;{Ly%pcf41RCR)DJPMqQgmi4Fu?g>;dj0~25qk+9OCp%?<@TGKRs=Wo-}dKo6i
zsw@~}L4P%7c`g1<4<O(L^znOQ-OrJZM8mn2b<SPFq?>sI$`9*6gnlg7aogmgY9|2R
zL{W?X@n8pWIx(>`FTO;(YWccU0D>%xQ#1;e(QWc2dS}Q}Ks4~M?kg=71%=+2J5qn3
z#RAmuYmRfy<u(+tjzxmVQ_h_Js9v<(mwL20k*MAd(bEeWI8G$`nnCb|5_xE7=ye-`
zJGTD<2ffj#${|=6Qh9Q8FXX2S3k!M{+llsUBfUjsO<$l=o33;O-F{VW&LUK)IXS(u
z-iKor_Q!lXcHMn_W^_B?o9e>>m>3xDphD?Us7uJDw~N8q3P9AvD_eri7&Bo(K{EdV
z%gJ&h6zi?*$<v{RZ+Tj%gcKALx$<NqR_Sxode_|_4<9}RlkJXY8++W7aQc_%S}HHB
z?2kL)U{vm^^j`awE4tm_1aIA`U%!6s-r;RK8lCfOwi^HRVo7okXij*?yoa%?NO~oE
zK2*a8W5-Zj-T;?`<064G(-q4vpr?lQ+!{7T#N|&rPa5ywE87_L+8OV?$iGMlq9euH
z3mzBxT3W|Y>_}3`17-rp2umQ%iT9}lCL(yQY86%Mom|Pz)~7PEUJ8|2o61k`!E|*~
zsNg1wApWnDA1{IbLwfnL&d5MTr6tc94!Qg|kRa?tLXZ@`OB$0}rPOra3C1SlgmO^5
zQCqN_#v!$9US$XK$KzX_Z})(iT(j(W_1eicB+KDq?IMj5v6&Q6PJ<?I=vxd8>tN&%
zO;<v4BZzO~G4H))-)XF6>}tWD*i+b=M>n~Hbc55v+}wN_G&Lx#e$|;!$KeajjNd$&
zh6F8vF+N_6t<8FSG$y#q;8JC{rf{B*GLwuzwqE`d_^BtroZ0ksC&hG1%aURe_XBOE
z4-dW7Gyx&<6_V#qh9@yCY9=}(ZqqHJNlw(m6&1I&;^(|D>-KKam6?!HQZ_(02{2vv
zrh9^z_3MSjDtCBwA!yS0?5o01WoG*9f32{(X(v$O$tWn`JkOjBra2V5E9!c8yYp<z
z#76MLRJ+)}<3it{i36tJV8^9i(#xe4ikT1DH?FF^+P4JM_E&NS%ea1-Nee31Ji_kS
zooL1!z21kDmhx;Mj>1>dTjocm*OsuEo}E3h2T7;`efK>H+i`v#><{H8Zf%fMTl9Os
ze43H^DK)A3{%FvmdEVm0gApAehs{Kc=SMOLOMjhm8py&M4SNJI9iZG*>vqRs!v)hC
zy|#|UU?Z7@EWPrxpP9Sf^7sbN*{aItna@JC4*9PL(!SKHFr;;jlWrX(c~FVIJsP^j
z(^(3&-}At7{iVw;;j;1J=#a`g0*YTeqLUZQwmQYlo2Pg<+wsjH>C&%TgnF(+j`H`K
zu{*~-H^)p|nRxbW>%wpQs%UiH6pQ0nen#jW@|gRGVy1yH03?*PQ~W2BJ8c;ufmhU`
z1)}f)yO(|Cg`Y3SP0UH`hSVyBi6%D;?M6+OgD!TelC(<u6*Mno^{Yz;EKNyD&&>8-
zA7|Sb754ZvL{Z&{LI>toQ(msJ^Yd+L=Z_U>(fi`dtPZ-CJHeC9Z-g}bT1)SK6*rd#
zyntFsJANT%co(%=UbfBGc+tepT-x%nxl0ol{<uv%m=b=nLLLMoj3U~}%*Z7Vb_BTH
ztAdkv1wTMo!;Y-BofzIztfOOMl1Y^s(=A8scugfLN!?8<#`fGHT3dTc*|ZkiV5Du+
zl9XZj4crXC7n9;Cv6x!m1$k^abroyBz>bc`S@2{-XD=J6*PZ1ifA$_Y9JV=8Lv~K-
z#6i=&{BUQAM|wv^;7t4b#{rek%|^|7cNj_Ql|9-l3~RacIT+JerN-{szc^bjwjq6D
zid_xGw7!y)BbCo9llHhy4For89NjOBQ_)n7OXEbtl1=g=!ompa3U1%Xdskl0eJB;6
z?x)~T6-WmI|8_7l*f`f&yP@~s0n)iM^K))>QbHoJIK{9!0b|K1M#hBH{?@-GlR>$q
zki!Ex!u>J2ID|)_eNi!!NFor&A^Q|+X`PuxA9bK;XUUw3eiTyZzNt6V{B!|#i{AWN
z%`~slHeKT6wa29HM#~ypd#(7+{56Mj*2sF0cnYe22`^(Mnr^K_wkVswW0+Zm76hCY
z^E7qE9b1gI@uHoN*`~z!>RyA6lqkxE<z%yj;iaG|)=Y)9qpPA4Q=U-IS|k$|!7LkF
zzR{r}OfcLN77^imq1VdlP&lbAH#{47H)A`!ZLWM`6S_4ElES=-Ta1U_2FqES)$dV+
zHpr2XlmA&xWN~MnAoJ=4`sxC3g(3R>m1Z>2jEO5Bj}xW^_pL`iJl@iOPk*bgUnC7_
z5cCBJ*iWAa&$;88dDRjgX_si!`m|-pKWfy!`cqVfh0R1HdTKWvIP3UQO@8|S^a9+0
zo)O^ZZF`%w{e9UsNDOmPpcO*K=FrxEyz1G*_-?JVev3D?ul4R1;kZ7wgkf{vm-Mnv
z0ZKm!gmx~CR<yG_rtCwogQh5U_OvHT`Gb9_O${j#Xn|kT2V+h08cvo|J#6Ky`2}e;
zL@~E`pegVQDxQQM7Qan-gF-^W!+=3`y`}4Df9Rqiu;jP_!-1Z|*%Q~=J@4(0@k!!L
z?qc`3wG$>jy%+>GCn&k$(GD$jPz==75+YSK6?)6e<MpOT6;e{YYODB_iMepVG6+!Q
z{Fbzgupugtf!QB}^f{?O-*Cz>H#dhbahxSO=WWM4vF7|46i|q}Fu(6)do}DmbkY&4
z(~b4zp|SK~C&C`jR;a7Es6|zb4++Y0Kt7q$e)vl!{}M&3#xA{F3*w<4)P!CK(anx*
ztb#-g8qXx=ic`2|eY9rD{IaXirFn3<0|L6z2O(+UO#MYG*Zb&(5q|5b;O;ty_2;%Z
zkFOuA-R`#Srpy-hsRfNp-?RLAzlte;g_-A_9OhQy@s>LRDc{0yvdrqDwv#=BD+>Ks
zP_CCcTX(*`VC<!o&K8fCQk7SywT<?oifi@fwsoGzZ|830v~S3JS2!|Jsuz7esI!YX
z)}3eEJ(*bfhT>h?0t)6B-DNsUp|b4c%8Ytde=g^4focaKgSo-O!f-k(*U1=jCV~@V
zp|3aaC7-QN=4EeCyl+=veOxGSeeT7a+FIDLQYxCs=HLP%14b_A%u=|#gJp7mB!{(d
z+6uquNjSOs793s-#b_GEBao#0lQUFMM(mBA9$aZ3N>n9tUXLxmfYj8}+^j&8enZJe
zPJgbQi=XipJb!+sy2P+)H0Z$ovauy}RwDP9YcM)G-90WzHn*U&_?`}Rcb>^gPJ4g%
za%(e1dt8zarn+BKc+?c`i^GJ^T+4{Q!=x9?6VQ|{%ds>gHRd~mmo=xNNGN$CUo}??
zV`6<QHKrp4z1pI-pV{rZWWqNY<$9Z5f0QiA{@w9*2H(uON)?s7dzf0T_ruf6DU-u(
zg|!)!gPpH)Dt*gDG<vaW|L(IPe=eFBL-ETdZ~x5I->~VpHqP+U=Odg?)R_zKgRcfS
z)VmaCoA7C>^?qX>LgStd`(at0NH<<j@$0WHpFLR~PNt~(jUyOrDi`#wALI1l9lo(&
zIySbI4^72^uh6he^mF@(dc<;3AGj6HFT&<E(^N|1H+hE*8x6(0*MFdk;ZuI@d<_*1
zF4L`pB%d(9BY9?*my6*gl)H12lcBW^e&+##Gh%|~y<4K&=b#et*ru#Jun)B&!F^B7
zR~Q#9ATYI<Q}>9zxw*5r&ZCFM=k;=*sPCo6z*0}7oSY#yEeGz)m-j|v&9`~ZE(W!A
z;S^s?3h~U)&*iQ<zoReib-NYN$?dYf1-(jMP{!f6^v5Q1*ho6fo$n)yPKUHxHut>A
zaiz7nljIT6%mcT*ck?11q}X<KsL*Cu-NhRry}0PkqZ=uqJ4X7cj`K~leg=P>;*-7?
zuH}iXb!~kP^W))D^$RB}^+B)M(L{9XUJc|B-qv1-e6iQF*m^-XA8zXdgZiz*Pp#7P
zVmINiC7{;%`wL!F1R)+e5s|}s$-VI0F;Q%OwrhI`(>R__xpk@7Q}ZdP!}x|`^msWE
zSJSPujA@+O_akkVk1Q=C>8u1?PL4bQG>jSJ{|+c+%!1=B&dg=aai**QU$wLSP<yM&
zoGMMw9>HB=r}ybaU<3~S!e5qKBMz;`UoF^pT!{7{y$8)s1A;U(5JD?f!BaFxDTx&M
z^W)D2er`th>fIV9-M39|yPknUx$h~C_>fO0^2NNO{fOn>(MB?t+8rKI$|y*PEucsP
zU0frjdS|OS(e2O*Pg61(=00z3rkwr;o#v616?(dzk`mtOLEzW*J*N&yE%w;x>CvZm
zc%auDj=mUgj8>Reok03{1_1!BKW6K5HDXEI4kk`?$y1avrPryMwyjcRXMgnMNo383
zF|I+fwy^w%#!c?K#-KBduEW=V=ry>jdfyHe^@cc{dVZEy#ub&O$(EMYDC$mN;2TEc
zyGlfUTGsUhj(rT5?71p5H~0EfXsFH!F9WUy>1SHqA}5F5GRWf|_9#l|UaRZ>k3&m@
zx%5^$+gK0j4Jb2zZ#;Yca?{`MiUMEEe;|hsH+ZJ4jmK9^;tVH8ddSAad5}y(G_K^K
zaa2SEJ3G5+8T2>dph-P>OegUFa*S!~;4TrOX$<$*26^&M-yh{28}>fD5G)Z_50B6=
zBmy!rAl?JzcU-rM#}e8zZBoS5y?4K;88O#;>@8dTNHe-Vn=}Py`Dko^6WIP*UZZ!P
zuJqfR;4&KJW;ckk9=Yzlljh+$J~oM<THj2P7^fw@tt19#6#S7;py}|7&zWmTTZQ4*
zS0Dt1bD`{N@?!?kU0w24-`Xn7S1F_Z;${#1!ZTE2p{EBt)(K>7FLUd-1``Wok`6pC
zpq&odM8_cGL`O$&-f6|2@43(4wat|6{-Zf_|KM!=@EZ==`2pGGnSt2hD8=Tu(V46J
zPLSoE1r6%n0H0hsO~v(D0jLi=!&IDH36B?@>uR1r+9KAelP5yM|2F%zQ?ul+Dc>je
z>Q=IT1)bP;mO)&l@f<=fs+qBbS7xV^zdu_A?R9h%U7S6<2F#_SDcW&S#ocZ55c7PQ
zlX<tT8838zEce!JCrn(kHVZblqp*$EUn-wP;lxh~ftbWvxKm#NRhEX3LGdK2U$XwG
zgW37<Lj;5QQ`D<1G{Tw37R$INM}}p7+f;DH=1#}BMwOnH0j2NHzlnvegjNqz)h0ka
zy$12zb5XTlDy|$WGgXxUhW~?>1T+gdiqh5LDlC~jaex_i>=F|Cn|IxVxDHvWDYroS
zgH)4*RB3T~%~_;q=+B(=NA=ljHCe=^yDij?_ZALxY)X0^7Bf){*qA(w{Q#Xr{WS~A
z(;y8(ZsZ{(_yWFq01o>o3sXjc0#y%RZlKi;T0b<QhlZI|jkVuErj>mvEBhUs{q^I0
z-RfYe>taF8v}aGz(o2S(%8-!$i^jqA18}3i^rQ=ywxLjSghKhyUg#F+aG>IXXH(nB
zRAEOzBiYwBt*uN}=^Rzl669Iv6I=3optS(3<_|Om!snnPnw0p5RrQZl!TvpwR;pO~
zO%ON-WN1hT!h<H7!DH+j=DtgRjAyxm{{64=)!038vGgTc74JcW&3NsUsbAI%c{C&e
zOpKJ!c?Nx{Nm|l5g#ICOpBh?1yA)pVCX3PN7A8QA5Za@knm+1>z<%@0_JvU(-%5Wb
z)Dr8<9iVe8gvOVExW|zDCb*E9Sr{g$eZIbzu?+J6S8ZP!PIVh^?I~$TWfzr*GCUd#
z2~oyKGV_qkLq%jJ^VmR0l8luhB2y8PnNS%rXO4}?Q06JKv-We&mvdd`yzhtidVNTy
zz5o0E-}i4=zqQu(_1pd=HkOC;$eqf{O7J07KMZ*P%<TMq+Nf+DvB#YuQ`f5wWU7E&
z)qkG}8r?Ma@3T2{u+dzQOk8DQ5&ct@onRGue-wdt<f8WNE@Qrc-8)|v6lg0QyI*lk
zzLmVpld+$#gZQ<`q3?9N7CpIN-=dvJ?4?VWSZqFR)G=>URlJ%GVN1X+j9dmR1G_hF
zRG3<IZ`h%P1($`)8jQ9(yH@gxTrIZmtJF2&J`=R7+PT3ZkNWH1n6FlezB=prQm+0Z
zqoTpgM3<)J38p&@H|fGu0M>{}@bvW;Bdl*b!Nar25woTpqSK`>P@%+07sZmhHb?;)
zDIth(cTwPUc=QSplW@<tz{2F<-yX+D9!EXh&V5iZSop`LJuQKdI^{E1S>=4^MLd1)
z4V#w49b%$(sVbr`0i^(%La?9bh%ug;j;%F0#lzEAVE@X0U*GO(UdkrA+j+XFpo0X<
zNKhPeIH7lb(QlIZ_wxYxdi>plpTGEsMf+tU^n5c{(mK>WyfX;ns5RzujgIa+bl`wy
ziVAbn<*V;kl-|~w)YaE}V)i569k7<7gVC6nN`1R{Mq0K0Z@H)R3=AjUT>3(Fk22m&
zB0Rcp`b2cYNI}6litAry3l^d?M8o&|Q>}71^^kLwH`pvzq+|w61F%W{n9%$XwVo0_
z4BD^%JY`=s<EMMJT^b>ULe#r<?Tah|BsEYDgg*A2zi=F~+sBU=&Io%2%d-FG3WzyM
z@z1lTiq|tE0ie5|`&_C!2>=-71h4CU6}~^TGJrj7izkIb=eIAomyY7z9!%$u$67dX
z$WznO9*QZFQSmw8rmG71(Xd7YS%QUulka37MwZ8Zb>&%g&q2Ac3!<T17pu8LGRoH5
z$*rF9<~Aacc+6oRYbc;wD7kzMc3{ebv2Fb5B!#f#EkQ<%EH8_X=%xTm!npLH^MEAA
zPe3ZKX|-V9MMqE1!QK1ybh(&m9lfgVxAyd2;E(k;bKjL>nu8Xh?^6;lFh4-(5D6I7
zKf@y~x_fDBN57+~Fc*AyWG|rjd&l>Cr0O23gu<g{D%7oV!w#b<B53I#Ie;sj;=o?3
zh4!Njd|FIWPrQ-9IDZH-BJP|p7xW$vj}Z8uDY0?s$EqBE6W00umPV-cISQ7?&IRQz
zXm#}RZ6*OlM9U9Q6d|2M0Kr%-eKv$FuI)reqFQ-N2je>c_F~=Ad5_5ph7&BdYiIuV
zzrY-D2XfpMqjMA@!0i=(`0#7Knp0%SaC>^hyEZcKBT#>~s_K)z0Sj;1>E*9)A^}vm
zlJ+?G(4c*qpaL;G`{cH>oXf{WKW|B=3i%k+UH<3#$OV(dor8rkcISBMF7_VJsh)5v
z__e=xFY!p@#r8d;w`)I^3nUB0ix2tj7ubnDWkdAL+EvjDYlEJaeRUT5Vs-({mX^L(
z6Ci_(bNd+D<b08_cAj*f6y90MZIjhzuYH1og4SlLXf1o2XQ#Ih{+qD$m#@C2Kbl!|
zzqInEX~zCWf156?C}1{{nJ+;cDqElXia9~L0Wm+IJbAL8{J;f9Y-LOwc=(X2m8&6z
zRDYtq#=35arRBPP1xNoZX`87+|0S%<toTixNDU1QKR>$O+E*{ij#LFo3Rtf9TiSK9
zL%k4|n_SeeH?;gl#eU4myc;%)eVV?cOYJo13h{N2!r#k31tRnJgo5#63q*~6eh@i6
ziPj!y*^_P~Y7F841|+Yd?VfTlWAXwRyRwo;CnxDk&|R%R;gK9Ck@~cctEPM-Puoy)
zy|pi}08a+fR|kk=ez6qK%-Fep5VOv!gis;q&}pQwHtwrV<&l8W%;MZ+O@3di^xai?
z-R&_JTO`&7Ly$`TWWy{U6gi*QAM#OP|3Z7hi>JJ~RZ8`YA2JQ_ywB2T8HmJA#k!Dm
z%Dqd;SN9fj3tWrRIs8G7Wfr6h?XEQK;?8g?>IRP^xJr{oFCzDghgRPkoVs_A=aXfT
zHwV*ycj(#r?UY<n>nr8r=7vDZGWf-#c2hnEiPf=Jz(50cxj<TT@7}iH{t`R#dS7*m
z6?0QpDgZC%MFZzVeuFaSH%to<B)B+Z472*NiGxm;o#5053`Duv5v-B|zoYnf{ahwy
zul?~nZ|$JI%A8e>WPCpek<4+fo1HIdUc|)c581W1La~E|RPB1!jq-eA!b&xg`E5s3
zZO6K#*XsLdau&oq9rU_P`*Ri8Bp<zXFKZu)<cT{z5ygV@0a*Y<L!TGs!EQS~n66g{
z0>k#WMa6W{5BivfXKQ1Qa`islYpOJwj~EK*_FYgYu<$Z`nuIy)$jAs-98N&JAr81!
zuGLlQ;X;8)EQnUyD)C)h8jGa&dS0htM)$#Vr*w?#2p2HoZeSE?7uW1!USP~uv*cs`
z>%!HmR~sq=wV3E~+bwudK=AO0gAs!?p{v^i9c_5eFaR5j9@~2cfV{leDqors%nY=&
zrdXDjKIVJYmttE^;5|dvpv0HkB7SV6ewgTVF+HH8FY$V^Za%MWh2<s7w=^~vOzmmR
zPxD$`PfKMun2=e+R{Rn!S9j~z2G>P|54CV|v$l?I-D2RJuMew_F&n9u|5BHpo!wGh
z?eA%Bi2}&O!$VV3)87v|wpy;}q9TzJ%euVy!FVeApw9pwm6es*LQ@M153_O*2_pJT
zKSxH&yf=y<M8n6MH%iBd|5=69M#SQ<r>FnI-ag2yFW0CfFMkUn9(-6^E32wje>c+A
zb;m#rjL7$Sc?S+0kmq>X+pF(pxhqx$HQ)qz=QD5be)#YKHUqc=*eXLiX7uVt=aY|C
zbL|hr4gFb1qoJjBnE<7mzpwAW=KSiayFP#a6uY&xbwh1!g?+K}b(v)K*%wQn4;g`W
zK4B0PD3b`@WP)12+9eEDiP8pRK~>Y9|9-4PIyIx@ke6jto|&2H4db<xtOEo}5aY)y
z-6%-Az`O7Od6p%_--BTH30FP*^Cww#wagqj^`OvaYZIJ;pVXL4I0M(h_zBA~GIDcH
zCIyRzSMlM)5;6r_=oki3JUl!mv>NX2A|ch_R)^-7p*tZf7=uDWps}{JvEh^~BTrw&
zN3{{DaY1GFL$ZhB80BBJSXf%B#)>SWa>DB<84Aq?Z=bF7oi;b9>YzT`T3eU;Y?YN1
zQ<wRQ+yC%QNil=`OG>H&!^2u_N~r<iCrzU+uKh*MdW^!?JtejfGRn6$MOQ`!s!oZE
zmtM4Rb8{mkeEISPQkoARgxum6{Cq}Bva_=j60V`%gd7Y6iK#}fC#0pj8}D6&HMKBk
zEOkeyl1{Mu@hNbAw8_QE$=U`DunOel@#pM+K$Gd|<pn7gZ>PoL;-a#WQciB}*Sb2)
z$<;M9s;a6mi)TXUVPY!w-dF{=jUg9?QMW8Dp@E%)hV9p{UrL<w81db9cHTre|MOQ=
z$G2}c_@CkWu@Kwt4xo3XImm5(>sD<|jqCdIEE+th&V;PG_?#!YFc35~F~J}iHK{4)
zoyfk3GdKC4Ic(HG^?2Xf$_kqa9fdYG=#TdH_M4lVP@+|M1)>|tN=-cndaaC%jJ5UK
ztgNiyU}YItim_kDG;<Yq|LfvSv@N}_uUx%KmSa^7x_Wpt#fl1_IkPX`=h0C~Z$OM#
zv8$Szv3%Fa!#`wc=mj4im1H@5_@oy1k-e;KU0u=7pSz=KLr{gTXL)ti;am(rivGge
z)vc}S$B3Uke{KMdj42>aCRPIx1L})Ev$H3;xx;^Fi!m}XvW1>Fdv+1zsAYXVQPJ<+
z-G{gSptS++`NN?Q0#HaXd(Z}B&3k)$%a(rx;bA~Os9EcxIu)^so&|XUBiRSFCQ#?N
z!h{Y-j=;*p^A<$~-?Ms{0ANcan9r}QKY#ulWO|&|l9CeqA1YKF)jF2FQSGS4$mylM
zUEE_yNmVtgtjv35?k7%Ac1{iklHoW6j~}Z*5vt9-ch|1$4<E?vIt*}Al9Lb6(b1vi
z^EfB;BDg6zCnpCImZxE1eZ9T1!HoFf(63*Z8f3dnlR$2ADC`A6Gadq9kY2F5B>1e{
zzCDka=_VFeUS2*iIOgaMQUdxWaBjhHyZneD8=V#k3G7t}4BG4Jet`;*te%GLj?}HF
zs2KF<5i}GA@6G=)Hy^`j8EF{4s+QeVa4`_ny>tl@w$r@&1!$+lZ+zLkUjGR_O+itS
zHzvh60np}eL4dnY8as=Gwk$BP7CTcwKmZ<)!y;cR%u*5Hc2f<Zam=^ve;gD<Lm-gr
zP^{b(sZ>u##|=9XbPbrWq@<+${{4Gvc_;)63D_e$KmX6tQsnPK`g6V5ci5`X^jVsl
zM~8-n!s5p1R~vRa^d!MYCF3fLQ`6HCOnUnJ<Ew|jE_{nI3^(_igaoM*^ehsd+Dmr0
z2Z#u7-@e7aD2DBB-xd%Sjz4R86}~n|@uo&0KZr-e;k5u3m*dJ$$Vr%_Yd(E4f%?fx
z=zC2KH1<cu64TQkX1sp>ya&6ox3BM~Zf<=1QE$oFOI5%(nvKl-uzS!YkU3jTLb3#m
zS??+&W6#G)CjfV1^95C;#ZP9;QKan6!psbVh-2zPH5r~k&M7(5(a~Wg#KcO!e}APh
zA11Xk-zZE+M@Alevv+hnBI#Xn_wHZPHMO<3$a1iQG+EEAh9)K^?2^yB&J3V5ctCqp
z0-U(+8>q^j)L(~y6U)l6RRT_zqobh8ktUcnQU-lyBU}gT9RhyT?s)z&Bwkb6%bUMg
zNr&Iz4+;tkTg>0%#X$Bu<Y*^eHPb+21*T<=e;CW=+vH@YpLGYEoECAyI@{WalxL8}
z5j{cUldon3|0r0NX3rjGKI1!&A2%Yxn$qS2y6d%I_jPn~LQiIk>Gf|iA*dC%4mW$q
zM@Lr|BW-tAS5Pm`^6@pdwEUy2yr<vGDEDUY#2~5t3nm(>tEy+u=7Gls?#p2&ChQLq
ziDfzi{n1l#eQj+9Ll}u#f-<^K`qZgYX#NmQ`bs^7B~~$idSv+#v-tBz8o>dXRm9lr
z4lP}G)jmpA@y90UXF%>a6Nh`bgfu~(f)5OQyqOyu96UKWsi&tm>-QWR))3&HQl=f|
zMAfGq?d+OcTd4_znWd#K@me-EnGq2OLaOH%7C>&j?B;g9>?Cwg4Gj&*#BdYY%OZBd
z<IUdtJvCvUafd<$Ha0d&OG^*${sQo4a$+Lss2oOXjLghD9tB|ZC6vp$KC#nSuRR_w
z*AydoYyWxH(sjfhQ(7(L3uC@*+qT(#lS)uSR&kl=vL9<^?bd3QQd4WjqZO~u{aG_R
zFoNK4*e7-QrPmrgt&ze5U*Cd)f||S$$P+ok<TK4sy$`=faRS=X%n|))9&T=;bV11=
z^CeQH=QK&YchUEEB4qmuQrPK?3=CbYXBmSZFtPhS$;imi9~2W8uU%A2yP_YN)Kra^
zSj!pm`TAh4FDeKq;hC9Z{|$)0F?SlafR;q({yu5QD4Cz~gXgBH>2&*cnD;!`&Ze~3
zJZ*z9RIGxwB(WIwvN&(aLhAYA2R=J@>=+a}wBdB7<uCuL3Oa|gf1`RCw=<GO3e(V-
z7k6tJcl2}|P&lb|?I5*Ztc187_W78P3xns_U?UX($`S!~-?G(TT-{Y0NSMoNx<!sB
zyT2wUB%CpCe);4Has9ZC78s?Eh?S!iV_9)}ZZz{AQxar@gD_PIP=P9JnWqkX+*&Q)
zT9XuGwY{dItQ@hI)oUt%c%6>{O_uBH;$qPVh0jM1OUo%L!sJ0Qv(oc-2MQY=ho81b
zEg4=kyjDAbSi?Mm;08u!9Sd1&Z<|FKCc))JTSzS+MTFKrLLvw0<BFyxdfii<3Q1De
zHnVIFj_loVD46vs=2A!4?dZ5liU>I@K$Y(Tt5sKu<Et6se`nB?ee@DdEb)7GEa0qV
zhn(%Zg&u5AJ9fEb_1diSZXO<C?TVzzBP=YNoBNYry%K=k%$(}@_B+xEAOL7BX^XK;
zLlcG^XlQ6?BCyIE>)bJkIGi~_V%gbzQfz6|8ih=h_`&!lRcLqlp8+Ea|9k8^2*$&8
zkB<@bON(SPE4f4EKG^g<EH>jS$$0VNMF^8{V_REzg)!xh9Zm$+4c4iy?2DOgK&3B>
z#FDugP{52km1XEf$i9;I56kB(_xX4}LLoxpeeG@foSIs_Xow1$J*w@xySfIhzd;e2
zm4!&bxzUe?Og_`D^+r-HWB*?^79xE~l3VK<OWG*Jzg6(&xRh6a`BE2Qka95xrUk56
z$3=^4n`V19=a!b1W@bKaDAH5!2>HA?J1eE0rwmTHWxoulh={XSPPL;_2s!yuJx#!@
zQAT~cpIt1c{}pXLy`ex}v1+~5g{ws;We9HW?)xe4l5hR6-y5Wk3&)-aQy<*Fe=hTz
zHtskCU!(JGVl0h-D5O^v4^c|V88vS5?A+;(!#wCQ4Cc_D^X1?43T4dL^=CALCc3OX
zEGWC+=PG*sU=$BgA|;ov&nX%i8HsI8(oFqn?&w%rSZHf$VKv&!Ps+E3sCszksbVyi
zVs}OPOx$*b)qb)qD7n#y6zaadY`-px0Q#dW64eqrJNv<)<cy4<@;I*GUM*32Asxz{
zJCQHp^Wn@;72?l6u*os561BACJih(1p)GBrowGCQKG)p8_XjSV;Byc=d7fk5pfg}v
z`c+F?d;9F^8`?TLrKKOy2y*NX#FmEr2|63c*92B3CZ@tl9c}G%=g$v5#cnu3ui$@x
z&DZXG*I+@F+n|e{tfPn*N)$o@+5O4Ath7}8{6<{RU0%WHf^HuVfnkG?>+8AH^N2z?
z22^{Gq$Vcb^LUDgvUu^~bqb1uRpj>wnAJH>k|gI!&2KwUbO>Ux1O!m72nq>(sh0P@
zXpg#(z<T4x4NqA~$PL8Ey7ajR?7u04WdTA<O3N7-F!$eQ^@z*M%X_B887xaSYr^gy
zcU=7*`;foLZg3mg^&nz|0tyZ!9|m!EL5YveMjO7ZoxBC}`k9rFU|ED`^{l*`A6c@g
zrlG;E6>xeV4qQ!~A%~BL%&V4}29*i0`HDPO_&4v^Z5<avydvLOySTWxVdVnY#MHbn
zaCa|4$n6)RrlO+4!a!_4OeNPp6Be77cXm@vObo(V9ck}X)HTz;!t4NmLfev^UD2Ny
z${ec#4(=*|UZV8t*ROFj&cz^sgoV+)XqbAkh(HV8xG#|ri5zL}Cu3y5U~Ixg;nQV)
zx6I5^($cyI^*^<=?3-3Zy*kH2C@LyKjSrAGp9M5@)PE?1x==f}=>0h+C@8qOnB_D0
z?W*sSvN8!bRb5$B2`Dl*Ayb}RIr3D*;hEA1lDfT`fk7Y6;XMAIo}P|=$KBn%pgWY;
zAqOkO#BWjqhZsjkM?^RHsg#v%f<bRC*aLEB&F6*1#bjDxYQG%M6^HHHw--|Ru#!sy
z_&v|Mr6YCYwZZraml+w^0TL}y8oB(lV(Yl$I_XDWdryyhYm)r1kEyS1^D*BKR^3Qd
zflbNbANaO%Wlh<A7Zw(<NkA)gN2Q3qtEjm6MRYV=MM}}<!!t!wa}nhZxqQ~qc~)Y%
zG(Z1J?SzDc1X-3*uY?K$n3=M2uylgCrRDsJC+>W2TAtmYG)ipL2k@4l@mZ86>E0tU
z!i$0y?=yhz6qpnGsvkcRksvcNnCR$UA$VC^PmYX0QvEtTz34{u-d=xOE35UNQAUvw
z5om){NOmBq!s6#SwEeQOpvsHFVgg<SfNO9U$leG(P8Cf})9BHT?5*zU(dF155fO2i
zfgwO9k=I1nePQgu19UGI=H}@9(h?J;lhs!|%ZiFtA-sp|4;4jEPY=0|we6o89)`y1
ziwQr<8ThD_>|}~iSe%=)u(E<-(_xl&e$%>e>{p|5Z*&~`s;ah_(P9A+@z7{B)z`zo
z>$$&wHPCAmQ(~wz_x2sg0ZqFGmD4pdv+AK)vg1o)Vj>dyuU{6Ps^nw8Omhh@9?&8V
zLq-0KfxiB*bvMjJm|0l7e|*}7=mBGqfq^?s$(-<~nfBRwA^YmQhzK?1J)kbCs!c!$
zJw0*~6WQQgxy0ph3l+8J%)nhgze~o(#^^{*O_NbY<mL+aS3w_$_{7W2{i)yeu%LM}
z#3(p1rD#UbNAa!;E|77@l{wQrdxGog>YyG+#{+Mb!NJhDIB(Pr7)aqZx3{%5k1wqK
znI?mbX#CnbI!KU)9eQkk;6Ma!{fp<%VU>jMqEJO>gut`7sAw;fP<>O=X->}9P}^f`
z0{sA357Q_~EE76tC_vt$1$SAbEvJE-&e^j72R5+@e5J1ecaf8;0W7jlnoLnmNEn3N
z1V#q2(a`}>ovp37c3tSL?pL@j&m?fObBII(b^$3L$47@7Ep-Gy6`%{~6Ma_4Qvm*<
z#n?m<3=bvrL6!HZPtwEPM*~{p77~dx-WBlY<3+}Qmn_#$dJwfYJVbB}T~^NM`A}hp
zJ$(xA1w1jo*XjcBm37g17>$s@_wP5%HxIrbH{nb83N19+F!)Sr`1trF^VDy>@u+Xz
zdsHF@;RXEg@bESgDGp@mpAI-lyY4NMiRiJSov{&;u*||75fC3@F_>Ayv#x+)D=upx
z+5rQEwsN+~6241szY3rFblqN7mWpL}H;KecBtSh+&h7Nn8R_XHLkUF2f&y0<ZK6Uq
zAVft)X{o8*NwKuBs7d%6Q3U=Fa0-EmX*X^k9z}|XS{#vp1fDxa4Mma6Qbcs>DCBUU
zfa&0;;QGGJ%;Y4n9Y3CeWkoMN{1hh|Kctj`j%UcU2y?%4@T6BA9UYzSD;7R~eg%ja
zr4)GnhwvzrQURZ@um7~VcML=^H!;J`>wtzNJ(r(+{rMp;ZyzOk)$qv3s{mUK4L6}A
zLp_d;8hv7!+x&xL0my%Vn*wE8^R$ytn4nz7sMyrhw7sL_p-dvm_VFD(-@limq|Mgu
zZMe{?uC9)LR*p!yS#bwD8Z%^4krNrF##oq(m-k@M=a!ZhtN}S1rKXOly!rG=CP582
z3v>_PplHJXLql6yTkmbbPbCvK6i4pfF_?|v>fz_mop%8$S!5!B*TqO`aT<^-8W?!t
zsDN2dhAk+i*75K1w_%6EK6z@7nE@&2Wl)~u_n^`TGaRv`6b@d9w+<=5J8f-kv);ZH
z$Hqd$yyNnA3NdRRfpWRS6}}5$$5m}eB`b(qSO<VBcpLzKu(fp-ppI{XdrG<52@b*!
zc-7&4pa5H*n#Rcj#D#h5A8@&el=hqJZv5Rgl|!sEjm>9E-9vN|XcK;yp(6!k6#Azg
z??OcJSue+=R?EoC&-T+oflr6{O?>Y{?V{;53AC?R>!UX0-+F1a`kF64FE2SMiHw<%
z;>C90yQ}$Sha*bS#j=ES#*XIwg>P-B_tTG{Y<yW>HoVG<jcDuRB7KcE=iguVo%0TP
z_Vs7k#`+VSh<)s`3$;t+N2h-Ivhv8`!z&n+9rFby1rzi5y1D(5l9F?w%1I#Z(~e#1
z#P8iHp}!K1p&e;+)}8!!juibjsPH*FV2*TSDja<^0CPZ9U3pR1k3bB0#VQjlb#-+a
zc0XXNs$ILC*T+gE6wNqN6Lt=+>YTg8;zB7!@T=TpR6*4YLpI1n%hU}GNkP*}r#ISC
z9CY6qOS!ofR#w8$roFS1i#sMN>d;E=N9uv8smyoU#J!a#&CUThj?YPFzzKsePffev
z;X~>|7GaVyATAxePex_sNrADte=n0j|L^JsUbyx@eOV)!!KaaS<&>uVNv?B8f@L+j
zv~r1P+o?A)nSJluDKI>iPG~k4^z-}O&l7#G>ZAa*6w#}6&%<wRZAr<=I|h#(!Ecc-
z>8;Pz|9pZY3ryr?QlO+3z^9ioFZs6r-@hEb$Wsqoik_NmRMsj0kDyBlYLL<V)NRLB
zLr~D+?!e>=VI^tH3GOgBXe?d5Ht%p!`S~quZKo?Or+W*B#>Q#}?EuJO^=In0;1vX^
zmxzc6sx+v-$6A6T^g_)1cTwI0sKnCqCG+>}#3?Q=zzm5gDWgAruuXfDa^1zw@<v(S
zxbZ3@19%9M^!HaxT=`Qb{!Zp1q};4ih9PDtSCkbbTRLfdrxKwcZytA~qYJ}@h+Sq2
zjehnlEj=ARMI~E#(g{yuypbh=SHmj~hjo2@9oIJFnXT<P^kt8-asy}^OX}1)1s;Dg
z(c&YUDkwa}8@r?+><Gbp5LuQ$xy{I^AJ%Gd#{%Z&9FR2eI7pCiFSLhWKxy7M|Jdw=
z94e^u_bbK*8zMA9%#Mf3O-xQ=VyUZ#*>2yj>FIvG7n?5bVtI(W=@}WvIXTPR7cIc|
zC)ao6M^JH=F;lKy`=hx7D-xbAKQ1gWQ28b;?L3Co4u4SVcEGU^RiXX0#{mJ*v;%Q*
zaCZK(rUtmBxOffrQAe1WYZvVbd*LTIFpi||;^HF4Oe7kk=1oj&&G&Odg240v2obse
zkE3H|7Ke8s+E8>DT3VebZRn=uCJ~I$C%Ud84_|z+o0aeb7*QCfa-{$wm~w&|5Yy!5
z@T3J-XNIV+PWJZW^Oe0r-Q?)_HQ@7)oE(0?5%%P2jbg90522y^;>Z>TWO0^Y{8oGH
zL_p*7$A2a=9(E3OpVSO%we{@sTS4mqK>FitJf929kMyHqTgb3mR!Zuw+6jHw-Q}2m
zCETlNXlQS4CV(0cD8t;*(cJtbGI9xk5XQl$TjzrxJz9Oor^vBiL*pBYE|hG5jfF);
zL>O5er+Uwa%Dulms^Zz@_`C;30HgKK?x!#@GNLpi+;g2DQTFl@k9!8oaMVv@Z#G+*
zy7y83qp1mFr>OY&{(Qee;T%!-h18IcCP27oZ@s*{P%W*o<r>PrMc`;|<`WSqDk<?q
zF2KtE3aTe0e(w%Vdhyt05-*gg`=n}EYdl_PVM0PhTig56(%54JF0PE{&l&Zjv9ocS
z2=`L6vH<Nh!1iVr`K5x&?mD;^ISoEj08LR7CyoBGz6N(2I|ql#Z)iHnIII*AIc-=e
zTf6>xHvG&;w=7UtBJS)e<d9Cy&JJBa9rg7rKR-0SPucj#m26G0CxOvJSEG+s<=+?f
zJ=cs@8n%xK+1X^nTWEg&F_zwHS1kSstdl6co9cemV2S67y8X&}md(w}Pi;g<J{+gi
zZA~u!n&(WGTv6NUa}6G1jg7nl0`o}!04>qXxk^MR?4h9n+zpqvr!0^7j6YJGih%PD
z8Iy(e;c*U*ffW`O7BLnuAiXf#nY=GZVigh+l9Y_E8ninXLl$lqoD=Ks?LDHJnwpC9
zta;_i%SQDZ+1g0P6^b}}X{o8^78Y#2%buI4z@Wb&-)NlDvYOoqq}nNH&4h%70hCXL
zJxW@Xbw>O_4hF7`N}>a2$p+9Cyi7Mkd%zbd11St;wM0C$ud4v<PDz}8^P=0SsL_Qv
zkbDPOlW(c7MwCeijInusRw91FCPHikM7fIhQrbs(H!B)0N1aG&1M^czfKRmQot>ST
zJ91HOV=y!@4j6(lnDbyz?bg!Z0eE^r=4!IAzV3-*m!QVL%nZo$kr}F@@!!A6)9Y_-
z<aag$L?sp*5pmXa#`xYb8Me^e+<6GaL?;u}qYdk9VETpP5+CSVS(DPzP`D+h#%yLp
z<tVDCINRBM6yW0GO1qN&<8+Dp$vh0vU@}W+LP?UOp62Dy(z`Js*Q$Iu<s_HkY10%A
zMAG?dMrYW4p`(k7j&{WyA1(S50mNP>CnwaVE1sKZhc2<T6dT&yx^;NpzFFKyOa-AA
zB@fCgXq|jOp{0k#<&7Knx8CRHn`LW5qe(~r6pu<PP-g$ZgE#T)DB#8!F(I(Dw1gcZ
zDmXZ79OTx%L&Q`HtFg8;879Ypf(9SdU*&X9n}QRCE(9M}^!4kF`S60_&G717l3Zzc
ztiS(dP0e;-?tkx>#}E#U0R7f*kD-D!6yaKU^Pz?Y%xlS{4B!bb8m?-qCrz1krm1LL
zEitT9k$)cUJ#znaJ%$*76tQtIb&G+2%7tWgi~z=lhH|feehBq0ic;rQ2T93t2!PN+
z^|iLn0eVJ9^RQ>8oGj=|&@cz!f{7@B6+Idnb2OwJfB%ho?Vl6jDxDVqhD6`S%|O<G
z6@#xDfB}p`FQSgqx*)>5pzf@Y7?tx19v-~Hf_TG@=687WHMk*A*CgLJRA~72?KTz#
zn@aG^nL@XD&Zw`N7kF)ddppOfKlRFs%F%ernH--$q-^Q@PBBbG3XF+)BX*o9PLs2C
zVR5L9XtI-j>VElka?!C$ms}7}Z8G}nmk2=LGSWlRwr;PNtM(RzobA**4Z86wclO`S
zw8G~L|4W_nf39NwUw;}No1ZT%eF7G(@KzQt)1|nR^Ixxw!u2Q&Wex_RMLsIwIW|1p
zmYXq|hN{z@TK~-Umu+x$K(@YVYip#fU0~6E%2(~$wL$c}FoVG00*!X2QMm-9gIFI-
z<%gmmi9dJl+-?RwV<3sB-UtcEco@XrhHl+I7Ygn1c61q-`)I=+($W%W^eVQ$4-*?F
z=S^g69BN>Z_P1|)KqwbqDoU}SNJ(*_5FrVLH+TUU%k8Jc3EA%3j4|PkZ57dHZeW;*
z(*!5tXG%8#oS<CC$wGwu-qQoh9V{dih%(aBl^Aa$H6fa3zI_XuRd$Cw^lrPUUV+(q
zf}X61L}iBS`0ttR^5yC>PZx?Ymn*BwC6EkB0`vrSO6~V!Y6`s~@E?4jt2Hn%P*A7?
zXo2{@Pa29+glq6OR##WaGkX+C80*A0MH#kZGK{hhQej{w7@Z<p^gp;jAUB3F=jmAA
zgMW?I6bD{gTl>VS0maMsd)PJ-VgRUsEVBat!#c)+oj{aES1HEQgl-!jU%!&wl@P!W
z11AC-^&pRKQdU+uOo2n?WHKKrBtP);Lko%#>>$n0@;!Hn{KCT3bQoUYGEtV6#tBet
zF{nlVHKw(D&mOb-=zpLYg5Y#$_VJPP{Kg;epbP}mj+UGo$4x*W?CZ~`Uy*81A<w#&
zpu47|qN-y^e)DFxDgu<&l`9N~4=;LhQmku8U85S<-rUo3YUn90Q=j(_H0aAzGcd^x
zCO!m1Z6db`6)N@#vJmCAzY2RmEWuhLU`fRLpkx^j8vj$%*r=kU6fVaBq7MQis67T>
zX((odPSjJJ5IuVIC?eir#Su6w;@;pwt<23`$<gk(Dw{7VH$a#YFg8G)i&|-AZ7nP#
zR={j;gaY2G^Cb!o=>9PRM|-oJ{||;uA<UvTjg6lm;2t{UFIxH+h4{9z>fIDiR52X|
znE)-Pku8B@gAQlo-=e>acL&9j5R}pYM^@drD8}+%a*^8uHMZu6J@|ZLAFlG>(_gv!
e|L>QfTRR(_K5s9s)``RuQOI9Xk;#-czV{#RR2nw`

literal 30523
zcmeGEWmMH&)IJJtL_#S=0Yyp)l}5S|6eI)$NhxXR?vxe@=@bzKr9-;AJGXR;bT^#2
zy`Sg&$9Tv2@_snq4r6%Zy<zX)T64{L&1+tBE`#J{CGf73UPmAhc#<!kDIgG60uczb
zeC#Xm%J;wM`S9(U)eALS1OkT;^#^TW(&G$)ph8GKd-~cbX>HO`=l1Xk_tvJFG*|Ug
zFJHn;s%o6~US2P5HC=h2fkA{reEU^iwJp)MjnW5_S@k{!><<ICuYSV!Csn7qDk_S7
zZN+@G$}@>_cjKjAq@b&z!>-$fI#;KFk%M86ZBIPA5v^LxQ|$Y`H_ZEfY9d78Z)cuS
zao6AlywoNc_+MjeT0?mGnb;$?TkxG!G${UtD0cr1sV?6eqJjSMx^(cJ!<Ud9-tzVS
z|M%Aax6OoEj3dv_%gZ}nVy;+u8*KlVGg+3~VNEry|4&HBsjBW&h5hR9VpGfVMdD%{
zII(XRCx5f@Dje2-XDTsRnoYD4vU^>ep9=4^QJciRzajeKyx8;7W79lo<Nx0SZb%jg
zA`q$zpqyJnUb-%?sePmf@yEMK<d>_t-AIrb6ZWJ1@UtHH$8?DyUd_=JcB6*bI&=am
znyUr|FJ#4Ka7!#DG|19r>gSrVaT>Q~O=6>ec)5UIJs3GVR;?y2oes^_oVB8Qh}A0`
zrd2Kf+D<K_^AUYRm2+T7GxzilP5H0*C^vi{FL~|3gBy3s-ZuJ2jcT{zuuwf2@YtuH
zo3k2x$*@sw^!wL4KSW1|yhgTzS`eS{qr@AcSFhuXYf+S#tsEWJwx)lZ8G4+=vn{SQ
z#ee^PTW2scL(%wpi)F1}y;r@@Ff+p|tbK@yXH)^`T=zCAdsUT{hw|@-Qnm(APDLht
z9Lf2V<hEIEJH0t^cWcu#?J0U!Tq^2GC|?K^v0W0$@To_L#LyTy^DcF*$ZLFjnaJwq
z^r_!C!r(N7d#(XvVJTBn`F}Rkak@EwIL&5+&luAd71f4Aa-H@XEiH)*9ml)((y>c+
zn>8FlQ+y7LS}xa$41a2B{LkBb8@=~ew@Yi^o;t*JpW^z@_4QHm+1rSVa}}wxxjJn8
zR9rFKwJt*1EMT7;N{IXarltG)rCl4W_soGSfvY+E{=rnWeZ)|U1wngnip$oQkn{aX
z$3|%>e~lV4F#VKSC?-{BCUNW!|9?mHOa>PWu(u*{dQgmYRsWA2pPH=6nAP*~5^*(g
z5s@I-!@6=?0z<>}!_Bbg!F`pb{~m~flqts}R!jna#`&)Jqaq{P#GIdv)>n;FB5f~M
z2FoLSzkHc~|Nf(F#>)uI>a8mSgWCt=CL{?zd{7AE6wj!)B$KV~_ik{jU*cWP8_HkE
zC-xO1UQ@G=IO3-`Nf*O})D1XtEr*P?8vU2WKh{-lHs~u<PCO$=AyZT5z(8LGCn+c2
z&6}@E3_1@!QLh&Y2t3feCV%_Jm)J!Q{D8CVR_Wax2Z`tQ%uGxl!&EApp1tHa4!$8;
z#Yp3ulUL5ixxbPok#clw<TL2Z`!E9=yFhhECVd+}U}N0U$H2KgRm|DtJe13Ph?(ux
zEtJ<Zb$(5owbB_(Pjp;s+Z^k+V7-mTAJRfKqwwNI;AAcq+3c(uU$SBZ&6mqJmOnLh
zztW+6dd|HwW;Ry)WF2{{)*+`+N7aCJ%xhgl<JC>qM^g0k*JBn1s)hPiJ_^}iuXOmS
zq4SmEzjuB5^k;Hhr&QXZvfsqL&2UwxXdh!u<jbTMwko2{CT@~~s?&;Z`%julY}^m=
z?j{X&^^-34-XE#}{WqIt>rC^aF)(8|P4<RA-fi`2@w!>|pp)<hW?WJal@5gtmf8LL
zJf0Mlj#-+<U-3C_?PjYL#W1RU)kT8@5x!@4n`HU<voRan4EHl@J&`L{ymhEF^Bk|Z
zsEwf8BFOXC&kxaNmQ#kAIbH>r1RXD@cnW!(m8thbWc?UWxJ|5OGGr`sxWTT^d*cyC
zE@H(?Hm3B-6M6jGXuIAPDW0J*Os}+Z9a4k%h9tXUS+qTl7h_47)W1pD!P?h5S*%aj
z1@S2<5<XdD=3k-Hq1mDc|2lezmhE+G3@-|;^`TtLcj>Z2tzw*)L#Yk6U4@LoM>9UQ
z%RS~}`ET(k-&DKo&ou`#R5>54AptA}FOKG?7^&|2ZaEx35jxh_RvxiEJ?urx7P*;^
zJ$7}C>&|ua5>G_1H<yUwm-9qR-6fd`yU5>ubbsaVv$1HD<vZ^#rHO}hbaZ6S>fNG~
ziDP{$>c?%=oe&)#ztTN$Q#6odZ#`JsL(OwFhkJ8+ns^Lr0bP8CS42gthM?@$9jra9
zuDU;-7q&x<1X<t}q(uIOhZ~cXy3HT@`ubv+-o(Vl2Kf7LAd4j2*ueZr0Tc}>p0cW?
z7VK76v9Bs4OuaX~Y$NO_{@|}+_@lde@BDaF94tNbO*HIyHLuo?rLeG2{g?5p*RN@6
zZ~+CP>b)#$g3PDfsQd8Bu210kqDhJbGEQS<W02Lon_-~3{PXHHDGSX$785HZ716aR
zcfR{?1?8`=U8O@jxq)%Vd$+Dygkp;$Kcu_EV;&D4^*YT^H#8)KiGe|}00B?vS?t6}
zO^x*8(7Dcvh3F&1j$YKOV-caAVbS3YAFtqK)@rJ8rzeT~;p?IK;fAR1Sef<NQa3{X
z3w9hXBHT+sherp!2C;$l87;TObkuB3b!{p{$bE?E$tw&z0|45??gT$hQ!<xo@^EkY
zI<zQs;yT=`R}6JXZ(RL|*@Jh_|08ZwOD4*5|DtgDFh>=wT8RI_oRzGs=@%j$gnk{`
zm=|H)RbRA+-ozqdw9-07Z_iy&?($R+DA2Z07Ex*{apcc?tcW)B4m5t9t9EtIMKB}0
z8L1F~GLrT4VbqK=>!P>p|8b&C(>?^(c!6?`u2-u!b{%dMI$=28FHA19`y!rSuz7^_
zu|vsT0lIMh1Ey1tvzxrUX%6cH=y{r1&fTNpMK9|Rh=?eHF3h=ub#x|c)v#A6s<rv1
zoKP`+!8gDNt=WtG&ozxJ2w!vq5k{Tx#o#(|$T1pKKWw~%T>hn=L-Xm-#m?!JCkBXc
z+!u8MGbwdXbqJ`?@1bSU#Cy}$^`CAiR+a(4F+fJF>N0t*Lxp%~MSO(AiXha%)b-Bw
z!kxeruw;!IH!HnTt(+%b>41Xbe+gjDfbN$5-IbL>M)WaEj;q9oO*BK1D`a)gXQ&g`
zpB`i>e*a$PLNs3TQ`6$V3^nJzyR0|Ybhq}*22B-L1EU6Ap9=R823Z6RnnA=6*$ecN
znq#_}OA1Q&Z>SjmZv*ygfe$}hNevLrJd(e1713PB_vIEV4QoE{4B`UE4(-Hk2Qt6B
z!q@-%9XCuz&ic!3wPze=nCl$YDT;2EiNz#)k-tE%!R+{&whigo*|u0(X6R{TEkq4=
z@OO8Ylf=5nx;u3URd4?~3#umF$7rFe`*~yQd1&6w&PT1n`Fa8Wk{1PJAZ+V#^Bndk
zHCH|(>_phUV5uXNsc!V4)z`h2?7*^f;3sAjiKIh;>%V-3&Gt^=Y&feM4abT)pXd>$
z{uf-HtkGrkOSVLIyNWv^mc_aYFHoT(hGNyePJe$!T3bIBK?}e75pmu7Ijlx5V;oyR
zMg2rhtp~$F@Q}>zHSegl-5Nk<;9y2kV`D8kX<YZXx1h*XR-!(1ocBH|S4_K>@C(m4
z`j!XfK7C4cunsaY-XVdN-z5EgHrI?JVrI3qYhWOi_lM$(+^c@2FV1R6esAxm1_nOj
zVHO9#Aa)I3L?mF)(Wm~m;a2kF`}YsgTi(AX`-0tu#^{CNL`9b#O!J)-*D^MC8hha(
z%@=3eZ|RwUQvdO>CPlgDw~2@l;fr{6C;oT@=(1F|k0jo{{lX~GX7hz{`pTZxFKjfl
zd{tDE#6r~<H$@|)u3Sk+gnzT|@3&Sqwe&#~VY9MTi@z1<z$2R|<KsmafIqwS_Jlmx
zB^X6i18<a<)6yxvLG<@qBF#dirQRp?JgLKNi-<}al#IB|*tvP-WXGl};rXqiRVKbI
zR6=D?(QWXZ2oEPgs73r<Tzrl+6VDjLC5xg-9z@oy$OHu)zY%S)^%^v88Y@7Zp#ML{
zp=QBM%-popomJ^rixzHIq4?^Rv-o@=g<vk?8ecM_B&OO%T<qasx$UWUQi=J^8(+W`
zP5wD=EIFTVsu<dyoa_YwRjCPv8Z+CSbP7IwbW)z=MY>3G>|*ss@=(<S?T(HiM%GtE
zC_8KyV&a8I`&Djoy~(+U;H9Te&1V&0%w}a|yk!+9<NWmLv5Wi9b~{E!Go~9+$sufd
zGj^H(cYqmiPX%gLh9{A|k-vfdr_{U?|N4{GrauPd_rAn_Q&r>g^Czuv5FG#V^wx~%
z<pnmrxbD~Ai@*X=q)f|U@%qm0TdvZ!Zg+eyn&+yF{)!n5*KuBA`V8fz+!qj#$s;fu
zvwB}Ie}kOdZJ(acuC6)QBsTGXcp36x%FOX{f6?;SM2p|dk~?Aq(RXlrwzinW0%Dq*
z-;OeW%b?~a884BvwyqZoNcix<YVK-<qsspwW(o?dZ{M$3;UdenGifB>y<;PmUTF!z
zD=I>_v1M~&P2i|!@1^3o6U)+?o-S4R5e>cXtKa`w{73<{&{?1JfthKgdllpS*|FDs
zF;SO8uAX?kd%_g=xW6hfbftuS#68<hS>c{?avGrN`keEB4vLm$CM&N43qJ{CjP_c4
zds}yNrV_c(`OsMh!_j)_<r{a!yw<+d4A(<uiwV_r!dsmGpqK@qSW?n}jg8#<p_pi2
zF@vb+)`YF`#2uC@FIwrXdMp!?PsZ3e(J#Te%IY6}{FoTGI7JxWzU}LcZoQgwhv&v}
zFEz*Q{{GyQXd1~FgJLPEUta(D+~$<i(JWnSs5yF1Oq9;<(q*Ne%wXjUrGKSM6(i4`
zO2;=}(=M<0$HX@3iyHs<ZSdgta}<PBnROB;3B*rV>|@W!h#*2Knj8E~VuOrH{yA3-
zwS*`E<p#dM_*V?^kcO6!vD(@Wf&s{GhyREwlmYykSC<ydFs}>@=&;VDTsHaR{h55T
z9&wZDD+&ypkdbn_^Wp*haaSkqkYH6)+`Y>xUmN&3<lnN92kUXdlyxdz9-CTSpF<p{
zo6D?+-8CX;y#1&X#9nfNd()}4<kik~5Q$lD^HFV;jLR$gTTJ#9=<Q$nY_N@*{@sfM
zMmJRDyxZ2+#-`VzZ(#6K0TbZi?Y6G3e}hS}zu!4VouODPv(uS%aq#nB(tb^DXGh!I
z({8qd=`Vl&{7DzNvKXsPtc`lpLI7oW^kUUw%+Ah^nMiDuuNPu(Rb{gE9a-X!@Hj;U
zg^k+Fi-YxXiD%Dbl6cr?X`j2@MG;vB6|2qajL$A6s*>#jb-X6G$wCt@vG5E=ZRu&2
z3iMig61iDeST@6iFI0aS_csOIYL4}Wit^nEZhJdI)=*#n%E0li2}z)FvPonuJ~`js
zgl(^8xy^T#JS`;;o&CSV!6YoXTZT3^Hmf5!)!KZb*s}}8qpZ$0!O8qAS5mzou(!70
zi6Mrgwwbg`_r?6l`0W0iZq}yoSnI;O770*nvcdNn(Nq$rsWQ}9;a3Pnu?gHybmp48
z<b;l|VPmD^k_ATHUYiWAanCxX<cQMj{mB}2-Y-GvesR3Kw*QiQ(r#mdw{nuX<_=J^
z7qQGLlukO-TNFS7>&GqUD>sLjZ}l@15!#B1HoV8ciHL}}j)Sw@oj97OEsUPyC5FAN
zM@w5WHilOhVs@LA<M#9CC^w6}es{Zve^bEqV6Du09?X7nuztEfsvXRNe&08c-Ad;O
zo#2ia8agJTZs2>_I#+S$vtQE5xqp**ZI`;@)1-{y)R?$NC~khj<Pko;Dt!5Is@i(9
z`kx2?3#6&n8Xs>`f3U20cRPHlgegh3>Np*oKSBJT^RINWY;QF_N5}F<Gpe+294k<*
zuJ8{VEK=|_)C9uKoh%sakC*6vvce=%n)4=gRIe=@%q+;P)cYz76+DlHD74+)h6UZd
z@@!$@%NK-F&V)$d`+vTzxH{6-^-Ds$21BE27L$jJ$0;8^S)S{kNH+CtH0^nCgvI0u
zy7Tt-*sp7Tew=PeEd|&9MO`|Vd3eg^$B%CJ+RTQp%L)3mgqR+c(MU!muJR_FJ%@sL
zsDvqj3s1ZDg5XQDiuT(Ho?q`28(R(3()-t3LQ&QB+{*~x@6s<<R$|eT8@+dzw@2P^
zdL$LvxJ&%oceY-ut+w0t?fm0@iS1z()<n|_9+bPHsO2+X1}|z>`E`XGlp_CrIn@6B
zySRk1|LSREp+OFX(+t5(iRU?vMCcDq(y#Xa2x;O-PGczfatt&1)wPrHX;xdho!wz@
z366hd=F2SQ*=}wFNYZMrZ?}ZChLXd6{VV_5FI^%Y-QeYAM*G#Q;-$-{MTM)p|GkM?
zVlGl{Oge4&!*G`MZuj4qKx4B1EOfdVpf&xs9_tbZH-(#krS{{PD!2y6|G>%{%jDC;
zS|iDUbQSI9zEn=n7+?VZKGME$a!Ngd^xL;SJVP7J|4RH&3Uf}qmPcQZ^*(o=T+NWn
z(xm$O?!O;>c0Xtcpscarp!m#lQ`<8{t;pnv5o(A3miPTTHg-Ozu?`2RJs&wCp-xe~
zMc%AupRt4Be^!3XdV`i0K_~ZF;BCWIM85i4)*l-5U*G;`E<3)xjY%D3;fxpJmKSJH
zIi;DJbpQL@DOWkJc*C>3TjF8j^DRz`M8!C;Ie$fL5C?Gm*V-d%e~k-Y9O)GYi@G6D
zn!*37ucV@3(l=47`^vOe_WFO;Asb`x_xdgFe>v#?S$QQDE&clMEc%HPqBVQ}{bpX8
z8nVe9wb=jt@c(CNm&11{@ZTfn?28kkUxrB!+y13l3c1G(H8H1vz}ZrwrL2^c!gr_{
zMFTN^hQ|#K4sO<*ZV(6^QBhN;Y1eujPPyhCJpqyU{_k(1x>M!0Mwb`o0R)s3goHag
zoh&H=&QFAewNXVYtmMh-!+@_}zs}9gwS?TWCEzveikoc=C>jmGCzk?!BrtG>!Vf-b
z`?(U$(!|6B7KM|}`N=*xpPg!<emg^6RiWGQ&Qf>c?)AH5s4ra57d$^h=73T@>ul`r
za2CVmV_x2J`_+*uDOi!O@k^p0h!RTk(cDS+9m~q~lBsIf!`0Ev*#OFm_0nk`ai&L)
zoc8)92q=Zl8VQ8=Hm7SldjEd_m##Gec8}QT?MQF^{sKO+SNO84t*vi$AAyV6n3Y%4
z;V3e0STyM{ZZ7Du$K$g1qu+R;%mDU*jygw`=7qemu`xl{gGZM#GBOK^aih7KrlsS{
zy(yb^d`!f_&APK?*09{B45fZOt$dyOKmBQ^dn>lbJBtz&&I_<dtqQxxt>HoczP!D?
zJNS=}k7s9R$;4207s~uVvHRtzPtRvT*Ujp~o+REP*TW4ge6l-&E*!1lo3)oUXSI(Q
zP<p=1aF}#+Q<F?0*GsEcs1s3mu1mqHBeKZtxHdL>-T>S5%p6une*2vDfUP%?JFGg~
z;CSe#VrLA~>R3VAn8Hob7lO}^6JLLdg9WBlZH$+0LPpfn(<5Qlc$EJdKG3s&USWS?
zf3%~grw1Pg=a&3!)In%0QxpxZ2-yAU>xyT`AP$0c|E$e?(FKBx{kf5<s%nO8^0Vjw
z)XSsr@-UMY@F?FKmZvFA6FF~6{|Y(&>ASy4;(tT*?ACc8hV}F%&C=!BvM@c7&kfPG
z%TzVw{sg?ltXc8;IF-`#te`^pChECq^c!p`LhddXXUBeierqYpS@s~S%(sS-ykUi(
ze-p}_<6ZixnDKZ2BrYy4J3IRmwS-o=&0<=Z5TW)H)NfE^er<J|YYGy+JS!?Ib6RNo
zq?j&qmmNHpO>d)eQ)t_d8YRmo(z|r^_xBt1ehvu@t+87f7|v4h@$u<R;-#+9C$+mg
zKV-<WwY7!QKGY?;ecQ=$ez-tAr$P67dOEZEufS@p8aHQI*-)=9D4{D7viiZeB~IWz
zoV^F6@D5=X?V2jnQD&CrlTFXd(^g8)N7HX@Yzn!*($LYtxtBB~CUTkYh%*)oobL_7
zT6ewCu@C}#ePVEQ<2CN5d9@cVKmpzwgwwQ3N7i1P?e^AQoZ!@TL^FUvaAx4=6*h|>
zxOJ5bi`AbqubUMinYC+@A|f6zK9GQDG)i$Vfz7GpYLr97LwpPAHU*wk)xB_Waq;jF
za$R?^?nx2$Z25RsO-*eI8}+P%WPY;Hj~_q6V$TNUg!jd%)M9Y1Uk7FpM#kG9E>U&!
zyVN}{vlp_mJslkh!k!nfhQA?AYj%9WA3kVItCd;JLUZF_bJ}yUBieDwIl#54s;cVS
zHyQ+Sut8h+{gsuKjuhDxq4ROeTI-Md@E9@yr!DX=@THTrqFycM#b{ttw@FAyh>3-u
z0YZlUc%y2!C+OBgzGT^h-uB3cy$PJFIi*uzH-pe%)-;JQS!ro$Sy_O*-{rQ;I4o89
zuxB4E0^-}ZtF~M20nxdsL7@dU*nuX?m9Y}@@mq2N)+c)_0|NsXIK=rLqXCp2PHSUq
zmQ8}daWa*%qCAO7NGN%%bmm(^rUDnOc%-DI0YeoP6_NafjA0alE@1xE(Y!<PXO<9i
z!uvyt59L083=E9pcidP*7PW>^UIH!!vc!vL!(%&HY8jcCF?EX7qv=^5J~RS)DG^4o
zFWwic<?@(;fq|BG5w(K#;?bJprNqL*CkS**+%mfr!d4NW%SA@Lpurw}AYzD!jEtrW
zJj`cUj@9;v=QNEOT+sk|P(eXKOY3vv><q<foi{pwnH4$aqsNa6kY>PL$1Ci!xO2y#
zn^b$cQHcPmyI{qm;n=Zax3iA9K2|XQGvo8gTri8n#>BfKB<wScQn&{42msL3J?$kQ
z0wE_@Zd22<?>^D7v9WP+PAdc7i5QgKpm=g?6x{7%fA{X4(#1PK$Me%e?Q$F1=|C(3
ziV1iuK!@YT#No7ON^-KMyu1}yIAd@nWK%H?sH$Ry?Beor?QRc0c!X>QJ3l|b-=st3
z`AoeJgG%nN@%5>|(@|~D_3=^y5etir20t7*X#cEaq;wRzL13yFGrL;A{^SJrhemSV
zWXQztAdOOZlY54ShQ5A%pcdo4S6@U=xcf~qQniNd4sImf%LHCqLno)Qt@rQz8fTBd
z*G6(RPvM(-&d9Xqr63Cnf!DAJ6EpL{z}V(uXKar0D)4#B0#y)lTYB=J2NRR?Sb16Y
z?f|sn2Q-=#cNA(UqrSKdI&L3iNXIgpPuC>tF)mHHZo0!+rpu*R6d{vjmsvng1ET?Z
z5L*eb)-O;6u#b$7|5-)d`Gj8|KfuSe38Ffysw&C+0OH~5j2+*@mkD;@ppMh-v94ue
z0r=m)eOp*qXe&&Lj*gCuY*YvwHZd^2G7>kx2w}wZ_%WJJ5ps;5ICv4(*4*3-UPI||
zbiY*<vF70F?9A&0M(OJ6qLv7Kq$m$&c0JxPs5x|jg;pmekyTD^*zu|4s25vK*FZh6
zvtyGrd;(BYgwxk}!{nTzh_MqQJ}@{~k3B$jnGjKln6(jc2E6S!+MZ`3#u@j3^MUeC
zuXJ2(kKL$;Ovo)xg)z8!2dawk^#g;>7&XQibS!)U2op$w;4f>&E@_0fzI<q!C^j7h
z2dTB5ZyC&xn=Uj6mMbL|T(;vg=Yr<xI9T4Wbeu)A!fv*~@7uR;w;syo*V=?R9jvL1
ze6`~t>)@pB+{x9fWF*GP%5f@p+J4Zfq?|QuQ5qW)^I5=oCzz!MVFln1xx(gS^O#u)
z4hgfqkrDb4tc*pgN>yII&L)Uk7wwL)hrnt&Ku*T@?*%M<_kxat9)8XrU(d!gy4Ty*
zb_D317#-d0LhsgyTt9#SC1zt{S_f{j_VR+_-Rb|%0vs4zT%2yYS$aNNEFE9ZkW2A6
zS;^Sl+3D!)WFqbaJTKc!9$ZO+F2mcmv;KJZPWD&10dOD`3WN)`iE0E>;e=q7kq_n4
zzkSPpwU^BAs8#J!{Oi{qfQnh2%$=I!H!+I<TLS|Mm6H~w<BpqC-Rfpsi6QO*(aU&F
zU#qIlAO#8AEWEI=V6OZ@j^7Gt#*FKYwe>bIu*1#iUN#MQww&;JlYg437@&Zmp<$tT
zCS#}Dp==yIA$w)H4b<weR1fH~F}*2q+i8E;94kYeB%3j5QEN3T`t&K9>3aw>AY_Gu
zS!HEM5U+030O-ldeYNKY_bT7Ef2eo)>sI-cQ5cRLY?U#nrmpS+AOatU(8=R%xsq=&
zS&^O1XmDc(a36_8N~JH{!x<$k>NczdJYXW0e){yOwKbdRo)x7kW_WCDYtIc0X}#>=
z_L^~^9@`;*ta!kr>kg+--v3G9jHY0p&U=kan6&6^Z$BQby|9PO4-d{7cA0CsRugo5
zcD#FfxM_6gk}SJ?yu0kU{yRD<s;Psj^T}b6U$p1uRCUKoDXzkX7X0Qe8N24~*FUX{
zVMpB#FD1Kxy;bR=S@Dobgpx_Vd}$Em4B0Z<2W$`dE{eL1Z2}BCn)4>WH1?hsq;Ebl
z{1L<b{!oMB+Ey|mqUiAOz7q2{h(vBnVpSURw^*t_=U^2u)B%9o-jf4P4N$Wi@<W3)
zmRVC_!>6OLYsq+Povr9hzgWGfs7P8$>cfW*YYvr-v#J$#E0CPVh?;?XLRBaYXXmG>
zbGg?Ncq_{*N!CbRUERT<WL2<JDNEV9ixy(-`}gngUnROFh-zpid0y<MR5hSM@@9to
zpvuUVxDFxQaq3W6ou8lo<42%n=X?nFlzd4{Oiar`Xh>oHEq}}XR=VPPgEFO<MfcMK
zI73{-bYhJqWL7^MlCh~|A$N7>X?w5Is;UVIOC{izEZp3q`{cpRHVbVx%ZAw9?kNGe
zSo;$d9nDJ?3M3BFXM*4S_!S*8e2G`D`XP#D2i`q?{P^wiw@tbw`p<g6s*X{iVL3Gc
zvAsRtYWe%uPk8p6bo$L~Vx5<U%Ig*rW!sQd^*se)ssdCc=Vo=FVsMbr9oUF_I~Y`*
zg$nm}u4C2BTAl3;$^r8U{qR9oF%ZZdijx7{OiXB~#jrnl0#!{}6DhE1V`JmRLp5E)
z-Df<yX$}Z|HAC$c*kM#H1-}C$J^crj?7W(j%vWjRT!}B8`@V~Y@p5u1-3?lVdRgc#
zw_+$6?;M|Vt3|2j0n&&#*w69-GXJg54j%(UmSiMd*~sQw671HF>g;Tpm1|{MIE9QX
zs`+2Cvp-sz1G0Rd5OpAbkQv_VHU#Am*j9YhQI&yAL{c)ylsbaMLH&x(E|ctMf%Y0n
zmX~b&o)=X+>Ob$PnYR-bac^*4mwz6140(VYh!oHy;9i#l(k#Qv*&j{CklBh2>Fi~)
zbQNyLPg+UCyBA-n7b2qG4Rq{@*06J`y6%n@=;fkE^E<2^ZMRY)<0xABIZ!Smgq!f`
z6Piw=KVF#N!JRx+gwWYmqvzQ?Wf_-rojoNo(WobBcDTcuhs<myS2Qesm=w_NabkOy
zqgzDsHNZZHNtVjVo{jSSdz4jWp~-Hi>!nhQon`5Az2^Xu9lO&+B`8@CMG<KYwY2Ff
z3o{u#MMMy>H|R5~OgoaYKYqkLAh)S)Y|x5|jjhE0UZ$);MqMHF{i}dNZ(*Svfq_cD
zUT!b?Xf&{KdUHW#!E&L4*58{|x&^bXaH<_)5>P`VB)=zf?J6%Xha_A`m{6@V8pha}
z?b*KlE7zhf@;T5l!@CJ226ajMaiE+X&iD{i5c_urvc=1MZjCT-27<x(CV#hn)(VCv
zxUgqK?4OS@x*Ys2W@$(l8e2bzin<-@+|0?Ar(Ijq5NRl7>x)Bci_qC+*1S~L&;U}*
ztA)X6ujEW5@%;JY94pG?Xiev_l$T*wpkBPfWgHb32cK(SVPa`I@_}#ziZT>EHuw`l
zu?zWh{<rvRI@h9&TdT82#=1+ktY^d0EY%AqNt9XFHA&8NcU{Vwq-10?3Uo16zo>l1
z=vbO~udUnD*;%>#*)^)OrMPwNge^2Sa&RTvBILgETObiuZsF=bCDnXpfKKIhy6300
ztom0ocAA+)n1=DPvW9oIWCj073dn8!1J$xV3-B}^uDhl}tB_)bO*D%P@eWlnFffQH
zCX8|@LkSxA@_wE@)GC*H9_`q@Pkx}3BIp`3&y%DO`7HzW#bEUKFrW|t7<UFo9zE^I
zmn<i+eKoOQRagx9tbPfXfk=C?vI-6e5CUic)WKTOMiC0PwxznD@XJk0Rtn|ub_^@8
zcrHUtO^ucW+A0vKs)onf)Hn$V34i&9>JNfPzh;{0sKq}l(`&`g7<|W-$l%r`t!U0L
zp=ZdV@#g;Ltnvl3F8O!-E^Yg`m%F{f3>x?y=VmoxNF|GIogca)uMnZ+rjVrGzGWG0
z%GQ`9D~h$jRQ{t`b$M~xVXD>IV^ONY-32@Z53><jWc0IjEnrMiId$}pdZKMoZhLOX
z+?=j~56~xti_DL-JAWI9iHOQ`U497cEe=!I+IuP-4BeZJVvaQp>E&iC>bDt*@9gSI
z687|n4vME9E$XpPQj0HaI_w`$V6a#`z=~DP#c@}uIj=uQv*T(k=rI4g?=l)@jq9RG
zW*U7xtf+Fze(7^uo>p~c*R9tSTDnmWg?V+wIET-R-wme6$Hzy-oy$kdO_m=#j=snB
zS+Qci0~3P#$C*+*OUJ4}R=#r3>V(ST%gFZEvwwG<;b})b*-o5S`sSO(YPC!nIJ!mZ
zN}S|wZ+24xOftK{IlZA`ubsYwQBkrb;5bRao+olNeTt)m|6~^rXj*H?Jr!p>qiU_q
zggE!ti<Z4vf=f_&dF*y`uN8B*<TB{Ut*acW_|(g*tN%?CL;n3ZeDq;e8O@AMnz>-?
z+5+Ehb8+W)0q*8i0hs0*C10<a^}oj>w4f+9pcE0qO<2@WoUW7S>=$W(+}|i0HW75*
z(f2D?(J+iJ7tcOMC(DpnM;2u)QJT(<C)!Q^hS>{-X<0)R+Sb%H`n<<J^)q=vYR){O
zyqC`I@fNv}9oCO3{dXsfIV_Nc%~^_qRRJztoaGj}4(`9&#iCui#M25Y!kl}OEdoFb
z8k^Ej%q%S271zzxNUg*_F0{CtM??Tb$W+)8QVx_WIpIO*SFZ4kNJNtuWLfD&=y@(y
ztajxS)3nO`tWqZSw{0$EP}VF65vQI`HB&M)P+=}%?CfYyV=Ql6eL~VF?O;ENn(mPj
zLtZRFNe?At_AxsdrH+bAS4Z)JToBTvxR7_<e7uA+UM9Ok@$X|x8pBaewm5Qrhu2OS
z4ZCIb$_6=_sjXexv){q$kSsw_0Nu^%{9)_6AlZzf4#ijfU!UVJd&jL3C!WeXL~p9R
z=n~4@nUak7reT00;-m{s@kJzNd>N$$O3ND4)`I%k1~Ng&=_Ee;4*P<}zraM6mzL-?
ztFITom65@1qFeg&$5?Cj2|GJ-{V%F7BQ9RJO?!Y7v?Z(SV5I04rinop*<Kvbq})08
zwL>#2{ODK~-*~dZ-p<a>?~vc_Pu=^S<Rynmp9XQ=>i(yWi+N(m2%5*1P*O)}x=grs
zWfx}R=jo|9b2JzC=;u9VJH5x<OHmW;3aQYhMCnDBng8v)-7ZcxmBT8--KB14KSkxn
zz7@Oe?d|*b?<3~s=aGrJ8*B_^d4FCn^4_>}XAGJliz#@d99s_jUI1^nBzNw#;oohH
zQ665g%U-;B`}WmMBdKN(7|X9_@@l|G4ktu+A6oysz6oiEVRy=$D>0U^C~jKNCYg4y
z*i;1pQeFg2fvNX!3h?gJUSS-OpmKF3F0RSrJ9$<_nd(JI=t<@|rL|@+G=oD{I(*!7
z5zMm!hO_1;n<4$;#St))eV6J&yONov?52H_bX;ugKZ6-z1K2ZV>d&KR>BG2oe^)~K
zqs3x(KiNb3GPp7(du@X)_-+S2KE856Yu9<#>z-q%)3~C;$mJbYtW(aEcB=`rW;L>K
zMmh*^a0XZG)_nE%AD%`lFwZ{w+N6=wVI=2Q6cW<vl4p~5LpI_YFjW_7HU{taK0can
z(8)I07=q*f>;pjt^YB3VphUO}T0;3hE9!71g47EGnu;;QJR>_w8wsYHNqJC8y@*&y
zh*R5y5}QscEMD)S-0>6S`%fXHoSDaZRSimYhN3@;Td-L=em*hoqdE}fCD^Q3W!9+N
zbJARrB2ms8#Zi5y@z}x4jM0=){MoZdqw3uu=pkuM8cYt&^qjJ)s$<oMQ#i2+x={Rq
z3_ubSr-t>tLW#+=lM{p9TlIYWr{co{?BaTz8T)4XX~J<Ft|IZD)X%2$LP3(Ax|O-}
z26QXkoNy?i3eNXUnBPrSaK?tHEZOlPUnO$9BXkY;4ubHDktR0M5b?B6r&9%k1LI+p
zWyIs0#Qgf8qx?84;KNcd$T2&3Kdfn0J~F0M$IZqt{)y(NUTY|kdfk-i4Q}(_zn~^T
z81^JRmQ+wxy`z!ISs&bJ^%Q(!&5B;IZDvMiq~*18BP(eLaOXYX*8RqI$(!_&5waP!
z=jIGrvr(o>5#Q?S>M-$0^Vt*U<x2|5N8CB?;DX#0)TtHMZCE2`bT_1>W7tJU=ZnJA
zt!`Q(QU$}J_(k?cP7U4E1e0Gjratpi1;lTP47<npQ&Us3k@ZhPwDO>HdRpG}Ub+Cu
zy|nHTwpzNtN7w7pC3JE-`~A8mD38BO%sVlEMC~@+3qsCX<(Fe;kE^E+Lfx!?fPdKY
zv0g`q$llGZN;X5U`|)44(}xZRWmDAp89Pw&A^Eu{tb9~lyI#F|1sZu%ad5!!&J_0Y
zZ#{6)8sHxdjg0|#_eRnr2tuW;@=2s}bywo6+qL#q1_L%t2BexZ_g=$j8=Kpal>^y5
zZi_B9Vy~?&t5I$>U9QA#m+H@(Mcfv2hFbb+4uU3(Bf#DoTF?=9aQ&eiu*GfF%9pS}
zse%c@OR4lMR}pXu?}jsnp`m#sgiCj4ys~n$rt(dx#Tu3@J+7j)T$i@{K2h#)j8q;S
z6&2O}`){}Bn%!DUHOH+OiTR%+)iR%DP!wrHrzHBGtXbDp5fCG_oMtgLh<tajb<E7x
zV2ILc9am8u)2$Kh4wu;-*Lp%Y^<*F=p1^BCRI{S&Ff1;Iqn#u_Xx@3r#K=}^K2BUG
zcsS8Gg^Pz50@a5%{h3$Q!?7v*zeDZ&FJt1MT7KiObO#r+CPT`P*$E`y&9eE>>vHV7
z*IQe7Ma9IBaz>=M{&jUCBYMhzpKXXC&Ac+9cM~n)#Vh2xq;58vw>R>}5?FtJzwyV=
zP-qEwh4pk8;+Z<cjqN>h*de(4#5d%uVD-~{e0z=9C`A#t1e^>hSlf~LiUvJfwbpc&
zN?z%-M<tXsX8JdW^bS;p_v6Mvl&N&I)L1>Urd%^1NO=)4^y}SVU!S}KI>D%aVRip2
z9X$lK_(y<UP<w_}?CR_55mg1i?kxTe{aicwX;m3K`rLJAp*<pkFeVP_S`>Cfw%@*a
z)7x|(!}Mh9<O#G<E}>*WyTfPqV<%lUBN3<_7`@A|s9pz@rg@<#RF%MOIwEsC&}C>_
z>99^OY`(_J71d3LDFmXU*~eJ>FZO)N?fN?d*=7eJ@iOTh+LL(P6Lkz^r<2w##q>s}
zKSo#6($YY>YGX)B1k718QXi89_o9{5J>`K45&{kZO&hG%&I#~-=l-=UJ#-J42aTW!
z;#^Uc6qv4TYHEVMnQXbaCF>nH5N*(=A^j=KCCI=4u!k{Bp{}jn6>{ToU5-}KdV8D(
zsk(H^g(3H4^qkE#_rpoY%i~_pG1HMtXgi2c*SPcB|K+gzBOlrRZEywJ?yKJ=EXF)Q
z8Ig|o2D)}7oguRr5|YZfzq7IN53M{(K09L{Ogzv|zK^Z~cILt{Jr*=%)?OO;F6{~0
z639T2?I8JOq`yqWym~eNG;QrGIk$xdRBg`1=zxA>U!!xg$XE7HdXo7iBqWOL>7fNu
z&%hQ&&L6{+Bm~(7j6g#}^QfCNS+=!h85%-IePRUGYYl(xDxl&2$F4#)neVxTM5%HA
z`{Am!_*csFyVsX1UOd;=r;+Y@`}Qpw8XBgL_si-9p0k;ga8o$LC?7C<0Pfu<@F?i%
z1*QCI#*yyIp2Eh*hwc(gn>H60(bChKOq5vz>W+5&1)>6i1prCM0a{lAr*s6(i2j0-
zkAabqCD1f8F5&H_TSwD(%_C^yyO+Lv`BLwLRn&@Q{Ak=0R52rn8w4gkS=-<DaJArZ
z8poQb0ZSPN7woXaa$4ws@6n@20s<PmKJ)YQKJVXOxpD=idLA7)Z3r%b%>CH;)4B?H
z=x5gr-mO)(jBnrEz^;(uvz_s&I5_O!h#{Ym3VXN#fq{tMcX9Vio#C_PO9u7?%{;Sw
zW*(j+OV87BKsK~|4~kCOs=t9}XKn|2!BW(MTwKa`jo<o=nK^lN7L1o#PQ#prm3bW<
z4UIm?l4Z)nq2&C;b&x=zXR_qXC?2y2o4~-tEWCH{>C-0wR((AJAVp9NW&4qmle-VT
z<PIU@9ngPLY}`KwjV%1@*FSvxm?YqA1Dy&}E!9lrvAZbpjs6FEd@#fjb^52EpupN1
zWohXmF)J4<zGM(X46}$H;{}5&JNzzky#?~>+g(A;06c+NE1ofI2Y`H=u=st3F;lIV
zWNI<6YJ4xwvgoF+gHAIpEG#UPfIRR`@?|2|BH=4msV*g39x@2fE5NpX{MrYy<9c$?
za?f&Yx>mRqsS3RiNAeB|h>|RGG&g9Afzw#lo^zGuE_}It3IG-o5<>n&KQJQhC0(Qq
z{|9MZRGSM8_a4_r!J(|G>HN2i(74!yh7YNr%P*u^4Jxa-#e>s;WCbA1_{rDbf6A&6
z4-g4KB{=}eiPCxDCKq?2blJj!{)nIXNH%h!+!k$bb<`3tX)Q0ib&U^WQVc|z{l{u_
zU_xCvwiUYy=v#jQQjg$~ec039-rm`1I`FL>)DcRTKlc%kOAH#LU8-x~FLaTQ1q6~*
z{IbsIyPz8l-ScHHFGO1Z9YVr^T2Id+<OHCqmzNhDrP865t$B`W!2@b)^t$l-Vq<K&
zK72%<`In0wAGl6|EMW-)wbEU=B;=;lQV|@`ol6x9z{JF~IddioXiObkN#Hc)a@v}C
zV_97cZGZ?GPLrWW4<5YdRN~D&ufyDh9s|Yg+e4+6+6Q!5k>KCNELszw5r&7~2sP*V
zH00)Xp?!HWs_8h{Y)yfsZFUX+dn>i1f{2I+_`Dt8WU}KFuYTK&P%&igF$)imQ%BTe
z06>MQYj}7DnyR5ufh^T~3T|$0PoLHU%9IHkx8-x*h8W0F&Kc80sB|ZCWAy<i)+*Nr
z?F~Flidh46IOq(6;0)W%RyKu4iHOvpG}3IfNGREzc@`6KadF8wjKcxo*bP70D1$aq
z4Z3fo<K_})aSpUWlsusj15ycBs3K?tx9ZTdNp;_e=Z%({Ar+3-spP7_AsCdib<SUu
z;FvM~`frN=Ixen&?GmnZB$??OjZRv?B{MS7ywTdDxnO9_F2KAR2%uet?6Fo5#?W+I
z1)l&8^x5;`7^WFWh=}%A294mF0jiJ}TKc})kfpCyqMifY6hxG=kQcyUfjz_cuXnqL
zwU^{|fIQ9^2DkXf7~@H_-TGJT93b^Urvr$}lS=*rVEDc9>;ck@4w?DoNgW8K7cXAC
zemw*Y>fdG7dXf<oo@d)IVRkYnU&0rJw15z+Ift3Jn)Q<L*N?sUqngNW-ed!B5Dy`R
z1r*GV01Nou-Lv?kD5UGNAgV8x%x7=n*Zd&rp}12qIuv51&bx+?VsG7Q{LF6#<>|9$
zO^_Hgg<BZN#sjtT!U6+z^!%eyNfnwy8V$+3w&D@rzI>sI5glVkLU|O&Wloxvf#5Lz
zZP)kZX$QfeOT)foKGZK@pJlVpIE~8c;Rd{q9{p+iME$w7P(259ATxC&_@@xu!T=;i
zt#kg)?b~+a=B3tdj}}4KB@X_+6NUk!3lPlN5DnL^U2~kUmRQu@$h<d7M30b35vm3h
z0bY#4imX7(>irSJ?u2dk1Wyp&KzYA;^CpyzSv$mt);BL2@>AEK$%dxGP8q*4nDLN~
zuA*luZ$ddY5zOHZ_{W#L0qryWO_Wq8ArT*WgqrC)G@6-=N=fMzpMe%YUN-uz8#lI^
zZpoE#Wx7T21vvvVf!ZeXY@F)%{Z1R`i9;Nhd7Qh9Oo)Bo&vFiVS#bRU7gq^HeeN({
zV|At5#zZ;jSwDXKh-OeJ431&iU!*nL9DAf_pYn6-jm&45ynu}Zr8<Qa9nB4TbM&l%
z|7JmBvBxlwD0<Iy==Q+y$S$g5ZlESBC#RB5TZHxZHo!f2<CWTKz3xF@Utjvm-``=g
zniW>K8C}10?&%Y4z%Fy#ycT;nO-G!xYN1&LK)4B5#KEx_^TuKYMi=Z;)_xbS+IvPN
zP%~?%Gj>X`5A9d1B8wg-GUgZ$rsKsLw!(*KDk#iVV0swvC8PL+hQ%28e!*k%+OwVD
zRrd#>=m>g+hipXweZ09_GkB<R7sy+X5+b6j1CoE5v3(#FYO!88TWF7j?xdqV5%ZxH
zf{>693^{*1GGPt+T%tvVqzoh1JLHPp^)jCyWT7CpJM$Aa5*G36m21cnagonf5Kvk1
zvO{|VCRqC6&g01M+_!O0&$7*W4|`VZ3ORmeC#jQbxwiUqJiFKlD6Dyjmoj@)<@MsX
z=2v&%OaD8ZS$H^dHP;NfJsma=l;#pE)Jx2Gp&8p-x+Kd}IVof@!NbOVgq(uN4(P7S
z#GjUKOU}6COkO!NGXoJ*YC77pn^P`1rYUFOcm30>%fZ_5m|hqxmH))TBT`7Eyt8o4
zibq#hckJZq=z0F~O=Q6>XW=PFO1(<vb-JZ%|E>(BHY7sbHD~Yo(~!ucUQ~t$h?GU3
zOX8CfMk?ahCr|QDPn*aB3h!~6*iE)Z9&z!Ht=po_j)kRb99GWp+n|yi!>WyIVq2Do
z{)1P!+o0kFsZsfnSFRq!p?H39<j%ym$);?K24)&=cG_wDijf9y)7oca6~cv#DxdEy
zR;#XIyeX0Ng63#lHIBDlp0p}|UH8jl-CmjUKT*Z!Ss*rz8ipYy0t(Mdn(O`eiF{{<
zn*Hc!pN*7Q<+XOLjs;cawFL4yEG{laQ~K`HA4>E%50`idMBx>@8|ZfJB4e=?-e5aj
z54FDU@u+esY`cRN=9Ikdxl7e_<JW7aw^0=F2gO@<YIbe;6!N9dq?)m1e3;>9d|y6&
zw8VLB!mvnk)I(&hg7QFs?Cfyt=_LGuWALi~%i{e}3id23)6WszpJoiPY(ZXRSqn&M
zZnAMVtXPuU$utM)EX!(=CGlYsfdP)R=El7J8kLoRPVZ+yfSdOLp%VfQLfpU6{<4jF
z&5QbsE?y`#6`b61AOf|7y}`e*H$|Am<HbwWX9sal@C<NrC&~qwF}%C+4=J<KQq3HX
z)<d1!^q&Cwy->xNy)a|!)kEWP-l2Kco$}0lgUwdMI2+ndpDjDcot>S3lXWb;XL>}p
zHg>!%UoxYHeDB)T@z>))<0KG$3JZy%M{FwHK+N6^ijRdS%*2zfjfVT~$0<3Nc#n3B
zk1j3=8fq1c;jkTI=&Rx5;lb!d+RSp?>ajc*$@|Svw)X*r$X=<DkN$<{c&F>v*$T$o
zJ646z8{M}}*k)hf?I3oR9@jQ9C=z+_=7)iSfrUkPZ9t*mvWIEhydPCrbmJ<?I7c9%
zZSjO;7a6;OjLgTj=iOAS$-aJm1(esbkM-Usl9R#0xpL1xZ`cdB7;HjUaVA3y`Tl2N
zU^pC^RM-B^Q)>>kf(bX`7|TUwMti@204xlQcUCQQc@AD}f5Xmevh!2TBqLxHkmo`}
zsar})N<<7PP$n$XY`ST35lw1o^^k2)zND5dvHJmo<0oh7EqN|Pvg|{%1P*GmRP<^s
zmbHFHgjqD(Cr(uVVNwk(N*cxlP~s5Re750xOR>Q030`LPm8JN0S`BF4OsKtz(_@er
z)B7-Yye{vx{QmuW-{J`+H9;x;XOa=V1}r5V-uYUx`ug8iHwj0)q!)V>3Na~fZ@X=h
z;Rj4wc&-(+HpxurVGtLEa=cb&7*C!SSy0M^LYBK$rd|wLGp3gg(ijS^#%>Z-xkMdN
z6eR}5OY`{DQ`z(c(@1W7+}ly-6wMgihdc<$eH5?N^wD@@NLjB*al`t(1C0kTfrdg<
zRh~~{{f@gF-75$GI}1Q@H5Mlo${Ofwe70<Lx4<{Rd34P&DlTgnh}5b_i^S*~u6P+p
zhXF`4V&eYpR5PuDUtGJv)RGYZrD?@a1(;hyX$?OOeuG}x6mYD!v(Idx!k&Vzf<CB2
z8%Z%_?2FK>Z8KF=Pb)_&IFYmpD+i<%KHrOM<4^lXQZ-~4B_qVcD46JoO^OO1$Lf*X
zy~_*c?k#OhUUpT<YFgv1pGSM&UWjV60!OPne%njk($s{a=?x79fe#vko&_NlH9}pC
zjsF0x6qv@>1}wWpNSMI!?g5>Kyu3V&Y2=-<Y0XZ^&{Un!LPbO%xEIbp>t^w297xfh
zzEqIP8Xi=%lkAo<b^^Ej3wnKiAe7k0yv{>Q6h$l){PMa7H}cXfI7lbeL<b}?F3^vF
z3i&phAPX0vRC~xshE{7R0i^9svB{z=Ps*FB-|kf=MBJr~+uZEFe(0>wiG+HNIdOnW
zs_WU+w*(C*+}o>6rX`KD&h;ihv>Ue*W`1@n`i)L;z8iQ}^e|)j5nH6mwT;-L4JQOz
zhLO%hp2>MxE8}ODP^=?vsV<6Cjk(GnNNY{?4K`QVBG*<^GqnYIhn$keRkr7!X0N6-
ztx3BT*gPZ~&Met4w+KJdn4{Z19$#)jcgw9Kl?XyoUnD>NDJMCxJgrpJ`<;3F$E)U>
zw1(QEKZB`F95iALAKp0D<*C0)AUyPUB0Y6&^=p$%=JTJ@rf05?5*vOtFX!^7Fj$$t
z(mlZb&OE&sENx{R-y6bY%A2&z!G9b~s<Z*ufda8>#_(?lll@H_{*iaf4J+BdFfW*}
zNaCy#M|vKurn9zIhBEo8th<!fY!eFy-5q%T+;nk{PR*F#zU4)AUEqw+_am0Uu&`vO
zs5r^uAFbJ(>J%O6ddq1|y&X#pwfCB$;@qz(dDaAnPTIdah$&%NLUbR*y?Znc*R&-C
z7pKLL9@F?=X60Yr6h4+Nnk|DhWj+@}s)KIj%Wq!7nAa&0ybdx+jc=xKPU=wer)dHM
z)1p@xq#Yx05Bi(>#iZWBrp(GT`u<k^tbF$pA*E)7;=j*XlRo2*4PPBFOW!+n@Y1O{
zDvQ#&aL<G*o9eT{-T0&(2bvdx4CC%KWo{n~11OzR;XI7<W2t{vt`6*Iuv?O(UrFIt
zx_StUO=N+|>?7Q6{DTjci$}WV(t(M%CL~_Qv7U~77>AF~D}6SCIrtb{H>Wl(PgF9J
z$nN0Av7w7o$M$`W`7rI^#*w50q8RPPBnxT>EG0Hg0k*c(a}DPg{8JvMaDk%IqV^oL
z#>QXoK4VogXDV?7y1?FqevjEjhAk0(I=i4KT3TZAJY!QYSvxykrj2FhWMc5C_kw$_
z&BJe{6RKO+4&{ujQMXtB%DSnDB~mY6Os>u?p0sYPsd1Q<txh^_(m#$L_;#%)rL{lp
zZ|nzdZ2^b(FfKm4bJ~%Nb4j6cL(&avvRm%M>Vg_$*$N#32B02vvXXv^U2e9rKe4iZ
z)3;RdNXW%`K%VQqeFm~#>-sW2cVmz#%fVU@iPoQ+goSth*d0G<2?b3jnLE9kB@*W_
zERdyS9~Pxx+3zGGn9=;^w-46*-1Cg~+lXg8WG|!|y17T0VhlGK`?Ad(|2r5{-6L+M
z=>cNm;F}zRCg(0ydkR*DhF7fA@&ft;`}+7KhRpu%Z0^habCkujWP$qLy0Z4##;1*%
z9KF`ZPbPoAc=y<_n~&9Uib_Yl(cgF!UrmjoWl@nech<N0eXp`E%(%md;UP$NPkk_9
zT#N;7Eq}*dsBq(mJ=!*Zb-B@C;;`vdelgYlWbNQ4wK()M5N%!0?Cd6WgEkdSHjwPv
z>7u$0JY<fO_M<SD9uX7c0Avz%M+hzzZTEL12R^h=J{bDx^ymJ!499_$rG>?}b%;Av
zLc%I%KFtq~|Aa6R5$?iV+JgrVTEZwTxe`;LI)ai<!R>Bf?SL6cOvAASkF}c7Z;of;
zU(LrSxyHZE>H7K(IZG#;KXGSEa)QfA&^LjBYVYcezSKsTFKLSPE^7p8R@`1|d2%w>
z=A6@T?RO<QR=(Y1^<QHatP6kkoenk6&WcL)PR<QBf{Q;~?`6}jcH!Ln{kjoSS#Yz$
z>({r{-|fu8<Om6imd0#1ObWt8Wbh-}S3aI9Hy#|%9ep#*`2E2Yt7;d&p|QZI*ZtZx
zC^sH&Zy%jbvpJYea*1=h&lZUxVRdk)3)<NS6+eEo>9G2~=G0|JU1GXL_u9#JBwG#I
z%UUcC&}I@Rsk(DNU7OCnxZ2w?ux9n+i=N~51B<Ggv$LQzIaHGCwa%R%u9nblKTI@~
zBhs$gczve>dajF5w;@-d0-~j%IWkW3H;&|zdsx34bDlWuan1b%=9#Vb|H3@rGzT4K
zFv;jT38P7~#Jwg+Lm8sF6dcBVP;`O$m#lb*8C9)d3KT9aZL4rx^P|4Jcv5`FVnbtW
z{m<{>M!`cj*BSWNM7DN1wQyUbmuh5CCqJ^fZ#tUmzEQpmbpqT)ID@g*4=m{YRJAF-
zbG(-x1EZU!<EuCXt+|?kVFAPH#vF5-o~?q1L(Y1?&yW)JZhIdUmL(Wn_WoFJ%{)~S
zy>)o33khjw)4}W1S9yb+U$>V6F^EMlb#OFqKZVPrm9y2%9vQPh1F&UkfFr9pl%e0`
z885_LoTEJJX1#54S#txGiH_a50}o^s&nl6r$dy+$M{tv7${TJpecn3N5EVmiAv-4B
zA5`XZM6JWs-DeC}!{kopG}ukBeh~GLFgOAMj}_7&7p}PhcZY`RYAc^MIrhgp14NFk
zPP<~kJNVrSN`}oyMo0oEf1Md*YWi06Ee&yzb~wPbUInJho+#ASv*W{TvwC+a(vGEI
zcyv?ik#Xd!^#`vzm!Ht$8APs1%gDU0#MYV(c9&ub;D>Q!7`gZLyHyUNe~(GAOW7xN
zq9f27poP81`ZW<QSnMoz!WjL0yEw;qTsX~RYU;+_^|EqDm+Gk%I~R(cmkH;;O~rv*
z;0MgZZ8R9LA|$p3*&67Qt}fDyi_IJHBI32OatZvqH*aqWs$|Xf!Hn!G=vCV$&Km87
zYqPVqo|}6v=OAng1~r#?-qVFpep6z$E%259qqrWBQ+4%(fbssdE7Yh#ZPu96j1T?B
zx7D%`Fj%dysC{FEN2=>HpXY^}y83Sr-I{!h)N`QwyIU1%6+j~iVw4Ba$E!`cvmmY<
zf=uaL_(|QM12x3ErLf-c<U5TdNKnS3xycV_Co)D?^EAqzIBsaL%_7=RMPpOb6Vecb
z0B*)2hWIl>smS();~A-(F1SPoWAP9EuiCyl9_#mSTi;YhaiJ)CWF}-}kB~@4MwFG2
zvX#9XBqUoTq>>PlWECOVsf3J#L{|1*_i=rH&p*%odOfe_pZmW4s0i2RbAHbAJ&yNr
z9JqY%(H3QWchRI|dMFK!H1>3G2nZ0{o6|vcA4CuC1#b`FmsO2Mtbws{sZArT<sixM
z$VfQ(o=2snQo_P*aYsun$|e@qfDydIy0G5PHB6ram(Qx^sz%FDnPQJ+NpDDq7-J+`
zz(P!D{ijV=M?RC`0cnQ#q*X#E^YMdwIcs<D9X)!04(D7w_BC;t{Vu~NWqw|s+^E;|
zm32LprI$QNg&4etGF`2@7W%$DHeno()v6}>4A061E6;6X*Fz?riVquYWN;5mE^j$_
zOn)d8$Y5QqJc3`+AJS~Gswp(Xy4G9^`*LMr=F=4xRz6y>mml>Q<YYaI8Jl*4w*28K
zU8?q;C>_n^<$Pd7Bw%*RlaDHNTGJozIwKO-_#v}?GEj;HJx>j%^2N;-N^V;P<n~?Z
zm#v)be)aT+mHo;bCP5e+jVul710TbF-J*2nCCk7F@2=$})Qr2@sF>~BsjdLl($)P8
zm_(kOH)-APvS>2iO49NBfTgNrZ6Rg#{0GxqdV$5H39cAwk~_>z=d=SX{p!nktZ3bI
zbqRihmQiKr7HNVdbL8X<?Iq%mI(8k_z@uX2v0XlS@}$I-&+q4B4mc_3JBTK~Dk_3;
z$xfZ)LtA$AP&`G^xjD7jqf3>IJq&c8!Xc*c27C2rGBvmDvMhydk+hHy)%B#SazgSW
zJye%ez7^{49uN8P5_GELDPH_)7cUL~W0vc_xuTu2uB8UP_MqH}<-*;C158Wbtn;Ik
zm6d}t^MU!UxLDcx@lFm#5`9}lM08uUES`L6Sfk(^-J@jiY&bT_;nKpNFRa>E3k+p3
z2OR)0HZ7R0Wpn2jhzv}e%T5k&kbukMW93;DBA`KwwP4YR#UyuEmQYN)#mTfz0E=|1
z2EU<gkh5($Xz2%zrraGFPl$a2!FaE!srfL;lCrVutbQ*Evae-a6$vRR=^ay5^~0Do
z=9|e{8l3<PimB)?A7B(ZjFBH9G}VnN>j!M4iB=+nWrbRCLxhu4`MaSzU~7Pk<dn;h
zZD=Qp*2Q(Y=LswWvn=WsbevYZd+-aU%g3@cv)bQ5HUWb9em7f8^52=W(bMxxDr4f#
z8LHB1TQm^%fi<TiWCnw+LO+`{S4C5Z0fK)xvq@hY27aI4r#^QeK1se99UewYl(|vZ
z&KnI}QFkVXe*~^iaqhW!h9|Cp!YHeqd}0`b7iAkJs_UUoSpH6%d3&#bqmEJD5nEcR
z>*x}JXBB=b)NQt2#!voOU++h&-HiF&0UDaYMtXaXNi<o|wTx&;mHwc*((`%@4E2VQ
zmNi(lF_Tq>0`KH_am@vp7Fb|#rVt4@0K?&ItUMz<bFLZz)fTpSdl|(W=AEP0O`;EL
z*humT3Ek78c`jD1*v-Gipj^B<p{Ec|E4GI-SCOoZ`)2Lc<by*BG4i%;wH=8Ap4SIl
zS#zAE(lvLZK`CCHJR}^SBt1hc!o#{tdUo_skD5%J>kJ<_0|i1{Y^<u<;hqy=$vcUy
za>t1Rr?Z=~9%0)wR}>Y*Hcd=T`yf;{7`=Tjh>dK!A5GjyO^Imcifb{;<bmTyOcGob
z+-ob}0JmAcx{HO5Za#f`<1$33_6#464!PK^qt9X{`+NV+^loz2rlnu5l^xdkweq%X
zGM<b&<Bt8;JUu;AOO%qodY!czQSy&EpZI%W!QFC@)NAg~GvRvIp|%#8?PZz1#fHYl
z*<r_Qry`Z%Y2%k4Ofh>X+5Y>psBrxA35F%E3{zZ~QtVT7;^Y;y#sT6U;|7+4RIGA*
zJoMh~x9oeVjEn6%P;v<KJUpxcSPPCePhPreCJJWHTMZP;XL4(RV!6Mw5tJGXo6IUa
zol<K%!p#eU7CVe4{FA$uz5^$zE&6TeZ~#O>i&9q(Fn*m2?bnNKY;5EpFX1$y1~Rvw
zz8O+qX^hMrI$zKv@-O;sLWIOIViddsGggRg0vltwJ=Ui^8sbr8p^kfDQrh!%%pH_S
zqFLOB;JCwfNN~W!t!gl<f<eL)OhP}IJfA8g(mt>W$C>lbeb0I5(O=PH8-nQ*^eUS(
zwbUMzMke5ripi}MhW7k5LwQD&F7?^GIC`sk28>@ed0&GwrSqe0K!NQxMnaC@xx8$e
z02S4$rtag%9>?RBRZh;L$wYzT<u=x1gUGp<Hk<p7aUUB56vRGL;*3EO_){U9<{YwO
z@*9Pr7eYWGFiLfK^(|xH<>CwS=Z{rTnSF-_MJ6;i0qL|YRo3Wx@*y_1^puo|##c{@
z*F?J?Y-4mxONTXNlk;{(objOIMbk?4U1x%9qUKfIeDLO16;)ML+(2MpWl~_8cGmo1
zQ>P=#VE0rP1!_3eCqe}VnFWHvglo+=4v!t)n~PlzOzPxDxow)U?wGf3Y;2T=<Q`4c
zHad5XXL6YW$}n<v@UpnLRFnYQc0u+4wBj8!V;;TAdD=uB8VOEVzKKfKn=FcoTjrzm
z_AJl1NU-O!O{8YWt*tO1LSh1Nis6E=fIw42!}l_`EWH%a=L8^WJ;%hnx!qIIUdoeS
zykmfu{05<u@DEu74~6gG(eRrT2H})^eBqt@vN<n!MT95V|0ut*tKdLhu~6xbgu0`T
z-j%)#%Y1BQ99+&BF?n<M;b2nH>CpL!K2z0{m%-}k+gX-LEn+&NZfVJ4yjV=^yN@5$
z*x1<SGV-{&xu>S43=Iug=!FCY4;Ne<A0MB|=kE%<9mJ?BNDou0;}Q~Ks*guTY=fhf
z*#EdX#iyjKgLDtZFJ2H&+)&arHHF-W+sIQ$NGK;Khp3mq?v@tg=FY_660hQ733+)I
zmmoX4F{mnjzV&JE>^ujrsu=ZaZf?KxxZPzoF(!lfu)1V-PfrijYbhxym~=%lym#SG
zZ@VAJT%dkfKu|FLi0~ORGZv0`Xpv}XZ?3mZOiWyD^sk6|jd$SX-BUl+6GoG8y3VBs
zqqbT6NUx(jv|-^95h2PPe;G71HC4zwGK8tB=0O6)i&y*Md$Goj%D~O?^71uw9I2J?
z{{gG%Ddp%6Z{D|<@q*yuEywzo<2?<oI`01cW#r2tFXM8i5$NQgsYy8UFgdv!Sq+UI
zmg7N6N+VtB?c29;7kFV9gr$SK*_+p|qvg+lvMtA2@bsx_|5`aheVf9%pn`(J(W7}~
zWyu>W7>-3PKeRr;z)%&qxds8XnYaDnNVVL&Jb4g-aPA@^B4+QM`sh119JWwnRVXDZ
zE32qjRb0G?OPTvCutK|pvzV5ahJA)?2yX{oYZ}rUKa~{He+~vr^f=k|-I-n8KpjY&
z?G2~gqRw4dSfI+rp5zE-WMXP=Ztm^r84mc$u$4Oh@#9C_1u*U3!^3j$b(38_;oG}s
zslES)fteZ1)yC23k%iPiL;zXn&yJ4bvNDvR(sFV{EiITRbBl{+mHuU>xgA|ylE;oU
ze)=SI<cQm~Yp1oe_EA%_$$C0s*bBN8HWEgfLi7<h=~;Ps?;sVc4W-V_%@s>-!Dy_t
zyL%PVy8F2n7j$%@K;c>4z(P=x-o#u-2(pOGOl~f&UtoF>MU@c|y`TVK4i7?AVoXfo
zrDpJ}695E+hK6G3oYH91w8&Kp`s<s*LViI(s2i;cleDwy>*`k5*OB5$m${*deGC0M
z`V%DB;^N}U%F5%%kK^Ca^hl$f!_yb)N@=_(;+mp&{=CoHf*rb&sVR9>wGck~ZyiIw
z1H}$hn8t>N9_WX`RgrY=Ll}?_-0%kcY+C3l6|4(EwY@#?&C5h~O?33Y#6<I;D<<@T
zqn}t1n{Fr(7#JiGb1>n-$;{5p1-#Cp7}!@|pY`&kZhrk7Bq(=OA0tj;>h{a6dUDe7
z^5x4LKjBGo6l&w<-}%OcL$D2nIMfvuBW`Z%Xkc@4b5V0}Oj|QOHyB4o25Gg#wv~}5
z4r;g|=rog3Qn-R&zkUtAQ?-pK6s%mFoVIOgt)ue}_V$=B;<Q2=WN&XTDk^#|-<01^
z%JiI0stmNAMn*=<ligzS^6%Ex)*w<+^qKq6)Pym&KF)xQj0}z%w(H@;A9{M$;8xdP
z;*y@2cn(^5C8g_=gWnyGE8K^(o`8VBX>Dy!H#ZEx3^10+&2>0=l1R6uW|eFBeGSrL
zqEs4@7W<LNfopATg$QnV)_ZGf>s9EJ_A!vl0PvFU-AkmnVI~fk1JA_=$?)4Z1H?RV
zNfJ+Ij{N>joFwCg`;d_E0PPt>NYE4@m0QE*U7`*pJpOX!z<~pZW6&rodwMS2JtQ_G
zMu*02XncGP;!&IcqI&{pX(!31aE0qjQ#?F8*qLoDEqei(ObG5t&p?0^Id~T!P9Nx8
zh>Yy)7crtHDy;pMoskMqK~ghIMW?5yr={f=6m0*BQaop5lrHV|vA@5+x7Q?pRByl)
z`wm<6p{!SaMuw%Dnwp;8*Z(xn598y-u@1X$W#r@_nAise1+hj!uU3rF2k0)hZ|}~~
z^<J5=?5^$Z>9M_Zsrbzs{0Vo&YOGsdpFb)`w2nYI5(4DIRs3~9?;UMPZvTCM8?rz$
z4m!Fx;@G;|5yZt{Lg37uoE(At2YC3{l~=vI?mM&w1_rKdeqh*#p}2BT>H$PP_#}!P
zKTfaMdyqpMrWyhXGPppB5G(9e0^>0&RR&tnR90Snt*x~N+W(bp0tx=8py13$V-u62
zZpUC<b#<~^)gs4^RqO1GPfbZ-(yoVn7?eMtxsY-2@@4@^cx5d3*7j8-=E1cIBOveq
z2w+n1UjZ-5Cdva?i)aLfCRA9~jd?^xDE6hU%3c0&henV-g8dGHId&r})Y^D|z=HSX
znOAc|y)VDVHw=u7s6F{|n~H^zmGxeG_2S-INnxVvV<W4!?@CExVkeg4TSo^@n3=x5
z3gH7d&x;ESK+kwt93WN~XFLiH*!$}{@CJym3G#gy?qAJ~Bz*1YNL37~M5$I#FpbuQ
zNZl_eh}s6H9-kW<96W~L4$cNRcM=j3{>uTa4<ufN`WZ118>TTuY+XE1kUK6pI^I=0
z=jZ45w!pdPwZ@Vw$Wh)Qcz*l#?K?@9{roxU?%kk>d7@@p`P2K>M=XB_ktX9Rg<9;R
z@F9_lBnoG2s811s7o=jrVgak9-Wy}=Zlnn~3WndhwKJs^q3%sdiHnm{9fLhKtastd
zmm*~~W>tYKRiCS?PiSf$VP}8e+shPWj+_oTvm9$%d%GJ-b)=A$XM5pXEGb!1T&$(8
zF6BM*XtGdduRWIzJ;=RkpB9#vmzS1oMGoquD-;(M#Rak(B8IuUzn&bVKYAf}i*fKo
zjn*u0x<9A98yI85!3%Sc<|oLTB46C9CL<$zueB#cd3JH}2P~Ta!q`}7?1A<ZKW%ER
z`R8=HVG{7w%NrR(x0a^#<^1^%(b7WM#o6@$#!yLTc*@dH*2lD?IDxxEwVSKuo0l(@
z$Oh+bd+qn3oQFcywTmu4;@PujU-yLN<k(-lcro<b6>&!Ay9DL@{QPSn^9u`yyHqmu
zuAYxoV8bnPr^ta6)<(;t{jkBd2$F?=10tMz?#1L@5;<tIPmK&xR!{XNJXu4-j@DMK
zwN`2>sziBH#MMwY#^BFj8M)qD@m<>-7HFAf++u-|Sg+|qSDoq}W~x0p4IS~QUb1Qf
z4j~R{X|*m7pUi5X{c__-rfJA|zWttS3-7VdA8-G#oXH54WrA1A%%{5qqBXP)^)eFX
zFBLoV$u`3GZ{Pk1>kzd4GwC*FhvdBBijltONtLhj@1rf#W$Q1I;lX(VdzG}xqtu9G
zdt!TQdV41LZ3t1bC=<L;#-)6x`l5}k?NIXHzkdw`4g}edaUgcWGOLVY9(4~AMnghX
zzza(Vzmzk+_w;xsxm>ygTU36(QjlEVy`w#|_F_REcS(9;FN=)9S_8tao}nR;G;ew&
zW_QSHRr&cO*@fo8kIPf}>bPAK6B8dJ1%L*9{f7=68r<>lPr*|B3!#;NmZ$E_(c9i+
zN9f{uSN+uiw7&O|><Op2t44;WrnJrp`%t=}&0g1xzjyD`%wTP!VkR{SMaV0!Y4dLD
zS?{+HOCtwD<sQ@er^3DGd<o)k*(*OkW~Ho|@98D2>53Lh#{LS5($dL|HrfxR&L%!`
zLCqC+MD`{-&}POmiXv6c?sAVKf77aG@|~TXiL?<s&)v764f)0qybwwhmX+zPqgfhr
zUjvDUnS~{@Yc=25kTH^nMZ3RdV{6B#fq>!KXP&mR4V=6#mY*6Ll%`o~FIijPiYP=8
zih2W4VIc^VUEZ=9ji?wN2md{#*<<c;V1MXzTceuP-c!-gzsbtPbi~Lrm8wzU>QFG&
zu`@ShHC<w*xwp5swsz;T@L9h7a`avG^?Nirj)PR5A3zt@Xmi2DL_gaAm4e)}>!&l(
zr_pKb+h61HfLYJd$tjYze1_R&tlebn8G&PXY)pedN__Ou46ok2dGpT=rG7qRb@he{
zZEJ|k!JA5N8=du*qQq-V*o;8R5g@%@f^A4V%+oPJAeIhqXuJHTO^tM|Hahysx~TiB
z<PR2RW*H|IOqnC;P`Yfu?IIu`V0NQKxb&xriAgVPGftff^<VuN+y4fP;%K(#lUCN&
zv!s9DNB`zN!$Vg!ICoF_Cwg4ed$UXkpkeD5U_%jk_pY&EVt&5RmT$iJHH#_T?>02d
z%r7!BVv>@+Tkch4y?1>*?y0O<Pj7D~HM7q<d8yj&Zm>dswI-?`{`%y7!|c=H=oT+C
zu1d;CHGLza`l`E@mo6<X{R8=NtomV;Fe4))2!~&jIfES?9fv6{Ll-|h9IW|Dl`}(^
zkLBDQLXDGk=r5|#ke^P6zKkuPL}3qBSo^~@wz{;mG`!#^w7dNC#Ub16sn%#O`S95`
zffhueW1^yrQm&7}!x`?4eI_BHZzTSX0E)jFZ;FfSE~k2LL(8*rPKyDqAYcFWWDpb;
z{rdg;K{j3%syft#9Km-ZBWDk7LT@m$fm7VbbngY7IHMEXnaDUiJUslVZ%_Jkc6Qo|
zCW|vpea=uJS_QO^xp)aQ{Q;!{r&UlHThYj<>;3zSfcjQQRDu@iPFqwAd~z$J5c_CV
zh<BYK-_w6#ZcRl?E7FvsQWbRtyPt2&#uQDYTCJpPVH9o>Fz?FFn$7g@h*z*GiLCYU
z^^KJr1f!e7#S;w=LT*qm-@$_iKaVYdxgN*F9#E^}i3?j%nVp$|?-Tjz7^Gp9xjHqd
z-ym-J_;Ghogqo+j3@GPgAor<|B|mt8qcP-)#e{c@<M9fWP6S#O@hb%7D@f!>bBPYd
zoVUGhQ~h|mLjNo^HTBsu+fX+eq|~><cJ}s7e(Zlxx2>;>C7<c@1<VM4B%)0;YUfif
zKW@Q*c?DJgAvz{B%BWw5<?Rkh%g7LoRN!h;H|nN7-f&4Y8A-kF1lASSJ8k%Pc216r
zv^4H-(Dqb$L!+{mmJ_M~N_xT1U%s3*G`v*QjUNRUp+{e?z_)>ep}ni?zCs`0{wA!~
zzP)>g88<?RrGbRF_=nMK)Hk>{w1Vipq0EO4L5VC9bZ=n|1bx2<FCzzs36NZ4wi9S7
zPuwE(!2IIbGaiNedObB8ZHx&hGGICL?Hkcokmv(Z=|A@Oud|hvC{_@CJ|YxS5Xv1Q
zKNG#-Tz+?3Ta<Ff%E}5+mmwyG3PMXu3)Kfj$m6^`)cYtpT(df{OHeAJKB$5*(MAWQ
zj{~{#6;SaNA@4?a3Tz2|72+cjWPZLg3U&l9v`i@}CdS4|IXUB~MqJq{3kzpqL31A7
zCkU24wfmTkw=_1|p-Dt+huXBEtxX4WgFBe}em1TOl;7&ug`O6b@ii%C2M323bxd|_
zFJ5G)4J(iSfq5<yBja;}92D#*Uc<vF5>Ee~U&X4yEC8U2JxU4(2Vx&v8Bh@yrw@@;
zT%)6t(<MwRSRD-nI+~kvaXsj}ybYS;`SlTr0yft?;M;`O1$70hj5{J8Gb}#D#OBdH
zv9hv)hth*ojY?D2-KfC|&lr_cXlg1;R6UVv4r-L1zCPO3S@}gA`vKwo=Z%e5R#%}l
z-Se}?>+02*l1ug99X*kj&?+N(z_SCTmcso9KpCpGH(eMbA(P}Ikl`NW<mGE?YiFk#
z@k8N))Lk%{dDOIY25ka8Jw0>==tAM?0?`z-5nwH8XCXe7zkEpu`gI8d3vk+{@VwEC
z91##GpK1RLmS!`0G3+pbBS#=<K+=ZGMri0MU*9#<4$ySrfE%Ee&1#1!+BR~QOJC_(
z6B9r1W6h*K8X6dM7Ff90+dqho)-^Gi7#uunWHdTCIXONq<M>?!4+(}^P?cb7q6$zu
zc`}ksaSO`?(}X@`KFq25I}|sBFyH(3?R8n%DmDQPMF=`Uh%W()oFofCjsV1fgAB8F
z4=XE9b8{4!c!&^O?cMzgHR`Lo(SI)XzxBZd;rL+WgkBiY80Z4}#4Y&yIQPFrTfKrJ
zn8=aK$)TVYc&f{X*qRIx4NB!KYp(vC)OLIST{3!#5Y*n{c(SvzmPOszLa4CMoIVYa
zlc}a=Gej9gd}X=?qZCg}_gtbfR!tm_quuGoN7y1q1O<r(5u2Nv32#KUi~b(dl!YV>
z9T5hI@KwUwUArcFzZoZqn`{U$u_9)CUguy;Hs5?-7!+iLJTQXe>VM^(FWSv!hF}L>
z2z*xR6${>>P!jt2oDflmL*XrA<L6hQ#Lg!ytccvM%l9HD$JcKakr4&$;zU<TNr@MS
zskym1{8iHOv$Huu6UD{F<KyGu(<uncD6AHK3h4=PNl6PZ>_LD;cRF#wlk@}{{>!$u
zsM|+|hw;DDKPpPf%O9}cdHC=lmKD8pwC3&5(9z*x0x1m(OXU5rn|R09uf^q3)6=Vf
zb%8AaM2v=XwvNF9$C8$mgFa&Rei8Z2J9qD*4L6H0G&9?r8B`Iv-`m-#Mo`77AesS&
z_3>H7WcAn3kdXOXpbYCHtxPxBQ3m2&57TONS;e3*F}ZMosO(KhFf=haLK}wfeadjf
z#bp~M?FXx?k~=jtH8gxyW;jj|R`%?~jt1$^7uf+R3A(HN(o$nVdc=E0zoi$5MnIco
z3u)=-LX<6mvmo?A-GzUnf7aBb+?oc34VcKp_&C{3u<dd6IN`Qk|0E<P;w$=*PY|TN
zwE^9Dcr4<m<eR@E!WOiov-opJC$9OoColgb&Lw0Fs8{iI$OCXp^M{uq#My|qjj6{G
zQSf^6W(gY$5p!pMaru+X%#-Yd0h3<<%HTU$hVBw^3(g_{S75LW;LivX=2kv`g&<^c
z<&0?dJ5b=@VZDCyW~ibHCkqf4Y{U9sxORf@0Z3}2O$B*W=zCd}u7z=V+Yp7|{B?As
zMuvtb`^9$Rml5rE!K{ENXso1Z|Fv~~IcWzz$=!87WEJfzf=NIr@e|iyjiuD|b#^8K
z&IM-X6EwFUpZ)W5&DLNEU969sxmQ}(ZhUG%GeAEHv-(?GYq1j*uS-g*Hh++Z$T!dv
zznrdV9fGs_>r3-0I1$8|8*eZ18u+f?q;Rvb8OF5kIsrkoD=R%vpji_Ld&8;YF_2*^
zJ_<D>e&L*+t|cTkzU%c9#H;$e!$0Z}{?_Ge@{20dM_&!)P`UT4RhM~Mdb%)uM8ynQ
zXg<!6rO3hXy}Z+H9iE}Ur)%2z*_neUE)VYa>7ya#*iW(UAcd+K`4T$OwbP*I3RjA~
zThLE$va7zi3ymuU<>=_Bk%dLUOIIAK;kJK{T(798sIfXBMV9q&Fj$!*uqnq1Ck%#w
zDwcx)sfK;~yd{bD0$5uL%B+@0N0|P-Ox^#i?mr>_x$6J+;r5G~GnsnX26dS)23*A%
zwJa@KVTSmeZ=u4IKvlzUDMb;Y{N&4i`uw1hixgB@EqtUW78Z~5Y3u99B_`4b-*UxQ
z5ijZW*7^Uu!L`pWcv})a8on0%b)EJ`&EEh2hcVQ}B1Q%Vx_t2%VQRFQ7TX^PQRY8-
zG{M0TTkW`{B%&-xH+k9FF<V_XcJ9GJ^SkI)QH_(@(C{1^ni9XBYV_WdCtRVm<P;S4
zc6QZb+ZfnMNlE?r^QW}DTmg;$Damur@@XtQ1Tuh1epi?(^6QIoU%Yq-m;vZCqU_ss
zpSJPYZ>6UBP;}wA78C$;aEfw%(N`YF(wQ;l-By|7EuWUEC&9m|YN1Fdk1&7heIlz}
z>&zKk$P9CssugUKb8;@|>CJ6*Vd1W=;WQ$dV7d;w3osJ^7S_zyS<S-fR9r#=NAtTT
zf5Q-_u4wU*O)<%VtxhV!PA?7ua4U8KnSVe))%KR(O?J%sku-7Wksx1#CJU;;V&}8w
zNA*QqM7!m^yPt$g8foM5R5=SvN}|HT?3|p4+LNlPAQz{#pJTJ%P=RIV=cgqltpI{N
zUu26x{u1#g$ad4DO0Z1-G&=C2zDPfZC-djZb;_b=&qh#-H2gp`Gr_nSRbgF-G$$uU
z<jTlP-Q9<#RRFi3`vzy_s=NE)uG-(%Kt{Npg{1!D$B$Weps1*M!^2Piq#r?oz{my&
z5wZXG)^>MafN>TJ3kwP}Gcz+b#lSOfxrz~t(I@^ILmu7-<uCgkU_{yv#L3$b!6ZVI
zftV&J>&Jfm)op1i)KyoP*+%IWbs0u!YC>G}5zS3a`yWKbv+7I%zC*n99s%P309EE^
z|1Bf0S6ng<GW|sb0_IVdvkbIOSIt^7jG*-ZApK9<`4Z<J7`k1xgr^ivErt}z8Ln_a
z1(?f4#wjUjq@uzlD0t>Dt%tFRii!$?ApjwwC!%_rq^Rh4ZH-`z`WY=P*l8hrgZk4y
z31MRJXS}~E=NHg>W-Z(8?4cq|7rRWA=6vOfE--AeAxO^_7Z<&4!SX@Kdt9`p>0)z=
zplWEC0JA}$m{_&`hQFvSQGf%&1){OAxQJ?LDx$@gyBL8(MWq&-8`w8;0ai9!Sd{CI
z^W{^q&NBVQxKS6IEIF;yL36lKpsxD*?Mq9ZH`(DeirT={(%H!gr-_5?ab;y7pgl~6
zxrw)WAEvM&vg#TdS6y8JnnFexllHSDL>la;#zt}-3}Uu`!;+p*J#{Khtj_9KL>)X%
zeX_DqSYAfl8IC2(Kxsc$g$LBo@CUeJc*9e%cQM$LvvYHBaJq(8<=-1~Mgc&U0i6&g
zIyepY4vDGVAm20+o=PMFw`A};qzav%`>k2`d%mhXnMI>~%zHcLx%#^pmh85a_^n|T
zmW>ejf62;Lz!nhcAMnJXg9isz4Fn_v1@Wnt=4MS_ks2sSpBvaC%%cQ^spG@w=<B0?
z-rh6bZ_%5!rplI3fAtQ9OD^=Ta6}w*#Y_(Mz>Gf%E4hrvk8z$M>%AeP8Chrb&S!qN
zaQC-wPm#s5v%?5O^Q)-9pr^yHWf;XNUFQ@F_T{Yrp|Tqu!o9r}I-;?iOx<ID{_Mp;
z2RMYH181oMZwz(i+9`YNx;0F^ux(cSw=i(R*zftu+n3I^U1KsoJq{C}U}*d^jWMIO
zwU$Q_GY7;@5U`0XitPbW{(%9WDuJ}pY70hyr|urS(N=*3sA7Jsx4YZJxDDkt%ua7+
zVQ6C~{=is}XS+Als0CxC+QH~A^YimKcBnf5a~2herFD09J_F8=f<Rlp0)Yh()vWuu
zbDdZ+@|%P(Rn+zHvz&Y#dfL*LzXE=vSFnRHZb?299F30l+_}WZ*VYQmn}npLyQ`~r
zv|)=q)yodAni<dh1_g8Fl`oufMTLbir!$utPr5A^acM}hq&|D${VqN=R-AvcB{ZP!
zF5v;{CM&BU@JZ34pZEkUx~rq3qqB2leiiNefiTr+-?GfiOArO(IbtdZJQ;18@h-)0
zn~;O)0cnYh+_%-<-=9`z#m&ph6$}Ow9UUDvtp>V26htWCvK(B!ybAL2v@|sxTwI_h
zG=6z;hcc#8fHoke(&3CjL4%ioAeY^{cryb7bRonSjQnm@<DMCz^{k2IT-5VK1BRL(
zn0tcyVYELeZS^<e9Q^o;;R$UD15tzwcESecrYNrAU1+HhQ=V>?C8KqI)|X$F+fHvS
znYvO0qXm?gK$<XhyJidDu!PgF=X?|u)dr(fC_Pb>ejjVz+}NNOGDQpJj-U(Z8L5Qd
zzIIy;JzJ=D1^^dO95{o~qhVx+hBO*}UJMMUGIV9kb^;8s{*?FP1&Rbn|7nzogHUPd
zz5M<P7CXk=KWldLhnY9sgNa93A85FUCP4IIhrq{vpURhCLPw_!i-Jwn{^7$gNI#}R
zMmx&h=B;;ooYq+CFmKY<cn}|WTbZD0;j%MY1w%8~0?$2ES`cjbyR#!oUnQDFx0HG-
zoLF@H(TN`l6o^jE&7A<~yXJeum(phW#&~@;M*|ffv->4tm35l5{MElz;Qyme`EO;*
z|Lljlo)~SBWuS|Mo^Wx+eg{d@|G6`gfdH^uj0K$?9sO5kGSIxY<-f`5+)eU_-?eV<
zbv+<9$kr+E-UR@BgT92I45d4IUi|8We^OdKuU;*~%pBecSRc>n{z!}1n3%zV0g;RE
zuLDWMgJBN_{D(pOCkWS>KcPE)YJ@HWbDxWLcG;M)W44R!zv*-bT`e*;r1N^GPSv-z
zP64wDSo%xyS(t`|<oC13kCh1@FfLGo#Up`uLI}!W;Or+Owf=bbn1n=LK|xzv8~B0%
zPEf96Uewys;^^Q&Nlu;&p2rCSbgdX~yIi^Ay|qz><^Yz&4IPQ`@fZ}saUest6b9(1
z%y1q5cV<HdU!VK2<I&HvW54GAA-TQ*-(`eC{{7*oRh8I31OI7n2gr)3kJ<*H1>*ln
zvR!-jq$DQ;*cqFcsKU%1p&2+PwxzEjCd1%#N{EO6G6B+v?+ar3hXWm+xZB8?;Lq5H
zXiY8j^sFo`GyZt(xE@G>ZL=~198Hf8zo#-%?U$>RH&3LR3K{0V^WJi3fAJC)E<m+}
z0B#GM2+XMSudl>ZR&Jr{Kgmf`CLoZ&NKX$dN{nDbgyXl5lPWj#^)c%}BAb+)48UQ*
z_Y=v6wUXuTM;6D##UG-H$A|}QZdAQ2EFzI&PzC~OCt92$_O-N}5xTD_lpY-&4G+p+
zv*@nr4j$yQjt3`2?n{5a`Ps9UHa6eplSyWDc7+_ePOaqNkb76t*Ut|J5lYoxOz$m%
zHYJq7+xc76rhp0+`^4bvSsF6ZVS5luuqFuKtNvRkSu%y6{p14Q;@GjXoH0<!9b#fK
zIe*^cs4&U<-bW%kA5B8}{vt2$l3+5(g?KimZ@mjMG6sp!E=?|s02X;N7$!hrQRkkD
zWc)ont&{Hw0Ny~rW8nP_6dqs*H#9Y&y&3wcHoLGO=Q){#-{ORTYii2;cVRcl0@(_y
z7>Sa?F>omw8_{wGyyGBQ(81aGx9DFf-;e)9DGhLBd?JqozjZO+5&xqUret&+e?L;i
kRsMVWDa8JlKipzVde!J3`DyGr-iSowq_%3l^2M9~1?GINTL1t6

diff --git a/docs/articles/examples/timeseries/timeseries_classification_from_scratch.html b/docs/articles/examples/timeseries/timeseries_classification_from_scratch.html
index 5d5db94e8..7e64f11a0 100644
--- a/docs/articles/examples/timeseries/timeseries_classification_from_scratch.html
+++ b/docs/articles/examples/timeseries/timeseries_classification_from_scratch.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../../../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -348,446 +348,666 @@ <h2 id="train-the-model">Train the model<a class="anchor" aria-label="anchor" hr
 <span>  validation_split <span class="op">=</span> <span class="fl">0.2</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/500</span></span>
-<span><span class="co">## 90/90 - 2s - 26ms/step - loss: 0.5577 - sparse_categorical_accuracy: 0.7066 - val_loss: 0.8512 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 2s - 24ms/step - loss: 0.5557 - sparse_categorical_accuracy: 0.7073 - val_loss: 0.8517 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 2/500</span></span>
-<span><span class="co">## 90/90 - 1s - 7ms/step - loss: 0.4871 - sparse_categorical_accuracy: 0.7649 - val_loss: 0.9151 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 1s - 6ms/step - loss: 0.4850 - sparse_categorical_accuracy: 0.7611 - val_loss: 0.9464 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 3/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.4678 - sparse_categorical_accuracy: 0.7663 - val_loss: 0.7265 - val_sparse_categorical_accuracy: 0.4910 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.4713 - sparse_categorical_accuracy: 0.7705 - val_loss: 0.7593 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 4/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.4174 - sparse_categorical_accuracy: 0.7941 - val_loss: 0.6591 - val_sparse_categorical_accuracy: 0.5534 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.4239 - sparse_categorical_accuracy: 0.7885 - val_loss: 0.6721 - val_sparse_categorical_accuracy: 0.5021 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 5/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.4289 - sparse_categorical_accuracy: 0.7826 - val_loss: 0.5279 - val_sparse_categorical_accuracy: 0.6741 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.4218 - sparse_categorical_accuracy: 0.7885 - val_loss: 0.5647 - val_sparse_categorical_accuracy: 0.7198 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 6/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.4058 - sparse_categorical_accuracy: 0.8035 - val_loss: 0.4469 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.4060 - sparse_categorical_accuracy: 0.8028 - val_loss: 0.4563 - val_sparse_categorical_accuracy: 0.8141 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 7/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.4005 - sparse_categorical_accuracy: 0.8042 - val_loss: 0.4278 - val_sparse_categorical_accuracy: 0.7448 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.4032 - sparse_categorical_accuracy: 0.8017 - val_loss: 0.4209 - val_sparse_categorical_accuracy: 0.7892 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 8/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.3904 - sparse_categorical_accuracy: 0.8049 - val_loss: 0.3824 - val_sparse_categorical_accuracy: 0.8128 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.3920 - sparse_categorical_accuracy: 0.8003 - val_loss: 0.3938 - val_sparse_categorical_accuracy: 0.8239 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 9/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3830 - sparse_categorical_accuracy: 0.8188 - val_loss: 0.4006 - val_sparse_categorical_accuracy: 0.8100 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.3892 - sparse_categorical_accuracy: 0.8135 - val_loss: 0.3849 - val_sparse_categorical_accuracy: 0.8225 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 10/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3845 - sparse_categorical_accuracy: 0.8080 - val_loss: 0.4014 - val_sparse_categorical_accuracy: 0.8114 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.3874 - sparse_categorical_accuracy: 0.8021 - val_loss: 0.4934 - val_sparse_categorical_accuracy: 0.7503 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 11/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.3804 - sparse_categorical_accuracy: 0.8167 - val_loss: 0.3738 - val_sparse_categorical_accuracy: 0.8017 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3798 - sparse_categorical_accuracy: 0.8149 - val_loss: 0.5693 - val_sparse_categorical_accuracy: 0.6990 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 12/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3695 - sparse_categorical_accuracy: 0.8226 - val_loss: 0.4010 - val_sparse_categorical_accuracy: 0.7989 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3671 - sparse_categorical_accuracy: 0.8215 - val_loss: 0.3998 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 13/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3598 - sparse_categorical_accuracy: 0.8313 - val_loss: 0.3742 - val_sparse_categorical_accuracy: 0.8128 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3555 - sparse_categorical_accuracy: 0.8389 - val_loss: 0.4145 - val_sparse_categorical_accuracy: 0.8031 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 14/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3604 - sparse_categorical_accuracy: 0.8250 - val_loss: 0.3839 - val_sparse_categorical_accuracy: 0.8225 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3628 - sparse_categorical_accuracy: 0.8253 - val_loss: 0.3886 - val_sparse_categorical_accuracy: 0.8211 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 15/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3436 - sparse_categorical_accuracy: 0.8417 - val_loss: 0.3815 - val_sparse_categorical_accuracy: 0.8225 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3433 - sparse_categorical_accuracy: 0.8406 - val_loss: 0.7763 - val_sparse_categorical_accuracy: 0.6019 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 16/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.3441 - sparse_categorical_accuracy: 0.8444 - val_loss: 0.3387 - val_sparse_categorical_accuracy: 0.8433 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.3455 - sparse_categorical_accuracy: 0.8434 - val_loss: 0.3537 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 17/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.3327 - sparse_categorical_accuracy: 0.8427 - val_loss: 0.3360 - val_sparse_categorical_accuracy: 0.8433 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3336 - sparse_categorical_accuracy: 0.8462 - val_loss: 0.5375 - val_sparse_categorical_accuracy: 0.7074 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 18/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3296 - sparse_categorical_accuracy: 0.8493 - val_loss: 0.5619 - val_sparse_categorical_accuracy: 0.7074 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3263 - sparse_categorical_accuracy: 0.8503 - val_loss: 0.4134 - val_sparse_categorical_accuracy: 0.7892 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 19/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3222 - sparse_categorical_accuracy: 0.8608 - val_loss: 0.3951 - val_sparse_categorical_accuracy: 0.8128 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.3195 - sparse_categorical_accuracy: 0.8601 - val_loss: 0.3432 - val_sparse_categorical_accuracy: 0.8433 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 20/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3195 - sparse_categorical_accuracy: 0.8590 - val_loss: 0.5172 - val_sparse_categorical_accuracy: 0.7226 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3136 - sparse_categorical_accuracy: 0.8674 - val_loss: 0.9287 - val_sparse_categorical_accuracy: 0.5950 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 21/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.3003 - sparse_categorical_accuracy: 0.8698 - val_loss: 0.3359 - val_sparse_categorical_accuracy: 0.8405 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3001 - sparse_categorical_accuracy: 0.8691 - val_loss: 0.3926 - val_sparse_categorical_accuracy: 0.7795 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 22/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.3012 - sparse_categorical_accuracy: 0.8740 - val_loss: 0.3339 - val_sparse_categorical_accuracy: 0.8447 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.2984 - sparse_categorical_accuracy: 0.8719 - val_loss: 0.3023 - val_sparse_categorical_accuracy: 0.8655 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 23/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2901 - sparse_categorical_accuracy: 0.8799 - val_loss: 0.3859 - val_sparse_categorical_accuracy: 0.7920 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2930 - sparse_categorical_accuracy: 0.8767 - val_loss: 0.3501 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 24/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.2898 - sparse_categorical_accuracy: 0.8788 - val_loss: 0.2806 - val_sparse_categorical_accuracy: 0.8863 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2861 - sparse_categorical_accuracy: 0.8799 - val_loss: 0.9187 - val_sparse_categorical_accuracy: 0.5992 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 25/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2831 - sparse_categorical_accuracy: 0.8785 - val_loss: 0.3124 - val_sparse_categorical_accuracy: 0.8585 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2843 - sparse_categorical_accuracy: 0.8816 - val_loss: 0.3731 - val_sparse_categorical_accuracy: 0.8363 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 26/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3130 - sparse_categorical_accuracy: 0.8580 - val_loss: 0.3423 - val_sparse_categorical_accuracy: 0.8377 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.3182 - sparse_categorical_accuracy: 0.8569 - val_loss: 0.5553 - val_sparse_categorical_accuracy: 0.7115 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 27/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2741 - sparse_categorical_accuracy: 0.8910 - val_loss: 0.2908 - val_sparse_categorical_accuracy: 0.8835 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2732 - sparse_categorical_accuracy: 0.8875 - val_loss: 0.3950 - val_sparse_categorical_accuracy: 0.7628 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 28/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2855 - sparse_categorical_accuracy: 0.8837 - val_loss: 0.5778 - val_sparse_categorical_accuracy: 0.7323 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2943 - sparse_categorical_accuracy: 0.8705 - val_loss: 0.8138 - val_sparse_categorical_accuracy: 0.7254 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 29/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2671 - sparse_categorical_accuracy: 0.8809 - val_loss: 0.4736 - val_sparse_categorical_accuracy: 0.7628 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2772 - sparse_categorical_accuracy: 0.8781 - val_loss: 0.3712 - val_sparse_categorical_accuracy: 0.8405 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 30/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2720 - sparse_categorical_accuracy: 0.8833 - val_loss: 0.4564 - val_sparse_categorical_accuracy: 0.7767 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2714 - sparse_categorical_accuracy: 0.8833 - val_loss: 0.4209 - val_sparse_categorical_accuracy: 0.7975 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 31/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2724 - sparse_categorical_accuracy: 0.8882 - val_loss: 0.2784 - val_sparse_categorical_accuracy: 0.8918 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.2720 - sparse_categorical_accuracy: 0.8854 - val_loss: 1.9298 - val_sparse_categorical_accuracy: 0.5104 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 32/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2709 - sparse_categorical_accuracy: 0.8830 - val_loss: 0.3442 - val_sparse_categorical_accuracy: 0.8280 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.2699 - sparse_categorical_accuracy: 0.8833 - val_loss: 0.3155 - val_sparse_categorical_accuracy: 0.8516 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 33/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2571 - sparse_categorical_accuracy: 0.8986 - val_loss: 0.3730 - val_sparse_categorical_accuracy: 0.7864 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.2586 - sparse_categorical_accuracy: 0.8951 - val_loss: 0.3628 - val_sparse_categorical_accuracy: 0.7947 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 34/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2449 - sparse_categorical_accuracy: 0.9003 - val_loss: 0.2799 - val_sparse_categorical_accuracy: 0.8877 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2490 - sparse_categorical_accuracy: 0.9003 - val_loss: 0.5844 - val_sparse_categorical_accuracy: 0.7309 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 35/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2698 - sparse_categorical_accuracy: 0.8861 - val_loss: 0.4860 - val_sparse_categorical_accuracy: 0.7420 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2627 - sparse_categorical_accuracy: 0.8938 - val_loss: 0.3480 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 36/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2539 - sparse_categorical_accuracy: 0.8944 - val_loss: 0.2572 - val_sparse_categorical_accuracy: 0.8974 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2558 - sparse_categorical_accuracy: 0.8948 - val_loss: 0.3793 - val_sparse_categorical_accuracy: 0.8128 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 37/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2495 - sparse_categorical_accuracy: 0.8962 - val_loss: 0.2735 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2515 - sparse_categorical_accuracy: 0.8958 - val_loss: 0.4828 - val_sparse_categorical_accuracy: 0.7503 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 38/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2474 - sparse_categorical_accuracy: 0.9000 - val_loss: 0.3009 - val_sparse_categorical_accuracy: 0.8641 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.2541 - sparse_categorical_accuracy: 0.8986 - val_loss: 0.2950 - val_sparse_categorical_accuracy: 0.8669 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 39/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2492 - sparse_categorical_accuracy: 0.8976 - val_loss: 1.0276 - val_sparse_categorical_accuracy: 0.6283 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2505 - sparse_categorical_accuracy: 0.8976 - val_loss: 0.7151 - val_sparse_categorical_accuracy: 0.7087 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 40/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2477 - sparse_categorical_accuracy: 0.8896 - val_loss: 0.4444 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2490 - sparse_categorical_accuracy: 0.8979 - val_loss: 0.3170 - val_sparse_categorical_accuracy: 0.8627 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 41/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2315 - sparse_categorical_accuracy: 0.9069 - val_loss: 0.4963 - val_sparse_categorical_accuracy: 0.7573 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.2325 - sparse_categorical_accuracy: 0.9062 - val_loss: 0.2560 - val_sparse_categorical_accuracy: 0.8904 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 42/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2279 - sparse_categorical_accuracy: 0.9101 - val_loss: 0.2943 - val_sparse_categorical_accuracy: 0.8849 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2312 - sparse_categorical_accuracy: 0.9076 - val_loss: 0.3396 - val_sparse_categorical_accuracy: 0.8405 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 43/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2365 - sparse_categorical_accuracy: 0.8983 - val_loss: 0.6205 - val_sparse_categorical_accuracy: 0.7226 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2402 - sparse_categorical_accuracy: 0.8976 - val_loss: 0.3650 - val_sparse_categorical_accuracy: 0.8294 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 44/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2314 - sparse_categorical_accuracy: 0.9080 - val_loss: 0.3348 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2342 - sparse_categorical_accuracy: 0.9038 - val_loss: 1.0563 - val_sparse_categorical_accuracy: 0.6172 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 45/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2301 - sparse_categorical_accuracy: 0.9094 - val_loss: 0.4539 - val_sparse_categorical_accuracy: 0.7753 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2320 - sparse_categorical_accuracy: 0.9080 - val_loss: 0.2506 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 46/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2265 - sparse_categorical_accuracy: 0.9090 - val_loss: 0.2660 - val_sparse_categorical_accuracy: 0.8974 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2317 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.4473 - val_sparse_categorical_accuracy: 0.7920 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 47/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2065 - sparse_categorical_accuracy: 0.9188 - val_loss: 0.2680 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2164 - sparse_categorical_accuracy: 0.9125 - val_loss: 0.6324 - val_sparse_categorical_accuracy: 0.7240 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 48/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.2107 - sparse_categorical_accuracy: 0.9139 - val_loss: 0.2204 - val_sparse_categorical_accuracy: 0.9126 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.2186 - sparse_categorical_accuracy: 0.9122 - val_loss: 0.2525 - val_sparse_categorical_accuracy: 0.9001 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 49/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2009 - sparse_categorical_accuracy: 0.9174 - val_loss: 0.3135 - val_sparse_categorical_accuracy: 0.8336 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.2119 - sparse_categorical_accuracy: 0.9118 - val_loss: 0.2401 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 50/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2138 - sparse_categorical_accuracy: 0.9160 - val_loss: 0.4732 - val_sparse_categorical_accuracy: 0.7587 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.2134 - sparse_categorical_accuracy: 0.9191 - val_loss: 0.2410 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 51/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1992 - sparse_categorical_accuracy: 0.9201 - val_loss: 0.6327 - val_sparse_categorical_accuracy: 0.7365 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.2120 - sparse_categorical_accuracy: 0.9174 - val_loss: 0.6803 - val_sparse_categorical_accuracy: 0.6935 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 52/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1888 - sparse_categorical_accuracy: 0.9267 - val_loss: 0.2356 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.2023 - sparse_categorical_accuracy: 0.9205 - val_loss: 0.2938 - val_sparse_categorical_accuracy: 0.8779 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 53/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1837 - sparse_categorical_accuracy: 0.9278 - val_loss: 0.2637 - val_sparse_categorical_accuracy: 0.8877 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1980 - sparse_categorical_accuracy: 0.9243 - val_loss: 0.5380 - val_sparse_categorical_accuracy: 0.7531 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 54/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1758 - sparse_categorical_accuracy: 0.9354 - val_loss: 0.2062 - val_sparse_categorical_accuracy: 0.9126 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1883 - sparse_categorical_accuracy: 0.9271 - val_loss: 0.2272 - val_sparse_categorical_accuracy: 0.9071 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 55/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1668 - sparse_categorical_accuracy: 0.9438 - val_loss: 0.6011 - val_sparse_categorical_accuracy: 0.7559 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1761 - sparse_categorical_accuracy: 0.9385 - val_loss: 0.2707 - val_sparse_categorical_accuracy: 0.8821 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 56/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1580 - sparse_categorical_accuracy: 0.9476 - val_loss: 0.4532 - val_sparse_categorical_accuracy: 0.7587 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1683 - sparse_categorical_accuracy: 0.9417 - val_loss: 0.2584 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 57/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1518 - sparse_categorical_accuracy: 0.9521 - val_loss: 1.1827 - val_sparse_categorical_accuracy: 0.5492 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1581 - sparse_categorical_accuracy: 0.9490 - val_loss: 1.4305 - val_sparse_categorical_accuracy: 0.7184 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 58/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1449 - sparse_categorical_accuracy: 0.9528 - val_loss: 0.1922 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1518 - sparse_categorical_accuracy: 0.9497 - val_loss: 0.7991 - val_sparse_categorical_accuracy: 0.7531 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 59/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1520 - sparse_categorical_accuracy: 0.9476 - val_loss: 2.0179 - val_sparse_categorical_accuracy: 0.6089 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1529 - sparse_categorical_accuracy: 0.9479 - val_loss: 0.3389 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 60/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1457 - sparse_categorical_accuracy: 0.9503 - val_loss: 0.2761 - val_sparse_categorical_accuracy: 0.8946 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1784 - sparse_categorical_accuracy: 0.9368 - val_loss: 0.2534 - val_sparse_categorical_accuracy: 0.8974 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 61/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1280 - sparse_categorical_accuracy: 0.9580 - val_loss: 1.6068 - val_sparse_categorical_accuracy: 0.6796 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1335 - sparse_categorical_accuracy: 0.9566 - val_loss: 0.2323 - val_sparse_categorical_accuracy: 0.9098 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 62/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1408 - sparse_categorical_accuracy: 0.9510 - val_loss: 0.9512 - val_sparse_categorical_accuracy: 0.7129 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1401 - sparse_categorical_accuracy: 0.9531 - val_loss: 0.3259 - val_sparse_categorical_accuracy: 0.8696 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 63/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1381 - sparse_categorical_accuracy: 0.9514 - val_loss: 1.2871 - val_sparse_categorical_accuracy: 0.6976 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1404 - sparse_categorical_accuracy: 0.9521 - val_loss: 0.2970 - val_sparse_categorical_accuracy: 0.8627 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 64/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1280 - sparse_categorical_accuracy: 0.9569 - val_loss: 0.3003 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1375 - sparse_categorical_accuracy: 0.9559 - val_loss: 0.3416 - val_sparse_categorical_accuracy: 0.8183 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 65/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1206 - sparse_categorical_accuracy: 0.9618 - val_loss: 0.4581 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1303 - sparse_categorical_accuracy: 0.9583 - val_loss: 0.2586 - val_sparse_categorical_accuracy: 0.8766 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 66/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1148 - sparse_categorical_accuracy: 0.9628 - val_loss: 0.3863 - val_sparse_categorical_accuracy: 0.8266 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1199 - sparse_categorical_accuracy: 0.9608 - val_loss: 1.2218 - val_sparse_categorical_accuracy: 0.7337 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 67/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1068 - sparse_categorical_accuracy: 0.9663 - val_loss: 1.2923 - val_sparse_categorical_accuracy: 0.6852 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1186 - sparse_categorical_accuracy: 0.9615 - val_loss: 0.1464 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 68/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1159 - sparse_categorical_accuracy: 0.9615 - val_loss: 0.2969 - val_sparse_categorical_accuracy: 0.8558 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1176 - sparse_categorical_accuracy: 0.9632 - val_loss: 0.1451 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 69/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1228 - sparse_categorical_accuracy: 0.9580 - val_loss: 0.2251 - val_sparse_categorical_accuracy: 0.9071 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1295 - sparse_categorical_accuracy: 0.9583 - val_loss: 1.7723 - val_sparse_categorical_accuracy: 0.5395 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 70/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1138 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.1805 - val_sparse_categorical_accuracy: 0.9251 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1232 - sparse_categorical_accuracy: 0.9587 - val_loss: 0.1802 - val_sparse_categorical_accuracy: 0.9237 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 71/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1101 - sparse_categorical_accuracy: 0.9618 - val_loss: 0.1439 - val_sparse_categorical_accuracy: 0.9487 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1202 - sparse_categorical_accuracy: 0.9573 - val_loss: 0.1932 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 72/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1133 - sparse_categorical_accuracy: 0.9628 - val_loss: 0.1390 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1124 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.4178 - val_sparse_categorical_accuracy: 0.8003 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 73/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1042 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.1421 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1068 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1517 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 74/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1098 - sparse_categorical_accuracy: 0.9604 - val_loss: 2.0644 - val_sparse_categorical_accuracy: 0.6685 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1085 - sparse_categorical_accuracy: 0.9639 - val_loss: 0.2126 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 75/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1419 - sparse_categorical_accuracy: 0.9486 - val_loss: 0.4101 - val_sparse_categorical_accuracy: 0.8336 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1263 - sparse_categorical_accuracy: 0.9580 - val_loss: 0.1689 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 76/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1230 - sparse_categorical_accuracy: 0.9569 - val_loss: 0.3055 - val_sparse_categorical_accuracy: 0.8946 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1169 - sparse_categorical_accuracy: 0.9597 - val_loss: 0.2955 - val_sparse_categorical_accuracy: 0.8460 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 77/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1028 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.1704 - val_sparse_categorical_accuracy: 0.9265 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1089 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.6333 - val_sparse_categorical_accuracy: 0.6241 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 78/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1003 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.3304 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1017 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.1416 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 79/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1019 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.5429 - val_sparse_categorical_accuracy: 0.7822 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1099 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.3434 - val_sparse_categorical_accuracy: 0.8544 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 80/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1037 - sparse_categorical_accuracy: 0.9670 - val_loss: 2.0288 - val_sparse_categorical_accuracy: 0.6976 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1046 - sparse_categorical_accuracy: 0.9653 - val_loss: 0.4430 - val_sparse_categorical_accuracy: 0.8280 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 81/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1030 - sparse_categorical_accuracy: 0.9667 - val_loss: 3.3225 - val_sparse_categorical_accuracy: 0.5492 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1098 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.1632 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 82/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0947 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.1449 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1021 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.3984 - val_sparse_categorical_accuracy: 0.7906 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 83/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1060 - sparse_categorical_accuracy: 0.9639 - val_loss: 0.2491 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1086 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.1976 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 84/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0958 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.1281 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1026 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.2123 - val_sparse_categorical_accuracy: 0.9251 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 85/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0993 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.1956 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1032 - sparse_categorical_accuracy: 0.9632 - val_loss: 0.3491 - val_sparse_categorical_accuracy: 0.8613 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 86/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1003 - sparse_categorical_accuracy: 0.9604 - val_loss: 0.4167 - val_sparse_categorical_accuracy: 0.8239 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1136 - sparse_categorical_accuracy: 0.9576 - val_loss: 0.1498 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 87/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1099 - sparse_categorical_accuracy: 0.9604 - val_loss: 0.1557 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.1107 - sparse_categorical_accuracy: 0.9608 - val_loss: 0.1406 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 88/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1002 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.1204 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0995 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.6864 - val_sparse_categorical_accuracy: 0.7656 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 89/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0969 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1194 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1013 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.8218 - val_sparse_categorical_accuracy: 0.7476 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 90/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0937 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.1397 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1002 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.2009 - val_sparse_categorical_accuracy: 0.9209 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 91/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1024 - sparse_categorical_accuracy: 0.9660 - val_loss: 2.1705 - val_sparse_categorical_accuracy: 0.6671 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1010 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.2869 - val_sparse_categorical_accuracy: 0.8849 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 92/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0928 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.7758 - val_sparse_categorical_accuracy: 0.7559 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1012 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.5128 - val_sparse_categorical_accuracy: 0.7434 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 93/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0905 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.2143 - val_sparse_categorical_accuracy: 0.9140 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1009 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.5212 - val_sparse_categorical_accuracy: 0.7753 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 94/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0919 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.1413 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1016 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.3146 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 95/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0884 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.1715 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0975 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1750 - val_sparse_categorical_accuracy: 0.9376 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 96/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1015 - sparse_categorical_accuracy: 0.9653 - val_loss: 0.3827 - val_sparse_categorical_accuracy: 0.8669 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1121 - sparse_categorical_accuracy: 0.9583 - val_loss: 0.2399 - val_sparse_categorical_accuracy: 0.9112 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 97/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0945 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1286 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1000 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.5611 - val_sparse_categorical_accuracy: 0.7101 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 98/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0924 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.2279 - val_sparse_categorical_accuracy: 0.9126 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0949 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1864 - val_sparse_categorical_accuracy: 0.9279 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 99/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0951 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.1800 - val_sparse_categorical_accuracy: 0.9348 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0925 - sparse_categorical_accuracy: 0.9667 - val_loss: 0.1228 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 100/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0864 - sparse_categorical_accuracy: 0.9729 - val_loss: 0.2424 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0922 - sparse_categorical_accuracy: 0.9691 - val_loss: 1.9448 - val_sparse_categorical_accuracy: 0.7115 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 101/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0830 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.1904 - val_sparse_categorical_accuracy: 0.9251 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0887 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.2127 - val_sparse_categorical_accuracy: 0.9071 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 102/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0973 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.1916 - val_sparse_categorical_accuracy: 0.9182 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0999 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.1903 - val_sparse_categorical_accuracy: 0.9154 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 103/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0883 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.1190 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0929 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.2647 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 104/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0936 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.2695 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1008 - sparse_categorical_accuracy: 0.9642 - val_loss: 1.1019 - val_sparse_categorical_accuracy: 0.6019 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 105/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0905 - sparse_categorical_accuracy: 0.9684 - val_loss: 0.1941 - val_sparse_categorical_accuracy: 0.9182 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0969 - sparse_categorical_accuracy: 0.9681 - val_loss: 1.1464 - val_sparse_categorical_accuracy: 0.5645 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 106/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0885 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.5663 - val_sparse_categorical_accuracy: 0.8322 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0924 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.2535 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 107/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0891 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.6871 - val_sparse_categorical_accuracy: 0.7490 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0930 - sparse_categorical_accuracy: 0.9677 - val_loss: 1.5288 - val_sparse_categorical_accuracy: 0.5118 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 108/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0890 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.7344 - val_sparse_categorical_accuracy: 0.8031 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0893 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1496 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 109/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0894 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.3726 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0891 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.2799 - val_sparse_categorical_accuracy: 0.8835 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 110/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0963 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.6424 - val_sparse_categorical_accuracy: 0.7961 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0963 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.4827 - val_sparse_categorical_accuracy: 0.8155 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 111/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0819 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.8653 - val_sparse_categorical_accuracy: 0.7642 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0841 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1203 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 112/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0852 - sparse_categorical_accuracy: 0.9691 - val_loss: 1.0738 - val_sparse_categorical_accuracy: 0.7393 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0861 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.5081 - val_sparse_categorical_accuracy: 0.7559 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 113/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0828 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.1299 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0831 - sparse_categorical_accuracy: 0.9729 - val_loss: 1.2423 - val_sparse_categorical_accuracy: 0.5687 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 114/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1042 - sparse_categorical_accuracy: 0.9649 - val_loss: 0.2819 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.1094 - sparse_categorical_accuracy: 0.9653 - val_loss: 0.3459 - val_sparse_categorical_accuracy: 0.8252 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 115/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0780 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.2349 - val_sparse_categorical_accuracy: 0.9001 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0886 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.7117 - val_sparse_categorical_accuracy: 0.7226 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 116/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0850 - sparse_categorical_accuracy: 0.9743 - val_loss: 0.9277 - val_sparse_categorical_accuracy: 0.7406 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0989 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.1669 - val_sparse_categorical_accuracy: 0.9334 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 117/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0813 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.7052 - val_sparse_categorical_accuracy: 0.7836 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0891 - sparse_categorical_accuracy: 0.9705 - val_loss: 0.2413 - val_sparse_categorical_accuracy: 0.9154 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 118/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0838 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1543 - val_sparse_categorical_accuracy: 0.9307 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0902 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.7796 - val_sparse_categorical_accuracy: 0.6741 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 119/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0817 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.1127 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0877 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.1806 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 120/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0846 - sparse_categorical_accuracy: 0.9715 - val_loss: 0.1209 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0880 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.1146 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 121/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0817 - sparse_categorical_accuracy: 0.9740 - val_loss: 0.1134 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0896 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.1279 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 122/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0872 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.6854 - val_sparse_categorical_accuracy: 0.7892 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0883 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.3067 - val_sparse_categorical_accuracy: 0.8918 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 123/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0795 - sparse_categorical_accuracy: 0.9743 - val_loss: 0.8494 - val_sparse_categorical_accuracy: 0.7448 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0855 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.1060 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 124/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0965 - sparse_categorical_accuracy: 0.9632 - val_loss: 0.2033 - val_sparse_categorical_accuracy: 0.9140 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0917 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.2169 - val_sparse_categorical_accuracy: 0.9112 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 125/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0866 - sparse_categorical_accuracy: 0.9715 - val_loss: 0.3366 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0976 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.1407 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 126/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0792 - sparse_categorical_accuracy: 0.9747 - val_loss: 1.1318 - val_sparse_categorical_accuracy: 0.7462 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0866 - sparse_categorical_accuracy: 0.9722 - val_loss: 1.4019 - val_sparse_categorical_accuracy: 0.7295 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 127/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0810 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1230 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0877 - sparse_categorical_accuracy: 0.9747 - val_loss: 0.2518 - val_sparse_categorical_accuracy: 0.8918 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 128/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0839 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.1734 - val_sparse_categorical_accuracy: 0.9279 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0942 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.1465 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 129/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0845 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.2233 - val_sparse_categorical_accuracy: 0.9154 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0904 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.1546 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 130/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0906 - sparse_categorical_accuracy: 0.9694 - val_loss: 1.4223 - val_sparse_categorical_accuracy: 0.6963 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0953 - sparse_categorical_accuracy: 0.9705 - val_loss: 0.1201 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 131/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0774 - sparse_categorical_accuracy: 0.9743 - val_loss: 2.8725 - val_sparse_categorical_accuracy: 0.7018 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0846 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.1980 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 132/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0748 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.8038 - val_sparse_categorical_accuracy: 0.7781 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0812 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.4080 - val_sparse_categorical_accuracy: 0.8599 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 133/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0739 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.5711 - val_sparse_categorical_accuracy: 0.7809 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0785 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.3284 - val_sparse_categorical_accuracy: 0.8835 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 134/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0780 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.2510 - val_sparse_categorical_accuracy: 0.8918 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0885 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.5050 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 135/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0801 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.4907 - val_sparse_categorical_accuracy: 0.8585 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0859 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.4296 - val_sparse_categorical_accuracy: 0.8141 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 136/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0819 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.1151 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0885 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.1295 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 137/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0840 - sparse_categorical_accuracy: 0.9715 - val_loss: 1.2419 - val_sparse_categorical_accuracy: 0.7240 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0892 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.4052 - val_sparse_categorical_accuracy: 0.8031 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 138/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0774 - sparse_categorical_accuracy: 0.9743 - val_loss: 0.6905 - val_sparse_categorical_accuracy: 0.7864 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0835 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.1160 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 139/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0852 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.1253 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0877 - sparse_categorical_accuracy: 0.9708 - val_loss: 3.0631 - val_sparse_categorical_accuracy: 0.7032 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 140/500</span></span>
-<span><span class="co">## 90/90 - 1s - 6ms/step - loss: 0.0666 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.2353 - val_sparse_categorical_accuracy: 0.9140 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0867 - sparse_categorical_accuracy: 0.9719 - val_loss: 1.3638 - val_sparse_categorical_accuracy: 0.7143 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 141/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0657 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1275 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0857 - sparse_categorical_accuracy: 0.9715 - val_loss: 0.2599 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 142/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0635 - sparse_categorical_accuracy: 0.9788 - val_loss: 0.2696 - val_sparse_categorical_accuracy: 0.9029 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0771 - sparse_categorical_accuracy: 0.9764 - val_loss: 0.2860 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 143/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0688 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.1737 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0835 - sparse_categorical_accuracy: 0.9694 - val_loss: 0.1252 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 0.0010</span></span>
 <span><span class="co">## Epoch 144/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0615 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.2506 - val_sparse_categorical_accuracy: 0.8946 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 1s - 7ms/step - loss: 0.0674 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.1234 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 145/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0629 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.1486 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0680 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1147 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 146/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0656 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1651 - val_sparse_categorical_accuracy: 0.9307 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0702 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1042 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 147/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0677 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1034 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0742 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.1511 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 148/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0762 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.1455 - val_sparse_categorical_accuracy: 0.9417 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0801 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.1219 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 149/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0657 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1037 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0725 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 150/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0686 - sparse_categorical_accuracy: 0.9753 - val_loss: 0.1306 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0738 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.2021 - val_sparse_categorical_accuracy: 0.9334 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 151/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0667 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1227 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0707 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.1358 - val_sparse_categorical_accuracy: 0.9376 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 152/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0677 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.1041 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0727 - sparse_categorical_accuracy: 0.9740 - val_loss: 0.1989 - val_sparse_categorical_accuracy: 0.9098 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 153/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0638 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.1837 - val_sparse_categorical_accuracy: 0.9307 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0671 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.1250 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 154/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0608 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1237 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0687 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1973 - val_sparse_categorical_accuracy: 0.9237 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 155/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0662 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1192 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0697 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1190 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 156/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0672 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1262 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0731 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.1409 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 157/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0634 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1035 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0719 - sparse_categorical_accuracy: 0.9740 - val_loss: 0.4024 - val_sparse_categorical_accuracy: 0.8655 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 158/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0657 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.2735 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0722 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.3254 - val_sparse_categorical_accuracy: 0.8821 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 159/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0685 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.2094 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0706 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.2014 - val_sparse_categorical_accuracy: 0.9265 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 160/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0712 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.1137 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0707 - sparse_categorical_accuracy: 0.9753 - val_loss: 0.1128 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 161/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0675 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1544 - val_sparse_categorical_accuracy: 0.9320 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0727 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.1118 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 162/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0610 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.1070 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0660 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.2972 - val_sparse_categorical_accuracy: 0.8960 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 163/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0572 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1382 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0619 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.1079 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 164/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0617 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.1737 - val_sparse_categorical_accuracy: 0.9265 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0670 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.2169 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 165/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0660 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.1737 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0690 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.2980 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 166/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0632 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1408 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0650 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.1257 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 167/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0620 - sparse_categorical_accuracy: 0.9788 - val_loss: 0.2021 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 5.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0656 - sparse_categorical_accuracy: 0.9788 - val_loss: 0.1451 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 168/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0669 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1003 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0750 - sparse_categorical_accuracy: 0.9736 - val_loss: 0.3402 - val_sparse_categorical_accuracy: 0.8350 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 169/500</span></span>
-<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0578 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0664 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.1295 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 5.0000e-04</span></span>
 <span><span class="co">## Epoch 170/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0562 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0970 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0635 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 171/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0535 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1347 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0593 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1230 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 172/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0558 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.0975 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0591 - sparse_categorical_accuracy: 0.9799 - val_loss: 0.1079 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 173/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0536 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1328 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0593 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1073 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 174/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0550 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1243 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0605 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 175/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0583 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.1018 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0616 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.2084 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 176/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0548 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.1016 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0584 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.0993 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 177/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0541 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1646 - val_sparse_categorical_accuracy: 0.9320 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0582 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1498 - val_sparse_categorical_accuracy: 0.9487 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 178/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0522 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1151 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0566 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1165 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 179/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0538 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1049 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0566 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1714 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 180/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0531 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1178 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0563 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1042 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 181/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0562 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1024 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0609 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.2017 - val_sparse_categorical_accuracy: 0.9209 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 182/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0525 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1600 - val_sparse_categorical_accuracy: 0.9376 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0559 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1059 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 183/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0554 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1395 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0597 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.1279 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 184/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0514 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1340 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 185/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0531 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1383 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0570 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.1022 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 186/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0574 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1325 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0633 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 187/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0532 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1442 - val_sparse_categorical_accuracy: 0.9487 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0576 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0969 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 188/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0533 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1016 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0567 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1043 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 189/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1639 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0546 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1734 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 190/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0583 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.3468 - val_sparse_categorical_accuracy: 0.8863 - learning_rate: 2.5000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0639 - sparse_categorical_accuracy: 0.9799 - val_loss: 0.2847 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 191/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0496 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1010 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0551 - sparse_categorical_accuracy: 0.9799 - val_loss: 0.4059 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 192/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0536 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1100 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0613 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.3821 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 193/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1388 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0559 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 194/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0490 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0983 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1008 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 195/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0499 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1001 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0557 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0996 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 196/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0517 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1001 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0581 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.1143 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 197/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0489 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0544 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1027 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 198/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0472 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1672 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0533 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1310 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 199/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0508 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0990 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0578 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.1236 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 200/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1290 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0582 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 201/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0480 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1390 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0551 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1327 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 202/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1194 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0543 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.2115 - val_sparse_categorical_accuracy: 0.9140 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 203/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0508 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.2015 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 204/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0449 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1227 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0508 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 205/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0487 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1194 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0542 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0996 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 206/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0491 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1540 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0559 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1172 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 207/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0529 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1023 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0570 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 2.5000e-04</span></span>
 <span><span class="co">## Epoch 208/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1295 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0521 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1000 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04</span></span>
 <span><span class="co">## Epoch 209/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0455 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1169 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1286 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04</span></span>
 <span><span class="co">## Epoch 210/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0489 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0541 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04</span></span>
 <span><span class="co">## Epoch 211/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0475 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1001 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0509 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0993 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04</span></span>
 <span><span class="co">## Epoch 212/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.0986 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0503 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04</span></span>
 <span><span class="co">## Epoch 213/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0483 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0997 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0534 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0968 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04</span></span>
 <span><span class="co">## Epoch 214/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0490 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1029 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0532 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04</span></span>
 <span><span class="co">## Epoch 215/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0464 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1445 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1013 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04</span></span>
 <span><span class="co">## Epoch 216/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0456 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1137 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1244 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04</span></span>
 <span><span class="co">## Epoch 217/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1345 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0495 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.1141 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.2500e-04</span></span>
 <span><span class="co">## Epoch 218/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0501 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0547 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04</span></span>
 <span><span class="co">## Epoch 219/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0445 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1149 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0480 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1387 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.2500e-04</span></span>
 <span><span class="co">## Epoch 220/500</span></span>
-<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1003 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04</span></span>
-<span><span class="co">## Epoch 220: early stopping</span></span></code></pre>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1161 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 221/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0516 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.1338 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 222/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0525 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.0995 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 223/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0504 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 224/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0471 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0994 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 225/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0536 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1145 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 226/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0487 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1047 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 227/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0529 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.1087 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 228/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 229/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0499 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1064 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 230/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0967 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 231/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0527 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1730 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 232/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0496 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1109 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 233/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0483 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0963 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 234/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1158 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 235/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1057 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 236/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0502 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0982 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 237/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0501 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 238/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0487 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1031 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 239/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0491 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1139 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 240/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0478 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0990 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 241/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0498 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0994 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 242/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0490 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1118 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 243/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0523 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1052 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 244/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0527 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1055 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 245/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1045 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 246/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0518 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1214 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 247/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0457 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0964 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 248/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0450 - sparse_categorical_accuracy: 0.9885 - val_loss: 0.1161 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 249/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0492 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1282 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 250/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0510 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0972 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 251/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0506 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0986 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 252/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0503 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.1075 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 253/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0507 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0999 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04</span></span>
+<span><span class="co">## Epoch 254/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0478 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0971 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 255/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0464 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0954 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 256/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0954 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 257/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0472 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1047 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 258/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0448 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 259/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1245 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 260/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0454 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1113 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 261/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1697 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 262/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0450 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 263/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0439 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0975 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 264/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0498 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1111 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 265/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0523 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.0983 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 266/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0459 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0997 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 267/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0462 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1002 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 268/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0438 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.1058 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 269/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0491 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1289 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 270/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0430 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 271/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0454 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1096 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 272/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0455 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1262 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 273/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0469 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1129 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 274/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0429 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.1119 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 275/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0430 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1392 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 276/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 277/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0457 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 278/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1205 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 279/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0473 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0958 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 280/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0459 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0947 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 281/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0438 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1216 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 282/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0419 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.1008 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 283/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0481 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1129 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 284/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0480 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.1418 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 285/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0467 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1058 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 286/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0449 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1039 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 287/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0450 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1015 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 288/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1156 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 289/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0460 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1000 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 290/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0482 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0967 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 291/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0433 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.1002 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 292/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0506 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1102 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 293/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0473 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1139 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 294/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0459 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1005 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 295/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0502 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.0978 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 296/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1006 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 297/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0978 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 298/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0953 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 299/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0410 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.1107 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 300/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0423 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 301/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0457 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0983 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 302/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0422 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1233 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 303/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0417 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.1175 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 304/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0467 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1007 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 305/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0466 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1058 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 306/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0402 - sparse_categorical_accuracy: 0.9899 - val_loss: 0.0999 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 307/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1130 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 308/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 309/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0472 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1179 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 310/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 311/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0448 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1012 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 312/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0442 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1172 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 313/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0438 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0990 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 314/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0432 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 315/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0980 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 316/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0429 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1030 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 317/500</span></span>
+<span><span class="co">## 90/90 - 0s - 3ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1018 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 318/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0423 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1024 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 319/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1197 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 320/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0405 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0994 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 321/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0442 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.1499 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 322/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0404 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1253 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 323/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0468 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1081 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 324/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0418 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1016 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 325/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0420 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1290 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 326/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0436 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1270 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 327/500</span></span>
+<span><span class="co">## 90/90 - 0s - 1ms/step - loss: 0.0417 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1009 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 328/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1141 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 329/500</span></span>
+<span><span class="co">## 90/90 - 0s - 2ms/step - loss: 0.0422 - sparse_categorical_accuracy: 0.9885 - val_loss: 0.1198 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 330/500</span></span>
+<span><span class="co">## 90/90 - 0s - 3ms/step - loss: 0.0435 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0988 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04</span></span>
+<span><span class="co">## Epoch 330: early stopping</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="evaluate-model-on-test-data">Evaluate model on test data<a class="anchor" aria-label="anchor" href="#evaluate-model-on-test-data"></a>
@@ -796,20 +1016,20 @@ <h2 id="evaluate-model-on-test-data">Evaluate model on test data<a class="anchor
 <code class="sourceCode R"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../../reference/load_model.html">load_model</a></span><span class="op">(</span><span class="st">"best_model.keras"</span><span class="op">)</span></span>
 <span></span>
 <span><span class="va">results</span> <span class="op">&lt;-</span> <span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/tensorflow/man/evaluate.html" class="external-link">evaluate</a></span><span class="op">(</span><span class="va">x_test</span>, <span class="va">y_test</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 42/42 - 0s - 10ms/step - loss: 0.0917 - sparse_categorical_accuracy: 0.9712</span></span></code></pre>
+<pre><code><span><span class="co">## 42/42 - 1s - 15ms/step - loss: 0.0980 - sparse_categorical_accuracy: 0.9682</span></span></code></pre>
 <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/str.html" class="external-link">str</a></span><span class="op">(</span><span class="va">results</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## List of 2</span></span>
-<span><span class="co">##  $ loss                       : num 0.0917</span></span>
-<span><span class="co">##  $ sparse_categorical_accuracy: num 0.971</span></span></code></pre>
+<span><span class="co">##  $ loss                       : num 0.098</span></span>
+<span><span class="co">##  $ sparse_categorical_accuracy: num 0.968</span></span></code></pre>
 <div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span></span>
 <span>  <span class="st">"Test accuracy: "</span>, <span class="va">results</span><span class="op">$</span><span class="va">sparse_categorical_accuracy</span>, <span class="st">"\n"</span>,</span>
 <span>  <span class="st">"Test loss: "</span>, <span class="va">results</span><span class="op">$</span><span class="va">loss</span>, <span class="st">"\n"</span>,</span>
 <span>  sep <span class="op">=</span> <span class="st">""</span></span>
 <span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Test accuracy: 0.9712121</span></span>
-<span><span class="co">## Test loss: 0.09173314</span></span></code></pre>
+<pre><code><span><span class="co">## Test accuracy: 0.9681818</span></span>
+<span><span class="co">## Test loss: 0.09795157</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="plot-the-models-training-history">Plot the model’s training history<a class="anchor" aria-label="anchor" href="#plot-the-models-training-history"></a>
diff --git a/docs/articles/examples/timeseries/timeseries_classification_from_scratch/unnamed-chunk-12-1.png b/docs/articles/examples/timeseries/timeseries_classification_from_scratch/unnamed-chunk-12-1.png
index 42a808b283089d92bfc1c96ff707dfbe77c5c4dc..3337ad475e71c6b075b0da602b91a9e9a58b3483 100644
GIT binary patch
literal 66684
zcmc%xg<F)}7dMU$-JQaaQqls_AxKL%NOyO)q|%6#l$3}xLrP0a2}nzKch}iG-}gP&
zIln*PoVhNTVVHaFd-mRItxv2yA61lOu+YiSArJ_boUEi81OopD0)e|ig$K{<(JEqr
zJ2YomT{j2>1OMp<ZfrjA4g#Tu$VrN8cxUd-d-<zR+@c(fP|VExrPh6}<_ihlqj~cT
zKC0Rr{ln8^i+;)|E=2Lyym@<A7QYv23fXE3{pcOW2K|D95U)?(yk;s^Joah33jM~n
z8TcuaPWiBLQ(u};3ReP^CXA4=t^ncb(b|v<5(-Tiu0#|C5ft2^zVOs=e)>nj^f5g}
zJ93R)nc{4sES)86Msy@^oqyQ0>a%;4m9cJ$g)VGHe@s04y@jqS;r=O7;jk7WwD%>Z
zSx#2$D!(}H-YHVDd?7<pzbI_R%?iP|#GZA4nGijLNApJu8j_5ONiKNff%Wk4aPS(2
zvY8b5!s?068n$!=@b?~X#*BU4NIZAAMDB#`K6l2vCvUh!$7%DtB{wN%2=!<81S=Kh
z<m>C}i}WE!p$xTRbJ8BI8UDEZ5Coorr%#v=1o!{^K&l<$@;RBAneP!$eCvYRf*v1q
zb#>_}B5@`9#QX@9$mkzm!K_9WW`0u8{yvO~cS{ou7|oNS&-(E60wxM18Ar#1U*Q<^
z_#flrl^-AOpGCabmMoy7XJpK`LWG1x6h3}29CHEZzJrauEO0$q>X<Bhr~HNI1s@+D
zJ9~M-Y68AiTVR0wd`z?O-n3ScQr4%qH(&~KlH;C3%k*m1iZyI&^-3mwHZ{F-YIa$b
zHZh?YIW;jc;hn+g<P>%)Qbrx9#{XDQpe~Fsa*Ek`H!#zBwStlAu{-Q!Xw>AnFB)(o
ze5eU4C+M!e(9wJ+lo4PFn;W(a5Z~v`kff~|sbw$C8WJUWsNNh#Zr7UGJI#7HG~0ub
zJY;nke~5`$^9lUd;KG%T*?x7rn)K-tl!79aRo67hVER<=J?p^izSXmV>WjwUWRI;O
zQ6rV?O*Z1_|IW};&DzO%F*CDwA&1a@+8n?>f-V%eP)Z+CR*e=yoh<J#rctaVh#ps3
z%If9i6=A~bFuS(7IrUvNzbMcysiooPPhvvC>3#Y9I=znhx>A-{Tb?hUuum{An#aCp
zWMt@-)s5JD&dq>-Y;10(f~l|EOqQOXpLcU}``^;o1&99bamg$xDN+2wJR#rflD$7y
zzu4?U5Nudc#4U{m)`MEFlCrM1d?YpnF^3^*qAd7yeiwVX|4kU~iLJa$$MWhbx7}pv
zCnfQx^+U@{@rL=oh?w}kL=7IuR^^9;K7~ei96I*X5+f91<_CAUYJ2}bc)`^tY-0rc
zk6jG=oS2=bo;P8Ytac@lHt&Q-xG~M@XCRfOB3cuZ!<IQSdv!1us8#(_6lQNqsj_ZU
zj~MUUgE+|+BDcR>H_bUb3!)Oi_+PMl-k{vGyi8Z%wUHr|g3Z{MOxnL?PM)>mOtBD%
zgU#84xPOXxFyx-Rw}Y1TSwp`3LyoZYAxm*wdBHwzo&U4Q7!qaer)Nrg-=b11|Nnom
z{_lLOz7=eLJG$*2O~S!T7CSsV9EyUI(r^A>NTbqJe07H|^Sf`UO#IL+Qb=Ff-=`oV
z()@ofh+e$7ll1{VA0IepXXgZ2y8ki-I4S~9s;YpyxfHzV!E}|`Sb-dRIDGqHI(Jf1
z5(N}$IhbaWbm5a-ufuwA9p((Rs?w1c?B=dy9pH?aw<JK!1>p;#@ny!)-x)(RkGedN
zT^W*!pPmItE^ZYv&78p$rB?ixK{463a1|9LShGZnh>%brlYdmt0b)H_sw*!3UW0jJ
z_ndboT)vQtEVldat%u<J_337Szdy6aca^ZF0dua<4&zge3LB88<}HITQf{j|<`NSV
z=_xjCghwz!jB|_-gh$9Rk-?<qWMKtsFk9$OY8`sbefnR{W|93UY062u_h07SO%DO#
z#feD-Wnx3>-5xIzh$9#Eze?hpEYTjh$pK-Vu_slm@kQa=%9T1b3rmT_WxQ$|&8MVR
zb|c5Lt<j3vkn`LAjSj=ee)An(JKha;#9lVT%hNf(OJ1ji=7KjOL-xFLn~Tcbdh@Le
zLVNaFAlta{=8szE5EX=mhR%aDM)G-;Kf=U5dC0dp>~mbmYELZr=;$Z`g97BwH^qzy
zEb<ihJZ+f69IyM~`}!UDG;Ytcqvg)UU~bjFwy*sCO#+@Lvs^B9gnm+zM5XBpOa^ZO
z8xdvMMy&`pAw><kXJbLf?EGvEojW=@vNqv@wmMTDP+mF6zz$PV+-X6$UzE5LLJJM1
z$_;SN4ylMpBG(G!%HAE{k)PB&UaiKr-JL`W(igAR!v;RTQ2%ccO;_-(WsD3rHa0rU
z)@=VyW-&B0q@kgqr|7lJ%*v99gT6@W=OI&+vjRE9bn5%kRjO>^*iVn$6nAuRz5?^l
zammthiR+u2n`>*I|9kGy!#!+OvCjK=WxH4DHT&-Nrclts9p9?CCODuFk7C)|%);DU
zK`elH+{$F-uaWn5v3ilbARb6(kL7uU(2GV>3ZE$H{P#__`BcKf6Sf{7d%($0axSWo
zdPnnR+8%D~R0-$m9Cr8ink@!WOk<@CjjXM$SrjQR4tkEw`28zY^QHe+5rd`%h@tA=
z6l~_|_7k7We3oAaDL3e9Ir8Zss)1^dNv~;myx+r^JqIiq8H@fK__elO3uUsQ;bF(I
zf_{+y1>LunHaAH~NVH3<J}82Z&u;MZe;;+NFy8quxO%U{g$zC?M{pAG7Xku8^ef^g
zxUxXU@y?l5x4OWv#rO7N-_6U*igaVjD2Q!R=P9xIBsK3PM8ROEV8+WHa|At@%eG!W
zW%X1Un8yEdwsi{n3*x81dk$V-^&GsNj#csAVq1=7aRNz`+on<~yTRSh!0CJ$oS!im
z;G8U93MK6ALHIFB`^Rp>u-&hWk5{_?%L*S1zUne}^<1p~Sl+{gbaWIK7yqB<y~A~x
z!JO|-RgfU&+Khh_7Z>j)+f8NyK&YmZGe&`7)yJ<ngni_+&Nr>D`&78sMcI;ks^+*9
zt8J<5=X7Jsy9!Er(u_@?>`f$breX+3J7<hOd;Nd)kPaVhpxQGd)`ax7dH8cJY{rV$
z@4&utJ2hHy_Qk&8#lU%nQ|nA?fFD2_{Bh*mmV@CKBn-;g`lbDbhW{6TA%}K|j(D2u
zUKB_A9Q<qSjKqly3v=I{9J*Ox!sAMhF-c>D#ge|H-Md_LT5O%M{u7LhMK--N@j{Om
zlnapCnA<VHr)e>C%9(jsnAt-aIrYpKGUlANLMql^KKE_;A41?9RL`-m2wmkbRI^F?
zCSRM%)<2NK=68SNv8(=VeB1W#cRUITgTWa?EiJ7S#aR~*4>l&I%8`&a;Pth$y<*Z*
zQXUV1&qNPC&eh*+SO(ng*V7X{Aw_epSZT8o<imv)J07wUjpCD=AcASPj^&MwZ(1e$
z+;i$#LxJG>vNumpPv3%q1s*rC@&Eh(01t&~{hTpn)h^J@ogLe!Yttqb^}a)TEfXpK
zZ=uC+)Ct!=0FSKBcW(=yh0oq$tuJvG-96#=EXjB6a($be&UawJMJ-u~{qZ~7Qf&*o
zFE?n4%4-O?y*QX}BC%sVF>P&amHiM}!QuYXlGzUm#}*P67UtnY7DI|;eA#neq@3gU
zui+S@PR!?It<i1shi1tnNCH7D&!0eu?@Wl<&fj8>-ANJ8J<?#1d+QzM>iE?YgE)=b
zgLgY4_GeFJ2#U@3t^i!`<sG!-zG_pFPLwtDNAw199C}IdI-_pdYbm3lf#yxb9XTF=
zLWC1yBv6AU&w!m2a3!zoChg~c)_T(>27Oau(gHYC0$1n4f{}Nwt=eKBupM=7>JG87
zS<2r&6wS=2@|Y^<?mb?9$6J+F)4wsrPh{ak*6rqh+4PpgHG$SU=?4}BfYYN+oPgmR
zK2l69GKIinH0Z&>f%g?|heblto1!2Bi4XVSpnRXxjYkP=2?!^qdWB(2>WnL>7C4bO
zVz$*hmM<MI_ExZ^0YH8=$_i0qzP`C}Txiy<m>O6=`7{g?-dawJYVTUVnP=^TWME(*
zsB|K8ZI`1mRNY-&)0GxKctH}a`5Ll{Rb$)%zj!HhQxo*qblA$W%?df1_gdVy{Ywbj
zq<D6-ezraSRuCHuH~wp+-V6poLEp)txioGnHxhrhgOS_I!&G-#I(mAVW+<fL=l(!H
z%Qiolb9o36)alFFnL8q~=nV}g=kw?JlB<x{VAPMt<>?sBriN*1Z9lt1!IjbnE0G0^
z|NWtfF>XaoM@YP!wIww1<KAy-dVKdBB+AC1$H3E#p?9^m=T{dO`*Tj8`{O8t)T&Io
z<3(?_j0ONp=n7QnIR2(utl`D$I9+2yFi=fZrbnOD|4?fXPtNBUV9l>r|L%TZHWqUn
zOj~c8?M(H0k{G@O>UBjf7=pFs<>TwkoW}tnfE#wrR{tDwL$+pams-WX{{ndJgfy#D
z+};d~eqVot9u}nD31orK|J6OuusG*wlKjyIu<p*z&cM}0Mn<N(XKGh&2h#Q^c9m`$
zr=9o++{Z%!8v?_!2M}N-M`r}qEf;;jH|5v&95P)!Jt^P-6W-1_6xe2-)Bp&pw}oS$
zu%AkC+~;p0&qYNS0<}h1v3*+tWwfvt9HN;uNAstiUM4uo!q4yKJMIJHv|N+9%=0_n
z`5#1^P6Z^3Hp<lAelJfdnwV9m-9=xn_XRuPFV|$K@H)i(atq=Q^jTX(f4{4mfn-CU
z74o!JnJJJW4J+DQR(sSV`Bqj}v5*mZT|i~7o5kyO$zz)RQA<0`X}iXlAJN%SRT4iH
zncwE?|IstW!pOhz;?pk|XeXy<h9aR<gbBdEuw!A6_W<IqVzmR<_@)3YL<Ml-j+=rR
ztGY*7E3=s~o-`Hzv`_be0rW`>ieETQey#3$1YEDDULG!bd3qYcEMpaCH~A01xC(h4
zoLyc%Zso_*;eS;85}+AEO^S&eE^)jT3-Hk9CQ>JW{?iV;kc*klCvbi&zGE-dUR}x*
z1(0Mi0FQPb0m}yD_%BJ6K~?YA2|b%(^Ys7l8Bf}mFM73$kh87ucV3t02lw}vi*0KZ
z05*Q2f18(|50=osCa=T0ySuHet)nAn5|UKf$kE+%<%jQT#Wi{kiSJlMxt^%G&Htzw
zSNdRi`IlQdKy5+({ci|f{C{UzfIo8<vq03_*3JS6z`;tLIT>6&GkLE|d3ian%@2+I
zRpCdN-4y%TPUw$`KL;&r#WvFU$MlE%AQ4_4EHp(1yt?r`E7gVl`WZ0oxf^#B@0Tgy
z_Du(Y;0f(Wy%7Cn;<m7`AS)}m&WwvlNZ5RbS6S<2_QwhW@&9A7Yjs~`j|9#7I`!CK
z9(aFq9{utqsN!n!PwS0~t6FCc)QsE7;Vhgi0j$`K)C%e5JG20bfR>=Q@{-25?04Hs
zOAteNh_F>oFXt113^KA|=8w`eG+Y3n0;lQc^A|S#MJ<Z~m-DG@hc!0i)5Ypu1qJ67
z`c)G(HuK*U#XG&64-`g5kco_p!37r(f;db(-<_gzJZ9gS3}p&@Y3qG?FmLn#>O<>5
zDu>a-RJ`^;a#P^F?|Qr#CMIS~T%4%qo({N06SM8PVXtA7>Qt=&%7GqkI+*qn4-fA@
zkRkg*8mad=&{2*~5FionC7Dqr60MYO|GObHcpsZ)*d%WKBZ`a*bUFA}AeUrMO4^Pi
zcISL|<-);HQDxSvcWM0wpwV>aRpK6Hal>-oyDI?Y=IR2#RX@JqdAptnN}B0}D^xIe
zT?{j*=^(l!wyUcv_f6!*-i+JsWEsb_J-}e3H%PWq;uWHAhaYX`+QDMZga$os71(6V
zdXNfvmO7B5IIs3#@pz+PB52nqu@;SQ+fQHZ&A>$FYM%f!hf%A2rzIHZX*Onl$`=4x
z{?48kL%#N6V%2fKDYeR|>y4)hTIk7_j-4H=+I>)6jo1^r3Tdoxbad?NgFy1Diq(#r
zhoUwB(6*k1Lg3*qH*M@&S5)SIdbk)<4O-GMzA}3T;6zCZ7<C~r?NFypDl*ZZ=hH5A
zwkaSph~(2GKOUc)s4*uu`6F=p^9+6~&u^1=`nz=)Dd;XZ|1$A?1qY<Y`g}7dNYv?I
zHy93q9h=PkY$>pyC_vOD#9@50+<;sLJ6BnZPSkpL!}OFuav|_`pUB2Wh{S()V}sZB
z!JoViN=_cV^9u61I1Co8Rias}!E2ub&Q6gRvic~epdi_Z3R$9e_Tpe-a&~p~wIwf9
zo6)Js>>G~AAkS0MIGkMfDQj$KuwQ5vU||{P?q2t?I{_T(`gFpsopW5s1In{SWLew%
z_;AZTJ{~`O*E29+x3@RL5!9PH1>m$&mf*~fSE__0?D~L3NI`HV5s{ZoTv=WyPap%~
ziUbsy#~SNV(-rOfrGvP?e|gpV7fY~8bYYf5*&<Dqm6d=MD1PDbamk>hq#U``l$P$<
z%&0eBpyeFwYJ`!$ven3y2)A<d0qfy(a|DnjAs(Ky<5&b_Oi}<?#KeBk3b=3macaBY
zI6M^dJ~GZ={oMpWfUbaTOisvJL4684=FC}ief?zu{aeE}3$Ow&;?d(Ci~O%oP`)XU
z3AnB)CU=VF+kn^w_2~ZAa(Q`K?EY+g_wZo3GZK{zW7k?%@*1p8D`Kb+Kuk3j<O`0$
zs`-3a6l2Xkr&EjP(ODvX7pt{xOE4PmBkGHi3VT<dX1l5KTwN9jD(YR*@`eB9{BFJ6
zf8ZfWIwvJL+2eFSB25J6l=#B1rpug*Xt5Qr?BLxg=S$v|Rp%4$EJ2U#I|rZ+W(j#I
zFpPs#{xT5%OMUxQKfwL<{pH+TT<rP{Dmpq>gNxbxF7Q<D;n6HVG#9gytavkwg5o`P
z{&qYB;7a|A4p^K!JtwCFX)5XuUR)877CQy)JF1T^v`Qv8NC|utT^sLaM@qCS3>|ng
z{Mc?6hKBZ!D?ImSCmxF?m<Yx=w}CPc-ij+vRHK)!aB*`xUu&oRI3bqrj}(*R>@x9t
zX=$m57ZnGmcVYmQBl6Cl^lU^3Zavxk;GX~^<L309tdY@T)nef74Hke7FQmRNdFugS
z^3t2>pGZRbj65faOf5~+%Bmba09*u&N_V%s_d)t`PMe_?%JV-*@6Y5E$l_h`KEyq^
z9{-xcsWj<CmK;VL$Xga-(JD=QY1X4o4o-CqbkVoHG4|^#!k!12Q;YeU0J{O0<y0~W
zxY=h~4QF9Cetv%2j%F~&F9@RyqlraY4G6oziXtH<=Cz+bTIZm<^#ZH;{$YilRB=Fz
zd>SC<O~>Bq%Hcju0U#(^QceefFi%Lp%>WwDw+RtJ7fI%T4QI4Y&Ll?V?7JI3T7Xp`
ze*~b)H|JAE0J^_)S&`)9s|OHgCdjaKy8aOWama@}e)GPB%esRt&$BMZ9Dg8*82<ar
z6ead}=LVqu#!$xPnh*Zd8;Tl5&YW@vjegZEF{<?6ESM~-v=+stXdjaTfVoP(klCoU
zSrH&(4AC20*tgYqgEX;v<s8up-%AQ1PwzUL-I;IuAagl<dXl5b!J3x=2+-5EBlCPc
z#COZ}_G}y0-%XM9&2%!;rR`BwMa3D6nF9lc=zb(l6IF@+e%D6=h^d(wc;V``VdOJz
zOJNWC8>7H$i?jK(#Yq3-vd{s*ps5zKKYa>GP=OeeUR_&QS&4CskQn*9IBzYjcfi#L
z>RPopf053A#%rHub$f}7Lz$VJd@hJnXEl;-RitR3-6DVXEp{a1!>!lM%nVs9gOHHl
zpIMJ9Ptgr_Xwc1G*}Hl}#;02Oh)SmBvj%YWeTjCQo5wqi+pKAHId6YTi(Z_KZ&t^7
zAAU?w&abSxsjoA!{0Y2WJ;6YTXrJ*JZ50U=^7Nl>gq>7a$mQqdaXK@ZyYl#3ogXYx
z<<QK0SABlAk3vPYw$3}ThJ=XNOsy76_6o4a<Evf)w+(yWCK}{`R<?s|9xhiv&MQv6
z0q}cQXFrqr?+R3Hv)(wxsPB*flSS8MRF+%oh9duPK!LuzvTMCMU{uQFciT`{|D8Nn
z?_{(_q{^Y+fJ;JhIV^NIeaGbimdgWD%uq$g@uA<4*kzgL-gIK4OPSYd55ku^zbwzs
ztSlC!gln50gVoSNJA|H>ITrp#k4t_T!`-`ca$;I;<h62rQ)?>{(^Q17+UCh7^RX*^
zbq({m)iaW~TR#|c@(*xzD7^|bf#daQFTt7&3dqXj;9y|*){&6=T^FN86e-w=;Z0f3
z*N%jNDtFa?DMN5@aAdI?K9>e9zURu@>E3P6hjS=)w?$po`e@B{KpKx}dih=BnV%O`
zhES2~`fm<5j&ub}-V8S4w|Eyi|G@39ZEb+1da(wO*nt2UDv`V7BPu4=RuKC1n3EJ!
znPR(!0AM(vVf{ah7Fhqu$HMsx=de(}Nxrj^ow34!0=ub;1EHgoNoO$*qs=w_Rtcox
z(U+A9&z=uF1Rf}Bl*5NoKC>Z`=o}Pwf>%5FD_u<Z7<4Eo{s&Iw&ZC$+omGVM%4_KH
z@k6P~vv$f@;_OyPLfVN2DbfIw&g92c4b~E4Qlf#M^7PN{k!fkL#c4jo!`!#-N(2`Z
z&9VfPx~rK}JdAZ2<2+0SCVQqdIwnfARa@cz{iC|y+rIAY!voSk5KD8|Zx`m~GECB}
zS`qy&5B95e7z?b7mreWDP&vG8Hu$9csw|<RAv32CYGKpyZ;GJu`KJ2;e4o^+s<6j9
zcWU?tzn3#c#A=m)cq5gk8tryOYwX374MuM1cf8*FD6|k3vg)xBBiuhS!Z9tL?y_^$
z--A_+KJh9NXTW-L#Xq+^wT+4&?T1#l#CyA`R@L9LH8u#;jEq(tAxJos!rm2cBDJyv
zw~@p-J|GcCs}fFtUtvm>Ywr+4sR>a-lyJ{NzSf~4tFqRh;#9*ptTx-n2_Wu*D23Ha
zAL+tFbeMEp2Ol3tj5w&guOY7`yIwBojE!4FB}Z9gc@HOAPzZp*G~gfpXqM%(0DcJ!
zr<)mgFQl*PKit$?^<DrdI#prx*mAkxXBi6Afv$_J4<94sk+Kfv{XGEsqU6|eyr=ft
zczd*4(PnyQ&T15af}_F?@6(7Lp7((xMJjj(Gdogxe`)FHQ>+ebX7gQ^32FA`J~#P5
zb)B#B3~{f^MJ6CnO(MpFz$|*wcK_-*1Jye~%kQz3FL&t=P_}J)J3Cjc^?V;~HL>aG
zlmvYk@~xbB93xtqrm}?5cEdLc@iPVJ?M{?5G&Dd40O}*if85;M>gwvcWs_3ONRSjb
zQs+sIPcm8OE8Wq+={df<q?*Z()MrO=rhi^IG3-FfPYTg`XbAeN4Un|2>R;V3e{oV#
z$A86MbDg~?GT2z9hHQDZniRdoSjc76)<~)jRa=PSEFbj5`(QdXAu7grXF&m9tdYd+
z2-S=pOi)s#RAvr&d}w-=rLL}jR+eJ)HXdc3i<Jv+?rn;dS;gPKtKWF6DlypA>I}T;
zx8@OTtyf?$)4fRNfa`T(`kO1`G#C@6F)R5iKqSC-)Ued36*}qGMbbAc>D*`FHI<nO
zEmXUXzpUP*Ap$7&Nt*{$>S;hz&ei7}VKe&)QNTi-`_CyuihL5ZWVDMBvpSFI0EBEc
z5K8?Vi5&f+@U>s<ZZCfT`aSYek4OCeJ2D<?W8RF_)z!n#EI1@nUMpsN_S2Oh;dhYp
z((;s=+_gBhUQ6eXYW&dHKGrdCbv;Svwn7vqt+OD%xscJ<pW1o{ax4OM)Jk9C^Pv9z
z&~7g3KS3LV>G5VC^0v+cWd^?>KtAF`WQyGn@h#r1$`<JoVjzk;;GrXmo8g@xy=|51
zBSDPq&Li$>VO093F7Rs<kIchbCoRR~l$E%%r0q;T#bZN<B+kuLch>}88|{!LwSUMG
z{B5FZSGe-qtsF`caB2xl%vWs{&O1EFc-aE*P{mIn2*9{$+*ZTK5-mZlW1WR!!RxpJ
zNWzP4^6ULcj4XsvB>yfCk!1Y+?|`X`fsXDA*bE?>v}-<c$H&LOD`|qcA!}FgTKxXp
zMwM3R#vc_tyv#%<7@3c{1&vz)Qqdy-8awza4McCpYiDahejO4}J?Ng6O><l2YDi?E
zL?r6Bn)?>L+~I4_MR0ciQNqISgjc%z-gI30U-Nt03Fm{oQ6D!==7~CoxeZFOkCP5Z
zyS_2c;*XjhV^IEYFMunA(?nr5ZXenU6g!9WoeAmuDw%jnEHWN%t_W&Wkog7woT^`4
zo6UdG#M<@R`m^k@kOI=VLQZQM7ZzkVCyV||*+9UK)G<so|FU@s&*d^9{bT*kN}z<9
z(_!V8S8hWa!qN02v}?Mow&E)95Z@;LARtN6)EhK(O;6R>={a?T?hc<GKsUSH9HE>2
zr<8YMn2ElvbI9XXZ`gaMN7F<|+$?k7FiL*zO&@<=J=POX^%!z1s_BdxvUKlA9z%&j
zLNb{Cj7XpiVEBrguZCj)N4ZwodF@R{G}SN0e$WBVwR%3IhBF~>?4{ly$>6DUs6rRQ
zXo$#7)jkPC9J@ImxjA3;_Y!b!@RYiU=QpX83?M~2zoh0A;=n<<G&_*cVcpyeh|bKE
zA&?x&{1rmK<fHL}3#{UI?2b&VT~lUo?bfgTp}z(?jN9)Kb1n{FCCDjf43fS=M((V0
zq9yR!uX!kJ1^ocorzEd)x5$<jJ*F-|b(D@J^8f?-it4ilvs8ys(TBA)eq@m^TFh2y
zeal8WGV!9W>*#fRh`k*H<m!wyssWT_WMmo)Tm8}}Y!cbsd3a&M(NYdo)`+7wIq=j*
z=3M8#b~c}^>b|oP?_R|z;kt)FmJD;@!bc35W35PWvQJlhsIo5;k3@wJ8JF+rKD|bx
zqDwx?LoFtB6@c*b8$TMZtx@L*L;jX|SuMQH61<rPay=d)rjZfEdUOY@;%;qcsK%yQ
zC&$(dd9KNAy@M|3HvqzFl#LOf6wFWLPUpuYkMFJ2{ZH|L)7f_ij^e{{j;wRD-MJqB
z*dR=sImTzL-`Seo#WuzcM6_A7`{SD;`w0@RAd^11o5RK<RQ0UzJ<44pC5bul-wCdK
zg*_kRR(lh&!g~})z1p`&Q1b8nj?UnNnKkqC^8rr@e7L*L#vnF*Fs<C1EX$bsTMP_|
z+aSzl_w6yzY{8uT8we0UAp)F?HAWEYgIxr+kH6PPNv^ir$mhL5SU4k|A&ujyIq#g}
zKYmOH>X}Qv!0TcKB_#>xXZlUP5=lWTT`yQ~U2Hbvr~=MLS1ANsOP^oHa6>Jg)G9w)
zy;4I$x1kha@xE6XZi6Y%XnMJ%w*5aH<|KTa3f42#$l+WA_PihRbP2~5Hh7Y*Zx;0|
zAx>zAFYUdx^q9|(uWnkMxVpA54yCgsLnEf|{XoDO?nvE7$H(K~Q@t*d3IrhkJHO-J
zV!dj~1Ykt}J|BI3=WJ|lzLu0a?nh6Bc;%q1ByLR#;c*D`*(84I7<hEyDL4@WA{Y`9
zQhbF>(DJWvA8squ{*SeGli$^0zF87wIsI2_7w~C1$*b!^UL1T5F0bhCXOCCEpx{)R
zz9Js%F1m*xa~E~k_blap*FehqP@cu<iz$gWRq<Siz>MKFE~m|1gvlE~zx_C0VHqx@
z@gqUl4bvR|V3YA|9k*utEV%%3ltURWk<%*cv-QxOuqSv<on(3?iwfTpL{aY&nv^uY
z8vBY&F);8Usl+niX1l2A7CWqjoc-=ugS&~EvGePd`?Y6_v`nNSf81|A1B@-)*!0AT
zu20t0iqB3*#40q3Kb`jg8Xm+KaC6qZ&YWjTpQfN_r3kAltwP<lbhFKUIGQLCY}Cht
zX4>FUk}2XSe$#+;-<4+fL*fL>c-YwVe+Q{FdD0isN-+3Y(uY}Pb-Dmh&(U_r#qb?d
zSmd4gw+dZYk1zJ28L)%jX_gT)Yl|zmkEr*Ou<9JG^qvdb*8T)pbM$#Hbu*OLK}UmT
zB}rM-#}$E0?FBseA^Sh~*uYh71TG8AXVZx|+5*^Mt5f!WY-?SUj#ocaFFUfEln+~o
z2>+w=57Q#RTbmL&;DuB<CSRRclp6|pm0672m82M)o+z$1^fc-+YL&IWJ9plOXV&gG
zSUmPb@b-JoiEAcJ!~3ndVuTUILKl<$?1yIkOkr#zG5h(yuG^m&I|+%2l`?qufpn0}
ztVt%~TXSxAa&x|G{RbD~xcK{k4_BK#-xLdCtQ!_qSWqCW>&!18pc|whFaL$t0T4Dh
zS=rzca)?HWA@FZbS+o?=xh&QfTQ|nX$APwn6JgTxa3R#A95!0BUy_J6S1-WN-+YC}
zLl*n&*)vg5(fC%n(%Vn6aodyz`aK`b=%oj);O^tJD*)!ZuZb+tDytrr<cv1`F>}&x
z?QhJ3Wz>eJm;o0|9mSfj`U96N1u0W;pr&rsmMfxqZ*IX5kIM$BkyU~{Nz4jguTVbO
z^q&Z)30H6@%Edi!g-AFIg}+a_qhYF)Ohob^iQ?cNxi~?XND_b6ZoMs;Z}M{LOQ5le
zgZ3>{SBZxr1N!Wem_Zwsz=uEteGFc000{$7W8&aZ>zJ4r{o310mhJRq$KfxPuC>h~
zyK5HO#hS36VQ#Rr&P<9<E_G3eN<M9{@THy)a%b7#-uIGCB@u<jhjYZ|BW9O}9|Yfv
z-s7qv<3Bhaw(|Qx10Q64K6K=){_-g`Y-H!+0_s&i>3ET{(awJ4FcqbM$I0(rs)2Ra
zprA)EJ!X%hVG%TJSRqLGl#w{O5>j;ZtwdDh#KaeaGCU@~!U9BYe@&4P^q9_%fSGLa
zYz49$Xy5{sh6N=cqMm%|qO6?C6Dc7(+ptka+5H}sWH>(%fnN~rb?px5Zm6}L@cs9V
zDGCJ}DyyXA2!u>yW9nca)$@=Gkco@SDhQw6k3C7b=W8zj`71Zd_>*_NDg?YziV7_P
z3PHH5fbEs5D{njdY!i5T|F2<A-+vvo1buZOsAP3qApM*%TH}>t-9j^I9K%cMXopdY
znZ%N42+wY@F6`S+${_noTdj}fMKYe6oGuy_L$bUsa`23O#9qZ4GW70YMe>8eFDO=<
z4xuQ<vzXq#g+sT*O`ds*@1P=TWqPj&`+-)!(X%MyEg;#euV2e`b<A}Qj3z4SH_VTx
ze-nOVY@Od=k@N1&H}ReKctn`6h8PnfTBY;2_j^saa+MSoO%BUnx_J9e*6I?PQDiyE
z5!DAh&KtkXQI->$tIIDOM<IQDwbL5=ouF^<2)`RFiEjl3q?i#q6D5m{ZdP6=oxZN=
zdS$IJdwR&;?8iO(f6Z|ToOGh3diBG=SR(lMFEKe>`Id$nH&MOpIY}q8r0^;FY4-kN
z?_MJ$A!XLRyBfR|pY@-wz$%(t1f6G~#g2`Ru1oN$+N3jLnT&10CgyXbP9v0nq0_!c
z+o+VBuMV{8Kznr~HGp!`tVjDM9oJrG>QDFjXI)fTB?ZGe_&BA$-+R?QonX=ufHp-$
zL<D41O3J{ytK;C{VBhndEPj`~f%lzP4JeSobPtP-Ga!ovJ>Fl2P`mF<R{_2X0b&N%
z*_0wGnnN0Hy=gbP@~*ATYj=0|PHc**6RU=wkfRpgna%y1QRMssr^q;;2((0bTJtgI
z^s}*xeMT_y@<0tYKao-HZm6IddNXOu5>N4rj7$$iIUE8qp~(!DDq+iPTcpz_LVMO+
z{)$;o%(K(WA#2$}RPbZ{fA0B#Jeij;unv3`pm9<KzpHlyX4BQyXuf>((e21?pwkg{
zT#ms$-TnE-{QFlw=s>^U(Zz|E`;m)^oKBs@`)}|();d$OapJi7BUCMO<rePl_Ge+o
zqs&EVSwfQ)y7{;3+TK1?JNIa_ZShxOZrO&o?@z5%Z`nZZyumu2QTZGl-0$w+wTV_X
zOU+2`i-5h?+2k<VfF`<+W4!%uriDt_duPNhx_hjn*KX?E?z;|J(B92jl5+MJq)r!o
z;7?LLZmq0@_@P0THsd4vV~do5gs3Flbm^BRJmK3JkrVWw%aHUewr9fYz;pu&nLgVs
zUIwCg;+<~LcPONNMyhBW4i)X`CMPfd<IlZWjZM6|Rpj0rAMmf@cBdXM=1sTU4K$+G
z*4C89t&Dr@Wea3Bhs*V9Z9z^17XKtLjcRIYK72qe_V9QGX$QWoN<F1(_0kS}Xzt2^
zL58n8i6q?@1+TLR>(H08=0D3=nyq-qkY0j#eMJiQRaJd=K47-ZN={*LP&P7@AS49W
z1Ad*wcsHdZ6vYepGJOG|CZsy7xKf;$?t(xL4v8JI<Q^IKO8^}|uc<sMO4JJFfi4TF
z1PKY~`tt_-Q#n>sFNm=5x6Af2Ywki6>&+w#GS4kx84{{<Q$k%c{c`MB&hAGlOy;cW
znkB{PUt_4sGvSPnHAG48Z0yw(6qIc3_sOczUb;{o^qci!ND~BJ#XY;Mo&HL5z}Vi6
z1fl!%jtmPCuH5nJ00jgjni-XWDOPaO>FSB_^XG5!N4vVa&(qo<*m1ehn~~w+wGML^
z{kE&15AV5jOvC;*FzSx}Ih7%x=jP@TZ4NdDY!aiRZ(u`J!0NRu(aP7Rf}l)Rv;fn~
z($aFVa1kMxI@p3HwR0W;qF3MD>~yST1{Jt6smKOAI^fpRe|FJ#d){KTEt@WiMY|m8
zbLj|x8*lnt-8u`d*2U;|nSH@}r$r@Y<&nO=r_MW2oU?s56d4Evy}B>o;^K#(T#DuJ
z!$AhV#%QQXGrs^UXklaML3puyetvDc(9`2>Qh!+ck~d6X*!VBH#FrWPwl<|)iaYC#
zxHVYE+t0tVa6S#l7Ivt~J^M(f!wf=|cqB{c@Nc@8L1s@Jg`J2A?~4~UAn}8$0YraL
zs+E!7%@$o&_OIgUe^VU`HDu8ih0Ou<0Fc9FeS>#F5zfwEmzA!M%2}=Qs5DNb%>4En
zpxFVqlwNRY3f~$U8k(D%0Z`*!9ud2L^Xsta+d^BA(JAM?ysxwc&ZT}R&ATG6&+;wM
zwcO$1hqyROc_|g|8Rt=2f^L;YRvzwJ4@>4>RApf%{58)Fw{f-Wr9ZLrZFDN*U+hOe
zx@?CsuwhM=W805gc8@;4vS)&5lt<}#km)yi>lKF?J~|qDN=iy<X-&4jN64q+YG|OQ
zfpa4XF<HSmAm7^gmoF3Fy9`uc!5~pgf}*byQiOzqz*7N91~Bqi>A0RsJlPLU0sF2w
zq5<VbZP-cA0IY3)2ms9^t#lA7_YP&2mzH$GE%54}lH#D!v0uE?c&fCpWRyyUY*toZ
zut5mXk<d@zmL?VdMH^HaivtNAIN;(OMrY&@7@ASMge@}^>U`FB3~6J-9!ABKh|f+9
z(z6f|TB`!A#W72mXz1P2S#e>2ub7W913i7=sCNFJAZh@}BdJU};A<QavYmYQPg>7+
zLCJ0?QdLJpT)DfB7u)jRy4W8)Au99YsnV}mz%Ap0JPLjO4KYp9vYv8j>#uo?Z?QuN
zjd5{7U{L5Px;N&{A?Gp&pjJ5r11NC%t4m9}yP!K{ViW5(t5NG6pEA|jn#tPK!0M`N
z>&@?<h-Hdf?mq$DLPkVHL_)ehovdIN5t*NuIPcF_1VuOg`%<L{Xy`Oh-8saNT3cV2
zl9pyLY+`Ub=M)mmR+bm^e9RrS9vm7vSM9r}px{TIXcn1Yi{ctq!aHDp9~ZZP&74lH
z!SqK1q5@+!b^2-#!KJnx=km|0Ms&;gfv3%$Ex*#$6%s-%(_Pcea!Dmhn<)Dw4(iC8
zfr3fuERXxrff*4lsb~2_>h33;;{nTIZ_wGO%p~03zP>&H`0`CHbQp)^3sKb6U@%xb
zR3M~$UZoZOw>H1?wHjC~K$SV-M2aS%7C<YRGT=(*48pE+>i&^q!V8*gmf605?%1xq
zY5g|cB;cS#Mns5SE86prRp>W{bVjNkI;@cxA6GZp6sJaFJ`?ffDb;RVr$<G#Z}Cjl
zV8*JroD6XbGVLLOEbaVs+Z^TqE%p73Fd!jK1?l+!HiK>;BqrKBY0YEU>VG{}_}06g
z?c|m94B7h*?-F*=E%c={!yr(;MP?<MHE|#k62Iap<)fnn1x=WBh;51wjrcD+{^WjS
zI1R|(Ztb{gH<#!65|f9TJa#jdcYoVLpiZs2H0NVX3Q<+tju9GF>>P-YFNDK?XzGr(
z(r=95vT)uw+m9uu%yBvS9En3k%SI&POVfZEGGH&_z9b}G*<brlO-=2``9bK<b6k>3
zhk*^yc@}WbOEtdj4qR9MKIgt+JrVPxQ}!#X99H515Kj6A2058fCDiLFAP7{ry11l^
z1wAY}jr8~H{&e34>Rx767Pp9SdC|8XgGODYUm}AXADrD1oipp@HOG6$-xa$j4%|~{
z24XYES+r=%JAM6RCQA+}K8C1>B8suvJ8^BIuQa}CN{UGlG_in3$4GMJQoq=flT&<Y
z|3Kun5t`aXF};}k1_cKvesFMbx8;WR6-9E1aX&4OUECpY=SbV#oKx!QnPfCz!z5s<
z%U)VqT4x7M8YfYak_1Dj26{1}P4UqY;(!FRRCtttO+QdU!~$;Id$+mqR4MD7vh{w_
z(bE39;d~M@TlA$cukl#|Uq@>lorlMdBsygD9{b;Bmd-B@ca>#`GMJ@fQ=~Y}L~)t?
zArTdPM$0!Ph`}cya1AgbKy$V1{p-8CbHK;mCD_{A%go7H1O?&yhp2`Cf`|9t^@!4+
zB@9rp)%b4mUffl!{gxtCj4u}<590W0yAFC;p|P1E%$hE1eQ&}`FK1wi>(v1H9Sz)^
z2Mi#h(s~*-{BgRZY@UL%8fh1Uv<5o6cCDt@<E;lF1~%tlzW&AeZKrlL4+{uBR6bXI
zn^{6AZ|SSsc$MVk!9=NrqZ0wr8Y*`2)s#LgHB=lYA2f)6$nyqGm7v)FW&R#Qou<H0
zrq$%U{Hvg#puPRIlhYySLDDGKzb<Y)8{laB`be$u1JihL3$H@M+1%Wmj*$^G32ge1
zgNWNO4wbh4ix~39>+grbM<Zwbwm@LP0#nqMhYa-g@MdfdWuzu2PXN>eu(xPqp;A$X
zpvTUaIWJLc4B&|Gv^k3!n3yaV<plb6_2S@A#g$!7Y8wT30D}!^YoMg7-;4LPg<2Nh
zjSP&aU3&r3ULPQA&@Btf93S81aF&pgbW6zQo6IZ&^Dzjcj7&OD-{|O^c=1cpkjyM$
zLUg|H7&F5&ZP%3k%#DpiR_%ht=Hp7!2~4WRNU^ftq@v7HWy5!l9~Wk@NQQHFWpG)y
z=1Jt_-V?Mcp`(TwwmEh=1GhV+B&esoBOY0F!6xi`lCkdLTG!b^03PlKqseT;ny>ne
zt|Go?{G6sd=B~otxWobi!vnXSU)w?Vi&Bp0{ZeEOH4o<IFf{|R$AZL~29GUf>;^)i
zzn>acYdTyg+&Dw$5XHRcugsaFqu_Yuw+;_r1mmCrV_rjg8pTltLK&&?)(g!(i>(2#
z$c8{miIQY*`-Eol)oUS7TGIns1~TpPudWsiKWTW883^FvLCTxO>{}<jCxU!G{ry2A
zgV%wK&oMJSJslXeLt}+k2Qq6LrbEiWFchkzZBQV}&jmI)*!cihWAop<S7YN#2k$Qq
z&3bv)%-@>;INi{|WT1aOJUo(tyfkzwA3J-rBrb2uJf`+_BCis(YgT%``W^9{;}Etn
zgik<_m6<sP+Dd$Gb5*7~RcoD>B_N;)R=|0w{qBv-S5+ZPt99j%T|3F%O`<=vI_ucl
zV()(Ibpg-;+=F5;l)zhBoUTOEH*h&%t#jN~Yoi=FML@wW1who@{S4UYz}Wz*6)?uL
z($jT8!7D8-9m?R#ncRMJ;);KVpu!8CDyO{jtkWwC)Spft+5lR>P}<AY6usEvB-LD6
zh`)%b*5)v9EI|j;rcW!dF;Mj96SLMm_HCuDb-$mQAG<`Xi*+nganst7ShLF)hzvvk
z8~kvn9|JHnN?v%)lI5-{Y%j+;yQu@PfdJslP-HAFQcTbx(gL<*flImp4*SXqe7iB{
z-NWr6u*y)5%>KSGFGy7_7Ja-s1$H#pMfIGQmlr5BB@@z;l1$0J!O@x(hWF7NtqJ|+
zV3Sv_3ai0SckoHQ1pR|qQol|;e=d@?O35#fuP?w^TEV+Wmn3v`<^N3Wo6HoulwbWA
zYjR$$F7x9H4d=V~79yx}bt{`9!#HSs1P!}H?XdNK`Xb3uDHb7?*)1w_QyEq|5$`iM
z^({_r$zs3B69pwRe|1f6kCY1((0x!VuD{EX`8R?5=8IOw3r+Lu8^9VZ<Sb@TE?^%o
zU-_E7A5F93WUxCSAb-WilQAC;{avncJsQ5Nb+=Ws>D6P*SzXF9Vxv23%(-<5MI12Q
zrfr^w)b3LA_8gX&Pz)p+Y^=qJ2Dor&H5!+*bE1~%<*%ZoJwuCrX3=>p$6;=6JWpnB
z*>3)ekGq{q=XmwR(*ZzIptTd#`@fnjkBH~^!rR*_qUdCp1Rw#Sd_&{vLqJ#0S6#Oc
zua$ULR{lJQX#9xTe+M{V%(IVC$&^`8-!tc|dq4IxBp&MzF_`97xu*1`x6hW>IlbV%
zDR;Mbq;V2wH39KDLAL#E0`8_cmyPd5NAkcQ;@7xK`=KnU$4bw=eV3<A*4A^ypVKYK
z7~}q!c&&oVYME2LY-4;>|F-)<jO^84TT-kg`UV#Y@eU@Y_WcZ5r4&NK53}%YI{YWD
zG|73=ikSjfC@5>2s{?pCHIPfwuT%LBe)>(wBvxyiZ`dQd;gisqi83=ys$Lg`GA$iY
zeAhU_ksQ+FuOz{t%2PF?V|<_by10wP$mAar0FJxII(39W*)r?D`vxUVa@F!T_`#5q
zwlx1KB#)zvkVGfZ?vm`B2Uo*Sfs~9+hNV|8?#X+wAnyDOmH7L_(NR~hcp5jx$B$`k
zq-_qp9rtN4$Xnvq6}@LOI-DfB>71m0zN4M_N-nN{!e_(7b9N4^pmNlIBaaRbpODhC
zd%p39Q=>Et1*e_MdJbEtu4A46Cuua7dpJ{~NU6b<$fwb}o`Q=zAsHzg%Ju9svn*Xa
zt$?67Gt$6{k9uST_D!DDsaz66%MJNw^f+43-6j?e8o8<-LppDQU^92UR6&Cod?RmC
zbIK<)-Mu&t56F@-iuf{gBaT&=H1PYWE?200uZ_1z68bA`rFujDZRyw61-=n8TC7y>
z{WY&|u<qLHd+rZ~`g4ulDM*uCM_W3HcGIB>=D&zsb)6USfEs;OeY>(cNZdgiMcZYc
zWoX4U?|jLOiS-J~|MeO7!Y^0bj|Km(MrV8jq`WTgkI~fP2KdMb798T+sX<TNn3Vq?
zqD1wV>*SSC(d`I950H7sG$0M0nt~?-mKN~CbVaro&|hIy@x&vzQkR}=x)J^MV^Amx
zBjXi{>8fCjJytQ{D?77CLmr!`cl9cpY*3MqCNEAOW<vSFOl0xknGjvKqw_u)D>Huq
zLaYVxTx_pHAg8-j9pMBs{Te4|M(pTl@W4=I<XqxrS_7*Ni`?W^tB<H}=)DpKiMF{#
z$SS874JCuwW9tLD4d{YK!*liwmkjx%!j$an`97Y5PWYdQN*Fh<yC5ntX>6sAF;}N~
zh&WDmq!LOYzU?b0v>mw4;<Qqxw9U=Nyfi}Od8jGjFpN(GCR`mu2($2nJ0?7@X(Ibk
z^U)Xx2+_AN5{)2?Ryrgxd|$q5y#Y83{v#sWVt0#m*S|NO#30?b=u~1VST;1gW)Gdg
zgoL0%h%pekC4=Gk(e(AH({d-<hY*M{LP8R7am2)&p=Z8V*ZGyW3<z{;eMHXgVdc2O
z7@+?p6i$QLS(u-=8IkK*Htf?L3-^`@Plt7|UMv@lu`a7_gV*ae5=>?hod3MS-XL@!
z3ajg($Mv51-gifn06GVDhQZ49e`Y0{ZSWI?{?0xi7FXz>+F{%9&mSEb?RZzLB2VYz
z`=%1Mx?ZEekt{KMjAmQgV|?bJa?StUpF%^p^GZN4j9+LYLjegnDM?`?UoE16Ur3%F
zJzHCzP9AM!b|;h)VhoS2U`fzvtJ|}*R?1Cwjzfix92#a(%aAl{Jm^!x{+W5a)=nwi
zhAU<K%h0h>xn&tK*5dm}#<8*Hcqk{vibyyDA_f$y(uD1ehJ7G2@5@AzE9NClMHOG8
zQR9b`%AO~uo+%VrAZdhJoNZ_eqdubw%h57zf6ZAM_-Gg)4)+=T?z%`x+j?u1yv;#o
z#<^wm`{6WNDtoSem^h&aqcYe&1oDQrK&DvaJHF*rgcK<)Vhv`}*egpxi2lzlK^Fzb
z+Ar~xKcX?XxH-*9Efqrh!_wU<&EZ0RQw!it*wOG1zQDo5g>X=r7jzC_xC(%IGEb0w
zog{8JXdc!LUq(!fL5e&xV+?^IS#rlnrwuuKbgU0pUtw!t>zqILzsDo}psw2BK```g
zB@q6v?Ps>=<>zqJJ8Kd$-AM!uhWP(y0_C@v_yI2g?q#i>gNn)@fERE6Iz7UWdXr90
zPCm;QYA_Q?xqyDsr#4T}C5R*rcIbhdth~JGs~u`a8LH$lCLL_LZ&xhlYy*f{3i4<!
zE>Q#O+UF+?8Of`;DophFwH?mSxl00rjox=KE%PTC<4GmLnGs7W7v{xPT+V0e%Y2ml
zT_xR-w?yAD9H$m{s`bKy0jBm#XuurW$yA8XSp3(x?mHbmUS5-g37(Y9HVel;n|ev=
z5$h01_<S@>%-6XckYYl7G=9UfNhSs9Xr?WCwaQZIaza<w@)AUwkB)vPOCLQnBq4p9
zlq!`Y=?(ixsw~?z8HC9QuHG@kL?J|n8r9id(pKK2rGBV*``aFCKz`44sbtW4<%}Kn
z|Lq0%l)gEnq-5FnZ)J~p&HNb=GbxrYiFS(;iWg3Fd<DHIHa51gPSPm#k=pApRG5S<
zE`&3oUnRjw>xEQAxB_<8@ZJGC$OLH}r_$*XH98MokO;G(PeDw+I!Pb#q|%0rNjWhQ
z;UHF=oWw8>!cfhUXK|s?7_M#ts*)x&5W^w_NO9P>Elm{b02eZX8L!)pzHDLX%&)YV
z(*CYW%-y|TJ(4i8SEu6I2P;UKLIbH-GOqaACA`>F5~+^-o`|f`uItXL$olit$r3a&
zdSu>|_-Oi>x#RhBkqut)2G`GxT0^Q8g}S;|7DPUyi7Q~LeR}D?rrY-QSGXIFV!A%C
zUbXjeK%W2>i1`<V`AhtO^x4|lf{l6`!&!4JewRQ)XAF9a)H{HSxD3R57Z;a@hlg6b
zDZt)=N<A_>?CI+2>ftdoG&Hg84m#$2TNX^KYV4+z@<;dnHFDF_gT0^;Cj4<`q&?<O
z{;`;tm^iNEV(b107jiUxdq0g_*5hk!(o&6rFKl^C@`K-0NUV@X7;R5NGodvMuvE2~
zT>a_4-(krjB90c8*8+kkth=nq{J#s}sm%m9lA*LUmTxPQ@=GLA3R%uHn9+wU8H*KY
zmOJ$`Kk*=`#U-BzLy$(u*+R~ZqbowWri`7F2A)OWA?CK5b76$2(T=7M%4e&e;Afj5
zsS-+|=ArJAaI^=j%F)M1@<^(FK5hnkWK&*Z<YjKs)urXi%coZLDh>Fsf8Q*6+U+%Q
ztJa~)0{~<Z*gtEwKU)hFXf_}w0cDF2T?%YY$-ZMFg3|Z>tnsn6-BB-b&|^)BiNS<0
zByIm|bhE7`dkv0hX>JC4=U$P;YA_EC4ubYUU}J|+1J$;%sR?Z8Uj?lB#ful9F|nhg
z!xAO{wo-xpP*qh`N=iyjE$3P~I%!2kR*sJ49!ilK%p#!W7QlWWNtoYAyy$(P^4ol-
z-CbLIY-Oob0rfqk4xtXg+H1+JGJX4ZTJcp6@ym~7ep8aM(n;3#RoZ(|Ex?CyFYr>T
z;64rHH#DqZFkG1k`<MBM5|}B#?KfEykUe9!UwcKUSumPt`cg_&uWVA8uCG)Ld3J6T
zb6_(;SCjd<_>nGepW!flaqoI^Iet}}gyFYw3r!Y;{k<!wyakUcq8uzHrWc`oHGxo;
z`|YW0)SCo+(RdTxD7rJtSvfk+F`oE0={7x5bj&Jt=E9WXxVcIfmv4Xn%F5#U@nb(6
z9$WnPwQzDj>xQd^zhU?Sq|(!0R0`iWT8d`LPLa5Tc}1Ntp0?H!Ba)iw)0+C)DGp;Q
zolOdnGrw>YE#!*@oUFHgZ9nU}ODzK|W`q$9l}X;J%31ak9coEW-qV+GgixarOR#UI
zADQNKYyW+XOZ^&Bn%4Uqj+hlLuP0JE81fqbt8NNJ94{=~!a8C-fleG(0z1+~H^h4~
z=WmU^*<;(`mQ=6)&~G~NKdf(psUZ;-Xz-I8r5S(Swf5dRh%OSt;Zm1gz#wwLCXf3S
z;kj&>ot3Qbt%^{CVLFIQw{tH}a-XlPtOVYAJ_gu_te$^|iDW1`H)B0ehL{a~c?B9f
z&Mq#1VligTGkIVPw%SWcNtsr81LeZWoI{v$#I)+~Q17SCWt~zy9}mo^JlRjB7|dm*
zr7?L-jrvOWc3P-Xs!ykUr>QA5UZc+*Byd<HUnm&#&@7#ghK1$f<)!@g?ds_@Sa871
z2HTh#ybcX5Ea;_F)rvoNE(7vaUCouHWW`A;`{}bR9i7}41qRrR!gvk%vIto^W@hFe
z<>lZz0Vc8ER!rH|>Xm8P&DUy`(4$JwCZdK*fUhu_+KrWdF87H?5hGlJJ+)-QHh_s%
zGvAK^4>iPM#<+?TCNGDJnDgfPcj2?-3Fs${P-9&!)XBJ!k*V$Z@H>f?(e&Qh%2J8;
zk+H2zZ+&bz6-1CnN?zxph9EW2rI%?$2+mpoA(HMHL#l;_re9wdKKo^{nwd!s76l%n
z$+u@I7CwY>FxJj$_@}{9R+UfsD3h42Ry=90RHk(!-z|-?|2)-#C7BZcV~_bpLRgP|
z%IP&Nwu}vzDxx@^xt2$5ygu@48eGEf6Pn2R=e-L*avBJwAht=x?~^)*^6*h||Hv`P
zmX)3)$m^v1zo>fec&y{^|NkOnlaMk)_DXhk60-NoC@W-U%go3g*&#c7%ifYr_9oeT
zll48lKkv`y_WNCbNmshOUeDKgKF>Li$Nh{ZvZg41@_I(}t&&rCK#P`X%zTv2zB>9@
ziuSWR66%$|=H+o=k;E=28Kp@s(cQWCtwPxDtzXMm{5@tINz=#(<B84~wG(7-uV=X-
zbQ+(&N#uXOw|9%s?nN5M_^caj<Hbr9#VaNk);o*eKOPr;wVF~lb>p#!%-}Vf&hKHm
zdUobUdrc7BF@|j5+Mvhcq3$<2FaVKTo(jJ{*UhJA`4`tKUM%M*S}k76mk1QG5!C4d
zc6^o|VQcJv_Bg35<;~8xZNg6y&@{<EpD36tuXP1|C%mi&v?HaAI7sFKYq=9-*^Tah
z4~m)k-dx5aHw=APF1fn8x<hBd=q}a6LiK#2PZB2OHQ&AkX1`^N+uYdD28W=M(&EmJ
zm9=&8kQs?TBQrBBIuPU4)=ssx`3|gVXlR%k8-pw!pfpJM=qTXQuBhOmr|;Cpo}8Q0
z%O3@2vo;HHtTFIo8X7*hy4C{O0A4Ky3wAYQCA869@1fui2E4CdA?}rxl|dDjhK2?N
ztuJL{($mumG@7cb;|)n*fpR_2iiM5{BNNkWE%l5czNb%3O-(o73&a|~($(c7lh@Nr
zNlqr08y2KcU?9vJH3vmE_&&z#QVVrP`zRfRQ}s>X<-=pjeP=$YY#UD~{HE<vNRp3D
zzfo6PdkPH6%RO<Oot@LK`8IIJ4vv^&Hi~)v@RppEJE(|Fbq_b8RUI4^2lQnfJebgB
zvi5CyVWbNt5$MU(8g&u8c+q)p1b7T!(<m{_*f~74$yFpgUe8Ir9#x0$K;xB^M=!c5
zV8NN&x?9NPm~B+fbei(G`{u1?#)Hl5cZdSpIL}I=CNVkLHT`!#trg?g&0bX%hUpL1
zT2AY~>)1WWw#@J8JTpBw4P5;1&$GNNJa=oi`QIOZJvM<7@ZV3cRQTpD*?&Gun}6u{
zFuDJG)11H!R=FzIgFe!?ov~Aw+~1z&_UZqKefRgcWY)6Sd)vfKXk)AI#6%@2nwF8^
z)0~uwr^Uv%g`eHs2mAZEj6wSw2S;eWi=kVp41^oUe`_a`KXv@(b%?hnw#Xh5u+T~F
z<6<KCBul3<TT$3QXDOZ*LEH7;ZxaUtc_F#7&`M7c-pgxD`do>p#am!a*-(pzmG#~F
zudIQaJy09;jgOy##+r?t9Ug%jtiJO9xq6oMXb$o=Aaww3K-1nzTx8_XSFu|Nkc2`z
z<t$kC|K3LD(4R$3h<S0m=>&4G)q7C6-E{t}$WZWUf<(~S29}_B8XuUClBVh)uBym2
z*nSjW)PL8y`+IHeOe1OUG=uG)eO?c*PBQD^E0v$MxUa3PZ{YiNBg;wKa+B)3$vK|F
zl|*F5#>W5l_tDQ9dr8ad5Q3$Kxy1g78z90vIyyoL%1V-BbTTo>T{yLKX|xYH#ev`d
zS(@TE)x7bNJ|j-XnC^LZ62Pah>(!GycrcaqGH!l`M@T5SbLsr-tiI%nVf;UL&}7;3
zLUj;i7_@%G&}BP4C+hC$0qa}WqK*51UL)qmpbdhEE&$#PxFY_BkU-#)3w(}?)6mt0
zjt~e<A5c(08^_My5UetwP=qx*RrO8>T&xIC-b+bKe+mmL(kP}tWDLcpr0lG$tSl@<
zfD|}B9<&0#;GH$PZ20I1_~1}b2e%7=ioSjO=Huf7j0(Uuy?FlI*u><(uAD6KyJqcs
z#_-NtqQghPCjtA{S49ToTeo)Sn<(+|Ih^+vpqaBXRhj?wt5?sme?Wle<$fpN7Sb6$
zNkm0QmmBvGXADh%BNyuRmqX02v)DRqCZ?w$$c)QPZ&(hT{K)T^nw)Hi%)TBQ;i;?v
zA{CsZ0`Jg(uHZ!6^oZx|poje~0YSkmj^*<D=H8q0a8?C<O--|RlwQHX!48HF4#!Z!
z#-$YQU9|b9;b`*oY|ztJR{p)d{+2TV60$#C(Z{DdHf^or1zOLdXj2E++1MHZP9mNL
z_3D~QOJOj0O3?;|h3Ua%1uj9y>(cUadueIu=g(2a@WaBwWF1Ws<n^VMK@>>_zC9@J
z7V9?^DKhYpg>C$Dd6BkY<H|!A1O|7hj3H=XD-$k5klNg=14nRE+3ySE<ro4jxIf+9
z-6Z~yV+odey!#~iqTKQ&yAzEZudV&$DQ$+<<pTUYT*?R7{y_HP$c?8`{5Ixo(2%8!
z)ng-_mr*<(<CD&UtFNJH@O2&Cls?gz5ZSvy)TaDfyL@vJ{^%YYVEAQ>Q(e8aM)Op(
zYA{VrLnVi2hx=MNOzjT-6cs@MIPb!##{o^Fbj`$8+TLkAw~ur{(6ghLgVtL$CYDy(
z8fpF|oh<=Imf&323kB*g<fiRhuUxN)y|x?b$a_G=v!581iSF~2K_+K7G>`IqQ$>M9
z{w~GZQ$8+Qj~k^sad*E)h^U#@8%4Rid($N7d2FL@RgiQz8^w7$P0^SO2NS3Nn{xM=
z;2WDOh-ndjC{MV@jav+v6>c6(KrqHJXXZ0Y1oYPtxaz<}`_IM=R!Hc5?QJKh(eVBH
z^-DfQkRQ}&yNwt9Mx0<C#Rl1xU(7q7CHBlP^f0j1{(B6@$G?NFoV$s*l8}rzI4du2
z<NWchSYuvz!aynE+p@5>rm3W4nEY^geO*gY(co%AoP{qbX?(2ni9&AwBL-NS6yX|2
zdq`eFAC?G_uiI6mTi3FU$Jy+qH^gn<lJc(Fqjm&bytiInF5TPc%W|Tqw#_xqs7di{
zZRNm363NOeh%)(menGoTL}*^|u0c>RGb-2x6&d$M|0)LG7bl;qft5d#SST5!+|Igs
zHA_R$oKMIp7m}2<R~V!JFpXY4^T8ZRlxb+uW9Ze$*DA52(6@d}%2MI0NV4%~Pc?Te
zUT)`g*HTco$3vvkm&{T=Yrl8x1?a<H+|i9P-&}H`X4bH-a_-KQ$$0ovFH)_rV(AbS
zXnAn5V?^;e>Ne;7G$G$Spq7^@nBr$Bp^KZE9>Ud^!H{TpM2sbduN%cRRFNU|jVG~P
zx2Q$@rL%6<(9cJVkK^7LYCdTzF~A|73L98eGOC%Q`lA>|ZCENv|Hp=f<&7apqTxAN
zcPoc`&}&VSw^-qsn$4{ogwiD}%zte1LnZITy~(~GL@VVpn82#;HbNI&ClQseI*EnK
zlp)=_#L2?^+s00n30FFZk1TYu_mMO)zT)Q=@fUngku`0Xs>atans_CSF#mt&a?O37
z7@11w5(ADVl6HM}w+VD-#H`x&LQCeK<ZLeeE-`#SiKD8f=Hl#pxdQZ<95BSquXE?$
z4j9w1J(Q>Db6o$myc8}X|MI0h$hN?~<mw9L-r#@ym_?h{uU|tS1c*FNaClbEg&+<8
zI>ljyu&t~H1r`r<s)*SB%#N<<O!NpoiYl);pW}S%C7z<Jt^Re)@#xq?Q!g?{84m-C
zKMwTbvueg%YWG9MgUpwf5|p+3Go&!$?rknQ($R-gY0~r^VPf*7#@%Q|OFpF#RzgQj
zzWNYmx`NL4xlTg7oJAVnQ82};hjDZx)4egIlUv)2;`sqX5VbVLb0jMCKt2byu?^3d
z+1&NfWLIpEMu%IO-6bo2k-R+#SL~glPD?3VCdi#<8$Uc{W3w9kDh5L5ddM$8I09y)
z`KnJhcaj#)uom~7p$*p6*Y|F|@rpU+TV7rsJcVdzXs#|U4SQgc_Sa+qz9Hdr48K-T
zgMs}yHhUd5a|iIk4qjh<>bckBaw(Xyko57sE^g=WaCFb0fhGbHB@y>0ovnk<+QfP0
zJqZm=O~1Ykois{M$hInyRyf2AHSvs$phm~);c~RE`Jxe#q5M)J-5{4%q3|S1h8f47
zXD$V?6B~=lxFv9LF~i2j9nXrHCfdK6bRS&XwiwFIX=bsJHZHVcdkjIe55H~%p>qs}
zcxUs|8m8a(H}2(Xdn64Rs9B#$6qTlE?=tb*3PR~YY~}?;rsz*EBhV6go?uJk<BE%U
zYpZ-4;|u!8qKPY(cwe%8*+hfcSTH5Qu)?JLlZlysnpRat@o8xKZ{!%dk*=0!Of@>n
z*-v!Zb*)Ss9B$Ef);-FCQczs_+&lcTgCFECB1>}XdcsdBJ_-n^Zr~tcK1B+s->VqZ
zp<A@)W9g|n-k;gu!$7Y%K5<vn)=M8g#l_`|jOlqKjTAs&TLU>xw^<0*(exE<n;Odo
zDOt4$>yoXDabU((3EHv7Ts?$ajg3%c;c#P;#OL9nP3^r~$d%cJ;j5vozuB%EUpy%z
z<r9e4T4^12zn?na-IF1aKb|P}nr&Qa*d%d3wrl6}XIOQL4?>x(=H_e;LVSJOC>t<&
zTJ~K>CR6(8_~y{yNCZkIkJV(STE}hkQqA`Q$;aL^;KT;6kxtc~2%JcZ1BT34SXm)^
zEtW1h!IY7aK|yZmDR-umjmOvivdbD>RBc-~f1C1T9TXSC(Z-Wd-2%na+~0U1mjhZa
zqb1}k_7$S)%H7N%Gnt?uWFaB#7ccbrYy}6u^%W^Uuj7B|EEt)b+-qt6=eH3g4U^PM
zht-}lN!M}B$O7el##+gfy<Xg!W>{^3H8Yl3-LI(S3%_C7D$cO87ITtF=oWpKP1IBC
zpkXYWRL&mH$IiyT?bmIjOd2ZrH=%q%$;G%x=3VD%aP>&E%%eWYOsA&VckJ{QLo#0u
zS+T*jl5cKOQ%!%IklJ1)zFJfrF>-crUS~{%rQP|ZyI+nmKVrX{jbMM+T<vqx%O+Rv
ze5*O7L|Kqsx-F*h1_c=l&YgV=#ONHj{8gSG(s}Xw(m2@K>!mGHUl}iEFceZ`EG;T_
z1IeeU2-08O{vx~_msB`pCW-o2uaCVpU-|UJQ3D-inj%|$1E=x&8umRaK=>IOFN5e{
z^G-p?z*GI)ikv>Cj9lHExV>t&*O6Yojgopnw4|eCRWiuc7C&9}!ZEhGA~QLuXEy4W
zfm=uQx4l#qidRg923kCdv?Gaq$|9Z?B<p^uJjy6tSXd&;y2wEU^Npj;U1>7by+h_M
z22dw?lxwDuqx?yifshlMhK;#TCEuQgI&FAyc9G$y)6^wR#RDkqL`ZFIZDFJN%F=C4
ze=mkZaL4QQ(fsjExtcPJu<<rJzH6lsbsI}4qaFO|T3=sZUS19*<<$cj@ISs?m2G7^
zyoH9P2jpnUf-b)%Cu{VZJm%)+KrZ-de?Q=ujF1tz3(S$jzkWe{9)^qw5N`X<g0aSc
z9Pt2}j)j>anEBZ$!(S}Ne5}h!{rA#`?|_0*Nl8hi)ZoSLjxR2)WG!FU`Q^EW)!B5H
zoT$pZ93`AKwq>FNzRx+Tzkc=jQU~g0$@H)GoV(vSb@0EfDf*5x+Urv`4l!07_5IkG
z{j*WYH-2*qv;<`=%qw=>CU@g9(UDQmqr4(LVr>$iMX_ii(6HWR-`^zh3he1p$wwu_
z4bQAFvCmasWsHfS)%rS?`T2A1moIt9o27o|^#OS8f<<%UrmE_P{Hh<zhd&j}Xex|n
ztWO@35t)`9hD3%!lZpxT`T1u;w?R6j0Gg%!vY0%~<Q7q#DOa~07gI#-`?ho1VxnN7
ztf&i_N?+e+Majs?wF_^Hc0Wnnl6yj33Z_SXftD_eCr!{&I<tDjbBXSU9DL7_wx!Hc
zLZUrI$n9VwAWey||BJd&w#N1p9`0>dp(cedrAig1*D4}D%V?vdg3f#39sro?&Rj#j
zhbLD>qWat1|3N*C&VA7}GNmb^CrbuSj807fsLx=j{v!dgzp9l`QiWW6X?Lv6sCE3w
z<D*xW=l9747CpLs##nuQeEtq32dKX*ad?94+q_7{iOE}pMDjq{T4&=fe)~}vd9Bm#
zb;mt@T}RtnZKC<80lo{a6Lq9W-ewfO`cBRA-g>(WKOe#q1b>*#z<2|Ge@$E4(&Xe>
zI61PPp)JfMBSLFo@8gmKOi>ej0Y<Hwm%JZe0JcQZ@3UKQj;!>{m(yztAwBn$l#qga
zq&_;5N4S@NS9Sj?jU82-p{BzXBtEBNIpejCG5GEAO;S-2TW7Lv#NUaW3L{v{V_Hv&
z1E5Su0dX@ty~>A%fq{XMv8%1^mMB#1Wo2a@9Zz(s3achiI;{`Dxep+SNLv2a?SZ>Z
zH`hD+ZyJ2B*acau_0{+1s%=4oF+W1J))u{rSnFM(@b<v#VPaps$M;FXSXU1m%Nub-
z$z5ILclTIWru(N4W#uWn%zbUmWsiQi;lVx?-xK=YvEm*k@-~rRl=*^DF=yfz32`4P
z^S5b!hVM;+$1M9fhL=<HvQUg=1zr+J#I#T??yE{hcN@IN$JmMaT<R5>eT&F3K}{xW
z$a!@1*26UCqrDeS7sYJ4n-ptFuL|82)gS)08>Jgw7<AR4%(Rnk?JZ#yoW0!oBa$OH
zlE`{YP9!gW2a5sal0HW5UA<s25us~$CK}cUp5leWACWFh$2;`*PbG0?&l$Fr^;-J6
z&5sk0_YQu9y_+Z{TD(K@grehlVL18uMt(6A=S};+)C?H39$V}kcKUS0AKZ^+x!uvS
zLTP$&0fbI4#OemW#VAGT3{U@t-n4}fL+pLHnFWasC`p7oE}dgXaFs$0ITtrSve`{y
zG~98-)a<pf6}vQ$O~R(6luh)RA!B*<C#xVVC<wR-V*nKD|G<A6#XFVf_XbgK3Kp-E
zrDi~&|K3rnAML=K7f|8aovqWjL-LVMu4Oq*!y0jBcy;C~Hb38R+sSDS$Jv?>LB@~n
z@7>xm|IM0&s6mAPzB^JgbpcXYxk1{)=r$3AI1(Z==MxKYaz1HLFcq;cRm)<tfmbRe
z3zuCPK0Zo`^AWx-MTlexHuVGQb2@i-a3kh1z2Cr$YTnOSI5bQfs%GSTf<SZHY3?IG
zcTt(Ha8zTUdsP#bG|qAM>hh9iYYX%@yqz(|CjDQ|hx+vmd7c@D|B$;w7DCk1yd=if
z=0;QU_7*yFst0Zj8TJydtMf_3X^H7Q*JH_3J~9QA)Nr~!n`^Q#`gAeA6v39jNPP6Y
ztHjTJB0BUtjcET42C#}nf~GLTnwy&;+tQ(Lht>+PbbuRDZ#-=tEN3H#*Tw;lzfkqE
zspr%4OCDCc&w1<v`Z!LG23jO$J>Qe3@1P9XPE$N-t2jAf88YvD@!Z{x^R2V9S(@Y~
zHSycN6=}H#4ilxto>CFN%m{7yb$tkJU5W_Oa#S~%<6L^OXvHx3iw{~!SS$Kpv;A5l
ziOoW%R><;D)mvvEAn@vc-BqL>R81Dz)tmKA?v&!^`Q_G~jD2}Z#KeKhWo59sSaV6i
zCzBr%JkE;hrM%oiKicHpa-C0h5j?TC`@ZWhg`LIu{)pp#s#D>O!O>1I>3E6#Z2_m2
z>jre})>W0X(ci(05nUV>Y>3{($<I|0s4nMBm9cGw2gkP{K*#XB{nh@ulutllYI^$i
z?b}f3=}61mlU2%<VQL$<kBIpc^?DB#y(mCZ39;8#a4Xfl0=!UnIZ<mJ+q^$4>~mP6
zj1@@sj7>D%du>ZoNcZ^hP@$842zj`4szE1(<@Ft<AqJ+_(u@)n(Lx_znzAR4cW&v{
zBMON-p0ACd8sgXp-qB?X!n{L*os2t~aAtzkDDM^h#K)(3nT7_(oZG|$C#co?!`k3|
zCo3@)I<5yioCM3p5zNhLus#WW20G`3Rz{3Ic40SA*zpHpDrq8uZUv*#-JPmxJI;sy
zczZgcYj4s>HU-_yEhGt%hL1<Vb?GqOaDeF^>2-A>s-vii&Cag+IYYzx&+0?^+eKC+
zLd2bSZ8uO*d?Ftn6SL`Rx((_2{(pnz!-tEP=ZDY?U)^_xMB^V!6(9%yJq%P?&wHHh
z8Ge%Z1%Cx}6MtliRO-#AQ}-+!;tsJgbiXhkq0u~hSv_<1x8+F`TL8bn`-4;d=&R4P
zqIWXmbQ2+=rn_S=Z7yjUEq0s04ehT*--@0X(usIuoGjDwzTRAJw~^`tS*B>l!m_D(
zc}Ib#;Z#shk?3E2URS#%FXySKEi6>kwZ4@jld<op)sfaHw4&FjWXv(hvPng_)_0V=
zfG?XEbepl-ktjOH+xYQCKI+GH3CEq0mC#Pgrxz8kZdcb}dCYMccN0c;Re-`zXj=zc
zAR@l<f{KrfB-Ce4NP=2v*`G44q%()|^%jbeF*QADXwui`%!qh?@;i}%lqiVls*sX+
z2!>EMq~wMw9nT9ZT)VQzuL)b*N|#sVlAoSzjAAuIjf>aRXt81O%<)locZtE{gOv0N
zlv`*muYa_MKa5;R7M61J5xy$15tfyd%9MKPmroj7gd+X>XIdpvBOueH3%;Sqy;)RV
zt2lF~o^q0q=h@mCtj2i2`~10|QM+Q_nyqsV9vf|1?St?gU!*^ddj~$P6x4U>KNxU|
z(*;Nc@)G6djr&rG4s#?KK5jEZiNlI{@Z2v#+K|he_6~8F4zD2(O%-Y{-4}j8^G6?%
z?|7g7`Cl!-9tt{MXBHNa&J<D**qE3LTRtXzk$*|>D5<hhnYzV>*G5T~jrkmqiLtaR
zTehe74sWG~w<1?qqT=Gc%^m2qaSG7J;pL6*qheGf^w^BeXJx@kCKe!9H7CL5*m$Tx
zMTL-gq+aNy8R)OHHi3bd^TeY_S(`z9f%j&J6ERU`45Scpf%j(_7Zv|^Os%>}gmMw|
zlYug3T&f8MmWv0&KP>AX73nqn-CGUa<>R-kbu~V&WKIT{g*!zYZSg(gr<i_yGp|zf
zMrM&OPqMN_7zziISQq8PxO&|2iEPVDCd8=tzC>2mze!392#Z8nWV>Uk#&~-4>991q
zVJ|2yXUs@lEk|8-3hN_<^8S&1Trc(;`i0G2;_~36(>D6>ZsyqcW@_o9?`lu?UhAbv
zQ(xJ16FzE-sMp!1rdh49`0hNjmWCsan`vD8-HI2A;lPrd#V87YffwP70<yes_|3co
z9`g}jY3@%Zv5X9(EykbmBYXpLKEnOyw*#wcs`~*(Ur9VKX(_Ai{^fwkjjp=-$$n`D
zAlss!Ffma@iqm!0JTd!)&Gbl>X{Pqmj%|r%$%oAW41F=`WDz?;T7nswA5qKk!l-?T
zw?uEwGM`-({NvuXblgYr4)iZkS!jO@!OZXQu`xj&6S7w?h0st1KQ3i^Zghl{q9RTL
zS^n7TZF9{GU7-fZBMAryicUAw_7~~n9}}5iaXmN+@60o1VDt~T2Mhr-G;9f3tj}>Y
zRQU4!17wuN{@K8)$oe>m!cHmZfl}!IbHMcC=-Ds+kI>WWoU}KaM9z_K`RZFo?|Rx6
zb#69_=41uSIaTgQMc3LOMsB#6P6|$t4ZneAg{7Ygy`pB|?8U={H{-Zp1jA0qBb*8Q
z`$@P6i;B)nh^H!`;ITGwe|>$|D}~c&0W3gRAE}Qv!;_~UE`Un<Nsh0{!d6HWK|L?>
z_o&hZ(ygiRJ`s?_<;S*-<JIst2nylikofez@4Y5?_mt`igF>i1P$_WJFXrVPLgWO(
z&by22sCamLejEvd`S(N@_MfFmW~|yR{=vpm0NhmsEjsdRjCKM({+nr+=$`%2f>q&i
z&64&O%RB}~$;$=P9>E5gmoIPjo|!?XNE?bjEgZ7IUKABYAfmfQpso(U_eQxk%gfu)
zUMRI&meHTO{Ard#K>WU;WI}d$7TFz@tUs$BnMUrS+n7*HeGR+OZZap7Cg9_kj^ol;
zr4Kbyz{Y(-4x5`Hse3uwc&QREKBo5ePtKGF*GaVITzkq$B~037X&+a}OfFw3?I;a7
zBBy<DtomTS(ZJ2TwE4aIOz-l`%W-OY`Sqzv=F|Z&Y`}{jL=;G8^B#NBy1FyvlHJ|V
zKDd|C6!GPY;M=}aetv!x*z;&;kp2Pq4)d%HOASs0%_`#$N4uV5!P6?J(b+p+MZ>{i
zV|{}(AZw_#iGK>!{DENFv-bqJ<X<d*;psN^j*rI~Ph#C;+hSwx2uL;PQTW;8tBxfi
zg1s@%*X+5S9ST{g#kj{mr&gJj5dJg)qPCM;#BC$7(w6OZR+f$9k8PcQs($M^Yz&Nl
zKmJ-y#IQqokc&=E(uA&2R#bAzM>@KZ&z{YG5FgySY_of#mhOKS{kx*U_N3<78en6G
zEWOzNxVq-eH2dth`zGK6!8Pl`KdG2sYQxAzyV&xf!t$2e?U9ixl9Ov|{e>@<IR|B&
zrW)}f)lT=epBh{VX!e$rclK>goIN3CM?C+%X+6k*dAL#%8Cv50SirHRqAM^6KITuU
zXE)BkCW$Am_QVTKx80!SdcgG#jo#F9Dr8q32qMj56OA|qEhXsaUNU~wdJ`qMo8#$f
zq)=8@oZw!t-|_d`q31_n-G1R-j<_o_)nJweSRU)4dI&6VyHMp4L37zADfQAfoW1Ck
zUsqrjvlxE7@clX`>Ix(?H2z7mSz`T12J4u7{LeBNlSD{K$6hbs^YQs)FRp!zCpOT+
zN1<~MN!ySAi|TrWdA1(Vd-UGxc^xjqj}zR-qcPBV3E1>|y)XWLJ&3Na#D>De{ZqtW
z%_dxBMzUo5`xvJ8N<_O~Ng3PNdTXL+E^{GijAJ77<_7xDmtOEyH(jyo%#A!Vheu#F
zw{U?3nt1>X$Hc@0&VcJ`e{$ULhpDqC{}_&uL_Pl1HM&d;WR!pE=(#gKC2f8kToA0J
zt4!zY?b?^|SWGxz0<wFWo@ZU=k&)w0!k2nAJNl0jwcFEbdED);wbj$pl=pcC_4jVF
zN;K0-RI@dktZ!1PUI7E&yTyyWiR8}ouMG#_b{`%e&%(Htt>AHnD5+AX`}6fRGFhPH
zD*OKL6&@7i+R_i&&(uIl<QuQytVh3~Kw>$ec&G;KuD@L#Q>4U5Z1)@;(XnNurJ-p~
zjZV$YeGF-KTKC3_9^$sy42O-jMv-fDs>hG-V3dpE2U<ndZJX)?Rs}`u=d#_A`|SW+
zs;WmJXh-B;zm$8EKCJZKPt3l$yo6Z~cj}xOF|ks%pYl3E9UL;->GB0qXO}lyQX_cV
z*%1SU-`Wf7s-1HDQrYu9<M64DS5v$kFMf8Y87QG58FKb|i4L3clb5IjPwN6wsW(za
zjzV!kfgoI8DbIOI4c35S>!02YAfnkioi+FSKENLucK@&5(w7-GA-5!B&i3QK-^<&|
zPPoYF;_@hjYBdzmKar&2z+D$?Fc`#33j}x0_2126*M{q@S)DlOv^XNK477Y^kGLV!
zWpj5IjW~0qOHa39z8{_4JbsVYn&-XUjG$9kbhHZKETI@s)9`!c3!lH!Uy!RqMnCTn
z{}`u+f8j7P`c?w=v`0(SGM^nuo#XH1IYsq1H*=Geo@5}&hJ_N3CjxktM_zDz+n(QN
z=CKkz)HKRuf4~b@sERuP6hl6FGO8MF9Yi(!YZL*2=$F+)4`_qWTCR$8(W9i($36d*
z9j^R3<XvAS$l@ZXBPFQ$pM3ZW4AVe)@_Sp;;d|&@=$*ND6`9fsC<fD>RZA=2w>Q7(
z<mBWu8A$nX)ftM8ok*tc33o-_wt*v;9`cb!AspTUInAo~V7GbdIXBX$XS?;6=p#>a
zDUBIL@DY7OR@5!I#5c1}fg$a~ISV%U6k$a|qQiV8c;o_N`7Pa9pO`hqX!E`sw*5%T
zs`6O^;+2bw3xM@WIE?~Dss8m`=kv5Y19{alBWb0*MVl|IzjRvTu*F?m8UnkVUy&}C
zlf?9U(A$+AnLB?bXL9;xXWf^}Lh?2&Q2l<nKFs2;$s%CfB1}fPR4EdkU->MR)3>Iq
zH!|Flo1;Weoq8^7hTlQEJeMuxvr%eAA&(O2bgHCnz=1$VS+FT=>`i=SJzIMW)hg^Z
zzRXPLQ%XdnS!UBbNFnv5ZY3rpoWo$B%S-pOGi0ywVEG)kMVSx#9rxUF21N6D1~(%U
z1^7kq>{q<02C%nozvwjW_q%n`DB|1<qjp>g3^=_90<>A9yUevMfH(VNv&(FVK%}5E
zCmOu)_fK$%gP+q9l(K9tEFc;v3a1th6<U^$PY>3>G6@1r6Juj=X-at3$<Pu&w;8XL
zH8R<6u%w6lX_{8A?@`6Km(p4E3Nwu#raL=V0dc%GbT^Kjx(ZdhqCNL6^moI;!$pS6
zaH?l`5)w>T+z)@g9_ovaEEvWc+9*?gl0u%Nh@D1)=rzgAZXLKsz}UMYKVQgKZBOq-
zD&R1^J*}wiqt~D$Qqgx+2fJ8BEbiISumVyD9N@r>Mx?>U6G+Li9nAPGuf28`e+-f2
zVYak79oYFvuVJIX?+^?ZOx>NKr1!|kK<cZgc{_Tl(&7{tH}fp)A2JUR`d-p1durmT
z{&$AQ3;(&69Q~03g6BRwB;cNEc*4O^GC!`b_3Gtr{h@qc$Hsh<h;-C#Jc`Ub=EV1#
zzo2inT5JLv=RL<wfe+gPvdJj^Z~wN{LQ`I2k{S2i1?rseM3d=U*ftb(5w1sTND!t<
zZ(3a-ca39%U7d>V-wRC?<gt=0ulz&aA8m!cz!S)iNx~!hHW^%8Yz>d29GyyEl<-c`
zg}wd45|$4cua)k9UNhkcM3!<5T!z~QU$CbXVp)pv@&rokxK~&u4shVfSAQ1~gM%RS
zE9mt$?hsG28OfFdGD)#3t|ujKz%!*0N|~9<95MWl$6{ysIOjNeYm=ZEx=-LRT-C&0
ziSyw2__*)q&xxvcNnJ({o|wpjy&j9`&yD!Cl2``7#6hw1Wh(^XcPVc>x!RYm<6KL>
zywX>1gL)F?UC<#lOx_ui>T*IRg^8h$R5D$%BiqTNP^@RZ`)%Qd*+%wlnu5z;IsvyY
z5kKGX7N!a~<P{eeS8C4z_|c73-+PfJGM0yr{tZCWxJ?KN2`}OCq?Lxa&%07SP-^fP
zu{BfGAeRrx%4mV(+F+WJPF1??-K}X~I=LYs+<OB9@9j&k-Gv80CjoZgj%7Ol3iIu^
z&|s7MmnmD$AWF`DzeH5qxjc?@20{mtZoPBq?j`CTo~Wz#0UoJ&UP>8D9kf<Kd2C-Z
z)81_!azDW5vYJ!_jFaD84o!qpML;42^-Pb^4As5!TOTj~RezUR)}Q%f^Jk5{o)dTH
zzXeZYI^z8oUJGE}j1l#x2tD0{r1E0nNIFwkpwf+344>R-yJf)%>1)}Sd>f9Ni!C$_
zHhN`-Ktjw`RCg<R`JF-|@Yz0m84Cw#t%TbqbR(*0XFwMPL6DA?7C#J;fOdeb9)B@_
zjR7}oYGhPlKH3d>qgoEwBw-N(U>RNj!n}ASRPL)857-h43O)N+5T;aArek71`7vz|
zkS_#@|N3P4YA8j+!n$ARCOeC^CIE6Z+V}rG_F>MH@1|*ZP0baXVK`38O>FjCe?xSi
zlBt2REjSH=zzzJOM+YS>OP%2$5}AX^F!%B}W8&g=r@ym5eOd=_ihl}bVdi8-hCeoZ
z195b0Y-}kao*oc=LAWdo&H#;jucl$MgDndcxdp;p9lbloClmq<O~;S<hrqbkD>Zyq
zf*RULQT27mlMp&|U^94YVzL5g1URIOLwoICsQ~WqJ5r68*GfMBR?Vu%wXH>Q5j3ax
zqYsb7w4P%roH~(y%v4if&qx|b<PN3+BHSOV`5US@X;;VfnPP_GXIh4RU*#g_Y$lrK
z9xBXZVG+ke#<i5Kc|mSh1{KAlis9gUpp}N14Lj5U;Uh2F1RQjMM9>!zu3dPtJ5HJL
z!^^PLklg)h?_3qZ=d|+FGyvw+jcGmw4zbFxiBI}h^$wOZIsn+ieS=R;hfdATz6;eL
zz{r3;+wB^m#-t{CDeC_CuE`U1mRoHGU3Q3x@2j1Xci<FJQItJuet2+jd3EJ!O|=2k
zW%#X~Y;FG`CN@8%8us$7;S}O+O#(p2#RWt@RgI=iy)fNpE>!?$-Wo#j!v5ePxo#t0
zWAy?Im&w)o$1Lq(*Y{5T-4-sKcW}^{uDm*UGxjN1>X%$rZ~X^SZ3oMZr9x&6nC)}V
z5k&PMY^0{9HcRv%jj{9utXOW0%=>k;(CpcR=b$5Uy`HYG7mT7U#BeA8q5&wZh=ITh
zxOQr4<l;*lO$bzi^t@alC|>K)FLBA;LtFET`+9tqDuJ~+4}wp<VlZT;9)G)Wn}!ZP
zcL_!q+-ry20^J(UM~^zj$DQC3tq!E>f47>|;qLjdYYUD1evi3Ty945icag7VzwUM<
z4Y&F}{p)&b?fY&Z|3hyDrO@X-D)LcucDYo`wGPbMOc|TJ&M5M|zlbd77ZDsgzH2xZ
z$^EG?uBKYV>7kL`H#H!MVB_L8l8B(-w6<{QWPfPfs+m6qAU+ixvEd>De>h#MJ9(2n
ztVQ1Y*=>QXiR=1E6tU*lNY7i0K(Lnh#5ByoMO#}tSV8!Cst2FzW!vGZX<wpRIfw~g
zYFE7nQ8kyGTL+hp!x~4nJ6di*l9%ax8uUN^9G)ZEU)xGa{bcSBKPJ-8RYh~$_;`Km
z4guyuYhe=f4nfRRJi!4<hH%xtqjAHOF3#ts*=}<UM0f8VDv2W_h^4qmip?Fgc5$87
z@9MuUEkIJta<4LWAhNTc=7=r6eT27YJ6q@QT~bCbvI_u?k0BGj=h(iqFD@<)Gb@yH
z+jXd(J|*qbLH3fclQ(r<oQ|`Y43tQZ3%wNLBg2*bVM=NCm-|3%<c^?_5a>*=HFV%8
zB@(QSfm=RV53v%eCuE{Pt^u6TRcv9|{xnE>%UI&lt%zf`WaQjr-+%;Tik@-I`fB^n
zFMImxx8WGRX)z6CqJDn;_%iNpM+B&&x9}0z6P0e`S;5~l@GeehX=x#%nZr(V`kWep
z51tEqEv@_1=?E{{{ayBAcx--lyPiBeM{8Lf6{%*Yr8OE&1NiaYIX8q{w}brq=vbYz
zPbrIZ^~&9WI#2QxspWVueKc`hrSW?F=q@wNX9Ra|<+X^zhH9lYc!Yms;ZZ^$K`vhv
zoUCbUG%@M)q-JEqD4cH9)`~EWj6S@KXzVhA6NJXww{HQ?2&zDf*za$LSKuKA%_Nw3
zxO#*+NIb_jia)FCj;}Mb-iQ#2fdKwh=w;5k{CxFL3S$MGHOWPF{o;<-NU1k0%F0Zm
zF~p<47I>xLGmpQU2vmBr_eZ*ZAo$>S6U2>&rZ2)9&-Nastgt;8yxC>ugMDXzRfV~w
zW!VlW8NZ74MK<dZq@Ea~qoV*?xiX@@+oQ|MU^FY(zcUSVuaEmtTRDdEVK1k?i+7VF
z@Pm4f#ruv)_XO*I(klq-)Xo@i)L-n>f$9GNpUqd}X`Z<@w-1tGW}|sTUVtK^Oya%G
zI=#@FD`-55k%5t~@zsrtLP%z9WZ&f$GBWZ?RY)?w3pEKEp~H3(>Q}z-IMx(B)jzwt
zH<yVpIL(ECA|)Z={@>%?)r_J{WyU-vBrih)o`&D6L7ht=w`Ogy+1C=FYx05}B9v03
zF?LmkDMp+M(b4i*+F%XuO`<}wK#bu(_B@QC@z`s*eft^B!bZ))l0xx^?^6Pkq+Wi;
zyK6Y(B)`qAuDO%-uD;ZLymdzMK9r$bN2~I33a9<pM?A8^On9g+ZlT6#f(&=nS2EIq
z*UIZVUKsiM=~A}Fw3d>R?P4n`V1i&*961=grhyfFJ^ET&&~<Qn+U>}bJaG^C<m3cm
zfcxHpH#iTtqsIRUq0Ob=y<$?%<q{C61+Of(@H)^?nz-{VFG=CdR_A$h4XViotseSq
zw?hVV?ZY1pRwC-3iEVdhud|dsXf~7+8e^by25?uLp=!UO0IYXD#EjPQ2${S0dOz?!
zw$1kwYfvP6DzL0?eF71_j)24&e(s?jMV(}*m?#w{W-D>8R+Xg|K$HPo;y>-w!ud2u
zc4siHts2&GDCsE_$YbY=kOHs}k)g^eD*E_jh=RMu`kMb%Q1F`{Um9Fd{rp5eTn>2(
zA3S(2KEgBcQwDP2B?#GBW8$=lGZp*4G7rzpc^LX5txaY%8xFoIhxWfh82qg33`;e8
zY-bcD>GV9mE^T--<<R~RgIb$5gb2&`{gf@Gd;DbDo0Z`iD>b5T_b?H4PU#i2uiGzr
z=E?ugx$TZxazjoGQeBuFT5;pBnjc^U1%Y(QF!ApceD+qqyP|>1RVmV}F%0EsIPx{+
zj_G-IE;R_=O#Zv>*EDbffTLHVL$+6_NCG&amUm_yXoY(8ABOMSiQH6tjZSq+&}*tO
zEU}rF-xR1Q&wo@OS>ti*Wd0Q?2>kc3ynJsVD&BoR)}$t8Lqa%(!;RDVV1xiEFud3R
zg=Q&-hX-n17^eeZ3mg=`M?Hq6*og;Pw$oK(jJH5vA@%Z^6>cWy7yj9;hKIl=XJTZ8
zy545bL*xOP=q`w?|84=CFQ8*<3DRd;0+_q8&YN+<5R^ESb_I%<#qCh$xFx5ZZ&%VZ
z(sr;d%&V%~)y#~HjAX^gNEssr1?gB>ak@8585v*c#!5=`_V*S}DoRAjJ#Qr`<xpn{
zBEWP;L0Xu$b~4ygw0IcvW|;mvK+t$@7~i|H=%5<>+G2nDer@k^v(dqS)zb*>DT&b7
zgS+K+e?PO6tPEf`SAc{KuHF<TZm8s;`gX}a@`F$l_#CjtdVc;~+t?xaZ8lFcI3V^j
z?$Cue)`0*MsYEa(%cHu+kZ}88`Vku>mXXOAp=ja{i}buFk664j)Gd;cE(8I&FjZsp
z*!85fj}$_Hw^}g@8WJMl7QHD*3Kbbh{H5jPuRUhsEu?dB(Ap->?E&~%qGfU$ulRmO
z=ggVMt3HXodD<sW%2&7e1I@7;?&$Y>OWGeDc0rC=!s52vO%z27ifz5ax{FEE)MKfa
z%&;eNe%Qb=q^@iMci`(%;3zGKVTPbZ5Mmw_<C3j4+3c4q5<20rifXY`bYOeqj~B~7
zUKdH^SoCv5K}LFigQ@;q(*wpjL2^5gdp)j;anlS7L1aUJ(2NVtSe+Md5MIVG$@f3j
zi0kiATbn314L?rZ*w_GpNN0Py=H0UiXWyB67nLmKjvqhtYV3@`2v9T{vxA~an8ukX
z91>=>zHZ3wo=qT&D=)jW68qPXCGNHXoV}UZ^xv}IQTW2Qe?D$O5$<F+)CwnNQ01ed
zp-DQf*DvJX-#fBW`78nslY1vt93=jKU7ws<;kjFX;}(&C$76qTwZutO>+L-P^~%SP
zVz!0#fBja}(E*m*y8&&JUlRPRi4k6kHoyjfB@Nh3nBc4VMnZ1^>MkUYF~cd-Tv*VN
zl^Jz)d%C-`WfP*-HjrXNFBSu!5I1I|kB*C5m0jv81j9ZrCr`*hi&g6`eRovz2GD%i
zbbBZWAD5Xh&bK<H3?364?oTKY;%yXH2g5y~|1_B}y=#?X=7m_xuNGft!8y<`tzeS`
z{yjFVs*q7A>~c_Y`$yFrLGmrNxV<wYc|B}BPq{zU2}OJyQco-pM|=ETzj^u_&YiF{
z8_3<R)4uT8zJ@*RS?fkkU`MFPxskY#XJ@w^7S-~;JkCz%LyQ+`_+Ea7Co$>;Z}>C@
zbcG0rM?-pU*?|6vXq1UX+i}cH@aYG;u1`w?P0YOJBZ1lLM|U!Y$;rd7O0Ij;QFYmP
z`S{Mx-DMCk0`Teene@>{=%z+Pk#6aK6aeLit2>kBokK&m?X?aF3SoC=P3rR48@}tF
z6?oc5MpVrl1_TMSvOu0CA7u9N=b9QRkJa!d>>Zb_kuX8~a}2d0@`WRfAhNOXJM3CG
zM$rk>E;KA6!Yuzj1ST2e1vkr##@<5VsqQ8wrOx+7KO-UdQ0~wWJ4VXD^aNqSB8R?*
zb1Bo-^fa890k}VY{Aip3!u4$Fn-|*JiJ(Bs$e=;g=z6#f<H6{RMH{}h@uCkd)oUWk
z+S);TlA85o{F9o8g@q5&|3%VOmt^yCIJB@|>Nbd(CF+&F_EXP4J~=7+^kB%03u-@L
zwG|T+1B|50%erfOjs#iw#Xt-TOQVWyX!DrVMAl+){aS8w0<>jZu|b#He*%WfK|{Z@
zwo0?0h!~+8E~@{nBGc!sw6wV4f439bicevU!iH{SY%J6h+Opa<Bn^oB$;r{JqKb+m
zuDI|CV#Hw1JRo3Rgpll)se!J&Bj7H)Z{X&nUW>b@@GYykuri@vB1SqfR!^CV$82ay
z1IM;K!xm{QF+GOF+AtpChaBJKW>p+&XT-y2Q9r_jB%l234Uv!6hNM=SSsg4TzEvY!
zIz4s4#*=XH0GJCS3yUSphUCs|w%WwLt+wGS<KR6RLMNA6n4Ef(P~LVMr)^u$+#NJ{
z2ajI%TWQi02fKt)Pk5#l=-4kn)bRB96c;zX>*tcEng@SeU0y!knmjy5DHym*N!eui
z+Z6g}3KZd=uB}I7;JRCme7F`JH0!&;qZBTX?z;R@<G7s&wH`=lU}$$#YU=L#I>rJ>
z1i^d@23x%fGbI?5B`3exM6O-vc(%7VD^;s(JwZ(J3$kYTc}AwDPWGywH;>iOmX2Fr
zYm`nXQPm5w%>B)X`#~_j^5-4&hk28rW<(XeICI~~gt$mcWOZkDCgBMz%#dB)<{qq$
zj%4%`mrA7O{(ju7M+1^nW#xiX-_PaR0QVFT9qRh<TY-DWg)5Z0ZYxdHoPh3uGDBEU
zknHv2DF?4O-dMKUoRU*^)_&#uhcVR5!>-$UqEz^>^<|}}i!;THNuj{`>Kd#ITBqci
z?RRBag$N-{NPfQwtOYWmDwrbW*R)gHdv8l4&t7)GuHyis^^6L&$VxGHf4h4CcjsPX
zCJzj-lh1*f3)1sQW>Z@yjY1~nE>QWYe7taac6m5}2Wr*w*rz;2^Yr+a*%rC51INX&
z@q1iWTffgp{9XIcN475_`_7b<0uaqBo-ms)iDtn1BOr=+Eyh@AY5zd!Qao)?JJPNr
zhzJUDnCR#eO`c7vOxqKsG+yAhQBzaHK=I)n)dw?abk`A2ncO%BEasE$19^|RayGr`
z^EK(GlnSAfvXPl{mKdcPgcc6^?L2RH7ENLv<Pyc`6Kha#KkkiiGM4^S@V{Ds#mxW<
z4Yaw2V+)Um@3r0eu`t~b@n?6c1>38dYsw8n9)TjJMPT?3KQ4tJ-}`mWJyc4G=WN;)
zYrN(9aP*Un=XhAEJt@ti4I^Xn+vs-Yn+7;SDp7s_sQ-Ybt7Y<uW)-HRqh`6l)_z-n
z-E;OA;3yk!`Z94r1TH9i6r~I2K&X*XR5*z@;B#5vmfxQ-Wh0=Fu85PqVA`6qegD%l
zsurf&vBf=wmR($i7VK0*)^qOF)#+silb7TfX{V?2?_dV$iAfWpr|g&fs}!|_uC8mA
z<&(p%Aq)IZ5@^0@RwhX+`_2RRr1GEI)aXXhhDt7Z>K!Wt^elsxs#c3ij)ho9?f5^2
zK~rm<&CS82vOaB*-B7OM@7Yx}WWex(aW-H#HsjiOU;EuO0-T$ABv8wJ!cE7)u?N$;
zUzeUishWE?_|c<Buys;LuF0J{q5C-JKSBNH-jZq5TVH;>Cz>ALwoS0G$r!0g0wVg*
z5cED33mrE%&B|si-?ycrDW9W|BJt4xe}MqZ@@z~n<K|c|=;Y?F2rI0D56b@1EiFZ6
z+`02f_=>_KhAO&CPnZ-AsH3ObFNcqq<v+v7VzAKbH^}3RJ;+ptM0otBH(@87qE(>m
zSEZYiNb07(=c|k0GI%T!F7OZ&mtCEZvB4HjT5HKKWv8wi4z7-?IdvDw=sXLjK;shm
z_GP=4vh6LSecY1<azgbcjOR)RB_)8iyV9e%T48Ta&swl~y#I!fsI`y$R0UcEr}J}4
z#8@LtQpQp>r$J-j$4*KR!0>T)6CS=n+q|D&UNt&tUW(W#c;|59qn+AU`*wOh4#aKH
z*O3FGk&qBD0_x$(fvF(>ri4#({ABup1hD5s+88(_&x0tbxar!QRow8E=H1@??4a+q
zHM$LYH}J({>~bhXL%r0^)wMtV@iVqKN)Zot3FkE*X5QC=vo*ADJ6BWt`YaayOmH`k
z56JHnm)x=`sCT^{PEW!pM^F_bYWSr-gk;(We(JbxfWQY451^RDa;c5n`%+G~FMixY
z>t$pRm7pa-dr%40ElrkUo-J;U2ccsd)9judtA}vj#sBWO9T|X!j~{sJBC$zCw|IQK
zY#G4IeX@<AS<EdQZKm>gSXiXBe|1>2kfOd+nmCkaW+qDAV?L)lG>lm(kx4EXE`Ml<
zFxZx~w@1^ht8m=WMGS6o-;}|zvflh>P}_&tM#~BGn}p%=FS=;w=dzmBdre<ZJgmCX
zQL%`>6sU)VTK;qX&0B_cF0I3275H*8H8Sa{J@)&ZgME<4E4H~GtA_wf`5$Acb-nYn
z#mCs2T_v5C$1qFw&mW^t5+G%VK|=UY)`>-V0Z<jFt3DGKrx5WVqolNc6(1W*42Ed<
zvXJeB=j6f!hS38D*rwsib8FUARAqG>v1ea?`-x$oC8|VA9{~|kp^j3^Buq6{+{|+r
z@xDbCc=sKVDp|E+{*ChUnQFhFTM|fG$e3l(5y|zGMF+|fArU`Eg`4nLA@aK+YuMSP
zuJW(8pyu>5cI?6c>MyX-B}n&VD0^<MyK?i2<kv@uJ=#4>Q+Uc25Mqe1=^8X@2z{e0
zgDEKO>G`?;)e0%8V-i0NJeaHDui&1Z$v*HF<$zf1S5*ZTO~{|$?aVyM-$e>{GP@S6
z^n9((KnP+D;Y+65AGz<SAa7KCEMyiGoO#_D*4lILgJfv4&bAb76g+pp(uWx?&;dAB
zp(NC6^1yN2Mj)hUORS<(QgDgYcipNh$_ILS?CtEp(xdIPupq+XDCe|FhE{&^M%$F`
zO=eNmmR)Of4+bRn_Oa|5`_HqovteQ&%**Pt-J!7MdoVTGHaYS0r&Q&898?5&qvPXA
zD#<X#@c-1MZII!Tk|x0bVWt>N1PoMdX;}p0bt1YHTigo?2`O5FH*ekmXC98*pyGpp
zm|IhoFVhq!rl$U$pO3m>rcDjQBxJcH9wC9dIxOF6I6Xci1-sBR8Fcj3y_ue#Wo39U
z*?%%vUcusRmaM}|IbHQ0^FG2{yOM&?>Ac0dMU=W_Zr+!#*6r7?mYMW{&6#iC7~BaM
zlKP6^3l<h2?`<``zkjraqma6l%wYxRa0C@0CM-z0AHIOLJQ^;n<FG<TH2?N1z7bSm
zSAc<@?*$raYisB3!dk0(GBMbR#dh7Gtg#6LGp+W{pm?^)F+}OJ;v>3X`?np&&BMes
zXZsPe!7=Z7C(7wM-UP{T*Zs0Y3&o4i{jxte$XO|0k>}%WYKvsGJS79d^WTO{LhMk%
z6p^Pk3!*?h1=BGc#~u`GWIK}K=H%sJ&Mux&n_DUrJ4!URc~1@VBAF``W8~41HC>%d
z!yXMCdowO=OUnX?vQSvzCjSx?7;Uq6ehHr?ItUXNk2E48nO=$EN9A{W0cUqvx9JB7
zy~W9eIa&7+BT~jJFjeYLp?0vFTTkH>AWu-Yf)E66OXF4{o&_l=0JxC+xJ}~c)|TaX
zf|$W9nw4IF&_(l4bi$y<Gz%Cmr^N0Lv5k_?#!Ea6Hk=#?DsmpI)nFB3Tfj*Bej6b=
z@nfWqbhGM}p8`D@JhDwbc-V|FB9@wNP-;E<2zLbD%SjUA<%M|jW~ujg)ZoXS1TtR&
z=KP>33l$76BvrN0Iv0oW17jw%8{5{F)x*Lo`nD;2gx98qlg%-Bi@yFI8o_!*Cg(*E
zmMw<y;_lav&+e*{D?_b>AId4P`iwazVQQaJaynXHicpfOszFgkpD`zL9iLTgs-@Gy
z#8%jL<$8t6`u(dy*1cIx)gbXy=Ka0csi~!_E`MC|zeWApr105XcQ=7d>7!;Ls(|M_
z`ZeNKeQKQz9Q>-Q`BYSffkaA9o(!t}@X1mv^&+%f7ORf#EfxAEv*e$he^0itqLD>k
zVs-c2W->ZXH~&~#`q;?CtB_pK?ZDRJd#$CVrH5AKM$uQ3?_XCO)vXex3f4AI5MNOQ
z{Ev;w=h{<bo$POaz@4dHnX32LB=Pq?XiI(NC!S%?^PE~qRdob*1o%IKi5scWecZ(s
zi=jdpPVyL8KTEuK@|blEjyBNjV(#fCGnxJ&tHZpv%Pazfj#!NnAe3xd?$omd?cW~>
z%fLg7a%x2;XRM>^*QYV3h>dSA&QV25p*U}Fm<U?Vp|cf5{Ozq&*nNDFZt#QUXMY&!
z<JQ5!lJs=sdQXJFtu;g#IammjCMSm5+y4w*|Ac8cc6?tNlX!s546GC&lTPK<rIwkG
zGIa$Jm0r+velPt?l-hG{?Wer%`@k?t^h}Y#m5rchW9IFX9Ob`nEslpxPEE=aXjv>W
z2cVn7_E`hXVUrV1Tq`jABuQvK_u{0`(7^0ZLNdRgKrR0Zi;v=I@qD(wLxtx!T6fom
z+J;E{wZRs7K$)`<(XFAA^$UenIMZ&i)lW@iV91mso=_%KRg%t7mg$jB_7M=c?x<M|
ze-qJ!Vm~&A-%Gqt7VOIbZ}9;%irsXB&k;Y~Fdan1{D8j$1OOgE!q|FqHdos1fr}8a
z;iw=4%usc=7bTUxISLCa4)IU$?Cci}XbaHM5(w3c-5n09!@}f;JsuqIIoI<vdT_Xs
zNwuf&7EwHP#HQRihxrkmOhyNt;z~hMpQ#D6MgoIw8WMj*i0^cm9KSAiwJ0qV;pKn!
zoDwe=a%wP7ZNuXa@g7Rl=G%cWQ-zwPuXmSnb}igK$6UYPIRa72#*UZ2Y9azBmkZ~C
z@>^uC%yf*8I#~y-K_MZQ1Is3As=~O5w;Kehc07M{hGo*y&rgwYh8Rk<av-GKUrl_N
zrSsdli9_n^?#A>?z}Vz%4&8kEyMLF2i`IE<VY1wm*Et9h^H;pwACA_PPKkQR$;kFX
z|JAlCRN)%LtuJgLzrbr7rdL#3uU<37!U~?WQ0IL({jUxS?d<KXEhxuooL-&5YF${w
z5Sl?+_>o@#!%7E=))`{DQiCKE{C?AmFWyaCt31-bkA`OOGKar3-pVreZnWXcoDUm(
z&|2u|=%}iy>eIbjtjM5i()+QaM^xifaso|3LBY-KVuh^ajvJ*2D(C%fm_Xq@VhVR@
zbnp{CidZ8N#88>6Uq64M-%K?(f30HRwu7=$$cvjJ>o@1>qNpIS^h#^%G|Dc`IQ`3!
zi=c{-889^wc{|;hzta>HrlG3pRr+|(8Q&9$#~PJ@(b>(pm|kHIGldfD!|*DoGlt3Y
z3hD)cy0sHTgm^o%sCAC`RYZRdMzWf8VKy|~k-dVH)Y<;pV-jAZ6k%!o1_3Uvij#+I
zaah(tbDoDMtO0H3XY24}!0(@{cqvQkK}XOKIaZARD@bO&)#$x3XDw&KE0Xsgx>xT8
z0+66r$b&$(PjJ2=G$f=zqxvcs00cgMkK*GIh@<V)&!01EKHmDn883XdmDgK*Qi~y8
zJx7t<hw7(ibRI@0<altzHP^^JJ3BMuDzKK4mF@j2EOgV~&G>tNvExvE>>bZsBWIB>
z&R!lZri<)Ga1S<l6GpTrME|+FkS@=d4reFbgTpk>F0(?#ZbVYjMefI?H?#zdN6(Z?
zpbaMEPlrGlamMR>{`}d!Y6)A9iH4OGv_e-v&7_T#g28rPxx?N)s8~Hevx1*(Zh^OS
zeA0hrmO0K_w(%gK(K+?=>jV}x1`ifuk#7aAOoVYCxI_mNW!8Thyf@(#c)Xr`KSX_m
z-HMp+)PT!!5|_krspSgxmi~k%t1rV9^C3HkNWIO?-TeRPdJk}{`!H<yHnLa9o+Zg9
zn-D@)WbeKA-h1y#Rzi|pBq4icMfR4J6_PC@yw~0HzVG)Q@ArN8;dl;DxX1th`(3~5
zI<NCOPm|Vge*$;v=FGQb)@d9^hV-ZyQ5QU`^xa7#8y?@XVeow=D@3b=FJ6PSV9n|=
z&HcvclHN#d;V_BE16~eAii~V1U!G8gD`bA}T{@H)+)K+tnz4FpcM<xD;F7VsczG2L
z+W?;ga`k80oR)=uP{?r04ZO}bBu8(8=EL`U?qpL_l9raa`%l~t1}oOUZ(};YFY-}n
z(O_;WjTnfRXt=w(`=kQqwp)6>HDNqYOhY4P%pjlgHMhqi<skk^MEjfM-=;dl#n}`I
zDoTELn5#%q1{{wWuvBeNr09kw$kNjJ)@?LckIZa(j<=VHV=Vw1`Swa>RtRVlwdDT$
zK8<VNpFbc%Kg}r?vDL^^wXm{^levj_D$fK=qDBzJh%#5##8sin<~9v89kwTq!gsWv
zn|~T!A+1J1<=EFxTmE#)?NEldZecV5&UEAE0AH!UmRK1$QjF|Z)l5rIwiR+=Kpu+j
zmcqiWg@qP?lkjuoQ%f{UN=l%eH##vvfQQG&$LC-%2td~A$_n%ozUwuhAS9V-8##Uf
z1dAGqM1j}mN&PD+Kn4qF`3OXw)ai%9wHiquZPf!Emgj%Z!z$kn8+d%8VX+V!9MbgO
z3Izk2ZD+;SdJcmG#iY2uZ)DqYxg6)+hTaY{OQy(XVJK5MqVm~lkZ3e@0M?cOuiD-&
zMNXdn;st4NaByPO(ZiSAx_J*MqJ}p-a2^)qK7s*TKxZ><cXt1cy3gr5+3}A5T1SY2
zx{nXrcR&2W!Q6WmPpe}0Ql5KD?8KPxW<ruYRKP~Z>4Kjl=4<BQkU(Dip{B-YVW@q$
zo`v+QwQ(W10YIu|eG6LqTcvWzZEe>_Mi?Xr+1Z!&mO<DButesRSV09%RI&Hhf0q_F
z>ltP<6+dISl;|D0`RrBol+%oZwLqC@mSw+*DhsI?$#?{u?u1RjpzGJNTpV~J5i}k=
zM~V|El}B{2!yaV{W4IZWC2p0~SdIGZrsf^oni4I6mM5LE{Zlakm7#hI24ilooU==@
z!T0hz>}EhR#ZBAMF~X5_aO4#kJ96qa;f5ys^0z?{)q}P;3~`nlHk_z%HZ^#2k;nUI
zWudlehU$z}tLOeMXGndSkwMdP#m&ZMUsHFQbKf7@KA>2MkcadU$Cw%>Ogk>aTh|A9
z1>bKQUV^(0^c5ADo-;~E1vH|@$lNU1vMi4~rxu7rLr_rF7&dIChAt%wIIc%U!5Ynz
z@=Y9U=_fHLUz;?nDSgb?P^HuKN$0*!|1q9O-7$T#5_mfC;EfuzqaXml1ah)#P79HP
z<C5#w6zSo@!opIqXFc9RyM$l9Bq%46XU*91H8?WI%1HI&J?}KTe*7EPluMwIzp~y8
z`c=jE?-%E|ELP0}|MU6t=b(9$mYG@7Til!gt%j~-0f5KW_kW1$3TK2bt4QS9Rv4Qq
zSl&%+{v4RT?6FOMCAPYB59HP49zL9F@~;KfD8SODsEqG)&~A6m2}aMn61qu*_2X|>
z>rYq2?s?bK%*aT%7HQx>6l{pfNFCZ+Ut?|$IEX~Ec3w?-iE5Yufd#K>F<1k@juU=d
z{<&wlJC)2}6*q$3$!bVGgF1d%JAO_-ep|9AWs~Wr=dHGuiNok3TmM_8qht)vUU&C_
zun)w+l7047Ok<C7P~ZYFaqvxBBr)eN!mbSDkHE*nqM>dJ&XY-cUm$H0;&=7&`T>7X
zAe@yzbqBwin3xy}5f2+Ht3vE%nEEdSO}o9Dj(Wk5D-Gzzt5Y5Zks)6B9yrbK4i{i_
zxvZ1TBiMO)-7H=2_8SK*&1b6&5AXIpK_G4|2eW)SiHU)Vw(Gv%{$-4ZNRTuXuIT;!
zp`92Am+faSUzUDdlpm#eb)Rjqw!mbrP);{?DN0#l?Z*5fuAQ|Ng=!C4b@iET)wxY&
zHQx|^c`Wy@3n@Ag!<)}+ev8G%#x|QSEl!{v|8;dxArhY@hiGRju1S&v06O?8jD7uT
zH<)uDxch%E{w$y@?k#u2Y8kfh`O*$c-Ay=4!SKf#I>Ol^u8v98U?jyz_*$)HTxHtY
zy&4CU5CAgT+iy$eg+$-+5+M6=i8*Q|W!2?kE{)iQ>_yCFG0FfV;L=89yuBS20oF+4
z4W&P{M$k^Sf_KH<2k5<CGkRF@AY8!9T|`?g6L0Fi5q|S9R%>*bppsm7WG4fO^JmwN
z;_H0^&InAo&wiZ}W<_f%9?+-KX(FS*qYyh(J&}Fk!hjW49AAo!8D;7dh(-AC5nv9d
zws)=z=mSDP!-*UzQNNS@(YqlKV|lJ6sih~5mBs5|e`e3ypeZZ|s(bPj`miif3rm=;
z69;$P&m7cHj*&%0qQG!SLTv0KB_)eW9Y7gdbHhxh0JqC78VtG7_PVInwR<Ax&rU;K
zUCCagM&AwcGT-$wU%`dPsGW!qw$v8|ZxP`}ZkMm*THn&4VV3SC9K8ZJ4iGAUObr@?
zS0g1lp`oEb-Rg(!iH`?baa3_cyLE;qD>v$1$-JT#!wO?qXy)Sla+rHFy{||)>sZ^8
zpY4yP4|#V8f!Aw|v@xuT)io$zHZ@X##eBHDzXS(wu}(GXS@3$7UPc80fdDsmv35;_
zZ2r-CF+m0lYEw|58num%Zb4aW_ejW&G)_^ht&Vm623?ES_b*`zN5i9Z<@rs%n=kql
zwLt{>m{#Q%9({3T#4;4ZI0=tK=<l>w71i~nPujD;iG$`l9B)P>mE*{ceT&w+hylaJ
zjZs~;;L*bkNG>l$RojO%AWLt_`1PB&H?-t(+s@~;#+s()`Nip<s*)<Gpp#_0W8%xS
zooEmlN>(tFGew@G2nCWflTv^-Dh;PW!zK-16*kHOWXo_I@=-Y+H=agXtp#go8vnqy
z6Yp0MEK-Z!vN`{0=WW@%LnoI%EXjaybSy3(`S}CnU(4G*W4|sUY&xg85d9rLnpjZ)
zr8yXhw}9b`SFhP8Cy@2LIrYhwB)lyHy2ef0aNq*VAyM#>iS&g_zzFQjV>wyQ;c+8V
zOS$(;sp=c<zCVULcXB*8F)qN#H>NW+b3<AD65J0c@nq)|w4nD)fQwsJS_)<X8YIk#
zP!c80S5pV+UsG7sZN}Qi-P`UW{pw=){#m_<3cFR)G$q`#6aPKp4^C*p)41L{cKi0W
zs;;hkaKZiZ-UV={n>#xbKF$r%HfCX?{bH-IryMKe203+qzY;V}z}Kx<aID_L(-ZUo
z`Md+r9}raos?Nj1!?JX;)&!lCZp%J`!|gf69?TGXk+aY=+BmNWXOB$;q56XobiD9(
zRB!J;$+>JqSW^yOw=lI?NP(2_H$?`eQs_hKFf>|sx1m_L07;;iWV^V}gjPJSv+JYK
zpKaQrB@9i{78ETHm^~Bc!T$s^&XKNHZ0ukpbx+;D;&^Ci2-loBq+mR9@>I`t{V!!f
zbh;7EmtjHz0;l%ju<-C(NBFG`!DGG4m<1!yV<;8WgE#*JOcV~*2b1ID?+FOhR91pw
zfEsHmTp}S*1(j!_f;J6+=%A9Ye{cXnoA&y3wH-#YG%9IqxsEul?mK^e72aCG?Oj<I
zsiOZb(VNQV=XT_nG9t+k^6=n+qJ5+87Nd09M4%RE5P;dJTfD-~t#qVBTPeXGU|e2~
zEr(rMAYyP9#O~k0G=4mbUch_0QM##qLvv8ZZ0L$R>j`IgNA!~kjIECn#&%J%`TVE9
z5@AKr>gb>%GW4P2LL=(M-bg7IAMg@~x&whgMO{v&m&L|}om`2AN$|F?(8|IBehn=D
zhC4e~fgub29`Jw?Y<QUG)-7`g$6%2G2j!oXJC8T-AC_=Za%>%7cSC|e{hf}QS`<!O
z&u%ogx&T<FsG+Ux?Kfebb3sR4y$ZyOLM2Vi%y?nCHgU}<L!<q(Tc!7&J;LW*KK;-z
z*vy`ZEtpDNaEWUz5NV-zECjdB&FagYDZLV^oOg$DAfK)END~vEs<%D>lfaI)-6bWC
zMYp@7#H|Z06vgltEHALdE@QuL+6RPcl%OMsCAm1XW`Ai5M-%X-eg#j+;^&Sw8lX7F
zNiU9df!Utg2YoWTwP6g`LryKNkaD^&IsJWozy*B?vpsr0WQfoiUqY&Nette`!w1k3
za8}VIfnnmkGam?ZcwnFhM{~WVDz$Hns(7wT|Ets9>3APDqNDoYtLD_yR1h<Pi1FY7
ziqZBS#AQ(7yd{_P9i;r=T+YtUo*9h4)x=?T;Txx+hC$)AGJPOiAhtK%Liw29zfpc`
z;wyz-X6wv2w}()FMm4uxE@%lhnY};_MSR9UATXkUG3QDDgf+F=_c`c?PVTfAODJN*
z`eexOKIFP%$S$mB<Xc}_Z*bhq>LrR1m{4Amo8x59dhK0*+w8%zHL^pPOUvuF(gPWG
zaZvqQ254xuO;`0!64#4*!{D;Uz2qA3c<e3J)V{}?XMA-&YL$3D$nCN2bkRps^aoF!
zsbXbH4Kpjs%5V|DX+;V$H2NMOnOo5Ltrr#CC&~+G9+8A`7f6lbTk5DNkPqM_@i|f$
z-O$z5J*g)IQ2O0F_JI7nJfsc`h)(J1X3Cxirl>!Ebi-1C>|D>jEa78Zrb6$~=03GV
z5}noPTE-wlg&bj9E5h#x<D7uI{SUn2QI5DtD<WnJ+qUF`{W)~acN2+YnOInQdaNUs
zLP8<SfCLjXx1ol)|6}Ar!?*%qX3fp}a3&6~rd}AY`dY00Vk=`w?>)M^a@XKO(UIMG
zR_DT@$5Gjl-5bdDdzr2hhPw4`$DN9x)o8PsyxwO*u?B*HrX3iN&#z5>`cPFR3e%7+
zUS5k5lrx_qr`>@W;#~bDmH|tOFDEAlCU&1G6aWwc6ktrGk18HCNnk!miTMDM8lP{L
zjr8i_pn1Pk-?@Jp(AbNg(hHp06Ip%z*>Frk7~sj2&3D~ZxPgEZ8R!Onetw{}q1IQz
zgJ+&Zb*pIfQ6Q!&J@bv-YcqUy#%#D$nM&>4cnnr2trLIf!lh7{h*zv?4`_8Ou*So#
z;nL7@EkJ8{{nhIk>Pv_YiPe?SuYFYww?$oP5-6^%uJ(xv3yd&o<*5R15!^=!DS6+{
z^o#8)@0ED#qSnzM<Pj1sho}jO0eC_l<MW+ttgKEvIwmGqh{d^`(}Q(*$K|!jPfFPD
z-sOSfQOYXBG($Gtb-bXWg3z*{l|Q6WOi{6~!po?-gIg)T#Qh!*MM4@u9tfZhiz~%5
zQ?+}ow{s`-*f+-dRxSy?9lxPZGQ$V{a?tVxAxdg0svorl0N@L`uk}MW0J;+JJ&+(H
zAw&!f4`X9t=`{IyHbsL{LqS^4xZ-%wSC~>cst0OQm)*_RKY!YQ=q8{}aCU;b4i^^}
zA|@_wbEF_1oTtHp49=>(QeEiyfQU1)%y|jV3H9)^UJDR;20>1MK#K2jSwFek)UT%<
z*QxS8!Pa8m7}XWWL^6Ny*NiX|(_0{t!vjVdfHRz{&@Lq{T|4E8&=-~ejQ$!}(I^Nl
zj=^N%(t?8VMRhVz9yL}Ze3c!d6sol|JE1dFOmcX{>~h#|W8C;EV)PH@iR0~`?k3$6
zE!Ik9?_J{hzJ1Gv`GcE^xN-5$+uwgLF1|i{W($VOvSrHn{M!K8DW<X4F+ax}vf2UF
zB>;Z+rYv|Vd9o~k@CA#@%qt!o%BAu?6y)0=vQ>&e95nQG0h%p*pDxez<<~IWCSy>%
z?hnR_Xqo~WuP9S~ettV)*zfJp4=<OoCOIZ1CO{)~Zhl^zH{UGee{lhz{pwW{YQWij
zRSl`_%<-PAtn3MSK|#T$NP|>f&%l7uuXV-SDL4H*{sgKURR@gQR#He!9v|#&1MAa4
zTO0OlN-qm(oK&6;Y`HpmjL?M>Ma+B%v!JmmF1`j9RW<K)aZy5de_?xU9|y5zLa&i3
zpyd&{;_>V!wAw$oPLFUp8Sc+*yic6#u?X<ACz&t!{d@if85wjfk)o$RemvR1f(%II
zl`y%I((6(?I7eU{O5>sj3h@$+plU%CHnuW2*nUU^pL|c2f_j(~$E>*oEyRHHj+KM)
z0r}a(-L#^JMEsU{J&~svixPY{yC+7}_+T{4gaS(SX2XarvaPi>WRH`K%nJGiR#s`S
zW}ZIf26b>qN3UT1#X-Oy7AutqKZvl}8|qOWbMphaYN!wG(<kUX0B2TMcxlc8fe5Ze
z3vqXj%LBS+>(iw_<z0jn+>6Vz98dy1(y87D+X@=;BF*5O=#RWCT)3ZX)wz~Z@1~Ob
z{4M?%=Z|XYScBqQ7e7z-SHzPw>zmU6rYHmx2*!b%YIMn%7;uJ&h$!pu9vqEd{=JS2
z=nt#vJMB<2mCE&7$AL#<{AIW&0rSWX#`<t$4}EP<r;fdLAM@f-p!42+TO_FS&WuQI
z=SP;Hi5aTvI!Ov$#^aL`uEdX!)B4q(!6AyInIo{Uus(x)TywMdB}cgdr-XqKrJ<1~
zODZY?`*ps|;Y!bSAAP<I+3fsEdQT3>t6ORvB0|HL*6_bot3xgL;IdC6y8{JAFk@u%
zLFD-5ODZYl3H8aRaqx_};AFuBiEWGDnT9CAotAp4djdTC{Mo|pR?zU12tu~=!RM-P
zc@JhAKGys~7#jBUV+Hus*7(>g8^wL`?_*Wb)QV44KnyXabZu>@tZwR$7Q;$lAhNNy
zU-lO`An3M+y6PuP_5hEmT(Bu$3zTkuq!cxJenLY-+Y)g6ukkofWfJy!bOSVWGXx(B
z)2L`_-rzAGJ?xyY0FVgy{639o1aDD5kC~l}#C-~c#I-aui0%or8#%C(;dXF)cFF>l
zB<B35Oznt34r)Wl{GNjcnV#O}$_gnRow)4?a84$k<cJ3<<PS50#p}YtRn=$!m}VlU
z>rKD{QukzFjqJATI#qBQ@glSFuD@-kXEWmQABUfxB$MWK0uA32#zR~8iDS*)4UhkZ
z9CH1@=H7AdDC>jo7JuE}nf=?-PEN-61R%*>5q-T$)dP~a(9q}L;D9Xw)WC1!OE$U?
ztENP5xx{_==)Rc`?5)yE84zC;`b=*m7M(pSn?!S3GWS-&(!sJ?TkH4F?_rRL5x-6*
zJC8`6Hq_rLF$jEbMP8<Q3xtNBKi^;bMvVYX>o6c?cZ5ixP-g*aH7$t%33zrD`$bEa
zXiSvj@Njc8{CaX?RS)!xK@~$<{JNy5?<v8CKlXfBgI?qcI_J!F#zS~+vi?>~n<}rY
ztgNK;sj3QYeXeAlke=F+obJO=85{&39(6(4{l1XV&<B%?TedJSv=5R<`V#`XBEicc
z^cVy*SkKgks;RNicYi#D)Ky<Z686N7?r0$n#p38ytipJNIplmsMn;gS@KJF<_TP4t
zyC4E&Md0+ZG9P^6OJODL413v?)jSbtIt9XETV!KMThEwXM{YM#gO=%V?G&elyeFKP
z2t!_9UO8tG?ZKE<MUEt3n%Muua&f&DQV>tBl*zlgf945M35xovH^+e$8_Zy|@J6__
zc|9^B;uVzRP@(f?Iv~B^x)MiibU1!MS0KOmsgc%wvEyLgNVDU#_uuaJfGV%8&IhbR
z;i2mD`-rD~$|Ylz1{bk3hEn+1eDbWRVe`@YTow>)VNA9Uc#SW;KEd=5ats!5Tu|ga
z<sbML6^7SIl442jKnij?X&n514%w9h_!;I`Wl}<`Ltv)&v3bXap(twWXLoYHW1z^X
zHI^pF-C)>f8tD!^51&7`?fvy}1V3B!Y{O3dYHpp(_^l*!il%U(JQEH+e&Eq&YGh<5
ze5UH^>RUH&g5`;$iOC0#jS)Y`m6x{t89a<B%;DY=J>PVH4J&qHQG2ZQXJ)2zh7Zbr
z4u%0e|K8`f?Gi|ki$*Kpa{B;0e4tXv6!ZPK7H(?_v^RV@Z<f!svwa_NehyXsZhZh_
zp!@f%LzRracjr6(KB{D|wD7k*&HQSYsY#6psdZ+!{1H3c?BpkMbc{^Zf3B9j)+nbH
z_W&!{TzqHPJ1tom9U1hDtMr!gF=b_2FaJvERAS)ZmZov2J6}!$T&!)ave4+8Vr{#&
zxjFMn`U0RUZhyKVN)y{7AtoJV6lnf3EMyGcp#hWBrwRqh^b$ak9M;Avz6PSKzHRfx
z;nssMKY30GyP9nux3{;iYL~aAZ6k<k+-DcZG#Yaq?^BvR@_95}S85kf-GCc^8vpFU
z`b1Q{QIkZo^e(6|4O!cM5T-$BMV5n=Z%kz5!{sG7h1*6_5eUPEb;FZ*+5FHVazsdp
zZa~3VpGnW^K4fh3AH72&*!o0-&(6>9Q6xO(54MdcDY<K5EqzX)swLacsf>Kv1^fGM
zbjjAXw$qFGaIOJTd*tUU@LA1JgNotxlM<+mVC=T~<Hr#hqHk}{#s<2c$uof*ub^P9
z-|T6Cct&s!20J?&8{1a)UZm=$%H8dak8Wb)yJO=<BlVi}*<ES1ApScpT=>w_({s5Z
z4BR$EMei6i-~^txh5N&i0@}V;7iOqR*b#plO`{AvFNo}Df$25TnzuMlLK=r}9w^8p
z0T@Zg2S(4WR#XHMrteLB6$qTjYxk_XXk&j@ysr*Au&VB!#sZt2f+w-lk0Aszr#$MP
zD%RMX{L5dG1bX@MOzmCBpnQtk$(;az^S@vXZct-?*4}VJol>NdZC0s++<(I*!IOW@
z!FUErj1$Qk1I~&+!$>&>dSH$uZob)ktLEsq{d=xKR8(}5lVn`Ac8Xb5n@5N$kr{lo
zpV&5SLoP)ptQ$*6k4VIb6%@G;YdB8GH0w<h@#a6XsSM`-QU4($H}Pv*8;VvrDmC@f
zK7Z@R57pI<0l@-Uw4|g=c!a?pgQ}aFE&!1EYE~(*vTi*4VFEnEZEcEY%8p_?%^1`W
zi05?eED{t~w-D_5b^U{b6;8&UjUzU<tl<FjY-kJoRvIQ#&Ic;tky4C2=C>U7<Qhj<
zOyQdXHH>gIb#>|fDJ=iWlBN-slwOB+k`l^|T}E(>IW3xee^hPH_PmdfBM1<Xzca!o
z3L=J9<C4{3fF>-iia=>#!8;8#+Qr31rcz;L<sQ_7UOV%SR#sYidVZIGeV@t8FsBUW
zO3+hMp6+!4*9q!TP`<$HfUJ2-*+m1{@{s)Z7#-MqpFeB^%MN-|DVlv<wBix#sddB~
zY2KDaY-Otow-G*emE^(l{2F-$1*u+OroG(#h-oBQP0i=t{9sfiFc<6fVdf1U67wnD
z%W$YC$=pn&;Ca(aSzl4Hv+ugR<S8!pnGg-5Y2oV<^xx`{$OzUO#)PZva%CY3M9Yy!
zTgz_@gsZi^w>kwyFDjM15OV0?)vI?!MSlPT0puQ7@#Xrp15lj<3I<cc)eUfxBU3$a
zbOt!m$z>T@ra_md>(_6|er{+84j>yZdFcYwmpkI(mvE(UBHR1=?2(m2@PKzj#}e>$
zFt6%WS@F5<imK^yXtJWbCeNzwpOQS%SO%k7$ZIDc;E8%WH7#Kyu{B6vRStjQ;&kSw
zFw=^9y|cBv2gV$ir|v-4wjiM7TdBPvng77^h^TJt`*(wFo=LzEKs(>8@-x)O#kbkR
z7_MC1%u{9}q#1~I=Sef|>`^4W9;TKgDsR(2ik!Emsy+k(GsLIrYH4X{U3M*ym{wvg
z3nb;z>Rol)nqRDF*aJ2Xa<YUQfa4NjF1hiR4PXZX{?ZSs6}=YwFcS^uKZrh#zaj~a
zSFqgQ-uX(9rmgTo0pViGlTJx}XjVn#8)v`?{I`3&yzNE$py7~+wSW_!E@in|c6tEp
zw5+NhpB})Ti=`C_(>6wY*3~#%=_RqYUN5AL>$L#wVoZclj@Wf_@*?vYyB;pwG0LqL
zFniFgvdT8KN6Q{|9?XHmArHqKE9`IjxYFM%|0#;rB+o6Q9C5BxHjKe+LCCsdapYRp
ze=i0rLgSp0lyqfjNv+~aHr!d-Q&3M|zv%5-fOnuM<txHThvqyCD?^F~dl0dhZXARh
zs0wCXDo$hbN>AJ2C?jGaMGQsu>R34}zCX$wj)PG74ZNa;i*Kx~(w_PbfF>2acmUSL
zS$GYB&DM8bZdMkSld-=`AXfrVB`h46=m5-Xx-=T31_*E8-f6jeS4%M-9F>-Lb{76F
zgUZ17de1(f=q+c~A$EaHdrtum6%}Ps;a4m){(XFX`LDnWfMFpzl}%eAo%8lHdDfsO
z{P*tZLt7Z~<-O!=&?|+=H42LgZmWgG+s)cCat=K{$bcd1KysMzZC-MHwoeRX(Tluj
z@EiBu$PK;(bY*ySG>gx1W<ldDX=L9mlLqnY=O@dqC~1#R8mHG~!0$bY4(1gfFwfEx
zL)S&p-`{`gLiF%A8Zpnx8gwB8HtS$kBk_P1S8HO@EQumVgx~V(3qMh;$aava)X~$M
z5RCz)>sJ$9*UN&?3e582s}d8HNsOAA-xd}5v+H0<2QL2J>x_-1Prt{Q(yJ;O`-EAJ
z{}E&_n{DZX#l_Io2Gpkbq6RMcKM-}mXEh#x>43&P*ls|_a|a)C0Y^Red36nC<jl?;
zfSjQL!It_Ju;v69?CdRKY6d=OsQpCyATGkh_}IR(oD-3uez%2+GGStrvFw#IHacpn
z|Aab=V8l(t&x!Z5Yev7*rNSWkeCIEtrsR$B*T&GAKp+Co@7D)Z8ny3%yn&N?VOYvN
z3#;Gz)S5eVlW@n#HMGqsWQ&>i;d4g_AGCg@5^HKtV?PsGg6?!qalR*$876mzG;5!@
z=ykmkjkFgp08%Rs)B#c_ddceV-*f;lfy)TgUTcl-i(k5+V`4Vc*8{nf7BNv_T#;AG
zfbRO|x8aHF%}=~22%Zq}sEd)WF1&Kd$G2;};^N|zl$A}_y(k1|MxLbmw$Rh6x;71R
z-wJPW``A{GZ_xA|uAX1AtejlOJ7JSa0@AoT#h_CkRpr#%Gj^_ki$ZmF=T6b!ytR4N
zw5sMc1Vehq9j%{0ujk)Ab+6R<4s5J0=eMBO#i8?IF)fXtG0;8G-Y2|Z3JSfaYY^ID
zSm&EW5^N>-O{i7?JKS>y5nyL~`@U7TEP51INL9$Wj7aUaWJNqM_M>zAS5MBXtX#y^
zZdO50?Cskx6B9M_&*OqGPpucfjxWxUXC%<y8|gm3IxIn5a$aaoOiBX7EtoA+pR2cn
zNtU7M4=A}E$hos*>1%%zC@_ozBiO({nZ-`xsr&#1)3i52`j$?u_`PF|<vvR|_0LSs
z4zp^Vq=GK<>fFVv^<j8YBi0o7=CxMeuBA-0md$^(RD>ciy%`5tNZCK(Uz!3%1L(#B
zMhYJZCPe{%#=*r!dRIgLKC$c1xQ3P%)Brg<zdWv@86^v0?yoca{I@sp`s4j8jT+Y<
zUt;U-sDuK1xwHjrUV>=#b5#!yK6YIK1I|PG69JEJMIZ4ib!bRQJ?#6@UqA8g&p6#b
zshkO?pr(e8e4#$9@{-T?&mkp{x43eg>9%JRCNDZcZ!X*{sUvNhfr!ZwKm6_}0wOw>
z!RMiq&ekOtu<g+G{-)x-dm6Avg+SQYDl(NSO~}H1(t8f)F^rN}SutN;7vtgrj}BkR
zen23`?y=B{+bPi5&{5&dKHMJafbPLwCRc#?LTBeglG{B=boWKHlRJkC{SiM43UdBX
z&NQq0XG~1RuF)k#am=E05Yyj=5a<Jhl4?R2;3A6OogD4C4zAzM)s@$}lVJBW!nv`j
zJatNHZ@<+)r4GlIBXa`ue?tAZUaD(opgYbWqEI4H&GydUOED%|olLJk?_Gh3K|YBn
z;C!yHtw~MEQgCBA-6=hhmPUS>r=_?kARDPai<s;02UYyUHWQmSO6{O;3O;aJpkoed
z2&$?f&W8sFxc?piZ_Gic9uIl~1O1U_c=hg<r_1_#<@$m(`Qy3RKysG=O{R_q;d@eZ
z3I)V0FaC5;ve(Enl{UDms=`PS`Y@)+>b|VPXjVps<Z;;_Pea~&;<GL9IRQPR4?0<5
zv!+IvI6Ln=OGn!n%qu2xb2sS_nfh~12ny9v_~&*!4&OiXc%ItrQlpg&o@t~#hRNKr
zY$^x<wp5xsE(z3v1EZrP6=cAY^94KwV&C-4OzHjCNSqzf4m}*G<^}ptLcm@TGP?#G
zILpi6c6x}=a;c0fA&pMI=Y`+#{`C~l2kw#DdD0$8$zmIBGFXs2?QL<I9&7M+2H4&*
zuu1UOJa&y2QLyPq-p~5?s=z9YTjpx^*=wJfseh(Gk(QR$@|Z%<emH=^G_N@y><G?E
z?i)AyJbUqC1q_XT^cg@y5{fa%2O$q`U2t^}fLJv%F>!2PZnnDTW8#3v%xp55RB3xr
z^m6FO#iy8QDbx;bk!+RzDM00T&d+|dsJ3@u(8sY~#}BH66#b^L14UE`Tg{ae2ezh3
zsc|HXr@%IQ*0&0)6E2?r$!=bLzPY<y2%im16vMw%efR)<Ta4U6dLT<|OM{*Q!0XjJ
z3!uf~bFhY=_3hFp)N&N|XIFQ(zMkHX+lE^T%F3QI^|XcG&=H?K_KMB@fzX2hL)fq1
zz8&rF^Kx^mD&pab`0lT~?T~A0y7T>W&}DjL1l0qWw_zgQSWfb$@&uOG207P*mP?n5
z&2sT32~R@oL4lZ4?=;zb7$!99lFd`yOuNra0C<uq1tj6WI%SJSZxdrfV*aZ$%KE8Y
zf^uq<u4igmn!pk}Rp1@FW-gY$nvjZUY9hT=fYpz1adjP*k%TK+Scrj$kTRX?G6zRC
zcCyJPebw2Rx3(=A$&?fn2^2gq-3Do{A7Lh}Mn4gY7SL=hH{9SeWp7SyLO$-(<>;B&
z48dt=da3~26zhtfnC)*4x%oyuwK|E-f0Gkwguy{kEZ}6wHUT<RlSKn6D^8U(1ab^S
zNQg^d_e(=(&}zK$Lz&-XiuuDQ0pkBYM&YqjrSVIbfOI%<ArU-+o)ILYcpV@){Bw1+
z+UgsXGP;GmeO68mxZdUF<cNdb-OM(4+&_kMhTm0IbI9gX@SEd{y>5D7lI%wf$+AY+
zp{6Qadq$+=u!uXP8NW4Jl&lvuf;?I(q{kzc$4b;8U4#Z4;3_~mN1B5v2*IHak}yzX
zCnP3<sVE4zfrcpn3@IRC>GexT`IXv3GBPsIF5z(71h~uS#8G%^O3G^OZ+5N$TRd<(
z0=i!)+@2qMcS^znOUmBFMAgX1$<9tszxM#>0C*Vr^ZJgyzQW1fit_Ts4c4P@D93dE
zm~r*1)OlBDD-NDWPNQB>8^Qx|vYKwp=OOz8Mm|`TKflPynFlJ<PGM-N%*|6LBit~C
zn8HT|BY;}r)}N`Wli2jkeK3_iNS^Mw`Bm?~0u+FM+sh5Or0@~$fxZ%eTtJqFo*TS(
zfvfK^n>y@d;<_uMuo%hMbzE+?AR_qX$T)|FM68hjaJK7aU$AnMZX?ZAFsY=*X5Y-W
zW4tpC>ayhA->a*hAU#MBeGmM@fWCKIF%2ki&=Z9Pw<P*&zG=3<|DAe~3Ve+Z4`0WL
zR4-KW1=e><OYn9MCIL-Pe}4e{Z&6WEL&G^}Bmo~DSo&}B^C2^Xfd9X%z^m!Or=^^h
z1^|daZgxj0gCSKxtv!^l8!}Db_Ayi`f%dd*l3J%*Yb(CUw>$^FV<33|OaB_MR((Nf
z0vc@4-s|b>D{IgPJsKoU;rDmpMjm}QEavVPDP{iH+KSh<0L(19rR54Bc~1`y$gvTv
zBO|<wjCl~>A=kci5G$Qu%7C;NN_W@>d(M!NCDdy(C?6be>vPva%j21(wl)(nPiB7_
z0i<6ME_5>&!M0YhG9O)*+Rp{N7>X&`%XJ&P9Ji&%9*><;@Y|6Z37A1eI5RT?>xrF=
z?bfaQdA9}d%2=AK+q)8IUI)dCz57)kKO!4Yti%SK+NnRVavuvp=0;CKl9HPnygkDo
z5O2Ua+1XHjt0$dW$YpWH7Z;R3+rzH=)w9e>!Wr<?a_;3M-{$n(b@&ukyqVyUTS`iD
zPf(DHjSZ>*18D8T47{r9)97eSqt+@;m;wTh3?UUC(gLBo+ZuM)(eb+`LPtl(i^VoH
z_%SF5`1|@oHyavY<HiH)(Hh9513g{fM9g0<A~Wip1?>wOtS9O3-*0a_E-CI;)l&V0
zRJ);|0}0CssFr9zlMQ-q<BI8*yB&mJ&7`gE4MRQ9i8($vFt5~slHT)=F1X`Cm5jXF
z4WviMK+`w}ojVv*KtWPW;cfgmJY<K2LrN4^lP!^zG!7UhjpkTE=IhW~H}>PjSJPhN
z3JeSs&%H{Gw<?YKu<E+oZVX*=4qACMETa?Pyu@WxUOzUIj%;}u^?!iyc3gkQ<&)zv
zE{fO^VfOD9Xxx4HFx1%zEx^u&1><~EMkt%G_V25CrYgV`^1qB=2woFuE})_DcK8Ft
z82FqyfLWte6mNrV--{Pe4)FyDMKg>|4JFIw2M&{FR7CYC^FCrCHZ_-?Gs_C;A*X(3
zjyA?|+;{zyJg&a86mX8{<m5qE&k&Jdo&~9yn3@&Kr(#&e30CAv%(oo2q|8(}+qe^y
zm`MZ#z@fI^m-rt+6rdS^+3-5BsI%bLtT3<<5s8T($f<K&irEB+AC0#~>|{Yw4Rk2T
zJ?$(ltAUvU*Z%VIe1{5uE53NIqDCr@Va-3}NePpciI1WafL~J)Xb3E7YSUnlkbT}$
z38g*s!AK-((vsew;6$QceHnRzLKJZJFQS=HOq3gnMoKM#oy_85>#7?+6&YMHs5MD6
zcMgEPtD~<!IW|U0PF`z22@)^TRQN#OhV8o(3&|v`N6=G+bRjiW6D+k|Ts|=8OYw0E
z3jTo_3DzA4?Z3;P``WHOPlFX$^Sc%JczCMjd(Ja}?_{U5Q@d}KZ)a3Ocb0>e0C}e_
z)IACQCsiIyP5+y_N={Ce?KtC7R8nH+<opfVZqXgXLw7IXKmqhtp2@}aJ_I(P6Y+W6
zMBdFdfMj6w?f-_`(L%sP!FyxmVan3QIy*GtS`4t?E1UlM+&zp5tp|R8aj5}9IrwPJ
z62QeoaNEHu&zBvaBPJmUCnn&3F3)ssY|(m#k3bmwMmEaB!uVX@fODh~p%FGM&0<@V
zt^d`~m%GRX^|qW#UWiB;_^k}Y%~yr>FAF+HhGGK(1Lx-E;Diw2FnkA~ZeA7gI?Hpk
z-=_J$DL2I$tgth|SMkmJ_xG&_Xl}lUg=P+PbE>MVHO;Ywc{6FmP5-_DqlwR&Et_#X
z=YPt!dg$-*@TmFMO+hgT0R&>z56c`Vq>0{=EzMH^QQR4r*6Zu!WMo?4!)Ij`<vcbz
zii?NW=)SH3R45pf(-Y_c49t?8E7X;3=i_rg0Y6Ots!s->7+~5D5M3c7y%@O#oEk!E
z0jSyD!4%<w7XNpswEM*Z(AnAV$Kk+2jaKMD?mExwzme~_4~`TB4J|DM$K6EeK_Nl_
zx&xeyY)}Bu#gF%b+g~0L@vZAQ4+89jCJALEIKjarwSmE_$+Mx`Ztt#YXd!?*wC@2r
z?dagp{N4c#VI?aIRW0`|7S|OxA%CV3V8VT8Bx|2rT0A&~#vBm-m~#ia1#!^Bpy~jB
zcZtiB4(O&sO9w$jL6MOS(|JYSa41&4qUzv=sj|V}fa!)j2);^8Ba6DrW!x;blpHuz
zN%D^HXks$5fGt$vns#lxzF>*##2p@gR#ZfksR1W>k40+=69)$ihDUV1D<~OmY*c%Y
z=E|(ZCw_PsvMXVL9$}i%+#IARxA*8v<J4}bKeP+`gLsS=$eCy$D1tcgC0wu3(b4Qz
zuMj5CrLZH6`DO*%4OCND-qDttRO<Zx{aaR67Uaf&$mP?1or;PzRpAlS^ZKurn?HYo
zW>4`5hnHXU@i~F)1AFzD7Ve)q{b0LpdZdR;_;$YEP<&lVjE5IGsBZ26O-vTjprDqD
zB(Ny5;S<ko8Jo3V&M`Ex$0R@Yr>yv2TmY_wnRC(Wb{Q~E)-KRHHa@<3aQfx%J(;Qa
zM3o|Vicra+kx;%6l_EtZVzZi%$;ruz@^a=i33O<RLU`-o{`;-?6b5DC{L&*hQdZ|*
z(gA`lP^w|(f(K9kvr8W2n!q^%yej`V#KF-KF2MHoHvG3cfl|GJ2LyOPp#hjOkmsFr
z&3rBTkA@I8X9feJ7z=gk1_r-8H!Yfy2>>GPCosfJ(|Vd->#{}W*_2OIS9FtZ7Q|7{
znC?Ky=tpzk!NNi&!sZ6fHPhKE)J?MURjhdqvi~RmubnEDhkneNwJ2u9-K_(6WjD8d
zKsn*=vlGWcJOmyf|MjN^oXIgU&{Y5i%H`!HL@LNY?^*Q&As9aPnu`!L)ZPiUJTSzL
z{;#jCP+)xa1s0mJC@~tXXxp4<t(Q}6WhFABf|0~j1x-a0M5|R5_IX>9N)dEmYdsk7
z|7Qm}m>1CAe;sJ37P>5W?~>vqciV4BQngy}cAD|<ER59|IDt{>zcjqdtdbi~UE2ZF
z5qt9Wj1x<}g<yhUTeMP#ed2pDb*54dkWRO2kr$C85kMKcp9$Xt&Awm(&I((1;9&g+
z+QG>WDrCfb-~tg3AYMvt6G=e5eSCZX27zV7Pc<Rf4gKR~%N}GPS;;D}Yp=8!eglC3
z{F9_vQ<2l+xqNX9r767@#S9e4QKz)hPCEEG8K{qda?Y18GEfjhW@*dEzT*D`q(gye
zMbg3pjaZPe`!D8?pdt&23L|6`hbhaCI`1G41PT@$aJZ*rFu4M%N)!ZtPPc0pHp@Aa
zfFDmUMl<33xz45+g1#QN6f(vYKz9BjHPzS859+=1lN0cIIRiS)zo&SwSIs&Qj1%EB
zU)23x-{BC3`b=!G5wG97mpqTz0%i0EM95VNV?{Z9wj;eMcc6m{^9T-3PUXWNo;s@q
zJiI|cg@W+#?kXEzjv}Nd@W08xMxsKOyU<bH`QslJfdsw)d!76K9Wq*If$-pog8YYU
zhg9=J^Y`owr;J#UE^9A}imbPf0lj>c#c$qrP;_4J35IOCeb-{(AoxU)iW3Yq0z^lo
z#s(Yu7&?d1$rc-fgYT>Q$QB<lBN-VPxS62T0$BgtNM2sMu`=vQ{2P~cR2nB`*s4oh
z%Br@RH&YcLtt$sfTR4BCmLD{m!b$KG0|O4%5eChzn?R2MEOl;rT3276dV!dlx~#Ie
zS;;ZE)(RLo$VrwamJ$uS6yrjWWfY}Ha!Q~Jw|NF?YS06MfBRJwX-WYFv6aKOx+&21
z&insvxcQjlm~|a*1BM1P5_k;RuqYRnHyH6zDKilVK9**M!}#iSk@58tvg6H?Jb%KI
zr#L*$am2pzkt#)?ass9FRKs`pM`RKG6eql`xTJ*9$^lqSW8!byEqKS>ik)AUrpN*X
zZmv|5?f$<9S$s9n9uuP?`HUYv>>RwKB6Z?5McQ2b8ZzXPf?HB>r!O1+{|1xbj~+e3
z&d^7QXN(j&f`^@=Tm;;gzuqN&(YHW27S3N578__H!z)Vf7=4)pxJ=wR9`3E!<8Z}2
zs5cxoV&D~?UU6!Y5Qt<GAVA&-3Ky6HfY|*q0Y?oDCz6<wvhHKOnVMIZgxWbC9-f{E
zGG$q5Q5<KyK~J9xZ6&zNc(fvrLBoKr4CJqNc@-5`kWANlkRo%bH*W1ncTs3m>FMfj
zgLE!Tq!;^A|4Z3QVG#qz8E&s(OHdQg6Aup^KpzFU3T=V=tN8fw&Tu~FW?eXZtVQ7F
z@Bj}H&Tc$P-Y<iLJ3tM9Vd4oJodwD|gJzrIn;neO_?QF=N=gnJE__>%`pugzRV_4b
zk@2m=IO%_TR|*Sitr*CCzkY3xuSld7ofsHUNl^=Q9(X0}o?(uaRT!?Z*<dl;fS#O|
zBp(i&1FPj4TGRvYA~+8k>faoHfDaY(6&o{B>Mqn70s<HzkiI}FV#~q+@4|6b0BFai
zrfT}LKqd1Segjj^vWO2RZK%qGKMm^KZZk7OH87AR_?ROc6<DGs=4c4Xg0M7@=;+eE
z2uSC~LyJZK>9KgA!>*+mjr`n1VUVkXPCbCXXMc8*O?3d?1vO2raszAxv=h{K{QBTz
z*4NbqxjuM8kRi^<%tUF0c@zvNcL7)6&-tnTL|wgG?sv3t8i=0_{5^%6;S_Z1IZ%wF
zIgL?5M;Rpsqx1jJ0vgB-y)!npw6H{u#sHo>ZpQz|djOhpZLQ#Cb#%1cwm)~|o6YMQ
z5WgZ9BD_63`r&GlBtc~h9Oal8EIBzjn6(WUgy5*9sX4rH2)50|t7Aa91R&+|6!VM_
zotdP+pQPFY@V=M9qDYAVK|y2!Tmuq?DSx)jxhahO&|z97!aDGpkHq`>%A=iytHg$L
zpxOXmX7*$D?rxCY7YD{rQ;KXAV9UugPxV;vn!?pI6OixV5`f*f5|59hI~CR)XD*Ow
zj$Q*o8Vs^R28d6|y8>AZY#12#1LP_|oW6x{(1I5z1I^Dm&A=O{1WrSM<zNT}4s7%g
z$XZ}32vW<Si?fo|F(6q%K?Z01(>)vmgFIQp!$`<DnVy%b+fy$gSs2rJOvKo^lG1$e
zoXb;(kOm$d>9fAo^Qn2z-%^DD{$30v^)oI3yp9+eYM$Uc5Jafyg-@_3J*XpN_}|&m
z1ccK6eYSv*keW0$CP-G4l%OC$&1aTs3UnE++Gl6aq5bL8&I1M~0p@T(9SrJ2vLWOo
z29`N`I9giV6O*#qP=Vb?->{IM%83IYjV^f-nGN;KnF-S*Nis0}i&48ym5}hoeCGgM
za1Usk5X|D0w;|KmdFFAW<ZQa>-MjwL(R<?J^WEJ=aF1XGj;Kn)><*-mh^GLY+1tlE
z!+enM@h?im%Id0ZanA-6Ol@6cGc&FI3|Q0XMFUopRGvSF9+0df!`jm;DhN7ow1XZf
z_<6eRE&>y%?IqjKm9@3B++2DfG@6(|(hr12pf?UfeK0jO1<gcQParF8|D*9LxaIn-
z@t_C&Sx=E+X8)U-r@SQ~3ec|CZ}j0w@9X6L1=tT2PK*-sWKt?*@!7OS2j)QQpc1*|
z1T?n1<vWO|2Tq)%H>A{-P_?bi2CV+QF=ql-YvB?V4tF6$h(du;qfcdVF+7Ji1X74&
zcEa!Uh`ipC<aVeT8qOgv<f6L}tYWyMFeS(rop1a3|H*g?kA-PuGUOJ1j5QV?Wfc|_
z@Y@VAlxWP1gu=#V=mD!kTybQcg8qOinwa3<H+;YSDW0^ked5cN%4_9TGL%ynHN%J^
z7rp7=V0U+>5E~~l>_a7r8kYcq;TKHBff{kfwcC5~i5ZnBp<Dv0#tt%<-Ix4<gekKB
zo|sYSh1Wks;f*WH5@0qDh-8*k+yA|MOjlk68uGP?63^+hO2)h@otc3tlF}Qan+$B!
z9*d92^4rJX&rD14mYctyNH*s)dH9(1Q!f#Q4$9JyT}$O5LSQQLnLIn0B1iODVIU!P
zH1wVNTr-pkRauzUa_<Rby)REMG4io>Z-*r{vIf6+^VxrNL^!5pZeAAvj7`A0YFdmg
z`9tOP1M;|Z0c~W8NO_wEbPAB~4*eA8|NV|(H~c$6sQ%nnqPl1{CC1h=jJ*+8Tu{Jd
ziXRh^p&An#k)ecxU`Rz3PkyOz4d)s{eP~0nh)vhwc1nQ>cj}CYg5mwh8UmuvrYOke
zGY8LDX(C2f_b%eFJOY}_8hTQt?^<P~{xU|{$=L|KZ{;qiNs>ywCCIHoLDh?&kQx=G
zz^bgQb_;nYtzSpvN1D0`ZzwaKDl^s<hx{!>EbocW%RMSI@jG)9&~GzC3okL@4(V+l
zy>>e?OpOJBcyhOA^|gkH84svs^YdrO$BbjiH`*m=<AWcW;luLso9pY15-hPy>r^aC
z92U%lIQQHm>DITJtl*lgv|!$*;doHOl-_uI-BNwu{ZYli0%6jacyZaCbqW@|#1#YS
zYu%D1Wn~ujmD9hdLSM%grSv^&w0l?jT;n*&Ku^j~TZ+{`I<|Ag`!;-yb@w7(j}52l
z);!a%P1V(5%j{okkwISk5|B|bCS*>3=3aGIm+P)BrOvl2YeL}N49_S{eAO79(Qq;=
zy!_+OtD9Fu2p?5(Yi2A;LrsM>K-pJap0)o@daBvD=9u+e_>#QYt6!ZqJ=6E=*RUfY
zJiCmcYI~k*hJi{DkV1lGdwbi(U+`!?zl)>pURO{()~+}F6<yxgzwW72ZvN1E4~-}q
zf|*!t-@T`<qDQzweq3|q4XT?O%Ej7^y3l9+<xTIJ^20hVzAhnG`~qbn)BgoiylzXx
z|DBSZ?PzQJ2uOj`x+Gt}evN4Vlfw7N^LdD$BbHqY1dV0d-9&}RFx`DEQPJ?!bm_F1
zy=YRrsMfJJU2hVH`VAfwHU?|i+J<9-Z&r!M4{BZZG*uQ2*3_%X`Rr&pwN-z#t88^m
z2oBStOiU<viVTui{yH+cmg+L353;XYj)}H=(meY!(0}jN@>YU3bSAA-t=!ahezva0
z#kF$(Cb;=J=2sYc0yfI?rx6Yo+f8KyzE{OeUx=hg>Pr+(0uPrqN;PsXy*vxf)76W%
zezNJ;)cxi36Lk#o@NE<r43q1AcAY>x;N6%ixoa~wNLiJala{hYZYrE^aW(E?4+Pl`
zG^6pAjf{M}tQfaTWhwzpf`aJ@6wa&KZv>M&<IK;SUHi<k()1<%OgM_r5Coo~MG#3b
zlcov&{BLFpRN`E~Gynig80)DIu!M7Ts}lqq01asY>j_98uA}EICmvx)_icMg?DyQd
z(>$Yyg2ng(fg*)9v^MC96G6lvi3lM^T%-_(AoAXB$i0v>HI#n$hMn#?Ov*jDJ3Q>S
z>}j+B#w>vWg}XYwx6$T3(&@#CY{$hJmA`EfDqu58;%sf^ZG^t0(<Uqw9C<+!@|^yQ
z)BMMRRr+@hc6H5p8xQoWUs0H5P5(*rMLSDUQTU{#tyjioPWWriz1hP4_eQwo4H_T!
zs`P3l`Mze>ci_7H4Rw4ORdwVOrru6{Ax73^9VrH<eI~J6c{z&wsHFwERAJHaT_>9W
zt8L>u$}dUx&CwVmhK_I*+tgH5fp9N`=;`SJci*9{Z~?aXxHvs75t^h|uU@6qy%U-L
zToC3el1&zG3Cfw1xu-k9SN>lkf4m4PL9~Y68xY3qs{5MxHRJ2&(n^GNkmjh+daLlH
z1b-+Z?}aJ@>g{ma$}8thN8R37;sONG&{S;c=B?Emb(;KC)|Fbmc6QDK50+oK7V?(!
z(3!mK`kZT9+BMdv&m2K~^T|!snLDgfZ#CX(7&~YpTmzplcL}c>EE`xFSiY;XEVNUC
zKLqW|Y~q`$QrOqAw@Gf?#nyNmLCm^YfMAS}eBXU8A-vF(8|<az_G9Ypm$z8whx%tq
zEd`GY`kJaf{4}<XFP#&|Q0Az8fqHg@%6PM&Rz^<Y1<s4O2vr3ZMucQ2!i7L)VKBip
z@c>~?WzK4il6Z|mopMrWndUGpG(=MG7~=-k2E3{o>l0SheV499egE738lI+J$3Rlk
zH&Rz4y<4#Msw$2MD3JY`%0%e8X^ASs1Q*x%9c$7q<*SAX1>CLf9kgK}^1e>gXB}IS
zrG<5Dcx>KVz8g2f>m87l>PU9uVI5eG0PTUUzw1l@rUgKPIyNRIgWI(I$lA%qhA0F_
znZBkK1<Pds+x|?|f_0hII!Jj`h_LwO*SfEqo;#1_e+vhzUOjNFJbU?3c9MKuksw|3
zgfA7Ynq3|$qvYp#T13LZ+SP+)_EtS{8@QFAz&`QXb%4@5f^RTL8GGsNse3kYX~l?O
z^BB81l{Kq*P?x4ME_eAJ{il9m?nPGa0U@*j;Tv~VLpN8GUP~Z|k_&@;n^*dNjLfTD
zUsiuE%goF)<w%H68xVZ<I<UZ!p8OTtJ7<kxicI&R5cjS_ZnDJ)oHO^Lh?uu^whX`-
za9Q+w^o3&5o}dX8*QK1ND|qVT%ttn%pZ@vY8cYb5H!Rtr=G8{^MzOYK_y~rGo{tVQ
zg9!I#i6^kR-W!`8ynZo4El$klv-VdNlZ4(Oj6w1z&NcVT!UZ4GyF$Ob8fLdK5Xs%<
zwX?H^)4v6@?egXlgD<v>w**VGx+M*gK?&%}t-}v7ko7gJkc6I}E_punbuKE=%d01o
zSBq)aOn--ronsW-emI2^;(9rLIzosukp1qRRVyXtK;7k|{!nT6Xql}vTlHm0t{X(k
zf_f^Se@3%9pAjA!5nZ@vpsl|`{0ygisekN;!s#iD#lfL2l{KJ1go99SQHT({68RbY
zdLy1j$Yq3v8jEp3OUqF#liIfOJOAsQrtp!oP8sEmQG2&sBTNOZ#`C}+mDSEPIfwa@
zZ{JW|$|w2G?u8UCG_R#?i5XqcJa@5mRt-hHtr0Bv=EbMOZ!RH)AHK!r&YgDd4|lY1
z-lcXMY=ZorqoO3Us0c=N?HwHWtOXk8es3Onf9QCmMyf_iJ=>s_xYRFpzo}!18*1D&
zv8lt24e3-^&GFZ`K2hX^f$R+j^sE3A5ES%=qH(+YlMZ{jvwH!V$h;5}kWwahW-hv@
zonOJ+<k4$+xiZs?Av=nE852E^FMmJx2&thX2+jSezUNK|v!NlH&Sn27IC>YGZ$0cL
zq;|L<;*h?pAH*8>rnK~Z@Ql;)dwr+r&qs!w^mWq@>>XVOj?$75G$qB<zYphbkPG))
zWj}q+$9Q|gKiOGd**vLlwD8+R^1PK`%}-Zdy#e8Z!~La7Z9Cs8e!GD|I-5Z8Ym5<3
zOFkWRhHe$v>Unw9EbKg1QZo2x@hB+h61e_p9QqS}mRXL6qUCzkn}RK;iQ{&T2cuqs
zPS>B)5g=j`6Fo(;LnU7?e}k6uLXf%eVQP@3YWdV(^KQ(j<fWxw8~ob~C=>NmeM=XA
zzwnsgAwIpBZ;nl=9t)CPN97JAlnV@O60XWTHtC>C8gLb&0dD_Vztv1Q$I5*gINTU*
zOBEqXyN1tw4hwhRI9E?=YOo2UMYv?mNq9+_tmtq)V#FRo8NJl|`jNfwW7X%LkHHm(
zbFqaV^B)^8yr9%Gd%iUNVeu#WD4m0jiw6N2O?JpNIiW>Q+_2lE<X`>8Nmw}%2o`~b
z&-!S$y1s`z$yYStshk(~KEE^XG<{Xzu^FKnFmT*bfr(*#!mPYH%p+SQjm2mo#x7Kz
zG%?xRaCw_-1qDI=1tdQ%U2CI`&=tr#-<UU6?HpSxzi2rzVDCw3?z?{AhGyP%dgpsm
zDy0<G+zPxQEH%xIMeVe3V-n?SQe%vn^N-YhG(HX2DJ6Dx`jfGfDa5VZ7mdU%NDDuI
zyW<w1ZjFI~&m%%5g~ixm(&qZm>_HA70iW{d5TD+XE+h!+4O-+&ds6CE=zo_ui_P#K
zsb9Z0GG!7NT=rsSK{QZ6r7w&~O7IT5(98n^72owcPl7lRE}d&xV)vi&pep%#QmNmP
zq2=u#4}QJ6#l^lrLyR*xCD2HfAv7)Mg^Cq6yhH@=TUE#9FVXsw0UqB$l^;h4#?b5L
z0V#zp_QmV*EtxL5$I96y1k{Yd$Cav29Htu-d(H<0t(()H+Ia9m5+VrKUV>r!L|({`
z2$6AXUerD(ZDjVR%v=On?qbm5^^Mv5eR9#ruWqwh73UwB&){Mi(qb`sq_l=DV}4r7
z|5+<{VSWdL`Fq!17567B>;#JZV_K8By=OW;vhyw4V(v4dJ4R&W&K|t*Vy4#f(@}VB
ze#{Ly0bLTdI9k-E?fK?nf3A8&*PS@~q%7;UgD;tBCCofnkiQ*$O-|pSl9D#>9M>Yj
zMPgq)Z6vE0Wy_kLZB~#ZdG@C&7Gp#^7A}DW9vSVj-{SR!FTIi_I!~<^P6YEB`~Um#
zHTW@&!bPo$m&6H6auOr{(O3Pg0+Q~80eA%#_WF($N4n%YOxrr|wOSUO2ks^@J*wa2
ztBLwoCABOB8@~7X?lC*7`jimC9YET!WrWpPFm&yKNkZlUR;}K9m|=T0t>ph9_Kp1A
zrulFVF&VWnzHhI%ir=spRZRZ-(WB?+2*&<G79Zb8CpDMviyIsGrZd7fj;76E5VpLQ
zv1_HmOP?SR|5eqB(QkkHmC1|q*s4yiBwIp(R~$9&E;Umst<z>5D341R++UA*pL^`g
z;nsCZ^(nmY7JN?RX}}mk#EZr*^fVS7gWy<f7mGz8?_>M%gx?Ro<%N%Mg+x%`5^%zf
z^B?Xos2qN2H@lYjcCY^N1Oxmb3jE=Q4R-ABPQ}mPRO~rt=BMSAd<W<t4dZVML(9Cr
z^=vU{_d4D6M_YC;9G$P{4*ryZ=Pbu1qv?68W6<co*I}R*QHryA@!sSsmj^s04l(?w
z5q`9u(`i5y*?w>0@qBdo)eWZzqIz4?wooPIt?_Q}_4|@BFS5)}&lK~Q;g2?%;C)PA
z!@%ED&Ot%!Pa4bLY2g<kkiyCvXbnU2M7gXa`J3N`8q&CY>(w$oV?_N;b|KZt#Vi4R
zXo=gD?$49`5Lm8$dR+?3bCC)sNpRk0IrdHth&{vDHnuTC#Ee|bp8@|&@rqv(3!Pl=
z86s3CZ(`KVIR~H50*M^vONpg&0*|M`AhLP49anosXY^CLkstKTk2XfF!AfDf{Fjd_
zfECaaKt~jfW~Qd{18)+b6J{nRL@P)Yf^lxed2D`pG+Uf^o7w8O7TkjMRW6A?%GtIa
zH({l<;nYMg&v`EZzr`My6^;V45XFHQve4EA<QkZI3xs(B@OA+^vl+wby&r(X0<i>&
zfz1cuNIA`h(o#E+e?Oyo_dojj>aZx^t?P%9K@<jQ97;hzN+bm7k(LgTP!I$}+Mz+p
zK~Pd9lnw<12>}IZ3{dF~rIk)8DS7w!d(U^B^L^*~`~y*Dn0cPL?|tvR_F8L8INO@W
zD6PDbCTh6zBPg6ijRu>yfO<O)Oi#h}w|o9_1%1PfO=M(b(4<0QA3cIhx^ww!G~;IV
zkC_>>!hWE;1Gow!4WPFLaJBoSde*WM>tkjnDF)<~w0sU@K~(fT*c!ZzY5G3%FLR-P
zV`N~kJ|lnV1B5&Iy`heagM)MK!;TV&F5Ra&_qWxhQ_2TIE4ACB;Nd}qNF)o2isFDG
z2d!ccNen!PBq;rGe+F<=Fl%G=svy-t<ADb2{I%e+o`xVpL>J$%A}lFTs*iT6)_T3Z
za*bjpVv*g6F|im?uhN%5&Mi^0(9_$vKiryV?5Bv+SJrFwDWSmv_Xx69IXFS$^bwSi
zMAKEZ;ko$`K}mpdrN9;eE)IySU{4>(CR=qcYUw}otF=>zn1sZxzuZ9^H$VSnmz&Ut
zgszt$Sy;ymEor8R(i1;`nSEv0jEpXIo8IDk2ztdxNRlHyol<|?;*ni1fnC0S>tK&x
z*R658!|R}IrA}YM$f#|n065$~J*&kHsP2j{HJ)iH<v{J83?2Ghib0XW`~LLtW2Lze
zq}NN0wr@-+I5WSh@WmIcdUa`W4~9&Q9UhDwF4X@av_*F(vt#6{ZhW}U@00@MisWSA
z(xoLQS3~Q~(-@4)sThdHL9hZ4`oKyxICr<Uw!(vfKn^JC96#8s=Z@tSQJK_N<=9UP
zNHSSZA3K8z;eB}IX4%kAN_G5Fa;UW`$GgOfTxBDUNjcBfFR>zqM&fo7a6=z+>1k*s
zx0Uw^&U`N?gg22U6ui?jC@kR?7s=h9`ow5~k?~%I*4vQ_1RfLYoQst$fDJ}OM8Lvj
zWoI7)q!q{;u*xL(rLU6)0dbE&S9Ea&-6I(J!Kh24yLS&hKA^qyt5wf9cZ+>?fE>5@
z`)R+q*x!vG9b1s*?*7jFda)PiZeZ^@m@w1QTBki0H2j-_Xy=yMIKFy0xU_XLaQGAx
zA<g*S*=gDO!69j>a1-nEETOY!eX994b&R@6g4VYPG$W(2g@sGIV~*?~oidHGh+(eo
zrOSGB=^b|M*Ga=Z=$V;0{QaS$#7Ryr!y!_|b2Ee4Cl}{sF`m^U6RtJj1H8+41vV&Q
zk|O9ER_!j$-X?97UXc?rgY&T^ts_z!HqPwB5Y`WCJ?VcoT7>I?kPNP#7r~hgBs1?>
zC^I=;KfOBEDqYC#ScZ{)wYgNhby@Txgsn_ZQa{#cYWe+DpgDph+5*N8Lo1=>t1GeK
zO@`o&%-~3xhIgA&+u=Qt2TQPBV4dGIIT8dME|>x$DJy#brQL*PXy==?wx=H^Cm%Ey
z`d6}Q`UsLj*!@Cg=GvMrf0*XC47$o+z|jg^i?HD0<_;BSzz*OqKi)1PGe_gsLlZvi
z{oVX|g_gRDNYTHz0F==nhuM)oT$eX6Fc>H_V+5twSN>u=;n^COHGhrCu_ADYQU3J)
zO3jIH<<1K^jYhpPDO0IGHURzFy4!vE0Qav>V`qnVRAUVGw#c>00#>Ofw@<Q?!$HHX
zNRN5Ub@yO%0Aj=qs(YQAoFApP*_>lCJjDA`*-xe(Av~xtKu`iK&VYai25&$j>Ky<z
z7`Nj~I6Iun{H9wgocu1!cwn_olFl)!@IXRFiuP}O4Kh^ddcQUC0dP8@Am;SV6Q*N-
z{)kKohHX=_*Q(M!DbHidriCN)Xw~>rxHmufFE|b3$;i)hjyd&w?6xw!a@)tnVLbDN
zWm3!PX?&*^|DelDj^21w$V@xgcdN{wHouIkqX?fn?<^aYXqjmtr+wJpx>}U)nnMT&
ze%}6m0`J2(eNo$mXUDuoC=b1x?|9dD%10e%y6K+5{<lhO-zjhPFL@?)5;6A>(!4ZF
zo(!q?4jDHdzxsijYsuYXBy$|qw_lWk1EROladn4G?uS`Yxv#nLs1VC;N9GUqifpoO
zk<Nhw^&9GJPFanZ-A3E?AuBDv5<MtAbJ}w9^QHLJkaJFi&F$MrlkXTe`GquSS<e-}
zdM#PRw!H&se<yWMq^UJU(6u@BjtNC!(c>|{fz5{O9G2Eh`y{FDWP1zU_IJ<9?2~NU
zsXf;1L@z2!Gg;%&INOD)l11zYb0SxVY(ym?FL66Zjzux^4zfiPs@Fos?Ck8G?rtbq
z81Ac~LQh0^{I(snIap<f)%GTCH2=#H90%OkQxN`y19u2YW|b%wWj<L5;XtUoeEG65
z=*(cUXUX)X*<G?DcBoLh7f+fC)9Y3OepA@yG-3$ZC~7NC1HuUi7N9{_36vU;q60?&
zmWqv?U6?oqn>kr+O||eyL56K%tL<aly<Rjn&yVu$I4?}V>?=qNsD`Ko@{m%~LWT%#
z$42x~ZXt~{GHO?4n*6h`*j>mC0+i8^cf7XLc`38nKmzPolM+iJq-J~6ZyU~ai^JUk
zXimD?UgC|r>K-{$<mdEllwrNT0`q-_UH%IpX>zBfcsuS5Gm{A%eQ5CCPTFEWTw{lR
zBTNqHw2qNw=yyehCL|1xd3_VxiAaToWfDXB=1_sKK0Dh^4H}${6w#xDUJS4byeqyD
zythCUklgn7c`5P^R+g*h#=q}&$=hqa>2iZeWJ}{n-F%N)?y8=i2;9vCaWG7w(&56t
zZ@J*dR``<PtPro6#Jj+KrV3T2REQ~6^_G;;VC4#qF;kl+dKVg$yUHwI9~5quXI>xr
zl{3?*`p&Uw2m^Ocn&NGcnzlAH#Hk$U@XkzZ%s;PhoU0fL&DZ$JzJhLxq@9Hv%j<+A
zwJMNEf`~}KKOD`@UR*p>ch_%FpOem0K%yPDLh^Mw3akEl^S~)>XPd-xoD;7*NNVP~
zKW|*W+gBVw|D!1>r*@<JvQT0HcvgYN%*o59IRY4=AavJMQ$vF2`LA%DIdgrK11l(|
z1%tRCQsdEg`?--=n|GUB&*1wl;e)qzOrob8#vc`b3Xxe!(6YN9jh{AouB-=Qsd$^A
zJUd9wMq2wQqr*VH5fivSRZv_vY<o~xG3xuG8sH8Nad8ij%)@ae#K+fF%+zm7fM>}g
zqN1XprRl_+SOp@TmXk_q#*ijGN#B1y`>_I#)<7XBLWO~FDxhAF=3T!jc1ZdBWhn>Z
zwY9RHtZpLvu!D7Qb0?jO?I(s#uXT3fcTv?eWAUGDm^OIq?j00oua<f3`0urtKny+o
z=4^J%kugwLHG<j!;Q-=sMutK$_3$f!vuEcmX%K+iOUueYX$ex+Zs&Uhsx{Q_0xh5<
zt=r-mrNV}T1iDr1o)4uSUEvi`Dn~lyNBXxN-@+UvDz`rBrLjcxI6@jka;|;z(5T6<
z>42q2(tw%rmgZEX8(54fpfJ4ECCkY!i7~Ay7cwkfIUcM`NeOGG4f32VZCnZ5C=MK{
z@xe)+r^Vhhk(h~`u$3Ex<I_n38PBN+DGVriCYt<=S^}$<S1NuOj4P}5B}|(TK%T<n
zUSJ6UAAm_S8(3xaN?}>NaITNUvkF6E4Q{zqASZ(EB2`%S$PpcIdWAuW)gyAyV%C;f
zZ=u0%#B{`#GnmHdt3M{>W{T)z9n+rGL?~|j2`p;%aL(mV;a94n^9+_r`EK$(8{(Lx
z(GYoQvtyWqn6D|l($7wWmw{_J_Oay5gx~Ef-lTQWhE>K54N^r8{8Oo9DGOaBmg`H(
z5CmtUAgMBsYd79eg^f6SRgWO!Yu<~Ve$|lKK%2gMMI79?VRDJ*$^D&<z#>B4)%G*l
z%Gw$p+z=<~H?-uL*a8Wx=VwS$?pRxA2Qvc)d$q-DY@)69<gPlc#;rHs3XM795c_;J
zh2qPkSQPjB7qkMaqjREiT7I^RpC8mduD@t;WnnWw*VS{mUXZ%S05UDyJM*NR(CoE^
zo5T0C2&h<;>|l}7KRJyMf5fLr_ls@M+RCBHZF?GzKby>flH@q{-kmiXLKP?{z(f<i
zDn~H9sqVIV?Osy(?^)!W=v#BxS5?6BDBfG0qL%xeQCWHQP*nTStAqpat?i-yz`OIx
zM`mg~<as!M2sE5A9&e^eP_KOwb7$gFpv5C8|4O0`INz(XE%y)?7^48I4SM=V%3kN#
z1FtWjUS34R4a^^aV*$K?tB?{d#5=vUFS~mEdY$vg#daTm|NR~v`8FnpgB}TL#`kPg
zxjheLF#}2~6Sr>NI(Jb`dc)<Vxr1<s`>)E)9_k?6;jCMoecAgsK_MaVt6}q#ETY(Q
zb&viqkylvwPhQKpG6B%oadm@hrX^&};C3dr^Me+5J&c5sio~Nngamo+LS-U<NQd~-
zx@?bK<lguKkPw}K<VAAIXzhlO)0p>nSgG(;<qdfP^Xn#c!-9ba+-i^I7aoowswhsn
z=NU(#`48sw!B83Amp+a?Gm-3i^7lc)rM(}*UTy_~u~NV0x1rc^w^2M!9M3hkP+Tu0
zAaD%FgxCD&wO8H{E&@4TS<FY#9hGK9RA!MN#C)LELr4bidf+fVud@ZE;*HCpu@vaZ
zR1xy;LVWTAyML~9Omdr^zdp$T#XjF)h&ALqk`E}V=FpSX4n%hcxZ~)){aK4%$b2Et
ze#89?RNIe3YV)U^Dn|N&>+_WGhbg&j>pQN4kPn_|B<q66Y#KwlS{YxM9NzM`fQ6?B
zg0i+Jee~}32t>vOsCjLH-UIyTk|ykDnM%ja?|U3Ek<T|hzP_z^z*jFB1r07n$HujZ
zF!nTX&W9Wxx>!T?fq~#0ch#En=2@Au!)2?g<_Vz>Df_A42J;Eq2cfaH433SN7UTgi
zSPOLV+B!O5BI_<`z<Kkcseg!FqZOKTO|#BphUf475jr!dm<?u*^)qRH5dT)H7K)%@
z#`8~Jd*=D3_U5Qt?q<*S3zwMlsx^Tdl7y2~H<1S36^wIFmvlP?p&cr63;(rG{KYjV
z`mds%8CVXYOpUFjR^w~@$Ri@JkmsEIdo<pMgCC8J#z_78NUH-Cr7v8(V*BKM-R-Gl
zG4WVAI?0NFnrpnxjhe6!;w*Vw_Lf`%{opb{o8(frV++D>AVA)o2xWZFDx~&*Lr&*s
zUfm(|-p9B1qNutdkTF>+vP}%A9Pf-OzU0QKq*Th27Bc(P!KvZ)n_d69lAG1eP{t_f
zJ&#LTx1?3p>k-4%WjxICuD$-ZvOJ@H0>Ph4&f?9yoxgDO?YJ7;M@Rs`IZkeGQM7mf
zW}q|xvB))y@<cm>Lx!`InWL_Z^Ipzo`PQk3m5BgXz6#Ugxlu_oN1k!V*Qxgajv{Vn
zI7tPN01#OO(?0_2<s0QvjPkhfunl(@Q)!jmXY^ay1Zs_ZNFj35d~XAf8Hgi$>*<Fw
z_TA6Z4;JliM1{ugyq;z12mxH^Bj7@;?97DxUsDiLYM8POZ}1W5<3WcFx?Uz(Uw2p~
zz1+9>YHQ0@uE_n#jl)HLYT9WYxlI66&c7^R#B7fR1}2Nc<{(QEWZ*;)-LPZp1AV4j
zOOLjfC%Z(TYWiCUh*tSFuKjqTGa7_Jd0eZbsCcw{aqfrPLi6Fxhg#Jyin|`5v7g<G
zuDfcc%N-o~p^!&;`lIx^_WW(S_Ef#mZ<9W=dV+F|v+%**6l$bmQAT+vp=G?rin+N?
zqz_arIi-98xNI13s?5%DhKQL_a-W-YPT-1LtK~08SaGorEE|J$Kd*k~?YK+rR$!D@
znX$iq&FbrdFZ_(NWL$wauAGfxd5>-KF&ABWSzSH7FzP-Udr?3VeN-gjdka*U8HNI%
z%a->CXC>iH9KGtwLFaq>FnH9&i-@zkV(e<&u#-CmZp!r%W0u-8|1D>7t{|ANK+L^(
z(RZlS26C5wPoa2t!cI$IIq5z#DVhuIv~>JD@O!|!)1-5rP?|FH36M3z9C_fofpcCZ
zKvKY_2WaYAT`33!?tOyjWNT|X>NAr{2=X8&GSJtbh6%*pYm=_emGh3b?~EP(*@l==
zP+Iy+<0Kku>Ri50i4+cyGzhCunaK%^lzI&$y#W`n^TjqNHRgVnd3`OF0k<`aKfe;e
z1c_pSRtc4PQdodY^*<9JVafkZfJ9_vudQ@Ix*yB+63B9p6oVQMtWLki@lAY=)QtcP
z2xETe8`!?5Jj6T`iE9rgvP*G@#N9F_rNSUVB<SM&6Ieb@V$L}9qZ1l=3ZL8eL%2-L
z&$%`ftJsX5YGJ2KAw!^}Y?&fD9_iK;Oj2=lk{_32^iD=ccD)sNYkZ%2Qc;=C+yZ1_
zJ#*zljz|XY!=5=uLb3|uXo(5f$08Uu_T?K%Nl5_@6fhf$YR|$C6HKtu5t%=~r+`it
z_29t}_!X&H(PH5uu-%4|fJtYcE{{P^x=KcB(`Z0IOdwNyu9Rh%VaQ<!s3`|23f&%z
z?xdt?Qk>GXmT9=E&|}!NANexUZM2teYCll>#~tlu*51R$%zKintTPX}HI##)i;N{5
z?p=L8`o-;vLrUMpUXS;4?psR^KQ_DC{7Xy(7_tq@bppvnIk9Rm?x>5e=yM8r^5`Yz
zd`FgmOKiw{L)XD2&x&uy5w@6S=ZXpf{D*i$@t&$ZgX@sZ<<+=yC1Lb4*ie9YaU*1k
zI`B96kYvR5_w|Kxc^VqVeOtO>B+gYBnvlf{zhRbcsRTrg5ief65~Gj!l$AgK(@v-;
z-=aR?#XBjOgIAQ#LLR@KbM(@bIb31`q@{K#_9>!G%m}%5P<Y%qs<u>&5{FWT`@;6l
zBZj%?y1Q(cBxjn?z*N3&CIczgb}6E!%BG>^to3#Bt!>iY72$q>jC5K1QY|58O0dzR
zyC#&_r8vylQaZLWBN(6a41h=pF){Z^#)%{6mKt+-k8&y<8de3wb%VwvR;=$u=!<bR
zaKCJ8(*k?swzg|99kZGr=4X>ffToU?bmEy^TGxL8<Ti0ai0-v(toc78MQ{mODKOy8
zI`Wy6+06A$h?nyNHZ~S@hdRViGf<#8;-G%u)cvvb*1yPHF6feNQk@7I3Mzyc!DD!9
zSF6AA{E<9;cOs))CH*jFF9rc32)EK$8d`04<&YKNI9%X09#lwhb#J*a@67ksp^d4w
zS&pSnucT*X%&A$dks{b|LO#od;ePl3PsV=F7Zgwb{5f|m?jL191K49p&VVjkNB*FE
zbg1u?A(<+ylAgI^q{N)|g5}V~wm&R+V)HIDvk_h~*2cuh$e`BdD%J9n`h5&C8!pl=
zJ)OjB!vDW09P|>q;BkN?&#VIk2{>DCe}9r_GBiJu+f+~#MiPqXR{tvt_bCY7#v6?{
zIH1E9|1<f+uOgjbT;ZWY$H=yu-ua7qX7CFkS;IiGMnoCy+VE%<8e@O-tZM;t3E@cj
zVk}XZyLmsVPXR&+1tk^Uxm;mackNN&)Rh8V6X$QgFRVER6Al92MF_~B5iY0L?MHz<
zzFg>H)t%;VzY^1@#9UCB%g5oVk&V`@Y3%YjR9{Hj1<ALE-a2P_yio}Q5YH7_CY&E!
z^?rT#iScT}2%)fDQr~us1<31(pG4hJMrFprDZzP<OeK0#z$b(|7i|<@|J`P9tiG%C
zMD``fO~Ro;61!IZn{?ok{F-)pn~h0cetG?y(cMM~nGbjF-AanXwZ8SBrOH<1ef9p%
z3q9!4oaQ=r_OjMV$C1sKPt*DD25MY9xXXcyb9wLdo$ZEu<?@5y2i+B_f~wT9O)3bw
zUj8N37qN?U*bxQ>4C)ywCUT>--hx+FIPkRrd}WmuNRwe>k{g!y#l*aS$ArD#KcL=B
zby=!8e1H-$*CWm@?X4q;Y!B7-x=%)q&B=bT;VEBkDDy;kWkk8)te`5@)o9i$Jz=}e
z+P%J9OsPfuQ|7!v<^DJ|nJ3gKBuLD|tr%wZ&zMi#J=cEGXF4B&+u520p4EVYKxo_*
zut5}vnuZI#nv^ywOg|d%;0<`{)_;3msEnzs37(#t6mrSk^uJSRJpZj?Uu{hD(7xq(
z`#au;`4GUtx=tz(oG?$T;Mdj1I6f{t)(+rLl~i#Mo_8q!aXq+eir9XR1Jz`EY4%Jo
z;mzwB8*e|EGvMQ79AV2)Lb5s}%@jUY$3-qbTLs3H$rtrY^gel~F6{27Go~I6ynBy(
zb6jDgzAxDAhyv3LMiMz!q+>`)8ic%D^Ck+?#S-xnU__-1B@QjxiFtkssQi&5ADdcF
z1#fojFGe4(w^T1zwRGOau`<z8c0817elEJRyB+n8<JKJWpDq(<&_O%5>YaPomv1TB
zc51_~ha4fza>U)8gYY=EasdE0DD9MC>@f@Lb5^KUJ)2yC60zoOYWEF17&6~DNV+y9
z|1oAZmZHoPXn2-ye|AQlhQ{mpR$9WabhlO4ibvgRw?Ors%u>pD=NK;C2l~Of!DtPN
zz5?>vNu-_m+&Ifjq+7gb{A*<DtTj=TeTw}e`3ecXL4G+|74c8n%C4EL7z@}_Sx(`9
z#HEhLb5Kj@n__j?vUBe=^lkiU@Z&0ZWNk97rt3KInE*MCX@eBjTqu!1BuIq`mT0WM
zoQ0a{X*@(=LL~JFV~f^pTlp$w46tS&p^}HI!m*fPK?(z)CBTmSY6q4Mse<RvchjTJ
zwHvjIHT6#4=4g2Om3az-wD<O>78IBTPF6ptePWlh^+hv$g_U{8&-F)Eqy3AhcG`HO
z+<b?J^DL6CQADS;W^9<qkEDpd`C^pwGO~hB#J7bsTmgjurwXm1R@i(p_bR{nG7#;b
z)YQf4W7Sk|NHTtHxi#u#HR|>JZH|4aW8abESHfa!`D&9TC_nUM#z9N2eVzf)ght7V
zBlLTJT2?c}P15rsRjSnF+Bz;ADQR^$NVDe`7R>CXQi>tPJq%-?wbSS2Y`cIctQ2(W
zc+Sc);7^1b#MxIo*Q|b_V~dq>V#DjrtS<=)jTq~DX1uC-)uoQgEM$4El`R=0mRf3G
zMbue(A59FYN_W=o{h+42KS6Qx%)Fe2V~^3pZyR$YHsj;O(yKY2XXHdnydam&`_qd;
zXYBf<OygvCov^oh!k_f}T^d`|pYm<FqSvZ1CspQ>{_bG!jH=iHD|2VL?9AoEXT~Mf
zmA%FJyF*zLcG0F`D>n>YWyMN(je-Pa>;WTPZte=Er><;bV`CzSteG1a9K^uj!1*+0
z78Dk4ddmn#>>?crU8#6<)6=@r&HAhXT0Q-n!m=*KyxE0Sv&35{FRM~%kuS-yvoYbf
zdj!f^E(}-Mz+(2SPWM}uT9}j3;~?h58jL7xcG%xl<0m;-JT@R{AYSCA)mJ&np6xcC
zf*X$$^GHGA^)-~Ryqv@@PP{nvqA)DRb({`G-l=oeKGGrOB_u)@Hn&}Ykw?}pHE=oh
z2Ws#0N4$vogAcTRyjDF89f<Y2KE`^!;;%3BJ;{3$?nIH;VK#6^EHgwoUZ+Lk*AVN-
zGV0}Y9g3X;V{%owy$FznSu9XKBvYMvx|Mny&Y4-NTtoD{1VBB~J59ZT2vR8?#lZM*
z<|uv=hxLg6aYrS+Ek-Vx8u`wa`C_AA-`7E%Pk{45gz^5!fILafbzx%?h^ZWE#Pq?)
zM4UcRcxU%z;4hN!XtMiun=}6wKPXEfW3sN<iD98SJA<U0XMkKsOYkzDI7lH};bq45
z1=m^wGSmBI=crihh?-7uwJ4}+`kF6xdaH&Ubn6~7=5VdanqLk5W&JZ=E=t@chc4*t
zu}HF@gn*3xw){x-xOw@nYT?3yf=jJ`R8iNC!=d28ZjLUmJ9txfKosOHGr5D!tc*>d
zqv$jG_>*oO_i5i8K@>YDX|LW9eq(q3vc?TJs>;QMH(<p4ZEx3JtEVQvDY-tkz?d2%
zd2d?i1P5X+EYIRkMBI6&^wP>`MjWbBEIfYxCuvX<q#klcJ>Mo$L`Hin8i^$9dpE6U
z><^#L)`^IG@C7t+lGco#88dEv?VyV?UX}msFX4WX@m<_FrY8Su(zhGtIxQx+kskLm
z6d8>R<XP$EA&}xu(*N9?f^(og5Vf}eZ3V(vU&T|z8ytL(goK;~S=+&vo-p;jE+DPb
z(h}amV`z>(D>OS$HCY<Z&Fvuj<jqH}Lc8;WOUUY=bqq{V$HB>D6d!NwA3j~GP@fqS
zVV#1*0`c`v(Db*s?t-)rQDtferxx!nea*-V4}P>&=b60v!7A_b&z+pNe{N}wyxwgx
zO=m|W$ysO_e+*3|w3!kwOaxkZ-}oHMDO`8ptf8TCZixe5L?1m-N!kn>DSBcyg+FSd
zl14I@u>k+;eCDgT>%27i1;g5=wlTW2Crtc_BO>ggy%`ug=(#b9;mukL3%9Nb78Y=O
zh;LYl7rs-gNs~x+iwNaDW6xI`+8cnqeW+qkmmzsS8>hL~e&1U1J&~UzGzHP`;Y0Mt
za#)ihXmJJpufS^Bz`oQC2<{&UY2i!5y?-YvxIJFldOF+0kS-?DY713zwlzP<YU&C<
zikMNUa=&UkNrRYZbx+agWd|kOg-!2<sWWWUPOo^{Rk{b&%D3u6+Lveq>cSE_pKx#W
zo_KuK5u<w)d6*T%i?z0{+&YHkO@Ul%Hm6Bhon13u_(bu*6=PanzMMXz4XgOkpP(U{
zEO_2$EK||FqgrwGs-{51gDH{M0us?uzWCxxWSlnzphCtbx)#vy=I;2Nsd&Z7cEKr+
z=lEv%>tL~7gP4;tY?E%Z#fjds4NRo_KYsOK;_gHod;F0s*o?l2*Zj3x0rA5qWtBUA
zToI90G&B>Hvpm?(gtvt901q*(*F^0yA?PYO?^+{B%2A=Lur~-Nyfooe0p#(7GMs?$
zFIIiX&{ITXyS0^-LqAzj7&&m-wOa!FXU^Re3b=da_s~|uCYjL4qE{A~aX4x#I|yeY
zH05Omksy5yp=xXmGf=&usKV9dSoYrofiMjMnhrHn(WR4J$NO1}#8}QoFbM8$76-~&
zeMDv7bLS=)O#yNaAd9totdK4IU0m}^0|wegQgz4IZ1$T0l|i^>HKa?i>M3~t)`5@~
za&{H+IgRUnOX7(wn~P-~kVTj^mQuncVQ&nuYQMyPjbs+ZP=$5zRXqi58QA8H&eC1$
z00nS%Ru;hPpN=t|;{dN6!hi1XtZVtPeJuw$+~WEU{BUHPkN%&#l%`K$bLb9E@X;fP
zR?l_cIqt-kK2XO&AC`r+c@etjel3C(sE|(E`Uz*p#BWR2p%flliHVK|9qcpShm}1c
z(2L*ezAUW%xEBCQm|K`4a(_?I8$b&HLw}!Sc@94K|Hi!mZw9yeP?9ZI1o@|3v$->q
z4zg@$*N9w<_Hk=pUtd4m$dxA}CaQEKyyk#i`dyeXwzI$9!E@#e7{oAonZ`no9-0!b
zU~E@ke}9@3?#Q;;D)^ipRMa0FU%eo93sMwl_go`Ta{3CMJb3~yNx^_YkpE!XSh{1@
zfBDuzDr>slo8h!|a=FR#MQ$!=&1evC1>sVEpUUtYMif8~?`>V(=+8S+7^?3siurkY
zzb7MvH@?BO8$xplD(KYITDuOmh64tPt8iVCsoKeHM_}*3H`!B96SH`s>6kvchDuzz
z@I7jYMeLTYqvIm@S8lD0rGtP_T2KuttCV#RKht7?(6C%#d9DdY+pL0n%R&Ua3Xnj~
zi;A*)j|X?iAHtvm@@=!H<4jZt^7jxXYM5Gs^_V^7&P=$5IF^KU1R-fxS5h==;`jNt
zTL0*BIfs$4u{0){5kS;buTd%QRStMDP@IC>&@=eBc2B`Wz{z3m0%5FMVq(~ypk_%J
z3BvK0oy&;a!Qr4e92wnXQ$FNUdGgyEn31K3PV<>&K8WG^Zm&2D%0xJIi5eC*Phef^
z$v!N32L7H1gGR9hSn(0?d%}ajJ6~Iy3ZbK?2MNVQ03~e=6*&_0pQWuzvq&|3x(Cx2
z>R+1{{`1+??#>s$R`Gp9P{C5ro*CDsRPpg4xY%&3!|>ML;&V}Zf@+|l1_*b$tV1rM
zK2!0mu&_miGwX~DxV_HhrrSos>yiH}CU|QAbN4TG5qjXjPNah4=l=;Fjl&?l15;@i
z_oDzu1KZ&LJk*&1mf##X-TC_NLas%P6xVJIgCX?Z_DUdZ8kwCRPj>PCu(RJoPqn+d
zJ7BwdY;N0k`Q6Nh13UDfC=l2hZ2qf_Fy&9+dd*W2+wmSnl!qW_#qiEYLBAR>lj;RF
zerMIYfnhf0J#`!bcf%QA?_j8!7l+C$0uMHUssW}-fQ}O*O<-*eDvZPyg5eCn*RTho
zm_?`CE{qYJB%#?3d=Qt~&3>>1X?rU2Vb<<GdY<KPOfLcTX;M-W^p=Pkz$XQKOypsM
zNiSDs!*4<e%P?@GOBJ+#wGf&%BeI09mRN$M8c5&ZqyRi^u)~6oAqUUFQ_$|hSZ4%a
zM)0P~daZMmvo{m~)lRKO+cH6mI`ev!%5O{4DfK(k$#D{?hVfkLMn=HCpLHMwmgdX+
z+pn)~h(pU*E?q}h7n<Ux6HHM39|iM<tMvikHdH&XkO~@EIp#jtl==DjhDIaMWQ~KF
z>6*_<Jr=GM4=i|AgZCr!DuJ4l>pWDdY}5zP7PuVG;%rahb$KkQ$*~tg;omZAA8DaC
z2@rXV-0mziatVfsfPTXbNK8!Bfl2XSc+%r+c0ff92q1Y7`@yU#z$z0dU?A@gtpd69
z6`jD{CqQW5_2Ix*olU7Zo|)Y1P+WUOU;o$M*x~3b2P!j}_+JtB2H|yIR#jazGMbv2
xilTzUiVtoKEVs;(4+dxd`(Z^m-U#9OATaa}U1)`g3J!rk>dM+mMOe#_{{g?2-V6W$

literal 57721
zcmb5W1yt2>7cF>^6cJED;!+aQpma-@(w)-X-AGGIN|&^BcXtU0NQ1<sL%QdO-}`30
znfKnTHFquKdgC9zIA@=I_U4P6jQDd@0#pbD@?26vL;(VM@)H7qyF_^cUfFy7@j3YQ
z)K)^>0RnmU^6>|5V$u5+0wIM+iU=yXrtU48cw$f8BORFxo?E(0kWyf}GkuoB5j1~~
zi6EcciTM55D+CeuL+y3i0@dx0f;3s=A-)pQC~Vc(G0~MYDf=uZJ{7lL!{GFPmt);}
z=^l|a#j=bYr0?CI4=1sNQ%I5qU<#qoNd0Jn2fxYIr^FGF$-qmYP#oyLckGpHAK&=i
ze|&(x^2E)}jnPazOO6wY?lzje!6}1@;F8~uJXA?P#0^DvK^_24V<-kr5OYUWP@sKp
z(a6U}d+k!GL;rdT#!KZcS|^&YbGfiXN(Xx@e~9JSNh<ct70cOv8!=9-&QU2IF|Jy)
zc9FCw&7IuVI%C$B&Q&N>qMbE%;DrA5<e4;O1?T@eOf^56H*V^ho@m8hJVhIt-Q>S6
zTs*b8xjD#^V#$-DQf!3Rt31mYnd1lVC=vL0^-OU8@4J!jIF?QP`t_~tO1p0z_j4#G
zGdX$Jq8$oZKy$KApk&UvC>S0SlN*@=H8Ycik9T{s(h2l-F0aAC%18-TFY~2kWW;);
zAaYsy_V$%cYv3$yY>x+>Dc2LWTn_y~zxR6@Jay><qxsi7TbTC2u`3wkLZj{=YGik3
zrffN1mgK*0`mJR|uGP1C-JI{w&nyZ)Uc}&5H0u9;9^^Y7KM?);f0?MBwQ4p0j&jFs
zg+QSf*x0O}+95%D3uz&b1K=ZkjJL{ZyYha%*6NLHx+OE7^<v}30q5fsUwMS~p@^rj
zTRC!TJ3Bk;=%i<4Wc<4$vh_n6V#MI6sHiD(?Rs0Xu@jAwX}7gxHj9agiE54gl@5RH
z%2x?SYy=Tfx%Ob%dQO^ygM*tC*ZtQxBi9U5K4ObX#w-v~kf3#$)@ZmH$6A@Jxuxsj
z%^I({>7+AJcQJFP$)wM&FU&&vSu~H8c~K)zPaOpW+SIFzt(Kavz#NPuv%zEH;T0&+
zPMOz`qA<>eQAlP}Hx{wY{Na>I@4ySvQ&*{-6_Ljl9bXju`)1y<a^I4EXt1`5%YWcR
zn8??%YM{2t4vO5aJZrj?rFcxu>Ad%Q{JUf<xC;&X1o1Mhe3`V=2T563mjRt$<~86f
z^ZSfuBQ<IXY$+oLGG8&`rFxjdEvvY|l5b5jV<iadc|G=c7RY~B;25ex@stKV?!Wsp
z!0Qb>w1oEY5u`l4p(94@o409e@w~DR6v~>}-44xrV9lnkv^@T{_yO!TvrzGl$Ek8T
z&~nKqWttz)5pNnxLypK3Px?tIoxmAY^b%~&$gME)uVn`gFue}_;q<mbrGBtZ7Kwr}
zg(OzNYA_r$uIlK5Z+-&l99(SkSzi1C)(18*4pbyBPHKY(?w->V#g3z5^`^gH!sX5D
z089l}u&1vyD)f7JG0qBpON+yXOEqeW{!KXzZ*rjcI@ot-hs(>0G~h_}kcj^g0sfCG
z@4q49|Mv$LiftKi;-xlXobiI>sj=zl`d){xajNotn6c+Kupg!Rx3~TE@NyMs{tY?r
z*idcNF)x#3429$YdJKk%N$kIPQh(ajL%EObQYO?vhT<nZ_mx5tTU51XQ={@Zh&~jO
zl_OO1un2BVvXJcle{<QuBXMt-vQIKGyMD!YzTts`iXPmxT9k(%Xsc(zNFf=+>u(3`
zJ8Ay!w8KSPvClfQ!=mH=^NM!?kFFe-iGxe1C+9PCRiib|(Z};EIluT9EB}8F#Qzd)
zTTtmDVqQDJM$<^VFFS^YicQAS!RGrHp|GV{I|b7z-@dKrh|W6k8~RZG6dno9)&&aA
z-GhSzU007J<_jIwmd~L|W)mn-%K!eop63OK8TwyP(f`s53JQj_AwyyUeqoc4ko;#w
z(1Xx5_Bu3sz>xOxyhNjBhiUG3mNOjxY)TwnnO1q+h#W*Q)Evp22U27V;=Kmj7tPH)
zJ5I%_WsDMM?aaf?ZYRdSEl~sg^u8rx#$XEd(-pd6L#blTuKsSYr;H2@S@cJfo$}q-
z6&4oq^YcsP{%z1a*Q&>Rmgu+U&F@~neSLO!bMZ>_IGDt{-t|~NM=XNdaqFl$CYv+@
z3*6S294~qyl-IRfr9?w9U*<U)8n4}-fTp$nzyZUZsbbZLw+8;p2e&6<0s<Zef|!}|
z{%ep-C0loQZr^x1Rdg<w<Dop8Qy$mLro&c`r%6xA0&ZDwqWp5C;%Pr7v%;;cu4c(m
zFA#|{vRnSP%`{{2<bHAT>cL{8$i!yYPV3NK@AnmaHq&YfTN=016Wi-kv(p{_Xt~pe
zt)E%KOq6V3Y<;r-*h%EtZwx~2FV~{LHGWLzo&Dd(O5UiMBi_T+)#L66<0^bgVTi9$
zmVEJ)dAiT^vqtx`9TgsD3JMCx+Z}0`;tmWJylxmr|E0-ANJ!|uF%0$xMAmwt+dtCn
zk9jqQ0xV^Iyr6OOn!m#dHYTy~=`j4DuU72ou#GFfcW612+v`amN7J7B@>b#K_T;QU
z%ANN$a>hB$CMY7r)|x4qndJ(Vil)p3F|{h6NydoCJ1T=XZnvQ8@3cK(wAmi@vxetj
z5nru^po@77eT?}-o%LesgNrqn)6TEG@tmAWdv2sluUbzxGGd-CW)OK#Bn|Myutnvu
z7l|l*Do6fZQ-YDv{A_B+`c)SC``TH<d61mD9@=DEIg{-UyZftN@vp8A#Bx|KY|WJE
zwq9`OlQ(mMrI|BsS~ssfk!-Q;*x20M+_sb+9bNA8a338V&2BYUpDSCeL>n&^9~*n}
z*X2%&Hhyr`w*O>*cxHx!hetDgHhp?_K_+MXh&Ll8W%q#e>Zd8IO0jY}&UcXBIzC04
zJZ|U`l_BQv;z~QHco<$*eYkCRcehrXx7VNUFOb*d<hmaFY%XtN!lI)wHSAI`g+gv|
z@>ZfLwkds*oTuoYgLN5DFDf{uz-7PQUvIZYA!)wc+LD+!GhLz?#)|uxzI7-O894J~
z(&j*N2kGCUW{ys^^oLGMqlCd@q*zy0_~}PZPM+g-vMDbwZ_{??T&R!>QnT{+3UF}D
zzqB-_9WP$II9?4Vv7W8W1qfj>@8h$lPiZF99#gklJa+YpT7|wS*e@U_z+kXG=Ha}L
zDWU)O+4-b<+@@Gz5BVQc#D=~-LV@oqTl6@OmyXTFE&UH2iAIhpJYKBC*PJS)$I~jT
zZ_&rAcBtxlO|^JjU<lkYQ&IIUs)M(pJ#QnblsY>*r^|GiLiNLN8H9v|!M)Pduk}UA
zHskpZuzX@YAmZEX_<ZMx4q^^w1I-pn-|6;bUj$*kLLo0NZ*LTdfI)Bg>j+U)Ms{}G
z2!hXgnKR^>A}T5gO<0eEB1;Rt(de|Bos(0e!Qd;)fAt-Vh+^Icn!!)sGYQzt14P5*
zTaXkKH90LoEIE)70T;%*ZKIWbKmMLC;`fl$Vxv>r;iARRVDA0>ea<6pdkl>fBw#Mw
zFAf$#@^@MGQjPge#N&LrHJ&43@U@Nd-Q#28U&f2h*Qb!AUC9G*jK}?y+IMp#rOsx#
zm0IzRuc4s<;NSd7b$VQ|VZhP^@U(bp=K%XkjvAYHEM>$PVBlb(EKCGJY|+Q3;4xqr
z^KY%?DhyQq=+U=xiT0$9mugznIjWmXlq{Pej1c>W925(cAX@PedUzNZ7>TUPKlpqC
z))v8q{z>3*Iq<yMuLIbPE?L4SrC18#aihkWbdb$|g7!(qD2dEkvTN$-=wzAX+Cty_
zpHiJ#Z6?g5c`jYo7LAzql5*Yk1xR|zAD{ZIbWUqY#89{)0n`S&%I>cP>x@j-2cGG0
z)P1o?xj0VBh^+EI_~(Bl!a$*p${+uqsc?*mQ=``M>ZLvAUrs^HAkP}yu*A=L&IgN&
zJAEX}9*2A`!T+EL>!|R0zj;mP5Z35P))Rj;B>#V_z>N%m3a$zBnnR>ZgPAB!4<y2A
zr>&nI|G-UibXx^FG8xlkC=Q**!>xn6yZenz8#+3=^S_B^3P6oi6<1N22Al5G-K)uY
z|K-b<(?8V7B`4bM&&t4;8hJGIj(C@IrC$Nq6@dANK*_fGH5!s|?!jS}qDl9nS)Gxz
zk~slCF=B!?&!08I6!r;s7}y~TVVmHCkx(wbbDJztX~%7Xcmt6;l(^@@rueL8?#F*b
zUGg39C1cJH7I}Q`Z?WFU)bl(8yQb|kxS{}^GV$q$j6t&Bc6S6KFJc1dRKZ4CAQ*|~
zf4Dutz#vjrYW>Tz?0wONAyChQ4!-6G%7&hAi`J^t*rN}1ma~=fQ3BSw%&)}OemU>^
z`S}HflfX06@o#C@TB_IeI_55W-#o|4hkJj8D!`z7I?8ic&xQGTm@K~>wZWkwLVnLn
zYhm^ec`a2YVj?28&^nN8Xa)udB6{cN3EzXjzCbj2#!B!UE$Qf3RA1=*d*A<HLTXyh
z&|Ef)={@T?w20((OPzY#Q4r(O>JsVICt5l}=K`K$*;9SE6phm;jLV?c-27KqpoXGG
z>eH~X;Y?Y;`-(aIqUZ}V!5#e!kKB5HZ6O)c=(s(RBOcXeEeC+;{(PNgi3+R+3NkbH
zD<xZOX|Ol7njG=n&jt53;Mj_rcp@$E(1LX6RP?8L%lF2ws=B(mKrBr?Oi;1{p{qg|
zQM}?8*x6oE8y)`0L#njPH5RHaeQuG`iHvFdUXOXTtmS-;g<V=g;(hMmiW{Ur`vUB2
zSvpP-KCpSfIaSYgLH=oLZBpc22FLIyy+is+RBm_j<M(ete<VJuA%GTE{~A$Hy)Wry
z65sXtxofmZ0ixyi&`EA6h!D649g8$t^mKH)0uNVXgYk4Eysj$;^rHH^(<OK;=x5~2
zj@=aoed!gs8YTQE8BF}V*i&o4CBd7+=%}df>iCcM-tb4>RwBl6tv5n640m?l*~P?;
zGG%1C&F5ht)jVh^-~EuEi;L?(X5AfxJ_!#9#7!*SXEa2k!F~gmL8Dr~Cv>jHqAlF^
zG4~_@MAD;Q0diWidl#W<nGT;$EtG%*&i8vai^*uU_2S%OmX_su|JOSH`^$QnsJQj<
z6IjFJ%EP>%26dlU&h}i*F4hbJsK!2OXe6r?69EJ!ect(4y1ONrypLnh#FhMl;S&;t
z;Zc?n94y|d`Iq;hx+#?&cc`rGe&_H|H(!P*5$1^$2_lP!SS=V6aZ%CSygVcX1Y77u
zOx2fcYRQSA1v9v}XgO2n8%g^OX(QUM=_Yt$wX>kSf;}5#4wuRQ56Fl79aixKpp9Jj
z^XJb}xe6sJP3G&uF%<IHx|^I*a&kZYYOQ)B?l?FPCUHkBrhi;jw>UsA!asqS&F6l~
zsZxXVZX)>die9U}y#KYp7d^ZLql}yHT&jpT2xL4wHvoYc4JDMQl*~!@gTjM4RJ0|h
z^Q%>@i3>Z2zIrWb+6cq{j9^liBmV{K#A?L_oHf53fcQoqzUSvReHX0Jxa)bPySLah
z)7NKmdwI+Rdr5J3eYQK9A4MUF8rW&cE?~t1PHuMya&}b9Ig&;#Qn~we+*=wN5W_`Z
zUN9-q;sCVOe_kID5OCyk)<?)tZr!!cT?6K6Eqip+mUI2e8BiefxM93id`(piuf|R)
zM~rJUHq-^pYc&2_prUnQ-=^(1a7Ip=*D#tDsnPW!1`mmfibimG;bP^J?F;tS+eJ-3
zV=^C0le*G^_N|VKCsJ4b_yBuiv($_akCf&99KzBUf&W$?0p}io5e)fM)JVXJ;F{fl
zYD6@Qql20Hh3hXe+?=y29>=Yp**42L0~df)p>2-TDRXhj4u+!lfihbrgI^mI*;&HD
ze|a%rZelSAlAg_Tm<XSZ)QbcjL69;y?(6u(M6ieW1L6cXXeB;+DR$r_n#2356fXi~
z75MCxr>7^aQo+grr;kR9`&qapxmd*F&F=ADgG2ChLiT)B8xh^^wD~ZP<_FU%uysZR
z?9pO4>vnk_D<gwXApMJitPl)f%Y#QBWB+KU$(rrSW~V>$LfY2hsvH07*PV+r>5iMj
z_~9ui38G;MGbXanP*5apukyFHwkC3=1scn(EG_qLEjY?Qz4d*{k}oPIhJ=hPuoN<4
zJTy2+E1w-eNDKj6=hfHkj{C&1Pt2sG5F&2<rPoTOT8)loE^pj%l!}y|!X6;LgfsB)
z@Nfl6MIj*};!{YzadFsj&pj@MO-;?sch(s8W~<)RPtH$?`%HiW3n1Ubgh%jc0ctck
zP!Lv})7^R_rEr{=sVLW;FfQtY8VinS2{ptLM2Y*hvoL_Ju1Ggu^Ald&kNX4MG~kjF
z5`qF12$Ftv+;=!)-8WSrzjo|#!>IcOQTk}R8@{Yg4S?QN5_oX=bs*<pC>JVhTn9M`
z++P|L=0(iaTJ0WizOz5yn^m8&{&cU8B6N6c@F#^05OQO8B?}7+mzS5I@?p?vc0FdP
zJ3T!Ozq)T4N#SsS&RC2P<SJZkG0J0SDRijcA|l{f>v(naf%)jx-txH37))dma6MY7
zudh$%^Elt0gn^+xkfC@6mV#K#4y4_V8tc59EXp&D*R4|P;v%K&j{S6h6ohG?gL6>G
z0^CdRRzIMu>~O8Fik>G^jLmY+mnFaae27uI5&K*a0A4#5K-ZB3qZ1{+a3PG%>oN@|
z;5q2Hr{<Qgl8mLodxsrgOv-Z;5ssXdl{I!3k3*;OX&PGaOTwcZ+|ro9!|k}XA89y=
zz<pEk45?I8{ztRl<z&8~3Z}+JB_YXZV`noNP2EIB@9yiXyZb8;<?IE*Jy<hGM@R3B
z2J5%vA1NFSCEMF5kgiCx$sh&FM`uF=18!#0&Vwov68)=ORp5ROx3{@$r;1>s){oW1
z*ArNl+!J3hiy94*HWvW=mapR5Z$Fo7EGILAi}wQeJ@+mBX|a=5R|rTc{NtcHlZCF%
zRhh^xKX?cCemuTwWFQY}yV^`)bd3hdAQTS=`jBHbnfHx{1UWm}$hXHGK;O|co+h5S
zo2epYiw-|T#06Ktw&C$Y9gMT}$VK)G!Nhxd-sW_Cxx92$Dnf(M;#AK8oNuF4^j7bs
zFJhI+sf>*GEkc5))m-&ctb9M%vDnF>Dr2snKR6*`a@Vl{sLeSVRi4K^?@C2fYqnh3
zdH8Kvn^@~2kDZg94T`w9uV0TZ_i1Qoz~rNCot|e30tDD4J*3DPLy_e~czT7YTK2vq
zjV^PkUmgs(<I;SilLeUKZiHL+YzN<r0YksC<*wz^<B^dOFY6m<UxzUwB<!q5HSi4x
zSoS|}&SC*hT7f`d1Tc^XyuH20u83aYgCRDy5kNp(y(50R+lqmTYQMw}GDe#AlBj7C
zNon^rquS4Evq^y8lt4Y^a=3&V_-DhAt{#jRNxA^x*=yT96n3j%sX!I0R^4}PPP@~P
zo#~P$lZ7_`Y@(v0A2fM3P=~5DqG>P;Z`N`J`O(L6dtM&3#J<VaaO&0}ZrIw;ew7Gl
zlCVs_HA%p?bhbyW6pCxO^Mo*|S?$f&9RglKPfrhUB0lG{-vLqXFbI!EfDWOZI#_HX
zVfdT&_3P)K?;vuyJBPZZ8Y%Jq){A!M7fm2fXYk*N{=o?Z%Ri1*nU<W~z@o)Hc+a^&
zseC?l#@*TcCm3R+=froe9)0JFO)j7!?H?!4U>BsIqM{-r%aYF#Yi(@>C8tZRO_Kvm
z2E>-_C*c6(XvuXP=PMVh>@T&<kB!;P#0`Lu{`dU+;A%>B>^}bY@82LZ5RbB4(5pJH
zb_Pb3k-@FBe}*Tqod1k*{rcQ~A>ipy0)vN`qEzlnv9&G|G^DY2K8#Kp;3&dP-Xc{T
z<t`Xq%v&=g8bnJwr#<-wy5$SyYV-pG1G&<P&QlTt$7_8}qe)>CWHyFI=me}=KTih=
z)Stb5+hzR0W3FuL?3$R*oi}F$?BC(=)^Mr#3dh+)T`zoG+|AWhc`z`E2vm<t0^G*W
zr52A^_L7@I^|kq+J)bfSHkX41fIw}2muHDb5p$;<n6<GvYz}vk_q{)CI6lfk)%E7m
z?+)oZ<a6A@bU*v-16eYy&|6+Ii6`_`DAc-KYBG2e8JyvRJU;HWekAS^fzLwada}u)
z$-!<mv0fXzIZ;!LQWkYuUH2qi;QF;x|H}ZV{4j2SrC|RHkywgkQ5sFmmoKp%JpNEL
zH%q64e_ZlZ%%9nP@PYavp3-;u9{*9J9NJNvjy2*{n+{uR<{S+(inqKsc~+)TgX)0$
zzVLSAx@M>C-rZtolaRw&Dfu)hKOg3h!#^@y06$7slBu5?LC7JK%31OABdD<LUeR(A
zbNk$T9v0!?XEsMAuC<T5!}AL*`;hVM%01ktaM*ay*$}ZOXI^yI(EV<)2+DW>aIVpO
ziq^-+2iCGN7~d7IhJZvyS6q|dR|QW&Ny%n4$66|#2vWhD()G@hTd>GLrTv_g^3BEO
zuR;%WBFnwQneD1C;^A%$e%}~S)cvB#rPTdC*`my5ga0O>lSyOSBODhtDHR+WOGCc2
zt2Frz|9B#xd9<$P6M3;V`#!g*T4RGUG&er<eI!rznXkjQzA>gZZ~7)DtGw^75pCFg
zVo>A}hC21XuBJZH8;BheG3y6tOtgG_cRhr&O)iJ=Q*?JubBi1{y2GO*#Ai>F>ma_l
zVT_~S&b!Z~NgiWDZ>aL|3FC0;^Ahx#QnZMMOH-Y|5o1Cg=e^$g`RAmOBm%7f3XjfX
zfbtns=63F57(wXIi`3p~Vz$mUTB%CWHn-4KeHe;N3x8iry8cB!oqF?2OoufGs$YcO
zNq4>~#@JeqT{^VbM4f2JTpi4(x4?;q`II?i#WpUg6>m20BdGJ3Bos<XL!<3Q2VRG|
zWq+d!Z2&_QA|%Kiz8Dl2x0x$7{S37zQ$a<JW{57{y)-&s#A>$E=yH?c=;VUSdswbe
zYGxugfnHr9dY14XDTd-FSAT~~qpQ2Tw`b{QJh|=ow^=4#EN9iNl{Eidizlf6%xglK
zj$Uav-rnAV3Y_<`XJS^P5u^to?pnR)EB4!bH$aiAw!nG)9e8ss4YRkZJ)tS|soo6$
zo15+#OMdR&;$3wVN7Zbz^Zwiyf&Jd*KuC1#qSsJw5}k@6s!&kZ*Hznv0oD)m)lUU;
zKd~bQ2gDU=QI43mq{N2hljPBm)=-<P^{VDhJN)1n<HB%ubx8e|M&z+st%J=|=UFB6
zDU+km?IiWWz=56Q>Y(XROoZrt4%P2Amzm$P8r7!q(RbX)N)tqptij}q2NJJxm4e9!
z^d&22V=Zg3o4W4fh?O-83yM@!XNoM$*x1;TQE4evSCq9B6%_%^<6%GSdU^S0czewL
zoxt7cB<w!nABU5ESVc@sOcg7cJ`4I!SVJ`f0SbkN>eKoa^?-Ga``6Hf%@^vFaS%lO
zr!T2c2&tzf>|!P+9>ZV1f8&aS8u{Z#n;rcMegcDLFA-nn>ecGmE}DSt>Fy1NTn?3j
z!VO!vTC?lhE@nRpBNU-VVTqdgN$BHv9oX{PqL`FwH_^O3`in#^Sa_|;GsPU<X%1py
z;K}<BfA3b})+usPxiPdjIciLz(o)!U+>2RUiGzkC<vno6lAZXd8rSkOva~6(nMOhj
zI4pmci<cM~8Y*qw69=IMPt95G_A+SJ7EJA!?A>(+K1*cMwf<Eg-xG@S>?~CfFk9H6
z;>+*|l2Ue@f2_`VROYpd_iLw|hm6Cj)FOPY45unBKSnf0st}b<1zG&sm?|<+CE?$n
z6Zo-HS+%^}Y<;#fg*ZTk;>Rogf|rl)<nLd=L<7S0L|8jl!47KjF6pr|2v(?d-B6X^
z>G}ESz`)mkOj(V_2y^(XrL&|3{ZMN^Z>?%?F*9h-*w%kfqxzWqW2x3@t|@I=lgR8j
z;qH*?09%q=-d-K*My<a%4jg4AKGlQ3tZ(}p>!0P}Bx5)BTF$pE9%lH|%BV|AkaDM!
zPU6ET?J6XaQ~#;ZZAF=~yQ1@-vRUJqmUzw0=0xG4({7(epFsMDY7TD6NYCGKx4=W7
z`NI+ydgTU-+~N3P?WE1K<`b81K^k?hUY^XC&ACb`kbBX7T)Dxd+xpl}?DJPInFgR>
zvBjefP-&pUP^ey&cj6c&K9)s_0y0a3=T#+$(15N;_o#Rox#pVgb4!V0`0aiCJE1dy
zH+u_pJf)ges`LrI-6E;~)dE<kXjaeKPnxrQ`IE#unZ%0~{j@!0ZO2^ctUB49;A6em
z{*V9{1xvX~1l!FyX)`gqmFawgqM(i==)`(RAc9V)WXyU|8_kRY=(G(DvZr*dM#F*|
zPY~lB*g+FNm^u`#3#{G5WxNxCpP(sUF9i~I)N^8`d}IDtXhhV*N;#c_eKSHw2CNt{
zaYBRM1bwefkC}DSoS%qwYp`(HI+bt!yG{!zYFW^*%0BHx5C^v2jaTA8om2UzOm@Ee
zCNQuwBoCe3_Ej0LZ*IP(Kp=VE(I8;^w>z3lIOvD^jcuf1UIz&19v26}#xlYp@VEBW
z57mW`Y7GW^drUlp;Jc4e4_vHR1O%ZYnqQOs76n!l4LsX0(p^b2JpT@CqALIVKAT9V
za^KAi@}0N7|0ALE3FjLxd?8JOWGtYpF1nF$!!}?UKDeR)Hb)}rBIU&SG3Bmky7De8
zQAt+d?uu4z#LU`vST<PK7(MNj&)F^*NAQIkEROp(%1&M-c|}qKEW-dD4wh-UthF!U
zE-snPlxo9WNTZO6P!+6Pl9ViYn=F-hv>Gc$PrckJPbk;$XUK!3u(exC)u~xiyvaEE
z+jKOgTKn^z8Jl@-JgpO(xs8KO86kOJ`#kKj?9H#PAYv5SKn_hWr=#}fxVZbfo8X`z
z9j@}n`Yu0`IEbc9`_q@q(ZhE0JLB&1NFtsmww%xr#RC$a8y-iEg{GNo(J&@;=bt8P
zm`at(I@V(~7EavSpmlYy(4e^QuQOUBj{KYeo6quhS+3ODXxir-alZ66^Qj^%i<wkD
zA4Vk40I>+o@cZ?pQ4(UNXY9)nKJ~R~8Lgm2;lsdLs@32({S|_Vhi%mF{YR&1@xB9@
z1ZwpY4_a3vn*oY(&M3!wWg(%?-xUTJDZ^H$#6UXn6zw)ckd(6P*Hpo-IWh6U+b@48
ztIkqqZ-WQyM=fP4c>esT<t(-eBO~+;GBPioa;n)mvR(`}{bdyIm<^N=*aD9g6nFYH
zd2{vcvM`oIgr4AMv>_!6%yFUOQwQ{r?Ks=@%`>k<$H;9F*KVf7vQ!7o$fS~^b2v!w
zUEQ*Gm+VbD^VUU~O^~-EsILGM^>DNNaKD|O@k3=djz+O{RT*#jS@vaF#e<Cbyh@?c
zU*0z3kz}xfQdkt0AKW9#%F1-gbbp?ufx=Fs#{4ZBJSjc>4^V$bZAt>DZ4Qbwz@P%~
zf4?j89keexqy5dN;4-~M9334&xpv<?zv?@HP=ag6;pOEumLZ^<D;3{I7X!LA-c=TF
zt})u<f0yP;^NqWH_%J;FleyXgW5v~YbGFJvv&KCBkcG?Re6I^Wn3Q>|y`|}7b3_yH
zr!48t3`6YGW<}4OM@qduB4n8*GTKR<kx-p*RdLN(vV~2U#a>kppy5GW9ki?>8f(S0
z6-$ar<bwyw?(03T{w_2)VBl2>6lr?&hU2p)OQDc~a^xwr>sNtB&Da7WA-m<})!uCL
zX|?i3apkLnsdum%Q@mp&XxTO&FK2CTo%qnaYL?OPeoDs3yL8T@$->iNmI_q|zEg?T
zD1{ES<|DD<T8_q6kCoeoABBp5EPkXAY#6n=n-1Y2L$8^sd3bmLOX6EscX70ek$6SG
zVmy37%OE7g?7VPQHj}`peGRs1tNWQbD67n8O7E!OjGcgf?&t+s9HTZtoekgZs`CQY
zn^Uoo<hMTe3prd3XQU$v1<|e~iHzyj`cpY!-yC;-nKg{#)fftbQbv3<<w9gFJw3hu
z_Tkx(ks+@Efl}m2CWdeH&+6)G82<@-N{Mcpcb{ere3|QhPShh^<UH^avKQkxtIuci
zPPbZZEM?eEMKgi)R)6@W=d%3PUgjQU#&pMm?TpntqwT}J>y<`0Q54N&=Ebe~Ow?4J
z<@Rh<{$ooc<faAHq_EzQ2ps~bTd|yR>pFqPjot`Ccx3cu1zNlTn!Wg;Kc};|brzk3
zBF$gp@7<H4d)?FSE11fjV3tf}w7TtnklIPQ?B*F#B|m@XE#U89f>Cz0)BOc&68ioZ
z@#zD~#R1#R{v`+qt)TEfPO0-V+H~{+tv~j#S4>NB(IH6=a4Ve+W?Vt3si~A)ZUw3&
z0tadjf`_$M^ZYkt^t!E{D!;tH=VgkJ+S?b=T&}FFfKmxiG56!_06-sHWH7z(!C1CI
zD0{E)Cm8tx5Y7d@V-rB7qbj0{)Q3+0irr)s3Upoyh4%LL>Ax)ju`qc+xe|dJGFQj>
z`qm$VzkJqmR6fabD9!;kS~Fkyoa7YVcVDxP&8G#lKz$a6sYBlyTU7VFw5k=;q~|E9
z?VCZ=^9`jnf-lL-k|E5JS3UXFcKanMh9Z9J7rw(QNc$pB*H~H-x6_U;M~)LG>gVZI
zYN$qV4yGwlWcGV<=$Ha1;B~#PD8Yk?DQc}AFgl)Xv(Pq^F&OS{XL1b}k`tATd2-s-
zT*>Z7hj(|Z$&GmnjUALh2#N1ok}=Q^Zx;=@=9zCVqF0t|T%@Yh#@SyAi+$?Fjw`cW
z*hIm=kS$O`lum%Zv|QQ)QjOK9uLk9gFDRb+`E~`fFEx92^&PG@WOj)k+}`g3ZYzV!
zArEvj0XT4?ilZ<ajH7vIuq_N!Su9k{S6407TU7{o-5d2e@ghFSq4a2>BWd!XK<94!
zit0|q>$6)JvmLJ*I^>sm%81Gl%<%8}K-W7KxXbHnes2$F=X21i1D!6=xaM)%VFiTj
zigwdz2G?x8*L;6}|INiA1|j?2%!H9G?EIoDNJLDGfBZ*Vn?Ot~$-S+>0N446Dlz@M
zTE5v24_EwVUU{wC$peGJ!rIXIN-4yA?f^oS`RBz}Eo6l6pCT^H<y#fVi<`36u+paM
zK}5vGnRJ?e>sM4;%#?x+gy^$^*v<R_midzB;gV>B?$hoj2lBbg6(eXrb7sGY6u5H=
zAlFqXW@Be}1~<`WLXdeO^4@p(i-909x43Ci5G6dJMO=gwlClaNiLQ#`lRxe01~{-B
z1I7U5`cvnb)P}0BUn9jn{>*s=xxKJ3H2gHoxi{~>)<$~8f9meIAGz4X>_m0yuJy&L
z#o<O!UTBE@^6YZ3zxJ^0f!|lV$@w{_&x^xWjABH8<ZN-KYBOYrg9%vy#q}Ao@1lU0
zYVmcBV5Bd)jo!Wf-g2<OXtp(HBrzAKi*t?nl<`=)7l^+xl+rQ)*ZKC;4;an?Qp(!e
z8gPuDp>(lkVUwwV^W)Bb@@6sP+x%Ov>(eodX;C$<g;0ye`!>+by|u6a&??1i3x)H1
z`8lEI`K(FQvJ<F7wkPusM}`UHa^}MESyG>+!ZtTSwzW44f;()Da;qW@VHgTzBjl#<
znA~+t-`;sZ_cm3_D;2}al4r=^cy)LYBoiDz<h^sTs+&L3S`RikQ`_x3e*RVfbpS;-
z;6g~SN{s-1p#S4HDr@I)9gu&3lm-Ymh=hd1?*<2$P4?QwvX2knye;TsQdezsB-^y8
z(0}=qgtV_QGh;@Mh7ad#AsT`sx_iS$3m1)k*sNUKNm*)sx)`t}Afv5PgvF@+gyP%Z
zpQU{IOz^%GS=$!%?ud$SI=XTc%>Q;&vsx~}<-UAQaL?+KP_<I;07EZQL59Q=2vXYF
z3eYNxrB|5g91Q$+Oy!D5<FQ?H@BJ<r^;gz<-gq-Kw?GH}BG#DAX8Nja83752QN42g
zS=&q3x;Lddb{|q|K~j4|3^aUv_Uj=k#a?%3GwKOI6ZZg`Vo}=7AwE8SFLRC2h>&zX
zA6-L!mfSqH<ET05ai8ruhm-gdu*tsL1Ms!UY4^OF5u|oD$1OR~UZ<S`OxY_Yoz#?+
zn<bA!!2HXkakCXYkX|DH;KRf@->)O_I+X``J0&Ar)R{e@w7Y`b^fw;SGJopV(UEVp
zdR<7620z!1RnB;W+mUZB9hU526E<#n%YQ%MG2=l*o*tfEzGj)X9xg6)J|4!9l^p}u
z)mSbF1RjWhuL8m!A+d10xBA^E!Ce)0YrWxC^Dh~7dpRsBmk->x+FQRNhQWRnXm4s*
z-etZo6bd}*jo2|X3V6n@8<_1Ur4F~HL56L146#q)s5fHn{-J@Fl5pH^xKwsA5G_Kl
z9dP8nb<H?^chNCBY5JG@<)7mb0*Q&9`V>1mJ5Ze&zSFx=iKG5}=|ObsN%i4dIk=k$
zv6C{Uwhs~#A*q~CSzzxA3HVGlb2dak!(c5BHo6EJ?`I4tX|UNRA(4^2{j!jD!#USe
zHUH;?VEFjbeSY%|8#`q1dwCZsVz{}b0s6brQ1Wnt?ydKeY41Sekr!T1b#w$(^%dW-
zV0t}8#&Bh|zU;;VI<K-CJ`cS=hDv2m<)QDqhp`a*03{2ALnxVmqy;TwDCqQkAtUJ;
zvl;7j3zmuxeP`Gu0D<fT=`OcmwF58_IWSpn!ZG^rnaAa44I$+6ilpEB@=SHCqNL^;
zS0jS-GTE|r2zOqz>F&osGZX*#&IV$F;3qk3Oec;P?<Em+MN4RryT$f-h_EIn`#jDs
z?W(cg0u>M@2Oz*)OkCwlHFnf;#sSIQ`JA_sUK8LJ(AS70U@Nlf3C%6-tu0lnP`N`X
zA!C^|WXD5;Sub(BpC%f-GB_|c5?^erJ07f|>&TkL$2XThQYlt~zFN;J)_$F-thO~D
zKNv-$Xl^^;E0I>D*HCUBzOGz@>T>IPy;U(0PyzbOi_07>v)7#gc9YJBT-r=eBqiCL
z53f}vjsQUL_xIPVHa))HE%R`7H8e5t@qiKIA<-pv=y7PZ?rB}YzX$zOK!HXP^A)KS
zKjL*#8k%jjJsiAucBjl@5s{7F<QMJtfWzRf697!M!WU7Pi(JvK8Y|6M1ATq3yG(_(
zi^+QL=N{f-BXhkPHOyvDXjR09RPxcGrYcmY#}-Qo)bg+IM$^JJ=sNpeq4^6V_nlpz
zEJT3rH2TS_rL=cz2fHIa8m-lO1DT=~s^g3=p~y9j<_bc-Q}*hZD{KA{yW%xw+8~>Q
z@U0zX8j@U7ae|!6=ikixt2DyFhqVdo6{ss<Fl1ktn>_X_My=mg_ni?u&LW0JqhXfw
zqU9w?yf193A-<=MEmCF%>N(U)HM2~=_r7Eq$+Zc7h|ti@%_FJNcwp8%nA>Z}?uJi?
z!pi5z(s&XR6JeGlz9Yt12&-SPbYo*;{`~m^h_ZWkp*<jvLMP_!ue3BZEtoQohrxY>
z83`_=y(Srr7kikF%Sr5YTP1dD%>>d-{fd5;lvEzT4gF8zcY=}COGP-CZF9)*@G#i)
z$p(zZ$O!lmy`b3<l3lR&yHew0*iVHB>G)GQYPu^okBz$I1mzg(jm@6;GRGldAI;qt
z0$zg=6|pbrlq;&}`R@#^VRc}i9N2Q2|0-DJRWq$n6I$9qNbg!e<n%$;WcQh<6b1N0
zI*z&m^tUqn{N$U^Yi$<!E&B;=n(Y8{51OZsT5qc}jmyiGAtp=`lI=A%y-ox{%u<~e
z?o$P{C3iwae&IGa+utoFA4X8=qS&qM{;Ep6G#)|pmF?qC{ga%!@L>C^`GK#PosqHh
z_@|!fB)V!@bxkr6I?xp0b69HuTnJQPOJ_&p!7wzNIl0}_0n^C7Q}v&2pL!-1;ctLe
z!3(fQpq?_H%p2i3Y=&R?Rj8O|Jl3&m^J#3XK!u2m>~{sccdO|H_Dosb>NmiWIqyz0
zfYYrG&w{-G!DYJKuaX7Y52Cpn_zxL@-6Hg<n&XxVNQgZN48eIL(Mg|sKc=70nW+Uy
zOTE)5k&ujBZ;)NuZM_+KpyzW$P_cM$cLJKV#FP||{kfVn9vADeVIhgF-8vg@kX29v
zU(?VOg47HeLzl;E+OL>;QW)$uPwV)W%RtLWPA>W+H4T))pp`40s{r}|d5?wS;moS7
zh^VNMiHSG>UvK5>Li*3kxko@gPVSONVGYiqer&QN%GvTp^ymWwZv)m=fl6^pOH1Ga
zR8!w`D2CECh(WdX10{OynLB%%MKLN`?Dy#fX_*Ee0m>QUx~H8Dalf#GtQPXL69Ex(
zxco3n0@E87wPrkclcj^;=WloWs(=lH&OiKgiKng(RQ!jH&rPS0q;d84T)*dcnfF+f
z8H|l3Go%b3ItrVc$9G>^n3=hjAuoB6aFiNcW3!ruWRWvpv2gCG)q|1OzgD_+AyP8G
zwR0BQ^atv79${{1<kbnRueaCT&8_ABdKWj$=r+*QlNFD{MoEkHa1AJ!CcaCy2>QQ0
zR}6U#Se_oKt)DM;@5AEa4xDy>0O|4g8t@Z5efkvizW`R^1#*4R20L78NooG`jN*;K
zhJq(6LYC268{DPc&j#Y~qiIv7tjGJ@q?LTm4*}%C79Kmr%Ebxs@ik1<^ynm=saN%0
z&jjuv5bZ|&9)(kqkWHIEzrU7B-3QIS%sG>D+bPKvUfK8eEVD{U4E|+ELDBv#_?vWr
zCP}``r#X5Er^Ajv-P>i|h9LV5ou-U>ps;yFY1gLIzX*QZNmY1-wcKfjwD5j+qVw-4
zpEd00ALn$06sxe7s8`7X;X{+N(}5o7gz*RuzGiqnQzlrrCC><$SUntCCF;hrZo?b=
z_lV-Favkz0K!Suw%Xz=v>$g@0loOBQ5E236SFif)Qh`{*swO);Q8grAJbE;zxlXkp
zaY;INxDM5>k~4s{ii?ejNxq~dBQfy+Q<10n?tQLQ8~&S;``fu!7FE10w%gek)snbj
zJ(U(r=lh?>LsB?w*SN(ZIvNk7V*~lU#*3aq&<Fzv*uJKOy~ttSR{W3@X%_p|5y6`)
z=){MV<5dKKaAD_^nyXvQ`fo73yN->3S?lZT16bcHUM1?>W_2?n^cl<<6x((Zc-{-2
z>EYFI`CFM&kY-A8u&~yedDpm}errQ%exmDn@6<F6Qnh1g{q+!2n`25H1c!E|+fKyU
zm-Kx}5H%=1t$R9F-;2Pfaz^%kJX(Qc)D9rSdQPB!$Bls0*#(ckjmM7~e7@EhIDOWM
zL2$yXj~#>o!~&S2-$uT%Va&6y&8($k5W9MK^hFW)hrJ?|k_Z=H%j0xMXmKawgKus9
zxg(cHk1eaat`vjm65HbaG2HXn#lcFV0i3Eq&v1Bn!?Ox6I_t$wO`_vTMv2$$q<3&Z
zQGVkgx=~%$!rO9MO$xO8k8Hw6s!6Vgmj&`z!5I1-uWw-j5>f$+xH0FSmYUsUc@Q@d
z2hLf1Q6oEn{0_5Vg&GwwSduD;eOQj%ivUjo#8B;Z<IKo6BAt6GU%V4XZO8N`->=Ta
z$cH4t??SQoj@4W?eo!%2seI32zFJx#VOHOnbK+xOK+zP7O)vgV5Med3`j;;aZz5rF
z^hQtYX_eRmXX@-z?FL1}UA>--r9=@4-XU#qK+G`8Kb4vcQ-%qbPervii$ZL<sJ13$
z{mzKd;HwtpXUF>XY@zjDvX^EgsSeBnM4IOsdyDostA|nTqy<x3Q^=4!&vR>yzh@tW
z9jiFTGs$YLk%khQ?Hj7|?;}U#Til&eMx3`NC?_!lgmXK$qAFau@;>&O#CBAwHJ~LL
z{LCUp7ZEHG+iwIa&KmQsnOCnEGrKhFUE##Y!P4BN5{vS$dzJUWPvw~b<1h1YXhd$9
z?eUXy;`a1K&Kn*CI2UWZQ#E?c@ayODRaEFikUnyn7bDBvfhPsJfameskSim;h0i?k
z%^>0BU4bFTU#rVDl9F;DJ0pK^i>6qR^%H^%M6I)a&4y2A)XrRQON`5cmQ+4RDv5#?
zFoM+b=G1*bR;cikQgNjDRK-CPNic@je$9;5&zAc{O96N=b5L49ET95D0kJQ`P{)>c
z=Nd${uAt{Y=mBUE9V3kWT;%OL3)QxmbTenpX62%n+$9OHyJ~Z`F-@Y#iS%@5c}!Mr
zADhD%VgjF<#(=3m`BS_Grr$~ba*p=emVZ<4(6d9KN(d~=_yQ|2uHvRt@31%SAd`$q
zUSnavja{^>b42MJR7X>vTb8*jn8G)95NZfT&w0_2D_`*vvRl^I_2*C>p^sr1;^>VG
z@#d3X-NZ=|#uOP?w>5R;Cwnt<G#jU-^Ld1DeDC?mhN*=u%1%(2<d@;oLKQ*g8{~B~
zDE<9~MYXxr&4z1wFmmT^mYpKK?h_0m1qDnM^qk)I)%IVKAgeyx%r<5)G|4h%_;G_y
z`Q&R{$OM@j?f5#|j~m>>i{7P-)qQv$A6ARV^&0>Mxrr((<tkn-Sq92?=~Z<slO!d9
zqG;Rf8#nyL!JlrXqrYM`=C09^3Y4qcPi2{I7yU`IAx-e;$n?f)R^{`b>bi_QT(eSd
zB;g~8Q$Z#OYmU5%**AgUKm&o;lMF;m(4PW+pH4ulfaVeqqCYfpuM3o{xM~4QUPi_;
z(62k~{jO+k=1r4%g@u(<u2F=1Y37=4@J&&208S&KOf1%{))uW%sdOb@W#oE)Z<j3B
zpSPOdJYFSh1@9Nik?h-KM^v322RMxqm5yRAoh+p-HUc6FBe-7a^<h#tXCA!X*P*yf
zHq2-f#OtQjAVm1SjQ;)u8H1PJ7^iPTt;CGGr5j?@iRr^prxfz3?llzw<&zQamRuo3
zC?6UIhT0D<)ZE|MloT&`eAZbePxjCud6_X=6C>FQ>aCYh08I1HM(?dBcTW&re1h7z
z)M_^x7OM}%B`2HoV_UL|_Y9(^zoHmF*tMB46-f&QQc6X}_M7imZQ+9-QaJ6hhcXyv
zy^+8`ae~zH1P3ab1xz`>{`Wyf=3u&H9<=;CPPdLPuM#dzUO}%KK?wx_KIn3fR$2mn
zf{%|c3d$Ap_3=R#s{iTJiZ2{|>%;xcZ-G&Jw46`s!PLsnj;c8Kpj~-*C-i=DgI(mk
z(YZ$Lp9^2EcyKY~2o7XIi*eBAg5o0@y*S?I9M71?M7Z4(INhM?@z^XBt*oUPzq=}$
z(m=#u<_;7ZIWaR<R2Ep~ZEA@r8(Z8<#cT=g(4V4p4eQF1ryfQ}9%4q9S2}4f)1Z`i
zIm8@;@D#Yfq#_+cvV(=5`s%g&+T9m_!UtOpu|v3UMLiO({rylIH{Bo#T5K1cy<6ay
zwGZ@(C30%&j8U+UP#Xh0^8E6u;V-(fl5J@PKTErSNkN<VeQgFD7%X(K$Us__2Qt_`
zAi)Qw&MsQzA|T9>%^BylQ)i;()JZ*w8~yq7C&2B#zK};<#6B3m7N=d6PoF*k)fC8*
zz|A^fSOh?#tLr7`Bb2D@0x1ilZtLFm_M<Fidwbx-Q|0lIKp{y#bbWoDmz%rT;P8fl
zVPt+j1?Fa@B9=O^qgCy@#wi)&n(k7{NPvmiUTLYI5Ho9ul&Mn82kaE~RfzHmboJfP
z9H1?Y96}-w&1UKg*gAWOnW?-yF!=QN*F565Vcl-L)cY8`Z~N%XKVpo$*l1<rrD7;z
z7s;d)d{x#`SM+fZBK3IVP<<lA^5ybUMrhMd6pGMP9G;e_JaG+Drr}K2!=bmRo;{__
z^)up1sCf2z=pB6*t@<=o=%{pc!Gl(DocsxNtQ-wq@=4eGj!N@%cswMGF~?PPm7Ze=
z1&rY(XOR5e$dt@jF7NLhEY|G`HbZUE|8=YZgURi@hetr*zW4iW&iLiDrVX6h|5JUO
ztx&k<+{F7p@0RYu34JtZK8lO!CThNk$PW(;RLojZO2xU}o&!Y^C}@}K?S!#KgS)=Q
z#u~g!y1ly_il@7nvnT_kkN`iwPL>>AL@z*>z#<3ae6YbH+%QT~(o6u>V<?D#0}b#-
z+0^E&$(fm?!ChpM3P7K$p+R6UtZbi38j6}QNqaRfrYIS&5Jy4T_cW$JncAALlJ3&A
z2fuR2&7;VbpK(%ddVYv11WsdwL0F}HX-&pird+3w1v<0>o)zTl3{*&Rxfuti>ETiK
z(Wuvc*D)zq8*vWSDg3~egiTMlp2ha9gf@MW#ucY8iO4SqrVK4sK6=X4JK^qyZ!`8T
z{W@dKx{S+}f|d2xhHSkh58>DeqgL2J4G>T7cj^K$aBcDMKtmOrtsZ;Lb<*U(1Ab`V
z`)Zkd2gWz%>?uy%X(zqy?e76$uTre-gh>C-R{}e$=8TLrVGhz$OJLZsbZ;SU$MOJ$
z_h41qGLI^Omop+4AeY++H^1bHQcAvjw7TRfY&Z9qvJ#*n`S?5x4}Spv1_XeTv09@<
zr>xE8Fdc}M)hmIJO;Sp#61=9P10+{^cvPT+25bQM`1lnS74$>y9v&$lsKBwdx0BM+
z(&FL_>*m3ZVF=X+kAWD<IyQ^E$?ecTNr_e_2I}XOj<ub3NU^YBZpiGTwb<c4qKTI9
zL_hXa7r)0KU=1j`fl>#EcR}4G4WgTj1LwN%qoceiH5K}3&H<+7f5!5h;vU>5L<%%e
z?(YgHjuN@l*ils&z-R>gk!`lCLf~fJ6#a91_3IvUe($tf=>JN-nCAEBrV3O7o@En<
zsnxfzaY{F0%UBA@M^iE|;6c#3K*4_^8T+rho{?HLaQmN5yDP1#QS6C<XHsY%6dfEQ
z0NbL+0N7a`Rhn$Hk8T8Dz$#dIH0A?G)}xjZh?ftPITKX|-~Cq$APok)@jDS1Dxd{#
z;El9=1=-|`G*WjfP56)9J{7k_q0o{&g||xHgbrcHGLU?JbaO6uL_Gl*oFB#Dv1T21
zfN=&ARbGwv*RzTFb!zmCvxQ<gZ&OAdz4qIVrS9KKH?+)@fMKs`0+gXOvzGt$-m4|V
z13zpjYtiJky@P{M%Exr30*74rM;~k?DtiA>bSQ-+VCB@RO~=<rZu4a`7&UA6Km|WJ
zIf;dZwTKt>Z|F#yUZ98dyu`vf>E>ty3gDpUgii{908vXT)xqiH+c&(b+2Iqw=x%6N
zS_J8(cA38GuKXdCW&3ElWm*A_U}zs#83PPh`)r-r|5>OUS~Dw0^5m%3xk6X=EdTO;
ze+?T&0?zOCtHk@i0|zI{IpdD|b7y~g!lIVJYE4^{Cy3Y_503yHGD->xpdke6!Ft0P
zv@SsA%-*Z>dj~39*?MOSGzOIY;8eYc{pUweDUScAQURt$O~Fcfy7@9Xh#^lp6Kpi-
zfr&>am6vWju`JVv%*@OT4h~L~U<3be3-4VjmqY49R&h_`3vGt;GGboW=&xVp!KUha
zLk1){N4x^Y!+1CGc!fe6Os$u%xwzaO?(h6afuR5y8Tn#={^GcCAFMjik_9$KplL5q
z$PMlI1gx7ttqH&=Xg((;B>^F6kxq-d{5selk83(IBSTk4Ci0)0tKrtXLMnHH@*|D~
zu)%>pK%iTu(*k%}F)^{$+ZW})6a~In0~{`ZdjT5H8|fvUuL59ph=_<@zElA8tZI!J
zd&=>H;+M3C2GFYmmY++HIx8|GVPWC5KY#X|A88U67Jyf!!LqWLDgp}14`tmR8(z&1
zKezg#NZNpZX?QD4Bk*;%;)9~+M0(wPV2#Zp40czt6Q~M+1UEv9!rLD@`|*U4fCeZ6
z6JN_yXOlckoGsV0e7H!9)5hskd4eGj;O22%G$kpP^JUdj-PqQ(WE##p49OZ=d3yq6
z?y7t`KyR&p*pd)RiUK+^8g1Tp1F>%&yDq>)ISq)SEq!3kbpsYp^O|eWenm!y;=MEM
z2i&lA)$9{^c!A3m|2OP;FVH#c4^hP3-5X`{c|h;|uaA$`()^8xkU|Nr4M^fzB`RwV
z;+#N^`)JGp?vGi^S^yEkiTNI_MpUr_z*5z|F_icyC%gPTR3nWLXMVq#pW&ShOsru1
zKm|<w1DA40L-ebO8dLmV@6rmtGMCAT+@&J5(lJ?i@C7w!r2UePi-Y}Zzb84?%ZkSf
zX+QSnD4%$5T~F`Dnv@wD8YY(*5C-{Rc^M5_JfG1D9k;y=xptb?35}q}cNLgXs|wJq
z9LFYBd(F)-B3^<^d07zeM`7j`7_;Vx8c6*GI%Mt&{u)EtpMgUpyPzL9;l`jp&*-B9
z%(|ulIkx|k7_h-JpbM1ykE_S$@Q)R+AOW2zRKS~es1m3_HGu;a_)^0dwczACR>c^Z
zKu2W=j7*7&1(<<UHo*@cK7a@wOZ5?$8HlYCqNAAsIXPUcdds?FXs}ce(kOmF`HKm^
zerF-~gCIR{bpb_P(g*sXoe7262LnL=W!E>d-&-Eix44)|y+9O|tKirR<<svft?2VZ
z^f>$!fm5OPW!w~-{58Qd>QHJ4#pKi614d2QjR6+Fj;KEpn%nZ#UCr=<y>We$!{&m=
zm@u$=Zy6alk&~ZTsW;@5M*8-|&2w?{@Q}<l_010(D#FmsF=M?O7C(rq(ZtSfp?q}n
zBn!(W{iX4N3Ydo{%#t>6&>Oj_JP(>GQHA-VfF;xnyN1A%%3msrxloBaqSxSO;j$F&
z-|6|`KJ-%1?6x{eNZI>DP83<@@5tHuWMb76ZD-dumEY?cK*h0iKES6egAERRG)I4W
zo`IADY!toh$4q2Ik-b#x?CkXP;bX9|Z*PUGL?!%t;XcaBUK}0Sy0|dW(3mYYvITb)
z(f~v#o632-K7b`HEp1_8!I<cAv?Ayw|8;oq{N}KYiJCg(%NMts^AZV)ckwE4EA`c$
zr}H+&Uh`%ZYEAkXXBpgUfXi%gs!=jOi#T;@y+Z~;89XePTlw~9C+$i{a;ofuIp+c?
zsoawB+*6Wz3}M_m<{n8&yn_R=g>k%8ig^lbGm}OWW?BT=*-9wiK5z$`XZ4iD&`~Ry
zzD<I1`;TN!681^U6-~VijFHKWlPES*DvIv;z{D`KR?b34i&npII$@9QuR@wYV9$jZ
zuTN*SI(hp9DPDsyaVVaah@GyuQYt$d9{Y<{>61XI&kepz33Lh~MnbqJE$OSEg?{Pj
z?Vqw}m;v{_>)0t8y-)Q$H#9=fizu?a9f;A9K|fQoP)Um7C8%v6u(1pqR61IT;2eHz
zZ0x<c8ae?1A7JPQR;@{BGZ3ACDzd8T1jyQ{3kOgM9}O*pgoHCcYzF)LIqm**H>J!F
zojR808mm36F{E|MFk@+S-fslXC=hELC#EL!E9~itPr0DT+WR3Kg^DT&9`5c3z)%OQ
zg)b{y-N2tEh)tK1TbS!+W@Pfo)UNt5Smd&Sg9NP=DyjYTvYrsXnUQ9He)xJO+~sAU
zP{02F#nx8`RsBWZUb;c0OAw`z?i3Udlo09eF6jnAL_t7Bx<wkKyQHP1LnNe2y6bJe
zzxVs&&AdB|BMyW2e$F}j?7j9{YkS6#>B-4ekyyVc#-<3SGcD;}av}-OYB%0n{a~D1
zUQuG?CY?zV)o!j#u6ouvjOULLFQ2KJD?K`D9Q>IUGgm%WIWJ$01?QopzM7hsRlInt
z&b=rN=0&;oOfM>R9m0kC9Qs(3Y$Z~I{Ud^VdNESD+;gw`OHXdSuikccnb^Ez<?5PP
zSzoP*pUm(wmsQ7mC{=W5Y_y`Hf{&N?xtyF)=#yGqb$6Sd;<iQG_o}qFKm?sV9RJ*0
zS{f}`QGl11jfrVzbU5op?m%=~gkD{JLp{Y#p3U(8XSJXo4U(^{tSk~zQg~|ivyHyf
z)vv<sbj9E}M0$e%f~6&s<SmFf!{0c``K(9Kol!)nw>+ydFpB={?98_Ur{+Uhm1m5>
zNqsSs^@pbX*M2{TNt+{6UHQ0($m1i(xc;7HhIP;fy9rMn2~U|h)$oTg8*-b;M{B14
zGUoadjyD^vb_;brCwsrLzD4)PKt7Q(IW974vizty*C`uA@?N1kXDG3Jyu2EH)C=Yj
z?3yHn?At#!=w75hQDKN8->;Uh(00bk9CdPjcsE(9iz&Z4>9crdbWc9rV8(Pk`CWVG
zm)!HicOQq$cbP0ibruvBE;)H>Yfrhm@e4&q^Tx(xDw(hz;B`h&Z_Ll%zI#_OQ-#mw
zX{xB_-cTD+N7jcO!qB@7gCO2ZSy13sFOqd3T67{J{_3q%UHw&cWP3Fot{!MIBqSsN
zx912dec0{eZ0w$r>48%1<>Z(muYWK<eTEi>_xN$8O8U^S#@ZTz@0p;>UsU-8RP%&Q
zo&bna_(St2*l<3$a<=o09335Xb*WMZR`yTdzkfe7Go#QsPn)ao;>93{e4gEv5S^-8
zDRhqy>x{Jd?f*zhD$vX<UT0&OJK8$!aT%1q&t@AAM+R9wsq0`@sG_F#86OI{*VLrw
zZ?2~EPidQta+`PLciHJm%*HmSmNEW&i;g(3QONQMkSb*Vq+Vt@a>0TXrieoOf=SCo
zTTwbXIEFT({P|DwA*K<!_g~~PN-cSFRdSW{qpkT;yVyRdzGV8y^aYotLgil7$dFgu
zxu05yTNiz1NGfBuTK5~4_b7?<CFL<Ma>grMxjZRYSc|ROErU<?5_xf6BLYm$@qZuR
zjT3Ta;JGix-c%giI73F7?W<%U%EJ}X_`0I_L2k8bY~N=lBFU!C<_2P9hJe1e^3fbo
z1UAFw{YgT|uW9e!e=I9&0XsBS4H0?0soR!US_-V6%U9vp!E1B(G;5Iq{wi?GKO4-X
zw7<em=f}m{U3Itp^ZwAr@HZ~P#ihuszK-7;A04mb3dQSL%oi{miMHAKE1eaDlB2r1
zDn<Ka3J+muR=D<+q0i|}PQliDLXc)%C)~SX3+yQ=8-xVWVnS}#X909Wr)RIC=r}mG
zL4=Bqjt+%w+(ZUzfOsYUuwTQUzNtJ_^~>e4qC4>gA38-&_YP~m5fO?TDA5H66!n;s
z?^gGpc~J|On+#hC$l!jrQcmm}@_`VJT~S-09izhxp}Cs3Hc9z~q^E}dJ<}2@7gs-d
zISA->Z?u`ea=%wmyroQdi<3O~j+MFR(!Z4Cb7@_yy`!X8JnMfy`ZaS!%70T~2w(n|
zov8KVo<{ETQy(6rqS(U2h|YmUYgcdQVT{S`C~1gM{K0|G9-q;8ej<yBYs(te;!U_0
zEAq9bb#mO{?;dtXU8QAu(T0(6H9ep6zd-57A^1@1LFQKT-2oMW{IEC<3p>8N!lL{0
zk}~s4^`eXPv^ju$h>3~syk7)5sEA^!pPhX$p3V=$dhb0lIgY-hWC9@(r4&&uTkDAN
z_wT|`!ru}%dly|^oWHvb>CeSdtN{xPO9Ux#hBGM{#XSbELk@unTQ@^PT3+%FOMky~
zOui>{UqS&roj^?-o4@q_mYtr#fxi_JL}*OY+K=BVOSdgX^6=r{cnCvk^sel0FDzss
zt&d&Mgeg^7rKDZL!^I;FPCYt2gcxf#T_O!DJM?J8U7ntaiAh*^YIYX%Zy@9cF0wh`
zbvNPh)F4IW9_C+Ym^q^#ByO#x=;_z&nhhyCo0#0)9{c^AgJ&VzGZ@aBO7Lg-%wC7z
zUW3VT25$+xv@}1fstk>Gw|wk5rDZT+j+N-+C{Sm4_HDE#`fqjp1!-`5;RoD)+`VTk
zB!^<xFNuSNe+;n|$#0tH(46wKLWm^^TXWfIYn;)Gq_0=cQ7sCUFL<M^Z{ER7pDB^~
zZT8YWE;vBqZ}x`t9l41BarkA}?VlwH7F=CiN=r+bh(fgs+1c5%R5c=9Uun<QTppHJ
zIn!nA2aUfT=$uEP4%@57l_{I?1&zWL@S%YU5-Z`)pq7VCe<%7;n7-ET_h;_76oH+Q
zB#v4@Uw9uLe)zkAbf1KlpF~3MiHHMd9GM_ly0`KRB;h{<)-2I1QC(1O@_XzU2Kk{B
zTR{b9rs(hD5Bf*&G!eG944kEVp^U&rd;Rj3FR4lA{0>qM7OG-%VWKi~dS>vnE*loI
zB&~<%-z#4zpb%g#Q6|uIE6cpv+d{n)#6x=C*%=n<ajC2^a}&(dk#Ey)tLf<2+2EYX
zcG-?tKYj4CA2|%I2Di#jS{~a+nD&afwA9c7!-@~p=V^Y4nF!)rcAwYXoBpf=|HRd+
zpUlxkK}jm7*zlK*w8q;J5KawhU6{GK0nAX0=QaaEbGS80*9y1@!NwHzJ}SUSjnbu?
z8Z3+|tlaq-*4At;;}pF<cwWD)n`CcpTKQ{Sf&F&;-l@F|dgauV1^NLXtp8-r;48sR
z9@bp-ETbTaaeIGN#Y)@C%FD`g)|_9=u24N@F{lX02Ha(3TSqj`w!{1frg0%5olx2@
z0Qk8efi0#PAyVlPM|Ma_=@=#B9rZ=hNJabjU}S8}FD9D4yjYB)-06I&=kIwH)B-`-
zihA6hpJQ`hNg3@Xsomh2x-t=gDp)wIo7cBhx>D47vei`Ai?vSuF6SoE5nkuM7G~n6
zKW~NS0%}Tyzp_50W;ugg=bl!22kjL43by<O1qKBK=)ZtfO_(8@GSw)=O;2pn`MRgI
zkkWDuzlLgok?!yGVHG*1K8Co2(?%9hycKLl_kP4X3i5THmOUQ(&d$c>bGQy6J-v{W
zzaZFx^5TMUnf*r{?$ZaDaRKITJ4_w9QMbYd9S}@J%1pmT>!67T3r;{KsI0HwSQiut
z?gRt%+xu&{&A*H~O%|N`dW|emg{d#m5hn{82AP3UuiH>L<Cxf<M5YLO;`$(c_~0mV
zhc!?#Bj}J)#y@0mc0M-0+MV?A<*R>en$L4a(^T%o3M}bPfhW`h8s%9UwhHa7&CP;O
za#1}?Q2ldpafMrvb**UDIGIU~Y}wiU5pp>zUB%$Q2@??eP^g}F_ItANA%_whi`Mbn
z`|QkM1D?c+Msc5I8jfL6bUi%N7_|vYo7<0MtWNK;SU@Sk|AUE^pZ^HbR6o&~3g3G#
z;wM*!sHug&Rxk!kFOEklrI?mzBA`>SoL`vl=@z4|rm!|A2Ht`<px<tkq1pc5o!6`x
zvgao!mR{h3P__IR5S{U#gWB5K-m)Kljg5%|#7mW&rqWzUzv_ZnZfMcORQ0BHo|cgk
z6$u-Sd~JE__h{q#empq#AtSK&O3cOZsh`LQYYRSpFl2g$Jh!r<@#4kpQ{KBpcRnSe
zIM5UrpbS(_)RJFaDd(rlEe{R~u(R94nba>S0Tk+&n=^z1y`z0TPEA2y?1u9I`QQc=
z<J37%(U#U<MH1XYMuzWLW4XN9zw34ck(FDAKgKY$5)xQ;*H`}NaU^QG@>U|eYHKuG
zl(pzdCbFgF{)+ycc&a6{r=G)#g+2%Cd_E!zKPAg8U)zk&%>@LdE25)=BMkvt;k{_h
z=TtT(CeBLI0kpKS_t&c`dDo*>>m1#y{13dXcbq1KXs_1Vq8KThzkC8$E8Yi(t~YK`
zUT~H`N(q@;M`x#h(!YJ0Z%DKImb}`-Bg1)xrgcE)T*bLf&GsCNR&aSftDkyI++)+u
zd}wN7$48}`jw2E=PGT<g@Lwv02I@{=+7pZA>7HJ{FDH40pVBi@B4XpaA_ol6Lz2H+
zjs3oPQ9c6R7bpfle^<{a$GyY#*Z76zPxCSw2|u6JU-#mlqhHh<e=AVc(MhCzmrqYu
zzK1|u&qKH4@bIwE?gCKu5Ng49%~u{28m~@vpc@w~b?=KjChEJI5d6klVV`2W4#p{;
zGn93n?!P4MMGI=oiE;>E_-a%Tq^H?}Q(f`q>6>nO9|01|@z+vo9ZLn%)g{YPZ=~*_
zYClzyUHQH`Op1bTGQ#Py{XUibaqg2`Ok||~WUXFfjz;Hk{zC<sNPS1w*6t-iZzNs{
zc7sxVLHDzRwc~ARJu}WzjAONMtVb{4{pdEmh0VDg5FA!w?>dWP;m^er6pXgF-(Gjz
zJVMmqh4K@Trt$*GRsSw;?pQtx2DON&@49Msz#tKJCJ2(4seMbVC}$p=*Y{}xP!<z2
z1~OTYcAz37MRt~zm7#zu+*&-&@?Sf2^FGh7`C<1%1{4#1xt^<kdio_fd8^dOE<ACx
zm{)7kG~7ccsv=x5TcFPhr%%DAyYg~aeMGVqzcMRV@MJ4@n*yJZFluvi6YNOB?}{b#
znHOo6(9zL-EeX$O#N*5ES=i0X&u?lN)9^Jc(d3TD{Q)fBQsa(;ZLvS8#6;rVeH%6w
z!($_Ha+zjdqP-L2E4jIIw4x=8h1SiFPPM0II$N-vXM<!~3xqhi+EbE69%QOvOD9zL
z0-ZCf_z4|YVJba)=6$kb1Xm$oK%foTKiLwAOMvQPaV1inRk6u8BrRH*iOYmLK_sY)
z%EA;}+5FIhns<(xyZLTmI<B?c8z-UZtyV}DKJ0zBeTDguO-uE*=Szktg*Z+yDe#Dk
zzbDo=+Peug`S<VsCCpj%^*#U}g2`4zNr@2AIT3%QN8&60ZF>QMsswISNmMkn;3RQ8
z4S}5#&1y1hK0i%FG<M%69A3rbC+ZBS+7p$8sw^>VEm?)ixtv@G*@&S2M`pp-U0^VD
zca*mpo}?=}!_R)HUa<#FuwUX%#fao}1;<SeR)4Z!i7R*e#v<A2VWJ9_&l(igr#YY!
zDCr9N`whKG@KAyR2d*~<8>{=Qtj4=HrUjY=6d&xxlvR0OzXcx0IIp_0vf=fm?nN;X
z!ua3T=xv{%KSq2(g}Ke9+_|dOuC9j$q?gN_@y~;$26+uMP?06o)YOK{3%l_76&_Wk
z+h!;;g@4M}HjwrGBhdH9-OVi`JRIyUoUE;_O|%cqct_&qoNBOxzgn?VfGuu94_xn?
zhO#0yb`OaqpI9sjuCIp@hxYgNp7WNCePN63lJ70}P<N;tuk%`-_|}`&qd&nuNSMeZ
zF^FIJN?q|8W}~Hm^jb~9C8xIHiSuLSu@b@KkcH+3kb*9{Y`(eh!KIGCr$$Oj5u+CK
z4w7nb#jrH@ck&6StE)>-H&RoBm9H-|+Xv|Meh+JwclllLmBqNe4Uo#>@*}1<)p^;<
zX+qx6c!w?A-7OCaTLtX>rVEME$xVv2k7`by_V(Z7<H@7CMpU7B`r*R|qW4eksgSc@
zeVEq0;G~i0-KLvUiaYmJQycxT!&6++9%@J>^r4E}e-k^m;E^j|OGHMdNT9~o3XHo~
z;#OPh6ee#8Xd2lKsf15jesLNe<nU5A%<{bodtpL{70?-^9lmK>OosU<tYqQUp5T}r
zYQXnDg(^|_!h#Q)6jwz}62RW}IfFN}8K8dB)6)Y7sFb+4ob2q*B`5Hh;&Q|kc1Low
z?aNDRX-RnS&z7H(&*rPMvx}{7V{?&npP`Ml<yTCFG5#9e8Qu&`AMvKHsWH>*bY6;Z
zsdU~ic6jv8*ACf@_?<WS6Nv8a+!=!ZVPs?kePr=Lu7sSN+`!j=y_>%;9euv$1~g^L
z#)^-TB$Q9fv#4#Y7Pwf+Rh09msn~qwCBlXlMceGGgaSjzQ&ehd(Rc4v4W;JFfnRLh
z4Lm~(R&^r-Oa{fbj}XCh7FQ6Z1A^(leU_jw=Dc-|`l2b_!T-cf7`#?MY)SGa6+Mxs
zug?tfUFgnSeF2%-fMSZ!zFV8nmspOe(%j|h;NW(RS!}TIG%<O(ZvlE9kY97Tko~)k
zXnu_N(TmIeJvZH(Zw43f{z<9hyl%13%cMjNfifY_E4B$+RaL`D0>e^Btg&%EeV*1a
z7zbHrmRWAf%zBiO#L^OD$poWK1KrfG90wC)NbR1HHvhQQE}b@%usy|a=RLN3XnJ-e
z2G>Jx7jR8dQi#5>Byc|v^g8O8zn>l-4|=+xH^Xtym#=3n;tMJ%coO^Y$pzfT0)^(B
zP;BkM+GKas{R5aMEv9C|qX*C*bTNRjKqG~enz{jGlrnr_-xQ&q^4r@!9!~s_c4U`)
z;KGNY_EsiT-7Z?Ozm4&8MqGUSjdlP~S&N{cH&RA$D`)?kRI3Y+N=GE35E6cCl(bR$
zq7l%80$Yl+g~owLS+ls*U!q30F}IxRj~Bw@WAf#k4{GH$x+Cp{X>3joyb-@{TRpu$
z;J@{*-S}=KMgZ+QkgEQxzxF1uD+JEKxL@tKvbs7$wEV`}ct=Oacdat?sO)>|_<4Db
zr*t1aywLm=Cbhb@282`)b2=krBB<isxANpOjY~A4+beF-Ke;Clh0ytNt>fg2c4<!D
zCw?hFAbnC8ar@cVya!Ec>J3q>h=4A1q*~QHHg+j%>wt16$Md70va-41yOdSNJsr$<
z3s-vMO)M?rVqz*^Ey%ooGL-%}WNHRI0B~%h!8^s1mK&~i8*iaiXBBY_B-6dN_n)-1
znAow`hu-%rjeX|>r2;S_p!|N%{TcF_i)Xp9t$?^gccIL%85sdtD-9dggHv=>b@l!v
zuAryS9jSit<TQvQ_|lwg(|2}M;(gf_0nP@K7#3F6P7uSGbrbbTetHU}ZWc6e9UQg+
zmW_Z1w_ZECV;|W^X=JkSIOEFCk;dzMzAv$TIP2{6bZvDtE-vl{$sJDlB{9`E5cG))
zaSL@Z6lx@JKdWVkQD&@{7#F^A{Itj<)2Wn|fAFa%hUic2sbm*WJLYEpJxb$y(}#<P
zcX?3@o<5e>z8Hq)??sv<HAuascIobhEkDG;O>#SNR8;IgC|fCU!V9RgQ3sQ9tF{#;
zqVCCZT-!fL2y^q`Y{k`qIF;!}ld!S7O0tm^O7c<HLh}k}+D;RCr=o`$AtGg^zcNMG
z*p$9&i@P6KiiQOlNFZ_w;w1bVWek7z_y2=k=$eq1nThrS>v<n7iT|w*L2zJ&bMZzh
zJvVo)lNg+-$v@NIyLVdkk|QLj+3@1(d>b&3ipolRkbkcS(y|vCl*vRoh!hJTZZ3}H
zFYfCzz5&+;>dA1YnLQ@7v@)8#I>5ONX*vQ-bJ$%rf&-R6t@L9mC1Yb^dRUtfl9H~t
zEiv|8y#oR~5Y#o@cI|-4t?;tHzkfLV;J8R1D==>1-w~ipKkM9q*q4+PK}k+?OmnpE
z;-MfsHSjSe23^1%G3Rt|<okOc%bbD&+(uW*4FRNTm-&mdpFauyqS83Rwe&n0Nu1^-
z<2_4yM7b>*MklN_HfkW45S2S#nbgSOtbinOOug<h=-YVFm|b8ce!w^)ZwZ0BTF&m{
zry*Wd7LYBQfh5RE{P=Pu2|ImkaImICSd(x)k)&RJ!OkZI&Y4ADGG#_#Skh)1%JuF0
z-&Aq|-M{W30R`04s|J8rq-k$?czD2elcBw38Nr*Z$8sJ0^ucHFaBcEFws(9s?xyw?
z%a&zA8<Dpd8n-!ZcltH;Z6pFp!2XqeiNmwR*jRV1tRFuI5s$I~z1E46-H>u)Pf9>}
zR5Vkv(t?7F%=Yl+#aGeY-R<J)3SG#PS3%%G6tAHzpRQb9Q9*x4PE&Kj2_3}*`4%-A
zPS}l^WNj^UY%Adujunpa|1TFH)zb&KL&n1-aq$bj_8cTZ$EkIqZN^;Ibo8~g0B@x>
zy?{XSv-^z&Wp8}sGg3}phSJhA><Zq=<3H2W#pVxvgb}DIqE0%T$C1DDl|N7k{M*de
zaCB*V^Fv}vC0|(unVodd-NI%!ODR#kE(6dP11gCtd%wHbI4fS|%loIGr%lB+A9<gd
zxdf>F-u^z`f3KdNo(+T8n;<-^2pt$KLJ$veroT|Qm)i#o_)fyok|}L<X(7ZH>%)cP
z)7uUzm$rpPS!TGYg3@dxN{~8cg+7wot)UOw(3NU7FMAP{P*wVQKf8L$@Z~}e`C75v
z+Z>S!6EeC_mXLY7TjCOt>gm*V<i{Xtb=m!aHXPey_q(!?;hu18Pco-b%O0yni5(AB
z@~_;Dn61RYDv2b$V$-$5B}w4n=I#%Hq??$_cfI}HFg&c`<IO=T)m&AekEE&G7*?aK
zj-)D4wfCTY%Py(3zZ`i8cADN`_X*AAI|6oROWpME=r=Ay>P7vh+S*Ld`jZp5rP6nr
zQ~rP=>VCivvASR>qk<>3r<41ZB$VtA`OGv`a-ZJ|iMhp-ZFFsfSREEHUq~RJ!oM4N
zQ@g-Ux3jauzz9*&I`qs@wN8#=xGDax4@bu5bMo@6RYjg6OwG+>Vq$7*YlkvFm#nWV
zsxK2^OQY#3DsGwC5J$&fiWco?{-xBiiEOO02bIddEC-E~@(>j+<K338wN&`j)XD8z
zQ}+6#xQ&L0^!3~2Pn><A@il$8fipl-3BZ$^eh8q5v*e2e8Um^s+Y_~tWn*_9xw^VO
zdi3av3C`08t%^w#a1H^A>fD;dq39cUwC!a^jE;wdV5RgCz(;&g01v7I<`9%e;8_Q^
zmF_<H|0Y4>437ur){|Kpe^CJ6v<Fg(NHHX54fl>7!t4P+0BOo^`Zq`$0%^XoRKh+!
zLv4@6ynN)9e>FE70mvCFzf?06`YP99*wo4Lx@3lAQmbq<)Joq@;JbkV$S!2F>CGF1
zqWOk~*IjVPaAklPA>sPs=IK31O@~wfHYhI*964wyZEbD+fe#>4w(gH#p)KZn<|1ib
z%AR<soWs{s;VEE)gJ`ne;BY?l=@VG<a=EVj-Z(xy+f`{Bl6cOs?fg=s=*M4LL>kYJ
zoO7*wWhM|?@~^FWczTZdnNyOHK|66{VUO+;rg#V9hxzR7XEqVC@T#db*-=K^uB#mI
zIkeVY6}O#<DCphjiEa6LLf{(tRqE%GA@`@$qWt{xSLdm8;^J3t)3G{G5hqKl^T1MT
zsw{n-NsKsH-v`&?Na~3DvJr-!oF_g9Ym|*<m)SBIp|o_Y?b-4@4r=PmSjdPV<o1mR
zmgYX8VF#=|xaVhE(=~*TBqf`H4)vWl8kdt>bIP<FOYSD-iR2WuzJi=RVE_ywD}uK0
z!sIijYMiq**(%G+3*y^y^YXwAF7XLnNmUhep`7l!UJ~3PBYUdg=j&K(@LJQhZ2{>c
z6Y*n%rf_XhWJHo45zAry@4@!x{0Sl`0lD!J5%~DjKXSO9%Hg}XUR5gu(gM|@=(ou0
z&W&PYX$W)%S+$fvxa+bLnUlaBNF8w4)^ko$%+6q-Nzm0-Q9-t$RA!neBTUEG*(4|F
z3;Lc?^=ZD99>>UXM1eWgXYSD@N2+4><9pW&1i>voejq%z@7)e+ZI6<Dh}?KyjU6r%
zq=JQrj$T??LPJBNXc(h#S`6L)S{fz&bz0(~L%*?KcK%jTAu&<uhLjgeZ?EIJ>Fi?+
zn>;dd^3l61|7{<<pW_cgA&s@ESMUP3q_nc~==gXRl)u2RF;{8^MB3NK#|zpGK<)qr
z94l+S%=#te4m8J7{m$fcbk4zg9vsa9z-DJ<E$i>OyS9b(<6UR|m!4JwdL^4BnFkFm
z$-gZpvF91XJOY7J$`DnvGlGRn_929zzM-M&{2cmu+Mbo)_b;yiav7f#?KAE4jb{@U
zKI4&O&Q+y=CU#8BOme4dPp{>lLT3eS&YMKbk5NfqqMYWATw475s;a8(e}9fsI$B0>
zr-WCh@NADl*YiLVt)Hu^uBz(8QvWF~&g1;Zj*>DF!u&W1DG&^3vIMD095^tIQN#bP
zuX{P~XWdX&z_sU3t#4Y+SEJ?L_*jxl1f`DYH;G-_4Q{)$Pgn~WxMZ{%gaY|(*-y;e
z^V<^ILPc3zi<LRmW$XBk36?%Vty$jSD5<SAcHP<P&v;4(dLJyg%hN#V48Bx@2Cq@L
z=Q>I-fI^k9^FA!>8Mx1O%>Nu181GFM5fuEEm38%u8b4Z|4^p7+m4UGnFWF8BE7Ci3
zu|2Yr+LM+x4_6mkzYbysR;TR#i!J}Kg4QlD#KRmg`Q5GqKLU6t6f}1kNW#GRTEBW{
zZqE~71`ywIli|wBb}G4cF6=7nA9d20Wu_81-0%Vlf>{|98!rD9_8+Fkvg>J|vjsH^
z`dys##BvBxMKm;s1wfJuslj%icjV_gn8EMfAL(%tAd(fYzf|3(q}-_TyDt;bQMM8V
znm3pU)5nY*Tnbo+R*}I=CZfKh7%-+u1qTBj^tzfFGu?M_pvC6c_YbHMQq2pJB@nY3
zX8&3sb&yRFff320u3?cD&{?!J{U$iLPs05!a$z&FzJf8-H+SxQ?#ODy4{5r-&i09e
zGn&kij3)*4I>Rs>&_lFxtt~8$VH(M<M?J(6_sxkH&W<2r04@&{tuh7+4pNyao}&8q
z<*0xm3ARP^u74l6lbVQgJjJJySW;KN<9J;eLPGQ7cWEefiBhMaz(65UcBp)6p>D`{
zk>h^H;?*VCn&zsmLyWy4SS>BN9<09TPQ7DiUEu}xlIgFM(pCP<l$W?E@~10Tw`BjB
z8f9&s|1dhfHC~j;kOR-K7^qY_#A+s6iNSB*S`*0vUvRKa0-^c)iRsdt>)fRmi3uV_
zRz5&5i;9W@KP%-IFSyJ%;{YhV3-)brDu5ZcI+8owISM!TJ1ZVTdnD5F)@LNaej~v$
z`5-uZ@^yMz7Zy38J>CCXmvKp&%45|%)4c;m`b>li`N4}bFT!wKZR``8RoS1WrKi~g
z)_cBsjZTx-{mDkHZ+)|dJA&jhAs5Zc&6ds>OehV=F8<zp8VRgYi3;0O8gPCe`Nm^u
z%{$qg_5QEG7dwUh#4AAb{s42_!PT`whUppIrwiBMqTtR2tEpJ|Og?f7K-k#9IQ8GO
z5F=xDg1{lvi0W!;T=BhcRU#L6J)COl(?^Wy?{q=!#G2ZXw?zfEC_prs5_#LfS@EUe
zd3SD(>4}Txg}C#p4x6_m2GThh+|K-BANWWyr{XCLc4>Yz9)nb`xq@CD6(d!6a=jz`
z%a<?GMNxwlvQHO_VELgK9XW6U>8CO_bz-F?!$zZ{QG3Jc6h9jntF^qr8XdhEuH<w|
z6ZQa5e>a;kUCi>9e=>XoSn+%|BmrPIqF>{2H|TPA^?0rD&xYr-!otExY7v?2Vc@4i
zi?!8mbFjmEJL};%aVS2u@Cf_qMp2W#oL|tiZk)~|_U-NkpF#gEXu+C1K5iWw6JXo`
z_e&suSdEr$ED<;P3*2SlV<I9WLk>_`@;cm0=4)nSAr6o!&Z``Fp{{;(EhmnPi-V1Q
z#k}?++*}PXa_ds$lyBOb<IRSgrrcWpL2Z@j8ew*&SF2Uu8u4X;j}MFp0H(si!T`2{
zBw*$#_vF#h5uh=Oii+?j9OiK<;IzDQcD`{<hc&TQp#^gb#r^bZ8O8nWL0an)mgseG
zI0E9bnVA_+g^=&%IjP*YKiLZ2JmyrwXMwbS)Iz)(ay1-;70;uPtM#<~4raD_wCn7)
z)_<>TE^S4o9u-$`KYz#J;wn3!kKpwC3s;D<lM|;OccmSANZH}kfFAe;j}?&fe!`Mv
zL>QK&(X(-Ij6Y$szJ-a3{5tJud|BO-9#tDLxA=v`^RcrvT;JU$o&Q2MU7Vd08*L{^
zefz*;_&EcPQqQ9~7X&7mKWJ(Rj@khJBbH=+Wk_Q`^GIuLGn%zAC#<CI*mzj|m*sJ3
znM}KAj*FW@)Md@jFH*Oc8#q?>Xo&(9y-!cLh(Z&xqUFu(2tK#Y<NkH_uXQ3vfBR1G
z_x(*%lZ%Py=x8{$%ZrN#a1uco=o(|Cxm{^w^?z4GGOyha?VGl($)n2+Iz+(S9Ju}p
zil;i**(C$lmx^loDSCy?I9!f4psRw42KF2n!`{1h4|;tX-*qReUuD$Pcq&NsW_7{s
z5VO2&kifeYQP<fWC%d^SIAD_f@}<g;1?032v3LR7)ytzo9Va1TZ0TQrW=ZvG%`*x|
zn_D&y@)t`RQ39!S@ZW#XYVwX8ti!c5|DrOZ39Nn!3gg+kz40M@8`2>W_S4lbhV?*z
z?0ALi<g~MT?5BM}P#-Rg<yhY7@abnyYu5r~)nimpIl^@UnuQ4%fwMmRlsZTC*PBL_
zm*Q76TH4xlj2IgNMpFkV=w~!-65=RG_1|M)z<_0A2}#VZTZ9$25V2*eSrO+@q;s)Y
z%Qxd${lx~^5r>S#NuS1MNWW>U=d0h#JV?Rj;pe{^$}b?mAz_;^8{L%p@ePZVV-~!5
zxN2$P37w8h=2|56_4l(gGs|TNW~#CPeSWsybNFZFXzbfiN3bJSRa3i6#teD-U>#s1
z{S!}RCP6}c!j~@p0$Y%Sux5!TMG}tJFHN-kEbNd1>S|?P8mBsE{Clp|c;U91a{tmR
zq5f%mUIoH&!#t;^Br0n7W<W>V=lL-hR<rrB%M|!P!SgGU*(j@x;wNATl%Qy0Vghz}
zwx*`Mz?D1e9qBeLjbc>F%FaH%;R}Hd#<zq*6CBLj^Kx?DMMi>9!{m!5_t2O*M0c7t
zf*VDF=RUjnip$b|U{H`m5v3G@d9)P6xC^D^;I*cTiU=<4CzttG7>V%*bNdHt*VjXi
zFxvwFk*SFZR8-Uv!RKrw8$p<z0s*v`tw=$|uW%6c-Xa@o3t(dFtNUC-qYB8VRx*Mf
zqAAr3l6EfxTjzhR9YAT|KlM>~&QVaoE#kS7QW5w<SplGnK^fbVsmz3D2Gcr*2M4V_
zW?hW}Okmdb?lzp#-Q#1hGX&RMS8ThF(5xUQ%&TvUb##6F_u0WN3gU4FoyPI|(dggN
zOH_8RpBL)q+Ldd_AH0jCe-j99ysv7ejW)*9wEFxA1EtcdYig1K^~o{=m=NA-AMRwh
zzOUo9{f!u2vBGM2nMX)W;%&PzpA35xOhX#wKS2?sg0Tvz&-&8o-f4`M%HCOfT(bD-
ziFU!oH6vG$)b3m{bv{eDvvWteo51OS4<&{8Q@t|+sdVL9r%89-FzFPr%U*uEoBZLL
zd7W||;kY)G3A4g7RVLv)e!4F^X8Yx&(~9nP1U%gSMDCjCPm(HfR&LG~+ey74)OE%O
z?gb|n7z<xz`Q1?;A-dY>|M;Daenb|Img3^)<L1W3#oYw88AyAMmWUrIH9hm{1}|Se
z9armL^%&D2poH&$xxNloQ_1{K{(EtfeE`gde@5}^)W{RT#;^8#l5NlOXym>{wtd#j
zEmt|bb})3o&ZH07YF%)dd-eO3kgm1w%9VKW)^Axs!~;ggBAu##=34Cq{C8Q^3)05S
zM9G24_7k&nTv7(ex7;m*WInH3UwFsL`?gg#kt;OMdqCM8*Bq2p=~NdRZPK>z@X%_(
zIL)2&YALeIs<ZaImTVQNdWWC2;9Fn@y>7YXZ9o?eFR2Q2Q38K-r+qV#JvclBX9(Cn
zV0Fp&QllbwV4Pb>sAeI-Rr3yNFqP!98NK?Q*SBo!9^B~y$Q~rZ!;o?UC^9nZtC~Lw
z6$K2DgHyyyvGo!^M3;TG%E16U@4-NXK`BMItgb*e(`zp~YPY^t`}=pk3sn~7Tw2T|
zVNd@tm(HdJ&6QLq!F7~1M<2BRhxr=yJ9Cf23<~hO9Wpre+GT6yz5{p8lIk{1%&kov
zt{tZ_0W8T@8BVC$tgtjIlIVf)48UjW?tu{{M4=#|0)DcOpINKH=PzGS2P&auZ`|6<
z1rsiK@L*)zdi7%*pV{rHQ=Wt3;(&mFGnaJre8>Td)+qyJHhg#H;1lvDc8Z&WUy?WK
zR9xNaS-3$rK9w!`t(`i}vpUVaJZ<%J3#Bj_yPhSFS2ZmK?h?9JSob>vq+Z9UD_XD5
zx(nVQa!(WRp)qdlmb-I$(bo3F!NFl@{ugbuJOPb(5+sd94J+080(F{N)fB6JIFsA-
zg>&uVZ*;%&bE@J}nWyj5aNX0;(6F5hJwN8MN7k;z*qlDBH0f*_5k3_Kdso5RZ;!U_
z#n1{(j*T7h1V=J9G*>@DkZsa9dl-mOM}QteZ^`DKA##90t72Pw`)K?E&X4^9wqA04
z1Pc1r15CON-#+I1!hgbgc9)$SYN!}VqGZw$IfjG&9uy(^zK=5I2e<8CqHNfzXR3Un
z?7jIsSK)x<&|<dy`lY<DWnhrnYoziRzzeeu%7>nZeII)7MI6cMKZr;WN%f`=%$^LV
z%PS`18&qCtC1>*Wz1+%>VSlP9{mdP)bL4rnskJ4VZtxnwH>EHVDj_mp1zT+Y=`$|T
zZ}KaIp=Q(5;wfC@#^z4Utb{S@9(xm$HTYXq!s&ySIa>X>s$+gfvB1{)Hp=m>lyr1-
zAagX{nPe=9j){l|Jwq&VqjkY!U$D{@@9x8gg(5oqA#dK?Lgk|Oh<^RL83T<su_S|-
zMZ&HS?m}xTtI=We|H_aoM9s6MZIuJstXpjvukJ7G<2aa_*1E4waBLTrR6deRXDcc8
zCoEB;%>xW*u9Fz-%HAYafp^fB&dXsg|JgXOJ{bQw4Yi&?y*55czJc^)BmilWhK7*9
ze3_dItMPoz{?NQYUvJ<DVv_jhw`J!?ft}k-b4{fcbqSZ^(hN#O&#-x?3M=L`)KxPx
zD+o30DY&M!Wb69TZ5GzowO7K2ior-}73~@AE!{}}Wg?2R*HbW83yM&}n`RwA*fwst
z&;OTNHP(a{NQ?bOxaBh)e)2yP`za}d*nZQ!+>tvB&gI_cmSU|lM0ty+Wx^ZUT3UtM
zi<IS#>`Wu0m*-QHlYD7Lpl6#mW?t^aaC~^N2&tvS&k8eJ+ly1*JU&&zC$-O0*kiMM
zh^)9hr|N@!<Xnkwp|mX$kFi@l&8{oevZsm>3E661A{$)WxeC0jO<jSkk5@~^)^<kX
zv>`nKAb&^z_+djS=Z^vspWrg(R5J}j&=?r9xbV(NX+J$xFVGx}m#op%=VILZ*A~;?
zkACKYE!}2#`oO|`s(StmPY*wF>#%Dp7luU)ervPb5$j-y#^$?+N6~Oqch3}cH%t4d
zRy&3*iczCS#cYo%_-p^@mKSe=SlDDzr|bNF0cHhHqMit$Mi5Vaxa`k%OyJNsTcCd^
z8V@D>pAGTYx2jz+Mf4P6JlOC9E*l;hNq~tt(ctZFLaCwjO~opxyw$vHqezewW{-p!
zF|wLD=}^zRv-h0~l)d^jjI<M`%5^ng=4#TZ3+#)6f&w^}fP-K59*q#d=LVV&paa`5
zVI?=$s_zwg(602kD|90OZWJQHcy69Ly$Kyozxq*<fI!+AwVoN(o|&ha?}ye=OCD5c
zXI)r6EzVUH;qOW5D_tqx(qzNH|NXjkENc(}YAcFx^$gr`4@jl=R|Xgv8Qmd0O$pPp
z<W0~nv`<J-v*oYrTDS6DGdwr{wdN9T+9hH4lp6jPEkDY8olb?H4Iu4*DMaEjMj4Xb
zf}<eb=txuSnJ^Vt?oUom0;)+V=Ea-Xrw=k3xc+V)NTcuF2oMYFV@uFm|N9{QOc4F8
z?DrJPmaj+?R)cImCm%n&bDE0x8)V6VK_v3on46oc`%{AZoO>w8n;Ic@8PUsK$8e@D
zjZJk%%)g+2f-TY6DXWmV%p46ou^8Qy3{~?U;z{gwk@&w2Dq11llIemaS`R1KFi3_!
zwL2N|^sV8pGfHv#Gwy1bJYJqZ5l*nRM&%}e8x+35{waDRtH@U6T_Z_KPKHzuROLwt
z2~%<Ed{$6$>5tgG*x*;rbvbn5=$tt;9t4cPe#AY*8xJKInY}6}=cmyklSftOcQM%x
zfQmTNQXZyu_qHV9A$}}f!W73c^S;kLOMpZk8qBBDzT=G-)%*T5s%xl^jfXh2puD$U
z>3J9;?IlwT?pu_^wV%c{#aisPF?dmuHr!UGH+bg%0Gc<`eIdUJe)HyFW1NGJZ?vuL
z+lxNyQdJF&R0)6mEEOI4Mp|9nqM5oirdajd_nurzEnY|MZ~365m?OeVf)W;FX!)-B
zv66%COQoBh+9Nh}Nw}MGp5(BC3D$goTE3CdU}mWg@c$|pwXBs1J?_U!+<x}=!BFgI
zscOxI#E|<H78<AVFHbeKwJy1jtW;q*q!=IU(hKdNHkBZ5JaLkL_6!7Mp8=X&Sl|Jx
zK;bg4C_q9!UJ$4lgqXlDhL|)qHU>#?SVY7OsFnkO$q!Tvfh<pj8&%5-T*1|K-r?Kq
zc59Ywu`%-b8WxM1E*r0VpO<d~tF7t-`Q?q=aigQXU9aBbg@J)T*zZhDx!Mv!d-fh7
zW3P5C<{7&0NVic4KCp9;DEWi>BD$XvU|~%HRHZO4mAU7Zd7>D7lA@`ScM<thG)kU}
z5%U>KX6E<r#8F_@rNbR>?jj8Tx;jd!;}#&}O?WQ2uo-{lyv)0KsnzIn>N)$JjOS{s
zh9~f4_IkY1O<VrWVp9t2e_&PtGY8@0>uVFU85|OFtt3Ph930%SU8YlEm&|gf&+fPO
z57WE~*Emo}jE>P`w(NG?7=}gIjDx!i^j?I0{`s7g%eL%u5q6+Fm6u;Hvqv%SmC~On
zi{}CE#>OU0ZMY3}R8dY2CqF+rjLhaW-l+9TaJ@s%GCHsnt<lX&6H3ol_TV{7dwAc`
zV(}lL&1KuM8EG7jpK9dR(d3b3u(sE$0nH~S$Dt{H(u(HE{NKKP0~4posVTSZnagKY
z14#qDZ_98H2S?sc%MaEZZx-*PCBW&s5)mr7sJCExV`HAR()51eAD<c*?fP$C!(|xf
zok}JMBJB2oo0rndMljuCUg-y%c|s}vTRcXM_c8n1v(^#ZEb}V+3zZiyc5`*(b=j+A
zSSmqj*c80^7HFwnt$#ykI!u>k)ZP>Ov+ui@meuZh>YYrxiL#$74a|W{5ve6^ZweZ%
z&+H6ykwKN^;Mc5OEj!W25j4RWgzVBM+dwVezLB0)So6I-yT8}J8A1Ss_Nlah^}G14
z%nvRT{H+aj*Dw2-ZZ5{I70})^nL~{gshF&q0|Sqj*&<`~3LeI*uKQA7ri%&3INQ56
zd6e|?S_%sbSGdYRl-`-Ut?l_J{gK=&F|(Ihpab@}<K3uoVSQL@hx)pk1#!f-Oq*_)
z7W3niUWj9YRQkz%Cggy=m@d<b0mv`A1z1>kc*=gK2a%AF&}cMy?9!R{zpX(N{ryj`
zbf;$;&k&r<3My;>#sT6E<MX(k;EMlFzql!6oVNVl{jxlB#w8w*|3a9&mMu5~HOm4A
zY?NB}y@?TZ`}5RBvc+Eov4wmAlxEQx-e=FOc;ga?!E&ji11ePjh8p{2tKUKr-0N}S
zG5Z%yIiI6YcQ-blx!wA(y)=|Ca|uiLp<~6PTVA|Vx9plZ?TlFQ&v}T&L`A*+{qbh~
z%cg4G^qCIeWddx_xi9gILIdrECc+tqiAb;7p#IhLj~|Ax0tVV)|DB3wx!=Fz6A)aJ
z3Q;XG1S)-1&j${sD1hAFViz5v3br#-zN$E~h}%M*tu$?N4i_+SN6=O3!JX4f4_mia
zS{u2;5ZA79$AEhPMRu59jk?Q%7ifqi1QU~zE-p@;`(#d4<9+1x)1Nt=1s<be4Ot5Z
z554>$v0->Vlskz|m@(i@iCA?x3q-@I)YM7Y9y^6jlR;h~1sU1IVQgxNCkhEi5|>E_
z>F9rHht(~ZFb&*ypw=|h*TYp0^a?I$^?^<fD2&W6v*25pMg<!I$~ixu^;G^Q`=6My
ze||3Qy(eUxQN3e1epl)4z`tMiVbNH@v@K7FhdTz8rDs|WZN#1{@Ka69nlH22&fp^=
z6mMusp}{y{WGt?&jn6**Qs*aL+^zectkJ{BgLac696v<-dLvn6;YF3HBILQZXybE_
zXm9(@9dVl89Kv*Ez*lR&ex(C7vv4xi-J6EaH3GPS3ZP7tlVg7P5Y`2<5<6f70qxG(
znnm&!^dbR%g18F<12OUNe0Tr0V`NiNQ(v#K<^(nQeZ{5ZFX&%@njZ*C4wl}~CwBWc
z%}f#oO??{@B;X1JVGxA28^d#ekIG-Y_p=kC#vd0)DI5!c_<v`OGWb5c)pn1>-(ICY
zq4|x01QSt)POYYnj{E$pj4#-~$eB3uSGqq;fm<WZL$>BXX~bW|({DsSVh57H<^Os1
z<9Y~H7plfu^E{%tj{oHX*oJZ;Ar!Y}xOg6JZSc;EtA||EeN$PxAwUrlLVpT)@kg02
zNdBw!Uw!@h6&V>h?s*m{(P0xJmD^5o^YR`5Vhhr6uj?yccqj_<14-5@7Zt5s&?Z+(
zE(IZqvI$}itlr>Kg6XO{RrY;dU0ebJ7`F%sP3-K%AfbnDFQB)=f`YsrO*9|l<FSNM
z6dx{Atl1BZgHb)BKmNoMH~v74M@*a9>PKfrNCB`>o;=jUrA&-C-Om0udoA7YZgjoa
z8ZGN;ttxj!FR$H3r*^D{qFfxN!2h})d2Yn{g+{K!pAyvE-V;MGSakO6l(D-xXmJGh
zCQy+|W~$i4oM8n^A%GkY%aaNSH}GemZ>dtZW1+~$d^I({I#x)DsL)PMNXXkwTy#1G
z6)s;VWm0)LF9!z)0u~>7PZS}oKYq|c9t|K6M12M`bt9v6sdQLs+am)un;Z$4KP;2M
z9Nj%Mq|$RA<5F#uvKJ3&v+w7bq$Isl)b6FIa<1Uf!g84f@8!|@C~Mv05HKUMvDr8q
zkW|eZ2?>=x4}11Rp7_K^wdZ5Z(C%3Ha0tG2=}&(|uvD&6QX0^A#0aQsT^XpjCzUHo
z;gSfa#smKon%{1M_ikr&rq@!$Z%YkK5A^lvLGE}-yh-t^GAn7lybIlLU1*|F3@%va
z-&v>Z7sGEyUN2|ynf<=9VqH(`H>hkHo{Cjp11Jz$uW?)^d3kwYG+YW-1mH11odSit
zR@t&rhH=iIN#4FH;kT~b_P1}}f}`N<(9j-8yMe+AbO97)Ev*{ZHR7oQrra6@n(3b3
zG0@S?O-(IrYz*o>N+fH+i~&^=sBS<9`~6jv6y;LxVKP!1DN)KZ`N{Udw<+T014scP
zRZ0_+AE-r+f=FQ0tfi$T=mlVmGO%yf4<zzb^TdQaMVw~Ch~yC@Y-6I%gt6}AZ&aZ>
zy&}w~*Mym=LQYNHXUcseowoBc<~=Sv`UHtlhMlkD3ct2@OFs4Al-|C+DF7n`IG1Nz
zIt9YNiGMuaVx@JM(nveqwaq!tQps&}kxNp^y&+9O0b*rnIJdB1VrIq%86;57mR44P
zCZ8A=2SeTfbsYz4%jl>paGz+BLf^l)y46D&!;nql4YMAhF#{bHm{}OqXKoWC1~qNu
zzkmOLAp`coMA*T=7cSDvG#WJqeoK49E3@*lsO7n}4+UW=OF+v6jeJCtVoOJ1n#wE7
z>wydz$a4IDwLp7|zvs*942pn1-q7E^WRa>PEg?xN(r7ylraQS<h~?)U$7i)B<-v!a
zxUEVPLS6IPL!W#F>@fNAP(!ydOK8Z~cLxISy6rtSwoSMgfJ(~ASq$Ota7{H`nTVK}
zoPxsdO5`~J^wbJ&#h<K83@~y@&WS%-mxN13y$q|Us#21d2MVaXygY@tPxW#dI7#^E
zU<O)lx?)*ZOz9s-e!6|ipFf<nbqR+KnsCJ;9*(Cio@v7??zYz@RY*742qrdqK=b2N
zTiut=NeK36wE_;NZgnQ4w68wJIf89PN+@|*e9S<Ff(*l^-tY4JDwE54V4OD)vx<aR
z@4SaiXJKwW1`!O7;%Sw=NjpW(7x%!JZ{D81EJ!wZ5VO`r9mLt#MBar$Ms4iiF#A*E
zF$8p}A;gHgv0MhpZxD#X0$lI35`<@y{Dz<i7gM25)%wOpVqzj#0KpxMOD%i=RuDI(
z81UdA{ie*d!E2>T^dzH1e&-e2Ai#b#wA90w4Jr+7myPkS8#hx5v@P_&*5q3j;z#0x
zzI8%GQy9J2Fi-Z<q8aO#U(XAzusCQYieh@!Z5I=F;bQ=AlKfyp0z6eeJsp|b;?S*j
z)UV0;U909S9V~IN6SQpT=2p4z?o-t8>}j%S1xS=2DwVrn1S1Qh9N|O7NK^GvPw$4s
z3MHb9tSrhzr5&YuVS9b>@e2ENV?)H|=FJ87K;dIkCdqHIUzX)lSi00yRJ`6Q&}klj
zDt0Bio!ceseqS>E0dW{yIW8rYWaSbr6U9b{x;ro!!l|p&j5Lyp!rZFF<?_rnm|L)D
zXC+h~fm>1QIh6lqPyXZt5unWU>8W_dv>HtGG3CZz2_y9NyAAH|4Pu_?xOheM4g9}L
zGMcwJ!`fjC^dtrb_`|=$ike%-H{2egNl}Ok2nzYtfpdpTzv)U)7aYdsx3^QpUSZcB
zUF`mvf2NwJ%O3Iwaq)_Pa_oOFC7HiCBgSxjk6ODX^4metd3bO@F<Rh{tvzj$qq<#v
zp+Rrecj$@~hjjuZk3$zh*h!$tazuQ*-IhW(nDXU=bzmCe$psJE2-fbv?jD*dHW4Yo
z{675HhV27xZl{AWyI*(kN5-)6@Cu!W<%0k>F|@=)xE%>K=~{i$7Y762$81Q5zyK+*
zdiS&0N&szvlVH*s#xYNPFF`klP9@v#5vt@m#E*$x0f!m*ujdNw5(jg6{OZJPN8UM@
z@c)-Z&;I`#kmAsAlBw9Fk>s-F{U=M<M@2v_#%#!mP5=2gi-e+P{A^z*+N0|{(A^0Q
z;f`O|?(iww^>BWKs!+7Va)yMzH7!~v)htXx%@;n#L@gojRP-#JNRki_uoRj+U!=DD
z`Ev=}ZYa42Bo1K<e)2jEfNgk5LgpbbkLFb7{()Q}T^TrV6(H*o#(|OMvkz2qMsI1n
z?v!=<Tv2fdoGcKX0dT!s;Zl;Hzfr}N_+Q5A09ERJK|w)w_J|v0`2WDO+t-RAC||<R
z=n2>%BQTwK`S2kMN)Sy5Zm$2KRHl=b{(SVFJfaqWs{27)Q!O<9FEes575mcFA7@_G
zzXdWgatKP_vI9@q*D5Rj?LhHP=++j}(cSrXIhJlfBO%e$v5~_M{5oifLy89Xl0P|P
zhDxrV@YJ|iJ>(6m2kg3TKXP9pw4rGNjVcFYj+<vM*ZO}|(F!NjzsAR}1rII_9(j7!
zq^72(=UD17ruHU%xK!IKE-v;OqYrqQxs<;j)~&{Z%_$>W{r7sJZofKb{g7a8;5doL
z+1!W*Rr-mlvdXlXTpt>1(RpKZO=f&njq-Yh?(klAJf)U^f?bKq^Wk2!*y;8*-q@!<
z&@@z0UmG1c_ismL=zgce=WO+9Ub$R%<!}~E+K-Pf>TT6(jPxg_i9iD=J>Um9k3E<K
z9`lSVkQrRKX~y_ItX?M2sd`j6RebgPdn!B2Mj4Bn0T>;?PD|T&dj(+==9$tju8CNV
zK${Q~A3x?;=OiooFHGt9c|EM;pfTvWHRbvPA^JM-mKXaM`&tYG1A`F8SG3+Z5lATL
z<xuRvmwe{UzyP=#6vLP9%=#Yd#ogOJBvEK_5jPS}rSbcd3?BHsb-YNle~gn|SQm<C
z5C~V#C5zE~88Pckzn!_}c}juiA;G7@lZ=O{z*G>myAM4+Q*KWW4>tY!6PU$O1C;XE
z%x8;XO#auG$Do7&*a{AeuKp>cBj6W<jfLfTII6Lh^hmkValYjZQqkir;q!kp&=tFa
zCX#$D8teezm<Zpel)_BkGj(W&*)JFHJGLjwI?c-(4##v9FUuy>o-@4snBAF5?*1Dc
zO&fLI&F_jQu|y7GU?FzGw2&f66pb6dnD0G4p1re40cn2F>!R2Fq{41zr^lBD;$|XX
zqJ4E7WAG@$Gec$$AQbo!ot;83XEI)%z5WsQ&}jFw!>mUGXz^%#cAD42a(`vhY>IH6
z4oIZBMSe$tQQ3~L_aIgPakt=QcEqKG$MHYC9CBR5+zJeLOeP`#qp!;pJ;(ap4oVzS
zcD6WKBn0%4Ax*(QR}xSqx>?#%r{F-NW4#9U*9ZSBdO}kX*R}0u|N1`MKSf7a=BYo-
z*FOwqO!z(u_Y`|lNLs?jSjvotXZ+*9M)bcvOPpS~sym!@y}UebaSo>fdfmLKv1=>8
zZ;2=j20a`!;dB>2Q3oSz@x-IXvYv2Aa~pfqf0VC%OgtZsE2(n7Fu$g1fVojqr>W7u
zYFMd+H5XN_z@RLJaNuf@42FS+goNz>i%ye;d`SQ!a&xw+31SG;MD0Zv3qjKa_Oo4t
zPw4NxymZ+<IU|~szu6z8SC`wa*C~6Ubw@$*JYmzS_V(;V<v~T-hd?<CZpv3>xyL~|
zEk?Ug3C2qx6y>!=B`2TLL3^HsZ=WzEI{F#P_rN&(w820boQe(II=8|T_GT?DEvWc=
ze*U!lmDUW?oS<_CK9)1(YA$drwkDIAV4!zQ`8EQq)Vjsa$8Tz4uw#Ur$k@cB=bkdN
z;0_(SgTr=9Y&JNJ+Jh>FCE6P1<L;qFB0icCGR$^Qq(osL@YF6=_>k8nz=|56ii2S#
ziHC04;6Acy$-Oz#Kv-XIcwUK5>Bn;MA?ta3smYKjxO64Ai+Z+)g4F!NxU!{c<Hv6t
zXi|%A-?uPm9ZZu%(JUvMDn}_JXE*o`@pxXW+kjM`8))FEQ*Hspq(_@m^KcYk8oc+i
zSQU8MpMZluF?O(GwobK!grlzQhPs)A+LOD?3vNko_H4gL?5K{IXI$WA3MJ;>Cu%Nf
z64O1TDl0AB+uO4SJ?x^pMxnMEjL+`0f5GOIo-y&ez7RaBp{8a(`IQ-(akc@zXJPFs
zzmzth1Cw5a_f~HE^(1Oftogr5u#>}ITyAh(ntB)(O>gSN*R+PDModp52XA%~@{d-|
z?fQL1IKD9Jfg5T?W;mpP(WAMe5Py6iS2PZgB}%}gZ3^H4-7mAfHHs<?n*%@h-&PTZ
zAoB-8fKWtOczAe31pCFq8$(B)_~&TPriJT~aY&F5anBnX&n|#KNhr+)#v1=B+^#N8
zfrdB&*aLikJ);l6a2{P3KrpmRnKRhVpq5D>BQ$IN&Q+J7k#@!4cfQ)&{ak9>CI?`C
zm+4oYM3vlcWepWl>4sz-0GucV&@mHXC-%u$yi^CzDo(Q1UYRY!sO>fvReq^=GA~Or
z2Bl{*odEX-tq!<Bf|3geH?1b_xxnkXF<@FB$wkm&BC{%0&j}yht#)&Fha$rB$&)u1
zB^U_BbrKzHnD=z=_lsOr^Rh|nif#fZ+_AT&f~NTxL@!1R{I_-pG+%8uVk$q*J*oGq
zAg!=Y>{1<4cLJ!_-(3Uwc`6#ePRjwa2BknuHrEzZv|LJzaDoA_fZ&dajhz4mZjx*d
z_2G2bIDl4x8=5q~?+@(1`PhPEK#<#TwMlL{g6g(y3vWtZz8kFD@V&iS`x04Ya4D}_
zBX_1K9gkW-@4ckR1V(KkV;qu?TsbM`U!YK6=EbL^KqK+8v@NSfr$D=qKmNIkxd~a8
zPlp<_BbBTjzja;vknSH9k%`4t%82fcQd>D3{CGNg^1EuGUJZW3vd-rtg(i32ILus|
zfaMyR2D!Yb0}lINrz<|2v7f`P<`x#eZ7b1mNN~xypA@Q}pP&1~hEMgs@&p@IbMs|L
zSQQuudrARk(1YPSQ8>VmIy>I}KXrWvJl6f&_GOijQ9?#2Wki&{G7=#xBU1J#$tu|+
zD-lUXc9D=|g=8g3vJx^186_(sO2m6y?*H>V|Nr|w@8@&h_;g*r>-vrFIM3rej^h-j
zB;_6_ddVXPn>5>s9c-lGI<tbc;TPbe9KSE@yD~+ZN%`wY&ZAH5pQ`8xHFARr+jFPt
z2YP<BJ4)m{e7J?2objYhdU7(ARx&B~Jzd2U-@s&Vc=<A7_zm){p|$m`;ZECz{W%qW
z3?iqCpG}hP#r7ml&2)BUDG}|Iz>7c=n5FqQ72LT8SQZj*a*}n~E2q&@I{%)n%kdRZ
z4~c<!XhX95yBc2d4}r6F%AUt`^DH-pn1}a#naY8A<dX*j<$3o$>yr4s6F9a0d^f>y
z_-s+~w`qGeGnINYdkKH=fJa!kP+?>3GRmItmHPWF>lQE@)Fu=0P>RlAnL^G@>@Nf&
zIAf*D23FSERVP3>rf+LrXtWFkWdYLygFwdRb0A*?NLh1GvYITP;Rbh9$UpnoOkDGJ
zQ<by+0aLpp4I4x<3>JObK6bSWeX8}V>U?KOKSYn*M%?#25gfV)^r6U(<7qgdX3rrr
z!FAG;{V>q@9Gvu`Wd2l&v(cff>d&xk`i+E_dJm{uLB4T11tU_lPD6n*+!kd)J$o~H
zu?@B_d_OmwcBHbV$~0qT@%wO{bLRxjZ@V-!*~m{PRi^AzoG&!ebs3+Sm>8@z930$E
zIAlLzK(~$m*)y}B3y1t=k*{qbczFCmK^TozkP7xWZph0B4=d+(O;XX$E0n`NEz-GW
ziHZ9G&e+yVW|@f{9XD52CNg}MrW#9#Hk9ymkzVBgb=KfO@Q~-&`~0-pH?6S*v(NGQ
z!H;^sXo@sWC5vDDTu{ON_-OcOZeFolJ=W%^v{JjrrWM**oj3nZY5IJ1v^8N8WYW#A
zw|VSxxtbV-wY;Qwmq(o{brcvFs7`CoorTB0IQ{ut7i9zGr`Y*-T+CrAl-yLihgeMJ
z#Yv2pJJpyaFO^YQP5jsyM$6&QL2+QAOm<;w)54b)Y)~JO>Jq7XW;t&z%2EkGHq8Hk
zEIK$SNE>Yh5=@@W2uanhey>7Z^R4}T^wp`s`;GntjYYRfEDRK=1Tzq}czFpvHZxp`
zn?812?-K%HjHND&?w4w*ClBn*6(<mA$j0k(EQtMcZPHNb4EQ~A9XT<=d?u&v^>*30
znN(>Gvs}7~j=CzF;^1_1X;?o=8>2@<mKs|$3lj&2JZvkoI&)9uX?XNsRaJcr@Gzjp
z$63*(4G*X<-OGYOIoAyUQ<(dJSeWHZ^_r-N2vI37T=O$luJafql^b&#>&{T$vPRiq
zGGjL_x?BfRCD0|)O1Er{Y=OZ>z9T+!rx3tPdwfqlQ?P}A20Lh-ift>wyD-}Dx%5QF
z`0Q*!@AMaIjT>9W-dwEs-sk8LCiwWPLQ!8AAF8(I4o?*qnTou+JPPRqo>BkA5GiQV
z35?zdqMlHQaQw0S(V3ak0{L%USvcv&qawQ{&VyqR!5Gd9DbcS369PLs?n~R%E$I_p
z=|J0rG6thKWF=}Z>QFw~7Lew}D-Iq;f1`cYa;6?9X9g6CGQ|GLYJ)3Zpvp`hdj{C=
z^j7|$GX)Rvjx_DWv@|Vy35TsIs#)A2QUYu(B|}3UQqw<J9(m^x6GucsNQp7#B5vIh
ze&+Gu!HoRN!x&Yd-!4!0>cl?40$J(CN!Xb3Uw(YkM?(Ekw!vfSM`o>JY$e%EI&#$v
zS7x4F8ZHpxlLFl1F|z6meBz>que6+8_3x<h@$ogg62&SO0t~%>&GawAWqFrxZ$kr_
zX)&4N)vFTZJ>&#RXU_}0S5G%7QQm)Bh$<bzVX%~cU=7qfcS-TaKo^)oe{p9B#Dcxs
zw4GV|$xNvcyVqee?1XRMzAt-O80yGIpZ(kN@p7Wv?(^Q>;wusZY9Kq$q*`{hwQVmc
zQDnSk$t7QNru6lGjrFBobBu+Qi4}n539f&rlEu%zvzUvXeK)oL_Y(74;q>D;5A-Ak
z*|>b>V{yf2PzNL%L>2@M2{B-gva-U8_HBBgz_CLQ_umyH|LmtQ{A=yX@>$z?H2j0u
zQ)c4Mt?1HEb)TM&oqo(W_{1dPoTTMi(XRtRXXJSvXdm{o&A5X7=>o6z%bS%JVl&AS
zLe{<7i8}M6JRn8nm0X?e=d8Ve5s^K^6;7`lA74X3oTi{RV5xX5a`M!vQ`n7hE2L5H
z=r!|cE51gqWDMI>e}8{nUETUtPM{VtuA}4xxXglFXWvR0n~|Y_{$kl}kJx2@2wh%Z
z9a)-L$=g653C=y5A>Ni2VjMS2^$OF^DJ+SBV_4an9ocBbKUu(sI56-H9~ZgU8Oq1R
zw6BTwsR1jzj1jOt(Y{8nocn4fxrT}WyzZ2~J{%!kflC5GLKO;;LsMg;w45%GMi-ZP
zL{CRVMV&uA;}%9BGl#CROhRW20HoPVeC11nF2tWjM?!pCm7m<mT3qwh_3rDY0inNg
zlzyd)IK9+S_&XlMll|<6xQq<zj;q1-1j#2A&3?LN4u;oxTq_TregZF4?|1i<5Fom`
zGr`T+)@qI+8hZa1YF*lZiOrt50H~L?)(%JBk(<j$AU0Ff)I8Y7Pr$McsGs-m?E2?=
zGB4#_1w0N?+Ki2(K3xhtH|PqV7a5=J`}`Dr-`R%zFRnCQMsa5+BL_4Kmt=h&8y9>4
zZ;?2n(61InnDS}<#4K<OGw#amdSuYQnh`I4@%urktn0W_FN=SPx}iG@%qQ;HQ};v7
zgRW$t%jkJNp;YUf2H89Vs_MdC=JgMVMAV|!<^=fw(KVo^+~crBq_HpmrNz>&k&!-@
zEGZo@=ihc(w%?2U`=d_hsN}!gA>E-}Ij=88Bvxfg1uNg@^B==TASIw70=lZ3n-jXJ
z!9B?5(e+zL|9sn7y4{TJx$A^8PM;VP$8NDUNw77sm-_*vJ6(5ytD*##g*wGmGk;U5
z6x1O~?RRk_XzEIuO$u%^l!~@y2^(+}Y<a&D_BO3yGlqq}grI0&9?>#XcYXrB31NXd
zM3mM<vEk{iqsoVPg6QNkujpodj3B}q6*@eFwW_rL?0eIs*<UZIg?-EBUc?*CPV0pG
z87Zu6c%%7|a6swz7w_s#R)59ojD5(F_WsFtWro~;o&L<Pz*}=*vCppLVW-*oe2Ji=
z^GyLcU$Az&F{xdtSN*kP77`7!rd$z4_P2aLQFDjrQWyr65|IAGy4?hHcQ>~q1azj9
z*{6##07kH}u`zxkvY}3ZMCOs}*hZ!P)6d?9YFcZCV2Lp7Q#CFysvIr8Q&r`IhTP;z
zE>l0nJ5u-Lz{0)AhwkYmI-`SUYepw0y+&SAsK-)u>{;dkfAE>xZik+*acX!qWqVRB
zyfUW^_-<Fan}u}eP;i-fL(f4!Hu0OtMJCP8r~i(e@XG2uf%SUADwcOWQ-ZB*X8^jR
z2iGfZuHg;5>#;d)zU}=!A=VH>{rK_YIvwYMkdx5BAm`GiV&vfP!}!AFTKV$__InQ>
zJb07%@WB54;uqe(QkN|thqudDi3{X&Iu0AnpQ5@RqKoKthRa7IQa766#~LXeCxZZH
zk7+>Ig}Ei!X94IL`epYsPq_}Ah=`Ew3d$!a&zFT()dNj<6h4zSASWm&h~89KuHh4M
zIO!xz2Wzg*CYLt!H}BR@>3aCPj?5pp3o@ChE6*`45ORamRAEw1`dW)XFkbrd{l2r<
z#Sg9O^{FytUT$s+DCcEnWf=+>^82TL-N!Cf=EeWH`)hWveo7)~ntqCAERUqP&j3Z-
zvd*RTjE%J>P|qNKg9$Q)sF(j@n<P@{7l9<9y{#a}wzQy{%2#bS+2^Ct=qSPLyCbFT
z>w7<ffgsWrqx_zqlIjXR%Z?ku>tuI^f^UB0A8W$$)afX3OsQp8)CW+l#4ILj<}og-
z=Y#U~xk6&5(P|yB`1kprTY@cZb`A1o_?}w6gpPYtNz-uGmH8(-vmH*ug8j^O3{3sI
zXbt80V-)u#ic_I>X7yo0o1M=@nukP+U`(Ry#1~rHr1w2u*l4z#tGZSt`>blCT>yUF
z??AQ+7_2Hi2*0@2meaz!`jeP&&%dm7n6G7!>CfpTZUl(Gm|EPJ&0u{vN_U-HVPy;<
z_XYw_6kcV}$qFbHDiDu)9AK1HSo=rAY-F)RfsTg8u{*aj#kqEJWW-X8RqZAVw*JF5
zZ(@SPdmo$JfrIDm+nuOYV47m3d1G?~V2=-wC1CCNncr+<WrEgc1s$pU4MP{DfM+lZ
z8qcG{0q%>2xeNTaJT8|hdIqqz{`LIuUH;0RmqNOQX7axm83}>hoZs!?R8=B~9ASX-
zOx<rkgI8#keQ<b~f`H@=G;m~7@>jma{QbyGK2lh>o;g9o3JAsL=S)AycWJ0}5nhzc
zsB!u|#odJ63p|To=C^@^fH7LPwh$@D`3m1pT_}xg1|$j~#(2MNOBl^=xA8X&bac0H
zgUA$-#}M~XiA&z_v9teL{EM;)E#DS#<C<Q*dP8@8J6kzm+#ParRQn|*nXCQw9Gzcy
z+tDGc86anWak%#Hb^DI?6xP*<oF*UYque0`LX&G@^}<{0i`v-xE?znKt<SOTt+2m-
z-b`f&?%#IKqrQa!;&0w2uPLL5vO28oMN7oA=G6uVD9lX-6F`^a4eJi|J!W5OiJgUF
z_*6_LTL55l(^|HB(6ykT#~H9zjAjPv>S#9+db;Y$<f(k40)pcD@Mtf)gyY?6I|vqY
zawZ~$YbgmbY3=DPoEhg_S^;w+tVL1jV_iU9wF*5sI(^!&TQn{$v<No@UVB0Rrv3YU
zi^&XrU#PAim*?~6P@*p%Y6<z4Kp>EreMIq4o&uSH?6SlqlC&z)7xtnGp1hKX+!`2e
zGbmHaA9Jw^$onrYfL8Ws9bH^RTSgOyv>Ubw1Z=l+7<Y&E<m!zfz{K-uL2!t?QXZ%y
zEFj+O^kf1v08i#NGFvzhl>h|>ZzjO8Eht43(XcxLBygKRzydlnj7w07QqJ(xumzz^
zEt-82is##EK;7V;bAIEf06PNV?%z=@SInl9C0B5T=Qx@U8z)ed?jGk?U9Z)}n6v*&
zP6+IAphyq+t(fQgc;GlCw}BI6?0{Ocva)9LBy8{;(Mka2QxJ?1FId7#L7cS>C@@ta
zkt~Oe@im5s8vh<sVqO6zWnNw$K?H&m4A$r9y#`zmljPt9e;?<_UB?fD0%&ujK8s8i
zEA*ks_Ed7`$g(bCEDbDbIO?k;TeV2Qy04Ew7g*CWeya_dmk^5IXZLcWHOd)cGFA07
zb)3}o$=Qp)W$-uuJ1^52(f$C_$C`aU+nYd@p@X+7fJop22({>TA9<Bm93zU=)DaNg
z{y)SKf6=spUE1wcOUuE%dta*8qY;9kH&RsS1A($2G3qk+nH7h!)%rC}&THkL;I`_R
z1w`O&@!>TTTRRpBh^T^*U1EjPIGT2Bn_8Qm4&{i~Q0Td@k@c?ZY5G4}w2tJpy~dwv
zvD>%>;v?D}yJ;mtmFq=l%K9mbf6=&0-`UER)_;DF<L7sD%-0=rGE09ol;K%Jc6u{1
zavHD&7%%`^Qd#p=#t&+~h4*k$o1IKyqz%h!J)N#%&vf1p-IS<-i&EqI>sRcut`?DC
z%5~e78Vg=AieZfk8<^J2<#42FkeZi7YLgt)pOImWYlP?rU*U+r6IwqAG=DcTn@fos
z-H<AG%UPTw+!Mc>8ZqE_1c?&sm;SRHBYC-?E3s)@mN>1(7i1k!8gC^VXTEMi%;-a6
zu)o~MWMN<+f~aZQ6Wzwdz8Q~BVgn@l7~t%o=7mKL_Q4^^Vc)TC<BnTcpwK*?*op#%
z1W($D{X3*1u`QsC1nCAIIdn!H84AP=$o`?XnXzC3HkS5Nk0w?Owgp&Irj?oydve&?
zI}=-Tp=h*LR#-SEn(~s{2Z|F;swdk!h3;RteE#D)8m>Ok##<?+Lu-0ni2PQBruffL
zARf^HTGYBYJf2XA7cl(%I_f&zlXLk4HIbTQ#1C(``d_LUC<KrP0^t)wypZ%mpc2)g
zTjtS8>+3ugcYh63cY~*SnwQu}BsG)#&RJr44@JDt#K-1!d#Iz2ppQv+IKw^_y|{@a
zettbhg!jmm1Uxp(@O`^DqDmc(I-}2v-t}QpP_uy~tqp!;b{f9v^S2CDAt^Z&S-8jP
zyLYh%aiN6^kAi{%AK!V9Ul)|sV{6gi5nuMas_GV-ByG-(_`j&F*VU~FL&nZ?;S;E!
zzvgZg(%=(h`XRQdR6lu=hL$#6{Gt({y(l$i12CY`CsHfpXpKp!os{|UYin!1^FOhI
zk$a}M=S5;o<Gxq)`Glq>H67j7K;TQrW4uNXT#^y6GD)&Vf#u+Cdo&sIW^1h(#n$D9
zfgbB*u|tDiyK!~7?7DDrDeP_0a_V*{Gb>DF{yH%M{)PumA3fo1@dQ{UooEOM95uAG
zmX?=?O6_lDEo(1kC+aK<LD5rFGgg7S*?YcA;J|^``}3$`pSjHKii3_mf1lj0wC!@X
zZW9hv$n;OEc?xiHauR<fZ%xT6>Fj@4ycU1&GZC*x>J8C&a%$?Q%;VGq7E!yK4P-b3
z2@^pztN1f#k!se3{*r9;>OhK!jO|O1b$^L1x{O%fotbgGJ~M#j-9qGDzYwr#wy4*<
ziHmZ?YaqCEN9vx^0E^dvDK5-FPas|*C8}Li3yY<9O?tceF&#F?LrsfvO|B~|D^F-o
zk*7}xF;S=ay@8nkK-6z**A-_i^z(Z{=vNwYXIR4SuN3yXS!4Ji*o4?(40}UJ47Nf9
zX8y3WMoM+n^B!E0fF7Viil5j+m<3O2X<b4A0B+-mK@JKF-dzzB746dVVLpDly@Q}0
z`;F#6#NM|4T)$%^VBgA=>aB*o71GITJ8(;q2#Bm@cBZ!V@JDb(?Bxz=cifeT`Qiwx
zclc&w*n)fy)CYl%`>A`7FyaqlEfevjh=|Dk{rf@Eg82c~60EfyZ{IrLCf>Okg=xpl
zix}cM8giT|pfE>4iI)N~61G$dI^s!kzP~GRDf!Ma(s)X4`%*+3|HOr!=w#6t$&8Y*
zHxg#;s8GCq|Lv=xee8gv&>;pEr`W8psTBBj^5M{s7<s@l#h_u}-`bKyC!kse-wftb
zR~!V1v)}LlRw^VZP+Tq|=@MgY8%D)*bJ1fW9xt#HxHV=-#l>DH1O-?ZC@CqQU73v4
zx5iqgoO=W?M|gNR3+i@&T8R-PB6>_`De?a~oM`i|rZ)GfQa`7QLD_%phg!~Sw*o|$
z4#;Y;33{VhSbiySX^Fpf&La=GG4eC8Ti?>b;%lt+#35qEkxqMqg0r0@n0sVcU-ati
zgHrwhgn?D4sQ~vni7xko=cEOhG|!&>g!nZXf*Yq#QqX6<h#}sD)+hVe(FGTEOQam`
zZY%<8e}4_X+q;9Hm~}01iZ)ep@EQmpJ60#mx9K1Ko#~%-MEgcL;%}Wm&AWd_ctciE
zN8^a6#}x~-4tYs^8YF5s_b@PweE)t(_{ObU9%mlKx*FV34YM>5FhqUH;;k4Z-SyU1
zmOJ+$e+;3&?pP;?m|-Lbq)+v4A2^JeUcym*{o`EAJQbKVBoEsN1kdcTVd|p-lab9O
zf-NOimYZ!%jPC2c>(_~-2gi1@b-B+XYipA$O#`+yTN9aKT~vLt?u)XO1%nkq7~}{%
zxc8qD0~BYU^7J&?^al6EYh<Il8$j9D#m)g6JsRfb!#^UKWgnSZ?7mPo@KQ%qP>?2?
zGdR6wWVxVSX0D`~R7pw6<*S(31*(9wJOIk-l8uXU9gE!(^H2L7uaujQdJUZyd!IC0
zT(3%3c1G(`g>l)LOA;xQjxnI8(QF}0>&K>w-Iabc7fHU%qb|Qt)fqvb<9e8b;z=hV
z5I2TY5V{0QUi;F-tafj($EKJ51SK3c@S4fU=8MSiU1&zc1c4$E!YKJ97MGYPiNcZd
zdZm=u;~}Va!CTh&Ip$V>J0-<*iqL-Sa{b8I7_1hKA<<PJ8xPtiEZotf6rp&z{(jda
znqy27>tvTb>-7>2&I)Yzx#a@L=MSa<@_FDqa){8tYD;CM-gV3JF5S`JFSt|OJUliS
z3kMakr`RkHF~K}}8MSXB_ph|$s4Unj!1r+gas0>M_>X`mEHpG5tWwckn^R|(k4_%&
zt}KMFXj6{~sHLS<U{sL&Z&Szk`ISld8lv?%NbO$egyJ|9Mx64i{iZHx(2l}AI0GGa
zrC8YxadExKD<-~Zkkn`DauddKu(1VpWW>if?7un-!!T4%zjWJRz|%hx1M*I+2_+;P
z(5aQMco7I>{eRAjnl^e2j^=1JN5Um5TZOn;n~I0>B|LZFj0N<b>Z?Q>Y%gI@zPLyh
z2+GFDHV)!!x58`(|MEDZ7jwTQL*)}-u3@>dZ@o)GKrS%9wB&Aa53x6N^H@v{oq75V
zC=T!of(w9C1T=IkBIq(qyg9m+#DxP${f!&9>zuHZDk+FYmwHfNh1Mi8B7*c^`)K9o
zXGr33zv1TgvZ3K)*+t&MN-#ixUZ+n0%$$xnw^KPzV}DxPH|c2oJcB3NdgPTEOlAGX
z3E7ub?UgAz77_e;6%`9=qTAWMQl(_MK7dY(7W;^h_I3`%3l`3m^sKDUv2Jgy)@@9q
z5gP(3qe8R6x_|(C^7qqAtWjGC|Js>RRt01u?)1qMo1aYD6WDDKT!3~VFuqtekZA7T
z&s`<Kig1bZC^(r|r$Oz5p_p#A#;)s*mDs;-3*WoHy`@CVekZn`;Znj2M8N&${f}+q
z7PHS<+)hDo_;{d8uJ5y6hw(VyKMV686x5K)<aC}VV*HXro(<|0*Rk=kJ!Or09rWP^
z67dCQEa~8G#!(Xu%-&FQysw_c%^o4!daZ}5ht%Q|`I~2ccE9I|3E_<CRZ0g__U5*a
z-)3@-do7kDn8A)G+gIdeI>{HWQDdg_4`7s1(Ol}TFo9Tg4Vx&iA>8}Zh`+(c24iOY
zf#Uu9_nVuW2{CvStgHgUFev<siA&&;mj}lul}IY3-$CWBd{0gI-Q+!`-)7ssIR+7P
z1VbrY80Z%u4i6$KM2e32aW29CTO-6nwHUL2c%im|L|6aRDLi)UEa>m90LVHuH5CAZ
zjCCE|Ey+Kt4dyu{gqx~w-eS6XyspJ3-=B!8;pITwKm?}}&Aj>L<a~i{IoGb(LAXi;
zm$-8bjvP6{O6$E{ik6Wo=AQQQUvyK0o-NL=g;r_yn%YVnRoe9+K85Vqe-@OjZ0V=?
zIsUB}(x8)vB}>*ue-~@bY3cqBZi=n&SdReyG=$6-0V2wvm)}TmgboBG7`w#LGO=d|
zQm9Q@)ay|hyGR8GJ3AD-BPMR{Q#NI1&T)_v=c?mOO&A$*HOcKR`ixa4B&N$SOkW^6
znazOlxH4s$XjITx`a|pHI~;l`QI>o{dB^o0NU*(==_3RV)Ner#zjxC29}vg&7DhS=
zm$MSd8^wt^Z(aTg0Qj!kG7~3UDlIiFzJgMkgNdEELb%BYN)Z>6@hkBohvI_{E!#dY
z5V$14mbUoAmE9iTYFZ|xn~%4Qy=GRWynM$ZQ)Fnnqg0;;$yU=zUD-aVXflGDtu+fc
z15&sdXKeYTP9C^(tUSvoKT369T3tK3DrON+T0cb}X;@wx`lUBo{j*&a7>UvxLsdRU
zqSv7eMwFm~EN+j_k?=5(NU$ybOpwbarakxF0x7osuV?<8cx~P^hf`kIShXGyVclK2
z?WC&Jk!w8~kD06vlM##sbzLZuH1_ky#VUjP`=B$NRZ=%^kH02kz2gCigNKvCI~B)g
zS+?KoCyymRu#+Hirr?3@qqypCv-`ZKLmYbcdw3+uUBt~2yR1R%-TU9mB0PM}#$WF3
ztpxoPfRIT-nf;fyA$PSbBt)4+@3vVKi!g~A3v%^^_n#yuaWs0#Z}8{S#>(!Mg$wFd
zf_6pyFRZxjb{5~g(M<lue<LOHU9a-4fcx6H;(})%kDhKnjEmN5;ipjBEPS{CMqm~?
za|KB`h~m7=koqwD!_|8UWHeA}{g?ag1zVWvN~)Ebb{uyFxx=*9OpDTzfM-h}aT6cr
zl2oA}D2FREQqq$W$SDc8Py7BXtBxVz3USqUORs*bmc4L_U*0P_QR7SVVtV!aI}`Gr
z*}NT#RPB+cz4JVU(<^eKvPS#!*KVDS{Bo^1)w*~*NszAfKyZ&;(SVzf9l@$-fLsqv
zOFrKp9b`;3x<7A}@9q#^s5<-5^Vz{|JT?7RJ6^Hh;qSF-K5Y7Aa*^|HYrkD7Pd(Wk
zb@dm=n)9X0A61n{=h&a)oEsQQpQ(|>e-W?ttrnVXjCPdf963U1Nl93p&n8t4<W}+D
zy<2(wdg9U+ubhSaf;bh@jn$VslnE?cgdN-=`ybrTC@fLRLb0d2sVOI<5N$B;FMN#)
z+6S47fB+Q%8%Njs_v3#)7l%US9Lfny8Jo(klbX6~Zx^;;XS!H+W_Ot0u2fRK;NQQ0
z`)rdvL$M`1mPaF2D^4ZGF_UF#*7nY;>zXQh<nLTkZ>fH1T-;Or^3FsJr_qk;DkxXR
zKY3=SDCZfvoNZoZNTH}dUzX%pE*IIex9@RgoO9{6%=5d*m0wuwVj<;{OiFlRQ%Vv@
zey^g>F|*BPr<+iMZ@y5kV{_Tv%O~yf`66O@PH<_sT~d+?3*S)`Qbc1(IYQYW5@OBr
zy)*Xu(#}BgSe|R-v3%FK)Pwk_@a9ZD4T+VDdOH+jr$xf(muYYFSnM7x>{KB)@GJH%
zQ}wUq2)cNM@`>x$_L@22HFTJ5p-AOX-$6obqq>7feJD<AN3guS$~JPnr|OxMx<$4z
zf!rasl$tDI^wHdc+=2}!!_V)~E0~&;{q?mYqYe_Wz2-V$mgm1yX<a()gypG!JT`v7
zvAaAl(OfLKv{L3c!??%Jk6p3}`c$+vX2Lpd2w$lmlK44}Skr+Sjp7wjHS3PI3o^)w
z>U>v9AfrG5^z3pg8a|%B%-%wuP=xy%IWg#!opWmPp@BDF{28;6CkEaE@n^D?^<wW^
zGm&E$J~fGwsJAFfc9aM<#1!6AZ&%c}P+(NklPURe;c<doWaIg=Gsl|+D1(0fejK<k
z{70~1UJ%`vDJ-QDZXevTJYq?<Qbf&XGMaVDOTbGnmR$YmjaV&4A>z}jB)?rDsBRIj
zLSL^;jvHYaC)jZKW>69PG2<}XZI*jD?!@4At`o78v3wB{+Z>6<r0|>}?>rC@#7Cm|
zu=DBS`<}GKCm$9TW(*6pr(XmfST|I5H)C7ZUKz6sTU&VBxLyBSZw8Ng5RW<`lW#z!
z`M?b$y{A+Q317C85yE3_7{-rjDMvaOlHAJ+h;Lm?gb|F!#_K9d?pJA}k>}89**zqa
zL76ckcF)W~Ug#To7}DP&3CFL9_WA=GU-bpzY5Q5z^w;7Qav0gq6nwA{Ap~}wM<jFn
zkf2*y&ZYZr;*ksq1>8<bGFfg#7-4>C?eLKo0UB4R6XdcU(C_DOa%>kjT@?H(7b)K4
z*l9Yx@pwhTw6-zXxooQ71L|)A8VlSzw<$92-^NpUHwG>hTm%9+4O5D*-L?1)pWnAz
zi$X-m@2X||C0Oh{p2Ek<Lyyb74wD*6hV?4j$xbiMteyiIl@`SN$!oDqZs2!T11n)+
zYc)?lPvs*p@&umx5w;O{VvcEURM{qYJu3^c8ufN^I{Q8;q-8QIMf~x|_oJD5!OT~V
zW@N}*EILDNrMV-VE`vhu)1ecJ;l=a$^&+>TUF3WjjFnlqGBW;5N6j<)|5>$au+xJj
zwD7{EOs|Lk#)TL_EOa`D4-AN}QiuMPMJ%4ol1V!xE1R;3P~z>_^h}5yDY#P}y-Pji
z5)u}6fS31rXy|@kUVLWVn^2AHg=Y8k^3c)KSHn?{ZOGBQTDY$9M8d7$LzQaRxva@2
z^;de&Kes<(o7A>@i*<Rw+ptZ$gz0$Q^Zn`qQo1I;r|n~TxI$LubX;#8N{QhiRe8ce
z$w1BYHjOo6_@VDogSBY_i?*3I<+Y$kBm_mi)XL8?@i~{O=HG@^oGl{f;Uecz60H6@
zY3d<qzqYRJSyM6Hi<Ll}v*7B?$j;uMD>_c>ibrpgh0x#dyPIUmQaQzKr8IMW{bXgy
z(SwCxD()QiE&ZKHff!+X#Kv9z{YvzR!eD|_SGR#o2_yn2^U!ZW-*d!z`i+7V0yCsd
zXwNLaJh=Dtq0ce<*a@FObqR<Ova!?HlfJ|D`lY<TvWnlcW0A3~In2asP7O=!%+foR
zA0@v^eNDWDmu=4oABDqK;x7)lmwHs5Aq~56Z{tMOnApAL%Mh!s*<Y})Cn20{C+kVb
z==1T|A1&%WcQIeTg1y8Np%uVmK`D)`)X}G21U2lQe{W)-t)e45HtkGRHbtMJ^|wW}
zu~Kvs?#x}YsK561)&pG@pY2b3wv*`@%&r@{E<erRiR%^MHFmk@w!ipsv8lPKr*#aE
z;mfPv*UbMpP>y&YM+3`7&!ABq!--h$UxlBBC(d$g#%q@K*Uny}x6b+g!Fx%Gu}8MT
zEm8m6=1Z4}FNIj1Jm=l7JavulQMr>947=QkXoq$P#a1_t1abGdedJphQVgqBc)!+K
zJ2|UTa;tC)O2r&h7qp)dSA0CWRW;bV%iHSSk7!>UW_uln9ahhl`01s!FmK~_S<KXb
zuSCk`6}ZXaxM}t8d%}(e^O!Y$dfvQIP9d;9Q-QPG6HKu+GYH(hsR3#h)!VU;<rb>)
ziHNS72tq+2$FgH>v$CXwfYIE&E7kwJ@Zjyc>bCP@GB&aT@o%k)-}R54AVLy`=;EAU
zE5sC;C2^wMnOptF3;qWqS=m$KZ|`a`9+y#j27-P{+a~Jn%HAY$lWc1WnJYUlC25*-
z?!V}n0OUbuCAe5fpi@w;mf_jdd@`xPEY?5#@fvrURVKdWl;>oA68dnvf8ffyPi+>3
z=R!F@fRwhsvMO;uTiKwMS2~c@&97)4w90E!1r!0-w|~)MFMFa>v=l)nk87&vaj}m=
zXIJF-w0ND<UITxgeR$6NxL|jlvVm@&dk=JvNER;ocs!FK_PO5rB8aT>#Qc(|`{cF|
zzV>vZRCPCZcH&JB#m$Qo5^H7ep&}TpdX!2QRuPZ5f21Bqw7h>_Q*f1%GRXU)Tyx9i
z%_Fk$m@-+oA3FIRPZMtL&3FB98L|EwMEC}G;xw!~)^~K2dN}3pDF5ekPQAqEX#e~n
z{x@Hi{^dETd?~;8J+24|<F_!e=aLrn_>GKEJgDO>l6-%T1C6+KZ(v{vY|)%vE|+mv
zq8F=sB`QsKcJq~Hj8p+iWemB9qqP=dx#h3NC>{VdC?@_p&5hH$eYVGV?(&(CsJoc5
z`J4qva>Sf>l3SvEIdE7&K=6MNcC-9JCwXZAjH7LYchgJQAW)?O{rSHj0Sa<*seKZe
z8Q-#Sv+{;Yly3W){mgf`;W#-30%6F^%l35nk0WgI{%cZAt^hKCQ9C1O27Hr#))PjK
zR(l`|tNSyRPXq#?V?&J(EO0qFxdU3sKk={66*`Z0TZl;U03m%?E?+~WLq`cUVL&Cd
zeRcbo^(|{3O^aF8%X3wGjS3EVuQJ|=8d(16c3E#Ton@q=D4U(!wfR$=bvypPu*Wfk
zo|v+1scH<m6YcUKI~(R5B#N+5$G1ZqaEBUJo{?UB8zApTXk<S!Di}Zt?>zV%T6^X_
zdxoBzll2k?=FrmKZj@&LILG~jBN1R=pNQ*ya8oKe>vFXl`$SNC%d8ob=-iR!3B$Te
z460GVIo<4ThgIw;)G5KO{%6EeY(<%i`-*>VOK?=XqZpk8!G*};K31;ZDgC9vu(0={
z7%Kr~HSe0*H=&IRaRD|!Sy1*MC&1Ya?Z<(tK+AP9r%T+ikDfqmvM@I!AEnw*^7xq5
zPGy#`_mO8Pxud<4qZ>tTUMyMKL&%@IBU@1^q@cd<wXz<`xW}D=fT;z|i7Ce8FY1k`
zXc~$HHHj_ii4`+RgY^n^&~fs~DVloC1K@iIxc5D<bnvbiOCJJy(7DVKrCMNQI{tE$
zlT^{nEWO_mzLZ-6K^gPf_uo(i(8FeGG~3o(evknlHyk2!YYwR6KbtIR2XIB^?(i=+
zCO@bx&Ym8B?LPg)yzP?tA*>vBJIU{_mCD~al_$nRvw6qzdK{w1&K~JYS3jzzI>CJ~
zePVHOaTD3=Ixn{QSWVzuC5=opxhCLU2SjD?rG_4CvpUd`ES`Ti9(^W(5|VI%5FjS@
zlz3ao13?`Cnq$1)4_<YKtOqUOeOFgL$~Dh(A_R7Ker)=5e<y)YXV1i;?9Juj{wRRA
z{{{LNvitJCLjT{3H@}4_%hpPM!!`rd(bsnkogzRNI#d3NZRfjVALd_|Z;4WjHIS`f
zAd3p%#V!=v?-Q>Nv$=Jc%m8|UVhG$9UJ!8hhbGkVbGU0Xe*9=!*gM7_zvH$}-uTzA
z&jkp@XT*km8*S{d$6$7Q3bK{u7(icw@A*e2fcJg*QYmwS;r!9;%zuMLfAjBJSqPj+
ztx=Q0nCgYD2b?%)pq_y%fmSmfC=$%@@W){h3$(3#8X0jp=by!=bEf9Jn1n<*bjMHt
z6`d)-_`En&T?uH@MxnkgS$g#N(I1p`&WXBN55FySCrM)=tBgP$^Zz1ExIV0;`!6m4
z2oy5*p~|@-f<hnN61)1Uc0W2-$GU}vM8H`R%*u)WQtD{<Ru`Dpt)u*lnnYqEWzc72
zbmJD3@#HPZGQYZgD<W#rpGe)Yxx_j$MMP58n{W{D%c$L=b|@kcFV6TIw3n?0l<{|s
z8#@L{3Kr)5*FT%xznc5)#xX|j5Wa<(NY%141==~QKbkl(e!=a53s)3MdCaZC(e(1I
zw}nC<k-Ty+oc*&aH}y0FSQ)0vL)k~r&;?aZ^h4D6eh;XVC@SN*Vl1!M3Y|bdDLWXk
z7*Hug%uF{*Knw*s3U%1)XpYr>`wR{R|20I4sOIQSQW~zc-7X*mDim$~DW=^QNUz+%
z>qAYOK0$(#`W84C(NI%sYH7LK34~~L{2A5Eblzok#I35LN`@MreI2MT&Z852Yfz|B
zMRo+qrSBcNo~D%vTvrt-@A#%`sbAgPp9ss$#A-JbYySUKy9Wmc>wb??6VF@lRp8Wz
z140DFN*~$7Nt?S$j9-nxW20&CN#UflDMxx#5oDAPQ2$DK#b>V}(4LU_VcBhR1t!#^
zv`4Mgt}!4%$tk*T@4#}Q)05~$DM?95A|cI(8m!_|d8pK@*iwL`R$!E$R*?5L3Gy4W
z!ko>DMs36kJ*<#WrqT8rgxd9<%=)K%1GIh{E$vme^!F|XH@MW1d|hE;?*<9cD3*t{
z)Rp~X<<`Zniq^cHr~ZanEwMhK{i)zm6d%MF5kl`!+I*Jd!TD}GAMGt{4ShaG?n6@Q
za2Ygx2z%62RGw8;wP=oxjin_#vT>A1$&%Pl8AMj`RWX*QbN5YKljt$NHl|az@=j+|
z{jRWDWojtu+|lw}rgP(7`5mFn8jJ`L)Pg`~;`;Nou=oFz44K)XH#xQ3(qxDy1NaC&
zz4J{>>C%@UVo*j$hr~1(o6o*{Xl_|@BtC#$ysqyxanJv@V(VPri6_X*{DfE_r&SSE
zpSE+_nWgbt%XO`&%*?N|ski3_-Y6Z)o1nYj{!;fzN}9bvfs1MTLSm?ifq|^@NscNF
z!Iq(FOo#C)F-q>AsfaKP{WEVBE;ULJUT_ZSxLw|SvgvAwdMARUAH{z@9e=m&k$hbx
zaWKi>La5TQHSDm*3^s^!+WI^h9`39m^xoh5d=lScs8cI|N`<~N9H<uW+{-=AsN6%)
zT;%)GJhoPwR-RnO6ZPSra|B<Y*mq?C%qqyl57XR<i|fX<p@P<a_iz>fU+uQ*F=QrY
z5hG6?98UUWP*uU^v)zR_M!lm!!9!xjD=xkSz5;YV;ND)od|4KK5MaNc-@rX?yrYFx
z_9|J}$ooSA>y@9Uq;R3^C@p~9LP<jtS4T@jQ|&Smgc7tsyf$@MjD9%DwS=#Fgcoyz
zN;E4^qJuHPs8Oe(VV5exmJWQ4G!W&6<;gDl#)w_OxQ{;({5J%U>VQgsBLX9=3z!Cf
z*VYIYU>3n+M8}E#{`=ymP95hoY)g(9%2u%Y(2**iO~V=_pn$p@7A|1u7XdZ}aaC`u
zuOgOOg*Ir&cd>2nY3)tQ!S{dY%@A30bFz9lT7va<m~L@lfml?vc+pTg1Nw`fTktn&
zkA?AOl!X8gvJrUH_lCULIH*8Q2(zSOHDXX6cRIPyTW0uH$KmwEp`5>rvX-Zfoa*UF
z1MBJnK&t>t^4#KMK+UmOhUkyq9(xuZPDv19fm4hlU+$LlxP%0zUArg<BO@a)7jg5O
z?|X@-Y_*5<#lII}PLFTtTy#V%G~AIUbd3k|@Y>I!*91LMWgY@47lZO1mbMUjt``J)
zE*g>q%!?Arm7^P>mQk!UON-JVJ@;N-BoG2a{&+4eeGd5TGO*M+2lum%Y#pqo0=sa#
zu+|Ntvjesl3e5<9sh{38s#zqkLV<uH@~rXNF$nkmqZ0FGJIcyUaM*K!tu$6N{`8eQ
zEMc7vWjo?$CB_fU6YD}@mXzEe9(<`DhT_O8-*bP9085xP3qUSQ7Sa%UQo374Ct`U>
z!!voV+3Y;76#F>yJOg<O*8tCC@6&^wJ^7)NzC*X14qw_}$miQZn&E!Ffquuint>N^
z5eKuZdX1(5OURhTL59=YC46wvv*MRR=uw*hq`-;=!ijG#*YV1_rf|S=ZE}uwng+Vj
zvs4{A(&JFJC74*r0CX-y47&I2+xH~{mK~LzKdf%E-Mn!F9B+78*c|EK2#LIPsn{3<
z;x`}I!T2rhpFsqt%g4yxBt0cmlmVq{l&y4_<!)ul6NDfhsvRWsN~8=tdZc=>(=?p|
z8WKuHH)0JLFU9g{#PX0Fd-L+ugJ;o)-K*u)#GH!$6fV+m|DsV1DEL}_)I7gOW=S1|
z0Erap_TRdp1N9FL1a^n<Z`9kt^%%^M$+fe3e2jLbNt?I`wx`YYy8A=cm*%0MeOz3h
zG1%+t>z5y+I0RY=I}=j^$R%|F8+)0FspKB4Yqf|Fp*e+taiXvm1~!F#KQUbVnVNwO
z1dM42{9_8F6V_J7agmVot(9sei(SAjp4gGTpPYrj6@p;tC4q5Gn8f1l^{sN0fxf?w
zt+NKwlm3c&c<a{s;lfKT+U`L*wx0(5q~#q3Z*c?#pDNo!q|)hUFt+tz)sC`cNiH=J
zWRf{}0(+AKk@-;~s7dK+AhF`PZSU2D8cfrVsA_>D`E7dopfJjQEnP-T<lKi2wObYc
zUR?!!jt49cZf?ugyN5%WkCwMv(}^BAV)W!Jzj`c~SCE4qjIbJ+J6bNQwu>FsxkTDa
z(6RiUuM=IKbb#CCIwlan+020i7+Ux%MEUfsxBb|C)_lq>EbC?Fze(ljjB?+q>$t(5
z*jba&W6G-@tDSTCs@uO5Jk+$<O8*V7LO-%Rc^kA~{IA0Ie}~8ag;`-AibesvFBZfE
zX>Oeh7WE^_PlTi*c$wKd)^(Z5XLY9Uno1r|D;!yp+@CId-4RtRQdF^mDb)D!UYd@h
z*$c1PoCVK5n{N@W^L3P#N0r9*Wl5NmU)ivK<EIXTzvYd67YXgHP<hb)nhCN7w;tFa
zx;R|+X9+obY++t(nKie6UdV(0xYxKxi{{}?d?miEbWUcnuNbr7(r|4Jk9tyQ+%c(;
zw^6z^Vf+vG5$1WLtx|RYtdPoSZMdd;+~(5zpc;?Ut_N-mS8Dz2+~#k1Zga7v+kBbA
zHe&LP&5$ij5jRQ>Z$#aWJu^m3OAXZfOcmcOqLDI{@{^{Lu=Kyjb%bLV6>DtCbiJ6j
zp*pFuyk}${``Wf`#h3Tch6sq1z3$G+_;bB%rfvGs@XnLM9&SYN<`E5UB*bjsq>AOJ
zDuwR|3J}+Ca+(@0=hMz$d}LG_Ch6+F7w?;&5s-RpbP+WjN5{vdU0f>LO#g-@$X3Lw
z-|&6uocSa|x8OMuy3uX4>lObGl1d1lmD6fTff2`E$|)hX@buUHLJ+Ev7XONifRF$T
z&V76LQVp;-H8sf!?NVm6V6jd#8*n~-xKeuRQVI=O7~!e~Lc;f0=UIqh$4EmOD+Tvd
zuJ}VUvYzSQ&zq2d>av70^vau@Q<A_&t0XzE1*lZhQrpZh2{+Zi-4d=~7+kp;zp3<y
zY#g29I&NWN#CTj*&GYby1+S7HJ~2FbKIVBQA;(G(&7pL-?AQ+8)|iy`chWh$`?Hib
zXjQ|ypo$zx`ftg{=NFEEt@TnKJtGRo5VaBu!qr}wJquvU=`4qPaI1;`1dM&)&W-j?
zol82$D@_H<u;MYTo>VIP&D8n!PSZ$*`N=)$w3UHzMEOJbpp~Y|cP3Q&A^3gtZ{mll
zMyq;~Wu3LM{~wd%(cO<%=z}iqtbaK4{Py_tW{xAU=l>IT^EHUPe&VMh>>Qhz)6aTK
zG-hvzt~&S{|Dt+eb2$ex5d{7q-h2h;vs3j{zLxyf6I2wJ7oR(9IBn|Hs9p~RS%bU|
z;U66>tr=8=RaL9Z=Ds61K<`BEsQvnB4=xqg1M05JI-3VXyc!A$riaC~m_v6i(L{Bs
zn_o(*FDZ{cGfw>Rwf&>V3A(d`%|p*eh%u0XB{J34|0fM@0f8hlp;BwB=`4MWJy%<c
zJ2A|9#DcLbfw%+PEvQ>CjYX&i#)%9#TtD}|>dsF5_T?DO%2dG*m7+=+WO9ZCl<@6J
zD=mVkO8Xz5%1dS1#yy}eP*&VaP|ve*9yXXA`o4J<B>xaqo&f#<zs025(-nCl!nir&
zRcik%u$p>LO*wKX)i7Od^8giDAA43P?4iw{gn%9S252iFbA2b?#tx=a9l+WC$=5p7
z?DO?EW8iSJjzdz=8~^HyMLJJ%HS##+3-+FfPi?wq*G!i_3WxQoh0B<4l20|{t3G?K
z&Q`Q=Z&lv4gZpc{{N1hnGie{E+7!Oh`S0vzJq^YPH!I%t4XL`iq`<#Ne#rw0<wd`*
zL&1Sv0Cpi=U6--uqkIkK9Rzd)kqhr1fb|-ys{BTsN855nUU&7c!&jy9uZdA%>&^t^
z3XG`lHbMWO!2^&)O6nPSED*Cjht<gcpiiEt<RY?hhY@2F+K>7EMz6shbS6UtNU(o!
zJQwx^;Fymfeumsb_g#Am{VCsz&dMjc1fB~G%06;B`*Hb0Q8Rk*(CnnA7lA?p>Z3SK
zmk|2QG<~Y*{}8d=p^k21B9@gGMFa&;9!($l`V~%SL@)^t>DjYqV6q}Om)<9;Oeg{u
z?&CJutZoh6Re6EKJW8=D+f>wdsHl_b(eKxb-5_J&Q77G|D3WqiOKwRi|4na$vX-4@
z{tE|pziRtS{l8P{0zfZ4`zMuNIh;!S|BdGwz?3FYJay_lv_2pcx#A@LBfKLLZA4(3
z(cNtT2MiFjr35N-a&odHzPS9W^wnDtdsZ+hHdfhDf}%xWw`#ozRf6TVh9e>SiBSTV
zBF{ET5_MuM6pK11vCP+1!s%Ffcc9AbTlnkklu#Kz;#?jJrN$y8<YtbI`pZhUM#$It
z10GKv#-Ot89+I>0<A*DLWqK7!3#0`8%_e!V$;1(7p&?j^u~M%y93_qqLJ@j-)|hQT
z#rCLY>OUbd&>E?O6e%kMHfoPq`FMFvE_tU!y}q#z3!}e(ZN5I^wFxiq*b@^ch^wa8
zkX>o~w+wH1n|wd3y+4GWpcmUJp+uU(!#Na#piXJWalIX!dzNER$K9{Km#~BIf;k|4
zNq0fGmnv3~lq*H}VOUf+znibOj}L;3_bV5}5yqX$lZrn0n=MQLH_lNaGV>e0YW~L$
zaL-8ole`Dc^Cq5#Ve@}8YAbcY=Uto!wN0=E`W3xR{7e$-5}p(A7-UTgFN01U0Lk{+
zNTh=4oIos(9vjU!|4xm#*_vNNDHlZ``GEk7I>OuRxLJo@Q4IZ&D;@P(=ghqh-*ZUl
zAN}Ppfdo@Qh|o62pH^>k#qMCE#X+5JyDVuIDW{^&750{4(I;UCp*;Ejm!{;Fi2(}a
zz?r_WM2!#}$N~Sv@ih1L{u=Zc-XFp^l$J$B3!z<&nYl=?tYPk7*+59ACoW&UFk@*H
zv5!64QtW(yaf{6$bB+5-A)WAos>wfL7iv^{iIQn48M>TK-;iTDUAFf2T}e>`^<U@r
zv5RFW3rS*-zunEmekg=O?e^W&t;tIlPt=;_8VyQWgg?-gD<?)&|IQ1lsWG5eUfwBR
zbF*fv1{H<R>O+Snrh5w>^1%OjQLJ#Iy6+H}=c8a@a_9?ZPx|n^QBDmN%KIfKS8&@G
z_s||*N7W!x%I6FsD&ls*eHzI=H*ZOg^LeLVRD}5(YDJOq%HO=g5UjP^USPO%!-0aZ
zR$miQw|<4V1-NY|eu>PY=HU;o+mvDreEgB_Z`Lu~NQkc-RX$mGrM&hXD!65r*%GgI
z_7F9^)j%3_E4f~E=T!+k*h3iv_8R|}j1A{G|KJq9MXA7z^g+h~9!AHmEXJhwx&oBD
zew?_V)PDaYzply_{tZ=&nOxC6=IGA{DIVvJ)$kpoo4F_dSw$#7J5q)wyrFK?m69)f
zhvp@I&-e(T?Q!qcE|s7G(5{Z;lx-KvtL_hX<bPKd9sc`8ePQ}&ir-UOa>BcucP@mV
zyV8Hj8|`4d-KKKbpfu`Y<zsg)LSSuh7}-6#AJqZhNe?%u)>|mgYS)$>pH?Ig{8Zu_
zKN9{_!VVPs?~j*Bh2i1*RBy5{sJ&uoVo9QCSnfZ*^L^L1noG7@X#)m_>KP8dX4x0r
zN4KYti!v!G34<yXeSSY&CneCcotGk5&BdQKQ?Gs>o09%o^tEWTb?b}6WKFI&U2h&%
zos&7lvpedS$(@R}Lj%8iDyiG2xz!VFq<PfoXtW2{&)?>0(Om!IZDuwEJ!;ueRze`l
zGd9-fkJH`1b~L$yE2)}w_`oeD(;%)_QzF*L0MjSjQ?%&GwGw#8h0pQ4wn<VhVc&Z@
zTuN20X&bx!xYxC@ppxn@ALrh#%e7zaxHmB~w&7a4zMz%3P*v`;MZoWdygV9Vwb_*}
zxq1%LN(9%xa7}e8{UKM_Q6{q6+OE_jqyLI*AGy-KKEG#yr1_2OTk;!KN%9-R42_Jg
zF1B8Xc9q-r8n;R9X0iFY;el8llfBZ{Cd|`gB?ppsP~Mo<PL=8XpmfBQz2?uG@n0Ow
z>;3P<9M+Dp|7r;}DiJ(LSI<(~??^-;MJ$6@N8CX}JmYu;|1%bydCdCGWo^rLHO6-?
zA?WV4^fkqqNP2>aa-BHqM(~)_7cz@LuC>3nt3TD}`RfVF`v3Xe(Q9K;3yG2+*;aZ6
zwFjxQpIOGFqwQZEc3RK9z5Mg(Qcg$0lfu@Hj$2;D%N1kqRBB+L5My^xZ1}+@#;&K>
zz>pRf$A~sMHrDm`CbI_DZYjKxn+te6KHT2^SGX|2#v~5=U2tr-kUF;>jYceAOUcQM
zxK{2=&9o<VdN0|Ws~Hv@pK~~#m&yzd+edX?sync-k@WmYCi3C6{h30MH!?3c&*ldH
zO78vmeQAAW`K-H-|5YEE>LIrEKv@==kXQG3yn>YW{#pMM_4DVIf>JVq%9PWy^Nd_W
z&0B(<helu3l!bkLeN)t2@SN_#=vN1fM5~)puftiI!WqU`e0)1<mA0w=s9lv*Sc?0A
zg8AOiFfv7E8{&+J{XNP1^4C(Ml5_p)M?bnR>)#dD{Fn7J%yUQyzXLFFzHRAPweQ$x
z9ptipv1{UbrG>|@2K};jat!Nd_!J$fc5|^RS&tnI`+a{DhV0wQw=O)rg}^1oPI%s6
zY%%rod)>)>d&hds_xu0ZHn|Xq-oGB?+qVYpp;*ZIox{A-`X$Bnm8^4ueFg65tYxOu
zKypvN{>RmgtXn%f7B?y#|J(~(@jb&!N(kS>N=x`l6}@LVt-_e;=30-@;`iB^1kGs`
z0zpq1hh5ARYN_z7bUbpoowlXjLux|2U?&Md<yd@SV??I0iOJ*-Pp~{eByRXV)Ao=x
ziR6kV`;qQdmAuJd*6#@|)zjyOtB;Qs(G(mbA$;0x*zS@l<4*MOuCD$xd8NF(JZ!K(
zMN9k$*|(%P$E8Dl8f*Hji>oI&yIGQ1pK6m6T<i^`8#+IIg8i$S3aUwMZEZ+ySI64X
z>o!+1e(Mz}c6iOAt(&cFBaei2tHry?w0Ba`JIR%%87*08w!FT36f>-YrK+a7>Iq-)
zY<tPz9riQpH2H(gWoNZ>Hxi<}0$&y!`{;vl^mgZ?kB&iH-!5Mhvn`suoTe>LC$Tre
zP{Oa4dP&~!sQI8%MZe>Ly?2_&uU2hd?Lr_li_hJ@XdswEdTO;J<#58?ySX0|vlmF;
zv3=P&XjOckgNMfUoTuN>`!^4l_WL&V3o?06ndE(snLlyO;-oHbvj0}LCfDb)T+-29
zvl$0f9#jWR#iVa#`#W&;XZlgu&f1)}2VcRlyvA(r!isN&LWK7WZu3H51@qU}3qM(3
zF8u6-=(N&(+9<AnW@UWo_UQuW=BcYimR^6pLE8bBq)E{fr>&-~i8)Q9Y7gFvv%@YF
z-I!In-@S`TOXIj|?<~>thK4{OOzCA48)|N#5OYvK0J0;<Y(F3dG&Jn7{+~Bl{z)CF
z-FNp?C;dZ%Oo-%0-70@3X;lPo*B9t4K2v}$V}Dv7eynF&<c~)c^RAr1*{-oKr}6PF
ziJfz{J+~u!LSLcJu4<NEM&e2R8CucuHUGMgvjAngWlpd>|Eo&I@E7`z19#LjI`}8u
zi@fCa=rJxy_kHUX?mVBi_vVKWMv;w~9TRasW%}ZFUMPP4UcbUw#c#&x?x`E`sPfeQ
ze6NXGTCdCrxqb+hU`vCpkoVkUq(niTsTlIU2><^xTW|0p%6D{hJX_(E_Caa1b-eiw
zY%@||{p=p&dFtG`*&m)jY_i>Bs;CAI;pBA(2Su`X97HPuqkO|@M%M#?4HXp3rx}0b
zJbUH|Va=JGa8Z<u*+nb@Kk$!sAgp(nrOP(`H5?GYMgLBwad3;i06yoQ%t@$m34v20
z*~%(B5mvUg*-c~3Z{F|;3H8WC51p&^^#XPv*YPWt?JY|}?=)<Ktx+>UH7eb2*(A6=
zF!Ok3X(=lAr|3htkA=zMR>kt%ce*`2H3cW$mj}g^wR09jna$wDC&Ey0w(^LO&~|ry
zvUV4G1_ZYMKJ6Sh)aq<ZoE#j^)cM!A`8?g@er$IG*x&C^e}gC=6ix_tqce|x!`>)r
z*J@mK^^(2)1h}xe6>qRXT<R|gou`U2hSUgZ3zYVcA8$k!rzaVIZ-i^FgOgKxb2E1)
zIOJcy+7*m_OB|l_kYcZxyValN#Aj^`qf~ey9AVhbA_5H<qmVKVht%*;V$0YMV3FNI
z$`~R<=x{+ag6TfW-SC=03rzUC-PD!rA%25f)!^HOwgY#XXRltwm7F}#1sja`owt5o
ze}4Zapl@*jY3UW{SBPCmq+IvnQ^v<_OcF4#S+LRWc8?6<QHPMgyxcLV<VNQ4H;xj}
zD>}i^9na8Jiaj?)!)5zchTDx+KfG&H#urz>$ok+qs(K5bzR`j5;h@tm*u20qDT0YR
zrwb6RdBEQ#kS@TtWOa{JL9=dHjX-Hd{P6QoNx!9o>f)lKqspK`kSM!~%70A40{7Hs
zotrm?S3C8y*E)3Wr`Jy?ckX=(vOWyFqQlqx6|8A+(ijmK)L3fRX`7I;>prEN?JI5=
zaiQ1K)YSC$mYI6E<J0xv@Dq%U@b88y9VsaEV7Nnba7M2}-UqqJ=9BhDVh&IKcqeOA
zPtmUsU)K5ILpiczCHmOd*f`mfSxyXSDtbmsizu$fqt-Pw{Rkw))<j!N%kO*T1dqD%
zV0T~`8HTgLb^cw%Ny34x<bVAY-VGBUCPCwS?o@<uO@wU(f}%Tq{fPu5eUs&|70?{i
ziB$|>VhqI#45t8#6&e>So5X+-;*N>c+TQLD8@Tx4Go^M>ZgW`}3iO>Q%Onb#9I<P?
z3o8xv*assc1V!Kaf;N&rq3Ezfd>ZmTM)V=L9>7Elo%qb@>1nt;Vq8Gemz<Qeg0&9b
z1nJ=r;-K~;x2FR&d6+-T%E-)I^?`s09rwUW0Z`5fl><gCx=uLw*%0VCiJ>nw-0)EZ
zF@EO4&2X;L?F$wW3}Vo1Igi*-GN7TcBV0-(QZAv>?OQE9n604a*o0dpChASqF=S?F
z7vBhKG;sE2yHbWu2>gN5Kbch`QDnz{{Z9C9+t-qkBVYE6m=&H*_>!E=8og?J%8))p
zmrq6I=3!g9j>TA>P-SkIZOypKq+L@~yr4@Ud}n+zLiol^MNK{IHNlbC^5xem1YLQ8
zs_sCBqobpB`F_F~Oe-zGB&8aUp;ui+$pHF@?!2<HGXJ{a=_R=MGof)bBcm^L9rtx}
z5wJQ{9K2{pN>CxO$0MVt8eDG`?dY3bR^*x=zoRoSyNm+3HX36AN5Hs8H*Iv-L&Kfl
zQ24_LRcWvH=nY=fBKNl}KzA()d7&a#k^f)s{GLZkt^~#Em!p>me+_UiVS|6Mu;!sV
zJ<eYwn-pjFs9`s4p#Kzx-F;m?adE?GF2XzEdn0&-&||4VfnNsK|1|>t<JT|qxGp_*
TdJegH;#<{^YpXm_wg~z^&-R60

diff --git a/docs/articles/examples/timeseries/timeseries_classification_from_scratch/unnamed-chunk-13-1.png b/docs/articles/examples/timeseries/timeseries_classification_from_scratch/unnamed-chunk-13-1.png
index 954bd4c1089144ab346266634a3d623ce8eb3bf0..705c81360a200177925ef221bb9c849e391cd11c 100644
GIT binary patch
literal 61201
zcmagFbyQVP*gv{y5Kusn4hf~DyF*&KrMr>tl$Mef>Fy3`L8QAIq`SNBeBbwX@4A27
zweBok#6Fy}&zYHLKJ|nte3C$aLGS{CAap57F(n9s9fcs68)R7Un*(w=bnpemUQ*K$
zf>5#k`wug|<nsVQWRR4Yu!`HygEVI$9L<#;U)F}>yyj*?MP^LYgQGthaKf0{>Qx$5
z%{Xx4Dk@A+1eOJiMa)s~z~Rs9=;l|iDvs_BYD$g-crx1cw+6Y*wg%H~hi|)unb6>f
z{&)PWK)G{ug^>PdY~Lf{5^??S&?EkAzxuz!%h~?7f8hU&^QpH_OT;%gFGJ1|tSDk7
z4XbWK&O)d#kg>uxj*q%I5lA;lVOUb=vBFLipW5EW^@(JifZwabiz^aDUb$ZXJKoM9
z=W^p)#x0HfnDgb==PHc{g#Cw*E`6r91NMh)(SiwXqTyGU=1<HLDcy!rdE@QP6f04r
z_VM^=A6;FqUFqpi;9(iLAkL!y`{v;OUpMy~0qN!cjA8Zv|9A5L{ou5U<eQT0?9S2A
z7$LIWrvypLo@ml-doDyoM43eTTaUwk|NFuZ6F0Q1M0mjoiHRiK4q}v8m)5d!a_lTD
zFOZSVt*lB)O702%pU1M^g2#FZ2!8l*obz+S#Kfe$yd2UuGgDVn%P%NEf-3adO<7W~
zUzp5R8t7HRAqT|t8Izx8d~bXt6r#i`QrTNsim*cu61mO#uKVnl^NkD{d><93>)Zc|
zs~-}|Q)*vV{D=W&pi?lQ;!HY!%zUCiex$E&w>5UibYj<OPM0H<VIesqV`X->SD)!)
zB*CT0J$UB!^@VF?1#GHVNnh6wZ8XRMt2qx8LgcYv@{Vq^+Fto;v@PMW;-N7np+bBl
z(ZBV3Im)#gK3z#@YcJqh%ZQ6_B7JT-94K$R9|@6t_v{zZ@aUH_Xj<c2=w1ruj0XEf
z>d;e^36;N4rUEs1i6@^no%rxyzuXlSF(GUmoRrj5-x`-M_Wv`UT-t(GsTmmf9k&Mk
zVBjn*Enkufl;-EZb6oBC{XgSYSm^0(jfUQ0?)cJDWU5M=or<bzb5oOjT>o`saj~VD
z*?U{1+crgIWh*<o=a80fZHVx&!kU`5U<t0gO20w<Kg+x%5cMTk{y#~7WM^kfM3Z>m
zUr2fhJkQmbot~bynpM6)MWrAoCm<kzGHYsBnV3drX09*yXI2{Rxb)!ouu#C$Yzr!A
zYD(pd)z;RwecV(kVxy%k&d)df6N;&(rlyyJQl;@3%xziO<Iz&{>Z(529{c+X1@fd2
zGAgRfFMF5x*r)nTJ+Dt`u_iKi`zq?{agvn8#Az#Z9h(lgjGwsz1Ex`e&O5tCesVkR
zPnXFgF<w#xH2W|A)w{B3a@+h9_Ie+uD6zO-`}#03Dd~8n{kdS<`CzuHw3Ob+XuV9k
zA)DG`wZlI={Dp)>4>}<SSWuAL?b#-H8#LJc8+~yZ{GNYJYs`j!ay#!$t{kX3I5?=O
zB}POHboj%y_d0j%q8nTvE%~~VLXa_Ljou4_$afgXKGe-0Qe=<PW-cZCJ{6qbJ{->;
z-P7Dq`)V?w-ACi6P-68JtCoa|mbbRyCaSLNY;9dhSo(nH33)~8<m~J@?Q+Ba@87>f
z25r-`EQRL0FOONGx0^VRcbE6F$R>)4V@1jZ;5rX4Z$d)C=EjC`!O6*5m!F@X<Mwd(
zpFfpz)?neK=PxWQ;4)}U-qN2<7AYr4*@*;S;IMPr@p*ise1E*N;|AV1uk9l-+oS{`
zAv{&yj~_qo?(Zdtcw)5wyQOJIZ%ZqynF_t<)sCl)@2`%Ij#@`b+S&vd800?vH(o+a
zXxhy#Hp5d9cAdxV3>A3HhJOlX@VhG2D|AzEHyK^)D|SuADO<hu)P^Ix-<nq0%9HG8
zW^LIPy0TFze}DGLDM1aC9aoNtwmrmSG47R6Utj#?c&Ay!Ydxq-^_~>%SK5ysP4Msw
zWpb$zFEVB(U*R!jR#%^m^JJhtrWw{$FSmJpDXM5qO5eI&aAhM(tF5bBdtQOd%F2@J
zc@-^6_3CKFic0a|@bI}q;rXoscHWq|UUB`ij(x${@mJE(F*H|#M_nmd+0x=-0uJls
ztgJP42ZEvD;g@J=@g)^+XJ<16ytNYJPB_CNBIx)%4Z;0F^zK)N3u*V&xKm~t?LMv3
zA><~smpIaGU)KK?*bxY1_?~U_EiQhRlar(8t%)3HW^>7%nZOPCh-QKs)V#l7yVAyR
z&yzGZHy37*EJi-p>}uz!b>>8O-jla!$)@GZSrRv5#=LQVef(kAo%8o1Wl51qWW@S@
z@ef$#eVc{)G#+PKTH0`JJx_zd-p=|c2`Upu8wO@H+LXb~C>QwwTEd@qn_FAU4V)i#
zcV?5~)8BtDEGiOz>7<r3G2G%QpU!6liYM5!3=9mYxQs2FqjQpqZ(D9e!;ISNtY-S}
zuFf_GMs;`(rlxE+a0%vk%XM4teg`7eGobT^G{Sa#y^j?M`x=@Js}q<&r|#k5@d6%}
zl$2Cg_mdi@SxzfXkmSzlDel~Yn_%=3`z$*X_v6##sUeMv!2TmDS(Yk}f%0D(rQY`}
z)i`EDq-^Ff!NDJou?@hHfl@W9cf)0xR}&bU-I+hjiOKp77LuW^eHGypo1^;O;^`yL
zZP_85Q*DO2S_UaAER?RUvdw*IR;7V&kVPH9^(mvzm_@JMXUNvJShwcDR&n=oQG_;D
zpV?=;T&MY|<bLnEBkiNfYQQ@08j?HCB_b}-v*=GgcNQwDpx^q|OHG&O7H$Cn0myc*
z@Al1g28WkiE}NR0?=vunKIpZfy?lx2mnV}HMZ(|I(!$5b*V5LeD<##}+uJ)gcf0yK
z(Dh)*u*C9Jo@w<4&MP7=GHo50u1atfJflAxQWPFDECh<EMnlC&UEN}A43VnZoY|k=
zu8>!BG&GO86JWW7Yz;T@*<wSVy))!9YRo&oqCgn9xJf^MS{oU$x}R<6G`pbNm8Y58
zvK-m|;U2-X();VqJ?kKz-F?LYRkAX>cm6gqH8lk_Zf0^44F#nr%tu;m%qiVIHa+oI
z`1oV?xMMm+BA;hfeTJj8>QH(XoG7X^C7#;kGA5I;A72VQM5xz3hR@Yoo%OD&epp&{
zN>1;3{vI}?ElS-HwGh6u$!FMk=^%8x+K|Jwvc$?v5eBxhcRAQE^X+X=$vv0T+0BO%
z%UO|Fg0%VGt?$Uxn8LVo+DWd=H3Zu8lvrkFW->A|h=He|sG<~y$L4Js$wl_UcSg2k
z3pZ&&`UT~|FmH5p*$0I_WaSTk7B8wS3k-zu7s4wnKg-G)5Ef-<$SX_h$*{0tOq8A=
z{1HkmED@zRI=#$*!DChAdWv9T&6psa7x7g^t}N=#gCJI6W+<PQ4h{<GilvHkQdKQJ
zG>v9w_fS*Q>@)tv!OE_r)Tnpy`;(4pgmCnOf-rgJ671RwvwPN*<0PK>aj8EMbH5mf
z$`RAr`sbc>PeYQ!_kPvCVIb0_h+thAJ`+)UYBPX?(Z5brnHgFTOjEmky&52j$jH_9
z`pH#K+?U|9{lj5sw_&U#<$VBHq-SSm8HqFu3`?z^4RlGj=lV16ILxFJH(NcgjhiX4
z!i30f%v}uptyX&R<Dksk)^noRFHaA*%K<qpEf1YRs90gfyW@E-E-uy5<$CSuDJi=f
z8?{cm<Dh6WXxI17%q*t!)u(r-V?8Bj7L^T|EEJdY$4cVp$eIwS`pXVm+3<3V+enC<
zMTVst7CBwz6ta%!(bCb83i|wfc;`|Yc6yYH9b1*s($W$etLWju)0#Eh(~T*-D(1m~
zDSjCoU0PDIj)OCa*#{4q(N>9S+8ms$6&Dwusq{B%bNY}>K6|Oz+Hf}OlG!V%ZO|4a
zONn)%Imz%geZ32gjEvN6B7kD1CbK1}lu`aXOmm<kc4;aT!_imdE&W+;%WC;d!c?!2
zE1Oq1UTDKdk|36gDJfF)H8j&0_UQO|>X+Vw|1Pt^)xn%^EA40d-VTHw*G|Dh)eCg+
zT|+@lO-)fz-{9akG4iEm*W;%wI9e6jp5Aypl}rW+yXi>)l)eQA2bbEbswT9z`&vvl
zqnN%MAhNkPw}3hZB}N$EQN~K<7RYDlzM3uiRDd#A#>UQ0M@MI4V*`%YI9qs$!DKO!
zG0wWn4;QknEiEobi=fn`rKJJzsy(6nDaz$lA6-+THj~%GSANf{#a2&s0UFz^*2}4%
z4=k?-^7He9j0<;7(Z?p{;^Jw)OBZM*Tl9_DZM;b*Wc{bG*K9<w1a<JfoBpWNEhhEl
zXAv_bmZY8<iC<8WhwrjKZ76!Ub7twz68n*i+Z-(P8wP6Oj&*K2x<pr&p?Dlj_}zc!
zn>5&A*!N~_H+gu7ewANE5X1hwjtHX;Fwh{3mY~Au6B9uViBKq{tda~9S(BmELi-^^
z&R~1YrlwT5V_eK?zDX=gK^5fjrn#bC&C)xko+wb7(!7tkI#tVD3|5(}R$X1DxWtun
zG@_zNIcJRL#tRJ&PTZX(T+NAD$GL7(GIvI`ZV0*Gw5AMuV{*6l6|qj>rBgpo^99bv
zxz@Y}`I?)ukvj|bueP;7p;Cn8g0Qzxyt4~CZ3#*Vt(elTg0~K0n99OXv9U{oqx(^?
z2i0L3JT|kkTEDq<y`u8&8)TbF6&SLr`tpQ<{em)!%uKT1l?~45!Qn54h{`3&iAxL6
zq0h06$paK|eaHdS$Ovg(ZD~zf?(bg<cPI#FH8pGN3Ae@dsOKwD;!XFj?=#^A*$^-M
zi~A%Hc}Je!*|-|xj~aFSwf#}`;_#LcAw-P)_FL+jI`L+IfB*hW1>hh3eSH8Ejtmbo
zvazwTvQ8-UDjx}+B<cCMU+$@HWWlKw_wO42O<^^gmj7{LeT5C*LB(}3)w0{KBK^s6
zzx?k@etv#s<p~7TH`d_%MMpS#lkka+)%_vs;UqLGk64G7VBT)2Dac@Uu+2N$OkNn%
zcyTEdy_}IE@`AIwO))n{r<Q%hcq^HfE`hcfkSyB_-usqq<aTwGC6V9K??V`~g!FXN
z-(!u`a#ZY_hD>sV^YXg9{32ImhaK82hAoa!+a9$Q67`8EEcg9~83yeBxITDz;r1zq
zH1aSNvRFyT9KJSgL_KTFGMSzQMm0C4IVhWh5m$WdQ#`V<gQHH`yTG@J(v8xzIXzLL
zlDVB={JD9_IPytHE=Af$QFDEzeIz|sB}=2pL|?C7(v5~!5FnS%MfW(fL27F1-jNY`
zNtrl1izappG^p0W{b@*6%GPCPqY64{$+k4Js3WMU(bjPs%@mHn{qPPeY-(x>Kuk6Q
zo4?=iA8&su{=Mr$yMQ_<4h({@!$?>cKa0~8)jx@wMCRn*wPs_X^YEMB!~{hfIc;qA
z^QMSlk7Z6Ypzk$M2)JkeVh$5Zwy_!eyD8^!k{cxCalkAaC40yHmb<)Zda=(v1<NQr
zZ@2!Y!C-T$$@m|ldeTmH|3X~PyQ%5>wr@$r#rr!u7l#Y3hx2v1qDcHH-1y=#nek{P
z>^@;(VW-zeB!s*FLE5V&PaSvea><MA>R=j-Qu}&e9nFdU%}5RoNh@=nLuMlA4Zr)-
zL{ZIsqI=nwFS2EJ*;vRhqDYi8?1Pcte7L-`bAso~|6n5>*RA(N3xX3rG(7v;->rGu
z#EAN>tD6S8pN9n+$>pAydVg@WG&H^uuv-GGiHV6x|HcaNlXvgl>1~vEAcUZOw)c7u
zaE>oAT9?b=yy_)+@CVG1r8dX;T8oUtTaTQ@si``{Nn}^J)|Ub+J>AS)4XfUcbYViX
z?0=?YHFt#^J`$+DETZ=0v2swd>1x}kB`?>n&EKm#C3z*?Kg}0yqtzVn{*7Iv%y<3d
z)VAfR>)SKqao(5h`bY5=uhPY2GcF2it&daLN`wPBZtnL>N>tzAbQAFsv3W^;G@7_v
z?5s(rfXK?5hBcLS!lYyMHEd-{ii+?{s{Dn?nR~^s?nn*!pXmN5(O`<_;Z6U+t)x<t
zrOw1a2B#UT2^AIXrkoscpsZ4^0y6dd5H6bo7lG49Q{}j(Kt~6MGi=dTS2+fFcy^`4
zkwUvGCT3Y1b6RT~?E`(~F5}I*`hhKn^0pCQW$kDvo8i4#CK>0z{J(<2B-pTv-O|2j
zK@}p|K5Q#XZt0+;zfrJ$a5VT)`<$<cc_s%2_P=FqMSa~6?Ft$6BwFfU%cD;ZgsHz0
z=Zh3u<jXuxoxkHG@IZP+a^TIQtE>C|V(JoS;~3YM+R;U>(dY7L^0fK2a&ht!UzM9;
z$8y-~_dXt&fW3p`MCw7o%l;-Ny!>cl$qN-VLNiUyyN<nEsc5M6{j^waWKTt$$WHHD
z0<@;%^TyT&5qCaa1qBS1c;~a5962;}oQ)*jIhg;BvaRELDhIs|fbD8hwMk+&X02V4
z>o8M<AU~Qld6$sjr51Nq0Ri2(F7j;274GxL$45_|*Mx+gwiAd1vF1$uM$b==$N@kt
zuvuwKQ_`F+F3y^qcXG<rHk5lz{|ReFTa-2c*a;YopUcBk=)0Msr>p=?ot~e+JD6kT
zNqgGZS-YS3`&!XyZGBsRyTh>RZzLh7-7+84J21dow^xw4CQ6bJzhZgVV!tM$q@?8K
z)k4o9p{RI@x@_9|`~S6g(;6EaTa+bS(e|QYWLt3Ye<oj8ICW)^m{e&H;o)hhsO0A6
z)>_S!ryn49cXt!L?CI;HVP>wn`j=80oL`gPMVb(w(cAmf?L8V)+7dGHUe@=<>3fQl
ztO>h3rNBD3*JBC1nH)L;;-ji+WL~iYU5HsxJ{q3d>~xk$jt$8w)_1*wrJmSOa;Z9W
z6)fSjkNc~qs=I6m+}zbFvhSbrf@YZgZ2d8W{rYgR6ye1&ObFO>vNY)v6hg(k5x5z6
z)@^jWo!l=+!(PXw^E0c&M3<B+*Vq1N);Guy4y1&vrtj_4GiM8nppHo-`Hi~^-u$AS
z%Zpnf9pd+Lmnf!xA$?bFW4|c1PckKM0#BW$jiFw1YoFZm28YdllYzuT&Pj){>6RaH
z&oT~8KVq4~vqiE-MQCYlkFlllsOWG9?zlIctJBhS0OtAZE!X>pnJCtL=zNv|?mo>u
z$#K|G%t1#~R{U}eQxs;^ORaosJRwIS`s)0A;iH7>JIZKIR<$fe&K4h|{L-D=qV(la
zzAt5ZK0a+hLEZnlBRr8RAt7gHXCWcK+(My&A$}&N%FImt^R1x?oSjs}MoDJtht^*0
zz9LfManX+-{{|wxnw*+S`k*(w>CoERiWKSwsGOtAAqlG~n!gZ`0h=8*rGab-*uQ6|
z7?3SiTHNDOQt)5DW|h+U71YI^$}urA@<BjAfQLs*PtO-1;Uq>Kmf=s@`etb=+P-m=
zt`|FFdJ-62xk|ed6K>U$TR?HmIjlx$2Y_~TR6$(4dvhQO6q(C(gm~#Z<Nz;hN5a>y
zUlS3@7Sd;|ZVUQ8Jw$)vW?(oR%aH((dLWTOSwZ2Zt6^CW7MF;{{`YB`A8ORI^U>n3
z91=p-sZMODC0!tCo;!=W$$Tu^2PiY~yZrs);^I|RRlk1y0um)at8hp!#l}`H-#0q#
ze%DeuF!W6QS3hhMc?eXV<!TcdAEkd9%0{ZRmvz{HxS0O5^>*jxgj+J&og+kSjIVXZ
zP*4dH!8AxN`VNbT7oJI<H7cta#(=F=QVa_VIQUt{nd}1z9-{2qjL=LrtfK6Q+pL_3
z++6y%vGW?%G_lmSme`V%j-?EmB5S*@+zg+OkVOMie+5k;^LSoa1DW|~xa@UK@NKI!
z*EV-{;0Wpc#gqb0_ivB+WpAQi<fpYE@<(Q-s6m})XAU|#OUuUzH#avskImPw2;*nQ
zU83FtHDzG~8R<Lg%V_)5XD$!_GG{r?lIcOqr6y;?Dz}BwU3oJ(VMu9@FvsZ|r~wZ<
zV-%(up>}%j(>3_09;9xXtu^Zo4-bJ70j@d~GNu|?xzn{5E;$!wWROCXl$4);IUtBD
z!uofN__o(qoSPfLp>^#2yPWbS+7<gJiuv{HSB0X<rY0UiLBXNKzeFhDNZc#P@KI7x
z0d$3xo&Ax&`y_R-@k<vMw@*t7GgKPtQ+Ig;=j?nb=#Y63zPq#Y@Y~KCtqY*Mjm=F#
zXD5s>F^MQ5_vfe9s;bj^n+1Vq%bppfTjBp02awaBTrrX1fh;XX{$=Ljmp0NRZ+c>P
zTxfJOLB4cedANJ)@iH7@;FsqoZ}Qki<36mp20-_&v4($IuQ>!RhFC}Ymi*4-tmd|g
z&Um0^zzH^BW@hH*=KeC0A^3$fd10DFO~bk{Q{jF0^n#Wtd);7F738;#Gez<3qUlBi
zC3sAV65%W(<?Cvwm=k$bQ|eTQVF@c9`{0p(lh)_h9bK92e+eZDJl^dq^Z068DRK;T
zgGVW9Z>|U|PuZPc$@G8vM(`K=#o>n#YbWQ}_i;yOl&Ej$F$9tKOUiyxF<LZc={o4F
zmu9$zbsM6iM*`7j;72O2>k)$E<3|q9r2%+xs|5;)sQ9ELy%x7v6-`Daw&lhz>jj!L
zo}uC49%mb2{{H?A4X&c1UFAXNOlU{4BoUdt;{_iO>9ziWX{N)+!usyD+fvqK>E*mM
zSBM8hk0O;Opjqyzk5DODFYVUAA)@W*)M(HHb7Hu^e{Z3oA}LAz_!}k*qh8x*T%YS5
z>ITL(eHXFGemJBck+q$%98a@5tcdOmk{dlmZS7`@`3-(ir0JSDb?$!)4R+57^KCGV
zt2h78{JmvUS0{a@iqTchGTCl)IwIoH4^==Gfe9506+?rCk<2SpAfx++i~vj0Uf%Lk
znB1Y;5WzVzD(WRWx&Md69hzQe)UQKCjM6z$?tJKo=v|qr<m?<Ah<?Szw8U6h5g)`b
zGvg#*U{_;z_5bn$^(96cVQKK_$QC2a+?XZO@B8y4B|KsW7ILBGUYWIvw>Mmhq`iW=
z)jxX@_BvDr5qIyv?<!)|)ymUj?s2g&DnF{LSvdKFLbDwLgB`ejo-UTBsBJ-&G*?3p
zthm5Nkx%29!oR$cv*{Ay-7h&l582s|cV@A9Cre9D|A%R?sd@dT&;YWhdauXkGCsii
zz9Q<{98}=+HsmPxEa1E-UIPgL4h~L}UJV@lU_2Xjj*fss8%R~;6cpWtRUsigKo9gh
zwlOy^EGcnxa(a-x8hH)}=dQSf1aM4zeSMLTLgZ1sVpkkAH8tJb8i76=fPjjJ_E)d;
z&?*o$iWL6;z|mCl*h<S1QUoNpnwpFtcTyvYuX91wO_*w&4>hm%jQpI>Zpl&J#Y<}a
z;)7xgV@4PE^0O>UbKMFm|9Bk1s$Z{F_0c%n*rcbYg8~h87_%HNx27g1n;RQ%93NL?
zXP>1e3}6`+7Zw6^vba$D6yIa-ufcNoJ8s`@x&J}&dtBW4`8fitKd>KG+Pp6>E{qcc
z{{H=&`Ku<NCmPX@!s3{I`etnRVj$^5T;E29?~|sy{Bej@{wmS~!+O9v5)3ZU3X0&W
zXt;D<x`1~RaA(GIrP^=vZtwxhvb8M->TyBFa7KV6rMU#*{Bm$@UG`zw*C?0T8e#!D
zCnmK0Rn$Fquh+bu;Q_D;)Oh~Llvpvnc;SYVzV3nF`#9~Eeti5mxx)r=Dd?7zl^sql
z35$(6YW`Pc*cB8C_x3)OmT4Q?#Z63}m9|(;KuM0r%Q7&pL#dpwXe#oCq*Vd<C_i4=
z_OM+G87|Lz>GGktI1&fPX>+qPLXiGTZELLh#XzMnwCZ$OUz2|a?-*C~Cg1gibd;I#
zs~<dN&CSg~HqXiF8XtGKyVwO_;Hjc~<M_+v-jq7-utEM-iF!pM)#(Et*yBSVa&US#
z)Qrx-*1dE?@X4Fpdd38>3<JZShSN9TNB?jdZ@Y@y2I*SP%2|!si0yJqn87S?X!7M!
z-w+W!Jlr~Bivt`G@{;&&$m3G=wm4w@>Tn@8HkLZPT%$@Pk^gD|SWoz1=YG0@LFn0=
zDk%u#+M<<)38T@jSgNhBf1;2G6U)|au;u4>9G{q_E-9K@GFt+#@YG7!6=VBuelE(z
zMe+yXz{EHka~NXa)2>%5d>j@AHnjet`wO}CM(wT=0)nhC^Ufsx`PskJTX7(6SAWe$
zuXv4v9$7mlI|UOMO~frih5>ArWZA7k#hm|C_lO95w7-X~Fy}U9SeAP>xE@34{P&|-
zq5yy*2gp)L0^!gFHR!QoNikdL&fKCBzVnOeU#}{+us{ZF>K{(er)8W{05%~d(reAk
z05ycU`4^Sb*(}XkG%@=W(vJCl-+)bo@51l(-QIyLWJ6XH9)BT>zpZEl0%G(DpI-X%
zd7sx$SH>{F-ElZJ>IUKPa|=Dc0a`vhtpA|tWY-E6S3qE3L#<24P1xx4p8IAeFu2+@
zCt7_6%F4<(2Gl#!AN{f`Kdf$zQ$-vJ`dtzVQ-8Hd-A@<up%ER)py1%(prjN7s`kjp
z$mr;a{LFdmsv9T_zZ|zAeXtM!A@?mG9vvOs*)a!*s#*4p^)jL%^TDjvm+N7>t**!U
z|0K{hKK|inN-qJu@2UJY{an!O&feL>?fJ@UBJeQ*=(Ba|_WJ#&pX^q^kW0#JJlT=t
z^mJyY2|jG|4m7q1cX7^6BmO>Zij^94<<Y=B>g)X@B3B6>CQ5;5ZrtFI<3QD1@{^<X
zYNfDgzqI-??jN#TRN)XH)YYkceFM@0|C<G<t2+-5Cs9!yVjfk^Qhb$f{DX!rI1-Oq
z{+-?k!%u!s7fsES=pd9}sr%cqR7->D;q|aQC2Zj%nK12<pAA)2Fwh+AFwk5V>TRM)
z`HFJ-e#xh)DJp)M?cORVR7YWVemy7)2}Q|{tH;Di=S@#f?*%TApE(?`s}v-s+D3WT
z{GKu~^E%t?d}Vc5=DdL)xb@-U^b`h4dHe?-#>UJh7H%MOMcO+!_|wV|8Tx_7(^|iG
zZqd`m!NbD?>KZC4D)3X2*{$A$U2MadYintVh>F7KFSodZ*~Y=aSzB8R4-Y@z9!V-Q
zzas^*a*(1C1Wit6xH>=Yray>-vi|!oqOm~kRa8+?(P$3>20S%36NiPl`Hf`8C1O{l
z*+_a`K>=%u>>Sn9%*?ZwBX}5qu0X2<j`(xSda)20m&e7<>A`iS1)Y<N%6~54@4)td
zQWK(PX4{2j3;Oq;gknM#T;dmB-u8Y?ii>+qOk7q_!0CB)u!6b_#EZVBc&FJ)1QENy
zDtt2J7)gCwU2SbtD4JAodUQ0I(~cxT`f;E5@9p}-Yp?@XA0();rIGZ=;w7O~Zy9ZI
z@t}+hJq-<wWBg`te%jd`XKat~=Twla8P7eg4(Dy^YER(+JY!mv4GU2R95@q7dgI6q
z%TgsKDK(|A)V)nm$JixoJKA#S{x=>q+;>gdURd~!ypTNTr>=?<6}wS*(A-$8?fpvr
zZpN=Ojjs&ZalsLdHWoB3e`X69Ib1`2y=ia_-oPOyW&b8i)*IIc#VpTmP{|XQXLBtN
zQfJjK%WC#DRK(Fnz7eqLj7y0_|FI`6Ep2FMs6n4tdJYe}2Fk*_^!2S08b(Y<f^&y+
z?S+ac(z4%~8U2Gk8cHrK6of=SP1YiWg@yCa-z?MJ2?;5}W2_AFWn=~d8X@X(G>7fb
zv$B#Q`|g_W$K%6;$izyJiLtwf$4de>>KktuJ^DUN%h(^<mxYnwzzYox1z8C@&TI%D
zAKwV}6;Vy~xs^FsFXwiNm=WC{bXx#K!+H^vadZfbUqF_Cm}!GckESvO{?+PKR+g3y
zPI(R)7@cSMpTvrq+by?w6JlciHfAw-eo4%OO%e<jjdaXnMzHgES`*`LrKY0tpz9AT
zVPJ6qA}+|%Y;9wMKBqZ4iCo4I^PSImPsQyPwyBAXVXB%;of6AirLDaD2tdl9pdk7;
z0}BhDQq~W*U;ikC2naU?dnYp3fh2;gHVO*LYx$o%w(G0sTZf=%EX>avi<Fg?%Bqop
zObGaMbaZrRb${QpWdrWp6~sgQ=N~7Lt(MmO_DEh~AvGW0=d1o_H1Ha$<FDdXg(zW7
zHs8yqUo5AlrSW@=EE}o0JPp|pa@xZC&Ck!DoScAKw%i3QHng~RaIx@4e}%n>oLm40
z$HtMO{BDriKvGvZ*SXcF()jJ-;sPWfoSmG!+U&S|F&A^s(oW==igNSwbpaR_A}iIb
z`3syt`AI*6+3pD3xbNT5(9k$+<}W<sVu*=}0bnxO+ExmKr}dvDE2xxJHmUyASJxtP
zmK<ip$Hx{5&&|mD^u&(@qy9|_9mmHL3#Y46Ew-_^EKp1+e1XSwPUFZu?#IOV_vKOR
zg7P0D|Av{FscnxzZN<{jb#}@nl%V`^wYsbF9QpnItC>bFa6ZUHlu%>$qoY0sMQ6O?
zoOB#jAha!f1=%(nHP|=Kym$LN+(&9<xsh9R^kv&%_zXcNqiv@mhh0p5Ti-I@_gSC_
zT}~VE0>Q7!mBM;Tg<e(l2sbI+onWa3x)Yi4sotzV*ILML_py=?xeb1o%3(uGL!+pw
zdU<lPlja%NZP>@c5Jk*;6)pdY&+CSXoLod+-VV^@YHM^*Q=7>#{G%BEh;1&txP5IU
z<>cf90wT$&si^P)dwZ6EK#Y_=FL$N$dk!}?2E;N$9rZ(H^5kpJsVX2k@tsO0J95ZR
zmgGr*j*>FBK{%_eZMn}1#oSzk^EFYL!7v}>S2=28_*+#}8E+2}vcDL;@9^e#AMuz4
zth*8qj)B3AM91yDiVJaPS67ChudTK9!TNemW@g9wDe$zw#0k8<I0brE#XhJ0iGo|4
zn^m`iy!=SI4y=D>c6PvT{YHB2mn24+btYLFZXit}Pj|c1@cHv+dhH6^&He=7(pgwo
zAiX4VlcDU2$%?ty+N!29>WNB780_uEYcTup?aZ$h+uSPf$ac04$uL_#k5iHogTP^a
zPSI3o<NOvJW~6BY04$Y9ry<Le1`0v>TlOEYoh~|T@LJ@W>fH>0lTe>)Z0``f&8+C@
z8qJFVU$gytV0(1~EewUpoQd*pNVzVk?*(~zm%AT0>FEcr`;>RKUsP3)H`wB{nJXN(
zw#v~C4Gp=_sr`OHuxG&I>k<0oGCKMj2p@3?-~-?@*he<q>M-pZ(I!Pa_~CNGB>6|*
z2vuwTgs+M0qCPnIFMt-)>eC&QyQ7m_CzH(H-rfX}^`=8Vz<xVWo`6Ss5BV+eT8Y?>
zn%8>YU!_@EAQ>5j`Xu6bT?!7vJ)+phg`+Lz2ArFi+u8~=4_|Ll%2%m0*>_h*RQxk0
zT><w-D&SRFRdvf&y9Qjno+x6F{Gs^xEt%_!;m403&CPe1FgXo{RxX0`iFsXm4;oqA
z_;g)d>cIfi9pqzN3h6E~1aksM^t7}}=weHq>S6sZAAd&lzo6$Kl%#$E8TBE@_<E-s
z;!XcuFPX3?x|CyIT9PCB1S99u`&DX_Gtg06>%%u<=Xd9=Lgeh~iv3nLp3UZ0cS$JS
z5oY2EsvK3^Ip5Hv=Xu{R`+oMvlK;uS((e2GxVfoJ$|p>36a12x01L}OVlWR2rJP5L
z^DI1gZ=d5!qY7(8Au6s~`R84syaUhF{Nn?|L!MQ4@k>bOFm&9(hCA3!uON^ng_Nh6
zUL~FxEe8Wx;$stxUl6eH@2_Xdb?El%M|;0gU@=SPsxjIDMG6K6rdnMQa2i2kSa<i^
z=k4gTo@5Ritz))5SpQXNMU(?U2+Axbz{ISwoT8m87c{p=K}Xkd{Xpd8tkW;p5(lfH
zE&63FA``B?NZ50xsH_ZKRaM^@@ZnIwCl@3?0D;Y^aFV$Rp?~lJ@$uL|s7r5sMJn)B
zT!!p`<K{-#ythI-4y%G3!h0dQ1a)N=ole}x^sFQJQirtqLy&YoK=##PF%}c~L&?md
zYWbCw_Q@H78k)iuQj=}1tu<Ab0!ct;=!nu0qV!L_KYShH2`>Q<z<%+Rz6m-_U!@Z_
z8ZjdD))UUf)%Cf@I0<CO0D|1Potc_a6&8k>wnM~1Squpw;h&}80%QXvwIMM4g+AV>
zMa)k$vY>hneU>$Q3_QG>WfYLb1iMqzYbGFp-HY10#cCVmxSqWq>FBa*;RB)|NQ~b<
z1I|wt@fy=W;w9hlK|m&jAKB_E-kUdw)*KpI!s6USF9UkUgvk7ICbW)mY$zl|Z4Q4S
zoSP2+oV%w7q}gDBQAlw|aP{tTfaUjTs%w0H{uaZrs&>6~k)Q__Tb=&K&i1{_CGtyP
zFBg^v{Qc;51z&2TUZ#a$#@M;LUTMq?`GrdZQY$a`$Y!_PvImFs$$E}ST*9-bNExjB
zXMqP%1THRrG1w(p+63BI$xnGELZb!{Bv%GPL)XK<qRve~WOLY80)P<_5fOLyU%>p1
zj*bSz&Ds-4xCLQZuqkMeEuse%6%|-gz5v;mr7bL7IFG2P*|oXl$!7@gUrOeU>#5PB
zW^f3n%ck4k8lKhB5yggnq+}#v#e!K4`*W=Omr<*>u&xeFKo<Cm-Rq-9!W}_c8Y?Oc
zW;NFic~lsoAZdQnOpDB$eR#wKNhdXR1PJAw&F0B3EXCKUB~k^)l5SQx<(-(}#FJBe
za%iOS@wAa?PR57xfFx)YQ#YKtq0@7~h<AodU%ey_HBjg}AhF1;5X%~@spI*Pn4qAX
zu`>UpB#QL46C~fjjt9}Iv_>m+z)Tb?f{yduNM!p$iwf&iK;)B&%hS2nJQ~DpfM>tb
z>{`*%vZyXMG&9p6nB-nunGZu1bw%XaQ2vSWy&h!(<4R@K4-sKSl?wqqmlu3hjn^B0
zfQ?a5rj;d~;}AJCT&{77{x=D%fz=tdw;gLrn}txv_Bkkl6k79sJ)ZTx$H$wKhBb3-
zZEf;|1Z%x0LNTuU(;aPkLc*A{2g(JXK$fS>b!~Qbb`B}P?^A}Ax&6T`Gtdm(PXEd3
ztw>bpLP__=$4Bz}1xiJu=er|oUh#Kn(s{5+a8PC$!ryn57W@#b3N4YqOEdZ3({g0k
zx>vfFMRV!kmi(Ug1wX%(le@jU+)q9F3gh2s^0nD|`3b~sDp{d;Rlfm#@4K6!R8G&C
zn3xzQiWA(-u}jNhzYh>M7V=GN4><Q~Dh*h7_WaYI+U5O6_74L_sbZa^a?`L-^yqCi
zDmAfal*tWb@`z~u#!7=9P~*axOSQ?%&a_^Nft<GgjmOf*iR@QPSTs;azwaFKM^9J?
z%>@pctwM!-RAZnLNC@8kf|3kbpDC_unzoGWEi#<A!9SI{@$ve+0T?(T$aDy%1A=nL
zg9W^8xDQ~9;E}~JB7%x7)f?>e>YXVR3O#6PonZw=@lUw2ZT3f2$8mF&j^*|Rv52U{
z10<1|$71lFLxpnhZ<iS95eaO5B>n}fLx2Di9N~Zd{E;DgX>2NFE=d%5w7%~6aPtK#
ztXBVbz(t)EX-ibl2{IXmmZg<Y2a?YS@jw<WUGI;DDx;pl+FBG|IP&STjO;9zy9*22
z1Rk6DT1wyNQDSYs9@K?ud|?c1abbE`v_Mp|tfoCb1!H~vQ3|#%ZS;*&o4jia$tu9-
zL$T<Kv>v{#qR`^z%NXA~IkBHB_0iGM=_uWO8~0Z-cQ*G#l3N?8NJUFqJC(<lD@e$E
z`_yZnmr=K6R$0}`%4+mrw}3zRgZmwMUfbz(E^}DjCCIwKDvWh+gmt4^M+Jpfr&RwV
zA{@w**DJTLmnkf|X5tXT{G+HGdG*Xc?`HS~y@ofUkEFQVftt#(HcXf>X?s)OleOYR
zcFU8S^_(9#&`nyn)ea+On|B9`b->#IKpCvoFJ~(ihx<-kmuV~sN*@XeyG)A}ih{$>
zj<x4)<q0BSIQGX_zt`!+*E7;<py45=K1NfIHz76wkihuvT`)cty><&N6St1V9z9^M
z-~fqU!~!Dn7`E4!$OsAucysY(R({!qKyIuy8SwadQ2zPXcklBbzpBRx$uIj8=tzg!
z9DaRHk@~mr?F{4&>$_VZhBC~uA&_@QLtF<;q1g`-4e|FoCaDF2#eM5QLw!9WJp5us
zkuzq*^3V^|SELVjt&O(mV9OY@go>w21b85M=;P(Dt)YZO!^I+BAHN(NmW?&komK-G
zqI?DF)+HRlaHY96@B7WWk7Ti?%6%Xq^bzChkSXBj_aDFX(TfIx!b%yB>_w8SEo2}(
zA9P;sv%i)k3PGC3E-rPKvc%qh02T1{!9o4;gJMDJB?;fGYuCHk)>qK#Mc9m$8ZQ?Y
zy=L{sE`eaCk}&xx7xrvivoa;tAxJ5ImL?$LgQdj!-2CG9m%b4a!fizq5ld~IiFB?G
zR$tqC&+Cs-FpPvOtjL3mVXYKo`o(1o6=lkE%fnO{V%W4^C70IiWN6rVGL&U<k$z+f
z>PTB}geXO{USQ)y9xrScXx2<hQsR&+2KnW;c=I}6lPIVK`*}kU;Un1z8Reg@R$evA
zf4Aq`z@G7J{|0iv_W<!i_B+%VQW%d1x#fx0FdUEqsUXZt5(KDMHe^)W4@*e*C0pIc
z`Z{<dr0dz^$)2ddM<hRyps$UOV#WX!S09oB?b-Xjeq~DE-o>A40P51;|6A<o+HPIX
zwwOx}m$BAyO9T*hk1S)Xq^0g(Sr$%b&PnB51(G*Ob=X@Vi#y-b6Cy;mv%5RA+-e6@
zK_Dmv3`fx`>!2ep@i5@Pgi<rKCnwxz)mZsFw}l2x0SHY*ObntVC8}(na?PSy+5d%#
zHJv#Gl^xE!_c)T1rrX{5X>0M33d;@`8|ft>Jd}#ms8LeVzewQ$5?eT7%s4O=8TIcX
zB|o2&r<WCze2)zx53R`$K8|RV+ImM3iPrf-X2x#8#d~2(K#KtUoKCR;rW~$gx=ec{
zd57#SM@_7q#li6v2oxtJeW0cF0QE*k230C&fr3U^N@}+tP-1U?KWEkpg5Y5o4nwa~
zCeLUIA~`I^b0cAJl<8#=u%ti+qi%P?_xx>;L9(@tQ<ch{>?afsJ0t@BqYY*dTTf59
z1@-L>4i44Kv<JiIo87>o($WJU+f!m$0LH}vSEE49=lkU4<MRZ-4hXOb)P(nE=!2Ad
z+&j6wogF3?mL{iNB@kKA)+T(YC4l@yBDYZlXi0&d>pKX~#O&Shg&!})FNOOv7wS9H
z4pZS(U3)@AsW8XZH`fAU)Yt(nofW@vk870QFv3`+?x*ZJNJPQI!+Yi1wLhE<?9FRc
zX)L}@`?52dMR+y5dO6V=@X(D<(@wAbF;B^z4`dnfQ)G1~dTtLkQT6qanH480^h%10
zflEmr-J{*&_QkHI7nFd4PigaWa~8I?Ao~D4|7d`tSPu?H1nz@)#2bF}MYtmhAT~HI
zFJ2EGS-m1jY^Xo!izh-d8y$z6^ZI+hy{BA&86V$BZ4j^WLcG06yEE4Ug3x%r5wFCx
z=Mh0Su=)?$>Ew_!d~Hn;ej)absed^ma!^b<$KWir|B!zt><Jzm8X_gt&E7sfAz`YO
z)kL8RY(Qmd>ieT5q&6Qi?9ShM`dy|mNg<AAFC(2Yc$_UwO!9jE4y|${($cBp<8$%w
zl()IR{){B7d-PY40<4PHcjyp<4K?jWeCS6Hx7y0NGI|bC&0nI_Xfl#efq)-Y<}XEc
z9RP~YFykLTJody&b0fpf^FWYRI}ge4P(>!gNmf6zsZ0LtyyAQl`vYS+$MXu4kJ0|N
z@0?sZx=Xs{$wTB*+qyjKfGal=AKljG<NSxB%N<pyem<k>i}z+b<GYUZ({<P=QW#I9
z&@d189kLYJe5E4t*xtp(hv&yP$YPlaFwi$Sg7M`m>nCc+3`A1B1bpe_ks&{_%$$Yg
z<);z(k-K(eMlO3F!dS^#+a(^K;bvR^lLEZ}K7M{h?9DJLjy>9jvj`a;9(>C20qzNr
zmA@IHih6o_FGK#zD+6h-OsrX3qqr~A2x2WAG!yU&ChHY*e2ws8PC?ONWuaENL|u}>
zW*Dde4>zYcbZYQ>{U53u<_MC+gvL;=$ra&q@n)?GH|uK;Nq7{<yOF8?mFB@fN@(j5
zR3mgHAjqB!yB77ep{CxtBC>HsUb{wyOg|=+IR}yROQTUI$>Yx$>*CxUr^_q9*qp1S
zHX{p*zQv}l{UaUVPo{7abulS*BZ->PL0lXTv+Tj)^gJsd?J{vP4~%Dxr`Lr=BA(4!
z2LpJ=d1BZ)ge21kdwZ@|o$~`pea2_$F$?Y2UkL=fZvO4hq|o4B(!bnEh0<W4QC}gT
z{EhJbCy9)$zwe^YX{;$AEh*yW;sW{6q^>pDR1O9Xj_K!No!J%~v41g}f~x#p9r{eL
zaBw3<g<aAap`juF=+xD`xUFOWD=;+7uB*G8tund>K{c&UC~Bqt1Gr>2FX%s`b&E&P
z4+-*Y_{b3_C&NOr88Au1T`dSFd>+OgXLE&xKsN;4FxznL$tfw90$)Y4WEEAT+#X+`
zp=D-5r8W@s`7WU)^hk#u&;?%Jg~i1dSDxx<uRCCx>u>QvkaqK3=t*?29cW)MQc@-)
zAXq#O?)xt+m4WQ@G&<(vI#v8gr$(<`U%I`N13adzEEq^@Ym1TZsQFc%P!3Gk&3HdY
zOMO;%@1*&&zglcEtHT8%RA}?{3GR96+h#jK9;=qg`T||!5AruOm|<tgN@|)2EM@`q
zYOba;vu^{9I*)ra=lqLlp^kLF#CwDPtPUhtVf9x;M8fL0y~b{jO3I?>rb8ivPRTsP
zkdSs#OkAt|Lu2~8f3*F-J1|1UVnShrg|=}29WASBmxz&ZrKaF~ZaUc-BFuSW1OpnC
zm*?c?U-H>>U`g@XctA+z!U3_JZ%%i!-??jq#HG{#m9v>m6|>)c-#ki4U<DeJs28TF
zVk>j;l*xC$OE&$%%2(-+mn6_Aji@j(=pcijs$@p&P<#gwv@U4=50(&0yYy@5ga{5|
z@??ZM;w_j=EXnlj85n9ZVlmt6KaP(wb^I|7P1tl(jx(#!3-EvYbx|RMXA}6}VEs5c
zI-Xx$k;KO->*@;J+|`Y6v45y#LJjkHk*H>We)qNPIm)w>i5_%bH8#!;4ar>*5()Y|
zOn2*M79r^9bfu)}b=hGff%d@jRM32Mdw(B|`=vEMDoKB~@lczej}HSrrOiTS^pt`q
zcE>|ls$oSM(wG@>SCS4-NKiewDx0>XHJG|=ek0o+lb}97_KQM5Q#<|l8<dcp-9%UB
z)_bmUZ+u*CBdkbCFD*5EA|jx*Ij(gEMNSmlTpzDzW~EE!>I(SkM$V00@b&vGZ5E#H
z-z9p5HEK{#xsc*7%zEuBe$6e-mC+npT6)|{HD*a^;iIL!Ht}+oNUBLh1o8BZ&3g2M
z8MfIakV;+Y&U@cClFV!bf}<0T0muQ8LuKI$yg*B_+r+)R)_G1T$toyF^0<<fl#~R*
zAF>rHc{pr`kj`Osb@lE<!E^qzsa>zJ1Gwtpai`DI>UO-$iGG<`3i~0W`Ds8-m=a6e
zwdVKqOGy805+qQ7m-VL!9Z5W)l_VXg&WNvI*qtFzy@3b~=7tVfGsNc*WI8C#<8s1@
zfRsAqbAMF^GyziCG|D#-@;|x0F!3V2lMihC$`BL;l>bU2PRMVBXXY>!acdh7AAjgf
z?|RH%#=5jTk1}u{DlF2pFcldX;BX`)m)Xb!-F%joKN1twbaeQQdt;V`sQ^kP!6VSh
z$cS6l&%h%^wLQYPtZi`Z1)0*MAi=LFajRQpzvR>55fCnA6B-&CfJz8lbPX-7CXl!L
z=7R`C@Xw|6u~M(VK1xYR+4@hroR*f6`MpSV<JE|Zm|Fk%S^Iu#)SzUoYe5oQR;J`5
zr7d>Eeh|4p@b4eKX*uanavawy>{nj+5#2z64;B{Xm!!-rK{|G1vntt^|6y!pp6Qn@
z4IhBzZ@+f3R>GInvD|k1w1nzoB_9Ic!}u8-arT#ITXh1DDhcdP&i^`(JF9UNl-R_j
zNFY$2ymjPsSDr8)(dg;l@(yI>4D}gJw!V*rkiDo8D2&|h+(uiTw~4Lo>|RMkb@q;t
zC5U`_qoU>)#mMK$V?Jc52?Me%V4knzIYE+}jja@5rQ=q_LN-YsR5(PDjhytl)2||H
zshoB@PCS#7LPl`Zw7>UpAFq#r04XIUg&crJ!lw}?Hu?V112hQB$jR0HJJlLjCK<yA
z0_jXex|3@vi^&TKS{qE6`9I$|sv5)lz76&5#v3MwOGk*!i;?;L<cpDHu}ejyW&hp>
zi(sOl;DZ3+uvc!pClBuX!u7(3kfW|Xz@O$1m296o>B)q{`}YOUZbqbhZj~cT%~QAB
zM)h_HGz|D4sl+`w|I+&&iCEBm!74T+Bm^vkzul;y62~PaWw_otXlW6IblvVmb>Hr<
z3V`^6UVqcd_;|HJSFmds%DQYM?t=EK@d+3|ZcIfp!`H^`{O{uCmY02idv}RFsH?-!
z{Ob}{0TlXdB(Co$U-_kqlxR5m7XR$;qWaDV$Pig0VD)XB^eEF0o@veRX<BQ>n1Swr
znFU!Z*wAl700RSu*Q|tE@Uv99ijv=nuk*_5-oC)R$UlQh*=A6?2}?{_9{HOHpvhRU
z{TR7nMhbZaa!HT6?});@#$RjRd9s0U4FW=4U7fp|Tkkh|(y_(GZ)BkdCF;SFls`bP
zg~4kb027W)I@dybG7vR<()afEz(QwD84N<jy&A4A6)>fTKR|FtKyG#J_+zjA@y$&+
zP$HU8yk>+?aDXC_t2A5JN_|rdA|=t$BRV6e`qr>1yZd{^zE8K?(FB$@HgzeSq>B<l
z-f0zye?cSJAHy#?I%z=vw)<EZE=K<0Lj^8Qg8c(qZy!uYVaF19eb<j30Y&)6CU+`{
z-j)YDdwYge8o*Tmf+aOQy_2J(cAM9&#1*liVEY0xW_-fpve`=Gmz;(`)VMyjRBl4V
zmtS)^@6w1tOPpq%B~F;wKYK24yZ}<_vsXjHr~|R!v$2sew{6%ahlfjBSnTR&)G>L%
zW8;w66IKKL2xKx$jE%W?dGBv-E?VB6ou7x;Ey4qEeEm3vvW_U55j!)a<$V4w`A1+I
zjMtU8$VUtYa>Z1!$+wuQ7?ek2IWM@~&8>p2Tr?_?A^kE=>%|vIBY_F%*to?>*dqXh
zfvQFt^s`Vt`J#kZMrPn&p`@?xGx*ROYHJ5U0t57|RkMIYUQtmIB+`9+d;m#{^F@J0
zLUOyC{ra(ssdRi6`{%uo$iuua9O#MyhBwHDnhhq8Y4i8?_V+j0ufc|AgVab=RFti)
z?a@-dodY;6(%9T9>jD?vVmw;U5FE2xO=nW~gBIWqx<q#S5dHMmC^ZBGgm-b=E(aR&
z^74A0`Ff)N?<|1c3LczMM`e}s6j0Ci!<bL6uG-|2khp)nJ+85~wk|Fy>G_6V10*1M
zLIyfI!kt#c;OMW}otsVbz!ArJN(HsPU!YMXz7-Ch?=J*NQW{rDO5QI0`Rs(vP99gA
zrvyj-TTN;#S(YU)#`#%;?WVmasQbs4^F6?yJ&!LhFVQKKg@wOp=njJ@bX%wm!u8t!
zwwrvQ;^MGf<dW}bQr2iG&p1z<%_R=J_ubj~mQQncduxC<&8oLFJzZ}y@aBs!qoqyH
zntksSPAuy?7$Fl}M^&l1`g)G&p48~@_bI7R$F~sqsgNen`{#9Y@_VIoR$SX?Y#rA?
zxrfKHaHd8B1$LJRRk?P3h7igb6X&=x<b59&ytUnYH}=m4WKuw$_TQ-+;DEqD@jd0S
zUZ_8mmSfPb?3`r+;a)ROZ|_IY{@D2WC|f+@zlK^vI(}?yY$7iERd@C4;J1Om(6Csm
z!-frnpf`j5bTe;hFguS^Lcao(3shGmzr@5uAS3Y?S|!_=VgV_Bm-Q3`i3ML5tpYxy
z-;;PyV>Vr`bN^8X)w1~5cK&2H9E*mACXPf9;T@1V5M}7}tN#Avbq5R}jn_4JW_pB`
z9dzkF^y2^5uey+vEiCTbA_n^5@>T|KXXocF*SB9S_#i@|VNK?Sv%K*Cd0-8{h(AWb
zLbDCc7oFTDCY#C0$w39fer;`^EFHRrmoPl;XZ)r(Q6zTa0fj$a{B<6%zp6LkY`r{6
zaCtG|(o8iz)f3&0cYt{I0>UGYHEhQprNf2+eUmY9hbujO;(GE)V&jxpQ=!j{M6X}h
zoanriO@ATs_F(o!NEB>9$JBre_wiZUXZ<;O2;!^|Ol7{DSWV-0+-g~FL@!k79iE@m
zxc8hJ^w4PYa^H|I@qdeCc)BC%uT*q#c?Ah6n%iZ(_SwGJoyZV;Xx{{0c!_ks{y27w
zw88pa{*D<7%qh4=bt}$ffCY8L2KMpRAA<K#7l0L?DAMjz=?3K3x)SDd(}8^S|MVRA
z1DIzy2#S5~S$0R7ym<QZ;C<a%wy^wyW-P|Ke{k8;SJhaoTB-lrH<m73{MZD!erdI<
zcQjH(wbwRF-afvc@2+IN$GhwLPT`MmZ7+KYY`Y7L+<R{c$qaxvw0F@sNU1+A6fgB~
zHwK-rAdsG(oxN!De07J8@%%>R#nnMb3#_h0^Pe`YO1Cf73i0Gv6}<MVK_)WBW;qrU
zmL8N6c?8V(avHJ}Zzhtv*-WdxY06%i%{9J$C)>zp#fXiKLAXwIY26);v)RnT!b1PX
zLw}7NYQ$(Nu!sYn&CI`fwE#3O;}~UH%I7NJzN5a6vP-vZ|G#MZ%BZTgaP3VfA)p|j
z(kKmzAl)Sm(jAi0(p?8c1f-;-yQRB9q)WO%Qc6;~?(6-&%Q%0|aENQKz1E!1`_x=t
z5Phf`<1}xbqyM?vxV)o|TjJr0xr%f``Y@=~bojyb^%o0G9-|{8NnoC5k6DyHek3-x
zl~+Q1#_Ha)>-Ai#vU_kaWuoC-zT{PUo@BZcFHEusaGUo-_G@`%y1w3Y%L4HipP-8#
z*^N7G_(y2Mr+)2E#KbGNso0Q<pZ`-9A)j4MDmXk|b?EBNeQ|NIu(04{PnwaLd5XVZ
zYKGzKTcl0%<43fF7Nn~Qd{TPrAjGrb)SNb){Ef-W)3aH1WxBur+w#+}<MxkC8ZXhT
z2Gi6gC{ymWt(};U*<POffhD!PR7qJGwgNEX=};ug2P%A8rLsCj3t%<;@yW#|<mVXv
z)8Rq(g;X>W@^EraBulT>NKu1y!>G;yni~{9E;bkW*GP;{z<GV7^GUXpl8J2<IM$ej
zI#@v)me!x62Pmjs)=JTRqmt-+`;y`++K&ORXM~={=Aes6@aLwv+H>s53#!enYZo5%
zrBO{0M1=IgrWZXGlhxt$5%5iA<#?PnU+D1hyn6L_=}|*F(-eqUpU7$Ui?eP05k*V&
zk`%RK)2i+iJVC$&^U1mBGXcRui;rMm`?HK%W8aS#AF+ucrE+UY6@`(GxVJ7Oznj__
zx^sQqu!nUv#vvRd@(avS;rlx1FKReLxUDZwnCHKN=c!1uV%=RT-@x>)@p3vION=x%
zI<gW*-q;XxGFF+!&icAD9S07@AhR|@B6k5d#1@*@x7<DR9|eb?yc=R5FMj*-@$K_a
zLd^E>-=Eey23kM*(MfS=>K50R`1hdwX!A^3jvD=YU!Ub^#UqNWF2Z`%dM|JV!Q+Qb
z#*1S;oBr<o#>U$q+S*=c$E|OFX4mpmlsWpM2UxczyT-?lo1Z>PkwN^rn$EB1LAt~D
zcLM`KuEa*f)=i_p5F>tYNAc?RFd^R;DaxSNl-;BZ^}V6X-CYU!6FL+xrfZG99qU<!
z>R9>Q+f%4WDV%hnJy+|-4HNvJ_1Sg~az8vg^&rHvx_$n)X==ahl9qW^=KHQ&mG63;
zG*m<?i-n_xwI$)}&T#s`YAGWV(erTT{kf*enHjC8k)KJpN4~~!)&;mRS`VO^KY5{E
zjHcJ9Q`{d3PJJ@6;+&ibFuEV2y-XcCP1s@+)+~-n!3mYVqac)SUq2Jn&x9dQAbJhm
zA>@f+l+Vyu^2P!tpL#skA1~JBoi4tp$o1L()#h74`S|uef`rf3;KxsffRx@y>1?g0
zOHZBEC!WSWfz_Z@mgtTHoG4T)l9rd}c=^)S#U-wZASODxyYr=mcN}nunVBmCAH=Mt
z?-HoGy1hLiF*8T;?eg3Cc@z^CC!IYwIGCH~C`pz1q!QMM@Q~Utj3|o8R%;tA4YTZA
zNdp6eDD%-WS8l{ykj{{)N>XgS&+ScXd)rqI6eW6^&;L0w+SWDMmjegGi1T6p+!M&h
zLaqAlzU0NZvG#Tp-%j8E$OCe}MCB}ojQ#wnalX&O#@3Kc1Ce}vdHLPU3^N%^DjFu+
zB-!`#7xIcy#=?|0KA!?)gbl2)K0Fm`(V4G1`h@dmvREtT%a@T-gLZ0}*axU>hGjW#
zQoUn)J}Php71qMy3h`Hc{YBYZ(w}2uJKOm~WB~O(bu7~>=X{{ISJdKB)gGOl)n+0D
zBfTGmj;?Mv=8rTbam6yIkB9<$2Dih<9^=9ISGk7nZ2(cAu*k$FTlJzL4AK)9$oKi&
zKJz0fs7r*zehTCHvo!eosO$W_XvMng&EMS<>(0*GxZ+FD2;px^9B+nuqfk6}JN$iz
zG0x&}Is*6M!<FrAMhF?~?9j+1sMtM~rNWa-;XBVLX9x~X!v3qOqT<QsWH614`rmN-
zaNRv$7OVuwWS^h9gRO^Gd6cr`XG`8&7+@gQ2++Zi#|zBJ=jZ1qr(}lxT%S5@6EB%;
z^<~f`Dl5fx<_3dSKr2p3Otj?jz#(GyGk4IaLiK%pPx(#qnP-3h=%|B>I}JX;CE@Dy
z>}+`Rlb}c=#M`~y7`a3ysUeXTq+0hU($X6fC}~1W7q#8lcmV;VQ?kU?e+Z>92Q<Ik
z?GNvYj*kAORka0L!`{I2^t>rrK^KsJ^vf;JX}WQ7o*18<#Nj=75aG7|!<Q!Xl$7_&
z(ylp>3hV0}GV1Rd>o%urUA#}pPg8lk*8Ah5cq)}mD!3gl)&t-CuBxgwPgAGD4?8~M
zb^mJyy)q$6DMpNpHI_A|H8$rAyfp)@ZEM4K5d;J=ikaf7-=bqv7%;h;`Z6%?;3MAK
zAFViI^YAvHw_FiL#>AKu<UCczu`bDtiil7kvmGOcQh=RmgZATl=@g%K_ZI&tc^4}o
z!NF-cH!PVyf2<bT;Ew-VS*e)p$9qV!|BI|^Q9qXBU|e$*+#VpOgQpK3v>x$ia08(8
zqJY<tOGO3g?GH&St?(QF{pIO$ftWx>1jKIujtzQWCx~aVThBOx3B}A13&BQI>4b@i
zX>75$W?MHmyk*hl7cL}okMi9mDYuT@dui$3uF-rI#~nwDxyLW!EDa14>6`oqOv!m1
z=@=NKRaL95qE;5F`s#IWGwPm*42Eg$O@as6fLYJ@-8*d!jcz^8d*fsLemEc3R-4mD
zCLf}*Iv}ndS!ZQUh1{vQ{A<&i7RS!<3Fe2f!iI!|8Gx)O@!AF%&vqA0F3%yJWj$XX
z)4t@&X*rQ8asT1NC>X8=!yElShEnv+u&~Nw^P@p+58B<k&F;sr&<u8WqqC;J^c6`z
zyS(J>>g>%S(r@<MB*DWBr>3LFNPOiY{%&?;WCRkO1^M~hyZ3%5y_ckpi*;{ed3C9?
z_1B%A+|8|)@V_QB1&vP~yF>o|5I#UOnvZXRvB|VAPNlaI7c-z~j{mLc!2wT&#xGlj
z9vPL4tSn=A55QVMfQ|~26z`s3Yk7=<QvGsEzGj6ffrJEPETCEhq8b3ii8yVNl9F~_
zbzt}hKHY2@jc5a2DocS547~*R^y}=iuNz#lv$KKC0aX1E<>c+A{U_>2aqe!%<(E^E
zJfs#|!NI}wzeNFs5VM(4$V32qAkHmz&#J^1{*R7M$bJO>XkArc9S{sZHDx*wiQm72
zJKpj9bTJV#_(8}YRweqJ&X-o7NN3*~1bq;5%tW-V^?9oi@{sa2FWu8S3?6KHbMe^0
zq3+bz<2<078MQTgm}!bh2Iq3%JmM2!5z8Q1T)TLqN5ggNfAzYy@*;OheCVNk=>8v@
zdudf+6IoHTeZ9TT)h~5DkK}pztx>37bFcg}D}zJgu5**202N<fe=q&oP-!)4TDS4Y
z!(}AM6}AlaSY~{mpD0Wm4JdS0&MCHYzEN@-^W$4Dviw~8m|G+V+}Fh90#5ud!?pgT
zWj8n9SiX*t*o@yIaaK_IJT#)}<VMhpi0Z+@#H@Eccp4zG$=kJ7Si+_AqUOH8^X84N
znZ?qN12W>t$@;C+y+xx&enjQ=1CL{S>>+yx2j1y|=O10T%HN;b(J06MZhQMuXQq0=
zeI0+MvSWbQG7!h0Vwxt3wBSh*z5d#8kv3;-|Hm9cd;1+RKk4C*STI)$tCy_*{(Z_v
zsIy6FVPu5T)${VDke%Y>oE2zL%-6P~W^B`M5vfChUKebsLoSYvJAc>ZKn$>MeKEZy
z2GJf>-TA_fZ1oVCV#1iLUK&z5wg(@c24tkBzWdHBhr@sS&BT(9(P%Pky1}_y?Tsy0
z(jmLpt?twbsgY-_hpQ_I3wwKa8Tt%!j5Jhz;o;%E9etw6Y{&PQl$UZ<2P9eJZCYg|
zrJbD}<6&{?-qZ#d`!}XAR2Wv*B4g^>3)t~va9p2H^B9nP)AnyeJTA4uB`0T=Ij;4(
zbXHMOX>k7Yt*EGr)7<l{pT5!>4IV@bvvCeVL488oyn+I6vNVL`n>P}fof{(rf%m`L
zVJ^_(Ob!=#Uo8R^tdU96Js7avXz9i+DeG6xikNbCySb`^h$_%~)ObA+l%mlwG4%Ms
z@Zj&jz3|@<MS~CF*niPoxtf`g;k+|tU}3><nbkwrA^_EL;)&I1uO5)!e*i3ps2-Av
zYGllGpl}p7nTpOU<X>QazYZglGq;gWS0Fn5qpDcej0f|4>~))I#eFh9*QM`w?)neE
zyGeo-v!mDnDJ_t6?$NI`htjE9LV@lcVO7QSnha#NvEU}Yr|zeL)f_hSNz1<?I@iUH
z&U-`@)5Yu?vi>?t(eq4se#?1tdmric%dmK|=)c!MmgZF|GlkH%mfKbTPd^#@-y6*L
z=gQFT&E8+uum_uI1a>;Jb~V$>mkKe3l*|umbweD@t({(Rk30T+fFQwnx5d-a*5Evh
zw|JbwkvV6Vi5Fe2v_7BM|L6^6q^}6>6HZ<C<DAv`*{A8BM)Qx<CmWRW$NtRLprNAf
zyEfiI@?Z!{8)LNM@BjhV&CLxqK<kmL9`)Jty_VbE#TLQeJ(swJ>Z(K+#oQw43VM2E
zE>DMh2q%kUZ<l>yqUiI#PU9lP!<(G{Xq_9ij*gCQOqESWPBw%-F@a{kqgXbJv5n69
zD&WTyR8^Dz-qt;Z7C3-sRvh5|u7?FjHKjN+(_zx553;!g`1m_6e=fG}*QhXnsu^xJ
zi;Ic5o7vUKZ>M9ruSge}G}O(LkYZPuC$?{X7$=`z8MQJ+-)oso#oOTV<MU$zWt}nh
zWB=TAq&=^zjnM7FMom~-)%O;}C5XjztcNo?KF+!<tzseM<l-b2#BcwTlzMH-u|C)}
z{mPx}-o!V}QScyvk7!<n=X2tV_*bVh3FG5g7~0y}Ms=JsPOY<Cj=PvqIf$PPZ=k!6
zOh9O_IYM&(%1t>u0CBQAzkRTeVP#cjVDxK>1<YaY+SO+ZO`*H|3Y`+NjxlX=ufb*>
z@fCsn9E&uJI;A2IN3+yi`8b!vBNfYm(Qy;g%WGrXDD*xMYy}@b?Z>`)Hw9Vg(m$AY
zkCOb`(?tUrunWvBEMSsVJ2?f5mxR@Pn6SUyi?O5gqdEshX8M}a($=tK1%)5Ul9E)9
zWCth+jfO=g%ZzjwYoDopkk0P?-oS8*8SouS4Cojc)$4CaaB}$^z~jGhF*6QQSV@YY
z|0Ju`{vY(sSY6(}_A1zP4pzDnTU;sS-8n6mYu9PWOKicRkihKP^Qe*Tc|$_dm&`n>
zC{OlV;}#L6zzA*<AN0k9c3c92D$kK0wO4++UhaH@-G3Wy&#t=LCT0@!IQN|Gv{lyH
zH{mUYaT8*^V2JUN`|_0D5O<|Vblsm9ABo&oMLm2mJ6CKGAtWpN8LlEd-C%(Diu*XO
zo$?tCO-{wO&eRme)>_zbX7i{XB{Oq)kFlE8hGJy6B`(b3yQJ5K3!t8vR38$ApO5I0
zLHk^CK#IchvdD(}^J50}^9c+H)GsUu%%xGDT@4#9Ro}j;Eo6t@LsAZ#j}`Y2I#`#J
z-<}MYt@I>a%_9G|F}fGUlWdxs_fuK1Ii!&!PW+#1Y-IG^&s=i%(>pNIlhrcQ`CMNo
zfyxBShWP8(5SN6nXlQu&;BYKnLA9fQbAJ|{n;St`@eW91`?@*kis_D)z10?s|N852
zXLuxArf{<ZNI0l_gzm^8Z^@XortdBTZofC7SCN9k7v`G;c9~dac39X7Q~HF`g(x1E
zT1WMptb=(GXaOVEcrr4w7{`$UDh>|k(HuEg7lBSW<10LM=nNzEaQgL92h%?>&`PtH
z0>$e;p_dk8bfTi74QZm}*F*BDTsE`$%RgUJgU{Vh;>{Ts&+<|%Dbe9MOU?U5@YKpL
zR^OYge!Gc@aQ*wXO5@k+s)|WG!Mq{k!GVXt2-f*|RLm-To!WBbGSbplS67ft8LfBP
zhBC=GS~7n3zj=%01ZIn5Z&FC&*K`ym!YDp{{)`30<XwNt2j~-kEd?1Rra>86-RHqy
zqHq9e=NT-GgoK2BCuq6{_K&2pr?XAMNjsm{x$W7-zlCu^-<<%+%;Jq^HBwaJaPOXG
zuTUiwRH1@`7aMcBR#%Hzd%rDGS|}i~^j{*HqvK2CGmO-=DjY6!KZqwSr2oa*zavxc
za{VQ_3%Uc|SN`#BzGkr-2~=f-$IaqPk(z?SOX!5;RpGgLf;MUjB1o4HO}}a&lo-&?
z)#fuP?@h&q9^v5?xOGmFKxo|fo!3>@-1n;g{@W1k?9#m|55oS7M#rFGH#LT-xH}S|
zAe;0-U6K5DXi!N_tvCl0CvatLP4#9E3~_V|3vq|{5~Ri6m$MF+;s%ERfCM+$nNM!e
zYjXBx)>j2dGkKDAwR0PV>Rt6i65n|pH`r`DdRyaPo$M{@A4n301Uw~+uI(O-m199{
z%v2RxF{FQFzW)y$4g@v#!areFsHPhh(*FG%fxEG#R6G)yl=Rtt_~3K@7I`Q{AG`Z~
z%UOGS-5FF#g=VLw{_f6u_y`NQ22h|Y&~^8#uon+VthMajDRmjgteMkcXRl%=WnyM-
z@jO2qs@a>Vx+fDWs)&mcxTD{a!}qJZrzaltS!}o0S-HuY<x`!Vrul&q&xxdzM7w=g
zn3sWSf=!0~)hlA?he(#692s#>)K{RxA2c5=r&QnEh-em-Pk~`!!(~E5PG(J~MNCfS
zj!-uvI*&7<rk1+WZtu{L3!tyK$Z>t$@vbfj4qe?vDq30(H#c2|U)ZDL&~tHF@SF?E
zVK-vWy)u2lpC0(wYT7R>hA|cv&+6W2bj*N5{nx0JG1BvMb0=M$PH{$nhEz8H;Jk(T
zZxRjbqm$*Q*BX_!0}1Slqhmyr*CmmQ^%tiyR6RX4$voGTsrNaEv9ZJu(!nM)&+a60
z${y(EGM?5U@c)Y~vOT&R%Z!w|_sXnQ9mOyBF)?wUVF(0Z;9XcW?xSRM_%HQ+cDvs^
z;b%>*`$P#%6jdb$qw|A<HYT1|c$lQgR+KS3OkWePZ;^FCAu(|HEzEEy+54-Ab+x5>
zU5Umupc*Z^O{dq8;~>=3E;_JB3thJ|&};I`4GEF?Tyca=h^86u;MM$9nH`EvKTKD=
z#|Y5iy05eQvUnuz+`(daM|v@2#%q<qJFZU#9S6txKr@uOZ+H$1xlK)<9DwJRH08Ms
zd{rg?DnH&i|Jj(YUGj@1{4FB?-0RvM{N!3%T7PKY%vU(e5nC61)XDz#MaleO)Lwpo
zl2U&&0lbVDeiUYo2D3OB&dKQF1SOlG6a98Sm8e)Z$S3D2R8v*DD5O>UreW99hLNC^
zo60}FSZ)HZ^f>w6>Z)U_3CE0+kmH!^<;)wUeOJC{iN_Wed}JlPzL_vC&4<g^$0mj}
z&TZ1jfsM3WU+$r0jt=AVy6Wy%mm?iRZ;%u;G(MRUB81+hNn01zKYR7+2$*w-M@(mD
z(=jHd(dqA|sqwOkEw9`O=)!chL#A~6i^x;TZ&!^bY7EsTazpbEMD|T^7Zz^&9b9br
z@xFGYJN4(!5nuO>ecl#Dd4P!mYBuAeqB08#F5HTx!pZnY8XT7nTo>^Ngq-ygz=RWn
z$sxlKqsBDX-_LZifvb!r@lER<gVVuXeYm@O>V=nN>##R?T{wajc;H(q7D(1G&#kVn
zL$GtS`6?1n6fZ>V<mJOMG7KSWR78u<;Kkg0EZ+Uk_@G(RVK$2MCRH{?@R5*(ML}Qw
zsM+XAra2<SsVM6&V|IimXNz>JV$FmnK4w7sk|g69?e)RS$Df&X+W~Hw=%bq#6qJwU
z%$H5tO-2crt*^?*Lk#fdy25k-XbP=b+oiIHl%Lnvp>VgbC-h%kE<g`}y+BienFt4g
zhp{%y-y3SXe~v(pG8J1C#v&MJ-o%15l7XW#;^!#%APbJ?p)8vHjR<B>|3}%T$~sNy
z)#DO1&|8!34!#3-8#=%4rApUC1oV&p^P6AY>6KafnnB=@h<R@gsv&T>URP%<aedYk
z-=<+_9G*i)tgf~h+jEgH#9W-~rf}IUhjBZwSfLsk5>CyG?Mq8%k3e|^pZ8U*Tp|aA
zv5E{hn&aXo>nNZ7Vna#f_+~cRJ1|g0^G;Qf_rF5Eh28!gnLB(K!icwE1&)cKvB*1K
zw~tqmE~{RzD!So}j;46<cVNhRykPwIbNIqZAyfr-x@xGGp<|{cXsbl~{F}tU6Hc2s
z_uTx)0+<+krW#drr+?{bDJhXcLj}UF*e#*SK<@>7zb<up=Q+}ruBAJr31S%V;Z(ij
z7`>P+7QP3AlI6|iu|8g?p0j(;*wPLPu73VNRa2X4B=ZkkI<LrX@`%JKJI0P@y9iVm
zXngPL>gxQq^-@GnF!Q73u0Z`ucXZ#hyvM>VZD5DVKa94Jl$W<z<9j&xg06edeSH<M
z+mpS;vSPgq!d0Yhqf)Xw^BOYlgn^q33}oNd*4BXGvd3KLH8%BS8~QPx#b4gb5gnhL
zEcAX6y~N>iGPLYEsoLO0q$@x?MomkL=WVNRf?yypKae|tLKPTIG(<_$A(nD~uAD%A
z6!+G7DhjQ9`hsE49>OVNVjrMul0C>fW(r)^hxa!(rthi0a3kh`XIR|W*x1w469kMx
zP4WZ%yCzy&(c4Q^Uz>YyWpI8!LR}-E>5HvKHnp12AlS7nR(j2D2hH2eR8${&jCr`Y
zVD7sPW4;euhkCIN3kaAM6_<QJ;Aw~35JVz~L4s3l<4x+A<~dhbY=!CltU!vWh!&W7
zA{^4N_!r&%KL)LZ1q6UDFKXH9&Qg40+<E}_H(Ui?`Dn8FxtS_0hug^Nq`9RwFQgB&
zG%b}D+V`}Npfq7{@OQd{I--7-LvU|Kn0}2P;Wsc%c;p-z6rjPhd$PZ<)E-!fZxk#1
z0II3MIodD@doNT6kIM_5X`|poN&=UIPfBWSX{p_yLf}V1BcJ3t-FRnp4cDIIW)*{)
zI{Wuf>b(Gwe3h9jsVIiu(iQ`NCc*Xug9E^lW>xb_cR+iOn$76x@Tq7j=j)0<eQ55<
zHycL&J*cNsy()84b8>QG@T}{5DJ2f+)bPy35z`E0XY*Ao%+1fwkB)7m%-`y@1!Nee
zM<XaCvT@cJW(pa!?F#P6B0-bz+V9`DVMtx-QZh2L74O3PuXD}sggjvVb;bf4?A~;R
z1SBJPcqXKF9oQp~0<qu^MDuPW7}(j5ArLJ~a|F5LWY;?EI_cSz#wk2JExGC(NvbdW
z65J&69lpQdhWPu3k0#~t(l%>eT;x9uy^HzU*ucr3QgC0KN<0+Fx0QeCVb|nOf+j-X
z-!TI+pn|ic&rpySs=xkd>DTrn`Gy#H$bJU5C@}+dMC5-H*0?XF$~XSc3vj&rsqb4b
z<L*{cy*qwgeLdOc^rLjj2+6b`9f@DVEtcb-oWzMh+PZ*`k)PiSHg`bj03ty+Ajrf7
zGF?X}Op5;PPc(ufWMpLHic4faF3r|^-2*<?JqzEX2NJTrlV<m;W{jJW{l1UG)r|VY
z_R0<J!)JZ!K>sYg;eU*@3mVidzR<0Y{u)!|wZ`+jh<T<A0I=i94T_}He*x5oN2hiD
zAgrfZsYB|Kb1}%u%TwQy`p!=?JZw4P5+pobual$S-Pic+>+9Z5*pfvsluTdk<Dwh7
z?dj=j8EU-&67?zb-ilxycYgkkg0(IcZJPuA*w{-$m9)hzcWkaLOt7u&fK>wnvoRLg
z_Zk&F;4N!pqV@n^B$zIMj*n?DG%^a8Li(+V5b;CK74Wt)o43%{^H$I^C>BHZmCD9a
zP;pdHwqfqdk9!dr5pi`Gfz(Al2wZ$)Qxm8#b#>>!xtg)fsC#vwBDd}M2M%Y_ZaDtk
zLdhSqYfJiughGdp`I_SGYrRByToI}N-oE^hT#u<;6-C_)_nX^}z@<bBr_5NskMNjH
z3|hb{&b?SYCy+FDYpQP>T(Eu8DAGWuHB1aEjAimmn&Wa}2M=VHiIB9X4%yR+(<=~0
z#^x%`qt371Xr@}v8blKRC3ajRkS!Sxrp#BlSR{HP_gp_`uBjFkR~Vs?J4%yJ$D2oQ
z8QGagMdib}`lgk95X=eJf5(W3xA(knSfRhNt!=3=7pnM$<*)++B8o;cmD1DTL#!z)
zPc0eX>ro=g%II4>-ivJBE_{Sdw26W_TqJ#L@s|Pgq&o@}MT`yi{M%lBJqYQ5+7j5i
z>|i&AP5$)S@(H50zJ6wQHbm?x%sY{iWAMf@9@_68h|?1Dd9f}G-&_`+v6z{g%Z;fB
zJ}yO(p0nZBYzQZ3eDPvpcsM`VM+|<|-J`z2*B{!y<9S`oBMUuPx0+CK8eh_RZA#kX
z6rmy|Ec_qzDJUs@`PVFyue>cy67V5wkkWzc=0QQyDY%TnI7o-W9-~uK<+;8NGH(Bf
z%}zf6<F9NDQVM@tZIGmVLQ+h1wu_7VUm3)QjHipmskKtd2vBBVk*9kBsX$X%xgTF!
zQenJ>O-15l7cGBmcj6c%RJADEt-ZaLaur`B|NUPJ(Px~;l)==5A$#);(5ou`KnyTs
zi$&dG^4R&#4d_!qMDsZBobi**iTH7;wa!LHovKlw3f;M2pqE!w-lfw=!(Cwh+Ws%6
z77_8l+WHSHq!Uv3+ClG|`d+;o<g=N@HeKhkH>+@~fGe4Fi`pV)4P0UVk7Vi02z<$~
zh4!u=?0QRD78W+0b`*x)$`&vE%g};x(wlZD$J=$WT_`t^A&Tf4&o|H9L3ro}H4sIJ
zAx1u#=UDu<?WI|xNo-$VtJmT)wF7E+PyPkSO-!gO)>dt~V9MroJERKj`Raa{rNU4|
zWBwONtA9?a8K)_Sbw@8V+mTI2DCc*ltF=J)12NAXN>u<LTIBkw1WD&4FG@FSlnb`l
zy4dJdYasi|rGHjkl2=zZzam}gW0r@Ue979vS5^XYC9?j6s`rb{sq;@s-blA$-9td+
z^ro!6#i&*IFir?qLne^V$ao5#7m0tXJ2e}NzemQX9?Q!JktM)JBBn~J*<w(ZO9B&C
zcGg#~<EQ*D?)pX-_V4j47@nn;8$**OwyIO54GQA=u9&zuXoCfYlz2f@G&JJyit6jP
zEIa#Az^8{}NYqD}p`C=(4+nNWCS9_#A=LO|f2e_qrdLML(bEHEr{Vy0N$1%OH3qYH
zN-}95`*e68)wFhWn2?+O9B#`SRt&@eOUmuH*sP!*ybecxhnO;1N(c+K8Dn+=33ihz
zX300fVi{9Y8i0dSr`CBW`S?hTx-u1w*epD6EVxpKO7!g4(Ozn3AL)#EJmwHvEh2ob
zbv4%+5ut(tD{vV)D)MU@<$!_1Kc+`CoO{si6lv`P$a4I6JM8anikc3e*X|s|GF9Dp
zp1t_!L>;Z92K&xbd1$G_{pDLHb}*<NP_AXZ8f`ecYke~`35^u}ZEchyGHBe>DMi{b
z9vU)KUm%0?v2;~IjYm~|wyP^Q;ROk=GgeB7@VnMd4%h1hq}%tB2dba~Dp%UC@usfT
z*Vl&>9-m)52cm8E^n|q03n3IljEARm?@7$w?;@ev=d^pqly7qKhQIbCr5Ce`2lU<8
zZwWz&2kxuQ6AlsS!}?|~h&TDX_1Jz4?ZQ7g+UJlBaf4l*%;>Z%0X$7Hs;{rDfrodT
zN-E^T>O0|oPX%C<6hY5!s-yLxit^`y;S6u6J<j%*Tpg4-k|#9cXDK)tVRIPy#rL}P
zorF$~L<@pKRdwZ*lxcYUw$by>od93@sN6x9JN_)WM$q5HqcR8Aj5l13;(m-&I@9-0
zt|h@iYX$mPw9=8q2H6*}$Uds9RM6w`mNlM^{!U!T$QLcDC#4$SR12HUUL4N~=Z-3>
zog9$sr8kO`%I14MEIIQd<v!2F6x@nTEYo~7rt*=IakwQm`!NZLXW^ny<u;<#tBnMt
z_eIV!JLpnAee?qB(AHnlb)@e|edAMX>;x3|c{?*oN;c#C`|{bWre6M%76U+edl#EQ
zejEhE56S$~1Es>=Pi{pO?Xp#9v}Vwpr=C~{3((=iIU~X~3qRuSi2iG*qVAE8l>Bg*
zZb~Bq#S_Rl=?Z6y&Nxt2qc$1<isZ+BZC{#B*Hh=+i-PgIW^%Id-)n0GNzr_rKU-&K
zFDLu|EFYbNr>=XT?aS+*KX+7>*gW?Z$ki^3mcI;eyG3@8&lmA9cGu+<I;-a8AAC@t
z`!JHb|7kEnI(z)9_w_P#Ct$P7HqlU0p8LSK{YG3)&bm8-GKRx>Yix5M&+eL8KQ<~#
z8`m-KiXikU?yJ2yV}ZtdeiW+&XI^)MiHSj!f-vL|DLWA#87nIZF(=a`QbkqOpK6a&
zCxl71D2CLrJt=5{<b$a|%CUkOx}|Izyp^afJD<&C+saBb1q7h}x+FKa84bj!w48yZ
zmjosH$J{;~paqcc+sGl}TsySw4^yIJk<yT_qw!5G^Qx{A$lR#K!$|5ZzVN%Bh#P)y
zlqdCGH1q%PVby~IC9DM(GHcRD8<jh<&KplG<Ovax`SEG*1pTgcgCl@y+8au#bJ~uz
zs2PO9WyZxpkXJ}~99KHAITEdnjE=^0X+k<!Sy;|fVm)KBvZy{Mq9Sqzl;4n+Ox9b-
zSy?fe)*}l!9LWfyZSl@T;Gr|WI2q{m((FVNx^tI~{>7MzWpA<ER4%mNo%}d})`5+a
z9Vp}MtG8jo!06dSdSV;ld$5DM?auZ3Da{ngf4cgLua0==)%5h5rBTGe9t}NcJoy>&
zX274GojYvIKbkD|e)mN%ysH5&Iw3(T`*ZAG*TlrhRescC0lfRW+?o>u*adj$(Oi1n
zvtN=g_2`gWGZZ=t8HSY9ty|Ap7K34O23H28Z=MiOnmv|<b9f5zYM}knha+o$l3e5u
zRXW`j>&e>D-&zgCm%G?~7xyXgxBF1C<$oQx!t4*}ABeze&l|RU&|m`Z+9~)g0b=|C
z0@f1a8w>@eMqOY$GS1><HG_%$a`2<-2Z~QH+<Bbt#wR2|b99A3tYNHFkpKCeS%<?B
zF+&8VN`ZgEp(8uNz);pW#OnFSChzMutSnvYW{Tqwb9@^lRvu)gyHZ_@Shw~e%i53o
ze>nf|GgTcOI50>@Nr}~dRT4@=8Q!F4=avtpW)KqkN)uGp5IC2<E;26ic?@IOla^`n
zP@?H9FPQ!sK(xE>98#3>Z{`%i;F(5lE$`U#Kk4IJ+m3kej*p9~*dG5VMXa8=<n258
zlzub082QA499B(L&GUObgvvRx+`rjS4lyI7J&JYRR=XpKIPwVrlP)4)1)v5Kpbv#o
z{@C!a9Z_6nzu^E`t<PjkAtWbmT~LKzrV1F(H(~~TP*7nYeyNi2{w#{<5*yG3TH&PM
zVC^-B4NRf`$|R?sUkFZFabOB|3?K`$Cu|?fK`CN8p-(BdJfSIc(~f^2b;Ii!OltIU
z3kbl&Kv>_M*OWUZS1tCeN~aC!Ku*06KJ<X9BRxuZpYKtkgb7B})^felTZrIDM#>_1
zv{>&{xV)??ZH_^^gZ0M?8BrD~rMW2lYpjV}j5<pxzocXjy3qlH#xZFs@eW09Q)j2W
z0%K^|H=W4kI0IN1ev^<qU1$<jTR&`W@`<I2R-kj7s>dd-_LZLZI(s|0zt8Z>6nL#a
z+-}0>0oh9Q2$Z|+uSK1bFl%VUEdfOG?D9ZP_*0Ob-m>bc7X@7A!+)0<3XZXFwqJjk
zc*%;nCaV9`lPUCMW@mrBdnF~Uh%20l(J+NkG@Ehv{y*Lvf}amimsVHDpb<%{l7WlM
z6-6JlWKHr7^U+^E*ZsH8F*VuEel3T3n+@Z|y^z&ts3)WzV<f@(-5vQ1>MlS(K4(E*
zfFdY(H(_9hCeXFbVhGdx9J%4*otUBnZh1tr=jrgoPUExl01-dL+wX>hYU)Kn-s;>i
zugM6wa0m!A8?=7}9O*Pc4wa*VL@<SpPC*yeLHhMjW6++<4pWG{<y7H&n6FG49uJX~
zmFWK`_H>Qm2rWF?Y`ohtv<obj&K*|)X$toT6UkjxORZQBA<h7_o;kD^K@zoDOHxGz
zzy25%L7FUc9#FLn-Rh$=RhEw)J$hH+avF!H?EL~1N@pa^=f955|K#u*4f2(82l6*%
zoa{!v=7ttG{*Lea4K8Wv|Bcdm?93>7_wxQ{t1Q2ekDT(A0-;f=>A*gI$;hBk7|I5<
zgAfyH`+X$w`7s#lDQCNvY?+vtw#qzIaY4`_C7lQ80aa61zY3&a(<8UPIesHbhtE*<
zHSBr6JPx_g<3x8450o^?A3HLXJB5!ZBR>(d(<g7Jw40#_IXbdw)dUoiIXeC<ZimMU
z?02rVXG@udPDEH3e74XORzf&ZW+d?;IZ(`RYSO=oT~LsMh2;P^x9REW@i-$B6LyQS
zJpETQXKg<^Iv^!<_aoTS;%uPk?ZqO&g9oJ|{i#P6Yst?Ojn_Tn4a>B(a;&c|jm`rM
z{mh<q30UNj4`(e2WtWH%8<tJM8)}gMmR_d$y!IfqVGW#p9|8mW1PG3M;qG?@=pGJF
z?A1))!g(mix}+EwQpuuWwpWT)bG0lh=c7I3Up}&1+w;-bB>l!ifXU-zdZluQNrCQr
zHR+w|c}vz%CHYM8&<78(gHZ7?GcrHZ%%VUwGv3@If9cl_6)*KuL9$cR?}m^xCn3Jh
zi8hp;axG5Qj4S~6%9YA>r!aPEnY`JwDkf&U#_@tVTiE~Zrx1S)EFx^?6yp0gL(}rW
z4^2;Np14Gc)jy|w|IVhFNj)>OF)1-8A=Fc@!g+aRrNnYGO@<TEJqW$G@~^o=;i#xl
z79+sLPc|zm*`P{YO}*(d2@FZ!Ex8eFECN;SEpVvOvHBzUv|fPgTlkgo9bp-V+Y86n
z<#2D_|I;PV)9r|LZ_#c!Iw0WAwJyo!cTLRHH>KQHH=-8lYQ;rGO_Saul&Nco&d;RJ
z`c7386om9%jIl>V>Auj`<A)CiAvD971{iPxI{JjNGom0~?@?nje^C^Abq`)h?p0QY
zy)A;t5b0`Jl0|sXrO`g2FPnKU&3sF;Dnk`g=CDl9nW4yjFH@(WtSl;i(d9Fu#NObN
zUwaC!=*R4Yq&&z|>uXDt561Khn4)^baPJ38OUgV?o&L8Hh5{y7OaXJJ!`=el?2JWc
zw&kDCM95~@MdNk<r39C=9^F_^{Mn{J56U6*={BQRuX9$#B4RUxqap}uY8x7U*VfG&
zmezj?*mW{Ap^ovB4v_r&P(A9#8AK|$j|-~1JVT6nu_^IS@lecKjcYCQLRHq)1U;0f
z;Vl&4|I<h~h!(}&KGLXkh#D#`I!$}YAC#qcXA)AJt_HiFc?FJ9((7L+=`niVqtjm<
z&#vt3G`^PL{-vp+;%pNySvg!Rqb9XkP?@BB``M&144J~lre}}eH!l#)&CagO&+q$b
zoC8qg7_+yp9bWNyxqEEK4(U%6W)(+^Ge$;5RalG{K>ilaM&jhG(eE|?M)O7C{hjZx
zH#Ip!WDt2H<_=@yG==nigbg&*)J1wt^?7-DKoG#RQsmb3G*Xfw##_L>ZRG$af%ill
z;-;O;97W*yY$+4f&;7EmuIshxZ6di;B6wfvOy(}^h9|H<)6HWE-g2-Qucm?3sc=sq
zi-?VfFO8P>6)Apjn&>-&+!y-HYwx1qCnDmlsi~TSle}i@0XSe_dVnaCd?JUOu5Qx=
zHpF${7)LnX18C)Dfn9Po7V++B_5=&qkMo`4o#c<hGw|{qz0`J#=R9$Rf6_<y6!h98
zSj=G}jRME}G|5y*`JRUh+HrN^$mY5>Zjc!C6asY<ZIU~|oi(!Y|99{9$rgU#rJA|9
zT@%sMLc?M`E++<vH!5C@V%&{d^!WsG<U7t%ap)(We5&Jmd1Ad7BVcRy+Gg<HyWcBa
zmYc`Y4?a{>91RSwq<`~!PCWeMF{0kt!}(0;59ZZ@&I^VU$)O7he54JEA<f*v`On8b
zIWJ+*&5(s`K4N3!7*shYJX?@QU<vZ^;U14lNN_(`ehs52VC$$T8!D<6`qlEOML2)*
z0$OaZt~?QrJoBK?OxX2~g{Tj8QEQl+(@9Z($WWS|l4_BuRJ4SeD3-SmDxKb%+9f|r
zjgQxCum`{0dxpXcO^1yQ-=VH;GeoWDLCsGOSK0?SQnK+8(HFvWBc?xHwziWjjTPu;
z5JH*5?!6BCly{Wqzn4^6&z|H~Ruh72Q-fPp4|OJ5odw()f(_gNBdb1ms-Im0N8=1@
zeRmJ~8(9uT<sMa~O-P5p9?gaASZSo^M?rOQ*F;nt_n2AZrX5Dom80|2a4DiEPnK5A
z9XHS;(KpRwo*P+whI1yBzEBeWHz>5ydCmL*eJI6hvH4o?_DcESWsn$@ENABDYnDt$
zvP+0Pn6*FTG+skT_Y+Q+RaMqZ%g@<2ad-&9H#lexCe!21(XPS4T?vk350I3QJ3}S5
zs}b_W8e6B2vh09Vj06C;Ct+*5?`Seu#x`7s>pxd4v1>J*q?B~&obOR0{t+QYsCJcd
zeZZK_<4p_i`Uw_BK+@b}xnpp@syN=;9?1^5yuRG#BXo>f>DSs(-EMQHiE^83_ZrIx
zNE>e4afjvU_mL535j+ip|5b-sv;52B1EF4Q;<NQBH~Xs2{7K0<GUCrF7qMeiLU~I$
zl`51%yXe?|gBrjOR0s-m{DUd0lOY>iSe~u_Eo9*%BT58>4JuTFzwfzovRbLWn3GCJ
zw`#zcogT}IfYvlu5TU!7ALnHL+1~D3v)?Z3SVRi%I>N$kC(=i>wFRQR7W)g}a~TP`
z*)>Yvww<bvL5t3g&3plO>*VC4J~h9-(Ei>zaEHJ7TcEqS{hPT&a&s;wO#$Cr<-Xi8
zeE~z2#^FW7OGpFDjOM0#oZtrp%#!T%kl^5bS;RjqyetI2-cwpyTH^LMva+7gWdwG5
zHPR`GJ8Rb7H<#bTo2d-**)omPZ3Z&15sd1^jW50N9FOH&Jky)(R)Pr_3k3COn4Lim
zyo*f)dK1|6CK~TSgZ=60>7yVqz-zGTmYfG60Dq;O(8sHY%EVSH@dIfDlheOacX;ez
z3)!-`5T$q^WU7)=parZtt;vL6XwBZ4%O;N79pu)+xSEexBeyfHoqIS@J#&x-tcMeS
zUcaWPu(!edH>V-PN>&f<v?s4jZKEDmNcXM3zk~qd-wnJHN*eLN1?n)3UvM1U!&H9V
zv9ZTO`5KNgHHx&e#cRLUS66qSSH7~+5e^0b@0ThejxImKY~kv9t+a%LhK|lmRgbbU
z_SuP3WZWc}0h--|KZCh9WtU8&8BcbCI+Y+i^fL&umaZ=l{QOPLhD#l#M+$1<8{7>>
zZ3GRa5PJeG1_V(x9lE96T{0d=Dk63qL06&cv3%TT;$w~V78~GaI?FlU7{OkO3k)aI
zq&4w=$QR@&Y<P&YhH2?0GUVXy+?7gZ+nwj+hXbFM8M#h+?a>;b9`M;XsI21PY&n>o
zwStaG3SjNFRnKT3;V#Yw0TB^TndR>o?5e71CJ|xWsCUQqH_RUTPa~mBV&Ki9ZG+RS
ze6a(hr39E4=jKS#DZ2+c+EBoIb7tX@!sq6f(uCAndEOJ<8}@q~y1{(c*A-NdLjuA$
zs%+0^;5)#{{Man3Wwk#X{EQL4XU3^Rq~!X)$C}q*k3amAK3!*Kg^GEn$z2edgS87U
z@gNj>Kas^cCT+FX&n?DL-3Ni&L0x?igjQ}(he+WQSEJEu-k>D>5^ew}eesU{hZS`y
zPR`S>r{yWV!J#pAh}FlAYD!PrM+(H~mVa)V7}~vKH>op1qsJ=~6Rog1a}_zb#bU*K
z`0%~?X6pzh!u_-fB2RD<Dx+!{oG$YxEiLa=*FT*+iUq;QSokY(lEsy7q2vB&Vg4vn
zZ_}c*OzYd_8~%_bQ48;4ESjiuw|nQFH}Q?gm3!!j%r4y~_i=z{)t0^=wao1Cu(0f|
zwV)rfrK;Y-4C0aD`f|NdlIq5$<o%2kFzgvM73Wwy*@F%a7%WF`u20c8KSDo1)&wkL
zCEXx`YHyl3z51!|`rKYVG4@pH)sN6bsE?I*J*mx(rSX^6U8qBO{WO-%N{7RY>LC7C
zWo#_rmjxe3TDOOmW}{0-4B+>M5;w~QDi4p#Ic(yT620{7?6tKO<)epdYirEk{w`Eo
zgN6|QvZD+F1MGq|L@=$o20XxQGhUYamY%J7UJODq<2SwJ#Kc+1aRD<uDdi*>vfYvG
zp!`bd^;3jDlshJ{neLyPjjq=`KnHY2nU79yeZ#1vrDd@=ZE9IkjBy8Qt`>;a;fOpY
zs<c#Hy(d1GQIGnp0ZPXt6>P3&pGv){|3U)w3EiuIZv8|KcIGXiqf#)J_*fPUzo!Aa
zX$rZOrTYg5>E4<?%Pv`xPj!<bov2zK_>%dTNxf-E^qbSCbC;7&yv2?fS@*#>#<K;1
z5T11y1|C0t%*Dmk%|ZC+5%_hRD=WvKLa(PMe}vw+RWS&TCxI|R@c#y3esp%)e;*7p
zxB31MGsal+0##&zI%{U8r>BRM-#r>SP7>JiA={H02MDJWTuLEscfw2u)QYq^-oEs9
ze6tnm&iei`65`D3;1UG)Dw(d`%(j|ckfqi-TKXsC&+1FXKPOZ&6cr`HGa%RcT)?9k
zP2C*GPxznf!4V62C8ZHymA=~_(!?ix1Jw3$TMih!_r6$MH+Wkn(rbVn;J9HD7Q+v4
z+B67fe~*umk_3I4!I{wHeymkvqYJ-*^SSC-dxRtvJar!A-a&>V5NW9Z^M5M&D5$?6
zrS{!GiTdgE2Isf@NT!*8W-Qk)vBy+QI|Y({*$ar`5{-+wUg<Y)N|d<aXym3HHWZ7k
z%kRv@ckX&eNzh3(eTj|bIR0h8q>u6IqSplSLDoOhkWwkG%I#}ReoG_C@p$<ctC45d
ztBiX>@^+ba=vNKB2vzXD#y%`?L;P>IgTNXWc)fxySA@b@OuReuIygEi;>vogkIwPm
z$xi-5b8~!f;8ZosvuEuPqwoz1>H#WcJ9A+%0I*~NZD%qui`kop*YIolIHak*Kp`_s
z0`E*rRC$^s)0aNxD}QAp@<o%a6X+Vm;&!{y5gt}u$uYCJ%Rn%clJThq@coefDf6R!
zfh`S<D$k>JIWLz#xk3+q0BTbQ{}2H0)cM%=|0dRzKOQ%*7lK6Np`bIue$(XRT#WxX
zE9>!emchI@l$)-s41b4YFBY3sG&nPA*3?DKoL{h2AzBMebadwK7sdgB`6yjaoC^kW
zICL2SW_kOdEQmRYiHX7KR*-#}otRLo9)=_zd<LBtoZvgaA%;`O;mP%$Mydy%PYN5H
zM16J|QW)jw)lyM3n(wlFDcs#l6yA?%<ktSIU2FyRRU!M!ed5U{u>}4i={Q6S`MSBE
z)&%f^K8)8fUf{F_Q%9`)I=1XjX*k||n3D>m(Kf>bd(YTv>vhA+njZ(h{>zl;-2w+(
z3!FLxXI(&s#0OV9LQ5!J0WJV>c5&g}N&CI}sM;YNDLWf*wP1re`}fYyyNv2krCO4?
z#PdE;vXY+#xDU4Y&pwH4L>X!Ul|<0iF27c&*W}*ThPxh1$F!jr@y^^_>Qa&6Og?tK
z;l0H70_reQ(wA?AuqD32(b>$5{@QOmZ*^}-71h*krseV^ZVI^DEb`(=!r<nBWprU|
z>@SF{RvZmLRw9612NGjsbQEqZoNGsQ6ecYx`2Zc2jYv*e`N%Tc8wK1MP(akOv*Rr$
z_9I)hugRL1Z>V>HG3U>8MQ&c6nRf|U2yd0PEB_bqbfyaHMG@tdxmg!xx&FXt0<F?`
z)Y9u72bohbGWMDO^8!$>^*zjyjkmXGeSrA77wm;bd8v3}+t=clZm0|Y6g;Os|AvWX
zuRv_#yA-g2Y;Le@jNYHty#0g<b*|@a^CgV6IyQ=mktz(N>=r7uzgZCxk{31_9zZ~*
z&Q~TJg1!slWb=IKonYzm=l(G<6WiN)KEh3j?MvWL8PCXo$5$#_$;ikE0mzWg_Lpx)
z#xh`X<I!ZnkD#PNoiHbqKn)HSuNMaP?%6k0|B1_(a5+B(^Ke~h>3*Eo>nm^Ly;i{n
z0>M=GCrd9mIKqO0SlHP^gMyy0TSQwK>F6ZE9s$Q!aJ*m8DfZIb^;NBr%KUe`OXAr8
zcwNDaoRMRy{P5oi#9r_E;XI#C*v(_kuKI$c;K0DS!wt84a@YtjB2Fufq<54<NJ2>U
zPdKKaPOh%>2cSW-g$2k21c-}=<1M6Tv%3a(sno`}4;YrgXQ#c?uHFVa>|<^sQfmZi
zkw!hAp#H(F37_{fIPq5OsUi>&YJ_a;k$?5zOP*}pQyodOGg5UK3Lm~JtdSyE(dB?E
zFrPmJInb(&d2ogLW4fjdca}cBxw(NO7vQH{SMX4a<uR7J`lQwvBVovGfp4a>%ejjL
z6X9(eQrCTAiK~wMINwZg^1#{#=f6O(dt`DlJR}5891Di9D9m(lbi5JNK<t~d{9gPB
zCp$ShCiSj;WqH!foaz+MVGVs2jc2v({ugsHs&mIo7LD=d&rGs^p8aSR$F7i;(WYYv
z&Q=PFemiWgs9ERE5xe#pIQgEdc2~ZX!a~c9pkFptvA-vo6k)j_S@jwrd(L=m!0mq=
z7#H_wXr4dg^P;w*!BS@^%xdgw?D{W{L@yYh86iG-jIuBp4_vu9>B-2-$|^`bS$~0e
z(D*LydK7ACs|t>^pr1&xS-J_8gpH`^O7$+RtU*?i?ep_Z0tJ_$@Mon=FCNcylC-9)
zm(A+DJ02T%%PufX%Q7y=;c3h-8xJ~KXX-r8;+-jAC{m5VAbLJCH)GVJgp=G^Zq?&`
zAY%0M7t6=H3kwTt%D>>%gYsao{()hf-Mk%)JfgI3ny+XG-P{EH@7^F)bbzW1O`NF0
zS1-=#4E@eHY2WKQ7b{594d?Y$ve6{OD|{Re(W!@a+N8NIA1-^5^ilzff9T?z0ml6M
zuT@pq^CM_4d27B|Id8|7)wvlZa3;I0%^1s6{5%tb11HSGaO`u#hrc|;APN<9H8y${
zwc>^L0JY4x=Zmh~z)L4n+a1gG0##xTt2_<+fz&D{!fjwHfJ6p8Y0>!NB1HMku(-4H
z2|SdqUcCZkPfKxA%#<|l>(?omO29HX*4Rqd+`1;}&Zdt@Yw)|G;kVWm=l8~b&ct+K
z{7#Pt7mHzF_qrkYg$qp7RDyWGD5rxPs>;Q2tHpsgP@Wduab$A-a6|K!CNx@;DegB5
z+`4%lr{yF8X&LV~Kc94M+8-Fib$(Wy7#o9J9$eaEek6o!Vs0|e4gWxz0$n+EFO|Lt
zs4?mv!^Kgce4H~?UD<N6xyh;lT2X^=I5s%Xu5Oy(TVOA9c=+JQ#kVAD?sG#wg)BvN
zGfR1erHRw8SfS$gu&^5KRz9jQ{8`_;p`~y6=g!f)QndKlatFK>0*-r2#fde6&;OR*
zZ<>==_ZSLMkClIAGgmuYq)a>5(_U`Y&d=bCCt*&lK4A4-j4T{24WJh~_J0~5_WRbv
zO{k8873dn3Is>JYZMhzEodMyo)*w&iBh;t%w(O;&EA&>Yhq$X~P4M4bukY}&2s<iA
zgjkK(8eU5fvu-%nL#YCoW4ixW45SqavmJXRlD;pqiR;Ki+6n>yaQ;bOzOc^EB@LJ=
zQyC7%znuv*_G|lOG2D48dLzL&7QG5s32GqgYHMG*{BbWGu2C&ido?%bZLEKz^;MQe
zTBeQh1Ky)>GPb>$I!|?d)wK}K_@86n+tI*JTaxo2-gV<7i4Ws|-bhsS{8W<Te<3b!
zw{PYyM90nxdT@G~Vq3diVX+wRCWIFmevo{{z6-c~iKXhBecWMO9$A07B23>iBYGD(
zZFCb1C;vcV_y$T3t&PoGDO5E8lnkkN`2*R-^Ao75Wo>K{Ew@i9Dx#6U%HY>Cw%cs1
z{Cp~c>+3BY++3farT=pZgci#43d(uI?G#vkNi7z>_8E%jWa<6nfJqXS0v{7j>4usr
zosQX3m0ld)_D`XAL)AE3v!=RHQ%S0YGM|@L<@ao*wEW!Pe8l;_FGe!f#F%`DnF_&9
zWL;D|@nH9KEBPwfC7$j5<qGA%><wObTv2`C$u69zPVbvjz;MaRiU4c?S)$Vj%+U~;
zPtK%GO-;AF2^3t9L^sNY)0EDyKXwBf0GekapBt<21D~PBOMsuSeaSue0Z<fx?@>jf
zvIqJvTkmuiQ|D$4<hNPnBMhW+s!Hd9fSdL{VsUMeMIrff@OzF-3;ArXylamwfdffE
z9lz~<)fun(p_BFnw>Q%_-G=acOc;Ko-v8ZPP}4=1H~V+wm=g4*-^v;7L3B5h!|Hpm
zYMNq+JF|zWjh=eJEHDeORS)&`5%arOhK9-|M{8p5p^DV}?eAVzLIgK{&z8nKx!^h0
zrH7ZUr{sQ}boL9_QOfs4tmm>`C1BrZw$uRx0lq9rof%=}b#>)!ak1;|e0t3Z?V>1e
z=No%n4?r+zc_(P+5W@3*2&%!V-!FQ@RHN@Bb^&ZhX(=M)4iCyi5p3k!1*-tiuPujJ
zU4KlaCssFlU)L80Unhb5$op!R<HpO_j{b)sm^@=*>!GW)?)rVx;jcCnP}kuauDhi{
z!I5n4)&Gm5y9hnHsah@aVe*rkGce(^vyw8TE6{lz8(O!dWDb4wqGRx8aYIHhJ`cq;
zFu2_?XC<FUq1afE7|Yll&(VkBb-)O>Q@-_xE|Ay7)5~kSF_<R{I{000u;7qF@%C$v
zSY_HtsWRBUC!a7u#ewgC{?DF0E7Vt1;>|*p$H%`gUdsykf%t~8IT}&%m;+fs{Pzn`
zz&7D-C#!2wg9Y1($BWSGVtJzstVr@HI-GHlFz~KkQc7@bA^a%dEUKaRIzHxqe~z}J
zaeNAChIPs6k$+qF?|V~@Gx19(!W7`Z6EZ}4|M|Zc+c(w-zd`;F6V_Xqsvx+wN1K}&
z1S~y6F<Q7<#?S;b?K!YTdFgV0q2ig<=5oFLeK-UF7z9*LgzG`$9D6B_n}XH^FQvJf
zg?PQ>x7WFQW0TwkxdC1GA>&B#;5Y-&wv*mw92dlWZl@ZKe7}y~BK_KnHxDb`{JQyq
zR2rRI27Qg@!vmSs1*-{C&DBrF!@bGR^rgirx3!?1Uxe}LaGK-dsEPx5>LPi??2uu+
zMRCIK<E~R)funQ2A&&&-_4y;Wlv5N&HPP^t2l$?+ySmxOAr$C`a%LYyal*(2^<o{{
zPEy&?<vYcp1Y2%M{+_0w&7?d-{8(9wMC-k~MH!VY;bfM)Gl-8Eb7=4%104fTGLDJK
zuMO0vxaenFEklwJO$EAgb(V%Kyrtf2wt1d+ubgNi{4vm})Lor2AHI_kum?FYVflXO
z8en17cyE#MrWHkKXZdw*d%F@5QKqRE?L597HzY&S%8|c)`%hH@#)1iEKZ<V7bht`j
zp-Wsn<eSg5M-post^a&|$Bgq~@2)R!c@R9C8COGUZS3YBc@pwvlKbvtiLVBe{bt)~
z^zT!0#0~(xy=zCr&dwn@3NrGIPUBf8IlY3mgLm?}P2%s}-51F$&m@@2Tz%M&%$%N+
zx7#A3u>Nu;#7{-s`0x({@Dx~ZeQmB*qNiJK*g;MeOdDR|+=a7-<_ot_U*Y?(*6{E!
zH#khoSb`F{+=nuA)rsB8;(TIakAlO(7EIm^k2XnU3T;4>NSd(!em1o`r_Y@-jj^(a
zE`L!Yw)5Iy>^N1MXx(k1ARN+aDF4y7?Q?sn@8#80T>L_~nmr!8`dkj{Sn;wzZ2>B-
zAu+P#-0Jv}$=?4zZ2bo~*ZuoHjKAzXLI@!t$;zG)LLmy-WM=P~m0e_Ig^*-M_Li9~
zdt`4Jnc4Gyy1w7v{lD+~xI2#P`h1S7@*c0}IM2uV80mZpaT@r@4`-H%U%yUPv%56>
z!oq%mNmPOYv({b&{oCq?II`bF-FLq(Ywf0%WS!gHd&CkYC+s5Zu~-?{GSfw`$ri__
zON8vnN#i$f5622W-VsbwHZ*BY;=<T=NRd#{(7^5iS6FQH!s73sy(ljJ@;rxI?*eYu
zH_=6f2Di75)JKEK^{PND%|3%YQHoi#sKNeEj<4h?8z!PnVxN51E990M!Io;871~2$
zz5wR1@+T1WP?J$rm9p8=@gVtq7#vyCG1f=}V6Sl(DGnm_c3x|#=~!+nI>NyV4lPb9
zJ;pu?@)t|BVB0%q!A*9(sf4Yd^))!Wnd)B5x8QA4VrxnF4Wj+&0i!jCh{7;KdB)Rk
z7AIWOvZ|`1?^^g74J!jz(e8MNmv!?Kqdy!XrJ6IlPDB*3@4;~TF==?$t=Bo5+R9qo
zdt`7h+IwDAOKZF^hv&!4pLq*QM$Di|^AfHQNf|=Oky2rBVP<3mZwU<9@Uv02eqa@F
zC3M3~(x<rM)3<N;ekS{;=`}1gC5-};Jp{YhI*0gO_~=lw>BwWtpz+AJ_Y}-7f>ghZ
z_co|G0;85wzYa2b7TR!x{pdHS$K^AYz!a~!x%nO|D;Pb(VgaE2rH#$SvAJNB@}ILy
zSUVrXIsVkRlSl>PyLMGxN<zn(B7k35xaXPdxHgod#2*J_pApcfg2gY`k9q8^q`ehf
zl1uZ*sh!_Z8X8G{ReVH=iM)_DB%U%0FS@0fkt1{Z+hhT*%#^M;DIX%Y<N2dcV=#F=
z>+CS&ysr4>;CVl>SwQ;INP)gjsS1|mA3_mY(V~t1g=nkW0Er&$ENHo%5sq>k6_dP|
zo_~7&^x;_0`y<bb6f7bGSw`e*#`zW00hBav&nBp&z#1|qadxQnHleGJ+QvqZ%xW;G
z21I**+f_&TT0j^8@HjC?^3d?`K63MUx5X9Qp~3bmr8Xm!#RE}xG~Tr4`Z`L3+nrMo
z_D7_ea3>g))N9N7-o#{VE;ejfa9?14r&!N*k!B_6If#be?0vc+5Z!O2(E2_!EKJQr
z)#9@rn~<Kiz>2kR?`e}ZK^VfNpmg{p**P~|_f0Mq|M(63W{H!9pL0X?qWFIFyM8i)
zS}+q^IO<$-_PhwqUE|<g7#u6W{GPJCg99`JBjIr&$_uaN`Pk&>u=EmEhI0xqft}2^
zRUM)&EcJUIEEPfPa%wLmy@^3%qqz8#h=}Obt6{9M#lsmNagT$%@bDgZOf^ocUn;|k
zt7?CbSIg&NhC^BfBaKb&EoYox**t-Hl~4IzKvp%>m-O7Im;`=*|C;lhtKGcjNB_i+
zB-~WQeqP$?=pbh6tR21SK%9>0&ySf(%TYj^#UHc*JN#TcB6_-ZgWqyaASu6~KpbFa
zMa8hx)YSQ9B`csQI@kb9*yNI69fYrFPm?e7lGg_|pm<n|wgYC0>BvrW;l&*@D}@t#
z_tmm*(qBG}?r(GbeWL5d7QIgqd{4|!Y6zE$|C5Qa+^D2>$31B^wc(?Uxk7WiLJi{b
zyGtu`HdEC(!Yt?yW6+sseyKofzqi*>HC01CG)%Pll5f0`BboC`=6ok>%zV!OWFJ%E
zgO*Agt-#~BoTC{48rZj@X8TsRgTjaoF7hg0|JJQ`iXx;G6cGVd$o9oeP_fHe)E_%u
zA2Px-HZf_8Op{>nExIr01ir!!c6O2K3%ymfN7X0t&ie!Nx^8a_PO5!dWt#V#cC6js
z)I%f_#F0Rh1b}{V9^T9pkb2xVO5o)5P?n;$7EZ_9+mgi6?tL`j%XcB@O@Zek&9+x8
z;?hCP-X)~YeDl4g*fM&Z$Km%?8qb?5B6lC+MZdMSIt|)7NakHl`ZY2<JoN#oJjC+A
zg7wVA^eBpR-qx7=c=g@KmIylZ=^AIBtzMY*JXukdfpN#1U>`pX7~yPRB$t&ruTSKW
zhDMBX{g@`l_c@$1CWvMtPcpj}m(<6dEYdom713w=Z>$wYa`AC7y<I&C0=6lyIo4Mu
zt9XphF+3k^yKl3}d&vwDqd!CCZR!_=M>^wsn=mg=Ykm5ahPP06G8XdE&;gtYVd{yB
zgm(=;6%Ab|WO7pZzU8D)o;VK<aLOtK4A;!eA5K$K_o7(wYpbiPrxjozfJ(rrO$Zha
zvi)9s@}QoMI99Bpyz!KgBwlv&<g2k<i5!>QtCEHFwSr=*ua-JEf}bi<dR%2NZ}0Dq
zD?SPXO{tRr5#@bc#D<&#n1N3dl3F|3CnwLEcIlM<hW?zh!yEmcIk=h|j{T5$2`;gJ
zksXUy3}G>k7mOs0NksHf2rW^!FtCfkvRdP`)7|qX>EQ5!!a<wy^!I29`cL{4++I@_
z4RcxJ6SN{OY_rCB6ojGS;c+%yUj!km4>;%o?w&7;J~Z<aQF2M7O?B8nXl}GICCy0)
z5vqaY&2>**uP6x?F+*8~9$xM}fv;M`6BD*|DJir=!{_G_)jr<0h9kg5*7NZ1BjT_5
zX0~)SPhJ`tLWO{f*f}}rkX4hj<=1L<kF`ahAxk5ClJ>VUAAw%c_XZ{ZH%;)CXSKCO
zKJvVJdXl&=!6)Z_32a6$@Rka3yRLXedl|GuudG5#r7N?nkt8L=^uO}RqE;CBkRk?}
zD&xuL->U>kI&AZT9G&-xe$$w<6IhGdh{g&xzZF(i&q~HO^E91p@V%hmV3giOr?pj_
zna=jR<43=S30n}{EXv&fEuYlWx>E8xfXD&T7QhMK&DuIYER29Q;*Quw^wI8(S-Z3Q
zq(4pBFDogq(Ixm0q_7ky79X_tF4KyONvfT&5c+5pLD9g2GN|1}W;oG|7H-!wiL|s}
zprf}}od408J{BH%^uoWCL<p$_=EaTYSyaklF=g8U&2!R^9s!<;4`L!P+r)j6`yjY&
zAw4~PG!~eFWK?09pPpVre`p-c9LC=a$jIoLIn$})b}rikyIBNTSzibL4e$ibKdV?Q
zB}0hucS#`EPLUHRXLbyj4tkP9DIH+Sb7$`o(kd7Q9f%$D8Uh1a#I9@ioIOROejkPc
z13CBeld{^x{A-~RLtJ7rR~zhBW@h+Zk|feHZ{9R9r{em-ewS;%<jlt~5i7`zr<*!^
z(XN#AEdi3zpRB8c^)RjpHMU>&9oSm<1U_P`n*y{*t5a#H&vEgpU|n^{?gUe$TtJWR
znAhZuswyYpA1$E|fJHTOJdzKrMf{8Vl|Y}`#W}BsyLW4Pgxz&>(tc$SVt;mUH!MTS
zmNK#u`l|4BYPp=&VqTOpH_qyzojN)H+6uGsMMpyz2n8hdw?uBQaf#{rg_!!MxE<q~
zVt|SM$0NI$O|WOnX2eH-NbKwn7epcDyz_!?qa4<RktQc*IL#fgw+n^N94!k+o<&tA
zOA-a{Jf?dKe04@61Uh@?fEs9xM145qUA?00ZcfGaJp+b%Fd7yv-Pf?48xx5X8u!TC
zcr;(lc)7KmpVCH>Ch!inzpPI4eV1$Lay&7?%(dH)Ln`XIGCz=8#DV@W@&J8<d;;H!
zUj#5O0fAAFnt^Sun<(ua^3_k`{+xH4@bSk=mgz2LZp6QeXmcq=dsFnMfKl{V-dy`p
z_Ap^IaEavQZ&m#@A`~bu=SUc)Z{R{c&)U^to|L>Nm~t+?-mi-{G&B?t7|66p!L~AO
z4NICVL7lZjGJhIAUQLSyYN>W!ki>18U!T8ys{sb)<)uJSl<ip%ytW+UCZmsnY<%0o
zv&TN_Zj6@Kei|0})wvFXcm4(p%Din~5;2*BFPEC0$LH<)Y(?K6WCk6Fw8hifGe-O6
zv3r$uoH(pXc$@KRU4=#FHcP;HE0DPF%%cDG4$<2xP9%8YSb;q0BkCKNr$vx@nYym;
z=2n+S_ev{v@gwxCtZS|_cl)X=Ep&CEnuF3!d+cSHhG%i+rZIbo79-}>qt?<F==*IJ
znL?N>1D4dJp)I@EG!Nl**uqX_Vkq*@Tn9|<dMa@~YkNB258(6tiY)*{!wO5!JnORl
zCodO<<daiVAki!+#fa`yeenYGK{TX{5MZD_v`jSI#*nr6Nb!-4HV6gL4dv?n&C>9@
zLE+zkAUr)6#LJ~?4Qy|~VjOO&P;&M!bN1t-9RAV_YeSD_`L`Da--m?(Rqv>U++}O}
zW=1~N4Cm?oiP}Y)6hN;}aCCC6w4zJ1-XD1}XvG1;SMQ?gShPEQF+6GhQhsYiSfyRw
zOh$zuXYt^95PU3YG-&vkfpGO!w}g9O;-A+}O<9-E`$H;3C;a!%7=aNA;|p?_j51ka
zqaoYLpNcF!Y7_)PRszAM0HW(mo<9A^l~xu1%AvC6p5-Rrg_w~|VaikDso&a46iigu
ze)QJK)r>U8+?wI}b9RCZQ7mm9<g2Tz5507(vrqU{y`Gp0)#Z{5j>IV>DO&V|Uk8U^
zdR?xB^ID*`?Rs^fqdu%7DE|%h6;x!?liyMbg+%1<Z4L<_2yMgYZ@bup$A`B0Vz;8?
zsLH$_dQF>TG|t&ElH}Yh&_DWwS=WU~Fh@`9)Z~>eg;+e*B_TY1dfv>X0Ldmrik6n^
z??nW}tn&g4-_>LY&F=W&c$4I!@MeVq*102KCFsLo8mH1$2Dy@lOkIcjtb_ViHq!1(
z$w4wm1DXOaw}z$ZK!POZ4exufie;E+tPgc<?yvfEt?1eu{xq}~c`CT=raD9|;YC7N
zHQEi5zg-I+4vx)nPLI%a>Ohhm;RtV}flgSmuaP!!tj^cB5ykwG6B2nS-^!)1DK4dn
z9oPi0X0pdAEmy~I;V+2UE!Nj&BewaLUDuu4hHN7bRL_SqOF786+s~RI-j`~>KQGr+
zvrL*H3f>LXDM!UZK)No+H8T|F$AUv~67)yx7QJQWbw~q+{I=kyY)hhtsivO!<+cE{
zh>SMUlCxtx=)J@Hb?q>zSf?dU?m&GQBEpsoC<z;ab1Oz-I28Gyql<LGVWQE$O@`~b
zF>dlD{r35tgf~82OR|%-gY{A3*5kZR5D^7PK|{-R<c$Ifw<bfBr`I+y+EIR;fup-x
zkpwpKeLQA^JQLpgQx-pZ>@V~ytplS#I9C4C(LA}9NTD{2C^s*KjqnMHcpVT$@GQ*F
zYmEs8832|l?s;TqWAk0qPc00{4UJ=$jlrmLRU*UVYwKgJMue_EYl!~g$%kS{;(ZxG
zx%W?-uzW*sqW#w|F>u2MLf80?#(h7qlu>TlNd>|dOowmpg#~kJk4yz{G!_cIZ<>E*
z)X+z4+EeYimBJP_G%_*`ET-)2m48{~R%6_EHX515nj6<gUYVMD?>LZ$f<fm8IB0U!
zzsSLC=|{QC{$a$$WRBuEu+V&xTS-FIV_zKAa(ebGnJ?IXQ#8Nv^k6Uye|y6<as@6;
z|Iz%JudgWWE_;F6l`BjGSIBj#3e~#zzf1w((N<OwbDOSZ*YrVc=<Rjbb0YX$iq4i#
ze_9nCJt#o!4*nXE*Y6p8zC-iC;iu0@%-zfJ$n%%WDsO)iATKQvV`7G2ysE?j^oAd&
z^Gvfsl;JmLcuZ9#B;HKwrRV^L3ctLT0%q2zN8?|pzLiF}`XziqtKMCY%FbI+U4~AH
zm<<2LNZ+?ZUuR4HZ^E?j_J$>K+Uj9hLffl$?q07j!`1gQvVY9n`QSD?evPT|$(?Y@
zBKH!rh1PFsd>04hT?`C$W8<K|G}w5}#e+4|LDAIw;IUw>6U$vxlt?*cy{GB@59-gr
zcZ0Z=hDI1QC||4Qi8X0>0SiUQNW7X(zAZZ9_+{j&qY53G^MtkHGc#T))foR+K5TS7
z;oV6d@`uD<*{|Lc+@q#;&Z~KM7YcrK^?uJLq~m_WMpCzc_veo8k7AE_NX3D{vau~e
zaI>$I&`i9|92OQ<R-QHib7nKs7XTP|4;}dKG|sY>aO8c-eaO~`*3Hd7X=w8$>v3p5
z1)IjU@y>ir{Y*e^=WLjG&i_6tc->l`OYgi44CMWWnf|Z24>;&)8V--%_tqRt2#kJ(
zKyK(4pxA=EQzIxfkV3HEyu{!{tGI~#XE;*Zpwx4?x5rj*pWI>yVNyOtjZi4w{8W!j
zo;D|q_B)z(E$rXzpr-YCWZptbNP+0EEPB0I{!z-espJVhb^x-1D=(P)mEzLD{glQ0
z?+@HpOTQC~itHhT5-;S>RV?*XwNQ77kEWE7Eglb&O|_`FL%H`0?-4^(%f}+(@|#Z1
zw;W!r1?>awjVSBuU+yl+!#4v_GUT<y{7L6$$BtlX21D<H)SPeMx=c&7`vN_2?*EO?
z9rse>L&YC#U3ihy=eN?T6{0DwFj%{>dVPljcx{)^=Yc5`ICzX&KF#r7`rL1(z^syv
z>yy*r)72@RHE3yJF|^nvcMTie4{UdG)0M?|n|3%sZFe2lN7&(X1QX8@QTSa_zy=jz
zOkAAb4>U?j1aTwm=<NK!Ory%`-0%j4JzQ2@y@D_P-k3b?W!r~cD-I%L(z!G}ulr^P
zqZ(GA=P#h1Wzh)Z<B>~wUvyo+2MkZut4@9+4s%K+zP~+9O2E~=YI9in|8N25QJBjM
z=RG1m?63k8o25OtTA279{YC^7XZLvJoST~qt*;L^&v>;j?`ETW0?<`(a8tQF?P7EK
z2j1W-zbXtj{L1H=8PA#@Vf(S%pqe`(JMMAzbiSUM|0{VmxWjW@S6+TSBHIhl=Q)Qe
zm>T^B6DCQ2)qAT}Js}@<QGJOK8I^q(?T-nh{ab1iFJHaFDsXJfA+@V&_>*463_q0c
z#PEp1a8rK1>(86!9htDUY|LNP<-i1onm}3k<yy`&hOy^*&ei3FXrC>wxoCYnw&>0s
zJum=aist$#3=&4<lW0ilbcg5Yu?JGp0v0ak68ILi)lnq+D(c&FN^+ZzwZZEY7>_ph
zHA7!l0i2-bb%Cj9{<47e8w&|GN<(iXm5q%>UIs_eEp7&4Y<PN6xr6{WD!y5^J0rM5
z^6UfWJUVJo;Psh7?cuqzdLZFWo)`cqBa|txC?}`vEQlcch8?*BA**yDq3Dt-?D@Y5
z<AU?qAN26UwE^P-z>VQtRi%U$3j520KJ2!c_KJdQxvX3v{I-5{*}qs&gADHKJXldd
zL3BL0Q$$srrR{2bf8>_dDzKm;K2mEgcq;zA-a1zX(VB(m3cqa5iorb!coY}8Z^^Do
zx!Yy|XtN7B)ah;V*_hxnII8b6Rqs>Zd1&7<XBW-ZP5<vcm*t$KUquCLupXelCM_mB
z;$-nSk6uX6yNj%wS%&tOfDkbm)vd4;m7SgP{u;38Sl_+4niax$33zz9gm7GbJs+G5
z@Nb?I^*SjnFF*K~Lk*$xdBPqRD>dph%fF7-3+h#=|MZGq)|Tgbo*Y+}@SKC?US2NO
zbE@C|GvokrwxM+MbIUoF{u>!QpHBd>a6>MdDf>&xuUX()!78MB!+NaP#NoMv9bbt@
z!z2vi{yUo@U7kwW<^We6McxM)O%Z-y>$!y@=qq7pGRW(kzTK05?!AE0AT4{^7kdw+
zy3Y)<VMD=ai536Ny~c7iRoa4_W<;T^^gt^iY~WHxtaI<Y9#(2-*Ake{d4lHi2~LSS
z7rw~$VnMy@I$wpHf`U$kO>(ZkBr&>qFE8($pu-||9-%LUm5}pNZC8bnyffQFNA=xa
z8@~4-mGgs~8^irap^^IPN?TT0YcJJS?+2es*=yE8X+WBNBCefyqoa_lWo+yih>jr<
z-<N<hfsOGW2*DOd6}Kk?uky-RQ4ktrAVrW-g>d7bqtZv+5tmCIZNAYww~T7iom_L}
zx76$YU5P>MIl;|{Pq{7|Ch9L<-!Ux6^;aXH4YFppoo<ksaB$lB(vo)TrO8H1UP{=L
z*SBGheayp=1$^LiZ<Po7M4$x_zmpDZ{fZ8qjyxWoNMHROgl2WtP3^6#+rOZrv(=Pm
zdPt-v!}k*>(LP07?;jsNeWD>I{=DWE%O2XLt)<gCi-)`{Msd!1B$8Kj-+gxxidhKg
z09T*+okWoY9H_#DjL$!L0ht=8P^6@!$;ru}j68<JH!vu}?8oZ^Pa%`qBJ#QG#FBw-
zf~WP+A6U*^P7m>p_TN}xMi8&l9M7~62e_CkD=Xi=ecPRvHx<igG(*9Cglfdz^o}r2
z)|zcxzx>vtjOP&L`K;U*HWqquxE(Qe0F+UXs`C>fH3I@Z2|IT-HX6YDK*J(bHS@%k
ze7%iT^@Z%V*%GBTTLDV~@p))^I!vBM3rq2Deu+B{KHJT!{~U^^Q!QvbfUbu14IgIM
zue#Y9GybF$ThOl|Z{q7VIpdbe&D6<YKS<LWp9{Z>h^V)lYXZ++NHGA&xB~Q3Vm=p>
zy}bqO-|sMzQGDPTKzSo>HDosWApKGZgl)+Q2^|YN7k2hSPAkfdbcYb>#RJ^p0^VVp
zPwwE6#UA%yge0^=L!0gEjX727Xr0H5AE5RbfMOvmb4>$kR^F&QoHl5%mwQuFKYnB(
zc_&CiNE@*={B0B}-|JML7-ro>JIlK0quvTTU#q_OF|9^VOe5s52=3;9a{%iF9d#}%
z)%&^ir%u&dB#o2qK9}h!*Sp<*w3q2BYQs?uOQXi<CHnBNh)kG9rCr(wG~jElv=aEy
zQ#~CadI(y2h^O2+J_`xK1s)50cC@Pr4B3sOGXUCO1qIHpYw}4x+iyri^__(Y8mO&6
z+~w$YvZ5+0pFc4QYdLp~D|SuBYvH8~ijxCo5v9#cBBh6C?4^?$7Sk)}7F|z>0_eO0
z3a!%~t3y22KsESPbKMXcw;H&LBK>#l`QH@si;eNkIcz+<uI}z3(54IydK~X&0I>w>
zCo-x822pWu#`abn)XD^A{&ORxKTAttO9F1xR0{Q;V$5zR2>QN#^4aV|W#z~LNwCBY
zkBGQKj16)@K$PDOoCNxCdTvNC(n7>%$@&MELiANf9S({_R%<vfr5Q4Qbnh5wzkJ0Q
z+41<U>uItOWbL3X?ZIdi-jt#s7d}{g#y$WTl9A-G!L7Tvb(ycfXWT{p+BqFJ|L$g8
zMco_@9V2Z+B<LIm|8}m?X6~H^wt#KM6TDTJ>EX@yGzR^mCz+Zi>$G#`<K<O-I$9-q
zG5=*irw&0fZ%|2VC9ajv+`q&jWzM^_A?Hd^e!*WKoer)(yC=*aAnF|{bY<~6(QrR!
z0HEoHCTTdJ17H;h-X>9UUm-9WD15BEY{#UbcSku$v5$gDCZCGl2&YZq65qBv@wy*$
z%}&r(B#BP%2T$E)<nn#H(_FPDcx!++_0<p&V@uFWP{f1ZB~T~tR;%D(Vtuo`otuk6
z(*055_bU_rxZHx3r)pgwdWv$JgKEQ+*5-+xI^SR;$HlVq$|8QzowlGaYpd9PW*js@
z2HYu7-a7#+F-mR`_Vd<OM01DZH7RH3G8%0je*W(T1t7stlx;uUilWUhk{-aQUK;e1
z1Q-+r4O0iqmWMJ^-?JEhvD|_$jublpI_urzGuVb54;DFyv4MUG<Fcu#sU&LPcIK>q
z*fKbmA>KA<D<_JN2G}KW;%y8JOOt+GEStwN^bgGM4$1H%_``!?<=g_cC3yPgyjl>B
zrNQ>XnO^@kF*X=Oz`cHz!v|dvfNKjilPP^>yv)&eqvTX?zTGvOtx@=|AT++@ws8Fa
z7lfLmgzu!AUUC6=1tU>EAkk!IWOUpo3}mJW_`r}HFi0O|*QP+`d^)<E0X#x_Vn8$T
zo9iwwPUT-?eokN57ybjn9^+NHBXxVhPvB-RuJc+#>PLMf9cX1ImFdJ@laZ22F+@Sd
z0W+b2$*!dK-CMB(?V4ITT1+I-c9p5!3UPfC2Th~=QFiU|nBLa`Dk1$GASKm)GP(gL
z^;Mw5^XK!+%W=Ts49fLdI1=p~W{i}RP8Rn00EwBj5n0MCNMvfN&tueq;k3Jt-+?zc
z-F?HK0@U>Y_R)DwHNJx3lk}#Jz81J{jpc0vYNXA@JD|gWKcTHwl2t5&`ci60*wkoD
zUH08SN;t@f!|qr%*+c6Ge(yk%hOn9EEHTj7!pao-GFkPm;FZDalgX$D`a)rHjD8Z^
zhvMjx?Cc5N++fAnSK9iVWnJyb3r1q7_Pv_sP#Vy4FCN@;WKEmuA$kNqeJDClKXTv{
z4}KdM9-5VQxBMHGja40_y1Ry7#}WwA*zURYU5xKxgFWH#a^{^c2XD&Dm5hEscp%#b
z5zy|OJkNf8ysfNHH`GyNek<anvdKb2O-qk6Ze@O_hD{-h`bM@e!_`GV+L36d@JX<L
zbo3B(wxF8F$AmBy$uxrwjPGf_p6jd#_DN;v-<ug&Kzh1jr-H@3%Szu|^gpofUgn_$
zOE(~p3ERy!fE)%^YyC9FJL#*G2sn&@Lw}QohHQsb>wScQc&u9M$OwvrE3Qy$Yw_)>
z4R%u-0pPvDj1&|wuMamNKF(^N244!QLK@BB5ikUAYHo(y3NSVKT2SC~v~3U=#Dqne
z>2;19-WA)Yh@LV%U<4cl0scUef;*=Xl(bY-kkgKb*xTDfK@XG)K+8<*?RSE(ZrDd>
z2CgfQ=4sxR7k3k#9v)VkDh@;SwflQ2X<lB`@O#@-Ry)Qzdjtf>arLjGxvZDR=ZgVI
z_@4&TBzm=%Els%+%2t4ZP#S>B2-V@){=XK(9jH!zTe$#(5kg2Zu)uu>>d!H&a_~=p
zDdJ*H?tx=Kd@oMmF(pLzB>0{G8(mB<$?APdxe@QQ5r0jpvkMofK>mJy4u%nTA}F=m
z7EXH;hTZoBw^MO=sqiU39<9ep;SxMEuo)8IxOcaJG<1La2zt*t%w4vUsQ7qwJ-unT
zVaW?-hZUgYCEg{S-QhCt9Ngwo3-*Tt-HJQRj`-bAc)LJxYX5B}BNLRIJO#rAXi!5#
zLo={|fD1Ea?~~OmAfhuVpj@ljn<b)TpybxE+P(b8ie66WLmq!3IKCeJ_K(AKUU7x+
z52KohbTryvo{>yJN-D6)XzZeZMk3GPwe=RBT7ba>YwjPixS@9~Q!I-f;V!$s_`Q#p
z;(g~tvL+SFl2^1fHSe~?O^8ubQ@48%AlqpW?eOitKHemHU#1u`k-^0tn%1m>%R>qc
zL&JHW<g<ef?bYYK&;dbQ`N;S<6#~^JV4fcpOo1h>g0l7g{rlEd84!ui;3f&ugv&9@
z*UF+cw6*ZUiE`UpV|*$mPFK^*^Dj1!ZZe0{g1?UmQCZn{W5?Yvg#$KyJ#rEU&#A<I
z8O)>#+xQH!;6!?2>{!9GPiI64R)h$8VxNnqw(_D@z79i_)#W+A^d<_j^7|?-?cR&L
zNTXAB35KZgCxJl#Ru;X;uRf~R(B~P1fWV5s>Y)<!rD-#ds~Y|X3RO4ze?Xxt*Url&
zM*?nxE@5@KU4W0D|4G{fge$^VlYy1|=@SURuiv=w7~+1y!ZNU~{JC^=^0Ko2%+2+8
zx7W{w*4M#)1;SnuzjgIBkKAYUyn}%@%zLB8$(rtuR9W`{tA$}Etj!Nw$uY15K)XL<
z^OAFG3))l;jPvEGZ?>QIPpr$7vt{XN{+bCi2QVLUT<i~rqeRKY#Bn{lPD3~v^x!La
z8-NyLL3<S2SE8>$iNjp?0nn606OTD6wrJ$jy?yH?N1mj^H1u?_9cV)r>x-ZQ0zD0s
z?8o4JNQT?e)rE%u;c6P-c-Sv`)lMA^4KKmvh)(~@%D{3eh&Aw%?|Vg-nV7n_)Fwv(
zRoCI~IVtmP*~W0W&&sosO*;?7mg)!yFt_E^tffuLD4RdsU5w_BOD5wN--F#~Kt$)8
zd{fjrhMF#GTpKNWAe&htR)!BHmpAtolv$N-J74x5g8d1|kwisB>ps{C3JEPixYXvP
zkbr<IbmRm65DIyc<7@C+{W;3g%23W=!X6e7@f@wKuU9|3m)eUZ<`o%^URen+Qypjm
zpay~Pq?)a|bue$cH(?ti=NH=beMA7Yfiia*<sM!j`MwrpxC7eM+12H7NtQc4M{w1K
zTAQ~nTPs4l1oz7oIHP|Da)O}52SY3#o)WkjYinzPcX)hyD*NaWp!D#b;kNZIZv62<
zRxrjKD2}5+gz!LTw|$e=CBawR-e(QJZ#Yn@pVLSQoTbVtO)&MjDhe4RIQyFB__ej!
z`1?k)?KOCt(s25)gPCC%MD>LUCm3k;E-rCfPoiBu*@5*FB=3REDFC6c@;qoo@k1ds
z&}QnreIbM;THE;JW2S>SenX?Zi6)aQL)ta$HY>K)j|mpz!DLSac~SoNA88(#TZ;8`
zbld>1M8kRWU^sz+J;v_UZCFGvPRB^nurTNRJ^3(lxx5@;OQ4x^Igmx}BcD?Vmjc3K
z17F+T?=>vK$5(#re09AtuT#W=Mc&{7B!?2htor^=sUs#PW*bG1exVD~tQ~dR`%Qq2
zOE_K?Y4pE$B_?4#&!Px*FaIWjQ&1=H9MV3(D}C^;uvEYqxM{AUnqS>^XmH!Trxplr
zTZocle305ZXGcaAJqcZk_{C9rULJl#o9o}-J5Y?ltP-a2GhT<ue=TJg7#I#FmFNuQ
zbgLXppdZzy>4pWMU8}sru8-i6!(2R`Gt(ReXQsX-isd1U4NungU%uRyeKrS!d*#Qr
zS09JTvgsNHMaAoV3rzoK@L1u{h6@kMBH6U44iH{MT0irK)&I#xmoqLV<*Q~xYd#WL
z>@Ec@ph-eGz>&Yi%GlmcIwp1f_~C86ls^+&uYBQx=J8nZX5UJ~Ttl;X@K;a|g{e8<
z|7uy-k$BD1(*3E!_<z7`X%ndHVSvuf!vjv`@JkQ!a^MtsK@tw~Xx4?YI^PR&B_g7G
zcq+Qd(aQ()?2Thpmf%syhJytuA*&li*4A9yrX^#qU%$pBJi9bN`qlX6t`C#E*(BT5
zLJ6L1HkmyPeW1CT9t4JyTQm<4f6V6nZz`F`UZ<#6>puAd-&_M%_t4&!;$vW)pZE;(
z0d<xD6(vw2`wt%SIlc-<^o&##^Nk$35|1u_UUbGaNDcjc4Tb;a=TfHta=nBxOj!oa
z6Qzu>RT)eVwqNanA{qPBPw*%>oSrCp0ag5siTK|7sL~ah=10$Z+p_6CxHWTfa*B(K
zle~+8R`bvyA>>L|B4Q)CT(&5sRFdUvZ#Xm(sF0%N@{iS5Lc?kM&HEfF!23Bl&M)Qz
z-BeTtKd+cQYMcD3%96OBc7zd85ZG+ZFQ6%D>4Enx2+9&@g4JZ9gZInU;90X{WMZ0v
zwZTZ@4tmhj(=JyM|1axyz~hF{+>jPS&^b^kUYx#XY2x9qbuoBcrEDxg|5HQTZFSvy
z$(TT=CSuLb3{^E43R_{=q&N1dAp>9?!n<@c29^)VfrI$m8T*%GS>65Y%9wkNR*!#H
z629n(IjAiG92I+Dv!i-T2Gm7AeuxD(=VWKwxwybJS}NH*VAp2x5k@hUBzdsWGX2>*
zuh@W*<=@W>o<9S_qe|fT3knMZwOS=d)t&ENL!ny6bsPn9M1i|6qP(2GqO6e-9wGYi
zr@Y%V)~>N33_8_L=Eu=l>i+67niGLRL7m^mw4&S`yq}>)2Vxy{y!Y;ICAlIA(950n
zU8v`<NWOk8tgwqlf^)Jbmg;u#Ms%Ew82enDtbPH{Y5U0k#qtBplU=jx@EQ+?`~dL*
z5&Ix(EVSux4Bq2q;~OorRChbWZha3XhW1ViBLYV;&8OcBE43K;hX+DR%GPoe&ApPW
zq1Dj^Mi+Pp;h_8F8eRRkMTwCB=qunuI{p294FT&@xGbRJA-nNW3xpYLlf_HzCuMcZ
znMvi=mL`^?>(p^%@%Bs~#om2<`D)|ySj9KwN(*qLgQ}tBKCIgE^XKPeQ=n*!qd->*
z%{(08^<TgK{Pm0d-o1PD^b%<XIYR90`PLI!$z}-BP(-!dTKoh@Lj2}NN%@P`6{6Q~
zW>t!|9m!_x?ncnU+28^!r2n`+=;rL2cfz10<pU!Cki3JZJqXa)*xA+0G!hZCw|RNf
z$}jGsP$~_}7gsz4Bwmh@g#Lqsu=We3)UKu6|0(4OVUxETb0hF^ueh%uy~f9sih9;T
z8e}j$JiPXzIXuGPAFjNM`Uuhx!CXpuGmpjWAufW<?i{_Ws3Uen-<<R#ZOeR`oV)fG
zzC2=~$o;rZIOt%6K|~`6;bhG(xu6yxql&8qL=(I|wN3Z2Zof7*{t6tH+}o(g%Xcj(
zff>NdVW;6}YH|uMO3S_doD-@Os)VR#s;Z0E>14A8)ov2PdS$>W-pueb&~R}n|MqPi
z4&2M5g$Ov#C&tF$Sj@|E8a#NR(x=A?bN6ve1LL$T?U8mp%~nyDzaMEl%oA{<+Uq|b
zF-0gf&4vHg2+!(#wV+8b>4UP7@dXF|Rw4rb=j!H%x(gi85iU1~GxW!jCu(hJ>9{wi
zOiUPTlxvBBWL67XxfJC#`$8*V@$i|igqa-ur?N7K(f$|C(y)NBzu*`5%$cRPM$lb0
zZ{0n_llMLnd$iQjSzKA$zx99&J78Hl-`ImX#`@~iNlZ_^Lj#D?;lz!E{<5r@o_1ge
z<#2r%k~vsB`$ARpJ3+E;wJ+`7cm%<fu`+Xd+sF6&V_-7&-(r2H0(ij5QoQ~Haj@8f
z>*4#En^qqDL}Ye(`QZF;rlzVYhb!ox!pM6fCctrP!VlCusP8d63vA5B0r;W*9tneY
zP`g-v*d*-e|GK-#@_@Dl$Y;zE4ly;m#0lO7+zSRf`5Hy@6*JywZ;bf5XFoVx3ku6v
z(=V;KFWthAAfLf7zPtOOJRZ_e<p?)G2pT}i&!`!qR)Xog!L}Rxl6!l4FcJLbI8J>V
zTU%QLgCetj2K;6}KR;YNJcunmIabLfyzDYg<{g&-uhEs&)w}NIOQ2MFs^x;r<h<%l
zPL|}WmFB^CV;+QAuaRJ$W=MSr(w*jjvl<^0)MP^S54B$o{%1`)+e7quJf}X;>F+0W
z!<PnM0^oE!l=ztbz6KiLi3D{EoWwE7$zJe?f#~708&gtJlB?fH;VCkARM068Iy|6L
z{-|AuqK=Z|1)XF`)1k9y-2!7hR939{>(N40TpqMYLlaYcyD_5i(u&f?AV{{Owf-i+
zjsSaiR}^Fg*+$z8LObP$NPzZELIQaw4Oc0(kOSkDRWO82aX}#tsqm$xF>MQc$!D=y
zL!A|*;lM{a8&v`Nx&whs3J~2k#@g{|S1}MC4&9fSn5@<}_B0av_Y*BX_xmbfzA<4g
z4KvMtJxoDKMa9Z<O&YNEdl05TdwKW?YZWsNy1Oe{XoUF1%GTB;5RfC3E;>~#mL?np
zA$*k|Of})Qv$V7nPJWsNVSW}G8XxD4EUyy=OP#)oc9p_?l%Va9H&fb?mq~>n&HRI5
z%2(jCA8!}P2SIWPG=c;LQwIlb1ZI^mbaDZ*67WOeIwZwLN9glCk6N;ZlxwEgU7&7v
zEDw6jm6zD{ZT)u#6c9q^{BN0VxjlcB)gw)_`6jHz7%kV~<O;_a4+8^1kV*l#oAmLc
zoTYF#oO+NO0ejrZ!66a;r!)4RiVEC37B64ULR%{C&dScNI9xD`XUuMG13`q~>IOC%
zj>`(TfyE%k`uS5@s5R~Fz%p2u0~;UO8?Z*Uq)K|uk}3CBU*jyM83v(G$2AB>N7W>=
zo>OTMR+(h{g1Z+Y!9W54%<|dQ)tt}2$)tH^OjO5fr=&7|@dd!mMz=*?17J9Rk#-)0
z&cSn!9nme5`>U=T)4Z@wy1i3Aa{lbrv;AhnqE@R(^rQ;kxRHJ9q*T|x*`;Zma99$I
z_PB<sB|eF2FZe7N-zLCF+%u-NuV!8zm7IJI!)NGwK#R7&umGNV?t3dyko*CCuIlw9
z3y{mM^pipYAu=hZSL^0Way778irklx0)GZKw@T>OLEH~m3fu^JeTCz348DDTybL%Z
zl)zr8sN$J*xjpov22tV#^Ekm)Q}8NfnF~2NIsPNT-{)~;>4ZtBslQbtY90Ok*Yu3h
z;_*;XMBO1)^2>jxJnthOmr!SD2ILsQYRA#uKCRyjmq5M)<g!2jihCj7`T55Z-WhI)
ze1Iemdw!_PNV0bI<0nQ&=_x5R<++e3!yRyj?k2kDC|=L$02VF-3*diFOoChediMVI
z;Z5yjFl#)i8>f+yYF<p0jF8@nm1A^_$JEr;{`TLsrFd%{TlW8}aCuHuq)`i8kEDbW
zw$@`E!1Al&|LkBy<Ayv5kfbKr$(0I@GogkXANWaS`h|9IQUDTV@eq&hc9dL$4LXut
zFgkK^Sws%^*UJ$UWZMm$fTQ36WFlOiZ~1^)>gEH_%Kwhue){6?&+Q!?o+v7o6&Itv
z+1+&jodg(1T`e^=Ryg~TUR~YITs(MZnJ7Vx<FQN~>R^KL{)e%nyNWp3XvoiS;{hfS
zYI#Gv^`8ZrCCo1ET!kgUuSY74g0)=g)6Gp!0S>EG&qwj_Xzx{uxX8$9ur@H5gU$TW
z#)1&zcoaMsfK=1<MGb;NGX^rvXHPInpFAS{tj0<uIkF{(->jghm=Uq+RQ-3RlY3w|
z_7CoJhynxkNmsC{loXnV*5d*cDqS}6F3JvltDYFYdEuhpkCGhn<?jr}nNlMShD8#>
zIPn(rB=58i(&66&cwm5{AMa2B$&szCK}Y@Nr9d#R0{?4}&jX9j=~q;u9GrD>a$J0T
zy5N4{xMdIdjTH+-IlX*8x)RZXn^#v>KD$;;4MJ^^?RuP~bs$^$L4eSbL$jV4th+a1
z^!n$IEJGC7!N5eEWkzr@sXw~Im?QpP7j_%m#<%Pqm|04x4GXf@0`$)oCHiMK)0^cx
zX=u%}_V~X`%ujp&4;LWM8N;*u&CI;n9fgl}LKd7y?}M>?pe6ilA|9f2!3rla98syG
zNienm<MiS);~0?58NKjCWz)yVV8W1(10N5)1IxQ)xba<&ZqsZ~2TDR~4eAcBXz%Uv
zg#dpEdQVS(Z6oLn0U^2bf6Dl)Xz`gD{WOESVSp+RlHP}VC^Km=i%pERizz}#=aQ&}
z!0<7V^^-68W%l3xNP*pn4iBFq_)*>W{*<Z61}SZC`i*?$(6wtkNM4qVQ5{v>1j-Zh
zkxSEkpdG+(0|A}?CVc0Bc8$UjoZj9L(+1f~Fi7T3IeXi@&&gsFBd1e#_g(pjjMi>~
z*D6>8FE2C4XO73gRVcM<+xG)&X!xO+yt;aP@1J_W#s&sRrgpDx4yC$eyY583&?gxd
z%dDMBqQ;J|JXuWT8PA<q{#aY@;dHXZPuE>t8`S>xw(By`kd{3=1s@F$j2?d*vDurq
zU3R`?^8L6f;<XPq0*r>TpMfwK3N#qyKbMrSxR>F3Q(2>_x$r`G{LkOK{g`5*P-cx{
z(0?<@e~kcv5Wal8Z#wDQ7SOpVH^D_SU<kk#Fwo&y>wEGuGq-kjT7U{@MwEq<`_~+-
zNI0NBa+`#}#FfEM+2<}8z6LUR;!}V9M*7M!!@roI1$;X{r=uYf^j`~watX1Zn-2p%
zie9DNL+~%2nMr+ee(+)u#HRp*K+TTOlZT4uG?c=b-phGB|Ij_kc_91$1}+wGiZRSg
z84hb&+!py<7!YBQ@><ybW?tEjpqlKE&94-()1SG+Tn_lPZgq|y3UZsT$O=q`dK_mZ
zZdst7m@m&)5q4gD*OE0V3eB|pAjb-20$cI=(eV;^2OD!y!ma?G4I{}TX=yO{gRPU4
zkN~&pUs(A-RfXRL>l;zp2nl+K>1g};)6+Y$#Sj2QMK<yUyGr<jKoT!B;gtOk9pnOl
z(8R0MoM>!PC@^C{<nAkBjWjE%H1O%t`<c?&-fl;;It6K^{{#6xiP7ieXQCl{Lx6Uv
zr`<=5d}roMW==~AL9LIp>rN8j<l!0Y18MSLR$Bxe)N<fblvGo53P2Wc`udy_^Yg7?
zwq#~z_WJb+1rgBhY+*qH{)2{zZ}zJzif@h*R4bs~hHION<Q+uz0LrB`XsI35ICtX1
zvJA{(;c*<7k?NJFA@d1INv9`YimtFP;v`L8CK|98Ny{60qECcO^Ju;Z4_7?(d5m9Q
zlxYj@k;vzQf_kGiLYOlH3>NY2K8(W=Bus`BH&9>n2#Nz}3Fqu8;U+C#UTROYvl=U2
z0^3ud@dNt>9yv@(#!AgqEQMeA+@SgH(>OEM>i$2Kd1p()kI$?331{Q2C#H22$U~Tn
z{Cz&bkRU7YX1%o5Q$;NrPXJm#9Ra|83a|M<+aFJR*Zc?~NPz(F8|Rh2Tfn^HQ>%my
z3-smD$;tWYuoS_nU=w(~r%wgH?(_d9c}Gn(0W|1`Lk)m>2Ck1l!qg3cS>T%x5)c6F
z5@VCX2b2RaB!c`zeOcK%un7RQZuJH=D<1^cL1q4oh0L6)kGt4rdU<8#2t<D7_AfZ%
zA=jlBc2sHQXm5_izjDsk4a?m}%1u#nCl$$7QhG~~(0pF86hyumRHpQRkrV(EeJA|a
z(XFR5ar&066A|m_1EI2Uc_#4c&CG25^~)NHJE)5lEoBqsN3Ml~K$?J(>m?}ZD1+b@
zFoy{t`?}J(*xa_MH%tF-cWPi3)hi|85Z>HUjR_78rW0dTsd9y9`t=7D*UJWA6&^x`
z1DGE9D{6X=rfcNn%bS}iA3mTWAWc?RRwIt>n%_MWCo5w5;EmhE5AFu`8{>Q;A~n!L
zS1g}L*T68oumlU?;wdO7P<MvFKl-;JFzfyN`SVD{?RAPZ2sQ=C4s2hp%3M_6NQfx{
zbmnAp_z7Wz__>t*4~>Cj$?%G@=Q`~D0Jyxtqv%z-w4wOr1oJuEwG0z5KgYQ*o3@Si
zNocC`y7GnwOiiGsx{}F-QLV|sc}butD+tkiJ}~Tuq3^=ZQFnLuEvX>*VPIx!4=Dk-
zVM^wdo{d9(3{YrF-qJ5Exo@>kTc{tEn275=*w9XSQgw$(SJ=-$@Kbfn;YTqK{xBJj
zBd&bcOK&sxHHsf@J53&5EDEVO37Y>d&v+OZB!{ov0*xO^@x?WOJ;6&1bUw5XJi)<1
z8*J>LW(HW8g;y}cy^TVllQWm(U2^+ZX%T88p$(K%eQ*(<eFWWF=u*Davs5dotD_V=
z5|Vh$KU8C6GJBuzt7}xEzuphZ8C0tE91ed~4X@P>+zVNE_bNDu_liJh1xYA@l2IHR
z*6X_%k~4l^aHFzsCpe8ww@Nnx!q5%d+CYp{@<o_%tvDN-LjTmQT@x!S{3R0vVYr2j
z!NLxcx~Ras3e~gobAY5-^T~m3BWltav$A3aD8{4KO#DZZ{a??%IHa|wNY@&^GRkCH
zCt6H`kcv^pPgrlVp(sAxtcgg4-3tff?^^{a#7l{=Dg9eQZ8I|^6!(vvZ~b@Bf3=P*
zBg8@`VWI|&1lST!On3l`6D)T(!7s+r;*F;Qle1uF;Fq1_Knzp^EQTF*tgFsg4`uPs
zpBn4m*lE-#%`dezMy6AfT7wrE`1ucDhw@UfGcrn7?-yczpHjEJNk|@=k(G7F==i34
zf4A$6;W@KI<(uKXIe=HVo43;Kfddg6IY?AN%RzxmPEL}M$8ryaw0P5n;GqM_`NfAH
zq9p_f3mY4H)>8x#q!Dz6Kl{O`$sWWcd`cI71P01#nm<f`0i3-!T#VV-(IEstM@q`H
z9|j&QHskufh6YHIzFHIF06P4JlMGmrT3S?u!P-?0OK|*Mr;7VB9Lh9^8#1$f#EW{X
ze`Gw4vDXR>frLMd8$`40Xq4{Ti}pZKS^RwjI7DL?^|l)qkJ7bw7^s@KYq#~4!3W++
zv3=)O^OsN6=olDfmAvc#a_lMs0SW5sTRc3Dpaz4uDCn!<Fc<{g4scN>?W-oCTGoVy
z(z)<_Q)<VNBjQOzYDI}lX5`W|MeGwVhdnv)`KOH_61|oVa^SXBAZ(#gM1NL!m7TrK
zY*7}GQ^UHmJtsxUN0n!d%+@750$||^GrFRw9dqM1e@#j(^G02Q(dnwwYtUoG#;U0>
z^}<I`VTplz%E!~x+S(c#2w-PFcu;Z$W*>Qa54M(hK1h{JE6r42`j(r!Cj3vtZk;GF
zFg%p}2j3T<c|*_keLUp#Eih@s)$*O+*!YoEd2|xa?j-EBu{p)9H~jl|c1FhV^0Z)J
zaYaS&#No@|*u<e}pBunr@W0LJnV`&~1D2o8Xt!tW$UpYMylCu%AAE?9emMdj3x%hY
z<RaHYy^(+g07odnatg#V(BQ%f_xJbLo~ht&X={^e&{-MALZ(rb?JC{0EnRO~tF;YF
zqWt%(IJF)iqGnD<2{r&Rj9Wskhv4N|!zb>|v>_`0u8H#|negHvAmRVLoip*0c-sp;
z{nv`wdzTStG(N|dr&;h225B}P)K7MGfxW6c+$i8!M*xCb9Gw1<5q=&X*jgX=0^nYQ
z+4$i~zY$>Ns;seHOFiJfQd=uZPyczf8=PpkDc=K>0*4|L)-T}#5T)&fL>1U6P#65)
zN);c+O5G758mIz8-oE`P<d~_<Vik&m6=d+{^iNC3qdR%*n5&(yyuwZ(99~$bIUrwz
z-g7j(I}5x*bHnArz(PZPs`7>N!fTHyTF^L#Ev>J6K$HY|C<dUj25^MIYe8#&r7=k*
zr=_ifZWit~Ie`Q4WIj9UhB=600{5fWHOKM#-Z<FU5JnDp^N{a^fk224OwQoIhoG?Y
zq@8x*rXt{yz~=_9b@@vn7)HWW(zMn_EopO$&U*vMdO^oa%kQ0>pE*l+HXCy{{!LB7
zFg1n4k)NNRH4NSy`f+(%GEE{a-hY%AQ$ZJ0{#R_NSNB|I*4xWx-d7zb@O?4f0HJ+t
zeI1-8rQlWvxokAfE&#aHf_C(X+~wi=D>$^&UoXR5NPv$I^GD>3Ogf)p^-+lJ9UAzL
zV19Lvjja}R4tsAk*lZjvQ6mtPNijPlP5R8?(ma<?HV3AKw+tyhCi=eFQq%bjaJZUm
zz#BP6VfawcHvXus4I-lZzGep{s_doK!^aHFH$@{;Qov|@18g4T!;F9>l9-qXUnGsV
zw<tikfG`7EHh`K!=ji05prk~{V|uj)K(KVhaZu{9VvRDiwY^;FzYV1|#Qp+n4*tIB
zFvH0Kd+halFTQ4FogN*1y<7nP<c%9QiVxEqJib{Fs*R#be5~r&?MYU>8D6})w#Loz
zDT0yo;086=KawnI*`u2$fSxm-jZJ8ImGAbPpOZ1kL*m8tx6)sL$#6C4nt5jM^X)at
zpc|Bw3MryO+}s1by-NR;O3Mq55NdRFFl}tG-^@5#Q~=dbS3D;`frv((=P{7NU?c-;
zT*UkAXt|ZsGUM6*;Vhl;6h=b|&16rVq<yT6(g>GSCo9U!u)EgzIZfXRVORhxQ9y`*
zXa4AL|Gwh4r{L%RL|z`6ZE*L`H8RH`O~oNDqDDTad9Nj?T#eJ0SQS0uQPil0@v{e3
z{_s{0to*-8NO?>otWHJWy}dM<knXG-cW+H(=jZDdzI>SY<2Kp#O&TH3Mn8;@&1dO$
zRXVtVe$8{!bzbY&O37Xy_)Sihh@*#5@jV$%<C~x~YNmEp;NnG<p-p`558Y!V-&`dO
z5rLOzX0`(3L$LOPy9}-dowMmaN}cB%C?754rVMOy187M}NCsLJ(GM?>LhoN8uyy8k
zioc^t*7^1R;nc0>t%}153&Xi(3C^8_-=x5=Rk}&#+Qfum)6Srh_>O>0ZQJsZ*2l|2
z*Z=K)1c3&|oq&)0U!0`0J(qCdu0^{)6mcP3R%~iM^|3;FceUR_9OCB-m5P|p!+zJA
zfJC!V>0yhm<^x+3h@Wwep4KlwwdQcB$7Ln>R{FwHy=UbwK8!68y&R9Mj%N_&(32`&
zu*L$FbPV)AMlfuL?gL`Yz@yZF3z99#aS6V~f~V{Ezf(pl<4L9#ue&p&<GTM@)@2tK
z+QEEQf!PsGF#SuT*D%_jKwFmRw_|D7-w-OS(tZckovW+?j`W#lQghW!lIQO0u4At-
z%WT!u)Moo|5D9s_QfDal95=JVxQ3{UQ@kH6P+j$YQpL<P@TG(K0bD1r22K_t#DRki
zfSi_j|CdLN!U23=TKmN5&dcn(-k_jq4UU5u-zFzMs_^-EZ_TEbhNsCLemuNT*LaQx
zZrcqGlPly_zrp{EdSwa=k&wkQ+XF0<zv4{eA$&P51q8lZsGsS5GIfJtidXwyEH*y=
z70(oOYfp#!uS&UuM5RkB;6@TcP{-fDY|snrum6Fx^z?o*suz}TTMYZV{-E7CMH;x{
zd;X}XXh++rs;ke8_M@#X@21OK83L~-pvOJ|^Hry=6Dl~V{=P+I@37q4ZynbwB5hfP
z=nQz<wv&|x8)o9>zn(yC&a)2x@qg)rPEew^S9AzxfUSAY^mIz|9AK5;O#qOlR+>Q>
zrmw_oh;$jy1;3J$!y#40mk+WUDfb(^{HQ?>Wr9LOQRQQRL$9Y%Y}UEDAYYvF7g{3w
zn`MuHp`lk~*aDYMS*h`_55sX9Y=DoHdB*zzA3R$~bN!0Ck&&6HrKyP`0kv#!uq^nh
z&@4o51=~J*)(_u;?@skUw;5;^N*$PgLg#ewcMPbHG7!~KeovRrZsS3ijAiI;8h|1|
z6QgwhzW?s7Txt<8i;MNYC2Vo$HurC)!2nirM4bFSFk#O<OztK>y*$aZGc0n0wFM0<
z+>wf@S-=qR+*`SZz;w5|s)}#jvAhaw{$bBOxQjYt2*|dm<MMl*M9D~anJl+3q_+_%
zbomtm9FOIv99P8oP$**{q4)m<lpV!J^Kf$mB?~C3pexnY(E$S%0tE7y;TJ-D{rd2{
zCSm(!nd_gOXTygVZQ^%$G)<XrMFwOT+<%C6d@D5HAS+RNq+N>a_U{lmt*E8v&-(Np
z(bF6HkX&n=#1)@=US)SzUibUkNf!Bs=y=%#hKzWqG9iq&<ar)5Cv;4Tx5^J&c>DJV
zymIJSKOXHi;QsgY!7cYc9zCD@%X^j_9uHln+~i}MT-Z?-L_Ye~2O5FTNkQ#y=Y(zj
zhD!~d+pI6pVzi|noUEi%+f>e_(g8|wl{0hg%BKnf7z*r;$jHfM02Z03u>iga@P<6#
zB8BlBblN+3$l9cR+fDh+BxVzn-Q#0TG|;PSU?V~H*V|?k!5{!m1AWMz?uy%Dqk+5H
zrAAj83>u*B8ogZIAJxA+F(6hFKX-y@30U8ObgqEIEtFR=^!@uipNLr~wNnVnI^GKv
z{MdQj-o+_?w%v?^KPb#b9*Pp4S<9N333W<Y)p;Gz3Mme2_j^RPoNn?AxZXFrOmOT3
zVBOMD<>J1Apvg7!(eKIA*T}es1?=!*Jheq=!mF!=uR$b`t00Yf`4zzITBV~e?!pYc
zTzNS^8(X)lW~Ict_wSvmbS~)3;!;yNBRI*gRsH*p)_Y+ylBap~Hh}M_%Mm|)S1Wed
z;z#aXETWnz?+NRQckRR86G?WRa2vs@%{+0p?zvl|!-1xxImkQ0{pi^o0JW_DWj=7Q
zuzUpcB6b51S198eM(Ko%+lgnw8tUXhP6HOv$3A^#R#tg2WL;&W9s1(XwZGCno7p_w
z_~sh&0=Q;lP$Ml*XY3k%bm_D%dU5fR`ICdHYD^IxR?VtcjE<JoRj5&KYHvr!yU<YR
zm9-5F3_u0?7V3&K5!c2Q8<<2Sc(`;JMO^G2okoc6zMZy55H2d8-sYWTTCcr+Xk6bc
zV*kaJ1qL5*_dZXPPcUDlo%|)mWBSRxf4K*{Kkx>wGqo5|TZBOq53e09A<wKR4Rd45
zAc5iE1-!-8>&1pknZKXHkaI|c5J6__t_K>y5D2;jSK(xUv<|J!L$TDUz{NFPkY-+l
z1<FGZ_ULSZw#?HX)+@Y4sEGxgRw6UafmYMi(?cEwMSE^HrVqp#nYgU71z)iJ>FM;b
zffU%%>30Zngz1CV2Gb@u$;RONsF_NMkFT&_kjW^rE7SAbX))3I*PXt#>2CP2YqSId
z`MiI8TCNe_H9R!rwB!P*%EfX=w`d64;I`g>ih?w_{&Nj90vHLhkB|SB^uGA6BV_pV
z?abM-z!M`B1i8HfzYSHo`1z_5%-tb>qZ9^TF)?HrMF{dH>Zqov2!bZzfML_Ez9PSY
zhWiR;p~6ED%6IjFw?#xm0RDyo)TQP;TIqrNP76hBD=LD>%-haER*9UV;uIW<0XU>U
z>VuTInE&&8l!A7%UWZc^P@w^z5@wf9aQv9FA;?R{3-WOALB_wqy_jDy9TFBc%xet~
z|9{`@x-Kg`NvK01c(ZP66Pge;nX8vu7{!%^8pUJlhwH&#%@zZA^Y@QEXv5(>P}|np
zgHH$Cr*-V`ER^Gbg+Lhs?;Vh=d-vqc$s}CqJhvO+k6ojq6F2E3OZt`RN`r6_`Vdi_
zANTr9gZUXe4;Ye9+f;DsR4SXZecp$0Lxkpm>Siw{yPZ2soxWZD4Kg2fLDTM!_E7F}
z)*TrF%1GyZGzZrUd?i}?dU`2myLylR-hC&gKb3T7tEdQj>~%0I;=c{g08S<-My`gG
zj=f^8;2{ibBbG3X(mEibK7L&ERt^@}x*0V>ECJuo_O{)%s*98Lh?^pDpFZiRs($6Z
z{@EWI`@8yjk!`e5ZFFAj9#OCx8kEnbGPn(z0^q<D1NI>R8F1)`A9pChlh8^Xw$LrJ
z7-s!pguJ<RtSy-0b8!qe0VqsoE-wy!F8-xfxltp?c4~cK^M*_P;k1X6*nZ5!##8V%
ziir5X7Qxl>0>A=tUvLNrra<7*M(1-jAkTSM5fkhWnrE7dprfCkH=MCQg}NU=%=rIb
zYgZl(W#5J$^inF?NEu5?B~ravBdL@`$Q0SP5NQY@=_O@Pr3KkbS+Xx#Vi0eXB}GV<
zF|<)*BC=F`*VFg@@tyOXZ#(DnM<+AK%=7%7-|t?o`?{~Y;RpyT`ajM`hr#yvP8YB}
zQCUMZ|K`45&yvlniE0qsJEYgM9Zi6rg)Ci*p`Pm`Kr@L552xN<LJ&n~u@EG^Y}|vv
zilaT!YAecB>l<coCkUc#tV9+JDTqUVxb2WwHql`Ua$PS9A9}HJT8@bRtb}GoYf&ss
zh>XnlVq28=&OY0agwqdx$}Yc!phNC+avH`tU9S_fL`)2H|GRR6FfGe)G(lWv!|7n-
z+8sYGV_a|#oc_NyXNfE1o9$t>T<8;quzabGND`0!byj#H_G@ak#L72%odXf4VGNha
zHRct_g|FmEam8=%Hc|*ez068k??iRWF{k`voZm0))@_qpo;cBNlW(Rxl)Wm(y}>Z^
zVOg0mdqsAA5%rr7-6u_7a^0mW*1mu=*LlTaCt~f3IndG-lWiM1%^SQR?eq7lrkjgN
z%&*inW>~oNx5oxbX0k>^Qu8kpUDi87s5x9KG|9g3b2WDr%0vK(uhyq{UF)!YBNgWq
zSbK?jB`nOX%#2%>vflX`<C$3_E>(Ty3M^GJg}nNWnoPXXa2Wla{%-%Lm*>?X%SASR
zW~fz?evu#omIiWh<RLbD6@SY(NzmMn{VE`UlRk)d`0&n`sm$DGYv(rEZhy<vR6O<O
z9uA}Wi#y1OTPdES-WnRipWy8#u5c0acNo&Ss^^1t^vG|NvrJY?_q`rtitSU6CMOYI
z9!@J9khI{%(wjF!ol8-gLiqdbXPywo!0zz|>0GeAo0@b;^AOKovYh-$@!}=auMG%Y
zQRs*J?__MZI~;S~!tM~{Dc#)*NpoJhV+G!X<@|lbaFyJ#R?U%9V(u_e^yM6c>zR#|
zQ|c&3o0WcPPuQ{rdQTReSVuE?UQOYx&@TACckl60&S}<<Ih9#MzFXMz$`rXb+$Unc
zl{0?Q4x1;mJ1t(gu#NOgMiDhH?O(Gz>1%Odx$?Dt`%h11wD>ifOg>Ot(bUkukdSPY
zwtea!&uJPPcOlCYUcGvx>_g(oT)H7!iST#PH&6mh^WbP^ysiUKKR{Bx!gLEZ=pa}f
z%q5g}cuqOgcBH~1`DS!9f1<ppeN$9W(4<52n@4A)9eQ|4_B`-bRqqe$^1CdjqLq_=
z)RWwgVG&;93;#jS49ZR)ITTia)T1DSX)X<Qu)150<@G!4`rZWuCS%|X*GEv3qD}W}
z<@UAuRX-kC03W(}S0%=QWAFx&s_f<b3<CoLx2=`s<5jz_`#ekE#->julFxyWd^|9v
zw3Os#NwmV24-5>zvW7lIf8S~Wfrs|ZvB-UZ;XIjqimNO&lMR2e?8|vU!SHwWzTP%A
zcPnNd9lyLX*Xa2l2t*V!C(czFg8U~;?_V(Fck%7qnaLa4fCOf2EoOqz3hcP%&S7I2
zL56`b?E1SNhk=Q(R4r@kfpm|(f5<l}+hw-D>FLSx3L{V1Il2X%n!apV4fDE07_TM}
zNw#W_LR@xnaYxC^ArBDPrKF`#A|6Dcj<VItKLiscW)MsvY(!u2A}fE8<AQ5PZ6Q_9
zFE%^QtX(L2_CQ77F4d_x^sfnuR(P^Zo>{Dqj5II;qEQtgx^sr7YJ}guM$8~KRhh!a
zV8Kbw#q9jlH_fjVQvCVb0!4;zVP)5fZPPao)m)+qMm~LdfWCI)@dtH1oRW+VzTlLc
z99^8LPq{zx^14|Fhd@kkJEYi)w9Mna>(uN+Kz;rk0F;q8_s}M<p1lr~Q7=iY*Q*@c
zCQ$Q{ne2jxVIjw|Wur6S@kkF~mV&Osr>dMQz%ro_fJ!BM1P`>a?#e`OhuysF>Ub)M
zbqeh*CztCg%gRC|v!Pm=0tg++kx$x-r0ZuAG5iV^VW|`%(}M>e733rI&>+Q`?V=Bw
zdLZJ*s34srwV(LZ%FPfmzHvX?uG8;X>l6IHO<sf`9v`3{-n3Ixk2e2HrZz$FxoBzZ
zK0>2G(-$f!aLgtEq@$x15k-N>1r=|sEUKGq)9*zAu7QIph`!8J91B$QkI4w0_36s+
zWAv-cyba${`Lv~ljbMfCf2+fxrYa0K2=q7y^E59gr<oZWKjRI~2WRf+kt2CE*4{V-
z$7G_=qhwLmjdCAYAfZofukK78cxTdv6?_+zbTC+GY2=++XxT&LQA&a=??Fo7IV|ve
zL&}bGXMDbWmP*srvP~)N9eYf|51$g|6aEU-9`$?X4&>+OcYoM9KIfJQ$1yqUWP)PB
zgzh{-U09u2icU20$0rSTeY15mVkG@5H3$%I{)2B)=H>anyxf$Ig9su5V3rpu^NE<>
z0=ZJ%k!#)IZ}BrL?3;Ka+@kvRG8w+C*htx$fPMe;BLB1gJ8pAaEuua~Fh^FrEBIn`
zargHs%rx$0NHx<)BN)RO$W_NgUg-B@^vbHiG@aW9`ubna^T$CZWLUb}?j7&h&ymPN
zO6>O#TQ_I+arpbA+T1w9cN$T?pOKN`X5#|*b++#@9i1i^pSf=(Rd_luG7sGj5L-7G
zDp^tRduwi0$N`UWHHY&HufCGotfrP&%t2%a)o=^~A&zGG-E3$an~iK5F(*CF6pv>{
zwG#S=6y)VT_su4u63YMf?MD$F7`+vILJjC)fw4`4AE_?+FRb=IxLLEj_^C#w>%1>r
za)%1LoSU1QIeedsuf$x#*SXel;DA(E<RZPhx*#pfF}9cH&=B;&RY^zQ&Yd@^hUu|`
zYs#6)WTNMSR-)qHvg$2#2Cb@s9&eG5xLaA-H#quH<lY404RqP1&I~@jL@n-e2G_4T
z5TrCr7NS!>UNzr@NZ?=Wi88CR(YyiFefyqZfg$8ZYJ_()W);w(i`>XxAyj~`=s2J(
zQ(C1Yd-O?5aF>P8kR<8R-)!RLF)0}I%~&_FY-ySDM9NjQRy^C}?7ObXl2sK7hsWkN
zNsM)jpmB8wtN6n3^dcUUv9~w$_U9ZM?aJxoMzdU|<<Enp(<%<Dxe%qHsR<ESGxiF6
z?Z3U|e{n1S<IC7qgyd>r;Yn8aN>R~2zBVfcU((OwP<QwtPLs6AErF$~s4!!Tr}n#*
zkpoEB`TXlyy>p)|=YZ@OV{xoJvs{aUP+P%~XY++$8y?=bHfZNm|3#k-zn7v`!DhGV
zjg(R~Jb%sxq92qz<Zz)n5dv2_@<=hcXPu2bXA8WC3NT7kH?1#pkt_<T!=I`iQ7y$p
zMRQIf$_z|7`l_>2`^YOJFG{3rw05Ubog!pPLyrnvFE=sF@fNSt-m@nFZnOIH>-(*%
zfp4z8!Hx>z&PXp+Dc-B2bD8|qb?c5pv+t{J(v^^q()Y0^$FbX!U5mod-*bHz%lAWq
z*Nz_Vy-rRK`c5|m3_^y@TVKgU3uhL{e>(O%F>W1YtF%d48Zu<Re%Cn&b67uU{LtxE
z+t6p!=L`*_Tx`*cjo}{{<h4$Mf7cIJ3>FHDp4B}G6vMMWS!2Kb4reGD!56e!fNC0Y
zx8^ja)*^#JGKBOW=Z^I92f);Ux+{p5M=NX=;wz&?ls4+gu8d6RF4pZSaRK2dJ08aF
z2DL!@J{avtdH)I$Y#4Q0s(y^2-4x#fd!;{mmr+0Daq#;$%ihpX5FZ_zjbIe-o(M!w
zc?Yc|Jfzt^Gdp|YR8cnzL~}9VB2Z(48-Zf}28k{7aRYFMdTB|pNCX$6^<Sqn1hgCm
z?WM&zmVjfYt|vS8OUDR8aiY@jTAoRE(2oie`;zYSRD}i0b%DYJ*FL_vcnhi(N2m%L
zBm--iuCLnLLk~d9x|aFn6h-dDHTHh6X|Q|F2c{rl0%`)yOGr-Ut%{9{%Qf3Q$nQ5*
z8|PAKOr^Sa$CYCban71N25V1f?5k5(XPPtj`}l(T!ENoi5~<Frv8h8MYc_6_qCjl`
z;?F3V;a|{T#!mGV*lDAVf;%Ho*L(NYG3vT$<;`Ae^Ni$SkYuA9S)P_%zx#}zU_{JF
zJ5(q{AyP^C!--K(D^Zk_iy77|bWC%8VB%OK1xO1Wp<Iv;eKKG=p{Oib?@9syc<|7Q
zMT;#q8p>8p&CT?8QKl&0g9Uzfcz75xgBjlD1hFK(NL=dH1#@P|uV*qn!yC**WK(Fq
zTvkD$7~#SZY0CR}H#@ug8HP$aO7HN9(VH$lZFeu=i49ot7yG2f9io=|VGD@--NM#Y
zM1{2t`7#4nLN2<OR?^J^rTQm&9N86Lb@>Fikpk7pP@ph8_f0g^ZNz=3i)tfb_cp!R
z<-@I8x5!b{Y&2!WL~?j#fZWQuzw%F~TbD=OM&fmDZ`u_aeU7aHNT}~6a!M&KLEN>(
z;)!L#(t&XBd~UPxAy22>;q_jrU`wyJYck)&lVo|OtzvqtWZ|;T!=kZk={H;=!p*-l
zZTI=WNmSpvY^;Q$oW-<fV#f1Y4nlqF_I_CbEQ|B6l=6QxVE?U?m&@(U^x|F`3F}U%
z#kCC;bvdtDv;F}Un+?dJPuhv2zaY~DDPbU{*|p3Ua);RB*}+o6dYEzm8Aq)7N}0g5
zd1%PSVRr95(>WwEj~BEEoVP{@>2|8jU$Gh-9LVMLVFoq*qPjqCrwV$%QRM-dQI{>F
z->Bgy0>pqWROoscP)LEZH>95ciu$Dao1p379N@4%spJl{m7vXhT$khB<uWocnkQhJ
zgN)lh*`{4Z-T$m=A{wog6P^QsgSauA$^{SPMJIF0>TL3TPc|&-6~b0Sb7Al6*Fqv9
zJ5P4VWVSclj7TdeaBF{X6d(>sW`+JhTwL7kg0n)W0tYcR4oLvYH{hj$-|ADp+sd2X
zhn_fxr1QlY8CtWB<frz55`^OeB@mpSX)xn;x~`!t4r^P8?Tux<etlW0^QQgMWYB?|
z7Kj8J4b$&F;R;{Ac!7$r*46sK^6%MF+dXVCgjZC-IVMB{P1iv|zuWCy4XtBf6==Oq
z&~*S~f~p{pBEZLZmaOroePMe?$1py{y0B}c;_Gv?{!Wkl@#{{}8c!0yv5aEMZpVRO
z;hMnk>Ywm&cXLCW(aqB7I&#?%LMoc5&V;~=d`;9!nd!K>>XCJpWz}r27sDLn>1KP|
zhM*%B7NUZJ-(FGmBsXlR!UFyMPa*Qe%gd_)!j-s`lone_f54P#|FO;$J@?Q`e6g2*
zdbZB4A!s}em?Y|6f{{T%+=tp?3NnOPsuY+pOiD&13&VzdwOxgeldOp8sg7a}+n*(=
zO)ny>5fUTY`1UXYq19rvYe)&Vfcu4bS_H8X5>=Nz)uq-A@2!i-_%`6$OV14K1#u1L
zKf@T+yC@(@<&=-Ks{BT9-Te!t@jtCP+2anRP6v`%B%q5T{h>tF={Lxg;Ro%X#}Ac{
z_X3*CEgyLVcfTRYw{`L`@VxN^cSlU~lQ1}XbRyjH$6cgE0K{lCTE}P`ht<0M<*GZa
zOhA>M8x))KMm!9u2e>g*mqMPZuq#OMc_8Lc4al*qQd_fz*=}Un+|sf#64bFpqN=Zl
zJVXJi0&N7A2B8EDD(S28^8@U;XHN|b7vd2`x3>rAyS5Yi)tSU18<=j8j6p!W9^yMY
z{Q(IDdTIMHEeyTH5)uIW*zggzV`98J&Aq0dzdu_q;)TLl6-p1NU(3j3fsFv1j6^uG
zb%BNKds`>$l9itRV)6#3mCy>lYVpsW-;t-DPYch<%>3#@#})A{n06|0JaHnun<cpP
zP@Au*jG#7vl5Q5?!i8T?LH!$rjLHB`;=+?{6#w0qA;yE~82nWj6fjLD%;!mi9gvmP
z#PW1`QzRu%{+vOAhPM%?0;JbLU0qwdJKr|An^oyOYIE>l7~3R)SmK)HN^nPFxDd1=
z0B%P&%Aa~u1il^E;+KWC*<j2UDs1>;GE?yOp0b=Bi-u@Q$kUyZJ=<wLO1YiiUuue(
z`~94djsDc-kQ4z_`ukVHO2@2X@zyLn{##K|vZlp4Y;mHRG`z{8(iDGd02090N=ubU
m2#bWu((8Y{`OmP~99I^1Y3-|h@oLzEgw{S?jhsDJ0e=In>AA%K

literal 55312
zcmYhj1yq&M7A<_}?h>S=r9ny>=@gKV1_|lz4rvf+>F)0C2I-Ja=?>|5+jsB%|F_3*
z=x}%r-?#VLYt1$1T!tztNTMMVAwwV#G-)X@We5a%0s?`$MT7=_b3`GJ27W`bmC|s4
zKv1y%{eqfW@qUCr$RW~VA}X$_M`<p?3db)4Lc6OaZ8zy*&dNr#!>E7BquN|TC{hN~
zCk;;#1C>LN-BhL~NHrHPbyp4_pBn|9?oIduvrlhYpR*kFcAEuv9i1|6n##w86>;HE
z|Fa@aU~fv$I{&k7PZ4m4U;SsTpwsZM{%19l$l2=uXF2kH)3fTN{meM`@hDP?3K`a4
zcNZgkU@P#$Mn!$L#kwsIF*+?KqIcaJ9hl&rEV{)*6U0!l&C9#Cv7=R<s&iL<$dyEm
zN7^Vvm?GMhe-(Ua#PQjB%a(^eesSgc@%fS2KcHvhv3Iplg+68U#Ob35Z`!lNsVIhU
zmZB|>whc$hz*eYOww{&L@61V4CMKr;e6bmarcLU+3Vr;@_BF4xOu=+YN{UC$LR#t@
zSAJ3)VqdGG|GvN#<mdW--*(TLwaf26Yw}p%ALc(RXQ>Mo0sj9kiG~0q^#5*?_5ZUo
z3fwyzbc~EMQ&ZTx<4}++Mc1=+5&ZDHqN4W}bEPM*3I6j<TP`_TYCMX#qobo}$jD$1
ztPpT;aO&#nV4$JREi6Wchno$x|NHf8vDO+J>-J6i`}-vJ?K``>OG`^IzDY?M-rnua
z&AbpQ8XEQ_IqXm)GqXD!G-1qdQdIR!<yZJ;uKY=I&$DWdXcW;QJsa+63$V~o5O*CD
zQ3!eLP#g2KDcBZQ{`N!#OYG3AlCoRUK_mN$2alu2UJ1<HmdJfP$ME$`<)T;`c^P?m
zE1w@jMpLF7=LK>!pFGd@rwfdYjZxuyy1F)Y7bj#3Y${R`!2ye4A_rcSpzRa!5SRbn
zSAqy*!luj%h@Pg+^JrIG{W^47L~mxV@>p53<eqe>Q`vLmO&NuUpa+W<O!pyhTQAc+
zf3&hX{zxpVrj{^dbltB>FVqmC^Y4U1#J<SNj#O~vT3k(O(%akHgT4FD0r%CZEb2zO
zqqNl~gtfMMvXtdb4TVMyY<cVtPmkyA)>iuYiKwaJLyU}#317Xsns5AX7<Z(mV-hzt
zH4S_xIb7@Tk(M56_j>rVz1@fYzd(PXB<~oapZmC`Y-VlUU$5d0v6RiDjgF1AFg4wC
zGP&swjkGGdqPn=aaB^}255sI#G&nfu*dWj$)Hc<I_}{2;@%#TnrC>K(<v%y4yQhat
zz&*F1ph3UOzdl4f;sve}@BfBW1SthOJNy3a225B?eG_4OdwT}AK6}#p_wP3uXFp`I
zGBO_h8HyDb7uO+z6Q7Kw{pjxIR;F23+}zw;BB7?L>f|IAG3NW9pBXXMy<z*s&=9=e
zi&ue=$F*f%Sss5Ruja>=^V44RjQ;-qvDcfP7PHlFKjp_IB$Vs6CF8iyGG89|za<5G
zlSWLz$LH~IeeAy%jHO1y5Ssn>@VCoQx7Wk9RR%wsQCI4Fhn=hD!GJYlOIbMZ!do5q
zq)`NfghLy){SkzeZ{C~-v~hB9{CX|uSet%blaKEodzmkv!pkxE>zC){PGo$%>RP*3
zO?5RiG&CKxxGPRgPcVk8j106dIJFOtpP1eb$<Uy1vSMe%#K#+(n$9=atgWxFS4vwO
z@e5{^e!08YW>>{Sp^boo&_`opVO5!oCSMy?eJ*{tIgORdoy?Jp9s0AlxF{J#N=-#2
zwQc<M_iS(k>37Jhp`kc|h$io+=JIlDH8nM7XXo=+Q(W-{Y-I^VnQ3nAjyXwxVPVC%
zamyyp=hni~nUD{wJ15W($yjAFA#Z;3nL@HwvsYI722~wcZEWtCDB;J_m6h|PTOH(#
z2|XL&WD(%t<SC#7#E{L5bXr}NO5Yb37uQ=Y%}q?`o0}gT9x^a9QQ;!r94*8qCB0>0
zIzBzEC@x-X{~9fYi;s^_MAR%C#y%#D0ijuQv)$akyk1x%<9Ed*Y_zBj-Dq2e4(=NP
zLG;3|n3$uSkr*dlWcqM>tKH`QcWWRjFfcH+wt<IodNhT5cV|a%xyAu}Xfk(36R8M1
z#nggsyxiQchbVlFzbM`**ZRY*{Qk>>!&byRmPbD)H?p!Qm<Nn+Z8hkfURH$(h(Z}~
z1uDvCm6a(PI2MZN2spW-rl#r5tT#}^-Px7h&?2jGk)eG>G3ld)d$;&HihbTw%5r!Q
ze?!7)pM!vnq5rvl8@U&`Hu7_QPnxysoDvroH#aX&Lqo&N%<RcxYn7QtMpjn+yUS?_
zT@<9+W=|M$d#D4yVv(PpztiNftJ~^o=MfT6m3lNk;o%BPiy4|gBA6T-EBf87v2%*Q
zqJ8ql@CoE3X)5gSKI{i4>!o^m%6E0?J^Lkvtdmk?5SqXh3~&6kcKG36N$)C>Ok>Yo
z>xEevxs-~EHp+wfARM-h?eUQ+#GBo_dxKIsJ{ba5by3YR%DNK$HG7h>S9M9Ax?k=F
zuy{4$BIULHe)x*25IVkPPa54tQHuLE5Zh94p;V$M)x8x+p`?Kl8|lv+pzvlrmZ5}N
zDz`y(_UoE)dZZV#?$0|4umkk-3CQs9{XRk%r2HZCZHBe!WA$Dh`N#7Dj(Zb&rg9Pz
z6586S;NB-CB?*&H%*;GGdqhh)9?e&&SLlWYvZDvrG&B$-qIvv1z=L2Sew&+9^YC~G
z#i5mvk^=eX+qZA9Xe7B(IroCHm@dkrJEq8iExV6yj(>i{le0(1qKz{U=VrCncQ~O$
zaodhDHYX?)dg(!2wY9rPSyI_DeabPN^c8DDag8-Jk`AICmUVu5w!hRQLaWUD+8+JM
z6CHiW%*I?^Um7Ptg{kzDQUo4?6_&vLE(d-ANsOXgQL1apxG+wiWV?Q3fqepMwH4yq
z4)H>Bf(VDnIZw{QLtqH^uL%?=v4uKb(`7Q1d43o6mBe%-;;hLpkMh@+eizGvyEi07
ztFES6J&!G>Y+{wRl<MX(4$D$gh3KAkWMwTwiLKVluVp^V@<%7A$=?$$mQzvG>f867
ztFyvBwI?Dit()GC3#Ur?igKiF%JlSfg75H#A{;^l{}-%0-Z&gD)@hwEixH2l2)i70
z1x!s%fefH?oLg8}*x2|=<j2pSuLubVqorn+m-+2Bdzqd7UTlxl2$m3N-K@Cp#3jU0
zMG9N8u?x6g?Gco;f*?IvX=$L~G!9A{LPL2MRf(Y9AK9Fu^~3X~z9xV1_~`NQgmRZZ
zM8nX=im_nB^+3#U=IH|8<YnO!)MUaab(0A+Bjfy)@La6Wys2vY2J2A{?%9MGN5xM=
zrDZ*DSjQAGAnCY?sbpK-1V{((%j{JSgwbZj)w~!q7~$ygFkxSc$j$X2`k0}?5<D-4
zBpB&pXz&o~rjH1Ja(iEh2A_ki_k>ZR2J#f&M@A;cV9SxR<Q2n1nVN4$PE6v4h$X(O
z=1(YZwB3Sl%P)=@VkhAftSFp+&GLgPI)UHa-mLV{e~|X@Vd-E<YSiQQb4YgRHrdBU
zR3}AsyH?knj$|3?SYuwhi%vfnS}o0(p+EV*EGtreLw@|sjuE`D4@8q65EpY&cljQt
zq9h};@NflnPz)Vy`XiqRT)d{sY5UgJR%UniFLiUxFqX30+?(7QZYS9D2{Ir3t%@9x
zaK#xI7`C^!0a^sNcl@>a58GR$hMr(kv)VJ)?PiH!46^6NrVN`R?PkaQ(;uJAu_GSu
z^Q{7icLY6;PE8A_sW)C8x0`DS<mBXd&1RE1Eui;YvBIq7T}jy8c_2sbcm4LG?zFVD
zb}*z->gwtRrKJ(%A;ERqqezBx0+DN6gIQsH?P+NZPCt5%5GKD$L`ASLS>PdSJJw>f
zDri{Q#9vi~$xvNKCvKm?jmr$j{n;*pPREX<fsPZ&iB+8Z7%F9i0?RM_s=9i1l<6qw
zo)`VMwo*DRZhJ)$S!OvjM4GImn%c<Jm>MVYFVuur=un8fPfHj^0LSc?Fd2VcTG<~V
zjuw1+dTVXd0^^Z{NDzc)1*KF$Sp|iY<t7JLe(!~9<DH!yh@zSr&D($L4gdtnmih<2
z#ba5yvGNKZJuSUTQ+%voQ7CQfH4+}%b?~{nySwk-zgt^d$Hc@aD=QycS(};yl#Yyy
z4E7oniR1X*00fK=qN48!&C~~4WMYRv=_8DNIXgSs`?JznuhNW6!{bfK+BIpY@1H}v
zUlr>0czX_#4^iX}C~j}xHW>|JuLLb-=jYE#egt_cQyj&p2v^eSU*JONpXU_Fj_XWK
zOx%7^&p`PubX3|y8?}`3^G|8koTP~LP|ke#2{Xi&;%-Gp@bP?r42MQezv}Z+z171j
zf)YWa#*Pl5fG*RuuWD*@t8E_d-o1OVuZ+o2t}rpNA^8J=7`(p=D?e#2rXOO_vri#v
zA`6S#cZ3SqFtyUT+L0HczT-tt3=yT0GQy(o_Az^tD_aTYS&kTj3NJ5F-xRN|siUKQ
zXIGg|1TCeh$~4_c_m*kJFJip3%wpsG7v7)Kot;xc_7E0a*Of^AXF3UxKFBeD**i8K
zZg>kGdl_0BLd#x;Hk|6cK9!-w+yPe~%l>2{OEfIdO*;$n^{~=(GP@3E;DL8OfrDET
zbDvfr6;tQ}V4Kx54vFLs%xaT}C@2=@=J)+QbiDtTrljQ8>!XE@jSZXe)C(@|rr6jD
z4!dD+j6P>Upp?SF!)LoK1m&!7+pIqJ<>j;ZBYiIizt#}tY_DGZE4%I{UnIrFjnjFm
zjrCuCB{w5|#5Uzv)M)Wm8xCXQe-8N?3Obaknwl1;L;Sn_oV>ZG&z{d`B?iS@_57q!
zia!TtXU@;h*|@oH&d<%HpS5yra9_PDEGTdRbqPE;z*u8H*$mx%TF!<HMQbh(cv8jH
zp0vw@#pOtEjzky!mZolVCD;h6&Am$6xZ_t>kOgOOQ}<T`_3aH7Wo8|o*)}9G{7myQ
zX7$zJ;5w%MIUIH_iz}@oQoV2|E@BhiFjkf}uGP-@d-+HrZ0sg9v`xC0$Dn@P_jnD`
zvA!P`yw*EWTL%@@cpKUc2tL_f?M+_mh>MA7FA`;n71*Q7DWvjr?Oxg}H+F5<a&T|}
z(2fZ!EiK)(buOF02;Lh8bZdLZ`ecG`jL58~2EW$$qrvqMCMU;4)Ya8B?2mx*ZEfXe
zPhxcb_?DTu-Q%V_CT1#EI(CtT6#!>@9%50^o^pH}4}eHZ)GJC$OM_4eF9I6QB|Bjy
z)w^H^Y+z}4+)=@4P+8&d2?+_&(G}+98G~=Yp;dVNVu&kFNl7W>dCO`vh-qlJH5OHE
zvnF^G)mKk1vt=FlfL6(uoJz#u_yaofz=^xMBbhx=7?EIeEM1^QzYCgAZ3`5c<1v`x
z&x~ApmB0G^M+xK_N!aukQBzW{*zS1&+AHq9Xb%dK=yU6F;xr7CVKBuGi^1+4j#`|;
zCQD9MB}8Q>rIn_MMRLXCg$ee28O5UDAr9=uMr@Rqzu-7X38U9i%_HL4brR333DPt}
zx%|?FU@R~Hy1-8j20oJtTZRfI^jpjjXoNs-cHB~2QSnhqs<G8-WV?zPl&geWvCBLL
z$?wESklESU!?`jXBjfeRhoglW5R`cVgr0&Kqb4r%5x4N*-@I*i7!g+Eg{KBkW^j1e
z`-Q2qxbJz~_x}lJDv+ZV78aiF|HdXJp8k1@hldBy<nhO>>HUuxf^9CRs{jROG@`5J
zP9{hUD4;Fd7#dDLiwS0A{QT*9yvTW3%<iGO-D9)LEaW}BxOo5CF!ZAV>#Kx>1Uoys
zNxu49zQqbJBA-<&qYq|x8@V$8`H4r$w;=FAciz?B@TN~&{`wVGy(%Q|@Y`N1{B{Z%
zM#IdQ7Y$Nvez+eKb0Ef#e&>DHiGv(SCqs=HMwBwrs2wLa$<Bd`J)~hbuKl<6hK6SR
z$b#Zy-ic2dzinT#xAQe!=*+}&tJ!ou29n)%@vxt0s&o5e3InDJ+rqntWZvQu=uQA|
zG7Eny1j#BE&HVZEr?XR-Iq~q%pIXa>>eKcFpF99ynwuA=rY;7a7}&bbg`oeJp&#kH
z-rypGjuSwdqs`5!Sr+nWsoveoP_Zw&H$OY%wY9a)%=R-y0@`;5&h{pAUTbk<Oe#k)
z(&;)q#+8*F?^-1%C##iyelt8~IXkri+89uE%i(S@X6HiaZ!0(Fk<O7lK`PF2t5ILn
zOjj7iY>^gxQcSiqX|vtvuJ>-#E^lbKQ8YLVuST}J2m%+E647aQER94$?AwR+OW%Pl
zwj@5TJHAZlH}soD=5i$5T!%X5ZX$wtc?%k{0lva+DB)sR6Ihv8?eB;`hGg^m{b0Zy
zR2+mz4G)+*Nz%SGHYWe1$PPDXl&@I3JCU_`bmN8lXKr!H7OvZDBl^xYh6KyS{mM37
zk>}{ZO5@V|H7*s&jEYRMmV_?KyMxp9jSV*MN7r*q5Sl4W`isSM;~uw-VSPK{VnI0)
zHJ=#+V*LL;S!ufR19aCAv*tXtejVE3js+SUGAJD44)BLPHt+cLpF%$Eh7M`$Y0kWy
zPmWiy=(3w@nShl5S)n<yPCRl{_w|#!ynLSl7DY6jfPjvSjEt66i)_Nta#QTZePDd`
zm$Ri>-*ENX+FH<yFV4^N7<9u52(*12Pfr7__VH2!+-j_}t0zo@laAX;R;K_tE`_JA
zh)CDk`FYsIMF0Zt3JO}CRS|bgtkuRj%WsY_v1~Z=Fj(SlJj8CIpMcDP_9bA^{mC)Q
z@XnOG#(w3>sZgvtHd{GIWQ@L=Ju)(qE^K&kFphvATo89(MwU8G6;<(5*BdEaFG>s%
zI@~bacz=Y<&GSM<;p0uTQ-}-&RFc0k9WmB@d*RQOiOw%lA%jMw{tOirUoYUO-tH$S
z;NKM^lX)BxaSaGukzz%?E-I@V8X|=}Hxe{mTKDdT3xJd%D-qeq<;_7D_3~~rdZn}p
z`TS#kO45<r-~6KZ0dnluT3Rr{JYGiCJ4SecK|%FGzeh$6+h+?96WNAND|Fk!dNves
zSK2&W`d5|;(>DOX<7I^Ng87$g|Fv8Hnop2~qb60my1rBaHY6`EkA#FI`c%ihqO5G?
zy<7Gz-ixETdsy*xsYcE4$Ox4F*?Lzm%Qla>AxJQ?S+iog&T~(u@9E)!$>Vw95zvW$
z3V75T%Zj3<P_nts?*=lpBZo1Q965H4!a;@s0v<$l_G)8FBA2Jx=H^IFPWerMXD%(n
zdgloreFejZx)Lr-g&iV>3;4L|L!uc*#zN&O3T(nWMPE6QAtUlnVpQykMaFeb+OU&s
zTz(ad=x(32bP4%nzDwvlP+1T5kG^^HrpJwu-p@9Gq1ADJ3fxBXtuIZ`S<^>NDEN%G
zUodP|Ti-h${Zyp8y1qV{E*KAz-U^UJOCh+sO&|CSLG+BrRn|7KuxPPf;RV5UO$!eP
zXO{nbY;5jH{jR0E`Q>1?#OF`?RqHw(3rkc~)bHQFU$W`l0qLWl@I5DIYq!MoL)O{(
z)_^6K$DhGyO-)U($9j<e-UAwtD!RM7ZFy38Fen|BY&p2N#z?q~!a_x;!_z9V{(a)~
zT|nO#CQ>dQ%b16e>ae!Zq1cG41}duZ$~rMf_IVr%0kh)-F+iwnY-|Sy2cY_D)S6C8
zqXlBfkE*|`xk0sq_GLN`#qN=yr|P)o;)a4`6xaz$ewUpx?cAm0C-o2W9TC6fU$-xj
z4GU*q`(zhF6)h|-oFHeij(J-y<oPCy{SKPfPq_09SrVf$Z(CJeUEsi{TqO&hRJEYo
zxHuJYZ^1~ZQlqMf(ozR|`y*;XG5!-fyHF{r4^Y2I(s1$c4i65H!@`=jjeBZ2A>>)5
z{$I=!qT|b`O^q+%n1+jCu@{1-ONH}vrF=T(oAo9R|J>o5#2B$dez{?ao>)}HNKC{e
zIJVij{H&LN2_8B5O7iN}tyf3l&W?$Y%kN8TW(cOb^7B;V>Eqqy*47prbQj3Yy>%ac
z%F%?19kkYbP*$D-#0WMvc6D2S?Aa|h8H3GG@%_Vt&#E#xCSc`2BQP{K=L{NzZxu(+
zwoS{AkDjWkvo<YXuPfI9OYo}NnWXW%-%XNi99~;%D=HdVUl>0(H#Z}rrH{4XXU(ph
z!9gY{E-tS4`1rqn{{{sGfj(m_^*t7mX6FxEcfyym4N=mlo0?zcpj9X-nVy=Wr>9p|
zQo^dBto~f;?+;Bvk~YpTe=fEs=||pD#~HkI@5gf6x%JgS>xFA3>uG6~nuh29>mW!#
zuz<qH6rYHhWm}!@1}Zclr8&F0OlqDU9}je?Cm2f`td{Z%3lSlJCe{}dcTFaO?kq)^
zglaf-@@Amo=5_%*u9S5`dC3ys7ZoxD)4cyGbEY^LKJLlTFx2EbXIDW%sNz&j|B_`g
z(xQ}=`DRO5FoZ8C$ETEhC_oY`{2`V*Y1*A&LO~jpElmBG+KgTH;Z4doi?M@iS*r=D
zO-KSfG&qx>hI$X@(-V6O6j-RWc>;QvG&Dh6IX1Sl)(6|z*r$?<C=O_Y-0umeH3XZp
z$pRIt{7~7Uq0-*of??U^<>g=>X=rGwtE=A~S@oe40!;BBp1acG0wZi`c@!!CT5`{H
zdye~v=;(IGaC@h**E{UOro@8Z>q!n&4SD$tA)ja0-}Ng2B(%%lU3rW>g&!Hzb&f$l
z{a5>>G!;8bk#03;JzT73bG1~v=>lBi>{8Uf29yofEBAF@uepEnyImlO$~z(TPwjmk
z?|iDSuNTTo7@jWp__5-KEhc_cUn-5)Nk&9u<Kg}-J$-db2jtho2k+IUyUXR}<@UMi
z7C3~c0gH;}$Hzwn3}Wa5B1}){<YeDB8~q0G2nZ~C?T=hx0pE6QDN!Mr<<5&~6M$1M
zkBWfi{M)sir&VuB=y}Fkstp4<JM%6~`}^aKsCU@H?s$ft$IF|+Az7hFGso8m;(=YO
z(~%Jo@D-XcJ3D93PjyTwc=y*MJM|`Zjd{=p=uwGl!#LhtSMR)z1#vG3f=yv?h;`s(
zt-hb@Dago#G3Zi_$^E!IkT*9ko{z19`QkFhpkB@xpB<6<=efOpb!`oRZz*Nv7&)4n
zTYkHDTpuUu(I<DHc+F?%%=TXfw<8eYq(lYEtKMwSZ<O>esyIB!;G>`;+>`q@wuCH6
zMN3QAXS4^T&ZuUUz8Vf&a{mGS!#snmIUZ%rvkgCsPL>WE5!Mwp+07Fw=|fx`)ifbl
zadGsbIfHRu$Exwz{%-`xH@ar30-ik;*4Tj#`)y?Aoeg@vlJNagNu3T+TI@J@G^Et`
zl12vaPH*j^rE2xn^>uV~<kff~0L=hG1)hCs`b2zXh{;t+Gg?uz{LS0wt19!AF3JKm
zrVegz4s?-ceh-tqZHr9;J3^1(mL@poVi^ydSjjsp{1lMIk0~h;MilAZb?02Sx;wmM
zU~x_k#!XV``%)1WinZ(J=<Hlxu6rvf23uUV>%PT|6xE`uYbE`LtH``mtAUw@X6Lj0
zFcsw!%Kqs<6q(QYX5SVsy?Oo#!PJ^wqpbl*5@{(!FXMhN2=%8+zg5(Zs?3iGybL>{
zvP6TgebN;_eaa~>|Nc^bVW^~}^bVc)TQU87Gt3p5FkNe-!w5I5<jpzY(>5%ALj|yf
zDe$>p6}om$n{t?12Q_OJRs^7>K-Ql}54*a$8mM#icP!~x-~`D+#7xbzm5UBVZMxb6
zAZJI$-8qs`zIWqVG7g`DQO(xS!m=$Y)_gNXZJBfJ2CVlC(J3bVKhZKW&>P(7lE*@c
zw_(B}3p(;de<OD{iXlzPm895@mcFOTCMA@$dcFk0pF{<$_+&^($j#{*-~0D<+uxok
zz%`~G&1hfzha$a<?Y?yR!@(q>IVCLw6u3b;w<8!$Ej799=Mj)~q@{~0D+@ETiemb>
z()T9S1neiX#bJ`o;|Z?<A8vxQo9KItw-|&r4-ReR(WGQ+T*tqp_ZK9q4m<K9-e$my
z{xD%y56L!Wl0zod8#CIMmMxNIRW33AoXpGR?EE$<X~^ai$y-+oW9lr#72KPtms{yL
zv4n(pGr%D5F_qnl4#lGE;_b&l2h~3GhW`Fwb3CKwrmekMnzfUgRnNcElo#{ilZA!5
z{0gjS+iQ&`^?@cDuTu7yc;_j_mb5h57$tf}hToHuMP+4wXXYRf8;5BRx4?ogTfKaU
zK&tpUmWuUyWcPA+rBR6}<%fn=qSl%|9fM&>G1c(~-k(iuVq$GQy^gDS>1ZjVl5DZw
z4M?Yw4kruCN9|}%Acs*=Q8kxZ-rh%A{hb0Toe;<Dh*A-)1RBXDMPPB>C^;6CaE#pM
zxvN!2^bpXy()l$Wea^!<qXzgK_Y}{DjbVwIHI%lkK3TlHoz7QCBO<WZEUSQmpw3Rs
zC#9|2X~04dFh~LUm#av3_`8Celk<40Da}tJ$0?uZm_S6yvEMHM(-HAIDKtGE1QHcx
zT;-GRl}5wLidlaO%}Yto^!N6B%iG%<!U|y3pFor?>RdmWhnep(+qOIz-d8`NU;p+i
zPyZyo&@CGLA%V%pLe$du;j<>}@jNugy%>deZwMB^)oHcghK#HiYxnKFvkK~W=E}6(
z_;rPag+Jd7H0^lJ*(}0p@<JeU^_e9I5Z|7z&Ab<~KLO+r;Xn4bV%Y2FimLkwi8aR8
zj7}6#B9kP1&cx3Tqo1viE>i#J1)%PGetyO!Nz1VP&H9W3sO~Ene}8||icNmUx&W9*
zBK5MTe!h&*P$c7ti6NJ6C0x#=^~o;dkK~xQ_xA~cKE=#kpAUG6gF7d4UB;5liT&0W
zZznHF1+Qz^Yt+<G^KdfW7FTH1|0_h~n3l_xP||K&#rX$ThENay-t2CyonNVwW1owg
zYC1&*Q3Zt2o_PLksH^_s?o`%g^bzAUX?boAFepIZ1zeG+?x(&7bYegK>p%u<gAG}Z
zxKblyniX$W6)nn~e6@6$C&L4kOX*l3DJl{5Xi9HaHr%{V?3yyN7ZIH<?^t7{A3j3(
zEXeQ{CrfdBg95R%vhMNH+p+SHRRdXE{zLce#j*F({c*|H<D#MA@$rmOZ4dKR#{0}H
z(IC|Kb;i@HSqg-lP9b(=$brhby6JIoMn*<6369}XROx&!%zS)Hfqjck8y~Ff=6t&-
z`2_^9U>7P40vgd`hnCfqPmImQdbdb%aY?EwG06&)i$)Td4l6HOK^NW^PEbPK!C+xv
zjUo6+Q<HaB@kdJgN!d}WnU;&^c_N)||37LdqSeQt`xRvV_9!sCp`p<{IX7}8c2gNJ
z5{5&ksRS%9UoiCkZ1e=T(9;`CbD3JZH<~+*<`j-)EWy||Y5$0WCPrT#=;|VWwE?|v
z)<PTcLfzlX@y>4y0-2a8_WWA}U6!4_38X(16civY;@E9duu`S2(TrjsN$Xawo6R$=
zpGypl+%aio1Db{$Q*C~MRL~8z)@fZScOWku0J)mtlJ~^l6B6cjM})wA`zMpOwmt$8
zgfI8)5@7p`QC|P7IdfO2CpYzhOM9O3;F6LKuGH|si|ecgcgX9ZuCCzxt{KsYiZ&pZ
z>M_mLHRLi3r$B#P5CY*1NKNiGO{)ESwgL34Jakfc5C3k9;-=?k^>Xd~_LMsz6yPs$
zrv^Os4T&Wbx#?c!dzVvhkLZQ*@u)g`YJYMLqOu;zL>4{gQf45<UL8G5LMkV!PXRe1
ztLik3YYdecG+L#m2J2fD<o>%xs8DH0BXJDnq8ej(iZ185qy0U)$TuG1>{5Ca<tEI(
zgJ~k)S<1*aZMRVWq8b}(m#=L|Nt4xZIn1ftaBvbi92E{^)D)_i1DzU57!c))<u5G<
zM7+_i>!)!AhCxhGZd?T!gb*?J??Mz5L;RslhZ_prTMfNJ9*?(GJXrV{<B1fpP{K(f
z7Uhft`>zukSYJ4})3C5ORb*@Yrja4oRA11-DP^<f3+XgJe+B|B9^_=LW6kq#H?*t+
z<m|hvQL~ZsG=0Y1#)i4M`2#l#kjHr|UAt6pdSc0jk9Jw^x1xl42|~z2c#v-_&XZ8#
z4IknymK4%)kpo{p^S&H28URi|D=Q1Yc%XFg5RU=Xe}4WJpw2_?`MtwKpRL~hehJRd
zj~1suI;nTr6!5qv2HKXK(&V7V+J((+YH2BBWgP+p1YVGsg#|;>Bg@jY5OmZ43L08j
zaWN5}^HD>E3TnIz2`!V`sU1gQQGir!fCx$B*ltUr0cf~mV`3bgoEYfo>2QZ<iassP
z9q{(pdn{3Mm;2@6eOrAc<l?+!2kobCYWfNrJLboa&M#F#_oWoELy62fVUdy4(NY2M
zR@OK;Ai?Ft#LzP^r0tC2pALq2<qFa=F^yizA(meF6&F8lk1QczCtkp@oQYLd!$Vlv
z+l^<K378_kLo3>a1PN#U`l2~9h$$mW92XC-nG7Xdl&S*Q$~AEm@N}|MQ`dTJsP)da
ze2$M-LTVMdPqx2!=w1-(ogon9uu1ujicgvq@W2Jt76BBKZ~_*5JiOT?Q&6fFoc?C)
znpsm)P;73GBm&AH1;A_q?#b}5u$>Zm=@_Z+2gT_wWW&mD3ya)lx&q#P9h-5@Sfa=6
zk59muQGA$~@f?%W+pNHGhLE32*Qr#0E>f+VWpob8{S^cgj7WtIS$;a))Mmgz!hT!V
zY(nhI5m+jflibpzWIhbMV&v<#bYb;TWh>RjBRiuhHD4&7h>tm_s9it2ktEN=MqF@0
z3zOI>tffMVLV)n8Zi6ITwf^e8{hwir#QwcQ?Xkh@D~yTXzxC=IzFPFRcyG~YqQ0KP
zCKK8oBcb<WJ<^>~VX7-#yjLAi2pIs-cE-n(e#o^|nB3OE!NNld0T%XK1ZkF7!PDfx
zya0czysaZVC1s9iFnZmSE9gH93+af_8%!rD)YX5FFEvEfv$C=6%hjoV`ZS%U63Rfh
z09tEijJD;Dj@!m>-+KEaU-Q|RzXsg9?I!NtzKGer6!N}>dBDI{Utgb6(F_y}yr+vI
z2ZT|#^)|ieeWB@*Zau~Md^CEvmEIpvs9`GfDwxN}Jg!2bqm`D<!BJgZIXXBv*j2^&
zY=aC|vZ>b%kwg?k^Z5nQJ<M6CsnF|KbwkhslzffHjlKOUhECn40}FU&Zqtd(dNOIM
znDFqC-84t`tU*BRcg~{ip-vd{$Hrrn70T_N<CvSv7S_^0e)2SV8S;krZPxk6ar6pY
znwtOQw9idD7Rq_`x|VZ#V!{+<RHwtcP1@B}O)dX2MjRMU-YLKbn2{Bh1bDdUpFj_~
z3yG*Uy>tHJh{q_i%?%x{d<PF$maF|~pu0RSmy!g)<8pA6dp>$*1`&M{^#HH}P$QI*
z-~U|RF{qX5x5-;8D#id054e#M{i7!q&O#lPbNvP@%}&Ro_>x7v0|UWU*GpW5J{JQA
zBNW{UOj<LuvshWaiHV=?kBaDn-?FSX2pIH!$SQ5xR#Rv%C=!_(NDhZy22cfX_Dk-o
zRffR^&dDX|V}B|i!W@Yt$<LP8ZN7eOuy1`ZGVG~6AnIe=QY+WKyMt>7#di|_#Y+?c
zkibN@^wME+ALK@(NceV+kK3fh)l~hf?$0k_FtULWq_>MRG&B_0+tAU`O-xKOZsz9a
z=clH!v$K`KPGx6LUDFv?eFk^Nkv6l=QlY5az#|j_5fl_WlqyV*P$^O5+1EL#v-sV9
zni!v)?7s4KmM!s38T7`tC9DQzWgoTgoan(0r>g>f!mQTIjpJLdI|6_?2xN0D4Gp*L
z;dns7S`92^tMCwi2Nd<YOYcr2d2?$Xr-PXrv|f{%I+JH4s3j<f@)|tH9J5aI)kCxU
z#h=jK$*uK0SQeaeCJlbi+q3$pcKkeculp;>!$<OSyqQ`52Ofy;kM*yYJKz9!A1wE<
z>B17?;-sXdozBXvbG$Yycxet*ERV7dG%H;oxPdP!u86+Op;8PiTgE1u((EP}jxiEH
zEPj^*vQnVDETBs?E8iVGuoe)(3Pfs}n$2JO!oG2~lQW@dacgmLqP}A7Tv|$zqVi^f
zv3dPDPUe4-Q+pSn>8Nqi52R&x=eQYbsvy2VU?!^BeW@F=@yl~_L%EDhtp-12?(h~#
z;-2~UDQpzc&Lzi5|Lx@FHdI4gDG2)dyCifn!B!^5Yk<Eo$OLC*X0$onuye8_6t#UB
zo_uRl_$kaZvg*r)WxwuZ)RafO7Vq7tr)~A_#c676{FTBTrBDEvi^tg#b-)b*pW_!L
zWfc`5VEozHk)Vj~J?#aca+7QAGq#ZP(R?_{?l)d{kYGt!^?m2<vdYVud3YMDswPK9
zEN;&>gmh)|>gqV(yb<~EVe|5422hQ4v9!EJB{b@4<Ew2AqoXw?%Z+xT^!Lw3hA@!N
z4;<bndITz}sy@9t=>_t(V&dYXQVM}~I1mW>@yEqKH(m;v6wy*<aSX5|09a9+q)q%b
za|k`AUtL{|X7~*q3d^5<nB@gnbIXT43xC6*7;5f;k3snouJ^F`EO3zd=C@n(lh;Hg
zDW+CdY1}rtRH4vyb$*5`CgbT(Zm+pSDXLpH(Nb`<RCPjnTz-wKsiWWy(_+MoK64sm
zL430dqoT(94m2?l6J8PS?eCXa8}wv$K_FUOcxgfp711pGq@e9^!w5B^V`AI3UzL@*
zLv_>!gbu)mF72*JsG&$=kKkB3uynuu(|m!S^$Hyu2ggR^CjmF-+OJ_eGU<vUJX}1c
z`V;OhU?edS2pLQKVf*O^i<LForytX?(NnPr?&>-gCD|%d%ia2{*QaYAz3!Gp)gK&v
zg<^*Q6ySi6d$xzy_y1JtITOEYWca!iB>GmxFTY+<OTDm+Hs=0HbEnP<P<(X&K)9nF
zF9D)G>PL&qDGezf|L8AQi@ivvCo71)i}-OogF5eRIJgFpM4SgpzZ(I4J-+6?{pU}+
zO~<;$kD)&XJwaaQlsY;q3@q3nZGW{eb85HU+nrxpvJt5DDNJxD2~LrTpO~C9v$A?R
zS@mWTqrh5>u(5jjPDS>`mS;IIp26h46qeVkIQigP-P$l6J$5LU<-9Ey6O7_y8Cv_;
z*x0d9-`08OE>tM2r!H-dm6w+nkiJ9lvXlD)sj10)7rqScCXJ@Q6OsSB-W8BmihAP(
zbavY63hD?VG|1ua@^nEj-cT{@tRFpFZC|RNcWiMqQth9GptQgF*ss~mtHHUwt-v-o
z@odC_S5VNmM5e&96%<&Hjg4hN7OV88gb=TFxWtw+BEAq-J2X)9^FL-4y^pX%@V#jl
zG;R<bGO`C!^4Kjwr4s^V8L92${7-1(GIRe&pfkPdzyeAq$X<yF38X^ap24Nx0ho&l
zN0Hb!XLz?)&V2&y`w*rIiXJCR28=JFc#ZiXlcM85_-_jmOQ20A@jnMaIe#ax0rtnt
zdkJttx*gt6v_jJcFx+C_qCO=I>~62igkx<8>8bFfN@=KO=8XTI!+Treoeu8-?p}ai
z2XO3}y;R$uuC~eczyp?ms3?beEG0=(H8we!SHIKOy7kiveBcKxkD6yh(CDFHkmz`y
zq=LfU6@Y*Q>;Ksnboj?BEr|nLP0h`Kr+xZ5{{Xir0c6+n>+9$5q`CkA6B9>d18c!<
z2o~isvtGN;0Z-*;lseAYnVI&bO5o{KZ)v`8ZS`@I|0ncnG{YY8L7>|Q7H4Mz;6r~8
zfmiYZri7p%`1xw2U%%c6mTM}^EiHL~i+g>2z3Fgyap8QrI=XEij7rGva-w}Mf`%5E
z!fj*Uq<wC(2_&Fwf(ppnB0irEE`GPEfq~r?mIFP5s?X%B(jri(s0wp)9UCY(3#s|_
z9efaBrTrZm0?yf53-y}i0T|@YomcH4AK$)(gY`Sd(f{qdX90ERR<JX+KhFe_8sXvL
zK}AD$zu$nFQL!ODlFRchZU*4k{`SnkPxzk(Y0&+R<uyD66+Ur<I(EpC>-D0iS)P$M
z>*Z?+1mH<OZ~3RXY@Zm;&1=Osuo6g!rwKfmwPm%pKLaV<lIww(jKGuc!&LwCpFoC9
z?F>&rpRP8KQ_?=cx6B%rE8`K<jZH;&5QO6`E@yLhbO<BHJiB2(s+|xnds9)JYM7h^
zCQo)5DjP=MfSVZh*u>}3ch5p;o$9~@L6RT=qyUG**Kzw-%lGi-(W&?M8^bS$3SYsd
zT15%7(S@OFwnIbL2jm)EMF-s9-#@*a-#<2?YF1%$*}zE`?QKb3U0o5AkhD0u1MD|%
z#i#$kOukNLy_Rfb?hec?Aglq+2b4L8>+|DX8lOvCd^`c;=ZRS-dsq=(97NDhBj3YA
zAg~ltvJO1NM3MbQ>?_dqo%K4x6kT(pd}vpXVd2l$C=fv*`^*09iIh!wSYVpk9Z?G?
zwRAi~S}FLrbQ&HSTlzyPcP}3wvqGQnRopikr+w^ZSY&;kVZ5>Q;6<VzasGGV&NJMh
zpqmSUi>L5ROr{X)ns2Ns48gMd8kD};FZ$562u~__wtB>3w>y@E@>N6@K`cAG2Y#va
z#%yHH+{Q-ecE0LsG=}dunUbo3v9Z(NgV~4()c5b3xH9NTqjoo2pa8Rk%*kt;SXE``
zvGxaq^ur??nx<A(^YAAT-PYWK{CqBagpCGk|5^?)fmis8Y|A8Dz)6D8fBM1E@rwO*
zcr>pj<i{C#etWwvFdtE2Gkvbq52%h{jIU-pK3=W#I2Palez}F3ufS8H$Tn-(6bT2B
ziXvZAQ@s59_vyu3oWgSf;ful?=b@k&GXlgEmV#W$jv-@uN{$x$F{2}ykYi@-4@!Ar
zngMutK%Y|)Y=1@WI^Ge0yjp)MC@(+3SPs>daCg54bvhz45)f>UktS$pXqmZ>`)T%>
z4#45;f{4zQLV}M^gNr;6`T9m2<6oQkLmgs92!Rxc6crWe;1L5G`@{e$9WDgI_W-tt
ziK&chcQ`H%Hwusilh||&9vxrAQ(j<$dw|%59yRb9h(A%Mb5XC@zO<Z5fTIet=Z4p5
z6}Z{xL15*AJ6B7a?yRn6uRO$ng~mkG{{k9nItGT<goJm*w&Y{h+!OCPIsb-#=Rsk-
zhnHN2k!5J~c8d5tLson3K!4}|Rc_7!|KWYCDkWz3H|fnSD%h%grPMtRN#4;gnY_&(
zLXTbsJtklXX9NS|<anWGV0f6GmiBAsWDiJmKuV#^5GW}vC3ZeG5AHp~Mc&@*3kM80
zEOb}j*LYWqJiQL@<0TN&e(BG&yHQZzK?=2C;Q<KP-exr<G&Gu%)7&Q!WW-}ElF=_!
z+SY6LZ@&)#hq*t@mJ66*sLyTEczmJ9*J`cM*44FM);_iTiVmr{zYdLxy1BSGu+2_L
zz*i>+c1~F0^4uozvCrrbVXE##Tr7tg#HIzr-B0!sxKRYArbuWJb~Gj>X$V2`_6WY9
zFf)rw`mn9JeN|LcY_#7F<H5^Uq=WSZx*h<Vp%&p%va&vs<iZ992EY&;A3r-TbNA?A
zP_r;JGz6$3eo_`LF6WV&-E}T>>$R1*gnG|A^-Jra;bHX6%FW^uVKwyh-ud#+bF+4L
zp3fOR&xD6Oh*G&NH6}Xko=u~7nT?H&xw*aF-5)eH7n26ObXAe;FV1)tt@k&X(I)5T
zKWk_p5t>ab!&0%gdQRFTe|>--&kza@jTP2~`h!Ptx{Pn5j0o$W^zLgvFAr2;v-9vw
zcjc1MZ6-S#Tj*-FM(cO>%XzkQ5ul(94u8w1$^7x=jrG;N6Yb4W;F}&`JY#=1XjHv@
z)WkZA5*D7BO&k~ZW+!?jLvK|H84;1S)tQM_(E0WkAs`O#us$uBiccmoj!on5IN<Yz
zKfEyWiNRLohKa+^epkqO{5lgF7%2X0dVtjuwpB`>^%dZ-=H})$=WW>56suI|>w1`_
z5rFwDlbr0}&`%+Fw`#7%+1ZqwlJcGLa2z_qx$7eVOcR?oF<@^W*?EW+med#^5u^g{
z8b(H6dUN2Je|yamoe$RlLy4|e0H*jk;zfP&;ywcuw1e}B^W~+gF>5l1)3ZIPRA?Ca
z&aQCE7<n|Td(a0_9KY|RGE`8Izz=44$VvQy0b-t7s%2&-Pkq*WQS)1{Q3D`N*zP1@
z0%62qr*lRqGt0ZZ^a}WMI`-Q|fUP`e{%fWU;*v{ly3h@!0?djCaBXCmO&j=9iV4D^
z$PP*cX9keJg`4AIae?&|wS@>PD*luHl2cL&<dUYIv!<khSn_3-(_kJEA_Z8Gr~A6w
zzAJD+EJ!8%s*o(#&Ub#9ozsCffRvO}hdv@EHg>Dl@;$08G$iF1>K`NNsn(#U1OpKe
z5O9r!_WI@5<ClPcP9qMyd8O6#5w=he(?TEMvLUEZbVQQ<w??id3snxLGrfRQY3JOv
zMxF6=t>dLkmEoDUIzx=23~}-1YX8$uL6}lYVObP4=GWHNT%i0`7(dZshpz3eT)fBg
z?ApRNL5;;5FsfPV`$+ghddzVV6RDXpP2$CUVHQByFm&QU)a7U}QTE$ya3wsEHuxK{
zkoTkHMOv*074{Z78L#6WX3FSwy3lxM=la@s*Pa5X;*&YG_4TVw4h4XT5fs!dsxMZP
zcDgxP0pc(OIH5kK@=Q)l2p3E*@k<n`97HfJtw3{aU{+rJyyyHVM?jDm?KQfgQ!QpE
zc=0j8E=~^Xfbb$x4zx^k?OQ5(1jL(7`lRS0xFiTCPnNTPTACd9v0*JitW}y8&A)rO
zx@NLCIPB0-Z54QJz2@hI0D;{9)B(<Eq55ENm~x#K_*EbOs<*iXT^FjhN31I90lB!9
z3ok@OnK?NQo4ui@q?!^E@WmxUPl~Dw^$t}#i)iyLWSFpwWn&pa9mXRGz<B2)`2xIM
z0kem~IwUmigGUSAmph~5Uhpd`D~au19>0EneHCz0bNGqE`R&`H>ma9Rmlf4tqobl9
zJ}i0z_0bwdf9Th*Z&P3>lG<4DQwwlU-Nc6<qEg_-yieW3-`T=flW6Hfm4+SMH{&!m
zKD$}-v0AarzCYcTlA?;KsIUQLX`+N1xE~>i0^UY$3JO{#Ph`Ely;M9rw*`)Y=UX7B
z=Ijb9IRexXi<F&Px{M==KscnKS~D+uePnit0S5v8e?S`?%oG92G&z~bdQq!2bfNr^
zyCVUa$jpq!#2Ax(`m8Ra{alGc$yC3an+wnUFiY=ctgfo6>6+$lSC<?@jtK1wO+Jx;
zvLSRC=Ip1C>bf_PmBto@FkJ}zEsvU&hvRS}i#kLQSNY(&lFq?b#q{c@tFvXIV7$z-
zvU>iwf%NQ~xJf<<J_O>uuGVmvee9#Esw$hv+@_~4q@ZK~3dk2iKXNIcB0+?|eAxs(
zXv2k6yr*N}nRp~Qa<SA#5S!ws49NIwQ-3L!%EM+dnSB;#WNOL7>7$#n2xb$zG9;s*
z&^sI+=kG3eUD|s9Y6DD4(C?Y%kr7bfB$bq+X+Pl19|e#88Z<F+Y7yhi$Gt#A>PJMn
zf1knGAkf-b%5p_kX|vXT?elwWN66cvL(-fs;shf5`1kd>UdC-nZSB=eYLcV`YP$Vy
z*LGFTqO!f!O7ra6T86UnH*PjQ*R#xuii+SLv8r0MAM?f<I4Xe6Ydbzn*6&e>0xOF(
z>XtcBQ5&)us7<U;nAh`;jev+K{6@ar%2{i(B12Y1WhNrF3qS~K?ZCI`ih3PV(gwgI
zo_4xX(eZrYg^h9#e|FH<g0j0*@C)P=i;P<aHnz#9DZJ0MxUkTlJMtMHbsG|(u|9qJ
zWOzTou_pw{&gpDrp`(AlYXS#MQKqJz$MN^O3YJ_cOt{EPeA3!Y_QuAyUaVX}i7YWM
z$ooe1r{hB-Bd;rs-twUOMnpK&n1GH<mzW*8)`^_uW<1dj=<OC3q1lC-QUvojutx0K
zO|Eu7VoORGQbzF+VRh&gr_ur8Rr7Tm3{C+0*ySyIQ+vB0D{EzKtz)MzRE^0fU?;>j
z3oP+LcD$d??->*B+}h}DFDOy_nUdnODhe7hFv4nRm;)wT3wCKU@W{x3p?*wXACWAK
z_|hJa){Wn!TJu}R4k@7sJew&c^{XC2N2jTRjn=j{O7#i^XehC4ERTAZ$CM}DO{rT1
z`>@Vkc|4@=qyqP!fXn|i<GH$eml}{{(7}vk7(NX|j-BiCUmolHCTUmQ4gDLW7U$z;
z{o)c+i|gvr&9jZ3WtX=&G|g>{B|pD>L4*vW^0t##l;Pwyh5md_h<nDrliWtci-+~P
zKbQZdcuz|pYe!`zD;o~_y<KtMu&^*N)kaNCU7(P5@Akfd%xI=iX&)aS3?tH%BoN@@
z{$tScWu!cIz$}R6d?i483~P03Tosj-nD;-Sz&uoD5x69OSH>5|axya&vAtQSsl|;G
zxPfiO)U>jGb>@wkOvA;UKoXbLA{bZ!VhZD2`5hLw77R?l<3tMB%;w8oqF1k$TitMK
zogk1av|-T~$`QWsK4^{im?JHMJ0LFfI7%-8>t`@LX}I1%sHt^&SIW@jQqblx-Ee++
z*}`c__3I~z!&9-R*tf()^(6}<adEi4J-^S0$w>tG_})skpv$CjUZqe}_IAAb0gzTB
zy^NX8O2LG-{4s$h@5Br=MA`Kb+%i+?(5wg+R};Bpj<@V|m_bqBcB@WKPSQF&q5W(D
zxt^O#g&jKcW(szzBXmvN_HkLo1<@avgHK$2j@7`-Y(xIzAvz)~G<xu1!~zx;7U;1E
zh+ajt;Ej=EFEqnP)uM7*rm??!7pRdG=H&?0sjc)Zra_&;qJIe&z&ZOdKi7(kgS@F)
zrh73#A?J<T!r2a<@{b!c>3|O+ql}&Xq)KYk(2ucMn7wz&=@En|K~uDIrK9bRSx<(I
zwY4)Y7gRoyEi(8X29`&_y6ST;FgZPq^Lu-0Ztl6A^!{r9vTq*Wu4}1&U<ibb!5BX)
z>$m>?tkFm?0@qmKvFr63%t8_pE=)|Yf>A3UCy~tQeI1#Oc^I6Q*zL7vJl-^BU1bVP
zM8F(JH;5Vxvby5F2QhJ-2#Tl9Q19SOLuN6uItz*f`bSw=2V2{{KXl;m0G$pht(BlO
z7^z)fM>InI@m7Da0PxM1;lvFxBVF^j5dFBr6uvBn-7%|_H(=J4L|fyTk%gd;!<G1*
z(aG$%4;GkZR}1kEqla`NCU(4IKP<CUFy75A*M2Daj^yvF2$7e6B9-VpS{lJbK=la~
zdcN%e?*KB7$=~7fetuagDNnxA`vRbIQGhN}EITD-DT)Q*y5HtV52Zfu0;JB?ybB;^
z(E9Ol^DWY<s_8)~>+5F%W1^SWQ+9L@M${|My-_X0_er1+9Qd@lz@(8iOCJvNKQBOb
zw!Qw)2V#HQ84hD3qX<XGIaG%P_lzKmLV_C_6}9b~8vLJEcbjw4W@bgRbH|nTXb|CF
zIjY1v-GL}eq}hP$oRM=qu+Bt*pyZ^Nw&CB`5iNAQA*ok^kb?#as3@Q+Uha;+M5Abc
z(ageDY&!bxmCBopZT?#k_pM%hlol|go12^a{Pf`A?(U4~06Ifp*Z}}6s?-Lyq3Z0!
zN|-Em{M|Gdw*pkw_5O75#kd3S`}b}to%pt|5zLBGBGCHm1i&2Y^`Tdn&AT(#%7u#C
z>#{MSp`Gyzp8+fWy@Rfm%78_pcP76~U&;Us^xpXeO7=U4onIBR=Se_;tD$BPQ9ut1
z12mMsPkG#gV#SZY&y>ilok*Cz4sW8`3S0(|#U3`TRuB?L(geNk9S~^OFN5=F^x8e|
zT5x8=yZ$%g<i2%dPYX8ylqvNOCFv2kkcbXEyZyhfAt8~GOlX4jz0Dbz;$Oc>hRXc{
zR)HlCL5g4aQ%l^vE<=dHFc3OFy1nT{8v`blM>W!q%!wsz1{AO-!Py^pym1*B-@JQT
zERF}Hq!<MoFK_eSl^vOf1#ai@&I$@rg?s1e#_O{?PfUWNJy?*_Gd_SU-__l>P6mPe
z!0gD7xYuBd{Q@@Jnql?&*aWbcO*|(2A$jQ)A4i`;t}_6}{Q4Xyt8WTx6Z>iGnYvuC
z+4*?!GG|#>x~b=FbaeEl<oZ;eY|9^K%}<|d3<tggqyszyK0ZEz8EbboPNZRiRt7rs
zFSp;8=q*#e{leFmf<VB`Ds$ib6%C=<9VG07@7lxqC!jBDGbf5sc-`19?p{T#hlGcx
zT=C%NyFx=!1#q?mR=T@4C3$<nAk3Xky$b7dcxYs#mASe5Q0ed3=wf3B*yfB)c}GXb
zZb7x|2@ELAunCf-28NedEMh$^`TGazH+(qXK0t)`QQPBnEQCsY<OR?npuc{cqV_4*
zfg=lhbIUk5)TQ^e6`dp~Aox)NRj=wx;At!^j*Vm`oWIZ`*NbMI9Y8xeQ5g{bQnI7v
zvcHV&Xn`M~c7$T8s{FDFZ%P>VomgL?np#4{#n;b+ruY=bfcDZ=pXS@+mop8T%ii`L
zRCt(PY4y9uH?=P=Gqz4nPwk%n-W;?S_%CEp1D8w4SzaVq$Sj|awB$!IQj#J!lWD{#
zB1eoJ-UCC=r06;U@MkF6Os#PVlg}U2U@Knjl?s9DnZT^$p3@5b`KTj-nG|U0F0UR-
zZXI{~I5_k*#5_nrFX+eu>_=fqr_0+$a|6^tCfgeka$sP-m(0w<0^@Vz%#$!C^4?5j
zO0PML-Y0Q)Ldn4qe8~YMym;5SCpa3DiTGS-V4*G|d}jiPH5Vx=I%fZT2>?bQd^abg
zS}I_W3;Yieinzyq+ik$+3Cu-eTwF|b^^3J;pB@ssfs06|$PBZQ3<7;~N|KVoFeW*5
zsYn;MvNiC3X!;Ils{jB0OZJY8jK~hzvNMytg^-A{_s*UnKFOB7M?}cpA=zZ_y~*DE
zpM8Jle^2MsIn}w>eZR)@`B+cuzjKK1-)-^-tdGMCjb&_2K-RVqPTI-JEG4%OSph<x
z<lQDd4goY#lTOWWwhWVSL`G`|e*XMy%j2c#TRS}|iZ6S-d_K!QFA;GM=R0LN8Rg|<
zb|;)WjA+cv18NzqW>7Sf@?w6%O`VdGGW8=2*dCkw{I*t88p7UvT`j@4il?TRXUU);
zO%rh^e5M8@v)Jubc~Ks)d%d;Z*VDUHH22te`0fh7H2P-;XkM2Ilr)-%UZ;zmMiSRh
zr&Tww?M>p_p>T6|ugqcT)Yks|s^ji~<4<*|^sLTu3YtGxr_&OKZ$Q=P`okK@+CX*e
z%t=<MAvaeBjxK51eYg4t1e9N%SbNkuG5q@V0K3lA>F}*y&%+N@cSmlzj0;NC3=FP|
zP|y)QDZ*oIZCN=v&+4~tn`GSG&w;@Kj~^)3KzRz{p8T(0Arhe%DjEicoc<;G6Xnzj
zc01C9#I|83p9j|!7#Z2wPD>pT2L}$%p0)RdWM0wac(&|xtio~=yj|YNT(B$o{4rFW
zFkr)paQAk+?yx#xSPU_rwl{t1L|q$xPfe4yvzCq_Tjya}N#$tbW#h`PhPbbedSgNk
z^q3ScLYKon(+zs{dBOh2xqc}*)^o4_@;@*0RpI_q9zNm2#I;M2G<-BfZr1a?>lN+w
z3G9QzIHxVBm2{jHe!Nsu8@wca?1#9xfO^&P5XHF6ZGF<Ylo9#Ya?uz53g{8=tRp}p
z@pg4BT9zK%Zb~Sjp7-Si2ykf$Lydz7(VG!r(u@7py7VN(pKX~w^Q1MVjg(f#JMCsz
zyqNnu3r9SD)(ImM)9uaWo~yN$c#vzatgEA*e!meC4a6tFK?ZVlkR4nf9E2CqVy;sl
z5Y_KR8V+qo>hui^>PifKmQI>{CB~TWXGnXBeO@1D<mG8w3s>y5e7w(75)^qyqx>+6
zVo_0XvdY%*u=BHQcE9y=ENtwp>6$}8IqMle7S*1HNYhSeJ{qDXCC%G?s9w|{#;2B{
znYx4-8LLGf{&RCVDbPra^S+-4r6Jx{mRB*z816sM0_uk<7{!zQ^mu<?m)D&8Q`e_7
z(&o1ohlM|Zf@z*b`a@AVRq1oGtxH9Shv2;26&BngfN!Q3D@oYrZwJPE(On+VHm`>d
zsb>_JIkK)CgT6NU`Ws=PKTmY!&zb0)E86Utp7tC_mjJRKP7pFbak7(6YG=I)FnxhR
z2Hbda0H6YG>s|Ds4Qx{$-k{n1^-K1c%c16KveFVU0CL&iZ1l~4&bHfl%?1gTZ?D@v
z+*TlE4Vpp!BJ6!75V_Q%ruOpkd$?y{=aK;(K}!b-B7M#m)L>&Q3uoNVpLc?s^BRo0
zjvB-1u7(2dZKP~$s7mg6%116~qqZ)Y)(bR>Ul@`1r%2#0JD}N5bY%CxLgC`%JUb8~
z)YF?1ZrTxgfAZCwXSlhb((<FW3OvkDv8aN!zP-VLZr`l+D&1YOCDIL8B4jo6dkmuu
z0D1fdHRj}G?IsqQB3tgXa(4C%YR<uJL2yCMsuWtjY0e<HJV_*428!@w;_yhZK?3@t
zrZ%AA4x;%=Fo^3NCr)(t3J9b2AL}8Br+qwvwNv0a9UC7HYgLqqsk6WT+YReBZ-v6M
zVe(t-sp@a|Y=)>-cvp>E()05mQ6964m%k1Q4Af7pBICr-ENtA;zCK?|JHEeDfF-7{
zdgLnjO|#+Nz2-1Qtf3{e4f`aGq6w)I^#X{qFoKRPf7k3A9p7}$=aiHN$2DcpC%o-R
z5UO&u5jTsMx6ZhaC%pt>EuT$M*4X7v{CxhB!n%&4Bwg9sQ>~Z0vpIKwGJ`9n!Q)Qz
zARj9!pDmv4_m)|tZ1qpwfA|u>kg@o<_#r0-ViA7_(q9@q-eskx&Mvm&+TBGT%rE-#
z#Aduyf+hB3eNYN}C9iO3csSt7lav43WMs(?0D+@dh&x%&VikC+r5DBVSas62+94T2
zwpxen-xWn1`Sc!X@y_1iu!`8o&i2bUZ{85yy_>Pc{Haz76Fu`0#MD5s3u#JV)Vfv@
zQ0|k>qh};w`Hdu=^N!{+BYwIJ2}|1IUW821wi7;~JphbL=ZF3Jq3BV6x4-Ayipd-;
zkVSnsIk)(j<^lS0e^l&}Fox^u<i~nH)=%nI`E4P=;fw7wa**-mI9IHt+f;?Q<NCnE
zl%G7nl>_VXP2Qwo{ZEb7WaXbg*&Lz%C2EefIRcE4@>eD}TV2LzTaKRNY@1_~QOQEi
zn_=<%N4waF!?t(O7L1lYQ1trHzF44<m-?7(Q#prCr!+kY&+}ImHUs4c8L;|B?hQL8
zl)n*D`CApOAT5oOQISC66Lq%Ahd`t}-1(!mx~f*xTT(j&U%EGw$}5jAcXDzLh28@R
z0ko%Up5lv2Kd6toWx4l%#VH{;Klgt9@uq$4>#K%~&Hg6;jKQ3>M4&nJ>s+iy-osO*
zrK4jY3{6X$104y)rkoexChAX`x&uBa`7UXY_U(*)OY0l!da0`m*S6xxT>Nl+L6I5d
zxA~N!d2BI#L&uz9|6>6<MWu21XSf_O;qAux`p0s4toN`U(^I}b-4&h6AaZl9VJ9RU
zErti1klX)(_W|>QIPW5Wv(Zy^crH!|-!jt0hYN&<EuyL{ezGx@do)|)#NT=;eQng3
zV_?%j4Fmq2-x2=QULvo_NbcVkJYG@2Y1`hmsEXC5jq=U7i|{Qrn4P#ia#3T6^=e&f
z{$1JpT7Rat2L3bd5Z7{D?3XW>-ut-0ezDF*Mj3rXc$MUDDRk8*$_~PaR<2yB3q2B|
z<)n@UTJ$4{BCrrn+;W*;HzxUfd~VO_o=dn3F0VvbFQliKcDuW~4-A8Vx}~1wp5?+v
z*(E<hJZU-r_+?LSMD17bQ^pHi5#r|Nk2iLTxNW!_6j+SNR+pELM%uP_cD6XHLqeW=
z9^L)Bi?h~;N5+}HX#fP(Yjf1iou9KQ|GF2#$cw)xC&30yezANQs597yl`k$fAQK5X
zCSESC`@CkVR2Qx>k7Dvz+?ZI;T%YZUWo)2WAwsCWcJ6d!6Nx!)ECYc%_RY{0x>5M8
za%L7#9iIQF>pYGN3*w)H;L(CYYX%hAGCR|#53?4!mh9O_PF*A#wct>o8#*IB^x1T*
z)BI^{fA$m8u?JU=0@yU=cd~V-QB^kBhIJ)hsVXOPGR;|HHvu5|?}6whXcqt|ESe2k
za(n%HJ|fKr)yr$V^ya1m!_v}{DmW!E@qD$HLPZ5y6TQRde1)W-OmlH^;*IXwhAR~s
zMEFiBqbWyr)q&IT0CnTqg9AZu`W#)jV+DTnud)qrc(pfKSrm*PQ}CA+xuwkEhS*O$
zw-pI-XZ@!M&e+;TnXZ*FU$Nr4Q>`>@i@gXq)5MJL;NS*7$Is*12q$@5=-!;<0@uvh
znHxHK$o%~4<7bpRN9yKw|2qc*v+kN}?sfBNdbRp3Eu)|2hhipZVv#Z04l7R<lg=h=
zD)JTvE_H)$pwj*DEu5kN%-p4uS+~>MP8EGmGFy!r`S{`f#Y}#KmV&ldVxQsors2zx
zk!rAUVl;W8zN`%XxmD%5V>MfU{v7|DcR>Jf(e3Ip4v}TYA8-*!&XG+|A|coeVh-09
zzLh*`WF<`dI-gcDAImbkXXBi%4%<t@L!b2^o>7*CC=yJ%+o$*cW<1k(8$3@SmnkA*
zUN}=uS74F7Ll+Z2#GXR8YI4VSXk2>eop0e?TX*ZL?d@%NpOP(R1cD^>DK^EEJBaEE
zL7-!@E{3j9!H+x@<ABc`!^s-{TPb#-Q>FDH!q;eibQiQKz{_533Y7ns;&%JVWe1;9
zpwwcdkR0u`i3!Ty0b<fm9@X=PG|sX<u1h86P&r1s{=!aBJx!XjX9dN6;=#wsl`S~&
zKE<QwzNLvhGI^4zPko-65qQU3KtS2sEzRMWP$ENh{!A;h)k}vthfB$d<~X1TyVYM(
zEp2pJGQ$oaxIBl$O9vg09}cWbh9xH_E2*Tm9%vy-#Wqg?tvF4Y+rJfyPx?V!)FuVk
z1SjX;&6r2;)OW>oC#W^@JTHn^wV3-4&X|6)?uOBC?w#F39{tjeHTve%sO(RKvV|q^
z4W~>>^3`^D@?r13>bwfMOOW02?-P;*E=ta3FgxZTBj~YEBcuuIyJl<NpF$z$L^Y|s
zJ=<^zRSg95Ks|j~p+~H4-u>&-25X$;A&K2-UqS%-rbyq(&XkJtCh`L+_V|0z+9e`(
z>AO;1#Q${${APWG7A*2W<+?w`C_^KT!!J(x(`S+0|GfC_Z;IhhkZK*0fsPr#cK*Y2
zC!vPTu)(@h_4tnDZkypBKf831$Plj)sW>k`J#ZxmDZV{NUgm>B(-i_K!oskgd@)Gd
zCXgT}c-c1iVf{+om1@9@mph8N&LQ+_Pb@(m72zY|#ywiehxlvyeg7Nw$?i;1uN$y3
zB&4M8tgV4fKMAt-j<pnkeuU>{)fxjgpi1!iR_JTdrV5{lnb{ev3IMDY1fD`I(SM9H
zoD2$?2KU2UgF2VWLaUQO?Dq%DT})Cw9!<wGZQa;0@}{z~vi|SU(^czkqV1M{-9-z?
zXk_K!*n>k2{ED$vnp!)VgB)Vns|PD#0YU8)hV}mVR5M>O0~ch_Bs?hf<QrIwG)orp
z`)MXaM(sSA25`{TIdtT>ekNzvd)-0oogrgC*^)3CtXbo@9G}>(K24vRe{GEx4H-+6
z$LN6(0c(jH3kz`doNY>}YiMM=<8amr9{-L-*;zCrJ}wK?uFD%A|1J$*@o_=>p>U$k
zTMeF9hiv~|n^W!JMQCKg-wv2xu;Vv%cwdEvx$XEtdJ(inZ;XwX9nPDjLJAqNARK3P
zwQ_4lBP2vKr*)xF+%5mBNGgZsyqoC{B)MC59=-$H6~nctyV-K$nCR--0?IXMlJbg(
z8cL?<?uObc-s3Ui86jMY1x~I<m6g&Xj<4r`MbTB>9|Q{U^|Bc!4?54`8`3@>(HVrD
zt7}LgE9YCb`reIi{nkAbXo%@1A8HYij37FM`13uGp5Fd#!>`3F97O|D5@R$GM~bV1
z13BN4wo+Q8e9k{7_63_Eqk2JPoYvJV^&53Yjk!g|hhm~9q(>X*Kp)f(JaN!iX@Q##
z0x+zneiW~XVEOYtfjfH>c}Z)4qn)7K5DB64GW_B^`UsalB@)gO&QR&vQnwrk6g$cK
z4v0sdO3QC@6#oYw$$QU1zrDP+W@u0e?Sa#5t#eXjX{wc#)x0}n{<~Bj9;;TFEul=I
zgTsHXVds03pE$IoW8<h<SUEJ9k$jb#k!zCGE<ASbSNuV`hR4Ak-PP)ys#(){w%2r<
zdi0LB!pXl~LH%TR@J5RVWstHW7|wGROMzeaH~w^!a)y$9kTzZ0{h8{u2ir9__lNm#
zM|=CT5WlXK14tSYGpyh2N`L&aVjUY-DM^(ovJjlguDjEM$ITLmao)ww!K^Pbt%I7c
z#<fP8M6c;WkE#}j%0(J$-^M=*J&Mk_G4m>EA=M55bE=_U1ts`0j~$E;ij!;`wWKTs
zDlm%|@*nfI`ThBzUZAL=!f(vE{U8rIPh>cuH8=Satjwu^RBjP053aPRvF*1_VZ0@z
z{5s-Hp%vA&48^F`;K)X$F(|QPMU|sEHX>yybu~XXlNO4J8JPW%3%&@bNlvn|YhWa3
z_|~C7`zThv7cGotGKdgwc}tpBO-E;3l^e%mB3UsD*^g#xzV~!pI%Bx&pIdF3Kb<2$
zk$k-M>3q+9Dn`mV{Qn{0iBQ6WixR>bHEn)~045H!-4{N~ClKsN6rDR(o?B4IPGIwh
zrGeod5&~@?zBkD#doD;fJclfbF1TN(0a<@ieDS!HDk5{9!k_nEj2(@k>r=+J4VC5;
zM05yd5`|@{V3nDBatXpsk}O}En8K9v;uRQhxWhT3$LbrLAteNCvqeRgz&O$w7M=@=
zJlF%^9<o>f+=YiUuv!2fp8KYIkbTLKCaiF_Qw4Q2ax-j`+*c}r6Nd{*F0)^C0WlkW
zD?7(SInQlanv<_NqaT47NR+N<B)4UXuO#QgvWT|(NGUipHFXJoss|d38FFsR6anz9
zv6hB8%w1VI644$1hy87iH)rd=9OK3S^q);0M1+Kk+uM{bht7ILS)I=q9DTm2jYKrv
zTx^X|R5g#`+c1JC-2*s>Bu}&?ZtttJBCiX-@{u^hyqUSW&Y>Z7%e+XBBNWLF1On%8
zQ@MTdMF)^8e~<4FVs2BhYyH(^(W3iyMjzk(O}{2nljZVgLn=qznM&DU9zn`-MiC}I
z#GsByCn(L(?=vGDwbf+)n1(^Cws^J=zVsrk5FLo<tF5Gx`6N@K)<peK=(+brg1g;+
zHTo=S%(lzR>~i%=N86gJs`F;)OfQ(9%+#EEI@#5`3hS}wUB{S**v`1Px_;{_Eh{VI
zG~yngZ22{?ylkQ*hOP9gySwi9Yl-K!c2<~o&_QF=@B*JSyd7Ly1#F(WdJlVd7IcLW
z%}rG-q?@q0&}?Q<5X>yw?de-jrkd?Vcz6zhnMo5CFVGAQ3W`v!AwBuqm9LhSt!{p;
zQmD!dTGyAFnwqy6jacX(Rxmx@HsE!4DaFg0lbuBSy>f7@D<*kc<GL%J);K?nH7!c`
zMOW14Mjv~hMs_LWt-z;EBYD*0-wX$Bng~_$FIXOzz!6J&pKOmrNlAY!{UAv`T9^|a
z^)NsY+4rf-s3~$wo}oH1*0ajFot~bad8H@g!s12`6%6dZ7XVV=Td%KL;4AK{&B1nY
z`RA$(bU>%62iO%hckcHq_u<}3K91<WMurj^t2_SIeg3~to08$DylU7O7;eX#T0@07
zY$TQola-ZTGPttr@hS<N3nCFno;OZwae$nkA0_0eq;=dUZ~H`psAi0!XJ8m9&&BvZ
z&6zU~_OI>;vH>wRwy#gKye7nu7Yh9sdCTCdM6s%Vz&|}cuq{PB2H~R;R+I=tq*NaT
zFv`8p{ArXh<OM!i5d2m!f2Tr#jg4qC*%2|Fxl|4P=^gii;%%?ncn?+r+t^zFiROp>
z#m}Us#1~GRWqFC=q{aC{{_;6V!mhi19~D2zp4<u@XJukT0$2^t+n@WW4eJ#_5seqm
zDnaBzt>OJZ_`Q%Svshi#9*7Bhsq5Z!;wA7<_w-1xTzd`IT8NpMa!-KuN`?5IpRAil
z*CIF>G#j+Rl{<MY5V5@Y2c-G^Q$pge2@qDgHC8fegL!;kA)Z&$Mrh@5%W!#fyzI@`
z`%Ce)5(MYFlcRwFRi&PNdW^s=77bh+3hQ+K-e1kzO^=sA;n7A!_@pNV6|4QElnX7E
z_}Sg%%V5~LZ?eDQyxi7Rq-`nCEcmaf-jwxt2P<eXBZyDh3YqX+Lf@*X;ytTM>TIcF
zUTOVRUPKhMAzJ9b6o`tyYe~XTEogn)|Lb_apf>dnxYywRji`@2(>}kvBt5?zThRSw
z!maAl__-^sNEFJLD2?LD)<3f}(YUG9ukN`P+#p#-XtD(TM)7a?Buk&RN(7W4QLmDZ
zVHNqW<-3$|(Bp`zutdp<?_JB&rK>_&)W%53ACy^%+U_cCj7WbyE_<iJTn4T8*GN-P
zUm_2ah)BI$Q~lk4)#%duJWUquuR8k&L(jq#rWnFW1Xt^`r_V|4^v#W3I{VZf#$D%)
zp{5|wb0d5sil3=9LZA{<pIs*0Y5lim<B-guN~%In&^(^dzXlxX-jthgNn1G?Fk4-*
zMd|NstKYu^Xm8{JL|vg58z3Q=@1?6#++qzB{!`5!H~Udwq5fqF9%2R|=*Z~vmz5SH
z%dUGd^5BwizE%Nvwwl%n=iaiE%L&Ksj%B0KW|6w|qH|DtDDqvzC$8=1Ub~8K?u37f
zxV5f_9|-B+59A73?-#l%{P@THL>TTNC+cLpi213i1iPmtBL(H|k_-9%gxYAan~UV2
zlQA*!UOZqZtqhR5d|sk^9b^Qs0&X<pop@|i)ay{PB&`z2)L}iYN&z)oG5XoRu&T*7
zXgFl0rW06XqG+#7uFWi^XbApg8G;B39BijMQ`+4~V<h427l-{~aIlYCxq#uJ(az*r
zuSLsZ^sDab{u@w&a^7ZuYT#YK$8%X)13lGy{iMi<yKTXn_N@`P)|+Dk#1f5q`4#ST
znb%kYP%c5vo38Ee%7sQ-p+efIF)=T1;hl`Cob!6+)RsJs?e(d)z^=2k-YFfPFHm^^
zAPPkzB&xtWlmFS-DEhL1@CENKWMyn^ZGi$+65Bc-H27C1J9@UZdr7>i)`U5|)K5L#
z_7@?rvM4=09on-%8EVv9M0IriK%p+3ZjqXlQdx!j6B(Uj_B|_eK|Eh1oaOIp31oum
zk7^^gSXIp(ttE4MEwOv;Jey#JxSlpY&BXGNcX+`>X=@rrRl^Xigj4I})O7rW6k#GC
ziy4iCD9P=3@8H0EPrmokmDhGwL_|d2abFu+;nm8Get?#rST{D^;#<f497u+oAo%s-
zdJlevwr@PXExx1dT}AO5yd}2NtcfC-rDvLgDk%{crMibo5if_6OcDuD%r#Y8_fymh
zH23RVUt!$bBKtcL=khPa_e?CT=KdhD8a=7Qe=$?5!5Foz>^0s!>T%gnfu!+nopYX}
z+1}H-RZFJ{6Vcpv1Lp@QS%#cdaeKzc$z~gZNFtky(pAYXPq!I2Q}i2a@_B2&O8PA!
z210w2Y)uA2<;I-#F%BZcF*{wcI_)XgFRz=BYzWME-iF)>hO|TF*?Oax@bX9DYJ-h!
zLqQ4<09loncbZV14h>Ij#JM9UPQdRc+T6Oug+fn=VJo@qcXMcKxoPJZ+dLNjKx-Jw
zq;2(>T~z-?$?o=Kl!T#;^@P%Y8fi9V82+9p#S3QA748LJjy6%f7gf;E)N>rpd#B!<
zJmBT$*Pe5RmI^e={QOmsRKniQ$sAu7267=Qv+t=7lGS!?qvr!+;^Ps%LM3C|&b%bE
zaUtC{2xb8j%3lc0;o&I{!X|}%qxD{=QNOG*f7wnq=w2sMx@f?Y{_%YDFS@rUM?!w1
zc4kB`+tl>P<pkg`GBJVd0uT+Bd_9E>{U;EVP8j;K!VDtI&i;GgPlz|Lc*)Ih&UoCx
z*KCgz?l@D*HlF+cwE)u{#n1m?J5?FvasbM}Mt~PPJ3C*^qH2|jjh}ca$b6HNDaFK^
z07Dt!8>;2u<z-`KmBM_k1g3&_Msk8@=*@>c&DGdIR@|)nN(LEQs%>NpIbcTK5vL$`
zjtq^$56PMB2_wX_mW0P8Nf+G@$%-I|26%6#)Tj+Of)UoUz7*lLot+tI5umkv_wL<P
zemE};Zgys-aet~Pd?X|!q_h_$G2JV$vmu<$K-k&%ry-4~I|jlbZ*;(R9v@e`M7#=Q
zf~-vDye4o6<cSEk(tM+P?<1y^<E?pps0Ta}$ft|d3xFH7_`a2Q7eb{2)Qk{_$CS6T
zRaF+XYWb>G94w-LAC8zQW%Y4!ZTA~x)!xU*R3m@H0K)7X8|Ib8MF<+tY2*?Un`>!7
zLV#UCjXw)5aLG+sxvyXBc3M=}QSghC=e|FD7cWG`8KeLCn2vXMzPVWU>XpYFEkAsn
zP+_SO>O*r=Y(nwUcj>HP+H-v!H&}+#Q(98ezl3h+UZNtTdkZz>JG~8h-IKs&T&6R2
zcO)n;k0~EQhTTPaPP~lDs<_Xu>LpJ<#@8wQrWcqElbNv{*?#G$Hy7Of1T<1$@S<?}
zO(M~VOP;tufPu1Eiw#hOgYtLS_n4TNii`k!e1V={YpxphLuMu|S^$^%P&OJ4qK#81
zWp<fyU6x+mS~wBhtxzv~S*_2+d?q@BhG^Erud3Fos&(CChp`3f1<-GRAkgCx!>~|=
zwwSa8jVwBjk(<-mdaIPNv9XB>edJOA8ZL-16jyfSbG`!~Gd^BDiTEH*>nCpJfIAL<
zI6pLP7gNYU_#t%I+^+Jqie60mjFJU&B`PX4mBLPXKQsEk;}q4G;@Pu*a3!obFSM_1
z*mXC#iixx{_2;7(T2Kju)6pG%$d0Hv_aTn|ZBL2NCp$uo>qR~~#z>Rnm8B;PHmbzE
zyg1&vjxR>I;_Fb~ZSsgcS!;&Bff(llISLfGOdiaZH95kDN>I#|rAJ1T>GYUzkMM)$
z@#gw;4$5>$`YODlOH4{qXTC=sk^6PpbU+-+{^+_tO7w)5m+u823tBwKO>^$S^?xY~
z-6mF83>yZiQvEl2RW>QEjA&+m?D0;(18UykeXGqJE4%pWE%gVy38&4E(a{<!0<=+P
zGl6M04;G)(-DTRFoc&>~2l5w$EX4To2~#Tedi<t;f3nTR!*j`uK=$)patpXU=Zz4j
zVUGPh+1N5ONnQh<&uXljz#r^%G9L$S%3s_T@?AIftT<$p{=LS37NpN!mD*o{$FYsZ
z1#~~K{UUc2-mVa*GJECBZcoNZ;M=|xk~PG?)2x+-8!k?hZ%AJ$v;^DI=v+R1=(X=H
z+&IF{o0@w}YQiYU5;^L8H7YiUfmZCgmXWKVh|TFDbNU4cu5E|wBPBT*Is<Y_%CC*^
zfy?1yfesM*VS6Vh%{j;y7C#mvcR~BCoTrd8#8_n(BTn<j<)Ri+4H;7K5UjP%R^_Lq
zUkXCK#0+nhQ<6JWd@JuFO7{kk#=Po$92}8+(Sx3`cgTqSu$U-IoT%NIf-^x!un$<+
zNwTIrzPavEmdB5kuK8b9*-(9biH_4T0;I_kE1l!6U`5JKaL`46aqhIQ#|xJMJJElX
zf|)l}AW-REA;;KJAj1ikrX6%*w6?K%+Fi4f+kWULel?M!^)JNZ{InQWo&+$tQ(^K;
z=g7xvI_PNE+HdYqKfy-O8-}B|l?nx3-~LC9qrnm@<UXooKgb;2buYYKiG4e%NfMM?
zB!!Rpy<v?BxolU1paz%XNkUQ*1muCU`BkH*JNVMzG!*l?a01+$JX@Ox83ED`vAs$5
zm~KPuzaD5fleNyQh<fj9@{dXg-@v6Q#p*W^Q;6ou&j*!>6J;GT2wx-WJ!JrK+;uds
zzSm3sCnLJQeg2t&>Q7bYA<EvAcTz%JY^?dZ?q9chh%5RRld(Qv1VZM?M54a_e#7#&
zN<bWf{n_%>(_>*S;L9F)Hp=G|9-c5zeTH_`VRnzf^giJu05QGBd(s5AeOz_~{+{i7
zX^(*n#v<Qs5_D;A;@Q#q@c<PyYVlM`MwUoo*9^(JTQIfT0>ZHFcn%=Dz&4~HYR?qk
zZJ@7jvc&Y@Vb^6>&So2^%Si;B%%)dyXXASN-Zkjk3rtQ-)NBd>qcGj*RVPjZ$;XE$
zCokrzowr7{I9~aMFkQKuq?_`<bvrOHP&h?WXcOMs)g=hZ`cP>sT-@uU#conKMwdR+
zkycj*4Eub0!k5HpBx&t~S@|q$5Y#K|xYU?0MoZVK+PoWqzZgD}+uC!SGR{%o00l;Q
zc{vzBw*({pxd%Wa4sLfC+`y-a$yaP0{`duRR`>7IxVW`$Vubj^*8u{_h97a03u1B<
z;&(0HqNdbIw#Moj-_*MwHi~GbRJB7I(o^??pY81{j@_xeSk~bqj1`Z6{Ag@R`Uru-
zvfy@Q;7;6LT9V38h50VQ_|&hCeSrW7eN5$jmzZM694mIyUH9C!0RQs82f`2qtfJBn
zHD|-lB>3aKc)vC@c$t_iKj&Be@}&0|awhm~Z5X28AS1sC!-{59xq)g~ETRyUjMvrs
zoZhs|_qxPx;RaL68nih37%Ad|w8V&nIHLY^HT4A8KY>U?mNmbK#|U4QTj(-*Lj#u_
z|4o$mZi_@!w>?|C!xpK%8eZuPF#kTOvC%J8*_!A|mmS~*DW3ERsTOF$MFYlhIPlIT
zMXS(_yE49qEtwK;rj!Ir8-MVdRYCJuT|Tw72h@>(7a5VRy%%B`XUYB=M%}=xH{w4U
zrFfYi?X<^$FH}I)j7h`@4l!@1c;J}%EdKl?{nq)^laPeu#m;921uXaPbwv+T&=3@#
z_jVY|uHwEu5OknNfIH}TeQ;^mY6%<eEStA)(WHX?8}7I{*Wv`iQdGfLP*MtFH)yUQ
zt}uU}6*LT0y@hv}(T~wjiAab~ZZo~T#jE&Vd2re;9CSXV<@H8e^civ(O6@hieuJIW
z_OD4^vuA%*kFmt$AIpbxzxJ_U4ieqha>y!-sP<lp_g{=_X#)+|)7mPXjOkaJlMfF~
z_edu;319W`ZjxY^qbuWuD@CxB<048SVe+y7fsng>VZa_i^I<goeq@Ikz)=c{$1f)n
zdOqq{0z0>|qUEj)1h4Uw%YY98q0i3dcCwvVtK`XGysMX$utXN{ik5_<DbYkwz-?r?
zi#p<8QYT2qp_~L^t6G7fZ2$<EF+;1;14x-;RW)_28JI!Pcc$uy`l_)MBFN4U4a$sB
zA@;yO`Z)-yn7ph@>M|hJEwa*rIIaKm<fKHuep>TUEDa%20FrM1R=GH&Ba?qmmD1y9
zeoXcHDE8p*%jUJi|5iy`e;OhX>mR|%@R3TG1feN#MMv=9QBgHLL4IADDwh>ayy&F(
zH@d8gSC;T^ergwc5WNSPBfIAy?c=AIC=#<!UuMxocddVW<yL);+#Kk_zGgd}pJd0G
zsF=^}b1UWUzEbjr?_jv|&v<!)dI2w24qBv6L*Vts8Q|WB{>Y?m?^m-i85!ue!PZms
z@cisd5w_u}V7Yj~2fQ?cog6RFq{Plrk@lzJv3MTle*$!7<mwRx8JVXC-}7OBxV>+$
zT_2}uO0gHeEc3pew3HmQdtW#B<;jS9GQgIN$y`hNM`;Z@AA?x6pS%uq2E0$=Xo1|_
z{eE``w-l}Ds}?b}S5ZtpO_D_n5kfE1D1NT2M>kfCCbSzD>s2WaBhg_TpqdSu(W%#<
zJQ~lsPD8rJF{`Mo(%9}$BIkT9zQUzmPyr=!P05ZpyY0=z3j8mSY9m0px-9o+x2*Rg
z7b2)QfSv~9wnyj+qMb(zsxBud5X}jP_N@J{@<)i{{_~tvVBp}e-5AQrJH&2Hep#Yl
z^PQ20d2&p;4vbF?ns64Z8dhVFyeLi*R=q;Ym7C@8e@lRIqkqEeRWkN2mv<BMSbdPO
z!t6-c@2!9n8wl@NSi&ST6cf4IXJ!n%mOkGJX&o(YOMO?3hFg?mjerECA<gG?G<rnb
zegwK|F;P(q+uJw-H~l5nj@FM6^Pe=`8CTU+q82-;eZIQvLEf0$3te|ZVG80%QjB)o
zSC9VpS<%t`VvlcrZmrh2^d@j+8FfByq&6nDA>;k3{oiaTp}7zqO%x$QsGJN4f3f&p
z`g8^}Aq^cJn97lumPSoRw!XHOw`KP!7m0zgHYPrPt<IC7M-PL0V$ggzd~ufJ)Y0C)
zfQ|F`F2uFJV17Koh9F?QXTp77lf_}$-T6n^aEJX9nUn;#fE0p%hHAK^J%w6*k?y?J
zuOaljKvrR-sBya=Dh+e5BL4fYs;Y{b+eG%-@^x}wFFLCwM8x53ymuXO<Xsk~@7&))
z9xT9m#il}Y#hJ1so`W;I1lp#!dw&qJr@1O=52%mmboA2Z$nU=~%fC)4l+_3%#51V%
zxqa%ifwjp>ZmrM5&HeI+RY4;wi2h1TKXtEw!c7=#;*<iaqAlGf+#1IpS6|gVQe)08
z+3nrgHd|AP;u*5{ZAW`cv6KAzGA={;ajlMS@>BPc$!NmRpHPX(<*36%Fd70IP%+tQ
z(Bn`esSIn7K7I*`64A6j<D-2()AdNHRP)RGDwm_Qzt))TuYSmS{_v-6TZ(6ywQb^l
z7=>K@^`U-b$XoUl8fTamw#dM3wt#_{r?%S~eGH@lAE+t}EGE7)PzvERdZ(*4mpi?G
z|NeLfZvHtnP2|xh1k|Th4bNU2^yZxI!ogHbE}R=3lqkU8)V^H<mCztMo=I=wY+nx<
zM&M`V8I-d9V*KZq-^~ps-Lz#*9fU(Tl4~8zpD8NhC?=qQ&!9`t;+31%Fs?7y%kJ-t
zM>W!Y2aE|)kdGhWpLY}-eoVl+aevUXE|uECK<G8m^Ux!?L=o-oafX=*^F6E&fvRdq
z`s~wk79)4OzKL6)z@k}(8B)S#>!EzzTsU7iEpuT=3@BQ=SA=(ST9Ysk2%hqfj!bV|
z%=X@I_gutt{D*VIl|6jUt-IbHuFrigu3(83mEXGG<Tun`HzmJk1LM~~EE}Ml8a+Aw
zSiicIkt-oFvE_@E*;^z+IeZx<1!pg$6lHvT{BO4czp9i%mu|7z*W7Nqc*syB4WBp?
z#LtBEW~Pf|-BtKFT@Rtm1)7s(Z=M?(8XANI@ZvHr3?xFN1GMSaKpnzdIxSnUD5_2n
zgX%5nL$?k6#&EuC^yl?rDuk~Ltv#1jP7P9!ED9n+|L);MWvwD3=&W{L^n>suPbtyi
zrd5ytt4-Ty`U|cEc^pnB5o+e4h&P*xYT=bi>fPp*qqjWf%>8Cq$tfu?peP?SU5se(
zjYTCV*RRB<y`R$EbGz<vmi+`Y5cF&GgqRUBbOacdcS3p$8&TNUa5)S<ER|OCZo+Z*
zt=^l+<Ef1*cF6Md)sFX5wnWWB&9IfQ57~91Z5?BRglGX}ppr^d%*}9l0TU06iBo9^
zQrGsO@=iUt7E)#O8$e&Vk8v;~?kwO$2ZPW4TP30Y$a%Ks*p^H+9Iil3Osw}^w}x@G
z>H6eLMuriT()RXc(b2>Iya`D+3MPBhh}UlpM9adb^oiJ?F{^SV_%O@ybEiQnp5w1i
z4{JSW(?%D}2vw=@c9^wTmd;wcB?n_2QkUEGA|6Dow;AESF8APF6uC39Fu<r8vh=?*
zG8i%5TeXMwqm+7UQfS_!r$AhcG0~HW-v=y@05L*4q`|KbQxH7f*i;H1Ka7|0dZGTK
zGe&^`!wyfIQp6&mv4zt>3>)QKGg99^s`I}U%+J!sD<LX(-w+PQw>@Zn>ukjJD}t|I
zoIu)9wKanR{FL~K`<9MgqF&_02+rmNNRb}56NHmm>%X6zR?tC1Js)%MU6eQo#ZSC_
zAuJ@Mu-cgeKa3{nuOe+j59!bSphvX!E3R6s++B03O-vD!iCr`qB6lPj-@wZ9gA7*<
z<`^X|Qt%o78#2`uU(!f~5jQ8hY4he>Z?kRSm_ke_Rgn?ht#(+2vgN07tX~GR8*g7p
zt{j%XSsG#IUKm6CdFvtZRXn%u%Fbi+KYo;P_+M7!^G5P21pR5|aANGtroixYG(_!?
z|3_GH_n#}|bT<X&cQHnTYWwrW=E#xU@YBs3EJUEI;8D|UYc^p{RH)lHb8^d97Za30
zazA-zEt-!jm?eLMR;ZtR`NY-prE9(O?a7;RRZ&$GiKhU8760z)dI&R=*2jntF4OP-
z0$2lzIk4zsA%IFMyrz}?<<)&AOdc&Co4cBsPLR%H;av+QB0#+f{<LCm+W0I-!B59O
z?>LwQmh`A7Jzv@6xuP{tQ8`ofxfY9Od|1LXKKGaQlRYuAM++BUZ-0C8uH2(Mar!o{
z1LRZSIe6wTE7IEM(iV(s#&pl$vmYLxbs&Cx877&hv(}S`r&LQOyo|9dy^nuNJQOcB
zFe(8gtP=+V(HXbqILoR14HCAVQw5Atg-UaCJ79mELy=yboDpt_{tVq@{#JUUFi7H_
zJrIlIP$Ujzb^VnPCHK@Sjw56wsOwBWs)U&J1-;@IR4Zkt*4R;WJjDb~2}>?IjG^)L
zKq&<~%KyT`F4od~UMVbx-<-!Q<xHn@;=AnK1ceY_A8zDjaurCM8#6*A=NR`C!WTdz
zi9_(6z8Ix0erC-ADrgW4{F(cbq6Y)!uox&O{qAIkGLX9ahl_aU9*KqXT$JlxPkeR3
znbeN=WyFwfUPT}(ERHufO>u+6X=JlP2=N{Z+Ajv4Elv!li0qL&#;wF%U2J_Ls;>u`
z_03Ia(D~6)2v?boxiJgggP`H9p_)QiBlbQI(B_crb_xOJ7i}H853X+N-|{!^2V_S_
zG$)erbG#$oFEUhN@KZr%X6xA4soo5i59|mS_Xmigps=vdE}8SYXl#)d_}a8@8b9MA
zHK#}%u9SKSvRzK<lbjFEo_zbc1=M9E<)-jkCok}a&?wz!xyoSxwEE?5`j@4;2+sB8
znzpV-X%94Nf&+APngzVce$wWI7BIVM7LQtM1}&YHz3BvVDR5stFsbOGD)lyo1j~n*
zgakCg@%?Kdfq@m!Fi3P<Rs0>RwqG)~q7t7&^rik5aQV~RoGziP$ts_70Fo7b_OX_X
zi;nllkFK17!n%(+mag&c!++tae>KvcdKJ#HYAo<eXTnC)NljmFqgBO1+G>4$=Ad&e
z$QIqt)M-dV5)RbKDZn@Y<a#-E$lwv*3^8h+ZT&m9Dzc$8;EL~4vYC65mHl!0LLn{b
zw*Se;typUWBG?GVF8<>#Ql6XPfK1wA{6k3}^fkuI@??}42;U+|;rV)Pn54-<1yDLM
z4|YEDZF`3Wl%2T3Hvnh9Zc%UMG~0X7;23_fWEi%D>DeNfI`0r0wuFP6!5_ZSX?psW
zYeHJmOBn{R`kb$jRy-z=AP~n48U0AkXW?a2a(n$8a5#t>yO}onnb~^#j-qWd)|!T|
z^EGFB=HfMeP7Woy-cF|-yd!JLPKzxKVq2%t#KPRXt?zri?V~;u3HSR3DtpNP`9w0o
zK1HEkRXF<av(YM!I!{fT@;O2Fh9qW491NiX!GzCB`XcScrB$1>h!yW2^BI{+u)2C|
z{&|Gc2Gg$8I5t$uCbna*y+}Bc@F|sP0#=s}E1P1_m}BK>eDVz>82R1WVqn;yq5gNA
zS{I%l-)sL4FB921j8st&gx(Qg^I^}!j*euBq{GM*WL_|1<9vm>F#eDLQ|kh4F+Ivz
zkKtu8s6`b;iSC5Q-H9|b5V=i^kB7@k3iqZrf!BQKKd~ETlJJqoRu%n_Jrpu9;NwQD
z`s%@CPtUJk`57|atkH(6*z6k=YNOVl7ToS4=Bpn@t#_JUI_elC;L!f&AwU@vffM%y
zSWW|4Xri0co$nH1-e-ywcfa4a5autlsQBVe$EVm>RBHo2aRkE70kZn|<OK@bS0DhX
zq@=IUggYxM%XX%=eZg*eW@bw;3AuCl=jU$E7SqPRq0%qi>o7x?yN%8rbyCz){b)Y8
zWCTiSfYd7Hk;bq7)O+DSTMkKKNUry;tRmZ6N?}Fj$4Ge3c}4Eou9sMW)F7mIDkF>k
zm&sq6UiXR5!Bo`dP@?6)hCROjP>QzUAt4i1U{zq|?`P?VdC9C3({fO1^9x1z4&k6k
zI6=Li<N!_c)6B54o2RDg=6UXpP#J-e3go-jgGLFOl4ACOs`~kvgN9C<M&j>y-S*$X
zIHjVkv0aFsRAf|{tc-3E$5v~41V<@V)LESB)qmnTs-~<0DZb9eoSx%7jdRlPhzc|>
z4ptPPbH)qF+R`CFDZqwrR9;tCR~WK#7!4H12dkbdkoSDy2`^iMp$S#L-*5&cdv7wA
z(WofXU)ycjY#zG)-8Oe+?}Q10z{t(K$&r9F;4NT`xP0W9dRUY`%yF}K!=nQiO``I3
zGQQS_2O8Npz2s-iq6lB;0;T<;TQorU6sKt&0nt45zl4MwWx&YcM0MAnrV{9Mr449$
zw;P&RHRW*vW`B$#I7ixc4k|5UG0}51hg7H-8odIdA30J#RY<8KlQnTWZ*^bGEr=Wy
z>rZxH{E3<J^iK9&ch=~+c-diA6fnIG{WLDyyk+?OfowEPe&biR>l<!ip8GJgYfu2B
z_Sm_-C=`01x=+9Ag%aYSn1{i|_Al53<}85P!*e3#QnDslj*)3?b?S8e^u{JO%;V-B
z*CnwseP{^I6tNib2YL{<D&%TTY}xl>9ReMEd_?Xh+FAAP_U0NSElxrJB5lAM4Lb|q
z=4uvr(!}M4G87ow7s{%i&3&(NLS9wQd(XB03a6GSx{uZ~Ku$)BFcc+05rH7YynK;s
zO|bkP<UzQQF_I+W{yik5a{!kjf8yJ==S2uEWqxD0$K%N4^LrR5p>wZuDragR2CP|!
zA*x&mT+Nd$w~V6%44I`TI<#lNF4@;4SL%lPA&_*fr?=@|QRu<Wq*|fs(@(P09~kiI
zKcgGVMJm2JeudNQC%=)Mw<bwjtyW;Y{%5c7Z3m1^y49~hxwsJDY=9DHONbIhViTEx
zl6-Ar!yQKB6;`R*=;vhTB7)^E@Kgna?li+?0HY5fuPmitv+}Hu%A>%peZh>XeJV!8
z`--ey2ss!j8kHmtH0ZU3nQG{usgNo=%8))#W%Hsx7ifbvg}W<O#<DZL62d|T3p}Ka
zy*vtd&0&|ROkKVQsg_1sDu4>3&H@<ze^BKGDk40^f|8tw5ZN;xzo)Kl34O!E!{g&g
zEd<qA!CqBrOk7<V@_Etb3JcUL2G5Te+#Q~+pma!;Rm?}P`oA?%*CX$ZRd&u9o?ho2
z7CKhAe4(qO<7s3IRbB+O$MazpKx--j6%aJGa}uK&<t1(A2mcWI>*4YB6uoNw-H*Lr
zD5gfPjf1$g*cv(Fpn||}A8J+M#$m;(c^oksr)({iRhgrq7VJm!iyx<<#a#OTkaMW4
zAG5Ks*wVjk%Iru*rHrq#pxfBUYWv&B@Q-d0@#=rZGIFQvs8J`GB`(|hJKi0{*DjM|
zlwj7B(Ks0%8om)?CJfj(L4=XPRnSIhGu>=jnBHx)_d45C+bkvuoOV4Le$JJnzA(?c
zL5+ymt`ksy2S59FS<mP6wRE2Wxg{2Wr>=liLlv=k@~~~-`F%8~Y*}$`_b#(az%GJD
z86lGef)0b?Vv6lWPnUIZ#IJ?@vELXVZ1w$Y+}fBzL+DpoZ1G2cM%z#+Mv<|5h1!S7
zW*PV?KXDkXU5l6P0@)?%1z)GasnOjS<EEFfZ&t8v4pD8hZ{2r2q%+aO7g~(nOUZT>
zgEz(|*Z*LpD96jvM=ak?Bw1#1SmCrK8IL=A8*L4o{%y_W*B9%{Eh_7=<J~rvV~J%U
z2`5g|=N`kWDGpF9$R-FiC47|l8=?fcZ?5i0)5b=XG1FkgcP&IGy6`bZPb`x}z^HHT
z#OZr&C0g=@KlhyqwtuJ|eN#=QzZc6KtF>pXoZ~iR{z@^JDPY!L{`_`1R*}(aK=Y$a
z77So#tWwB%)L42S2kjpPXv9CLVgo+?dstue$N7AWh(e`f?8KMk1d{$0-yc7Igcw?o
zsC=XrdurFBPx>b79@d+&(c+IhotP`80?`cQ2Xx9=Kq3LiDNj!b&O`v~4bUS!M}=!d
zl-T4hRlSNVICz~=_akvU(9s(1MB9v;g!|B7_?zL%3y_OLyz|dxxyWFTeuZw&ysOQP
zjq~@d$57n{O-<Mv_Y=;p%-;)hTgON)f*5!3mM%`YBLcWs-E>B~h=jI7B0(<vP>)?D
zUUrD#J8v1i(v`iv3RW;|xBA47%8KxgDf`8wr_!L4(1E)i@lQuK)<av?AWV6?mB!RR
zKYRVulwoji5OziOTEDMs3<}C4r5?mQ1byu5dqLa?0;XQrF9oNirA20aQ<jqpg}K*n
zrc5VGXMZo>H<k-lJkg{DxgAgTP$91TAFD^pE>1}lDWj3fw*Bd9O&f-=tUt(P1ZKU0
z3XPJO*qKA{zOnIwxey9|$ihPeL6fs%kcKhm|JMR+Bt}2!AD)XRs&rPR?g!#9d-*yb
zJpAaW-ii2p&E(y?evp+%b*!_H{F^>DF`+wlmk*JM<?}Gwv};*cT{l=Wi6Pch&a_qK
zg(Lx2Pn@L9vz&^I;TbbcexXTCe;SOT*Q%{*t{@9-&OGPxyLsBrbJNb*Y~MVqQjYGJ
z_(WF)NqqG>V7eU_VZuEDwl2J<UZdYLMcyokqz}vJ@q*QIaO7sP+WyUAcEM{K@2q+e
zT3TbcBx)n`M}NouRxDuCVCnw+J5|B7KWB*FA?SPUu^EZUce;q7FnQ-p^<?uual9<J
zy+;2Q0GhnOCRE_AC5#;=$VR&HkzVl}NUrz-B)AT`{Q716A8-ZOZI>92cXOYfLD3~(
zW{Uo%<6TV5(S`L?iGeUK4vrIrPo<m412PIEgk242V8#|asZ<Jnf09p=1B%Q%So1`Y
zr8i!?TsTy+d7C+g<eq~gOM8^Ob?@7WyO?Gp0`P1`(*v`1DJdy;S4l-4v6l@mIrTRY
z{qdkT>P<S!8myZ!_$8g$+9xq|db`B~*H<$?0?|CcDM53g|6?C66pcLP#DoMWlEd1K
z&DTOT`AmL&3a6k;Pvf|9?Cs3sij#V*#X|joelztQKEr3L#H-;NY%jtx!Juu~oF{qe
zFI68s&cA2Lov3MgB3Tvtk^n`bQHpsXUl=Rs`^KM%Iqyp)+D~kguFWGO#6;mz-PjnW
z3@mg+pkC0N_%#4~E}%2hNRwH_<KED8<0Mj===)K}7Fsf~y9b9hw^xTCMXos3yy2js
zN!KlZ3-T@-qYOyRoy3Po07@VFD8}%gU9|gNQOh$-u`Wj+q@T02hswZX(o^qJ<}Jju
zkpH)0ex;wTc$EG4@d{#OE~#VWJ$f)Dbus%)LXmxb7E~1e>kDo&G&`t7hXCFWjm1Cz
z1~8+PD(AScZ-dnK2rEn{ZCfzAc;xwe_}@$8a;;cCYzYvL&#K96gn1u+Qa}Ey>8fR|
zkXLwXIhZ~oau-E{KuH|)c|e9iC_|%rM6_(CM2kRP(k40N%fri%tOYm^n;YyMQSl7l
zA@vk}^#Z{HP5B&k3YQ*NhjZda&QuP?dssy_>N!R_@6j5F0(M){t8)08QGyj2Egej8
z;T@8AW9)cwEQXXo`r6ECev8oJ>8{1)wwrIZ$Hcr`?M17(TAvmS?0ESSjy0u3?iEjW
zRE!||t<f8R_>S)@1+|HmEpr<Z^plE2UHK#yK8PCkli2%}?g|b7b4{!aqm%#U1)hS7
z_s;!9nyB4Wfq#iX62Z^@Jz*RM%1VgvqbHOb*+6$#)%e|OiJ;k;l-m7M+eb%)5eUR;
zv868=9|ZDm*1PhrFXO7qNlVWVMJg6-OAGFqpzc}6u5uTf|9NWlg8IpDiZY$W>*!55
zvhY<mz|U>xIh33TP#f-3l`Kbi=#V495?ctf8~~{r`&VkvAYh3VCnO}avBo8Fl6{XW
zhB;(%8LA&p-Tz>}Lv*gNmvDb$uf<cPfEF{|5)gHk#}YJ!J8cRvzlVvhfI~cOCp>=4
zi?h{Xd}73NS(=#i>XVwPDxeVv1Q{A*E@a@s6fJVmtT>R=&!J))9ebn?6S>$p+Skk!
zXfPR|2f_X)jGSz?zZy8mDQiQ!%enn`DGx@+;OtX~-(GSkKGc3W@nQz$(wYLq3aQ5w
zuYbzAojCbhen}GhOYO&5SRvxa5wj$KAV3Il-)}n-xS@RVB|m5DIgCaKOzXH4g3Z9-
zMf}~C4WN?6MPq<@anKOH??NDI9^i3|Au`zYBqrR6Pb1`;IT58x4mXe4Gt-_1W1_Nn
zN!g1Nm^GN1LB`FC7a%+hfh5c){aY(5#7!n@F!<f8dllpzXPf)nl(%*MO?B10*KLFm
ztD5qq4_uk!t)C5^pb-HM5;N1({_|()#5Tw+EzY-JzBV=<QfgM{@y3;rvD?Tz4O^*=
z#E>9BLBL{=%u#=2v#VzQ9~JHq-Dh<?*8|4J-VV_8rN2%t*(r>LnX8F+DbQ_pUU&8d
z9&b(lurBH?uY<^?ni?d;ez^IIQ1zOo?5qEVzS9N~{*yd&#uMfCJHPw0H3rU21sHwY
z)3bSE2QF@~zj+n~HF4rh)jDs1*bu_iWt7g(r`Cf5prrfvS;}Gcfq-O=h50P&7IDpF
z?rTKY^pJ(_&CYWN@O9`^P;%l-i{gQo4cvS?oqtiknH=mqW9aO5SEocr+@a<A<15^!
zI#)3({1ULaaxs2tyAc$<FQ8xMmoA+nhi~S?2MmMfel!>d)UXVx2ty%ZQ@Y1fUhF+G
zB2xQ<$he1)EdQSvd73D3T9D<{Vl8NF5N^a}ekMy5pW*0@l20_tr~cY+{i)Fm@b7Vs
z&&~Bgh|+gkt7`qJkQg-LqXXxM^f26WmVt}3Lp4n!)Tw>y3oxiJD>Ji#S_WJlBkkDD
zU6>EDl_n4CPVSs(hRJ@<$)``{P}Tmk`72$8pKH6z_{sb%AXgar4X0x38<;_@!4!3o
za3>aO6~Q$CDmNf(vLP95>jUg)a=IA1Kbne)J&^Nlt@x3SlI;<TR)%an{r-=K5iT%B
z9z;_iFc{NZBmA2D7;L3YAMvQZmzSr!V9T=fLhX7^h}x?3Eaw*M8NwF{@cldr3Xc)n
z44F#kO+Bn89Clg-RSP^;OzLWDpPW6iumqWCQ(}UY9fpX4Hk;lL=dBk8&jbH0YbZ2i
zK&KmBZ8|*IIHScv{H*z#>H-xuN=>+SOOjTp`;-LQ(tQ0n1T`dm`C`e#0`q%PPOF>x
zmcI3}Ud3}D5Xin_@uD;113|e3>MtR~1%_e+d<_|???#=}>CA?}k4~3x7Y{N2hvlN#
zJH`e!vSv~4-Wc>i9UVe?Md^+pEzHtS&X=Yo2<6V<{{A7JLImf`)6M}o1Te>j3wkCd
zd~#mdu_%jvTtDW_QUN)7Gz4KVs{O?9M_j6WaziD_tU(Y6@_NN39YW#b_K4&Pt#u}*
z^o5xOraz7BvBgX4uC}%}Ko7w1$pe+70H60Zv-LGVJ%QZf#q1D_7o;7${GRY-FMEjU
zWph@@Yhz>%Ltk}F#NTxz=-HhKi%(_<K3o|#-aimxlA~ud8aI2>mQCU^#nxN-$(mOZ
zhOj27JUr1Y_x!!}l|dnH{a>T_Nv1HDhZ00E^$sh)qv)OkclZ?MPk`d3!|q(4C^IW-
z7+)IK(M9CJd-nH7<DWheAl65|qxKdE&Yx{89;6o0L6PyQC(VbMebnG#6Tj^f#1ETw
z0#Z^^gU%9r*Jq0(sO|hgv<MobaaH#Bez8Gx;azKo&QwW#kcJGxo=qVv_z)d<MOx7|
z5B?*iq7sE<Y#7O6Yug04A&A8IWhr3*2QGJHlAj;af1Mi4_nh6Vz_@>3`#ExOft}|-
zKo95*cS5H{BK#>n%VAmS*$>DL=n!c`LqYb#Ky#}=y7ot@Kpc}VenAHt5uF}ZqD&UJ
zs2Jnz2FxmQb<Vm621-He(iJq5U^t%E_L>u{f8sO)&NP)$dBY5Za>cVF6n9{-2;e=8
zq%OUN;}HD5V;GQRs2&;_`GD8#6PW4&#q3WJ1_cEnF)^RVky!*<9+A}(VQm@|UzivS
zBS&?s?MUQ|WEr(&O$V_uSHDNdY!xd$Wvxo@4BysC1;=vtTLyt~t18~A5f3ehod)2r
zaDyQhfXPS-{@tIUFwt3(p71V4Amk_&8`QnXYT5V+Wl2Y0(#Y3pnz3L1@>y)his#jp
z5QxtGB00GLDM=@)^pkt6F%;qQc=55_ngWQU*F2<2WHm$PwUO9xLYsS7B-w#I^i69Z
zCRkcH*x671{vC(x9WygCJG)YufMK9>Nhl~-qh#@bkW()Zpu~H0Z8Wv&viZK>q|t1u
zm=q#5O7vl1<K|4=sWwFfOr~k??EELf+MW3_kew?-FVI+5?WAY_$gltzp@Qa>QK0sY
zQEJTo;s5LIt)r@ZyROk~U;v^Rw5W*EAqdhKC`va7NJ@y5NT*3iiIhsIlr%^yAtE3k
zAkvM}f+F2;=Jq*byx;eo_pcM*A7_v8{P2AT?0w(&zOHqxHP@VT;jorhmzN}o<kE3m
z`g4QI-{kh~H4wb8+Hdi(9|g$=B({x-+FbovQt3dt%f}DwAz2CQyh}Ksb7EfB<K>@D
z5ljq)4B=$vDTTBHd5pqvL>?o<+{V!YI!eBb=wr{E?}Cfi?aH_S<)xHxR?D1b8rhQ-
z8Fe!Dk*E1pm)&lMSz(hOUr=tLrC_MxDF24*7DydEx{ry2gCi(@_m17$KFWuTuU7e&
zzGLLCP3Sq;by;|Qp+ld5&NM}-I|*hq5VEzfSyhwdR(kJIbL-waCVg=?55ZTjz&YdU
zXM?kgO;aD3W}dBoIvlc-Kz{WzoA+uc`99W1yEx^8@Y(Lk_yWl31=E2-f{&4?#LA)&
zZLSs`Nw+hq!F$$@aIe?tv$5BUmBq7XzPZVo?OY;d?Jl!2ax4f8+~+5?J!Fq94z0Sn
zPS>d{^5;5#H+R3jCuiL%%b9C3durt}wTnC9m0>6I$%p!Z<ei_B>}iST@~yA<P@?Mt
zR5@^@BYkXyuOI8T!(6L71WwK{>uWmn^>@D&9_|lot8<+C+g~(M`817(EL|bxEx9qq
zM7j@qu$X`Q{cJ0_@hq~@2PrARQis>8Vy(bMFB9@~p9-x}QY&)#93060qZ8+G>N1Ci
z&=fX%fyIEQta5GUPd>JwLxk-MLS%9Qob&?}hd~C%xbAW4tnFM;85khymg(k?xPQ1U
zkHSYL(RFjZSc+*I;f0(aT-LU7oZ_}A>$=f@+{!Kec7gI1*^^U7es7GZr>eCm1v(C@
zP5G5lPX*Gu`B4hwB>2+F2hh3s)5)F{BsbV0rR>^QHto|;KY9Cxt(h5h?0}D(A&n_x
zb!gvKtnl4NnALoJ{xH-<h&^xmJQ-DJZ%|p!`*``QAnAhWZ=56-t=ShR?cKa3)~(VN
zNlGRjytUb(?{y@2^=o~7ePiR|?5t6LVgLII1K34^lWK2k%X_N8B#8rKUDTF0^;zw9
z9yy{sr5Nr*F5hPE$9nbjjVa}sp@R~SjB;Fr;4oL-UPvmi=Z(+))%8tcDkCv${@kbX
z8Sj3dDwylaeiCCiWnvMx0`VhaUN@SXXs#2r$iULmtKZ#&o3))iQ}Izs<&9TpV^M~1
z#GOksRcEC23=_68e59`@?4=vi*AjmbB<dNSZVV@Ib1N_GKF;KA2vbh^bV1J35)!e$
zc2iPbOFa8`PA#zg#_{FUcWXAD5~nf*IoVE}xc|#TPHK28E+nMF0g5wLT2-<jeC<TX
zHyCC@kYD6HV`O2$o|eN2l6wB3*$F*{k+Bw@wcD+4@2piXP~R>;IO)LS{Ges;((e9X
z9meiA#f4^>G7T%M)23>Lw{pEYyiWX_a4_!G<yN1gAOCrC#Q2g<p7{kg$554PHNov~
z?+bh<5G(^yjD<cMouO<?RiCU2=Rhq0R-k3lyQhdCM?6+cw6*CdV|?|#muiQf-Xz^Y
zOSSIl{&my}jdEJ2b-DZ6L*Lk2F}P+jdo)Lq#U?_@!rif2bfLCdTYygUU0}rl?8hVS
zYrm`HCqZH5cVD&79O`@y>EpwS!J(l_hXVxGayc#`d_(P}ZcF&D0~nSkPE=G@K5Wy9
z0Kz5ksp`+tRKOS4`{7f=5)_M4pAWQrsoa1z8OqSCV`9$75BdRvCExXsZAmG;>r%ey
zua-Pj7ZR!AQwO;-)pAq~95OG36CeW=Bl_h&ET%AWLk3WAYJqlUWg~_`BVEgIl8csL
z8J0p)gX9gVLdrj_2hklNkp5hmv002lA<lG+M20Uba?_z|;7sySf=3P?c6N3qXB2xr
zr&$VyzOuWnR-uB~GZlA+-+d-_Oy9@n$uHF$JLLhZP)f+$2Hrc|28x{A_L7^s5kON;
zp;t$IcKbDBUQ4+q-A%DeCcJxSPJgPp<LyQw8ufDJunZSBx3#PWh5QQzDbeySp}*CE
zfH|H|DiM?zU(9F1+KG7ay1`}ms!-}{upXZ7+=Ar?>0>4sZo_E%)TyeX8K|-ln;^G9
zG0<)+jd48*)NBH-8)NyH%(Bb-_2={{?{bFUtD3swMc1I=sP2Qz%FgwZns(llL7Lz+
zcW!#i;|HH2{DP?x64S`Nz4>ZuQ=e2Z{&_1{w?9$knxo^Er?Hv7n%d`E48$+fb%>Jm
zF=Dv${CJ4pZh3Hk$jO=Cz)I-nm?*p2+oxe+P-=vOb~Ze$mYO&EnfI%&N%IZIW8Y}z
z^(Y)4i+QSi;2z`FU7kpegFafhq$j6jJKv(k@W>w|$*{j0^>`2|?jo`=+?w}if9N4~
z3$txrkIfPX);(@Nsqa3V^Vb@J2kR})@Wra{n;x&1h8{k6P^7SvFcOwhsD>^R{aN#G
z(6sz?BSquFrbie;5uw0BHFhq~r{c$t8z6NCJVEUz1%*v@AP!Ft+y_Rc6KkO@=@Rdn
zAz>k*lBJYC5I!u;rYA@i1v`taRh;Zz)AaC&n~|r>>MajuU>Fac+pn2^eQo~M&qsS9
zVosxNDP!o-y<2^(?knTQaklG;Om8HP=xVNVu)pJvq^{&8uk7qSIZ9p$8Dx~(pEyGr
zkRP9Iwqpn{CO4&bOp5Yc|5E#Ar>SKpm9b@~hJbz?o3mJ~lu_ya!t#d#?xb97E%XHq
z4KbAmRarJg5Z*a-AS7HUhyk^x2WFmV(=tf7YcYj9di3akGFrso0f1T!e~QWR@lHq?
zm|Xu3-p7=Lxs~Y~F8NZ(Mp4|`Z>{|u>-ig-_{8b+Q;jq$%G6u^tAT6g`Q5<fh&~8o
zZcB+G3%<ToU*%UFIp`k>C#X#LEIFG^+Rw#GYzc~o`m&Sm+*+7jZuP73JC8sU*Tlog
zd2{-(CCJ~}8SSHbBp^6rqKgD?flMNC5+XjBv%SYK!rRL5px+}ynkauRbW<(z8`Qrm
zC@7$S6Qu5$_qJLm|LVv%X=brs+RwBX3Sn3>r76c>fx>;};G#%O<MPE@yfjb72n$O~
z=R6G;*A9!s{ORKl<g>_k_Ts#iFJg09IX-EAF{ZMz5)y-^IHAi9zmyG6n3%{gKU-2T
z)+Ti=-Q2N%?XE)qd}|(gOwfs6(^Eo)DwlUl1P8vY+PNJm^dGiPq*InTgU8?O54fSE
z#EX2NUtkB@IkS(8zY)QCjSC7_{(4)vxR&-)(HAJOwp!#1E;Y(a70}=g8ph_jD2xvw
z_#r_sUS|>&CFX7NFVC(Fhbs0L!0q-gq9W=B&=N{ZN`4Lvfvp8+y8#GrHW2#kirtZL
zn10c-v&bjTgvJauSfSl$BPmHIe<-Ine@oGI;CLOyQ4u-V?Txb?>Jb)}(|z}RPTEp0
zGNnb{^Vy=TH6taP)Ti*+@R!CHT$~$l1U8+uxES-{Ky%P3vSe{D>;6U*oP}rd2c2C4
zpycPFJ7>Kf6oWAkPy_mxXJ}*)wW_}6KR!K9B)uhw7-StA(c6+X8z=euK>MEK-fXK0
zA1+ugZ4NNM<imgjHq}lAEfMLt*&4}fZkcVfzTO{%6|=N1Sd7ehZ<k(n-`sK-ov>dX
zBq2~4TU5;OL0v=d5hEk+F`JvM==Y_jIo4NAUi;T!b)joe@JDi9KX~kzexgh`n)&r3
zv)_-hC?>~6EgDIi7To`$!>Tno(oBAo{x8n={M#RHcgzIX*VWQOPYCPo4nM-H65jkl
z*4T1?UlhbG>DT!kXS3@MD2d0v{i4op;_LGD?~RvvgY4HZuEo2qzjZBlC~_;|l&nn0
z(%nQQVUbe{ueC2p{ZI(1XV?6^lm4$Y>=M!Dg3iU()|NHa5>l+~_-=7jJSh(f2(awV
zV-t1h1V<@7YQTyjMCPu8>xVDO(?{P|#6pRt5^T%#=1So7F;U&``kM6QWG#A@GELtP
zsUIZOaM7?Z)~u5_$ZE6f?g_EhS5K`(m6dv)a`-VRN^Z0^HYW6h5tK5YYumcd*0KQR
zWImXp#vbLi>z$UB`s>eCAv9OMIUOJRRc-hTVR&e)QORFpeyr-5=eGdO6d#u4evMS$
zw?6S3eEXfu?#uW8c))ilz8U=0ziLBC%m=1miiI09xpm)!z)Pko`4e7I^>Eh9&gR9m
z7uSVf10G99({egL%6grUSP`X6p*z>YwqCrCA7U#WJ+a%lycTNTE5V<6vFYE#pWD;#
zWv`QBH#jHg;^sbdqwD&|ruuq}4Vp?MaV~_4A$oQ(ks{`;{D<884rqy3=BSR1jit3&
z_(OLj)6mKogw&EKa|9K=vGEV6GT1S=!UJiwU;UdWn0GIo(GF~^RPCG3P>r}AAjO1i
zS;5cqlxA{V*y_|+v=~z=PpIW=YWjh_`P#pqpD|uEHC454^gi(rX<mbZ-aV%~wzjVV
z0=&v=zpz%>zfHst5Ey*E|Gjxhg^KG>KNlb0_w6hs_f+`v2jMqc)iep1t*ck6>Zhvo
z3eR`)gMROB({v4<6dWTXl};8za=Jp!-`!xMeOdEF<$-3N^7VHeHNn;PEDsG^6Ye00
z78ch&e@*^(Y3Z1L#;>}!*O?DUY0RKpjU`b)y1rty%X^fI`3>z|(|ITboEvr$vM(l4
zF`#RTG;J>#8QO6;#^4%hED+(cmywy&M)wqxdQYS4Zdvz5zLPN(+3POVsnoQtOgtgB
z@sZNmQ(J$6%uG#HPVHx6@;+fUhQ#GCgMlNU)n1H;lwzWl^enlf5Yqu#x3QXIfnHW&
z=ZDQL0Z*pX@z|J{zy~`%zI>r=%L(x+BuGf-TP*THmz(S<<UzaAx3)f%VHFc><zrS~
zzPw)@h|ZbU^h&K#{_1dTDA(Mrbo=jl-9Ks-w^&)3-&hAN>Z?0Ct#9@59~H5GF2Ve4
zr@QNF-iOkoNt0W)sO3s+e_X`ob(<frs5GvY!VP-}y34nis(ALtp3xT*pN~5KceG<4
zSyp-5o6BU{g;2q=NoH9oL|lBGJ>0eJRe}ufj3(ADHkh@$Ocm+aq9^~u3$Esps%i;N
zj>?Y<&RL7v4f~D<^Y8xje)MhYk4sF8x*r;?stb#il!Q1}ZoSmtFna!0=7@K3nc-y(
zsKm<)EH_cP_N}|S%F(QW1;B*&X7u=Z*5UUR>Mdue?h~$s7Abr#h54}Z<bh{buQHw3
zVh7??C3`%F{p86ZJO)seZQS+@Kw|V~=>rxpi<85zd<TxyO9T$h>NpijeUTbPSM$NS
z@EdOQkG6tDF&=3D%mNdWbk5rca@tBtA8%Ek<2{h!64E@UEb~%hlI!5+ud%smi^?x3
zLr^=wVWQ~8><^?3a+T80vL38%jv)Z**5r^PF-nr9zLu5zz}@vPtR3sOv&btbWHuKL
zybVk4TbzmlI^N@$ru6y~>LM5&1t;5XOV_{dDf4)1-f#S;Fq2B5bynKAUI`4TMx&NC
z)mj=xJT)AH3g5K0JjrENO{RabK7Z458>NF?w$9M?+S*;x#oer6O}j4NOYL|SVE=+V
z^z$KNsd4*Kq2dzfOAadk8t&^iUu+w~BA#D4kg5~xTNZofluVjOj|T%G`-sU3+mX!{
zmYclhX=<uo4D+*#kcgT|b1m};7C=mS+w|)gdq4dRHZ_WCp0jH4M`oTE|2cB<2A8WU
z13<bkKI`5eQ_+Ha6JM+Ri-U~0T$lb><*;PJ0p`2QC}&;mYv0#WQoDAfs0qGzK13)<
z=afu*lR0ozv-!rfc8DJRor845SKYN#CtsIXiHSS!v~B)Uks6hVPu4T@Y@LO012$fw
zhmOswFMo}b9OzofICuS3D{>GR!ZkWPvEvdEc}c~?QW{$w_?q6Uxg|Mm_rAOr7mNvi
z9ZlBRj>JUrbWqB@;iE}PNb-maza(|S@twS0xPDv)CZ>#tPM&*n;-qK52M)cw@$#(=
zp0ton9OcBZ;5#1xVZ?;vn6|sy4vVV4W%hgI%WC1-xhXDN)?@m^N1t%-6rzM{X&H`a
z^;ESJO!?Zx>B2C?&!J6cxYlOAn<ZI~gVV^j?CqdP{2Puf`M5C*tycW8b%H5%r8(5&
z!}UPobf1D2v+u3632jrWc2kw5ZrKdfTGijW^Nkj#<F)VS-LCoQH>7Mu2&2V^0s4@H
z{56zC5vis<WO_(l-_fP57ilS-WnQTz%TQ<>@=(3pthe6IB9F{7Xk(GXVM;Dpu{~AY
zzfBDX=PT2O+O}}tw|X1k?|Sc}f9HCI5Klrz<z8Ylu;EtNqZn6LomxQnc;r8&GISPA
zPz~lbNrYZyo_1E+Cs%kz{I+K|Sw_Wy<5V7#Ln}G{Euyi_8$bPd9_f2|tfVD^u6s<}
zgfy)J__6sM)oLF7#KK{xngK7SwB#MErw{Cbe6Cnw8)4MUqT&RVzu#LtJ^*um0ReJX
zuNv^HcO?tboR)A|G<M{sba2epAG=-~3%o=5(ZwIs`tF7y_E7l?F6`U7V|X~Y#-B#K
zuC23e8-bvtjpu_HJqcSo{1>x&Fe4k&0%tY{?iV`0Zrdj#Q>^m#rf<^w5AtsZEgoO`
ziwB8nM=?_s`~q4-i;5ih0s_5eXP^884cc+SHK_|~ho3<A5-oh!6p*D9aLawS^?W~z
z^r@$=N-A7yH#ZUMcWsin!SK5n#Sh`5h$<O{x8bk$3|T3n?^V~k#5+T<rL%lHsFc=5
zUTG`3wUkrx;0IfZi-?EGYl9ELW;arXH_CSq2nK6)^>&yfqg<&iaIEvJGa&NloxI$^
z-?#3_#3~FZofg--eBT1USQpHvN1P5BNXtLk^4{TQ$7XL&ZhggH>yEOX)%BITWHm~(
z<dvbS)Cq}`eyhU5Q%iH1f4fWc2vm!RH&~k^V`F!Z?j-b^biFgd5$NgRcHu$=T*tZM
zl3VMXD{`PQJi}D^sCof)d!WvGGy}9ZoG(tOCQr`rPT*{psQj9xb|)~>=k4&^S<LA$
zH8abl++UXr3YFkX;SXaop`PNdST=W$?k32{rKtVs&i9`@d`hXBEC}b{*?C)=AHP>r
zb=PxzuB{DC>xT;O+__VW^W&xbq`8L)9FJvagO@U#MnbF;uM<qLz%d_-M2aHr?5$@N
z2E1-pS$&eK|0L4pra0--N!a0N)UDZeZ%aC@Er^!?%4?;w*1gNn^h@33rNJNqBsF6g
z7irna`>=u+?%|lV03Cn%=E=;V^6RubLUc%J?hY0BHr<{c`@kf=q-<M{yU@0bndEp}
zr?7NvKr^L}<bnQriKs}JspDVpaEg-~zx^CJEU+aSOW?12*rhd@c93A%_kICK4DiYF
z_1SvRoPEb}4aC%8?_QirXg}k08OSz#h#9tstomD&9Ww({8#bjjIuRlm3kMeSXrr+y
z%HTjhKU%xr+Dac32KLh$<)}RLr5GL#RB%b<qp^_rJh0~M>L;^eFQ=eDQCz&SCV$zf
zS8H3Y79*pe$;|dtjX>`8Gqyb%nL~t+Tg0JEhz(Dqn4Fx5T<1f|*c#}0Z9&8jmunUQ
z0reC$s_FHhW7or%%D2I(<)@Y8EXugsG;@dc?4176bZ|#=H#IF;%lCa!8NY{hN;pJI
zY^a)@lvoe{p0f*L6qa&aoL~D9ajEM@K?a!ke3(hBj6p-FPnI%S-H`5M==So;%0A3o
z7P`Y_%ZeEu%*@Qf!Yf0yhjRFi-OK+m<1E%+Zo>fgZz+5HKB6t0LWpjnkYjtgj5)1;
zNiEq5%mxZ%)t!fDIRj6347<h4*7mum6Zcw|<+Eyo<=NU?l3NqI-dx~TkX&~4yqbSO
z3e$jz-TP9p2>+F>_4#;O>ZY?a<jEM81M6+r1<TA#x|Vt(k?_~;(`-%ebMJiwUq7cQ
zcSM9(`g0yFB@6TGZy;}s5afFvdhs>%{3NOS2pp{lEN~DuKl>6r7=Q)`no$)YpJt$?
zMUUO4x9GJ-n%~vrmeSwS8hg^JUaH>LlN4fGz-b+9u8>F?->3W4PAf;N7uM;y48Kgk
z68CMUTv`2o@4n_auc66Foj3Z5GBP4V+948|ic+mBq1dkrn2a6h2Gc)cOL5ngr;*ey
zYf~=;UD|w-3$J5&fp3eHgL`v%JK^sWXxS$_vN~W*cJVQ3^$7WHjJ$^P9tsGOojdVw
z15~px@N%@Z#c%pPdEz=E-7eg{AD1ehq|qTcQ5*h=i8A=QYOx9r%kIoWrT!XefyMOF
z{t=Xv#YLOzb4>_?9L<z~n9kQ;OXxThEIl(*wr@c9pcSG?cSyXN(s{p`;gQRaNlv_*
z6&>n=CRyY2#wBiUtCm>no&tF$w(`jBg#HW14`2e#kMKbB)Ll&K(Uu-qJjwB>&8Xqz
zwfMbWVRrh75p&Kx?qZjwZtAkQ?w5#DlsQF9{+Tx{=Ef6Ghhp6_lh4^FLaPtONv1f|
z19<6JIzvO=vN)AE9-h$rE$^EBa^ZnuOSDu*m&q&@t*!q&;pi~mWMUHW93KQ9CtHk>
z5Oz$9tDW5XFx#5A!nKZHKu{#vODtq94WyVWc_(*C2fR2FE+*DJY`WC*F7fd038Z(>
z;3}~KJ9itR*!A%KvO5#g>!-Uee|6u?OW0sF@zY2<6*4|x^VulyN+n=Ag8%BPHlzE#
zjl<MZ%*mq@^Q0lRg`aw|@`B5?^&nM%sdm_l7ZU(zzdLVd3_e$E;y<3BcJ+reOR$gE
z5*NK$&Jn<44@=m+sOR<-zyDsG8?&g@yh9pzvawv;GlW*`?r<4(Ev@lizbyJ0AbvL$
zd+F~lYi*sWktQ&7X24w0@lT{;>@6amIH7P%F>=4oiObxPm=E=wBgzC!N~d{Sn!XYj
zegT1e)Q%Js?R=S+nL^&SS(NJ8tf*qdi!f;0NJA58KK}Ui>t%R&)wQYphW`6>PKRRi
zM(LPe0c|o|CN0K%)(SH*5Ctr`3nunOHI0diMw-5y;Hz$J$_MrjUrY{8PB~fGG_-P@
z20UskIHL}Ph6sp^q*NP>j~f(f{@A$Rc7;|0TNE)sTQR>P<4Pc1zEw{<U*%<06%|a|
zhGIpWESiik*eCb<$DTcVggjRB;08m9bUOW7=xGQu&Kgt9DZH8(9K?uD1NZYUPNjX<
z`|7qD?fWU_Xvc~(scacz&m<;6wKX1C5;V$zj~I?GyQ2v=LF>K}pHrN<9dIC~4IVPa
zoflU|vyxiLc4Vecw=W?5F&6Fb=`^a0xXMgkdHBeYJNPra72I6#XDlqqum+0qw{oq-
z48`Tomy?2jpl*fK=&a}O(b3cFjBBQ+-OGeO?56QHw0X8;NAuUPR)<>?QfJd2OxoEf
z#?n67$Hc@KY}P+%unkNtA|7Y;_3QI^xjfz$W1F6Wz@!Fd7Bh<;&#mtyJ9d#9i1rY#
zMX=1f)5uaH&gSlPuhdQd9C@1)iOwl(eY`sge>(7{KT9Ez#xlEj@gndM7->Sw4VSL3
zQBY{Vm@$WpA-*~LjQ1roUb(CCYZ4qjzGdlyB0YWb@q#*b_s<&;uZ6sF&*O_a0S#?5
zLHiBO%)tJX3&4c8l()Xo(bv)hXvi5h>1oNmVI;l&caZW{t7KDbTZzlv%xBLVmp9Gs
z?u)7!>N48zQ(9WeRV}!2q-T^}GXe<8|614@8~>Ee1keJ%2()Huy1HC|q)$8l)U@Iy
zS9EdZWeGl^J*a%Ah-25b`wMj9uJb`5@oycEu8#F*`yJxE`yJj8P^@4aFpS7xlcd8Q
zey|zi4!0f_KJX4a9hg+p>c<nbY5jtiV4&WwZ%d4WNd3_WW$U-Yv_W)Fes|_PY%qu*
zTC}K(ooMiVlTcH4J-495<jNEG(oM<WI9*oO!+c*wvSh@M-nm4Pt{u*<{)}Qu@#@vT
z3kw3=n>V+SQE_%Q7qPXadi?xJvhw5zzgZ*m8~9(%&ap5ZB)xXJ!~E>U*9~Hr$ul~;
z`5@kFYZK;r81yQ<W>^G58`rP4NZ&6tOObihHoYu5$&+i#wfUcz*n+!8hq|g2tebsL
z-GT2Ln2sweD-OR}YpH5j@6ldM{EjH1H5&kSlv<h~rSj0=hk7cybDn^x{7)+;R$Q8B
zuG9->f74B1jq8_u_YSwp6f%y~$ZAuP>``t2>bGw;<<ImK3$4A^T+7QYz1Qd%GMvRH
z3kMh0*VMmE3xq#g-b;zQnemgaB)L%+1y2eY%*Bei*rClr&C{>zTb_x$*?yCOvN`0^
zg$oy|17T_+N++;ZU!KJk;-{Ix-o&xAO-s$xbQILLfyEZQ2PS@g2@4!G`emF0gnac^
zTGnAw5?NuP=;e9?Uf7Eu;jV6KvVpO6R{GYaZrVP#u!G5J!m}%qtFF6h6HLFZJlP+2
z>=xpClurH$MINR%w?g$9)~m#7`y0dj%F_?{9P;wm6LESIR;DSrxpR}M57$xEZJe`y
zv+IKW%%|5b%0xN)^lO+zX=LiU;K{;?4!I${C_nE^WR<~B9$6!YpIPV1ScnI2e<=dq
z0z_khO<iTPVRe3oSit<nlUHN<K4Bjnd_%zD+grGf5KW#0EzNCgY<dsU)7NXW`y0`m
zMo{GAn_W1T_*Ovt*eOZ1+r2y2#Yv~NHOj9(0Z1zDb|qFXK_hbP^7RTSsKC)1tdTnC
zy6Il;YY3`{$4WA+aFUDXt{qP(_+7qY8p=lEgFZ^3|J=6cF}xB%n}rkr3I}oxJhLp+
z!$kU=LV1Ge&t*C<?tZA1uCC^?(Sc3>^6As3Kt$l#)n)<bPZ3xTVP@)|KbsBb2GaG*
z`{6t&F23>c{tlVMXH8?rglr8^e`MKwt*za=e}Bh>J$WUkUP%$ytBGrOfwv-+!%N1A
zf*z;syz}$ugl4tFdlp$sEVMn4ec=`~QKxRM8OnIgj6`=KMZaALtj3t@Qi==N_emc)
zx~-pE)C}a6Py5eHC3NJT%h^XL2^@|TuOjhzWNw;!r8;Z!P;-Sx)rW~bc8in!O15YK
zE(4lFZ^CP1K@%J=EF$96b7(`*A}V)JtLo@*0*Zx<O2}(-o&Fpa=q%7%c$~YnB*{w-
zHvwNaZ%b`Ml1X$w>zKqCReXW-C(QCam%>uBrP*$2XA~-BH#JEco|Juu_R~5VFYwU7
zE~Rqw0mTDWM9BU$u<^k<(t8sA=RODeBnfY2q&^RYNUh5xPd+_y$uo{W6g_86o?zYg
zk8;BZBjJsG@kN8ItgK($UjDjeBQ3T6ot;>k#*san9=kh4OUFC|@66J}^%lT?{mpmF
z%gd2I43M`y^2IP7eZ5bsBY|KLH=&hA<ITqSTwMe;F20(c4^B2{Q~=BeF9JXdA8L&K
z!Z`x|swr*~G725P)-S*D@YIVf5PZ8CZpdos9~7i#GA)%m8dKz`UTFIR{Yx$v$K?Zn
zM)fzVB3n748GDauPsVFn&v-w2^tmlF=IK+VyWgG3&ao4YzxlN7l7G3WuLh6S-=^3d
zbTMUf)W*B_(Ip7}*i#*Hrn<JG1HeAYD~J?8OdA^U=ocy&8VLmX6$QuA%uF%*{XSnm
znV`Z(je`oi?j|48OWY;gFLX7?f<$H1n|x|th}>}SbB_Cz_U2=&teW)(5Wur{=qQyR
zzN(R?usgb2=J@gBgINs&45E%-Mf23rLew9qXlQ7tW0Bp(DrwpjoAE-hVV<oGZ5+6|
zW5gEO5Fs724Y*N=V|Eqa3rjq>{(c*0_PM~*3elaw6cGa;EitB55;^63jw`nHHF?t?
zTb-0&3%tUr$z%uo7qu_sqo%Q+8<p4BACL)WzY{fl^jAlg*Z5a9(p#6jZY(S;jEGQ#
z1r!!85xd?1HBPfNpA|$ty9Jja|8gr_2G&h~!5m0Yes2+dvuwlwE2FbRFJOV8jn~oI
zIq0*gpH%i5DNZ?(Ff=>`X~=<-V`hndisG$&Yc_Sm;Ai{}?=6g)n>_oYHR&-@d|~(g
z*Sju+J<Tptoc{aQ)_Sg_0ifqyv!=2S=9w6S5^z1YC<ti5Xe$JD;239>ty%!+u2M{m
zc#tw@d!QMfPKq%im!(keqOQ1`qPTK}p|R4?KKE+Z+8voB4NY05{;{#&pQ`5Svc%fN
zVSwe}@Jsc)!1u{pzY?f@ymH+WmcZ|b`21ar!Lm|}f$E`)5NGY|oNY;D_y9p*;nF(D
z1C2v7PgrP5?I%>upTCD#ce{e~muU#!)E$`!4Qf`(>5;eH&M^wfCyjZ?DwVoQ14#&k
zk^5i>ycD<%v8i^srw3!O6HXHVMB*;9H&L9$c`VYVt#A%%8!9gkh3UqW1*yHO>8S4#
zN!`bUYW`a_5(AUlPd*$v%2-WgeBAqv7Q&a>+G|&@@+w$uqW6a-2q~HP*|V+fWI^<o
z>m-8XhJVd3Bs|l492wfCMlYp?Ue}{O$s0%kWy=4utM~J!kl~Fo(8wa%3`Isp&fMnV
z$wJkKwKEi7S}+-*(zd276QrQ2$=((J*Dfy!C~<9V3MLHbCKCvRWhHD{{2!R;n}4U^
zDauB48Ezw3fd^n|YTR)nk$uY0u0VnK$ba7sY}-XBk;mSFFb#SawYs0KPY4EA@D?x#
z60;lrf(D|4NFTm!$&L9IbkX<I@;z#^ow}x$;wC9m+d5(K<Z^_|>C0(tq(z>M?S~7V
z9jeR48Ezjd?qlID<uo0wJu~p}<N3@(+di&^8x#iulo=i#=F~0v20Dl@V8!_p{6Tld
z%VhdC3}^3V6r^h9`D|DaG4&(e`Ag$%YY3~H&7tq97Kef*HOvQ^<K3E&ecg>A8NAV+
zc|&%Ny5*bUS+hU*5{XOjGy?B)w1+G$r_fZQ@qPi*F*UWr=w;2F&-k;+DlDIt!9?}D
z@XM5*M+7J8w><NS{o$oJk?R*HWLVCfTk^jl`Asv;{MLN;*JwNg+n7Cr8TKMa{i>v-
zR3_@{<+t4v;9vL+VqPEE^}n0redJDyg4Kwf1r(;nf=^IP<F>1qoM%$=s=w6Qk^VnZ
zn)K}9Po|D;#wc9+u{3{+w8xAyCLn-(c7ENdV83KJ3S>LthXAW_vi~7w(H+nj%eXv3
zjdLvfz{3gy-PWnf-AdNod9@7<T>SiyQ{+0IZkjGzQ(y{#o5I+LIOtdCwi9Oq&&DPi
z{}RoVNo3z-eyx(ie6ZFhI{Z)yofRTp3SCCl)#cncr&F&_U15hlj?tQ1GEV49zeL_$
zx0)^P!jIQvD>^#PG4pS<R*?-4o-aRYnR8bZZPor%%(te#-MYLsUH(ub<MXVzk^(>p
zWU`EQ_DP>Uy*{Fg#_jBn8bDU=(-K6kGavw*6_V@rNo5nj%90R^U^%AmvCvisflC#&
z;9?5XrR2#CR^e;$DV&F==H@ajLo6!Iu}<a7wj{VVtqaFZU^tfD=^~5SW?<D%^%m#y
z3ZE1kje2r~gZRL1q-tcWp76iAt#jv6&(?bRWAf8YyqjL+%3ptbJvrC$)VE1py<~F|
zjx-2w4tMTMU}PNPBRYSmA<=*@Um2wzq5yueG&`HBn%s!<6g&l_XizS%1F8j{MKxUv
z`V!a`&?n|w_q|8J1V#v(K`2si8^1vy0V)2K8avwO`1O8KRc}zQ5bld$D%T{jCKun_
zsap{e-||>e#!oZl#>%?Z<3h!28a->aMmfl9j9^fM_Ho{@i)>v*g&`U};33b>%={2R
z3~+x7p%nE0LM$vkzBqe{$}9#Uh{P8VS0bIj`)Wq_9dX=7_xW#Z37%<Tu%XaLrJY>E
zk$}lFf5Cr8LV?i*%uF~@;IrllF9eKb^s-@8l^n;BP8=NshC#KBB+h-C@{NL>Ugw-h
z7gn1XC5x{o*bmPcZ<M6@S-HLA1D%b9TXT+QQ#(V0=+dszW}&9kd0l;ZcIeTkLe7FV
zAaNLha6lPO55Ir^hMJxe9z{b#`Yw925Z>ZQJjpb)Rp!=2G;A$0Gcp1T!Weh!`=za|
zE$m0nG>;G!w{8*gHo&IDhC?Bo&QTyQZN3puq9czJ_p~d{{u0~dukPFy;rkE&HaMu?
zEW0to^rLKH5_9f(!eua{>+AFEH_3&CPFJo}<6|*!WE4E<|6~hvEPvgvqfrGu?*PCy
zVw9R}x5AZWO<gZ}nuT)=1rqUy#A?R`xtlj{{*~p+`n+b70wpAlv$-4Z?S;3>VLj&z
zLR(8y#gY8}10nOn!4EF(t(=;lS=X{~+i{~l88?8uZxJhN`N~NGVb9}F$cHgONi}s}
zJctVrVdB1sse*o)7+{Lip;rARD<d9ABhDb1gpIo~PZfuotBcD9axnmrQh=F@0PyF*
zga|tw<K4J!c_tOvxtuH2i+lPM%WuL<)DgF-`8~AslB?@`+Tc-G=0-(*SnZlCZqoCm
zF-;CO^DXTSs4?@?<>p~!9Td4-i{s?A((}kjvua~p>h$u{AH#Zn1=a37g5au1oEfSJ
z1mFNR&*Z}NK-<fr_wP+jOuBR}bu2siUIf$({loPWZYHRtzy@wcz0b9%xEPkmow}AS
z^n!6sPL7V;%@wjsvE!qo8czn^(Wutgk{^>G?kh||xYhc$8GRc}d-`I*nM?EM*Ltu@
zTWv-E>C}C^-zOJp$o$_v+fAu_bv%&1ql;Att&UG}9x@|HdN5{$7GVJ)0AB-KJnQiH
z2YA2I@dPVSIO^`ce{gWd`S<VPuxE0v;!vk$8(zZ6L%2u#yu0#$ZvLF@Z@gdFJYkQV
z8zXgecjsKg;pTMx{aLWti4}TJ;g|>of$$s&f;37R7#Wt~zc3#Js!Y>ilWPj`13Eg&
zDQZBvIVKu%_s_$~#qou>5Bqzcx8YccQeDa_sDiMfM?{cnJP-Zv$%yahCU;EOzu&fF
z*ShqlG}gluH$MF8X&DQcd!bbg^5(4>yJuGw`bu+=)k4UlHPwpV(=M%*zfIq9)EUqu
zA=08JXjqA9C~fa%6@TMkN{fnIxc<;oDok1Jru(N(BQwbm{j1garo9{{dP28z=C%_E
zT3o8T1JNt_Z&)7^Pf@~#mUzXP$u>4P!Mka=E4je>n$EeFh1+?nX~_R%W9f+9rx)Fo
zXYhTXs{>UkUVUXA9{`P^EHx77@_DOUY!~>~v((XwdTxcAOB>C=&Sz%mtf*{+vTR`_
zW4k|<_R1<7q5UK#8v#Fo6)d9vp*@_1B}@b@@02ZddUEfdN2L0Cd;3dSqZN{MitS7;
zT(}<`tSBY*=*|#!5FlbjraF}Fh@3Ny5drk)xSjqQ01vI@oI+TunrtL|bQ{46+&d|M
zRPuF6FE=WgzV~hO#fP_jJPl|F=O+ukQ0zkHgH_elj+5=bkBaKQw}&w9(c{OLrKPc%
zF@%;9V`1>3p#(W96-C7{#*I=a$w(Tv6OR4Cd$eZm$e%thwOi98JUTi+-G5sh4f>hL
z#j%X0k$r1tICf?mA}Krd1`DG#O>=r726mf)PG`37*g^aUiI1|odx;bin`i1GLOER^
zIEmMoP4&ic_Jx}()878b8_-|^v`>((=Xvzx@l(}fb5`61Y+RrF`(y3wHaAY6>u78Y
z>bX+I8^9%KX>1x5bu!x5@=U=5*P|MKhoYNZ8V~3bfn8$%jn(TQ3N2JWI0>3$=ZE9r
zGWyp822qV;*>i0=0_A|j%GS=V8_%ltLHVId-UxS!_JDvx>0MObhG>hfEpePE)q`K|
z{P<n3`Pq4`Jac6%zOQcXu;>ViBg%W>sX<;zPRlnOZrXIh+Ysk!k%BVav{fh2v}Mb!
z&FOM&g~yNY>f{4i&6dk{GN2jlTMEi(`9NRL_AdO$7k$H@90dx=#-e`m1|~Nn5VSZs
znc<trRJ^{`ex|m*UNuFHN3rtAp+m;&4+5)t^0YH(gKupsD5^d}?_Ws!ySJEMJ0t#7
z{gP?InCDCtR+mln!*aKo0;HewdbtsAsGe5XI`>aSwWVIKm2)z&n-R>{F2yE7(B8d!
z_g2^Ihj`s#<B>h>ew4~4ml?qH*ROI>{tdr=TA3W=*bqZD4ULr4VQT87S(b2Y^0o|}
z$Uy#!U5BWtr7r~f`s(%tjcCt1KqtMX_JLw%h53MzK@12OI<r$F4XB8z&pfy^bh@eb
z>k3NG`eAoz!g<ak3~+2llLZtTYinz`KcLBn&eF*@*kffgROBBg!O^*}w)9Nqve&@4
za`CfMiU4h_-n+l}uCKI8_TE*Pm-pK<Gaj3tKYzZJ7d=Rta@%R>p(R1-bdH_u<EF7-
zG`yC3fS=LYd{lgS)-SN-!i!dvD1j?0KJ8g>*w*LPx2{zc6%~zpqPK|a;wR_-S>6mB
zBtp#1MKwj`)an?cde()Gtm~EE_7M#Bns#iPR9|P!AA$Qn-G`BTR>%U{uUy_(?rYyv
zUuWUb`iQc~Kz1)_5Y8_+Lfyjgi^m4sPU@`P@YVJQ+s+pc{`~olDs9J(upLQ3KmMEC
zoyfD!eX$RvP2C>sDy**QuH2fQ9)M&=;f{vNJiQtb?cTf3tq%Y;-#Yv9XI6v68I0~D
zgc+icf$jFhx7U^6GL!@QLz3<_Q$>wGan3>^0)*4V)YJ|gYC?Zg>xKA3EDGz(L%|Sw
zWi*>3y)$JvOZqWf-*d&}OJPrnp15|w`sY~_Afi7+C<%lT{}gcsEGc541jSKftT;U#
z-FvqsYYpRVB|ZBk*Uu}@rI^{gl(-Er0Qo%O-V*9MG{P_dat^Q_w1v1GL`EEg_m=oG
zOE$v#o)8Gsu0$??!EG{s+=3!*0RufyIZNP);V5F)%nWEdbx#M#{Hx;JTT8)tHz`LS
zZX;xS;`y1Q&-Lm8Ob^9`u|H7)xw^UK(vj6A0|x=H*i%(?iizoGpFaoGzboLJE=>>Y
zj}~gYLS3+nz^aK>hZel3K~qJ+e&fasTJMz~KF72INk+bHtS$9?jV?{O=6}li545n(
z)0@DiF!zHNok~KKfy#a#I8FHZy%4(dW~K*CoE+M{bt!<Qm6jNX(~j#`)ehAlaZ-ML
zktFjH9)!#D)=CUQ=Fu(s3w2>c<@w^#$w}F*-jht<Ehf5hod#4uvgk4TMMD9&3P9&s
z%g%3*u@{{Z6pYAMQ({C1X|$e<x?L{z{LSS%zi!?q$kOg0gdI&POOlo%zOw16o*~DK
zj1%tZZ#Kk$JyiBXA{6Vli7~La5v#+J*WsHv@Dg_QRICKNm_uvfM_XmVsc}@;?&ivz
zz4q!ryuO0@ITQAdJH+)Tq3R|Ce`rs<1(94}(K?Qz4HfoH0jf0Qx)VdYq@?Ub?d;iE
zSz#Kg>w{&<T91cH9A6nD>O|}@f#C$wEnwbvbpGmq6_fQBRrR>5`m9)n<Gb-022m*_
zK3bfo-++dDqu4GHe}fdzGRa62sk#@P;91MHcI84Fs9U^&j0?k24f_f8<>Yao89|`K
z12z0_WEYlX`-m@OB3i9rp}28fzp`zcJ<p%jb}k7C2`Y+L9g{9%s<Q5cduXCXmV=Ls
z(P39#E1_>%(cjPLUw9uPl*z1HKMv9fK0B?blW!4zN-;wtO*(+)M}NH8<He&qdL`zy
zsyhf;d-0I+w12~a3<*7&dobe1jhJ9dJddd`IR5&n?^SM1_V5TXB8y?EJzd_oI#j03
zVYHQ6ngYR*KxqHi`oI{;FxDnOs&XY(c#yB*Bt2)-|J8zzF!!rFe{#meKSkMC#MAQ2
zdNJM!Vdt%a2NP4y^G8e%D{xqz0vcu8*!o}yGl{S&FXB_-W~7>-Eg-16?KviLZGbl8
z1|H$yzDW>=z~Zh_fJ{BP&7Z1e>=NsVvbZ=NENoUE;Lq^gH#JTA@IjQ)#KZ*5E58Oc
zLi+M}e6Ymo_{sXX!V3>~l{dX~S^jGXHes25z&m$*kBwkd?j#}cm~WQGrJK==!Z-q7
z_c?`&;*L{V@TU?yv^|UjNid3Ki4HabL5N6?p1qrZK*{;%P&KIz-sG=dQUn=J8uYEN
zgWtmv=Wd5O1OOY7Fb&L^A)J3<_)CldJRYnr30V2bo&Mmq#69{0e@ke=6`+Pe)v$_(
zfGjl);xTOF>qOv-VB&|w7A#L7tMjNR9RBpW0GY!M`>$YXjM50_5lrYD9UM$(MtzQX
z8VJCDkPvn%iGt3--kw3&E@5VpxYr|g<NsZEJpIOA{7k=uAszqqBR9eYTK5#%;A-9g
zElxph0kcaxx8HZkdI)cYi|9he=j_|HqvL{%98(CY2h<na?|-~PFIdSd*m2SKDtmbM
zzmlT;_Stt44=$ysy__+J>j!+|?M3(*Ted?mfwwY&N3%`<*5iV!cn6!zmD8a_wzR*M
z|Gp?g8v4KtHFm2%F*@9lJw}!-I(y=pB5QA2j^%i^tJr2Yw_0YdJlkg~xT~P5^NQ|n
zwlh6}4GxdKXBT&xcT$-end+>!bly7ybPp--;r~y5jDYtbFDpAlEEB>+S%vCCP>#36
z&#~MOO@^3b4H@CT!^3VTiL~%Li#B%clISEk$v&HpU<nixf!C2eq(OGW&U)LfF-u7u
z;Q&dB$cG}-Q66R(`I7EvW!`e-z3U4Nsilb)pGnkHN<n*yC<qb!V9~gmHTHgR${Km|
z7)Zo0vE~UoiA#tC3W}ue$>d@D_MgEBQrv$w1qtzc{=ff2HEKsBQWFU5fM=k~00gIw
zh?^RaxeDFm2SCKdBG=oWQ(y3HQT|GBZBx@2@s!ObA=zy^3V=81bi<c;J{K3&jaR@!
zfy@yIoev)D5E`1ER&=rs)l+m8@B5IZ#7dL~5iBccC<X(vvZhz{#IGpE&=}ctNdCO*
z<m5yw2njk$wuDG91Mwo_=!Eo2t|PcLgQFbc3?ebsBX!|ujv+Ea@mU?X>!BeMTEGTG
z+i8AN@=9LIXjDLi^aJ>9G`NsBVGDu~0ppD-dW_*fDX)wWlb3f^un|ZOq1`W>F$j%D
zbV3g)W6C_x4>){L;_Pqg@At$uJLWO33?MkbEmr6b6AOzpCsm<ETV2358gEKyS?>o1
zDpBs)3F!=&Z6^<tuDF+RRh!aO8}Qm5P52BtK|@tRl{eRcJv81qRh=-L9@XAM*@dM}
zFX%cSsO2Jy853CXQq18p{$_V^I5}^wyGTiWhC1`c>x++ZVjOHnkV5yKJ+dD8)a`z9
z?7`8OJi#^4R_i9S(xU4hpJSDSdh-hFVQfep%M)UZP0snn6e@gz-PwL$MkS6tvl6tl
z&Y890jI4)ApC7!@G0>0xT;0ug(B?tamIFx}j<V_p#xS(4j!Q~TjyeOcGg%B5|HmKU
zI{%L=VjJ^@NOkzfd>InCO2jGhwb4N%eiTy(D{7WeqaH9LYH#8SMor7ZWfEl)zmJkq
z<Y-05ft&zdLZ`cgJ||UfOwDQzpZ7^La=5vJe6j8j=%1WB&CGnD8LG@DHeg*6&bxe+
zsi0(Fh(_=H=uxmIM&bE!>{vN!eeiq``Wl~~If-E^K>jSFsfpitXnyZ+{xz=f-d^2a
zl^g>8$E>N@k)F=^FWk@<s&Nu=+LI?MM~x!KBc9~u^7pu;A3ME?T!7I2dJzBp|KpFn
zMHB|gfJ8u^lev1;yd(1laEO1QNl7m(>wz(Zm!jJ(l*;daC?BX8ntAW|yUu|LNxR)^
z_db;_%DAgs+99Eb7B-rY75{4`{rj`{+%*1DX9cPGr3Dl%caA1U!=|wQ%a=is$0=v&
z<Si^x^|Ho9;Du_%p<k#5r0=%x6Orf_0&lM@d?B*KRT-^1vrqh5GG>lka&Q1NDiCbe
zc4`^~HW7um3$c1!cJIKr23*-dycnG-8uDG4{0E>=Jp#(gK|iSsjh^0QmDfsdo6S+b
z@?ii_%}`34g{mWTDwz!ZK2)41m{#v}G#%}68vk~`&)w$4fj#yjeQxw#FdTW^Y<%r>
z@^C^$a&VSTVI$v6|9|L994j{M_SiJE#Lb#?*7CM0BVB<SyNN)6e0Bt55HzX$jPjgR
z)JKlo%GV|yal>}S#MAO0U}tVL1uQzmf3kd_NF5C#T1~GkxH4*IoK6V<l#2d9=$se4
zidD3<%<qQh0SkB`S3NWXo>XAjU0Al^-LXzV9GY8U6?$6|_=8~W;^yYmo;oqeW50NI
zWAV1RIctYse>`ZAd5%XmT>Rcd&QErLg+U+)e${prdKKM?N(aWcM19}Jgfz@7GKWfP
zKx=`m2oYOx-z-5d1|3wwM{g{5e>_Iu4#Ogs_wGbxy?giFnC*m*qd)%NIZ*x|o=5+?
z{#@Z>^#yd%0OygBlDfNt$;T8xk46qG4Yc4;%|s{Z>5e0(OGp6GLF<GDX%HZ2w!=>_
z9b|lb9HEJahlj}(GYpZiV#sY}H7$;;k1tWwpMu2#FYi;|{}V@|2ip__yE`&;oZ!JI
zs6afmHT-e-V4md*xhqn@Y7sz-luw7YA=yQc1F(SP4z3x3lo=q)fnN#@B2;~*<Vd3B
zPV>`{n~EioAiH90s=m_pcf}nEn~hDA!_#L!=2n<OzTl*(<i#j32sr=cYz3X!k3l@j
zKfna5n<0^leqBbDqf{IuKK}!RqYEVPt@6nsYCv>oI|Szp?ja|iUtGj&NxH!5fNfGt
zAsT6IlMBR8{_89@r?=Z5JLR9SG!lG4B6qG6pF`_yNK-v(6pCXfN6V7PN9!c7^sO=~
znA4KP6TElt9wY~7Fk<k}V;>(lg>>hqDa43i^J7Ba9kht=7$aH*=qLO;h~!SIrnTWS
z0&4OI5^-5-?8N}A3TSi?0(7t8SX5^Z|98qZ41tOm+fZdna|HtddPNH{u?1|iQp35G
z^`A}sBeWcS7yeri=QfBT=t`pa#7MK3f>w3aF2pZU<cx-j+RH+pN>t|O&UK8s3Ju%2
zT)41}n8h-MpmzX<VCKUxsNQm_7%BdTlja{OvgGFp^MZ@Wk&QI(M@8vex#HbW_3v8T
zG0cznAjqBZA%ynu$OpuhDzbm5>;FOmUj*n9d0jT2c`IE4<^|0)oU_R9#=*tS{U^CK
z`7HmF)>Qpu1-JD%i6PYwcnbqslU$;#040MceS2hMT;jze>a)z3j~w*B#+ekn9hI7m
zJ1g;o<czoMS1E>_<8>ST)Ehp}p1s{V5ASO38_XHrq-3mrg38M!JGSAn>LTBDRVt%2
zbX|Tr?`r3mlu$FA_UiqDnB=rRaE>!%qeCv{ctbLdC=#EjsGd>Z3n8I3p&B~I6QA6I
z6yl1CF@O>Yol|B!-YrZT7kK;)whRDBVvr#aeCq%o;Ab{Fv4=po=kUMn$M(z}>;6q+
zQ8VVC%BiHZmek~tN7d`9sfD4gr1B@`GUS!mP2Pq@_$SgvyW-way1iWnl}Odj(U3ER
z_yXjGC>+FJ{3A3v8d;T#SkD^4(x|eaN-}U?K!bq1DtPQJ$R1IxkBD6DDny?XFb}$D
zPbZI$Ttv$1wK~B9sTS6c2dZ$C7*XH>cU-8GcvwNy%cIREA|zxRL(=*99WhPSGUF;#
z`<e!Tn>B)~a$sO!^iOhUCKM%L&}6EHk)iQ&Uh!Gg{8U%V=pVfcY+(&L?D#VSbz50t
zNvWmTZk+;NB_@kg9X<?l5l|T0qsf*sii&YUcL01G@iPaD@6Vq<MWdW9P^M>P@x(QG
zB^t)Z<i1|Eh<}`$oSkh8m<B7cx%m!s3fu1!_I+3%gndxtB>AX40^t4gL}dk-eQT?$
zM$EhEG3<3}aF7=}8ejrmLoah7+yJDHeDe*k(e3vK!~NaI8jtGU<#!z+e#RJ%Dn>>|
zu*SgQ#e8{1S=mdK+>&+OT;a&jK~-QPB`4ag4~r;tg+)X}ynOi{aT4xmhcbml!(@m9
zf$T+MW_mQa`7(=LO6~HSKXs|bUkOB#doTpSK#5hVl3TM*yCMYHC_^>6l5=x&NJRu|
zZ@wc^z<_yxorCrLNx<x(8vF0Iw8$LIIKA7QMWdq&b!}J3%oOnm@J6tfM#!%M0QA~0
zlEcJ`{C|p}jSkW8QXr16=7B#t+9}6$?~*x>Fya15xuVr^DuB2NyZmP|iRPUnGS?@G
z9A@b0PrgA=ymya~ITR)jrZiH2DoSzB_0G!59UZ;f^@_g~jiz5DGiy5LwO%ncR})BY
zHdTB6Klk&$WXV;;3qY0tl^@WT#@gCjkV9t{la$zn+-bP}n5r=rOAc&r*cL#q+bHI>
zDMsg5{J^jL-%d2i@Il#%3W}7tK@E=nUw$l0!na=<&17PEX7XRZcJG}r783gJSd0Kt
zncm<Mpjk);U8=0Cj5veQ{Hzpa?v}v!W7p1|L<!%r4ju$wV{smI3ruxT7<Xh1NFVcP
zO;$Wss@L=*ZU@2S+V!YzhF87#ep+G2;rzQ>;I$GB6;**rJrzj&>&t*wz>tG)FwLPu
z@k*?on=513X$O^|3x`AjhdNk(9ypxQ_6F#K_QM?54=8p>AHM;O5m=#RPXSkl7cC)-
z1Tm=2_dl?+w~x;uWjniBu<<Y`u@f5&cR|^Vp-12roWtx_&<W|y!v8eK*Wk2<ZSFs2
zbqMAzfMgBmDC+F3pJQXvQb0$)ASqr9-hPj72g&a9v?oGqv1|kkKkZ~<=j4PI1?BSa
z%x0+-`(e_2Z7#|)<i@?9#^x4WOEuDt_(@fBp>|~tuhV*kHYIhnEg_5&%;Y{8NGK=-
zsidG>i@!se4*Ecu^UTGxHq0<81;~hv(4T7-tdhdX$=M5G+tq(BOUrbPG>^rp>q<&b
z?_?+t2)UQ`<BBL@_y-jG@L@EJC2;72|B9Z80KO-z5|F6L_U@%UWAsRiv&?<P4p^1n
zxa8J~BKY~_yDD*9A^Qh}w7E90_5S^PyA3D8`L0J*J%qY+eEW$B37lHl_lN*!Q6e$y
zPZ2-yMR-h1Mw<niheZ_I3HKiCAPjnS=Nc2scEW`Xov2UyRf_hma~>fWum}<E#S9V%
dj`*EU5|^XAD<R!;#B+ym?XrSY=0$@C{|CAd!9xH5

diff --git a/docs/articles/examples/vision/autoencoder.html b/docs/articles/examples/vision/autoencoder.html
index 2713fc0d2..1e7aade6f 100644
--- a/docs/articles/examples/vision/autoencoder.html
+++ b/docs/articles/examples/vision/autoencoder.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../../../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -236,7 +236,7 @@ <h2 id="build-the-autoencoder">Build the autoencoder<a class="anchor" aria-label
 <span>  validation_data <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">test_data</span>, <span class="va">test_data</span><span class="op">)</span>,</span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/50</span></span>
-<span><span class="co">## 469/469 - 4s - 9ms/step - loss: 0.1371 - val_loss: 0.0727</span></span>
+<span><span class="co">## 469/469 - 5s - 10ms/step - loss: 0.1371 - val_loss: 0.0727</span></span>
 <span><span class="co">## Epoch 2/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0712 - val_loss: 0.0692</span></span>
 <span><span class="co">## Epoch 3/50</span></span>
@@ -252,17 +252,17 @@ <h2 id="build-the-autoencoder">Build the autoencoder<a class="anchor" aria-label
 <span><span class="co">## Epoch 8/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0659 - val_loss: 0.0653</span></span>
 <span><span class="co">## Epoch 9/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0656 - val_loss: 0.0650</span></span>
+<span><span class="co">## 469/469 - 2s - 3ms/step - loss: 0.0656 - val_loss: 0.0650</span></span>
 <span><span class="co">## Epoch 10/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0653 - val_loss: 0.0648</span></span>
+<span><span class="co">## 469/469 - 2s - 3ms/step - loss: 0.0653 - val_loss: 0.0648</span></span>
 <span><span class="co">## Epoch 11/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0651 - val_loss: 0.0646</span></span>
 <span><span class="co">## Epoch 12/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0649 - val_loss: 0.0644</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0650 - val_loss: 0.0644</span></span>
 <span><span class="co">## Epoch 13/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0648 - val_loss: 0.0643</span></span>
 <span><span class="co">## Epoch 14/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0646 - val_loss: 0.0641</span></span>
+<span><span class="co">## 469/469 - 2s - 3ms/step - loss: 0.0646 - val_loss: 0.0642</span></span>
 <span><span class="co">## Epoch 15/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0645 - val_loss: 0.0640</span></span>
 <span><span class="co">## Epoch 16/50</span></span>
@@ -272,35 +272,35 @@ <h2 id="build-the-autoencoder">Build the autoencoder<a class="anchor" aria-label
 <span><span class="co">## Epoch 18/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0642 - val_loss: 0.0637</span></span>
 <span><span class="co">## Epoch 19/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0641 - val_loss: 0.0636</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0641 - val_loss: 0.0637</span></span>
 <span><span class="co">## Epoch 20/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0640 - val_loss: 0.0636</span></span>
 <span><span class="co">## Epoch 21/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0635</span></span>
 <span><span class="co">## Epoch 22/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0635</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0634</span></span>
 <span><span class="co">## Epoch 23/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0638 - val_loss: 0.0634</span></span>
 <span><span class="co">## Epoch 24/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0637 - val_loss: 0.0634</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0637 - val_loss: 0.0633</span></span>
 <span><span class="co">## Epoch 25/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0637 - val_loss: 0.0633</span></span>
 <span><span class="co">## Epoch 26/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0633</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0632</span></span>
 <span><span class="co">## Epoch 27/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0632</span></span>
 <span><span class="co">## Epoch 28/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0632</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631</span></span>
 <span><span class="co">## Epoch 29/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631</span></span>
 <span><span class="co">## Epoch 30/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0631</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631</span></span>
 <span><span class="co">## Epoch 31/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0631</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0630</span></span>
 <span><span class="co">## Epoch 32/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0630</span></span>
 <span><span class="co">## Epoch 33/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0630</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0629</span></span>
 <span><span class="co">## Epoch 34/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0629</span></span>
 <span><span class="co">## Epoch 35/50</span></span>
@@ -314,34 +314,34 @@ <h2 id="build-the-autoencoder">Build the autoencoder<a class="anchor" aria-label
 <span><span class="co">## Epoch 39/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0628</span></span>
 <span><span class="co">## Epoch 40/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0628</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627</span></span>
 <span><span class="co">## Epoch 41/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0628</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627</span></span>
 <span><span class="co">## Epoch 42/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627</span></span>
 <span><span class="co">## Epoch 43/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627</span></span>
 <span><span class="co">## Epoch 44/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627</span></span>
 <span><span class="co">## Epoch 45/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0626</span></span>
 <span><span class="co">## Epoch 46/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0627</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0626</span></span>
 <span><span class="co">## Epoch 47/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0627</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626</span></span>
 <span><span class="co">## Epoch 48/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626</span></span>
 <span><span class="co">## Epoch 49/50</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626</span></span>
 <span><span class="co">## Epoch 50/50</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626</span></span></code></pre>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0625</span></span></code></pre>
 <p>Let’s predict on our test dataset and display the original image
 together with the prediction from our autoencoder.</p>
 <p>Notice how the predictions are pretty close to the original images,
 although not quite the same.</p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">predictions</span> <span class="op">&lt;-</span> <span class="va">autoencoder</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">test_data</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 313/313 - 0s - 2ms/step</span></span></code></pre>
+<pre><code><span><span class="co">## 313/313 - 1s - 2ms/step</span></span></code></pre>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">display</span><span class="op">(</span><span class="va">test_data</span>, <span class="va">predictions</span><span class="op">)</span></span></code></pre></div>
 <div class="float">
@@ -360,212 +360,212 @@ <h2 id="build-the-autoencoder">Build the autoencoder<a class="anchor" aria-label
 <span>  validation_data <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">noisy_test_data</span>, <span class="va">test_data</span><span class="op">)</span>,</span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.1004 - val_loss: 0.0939</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.1006 - val_loss: 0.0941</span></span>
 <span><span class="co">## Epoch 2/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0936 - val_loss: 0.0919</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0938 - val_loss: 0.0922</span></span>
 <span><span class="co">## Epoch 3/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0921 - val_loss: 0.0908</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0922 - val_loss: 0.0910</span></span>
 <span><span class="co">## Epoch 4/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0911 - val_loss: 0.0901</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0912 - val_loss: 0.0901</span></span>
 <span><span class="co">## Epoch 5/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0904 - val_loss: 0.0896</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0905 - val_loss: 0.0895</span></span>
 <span><span class="co">## Epoch 6/100</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0899 - val_loss: 0.0891</span></span>
 <span><span class="co">## Epoch 7/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0894 - val_loss: 0.0887</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0895 - val_loss: 0.0887</span></span>
 <span><span class="co">## Epoch 8/100</span></span>
 <span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0891 - val_loss: 0.0883</span></span>
 <span><span class="co">## Epoch 9/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0887 - val_loss: 0.0880</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0888 - val_loss: 0.0881</span></span>
 <span><span class="co">## Epoch 10/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0884 - val_loss: 0.0877</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0885 - val_loss: 0.0878</span></span>
 <span><span class="co">## Epoch 11/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0881 - val_loss: 0.0875</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0882 - val_loss: 0.0876</span></span>
 <span><span class="co">## Epoch 12/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0879 - val_loss: 0.0873</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0880 - val_loss: 0.0874</span></span>
 <span><span class="co">## Epoch 13/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0877 - val_loss: 0.0871</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0878 - val_loss: 0.0873</span></span>
 <span><span class="co">## Epoch 14/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0875 - val_loss: 0.0869</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0877 - val_loss: 0.0871</span></span>
 <span><span class="co">## Epoch 15/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0873 - val_loss: 0.0867</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0875 - val_loss: 0.0869</span></span>
 <span><span class="co">## Epoch 16/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0871 - val_loss: 0.0866</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0874 - val_loss: 0.0868</span></span>
 <span><span class="co">## Epoch 17/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0870 - val_loss: 0.0865</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0872 - val_loss: 0.0867</span></span>
 <span><span class="co">## Epoch 18/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0869 - val_loss: 0.0864</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0871 - val_loss: 0.0866</span></span>
 <span><span class="co">## Epoch 19/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0863</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0870 - val_loss: 0.0865</span></span>
 <span><span class="co">## Epoch 20/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0866 - val_loss: 0.0862</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0869 - val_loss: 0.0864</span></span>
 <span><span class="co">## Epoch 21/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0865 - val_loss: 0.0861</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0868 - val_loss: 0.0863</span></span>
 <span><span class="co">## Epoch 22/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0864 - val_loss: 0.0860</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0862</span></span>
 <span><span class="co">## Epoch 23/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0864 - val_loss: 0.0859</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0861</span></span>
 <span><span class="co">## Epoch 24/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0863 - val_loss: 0.0858</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0866 - val_loss: 0.0861</span></span>
 <span><span class="co">## Epoch 25/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0858</span></span>
+<span><span class="co">## 469/469 - 2s - 4ms/step - loss: 0.0865 - val_loss: 0.0860</span></span>
 <span><span class="co">## Epoch 26/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0857</span></span>
+<span><span class="co">## 469/469 - 2s - 4ms/step - loss: 0.0865 - val_loss: 0.0860</span></span>
 <span><span class="co">## Epoch 27/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0856</span></span>
+<span><span class="co">## 469/469 - 2s - 4ms/step - loss: 0.0864 - val_loss: 0.0859</span></span>
 <span><span class="co">## Epoch 28/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0856</span></span>
+<span><span class="co">## 469/469 - 2s - 4ms/step - loss: 0.0863 - val_loss: 0.0858</span></span>
 <span><span class="co">## Epoch 29/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0855</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0863 - val_loss: 0.0858</span></span>
 <span><span class="co">## Epoch 30/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0855</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0858</span></span>
 <span><span class="co">## Epoch 31/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0854</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0857</span></span>
 <span><span class="co">## Epoch 32/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0857</span></span>
 <span><span class="co">## Epoch 33/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0856</span></span>
 <span><span class="co">## Epoch 34/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0853</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0856</span></span>
 <span><span class="co">## Epoch 35/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0855</span></span>
 <span><span class="co">## Epoch 36/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0855</span></span>
 <span><span class="co">## Epoch 37/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0852</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0855</span></span>
 <span><span class="co">## Epoch 38/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0855</span></span>
 <span><span class="co">## Epoch 39/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0854</span></span>
 <span><span class="co">## Epoch 40/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854</span></span>
 <span><span class="co">## Epoch 41/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854</span></span>
 <span><span class="co">## Epoch 42/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854</span></span>
 <span><span class="co">## Epoch 43/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0853</span></span>
 <span><span class="co">## Epoch 44/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853</span></span>
 <span><span class="co">## Epoch 45/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853</span></span>
 <span><span class="co">## Epoch 46/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853</span></span>
 <span><span class="co">## Epoch 47/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852</span></span>
 <span><span class="co">## Epoch 48/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852</span></span>
 <span><span class="co">## Epoch 49/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0850</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852</span></span>
 <span><span class="co">## Epoch 50/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852</span></span>
 <span><span class="co">## Epoch 51/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851</span></span>
 <span><span class="co">## Epoch 52/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851</span></span>
 <span><span class="co">## Epoch 53/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851</span></span>
 <span><span class="co">## Epoch 54/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851</span></span>
 <span><span class="co">## Epoch 55/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0851</span></span>
 <span><span class="co">## Epoch 56/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850</span></span>
 <span><span class="co">## Epoch 57/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850</span></span>
 <span><span class="co">## Epoch 58/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850</span></span>
 <span><span class="co">## Epoch 59/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850</span></span>
 <span><span class="co">## Epoch 60/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0850</span></span>
 <span><span class="co">## Epoch 61/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0850</span></span>
 <span><span class="co">## Epoch 62/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849</span></span>
 <span><span class="co">## Epoch 63/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849</span></span>
 <span><span class="co">## Epoch 64/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849</span></span>
 <span><span class="co">## Epoch 65/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849</span></span>
 <span><span class="co">## Epoch 66/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849</span></span>
 <span><span class="co">## Epoch 67/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849</span></span>
 <span><span class="co">## Epoch 68/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849</span></span>
 <span><span class="co">## Epoch 69/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849</span></span>
 <span><span class="co">## Epoch 70/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848</span></span>
 <span><span class="co">## Epoch 71/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848</span></span>
 <span><span class="co">## Epoch 72/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848</span></span>
 <span><span class="co">## Epoch 73/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848</span></span>
 <span><span class="co">## Epoch 74/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848</span></span>
 <span><span class="co">## Epoch 75/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848</span></span>
 <span><span class="co">## Epoch 76/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848</span></span>
 <span><span class="co">## Epoch 77/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848</span></span>
 <span><span class="co">## Epoch 78/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847</span></span>
 <span><span class="co">## Epoch 79/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847</span></span>
 <span><span class="co">## Epoch 80/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847</span></span>
 <span><span class="co">## Epoch 81/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847</span></span>
 <span><span class="co">## Epoch 82/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847</span></span>
 <span><span class="co">## Epoch 83/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847</span></span>
 <span><span class="co">## Epoch 84/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0846</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847</span></span>
 <span><span class="co">## Epoch 85/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847</span></span>
 <span><span class="co">## Epoch 86/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847</span></span>
 <span><span class="co">## Epoch 87/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847</span></span>
 <span><span class="co">## Epoch 88/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846</span></span>
 <span><span class="co">## Epoch 89/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
 <span><span class="co">## Epoch 90/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
 <span><span class="co">## Epoch 91/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
 <span><span class="co">## Epoch 92/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
 <span><span class="co">## Epoch 93/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
 <span><span class="co">## Epoch 94/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
 <span><span class="co">## Epoch 95/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
 <span><span class="co">## Epoch 96/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0845</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
 <span><span class="co">## Epoch 97/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0845</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
 <span><span class="co">## Epoch 98/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0845</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
 <span><span class="co">## Epoch 99/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0845</span></span>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span>
 <span><span class="co">## Epoch 100/100</span></span>
-<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0845</span></span></code></pre>
+<span><span class="co">## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846</span></span></code></pre>
 <p>Let’s now predict on the noisy data and display the results of our
 autoencoder.</p>
 <p>Notice how the autoencoder does an amazing job at removing the noise
 from the input images.</p>
 <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">predictions</span> <span class="op">&lt;-</span> <span class="va">autoencoder</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">noisy_test_data</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 313/313 - 0s - 689us/step</span></span></code></pre>
+<pre><code><span><span class="co">## 313/313 - 0s - 673us/step</span></span></code></pre>
 <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">display</span><span class="op">(</span><span class="va">noisy_test_data</span>, <span class="va">predictions</span><span class="op">)</span></span></code></pre></div>
 <div class="float">
diff --git a/docs/articles/examples/vision/autoencoder/unnamed-chunk-5-1.png b/docs/articles/examples/vision/autoencoder/unnamed-chunk-5-1.png
index ef0ecb7d5f34e5cfe46b39cb00999cd795b79dbf..7c791b98ddfa211cd9ae74c8d8f77faad568c6c5 100644
GIT binary patch
delta 53795
zcma%?Q*>X?+r`_Yu^TnEZ6}S@*lyI=jd7yJwr$(CZQJ<9PTu@3-uwSr=W^CMbI#0~
z+0SS1=ePj8wFtcG7dW7vu-d@i7H*vF{a4CaBG}(QOA(Bi@P{{Hi0?n&qBtok$}DIw
zFlyo~LOS2Lnf3K`I)jSK=W(vg2T!~6eO;4Eh#xP8uZ|Or>5eXe4;}}&c9HI{r+U&2
zM*q_T{Ey@Q=V2co^~R$K40_cD`y=xf!R%3%q=h-z+4=eTxw*N8h4}?Jgd}9Toz~|m
z^PT0|)quxx<a;rk_0(N_=UapMc*|X1p3z?194qUPrzSTxHSBdb>BnjZ_@Y|5Zf!zR
zSV%;0c&MI)x`>Ff<tWBgc~0t6^KvqlDb?3!5ev0S?e|>ZRA`lX>-mK3G-x4Kaf*?#
z*5S$u<NNo3i_j1l7?5oZGt<=T<nw#wK26jm4iH3jtkDF~C_zJ6T6C_uIJK6WUtW<{
zC8He_3I5y}?XrG?F7Vt)Uq`3cY>v=!V9HT_0~;)Dqo$~;s-WWcUP?J46=HSprg!JL
zTx0yj`7FZZH|7#?X<o67rGeYrSyOX=@$4V5XZN^9=@5`TBa3`2N~O+IE^r~V8lqwm
z3<Pl~%1_sTpe2WBEKacS*x9)Z+-{SW?RLRePI&f7{av}#-=RFAni#Z#)1c&dQnT`M
z4!+K^G~6SqRdBWHI(NFB&m<cL-cyfSEsymMNVMC%l@PqSV=g6>YYTtF=hPJYqsxRv
zR_<3&$e`O$U+dgvf0t5w&gEeE#wS&y1+d@L*yNDEY+mE-beuJ^fu@vZnXawZvyF}H
zruzpUbbrea<qpab`&qYY^IPQ<LL%|wfRkOF5WM+e&ShO%a9HN$toDtN5trszc%0<a
zKHw4P34D0J2omvg;Fgbv|DE3>6MbCz81x9NV$geE)kI#ApS3YSPgrE-wE}Lu0OeoW
z2KMHf2d8O!g%5m{X#ypcC4^*Sy+Xn*2jU+)|5%USsEHL}TVyQPz-C5T#A)$tAjSAY
z-SBZTqvF!Ozkx0sS6Rew=g{&*Tr^BS3O&S|81(8RlEPB({cpZgL&<Wx@P$ndGf<Zo
z72BAf7kQu(xD5`ED2vMo8n8ft15YaiZ=skogrL&JSOu0EJGc<43%2Suw-^1j)3deL
zo}HQNkrQp(6B{ngl#>h9D6}0TLcNDv;1UcQ^7Z5{FK%nj;cC>3pmpzwvN)?gw-qq8
zv_E(|%pUOFW6ZQj5aTYf*K$dw4EDNZ(ETYqxrS{3%Z)+*Re_4lZ#Kdiz^B+}V{dG5
zzQ09)4{t~?9`JYJ%HV^ycDK_#(A#wOXolRgwb1`AE+jcPHq20?BBpSdG=BFojkT}S
z4&G}RYhxndg=Ix217I9^JvDW2=*;+9&Yc3ck(rG|O^LZ@o^;dC2xzxuZ9U(wrLecc
zThXt1bIZze;d=O)*{30Zjwf5dc{MiBG0xb3$;``f6J{Sfu3JHImbvO0+A4m%S?i@M
zIMmu}d)T$UeXC9G*Ld5<qj6dLj^kPH>LP9A#U>p?k7Vn%_{Xy=cFT5GvG_0#*2s=O
zS!1c`IS1FNEeq&)w9WjB`^OgOXsJ)x##7<>CXl~^{Q%M(t<e^kMg2G;5RlV?T+?lL
zT6at8nr3FKwzuv5<`nt9y=!v5e)*5gD4rbwJ~BeC!}H;NHt}lWB#qSej3JY|&AE_s
zvhsrWbwR<$=^RqgK5C$<3NC1r>XOfPZ||Q)h7XElr8Le9BxIoYzyL)-!At6Bt)2*^
zD~|@jtXlMr3F#9s-RYG9X`xa?k5Kai0}=o0HeHRB`+cgq`f}?!OvJA-+LH4x5puGV
zV_uK9#yPDGglx~<coY^ap7VN}PsZvxUUw-7I10+W6r}aSI+J^O^|>$WP;#iXA?B-r
zU5otuOk8hHe~Q`*82R2MvkG^Ud)@c^lO%!L%FIH#534(X+jd+*k)_HOWQ`FUr?6CR
zVrjPxYs<&X&(@mPP;cX5e{w!{_7a)d@#~&F!HC=+YQvt>Dvzr1NzK`7*?x^iTuKsY
z2V4KjO3EE!Y=UPE<cf@v_*~FbP>-3z5Ahq|;8|H$&xpl-+3R@8-*w~ZUmEiI@8#v{
z^-g>+SU}UhM)NljQX_N~RwUf|y4H)EEe(4&Sp{QZtxFKv?aNzWWM=2DI}WwIJYJ+b
zhid+kT*97~tIy?rTDCt+CRzC@J2*y0NnWp`F*wvSFigY9do_RRd)=~-q5p*&gx~bx
zmD5??z{$f#|9Vkh3t93VcTeDXadK9*OgAtj4xmjs#BPC#)Y553{}G*}qN=jEw!y;4
z{e;;4el>f@Fbi(Q_Agm-5`V3xcB#_>?uEhYq}plj8G%$emGfJJ<!W|;=i?liT4vyM
z@l(Fa9bNhxN$lmHuLqX`UmgkrT2f0xYxT(dU6zu}r5u!AkMusbS~Fzc`4?xzuS{m5
z^FaFx5<%zI9^Uu2lB^1-fLc@~8<XtSOn%R2cyEFeOUh9h>7ZN38dl}_t=E1zK8#Io
z%YcY@xxcHjcb;N}^m-4TxS1L&Ya8=CoXkvop}gV~lVJ#d*;Nwv{)lWivMxsN=0E?p
z#~SR<A(_n~{~+q3P>Hgg3l~t7r(f`f0prvbcmrIo@t&?cGqYfFo*q@L<0#>J?{lcn
zQDJG6WjvO4!{ZFJRIN4O0c@8)IbWWf(>$N9z0C7EYnvN#D4CYV?K^Y6+(o|MeZ-@b
zuo4F8)SKU#muU<B5^6#D=EztUy-`tCY~pfOSD$`!9#s_gnlf?4@C`i4j|FtK3k(kX
zM7TZn3*0$0uqwV7ces=h2e0YX!_F5k2rmv(nH^yzFLSy+_8U2w9TD)`P29KGm|BV{
zcuvl-LR;{A+hXWPyaGHci_`KNX|ecrgWLZ14A=drC9J1GK?~qr!6>;Q5kA5H!cA(E
z;$G?5==i{u;6_6u=s7%vGTL2~fQ2QVD|FD8J0yaQ&P=>hvkK}V3Ag*H(f7TtWLJ8-
z;S@%v&{qhgc}8e+b+@TW8eC0J?4d!@BSU_JZ(As#Hx07*UfGjM_B2}0g;(3OzIbU*
zNS73l+}I;GEU|`Q>t$i{`AuHItB9&*zpW569GLB4rKqXZ%eM|t(R{w)00`;ef#b62
zoqJnn7w4F0*l%E@RaW3idf-U7nVuXsPDQyDd*h>rk#Fmk5y#Xa@7M}_?{m3}X=%P8
z0U5#Zet~)te@pAqa$-^n8aA>?C?-}oTQ+W+2{2+~bTl`){I}m>z&^0>o|m0-*Ja|L
zj)N!h-Bz@4<)1~tw9xv01J+8jQ!`AAJ@{c$7?K(gWC-bg<P;OyRSqp)rxWd2;@b*W
z#Wo=&DXrog6JL?+psM8L?i!W%3;X$+FCF*Kw)w6*#ZU~fq*e$M;XjHUK+qwU@d=yR
zIUQ=3QCA8#EUJl>O&e{y1m7{@lM<8^S66so?FPR_^4!k)5oQld071|l4(DmZ8|OW=
zoqgGQ?>JrQXd4%|VZX-8EOFH_#YB}3y<s@d5a~HCHvkYPF*Q9E*_)%FhLywO)?;g>
zcVLs8oJ^QAM_h-j8@B!6{6XJWHZvcYPN99oQb|8GylCmISVVk|=5%xSdpu@#9@mM1
z_Z3Guf{(4Kprgk<pzf>(HmV&-rbGOR<_o?D=x9n#=JTZMkPU0P+&8*|i?>wUDxKa^
zINaJf*vDL}kr5P?o&+69(tv87z6J1y1JN-F$?0n9nhL9P8!VjcY%gq}T9(;ZR6`Ox
zHV8AOY_AJM6PdqGk|5?+*m^gxz$mb_E6cPtu%7SsRS=HA(i!XPjLp+GUT6R#HtLm{
zrZ6k7rlL95+4Pmp`;A2Ecc;huqZbDLbM-{ZYn(~<hU~&1lDQ@3Zq5rCwla+>YV?;i
zj_MkRD-et&1_pZKdVU3*?;{*yMTQp<N;N|*Y^XF%iTP=&raUAM|Jy|`E{I^W(;ZHm
z0N=8FNHzk1zxJK5pnfHuKn<#mVnA?5QCwB2yA6-bY<KA&zqxpuDzqC;wy}o`87(!(
z(OhYLffT3MnAl;_Sj&c*h^$L6=@+=R>-g<tJ1HyqbwMwetlb_VC#KrY#|AHpo`|ki
z8r7oD1&Y@#Au2&jb8%yOes)!W)1RB!X0!bRbZh}^nG_IRc!rZDUuAbJTFGIZmb_B)
znJtB>BXu}ayFE6AN)1sKnGtZitsIPm^Y}y(?<&?@kDlVBu5!0{Kak{B;oV)liF<5=
zeTfvf4Hqqzd$ZBzuGVuXtb@iR>1XKf(r8XdN(+dN4+Uz2mQ<;*xc;T5yvWGu*|#_{
zuu=n5B%L+Y7A}w&6#Bd@Y8A=gUias7k3+u!IUPZB<*3E^n}QcMG_psHA}<Jx5qlAQ
zvceo=1M$iiL>0W=-2|tK4Ilk!$>U~YM=Q^&(`StR^N$10?J4lXQ+rh5c04(p*Y9aO
zwEuLTaHhq~#W)EGNh+J9MLi?$JKn@)HF*H-PQ1s*r_ivrn&<m>JK8W&yR*!*-m$r*
zPgx``t)Z$-*>oQ90al0~U*>)40!hRHZPG|ZS$a9o!OG)sRWi7~MNvJB<(gklw=P`t
z79en^$AkE;HujJakr1OM_oh=W<M=5}pSXS$dzmND(Z^~?i-?VmA(*k9XmUS8g#*7L
zk~3=>oSXMCzvJI9vsBhWe!D_d$9;K9?dk-0_}4b)7t~V|aj|TB@AG^HWU-j|Ra?RZ
zn3iLH782+FG1|vKi~p9{QgG4TD^5qtpwoPT2V!KVXQQdCYbnmP`TI=AZ}V|d)B6@#
zJ8Ss)Kj`jMY<BQnmNR+t$jPX?hJjzw9or@c^M+r~-3$~pF?3PP`q#N32gK#lYU(<S
zfV-ANJzI5Up_9|snUjHtj%X_F_iLFglR`157EPRzQDy^t7rkoMY1KFk)Gvh@dnSB$
zOT5TpjM>HqU|eB2B?%cm!&kFazxM40KLj6d^6JifYfc94GqSI+f?nJ`Xn>Q*duxNH
z#`fxhvMuAL%+mPch8$=COgM0Ks%aa6mEq2+eHyh&(KKpu{OM1PgNV!JdEr~0dvllC
z=y@ps8!0O>JVqC=j6?6!eVu6YRe)=4bwOSwB@x$aa(v*Io`#Trq+jrTVLJI!FE27&
zB%`GO9xI!Gp_P08-NCtgHxNl;7^B-hK~r94(epe}^$>C@`0j3E{-><77KMF318TFb
zma~ZbjV3HopA=3C8<IZSN@^v5q6|0PChK`E)}&gJ3Tyd^481<QJiT4mQ0WqszG(CH
z!y4C-^oBVj(1kmVSw3<5(7-s4j6(OpA;V214T1>I9xvlbM-%Fo9H95twqtuA=aG?@
z`$E+GkF36#il-8egpS~S0QUv53)rm#d$@%Jx&KD9(}q!{FEktp9y%7&v>YER%#1VV
z$=DDvVY5SJc5^tY?W^F@>{8y`*jZc7%t#94lJkIFVRh{L=W2<)telKov&8m#>Xb#A
z(uBOvO-zRW`?<*F4>VAn|1;TmYtWN7-Mi_VD?5A5sVP|*NQk)L-+O6Aa#><}0S77h
z;oaqPRql`|P!|#pOG}G(L?@Y!+X&nw-Hx!G5`{*^OIx-c5INu+@*ri3^ph2KQc;#y
zSeOdMg8B94a*~tr=e<FKY{Wn17_44lU%*<8fa_1unA8vhfModDu@sOxwBu9f#1Mkh
zK8#%|Ha6AYJ;Qr_G&>7r{V_WSwyD3u&+7YthxEiTm3d}UPCq0O1}{h|?W(^JPKpz>
zQ1NcnKY7Hn0XlB=t_yi*vN+_4{BpAVZ@+##{S92Wmn7GalX#neIoak*B#Mo0zxZRb
zI^J~5W?Qf+u)Z>1>h;2<LX@Dt=EsIJB)>1DuDiPx99rQlZ0K_dvCl>r5@=ka5Z8+8
z>l$Va+TP^%40;!lGh9yHD}v&mSQxyGBW*HUtoT~-u@~~A6D3-asmk^$l+qL{Q#AHB
zgfZKAI=G?D2W}to^`gxe0k$bsrMNp{*v){C;wQ`>zy-}hjQmWs?RBUG<vGuvb9)Y2
zrq-gIisn+_FScIb2e)D1)UMA5_+#ck%L(kP0l~|9lcjzZQERd*Nf@D5UF63fmlT8w
zc9~#)r|5EK*pZiClR8(h5mI!trC*s|L3?WH9Js><PWO10ZL)GQGP2onU)jh7vJyYw
zEC&zrfqp)iHoIxdO+<x-%qsNo%Ec}(cq%LibOgn^r3M|zx2<5f3I0ffnNWT30vi{D
zKNnNG+~-x>yj)ED?A&0Iw^7pXh;R>tpxt#x(PKOPcYLQp+pZ5&wGX3jpVOkBgu=N9
zsXDf_?RicykvE!2y-pcuxbT@4_`AWsvl4*{80Pj0gTHzDCucV@w*OlKlFuyYC%v%V
zKozy~C|3V6tI@*=0B{sMgNSSBt`G#v_LQ^^c-LC0X5@zq!7ZMPs{pkZT3ZhT+(Tz)
z6LBpQjv%)I@&c4>?DUtbi)VvZ%CBj;k61o~2*Ot0;F-P#Gpq5=7RUIE-1e_?Ts&)^
z`~gqm_jBOjX2%Dt%bY#L1k(M3lXG1?>%3nPk|_VynconUlf`caZW^YY_ema+8V|Av
zN}11Myqf|(v=)2iB|Jh?UAM_?_EO<?lq3I+4v!C<gwQ$FeQL8k9hU&><nsK))W*uX
ztg^h~+cMof@K48;U>j%ii$^5<b&EZ)pe~J%`<#j7(!+?xhhu<rg2&G1H8b-Z)62$j
z={;41sqs&R?%a0E)%*a7ZZY{dczWBX<04|A;5lWa#z4DdHj>|cspOe?T1PJ~qTL~I
z&iGrh7xUIt3tyKbtQ)(ozN7>$8>c<){i+VHhj5SKwun5=FYQ5}%S;%wo<>w4S9r?C
z+f_x>%uZidR$s#ryaiQZ3u?C`SQmM%-37y!Vk7F!ziT%zTOEQFjrE>@%X9g?WHel>
z(G%P^Q-mUaKTes$c{k?1()$C1xWx~>W@A=VOiMTBnf^{*S=3gLU5`rIY%Ae(zngef
zvEmz76tyZq@$_x}ShezBxtckk$^K4PGxFb2_^H8<&bH4nZ;#Bhr7+S$d{{bnhwyq<
z`MJ24;NIvN=td@8(~}dfi4jQo9Lv}JOf?ZVoH8k1Qo2|$Au_p?IC*bT!Q_4f?4YR4
zv3i@e+WegAtqLmop`2P?28HNT=6OQ*8g%F#c=6y^&o-}`fM#TpM-KFNxWvb+DQYav
zEYAwCa(~6b+D)HtW*`Q$S;=IYUar5RAlX3v>pnzjcEoXI9j&x(;xTiu;yBs*6)8ic
zzxNx4e;r=Czi&UT^Iwmm#@ynH8d3%x8oqr|AxmYWGwG!an@_FO75)7SsHvD~5l)<_
z^VZ}WJ_me1Eoq^??FcYI0$RRl>f2Mps&s&p>e`)OnCIZ-c$Z*Yay>mYw<@Dz7#aR0
z85ZpC81Xmh#e1;=_|Xn$h!?*Mqu*gIuxrX$uv!Z%QWqCxKth_^GI?9p-b&Cxh@@k4
zeujtpPeN!>-~-WH{C2oFx?|#C<+|tWb`JB3X|hL{U=1nMD;m&{U!LRh_0@8b`YEQA
z{6Ni4yVZg)WZJjnCA0<8E3Xy*kh;1K8$3QiLrGsMvcm@Q#Oe1{3FDLcZ-Um~O_&{$
z?kF#}c8q5$WF2lw%FEu70Ucot@g{<N{tg1!_+S-3rA2%dN*gX_n7Ew#SvaYQ$Vdu{
zavHEyC<N2^ftz=}0gj)28ezQLbL*OFx}XCU154$;-Kd|PJQ?rV)5A7WC)@$hi@mo$
zEgZevJUFc-64GBOzTpeNljRzlJKP8g>9T;~c3A2q9=?YgG4pWEEKYx$-~)X;#Ug*K
za##zqFO;4c+)f~c%XJ3AQesUDa-3(w4mD(k+5KBQ2bLv7C;KKyC`<KSo<lfjRpM1S
z8Xgd3kRka2iXR3Dy}T8jx!E-!8S|c6khtJGf5rv9n)L=;xNE%Ha)>)B5hzGC{RfuE
z>;mpY@Vo8S6=|rWDZ77KOb8^xf7;n{k-gpt!6iX8V0uX2G$L2Uvm3o=BNxru2)ggu
zDtsO=-m(Ick?-~dy(eWBX%f?|p94ht^8X~N&Nn*U_2vO}^|U11j|p#*xbqj{z?I%`
z<5paNf}N_irdMFYu!gvtdWkPtXhwIn8JHY)>ObH2&QyGHkDP#!oU^*T!)>ICCl}Wy
zF0Pf8GzZ%UxNsSoBEc&JOPx2w4i{GGFQI^%Deb#BIFi9*J)^T93IZGtW~DbW8rxES
zL2)jyHZcDnsVIveTMrfSv3=P**>vn(T;07fk??$SowHV^#;}vq75&hPAm7+-yU;VT
z{hYoP^jOyxQxd@bIPP8ALA&c(RoF`v>seCMgDdb_qKBAzE}@QtYO^hjE0w>l`2rx~
zH+Gz0p*<0|;zF{$^`h$Cz}pJ0w+3f#+z*^6+743iMY{a3JuDbi2^?iVoXp_0o15u=
z(^dEQ!$eP;^aSGZ*mY2_HF<OR=1ilO%bDO4oUZjH&9ND8<V3UO1wm$vc4BEA<o*e1
zW`Zia^pSXWRP+<q_8v|3pbRkE!hp~aXavNj)BSC?`S+ct*#q%#B)r->hnxS{pHN<z
zkKH$qgBxVI!C3)t5T&c|E>RsQ0u@bEm<fC38It@D1)YBJiJ79RosNUM+NK(>U!^z+
zWNS)EXpJ5jFC5%e=kE57%kk^7(!rmCfdHtxhzy4|tRK<01>bS#le|7&0JJhqasl@*
zH!kPrJpw;mlDf)B$i*95qaN8UUlD3b=(U}i4%~PsiHT~f3Os*^#}6Ph311Rv{OICm
zozjI`wUQ(7^&Y_c?KS*-HoRL9h;)2vUVNIGQQzwIp#OK|6U05=cT^`c7+UQoGa<EA
z{L~TWLXqX(Ay_I@4}ggj0*3Kb*Laa_H->Kej&Cr}E1L!1k46_o=gUDg?;$4Mh;Ot8
zQjty^s(bnU;sI-SZQDjR4i|gXy#ayur^jdW%4+ihPVXfg6~$j+%xXJxVN&vj2#ahR
z#dVJcRWG6-cOjWmWX^a~8;8!8${mh^`kzK4WUcXa`5!I>g2JRd0VT!RmB8Tpwp5Z>
zqMX~kCD2bjHP7KwOgZ6;re)nap(vcQ#r4u=E#t(Twy=>75(yCXCmL;P$v4?MPpo!A
zo)-XhxGg9Hjct^eVDC^*ku@AYMm`nTHH_rYx#A+<-@dhL-*pu=B%94HC={m8Qq8!Y
zj*qd$2RF1CpA;I{ukI?yC*~kK*<L?&`tp|r6}-Lu#GG1+2OWLSxUu+xqt@xl6)iD7
zQbB(6>lTg_^NxP(!5)&$%x~;4QsuBS58@bmYImfRoGWkl2VtRUMr6U(gUB-l<*a``
z<3DXoEJU3YMZ5!hHBfW?p1CzoO1{`FTg7NK20_C}l^!Jn8Bh$y%8G8fu7-<@-#Uol
zC~OK5y$dNc?sKCC4IbG@C8YFzNh!!2PEF5Z;;vcmK=Kof=9NJP+*ED9J6W@Web4VU
z=3r-DTGUaC!p1vu9Eat#a9hEq=Gn_9BWtimK}E;-9=Gf{^?Hr)0j)k*?AN${8OwJk
z`bkDQtTD#{q>BymA(7`@??EYy)1f1ESJ6B)Wk;k`Exjv{=OZmU(-V50a=gkshaVKI
zJv2=7DZ$BK!ZHiWU%~KAUdMf3Z^NZSLSbP*SN0BeRn^QaFy0P8&%m9v`j~Xm45BQF
zPH&UAbJ_$RPgb=~SZapR(=~w~Z-h>*H3<T0B4HD7%6c14N6)zdf`JWp$pA6)wPwZ1
zS0wjbv`p%OM2_VyO$r9YA>CT1?)LXx4yp=P=pi(U>3YD{*0j@b&(18%XnU^R!zQ@h
z{`D2K(TkAucFJWrTyL=}y2KuA-WvKqZJ;VJ+QUjx7ajk|Y}vD?*jpY=^NqPaX(?!s
zr40m9Gm^>rY8~A034W(n)YfM|JN~K~>^I~N5dv$|#x465_IFM>E)H$a2uD&fnSInC
zBqJII3u4|@9v|t|wbl<CR;vl?3uqL;1<|M?XXC*#^UOb_W;BNhrNo8df@nD!9D79|
z=XE>qQGYf8zI@5u8x;)&J$n@VY7;+-B*B0&S59?~X$6I^hdwW8XlDjIe<O5l??Qvz
z<G~O23W3a{l40P9b!he~?U>(Kl3y%_0O1wV9E0D^l}#sCsbTRggS3s_;`pdr5@Axs
zlyK#PU;_F2vTzVm;UwdEui?!O&((_>mN^DN#6q=PTdXRamKc`1V1fQYD6?=+nH6}D
z@x&6ZndhHS9Yivtw%*n!e6#B!aT<rDCbvd8PRDjDNVw;?;ERxf(%=#M{xPfZ@#gID
z{tgo%)>R;~o|^Qt(ILQGOC$McI{_tUp5_XhskEq>Oa(EcuJvxq>^pSiDr5mL_>A`T
zvoHv+*U#K}0gqe<H7(F^k)cKwg#p(a!haIf$Fr*%VhPz^p@>n$i2Gc~V^M|qtPoVl
zTs4YOoGa7$@~FFmout05b><+(Fj^(7Iy|1muXabv&(%6#!*SlZGB8bFcv35Z{W97j
zAQ1ZSOxFvr;Zd^O>8T4DE3e#Wd*CEzYXtkghcdi#Ljg9q18&~FgG)frs{#~+-slc~
zYu4la^fkU!aKVN#!pWfuWyTnePI&%dw|+nRuyEMiOSIc_k2*v@3x!g9dl1E!E7b&l
zAQ5D8)jv*8iK6p5PXLZ?=F+YU@O|ePvtPbhL1O!uO)Gw?nP(M)TU0$08YPMEP)#Hg
zM`{~Umfxck1dQ}gw+S#36#(r`mMe&Foc+gkRRqAyPbJx;_)q8jGMM+mmm;d4>l+zb
zmx}XiT6w8+*KQITaxiP7^8-?(+@wL!?wQ(T>z6NtUnE5Yl|f=*CBaPUS!d@LmNq3+
zG-I-I5s@vI`WALSRYH)qWCNoAO@FvU^FgE*`LZjQhzE9Lo9wVyN&>>@YKL%tUgq;4
ze%d~GM(YL0eA)STD@ukwe-}nqCioi_7e6&dM{$*(g6;K*WK8|*((W#5QaIw>!{3HR
z)`j?ellHK14>VgGsVi#fD<<MKwenYN7XvYVDizOKIC^J%kwT0)r*NP<<VTd5xudf4
zAckzorkCFkz_Dc)q5?>`#`(#ahNgwwXB(O@@T_0==82bTL5Dm)vLG>vleTn!Ryz|7
z2?Nh5B?ju1Zugu^m(TmL0V%7ZT+owi*ZVF<!|MKftO&9zZe37$MHLojvz)|Q0lpv5
zG4dTe%Q5ol<oWtL{#Ue|R}>S=hP54LTx4u?>%HHK^}$j@egkjo=X3~c==uM?+y_BD
z@ao{vMxI<Ipl@frK`Nvq`KSp=J_PN@YdvI=AF4P<5D7{T(IIshJ6~s&kRpwjsQkFz
z9CjXin(vE)w`}Vnbl!C6x}EDU#qS`r0HQOr6xZj1<K_!DJs-oAtiBdS!Xw;2-2HWI
zAktM(@RD_#xdA{??i(BxnwgAKD@5IsA`yi{xmhs!ozap%H4*l%C2r^{TMTbW_STI1
z1YnwsBRQREjW#*rMeSRwNQ7ZFIr`UI0zSX1**lb;8_S8{RI>%@(Cz#iyNVve#8#b&
znZE2FbB{gnPOXWCL)jlEMMJ??j0zrpWF0;c?x8se*+BpPsh586a{RfW-Uj0CH@eH_
zG>+-G?Lw_LzT$m<VI(eNEhA^MlhibS;!(oWPuTWv!ubv<Jgi&k<BG++X@{UeRwBQ&
z-$j&4a#~-|nJ>+9QtQX%YB?d%kgRx(J|FKrO`_o{Y*sY$wdC0h6Tji*HbF*`&NTb0
zU0G{3DL`{<20AZ2-1>q}v-~h_Wanml8f>=AA$`8)07}X)uRUxRSCGVl$X5yC=Vqs1
zgCBJ`vWHsmDJyPhqog1qBYL*;ZY*7IMipymb(RS2aTF^3cs;Ku+((O66R;f{IiAQ}
zD!Xo&W?;!}FJ~j;GJ`>~S;k*fSQ@K>B>rgU2V9%pP|W5Om~KLb1vUI`?yVdBdJL5W
zVbxS3n`$}9q@a1bJmC%H8`wA&=9S|yyum++>8XkP9tviSM|Z_uSe-^Jw@pm42#GD>
z`d94@+%D)~Qvzj|njU#W#%Zmvo*5Sqpr~^Qo?6uXOl0H2Utbm&L9qOJVQo7h?(ky_
zK-a^$88K~nH%e7^^i?sI(BV^WYq1^cC{h^~HnhdHULm<ypa96R)BbD5<N1sMnD(za
zBauz$*@f9ExZ$EB>`bR7blh}>6>eXGlrb6L#QpsCU57Ze<CJyy8hy#Q(I-JzT});1
z(<3#JL!Rn6QB<;k`d*$)qmYwwJM>!*kTcP80MC=h*#A>N-p`8w2Ll2Ve{agJzy|3=
zrT)u^`N1U!0wY3t4+u(2*@&)<$PiR8>z4t)9CTx>zwW}pj66MG>5;wi^ckGtj0xYP
z6{=}PO4j>(<B5#*LETu^ff~H<3x%7BG4zd*g^Br#txZv7WhJ<(mR3S$ut5tCVC|GJ
zh-i8dg|??}l2f?W<aNifMwnxO9<@^Jc+{7V^Hoj{odYSC$&8gG1KFc@MBWjO<yBeL
zlVfW;>D$n*#1B_8Ctr)DiFM4hr?H+B_BqFa61GQnw7rN3!V6PF9Y<G5ef*$ZeG%u&
ziSbfQ*&dS4_{`Ms;EWK<-W3}>z|EXp@L1>5J7>^&h?Jlzy8O?CEE$uQ3xaPpHnIEZ
zN<*>#P_F+w_JYv!aH$6FAo+oKN8ha2(Dr09_f>C0E)uD`#?sMIMMcHf*jQ9lP}TmI
zOVuUap>$!`L-A0E?GE#j$|kn6j`;9cql$jx1`3VICMd3_KwteH>*O3Tt*DfOd+6{G
z5%xvUjoGqz((TH9O*bMTMa33k)^}N>i86YK*rmv}HO4L@=VN2{SI5}0Vi<9ceWJc{
zeH32ubmTN~lCUVuBgA1N!=i#eQ1bxi_q{w!_xMbfEZk2-2%#^IVNPc^;>i&x#&7v{
z0Z@B~{@Uz{J@5(Qie|v(^yeW>K=HGM)iQm&y3g)2pd{auUEET(C8N1gs#d9}q@<*{
zlNqq_(0puZV`b-(nbO8d0Ec_0(|NzTTX9O6?9w<?dV=53c7U3oJ;li?J~S5AX6US}
z6oatm9g4(DUnJZH{+U8@qDQvJPH%8{KnlFgYH_kXf(gVUvjZ3dagGCL@b*QUD_L~O
zC<a6Y5P%;pi?in?<#)-sQ1x*Q!L~NU-)=S^Q$>_%U%!W7zeH-bKJvRpt1WWyEG&Lr
z#Hb>V+#1;D2|ES8jeR*LHBG-WOy9)JE&crA$Jo-W1za)`l(grUrUlq<lCwCtnfOEU
z8O!g`qXaSENC8^fv#Z^ysVVV?OE=w4x8;+#<gc3OL&MPNAC10v-3tnqJ1DU#)9kzs
z`xCpEKR~D?gd~;~qi!dLfMq1wi(8H(Bdw<LR$_M{-i2^()_PyS-x1~RRX8kWyQHHN
z&~0JmAA<t<d`Jb0>l#JWOo32ncQpIW&4&q@Mho>1M?gRuesod{%I-t1169Fra_uVA
z`k_bfVDVu*;-PUde&3i>6sk!h)zo@7|B1ezrpFdua0nv8%@6m+n0`<64qm41<(K@H
zA3S$d7b}~0DqjAnZbn9<^?KX!_V%{@e$8DnYC(W0W*ssI939nF0Kl+>0{>-_jygBb
z@hC3W88B7!>P0>|&d%DFiL1;_PGB8`gnzVoKZHAYu}O-6qj%kG)><8U?U~p5s$XqZ
zbq<gCrsa`*f%mR&V^u;uAqg(dzce|xI=kCaI*T4Ct!hFSk^JLqak*VdOxxQ5l?~?p
zw3BNKapQ4(=0A_R9R)dAacW9{hiPhBUEz@%AfxZw-BF;^@X|y>qUQ#=AZTLXqfk-m
z`j>|WOHI)CMhbY5LsxHuEnk0kVS?DaYjV2E2_OFlx3Hk9YC;=<g>z(KQ9?ZuWOMKb
z0);N2+Y7b1elM(uux+zv<5b^3w|8r8N>sLPGk(|oD+svCWQgm2Lhh^FK}JqgS;+-}
z$8SFH>YCvKw~yR-Msf{o^D6QC`4nRRAYJsF>smdgpWF72V-E&@!6TimmS&fbM$VQB
zEzSdvSN>rG?+0%tl_7*6Kb`TYynEs~xV)Ncu)n3YLku~mQ%{aKQIIr9kKZ18nDaw1
z%VZDZP$VuXBSbaaEl6zJC<S;}j;RuW20VUCBO_DO-?m4{dzwXA2errqP%M08RMUF?
z`xe7kod5FB=I>;eHr%43v;;UYnf3=B_4u;e!*6i@Z9s68(RFu?(Nks@Ma)}zTm<sT
zO;GzcyI*cC-O`rLj^{{jZg)f+w2Z`f6cyud9a~zM{V}&+0!@8=^W@gVHXq>u8b+n0
zgM5|$pwdAM>*bT43XVX}a4AVkjLXqj<GKiN&CO~;4IggvM_|05am)gVC1m#a6B5ui
zN()>bGXul3AJZ&J?Hqsah29xx6^WIfTUNDBzMeH}m@%OCT)Ok<`G={aqZ=EO<?FOM
zQ4ftuSfQgXk4=tGjQtcAY<aH)u0i!ix45Zf4W-8^I@i#_lRjO&JzbxLvzC{37n=hf
zmh7W^nTF98N^w;5zgU~noPn>kHxU<JMBDNa)pZ!9V{)H?v@o2%_&EQhg@1FVzMgfj
zpAs7LHHmvRmM{+##w4I&qHfT3jl$G$n%jSGyWITi1x{B;Nv*4M;|1ac+Q02xDKW;D
zQxA`N95{bop6NYdTb;VE-yvm3{bS|q6YsZYZSBO+a2<=RFMM<Hm8yhZurOV-v8oo$
zqNwp~PeagmMuI{@pyA+PtT52FK6%e?Lw$q1!fevO<}p1-jW3`xu$^ma_sEqdsQ&#q
z1TP}e!GBji6Byhz+UhRA?K!f3wk7(s*slp;*|)AMxQRvr5sHG2i9k{bu<1P(SshoS
zqSpKdS^*lecMXu$VJJp`yR+0M`5y;+ZQ~76uM3VwOdQnj0C1WS{%RZOsa&K$ZOVlk
zq?+0rfa}m9Q3qQ-)7R4!84Dpf4-8pgXVf_$1xZYNQ_vwnGwcK6OZYtl)<W=u+!d_<
zNO`bI=vi~C<4>!^?3frC{mv@P&CSowF6dceBLzi>T|MgU<yf%Kbf+}UWRNT@UJm|Q
zMA)KAY1iow7Nn<eG;Y?9cdz!*YxhaRu;xfmUidV$PH+;E5KNM^TA#(qVOYle9}C*?
ztq@?4uAFMvS1SQ>lD?6#x?mI+xfiI->Os$bzzILW(%BpOIVSaTx2dcqfe6eYpOI-V
zAU-?f$E<U)W`(AlSZ`l91#Na2@S5YkRjbw?Z;FQ<blidMg>!sqSzJmxF)R@g4iAx>
zx~pMmCCVgwGG7Yvf$n}!6=?ZGLbo!IuWz}J@#Chz=?lPMu(qfZgLdX-o{7r=V`TFh
znD;vyo_&`vRatlRF)h}{#a_@*i=sg4LFG9@O)Vftdw4SQ+Y={IFfl%NPQ8VK@Yi?n
zvabA%kN!?DCh*CH^ey{(ht3DmZ5h|osh7rGVb7w$JLcPhaeX$s15vPyss-$DaVrrS
z^{4?nPHSKn*4_9swk9tDV6$Z?41>wO`?T@#;K}krKDoB?@TBq8$Z1G<C*yLxJ)j~b
zO$1x6)*p6Bx>t%zx+19Ke+2$T)WHqC89+_jlz>zE1QYG#32N;GJ=O}CCz8M0KZ6J;
z$S!qV?>Ix-Iry8MyI$iXXI<7B`s^iapgBWlZUFJ-<Mc8I5|3&jp?%99DsRkH<(!0|
zU=XrPfYC*4*LQ6KPZ2{WjRC)L0lQQtHb#N5>L%36ug&!)^s~6@&qGJK<^6dd(Sq{E
zNB1c8>Z#}5vP)JvOYUa=35elwKD>&*S}0Fd!So;!8WD;X<Y@A_9JO>6Ge~;<ogHv_
z08~|9{i>l0GqMCj;aCw<GCFs#ZfVIn3*d%Pd|mEQ3-rM}Y;3?P8F0b;z->1cD|PvS
zGw*^NP6AY}i-s4Rrd+_4S#_wE&kCT~lJsy>&^bIBG~@iM3~|jhmj%|nPP`qVJg|tI
zy_fEVin%k5jmu+rdT>MdY%B{AVSeod+|SaE=&Wi@NzvL4ueTpNjlb_SD-Fto|5e^^
zysn)={y<&xc_F=BGgxK&ZRGw!w2<J*ff%!ljm#(d#FNQaL^@JODT94hDXf9Si6Svr
zt)Zv@o@d7{t1B4Iuo1_0w~eU>wrR&9Z)|6MF#1?j73JQzo_@}d#%Cv@XBapK{6Tb6
zTqBcf+WDC544)sMTJCo`!moF_az{&lEhwutIorLpm9wxi(-pHXN?eE0fe?HasJq1&
zam4L}EtRdU>cvsJ@jj-)q1-~fyOxpU8`+7<8XW7hWf;>5-OTF?fO=}utLF+N-@J8X
zjW_Ct;QB$i@S%csL(m>bi{?cIpi@R21e<JSd3l&wyX}z3X>Gm!8R&1iupMieuc8y|
z?dl_^Y<8F=s&zo*fW~Rx{zAF)qaEDUonma2sPsUxDl>6aWfjh3Tn6F6qSQJOZO0&o
zcwpoH1H&12^srH7PWv;1O?PG;AVQvZs8)X~<B@`WVqQW!<c5X%w|77pkWR=%96i8T
zaD2Ui%)d+C<|Qmk62@9+HMMTeYbeho6y)ZcS={*IY82dLAI_BKJ0riIPe?&DHaH<6
z+H!Bx5DoH>g=LRE*9@HGbb$<@FiK#({q@sWI`0sB`_yAX-4KW1JSWZVY%-UO+P2c0
zR5pZ_lJ|c9bmt^Nx2QK5kdbV|I~QNHIS4%(e6ZPUGH^KXFP;|EDPNa@{VccAez6*f
z0C4xsfSYEA)k&ZrEtb!n4`e}Bf|U-8Ch@tESswkk@C_14;m2vE;pvzl;vfVRn95(o
z%(kO5M*(7XE|$u&ih5@w_(rkax4z#dhQ`~cbB`20y7Y;%1k-^)K3DT64i;g*v1goB
z>ehYPG=$&cU{<sXp*be#{JZ-e+oa|iPxlt9SMa<Y#;N?T$+APwl?HR(fHShya*?Q?
zd1G7)?f!?lwtf2x^toc!Wpr1J`Xjn3db?{pGK$CI`t(YVh@O^`jHIBdl(vX>YJ7Bn
zWpB#Iv)$l3=X)%Wiy61+ew*VikDHcfc7mVr&dcUqXPFuBvm-m?8|FMQt674+xs_=;
zSD|;(oD*4kZy|kicm0BC=9jrVk(aUTscv-}X~*f)L?*dc8{KMtFVjgxm@iz|q56Na
z@<~a^HEXYJoWhwtu~5!-ysCiZ@n6hTavu`qqzSx&y9Q^V6&k0XxQ_e^!8EGN+E>bv
zxfLm`%I*e1+Qn}H<`z-rXJz!+7F68`9>1NB?nI(&^3nMQJqa0oOLAslY<zG=itpdI
zmCg548@x;I`(dl^s%s|Owmk5##`BGL;T><9uSZKA=~|qWn7z)g#PZZ)h+a%)5Gr)J
zv5ER*M77_5a{EA3a2I!unR)q;22VFmb^LjycoYNie_Wr`wCv{oykYlapYIHqg&DF)
z)#<onq~dGLD?pbWHCVew<`ihqQ_=J`+a+d{u^Y5zV}jtL$(gy}F+V@UNr+PtMy4WM
za*<PVl{NpR-Zf66YKxM>$$-g2g9?sED-Cw|;q?#jnR@?&p~CI5uq<G1eM&65h7uu(
zV_}7<Pp0<`A2LE6iX#{VElu?q!SlzZu|1&akkS{}#*P*~pNFXY41;X`<+R9YmG(_U
zS+sgR<idFGFhtvE{s^0GFXH%i*co_&{=O6~&thk_L0y=q|M&jnd@#7V-ZMxL?hpbR
zRVWuoum6l~$bngNWw6XEMPnF|Q}o|0slBqOL+50b?U;)#To47zv%`!J0#9z5sybp(
zuDitY7xD}dF`w~QoIiFefe{%sj;$}0?B!L6GgQ+qXkz5J_-CgBNKLEhTs3kkVo82~
z-gxu-61PX8&c3%I>DeMlCMofX3CG$xO`HK)15VST%PdW}qxw{4NzMOA8~*DXXlUC(
zf8}k@bCvamMQG|>foSu{#ouVxkA%GMZ^Dv*_S^viVD$sxD$jITcrGc5(W_1o(3!TN
zRrvM&rC?=8>3KnfRX&!e);{Ie)S6H-ayqtup`MJSWL;Twt4~QfnDw-x;VQU4w17qW
z3}#5VH4OegQvNTzLRM*v<PdC`X${G5=M=>bA%mNO7iS!_@%o1-3Ci;Sq}$6Y+#vWo
zleOmgeG%Y2ygP&;)wPtY6#fYHdD@`Om{UqYS4|_MN_c<3qdg8sE6UGWeP3?=h>P8<
z9a|YtrG(>rduM4o|K9srp;yB=w*nAN^j7^?`BIwo%J^_VN9el9(NJY?dxO~bXKodW
z34!DbuMszgD*9no`+)WRVFb=ek#!>SUKT~~?z<d-w(wF|ut{2T+GS=tYcfuSAGwn!
zKOsaF*FW8*5iVMn11bSG*;wgDH(&0$V#~`9b7N-N^;@TNL>J{^$R$|6X#xzatn*s6
z6r_SIQ1_oJe_Vg;Rqm33agog<X{|#?j9QuZ-$C#Ljz`gyeFjB7WkjOCa3I)pK9iRX
z^i>ZTAnMk<g8V?jy2qz;TQx&hUALWDs1AHJhBVgc#U(PDj^x<j6r+x2wr*D{M?=sU
z+1?}-zHl>3o?E>{s%W<W89+Nuk`8q)3@UNBxd(95Ut5|QtbTtNPl3`{vvp<}+EEnL
zbzPmmKb!V*kJ}?7jJDiuVSDV30uK1!E6~>}9M%?2?d%nJWUn6I3ZoNZSXuv=<sq=W
zn{v>Cfue5wTS&yWk<Z!#Cu3$d&=PvDDANiq#MA1iIj_7pDs^}b0XKPR4X08@f<bcZ
zn!B6pS3NzRO81_NiP*UsuKwNWg_+x;7mo9&n^a6(mq$5Ck>NfP^3pcU&bvD%?GFvj
z&pfp+S|17s)ss%;DQf4!p?CV^TiGw^5LjELSq0?EWC}V+vgormAuSce^yY&H)^@OA
z{hTQKqGCp}waI>Lz+FYXc##3DJ|!qcMczCyp&Pfk?3<MsxFDpy;x{AJCn^P^4cWwr
z6s#^SFO~)<tKOBT1@tL=MfQwwEoH=X$Lq+6l4K>`fWmi;te1UMNOxyRxEeYTvGFT(
zXjNFq#da(vZsn#}OpW{>q=5uVS8*JYs^9#yUqsjoWwsNP0DXRXRWWSX7{}6sVQ+1j
zUy_rMwu!UM?Y1PG?5!9yQ9_4MIM=a4HhNs1rj<2iKaf!O4ZcJk5q!c+tF4;NLxx&u
z9B7_)lPjE`SCi%W$wVGo{>`5adBsDL3za%|9B?;4jmlQMD9S(v3Ht@o_nXv%0!HyE
zgZWd;NV)+?z^aTus%MBIJI~M_@}dzDq6(9kDANZ;=ovAKz?=?9M?P@w#Yl6tLT{$?
zpiMn0qLSb!(|SwX3;F0-h{_>VR5k1T*Z#1J2{CTV4TH*uUo;ww)a?4i`M8+y*{YRC
zsQJx{j)|nRZsmQKy~5d-_&YPK#mbbXg(Ws}r8@g({wwPBablh&fV&kve_GWL?87QU
z+>Wgb2PVjBHQVIEmWQ{#%t7|bC9j#2Z71XOpQr|LINScvr|0nSBi*iP|AUg6sy3^p
z8R}xlyPdNQ_Vi`>E9LSYu=^eMgp}QZF>2cw@BL-xtkH@N3s3VR%gkeA`O-{N1F9_2
z;8gM%F#mL)T0Z&CS<G_Jq?p{NW6KGhN81CtQlLuT!FiQDEI?l;#;*Qy(A*iC?{nhx
zNi+YXKxqF#5SG@J*kR{B{0@3`Jnvc~z%!$lf_ZRk@a>;2_ZSQP$_o+Lbo~|1eG+Oi
zh8eab#eEQZN3?UdUc$uPVxtejU7NoGs*)Q(9z~^7rZZmVh83gNFUC80rm(aL=rdlt
z9an*IFJ;bn(&cfGm$WWL=6HnCYlSX1J$mhqMbt&g)ipx*adA4iNo+<T%KOQ~w4wM&
zJU4saRN<QkO~3=b4@O%%uqY*Irt#Eg6nDRMd@KBUntIlA!65OT%lSGxra~_k{96O?
zBQD_;ZX`+x_z%x8lR?ylbrtP^fvg`!;V({DhSpD?bQ0U_Y>7O8zvBr!a#s&|R_G%5
zz{e0%`>jV0gB3oj5F&n{byQDu&5m(#HNpysLSv}@>Tv*#?UEe<m8Q|V2s0T1T}G#O
zmUF9{?8EC1EXibIAz%BUky(?Lls`a#05#@b&d{ML`D?rficJiD%(CJ|JcmfbcgN1e
zRV=PE^$vy3n9J^~lUohM8h%72>MY!l#b>)8%J+v883h%k#i>20o{H5-iNdnV(so)t
zYS6Q2ttU^T70RAL4!I7`We!&di%3JTnzq7CR`PuPDv|-SRB9loH;BcCM+(s8zi;!f
zykx%`=PkQMgKJ^Ockr4wcT1A=+1fG2_#TayF4&d(wc1%}={fFXvAP>SaqKU;=p*O!
z@4mLhX9KCJJfpZ@5{_L%xK?={2*DiR`gad{_NrDR3K6M$>s$pl6OC;j@zQvR$)nSs
zDJmcR3p=RFifm5tlF5szCd_~v<3WL2Cr+y@MBBrFA7#k`7fS-K*wpMq%j+*+FLVXv
zV)g5h1+rQA<~5UUr`~+4xyMqHO@}#X2|-JldbLjL<S6-m30t1y^d)6Bq9j6kwxbLJ
zZT*x$ziTjKJ>wGH_~^v1$e5<5_q$4V+1|e+pFLtcKbY6J2M^IAPiuf(p*05l*#y0+
z2M8Pg0MgQ?l5W?<t|ZH}icxq$_a7YTdOE66@YiNEN_e7CZMDquHrk!$9Z0<9Bi&-0
zcn=HdEd_@aZcw76e-h@L!#zbvxaFk_xfu9|?$Uf}pkH&9ae^^y9k{a*CR<e2_hJrr
z<Lul1+-nP%+ka9ONM@ij<FQ2@&xhN%=(xlB{UuGUjGUOLu)sv9*Ej0k4ktlsG^-6y
z@T!RzuZ_V3VQ#9p@W1`#k{=nKrkEm5?;1d!m~{^Q*>iTlImcqSL>{bG6j!Au3d?ib
zX91+zr2R9qLN2G+ApH!O{T-M--Jb0W^EDr8>S2u|9}vT-0Wx3oc=9VLyT$_!D*u}(
zJ5pOllE1_1#;)3HPHpOE!-wtaHvcZ9(BR9expd7o^XX}#>=BLh^;A!Ak|SKzSW4BD
z(P;!9BnGCA^{yyh;Q?A?Xbge-YWh+kv?J>g#+>V`uy8j?p+nEr#q8K;my&gp-qu^%
zRPU{=vyw5Ozc8AF%rIChmU1n$lue<@ce!fK1#gqp1z&oExW1dXb~#9Dg09T!gz$M?
zB!-0}F8<-WapcS<861xoHvQZM{u-wEuI((=M8n4IO9|3*J_C1~;;l?%2``_HKm2^Q
z+kNIgn2+SQ2E>v<yK9)ybN=5>-N#NtH%4`VW{<#m+K!#Ic2#SAbpR{HDY@oI`03)<
zi(cAqe;rq_kmo|3i^0Rjo)C1~_g&1kXHa3WPs2*i;_!52^w<Wy9X)oD92DYPn@#Kf
zBU@YAcXP-DS+D40u6`8+ytiB%fnD@n$dXwSYd!uFDhQ}cN(qb^=d-z9G7>NzgtB(x
z8f<_ZG<wm2?6ix}xQLe5ZJytYwc8$Ikj8p*-j5Q-SXL|jY5%HJZWYy8vU>8rTho_}
zgtZ+0eeR-H(9S+f1*85L`;C*~LIS~-UKQgLFK#O3N-#$lz<krW8})H(meW{_8<Tm_
ztix+3Z<X3b{{2^!;d>x{8;gW`P>8tL=@DQ&DbBF9vpvBNn(}z(gu5x)?(PVgW0b=&
ztfx!;a)E<6_t31wQ0CTmp{uR%aLDzf^NJnsb5OGgBlnu!)iyyN{7?HSFcBf{v!UA9
z`R2WLZU=VD<6~&<WwW}uI^d)xj?t#o(B0UTYFHw;oA0JkQs-m&r)jfKTv|$m!Y^Qa
zZzjF45@%q>gf#yD!vt5xJ6u=W)fFu{bxVT}YgTffq!_MNp-hvoWw!=>%a0;}<<a+Z
zZPLIE>{E#5VQSi-l??gvhmqDN@m9!0q(x;z{@P&nSdBsB5H}VPI6wz6TnwO}RprBO
zUHbVgD!!AW>{vJ3;@RP*1Ia;Y5&xjJD$1PaMusU^=}W=j=nDUIwh4zWQ-{<T`c-HV
zs3z__2kcz&h9hfAU2y#$O>Y?$N82@RLkLcAcY?b+1Pj643BlcUa0Zt^aCdiicXxMp
zAKc}e>;9gXwfNuAGu`K|U3HWxh#+HwhqVd&id_CQudI=U@h}GD(l$59Cms7HYuq(a
z#})-bVbJ?$Q?|Dqvts0Ny_lEZ+bFNYjV<wFZWE~RrgrPS_FOW|9s5ST0L%uS3)qU~
z=P!np`GrvmW?dtkWw8|Wf*sa3+)_Iq#1Cds#~13$qfMv_j$Q|W@xduFaNC_j$MG4S
z`uk%7!(G1naVt}H68|ME5Nnk)pL7Jh!BNO}g1<wz96$4l%nXkb7g)P?N#fQ7o?%md
zapeB~9gTf^Rk?PcGT2%Qto^QYmw)@QeU0(;OtJ)5vjtkb06>rl&FrPEh100!a`;{Z
zj%H=5)7%k}?AjXT(oX!Nh{L{{o+=?3K2CQAhZ#?ve@bqdQT=*pTgA7k5&sV1`lt9$
ze~<7F&?=svhIG#`87WgyehG?r0`Z_}eD}SaBD?*=+`OWhk+_H{@aC4(Rh8XP*NqJ6
z3%szyC!QJZn%vH#D==93x8&AWAJC7=B>7&L|8{<y*LukApWK~$qy1&u0!&@S_5P<l
zaSxd&dz-3{4|^#YbL8fZbQo%t$i8AxNu%15+t=_5fOqyvGG=MdAKrsz4)$MK@kV2M
z6FQM0Tq#>B(hM4aj++ha41O>zUkSs4`G@{ZEw{Z&v=5i~^Ya?`a=tp`b~O~z&_6%#
z@oO-mSdAjE`G{S+Wj+w73h5tUqpET700k*(s%>6~I-%c@CqqRT)kE9H#1Z=hn`c;Z
z-SzI8FTbLHK}}Lw6!kREEX3qOLf+xEjf1){_oeEc8d!A(f&d1G;<%{6MVZKzL+kFx
z>;EK}$2^2~^3P1Km2>e%Vo+UCm!z0f;e!-Q27Z1%5^*&-wA`|Abu{OYD2NKPRj$xZ
zk!-d3T=)B!ND;RNk)%NJLKpgE8yN0g)3h{0F%J9`O_<B`zWA0P&U?a&;;myXPdK~G
zMB6*m6G}e=oI0XLO>|afyR}{E^vq(_xj{>OD$K<qQ1v&`HWKW9Owy!t5ElO%Ww3+t
zzFwliTKnLZu8izAG<?gRC&<V-uu_FS4S>TyUA=OsX6epdc(cN3j?vLp_i=EiLGqfs
zRwsShreDjxU0$t?iCh)^sTabV?~e<+z>OJoWKqTiRGhB62Iu@2xa#f^WEjspLW<VB
zlKV~jm3N6ZT%g_4gWLa0W0lWWX4=`olzz`UDCZ#Tt+n(pf99Z45Mm9pxlDa$nN?DW
zejyBuPg20YTwxj&>40;}4V2Y>9~PgYuA+PSfo9wu%y%~CsJz!3KvwY^XL}N^4neXh
z1w;ZI2oGa*gtuEOaJf288MAdI8OX<3c$Afs+Z(J8)~0ug&4O{)t7BWMYP4ZTeD00<
zS3#At+D}_;vWD!9D3KLf|JN4D)qKypVj*Zd2maQj&Da5MdouE$M8&gBr;x8=iOIJ3
z0;0r}Z}qaqvswjI^4`@fo+Yg((I@WPN!%~sY8%CK$_S2uWMX&eBA@9QQH=V^;|Ydt
zPF+_+DFib8W~Zr(s)E*ElE^WJahZUNX9zONuBV3$k7djkm8+g6WCI&>b11ojl$xAv
zPqBDM-CwgoIJUu3&{5U<04yj}bqN#o`_7@6^3AD80B3xMJ06h-ijOmdE0+$I!3R7L
zOBW08VvUV&Z~K{)jf}A>x5P7^B?o5ZM@bAMH&YscCH)wj(`*bw?=T_N!*{v{@2r-*
zskw;K4}~oIcEtKkHrCzFq__oEZ>-Q)|968gDcWvnv$}m4>Ke8$Ap>TZ+;_65rN7v{
z@1bcGpajpc!`0rI+oxm4ndC66n!1yLOPuc{_8es^D?1OTZyMmZETUc$+5GTIKj0)~
zZ4)U>!Tpcs%2%1Yz^={TaYQIB)UXfdt5l;MVX@|DxEhMOvi9`xIBlUL;okmq1(E}Z
zn+-7{x=ZCajsSQy=0(&1t6&*nRmYIv548GOxXVAK)+bpnL`58I^IMvCbbQCa#67J&
zZ_BlT=VV%(c!wZ7`5!KzoVm!&D6mBn_J>b@Gf5oAf6=0v93SrUsbmJl4H#ygS^$)?
zyz$;@X}%fY`lTZ@zWl8g3>?^@)nw^=_|1V$5whjNXICo$zn95CdJ?LitlHT6fkuxb
zOZA4K&P;d;DxvqnBHI1tumoI(JgGpuQi!70EidZ6IG^LU_Swv2sheZvd4C-OpSt}U
zWABn-H#MfxPD^PenIx~br-E5WAMv;AuhW8m?6#2yR7(;8rZz4kz*=NFSY~WL;|e5m
z<ME4nKm+}-fAc@vn0{J%F0c=|I_A)wuIeb*U#ROQNQykeC<Y)N135Mq=N$jqY6$7W
zX!!MuOr$7$r5w3gvY*<5u6xJl&kt(B9XSGCUKx`s^8rNQp`%BWvzlBw4`8vlH!($s
z?(j5oNqkqg3h-*8F1u~i=n&=4DR6Zaul&OmX!749y!tV<VLfJAn;=kL+3f?6s7~0o
zTEF>pAf}3L6&4qI1D`Neb=^KntIP4zV&1L*U#w&j{>LCm=bDe+pzH5z%wCV<B{$tq
zQVv4@tq2KO`T8OojYHpU<NJq4p7CQ@h2>V}eA(%^xuIjMqOC_EH;!$ZtH|%#wTpa1
zm&OGX_6vX79xPx?-?=Wxm}uu;vY&#oI4C&*WBnywZ;luEaPmIBPt$C-nkF-w)%OgE
zVZ+1OJ0e-*)9FIcJ2%-~gO~39Cl5nC^8aG~Rs;@4{wn0&L~@)a(AxoRd(I!Blkzna
z0@e}ph$Dh2?8?ovx5W)-f^PsZYYJ>S0+WGWh`s1k4nnM4$}JY4C1vKJcpq(Hwu$rT
z;(H_TGvMk-%3{Syt|et=Gtshmg5)o{xeIUl)wO7>!d38=<;g!P3Ib26A3gW@S9xV4
zQb=uQ#wMi!O7s?7lWn$jU!?u|m}rdW{g(z#|Gxg=sfrK3xW~iK@Ir((Wr;}`a{XgH
zE-XATHqt9BCf|<R`57KzR93p9MXeXDvY*HabVil~{xVFMaKRd&Me~_Fiumu(Dn{kK
zemfxvL>GkmY}|k;I=2f76d&jcBp8PapzrD;IxVJmzh#3lseu9eAg_Ce5Qjy&*M6zj
zfG0erEu>%RMT;M_+pjcU=B3hexnDMR$lKIHag2B;n{37Oac-<Pa$IoL&qRCC4hn%e
zb!~PK(j^n9UrncV?#S#9wQx_0ho!K6FK)l(Z70sK)!ie@c&lixOd}`Bx2NHr`pu87
zKfyLxPdH-*fsM;?akiSit>1PsjIdW~P)1^ljE!;P)wt_-)U`T3pI*TW9up>2?-rlu
z_!;6LMjZ}B@!OZW7zfE>ybPz7M~46M-??aPqht-}7hQ6_<^{_3Ht3vqPltzT!xjC;
z`?%Yd1Oamo+yN|V`>ThOemOBps%04i@Z*7-Z3c*58tJ#*kk2?@^L90s?M|@Md5ToR
zt7go8v>jQyONyuebIzI1h3~+l@ig!z=lRi<p9_Nl*==Y0no%va_%OfI!4K?jIl++{
zLdS=GY!n<~@)Y!WZq3f^?69*w9OK6{h(9#>1*_X-|5HZZ*Dp0T!kFxDLa{UC8faJg
z<q*&$x?77VUDhq+zID~VPr;x8b~X6nmAmP?4r-beePt%U{(kB_2S-b=_VV+f9&nGW
zFUrkcjFy>o&o+O1!^((9JPL$<tqZi7uZHxQ5Ta^o$SVTkk^^>;7$@Zs9$iD4L6}Ec
zm;(guN;fu3{1=3%X(1T*sqZ(oNK#5>9Y}2#_R7E_xZB{MRkxs@-1<1Grc-DHQPF|z
zhmDh&koy8$T(`(Zd;@(HvBEE!{ogn^MCk?_UGr|IZNjnhJBflV{{mA+rl52xB3xx0
zl*Y3pB1+s1><BUFVV|aDnLUE3a9o`qcN-O_3=#hg{#v7V(hY=l<4NbuEuw4VdBOQg
zz7E))JHyA#@c4wL6H?tbqY%7xt>apV#WB_qQg~LiHihc1%UAr+6;>6sjk<O}@{B2=
zv}tbg##cuk3=V;L@c^ENv+at$+b%<H#WW+Icpce)j+zIm$)0;<9W@!<p+LOd>3iC+
z?k9Ngr~8lN+_=|9fWcNl7O&IY-%S0#?<&`N%9iUu<+9Ovc)Y^aG<ZKj|BF7riqzD7
zIYksAc(waQS-rRI{wP6v;4+a+_n36j)0o;D(LWsh`=LmfuMa5EWb0$Yde=-QZtkH@
zOa{M_(dH7F-UZ<2_>eQv;I{X{1#Ay(8_GAYQZnB^i;-}zu~T)sN{Mi3$6|#yJO2Hp
zxzNAKU6>73C47w+q*1~OCWSB&&Z%LVZ17~UQBovT^bY@FP-In>whbvQj4VmG{nM;j
z0o1oQH+M63MgmHG=U&oC!ZGU)d2ZuR=P&KjSIhgfp7(D<Vp{iz)t#H)+z?`e)J-qx
zEUe{b&V<?O1Z=8;xkoq?UK|<IIUxeN(#3Ec?q%t0I+kCT{s<`0mxC^#Hp#a_qBB(;
zm{c9wzj0!)UO-Zne*07uy69qqcti8-TJB}?FI@SlAO@5&6?Mxl>a!r1Ynd3&|9nE(
za}Haw_9c*SegcD+T#GGF@KRIt0t-gLPK+J0b_3_6b=l>hP6IP{XQ6fe@geCp2Q&o0
z*SyVgW9Je68y6!_oOCkx$DOgC+~q1l&3leJPa-a0zriTyr_LIz3uSi>b%=-d`uACW
z4ls+R4G79Ro?jnb>=Cg)4%tq<-)wvt?LDujK%Lxbx3uxm)G%qwhhwHE!kaOyObG1e
z7uM#KN7yEx=cknnLuk1$c9B&O{|n0EKDa6u&HblHt@=~cHmUWJOeOp_smdKjAZ&`J
z^bDVX2#*+_@E8Qet7?LFB?yBM_9vJx2T_Rj5oq>G*9kdAc=bNUgQ?<mer9Q8o86$|
z9~JhNW%a7xw2}JFm?Hwr9?PvARYY6e3AU3o_R<EYbsmq0hgo=@0-&Hs)4^`hzaE6g
z7lu~uciD?fCyeGY1S6>f>!h;uxu&PbBzo%s^xwVut;$+NY#Q8YrFJcX2uRBmWwn*$
zfUuU*yv^><A-#BSsf|ACg}s@!!)4>(?Ktu75hQogN$F@fe6Qt!e<b|JNGIPNP48PE
zhW1T$LpT1;UH&Ie<)FP@4fa<TYXQ;SHP!9!mPu<pQ@aKO`87@uziRvJxy|gHOu(_p
zpVd<?uvUnB-dFBN1C8|++CSG>x1rFbfgUvn(jXxpVjKFgz#Bs|*@vFymn`ny>(+h>
z4u*EaH9&6K<j2cJcXjeUn32)ixT&<h?ycb|<6^KFUaSaY##yTyLcUOoMLW<c_FL~_
z(T>@WsDhzN_|}bzIHD2Bfzyc;pv{~-`i=8?6o{|B_Y)-)oH7ztrEZcLdaAzwe(S1!
zDqr6neOavZt7!^+Pj?YNHgRdSAHB=Q*7-Tw3csex1uC7<!*l+S7B;jXK?eoW1@lbc
zo#ovwr1v29$FkZZ^lZcwnlbTu!UPWFA7X3FH#px%$pe#9e*Qj7J(Jm@kPy6cajj?L
zUYuJdgYM<yD-EK>`3RZ`vmkT@BKT-=`k$J1-!u1pYq{>Z<SJAQgmkd{WW9jh@+~yp
z|7k#^BT0yxlCH59x&>kjuwgs34yX_5{;e@dBA8!MND4`bF(NfHdoXbK9#up<qJhkP
zNG*8tAK>7B43s^gB;p>(u0;PZ1s$cm?inX=n&wA@{76Z1+1fTB61b!hAbW^5YTT*Y
z*;c)W0px0BOg*fOnmsMLa<tWhv8v1Us>+<*g495^&ZW{~-+Bs84i2&lFRxt`LHzyL
zU92%MLGDIbHwD@<EK@O{X8FnlT1DiamVDXqWJt=xQWF9&9*Yl5<H}b&-YSae?use!
zJ4MS%!#jLj1R`kE0ySY_eMe}D&CO4EgG}NZ{(<8%9#22K9=?|~H|CWU*+Ik%Uyr{d
z(V(1po!p@b$`QJMdRk-kLkWcq{zSXt)S9}~t(jKowwipK5Q@?R`4?V}(3V(eL7q!%
z!$zjJM>rI({gNPy!kx7kaww6NznOc@l!Pas+o)z&Mq?gC07eSoL2M%Zaa|Va_f>F%
zXju4ISs!<_hKu)4O*Ftj<flf-0wWJ$nh%-zJz)dCv(^-5W@c_~c6NUF2sj8avF%sB
zVxg{rO#$#Du+^U*MC!kQ&~N+#S1AhKgySv;l!5Ld^4G3@)Xq8Eh6JWuOu8cWV`9q{
z=Ns>$MzaW(K>i^(7@Ty>qx>#k`0@@)%(8_32V}b^rnG<^{-Zb222*QvAr>B0luh2K
z@S%FO{U(GD?;0{|^cY(VdTNxe=wo%-lncw&kckSS4bYF!P^rmr<|;eb7;6Pr4KoRX
z)#`XmB5dcSz14!cAtdMd#sJk|oS@rc)5hi@V#rSxV1+Gu7yr$eu*={a|HDv&$-vG@
z9>qsmH}lZK3C&FC-{L@|c_6o-i4Ev9tdTW|`yrwa6Z@e0!lj_43O(~jJFHr_+}%7@
zBf_l{_&&s-GxlC42oYo?AHpw%RGn6f%%H$MrSsI^!3i{ev33UHxb+I-mru~ql~|nO
zrfRJMTRotra`<ea_py-gz}y2a?s<caqgPj3p4D_#cG0EwPzY{<(+^-s2=Xlv?3*Xy
zRm^8p$0KqX-AJb}P|zkZJ&u$&V4s1MR3z3HPFh{npY(eV-1lPA(7#{4e2M)EN%=RH
z;wWODFh2V$d|~6(qx?e4qhS+3C<9sNaN~XnIR03|{#wbW)&{ufSe$T@_=ZUl=%(p6
zIv@2uwKqPr;ub`SrCNXY_?y_};i?s#5IZhCMQ`!Sa2qRjRDs;Jxh5tj@+FqK<WUTY
z*@KUS6*l9CXuBdwqVcVezQ8RfF2&tNRX-NM;KC62Y*L$d?y#@MO(4kW?%=}(b3;M_
z^npU7d<WQyuxg2`$^9W$2^SmEnS9X=iH$kn6b|ySMt4?uaP2ZLsbcca?Z%N`SxLT_
zQ-c*Ju$^~tKkG6`d;7iXhu#0vYA1le%;x?;(c5sBg#3d#;+Y4L-`R5A%+_=LA|2Ea
zJ<PNJfK0sg`m0DWYY?pQUIxde?+<SSWiA0+ut<-^!{LaI{+)=~wKpivGr(_wZXspY
z1A;Qe{xs5Wd7)#K>U`i=aTK;3ckC>i6Qb1RdPRq8*E1Qv8TYh2Jhqp+5j~-QW~%$^
z3+3<$SgmD``|i)|&ZiBUn^WDOWu)uwAX;XMi+gL?yW0v%*g)nddM|AtrF{b48T`R3
zZ(l}vHh&L12TXgVD-ezxBd_wg>)v&tVSh^<8@1B!8tEsP9_Hhk-<q4z_S4z*T@1{L
zY^_aiMnxf*h7Qj8j7!ia#HD=Xd!dfM>v;9WDu-?@aDCTd_!uLWVJRSmD-@Pvup#=R
z;HlS1j1Lttf4ce_;)~2*9Af}X$glN1X+DdA;*)k%&J|t{MO(##d=CKL7$6`Dx085`
z=UBWSX)r>EEeYB<=L4=BAUGdLNQu`UxlwZt_qOqk3_yq8jCZ=Cme_C0eiR&DPlJ-g
z97{O);?NoVwv}b1<V@ozX=nBt%fMR05a_)8o@*<q0w|In29%vtdhx*gpyy1MIpIq+
z0ihq5--<8+mbl3z0B6B7410C~F3MXii$A|XxB&b+BKxb;V&<tS*+{rMN8Wk`F?HXN
z02czmZ_p=;jLCiPTX4i;Z{aP_vf5FoSy`xGf0_llO+PG>ko%CcVp*pqsk2u@;Oowy
z)Ma98EH=5Ibf)_%R7(P3{%!82TG7|;*}4&NuaS>UK)O_qgw^DHLdltKLFC2fsOURB
zPyaQ({`q=4uSik^)fR&Ui_PkDPSSt6xt1lchk^Q~f2$_-lfJcoTB3@D{+YP^cX6nb
zzhLCC-9pndv6uK8Gjm$jfSyy`JSJ3K4adHauz*f+nxsXe$gKlV&?#hYctEf$zZDHh
zrrqb2c*&HVA0A70G+fv$ttAoj*NIV|(5%h_wQ$qRQODRa`3JkeUX|An-shac^2_|o
z{Ta9I7aUDPbY&PO)!VJPS8025XG0Hpvg2GL+_X#y3D%<{)1G6`=Q)XM*DBAp)l_po
zrR6TY(>;D)9g_>-n_*^0^1#dF+f8W$%gJC0Nbb_7_r)&nCE0jiQk|ZmMMZ9H`h_R`
zbE3+&|860tPH9`jGP@4Cr9)^$625EK)~Xqi-s7sN^p6)xb;^abKl_4O44(JJ-RF=X
zF$Tw=rO%l<g|#$3A2_V93r}ei*GE06+u}pb#zeU%r6dkyj2#oJk!8Vv)un`4v&uTk
z?_Wp0auh@$dQJX>EQtdB^)3kZ^Qe-o$N8*ud>k6z<B(HaVd48_syZaK)qhib2CbMe
zNpEEh^~_+;knU$Gx<S+(&&5nm`QvobB+95z_zq%D?!^uRh2B+3LA%iGNI&naXlial
zt>w{92ciM^B9sU}<a1W6LJUTuaT4`$sUzF|5V26}2$1;G@kKOmH<sjJKS`Anocp{N
zwIZX)BDw^Def*WfQ<7h+rn+Y(<kXV`{Psck-5+GayLnlS9U>bn(XZ=vU!P+(Y;Q>@
z@$H-Xm^_v1mqbxzbwGmp0fde|OMQFU8A=)`vKGLiP1y+KyL#>=wM2n2X}NYBK;PET
z#j*&IxRN~I_{;}0=#GA>7I!BjY8sdH`k^3h@%4d5>gK&CnQrkJp9nEGHwPL^+K&j#
z$vq8De7MYB*fscN`{A_j1e{sf;Ai3B`#=yFa{iCF(%U=G5KbJqNYy&LyK9gm;@Sq}
z2f97+!G;deM0fA1nPR$S1Q?ad#{d4<HuT#e@{S|T$u-;AULzwbDz|_bs{>!R;j}~d
z=7(R-&Wr;^1Jt4kSS@LZBJSVpaG7{)=eIZ9q#WtWINhRA^M>lOX#85Ck+pTVfRn#|
z|0rM@?DbC+xD6Tb=dxIaRFK@B4l_X=xLi<a<2d|0p748yw&i)iB-^gD3_P9L|2Lgk
zgJGN<HLaMuTe08mba~Gl=dG&>Ys+bv>7fQrzkc5HLm!&VLS6y{a0&j}{Y07KRSn11
zdK{i%q9Civ4SM0w?Z89(WVy9)P!sb64G$k5(|DyG17{SKlH(Fl-T9AipizKFMvGT?
zv048zkzOfonK4jaCv6MkAF_YgjgkDA^8FQIUXLcWgM!a$zeb35=q#q*p5OkraI-T&
zLP7Tmr6cR3KDV&5rJ~ME-_6U~+S%FI*bT0=5=YQI-AjmJdT4G2M(g?pz1Q60Zp$s|
z1woI<sY~{8e!pj9(k-eMQ-~hOIKv$_w9kE36J<ZHBdR>SVvFF7kG49Og_)?%UGD-u
zNc%R{vR<*wEQF@pb-S$}m6z=q)OokIVa+Vj<lZMETyzn<g81cdgdi}slzB+J&$BCQ
zpPKGb?d__LHBtF->h%{l%mBli?}m!tSg?VMr<uYQ>GU~N!-)e$-Wd>NQ^w3Z?14+-
zes&72!H+05kztr^4z=I>nQ(fi0nz{ZQ_v#w3y$>@#XJ7Sh5TB|#RenwxQN6XY=57z
z4^->c%yeBIy1c}n$ros`vffp2s-L66JpkS(8!X?CSowPkDlBWIT`sIrA`(+3_vH0=
zDQCaqbNH9S>OdLZssoKrdl+Oc1Fn3X<kf>MmD{&eD+fx}<Dgt%!7yMN1q(}?Bga+H
zula!Vqx+0%8$zTYM*j$GRa$-mdW70oBhdh+48RXBVFfh+Z1JjMT%7gFRTU;}*Ozng
z&~cm0ejeC78?&ffRzP8{4id$db$5?(BSDXkg!ou@KY1fYGmxQK4+#nS8*IY`RI`XA
zjZ}F4t)Q^lp>6bI3HdgoWl=l}H#<2|v=HpOS{W)|Iu$Fg120_}41e*PyX~F$J$wDw
z14@CN{U&m}K0~O6qN=jg+&1Z*MSFmb5nbx5E~B9hf>4)Uatc1l=|$kLe~;T~5d|@c
zxjeBv9~Z6tLcp&ctQX`s<W8yT<4!e+2Rs;Cc4$!@Gy3^eO*T03D>nKEZmXchj{Wl7
z)J8w}bn(3+S6uqt$kHN|JpXnbWV{Qd7%(zp4(On|#|(uEdV{_ODb{&Rzr&W6D3+(f
zG3MJsxGu^aR}lR~D$Id6a{Nnoy86pBgvf6avT(%I9*7|A$_Q07_*_!+5m|UT`!>FR
z%<q>p*-Y5LBAO=f;qzygq*uen>^K+J`<@tJ>H$u^IR9?iuEz2W4Gj&9C%j-VG(Fhg
z!9kCSR-fT)aN%`@esVfY>eF%})oJ|d<J(eF<5#&DTpD|x-{z+;4_;2|ibf-<_V&E)
z(WPH#0)=<qA+DKO`uC_Tx=m4F)lbZeMJ)cABQhu`eNW>4g$=OvF<_<T6<7Oa^X^@E
zxCo#YVA4TuCBjQeRZ(4-Wq-l{+8L^EV1O{0cTfmMr0BFc4=(IXLqhFGKt1kv=_Ngd
zKoH20*AnPLa!!bEPp}AW&S`$3vS4Z<;#^Qh1)3J*XXoeV=jP@X78d6G2g=WbDq;AK
z!o$+roW9G%DQ6=%CpIL=q}zrO7n`7`4y%)Fuxz(AIW+<Kc(FSBc0QjRU3MhDoO;?F
z!o!jEX90y+%!`787&BqZTQ`~TKdCsD7<hz$s;(Ndz4Ko-DZ3z-G-#m3hN1l29OxO=
z`k=c9h0P^#p7BR;Qtze<(u&KydgSn6{N@I|P?&#lLle2M6Y8DGt=TupHDlxNY_6B^
z`hndN<p<ex_+`y~XPR2nHMMZ4UUK*NB($D@J8q|3k?jBT5?H%NM99jnC8ua<Hrkn)
zm>8KIZEx@U9UxGu{S)+n0lvyklp_;4Jls8WVc}&ZA_`EohUiY{QwtXr1-GTr85lZU
z<ieWQDaHJX<(nTr1}!nXk?Ds{l@FqIJ$cj@6}&vhIU5h%)^pFTQIdTh9vT?zm4rp%
z0Lyg;t`?*k+-G@_)bj3`w+|ioZuAVN9|85_)X*l?Bc_(bn1@Gxz^YMIAbWH%Qs@4m
zHE_ANA3{J#CVQ9Z)A#3D6a>5M7c+!xOAdfG*Q<f?w!4{(w+U;YxjfT<1REb8ND&dT
z9U}gp6bJEOIB|q*43=F;Ajsj+pQLtfKxo`UUHRKlMbTkmjohsfu4i}x7XjF9xoG1Y
zpa1*gUvPj>A1D-<1$=ae9}nf$Nb+T+({uH7@=nNLL+Ah63~n{(ZZ0FO9T+LQWEIdi
za_yZ%%n^73xT#uuIcoo!oa>E6u#TJfayRGKloDCm9^VFOMl>N8aaRwZr<V+ezco;G
z(BSTQp1?$-@WK1{{;X0=4___`v65@Jiyo^y%U0`nbAtfnS9|P!?!ve<W74_1MQKMv
z2QdFQaIh7za54IAwl&RDtp%a%ZU55L@SXX7Q%(;BHZ34{wRHG;a%2?gK#uD@j(mCx
zitt(vcNgdmTcDU~r9`Ra%J1aO$SJPnVDJMO8$b++CZ)Bnvt?yz1oD{VK9>LKth3v9
zH@<EbG&Ke+wIoPgW0B*!l_ANV7iu2kz=E83Hlk{Cr5%+D4~M&UsHtP=v0C4%l6ez*
znt;P%fqS|a{Dn~Poq8v7tEBIBo`NDj-{b%jKt~HwR#IE5sLUxb(YJI#k}`kux7~!K
z{}4INJ1+_Xv%x2rJ-WNQF>wW<u~CGpE5R&xB3>Yu4hq4Ug|8(Rvw2<47aJ~uj}fV5
zP!CT3V|ITR5K$MibroO6F*dIf>hRxvgDO`3&nF(YpaGNZ^tu67&GJyFEcc$<o70!W
z#fQks%uYY75~y^r=;mUpbG2eLNZ0G{C7^R&PO1k{_kEkViOz+9urnPK20<Xp2O3#&
zPYmdtQ9+5<tuZT#qga_+Sea8$kyqBW)IB}y)^$*WQRMw4nq83h-T|O1!dkmc@VlEI
z(=DQB9mUs>F#VNj>~EXP%z#H#ZF+tv1ZIpV$@iGFaUou`XCeqkQu=-c@_wi-=4tH3
zKc51xB42ykJ0?WPqocyE5M+jFt&Z`DAO?VctqdG2+)dP6<g(*zaofeR<1U?+hivSF
zf>b~S76wU#a;kyH<`%^?w8JBkp*u4BFylI_r_d-3kfJ1ND0`uwf#r)!aeL%Y-*Bl{
zHcnGLo$ns-FW2lGx3g1%3KAxA_AWO+nm~fX5O!!Ta{A!}y_jX)>0dnp^wKjt;1_`k
zVm{KzS!E5I@{qRKo8ofv5u4!}Ib5HfUUCAHUrNe8+>vqZuLT^&N59H9cPGJNjFBI&
zAH~%c>K@C7o}X=95e_Zb-0VSZjJqPvRoG^4LA(gx7bE-?72Z{XqM~3uD;beN0Dp>R
z=eSu`?L4M?M-Q)J@&FH!Lm3L`9bQ288xeH_PVEHf58h;A?D;<UWlt}i^b|tqLN+xm
z)+GUy6Z)&!a@&$q0%2(1?`);`YdH|pZ0N7Ey@Wg&uiB*u?6JAFy4N>9Z4HJss)D7g
z1Sa)5T$p(YG(~&=pu-iv`8(A1&|Ztq{>CpI`x|TLxk5mDGgA&I)`JvKboT+0MO<Ak
zO=T%^uBG@D?hXmB&f=53CHFa);b?HXM$MT`W^`>|kf0~T$LMV?uW*N=QGK;MA`z*g
zyGfeyB389IrTd#@IslX9%CQi=pRco_yuj^?YIp7oujYe~o0a){kimn31Progn3{4!
z^*(x0%c`@T9I_rSkK$={OTPuSpyT4l#j0t7DS$WV^x6Kb9PXdCkQ?-#(cZ{p?^=_4
zAqGCwjE}?iN|oSi_EVRf7-wYp(ba$`SvkSz{e*R2{i`yE{33f0-|IR)E#xT038&rL
z>zzsO8l@TAS&)YT8%bim0ZK&rF0^}9+e_1bVy+KL@Zy`=R2G;x;R}?Imk}Hw0%ma^
zZNXUjQ10y|^^znkH37Z}V^}!Pke>m{t2#Q{N%=WHbloJU)v|6gsW+xPUSIT3;KQXQ
z$7sKjUoz*A&_=6tKWjrsC0#xgrrsC|dw>~&$0ppr$+48UZWfU5>8Ig}lUsGdCQG2k
z6-c=i1Kmg$yP6^D?SZ3?D!F!$jZjsvTSzV5)C_O7KaJ-F>*YEt8(^QP<oaHP3gd1z
z*kr@o*3!;K5glja414Xq9I%OT(*B^DU0?(c8uMP#uY}&cvty^#mphuwdn;iE+4qlc
z7U~|3qOw1aCrI~a5iZZ+M}!m5q!3<yX=HLkfU4EGIaeQZYk;LZ%;cOWKB~(V*=551
z+K2)V|9v)gxWkE$*CErsHhbatR$EHxdI)Uu@g4f;h#JlY?8k?cSe%&i<I&7#^S;gU
zn=?TmB!sh9MQ4@Qv~md1bN)C5WN*2<@*_flH!lFb-2c1EzpsSM!G#IAizmKrNI|z%
zdjIaofdl|G3#E^^@}ZIu_?l}elsWd=EP+0R82R>|`^xRav9XVb*4TE5uxRHj#3BXW
zoKgI{Pv3!ly{}jxd9gOcCSuGl7w0h}Ev{J(m8I;=u;9*~aL2hh5;J2{lOur<&zCzb
z9}xosB?tF!bW&lb^?oqO-TdVW3jZL`cFnX<fS|386VSI<Ln9k3EdwU1(FLFISA4kZ
zU&x!T#EJ9W%x(a8lexj-q$Y0ec{PoALFVx(Pjy8dWnC?>^ZF*Y4T#GTwaYi_0Nc-U
zQ?f4*(Q*Mga~Ygi*<3u`)^&kq%@i_U)lq|xQdyf5d2|XdOXcgn{W3n9bF}$(_oVL%
zBydzticd_?P*qn`Tbf^7Tv+1f;A#Yr)<ns*!{BJ#-M|1DuNbattG;O;h_APwUhII0
za&U}`taH(oj^L*PME@ET=<ak7r-Pj4x)?<0Q3eI&^WL$M!MyrAjUes!s;;y1{hcA7
z#L=c`9v6>gSPM9fl$-EOkhK1XFlrT;e8D|EqZk{^h4`6ozXF7nX2K(~YATC~%1m5r
ze2DDO?cEs44&kjm<zHJ})pK@s2DrF9-rYSs+}}UudFO5O+QA#CJfa;tc0z;D$usT?
zzz1H}O2655JerAc1p6A%nfw3Ue*Lz(M+7ORQfqTqbx0XKopIkun%YPQV5;$+VmwO4
z+<2Z#DvJF+G9PMQQn$vR255^%{|*J>S{BaP1y!~9cka8g4}ORdfK!CAr^?dR-S<U#
zymP&71$WC`%6YrwZu8j=pZAxEv=m+rK=M$472#7(H`dktys*_lU41g@w4|j$egOsL
zaF4&b4OclJ<I?KwFr6S1n5^~ki}ujdm;Tk4?%*2;L)|0V+2F_Ig)F$zhvGNaN1pPJ
zPpsh;N<bQ4I!Yw;sTDH}HO`MJ&0-@NR+1fPA|h&WU@G$mk|pcsiQofgWl<;1p3ks}
zg6EjGXFR1}9=z=76>Z9ppTCOsSnmM0b3qscH_azC$EUv(RClm1uoSa*uy*nxac~zL
z|9oyH=7O2B-@U@S1l7{Mt%)>xhs^3)C53Z?Utn~Bj4C`>`BTl_c0m9n7MSwp4SRGX
z*l+*I=1r>memHUpurld1CqhLn9~vTqbhTs0)oFU>=Y8j*G)qxdyzekun!f*tS~(#t
zLi{>2D<c~ZUs8div%CK7A&>sv@<gl)>d`T%X!{4o8xJVSo1bzJnH>19Hh;W*SBykl
z*1AV8Fb{}ekq%IsZwS+2#Ek5UE9jVMM}dSS#LhDlo3p4Ki`RAR%C_&}yGx&>l8x76
zmZ^jeZC_QjGZVN0QJty%=4l_0*Q8I6GIlv_FZy;%;fp}3z?<45Xm?&b3)(P;9AW=u
zRS7N8pP^A{MCZ4~mITCV^fjcpN9by^tb)6zE{qJsEJC3b1KuyGF6+p?p4PDZgxQ2)
z?-sU)dGoOF_6+BtDSpAR5y=qL?2IdP+=Op<>g_gFn~_pu^q+ICI7k;He!O7%CQCY~
zib;ZJ$_JSKLi&S??d{=TH&`0tdi68I0S6@Kn=}t1M}>OS@zo)ZTaFwzNpN2m+aV^t
zZm%4;##dcMp5JKZmCCbxQ`dgN)B=sbKY}(ooX%Recr`rB-diniVsYsS-VVG6Y-T3k
zXa60CxMOAWKn|>RNeNKB46I0LCRYq#o05y!{Q;s}wYh!Nr|soHDJI2v^Rr_FKP=#e
zz7Y#2$yt~%idhxaX^8Ey|2@_Ab$q{DNB5|$ibggZ`tgg@%E!982}%{bcww6(UXMaR
zf$szy%m%Sv%2!+IDtW*P;jD4-(alzT{{8Q3tzSL{H7F2MclljWxyt6UyMEUz&8_)#
z3)tzyyS$-|a@t}cRfE=4r3dZMMkoYWcI@g5u8n{rA%G%c1Y@h7R<9q@`&y(3G>L@H
zND@O&agm@Z)XJI0accTTC;6vY1%E8hZEC8hKkYk6d#N7(gAf8a5mRTJTUW?_YghnC
zhhI5aOB@JtRX7%nWEs4xJ8GM<JQp^CfYoOO(aq!F_7dd!z{hX9LOCvs+X%~Z>L~Ni
z+baD9PPUWC66F)JNG=Vk`c=s6M7(&kSF<7tyzTo$gPs4t#T%jn-J>UO4Wawg0}0<Q
zgi}ui3k9yzmRY&+G^6c`Jw6z24ZJW3{RG_KaErB%ElTJ{eM_O)&8xmsetA$T1Fq}c
zQw!h2;b|=osQ$Cws0ySMVyu8nl(p-0$q88_>gCrSt)v;2{}<kjwe576FQ}cTvO-Ia
zyec{KOS*o^c!O>t*c;Zqa1riyk!f=YRB(W;#sxs`Ozmq>-?lx^a<Id#8hYW!rw_j+
z77B&$xOCmTXs$SbB+p)j{u$A{0!+p;9}WvsM9Is#uCTm?w(nD16IXjA;>W;!f5I!)
zi|o{Wqq;sO0zBWFD>#`L0Hm$Wqd{6pMu~%br)`+|;(nZDo13d$gTU@50(dZ5uBf?`
ztafXoH7oJA&c-S~SHPsud#!T$^SuTIKy}Q-vAM0K^hwIYaGw!q_qZ1VL^<EcE2JT(
z3O!ATL9<(FM%EkoKySVtvhgo|-4i7Jt)jHG$<G&zZN3Guv!M0W>FNdnR&VWW3{3Mk
z>hovTvv;lK+$zc|glmO&U(WHtXl|(bw!6~}#w(Upw<n&=sQlo*926X9DmOpJB^=a-
zCWEM%G86-ySyV8NO?lt~aqzx0^6^`#cz-jV2=`mjh>+ia5g0e>g5kSrcOO&tuyvcy
zKx}rwx%aH7u(Px{J3c)jAtc1dCx&7FQ0y-#sl<<bB0nOio#7>FVB07w_|D8og``G1
zGiO6ioM_cKsIZLoXw!~9+WOx%*q)My;iYGfdceHM&2?)V?4SVpgEbHEHY}1$v4&=1
zN?xWN2jA;{_aqMaIJ>PpAFdaCh?&1!y+Jt}|NPS}MF=KIjFb{+^SS9<eQSm{{mNE*
zdjoD*vBFRNt^Ip#Rj>W{*xCl-A7=|*2|UXUJNO}+C`r}!TT0z41cEskD;xTv@5$Zj
z6TjIgGV_n3wm5L8|M02ADJeRL<aHbEI1T2{-i3{gO+^JGBV)CG%qPX!Oy84d4)>e)
zWJ`c9hmoKh7POQS*$*cB#yvfwjV#D90ceb-&^Oz6s77S!^(ozULN|5}HZIl!C3Ri0
zL#b9UX0)A#nVpmM&{lHW1jJrB6s1X~^)E3sw!9zNhZrc&h3z)j;|lL($@FTZFB{n;
zOaQ<C!_IzsaB}5>F#St=Ry+cy7aiT`*x0DFj0{3%H^1j{MfVuaRHkDizp<@<ew}?1
zES7ZiT6Atr72d8A9yz);wk@e$_0p5?fdPL}Ty_Z?&AzSdZeK+CdHQ;L(bNs1(`;OB
zjKaoiJw4FTk5%Hv4IBt2{e|%BtdVur&xSaUJu0fHsmgclV%R)|7rNhgCdH2&nQ0kq
zI)137g_n0lODiP>4+SH)&rbLDmd|^1<g%KwxxIvzewdV>2nCj&9mB(~4=$-9Xt|36
zUM%+rCuXz7swuH@=L0M>Juund@b3C}87ezUTLM?}hNSE}-}5sTM2w6Z%D#uB6|)p_
z$iS+aOYK-;ZmNT&)I~-A(DX!j+&!rgdeCOw(o=Rq#U3rq5dJMGEiEe<_-0)C{DeEs
z;WscdvNeSJ1#!^Bfh(k+5tEsSPZX^sQ)86x<@4cA-aN-v=WvS%rg9I{m!2<j_{Q4&
z078<viuRG`UJk5BwS_x`%_=JW+=U6xu;H&=H3*B0PlhA%`Ll7dMe;>{%^%K#zS{8y
zK1>>Haj-l-ujAR>-*LJB<>scTd7J^>QY1SLKtfe)v;jtYjWRN@Rt}>TGW-b{)ha5M
z$yku9f9F6kklz9TOkgW3Q7zr-D??NZF#Wp2i<9)~1x(Zzt8&k%W#4N1jcn|AC0XQq
z&#TG`HQ2Gro-Kr+nuxXMD2Gg^Wv$~S`W;L`69%mB84CM;F3=~vdwDZwfE6`0UL^yH
zERi2h+H}%74(Pv2d$;(&pHOP`Ff_*e%}MH<;)bc&%lM%aFjUbS^<#>64c7`d3}07K
z3z!gs)n|o-UT^-n4*&_e?f0+LnaO*4G`D3qHbDL1RQ5kVHu1h)Z7v6ru#7{k{Ahkl
ziB@%8%JjyTxFE_%SIBg{<+*Sk{`p(VKg!)^<F^llklk{9LLk!0!}8OBpT+Xd-VhPd
z`Ner85j0~R9b0x;FuF?crye<wc)<_`T?pJy_`SBR9tk-)AVp*iJf1HKkBNl_pnbyl
ze^9+4G1N;cPKZj0y9i>?)5{<_vDXE_%6_W{jD+2Q4?G56lRiVR1?8jh;cn5<;uP6y
zZo@`tO3qF}&`vvru*mq97qKfneWGontDK*N)kT8Pn4}tt8%Fxou2sO!Oz=r;(snz!
zZ$vgc%}(#)vkFMAo<HnYh=+D1atK9!EsQ8CrecM{5wvGu0@j55vb*4gi<~i{_X_`4
zzZ0jVs-mK%w$dP@r6nbA2ulKsD0l#-NqyD{i>J(#4i-)bcc8ZHz=camN=uClkB*MW
zgu&U{m}OJ_F~V5{f`$~1eVP(N2{nhV;r>d4e<VZ0!^6Yi{f<nh)#~`@_C8ry3Nf-v
zYYg_V(H&`2$0h>Qymt?N8V&vLQs{TbjfWQ^Q~t#zC5429q@|^WBR>4|uT1@%+17ab
zwQa<S2j-nZk&~0pab-UQd)g0fMdo(Fo!4&FW91DY^LIl)l&Wqs)%m=ahRS^sJe^RN
zGd;pIU!Y;cMMp<}CnfJu-@UdaJ+|w!yCp>Bg@<Q_m{C9PKT9p2jYFFgjw6|41OH+I
zKR+QC<1UX}&in`t3MYOyT2p?fBBRX9&M(X<Fw=j-N4l%$nE#KX3>LeK!`BuWr{mmg
zR_hF9H4V;xi|p@X#;Z!6JKrHCAEzX<a0T7nIoC^`t4dyxdY~Wc!ZJwv37)0iRQ9zh
zexVnY<N+6yfUD&8xUm1*mO_H3OJGSA8514D@X*9aV@6PlJHbsJ3(K^!QZ%~FX0vU@
z3brWyVAhh&GTcVvh#w*~2KZ|zZ>tNQOwm~3;iVu@*)65$si?j)P;?LVO^hJtZozay
zT1vB_f7ci5Ec8OGDI+q-NXCqo9}4|CIWWddK}TCwoZBfeb!ByYp`@V{4~)a!B4ePS
z>7E)I>zkPDm6Q>wHR)SLAl?&g@!319=$Aa_xLa$i1B2!NhRTf4(Ug>0ogAN?fyDKF
z^{YC;<3MF(WMb<olQ^^%Exa*2+|BKGdI;_LSnG|aTWv>+0r$)A^>HdQ)2!BOlRbUR
z3{;eDD&i_j!}B-Q8SH^zFk3ahwR^Xuf{dJ$l8CPSFKua2on#z*N4sA}dgMJujcr-n
z)>PjMgw@};xR`vvnjiGRhIT8=AS{D*<T5`G4<8>fti5w}Qyo9xZkSAlJ<i+HrD0wz
z0qek|=Wu9hp!Hv5tnt{3%h@9Gt&m8U*1kEph&edjX0W%7g`N-#)KH(B<>cZw2JN1>
z@Uqa*&|EcLfnN=?J&V1@RhgBIiG{7Hr%b#KWS19rn7@}QC=Y9f)~UWLA9G9$Fn_1)
zk`Wu4?%$oR*{jSv@~+hB@L1RqA|>6{{=X+2^K-h;1)z85m@FJ>F!*IPwM`B4fYSBK
zfdx!iu+c;TdRs0@o-g12OGikxzJ&{wrahmX=HzJLIX(a1Bih<gTKWcrO5oGC^;#dE
z(NOR#GWY(9^mtlAFAeVm9_Gs{)S@z-&YGeXXW49ywy`k5nXzehE(z@Rp=X(oo*X5I
z>+5TH_^_}rsW5nlM<b(Lka*B0QoGl6c^jf=Comx;ET*$DxVAhQVu%ZW_Fu8sHGbIp
z(Y>!LIvX9|e|se3xFj{@C00It@A;A2-G;T95pXa)0Sonjei&>jf&X?Rc(&`c4#u^J
zEhd@<r%K0uIlnU|U>UCh=xjk-`-uIN?`zJ8Q_vI@QCk~6uih)EOZs9PJoLjY{<o$o
zQ0MK<T}sNrLdrqOpr@|Ly(LOz7ZkJ!*X+D`8(-IlM&P7EZiDq-YY)udiL^!3+fS{K
zd>1$nCPxIra4b$t{6;~`QvYdj-K+a&^l|+9kqc2Ds+e9bxc*S;^nJQJT=z)cGu_bv
zP+!L}yG}b;_wP$^=P$z{DjtF`aD_6E=h;<qwCv=vsg~b;k(-8t??h?y_Vlpw^8eg=
zADx+(laUXNi-)QI?W_Vlb9z2Hb<Nh(FKj9zTvSb5?d|fuEVVZL6iA(!Wv{!13zMQh
zpJn558@`_B?aB_#FW-HstZA8o=z4>3+S~X?*+M(RU8Q&1b-w5Q5fK9H2<oxL6Hza1
zXxK1d`>kwA#kENki$K>(#T7}6Q?(HWN$|&L>1rO_=R13I)8+~iYX7b6#n1g}>S38*
z)X=!EWjgvE%HQ)b><XNn@#uPBl7dv%)*RD}0`(Z7r^$gu!8=K=F=_^;Iu&uTq3-3~
z;cJy6ud0o|lH)YsIT@I&^9DqHL%6~V2^#TxNZmb;KOSz&A&Y)zm**NxMd0T#FQFX{
zZL-z=ytN#9r0za+CTK#9Q<^1R(vbH7gVrrQkz~s+{!INrM(+A#Y0GZ^5dVBa+J1Wv
zK_L<HdAag&x7fLOJ0BnGsZ7+~FH?|dCBXu)U;UlpP0BO%TmbF^?AfN3W8$ng>LIg?
zh8O|pbTI0+WDZ*3r@x>WhSM4N0GEmL_Qh`FpVa6uv)ws<itfuj{iYDJe3Ub{8n$VX
zfMi6J_%ceH_kDYKMOb4j&x2?eF_7JJ{Tp_^bNO(r#BAyYUvQziMD)K&?kj+*sJ6U;
z`yZ>RtA0XJe&Fb9Q50xP<nV|};*~$mY78;UxR7#4LSnrI&246*XH-%}DLo`0C??q7
z?e=9M@=5{1&LJ(jJo0atn4;m4?Vr5>^^hncp%b6MG3M=G=JW5c7Fxr(cCg_e8u5?J
zVS1_VykKIeR(x8<GmjwFGznnhb$%w`rBpfhi_Y3f2~d*jJ+O(hJxlmFn?q(|c#xcd
z7M!JP0%hN`7O9xESLt+NV12T%S2fpmHdxtvy)b4I?GT}ULb7x<Up?HHf!lEsUq)kl
zi+J%$P<D;J+dXAC{4ma~L)1Cb>~64aAb-Q~H72HVK_V2Ly)t)l;CJddx@p*szVdvG
zQ)YL}0%B=!)jvkcPIkQh_fPE-TlC%vFFcJs5{G_P$Rd5C7XPYuV_(gotPzX7V^UDG
z?B7Kxz)?X)S!BG6>T9(*yVx`E-afT()<kZIp~8Oqcho9%wGDQV$_$(I4DKR}Wt@C|
zO1_Km1Y~PhSw<((JT>7FkMFeh**lVF`cjGjj1^sHs5eei=kFDQlst^trY&wn94*Gv
z<a2v^f!%0#dfa^PkG~sFFAAbOSomK}E`}ZUCUD~Q=n>J53f$(mihp)ySyo0jHYV*v
zG8m}C>)N~c)2^u<7X-lH&ALu}&Wf3q8U`1_u0R*Ko}4!U?1he;C}}7J_<kHH0f4#&
z7AD{h6xYwIr~R~N6T*uwua~E5DAD(}gQmJU3?s4k4I-Nfuoi(;FaV7g=34dYd*KsR
zThD@nae(a=fgkW$zV(%jz><#%KA}T>@~BGu=Mbf!SA4>2d2xPwX=6^wy|~8KG%k4a
zGF0XM`DS48F|q#yE@{*-8$#TV1kTOsTr&tVVXj<8r4SU4k-H<)eqdJu!CPa^%H6p2
z0?(`BvZaPJ@KFI}7Hq*wP@kXYZgPD<j)+i@of>EE{0#ikGVE(dob%E5S`gI2J%_PJ
zF7`hiS38a1EbEA3tL-Etzv<NlAVsz!h~~rO2B(?%3tKSG*FUnQP6KE!3v-hq#9UQr
zWoZKf56J|$_;{Y2_Z|-yv#$zzw;#`YZ3Rtrc7;@fL%$>>qWM3z^*vn-ObvflR~6+I
z^w?VXnErJ+yGn4kj}vh-Gr&=r=hZ*j!SScyNKrtLDZ|&+a|Tz0BS&?lC;PDTcU9kA
z_9eM$I`>J$wig6FuK>BLil}t|MMeHcnCD75Di|O=xd`i{TK)T<@5C)_vs+C;R77s7
z{*S6n*T(bq>xvrO0dwnX@Gm_%S$VlGA9i+r?yl<Qx68gFz0TbCTeJOc&!z|pJ|`!q
zx;l0TocOx`x6x;3x7&<xUQ8lgd%q|%Bb>O$6kjd@UNr&QeAroOG-|*w7m(1YJLNef
zX-8*6o!t{9`s*1t%T0mF{w286nYoKzAL3&t5#_f1xn(5<eBH2XrHI4>L4Vq|9nG_X
zk%ZXsh%rz!1c~MV$QEEN+=_K&!Mo3>%BQIX5+bqd@V!iLZ?5P$se=(~ikLc3?_R((
zmkBr^*G62iy|aT-?Sn|mpaD;Jy4I|VEHh$fekLjj-h=rc@p7GSi6a5Swjkb#qUH2W
z>+33M*spE3-Kk}~2Z+ispWO0#IM%GG3jY6A%}pbRU1f7=kvSHRVzRWgq%6+XTAa-f
z;;V27lCRiVI8-5gr(>Bvq?<21Ppfl)e{l(TX|ES22$rh)@-hi!&wmeFr0_nAn_%8o
z$h&R*9iy`nQ*KF8sY3**??N6udvb(_Tu)6+kt;DX_=^&o(hGBoUWFX#fp^S@Lh|hd
z_Jbe3g-FbT#Ml=G*M{%G|MEG&{w#C^%hf}9-JZv~(N(m~t?`aO9*+(|Pf;LXQjY-_
zr{DI19V@RO)W_8*+LQbBy}6y0&B)vkYme?Eb}N5SBkQzcESlY59vgP?lHazJxmL5Q
zB)!#T1E3StnVGYz(Gw#7$|)S=aKuxn9dNm?5{FTx8yVI06IsL_;d@NL+~?frW(dQ}
zWN@un6b9%igU`BJCNLo)&>Rt1dkh>T*_~N>l>6PN%<2?ATylEe#OFXpLsyaANRZEN
zhm*7g6nTTaJ7il&nSLZditJ4H2*^al-Ayvp{nn#=*ca-{4k%2mZ6{m<Ytf<HRlol~
zp3Z_Tj;;;T5Hz^E6WrY)fe?aQa1ZY8(zr{I;7+jM?(V_e-QC@`-~F!LAJ8*BbItTQ
z=c&7@PLr!Tu}^3l$g4^YQ%D>x5n9vIwdVYHF5<tFw7fvttZcvo9jy;8Hn2nMfNpdY
z3M%r={hibO&D||35(;umuGaPyuKW7daaDVLbM?!}!Um!oT%D1-IhaiqR&S*pBho@#
zZzle)vq?;Mb)x$$LEi3%l&<@5C#MT}1=_ohfs2J#QAJG|cJ3}BcruBWBb(RO=Du)u
zx3RFwE~#s$z%BJ$_O{sSZarBB%!V4aWbuy+-%G_JeuJgP)km;6cQ(TFlyTJ&$NUm{
zkZZWHt0=2mvBOW@FC)i8NqId`-a<OWr-K<~IaS5^b+6(QxQ(0gt(jWAnTUg^b%FwH
zE7Pia8VNX?V2wwkB^(D76E^-Oj^NVigoL`1!@pCoN%h$(eH<>Zzd#tEvpgY5%K%y$
zZ_Zu^SV93F5pX2D3Ra;$VuMYy&k{7II3hn&Bvgk+b`}(DC8Q<yUej=ON|6(c`yTk}
z72~b6|NFX`Jg9}~xWpI=Yu+02l&d2wm*J?p?vPzu^eWv)U($-z^2EHs=QvZ;Etkq}
z{<!o%dioN}W5Um}fbGug@uRT}1$_Ij23uX^d*|92A=&2BZEzPbI@BDMnHp(^xpw06
zexG$q2zmaaR(p#cmYaF43)}@i?nY&Z#oMYev#R^9wzSmMmu6>^y}QzufffZk;PsA@
z8};qQ;P4dbN(=dJ7PmkgCKDvP^XOUn!N9C`_d3J=4?longMG+j6mgx1?fnmwn54VE
zXX<aG^3sT_4@BOt4xv1n$7R}x@DC2ntV;vxnhNEs9j`L6v4mKdDxk4PC&N<uP|XgK
z9p<1xp>3EW4+?CbNdOs~`pE$5*4b$M5O8&>#l9HpWT`MbJMrH3QS~vmmY$y|Nuj#f
zd%4^$1X#-12PbEjRkfZNIVdT&FKr<`u$n_?_C@qlB-p#a6(!|xC@Dk5eaJvf=s$65
z=j&(JZi|6P7{<wfJ$pGxXUu1g`P?bT2zo-@o0v9T^`_c@C+NW=-3|i$0~_1FCD5h&
zMg<~Xzv5CeV}GaJmsXb6=R4|y8yT+}D3N)fB5RGMEdz!|%+cT~BSH>twh!8UA=&KW
zAo;l?i%0+OZwmVUfiazqES;bggRX)$-*zFNrz?~e)*$K<$uNkIdK9mclGdK@!x}5=
zqSE}*5w`mhn#0xGqQ3+(ip#@mAs9yIz%K%^av41yOf|e3I@V~>3H$CD4ZY5Bb9D<W
zqw=Bve$ITK(z1vMPi$<#;zEzawA%v%C2gmjwgs@^Fa-Yb(YUTms3+dB@q9<kD9bIx
zDB!xLO=Xb{@+{;A%%*K>;84KWM!zI%_>WIwt&u#da=PtE(pVJWe6p}G@i}~ziVmlI
zJ1u#e`|BV1XAmCCRqfZsfw{Z#J#dhQ+PIE%Kfiv%gCi1-awlwW_D&*>F6XiJzq^k0
zU6uQ8+!1hpnvgqJ?xRqz@lvT2_a#&>IEuR>*i(;FH3?_Oq=1H|_giof^m;2Xjy(&v
zFdY>`N9$W(=P-VwWaqPB<7AK0*zjC#0vb!mYx0@&%aHAx?JD(82Lb@r#Kp$_wI>r4
znJwYI>B;3{yC=_fvhR$fnJa{l9GjOj4tm+K^sc`joufOj;7dm6A{j_8k*Y~)a|Dc1
zfm`Qhr>oH7=r}xFs5Layk{Nv?QPG~0-rbhNd}41g!@*gjL#Tz#^&SMyxTz>))Ek#J
zv%gF%KB>RoHs0D=a)5?%IPJS8yUQu#cguO(;J|(|b48f%5xRyJ_#InPe3++H<_7IF
zBQwAMxL#Nl_T=3!I;Tm}9@bacBhum%3wUyDn)jeRVbwgGL3w#V@XSx<yk-6;i#AUc
z#>D4}eqi>XxOial@;{?C!unCyt;T2v^~Q9WqF*86qM)Xc4iEyNdJSQr=>&@>jbIn|
z%7-&!RkFoL>5G?#w<OeaoPE;*ntllbVKprc&4i@inMAHEf;%3azopmq<yx|wT<o3i
z1s&Ipb8QvWPkPdGj}q6uRBE*AyoI_Ce-6z$q4gwkpB?=sA*-976$J@kEWc}Y1VyIC
z{+9myb+mW#7`O}YFHpQ8M`#)*)8H$bLn7m;4o<z=dRZAsc;4hG_wdZ*b-~&i#N~Bg
zdmnqfDa2+XZVSlP+gwfRZAdWbTW>nCF)e67tO%9B5n`NVz$X@R6k=|G6_l6h+7EL@
ztRagy_nFjIyA%}R*){;_lK*%O@cb$$*Tl<5R8t33)e0MV&Wv>(_6|AvbZrzdlt@G)
z)apU1^`sq@4u+!$de4AKlbXr#VIP3d_0M0wfDN(@VoJj=z4?_hTXUBmFNk33Eq7o!
zgjeQg*M5LX-luH?QKtu4AgcycF>s!vI{c%i%{#OvQL_@}xhY-Kiko&>LP;&?hUMVf
zj(|h`Dm8KckXR$|arhI|_Xw!DNxoFA7DVfkOUfU^ZL;xnJe+M~#El?>EdGzAoqe)p
z&)(DHHy_+_zxKzx5NpO!)RT47Y+L<C+m0bf4n*l^Wn6t2h$f8M+en#_t)K6^Nrz{D
z)1P^mc~g&06LEJ<`m+c}->u0CjP!TWfw*7uHGIxgSBmD(CB=t49FEy;96Veon2YQa
zTqMo{gXjJs5w8Z<ub%fs#Xd0J;BGT(V{m6B7hy+~^UdT^dP{5AF|HeGjHrQKA%3Ti
zbc+W~amscd11o;E%IOjrP+t(d!cZ=JyKwO+eHS;Jy)MM3vqx_zf6R0guwNMi=9XT3
ztNhx*K~r#Qcy;a-Gj`o7L8|7`vo(<rvkqzuke~)O4GQqTil0<?T3lh+>hYU5$xBQ6
z+X`^4t(d*pQhwyZgzG4Zte!+4w`5Df&WJx4eqIjh<H^=k)>BMR%0zP?USIARnwy&W
zF0brd7}>a>rZB}V<Z$#;0h$(g=LC}+m`b5V1XfgIpEu6x9LMH@Tk_pfE`r_A1OzZz
z;<^k_--{%uto0kjq+Mz%U<C;15o(k{Jg)U<_dlp)W<fz-3p29s-;k8FRA|^48ag&+
z(yxTLM3rd~x0zf2_l$*^m3@#C$F%JrYp-Ay;d==WIfWEb!3WLgfFMO?LSl;6cZYo&
z%;)Padp;GR3R4pk^sW8%_6(9!>k0;zK{+TW_A`{Z{-q;OqWFG-wqauWjL?ms`D`uk
z0V7UrvZGdi)0X;<eEC~Gjxyl0ntwAVDG9o~)NnTwlA#DLR+`Er8{4Y~Y}t<v{8RJt
z&$g^<XVD?QG6+yaf&W;|>;Fms9V%<8s;jGN9PJ$J9V6(@*o1ey_ShX}b`v>WV#do>
z{9;S2(2KPA)d|c85gjMc%Uniyttj#gcd>u3)(xyYZk7FMT@v1LgiFrK%ECLbVq_VV
z3TyMeC29=-p;kvntMza4vVOiV4f^N_v#i1=Th6aM)7${<CFQA#I|!wN&<gk5ug&#u
z%}Z>CRbi2tzvX4*RF*%430+D$k8>`Z=v#JynF6u~CQ3WV>l|Ytvd${Tfe-FYyy_as
zc>ke}elIXrPznkPN=k}~$~6f8&bVuxE^gYeGoEVydb^qAKl!bgWr{?%xuZwo(S|#0
z$|G;T1KhD3m5AWPdcMK_7EzYY3=cYCT<lEmu1*!#xUhGn$zf?ylvHvUZ)1CF4_RSn
z%av@kpC!-0Yq0Ox!{}f9zxvG-mM}o^m6PkKV7!>}nalVx^kEb$l1NlZDit<l_4Bf<
z_d8PchJkC})K@)a4LJv+%9xh6piLVMau-c{0O6vb-5lzG2)Zpmo$Ng*AxupaM$%#B
zRdkCTyk55gO+;WuGx4|Get1Y*ldvvTy<N*TdZN+~9=<*ITbqA1OS-<GvG(k2?{D`>
z!a^k(Ncu{n2@)dHUtEsj5d4I-xzzD6aUt<(*k+ebg<JEIeaTmrkl{3jxd?GHYFGxw
z7$|C_k9{-tXD@bI|BjX0jC?ne?yHdieVMJ=;E~*dHuTae?-SIk82tJBm$cq5l-Mql
znA46Yzve%;Ki14sknGLh%5BoGc6`{1B^<@x`4(W>YfDz}(`bL0yiR#wWV04{2+-#J
zprfm+s(Qyr$wz(Dyxyejf)kwj{bB@2NalKH+6p`1b)hvot8w8c1<gGR6Zam-Xfcvj
z#=mbzhy^QF!iH&sx5tf*cXz>)<5%eCOg;n8iO<T4O1>F>>49#?iL!+A@2%Ye(e4xk
zsy=ka)`rLNNun5;KR`NG+)|-7i`a%~T&}mLgt2jW%HLD4T1|sDz1{2iEe{xoA;~W+
zEX>dEmsmq-b()*qyzpxy5TNQTdd|8*waz!d{+~agh3IL++<N?mfQ)2@Rh{=y{68@H
zbma?Xf56U8LP7|ta;%Z?!`1%<Sw63Sfj<yHgIr^iQe}vWzYwv_S?Xt^DEV8nerBcb
zWU?5M#4zrphfxu(2J~+KRb#jCj4gTS%WiS)>FMbf6+2&P@>v|d4$K&Z>=B~B+WL?k
z<PWdH`iyg_Rg~=Bi#2nf%ICfsI$U;?stMH_u%`)&gCcC8p&~ThGgjzbvEFFEy*H}W
zASzl;&ak%D?^HppIvE5{pIr7%d=4S<TAPyY7p14ID?9LS7GM!9Yjl1QN_0oMG*F9W
zG_UykNf;efcYlM??^kVjbl9kUOoT9QTU%QzbGpd6PxslcMPZ>R9S4l$0=iO<Wu7-;
zt6U?qV|=vVife35u%QDofi(`@Naq{m5<IT1sHpPe(}nU`PP|q&rG+_+49p_~U7}$0
zL;cWC;fM8C0RZ$fCiB7c?f$r1)@!^a>oC6&y7R-q$co3?1C}N+#zRA*@UUi_ko3Q*
z8lL-!MMtCD-MI`z6AS(Hw#&*a2nU<V$xz`Cp^*6|v+CdtF{q509C6nxYvb<1xIPQw
zkx)SOh*kQ-{IOwVWMV2SEiEe>jReo?u2K;Qo#eJsz*HF_&&#EHC+trJNG!D9lXaKy
zk)BE-NYn?Da7&Au^;hYiT0fC6c!`exs}a*w)EQrJ%ZiQ7=~`=h@^oPsmJEltt6}Gu
zQValtQjg!<C_bn`u9yPkpPGtpXTje}KT5FCVG1AUhBo>=<5E&mVrgb(Veyx)Yhv-s
z0oI>hK#M!~T5lbNgpa*wGd9O%qj%SHkt-nE8}77r00ZcsocfHN^>3Desb4?SAMSvh
zmhNn&n2d#nfq|T&vj;`kS!rbR&2+jv`K#nt&HZ2dw}CHvNtV8DcmillR5{rO`X<|3
zm>3v1IBQj6U%!HU7^2USQL>TGDsIz@ny=V*U}NwXPvtcVpuO0^{?iYG^bFYJZAXuR
zQh3N+?*9k#gU(%FF)k=9Iy};B`sqCNwTnv1!i474@S0#?9^w*Bp%d%%s@TlT-2Cw1
z;Nb9ZQfao|d7Xc%<AWTEqE*aj%CDKLHLC2S4|49Gy<l{O70Mtu1O~!c$Hw}bGlF1h
z0snZwmEj>t&ufC<am&6l!xbwb8(vx*xz4P?>wE`ssl9)&o4VzGA*fP954e12oZa?K
zOB6Q_#m5I^8cmrpFL_@~i6_d%lW8cS&QY+iumGjMPXv{q5WC3u`<}D|D@Efqjf2GG
z*3B)W2eLsA@}q+XKfh)wY3ftgZxJR?f9al{|E3xPMgJbaiuySmV)GSlPhc__1+<nU
zBhHtwH93pXo@e=V?f)Z2N!B%_*VH7qc=;+WDfvcOLee(CVCiT2>X~A>O%l^1FOpaD
zL#qJ;5)Bg*^EGrq*ve!R2P0`(#EYNy&AbPmJ_6?aGFemTO=xPP(qW7|jTj%eAcS~4
z2|g#jSP|_TVQ5$Gndn}8Hu17ixz{XO+4OB$g0O<@OgwBquIQ08{PNLV1rx5~*G~6d
z6)MATfg9&D_F%V{Lmq8NxfZ-qS7<E*>!+50b*UbD+PuHOMbW_Tiyc$vHp?9F1O8$T
z?nhGBmr_~Bf2MYLD6h!1cAo<O|EKQD(kC(>$=dT78Q!?xxKhhEB2+p)>TmZWi?IKb
z|2jZkt!(+8gQ?p!c_<$BYPsob1EX#%4Sh`h_UFef7q>FIJ%TjDIUB#n(^6YxHEu<_
zSE4oDXk^FfzRL+&JI1^51)2!8lK4m`Wlr^c_vXm#L?9SS(^Ypt@=gV?atks%K%T}i
z%`k2xbFeyIHRC5K&vG^8;zNZE45avXAb*m1VgC=OWLa&zN2@0-W7~R1sFa@ysgj>+
z%&EcNiXoX$>@TFt?Le^m{JDhM!$y)TPmFnuhl12dBt*!yagmQ#!7u4l0zh8qu}9=$
zaGT*4art*Y=RM(ZYXXQP9ytSI1{R;p>N$IBOolt)H<kjlVQ7o_zW%-~OCN*#MrDli
zqt2gsS+5;Qn2G+NHE&ip+vHUQQ}B-D>ktK5nT7u5#-=9&^{K{fMC{@?-e!e-RsmR}
z6L<EIQO)Kj74pDA_o7y1v#naYMpK&YJ+T=H@D^0~IBasL-T_FkS#AUKvmPDw)mZO$
z?<pJh70s=2^iMd^g)AYxe}w6*kMOewLNmQz9t?78C<M44{I?-rSFdsfA2=ni)qMYm
zT$sbrdQ-x^pvMfmVmSDAJFhPs_x)B8S=5Nd#&nt+Q<o5Tft%<4_a3zI<){Ii_41u;
zqbbjEIr4h%$^l*;y5>3qWZE}b3huQ@r};4yuR>P$U?0Lqd)BSVlNCJ(c+d!Ma)ZLR
zp~FHhp>6KG9kCWWw75I2z9!Oc!6J!zFJN9=C#{+g^P42*ThO~!_Nf|SeVm`En=U!b
zDU?fLMIf~aZtk|Bbe6qEQr@UI>iGk2n&rl7@(qBB2fR=#rCMKDe_Nk=?7ls?>F(#|
zWqZ0KZeI0!Ju{XzxGF<*dB(~Ye^^Ml`2KRx0aMU~gEw!rzk?gA#3Q`5=SoA~LPYyH
zN_LdIDo;~mSh@#!9K$xfX1VYa<-hUE4m5$gt(G8vaJ`d}Gamo)%<+a!H*Vpf;C!Bc
zpH<Zg0C<)j1f&NxZE)v1V{g*gweR~PX+vi9_6VW>Xiw{0nnEXgh#>t5&P5FCs&QZ~
z9nbX~8zy5hHurm72mH^WH7|UdLqR4;e{@}Lq|EISqa@@JzwlT{DGT#-->Nzy$x#4o
zX#4mEPxR*c@KNjd3yu&&R#9p7-j@)^FKjW=fTp5zYyLec<YkenD&TCAupSm*(diQJ
z7mgM4qvFjn8{BJEBYdYjXR8<9^rqyl`D2VHm+8k0ZRLNx*<&aBt#Mw_+uY<^GwH!e
zr`CprmsWAT{m1{nIwF@22cEhj^M_LkIR-?2Y^L^%^Rn-#*Y!M0Lw)@+^0LwL{I2T&
zlr}zTtJVj3Yj!8yU#Y1k0$C4ovSQbUG;I|ULSCxVeC?si15irzjvN&<dUb*ks17wV
zXhC_d4>m(Lp88P-z7^azig1e4z9>u{tX6-k2SABC?fewlsD@DQEAd}ppO+gF_~KRq
z!n2B@u%<nee5SsCBcGu>oacX>FtF4EKREw$;EgP6EKWS?ljyRF*-y+p-sEo-?W9*1
ztc6i6*up-cC03>SQWfV2qddEQ{Gez5vvtbx6-ECi9J2dO<Vfj>wqUE*%gYCUQBnO3
z1=o&UbQ;%nt*V&tBTwqYHON*8h_6C$p1Pd#dsHi}`W<>IMubpInICxp)QLTSh`@sy
zkGEq3MUjmRo{W;Auu^ONX2Krz+;<tYkM=o(&I?~$Qj3v#UJkaY`AKc9O#AWm)o=3A
zAqWSFfU7B7Cd70yH1uYp8;tm;KfBm2^#3!oO|$X44ZiJ|pEEQVPG_ptqsWM@C80Cs
zDAi;4$!8M;mwyluLZ-3;6!es<DrC|x5J1rAA1!Th(W&bE%+%bZxS6Zdb+ZSenV)cl
z`7t37pmTjLG9v#0u7B#Kbm4fPSZ0Q5VMw9PLon{_?fpa1RJKqN<B6bur;_JA$!lBi
zx97XGWD-^ZBiC*yf-3{Aw>7A$Z7Q;T5w4Y0ZI$#i5@30LbnZJK>mTtX4;yufYo`yP
zeiAM{%m60LoUAsGhAnshd-_!tAJXt%HW+v#XeP_e&-G3cl2WyX!)k`SbHwEAioj;b
zKQoVVnM{vtp-WDkSF5$$IWu)vl+m_rzmG{HpK;kvgy+CRO~=$wR9$Ro_S2^?%c!*h
z{{ibOv>rImm`ngLjWj$XNc5r5Verr~#E2#C(=<rItmuQlz_T-V^r^Q$jRjT6mcw_O
zLh6s_YqF?y^H?UI){w9G4GjO#56O1GylWG@VJ*-j6C={f(ifQ%QfRdd|3}I(>t2C`
z-bR1M_l@p{S`hlF^E}v13yPU=!aDeZ(f}o&D)6>12N11wm=@CF3Qek-v9y}}l&Fe@
z{z2cn>tcRHKwQbcwXtQtw*q5J+QINPZzDlfAmXmOIyEgK$bISIVt}u53n#Jq)z(l_
zRaCu`&os@Oz(KV12jN^?8Ry+nJ$?f9<JUi~e~;5XWNEBtZ=^Mdfy832A*d5OTxY4x
zadjNv19%y3*DhelMrp<uz3uCf#^ofTQ^rV6>^`>?x++VbGU%8OdDwZm)@ByYGlw&a
z5o#4MtBuYyj1=^HU4}bWwuks#f5p9h-y-yfwLBIzGVaM%-Xq)cLA@Rx_adW5t*6WO
zLtD8mxkS0Oz@OhU$Q!#<w2a*3bbMT|_Nrs?fPY;0Qo$XwzB22piI2f!56eU|%;u{K
zE{4Z}aZFx6|6hk(LB`ro$8+izwA{+9_6Wl#Q){L+#|IvFN}Z!Eifs3oB!V)Y>7c`T
zeolbD&6lINTcsFRLQ9%t9Z_&!|8HTi>5|VnoRxW8nDTciMX~jhgQ06Rv0P<`%st%S
zK!VEL3>WCz(R(U6T!h)!hdAcW%D&&2>2=dO1gk#_V+czAi`e<kwo}!Be=u8P^9t+m
zjTznD8LB2v&SoENsV5gR9`cY+J`+2^p!QUa#9*D}3;IHASHYG9+dK=e#?fE8P{fVj
zk;kqObw`V>#tdADF-3xnwO~|ED-J;L%v#u3OJvZN(^S`1f;wB?r^+n+fqceXy}4*G
zy5(@!DhKKZw)&-1*^4IxqZYS6Bqo3MsiD#C!Qrm!smVgyLjqFZP2ogFPCX2zet5|U
zOYXlj*}BL7Y&hD#TIIfohDM~gSeJRsm?TH~hX;XaVwPFIp&ZQH?0J~E3IX`awg3L^
zv(qBbNXdIRiB)O~s9*|E-m8u^vWk41C`$_@AqjaV*8_79!-Bzy#n>a<dRVBWSLh$k
zpgt;NY?awMkP7hbsn89OP?y>7YH1qU%$#e!@=phS^SU%B_?G2EDlv!U1`GWRIe)A@
z9(tP?c)DW)_ayCA3=wMq?bB0I=)@9Rll9zNe)Z;*G@?W6|L}iKDvFKLfRt!|GE@PM
z*Hq6mKmNDuhEOfh+0La$QT?x0ADq;fi%ZLkOG2n+bc)zWpU^())ak{z@TlP!<@bUd
z!m*WMX`S*{--!R3%Z^hKNVICUFqCJwg^rGhvFzWw^3wgQQvs9D9_yVG7lRAKl968s
zTYJG%+K1_?J5cv4O&H8Zq$x%ILDjU7j9)YqHovfpl$=FXSYY@QU3I3;t{D_RnK<q>
z6wSc{za?Sqs;G}2`Ax#Jucx1G`hjKfpmYQ{e`;m8h*d?~{pMe&VcC_p{g&?4jo>L0
z)BL;^DGB;mRJEf&U9JZBIv#Mr&#Z3B!m2f<;JGQP#esuE^n+c81$8mGUqK(u!mKn5
zZ8?%v#`~%AZGO7mY5wqFiyN8aX>fMR`{qfRm#3~A5L5I_8|qV<M=f%@hD@4`U64n?
z(A`G8PGcu!cGO>A=q0wro~bs|&FQnV+nYo(%$hX-d=&=UkXL_XCgI?^j2{0fJ0uZT
zWai%}Yr2!VQNIq)c;YVXjNs^YSF?Se7<?%Ccu>ao>d~iz=|bXr5Rxj*o?Tp2b!G$e
z5fX*^U)=+D0O9?B^E`xr{Q(^xVxUjX*&+KA#OudxHO^}jc$^basIe9>ae!X>itT+N
zU8r3jcvWTRy<<Xfej(lA0VxG9u8%V(eqk3MSZ8#q3Ia0V$e!AlT6?wD<V-$W(<CPS
zKbU}A?pH)mwAb*StmR)NL_h4yDZvA`HxDanyQMyx(}U~nXH*7eoko8hK-t{oC-`tI
z6r<*+uZ(&NftA06$^JV2;YX{J)e02w)6oKU?I&@b2w+V3hJU*(s_O0fxIH*b!&KWF
z^f_cz^OU{kkZ{@KLE;0|vnyV4z&d`2SWmG$X(DdUyaSIzNEAZzhLv$?86(x8oIC;?
z+Prob3XNr+^=UAgp1{Q&!|@QderOg<uf#(pZ&5pSygLzV?pn*Om7Gp{n(7L`ULX0p
z{2BHdl;I3Zu}iWFR*-p*%}la$XuHOxq^|}r%pZ`_bw)`_l;7AExn6DU>xOL6-%0pS
za(V?wSK0!y90e%3771Fqe?n%_d<DlC>W$jX@k$e%t-eANlN*4<%ha;QhVotR)lk3$
zL#9iCM+<hZTf;P3h3uAJ`z65bqHAE!ggDPIsWIz)t!H(+{)t3T(j`eGym7}#KkELs
zDAzslloSD6jOV(PhpGq!yW5CQh+O-7rkg(Pl4@qW-SYTpXjSyzQg~EE(U&Yqs4XTt
zYFUCk8kYYO%tx7gxu-;^eLjl4tV+uj{I#qx3xfH!`t^%*$7|EZ0}a6CUDKHlcRiW+
z6PMxP0cBciQ7IMMb$H+wjg@e@t4~I@_FLG1=2S7M;3jU0;-Wyvn$~-u+s?1^FQNB*
zHn|^O;twzWi9Aa9wvEx@lkkDrQ96sS9yt7tOolh5K0Sh>FDryQ0U_L<x^!tuZ)UNn
zf_PJk>dPh93<j{bUF3jvVhgx*Z%Puin-L1vyIm0q5^a+%a?~)_m5Pz^zJGye9q!f`
z9e&B-eo>%$uKWJ0<-INPYK$&YcJeAbcNVvoWuz?=kCeoWQ7Q(79~F7J4`u<MZl3w_
zhaIiL?9AcRs>JRp$F+XL^TlOMn8ck&;KM~kbib6Prw#11H8nsF1sz2(CB38vN!ZVq
zyyjy-97*o;c*<Bkjd}|P5PgG9x95c)5L?8ft$+EIi?9Z<Z%}F3ktgrwfiEfu{}L0X
z*N=W)DIXh>0WxywZc!L9vQ|`_qv_t=Bsg`sFhmo^Hz*8hPgIw7f!Pgr8vj;HG6!U4
zu=8CnYzk@Cs{v~FfkdmOhu=}etvyr!n%6p?PM$@`-k^wZ(vFMWuuy`78|Qbx=s-P<
zv*^y;UB;E`wT|~wn)m<DQ8}*#uwI~4(6f;Dj*3dOyMuSYrnMI*e%Hd_I-LnVo}BOR
zP?4e{+tZUhqF&&K&+VH~#+<yexoPfFL!y9k(DOI%;vuzF$A=f`SqRn&I9#^HC#Wq?
zEFh6=wRq;}-Hj|$X&(&YcCZQvv4L4~3OBS*%}E^8P0QcvzSVfp{zD2}5m$*sY0>*A
z?W3viLt9BzS3_rdVQ!H}P?)R6w`H-%RO5n|Z{^1?hyA#SJ`#{-^|2c(TNRg&VFWy~
zAM6llg^l+2ghp8yeRMTfCzd`^L9kBebaWmQ6|E!HCGLI-(iPg)H_84;@9MNbGCUi-
z2j_Pzx|uY~S}(NgNcrUBCHkKy<tcBa`>MJAEJv*-dGsA~Pl#mkbYN+?`lqcMUm9We
zhI!chQ7%nRcFuY~{injJ^IyY3mOyq6XwUx7)ACK%UPXDJlh>jtipVS5w+4d2HG>Q0
zkd4P%C_07ExBuri_Jf}_d&%$PPqJE}L$t*P!>hu_+l8;?4{*@BsO|U;0#9H1Yp(jo
z$QM+Qvy6eRI~94Sd7MArK$j-zQbpOP{?YIC)PxcCnronZMi7kSQrPMRFhA-v5uo8=
zdGRnaHT<dfhPcyVqNsX*8V+%d$Tks}T>g>zOm@{Av&_cftwQI6^A8zsPfZ}56l$#6
zm@o-!imR{6&*-r)#H7(#6%_Qg{Ytf;!08ktF9V4mcu%Uf<5K{q$&e}7>bf1btT-)n
zC0d$bS)KnPa_k#P$VY_?Al{>-=y*nsZ0sb&*9;FSula3$OcAdm?n%xgAt!le7P!eR
z0CB4`=PYFj**e!V{lafdk#hu>pXc1BK|KD`%yKqqUhtZ(7^NZRbif|Y!1z-L)owx^
zQh-kn--O>aKEnkU$I`0$(<iPwlp>81NxP}iq0_U%EVenA+L3I)x3E!5LVmr|jx=;_
ztcT?j^R>I-`o%);%}IAlOvl#I>~`BI$cs^}w4Lq#G%TX;5^vy!Gqdx6H}fCv5EGU9
zrs`9eTOEERtaU%UIW%tZWt_PBBuT{UEfQVe>Bi+}WnPY;$aNR-db}$CmDi1>LrV{>
zi_M~x6pU@HKra>W<pLpGss+hPPPaKDS!;0;bdf)r_~4V1<at%|a?LC%+}$XOLYHn0
zUa?u}8gBdJ=^I7O+pdTPB0yOgE{^qg4UG;LW&?vl8*AIua-SZ;=D|diiD5yrA$1Ua
zU0BpsequbFgMl-|Ht3JCv@}A2$~KA)M42MmSo=I4*b9vP8|wn2U<(bMudY^Sa^D-;
zSWx#JR=d|sQKP;ZWhxaRrLgCJACw^%6A%54k0AN|I|7{~XEJ@DXt2!3lzsQieR6UN
z<9nU;=T^+;nX}=ha@%{u_cP$0Pp9nc{$9{h$g=DiHh%Z>k=fZWE@w{)F{xPv!>Fu$
zC{p83LnlDNfo8h-T%D5{l}t*$v&(SRLy6r1<uZU8>|;v#uEHbyYQIUfzx|)c>1imt
zILvx}J%H`PR-v0nDuiMbf5hWd73>6spOzdx8AQ0rA`RlNo*`?pqkiN>JDq7Cu4j$i
zQzQ-A<qB(!9yI1C9ZiS1-H--tkg<eIe)+dY0sB3H;5M)z`fYM(5oT+@U_b({c{$ZW
zPyCUOpd_rypx=6!*Uily2c1z@zMH~rBQ#&@^G(T1^-^NyW|&6cgUIHzO=U@O^Nwu)
zGz8DFAwsv+dX0guTqn=J<VWM$@y3<t@8eA6qAvS5q3^7G+%xmDa|zEU<t0r8z0tdT
zfcX<{XRqN0g-C(>;;hz<TUKVK!aoId=cgee9s0PdBM)}ATHas@^h$uGOYq;VWxu2G
zr@CQ&7d*EcB*D0l$Z@@vn3MkdXgL<Nn07sv2HbWxyICnUll|%$!6%j%xVEb=CpYO^
zQXYv(Ho<)bMoBYJyP8PXHSu|1$-DD!pl@iiJ(`B=?h-V;#Ko6u**M;=L-Y$0@7vJ2
zC8ceeU7X{hqsHVR-QFjNCAF5d`;$=;gT~?^cCDfZ1jKqSP9jnEkSOaF8*o^-qgs0-
z&wNwLT37OKq(~r1{&?`BB60!vf?N3hV2I5nfYlmy`dh%Die>#rJ(3Kw#k4m7UDIzX
z5p2OuY0HOK5^k-L2rBH5Wp;1s;zJ1MDvM2zLA?dobGMN!Xd?FpTc--Prj|Wvjn8W1
zM|21^?30ShR_b?EEDv)E)mM@b#b=g~F<a~rgdPL`-Sx6uWDM6{|5}@ql-N&=voY_e
zcl|0`qa|`iP&^*F3m4gv04^VoEz5cNM)q4{k8E5*-8aTtMhxF!P_xp*q;Gsd6H>gI
zf{}x+>^ucrs(Y!(vTE|hM)<A+X}cB#z46mBEI;?!YBgL;W?<Bul%_<LBB#}gi&13-
zJLS~)&<`;OxMdSqg797bs4G2)cZfeXL?7a$mS{>@){)%bo>)u_feS5Ivw5ea{6L+C
z$t1nCG;9F!B^lBw4W<{&))~q0>WRA|3=zqTR@UpxdkvwhRG$8I%{Xxz8GUm_!u1C0
zdmaaW(z0XEgYm%3L_yM{@BA(BJP4D%+bi8_gh8x|5Vl&ifw1vF_oGLm4$s5KtXjAM
zVnN?I)dz})qeVjo;4n|3GBM6Pt&>Efj<PVrOH6NP7S(rB+35rid@+;2ik^=uvDn`P
z8Q=PAEMscN@bzgmSKraI8v0dAmLuXs1;-DE5bbOYjkP6v^Gb{z;G(Q5VEar|ZoN89
z(bNPlOTP24SuJ+aP~;dKxp7dm<+|d4*E$~wgvX1PFCG94@E2Cg-QU~nchOL}BkM22
zzYiW}Hi6?+%_ZS5>!~-yP>G~aaCtvz>(NVe_U;_8xrD48i`!B7+)6g?lKk)+L!jLj
z!u3#8cZY|vWLqE0v=1|82-bJB+LBzdk63vD<q}sLt)K);%E~&10pV<=-T1Ls7qos~
zlqy^}NC1qh9}H`bS&6gd8M55GRzGVFOuhPu8W8@JsXCw$5t!DwV;>nlYuvg@5O_xM
z(UX??(o>mY9TNB?DbW!cDa9JVg`zcC>a{ih%jQpk>0-02==6)j#&v7rN2?BaDw3b=
zg(t?1Nf~|^xhPfsq3_nS{7UL*(%&x|%hc4R=-7b(E-sFStFVE>G4WZM^t0X_&bBmY
zh9CtGsL&ALiq!L;GO2qO&kqdI$OcuJ6U3U(6{RH{wKEzlr%oudbCDOq(1CPu7;I#g
zm2i($R@jP}(XQUSbG0^X^BAu&LVBpJ6g7;?#-&45wz~Kv@a5~B^j#boG9UCd6*<FY
zIROFVTPs_^p;AJaFeX9$cbE)5@zT4KsDvO@^^@<OI5Jd~;Y9=jj}rU;1&Ja@$;!Ng
z?`UMxlpm9R?USI(AqZ<5&qVR2tY^$GD#(KMT&?2!1bY#Ngk!La8QqX&!qcUk^p3NI
z9;PQpM|^y2V6GC7H>f&9vGuG*f603djDSlh?rW|tPrb_>%9}uFh{PG>_421WMj<g>
zRU}`Ssl$|}arjH$N0=?wQr@lDFeGZ)9{Wb@lpwRacj?e~Z|qj9u6DKS$bp_6cG@y;
zIBZVR4%wG~QfI%yBG<v17RAq98~(PvB~N?NnSPU(0L<k=@8R6)(calTrnCp3APqkD
zMefz|MVhK8!Ujz24<2`KPrq0!udE@Xi`<@*kqp_Yi=(DC!BSzl`cX9xnFqQBx(7aB
ztB<|<GZ9Q&5>E)6U-RbGe_Shl;6aOFMO7T9DD{}4Vl78&-uc!~T{x*WToJ0Lh{_93
zb_OP9#Vq=z7F3lX!c)C4gh~Jvd<8oH`o|#0ua-TZLe}MEn9xMt-Sv$zQT<XS_KKp^
z+%#pyEO9Rg(_eAXVlrHAK7<EHzp>e755h?dtdBFIiHIot{xZ=^c^$BE{0g;ZuJa^%
zUQJDs^;@ET0q`0^q{`G=>)f21KIwPanRPg;=x`(T6zjH{$`;F0*ysfCNbWkdpMLIx
zm;Bk^SapZ`xaaX4@Pw@HLs@8xs>12aL@cy{yj{^Na>s`nO4C};*bdcZLi`a-on5dK
zv$9S4q+8_`7Lo+0^?(?3=T|7*^XD&zC<$(BP#y|C@v1O=(G^JJhC{?(Tm3K~QGV(_
zub0HSnp$Y;+o2HZumE9?#?peJSNF4@w?k5+W1~H$`;m|5%bye{adyK0tkQYT?0J|;
zX;>@6EhOp6cu~o;Qb0!Q8c6$E`t3m?6I1?2H^bl$EvZOj7HgLG_RPFiB|2?WYxa+7
z@dU`yRNq?q{3wT`)YY7SI_l}&`un8X>t&06X&%#diiGvi4p_kO3|8-eG!tN}sjgz5
zR91#4ETv-WkO`9*JDNQn;)b;4lXDx6@OL_m`FBg!JAH97U*GD$BHYnh--L<z@w46;
z{WOXDGO-$2>!roZ<Hy&%27B@~w@;Rd1w##jVG`F%Tz{}QKexq^YPgZ>z4tf>v0iho
zC~2B%pZD)!sR3LNS+llis5tcS(QaQi{37Fm?Kzgg@JOdV>(i;3p+vUJj@SCc$Z$xR
z$Z&-2As(0^)ZyFCUS-GAX-?OsYtEAF*77=2H?O^lUe860ttUKc3(@M>R7l!*#86-5
zk52;S&xVpBkGoQW8?Oz{gyt!WO)!0Tr@{Z2RfA;-eE{`51l`=cy#xgw$E%3%5{z|V
z)(I{g>mE7T))!GkD(4%08THRj=RSPce6(I9q0gks=bhmnzI}lSK;)s+oo&!x<r|SB
zvX&*FKi{INl!#o{?~_BICopl@_(EQoTTD+=r}Zl>00v&&_vG#VT$Ou}tI2|kGAlAV
z-&5bpMG-LeCiIvY&%@-!F5aBCzS(gDQvwc(;f>8PzSda?@tp{FG1PjlL+*+#4Mawh
zsQ!+}!rb_2Xz*wC>)pHhVZSYcs-g;}>3eee-rd<-PL*V`Td&-Up{=_`Bkp%Qc)FS(
zL%opj`L6DuREemO$Y=}tVX4rIQ~l+U?}!n+Wca`t&!WWB<?;9L8bR1G%etsOit38<
zQ(V4Aw`MnOyDUBB83NQTj&OvCIab3IVWXNLAo-B+pP^RB#^I^7?Ut^G66|m41B!0G
z29in+aoa4H7@QGcC9R{Zm9V)Qe4VLp;iS4*;uGQ%?&dq2SQsiwSpexBDim5vA~wz%
zfdAac)cvZlKJ`VI?Tg)>Vr)e~)Q9(APotY~E6Uw^Avjs)HLrA4-T9SCfykN5Yr6+W
zA!E9nOw*Xb&taGx#8h9<V)nQ`r6u#aiQm%^`KyZ<ZreQ`A)}hMaP&)XCH(1gwXfk&
zjQ3VEeN+2!^D9_Fa~>n1x>JQ9rDVW59}tL$_pi_-4eIddz94^=K2btAl0cGSI!#|`
zdns?>PZu&S^860Tf7w*eZbKo?kK5rrt+B&1qmz+n+A(K7?|U#`H+m6&8p7w*{A4I^
z?l7prpkvOAqDuXNZSRYo#n8cm%UPo0G?%cU<K<OdnrlFoV59fy?Ap{$a&T_KARy-g
z8(z2mtX#S1K>fwtTkHHHY4_cjp)ET!2a)0D1bQF^ERKK>s0#Xt4anrIH@DoMW={<M
zYT*$~x027|_4ux<l<dhM^mwFVx4xG)l6Q85#OQzJVd^)hCvAGZaqxgO!QIDg-YgyG
zFU08l2Yw(K?zrFDXb&rq&+Q~31E>iZ&j^Vz#F)m+^?$9<69)DI>;L(nGpiomu0I=Y
zSYr^tCYI&?r6IBrH&P8irg!Uii{+CQ{wdKnG$w`DWmu(LX%{hMPN2!(tk;2!NASo<
zN6E&|I5^+4cvstHAU>ockjzu2gIp96@l+wDwNZ|;CcM!5BkIIf&_ko13^0sho*Ef*
z`Ta@QsbOMCg12AL?4*R-8uCbd54U^`2`MUE9;`^FACw98+Sotz8vCl|o&^C%e0cwc
z=LR7Cg=&yCOV(+|jMqUOX;KieA91KY9Ifdb-b)SKmI~~X>!WVe11B1NxcvJ+<JrL!
zT|_;qzCf5K4_-|IbGnmFU|9-^(`+*LjbZOyy}>UoG4^+WveNX(;=8%(=*9sazit$|
z$h5}GmfvzpPaF}n;Yfhc<g^uL>@-`89TTk}KO1F5X*sd#iq9MEL2z1BSYj(4F6QF>
zUyTi!Z*WI|IWZ#7h74@WWtS>;*1n0sz)%A+9~<PD$&n{tc07p>fblZm|3au=HGQ^F
z_Q%wD@mz5vg0l$A((Kr}nAz($9x=hjwv}-aIb~}`CiG7q<o*``JqG8iwpX<Tnp%C;
zM<+2a1KH*p+RgT3^U=1?x8dPrKPn~)mu8)`J^d6N6l5G!4OAS*2WMW40BLa>DVag*
z2FGtlN-w2^9nyj)z*wC(&9B-voVXt|(E>y7VQIzl#zJkUZdcj7w6XA_SBMcA2K#S4
zdt(ime?&WOgYViW?;W>a%VuFxSaSVsd9@BoPIe7G5I@yLB<sBlFkWfaUn5A<9j3o|
zh_vUizOC&l6L`W?yLi2Zh<)7*?aSBNl&rWK&dGXSK770v15WG2HNxZ#O1>J~GY8)`
zF7TsRRVF9IMF(YQf#p}K`!`e_gEv>qk@w@*jvUIEo%#(mpQuKb$39ECf%(qZE3c1_
zRg{FRC%IBj`-1*I^>7pC>z$CJWk+RETxNdn{1FDW&TSk;y*oN_5U!B_eD=Z1kbc6(
zt_fPV=KkDA1~hEg&~1FjMSJ~Q4cD;HFC+zP$xdmb%c_J<p0KbPiFLPt{%el2s>4SF
z=Q}HpD{9*_s|eTB>W|p06g*<5Q~#7ZNCT83@$HYa*%Z+Upu!ZK>AXCqv-P|?TPwrj
zCInVcoWZnG_5$f7q{JtOX2s*~IMCXljsC<4n(X)^usq^>CUEW}t1Piz`6=|iAi90?
z@=*Rf1kCV7$c}h>a}H7FzoJ++<5skSMoqp@z)@NtALl-n<bg;tuX+t9kYHk7MUoyV
zIfm2y0e{))fWeDekM&8=mtKPpQZ$UXDXoj6EO5!*lIMM32!`z8EjBFW5!LYZ^mg~L
zLaEafuyy(#4sbB`N;TVX!4p9y4%;r%)k<hy{u;ZC-xvBop$m4!leI!2XD<#l1gu&k
z&*rm`t~;^MLW{OzL6h%9EFXY2b}hZ8XU>}3@+M;XP8w5T*(lJjp0HEU;d@&1<IaI&
zN{Lc_{<{ubql8{5ZLxYi;z_K4TkpcvR8$DSmO?cuhPWl+sC*vmDUfP0JWcVEKFXO7
z{$vh(a{ny1MoHK;Y)wu_3wMpz(07(hPb@rZkTtDNYw9T`W0D?u4sGE%E#%Q1E{WKc
zQs(8F8tm_^!c3NzTk4w_pKp?qWU0)tb4F;P<^pnxW7p4_F0jy_R*};rZ1y&`l7s=P
zqYccs3)man&yd2;?jEHbc{$ar0!&BcA;eB|*S03Pg*hK`mn}D%QS3Sp;6Fg5p=?O!
zyRWQ_&E9uSC$t_#Px7fBAP;7Y%0=GPe|ZmH96$41A}B(WJE_#1brD9F<Re!09M3MA
z$)!Gt^7gqCvP(4kOE(_bb`C>)=g<v!X=fKU#zG?y!yHn3%$>GQROm>)hHe(upd?kS
zegrag)tC96&~cm7g5{(AOA`ism~omNM72JOmjc*yR5uMFWAwAzrmIl7Q_^1!@-EJ%
zLoRd95<&g(WWP%|y3lDSkWalT>=SL1M5CUVD6CFgB%_x5{U9lKwL>c`bUNV3kow%e
zS!u9kn!Ru=wQkf3f9uRXFI;JfnA7_<tcd{YqN_+pB}>-wO3T*JOc**ZBR4uSY6uH8
zD$YU)mEEJOgK#!C8DK&MOXko|(=7j%9qpIG_4T>TGiH10iazamjl4Dh@GFkZ-22yE
zy0hRpe<C}R|Bx&R-<t^)YX)E@@@6|8<;s8evaw!AJ0ju{`M;b8Xyo|y@=#xbov`HK
zU}a#Cy;>XVw5l2Ek?i@4$S>k&NN8zr&Mnosry>jc)jz_P;C0GcsI5YYX2M}#!q|>y
zJ~6JL44FWTXO`n4ZbyW3<tg?`mrG=yx4)&vc)YM5>i#~;laYH10D=o@Y0nf$!YBFn
z@TWWaIaGQPP)}Aa{^Q@y|Kzs~*J*t*09|g63KwGWtjDaE=ONihd;(P5FNqQP!Z-5Y
z<!Qgkd`^f3rIEAj*wNp<wAnpRD6^~@yr=BB0fN@@Md#bQJHCZggL4%DMIlWz<c0p#
zdDbgl&oehn>E0y0fC-tk8!gK>IJ9=J6auS~G5Huzq62f=0!o_Zj6{W>??HiyQMMJ`
z24aXb=brM)h{w-shRzFuKOx?I99r|XR8HECab@a$7~P+3t^ND_5he!IxRuSarVOAQ
zwP;X5X}6Gt-65icKme}4z+lWk2BlVYH(v;Fx<7JXaE%TJ6kb&3t$(gDSE`*&OmfKa
zh;BLl^(?5Icz=O^KB(oH-R%!~(B_;@T&;_bhf*ZcL(PR-iSH({68u1M#a~F&O4{45
zxso7eolo<eDpiA#3GKm`IjI)Evo0!0TlFsbuTn?-+iPPGB{few*_ZGL)R=m#!fz{n
zD4y0T8eo?e!1&`6ukRx5gB8(A>p8Rp)t?T!3UjMtKRB7o_h&a-L%K?>8~f@Bwci`Y
z<nN1YhU)U8|0t>RTz3Annd<-WkQ|0bBmK4|fxIE06`?15*<s}_qbOsyk8(GPG=ud=
zVdmK2Dwd52Qt>0%wg(G$Nwp`$dUQYQnoe34hV%IH5HR&wOw|Z(J2;D&0|kR0<qO?!
z%7Xf{hMuh&h9wCRrk5XVV9JKcKnUo$PG_GHqNSnh9`=JWx5#k33Y2>+{AkYChlr_`
z+;ed9UCY?!=ht6J@xOEWAw>~Ln)e#N-Z5AbF6w>^{mx`T@^*u7{5D*uad80l0cl+F
zqxf_x{RWa^v2byG7fb^xvAc2Hlq5BE#kT{aTHfP#UOaxF8k})VB|yo$h%x1Ns@=!^
z*FXR|Wr&Byqw(Q<X2g<y$e5T9Ur2nS(~@~{bJa1>v<l5?$tw#AYfnsVoe@StAQ+I-
z6li>gh878v4UBQuhJ-opmcQk~DiCTrKQ%IN1Jn&zNC}y`pi%XH#D%90l<C-t$kAC5
zq&`Rfv+jX~s*I7-`C@V7oh3c@&7(O4wU~BL>U0%KOk3M43#>LMC~uEFd}Nr2fla8)
z=HZCKqwksDnK6==J&UjtnRg7<{HN5doz}6w`65x3>4E69(shR5N~w%U2xq#%f5I{%
z58O5U*lUkOZgcFJzRfKQD&Cx=#ETrUCR+7A6ZFD}zqnG-l~jSM#3S~Dvqkfqy~jM~
zXUNfuI;WEMMuq`5m+D3E)NXbIi2ed=TT`PKe_n#S_*zk(1$j8Q9&Mc5AZi)Fvaekm
zTSMn1(8!#8w%HTHg??~=pUK%!UFGTz9P$1M^ycy7`DAJU_)pHo-4yzPoyBIfQejus
z97;Z}1)HQWahY@^+`Pv}xS0uR^Y4(VgzvS<5BEJf$i_|P_UJkHP&OX!joP(6n+Gnh
zs`}r1YkF%-dBiVXqY*W?+VG2rA}?G8EVDfz{si9;ex!b=OOPRY*<%08zZcB|fOET9
zRG(g~yN=IP$h|~2N#}oAM*R@tT7>5XwOMp(8|vyi?dQh27H7A?tTR-Kt?BC$&8O^C
zeFXFGKK>|?VPKKhTk}ew0;z=KDij7q>$1Ap;QY%;vOPoqq>sG@7p+^?#>0tt%IOE;
zNaW6O_2Mvlw%E;#iCjAf2()xS^6Qr$UH2be>{h(dNFEv4ZfDcFr~YX^B*X1)U{#`r
zyd7q5<ZM@S)0}nHUH6A{o^1VhZf+OozLoy~^su&p6MNwX8wgK}AaGt+GvO*Qp{SRc
zl!Eold$ZZr^44JE=Du}t*G6o2Zm?kJ=8@Cf<mu*ML0~;TGWt1))d^5^O3aslY1BBR
z3q!E0shL2Z`pSnTNEkCH4C8DlFrK}jhOHAnt;dF{ea){0hIU^&>X{Z$b%aLrp=Twe
zro@9j3Z40dskK=Fh>j0r1H@+kqN0%QoYf$B3>>Y^b^Y<{#Qf$iuR`P?3VTnM-ZXY%
z@hM1*Xgl>bUd;egWkA;a?RaLV_rY{hQNCLYvKxX#;+sf>yo}_qm$J6T`W(j%%B{%*
z^pg^%?Pf#8ORoZ{C#JJx5suNPUy1@)mV@RRd@wH342N1xPZIe_&Jb(*0e;{>%_o_N
z@C?ixF)ytsD`#Y(qwiGIRt>XT1Jl$wKh&Uj^V$)Tc-`g&0Ny&M;$h_A$sapN9nZX&
z8$(nAqbD`WNvje1e(*2ef^Vjk^ZJNHy4zO|Hy2nzU6*?9nS>-9?48}#F&X)wUx|r{
znVD}#&Nd*%pdrqM_CNs50PT3dal5~Ge;)kYvP586^Yg4(gNTo;6YMROl#_yx<s&Kq
zkKuf7fp`A^vowKKuXLrACzO=*6s@m<K3b@#$S61Ww|7pP2ZyI2@TA0^Ji=@r9Z}ld
zcl2zl@=yNT&^K&8--!sAAccngN>0eltn>UlQu0@qw<^eZ&mLLx;TAMmbrhE5mE{-b
zlo$N{`?nd)b1bN@Z?6I&ubLKIbTc@QU|tjIbris<fom=yU--(IH>W-=KtJ=qp3H~7
zwc8UFf7vsL8i%u^FP#b}W#~n|leB|9YlXMM)i>$RG~@~UqTz`WC+RiIgc(VJQLxji
zYyL|o<sNiGVN7IXWOTc?05`0bU1t-~8DEe$OO{9^>ivi98~=xA5>@^w0rR8+HZV$u
zln_wjHQ6P&;LOV<oHR>I(W1W`%*EoC0;d&I>$94ADydn-n0Ajho8H<ppb@MBLinjH
z{Ux`O_^OAOwgpP*k8*@I7f!9&<dTyY(%#Ezi|aPp`Cxu1QN#v-qH@l0;kK#Ivob#R
z9p&1dm9H@={^>l?&WB_p`pLvauKhKY24Eo*I+Xhw?iAKLo|gut@EVaGrGG=g!ob9K
zx28e}Wy}^W`@c8#z;R*rK@Z9^WquTrVm7k~2}&w75!rsN6S3dh)V6gXQV<q~gw(dS
zBWSgw*?P2+ws!xbY9V@)r^SpTf&F!X2c75~F_8jn3{|yprBI6S^BwL4*T2Plj2IKK
zbH5}DHLyd&SW)iVR88o_|7q+@!=dWmIBpxe#@dW!5HV$~h!{(#hKjLop=8f8NcLvz
zMApZ?jk1#^WM5N}B8m_VW-w$OdxPeGdR{)S{^!NHuHWl(UFY2AzVFZHEBa*@4!*<Y
zHGR~mbILj>%7}pA(kU#0$sI<OhW_}k)5=#g)DKyn_8&(){id4<hB@*4!-=QAYp;;8
zJNW(&3uZnxZGKrb?jXy@=!F)h)`g=lUkcPmPLk6eRX}s4s{m#9{=AjB7B<N9&f(}$
zY$;drmYx<eG{D_KKgh`7M?y9deDbt5UwOiem*gyYJpBR#Iw^*jjH*8wTf_JTdtJ_M
zfaTa!tQjto$VX)l?xMs^-d!W(t_#yFzxt8)=u<+239(q49v$4CukHiQxeHgsr-thk
zJ$>7Sy!mBl$$To|xpL_nPwZ24dDdOE9aRp#?%_8}(bKE)FZ;KCw)_8%m|*dg7{wiJ
zYJ5P^?u|uSu}Ye2=$War!EsEN^>Tuo_BT!)roy&yx^H7nex>qk(D$;_*!7CKr;cLt
zqe!pfeZTS)#(*exU5>F$aOTSg(d0+XwpVf`{|0-ISJ6WP?J;xm*wnx&9lmz@*!Dd4
z+jvJw&5%&)!4pV*NV!~#g*Ve~*^|Fde_PtppCOlKCfe!h^H=geY0!0OpyoxMWhC~<
z7$z@XYP^<W|Mko65jHzW8L*-kQd!?^1`IECMw4Ibi2Bn#Ldnp~Z8#I~#eVBe#gkoe
zQO_fOHTQii<pw<RdWp&VEfX2b+PF~Hu;jc*z8f7U8b;k4d2TE#IRf-b7O1uyq>bIo
z(W?a1D6!R#Q*9@m?B-*C2+v&Vi&bU!EAtkvancBd0%R-DrUCX))`*xfN=aea%XBF9
zjJfJtH<S#WBtdUDX){?p?sl;4_i^RdiUcyoG{EF0)-E`7v45s;M5%o5DxX9ES4Hre
zSV54rKZZpDMr<6xhoiXdvXl;9eDJ~96R2SJ{F5M=oa5(D>d0%@*5cZ0(qy-K>QzX`
zH%4ozuMg>Fg=B!3m?+3Gozf-W_>b;6Q>}Q`Bt6+zYnT>CLb(>oxwW`?#Z3F%X`Y3U
z3~B}2tM95k4g1Y#xGH!h#uJ!MTHneX!5_Y~Tb}gSnK;?|jI2L7z;@GN4&>h@O$``y
z#pbn+^<jh|HTp@y>3k%TXtsV^50gebk@a-h#8>)W>@9p!qPYB_2>F(5Znv1o(ecNR
z&&cEe2M06~8A}Q2=yky88<`vX4Z`X^`D4oy{Y;(wcYS*AL8IysI~+xCnFU>2cH(YV
zuRyI3Apl;yWZD#t`3B__9zZJmdAI1XJr6I+#jJ14i~$(jAN=OXL38HqOV-d8qBGiu
zFnCUd<(%9l91C0J$@=vkdJnPw@ed%?ZGu7qKr13RJ9JBaGEd67IwL@uKfxT>zvdS$
zt`;TbiAmG+vr8mrr_MkD);aGoinY`gLPY*Onk~WaJiVKP%3(t@sfL%)5Y8;4F~IcS
zjyW_6b@sXG$<tu6J2`$T-mnV(>DnLZy$%ySi23m=z5x)t%#M=)MZ=!As*AtPJ$(c1
zKsb{^-@bD<qw8n3xKCB9xmZg?FeUZ|+y#<u+N;FFMn8F!!-%pC01{OrYeCGansDh;
zr-wEk<3y%EcrQTSdu3dur2f85N?sz%%B{Y%R!ziPG_5@b20A?}r{&`&vB8rGsl%U1
zG6>o)E2(;2jP~<oT{9;3_$_+YHrDW~;)|y4#IxcZUCK(sYGo_13qTuHaJ5SM<2v7u
zw!{qK6<VGZWU@Vn3QW2@thK2D;EQ*?G{P6YaDTHSWtl5C_uKkEXadL)q37%ez{hyP
zEF+6|dCAeKM6xGP7)G1yI=kKKh<18ZR9$<a(IN`RwlW<)GA=-8DL|2&2Zo*}%*+KB
z-T3pH1NK=$qp%ix#O*>&zq$T8-qGKGya1DAbqyeges^Y>$2g7PP+S<L&d$2su^CLx
zZyV#{kl@!dz4|OgGniL5F9`+xT$E#}(mK~FD8QeUW@czN7qe<1yXW%n3Ws$3Zi51#
zA}Uk|<<GS(J)Iby?gJqF?F%wI@F+b8x`ku29v-YAaz~!#bDiXiu&iT{MA1A75N7po
z?OjlkC~xYf{|pt~xV@Ml5`y>&9<WQpol0>~@D5@ku^O!*#~=2kvkKO0hEaf>{Nok`
z!kAkjvu8ROA$#r#;K9b%aLv^DS<dms<KdzVcC3$j8C}j1H1BG4g2DbRN~Rf$JqCB;
zu?f$ZvCu{Aw;B5Q<8}T<o?svdW9RrVj@cXS;sD|v75sjR!Z1;VL<v;Oj_?S^mDcU8
zO!M!mUjAoQwjK}V)sxfQnl2*L6i1`q6&4DK2>!mXsNpwg`?zQEfkV*H(Gi!v8ECcZ
zC7l{#szr1Xk-{q)6<xVS^E1$yQ304gOIcIkHF9=sLGof6;ytW>a=jI5mQ}f-Op0fI
z0FfMz71D6XXifIc4dUu@=t6#U<=AD<Cr)jr+z+)wZAQ@rv^<?i#Ykp^ziC-v0aBL`
zmV4l1f@sMDI~eb}tA<dRVv&9Mpxlvz{iX?>*DjI+>Q8xv{01PDQp69d96{__B-qaS
zVGE~-VS__aaropzpL5S}u!R7c(B5!sK|UVA!&>z#LjuTQ5BXyTb-h5)_+%;uR^~Yd
zq)X)Ioe91~t6bNCn8B%g`r26Nn0Zl!?KL)G>#w}IBBw%LQRp++DAVMp;GVYzsU?nr
zZpvsPUuufkn4$(LSXxo88p|%_5zI6n6m(QVA9*b?!<)KF?(b;nRI@4S82-F6Wq%cP
z?O<d5`Wh_0z)!tFsO|hI3CCc{;yS@7bt_PHA*?Jlqw%n7a8dbzO?N=xLubrpf5^2u
z_i9jGgV75ht|1HNF_OKqCS;tR${GDwMceA+KDS|4$K0gJXoELR5cZ2s3~6>#P4A*B
z4KeD0q7xnabfjZ<Y5O%e$7Yx9<Ht>rmP|u{^lnF*$7_|`*l#6pX+u6xm3DCY`ujva
z%d>%*=?AKH4B2viO-f?Z9?m<Baq~2D)pC%L0u?tJThE<itux^C+gVz|#~ekUBN<OK
zJ%Kjy&JW4IKl#M_Skf{iGc4TLuq6}N0AE~kKL0@@2s*#^>||=c34HOBwu+kafHOs3
zG)sJnvV;yFL{ZP6;Pt-VyOA|lbl5^F8rU$r{KsMv;s~n#LG@mi9*fUwR0KU5c!f1I
zMkokey~(e);!7mZv_~`8cAm<Pw#>W{yx^7-7TQwMu&Ia5`=q>ye6N;pM;Fk^eF3mA
zy<je?nvy_1*>IgyGpx^^^autlXmyFjZ82o_%YT}}Qelp*f0g9hRpEt|ZzA)`5A~@<
zvG%B!;y5){j&2d&d2IhTj(DN=>10=!ao;41Rd^btFY%v0oTKp0iSvtjE7>jH-Yj=6
zy5s8ou&utk2L)lhA>as|ew|1WJh)wOev9;Yf-mXkd_{9iE`heP>tRPASG096f)-MX
zW!tMB*neK6ND8aUEPVFfYKARI&dZ{1Zo7MPbz$=tVLedT)Qi%d%apO?bO6|p38aO^
zsrU=GdUGFqx*XVhQuU-nuyO^e+>ekH7`XciD`(^9>)*D%yy-JRjL)n7Bmo~t@c-^|
zH*xO?Sj5yk<){4XdpA;&-4RuGErr*|<uaM~s$-G9Va;q%o%9qVwTjY^^;SRx))xJy
z^!=)dGdvLm{RJB{wlGWoCv#R@5>OlWw<U69#?hu(GT6PL%5p-gu5#`c)jANlr({d9
z-iF7m;bSZS6x3+4T>kkkDi<oqFR-#Ie9K`@wjluvpY%_Xm8lSd_6^#~aK&YC8-F)4
z9J}P@Oq#uTp&fbK+T1|PYQLzxEQWIKsu`ct&1m|s#@qzJ{L`ssIO}oGGJF0lUpGF>
zZC!V-=f&IIIB4cz;i)fceEjVpsj5d<rU}y*gBiShi(uWYNCy&e;m7O5r)$p6w}J%}
zOC?Sq2|e^eYBqq+IINUv*<6n%5f+w+kXZ=;K#~%ZX>!jhqS2IR7I+w{puj_~E(_F9
zAeCvxy^`bm>~1Xd)V|OlU9Mr|0c`ePLNB5tDk>^^eh_p785q$;A$71_&wRyyGk)Dq
z7&^eg2B^#)=Us-aS46^uD3nS!R{$#Khw|rSL;3lk0A0QcSUxJ1jEp_b!^LN=vzpp-
zM$c(>SN`gHzKy$Y6u@uVo)U8=KEJRqJe-zQ=!OBRg%hC?SHKW=t|o5knP>#FSloYZ
iM*lhg-$w#f`Hy^+E20m?Sj9^NTzWSRZq#Z&i1;5ix)SyP

delta 53633
zcma%?RZtyWw5^fg9w4|o!QBZEG`NP~?(W*Sy9Iam0KwgZySvNAZR3~!R-NZ_s`|03
zR(G#nQ@$}~TPgH$F?6jgG@w1dd=L9lPf*y_)+YXm5(W}d<ohQPQ5bPLDr7~~?-($U
z2qH2{zeJTW;?B>{vCxe+Cfgn!ye4{DcC#LIAzjW#pX`Kg(s<3TK=lt1W`d{hZ+7Dg
z*8kTF_`l!!zuyUZ?`pJNf4U>!vR-R8S&h*FfBIgyw}*v=m6VjEs)}V?*dNq(<!Zax
z=rURyO|Ve&JP+u(4A>Cede0V*Yc*Z;ugXb%7nMLo0V{~aG<RCT+56nQS6&cs+icXi
z5D=5mQ4wJhaCiCT=El{Ag_WkFlW_C13v$9BfH6T!jV8NKdapVfD+@MD%^stTXZ)@h
z=!q(`?DY;;S2s6E-`<14Ez6p?=Csn`TRoetkHF-^JD}BNFq=}<?I!S<9@bSCgO1lE
z8QSs$j+5hW!+i4Ieb}|rpDCrpMD#72&AM$3U@^JzF4Za~H6SJ}QBPGyU0PF9T<y*;
zJ-xLqv!R)kn}C87rrY<has1t=vqq~4bt`7$YHmz`+$)*mR!%-OtD#oFWo2b>r@6DZ
zt}=*}20#+z#3S2ijP80mt!}VcYPX>gB4fp)q6qZ^3z?vF><&^>)5wm?r)%M!T9u9b
zK)9)8n^KGeZM570qi%lBt<f1xXWfAc{FpUC?IHflnr0eJUl#>z*V=s!R|e`2LPC>F
zzJqyE&4=M#ZQtfM-GXh_2dnrm!=>mf@S1}j0D2-m&zZsFg!;&e)%~v;H97)j289*W
zG(BkYg4qAg&oX@Pe(OjFHrK2+U~i$K3Zj%O`Bv?gWK|1adSPJ^;=ggwbAjLPesIy;
zq7H{4*|RAnZrMD}>$!`gJfT7nu+j5TyOQC}_t8``dxF72?ne)_q(=^yJ_<OiiISYy
z0A-N-&ttkU*H>mDPx$AjyNt%OH91)c@lo1Zg}&jzyNTUqaDP(6SmoMFK!aKAg>H+z
zvYwi}vR|OT_rrqTYqZ8p?n8zfh&r#(=wNHLAm7aOt<FsmbkqF%DqNbb%wkg1TYylW
zXf|k35DA6u?R_Rs(q6C2gWvOHxnB(#m}p-2N>Ei)Rn}EoT$<TXd!?r9U0k1#J@m`u
zAwlAtZv6jCkX~xJVkFG6IbDa-9;Ie?xe5!gzT1cxI7n-HnA~L~(E<u3Dj}B75AY7G
z%IGi5k1<ePCNw;E4W!YD(_CHd+yZ}_J1X4ljSW4|cYntp>@*C+En3H(mI5PT_A~Jy
z@cgDb42s7=V}NQ%e3pW7Z0q#$*G>gJY|f@eH`j-IG}PF}Bx50@OXmijN^0hwNg26@
zR}El$AHPu>+L0%<duKaQai-o8DMEVmG?jnD`-Qxut^_c5Hhk*qEveZ(+h3jxwAHH<
zaFtSu_#JM2So3y0%KhmA1qgfKcio9sWOqQ28KJkr&v9uZz|T>co}QnDGa$)0svJ@9
zbbMcdQSou%WwnK!0iN=PlC9#9V}har(&Gd5tZM?&8~u|LO91Cz20rgo!{E8GwIKz)
zya$ACua6y`yGN)~20hJh`ldCa3VuWeyY)X-+++yDJ3KH{`)K9>31@u==oPH#X}}od
zV{Yv1ZCGAX%_7Kk6tk*1+gI)d^YiVov*2%e7K?vDZn2TE!66(%WpU@9ecr{@Sn|Z1
z5v>r3A(|55G2rFJrLL`!l#_4kp2})h3^AWj)xGO!m3b0<sYeH{Pm>&XqV78UOl6u&
zz+(5v_rgKe($@pH>E2=q@^Q2*?keM;*)Dr$JbXvle7Ad*kk!FAtzjS(fco$3#%A)^
z9G)B#e9lPFQr_g`V&xU&Z>j}9o~qjUI2=T>=(%|z`KU?T;=ky<p8Ym6H}JhlVdO{n
z#xU7GK|x!T>(D>9auxAgZ|P>PJ>h90ewr2j`F1QcuMsE@xNWWt&6Zb}oWH-Edh7Ff
z@A^0c)|<g-)x4IlU*v@h5G;5(1rujcHEe~EBjrZCwbVC-SP<5-wZP!xo5ka|gSxf7
zBT_r=j9q*-@7p%uu1PPZDzJvI`MjGwR6%s0dys;XsUY9T*u?GezK<sC!o}tl!rAW$
z6xz}<4OEBBw9g$Hpb3Ix|L%Xnh4;OC&@0Xpm$4sZue*YexJcHRX5q0ob-WQ-x@iCf
zB%Bn-yeUCEzw{0bDCl)gAUCRG8@ESYcG&{TlJap^Ii~`jXR9{-5G)Yrj+WiU?}tfv
ztPRhc*J*gD_wO<ul2Oil3fTW!{^h0jyPg#wrzC%JL-U}mKtLPLg862NlVU;8=+}^y
z6cFMct$`a<59bp5YenY{HA4DdMX81P8R+Bx%$C%DzkM8CT+WMWeQ4QQ@@mTz$_G!n
z_!3#3_6VW{KI%|%FbL#(VWXX-WxI49<GfwFIC#hzX+3lw%?a518f@<l#YWDG>Bs@e
z;=DDwpFCI6N~P;C-Ysc9mTwAZAljPfrC^|{@jMFqQxw>ya1^;XfZ{_M2|4`|8Y~|6
zbU3L8aY?Z*hka3(VD!3X6RkR+8SZ=6^v>G16*KskG`x?-Ea<PtI7Kl_ZZs66jh30h
z`{mLeNWh-x?<C3K6F)wE<PAHyk2b)7cD7IYIIqhv%VoPNE+rv18F0WFVrm9j_sOB$
zQ)`IzeT&L{jrMtESy)updb)Q}9=4I34O9lbkEwK_!U=$cq1O;fH6N+4^@T^~zmh(w
ziOAt_RX$#08|ih9DMa78aLgxRQ-AVUI{N33_-e`hM$xBc!DX}3j@QpF)dCpe5Wm?%
zq0jwsL<*DnM{jk6m$tgp=Dvm&JxW`Cn*H+R^6!*+3)#;It-4JD90?p;7<j#O3-9B^
zexXz{9sjZ?C#g+CNlsQ$m}g{cx{I|^Q0lPPkM}&P3mGrnVj}mtWs(!R4Z^8>$PpTk
zx%<nlinU)P+;M1~NA)@h9sqXBIab|3O9<_QVz_EabI!=Jgx<Xg&sb>Y$3*P>HmA;y
zPg!tzlX@Lx=I1$2u<l#not>drDL<7cSROOvr0?%|b^_Fzn^DBB_6zZB*o=bTGc~^b
z)V8e2EWcNpWp}M!ynvOUZf7dDAQ83K`|dZy)5XHOG_9x$f%-}jdjkxiNxiV63VM(0
z+n~PnBZP-ZOG(pN;HECOxWkb39OTCM{|UYej<h9i@_7lQ&~1l8upXKkYw8A|vL`2f
zaE~o%y%OK9E&NIfTB_TpgvrbG=;eAXVOZ(#7*zf^+TS2VNJ)*-ml6v&FLn2yWX_sQ
zw!M+DhSwm`r?*NP-~)kBXx@@pBxe@ow8OGtzTVIIMcDx?v%))56l(ZC6JUH3RTPzH
zyNQvz!uoZ2ivsy16@F&9*gJF`pFPv^2=Fn{-N;GHSzRoJQf-l)L)<T?^)#mESN4|<
zJyd71FRTr{#}4lrkP&Bk7IKAeoK<GF2jnMs?SFjV6JlOlOTfbCjlBIBsxM!!bA*n;
z?g;lIm&8ECW_t8LEF#4Fzn9_<KFL8mtY5m8<mVNb85@eUvC!l5!O~Pt@V4r7-CQ~>
z&89Z@&qaY=b|Rh@vbX;|+WCbxPfp0n-E$=$mIxu;b!?lP*nd08QR)z-3*tA3kI0Nj
z@lT76)cdY%3UrN#oKZ-as+*l<)vPr;y(`&&9~<4f%?y}18I0+iwt(R8CW3sM-RXS^
zT|O?`7@W!MABJFVa-wlg>gO$c$Dmq8$!LOH``7JYrf+Cqbhvkby-ZP(Uu5Qd9hFtz
zwiuWjW+q+3R_D0+rIWd)-0m1eke#Xt{sIo>-FoW(325jzsCx&!+Z?s;2=_l0rE|cb
z2(t5Y=fnj?K}A`}Dr7l1yL9=yD@pCu|CYAU^nkgT2Kg9YLyzsmCBgFEl9(3L47FJz
z&KL%qH)pZW@{>}4(JW@Hm+GKNw&am^@KBkEg&@hMJ0eb6NyFUi+Z+~!IjG45FMKRK
z)Z~1OfKg@GwoflKN6&NpNA_Oxs|`zAUg!5IlAL=%%e3zZ6JqDI-C1El0kFE)qK3+j
z^y!{M<5kQ&dTsld=wFE5>G`{In;Yti^Y9FaqZELj-6nOZV#2&aE9%*pB%T*1kC>j8
zb%xrv?@^7Q!KyZRDY6!*mj%d^9pxDoAOC1vAgBBC@So<z2m#;p;0^pt{3FY|!_`ew
z_vE<f8eICQV<P#BEuU7#QMuF&d*VCh(^c&uq5}<Fb3s!({^!1rK;eN)_(;x4z_U5G
zf;#cMb-C1!Wi9FdLZsF54OOt;r?iYma1bq}T+QU27gP|vdKu=oYfYMj{UtFeC0=C;
z;4w6HnqGfa`mSv$ujJx{7G*U3$+S$n{?kiuXxhI&+dZ8jJ7djTUK-4)M7*@jZ)ju`
zx#RGco$R%a2w~^(YO1rGbv$lY>5F}FM?tqQ2`i1A2-*ZElfJvCg55s3=Qf5*M29L*
z5nketOGm?(fK}&qtBd)vDI6po3L$zNut8H(nqOV!0}eaJ^J>a?2+Z!~Y0UrQWbX)x
zIpO`fd#Y#cU~6jVT-#JpMb6B+;~yHhkAVV1C&Golvzh7}WPFjqWw%jh#Ldc!0*%zX
zM|FSx5$+r!>ti%*pFRnAq$I_^ceb{8xqF(r+7d@Tf4&?RW}J{yh(f+~ZA>u+`oV2E
z+|zMi_7C+7$v5u!6B!K<BaLf=ux>$d>IKzB1;8yB;YMZQzIw6aRjgol)(L6!^c%YH
z9mZLoHx4+Kgjy2Li!c2YY1R<hjyH$bRZ-+uYGnL1H#H&D;xsHWF$NnJ1^HhECArhu
zZ3LvacAXB7NnuJhREwS-Qr|DYtnZ7HKmOm~<?!v!iG`4pb};(IkCGVL$ZIFI*#ykD
z1$LMtMdUDRT3jBOqP=<Nn6%7>y2ig<M-dK!yzE`2?Tsx~`qr>LubU8EEbKyavm2^f
z@t7nBP6PKr_jdBEgLB*QKeJO2e^80laku^WUE}o#8zeDE<-b=|Se{n}n3?bN>2306
zj(?;oH;%;=oQ??3BZrOeRwT@D>_k+oZ+3!;kTgB$^4LV7Q^Ztq&?*@>s1v^hZ8usj
z9(V+v#2qEh+mur~?g|i)1D@UdbBNC_%**MRWg~s}j?MFG7?_3!L?y4<vOODiTVd3h
zx@RZ)8QUdAbRL%e%{HO}Op5{?KG34sgPpSfhCQA;p^WVW18W~z{XCA}EJ>(g+tRIM
zkm~dV?j0NX^2sL#rG7}u)SfmG=k*S9&RGX|B{h&W_3wUB&WZHQjzbh+rmrZsv%NSc
zNlQ%|a@FmEdwJPYbo*hWX)rN5)n>aeG*{HJjB7y~cIi8*5}Sq!EHxqtoU1xVt&y|Q
zuY?>NGU;~ut`5CBxQ|5^YNT~P%#5@aFHCIoO#jm?+>9wcH)ltj4G-qxUs?U7a!t)n
z_wuHu^|QLLfSiVG>COu+D&UqVhHsYTQi_(4jc@<RcaoWw;aXGF()4__H{sgyh&P5m
z+O&ET#@E(6w;(Scpyg)7-(xRcxfZq8PDQsHvO5+i#%VHv6UuZp-9}GQnr9U{v%f=q
zDHD!V7#|CSCZBhSW+YnoA+^d$&(hP@{{DRaBX_quhxo*rmU&n<%46@?xU7U>LS90&
z*=zB>D#Q1X^*CCKEHW-7Uhb!Q-(^xk=tW{_bAJPfdtm=BfFj3umbLB!-4`bk$wK$1
zY|PE?ods6@_4y?#NYQSQ9hUwxsIdPNn7_&HkuhuNR=-I!9TNI0%_gx4k<sZ7Zf~)I
zY?{uIUhu}k@;W~Y2h{a_8rluXd(<8ILL88eZ(cz+A|HbwVs-m+`@tU0KQO^;$fhPP
zWJc$WRM`=bvFZg5fu7s!tf{m+3(>y%bA^y3$rBrGg`U&}Eg;kGk%hGjtWT&~k&v9l
zoUJxmZ`^v7i|UuctzL%H9cwm&#UHGQgm=AX-|3JKaxZkC#rh~pFMjk0oX1F#Nmlz}
zc@+v@op_Yiw5|W^i!cOXX*GRY1Qa&^G+GSVDq8}uT(%;-jEPPf7sAqG%+;k<XRrpK
z!;2{^mV2Ttvk<-$nQPqvgVoC}r?2=wczcqW=3;*Fu6tu;{ZjmeA_l%%F5-LP4oP4X
zGEkI65%Fzr>jb-a2W^f=mY~cRc^o6?AAu{fUU2%JkPk(-RFSWZaIg|nRD`Tu-$pv(
zn#X_#Y!}iHlB~Dln~;4xhOxfEQ_$GhEwUq0_a+qnk*K4MmYjjQq3RSPO=RD5FfP!i
z&N1;|V^1BDniLQd{6mq@)hH&XW$*ZbgS7<tM~*h%ZK^u|j6-&IdM56W<I!Op56L66
z>@7$8q>TI15f_6VXdk-!-BRP-kY^CGm<gDKAvnLLKMB2!(OlMRBOuIpDz2+;DR47?
zQOM$MSVSev!6hNW!zVl<@T2?z29Nt^pc}rsc;^1Swt0%A>0&N=A=tz?poNQ#>lYGt
zIinaFpKM#djtrY*_|)un*H=wJtq|CC0aJLz#mdDez%jMCIy*nTj&wLcU4pS0pAIa1
zvL2!qhuLuW%Nk$<-o$VpW0_+zlo?J=Nl;Uto#Ffz2FsLMmKu_R8rLN63kIWHE}hR_
z>H~Hfl3G8EkY;Le@iKyv$6%JK($4LeCVzx`ct2cg8p)+?#sgQ(E_}4QPKra_{k?<d
zGk0G83cKtdh2KisbI4$^kazzj9MCg~*NwW$4DBPBbyhVVefO7i(jpLVqaV3C6>bAu
zr@i&uF$(_>w9WmAc+owC6UkyA&~wl<#mxGQxmD-p79lFkEBT=l))OY-zD4-2Hmh9N
zM)iHlVCzZ5Oho$AK_k<a(93e*@Wt%nNAZ+k&BN`A;Bk25%f7|xtr_%79QfBg!OT?P
zuDEy^w6E#)+lu?5H{DxZ#6t{j#x^@m?<JrB-8VHGmk4xvZf;{)3KP@C>>as3%M8<@
z;xiuS{TZqbj2UVS<NIUlPoH9xz>XtaZ0d@(V~6K!%7af=cU&n>nI??M->Qz(!dHSV
zoI}X-K8X9q;?|Svhl}g_O#mxJb-{mgs2ja-ev;AqiEmj&FB1dDb8~${TW`OKAWh80
zi{+*o8Qw^Jb1$hXF(Y*G)o*GTr<PK0erY<^=@1Ejer!@MHWu00t$|}sHRVf}SK<wy
z;2r0PFg_Hs;GPzf?-Senrt%{fBkIi<0^8>|-|pRzJf83SoH0EO>i{zgSlU=tTR=IZ
z%GUZKSd%}Y*%w;C9&pbsDJbe}tpwZ1n_Bi(EP4c+kU|Y6b8p3g+B8^2+_WdNr<Aas
z$HTbc#fTZFpf;C36;0#sNAq|38%#4Nusdxgt8)5L+G3sk4g8HXM3nWZxruW@y&bO9
ztWzciASOb~NWMFJ1$Glz2K~DO!LV8QZ3e*S>mPvR7Hgm4?F$=RtY(`#j3AJfnsCoY
z^RDBd>Eh!gIoW+Q1E!eJKeWj$>1e60D<tKnpc1-2t`E{!w(B9O=%_&JbluWPJu3Ij
zAUr-ihNsg1z64Yt7-ZU_saE70Y{je2h1{jXi6|KAk_&GE__#~RQL<a5*ML4av7X&1
zP|yZt-Xz#V^Oz3e?lP`B?2v9*Ty0#ix$euDc{vi<zw2U(Ve_CgU$HHRK1IV3N%<D{
zQ0jI@2BxY~iGC~3xJcVCax+uBkWoDoT6S>Vudf-E8VHlwA^P|!RP%B+I_4a=F6{+u
zXeVTu?<jWwdWy3b={ZBMW4Z(vQxmk&j!OO3ni0Y05_t%$Ki+{gfS)WZRc!Bc>dA~7
z@9{roBZ|I&7jKr?X&0hCb2&69;W=Hcq^uw#HO$wLZlln=!oOpL7eQ1wjpTlTpgo2C
z7QkSUK{gh}`R^R`nqEnJ6e3D9a4KYM)ZwcY(S8F^B-WGULCH)+Zt+)}MY$wByqHFP
zjj~0n9GhjSb(AT`&vd-^B>Ww`rs+eWlA8Foe@epBhfZ4(x1eGnBLC~9z9i2d?R>-8
zt>f-eT3bRy?G#<s5`D`ol>cPou&_U5%|Qs}@H@bJ`O(W!qV`#kDmxl?>qu$;QePc=
zlh^`oDkGSXqpe{VZ6!uHrHr9fn*U&+kA74_>V8dvg1k)h43|Aw1R6{&n^#ED(qrC8
zP;2+uBB{5rO&WXc@K>&afq5&hCwc-QI)U?cWYpp;aGe+N^BjD;-u$Eo^Gz;%vWr(}
zAdb0S5GDM92!|H5l0sOtI|$f(E^b>DZv*O?xJiUO5FFnoHvA3j#R8=4T}ulZdHCt5
zmrfIs-!33tJd*?>s3M)NFU6Fg2cYr!o>F4CU-FfaPM+*-;GG4w*n2-q<}`(G`{FLD
zrnHZGz0$KX@d+%w3w>K(-d?ldZ_4N-W0lqnHsO>bdZkdg*($FY_P}#o9DnM+AOqg(
zd?`Zop>kY2c@U^*8_jY8PDE@3*|nNhA(f<CNcn;x*XP!Dl-uyM$}&DmJL~ESTHnNE
zd=i4UrHc)@*so0TI1|~8Jdkk@Ug>c<GfRj$Ef(mTeig)c<o;?uZp+^XhV6nFH9k99
z@|n64Ln_gwy^fsxYL$$V|CuRt>IjU!)7f|6Y59whxbAiK{pp8mO}85DK25>J!Nm9s
zzRX^XuBevBv6j!`VC86Xx$;K;K!Blfbs^LkTTXf*8y(g+{19mMu7nOLkYp3*$r|dy
z&fwB3>8}})#18@aiFJ`y8Twha-loKhwz}!zcxN_Mj6NC30c#E$*%~^aNnkazk%g1%
zgH`Qho}T#~C3zS2^9n#_^Wv^`j7CWo529Y+K=rKGd?${uvmK%kwRQ8I7CZtI7_0GW
zJGQy*`r6Ano<FxvC?Tuivv0-J_AAw04l9~LTQ>=M?Ob?0aCN`rzqYWam=1<Kwf;JJ
zGCkaiejumlYOed^q;GhB3VfF|vGrc+_-ZD%%>en8YJ=ICn4&deuj;mPt(L9A^(ZX9
zY#&bx?(pjeM+p)RZQuU#vj#6m6!$u{6qaB{?zTxC$Ao4mM6lg$boY7yi7Taea0vO<
zxdD|}QOMfV>(Tb|##u@#P%b)HINZ<A`Fs(otFULdhladEPi|=H7Pt-m@q1mc<6O}F
zmWP3h^+rxp=NUpd_**{T+Mo9z)rt^UPyEqqJwc?qJ7?EAF;ZdI?+<Yy=@I!aLKew=
zVM5=Gl@C^DKDxd?XnHh!BB~#q$6aPx=ZX*+*C(*vo?t?UKW;XB(4Vm%P&)U&nY`9o
zpGhGY)shz)ohk5b>VaDMP|mTR!%I?<as~&Z_FY;`F^5%*a?vqI=A~uyUq?nqK=G;z
z{2o28Z;9YcH<PV*NV_(?LIsS6y}V}_S;)ghvRF%&ox~XyCj^<{QbImU(_Q!dl#7C)
zJdEkd-NfGwR&Qg(;)o45q_ub_7q&H?8Tgp+hutwG^NZ8U8URe##X1EUHTm%5*P;*Y
zV*6x$t9cI8D-$;(x>a$_a_xEo;ZFbJ(lz2$c~1>V$JxG5wLgzWprvEqa=yQdg9VFV
zQVUj{y_GAoL;w#2x8N1>tg9y6v4J|<I$Hj*`VgV0!7K|VbUPV{_zGnU!XZ~Oc|J+@
zY)k(fN_*xL1g4a)VDEQpvV<L^?teqYrZ6;Ol3RK^mbVU;fNI<y1wFFk67KPp%jqrg
zwfLVyhDv@LwhAvEfBU(3D@4I{c_983dXJlljJ|)eZ{Xj=U~j*a!ce{BU0CvNbZGq5
zvE2xMYls<!PG7&h-owoSESyR3a@pl#%{%A0k-6IHC7@3dp{3~k-a}z}$fWb&K}byW
zJ2fR)@8>VIP&#4#AJyjdL?Jg)4xYxPT_p!{%FUD5T9|U2R33QJN+P<bu3YM0OsOSc
zJ%!Q&CAe%i$e3pxBP+DF@<LI{U*?wdbG{zRHhfekte4%-MEx9<FP|OZoMkJSbstcC
zvUL@;)d9`bshR2M1N(X>q#xnPT>h`E@Tbn}XL5fZugG-e5BRQM1Tw7|qMSLBj0-6#
z`@aW$YIB>tI)mA0^9&uNKEzt=74cxW#9@dbPQo-Of$gq&cj>lUs?G9?N(t7Ia?E+c
zxVia#@##Btu~`?L=!^obFe2v9wbUCD+&y97jR%0!Yw0|XsT;%XvO0{Kqk=fdtc+?<
zH7=a;FeE1@rzZ2TVdswxRYJhC+s?}o!P<zMC|uPn<d+1G+VUe`whiMpe}5pF60|+@
zo)xC&?Sdk7j+(nv4u;Kteb+}8dD{i`vm~^dNvym5om(D~laUSUvKm?#CvfOxIfcjl
zv=2Csfog;!+!3tspxO0kFqTi?h+R`c@UO`U=h^_G%P^>8RC4!K>0$a)vQLSJc%)yT
z7oVs48C;tmGaOOUy8=ng3|uXd+Z~A*gQdxuKvDDkX>n(K?);6`Yhhvi5ELeuDk0dp
zajAQt{Pg>t;~EtoITLf?jTI@e0}Y7S4d60$;VT9LQD|JlHJKPvT+@>zrEy2i#h^5b
zk^A>e7TD?Reh`Ox7Y#G}!S-LLkn)|%AV8-oP+^Fsz{2}{<TdUm?a4A)W+-3vB(axX
zBPV|)`9Cp9_%|^6Q_krNpY>{5YI>R$Zn#AiA!*Ovd<J5i&mD4%+~lr%9el8*0kEy*
zIgU;kRz?Tt#bF(sS?7Ia{`Ny0)nT(0fmbfBt3EzeQ%V=^W$!h+zvzQOALl$l`AIgz
z*OJuyMbo|$eoXFDj%+;tzO*B24W+DD{BQ6$-R)cd=vcr=icsbpOZD?78&v!li)mPx
zHr<l;u<M&zc795l;Y*?kS|P^R2B3A@!i?EI_#lbvUd|`$+=^jbKIj$9KEz@C2{)YE
z_N8pt$oEJ~|1(^$hKKkAz2y;66VEBKvc@H8htJK5Ap`^|g!Fe2)fy{X$tM=1)R3<u
z<QdtbN2(NJfv-M-I$5<ON^yzt*KGZ(^DCErSU6O82+ilDn(l(IPbeRV6L{u`4Rm8-
zp+^93utPpyVM{?dp9w^rq=or9de@^;Y}F^mMTLZCX-J9c>P+r6yzXnEiRN#6scyDV
zaz<f0q-JJjirRP^*VeSvSK%qy$b_Mw1+i9j)=}c11Uw%^Z$iaN_ED9U+ilygbA!Z9
z9egG!(`O0?g8fI|3eSBp0QDKU4AN`i(N~vL6MKHw#9WFRHktG%YV||r7O!VvkCf|@
zN+K5@SaEN6bNk#bxIFCnMe_C3!o&DH$fnGySgR3)`fl-$RA>#oNMs4&4#lU7zNE#s
z2GMDT^?@n?iKnY$=HBg~hI3ys_?JoV1N+Rn^*!gxc2YOE*tU7VW062eBp!Y!zq-l|
zi-t4GNM+l)wyD0Vth)R~&`*#WG`&t>+emXae~(zF=p9NMEjdnCU1(+LHSl~@-_W~q
zj_`e|4ws9*U1x$&EqRM{vmH<T&5W<h1F4*v`HzD1P1rxWxIT~PP!Z|SopNgQjc=~w
zYm&0hCQvu*7v~@VqND@WWYM^&GYZvb&*?P1ugvJz^FYgSIQ%#FKy5dvxlcCSD8HK%
zCc^7&b^WKy>?-&rgyrs=RI!Z*C1KZ`8>Upy4XEFiyN$5CrOcPV-Io_z8M|!kw<>JS
z;#eFV>0MkZ*ANXA;~x$E)X<{vUUOtmH0*on5X_v^!aFVnhT)y2FaR^V9VQu93=hVF
z+26KLlEv*+=b7Lq11t*j$D^lUO#GR(BrV^AI`OqUnnJ^akS^ECLb3jvy{NU`>y?yh
zNLku0_=y*|B_DhcTyB9t>qy-Ld>b{@`SC8C?)e0ce8y4PzwjG2rLBt<gqYXj=+i%l
zC8vwKN`H`mQ47Mq&X<=y32|B*Ty;Flo4<5Yld(`TI3$z4Yf5pwsP3_Pn($xIran?)
zbHt{v@_DWKU=jaLSJBp;omznrx3X96s}%^<KJRWtw<@b)fSlC2jx6yP;?|zfB3e{i
zWv2SHh+y-0YUf4+Q?VuF0`0`_ws{%UJ`!_P<Fxw<oVl35X#P`Z&ti1h1D66PijZtq
z$cw@WGfv^%0tZQ1r_o+<;;CAfo%vq`E&^+@gXuLHCd3fnq)4r8poH&6t`kIutED|3
z8R)@zIIUu`IwHS<GSbp<*Ic!xp`+?`uo$4c)STtEP>T<1ex!$zUUG`_(oOcBw`TK}
zGYmcfv;LAz5a-3)Sjme5t|O2Fb_SEm`W97H6>bnMhac}Y<xzM6Uy1$ehmN?CujxqS
z5lh#q2BCZgp?YHS=L3U+0@AgsEEXG@4;IVeyfMzdCob>tjO%KCt#i-bWyK|l1!Do6
z_^J_lr3Q%G*PLu)a!u5dqv->Bvr|xI^#wrU4|Z0PKeBa$Pw69d3Hbrs%W*oG*u;eP
zi@OO`1FSY`cnD;~T%Iz9?Tqi&hLAjN39Q#rphXrIx8`k<EZsRCutQx})xFPo1rEjo
z^Qs?5STxqF*DJ%;GzV&qUzk5olfF$yqtI2mRJeFSkKcwdosOqX155oU(uvd{_!T%9
z92|tzzq`X0Z;_HcJUuqFvd#UY2ZidP5|0jAyUxOOfuo~{EeuG>n;+2fpVa-`7(;F}
zBSpcEN7*e=PJU}&c6pEkC(yc!L(<+A(MmJX;z35H;~-}xZ2M$`!A~5>_K)uuDQGKV
za(qNe#QY1|#d@c;+X${^e$YC0%QjGhYYS&l7Zw#UaaFJ3-K&Cl@{B{>ZK9EolZ}42
zwZdBfodANr6lTs7p;;Gsnjp`2U+SH{QIefG4Y(ex-cFkY(hlCA99$hgrI=V*Sy@_^
zS5#D#N8HTp6o|dF3o`xs*s0~O!!ptmvJh-b7fe#?@}iSe)Fs7ch%?JaQNROZK`n@V
zW^;b!XCn5Q)4$tX9?XM;7mMx!eg!<R2}ewz7cFiAOu5!GjgaAA!YMWshH0PgpBR3f
zR80LAWTe{GG&awzX*%Tm?L>ouIuh<MUGV4Sft%cpBEWNy)Tpj~4?8nXZ?%bqbxK$7
z{zE>l>>Bbga-l=5%Tej;J^^Y!x(ZVNBsNrpqCOg;YGLnm<l~uIm&X3jLnbv_hs(-H
z9<Z7<dR-G^V`IZoo?ouOJw#|4&$EM}XdujeLsBa8?$wsF=iTb9Ij0Da$~<Rd$2}=q
zRkMe2cw8F6>8Yc#SOCJFUj>@L!h_9`r5QEX^lNC;WUD940Ck_(Ccw+lQE2QJzoA{O
zC^D=k5E%oH<h$oJbc4T#pGrKf7`JCu)H`s!`8a0to#O+yV{yi2J9H4F+%{GN29CA$
zfAU!l=OalKyi}qEGxrF9L7mvd85&YCZYe1e9z%l*Uty4mx`>6G)8bbD8cycM(Hy0$
z4jkOm3PA;F;JLUeU~IfMTY`asu^mgY(cy8Iko9+f)mV-6E6SMh+d32ItiOS<*GsKo
zdfbZkQpJdr!rEro>%MzlTP4BcFy>6Fsgmuo`8zD4H3hq0d7R!ZHhOaAU1LMzUpHr1
z?)fMaJcvz$V0w05CN_av2fv()4*n*AY99XVraycZm2Qd#KrA{9G;cRi^PA6B6J}q9
zuG%5E<?F+Ag|B02MOitIFa_&g<F0g7hxVRuk$W&fOi$&6B8+AV#T>Q*0Oz6f0VQtq
z;wp*cbl^NiP-me!Y0|4V+q@Q6pC^cznQKlpn3i+?vayT0irmz*D}n}}A4-TJCYdO!
z3Qf#?mTAa=zoizRj*rPi`Z9RBM!)=0W@uB~(r}S@QRs}6xe(6vc}eP<Z}V4@UcT7M
zBf)+5!z$QrKO_t@HFz#6`t$ia`_**!1AmjPyS9r{%aQ%YK~nJXRe|RU+};CxpnkUT
z;%4Tr+ccHM(CGAGjo=T3Or>v~k+TR!lTM+XS<`$Juvf8$8m<w#{E6}l7F9%=RyU|R
zGW>^^mOl~j5>SpNip|G^^eHgCDJ`6pjMUA+(b3u2zP72Up5F`qKKn1H>VDCOu!mPB
zb*T^A_V_&9bBg*UC^plz<AJiM*23N5D1R&X9eY#^0^IdDsj=WD`q>dyo{TQ|1EQwj
zkm9ibT)b`Yd-hvX3KIrDW^oY>RBTKvd?iFVF4tcZ3f3Q;7D^b$$=VKT8;d`B&Zr<#
zZX`zD?U=4`Sp)sAdASzm$9s>od>y#o?`A1FY+Uw(TaM#}s@3`G+LMHfEL#k2T*2K+
zJuy);WMVf?NAUU*A}}$xZ!k9O&`YZs1eWxGOvJ`Pv5?p-m(lL-P;^ub5j#haHqu8l
z>_{Mg(H{N`>T)8++4{-r6-w1*MJVBXE|EL2-UjxoP+#%-*`;!%!@1ehMo|zDhidyM
z6ceQ|$=oy1F+oAew---vF1Fme{O?a*x0|GuiV=kv^lRVBFZ`2o8AM06nS`AH0gx=f
zvG?5__#JspY9va<q1_&kZ4w~xwmW972j|YWX*wX#?0rxxKr(MSrr?XMBWXq%wjC7&
z@xo<BxK37tY<eNL{;KGVtsIH23hT+giVq38xUQQ?EFgJy=Mj;pjEJbKOGwDi4_Fhj
zkY+Uy5wdkP^|W*}j2n58BkqijiwFi3wD0|@2gGZp3ZdWy7?8VTOqC3`{;KfJR9@yS
zwT^TbywX~FnnK34XJk2b?Bhg?L@YMBQJ@^5nftqbnc#hJ%hx{cAI|k6#t|WOyY!2w
z+wpDj@p_Poh(tuXzHy|;a#JRMA=)g{+_t9SjqKilCQWH}<lYl^wHUn|N@E3re*a2+
zUVBfEe)IJ`8Na0II_WO>MO~@=IbvmyYl8cgd}pgv%*G0ha3@>DQZ0430%ihg-%O<3
zIrjmZKj<q3B_%Cs_vGZzXx~83NB8?pQcPT4^ix7w72u}VGeaADB$O;hLu3a>^MwPM
zA~0Es7v!THH)N63VGZDrROJE}L3@e=5~9+Jx4rXTWDay`9^(5hjO&3?_c340S)(Pv
zs9hdegb%mzizfU?V&ku$^T|b_UfOAE!qUS1vr|G62xp_7Gmfl3s+%}aPeIB(ai_wL
z)2No@7qcO_Hm%dIn_bap?Y<M=l>81ZCdMQ1=GgKA_>Jx@aI^daMJ#|8((*w6afV{m
zJG_h*F4f=dqvKMZ7_XE^F&Q5<Z~`Sa3JNDDHWd{NFE1Y5NOe!B8^`D67deT#WWR)P
zg7XZHzg>?!+ctgZDf!ErD(RoSU!%@-U4QQD+H7&#Y|4p>PmWNLMjkZ>(R9#BND2%V
zJ81U~z>Ed)kNsuGH0lG0*Bu4TJb#L(lYEnt6q6J8Z;oM$yo@VF6|#Tdcq|eM-kdFb
z8M%)iK*Q|oIFsYt7nknIK150WM0Q!B*Ra-}7?=4WLw(cM+?+Xfow`{pnvb?w4>Tez
zel`W$Sl6~y<>B*VUtgZ@J5jG(_f{H9Nq<HW`E#G+eWVVBD8U5e2_nMDfdm3~Hs{a`
z#G~LmN0r5Ff0Cpa426QC)zp0hIzL>Wh+$>8nGSd15K7eY%Mey^5}|P+UKwAJ>Jz?w
z_gWno{O;9-3&+W`y*fQaSgc17S3c?q(%abtj+p3CS;dJG>uufi-KxutoUi)!q=XsZ
zlM6%E8`+gH@^yeBSnL(*-fC3xZ|Sw9(d@O(?T}t3Qrggm%bi<%lN22+c0dLS4e>`a
zXY1AM&tAjuPfVe~Vi#>7hb1de9QpF06DRxEQqaK_AafWOX{5FRraE*>oUXh6wi%o4
zdXMNEud{(lBTQp<@WlW&k|Fxrd@z9s%R4M3Tb{4m8Z)r`p%j3&&B&oQ(J+T9-(j!1
zB#B;rc>f|UgO=<jd4>cHExJbgJh89_Y2VrD!n$X&D}Jka!iFO*m~KSSyolPyJ@Y3l
zP?2Bs>D#TB!T-}EFavx~Xy&q}diz82?e$umKLgVf?jGTf3g*j05p08)_(VSvzu%Xb
zY{)-pQdNMjsdp{hr&tq<Z8t)>xqjR~XoE6dXa+;Fk{HUpR%TtT=YQBInurJ5=}XTB
zWnY+l2dUU<SG8ZbXrLDz1);wtl4m;iuQu8C2n+%tubaL`4U9;my>b3d+_dy`AEq?3
zomtRNK$3z1QT(wrONark;3*mHyq11faUGKKLKSGRhL62Zebh8lu>UlRRG#}C0ofc!
zh$0b)(^Jw+%D88v2a_qM3fS3LxN1wf<lu5s@NUFzp=J)17~-S|n(N!ToxNc;F!BrY
zuFd|^P)fpqlYdpBB5R#DpV2R!Exn=KQyptJtb=ooVz6FsC(aw)3Q??n**S!X8zS}a
z@&J_HPUpZ&VftNd*&3M>Ib8h3DBgz##pFv*S#IYSzUR2EIEBkh4(I95)WX}8a$+5s
zV`%^ERjMWULiaU#%6Jf)Ns!l?@nKCDVl*zmp+iMobXMQ%nx$!vSh+b@Hs=wu@44|1
zrhCwA^;`sEWjl{g&1uiD_f7JsHy$^?Bm=Aym{Sfv%D?$HwEcudorz@zerJ95U0jyY
zh>11jT&crVC(G|eY#O?h`L^()$%BlOoT{UZWVXOcSil?C)YO;YZR@#Hf0+yCR^7&%
zC2VACo$I8C0iKZg5fK}r{~qxCJ`@_*C5TXwI)f2pl98mNI?Kg!=V6Mw{s;FIcm)Ff
z^?Vd=!eGC*s?()Il-Rha2x6Jjz6jpB1^hHEriu&Pe(AW07?kd{JEA@KTpd>)7#Bq~
zC3y*LzsR%@=ZD7k1XjO&r_byNBbIpoxtFoBkbTX4R1}x%{<LqoC`aWCd29*b4A8eb
zEv=&Rp(VJX5+lG=8L8iEbU>5G0SjJMn_$XTMSTp-r=NOC@R9X-z8i}=po!EB+!G>Y
zjg-x=)ZM|Vhf}$>2oLWy3X-rj+&0vQajNWmZg(Q8qJtSTPZN2^le2+me}{=pr1}*x
z%qVn%e+k;wK#XlKZ3VDk6Ya9g6F(3SQ5NohQRirQ2ow_zw)B)mapTtL0wyPABM@8N
zUh|{Ngc!#Ef#T6I_I8c)O2~sd?%!ijBlbBG>LTcmwRGF>c<8TO3|v;XZWq%;c<7PU
z$a0mta0!mfGd*t=x&k5O^f5XS!mN9D;tZM>vg0k9rwF8Xw~A*Sn~!h9ySR-tKFo^c
zbQizlKVw35vMVNHoUb%G0DU3vA@qTd<2HGF8%T9OCv$(-I7XJIw@I1`72xw+Ddg!s
z8^B<ENl-hQ?)IHasroQ$lIIti?PH`Qsm-u9b{;)${X=rjfgs%MuVmNznR;Yb!T3)h
zHWsnjX<%bU(W#5aj}R}iCknxO9jtecs<g&!M}3&2%-!e$z&A1V0x9nd5{FD&;EiM_
z>JJ?`Elk5dsup#?XY@ma_v9ibLr4pR-`unK%!VAAbl~8aNQ=hA$_MT&8TE)X3Qa1a
z+D6BvA;1(wp`x2U&sya&5^H_m>D#u8N!L&xnt8MZSAg91UCwGxUr}m^ud>XsOEsFs
za|<~b)_Rp4%YX6$6Kib7$<LSIF{p)uA<y)Yc$*yE|9FpT(PLVCFJU1vMOM>AllD9G
zMU&z+XtuF=@0F7CSqE=NfZQ$Rk^J@pvkzdX(Gl|nsHIZ(yq#U;-H0aCjl^x8;iUb7
zpjqFh+#-}$^K@u0$>X5m<vZHo9AY2jqn`<=tfiMm$OLG<9g3mvfB>iE9<<O$_A0-C
z&b#DL*5&5v2V=Xw=f$gSG&>$mBDLQK1vJ_)BSayVP~746@KPg0Q$mQ7-p1D%<sYFm
ziif0^*RL=#eqv>35kUd^!;8qDS3x@=9r*5cHu%2eby-q^p~&O7@T#;%ur~59C)YFh
zc^pHr8;~!oj93W2IY%Q`K_Y-fKO^U1_RB!WKP_c)$9@K%tMNw`z?p2bkXYl`4iugV
zLm<UWN;6klyT3^&3gn}PI(N{tS$%lVNW9vz*yq(!w({3I5O1)ggb^v(oIxy+vma*r
zGteW80NJSzbfF6Qu9Du=$?@ekX4mfZ{mNqlK+OX3%XQXh(r3L;MiQ3D3OxQbR5@K`
z0f`XmZ4luLKT_35w#fIr0Ke3z0F1Wbw_eViT5#s|0!HRFZZ+gO6w_vDH~(+uRFZks
zxGfXP)$p>Js`s7>c<!`8>LP;^=R;{rI{IjC!FQAV`X{+-mD{Ncoc&zgXpAel)vIti
zz&vfqqyN6loQSjDwb^@B_(fbo#N=x)K~7d6BOXHuh=}shOV6o}Yg#1{dpn;-#9mLL
zG#X=&l6p5`y3xLaB)O%~{RMMx;v0uTkd!Ne%s-MTUe}qGo^_f%kHNO|3D(Sots%`M
zNOiqTm^(Ec8wGrG`g0895~_`uQDy)c!1^iZHx!egp}u%hXLoDWTn6_f^zKpFLC!5S
zc393xk0=t{gxGBta@_~=CJ&=NwBYAf$RTx>=y1Yqli5Zx=IdMM;r+)-)|p*>$QZqK
zAg23;UX<Zn_^D{>jT;ZUDmULBrTO?K1uJ%lRhjlM7i)yxe5*1Lio|{|OA`JCSaCsC
zSmDvW|9-#poBM7jfhV=|p`k}YbLB>Hli;_t?47;URIN!XNxw04v1d1FzP0<gXLqD`
zCGAe{;q*dX<)Mh~L`jU5SQ&SOP6Z2ZlM8omLp*=3m9a%++)&<zwx*62NdSNQd8aq-
znMF0jn7jw^!J*6i_85ZC&$=xmpopSR&(=12UeY*OmHQ7Ix6gFK4r>y>m}-r>Sv~EG
zN^cX<v<Jj*f##DBJEf3MT8iGUP1d0M9sMud>@%}aY)eu7!`*F*gz|y4^6JWA8EGL{
zXj;{eO0ry{Wp4isAJIi)qUz@T3$Dr%#O<h$PkF$be&YshJIWytOVrH+5X3*OICv03
z^+^OhVld1=L=yeh=z{W;Rk8raIdGUht65+0Yh)>5U}Ai<@RI=vzWZtmSi(e|fsQz~
z>9^UCVCSwQezs?4GAuFO^aW+!E_#Z(s9v*Hr-*`HcT_nsHMZ0}q6;d^tSl?pgI)7d
z_NU3VLKbFzOccDHnDGpN#Uz>J-iI53%H0gSkCx+j7d=9IArW$=ugbZmnuPE0KC*1I
zLeEB?d0WV@;jiBo7!|LftaO?>pD?5C0$n%jB|QdftS*k{kSBDGGpR`^cE9RaH)ZtW
z%jif|ykOyno1UCL<ZumIcW<8Y<B|XNoSxrPRTzb06}hJkt78G~V^jDrNL>we6IZkM
zm%-4rhhGej3t7CxzXV6QEmDk<w^uLaaPfM7Q;!Ob4o5xr>r1loCHyRll&U9thkGf=
z)MQ#F%D1E#hjnsh?ef#7s}O>K@hV0Q!WV}sqc-#4k4lXF^2O+F)4p7D)&|lNO77C(
zmz0XRJt~-`;}6wED*V#TcC}r*@!>${yLnKa#fdvVLs6-T1tKQI`;%t391Ud{?Bco%
zRP6CM=mV2V{*)MYSGAO9<+&#62&}~j*TQ6<QIG%q2?`X+WeWR?w0zg^>lQp~C-c|Q
zm4Hx9sWOuTcYcmz9n5OcRvyI%>bEO7ICeQIJSAdN`usDX;#+-x!L~$3PiGIS{YsaS
zyEP#@@f||NPp%c^%=@{A6HN*tP7gW}d1NL0<7nm^T3de?3gT_S=R5h9_1KBZqmNic
zY{fVp<>B0Cz?mXdBc}B!$X<4Ecx7NznBjvi)Rwp4n6gQ|z2Q~ZL2`RYK6~-~!nLe8
zho)8hoQnn2?mu^8=R|#x&-r@vvHRh&%x9@-BBy;wB+pikyTl$#_+`T~-NM{GjcTMh
zAXD_XS+4rF^H7YOw71{U3sWJdo9)QWwqCMo>Y{#u+1?K$jP)UTRAYJbF*#ktV74w_
z^JB50Uq9ILUj;z&eq%~ek-owHlD`7_8S<sH7<d5c<GZ$zgALX-_#=%<tYcBY#`k4c
zE9PJrX-Gz_-KfZtj-|nP?{mURjqgkQP(gF%iO?E#d906`<{vbsoQ2R+XHAQLmrd37
z6vP{D-5&b|W^3ny;a<raP&><M%z6$x-jQKYXLH7@&l(tRACP^Y@>5G{ne=5!*8+}8
z{w7dI07mWoD#wuJYb<@`p4&-^=UNhDP7A{Lbg_nLi#FuT$6J!L;;?E}%b234ytp9j
zuVeLQB7F#{?Hd13vqZ>GN<%mNPfEJk=ds%;W}S)6Q+~UHv>_-_<sZx)beQi%@5{@J
zwws5<o}d|$jRmVOD)l9t-oL+BOQtM%huJ_N4M+fbS9kx5#^2kh2}8ek<f2wn#R+MX
zg?h>JMhVM=fVIk7tnZ_Gr;$XWgA_(v0a`y}HF-9W!^A_39U_L-lX)E=v+s+4jSTZk
zHao8NP0O4u*sPX$JP`36yPsZ--nQqD6j$fbWul%gDj?Br79)8_=4PPMxa^619uft9
zG<vPpMIJ15VnME1=T+J`pX^lmEk!-ZiIK2AFA0sgrgS1cIC2Qm#=P}tINJ!Qp9CVB
zDpw0B%-*15EMK)SmwqDvZ;ww#p*^_$<(xmD33AtIm`cXkGZcrVNa<K`6EsyLKp>)7
z>-BuWs*7hM`D|w&F;sQYqi|Mtr?e0F?7V=!K6P&4C)@@gK7DMSu-qtPd8yqgQQBMB
zDiZjr4{J?#6RJAPo`l<~4?1D#@gBRI$Ob*m&sj$L8EjEPfJX88hJHmNWnua+4o&Dr
zyM7^kkZH`<mTv@ZCl)ZtF)INNcQy!(Vt$AX{?GS){fc&8MlKcxGN0N(bA<p;+HqMN
zrEN>j+xVb`1-8!aY>ZS{Lus{_qx4ntwvFMD4UN;8&)54-eUzr{Yy@NF!y34Lva!c%
z5s;7+yg;2y-rvr0N{ahw6n=IaUi%}wQYn%9&NVpSb=cdbv+px>1+((CFfsNF4o~)p
zj2E8+1SarlNv(D@c7ETF4;cXpf(5Bh;gN1Rrm71C|DK16e6dUezS1y`T(oA<&C!Xa
zpMR|#!;c8Vh*z3sz4{mp5?dL1jWa8I#8%uuEpgXQM%no7G=$py3xnuUraD?LRwpw2
zET(-3RG;%4qy^8*%{mutUfG4*x$BL1&JnWA&vToRLSR-kbX#<3;c*X?;{W7bzeEG#
z--`2F+@P|?1AgFUY_z*O?<buLp_=|=>RD|}=r+P8`T0Ot*S_<<WAnr80>YG^FyeVA
zHZhRs$lbSlJW~B><TD|bJ-I1WistT@^suuJF=^%a>9y9Ztc5h0FkMqwfJxfxWY^H;
zCo%6`Klp1i7qM%I>kcM>p6GBk`=|AVl&cM&8z=NH^T%}Bisg^ZPWhP2ju316l2C%R
zo;<yKn^1yb6RafuAlkISl0LSpt+|reBlv5uxz9#D9Mi!s<)!6%6X3{ADBCD!LvwPf
zA;Py1C)72U_4VOu<6tCUEU0uymuW%b#D3sdhOvGSrE<6w?<Wt?yon}$=jA0S{E<+*
zejvBl*f}ajB*&)&1nE;{>BZVaUY!XWItkKl6L}u}>8+IVgFcSghpE|yb(4(jlQFb5
zPNg~+?K=B#pHh&L8(?biTAm$XW(U-^drF$6gw$Ks7x-k#mxj+M6_nU4le8L3xx@U9
zx}+tn@4`u3<Ol|^4sZ-mBiEz3S2Q&;lX3Bm&RiGv;@VlhK010i@bsaa(w?NR^dkIa
zKsV%YL6M(erXxNa6m)E2Rm93KqNIR*7;}SB^&Qg>@@K%sQ0A(;K~(s95!@zcMtTDQ
z_i_uNZIOwE={+>?l-kO{%=jh~?WE786PMs5ZI#Zq=1B;|f_`53uQqPmFMJFDUFxeH
zsMvW?nDlgE#y*Pl*@R&!a=LgV{hWC=k@m$YkmnPg`xT!<hz$XA;DR-v;kZviPB2VI
z+KIL>elFmfjlc_UFLSy}Qo7@yVk0h57#sPR*O5v2F?9cy<vA9MSNAnGC?G^vcKtTN
z&f2yQw=xdsdt;Y(#$P^%*-CTzA2R_W9w4M)tzbTiz?x{SOV+u|r>#FqQ{(nD(zgU!
zkx=Ee-5%W$kYKJMKl{h11?`cY{NQ6DRv6aWqNT5F!Cb5#s#6ZZoY^?SO^5EbntU2&
zv^2*bMvKP-aX#WnbGAm}XIzYk!f5uTa{}nQsthSlXhP`WK8v2GTJ9`xZ<p;m`+vN>
zRahNexU7o?cX#*T?(XivAvgqgZ5#pw3&9-{G(d27f=h6BcXv7cuXXm-zTY=oP6qSo
z(Twp`)my{gr)r#UWBjK1;xH<n+me+$d6Rd#M$V4Hzx$SHwB1N$ru>!&#T}h<f+SNx
zNN44U93^g-z+C5P==ss}z4o9DF*(F$5IFPdSFmOs7=G@kqtK8TIkHnz&|AmT(kEkd
zpi&5LH#+?RIB66@w;4{PAAt(B%WtWA;>_MN+G1--9)IFS&t?+rOyekL^t@{mkytc$
zE-l=BODW+dJ2x%8<VV5ifswjz@ZWNRf4Hr0PU}J&<8o06UL|;;@@ArtHiyB#FFWq5
zlB%cTSQW3epO6W8hM0n>BWBLjpao+T^JV+Me~0Hl>e7YVcPcge{5E?2(UE^rMj@8F
z0VW!v33L40q*?lN_XMdO6UyIE?%cWN3uB}p&RW!3qrx+67V9~)Dlpkmkm5^XlOja0
zwSAKkG$Y*Ju1Dp(+_YUBmqs?0<;J1{cf;9u90usXbo}OyCL7=JCEuTF55)Ok<De&6
zBZqH5y>sv=wCa8>-ObEypHJH0<(uc=v*;y>L3J<HPs?2_=y5X9V^7|X6pb~YBwhF_
zA~?4m$A6>y1vxp4K-WwKJ5_DyD7PKssjTh%8t)4(99^D4J~4ygA(Bsy0>@|nFYXp2
zRoR=2bk%~8UU8XO9@4nCv+nmOrhM?4p{E`IjgKvB?vrsn_uRsW;x$SCwk2T%Ocztd
zXZKAj8wD4^nG&gIPPgy9He-xbETi{N{hWW7($+@DElqCH6b^MH>~KX8eCm6nqLylJ
z3Z6tx0%Q6Q^Hun26R$pYO?RIpd?Y&^|B-qZI)kSmV1;T7+k4&EGtKlQYF>1VTdh$5
z@Z0`D_d=hSuh2q()h!J#7<>lZb-Au>XV26Wt#t^!;ty<{eE*gWFL}0ir)7DwE8^%C
zoxus-;L2XM6!uMpkVF!kGD&-n$q&!*0&}dGm|QEF=cS_&B)91t*ypBY0|GnKCv%&x
zf&3<rG@4GU4cW=Tvi_ZW>eooAn|v=o=CICafe6Hll!@Z3V1g*zYKQw#Ng4G%;{&Ry
zFatN0V0^-`i(geS!4GurWFb-BwRs&lMZ7`JC@cX5OvAx2^tCId`eSygX6)FNf@dv{
z*pCz7m<3Ao;Rtn5Tx>q&o5yVDgi5T0n_S6x#}y-arWb~0cb0_Aa?hi&g^UltdlFpe
zpIO>H{vn}*P86xbYizAYEFc#hO^^zOfI@(<Aq!2KZgx`PcSS`0kI;ekfqz5IG$^Z+
z9$I)_$Y#${Ax*=7KOsHDl_^!6G?BA>*WvYOA-Zj`+u(HU@00T~mlg-ZbAzi)`(y5q
zXI$6^VUt((cs5YV;=Q!hvg*gc!BB0g5Lv{4bt>20*y!lzzjZQ8<wzUnry1G(iO_xz
z_)$86#?DA043t2%$@`7vdRoQsnvWqB@#9##N-S!G8y3hIKxCWFudh`ed4)Po)HsYY
zN_Z%6?VH+cF<Gch&bqHZ4V}E@-}y~oTjl#bq;HzeJ++YI(ksGT+ddr-eD1T*M)Q|(
zd1ph4#EIFg-g>q7q`OU~Y2IuaMVw`+4*Tq=uE*Vu1u9<Wu%ghS4|f<}=zIeMOMVLR
zQfzIa6i>_OKTGr5dRD#Ypr&bc)dw?1SXVuYMk2$1oUnsj;iXx0roI`5bqml2g+)xQ
zP|rv0quXi>l%HJ$FgpM$MRa=tjN0;?Xk4b60lpo}P&Cq=XDA~Uhx6<jW9(=*CL(8Q
zLea^lW-6J`NGZX~3qfzIm}gFRu&|e)TL4bfb94Y(oDy@j>|sLUkJTT7VC<Uzr*eR1
zQ`x^2F>{x{wdAa++Ml&0C2u^#eqyv|ccnghK*!{Yn9ad|BUc14Lfg8Hii;`xB~_a=
z=Enq9R*+-ThX2*-l1W+a;py_Y-^2qCR|L<O(6{FrlxHTwaN4wLuBV9J&#WJI`@Q?~
z+S0HXS??Y>Yp`8DBRAWdC-OSNTBo85T<ZwA2Z?`v7Z1?1`1x=8Vbp8ZA(RvF|Eu#m
zL(#Ys{;tCxl|vtpZa$FLNpcBiukdHoXJGw8{B(n`8>plzE^v7bratpsGl^Gx8eE#G
zTGmS_zpHhj`~*QQ#fEK?TkMQ>azAl33!DOTF^+-WZsMH@%$Eu-DJD6A9s(WF*_kr|
zMyxji?Q}wd6MOIXC+QNjJ|lC{qQ*)vt)|(wAg?qwN(E5<&a1TcKMG-Tv?{juzKd7A
zR%8DIMn66hjiVZ|g^pLrU2arGunptXAv06l($p4N?qHp%uIs0Kzv!DMd8jNU9~w{!
zZd@6`#Ts=oNg`7dL}cJ)x0T)<)S3E<2ES+x21#n`kqvLaF$oG<Ub0Sz_5}&ftR`=D
zP`MapKCm}xot0!^DYA0CZ|btM5y$}9@Pp55L9;V6DP7l4<+OUJ6lt7v?M=;PTNz<S
zJU5i)DIdIUEL~-VIh!>7R<P{<n$2f`iR}*&;6w(VIAQb+5(szC4=pV7O6i&V#ARDf
zZb%wEa$-@x(xH)OB!bM0D@MD_r4~3DI?<1TKpG)Q(3Zep+lpnA7WPz|johVfx7T1<
zik9%_O=l8D@-7b(6X&bX{b?xGOarC;I(A<Vo5d0BZEVkdSDz_Xx?`7fUj}-xWsc}E
zU_9eeKG;na_!Gx!^)V^%_TrOrr^1|I8eSv#<QRXN7ly<Oj*IVFH)MDjYtG{T)d9q_
z0lZi^|L%4pQmQS&Kl%k_Xk6K~3^0pZT+?%~w>EV&w-f1^L}0Kn!*xXV8retwKtCHo
z)I6<jAga6(vIVv={4FtbxH&M9etf&aByXMt9gY_1!SN;{^_Ys+l-4*Y(*mF~_Bl}O
z)6K|CMWdo}bMs}Ooc~~0lO#6;LF~mPfOy5~9I<M5h=rM%p}V%idrMF`AJo3l(l~_C
zp*v1se};v(VG+^UY`GP8+^<dndCES5<ZNs5#nsaVCCh*y#O;8rI3llWG;_2lDsrC0
z40EJ+TuSjz!OC|UDhmuvZh`$Xyv(fI%7Riqi#sGroy8?J?F@|Vb1>p+&Wc6^P!+Vc
zox~6PI4EttFCnS*yhZ&MtEdo3G`jKODF4r4bHAuatUGsdyL@~kCzExY?I7ZBs7Dxk
znAje~C!N%@u)beA*u7GO4<e{!85vg<`@y^<8*!qJJTi71A=x8^Iw9}g+d5Lpu^BnC
z66(u+Pf5ila_;`VZ_gPi#-py80Kb&!o^XW*^tvh$=?dMTl&rK&SSMr0I~jXxT}pVZ
zs>NRB{j74fTA+ELT@hMJ>H20vo<<VJ=~_LM2hj>4r*HRbB!=EqY*u6c#<%0Hikhc~
z!+4NEaO6e6O7-Go9704Oy)dX7y?3h%ejlO`k@z{qd)V6Ck}~{;{$Zd9F8}qS8QJ4k
z!mK#Vh{Zzp$Y(6fF)(@qAKZFYah#pEFHhh@U+ne8A1eDV>9OO-5!DzZ*??3$v*I8{
zL(eZpBFp=NGQ-SXeRG`Dmh#28289y=Z-c#aYSPKJ_X?%b+rFG?cRg=9avJJ-I5lY=
z*n;J^yJ#!4de$H#=2;q8EY*=6z-0e?C!DdDdM2zq!CGl|LY$WNED$e+5863cLmZcK
zCs#&LW@A%cizB<7*5Xv9RK~h`7<@JkY@O%(dp)i<0*LUlbGlQQA_eUbIgP`fSKS-f
z=XAtitaMZ>)3Zz#`yRy&A!qEQ!yWe$WW3dlefw4w_w=WTshNPgKTS)p&&JuB^H5vs
z^<K9qaRq%Uv9IilM$arD%7&-UNIcn`8{5fQriZ57dK)86EehjS|3N^MePIzkAinL~
zQ{Sl*Vs#ux?Vps^$A%MX^6;zR7gvEjyz?+x9IkHE8~9}Ho>A4&F_ebnU0Ruk;Puu&
z4Fxc~B%ABDS**bP;G?!u7Fh?ql%qk(EaEJhewi7S&_zV{*9>Z+%PXJ0k|0tAE|4Ar
zDayrJ+=^lQ)94~VvO$e?)GX!FQwOgt8ui9i(7vp=#S7<#wS#-bL3es#j+0B|`jScI
zkzymFy)*@x^)}A3p}%_b1eFhAjE0=Ho$PCoIj4fY;3R;1gZ#^tjcaoLQ&Kki+r_Wi
z`XMuI0tz1DLvxQHJT?B!t^1miS|?{1jKfzB!4*A|jNH4e()dhniw<;V*0SKt`9^Ub
z1_u5W)4xpgjLo{&VXNP|nzG6|-nz~37nCL><adHQ&QJ%hqGdiAKOHfN5ItAP4A;IN
zgfDLr{BQ=8kKl=E(n##8n8#J3gLkot$XWsjx+K?m$-RgnF%VEHVn#}Bjtmz3grekz
zt4>bNPGvJEv79B!+kZkPiR1`1xeWb%s3uf+`ZUJuwgXj0XZX4m(^`#)ZIW=o$oU3>
zK|(<=2s%}_>jG_pQ3C;uSMdEeRgiqqSr&oXLkR%d3v7H`LL3~3p&$V#RxW6tTKa=n
z!S}al?Qd`GiI<F|ZWnRGmG*~H5u14IpQEGlHV_<qFm!6j=G`R%*6VQGCjMN<jFgcI
z*x3bK{tczO)a$=(4|s*{3fK@Xpwl+ex!IeoO?W0%nWGh`))<jdta0P!9fI0WHhRTc
zy9YM7#(sg;PuK5-p=T|N#PbQ8h3l`qWU=WVR$Gu9`OLO)(bro|v=t`?Pq4Ev6^F}h
zh)BBiMQeN?j2!<rFVjF5-__o07>f;4u3pzeNRT^q86)!NaV%RnjUcHsr=`ICQ(>oR
zB_Ng&pJ;I$p9I17{ocTHgR0ztFDwtBtYgp$3&g&})KU5X2NxL;5fK@gzXN#4i>vs4
z#@3qW_b@cD&255!gNEI_n_VGC4)zMIvm^dV%qVZw7ry4z$T_c>xbt-RYX!4BAu9z;
zBpb@88_iFifWe~qjW-^tgn<9<AfS01Vn=P3#;I0g+nn^-u+t(yLe*iH4%mIOTmQ>@
zk^T(BeT!~ek5tV|H7u|A(95xjzu&kiZcBz*0*@jqQROY{d-ahY>C6o(G7!H>gbzaE
zFuGu+?%`k(j@M`+lj=x7=nJwIwfD$x!q?yG@T6?5#0gqUa2^l&l#h5N)ajUq=RTKp
ziE}l{)PGv~TIDu|p-af&xPhN5Lg(W02W?3$apI%DdOC~;%SS_9C*O>n-O?+MM9hp)
zwe)OgvS0C@_a+PNPD}=t6gbVvd<3$%Kn&cDdH%|4p$oU?19AS8>s3rQ?BZOi<3q$>
zn$8HE@0H#1&IjUB^I+uJ5VmK?T9wqNRdA6!9|cWNfU?;K$3-?64Nzh^j<oZPir(~=
zm1arC6ZG3+i!BG*FyelG-}=t!jCiij2fur5=(L6w9jiLRP!YszGrAI>N>}lzJvs)y
z$!qm7t9g@x#+D-P2jSo=Q6ajPpbs~F{bzWloqLbEX8PSBqf||{9`v0&s@2=VN9+cY
z`k1t6jHK3Ncct-`6(B-5y=Uquq9Lc-h5kD~$HMyZs`|YM9Su+4{V${$6s<&z&Cfxf
z2Opx)Zy3G5u^+tr=t{nVlxo9LNi^lMK2c@Sb^B1Rs3bQwjOHaPvHIQ`oOqdju28PF
zj<^4dJ=17-y{KRKGIXRt`36-YoK6bfmu5H|Fi-A*V_ub<vIqYDN`TDhpD>9eT5CgY
zbF?pFj2SdWKYjRjfmmK2tSS>pKwRz8OoE?zSJ{DZ_K2UI)y=|pXj2-bo*f@KosRWH
zh^PT|QrE}SfA=W(JW~1&r4nHGAUw~x(eNG9qVUn>5Ci#QIqI-UY8Q<#OX%G9c=J0R
z0j##72hr&Gr4w*)EX(!dF4~z>{cUw__Xmdp#y3sgo?1d{XJ5xq?nQQy@jZRNFrP%D
z64968&uGGizi1f?{SOXot(ykQi!8C<vN1diM}1T0{@z!aM67ywRdWSZh6Z`wtLm}H
zQWQA`GCyd~!h2r-O-=kIanquin)(9y1Q+L0jRf#gurmPsusG#Gbu~OuUn+gUwAUMB
zRb5AUu@i)Yl4<%g{}AX7QzX3QV%vGm{iBVJ^m5bKCHrhBP>Mj5gq+;>&U|su5jvLn
zkNuN|s{)};*s6u8@7|R1```}f*<w#<lMp!NGW-M6^H?Jhw&ppuc9iz(N~F)DEw@G%
z8^S5r&<lXie5FG4cU{O<OcCYd4J8sN&iiF)YPGwu^&=tj*oejF0Y;|pXAi?cM@Pro
zTQFq8glW|C*FX7aPLi8q!rbBQpI_wOU+F4h-$*a?{@t^r+scWJrN0w;#}4+58$C!_
zB-bf{&?7Z$Fz9s>n|;jOdGRpc?_soUZf;)KB?HL#Zhok0i!A~XXim-9jsF4iKx+wB
zO9x`sQYWGRzEBUxG3~F!Y^tuu#J^th|7F6K7@zyR-~IkjXzhd~r6m3c%`}ly($K$y
zFdX&+32W794|OXA!+%v0I&N?8+`T;N=2P+ZiaMf@0}nh-xA?Ek1>h`xf>6dpXVOw2
z{r6cwB*Z&)`6kgud2PX&@qeR!OR<-IG@%9+IPNC-QgzQCSxFIVAW{a^k+%&xr$dRg
z#O5d)0^#GJN$~sFY`>4~r8)(glZi(iJM8@nsd}J_)xndh9;PZog_+0p&~XF<%)K<s
zLrC9-L7bKTM*pT+!HZMyK<QM^QU%a91&PwsH}V((=0&_q;tXad*LVeH)|Y43mS%z~
zc)$9e_#M}5>&0vja=+((F*0V540z|fs46@Y$X3IGMxRWA-gw_86Oy2m4#SljEVcQ^
zjYk+6pPt?9e7N*JvStveUGL!3b-!j#NwG>!K{7=_gQ9>Kl?)Xf0#8qmhzQUZ1d&H$
zVnR(tb0tlYOs$e<W8S^yIE~fct$D}gTptQ|@7<r?cU}v@k;0uDx{b5%;h!J7iSEd2
z_y+1pK6k;W-9qX<1}@}qP*6?|oL)Un|MJZ<g?(ZXI{>VIdQy2~IAjW{#<cwN{F5uH
z>XlL)?q^ansgWe1)eM~B^#E@2Vo-)U>dKnuP3gOfNR^GIKPR`M_46b|MNnE8;Cu!2
z3GIw6X@z|bw)ZR?%nGVXp`v+C<gUAOfF3Cv$(Lc>zJW)<OfuZfZ41byQPCCPQrm^_
zi|5N{*Rsi>!11Bm2rn^bi<8=$wxqJZcOcQKDccoHOSKcYlfXQ-0Q@=pK8E+z1$FeI
zUm8#(-UXTJiMnHKa<Ai<&y0F)p2Yi#BUU|yN`vh#Uxo@8lDpZeufGn{ll|kjva@vn
z5b88#Xe_a{xateS7xrmQw@3eTDOW>GFg5Hw>Dxtf{O9t0KCq=9+T05jWr%#Gdhl$7
z?t3;8DP@TzO?v{mJ4F?+yzspJU_+yv=?<F}&qVG+sd8wGcd>hN`d+)fdj}%&m^oH3
zcw@Xy9uGHT2}J(b?SdQ_hKSjg$bY{rq*{mkT@v?O)zJ366*T3t<{3oq)VyoP(^QA<
zg>&GO^kNk|kW~4ubl?%g`X3)H!$ruhlizlS4{e^MEY$!ZN~Qv5HkL&cUTR9{QAGcG
zJZ18pH*y!wATEvJFYVzkvBn<%c(H;H+m@MiK_U6ycXegXWI-nSNshC90diM4kSNnz
zFNl`gC#neOY3WSQ%*_h&LA6dpb<T{ZJe4Kg6r;7XKm#=N?kr8yk=Qs-qGE#N#`iC)
zVKv=oY`|fz*_NWFgd=}iiG&Z%zyz9fuO=c#S<8mETl?WNIhO!GRcA%VZzp05%MXEQ
zir+)CQE*@h<jn%l@IKyZ)A;ax${%FNyN$bhd>|6y>wt`YK=uAes{AtDs$x|JCDx6B
zTl@THJ*;eW`}Ai~BvRecfQpgxJ(A)m3w>!ts5T%%`zyh0;m70G%^aPCVd}6Imj(_s
z+|`Luxk#IpyjP>QwG#C_96`JNR4w*43O%7&<13+uU)hN@b?{^#v!2~L(SQ3nx&7Py
zb>4fym%R6$**uG4wAFY~>t&@u%?2i*(AUs>M=sv=zon9O-A7IM9Ua@0xdvewF~8@V
zgara|J#fP;aD5iZHy>HOSAeU664I0xxH}FAdx&eN4}zIx#_dYqn*44*>R*`6uV;7p
zBUzNQ9vTb`-t}0&WRvl2zrQ-UcP%$cJ~AErp&+L&FvEhN{L!E*bOr+>`V~ktf@!gV
z1E+h5>Z71lpX~|t1&eI`+F8h<1XadNJRdl#n)QB!G}TyaJkY{jIeP8>O-kH+v3mw`
z57$cyNe=0$C~1Gfvgkz4)S>()7Jljd?#iey;QJv6`kpd@DJwHQ$Dea<eN9b0?FS~_
znb+9dO%yho@QPN`cJ9+8yV&}dR5jk^2@|UpCeECd_NVv6fkJbch?n~liWQ{PvI790
zPkJX#3@+<9+fRH`_4~9*M_EPa*;Vx?+O)5k8Tdp5c2!J6DsjF$$7kN)pi>AaK&s0W
zd->kR^XVF%#aOukmqxhBQ$|-mEH^YA7k=*TP1RgSSie0QV-gkl`Qrq?U(Z^TM|OLj
zJ?RaPc<SSy{I{HOolh<swx9AQxLbkmEr^sSGikl2xk$til`GP1U(#Vk434`@9U6)S
z==M5@N@m|Xl=uYfKKW$?*{~+JPfc?Esd2lEW&-VwF7Ef?m9_Gvi?E>WdF*sGpt^qY
zWaeQ|%t@ChO(-6%^?dipnUFxakm9BMLp31cS!DOs?kYU4*;<_U3kuGA=MAvA8BgM}
zz6RSCW`nQC=Zz{y?kSVH(ncmjYlW-bc!Pr4OQOT+rfDA9emZum4HAXq|K;ERmmbXK
zE29KQUze&{A3LB&Yd;QaTvB~OMt>i?E@*x;<5#a-C(x8`y`U^hv%pSvfBv-Kg9;y`
zSV}Bx=0;GbFQhmDshc@Z0vHLay{J7z9X}nnFgAI;oS|47xJfG8xVW%skPob^Pf5PY
z{*mlk55s*$K6rxWu+CLiLAHt%m$`<EL#JPBq*5;GOTHNenUR=kjOG>yh83h?!T~w2
zi1c~!aluD7J2PAfj^)g(zUe9WIGE1Mr!}j#<|LL3ke<Ft+3$myz$ov7b}fxf`xD3d
zx?0-H!v3%x)`9)bGYJ~L`$Ci=IXCm9Jr;IqiZ1_WaOUMocOmbpBJRR}ka<puPfSQi
z3JC=NH;>DxDM&p;*hD*PGUhWcDD;%mNcjFW!op2QxPB6&oM@B3s(2qyo%Q>4p55WG
zzk)8#DdhDknbbJIKl2Hl0t0qM-bYURxG9`DxfKo!*<0X1d>zk6z+NNgZOOvBOcZ}>
ztmMzB<(h*%<VfbmIDL>+xL$`i6ZA_koQrvA`wcwQKoGL$kblFU>YR=W);k*%!iUC6
z?u$}jD6u^x%56T_!y>+>tg30qCm1pw7#R2~BP$!BZmR_7YbZsnD_=ZSP_V*g|5V!e
z_<GE|2|%oi$eSC}ZtAFRJl(YCJ|C&Tb&5|_J0pPt_(5iNyZ0$i+T%D=Wss+UDHbB$
zUiS#h!O}SBWW8(MJ@OvkYKqIq3PVRDqQV&x)l7fKUH8mjL2gX$zW!(ZjT;RCFhPU7
zzKb3W{R317vtU^&2Vf~?j_|%nZcoS9Ewy&_e^`>;v+B0={4d*v(lTKs5`%(v<LOq%
zBUnaD1ryC<%INGCvTh@hF1c1DI&nAC7K_xj=?t}dedpKLyGytbM$j_yozoRwfx(`n
zvF<+tEb;fN8tgCbuck1#XK9gzPE`Rv((x}hfQDmfqL}TavYOBSc>_@)_`tlVM&2tf
zc3ZRzh@WgG;<z<Up`l}+xhG89#@K$TbY+&=&A~sT2tVV_8`)`Uz6~!ozNf;hRU~Xt
zGuoak4s#?vw=4Sy6SKB{UG~aA#VkDpU2$PSNU;_-_BEjJ6!|CXy=_Aks%Kk`K%9*@
z;BOKN<54T!cQ!;{^67yZyFOM$uxTwY$n}B{HYIptJj}87(9b9bI(a(FVHEPTpzS%>
zW!V_2EqnPbQDC((ZzGY{y@@1IFNRd>t%hq({d>Io;#=hn%HJ=fcU!wVIM_clr@2KO
z_D?pku(Oj47FR@@qXVy=t^zI1_Qsq|09||TlEp434p9HI49}0*HmMqW=3KDhf*ngK
zN56D?{U$vTtad9etZln6aw{krF>aI40gY&<k7~w`LRB8)=q0}IuC4e^Ekk~dCG#b(
z@<Mv%HFGjoOOfD=ZK=q9>;uH*oe9P}Dgh%zz-?XzVvP1SRrn0aa`#=F#i`gXK%yiG
zIgU2WSVH`dliw1_9aT-4M-qfP;#Cke3UQ*s2#g?dB;~+7`m`AGng5F9@_9j7Y7-4z
zZ%6^e1Y)-({S7BFkIPAStn<5;zrmTIeRz0KyHbdC$7L6jC8p!e4MKOG=Jz6S9}RWr
zaP$74Bu4ougMjSFHng&CQGOE=;LXaL*3|nzgVxgAM%CvBxh1I9XHIL|Wrc=Tp`o4-
zIz=d=X1%Pagh=C57yI#K<wfE%oF$HSyv;EgFL$G-vuEq+J3aBZxxVkfp4dV;x{i*O
zj9*5%$u7wL5p6p@gj%)v=03YiBi?euYjgdEJlPjtP+=(QZ%lYx!K4%b5bkW-SyvV#
zWADky4-F17(9^5u6%-WY!-ZX6Ai>ATD~vGEmy^tingv`6G|lFJ!QI=aUT0xt?LJ%f
z*ac&B|G-cc-b9)WlR__v%uZP5_dW**3K3%3KyN7YP7YykV1^jK-@1?y%<iDGLl7JW
ze@JC+0fhzSeD=%S)WQldvot?H@L$CW3{g@T3(x#Qi>oH0pcsR+Ev$^w%$gJN;M2@+
z0>OGtI9#(8V%Q~n$~sT;#^5g~4bw`yFaECM<gGx|vN`#Wla1}n{IivK*DxYWx8IuJ
z|M3!V^=xUlMwdl$d)&kkr03MZ7_7D@loZ{xX)JWxL#6`|u-~qGC&3MWSP+C#o4xD8
z{4<pD#ncp{!R+ms==cqSev8wNt*>hQ+igf?9-exzvF;b8x*cg4@8*Rx0^8EOQ<C~6
z>7P5=`7>iLvA;v7f7p{Yw!K10QhvtN<S@of=~d?89I+A2UQ7?RB3CEa@EHO_is|{4
z5yKSjHvrJia;XX_?LP+pg&xrkpQ)Nu*4WO!x)G^j;13{5BuIzH%St910u6_nxAz{G
zsh1_<H~zNu6GZHH6Ipx)k)^Op-S)T4W%eDdrNm86>+PFg&;&-XsbRer(m^ZI*NeAt
zj03W6rzn%adrOqjDBk(x5cV>IR0pMc91ChGRN$hzmTfY~Mb{V-!-agw!ouv-xu2v!
zXhYpb%KeLmg0H8sx-Cu1kQFTaD{J?VlC_89mAQ$ofu^J|=%tgK+LCxNq}N^<6FvwO
zWP7?CS$fF~j{7@YaKiWL>ZqEF>>W~^0)rhvo`I_f2RuP3F*^{mv>JB0j66GvnW}j!
z0b4N@ZCk^>eO6{~r-J0OaQCU)slE1ZHQ$>5{iBKo#he4wLZi&l=MR@v>U}^UoMJxL
zJcU9{>Tl)x*&h}(;g8Lq#D(?6u=Ov>1O}MD5Lc~Up&|NRalI|Z#Adw|BwZb~{N3Zx
z`aX8G1h*i9Z1dU~2+OrULC82VLW+%t3v6tEHa9B@oo-SV*-t35w7dOqdA<>2<L2OF
z^>H$LN*5O^H90BqyRMdww50Mf`dRMH6>ve%CB(?HxfnCL((@l5vw<afuKVa&+q8&F
zU||_{R~DlhhRZ5fGG~pQbS4w~sVeGRItqCA1aC5-3}Y(MO>k+vlRA-S)KG>%Mb=9X
z=7os0=Uqs-uGoR*MLJ%J)(jWd|Igiblq2!?Z;VblE+6tC-0+<f3(ifA3H=t1u!1L6
zi!iv&<F(k^Cn_;K%XazxTHONAF)^N!baU{0c1KsYgUUc(T4rx^c#xm(WM_Hq-%H0P
z#B=DQuj450;)C<)!l)e@=-tM04*hrZ6ses&0Ii0K<Xp=+ot%g+(lz!$swnDT`Y7%0
zxeWw=0?L*(#a8e+Ua~Lh4alOQi3sjCLNXV2Cd1m;!Q=Oq)7G3gpiXWRlebrJ^znGL
zpOohf$+`W@wdlW+hRvXea9PK#wNY45L{3E$3`N%^B<|#RW2%n}P<GfkBl{FT+^nk}
z%r4z9()7&tKk<<p>5>_4s<H7ly0~7W#>&aTIbqyZ<xluKTOf0j*TmNFJi^aSs5!~H
z89DY8W};fjqPo^_uun>?3qY<}?VsEDD+*EC3J9~Wt*RxVd@!u!%M!MQS8a-tV<<Vs
z&#4vwW{<@|qVg_K^{gi2K-RI%A6L4J-S=d~p}kkUv?JFEYp2xW>ih*tT4+hg7gA~L
z1GIti&YGj8gCFU)@<ym0Opa)~rIu&$`2MLQzdFdv2(Q_ro*5f}&fTy_MM0MO?)wWg
zmYFD=6a@Xp_Lfo-TnPR5m&?~BL^k2f1E*3J1=T2T=YdxMF*#h^q<zZl(_B~gV~dxm
zg$8bQ7)<&kCA!*UtZNPAoOX2yl9I2Yx$<Lqk?a|dZ;c^#Cdm<R-i|Jh8>$@rS;+B`
z)4b>#o=a}X+iNfLPXcyOf>Rae^=d_mORX}7ZbEh57xy;Q50uu_6<x}?fWFB2umgC*
zt?%!H2~D6?|7bYIb&B%UumS};JnfBRx4IJ<{qlonPSn-(M&{yAk3J9Nm4SR>>`xy>
z5L)&Frf-v8^^maKn3j>o=N;EBk>V7_KbHBfatZ|Fs)-Y1v;lA{nHzq}Oyt6%f~R^>
z^Zi(nGz)UPf-_DR^&iGF)B9)9&@ai%NPbyLbw2{Z`<B+J_Gi3~t9Q-EkzMPe2lAf@
z4@ghRNF{&!a;;T$Gj<h*R)5xa^u3zCXsv9Zl`Zl<M0gMWTAd5c-<Z|yGch?69Wi$%
zz92=oc$z2VPHc<jM@mHSG`aq~cguvw+UmP<)qKxJP02#j3Lo2~+7(zNrUJQb{|7!g
z`LqhKXNrbAy!?mJ-N>3fr-^PsyLO`1**#x+0*(-u@86{jk16)(o->$Sb7h5Zz$F5r
z7wXP7d}1(SD{dDHYfGFQao8_&W?s+>CT0YNG5?{EnGkC=w<tr;Aw58)1f&za0H%~D
z`(ooX#ZG>lJJLSFOTxq_o?X+PE_N_L#Y$4!D4x8Fx>+~cOTf!nXqGSw0}WEivDZz!
zvbk{$<hk>2RDus-bI;ddX;BGcfD^g+Z&Xr09`O4FKeF6woa(`cp=Gbm>^qy_)a<8}
zOhP<dQUV+-qSS<&lSQ{6u=+-c4I4V_^4G?%pY>i{%?M8z^be2n2dZ;gnh}7I$g^*Y
zKn^GuV(*(#zH=Vw0cd#}w+>D)#mTeWUJ_DJTk6;>6T$pzbAOL&bYWRJr*r7~r9+Fj
zg^w*o^rPJENIA`8^jNT<Pj7!VmtL|R@~&12EK&>;&H$nPeYr6D<J?uq4mL4Y>Vb_N
zZ0rlLjiW`stSu}%M@Lx0)Bu=G9Be--HOOo+_a(9Kw8Q7Gr&l*}PWDE6fq!e$CZE^N
z#yxece2*ggy!C9qdcFS7rm~oY6K47y-nr3#`%RhXHg92V<&%k~p0S2jLZ-<lLw$8^
zgY|`wsU_pT?nUOPQHkRbQ(xGg{6<!HliJ4)zyFtik|6G!u4^s@$SqT6>e^18sSB$M
z@&h);UY78%ugQW!9@JEc>ehdxfJ8m*`MKG7UNM_6h<?<{Yq1ENz`)m_23I`^sVFy%
z&%NYJthu6=$fSi~O?n4|2%i7D@5S1_Js0k5W&DckP!IF&^Fkduq8$)FS5noL^|N==
zbW=2twf1r%1B8%a=WR_hdD!f}8Y1LdK$MQI-?#a9-^GRxu^XXi^It-P9?j{l+`cNQ
zSU*{7-G7G*1M$Zfu|&x+|3Mx+J??Mr?rv{y@9)u&gBH3u&p1wgz^W357(ia+hvNl|
z*Ori*pjo$S{uL;zDS&Ib3&ky5%`^gU7#3L^vJNW%8EtzR)HuCnjJ*ks)x|4rprr)Y
zP(q-3kxFgsjIBtX>n9~lzC|P5G)#gGmw!_yO*y|ny#J19zR7K8aG18%>_gjg=)#PU
zX)H%=$fc&tBfsPbX&*8yN*KPi_5R)-&u2J2;;g^HqS%y*0+Sl)aI5f6t$d%-L0A#a
zrqu^vcwOqDwb98l`^*4h>-Q=YRt$Q4RCxV)^5;O9v>H_dpS|g4QdGKn)2YYiyYs?A
zTDnJj<@0w+bWS7&QbE62ULp3{l3xy{N2jS(A7@0VM9QmQHzMN?zG~#v#y$d*yM%Se
zcNVGv>iX_^skQY4)$v<a1rJ0O;_r-N%h@f!Vz@!~P&(``$U@zA!%qwn)rZVgpNFWX
zAZHB6GS4foZ2wuxhr;5IA7ssE_CRn}Rm1QyBHlMhS7>SFwfgR2s4wnu+=XqWLdt*K
z{q-JF@|iBocLv9X<mBw4{Q71mQ=R_qERN?O!4DVV=}0Ux5yrV-^%^hA<Xst}!^;;a
zX7PcQ6&+y=*?mep3_;87kbJS`>iUc7<cPIh*c<xVzMAbYhElI>39|JjD4`t%^hiW0
znJFl{Mw)XG8j4|{&ogICZsExDcCj_*YMmj*TuWg=qf_+`5wYNsI+12_FT}1^xKJY+
zn#4SrP5u15j(Fqx%S<svAgIV%$pZO`+2leA!5(_VAHr@{lI$XMQiaZi<CMp$Y>o(^
zEfGE~-*`pA{p&RGYlX1Wk_=cS`CUaMwh-RDc*rnxiC^V`0V%@biCaAp`saM?xa0+T
zL-Q0bf#3fmxz{5%uh=%6`?MDy!HHVw0cPFwm8uPMPB$fgp&NC6dhw~H;}=kHqy0AW
zk^U}hVbZVB@I+P87Wv~KV#Sy0l>4fX`#|g;Nd}jZ=PYTR0|M2$WCRC?HjX8wL3|Sm
zemVF=B&1IV9qZu8CdOQLr{9}z@snvljPe<{3Wy2eh3%QQ3{|x+zUoGJPm8<L`>LH#
zN0n&vMY`h3z7`;3s~+?r07V;?f01mOMG??^+}GEqcMEP2pusRF#!cNRE$`ebq?%()
z3+-I0@HvPAGgw5-`Dlf!y9T6s&wOZV;X}c*I-U$)yx8dS>oDf6G-<c6w=IIU$38R9
zll<2~0G%ib6+5ywbX~dD?LyEGf&rk#DlJ~;vkG|x`V2`*N1(ES%d(`9lbM|bI@4yq
z^{Xb&+o{KCs}C8&HJ@J$zbLtD6@__ca+2Ii27U7Y`K_fSf~Vfw;xjQlEqf|jOyl=o
z-=j*M>Ue^D9Tg<p7YhB>avWJwS+hj_j{4XgW+gG93`e=Nl6U0hA?J*p*&L@@=uRtF
z1G3VL;*weMbVUOJW&RAG8}q)K2U9Ntb7ej8yBN?Y9~@{*I$z1Rm@8;*YgOX*N&SO+
z1$p{tPWD1-hk4MPF0S^J=<WeR8<w4k7bQ*GIr=-okV_Dgsgr@Ev^pxb?*X*QgnjDC
zgkE|kKIyEMZjhe`eo53lCI)8S59R74FKSO*l|YRC!aY>r?4B}bH+7cB3#B97K%B-Y
z7r+r@OcbB<w{&qNO!K;H-gL#N@S=kLk0WX8B2^n3lAH!l=CpsO>{#_gMeZ90-ijrn
zeT!Mw5#)!VyU~<K0oHt3#zi@X*4jTcY{!q&)ne<JEO-tHgxMjnVQg(<-QSWgkUvZr
zg=d!sE(0w7pIyQ9x~Dpgu;M@_TEX<Sg1y0i-hB+0gbBH+4soHZuJPDAur~&Gx9G35
zP@CU&S#LVlz-=8W(~$gE1WG+*e)hJF%@-1;?TBE4h$Xs`5@4?XTwXs=&&Ym#Vdco{
zt8BKLn|XWGpl|g%^E{GuKGL`7Hn6O%nznBVsCYGixYdTD#B^2gWVa0EOm%`ADBk@s
z>xHbslCNdP6V3hpdTy<j1Q#ANlwbJ^yjku4iHB~h3@#=2kk*rT&NPP!NR}e^tgw7$
zGW|<-=!0=TAV+SXxd`fLJaO1I`*`FZB|e2Xz;1NU-$h)7#1`4iM0J*SL?oSvt3#3k
zHmQ;sa_^+^ZJmVgiw$n~q&@A_J0GEH2x)o7jnVQqOj2gJ?qg%y#fP+*nS&?s@NoAM
zB$Q3O6q(iPqkLW<+B&m&_<cW-+TyRv!da;V@owQ#kRl76|6P_GV<~`O;qM5J$LO4I
ziI``0$X;!SmXz%6rJ^b<G*6qs(Z3J_g67tbpJ-Y+>A*5FCOPpxy=BSR7A4aTwb0Ly
zc>x!4_)Y!Tb;`e4SA#<0<;R0~{ORxRDs$etG2naABO;!_Fk5Ecq~bMa?tnDRYU}pq
ziSyhFx{YAh55|qK=&AH|#F#+It)P%}RgK+)uWsXxjs9d|Qh#ZSL)SKI%9j2Ddnq~>
za<l&yVL9fvUb1fXT@t$?*LV$y9aMAq0&k*fvWhMsNe}#?RWbWa|CyW5y)g$}(^Tk6
z*XEjN$S)t%wby2*u2bbL!gzfP#r+J=%*;GCHip8z+Yg?UOsGY_!H0bI@(4+318=hm
z-~=O2qhC%|S|!%zx6{`J-ZVNO-yAPNBcecb-+A%&uR|wX_vXLLq0;nbY0&(R^Fw=<
zyS|H&VcW-lW;-&lQP_}p!XTS#YFa}#={q)m*BGSx)5g)q<W4hf!NYTCw^4OASJ3vs
zS77aXv;rIT+=?bal}jt6Ixz&2{pnd*nV6Ws1^TBaLfvBrFJzyL>jP={5bPSxEmS`k
z527SJv>b7^FbJq5lD|g81^@f1Ip>KduKDx5cR>AeVv4i2#?AHm3Mo3T?-t>v)X&gE
z2$-9~ZW$=2AEb=V8jZcF(=yGJJsdSRzq>=(;6|HS0=XrN4`Sy{4Z(jN)34)n^Nh{|
zong{qhll?M=cTF&K>x^q^J-tI5?IvBmB`65qtIcoDJ(3EVA7_y9cIROo~JcSMt*LF
z?X%qZG0PoK%E?h%-3*~B64~GPmyVi7R8?MCF=&68RX>-DCn$;cfP;x~FrINt0}@An
zQYfx}_s}Vb2c3dEzrcEPLz*_oBR)ONAlu8e{m@O6kAsB;VBxttoK8!#vB|u4b@Qxl
z2$F^PfeP+xvJL59-C{qZqtFkFS2+GSecIkUrzwtb)praO$3S2JneyC5R7g^(T=aZg
zAHnX7y+iY4#pJS(iex_b3b$&Gb8|7MNVx%7otHc1?6+42@(;Gl4d;K?k?aUYzkpM`
zEse<J8EzC{wBjVFQ%p^5vQQR{?866<i{KF)8Cf)@kr6Q@(x)lCH2efxHW0bfB+?x4
zoh}z3rH5M(u1-X(xasO1gHsWH#j<8?T4^~wJ@!K5B3SqEs^OVc7lC(ly+~Ann%_t7
zM^3x$oc?fs4x!M5{Ci$DoL&Szx+lq_A$8?!rTG-t^*B!1AIsDgZ<f%MC+zsk5$Z#a
zX;J^)piyu6Sr(c~=j#(y!ud94qV~L~+3Ak|>}|_{a@4#19qxxRxW{x}z1F?+-iB;%
z7(aQ2lEVd5IYv8@MxpZ)|HmsDI=X=vyoroMG7;+WMl#$V1MBVP%PCY3UctC-O9)!e
zcbMOSsXv!7BV=>Dg68J-*1*5Rq-$+K#a=2`N<-wLPtQ|NbeBkNr-|xnoFE^U$(|r%
zdEt{muCUwRVZ^3L`k*t!sQY#?XCK8KX3-DHaBTWjiD?;MHN`ZHjpheuPKx?Vr>!d-
zM&rT4lj}cIm`;_RW9yM7YPk9r<=J3trK1!m{rdIKpTvKc+X6>daN$jQhntamzcqe7
zvYWz`ibsU1ipi-A^w7{#6c?2KcGH^JSa6}*E<C!zgyX|!_}@PsP}}k-;(A`le^D`3
zHZV|B)YO!e+>Qe4HPI&mWf)IRnI6~wP)MV4ja`Vx^}@<*fiv-B?b#W$-e1ndfD%{H
zLuR?r^*yAP`$lM7R?1ft{3r(c!I2R%0ctw9GOZ1g2xRTj1xoD<G8oF?SgkA1(+@}C
zO3%#?;Zjolebm%6XbN8uAjm{q3z&aYyd%H8B590-=Gq4nH%3KOS68FMBs(E58fy*Q
zy#HB6wG{Y+aydjz9T9G~$j`^u3^X<Scz3&TlHd@WphW!sZ2_RBr6L_Ts>=6v^$mA-
zqXwfuA!T()+&{h-#su~{SuBsQPUgresK`qyDk{pN-K?=am48~%xY-Y?X^T|#%`=f#
zRD=#Ctkmc(eoiT$dSQF}cBe0Xc!7nEj){pWzMvRY*=N2kHE9BS(;~a-2Eem*t%s6&
ztR-m^5<7Q6!u>z~0sLRTbMswP%x|i@!M`gN6|Jcj1M`%If~GjH=)+!ECoBp#2%=&@
zxHJaY$Py9~P*5Sl-qm!Mb9L6QyaK)pG0C7r%8tN5fGvo_jl$?FARwUMSDKI!jTxPd
z4AY4hoee=ohDgG9|60@Ec09?Fd;pXn+$SX@YHCbRadP~pt6m@>#snaNuxw+NyVucY
zd;<J~{7wWE6dHnyqm^cd@<rSpo!Dvw1~w`f&+VBy<mXR!wg2;0hW{EB1r?MgEsde8
z4;d&z%7V(HWA!UQ?PO9SkDO{z5ngDaF~x~u*wq`ZPoQpwVyaYZ<4YE>;t%$*)gUrP
z*2e`R{qAK%tbywfN4vPbymEGP0Re|>QyNL=Tc->9-GwU+ji(FsM4!HWSDR#GJKf*G
zLB|!Pcau|>Hj*?jFp!m%)z!T(sf<J260NK(pk`&Ffn<JzF2U&nfyOFIP1gG<uD1Q&
zXCYmn)MRsT|6e;D67W@in7+jJh(y$7c!@M(b-r4}qeo6v(MVoJNB<}<tSK#uO_Yv_
ziko56k@}7qQ^>CM8CL(c`urm|G!k4@UfK+WrH8T+v^ggdQ>NSg7YB!lToBIZWbZfr
z_vvJjGlfnG`L>RGCV6MK2n{q0l5Xr;tjfyDD?~MtTw1OX0^RRKcbVZ0{1Vk8|JXTd
zU9W;Ce}M00rH>9QhAy3_`{(E9_x9e+Chgv5b?NzWDSH?mAdXD>F|^13<hext?#r^m
zXkmQwe=ooxdfNm1Mei``o#c38VMi;Ux$(>4+f1*v_5go>|A6zg+uO?yw`K5stK3+w
zg1TQZs*D2Mx^vBHWA7Bvggx@<9hAz!ds3eD8BpJuG(fIhB1D0oNsxB$b^#9-tCrhI
z{K@^}<$2#hMwp$Pm32cT6}fhQ+2q^3^b%Hjd1?M6>j9#>`yUqZ>EYG?jv7#VCr{rv
zMtL#n%UJP<VA%Fkfz-i$7Cw{vn|3ndOD-NB@c*BP2n7X&2+N%<c-gR!8Vqd(PZ=zW
z2Dv=E!j)Cf0Z$yCp$tE|V@uiaUNK|5bOMX0-gIG9)@G4BKJxrHD8f|NoSwRlD*-pu
z429J-WtcMkA3(*dAmr7l*|=KN$!%_U>LqMQots~s6!)mzU^s~=pmR}DN<kip#6^e_
z09&U9n8*2zTRnI$T-^UtG+bQd<!x>C^nCQ>FUxyEURF{Mhg7LT&V1IL+x<6R^b|R%
z0{-V}lcmH(?QJj=Nox27;+z`0F1BK_UIr}ERUWw5A$T5{4<HqhA7>~%Ck|$R;TAYn
zG!cnMxNF@EJT0r8hV?Tz2CL~f014t7YUcw3YR?;3YdR5jSQIM0?ILaW*x$s4j;lyt
z6_iGpOKs224o{Dd&&lwKi4TrX&yH-I97>s)2K{k~-UiRdzuAxDefd55ExwElbKkm<
zPSS9wQSJi$a<Uz@Wh^uXB{!j|uC1l=&(Gh}+^p}_VWV3SXO}-&A##E6p8&70@=yLD
zRnnE#W4GsD10HzRM4U#J2*$KE*#<WM%pDuTinNeFX8B9}2L#l?TlFWErm@D=={O@z
z*8|xmCcypMYh?U6c&|@M#oe=#^)s+nbe+b`5%UvqpLY=g{@#>iRKW9M(VWP31`wsd
z#kVY+^sFnYC{K+3?PvW6P!#5s<=dNDd8{FP@i4nQ3<rk?)8+4IVee4>-KLhl)$I*@
zOpo#p+Z&N=&)`KL4A{VZVi=OY!#r86e@z{WJC6^F4xl3y!9Ymvm<pv~g{(>2PT9|P
zdV(DgnqVpWa)O`rDM<eP;&Quqf=sl{zyx+i71aaOq4q4P2Sfm$my4Xn{adDe=6HJs
z1ya+{8UeM@h2Ygcg?NGlC|H8I)kRrJEpzIUnr`K8c$G~)kFX+f3UdEh5J|I*p3iyP
z0KIIIQy(Mk<R4l1WJD-`$gT#TpTegjWOu^;A-sBi>{u)`rwbZy3^0wvOVtUgtc|cr
z`S0{21RtY31Td6+u>n~F#uC@4phV*cA~a8@Mi<2Pk27jQPU@^L|7~^$zpc%a{2t4T
zH%@aCO`c0cKU?cqgnWbC_~?k|UQShLzKfHnKF7r-;&XKJ7?WzW(%$93L(5K6`@a*#
zo+kift&=v;!R&K&ZRwG=ijK@u%zn^YLMy3?H4s~m(+`%T-l>B;tS8>Nxr?{gT!&@x
zA4x&UR%C)z)jJe8C`OW+BenY^Ro-2C$nGt=+zq$L?dbtX|4^^r4zKB=9zD8VR(9k7
zYd0fH7jc!pF*{8MQEI>hlP7KjA>wDKm1lxdc?3^K&oBVOIX#lqgR8=S3o#=Cw*ij@
zXl`0<3*g-_v$0U>xxUGI+4|2UsBZJ`{hny8v&|rb^!VJ{NK+qb)&1I7;)N~@)>|M~
z5X6Jgp{_cE7>(-2FgQAnJY(sxQ5tWx&8VEOuys56MXSs2>fWFyRYl!r^*C-VAAGK~
z#RNwMm0q5`&~IaEv8Y~+9F)PS7Epy~Z1wXem^qMmUM0XHu_Ry*^pq)UZ{-#25rt?P
zJ%^kv3uwbs0Kv-q>sOZm`VAikt;nOa%zwJ1w6dh)JlLrFGW_i1^A1`H0`2}IE|%tZ
zRQ_ryD`@+;uQ$^x2(o#I>_7#|<*bnW0oFeKeWD?|^%4Or=e~1607lA=)Aa<6J+5z>
z?+-4&H|@-vyd)_^-Q@%)@=hZ;&0{Q4tce04X_21KUdK*&)?nAjbL+A}2BBT~uwwL;
zrn%&+lfZJuF^IY6gK)fGz~@GMU+h4NH)Unu$H|Ne8JB-P_Ug?MQua>CNdNv`R#(tc
zdFf#<>-B!|aPx8nEDij(bTbE^z^Ac6F;y>J>;KupZkBH`#od#hz?_z@^ifwJVS!0?
zgELA$>QQns$RXwcZG8NO*B$p4m){q5W-4BEEww$sa-#srLZ@F=cqzK;fW*HzHnb9;
z^Ul*Y(8|@{y`-}dMC9*!n1h9*8atuOU`y8k*ni&Vlg@DY$E~~f!E(USpHc(SPJq-F
zMbqy-sKot%leM&@6j)taTU=ch1<PJV_&FONJWpSX|6B9r>e6!mV|pnsXk{)`ixcIV
zG1g4RAj5S&jy^>IpG3zGNe-W$EiOk;g^E;$s7mdemo8<*EWTh$`MO9m*R8brf=(j<
z?4{VpOVyoQ3wRKSgtdoQf?TVr>M+vJ%FWLCZ$A?do}335C1zrh>}-2p-ngLfQ+Vo2
zRJ5;&b)WZ9QBxHETBa*;AARx_Q_|s<@{#?Yp5vLDQP3&Tvo&=!eDu_cmMa2CKBuzg
zChY8;PEAd1ZT0kkR2CauNrpOLRrJd95BF>mjwwa&+R`bj;HClzRZK!#W%%}3S8=*e
z)ejzMtp8&sAfH<H`!GNU=JWS($;b0=UP+SMfTtH_1Za_y_3rt$-JkuSAibEL-;mK$
zx1c5(A5$DxDu2ax(*Jo&{7pqhx-nkcu=izZwWK2BE%9>b+Zce--X7f4=|A%9^o~0@
zP%ZRtUL!ph7w;%ww<13pUy$ZbMB8CmT1-j2x2uOG-|_Q427apM^vs7uk@o74;gy7H
z0PAx8J-GR{Yq@Kn|4${@R@!&rclICO1tv|dn!Qk*d-;Txve@$TJ`T3%m%R;wEW-z1
z82*{OvD4!TLO^ec$q7Juh=Cx3AvDqX6)8wGe66t!Qc(pww8W#*)1n}_=)Uy+$|2sl
z4Zw@a4psehRei~?`#y-*KWXIX{Kd}E=atH8%)HXozI?J6rQ`JH&s|IUi*M~4ooTA3
zJ~f^7Eo@X2FO|pq<{t7a8LJDrb&>XXAjOZmz!XAN9Z--}I$eSO%rUY|v9_POR@wgC
zBrfEh@%v-wcjBI%Dt6&kh`0T3R2L&hpu}TuWNWOi71Y|E{tKB*7zOsw80rh7IfawU
zasyVv^!a}H=Xo~%{VlAFhnliNFK749RxZ1%bX*BpBwIvF!`8owY1d-o<GyvKr@`!R
z5Mw_bJpqxbM1t$XqjI`nQe5Wq#py`#Ef20RE_VS%)p_g1T>^VG60z0{pXXHfm?-7X
z>Pon+0F3DUSKU4UB7Fq3IzouOSFv&Nuye*|A<=Km<I+a98XOHH=7c}Cd?M5Rz5|{%
z73C$W{g$l;Vj){hWtCcURV6l~y5$9R&o`Q4fuz4{`TxG!YVmD6-EH<pdfA(fN#Zr}
zPQE(iRTY;PVztd9BcleFudhrEKk43g)Iah<pQ7M|>#34?FJzMP4=Tg{_URFBZ7|2U
z%rl1bt+2TxOVHrAvpRL>B&YoUXgUkFsM@v*(@2-njWh^IcS(1Hba!_S-3`)>gmmXf
zNC?v1-ObR=x1aYozF)weVb2}cTI*cAtwrXTet$LzpRGe4?ipj)9f8Gb`MYtGU)cD1
zVg0YiJ&^mT{pQHi^e8a@wG}s4^h->ivG%7n4p7APk=+*L=i;~EfVyyEq=(0;ZA)%}
zWX7C(ikr+J+b%~G!dZ~+W9K$d$e<)Qy1O@VanCQz&T(^bwzjyt-J!)Pj8m?_9uqco
z@dz$2tZVDPQ2x0;Ch(Ui^uL+i{T%AWUvJqe4k9%MDzEP$vK5EAi-z}T@d0lQ;9r)0
z&W~ru%boXR-IQ-@SEt#9c`bv-6^_aq)10hazw4rF9Q?vHhyUC>I2l^XSxb(F5aErw
zW5^&oH%_PEm?*qr&$x(<P!tuITKL{78hsO!RB@MkhvRA2>|u-GTV5#q?ci`cHe}?e
z1@d(@)pwVWQ3=?p1VEP+SsmeN<6T=(OC_A@5*Sauzd`)M|6xp*6Z7%?PW~Xpg%AV%
zI3Kl}S6(goy^?~HwYiMp^<z*-p_P)qA_JY;;bj{Exjp1iot{dz`rgLI#=VZpn$I1s
zmt(6d2^ojNRloH!GKq<f9qDAFE;~Iy>+0evvH&(N3*YuQ9mtdCJF!sZH--PIuDsY;
zOwHNON?Whz9d(Epo=e*=9qhHZ2Sk&ipRC;cg4CQ;jI^~-Cf%jKetVj^1i=4*F8HUj
z3A(5mjqN&cnHPaT-M{%a?fL?h$;}8KtD!D9R#gd0??~@WzoPutK^8%aHxF8eUKS2`
zV)&txOLSpd$v840t)!&%%qfx}HZgSbwKN%R4GuV3;#!o^c2u~gh=FUhfz6cUqfr@c
z-^yvmlt^<9W=cae9g()-Et=9WeT9|P1yObo3a;`^ez)ISsH~atik7&7w{w7^kF2Vl
zy1d?YBc638Z#&S%NAfy}0s1YK5s}=fbWxaM?BHWkwKvx|sf#xnABhsYK#1*E#k<cH
z4S<pnENj<dhASNGP7t#1ke{%Q8Mn18!aE5^u4k~fp|`sRW7139&tP$4GV;Y_`8Fv9
zmcQrS-cY2@1U*q>;0)xA#S@^pMdjt}6zt2C@f*`q&kf<MUh!YvfRLr2BN4$Y6AFQ}
zZBPG_gMkq+5*rppkZ9qSlF4>*URf7YO~4u_M|<RxGWqsaez2I~Y3-Qz@r8?|k|r_7
zmhlu@1jF|!R@N9&p{+xlOcg+p<l=3t1K$Pg#79%So$W%U=9SWhMo@TxgW6`n(F=m6
z*JP8_3318eFCuCn=i?M848#%B2y^&5H5@ySQ7fZ7S}zQOMK1x_fVp{gRz4ty{`6M)
z6|G~<#KNwivZSklhnt<6f=1x5t2X_9EwE>^Zpg}NDt)%)E^eY&N%ChJ?7>^g8TEv?
zz?lMY9Yl9xRXtm4UScy1r2j5so!_xdmZHY}AsMHQ5`zrn;6&nL9N=MLb2aAW=&E5X
zX|gL$Ms~5WZEU8h>8$^kwd3cj^NwRH>#SZDF3zF%6!OQKC9Aq{FSFKSU~y4pYq|G%
zDFRo5y!>n&tZxOc27=EC56cP3pt&FC!z-qPPt_;QujzUY(mF><7;DbCjqmz)R9#1o
z`4I}`p=3dUh1t-m(+DvE6~#=Pr|~<(RMEf~uHYFB7pprvF*Q0bGgnISqUt214LoSP
zOONn$-<Nr^Iy&3GcnRf}X>VP@G9o7z>2>J4gA8|IxKsMR({}UccwY1}y7|H5wXZg6
z0?PMQ$8=2m&*`GLK&dRV^X^Za#WRs53!eb%TM`gYF&==&iABE?4dR~H6h-v)`0k>o
zuC3g&w!&Q-BKgEV-*Sh?qVL>(zV!?+0gCIcIA2`&yTNzH#gT$*I!UBkfSp=bLNhfz
zEDg@~OUwImd}}%|#fy^?8J+}irzTq`^+?L?ItKbuCS+s;p*0+F&7TT>f99pR>1zG^
z2^7(2X{7BR7PH_VY+|shA@&RHP)pnUo)f}so>?#2%A%WXHEj9`(upKfeweY?X6~kB
z_!}=pA0?000t!O5khTx>Z+EO1E@=F~-LohW3{kwDjJWsvEl{HmOm75zkAFr1DSnxl
z8B066vl^eLh<<?y`d_x^r$m`KzRwQDpiu4^jZA{Q0~gKrIxyUO0H5k9MF64@(kEEP
z1!xs_HN=U;gMn(`P`RIYM)+@VJ@!VtcL8Y0mIvY9f4&KtP|%WEnX&kb+7%SGrcv8N
zJ7J(X%PEfZIy(KN)sNAdYB>P!YshO^T1@^&RIY7_tr(i+$=_}jCi<^k4dUPdh!ZtP
zqq`2=@b-4L+OLe!)QNXs8MH>Sz54eA;bD?ud~7T>;poV!oNVg9V2#J>;?3xQZ7bQg
z&p+|fimwXePNw9Q+&$z2v(KEFsp&;M5Kf1!-kL)KQ^TV|l)lN1{5eweve9|o9X$nn
zc694ZV_<?~YsU&^mLVTfJjf7eV`S_xc#M9=6v!2Q><(&)x-H3u-jd<1Wgy~5SRbto
zQ1G*r@DMAn9eGUYkS)pqULx(A*#88H0tpa1Py6BRAzafjUQdx>4)L*%zW&QsPBxm;
zq<0bwyhxXmnSBdM1gU0D^vYm*UKx6Vc5!4`hKl1STKg~FtH5TL2YRh|a-SF)T><=c
zzP1K~g5a&dCWEiZrV5hRwYi}{QPgTMcEn9p&1&4g^3yCygY}J?s__sT1z5KPe=I!G
z!<{WV&!>K8L1s<~0wm)?M{>{IwjVQS@2tvi)~AGdI!ojM&OfWa(n26>ws-qdKL&V?
z>LR>gzJ9kDhb@|32H^q>hi*FPP7*&}ef@m06KK4ppR|AkMykcKsGWs>d}0GVXkLO*
zv~S}5i1FbAQcQFdko?H?a0(N8>K`pSo_~ufiIX~A>goW8lC#jpm=a!@tZJwJd$Xm*
zqK4(qFOO{~9J>uHTbldrih>doa~ZO8s%_}&FJY{SjGx#J@*7{>jEn}yh7LiKdf@Im
z_OMh3oZd+M?|Kwmk@hycXNYBZ`;~rm<1DzOE1C!I4zoKYF*rcDwk|ceR|l`V9H`Er
zA9X-%RsH!l0)DkC;OK=O=QkBlF@Z?^Ee!6Vt(|A_zVRzN+gDcBuWXHV=a!~;sVX!4
z)+c_~2HGI8{mQ4Bsj#@Wu=cm7;9cblM$N%xS?mHt+eXttqXh#78bwi=$G*}#ZWtVw
zlc&N75+iSKZyTT-rHGZC_bKU~lLrxR`@q67ET4!R^PMmjh}j{OeQDcxo@+uAd$IC$
zKW~hL0xklm>eOvVQU9H%3Ks93k)?@M*?BJlK*C&Q9hL}<D++g9KP(1??_9h8^AJ>1
zGLNDSuHUmwPAM18u|(d%WTL8mb#b9FaQ+YHuQ=JD^zWAyK~~?5X+R#C6_XF?x#!tx
zRB?ssOO?9zHvxdlB|u=cb@(YqZ;=O%&M>?^|Io48`^OLBPi_Yl)tOvw%*BNOJ<b(G
zQ8zT;DDrKBxzNVQyLZRH>uRK;B`K^n>Y{wu_ieXVfwt1rCVBCj0D}kfbxdn8_Vw}e
z0p8Twr=46*@8PWg2lTSIrXQk(nW-6(m}Iz~LHvmC*N(Azqbw_T)6O5rrg;Jg!ERtW
zo5Sp00d2oLvTxB^casrRAqoSGb#v?>mrGz@$Itt{{mR4JU4v23+S*!H*4{zK$jjIN
zp2pr}@Pqm>qc{62Cimld@mFZH&N^^fRCD7(aT!6$SjhDT_J63Ea+=`~!T!E_vOp7I
zkHJR|>Ftr@n)CC#JW{#`q08S94+frf0yQP5{^#=Q-{2m6Jua5({a0#z`msP)Nafm(
z7z`&FIqlmd$r)NRf?@QAV*TA;WMTrGU{OB}BR3dhK{kW?{<w?COZM%qYUEG$6MMmR
z6cvHN0>Xmy3=eq=oV2XypPBo6p}hXCH1Blw@2J(2CTh-bw|1VBC8^FX3b(>XN}1qc
zrq8jqcy!@>%!W=7PX8fV-|Yv&y~UJ&Cz(-C!`fR@Qc?`WfQ$1WKPxiC!C*^J>g{J{
z@YH?}?TbTHYGmZ6WmVI}gp8FOu?6MDYf3I^G`}p|3HGOwC*Y|}a`AiK-W6xeHqFlB
zu^9aPDS#N#omUfopa`_A{fi7#@$_n3c^kPW{ClU&3W;R2A{F*O@^Au~80cS|YLb)c
z_bCS^PmSb3W62B;4^KelNg=M?wwbxA8tRM6!eQO@^fit7uW=z=JY<|x+v0T(H8r$A
zJTH`8f8-w|Rg^4H+f{!5L#?ZQC?O_;C_CWzARGY1ld!Rgi9fnsv^;-8v_&Z{j`;L5
z)LR4v-et%?-Amvr0y+iA`V$Eb|NXO)YelDJ3lwAp5}ByugybfM`(cYB5Qv-oK{3!v
zOwWx<%lR<*#(eSN+Us-EW=%nr`MDea&12<${jKNWfuP&mG{H(m+T{r`fffA$j@Be=
z6VHX;&ORCjhNoxi$pQ<%wr<v;?XQ1iA4NG83b{HjfYU{2+7Bg=yyDs65n~tD(Wl%Q
z-9Rt->O7^>#E!gQKl(KAIKC`v;~$liB!Ar9GBW;2K|2^>y>UmvNp{#>cp8|LYbY=I
z*jDXpWFk5<w9MC$4oykQUzsLNg@6PM#_^Lh=GYqGR2A#eQhv18cco<oH)W8sATm;+
z!Lz-QHGe2pqjH}Cp|pyr%C3|FAN}$Y6A7M^gt2u}G^|f5j){qx8594+wqi&I8B)i_
z4rGSU2R45DKVM+LoPGXWX1*1AF4Li@^=fK3kJ0;E?h=Q&{JX_K#6w$-aA9Sng2o8i
z7LE-~JnVJ@!*>LPCm)}>@D<}daYTv#vH{8!_MM4~-NGRJz+X_uD{X@EK68BSz*{;N
zg`F~e3cIb@ARCcD%!?`xC^4#``6%BnQqAhB`q1UE?{haoIv6iw%JZGB0+KcO>!xfH
zQY*<eep^@us3T=pq!Cqk9A?7s^6C%(`G}C@WJ;JzA*rBYqj#(2Px|+`3w2-@mYM=@
z6BNvZb5KJVIzLdb<8cm4S*J=(Cw=WIzQKnEk3=E8J$d}I8;}2~rl-=#>VP24U=A@M
zy&%%Da#RNS+SRpgQf;ubVh^EZCn&<3hO0n!xOcoI|3_F-e6$_QTg{m;$1`kjnQ>^^
z@8n^L{KM5!Zr#8KP$7;pD-+5`Wqka@qobohSrGGK2<?9jVy&L-7clLNlk8{0<-YUG
zJSE_J=BJc*Fx0=f;jR1C`f9!3(6b5=NG3U9nbu@fRIqfjSevh#n<EQ`;CI}|ZDS}4
z(F>A8o|4ki(qIu8BsVXlOAmix*H?rUl}xMz@Rs*PT~ozdZOc<q%fECP<2MI%Z8-r8
z(0AM<iqaBzusGr~26p$S2`~IQGqx%tS$RoOQBm}uSX7}!(c$fgfPr}6hknD9vl;6d
zI`J^q9LG)ymJff_Z;fd2VLSay+O;kAOp-_*fh$A}PX~m3UwVI$%~l&2tepmH9mZ5U
zS4u%eO3@c40(D61`H>&)UJT-{bWcM;`JRswCGLvitxu~@6xx_*G6w}CBrl)i+|&6{
zd(7aK#R`AIjpW6~rlvmJqcm;64DS@WEgt_{8;Y$4qO6dM_rNGZRvqZsJL6vejC-<N
z=hZNuWlgaI$RW)5!NeiWM=d*Bu_Y*)T38g)H<W_l_xfQ2;FqsxOfXk$aYqWm*u)!W
z{`S(HxFWbt>IVmOo6(>MGs(AQXM|@;X@8fQS%r829L5oR$|wX2#!2rNjTIce=%PjS
zo?a++X$0t~x3XQHdtga}AsAvG_Y_OHI(cppt0sk&SBx=Q_=5SEkkGi3G~Gy&s6Gkx
zN}P0Le&)#P$PwtpRK03^sR6p|gz_k|oe{p<c~Gx{0p_YqB&)D_V6Qojx=#iNtRl9W
z7b3>bclp=`>*C`>)RdJq5#VuP)=KGWW`qsTk!3LmzvkS+HaDoc&8B*0^^zm~2^vkn
z*=Kx!pXW;>*xZT{{1+4kDn><~e4sby@{_d?l3}&*-&2$}F|ePw0Sz85ZL@m)^bYl}
z-UAVQIBKI-PssJS0h2v4)por4lJG3bzvRO}-v)TKHhOmBJ#K55zMs;LZW54_^5>hs
ze`Z~VnLRC%^-04MIFF#U_|YTinSpEiV2wS}+W8JoXaAhrIh6TWGMkXwvAK_zmZ3Jq
zC(u1*Ah?iFE$9it1}S5=5y`?!59`2VSm!r&p5rAYs%Wnvci3?U1Lq20M$zryt4da`
z^PZ3NCVU9-L2y9&<S&QF-TfCrP<z}th>Ha;P*C`FO)Z7!{V=Wgv7RcTzwO>w#rHP9
zza`f<(TA-22FtX7IP|zO-#`=#V?cODH=Bs;2E_7UxM{2l!ggWcGET*%oBl3T52o<p
zT~bNH+5?^C6w;(w!@kgqERa1|N5n_Pq3~^1=Qtt`n8Svhyg}OMSgh8C6+#}_fB+#Z
z8vHP<-ObN;?qDev9|k_j06;h@C`(G!(O!ZBqXxzgN@)|>gPVEwAojE<3g*^Wq_~8q
zDzK02P#U1~T)6TU&ci^@8`M}CmP+g6L!w**z4ze#*Bvw8qZsAd7~0uAYWVhy@_H+v
zm2PWe#S_-sR}vT9`G=`fh`oc%*hx)c)MGOXKJ<^I(%^m(&y}6%X-YqUPx|d0&m@AZ
z+!IS>vd@uynf7$;T+mx(c6J`^W6egFcqnuaFE?mPHTC16MRkII1ttha`IP@IFmvQp
z?|%R-0kVNT^}<_@Nf-u3N5m5slb4#DX#@j1z%l~2NELP^412^hNL6SY{+t$TujltC
z`P$Z3g?;;?yTJRM^^K2Z{-WX0xGIJJ8Arr}U)9EIUv=zowb^%yVDIdANO4M|T;&B8
zhy*S2zU1vVHgW||ULDSBF~f@f?!1>2Qv6OJjRL!>!}QCkzvno4q4*)^Up!J1DNWV`
z2AL#LL%!D%(Z6DUzB;L^6A+?JYq2T3{Ucu*aGNLr+mK2$eG@q)+vtV`v)rV1e}L3Y
zrSAbdXbJfsO?%@zK{*AsEDdSBcm&{oA`v*3vP^A>HR_1(DyVHCleC!;Uz@Os0XDqX
zZ<dIfutNJDIBI9GAv^``ccOj<ubb1zm<VvuiTP8i`?=IIL0{4ci%^?{XXb*Ldr~2g
zJ939@-zb?xXHlag@?wwKn^TDdU#y8M+s^eWlPsmc)PK`|R&UJY0;=9ia4JB-mxpT@
zo*qU>ld9dF9(`TQb=f?;@bJ{spXP)-7Q?UTnx>v5N`JJ({)ywh6o2XbLH!NA{wwq7
z$hx$Qk=$Sx#6(U~6!4(^t;66X9UbpQvY?AqS}}w-rCZvoeJ-7!pG=qnbR(c79MP9w
zE+272z*9aC@5u6LY8;&jY8ylwPuGQ=qN+SKePOze0Y5e{);BWpmxZAkK#;$L&OO?C
z_9b80@NEPRX1mk9cwVfJE5R6#3?5fmbyt?To3{g=X;#+P15|k?JK|L^BHaH>LDZiy
zWEpK?O}Q{Oq*R8N25w3E*qt^nU2lMh5*@2$g>=GAyIrAsVhAeeruoIm@^2|@`)>zv
zB#`P1``N)JFF8|y@^?wi^TgmHY$rk??|pD_Oaw^V#AE5?jb7xLu;=)tU)fU*m~YO{
zJf9!L&Gj#}XrIy*2z<!AbG^C*WOHdCHcDSv{)=pCGSsT|_wG3fK52fIuIkGCyTu#H
zD1Ru%$V~C?YIl%1eCE`BO+LFimhn<d^7$a;*qtzx4YChn9Uj8|zf%F2BE%rP{CL3f
z$tF%^swQXqELX#Q{vhm^oP*}4br6^#$h6-rAh<fGu9NYIIy#(XONMQ<{Jp-oo+KeM
z2jO$ofB8>Y8$!OPzwtzw8Mz#-0{;sm<CES~;<eC#U>oxAnPTso14V7gwLoHO;R!*&
zDRh;xw|L#UH>)hj>Hzm+x(Z*Ft(C`9Y;#<7Zl6S%Tn%aKD{1IAA%%ZKERKixIeOn7
zQ<#{)#NKjf>~?t(1|OAe{PzT~`Jse)oIxZ~|1%v*m~{K?A;>?EY%im+Bw%13ZFc69
zfE;R>WfMTB7wGV{bpI58msI_jT0`oL8Y&O!tgB1?V+@!KPf)b<YCAXD+*elcC<S32
zAitTIYHiO6{n%Ttpnvp@O`tbx?g=OOcs(C`yZX<CyfCHeGI*dGRpXOxg<RQW`AE`M
zkLx>mwD0db&z!_htVv-(SPar;n{Lo!L9t3>%r#ZTvuhjGwMx+LL2Z~DfVWTi(of!u
zza_Pr;~yBtO)(_7DK&Y&K4l(H-|-P1P6*kX81k8h>qug7<i7yq!kHj!af<86NxpK_
z=%~75UuDx_k8XPPfA)2_#Utq!^;7)QWx26_YBy!*uCYbl_VvpyAW6ka_Zh?l(=uOU
ze?%g&<SHV=v<z~#FRG*gp!I(T_Cxb3nxKd-bNe*=dM}{-E-^1e_=Au}#Tl$cTNfF*
zUxSNxuv65gzc#v}L<hl}N==Y&OC#!`v69-6{8)k{IPm7q^E0tH2j&J5gX9L{dmXSl
zUSF`Uq;3?HR`@!6o`k%Jp+UAD;t;&lh1T1+u^OYB-^v%Nlg8Zh-?MO!JkN;lte;;$
zDC^_$e+be5IM)q>5H<7~PnbeqJp*HB9drA>lX*oqZ$gc>hnKyb&N*{!q~>-W>eZFU
zCerOOk@~qj+R4hij5GxiS&4<+*tHT{%rgq5?G&h^C6;Tp@9{(lb)bud#Ki~$yfVo(
z*GB%rU!R#r`@jPRpRxYp`oT~#Sk;EKKDwq0_=OZDH<Hg+o)BgOkNzB?{IB%_>j<aK
zjqTN<t-G_DNsw=|qk7mFiNdHHJhyIvnXtF!8wid|tyq^?VLUhREVQ+=bK(2uLwY)@
zvJi~TI-tI<g;(}B0_3x7xbW%cH-t9Bz;kz9hy*sLftcd(C~W15Hork92`1q+Q)PKY
zOX07Q>P8|+eyDpFn|5YC@eyoEOjVmyD=~8d12Uw6eMSY-vA&f+z8BqG%XN&FoQ{d%
zO~*%3RK?la+nlq`^ypa1%s1(efLjNPLc&eUyw*nxT7f%PPf#6)@=7vGa)ykUgNnCR
zdaEa-1MlUt(5<=ir+yzPEv1c~$)Shc8=&a>n2#Y&$I#F)C)kwbpUekHmyNxfc63B;
zW?oh*W(=`R0Nmxxr2!yddtgcEyZUl>*Bt_Ce${HxbVb(kQ+VkDbM@I8ELBhbnc%7q
zRQBCyuSr2BXjlc~qHS3hzkI}Q7*9rfFdqPFiSH(cPZ3DncNVGuPT#s~MNdcJ{-;Hr
zqARqfM>e6*MquS?rf#=JD(qS~RXn7NtP=&N;Oty1e@}2Rn#1X<mT1Vb@Njrbq9@>u
z>~yi!<=^g@$5_z;>p#~`xqhA7P|eK7ejR%5pt=MlrQw<(BE$B>=`wo7W_2?4KbucH
zE9F`4cQMr3+guP3lahpxSW+yf2P!Qfs&6``r_&%T+r%{H55PT(@K)tTe`cYY#HQ$>
zS*Rz2u23Brr71PBI`iS)mT=~E>)~v5zq^6W{+%>c&(`#-SE}Ft<81XHzb#6h)Oz=k
zmFpEKBRu#g6hF{kmPR1|2IaAtqIzL^@F`iH-(t^=k(CB*cr7rcH+<kqH?^9<%MTHO
z1I4sQiuZ9?+)>MS#Ps-U@UyW$eAN}f3zpF+qgfMk4$uSehP(~#bTsn18QYzm<@oq^
zN<3J4f)YvUkwWV5MIk~#0&NpPT$69Q`auX3^h@XHyB-(|KSgDJdMfTJ5wW5yvY5#S
z_le3x01of;Do+Z2z01bZ`7|<zl@@lGOj^u@rR8EtPW12SIO7LCG}2&*Vch_<zoii+
zDu5e<yrS66!yW&4b#I3Rk$TYB0Ipu|CH!BJ5F_eIX3b`p<#9ccm8IR6Ti_sMZV%c?
zvBJ>V{dC-?MWY|+)$^=w2r#Q>q&#bub7qD8q*Jq_TrjX-=A%izOtCjWKFC^W>)pkT
z*N}X3wBM*#j)(*y`Y7V=`!Y=baWv#m!`p`?@mSn#x?>Og`#ON}i>%q@4oQjGp2ScJ
z4P1=+QBGg_|Gfl#-Gk{$7odfpzV+E}S_%gLi`C=kbLs%aKsH6f(@1>=MrQW$8HT};
zwM2f;aksk&%Ka=b+kFna_>Pb}F><>xMutk{<s;G%U<jG&w3dFsnoy5thbYw1nma)}
zqn?bty(%U1@VxA`h#ET!VE+__RCJnQ>lRKguoL(WjkeF?e$;~|)UPe;VluKab+u(>
zu6GB3G+dZMUpJTDD<{OM!~<}Sz{w9H`0(XU18XULPp`CS9kqAFZCp@kLS0AZPA+XG
z=j(`}{p|)dDSq><`p<ChWdeGLYs1&+L@axaZIw5)V_<elO0zqM!PKQM%Hr#VdAHDC
z%QU7C!r(6R9po1H7v7abgO@FU%jc?EPQa~wXY-RP=Wj25%Hj?Xl84@_|Mr4%zwliX
zf6dTH?;nxh=23s8mxz`+))M@6R=B%A=<g-HZ7Fq|zwJD&3;=b*#zw&M<oOED3{P(Q
zz`{sEAY?Nc&{+H^V-$_gFo<rjb)?}>bk^4`CEzx64-)I&5O6Gqs`#O<Tk}0?P>~WD
zfy)4ke!z6T=4~odV~xG13c;9E;<Yx?Fatrb+5K_ikDs1hEI`x<Ys<8jxqr#&foXwq
z(+k2Xgq4-NYyC6L;ER{U`R>0xrGc8^wPG6422`AqZ+kkbY-2464U0=v*E~p;rhCc<
zo1uTS0&z1Hvx#Je-@9uZ(1Zte=0;aNKr7PNV1t?E1eEFbcGUzN4jy@$i!7l%77+HA
zfv0X-5buvzuj5QyS$1Wov;4|NU@sTTPLMNLYYd+2rXhV$MZa@XxA-gA0l#);<O!?%
zXE7P<=!HK-*7%#?sfd`8^q0arDdYryd&2$}R7jydtf{RE+c6&@#0VVR*%Q7j_$#~|
zZ=Yr-z17S&#AfsmO|B^_iX~bK!|tuGy+2Yg>)F9pn2ikj4{hd1Ny~cQ3+Uzhzw`GO
z55oB}0$>Qc8?&Aj)JIL<>|Ue3(CL1ls?S7pXA<9t6p-*5_)sAiTDYZuBYgO?<fcJI
zFsrAd93>JMP7RYuIA_br;1DT7lgaO+G~T=Y1N;eUvdes(zE21}*Rya}Qrc1|kN$7L
zY$Rk=Bk^4)c7;(6LQ5w4QglXNTCO_M;#-5a4K)`yyTYGYo+2bS$Y1d?Y}X^^N_5OB
zCuBafyACW%>%SFXa-+1Z+7D~;Z+-`f$wITSloxm&U*#tL1pm<*DB1g@!PyLWsa~lZ
zEpRniGX-CmP?rBm8Q{?%dK<9vS3$u~$cqQJa^k%@q(*lBtmw6-oj{!WMWDmm{#XEA
zQu?df7qG<`@2nuE^B-yoPpTQF62A6aPZNkUo6}R&FzdF%rl|Q+>>ni^I<A92*#9N9
zQ(s;r0PCxSzwX54E~U-&d#FzW;5dWaU5)<7awwIWGm~h`q(f@ig-4_kWub!w6rpE0
zVf2L9=%_d)dBq7vz@$o;rQJj9mq9R`hFqX<v1MgmRm|ib7?<YtR|$#Q1mz7j<~cFB
zkJ<BS|De`q4Fb9C#JdD-ZWYr;9X)<h(C@qSM+`^<>XR$;j(;)wk#1c$IC=xCE++rk
zI&=HlYXfWoP=XkpmZYXF3Ajg>r^a?@1SGb|E_v<-MjD>t3$5i(3p6nZ<^S@X7?}5m
zJ$duwu}x`y+*y~pb3HCtglRiKAB~!Bgvikmv+)@FpM!z3H0ZC~xR%3Og7m*H_NPH#
z1Vj+FUZrk0UY_q8L#ltU<V&nacz|~zN**$#TkR$GRbujjqyNiyY+b7CsA*c-kLR{_
zJ53Guy*v9^IQuW)uZA|<ky5_FdZp!NXC6ELVvvsU^}E<FVV5LqfT>FDY~6)I6QBh9
zVn9>y3hIq0gXX4rD=dw?Ru^Ya!IetVVjkWjz<C4JiuLUQe&FRVhLJ<n>E87rd99o#
zf-L1T{bY@PHp#e>abIzivv5K|Xw@{9g4@l!mV<Eg2$MZ291Ol3W8@qB%V$CE<u&!B
zbV59j_rU?Rr++5I99~x9*<<ksWr0)TB81Lu7?F`OAZgj*AyQT4Sz-T24=dFGlEe0F
zeXq{9tr5J`#i_DZHSydyfcF<A+WIALHgUUXJ^6sRv^gguhwz_m+hf?b{(kC`pXPg;
zwgFCJ4hk4Xq;rlRHf%S(giHd~2*71oo6jvsIUtwNO-;l$84|LRgyjstlY9pKN2{Kf
zc-w!1nweRiB};#J-eM#GH-TDzk!0`5<oXs?w%Iv7-L!nnLyvKQIpT4UMobhEA=Ba*
z@j5I$L|3>-aK~0(*HG_DJj>yEh3>1eydEM=B3fU}r?7Caf$GMgiEA#1aqZE<$H3Af
zeTlf9qYE3U#Uo$<^Gbc!Kr$=je<mtMkvs=tuIbt+-vt|H<`a?}+Sq#$_}^b4aT<r`
z>OwMBpq82a_?53C#sw)gkrd>B;u>&i{5l0I*$Zjoioaf_FZ<$SM=6K!))pE+l?Il@
zpZu@BJqF}N<R67nBC~+qDe|V`KQ@H+5*n;>cMks1g#)9k-=>eOT<|AO!Y=d7cMzT|
zF|5$t<=y55jw)NjJ!*ia-r8ckG_Og9p>77@k^MVp<$1ikBH8}re8FA}OWoz;Fgb6G
zS!(QuNb<;J2?=H8-q656m|$cE(PWt9Z!VHWyE(4t*q<X!j^74Xn(vp0o{nyhNp?Dk
zZ$`WA*|kTPHJWXzIV}M)gh^t=D~Qb<sik?z?vc7cjZEhO6(%}DiGL$zoW90R#Nr?=
zN=jBkkYpeVLr?Ko*7Xgpx39k#M%m5JY4}`4)k8%??f&wHaz6OsXbms`8%n8-jm)w-
zO0hH3E4t7<jAe6G?JXJ`P0d^l5p#4y<+@$ky<E<2e}w?U`pn!ce1cPEkP;H4j#GJ8
zKf6Qtz5k-ylyEP*y#216q`7^ch&2DLsG-Es#JK(5%{suxudb}Rp5|mYmr=8$K|mez
zx~JQL`7b)GIVt*EY@?x>n540W?R#a|r~cyNKh2XA7e-Z--=q|v^}HA6MF#K!g}pJK
z5s)>F;Yns17lOvMjO`DGK&xLNPR$ktoz-l@=UpS#{t^M-BfXP3$(|iYJoS+^vFJ)%
z+psW`5_MEJIJvru<nKxEv!+t<T>lR1slmuApOG(t2L~g&puY6!v<CcDG?4kxw{My2
z7}0N$#)$!b3td)ufk(dH%&=vVLB6YFSH(5R?g%%8u0U-Ji8A~?13FBd8GB9uIw@Ad
zEwwP;p=pm74RS6wvzp^?vT|~tvAc`wQve-vxZ)&x<xh)T^Y59Fe$&5t=HJ`W`<wx(
z-^WNEypQ+lsQvKsW_A17U13}gf(%R&?FqHFM6L;{C+<>dBUX7D=d&-;ApBqIsY+Mj
zE89qg501%x=uGi~qOiJGjSSOC_72Pfj4(zN`!l@bSbX3%F+}A1Xy}SP7DvzdnvM-$
z`s`3^<^x^Ej1(mMnSD+7ngw+r7(EFD-}tR`yn3s`9>a7gCgi@;3YnlShzz<LI{=5s
zcJlf6V<fBA5c|c27l+o^v5)ed10OliMkhLjBZq5_zZ<iF0HG+3dtdUIFUvae%C(+V
zxq42gf+gt282Kz|2KH|FQNeViKg7GJH73%E(@G$9=lcvvnPG1!$<1l7?>z~W*x43+
zNwq0in&6#sEcd>|Ghe24^s8BaE$zB!U;j8G<u7yRVQz-9fCs^OM>IZ+t)t+YuRrG=
z%BV<V)6Q@~Nt)AN-7i1L(^=vF8Rh~#eTtN#9hp`5;w5D<kGfsT1O@3SfKs>ZFciXY
zX?{eCLJXlf^)s24MJTIXiyN;vANU3n5U{;<<V<t77Az|@;T>M0amxbJ?9jGF*tHbQ
zN8(nG=J8ArWvC=C*%%X|^s#0-tR&Sv7>R1q^_>wDr1VV;!7~@=zHG-z)?0_g!0JO=
zD3C|=ox0Kd87k60;#&W;dTT4V7OM5wJ2W*u$JswadGX4|#sZg4H{j(lD#AU<3O|$g
zr@N8?=%K2~7M+Cskw^{ZI3hAabtymLL6{rMvFzvC@E`hWd+$dFHwVGPUa?c{0H5ux
zeN)-*Ajy5f?`c3;WendYbn*S9#Xa}ni-`GgaelH_8y?mNlc;h4z#lzSF6mRZ4t~Bw
z!Nj;XR-OJDS)R?e=KoaWPKYHa9)&oHp;;2v(br$Q$J<c533`J|YgqA6K277dbEV>W
zG~d`9>YqLRvtk(NrTvECrTKx5Apbt@pKabZ&`q{*075_2D27H^1k6r#e!N!|b)3$2
z;9hg!!*-3(ti$;ME$R20?ABxl+Kp<b-5f15#hw0}6W+s=h$tx^MPbwhvrzAsoc?!c
zcBTH$rPcuh!ZqcM0LaM1!U2*_PPz~{@;~s6K%)a{Yzp%ie{Gt#9VdIqwus!IKg*!J
zPw&We@u+}7vvK6!1ciUIX2dY~VZ->|X}kL8=~tkFyYPs!ug4T?Xzm@2JpnVH;N1Hz
za(hCx;5))Ufsn|oBkIHOne{bcfwxM`1R`#uwMyTA%%7WWFlkL*j~q`^$bOB*wt3Gx
zZ7k1+JQ7aEqxw*2;FR&R!2@pjzDQ8@@r06qNB5HJ<TCZ8w}f!&6gbcD1rg$|gT*~T
zaCV?SgRE@Wcfo@L%P0C{2S74D_7s}h+1xFVliGP~22cg`Oh5KEHjo|~pxArJjr4<p
zBNM>pAoZw(m>)^uFP4;~X9I;;%3NJu{0y}vjh>j4Z(}SX0v@Am(KZ=|Xdr32qhLi|
zx|@;&o^6VKsgp)BUtWC1Yu85T`tJNfJZRYI1hKL1_e1!6+V(M$OS1tMF@b4MpaR>%
z=g~k<MT#X6S=%cD^BqqXeE(?5AgqG(PgZ1A4RXfLck#+o_UpGL)CcMp<o@DL5@w$L
z7Zc>~ouUl<-SRI7O2ufrN+9qAnbs%0l=;sau*jqm;=Z**r=?tUPg=DbOs~lOJ3e*g
z4+U%JeEV6wm}@tHg%xBvnjqT$tjDT?v%j|}q10~A-Zc<^gAwd+yY}=hZ{@9klaQUP
z-TD@R=(8nEEcTMJS{iR~Xu^Mg*LQ{29qeTRrXpS7h+b9Zc3eUY2R(qi+ME?|FyIs9
z#Y|>KH#*Oaw|`M=PdxYZV+6?frA|n^d?w8f3O}2Z?hPf{_IwfF{$!i(<@JCk(3@np
z5)^muZ+SVS7=`)HgUI6?7vz0Zi!~AD*k8Ot{2|wkp8)>TK)k(fYv5xSnMuGHHbxN~
zwd5wVR7hN#f0+e}BuK!o-ae3}r7`fKd(sV^!azR*<2~{(8ngpU7GmnG8|q=kLPC^!
zwZ^vb@jSE!i!31m4eQZ2WSZPA_6PHA5-?RGQNiLnJcRNC<l!U#6LP)xi3dOkMxenp
zTaK{mOE@~OQQ1y<9`m7W`w<=nOXT&asKK{NgdpapDX1(B1?qpl#n^V-p@wLCqec2m
zotar2ScFhZcME&L*`|1{{`Tlgv;QcPmyLIl_%yz$vVBj$>m^}PojTY<|Czi$`%gdn
zCJ`jE2exVd$fLpO6t_nMllwJ;Z;6oREkT&ep1Rn=Y-<|_nu~#>M{<uJz9TKF^*(wC
z?n~%<vKO5P802CmCZW*MBbk*04mJtyT<Sfcoa!KZ+Ff9P1l|h+ytKm%-baah6E%a4
z%n8$eqUDX~p{lS*vNt}+1GmuK0YaN;BEn4sO0?WCBS85+$J!5SLwx#_&Hc*1hs{A>
zfCDp;HuYGCNTru6e%3D>KYt+5JCc1yR_8_GmThSS+QP<6(Nv#PRZGRif!d&1@-RYe
ze@k#Y_tItwt~+*?+9+R@%z>FjTor#UOCXc#(R~fu@w#(CJpXyZo$}`#$B)w2?7osr
zz|PFZOIl4vQY<k$46EZX@g;lM;Pj^v_CizJF`3~k(0j_q#*TrLuo%@%Rx=iT!v~Uw
zuU)SMZ7ueGgTp)r5W7odHPw!G&hqaCS;47vWVCI2&{&AO!O!k@AgO%+3~F?3y>`6k
zqUKHz{+wT*gs>jsz&SvKvM+Y?QvE5~@=M`o>P=N=*k`jJ9B!!bIyrp4Q{&6KkzA82
z`&)tDPy3Vu>q3hqZi*dnG(T?9W1&jK`}OfbSj{9=;~%=c&&#n%f6ydx*x?|-CmyFJ
zF4fkUI;~zc{Z4tcMlt;j`_IL<2M-xb;)Zry;?Q>1RM3-nx?}ze29BHvBaCWpUwD5I
z`ly00oyQk2#Her0wk2_@5fU|0K^&?bLD_xoyz+YdII|zlXM>#lr&H&MUb^cS`@1j?
zXi8j`6h4HVG(nXysFGt^D`83YUaK_BXOZB%FaOKORr3J$kNn-C;EHi997duDxi5LG
zg1ICU-?K@lRJ0Zu!P#N)Irv}nULfwr^(ip@=cLK1>oGRKdr9Sg%So`6XSuZqDY-?y
zSCtbR`_m}}2@HC)ewJHsd=mT+hurQ78rj<>K%x3?0A!%Ke3spHtL7BFKYJtgZ%~;b
zTjv>MSCamT4b?~j)dp<E)4z5kZKeHq&tOfYyQ1HAyzhQse3&8yZM2qO&oQq7%wUZR
zDc0%&NPLE0gS}amq1XLIPA5x*iY?j?&>bofkI#$>{`Ad5X~6b|e9>(pY>fj2$*HT1
z3_ZPQc;*L6DQ&9-!BoCipWrEGlguaL?N}5ozEqQi#9>9*X7R2?joZTEsQ1)w?;yJp
zx=9in4n`_tW!uMgQm0~ME*t#ba=$m$Ru_}?aIyQgnG4XR&qi-KcO}~Khq)+yb_~tm
zCgPk<Q9JuNM~PpHT!<3yjy*wSP>}pLHZkf5VgFmJ`wvTB3Jlz)r}{38A7&18!akv~
zTqm1~o%<LFS{fI8VH}cF0G<aH13j3V#X1)U=xjpKEi){3{g$z3CMC1ocS7q9`TOI;
z@V7TTsX@x01*SgZdk#ikZeliyTXNa~ch0O$drP{6<#H%>%GmxPkM5xC#6lSldi%;s
zXlIu1lbr95`Qj-KWw&O$nDnW02_(KnWoCG4)52R`?5tnEaJDv;mUuRG7BU?y?eqW<
zGF+d3*sC|6H~v{{sTe1|GajzA!B{r%E*2nbYd~>g_B^}ks=T~b(l(VW7UYr!V87JD
zKe7aV@sS-DJpOyC_gW0XnQ@6`0Ky1}{6q`}W}n5%a<fCi@{)OI(aKe*u)&5x7RIL2
zR}o5`1^aqKlb_=sT4Vh8hDE=Wxs@zs6f8o-UOuFUI27!s@j=ZpZO@?mBjSso*#LZo
zXBCX&kr<r^KI7`Tih<TE+))wsmb_j;KhMVMnxYLxW!_lSJN-coCFFPO=5ow3V3mMB
z%ulx*;47c9oQr|oG<B|~+j#kLma+KT7WGf&)Dnnox$JYvTP3szgBS0?kGFN#4EzZN
zS5vNeg}*-iHXQ%U0(1(JdaZr;Wm)e>FQJqsNK=sCiyUj_PqKbAZ=O65`u*T-T-4fL
zM9#^DI{!U=w{#5@yToWDQeqe7+4J<eJ^g&6q~A%DqQ)P11w=a|JbR2i?b^B-sQx^^
zICD<ry3Ou7-CF)I{i~|1>D+9Jp4Tfg@Z+{xKg4tf<>G+mY30NK_a`mFhD#IQPr4>s
zqQu;FxUhG`w1vVoJ|av;F%^0Vjyvvh`l*flAdLZAx~Fi^DxI*Dnme0c6iG)ami-#K
z{$M8DCpYFV>&S{jfF0tTILGVt?mBz8H@>3*Jlocj9Z!pXfoER4SRWUP@1WEJMeW@0
z1f3m~tEH`K@EDl)qgYE?)p&<>lZ4{!b(`sO``CtA78R~$8iw4%FY)#pI~JeNqd0BN
zgW!01mE#fUTLt5FinzYQ9H6Hp1uWRbmq*qo+0%xVZ@|GPjGVk3c&^rM;+nYlXp*WN
zy=M8)=73450k{H7(fn2UupNujtVb*7DoZ3wJxjjQG-40^{pVM6dsnHA!`;=+2}fs!
zX9ATshr3e5Jvfg)luiBLh(r!QOm+(J*A5xL&xL@V{xY$&Z6u8QEpdfkqE~UNP;4BJ
z(gt`@@5a#Su(CFa)4u53M?fCYs*PSOea_DizgYqSjQgVyqJwzr)4d`}#7a`P&!W~O
ze*~}WLU?W>M=Hs_0!ffZR+>t%|LK~q`s;`J;|puYszH7%(;U~*Nm7kIwRC^6{7-@Z
zrY}MLqkpCkJR57nJ9JO}#pFdi8#J;7bNT*MmIGUpAOKh>LP^apLdKCE_8~j8b}Svw
zQbH`{M_Jl`st}!p%Zd(l>JA=vvXY56?jPRY$ug{xiq@m#TKm*wS30(2BEvk%@C*|$
zJep@UKGF(axo|9`?S;(?yx2O1p5O7D2Wf%e=`Smcwo;S@ocdSikM=`o>BHOwYtw`Z
z92(_Zfv3u{{fE2_yLOgfecGG7m~KCHTm8icL1?VvP-J6Kn1vb2GAh1w860ZFV%4aH
zy?1~x1GUm3l8J`iZ^j`{>^HekQ|jN}|5P<|n+=^b;qov0xoq83%roY49MV9(GYrzV
zB!Z>JvY4De;J&VQIW1SJn}tqs<t826pT9wiQ<U!&cXT|Uk$UFWJ!SwJ6xuyhLHU!4
ze1rupU13(R!~*1GD&hr&8|}J!DktT)-svl%Q^SuM)gkN`Y*F2<GgNB|n`!vRuEXCQ
z^C!RfMZLBvtXS(j_V3NQjh*RV2!a%u9f;w!Ij@Zd@i_4htA=9RG3@V_U7VWZ!|fPT
zmo}lkjXw;S|7gBR$gc~r>c#;u2c1n_uDhIK{2$)Oc9kvty+wV*CdXoMmt9^^A54ZC
zlTvMH5g*PHaynf21=`!Z;pWeD^qww}n4Y$}#^=Ld+7KODBr8mq_>U;|TS4|~U@UY}
zr|zhXj}Mb)COBy`98FzbI}QNFQdB~1Vv%_92vZP<qCDwV#(c%0y3lLouc42VviSCG
zm%sg+`P@zX3h#5c&`9l(4`<x5m!IBuI2iB_)z|<%)};l;XdNJZNpl+qrlGNDb4f8z
z=(%-y)3Q>2;jFD{@i#Id1zC{9gMe0<k_sTDc@=+6@Lx)k(68PgB)}K<uvKf0P51&r
z&Q(_7iwuc=xy0&799Kxdxc6*L7XZ19^uqDwXLwGFa}ysLv^Z16`&OAtsA_B+2Y0^j
z?B<wkDzn`pSdh%$=;5E#unfyaUUoaLt2%b$^MC5<e_O$2zzqP={}89)(gKH=H2%|-
z!krX2WjBH9W))u1O3%lHE8+AZeEu2g32UoI`EfT^kFvogB0k@(ZTtKgAQ@A)wqdu~
zk23}Qk$8W+e$n<6x4vuqiy`o(2l)RynAFOC8yuo%DlW3MJ2}P6Q(fg~3MAM8d~|_B
zaa!gAcfZYkQ8j|Ha|!<S6r!MGX<x8D>K0^g0p9we0(+nFv3D6884Xk4i%Z5sgV^D;
z3A9#?eW4iCOQ$BT*~I<6QCuzln~eaf(mhO#Ss@Eof*(YNY2*X_5oH|=jYY5pV{di)
zLNd_kl2^?5flGAST#7a8(1l7VuChS;sc-mT8D3i2`#orB>+U!>^^iQDk!vt(+AsEJ
zB?)`<Sn4P^S$27lmc<)>U{BLsAwa~_Y7^_p!U53Zn%IQ7dLC1|D22KC*{PWK(6POy
z`a0L<NoG+oFaBWLNOCQl3n?<%o&-tlYaR>!CbeRciR-vH^13Qd3;uw3Ha{0n(1&Zo
zMZp_Kr3pfJ004Y=^S6rrdT;6~qc?MSLJ4grHjeft1wg(0;NS2dLzj0^s!CTafkJ&D
zF5b>^m-ZcGcYOV<(_bu#ap*T4CbmV5hOsH=j=>x9^)XT=|0(D|c6u@A-?$b&1XN5~
zxMN>Jza^Ajjd78ll8lM9xo2X!9u^#zZTL+Uq^Tk?v$$U|QQQx<NyL%=V-b_@tnpA1
zHU2NBjl6b*HsOfwY|pfGxnY^jw5R2%QiuZLJ*QurNOUCJmQP|wKcV=c^TcDR7vV@L
zQKAs&iL^f0-FFq{V4(Q4zRw2#u?hLz;%Bu0KiA@Oblfn-C;9-~7bH^Y>KMQQU*EO~
zf{vx+H=sD8C4XbrGxQP^T++xTB0F_IO5HLh0SM>*F4MkFiK(e7>hqKH>;e`6^Gmcm
zT#Ze?KwhVxiu^+~iXyV2Q~F*pY3+>_C9fPcdEP4nwSE_+74At)AZ|j8Ur2HyUPB%q
zr)r+IM?)_ISMt836WdkEqb3xv3R;6eOJlT~D(ZeN76H~8;zV`$xuHakM(JbRD6X~7
zCx)NH9u4?SV;y_N@pK*7yl?-PLn-<?!cVhath2Zg$$R>8Di2b#9-JZ1x3#pdVg`ba
z1jN(f1+WE`j&7?jp<_$eisyBOE((8HYv8wF6&rjUJW7FLm%E_Okj%7(SP&h?NcWNT
zLoG~u-Y-(VfcZTKv+vd;hq~JKFFUf^cm`-qa~~~fB_a?v?yp@uyWB|o%olV$Ny8;*
z0nR|Gdd~qrjaAf~MS&aZ^2Y6{^VsXAjdUZLW3XZT+U<HuZ2H*_W2BVrFcnGjz>#wy
zAQk4;^5_HbO<wP--Ct^DAjNzkd((qWucf82nGpa1WAa~AccXt6RWBL=39G?SzK3?8
z1Sg`<FES!-YXk7RXhEYQz<s_anH2V3qP_7p-kBu2QSFXxWjFP=zugPd`6UBkODYt-
zzcBH_#3o3|$ogRj(7-lO9j|MRoV)MI-k&1b_wen%$Rf9U_r8D%n(&2b*_oF#!AN<L
zu|aCu-*pbktFLpV4;{GT1)5;Cw?dGI;<^c~YMIAm0tj(mSU2-(Czvs~TZy<=*8~Lw
z1b_QqAi#x(3dRSdB?02&GCv<Xie<=!Z~%<^ugu}7slq348)6Nx<WqVrXrQ_1kh1A~
zvcJC03qk^lutC=`hOFHj!kqIPbE;Z8nlF?rK(dz}y6?KF3S89iZ+S574p=u1W3vZK
z{^Y-j1p^tvDp-d<v>1$&4#~GrP#FJYzKN<|ZYflfpkqRaftjo=L0;D}UefnvzLwCg
zrZ3#eU{bO}t15<Z1-XDUOrqD%lpLRTRjjPEy}Z8df>JWt`VW2`>9kAiHIL=K7OP#O
zh%^(voUOBrcjpn4ksemdCxFkaoRy<P%xRhTJp2y3Me37Mqod@U5`wept7G10a<YVa
zh>}N5ya2*ILTX0GGGOpMv8`k<Fe$J4CB;KVNRXWosuO{Wt`{DmwgpKFT92Ns)4dz4
zpo}F4VGKq@e?~TIRhtd<=v)Dkxy+1_??h+u@ZmxJxy-j3djR<Mv0aD2+WLqfGgAl9
ztR7jS<wh87j|snvGV&%m!j}?XezHD7`}pJp@{}5GB==mQdWOH8uyle&f`=tah)mty
zO3`}BGgJ5ImgKV(ssfDD)rJbO332hL7-%3Pd_vN&5BXW%g8bYVuILUQq|I$gN>1Hv
zN$5T7)&rVNCF`yENf^6BgFDudXfLEDVzQ;ug$mLrGvo>CFkruA{*Tbhtbo=Gpc>t8
zf!O;PVE51$^l$rwG47Fwv2}_5q$-Kfv66k2zy8~#ka{>8L^Ca27_6jzT<|CXs=b^|
z&z&i6>_=wLeE)D^jB~Uep9U^p!HYjXDE@w?+zs_&#z<3HVd@O|uS58Hs39E{p-GT%
zb>70V)F2L^87P6Xa<cM*CO9UI;NJ_K-U+#dAs*-tmaW`#7NHvr+^c`(!h}PMdt%+x
zW9{_M|L)JDQNlCO5xj~PC5eLQIf8QYbBRd~9qh}$FvfO+US}zE9HQsYZ&3nG<JFuJ
zz!gcr4JKjc-*ONfbGe$42g69i4o{<eq&M-|B<?+eHG|%pIK^7nWOuNbBW&S;<i>9w
zF{yBrcawLtn(_;?z3udcX15z8xi8jXbC3JD8}H<hL({oV?#q~kLrjp>Z#w27MTHMn
zb;31YdL>(y3j>1CbccH{0X058^MAo72L9*pf=0sVSDdHicpFTR2>2)P*`ZHHXjh)!
zeF=gu+{kYl@OZ5!OfEWeIW=+q_ARC}OMIS-v)6|$YGeq?>07*cnNGldN6JhIjv<2m
zom+kTNuu34YHBM8l!wQJLI2@w`M*C5-G&qvCnYU*8`+$q<>cq!PDN=B!F3(&<8o}%
zz`a490EcTwSV=i>;6w7A_+NMyw}2zN%S?(1nXu1hz`CB6sMEq7@Xg7QkEwB>c7}e$
z(BbtC`-}^&vOg!x$?$(R?KgE*ox~OXux4Y!7~P<@O$Lg1cwTKYGB03g&kizc7Jr2~
ziIg6;&Rq0B8L}qfcOB#AV5@DasQ-=eZ)BY+A^p-IA;(zSZ4wJ13EltH5f^WxW|O22
z{R|mp#`Kj$fzzC&L3O)WjBFj!;k+(OCTto(gf0_!H+=q^aFc?HlDhS0N4uq&4+NOq
zcSZf0dIOE`FJJx1No^Y<XN@(n_0U%M9(cQQjb90~iur*(Vo^wc#&zLU42K=rVE+FC
zObfI0we^j?Mw;f3jQ%gAPKlKsAkG2l@0Obiv2xVxcz=6+`HRe?rx8K^-tN~f+L<3#
zllgbED60dky_!W>QP<+U`~B$WIVCl11Fsf-kob=umf+Xze~H1)rmB*>^u#B@_ij0Z
z**k3|DIrb<GNRQE^$UOwW7t3m*uPskxcY_0rsS5@cMMIv#rmTW!Lq;5JKoaLJ3#po
zNZ6N)8t8wT9`0&*nV%XL5qQ_j^|FJFrLnGxj0hJVl_!>go9*60Ouu`tn$fAtUO~~x
zxi1@g#^-;Oe|Y$9dv<2Dr=_|$Cpk9!zK^SuEkwJrtT;c%PAbxks~zeWz%_@HQL+fi
zs2{O{m=BIl&Z}tZop^&yL||zhHs+6Exr~Y14c<=nw^Zh*JbiTcmdmAcC(R6X)fFX$
zxOY+GPkCYmen+N=Hc+q%$!nX~Is1k@O?y$(F*5gKf5c;%%wR`dY0mSQNB2Ch+FKjz
zs>%rCoz4>q*ja*TGcgG*2WXqKa=3mwI3~5QrgL;2dA<BcMvn<ttpIYF$4-IquDas%
zry+i>5bdTwTVRh65BpA<9mMz-`!crDaY-oaS=hVW360MxZy9|3!^AIwn#`-d=8F8}
z=wQDae@<u2^ws4=x$$Op<V&E#Fx%)kMdUOLEY3RL4vEWp*)lYT&i(!U|GogW`!C0i
z6m;G!jI>qc#6R-Ae$Lv&;GnXs7#};*kG}c?Q{MpAisJ%(^BKkU-4m13NV#dnEe5^5
zA`5(BetvFt_Vvun^wiYE=<on|%x|hFNQ(;#fAG3~{*<Y{rs5ucHh2mHub!zfpd;cW
zG^~7*`?QW&oOAXGdYWEb-!m{g2CB<T$YzdM!LPjDL&49@A)!xBPK=EV5BB%<bk!E6
z#RT~{+gpNe6<G-ZE@lR}TWbS86OVReSlK&!Ka6^wSJBeZ-G|skVoy|Pt`7HCOhW;W
ze+~}~^!N63cXqTk)m2xN6&K{D#D+ical2$^Wvr(Gop4;c0PkBj<H796mq6#^w^Hup
z5|-MpWng*kn%Bc8Q1Erl9lfyfINF_nJ>BBU7BJZZeZAdX9qlbm4Rtk@<z*$HS;g~;
zorQt6`aT(PL2gzckhu*AWUh9k-vB68e+f$NJ78d8ciGcFEH1t9WmO$)GqK!ojqPc0
z*Y#i@Qn!GEE-J{$OifC7`Xt2P+x5~}YZLv0D)N#-yIJVTA>Lt0+|`coE5IpYY$2m#
z;SrJAr(s}jd)f0|Xlz<uab<lwn486#j<GL&77Tzwv3In!G}hNvSG+7OD#**ue@IP!
z79a7z+vUP(lfxQHa(hK#sXU|=e;qy(zmb%hiAzvYLH)4F=?ktt4<i#(vtN`|gN9*H
zWuISM`iQlqfOpCM?v9p*+NyFW?A)yMlxK0#aEr!mD0{WFI&Pq?swgEUzzw<&P$B5m
zj_@nMY2XlTqhes=5#A%Gu4i)Ue}aqm{fM}foZ{-1o)Kux&%;G_MAc&f+TJI}hWfkP
zTN-LAUzQZ+=VYcOKZ}cb5)m5oz~9To5u~pwvXY{LJRB^HG?)(bY6toatVf4^l@1zO
z*gCo0eh?m$lu_`qwzUV<uUf9pjt%y7G}l#@7UpH8Cnv@N&I9gvd$?Y8e{wiuWn!qS
zsU){Y1eM0z0uPzuQAKhE5au@$?*Nr5J`rhUDEKpuE?)P;<I-MKHur%Z15k2(_kIaE
z-=>gqYi&j8i`>k#=g<}x@hC9h&h1<7F4w?a-VoLa<Kt#$f!pwrIm<urnRu*PBO$ML
z$k@v6lAF){u%{`xrL}Enf8#7f{M$EB@UO-PyW1M7%3frrCB;WaJ__{r^LD>}<>J}X
zC(MrO>u4y;?-di=&B4M%PYqO%|0BPH{}z77>D<9XF`S?y1)d6W^9zYdE39cBhTBU(
zBOO+j?eA)-uLRYnXK_&xArJ1|zIEgJ6-Rs9Q&wh1hhT*?9yaEkf3Ws9DX7A4z=wgU
zZvZ#(%&?1FXs@D%p0TCvr5k=h(a&?s8gP=lOJJx9p7I~=fk%kbpFNFy^zfdKr|VV6
zb2e6{Mu)Z4mE@%*MEJR(Z=Dtuyhmg~@iul?rXErCq-W(3-m9ptYiM@%nom${MrmXJ
z&<L)J5La{@9_(rXe-F}Wi7^oCcfCDaE?)o(-q_%fmfC(<NfFpsj*@~5IpJ^`8h9K0
zZ<r<W4sv>y-5`1$!xIkIeFCGB^D7%#arHV>+hU-jp|Us!)L`!W-EzBn@$4y!V+J~!
zO7hT^W;Z7*69aU(kw9BK95_xvVzuM@7LZYQVv=oCjL=aJfAk%Wow@20nwVGJ)i;Q{
z;=sI*4EFc5)|J1=N{)>T4)DF{dilJqwVAQLj;4x&w1hCU!!t7gZKExya0=c`Onn2W
zAk0>BI%X~bahd(PrnXnTgJRN4K)XCV687Qk0(`IQX$G&dPs1PjdR~V`AoUNbDoTsP
zcR73+{Qn`Ke|a4cG2X?&E4&9xMcs)^Ep5fMkj^e3jib<I9#mnTMTg$M?cwZr_N3WS
zy#p!=QsRQZPY&-I>%I%91k{%8REV67+F>)>D>nlo64SC@lvY8XJrs6(OCz|+=cOk?
zq5DFipF3@N{D`imvOKuM1Eo?tPTsx&9P@~5IVZ2Me}cA&oy(o@q=MqI@~YZ~rq+(u
z<_7RmQBs(l68AXR-v`QGppDL0Ut4{@9Jtbei|m?Q^qA-yTBHUuCMGr)q0BDyaZEx=
zMs8slE`3Q#0LA88Zdagv&ivRB-2<wMGJ8aTY62Z4J|}PA04|}&iS=>vNGczK&Ux>^
zn7D+bf8?}`?A-j^?98;3=ZW#Lks$%To-UUi&YS>yyoNH^<H4ndfg0j{>*h7P=%H1=
zosy1;l?#?jIBI3*<m&0|=O6eeA}TH>>T!5zaNvFa+n%mhF4#dQ`w_i^8p?_?P~OT5
zo$Wx$3tto;ehjEX1uE$yB&~AD<g}xk?_K}<e}Tb|!Xh3=ggpuld=PN&4wS%La)4L-
zp@Zs55bYuYJW%Yw$7O9UdUVxe!Sjg$C8whnHfO=CkgLb7+upZ4-Q8TAuc8-OT`hHG
zMcKU|bS^e#Mp`PeZNRZ@1HNYM-vG{&2A091p$9LxN(T+i&s@B8`Kq&v>kW^a9yg%K
zf3ID+<m6y`%F^VhzU~1vB?VbYabf5>%eIRVR<pvp9_sG{Zq>srITX}%jI7*<XuqC;
z(J>QqtCKc%XKYScpRhDHGdX%hPg_$}NkL|>gs2cd4;MQN6G&WvBo<z0?cV?_T=yrC
z;NA`;c1)Z=BIUqg<Kw1g=9X3`PM$hxe{E%HVP<M_%t&ANpoYqRc^OG@5kWqPb&#}E
z!>KF2$ejEr5b<^-t=mpcNe5hcgv9sCDeO~HQP<Sc*45EIctBG_T~%ejf}AuQHxD-l
z8_O<6dRl7Ae`Ky}E_Nh(%y1Am)9{P#l~YnyQB~K_JfL+@3q-D_s-nD4URG+4f0!^7
zI~NBVGZP~n4HX5dg15GV2Q4|9w`?T=(tnJ*SUI?P`1l2cghj=~#l=KLgoT9!1t7vX
z*&)VvGSJgPag*=ZPD+A2&HiVGwD^G)LY<%^3&`8>3kV1b2@8veiXxE<3h?uS*jbsG
zb~4b>QZL6&gv;CU`?cm<KwEM)T0(1oE9tf!<P?-t)YLSzbaeFebab?|G&D5SRFo9t
zWIMKRBPAi(O1xz=c&^xpD7yccJ0ZT|cU<r|#|bhD5IGGEEiEk?IrWOzw}RM-h!A<>
nb(n|6Y6n4+A$d(0c#8i8h|||n(8%lB00000NkvXXu0mjfnnd8o

diff --git a/docs/articles/examples/vision/autoencoder/unnamed-chunk-7-1.png b/docs/articles/examples/vision/autoencoder/unnamed-chunk-7-1.png
index 76fc3242c989d2ce8166ca8e37e6d682794edeb0..f45a20d01404d48d8c2115d3e7958ff024d4c7e7 100644
GIT binary patch
delta 99727
zcmV*oKu5o)jRuR12Cz*7f4y~FTv-+^+!5Ths&FVM+$r4M-QC?afdC<d7zxB73UPOL
zcXv19uH8vmzq8MkDjIrTr}KX4yl-axG1Ez1Al!Xq%i3%IpbpSBFylJ7`2>dhx!Ra$
zE6a-fAf~8kZ08vgpPiAC5EBs+;N#(7WvrvBC?)hmUkHfFD5_}be;Hev=xM1kWW)sd
zJ{vG_&|m>UAt50_fx&|Y4&eJlL{3%L!qGc4Aw9orXw#U99o=p91@S&yZP|W)L|;fK
z=~}smB<2mtPYU(m>M2Y9t*`nYDE!ax|E2h!;r~nVKg0i*;(vz!FNHEdL&w0BYwzOe
z=WcJVugQ`T86c*he_?3t78sqDk{A~q9uny1?QCPJr_PiX9xwn~QbbftR0LTQj42>0
zCC^f0>lqv9YHO$}DauJnN|2wTM+Jq1MMOnKgoOlqTV53X2pjjH*o@qwvg*3wBgeKi
zl%#~Zm}*Glj|U(Og#YRN?;S93umJjoV&V)fBlOiV=|yQ#e|}D8+DviYGXn<;3X6(M
zO36w~ii-*h4jzcV-N1o^Fa{zF7|{1S|CfOYP|?&eFtN08bhWoMf^98~46m+l<?I`t
zm=G5e6&@NK=<jZCVSv}fAoNxxq-5n8^0HDAqCx@#e;6z*A)~0I&eqfA=xDRGG&R&!
zl;BfwQ6X$}e=%_h32|5mg9q`wY%vDg)X_IGwXnFfyrQaRXkC3tT7<Waz6$JJ!cK$1
zOuY>>P(WBrQbtaZrKxXW@9rO-kP#c?Zfm5eC`LarV6d>bv^-NqU0XwiDK9N9JeWFo
z(8(hxC?GI+FtG%_)jR$!ArD}xXlUyhnOWId7;!X|f8-^F2a3t7=~_5=1;@rjM@5E*
zg@y!qIawL8mE}YS3W%WB%1~C*Qd4HgNr?#!{$Y@in3Np+jkXR)Pv6kU*wj#$t&Tqx
z6Bn0|l$4T|mXZ(?5fb1xyf}+v=@y)jTUA+6URGLMR9KJ_;p=FM_b!-yuz&y<2~5xT
z?**eOf3Q@wI7a3+j_$re5pf}2juyIV@}k7&u#<%(WSMH(2By|#2HI*&S#d$IJ8?>h
z!O;WP4&a-<=<f|O0C^^|nvR~4sez7$vZ9Q*5Hh?f$JEx%KLQ~fAuP-nUH>|&@?wL8
z(3@9M*VZ@D*H%}Oml75DVUQqN3uqIn!tyb-f3$IMwzD$T*H&Z6OG+V|%gD;g$;lwg
zlcz2T31xj7@5uDhhT7`#;{5FN<oHl;M+-e{Leeu3COrc_%L|A}BY#5I*wWh0(Z$`{
zFEGU0iEG4Gk`cil;|oGWhNYozX6xeXYHOyafv%??1|Ykm(_aGJLE_l<f`D)Qs=qHt
ze*mQA6qw4Y8f+acH6=w^Nm0Q;Vsgsb##|@w;E>R;u<(e8$f!UMdvjei1@S>5QVPmi
zdL~x(R>r!TN^%l{==c>96_=8cS5#%|n_4@1_=WhnBVUlC!2*-Z%PS}-D$2{i$uGcX
zcu7?whk*FJA!C|qD+)4`qJw>0z>Q#lf1Qxs!8H);>lp}1DyV21TG+X{xx2f2czAev
zI&zIUYUp}`Pr>X12Z}4Ad&<ctEFsLt$r7(5xb?*(q-5k|Wk~-6@U7qVcLh;^xTLhK
zydttax|(Gq#YK?eWtFrH&?V{@7!(p39vKxAi*}Z!z6L{Lu$U}MQ_sxC*~{75e^ghU
zA%*vokO;h2B`rfsN3Wphl$a177i$w_4@E@=gUMvESn!R-ga!ESRVj5-m(bMG;gd$!
zSL7u}`nz)Blwc4iGk9>KV#q|oLIS-!17R7KmZ6oSS8$-8kC%s=v!lJGv91OS)*of}
zL6S-wb7#M(G@joFOB4Ae3JgVge{}Xpii?Q=zWK}ko`9D=c*&AdGP3f>4<_znWL*tC
zQ)_20U;m(x@W`0B#Be_sYeOxjq=1Bi8oC8seZ%P3kG??_HH~aMke^<hl^B5@M<*~#
zNl8grSw%&Kr6?y2D~k7%r8O-)qOxno%$zX1B0Dy~#e(OH5hpWRN)q6efA{hX=pZmU
zEXdcx*^X-l*OP)Yx^W1X5cz~MYDV_H(HUipWf@UEwg$*sgv$uoovF-XD#~MLAMnjz
z_V)zr-r`7G7)NOM{u3B1Cc{+Y7+Ko6din$eheyRFrbGw2+ZwZ3Qi769EkkP;zX)Vo
zXDfYm1yTIM2MtEwL{7ulf59(0qqLzCZDC($OFdOpRW$^4b#)b{f{di75TD^?*j%6Z
z!iI?prZiV%MtE4VdC?brgb*$vzh@w(sAX*D6`GKn8W$em;b>*7qp8YLAY6AU7La3G
zxQC~gHBD+NOAB)~)09Dm7nP7vK#snuGK--AKM3&6U-oy10mLbZe?whxc*SHGaA9*D
zTs{4Q!lUC;GU7r!?M-!*r3IyxIp&T&;Yqnk;oc4=YzE)^L#MZMNJ??Tgb~&G$zfi0
z#+n+M2wDhq2tY$t$2K6PyrpyHj8T<oLH2qIJRgp}lKZ-Z5DT~l#phHNq{Rez*qL&a
znF=Uy#AgX<_rZ#~e|G*!#f_5|OlmAj^tINJqv8&Awx$}2G?C@8Yx6B$>~{;i^iiBG
zi5+lswt_<?Fjz!No~4c=BCeg2o0nfuXk=`tuZxWdM^yox^txvD?g5cWk$x^*19b&n
z0R4lMnu$|zQqi#1;gz|GA@0@&YUGwzQ&(on%SaGAegI)0e>rU{ub3Qk)Q=sS8|!VY
zqkxy3fB;VUK<qCi4R#mpyW{P>!V>dRV}iXLEe$l8U`A140bZ77u)MaFXH-^I%k)uI
zS&<$VS~5S7k-dfvTT4S-RRx{+fN%Y-zboK3z+x$(dstOX9bd)BK7)lNWEGV)bo5Ou
zxOR@N9zOm-e?D&D#%n0aDQTk1-^nw8m*1r!rDp6Hn2<koOnq4vy0tAi_>RZdJm0<?
zi0>9b1-7|MaAN+D(L)Q8LR>Ah713=&q<h3+J4(V*?0d(H%B$&FB9A>WGSJh>%1B2Q
z4sLWG@TccwHB6m?67q+P8JeFE=xD4aMMeLb9335Pe{F3xTMOfxzwGY`+5jDnuAaVu
zp^-5P%^{8#rjJHFLlbi=8+#{LcV~O9xsfhgO$l<dmbQ*=-gNXu14U@|{&BfAEw#mI
zQNE6*Y=)vDcI+9vG#?S7BMU2P8ryn>CgxP;CZSuwL`xBNxg^@=vMA`1k(QE@Bm%EL
zd-W)Ef3R}$3h{M=h*6!@J1>j9J84w|8_%%hf}w>eVV>5I#z%&iQBYFT($&>Nzo>zs
zp%LJlzwGY`<^T&zE3S>LoxLN9sMsosQX)i3hM}aUsm;+hG%>T}+Hx(;j1BcTYz;O?
z-^kR0YwN%@(bH6tm*C5TNU9jx`Nd>aj;P5`e-8JwHBy(8g-9Mi-_`>lL>Cbimtbh<
zTcEHkCM`Auo!KZ-#737_P-L>`s7q9sEC<j)alERwk)^%6qm8+tj)sx~+y?ZiKv-Uq
zrDNvk8<E0G1=2}neM4gtGjmI>HQ-yn>+cHA02eoRPcI)|{{RmMOG7OsS<!)mAti(2
ze_kaOH4QCoj-G*$k)eTszP_FwJ{!>CU}9>Zt&ZM1j-yCOsu<Y%M4>pUGAAL}%}P&2
z3Ln@2`p~95cBYz+p@p4`caXQMou!eEI#WhY9vNL3{X|uCGsvM69Lz4zJ7)`(Kvl>e
znHlP8sewN)DuTZ8VDcI4q>3x3>s$6pf8Y!jk&tC5YiR2mp##j?&e7Qo@Qq*f_XU4I
zU{FX{L}YYqd?;jT)D^@BQr3}IU@(=GRn;^!+1fg~dgwYZF*Y*L*VE-_LpEMsN=%r~
zQI}BGxAuxiEgn{y857`SuFVvqL6`E7$1Wl!DJ=_Eu8zL3IoICW!bBejWegU%f6mw_
z`U79E-(0-}acGluKyeY_UKL-kyofB$BzXpTI-+&Vk`W;ck9<1=6LaLQLW~OdrZ4(?
zLo^^ZJ~25pJu@3eXr^qY1Ww5a!+}g(y66CgOuR07aaQIghI%@hYRdfXGzkeMJ+4Ps
za^cXTv`Alj6HR$xAsUo*1_}s^f8rc5T2Ja4T5KI%9c}DtD>0Q+)iv30y6AzcAuAy=
zxNmumsLshVl$eTeeZVm?xcA~1FaXlJYy&eJXIpavwu*wJ5Hh@^925C=hGtfF&K^F2
zp;3Tu_m;myqyw^Y@(PMe%d2WAQo{f$B_jx)Hn`ei(#Xq4FV5P*8u@sde<}<a2>f_w
zECxl6fGFP6#z0MmsDDCYfVLo&L{m{#Vlk2Pr+^lfB7+HWg&LS0JPKnywklIrLU>@`
zf;{aah!ckh7=7TM9ug3fRsfEHnIV<bBMh&lYh+>T<lz?_8J`OH2Cw(~MG2s+vU+G;
z!|>*on%wvR2NeJG!%+~De^OM{0k6sd1yN8G>E{`77RSOhC@#AqCqCHCQddc$pJ(VK
znhHxn7G2|m2Mk7g7*>M1mJT+%HM;USRQmm&0L3JdvWn<FP=m`rOlS~~Dq`i}<{J`~
zn2`_ohu{AHAE?Htsi_%)Ftj)`KGe&>jH7~c+A`phiIA5lS(M{af6lxRpHV5AgRF|K
zxg(0Pvy&qIU9FASENSrWP*5&T=7rGlK<s!$7B72GxZG-NUF0<)pAp3v{ZY7x@ShJr
ztAOK3xDTs~E)zq3Ru1AlDQaPvavk0M!191^_L9Fx)C1~<)>KuLl@t|ZrN&1F`+7Lp
zpx8}cM++`;IWkZde<Q2#mL~daRfeoMpW*3hH$y|u%+Ad(JU%kW$JNdPx$0`FDk{q8
zP)AnA)wzMx9j}P)XldeaU^^<Q>lm0Kn3$Lt8>5q6jV0eJULXs#qNJxND#{D``=)hi
zmJSBaP${c`gQ~^WH?wwh_YICr0DQyO{QaRB&{$Vf0bGcrf5e3NIN;nwK;_F8*;pHg
zR*-a)kdbF%rUkN!lDxE-An*Dm>hZGjM0y`yY=Do4tD`N~9EAXeI0oR*r4$q?5?(#B
zyEx9c35v@y)pZQbZLF;<%uI|7^f>5@R*;e4j~D15P?`b+e-H*hDH%DL{B=M05#nHZ
z93~)#QCpj>f2F0Vp`oq@v{FYmpCBNZM10#f{heVPpt-)LG%q75Iy53WE-5uLr=U0|
zH9jK1)5!)!%Vf1w3S4PbbX;npFiJ*JRFLO*;cFYScsK?o7S{F<CA+$~I6FHzIojLV
z+E{ZfEsS-wR1{^z1o%uyW!wZM<dxY5=C&@5c3cY+fB1o_aQ*ix<zZU_*8@WVfx%LS
zAV5c#t&aXZC<5Sr7?Uj_WK>aSa}118AfnIF)<kXvu0Pnjdi^h)n*<osSY4c%7~${c
z7Z#VEUs2cG+FV<X%OFlR7UaPTNi}MR5vMloP16ZcJp&>!W@TgV=;98w{6K#{A8$_&
zH+1kDe`@2%ig3%t#3gZ50O?pEDTcZpFr_@4?W};%)ema}dj{z6RsmpUu&M^SvJH$)
zEg;5FlosQ^7toKu6azWr(4ou87RvU9x>~9%?BSumaPk0r>v#QKp#w0!sk$IF%HPGx
zH83u_a`=RqOXf@&(@>t99O38gNKh6b?MjlUe~7Ax056UrE@^cCqmvzuc7612?OeP9
z!lE%rH!?gln3qezjvy5OX<C)Aj1t?3>+BQi=i$gT0Xi2Epz~kbxOf1LIy$luG&FGL
z7$N{it|{Sc@*bv{Tj&bmn4r7IA9*ciI2kR^f}qaa+5zyb-}QHexqwN{)p-fQu9h5Y
zf8V(L`nIlh`?sv<n%pw9C@m_;+XbgV6y@dRWM!dtB0;6l>ChZ5X}CSDEzRJhMn|f)
zv9*hDSbTaxer|SVT1rwvT!=Ttg(WitLPX|e04CLN%<VmcV?%wQ@~fsmGOqX?bsmEo
zfrFr<Z)|2|<KW`%O*6oFpniljQ_}!4e|XX8=;9h_E6a&W$}tEoIpABr>+cE+0n<iR
z=S2J1=rN5w;z~x%UVr4)xdWS)bdIYpNsA4|Tvbh$qJkVjh#|P?0#r^0SqCm@EqyZ^
zf?NyKTV-7f=YW`u;@Y9rmE|Z}$jwd&^|UkA!mJIFAVr)QKR{=Nx&hbKKPojkf6&9u
zL|a(~C_saHx82ZzID!#6Rtyb+&FkXf6A<d-%r#~!$@XH<kv~F`$u_oyu8Y`UPdig>
zC20W>964%n3<2NtMSpKt0hlqSIy21GL`6*7Ij*98`N7+tpWisSbH(iO!-_KE!+c$=
zjdfJ$g8Bf8=t5`X&?T*=Yhvx}f8}Chrl-M@6&)m_Vd@;5R9HK9OiT0d`nn-i71?n?
z=tNQKkM%;bsS<OO!o2NG*v$SYQ8cUC(!#>R+}zyEjBDrY4!Ku8Qg$DRGquf}{G!uK
zGNS#R&2?B3=;R>l&4B;Quk!yb;2l4?sWc_X(NIZ5-6kYwc<0(9cP<>-f3j@m_=eK-
zxG+CAJLs!oG87f$<>X|EFbZmDDwOXH-kGkcwX-)iJX1zwpp=S%4ODCDYLU%LiwpB|
z5<|W0P4JHI(<}ikH4Z)zDTQg#0j|L55fKp<hPDSmoSc<Jk(&Z;`G8$+ZDna;W@=&#
z3~S$znB=TNno3J|SJBBLf4WO6F4)7)n9Y=g2p^L!am(Pp{qFx&%m%d96~z15=`n?s
z&Aj4ETV}2~xNTMU)X}xY>CwSH*dbR{B&u}kn%I{{UKL+G9`E>GhL>Q$R*Ot6EXd2r
zN>5Er<n7D8)M;X0Mx~czB@j=c)g>(ry$_IaW2qoN&cMjn)ZCJ5e`DuhYi(t2YK&ri
zQ#+5Kn6&)LhEj@3i}N;+v7?hkB62DmGkf%0Fj?`3gaZWlOdJ^e+wcBg#Z<t^%B(0a
zE=N&_q3;lsT0V06(ypoF8Y}ZsBK?p*Y;LHd&O$D=s-}(}a-@l?H6%Pb$iv=@qbes#
zAN81%1BNGC@BAW?e-h(kW1=F$LeQJHGSq^8m%c<wai)&BOF(R9c`i_S?aVm1ego$V
zrhgflaqV4PAT0oJu(dKbf#ytfJA@?{)HF{Vj;XX7(mZ|@irW-qAXy|V&D790w|5Uf
z;exS_8bb;)2q?anQvgyB<KKSw|0>1<>Wfpt+$`Agg7Rz&f48vIl7<N_!z%LApos#f
z@6?qPq!pAk(G`oLH2;vuxa5r7qVlY`KvxS6x;SaWJ7dF3i$F!3ZDi%-6&w`c@9X2`
z>F#C^#|FglbX^q%>r%+_gNP<NAkG$MCWQOB+M05-*_s+^K#9a$4Oj1=NZhyS>+R|8
zWXCl(($m&Ze>Sl5iO#MXIeGrX#-apYYi&8g4}gCpoh+ifP8T~%V?BuWd5K=Yzy0q2
zRp69uRZd)hqlt#B0CLAIT?1n?O7hYZB5@=KFBxa<<W+Tyt(|?)wVIh<T3z2fw!N`5
zHPi)dwE?tqN48RnKt&wCaVm9Z#jCrJr88Acx^#w%f4QMa`9n%`Q{zH?-Ry|Gt+tki
z8pqTIg@tkH1<+a-9UkoO<?3KfR9kgz{S%6ZPg<~Pep_vJn3KK&?yh3PLZOpI0-~^9
zIY#Ej1k*!?AaRnUAgO=*-T$ko0~Du4c-tDP$qa&CNi#e5fXEPkFBf|&Qv+>+ae@4K
zIW;{?e{^^!=a$wqj+!uS_JZY|E#;|!b{zR$hS$YC^!zCuoUGTS)9-XdPLVBskWkjM
z_6$!cs;ex@PKgQibGHYApNXNquC|G-dtgjjLDlfu%HrI#gs2cd4@YY=s1iwQ+Xkjq
zkDI&x$oko%O5;3?8R%eVX=36HeXS6zYIH@hf6)QL0>USza6+*V@Nd8Se-#w~D4$#D
zDN79$mXuf0;22rh;-onn?RB!_NLWx#-N?=-BDJWtxpm5{g{wC0I<$6XOG&J|5d#~Z
zV`A+@cGF1<qiroJiHq*)YN}A5Q&OU{u=JXsDSJfQY+6afkg|fT<d`sjPd9Xx1G&?{
zf7&f5E~~75%(MwD^;Jb#Nl}4laY0r=R!q}2IIDi@ihbAiFP~hS=4Z|#yQ{oHc#b6P
z?lBn<lgNYDC`C{>{_C423jisRzK&+vEQtYwg+wK!p%_CqnGsFJ$orPnG;;|_E*jQ4
zt!wGJ9fwX|xp!=BM?+?SrBXkJ$DBQgf5fEm9+Tt~Mi8KybkK1^v`Au#W^`tG)3EB&
z{LGZNh#-F-Pgf^<8%r~%fY_|crncG3yQhyIF$CBGzQ}>(s3}ScsM&_)x6Ivg_W8N3
zb4TWeaFr?fmV}%H6guK$5gkEDQVcRO2_^Wy{&>tt=p`yKfDe2R%4k`7#pKquf6iXI
ze%F!nw;sR!_1>Z7ZKY9ms>tw){D$Ytp7x_SA}9eZ875rFv1{QRyQHe2y<aSH)EaB6
zN(-{n5@RAl1AV>Rob0W=A~VWIOzK{<XTy@&6Ps%aQzQJ{Y)y4kP$;2d6HzjL>A}0d
z-8)SDGid%#WlnsMTkmAiAWQ;~e^p?RJi-5NF$|1YK(LpCxvnY>vdIbxtz;odDOuq5
zsN|yh@zcAPZP<1A%(b55>pB~=0@04AoM#jQ7(=N{2KTl1=EKt<vOP8~K5n49qQ;OB
zQ9v8jJ3KkRro1>WD;1eNIKT&;_VzYbX3l}}xiu|Q=B-^cvwh@{!nA0Ae;|OfmF2|;
ztJy^sw=Uj$<I~N3i(88$Y{Bq47EooX=xcbI1VB_Apycr1?%V|fyqxGDn|wivOsK%B
zX+ZH^RucQ(*ab^1sv9|e>YPQZH}5{YZdOZath+H|0G!@Lk;W7S0q`YqgbYG|8%so1
zS=-pg)h|2%d_jFJWjXLCe?0=DQ}VLXlH#Jmg8V$)TpaDJ;f}`*F+*A=cTH<;sxC;2
z4s^q5BV`3CVNHj~;<590UV3|Z=lszHVb&_x@Hihp$C)(f3O(-EP*zd<U*--MrX8)Y
zsDu#DDI=OFOw4%IZZQVi!~t#0ys{yUquU6tzJl=TX@3nU_rSQvf41*Uxu+p2uZCrn
zlA=R=@upV=feTlkpvZ*SNMv<ycjVbwTbi2~A+O%d5&4q^H6z+vhLz`~L<PFrTAGk@
zE!s{|MWeg6o_%(9%be!iV06brA_z)DWtq@zufuP6V1HoC<2)23e*xd@C4Y~w1JJD2
z!6K5fxNd1|X2G?Ef0jAm)M9x%@R`j)4k;z8psc1>9M9L8rVpKIM$lsiq&dNU&U&id
zH76}TBGA(bc?F=l1U<RI0lwIuhny^m<#gDZYWkMWzLCj!6%ExznTg?Wzhh26u&s2R
zqY7JQZ#aIRx^alquRoMzVgiVg3=P`LlVz&1F<Sue?cVZte+Ux*En`F;+Rz=1TV%r`
z1KjPvnG)e8<O4z>bC*+CmK7W5f=^VMdZnqYODq`+VkHUvB?UV^yP_yNIR-Ypg(cV8
z*3QAv*~JlE>lUU)2D(Hyv$7IX)6mMvCp10-BDo+hC$6y$&gm&J6%`CzqYIm7tUh@2
zz=|n#IGiJMfBtF3>9`ihU*)5H7m#r=w{`Ui0eri+{2c<<C!u3W3X<EDw2-TpUr<<7
zTvA$Ad<d?ii_u}2m93)-S`)!xQAA!{PLxuZkw<3*#{l=LU_l~=v`9ZHg0$GxkkY*L
z`0xNvM+bX58*8qWCEn;9Z7rgK9U3RG5RZdLKxAT6f3S}$CWI&}D=R4}u~;l)_vnJA
z>C5+A+O@d7CfUb~Nek!oBY8;6Xvru;QYauQ1@H}D^Y;hLK&H!C!oXRvuy+p#je>l9
zVOe!vVu-tyF7lA+&S{RGA==eeHg=ZA<OM;}t0kor$@@Ck8*NT3ir3fjL=SyI?bs1@
zm4%r}e^J43soP+;P)kFNpu*!s3yS{5sb>5@FBiCv)LGEX#lUQ7GtZd(#>tDfo!&BU
zTxEin36n1G;<SY6pA3&_`GW;yRP-!e17gx~AIZ0T&)*%0R#vLhSVUGu$JEX}Fe)J}
zyP&LkSktH>dGP_DEiiy~jO7$?iI$(&Oz$U>e>@jQH(&NOytIayTX;rA^VA8W8fr>%
zQes0fBhG-Ws;EG#kwHZs3i9Hzq#_GW&?_lO5mZ~2ikhaSS4@6=`@9WDsNez_UJKmM
zr2OOvALsz=P3RyxsHN2m?R=urOB(^-?k#_Zz$q-6OGB`r0-{oLiYkUSwzN*_7+swe
ze+6nHl>G+uM(=M1vRAeq!Eu>zL71?a68(tL%CN1xWAp1K%<Y^wwrOZtemX(%(N>lf
z?%NY106lN$0x{%hsxsvyp$m?zz3N!|#OBqFpR;=Jis?;-QLcuHJe8Eb;yg64N>6fe
zLmS}#=6n2q4ug2gzQ{`A3@u|jPh`3Lf8z3*VZ%p`ozPH{5(2`)d|k)`NTnoVmt(Cg
z(qv)^>Ux$=-l6f(m`?5Z0Rt)7VEi4x@NxOW+PXU1M>kd>XFVdo-QGf9gDK6UU(z5e
ztwuarK8_$*YFZqwM|f&!<Am8OI!6&vCC#_Vj0^T46Opt+rVuG%E2ClJ6qt}Ve`GY^
z+rRDa2!nb-lBI3t0E$+H#buQ>we`cBE3;$$?2S}$8zrAI7Etcs=$Dd_Q(!3Ie2uW+
zU~zdB9jMEP_&CEJhC7~kp08V$H2j3Fj<%6?Ww|L)0iaEZLPp6x4bQ;em1Ak>8e2J7
znUE@)bfcY>YhY|vS^c<<QPrfvf03xXy1tc*Ut~(8KdH(@hwsD%f|jS8rip`JOlH~t
zj(WTu2a>(a0@6T_^^Zy|EG{W6tEjB18B&l;tHG1fLX<R>AZ~#JW|W31*#Id)Ib%*-
z6R6gRP(BqO!;UsQlwFEIMGeLBddRBc1aY_5)gUCPsH%hP?m_C73K51^e^%Gl1C_)0
z%;F&<Dzjt!><v|=i0Yg}@9LZoPSG=gB8;=obb1)@Z@u&H0+ivgbp&PA^to>6uq-J>
zPH#U>(A59{AOJ~3K~ymUUucBwPCQ2RsY(!Mu7QaG2SiC^#Dyi~i2R5xIQ+PymYP$*
zlOv`LKMlxT$q~M|V*^MUfBncHK#3g!x<F3{+>aqliJIu><7hl4IUAyOCmgkl$SUiY
z+PVcq(YXgA5sB6&R=0!79J<P|6Y@W=p181y{sUoQc?}~Q&(MUNit^IpLTH9aj|p_M
zG1MULOM)FsG$;3#N-*SL1p-M*;6#tg+)<7IPmY*y?vUZ9j;Sxpe~b-s!(4opyrc+^
zX-@-384z~|dXPB-I=5(QW#{ba7aSfN><JYn^brwMAQyvX+Ed4aK8Fp25_Bw~C#+rl
zA`-I!|JFPIE<hQc@Y58vOza^NuBj|7%F9YmPKXKev^U3$N_xl$VRdl{33(;Be(hbc
z0v_7oauS%p3Z0c|e@YA@S3i&tu;Am=(uSWhy0$1a!p9!165#ZS3-y(@AQSt35y`Ze
zBB^b`F*Gr^w6=3_cH<QqXlf-nQ$d)ZUZKTlPR`C?$f;-p6)-3|8SpQ?^6v;_LQ{;&
zb{E%HmlZ-`JPgiQ1ANBvRtKIqAt@!zP~#YL9X$gg0<n9Be_lSNmqV!9LD_pC;a>5{
z5!2z^<dz}%iNS7`BpXgdu>bgw=2j4mIJBsVv9U2^n@vp(bVw!-#RuUvRSAmAveY?7
z7Is{7d><(@)Iik3%{K%~|NJ{I`+EZ2UaG{gbP0^hDr*>0mY)grGk1GSBW>hy3-Oog
zB_y$lmDq;Ff9{C}+B#gp(j=u#jFupS;z(~fV%qSNnyYeR{2Yv-<N|7^{gv|i)?Mgc
zDx!>|t_sq$Qex2JLrdAyj9&V0oNPfJ4V^3+(1-8j?u)C@-~2s)cc62wD*9aaki^`o
zrrPqnG~C44ce)3ygr%knt&TBi`Dro1o|t0KCo)N*e=m!kHvGh<@{9;iE~lRv1~dpY
zrKEgbaKWSOK~6_rN{kmT@N;{J{#iKu^~`Kt{XAW;W|J1&5vEWJ^agyh_xn8p3%84j
z5dyL(j5oz$d__@KGK`F{<*C8by~l*%(TNT!g1+HNIb}JC;XaP$92J?qgJ%W_ieM5S
zkX*3_e}*Va&(<e4r+U=1F+&TIf?UkC_(U%UVzE=c9w<_)Ivd=79|toXCCtcIg&t{K
zwpCGLDv+!iX=!q_jycG4sxf551*y(^S6>eod#;%wM++p|3{5Sq?QwI)KX_dOsE$t_
z2GR+t|A>LT@z+n#LSbZ1_0K=*FS4dz3J{l2e<0|mk`m&I8c+cbOUO!z3lH>kwlPO`
zS7FFXiVWsy(85duY<LYrYuCV-w8EOgv?zZk3yw0_UAXtCDVj_Tf)uLAv6iPYDB~N;
zGoyX&jn%2;g5`0<B#0g;QXU5#_2$n0(HW&VNs$3=wx%3VTGr6i)M9IE>mYD&$26!C
ze`{)h1|tV+meN&blIG>?<L+c<g`5FR6|!<{`OhRxDFr4$KKVYPb9iv?W$>r9(aV(m
z7?k$}De44)lQ0JPLjdIXEZX#6XLt>Onleif`(tv-+D2Sw@8Fp5Kp!^;E;{FVQzPie
z3=4`Q!$V#^Bq6)JzAP&aD)UMb^iHKg8|PfjfKdbdc6}=s|7f&YMpWk~g}Phot4Pwz
zlQAe7f3@J+;F7L|xjD2_V%=2~B`6c(C<F_X>*VF;XoCfPlqm_tf55=$75>nm%1Ky=
zYjFP{MY?+7&(HH&T$Bi!fc}pg0Ps)2eWZe9Qv3gg;c=rlX3)z^ONdBgx7Wel&(jqy
zMHG%JW6z7f1P5$oEIoumKW_;AnrreBLtHI6f6RVn2MMB}PZLDO?1^qiLp^hQkHDzp
zoYJ9W=7q%*N`zGcjW2wBFYMKaCgh|i#S(%{gi4O48ouYD^zZBA?d|31;qK<@XlHF<
zYJ?d&in6#zhfMrA5?4HgjX<~4)Y8@geFfcrVt4Wd&_JFnG1r6Km^?8yKH%{$y}{ea
zfAk}ylqBUl%gBJz6X7o777$T#dI+`Wzs~UZ%mcp%v?_&&GAMeX=oNw&tiq>T%D3eS
z!?VpC{bDjo>&Fc%P7QOn;xI(|&Jo48EO2VQJltFyp+yG8{lQTQ>3JntalsxoMw$vd
zp*mUxiIyT2B?ex`q^Bgt$3%t&2l!&ue`-hzB6r==!6P6%J`T^uiwF-3fTBH2#-h9O
zK<XNcGSVVAESQj^s`}+*OX3d3@;%UY3BZ%vh_+radQ<iWWo8jDsVJYaFL6`|^F2c;
z^^^CgB8Yy1`$DmTiQWW)z1)xD=NALZI6GM3NlxMdA`-H2BkLOKbJ!ZHN=yYle>He2
zzelyKD{7m&1jgr9jhHy1JR{PRtD`8?x5|P!wIGWU8WiB??dk67<nA315*e42o{c<0
zCv#m@Szb|&KS4_*59kCA7GeVv2SZD)t~yhS#U}f!^7C?Yva>SM)8ayXU94d?9_=mC
z;hdwbCBEXB41RpE-6_E?IRzTDe|yAtBx6JKf%Km7@lAj{!ug-Ugmx*?>Y(tdtfa(Z
z;uawML*S=~lNvGoq`JNsfdEfOD<f?cdGWzmb&}|$VUW#Jv|ufjvc>rrD9Y(rx`iYc
z){dDzx+W*ui>sp`(03vzHBcfUJ~k>M3_4!@14F_hqGRKeB0&67izUTNf6-$x90$+f
z*bpxV3(WH;8J;2W35iL`DQW2$nem}MP8NFV3Ze{j;2^iJWLRD8(3<M1%8IgV=%6vx
zX7Rdq=p)1d6tdA3Pl-ExF|VF<=E>=*q;w)^C&G6mOivugy+H17diDzqN#5hsGjz8P
zq28yaLA0MK6OzARdtyYGf3ToHKk~g_ju-$=cdIFg4Wtx;c@r6Vo0xKGsYTD=Sb2n}
zK}mdkU4ER8wT}Ftzf8TwXJ@9TCMCp0N5a_3a8MTn8YhbV6~uYJ1;yjw5&h^~eQaV%
zdRA^>NqKgBFwDA<6(Bmgi|Z$hZyh^kbj!%*rpoNNKqoUT1tD5Oe}@+5f$17>#nWTw
z@V@*)!=v9`h};+|swyOiPJUULUfr13*5cH-A8O!_3?GQQo9`J~C7jT1*JR@<y_y>8
zYHBK!?djogeTy1j4y;}s6X<SZ*lRBDU;pkr!|PgmMP*esPnnO0J=y5U^~b3tx$d~;
zla-N{oERS)9mzkXe^|Kp!@WBqpzdPKb@mC1PDsrzD6Os=4l$q|6k%o9R$j4rwd3c`
znKg4p$MmUFCN-6(hr5|;%J9A+<ytz~TA3Q+5~3`Y)9g1{^q)Y<{s@VYi39@3Prz;b
zQ1z7|9WlNGopJ!-0GFXgbr7KMZTcC~>`X$kUq=^OjHwyTe}wJn{?$Q!Pu4F7*+S)L
zp|7qe@t@8RYK|y_7KXKO3r#6$Y@4%k&V*rw@&0!DO!_*)BSz5LRpwYY`9`MXR}F6%
zHngU)yreK6q*0RK;1Be6wYM_S(^96PZDQjV5S5%uNMoR=U73v?xQK+z+``hzAwUVL
zD@+b`wa``&e^k)6bO#NGwzdfq#*c4p9XGD7Ffqu<L|vLktrV43fFvz$@zK}OLcS`-
z7vJ$@W=WK6x;8Meu(Y(Yvf^^N)>fA0W+uka3J!)Mh8<}WNd_v<z=1(HM85$A9Cd<W
zIph=MOdu0eGYe3Bv3GQKb#ZnA0<xVgPw5o~HA9%&f6tWdFGdi+%LzF}YW;FxXwcI=
z$YDOl#*P6A`L$y^muy}-vvpWuQm~5|dI5SIAjOlSlU!`byz*hACbqSXX&KR2Ut3dI
zR$P#a3x5$I0X`ls4mJc(aN*?P9}2n)&@CMs7U1b@<Lng}5ucV@QZsZ|<A|0q<0ecT
zQIQqne`9B$BEce#l9KxR`i6$a#^J-8&?m%UKqVPPC1n+r!Bxc#8bIQu2CW!>SsXu*
zVjD3*7}Ltm#na2%$Jfu_KOiu`k5K#|C0v~x`GEfDgeSkay22=24^J8%?!;FRpF6v_
zxVXBZ&(U}F@e2qH4haj7BqEj2&=3^Lczd`)e|wP`QT-Mpt@L$<cLz9Ho9b(!$hjX0
za2hxhu4`iD;NW17)}fuPtxt4T<;W@X*X`Z7c*gkp()1`FPd67QtZD${drzN$kch<0
z!m5T*Z5>^6W_C=SIAPrAkxlir)#W7xx#-dd;lr3HYG8GEINanJ+4)6f#rZjz=vat}
ze~2U7cE+_%Xqz}`%GBu{u;qhY&Dqd#hbsjIg~cUh<pfzMjL7E?pqjK{R<fD~o5LGy
z57W{?DDBH~^;l39&Ug)Y0q%Yw(Xnyy$lXj$OHWHh5eE(_qobmtA|oRsXrMo!zlx5I
zj){$pi;G7NDvgAA_!1O`!pG6zW}$D0e?CVZY-)N&R(5V)ejyRAWM{#fh>i#e^aE)C
zeJy2q34#BxKmRuHZUdJWXS@2Q-e}-yzMwY|7~t>c3po?;?Aqt7+;M!*h9$Gwn`;VE
z5>Tv-o7=toL!;tTGV@ES>qoXunK^Iinw85I&F`AkIc@TUu`NyYL#xV)ii&{Ze}kzo
znCnCQMM+613HYFLXhZXu36neKAb<pCR~KygC~sSRdiq^_VsctWc5XpYX+}(d3$EZ1
z>ea~X45C3&gBUXhtt0wo=!^WrV!ogrkM2IKjt5<L!29tHjZMwXhvimLRWo!53Mk6U
zN=x9MUx-k^kHSLyOQBz>tgO7Ef1;8`MFo6KNip?1!xQj8Rdvmfp|!*68ycHNG!1WT
zsINys3rtzZ{iJ~&4!}y4>St8Xmjhop`2NEga)^X_Rat0=Drgv3y9Pv4`!p;hsjzO`
z%%xinUpl#O%c^-D6GjZFC@ILnVl}ZTS%not8%K?w+&Oo_vNapG@7cL+f7ANa%a<(Z
zp4BnAZS2V5bv49H40L>>D;nL==#oaqLF2HY)fJ@`)kEutH;)=OVbZi2bLK5twrbtl
zHLF*ySiWrOl1{Yc6Va`L2N_x0JG(<uPb4_~Jn>9=gfI;iRdr&Vl9{cWe?($d@!#j%
zL8KypE)BTm^eh}agJM$if6CE6eMHOXapT)2wzr|MV%(U~qehKt!5GOyOUtNHqeqV(
zGj{B_ajoOWPngg~0lboNC>X&XqtD3)+S=MDPMSPr>a^({oik^2c1)i(b;{()6UH<*
z46Q85&q@gMb~M*jk?n_${N(^iU`_0s62sp9Tvw%md)@-$vdf7(e>x=*1`AGIxMAPf
zJ6BF0+PPu*yv~W^T87tEmlfuu=a&s_8arvmyrrwwZQ8zj|KSs7P8>bBcjwkk>sBpY
zFn7k(cJ$=PF%u?DpVhT++0vy;mn>Pbc=6)ynN!<FH`P^_R1F(3rhVG%`O8+WS-)xP
zj@|nX?%%U(`_@exf7Y$;nlz#!Bg)%GS08KNSy*xHvBG6!TvCLe3k*3H!&Ez2MV2c1
zy`TY)p@0H!IP#I>!9`0qy=X{tJK*oyeSna-3<$3onA!vNym-in2@|JGpD}yxyoE~^
zFIu=@UU%1AIFshgrZ5X*HXKu3UETBM&7Z$u;lf3WX)Hote>ESztP6dNKA$&#0X(p1
z@scG=mn~nha@FcJXsNG)wZ3fW{5c(yT1O49tIA6X^RhM4kQe&v3;FLuu@}Jde-Z5v
zQzUb$TShe0)l`%e=4MtjP3&5|>*V#P_itQ0d1%+>)l25hnl^Fl$c7>1bt5NC?_Rck
z$HAk=Po2GRfBD+Y+c&OUICK2){@vR*ty#Ws?u;pIW80zCdhM3ohYlV*aNxlH{rmQ9
zU9))3lyOZ%N`^GGPMr&aD@Tr<ICbXSg-cg1oIQE;;J#hkHlZzFo1YNm3<Vi|e0S?(
zZ_7V~$FU@vF0^Q-9kg_+ATQ6r!~w>OH?(#x0sKw7e-9ieEFs5&Q3}@X!Esq-!^TYM
zTCjN8%C#FdZ{M|Z$M$Vow`|_Dk;VoB>(_7CuyNC-&0Dr?-L`G}_8mKS?%K@*^>tg&
z$HeFG0D5B2-o5+wA2@LE(BVS|5A5H!ckiCvTh^^uG`Dk7>&Tisbk<pDGkDC^uK-Rr
zkX083e@n5CP&^H3O$+zPtg2BRQ`*OkYN{VnSyDfK*0OC!E<bqx>PgSd%V&=t+_QDv
z@`ZDzPaNAkzGL2sE&EPfzT5NQ;iD%{pTBtZ?9shj*DfOS?c1?w?Xm@E5zm~zV&ksE
zXRh44d-u+r+qZAsx_S2C)|FjTM%NYBjh@=Qe|r0o3-@~NqpyGb<mt1AcW+!ick;;o
zU8}n$HkGAEcv@izGbqP^Ni6JKym*OTikFA=@bD08F|-hXwu2(j)RN)tb64ZH|Mv`!
zLR=9kMLf^UDIg}Zw4t?Q;o42xcI`cQ^yJwK7tWtMd*<}1lPCCboWO~bCr_O|edg@h
ze{<*0U$}Vj(q#%)E?>GxeBtRc@G*Q24_rczT)BGn+O_L9Zrr?a{o2(l@Z_n(d$z7!
zHh<RCma6O+A6q@vfAYb<445t<$3GybFJyIW{gcZ^PG7dDd*(EBR5#X+=~%S!z`47x
ze*666+ZRvn-?@JI^pU;rPn<br?($9he@<O{@aEUwD183#=EdWl+t)6gMyB704u_eG
z)@(m??#83Hzy12lFF*hM`SYhwPp%!`v10awhT{4OvsdmqdGiJR)xUjw`|QE3E9Xue
z+_bo34D|DXG6wQ+C4~nF1Jw?5?<v+L&9OA%;3}Ws4}#({3^i!N^NG!`YwKQje*nM#
zzioI?8K#CV_>vK6#lu=>EZMmK$jP%8uid)$@Y&NRj~_jJ_~3pIf%^#X|Mw7jVA;aD
zMGN=Y^A|5(z9R7Y_3KwJUp&WO`sguQ*6{Ij^uUXkFJHpPZ{ECp`}W=2H|Q~VuIKuN
z69;x|Sh--zh_cjRCnM$mD~8AOe_dqd6^V=yp7M^X?-b=gkfa$5l;_y^CBjhld2?rV
zPMw6}@~Pb`w;noo>*<g0-@JVO_+ihTo0rZU-M4+?s>R*QHtaln?)rmQA3y*6^Dn>t
z`rGfn|MKzu>t~O8ZePCux5ApG-7^+~;cq^E|Hp5?eE#_3`?s%O-n)2of9IM79pma-
zI~Q-*f9BRR`08KK;`#lLPw$>TybXrmxNura74qwOG#fh3q_uTvLMF^W#{6CCk~VV*
zNkKclYt3H37Z{$Up=V~}jN7{>%v!eP(D5@DuH3l$;K>VQ7_cBRA>c6v@!t<gqk_p_
zQszVk1<*!)_MG?_Rs(wCe+}`#`}ZFZ-lNChxkq=do;$W5S$^i&A=#1c=IZ_E`F}ZZ
z>Jd{~V5Bq(>KL+;q5?F*j!4<V3RC3R_{8Pcw$7S83pt$Nads_Rzw_|f>-W(w@bt-}
zhxdE#UcY$e*nwT>PTH`2|MBzJd!D_0|Km@{_{j3VeEjg{<<mz!e|N5&IkIQVnkC(x
z3s!GChz$Skk6%9h`1aMa#}Dp;lem6acgKWIv?vb4qCj8$39X#p{`mCvIok1Ok001D
ze==J4p{~8e9EfSNCVFf_Jd^6L(`CchdUS*d;N}m=x!HO}q?I;|pRpA1_YE)0($qJz
zb-{CU+vlv<1{?k=f7<X*k@c|YpFTl&$_M;M!mQLLM)sruriXnGHh)E#950IZ*y=wb
ze0Wco{z=b`OQ(<Q+rD95dqZKI510L)g0H^~Gk_^M`HPgT6%*nWAZ4NT2&x(kU2AU;
zkDWTbbH=RMbGzovU%qbZ-lJzO-+D;m!Gk;3FP%Mcc;C(~e>?XdJ$>=|orlj~y?OWH
zr%yls`a81jyVozCKDvMX{PBHT*DmRrK5y0517~j_!~gsv+yb|*UO07h|ISTokOQ!I
z&DOogFWh+W>J3^sA3y&>8vgRxWBb>2Pi!no46x_XY^eRs8(K!FrXoWFvk19%qLg}{
z1|h0bIAkOqf5r1<hKCS78a&#$E4Lp#b^h}8+dYq-A+urIKZnEmIdNdWp#PJ1a#OpQ
z-=<)D>Rcx*PCSD?{|P`n_Wa?UYZp!-%P*ZivMf2kPPd=YEnf~-fHO$W!mub4bbd1B
zB!mZ11I3i+wlkKlwI|*!D7K#6y>RKuwVSr@Ie6^!e}$_zZ{I;)7+hu#?%uq5;mnC6
z2M!-Uef~1sflr>leDnUN&%gfu>*tRj-o1YL?8)s*Cl72}yJYUP?iE`OoVoGn9XiHe
zJh}_N+QEIhcWm9fVeP67+xH$heeuRU`097a^1mX(qdWfcnPdCb&Ye(~7vpWkquKN&
zY|&Fpf0d}h1|CVpKrtQFayJz41%_ABG%&Yw^Mw)Vle$*##3%io`;VW4>9Fw$yy^vV
zCzHFFH0MuzMtw&f?C*I?f_Um906&Eud->%4tt;ozu2?gBY)wX}lfi%DCI4-N0fGbl
zAl>R{4+Iuv1w21q0_Ml70z;C8-=4A_7nFu;e@3=W?wq@5#kwuK_Z>KdqO~*UE?&8I
z^G?sh$Io6qdwl=y&1;v>pE-B_;^iyXZrr-v^YH15Hy=L!@;hAQAKtxr^`ht6nM2#x
zE}4TIz|H$lUq^=j`1;A+t7nhx;{`SQ@xnn{3N4)1?|%IB3mE=Bb|F^J9ycU2+|`6f
zf3u<gt+Y@BJ$DXDMwlfbK@l8)dkH;ve|NSlZFnsZ3h|50scN1wZ_Tcg=Pq5liI(+?
zHw0djS2TH#li)wNNn?Ke_z6H*^+(#=)Oz55hI;D5o9B<<JUnxJ^MZ->xlwK=N`0qv
zeL2zqNmv=$$HT?W!VokEMF&&5C%QUXe=u?o1^BA^TvseCJF2~-d)d0}`+3m|y6<k@
zMfcs?*DszSC-3g98`rO2zlLy?`1^N1{frF%^QVtLet7rx(am#5cC1@Gd(xa`oAzPD
z|Mc>~jdMr$Y+ku`<CYzJ;Iu%Y`^C#w(ZWIQ)AQHwKT?KAQNya4qpMPb9rSoKe;eA&
z^gbX)+kv1eCaqye<9fI(Q;i%p3HTzz8(P}C`^V*0kDNMx-5%<sf7#2*?|8TPhh8=%
zZTXo8Y}Svotv~VO&kwyrv2f3=YnRULT0VV5QJl90h{;Jy^dEfvZIl5Db25_SK(QJt
z1S^poEGWgn$Z#!n6|}}MJvJ(}f1s)f1?j7{?mu<)^2PIKP8~mb_yD@kuibh0><!rU
z^~>kao;-TcbLZBLYv=*w`aXRA`orhnQPB4D=T9Gh`tjKv<h!q1JZs{tB^&piy7utx
zA0M9GxpaK*rWM^wR&Cs||Jd2fw{P9NfudLx#)9Mg^6gJQW5eILc<RWWf0dmrWr==V
zEgsDVw*etHlqcvylOjoZPCnuTMZU+b&EGb>m>gLkj?b$ZHErSgeP=FS#V7r%cl?I_
zk$?04#J73Dia*o%1;SSdV0rkte-Ot1fLz~4_wU@idT{ORG3Ckr*1VxGUx6k-T~$e5
zMp8^zfQJKcuoNW7eiZ{#f0!1p3u2WF4FgP}$}Ao>cKU*KyH8wwcn_UFr;i=nyJH`6
z@oqnS@$NHN_9t|Ep*0GhUOap9+~u1+PhP(N^!x9>{q`#k*kAQrKE8YX;+btT7q8!Q
z@+va?`^PuV9@w&?d&-<8>vkMEbL~F3`1fw#xO(Z_WwdUdp>^}ef5$gZdywG|??#Kh
zIM&llg-5fYM;Z&#!7Yz0glS^3z>opnjSw|~l7X31Uu1YAD@V`3g#00+r!U&LAD#4Y
z8ohY^zW2W7GxVQKjl$AD;TL}N{u2%S)gO`NpFeqY|L%#+^C#4#2iqaPmLdNa`jLMd
z*u5RsJgllHJ0&*Ef7jL4R99V5Qjn6EcCfKB1IDr{y5uY!JcFW=v#Uo<TeRum#ryAG
zpvW2m_YK<*oValF;maR?r|}WFqPMS}KY8fH`Kz}dK7ad(_tCqD*G}$Pzj(%k&PA|%
z9=!eI?Sm`FcdnT~b<~vZRojnV>UsU!C*;udAcyAUxhuDNf1bR0PZ%Cj0K1n>Z7hfc
z#hbqaiaY|hfCMeE`X#L>y$tUalvpryOy}ZF2hmA}PI`2?eIRdd(%64t<=_53{=7WU
ziunBTM{tgwK0d!|>6BqP;ZFU`-u-eAvt{cli?Tp&5Ly=4Q1Qjw6%N5&A+#i+HP*>9
zFd`ujRE5{<e>!pP;_0LNcR=_BaorIV*FAXr^!dxz5P`jU_TcuFbH@+vK6LWJ^`571
zKJs?_%X^oO?OeOCb9~2wH9L-8y!-sq%bqJI_ikF=HD%7y^}CLqzxDXt%g6U_Ts(d3
zz|Mmw&R@It<jqI${9z@XIJ9QQ$l@4xqyE@4bdwiNfA1$d_t1`)mJk#9C-Dz2bEju&
z?TiO&jh()5-EJJWz_#xpvdWNFevU%+*Kfdy{{XAu)1R*Ozc|bBY5oZ=K{zVk;c%Z^
zPM<#G3>KN*dj9m{p5@aU3!>c&Ihra=IY|+L|MS29uYr&H+KPfq&=7R7wJ_3AW6FpL
zkg>6$e;IU^!$~hs^dq|ag(T)yHo-Z)d;6w!E0@fl(>ZlU_u`f7x9vG}f(~+?-oJ^0
z?gP8F>^^+@^6f{je&XHnPj8(+ynXe8j#dcd4?;Zm^e$1}>t4EM%O13VZa=zv9bM;p
zw{KjvdC$>v*YCgh@sH0?5P&maQ`gw?cu$l5e+*B1d2%u&v99j`<9}dyDAG{5dfY-j
zYVzFWYu0bxzI)%HW2cZKdLDVASFT>aaSKJY4<0>6D*!D3WWPVV?7i-ODyqW)8#4Ht
z*RLQC_83YV_#3==3+w08=bwN1{OKng4?VlQZ&l}r;y5o;I(hXK7y>BEPfv&l^l~QD
zf8^w)L}+a(<l6zk9>os~6)h|R>luQ=;M$RGGZxI7Gjsan_SP{Y$F@!BoV#G@s`XoU
zA2@dQ(v5p)!=lh@|IW>8x9&T3{>B4z3xHSu(}(wO-!^DHZvX%w07*naR6MwL`oNZz
z^QN~>>t4QT?}^KI;l?<744oaT)^FLlfA8>#vzM^hx2<2fXztQ9or7OyU$lp_ZM&%^
z+qS2g$;RX+yH2(>*_fK_Cfl}c+jZaHy`TFZob$ft?6cQe&-dBLt+D#e?{|V!9@)YW
zCB}#O@bkkP#bds{*#8LR>FqG}x{STopFf9gAcO2(Cc4H}7|Hk=s{fU`yo|Mg$`OMG
zyV*uiReUMwaBc)=Gg;8}FmT=~_(-}_5F4@cEc!2i#{;%hCo{<hwAdq_p>MVWKA2TG
zuS0K>`XV=oGa5P9k>ZHEC;psu+9L_x9<wPO5UFPg891aUl>Zd2a`EsTjCP2>mRgE7
zWw5DtP~9Djl^Yr>8oN4-U)2=oq=#}N{jDe^(B7P5bL}ZC&03dV6l1$Yh_?)|Hhhl}
zFR6ME_`G_p8oGGu`sZ~F#kbfBl!$J-yjM~l+6nw~yG7s$_iDUc2~>lmQJHJB?Tr9c
z+f9?&wf<Lc$KQno!wQ+*={y;K;NbKZ)JOJrQnVn4;9`k$lit~YM^n<Zn##=naj;D+
zfs{2JuEqYlP`TNRUk4MD%@bWm&~&*0JQZpXkd$~FMG2rkclO|f8jmLegRvKZ++NQ;
zEwkp#y8j%B@k!S+nDs%0!YmCDY^Stz&!~0i!Z^NK8bKIr&-TWB6&OySY&i10Q(Y6s
zT!;v`_C5?koLX!58Ipi-I^Mz9G&jr)sAcQkSI%-|JUq_)m9dIIRY%x!rd-mxk(kHj
zc`aPrtI<}o!E_@+(JK}(_;-ti|8c8)xk+|-w+-4lxK<eOxt%oA)lJvED(n7px_z0K
z$DPO)ayUDR?+vxb_T*=KJ+rheAQ+g`{r5Z->{Ugc^NG^&?{H})Mk+OQX0i&RE;`Q&
z1!Lrdfj7S-=ja`@>U~ycLL(7bQ898D!bnp8Ovv11-K9}VTVMs8To>DdBY#(g%mSZA
z34Zn^p-eMq?l8F#_B>UQYHzWmqL!B=@sE5CcT*jK(lNa!Exw3pAItwO`2pM4TF);7
z@zLuQoN0L|x^i#l^l?yOQg+05f9WtQ9K`~sUEt|-pkcD^s6zT{zRxu{M>E=FuK7Up
z_Q(nuIV<RTQvi1@rYQ^2*)z~-ntKbjdYz6Ak7JV7ZbU-B>G10&(7k=M3+u+5!&mLY
zh{p6~Qn%W1_JY#9Qm@_fek7UxH}mf1+tvoC?^K~L&{SaSY@#S@^13$frL^gLKUgJf
zviR^k%bsD*eF&v<&rOZdBtvk$XbwM$cqQ%m2^T0T2q5mY7-{i(K?n7JDNcy`MfTa)
zsF=y-qyE@q_IKK>0r&isJ+7+5E0$?gh&<Bu2ngF5S8E&1%Z{}1JzN+kTx8A>j`sMP
z(R98NiuhjGbeGNY7HjydGswkk@pm+Q?;gh&Iv-5bo##(6hLQ1~*DX^)iMF^ugXk>l
z(*MO-0my<?_=l#9(BHAKRaI3vIXRtxjjQCW1g(DFsn7b2q#pYe+fv)-*uC_nz1G9?
z?yH+!rf{R%XjyRG7f5zQ48OrigEsH`7scjfJ3S$<x2{Uic6~g96@E|YR&_MJglWa|
zY#>s&I?^w5mHW5r7-1HvRR!oF2{_JW*`HbkKthEOf5Li+RgIhYYZ*d|ELHy*CR|Tv
zNO4N|)89!N$ixdv0%a~Ucgwda<+`xL(z<P}9;Xk)Hr`0Oe>!77uFxu=Z5(OWHd~!u
z7otx!s&xOk!$j!laF>h14yF)WTxasRJ-=kV#<f#q8SmX}3e~tw7ls6Ab|k+~`g<J$
zZHD@OpkQlK<(0`f0h)At;awS-G(;_2;d7dt#qH@4etN2oXeeL(ADz@zh)y%(#dN_{
zZn5|Zr<*7I$c4)ghVe_`&%1~HL|8XFT_G2X!vSp2E|y?*gVpW&M6nfR=Qq@H9Cz$5
zP(vPAU7>a=R>b2i&nFWUVyOGMmgxe(RtdZYh9OK5lF>}vnY;g+jsZG2ia4%+wEwJ6
z4D*pT{#R4*m&WovDl=f`=ER6{pk6V9^VjfKmNn3KiP^6}8SitWy|s%?tl10q3186A
z@s7KxW}eyLpZx_xgr3{4)oRaWu`{YfB_>nB%{Li55(eOLE~ZE=BLY(-4+aL{_mxG}
zB)yVKWGM{txC~k7?D9U{D4|OJM8ir>>RnS43x?x`e0lI6E}|@ETB-G&Pk62?QMR&8
zL*h*vecXIQvEw~ZCSGl_U7xv_2sgQMPju0FBodOP@(}v?xR}15edT7(;kP`yO5Z8(
z8v+CIr`u}3FI%4WQ29P*o!kKrFZ{906U2@MepDTkkC^Hz7#FiGe?gJ;@8-MR1Tl8?
z)qs(t?QH79vD1`)r_t&Kq_lEYr~C2Ey!Fu2e+^9+P(zy#j(b|JIr<EMP5sGTY0&8k
zt@S>3oH*zJ7c?A}<%Gka*Nz^r>!e|~0lb_*)@*g(2%5VxUgBwG0~lM|SBDpM%%P-U
zFu~Sc2>D*$o~TKy4<HR9lB|e}4i@9YDe({-ZRBIa#zx}Ra3?a@O;)H3r(!R4n|~h0
zBf4$5c6yx8w9F1Z{xaw!in&2A(56w!m~XduUQy(KAm24;arrzceb>>LxWwE1uA`Nd
zNDvA!*1Us0t8vl(1Yo(oRf$Z!IKOt)K167m+X_9PcUtf{n+{4tx$_ypf7f4z!IYmW
z4mJq0Gx@~m#T49eU&VaP!WryZ%CrvU63VV7%1Kwm7~%g}U|=;KQqk&qx!gpmnJ67g
zMkK}K{KWE7_1_GIU+$#__ThZF0rZxPXBgJG89g|h7EZJPkz|aHPkX`EZls-5$f)qy
z*>$bz&h_65VLUJ>OehRYp1y%GLd!N$3}Mb(?>L)qJ4~DS5dKpkar;dMDF2WQ@Apel
zaWR4IY|bn@ki}fp9*iO3HRd@#+)a+gdB}t0=PK7^v6i!T(i2xhbnl-vckTYN{LlI`
zUXM^?taoMrkZegSg=a<Y{*X0eWLLlJe7lR?^nHJ3e_7w`Y;AP2^fJp21>g4AN<d03
zGE${*1@FiPL7=)$B%Fa0>tar%cAT)@{@bN{oh)f@=CqW`u*A<eLw8IU`o8|;D%Yi2
zQwz&;5b+!3mC*8V*>@{0r=UU_R<qTW@om!+?4=h-2Tif6v}?SNcO(%HTqd)4iMd9u
zjKm3~8x%9@?E6E`Ut_Y{9bdO2AKyYY+iRV^n-M4=v-ha=c0gzz$CALs?7BgJ(4zdi
zcc7$s6P19oR*Nrz$cy!}^Qu+T)K=A6MBpeJAtWRuI0;4m`?rD^eBw0%nV(E%waIPr
zLXRK#Z}wq+@)Y0u^{;iv!aO$@r#ONk0hNTIXcVD}PIivegG~@jiAHo<!vomAU2^2B
z?e&b`lgt;|Yx-F`))U|3D2w=puIC~cIGx|pbyi!iwC602O$78IA=r)t`aeP4QGAjn
zBMim8qsK1|O>m^JHkTmi5<Md+Mem!omKw0QBtW1AjLcbrMrc1LPWl@Ly}j->+07@q
zpc+BALqk(E2-_%Yaaf8gyV(hd_;;{ST6c}Kn27sJ$o+$a)cL8r6R>+6!7ren6>rPx
z_DX*L+PiDd)U$}MjSg3&DKm%Wp)JWvCk6@rCzas|bI#9_Q)2B6#P+3Mc1>~A0<Nf0
zvV%K|`%~x~<sN5!F}#Q=z5h0=jdlkVGt?Qg`F+IX#a&o0aswXnZcg&<6)6Yn8)Ged
zoPteydwPbrUe3rXoEAy{MIfPC`u(Pj6Rz_ydX2jyBm6e@WyYyq#l;ypN@2XB{5-Sk
z5+OWH7Hrx3Z2#I69r=#O<|rG=8|b#q_w1u95}vaAp*}AnthPDS2a4@kU!Gl@lM<0+
z#U*4XYiL5Wiy%k$f+n)DHu&?1-#ZnZoNnPP63a+odQg)0sU@MCV+Bf_kHW5J516!C
zZ8vM2{%iK`a=&H&V3*P`3&JS7GBAHq0IN7d<wX<Vo9e;4>5gE-n4>}ixEKM+N!q+%
zLt0|IDM8lSkZG!LI?FWPXG!XPP8SflZJV*iYHj!U2HGSs8Nic!@{`B~_>Rt*);EAK
zB31U<TZD+32rFkzzox&TzpH?67Vh6|KB^l%2vH5ZK49%R4Fr6aoL-*}p3fVWw<lis
z2u6^!NDh?}O=RlH2?>F~2`fWWF)2|=SMNA_65eo^OWuK$2QFPib`x<^?=D<|?0SK`
zCn@$s&gG$}kSPM2NUjcUzPss8!cRHg7LRA>U`YutGU$ByxU<^P$w|uZx?#BJ#MtOc
zGBvR|8|}us6QFJPn$HVO?<A307Cfb(Lfp1w=fKsoUlJUtv;fc-j1^tu)~mf^#lV~3
zS5#pwf&`0>zI6*}@s7*z5vcnGZwqCFlFSM6P+y4m!Vv1;_$p}H#arUFoxrp=g&L}I
z9qu53`lq#oo6Si^6ACHq4g`MfraMe~oP*fnL`?zjn}BiYs?Mj~L(t`TN4)jYhjNWs
zSB*wq=kwfp0}MKdRu^qGj6<6#i?M&{$@mW}*V)~yLa4XoH!yh%{F5#R;+ew0x|~VS
z5Dl#{bx56)_0|P_-XH?SdwVD;3i3_tOBlkNo0pbAQGJUwZN&A4DR^QcEWEA5!_(c}
z^ZkAJbkJMHh-uFBWhPyD<3r5g=5g{;Pte*%QN1|__!|vRQvHhEg+$~TKtwGJwJSV+
z4@$F(T<BqQ9FJSVvw3oHi~Fk%=Ot}yiS)CXK2&b_HxJ_Dc+(NRG)SzTmfNv$C*qLe
z?_8dr7}^?{2Iy>++n(F_J%HEB|NGL6&c#&G=CQI<G5D+PDKG}+vGUxiidg}vy2RY@
zdRRXd@ZSh$3+%m*8lwG`z7(Au^;olzvQK9X_3Y)<?6(X1#MY~pLE=VuMI~fb+O0{J
zCp~e<0qXz>D@|jbe@@(sxOKd}eF2kojaB{N&qyT(mMap{T7NRAl+|QBn_YlwCwRdA
z2Fdc)OupPUJYoR8#+9BU@wGo<sMLP!OfVoH5ck%#Q83hPA|m->Zq=V51y7{D<{G#k
zsM0KP%z;7_ChR{)vaFPu5U5mJK8EC+x{sr=z!ja%Vm7enKzTB9%brVcWNdBsr(!_l
zmm++zc4<NxMQvf3x4W_H{n13xs`8rm^PqX|&$Tx1i>H_$POF6SvpwtA&Z_onN9$+C
zfVoj&!9)VK8vA1u-JhIh!_k0;f0-;aKG52MbRhW%2iohKAG4QQ{TZ!#9#+@OM-m>n
za&uVvu>2hz8XInIjjdIL<yj#NCGtO~oD-X=CRbG1p}z#vTwd3pJmBQI`(4Q9)$76-
z2{%V*66y<IFE^OTT-Sn#WWrIfD`J=u5D0Fp4wJ6X7L2UW@=)oD6SBf1tl;%g=+!?6
zna8fnLV<B!^@OyS8+aybVgFrK-Dto1U}3moy!R_2J0@kg;dfc}deR>n4qxE3LukZ5
z2O_dc?AodLEJ2ej2m8_5<?~+pT9fzn^YMF5@<$O<E8=$hxIiDhkQ#IE=cUSVz?<?I
zt0C&VO$^hKU3I6RH^5u)b2)5UXr|8FxTv0knp!nX140Nnl>tY4MeYqkJ&7WQVV`XJ
z`5Po*m3^+4^O!-yeOKBk>gJo6-p$gLleV=}0fYnAm(L#|q%gFykynk=u)U*yPo4DM
zC=*RY!q-G1p`%>3Ozb^&p-YK30o27rb#nD@i%s@-DB+MTgkq^IP>}@cAY`JvjE9CA
z=Ziwm8bNW?k%+)5ahs_-Gf!Y{zWFi8M&j@LKG9Ezn%((GaQWmlmDXato<Fe=a#Oza
zE#S*Q-1lz_bDWL!PTRwC^mxru*wIItF81e{H9tW4SH2kqMIV8hgj988AO>oSUSEfF
z-pEQF^%x>-=EZF31p#3yQUOmd<mZQ+EGC=f7CCwZVcCiH&k1mcmLKDottqx$&V@~>
z-2rv?kCW$m<4wE!lAXoL#W>%RFfPn0su+e7iz1=!p&=QmWyUS5U62*sA-lqw@e4y<
zt6PuJNU6q&+r*6XqJ_Q^0mabbsTrB^I^XqEvj|A>k>py6Uz+#fn|Q1oqsDBTQ+|HY
zEQc{w0BMp4Kts~X)nE^g9O1SB;yQCwTOCedWhvz5Z12d>)Cv<NjbB<clam~a;x4c)
z+r+}v_BFs(o?q&Rxm2@`AvhH*_hpwop5o<+#b5)Ncfa*(-tR3AU~_?n2&}^a1w?}a
ziC<TPr07Z`s6}ZhBj9?8OQXb8eF&?<>JwfT@Z}e6h3#){vPv#}i~e#5n#XGhr(DK-
z67-tDpj42E&+!Tbn)JSjL@UV4D*3~wArK2ry&XKBOm|DLXL{n3zzQt=5ZU|^J;fu|
zR0d1C)7M9nm|w31G-`gwgO1t}Q&m6)q1SGoVc+(4TX|PkHThnpi~K#i%ll7VN&L$$
zNWblN>%R~CWN0`m6E4D;*2v&R`xoVpF)~yP1yeD>MYn^OVi)`@KS>v;<>MBb>K_ZR
z{!hnouzkLLVjAls)3u2AF_}VB<=b=jrrnnSRZ2ShQWOq!m+lBK3aD<WrWy+55&t|O
z!plJ{mm*`%<N0{@Vi8cuYW4cLID&6d=Ar+-h}S_Nf*MCH9R(A|?mZ0?FhKcZD&mB2
z@KM~fAa#Omgs*r=I!abv%AEe8IFp<QRixX4g^h_e3S(qg<Q1%4`#*nWf_G{N2KrfO
zK#h&PO)H>ov*xmQ#<{h&&GRwb>(#_Wv|4BB@pyNwC_Z{SOHO}j_{(~{frFb9d<U5|
zS~X<+K8F6!ubfR*UkODcZRgy-^fIcve0*J|o;*KzG!xJMB7OGeRC;yzD6c&vo%cU;
zT*~muiybC?4c^-E4`L9Pl*c9Z(nmioipKoOxC#Tj&99;w_&(kLo=gqDzBtdC<=1eI
zsSM!R7?60SM~-c9ciahFXMpcboT<y@{0VbSgz_V9a@;zrgx$?&{`lB9q&}fBoEVSf
z{1OyC*N`UE;rB|#x`=S&$m1(1>+8cHlJi{9Z~k=Wkh~f+re?>jSe2}^2_IVrO}9af
zgnxkf><Bdb${-T#ER6)zlRM9<(|A-aCr%_8(ub&a?;0aTf$?5_qe`?mOBKV?=IZ+L
z#!3LvU8jK4Bf#cUMVRw&7X&YAmF?|daBf^?2vsf=6CYgRgm!SjtlRl{lqS#Zncen*
zPDt`twkFhI;xYJVB<dR}u5tD*=+7kTDgmfu9r3+upa^t=zsdObaXD1q9-h2LB+c^L
z%6?g0S`cL6<7E9JFf>R^eD<UoytUj1ch0TIiG8-UgM_AlKT%FFUt{x8PPeP`U#gIv
zU_j{{L=111-fH|>A8hl?{0lpxKY0(ri0ri5-6!rEr+fXW2~ZhSGz(3o@wL<YfUc9b
z6$eJmmI5+<L2|Ys!drC?P8qFC=F$02YM487?~l?b0E2<1Z{&4kadzjg1!5BNubfuL
zL5Dws!k!-wB?hvM`xzQdN+P{B&=d27b;)L(BUh<5X&^b4ab6%M&mNvbIUl@RF-66s
z!sV|rKIuPp_aED)>EZ!-mQXt(Kq<UR9=JZ!gL-is_2T^W!c34h38r(3#nBRUv3hh`
z`vV%5{m(oe;AqHoY#GFWkX*6nx`C8cc?O@BllQlvs&}*6yW6HMZ}djBAW%T$O$w&D
zUd;YJj`BpFAWXphhopy{BUOsR!8@OUde$rdQ{wY8k&kcOE}bVTtH>!JA}0C+_7Id&
zbYs|X-|9)ua3dA+y8obLdy}U9*6!<Z`_xE1n?9;m+I9aIDVCo4c=}_)U6Yct51Kfq
zF{QaIyr^1)@Au@`oo<Tnu>ud8Mo<g&hYH=0e`s&-n8@#M-(NpdUq`BWYR@jiLq){w
zLAG52i~n`-u2cI%MFQwz7&{ji7k%q`G;Fo(ubWL16=kcYhCkhKmLI)W%+1-3D`#CU
z<8!VYr=5JjIw8W7Gc?kUf2-EHpQa6RGm~sKnf*}qGLgRgM=U+D=`B{v?)!f37Sttb
ziyTK2|8)!}$#bVd8RfB&TmAQeIed^k+^}U}nmUMg^)*tF)dD>JOUu(I{xRk`ntk|n
zvHPix^bY<u-L%&l5vTCU;0q6}NLgHf+jb?-eIe}jxIGIhyxyM97dK_4`b$tRFEW?c
zH}%ud4~%Wg5Gm0b<Ecm%ps$V;;$vyk1HerNdiq2NF_g1&fQ;1ev!QX~_FjlKT^mM2
z7&1l_deTNZfB|lf%2T>>8%=Aj<@hcU@b&cow&p$<|KfB{S#52MPJX`81h*QI+Z!g_
zUOjYq@ZCF|B47k&D#iyf5?km(cHj)-^9GyEBQxH94Lu@^HF$#tv9K<=`GzTeNwrM9
z6M|&^&D*bT+08IlC$Tx5A8)antjPxt$654;KMF$ujEcsIFwEGno$9#7INW`sjDC6s
za?}YMDT$V#AeHJL-QT%ty<n-B8gjB^=C7LFy02Wc?bcSurwJxdpojQTXIQ&fEl;A-
zbp0%*oMIR(bbO9pVE?S4Txc%Bex)Oey~QtiHQ@36xI4K%_pbxte4Ev^CDwL6l17fH
zCDHys?UGH3E#wWRIo~U}$6V&yPHkDHCLn#)_u<>X6EMi7Eo$o(?uhq7M%d){XDp&c
zp6iU?>307V)PF`lTo?AG(Mqf`5FqAhD=jHy>E48iE`u_UrM=!jW0LSF%%i0astzK<
zFD<F4RQNg+r%KDdbmS{enD-p){!=MK1e^^3DQ&<-ag1_YU!CjC(h2;o;oW0CjK)Hj
z`35TETgKa0l{Y(mxBI|%(hURvx}r1|g&n~2^Nb{{)X!U0N8HRvT{X2SFs(6Q$R?$j
z{Q>k#MuaOXD2@8PGhm*Z$9sq5{=u3_A#qrdBEkHlcgaEGK&^*Jlb-`_x3M6OdjXIU
zP$a}Z-xCfec6hl?o>Zon%b?P#nMppwm{5KCk_Cg&v+qqvrgMO2i|R?L;Pioel%_#C
z(9dEyaBwBy?wZlyhu5`u)YJ=wDO^+H(CAO4W3&^pWx%kZIAZnsS3oifP}GH|`!a)Q
z%n7Y}D?;OMsEJR%evxhp0M3sC`#>am6b&!m(yTx2s%en9a{nKZRN;U<_$BS2kYH&E
zspnQr0>dtF3ca^?>hV-45|8+g15mJJ-d29$LdbVtDBev@_~<t?=_W;Tmx<2rhLmC`
zesB{BjCL=|r;PZH#v1=hu4x_mA!BatIs0w}R~*|?a)!S~rsp_lSqR$QE5J=pW}=}p
zVnof((6qdwc;p$Rh?^CHVjavRE&kTw)mZUAQ6juMQCAlEh0DzrGd<w4CuU(_Vn5?C
zr~UbEmL>f2=H#O*ygftRd9s3jR6ZK{-XRY~h)LZ+`V#Lwu8;#xj-L&AZXc{xj6_l@
zB?gUBX>|zlaR8$Bzx`wlpoPtdsBR}@f3Q;UNGjwLGV(y$2|^9mM?oW9l+4X0J0o%Z
z@>ouSoZcu$s_?-w4KmN7$}*$_`x!XS$jqZ7K}>E`E(8WeM5m^ygah&mi?|2qqHp$B
zkXvctObuWAe{Ozfon()Kr{;=s5X1Vu6S}b}?eBERsMiULz90Gp$Sz#0(w+MLL&n+B
zx3Uola;73Gl9-CEIJeI!@X$Xv5aig6OoYTUjOW*_ZV1o;TZ{e>c1X+?$y+jtFpCbk
z)AKoXBW`tnJ(eEguQmfdk1?q)nth(zBI;Q*U~Xo`n9f0e#PnH05UR!L$*DydMc%id
z8WAEjgdZc!xrT<oA#RSrrlKx;+BVadh&Ov_%6SnNTOUf8*_DlOXo$WkYcSiJZg;ys
zWPE0jmP+4i<7Jnr8=P>RCt%n6R?2R8bIINBuS0<uGeP=mVWD<ktH+gorx8arlYr?d
z?X-H?UE}Zqg+q)K^?9=;3h&sK@H~<(7YVe8pjx?bsi#B$y~K8p)!!8y;|HrNT-fB_
z^CP|1_pM`pFJ|AjsP7+P`VXt1*@zu#Ccm%YI|c3e8Ir~4IUS{}z|9HSh9b3#EhMJB
zp=9M+oh3U(IzJUvzTGyq+SFRz{?b7(=P~#KL66!}7=QYjSDMZ;FL-HRB8+YAmewC0
z(;U{Q3nnEXR{5K$x3-cBv?bkleeHn88X1P<2?6?RQc^wMe+|EUU0jWWI{!9=Hzq(S
z?8qo`X+Ojgh->jKYl&UwQ%q7QO@c1m@hWm$s&?nUCo52jWeh`u+X=ehH%Xi?OgVA&
zE&${Cc4@FFDD83?lYBleVL3QQ6=5gDBqA0?bqx$bhf9am>sL2%kUKCA&7f(eQ4I6=
zlB>Y$4OX8OZxEZ4W^b(Nk(`z>3%S5J#ExRLld=qVG1Bsp&&^Ii89i3>K(iT1)aA$m
zg{<2dJS-e+U&yHi`B9}6UvC)y!<U5>c7z3*?G4xS2YQ~qk<t|Vr<b!dJniiKWQ;zl
z1E8W~zAgKoTbs*UYI#bUFFK1waab3iEh#_>X(-s)E?VVc>bF0ht#iv^>^5z7w)r@|
z-W-d!axq!b$)Y%NsN+zzyOqQ!tYARsMO0__7u>!!pyCLzU(1#0w!N>-Zj$8aE~f8K
z#$}ImQ!)e(j4Da2P#Pz)J7S8jG29z21GhO)cK>=PyS_2O<f(!e{EpWGe`}3=lttgY
z#QglYNL!~r)F_-TLFD&zhP#irSE{c2Q=0v+EeLG-a_6#_(R>RpU4Om?#<(!n77L<I
z)btL7Aj=)x*{yrsjU|JC6Ca6MKAF+STTpyL1go~Qj*DhsRa+zEBOLURun=bfA`Ns&
z>~y%nY(~9dmj;>|{B?Af1I9~8-*2v-(s}WHi&Z6d@_H%LUAQm7f@+3}s=a8h0Ymzo
z9IhLAsE!u~aQGPu90e^jEKY{6Oz?jOLCeXjDB3o35v=Un9UW_&2D4q}S_1+v5rq=N
zn+*6RpNtc6rlk3eF{Ov4DOD_R$fZi~lc`%a_<kLYQf~POs`!bPfIsvnSpD5qY2@Oi
zV2eX2&;P>&KDU(e4;Q_4s~`wvx!G9MM8z>IGA)Fv-+&~okj`Tca}vHUP0wZu&7FlC
zcGdfcM-scCO4#>tG^_Vr(Nbt2L5;^<(rjLYu4|T=mVpl>s0A7p2Qw*a8?vt;qvtRa
zC6sQi936Y#^UbaH_x8@tc6Lrq4h{$*=6Xj$7J})yE`&Tk2E@^lMS3QMJ3w?OtHqlh
zPn(OGhL4IIPXkX1deP&6@zAIH0@BVwhMe}ERdp`@+?tds$b`MZ$nvEEt%g7<y+H+J
zM4cZK%y&G+1uBK%h-N}3H+{d%Ba6}>jtBlNc|b?DakBJGimk|tSYkjKv3H~b*wghe
z-Z|Fh7kX2{dXyc%(Dqs`1@AXj@;Tr3M!);q59>=|s4aQ)?{Oe8oC$jSGs;m3(FnoO
zWD_VT6s*N+5!_gf+*nYB@j+lK3Yv^$jE=0V%nP&e0#*5?1#a^L#D!bfTwmQm$j|(3
zZe?w;xxSu?c710Zv7n3C!B;~rsFPo0KafYL+j_b(y4QF)*yF@Er-yrm8NR^V7D)f^
zEeRcgux%_%n)hobGUTV5ciaf($NSOWxJ}GN8w;JeMGW*HHMy>|J|10bmy|c?mdZ{<
zUjePvI3vqnMt@3apNX&2Rgrdge$*h0*=TpTZH;$b6mf==!0aS0d-P*-M2FS!f${8w
z)8Q}Aq1YUtYCaG>E>QgtA50w<`RGWX2rJ3^BX$??@&W?`6A}_4)u_KsC+TuN%4NIR
z{y7v5;@Lqw`r(BSRlN7*3TTi#&C`@805DVYffR}9mOld&`q6dq&pu3L#_vlpOE8)+
zRhi;Jn!j)2b88{8_^U9%ZzbA|Cd5gR5lNYOCiYg6UY{3rzpFMq!X$|4ID<%q1zTLL
zCPAw|J=<=;x^3M3SKhw_Y5MIvLG=L<Fce-H$A1$=eh}Gn_udbc8Xjb*F4pb#0Ck9~
zxIYG3PQsOb9*QTbG=c5CrYEJx2geIw$(bK8FtA62er4jAz`$<->GtU$-;f=0@R-Qy
zikv*Th=m-_o`%)TC$c+5O361{U{?u6bbmm!GR;k{`-f-Z$+88+a5W3o+Q47Qpr^pf
zETlRYH9i{iX8zw27xoEVrV1c64W04r6m~`hv246QMJIf`-)+AK%7->FWN0K~Vg_N6
z_PI;-=RqLbV}Sq$wEIyBjcB6wZIMf+seaQaLCmkXHoWggV+R+tc;<TXvS;2b&~=Yg
zHzdTr<1*7HcFMMmlWF&5?4edt!E2eIj*l(rMrGuC@fl@#sk<yI%mLCQ?d2NJ&7w%$
zi7Y{<m$~p=8d1p8o8-TXlSELgV<T%pN7W*vZZtabYA|>lJ&Z;m&0>@dkLaVaAE9NX
z8U#!#X)Ne?2Va%r@aepd?1{z$%3zIHb%nHUc&0xi=_x`E(2Vj;M4h6Rdmlmdl^|O7
zV?Bf4{IODR>Ra5_CGc~UOpYaQnS;w%B3zn^Ey2{P$FSy$rs(DZ#tRc`5wV1eo41db
zLsCKGm4s_hqx}sstaYCR#dTT@_=Lnx{Ql;5uKKR-Ju4J)4`RgMUCrftM`X;>#ql^j
z9sW@;;k>X*EoDW3)p<T+2}yw2e2dy3$~h!Bhs;M2<*K?XCxBHH)Y8hgys8QDePL-X
z$`ZPqU;5^gMWT*oN6x%}11(GqVRW^74W{qlFT#v5#Ja6QeO$pQTp|~N|8D|U|6#LR
z^$R|)OLRt8$?G12&Nc_**PDgfIW!u2doSGG4B1#osXOiht@g&4N+An;RvFaM%l#zU
za=)-fad*+v6F{6BrOJ{X&TR@KT}D1uVsIc222@SBQ@Nrh2j!**f3G*B<Ca(G$J^WZ
z8_BlY+m@9Yax!u=mp~HA(92zs_iJ+=6}0<pasAlK6Vz$ET&ug>+BTt4y3}vaB^Tb<
z?2f}&o1R|a<`R#y1-7i0v@lCyLyc8Fmf1DSalAo($g9z_lZk&I$uzDLBffqZx?uA(
zyuz*v{Al@wzSq`FB5su->gOzTr$5tHs2ix#A>^qyQt`2?FTo$?*-dBtYB!Tq#H^x?
zk57m>d7%tF2Q_CK;FSLRvc2|~q`X~NMaUKu*gw2gcc@vLt-Cu50HIYn92*YKWDZ7<
z?bM<Y7*a%dbl?c(i2o}<L;<63$!M)4@Uhs`q->`rQ1k2b1eC){j0wz0;lTDoN7cjo
z1Lig`Fx<;b#TV5`!v$62BWm|RCqAB1+M-B9M{Ytj$zB`EMH_O{`LRGc_-Z|w+BT3q
zK4{mLKj#PZH}4GMK2%yU%Re+t2ecxE!C`&^4+iWC)f?_0ojS)h*8ZWXNuCzVcYTT8
z8SYXW-_MO7xAK_n<3FBg9O1xvh8bzOK!O)W%q!bc^bJ?3vIGGJTMi+Tk#BxFs)5@?
zsc{-=3CU;|kYBbLMAZoZDx<Tkg*9Mr10-f&>qO98!KT-!&}#MVEoRhtT^X4b*TVzK
zdh+2|a|_EVO$<lsgO}q`{Yomv6p@VNX?mpFuH6NqXa;-aq#i!5JsR{}p0B|lq~v<Q
z%t_-b!H7TgIvl#R!O7LTwygVO)S5QYJVH6&s$@t}>IHrgt<KCQ;qF?3yW@~=ET5@K
zUp>}9tY2FZX!E^<^i{}qaOei=6F35qU95>}VzWJhY;{&2H_zCX<S`*PZHZ*pM}J^z
zn$~&}B2L=+47tBJ44cf5k<t~3JG-<cl!b7Ski+xR$xMuBA}-@mnn5B-#j)NiTI)J&
zeUV>q$9Fu`?J$;<R<tXxs_+j0`%^v~a2JfC+EMKU<%=`DjDKXM5{1CEAR7jlMHkIS
z+qQ%4$K_xhSc<=KNh5o5_Vi8q1w*rF8sDD$Tf7i-yCiOpc|R{v{$&8h`bH#TJu99S
zooBEg>g&RBqojUcm=wv{+oP$WD2fr1i8sy`-$<P>&*p>>HXP*R&2zTfLNksPr_NdL
zd>t7jKQSaZf;-f+uBjLhshtVfJ5a8m8M;GJCFeoHMUZ>4suC05lG<O={JQwPP;GPB
z)mq{PgXZpgZ9EkLa!{O<(_H-2XwHcLofJ?@AS-H~%3d+#tFMM!NEn7203t&KXa{Ns
zavLsBaB-C9a62YaqzG;b#Qyl<$?k*;OJWZRZ6EJD{k9WwKHi3nYnK8}m#Y5EwYZ;L
zeVGAdW7EGW=CBcXl)SimXqFLXOiSHlmMg^vNgxCOiQ&;YBBpFedlmP|{XdmM`Uw59
zopj~YMR|Foxp6RExYeX%3L%`vf2MW&ndF4{w^T&NW&~o38>nf8NH$6aIyd7drF~f5
z%e(2jJAA%?KEREY=Q_aDJZq<|oxGZfkBU|E@94aUC!3uD88VELn952J^e2jso|PGr
zUYk$UketJs+(6b<J3!WBb$LRJhU4eSYdf#fyB1A$eO2-kVZ<tZ#VeQ^hs*i?Qo#3q
zxMcE9=wWpAzonkX!Jfv(gi?TkLzDejWr#m}t(2^t_N4sq0O-Qxh6=~R`K{hHk0q;!
zmdR<Bl3QQa5nSMAVI!yH;BxyW>2uE6BC&5<E~fG&%oTz78LVGIDAse}K)w1_nwT5R
z`&(kP$IQuszokoLQ#OoRPar=Cs+eK@rKIMgXr|@hD;E?C2|2bsj9QTIpTrSA<A>lE
zB4QPLmrAJ(2V}xVRkKU23G%}pL5m_u67rr$_v{ji?w~iOt4XL#jtKIQ;}(+rSL|nU
z@jl&?3U}l)wFO59hl9rqj8NG*1d&Ma3~32kiW~bsu}kPp@*@^#u~1>~`Q*DaN`0rw
zKiu`}`+Jne?;b$x<<Z+FNx$$oMkg=b%piA?nN@iW1W8ypun_y%%J<M>Uk=p(sg1VZ
zw8wj*2kj`I<-gV2mco`im)A?=qgGF;rEF!064HVxW9;jubBMw+z_e6k_Q^q}a5kE1
z$CXiT_FD*ae)3)KJvcTtm(_p-XX^cHk|0vD-T4u_B`PTTtOS>(YOTZ7(pvIoVM%f&
zLyH?glDAHTLJ$Vt*rq_x(lseoXDCzHL2{o!-owF6^ZH#$M&L-luMqqD`0<5KrxUgc
z=iu`)e%z1^(+=5S{-OmR4I?8ZrQ|2=RbIf|hzPyvCOJ>VKkM~wf2YAXB7JFjA2V6s
zpN_hw)wsw-(9O6Mvy@4A4(IRzmx9KU@%(>*;Xndj1!z$3iwy}pvH)Ztjo$J{E4Y69
zSnX5OWw)bK1obuM5)Pp0xi0yA&bM_^3Wh^VnwZFx+!<48Gs<M`Rb}zB`VylAg;cbD
z5M#<q-<1DGGX^c<8K}+0a()jKE@R<2ByiN=&LhHn6@OgyC)0^To9-G@N-*?(69@ua
zup(b@hl=CCl!7|NHHBpO=;)9m5ei|L3k>(L64d8wtR_8nj92Rs>~DYcw^&}siwaeH
zYb0Xt@23V`>5I@8sSB<#j59jM@w(>*n?(x}`+I}J+<_kqYg+%^hE&p%Y@)lVONEb#
zleOs6-ckRmvT(J4O_zxWsXGI12@v9?eM|=z-<mdUk2>hzSLv)!o@uM1r|jJ8J}1|c
zdN*z}jx1sUh2!@9H5jAH#A<hkFj;?Qslj-(8-#K}{MuaAhLtzXD~ymC!We9&Rq4Y_
zUYrUI8aKkPG_u6LP5CAL^wJ@D&e%}YQd*Oj+ZTkrGOY5u427+q>Z0%%0652Ue@6P(
z_}eX5R0KnV#pNDa2>kmLx(?$REHMnze(FGOMagM@0P7o%JI0HDYC>fqU9GV)lGl=|
z&nXE1hsAvQ1HU1c)~R^mKx%84fobS=oe;HvnmwVN)%pc$&DANs6Nl!qI_Ru+Ag0K=
z>CtT-_X`X!`yA}+t0P6MFhFPZL#q8aP*^ZJsrDvHhB>3v^u5E01_dL{V0w05TLbhx
zh1n&s84!J!ssLxtkNDFQD6L5KJuP7kR5J4C6xNb($ag3y_+ZNlxu(2f_t6S<cM@`Z
zlN4E=<D~f8=ym=8NyEJ1`Sx=AOY{1mzoPDyo5A+)NDQiL-HZm#As~e}gdS_vi8vCV
zhnkuo#|CF6FAp^vT;q>3zL9&2(vcx&*nEPHmz<;rIwWS9z3PiFUEjJ~4rgZf5F;yw
zxu7*(CMVI*&?z@ltMSARCx5J7akL`Ncm)g_gUTePj~^T<5qVgQ<+jY;OqylxSL&{A
z&sB>txS_QKO$k@+835KggW?%EcV0AV2lK5}RBb#MRGFF}rvJ8Eb~{^}Z&em;!5Jp=
z1_cSO=Xd$lT^Devf;^Ym>-S)t`lJ|(t0xa|lhwbALr@#ej?I)6KZX#ve~WSv^a$&-
z#@AYEeTGR4P`@ww&jZ!?CEZbz8(KWFdS>w(IQCLnI{&jYV2-sou2cK-HLs4lGLzrK
zl=RB#Uxu7W{OC6=6t2p;FawOxxHc|0YV$$p7VVR-VPgamokhyHGg_gxBSH9W#$bCc
zKTA345WCPODXGAXv2wOHk=`D{thAr0lgCdto$ki=Gf)luWS*I^=w3d80||eR(J3O{
zyHfb>NPG&s03M{}dQo$yJg|?~<?`BS*LpSC^r&SoW47_CfT@kK)t7HNV5c#CwsFsN
zFqjBa{;O_8b$#CzGa)c<PFHa%ma1>3()?RNA~HYv`Izz1m9HxF;?d8b%f4qmBzNT)
zO0La@+OXZ-JVGK-!4S0hrBwRi2sdJt#*6U-zRyAk=;_2w<)zLu!A;U{H|#~H^1gZ5
z<)*l<4gnqy<}1`%)m;|$`p6vEp&dTb5{KgVEPPQIz@pCrTX=Y36fdeX$&Y6Qql<Rs
zlU^gzUy!V&aW(!sho=*)T#h0@u%o608TF_G0X{O!h>@5`7o;Bqm+f~-so1E$1=CbZ
zs2%}k2T?B#T63AWM|#Eduzo!^7rK=D>>?82LYDTaE4xl*BiAbyVRPQ$!=y`44+&>P
zJNkbaLCY_usR?2a#&%|055Pq81~^E!HxXxWqocFL<u>UUnk<hW*fpQ_G{_`7Ayw}{
zK7E~tM2Pb=DeaM&p0%@bmAgOCDc&WGmNr13`MpT^{<gH;_bX+YO584@C(x5~l;%g>
zl$FIudzGL-BpctYAB!a!=6r{iC=p~iae2ogtM!l9XX}T(@76*4;7~Bh8iSJUsV!5O
zt2u4mKO_<l%HMF`Y7A!L2=}e>d1QuQ{9Gc^r4+PFqkU$O?8s%INCz0<{}hsu^M(P_
z-a!C!wO>+sytT86^=HSZVdgtQkg_2mq6Yxlr>tD0pUJpA5syZ6U%JoT3N1xAeIY1U
zAZg3${S1TL-liLNRr-!Y)^2QFjBg>9I4~Qg8(re~#0E*oUvCgEYIS52h%s69lQ?yL
zs`YfH%jL@2>zbhB5fr$x({HT>egHZSQ5iKvyqW&#kl*PO>HjT|t}f}_!zOB#&mY*B
zl^4;9W=69&L-ps~L(UX3P$+%?VtG9bkREE2Pd5|y(!bV$HzI)uz^G<>@=SMHJsrys
zF@eJvVvSwe9Qkt)Rm@pTCfSXkpKTC;&*IhQ4Z*QH$V4tKK(OKq`MI$71n6laMSXRb
z$)h?ulet`pCl^%t`e#Q8H8ieK%~|Vl!3h-c_q=^r4~@+c0=!Whmq9UL{+hfxhJ|^N
zgn1|gi=~i*@nd28+o_X<02Z%t(Ue5Zu*J##5g9$HFsB=&ok93s3!FL#s2V>N|F$IQ
zXY!Be++QM0l1`hZQ=i~nz&t1~>-%#5Xdp6mx!l_dBGr#A$D$p%C3~m}8l&I6NHNbb
zEsi^mllWn|!$-JS%;Mv&ZOmyAusBgv`#Oc^aIqnORrgFQ=bk)#oS$mox-pbRIpNzd
z(-!VxXtcN=?z=&@dX-_W-NC$5=G)Jj@;pftN<Q=I9DR*&h@U3`WLo{6ga{z*<t$QL
zL1)TM2IAeqK9-0EE_i%rfMh3lebDs{&NQXN58|HecDbMWS6MbEqGQYI_{_l;vDb-+
z)GA`5_2(*1iIk9R&(gNz*d~dOoiq;Td&AvP{{)9O<JA<zR|X=M_A}8y^|*0AotQgk
z)dpndvnA&PIy26D;IyjK`!z~LTA3t!ZAk^9OKE|I>LNJzthhrE5_Zv5o$6J`8bLhA
z&sE(ymgM+SuI;+Ek8qMJa%QsR9<`e@^q2rEM-QGg1odVUD#drv0ckR071;U;_Nybe
z@AK|>T&*u;^Iv@n%fhHw*4HL!OS;Dr(y+A-`LJ-fn}WaTKo-u{fVcS_J6OEB9fzUD
z^ppw>6S-(}P?%U8rZyP7!^rOlPPiTz3Owl^E+;M-QrS^<Z&fQ#SAF-18@$&DS~b**
zHJ)ZGJKxmxty?0E0BhWaBy_=!hU}1p@@BBMar;5Y?%u^<3%Y4yVV^HeVCB0;@g=m(
z^wi{El(E1kfDWVUPm?dm2P&!VeaZDzg5H79ZO=Cs%kQVSN+fG7#=d5rf9k?9mcalT
z9Kjd=-_>6uWJvwpxQ3;%aDojWPy<RS)0Czxs0HEouvioQj}XjnDQ+F8R|4ax?lATR
zTf*bx{BM-;T%7CNgZj*p=3GGMJs=)dt_WWx$OyPB0F<RCUJ~4$bxo{@@DsaR2@4I^
z^0YQ5$57lOptyjW!d;&;zPt%BHa>cOSxF&blE{U)qN3st>~f<D;&q?E&%s3gvwfre
z9PI|f-Ro62_uF&x`Vd&Vvkk_-t$qYne`&b?tu6_Ni?;IE+fpLfgYUJFGtx6tl7H<R
zR5V402kHaFP8FOrWo0OPzuH+$56$B{l+2_Et8RKo#*2IHbOxd@8(Lu~HbpzRtFQMc
zf?5gP;U#CZOgVxbmZJBha&8#K#2IRODj)$_MsjvK)`8itGH?o?r(Qw}m3P!@af$S)
zq}Oo&JWzp(VM*sD8nvJJ#yy#)?MlcKY|ecL*zmi=!fd?VoDQ11F%fUJ!Z+8@<y9V}
ztLjn-8k(*x0k|?nVzEYuH_lQL#9ycpdgc-2zMiD-d5!mX&zNQTGn7FE_oIao3^;W8
z1)J8|+dC%A{Yl=ppph#rfXiRc{%aud^M{wPBT7hLK^@5%Ehwbo<ZSTSmH@@fzAris
z@I@a>;TQx{@6Q)@<)!n`ME-_v72UbY;c_@ZQX<(J0)_2P2vSpZR`~dqdi%OptmYoB
zKlQ#9D@);QTuVA>obf$gv5&)k-oI0N5@z6R+nW4gkct?NSs?gO4siZse}!`YXkYvD
z$eLDec!<7~>@K>J&*3-1%~<}PNQsgp04A%{_Z_00DD}lKyFC)p{R8jVa`&Bq$$Y3(
zkQucpHGr#kgPudE)oP=595haP6QRy_-xuyA$F^>I;MkXJO`}E07wS^Rtj4zcKo8df
z%?4XvwMSJ0&hE`TG<7W)(AlBWW&Es4t?*Mpadvldc>;PTOi1Fe8t(*v_^txjL(5cO
zuKD@dKWG31)I&ZTjQivwyI^|6-Z+_B48tdcC8MiSed@&#$Nw_5(oH3OLbs%)B`2#Z
z<qhuLgCzP=lqklU{Bt57+NpRPCjrJyW0DN`g-Y=V0{5!drIC=&^r)#oC-j0vEX|w@
zxNd+BvH7nhe!=gwdrXrF@}ErV@pSw;O`kZ4Vxj5neuEw31?4~#m~&R^w!2OqY9)R-
znfVHs_AS-J-zkGnI};1M_3re%3lgOctb7F)-RzPAi{m_{4i`WI4EHVk5xxkGrpXJP
zrsftZ>Y2&3(h`Pl`Em6DuIr==Cr?W|$M3nJ?P(;1>v*!-=&*kO1b`z8<3FQ)vPr~{
zD0euk9x#KFp|FW}IAcA1jxr(%m}qENSV@a@H*m^~mSSS_Lm@k#M`M7e!{^Cy;OgT2
z1f48G{cIv}kl)~`km9M|+{p#9^e<xlb#*kmDryPju5cXm*+H5_emGXTov3OT)G#?1
zjM&7M%33H#2!c_XXJApZ2f}6@GEx5U!H(Z|cb{1TGcpOy*VEy>@1es38{bGsDJ3NL
znjPjTG7tO~nH$ZZjBX?>n6a@j8$Gaoq<D_4Idu1EW>?Tq(Jw>6)zeY;yM<qDEBi)F
zwWqhfT(8*t94}8%q1kY;Ug#v!n$N=pq0-y0ob1gOXPd{64uA>Grv}7FD0ypYP70P*
zUUpUdazm-Gkh$i-L!nk4-S0uzTo(S?kjuqPK9rV6J{;XwICw<<0qbuW%G;3j*EOf*
z{eW~yIbomDs<Mg+)}oFzb$Zsnj-t45&>1;m^y{d6IEvV)f-R-f6$(-;@&lDG41%#h
zuS4CjKXEM2HE@{qp0A2&LeZ7qwUO=&x0At{Me-Vl(jVu64LS_>?xwNh{595~+4r+Q
zH0R@9dBt~szI}AitwM({XGkqK5gyAfH7a!95t@^jB4TAR$KBo0#9TIMc8eSp#qk>h
z9U>Jq&1GEbT-%;KsWdyWq{<!!`<F@3w~8P@DU+I^3P2Xpu{5*S0{Mn<(luE|KdyJ*
zL2uAuatX|`4jyR63hy+D*W0GXqk-;ViA}6#jk`J3T>oSbxH%76<FOI9gNbB;`gYcZ
zUx)dZWJmx))d;t*priMy;~89~2@x@U7_E3Fhh@_tr+B<X$<5RjtH(}aet5-Zws@aT
zkH$&>JC^<z(i+MaT8p#l$$^qCLx1&D^fUt1756n7?v(#()!&abQoG=Tt6M?zdNU)j
zu(CBd++88Z1XY=#8X#WF3>+AJn0-_Itn2@d6#oHRdh~ep{;M8x@HAtvi3vL3PjGxp
zS!Q$BS+~G|3aZVdmFZ8jLx^-2@aze$Z}ak$K((=gl!Km;YFI{a3esl1QDr7PRf5Hc
zjgc0ziS;7bPU@p;18g0n;*V-1Y|JmLQ!MaOCcZou$%^X~jwd*k!rnJM4Xw)hl)Gs;
z;mn^GoVnv%OtuwcG325NZ)Xgg5~>1;k>{`^(3p#V+KRK!+sB_-FN!02@uR<GwRhmp
z0xJ{o78^eCe;{6X#iGW0-~Q%ms0-wFU?;^HtajM+Zp<!{0wCNgM<nBIKC<LDYUOF=
z1d@}~LiQ($BHs*|9^XAam+J#c%mh=V-4=~;VL>r1W0|SpE`GfF+7j}eGry0=M`<sa
zpGBf=x(+4=SOiV+OZr#2h?^~G3OaH*faA5gNIJtiSM7$Lb*<orz^gHnN7()pd`RH^
zlsk34!{hmGI+;@E_hi+G)D|X-riqDYu?(^#IEsjA9A#Tdejk{QVA?iqT>W-PdwXDD
zU_n6vW~q&}-A^kdRw?CM<^V41oy6IOo0yR@n^UdU-JxKeNul?L)22z%jSlYz;OJvn
zkA6ii;f-$5lqts2q$SB@7d>$iB8SI-*$o$TFb+M->yJPmvd*@SECVso-hc)~t2#<O
z#bl^rq|mqgt{(R1Zljq2>^UihxNrXTQBmdPnQd(WNY&=eyvrqt2#*p6$#^8J!A>u(
z*{zPZ8;KgAj2S}8H0g$4`SS5P@OS7R3O6*Uq>Ltp+;OzN-6_=}WzmcQzxHr>hWFnC
zGf%w?o{!SYBC;_m?_;Sm)YifjwhycE%mhd<X@#vsB`HI5_jXc9suLw9x;wq!K~d1z
zP&YfG)i$Wc8YF5c8JeaVlLwEX$6<D;^3FBl3OpElF?q^_?5Ga$Z`x+S;7R-6!{5};
zEI*)WP<3qLtU}J(jBf}+B)m?Le5t@jvC)4gNkKdnBJ&<bmChcG(X$Dd$gjLS>a{pL
zq9=HWrA&hu#8zH=Q#EBL(Y7jN>(Iz8t?+)vcBf+))KPR3`m;QL&e*QKsRW}x6(M*5
z4}G>=+RZPWxX!PX<yHqkh{Q|Nxv<0a!Y;Rg@8|Pv=%U0%m4Q%oaotzOB~jFaEx4aN
zDL!io=#oCxlhsK*pkvsXVVwlAw|L=m&;1IsP2BBnYqJhrAGm(Pu7bcRf{!E7`4c2N
zBYbQ<HEXuZuKG}Hto$9TQZ#NH(iSeS<Ns#~4w2@pC?x?K$q)^1t-+OE(8)U94=3-j
zn;+L8)s46NJ(PNa*UKusA(1p;y26LQM*KFq#@fFG&OS*ys7=w5P?&vcqOr2$F8jfO
zWquw4W2Bs;E7AQd+mO_dB4x4bwT^RGB{bskTQ>vX^LfyXnPk1i_xNmLzoP$INWmrV
za{~G35*jFJ=IiC8Dr#<FU|+#SU*iR-46s-Q504UJ?guZkmZn6V75RDqR<>-B{0*eB
zbF>9`xfw02ZTM@}J`rmZj4Ti65I1A9#{tYo5<#!~2TJ#-_nYVSTi7O;iL52DOjmTK
zfg7wK>zh7O&}2;9QAhov)3-WDdk*7~yG&kFALtPetS`8U9gadJmxE;VKvy#2Rm}em
z7xBJXZYKNOdCa~fYj6Ab(p$o;S{Y!0jOtZ79!a>>N{?i|$9n<}TM8Jf$fN!bRcF~2
z$JRhu+}+(ZNN@=5?h@QXaCa@--QC@S2MF$(;4Z--KnU(Ib>DgB!}K5M?&?$3NA_Mz
zh|#=zZlJRskz3*3;MK{0yQx#s(|@*?-@752a%3OH!UPrT=e1nDZrAzQJif{IoAZqA
zx7+KAWfkDBfe^6!n}{n+Ysd06Bg}mh%f|QK@ZIlP)Yv~cqK5%_wLe*t5R0p&iA!j1
z9q+Le3>@*aTt`iSM;fh1_XTi5`umAIPVr-oC+qHSf9jqk{jVR7wt*)EI)$Tna-Dg#
zEFzN{7GZO)p|dwbC;7@8NXzCjW|pZ<0|}Qq7{CsJ)qWpOf34?kY?|8k2w#f-BpOYB
zLoF!M9$X_gEYL<{V?y?cVc54&4k9VJh`I#n9sbItwcO28o$Wr^4~a-%IGLlrcQouq
z2z=5aO&j8__&7P>OkuDUTz6z-u+q~&h1aLkg4oJ~$6EGEtV{hx*cvrlL)6X#4Bi%k
zezvw+2x9V2`mqu}tIZ!}BT!MClDR!%SgBa+CwU`%|8gfiZ1;5e`*{97OA^wL{6qIM
zrizmP!-w#9)<|B70xwQ=Q*0|+2uC<+t(wdZnFAi?H7~Eh;A}V1z{c0%BT9HW$wM(~
zb*U_?gNCta#AFGKor*HGuP$Q*9i!<UwSa)n(On&g4lyKccR3kY)fi16+32;j1Z0$y
zQ!~nVZ1d37J7Mi@<2Qub90JrrL((y9^-H2!93!XZ(RvmNBN5bS{h1PG_JcbIaCTz|
ztK?i_CaQ=z^138;DMqf6(1<fF3QhhHYOnPi{%&2rC-3!wb_o~h@o{?HNZKUU27Z2<
zLhxkQ!X!)R!q)1J4Ufvx*1_^1lk{cjgFDgl39U$LW7hjoDEx6GrHWD*JH;*>ZrWE%
zO#|N2QOI9idU|MRVL>*54+dgU!o~vQJ|i_qL36fi5#G_D+wE;CswCp>e5=*=DE_~S
z_<Cn?a}OK)%3`nuV=%=^T=x%F;QYjP0<CQpqg2j+E%Gj2Q17d&vvY`o0C~D%>g*Rj
zEO)^AKe}@vvWAMBrZi!@$WJ3N4D%<&2;|!5O1r-)I<u{B$J5UV`Q3icm)9x?Kv2v0
zsi~1!Ap_q=sM`YcJl>{#6%yf=A&qEX!!-(I%J$=om_T;5Tw(T}uRbL};)RN|<Sva;
zURTmx*IE|#X>dr^SX)6N?S`&+gNgPgm+A^c<q0^wEzG_Oe6PK@IM?rK0&dsbiFxka
zdD1LK;5zko^&tCCn11jyuzlj1#YXw5dT708i>bzrkU3sWpwkfna&1~a<5RHU+JCZ0
zlxuEqb_yeyx8yfa?Mwi=pls6Ls<@GP3%Z^o>z%eldIG+_AEqJe$<GZ4W%4)q?KP!R
z(;G-~$1m12O5t33z(^039paRY!nEuCsgb=>A8;Sk%HdiQN135j^yS^gXh?uBaP&7+
zC0bI|*6R1zE?&DU`lQq?0IAbn1z0a{be4Bq3*g`0+<r4$;;sNX+48Jd`uCKTQ*+|N
z8V%ULGyF^`8XA2HrGvv+#^ySu1?SEU5!qclTu&#%x1@0vx|gCy3w>s~|My4evYBf^
zoT$854Y&iN>EE_MBGu#iFUP+rb`MumRwD4&?sEC2Ux;Kx8n&YF82u$@XIhnNb2kLL
zUZKSD`B&C~@d+ITIdzPhRJ7}12E$Lnau%fF-Ih*vK4zK?2{n0PNo{RBP~FGDLxTSr
zEER(s3v|DKIn9y~SiX0cD0huvIz4Lncy?~Qwz;ONmYGIE`rF1LG8U@i03~mL!?ttz
zW9?-4*9d%m1olsfmxY`k#r6%*s!VIBSnRmn-OY&;fW)EP=yX`u8szz6=#APq6ftaE
zNj1umn=CYZf^f-~!{(ff^Ui-av>d4~FSixio<B)f8d~Z&1mgLQZ&HWaN2o7qiAhBj
zBDZ40+v%3--vWAsw~WaCI@I5NMkys7-~EV&TB}1}Tsi87tUOl5JQWG<eK&C}%!D#+
z&matO0%>AjEJwbGxih`D{DCD_Ia_V0Eh(a*9uW}~E-5TF-5Thj-Ml{H(#*hZg8A3q
z42#Khluie~b~5SG{N$y;iLH(yW27brYm#<+Ush95QSr;|E<qkXEB$WdYNHfE1fRyc
ztfZ<Q%owzxyJz+tyYHq?=KOHkPRjt<M(_VlfYFb}z1{83Hrjx;A(=PZIMOuHB)1@1
z^a}gfFL-L$fe=5T+&)UYs_`O$>~c|b>>Fl1QKM}zP*PD2>MxmC$Kt%~)tnt|91wdF
zhwKN2d6JA_b)51}6WQENT`C10rs)q;xm!LOb``n?*eZZ0vJQTrU=ytNDTK9yjs##G
ztB`QNVQglgAFuYnt#u(yORP{sdwV9ck}k?+F0~&5vLiS8u>Js9&o+h=d%$le=a@PT
zHnP#ba&V~B`YbYz!s3|7P<EkpIjRMCff@Vr?N>e7W|Nz;oP0wBsf4y%aDh2<hRgQw
zy&0?5p_JMdE^VR%F0*07albJP3lY%eqXwR3g=WMBC0CcTR^KH?V64?#eaP}2$(TA7
zX$Ww3RWmCLNh)TYrv)|3U9kJi_->w_?u{6HecrBU^2}Duibx)idV)*mkOmd7J_#q!
z>&aaFjg5`&=+R0a>uvSBe5@@M+)btZGQZW-W%YD+@rnF-7};wKz;A!xz62tf&JBQ@
zyHRrB<?<S2;r94>`<?W4f;oHyUgB`pOrD<vr=`>fYRqTK0-GxNEakP1sJPBHD9J<`
zpvNEyau1)8QBZ`Ot}=@zr?5CE4=^yYw=`#zy=QCrf~(1u=t0Ml!s}Yy5YA0<OC0ZL
zEY@<)F5EWe1n!~`3mpCeZ1d*l7F2(U|DMA6ywH4ypunn#c;7Osjg>V#cTV{O4q9kI
zh$ttMV~Qm+eTk>d11&zEVou#)b7NLrBRK^Ndw0*yR&33jA}*XgphW`uTB{a%t{UH7
z*!g}QBosc~oa~JzkpIowY;CGzrk&Z^fLL5EDQ<Yth8zzSB0<Lj<jNAc=F6|;s&ZDI
zCCl+FXr&WB;3HkRG;vrpE0f(0(vqPwL`qMrQFF0>tNzb-O&A(cdJvgG7MK%`Q2*-}
z>}f$m?nv4Cuk-C0&hN}Z1M_p^Onm&b-&^Wi%NpxDi(4uK!}<vMt^3Y%qd>(Hrlx)A
z4Zrh5j;@a0`Wndp{^0{3K1yS;pQed>R5}v&=*tI2^YP`F#ba-qNwHGDWV9tId~u*A
zsKEAqf7p5b@!gT_UpS%>rpY7UQUUDZnhgc%uW5GFNxzV}*bW6yJ?0%i<a2<!U<yXS
zSQy1@X3$i&O2=G-8(=N@y(31jD{2xr=T!~S81;iY3t53F=ihC=lR2Fe7#&Wxd7b=@
zs$peemw9PEQ0B>MnE$6WR+g5BLJkU9tZs;54A(kIcBN`x!R#79==;rchv1BjfklR!
zlPNBctV0kK5tSaE{Q>Ko?9@_#rX0H>H8;CHD$UqT?sQ`Fh&<r!<pjDR*jVs~i`6R>
z+d21Akt9$)^3<#&p&B1MPp_C8TY6}TNiWpZB=^Wodz|*2eQ_41$i!NXeZm)_)GeZv
z?7B3ywJ&gPzCij&0RbVH2o7OdB8UM|Q4qJ>QQhJ`yaZg<Z8UY2)Kd_;xy?0QMNisJ
zAi2(An-4(gB9OGtVp3oT?9FxxefVvRmVIiX`~@&W`PS1&I6u<Ay1D)fv?;JL@X^q4
z^{r21&OUIrwzM2Cxx66a+C`$8+3aEE>u$DsyqAp{Cq9nFfc)G1?Rk-2ki7a?CM}c#
z<p`I(ItT-iRWhQUVniUsVwq7X_eQq8p@~8W4^`c*pB-)SrZ0p>{@t`%c?cF}kheIL
z7hs3x>Onmx7DHN-m+iNwWTa%JqpP;SPhM?sdtMz~Rz6=}5$J7$FtG3YTUg30!pR6H
zCe8*0f4WYwz$!~(vPmYH$N_r8%z{}komWqZI5K(S9gG52Tqcr^A4Rh?Ku3*~l7`H$
znM_FbrC)amkRJ>RTTcEI=qz-7xlRhI0E|U|(tQzShg19Ugi9`MslYVdK~`cJ?wK~K
zmBA4mL+L<skYt9l4VxPhyzBbmXesRry;R_PQc*o!RT$idPgZJzT-RP0lAU=N@D26h
zO@cE=5N2{$4pNd*pmaVGt-p`<LvPx#1*Af^@8+C$D=Z%eHZ`;mDT|U00?89SP!^xj
zpz|F*z{*71R3_CM1zW<Ol*J#roaO2V>fODI1V@|C8WF-R;)D^=5c=R8npthZ`MsDR
z3tF)p4@B5|ZS~`%XKwr3%X$XLI1=)8yI8uVCdOZ~2>(Pp@k*~WZzU-#sa_e;nqNFt
zi&&b-)r-M@GMJ3~XDw%jNhP-fD2H2s!m-z(xRl^bApJ}*0>_pk^NVP_qH(%#0VnV)
zVnt6a)F4w4%7U@5&=3Ee<M>vg>r<QRiT@1YuFWZ+&@*Wf?=Qu_V+k1RIhCBDT_}Xe
zlAPhSs4I#E@lHWjBS=$|=-&|OHjzXL?b!okOAB-Tr2l;h&!B~quzeu$tBi~Dd~vJq
zuMMBadGJdZ3HAV|Yh|xB0w0p0?OKK#W8Vp6pkX#iR4C|C#LXm?E`B51*8N@ToFf?v
zA>D*`c2>_m98^p{E{g8P9TZiNQdTD@#l%>3<v2m+_U9+wM;D~is)L%dNw*WY{&(b=
z;Fv4YWp3~2YwPOot_!FceYSBc{81NG-o=0%Br(XV)3ny&fWB@gwomAAiWDx%HUy1V
zomZpc^u?^bAf~mkJeZ)$0To5W0aKUT-&NoqBC;`{Z5x&*08hOFU2dwz{sIi%4{R=r
z0FVEkRwb~UxxX_k%^OGsM%+yh@9Ch->R?=0mM{$pK{9a%$^Zu94qCP<GOk*tJ_eQO
z3@bs2Y$CJQDb=NeQ#kDS7^_LZk$fC<%no1Z0oS(`-^YXAo*r+vy$ulHlm78<?z*aO
zZcaMJuC*<c_AlTDl`VB?v3~?As>y~Zd%fc^Y`9$Ni9b8&2jr=7xj*_SscXbnbDg{%
z_whADdN{J4XW;e}UKOex%Bnd$*U3_1b#*1jHMWGb_yf~KOcm`gH>8G5WcjK9Rav2j
zk%oooS8>U}2%&jNbh*u$Ifj?tj1dcGbUT^jA0g4`>cK{IA^h<DjhLiLv?LUhuN#ZR
zE{K|X*?kx8ZFjvKi|1BY%Ra3EA4r_)5R{hHjPzW#0M%y=lIp;i5ZSI=xI!jDNFoQ@
zX|C^0u5PzCs3>tao{e0h0%Q{~Au6ikWDCipLL-lH6+Hq~$_45hE3JO>J@d<C-OmTt
zV9d?YfZzX%<NHhWf~bHYW4hE3fefdlvwUSu797c~Q(Ew0L$vTbEd_sfWqlU~H9H3a
z4U_dUfTi4yOk;toW5Yx6Z-|z;<g3ZfG4Adj3MV;Z|LV#qX2K<-JC?hw-~5$OM*jQt
z-d+v8XPqurMQuCVM|w^n+9+AUvAbv&B}%33sBj4Of(T>8CQ530Cd-9_hbL7rPo~mP
zmL<iRFeCJ?nh|$$6<e+A@e{pgNlWct#+@Fcz|J+e@_0X(uaWuAn#bSbO6~k-#9QA+
zekxb2gC+LXQd}PR<op&tQf-!tje$sb7mlP?4r)vr38g-jss#$UKuJr-=lW5pj}?u`
z!pp5g$D*aGeQgMtY)G$)%Mn@FSEN>wl1q1Cfy=~->&a~CiVlxA=SzCn#4mV#`@0ne
zK%Zu`q?Xgr)6>$@(F~17c&+9@QhecuuPWmj!}qcJEoElnq$8=RXdtd`;O2$URMN~v
z7vL}6T+zJmOmcR9Zf9RxS4T1Ow7P3@$$&x%l@gte|81@5V(0*DPQJffK6VGqM|jj5
z3xA^$+DI#+i{3yOA#stCWQWlaBxFznR0aN7V<)RE@_PP5idRwy{uEsUt!aysfy{i>
z633F)m0Q<;s+pP*BHihXq=r(>M>ML;pQ<Xw$LJ`%K~v~jS%n;RvF?9);`q%mZ)0}(
zb2<?Y77-p9Auef5-)rXtxCz``gaNZMrRpw7LLiKThYR}4@{HBNwF(aD*`67I+}X)7
z#*S-v-nR$)w=JcP7TQN?5WJ(^OuSW0al>uBq_v(Wlzt@ScKcjBMigv|bhz&v*Q5Gx
zreYV*v<KQ!Hc>Pnb9o$N%W&!bDy(b}HO7*FMuW4epOhkjmmP#5fk9aSi<Pdyg9N<1
zycRH67->*#SO{vUaU#o#yGno*nK?<W!I+=4k(^SD;!8cU%G>2G2wE~+JfXZ(cF4CH
zBa-=Kh)9{mZ=ocVva=C?Qdr)R->(*R;$6nAbd5JvHKl+Co#u<OI*8TwX<RL+FonCm
zRL*ZL%9^d)3^_a9Zq5IAp$yCb1w5Rb57x+DmS=fA3NpLz!;~k4ya2G#G?8qM@_a}Y
zGCF!$e1zwlN##+fDW<jG5YGuIEZb={<Ieo4=A$t`D3ezyKnx@m7>mlo^4Jw!W{uBM
z9>>iSa5+-mPQr#3WoW1^FS&|GiM;PGSK!~*T8$^T&GJgQX8cB=x>Qz#cK?LQDu(z;
zi<G*UX*6Z62(nYCa|pP^A<8gPU0vuY1vDJ>Ek2iKwC0nXh|0d4&G3ux|0r)Rq7j;i
zcxd{q&&qv>o`J-?NLgjShh9GWLoWPZO@c^wkLSs;M`(NWr6-Lr-i6bbeRf2ehwvI>
z=K9HxbjTqD#FjEP%uw^hnY79UL)3NCH?dQdR&_s>5M0=olVJg8Y%wuPO4H&_o~Ceo
zVG(Nf4=d}iTvOI8YAwA%T`>w1V@x&Hg+sio>)vmd9}|As8ER(jPbb25n8JJZs&cVG
z#d#PNT4w5e^1<@&uDUDuSK44HrEX-Qa1Y`=O`j066K$B&guqD7QRG=?SdPzODh)h8
z5hE4G-%JsM4|KrHhy1xGG|H^jLS<m*&w0eJEx)I`tGf<pV&C;@7o9iiJ;)9!sGV5$
zSCn_2*<&q<U!ij6ovmDf6#cw8{%ewCpP(ZWTcRJC1WvDZU(Zm}B2!aBbew5PO&CTj
z)Aq~gCE<L2U{rLMH^m~5%(i@pYEP{N1LU3Q@xoaTt_fh@+{x_q<%Lb3n@sJKJ9MIH
zWLQ1K9}dW3D$X{SJH#ktm+$PKQ^?wyN9D^+q8573|AqO|^LS{}cx8_@wxm94W(HXQ
zw|lbH*1}FdC1;GZnx61DSQbpi@iU#%C2q>msBLfR!1nuvi1*`PY>Il1Z-44P%A!b-
z*0LS#%k2Sy?ZKk*fY(;=Bu4RS_;dywY`m5R>@vL5S>6@&H>^R<o_6~Kqctf}l<*6z
zWc9f@oA7kjH?^uqE!qxi#3Zq;dXLOcVe~LAvfL{A__RHqqpJ}jo8S~0Git?Tz7j*-
zSw>p&Gx%?{$}7kYA94{67l#!?xmJ3xo?z(zbPL?|AE1ttg=jg$w33v_^Yi8f_;@II
z>c%$zbn0X@$KQvQzbT7G`PDx6Xp3gl?SAne0ATC=;dXV=T;ErJQe7gbMn1K9FQVNg
z6v6^nxKo{~A|y$rDoxL49gbEL-(4-_{KbMi5{P^KjQ_K04APQ7enkDVytwXmvaJ4B
zYe2%?+DO{Wt&)5a8zyHG^R}y~Dy+5JRYzKD^p8J2SwN4wiMKz;%#IUMktjqGi>O9+
zKL#2_<OskqSu%Y9wd+#7<^z%me4od%y~B3;+WH|o8BbclAok{#Mg}Hdm&3%$Fb$Db
z9T?G2U+msFp8j!94%q<iXGh?p*BY+?iR4Gno;h6vAD`Q4$>apHkE@f{RnBtzSRk^+
zY}TH}CN#8k)r^STSS*Xh<>voK+^Vap<|Ec}_DHW>O~F!IXm&#lmvhum(iQtGZy=c{
z;2+iS;?D@;%BG^1wx%OK3O#uF8#}Vhu<Kqgn2>9?)25c<EV##IdDL$K!$8=GM>56Q
zLYK)wdWbFPj_+cDb7l43k29olCB_4@P%kBByL>}mu*@0h%8m%KwR*oyj)Tq$;ee+X
z5CU-VH-B|gq?bczYIsc(o@@AP?5|HMFi>?A*G`F)r4A*!&5TAr;vo-qK75Q>4wFSj
z*DIk?SK!I79L|*+>&``+2Uxf|8hUJvT)}d^gO3>s+{iqMOronBIzocVuW#0;y5FCN
z(%_j7yw6ajO)s@}DrB&X?J!}SSE}=y>Np!+KxZ}NxA}wX35^g^lzq69mFe`kwyXH@
zI(^jw{8QUH&f0*rm0_GV-BN{+Dqpv^+@3s3a#gNb?Qf3x6dz`b0T>ZtB;-LJhp#Ye
z7&>05J#i)zR3(Mtxf8$nf>k+pTQ&t)WU$fuqH?<i%&^4l3#rBx7M}m^SX;QJGg}o%
zkle$r-eiY{v{pw%IT%Qrx+7=KQh5$52;gahrMhU7n5Z!da^4nVRn5=y3$wf&?8N(F
zY&2|9@ySOY1}CML1bZc#OD2Qf9ZRWra>20zle?j@YHGq<QEPgTp~K$`Tnvw@M0rUM
zT?Y*k<1Jdqbh1Z&WI}B&cjhcoP3g!Z22{V8eKnPm(Dww(s0d9^Aqo^6HP!x77rXV|
zEVo)^u;WE!OHa{M&9Zi-#hqjv3cu=dn@tX$>?zzl_TH|q*8?9FaSXngAMRsgh8i5A
zwrFZDE^r41$SEg;!_(|2gj&I!M}1|3)5W0@r9*4-aYBwoE$)^crI+n{YRM852gX`v
z*=b5TeK(7Ojy~p#_}<-&r;H|iJTqK0%Ez**KrWGkWR0)d`zjnvN0=F>*!cADJ1j#R
z{HuZ&4eR8%A`nL_(x6W&<yM7<Pw1N~2c;hASP^LnY3W0X?C!{_mWB-uw^wDz0w(L}
z*rykPZ4fO+oGA=YSAER>7e#v&z%Szt<rZ#B9?vu>f!Qct&l@!K8!?PehVEAMscES=
zy4Zb1>`^^pQF@x&sn$nl>~4&LI8D5)V(hr(u2g=}L0K|xMuW-!b)<Z5uO6G}LEBI}
z@YQlVSA1n~vZ&np3{#sAhVow_bo0|tU!bVQC<NBG^4Ot2kKVF}IAc-(QY03AMwNmN
zzdFhG?0P2lpF*aebhTf_h`u`Lt#2<&l=hU-a4$@wq~Zl$mWnPMw+XYaYF$6Kk#+dp
zuHEK*|GUxs4)L5Y!`aS8BQwgyT{tdamRfkw(2jFvEgN8kN(yZ!8MBA}6iH%^W+5q7
z?!PO^!zxCDY_?aR!{PY|#A|qNLH1pZgON_hK{8>csd3S8Hw$9lAzZZ5^G~*Pw1Q&t
zFd?sjC~9lgCq&r<e{7PwJZfp9vy!UQA+2wP)_(~Je|I;P%FXF{z=6bd{r0fCg!{wO
zVN<p=;H0>IZG(Wka6qx|N8az$xeovJcw^u)nD2~C$j>RE%so&Fs1}9Azk~MCtBx*J
zIybhf8kgfEK5-lz{jcD>FrSz#jA!=hH%eLAqLRxOqAw|p%T=Tb|Kt881Wa=F_}o48
zZ|X0jf4iubzx=+0@fc%#w*u2d(uYhbXq*!6DWktVwvf0_9*HKA40~UBxHwAw`nJEn
z_K8qf+6?<4b-Ei!nGjea<8SahYEwF0u06A5QG#Rl{);%@5o)1%J9V~Ea!r(B_bK3d
zTIBt5{^<T&fk>y!xQZ>(jyrN<&1`re!x`ik;SJ=c`wQ{Z@XMBe#n<tgzq#U=^SaEh
ziSg|D+#bjA_dB2ivY)l5nWAplw0<9!Zg*cIp&`UN_80#KcGa7ZDoewQjpW2UFto7-
zw?(@veP)+96OI*ppya;R|JD0Z4tQTAhZ&yRr|Giyw82G-pZzPLzxsQa0G@*8cVEMO
zA#pS&Z};->Rq_8?*qROTfp`(3L#m27JBtEfs{TbYx_P__k3)u%VY(X#lGlucAFuO7
zp~>mq`#qn5UN@ijZ{|>8KeD(qz?tFFVv)5)UAI2iyhlPaPxx!l>*w(T4q=ABBo-)9
zdF^4tG>L4Kinc{PM4CoPKYxoCC$BB_AP&3we9vF$s-&CVR@X9t>V`SBD&YNI*4Wcl
zO+`;RG`Oa!m8`A#8TCL&u(h(s{IUGi(!qeZ-R%vS8aI#7Rfg6HTKKs%GLdX;%7Y-K
zLLxQRf`s=H*%ww$HFhegC>gn+O=#yBg&?*aLNxN}LZ{t>BeFJY%^PO)VA1{VLeTkj
z6&wX*+ua^J3#PH{yCfgM@!L{9@VP;!R0`s;Z(@S!17pZcU^YMUOuSMKq4FF1*kyMO
z9VhtyviX)!HW~gUoRgFC7>fMZ{amxfN=Br8b#EXb9VAr5e0ErKJv%Ofe!e?f>q&Eo
z()w-Dfrx1uKY;NsuVJrW6IJSkz4A~;gLFh8br@4OEYX}Ha6bSDh<Drq3a^AMkkmsw
z5hKSL3Yx!cZgKJ!0RHbUZ*j=<Xi&Q!0KgBtx}lT}mgJMl5L$A(4EG;wh}Jrvy!z#X
zk6PnE&m$CdO0$B^p-B=~A&+=TerhTPIX*Us2qil{Hyfo<w!5K{*qXndw9#L~zpY#C
zvL1YDwf%hiO=n9wXTk*2{@*xc0p0&g4a7h#F)2X9XL5np7eyVT5)OENWQ)CZw?h2v
zG8DQ)^8g>`%AQY_*J5_5NhZhbWGcsEb8OIaF!Q+nvA4(9=V+oTL}2N*6}0$vaA-<d
z|Gs+g7e<T}yTAXEzQP<i5hE*0Ev0S_Q!kDDnPGmRFLiTNgbzgxtK>CrsuglL*52R0
zt6NY=R3@aoMh+LrTmb-uC}W|q!O39!|8VJ!m7*U!9dP$((YBNcX(7a))DEm<x<v^J
z8J^L|z=vO)D2n4FMojvCAIdR<bLm$)JoZXySQTrFD#9hpa$hxHemQQH^#iW|q1f`g
zeh;kCK0V#d#<@m#)}Ef%PRCt0b86z4nd36-;WS}*p{dkc0F1ndp~dBmDi9TvX%gf8
z)y<BQUOHAzs)4=V(93y>2O+k^sUky&;_W!$2$#UaQ#H1en%e66`l`CR8j$!-O)V01
zg+Ay;xUzFX$j+?mlSyDK)o8H_n9##N_asri85PDk4rTw{NkWqvdL*YK`^1er7UHfh
z{kS3Zs()7i^gR8(U0z0qQ~tZUvW&Rp@>h{m_w8hl(&5g~Po(KJXgIMVR&r+v_L2#o
z81A-u){KyFywMbrInSoDyD@>GP6Wmu(#PH2&|kbbr8)WoF@{=oCB;VpdV1cEAA1W6
z*=VQciL6OyaN%5_mUv8bKC#7_+)L1q8B%!|nU3#l0eAa+c(xuiqv7!0gOM3su#xgk
zS8iym!cL^mG2YvJFmSvGAh7_R24}(Z<95UI@gs4<mY=Wt{ce)|D{}TEN3kEN;sT9#
zdBApHtc0k?0-Ua|2FutN2KnFceT)oMrS@hAXM1)QrHmgZIHB>#r0;szb#cANF?-?<
zJLTEj0ICzCV;5Q^7?@%4N|MZU!kl%!j^~Ks@_ig_?l$gaExaQvk!*y}3ME-ZaU?f}
z3^#7!5CsIajoRE=#TL1b)OyB05TJuk64d@9gOigXi~;`cSBk|I_cjtNlcSu+UB58Y
zEZReZUVlE3!mAi|T;FVd&j2%?+cWceNw5uol#tZyD$Yl15)o3y*ndaAEo`z6qcDCQ
z$A)ReT7LO@AUM@a_*C(a3l(vN6V&QZVM9Qz3j#Y`;n6Q#5ZWD`zCO=T1Y7H~1}cPa
zlYBGLdF``?mvKKC>qndrm01Ti&|lcsa%vP@)XgL?eIpDu=a}nypN#e}61|3?vF1$x
zpOJlTX1(4JO-A7Td^j%U$J0(Qylhry?C&Y(Jm#XA;6fu*k7Nw!!$z;n2v27nTc@_v
z^fFI3WhF~ep?K0K<2+Ho+6a-BloS42V*A2ba8q`9+t1-V*Hb3sW(~8!!DE74FTss;
z%>_0#b6h;P-JEC*af4}-1AYa~;jw<;BOS4c(IuuB#6Qlt&ta580`%(X$r?mABl8A$
zwQeZvBMBZ<+-}*lRrco@2-pe!GQSLZZvPEGgXe^#+e^!zlQ4Joh8DDwun0uJM=rQ5
zXHZ!1G$D@SGHul1r|a82#S=yCaehQaW?ytU25^lX3-M5uAy;9`(%h%*Ns$3xJdYNh
z#m}kxmSq)m^drNPQYi9*LRTh7SQr@^l=8?Mqv%S48<%(>><Quq@y^+`;VZ#O63?Y3
z^#|$-i25-D)gLBb1YqDv&0nnO)mQXb3H_f77os0u1FL&{j%H)-jq_1RRyXF=b7786
zNu>~b-o<vgDKyh$U-=!*;35GnluInUBZ%0pS8Sx5j24^}R?!f6^BW-DqgY~K=dDrq
zKoXtTGvH`|wp%EsQ{Zsg9)#sGvy929#3vBtdR0U2{>H+9O~teQ#lv+;ql}zn9yy2R
z22Lp#A*tz*W4>CeFUV#7IlJ_O3eJfK&-lFPs2ul6(zSk=ycS^}u>#xe_YVg@ekFV)
z-Dqf_{J2E?Q;o=mcZB>1y$DTAd2curwx7WW$_S5<y}F;+=3#`k#1cPKlfzTjmhqDp
zrO4$yv>I0zg|e2gE~)xkr@N8Na`3QF_vCZ>E$cbx(VZQ5WNAHs%^GK~9oln+stv@1
zlGR0H+Z@tWq7$}O-32a5_YbXXqqaQw7ZUxuTT1zCl_E|^ph~+L3IDsIN|fV_V~$L{
zRhE-ct{L^40sn3eevp$Jt$Z(U=I11dCeR{Gat>YF5!Dh(PR~e6ra_mb#pT9>hO^%`
ztqex(@36Q%57C%Bs<Znhi|ovmxK|`Qvwhw}r)th5?Cme`4ul4efN1WU8`e|(9y5#g
z>P1`7`_ka7@t%BlI1wEC0M-TAdT#P%G0_~{J~v|(DAML<ZxC`$i}s_iL0f%?7+Lm_
zt&gxe@Ul*VtgTn9)>B9X$1L8-1O%qq;($PXsrzufYsR-tDC%_2g2X%;g~fQ=mqHJ|
z838DL0x1ljN#a_UrT9LaLk{7mj-Y1$yil#)s+LmRF;K{>7SaxpN=euooP=#xq+Lg6
zhC8%|3c5lzn%^;0VSi*#xMIQ6=8rrC^M2~AWamOiW$9HB4BOHKdsD|<$%$&3Qmh(Z
zrTJ-+5x?0pX!eGpv==4)S(1#sEE%p*Mzww4kB$ep;X&7?|HV$q(YG!CF8HyWVlQ$9
zYJ1#1UKR59p&Heo(CxsrQd^Kv8$T5~lSqQ96<G2URZn|9(goe)+m4gPtoR)EKe(&s
zslj*HQ3FLW*Ne9QId|JlUpqld^|`Rq&B7xS@6WUCg=Hh{OhLZO8zn`2;!Hdh+Mb3g
zAW~KkO5JP`57Al8RO~<fyBMOkmZ5ohH*u3i8s4GlXg(6cB08ToE$yy;bs}_mBb}0R
zzBH&DIS-z*#dqL`b9$oxWz-Y!yz_J1UV-?!ctde^%Uh5=oo_6(1U5H3oX#58(OgE2
zxeqp*tok=D{Xpdr7xPw2=)-Y6T{ItXnL_s(^d|A*sQN+q!-bCL!^~SZs9{|1a^74L
zickr<ojVmcx^vXoP|qHb7c95C`j@zfkMF=5PDl1GFY8WBH9k~^{`ZzFi0tmr=fj+M
zt_B+<Cd<93LTcp~>pB7ZbMT%^5j)3}q`4s(Y$DEZ5RoW-J-N>KkER2hJ^+Wqtyx9!
zPk-b&8lJLw{(95Pv{Agox(&w>;!)u5^sD2M5D6mDIm|vPDST(KWsG#@)!NMoM87NY
z6A9q1>6;4Q$^+3a`~hC?`K0<he+Au$P`|xhymmK(;maBhIQsmx>dPx9?2~;H%cS85
zx&W(&YlSL8-5;<6nnTCF#Q>>cad3J^4<wcdt(jZFeqtd_4DFX#gOaPG`W4@k)&Eqq
z-2tzM=j%B#yl&d%FghiA(T+UN4?(uhnc|rQ#6%S6h=1a~TTaVIz}2+Zltv3A+l-Ic
zB<fB#g{HR}t6Q%}8~*UT5O2KhyL5#H%j|ydw<mq3E~Ka~cgHf}z(*+zItgilhL55j
ztbg~MktTgs)VwWnILuBqz&>V@g)kprs4c%|D-o67*ouYo%FGr8hi~2hPYRa-7p23G
zr8LRi0uexLq%Vm3yevvsQELKlop_)pL+tsquCGr~0MD&tF9dBK-cc5XCRI&Dw6I^_
z*38PgG%7EXZ3|ToK+ED6_6wy1_r!U+eYViQm{>gJaoBA1eJ|RxWJTNPx_=nXS+DS{
zw_;L3b#8$vaTAlQ&4nx^VwZEqocWdeDP+h887W4tnn<SucGx0Ke0P(jrPP~FiQY%J
z@5zQiqsw0%Wprf%mg{Fr;teX=OG1~BL7v!=pI|CWMIj3(pyt9MXcn^;IRqWCd!o@d
zZ@kjn)XD~`{1E;0B8^UfM6c3k+}+c!31c5-CLmK5|NT;n*WvHUFN=@(f(aWcH&>h5
zf}$meU1SQCn^W5~()PEEiPvu3cmJjX)G}?g```EPDZocvvGJ~@aOal{FD1Hs5Wj53
z*5+*2vku+^oHgZ*yfvI{G6gjB!yS>}zeRGXKAaa)CkJj%lh=;G@BQhwU04~R<`d>;
z9$Ow9-XhxZ9eJ2l&I;lib(uIcavrvS_5E0!3wj@azje>A`{qQg{nR027E|rz{~MIb
zqEP}ZWT)hwN`tX0on>|M&z6fz_T&K!Jf^bA^XJP0;F(e|72iN*5-koA6?=UTCHuH;
z*)1pw+Uk7YKL`q{S*|zib>2ISI`^-ml?%En%wdRc`0G`paB8iz^Ba#!-kp>h**76D
za%gIxx*SWLBNQOYXsFGmoF9+iTVc6LrHu;7b898BtGd~=Y`a2G`Vb1~Sopo>)hiQo
z)`=a^DpwI77o@sW!HC%1%3J4+TB|4feba=%&4mV4E>QwoEXc%)q050B6&bA{D>vCR
zWgQOpBQUlzD$ml&EEAu$Q2gYA$<``qgmYvJ9|o#)IM)Ew3xr@@+Z^88gN}{6^Bi7K
zH|6rU8|ELli0o<3Fe#^2Lb;J$qi&KtP6GIi<*#kci*V5~a`v{AchIm5%*(5YCB~`9
zZxX<qHIgw+W+0JM^2uIr?C&1hP&~l-3Q*7f1s^J+S=i}?jIK42Lm~kWzqj)AdFxS0
z43@R>@V<Owdc@y-Hz%2-So^?1EyU7l;Z^7_opV!^3q#W4=7)h8zLO_5kJpM4*AJ|K
zD6^%ltu4rb*;&Y&;WImXdq1$hoM9*Eb}(Mz3woYAyzUm*O^k-0^-de4CAb>rAaqr(
zU7D1#85!zqtWM7G33F1F|9Z>MQ+L(g;)bn;;h@_26>~7@O>$C!$Pik~6o7=mMk0&L
zV2+<B;4(}}VU3vSO1&6_F;R{H;83zS+Qo+rW<%I+`L^bn<h6aV+2d{h`zoya^?EYp
zrLJ}8efLv>vt2|W=khOUeJssM(Sc}U#Ok-G4w7zWOH&uNzq1n~Kl{grr{~w#bFc_u
zBy(ZCp!6OqG~K?5xa^&SeH&1V?z=mauSKdwSoCuwnj++9khDamPL&Ff`&R6iYlm%#
z$aJj32S4=Xa34P{ReO<#D@3FrE~x2P(HRvvq4F&I=VIOvFfy*19})VF<w#&VkN|D_
zoB6__3Z(s9w?z=_$q1x{oKd=;L1|0~e3sY+X`|lf6T_|*o;TAh@z-Cg6GVdGMy(_5
zg-~_-J=V<T$J_;p*NuTsYiL4h<BN5e^Zl!fgOkf53^am*g29nvvZccD(H=_A|AM_w
z<2PTg-iAYn@l{CITD|44=Ui|si?*#fZ5A{l2PslC6o<#QKGM}RRd<%x_Y^fE_Q?x-
zh`OaOxgx;*J=t;3!yOl<2oa5Tz|i8#|F;JbtzxG8oIQwe0fPi+V1UyscFqk-zE&vO
zw;gadtKNV@`NuTdiwmy?<`7ik9vM1?vfVL5yxRiaTS7L{)9_s1N4zCgy1!R5k;m<!
z`>Cm^3kwS^EiDWp^sY42D1J5R-1KDc*X!#bVEI2B`hn^rR40G)=pteG>Quq<(i*>~
zvVwvx+=0_aN8k^k@m9y%abAh-5iZpq((xM%91HeNXN1aim?N%_+rGD;^<lG;G>G8b
z@-YI!gNtv5fIqBM+oOKf^KuqM#lW%V3whuC?)<w}%Z6_b+tb@{PO{ey)#&Mc{ce6y
z1Kl*y_Wj%53W6+1j{e^(BsCPPI5IjK(d@gKXAxk=>j59VnX+x<Ti|M>`nP*8mi_*D
z`pH(BcB}cPk?(MGK0WV(vY`nEYFM9>0N)d}$gpp-I$O4IVq#L}N~gL>bpwLS2740G
zaHQDDOo6Y@`&vi65a_<K3##*BfDb|=Zq`60Lp=BBTgy7GoQM*`50U`mcc6O2Pkyg7
z3UKG%tN8#<<irJQK`>AU&a{6DlV#m`*H%Yo2lJAWO6(yc-dB4!PPn<2DqK!&(v>bV
zBMsdB5EJ9a*<IC-Vd6ri_}E8vtG<W(jf3zW7b|m+6tSfczJ`Pj<@B%9=M*d1>D6wk
ze@G>jhS>Y?QZ$>ta>laj?NlSB`XEi?0H?=iyMGQrs*zGN!gI3^BUHAs8trrq`sSJN
zYKQCK)lyap(-oG4kR3`YxE6S9nE0ov-er9YyA@fJ(*ZWVp`1#Zx=O*H9`nhCUw&6`
z0^F6|J90Sg+-x=Vut@&iI3@eF`rdj3#{O_6I0~7k&dNR?p*SV;_<>18kB<_d;DNhv
z_&U50MhbuTccaak^bC_%>x+~(lu?pY?|Yp+la8;sb8to$)dj^@UQ9Ph7>KmXs(Koh
zjh}1WqgYNho#kz99v*jhcQj=Y3Ja^N*<^P!{daE5TcFQ)Dn}SoGi1;&m-+S*kH^6r
zBBYAB;gtPHGRdyZR|nH7TGUoxaZet}g+qOJ&~L|3Ju#3{^y_)>^yo#wN>?GBP@gzv
zZw1>N_M0Y5ngX2bwURzZueZ9pv$BgD#)>xh4E7&HM<tOM4Gay=CRE_D*MUyObp5If
zji@A(IP+!j1KRfBWldiuBvT3Z1U&;Y9pnsL`PkRkaor~tt?@)5q%J8yOy0|GYV|;?
z?O%w=4?t!#I*h<3Ckw^TN|~p;&^!3x*GM0vn(E>ge@i%LZnE~&<UOiD(5|MUWF#)S
z$;t(9R4QZH_MLY>6rAVneq~p+3z%?5;}bKc5V6VT>#3z+qUAw31247ot=Q-Ur(j)a
zkHtyxQU}X?g<>^v0xLj_&SVH%H2WSD@y@jF*C01M9&|jiLnO&*hA_wB-1%n={^ixi
zEZj~OOh%D>cR%4k8S1@Z=gf!8_otHZhA7ga;_QyP=B?G^^5p+pLNg*A2K)T%)wyOG
zZN?rT-T_0bk3xvONmgIS@RL?Y51t8I{m_<Qa-WxV)&1>cSO}QRMk>j54-E?og@J{I
zg?aE6kxR?E2!WC@MWcA_>$xf^lSZe)svbe>Vb3bVw&doetk6!4oBj{U4!_mo*vm1x
zqREyn4jc9zGbLF=9X$MDBMjup9;2<cX_lSK{E4_Dt&wP6WRQtQ!9AbTY4sL6&V2w&
z7?p&HxwB(x3KSL1$PA8g{vOBANY+wS*GMfgnLR81F!D19@ez9hi35uYN)Sxjp|UH5
zheo!!YR)bkJhOIpxtM6VT6aYhm9}SAMm7{eh_o`T@_sDBl^?|m#u3+(SBLs<d`vZ{
ziH#0$GqBdw_LNt5wNub@a<fmZ&noJx#Xhfpn_`y+?7zY8oVSX_niU2|m)93%rIE(m
zB(op-omMjiYL2tfZO+fB(hACXvC$)pNE!;x$4rxMZ|LZ=^MLwgoU-?#9W~YGp#9XS
zwqh7*eL1M(g#P=+m=DCC;D(Mq<6F{$ZmabiQzctiU28;k!{wFTLoN7k-QCV#Ue6|Q
zj9q7dei$}1a`f_y<BSg}+(reFsR4Is<SY_Dsmp9k_Ap5@wA52G($aD8ami3(3qH&R
z=MMHfPLP<J_Y;&E!Rb}O$vb%bB=h<~>Zr^vZQppB^~Y9~coL-tRz(}ei%(`x_r>Z9
zN*o*Kd`KF-PN&2+ajG$=%jnmke&MK2W>LRkfaXaF$;1|lh%YVdq_LkML|I`&{`kje
z#<ZU2{*nq9x6@NiA^ZYuX8CXK(#2noI?10x=At38GP6CjH1$TY;oFN9A(B4kCscER
zgu<1Z)X>aG43psR+>2UixCf-K)9uwFtz2whedCe@me{$zJ70n2(|;ja=urWMaC!#u
zKnHB)vQ)aex(K(A@MqBBf{%H8U}lSULlC7ag^NAe6ojd*f+%6qXz(edu`~*C6q0Vf
zUsFL@hd`$(h<z2T#G5IPHv0C82sZnxNf?Euq-SIKT$toPGZvsI`TqIOLWY@=n4Jax
z-&IwD{W(HkUubQ&_lI08=~<^A6!^t5kRL@trgrtDiP>^<^ldIlYKX;V#L-aCUC9%5
zO5?R1s}?h_!FA`kn7s%Z<n9MGIq!+J!_o=%eHW~TdSLfNa#cSO^7Y`meR#Pp@v=O}
zBS?wwh|9B|`q#(9%)?#5hn>nA$2}8;k0l<Diz%kv+JN!J+7C<KNm&BB2@4o}HGrs;
zcq22yB&`^G8ISSE^>#De{*(S^N>xU+Hw`pP<L+7}QV>i-;-gBm9iHvrlMrUS#F41f
zT{t06Ru-BqFD|cd4)-t3O+{iIn3KQN@lC+9ZHz+@z*#9?A|sf-Y7>3}vq$F02)6{S
zCZ!cpe7{U&f6_8kAq04OiUV6AVgw%eaHeeZX+`+hbRBIFXB$MT?GNF@>@8^&XeYP?
zWblI=z8dzlhzz5$4x+OS3{_Ty+3WniaG}GhgoP-65-&nw3w5s=AF{R2gaH%((=VE7
ze8;;u&^nhh^rDQ#(L`U~`x-?;4OB!cg9b7ID@Ao_;s5T=n*fAGAd#!pRELTy5GLOd
zWxWA%fxkCx=aNxX-l~RaQjT3(hbrl4)6g&%^oQ6brHDNkW+tSF3$#D`cC1O*mqC56
zr$K<#uqpPJ!w5>lGMsEGgo@7ZtN^S6@e@l5)eXTrZeVM8Qe+DM&~L1w&<To&zAF<~
z!f<p3N9Fn7=Q?=ctQSNp#6<SR!1RmG4zX}}iceMxovxLCJ{WphJ>4Vypi`Wr{dpPA
z#%;+*Xwhw;X4yzADT<H)xN0q6Irj#4(dBgTzjJZ2)6CTrQcO;>yByzNKleH{4-bur
zs=#NM-N>Udh(kNJq{QTa<h5^rXyW(gR>X#!>*$H<;!NP=FFFMszAuvo^ygR$B&+hJ
z<>lr1VKaCfxo-HSYn}x)jiGh)kud>>JA&J=KVCCj12y*I>L3p&Jw89^tw$1dt)Gai
zFX&8C5&?<Fh|Iy7RKGVgcnwrmi$(HptfPf;{d>x#VzA|Fe}t0(bpU^u6#m7TU2Z~k
zcK}}EaKIAl+uE=6RwUWOD<6J6(Zx_BC1=wBGUYZ*iJpS(@iyienA1Ne!~}n=tC*O^
zWTTOyW&@mE?h#R9lw~b8o8|&-U9XW75)je<OFQoj=p+nGv7K;*1cR8LwTZcnsf7(F
z45GVu<u<(Ic8SW613q_?W%ilR8luM2VUrMv06U0|QsZrsf>r(M!YtKURG}m<rPd_~
z`3P72FE1GDYLr){mO(ZK&Fy<xr;HxapC>%Dd@(h-)nV%-oEWHPxe%>I@jZ^{zMi}0
zGSB9qRuV3vR9sxuX~d_IP*#_fgzaA!q0lmr!_q?38VW0)hS}gAgo-?EU?@o|t0!#b
zfu_LR4N=%gxw=^k(uC-g71`X1wOI|^HZ>ew;^E+ZC>q%XdsCHe7`i7tn?Bu<m@6?W
z^ba*DLcE%&CadDAtT>7DjW?pWqDpc+C>G2&Gt?;=VV2>@?kL@O5Ok@1?Ptzmx^lCL
zA=w0bKPi#e50!;?<UU!n&R4q>ch_;z0;3IlJ{F5{c7+WCT4&lo`$BDs-2y`YTmr*{
z-s7W}mGjcYp-1!(!S_wYI#R}2duPa(aB*yS@~+zCHS6n~YUE_`4KNM3AS#Iyx#P+?
zpS)*IHE7~0WvnHgnbZ0%YO5qEBNZMI22+%H4vScqmc>3nn_Bp1?<>3!OW`~=fDf5(
zFotSXHfIWj6dn&Q;;@gnKNqgA_sdFYrs{d(K=TW*>V)lMY0dC>Ud72q!9P)J!`li{
z2hu{C`xZnl6eDUJadILot;JOq$A>A1D)KMszC5X9oF(lPpr%N;NjS&t4E>-IwdH*f
zP&6kTtiv;6k8T)RM_fS|eq4zH0%DuO<6AAgY@A<fX(3D)6j59_q=UA$s2TXVztxsj
zQt&bf(IRS@7Z%b?joZku!N?JwRE<YnHqNS127tU|JZb)8KWy%HQ=IR3@bm&oYEuNg
z$yJsHJD}mFFO1lvf_gXzzW#wFD2vHTdk$&@vd4(5UirAJsSoa0H=zMYZFFx=x8rYb
zD?`$7xTCa5ROj`$iIM*Z`Hc4q-w_j?&VaAsnA>IO-_GD~KfelPdjNW+gOY#!vZaz~
z3o<CYJ3CC<?Qf{=q$kOChYOG0m3~R})PHF5?>Ku@@`cZ@RgySdMkENsv@aoNQ#b8w
zBD@>l%qkb(LN2Rx;J*S(Q>T0&3di1~AuhWQ;+P1oVwl6$mRKJYLBX#~<~O?>Mdd^+
zab-vg*xt_O7Sqxz^!F_7y=*rEB=5k&oAb-N+Vkk;yUaQH=(~BUKY#G^0tD14S-ugE
zwSh;iNOi}M32sR3E?Xt9jf{b~^fFjIbM(93RjR?CcBp2Y29!)&7p0NMPyg4mqz`Y`
ze<dbW8)0Dhj{R?q@OKAWh$`GIHmwy$duIpq?)=BT=@A``p6#_Wkvrov@GZ$(|Nqa?
zOB^(YVaB%MOq+<&wMbP{Y*C0R;bjz`lsQVGcQWHd68%^T@*L0EyiFx^zqTX*bv;zS
z*(KRo<qa~|1|d)>e@`T;^17qRPmzn%+21Yj7jnm%u&g#9j@e{?dX%vof@#;WnnZR*
zflw)gcsFnOOpjOE7PiCs`!cIr;_J7;{}%D<AKCcMG&o3z(85IScmo!IkAvA<TpaEM
zUN|Mf5oU*upKz6G-v$NBRcuLt=9qrvyu#!teaX)UH?+-v6y^qn>fHCi*8y&zf-+hM
z+58w*8HfqSloH}w=)g^}8RMHCT^!NT*F{RVW19~qWn+2Z%eEdzc71zbgh@&&&@OI8
zS40(ylhn(l6_>Lm%+g;M`0)%ADmen@UreJAck=LW^z9J+`Qpo!Nh;vR{LJuGD0|m2
zNh3L<Kk!4mn%x<RmyBFsLIym-v2+5~e8Zg*iGX!~_%NqUOtQo#GglQ05(^8r)QtQ=
zQBQP4hOwv~YUY?3XSB;aR&lWZCW(%?MBzh>#a@V@T^#qmWcx9pbb&FpdFR2RTWnlh
zTr3JIDk_Rw>jI=U@&kZQrcC9dP$Du_`yUUAYFG5<KxuC$8{RhzoRk6b=Nyr<-jtI-
zYZF?P09Fr_hzktgf&UjK2#<P7#Ke)wp>ZM}gvnhi$A&)RFQQIaaz&Z0&F?U~e+ru)
zJ)(#CltX0ROEF(o8pOvo4TaB(j>p^pMpNTrkS_0Cfc{LD?C((XsgQSsG3#SdPUsDJ
zzc7d)^)THk7PPcdaV}%?A)jWkZuP?PM7yw4a!K)zZHicyd95Lem@pY(wO$%~U+3S5
zv$!*>AZvS~k(|`z?M1M5#B}U2%ZrpTz75k_>_bRG5nu^}5RrtH;&EhN^7Ym>cN+`k
z++J!YAb({=M_*s<*$Jvo718N~vT^`B2tqZtrB{*Wl8?uWqcZryQ~&lCJY0a_wS3kL
zMU{aSJqZVEZf2EuIsk1MxlDtrSgQIGPow)Sy2{v>s!g_~Z`=3h1$WzHY`;j=u9EO}
zO7@9>p&t-B0r9HKmzUSbJGXQe+^AIa3A=$nbs8XlFs1d}nebPK+tKBd{J$<y=c&cv
z)u=<vks0V*ToX7SEf#2vVii<|4^O2WK3|oWM)>(&65hT<;BWx82{SYl$Kv?G<$|l-
ziRo$PRhVqJq1i>PwxXKyTkU5ABJd`^O#I2=OwjNC_nE}|UpCnEb4xMgk|NNHq<}H$
zdHf%R7!8q3A!NB^od&p~E28t1%oRQElTknL(H#x9I2vhK1iAPaX10Hgu1^vkY!oVm
z%}d7)neY9U9?wc;7Dc6HgupF>8$(c9&nEnRJ`Tx4MX*-gR7(KJm~+cHO!+dz8A&Ok
z$&F73p;RSi7T1@7e-k4sh+%T64FI7zm#YTC%6DK5pjLp2L4vtS%_f<(j)3xG1Fho$
zfEcDt+}sRK*}RWl12b*W5fP`yTC{&g{`MzNr}<q%?l3t-XLdErvDct25SR~FD!i&i
z{t4weX%7SHm|C3xrJ}Mdi(_g)R-5#Wmh!J(3pBU1-Y6?uRlBK}IhfV}YNDdqY-5L1
zPxqdqLSizErRS$Uflbxiq`oiy<k=@|m}l!E=e<d4FcJOvcVGGClQsVjO=lGqN7uDm
z+})i3!9BP(5+FDP2oT)eT^kE7NpN>}cXti$?(VLq-tQmhrtf-;>aN|@d$0AZIl~1b
zD-Qj=h%bu|%SNX3UE;JcZ5)g)c1QR~2hZQHPxp^}0Qg(!L@z%FV)fJ?4OA4@6S`rL
zC7#p?-$UgTAvZOZ&~FK`xt+q33U}}*f2XhSAe6oWx7uz$ENz}6lO!IT25p6DKq}l!
zsgt31RWP-Yb%5h?f^9eAhGFdfh0U-&uL)EdHC<gmG%{nmAzq<LJl8F-J6Q7`B0~w^
zX_>(EK(*0|-e`6=9z2GXpL>$Nsy^%s$;Q6e+4uV*$&Bkk&-4_seTl9Sq!0eQVlK&@
z+}*A0*!}a-1!{<oC!0~KJ42J5h^pKQKS}#bGG?Bc=-!^FB@#jwp@*tDQI_|oPNE^(
zvO64!MZ#^w0!^6v_RKdoE;&@p-GjY-V>1)eywc*5&>7<bbR<&rwq0gm2HyPW5EFy!
z@F>XA-h>d-g>fV3@4&Cr=u_~|&Zc}woZdhv=u6y~?{37%-_{^)(NoXG{jF0_Z-+Gn
zspUA(h`h3Xpa)R8ui&-7G)ZyBEm0QmDbvIkgtYD)j;roYt5$}3MoCsj;r#}-k~c{5
zWAf1`_O<NOzs;(*BexSN$0@5W-cCD94W4Py)4~N7SF-6U%Hb}}61>1e{QwR>yU51y
zfD9v<1ozOut|XU<K1=@)C++(ei}#fNTH--`lRmg?JLrwU;i>M4<({!cB}J`RO)TPY
z0WlvQOTuF#2gJqsSoJK|6OZP_C4(s}@$W|g85Ftl*^Hc$*<vV-AyTM-dP;*ZUR_Rf
zv~Plnj)n=M(#(<}djlNs*^l#_*og;xVfLYe8r!nGEWq2`1jI@p1xcHA|EQ60{7O&C
zLF-zKz4=c0md2a~%aU(!PV`rm*T7SJZgDJ#O%LCSaSRnjmDiNUy;W3ETpD2MdEH);
z2SXoefjX26kQrIwM_s;5Ph$l^L<BBXn5`iz4H9d#!uP|@=zuj6j5$rq$hiVAWl8*H
zxT$T7tjx$2e~$l5rP(P6;%M)Lg`e11aqf;BD<{=%e+}pj1P<`5X3Bs#;191+z@4wN
z1H<7@0Mf8)nDE6)h+0{}z{7^a?4zhI2N0`bXqZgRbt7Ta!3F*K!(L_j4U+Fje=<UC
za+1dk@hf`w5;(anJOQq%P>G97({|8tAj^BG2vM}WGRMjkT@|b3OOH0y^N8jq+%>}P
z&6Yc<5rIGqTfu2njpPL<hC?+0HsQAC|J_=8Ka0au%;tlp&`;#*ZI24FjvNftVo09c
z!pejusC&s%9j?#eJ-L8lX1D|V8=Xlwr&3u-wFg|cXPI=fKGcU2S74%SAneFS{JcOE
z{9}S?3Ol$tf$2CL@mj1Bp4^dXY?v=NHH5@O&S^C8y}E@(_Djm{rS)l5lGuI()lfx`
zQVNSUF5XSqY1(Srt9E%5=jCS%o`_A@XE>M;uwC!TewFe*8A1Aw-|-|7Sl+Z&*u2F_
z8^PeI$lb(+M);P7<oaJ>qG-Y40aUz2f7(f{$He%e8h0Il9j%(U)W+f%mL)%|v}lDg
zs0R64DN$A|9`2@PeoQk9VGR^ARni;M)uc*iHt0K2l#j8L?`KQ&FHHSTGSp#3!G^@r
zDwos8ihsj7^?ZM~M){ZEJZIzJ=gu{5*1Uneh?xp&kaOA4?iAH5d?hJyw&<*lzAJAE
z=5T+w@1drN&5WetDUc>&?px<|fr9o?ekcdgGsz4r5$b+z)3dWSI59RU0|GH)z4ugw
zp#j|1<tBYFys1`@t<1RXXM&$F0-f1}4#;1jdli-&VIIH1MlDI0)SWNUy{m=7iU@;y
z*(JZ~&h!D0LJ9$vp*bu?EBo4SY74tsmvbNJ1op6AlBUsCWu*kj_zf`;QPmB9RYB~O
zwtV6XL#ruEr3bV7m~-sGtMF=Tx%SB@Q~2nczw$o?Yw<O$h>I&ly4_;1IH*y<cOFK5
z*U#iu!ZMIrC@*SDs4f@4oX6cc;@|?)jzc?}{(^n3p7vdS^#|oiq#D%m)+y$?VNi75
zM+`%S{-&~Bp5gEs>v){YIQWRHj-bni$4M26&cSr9h1bAFnxfMD%xsibXY-1UbaEia
zARBX8HT&1Fm<T}`xy;#MB7~UnXICmL^6Yo9)yKXQjTX28sI92>>R>>V-Pr_AOUWoM
zk3hE<DIN;F)K7w+BHD!xmdO@g9*|Ryo*O8%fcMiyh{3s3(uc8l!61OYJ@PYTp%q>n
z|4uBTqnfDnp=Cb(icV0wnv#Nn8$P%<(4~t2i*b~NVmL$*VL9rECEot~!q~)weh8JJ
z^VegxxY*iQK+iT%-3q!DQPBj$nS@bYk=mI9MnBT+6HBv#!U_ne@BEU0l*V=vvF~$%
z#+#O;F6K8X1T#{n!PpX=Z;|Ja=%z=197XUDJ~w!`&Dvw`gl3)HSYU=$2L<`e^=x~A
z?l0^n=O=5OuVPS;dx+%mXgrCER`GTX%tR^m*^qS3S8w}@^jE`yN(WecFEu(kx}~O#
zvw%Bml?BPkO9I#%+308w-c-eOX|~g_F|a{<FzY}YsUTYkz!Rh7epmkeNB#T%#v$t2
z{2hd!6UxfSoakhrOd13W#>jf&oZ`-#LqqAP)CVKMhteQqm#~8?hL5owdgwz2xxV&Q
z9g;IP_Sga>&&z-|a9UZ&x4X92TuQM(XukspexEJ}1OFq7UBA;L_*PQRViV&qO5BWQ
z`x?|h{*2E{yAfqf!z==VU}Ps1Bj_ygUrU+gR3#m&TP31%5T()N0~53ir6opUX6e1p
z2w$;n1?fqs2o`omGrpMt6-$lZmk-KQ>;sRn7jKK5o$R+WfIu$zxrP7d;p;F~9t_8N
z)*J#rcVQ#^Zf|a{??=8*BL<sXL1dp~l;b2#PsoQvM#UUu@lPs-;JMe{m5hRE!7p6U
zN<hktN73EIx}14j<FDGJ1QJ4&!nY_i`&w?LSUh8wb8}nV9})_^EQiEW8DzINH3cmw
zXBFHXO_J#O;95@=E#L_XT#Z3pN>Ex&aBxdY3d2x%AX_8wBt<_N6YZ1`(^VTBnqKc`
z{KP~<FT_PpftUV~1+huCFryULQ$%3i$~ZDQ{0+N@$dUwPe)tWojJ_~0+stt9@SK>0
z5Nq>n-`vQc%uF9<S*-z0fy_61N}AB4Ki(s(+ZGSJTmxk+iEtHsIN%8bjIS;d75m#V
zaikkX+OJ|&;6ODMyho8Rn{fxt2|mIxsD3~PBQ_Xp`a>1qB!u;?%8AVMMjn|LlYbBB
z2P6w|QOrZ*a<bvHPbnRMljk$q6<rU<4PH3B)nyjB1qL~s&U0|T8O>IuIJ?eZ7mS3v
zIxCn-j<+GlcBE_>P`$!K!$RT>vg)=#9O9z<tR5jjO`VktuFtOiEhvK|p;-^%LW&S3
zcFsri9`vXE>3^3qfFnzQ$Q)E*p$`F{L|s7LQwZOv<kur5(~DyA0J~EtpB!q@R6DcN
z`?hvE=B#G2SGAxv$EC`K6>Y_d%=!Wi*0Dim6p1FtRMGv7C%Z{b2d5#>^788|wOLGg
z3a`&O<tl?ckT^2KHAUGHEVq*B;SCodu(g4(R_9N#WC(YBj0!v^H+zrU92jiufZca_
zU5|OQGYS-;cEv#vh9T^AWbX~RwaB&~Jr?*?pf0Di%I!8^C+#w~XH<(t^{Qh7maofJ
zt}H++^KkDsL0J(wUR13uYO8Kx!ySs{GEo&79Aaf+Y1h|&tMibZw-KoF{Gmo<zI*$`
z@H;7Vw9=xOIrO1%Dq%mM?JLf~K>`~JCl?nxH^dh~6Ie}p_Agmx<(sv>QQ)5q(8Ri&
z{-I2vM?qV=$jGApWBtS3-J<)u;2xP^G)Tm!okFALU(-2lOi5?%z1AW71Tf!V_bYZD
zaafVpIHf)oPaR-`!nBbkbU6gfiwgH~)aSXzEp=L4S!~fpZmK$v+HImKj&=$R&GoWk
ztC0@%Ee^%h7pKVbL-9;$4nt0|+f-G=(?z$~<;hINp>^?>I;Ht|4zXV#!I5~7z3GR4
za)+L=Y^|<lv^%@5hJ=FU>N-$th?dyIqVb{rXd*J9L{<1mO9Mv@2Zq;CbApwZAld`<
zXhu@4-?7OFk>A@cF|@b-{8Unx)xpJf>-hrb)BJ@zUgsNHgp?_b6ug&!7V!z)4lKgP
z@58*?)ts8+mf;!A!IUQkGBB)B<6+x!^T~3mRm2Qs+gaAC)SQ1rYhr6@Y&+^?A5qU|
z0@Lou1k9OTgLM$iKlqo?m=i_f(Qy#Uxe@4n=#~<(gOH~n$oz+?ni`IxvIuO0DeRCb
zTNqOo_Qw}4hW4M5YCAQKb%J%lG%^qI@k%W$r6tI64VZ|WzjR>WxWGY>aspkWbKN7e
zBC=}n6Lr%3^0UIjW~Ae+`8nv0)svKUwQTchgYrIc`?Q}6P4;mjnMu{t@zFz4x^jlq
zujo^~XcM@Z*Q;%nNAtn~OsXBZo*wAKudC}5h}F#upfniy0CFe}WJW73FQ+*3?LQ#k
zlT4<H(}mI~z<2tA;Xfj$bPENe7L>yvd#NxXceb~7;D4DR+BXA1W)_CP@-iF0!~Ur-
zZ*3cwGUV=m{lboidZyi`MIeV8RRvK^H64}IS*?9j5tp)3`2yQ6dtxH2NKx00chPLj
zDTg7ut1ZZ?gPdO#o~ZIbNM4+3|5BNZL@)`}PDPWK(hU=Q?rI~>K`iB#nRU6?^cd8a
z7dKl6c(!Oc$`psa<NZ=x^d((Jm&FAH5?IY`i<smTWu+D5LL;KWkWeoj++c#W#j%}2
zA<5Pi65x(&D7C3H<jE+687|rCmi6_9{$!ds%aO}roc1ko=F;j*cby4(L-pir2Q}-Y
zoA>dydclKX;C=-K1E$_V{VtTPfCNl&c&kS}JVAXD^cv`EnUdq5Ex`qN+-MW$NneR<
zRXC7k8M`d`_^EpNuu3|`bFiiz!$(AmmPmD0!WoX2p9h9eeW-1UuFmQ7?W7u+6ow(P
z@?*mJ-2aH>Qd0HwUXIS1Ri-~P7|4fIwFTeXm_>unH>Rp<e9mu!mo3xu@~HcRp(8h@
zH3lcgme=QI&{esZLQCLgcK_hy|28h`!Cgvp+&3dQsS{T+YbL(+W{+o;t{$4lVuB+k
z$~D^B2SoO<Ng2uUvFb{k4S7KGn~w&N`Az-Bn2aXmdaPtfV~fU*jiVqQrkTBx#1!J~
zS^%%~&Jq5lAIe8TiHek$8y_2=Izo+>9%U>|{?b9mJo0Z?4%rz^>DBcPEoebRH`46$
zp6IQG)OR2Jv7~bJ4VX%<K+|vEvNCyOaIC9~s_1S_k}I*M9Kz_6JFdIx&p8J9AFNlJ
zOqY6^ISOXg6LEVuF5&ZGZN!}f@Na_*RVY=x>GE;$U{WRjcEk6*!Bnvx`~z9~yb8k@
ziC6cA4Js1>0!0M^DcNC$o?due3okRpkE*2IvDfKi#QHnb*g`q0j3o^fT@l<VPs=PD
zjxNKe148Inx&;#OR(gI|MgdaG$CZ3msofbKPNL3_g;j1JF$2f{V7ZYsc$4GsU;9Gb
zm;F14h(9lW?k)J-ygB~;$ILWW)=sV7r+CUkD}y3Ip7?~=O01cOZ)It<SABqDpi-CL
z?fLOfpukjjrcixH4ILw*)emC-fnq6rW3d4Ud9v@iP=6!A#~StlKB6)2?71rZmoG|k
zcCv!P;eN79OG|ugtu2uONHmYMw3JL}a{_92Dq8y!c2;^d)_cb;vitIt<nTWKVM=vO
z=poSp^q!ET&GM%E*aO+0m(b2H0|9K@s4SAQzI=2H;=N<tq|^l=dh|C80y)4cf<z-{
zw=pSP&H$zz8I_Thksh3#z)p&e6%*=$&vh`7pxAJj7~z9cT&Y8mD?eik{ADMCkq@St
zIBx!;M{Teh`(vTwX1f&sUxwhhhyBGwIWnLe*InYy&CX$?BE#sABnqBq>DWR{HY!b8
z{cUkw^&QnS3i=ee<u*oBuM@pj(GW+AJkuy2P8f_4_<e3VB%fLx7=yVyj7!@-mL9)J
zEL*FxIJwvHLMJ(1UaR(gpIx3l4N_u-SCRMl!=9)8hWy1ea);Wu8Y=l$c`ErSvHGl{
z)n}iUG!!Mcg}>N_Qd5w&O1II6%kZD$<!p7p`r;?M+h4)N%!q&%Jkjs4uIKnQNW|<C
zvKGwSYiLU&&*|gjC1s}01j5=hwJOU8z)B^G!7||ZI13Tio$v{*&bNC;lYbZ5r_0R^
zK9V{fx-xNqt~k3TJIYJnT2lEg&LR0v1|AWX5%64@U%S$YiXZ2o^}FV~MDXK==i%x4
zI#)O4VQ+tTjUE;$J3?2M#_Q8vb0@$b111shxbMj?<vG_DZ1L*dPLSRKq`iXHw?N1#
z#J9tD?a=qw{<a5)?svBJ3Q@&TC;%1+s5y4Xmv~yrN`tfXpzA<zoRrQA@D~>mQ`Q;1
zwi#81B|EUnWzH`%GQ9|6d4Dx_++KQd{?(k#%tZH9L-thL=aWiQcyyE^1Otu}*anu}
zHgcn<XOf@;@eF2hCbjkIv3-Nk>+W(HV4u%3r77zrkdp73<`)*(Aj#rbJmEsrL9rBo
zQF|A)xpX2)`>{MW`Y(N8t9@YnLar`Th>Uvl|D-S*A`w!X;FxVSx4QRe!l&2e)F4LH
z{!Fx6^xb?cuElSu)a`V6=vKQ0534thCuw8=^<wK1igBLT8(c<qLKM(iBB@;Ew;Zo2
z2z5<JimfRJ>EdKQ&B@}huY?(ewE$rbAT1Cilm>JJgsF&l>#C`oq|DRfE-9~b^Vcve
z5ByNjJ?ufftKk@M6D-yJbn=})OCDyY2`C?w?eF8_L^BVZO7SC6l2n160=Ls?5@fsS
z^W1>R`x+fH@*0wl{j2f_U`1=Lt!t~qVIA8RGY1l*Kw=_l0;2T<ksMC*Me|9}57j}W
zW~kNN5LATam*e0AoJpx3+0fssN@_J>geDL$(B1*@xE=G*VpY7lwg$sZ$IHP-PAlrJ
z)*Im4)2H*%1>4n*2k_=p)Qyf8H;W!ydj&6Z&*)Ee-QQdTuf4&;`gwgp?t_tJ?^Lo7
zny?(Q+5F6$C~DElQ&a59TXaYFSI)U{Gj&{9F?ki)VKq)7HYV}&|I9O$1-O|>uo!Wh
zTC;fz{o<%JOV}_5CmGN70he9+?*tu<pK3zBV)8m~eZjz518)avkJTGLNtncO-pZG!
zi$tu$KUn;oQmeU|%pYVL?DTQGI9|$qX(+dn$QJ796@&iVA!nV|9;me0_(drmOaH@w
zV1+=Jq#YuDoF~}k(QWd97-spjmyIqS|CfWu?>9E_VSY|hpG)14PV%HqFhpH$Ko6x6
z_V+I}H-iakk>E<3q%Su6X$>Y;JZFOTx13FH^XV_><ag`M&NWVh&-pinY6t!ByaWu0
zG0$=0yPbqIfd+p8mIDrrsYVVn!<RHM?WR_KfyE^ae<j{|2nuf=k*_dHEp<I3(zQeb
zdo(buS|=(2eMLCc-19WgS4^aIMvTBPW9Z}UX~4F_4)}ydKAo@B<UXzWW}nsEres8%
z-ajm*NpS36TR_g=?9@O?F;@i>m6PT6LR&!-zW248jFrTHWCBf260-8Tb%o6g10H?~
z>4~}d{`?|d$%#?AWhC#3Cu{$>5!i3P5mgQHOX9h*==%C&e8X1Z`0{0XW~O@C?5C%v
zq@=0o4;Jt(ODWAPMdm;A<dx+P<ELf$l?O6@*MgVl@fEjop+k??-K96HgP3e}-=o)I
zlcOyFIc~_*H$u)WSgwyy)Fo8qAHKG;MUI#H7lV{+_hie^PJWUPhzPXg$)9hY?wZ?~
zLEm5k!SA8G(`q%;cvy=mNc&a$9kl*0Mmk)Q?qGKm(2w*+`&+cIz@U7G<;bO*6+Bk0
zde8Bc`jdIP#mQ>(=f_uGuorN+R}twoi=Um{{>IkqFtTbw{E*S){Xso?MXoF4<Nk8o
zKO@D%nkL9SF)A0foH4~CNiD>H3e&uJ2}JhA#o}7V-yPdunuC#>D7t1LHQCzjNix&W
zz)~%aVm>}PIyN@)<3WhnmX?-QRwu{D#6+nDz<0vYiObN)b;8eWw^ILcSv`Z#veD#q
z0m*-+m>~pyI=tWG&;gdFo^UA~cDSsgHwyE|1@OZ;m2*+c`!x^=VWX)_fv8E13hgAw
zCOfPUh1`3F$t>@}g=<0$>%o_!wYv|-#@ReMJK8@rH`_TS#E7;WuWqUdAf<=6{-mM?
zJM<Y7#%vKi4UiWo?+ck7h0mQR`P3LKtoyv&Ux4f{_00|nBxXu$a@&~Z<Vk#Trz5Ni
zS*`Wz+=r)4M%Bw!n@%Q+N8LO(sz^R&RuUCG&)3?P+wDx8JOUbv1*H{?l`X*#R!@h8
z(L`eUA47MqI25D5JAdAL7yRM$J)|4@2_UoTuLd*?O_e>=<i%BPPM^+z0$Y6piv}Bb
z{q+eMkICG|#>z^s&G~6$&T7HUdb!5t;Jh84A3az5eLEf?ciah{`&{2X0W%J|pk{FH
zi}S+!nMN(`q^^4KjmmY>2iP<TWngAKZnaNt5U__Y34G?~^RoUSMsNr&{)WjcZ9pMP
zZ(@Tjz;pW&NapGn6H-dmQYoIhqw?iu8k-VIC|gtU=vZ3+S+ETgauQ!eg!i%EIDu<S
zx8bNOyH{sWA&uYX9=K3^7TvbRHJ_u&f%}60w81_C8;51#^f&6@i{*B+3*p=E&Sd<)
z_tQ}??W`NMBp;(8jg=W7Ah3L19pwZZvE+@1xEnu>WSB$Yr3{2%$5!6zHGR-X#@qE8
z2tybdcd_&r;Qx?M;kH@_{!8cW)~k(P?*BJl)n+lXKd#kaz0~5ea-ZJnnv3pJg)Nk^
zLp!z89E!vn2OfU76&6>J(gg$hsfa<hO1Dg5Py2;%&7Im_9h5ydjLCO9U_~XZ67xbi
zh;Y04U8bw4CIb@JFc}GUrqo}w*8yLMG{W0I(F*agU`c+~?SSmwQm#fEzMaTt@yov|
z;Oe(#50$WiMHVPOxle2Hcmb~B;T+_?I5TsDu(SgnjA=!;)nKb;n^;+;`ciASoFgB?
zqSN6BP$P(W?AAISF0W<)itWdk<7;K->+RNB>H@2t_Ue*u;=a-6;T)@+VIFBI9G*eR
zF9PwI$*=F!xdZG}Y?C+=GJcO{iSt~I%sTA`C+C4Wg%g<|xBIap6<S3Ski+)wuu7pq
zx-i&h|CG-ehs8DbpYIv}^X}&^kk9@0Tq=*<Mu*qKdv8dlF38LI;W;0W71~+b&_!Oc
zseSIt%iMF9gX^t=S1D()p`=ez=B=pU<r*88(=i-Fm!s~ZD6l*wiPK=~$l(QX@NrWP
zE#T*`byLs*s-Oo~C#YFLrfy`hr@mK`HZU8kHEeNylnt>!EBV{&3GKX-??p+n8X?8K
zXz`xBYy)X4q}{^{I_T{c`Tb<$cQwfeu}z~uqT<9Y7tYB`-O)M8)*5Ng<)v7=!0t-F
zrzHx{U+iNXc^{4gKcE^BYs$LTWVG=o7QbTM_wn`!V3v522{(Z+*56*x$pqaO!3P9c
zO%rKXjf)$gx5!g`<BR{pJI<ZgwkF84Dqhzs5&Cf*b`lC-8R*^*98W`8Gfi7c{mVLx
zTb)LX9p$jckBq%55?Sj7DvG&zYy!-Hb7^A+n6U_vGo=a)Ye{1XMMAoAj3yef76eX(
zR)kst9<cdw+zqbIx~ES^;DbzIZ}*S$Gp&K<PEbb^Sbj9<)V}a{-8Fp#H+gg9Ixqsa
z(dHE+e5H5@+>sYJA(&%w4%dT8B>OhHc8~xRshwkoruQglT4-94kE1i=PAxV;eTJjn
z0r9KaMPG8n{2BkG%k^u#)`OQ`oBR9eQLOInE7|E8Fv~)#ulNT{QQ7MgJ;BGnzxfcV
zgYx_ThXyh&IzOHPc>X%Po2_<pmEGLf$Sj1F3nb9k35d@HSwBWUII_(s;~bDmv&S4^
z?yZgGj67SY@v*Oqko0@1&Wio?cJ(xrBQO{Ixg_BAxGk*W{S^ZvozQT~A@tVypCpmm
zPgNP9Z3(t`e||f-pvvs@z1!yV+{+a7dU`&4$@CSC235H#$lK=0n*UtdoxgkeCp!Lo
z%lCTr-^ofC92_lmp@MU>Ne(`C$5Vr9nUU%x6<>Yd(YG8%TZ3^&ej(-~3$3|=1JxCv
zic;}$*XA`eyUfi8N-3sGX?p&fQD@Z?q^+P81`ndad%dSMy>TI4jCs&Te_Zav%2?a!
zv^wjbon8@kyQVPdUy*aC&8?)0{`kigppRJ}StcUR351>s9Dc})wq-^&;NYJqPZ`gI
zRToV<xz({#?M#LO6W(!)N}$L(n%`288SHI!s>R9F8OJZMG*LE5jjk8Tt%shH(ZG!d
zvz)w3Ccd^byLe358dy-H-uwWx!f*Sew~2}wo!&RcPc!Qo@NRY+O->(EvG|o6Z-Rb1
z$(pSMZzOb$=<ohC)+ByRcPC5wMGX2Os@kG67#rzXb1nM)1?__hnSiNdB5?4ZUZ-Tm
zU}47fqtmH_F{|FalfbnA_F?xGD`4C6v_IIN6F)haGY#Q8uKIfjvTll|mCGUNB^bbr
z5s6zeD0Avv%mY}DN5awM!;BSqR$IHoWTyxIJN&!HkI3OQSX9ufXOmiA^KSz_Eu-bE
z-&dRiv8E=(L=9Dt+1<HHqwaRx>--(#n!4TkiO2Qznd0W--OVwwp-}Hk_zBpIb!8e6
z8}U~6R@m$5^s3jZ(N6nF7>p=XwT^G+OVoAcgD~0m3vxxim9%lBdVrQI{t?cw!{?bB
z5Yq$$B=SE%OwVOzpZQ|ZW!L&4p4g2{P^|KW*j|XlYh;VHTh|{L%Ph!T(S#@CBg5rv
z7kaSUI9y)j$CUWyBdtsk4ko`xCq7>-?tPyjXvlU~K^#c+-MTu0-LWjJtCOdfh1KOd
z7q%c;GIhMavu~jKyLl|hZR<Z({N(=i)L%y2Z(lkWN(tqnc!}2wUP?kDC<JoHLPX3!
zXLKZ;wgPn0-+la=jBVChxgn~2(R<OJ$S<(ZgVXONRS;F4KGos-01lfun6mPZ9+;7O
zumgl3TVsfE2zt$To5Q6K(7YKr%nO{8P!r-9>I8DD@4Z>wae|OmRHux@#y#$y!$8e9
z-xA_^q!>0Hl>_fnC%LfC9vqqzoug=Up!Y}k755^s^;?_5s>190XeFrK>usB!<kybY
zGA;f<6Y=mXrgGyuVCKC;L1&Y{BO;0YtM!E}xNC-yqbChz_n9~22ZTdIE$38-X;>)K
z5AzASif(K}FKl@}PnT#6yl#DA0U>HkQ^EHuSgzUxJCJ96<H7kjBmM*l$T92Ww*SNp
zurPdhUF?YyYtiD;K1=4~$|p7G@6AtCV4JaYZMl1Y+}EFjYiXlNR@XdUSL0WN{%xRc
zy4PS7+gRJ=l+v)Wu$g{#<!6!d&1y#9I{s)wTgd#P4F%at+!+d>NYI?ir;$pDweYqT
zW~mA4faT-wnD}r}eX*f5A-N9c6;f^&`td|H*eUw3n;Y_=V57In$-O=rNBQ@aWDxro
z5<eHgFk?12&ur@y|4)*dsxrvsUhEJ+3=;sAYBJn<0}EpdF{fDBqGVpUzHG7ll26n<
z#7Hm}gqoUd{FOHQo1_^@DnGQ;fHZt~I7DaDD<VgNt&n5!!TLDkfg-6;li5lKRbi_g
z;Cn&id%rAb7??Ibd{yU~(5&%Y=nx-N=yrL$ya(4um_@ssuXQ*abnhHg0!9&6h{2tD
z1siN)3~tpwc>82e(7jpVr09FFFJ^8{?~#~!<7^G=-aS|~GL{4P>zKvA=2Z5XHETT{
zwp0>r?{0-fUHFXahUyoe>e>rWdqWh{CLT%o-rrW#Su^g0JwuP1E%L)U@MKWDHpv?l
z55c>ts5YyCJJUC#G@KLKRnE)`4StV7Pe`k~kOTb8EfkDJ`ugM;nLNIpHV&R~G40hU
zpUD;(faV$Dl2=F}jw;uLO$KKNCl52r7%O+9=kvCFI{x`ahv(MppEm)X%9WPNlT*O&
zyZ-vVJMw27d1@_y=Z46%u41vy2i=+PKd^#Z?#tLzU~5B})>L)v&2Iim%m&J)yE(2o
zQLfhD+I$cMf6@kFZG%z}5f!B8CH2~C@t^3m%c)gky2$v1l7cbdZ9d?I<k3dY4DsaP
z2o2T303ZER+T~}@6oMOY=ld%p=@b6pnBHQm<3Lj!gcWW9*E=n$$>}i-n(!8Qn6KYG
z!e(w@J~)T>6RN4u*I;=R75pr6Zu7{vXl7ICWI+~zdGy;Nw$HK>Ix2%BLY&R^J+}?@
zvik}^A5R+TA!@idrJ-o2tGRW7HRF7_(`d(XF`0ke=j6)D{xv~DWiUUauV626K|Xz^
zqs8dNR`prC$@2wiY>T^Gvqc}=Kd>e>v2p0Z<+npPKO(|o1ua+Buc6nH@z2`koscS;
zyxYIo&pDFx&8T)jKBLv}Btx8bLa+Vi5LPFn+WYya`h734qrE(a^yB@^>hd)o+UjS?
zbl_=G$M69!S*Clim!zDo4tf3|P`x;M%HN@t&s>Rq#n`u|`}acS|A7uB{9+8F?>spN
z`x80{@|T{%;0PlnF#SX_WLkEMBSEOzQS9`o+ngzR1E_1HrIW<#e&=y=7z6}UTNA=n
zUMmCjS`V)26twThQ#P_@k^iD(CYaON@=dlFytfrIPC8s)M)-CgqkvMOR)fL9meT^f
zvmoD9!?}M|(l06W+Z-@0D>^z4kGaWfn8AP<@eJ>7mKq<FA%`sO#LqSZ^hgrho+1CQ
zR~>~O5Q85**Zk2aWA%P%?<BuN-0U(J8{93Wb1HES9N){ZV?53;qoW0p#znM@Y%aF9
zu(9Hkl$E%Bw`WUMHT1M}G=;lf(cv`?>6jR)nHWU5i$%jk<S@ZCt%tWcV@()}0@(5%
z%E}IHAynu>9p>&U>MR+c+Tx0u&#di5Ee;dYsI4s%nL;jYrA{9i=_~hSd~-fG-L;LK
zpmtyHgYYF62}Dh!QrdBA@ep7rK9?<}#o>U#@ijWC6mMLo#rI&Vc7Ysi*ZrqYPZS~d
z_e&_vwt7??d1$b?p0|UMM%`1NzEfW}2OCRio$Wvm*9tl*XvPN>pDmA+BWnYMcKK+R
zJsj4WrUcO@q8qY(f4mm~KR;gjFG3PCxn&2`dljzFkR@nfZHj0()&+6Ad3YY7otdGo
zot%nsYFR<w%M<W}oT_xTYMm-v4knq+FXMC#niBZu76PA+->RBOAgC&Qm-#t5Erjwb
zAi*R8Z_L)Vj2u+a<cU>c!F|f>zWrF8s}q9mLbkf$a=guzHV0}2rVEZeVn-)Y^+2Tf
z!cvN9!V5lo&*iP^|HwfIsW?t}l`9U108NTiaVlTr2k^ocU|i)y$;{9NJGWX@x!Ogs
zfHX(|w#|a->e5r~RJ7A+_Sm?p1-1!*Z1|NczTu6y6YSXd@%5opZ%g-|(czj1h4=O0
z4^@}^5Po7q!^wZ6$Nrvp9N_KrU<h(W9zddotYQVvLymDd5{`af)gPp69b_Drn0acA
z)Fs9&VO*S-4KGAg3BhVc%pn&=fx-&YM!*0bm=Klz#l?Zw$eOe=E$DLHw>&%!RO!m3
z7S^_UrCM5|$pnI9y2Xojsy9VfW=qnhQ)uF5E)E4z*=kF9Y}cCIPz8MZdlDq98r|NB
z2RW{dilXrc<N70+s6Y9~QA~ZnF|TybPQWR7<U~?2aCI6Qa>%GYIH$KdpUt;t(6WNp
zZ*6)}cio;ZhQNQ5;ap#4baA#WHrhSvUsj8aLeNh&h1|9mzk&sHL<p|zy?>C#4D0Kw
z(+JYAqdsW+?<!#9{mzCa-?|9?ZY+W5TSh(@qR>J|CD+eNN6CVE`XhzkGnhui#>fI{
zwV(#8r|2sV&R0A5&bEwlJ8Rpyx?jj(E&Q=mfmW0K{|Y~e6+b&IUe0~s(OpPE9{aET
zrT^*5uD8z=J3*Z;_gw@1999t}G>vtYORkr@d!@6+*Y9`8gXp#!Rc$tSuTMv%=x~mL
zGtEZdwS&`zR~Lr0rGNoGtub=~Ni}MnPxqsAl1DNxCGuX;Y0Zv1_iq`r|G;|fCz-Vm
zcMpTGB<~#C_lZMiF<Jdl3NOtnQUVX>t2G8&vm>Y96lLWdFhz9_SABwa+Pk58jbb^F
zWlE1n9I<-aP9wRDap&rW6jB4JZ=L!dt5<$~?faZhZ5~XK^R1{N>WGf*jxZk*2=jgY
zJlZty&GM&b7mTl{m6q0um@$|@zs7VeSYFHZR3eNRrn42D4CDjZ{*3Q7LB3q$J+bSE
zbEuv*wCHniu4Ad|0Zr2B>v1yntF$p-M-Oe&Jseq4_3u_sE_|4YX7#%Q&GBDBu*cOV
zIzf9CZMnhmE`F@?_XR=l(K0h2gY58rS?`r%b9djUL|<4q@kwzm1A)cq1vk+SE&A(6
z@&A#4+Q^%I=B734C5A;OXi7A~AY=Vr4I&^S6i1nhOYAHyY)Zz$?&@dMF<`PV6z&k>
zlZzl^-OxA`y1x0vDt<+B+a8akmuAP43&~7`S5c1mZ~U(yfpbhp#3+1zs=<Ty_H^<L
zZ5%}~-OJ&@aN&Ng96VD}C~{M9@;bi04k%1Op0hJI7<}(t_jjXsoQ>tnj+?Xbkfi7-
zNT1UC1}7UKUV;HW;9ecWZMC(tv3n=%uI&(lE0o4u<6ZbS){4|-^Sc4k|7ogDDBg?g
z)_op1(`r}O7w&dH){WYRId&gj4sAQVuU3z8)4dv8%5=fY+-BeTe%z^j>Z8u&O7DgE
zC#c#op44ePzAquaSM2Y)jCGnf%v2auygP-7w~38`7u)5pBmLuh5IHx-lKa=Zct{aD
z=o*8X?EocAtPW8U%q+wG*qKIjIi?b;<WBqH>$wvio0Vptll`cZg$mtf@3#gY9ka?+
zk1?X^&Nesj(c<}Cgz4NrbFKFBo7;M_*Bp>n082wEz9PwDhwahp5%Le)C9tFZAP35c
z#v&9He_~mOiiYIgtqDg7&`6HNasN0bdLpl;$mR7gK$RKC`R1kMIa6)4Vc@58E9iA~
zJ(t>eA^bW!cm@5G?kKdj>>*`&zb-X&q3)pIX1@SVxMN&C7*G424By<Cfa0;B8Oa6i
z3hz#m^ODu_u?<ac*+dUK8pe`Y{&n?>Ir7)Rq)){_$|1sN384nRhmnbra>gH#Y-la$
z*MQesWL~%?E5jR_v9yH_Yx%*3HTYKkhqKk?YS;e14e@kq{y$Kl^Pk@40vTx^Y<>!(
z8(GGH^;#ab8})oWAK>Qq$kxQ7HyUh*+bnLTNbRpj-uy{Eim&KWJZKXXbXu&I>aLB+
zkxe>S_t!r3#g_&yBV-thjusyQ%D%kS#o`7~X4p%8AOq2=;LrQNKls8gaF7aJBaneS
zLu2_h2-fO7&T1N#W{66VRe%HqiW+s3#ZPbx=-sLyaO>Urq{c-HpC|tFvqllZ>$e;l
z8F>ovor8m&o!*Q@@%YRy83^{(F}pZj0x0uzCi9e=9_GheiRph{;^!OgqbDlTTI`pO
zQMFvp1)NcQFWLnfoXe<p!Ufvxe!FeNCiJC`nnj;3wV7V6<`Wkgz8$z#W(fKE315Pf
zfmUsWc{+QsqvPndOKWsm>yoT!!udMiGJ7Gz(%@HDzkDbbP3Xd^v66jUj=_Vk&8ZB?
zXoT1JjWQp(fAHQAfJp>CRkfcUl6i>=DXorP3&j9{Ys2Gl*F;Z50u&N!%e+=t`164`
zJMbAb<nctk<=>83qtnSi{piZUggLmZlwnvg`qJgg%nywGML|#@6(M56?>~C{9R|y%
z4F%V?Y-UsvkgNXs2fI*V%DQA^=HC7_RKGdtpdKjjh$IO)Hy7nK7{gGA1zYY>ojK>?
zaBr=%{bzWA2gzVYpp-&dv;F<&67l~*1Jn-?1#O?AAoA_DeyqRiXbH?Sfwf`+6d%q8
zE5RR^&ldmEPu9$0E;d?RuTJT?@|W^m=#~LG=C<R*FDEkv;70B$4F&|~Xs|lrCX_%L
zrO`FsMo$!5l4WLM9GVC5C=_XJG7|J}bq!E4lopuTUXUexSLHC83T~&(j%@unTw=6b
zy$_qwG$ImA^@3LMOzQ`MJyrv!AGH=(x<#HR-`7^S%k}E5l~2+LRgMP}@%Qp*gJ1Zy
zj!R{bvAU+r#y{ErK*#lRHPCeNk@f@__R%CC2uO)2$Y~n5?kK^9J9wEmGFzWQpha*k
z-pr@IgXCsr|E)kHpp9kAr|Ur490LtyGCtpD?T0y8{D};H-#jPq2e6+W^1Ew%_)jp3
zCZ6=>^>Dgejk*UhhH(BCA>~$)Ca~V@wQ~(Sj?IWPt#8ZYalh>%fJVOBYW0#3b@HP!
zX+|jpbIT$>Dt`2Nq2`oQ2JXINDtX5dOH4#cDpF$hX)g+a_LVnC{6B1~q*yF+Nh)61
zvTG>AMrP+Z2yB|asvvJ#-MP`0{fCe>LtF)qkCgiAK#^PkMM19-kR?7bkE;+_GRPu3
zO~)Ei1+=Fzq0a5~KOVBLn0d2|orN*pFXlyR`{@uB%ih#R1(e&gbw*fB=*K4(DI%zx
zlz!<l{l-qkjt#{PNr<W<)mc=x3$v+odE@$@MJ4SE*zIW_IwU6NZ~M!Li*|Gx$my8Z
zU-h=b`(uT;4Ym=y@{9_Up_dwrfVM)&Fw+jH=I@i`km#2*dhGC8te&=BtQuLX!g0M0
z2U5=m{IO&{7e`#W%;ps$RJJ0K&X|_4KsR?8oZE}~_|Gra>tKgm^{3{qxOL+sK0UG5
zr9=l{ks8OR=W;J}a*)?k1Nz_KS&Jr2+5M|UhzQXv4aLk&h?p4Z1<|<PO3iiQN|Wcf
zFl>h3GaP?+a(e^3g>=GhBQPH5WQxu>D3gVgs+;QrBSI8KLSt&{{}cqkfDT`}n(&B~
zy$y(?mgXNJ1j0=6xwvLUdUZs}4x>HTUjuc(09}!uO}Mfp$JyG~RZ>RGTGC;>?nhDA
zgiez)toWfl{PswG!~v^LCu&hGdS{2H``hVOI&s2;jaH4NkPHkOxfvT@CHb1*M!nfg
zIPCb{&b12Vn~uMh5H)zUkI=@+jFU@_K#HQxXO*Pv`S5VEf&kG*1p>KQ;AiTg1)Vgw
zR^xX^_|BDeu=@A$p>=q;%c$B!Ymuj+D1iqrw$=SynKS4$zYw%nMwAxM;efRq)$?U)
z@A8ZF8sEJf502EVt@O<J6as9#(+g<KxoMQ!p3gxZe3IV2W=Idm?4!|P&GF@c9GZVV
zvqu2&>oVerCo1{G?Qu0)R7nBZ6tomrnnZRGQ<dp;8`)CJRE}B@sY-gO!`17+V)u5%
z?E-Y_5m|y|&cEIP8<YF+RN4r73m<vNP1??~08V!Dzpt|=9}nq);D%r)BI&~;2CJ8f
zJhnj0W>aL&@xb!Pb{mi&#?T{YRdnOQH<L;jAlM5P4Gea=#Qb5aLZ#oX8VAl*qX*^o
z-X;5zvws))u`&%EJSESjaVY5Wv@Y(qB2t>8_gyMS(BVb4YsFx5&B<OsLCa3J8R0Qc
zXhq=nN-FI_9R7YD^*|R*E)c|PkwBf23iwKlw#L>*d8?UDZ(dT{G@fKlk`R5_4k^jI
zNJluRTxFG8R%{37taThdtpR>bCUSHdC2W@5n;+Axb+qt0NlTs~B2K|)x>)qtJ*|fq
ziXnRt2AWvZ(yCkOG+jsNppR#qpSNeg(43lFMth#oziQjaGM~HqUe-=Pz_WGP5Pwss
zikBye{ls9R<0M6nb0K>@y-nq(joMj-Hes6~Jdv~)=>@an7W_p^6HK@`K1p2B6{_Kb
zI;g^L6Ar{I>5k=7JDu0=18G^-bA;(<8V#P_NUsEh5A^*iBo+6!0=*86wcG8VH4fHE
zQ|Fm=*lHcbG<`j_KQpl}YNi|>LHx=KNQ=aYqJ75}@ZWtO;NZas@Lxt9;b}7tz0(QB
z3}7J`ME^xdfB|k*23taDmodWvCXLS75zSKcte@%HJ)`q>Ap7m@ak6<+2&ZE92%L8H
zmP-O*{??pgm$&s9o+IXPJ#e9ONhz@X_9l_9-<22;Iq6!S15xtvBQ&|s?jeZNvoSyM
zYaP-<juP&;%TQxae`l9o^)akY_w^W5@l#Od;%E$wbWm9a|8UOQ-tD=AcV~o2QGJ8A
zo$ZS%S^C{gBf*;7ljY}rwtbWfl)u-iEU#8A?u<!st4`sTOOU=}ZRb#mK{W{=T`Hdz
zfad-f52=bi<AdmWbObkbc>ijtD$EQGjBkw3YhV|RX@C?(dMR@3M{3#=S#qwSWhSCY
z;iG|W;KeXFRS*0!Yo6Af``>}Od^F}NbfPr9-Sy*FNZGIA!iDIwuGX$k47oJSUEJoM
zV+b$`TOt3XGz9Rnu6(jgW)-CF8SI9sO}l&fwisOP06DD{bu1i3^lY~FLY_g9tw=h~
zl5Mh+s>JEAp2F;fmfq(6_Kxk|d&T%Zl^0J`r9uJ5IA#AbcIv$IJU40<<8r=UWBb&@
zC(mWlERE^Q@`J{+DrH_OE9S2i@vB)VGktM~LEfFHEW!=y@3(1fo!lk^2s_Kc9s#z2
zQtPe6pX^R^@6ay~XKSry4;4D_G3o7*Rt+1VYCZ5%awNHJ`YIx6g84(}n1lQoDf~Z>
z`{gEBxW?<@T<37L&6#Erk0UDu6PPDnX##L4$(Fsdmr|~(!be&yj}p&(T3EW``wJ#y
z@N1gi2j5IZr~}@(6v4)kybWpo*ptkx%W@dq=$Y;L&n|C&aA$+wsGZF}wQO{{9RT8A
z0PS|vVSj!*Y@`!%Z+oKAZY(-F8$6OT76i5jsS{HvJwlqIYe4qf7B0YPL_~pCAG@kj
zL(mE3IB*<%>U@SL?Zv9hcJThf>~U+U2yuC^`$h|w8EDtQO>Ak-aj2JRb~@ZNq<h_y
z!%&8J#VC*87w=?~BEPkStc7qhXZ%Pe-4k^aR7mIdxb6$v8H^@ft8cJ*ehk=Eng`1c
z)FFwO;JqVEu802|08(p_?(`>a1?2F(x1uz1bk*=LQ0JzE;;-e_$Ntx83GuD`TfG|+
zYV`F__n0{a|3XO<`4_Th{rMjzAy;!C&<6JhT1uu@XBU;o@rki*Tynz4*|F|@hX)=q
z?_WCey8Jdt=ZwVdo1h*&Iwj%92f*LH@rvRRaTmL2TH{AL$e3$*N|>lCq_4FYs|_}^
zha~k_1BI{GbD$HK0Z`^LSKAxv9kfm3%*@3U2+1a`*J?aw2BM=;PuL6bIrRvXP-|Ue
z5fs0AEH%MU@6P&l2P%_L&i}jWUE<Y04N|kp>JL##W!<z*P^JtMGbEbt03-It{^a@8
zsn_QD0_kk|BF+e=fq%~>d@(7huJ;<miz8#!Yr~i;#r|(MRxl_9<H@H<d{RU=JzJ(4
zSGf9K`1;PO#`euazwr*<bSDp;J4&KQx`}}AG68}n<2TOM4#^y?G7)oGme~pJA8uD~
zqtTu@;Ed4EXjpZ{>9+$0d)~A?be~xzPPo`=a(fwCNXDt#qnw_|TWBuA6-DvYnvJKa
zY9`^4ZkqN&&2^20;FWp1i;s#bfB%g=e}m&s7(GQ?P#&|xPLI@yK}dL$U50SVD$rW_
z#+ydDI^~3*P@2?XymD2GUny32e+TjV=NBVdUn#fe@e}n{Aid}bHs4?=p%-wQza4nK
z+B0(E_-jN3)vj~Kgi^1zmseGX{qijVk8$Vz-@g+!ht}wlwmdf0mrHVj{dQp?n-r_$
zg56$mflp_D-;KQj=PM&d=!!Gz^Nco%0ToZS;oDtnCcOP?h`J)#YK!Ch?jKUC<rJ%d
z0*UPj2R!HK6Uza>NBbi#F=6+!6s*T$ZqkIGWswefC5E25mI^5@p>nXOyf~qbU#3{(
zl@TGzkq|9DIvDg~mGM3_WJAf4tAC1@tRmobrweC(huISiH@Eph`ieF_2IOhcFIJjO
z-b(V34-5bh6przXNo#rYpXipqD9RyFy8JYSF9wsD&H}LDb|`8J#BO#f_?ge8J_t8S
zqomyblJ7~`ZV|gpmh8l1G2ams=#yc*CLZiZk<@m9p43sVoyM2KYCu!^cWZWZd?K&u
zQrsIDo4Dh`(aa*+b(a7b*Q_X*fqKODnK|28zFe2@1P+4Tb52TXr5FP+;jh$EVC%6K
z|9nO7sa<eGR7mA&&^YiB=Fl|};ooun7!kDZr+?KBQY35D=eclSdvXaMcX4l@MSIX(
zp0O}=eG|WXc+O1aHK1^TSH&0&zpk31L==CHWIbAfE>`0mv{xDE#VRuNanFG-&M_fR
zQ=Y3a1-o6`Boa7WpFl)|Uv7cE_8|H>OhL!<HF$ORo18g54hf($!`i(#K@~jWHVsy4
z46hR5qVnj&br~aV?*&>r9Vn0}5Qct<FFQx8o$nH~1*%;=)=pgmAA1&~A%@SNJk*$5
zvu3Z4WZlx*qKNAOMfAmi>%i6L>XT+L{L9<!R=d_h?z9bmUG~kFzlJ?f-hNhNt3X)`
zEH6#AoZDvfZ7MqBQ7`l|64MYy3P|)yzfXbCOu4MhX+%;$DO)?ckp4tH)SRce?`-0F
z#d|nvoHl#@rIi*mv5+Jm1rHN@DxU-=`JJvGw@>>JKzeqp=sW<MA6|<QS%nH($0k@N
z#O2O!$7MX-y9c^G%_d9e-=XX~@u4%gGu-$PAq9`*^dG1m(wa`j^Qxjk*8To*$?9I`
z5Yq6=*yAPUH!>1*a|VJ|VZZ3tUmUaslJ#va^Kr}9J*Tt0-sM`!ghy7z^i+j`Sww8Q
zO0h|ur%u*B)n8!#f0h7IziPXLg0W9jbo$vx(ejWpt^f^sJ_@|B@`MjgM+zr=HkJG7
zdb!Wa<UUS2fTGX#<|!!_)Y;ftEA-st$u^Wo9T1|LKK%TC>nc1%zLZRyMHRD-LV)?!
zYD!WX0sRpI=|K5cPc++*KtTU-?oJMXDA6?73Ob&_wv+Qz?dV~0;;z3-daPAM<Jvqz
z<n@WM)qa`xUxH#0Zsr9aX}`!)!imbz%nlBV>8bq;by`$f)@a}8pv{05{C7x>=}H_5
zscpnt?I}Q=+BZOBNBEb=Wo~PQsU+ncRlpsB)yr0!x5e%xMZ&32zN{||>1);O@PFgS
zvl`@VtxVFyVlaZnbU>h^q$598F{!f<`&69qn?CjIM;GE`XBN1SSBu^4GSMR-PWe{!
z*;Lm-0@Wt9rRyq>ew?Y3y?9NLcN!Y<1z0^SnINZ}Qm4US6XX=yA9$vpzruM4tRx+G
zkFtgtl#>{jcE$)kC$h1!DPf4lPbFdYISsj#RY`ALp+sR4R0O-Tw?>r(eEH^Oc>XQ!
z;ZH(mf|K0vm<w3P4dg1=jtGYuc|gF?9vI{~v;jSdN{&N9f`vu2i=NjU?K)4xUsBWL
zJ(zCqKKIzS%Y_061C^J}><kdx1UZObdFEN6DKSnfDdfd$Vu+Kx2Jz*8hLU`w319h;
zD#{zP&!faU1`$J<R<10trt_5BYWp&fxWmheGfS3XlY(oMXUm_dlik@dpw+?{YPO-Y
zt*vv1`wIU3p!X3q6sAx331AQG`wS^yWmTX_QI)=8Q9jkj)kTIW%HUQ-KDY^bKsm1*
zAH4z4PqO7&le6t+m6MVGu&DvPlt$aJ-#Kck3pAa-nk#MGNm0%OF4dN$2?kE$+LZte
z=Bo`vaOU%RXr16x;=kh%r^_(IWr)n6>d2id^L?tp-p?7@SM4~ySP6y=<vL#)iwih;
z`1ltGVbR)1n5SiG=!fh(=%8!alQL*<Rp}d%vQ&bQCEZh0NkKtsgExj5o`lpcI#kF4
zW{Mz1GXa8KMnfS3>fZ$Q=;Os&O?R_zzR`tZU_s~dERQL#3(tq*iZ=nsuS-ghP776s
zeRXL)0H+jj4%eh#;)yT08j@xIr(-}a{WwlsqBKL%iJ`B_-&xs+D$f0n_G&VGGRdql
zGLbwey<<W{=s}|v*NwjI;l(Rfkc(hcSC(I`5W14WMwD{s;YjKlZwD_U1to-2AcOQB
zc=)L3_&Fk=*WU?Ght@bXj%Er!wu_O^m}4h*g4FZfTg>pgqYOlqOAk}S)nTHG*imRn
zN-zMm0{E%L>fR1B(|~E~-bHZ7t;B80>A~{>=os4SwYs|+xhQ+e=>yiPt)`ZOga%kA
zEmH+MOY0)yR4kb|V|GlgS8b2*Z<*jjf?Q}tbefMv>}>qKt6z8d85G=$;Rz~p?A7;D
z(RLfFE@w-A(X_k*&*AVjEfJHoE1f1TUdWPvem%D=b^F6>T6?couOu74N4#}R>wmpU
zjloYh5!LWZpjb&%<;YVMf-#Sr(tgcRs_dck(O3O{G@WByrd_m$vu#@wCTp@M+jf)f
zx^t6_$*##Y*>+8~n{2zz^PclNeSSW)AMAbawf^h6R2G-V>Q<x^z-wtM9H0^bfa@VP
z|Cc(u=B2>a+}8C|Oz8t;&Vyi!-}`K9l76-iH|S^QCh8<<YvigUvR|Z)2x?Ew*V#{w
znai%~63VIuct;>U4nBp{=|tt!LBly4C&=KVlO#U9%|L5O`rJf4p49CvxXRl>B+3vZ
z?9~VcpGSPeTB702V}Hh%xSp&7&0gwjazu~Q#1_b~2Hyj4oM`QynI{_*o0+eZV};Ai
z_fuYugaeUH6a?fBTc8F-ism4S7q7Jk7m_UHt4T8PNl(tL?4?6OJAC(|Xr{ItYhRs6
zhgG$SM{SNmaDXGbinWYWnLoP_d-dal|ApZ$=dTOHQ7^XsfXY`7L%RSJGrNR=bv1oO
zQFWDrnT7f3(e93saalxyMs6rqVW5+t|2OU|OsKK@30ue{*&oA@XC>WxU;a&IcEt`m
zKFc%cW}c4UCQ?qrvq|O4WyMjflW_ezb*wVwp;k{#D2BK4vDtCg7V7CE^Cl5Wl>IIP
z|8LNjGVzw4z~ZakM(ifAuwLZo086VWXGMhbOP$y5I+aq>8?+@&*%);8<o-Gf%;xn%
z3UQ-JwH$_}!I_`Xs&Bo8$cwBoVZ6{-21S~2jb=6o3Ho)a3)y9z&u?=Ousc{X&Y}cI
zcCsLL5l&tX9zX$BRh+;*!#CLBIcptPm2Qu{Na#5mSr^ddRUfdEGb+xEO42a<*cmst
z@QeAWh@LG1Z62RCKcw_L14*u4R_r2GbCn!)Qu55uM6_IQ;;)K5t>^O!9VRY5OkI);
z>aMh&4m8rf6UU9WG}o{iV)n?fEI*tbLuRJ^&DKlE47iv2_N|>1U4vE&MPP=tonlj6
z3XcvY1mx$tmjfp2yeor~5UmWq!^b|?4?i&DRmokVW$7$J#RA^~_I{KR?|E9x`R&zv
zsoKcY@3N{Hh>2w7X1Dft?begJ^J_GPFns|{V(ZkV`hc16SYYho{z^3D{cVmeGex6`
zT_XA19)Al`rz>gk**wRZYUaMl?7QCaM?&Z%?w76wa09LDe&9ncb2M1RVU}ug&?mc_
zTS8#Wk1^wiiuOM${`S)59H=gFgB=@P3DRGWhUBn@4W#4PnO$r7L*7u1T^|jUCSnT3
z?`hNKpxnzjH<#N<`te3)9t{xMKs5Y3S@l)Vp#ekhX(1Shf>*@1QXY|!mF{sLOaF*k
zQY-D7o&`X{Nb+_wQBzq}Wcq#(1pO?+#O%ALtAg8YVwFDK{HNs9^XuaT-xQjwP>aXo
zR8{6ujXrN(v*~ChsO84mx@XC_;(jM{d3l*5(BiZsepoGUmlV0nFF#7GSj5iJ=qj8!
zRu&gVW``8PO6uqihhXBygtD$*;=V|&%>ZV&4t&U#eWvVtCa&+qp7*zhtp7Qkj&*x_
zD#{<WvP7sL-5R>?m#mN5!A2vGyv_@gY%)_*gAFL2oyezK^?{L~J;c&XJ18CwFTBCr
zzg4JC@8kE=Q+++?PxF!Ph7pEkKHBK6z%%~LJ7nEHO}KQ};$LfZ+wb_I{u~kN#zd!}
z2l(8)o|0(QX?43Q_$iwNRK;@OBJxR8WY|=U?J=5t|5OZYcUDmeF;WgRJ~LZZ0I#C!
z8GOL*Nu_F?<Nkvv_k}w4GHu~=mA2xuhhi^mNAK}@j<(I+L#a-1U=4jd%%5`D^ea4D
zC5S#9A0Hovr6{#EqGl*|P5E2Uf?DG*7BJ97fe9)s_dy1B5mBx?btuwHHs9yV#sTm5
zmew_&@V-L~8;1(H?Viu8RiNgm@+-y2^M;g1mF}{hZkEQk9~b5NprD(1@3G4+1W}Bd
zIImWar<n(0XDXimV%%XwzG$xlj1>;#8L5LS`;scD+bOdxr1|~!Q|>EI&NaV0u(J7j
zwRwJerKTWU=@5488j>4T+;!6I!XJn{lZ?Y+G(XK<=j`~0ec>5SW;{gRJmYp`cy3_^
zr{rfCnId$MbshBm?ILN%)6tUQv54B=jZDoQu2X*R8zSSpA@TF=cbn1&2OTa;XJ}c|
zv*X4+K8=Pqp@U5b`f<B8QbGGiV9^euAP8c1<Ny8;euj|2AQV8*(?=p;L<WKv(2>iO
z{fvQI-e~K0n7ki|x)yr8pxnHiV12-|L=qTzojTOE8Da*h!vt)R7mA*iE!a8~>+*2u
ztY@Z?z8x*;V7Kd?##TUH;TVq3Z;to=N3`|XjCSY_CPmKjjDPU};D`O*s%kJ4J8wc-
zMtwy4A~CK=jvS;c1>_tH-u0`H)QX;PmAuD=IIqROwt7N8*SQm~@oS*?!$TvB$x0nf
zVM}yBmEe8+fuKmt6R(ltN%}H2-iMx#c-W~ZYT)I!mLdhbKOaNpbD}m9&^=`FY^=E8
zBPw;%Xy^aEL~#uQz+?=pH^cN}>~Jsnhx_g`HL0Gp!P_EHN%&;S(Wtn8GWf15{X!BQ
z&=S$#njRULU+)_u2xv)!AaW^rx1njzt4wWm0W*c+-w{D|%<3;2=zgZTc0KrF#o6{L
z$@-Z6?R2jWyt$z#=o2&FF4?mdYnu{Rv~uFl@)K3^J!AGeK;cI$&Fuc|2;mn8ByLiC
zGFfoqIQzrT(G@ksq;{cvKc9!Eold?o$xCaSAJ#?pNMEe4GGCXN8@M!5s>ig{n+~s5
zWc&XL3AK0@Sj$J|LjNv}J@1BJ5FQImd*%zV)Y+aMn%bOMS(xk}?@jjYwseWC$oK|J
z{f`Q!J$<L$6zB`aTcNi;QjGwv)pzlKSq9i%YqCRJzCr6P)##)`-J*d>O?(`Z1bu>1
zxqdi^Na7Mu@Pn_%s@h&u3vxYgOsh-1-?gJ%);*I*{3ppoYzJI(;|$SGAcFjkiE^}6
zTyAcHyFWldKWzR*4+n$e^KX8;O7ENR!>d!M>s_BgK>pgR+0pJwNk-La{gT~=M}v#e
zzrjNvT$EHx7~L<iF6-&y1rTH`5x2U!<Ot4}2N%cZr=)m91Q+K<1?0$zOzbt4IeuU#
zoT;=h-;Tzw>*U@%p7`c`MSJHXvKEqTv?X`(qX^h;>F$|FCbrHTgt`FDmDTk@Wq$<=
zo$bYU07&#iNE20)D+{ySO`UDHiI&s-MY3;RMLjwB_73D~xqUvo%FUjmM*YjX%`upL
zyWW``FWoK{#{0LRp?+p(d(q-~`XMV?j@{G<rQ75z=06(yf_VrdWf)XBxynQ9b^$b{
zrnBa1ib`yaVXKhfW2VI?6I__v1awvo|0Wv*yfZ7{<j?$JVC%9~m6l!sT7M#fU?ZhY
zYVjYJF`L8`e_yciK~r})pxY*q5a0FFd0@;M1lX6kchorwX=KF5t3ifTaZjCI|LD51
z=X*kYd|x9lFM^OU<<jCjvz@ehi>2$2*MEd@)=Qa?{|L9H=T~Pu+>Qj{fX@Dj5nK;|
zIai>g)YY7zMWx|k-Sg?=VI`x$j2ZIUDU%x^@Rm>lHU|o4Quu=mBKza4!{o-OFeL2-
zG~N+4XZn^cw?OTs1!U3|nVki#4o*V<CZB2%%DT$S(<I>f=)n~f2$*#38eE^D<e$hd
zC?WKydB_piRBCMXl+bDD{`0$}5^N1<uKfd#Z1Iz~HU+YpgE>?wXObGEbs<R1$^^|F
z<_BVYBF40SYG5bZUlkRNvfM;7FN42&-oygJ>(`b#v0Pl7&4D+)OnJjX!zAK=!@YG+
z84%bD!J`2v{Tt@$#&ZT~)OzLpVq<j<+<V;$9||QzgiK0(Oa`f|?bx5v-{k>V4{KRF
zUXh1m#1QQJbHBnlO;y<C0U+Vd6EfX}+9y`OCD?nhl-ZzA@#%eFZ(>gb#q8st*rhaf
z{(M!h!`G>(1{4X!`Z$wg_aCZ+hn1va)EeoHz*ZO`Io8xe{>1}JV<y5`^fdw_u7aUu
z@f!bb5Dk}$Zu?tN9vDhP;x1rP@NH^loFVou`aSf1>}UDMP|C+GajCA(+LXKd<DEXl
zcmizRsm$*b+NVK_0?ChoR-0u{7QWRDEu0B+7VSga#Onze)3zTZROoDMYzXujZITum
zAl7ylh$0aYm3B}P;<JKJ7sX@(^N1z8)UzB7LhVteFw3MyARyHoU;~NqoQ??E^%lB&
z+VR$%pHI)HkA`D<tQO6lR*IGMAWN)EV{_rYqyEBTBk$-=P7@40p7h@_4!<$Q)xXn}
zge0C0N8n7<gbf^D!T3;Fl9HitG}LYZJ8K?uoKsN)<1f&Rvt(arC`L*xa8nzfw?pfk
z{N9$Ym^$KwXF>~c*8qghm#gQ|#@OKHNE#OM_LiHT<BxVfS{v?~?<mej%X)J?c750c
z!~qR#oao=aee;Hqq_My$EAhBSfQyor86T4xE=l?w#_`E-uk3|vs@oi3RhDH24M_4m
z65<$dW8y5;U(1d#RQ<RZG+|aW=qT2s9Wu|UOQ-e3RwM})^Z<Ou6J7-NZBnUW+I5d1
z3VqODv$R!#SqqfC=+RbwTv~kzNW`h0DOzXg<eK2R$R`RM7Ull*;?OPcHTi6TxOm!W
z5DY#A4SoASsTOI1PGDL<M&ZH$)4()@#PaWR^#gmUjzU9ryQdvqr4@-jufApm;pvZE
zjKoAx@Wg8A9=O1CJ35NHZRYN$z%4^B9j<HmyLicn_)LYx8B=NJ2*x}xG_<-pHa0ff
zyTQys!%OLb&809cu}|Ec4)QxjH2!(~OSl~n2)XS6og%+MnOa@JzwE!WyUpZU*7?zM
zMTDQ`1raG0m9Rw$(%Xqjow6R;u<t?WPc$Z*4?gi80_^gy(tO&ya6jOqj|T$OP3YNd
z@@s=~Vl1_sTbi@;>+x{MYp7%rZ20M;ki02>*ZgLr{W|c+u{yc#G7%YBpK_p*L~uj)
zEQ7-tw!^V~DvRH{whR6)ODebH{q4$me6!%rh|kR>Zse}d4!QKJ8&SoYay^AWsxh1B
zd|cGiFhFPl{-<xgca3oT`sL-t+pFbUU>&df@=xi}3B$}Jj2%16kPBIIC;aI>u0O1c
zD#-(jk(eaieak+Z5DFyPe&Kn^K{tL(mhG%kL{_t;W%Gd3GC=X_WpzXvx6$g4PeY8H
zLWnq9@J<xtrWSzt&GKcygz>niNILHrZU>RX2be1u2#zbOF4kosN286z5J~}C^K<A8
zH57!tyZMFoH0<?9;*LL&wVI^KW#EwVQ5iM7=02Ct?M&E>n*7Jh(c~fQ`!KyGjVFQh
zkF}@4L;H1?hF4eB_v)L1w_<p5$AiE%)<HQ_#u&OEjYmgEcvy9Hb?CkRIbR<%N{Yyx
zHGtliw}x~uOBXvYl{iIxRb0sW!x%#WIko9@7$<p#%jL@5?C@Ip0TwvQ4q6wXSOIAU
zL-aO0ha4e-etTQbpRN3yeVM&Bue`sO!l%^|(lA805&mUsY4%5}uC4Qu(M&I>Xrfbp
zaN}JtfyGmQ{noV{JB<?pMiNLoq9!Px_5?I?p7#UjO6}JeXsF>|bNjtR*B&_=jfqcu
z1j3sS#Fr~>#|XcLmsC|R12DGXO=;LQWAX@2oT_PB1LBKu6e>s#;yQ^$5*8K*_2%O^
z!{M+-sl^f5Qc4rMXA7Cr+HoX$kl0%6GWjox8W+0OkK)i)GDg)2WG6)Zsg<jGh`?&L
zrhS~i;9}sO-oGJLYb*33P=Iy|mS~YW-Mng20m<nP=b{SQo^dAUwId(V0UC^BJ}3yq
zI)}}7Ey(*K`NErdIuhkr{}!fc=xBjq`|o;-*iQ9(2Ik-Nf!`*io!*x#JD1<L0=Rgi
zZ8WC$GU_fMGiTrB%?}VNbUX+5ZGm+}`r%i1O?xJWQF$asFj-T{_p<L*JHJ|r8!9^s
z*cP#HaH{p&-8b6Y0%RWDn%I=sYuyolYA3cg5q)nFj4jcrXZx=i+b0Skq<j&5#BDic
zvYR^72z|e+e=nuO$7}%Wb#Yytu;q=m!~VH(imO=1=(Tk$<}(JCy=`rm2hdR02K*3>
zP>p5@onBH(#M#+PAJ#(38yL6#l^@K}GbkzrjuH1&n|}(ow0dRBs)0~MpR4iFwP9K_
z1^pm{FIaJsi=*O9c<E|ngf1O9dc)@mE>jYC+j|~axTPiZNW)E4C$>5!A@ced)>52%
zz;+@DOU%Hz*pbypJr{7d0zSJo_?nWdne}jRx2=jO##n>#iSIz;6>QP+3=1A+U&qJB
zrbhL1qzXi$IrceYZ1o^1K-tq;Ev%Q~`R@XL(d3`u2koD0dGk{iPLUPA+QO)qnOEA{
z^N;6SWw|9UHvr*?PGud#ts}f{0J8A>wXh#jfAZC%;17ZvpRsN~Ab5NDv_l|vRQ~0=
z39n8DuLRNkSMWL!`=c`49kY*=<@JI;Ii$<hd8pERx<6nl{xsHc3HKdFSua18R#x&j
zHbLIohg?s9=AMQ~1bp6ijyEcBSxT_h=utQQUX0I7WGBPUtWZsTNQJNMsstfkz;wB=
ztELPg-tnUAYNdl}fwBQy4}Ncs?03`MUCbmEm9!#rD#@qzozSd;)PxlF@=KLH%3!|@
z;wrhj1~qK+9-H(t7(iiH8iXtrMQAGnv1^#tTkQS@n%Zro!PacFgx(X2T%%+mbX~#I
zV0`W{1+o!!Zu%Tp>F<|tYD<G=D9c#(GsBs;0V?T_Zg@r@IzYQLYFQia;iRl3X|B!{
zITF>qc~KQhB7zogsH`n`@Z49$IEIG=4yJ;aMT-fEb7rTPp%6LvLw$eY)%x^=@PX8d
z-}B8?b88{rM{$dvq-!-8UQ8WXYRwxaoV1&mkjJOCo`+$In^Dlt%lJ4pL+suMq&<g1
zU;#DQr*HsodX*5%FX^d4${flIstwxRVCj=m!$X*}9`v&zscqR64j^m|?oN$a-aF@C
z|GwaZ?Z1afD$t}vXMJ6TZP!n~VqFCA0Eo!yfnF)?K6WH{tFrfVaJ99?b9%$HOpFUV
zn=*>P^JQg1&fX==uNNG(Zu4(WiDfP2&CaOONcTWUc}|iSTTUJrS<(%K37n1!0$mr0
z0@rdi#J}MzRsj<`<!ev`OLm;=vfSp|f`H@e$9T9$^+OXk>1wkJX-lX<=j|1?up<Gz
z><zBvLDko<8ybz5JzeE?#qp2-*M+8Y5aGnVu<9|KFs69*?5khi99`J>&!9EGw2%s*
z31cM2tIlw7G`qRGJ3G6&);7cD5c9_&;xC+rz}7X0|Auv=nYM<HR2bD$R@2aSsXZ1y
z{WW##2eRfB=@sqF4ii}2`NVnAunZ8kI$f{-E_{8oXMkyCCKQnHZ44@1AJFjphDH|D
z4+)_GB|z}*D@l3@dir1GO<zYBwYml1Az=9U!~%nE9`0ZVaa<?`<gu9`*>x>z0&2B*
z!f;EAnf066KvU}n)qy*uk73mhZUz3;2R$>Y*`B)W?t3Pd<05K@Y4)3KpoI2`PM^Q=
z^KA0+l`xna{%PP#CzVwS>s1mJlePq??lCbt)Vne{K1y)k#a8wxVV4VqW)C36Ofpv5
z5l;sjJCwlgPMn40d@4F+WQjAy{#y2u*k3?qlxBD+s3-QZMkV5PD(@sLCebw&qP%!{
zBps4>vr^B3?FY3rU{NE9Pt!_6+H|dhY0(aSQ-5Ld_ss=?$^Mc!bFpCKa`=N2Nk_2k
zEm0)-vJH+h$2`D>RFMW*;S7+&ios{BhQmC0xALxN|H3~7kq<IJPrnc(eH)0xLn7p(
zt%GZixgCysGe>rPq))<_IgUs%0^q~jE|wW_9P@=`6%`d#RjX}Ic7h$3BK`(s{`U5A
z72jfW7cg6Np|O$eAWdCtTqmuJL&$7@dO=H2n_;Onf>0l4`kikZO28y7?EBE&7LcI~
zJ~*E~I@SvUBNo`1#hgSh&sPQ&%tmG}AcKa;F8H^E9Er_IpiB(9y&hvH-qMz%X&=Ku
zH(EZA2ebaz-K+sJTvb=;d`XLovn|?GzEbc&1#kcPO@+^tlFhk3bFcxWzyVCn<vV=5
z9wapen3I^hJa<F{Ag|Q2WGD;-tRM&>CY~<;zA(nH4EG^wsXIP{vplQcE&!q8!41Pa
zN8tc_Vog6DrD?$wi+Fo`{ov~1;^f@G%{{FY54MZcSv(}Bm!51~!LdDOyyL92Nx5L-
z)k816wZ6F_)CZbGfb~86o^qILrX*72PcXM0$}hTGFSAMmaDDCC9yX|T-&@o-s)v(o
zT?&^ZEx@?MCeIZ9>uQxSm!?P>jk1B)u{@{JZKrQhnJZAM9EwxNs+g%IiY#`4B%m6d
zD;FzO0J;wn$B<tqm4)(z4jhCllN#IbnhNF?mFLGuIV$CK*EFYvXI5lG)8m;d_x{vp
z1j{l$z)$J~>e^ZzLbhXF8?tT1mNvoC-Sg}nkU0G)DVwYB+_DA1b7lw;(@JS2q0rdi
za|{eNW0iK{9c#A<%-CAvXwAH?e3+~9%H<<>Ci@yW=_>#3h&NY7bZUNwpjW;(M!HY(
zHerK{T}eJR5*nIhW(<xofA3Q|(X3?H=qk<sy^mx7$Zv>M5&!r;h<Biz#6LfcDQJqD
zVbfeG6LpoR$7U&a1_dgzv7i#haebQbmD0yzd@@J!@tVWTtwZbiitdW~{)0nBB3_8L
z6%Z2>6AcOuf`Bv{s-_<u7L3v=2j@}jooDFE+nsJD7sfF-6WChVbnAMYO|jt)m%I=l
zGkg04yx)(ew!Iv4!8;F<kH{2#XmLG2M%|uxN3a5E0Q|kM`}|9?Fe}Lkt044?IjDL=
zPq_LjULx_lO86z}Biin)x%;mf=*2%F&zN21&)44qd}=Mk1}|RvC+(K4FVUy?VUBzS
zW}j~{C33o*E|xcAyre}?4!l9g;;4hktYu&TQ(d5#M|5}s8U*jg`5IW4zo~Fl507!h
zJ+CGQ@lG}ok4r|SWAQhAJk*bjh+jZJfPZ~sW8Kj!Mjz5jE+@K66%J<2qanD@>2x$T
zsj75ss=>o`chBK$CaM|e@OFLl1~hF{-!BmE#w@8Mpu<8WB!{V~N(&}-2*aQG9s#0I
zn4YPhL`u*zr4TtneRyMAD{r)RrrpT9R3<s>xR+3Fa+TQ~!37xX&YEeq)$6(dTz@od
zO(6tuCwLFmLFjP+6bL_x`IYuqakEZuENY1<q(`M3j4F=Q{D8h@@+3LoY*XT9pI6pQ
zL>J2zJgxkV6hh4WNb&XP{J=%T4X84=vbU5vmy}1Y4g3T4NP$7t&s1f%kKNheXrm8l
zeTvG#MFLf|Is^%ea<&CDH2%RYe+6ZwW7mm}j;#c|^DGWi6FI$pm62<;G4gRc43^)~
zIS>+S3oc$}EpQ}gC}7yytsKN#J9_p6yvoty!O%tZr(S-N`R5N{t>D5(0^`4}33Zm+
zLDn1E5_8;2Ch!D9wZ$BJA&-30i&pg6t{}P_<5*v|w*L<k6ose#$}{)(085f_PV56?
zr)Ox0pYF<_iwq@%V&W8oJ<~T+yJsTGCL$&}Q<k)2k<3nVd!f?7bw_6*>|xaVE+>P+
zr&7qm!NI~Zy|l0hD5+{B4^uj$lZ*HZ8c1`Rs<9PSEi37Cxvj<Ow?8hPZFE*R`wSk`
z%P)Ih-JHYL<O+aQ)a6WZsqJV6C+v<bgxW-bFga>XUi*nG+?d$LO_zAXGyDk9@q@t|
zJ46R7>?~?fjOfMcu9MvuO&_g+FQg_`sDrTO*Bq|$jPQ@Z=D)kjjs=O#CYG0W@cFHc
zK?R9<mbAbQlJ-r=YDT=MeK0LX(L3{k62(9?`$d=|8In&?IdhY}Fzog^XzK)lj`v{j
z^2#riwMIBy{TA$h3ZgO*eI_GiWyVKF8R&nPl~+_0$|$R=t4pY>%ggJFiK$|i{L^Yw
z5S1RDnBn691O;rodNT@;&2>H0k{uYo>itUVbloq>MxH#ZUibS*zf<Agaw5<@(6Fys
z?lQGpxaeGX9lJ?_ux+H~E%9dvW`Snl{5}Hy23x5vOd6LG5ZP0Tma4J%v08mlIz*)c
z?@a@yB+@6QEr$E*>jfJkF<fe9c37p;{Nr@$<XtKQSZka10p1@reBvCl<IbgiGaa0U
zH#6tjKnLbw%NF?BA}5PqA?<A)`@9$a41G#96!pub_qKQ-2~3cdy;bR7uOphIA}cnL
z@F!!9{60coUQWjVs(_D<eGkq-DKjp_A;_wVo?w(mo@OFv_yUm@*i@HN7!GK;?k1YE
z6fhV7Nx2|x8zu=}Zew*p*M48juu*EX`PvWa!=d2tI1XX~{{pYeuu3{jO5TghWhVJw
zqiq3FS}G#?64E3BW(A^1%4=V==JZ5KigG^<T|eRDr03^JD4nM@sZa`Gh#@f%lYN;#
z<9{v>436I{*;kg8OW^tsKl^tK2CR|%vH4*Ml+zY+_juWT=FA2)d{<<!!*}%SyBddY
z{WnM6WiAgkOVS=vfd?8I`Z~(IQfL38DExOl*(sGta_|eamF7%gzIk2+yb_9&ar|8G
z%VP%x%UAA3ryJKMPVQ-l*mAnwCN{2Sk6XkT`N;{xb`$)Qd6D&TcI)F+;uuSq3rHV#
z!1dq770ccIyTC@a<r?3&Z^ol8`M;e(mGok^Ee~Ju>mi&E5l(DsVIP^uxu4(}l3yl<
z8w1c}-kw<_2sTYT41(-Jj$uxY*f{>wd$>P7#C@0htN*9YL=m#%9?$nF-Z8^pN+%kG
zlDT*eZpVvpb9Fe5Og6GKHAhMbY&;5?2Ogf$j_jJI<Mjpxp1c}JvXW!70Turr^0%A!
zvUSXtR2kC!^#PjVIQQj?7BCsLe+>r|5Kpdtj@jU?w7f_J{7lMEO~g9NGoJ3uf+5@L
z$DB>GqzZdA4n`NffAqW@p;I~}<@Xi;{<ziU^?m|kBvo&<INdH*J%r6xviKn91KL}&
zNO1GT4T-5?Z3N$Zu>Rp&Arb_|cw?jj7BwG`tPxm{&`NAdF$b7IDj+22>`e<6Tchg(
zvh2u+p=Oth;a;6aZ3aoNk4-#($+@|qfCTm5#_>*l`F8Gq1J61(#x-@Va_l8?BSfWm
z6nvXo2AZj9q1StG`3`qFh~;ZA05j8XUQSQz%eFzml6pj%8B$v=zM7VT)QGBf0b^I*
zIAf*njmomSVt!dn$n$0rM!kKI`T=>-Z3$IUj(XnwsnZe*<<a>*wjZfO3d?$Z!Jm<A
zAZXap_1>^z4!_sS-LdfPl)96>cY$08$@EghFn2_1Y6*-?7lGI?bQr%7pbfXzE@CZ_
z;G;9`4V$UJ#sAQE=aNU=hhj7L(1|QxTc3yH;lL_`2-&#V6;xpPw0IvYmSQv4mxU}6
z9iL=Z5cYtLRx#J77lQ?T;CndiM7I)hcy2}F>sD3lED@5;M!R>p9he>1=mSF9=Myyj
zEcpESOIK!Ymfb|Ux4y^`AcbYOgI0FE4my5IHneboV>r?VSqeftwNAf8I<m>@klCEH
zx0Joy$!i($1NRqmxFqW0(K$BDwaZId7;7}ZaZXe-=QjO!SV%3Y8Nff%e*TlDYj%pp
zYADLnc={MWqs4Y1EOG-`O3}lx%`1F;jg_m(GafQ?wYMp3((4)!#2Gsp5=Xs*LOw?%
zuLl7ztL~G<dAmauf(!A?_PurGuQ3eYhEzo;tA%Zq;0Itv{Wi?_uQ?kYV`QR+yR_+5
zf?RtgraU%SiM1kg?PyvSc%}A}IbB^J4LX8sK)DkkKLiWg<)4?&QKy!t<MovKcUEtx
zlxeQeQm|b3kl%rC$2-@j17n{bdwGq!wQA#fP0*m^55bUo&e8I?zJ7gi`GuqGYimwa
zu%{cW$zi&dqRWwkEAfhYGXA~@oWWPKY*z9s*&h0@@-Q=wHuHDB6<BPFa%<paHC3-%
z782i%3;1$AD3^cA(p%%NX=Q+-di@7#dT{;MB`jn<fjI!^S;)G+07;4KoW{(i@=IJe
zY-OLd#)mYtE((z#Oj1lvlqSr~zCW@4d9%Y72?0Ty%i)yc)@U1(7A~}@WXp0o{=UF@
ztS~IyD>FrMrhnMmr4Wh?wIka;Fft{zo&cT@@q?EkYOLw<w~%Y)y{*Sq3Soa?ayPgg
znb7>2N;6>YrW4X$6_HxN!~A*%&)Z!<4nn*Ovn@x680DG92GswyysN)gr(R(-u%H*M
zj0RBK2ZR5!R3y~ZMMS`Z2W$1P+0EX0g~Wx>{vMe4%OeYi(;k*jE%4QB2NE<rkODf^
z)R#zjTpOO%*6@35^G&)+OPiZ(YPvsn|HB#r*i4EsP@kO0yee3cWjFrCMtQCQg94}#
zm=uyEpWO%93C~+2lAa`KWJBS+UeJ;Q#f<u0<z=pG7puX;iiO`$J4$<9)9Ukm?{6;l
z|46wP=6nU+HslB)4ZHWNK5nnVuI^Fc5a1Exq8~gyGK`^&G#F||?$q&>Hy2lD`S+fH
z*_J?<l7iH(I$lrZ*C5|0p3ApiNhOooBdFhf7|wQ)#*<oIzE5C&$tW(7ohyX1u-7^P
zQRrl3nr*ilo16SU1B1hrKR4HBo$DL#=a1*?h6!!x$0HHyRSMs-nOw03aad0{r=5O{
zPmNLv!&=jOiOfNZlfbP+=a!&7btB3Ego#C@Y=3H95dRC6{Q^<VgTYmuy%)!JZ^kl)
z_UtzNw%0*08re#M!0f?GeZDPkqoopc{5tR{3j&o6m(uC+pQLd772&NijQn25bnQBZ
zW@(c8Fl{<VzP$hP&pnbGV(j$%KwMMt=jDF>fMZ+;63Rj(GAXe5TGpF8!Uw<}JpqbJ
zDBm3%k(?NDDevhYwd|i;4ICT?#x=QEH&!!L&@TP@pmAQd-0_==nG*!aR1|?jd^36b
zi!1Ywrv)bg<#GC{0MTM>KosvIIM13NVYVl-i(B~zG^ZV;YS2IYRIoOul*Vwyrrb-?
z4$hmaXI-|<4lf?p){5tzi%H<LeixDmIVEJ}v(@s+1A@|VV%jb%quu^C2f4@|Y|m1`
zbRR2GnZ4E(RQgQAJ}@EX{+lZY?obTk{rMK+Ff&hy*yZOCZDkpjOc>>a|E}CMqW@z=
zf*5?DI9d%ib75Z89S?~VR_OGQc37j=+0<gD`h38o%g|)ErQGVWbO8wXRM<Q$ENEhV
zjd^I8Pd0nPM7wdHsJuNgIwV8FqLTP7)cKh^ha%!dfv$GDqGc^39+Y#;>lseX$VB(M
zOwhI9Tu(@{xXTOD3wXTPn42NQ<pTvFn+vV9qnnGvpoW2MoKHKW6=x=!%k;{Eu_5+*
zb6({sZw%YLDHmt{8-RF&ee7qdyw>0VwXKtgm;4Wdpqk>0kq$%u_iVC6Cl@9vep|fm
zQ_T*|mV0<6Hhp#mK3Z-NtGD*3WgwXvf86;7L8Hn*70z=q@#b9p6@`Pd*^PKpyMd!`
zLCcuPEyww`IQR<aC-VgtPJV~#n+GtG;QpW@S>(Tb12<+h0m>{7{)HQ47gR08fPhu)
zu6q}SOCaJI`A<A%lFd&4y7u#d-t*8O#tJX>QC+5x_kT^%LptE|s8DhUPb#<4(T%=#
zJFdgUsfg}LzT?f|Ci9wj^4Yz}{bvzt+26mnJ8F$^pGh;zmD`7yuwj{snej`Yp=8Hr
z=dsR@E!7bR9{eE1C<^wHaG0h4g03(Ov>wlGH~RO(Q+ImA$GmMe=exp$F-}-r(o`Hj
zQCLKvS-Ft=d~PgR_CNa5#oGnZB$<z@dhu9Aj3oqY;#j6(VD|12HN!~27~`~-JG~wp
z|1BDQK0mzc-e7i2V0jI%iF^zP8ls~M#1$3zfjSQWQtn1O&5%fn4QLVHay}-n`@Vus
zyrPzp^P!d1L|4GYD|XaSN9Y|@L^bbspx`gRZecYC$-#jkTB>43=HL8|<(Ch0-52*<
zc7NmC=1T4IyKf<#+R1;H8RzBGQQsAxUjy<={Q@d>L7^bq!8#L%*RwYo{7HxBElJ)D
zoFb6<SYycF8w*ovEt3kfRt`T=WG>=Qc4ezSHZ4$X<fHm`IW%}*TO<{M$dHCalM2a~
zKD3MSz1;uRF25mASI%C$f8~9D+*I$rc$s$YOKA+aMU18$p@Sb-t}j;;-_8IATkRUS
zRW+al_Z=@^Oh|y8uh~=gysnyol38%;QUIv8^J@6hghu*%7i<&WD4)Xxhxcm^xSs$>
zu^dF8bq)t!Vk`QR3y=DQQ-*zEi-IoUr(1J{rS{`oaC+A5n5))4!23F3F|N(2#9mb+
zy|d)XBf0p~Yuhe^;-PoLr6XJ=>ciSf$gX3O#SV@$>ZQE3$n^{&Ni+G9;jiM|9~D5#
zgTMbiqF`Z8%Qno6f9IViYR6%Kfu_dZYX2WGXea<3ADbAHFfBQwi_v4Syz#Vof#}C+
zr&C<fV%S>6Eyg}1B4%;x5Q;_8*|Kw>qXI}mtC>hHDYG?m{>%*b_Ow?6Zl0qr43jb<
zh(5||GwwlZdrndW!%KJVU?}M03jnT98p9v;S#Y#=GnW%ppI%1BqCDm*nzpNcE%lDl
zEM*dLif>CkD643ZwdAJ8#ycgkzx9+7lBCoH-klSol*yfp5q|M*j-dEov{(~4s-%AX
zP>#Z7R*;vMmR3+ONc^R%)Y9c@V}72Xx}kZ&?+uCJnVA;4Over<aJ!5XFahM`)yBt@
z&GZ#U8px^WXn<RNhTJ|hw+VbAvsO>#vc~Dkfs3gp{#mlG7Mxx5KpaTQ>GZt~Ohh2U
zKe1k~=O2?$gt)8yp#3cVWKAZ&tJH%uox6!*SS$)}#|P@FdTSARB7Yncj-f@exJq0u
z`x;1N``I+-EjL&R!A1{SbO1Vc@vts#ELcG=wPp3@#@WMNXr*XuJmI>clgpz*G^Xmk
zksa%2=CX6FCWT1f)rVKxm^X-gUM_BzpNHzBg%Q0(8{mVIGUt~l>&Oz3xH>r8D+hG4
z4No85JO-$A<ZMyG1E^YrTEb}#vFs`YHl^h*K&ZjbVc0yS;8WMfd>|6L?gB;yvkz=O
znB|Y#J$!`1lvWPWiAD9tpb5!rG&ppF41yip;P`m|WTzE6ZfO9Nh~AY;9n?NXlatN2
za&`95Kmq1}Ts$yrq%BcmWYVnF_)H1V8@V}yZ$r<OmWoxS{VOf$mtl+Xc(q?y|M5wu
zv&-Gg_+##H+oOZrEx?{oz*yA^zAMVJaCBm2V_gPvveA8^AL(Z&`|_1uZsgX#-S>qV
zeyk^j1kh~>FJ|1Ty%n}0wG!xux<nnTHf<>V@jB+IXsR7%GKm^O9m3>(g@O`AA(21S
zy96H^_l$@Ki8+QQBMwDc4e@qFmPS)F1gGqn`eW}<NKdr>1c1@_x)^ri+^6SKx`zS2
z<a6{xX9QY<jsk*T&oi{2y2^$$tIhh<|E!>^tCq~;KCDVTk6E}UDX07S$?Hx}OuD=|
z5Km-#;0R}$S1dcAF(tJ1S43TYU>6GXJtmF6(B|dQ!&@5E{@yd8WHdz1=oq`nX!*@a
zp+3jS+a8|^81UzEd1^s=v*ZTzhNxYbM-@CBKA(Bm1<VBe0Eu>@bEmx!Ie(ggn*1vb
zeWU;~l?X)+2P$6kI;Hyq*M$fPR}mPm{-NrF+fDkdV4V4Z8VbJH(oxo|PIP_MdcBjr
z$aQM@OQGFijS+);%cWqP`vNwt*Aap;fhw5A{5*RTi2wcI=;7w>T-yxo$PItD7d|TR
zB>Bm28cB*=Usoz@vdh-i)K#Y~!G~h>eQ^W!@8+Ax8m7TF<wmZ-zoO{6P#<L-!K5K+
zgznCZOB&!2Rm=^6c#uSl6hU2l1jE(eH_M;I_xs&9w?<G{juuH4y|spZ<_YH4+mrlu
z$}b^<o&ekqj+1c5GCWJMG&oTM7^L;p2PJJqJxUH&M_lUvJfGfvKkRaYug>uGR0gxo
zKqyZ~KR+fVz`)u*+zY*Numl&J!w6d6ZlIuJq@e$(vi}Rn$0HIk3iV9&PSQGT<0aP@
zRAvxtbXl;J!75yQ(IGM_W+R8yq7u(bOG-&-ss}iZ_gOzC$<EWB@#J_xyG)njb({HA
z<?pQ(zo`QZO?EeUnF$318nbAcnesy;T{`eu7fvsDaFEHBG5!zS1fv~4@cGd5JSTNp
z(S)U`Mk=`B;&(<}RSfwu6j~Q+9aU?{mv4fwt=~O;^lzXgz3eATvOg;91oxiq_e=)A
z3SjNl5GTp%oQfP<^|5TiEhRkjFNk~T^6$DrNYDgdJlQLKy9joz>{>n~F^)NeP=fW~
zfU0M0u2en~W_DW@zKl+7Dyj^KW(!eKQC3}=pPgAK6^FI>cwiC*yFIHC5KRhrFZnw8
zm&;{nLc7e((qu>#=ZA!wzG+9E{=gJlHlRB*LF;uNOT*~xljr^zJE{Jv=&ILozs=e2
zMnZglnk3*FDk7Dbgj<ftNlb~zXn`-O@(3YtHhJF@6JI}x1)WZzN7Jo1Bu8W-`?|&X
zH{atvXspCuSyp*hcgmo}*X8c|)B#h7977=%ZL#DfBb`#hZFoBW4+}dx>y9L060pTX
zw~}`~;7Tl}Dyu6cD~1eRJCX@vG~-@)a*FB3p$RLMK?a#@>dm**v9Kq!!K_NAY6Za_
zwTX@$8HfM%y%HAN=sFQl=M#~HY`%0w!_c;13#nthbGj*OV6SU%xxTx3xW2x>MHP;K
z6^=k#T7N#%k~Rbp66lmIl#D7z0Um!2ZA|<<%(%^`f@~?BRc54h0zXmO4}2Nl`<J^s
zco}|;PPS<nb}R4;=rZm@ur=JT3ODHKiauU56!i_{$w)2eE3KZeUdx#$bTU^=Mj7`p
znK0lDYz^*iJ<k%&yaG;{<I*=vOd=1$l5&m-AsG>-!g>cMlb}an&5QC?K(+HyNmgp>
zK7rN&Kcaid#Y)yaJ)7QG6J<)gOil^9rz?i|OF!C?y=6IM4i%W4x#38lj^JuOF0pyH
zX(InX9E}Y{a2NRTK(6g2ImRbE2I``OrcT9Dsl3`QCwVOmR#uoJkMr#fqA2NrA^|27
z6)HH6GmI#qs4+A_bHE4{P|24^H#8Yvk+<iel#r%6$Ii_<zaT1~8Vzz<Us5cWDOm2K
z#jDM-HMuuRnYZAOQGrK-6qG56+1+iPbBkl0Zm^13yh4sPXDfV?yO)o|q0})s>2&ss
zN;NQuT5{{GVH>d(rOYxN@H^XsawsmmI0cFUC<LxkJxyqbB!h7PO;jw<ko4j|9f7M9
zG=O_){cUB&Wq)zGWkBfH^uW0A_sB#()G~(W(7_^M|Ez7kUF&Q=5{7=rFfK}rR9Gto
zIURp*xqztF)U0U_7{nr${DN<j6-mS86P14e%N?|Ml^J%AJ9n3cMwZ1nwa}OKiJ{Sc
zZXS21hm)AK2QDrk_ZTLB5BCy30=7M)Pi=l~L>f{#Hr$3B7soI@RaH&w)5AuJ+hH5q
zW-QA2Z>~uCLH$&y*VTHf=TNjW>hIyxHPYwJp~0e>U7;6pLmGeW>*~TP;?DmvEnc^@
z2~UTZS)NEL!MC`DyIRX&HIXuQoY;;ji3y2dgXAbygvWq78R&FnE;T*J{-vSRX8ohG
zyRa@}z~e>sNRgFaR#HJ(TV6~TS3QWW;7Bl+GxV33lFsDxD%13E{~)-TeV)=lQ{D83
zdDVuB-#=Rqp;YjXp6_pkfr}aUvi;c&3&{aS0c{*DBRk0uI4L>5WByo@)dJv5;<fl!
zIB>J5QF(xu%VBMgvkk;XQTNm<km^GZ+Si_r%m>w_6x|+BHoG2^72f6|<oR>F{`3+e
z%i@(hiw~Mxfgx~#v|cx}CP5uUWty3i^LBLC$>j5l_W1hkJw+YzKHN1^KOdTAQuYk*
zCYi+((sAJ9(byRSf9mJ`!a*1dQj*rH{cpfA7>EgtNC+h1VpC;tMUauKI=e>1t=S~-
zx;nd;lH4UlZCXfUihJbhx6ZnP%4d$k{+G^^XU0<NcqJUct(J!V&#2e^v0E|tE=hvE
zNu&Rj<|pXmI7;V;2vYgDq5MsY5m(MfaeZ3RQ)T1$^E*FpFFsFevu7WMVq#7Srf}4+
zTc8E17<5Ok66t~~1?Ld2`-)MhUg+A%Q*@WrmxiNBT=B)Qm1=%LEyehF^hu}837Ibr
zF0bMYOreHsj2$>&{)3A!`XiI?by&P${?)VP+ln^caao`_V|YWCtoUMBMiOKQ=gyj9
z^gwV$Z_|RTC=uhGa#GLu6o07B@L!8PY(O=j98zn`+Gqtf<+X+dmHH7Gk!O3AVgR4q
z|DR8x5Lxy^jWY;SImHZR9upBtijmvs&rK&Py1VsJGyvdpto07gYz|?lo4uSMBnIgj
zmK9C28meqDeeK?ze#TrV_JaRtTFndS+?J)(`DRhi`lL~w$kJY{^Y$-fnGWgf5vb>0
z7$O)E-s+9dS?f1N1vB}8rMy;j$l=pbPsBXjv#Ktn8e-pc4sg#eP`^0NbIgBJ^NS0V
z!n}_0`}N4|{2AvD`q$0^BYu7N9ku6gZFl)x(Ovy#YJwT}O7LQyBN!(oC%v>jx-^ch
zu15p&?Icn~OT>1p+X`&Z49P+S0DP){hRGY9oScJzmSc+W?jT7r^&7$=OYiIO0F)cH
zI;KL<fGHEdXh@*lP`5?xwX97#&y#pqI81-#LN->(Km8tx%6+^9RTP2|Q!3yIdq5V;
z!&}#ST%!nzuaA(qub+!hmTh+{M}hxYal&l98|n#Dy;@IcyZ;C3HRTo|5G%cRmGrNd
z9kX9`8vR=~zj%bh>6Re$6E~HVIg!dYcknOJnWu^#4=C$IUQcfuhRN&4{jy9f^^F?l
zBR(I(N(X)e>!GtXAW67u9s4jXbEtpm;}Mxm;LaQ48-irD@edpNNS7p$AY~0O0%3(G
zYvbL1<kWw7D=5hqzijCk0J3ti?$~4!3$S8~L!+40Z`jjW>P=U&2C7~SmPnbgg)MO(
zlpHf(b2hu}<Ry#3Tf4#vl37ss5Jl3%w_s^W{yluhIAlE0&*J~ZbE<q0vFJ*I^;9Wj
zD=0-?Qo2izVIwqU3HcM^nz=T<OTPQVXV7bR0xW^Uwm0AU0KKu&3OL-U&KX|{zYy?$
zCZQ!U@U*f59n-q>iX)XdEqnL{3n5glu4OfuoxEknGo_C_khMQTBDv1x)6jAM*%y~m
z4axu9y}Cw6!{dcE*Gaif`=up6+PAW_k~mLZP>t=OiEBwK^`BQxR+Z9^7+Ia#%e1+y
zGKQ0uIdJ%GV<&$N;C9gK_CG&a=<Q{Iv_w7E%OyUv44<OFk8jHw2Fp{<6wWg(59VXo
z^yy_%GznAk8V<8mxji%;_#5ZzU^a9lgR8oG6`~bTZ$V=z*{x_yB(wGT<;wO0)P6S@
zIpz~KZm+@jVHWazL7>gi8hIq$QcvHt%pbUlehD-R%$qU=hO7i4Gkj(dC#C0=&~x^F
zFAqXsRWs=Y?~1~o+I)l(;XzQ-lT(+L7371)0}=Z5;PDPbcL#Sc0=vtprh+lzc+)^B
zil(N#B=a9XjO{Hzb`qo;X@z`Q4F?C?ZiLMuG42{<y|eRH#~t6t*6Ql15`oN{q~Por
zczBc^UsHd8=uI$risE2n9PX=GflT`sGy8NWMX5H7!l#7G7;jZpIUJJmXBH)}B=x=k
zc`djXBiGBxoa@(lK+21PdHQ&QjD)7J=*K>Bwsu1yC<k|1R_ph+I~m?x%gpY*V@%pj
zv$CXze!s~Pa4m%Lb~HCaHFil!Nj|aGt0rrZJd**K_95^uaa_P!%|4|xHnU8}r=e{_
z!4Sel)Bm<P4c?8apzYYA$MCiNqqOTakm5Q))ImJyishs%`2`an)7Z?+%mfCa#T`YV
z_serJC+*jD|LdpCD|uT@*gnuOdn_rRj=n}M)x@M8F`wnNvx^g(#c0D+L0V@-NhKOA
z`#})^xtU{i>?kD!%YenW9q_-4RtG45@Y`nbdxn%1{EoFGb7q^}ExO=vTKN>hQ0$En
z0oRmMkYAeT#47tT-0on!)TVhUD7tHC+<%=@m{Nh+DZh_@q9BBZ*CYve`~mIWwY~p#
z=?tYXk$?DeeVc@uN6}B>cnc1xu`yxw1m;5~O*4kUAINLJ{&91Dghz^#M!E)LQdTG#
zrMotX?8z-A&iS&(!^Ct(P+B3oeZ0~69-Ne#cE;~|a5HlHUn1+?Blm}(?~&2o2S|~+
znlV|yUeHn5X#Tyh<J!PWeD|FHnw^1mVpOz<*y9E*po0eG4Vp*~P3Wd`CSn)^FuKJJ
zFo<s`C{OJI-5a~Axrz)8WYo~+@4rMvTv8O%!A+e#uT#`pdmn)OlPiOKglk{X6|%^l
zo{t40nmQcKUdN!AF<aq9wvA|z-*x8JmnT}lXie3xB_<lCxZwwPo-iW+7$pSI9UFy`
zobw_+WBRX+{(qK0e19Ieo4`v2qWT3Y8;ZORwg(ujK(Vj*F3vgp$YhzZG5T`r6M~Ev
z3$7^x@SVCZ<4c!TJYF9!!H<70H%NGZ198*)%?<T-dJo&0Wu0{VQ_IpCGB`?4g|GGo
zf?-40)|f@}G3On9enf!^6P&Q6#Pui=Tm~G9)Sb62J;n)#Qe6P|iG2xPCYf#}h)HF#
z|6ga+7-0q0l^f1pRp)q1kw1dh!k~v4KGGLdPypdr`8NHC_fEDTp8n1GoXvi7e|+1q
zgN8bY^;QPRSgJ|PbB+*yd6~JMzwP~5I;4T-CH#W;=Tl)^0MphC#oqrVYG|Do%`RIg
ztr8=Bxh^L=7_c<*)U&qO%t*XRi4p^O%b*Sm23(?od||{;10@YGB?ltM=-bGua8j~b
zP5NWo_Oc=S6+b<_E)ND{4~9B*o0HS*R+Uur8n_q)dq%%Yxb7f{%KTDPQVW1p#Btg!
zUWhsGr1VwZ#a7xKWhZIDnGcCfH46LhYo6Fj#?jCUctQ<`tk3mLEfC%pE1)zj;#j<7
zgbZR{@5?<4-iC_nDr5)K>ocjsUe?aPA-AJ6>J!npu?oIRQ;}rf^+Sy<DrM|oT|tr~
zi4@vsJ^e$@5>r7Ot9uE7_~gmrJZDt|X0~ANr$Psr3?$*W9KL)<shz<xeZ{6V=pR{4
z(U{2skWl;r+V`aH^LVzm8z-}TDOFt%@SmcV9v$w2I!tF)IV&_E4!$u6POo5zr{Hha
zH^?Fj;N^*r0iW65Uv0yLz~Sk3v4j3w5X5+XZusak1>4xFq=rIHAi8lcxc?>z-7}ay
zxh?C_?lYfIm{nrw)P8~ztN6XNiv9J&3l8AA>PLp9@cDM?ko<(JNV-AT>T<EY_=d*~
z`K2i$Koiq}e^}GO*GWM%K;X$gCQXEFBT+i4Z3T&oxU&W`S!1CY)QeV>`>U<4*H!5K
zIU_*mjOlCFK*jrAnqSps_0);&+?kS$BK8&Rt|^S#vgS)gxx=>&zvsnwT+-ZN?!Q3Y
zi@td#mpz?!oPj6L@%fCj5HRM7VE1K2Eb)kLXP*b{izd#Y1#|&~^2DD2O77nUV2Xse
z?(^@U1-E6wH+3gN@T3RzNW3K`gQg$LuBOQV)M*%%RCMxEoidbO7PM$+vT@XPG%%|G
zStt^p?vdmyH{FW3tn=>obYqQ&9bhtwHt(6&v(4R9qa~?&AY>ZqideDGCLKrmhY2Bh
zpS@3qD1?5<sWn>A5RyGIjA<`>z8z9E()NY)?v5hyvT{h9y^o(4G&Q!{f8tQaqyc1c
z$qZ&>aVKqRPy=W_f|^mc@yFpu$zm)?N20IqqmH4^ZEptgTC>BNMxEO!kjhjsM=8L>
zLDecO0Y_%s5zUuVuPPmJ)x@omvTaO_n_ZUisv}{%gNo}h-)HjR`&C35+SdOcCQ(WY
z6=Jrm_OIh8RgAj#{9^K#j<Sx!z%i@E%3tD*B9+L2uh0sJdB9J7ThZOw+sLL10nhr^
zv5ivFAC@v#Uwa}whRYX=0ob)45%vC2K2L{rXeN5<sLLbU16f6Anl8~XMV}NJ--VeQ
zl91avuzE{ktC<2}C_q|2>&p{jsEn_z3?g&IC_EpuCr#Mqv@q=%klXr-n!1R3R8Ku0
zVKbzONK%wa(u&N>O4`7#>u_k4rbC>dum9)N!bi2L6snr4vT73rKs_+3Bw~b8m$0Wd
zK*#08Y<)?XjedG*R{nLc|Ap`%KX?pFOd_znkKCW*nq`;@kEM*~8Z}CKYDBx$elBd+
zEwln*y0HPOcypt-PgqsLfZ)}|WC9;1SV>h7#R>?-F|s`+!OPC*^zLsu`JVt^J)pva
zEgctcj6G*K5x;r<aOBq2ix+=78Y@;N`&p?APl#+E(Z{ne`gkQQQx@&_wa6(o5DoNp
zu`$KSSo&3?WZ|RYQ_?bXSFA4Du%)tke^blZ^B3EX)ox#(w<Oxn!`aT#SWi=h!ruQz
zZ=gYtR9acf(9*#-a`E!@+iH(?boY?DXQQ{T_IIB>*@Sh@4;?;oq-lS5Nlu)%m4-M)
zpc?<wa*C^(ItL}7r`F9vQ<|5R*4gHvRTM}@*qk4o+|i?2ApGfBIQvB=EMAtkdVSfp
zs+~KwZ7na|xW1?$YjJ#-pPRk8p^loOw3r|d#rB&+wv^m_0%9`C(9Af2XVtQT;?2A2
zj-2f1yE^>vCAd`(@#=rcy&>%5Q?n^I&dW?$06V|^C>3!0<c{zX*1=xFl$$Rd^iVH4
z*xT6(o!Ee+A*Q+X1yNYEenV;bmTf!s)E@!~r-2*&9Vhlzm9JZ#2ID;}aY?UkDe+7u
zHXhW$>YF=wh9&2$-CEOh_TuF$SFhi^edq4zog0G}FLa$bdHjDw^T}2$hIMeq+VoIo
zLpg3P5*0X?7AdDj$OTIxpws7Lr7v{Bujh}me{@Mh@`0w~x&r+9GV@jyfhKhWMv7uh
z_VVJD*<h^WW^ZYPjlMwOpA!=Wp`pZLEsC0k77iW(;qhrXtJZJXRePuzJ2c&U`WpL`
zd<1n<P{g=&{!D-Kp~`g`5w1oGe6zpBHvaRN1wo^0sKOX^M5dZDUs!>Vvz?^ebnNJ8
zs-sMc;>{5R%bBc_J$unvT(fWAzS_p4C(-MUPU{;NyG|Xc+qJnE43g~(Rm8bzlTVs?
zKaX2f4s;R&<1$y5?KyP1_ZA4;fx_L;&1+XKfsIMq*^Ym%a~FF1E)JZkE6<Pju~Y?2
z7#1{Rh@3KUOKV$uM=xEQ8S87OE62zB3oK*VIe7)e=mL<~IG~#E2lw&xjGj|W)fk;J
z+3PlKsoYa{@aV}iZKqB)A3u8JP-FFuvUMvmQ(}UBT<!2q3G2GzH5S0d#3W_pm4GH0
z5DDIdn<{^*>JB%b0Ws_0dyikdW%vz0yLbBvP)use3sQm|^klhbO@uJQxa}OZ;3Ai&
zDqkwtGxNym+WRNutY4L#8WZGcZ^lp#ohPp26q&WDuC48K>&ccA$B#C(kebX-?%f{j
z>qM6{x|>r&osE=)<}oO~l!VEgMWPBi=IEwalD~hUaz6}iU^jDT=+@2aR|f|!UF_?*
z(0u{r{Oa{vLw9?QRTZU$IOvEAQ;a)W5Qh2<OL7wf9rfk;Stn5%OblaE`fi5!p~U|-
zg|raXvGI+|D%!EPt`R<V&!zspp7ZBA+s~Xnwtp|UDrF=_26{R{l_w=32CfafJd3dS
z2l#)cnK^ieCM;c1Ot@6FU$}&BxUq*%U%Yt_UKNDL_>++v7du<g>$5&P&evL7ifckl
z)PDV9A&*r`VZLTh&DZFdBcNpB9=Wt=XZgCk^tcc&2SUye2(aw3rhaK9b?rm9u7i!(
zx%Rfso=ew;Mjt(Y`{L2q(6viFou}){mdAg2m?-ef7$@9Aw!+MuqKf*~9wABD>nipg
z?;0G%K1_H!Gca%&#idK&T6717m@jU$@87&U*27p<LYTa1XtfI%LO2DdtSB!?4RJyB
zAM3xS;ATr$Ql2g>#>w0M5WJdRS@A0xyGCTJ-Er{P$ulVHS8v_DbNkkfYl8!qFM@x&
zPSuu@RoRPULw%gV|3yv)=U#wMNK9H@8T78u;hF+c2yjGp_YdB@`+$%yfB)%E+Ijua
z@L*5dv4-87u_u+f_{6AQeHoOOhr_pC9<0LZ)&VK2wjHe5R#LDuA=Jwro%T`!9H=Ab
zP$o^oKfZbXXmkiQ9+$77n*7<DkAHvv@&5Jm$M;9>-27Gwmk!zqes%fYBkh;(zW7Z1
zoa+O9{TD9{Tp7G}{U*s3|MdCcrRLph7YAB@M|~9~!N@f{y>Q3cj0krVC4o5%*OG7l
z@(%_LiWO3`@=IR1^*}peCivhf0N@`#d~k1c<a&SS$-}if%ZsqTxr>#dItYL2O9RzV
zTt*Q9h?aIfk&E-NpE};cK6v_yHm3TE_8@$4`%+g+<KBw(If+3|c$_-nc_$h(7@eBy
zfwn2@bmGy5kH{mVYwH)EU5q}+ilvF+ey-L=nu>%b=|V-D@chb_Yj0jY9=mh>a&Om}
zbA5xiMjyZY@b|~pPw(HkKG1*LbqEz@A&&YoTpS!63$dmGus0UMl1xNF7uA#TOIMff
zI?!_d>e$QApWeQFa)0FZb?SY;2<6_j8@GmrNBWwpH|4~7nJa&rOSt20@EsrHW2+;>
z!~Cz`rZdmu;1!aPl@KBo>?bNnS&%t^LJCM-z5P&E@1?6ZhsPd0dxd{9{|H@Iw+7Ci
z0Yy&qg)K`#C9xCsVbq77RtJ*q<nA31la^P!ZC_Ix%KNRm_t3MAUFLBE54anEU);OV
z-+rQ@3S5T6Jgl^31?DrHU?(hyjvOVP5Vd8U&M<V8R6{*7dOnL+<)kHq`@5rO1GC}L
zQNK{ZDkNuX)5Rwb?t*_i=9!kJ){fr6q5IF@{Y_UUyGvJPK#!=RtfZu<sGuM(FR!4W
zsHmi*jLoj1QgaH+cQqVu$CKJm?_NE7bnou%>sPN_zS!4$p}VWA6GaD#BfIfF-sC%p
zCr%F3$H(}ANh0b+X_;HvB5$ICR#g<$vI|;LxU&frWq5)999w^fJ$>@%Au)qEcA#eG
z=Jl(WXCy_3U@EV(lY^ZtY^(!ABV!XY3rZ?#4z->GM=Er;zkKt7YT`b=eFe%lcWzxH
ze8pF1Cx?665re7U$dYX0f*!@tiN`MukDxfVr1B1lU7Ves6cg&_?qFrCtqe0bl=ua5
z<^fAL?e9jX{H1>j?XAZeYY(67#MGz1-#of=rTf&8+8w3q@|Oa<!vP$vtgWmpEiA39
ztZi)V?Cd>*qEoY1m4L$A*`BL+AHVsGg`OVW8yUKBb)diJTt{0g)XAt%s;$`y55AYJ
zfif{Y9G`f)CGnwJl3(#8eSAANQSl2N4zbK-6W4YK$tZuSI@Wvn>diZlIznsw*^|eQ
zMs5!DcAPraxNlcQY4NJ%OH<=xqoX3j!$N|CLL*}mQqq=Y=dCTR+=srbeyHglJc0E)
zZsD*)#j}T~oxO7L!s#RXwr^OOnFu%oyl$o-Jrnk67>T!|5<iL599D%LyR)ZXNMw9e
zh`*PMy`_JNzB;(Q0sxl+#oiq3JJ$vlq1aGtVCdnSzhB+E-qU(;&*s9F`8mtd7bnG|
zj~)>o77Dc1kkGJjP`656nzv>nYG#hKwD(-Oeec=3&wqX-WMA+--`#%d#Nh*Vdv|Ty
zylH)5ZW<JpX1Yq>CZ5J}(F;J@nuMP2O;r5y+wXsbmW!mWV_4?;z0LiDH-_%sM<+cg
zHjZBUd+6G`&~fVcq5A4w+saDT7Ubq+FUwq-k)8&!o%t)*6qRh+wx^+~6+2X5k*hcF
zKaxG%ySL9Dg8%aQ_SQqy73=fU<ANz>7}HNsZ^Ifq&|E^{h7(Ue3J#9Wu5KP)-oBo$
z;23{xqzAleeykhGCS~ZEw0h_9OJJR|zh>u_4cpPR)pz6mtG{0iUpjNBYSW4(X&ISW
zIe9Bqu3lBJB7b>qPS&!l?A+z~D+*Q-p7E%xZHLZ(?D4A)pZ^53(aUE~@JrKq>e#`3
zRTZU0D|0haV#5QxoMCAEoy23yv@guY$n<}V6Y!a+pcf5N`pywq8*5rGU%!Rr#^1cB
zb><&Jo!irO2JE!ZbGv;@DPA?LFD_cQeq-6@Z98`DsX5RD1CMJ%_i#1;=G_OPhkJ{D
z#FMexgYe-ScW+*|d~uY&i-m!Pf}{ZF+*v=taDzy^wuvpB_!;wg#Fcd|oG}#E)!Bd1
z-WFg6z_nlqS<RQwca6>4dhp!Ymc#YCx1khm+}Y6FJvjRE?`O9zoT!HRdK5_V7ng0{
zv%6|104g?@ZQeq<%GH7U9vCfOh5F{j+mE0B0&4J^S1+GKfpGT3!F^PL<mG5*Wo~Ss
zqpA9x#M4q8^dHf+_k?@W&<jZ!L)U+(oYK0sLDX)7GyGdBZ?9fHc`yPe*7=Szr&^9T
z9Xe3Iuco?s@18xocUSGHscmRH)O4&F?8gRg4&Q^c6QUoV2y2R0FCPwH>+3vuq`s=W
zFeeo-23n}Df%X3MpJ22?10Zp$28o|VASwgnB18PZOw33}TT4?z4ZP;)?{|Nmn2sZQ
z>}yY-I8<A;b;FuGx-t9v$+h-_+g2|QcJzrz%`K{^IYJJ$nhqa4eB|iy=98z|&USY9
z^bcIUaT~j`yn6fLkI#SoNjPmng>e3KQ{B$8!ra9%0dCfYs;Vj~%F0k6f<Q6b7m5Fs
z+Ktmo1bR+K!-yv;XylEok$ZnkUU|dW>qB>8%<%dhHFkM}m#IVGoqd5A=(M)9G&eV&
zIC0`Q{@m@I=gt#52`CM)-5>~<QWm4n@7=m|t`)U|WoxpMv8ggC06**ZpJ2E_Cf?Y_
zL>qc^lz1lC*Et6!WG#*j@dB$(Os|!dm6c<-e9aZrv<pdFw+kJ~sDgimsz1AEd&8-|
zp=W;q$FU?c+)2;QKXG|!&552Na1}$fdhdnaeqtm)e0TKT{fCdAJbM9-dmljP=I_5y
z>S1R2?CxOq$%A{gtj$Rdhl)-dkGREvf+;P^zle=xeB!_JepBkJ6J7>G^?<<y@+O{f
zE4DUv-l8AdKd4db>*s$@9**5bsUN&@W#ICqi~armePmp`Gyv0y+js7c-3M=3@RI*P
z^zYbI80G!x$km><rdm|aFH1z#tdW`&$zGi{<tG?!K;nV+4o<Q};#qiM4vPBsyv30|
zFl+~+grJa!m;?iIPnybX-Cw<<436ZOkdzgr)yL1@eEQe@zN3FTR>Zj*Nb0-A=5KH6
zz4zCjfE@(&uE$RSQ}*f4&;KS~jt-tXQNN=kFD2B?9Gvk;q@IYBA~p?YnZTLMKZ?l|
zhD*`RJ7MLv!`+}m`4qkIAF&lYUa(N|bazpVjEszqj?x5?2M-@Tc?!0oq(Jo_f6{(i
z6k+Pgoy(mk8g_qfT9ug);$ee<CG;|F;&ohTe<3>Yuz9oe0M<l&u)7ud_jy2C1h7(4
z;N1+a-M`Ei)3y&uU$^V%!F{{nq|Z#vE7{rD)_><YdgD*++mIFMYTz7^SyFTI@`Jyi
zM}Igre2a0mQy={^E#8T~A?!T(_|E07mi<*_V3Xxx4eNixnRD2%y@iSv#<R_5p7!r-
zN60<Uw29ZUrTYa1Q>^&jbsM8%apM1g#G}l=C*<kwQ~AD+;sL}%^vRw)dyex?eR|?)
zK_>+PtQ@|5qYt(3sEu11AL5D5d1&J>cLerPKPmA_;}budQ%uF!(LZ)+Vz`f!nU0b;
zFJR>0g_nQER}Sm!X}>Oz(szl<-c)<K`EbMD%FP=JSKuim6n9UqcOI@>w=~in=Glij
zu08&XnB`sW={(<eX%LTMu}bp?^y+_jg<7C%s5j{9KHt*^)4HCs$M^5vQoI5_y`7P|
zj3DOPgX)d3j;gE}AIIG9DK61Lk6FIND*dp01(knzHV%B$=WjXCadUVS*LEMqC7zV0
zBhvj4r5oZg1bW?{V_kBbcSt;4)IEDNHjD~`-fn2)icuSf7h*~h{0o?9iT|IKcvRfc
zcl@k{!XQ)^oDkvfYGb6SAkN1xAS41WJXTLxFqe78R8CoA&x92f2hX*)LesjlqI75d
zv9o{uHy^zI`}t5`Ys0p6%i=QER@9&Dz5VR3_s{PS4fb}lHqn(lUDLgNGIG7IqvcRd
z-TubIM^BtQeYW{f?JiU`WG{&g@x}#)keD<EteD$SP1{uB0{`w8L605Blg$|P)*twy
zXuzPnu}5s)X0o`$8jODsiT?zYH+*K3>3)CmgfPtnKfUMhy1&K_6y)6wTxhIj_u%f$
z!T#>{)?*F3%GaWjHqg!1R98h>c+q^wJLU=g1jQ~!;)w;!^hq-@X4%dw3=e75<;D5o
zpcj*nl9mO_7Y?9Aax0qqrLL_yaj_SMElrK})r}|4_FlXD`0d}X#;$fZH&kw5oB@At
zAK$&&bLRN|J^KzEX+GU~;nG!TfS<j3_vtfCdrrZ$r+8x-c#+i|sM%GqY2C`4jO6GL
zA6HvbJvBLHb!`I^3tLAwcs8-2{l8gRP}a!f1r8^XJHa1Qy~2rSWn0K4hpPJJW%X_7
zQ;ZUnZbsrEl`o$^Ba=;=C!%=yihO_FNn6Q3h@1V{lSdDRuV22<ezIwQ^|lQwmnB8`
zxmX%$E6a%TE@YiS-&j8}@upPbr_bXSQ-rm#tDS`*_(BVi2fd6Oy5xDGahk!WY7@M4
zL+zOx*9I>3oNI483Eb{$!w;Unhmq|7YD)HPuQ_xQW_hpwetrKYy1i<5Y}kLiV^7_o
z<EJ`%Fgx(sn~$HL-|0Kmuzg)tY|@g<oPxsQO<PLW7p`2sEG;EIGT6_<37Sdl+~eZm
z9e@G)Vm$NzU8_b*@{McyCuS|nXy**wqH;FXp1MMdP1A{|8giWK7r^m;iP_$-UNih=
zK2VTKk{>rVa{VHBhwt6KsW5+cNnEg}gBhkQ15b(6>ze#CJoLU8;NX?(v^kuD(kgo9
z4mM^09g`6gBoBI7d1<U0IDgJ;K~0B<+${&skKDx@`TpMXc(eTU^~b-7gT1r0=_Fp;
zJbU*yD%87<*KI3az6!=U^@p1oLN}mxv3*TisH3}Ya727cMovLq&a!`uB`Jw<(cvJP
z;o@LxY2)DR?(G*88tLz1WuPi0u#kb7|2D>Z+zDWhJK7N$;s?GcJivj(%NV*wW&^Vq
zwk$BQ{X{38TB{PNhGf4Xt);+Qip)BJr~E{dCT?HuKGjsWtGp<GX(DmE%1eonRrFMv
zcl&>kcv5#<4`#IB5-)#-@{SLBIRzOB5n@&%qT>>~vhw()Cyyxu@H?Xq@cR63a<e{e
zL;w6v|LFsj>vNORa#j^@#wL5@@$ea#|Lf9%Y*clOtsFf3LSvGX65``xq9Y^1LxThT
zePMR!;o~0^8W9tp1Vkw<c@eIEXV>sM^8#KmIdy#t15h|!!18}PgR=C4a%YUmoq<cl
zvJyP2zcY$1`FA*t<ZBOAIyMf(sm9sHLD)r8Imhu1a0{b%ZbI`;?Bb$>z@AlAhTsH)
zZ~Sk+{ts09|2)2!)#JoZr4%SN)l?Maq$S1Rf`@}%UO^UX)}dEk+`uzw?cUQj-n^v@
zz%hvA<EKCW{_}t1JLpdy-g`j!Z2bB6vs>qn@7<J_6d0M9vAl58w!H_AoyH~vZ$IC=
zaA<Q@sErC3aB5%>VnASEU=WJn;NT!4LL;JL6OtFF<L=f#MS|zw*)<TRdHR;)RJke2
z(htg=(TSJVcMi{7zvp-_X%P9Ak$9rE|3C$`ZY4AQ2P1!Q;$J>}2zkHMalB#Y#+8`~
zu)oKOp@fz{`iP_+>(9tLV>?GDo*I~dQZ>Q+7Nf{+n4u0#0VxyTB^w&LM*jMDvlxbY
zQF~*>s?=ZykHDyutd;Ax?%99*OwZNf$8Si1VpT43j-+X1W9Q)H;^yJygDp@aqGI9_
zlE9oOD>r|yAU!6)*-TSb;NS6yF!)*5!r9-&Qcsys#hD<?9b-wO=M<VzRCN>=srMeg
zc=PeAtd5UAsi4O-e^TAwTTBTbywua$SXH_*BR0SZkj#>x4>p4q{g{A0^xMEZ{~2?+
zgk)6Fjc%c@CM&#X-pt8UXR^$Px>!b95)==G1yO%9hly@VDsa|=4$mAhUDw!x9Y^~g
zK7ltKb>x53Y5w~!qC<sqjxfv|1yKvYLq;a1FE89!v1dOls4m~Q`|!nJ%dS;%t~$aq
z=W+^2C}<d1I5;{vySTc!dw@=@uYW*bP;h8?WHh9IaZHfAEy&q1$P%;?(PYxDeG2nJ
zL1}+A11lGQ!a9RY{6xI*MB>pUFQIK0n7XRsz*$_^+<)?FT){?P_TS6>pTwUNb-<g~
zc!}H7dHO_s<;Im6aY1e*{fa8)p?v*0X~(thtohs`a_WXw&ghhp2hZl;rp;oRzmS_>
z7`^HuLV^N(yciEIEvKkLcmXV0IFDIK-8O$Xqj>Mh%K$oq(~gmNJb?u19@Nx4dGh%2
zqer*;&m5_S;d^@Ss`Z;Is}CG&Y3u0j1LzUZfa5%jWESuUODSsTo7uXea3KT!x0@Th
z{(b?Fe;NaXE*nBfpJ8b>l~ARbIiCj@x;AbB3>~Zqy5lLPFuRzBwQpixX-x}YC9!`e
z?7R0=lQu5C^ZGMdKGeNG8NGF-rvtjUt;PB2u|e)O2J|wH##a7}ypvxC6FaXsOl)0I
zm#iegzmOS^q%prsNRXcogTlG_g~X)flvFiUDEcI?qNzt*ZfR|6XYZApBadFbXC$7!
z<HyEENABJozH{fw`L?6=yEYf)uPJ{i-?6v;@QJpw9k8eB>%Y`mTb38^W2q)6E~lae
z?gL&P?ryHmPL2TEv9-a9DOfQBkO59M=7w6J$iRm^YN!21)7c>Dg!l!gAilTU0~u)X
z399NT1kQXBHFM9HWyL!Ww}AXAFd@kP{EG|y>(qbxL=t64*OB{UH!pX$0Um!Fm367n
z0d6)%G`s!_@vMoy-l?09Wg+%Lvd02Acp;C;XFvep!-`f5*s*Z6l$^4<4k%vZMG2R*
zF5x?eK}3MKq7(ne`!_FOM|Jzw%^TORT^+nK(A#<X_`$l}+sZ0-RW}?yeyaU^clWuj
z&W`r>vxjz-fGx4UoU(?Vv88{VvnLiob9S(^v9vHVH8BGAou0m-k%_69g{7&Hp0>KO
zybKAmo-u`C*-KK>*cb4jk{(?co<X$6+C=s7^e|XR*~B$GZS|JABQ0=Yp~s$@#C~1P
zeWFi(nk4h#{TpIAfx5VJtw;7%mKH8gix2a~2d)$_|HpN4KOyZ5XDEMGE+J_Qt^;*>
zSrP7e*faslz61FOM66*;A}lGVqN#_S;W_6sPh%HRF?I<_&f8d72b0?SuNaAE;AJoM
z`9-!p001BWNkl<Z_x1K%IDf9=%&F#MhZ`Ge_a8cT@=WK2{wtSyyF1UcHXl8-r@SyL
zA=u3l&*q%my!?X$d;ov%V{H!LRFGIzLN%q5GM3N4m;i7`kr3wNL{HyTdihQ=k2x3d
z0BPOW+R2NdsyI=TJKB6tK*7KvC~5hIs`?|usDc{CzGsy3pT3NbBsB^ER<E8velR+G
z>-rTK&BG}Vlq)+^T{StfW12l3kEACUVn+q-3^fm3@fwC!F1~*No|YHmWoKey0bVNG
z0?0ZR{+`9UkVi;DURB$WcCeYtC!=ZM7Muu#h@+hYBQF?<|A-xNuU@)<-s+C_Gi|3^
zTU$>;w6vT)+jXJu^3@xF(ClbEcCZ$2V-muAoGq;FNn_z)e=j#j8*^hlZBRjzkrWdW
z2J0hn2}vn1Kf-^HfvmV7&jQxjzfYpAdcn;O7>QJ+;1f(+A^mHDX7%*$pHEiT#y4hZ
z;nqD3N1-hpeM)DZ?$^hso;U<ZSl!#V1oQpsrQWt9)mzu)rbhU<*qIxEOfL@?MF;%}
zNGmF6XDD`34mN<$`sZKB!ZK$LD;9p9%gVw$8}<auY>R&cB;?g}O>|Y|i2WEdr?9+^
zxwC&X`uue#du~5t^mQLzJs!IaRL{0kt(aVO_~`MLQ)fEPVNTSITX#n8J-9R2bM{1I
z&5lj$3bNA@qk?^0u(fPxcvNh7ppTn_m5IKlsv=Ac&~*+*3S3-^7J)m3gfuEOh;wHi
z)3kBQV+(%>gKkL$H5diC`-XS`kb$2_{J$omE25b+JkpvL9$~5Z8@E?Co@j>!#Zy?g
z5(^ld`tj%eNBV7tw4-AF*`qN`4DCE#yR9fEDb&LX^arG2!VJRF04SOKvxYYG$3PD-
zOzL&a>^+0L9Dq_G#5EV&h=P$Qk@cxlew{fN>qLL+nGuFRbEZ#52L+*T6A8W|o!1^S
z5>HS)yU#Qq0~+D}`r3W_k2Dj+I*=>H3P1O5^qpy{-M*n<c~-{a#MsEtz~E4#X-|v}
z!G1Z~%JO*o&o*Zk*u*oT0MNIPxU7<fp1Ojh5IEWpa_TtmoZOg`NEuZ5g~CpkPW*&@
zWAJ~<gRO>hVEnSevdY>cr@IG+9zK5Z^#5z`I>V#9t~A)7hI$|MHlxvKMw+6LMl<TY
zH&8{rOQ;KVfha-(geW$q$P^neO%$7MQ(Q3aCXQn}vE#(aCY$!GH)Z$PeX_eh_MUUU
z?|vgg*dG=aUVMJ~v4)v@ZaL*W@5_7sKa77o|M$6<Ji7hg{`U3n{|OYr-+ukVv1cX*
znoBdI!c}sLaIu0e1dPZ%l-HdaySO~?i7%>xS+8V+I>?)jbHgw{o6t0~6Zr*eqLNUF
z#t$ZGm`q|>$A&#;Ui<Wqg5zPJ;?ob`ym5*69A}50$IczpYiI!UU;q6-Uw`%E+n0Zi
z@7p>uJTy4a-?zHAw|8}4|G?nT@JKJo+6q9&kioo|@eCblaXM9yubcg{C;32|l1zkZ
zNc>5a3+h`d3sMcC{;u=scus5CBPcwvpsH<X@~ML-F5G<k<1b-16VKBSZ;t2xKG*NW
z?SK92pEwBhE2n3+4g&)?iRe5;P9%RzF?pC*JTZLic)vS(k&~NNP~O<pR9=vV;B=nD
zf#dKnGc_eCpG-pn0jMa#*04fW#sHZrB09f*aO$~>Z+-qXwj%$9B<}zG%b$Mq?i)8Q
zpF4Zz%<SyxQ>RXzyY$Ly2&De?ZvnIN*MI*{NbRqX)PCc-HADTqU2QFR%ddYP7#bZL
z=xnSk%1(|B3kl*Vby=+xxFj^XsCcY%_=xN+o><ChVtV^g%`r7<)d10AB_r3HFJqG0
zBC!fNA%-_?-**J0k3aqC=Q!FA@G1mc{Dteu0SohA{`}{Eg?h%H{`iOA{|*w|pJSGD
z`TWWKQ==V~dGVS6Byq5@e5ij<I~XRk^wuKbyRSSvKD)TKeXzZ@C=)2d;IN~P3-SX3
zH?y^iPY?uv#nr{xiIJgVcRMq#xTnH#+NWQ*_THDj`0~r2BmeAA?!I&T=CzAwPo6w=
zdiKoObLU^YaQWKxo3Guz1M&P9U;XwEU;pj*KmX{?%jb^notjuT($|00+E7(W;`z4j
zzTvTrBfYJ)r2r0K15=D|CsH59i=TxAdc#%E@c{eL01)Co4KG#0D$w&~BoCs*2uRTj
zOE$0$bTLVU_ZFarKlzNf|0{|k{S&i4qPKtjD`e*-x$P%E`2bq)v&RnZ-GYkDdW8>G
zBCRY;sox)-<FR&$ZzF%CZT_M9<bsOk)nfp#iw~1}z+nfso10O7W)k9iNmcqJ;`dgd
zjMoA!EocmTgIZ`@e%<PceWzae;Dh(y!`%GN>$hIH^3u7}$B!I4erom{@%q)PSFT*X
zeCguF3ot?Plh1$kyRU!q#Yb=5xN!0i;B))h8Y&92mzPyHb*z6LS-*K~paXhTiF%D9
zkc945Cc=<ceqK&i2FdLc@r`$}HGjfnnXRK6K(iEbd2n#BOor40`slbsnD&z5v=`^L
zZy((8R49&~fl7T+Hd&6Ik@4w0hflnC>Du*MZ@l&H2Y?-;gPv#rx%|~vzaS3(>{FPb
z|M2~H-+AlJn^%9%pL~Ai>1~^aI%<o7meB**3oXq}mOQ+U3>uPsS|ZaY$$cei;I8f%
zo)~Vc%1_iv-K@AP-jov)cM(AtkdR$e-P#MYTu~~qn+?xD6&R6L(u_>3*I#-0+SMzU
zaoX+V@uSZlI&k>tiP`fPuU>!k`qfJ>o;i8!xdX?}UcP_%7Rh}7@T;Hw=<Qoq&O$YB
z?dp~)vYpcNOKV$uN5;2}kMxjmBR2)|8<Id+nKHhe&en$Nl_b+kp>l=9@rz9@?P)(r
zt%O-*sU!gSlvSE=oiXeFqItpGv1*YWZTw~~FRyP~y=K$)J%_j-`r4aszx%<DKK|sV
zUwru`+<$*?1L6$m-S^*p`wl-{aPGu&`*v;FFhYNKxZKYb35hJsmOf;^Qz{fDD;T6r
z$SH5`+pv9oPhDYhxKw081t(2RmQvt>r;ooP0!XM^R|D)eHvwS3j+B7P(;EP}ZKG2&
zr%#<c@d6Ia96a#s3{DQsJa>Hdr7JgHhas!eN1lJ(y=~Ku{m0I|eEaUte)HvLI4N}I
zh5g&s^){Af$7z83tEqc*0tPMHA+;1lZ+X6*>G9FtRqzbN>eRuYP-?c=)Y=ggufhyE
zEl5d|<j==1fRZQ5K(?5d&S0Vv-)7yY=S^7GIwhyDg1CNU<JO(e>_2q$<k<^XUm<xb
z4vT+$%FTLz{G$)wd*{v@uid<U?FvM@r%xU~v}ecU*kD(4O$ibJ26#A9EUn3sCmv=r
zPYhBco_ou~D_94dS0kJEOb#@aq|zCDj+BqQ<RAkqK`P?Pmfkg!Bb{|c=}}65PL`0E
zI4mKrx?^-||Ngzt?0#zJ_UWmqZQG`&w{L&nv18A{V?g%t_FdpB+`nUdxN~Ik>BDDU
zzVqW>!hqTBTQ6TccX;Rep4t`3;iy$p-!-~r&-BLO?xxDZtOPco$@A@;I=E|MxE*BA
zBb2x&%ody3ID3la8a*hPDPb~N<VJR*7bp{YySq4IBWKY(6>wO0W0@V`y+bqr9w~n;
zuWRidTsyvH$8IP{pTBVB6>{mncNaL<p)mcPFids&#IYlX56tW(ZXW<PsoXSjbKwSt
zERGVLJXF6g!YrMt@{u)C7=SNq{f>jvqirkGb%D+%lqiMVJ3N!VDk?Rf9>?*4){5K&
zt<)WOoFMD7@s!bxvt{$d_=a_BM~8n0hey|rt>3t5a?7rnBeR!o-nskPyVuVi-UZZm
zjeQ$;J$LTLd!K&>K)p9eFaRVkRXNdeH)(iMK~3kHshM3sCQ)0Gn+y@07tkW|d^>lp
zoY=QzsC8u)66fLUCV+*cN?lwUkjRo?P$aO^GlD}#KKyMZM){Jzxs|=MJ5YZ#Y9eD(
zvX+-tH@5W-jZFXr*3lDZUcCJBtv8?zap%o9Z@=*d@%p8g&YnJT^tppGdw1{JKDBur
z3<N^!JwmO3jMdJ#`94oBzP};i8<b?pP3_~WjLBZPYH0JGW4kwWRc1y8ItdcIrl{U5
zSL>2;O6xm^H|?C6Ue{A!n5KW1Bl{GRhC2ExB7sGCaG<ZZtD~)@siApQXHP%L%QjB$
zeSY@JYwvyX%MWkN&TJlNT#;B%+q2>6qZe;~`te5}zE9$u*Kf`48}BYp*ZNs`Dx$M1
zJJxPL{LHouLmi+T$p&C~zMWs+y>WWqcu#c>@`$-Pkmui=gKxm;L0*3*e8#Le2@Vqu
zkU2lKdiFUy_6`_V!lM(?a#pMaW$U%$Q%~(Z@ci*JFI~C*+N-bLx_R>k34E?xI6r&h
z=;3FH)2FvilJs@ByBXPWwDLe+Vdx>S9^BVZszC(D-J4vAnuPr7uCeV0&&*7&uF2L1
zIvP=$@`Lig<~KCHW9EN_y<3LcfSbdgcEo`(o@zv_Y-*^ju2@-8v^*cmCoLVl1EZUF
zAD+E>`|cON`}pRm-D_J5<K@x0HN6x2PT#=Z4K(Q9xo}{jt29yJ0&F&^Wo>JB9-G-Y
zv8D$h^*s9q&$mMXdLs>ak!rbw1pFk^<G44oigS_y|1Yw;FYteQe!6q?lR%n;%Y{Lz
zQ6g7`8{*RPimRGB`qyll+O_AvkrQVxUV}K7Br+E+oW&vkr*}*dr;iTyb+@;Yw;hKS
zM7;jPLk{T#^FV{@HweloK-RZ=Y}?GXHJ#OY2^z68n?WH7v<SO1#IcP%YltJ^!Y)ZS
zC<N*{#A3%~6jpx`uT%Xp)TyX#Z0#H-Tjlsm*Wdc_t7rC4_EeKi6`op*DZtGekOIKM
z9GV)a&oxLLk<Etez;!?l+0#<P$y0GBKBI2Up7Zbg=A&D)`#1H{6HlkL5n8D+Bq6gH
z$?$9ht{BEnW{s)=CQIf;O`6lB!7hi5qli3&!OAd(6zzW>T|Y6k^O>2$$BrF6a^(5v
z4j(==vj?3%Hj2RE=7t))<>AZn%<oISE59#7#(p1fZ!a&9kOsNf;yMy{t?6y4T9FZ}
zg)u2Qim-H<tuyG4BCC4az=rAl+tzl~E>DgKa;IG+AhmVyl&B0zIpnP8<z%KMCkRTv
ztNO;a>^^^V^1_WX&u$%RDoWD$DPtizhZKO0%pW{3HQZbbT<MGWxvh0Wz3olVrXh*A
zQtAzkcM6U#?iimr|Mu0B`?n6`8&6X;(?yI>nCjV+?2S=|2-Fx@7Nfw$d2u}J1=HG*
zECe{ZfuppxxxKq@c<si?shvA^Y~Mb;ZQIn;BxQdE>1uCjs;{Yp!;zUpsm(E%H^*T9
z;l^~##9#pwTO&f9fy~0H*43RYbrr?=St$aGAtMrsNXRa20Jf%y!PbhLIE`3<JF<BU
z;`Nk-xM+O@mQOOW^BB<$ao5PIm6=frPgz8AK~2Z#mVJ950ob;6^Ta@NS*8I-Vli1y
zFRXv*XlbY}FJ7LL0cV(UUfYXx8CBimduI<nJvq`rqI%G(r76CNB}5?#jh-*?3X~x{
zxjloZf9t3ojmnQv@CPQlz5&Q&20elmrIo;M1Y9>GBg4Z(LxY0@tGhc<T9H13z%UVS
z;KqKLjKz24_eDWqfnq-&PYBUG#5f&aQd56jRZ$MQU?h`@R7xoQ6)04A_yi~osRdQ7
z{p+XK^){5GM}_!0+VS(~90)%?Jv9kM!&S6=5)+r0l2Z)KNgJk~o)}tHnHwJ_5d|ob
zsc3N1#`SAQhX(q3y6THFW7T4j73EosCfNgMJ&^TDNs7`c$bCrCIr0j`<W_a8-7$Z$
zrmw9Q2B}zz&v=@s*s!&^Q0->=H+06<#(aJpZ&b*!u)+=kknby0T0IEb=j0cbtgNi5
zM{IaQLqmOiO;rVGc;;ix5fxl{Wn^n!xaPa^`=SuAU}=D_CyCIo*`n6Prr^LzWo<)C
zTlET#L5d9xNyB0@OUQFD0R!_%5i);ILAM42K<L%S8gziZmU7w?8f`>uYEDsQV@Kbb
zo<^?h158E4w~crT;N~mMO*W_lJsmA5n+-|KK#eRVIVmwA0m^eY3S;RiR>hDkzq_rm
zh7MH;cM?^f@B>M743LFlH@S-Zg2PC_p8{197zUlM1?GW%rX<3%0|bZ%h>(AUsI+9i
z$0sJIWn||9U=R_5x!FK;hkDJ1$Ovs1r&NVs)<Qs3&d1mKzfA)MazdUWM_WrfXL4j@
zp;~=H22@SEht~5_Ad4BwjHGNy`4ugz*C9$uBN16LYE`^!1Ic9@5e8@(e;*GoZ>XM0
zq;T4jvq`E0A$okc!e4}>)rNnhtir1D(!zpVa;ya%TYlz^6Df*`6}-!jbdhtDn~mqj
zpy6se{MKa96(LZvaP$regTm7anDq8>wwyo5FXoZG=A33V$PQA)MHR?1=%ZqyiA51J
z=p!S-!$6rVL>?@Y21)|_8P0|u`u&dFzGzVdm|7tv_l><3DzJ)R&uV`_<D+}+);*K`
z4TXtnKfA@O0}Tazpd23Vs4Yx0g!noMf<+hr0(Wev93W#P>I2Po$QGfxr8UHpG#51L
z5WivP>J3f&q^$Jhgct*C6-Keav)PCPvCl#d50vNJ!SO7)&>LVw#3z6pCVd64y9`|i
zrUApe%Yp_d^{L=BnKysq>Ou6v2i*x<sD}X?J(LW^&?-ce5(|`w1N{9sAxdCfcV#+G
z4^*Rk7zR5rDjWk0TZ<(enY+ECSesJZGDMOsA))8VrX9S4BTz-8r93Y&T;^#f^o^OY
z6g(XeKXa{ID{E^IDwBbPe56J$@piReMqXrRB)5pfi9D>TqHuo;Xx#A{1}?<;vJg##
z(4=AW$w&mOi;5G9dfa<!&VcB`uVmsCU9~HK^wMTNmvHf7u9!oWW_aBZTMoA7#G4~A
z1T@(J#sS;iOdabxOW&ErgRzU086&bfw|`?talG7F_)ZTvJ)By%Hz&bjzEp@&p|<G)
z=m|z0&jg_!Ks$fQy&Yi&kd?<|of$>ogvSU3n-4J1)61<SA>mkObzUsc+&=ae38qm<
zXt$f0gCsJmx_5Hs?6aHuYUh^lcopcnG7^j_#qLH7n~V|&(`9^RxfuserP9j}*+pSb
zEH<I|4P+R0p9`zvlO$e}(11-Ea-W;=BTy!$6zxkQQ5JvI-)l^d!ZS!4pHtS@GuB<l
z(cC`vrUo;jbLY&~SDmn;p?`Ak=7C12wixM<0duIzkerLim=KAl6UWdY=#YsRiiE3}
zS##}vc=(tiuDOk~hkuYVJeoW-^crVI14%mvXLqO=>0%Pov1CgE13|r+N>7ky-~p86
zOq&E+=GlLRm5rTs#hLT`#(!Te80=nhXDaN|uzEvZV@ZZy?r9`P1*wsoq((Vu2ypcg
z3A}QYLX;eBZm@UB;w88h99=kua(5385!QRmO*t7|lJv83EHmV;fqIA}SQ&1JPsxNm
z7Ap31v;+}f5ECMw2r`op`~{1B+?{MK?l%y{s*QgUQRGAN3d;aA6poU{kG%zeF)B!A
z{6pSOA97=SDyJnJl@!O9ADH4LMK}|;*kgsC#MKy|!>dmxu-rs002fE7fwc=KqswvQ
z8v)|DATdy`LO%bT<t4c(IK*kiu$CAz2SCuw5%KU}-Z)X?inxZv<djqxyUNK+jUlPz
ze1d=PzPE-w8ly>L&X~%aKq<f*mr$u;v?W9)75fQ#&J3S#&8w+W;bG!JSzrKFT(q;b
zL8(_$J9lvi{0lWqQQr8Upvn>?XeVwjURhm~m7s&<pC)=_BazIAON~5@7_Ghmr^BQ_
z5|e(8f%gFc!DD7~b?2BFv&Ybq88cS*Mw)*F$W1{@?;G5)v;~GeJr^&C&nQP0iRZ02
z^rpF^w+u9hYTEBryuy<z*BBC0GjoaCtLmC73R0t05)Zqj7S<%kBaeG1*-D%el`%8*
z+$ou5&QNX2Eo;Pi92oYK{o{AxF*9qhdv(m%WlJl`#w*3Rg2fW7PRNYN#2DWM-wA&=
zwdRWCI!<EV+?-Q$w{Z3g(Wfr2S~a+;5^$aFMv*o@vyqcuSXy3P-@K}`z9c(dEAw)I
zQ~<?BF!{#`X(pPth{5T2&+}S&10D51Vjm`(LwNUi8^Wva<p^Z+-}e%YFcuI5)&h=I
z$jA6X1}j6Eaq})IaXOBO5-VI#;{bnc!<_P#{_(yhh#$R-iYhd5NXjWK2ZF1%uHJ#x
z%7T<gg|8#vfV@$knC>KJ+c`XUJf4noytSv?kXhC;xOt?bW<^S5uor!UzNJRv<7%vS
z%j#+mNT`fikrg=7U?kP2OD(GJUB7F6cMa7JV#dqgH8i!hclGuUjg0j)lw^NKEB&2G
z4(LgqAYCkW=zT<X7IXTTw1mbyPm<Ea!p4EkAV@|#{XZCQrqoQRv6Arvni%vUw^#;e
zPmJ2&2z(IPFI)1Y&=2E2!khviV^bMu&bX+Okh`*}cYSX|NroZB$6hFRJCj>N7nhb#
zPJB&04pcQ#P74>lq@#~FXqA7m0B?~a-_U%5F`)=;cq%&DI<$49y)rLe9pGfbe&@Gj
z#Q6T3H8l>ExQy3re1d^2@9{rQ55e3R=HdGIbP9~}71@~!Zw8ND#}g`3k(-$hr+G>N
z2QMktmTf4@NemD6b~MAzCWxsc^FmBwdQSfG!eV5-DkX_nSg@ZfOY?uAt0Tw3X$qkw
zuMzE%k<G*Iet6>(^D0`0Fo1-@jU}~ZXbZC7kBSKm1P2E|NK<BPib-SMpYs<CdjJ$O
zz<F6w2pm~bZ)YnO%rgwLxi!fE#B=0A_~-%N{<yTf5|HAHQj<RyKrA6jO?V{svs2SE
zvvZ02fzTDM6+5WzgFb(!R)h00GAV<n(iV~#B`K$#=fR)NmE<c%DoI-EK*zpOO4)9K
zmi*B$3bynB6+n^5mDgs5>ypFM-`h3fJx|W823T%eYxwa=8!mkp2&l}h?Va6y0;EFA
zl@W!}7%eF?KZABwU2GArC|7Frdh8%X>U8?3=-9Y~q-0<V0Oo%SH_FE&36h6~f=-57
ztp=_bZ}@1Q_sCM1=w`Uvy!1qh%>(6eS65dY|8QBLC4V#wD|c84@%8cX_Hd&GRepSC
z$&>dP)#dd*9K7Yalme2Eb<~k$D_ri&L2{Qq!NC*A{{uZ#XYds(4l*PU*$#4Lm@X>L
z5U!^C!p%n<td4((PE1cp1i2xNQXYa7n%W3mm`Wi-WDPXBef<I16M~)k=s0em(o$%M
zGJR>j$e0|@QEK=iuoRJ{#1>}79}&Y^eXuMjNE#R@5&MBcn>_$R*yz-~p68FVgKubb
zR#|iZ_)te}QF@G8;>O^%bZ<~V60#>j%?{D66z~ciJnDZwd5AJ3SSs=N@p5<dmMFpu
z3F&zy1z9PSvze>P29X;bRhT_+C<@E9cs0Z%re#Az9>LTAc|@8s);m~=qQX)|m|CU8
zaufND5-9lv_=7Ffnm-bT&?~Yo%FqynoMR=nvtpx?_tm!Ja0d>4>V$&Yp7p@lQ<X1h
z!<#;7gh7AgFbRS)XwTlw%g+yP8DbRN4e0Iwd02FEc2Q+xB}NTFKF%&M>_MCj$p3Gc
zzYNe^nFSE`0VSUuCA_k=cOcId$8O8;0f_zrE>=21Et4TAW?@$R5iva&h>3yTt6G6L
z2?raV{lerQsaZnEXCI(VDQz9uc4*he{^qi*7#M%4!7yuyFg^|j#X3Rx8Vi3Aq51og
z1MK4D05<~M;9fpKnyBR5lG;^$EtLhS`cQw@WtJR%hmo!^&=PcbA`>!~mp4F7PA&Cv
zG^Ydnj!puSXED;FkU!=^LM3S0s&SixEyQ|1GL`@qk79BLePmcD2RvuQCmgBb{r=Qm
ztjm9_>e;k!_RtPG<tin~m6M$n7VWu|&q5el<7|nUm7TM@cL2FufXs-4;vm8<RG*Yv
zR^K_aslTZ-!!W0o$DiE*UvE!$Z1K5ER0f!)?gcJ0WH01-4SB4kQC-%=$^j>+$#2wy
za$Pvu!&=kth0zmFkGuxz-@XX9MZ@HtM}>cJQNB@_&)3EFO^@|76lX*${k`1pIq`J+
z@C71b$dO8tHldx$y6>{E=#>1jy7qxpRV&h>lmQ~7UO|>C3gjRsp4<|Ud)V2C{6ln!
zIVE)+4du%-<8{hFPq+%;E)Y&UGZVh>gGXMaLh(H1Laq5DVf2kBw_j)odE}*H$_amL
z$-T@D$Z2Se%-Lpq#K*t-Pzlc{Z625y>8dX!VZ99Sl5~iUz47LZJBK%n=z*n<<ln$S
zyF|cGN<u&YtE9T6nja+ue69zu+Hp~U2XQ+&?!dF?;v1}u%UDqfBR?QE8=+J{>lfh4
z_cGWUbriWr9&F)O{Sh!Z7=-mw2<v}2oVSf7<-{RRFF8R@aKI1}y3E4pv0h{e>Z94E
zjlHedE)NMXx??%dn^HbuvV(vn5sgInG$Hf%cB77GT_0a*XgH$pi}Es(q9fFDi0CPe
zkpi2rp5(MUL7B<gav6|t0yA|(O+^{9o+e`72Ix(V`*qZo@?-2QtOr}TReyg3jLy#S
zi9EcY2%q73K}iY){|zozWV^GXt#%xrptG@=h1I2bfQ$<Cau)jfe6CNcvrYiL;?P$(
zV*yfD)bS)BU<f-7e4mQ*mN77lqtS-zA`MZ|v3jkFs+~D-q<c`CK~BEvs>+J;m1QNx
zML8+45s=t@v&&3grX^;`(Byx~B@u9c5L>`yUwFgRh?VYKkh#>x3gJy~AqPSJjPVqs
zI<(}Ml^;vaN{NkB2aCO2jgA`64$hrrFUZ^c(ER-a0>seD_xECs=V<m^P!U0^k7b5M
z+l;K7ymX4Qb)lSl5O&y#{N*s8xw4|Fy0#w3Fsq9)<HLh}odMoMw<doYBp|T0$WMa(
zM-IYb=>Px%AxT6*R7-bg3%l|QY`|aRY*ehLbcU7?LL)z9NHF-`2k>&PF7%$|y-TN4
z<(fz>uXD_5rF@l#CDMT~NFvOX1QHWc0O<3QC&7lBG*+m>aLP6%op`*Uu()((MRhUH
zrcGZP;#lO{GbCjrK&OALt8ZvccYSe2l+xdsO`+irWb_4CN^;<<^<WFT@(XO%)^q!F
zoY^`dW1K2H3<QXz5`S)vnmqX6c`U^s9}>9AAJ`Zj06J?T(0I^3jVb&u7*&Np=B%WJ
zA{-ne7q^q0CCmMY>#c1${$W`t-1ylA$Y$Ko+}ci}$D9Q0;IV(QlBJWUBvcodUQk-y
z*xom?Vav|-J#{M*!^F-)__>I06LK(p%eg;tK^K0Z%~UWFvYYaOILtZ(D%26t<fYXp
z6~R&o%+Pznv1gbZ#@P-*Hs1f=sP6)E4#nJsglV-}k~v32L})cCWr&>YB|lHj@r*hQ
zahi~ddj~3Mai)J6xlM;h*Y(sFryCSL_I%^$iNzN7ZvOJHsAS+4>|HalbN|t&)^%27
zaJ)UNvBdWYsYs1cTKVmyN&deXE4;w8YOqQdlf*H_(;FXMe{Xr@0mfHO3{g>0(b3T{
zF)`%h!hnX(cyfyI(x7up%^bXg!xMAM8hiVPM%Qhe*g8vnACwLor7o;#ADVdT(CKSO
zcZ@XUM+Z60{Y7&zIX~F_?FYv{ruKv5A5;6m@sFwf;P}VI{ukbGoCGA;NSFWs002ov
JPDHLkV1hC(f$IPO

delta 99907
zcmV*BKyJT_j0UHT2Cz*7e|=?mlv%cQ19A6M#kJy|ic7`a-Cc-#AT9(85In(yy9Br3
z?(XjH-O&D>eO{?bCp}-&%tJGGX8pLghxpk?wyeGOcj^EgLvyyHn@?c4AIH{IM_ESr
zJ5dEq6MN5)`0R|7gqVns03Q!WYZF~n1xdm0`h#CoT0uoi-^9vPe_u;gQCd`h=VOBg
z58>k%5EK*?;O84Mco5G=gk@FrES<bV6Vmfahc}O&*x5ayK0n@vts^tQkLV9^B|U5R
zki?<G@{&S5*!s#6f9b3K2MYf){C_F_XZZh8{Lk?JrTCxW|4X3^(9ktBV>`Hd`nfw;
z7-%x3g$IesYZ%$Me+5RTr6k5hhld3EdArz}>8mrOga!@5mJ}8d6%j_(1Y`1xNXjwQ
zwDnC4^>j2;l@w$pCB(^(qE8hN6cQE@5f%~@=xccq^doHDgJLss3rnkNn?{V8&`_Kb
z>T0GTg+Dw9VKDrkzQ6CF!F>Ga8;Xi4Y8j)ij!7>}i}G_ef7fA%aX)7;pMa2vn1rN^
zl!TawkO1Fc{Otx09)d9#VbGxd-}%1`41kKJuA!-wtrN$=${3Ed5Hh^FfwhZocw$0a
zOjLMiaG<}tgQX$f6GPCdic8AKDay%6ii-&H5B`o%NL*S$NnKlCkEN@lt);1<uA&4V
z6%!G}Mi&(me-{^njlegAXR$>UwauJ-BU1~CO3KPBtB2Rt7pFyd+Zw3A*(L162WIMP
zpuzk?q7u@w3QSD{O9yxV@Pv%mAa^@sO$AZ<QwH$~iAl*ZRMd4eR2XtnVnTe>#e;4h
z0Ret~K0aa#e62P9CLsr4sA%Zu8=G6(SsJr6l;k9Ye+G-nsp(lddIiVEL`Ox2hlPd&
zc{y7fYb(o&4CWU`%c`iXrlqE=C@U!{$oJh4K~YIr_!k{rmcD_Jv5A?Hp0+yvsHm8j
zxP*kHl$4~nsIVYEui?d*EGxI*gxt!C^0LyBqQZjwln7rZGkkWz?0o$EU?ea-&+iLH
zm1nAIf3b`$Y@OVFgCgQWyqqlc)Z|2nkHbk86qjMB=@^>Xm>cS-F=WI9!0yB?B??y$
zJUf7|y3pSmqycgaWHnuVV>3ft4P^ysF+pT_RhF5Zn|}mCI6_#MFM9rURpms72%?o&
zQr9ssHPBI4l9LqS|89r?+6(9qs>1d$v$Az`f3deVGtg0E%1KBfn@h{c$jVA1%agY*
z32|iuTkpv9l7^b9vZB1~^yK(ZZzoHAY(mmA5F$MT9?SEKN+Ewj&&0~c-pSS7+b=N0
z+nH^wtt2gsKa3v;VQHp@fw`TlFUQVYUjsc)-wi@`N4LK?dV|EU?F9f|d8xlGhy$c#
zf8`m<sv6q5T53uPG7=&JLquhjbxhdK-oYWEVPWAB5s^`W9u5|IYVu-3geB#bwe(G`
z9js0CG?iq<1<>^?C?X~)EvKNWZD3~O<lz_M=Z<_qmIf0{E+;21ub?0&4L3hOkKrX$
zjU5BxhYlOvTvMK(nG_xD;|gwsBG?Jpe;r%{(f*!+poF}Nj*+Fko1444yN8E|r>7I!
zgr$a_C-^9seehs01@uli`-CNg`8ZqQodmDGsJNuGtc*12e*nJrT7Odz0f<RR$;c@n
z+oPvhT0%@18D2(7%Md-Het|(Dq2ZBHF|p`mSs7?3it~xeFg5kfZC$)vY|Ql3e-$P1
zc@h+cbyd<bvU2hYicX0M@o}{=MfOloP*h|vm`o;oV^JY~o_AGJ-OM#KwWMj%sQU7u
z$&vmXHrx`5#LWyIoQNngk&qyNAJ0HYnyF=E?c^04=;!0*;pXDxU}d7G!G!%s*?owF
z63fEHFDi}e_rcageu=!If*iVge<Z|2g#llEvA-qYtq)$Zgru~L9P)#Sw-{MhL*LBC
z#mm<}C?q^GCN44D&(+3Aiy^@;F0Y1O0gi7N9sAKQsDh@ktq1bci?R|U(5Istn5m?s
zq^zu>qQX>=m4Y3`{mD|AmL5^r)uU&QZz|7@4RE#O`eMY*jJA?Ec;$UOe*-!Qj1CL(
z^>DFgo5S-YFNIzl!X-pLp|qN@gKu<3X=7<dl#iVu@)qGSLUv~;GZ_kU*x3hs^~L^{
zfZbahX$#>94ZnZ<e4^3}HI}iJJ;&20AUHfKE-@uK(A~~Nn<*(E!O$|YarKKpwso;K
zP?s0M3qFJoeG^#?6Gy-3e~glb3Uq{hU99v~RaMmx)Ya8h81m8*B7!`Im)2(c#1}M7
zTsWn*JTt<>N}C&f(N_rJ67qTmq6%6j_Fkb0xv6pC0Ul1)Cc2ucOnJg}r(ywFZA<s?
z^wQ=@&82B!9CJ-+WOxyAX?f)6t12@U<>3bbzWQQ+gBV2Ik~q`_e}`98S`i*>wj;;W
zFDN`ZJ|!bA#M8k{S6NCxN||Nh<P)Bhn-uQtXsWHqvwrCIb_q!-Y8c;Am6sgmWpARX
zp^2b{K!*S{WOVHUQp(!8R?Qe$krw2jFVFSi=qI_qO9-(5Cn!FrGCwUQ$iv=@rOc2=
zfg`?4NW1eX=-K-xe-$-OS~#h(FwxgWSC)!9)U`F$P^5`0k6oLuv9RAP@YY9hwgh&-
z(cKCT6+fS_q#RQnMMP|SXE!gupwP(JP+wPDQ<kbcy6N@I9oz#VlOp|G*@o)!+yMGJ
zNi|dF;H1L3_NI#5#1MBILpAcqtEnq9<fO%k6F-PBkgSfie^*Qny6VRa&yDrA(Ur$r
zj-MZ=d?5Chlmfep^gr<qUSWwtQ)7a?oU9Br8DK^cA%1R_hEGn%+A}JvvTgdv%B)Ba
zOD*Z|$jDwpS6fR%T~!6$_<*my*54HH3NV>U=p9y7Q^!v+vJan-xQv3bhOU9BCEMPK
z<Kg2U<l_czf4qj0tdb^r{GB}mxcOZgl4>SSfeCrTN7t8Tp;z0Ah2MDm%=4Vf!T4<v
zkk__w4NlA(Hfnf&QV7RVM*+PyM7l=|j-v!@#r|)+h@6_f74p~<BLh91t&Me6;o?T`
z0dIOvM#IcGC?Rjy=;3(@flelBl2r7s$<o!;(b3V?f7ZhI>Wlp?K?k7A($hCEG%_|p
zp*h6yLiE+BZ)9p=ZR_C7ad&ZGTNvwUt0_Tl*2>Pw&6|$CXrKtq!9Omyx~--tEy~x)
zOj}Vw0Xz1J+%z8%q9Y3{Xqwo0g(l`y<R+n4!Bk5DPPqg+<}xVgl9rN`lpq4HKYH~j
zbg*{ze+uz+gNRX`**7nXy*nvYLtD?V<ow|UDPf*Akj6)bmzGyj)6&z^M}MfHk&!Xr
zt1tGq1Pg$rl{MSe&fdWZMO4}<3X;M^N=8vhO;d+uU}S1;#kONxnVT5tv$Qp|Sq8>t
zmTWsmwyD0RlAJhC9z;UL$lfm|tD>bkFFD-Pf6iE4Rt6$@0R37If)HI;L`+;!!@v@S
zWie^7A?VIVks>y_oV)^qNk?5GLS#9B28!cVb&RbX+?{MKjC3`W<l!}-Zw12g5=>ol
zC*Oz^ZYq#YDjOJ?n3`Kyv26ffd#%4IxBy(;+&#T~eEkDF9IcGBlw?E(^Fc}m#l1=@
ze`*?9IxKxdV`C#jLjwbSeS9~d%fZylP)8lDJC37BNT?Xv`9z^Osv;*L*v(pBMG{}w
z0Q%CVJ$8ngu92m^t9Ot$$KJ|VSDhg(D~F7(jDDgjdKqNV4Gw1K@0+uQN}wv_kIaqq
zwA8?#7ZFC^n2-D%?4*jxs~cGLN#O7ae~Zf~Dr@K%7^4f!#@@-r4e*th`rCp(ATTH-
zEFv;GHa--xH0ttVgDLCC$tyCHlvUL<G_`ef_4LtmU}|D)XrQmh(t&KeoTR7_kE1TG
zY+&ORky=z&k{J`=Y@x#tr9qeSkjE}8Dj_8UPp+<ki3Qui#?sUP2W5&(a-V6Vf9MZ<
z!2xsi;>4v*)&a$Yh5A%{!Scc~IFsZV<mrU=F;iNYFg)_@3{5SNy9zNX;Hxh5w}xmy
zY<yyJYI<fij?m1s8R9r4BLo*R@#vxp7&7sCXyL3aOpWw)HPw`P-DwiyO8RV%u;hZ_
zg=vw#4yKxNLV`3X>kQ@>62UoQf3%;}HMF#K^>lTxtF6RPQdQT~hTBCSTn!m<VZQ$5
zIiflzr>MkGfae3Q5x%~gW6&T->uMXC+q&3U7;3A?O9&#vOUN>iZ)ap~ZSUgY6Brr=
z_<F1S4I&+ol`}NIsHCj2nj$p}qEa#f;Aw-aEh>e)e6(;jjyA~0(^OHEe}=%1d&go>
z<Oqo3J#7uuq>1_`BnD^;Qb{xwWhEv9Ie+qKQz<AiAg)jYvx7%rqOYyWkP#Oe+`k}C
zy9i>$B?3kt{D)8C7nPC+j)A!mmDD2)ucc>fY3J<W7aSR%3it}k`|Y9_P+CznytbjK
zwXHfgKEM&hKLc<S1SJ(ze|5pDazsHC6h#L395E)#k`ok{U7iyk>}I8>BtF3B&`C5E
zro0S##`y;Ep)(9SL0wB1o81OI`7A2^{&#?45(yav^d6|eV<0Lxgi95%c69R%iAv1K
z1N_Zz|9=Bj7}eF)!w`lSWyXhkIhwOna86qqTry$u5haUqY|5Dzf8;SLMRSl*(X((u
zF?M!Rq(8^TSeq#Y-W>|c#mKx6x*muVufXJH?+KS%O<NCnjmT$2F~&d?E+YKr0njSo
zIuh!~>Y~fUke`)>cu$hrSY~V|cR#Q^;Hxe2w}^T`?eOZ#^3vkM{H)aY$Y5U&XIm7z
z8R%-kLoQ1O%A#Zyf8NT}KwDK&MvTYsbhTSiL*Lxq%`ZGYGRTKxZ;4!WHB}WAWpt?{
zE92_iVCs!mKyS1Z@h@;3mDF_&%@9mYO-)SDO|Qn3>k}`Kg<28PQxp;52L1ihx-?4%
z181m|Rlq^j(l#)+adP(!j!Xc2#by5X&<bd*tu6;HL{efxe|#KpZX%%aWru96gF`Dw
zx`|86F)-5tSw%@sN>qUR{1Wwe895@o4+|UM<H2#VV_TpQzzD|xEV`6}B1OWhM|Ky(
z88-nj8HT#9k%g^|wWYbKv7tT--O=*W;=J(!9Rx~IfZz|p04OCRCzH4C2R}j#ERVwk
z<S^>!XlrR{e`;u`s{yUl$;~GS2qqC<ccs5Ej0LpTSC<UUNQw@Ph>lB2&CJO!%1Mon
z2=H{aMbR=@EtLdUS`}TFnkbBtmJku(I$rqM#w;F|p{b>f14PLjS63GoXJ;n|dplbj
zww0xco|cM&v?xE138{>mfViBpwxNZctCKz3(iDE6e=0oxeM))QmcaGEkVjBtDnk&U
ztEa7wejgM8@P0BTTSCaFqOQ#{G(mxg0ZT^{xfQtn;K1?vUpO}jFuJj-C^Ip_-_0*9
zE<LZjwza*rrVN)soNX=1ix-k=)CnVQZQ7fr6QcTtL}JX^*1^fu9cuZ3{(e5*o*r)K
z;y2R4e~}g8mWzr>;HUu7v4WC{>iWQx@^G=Y213^WtPSiLpvzkYfSJLn8tBP3G%>S;
z7(+oyl=odgKLS$>WRXLM9w$2}+Z*X=sWP#LhyKCY1Ms!i`kO*0U|e%merlAzt2HMu
zF1w;>{LH0uri^YV%T12(b9W*ri;#9DNmN8te}tbKM-h)SdjHYQ4p+MYT3dTpuYj;<
zOwx@E4-Mw#Qm`Wk#ebSsB_yq+ZOnG@3H9@EVw(b;iwMwppKV+`07o5N*$5gMICBgU
zfD_w{a5lN0Of$F86T&h@Z;wCnTFh}WT8;@porR4f;A^k-H-))?Nv%~w6M{KbEF0gr
zf4uq$^LqAeSvhZV+wj7)s331woCZ;llarN^f!c{Ul|rXOb9kiT^|Y}vhnpH*sX8V$
zuD)UM>G^rN*_ml6NeOWw-V_&>%n%3?nU_JBRKv1x@C=R(^?}N-nmoz4;&s%y3~mG#
zg06vyxwWmMtGhSN0ONxC5mF3IL&)Gof1|65ZLFg#D<UDQNN~vkUwf^;DJ%j^8(EbT
z?PIIYF!6{h9yxpc;hSgoZ(7<lw!SzmHWYJJHJJ+XvIHT9;HL9aIT>Ufc%-!q%xwvB
zEl_Wj^(<WiVls+qhF4XTp=cpDJ0aB5-b4$tHb{aLabtW3ofYbaY>t0aYILB7f4!-W
zvNTYDhV*T_p#yOQV|1+;83CKu)x#$s)W?NwqOBy;he1dF2nmL^i5+xZ#0GoXo9QS?
z@eAX~QHx~+_^J#2tzji##^|cdFpjB;sE$iqdB=(aw|;zn{lw0dv&YpHX2gg2y4slN
zs?Y`XK@`!2&c>lfT20T?#>LCkf7V=IgDE32L|VhlB{->|X3Xfe)~5Q}VU^|CaY5)t
zQ5uN#Lb9n6bCbfn9Za<u15u)AR<)I-rKN?1g}FJ~-o+hquRNseeh_2mm^=GLrx#~L
z`@2}^GR4u&LDriA|K_Fq&jLR2lbcIYf}D($gw<_Ba+<o<9lm}3;Fjexf5$bHq{oH%
zx!FTs6+=-$UQSk4h6tmehNeRK-r$|-nc28_W5Y9~g$GNj7}`R$rnUyzyrigLXij3N
zmxC!j@%@@5pryvqCnBXFEjoY$oE~9eAt7jc5WvY<2^6`><CYIN<u=w<mgZ)rCcv=v
z4T(w4Dxj&fbaxe<ETX%#f8v5Y>`k;85)k2I(j{&g{HNFcAH{4ydu@KapS?aqNZH&g
zzNBsD+5_8GcTXKvQ<NSZ?1LS0RRy9-r>=>8Y2;P$)Z_7q?_+pzCLFcM)Pnq>Ia%qc
z$%))^*`GR1oXe>6;;aPXv(WC6l7ij`$ha|8kRNAgY+`0%#kRF~f3&l)wlFh6vA&tT
zM^H>!UPVI*MWw}g8_3wv$s%D{6_&XJ`n+JW;tvT22=Ey=F!-m}{vX9uz=(>hC@(fk
zK~T}aF(|cc#PntJrjBi_7@894hx}m+BVBbSa;a4{b@h=WO+2k3;n6`J4(2RXSrPiG
z$DAB6JlT5Z7m<`0e;*qY6%iJKR^Hl33;JF96Dh?Qx)!bhv6*GLK;^YJXW{w{+%K5^
zWn|8FaCL>W0Kn1C+QJl?Gtui1mYiSRI<W~;X*Hy{{3;Z;$xB1BNJxsIVPN6l9)Q9H
z6J0e$Nys3e_+C~XNI{DK^xFTU7zd~?N(pnb)Rq&F)3$UAe@iWH7~fV`o|gtq6gYjS
zt|TududIolSQMrCheXCDXXF-^WyJ+@ELrH`qz&(a4KF1O6>)82YiF<Epa6egA1_aL
zHwU;jAdaW&swh~OM3x^yG|>TZwjeVh+>c{t#?sN&)KCLTB<5;xyn`Zf-=?p(r@ON~
z+rn61M?=}rf8HlLyK=<j1rr+!6MSuSWC=e2en~o6M0uUA_EskP5bbjly?}pu?f+5W
zlx<~BT!53Qh73P)$E`Slu^Gif(-R_bBnOL(Gk0>Tx+XR*KImD^%qyv?ZynRoSdtp*
zijLYK+PNcJsfD2;j#r#Y-C1+%E@bIURg*5A;bLxRe^TDClHAm|P+vEDB5$jsrJ=?$
zvqfQHTzWpV)<uU0`+IR5ZHQ{Co}GU}QPZS_n-)x{$qsWikjLFs+OSdRWD&mz>{pht
zg$cp*kS0i+Bq>PppI-Za6t#e&v<PoIBQ@zE&?{+f?;a2t;_v0^U~OioLohCoKQF7M
zZ-p-Jf8^Yf>c)}dr_EltqN}YeHPD_V*T?XBxQCuMrGu08dUX1oj>svp#dqS$`Zk{7
zDTTEah1n@Fp?>ZTK=3m)GSJg8wQ~=QNz1Qns;MZ-O-qOh@$+!9F^4LVl#X3sYSq}e
z>kqG=J+dUu!$c8X>`YBeyrG{Jf>n*4C~b6se=vdYi7A{=ECl@1YyXd;9028WYkg(O
z!9o&pN*XL<OFNu2*G6ZZ>^Kq<kX1Lf_lZa?tZ8kZGHcQ5O}z)#&1@@<bvIVThG&`D
zIFsFUQbOohi%8(2ySkbx)aR6x=qxO~CuqtZ(Kee_+%T*(KPx#V%-_=uJ>@{|G_-LG
ze~Qa0tsgyYd|Q2GVOCO9Alh7zRge+Yv<uFvpSp7I)qN`_*QEJbFv;#JuMn;yNxORt
zMTkk{z-yEwC>;OwO_TY6lt^DEa~-DmAU;76aVaRq&`oAUQ!(<sWi-uQLy`;Y+NaH1
z*0bZ_smphduIp^b46srf!0?!}2a%W*e?DUpJi-Y4RFe+6PKXvsOwo+aENiZ-D#^=C
ziHivG_wnR7JJ?#8I|syORWwhSy`p>ixRznS7Vt$5Buh;}f?v%pEU#_umNU=KZk;<K
zH-xQB$+slrB%sg{CyVF^LV{wDkx3|l|MkaXMnWG^i9tNz`9x_gE3cT`n)cbtf7bUN
zK6mr++h6V;Trr^}%3c*2UV+!}JlWF$6h{Qap(VqV4LNo#oMV?zHFEHaMUGlyO=U@b
zc3NUgL};L|mz%SLjaOtwS<9sEwYxVgojtL&x*#>e-_6cUR|SO<Dz*{D<CYz`^XuJ1
z#IHf~e=2g~gWUQii-uqlfQ-B%f5{X4?-s+rhy?_DIa=tc;vk!>pwLPd5|ok!ZjVYX
ztRFYMd-;amL#MCaJJ!?Hm=%alJmoy25WoaVWzx8>y)Pf029fQtb@g!r-4!)OX<>PE
zP`$&G^Qy~=hGwNAvj+$GpxfTT*4o@9Fg~}sZOZ(0i)VI>7*>!L?GFTSe{E$sF+Mf>
z$fEWod#-=Fv3E&(QG^{BUe^+;Oy&IzPm=(MiUX7!{@b0qV1So19b}UWq`-g*teOTC
z-(@7Q?~Prs)WX^k<EGA8yk_&RLp`(FN@Cqj6bHfWO%!R&P!IrLB1_0140N!BWt4SH
zY&m}60pJT7XerBrKj{$|f1NTkD=jH5DlEv))6Lb%-Ui-y+z>OYZSuTn?afvBY0-gh
zIBldXFDazy7+ExC!On|sFYR0~DnHCd1sfjc1L!!D23?`Y{Tj+DYX8gJ;li|&H5Qc+
z<T_<U6NRZcx7sbLsBP+q4(8C(VU43E5MF&b;nmar8c^<magS}^f0uGkLqtvu%PJ*B
zhxp<{uL=Se9G{@bgxE-Ab#Hg%+1XfGm>MIm-rNcKllj#x9c^`GLsOyx-R-PQNx2ps
z=cvL_^R}LOc4o_**4$w9#zP_qN<*cY&~2~FYj|LPV9Vn?6eNEEUu}`UMc4yqRx6*d
zgbc1*nwVR%?Vx22e>k;R-VS_b3y?!f$;vOS?i0uJbfy_VXPPne*a2xyV1T=xYIn^^
zi;oEObVgnQs4hWIZg7Av_U9odi()xlZA~=;D;M9$<e}vaRfU;};qbm=PCu}%^jx9}
z+GcM!cAt82h}5q?lw@K8h>{Eq+RKw+sA^-j0O0Gb@;3-me*i6GL|)p^9gSOL!y*IR
z?ZKH6<|gC=LLqaPQ&5@}8|aE}RGNCFsiQ}18B1a(3H~VsJ3hO-FgrO04!xxn+s4k`
z(aFWt2|eqUX2ynkL^rdt5<}C-+Sw;GJ_91TATMXOi7w9RDKQk}4LQ*TtuxjfxUqlb
zlv*6lkvadgf1-3;i{q{G(Y_1FxLDY6d_n+UZ<W76;QAzVEJ;Fgo01mdc=-i|Ma3nh
zWyOc!I=UzwhFRM=xuQK092Q07)n!E}g&8??SFj9muL>3<Qj`)NKt+%iyBbz9G(A2%
zz|+am!QR$}ZEb}QI!i~3XkdrNNi4+U=n)W^7!~Zpf5C(hWo2b0B_$@4Y2qH8-#mTA
z?u)%kI;xX>%o((B-T;z^w2YRtG9-lpqEY}~ahbn8U<NW>&JqI7ilu{lKxh=?;|ofw
zh9-u%Tk9bYneLos=^LR_ZEb6BWkNm>B)wWfQh|K0gT2w=#G-ipEl>2&=huvBsjVo;
zOo|GIe@ERGyM<aBY6KM?Ct6VSFGe-v2YR`}d!){UW-dj{mNxf{$!navWZS7N^T$>s
zc$qTj@-9wGnEk=<n3m7SFRh|)#R-T>$9*JUbDh6A5Us3Kr?Ie%imsWxdtg*TT6TVE
zRbBJQVMF5sKwDrC?HJ3-;}R_|ubDniBzZ25e{R0)Z+IyUbGPt}^46*2M>bR!=cL4j
zVn&>ywyJ_WtwshFc__$>$&iXHI6<!@FG)~snJQ|UR$eiA^&Rs!9HxQ`WOyxbKa=v3
zBYdC(urHy5=%AKTGqU%IN-t>ye7#lv27yypG?#{8K?Ovm<`k9>Z)|Iy)H$juD+<&^
ze<=G6>WjYL3}mnDJc8pg;ejyKW{3|UMk}pt?H!v}JAQ80#4*joOY_nRijR)6j8Olc
z7=Gw^Ll1}%OH-90D*;__WbIYg#wT`Y?YKE>_N<)VToA=EQsAnj^cUx$fmM2viy9^X
z{+;XiABQ1aWnW|^F-0vCdrxG!yrQz|f4Zg-W5zcWr-Xp8Fi#iqAW|ubIOSL?i!_<2
zyt=-Xvv+7bG^SH0e$Ze_HW+^gFnnBI-Gq5v9itj6k+U8V;O<~)puv#h(l2R{l~!Xe
zEgwe^Of@YQ+ao-+q;dT0m0cr=sFLQ}WX1)1kcmiIAybf)u$9&@bq-7zI&2i+f9tRI
zH-sU5Ai>lzcLYVNf}+xj>YDnd){5*{KL=w~+(yZxj0KcCxcVifW#tv+alS@KfKN<L
zMHlMwAwDi}hT)ATKA)#smNfkMd7TqR)RyL^L<N90B?=iO`ZYWQe^-{Np=V<4Xl+WW
zXwr>#)||lDtkU|iog=GAg(DF;e{}<ESHH-VNPkk5i4Nb12Lx?TSxr+%znIL@|6TR?
zI1VOznfaxF9_t^KT2NG6Qd(Y7Sv@R2nO1`*rG+SIDgoRA2h1o9Rk8t6oN~sT*``pf
z5vF`99)=xlcqqFRf{Gf7<@J$O#R%eVpQk}kLP1p*+1-QGEfpjTudJ@4e-A2$@tH-#
zS}L+*{2Yu_C5h^sW8dnWAWqRUfFg{u&vbei@K3GzHv!7<*g66-Y6fgKbXgXcAg8wo
zfhXQstl$6uAOJ~3K~yxtb|*fK=u;IZ?p#AtLl%gVNQ()H%M$q!J8<}MM=dp{fGbB#
z8-5y)yOJY(amNObGzO4Ce}ED@1ayI(j<_E~h!Qo?HNeq$Oma3v?anxA7nV`hHM4UI
zh@x{3L?ROHO{{JQl{xg3VJGB&UOn+(6a5E5LUJ0$ww|F0Ipt*~MFr3dkscH1W^1HD
zyq5$!mS|4yE0tix!U_Zul)#BTmARuF0j?Y|;oKp^PaR!fni(79e}=jEOgRZ*F4LX{
zjxr$b4)h>%26S%G%-Y_?(=RwYHrNv?Oz1;IP=Q<wnrTm64+bo4Ae5kM34MkQ$1fr=
z8}J2-_QmV|kApHi;ioBRnL0ouTwPI8I5aCgIUy#<)4>8WD(N93gw@5w#pRUX`E}r6
z1w3@ZWyLXp6*?=`f0Pu7T>W4|z=DTUOB;U5sG7pm2p<QuOMufSCfHxvf=ulDMI_T=
zilnv$%gEHi%EsQ&#f@8NpsAJQOa&o=dWANp1vxuIQC39<sDMGy$$&3dvHuIEM<5fL
zqExoKsHUp401D$_aK{?rJC?gU@WcrTNhw7&mI>R*Gaw=mf4gUB@hQC=Le&n+-h&DE
zibsx^4(BGf4a-XmcC#YcaKZuu$A>hxf@s8{MNLdhOd#8AW@e~MGI=OI2)C(9Kum_I
z&N8;NXItR+NLf)0L_OSmL$LJE-&v|Zi)8;ZLARGGv8-GJ<FZN{hLz@JLjBC$!OB<%
zdEA1$rFwA*e{5nUZ6o6JL<4Odo?t1G(k4nvkU?>zuN*OL_(`pmIWc~YCQxz#wbOx0
zdHw4ybT1WA#!*)VX<A89Xz`(?>}f_X{V&dTAdiM_77ggbcXs#1)#$&oQvcTsPv%@z
z4A|}=iMf@{HDyE7a1&$y=^nHamYN>4I>w~srNsn$e`1O~kH{p6{w#Xh@DrQMG9o<L
ztN~^i&>+;5lJdC01(&i1IUPAkQEt4z%k3fhXW{bKH@D;XdAefFCM|d)%%B$N4fq=i
z_2<c}|1@CXc2QA6Ko*7ZW;l#5FU(4Ykr8%WHF&!Bm@quL(LqJfH#{k)G$%3K$H{`F
zBHe%Rf6Nd8VNBu!k}KB05Mk=u`NZZ_jhr@mcz#lltA!Si=;dH6cFNNOMQT-NgZuB}
zXs)Y-8TqQvBaO?pDoPA_l2s!mMUK|70C`R|MQJess`H-X>*4CaHaB8vfn=MJnU#$L
zZqE3;l^Vot8ssONMv1%^m7f~?Cm2MB%>B^+f0JpMFfRIh>c4I0fw+V`K|hrc7gNxH
z3V2vTR!UrWpr?zi1+u$}qKt$vA6J7GW*T6_YZ%#Z0%Ot&steMh{GBaX%3yb)zN4mS
zGBpTNs3ON&j>@2nYb?u*_H{5(r?v~0#}O4LdZ0*o9CX!NxcEnBl;k8u2DsUou|R2A
ze?wDKOIt@r7lDO4ra_fhQv);@Sy;1_t}>G}FBczoXM1bp3}~v5m1C>VmNX^JPDVde
zErFk($2fh>MSqBooB8Mm{(c~1Wg$F519}MI2MG$04a$6EZ~DK%@EQO$Wu^l5$7Gdt
zjM*;U!7<^1K5mX|bkB3AM$nNN78FN@E{D8)NJ4g5eQ8!4ROXe$>61!>&bgWcqXzix
z2G*|r(P+1{ROKawy4x72NYLAp9w-`rvt--ilCGtN1+-FP-BlDNC==o+1Phey?B(WU
ziv@j@DG9~TW#I58so}K~Lw<~iFl7|Ng7`lK`+)curOze8g)om{Nfk)qzlACNZbB{$
z{U9+Ad!lz5A0_PfrH03i;+R1%CnYW{h2369cRx=KJc=kBSH_+fZwU_A%2;}T2!(#$
z5c;)N4^0f=Sg{xb%nlMjL7ygwj5!e9jz;<x4jzF~$vGv%$;=CrE0hSk1R7s>_+Hqn
z4^7BPPl_c3nFy5}O*Q<^L+Rhw$J^V>)5G13<797RX=aQWItntlM~6)OIT24hgpEMA
z)6~+@1$_m*&ocaALT#O#;zCq^jj=_9DccFr2NggV7k`WnfO$a737|E+$Ol)H44ANl
zw+zrip?y>mJIwUtG<sy>7a1PkdEobeR;3_O21N^smJqyP6&~GEo+D2fUfbNsFD9d;
zer#P)YM8q<OHsK098vtr0;kr?!_CzRT4Ye%9~_mCKD0P1F4)7?SW}*VD^y3TAkk8!
zBE-PUnDmsy_?XDB-~eB&S`BGI<gQyedIW^W$Kly{5#eD0P_&21Saeq&NL^!5Mp^`i
z2@`TuRX>+(Nxklb3oI?meH@7cDI!dqXY4F-L0OdwglK?lmn6}0Kpt#bqMwS08PEiR
zb)JZyMNCc;;<fx286L}jI6GS7Nls$?!s0UUBI_9$u(UN)l^F6oYVcHkk7`+0(6Mk0
zjL)rXnb=a65$Va+RS@i7Wx<?UkVOd%3h?vxbmus`dk2I>#wDd^BahJ8LQhqOTa@EX
z&=Sc5I)Q_Q*uccW(2}dC&X8nkll@hBLvwSovog}t;zE60ZD2Nk9_=mC;hdA56@KCw
zioEzN!{e+r$f&`<E+#{fvOTHah%HXG=8@fbw6-ID8^N4lMG~^Ivc!p&Ciegwb7>gS
zCr1dpVu3(qAR7_N1oYrE5TW@u7(Ni->11uJqar89hgBzuP8vnBd5RXSrBb#y9|J`>
zT`RYc<bs;f(??Z*=R|w4b>;c{PXwg~N+iU`Mn!}{$E$x}NLWO4Y<yA#h+k?kCAlej
zEQaIg85|qp<!Fg{{v^XQBt9WADLExAJtH$d)W_LUUtL~A5nVXQ?JKUUtr=cjRasG9
znhhN^W;#r6*A9J!ID$epdg3W@htK8JQ+^#dpIFSDYUCz=uVM+}z=HMqo@@LuY<7Hz
zNhs2Xnce|d*B{iS2!4^ehL$E4=7SbAOhf~vNt^}~<mZaed;wwrINhx#FFKe~2<A>?
z<Q`&5RFp`_Aj7k)J;KwVBtEV-FV4qCS8m9krrzSSGt*O(65^sGVQghMs0#v(6UF}W
zV%*(J@i@4DL_a!LADftxo|RirT$UXl46|-z_=%40qWbaU+Q*C@)i$EFxgt9*(Aiu|
zUXYg1p~ZP%x&}P)^w>G<NPUjsiBVnT5HnR|5o``(8%Yyq7Hm$<Wgrf<4Dm5+c4$-}
zmD(uUAh`pG4FG~-FryEP#A|7zfJGOFD=1!3Q&mxaVk(j%L~#EOFmv~_fz_*H0^Mzm
z`po4W=#MA;8`|)CHeOL#6|GYi;9*a;y0QatYDum;uK8qTq$MZD$3{o;PAL}Z`^kMf
zBB1VK!glcqi%v+*&M&E|ZGsrk9*VHi+SXpNLu<y(oil6ZjLzv(r%Y-tOAmLm(3IwW
zL&~*(bhfiLGr}cA87!wcV6y06fvQvB&VFDM1KAfh7%EU{1Zin<s;0aG1uDfriN~lD
zfZ<H4s)W`nWq)EWBMk3>!;ZsJtQ)5XbDFg@)v@WR&cA<);mH;%Crbl$1@V9Nc2ILf
z6=`8uOSjOJ;>HPcR?Qh-R}k-SZ@^F>JP~PsV#J6ddZ}2J&c2Z;d6i8Kb;GMG%8Co}
zKpG_pF8)Aoj)S$SzLqi#9aCGkfT-kLLK*`_?aJENfs07U%q=LX7zUJ}+JfXzj-`&g
zfV_^CJ7_pem@t0)xN+_6W5?DOBnCN~s!MU7WDzNONYdgK9|K)2<f~FQ?dQGm)Br?(
z30W9(p>JZrwuT521&~<T51YsUS_-IXHyB3bOb?i|4BE`rYzo%s%R$Bot2mis1z%G$
zGjj_IOOk8u?83p?pw5_r3{B(4hWel_3^YgK0T?Tvk05}TGjfX52IRodpr?C~!+cCk
zoB|T^YQ}Ue-MnmOdtE_Nu&cR=k)b|+3rO*#=p-8(a%frI$cYo$N4K>!*4I>5losXZ
z;=*4<NPv%rtD`Le6kIra_=ke-0(48qh6Q-K*t&QHM#QJ(7FQ3iYiwy7J$C%Wmh!9^
zAA3U;aVBw<6xY|+H#9UhHZ?V)j}Vsum1Go=kWo+uR~0vC0Ew3xv|{pkar|I^eo`D4
zm-=iRiHxroz|+&i!`<D@jl<z!KN&{ey2C#JIE1jPE63dfPkx~QQ$hUv{Cs_VeSCb#
z@8s>{<Ll?=AAo1NMnpzaW4wc*ci7DZddf@<bTyP<=5v3J+UEoRnoc%m23ja`9zX(|
z2F`@*nOZwKIyyMm+uPgO`9x=bRg9Rjpl8p<B{Rm=m!wCbU(nSVs~Q0L-qR-_BqA}h
zpt51)gwA<$W_C`UIDYJ?5zX~ARb|Ebx#-ac;lr3HYG8GEIK1Q;*?EPfMR_@y=vs)0
zh$Gu}#<q{2Fmck9sna{*$Om&Qw4vh;R|@hAii%6i2(nNZk<TAQHEF|ttYkF}Z5DU1
zJxognp|sD-)#Jo7=%8yGniJbIHa0FUK0YBKF(Dxyg&T3PyoezX9UUDN6%`p75fvR9
zmynd4nnnSpBml7=YQsqgi3rGfO-W5lPtVNC&dD8`S5Sz;m7MI%40J!nMWZv~>xF_Z
z+&3ukFRb*}flnK_yg1u`)j#z{15fh>y@|j8e?MQynSf{4F=y3|W4kvjoz>A=ou86`
zVr|^q?&TjE6`zusS6o#;qJ7HD`ODU>TE2L}yjfk-CXXM})?7clvb3<U5GX#F3WK>m
z#6J`lmym!DR19xu9X)<>=Ntr(;G8!Pj(n82odG@lE<Q0iEh9UBH@~nXBPPHVSMUh+
zYGigrqCrxF7&8a0BL>KDp8jDmpI48k<`wD~Svk1-hsCDm49&|gEGjN3D=#Z6D=jT0
zep})XA^w!Y0)+hh{Jgxpq4eIQfM*0$0xAHYO;J%%Syer3*zlU#y84F3rskH`=EjCP
z2v{mg3UV`3;DUsI+KIjfQ~F<>&%X@(8sYa3XUHKE>QiN*AtJ9~Xu}DJrp{?tNK!%V
z*qO_=9J+X7@0Qi`JIA*SD=*H^!D2PBDOm;O!y8AAo7^>b;qtW`x9{G$ZPWTSE0!+o
zp4B;d!k7_FwbjH-40L^?CmOxc=#fU(L1W$Us`8TZs^Rs2O|2uxj-NDb#+><!m#^+w
zw|32{l`EDnTiS(=d?I>P@E{`_2N!o}>WKuWpDUh8j}WE-@ev-UWNzo?ACZ_<^m&{+
zei4%W0eVg`skx=Y>l&I`MvNRirXBy{W5*zj9zAN*sF5Q_wjqodF#@!OS`eC>n_EV-
zjT#M?7B9ws6BtKf{Dh8)lO|7|GIiRt>789&GiG*8pE`MB$N2U!BN}U~N(*w*lW;#9
z3x&D(cz&VbK@wOK2Ped^w?EcZY2co>z_{!(;*Cy8gu#MS7j4*k=Jw@N2X}5*F~4i#
z*tRC(cg!mt-aKZ~jQPt}_iWm}Yu};cr;i^wuxICg)=fRDmo1z-V`>Nb%n_r<Pnte!
z-lFBpmMvSlbm@{MOS)%HoiM7owyL<Yu4Qz`wAl-muUfl))7Blk_8!=`yLbE6O&faF
z%$w9wo)P73t7m|<?~upjfE6wy<B}r$Tw%zuD5l!Upo<y(UC@Asf-VInT=~fHV56;@
zUO23OwFB^%7fC==8tZBT!80ziu(}C38&jrr&6?A_05BiW-92ya+&Ob*&z?1F*36kR
zXUv$<h0)pB*)?<4oVnfe7cTk(7V==xqQy&=E?d50<;qp7R<BvJcI~<~E0-@_ym0=!
z*`1TejcTr|D#dN$HV}Q$BfY)=3Vi^U|C4BchnOOnQ`OeeP+MJIjIN*Z=85yx^q#o(
z^#1h=Cl2;*UbA%mtZ5U+jA$5ERy$(+^zP;BcN{o!?BtpAm#*Hpb^Y@B)5i|&+qHeu
z+7*lD&X_V`Ob4`DuiLWg;DH1C_wV1gZ|~l%YnRNKGPZeG@v!FhsdGVa<?zwtCr_V$
zJ%91?`7<Yu9N62tZ4)~3HF*g^E>Ms$z;8EXm|Z>nL%1AEvgtyLX4*kZrwa1&3{4$j
zym&)<*HXY=UL-+rS*8Zd#0oMJStYe?6KBm^uz2~ZbsIKs+rABB>(;GXHgDRral?l7
zJv}|^)~#K;b`4-PV9mOo4I4La*|z<EUtq`1-d(%*?A^Byt;K-@2M!)QxPR}io!hr;
zTE7mhSJ$NWw#MO@`ASwd{_I8m%fOWZvg)G9ROlxZPeV%6(mgV(a%AU}j<F+~>xWeo
z*N>aEeB0qm58l6ea_`2aGsh0>-rBQb(VXcM$Fz>?oWF9*-s6|<+<Wlw(UYft&tJTH
z_UP`-s~3>@_U_oUZu!DFos(uRSh=zH(CN!}@7%e4`_`?SH*cIduyxhEDWhtOYDZ1&
zUbFr1`MdY-qpyGb<mt1AcdlPLd*blE-Zk?kHkYPHcv@o#GbqO})$!m$FK(ij;^kpI
zJUql&6m0~c?Vt!WwYaHc?wVbHfWK~d39>6NC^oaCe)QDtRqHlx-MRPR(UWJ+o;`a8
z<MgSMCypOKcJ#>M!-ozbyW#Cj;J~57M~|I2dHT$NIE!)a{Dq5`E?>EN?b@~L1g>5>
zfA-YzqlXUkZeF*1!K`T$Mh!1W3HPuuP~oN~zZ8_$${i5YA2Paj{>i0(Bc?B3+&yy|
zx~dy%Mt3gWxc}^(SHJ%F{o5B$?%%$4>D1vpThT8$bIRNmoA#c(`ryqkzf$<|!<!e6
z@7=n3@f0%sMszvMT)cMs!L!#Nz5Vr<pMU=8ryqa(^y$gfV>?#P9^X(@KYsSA-V--o
z&|m%Q$G6WO+`N4D_<>D-OFBnGKOZP#XerA{2n`YfsvYLuQ>;sxV`<F7RX&051jM8j
z)u0K_CpNEkLU+&pOMt(;J`|J3Cn~+DcFgpJ>vrxteEiJCYq#z{q4D_9!~6H{-nk7s
z_{R0?*REZqaqY&<+jr0&e$*F_DZrOMefIpt%U5sSzC(D6@#^`1lSlXO+`M-A%+bBu
z*RNbOd)mnAq4EBXCTbGAQ&s;i@O&2;IRyqD@`$Ir<LWz^pC!2lLxv2NW7+y8!cg}4
zb7yr;orL0YwDGqdJbUx$_wV1leE#_1z1ue~o<6d7`^MEvx|eU*dFbr52d_T<_|s26
z|MJVPzy0>}$M>&)pFO&F>)LsE71l26p0NlFf8+7{-+%r2$B*B?fBX97-3v!{u3gwU
zw!Xb<$%cKWZ$5*s{uyna-+uq}?)k%8VEB!TrnOZfzn)98q2o+iTbCwe!VF~0-xU;-
zR=|_!TtiaOiJ!N258!hR?}`jxTsO9JQO~XeM^2u*eB<ta!)MPaJbMbZfB4}3y?b}b
zON_#8baTPQ_3V$JzxV|*`fITI`wukUzIyTW@dIjAH>_GRx1*^f4HQUa`cvFKADD=N
zDJ?Kk8U=NVG7=*EG{KHY*+clCz_Rs;%d2UhHG9@f<hOKApEhs#`kjZ)T)Y4B#q+06
zV87qFcH#7Y(fz$!dRA`OzVFz%YxkbLegFLr$oR<eKY#r2=H=5z_ikT4eR%hlwM)CZ
z7OvTL02%(>?>~R~{_U%0k00CtCvpAq?#}UDXj2@5O@Y4p6WTey{{HFhb9Ca*9NWKP
z!Q__G)KE?zF$ZGWtf{^>A)ZO~*Xe1)*m`t@@#E%y4^>TFLp!gCw33E#GnN59$M9IN
z$vZL)op@~cqo>YYxq0^yG8x88v<T0z?ZNmD9^6Ovr*R*@ch9KLrN4y2D}>i?klo*Z
z_@0Y*Z@}^o(W;(5d3euubW3(ZHO$9W54zg_PU5)_%*}DqkeK{M%GQbs(z9iuk_81D
zVIf6-JsWQjkDWTbYsReEbLY)pu%c({o+GC(-F!&m!Gqh^E}l7lXz$J~JNF$qb>Z6W
zhtFTVdH3OmPe1+g8?x@Z*Dszvx_|B5vAtW@EuA-g{_3s!&s;}_|LJ>p1#VtBfAYw_
zotxGo2VlwCt$U80zy1JSeeb{j_~Xx{;V+$kIl8Z>dtzf@Vt@meW<#BC?$9zqH5C~e
zm_^976QR`mGzd|hf?*@@D4x$SJfX{umDtBjCk%i7%FTO^Ur>g7^$Ki{&izxk_8&j)
zbEiLf`t&*6<XpVw;tj@IF#Ct^f8gQ+SpM0QM-OhHl{vh3=jL_uIvR=+109V9nA!7x
z`QQLtKyns_MVX@elOZcEG=v%`rbM@$G4*Ub@o7P^_3Z9N%T}%1w0$@J`)|;9*@HVb
zuAD!8{P6xm$4;HQboJ)lhfkireDnT?AAkAnmmfcVc=!6{vnRJMp4h)_-O{<!x>s)5
zfBO2Pcjy{_@#xM~^v508yKBeR%^TK#t=_PG&*4)SuHS{Peupgo3o<-<<1d{)x_8~&
z@wG!^ysf!3oBo6?dWxwMCCrIOQYoUCj%v9Z4*2Va$BccpGgkeg41eJ&dg5P_MntB2
z{Tdtp1-|zYp3;El8(-xE;T?hZaN2+P;UgE{zekpT`TQw)Cid^zwsd-1MS2*2#|)&U
zAZ7nWh7SV-2l_#})yV+}EXwkDe!Mu$k5>hTBonWmvOXJ>hO0-kPwtw#cxBI)U3>Q*
zMA6#mvllL3y>a{A!^h8FK6`xs&W)>=&YeDc?!u+ZSFhi^b?@QR7jHg%{P{O{$nhS&
zclGqa?dz7#K@Q;NeW$J=!+(5#{p8LS<iVhDXUC46J9~Rk;13(;8v5!d&tJd${?pH3
z`1|;=TQhs?u*`6dDVJtL|E{!913h;RN=BF`L$U-F@Qn>Ucb_*~mhd0hPN2A31BTyy
z@Yrc|qdj=~3fmmp5B(o(e%JuS4M1PzufgbG+&_T(pnp$T{Syy9e*fWr-CHm{_{0~^
z96!8o-8|UzUe;U<x-S6E9VcOBXde$(drKqG92DWBbWikjwP55RdR0^n*c>b@JF=s*
zdwI|HeaC5>xp3viorh0fzJ2}T89Yiikxz@A+AGArzx&}QWcZ(mKjqPlvxj%|ESWuN
z&hkxrvEhGs`QZB5BfB?$qZnz+j@@uupwRuor7LLT+<Aa3|NbLocoa3Po;j*AHP}&~
zOS7TPO!M=J(RLuHO0ioghJ*}5jT|-!_zc4<sNtsFFibol41W<lWzSx}>1$H*{J#6c
zp~g1mdB1tD^dHD`j%@uSFFt+z0h|8y^T+pZLmK4x)+JM0iW2>Q?YIosFF+}vASWX^
z4iu}gLa-9a!Gcm8j11ROS3!pf(_^Dj^DCQCkiL5BzLQrjT{w68<gp`%_M`XwDq4g$
z@O!>~`5fM*d$(_*K=i`7%h&HbeE#~wkH4Xy?I--VJiCp2_nsxQCeB*AanH%C58wX&
z;o0qr$M$Sm*}Zgs^~N3hj-I)6>n6lQXy2f(e(>bw+aG?yhQEH{<l)_`y4p$;{n%RE
zSQfVdAvcsO=t9pUCFMD#rO6&2F)@mKk6oM3F}x}!p@t?G3?~eaqB;1}2($M!>Hr{&
z`)OcMmj0<PNYj6K|N6y~hj(vXyL^7{npvYO(nFlM!rxzifM!5#W%1CAq?oV(4@cl&
z$%~WyDu!k-EnW}ADit*hF@-9#s160PJ-d!yd3YDSJ*SQy*t26Va`A3GeDMzds_1up
z|L)Z@_~?Z*C(d5Faqr2?_n&_I?bly_!2$cLdzX&wTEArGgqcg$?>=z_8UFp_>u2_F
zS=l{h&eEQL9S2Wey$>$_-CNhMTs(UTE$%b4Z+`#y=IK3T_(Qu^Om8WQ^)y%E(roCF
z#sYNZf=d>{G%*>Nj|{vUL23e}BG!ERT*EuKg(T$@hCg)t%%vN5AH8^s5A*ljL)+h;
z1DlnLpZ^R*a^vI24-nErw0v~)f{vP;NDj0FF=Qowg!%vfzxkg7ySHOo>naPgQ)0t>
zId*1x>IxD9l*F{7t+hEYmQ|Is46PhJgQAkNt42;+yy?J&`|n<$$Qp;3+xH(of8*iH
z?|-B5@hu9wubew^@c6kaw;n!!`-%IZcMq?g*u8$qjPYHIVf#FI`}^AmmyhjSyI|_b
zDc!4ow;#E9@Aa>r-oJcu@8;!mC(d5JdGE=q_k`gg1+Z)R)W-ZsP`vpopvWWe3W(DZ
ztDoPAB1GwV@faSq^y8PLEB+DNnX=+9pMhWhfIc52%>U_!_b8@EkLcOn<<pu9V!g}^
zK?q7p<ckbX%$BXKD9i%6L1<afR+bYdxGOAwg1bU!NrHFd>=_u5FcegU*Y+O2dg0WO
zeLEohg1GK5it8Rce)|07Yly(!JbQ5K^4Vhtb{#x%{@T5#Z$5HQ{L8x+kM3NzsB2v3
z!nHe&T)6Z6)6098Pwd&WV&0TF%hvZEId}8%yO)peUcYea=>DAtj-R`F_sN@&;Q7OU
zPC9;Y?TiscG493#v1jNeFPh#@cJ84QFC{K2{P!~+g5t8!5^jYB`)fx{oxghHw%rHd
znZLu$DnBQ3F!<j8f%|0t=}G^?#ZKPqJpMhonSTC6Ms|<S?^)H=TAbiBz*N=G2fpfS
z%JVZpL(tXE(pXoGAuY;J#>PhG&{+<DH@zIukLd0fl9*f34EOY|?VEa5EnP6DYwC>d
zC9BqNL+{?{^OvvRzW@07>!<f`prCty@0MMMPF=e7=+zJ0H~#6(bBDICS=iYQf&2l8
z=bqjn%6r|*)^6F2Hqfm{cdlK)Ig8bscON->?f#4JfBz8*0&oXxnm48_-qUn{Aj8vM
z9-ZIlKfw6!0bhVx`X*M;1XMJ9#Kc((mabaAwRiuKlc&#|LyqX>E7xw^x^wsbL$vi@
z@NR%U0oorPdFo$@sQvxh*T}d}VGH5!0NEULll=G-mDha!^y1z%Guuj&{J5#AFTgNB
zX<m9lM4*=op(ZCMDNJiiA>R&v2=*v;QdH5xBCwtzC=9L{F=58S`EzDYpWM+tdc>Fs
zQ@Z9ZT()}s)?NFLBB$prI<P48+P8D_x~+SUp1b}4y?;lj@&EuJ07*naR081D!(aF2
zG@XNY9sRq-W3#bs+l|$@VPmtgIZ<P)vDMhN?WB!u+sQreZ>@X&f|)gE=KEmp&+c7R
zeQmBt7w+`cevDA?jxlJ1WEn~;IJ(g4^c$b*3?+p{G2d+RTij_K2e{Apoj#W4ALI5s
zKj1PWvLc5ysk}4TV4hTutQm>Um38^-+&?xU7c*>so?@eAr5G&p7MrY0TnsY&wcVI)
z_nz*8gheI!MZY)5K#S7)36@ZQ^n@Xs_7Ov}J+k{b5KoAX+#wp`d$)CA>+~t;`LKi4
zCLsKFbuDe`;j?G(73l5&&d*f#>A<;N5N~n767`(sclwPae$zeR6b%N4ajAWu547Vk
ztMHLMnoQH_A4nfK%Reb*D_GIaz(27rV+aLpkfZU0t*kX4>s;d|+I<M-*u1y#w7NkO
zO@_y8;OQLaO)byY`*6q6rP^I7sPFV;qu-)hxXpW)oj1H4XnwpH`V)XbYrWcJw4cv6
zFJR8JF5>awQkFw3F-Nk7*_lF*5xJwXGHCoBy6A_UdykO=$sxe=DYNCc&>TaI+V1;2
zwrJzc{d1+xcKE_KG$vcn88ipWXa;rI{&EQtV4pFFc2A1G{9LY3F9;qiR0zG_56-=x
zmV+B;*xyP3l9_nvly0vYi;L8;2GTeL4T~|qwC!44`ENf<b25UG<5A>egEd-v-}V;l
zSlh|7NfeNr&GB*_meyG?r3A4>Fc4j8_YSa9lGIVk|B!H5E$uX}KIC+`JUsQTuX0y_
zn&JIop<(6?zX+tyUDV4>e|N9<v+a%h@dHI70W)skWzBb(WuwdSd~ER6LXk+=Yws#A
z2p08hGxSZ8R!RYPnbq!~ZW4BS(--;q&HZ1qP+L_!`avGjRY}iJ6IVS7#EZ7L3QMDW
zhP_J;97`^DA+$BPh;I`U3{|C8)&v5M+J7gOZx2|s>wfO9AWP&uto@SHtPVOSNyOq|
zr;!T)qRuzb9;g$1S+WbX-j^EX4nddYayM{Xe{|B~ij(#MJ*EF1tS{5TV4x@5k=NeI
zPm#p%@c<20MIvKh7!GoTt1HH#3jL0&OV%t*%pR!WEso$vQ~8;-?hDc1M?p?bK~a#O
zZ?dz4kvC0MU+A`w<QW$(X}7*~U!LMJt=j?E4d*%Y<aB(VA1aZAeVZssd~L3V>>J!m
zL)5fgaeca5WYDbA?eck^E0Q~8RVr~`E#{Ita8txx)Y4n~$L2nHq1vqf{xPmZl4pJW
zc6>Zpuj3q837h^UT!%8+^RzyA|JjqGv-=m>o-##4b(G_=upl>`P;-rsmhb7v>^TNN
zt&sNBWa*8HA)N9deEyJGzts&x6@6>&@Xt_3;ryM1IV$4^QpqD3i}&_IZIHLy*@`>b
zCgETEP4BxqDpMgKkODQO+tXz}@VdXgFDrnce9gk?<&XjaEjTpDnjW1RW{ya4Nqhh?
z+=~!=-WT(mb$T0X+xpT*@93}HgYA6)v_v=stq~3{Niy^{1RbZwY(r;nH|mYAJMb(C
zer~wGtQ5GnFh;5>AI4lP*LlrWjP^_fLT$0RNK}aozFfR#{GI|8xeY<j9Ml18$1@9a
zOMfDpi90`b+|S5+ez`jizo~fUannP-(WRmwRPfqcG}Uq=t>sd!z%4y(ati{LyNEAq
ztiIX1SZMgayanO#M&eEU^;#?k+Y|&Ihy|VAFu&H1>;nG!rzO*#qAB}ui9wSj7=yZ)
zb!f@B3EPg?>`So@D+zZ`o89fMKI8c@D{C@#s>e8Bk5it#9bm2@4R8vZ11nrJc=V#j
z>Y_1Frsy)a97!|Ve;0Y!8*6|N6jw`?=7ue>ZnK?^#`b3WU~-+mdyX8~)tk^i(l`BH
zw$I0DQ0)x5U2e9fq8V@|GC7M2oG%i%GQ3bTd+;`4mGnD2eNK16eOH&5vN&wt!rE@$
z?w)EUPHLUKP;~ts1kkZ^u}EoVkW4+=W@FsDIAaS>HVr7hmx%|Dm;`|=qsg6-p#}~T
zy6kpm5Kc#7wTb+P%u=%om)Vfg%0ABFg$9B?6I3>W0TIYa+oL8ZlDef^8=cOFC(jt6
zZWN(zqX>8AcXNWVzc2ls(i+|#NiH(inoPg@>NgND$=^Y5#ZapxVeV8RR|0Hcd5L2H
zTv%KdRWJg&6ATg|L?R%kUCg{u=XXB`U0<SPZJ&X}mnQeP`37Uhcc4VH-d<_(!~O5T
ztVf6J^wN5$BARS=Y<H)(%lSgrB*TFs{=AxAQZg>y7U%@!bRE1vRPrmi6@EmiJfi)T
zruyDH?{#Qo(YK2JFqqqJ7M@O($or&dw*j7q&IV6+4B8kay8<GV1~nZP^G*71OS{JX
zwhnhMb#^@;N~L{5qARVgw?m+++07n2LjBRWh3A1#(C=XGULZERyEjBSMY_1#-FkSA
zE*iR4H*1>3p9Bo%mkn!SaElPFZvz2HzQQm0LX?Lh>Mgi811IVE!<Hd7Y$5_(6=yPs
z!(@fha3=Ot8)$Gl+`etzcI|e&-&s~2ey}y@hN!wpi9g3Ei3bk!P2a|OE)Dov?N7^~
zvv$*M=n<U1?<)J_X)dt)E@mStZ4Z1x@J%Q+Kd@ZV#=6dK=cU|66pKr{`AGPeaOmUP
zEyG~Q#Q`jVu8cCs5!3a8sl@9~1HWJ?@FptGv(`J`fxx))-NZSkNcvOlVa2n|cEiz|
zMMGcA10smJBRYNS^<`)O+e809?k|`#cm750M>C>(7?rw$%{ET2g;RWy8h1aBg&fX6
zRrg~~*T)A=)wa^5?$^dv+FsZYp513Z1Ak>OCTQ7a^0@bZg1(zidBae`Z7Rggb7Vm5
z(NSU|5U;`Gco#YT=VunDqu6Vz3WavMC1^kgIt-L@c-{84K8Of3m@~PGIvbnvT5a!I
z!c~i>Gn*}+@qIe)?R5D_<Op#7b7KuiK)uo=H%)5QU(U^g=6Cxvm{WeAAM1-k<NWkr
zgplB;A%B~eG9m|ksYSfH-{W1bWolVK;&%ralp44(BA{|?J<_uSPd`J7Q@By42vO}n
zd*ZH2gBvpYBGQMN9#HORCeykt@u0j;19aOVO=zMwHa4;Z+<wAZU!+WCaNB92>Oqcg
znGp(?6?M6u6;@poUBM6wc<)aY{3h{00*c)S@0Jow0e2T=t~3L4kBU2KRH)(Wc0s@%
z6IL6g<qye6XbtI7F}Xdo7zudiLyJ0LDgT()Qm%^n(!YN*ATR`o3^5Ne^*V+uKq|A^
zZt+;Pkwu`%l+NM07S<L~cCy`gA3a$a2a(E+L4^4a%|yo<<P;vsj-J`N8=I|PI+wN=
zECl~5lCmpynGjQkX@D5g!AQ3#8UvJQ7?xR`=Dq&yT}*`WrP{g1M~e{yI6i#AbId%%
zAYluY4bk{2(BUlODKa{I-o(jwRoO$aHRCEtF-lzWW$dRqo)NK`^dYpyBB>&AShQOl
zdf%RJ52kw6xR2PaY|1K`8Okc`350zwb`~?dQL{T7c6%cq3%3EV{!4B60-!rkP*elW
zyBZ+j&5XQq4bXrRqr?8fC6TQms*x}w0!B}b@qttG)sjm>b`77AW_n+M)*64c1$L;=
z<odMkCSt6tZHWy-;E++bw#wrD1(|@`Zmq%UKB0AfxS2~0Ya4~$GLK%oO64|e_ME67
z<63w7Urh~bUG!IPt=F}CSfHA;UmSmn3Sm#l-e7@^QD_7kvp)Qe&{j=PNZf=2(T2d=
zBBsRN5YEd-l$*rcQ}>62xNi!#ixR|Fi!8YDL~`Dp7Nt<-8TR^`x?1t;=ujon31`{q
zQHEf}+_s?<jeMZkWJiKFSfLo~(`07=#q!3MM8NzGv=P+SNzXV50n}5l56mnI_fnAT
zt#bG+Mpu};V=`O4F7deEXwKHxh1<Osqg0ad@^TTXD;aMkL#Cl-#y;X8dkY|$6#V^4
zNSPEBAZCjrKV5!OP>4r*#)%=YkMR$2v!+wBQ88guia7powVe-R!=QGbN?+|XoJ}xe
z&=3vV2nbC8X-JBwfr0`ef}EuEcp=Vixbjk|P-qWl!49AIAhG@e<J%LEy~*R~<aRTl
z{CVfN-@7?R)QVkTZBRs0{D(@&c<a1rJkc06wMa62l>EpD%XAFt&Q^S))?>Dr@xI}B
z6DJRw*buDGlGy$18`dClTJB1D{~QM^I~zx{#OvfF;)9M0pbG&q*-x?rZ?uh-8TH#(
zTN|3Y6G6NHN5CQyiH06DJ+WC4)!;8^kVTg-?U+vXnoVV!%n=V-UBRSe>c>*O?`ODN
zb52JA@+UZ+-(&?_e_gAyMmLzJe-a!Cjo`p9%xFi4e(fD=`5y`XznZw&2qjW;hEccZ
z!Qxj8SbK<oBx#WYYHm6vfmlNKnLz#9n9-x-Q~d@PVyB}FLp2|A&H$Jf&sJ|Q3XafV
z$(}e_79I=lCO(4ozka>jY~hj$i8yv&oz4d@h2O6(_Eq;t^uM+k`N3Yr(oPAYjxQyo
z3)maO-ZY!to7mir#&~K!PGqGU!Gk26JIT-3)I%BoJ=zPFmK5ST_t6Hdy4l-du(Z_h
z&@T4!-NZyiMa9_}p@<vz%jLvG6o`6}gsJ=x9vK}T5e0{ci~>@hT|c<EBZetXR$E)=
zaDxhMJ?W`_Bm=@JqyC)LdN()kN#z@>lvnb86BraB(U=61!0Q}r7;X9+(#I-<fO2R(
zghIf=IY?N#bT7^rcoG|AH$@@56j|HV6}d~&P+LN?t*&nyU6a@LF%9eH8J-<Vz(lU8
zt#5{2fgNEMHDBN$6=bfga9v5L9Ionq%8G$`tjPGQVi1SBB{JQ!6W_@S+lgT&iM`4}
zUQpp_%=0qgK|(-;S5+q!*WDb-!0YEftp_k@uOxi;O)SJL6RD61%AB(LpOe)kB-_KD
zMEJ8OHx6x2xc?LjotPLKAOFi2Liq;5S8_S;7H8e8EW-11xX!N(auy)X{o6?jJV)N;
z*2I0N{YV`(x1fW<>CLWKn8YB?V0nuzCrh|m1XhZ=QJR1Ulw@dbTrcL3>-hJ008uFV
z3vWfsIm|e0fkXKBbwWX00P4Fpt5gpUCpWaMV?!^;49qmh_hd!P<u?{J3i`5+->~Q6
zA6Z)%6&4hihH;6g!6lP0-iViuh0>U7MxSUmy+1|&?*4>Zh$8qsMZ8+;WhZSStKA}y
zt%wjq;G3>@&J^vsn8u!Ojh)Q~Jl|vB*!}pYl7VsXlT^uum1;;T1jaar<ccRHGJ8sl
zUN<6c%mpef(Q+IHnxN%VVbVx^b!AzAmzSTPk58bTSAdU?pZ^~P14e!n=o}mxIh~nb
z%v)(`yguq^=u3~6;F(K?tp@Ticps-PPmeDw8NVUX_|&nm`#^$8AfF)t>Xc#nmb5~B
zU~D$x3NT;$Ld<`{#&j0NqbOp`WeXqcU$b@}vC~F{NgOhKh2%RT_5OlSn4F}ptnzo|
zYinm?RX!;PTY`&%nFP#zC@v%vQ-5L}V6s1z$Yby&i&JxoSh&XQ`7EaR=1dE4hsB%|
zxd<3p;P>pcG&LbiqEGh#hJHt<HK$onW(B5%cWPRS*-3r#anXvaKI}Fcpq&mTW;CJ~
zv3icg;8>Blr$3rnGONA2yStZ_B@oLORY*>nBL8>Vz}b+Jh9T3%9d)X`Fz<af*QuGb
zElk>fxycHAceeVb1~xn-BW{$-rUT%Cx@N5Zk?{Pt_ZN`=RRJIIqinencBO+*L;P24
z|JJSp7s5&W*A>dq#z9yae$UW>fHh<?dE7urWQJ4i6Z%LH4$r8Ma-z~mCK73=e%%ZU
zw+1EHCo`fe*+xWOO65GYii!X=v;7r6=c#=rZF#l6rMxqbk%2oYH=OR<2!jm--%v7x
zDvSL{IQ`FKUBK6HJ(Df@PrySP2qFL}SeuZlfgA(1HIAf&?3ruva+TPGvt8Gkru9vg
z=#joRe2JWDqURvkk7%}rTns9Fi}R><T|D77G&+;te&75^xKQaW^zm`({5*{(FE8%v
zwLwA%3`u=4E7jWgkjRff200wOqisv8Fk?y2kcg0`Y#^S_9Sj$pCuqT;+Yg0*09Mvn
zx;jqFvLF^A)T-C77rk~Aul1K1^zxtU_}<(k8*_h8%9cU&@3~rgu#t=YQC7xwo+bik
zUK8W5*ZnqYY~BZ3O^b-d(N?tMiZLis#oT7-{dtvfs<tMIOBH)}Sz`4z{MdFnOMG?o
z9DVCw5CKxYHc!})eoL<y5Jcn4l|7qarNX3iI<XEhk-^1nbp?cRy7kGwy<FE{(J@qy
zaEDrIsTCh|<yNWrkU8oboJJ3G*`TfFh6mIX(T~DbZ}Z3W<?}|*+Ft#bqiERvPv3FS
zx37{|e1T6e$fW;GBwAh$9+rue4lI4MLtiN30(f<In7u}E!<uQ9rQnR2DL(;^;AOiW
zEu^iiC&I=W`u-K<^YcXn0TRQM_jcP`dH-p*K1mk^c=c9L2??Nzt3SGzUK>__3~Wp?
zw3kHNLe*(eMh&rYvvG;X{z>L)jURx@t>qzY^x2DAyrqsU9g9YF;c7Nr<>zfB4_yt~
z1<w3^WqN+&XP0ST5qq((71-M;SDUcn%I^))#`7mfG)Sd$V!;M&8)c*NoBWKO+3Q=G
zoyVzOZhL%^B%IoY1eLg}0wrFWlZ1L%3g4i{!u`T?nNm#hU`G-tq##G|x{km^chPAt
zj_+c3VM*&$<Snes^f>k?h6<?)+OfP<0cNyJ*xW5aTs$01DfoTUTwj0vN^j+#QjWy(
z;^A-);=NjKR`0kj%eH4iJ_qy;7Y>A<ptVHCVeG|5!+-YSgKaa<<G5sZmzsJbogbt;
z;iN>7R{%}r<U`DO^`+JfUsX{X2&NF%4}MK|RdM$7s<Lkm+^2TC6`El~T!|O(#Vj(A
z!Y;@8a^tQw9*6n0u-Fq#?@xY$6B5>pyW>slWR+zaulxOh-jRJbB77!E9@hT*Do?ZG
zh1M)z9XZ7Utdw)vNQ7T_2$#m@XgDg!=7Oa3Z8~r%m2bd#&$g(~Hmiig{eAJ+>R5@6
zib&O)Rgiy^>_eZB=E~OSJ1;DdV=3mUs2(0b#hY|z+hN#o_oRZ^OA?tpp}RIOSN}77
zV!eJCwJJxT&U?P>7YDx-Prxw-IfRd(F5$m}p&9&0NwlY~#JL)W$Pro+Hru|DA){1H
z?6b@BiycD~S(q1q!uhe}$mWGicRmqC*ir?9zoY?|i^_kTL53ND>75nO8K+<@s_F6_
znwxA#$?ovD-Ht@<Mq4X)w>Q`n?6O^6NSVXDL(;(`<I&iY<E$756a1Dy`MVcKF4wAD
z`AhW8-r!V5`6bJD#d1M8)&_;MHOK;043oYfpS9V+7!q~3Y=!7q&v`Xfvil#iQ=xSI
zX~`4<zgbeb%yf0cT^;QJ&;Hau>szat?M}B}4;FWIg_IvI^aQ+Jrc*$3CixuJGI*{0
zYY;<Bp-kc~F27P1sbZ=eZ#5)Y;HDmgD+uaf^94uzE=G0D;e7x31u`{Tqm<^pqE?O1
z)=uSHT{f|JkFBW)Y?sqO(zA^_B***3uI8pmzsbpBw22Omk{vxD57w3xK#hU-fyfv;
znv%u_UX{|u8z3HoK3tY~WlleXuzvSke4{zQnc5}%o6$Us!5V^si=K;&p0j6sv3F%6
zI7+jYp9az;#qwwcwpb%3yYqq+)4@EC7kCOonONrczGc4R$aRk?oyrsbJnpnPj5%NJ
zG8*h#eiHeOq7V(B+<;vZSZ&QA^v#RMl(N{B4ts%cC1$Oo@mlO@uZKS7t{0!NT}_7t
zAqsE82VLxf5sa_ixS1kr>+6-f|G{)Gss_jcD52!U9NgXA5z%x5p+do7a97<o`J2n^
zLS;j;e$1VH`m9*M%9JSn?UeRo_Z<7p{_rn#ONkn5z7>#VQJ0IoYDhSME_crs1ojU)
zX;}PT?qK2Tsk-i)mCm*J+d1j{m(hI1cV>$z$EHV-(zxcRnFp?|a)X-EWe^ad)Iy8W
zR)#-+<<eUZJoh#ajPhjCZf#%9sA-U5EqXW#1w|3(g6*g`-6>V-HSFJQ-6IZzWNpjq
z8~<k8-GJ9^=f5NgXHf>gCnTA}%mn^OqULlCSSyi?(DhP@#1k*>AGuqO#m!>GQLCUd
z4L3!7Q56t?QBvJbtR{m>We`a~_jCE{hyCF1BI5Z9)0X=Y*57F?9}_Ob=vHR7;)t+-
z$M!2XNt?`E%_a~s07xW7=+HDXS%ITq$(Id41H2`Eh;?u-Pg}ETq$Dbw(2zq>zu3eH
zz9?nqe5OW3pN&jr`Sd|{>YXzh!jLkdFy#EfNF#9(?4<bX5Ji8n=5ey!y&>GW3OH>Z
zBulV$KT3RNm0W(_vmm(lOKkN}9)3)IIJ=x!{KcY%ZcinJ*WsUOf$?Gb&h8vJW`nPN
z0H{UJ@R<LIVvohgrubxZmGUR4Pwz_0($TV(o$upnS5k3k$N`>%jMX|kU3Z^SvWMPq
zQBSZBQeT)-*(EST|8$3R&`ztl=^Md&I+$}%j!{uY1y&+ZD^Rt&i|l-VT_*X1PyCx5
zw@Yz6`n5%1xo0^K%=2*jDR556Z>|AP20+xFrYftkUF@~7&vg>0@wFu>E%X<h^3J>f
zcAwtn>w7caEn+U#oBTW<JC^U0CF!>Hn~==d6c1WM-v*fU-r@zV?L=igUF~%JF+7Zc
zR_(XyL;<&e==5di!lc<63|wnD9uh&|u(zOuWuxQsi9uz<=gr?#rHWX)JC~aQSo@V%
zgaE?OsCMl~KL$8ZjZq*==Id_JR2ntjH7q8}LgRVWaeBglwD%2xW!&O&l8Rzs7DIcE
zn52oss_<|KCgCV6=kudebHU(4G+TTxC2S<-r?{<>`&^H90<MoM&%3YTHh!~f#NRlK
zk_G^;_a~IFKmI=7b;Q+G+?-l~*n+gCKsXxJ34+&fQ0qfYSyX7z=eP9b+=9;WJ6%mp
z0zt-_uj*DKYDrCIac!2g!%xNiieENv#O-fevop7vG75W|=`7xkn<rCa4Ti8pt~-p_
zd)e0t;nE1;OXee4k(^e6$OUZ?`HW$ZH~D%3);yk;$c|bt{dfMRlui)90~%)i7Ry-f
zZRXMab>LF)(R$o6XPjeP0WQi<BFk;OWbuVN@1xyz=cT@69rPeC&^`F@dAzU1>k*_d
zi?L=WrJ}J&=l@JZKgbML319`FMlos{;ULYnj=(e-U|<MI=LUh+LcG(S6XP}GZ(bUt
zf+|bA_H;<c;T<5&%mr{bEwLlq37bDjnp?gKTJ=|`lo{ULGYw|J2cwH?W*k*wnbu|y
zShpBb%RQGnIKYit<F5QS{Rq&gY3g8wHO7mR!Ne<ss*-#F7dgm~ai`hBqk?=RBvvdV
z7zhw0k-*6L!RCG4<}1KmtvB1AThNG}v^S*-+|AR5e_oy00iV}G1ixJ;s~D!0A`x$?
z6dAq{Ofg;@5mCU&P&`}ijE^b*h8#ig#KhAMaA=wTw|9{NQT^mewHQSxqJWIQtS8N~
z0a_0)X5@s;Jv0mZJwLCX9xB0HZV1*kHW!gDjc0l!4g~j@-s-$~RCqz>Yo3&i8KTfc
zE7y;N$7BmR0k?()J#}{usEABf#5*zOhf+>-BfZlz18ZYizqON%>+Q=@5Ag<rzYq-Z
z21Q;^G!5El+^KA>@d=%}QRKgk3?$yY?B8aTMTRL(fiNLSqIDsysR-4qA>S>uc)h&a
zAUi;`j)b-Cz)Gw&h%LLj&34&XD`nTYz0tS273`61prq5{zA4C}Y8`lfVJgCX7!Pe`
z2_@d>^u(ewmT1Ax`<9mByJE>b0SL^TeE|pQMqTe+Io0EpY5TNDW9X>pdRcONY95tn
zOE0y#s<|2Z7PaC@E-VZ2OJfYBL6gJ_kd$E07*o6&ya7H2zhI>LJ9pf{!~Z;eMI08>
zmp~?BTR2BKgE^CK=j~H+FVAQGxR?3Prcj%k)8BN-`mx(N<LxAiTw|z7zgpfgOlBhx
zfk0It_>zr>dj_&422LzqS%Z!Z4q&JL^V*a<X;%yvn6Aq>JL+03b`l_Sa&AHS<>~qT
z)FSp`z0J+)Gu?CXm@MW+CiKhRYnPcuEYQJkC{s~2$qECju{Sm;n}OatmIV3MB(cEM
zVS<>^&O@@^kjqUmBPH&!qqY-6{iuJi^e{#I75uKvVTIKLn;xasVC8B$e14YzWbDh<
z(UsLkP)|zEw`KeySHv+b|Ms8S;pcINPGTY^-j=xuh-nR;$m>+DU{j@PLTh6Ic!P{k
zYI|t|R&FX-;?kcjnfPI~BWpY``Zn-%1bm^zDU&DQ$A`E6jTFz<-R~d6CGOQBF!rdr
zU)yb-GjI05bZjMbEONL#`<j0q#<3B{#!*@ZYV&E~CAOr{CE1^Nd}og_kWmx+OD9<J
z$Ne?!wS?PMeW3L?QYl?(ZMRMWxU?`o--56cWeYdbe;9&m4;`d4)E1lalj1es&fukG
zqz9Gq6tJgHhku(un!u;I&c6keACsN8uYZ|;GJb=*b!{>&9Yx@v+4iuK$f2@y2_8fF
zX)sO);tLQZs;<IcpL@O@^!Vtbb2-Nzt+iEg={yn%xSwAn{w?f6pzZSmAbjHq-Vc?2
z>amyePOXX|V;M+D3vFi*Rn8W{wd3*fn@M^Wvwey(`k0?(={8!fE=zZ#W^1jc6B*JA
zS@Fl#V0w5j^J7@WM#+s%$RW5k&jp!HZ^Ey+NJ<3e5uT5Lo1-N6bhkVF#$@|ozpl2=
z*=Y2!GvmjIV$E&>AMm09{{2{xP}0*E^LM7d1$P{0%j{z>6VMR%*;OTo?1@BWQ?ThS
zoLeedMFq?Jn2mHV3`3wj@$q?m+s?AVQ6gDxDJ5eWTbBrL*#AHj5AP<h)SsAQVMcAP
ze3j|p?yAJQxZc0z$@01ye>W+&Fnh_6K@Sf_iM3n;tw+&^$Yc9}i@br@*M!P2Ut<wJ
zglo}+&$|B?6D%%6Q_2+l5Ecdi)9Ww9L^(+2s>w`<8vdkxXjC*e&$ldETh3b`EZxX9
znxpZyT!f%-TKef0BYzMM!KfSsg{NS1{2Z$@vMh6%G%>_xQe7>4$4Sy$BdTKciB=~n
z9Oq+B#|zMtH0=4gGkg9~50bj-&1t9L@Abs(hOA;>(`?=(juvElC=Qg{99rAtztTj&
zG(FEa#xUC%xSu_Q>V#@=7QN!jDrQbG(3clmSf8HklMoUorlyaso$h`l{j3~*7ooTx
zpo~ux?8vN1CQUFoKResmIXO8vxVXT<&q&ZyTLfm>43|Sdb35cU;5s2Cne;Wy=Jzzo
zN}Z3sucI|oBX0_N(c_Tu$S3Z)PohkuE;2P|5O;F?JS#+Xz#aJ&>Yw+eifB>8kKdUA
zavqB7eaIjgUWc3a)pABo{2b9y*87dy(M2*xsGFVEl71q_u2~V&q%5;imqI80By+eQ
zK5${3p&}0!;2QFii|efaR$G&}<HOw0z`!@6Id4FMTc3>&IpkMRiGiUdrK6n10X5X_
zfT|?oet<yCAF}FV##pJqQVQkS1rCT!W(Ecu8v+7QrI7SCR*aGx6CbFl_5I|!GedJK
zpl$5rg3opEm<^oy;cfvwtsLx|I)v_WF0hm}Ff_`5)PXmVCC&lKcPGH_4HG;}U0{3H
zY;KDLQD(@UYf6{qA%VmBXeZ>k{Xf}j$NLijlk?;8BI#Dl64P-7gA9@zx6#QWpt_@?
zj=D0v0#mGkf^Fzb$(tE1O&8*LU8V(V#>KQg|CtYHD%mE`i2FbgRJQ~clQ{w!uptr>
z!!$+a2g(99c*2Iu&TBe}YRE(sa;kfm40M4W_!0CHETz@chO9`S-#IN?ZL|#G*2FJ{
zzxEq#zBsa!o2_0K|Gsig*_MtdMZ@1fv2dH1(km{%(2-KYxHJnZlH1V~u97oR4-7+>
zl3ClC$l|TSgnn=^=`2A@r2}Q!Pn1bbOq;$jn1g<g@Tye8U%S1ZpQ>Ct&s{;qUiQ*U
z1bI^7bTb~7|7KB3Ltq35x*c9rnbyS*uQmv7fqpZk!FnrKdAi~Eas6zISiZA<CqYwl
zoi|$QO?u<2pg2A;B$MsP*@-qDwUZaY5M3`Jb#a66Z43+$U!zgg1S~Dvc!>F<)&*s_
z_H^70y&=QO=?(w>diLhk)=s6EwDm8A6w}j-wvzLgj+l8M{b4efywaO-?0|4x`GX)x
zgoiE;lhYRBdpZAK!lQ2R2Uz<JV94?XRmm=Mx!XwgRldQWx{W$umX!6AcLq#aR8miF
z8Z3HaVPuv7<?aF^1dtyZVW_qKI8a4Zv_GK-tuV3hriWlvSz%eoFWIic>8$YNtMI$p
zc>mEkK&_;L*D+%rA6wA{=`o`xB&kAVE_tE!TB#G2Kj?Qj5YG<9k_dp~YyD@&aC-p@
zGt$_n8VZJe_TpXr0fJbkrDgOp85x-yo90%RSIXX<=0mIjGR{OqIUVwTg6XTg&(ao@
zgTc@qt^d+u;XAyLApTCZq2#5xBJvP}V5XPVw8sSg*&V>+)mSYN!@(jBb;v7k`YCPr
ztvnvH#?>Yad3-#js);JKO(5Q#EmPhlzRr@Un)~ol<8KK8E5y%{kQwYLKlIsJM|fF@
zo%$UJy|4j*V!T^cbru3PvAhIv^i-<_X3(N!0Lok72fj7g8NrC9A&x${dbxi?8X`{)
zWn;P$u}}*R)C}n}4ywK0*Xlf8uC@Nj<=!QP+&U?0OohqtH({iiv+$O=lJEF%19ZRk
z&kgN%w@5Nn^-_rm_Ka*Qs8Fw?WeFm2mE6Z+nB!Xkh0ayQO<$QkqJgT9r|6KC^==PC
z$bXM8-u$US*Zha69^?5xI-6puzFOj?v7>Rm%Q<3MJJaT$<Es2?I{_JC7Hxl_$7qWQ
z&&^S=IbN57PlKTlhp&rMk(HX0VKHfq)kglo<%1bGT*9=V3V!9)!7pVPSC#W3tSk0A
zM)vgvsyjUSJx=Lm`hIRSnLKVlhQ<&JH`*+|g#-qJNke{fke1fj96=Iog7mL8vKKET
zCXA4L<||QYDH%<Qf)ynF;+Lj6|3fpC@Wi@`3X*%XWAkonh<e5je+(TX_Fw+ezBjX=
z*Gu4t0OLOE&dF4o9s~N&+z=FHv1dtaI3Q>S9LptXgh)wA8K5g;C^sXI+UmC*-fWmL
zdFHv{2kwF$8JnKsCB&slBv5b|L(rZPQ)!TrMyU|ojZ_r0va|5@{T(HoyQ9%lL=SQI
z8b8?$`uuph*^WjWezg11X#P$;=jgDzQ{2Z(!obwVstkS+RasvG&>nZeKMcT-HnEHV
zliZGl<J?@$$GRNV)C_CO%Grk**~_>v8|FWrsFU3*_&l)OoGf{Qn9?n`qG?#A!XA=M
z#+n)T9auL#ASp~Zw<+0#o;Hi~ZpiU9{6_bkHP3$VH8YM5>;C#2K8Emja6*+*yJGBV
zE|PrtM^N_dhP824X8e{ek=jAdI<*Z9=qThAVC%z?@2kx~lcJEpeJ<pw=JkPft@1C@
zYx3DY;W`!hURYg8PBF2lrmlra29u<==!#37;>Id)1AW!d*jfwz^H}03jEA@z&BKYY
z!t@9&E>754Ur$slHz+TG72>O(^GcUc4-RrPA)z>j4UV0l*E`j5|LwRvW^U#HU@%cW
z1?Rd{LdvSzWkweYxqhv+6yRgk64J>@iR$!%gaWUC7+YF#(kgeTQ5?#TH*y;jI#5vX
zm5fS=p}Mr8j#`**U_GH_k1akpHffHmjbC3@9a87N211R}0<_@_UVOOD7aJO2>2zHF
zIF<jgOC^_bcOUR+E^VuynL`AKi-M^wnFd5v1tqbZW%=hwy0{_fF=-EFg+mP14b7cM
zxovEZK662jg8J?O_uEf|I|K@ij$MRs=8u6&kPNDm=C@Nf*e@tuznK20ud~xI>ec?;
zxRD)+>Oypy7NF39!t!ELcyUEt?hhF`pHb^HKG-4i-mhOF9<JARE#Lr|a(u1U7wN$g
z(*v@7MAL1%Lh4>gCdHN(Sr~AES0r#*vm>xTlW_egHR<6u*tOTujMow##^w3Xli5l?
zsBD7<%WVj>u(ZEFl-4Hg9C#>L=%CHGB7WJ~St&f+rdyM)lMcu=IPYHP;*R47Ep=r8
z9uO)9Pp$qOm;cb|yU2hrRK8?683Y)l7&0p?(7%V@`4BnY-E{fkwZy%U=kG6Hn>KTE
z>9&mc6-+`)<j-^A%(LD7_`pOwTgvHPiTb!W8PO|y_9j=<sBZxQ9noENxQ9?Of+Kv=
z1107-8vf2MCh#imIwvD!cl<qnYw?hN#}$&}Hn+aC320;dPObxx5`Vr@RFW6}#EaA4
ztlxc`S^sZDw_aCl|IrcCYFn?9j(-|CPu^jRtTw<G9!nk|lgZ!K=JhfFZ6@R5gl|0-
z;k2OPw?cUk2nM=Axbh%SaFqw?$$o?SCyU|5)?n5U1vP3WS5C{@%k$}jLCYtG2E(UJ
zM0XBfliBE59k53#9m4qUXS~cl+tcygo+9+Iq>6>mql=(p!MGx{u0s3*l;}hFS>RmR
z3%24{Bgfo}zk=>Y2Hv*BDxL&?l#}4|v&5voim=5`7i7n`a-F@Y3?qY)EST;dC$}jm
zd#&d)mGmTC^~4~L<G4?xEvgylsOx_hc|D#Y6^@F4`3}r2EX*its)Em;$(+X%J4+x2
z#EM1LaEPr5c}pKbiy}!9@SR8Z?!JbI{5v^*`%Vc?R*>K5J|6!OdMVo<X#l*R_m|T=
z!Z2s^BV}%`S+d9}1P2704Wzz#IB6)=xig{xA*H9D%WUb;;^cYrG};yf5|ZRzUB_`A
zSVGRyEI{0Km1SzZ!*bijk9|D2-;%Of^|M^rp!}-BD8V&aez4V);A-ZLc#}_7+xD%y
z6=;H(4T+oIxt6QnQ5A2Ok7XrD4D(7DdwHU{mBF$p$67(-78ETcCo<t|PiW&GCJb_v
zyvC+al2N0OZ}vfJ`gMq8r~@?veIZrJ0!m1N&p_Hk@zeSHZEwbJMWU6)S_=&;>zev_
zcG`~k)8Se;{4nsQjucL(fShpi-Z&Xwp%W_cU@QL@N0A+H1mX;REur#q`}LS)iABOo
z`)Shn<$*t(@R9lj$An>!GP4E<KBaNuNIQ~C?ntuLY0VEJpUVX@DAff;U1bfV3{+p~
z2Y`%_IfymH_+#{M^65`}Ti&r+DqxrCP*==8UySK<6SIo`fEzj{sc|)+3!)jz6K?@{
zn8|_SYAVxXOuS!9+A1yjKk9jM*c22*Rvyo`^$30krByXIPW{sw+Yxzv!t=?m+p5}^
z$iDv~Hjx7!Cu8NW1NK)9evMF;$a%*WIuD@qSA-rY#F@^V@AP`jo9wK_vXb~)Bo#Xe
z9ybbIM@Q+8v3-e_f|{Tho6`g1Z=j_s)jPw-+1X~Yf8jigdiJm7i^J_AmFF?MN^e(<
zk7m&Fphxgyg63kerqG)FBvZm3mrw35v*^xDv|hC&*mu2_deWgi@Q>eYF=s}t1L^>B
ze39>XVDf5=e%ouHmHz$1b)_8W@Y1fW`9n#yFesrO)yEVQ;LVFd=~iU&^V~S}#4Dd*
zs6gY(P@%oK#S~Z`y4IS?InK9^nDEMS0~aZ}I<2qm?vrmX9a^AeKV$r_1bT8ZIl{|)
z2<7+~cAuVunqs@{<7DR)JMpKNu3x}86UX>}nFb=^@QAL75w2zYBbXSqvR!j)%JqY}
zEu{Y?-B^KhGlKa;2@PL>f86HMa;|+(Kd{TA8$`ZMv0DQvM7M0loMOWMqQXuQueDh_
z>X>OX#5PtDex}M>cOylxTNs<zl}2je3mSA>&se6T#}W@csiv|6x_ixoz%q>>TS-T4
zfyHDK*6m4TLZF`fV(9Cu6KSk4owd4D=W)NVP;^q=L&OAaPN&EE251|EmS(UdRNYtw
zIyec3gjm}}m>Cz`7hqT^%Gk-1+`c?B{_&l=v%1UZ9isVDVX=KiuzmRRPc`}I@K92B
zAZ|a+lTuKyx>Du<ONqrUK#-xzfm7ROFX;D5P-*_OeS97%-Bhi=pq)iPvS(aIHW<ko
zx0%}}{7o7A%&R9!EkTr4N1HsAISSn%(o}KwCT6W~?%RqKXz>B!#qr4mxGLI;I+cbt
zes$&zYF09)oJ#k_%;X{*89C+oTH>^nzbFu+pC7G&JyjK;N2xLmVEFpOkr0xF#mFZ0
zqWdwYAgR*@98ch64@GT}TM`m59S1ck#J|DJoRAJ(!GFyAv(O1jl2K8V#3d!943>jT
zzsrEdQOTxX!5+&7lD<%@x2?O5f*tv+Fh|m0D|Hm1Bw!<Ny1R!1?ahQnD{AOlp{}5I
z@6$)RC=wf;T9*w7O6)WuF%ho4i^+(Vzto*Qp(%kv==dr!0^@c13P&)eX|WK_<R`Nr
zjoIZ+4j5s$dwW3MLX=BreFaTWmWUG`?Rf7{a@G^I0#3qTn7qBrCqo_vJ!td?qdT1!
z7qTqZFh6(vYFsc+y;S<ipAf7Aw@3=X|2+g$$nnz1G9b-cVt>J{YOMn{plp+Ea>y=6
zxTV$J&el(?^mk`?aVG2VzeC>^H@5H9ySN*}94uC%1MWq)C{cF#CmhYr_jkxh(Ghar
z#v!Z`4cFsakY^oZEKYtQ&IGKw6P_8D4NZxLL*LsX{;u3~ymI+GKYc&!O}2h5rm_`r
zXj&>7@&N{T!e9EI!-$ZbiY9180qJYY+hHrgd`ho>;1VWa68w6~8sih;rQb-0ZqPsK
z{f(%|Grw7{K2c#e-;u_qtj6m5J-urxe|X&Q9PirDxK*84(|$v^Y2cYxPr*!O!xm^2
zhQ{`ndg#?9oDWsFsL3Qdo)f}5%*vvD#NxVFumv)8*9AK|eBU8!=|St>r=bJx!i$2M
zFx;iN-d?H#8=O>KcAL?%Xn_X`a5SiLLD#dHF0R|tn|vXr1<C7_SUK(CA(p%vbWT0U
zvpdt!^JS4$HEmGvNK&4xTHsrzDiFL1$0ih>o;~no3Qc3C(Fs*n;_AKDC-{@GX0s7#
z7U(lqbFYF4+l~hLz&?@ObnHc|o<9bI2)wnwsfy2XYe)r$p6eiMNWn8cE85N%qvR|?
zQl*5Fw5s$1d22t4>z%LA!~f23z{uHodkYBY=ny1?Q_!)moyNadJCpOrq*9X0JNx8?
z%8t!Vo<LRyI=}zDL?XxWa;WERZES931%~?7(w5DxMhM!Z0wdmcLOLvYOa_g_xWOt^
z7dRUVlUM!?DeYn>NL(4MCHQ}fsB|~=q8mv4+7Do2u4P2R^6orGuj?p+s0O<myEwwW
zFIj~$Upw4S@2ZlsxPHVFmsVg0B_F3UT6BTB(M*mRL$kiCbHWd$d61wffHnXNKs5uO
zV`!@7gjBGc^T0>OzE(r|9q7*}{)&ZH81i)uBEw*knzlX9{c!NZhUe1%c&YqQJeGK^
z)Ajvi-<M$(golpa8`(x$Ak5WH`_5;-K8vik2(YIOYTGg|NohxXdE)?G{};^ArP$+*
zTOe5M7oGW-`NnmTv&LB`ufr+uv3m$=*^6ltK+`XQw$_p&+TLlY$hOuYbDrCh+{g*K
zM7k8d54YL0f@G687m7dnkYe!G{4nO+drx}WNKy1Ml~Tp^6rDEGA$Jmgaw7zU2*p;W
z*!J~wrV^NK4a>hRa8HL2BX4v&`}kUTW)g@eEJGn!{`<8*tzG;1(yI<s{@O-K?`Nc`
zMXT;EZ_I0pLK_aY8u*P7GAnNm4QC8q0O3g&`)u;I8bE-?Rqnr#{!9?N-T}+(eEId`
zX7~`*K3lj;={uzjyI2;kB|-%K{v~l?F)GuZ<Tp1bC&$FnpCRyx!BG|hiPE@lc|)QR
zPq$k5Sg<e~hQBDjINjX<&-I9_I2wcPD9t#SbB@u~+6}vN<sW+MT;Ug9{T~=AEmrCL
zFH*(Q|K(87M>xhW63et3a%iByBRj{2MIr2A_1?ff{tiI@ru~q&tRUy6=kbPwBpHDG
z`TSm964VUKoCSS;&dXA7^p_A3vY#1E2b*57VpP6NQXy?Z?Q9Q#oC_>VA@2N_7F7*R
ztS$2E7)z4Ho0cmSPc^Ox<E6jM4PBCmKuI{iqS)^}xzs)`b-VwShTA0xq9*YfKHQs(
zx8#q#`)2@GRx-%Kl-@T(L+VN0CLN35K(XQRV<$RiO!4?|{v~uP-Q*ZMGfow{cZ}xw
zJ~vIMPU?b9(v|^u&?EM`K0*He^s>8~VG+~}@^(8qtiPo#Z!=3F2%7Nce@5muUw$zr
z4}3c+GcS<*!M2I#i!4$fPk{VSol_)1Qa;Wb2BN`P1d)_X@%cL&tmr=xD0Y9XcU23A
zz`OW{jz9L+wvRElN~y#qg-`>XI*_m$Q_OJ9B!1bu-y9}^z7(hY;pW+S<peAo|7N@k
z4^oe()-Oy{ZM!8CTiE`tf{Ztq`({OyGdHV)EP~s>L}9OhlFesQ#8hJ_+Cof{+w0ZM
z>^Vai9Zx$W)r`K98>Rw0wjU{$WMOR2xa<x3FUjqUo*=^$nOgKad|4$3KFJAF(l#+g
zebcX165eRQO#vCc%0^-aE<7IXXRwO&@Dv3lb3tL1iHy&&MfKX>&m<W^*CmQSCjt^Y
z-385T1n^V4TM0{z_ezY`Cl^pWV}+$6#T=-`<63yfr6Qjk*$H;a5Ur?OTwLnvSWNkp
zjyG3`&m>A0)YDt&32~~k+z#;5RXPpF|L%X=TaGM(kgbofQ$*|ao@R#=Yq%^7OtL8Q
z-;9iqRtE!Y2vq$0>50g6%qS&(J+*{0CWuAP7n5B<wIEUS)7x3jjx6FimdvFHe>d=u
zj2HLb*%YmZFtq-n&|E1e;kF(aEe)%`HK6K-`JY9GxBEb;NC195X1wwb2mn7ZJ|!cE
z^5_Pz&Vt2%ecQ$>{Um$w*ohno)g6_2#DKBFg3%t^K&$XcFo4fDOz#610ncB*QlPav
zUiCX!I?dVW{PfS|vCmoSa`Cx5z(UM0qO&NY=H4X`n)%cqO-2w>6^JY0pgduvV_k_;
zSN^W@mvYml>R8o5UL0FJu+JDy0(qC$utyB^sp943leJzLb7{aRT2O(SwVR$4W-o8^
zg?2&-87QbHKBI+U&H{lv1~ca*?DM;f{KW9*v*2Q?B#W@P3VZU>d0`WO!?%m>T;;s&
z7FrPs_Qb1ypI<P;&Cpo}Nh3k@#i19VB{uY%Cq3VfI?<Sl@`ud!$9eDEVDTLTR1^S2
zl`-4zTgLh^+tq|5Y1C9pD*s+WN<>fS+Xo?-Wh;Nrg;`{}m#l{NBBhztA_wjHFYS8;
zSlph7Je6HUOQh#RRm|42Noh&+WBtF;kj$0a8;3|b%M5;xP^vw(=^u5+S4FS(r2`n%
zZ?8Co#^)&)#c#Oopc7^R_Q@KesSgk$Wv6R*IXJ88TDn;<g!TPFOpSwYGEXgr0k6!Y
z&J+@!98}IzS6Iu<V}2x3b0hm<mWv%8ttk)Q0RFavNPtr%>|u2!qygdKd3kf4WrN+U
zen<_~o^CUafK(p$1IJW)4<{L$<O0=C0w3WU+{jO3#ckGSuu$3KLS^|PyfdJ|aLQDX
z%*w(29s%X*n$pSM%p7UecVMPxLtQLTrYt8%Nqc>S_^12H%I}~5yuG$&m3Xg@vIT&j
zAP`p;SK9UzO#d^gmdxak0DXb6+vCf7If#nyGm-p!9TzX=tAH<zf-@~L?|O#>7yDo4
zRs-jp#`9s9vk7Kl1RZ>%Gw^^sUHbEM)$8uWg#0*%^fE`d-^pCn;C13{kCjD0TwQjf
zmUQfWIM*eIW9;U8evly1#2x9ml+0WNFWAC96%=2Iap`z$60ZQqjLy$XtS=-O=wfp^
ztcV!XW&V^jV8zHcUQ5~NRz(UjrTELrZ^Hby!pQ5LPA5H}fUWv(i9k?TpBpX&arG-`
zial@LwnWnEXsNDa6daU-<}@JMWLSz$^_A?;ZXXLHwPYr`8?oX3Z^G@<pH=0Iy^y_g
z($6?-gw}`9%I(kPZaHn&cO0zSWM73k9lah;pcMv1c-ZGP(Dw0v;c`BJ-y(yOOmBWP
z4yHNQ3@$A}LP|<&Dgg%Hn1Z|;5<`hE!EGScOr6-j?-cYy_zLM>xdkup&MPvNQ8SbM
zDPoG`xe#<|`+V5dByrpUK95H$Bc<IdFEe;Jc$S8>b-XyQjf{k1_u9JNdA@G&w^WcV
zMzZ05%T5{ph1De|;-bAh@it(HqlzqRM@cxDANsUNiZM-=Ukb>HogduU33_}?jT=~D
z1uH345wEWN)ks4}u{6h`3&7j`QIpx56EYP6LS%x{b?e4&RZY<NR25cd2s<DtKsn1{
z)d7LH)0R9aLN&YSG#_1&fQ$C`pOjjdy#L}*M19U0bYNkRXJOMc*&LC_OD#`Vn=b!>
zv|yndJ}-~mhJX*`QKE%AGtp{mlLd2p38{q%?b=l-;`ly-HNO;gbG3lq=RnBRoaN!R
zI5FW~uGV*KQ`anCF_ZZl)h(ep6-?+##wnE#n8+YvLua{CJQd}3_0?Ftu1^H#{LKx>
z-L#=TC~N#)o~&P<C8KRS5=B<VyC*o<IoY77w}1-|WB^i*#vV+WCiL?@S$y<PVdo1K
zB6q^zg{h5EMYSaIVLTb^7n!20)+B|szItnZjSD`XmpM?ay7?u%L<J7DDiiEV)|DB1
zk*wNH(p3*xn=9F1k)E{EZ98RN2j{ODjCMJE)Qz{1e|TPNWnzhe4Cb3Q%9RZnF}w}&
z!QVhBUEq4u@<&T(G3KKCgYBC4$;wv7ChNz}&?wRPp@x|h2&{Zewu`4hO}tu<pZ;UD
zJ<wb)_je?d+Wj0{1#ZTV@0v-3Cw8D*6&_E_5t5=2{&-a_jsB4#ra0aM-!;OXGZ9rR
zM)AR5StL&Rz}Zxi7;iI-$C0%*=u?y|Uf}+rA2=NGIN<O{%SK)2t+y9fvQbxLgiqMB
zFexKT*h$k>1-4XJ-6E&#Pd1iTb<s|Z@_Ex|;TD%qGk&c66F{GAC;6f-VjvB-BsftG
z?Ms6|d$jrjz7WIx`}G`WjOlfDU!0&lBQg=<d;%{$B0+4^T>LsG(B*i6r?Jr;j6;pb
z9k{|^TToF=!d5r_=OtPEo6`+kXkFhR8<?YQgmDctvQw}mrOi$~vMHmb8w(-{FAS!g
zJ?l>)w!H~Z9IZAzLC@O!*ZKU^`a$rR-Qn{&-aX6wGB_`d%1p9R=VIro$oTX#!1hLs
z+B9N888a0n49O|@FNBh;k~t>=rESah7O-`6bhNj(Cr`hco{@$tT619JM>W!c(VPwP
z^Kj5RSeC9|cKi4|!uZ__SUgswYbFxykB)H(#{3a9zR}*oI6OSxJ~=bAK{oHUHdO0j
zskXmFZ9GhW=5WqmlTU)8j?pmrZn%y2w#9073S&)yEmJC@EhVLaO~`HLFVsKT3rOLy
zeM!?~zmH-{Uu@nz1Ht8pzk(9tG;5;#ZXrx370<P0mt0%uK(8*B6GGH<Mp}-@`ip<B
z6N3CmHE8ci4$E8crmr>mZ*qASW$0V6Id;kt_W%xIprq1mb&%6eCbypadLIY=+wZ0e
z0l)2+phFOWK)!Mt)}LPbjvd7dNFS>!WC26OQ0h|B&og}A-!`=!^T*J}haVn25(vE7
zC=)6_NTGSvE<;eny`LgmL|XP3+~Y!GBqX~mM63;KDdhapGpsf}>T-ak(7E$%71UM;
zI=uEK;@ATpSU;ptkvCd5fK=Y|u$!Tj)_%#Wio%4Hjb)dh(EYGltcMYxCtcdm$<oX^
zw=x7b5M{vjK^SC2^Pt?BNL*;TR-#Yqgu0Wf-tc@o>Ax)eehtFJ^{TS+42_E+-$6d{
zE>qXCUP`=HmD=xOWoswlk(``cR+T^q(trmyCJ6OEApgIXV8J<J#74hv_bg^(C=*RU
zA@8~XK;ukJ9Ripb3-WD@#Se&5AAH1&zlTMX7m<U?qhU7W#uXWKo!JF}ybhDeywm1O
z5JBx_eR%WZ{WO$k8l({Ndp^6sI!|VeDL_`Be(Nc>Ji#-VYs=&tkhFs`5Gj#{*{34x
zt2pj)7+x9##h?bN$KJAlP8D(G_K9>)n+KEFTBS>U07a<971X<DfnN9GU%Ys%hp~)5
zxA_JZ71qU6<<iINjo;9Evx2lslrZO=!|Beo+|Q=2?};S-7E-(=H(v36xgTO}me!)V
zk~J=YQ<@;K5&S=z&MGXfCCb9MJHaKmySo$I-QC@t#vOuNaCav-!QCB#dmy+6n{#Kr
z;i=!K?&{N3d+)XWmFokomS0j;hdkQvesZLDjr)4^4s?he@ACzFeO$~8ORAFuT9b0F
z4^Sb_JPScSjFV3W{0$+xOBhewRdShz1LUDC#RyT;5K&Ey>D_RV4LMXQbWo%3PAg6A
z4&Q(wxmkzz!%Apj#+A_f%^zwKNh5&(cQr3JmG1i1bX-Z17thlDdMLsYaG7Dot^1O=
zm*y~!Eetz9Pa_{UhpXgH3e<r4`T(RF(<oM6ay*dsVA!3%nk|llAmlbZ$=&_yFX7Mo
zx4l~WlZ3C@@nsEkZ&M&0?Nvb+%d2g>tNN>}YNf{|91Al3%w#(9kO<9m3yGIC(tLMM
z6$?neozp4VC|18f5Vv)!G%+Rnev6$^!0yC!7iwgb(>pWB-~aBuKYkJrRO$%=Vy(?(
zX%KpcNTV<6ENP+RF>$))hH4)1rAZy>I4=r8h#W!ZXj*GMumAgXcE_;FOlMIswwF4I
z>5q_X6b6qp^WX&<5*UY;;7ZQy!=OWm)~Wn{Cab?`+Uu=1Lp`skG=Ee}KEZ<ir4eb6
zj4@9Rxsd2@zcE@x&N%5Q<G~s;Y=r3&Ac8WIi9sn5(}^!7obtJl2d-7Fm?2}~uwQxg
z;-qG(82pQhVbd*D8n5N*a@sDh`z~I;=k<mhTn<i3wK3%jb=#?V$;q(I3&qE)-hdGH
zr{}1*<64sMrN8pSbmQS_)S4cn=dLz54;DV$*|CTGZ5b{Lw9t}3k&l`RT_}lRn+Hvp
zBw?9RM3M|B$rVp!GMSyCYppasjsd{MI-@S%(*JehDiZVi8}D2a(ev5575CJ(WS#pb
zE->2b1w22_x4OlW6SanA{>E1*2@p5WdlL)JrN1a4uPSl6z)M%v)=HIqGmWrK-YjxG
zgGrweD!c)Tijra&HHZOxGlqOn(CX63&bDBe*Ts~HGF{zF0x^5({^u8a2sZqYM_}}}
z=7V<8+7~OoI>AoXQ)980{qpeAXqb+_&(1P#X+GDjg~jRN{J646ZLt&=uON@~5G4+#
zELfrmGe0NCs$U#?Qp3mMe6$bPLBA7jcG;cG5dCg*`rYX<*);R7s00nfjaXI+7h=r)
zc-h_FRZmMjq$!?A^<YE3!OwOgx-u;=)|r(opE}RsL3Z}h1lX9KNwnkyDOK<+c0by^
zVZ|G6lM?W!kW&ntzX78j>MJCnz<>LknQ=V<{&)NHTLkHzoL?v-<FfwS9eKU`hGIH%
zZ?7b<;L08?n#{MEa1hGIxP$_P=;z1&S-+ifLmfLuYm1v}Lvv#j3pXd%((3YpGG$LR
z&66y=;K^8s&gA2LKEIFoPQvuL=KUk1L9gj@cI!F!y$4sC)i892&YmuK|DWpPS}Rjm
z>mq90oWiNakyWxHQ#6(o4HIp@FsdVswv{hrj93o57Kt*=4ImHqP~#(VBF=`#$OLh1
zPqW!a_9vN?M%4U3A3^Vn`(A~~<2DgN(@AA~g8%mAFf6uWq|ul2XB63XLc!Q`%jSOE
zng}<20G5oQY`bHN6IuLEv=ohbt`>LaCq(F1@9US9Rp?R^WDgB2`zOY#3$*m#OI-(V
z(UAOHb*EX}psqTb$IY4WtpNHJhAi&tGMZ3p#{L6E#Z*5!OtTJqPnYhDN{p{}nE328
zb=1}?a<Vd$#;BY=4%YYVvS<AGlg*exGnK)^O(fbc(o)Ds;tO60V|IUjdwk5#HRF^7
zU&eN2Bsdzi7L-EN^h~#yLyt;SJ_|#y@XwQ#ey73j1pRDmZSE@n4niAKi+twMBLGrh
z5lqt-l0<Qs{gWs7%1{gex2eoLX|LlWcErZU2HbrSBE}s{W+s=4_97Qgwc}0tBCJ=<
z`FV)^NgBW!vNf9PDk=v$Sxq$jlvH!m8(?_Qu9J8?kygu|wR*}K;vBvR-E*Q~_S0)I
zGu=?2BsboYh}xKzhUKN@czZ$d6jyq^<Jme|+3)b~ZLAz%3hHQ*;s3is)<?QFkZq>}
zfTn>NSr8;TU?(it<>C1mk@xc~=NS!^01_ycSXJmgl!Beyaq&qOVDR?=r!@5Q>&riH
zKi~E)UhN-b7t&N97jxrbK6FVzxu?V-&}LQ9)HBOhD|$#|h4x!yBvAo1JPFS-g&+j#
z9?Or20|0RN$C_q34mnDCjG@Lpv$?>@Wpz^rne#|e9^9V^PcT`V7wEi<85*VkSRd8P
z!b=I^xs8#f1FM$UVKxA$vjK7$L8nNGHa1Q*V{!@iUXsaLeA#DoG5`Gx7=b`DBg8Js
z+xk8qx121ILT_&_*1Ly_iD)6wp_S<&G&qPrh!@PV5z5_NKg2M4e_-CsE?Vr;;T(~I
zT7guw%i)SkQ&4wru93c@gP)m>r+)+g%|_Aw_RuDRKWX&uFuSww5=sX@RM$JO;ni?T
z!+e5v{8b%&#QkQAUUJ>^^s+%`c6S&Mxp5l9yqR=EtsSgFnqbo;k&-aj37*D`<<+Hi
zH3$&7o&R9INpQ-frcjlYp=|FBA?^q`{drr-qST4r3%veuN}dJ{moBt84`DW?(1RHr
zaXbfxHUeh!dN=#Mo^XAWzl(u$XcC;NJmjxgk@}n0zFfJTz+Dl|RSX75x(y+nN&9}6
zUNjb_UUyA35ATn{7y7E~&Wa6jVlnIbiE8uXG-W=k;SZ14?AVkLLS!rtMuX6OH#%LB
z8P`Cq)oveoC=};y{g4y{g6Qm3EbANXHdyCe>{G$AD<}Ge)1j4>8FHTgT3QbCRe3Y%
z*+o-f&cfXM>;WD=E`gw>o5r`S58lk=VF`kr*1ff90HEFD_xbT2iTV|W`O@e&Cp+~7
zWbIz?m+1*SL3C-KFNj@>c~6);!>JDr1!Rh`lB#C5O>Asz#&2Yl;U_1>P^I@Q8p=r~
zU(xb~o0}UM*hhB8)#WrrB_VCB7TA2)so?F_`c!^>to^hgIXwc|MIxO<YxOpoUFNLl
zFe)HF$V1H_nMeu}%wDG!Ex|GC1yG`UmN}xXbF$nkKUtgGo7y^>*jNCWtyiaq&}591
zoYO>%{}&YA%jAh&A_%i!ZM<hjBuJIi4C`Sq%2MH|xc%{Gt1Cay_x17O2e+5vmA+89
z0O#8LfQXz7NH)q2pFYDCEm3}=$#p^kUxwWWAwRXVG1N(2*TqwnqSCdHgqfN?l<WLZ
zMOb&g5eZ^mdfhvM@<<D(OcJ&|=sK~5@MZzhc@eDt;ibd)9W(FuTJy!<-iNK`$^TBQ
zoE~vg)w_u^T|IzsO*a{Xm<5-UlmfFkgGWGshbIPF{w7<GS>Bx$o)(`QniBg%Nm;_)
z)iTh=L&j0p-#e%rlcd@sX^0F8@$31i=$8^QHj$U>7<&6Zzg>=*lAhX}41f9Gysh`m
zPYWu9-}F|5Vo4eL)CeA{)Yif*+-xpVn`JpEXR~@C$4u7INK4fVh!erMG%X}!l)x}D
zpnm%RnSJq5MEki5oXjThLx0ZBO>GL8B2yVf4U}<?d7Sl}(XkZmWq!x>ypMX;<a}3m
zeitjoWRK$DoT;IspO}_cFeh_v(8cm(9d4jAa8-oD;SbTmg}~ih0Vj2Y5GOA>NLICx
z!ax+iW|?15R+B8+s`0NYhB3(CfhyOSGUzw~y}duB{@6Z<%y_KNHTprcXDQFBT;A}{
zw=KAU5U)J$75yUCssWQ)u)Ap-y0!E)t&MYj9;(pTMuv3)h~zU<n@Uk#fq;~)2gA>0
zCMAKh#U<fDg)X9Cxlw0$Pp<y2pW}1ccU(&35l&lBoRXJoq^hE4nEE5>3jy&EU=RYu
zCx8X7;eB7ZPyzpRozsVPBQqoSeu$r9a3C@e@PC7Z2n`7l7ZV3sGMl+*@kVNLa<Pt#
zzt{^QWUyn&jH9&V4d359V408uy+6}SpmzizsRid7PQf9udcqi(&e6#F(@y8GhP=yJ
zFZfEMQ;6YlG=T{1LeUr|o{_?AVsB95*qZy0RcL$`8g4H-Wp^W9!Ht8>4xH+orWk@F
zB9KVV%BsTnI#&~s=U@d>pSKB&f-a}ToLj-WLKkjBY7Vub;QQeSYn(!ryGUGuJp9l^
zC;HYlH!$p@qb3bk`v4x&_}1^QdWyX0f?sLe=qBj_C;Qnc9&VOc!{hh|tqxjn#7`$#
z`X5_eM`LND!oNVTx9bP}U2QN44wsHhQ4c@CRFt)O<1I1R%7TAlk>>sUcE$#=I2xZX
zaRY$uXn085Y&{KZ6kW}Ox^gRn>!>QjVd!|H>IK<SsuBr7!F%`ybDjQI7JaH$y1&`2
zEJ+h;N-F+)pv<TsB0pp+4=5Q{E`piy*EH5JE%GWGgL@OwdmBrMZo81-uRW#Me)?|U
z!#%)H7~&7%3{DV-EYEs+7w9@f6vTBVG0xkeSzN{BQo}?D^@8q9;cNtnNNoR2Ipc^!
z<AeIoP}M<TNrcBX#pN^uLJ5RacZh{4LIR}?bv^Jsw?kQW0|nRx)`JkyU$cDvwi(+F
z1+5GCu1E_xoN>r1fLo_KpG}G}>uq;jj>YE_?+Ad*U$ZYeJX)8r{tKt`;T=~ACiW~F
z%1Ypeli$P<&Av|=Wfw(;*gv&wbSV>nYfWVOUJddx=E)@E2`R-d8X>PMV`<`Sb-fQV
zegLe+6mBsF+Qiw1nQ<{Xv8xySZg=a{x=&$EaBYi8UtRzrwR|ynh-1Pi`U$nop906f
zr?7Ulbt|1s`eUJjTd;-Q#Q}R`FAz+IH41pTbgsz*-PYAeosuQYOR2PKq@Z2aN?W2#
zLr|CZ`JdN(BV?2u{+0j*CqFy400rPz-&0pUF*3KYteXZiI8;<lTl2@tMb-pul)&J{
zW?v#)gg4zr1ooq2FR6+Q`bQntHvl79(#gs4)-Gwjd_R352tlg-ZXXFPSwLz?qZic9
z>CW`q^?4u{CttWb5FzZkd9>}G182lH3sjA8lT`G|Q2xRYV$C3b-L{;z_l-e3XnZ_a
zO5fDb!_G^}%|qpze<m}{T5uwZ@cf+`-2^$iQn7ANBGC&@-4ag&Zi1P&<6V<rM&8Hk
z2@WdE4DohH;Gd$Ibz}`d^%7lZ=sDv2@K(D6CT_Du=)jmrQ(YF^(bEO;*7r@#2c_V(
z6R>=;0KB^%Rxk7Y4vHoD97xPt+vEG1-yZuD?^?eCy#?=gEk8RBU{^htEyH;U##}(C
z?}$;JQD=Xxo#Ys4>s&Hb#W|*hk}AX5PdGGy-TwyT%cqThUid9E{o5W+-!2Iu`!i)<
z|Il=P1^j)%a~@lC^acs^#^~S#h}d8<@wv2%8TtL!lXx2wA%O9A(Q^Y%UKK-2I?8?8
zp$~FRHNwiE*ih;29GC+7ZZLdDD>EBMhpWTmBb+bTSi82vBK*V%;QiDzq^P#zig`Ov
z@#R@+-E14_aN7;W!^iJdb;!LRM_aLxB>qiKdPa6SvO&3Z94Ak6zk;km5i~N$ZtROe
z#l&^0bA+1nZSKf%<1}^EG*ducD$Ka-Mr#=4C4`I)bXhhsjDQRLWCbfjPPce~f$=dp
z<ydqN@eOXiDz!@F5I5siz|HutZ4^V^Mz@Cnog+%gmku4g%5jN(BKg*+lt40@e~KJX
zQ7;1jWS!~0R|q8citfpQptouhF@9>Zi{khSUf2Drd-e8Z83CGPCtvfqo12~9XUI>S
z06plWlF}6_$!Bg|XScQ!JtKS>H(V?v?fcOPEDv5~j7!`gaT|gzm1iEQOV)5liAoTp
zE5x^N355P+s$+#C%CIuOA>&X|RmvKJCmYhEYyH5M4$~umexp3-NSEQa9LGP?g<Cp&
zK1FnieJ@}>J6g?u@>H+4shXahoSc)Bnwgo1c6MP56O>51D8pE4(mO{E$&3*yHb7o%
zc7U03U`8gEM3kF`esh7jpEHm;>f-L)(AM1A*cbsk$-|=T<zcDuIsM!nR?{o*)XY19
zZ^w5(sN0zH*M5OKJZ5Xy4zEeL%<wIm`_E?8A;7VfkSGysVU0yd@iYS5q^=U%@69ud
z+j+%W%jlTl$te{@X_e`{9!JtMI2Pi?qfC{D7kNAv3d86IWPI4yq%cc!%a|y7omRu8
zehL3_G4(Uj^U-y-*7lZAcb6xGcsi$(yKQz6BZX_oN;(61^RW2S2=EE<l9v~>02K)@
z)zIoPio1tJq8VN1M(`-&rh^qfz1k@!GVnnEDS$0nlvtih)O@G{C`5TZo?fRHC#$IS
z;wHZr4S(RAI5`zi$ge6fDEV_ic?OTLD7#3cJRl^Xpx%<Ra&+EHN5}G@Bu2pVz_Bcu
zhw<|EnSkPc{P@8J0UxIbtp^`g5iyc)2Xj-Fd{#@SKj#0<P(~qE{%z(C6-3bK>P!^H
z!v{pE6_VNs9Jy15nV+R{KEz46@t1{WANz(&P+J^kEOSj~30KnzQxd9QvYMmHtpV|@
zu)&HeUX%(8G02OQEj0l$wP%Cn%sc+qe=kllo<Q|En~>g6Eg36Gm<5eBz{1%i+EFP9
z!xzaJYUOj{P>7`$hgKGrq>V5)*hOZX<tp&`p1+l5kf`Sfsb+wMkqiF{N=2|=8^CB%
zH5HpDC}*|b7CXy8gcD~jFRH6OiNgS0?$v*1`fIqNigr}x6mrZ&nk2EG%ZPX8N~Rxz
z0dlS4i;&Ptpl&6HboX<QKA>d~DsHH%47iDj?{UyrSJ~$BbuED;1wiNLZ@Bvgb~Mvb
zkCcHr*Nhpa56z{J2Wlnm7oiJuzqKxTUC;Jg0rK4E{h#4nKq6L`xuc(sesN-5!}Jgk
zrbLpsOHda$uMUMk7s#rEJG9oxqhf{*%G?eLK1sb1SlCE6RLM%K<@AZ}uRGRlB5@?6
zp`oCVS6_|bx*otWrvmc2+e0FFYbj$Mxf8uyWbk|}IFVDIM=1GckUwsB@6*FI#)Zn7
z3O<ic_r6BJe2d6N*zXpZW%=oOOZLwf^WxX8jLB@w;agRDodv;D&f3Or)h{Owpu!RL
z^@AjC_voogy@p0VO&i(7ltMCyy{5N4Rnh6}HC_wkoI$s<|3u-(`<%(5fcxY@&_RP2
zOlw{X`y`L!@q@e=b^y=pA6|NrXt5TupqZTQYHcYv6ugk)&vD_~F;+tHMfww_tC~rr
zYcCdMGF3$s)8RDtpu)^VgY0}z5-!%^0Ro|u1s*z2k&On13$2b1rG^J1Q2^VG)mQ&s
zFaNF05NCc)VijHwm@$?iQ7tf!EHITMJe{uRh!I%7HT+7M?;Go8lxhtUx{VKWFZ`q2
z?iy6Sc$2JH`_I4?bI}gRp0BmFoRxc1#|U9Pz4^a47M<$mN{{O|^aV7n+j+NXfmZ!r
z>$tq|oZ74QZxyq=`G)e_m}@`57wJE!vw95^JoEO0$8w<%qg9n5o?JYSif_SM<4g2~
zB@Qpd8<HYO5my+=stXIYFq(DX^ES}SrNF_b(l6xrn8_PX4dp7$sicQP+54ZnG2ij4
z?sk-feS?iUO=UYdh@7n2(fDW|Cn+_y--|r-TXgVD-+}{B2K;+D4lYHe%^TEkfojbu
zlH=#j^L4j$)AFf_ow4qutwAKa8A6;LpRTsQOwpcie4K05^Md^<?Dul~K1!|i{XeH%
z9yW}=Mo~`gjO{Km3N@Vaqm%<Gm9tt*qONb}7kZetaHvGkFSIrWTu99sgGOUb3nrdH
zWw!JtRg8&$ni3mL6M%U8mOaIE;4LeUWKBPz;sjZq!JbfmfW4Yu^Fk2x`5yl+cdizM
z5~&6U4H0Dzp1pt+;T#mt^)FV3*ntTLH(zgSrORX|>qn&4ENu+fPh^xih4IV|>&YyK
zEsreG;`1_415>5|!f%rO&2{hlk=$3*ufhR0XBS<tIH)y7bINE}uwFm9ahWI4wRjST
ziW}?bI^Ihr694?&gebr(DXaZnBcPFnbyO<4>Iy@b%E2f=&QAY3yGqVg8OzEkJX??!
z-B|%CSjI`s!N<hY*5AAVHXxeiJ%E;Ty`pAfWm_Q<#I)__=iqcDUtq>|QVH~soaJa_
zXe-X~bvC+Q{NoYhFDOJ?DuLd|G^}Qu7R9SJ(uw}<7(vIkBTj~m&8=sv;h`%u%rfN)
z+Xy!aMYE@s6t7EEA%B7Uzr)q5UNe6U*9~*=E+gKg!aB+WaQrEuAZkhOoD)fhEZ~Oi
zR!x*a#C5k#xMtBU*9Dw(RITQiQMyy%VVjeGv^yh9zbw!GSk%|j$;n9i5*vKOF^bO6
zEC@l4!G>V_yL0uDFYtv}&4<`8R7Klg<M}LU2!~(I0HcVPjpjF>nO$4Zsroga0be;8
z<4_VQxBO^=n(XflASA{yFd#4|KYpg-pG{3HD0*?t2TFqSD0RAfKgN=I%jZ$3K_W6w
z$PXBCMmxw^rcprmftQ2Ph3wE@30sRB@2PYm1z(lNg&?@lg&x3(O-C(yUbPS^K!8!Z
z-o?sFLve~rCTCPU36d>LS~2GB2*vVTBD$S&0^_GDVyaFc{F;-|_?zG9)Uw|!VABMt
zV_lWehcHqVg#>KPJBq0Mm&Uy_w`QM-GZ58B!6%znO~_1;1&Xkg(gOS3QI@pl*!qGE
zoir|Eum7gZYO@YilG;|k;dirlF#0_ojg`aw=sH63p<|xdvVedt<c;vx;)!aANStN+
zuf`7w`%^S<6rE|Bq8`4l+#!nx4%#4RbyZCj3voRyGuBfEOeK7#;EKOzj}|?#$I~{I
z^-T2g@OLfqT3<}48<S$re5rywW~;L~K`))PB<@_!Pvaf6eg0GkOiiqAkMT&~nC_4m
zu{1b6kPD2G7s-`*3GkjVNV7#@i}2zkvBTjKqeE+mH$(SBZ0hH)u$(PIrFlWgk$~K&
zD)-mrTv}ndxKf@#;Qi^|VC<`=mu49>|7j(C;mumfRoc_nISR<;q$Di_mB>Cs=6c|4
zLk&XkusCE5gTkkZr<Sn;!-D?a*Hl(#@xxCy1~Z_xmBKQrQjdgZeM3Xe58J)(CnGmg
zAG!m$^fZ)%xLgt3SbgqdS0HG%-weob>Giz399;ZyoHoj{7|rc<8x40WiN<T-VNIQy
zmii?NvoA^16fSJIBuZC(XJ}!2*zHi&o5K8x9~xpiNSgPiiy+f)7@Nb>Nr>zE?WMVp
z`a3bo_Af8XlZBOisIPLI&^F13pIaEraKW%d+9Ik*NN^k;xQX7J78D@O*;#S6auFts
zEp4j0Jv?a_e?KQP$IRloXLS=!#QOGu<Gg8t)tb|DD%Bm;Z0uu47+J(VZ&eaocmAEV
zoU@JqJrtu(*N2S<f;PvUp2&BpQ#^g1<l<%ylet48R@pgM^=<4&4hrFx2oy+y`Tge<
zp?&vs@{J0TU~aVz>2MHkSKndNN-4kMmhZ#a%ObGy%`Ug|q4dj<O=&Ihj$h(xc#9$%
zd4KP3^X&!t0!1QdE?R&qI*_0!W5~aPF+3%$QQdd{@6y2vcGgix8Sko&Sx7}>c$2@d
z#ACgVNRJDx@=Axt$<nZfS=nS{Z%a|ycKvTJfa60(77V<<2KB%Dd1dOtYL_6WJDUg>
zl$TaXZ!85`=*V2YmnDC2`M-q>6p;%eS4PN2y*#`NiS9F(-9(L5U<5?kNnp?ShwuS>
z<=H%eZqUhb-ySeRS_lP3NK%28*sx86ePlS^y;R6o2|s%aqru08(TKt+5HD4xs}lsD
z5B8!to7n<D$ipKyv_D=cwA^cW+d;DlwFIlRhA7t%XelSe+~IQC-!x{Yw#lh}x!fF^
zts1?Zw<%p234?BTQ7DCLX~R|-Me4p0Kom8=QXkA4&Vd3cirTLHJjSQ;Whesq@_AIt
zpW>OV^?@;dY2??$lbNj_kngWr(~NT0Tw2Gk2roGxPF5m_1HJ1CQGHoW@U$i6C3@O1
zlml7QXMzfxWrj`Hf8YT!-^s_PIPX?!3>#a#ZbE!6fO}bEpeK?fDVHO9s@D@(NqYOq
zR4rh37jE~v)BOx*|J`}s9Adu&rMzANGsY-uF&}4+qZ~lhu`GEdRB&zF9;eR-E-!U<
zbkGCBc1V9b+I&TH-F<??_a0=TsT~P|H@CF-om|VJS91S0lMf-fV(oQsWybj+sl3TM
zfr~+~2Old#Vj`BgA{M||(@mD!A1;yf)p5LLn{kwzu|@&4K@|&Ein*U}<7CRwrY2v<
zAnOP2S@h|4QIjpr6}rvv1Cbo}CBL9^>*qeGW05+6*pv_17+TejU(b$S4zbyU#&@4a
ztW2X}SBhi_UU`wU9+MTFRMp6$y@!$vDxi|_y5~+&7F#JZswH=tFn~Ncmf)m*!^3rV
zeAX#n7(!4O!xrakDDlWNMEaLn0VmeakYP0o@Iy#?z>ZgfBnD}FCNILz<-p~E5+w;r
zxFVT5vZtA4M`@h|?xnz43P+V2Zk%mBPgReyyAl)?_IIK4XZR%drI0Vc`t9lRa{MjU
zF+_LGwiOn|B4q;hthjb#Topm~fomUBMkW-&7nea3OWuUgpWgG=2m<Gq*R~C(SK<w9
zbVPSHZx6v^eh=&Y-H&oq$44O8%L(+VieXDV931fbmlmA3@vKl;E?8CakjkY7pzZbm
zcrR&Cvvvf!JiaiJrH832GXrENfm=WRUo>DN;t=*tke8R0REUd;kBw|>ba^|Or*S?O
z$!C^^CVzdQx54S5EdG48R;R~xxT(~kr4k8y9_AN~aSt1(bC67^Jg9oE08)h09b|1{
zTT(AB*N{w3g+$$NWNpdR`#aPEqOR<A(mJaHY6P8md_d!Of`0e=-;-6POGADy`<<U2
zh-Y^P-He>WzX<jbBB6#FUi~1{?6D<|Bq>Ug$STnxw2GnWQ*CUGCoT!lFcN5@S;1$H
zH-bz@e)jeXZ5!ql6oW)JmrD@BlhRVOl+{fwfxfvzfScW`07<{s^pDl`d4LE2xnQX;
zZv<Xcx5N&Hl3tnD+09d-nP5Luo{Z)L%S;2QfHo_o8@Ks&A#LKRZtwHUL12l!U)Y!X
ze$nz8i;pG+KPAJCr?-RZ|Hjb*4Gs=QK&n!Eb#xWn?<Z$M&_6PBQfJI)kJfc*10UkJ
z00<uBN<vu_O@lWw@_|9I5J<>3f7VuXBA9Dz3{k=*XY(If$tDN;M1;sGy;Wz~*&A8x
z=Vt&QevC%C<ZY0}0LIhXH=;~aUQj-+zG$t<UbrDCG~ido9GKJG2)DW7)3tOY+5X#i
z3?)tcq2ezZAXZldS-P71Huv|L!4nu5qd<R7@AtQ)-17|2WgH49K3p1vKn1}>&YPpL
zM_4NHJT{DQJ)EI>eFud~t$6IoG5KgjRP!@+@r(EX37dD)Q^Yxd1#wJ(pOS2#zZ?{$
zIvoDO+tK0k@&X+mE?K!}$R&LqRL7Et*}yfpv@yQ`Lh}@s*q^5~y&}rP*gFtOFl)4w
zlGT@JK#lVr*9)qEqL$B^8HTMhGs=1jhzxpx(DjJ1%k-15)SUjK_g{j<B!II2_k8=Y
zy!gshuPt&0aXNPM-SV?<Bj|4ha`R)EH7aJGGej>KVMwy<#)t&!nW;ksE%O9BGB$Ap
zN?;>sJ?(`yDNymaQ+6Mav*xhxN=RnVtB{b;V2d5~wM>m|uB?*-5LeE&lFr83j=Cy}
z@6QVoWC9MRs%)wS;vGZQ>P(~(F3p}lxmKup;;aKMOp)NqguiLHIO>W?ppkr<^g~-=
z2=-_Z*ywx+LUQM*@^SUThbzRcT=6*X5A=b03c2$QH+%g3Pc|-jNr+GXGc?_C7HY3R
z(Z*e`?poMpABUsOp2mf1#A&H(E)!1u!h31E=0JvD)8q4nmZF4!`ss5S!i$^+{Rgbh
z_3rxi29l-wdrC(h+uJ11Tug5J{ML2+3~8O^8Gf>AT*udEvli~v)Z?;|aMDMVj!sai
zj;_&bPp|k~wIY*F$;(c!>(hT(V_QDYZx`h!ZOnulP4<T3F1BH9MB+MecYaFgfzP6d
zA-<ez(qc%Nqx-s$SNjbsB;~2;X-e{r4pu~h2}CdTMY`bI28QsIMC?*mbI$-H{C1=4
z=Zs`-^LcfQtLg#Sz#rVaXOlq(WKaZNb3*~<j(RfwnI#Op*8eeX)(C@qC?#MZ`(~B(
zup;j535}WIe@h+{C?&X4DGtewqSXX6NkmGJrN4ZMHz0SloH6p+eO?EGrG$Jx9#0Ah
ztRCz11iHxRx%!61q(sBsU#Sf-LkK4@9*EYyktt{kNDn}1`)@&1^fQBc3rxVvbQ5mn
z7ITp*Btz9i6iswA4BYfaLoT6-Y8BXl1EGn1K{a{bte8BKGLP5;U=u`5R-%<cUmi$N
z5np;E7Rf*qC`HwG6~|W=65C)ZaTc+;BJ9RYLAN3v--yVUhnAvx63WPh#Aj4>XvvGJ
zJ(U5VF~c68tNA<buOMP0{$9vnjik-8xZSv`d~akp4NBEt2?Sl>rdZ<LVZ*#&<X1sM
zMaRR8lqHCB*ZsI3t(EK07$~SwlXWD%?dxv*I1HB;hHJ~LR-SHyQ&mj^n{laOr+d-V
z)CMW#ZtzhMvauaj1X<n~<>6zj5;m}W)En7nLFg&rlFb~nXCMlBk4JAI@O}kY`h5Bu
z2CBX&%(8ghl|X=?5%62d;Ph+zJJ?)nXa=g&WtKtS2lPmC+E**{3h~56Z&njgmY9s7
z3`yBI+v{)my)NUY>#&p=GlmPg-%t!k=70TZm@*6H)FD#+==4w=?S6^v^vif;{B!Zg
zu-kesq=S*29s$&>YMK>2dIF^lP7amWL}=U@-JC7&f7>v0z(2M%K7-J6;a)-d>T4t8
za-5B_$O|Ryr^crJq8}~A>N4Sup<Q!=mLscw<2Un!y4lVDe7Ihne`&P#3&q&Dm*k&}
z8H3J?%Qu*+Gr(mLmrCIyLRVSAipOI8iXx6>4>!YDeFZ{G)?~Hv@w4!)z!Xo2BohTS
zzT!(wSH2uRTtH#~>Tw3$&8@Byr$0?c^qO_LJ(gPlKwXQ$(W#DZN**`Q8{t6iD*DUb
z?l&t=_8VID{mSv&q*6SZOmIuE3+%pUUDWXhv|7ToCGrU>O)V;oTFNnC^cqgh>Ou#B
zEjLNf2@119*Lo8ryYjje_P_mm^n>5Zvo=#Ni+Eo`zhLgC{Ch8!Ae66^F=8>Cn5VT+
zwdDP_G8KG3UTh?tcW0yq{mHU2`M<BH?*}ZUP!ox9M*VYm&P()K=#3br)BhCoy#8)`
zU60K?1l(oQxu!%==8ZguaPxLnuyVkqGX9bm2nTJei`t%^b|)vQs7o@bSw>Xk;TYFB
zSU(u!Ig67o*GB)h+#&KO-@ck`pOHgu4<{kd_s6Z*P&fm>^TVH%jFLA(>3#5D4o-|a
z+SkXF3nActfw#(Jm{^-8Ooh!Q5+Z8^70)2+YNbTGqI%Y&uMcr&VkZ+zodmpPWJP`=
zg6yFIh?ZZQ<5rW;V#LGwO5JvY$La?;^{fFgPmhzCk)MIe#P!f3H>-iYv93Vcb5WTo
z*$td@w1ytVkYqSC^s{$66y>3YM?3@3CJ{rFn7VFeB(fhJiMnuuds=GLj%qj|OJg!e
zG=@@b5=V_cW{h6OB~QS~M`+~tJS0Is(8uk+`S@5%%Se96VR<pMtjWgf!8m;Lfq4i;
zaq*Ovn6}n(8th#N1yqge7)+BTHv()2{msm;7nU_qn8F{kO<hBuFs1wEB!G6R&1&_r
z_6JwZR@3X;IO3|Kuz%n=Fy@Zcomc?A;;9P6#7+P92d0O|h7O$ID;>otvfA%K{XN_4
zW0>wirQtkO`!2Y;moj5?=)a0#YCXzpr#s7ICUVCYY2}E!`j%oI)BKo`gnZu)$LAKU
zX7h~rTYff^4e!K6Yy=3$QKE1(LGM|W&^wyjg1e&U$y@X%^%ooeRdlBo)XQl3GUA&_
za}z%Z`#N@m`77H)SyTKf0Z5Pl5%ae=Jv~knoK_xvLbCNEkYFOo3AAb)9PLppL<`_o
zxD1R9eMjO7q~#PRcp@R$zzdXVDV9^8V_UT%xltn<d6Yr<?!wWo8FDI%H)E1T(f)`>
z^!}>(6Kl8~Z=#?#l&0txXkUROE*=SYOgzN*fGD$hA*r9?vZ`V~p+r{^WWhByWvL@J
zIU?Lq<FGyh#4i`EHGZzGfBZnr>-~69`7A_^#Yfyv$`SmRZ1RQ*mqN;b@fR^sHajyW
zI9JKs7hYvKvg3!HtZRI6JqdUmd~rjzP;ndtt&1N!{;r8BQh#T;Y>3^uk7P+y;Mf`?
zo8R%k14k9U@$!k+W9y+LsPj~gbdK+L#lHpy6pH6#fJqzdPrSs@`hfeb>{xSz?@Ohv
zxpGQZRxY3g>0RMl&_8e{66pj(zRLP??+{?xRK4Mw0i=DKOg<XCq7d^36n_58?JvaP
z2Dqdvnn~Da=8~IZsv%L@(K-A>6vL8GNoP3G57=L@Q`9ZAyI@B_RvDuEf($JsJ|jw`
zzCwNfY-!ZG15}Yl$2PDyW?B;Okx}0A{ROu4MNV>tC(?nHtAFBJ=FR&&#Bve#vW2lM
zqh9WG`PtfeiE9EyJCrAu`Igm7tVpL(Vc;c7R1HKeA&U<32mVo1>A`u{nRm-53fVNE
z5K|szN5>!O>i~^2IoY3|>GTGk=M9$67`D4x>;mCep*W~$M<#}GIBo(`v9+W}>S6m<
z<aL(LtqV}22d7JKF=fdu&Zu$@c)ih!z(*TA;DqW4u&H}qQkI{c^##4-@z;GLD2Bpd
zUwu0H)=2zO&-3VH-_#YsU3d$8o`<kFn6|y2CsL@tgJ8Y__5SPeLovbeY8mXhO?1HU
zW$18lD&wMQh`@!sdnyIm{<kdaKi75~Y|?+88&U_BoMrj)WHVd3L&$grDv~I_Ads;-
z1{OBbu3J2~Vb^aoT@96fe9&vv8xr8Q2m0$7(KjiqC?}&!X?t}qmp-&mDV|`H$$Ah0
z<ki_e&{#<gkXE+P0;kk6dWey(!QSBhNQvT!C}<(GZ%l2#7^z1WaFbOlIG7x3+(&SD
zaNK9gPDXbVGG>9fIrGDhi>TukYAMEPhp>GE7-mUXH2rFgfD>jr{AWbb3tdFUF9fH<
zh8P_cBPT60$u@2C4dzEsTxVks!2N3HrYsNxbp+7aStpIKe;dPrf+!iz+3NO(3`x1Q
zJ9%{AbG|&DK8Mt**Y9=Tdo%1ilWhM%Hz}i1OuCs}t!kVNzxa|U*cz%OqHXW0DXQb(
zX5(9u(V7>st1d+AL5|ii>RTgkMCTv2OET@ICYx;+v`esm5F*i~^Q1mL<OVMjyp(PR
zQkMNwvpp@b|5dQv@BCluN9>6wqa9=XQ=>ALM3wQ5=%}kXL$G!ARLpJDLi%3tBc8*;
zuaK+q(t}VL7NjeV?4K1D#!w@MD_ou^C@2YNii!yZ%$818R_7VYYwg~H{gbF)H#>YT
zZh`E}?+|x%fOR&K7r!Vv4ar<^dXm)x^oAsAqX9{e^I6e;h~zq}mX7|%p}3xAimGI+
z;URve%D#3C2zHoDJ*4>LQ=cc#;?Q5<`}qMcsSx{{fc{RcI8@*-iT0Da%6_>EV)0Rv
zoVBjcc`~C^JqGjJ1%VcGzOb9CosyeLTx2Dia%QgtbYR;W47gR@uiBp!73slYAS`aw
zDZWpLV+H2PtW3f<zm}h^UhbYAPq#NWg%U8}=vLr)K`Y>uX$I#gsNMd2-hR6aSqHFQ
z^?xg7kla{R56MA3`&K5Vr9v+MVsBmK-;)fqMMnoHS-9xi3ffzWd@aS<|BRM1@QLdW
z0w#l!LB43v$ht^gWZ0+d7%e+B=#8fVW@e!-y{fy!`712kil7GDjX)3rNAnXHYjCMv
z^bmRO*<JbfuiFg`LfjA$MlBdT=Ry-k!?bWkBU31itJpu}>ki5lbN^xe-5JA7)8E3d
z%8va}-ex4?Y%L)nL4mHKA{{`8b&-vN9`3e6-1ENoFxL}|NR4{A+0tIg0@@yxRm+JE
zET67WQ6fbNl}BO45{La<`wP^T8=e0mM#_$Fr>u?qNciN=gQ5LaH^l9LX{f&_Oz@+(
zsp7K>*f4cvcfbRwawgqu46=wANsV8X`o5&7-SRj;RhRE25V=<;6zF{NFAWiR6B}*W
z&p*ZJr{W_gb_6d)35|9@Di}v4jBjn(2>*r``k1j-n5Zsq8^$u9hf<f8mRed`?muU@
z^<d4j94-bA*1}`l0fh8_f8@xr6%}x%W~8R3^q%yd)Sg~mQ&U+^^&X>;<R{%{1v4*!
zR`1;8vye}QPuqUOt#$N*f*U*Qgf?L&+f|k}zo&(_g?fGV)<E0S)!2cGMtu@iDE96n
z1NKy_N>$a>x!P)K=`!@2lUd%BaXCNzXG;Yq&k$Yi6SD8o(Dvlrn@8#`36l9u`qSB8
zb#Z3<+@Wf^R+G(6pMwF@OZEs?oro!)hl<_r_1_}Of$a{fE69jcX{AsbpCZQ_vln01
z(xEY3#V?kieL*bBaACjuSH52J&XZ9?nVmw?vSDEAY2nLbVT(F=QK%N<v04-TYo5I{
z5cKn2jle?0H?AUH5G@!iGgpTd=^$$Gq~fb`HC2|xC98A1L{DOMRoxnbKNv}<WB8ht
zg0^o`ThB04Ykm1cpVjT*!|qi}kv`UA5!1{&CY%>@s|FOz;b8j`hxH0}stJEXvgW0)
zd6qER$jblJkXm$>CCAm0%PZR5Tk7F4IY67XKA(quwyzKALh{#&rx)2?L)?Oo=7FvG
zD_nzQvaO;MfS9M(*8oR%2+%Imma@Xp;zm_sc8ZW(%U6>_pL36)%ldiS7ypO}B>3rZ
zfxbDsM1h8s0a>mEP)hJs7AAAoCiD@wc(4ODKvghD^*9E=g)0-*nUj#r^S#MJWaE_g
z9A$^E+fh?8xvg;u>}U0pk2!+H@!A`FJb~`9(hsnr6lwQ2qKq`f6EG1=#}k4y6~00f
zne**jy_I_1pU#GZOY=VP!3p0c$b<tELRzPdLDL??@P?K#-GXcD5_9P2VCo|$oyl>|
z{8lT3l)Fpr3JwkclJerBGLe``#mdUAY<-aMXF1B%cDL`%W+kQwC(qRAxa!yP29*Rz
zTzQLNgYUU%gk*J=$Jpt*s%i$DRTRS1<di~FINOxa@YWFKn%s^8o>$K!BaMH+v16X|
zKw@%A&v#XDZAh+n3OWK#8$(t5Iftht4Obm?+D%8LbY3&yjXD;XmQ1>Bt~PhwS!%YU
z8b_4j?RwUe@JJHBK9vOo&FK5qVCbtkC#Y!{sKDo7xW+BxEt>yus8fnhtL%P(P8%q0
z^vlp%A3#O<-Sc~;jX|Ws!IJH|kas;Z1au5Pk&jH?7#bH+MRoE+XK2}aS5-D477W5;
z7d{zHxeakT80XLYpW10o-ol@@-P#N=G1IfWGvL&xh=)=SnBOvvtV)L>f&+HaF8Z0i
zIQLm(#OH5wWO(;<Hq``eYUTYrL^v!xp$UvQI0;r@P(*22P20QdXEB9iV{_>|8v?<;
z<+_@t+e-u3Y{JP-d3>0Cqkm&@<-t7wnFXc5I;|paJIv=?lOcy43*ZPgs+jbD2<6pz
zN~<u8D5)^@4`IuV45da^PsX~@GSr5q!KWSD@ktymGj6+Fu7?}Qog)_Ic!Y(Ahe1I@
zLqk3L;MKXWtX+}9N{OP7y!Z9qffkxV^TxI^R5*N9r!#MpyK9!hPQkbd$dPsSOaf^C
z?B?%28BvA0e4(@Mj?YZZNv<lZ3zorYnGb*gPy1fQ6-g0ao1?sGjo?a{il_%(;^&)!
zGxNic-_tzso2j{kgtxc0wl&Ste9)l+uJ`S5sX12a(iaPBZH|nJm_|2aG0=8q(_36v
z6qu5foSJyacp}=oX98VLxBcv}R>o6prY8QC2@Ne16&)D^O`p=NtY@4YZumkGM536q
zoQy2k>x|swS$742ZWpvf>2W+AmErgA2oF+RAV9*#l->Aj#gmt3h&b@4mAJ;VAS9-&
zt}rW&DDpm;(-U-FMIWRt12W>;TwG8t^-gYJrG^{H>s(umohA~j1$0J$I-e^w?6YuB
zCGj=*Fg5yT5mXVtCcYote{GjYizkHKcdL$RP5}^+7t_j%x}uu8V)Cn;KfO0%epX-A
zebwc!CL3NQbY}sN8ypQ?cPX!e;~Y81Oj;Q4y<1>QHY5YwE}BfBUXz<F+8Y%haDwTv
zA`Ti8tS9gp4B1Z7xJH#3u~r?DlAGTOHOFyuoOb1u)>BX|0NK|xYq-tWOESpn)cEf(
zD%HpWT(h7cu1`oI|Ls=6TXvFODmv_!;j<>N8?2PmCEB1$c$!`HFD0WegXe{P(wnlU
zU85P(`fjJk<@}={E{=q0q$co2cJDvl9l}-_1;@dJJ%fNBWdA=!0ee5NScp2PaTo~;
z8`mH-VI>?Aj{!?4;iug|VQJcKdO1k5^3_w%*rM!&!jAZqNGIp8e0FIFF9=SSeb>K?
zBEOiLa&rH5u)a5j{9S5~5@E*Cb1N8?o}y#OS58NcYX|wt!UaoqxMG`)qhTgM$d>`4
z{+tGu26ITWrX(%Zm%k5ES069C_bcI?<YYNOz}1)^?EB_ycn<D{nwQh(Zd7L2(iFP!
zj*rbcZ_}QbD#h<sXJ@abR+g3U7`E_V&%gVA5Z45}TNSq8<Lx@y6;+aq@JEs1B&BOD
z%yBj~P=bCWOU!eXbpl7oo4}@cB{UHSA;BKDw0BUOz&ONn5`Xws<y4L!&R^fn&o@=v
zgP{`gw#MdoJTCh|Q#H33cF{iz+M$2FNlEv$(a~Ks5D`UFN2m*-UO6~O=~*{tp@aCA
zehtzW9el~s>T>@5N9yFM#=c?#$ph*^&&LK{g%lKs4KHTb{{9b9?&tB}Jsd*3jJIM!
zrRgMOOtj*A?3yWMUo{MRWMNoPKfWd#SOhq}+8cye#U~wkc}=C2J-C!$5jJo?q?uv2
zPIeP-PI}(jiuOjuH#ud3GHKvXE5*SqGj#yZ?BHx7#W5h1(}UknGz{KznPX_?Ue-(w
zatFo5AY0>OQ(`fSyXjfY1~uUFL8JyV4_$pxZF^Lr&0lG{+w5(<vy;)>FNh_DY1mJq
zCLt|~!I${#Yux$KnHQuSN@(^`$O_1T{&y2z5=pxUFeo6{L<00A*ND|m@0xK5<{APJ
zQZY8pCR|l(R2Y(m?k?+?W)e_5e-4d<NZy#HxKqFJx4-&zY=}FSLbTOUz(FtC77@vy
z1*awJPc{`m$ZBpa3Ul-!j;Tmj)z#VAn_TT5eLY4D5ZYH$<^hk#G&CPpUS;JNcKoWi
z?A!O&6;ad@1=lU@j7yW<E_s|_qupqyuy{C;qb=+gkW&~!itl6L;ZX4HByM#PBuP!!
z^N0{5KPB=u{$!{(a@T;Ze6X^psud9?k?1J>5yvH9r)|&5$~L#yuO5e22`&jxHa%Tk
z?ym12A5h_LfyOtur<<XQj)tpc=PP7XG*m?yOb&axLczMek&%%S&M=B~vM=h=9T#9?
z9{K}owok<EClQb&K+F;E+=2|clpvRrniT<D{>U5s_#Az~e<dW~Ft`l!_JDhSApFZR
z^5jV{nn!yZ5uDBLa}F7e9Z&lUjMPh)_8Gn0HS*#07DP`6$Xg7?76)|ITB<{3oEd5^
z+Y}=|v#9a>GKdVWDuP*44dfF__oO5jiJ8V3<!l1M!$p{?00cCV1GLfc5<WT#6smq5
zNe2Vk-$$kzW(SK3##BrkENmPtZ5_ekko}~hobEY9t{@@~mVymNMf4Pc{j&)c!4g>y
zFj7^d=8vWhwX@yDm43)#NxFDL>?0@d@k3J{#^-Sr3J5du&26RE5!={Qoa^y;!$?wE
z;FL=)Fd+P;K(49^2aQ;K2?@ArwXke9@kf-*bQVHy<6p4_edA8&43nTsY*v{aTf(qZ
zL>~du#Zczw3?V0~sQQ+`I1b_J3W-HR)f!R5AxKHeg&{)&<Z2;Iu1hwPbb!HQ1n8|T
zG6zTZrsF%O-z1j?-4-<Qah`l@0#9XxIuZltC6Yr;29IML2St>{h3Bz#umV|!6_t2j
z*uK($$<qY8tQR9RB^QyxaI%g}kn);N0TCl9t`k+|>c<j(dN#5s2hR5sRRrtaP{U<r
z*sndS=~@3qY`}?==ggA8J(Bp*uiw86>LR9Z$=4Fu%saS%I||v(p6`!L(pBcM)%Vf?
zNYZIlDMe%nkVG)95H~v`uMia*5|!4yhDBJU+woW2+UV1YvFnCJ5?6D-jxHcm3b0LS
zLoDEZ?CJMaL%aL#lLfIxtOcpiUC<X8pW^1|3z#?>@OJbN#i>h3{&A6FEoeT7P?2_2
zfp&1C-ib=}E)aJ0Xc!>|M}o)w{VGv{oscE){X*0kG~y_M<-wp2VY;AVb<@SnBxo$b
z9*lC%L3JxWbo3EwQW>rlN5ydC3Be`iBE%#s{7pxEbc0=>)%$H~xV9qg%b~eEim%`I
zg3~2*3(9an@e3p{QV>8wQ|=Y^tc!h-OH575%g(_CNPk%SCx7hiOv2WYs`?*FJ6a=<
zj!bDlF**nTdE4nT=f-3&Q2Hb2dnw(ih7fRJpM8DW`I0gwAdu&piA`uF4=kCs=YDn;
zV$gE)qj5@C67c_%EX@cC_*^c96Y^!-7;9LxG3m(HXgIeA<L9RE<Ih9)xDfWntta{N
zcbTEP$Zd2=|L4Wp<56?f-M)W~jOOv#*Q^|nUQBxwMnxcq2^~5d2R3FOBanojU65&N
zc6vo!>ml~J<3+s&l6`c>0Aw$2=Hb~8pDluOijQ4B%nG2)_4N4tU^6H4di-B+2w*i8
z=;J+C&I)*kr4`dd0_u<6svl0Rh0ihqlip`<c9uU*o<Dg%Knb7u2cPhd!lNLq*Ao!m
zhudp6^iu~o)QX@$kNtiH5QCTO7Tr$+!x=MJv<(&)gdAv~x3h*`T+X=B;JR1yud%d)
zHm7OBnAC@e9`NUN521NggNn%~@R5%^Aq@E;{~|VTpcDiaK}o`BaeImQ|ATH{$F-aA
z!vFD}|Na&Dzn{XkpKfoT#s9qo{om`I_>eK>A!B*kMp)Ad=wS<u7NnL!_*s5)u-i;=
zTB4YQfJCJjDa}=saYVZbw&DQ$2Q2XKq%rL@E+)PxkQ`wJ?39I$XN0Ml+`h7lHRA_C
zD=Z=G=IqiUnu}B8!hGZrruRg*=O<Hj9J?Ug1^7aeRz?ml@b9yOp!<;Z4is1`EmWsp
zeY*{_<IXgldEk&Yji77Q&t@b}8(ec+qidSo0MMV%sU26k?23;;{u*%`qV?f|GlmND
zeHaYiSgQnP8Punim86uElngB}CSKls-;$)#R9omUi7yYvYA5MRgmSHm{`wAat!bsk
zV=!6Re}ioLN9X0`K!)Dd^|SruHE-P*#R0~)aJCc+`-JGS-zLX4H@4Tes7WZ;xK7zA
zbvhMBBfq~7;=JaY-x{~LGpveu3gD;KLr0yY9a^9%bDHmx)RFQOQ}m5aLPEKR!p7KW
zzfxf<*z31h`+AtTR{RL35|k_q2P03eU(Da%JHkoSgrxE>0oiW;znGwDEpA}5C9J~O
zh#NLx4vprc$lgCOvbHfI2hIz}3?}MNy4E5q7Z(>B8wUpm%iOXmtUlweuy=%NQ5({~
z!Ksb;Ep8ezltTu2zG}o`@9=`mFG7!~Mt?!ce}ZfZlBtEcaRv$ti&1vLUAbfB<RCo3
z_;&9(K)JZatF(M<HiDybQ&o73{Hshsw0$I2YG(I{tOmOH)^H09&ZsA%{Sfq4`Exw@
zI*tQ-wIbPUeL}_U@i}AHOl5aErsO!VdUA8Qs#+Q<szbzri1%g`X8^J>$eWsqm!D~F
zSxm3aYz|CJi%b=v>Um8#u=P{a64}xgthl}i1hLtos>dwrjTQkOhF5`^v$~%Qj1OJG
zDA1|eLPMgZ>`g8JOqEwno^S^l^~?@BkCMH8O>OO|4-dF;1r*P+GD(o?GAkf5HkT|u
z#GR*!iJcl*-r3sLSJKc-^_n|jFY%@WljWY;8ALDCHO@^-^?f+mk5H4<`8~9<EGz&b
zO)2&q)Ghbj`EGOpN+g{nDm^4;9FUsid=zt4NzE^=IDdYA33u+6(NPFZPN6;ts!9XN
z4##2nZG5-aV{P-KfWPh*ahX~kUXR{GkKm3VD26aE&{}3=DZK~o3d4WZHD{;BH?(2D
zfA$B-pK)-aB>ifYvc^DjPeK$`%gYA!B&MewRG>HDg=H7E+KH%7M5(N^2BP%k^}i5f
z8>0d=pVzPEg~ox7W7yQRL$eJm^JM0sS+NGnaN0v@qNs}B_3IEw><j*&rLzZqlpT06
z5pH(Zn25>fdRsdy7yQcqAD+%BJg=^e)<KgrY1G(gtj4x&H%a59v5gh9v2CNV+1R$(
z*k;4MzJFhPpS-89=bYo2W85RUDVa62ML0dI9}m)nSIK=83PB?dR%yCTuzae%A{BsC
zP;_Pf+&!a_XxjhCi&rL%EV&_SaZNiS?YO%y6r?64r@S~rK|-1TLrxft!Fm9%cMSvT
z|Gp||17&}|ES30t=M_@Oi(wNxjLy!SkNgrAKV*6=_!)4L;-;J;Y0_qD54OWs%~v_-
z(*znxXh=aZ7tky?wff6s*>4UgHk*gL6&G;i6B+u<6d{B7G(9(Cjb{czv5{}$dIgI?
zcVUGwc8Z$rtd?dH5iEjF1_zpKMfAfgs_l1QTzS@muIAI&@(+{;UN}X)V%5Z*EAJ%v
z@W_GuXXD`P{0Oy$5sqN)(^ZL3)1=?Lw7s}RIKCH^=Gb@OcH&~vE(2JpbL~zj{rsCX
z9u=Q3)Y5=SM^g4}y1ltlM~yx`E|zHMOYG^@je}-J?F7D{oevokRXXjpQ4f4BL2&FZ
zn1#@?RD)>)OLc_^6icmI4k;yt)fon^ElIhVmpTSqL{Z8v?En$FYnr+(OkOUAvhrMW
z%d^9M(edsWC*4JEBQZdJw#fKDmqBA~O$RL_jX=`rBU9}TNam61`wenNe#HU11aFw)
zA7K%VE?xL}5Yv=YcfURINQ*-H<<VWNruPO4$bDP<%G6SjgjsMPw6`a0X?fTsK;WT9
zjThriQiU*NTYiTjPR+&oqSC1K;l6J1tr$;5^uvE`$;;ZQ0Pc}xDXme;*n)8GwtvKo
zip;m5q$ncDE&nV9ayZST&yY}WXe56(Yh0B@k*I^<U||1ZDvG$_fBLu%X{&fu^8n?5
z`bgB6Hj1^AQw?W%M?xJi+o(j*g{MsH>HC76gl=kN;RzPoZdglvm0t`yT>SAjryGg|
zgr<Mb4FxOofFU80NyC2JAx3e`@Gjr`m3KAKCn^-ge!|nUA)3kx*h}*$uixSE!TKV~
zt9=t73yqt0s&_+8Qd>o+dy0mu&}6$!J=|<Q^6OX#(ieT`&7Ivt19KZgGwa{Q74Y%s
z-o3DQuMH>~KY0}9YUc*G(U>Zzs=6jp?H#&v_PYF`0odxeGJ0Gag!tSV1WY3>BBCV}
z^(rOYM9`@)+BZQ)O-T<i$jWZ05Q_kTyT<9r`F`5)UJ_JL2{yhfe62js`VOH;RjIj4
zK4@)3RrDL0>3;V!=%<;$Qi4jJVRA!~ro~}xD5|AqB^_JrUqNW`_eU5U`%sL3%a&l$
z4-<?6oIhA~cK5<i@LWaKs%v?0<5b4SCMD&3tPZN4z02@|ymp7SddO)uC_)&GjgT04
z@_*H?>3btLRh{8zbiKbanJaI=WwHInHTw)_|M#U=9vigmtaQ(hGIH^Kw6kx^5QhnN
zLh1^EzZDtkFUhgmBZ`X&r#D^jl*tqQwbw8XWW46jAk@X@iy9<|Yr<iY;2?b@BQrJ_
zkAZTW$;!bl%gMEy_#AQ`Ii1=TmIPZ}JjG4Kz82*;kmEgsCMZ%_m1kw9Sc}51j%#@S
zMz}{`?YEZw*hU;>d|UVd$~m&4e3%zWOqXClEUB63)V%6dksXE!$%j?uock;2mcxVt
zHgTnGU0d~~geM26X!Fa>Ow8)5GCT4v4Zf!}$9luU5)YeWU?j#g>LPJpM1m^K;#(5=
zKC~zD7r_d{AA4h;`3+O8N`&>8g}?v4`%4*ok1LmTsicEzVe2u3Qg-4<o?n9Fi@K=v
z8^us+suK__bs365?l#Cu%#2S@&Ms>JVqsmOYP3EuPPtKXy&T|2rT2=ZdKox>^VM*q
zhri4$mp3T>(uq`tI}B#hh{4_hR|ywkqnN<lJcD;q=g|#hN;A^T0U8hbVwzIWM}VKM
zEuv6NLNml)jh*aQUkbTwp$u86@igQz9EYieBYdG55`6S@cQl_o@sGQfjL8i^Vo@RJ
zzafg?<F$guLD=Ys3Ay|fE=|kg{ZIIu1GZFUH+e=kLSr*^^uDvpfGbfpg<vP<T`g(f
zlw1T&A<Bjv6J~<U^EO&@ql<>k#^g0%IG?2_BNF4$i#4O0Hf-t_MK>4rXn4rY-2hgj
zg2uqg$V?AkpeGWh@>EbsLqrMyVeZ6W&{U{s$BV)hBFvL=bo20%Xi9aK0`(#e6PYP)
z+Hejx{Zht%XxKp7E4Irnf7&jBUb_>Z%TS{e*#AXcFaBao^L^8(3-c5=mAlkd*W<J9
zlE7DvffX5*%)XGIQQTIE{iw_aN7uWLGA<FZ!$E1MLUQq2xBakf)fgGTmIm_qy%H+M
z4&hq5j_&+WHzQ9z#!@j-f>`sA3mt8hsr3ob&jJY}KEl?0wJX0<Z9{*B@_N1Pcw7#J
z%1$(>hn%6RSEE{pFS{Y)UPb27r{SA6{O?u~vSOk1O4j$(X%bW8L<aJ^Aw*rdrg)Tv
zHapo$OuqMhwYMKp!0aXvRqBt*-!($M@2#aZ4Yfg!laqvo7sdO1aE6@;>!aOiB>V2q
z;Op?wlB3j>*2iYhJ5)Lpa!CThaqxDbgJg1%R|c%rqf`gXF+xITY04qk{p1pTsBz%t
zj_7p*w;i_w*XbJ|=fpMP?>g%!$AR#g-ii`<p-xG#HrW`ej|I4#_BQ=BZ^{kkRQud&
zYirqdg9<i9UX)04?aYJ?!5&SDuv;Nj?I5vG=*uX$tj<GJeN9lHxWJ&s%rLVwIXDN(
zki@0rJvBwNeF=e5&2Tv-R5U#kF_J@Srjm?y{wU@zWHdS`%ZdG2@`GviWV}!xn#ySz
z7M7sbGSDHc4Tw(8^b4}ke`HUim%C+?YGoMe?e`ZGGR7!fYxyrjq!qTfI7@bv9u*bU
zTHD5^f<I-8{i~xruQn~H@$3E-pYAJj>EZ_om`2j7LMsDnOGBFjgFq%)Oc-1b3U+xy
zA2QWX=synPt9HfihH|Ol6PB-~QrFYv4}PpWeg3Sh0aQlWyfbC`A|InYU(vqLzdoL4
z3_>2#I`_4&=@Fde{LL*c5yAQ%<3?@%`6w+JP!s!M4{=E#REWa$l|Cj*0(mIPW9lqN
znt@D~M#XT_^f0F*W>8g_<BpV0)-s83VY6p#ZVBR0DfhwrM5>n2t=UqFqicBQ+3_lL
zr1Ut22~alQM24Ei;LO6v`dof;Ra;!Zib&5*FZIYz%QFxncvLk19J?@Hr$ztD=HQ>(
zQ5l7`4Af$%1lZUyS$?o3`YsiOL4Zeq^EY@+jGS~<9U9q*oF_RUE;fIQd1gk+OQ0FZ
z)_-=m`X~*7%P)OhenybF&bKvR)6mGaBc&mX3DA(R@sP6fbd9eF$$yJcZ82_E=fdTN
z{$pTE;88R^IeK(lTmNUp-2v&mSM08+(O|vCS#ZHqN$yjpYx2S83QE%A^4#3Y$|8i?
zZIUqta82!9c2cs7jnSNEt_9ud2p_|xWfbLQLMWaEsUeyN@7OR3Q8A%S-NZe(aV^5C
zfgsyKC$<LmTe)(Ab)P^oQK&XBBuyW2hZrIBXJny!xR;TQhsIdUp+BV0fb)}$wm&3&
zCT}7B$!7xMZ9}U9*wz|rw<z|oouqA<T!_Wn$suM(vv%(Oz>6}GyUd8I=PEsjsm@`g
zRdhU}-L3?YR4E#1hi2A)TsAxs$H6k)02sU-9Syq%Bgk>vCGPV2dcrDb(e92t?}>|L
z=F#5~Lr{d1yAIQTW|Nt>M)|{YZ|SuwrBwg+UMonQhY9sZhDCt#Sw1H~)`Pf3z^nMp
zGT24g5Sq)uhA<qTqm&T;X=>3whPp2+aO=IgmUC0{*YPvX&VAPUz6Xc42zfm!&{E_N
zNnns9CHwOaoGi7_FkPXcMBekuv?`@Mjt+M%mmn{{z#7;aDW|Eff!tnMTA3Rl#dY3I
z&~{oS*T_%~!EEF`G4xWeh43ak0bwQS)*17A_cU(=UiE;!PsRUF37Q}kvoZVuuAb^K
z4iyz@PX&_SnT({Wytp#*CKrY^a5h}@n7#glHFWa$pm${)dF7=zaI^l<&z~zGf#x8*
zp!79}?3a}9s=c;P@zm`#O?cY=SO$v&xKieo_k9F3b<l07=gLZ;t%#zU5%K2|>3Q_%
z@w7G7V-R+S(^zP8jCZlIlCuOYUlLlv7p<3@9L-Gee(X9wVo1sL?M2!EzbB#}nx>QX
zgPO%KcAR8?z^0|8;ow8yX4M9iC*aKI97OzedW(2Jj$_z!|5n-VWl`lY^+CZzxBxvU
zlac-dJj=rrsc4XrkE0=lXt3YUwrSN9swjND@87t-eDdVom|8XDf`wG^Uvm)^7#tO0
zAD<TF@KO`#7poLk_|D2p4g4<N$Iy%@%jO1#kGZu75)QV!!`f`3NRRR<$?6%WhP7av
ztJ$1$X-|JBiimw;MZX@slhQ3M$(_i%T>65-n+sVLembdmfCJn1efJ`-f)hvlG3Z(!
z8dim2gj6)xP&phs{9Aw&L4+e>h<?$+!v#8p3c5t!l?Vw7oqx~NF0hiEkdT0i4f36E
zk&L~vFL&F*lncQEsZ@($^1e?f-H6d6FfI*q>WQD_=_Xoqj}FeJ17)R<`^i}2^8?#6
zD<WMI93Xl)$$4n_o2tt#b<fl$8qEazLFXA3f1c+=+P=knzs2Vb*O>G8)2{yY(e?S^
zjUaNkpr9%Epzlm&fN39=$I?S|<W2Y6y`xXKG9(@n_Av^-_(<0*G-PmB664kk45QGZ
ztZ*zr(JtGs9kp~5J<H}Uv^=E=yt%4}&8X|tX%-&*V121!QL7(u_WIhPKTm2)NeW?|
zli7aheF(tofhZ#UyLW5=5)=zdN5odtxa3QGRH)_+d}%O!fSB-KXt|lcYASiK?xa()
zV=|Ib<Mq{eN-f3stSu3+<bA>vK|E1Sz3+4*`zL(Lq-ZJRhx@C$yBm~`h=|uWj&6-j
z9GnZBY#hvW)wPWcoK*gljW5ezP{KD(<we<;2~Lp(eG8`-+42@0Fk-`*Q@DF_$p*H5
z&*zqxR+d!A0z{E~sYp|8Ts+*|@knMikkAOu|4i&VJAJO}=CZZ;5>>l4W%qRU&4gUa
z%j8|w{kAyR$Z>>SJKltIKTkXK*<EcxG9BcJZzv$WcttLa56<u*_gK(=lHC3Z&LmjL
zd~8Hn@g?1|b1s*fAAj~0B+S*{{s5?0=ZZ}PDRRsRffVKW>Zh*zyHUL2dGL~~xR9KR
zvaf$Y>=*1~GcS~AWo{^U1|-pjLK5t8?U%JzZgC<~fv3NQ77g94!>Rg)x3f%2P}}=k
z5z2%JIo8GfYwC0CF1JYXLNWNr9GiZBW2UMv!MN0qH0dP1`q;*>jOMOtfByH%KKK*&
z7{VA$Kt+9VhMldU+QHsc63wDnew?1Z0-|j1P!7?UJPrsJqbS(Ji29Ig=H_Svf?2Fi
z@X<M)6Y6s>8vIG9q;0hkhaqwb72~+wV@2}+3rt*^9y2W}05V#$q}F2cH%YUS_qIBn
zZ5|VIAW<uVaVE~F7jnSPqTKk0na#!V5gbs8hm}wYtFs%6Q5b8I(U+*@)^Zab>FUKO
zQTLO0$6TgcG9-(c>ZDTY9top<yuvzkwEZ<egv=OCWv0%_@#gwdSeWbxf)i`p&KJ=a
zenJK$w%z{6=GGL(Hy&d`MoJn{dGhqB)%1+RZpe-eqRI(WIbnKJga1*Q%xo200XuuS
z6_6ttFM(b3zkeMhCogxWUOV&_dkpptu8<&Gs3Gi>3%1GFLNH@@joB0;JpAnqrGEfJ
z7(%#+UeaqqNR+JH@Z8>WWn;ELE3lZfpe~C&mMUQ3t*$Ms;VAc8kecBKe%fk_J6zCU
zk=Wyu3?0EojzIE2xIRxPEl!~Ib~M5RkLM>ADy-{w<*?t_WVF=2TMC>kh8h(A!y-_1
zx~r+*J;I|~QOzr+BA^J=lJGK+&TP)FD9KCwpx(xLi}EJ?RD=~Dr>ZtT$IR5|XnzAA
z6`?pjAtfIc9g2t)u%B5NnpK4GfnW~FBoefM7J=s?)fj?pvs{XNpd7~e2J-V2X_l*m
z<;+VUNmtPqYlJT}h;~V}cpg=$H_MDM$e(W;P-z7-kVi`tLyx@3HO6fD-~LWS(qT^q
zU_7|iUYW}i=_{us{!E*f$Z<ISjFTEOEgl;z!w4xYp-CSU;F2%fIhIm<B#K!F{+vqw
z;*OXtt&?0O1aV+C0X!`p&%t5?l!XVo$qCV^OcpEU14S^Ouo<-*z57ck<m-y&BEHfM
zMSOCsTTn$Kp6Jd6@|ma956futoBaa+09v}1whsM6aZrxcl!JpGytuy|mJNqp=g1Kd
zAW|y$!~Zib;;6F9Qb|Pz8T}N61;&;K@N$FFxkP^hy9pM2fiwfdL#ES43*3}%tE7Ry
z?1WG&0J4damfGw%rfX^Z#kSX;ljO;40g#?_ewKa^eZJh>X5lICAuPd1;vLC`SfS_L
zi_Q{GySx;`aBVMwM41D7B9ZKy%B(n>eEL;@q#CY^Eb3U*@DC3+>|Nw|`6+=Ozc*B`
zG1{yXf(%_5U{%b<_SIUu-EcO^hS&9MdC}Eiv1nfQU^0WvR!zY>Cp<NX4YX-hJ-wL@
z@O&4+!W^QvUVkyzX!d?d;3-u|j)xri`^`~Ach}huKcS7R^&V21qTy!oT!*u>k!yNW
zLRLh2v4?_$ry_@zk#q{zy9;^Z)|BQ?$(OW_miGKQfNmuEL-R786-~$$@@Y&uQ+6B+
z(om(<GH|2+{uJ1V4bFx(I$AxRnq$dS^8OB~a@ISbM2u6Hr?Jtp&diF48sQWV^|tW(
zmNF{_exjnKDf=yRAUHBoy8DePxEK-|g`(zbJ;TVLLI#V~+cxG&)9DIu+E>DQ@Kv|X
z`)uGBfc`w}-(;{*aD9rSufy*FNgZ?l)>`LlPvyd8W`KY(Bvo&NwCF4brXzi|=YP-i
zw4L52{7f-Wgmcm}rRgS>L;Y>=7d1wStI}{e$&?&A`lewzNo9e#?tvT|_|DR>)54?n
zD_B%JrH^Iko6*5BIvO64@R#|=4>=;tiyEl-K-41xMv%lKrLHeiF=z4J(z>9w*X!To
zl17FyEIDHRBtqg{v%=!vHVC2^79M*L^-y1n^q;<PXv$;P<Wt8!s3N{14I=Uhde9qQ
zw%{50Rg@nJf1ovT1?A=~{KwFcP(_7F|2&EHxu5}Y1}hHRO{Ys7&)4I-^rmxNZZGFe
zV8MmqlUXFA46*}<2G*AIWam;0Vo-L;=)H*q`EdIWKWz;mDR*aG6-zS(EH%q7(m(&Z
z#44&Pp(hJ+R3|?pk@OLI2^o3%SGFIo$uzPmA*mt0x->EzuG{<{c*uRw=^DmTR|c*=
zG>0%bB{nrNo)@Y|-9&Ik8C4h_8y5{Q&e25(irIjtxRZ6u@aS@QSsmnLBv`MrFukd-
z`9+g^p0vWEW$+PXD~2ICH+(=VB=Ug^EU(Jrq1{D5Bwl+shzWz*LMyW4i*Wz?NMOtf
z0D^-0`ozR`#DYIuf`3kzWrDFO=Efr5r_+Do-!z(Rms>8Tu2|2KD|1#a);ob|-{Yxs
z1}hz`xny&3&y=!or>8}IYxiEMQ1~lk{Ix1<Nn~}H)IzB&iQ=efX5}sN!}}{|r`$R_
zBy})o?t&!m$N*_cSLXb3yMHt<Zn1@`RnoPNG|Y`uK+vW9OIeAmDxo1T-Azc#S>zi>
zOK+(}G%m00%g?mX-4O&w=0zZG4?9tLW{R`H6^g-=xO|p8zO3M`tACJSu+z))VI-Bz
ztFb&)G3x#!$|ocT?%D>MjP}5xZadQ)T8geat4-lv_p4hdHIB_{VW4yBN`9dZr3kH!
zfcnd0#_7q=goNGQg1)|=ABqBEV-v$E%4>G078JADt2~CU*(-EhPq_j6-QDGI&8p32
zm*;a3$xFQ6;dtKXua~~qC=`jVuta(rbEL_K7uQ`a%t*(KK**Z#BRmYT_%rr-<pi=T
zhXx5o6dno2CKBpr$b_#IIY!%KY053|-RBEW^&~Zs?H~h)qmX!V+MlJ-@fI;6L~)Xm
zX5`QTooX^=naS!tqXoFZJ6P0=A|dq;O>5@6oooxpi*sOLsjGFOJwB+~@VS0sjoR7l
zbay|QNzJ!e)xjHx3b|*~6wiP=ob!l1)ga7iKOG%F)VP0M{S{Tkd7XFnC73YeGE}r;
zX(p>cdaLgMw*`hGUm}v8j*fxm>z8HFC^MwIhnZ+RApg4}n+GsnSo`-Wxl*gnceW99
z@Dp%5?DrH}9p1r36A5~FzV8>-4I-be)Y|&$S;*uBW}R{leKTwdpvZxG==wn0I@l{n
z&p|=T!#A|p+rN<}a&UO`<4ENwC=i?|wr3*UN}lpVeTlCPr?sNJp3Q?c_+z-GFxfSv
zr5TAyL^Kd>bPQlcZGiYx7)<ozq8QCqi|H9mI?W0&XLQ!;a)PZunE4-5h@{FC;m2cn
zT+O^Y+T4PCT3x-%!<FpAcL##^tGhb5rb=Bd7n7ZUl{_v+Rtir~iOrOPTXJ^PU80{j
z5K;o<Vn?f|cGx@}D}QZ|`2E81QFyNfc->(oD9xg%0eS{v)(+R}h@;seg;bUnUzPQO
zr>9}nB87B;e8Y*hqWyl^<SO0SR*RD;ejB)G0zsF`*XxFKFs|hO@jGz;(*Ga-G>hBS
zezLJ}wyd^CF9L(>ZyzHEd0-9eym~Aa-U4yGsmCvJlSDSeu^PjqxJ*3kpuNuw@-FNk
zC=3Sh;Ff6`8mFS}T3nq43GEXD%dG#!EsH<0(xt>gk)SZ6)j>I;;G5Dv``78K(T28}
zGg@f3n5_qmuezRY)R|vw_}JJyKiFw;HL&oGjDqIZX-?YqX3yh*{a@i&jM}Z`rXD9z
z&{T};zSl`Rk04N--^cx7An~4mv^Y}k65vQI4nRkv3B<yCS@3VCSrT;M7nC2mZu&&1
zZ-t{4gCDGjqtqW`FF|gGsTo<8$>-y0d=rN`s*}W^SL;6AV|HC|`CB&mb99x}dNcS$
zdR`Cc#o2sL9k18(_)X^<zy|@~ZF{e5u_~Lt&FOV}c_;Y3B9&J_a9~z0HTsu4;9WzC
z8&TX&<APp?iSi$_Gv*M~&*eXvrruRz^Xkce{rXUt&>(-h_CkiXlh+dn?ail4IP61+
zN0=SQ{e_?O%M^Opo!rC6qZ|7#P&tf`1JC^?N%LdhS`9b-!Hg4;pfpQ@)4`;Evwd#+
z>$d;G{!{H7DwPlK=3)7@^o<5^zSLs%Ky<BgDrkQ?)_($a1U!L@@CET^yYJQQ^eeBV
z&Vd?*SNf3B>>5}~6Nd=-Npy!5V_1_1W$hg&pNaLg`B2($YEOplcezbUlfm%yd_{WG
z#d7<90S28W>y3Jgn|%hIM%(qE)TjSKR|LEcPp2MelX`*n=iT{Kz+_gZ_Z_$&aoTNm
zkX5||@-8xF^13+B_DEB}cO+Rdy(<nk0N`hYi0=h0NkTNVk8JO4j;XOr@ylc7kS^Q-
z1)nG3c<9r}j~omyUj<J`UGjRV9Dh_I%=DPCG2LS*Ej+4<<($C6#sL4!e^#!UZ4M_3
zXT0_n{N9h3pKAtzXyT@(3abny)B~II&#Br1rdvVK*o=A||3g}E+O7LsJ}#GmT}(S%
zZ_%5AKHl&5<7LU%jhxZ4H_UOnQ|!#U=}Swn%)A5*#pbl{-*b29A}t5*h<m_cK4P(+
zketMDvxPj}@O=B9v&*Yl?1^lCr>D8loxvyqe$U&b8sI-{3YSM(fz27%$=x25y9Rzn
zp_9V`zzvwPd$<q;U*^@$U=w3W@A+wE(h+8fe`6X!{W3N_xFnx;JD13&$6<_7<FHq9
zv5$?*=^5d`Epdy<%`b6NGXu^Z4&Tijt;J%h7~%hrqKAIGdT@1un&U(4{v`6$_eMmJ
z0tGY%%+0hGExevZ`#$fmdb!&^gyAOQlHLoKkey)LaeERH-S-YEcD_cvzc3mR48Dig
z=sYLSn?2!$x#(IK@km~s-<x^3cjiuAHAOh`@qRh~|DNl>L50jt=hypJs%T<?X1510
z8@E`s3Gv4x{?ra}tz<OA+M%`^*bHnEe3dwL8xiR6xjUVHUFEDK(BduTp_@|<7QOh_
zAH*v#HoBpu_ssw`xPE1tm70g5$yx4f2(df>X^$TBx7X4?G1CO0bQ0RPBb5FB=OyUd
zob4Z<4L%-|fK@$qY$c(k?&EbfAa>o>*7~MBy0kJ(M_pKN`}Xt-U=#U1oq_0c;*CzP
zL7s=nLG1fVOPqMZbBs6+$C;kU;IG|R_xoqhD7(3WvDrTF=M36#Pr=%Md~#q27)Qn?
z@2OVPS2hVc_P(>}4fsuRvvy`P1CtQ$?taPtB;vbFJ#W$ogQXLEQy*Wiz`Pxipr?EG
z;~Vmlo{>GQ%69+=rQ$uiS1e!bz5-V!;N|c6xK;4NBz`jbc|mx=^heegqh8*P(QY|O
z<qS87alRr+nhP^5jfiYeQ0yS}DQ6Vs!Od*ffrkDbp`GcGjpbfWkY-29F7V}J$ONLY
zGl7C<kr|mh3j+_i+}BhtyR!|ahmgt5Hs62S%d>-u*+8c!NS-O)4BdIC$l~YVoS|T=
zD0h8ZX*a*J^@4nVdOJJOWXuxuk$ZdIJ88P?&F0M#O%yu6?Y19Ovb~s(C#ogm?2ewh
zHwl(0vB0cNWC<Hou+Z(W*px;q((m-S*qX(j%;qoD1ug_upg9}?cwb=j>6I}Gczqe}
z^=hgFh;3~MvhladsYpkp?!0RZ)$HtGmHFKOgz(>{CNOw;C1fK53*!Xo#24oI4E^KU
zv_aN9%t_>)=TRJHYP@V;zOsWh<vEnt1&m!~KXV*LGIdL{P(7c{u7bR@mNK{O4$(^U
z?IE~_bNh2U$Kk3}R$H-p9j$ND<7<gv3(g<FA^l}%7Q1Sr!}scRxcAx3Hg?bR_I5A*
zq0?o{y>apDfUZaVs@K~HDu=6Z($i~%NM#(h{x|i_o@FX}(mIpV-ImdBZiL*x(J|8Y
z1J$H{yNRx%Bv15@CgaNq?)~nIOhU!&6|W{cg5@bS)-N;jLUDvN;CITGWrRjJlWYnQ
zTzw>uB{a&k3X6j}b1vlN@`b2sj;s6?Z|7c}4VQI-TDAj+P=`hay>>RFBNlucK(d0I
zw{c&2PL%as>!&O|fuPskL_DXyp_K2<%HBO4$O-X&I$o!;fB6=d-RBupj2WHX;5_?w
zgI%RdbpfLd0*P$(I@f&9x7i(8JG_DCl23zfBtq!eu-2SAt{DGL!%eKzG`Fp2bvJF>
zpOD0O6s0lwWz9Q>cGqHeiY9bgeFwW|cT3LKfRDk8R?hra{41(PlPT*_tL?I#b@1ck
z`RQ14W6`dxCMlh7`Hh<9y(5`CV}eNdb8u9NIqgT-MA=z_rJgFHvjw$ke-i=x4N|%M
zqSOo@{){tfX?qtk{HCtMxi)2p#0QcRQlXmyX(;L-Crgfrjc#S-&s&CF?szduOqGWJ
z?spc4v|S$d=fe*ahy`bT&+dE8s|4Tv?QXx_-B=>*w3E$-4e2$zQYCz?TqAMYs*?o!
z8~-@J-Cp&A;o=UD_oEXOJuV=th+&a!3Ti?WRr7FIXv%Bm`a?!&^@Sbc-5>g8N~Jau
zi?y+$q&r^Zj5ev->)_oWvgCzT&MN)S;Gs0?9tyFRgKw7?RI=bdXtT{*LSYotFPw{5
zea-m8-l4yB%zMA_k3x?rwZje;>?TEIaoKEamtf)=D!@7t5=>J20bHxvX@{aUVq%b(
zX~QrYX_=_UrgnKWgAo(TKgFj~CKVK=e-cFl-fv5(qm4u@*SlMp`2WoVTz_o-Urxiz
zb#J!fSHd5ef;C*=(Wk;5u?3Oyf(m!iBO>5@y>-QR|1q~Q^Eo=R-TP!K{6UW^3om4j
zqmg4;Jsuky2jIw|;<dh|>-G7l31nB5S^)X6`YYzBM#ySx|J`2AB9u~&Yu7vWPC!$w
z+Qm%m(O@>kEhRl>ab-6wI#((b+t#o0{(F6hz5w4nvgT$e!zU`t0nfCq9I}f=?kttx
z2DM#~ZD=x{yRQ-dx!#EdJr)9zQuFJ`^bq$vxMTrwMJ+t-G^Vv(G)u}TNYHmmsj*)y
z<}MmQ!aznBo`17q2IAyHxlQL2O<|TxK(|r&C_byt@V+Rv^cT;6Yr|wVe;QA@ox{re
zZyx%t;5gal6Df@o^KI`z7UVqvm*@3bp;Z&{TC3+4c$)ds)M#tR>v|3@Zx^lhd{Hiz
zK>Ctjb#7Sw<t1ciZD8S{yA)%yK)&RgKoHEIZYHYIQkx$Km{}@Jt-jw!TPrTh7cT^R
zLkOWchtxxEc$at=j`X}T9r@s6h`{)y<H`0@o;#yzn((a^!8>kdMrm%Om%Xm%_vMsU
z+(=ms<7${bs#B`!A!rwBh9VNW-v$Q60IHzT?cDM9Ej6*1F}t+&5-n|6DekgYaPh9@
z!gwl&gR_P0H{BF{YuoubaS-0q>2@;mFFyF*cC*>+Cac%Ha?RuEezG_j#YR`?94lk2
zELEV>w0A+zX4f9vd(ZFgXC+r$t-sCI`aZv*W83ljuKwD|Rm-uEvn6!XK}VPZy5Gie
z3}%1gK|^>>Wh$JHbX~m${e?F%Il_wL%B%I=xCX0X2zb3h*VOEO6C!0%oWF*ggWmV(
zgh@fcZSE+r9j#yx<sx&X!^BZKpb7U-?vj*hjuS6*X<nWH^b9+NIfU7WpS+~?aFHPG
z(|;MI44Q)xg}pszmXjI8(pCX1B~1sP{E09{Y9ZKv>=C8uGMQP<9``#@Y9{ZK|9T$S
zRa4Mz_T5|tjd>Y7ZEiXzGAXG%3%)m3`1UVFzv3kegZqm1A_+J>TyIA1(%l*fxjo)S
zDn_S*SF0_hjb%?JL=P$jz1M&3XfKQiaauu>FBnyj=}l_7)^rSCW*h-12@q*PNyto`
zVAa;YB<R6O-8R>QtsBlU1+z0%IO8{cO`-!XqL~cIhsjedo<g?i!GK=uC-7L3MYw?)
zuenG?>M0~9;`#mPFjVz`4PEdjH~z@Y{jqf9%kB9(F%!=hG?pB8A?BP*5Ge>Ir4u8F
z;Z0aIJj3$nX*cJ%V}PC?^jVx0MYUYK+_dc`YZ2lnZrROHZucFTI^Q(yHhdo08g{^j
zgj$oQ!?oYLYctI8cv$6Kguu4-^Sn;-B-$XT{@C1(S_$DMzn{!p%|$i2p#1Enx8`RG
z%xdG~>kSUn7?_$&z5KKJyT=gR4Tr}AaaDBhhv1RI7cVFcfzx@Nl=zo>Ku1!Nm2pt=
z$;>Yz#Dtr(lIROQtKdNIE`F5fZVS5U00#t~k<G>S7CKsDN~}JIXV31)nwGwfp0)ta
z@$n)GtdEk0jFwtJS^+Et6tj59dEV?N)*TTr!OgZOE?&}2?D6|b+&r>0v(9_E4fk$`
zRz{)#TVsA4_+D!}xaDLH7U;AA7nNr^uEbxiCSDIN-n?BwvvQL54gz{@SSjVik1y$7
zjE#BH?ry)he05I3emNzTulrt(HNfd{XDMgf8>dIIwFzMrH~Qz5d^!ua<xCP!*H=j`
zXeS3Iak&jWZ;mVACJgqUB1|gv_uMbJzkgI}>3cx|oL1@14*n}VaB?<Fsq5wxy9+a*
z&EYe&HiI`B>w-A`dw4FZn=O#n{DqTrX<k<~s7f$jKq6A*6GAu%iSEx7CcmuHb?7hP
z2F-sueye6GPW?NhAfY~~&<lqf0WayRbq0Hd?aQHZzdCv`=U1JkinGU>d`V<tzUoe!
zi>-J7yV6MQ{pBe%lkQ+4DHh-NWp2Qfe23rhWlsB4T%X&m9Ep;~jPYCDS=UjS9#2-0
ztp9KD!4PH`(Z#Pu)DOS5rDL<jPgIYN?U`|SQ}u?<(u}QQt^NV$>NyimF{{(}9me<l
z>;W0OTe9{0D*|1nN!@0fjN>Z0By#=N6g41?;766{#CIix=QKW|-{!IZ(5Igv1+r`v
z?m4iE7LaWPNd6{b5}z7||5Og7j#K{bYsB?L`qAlD%A}+q7mgjT3WCubpS55BH3%E(
zJ0hm>K~6zH%`7%M=Y&NHM~kb7%UM15I0IPB%wvD~H@6oQfc&1P&d>9!C?wI6-GM;S
za3Xi1@rkrtr_DP!mR(`gYw-o8PubyP5)9HOO}q7XJA%oP=vcn*1T^8=+A=~=G@?20
z&@zqusSfbkafAQFi?Jh9*~&cni`IiDL9EWfTfRAnH-6+91oFP&+5PzOubsE4QjZTV
zALdU}16#y+_+8`7t`gLL!W(f682LF!USv3qDRs_f2!Ts``T5P3TudY3ujDJ2K<%u!
zwjr&r@Mi}LgQl6e=|EgYtGFfTrV>z|OMiA?3(@~RxxmTM<Tw%lbAx{{kvh)=?QcT2
zV7t`nq`2ze3r?K{+#v6}Xa8#g`?tNH>QfPJriQJtz`<6vGUK{ElUN2U+8T5oulzH%
z(OG1$N`rrYpG$lOf6*0dHHRoknF4Ez!@82n){oJL(^D!BJ=?Sr_X5+n6g%c)`Ut6`
zN3u=l{O&KO{U>hvdyk5T4^2ypU8Q%CWlC>c&29%tNLF|!5=Fr+X{ElRIt%#pOne-m
zK&f}s)mAV{NzSZ+%>jUtR1P@V-v?n=`K!Og`p63@aDqr>VwLppkEmmwA{HPp(6RE!
zN1KDCEx&mR!F4$Js;bUH8I6Z0(d%?rj<+H$#R>>l^4B5}33|adJS8_x5-!zSyPioo
zPBmGtIhDV1u$5N>4uk}quFEgDpEPV?2Rn~@uGp}%sEh5#*#NZBTBFxYxj71trx%D1
z$aF!4HhZ6wH28e?x7G0*FL*uoXXLH9+rjN?Cz+S1=Ow!DV%HLgYG?(+{d!zp+xDwp
z(J-6n(>N#@yEnUql>MNLyd!tWy0&K>WoZW&ImfE1hC=TeW;IbUAnOHxdSWD|{)Uir
zL&l~0Yk)gK1$_CH=+0{VO(g<)cI#kInAY+$CVKMJTwEG=r2OG^V%Z{7qXX~U>ChQ+
zgY4-<dz;hN<wD|3v6_6q_V|_)XBMBw%i~qPBnq(rNz`VO%h}8TDEB$ecs*KV2Lp?s
zq^KP1rMfz{Fca083*-2bBBKy5qW#utzGO;(ZVs4vfHlc_t>$71zMM@oF!PuN$laN~
zAl{g+kZ#p;IDYJIU*)XO>-h0j4qtc_o29{1%LS)E^pB1p8<r!Wz;^b4;d_IF;p@<x
z5U&k90u*wODm9B_C2GoLd{>~Jw1&!0VoFrFs}LzVXul0ulU~qriMtIed@SY{k*cV$
zDZo?%*z5MT!GtGQ=>CT=0XxkaEA`qP|J63=+!F{m{ku0-2!EYl2a8lI^|+i~Zi^Ci
z?z3B;P<vAk6tnq!-FJGfd~_DUTB-(ze?{$7t*(5d)lofI_Bl?Y$GpEx)#UVe+F9s&
z`lbAv(ueZi^6zjzVi<v3<D@L*<wR9%TQ0yJLkSx!bSx}$A?{qdI&|y96;^t<{CZ0_
z5tCy3pkZlQU&+TQFg2{E=g2j#e1{pu2y0AsCNIC^Bzigr1KLMo)Ivi(e%krP5Ygkb
zE1<dK?EKSo9+5t?O&M1$!5l*A5HUcafP_L#Rw2u<OAnpbXe^>-TvoGUv$pQ+@(tks
z?=Zh!+idYKNG~`SqgTY@&D-bwI+PzjX<S_HtR~z||Lx>K@nTa%?&grv;U$S3)ElXi
z=YHtP7cP5`laZ3sW4txa*Kvfh#SwD%HA?#@92akJ##>g%{=*b5H(+rE@NcE!4@!2M
z`?v^gN6!^;_m#7jTl%qT!`VhH;M>1FkQz^=wil=p-VsMVB_(=wM~9$#WT2J(3)Mu~
zM>;S1SC|mv@f>+Bjo6Qu7jJLxLl5q_e>BDoe(6?SN{|_b)s&=lSTI?^d0D84Ox2E^
zGb;-l)61Cg*z|fWwzK#AyL*E_oM)frKW4PMKM(6{ucKUS2*F?97tRK|0yer0KPKW_
zoNM1!pBE>IH#?nnMvek`b#(R>v$d30tAC6~iJIF9p*;TwO1)SUsP%O<t&I*n?V9vD
zTdRdtFicIH{bm+15$edPj&(Y&a^s6)?56>>eQnuV#PQg}xbFDKvi{9?wdUwrGoOPq
z$<D%frilvWVjYBnjEJuRCtj{GF}D5N|LTesG&B=-DSE}4i!xAq*IqHBqCdzE4bc(m
zv6^bA^24KLMq<EJLqSF$we`8X=Ul>!L~X0|-F5kdi1d;Nz!Lm{Hz$uZ4WwG^@Q*4=
zs}0Cvz_35w{|t87&AT2xT3jE1h!YTX#DMR5Dj0Q^1Sks~rE38rtAC7dp44R*<9dr<
z7j`%SU+<gKtuw1L^Qg1+HqYsD@DN!iI~Y6byR)&AyaEn0e72WjMd21W7eJ;`YMH$n
zk`}&<k?h4`#)w5e*px*WnedalN$3Gz^aoptCL)a>Up|ZE0(<3#N5JU=Ty&NQ+h*bB
zkqkIK1|%D-fsHl~XgI>qeOr~qr!DAbSDV#(TlLYrq88TTIStJutla{Npl2Rvs5IbG
zsC4xjGivZR5zGB)KyB$2#Zm0{1s=zjR}0;1CfjeA?l?WOHGNd#B3vO`5K7{SzV$dI
z#En0W56&Pz!0iuo*{Of$EjGQ0TDJ*~%H&x^wmTm-27>P|)?NN`%^SQf1}8^fd=I1?
z+gShOYP~9A1knidu(g-JnmkFg+F9qKy;Bdr_s=3kA*<oXcw?Z=A2;juxmGjN(bKe<
zh>wbNHfmq-XG%Q#De_DVF7PwCX{ks*I6L=oiDj<a-6A??=Zdg^eD<#$ew3*#CdR9{
zx6e14cEE;eMRH|()GzMCX`}f2y-{q&A%#w#R|E*GOHXUPIwK3GoztUus;DwN^^D}q
zBpme5a!5#_*E7ZIi)!SttLUuvO9c_SetLK%vNtGrA6b-ibw#JB9f@RoL{heFAvi?S
za6?5j;@Ui7{33!%^7YleIYQY}KYIKLCXf1^!UmWx5BR`E$0W38uDLEs3pk$N4uj3x
z+rwoj@1EhR_FhfpdW)?N;N|$L+qr=I%YRd&?;XXkyT})$t3LMy%>CN%h8u?P%Z2l*
z`wW3DT1#AB1G_6SN&-$z?AtH6<*}WT>wVq4d=v=B4=XZpgp?P5)^V&a>>VidWI^Z+
zFqc=OnyzudH526vNca3x1g>~boCnVAa=ys+EFVl}3HUg^-@C+B6AR8YTAd6u&?T`=
zTBUH5m|2@@f4=PO6DLKAB9!Ws`sfIV8p;rlfz3wU{k>EqeS^y@QmVoM#J4^<df^f@
z9f){*Ce)5B@S!p}<@H5Dct4uyhMbfD2;Dqd2?hTvveFUR@atMu6*MEr?_9(z)pZAk
z)z9~O5X<Y$p8vLgIbm0=x4u92r$+;AzV>Hh-Df9BV;0}qjLs+tKAJIc+YzmExq*O^
zqydW^$-P3cr$10lo-!N1;X$gkTvZh=YnX)lb2GP=%ha@?F0(;%2tmNFHNpx&9!vrh
zfPT}>{)>-?yU7bo4%gudk_LPG9B~<Zxi4?IW$P1bCtCR)>yUvDe;4NOXTkfp<qn6l
z>wa*Yn$Ff?sU{81{Z({Ls8t63%Zd7bPx*;DiQ1Rwo4He?fnvfLM7gigh$X>u{9EuB
zD||vd`myKT$>74Va<GKDDiA#bO(jeqIrv$P{$=~748KUR$Q{EVuGZsj;k7tq){ta(
zYo}O|$Z-COGDl9j;Q4%(nA}G&00Zu>E-l&4FXrNS{7-aFa`(W$JCi?y!S2iHmT4f-
zeb5v`V_sboBU(x^0^#cJ>e|`O$-#B^N5phiR}@$Oh>hik^5ihbO~6n7*>O<yvQk(C
z!E@pJXAALpReYUqZp7`Ni>ByeH|S#9*1Yw~`W<FXLA*kf5!AqT&jakEX$KR(B^$Hr
z`<6ls07gzpUc|g1GExh~sh-&tMExyy>QblX@ueRGK~bn#kP@+9XIP@3Nwt+1<~Zw^
zIX_erg$d5epKm!z0F}xu#^zPuIto1APH|O_EsNN{>U7>rrIvAh@k56?m6eAq7JR!O
zWTl?wR3zljYja)LzWWo^NLG7)_b0mZ{onQdzd)U&$#k{YY&`j4O`4N5-lv$G4im9&
z-XCJhwmzNwJ>5UriJ&*{eXjF0r>RR!^k0u?+d?Q0QGejg`U0pDj8T+YZWAEB!vA0-
z;&Iv|iW2uu>Tqg&etuA+dBglVMut;Eg1;m2T=L(%Sii%5qQ3R`Ixl0c`O-iswi+B+
z&o2~ElgAnoST<}jp-aV|2u~t$v68w4-XaAfDsL82jc1P3?>q~kAcl0V|6qr-ok)PH
zsx8#F>>o>f1`Z8O40mQ4bRGyh?RQ8cKOY!GZrB%@Wa$b-*gPJdUO$0N?Afi|a>_4P
zR28avsqr`x*H{c5v4%=K)vkBZQc=iIUy0C`NknOw7=(Y8eV{il-?2!P`zYH_Uh~vv
z$b05H_)s*ZM;?C}23tQo63L5_)#`k5W#w(}J{N^_4=Ai=UhyuYpq!&OZ8rO2_7G94
zOqQ1ssi!1j5T5QB+1Z%=&KI7sfOk;-O!^ngTn)Fw1tCS9AL2+~mpbfE^XMP@E_-Ee
zc`|%XfK$g<K8I!Jg$EKTI3z$?)=j_7&n4(AebH{!?e=v3kK*RrRn=r<YKEt?;bt25
z&b1L=9w4bU`K|ro1$v@ut`0;LGo*8Kpop8!6~MsW;dGI~_8r*@fjti-6TH9S_{XsS
zWAqvX(3@uxg=T5)!4@>(>G|L-9i+cL+eO0s8v^;CMGzEI3;e-~dTsyya?jcDpolp;
z^F_Aw=Fuytb+fzQT^HV?+pI1y*kpWHh3qi^x=zHAwM=Em-IzXJi0}=5Z1jOHVP=?L
zOs4f=LhJF?6YE6OdFtAl^5bmS`Aev^quzKa%aC1TbkWi7<$AHv;4~Q3m~Z~oaN_>J
z{C@DF)2ENNe;j=^w+HQvD3-gl!^3B_1-wVwBy)W@U%z?g{F(|`-%tO$NU+UyfWsCN
zICm{tnPNZfrywcJvpd?xK02_LzQU^ozUt(d(#jrd*}+DjEp6VpP#GV%V?^lou+SBK
z-DrFM8+xND?I_Ys$sJXU>^X44*7Ni)UJ~Ve16-L9;Jqd@cpOeIyEE}M*d|M>h*Fak
z)-5E`PQ7RRAekUmf?1iak>rTI4G#AK-*zt+MDaDgA>Q#M2n*8>1meg1Kbm96shgJ5
zj!TBauTYM<@CF#H{t{%Vv*(-6uPj9BG+wN%y52fJH~fi~p15z>$tC!7*9vzA2PO<v
zVhKU{q{8L;)Z_98Y%_4!e!Mc4j3!#HX$4E|UUroxWCxNEgiNvCuyfim8zKPEKc;=9
zGO;;U4m)})?2;m7fU-dNW)@ipslnWvZ`J=b<*Wm;ZrwSv5j`TzffsXzAGkk!%LLVz
zaOBR=e}}@erACjtIFDJY(lDnuW0BXGF6C4)YH|Q?fZZ?axp4i>rWIzW67Iu=sQ-r+
zb4D5AX4TxljbjXUef^kUK#)rP*Wj~&hFml}uz&|VwbBGtuEQ<$m46z1zkeGKr<!u-
z0{gUHT9s3kvvT3NO_y?(+bnj17Vbc*xZWI-gW6XNeJv#<UYF6iudoiEWQIy01L4ZJ
z#I&gfAMn{eqz02KSBHFHGFacXAJAe)M55b#%nJpN<>sF$2_+4I!=b32IVBs{wv~(Z
zR*>l=O>HTnXL(kNx$By(#?K;1M#|Uq>EN?EQ&M1uSFnZB%Gu_Ww+c*P44jz`U`nOH
zV=#;3>Fs$Q^I_x#gDNqst>EM4ls_R<VRsLLXJgDrM5UEhwd2k?nO~@+vmtiW`|W*y
z{PV8E<oRa3^TJPHCW5-^FHXv0`doDmyb^w$`FI9%O&$pYn1~31F7=N^VZix!859?o
zSMXN}XHr#dmgDas66-?ul~h;{qgX@4KX;FuFT?9d=leJc=T`_}GCx*fiXt-Fe%wsY
zVpp!UJv{cWPQJW__N%n}bMWrlaaQSa`6QT}r7u)zFV{8$9&c+wPgm}L;Ci)6Lx~Wx
z!h3i>5grKWHNRRx^OD;xDkmmm?C%{v(2eph=N64KU9c1+EY(9xs2okkWJdVvsYwh!
zrh=nHZV${=6}yf1g<m^@9loG(X3vQV*+yrc&Bj{!8@WbQU5+Ck*wI%Sb{$(t0agPF
z6W#C1nF|B#x8>2AAys7+B~f7!)T22?x%70TYb}lsqm1<rULx)&<=vU57f-G6HIJWs
z4ywndS(7iIBH-CEhEl@}&|0ETnH9gZN-})`?p~zFR&=l!e`N3*9~Xq~NFgMnp&y@^
znM;pe`j->QAI@yqzBlX)-(v<%(J7(Dp%Q>sAL@Wj(Z>&m0T5m-58^>Ct=?4B<TL$D
z0(38*GgMeU?`K>Hc)y%dNG|{vJy+I*zU$k|E{Xq2V(E*&+qzPY{~{L2TGiDTjfE~H
zjnbTBcDfV^!FgHsd&a<oyDO`VElSuaiShcF*?>SwskHmQcVQ$yaIQ=?FsZlddbJ{7
z3gC|3YA-iOiL167!GEh#)qV~WzcG`~*5&n;6W3ODMWFWL{f2vF5lfcJw{~eL_s}pq
z{ua6BLtSt#gl43zoF$CmN~F%RbWcICBFB6}wh_0bdGJxKM&d-c^v5!sxf)0L%Iez(
zQYvELIsX&rDRaG8X)y+kRLSfLgn}%b05=-^S%6<6Kl#w+BEI{K%@|^XI%a_wS56uv
z-*y<!e$+-aWmg0vS%4p;5Ms>hW%BtNW2Zd^>19`db2#?W<Dcxt43QV<5t?+bLF{>U
zJ08bZWMq-f*Grw^t|-esW<$cRL#7l#&}zu>w={##VGXQGHuKB*Q%p7nNjoYF^z!l}
z9_rakzO6VE16KL5UnK0=7S)q>iPy@giVb>q;ShIN)H6wVVH=dFzvgxTB`9^biQg2I
zf2k&2{J9P?WI5`&vddoUa7OzHw?FxtP%k4BXY1hPY;SA-`1H`g%GA)(B&Td#<;cF0
z$iA(zC0@}zC%FzO;XvDG7=X?M&J$v9k5Dw*t5Yzjzl`|q#JSkeV|A}!vB3xbKQn<~
zi}mr$S@GX`(Vu#nN4(V{LR{~=(>^#6&1=Y~^$hZ?Cj$wyF#k{mt(jt)#R;so1K|w#
zJ2Vvs_JErUd5FNznn>EYq|UF!vu-Tw5<xtt?_GDwxj8Ahb_0Dtp$(_+L6?;PYa=9U
zzAe32)#ta3^ND*6GYQYBq2iHPxvb%TzGghcyV0j(NJB|fcChs!Z+gs?lEpjKZopSt
zUmU%2*I!{1#p@&O*g0bF`-I%*;MEpV9Nf#UUYBBHHgB!hP8bZURaw{%Bq&Dm*nod7
zo}Zs?PruLMNDco4p18CR%Va(w70TRbWtm%b61u&hUh+-ar-<RP<p}oMLYqT8ROg03
z=Jv<KBE>Z!;xc>mck2+8rcJ=`YW>tismd0tHNPqS<y0j1TSx&$rh0A|5)8E5LDr4N
z)^`gAS}*5zF&ZB2icSbqalv7dE5Y9h#t1tr99m3ha97&`x&!(#-JdN;2zqNJ!0~O>
zD?FmeJO1G8W!gcPt^LW$UiiTz!CH;&(HhOXY0Hr`vk;B6GCa+Q0o~7$ia&Rgul=78
zF~p?I*doY+`nWk#d9w<cp?a-Du$OH>)*7Zs$prG)rZNS<aF7y*YlXb|uWW~=Sk>e7
zQ=gn&ev~H=vI#weM2tZ|fQE**i&D@M<+=a@6>C#Ej4wWYXBS}hKoXUuvO~LH*1_o^
zoV<s9k9288p!s<J5JPD=$gTUuabQAw78dj~z<N3YE_@qTY?Wc9tGZlXmhTJmpYSc|
zKK<v-fc9A<v{5S6#-esVa`fgj{)if4X!yJ03mx#a3u#*u<2;B}eMC2Ah^k-N3L66|
zN(*xij-B0a!N0y46;oeUdNk#+Kn-ft-F(m(>CG+e?D(wCYmqSnV&YZYi87iFKNbxW
zl>iGDJv$*SHZsj@`g5)@;UF>1jGf73t>ZP~7tOYk%Cxo*y8MCfWKH^m__486$8W!h
z>fHc}y?JMn-&H|59ae*?>skv}h$H)b8}f8%MUfys5{D4$@ND0}#>_9#!||_j>*0D7
z9t-U3Yypb;N(Gb&Hj;4)8txXk0?}kR-yMt0ajOKD>qHjVLGC&=q$wtiNfCq7fHIET
zWN<U=l}VsTe3gCa1H2v&uUnn{Hv>W@0Y=gs5B~&-Qx)xDB&bK>tEzw3&`n8>$Ho6^
zR7pX_m9iiQR$3MWw0^y&Qx$HnA4;BTcKj-7EfF(B1moB0Li#-8<XM!({BH+;@vQUl
zRy;ZC{fse+it6=O9aZ$XtsX~{ud}kJ95q^K>ai7M6d`TB8vW*3U{)8mtPzc68sJP#
z+ZA}9@-bN8gfWp<Z5|cOF+llMpw}+_ADYg=Ewk@^+u3fiF>$i3sU~A`O}6dYX|ijw
zZB4eDY}>Z|KA-RJc>javIQFyGUTfXgb)JKgQ0}~%c&C0N0nu5VxwWP0-VX2a=Vrv9
z4ehC0q`4kH;C_fNEbD6btHWIFm#Tp$Qq3!NB)<CQ)EsxQ!Qk9WYnp>NZEj-xHw>k|
z%Rj|oceF%Cb=#$D4>Hu*sl@oY8tbh<Y{i#01#g4#w|iK|cfO%d3FM(EHl=PzsFY(O
zD;$ow$PqatBX%Hh-=rH+BlVN`)|WIyj2<-qjPLjc2RX&+sBCyur8E$W@{E?l0^@WW
z5r4lJAo9{(0^%Bn=baV&14~Jm=Ykj(BJctcPDb$P-Lk!wmam`ouEsP<+5zBnW#BJ0
z$|d#XOZ%`<IGZ74{_G7s)r3xs<8AQ0l^TOyYcrlki@#?WLEATPVS(zhLNjZF-6M%G
z5UJyeG;<mUwYzR5VO_WUG6c?u2-B*oe<w_?685uW_gLH=IRb1)`rNpWWUbM%r~V{<
zECoy<R(W<_F>fGlxJ3qQwiQ^tmVL%(bN&*frKVdNi1I}P)pb$KEm|8bY|-G3Pq4hX
z3_Ct{>`Bp1(n&^I<KHM(r+sg`i_LiqA9q&t2@R6Kmx>b2&E1ekJI!Tw8yyQZLNvI!
zF|C<aSY8#I|2C(sr7cz5J)^R5$rJG=f94uM=4#>S@xGp3Ma(!gJ%9st82$0=;zFk@
zmqyXn6emSTYq5QMYyb1;3AJLk8g$+a{?>c2AC=%B7@!Q&RQMl%4Nq<6`eWjDs`om|
z1Xo-z%W7#3F|whh!FUR#any6S3u})kMeE<5V)y+f^d=27^WdB;p`DaNcyzUQ6c^pC
z`BxE68^KA``nFdc4TOkYoP!D;If8QIpP7OV&WEky$e0qQL$Fi_F`1q2yC1iY7i;al
z(6!C!26aPfzx>49?97pjTB0XxT}?eL>+;$IyD+>&NpwT0P8WU`jLs)T_gH|Ud$#;R
z(ZDOf^8re5B~N7Yy4r6J7XGyo$?cm*ndP=%@wFtb(z|2?Ea}%x6Q8~mvX$f@-NdiJ
z{UKSyQvOZM>vo5rFkD@hy;x_lS(M~)|3}#A{2T`t1059OS5{^-dyd{!&vtd?FLJot
z04aRBQ%O(sHwR6eEJvL%6#?V-j|WL0)(CtfI^;2?I$M?avWx=h3#_Oz!ii_mK?(8c
z)*i$Pj&cCd*Xp+VfqWX1p*^_3Xn?n$fMdfeOZa)R$@_u1|5QBZ<974UU*HsHGD8t!
zPAPFy{Fzow4eq3Kw}yn5IGzwUL{&v?c1lgXXrEt@I@pMh#W#-D0}GP?JASL8v5u5u
zBwJY+IU-z+rX;JydUcv!;WNjG*Ik7!>llmz+ISh*mpMPo72q<FHL&E?WXWOly*V7>
znj`@ydky<<$Y4Lj25IpELrR3SK}{<-pKBKNm8Hly6&Cs6O5+3UhC&CM^chK>A^m&w
zi_EVArwSj#e;y>H%bV9?HU!BNWwig<N~2*5anS`4&R#O%TZLVaR^|;ckQDIDmHtV~
zNCBaMd_#9ob<#xVHvIBeqZ9m-p?G$F-l@JU<wzv>n2FM~M)z9-$GOjll|vFi=0L#h
z*#8k0jIQ11cCeuwbYmO5*zt{?%I)rMKs#Co^y^<i=H6_fPGBgwDD1aGR?!@hf|QxL
z<*^^<A0=31kwJ|sOcEi6hKWfwYdE!J;5e&vj|&alxAdN%mx&7XZSq5kvRND0+A$JF
z@ABDf*iEhZm=?g87qf3_F_Az2SAjcny+rx>;?P{pmDVp3q#T(X_b0#DSVg7HuaI>t
zcdz~tDO`Snwu+7@j+lga>-fix2eo<uvjU0gW_w)wok|A`LZZE6g4O%VK7D8n(DfOc
zciQRnx)8-iS(nOJ%F0x$uLH?LT_yN_J1&M>tI=ls@YLH6rcGpFi{cut#IdOMXNac2
z?XbAJj1a-Kw*Yl?X}B&EB1^>2Ysw8w8={;jltAmHA4k?a=-&gL&do2r53VBDN8AGw
z#ta?@<JA$5gxGHr5L4(FqWqi80LtUNgp-sah>0%+4-anvckO%^C40~D<&%7Vs4@3W
zR^*JH6})WJKNJ%@h%f6=YGALCis_vMc$2`P_p^R}*?;)^A2$EHLl3_RjP7d8nTD8S
z>Q4Pkn3LKWx9>R~tb8^m&X#!rId2d}sN}e9++uuh`oraxdlCZlCejq}ZIBe6s&)Ca
zG_ci$i+~3%*Dv4f1TR5dz-_z}@osHyNC7kr&TVmj`DA&-QdBjy`rPG?t|Wyajru<n
zwiJCX=?@wzY&u+;#>4>sa6eLIh`azA1R6tqH`%AnN#EV>Ew0T>@Xt-3g;uNe22+Vi
zp2^pX_kJcgk<I8v-~<F?&Z|B(t_Z}4DGl9wxP={Uj1~NqSO87nDNyid&LxTgnPcx0
zqOdSVjz#+RA94Gb3y10-mF_Hru?a{FuyA`TxUX3SZbIV7^!&@}S}S%K2y@kXM&V0;
zbG;T%CUVj^qqbQdZgfFA@r{*!Qa1ufRjE#BHr$P34MoH+K!XIL%WQQ6f@{kHo*L{H
zLuXM~W^qSpQUJ|k`*A)HSUb3v`psISd#=}O_~ch%L#BdM>3elo%)<^TRFvX@UN3zN
z?{oCZO(0LIgUAFG_w|uVuTwh7_p`!XoRPCc605~rc|)=;?gtgW3mp;Tex`w|>|d1M
zz@_h6gV<670Kz3btD|m@m@cnhi&G>ek!1A;xoT|-j{C|cHb$#*--P9AMfC)Ztf9aY
z=fgljaJuTYjZXx`#Y<hxLo4_rIzjg{no`l&HPWgp12L16;vw?E8%s}^;6W<WgCsRD
zP@%Nmeqt`$v<Bgyc^c0%n`!qhaXoQBsid=n?R8-~pke+A{CK-GK9Fj*n5;}sRv>|4
z<)YM;hhPsW`uIe%6b*xw0T(3WXM~^}g%AWy4G^g}L7@1Vh)k4LQL6E7@d0=voiB+x
zS#e$}gA;}+YjFsx3MBXW_#LHHWqBD3Y;Ql5dPp`cDrto>+KA7zCgwJqKTlQ@PXB~(
zQ9Y#r37#iQAL)w78Ncz`Wo2bI$@pg>LKYfb|BvqTK(+TO5FsWz&`)bGH8=y}qItVT
zdcB1sw+Ho`ApfG6as8sU>5jA`2qe4z<A1Rs76PSsCMsb_(I|tOQw%YMtDyJvPeQ1D
zso0Rd|Awwn-l(uh&~&F%iRyV{m>Jqo1f&v;mfRDFedVY{toxm_WAwmIAOd`jza*vo
z)bP0-Ch79|xWaBTUpu$7E34%H<$1RE+*+R55u*p1lzVL6zl8U8q|b6)GVm@g@m#&E
z<}dtgQx5!w6%1l2kSc+rrS$WxsD%cswY7S<KR-c3{SNY2CMcKb`8nE8pKo;-19(QM
z%0Qmo8$YKEeR4q^ryIa!l%2+y6a2ZP%vw>3m&bg(U=O6Yty5J08JnsB#fn*Q`tStI
z7+fR%mLa2~Qt6vKNSdp;gHzB-Cfv3tq#YQ)dq)+nXr-1*6={7ph$D2~n3YM}6zl>Z
z>1kk6t~5ZA_R~k=uIG2Ra2oL%zy#HLa)g$q(z^5h3i;oPb-07d?uQv#{_(BHz@PhX
zzexADl~`nUF#7my?rM|onXwAYnb=HvuGc5Gx((GcKz%8#hn`{W7!~EI@=K~LLQ|i?
z)>~3jmrF~N=?j<9zO^y?9iD^nm+2Afr*r=(0s_Cp;}0{=<e=6T?WH;nU=+toqt`dG
zw&d9)F%k)yc|df(WHw%%{ZVeo{YZ4k=q5^1@7$L9c`&=A#G-j&{qYHl*c^iG<~}g}
z-Yoi{3NoaqF0C=EINS3Sv8mtdBm#N0T?sBjYOdF?w_ghCIS|hI<?l`Sh<_A@?ujH*
zm|ha${-JpuRZkzQMOIE85XWYT<5n$)<l6hb-3%Mbhe@Mt^%;cxs=&40XUb={0gx~M
z%-8k!$4}>{tN(<h;<lR8+PA<M!R^}&M~&G}xHJN;o|Y_1Gr99coy`S4dQuk7flXa9
z>@#(6E#^bmIgpxGJUCbKYz1OzXqFNGDY{F?rK)|$kcft4jUa~^C?Pe_{+X7Yi4U*;
z_V*|r@M)`}q2`&=`kfrCs&saHsj-gfucc_cY-N4yXOotnL`k)~+>}%ScFN941}(ss
z{cjI>4xIBth%%~No%1&QcO7wmRX5Ku<Uq!&%#Oo>5Jog)3=FT*XDh7n=DKxM!<YW2
zB{-5w60Yu^P$<p^;5VVhcF<XzZ_j*e?a%Ib_LAT^O2r)q7wAP^7HPr_J!N|~4-fmC
zKWLa()2`t<e}G1tE7S^g36*<+UqtEzmgSZwC-9?lS)KmWOx<dsD)f3g1exLZ&m#w`
zOYY19GvhyC#!Q$s56$AQ$3%@<Er==6XJ%$@M(}xqi;^1UfeS%@10|W{LSw2Rj{tmJ
z9L(lojb^Ct)uA^HTq?v$b&=|CqNFg~9{W)`eY1|yy5?>0k>R<?c2bRJp<|d~pChrX
zV$c_~DqBT^FkVY|R{z56-$_e|i0=Lc!)_3G3d;<`6!t$E9fDKE$9InfEEE2uM$IOh
zEup}g%0$D$2DW}SAj_@#l)2bzkO;Yd66^BkoA-)@3Ut1|oj;!k|6B^E(wBN!fHY|!
zLakE80WG^E=94ubYRPI4J^KrfxrrrteM5uGJ#v)XxRTuE*)a(|Rx(IN_a$t?(}niK
zV-3U^m*A}Wi0cG}0$3r0@-iV!giZZyM~`@5`rb}}k@@H<;<4ObV#7o|sz2`Jw>ety
z+O4@81jsnjsAn44txyD=dGTL-&Hb?7bH)h=@e-Uo<WX9~VX!kgE8U)M;7GhEOjsBX
z!V-$mQocS+d$uH)jZ^Hwv#Vkm_xDhVZIJ7W+iH%AIXXT@U*S{e|F%(2uhQvMQPo`Z
zCi*)BbRoOUC+(R^c;WKFQXB6R^DR2V!lW#hviQ0dY3xC8?A&o<yAjzxDHxa@VyaIu
z*Vhe#x1(8E(bLPw$RK)gZN`>7VWpndIs~B5T|SBT(DsfDpAI40A6*9rB}M7!sOc)H
z`mniM@%xILYNB`@(t;7^s)s8J*|(iLiVyn(Yf<#c!OhyowBd$#be`}49xT%PqbRnh
zG@;D16SHiXSsmi=sg_sJV2H;uK24Y->!)bm7oL#(x_^HDG_K(bvx@Opn0p4<^cH)6
zy<fwM!1IiLOF^sIKu*9lc2x<wmDT4J9eYmp9g=hkbZEL*%Q(hXrloqhJ3e$`Cfl+D
z8BDbQ!1)nuSViYzbl&=q|N3bf7+&uG-go!$26>Nu5%w$*@>zp=`=&M{V8(&DWt7!9
zDeOlM=~@I9bU*a11{`9tOS#0pBiosxW|oq_f$TeCTayLkFq!Ujx43>!2U2#2^D<b4
zA+B~hcmzk*Wn{=2+<vGE_^1#4ZV(9o6{v+M4t)(h840cEJ1CgPi(MrWB%A>!{bmed
z{n!DG<NLffJ!&SqMRGWfvEULj)a<!5fv!^f2qkiL{Ju&wo;;CEBqaPJ4W;Arp<wsT
z-$jNW>1#uUj}e<2p}?YYgs=ZE?K<TosC*pkHlm*r+>wRoUn{Zk@bG?<^YVHE4zblC
ze=`f=EkZLIUA5Je6T1>plL$+Pii>gs!Iy7?tBD8c^|`o-2DJjD2N?~d2p$qMD$DG^
z$4}JlLI?Oc>WeWH2p{`N5BqLIa!y@4&onk(u;MlvUl0Nce2=O>gCXBZf{25H>oN+M
zg>dNopZ#XwO*6Di<v*0b8L@c*1e)sv{pKMUQklqSqFSO{1~`U05!vV{3)bvlr?aA7
z`o~Yw9M>*3S^rIe^S0&4dxS?E)j0#A6G<Dh2h(qjKX?DI*&|4;!~!?0ODN})tF3};
zj7tM5av4^fnnDONtnKI)QjT9rOH1k1t8}ml4q`Nu1{*K@l}2Z{&c2%g0-pBwe+<TH
z!cK(nmZ#@HBMi@+Vd`QtoEqo(2Z}<o^6Gk*dz5%=r$`%QitDafa(KHe!@k0@zml$|
zUxRb(7^waY4Ra=2yzBwFuGiT?ctM>-@sUU56VS5G1M=+(D1zIa@}?_}y@X>4H1edw
z62u>?)11Exc&_i@2}fLkID+W;&ClpNWb=f+W>aK=Dm-i2)zmXHasl-p18ZzlMnf!$
zJdC`K`gXeV2g-m~4ZG=F48k0~FK1Ih(I2z^@TleG<#bM)m39xOh44ZHLHVSYvKO_g
z52aByu5$>Cs;0oOqR9G!=9JLbtvnK1H_o_v?%kxB`nz!1;RzwY6T>>m6Q+idZ_E}j
zGXAJuZQT@X_vE$5DVf=aZ7h;}T{_aA$Ii+9%;`@T2IEmkvD@YkhNPBmk1V3){qdeT
z-mp)0eH1^i>&<eKmiaw@9qyzqEHfmqB`drh?QrebyNr{}!ox)F5f%o{?MyPL>~M&w
zs{{Y2Q|p(nvAQd;v0Yb|qzzf^OJH5T)I~M8E-c!&#$f&2+HM4j_<iG5uAz3*ei*?8
zV8u-0e$S>(iA+*cGLA3REBi>EjDY++kz=amQtY9*!5P814H1C5_sL3qe+j9=U9O6h
zfKMcu*@v0n>8Hh%4#|^FG<dB=y5l1Wp9gUUt)~GpG6Z0oEpeibs_+gvbQ9k{I{&gN
z{kG9Y%dxUZT)#Dtt4kiwdV$=54%Go!l=`z@>GF((=qNne(*UJNFLa!N_w?Dn66PiL
z?j_W;26R_vK`V{mIF6&#^dO^=tf#i==gHOUUQ}L8!qIRk*yTR8?s+Ub{c-+^Fsak(
zdD;^(83-(vBQzCDwh}CRK!)30Iw)T8*O-BVvznv1B(t%a^<e3gAc{gv^X_#1@g5Eq
zW*a*rnS;kncwxl5&b6q#$n4Y%{JdLO?Y4-ZElSd0YZ@H_Da=1P*Fd2k%NTrWM#(NR
zO<RQ)-DurqZ#G=E_kPIe=S<Hg1!WABAG51C?m*x&IK8FL7W$en88K(w4gTQ`F(QTj
z0xb?bTc0kGkGG}`VsiHqDHr`OX!Jz)0E5UfK8-aDUiZ0cVP=hSzaC559Dfv4T?+=>
zyXsAs_LAm;yZ+(2o1ZwA=~XU|0-o0&%kLMBqlXHF%T7=IEv=2(kJ&!BXB9#&`c_(z
z3V^xw3?1`^z%NgvIl{$i=_u{9Q{$@gZ&L0_0T#5l;+U!e;_3JIA<N#z&fmy<ioc1Q
zm#(%xL4ok(7$4FvTEPBtjF)WAxALBf+3rCBZcbXC^@U&1Lkd~tooj0w9>qXxqPzEJ
zoGY4%@yG3}Iuw3!;mh3gdO9mcA0&T{DG-lC?T*EkJ3=tYMuOvZvczloVLd1lS?=j>
zE5@HwcBtRGAw356ub@2?Hy|kyB0Q!opQEuH?3smHHfE{b<h1de&+*okgs!xCZf5!Q
zX?)gqD<GSZR|Cp6cl}*q69Ve6-#??mq_mvNdVJljxuoLgs#E8mm!WfL(_fB%oPeJt
z<Jz=eR|;GD*)II3A)h2j-AB^ZjYfh)APOd>sH3Ji+C4TnJUl(!)3Y&w;_iCIU&mIq
z=z!{J0naDuqH+Zl#3WL>8Q=7I6vl?b)a2~^Xn<-F+5Z~xlYQjv5Y<i>MSvL0<fH5-
z>3jz(8S4}zD(iP>ckYA9R2~y=2Vh|Z?aNq1ErRjgbr$DKNoL?M5m^vtHmQD19qShw
z8aoA{u}hA*c|}lir@`Uo4mB#+$X!#}abyI!B}87=`lS7HM@jFoL1JzaD=D*WF0_dw
zP+%{4e7(c%vxj0RLV=KeslleN45Wmu0RwZvI}Uj1fHMEXa+Tosyaysr01FFjAo^0H
zo8!H6J?o5G6884*Mg>ARE|~xHhnx>bJvD6e&<>^x%aj4vAxHDGFbVB>jwdhJ(ZnAS
zM#+_9D?xI4WdtjB_x>47N+;;7*?PDE8hi3a_FEJ82QpaH(E)V^qCs5|f51(L^VN)3
z9_J80e9T4t3XyP5Gw>pT@l{z@185(D$$_x#4R=4bS2IJr;Q3L1)#1+ATqwD7v3DQ-
zUqm(xa#0|DgzVV23TrUb?Sn8B(6z?XSmmpNTGIoPz}eR~|4}-{bT6U~n8_X%+#o<c
zF^`6pmX=Yl-EFAooBJlDkATSA+F0nEk6|{Sx0=G1?f#$nhPx<WtYV5IKMXyfhU@?f
z4LvzYeR__qw%+Ob&Pkjj``aDCtS@0!7zRcP4~{@=9o>qATJ(jl!8AN=IcLwk>b(8=
zL`NrVU`*}^bl_7A30@&FSd|dQ&sSIhuGOl!40`&t<iDI>IhXndW8c)+Xm}#8pPNt6
zjtZ!=OS6ZCQn48UNYY?d$}F%-h4g6fG2sTooQ)r-mW#rGr>ChQXmD9sD!()hUKy@Z
zdDbg&1){B~9+v6LxV5?Y^%EOB2Aqt9&zgKCh5pNEqbXZb-IN@XUj8>fFcWXhRX8Iz
zs#?l}1%=r94~#eOHyxA_uL4g7RE@94smm?T3=a<MvoM=L3s80Oo(<d3G<c!Yi{VIf
z&&-b!t&`TuNZobi?80V{1`jQ$;t>?Z-O$=cmkrh&jDh+#V1Jl^jdNlFS+^#-hB)XU
zHW|y8&S!;SB=J33k~{%?7d0$fn4zDRl2a%$>A;}n@A2)kqJYsk@fX~!!`=PkixWeu
zXaos76=p*K2NBFH#q)dNCAaVO3YZ9nbxC=ZRl8^9?L)^a=`Y^Uv`+BYH_Nh0sL3Ou
z<?{Gs#s_L+41Rur4#J#%?fTeMf5|-Yz-uq2uhn_t;oIXuEBLQgW7}x)^URI6NV);i
zDgwA-a&K`DMwG?|H%Mz<2;svy1e0S6Aj||WUco6axzMnA)C;$3Rx+56yw~MThM|yq
z1r~$EXv9S@fwgZp5sK;}iABF~MX2{Ji51F#n3K>((2%c~JQ(8>b0G80<>jo!I^!q;
z{w9uspV?W3qy8EW9v%e+0SW0U1g|X|isV$*uoPyC-ve`Uso}Xe{ngW}m9M%<&!%<8
z8S5QDYr1?u<|n^K0NM#ox?Hf<%pR<@lY^}YB+a-$P5{3xW+pz2?;J0OAAc}t2E_yh
zn}kVRu|q=R{WIO%m4jk3bL0QTEDc7#eY=7Lha^$&$%%jvHu2gF-Pc@2O;qD-aOqAE
ze7buM9RuD+(1a+Z3WXWlof^uVH<Kc{D`o*E`u<^751r}c@2U#4(Nipea>8n(Ydoa9
z{iEo*oFMu&%qaO3YA=$9u<1^xJ0tajf6e37mA#vrlatH9K8GuE4vWXkUR_=8HUbUd
zqKdle;@rZo&Y7vDHQ!tL&8?|Z)UFO9Py6Eh(xSY(AA+9y>iyzSP$9vRgWtjwzNZ3x
zTqv_{oY?hGNZb^8x^^(T<K4HCxMCmx;d_qF**`x8SXJgu6cPKMx~ijKK7LpIW1zfc
zlDbYwRmaTQ%CtxMtNdg~=WEFlr1ygMOal~-CuP*z0Glv5@#bG13KTI|6R0J;#WnY!
zul?p%{Bu^$xwe)$l|?iR;k-T|)E|Hc$ERz{C=Z%)I*GbeTIicv$(&2b-PQWvlG`$3
zkoGYYnjGV$#&<OBL0O)Tj*Ke!tJ!#HYAz2a99rV9=C*#`y&QM1Pc*`mSfgAFFE{gI
zaX``1&$6jkYi1K}n^ZFN4T+Yd6#f4!!A<7hG^&{qX}`+z0G<K_Qh=CwOK=uF7+l)0
zX8*NEq|`jXg#f%G@u#vRNjIOf#Ymcs7a|G~J+G4Q!P~&e%BVWNw$>^i@8zAKJ1En~
zm-jbhY<4gnwN<NZH0ELhiC%(F3=6(j^$M8&c$)0P3P?)Jg?wO~q4PXIiAH&NaHYv#
zz|EiNVYPv%L>z@417MgDalZUX9EhHyK|;E_zXyE=K(NOzdsm@48j60|G3ui8aDR}b
ztQrq5R@$n!rs9l#gV+_kH9~;fhP<_a``^dmwq%8FOC%)r$t*Cj=<!odW4o0)o7ai<
z_0yz~R(yIyhNox;!twlASWW&Vq6u&)NMZ4A3?*sJp);mlU_pqP^g4k`Ar)mc?Kx2z
z&71@Jdw;QU<>W?uLK|P#*3@)wd_wU=MtMb*UCRQr^w8z-!v*~?qxj{UzJjEf$?|lE
zw))o~53OT5ICLtAU6T?_ux-o2n5w;PrvANU#Abs5TjOXgV>hNQQf@o;0-W0MAZmQ;
zy6J)OunyA!fU=;#WFJJ>Rt4!M6$?x9^71P3^2$J#x2jz5pN<t;)Kw)mCtFw;=^2fd
z=YtHZw-4A;JDI1h#QYAmEvG%@W<i4AVX<!+m;^ob?LIyN{)afxO-#l*w9Efmd&T#-
zR;^JgqJzC9q+c0u4+dWX=Z#24`?a!bW&Z6SsTuH4ay=mYEhm%S0$+=}`88x<@GDhe
zfr)A5YBP`7VU@T0r^2ritCs0o{UhY41yZZ_CcAF6hR(Wn`_F~3Cw&|?WGUP3;W7Mb
z<e%*Q=DzUvR&=Mmv-rfxb-XNd$fMJ;-4SMfUEpC#8afgp;+j%QBJyeyS_pT}!pH;y
z2U~!(iMgdgUW_d~9=t&_Qhg*yqx~*Q0b-HBQ?ynR{GEy4$?JtMoaH=BR%>|R@1t7o
z5NNVf$_h8x2R)QZZnxZ<o!WB?bjIMiwaxA1H{QVCQIbDIiPt=)#Hh&sp*6f?S*3yK
z(f_30Vw^u6P#vB5G8GBIMj2V~z9Relv;rjEnDjn#rUptZrDUbY|7mqutje2T5mY}S
zDd(?McTEQrPr`b&ZEf(ut4;boz=Q{3&y1BO4DQD(1mljUffWceeplnHx$i}>O#j)t
zE|&<U5o!W;C*!nyZLjiEPZBbR&`^}LIPm_`<F|p1t>O96wy{xBH9VmzdVr0Iu_<tP
zLV%H(k~UDI1*%Cct0?-m+_Kb|k)ZH510*jm(Tz!iFZ9UBQ8jxOmE1KfJ&U4YmwbYG
z?na0}Zdo09jsR#`=?9a`|FW}dOc8_ucr(LMyXSO|M2|1*|KV^(mK$TJR4U#%*~Y}h
zZ=oc!`1)j;tR86hAR{dlI8*m|1$Z7ATvkRW(a#q?FVpu5d}Ht5(jLncIA3%~-7pc!
zX<F9#6=21=JDGpKMcH8UzEkIj$V9vf((8CoPw7RbMyYE^Xo7%iDteF)&pH+LIVdrq
z8Lgm$*yRowDy$5ooVe7KNMDYZri*ZA2!oVvgP}zV6DKZ<GqS=sk7uqAutlbLn9Waw
z-+SQP;dbflw%O@ZTzq)Z;iWFWSM+<O6w$Z1?yF+5a(LQ_W!ceLT(VIPgE<`MiPT5$
z&8LG1ji`L@;6l&}7i|S2Ek$t&h-zAxZ>Dc)@}Hs%13kT!?DF67%0nAeABOg5f@^;E
zMh7AOS(n$Tr#5IFl=cJ&!}?gG)quG$?A8^nBE%7&niW&@jgCF-(rEO21LsK3NzCY#
zX<{T=Z690x9&7`_lJ~NoEcI8zW8e#Wl+D_itcyxPQ1bx`5<)4brH6HBQ^N1gvPkA{
z8tyrWXt8Q+AY5R$RP+EwD{H{n42<oxrq>9;tAiV)mDf!E+42<d7i3Zw0wJ0PA|@hW
z7l)cp!oixoMt<0HQ4()brlsMZ&^(tR5#i9(`_`ZhJ{qRv!!}w|i2E($U~t;y?ZktO
zJTFCRC{-JA^qgmO{F`&{aBa2Q{XcJ#g{ymCLdLN|nkpmjm{mn#slBUdcjKSDLy@I&
z8G{ib4!jRzv`YYUqQ=&(oyw-Ba+C^9>+=4dC%3C^`OOiADG(7GOZ)ruXK0wVa$m2}
z^-Tl)Zg2a!T*ID-J(27o@b0l7O6KtkbiVxBb5Wv#b&w+9FE`k4ww$!v;Pwgw>SNx6
z7#ZlqsvwfBV69JTOdQn0BOxtXKobWkvm`8}hGdk|Cs4?@xMWn|$|-T=r%Df-U6Tl5
zKc*DXR+>|BG1xaGHtBE3GnN=jyzji2yk8v!LM1E4WA7h=0(~RR-AsSUW<VY93TG^F
zZV9pC9wnqY$KopcQ%91w!Ji@&Rl?Sa=>0ImKI`B4uQ_UN!+wM#-&4T|JNJlBxUC1Q
z*&t2t0(JT@&GS6s#(SCO<`!KW&^`>;d0p<;7z6Z#)t7V02ZbSgr!!9^5{$<P(J0oe
zgS=)3#=%Onlb>83)By|O6birrPlX>z(Bj4f7q82$pvsCawHYXh15@d{JgehitnFE!
z{M#QUw!B+SpRm!{PgOPycG{kP?k-XdA(greG=G^}Ov@v(*pB8siZ1>{x9<N6`eRiv
z$Rgj@>pRg=g>Jidv66a$6F-=?ma&o}DRQ1u85<Rlg@nL<6D>>fb+CF&%;eEi^(t&R
z_grLfZrK!3_p-CI1G(=2;Pc&T{;O9FBf?;$J)^#1;$yuoHxbA}_ofr%IP0ve@;L<1
z2EW6KltRU$5w+`W?F?a`QOSR>loGfR@%{8TmX?9dNz^xsDP%XFo!)3Hix(h>@9R+$
zR*H;qc__H%?j|S04uJ|VuAvC*79PqFm!=|)qi-UbkczosSH(6qcyn1|Al~yu0xe*X
zkx^0bxKISQbMUhV=z<u;@(Y8!balW{P~Id)z(AF)zDNvW>NA*boAvreK;KZrsC;&t
zd+7zMexoAG=EA~;@41%b(EW>(_-kW@G}5DE{giq#bQg~&zofR<6gc)HKczg}Z0v{K
zt)M6t_9^&oDpyx5NSNA~nm~ffbeXTycCmI!t@*nk_vv);(GJwNcRx(qHvl$JinJVi
zGn{b8rdfh;f?~1(eS32a_k!$MJv<38(}p`F1>G+4-cf0pa5G#ze6Fc(mAN@KR!AmG
z5=kebl*ow`W)~DbA<}b(+6cC93I`KpED8nYNo6Q<G2|&NxnWkE))T|)e?O;ccV-ey
ziYIbD-VIe%UF60KS5{gP_krKAH;ol?GLctKtRNoezTuoz&ThbRxN}ZXJP!LW;XVJ@
z@h6t4yH9Q!n}6G$-PZ2SkVXtChgIwGZJ-l>_z6t+;kFiR9m+LKU%U*WV_lN?6E|+T
z)Xg{nP9+k5c}NTBK1iR^wvdOsjs1kRdqBr)FE)gn=Ld}?o@U!q5HR>H=BhE!qx&oy
z-aWD&!;i2B+X<XPA)&@Gs0NDk8NLBg1I{A74Qg<ZNkK}lgVF0Wx_y0514rxu?5|ti
z(b9In+d7|<8a3lX{mdU6s9SD`Pd3poLRe~?V|xV$3|}<7eIZndsF02<;XgKvNno(h
zsPxpAu_^m_1dWiSZ9rq~pLzzbKnT{)i|h617pJe#d<$R2-B(X&;cBBJq!A>xQu0=D
zFJl@LTEQcQ9O)24a_Y-Ee<u`nrbVJrkJfm6d`EDnFEq8j#H_WRUqG6}J{h?ge@=}L
ziG^_cUeP1_xS4nsPI|5@@q)PAr_YUH2B<EyBBz6FpKA?XlfcHC&nqGdA;0t9QSI(;
z@vorIAZ;8mHa-Rt03o^&9cDsIRDlQ&2@P*Bqj34jeU7Gb0+8`~>#XOa#HefF!?Zr=
zM`gujdgVM>&zs1$c-`F0TP1pRpCH}4H7uwl5{_xN@sEwG$VD3I*nih()R`Tjr?VOL
zzZos`z(bdB1$GdFGY|LN_xM`P64B2g$V`k5r%bHeunwwpcZ_oB_%9Fjie!hba6znQ
z(eUK!*pJ_j2u*^e5LT8Jr$;Bp1i1Kkf7uP5!~fuwFIH{Vun=6O@w7dVFw&JbYb-h+
zY$YWQFiPC?mE0x*TtrUTeieu?+e{GOmr+S&-_vt808a^zN%oK&*iuXI$gfl;HWQuX
z&huQlZS#CjZ_q9EpHGHY7vo8-w<bfbRnfOi^Kl1RA`wuLWX+^|k>Fn)%37*xore$r
zHl7Cq3Vvyj2m9XWucYFEIVDd_d#%@4*{?#Eqn0q|g#`V~ANvqvR&bd<t!RZwxRH2-
z4T?jEfFSv+Ab3v%h0$&jgMw~c9V-Kc)BM4&5a~%h+f!n5J~#8(IxwqygD;)%l<c7>
z%p%Y%oG!gyH<HZzA2za2qxNQ0Uq50{$M?&d&V_=XxtcCf!AWskxw3VIeC!`hPyeKa
z_i6v6(23f6#_fJ!f$|j(a#`PP{PL^ibM^hj2q4HmQ{@hcx+)3Zv%A1j14UT(wGmG}
zPa>S+-^cc_<q0j`@4BGm{EJ{-j30a;6+7_Iv549z84Xk?{Oe>EJ@wwMOF9ezwfb^m
z8^rfaA+#m}41|-H6NLBx3Y(bE^Z*H)?y{eXx{C<1f1IYoWA<~iVr|xkCTpr-aty^h
zfR9#p5=1Qj%SfhAkFDj_0)OqXm3J(gz{+CRLTIgjOoXZKNOi_*^j}`0@5<BI!+uF`
za?r6|et#eSRi3AFN2y6YLt7jLZi)i@2+<~a<4t`!L0sb5lJ03E@;i8eh6xNp=e<k#
zIm^~`K`*#D#{tSH>^}T`RTJm609_f-0VrFUVA#@Zrt!Nw$FnRe=tMdC4HR%Gb(cpE
zZ^=;LgvM!XIFQ)hy~(+M&-I5}R?2_9Lz3p+zN||sI{QK`{H;8ykwK`RM>D{)xqq63
zw441BxlUf=30sK$o}BwWx6RqdE@e%1|I<m@auue(dReue-m0{^5WjU7uS(%dC6F~}
z$VRyfe>s$j1W@QI@o+%ONLGWe?E~BBaZ1xH4q?OeY>O3}^)`Cj%dHPtl9UacWAahS
zG$bizKhvx#^f`ya8E``Te#%S9DW&1kzMGTf0wnB1Nk27oN5`hQ_{ku>oaY)gI#**_
zTa)dCyq~W?MJfNztg2I-nEirafcNCbw{Cf7qdiCi15p>Y#n^rhzL*T2*baWUxx5hS
zk7_TJ6}0dgG9yz^o+e0$)OWs7RxvI?OjY3BF)l($goO+tm1pfA%Ey|)Joi??w~Tl4
zBnIuIl$2OB5|S={4kw$3#<h-;m5#ZYd7(RE$}87uq((JEG|vMDeaKV+0Gv!j1U&Dz
zBvwaBP$I}7V{p{Rn`}{UI|)d=@yC6)skxS+Ifjs+x7`oUgq4|GbaOVKzF55M@^*i9
zg!96l$38Uu-i-h68=-*a=V2~XuGKLHT4rJc2VmrwQa&AOgFO65LggcdCyE|ibwrdt
zNuOI`wvUgS2_^c98>H|FJl@5^I=L`^2`Iyd)5oXhlP5E_M`>y<i_6!W3%|EoteBA1
zOO202C-4w1<JDUPHHo$&Vm4u_lMXB4n3E~Kr;~CwOZf<3Ad>g!_sJLxNmtB2tFLE$
z#*<?4NeK!3V)J^8ErdEPX?Z_5?+j7U>j>c>g3ErmXHPuW#5}bC^18)!9gU>h8G}lr
zBHZka-@;X|$*N2(DX?#2P3=+43RRWO#b|VOiEV8e8RetJcc2`47MCFsWw4R7XQzXS
zm}?H9uQ21>oc~SsUokLlh<n>xirF?(`fGL?8hu=>WiSvdR!;mFAKB<UR8)F?UMYI0
zYC{d#@P6T3a&(jg;;U;ZO)SR8A-AZ`RPysH3v;WA%JM2oNSR<K{en_L(a0wGh^zmh
z!mnJoqY$849)rY+YXM0WYt5%ev{P>#kZE0dwFu<qoq7V=HDnu$qQC9<H9oM>IG1|(
zXxIb3{KkjTqh|Lx_{#!Al}MIY!z`$SbDbZk6CR6C@x3P%C`QTtv_}{zIo9UY#w!^{
zaG1ypmVwf_WKl|C-xlZ3aR1#$TWExn*z9{L<+r+TJF1)BV(_}rS7>I1fBj+zC-A;W
z%$fa9>TU+-D_llSAw5;pG((YE(t&Bwpajf|=gOBkh0rJO_RZ<@XQnXtIYOK(Sy2o}
zIxTG59vL=H;1DO*wX>t#?P^&&)*l8Zdnj;`k#M7qe@en9NOGwQ*$j@F4vdYA+mRoZ
z6kC77zI%<pmv03Pp+kAcFpr#kX;v75a!2ac&tO!*(9G@}`^Z)fA$Hb9aK2~6V1n<3
zJOM|Vp!vJ$9=1{9xxWA(C#c1JXn)+oOXDCyclCY<Y<3D*(=Fp4R#9+c`OxJ-CaQwO
z;H*t9j-bA>V|~+KaC<KJyWNmsfhUPiZlmy87>@D$!A;hcq>J1z&<$gf9+=#3STQ>(
zsd}8r5MC?LFfEAJ{PP$6D@h1bP%*u$BgiG4guYKwirkO8wY3ZV?#dqh%fDR}<R1z-
zZ<hpyQJ9YwF4S5z219cMbL(u0OC3Z!YnbKI|1lb!{fYH?GNf*fPb8e!^haD9wP}K-
zF**?HL}tgf(cQ`G2?1!qsz%H8CF()`5Br?1>L%)uu1cOK*$71Yz}axW*qYEKbI{9>
zMvCK>k~%op)5lb96jjxR+=i&|tHQ2r{u9_6S04#@+CK;F2aeGhUvkv@IcGTcvL(5{
z`xWOG<|f5|Io@Y^8z((ae#VvM0WC$H3fHYB6Jx`h%RUo3Uw(2ppy;F#<9z>zE0;rn
zTf^`rSxE)-iX~Zngvl-NlX5h@o$PK*ls6noqh2nj0dKN4tC>Aq^+_m41ZI+aC4eLN
zU_1?h3t0Op0R~<dI~ARw1C4Z7TSw~utigOXRqR?#hh6kwQ3`5p5C|_1;4-X@#NPz9
zXT5JXb{B)pajg%%(&f*`!>^f5;e4aXyh(Lv!&$g|t<3Td%uZBdVX~3-;H?z~hQzVH
z)RTABm8At2HAWME<z?RG0|Ore{x&(HhCx20r+xVhz?(H2`(F8KO+|$d1mV={RE*6w
zW65^xa(z+JpI)uop@0TqOe(BFufi~2@F_p<Nz*{vDV$vTI0TKp%C8bjf;1zRQN{RH
zoGn|kB1GGuL{PcTl4`p)lf{JGbO%j)<+I8IB_j|wz3B(>S6>U$iR~F6d?jgOgW8Tp
zy#DcWh~pXsO^wPEKZQcvWoRmwjg4hID~iBI)>r$-FbpzvkrhD-2(X}mTQVAP&6L4r
zbEkoG;jW4X0}k~YpOofcTs(?^ZPr?O5p2FzBLMcOH9(rm7M(U7HG@ahHLu|4X}S+q
zL;|wu(isgyvuGx$mgNp`a4e~3Ypij3xVpP}0Eu00CBtBa!$_$IIgR`%NR;wqi@P;2
z9U?h2ck!HepuZpyLhPzvb|<V>31)!y>z{s@UA%*Rp@cF~4A&a>(t3zvIXDh~b2q%n
zmOAYnV`I9#S4JbS;|h^;3womdEiBVdtxtT{79oU6mSjMM%lQNN|A`QoU}_yd4qMeh
ziSM!nuY;Y*gV5P28$|i<9O!yEz6oqiZRZa~)MRvb-=aqL3t94YPz%hd{jQ;hH6vG{
zDgo2qTuDkafU}1*tnJVH4Q6F*q8F+q$cVz(-klvCT}PWjiORBI9A$<i)1GT&-<!79
z`}2?ekdTiI!gpXM%#DlOuBvwZ8Xrvq6G2{#o3O>>89E1bhdL^3X;{oJHN$@@SW&}&
z5M+x*IYprfb(|y;5X*v~yQwQ{H64WEO)D9WPeMcA+7F02VNd!ULHUIvszT>X5w8j!
zPRehm<yn%lz|01O#Jco1`dUoKq<7xV^>jl)aUD~g83=rhsw#oAT~MrsLDZ04V+mM~
zjtD_5d>fa>EoKyMF_YW`BVs?w@V6nG5D2fOvT}YqOfT(%p|}p0@Dp2A^)4#Q1rdgG
zn<$+=C@4F<FHg+f#1sb%NUU}W$tb`7%Y=<Zefs_VtB7yL7T=C#rVBA*A7rrFDONnB
zxx9+9nj?_d^*6Psf>aD48AUloQ6%bMKKBBdo!v~%K)3mavgYDg&**&5;Mxy$G3XN~
zQv7EqaVD9~8l%nHn}}p>;iIQ}YKfhmDk{F|MI8|}giuHRjI#_4ZxLxVaNJ?gy^t=r
zt>5mK?JS{8=5do$#M`YFr|U?(ePZR9#U`WY^-cf>^J7}s(HKdg-AQ96xPzL`GL2V}
z@A1M-3oG$QJApL+U6)bC3Cj=U3BAa0BV90eRTax}y1??p`T}kml-$$Iux&J90@3SH
zOKp!}>dNt7*%;@C`UP~+3`dgD7zS!FuRkp`p}*?K<wz@PL9HLM0VY~Bq=lCH`}kYu
z7uEnOE0-dT;i^UjMw7+l_1}92qV<lz$qjE)O5(I-8(M>Ct+H^|9d~dr9G(p2?z|a}
zTGtymum`4`-^?`*R2vW?F9zuBb{B-!E4&}JGZZ%;lWYpq#0B;^)OKsI5FVrv&(WNW
zo|_>UU34qsTYQYMpZr&21Q`+6@oo>^c{>2>6f87>stGiY;(|`7XG3Xy1dj<EM}oH2
z5GSea{e)2)S4HSu-9p^{AwGw6!4FxTi0_K;m4$WzU(sq2@2p+H@zChtkwGFRr#g^`
ziF0moK*IC}Cffhn>Lyyxyki5IaK}v#Hw<tsuVS}Ttw^R-+YO?I>zMmSI#iD)4FmW%
zdATLJLvmR7{~lYqATIf~#|ih#Sids(Q4S6CfFy>XM(keoOtRwKO)#6T66K(ar#aj}
z$mGLIpx7gk=Gse5e7irUBZgW^A*f@WiS57v;RhY)Z13-lE)QARFkn7UBUbV|zBSr_
zRvR^&X|2K5BQzomp>HPP+DdU3A5bGU5LRD?$k5ddGW&?kHmgM(u>^1BCM?`oT^~TN
zn1we|r4=|FuvkSb=YzNEMb*O#gH;doAxMH<O+U@0@=yMAtZqppWCdPb!mmQq{i60~
z&n%q-y)UDiHDigZh9Sr*0?jN4cl45zuB3i;eMjhZGUV&VoB*rKsEDGl1aLnd@rdL>
zNPb=Qg0aiejfcm++Oym<u>zrL>}h#}0g*wWqI{IXlzWJu@CSIY+`nF%mwB_U|FF$t
zHv$i|D<lX1t6eq_qlZD{@Bg0VN~NU%iFd?@J2UGATn*nYg1~o!Ki(qR#t%^rjr-mC
zQY`4hZ;+Y@bAFYW|M41}1f+LFpG#keA!7zF-Co1Mb3c+(i!qrB3=h!%G@dh$o!H%j
zGA#bC)NjmHVM9L1@2m>xLlk8tMh8c`#{qPVMm{zi&{7qo52p(3eum^Qe{%q@s+v|8
z;ho@jv*@!iX*fra*M#g1;czVY(e42>T`ozphoB_3+D0cwLM-|O*nPme5?gCK+}t&O
zpp9v*|1gl&<uoOe3mL#`6NXGBPl`pm<+6KOEf2q4poHS-CSV%-tB)=A9wpEJtUji%
z(+2f~salnxxYcKY%96Ah6}_#IM{EBi8<^yPU}SjJMfko4DMmBMxVMwQW{;%RXzL~=
zCw4lX1*@Rz^AzX`ur+(_f7kVFPMByOJE6T5G`tW!R)>D|;9gDJPDmE*n)Px<E9_no
z9ecY8X3Ui$xNR~W3~AOhAO02q6AS69mGWdC_?1VZ5i%JVgh<3fo6psglt!J+R{s09
zNz^8j)qH|3^>xsW-@eKQEj>ElJ@P}+JnvVvK#SdOxV;-2AiACWCzW)p@31EfwnZSm
zpC`c+np&Q6!ZXn$k0TQnT5z;EWl~PxFRZ2?pN-`?P5dz`!8eur+KxtN&M7c_3z|7B
zaiQ@#Ols5h${CW*-V-)N7bx&a)myKk-$=!`tfu`=F*}6TPc(^ENky4UA?B@fJ_c%J
z391I;`N@3+bfPWnTCrICZIfEV^VWiBP)JC?{p0?qE5{|EiNX$6&<ag<TS_0u3(8{`
zprE@{Hn)ICsk}?eD;nA<xF;6n<uoIBag3Q;ET3DxdY9{2d~UXGZkw(1^P0fRR^uH=
z>f?+p6*#xgZ$0~4Rk~D;bc$uZ0K0a)SHrY<Uuq0Mqr1f3N}YRq#=T|(JwACt+Fy|^
zu^QVEqA=tBo_L>i2_I&SjB-UGJlshWHCJ#7D>cQ<7znGdF?#LqLGKyNc8#aX2J)a(
zE3ep%5mFD5Naj%Fv?mnDV*yTDLC%)7&WFMvBX*9x%>@Q7<__{2ExfNR7!H_&gC?|z
z3j-h^hKYL+-p7oC!32{G_f{Tm$WL>f;#68<Vr>M;tBH*bI!F{z4N8z^@8Q;Y_kG;Z
zuW!k%xIMZa-aj-hER-{h%gIGyNxGR1!1CasV-Wg15593f@h47@AFRtD)=U;jx9Q>G
zLpxch8-4+;XC*mUb}6OcEG&-@D(JRE{=kl;g>tm-=`p0y0|X;DyyChmvXn;0?$z-q
zSKH+}z<zhKekf$#c{g5`ubl*|QF;~SI`eVpPvV20`u*9bLIdM}xN7QN^Kljf=uYCw
zH)M8;w~>|9)AB$jD&{3&eHA4+naTfAsUSD(OdLSfAEg}+KN{1^ef_dZKOlltI`v;D
zoS2xH(NDDQh6Xnz>3<^(71rlI?;smM+fo%u&^4o)E;n%DzOk~O3p$xD@kAu2Cj_#{
z6uDpvoJVpPL*cL*EFf&1hb5u7-8q4^26_sG*1p-Q?366}|3*{7`hqQ%jZ`k&-D^cG
zPDN&$Kb<TP)!pMH%V1=c3~V(=GQ7%^SIxNo1{8zWV)z&dN93<K)Qy|_qsW-8qdv|}
z9m2~E1EplAekSke2^XjOt>jw82eZUoxgdXoiAk&R_<P6z^@^sEyv*I|0UIRFfb0s(
zpcK7~sk9@G<tHS=$NzKCAuVl*LtZkZd$tX<xf-Y(;R(%p_IG0<2JY7{%v-##$BuvQ
zQ!6AUeZjfds3Gs2m6l0kA0fE-BT2cj04emhPLy!$`-%fKDtPV;^Y#A^6a2H<={D2;
z2hRfHOw=2w^6`&Dv|#$4m3^Z?hJ!Zf5%<;r5Z~)&&6Rsg#%8&oUp$yQxXmrJ9<qAF
z>A7gs5fh&jfXkkg=j*=4@PK=n4q4D&OxFl*tRPp<Dvcl=fA<p9TN@nse(If|nDLt;
zm)jcj_5A@`&hxiu+~5#1PIaEcMB{@2(Wz-JaVIqpld0Om`c(d9LDq;ynU0PY<l-tT
zwK_`G;`4Y35vQ(LQM1o`Imx8X))hE8Z})7edwZ${C_+$)XqK|OxcESsPc;QyFo`R|
zjJ0;}NJP{I_;^c+#w;Y>>+1f|Xl21D4R!^pj$7^?gE%u;sn0Ou@3K^|OFG^V<QO&V
zib~3{$$ygiqir+v$v831d@)>>V*c@qNTdlGVXu-~Je`rr%y3@tI=(2cW>S;JU0>`T
zR>wX8T>7hSUHf;P4J;SItU+7riM6SUhB^Wo8QWn5m@)Oym3r1MjbqKi)M8)B7HI3&
z*m@*%|BWkrPNnd{cGlk>#;f*Pt5ukpU8195C#1A`8ckHWjEd?+et|y7>vxLq^M(;a
z^_S2EIX4lc6Vdcf($1m3;^6aLc0HYs=eI2a1dK@}t8HfL8x!X|AYY1f#JwB43bsME
z{14iEJuh_BtZzI0GNpLS9t;m6VabwV)RXR8T8ef=3_VCJgYBQ+Il?t6lC%A8td5Vn
z@08H@b#;R5O@=58E9N;``-)|tP|%@is-|AE+?p#|L3xGI#UP7iJHY$-=>n_U+Di!_
z=;EDG)pOMG<_sb=cSdH*&glGUzq9g7!$&j=z@12YNGS%6rbRlOa#=>zs+N%=F9oc2
z-zz!3<Gcl6R@<8Ii<X<`QHOP}U*~%&BH3SQ6-5hfhv6+Fy$!St37j{WMPIJhS?s>J
z__fGKAltKoE13j0eG_Odf`v@{!BY>+&tA*=3pV`_Mz1FA*YIu1{zukRP|B9`d2j$Z
zMXig98Ge2=0Lv!yO^z#l2cDIgR7KI9(v(=r<!5qE;c>bf32obw^IyB8T}jd7n@1t$
znvZK?lKN}Bk|><l3GBOU*y};2w^4#F4x_Lxq9<SEWD(Ltu|Gc|S~Zb4i8`tQ%vj~=
zzxFrq+G!cJSv5vHtpZMAF%3oiqRJSP{oxOu89&<AzF}bZ`G9$9i|x*G9T;4}=R|k5
zd%73D-#pKVdGNVePc8@Rx^o|&Pe}=p=<(y)&~}T-v(aqt<D${3W9*vtum(1#NOpax
z%mY&(pmv=jGFowHf*ZodB^v<;#+JtZvv!OROm8fV^p0sL$Y3>Oh<a!Ar{HFWMdpM@
zYnEu4h6Ez|IN~&Kx{>gJ?@hr@!rx-Va(w(p67uil%Uq<3@gpk^zG@D2-{#AbSR6Zu
zpX=rn4O)M&{|(g&x2Qx76@>=~tOtR%r99{tS(L;d%9=gcb1tjL!@w{-UAcw%@sU9)
zr7bDzjJJiYuYm)exGICsTo{99i|=7i0u~4I*4*RUJIFLu{j0^jtVaLy!{_w|#M6S*
zVkSN~!N?$G0E=bT9VAj%E=9}h+Ts63*^H{{1gI+MlQ=m@a%`~)|5B1x65E7Y)qo9>
z2o8aPeR+0oY2(%K2jJt|6GKUbvdrtc>(N>dd7KVvat1MYWnAOw%B=lb<C^czeo4w_
ziJ`u)_atYV59K{FW&ghbbUus04&1xdakai|@7g#Y3l%{oy8RwQutN;1>H!-Wv@|0w
z*u@NEBk6w;D<q&7+R@rnPeTE0>ge)*>YRm(B$c#H>|DG87sn)|WUkwPlDqFn$?3|v
z=E{<NThf;XxtIaqT~|j-6I>+t!2X8OACe45Lr_UiUJJtRwRmjx@ae0^{oNf+m(G<P
zr=l=#O+v7vf&8L5#72&Ti&1RiUewMfaz(VCqlua%4-1gLfb%nq6o8*L*w;XeDe;s8
z4J)sRteT#=y{ngBaCl6Ad{XL)q-F6-W1}J#2YR~#?nq4mMh;Nukzgh^c6LBHadC0;
z5Ecsn4|n#90BMRNWfy9jI{<_5nTjuOpFO<S)!tZp{%B5Gq??H%KVxP!h9LTQW=0>c
zh-Jzmeg7(QN)1E<yqv8~Ffx{Y)hJo`h}eXr)XX&-wrt;X;P}aZimLjn*RPx}-oI`2
z@<?BICtC|6T@7Uldq0Wh3*uKsNz=drlSQ}fD?ZoI(nji@J?!o7XsNHRz&hur&zwD5
zaU>@r#>-M&j3Q7Ce`-0zR7{)#m!PNC)m%f0mxb2Z=Al&-Xb0FaPVN|<Q(VHbAo+%R
z*CmN5=~)|4IfANxk&Lv|74cD_e(nxd#=06R3Sgzg%gM<_PBsBDCIoy086|9G;Od2S
z&GV0ylSVzy-hLtT{^`@JC;i>+4K)>|d)F@ucF>pQ85iOD;gdVUOIRCw2~%#qbkIY+
z$RIB#OLSrbl7^V((icSbmTlW}ckS6%aJ2OFh1$mUPEg)|K6!Z8=2b~B-op}?^y-%O
z<XpfbETgJtX73RipRs9gVMYD*n_b;^?)LUSc-Y@_>w0U`<?0KS7prTq7}lwRP01ln
z2D03oBr0$&EmBU6kk>9>44pn73w@yr{B`~~YphEeED8anH|Qjc?YzR`lhanO-@JX-
zzC#E0@7<k$w<Blk<~3={V;B2-I9iz)Xsav9N{B$Zg+*xk3F>4pYSG0zD16C^tj&1`
zOU_<uzSar04X@sR!MdiOK7IW7;$hGA#_BW0*qhHvOOk8GXhGk99y1|mbPZJ)qmIZ#
zLwbZ22szt{%S^_Ojwaelv?$&j0kE7&%Q<=soyCQJMMXu$<>#u=>yA$Ap6gAQ&Xye7
zy>(UMVlP_*Wif8r<dbIJ&*K&$UYeNH4S7dT*S6mSfjdyR>$`iq>jv1E)YUgMHMh23
zyWV-JWLH+Kw}lE|!mywjLnUA~x0IHZS7gej)My`D9a%n>pJ5rx%E2ojN*92{#sSrQ
zW8BAo(`b~1!isvfUg68KHg4Uyr{HLD+1b-&CrUt!zaVeR+H|bb>|kwXpbdqdxP+t>
zZe10XRaDghn{Hy`>>UC{(dOKO<K>l?Td{M&(95?UzY@5cj~_k^^><ypbhh}w&NYdl
zuBNJ@3&(}^J#0HiEx5?#sq#n#dnO(k9Xr2&B^le+rzb{%6&^!5be@>DV|d!mlDfLu
zn(C^`3+F1T>YC8&e*f|FN4>YMHKI!z-OY(1PKJtts4k#v7AOgmIb0(0+GdV~bIVB>
z+`w+8zwh4NJKeWBZ(P6D*4omFa^4O0U2W$NZ%GQa*G9J{$+#njBNOOPn3K6Qz(G%c
zj-O=$rNJ}|t3;jA5MxR_>Bhz_sbTIOwrovq{=s7<r@&gP;c{)w#S7=ooC1NiHK_@a
zU~>upb|o2EoN_gFO)VW=eL$I^V=gc<VddJbyABqXpU1a57B_wS;WG`mdiUhs^@a;4
z4(4u1iSo76A1~FbUq&qCu}Ue-*X*c&`5GN_7AYFLg{N#exNGz3<d|SjdqU0-2(YX&
zCca5IC0F|H-2of1<|}oLZ8vWBJsf)V;q}m9-|ZW1jkP6tt76=Z<$0#ld7?WaCJqq=
zJuCO%xb)5WMHiZGJp@`Xv7PDcyout*4R9^O!hf%OuAJPxD%#yhMqG%zX=t^73m8H;
z1|_W7wKg%>8P$I*KmCfO)r2JE=)z){yzQ9a)%1)W{hjuK@maaYPMkhpT@MF!ch}91
z_SWXcE0yKP_wU%4nH0M?(96Zv%s><89Ds%7R>IES(K|FYIST}S&Ro3Q3}%#se)*@*
zUw_A56|jbXbf@*wsiV8I)0PH*xteRr@Xw!2C>)L_A%ybsupjN^!6Kw)<)5&A-zn^b
zw<N^V4xRRrix#4ee4!F)8V;%*5BqN4Y`=LM)#NYVfBEybPw&7pwg2vDDO@^eC-@D!
zj-9=7^TF$H#GivY*N*EqI=gP&zC-96zx(v{+tV8tk8D~NVD%mKRc3yF1w)sx<m`e?
zD;B#MD=wPDa4i}AFOM-ObqE$gWiyZHjP1uR;7;V;gC{QtzlD1}x7+J4lpWc#ZEb2o
zH0bhM7;0(j>Kg)>l(b^>@bXz4pSEEq5CogAb)ggT#ar5l4)7PBz@FyKKv(01;`}X{
z@u8kJa9Og9uK^tn+?c_C=+sOXv`ty25sx+$-#pSfHomdxTMzEqvL<C|n6HbKp@ssX
zNy@HZ9hP;V>h}9L&j$PN+-z?`U*p!jhtJ-8{`1Sb7mxezbhbC0Mnze$gPt_!!i5Xj
zv8DpBH`rlGCM>Um>dDxY4Y`NPs;+hqzWMg`!<*-i2YT;N@B4LsDEDrIZ_~iFi^q3n
zM0=Vkjm{<9@z(f`kMg$BmgZslY0@;Nc?)?3#es9gF;>peh#;lj<}DD_u=0(|+ErY0
zrR7>D=5mvn@4b7Y`BDXxH*3<8;v$26QGsu5Ywzgn>W;lP2`A*`nb|uJ6rZWVH640a
z-hCmRroaC7`NP|P*Dr=14?gH@yil?a6-lszAeK+#FX=;c<S6lks4dGhhM}W`D(aEL
zm#yBqJ|k&Kn4cSZHZU6=9rf(;mcbc&E3Q9(^5AaQwdTuJ6*b_b`uNqyKR>>F)Y}E*
z*CV;>S3r-btfZ)@pdc?VCnqN_ub`l)sD#a~A`&yQcO5!^ap4M{)PDU)sDHq*dHq^@
zYfDp8BZ>wTXOG~0yzzGuPn;a6kB{;NlSI^u(lWQSMc#M?J*<?}u?t+bA-}A?1&+A~
zkDtHBnI9au-**dr?^8twcWqt2dgbz^QK9~X*=p$Gh^Uw)aq)@ElT%i%-LmUo@tNv|
zYn{Y@{NW3K9q(U1e*$gI4b&2&ye|vKI^YugY_rGLYZM1PilGyaUm6|(F>FcY6&$@R
zJvlBa#MjN<(nw1QW^gF+Y_ewlDLYTLpi}-v>y?`G<>;$4V(QbMAj{Z-O45Sd%~>e`
z@303)D=SM&3v&xgODk&|TU$F+-z285&&fYlcA>t1t^2{V_uqc|j13nD`g*!MJKCBX
z>Y!RWd#db2apB(0nF;8~(^n#<hr<(3w<O+FO9GSmxvaz6x$z2GHbB<EIdsM5gJ+xC
zZ*<?iKlt?JTTEqt@@R0N=SEB2h4P|<ySHy%zd9{BAr>b*VOi3O)U=FMS!>p9$lknj
z-%&7s9B%8Pi+S9}K_B<#`J?+io#;KPD9VSt``DZ5p>~*i{;VG$zfUFJmP-5tQgc`Z
zcI-|bzQN(K5y5_*&UO~YdTQYI3IJRR6npp9wdOjo2*rkCoqbQ=|A{J-wi>8R)@5a^
zOkNfj8yOM4I4m>-Xsy8^p<$qI6`!(t<Bt4)!zE{{uC#UaK6?4_+wa((Vdycw=Uc8^
zsytIxa_rE)-8;8sXC^^mX{w_*I`K4?i(UZI)+F?FZ@hw*4Nx?3k4)cw^a2(ie)#11
z>-X>8yn6Zk+0!SF`no%sYEkBi558_y8j<j|>o;x&Pqo}#d-C@kDLDlLkFL8|`r-9|
z`%gp{2bkKIkNa<5Z@GN&^zpr*+l?A*9dyKkyYLUNU4X<(D5Ate;f51WKMMA!96{HU
zmyd@FI7S=l0<W4M>qah+H1LSqaPY#7RxnK}Jh*53{-TPyYdw$O{t24fr$KWYKjy2^
zQ@(!fnygis8EGri(lgOxzji(08IL-D|0~e>4?cVQ`P=W5{KvrE&i2Mj=T8+K&d=Sl
zE^|dfbeO-V6AX>NlXz^IHo|P|=SfqhjTG=1ufV%XkRDgH@Qz)bS6qYM_dYC7MwrH-
z)DO{KxrIj$?%$KUeM|P{tvNe#L7@2{R%k9caq=uNp1ub@t1n)^Bf2<T%)c9ddT_g=
zvHEQ3;k@khgfMS=6WEdPbAp1z`1>vpiPti=p%XuSK987^j=2+t!n!y)*x6W`6I=_1
zkkx!~J(uX!drvjjSDh(6vJa(b$H5a9TW&pk^XJQZt(C`Nz8<kODP!H%y!}Uy96ktu
zirsm;_mHk~C1qzTFV#1<-$X@!@uO$xn)~(#_$>mi6bgj;%2P!LP(`^Cb;O<yww7i_
z`q~;Q-$^_z)j|IwT7+TTJ!$CbLDR-Rem%P4yYG^6Eu_J~tCuex-zO`c(`BW_$B!J`
zzjxmOlyH>qvQuZyp1V+4RgJzjIx6n=Kcw6VzR+0eH;?b#XsSM4xPQlg`jtyVF`5i0
zV+-cYobm&F7ia(^Zq*?1GYLdxKumbBFPMoLYHMj~sH=k49R2&w6V-NDoVKI5w(@lG
z;l0~8uBIEaKcC;ea%$g(WkC+!ixV@q<QJa3Tz9D&UDy?8@OQsheW|X#v8An}v%9DF
z0Z2K*=;9mZ5Wp`8Ln|zQ+?<`cEXv>2%0NX$Sy@R53PcbnUN9o@#Es6u!&oBFb2=JE
zJYGQy0qHt~ByBoyrs*yXl_I<EH*aXbU^SlSl$}5sKYjLGWmOG$>Nd4pZENqic?*B<
z=dUpA;uAI;#7>j1Uk!A()}ATM-<Fje11|FFG6KA$3i}W6T_6*GZ)9z(1wA@S{A}3Q
zIRz|9TNWMc309q$UMnLbBg=64nk%AV8=SQH5IT|%?%oDffBKgFCoWy<d-(@&9CK2`
z9CdB|mafVztZeH8S20wpx3{8ijYslet^4@tGd#I?k0B@D{`}()AP#-~^76s0mg-YS
z_iW0D4}*$M4C|49ivk5xN`#+l0rT+0f9L(C)K|yD(qTkQnohdJgLm3Cbj9Bvd@@9z
z)dZdW)w4$r?sea6Z)s}0QeRhlsk*ABw!WeHYFh^$PW0UE?E}UL_)onf97e%c8YLdQ
zWnaGNztI5Y{OZI=e^+aLRVe}X1@lNn%klSJfW!ms9h_u;iNrJW!W<O!@2gQi0mF76
zN(g|576Womn#$}wdAuMGj^rpH_8-4+_3n#59$!0GuqMV$Uqa6{I%|JL`=dX8|N03U
z)Mw~$!#2vlfBTyNcZSN+f}GU}A+BcNj7K8%ge4WQX*lyZ&Sd^k(6AIU&*;qDqM94v
zABN8A&tHFkCmvapeWD+`d%N@IjT_fHI<B?1ciiae?zz{GpY);UFJ8T7IE(&H1)%6b
z<m%bowwkg7Ija*xy&TMR6vg>iXOTkS6Mul;2JJ6ICmuF$7BGxn5gX)Ysi!2y1JWXZ
zm68PSW^nEPdA_KYU2yW|L+4Hv9fFfSHF0&$!ScF)j{aBZjlWd1JuTct-)V7bPGR-U
zCx1YX{&evEJ;vEie)B)R!OQ|n`8^yQ#Lk1y`foN>orLNpF2vmm)`c_XEWj_CvL;v@
zqDp7#-#z{`czN8lY)Pc3q`a|PWZI777dsxJ7ycD~kQs@81BfY<`R?vpU0t0h|HKK0
z-ts4ZL(i~>Jmj7D`oB@-Jkied-@DUVU3O^un&lB-+@}U@9Om`Ikp2e<_z*|^@WjvJ
z5LGsE@QY4a8s_b2s;wx-3m7?g;ia(W2+ORgzpzQ_IY*@LEUvwH=ESiBySHbr$tftU
zY=+|Q`JKiy2R5gKyDeU^so-?O?Pq@wv%H&sZH-s2p|S>7!k}e_UX^d3-#&lTe;f4%
zaQ|I{X<b|Wg_B42Y+aKA?u3SF(gK)k52`mt+A1=rx0(Ar#U(mu^NNv@rvF;KfD!ae
z(7<JJ3OefBA3S=BUitSQzx@6ik@zpvQ`~>=?%f`s0^KHe&5t1MU_$^#V;@Lp9W|eS
zg#kAR7PmEDJb7@(+T<udM@!Onj0B8M{5NFW|1Id^j=tk(vI~JwUC@%nelFIA8uDU%
z{EGyI;f2TQDQt6@rcdUOG4fclCjV6Pm6{8uOOGGS&&4Xdcb~lb^HtxqniKmruZ&6E
zlwVri-uv>8Pp=;L-2z+zUCGlm-G}FY19z@9RGls?Iav-K+qLx<PZu9TRYUsn=wL5g
zV301YMrPJj)An~83wrE0T-lPrY8}H5ML|6!bsa;Jw;Vdx+E3PYpGl`kl=+XSBYpPt
z(Zl=q`}_O)`g(is^-=W<<vxMmcYL=~2mI5Ay|-cLU46c!VEfu+umUpB08>AIHs%rV
z{4sp??*$|A!~$m8gc%sKZ0i|{hqP*PV*GH>i;7E1$$;g{LZC!)E13BvZaQ3fy&Z-v
z73HPJ%PZ^KZ$EhU;m@~&-7ObS9N5k{13o-^(A{?V!pWmWWoIweHn!gAzJ~{1AHRNs
zX%F5*ZQYRvUSuU@g@^KYZeEvvu_8V)*xSX%L|0W-Nli=N*xbg!6`oCOX#Y1mO3E5}
zxWFMPl|M$+E6T1|Ov^54S@wamExn{^*k{Ti^vf5hWv~<}W_zM|@Nn?a6G;0j>^6g6
zcgXt}Jeh|x{>dN=y_@PP(b2hg>*}Pa09Q+0byUyram<@Nh44`Sv57Z-p%OoB9=E81
zmZ`m$i><i<_(Crt4|-`?bjkBV<20R5#X2Zud-3I-+nwl$x>8qNS%-0BuRg)ZwzIkB
zOws<r)73D`d-vzN$9Jz?K380@eRsjplG7J1HMZTn-8%rQyl=1iuU$H^e{))N-15|n
zwb@&D?#YFT*UF@X*zh2KUw21nCQ;VG>CDR?1N23C=Ko!*MoaPyYx>7$ElY#V)vf&#
zHtav$h}$`I)_)@M4w?UiM-eZc4LwEi1O=+-@XP+@Ey_CiwtptKGN0aqlk9`Oo|{+e
ztIm`h*|&2;dO`%2g_e~N#{K(n4&E3(`gdc*00*yJr_SLJkW$uvHM6%i#V8U{0rH@i
zk(0u@fvB?-(6C>ex#!f?fd_aa-_d@x<JR2=PhPzH@+WbyH`Y{C<F(DpkAI><y$Nrd
z*26fb^vp$u&<&_v?BAFa;^5{Jv^X|lMaJ6I87o&TPgoig83v*m&h|DI*7i<rUcP}L
z;eO7R`YMu(*cq6A`Oz`l<8Bf5xT77BA;$1SQTTiT6$_uG>-LsixzmSkd34r)`bx)V
zvLS!@@&ysp36I}(@@1!%aj=#`CGYLdtCug7AK#z5Icvod(w-IGuLUf#r{O#8``pVh
z$LPe9y5qVqqXn0EQIvOl(96n8iwhI85@BuU=yeA!+<5+fj4}Z4fA|Ei&;KMh>%%tm
zulhS`%MNVIj7!Q`zjgP4!tx8$@$l{GjjE%YlLD<(w2dt7-F-u%;^UUY#zaMiFAfU{
z3h?uR*`d3)Utq}MsMt8Xq?8lp{Ep2q9aEUtctrsNs}BmNY|K*_HVI>tJELaxd`f0s
zv01xMTt*jv*25>yU%mUtNIa=dM;Zr$tOMlyJ(=^*RLY6?@cQ{<)b-y$<!sr}z1!BU
zj0eIpP6LLDf{gIrGwsI|BWCqD@slY9N)1(I1z9NxQMlmYpqG=!$WeIZ#q>SmHXW<&
zc~7~alH<p(zyJCBmyhppiGvEbH}6o*`{&DhS1*8nkyb$X(iN++ckVk@cD@#y5PbOd
zsP**jv=D1$P9O{$nA^Mi`v(LB1fmEE3JN45WN}3FlK5rGsY`<0t@V|~dFC>(T%*4Z
zHqFzs7^cci@sh_VcQ%pYV#yo3N3F^$sqN|^NA<&!Ny8<n_(Z<t#KN7njfQyt3YB%;
zH(DEiFP0tMvt@O1Opq5U=Rq-)ofS(nPWhFg#yW-%{yzl0lA#k%4NO3(8icAvMMNpG
z8)m4ZSAL$PvCs1DCz=NS_`6vQL%rxqdH(vuAba<Kh=jCt+x8wkdEs(f_x)$@Nr7S&
zPI8W<VMqfP{1|&|FeggOT#XtXe<xE7nMDhK{)R|44ZEF~JNY?V=qeGaIOBx5qwOtG
zKNp_9qp0R4HiHIQGoAO}Mg={kq3zLOVuMqUn%w($ZnidDI(=-<mdwNmUqV(|h?|v(
za2uoAxN%QBW^zrR%PEK*GR*Z<WrVoq&6qfO2J?I@(IF)Piibi1sF}k=H$`PQ>p{(b
ze2%D&OZ3`;a~)5C-wDI3Kj}38NlEv71iUCZ_})JRQ47FBhA&NCmAxbX=*f!e`l~m4
z9z1=0tLo7D7#D4!8FM)niOZ|&o7+1$Iyt+zy19c+t&g970D)V<TkWVoHyB_GF~|}O
zS2xnG{Z}S-0V!2|OJ_gAI)hC7c)am{Gz~~n$00a*)4{VX9bFhg^Y+tcM#VPz5%`@k
zp~FE>_J<zevhM7OLwVU5i4o`%8&V?jGs%J74@x_(b!X1!7M4{ruyjJFj2w72Pnrtr
zM{a&0^r{OB3M}H|#paz-vI@$C7XTOgJSIUko1hh2k5%8i(>L%0)X5l$r*%AkP=WLO
z`Lkz3L-#r^pFIx4_vFm=+jbu~UUt5!uA$``dSf13J%ee$(ri3Jk_zg2rZz4poXLRy
z@9N6HEzuYtLLLHEGPDd4b|jlVW5#?QVCY)A`ZIK}#_5hH7rygY#Wbw_;@9pjt)=m$
zABWX#U%vbHQ*SwL`SALF=t+Nn_chqT9?9Rfc4hoxUl%KVO)R<3#zGD3e*PhOCx4FF
zth{0{v2{URvZ6RYI}@d!zeq?>fS->Sw2iod5G<>xqM=ODCwUc2++#9xi)#Q;FfjDy
z6C?5T9gmvXfd>!n_xE>QtvgqGX!n+^jXAptj+LINtgCNmY`)rdt>Z?2O>y4pSZ@ne
z0Wn!+O>iIZba!)gadLD3*p7`gR!qT)8GsCMv^F!)1VsiutOPW560N^S=@89f;Sj+0
zmRkS=Ej~_FJuwqxW|=RnYVH}Go^!aO=E~I@xBDMGfBT-O*+2g!_ahH|ssI4H8Nk)`
zb+ul;P<CwJj*S`1V?w-t;l)!V)XAA?cKr_+*wHs1Gy5V5Kwe=19K4Xn<TFrv;KPbm
zY^+$gT2fX?O&b)i@uGxNN{8^BJ5$}*fe}}9;(tT^Ddu+HyW4Z;cK5BW&i2OI3#U+@
znwNj*_=z(YE?v3W($d`2*l^`a{pmwFU`wngtE8@LWMS*%x{vvPhX4Q|07*naRDng%
zoa}9_EzC_#j17T(rwia@6H{{w6GL4sH6=M|5@tR9SDMv?6=^`YnvD;Y^ytFy2&6UE
z#;cE~J=iGe+0wIB^{x2AdP6knpGKYe!}I=ykf;5CUW&&L@1rKT6}=7z^ERzYUb5KV
z-42WvB(bU)W+l^q1H19cHx;xq6bq-I6b9Eh5{rR(SaT1n`hcDfh*-mxL`XtbSwj~)
z!*k4Mn#wAyY~&mqzk0`k5}4FJe#=Na1221{<63)L>(%Cl%a<;mKU039_~hyH)t4Jv
zJGySPw=`a^xp?mM(OubTOM+Z2@NCY})zdG?-y85gR%S+j26{Rmv8o6x6eTR5fiVH#
zjv_9^$AO-{$@KDlCM`q+zD`C~j-Cuv#qpZlF>v*=x_16cGII`<oFS28)G+oFo%*k%
z;tK_{Auy#~*IR4P78Pt?m%236*UiBaU+;p1+0=|FKLAEge+|kRY96}c)eS72eE>Wy
zC(6q@dp0wF@KP7BLDsSG_e>Uc9zk(A6)gkW!DcR>w1&AWu=?|h&NX%pyk;c+3+DKu
zHU+)a4OcGL)z;M1R6|r%)z&w)Uc1@d0|?EAn)9cM@it~jsJD~3l^tm;9OUQe>R@eV
zq^kugXwnj*f|zPZa*;6m4B)mrY%H^;5abr%#etiD9Va*6BC1mG4x+7)ej2A)J;|h*
z3ED9hpdGXOaLL)4rt9duqt-8E_x9I9kI~{oO-DBG&GqMs_vd86Xr2^y6(+SJQQ7(f
zVDyZyK{`XRl5(*Agw{VlJCM0qu<-L-7G|bdz|&+}z_mzRPF2TPM@5#{k1=rw$!VK8
z`9-FGWbZAhZtH!?=<7beeKy$JeZ8f=?otgVSDiU`q3Y7*hGxu(>bciH@aReZt+x8g
z^1_0ho7bi%EsY5BcE;ASAz=~GVFBK*_LjzaB;OQW=U}A32^3>K0by||RB90C&b--E
zfBxmyVFFaVi-bfa<W*r5<mMCXZeyq_$xkGI{-^QiifGk84lxyD=fI^ITlavXL2Fn4
z(_sr&dQvko^~C#*M;=cH(67_lPz#`gHOoP9N>>9k9#QQ?iYZL~8<o5<2Q4)WlX`7a
zJC8t5d!SSZa?ZszqF^LSWF55?Gv;EQXkAmn@Mq4niRhpp^lieySETXwGe+XU1N+W@
zjh4$7&!0Pcy8L8manb*4?@FVhywbE91yT0BfMP4ED4+^zp`f7H_eEAg7TNb51OeFu
z6mi2nagCZ7O;lXkn7AZr%$OLnFP(Hcd*>wGJ#(hheWuUMnIAJh=DzQ}->o9r_K#pN
zW8PnV`g9|F@AuvNF3)n|@^wnA1G!RvK%e~mPhWiS+Lg0U@7*%FZtY<2s`lpk#%9{I
z_jWd+Urube7PtQ{_EumMZ$kn=-?AWeSX6=`R4D^T8!D%cdG~+<RH+gSs%n~Hr^^#R
zD^E;de2V;aiTPC>!<(M~j`N-0e)VUzh56B3mZhHkXX-2Ux4-`Ir$2m75$Pv?_HP;P
zsLD?c#}x?LqSEKR=z&e^=9z$&Jn)KNQHgb>K@;FbB^trmfir3s+lhRGv{6auMB@ug
z8q_@t4vS7M?O3<>%*{{!&6s!~RDAO8Yu8>NIR^^qGf(5@4(2ugMK_ZF^S?iQ^Wm)*
zp54D?XmDUne_!wFo}Ql7y?y<EYX$~~dPvun4?cz*=EXx$=tzsxs{?%8?3bbmt_|vB
zkR}A&Ck=7bxuCA4B0tp-B5|EXy=tM9be5>1GmGoH$ENljJA3uD_dow51K0fhuFRVR
zl_>q|U;h2OFF$$j_A6H|oIW(QwzD!XK`Zs9N?A+H&5Vke4<hS$*$H`ncl0D*Zd!g>
zLsw&2ej0+)sWPjTB~=UqabD8$1!_ql024)cHLMmcv2_;v1**fN^Xk@Yed^-v&wjwQ
z#9v6`{>QIB1&HyB=gyuvGd+F!)Txu_UU=yy0;#|K9u}GZ@t^+<t^Fm^+OHoQ8R+Zj
zYHP+*es%xA@MwQ$L&b`JtmNp>-~cA6%PA}%rKr_M#izuCtK{Ay`y~q&F;7e{U+Oug
zL9ZGRTCCvWdb1Wn;vKw#!jp1VHukLDyyx(#i`Vab^vS0J%!V%G{`xnQz=h3UDB<GU
zZ@&8S<9A8ge{<^Cp>1Q`^+oAX!Jv=^vq*~>d4B+j$A*@&<4E~`?xP5c&nl{EU(;T*
zA`>LT>;yetl<Khrbx0TQ04M;9s*17_BSNI^c8l3?PmOfiJtwZb^`!thA$7~0Ti35#
zJbUuwsngSE&YnB}o9AD=^78eYw_b;O{_}6X|JNV>{^u_~c>U_RXZCHK90R6NePs!$
z=i65G4vwxL>S?KeDFJW*Croj@9Y!z*^Rr-oFF5MWmN559f9ic19HJGhYWM_t)<PH>
z*m=m*QK@-l4XXwxw(kcf$*tROzVkj7FkgT3hi||8p2{$z3iBVo|L!}OWqkA17oUCl
z$!|Y=|6Q2XojP`K&(s(o+Y-V8#0*loSjhVW`rzl`>=N&PMrhk4A(6@X<xQ(c;n#={
zm3zR)4r#Yo3^or~;(N-~kx3-)Eg%`M16f+|81@3U(73$X)sy>Az4XpIZ@-1T`RlLV
zc<IuGbEls@cKq2>(+DrVeCg7QFTQZ`;`6|ufRV=!-+lhU?Q5`-nwl8yZL2RY%vxSr
z)!4CmXnfOuXn#jTIZ%hRN`F$iTbUV2UU|9M%Q8rBpNMz7i><{1v!%9<ZUD_vDilFM
zL4kpwGZ_{c9hV4fFFCTkSk`{}49qz4@}(oM+P1#YO~8b^@Z!~LH($H+_WK`x^zkR3
z{{HhXzWR!M2M}zM@i9>T-+mK#^fxY@KlwCC`@~3pcVk&za%6~~NT}&dc5x5ndPm(Y
zOH}$Ky{}9I+SMI{lY?!Qd5Jo?n-x3a&6%0FiwMeqge)L-gJx`$TIyya=ui2FrxiC1
zZaFml^2;w>y>j`|i^z66`7DTo9DU}w>GKyazx>L}mtXkJnUlw#dh+<$7q8!b|I=?!
z;pN7ErL!>2Tf4fslDtmoc_lS1Jwp>ybc~gg0yZS1fv_^?dOMvh^;IA>kwWbXN#f_5
zTiVlol13FmMMXhRS*;Dzo2u^5n-$C*Q@vWa$jS+JiP6bfD=Hd0*Nku8edwv<C(mBE
z^wKM!;z6;4zeU3DM<0Fo!TayN^EUY)UcYsJ^9DfeFPuLK^plB^zRo(qL5WHHn?3em
z^G>BuxUFD-E+M<DsdwG>@$TBf<S@Bt2{oJ~y(GCnNxdxgmMFtPgt}!lz;1IAKu^w*
z3Q&o?0Fc`@y!GJeQzxH0frOdEPaZmW07U8zKK1PMg-h371(Mb2V}~A}+OXrm@pD&y
zZ@u^X@4oyU(4;P&IdOm-cnu|4ail}YFKb*iJPAb0c4#e)rniFL&bEo+o>sU9V)dFJ
za420o-`v^}9IrwRdL39vljhIc*N=)P&aiEoFLd%h>A|tgFSR2#k3tgxN^g~ozyqAx
zz3<7VpF4Bm66K5BzVp^wNcMf>4M5j_-@E~I5`kNDbl=X+>xNdhH&l{JCQ2J5akFR3
zxP=dJ2a5r%5e_(AKszi*51Lm)oAz$(Z!As)NmFoWTLkwFxyiu>ScX!>70o>(8;3e;
zSENU&B+QnOgfuiEm$Wup4;<L{#N)enZr`?b>(tb?ZQHl+*s=HU@#*KU-hAVKJ<uyW
zuw!Ddb7<q9qi3$Z{^8f3ef-{=w{Bd$c<$)V@$Q;}<S_KAsp}fvym#CB!BveFh078+
zK~vD%Idyo~<X}74o`<XOomf17v5m7>s?bJ)lbH&b(IPkUHhO|Hp_jXhBQA30&C&pe
zn*lGT0*6XXWPDm~QDsB>>cO#p$*JA@j~qXJ_WbiN0CDD3m|5MpasB!=p{JcbdE(g7
zClBo1vvbq<U{70pMNw{Ad?Zztq2T#N_Y%IH)xa*Dy7G}{q%?qD*!Yga+lJd#rtAHk
z&8SccIdv9WF&khgW$<&H=x-^{NzlpNMXpSkUmQqZoXwjiLGNpL5IDhqqvPv0Y}~x-
z;4$<^{@t4|pFO$@r0*Jf*YA4j+_kqp11$0Fe<8)diO1J>S7t{m+~i?N`PH2xTMzEq
zw6?dkrZ^`VDmYJ&MHKXQUcdAlhykr!hQfKs-2||ZT&0go1G$+XSZr~1dM<D%<b4(_
zM(GdY6{S*3r$sf*oqfZ96I*ueJM#4L=T4nD&&!$@E`o6?w6sqhIkf-rUE2XzHrU+?
zx>#x8!Wn>`+B1}{XC=k=4itQYkt{i>y?s<MSu0z?z5MZYT@{%T{!T{uRdaN2R%rA|
z*(G(IgBx}pBppayVOpdD)u&K2)X_&70V=|4sKQ@!V|`O=XLlce>C4t{+xPVJrJHYk
z{PnxnrVnoFZzxF2ujyX5=b4MQKKbZ_ci+Bq`?XhZOz)pqRhF*vwGu0%vno2)Za@0O
z)VhHVaE{~zSV3>++xM=W-apY@m5si%ZVu%7w_xy%9O3K1Ys`vSa0vQfvoouoH;<JH
zf=Z(Zc{S0A={ZGz74_}C!|S*1e&WEBM~|I2IeiX$+0*3ygKFo{fxVdY^<%>Wy_ojw
z)VK(Z5_I64Y?qopzya-yK&u83eC}T4NYp0eRdtPSKYZrk#?{qXk^YV*lBNPtei>=^
z1}1hKJh5-{U|U&stXjei6PQQD%EtPds`8b^E0*WM_@udiqo;p()8j{{FW-9a^FMub
z{nX=YTMFY9(K*#UllxC!!`%%m=<YoK<YZS#qS6J_Y*I_x*6zf0RX0NF1@#R<Z-)Z(
zhU#-8Gzu9h_(`Y7v^SR(WhVpvUu1Vr;Pvcm=Xfk&g7Zu5oZYddrPg=ty(1eoPwm{Z
z@8IF5j~{=31|(UJ9w8t46T5dzZQeLBHoOK$lr<oYgFfEW)p3d0102)M3=a&LeuJTm
z5^Q}}jZPh$8tJUcP0&i6IR}L_&?4N<ki<50kB~&dfnA(#P#UG{5Q`m`QCLZ`PW{W!
zr=q5zrE`$HD$ibc`SypeoH?+uyNbM2VW~yf0>IUOcjYqa@SZ=iwZAUMAa_JH8}bH@
z?cTC(u)DdM*;BDEKBIPI@A*65eQ;y?z=j_B#Z$I6LMyd~q^#tauwc^R*|4u?zR6C7
z^Ur3>O?tkt3$}4YoV^O{Me<9ln>zajN5<AqZl2n)d-txLJ9li~zHMsD#trL6M+VpQ
zc6YRYHq}*ElosV>B*l<>$qzR&Na6pP@;(n0`@OxqJUzie8th_=YDwKS($id7kP)i`
zniQoXAV1W{U!jRfFQ^2urUO%JyK0svhX=S*QBe@Jbr8$chNNurt><QErX?pBoqk(E
zvh&Ev=dYbPv}K@iMUvK66${llv;dScfB4CNt%FTPpp`yP;BAc!^t3m^nuavuD!CUV
z-YF=)sAJ;b`8O_~Jg{XD?|9m(nUgb#6nDzds6^^#Pr5gLY8s_60I&)`tyxRFfC8{$
zwQsP_5TBM)xU#0HwWF)2Z_Uu!v9Zz7wId_L!$SjotGm0<Zmqh4d>Z*V%TN+JNb3E6
zAepfD4k?5m#ny<>XCSk%vSoE=b8UH1-m(;<iXoQ*2v5i=sqY%zxMy-rOL=yjR%(Ph
zay|yhdP+iEbYwV=PcpJ{xzG(s*HG)q%qXQeFg!WGx?_0r{=LuuOl{dT+22%}X#h$r
zHtXqy)g8_CRb@rXvoqiurkdCGQhi2$<*JE&(?|Dg9O@udJ$Tj97T?Sgst{6S2?JiE
zIuvI-1qwvZpIp_m;BG=oVO%Ws@k2&uQhH87Nm)g8ZGB@)TSrHGds|y;YfDR00~qX;
zmBJ-R^(CV9+7MhnaZjb6Dev<FApE7i-eM@xJfz5uFRretEH49JFw#jysAN=s{t6r_
zJiO6Dqq3!MeA`%0eKAaz$@k9q=nRA(PyQq{4Oi3gNlaW~N_G(_C#~BGvRxH9@u4!2
zp9+<V)@)cmzIJ$^zqh-qt|&8BBNbUuoyBO<J(R2{fG3oa6s1$jJfWQ<w?IrzWyjhb
zlOw%tH9(}|Ek4(2qGrR^7RG&lCilOg99tWU*-Jdjys$#>^pR0P>J*ePEG#N6Eh}5O
za%E|02|Uh%{M?+Z%#5@YYGe@(tQ2TAjn0feQ{LwVLj=iDRoelVEgF4n3KCW-YU-QY
zstTBb6bBT`Lt`_G$#nn%T=_}ifnuX?4Hkf~tB*D40evlJ-V<6~cx-Ba_KJ#zj^2^(
z1~&Eqr6L@1ceXV*z}Z)llWfrViyfCxH5<~Hfg4#$a#CVK0*vR73S;RiRmYGne^pyU
zH6>LU&m_uj@nz5zQf+dr7;+T(28EJ>KP5(|L^9~CJzxRhX}$z7cuuZv%$x?~`Qrd6
zmYSZPmX?M-Tgi!_+z#M>A@r>YQNozX3jr6ngQbYdS^2g8X=ow9PDm_rw6!FqDM+a6
zA`>!TYPxD*T(AO}&nd^GWlPE{Z(coyC@HN>WXUED^XXyhPY&DgP(aH_ygfX<V0tE#
z!`GIaMY`N3r1^td8;Dku61cFktORbm^c16yt-yI>CPgu^#_S4zl&-?%c{y1bU_foq
zve}OCYKjFVb7P^j#L+7NOr9zVfa&e+Y&naGXKeyQG}z<#+E?ly$OVf*xdp`ibO01n
zt3rY)P+jIH@$~_#TRi2BhM7N8ghL=L&(+=v9au#k-V##efo;IrEqgci)fXmeeC_5l
zl#UgcK*xaO@L)%OO<|fL*vH9eSVRRvNajPphzB|p@N9=}5zGjn>d7tuk2=I}*tvSa
z5<h8KdU8UH0bUg@u_370NYT#=E(;jXxkKW4a}jBP7a~3Z>@evqfZJu*I+z}t^g;5X
z0Y-glxJ_m+1Ta6AcuOt0!5MkyeMpBa@q;1?LLwoV_JKlwTFf-p0nLU6j?|R!v7a&T
z^P(Wa5H4%GWFZrCw|A84Qi_@fNV8>Z=moK9+(3hiNOM_kVpyQq&WH@+#!_%~AmNLy
za;>bb!Kf?{EaW4!3YnLy{ZeuxQx%yAWaQygl@+!|4+9sHd@y%1E@{xo#=*sn{40SW
zT76WUaj3_C3Tz8L{4)NP+`OWzrT|1QZD#o?^3R@J_6X?*c(<+KLgK(+OaNrEv$eIg
zv4LH;6}4}tzpSu%pXJ}{J`oSW4^kdn$m-mI^&Lg=3TNZ5bf2$>%l-ke3b5|R8g-$C
zmAwm~C%AMxH-x&6>?FH8LJeRmkIgy{iogkrF$!#d-X{PPnNvYZ!qLvE+*pvgedHqo
zLaUU~Z8r}GS;R6_?mM)pw`S%DPjG>*EhWX6O6qQB(k=^BLJ!LCT(m&oI53w=Quv@D
z7x+t?QT&F2@&;zWss!yC!IIE`OB!;XFBaM-GjodeC6y?z>hBKjM<EW-#b=i`bdRp8
zWiq#ak9=q#Oyt~I*!pM^3hMhd?%UMg0Mix|A2Lu5RU49X5E&CJ6FV85G8it3n*Wef
zdm-2Jr|W)qu@{4sS(rU$VdLx}2~dSalZ%EP<2*P|#T_O_`k2IY9NCh>fICAl>j`oV
z+((j}dy@cHI;*gvp|iFqbGGmJUuXnD+%4{ZOpSf&SFh`BD9(sfh)wLMpfysE)+jp-
z0j}O6qplofl#x%Hn(3M?oWBrX1xFXAQ10#lRV+Zt%okcZxcdfbNYl^9u{=<_2F4+>
zAXS(lJ|z?0u@I@)(Q?5eY9>T}BCDNY?&a=eyX0PkC_Zfrk0L)Lx3CmILt$uX{KyA?
z)|+?xkqZ}jJH5zF@u|#9I4UX55QZw7kY>8{qzGq{7JHoVle!w~bGY>>1Itb1;^bgQ
z?X#>&uda?v$Sh#m_$GijL19Lr)<wo9rDrcM&PhQMrxnLqdcm3<-F=u4kMPTzHfn5$
zYrt+gV_8;qZfXo^C1(|U_e%}j(U>rQjahJ)IisY25Uion0JSAJkWL_&pEJkjTO)di
zoc`2!n4}Oac5xG7XKRC2ujY2{(qQ-(s=1@Q=|4f8B}mau(q6QZOSRKRkGx3!GSVbT
ze0_bGGmW53Jp)yRO@9P7{Y-)PJ_W%e7BhY4c$lik(2*GrKKDkM1Ubn7;{RuVYR57O
zMo6KHC)8(DBa77YRt&vq;pi0z9z@mccROC;m#WYj5>qpCNZKoFo67T3qt!AGyG2W^
zNsmV^_Ym?bF()chW$KwMnWfG!ZOSQaKt2wT{R02-z3_;IHG~yKd6{&P{II2CZ__8m
zwSuKGW}{{l6XSXl!Y16@nhnW+^~_?P8>=pH_6?3qU0&I`rnLfao$e-;w!qoQ&MPb_
ztEy{i?W`-#iq{2tIzTIc!h+cRBSV^-=FQ{76e071S6+VyVi`jNX9(Us+JW%uyXlw<
z1X{}7LL*!Z0)e%FV>R|;!W@HpXDnuXmy|d?6QaZc7tA=o+c3MVxo@I>w-M?`Pm`ev
zZ5)!aOUgjts;#T1zojBSB|_=r2sj`w^e3iolC$j$ojU<f#}aQXRv0o%o7Zd_>ZmSA
zi3swfchFBX=X_Ly&u;l#?LG~asVcG(DGj_KgPr?|x}Nb}<EyHvcMuQ3{;s~UrM;`C
zZ(wM&yS_LxS|xENJ)oF>TtWI++|YyE<BUBfouKi|lcqGWu%Ukw7?ROV|F0p0ISn%!
zi@1W`KVD*RqA2<Hr05Nfzz322QceMw$1ht83V@7F<)As!p-KX9kH&lIi!%(t-u4TH
zX}7gAIVJRQX?f&}udYKvRRh(ua1lm2k?{tdD$vhM<VZ0!xYuHTdotr1o|=xf3~U){
zugHzp_&J&Jzw;+5V*K*Mng&B9a@maq9AtS{{<*Cx_TiE7=@b~{BeJtF-WdXR9ly{(
zH947ua+*7~dCJ*Zw!SnwF)YZ-aWO77on76;sJsx9n4S#_ZB)G~A&ppQkgqFm^I)r^
zK;kro(30DT>fexmmxrJIaK|U+mbajzOEe5_ENLvon3D(ou!Iv0M@L6kCD0ZdQqPDS
z_do&}jg&p4!Jv6rUbu|fBU$lco@1CTtkKB9)GkD*9^meeOUo^;E(Z1`$Z{iNgVYT{
z!76Q7gdrwAF*!Az;u*3)`->bab|@zaj_X?7X9vnDjsdoR@3=x*Ohw9R<^}L)Qw8}I
zBUGd<W&0AeQns6;NB(dG1zUQ63!q5kDtI%)amn!XcQ1`NQPfke1(n;DYJom!!`gSF
z0F{Ncy|cTwpWL`|rL`eRF|E;(GV?O%X4S>kQ3PMJN*5W48-xhGJ~9f>3`xlVO#|f%
zH>$@Y3s8iAgn&<mMx)V!aV-33LHEeg$mr&{+uZa-ip>M(aaUJYq<^@~(IbC2!jG<s
zePL7V?d2hGbQyhy-r9F1o-Sw|ycGJBeA17BDqDP*!iPa}7cCF~NsDlY>I}K!!$FSZ
zA+Li%6{?SlGlXgAd*S9S4blKMJtYzBhO{b0FiL2D>caJ*Y9$qg5xI-OBEvrbj|IIU
zI*t)4ErqmO8~vq)A!Bm9VBwCyQbd-Ln42g5kOa=^g8~Br<o^CLsV_LR0nFE&Q>X6E
zyzn_Y_=H3+D{bnV03F8k7>&%0!)@uiVP<a5=+~}-p37rKt3H}OMX)M3NG_9jd%C-N
z$&{gghJ^IoVy*+jrm_L#M3+i@ygfaT6ounjJQ`vW)3RV8k6>!hyF!^WzB^cirowV%
zs79^AaTEC)WiavsP=rJ>SI_*R2!vjd=YohJrb=vQ#i^3_v@vC{0|#GCLViv6_^u5D
zZIyXOZ+P>^jCBCRB-j~~L3j3Up1!_t%8;Oc;BG*r_$fk>cG^&ZRYQQcvkQ<tNU{O>
z|D%uu0-7r`AId(E<g=sj7h8J=4p=qf0}%ZMTC9{p9T<oh)46%#4@pEqfSDNhy=s(*
zlW?#R)Gz*_;u4g6_I|pQl9r*VBfHl3HKD<^ryG`83u)pZ?+`GoGg_YG;14P^i4XaI
zfL)v%;6y;El6QbMDmkaPrnR@ZB0n`UMB=*ClHqr_=$f~eCxb0{$<+}Fnaj)SVJ4@M
zdpcTB0>7h^5y>+j<x$B0nU#bp@U+$7YYs6N&-<Z?1c-PvlQTp{goZHSITt>;4;0K^
zs?V(K-mriA$PUVKm6PVm$<7Lg_N?W9TVf=wvEUN_ekf+dpg4%I3yDn1DXr@q*wEKl
zl3|!J%M-p`KOZl#JFfWLWoiSkse3@n4Alz-T|)tDX)>2JvvNQNHTfGO!MQFB<3T;s
zUqsL^o__LLn1A~q+!h0yduD+nRQV=lzAzWtziqU;z9=JFCGm8>>x-wak1!yAB7qvI
zKs?~<RK9%=42@37E3IwsZ>=myi&FWCP<jPbt|*X$eDUOzfZoH-MkEQ=CuSGdf~<RH
zyk6yx2u6f3@-Lo;nK1alPhPD?^E}mDJ@bbm=pB!!UvkOIrBoBxlHJS>sA*`8=-<Uc
z#V6eQFbU5nY3iRG>Z&UtWqlxj;3X-Ej^FVXTsubytmr|dj`ZK4LA%h1pOgiI0akHU
zb5(H;`W}e@pX&jtcB~5UAZaI`JLoLB_yp<VG72hy@&jhG;VLDpegRv3w}QRNMv-0e
z5Oep`AA(RoKv^$`vR>lNM3<;04!L^C7vuyVnDq=$C?5Wn;69pF($K?yEHwN~&RCXt
zbE+q72T)T-8l=Lf4VlEtjV7LNeSG90VTit8k(-ee9j;M8MNegnl(>Wylds(g#!S|h
zOF@Jal&R~h%S%!9G!geUAa82AucNWZ!pff9FA_N~>mlausXqikxpP7z5BDd+X9QhP
z(n7(12Zt-F-C1$LhWU(t8=F~JRgw$HD1T39<6d9r_33oh37}UDeT5tgu(G0wCj|jV
z*fH>ZD)KF3fQ+Nnh3O*<QPHuHIyH4Wb6}!-Fq=V5zN*TK@)(G|xBvhGy-7qtRI-(&
z#YL#>4~^ZAx6Hs|UyKS(V%9{!`9We1AN$+~wnlt(hX{Xw$N;>52@d1{=%2Bk;!=l}
z!ebR^$;(n=BQ!x$Pgj#qO;87C-?At4ZN3;1iJzYoR{0W7o_Hp+=Yoz1y2w}_IJC`J
zmYthUakeg0a}UZ6TaiQo{LGc*l~px$Ack4BA~QZL$j6!LyYq^a%NN+=`$tE27<2pN
z=Qu#W#@S?8Pvs1MEun-)eaPS-$h|k<<y>9pIV)tBvQrh>2%QQ|C+&^1Qen!&8)<(z
z1a{BlQXwa<zrV2sfITm{5^SiZNT5<3iY(idbdvG>!lIIu<$}$olO?@vNMccM&ybXb
z0G+n3-hq)-bwwFbDv2}K62U(Z_e`)6#8HwdyW!DZ(pp=8&)m<kuysPkICWSk7!b*2
z62_w@7d~VjM=_{}1gVmMPm%*bXU&YR&2&#=4*wIZs)C_&R?$Hb>{evt;C8aJ<h>tB
zy|pdV9}WzG6F(~-)r{+#TG~nVn4N$dJU&*kbP~%#^l|C=B~=aWy+i9Z?;P*0El3QN
zIvdN+dBU21kiqmVXa13M`rzj}%#A@xc5|T+2d;y^QWG9cZd$EM86=khhh7YyJ;&s5
zv3@AB@%;bMd>52+DCRCWRHxIC&N)0hT&Gp5f)(Ul@)fhhvr5p6;xwTZ_wrZK;Y<^1
zn+^_-b=MW88<gJm!s2Mb{3Z5o5=CfKa#m4QOV7xE<jw=n>>leZ&tSSee6b|#38_hq
zNn82TtV#aA1s}WsTQx|nk4a)m@$|&U)ZaZGxlag_6GK!~RCIK7OiT><aiJhXXUd%7
zx-^&^6o?H=%qeZ?=^GdxTR*vF+dWV^oJw6--99k6`^f1l&+Hg#%!>|in)w&a#N_-M
v_l{pE6aR?DuZe#|<JZJLqVa3uAC>q&pQy4m^R*lr00000NkvXXu0mjfBM7u~

diff --git a/docs/articles/examples/vision/mnist_convnet.html b/docs/articles/examples/vision/mnist_convnet.html
index ece6a7dfb..0806daa18 100644
--- a/docs/articles/examples/vision/mnist_convnet.html
+++ b/docs/articles/examples/vision/mnist_convnet.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../../../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -198,35 +198,35 @@ <h2 id="train-the-model">Train the model<a class="anchor" aria-label="anchor" hr
 <span>  validation_split <span class="op">=</span> <span class="fl">0.1</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/15</span></span>
-<span><span class="co">## 422/422 - 4s - 10ms/step - accuracy: 0.8895 - loss: 0.3636 - val_accuracy: 0.9787 - val_loss: 0.0793</span></span>
+<span><span class="co">## 422/422 - 5s - 11ms/step - accuracy: 0.8895 - loss: 0.3636 - val_accuracy: 0.9787 - val_loss: 0.0792</span></span>
 <span><span class="co">## Epoch 2/15</span></span>
-<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9666 - loss: 0.1112 - val_accuracy: 0.9852 - val_loss: 0.0550</span></span>
+<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9664 - loss: 0.1111 - val_accuracy: 0.9850 - val_loss: 0.0550</span></span>
 <span><span class="co">## Epoch 3/15</span></span>
-<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9743 - loss: 0.0824 - val_accuracy: 0.9882 - val_loss: 0.0439</span></span>
+<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9743 - loss: 0.0824 - val_accuracy: 0.9882 - val_loss: 0.0441</span></span>
 <span><span class="co">## Epoch 4/15</span></span>
-<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9786 - loss: 0.0695 - val_accuracy: 0.9897 - val_loss: 0.0399</span></span>
+<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9786 - loss: 0.0695 - val_accuracy: 0.9895 - val_loss: 0.0400</span></span>
 <span><span class="co">## Epoch 5/15</span></span>
-<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9803 - loss: 0.0626 - val_accuracy: 0.9900 - val_loss: 0.0354</span></span>
+<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9804 - loss: 0.0626 - val_accuracy: 0.9900 - val_loss: 0.0355</span></span>
 <span><span class="co">## Epoch 6/15</span></span>
-<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9823 - loss: 0.0557 - val_accuracy: 0.9912 - val_loss: 0.0332</span></span>
+<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9824 - loss: 0.0558 - val_accuracy: 0.9912 - val_loss: 0.0333</span></span>
 <span><span class="co">## Epoch 7/15</span></span>
-<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9837 - loss: 0.0499 - val_accuracy: 0.9918 - val_loss: 0.0310</span></span>
+<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9835 - loss: 0.0501 - val_accuracy: 0.9918 - val_loss: 0.0310</span></span>
 <span><span class="co">## Epoch 8/15</span></span>
-<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9849 - loss: 0.0481 - val_accuracy: 0.9920 - val_loss: 0.0310</span></span>
+<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9851 - loss: 0.0479 - val_accuracy: 0.9922 - val_loss: 0.0310</span></span>
 <span><span class="co">## Epoch 9/15</span></span>
-<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9861 - loss: 0.0444 - val_accuracy: 0.9917 - val_loss: 0.0302</span></span>
+<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9862 - loss: 0.0444 - val_accuracy: 0.9920 - val_loss: 0.0300</span></span>
 <span><span class="co">## Epoch 10/15</span></span>
-<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9863 - loss: 0.0439 - val_accuracy: 0.9913 - val_loss: 0.0297</span></span>
+<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9863 - loss: 0.0438 - val_accuracy: 0.9912 - val_loss: 0.0294</span></span>
 <span><span class="co">## Epoch 11/15</span></span>
-<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9872 - loss: 0.0393 - val_accuracy: 0.9917 - val_loss: 0.0306</span></span>
+<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9873 - loss: 0.0394 - val_accuracy: 0.9913 - val_loss: 0.0304</span></span>
 <span><span class="co">## Epoch 12/15</span></span>
-<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9874 - loss: 0.0372 - val_accuracy: 0.9922 - val_loss: 0.0289</span></span>
+<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9875 - loss: 0.0371 - val_accuracy: 0.9927 - val_loss: 0.0287</span></span>
 <span><span class="co">## Epoch 13/15</span></span>
-<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9888 - loss: 0.0344 - val_accuracy: 0.9918 - val_loss: 0.0293</span></span>
+<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9891 - loss: 0.0346 - val_accuracy: 0.9920 - val_loss: 0.0292</span></span>
 <span><span class="co">## Epoch 14/15</span></span>
-<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9890 - loss: 0.0343 - val_accuracy: 0.9918 - val_loss: 0.0282</span></span>
+<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9891 - loss: 0.0343 - val_accuracy: 0.9922 - val_loss: 0.0284</span></span>
 <span><span class="co">## Epoch 15/15</span></span>
-<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9894 - loss: 0.0322 - val_accuracy: 0.9915 - val_loss: 0.0284</span></span></code></pre>
+<span><span class="co">## 422/422 - 1s - 2ms/step - accuracy: 0.9895 - loss: 0.0320 - val_accuracy: 0.9920 - val_loss: 0.0282</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="evaluate-the-trained-model">Evaluate the trained model<a class="anchor" aria-label="anchor" href="#evaluate-the-trained-model"></a>
@@ -235,10 +235,10 @@ <h2 id="evaluate-the-trained-model">Evaluate the trained model<a class="anchor"
 <code class="sourceCode R"><span><span class="va">score</span> <span class="op">&lt;-</span> <span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/tensorflow/man/evaluate.html" class="external-link">evaluate</a></span><span class="op">(</span><span class="va">x_test</span>, <span class="va">y_test</span>, verbose <span class="op">=</span> <span class="fl">0</span><span class="op">)</span></span>
 <span><span class="va">score</span></span></code></pre></div>
 <pre><code><span><span class="co">## $accuracy</span></span>
-<span><span class="co">## [1] 0.9912</span></span>
+<span><span class="co">## [1] 0.9914</span></span>
 <span><span class="co">##</span></span>
 <span><span class="co">## $loss</span></span>
-<span><span class="co">## [1] 0.02448307</span></span></code></pre>
+<span><span class="co">## [1] 0.02402576</span></span></code></pre>
 </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
     </nav></aside>
diff --git a/docs/articles/examples/vision/mnist_siamese_graph.html b/docs/articles/examples/vision/mnist_siamese_graph.html
index 82fa4a6b3..0a25d5a4d 100644
--- a/docs/articles/examples/vision/mnist_siamese_graph.html
+++ b/docs/articles/examples/vision/mnist_siamese_graph.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../../../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -238,45 +238,45 @@ <h2 id="train">Train<a class="anchor" aria-label="anchor" href="#train"></a>
 <span>    <span class="op">)</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/20</span></span>
-<span><span class="co">## 469/469 - 22s - 47ms/step - binary_accuracy: 0.7565 - loss: 0.1644 - val_binary_accuracy: 0.8782 - val_loss: 0.0989</span></span>
+<span><span class="co">## 469/469 - 17s - 36ms/step - binary_accuracy: 0.7566 - loss: 0.1644 - val_binary_accuracy: 0.8753 - val_loss: 0.1001</span></span>
 <span><span class="co">## Epoch 2/20</span></span>
-<span><span class="co">## 469/469 - 1s - 2ms/step - binary_accuracy: 0.8886 - loss: 0.0873 - val_binary_accuracy: 0.9220 - val_loss: 0.0622</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.8896 - loss: 0.0867 - val_binary_accuracy: 0.9248 - val_loss: 0.0614</span></span>
 <span><span class="co">## Epoch 3/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9246 - loss: 0.0590 - val_binary_accuracy: 0.9380 - val_loss: 0.0485</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9261 - loss: 0.0579 - val_binary_accuracy: 0.9409 - val_loss: 0.0461</span></span>
 <span><span class="co">## Epoch 4/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9456 - loss: 0.0436 - val_binary_accuracy: 0.9493 - val_loss: 0.0398</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9474 - loss: 0.0419 - val_binary_accuracy: 0.9529 - val_loss: 0.0382</span></span>
 <span><span class="co">## Epoch 5/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9572 - loss: 0.0343 - val_binary_accuracy: 0.9582 - val_loss: 0.0331</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9589 - loss: 0.0328 - val_binary_accuracy: 0.9603 - val_loss: 0.0316</span></span>
 <span><span class="co">## Epoch 6/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9644 - loss: 0.0283 - val_binary_accuracy: 0.9661 - val_loss: 0.0273</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9660 - loss: 0.0272 - val_binary_accuracy: 0.9617 - val_loss: 0.0297</span></span>
 <span><span class="co">## Epoch 7/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9707 - loss: 0.0236 - val_binary_accuracy: 0.9658 - val_loss: 0.0271</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9708 - loss: 0.0234 - val_binary_accuracy: 0.9665 - val_loss: 0.0270</span></span>
 <span><span class="co">## Epoch 8/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9747 - loss: 0.0206 - val_binary_accuracy: 0.9692 - val_loss: 0.0247</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9747 - loss: 0.0201 - val_binary_accuracy: 0.9674 - val_loss: 0.0259</span></span>
 <span><span class="co">## Epoch 9/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9771 - loss: 0.0184 - val_binary_accuracy: 0.9695 - val_loss: 0.0238</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9787 - loss: 0.0175 - val_binary_accuracy: 0.9697 - val_loss: 0.0247</span></span>
 <span><span class="co">## Epoch 10/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9795 - loss: 0.0167 - val_binary_accuracy: 0.9697 - val_loss: 0.0244</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9807 - loss: 0.0157 - val_binary_accuracy: 0.9684 - val_loss: 0.0251</span></span>
 <span><span class="co">## Epoch 11/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9814 - loss: 0.0152 - val_binary_accuracy: 0.9726 - val_loss: 0.0218</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9810 - loss: 0.0152 - val_binary_accuracy: 0.9710 - val_loss: 0.0230</span></span>
 <span><span class="co">## Epoch 12/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9831 - loss: 0.0139 - val_binary_accuracy: 0.9734 - val_loss: 0.0210</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9831 - loss: 0.0138 - val_binary_accuracy: 0.9726 - val_loss: 0.0224</span></span>
 <span><span class="co">## Epoch 13/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9841 - loss: 0.0128 - val_binary_accuracy: 0.9748 - val_loss: 0.0202</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9854 - loss: 0.0121 - val_binary_accuracy: 0.9724 - val_loss: 0.0224</span></span>
 <span><span class="co">## Epoch 14/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9852 - loss: 0.0121 - val_binary_accuracy: 0.9755 - val_loss: 0.0198</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9861 - loss: 0.0114 - val_binary_accuracy: 0.9738 - val_loss: 0.0217</span></span>
 <span><span class="co">## Epoch 15/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9860 - loss: 0.0112 - val_binary_accuracy: 0.9730 - val_loss: 0.0217</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9864 - loss: 0.0110 - val_binary_accuracy: 0.9738 - val_loss: 0.0213</span></span>
 <span><span class="co">## Epoch 16/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9868 - loss: 0.0106 - val_binary_accuracy: 0.9758 - val_loss: 0.0204</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9881 - loss: 0.0101 - val_binary_accuracy: 0.9754 - val_loss: 0.0206</span></span>
 <span><span class="co">## Epoch 17/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9879 - loss: 0.0100 - val_binary_accuracy: 0.9754 - val_loss: 0.0202</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9883 - loss: 0.0097 - val_binary_accuracy: 0.9733 - val_loss: 0.0214</span></span>
 <span><span class="co">## Epoch 18/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9884 - loss: 0.0095 - val_binary_accuracy: 0.9773 - val_loss: 0.0185</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9881 - loss: 0.0097 - val_binary_accuracy: 0.9723 - val_loss: 0.0216</span></span>
 <span><span class="co">## Epoch 19/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9883 - loss: 0.0096 - val_binary_accuracy: 0.9747 - val_loss: 0.0207</span></span>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9891 - loss: 0.0090 - val_binary_accuracy: 0.9749 - val_loss: 0.0202</span></span>
 <span><span class="co">## Epoch 20/20</span></span>
-<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9896 - loss: 0.0087 - val_binary_accuracy: 0.9764 - val_loss: 0.0199</span></span></code></pre>
+<span><span class="co">## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9898 - loss: 0.0085 - val_binary_accuracy: 0.9752 - val_loss: 0.0205</span></span></code></pre>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">history</span><span class="op">)</span></span></code></pre></div>
 <div class="float">
@@ -290,7 +290,7 @@ <h2 id="evaluate">Evaluate<a class="anchor" aria-label="anchor" href="#evaluate"
 <code class="sourceCode R"><span><span class="co"># compute final accuracy on training and test sets</span></span>
 <span></span>
 <span><span class="va">tr_pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">model</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">tr</span><span class="op">$</span><span class="va">pair1</span>, <span class="va">tr</span><span class="op">$</span><span class="va">pair2</span><span class="op">)</span><span class="op">)</span><span class="op">[</span>,<span class="fl">1</span><span class="op">]</span></span></code></pre></div>
-<pre><code><span><span class="co">## 1875/1875 - 2s - 856us/step</span></span></code></pre>
+<pre><code><span><span class="co">## 1875/1875 - 1s - 793us/step</span></span></code></pre>
 <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">tr_acc</span>  <span class="op">&lt;-</span> <span class="fu">compute_accuracy</span><span class="op">(</span><span class="va">tr_pred</span>, <span class="va">tr</span><span class="op">$</span><span class="va">y</span><span class="op">)</span></span>
 <span><span class="va">te_pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">model</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">te</span><span class="op">$</span><span class="va">pair1</span>, <span class="va">te</span><span class="op">$</span><span class="va">pair2</span><span class="op">)</span><span class="op">)</span><span class="op">[</span>,<span class="fl">1</span><span class="op">]</span></span></code></pre></div>
@@ -299,10 +299,10 @@ <h2 id="evaluate">Evaluate<a class="anchor" aria-label="anchor" href="#evaluate"
 <code class="sourceCode R"><span><span class="va">te_acc</span>  <span class="op">&lt;-</span> <span class="fu">compute_accuracy</span><span class="op">(</span><span class="va">te_pred</span>, <span class="va">te</span><span class="op">$</span><span class="va">y</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/sprintf.html" class="external-link">sprintf</a></span><span class="op">(</span><span class="st">'* Accuracy on training set: %0.2f%%'</span>, <span class="op">(</span><span class="fl">100</span> <span class="op">*</span> <span class="va">tr_acc</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## [1] "* Accuracy on training set: 99.71%"</span></span></code></pre>
+<pre><code><span><span class="co">## [1] "* Accuracy on training set: 99.63%"</span></span></code></pre>
 <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/sprintf.html" class="external-link">sprintf</a></span><span class="op">(</span><span class="st">'* Accuracy on test set: %0.2f%%'</span>, <span class="op">(</span><span class="fl">100</span> <span class="op">*</span> <span class="va">te_acc</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## [1] "* Accuracy on test set: 98.54%"</span></span></code></pre>
+<pre><code><span><span class="co">## [1] "* Accuracy on test set: 97.93%"</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="plots">Plots<a class="anchor" aria-label="anchor" href="#plots"></a>
diff --git a/docs/articles/examples/vision/mnist_siamese_graph/unnamed-chunk-5-1.png b/docs/articles/examples/vision/mnist_siamese_graph/unnamed-chunk-5-1.png
index 58138957cf44a33e1c7e6c6de0145d80162a880c..915daf865aa65624244e9f7cc9e47887378c0cce 100644
GIT binary patch
literal 31048
zcmcG$byQaE+bz0ilvELrMnRF5Zlt?Gx|Htjl2lq!x&)-VTTn{6ySuyL+~D`yXMcO|
zvBx-n9LMl=yw6zAdhT_{b<Jzed9AOq(jsV&@g74EgeE2`C=Wp}=l6e+VZkSF#f>Py
zKaZ?LRc#>%<>~!jn86v3YX~BN#02>iof5Za9NiR^CXx2nra8zX#mN0g5s={@Nr{sn
z%PV5a$>u)iYp(q8VN^&}7DwYNZTf(wa&bK^<x9%nfyc9?Ft5ZYNJ+zIv-)WEh;cq*
z!%9Zf)I=^^cwBFzvpO0ivqxUr*sM<5exdI6!sMs0l2gM0|B7nj9wU=t&EuXBT`qS<
z9vmDnNm1Ql30WU3{$@#_p{2Fh9L|ZoR%l?r<X`8=77x>kSxHPxWSw&xS~?geoNjP+
z<mdlY?|jtN)%6$!h3+C7PoKmW6E@oASUQ`Lib?|O_a)NN;i0^O0t^W`IXN929WQUw
zEiVnW#7(3*O9Gys^Vf6=)tHK#@A100Vi9CwgT;vPS#-|N)QP%Fy-AF&w2WraeKGxY
zHysn0(wsSEv4rHJNRUHngS3j2i++sqN2iW$oh<zc;dDE<y7BptH=;vNp<SU?00zx&
zxbVvl{Ndx{L&2t&&7h~#?Wbb8Ye9r!^rNc;TJe}Hk;vB{k>ZU)9{uQ8LQnW9a-S?=
zvgOI9E#lsg2McG+SRg?Gt-4Hj;K6Kqi*;M`KTx$>OJ|q;eq5Kx>zl7tgagkvB~8$G
zL>nv73JZDZ;>LDo?zsLN=umkjFV;NLPID=BD}9Iw5<hag^=9<2TDeC58**}eL&G<3
z-WVDh$|SOVTdKaiQ|#TCYihE|$j{IJ{n#{|A~56<Wnj%I`OJxH@>b(5X`U_&go%Z<
z>`+&+BWf~>&Lp*U;?gsR(lZ4EQ3RSOqS;6C7249gYhVEPef28C^X}HLH&IMRChW_X
zVioGx>oSC{_sD5m&kp+DQJcFSbM=|dW?-PBJJe`QI;4W9txO4od++4rq@)yG$?eiJ
z^*PJ9Y(hg$E=?Eh{wa>Q`i_{HnP=zxvNrnrWlzLSX!gC|`eHt|`nQMFc3V|^F-;g$
zW2(%j3@X$V6cn~rH%P&xzw*5$L56MpiH{ONrmv^BJyDh|5P<G^dz9ki<I_+xmakg2
zC_@y6_Sj|JZ!Pw=_L0aeK7tQX8dUDEGr7_o%ZPe(z7&Q*z}BBohlPbzzyAxR_ByiA
z7ky)6!}G7-GoeLmDpm?p8VIurC5j4Pqc<Z?)pV?I<Oze`e(G$4Yj?URt=2Ukm&MO3
zEY1D!psFwec81FRV+n4~l(I>i+l#RBa;BHahby+kdT}<|--Ahzf0gBr+v`w5-|eq4
zc(1MQZq8T>^b2LclU7jVjdUGxb(hZ0tqkWVto0^Ih>L^0UpL6!wi6@t*`|&UGw@I*
zMeVd@6UJRo%;l<p^^%cp(<!uCLolXg2UhH*1&$~~wWg<U&QMNY-(8_0!_*ZbUxa3N
z><d)HMVnuDy<K%~Z9j<LH-+v%P;at#S$4fMyE_aC11$8>JNO-BwLH(f#}8*0f~;&Y
zHBD5Hm|J4yq7P@~lF5IxVK?7CM5V8))#Tx>tGiH8H9J|T$!SR>g6V6R>@3{HrMO@f
znsD=R#@-uxuOYqf^ktru7RPD2?G>23X^X`JJ)&_>kGW)c+x-bO)42o-hOHAptmX}}
zbchJUy6!!uXtx$0jV*yJ6I2Z)Dn*lW5JFKCgo-LoqE6Lr2aezW79alJiblR_-}%%Y
zTCx^YE9@IPzT?V$XkImgi27<M)}G_hH)qY6CKj`CS(o+qy5>7XJ{L=2ysOT$3G&u1
znXo>MFu2E$QC)<5cJ{}bZkcJ<RK)cJ|J=3~x!>w%OD92I)f)2-PhumhXWCZ4WU?i-
z?Z8$ARnJ`1uq)o?ppzOv9R)u(!@s}hBHDMNMGZcGEEUA0psDcu*A|&7kCM`}xeTNp
z`ls!1q|Ha~DdN^XW5zAPydT|wft);+vZPk(9H;fZ-kco2I4YhJ4kMzm<&uq(lxr@9
zbm#-dZ_n@tO)7Z6*2;Daoi}pXtWD9rGq5)^+h#^NcH%<8p}oF2n|t-@)!k`TlaQdG
z(?yrKgajda!0(R-9yR!=E(nP3aP;fX$7K3rqoT}aYMFU>8ZGA<Jqe=87Qs_>zn2Tm
z6<@UR?iSLJ?pldR3N_dEw3px~fX2Cqa9aKGj@z6!-itiz;NUI}mMmlFgKO_Zt=pP!
zN#K|C4HwV04HC6zI--r!<;#vVR;z(wdS#pVxAgr$=`KHxQ+p_3cMQ!Z)zgjal-<dS
zyx6{5F!MxQPQ@}QJg<a<tFQCWJb3l=^&75s>(VKtQ+V8)y<mErcgB2iB$;3Q8#xU2
zaoZANcBBn^%La+`n`(_HFx1f8#YH&Yn{j4_Y4XRH{l(+^R*N>DyGNEoD<y}TxAa0|
zor#=g{5n?8@nTJTVzp7r+hQgOT_0Vom-WH6!?kvC(b;kp2mfWFw`u~X-ctL{)x_-h
zQo-=H<fA?qcET8{RQAOg9HM)qZa*vJqm_1nQQ2caMNMrsl+`oh`Qa;tSC8;Sew)QJ
zQ`4}WDT(dMUhJT6FWYqA(7v%|`!WP0b!*;oz|hxOkM5MzX3bCpm&8+*YPFYzMlF@X
zjM(S2X*_EdW6!ZdaM6Wg{2Uv*P_^te7uWS}-QMZ#v)`qqrAKRhZ^+2t5fGRU$j`l3
z=P+=OrTqEkAXFE>O5X9Bni|g}E^E<xeeHn@*R=+ht9d>}DdwDlOp`n@b2R<3Z<BZ*
ztDc(D7)w%hqQY@B{5?t?k|3blvQurh{gv0X=;NcWDm`brpChyZ7BxH4S!>lfZtR7R
zH=Y?%Y`2oC>I*``ojRNudW|jN&wO)wYz<E<;IoHnEM_?PFu^u;&$M{^giELDnJJBa
z)f3a53Cku;`@VIO&}e<k($i9rGtHZ~dP!RS8`3Ep(O7t}5c~7!%mYG;dSlG>lTzsm
z86=LGl`_s>LzD)+i4)ygk?N}UYbkdx?ber9f)<?{5#))|R$Eg=HeY&^jE+(#o?IId
znl67=QfHM;;`;g=zH0?OJCcoX-bP*;X`E2-I)yiF7qyn&GZa(jGvt*&)S0EzsTU*l
zy3;7aN76<%+zd4$`1OPi6b}+mABMO(r!FdV?Z&`+n5ABueOzter>m<=^&!8brza#d
z6#da7ximD)L0m-cL$r@oax0mN`Up8WX{Nbi=3tIVKja;LTV15xi5|LyYfdIqb|z3Q
ziVfi_CH?v9mDy}{iFvWL^1?-1UHc4+b;Mt-PqGPX7n~_qM}&8h7z@t3f4~X!sEhqQ
z#FUhnxV_SeT*1L6JO>eEC~KHYeu&A*eVn^aZQJ;Tl1D@->Pf&7C5HZyQ!q6Om_wyi
z{96CtZQ>k+`yBd$LLY>cj}cX~16)${Vq#*5cs=Sh>l^^CnvLB#a7vWU?$kTHrB%+p
z7`J=&XPt_QO1Vg@U})n{@QbCqnG$Bpfcko_gr3n%37kFN_CN1j5Kh;>Rf(F@Xlg98
zyO-*m^*X@{?+(^@mClm*iFB;WzGG8iiPKjoHe(QT@yTbcZ!{X%7%^pwXF$h24nrb7
zuo>#>=;&BpUj8Fpq^DXbhk{yOoK=7~`qM(h+}u1{EOKGjT2}68rxnBV_F~PWB120_
zX@s$l>qtO<M3(SwGq1?yqH36`s6IkY1^%14MQ`Wra->XolGpI&xp7!yeZ5Aj;ToHC
z#E-3d5t(h4hMvv091_vdvS59TAC;(XrI5fSMlN?GH)M`iepK<rB7$*gRU(q@+}hvY
z7mj{{jr~DWvtTE>1ks0YqN6d6Oe~(;_4G}<ZS%(Ea$Gjp)jK^6p+wvqp10RML<gty
zslmahWfN{ab@^INx8DTftgZ+lD8=J?W%#X_n3%n`y=cT%Y04#~T$H{uNX_i*>`5H<
zApXAcg}fwU2yq+@nl4x8Kgb;(N~u*@%>3z~C@zPq<JO;AH6F@>fh4|seyLm=G_>Ky
zu;l)o?FBSHrp@qiQZkuqB4I7v7?0NxrU}i({ElA_S2%)fWAUu&wH-$i2{O%5w}17F
ztd=-sS6V)NSGSnZi>TNq$^tXH{lz=khnA0P=qVXiMSF9V@)Qetbm-ZVq?P7S_%IE6
z;w!dQPR(2RJg=taTu!&0J0B6W*UZFfaVe>&oKKn-MaRZsG%ST)5xaD}7Pk<yfg2Zk
z0_6(HA!6!HP>MnG-}Le*3=9ltd$?iTFTJT-&bG7?d1j<&1rwC6mm=3G`D8sP5Fl`k
zzJ7FtIT9)wx=7U2b0Wf>>T^c*S6>!?PjxYB;jrZ2RgtfLMk3l@)!_fqVpVb5ZvRWh
zhp2sBX?sB8sivH;c<)eV=8xriS;e1@+6$_vi0`QR1C3t!i-<n?_E8UO9SuP2^|)B>
z5{bdXPfcrC)91fX=*}B|x!}3#=eI7XlzbEdJ8Q;q&OP&+dlmH*dCrH3qzJv(A4<G8
z$3>fJ>q8icy}eDhTpQmuh{DYy%y5KQ-xpB8N-0-!i<?k!YXp!fs}&F>Bia0x{y8_O
z;QJ=F@cE~#oQR00A{~AUX6{P*2`uK&dl|Xw-(#Y3!{klY<YHYM*0Aa6pD{alnNz#b
zW`mPsqoWC_Xg{-<r&hiW4R31`{COsEARZVPD9#PLJz;<JxnUM#W*)cU9)o<wITplR
z_jcPfgRw0P=WskdqzX=qXcKN1*Ike|`i_N78{jXIrt81|rTebjrfz1h@oG0sAmH+N
z<EZVKjMGKQcZxvuDvJ{*E&+d3<6*oq<wuuza=A}_tBIA0mnub=ke7&K-W|=|5y@;L
z^9i$kWBs_V6F0`Lmm?gNAF&)xR!27WCEdl7&<g7v=3___D-AjA^LL}i5H7zrMc0DW
z_RWpsmq3_U#@c!_tGnCUAfM;LfyFqjtlw+H^QqH<u`U(@h*`coRheulOp=-7`e_-K
zd>M4cAjjl>fD>{NmVha!!1P{93az_TxPkQhsmY__>|~eCZq7emnS{$JjeD`gG1RIy
zosVKYab4H@)23oPA7RMAK`K?NcP>AscV{HCrNETW)K*Bu(t--?==R8GdRilIWU|6k
z`I^46gnk_#lb%mTNqOLnZQQdOyB1^lyOsWO+<`1nOq5Upv9dO(`KZalef<W5!){&n
zZnrUBpHytH+<2(5stu+cjWgzC!Z2mzj;}&2LL?^o$?PPGHMNpNgk}VlM&ZYVnI7v8
zUM6C>*qL~8`Pg7q3lD$)<d*pIEgd&3JHPg^WQ$If$cW-uLmG<2m#<m&zUvf}U($~W
zOfiMxF`r3UzdyzrP#DgmYpP%Cts{L2%cFfeJ6~S%O2kI%c-Pj}w&`Lu0YoVPg!;Nk
z5yT=s6luAGYWVi%Dmyci`ASH8pvHDh%W1(cCC^mEA3i%fTib(Xd}R*f`Sa&V*^+)^
z*sww^zs4NsoOU8CN#}l1eM&$D+p2(j{cCsUwMQ_g%Xdl+bw}Az{5FS!kNHBu6`}+0
z7<$YmO3AO1JRAF)SRt<4p+CIt*Phna)+fYk;(Xfj@;2M!B~ejP9UY=CQZ+EC1WWj_
zsNmD&qjCiYD8(w$^&)@Z$=Ud5s42LnR>P;KLy+01RfCou`-{{`9NnI0AJcP40vU$v
zFh3Jso$`%YK+r@}NLa{5-1NkIIfHlx`KZuvaKS)Ss4QY2*uagrJz$M5Z?E>IDoj~F
z{R!?qs%mkdprCNNIT-`#D*&C4!*=!WYEyXL{u()^2S_4eJeQ&|D+f#MW7@jfX#X&h
z*C_t+;zs13Pa@wFw^42!`P+Wtl9puj^N0n0ZP0tKiuL=gLRNKY*w@l&-SYxa*2QwE
z&0thgbnB<9EI;XW=29;W^R$~MA$#pjR`E3`?}t&?UZbBkg}BI*S3(F1ik)$czq54u
zxA-ESI+~|2SR}?}^)L}ie#4AM&)U$5XLu5LHJ5{z_o2XL+XsWMC?#T8cOYG8mQKUt
zd3+!7*;=Zl*!@6{*5TT6dhpxq=S~AnGOiUE0+8QwPiwk%tWx#TluelN;>0$8lJObh
z+nfHdZj*xZTb_?=?I0@fV~Gew>_UFG5;bz>iGicn0KAXo2a6bpf~wdS>ruvrY~bTh
zxQtR%gli;$kF$_P<PiBs^EGNROCLjs62EL^xb;8cLda>x5WOsxM6;;e_T4R!i%F-t
zwsbwlu?xwMn=CXL9*))o2V7Q*X%ItckYRUO0%Qrd2Ut_>Pu4enF<zfa+3__V_Q}^R
zsgFACX=|HU@r(LVBs(}uyG|R<{Mvo?#-n6{loAhmA5+BsxlMAYgj(Trs$}pSKJ|rF
zox+&g)o76CS6x+0o?TA+&8VrJ-RYVm=03&knYy!3<vaAM5HGl^p@7-z{Z<TDXOWbl
z`Zqz`_wXbE)%Cal*P6{P!BaO>-<w*ZRI<bG=d5$#xfVa!3+bAf32EizsiV5?=q0<P
zQzw%x8TLny{Zr?I!h|4_sI&}R;S0Kmw54EwOtE0yaZ&$J{TFaTFwK>=2xs-j%Ih;o
zr+e<syWL6lTIMs4pe|~Ty_2o6z2>VIs;a7wS_7P#M4s}m+l*&ezNcCr=w`5`R;aqb
zS+s&klb55&<JzaMPJPK<y9pgEMD9{ygmu`|nxRQaqG#ne#e^(XtHkis>b4dpYW$?|
zJpH5PnS4oq*OYWgbuteyLs?l_9VkV{yEPJ;Ezg3@#l|y~b;24ui$&%xF-;4=nOJAc
zhTcC$>we+Uz8YSflJT?>t?~J0;b|o&<V8xB{HbpJJd_~e@6lI+*ya*l$p+(YjA_HN
z36&W0;V%w)_kVt1Xg}c>Rl+^KH3A!QBcXB{&%|RhC!2P{Dvq{etms0ma_Y>tN_D1T
zFKo5gECJtajn5KlfYF1(@@#cmhCdr~AJ<|vNIMpMbIcg{xhlbjY>eSEyJt3C{jQ-J
zYw)gSc17)OF(t_JcH<WTpXw_5b|;UWj<Z{K(d26^s-UvEY4~|WLQk{SB6=%KOs~z&
z#sm-d<wDMK_vgr@cc1z8938gDVP<OKpnb0LuerIofa=Jz;%J&ITSL*hoAOeR8gk-C
zM0g<_?`^?|@Vof0CyO{;!wvTFHUh85-Mnd$$ptJXzXzhvEM0*r+QB1P`KNWZQ`TYo
zN-Yybc_xVq1JHXo)JF``$d4Mi**awROj6?*4NrHb&dv{(;O=gUzzP^EbMSZU>GWSE
zm6w<2N^NzT(kY*9*KCBa?ok%NLnMl&1*<Ln;UzoIrZjY?0<{`kPBusKgiM8yD-dyy
zJq*wH7qnA&j!Xshlmf`0RK1JJ(j%E<?j)9N?6;%o_V&nWqewnl_Mn1W>r2_2sjFhm
zdu~xSc_$}d$;yldsd!yU^!9e~M34oYJ~|7Md$)-bg!%YA?(wF}*^Hz1j3e~)*}}+J
z=<Qh<g+4;NKY=wHPE?e6ZUw-T9dNp&Wvm`4DvBidK4#B|BVJCG>x>|$d8Lc@2c=g#
z9ehma!N$R1wck`+NgXk9Kk8xKad4HF9}IrM2sl-M4>Pwv9!=Z!{r>YOSMF=2o~p-5
zdy4LWIs_4Nx*z<;>EpTDUdc|famB`tEi5dIhB4xQ?F-+8X&|k7RiOcXHTcvuTQn_K
z+ba<Bu}&`h&F)Oyv!_r0gb}ACCu^yw7?r3%Iy;jU#>U1~GY(Vumi6CKQY@>cL_+YG
zjR(n?AMZ;r)ND^?Qp&lN{kYBuq}0!x`_{roSMgPJm3=0@Xysa=x%9N;*}Ih|{5?O!
z``1=hGPAN^-;0ZjYiT7vJ-(mOu_9u~Zgco8D{HLo_w@Ai;NW18!bk=Df6Do<B9j&c
z6EW`*qmYzvZ_hf<t#xlTE`M8*q0AkU=%z$?0?DIYHVKKGGOojT&7CtQM51Al2PaWf
z{Qeq&oVF@YzFq)ozm^QGqAIn5u9i?jPK)v479%E$xkmT?RKAjul9@Wky@8DO2y%&E
zt*v$TTZqt5whXWHp`NH{2f*XQ6!|MXNt{(@7`z1BF2@TOyp&6d(7uAuitQU2;wnxk
z)ko_rtnJ+T8&2)c;%La?XKtuDIH}`gH~ZrBgd<6M3Kp4^lvG(mzUlY50B3pgQ=75j
z%}KtgmHq1gN<K>_yd4Klh>ak$Hd9eJ1h2wmB=<Sh2eLPBkPr~wHn>8#fCmG~GCEbe
z+A6JmDUAZ0G4YUJ^=;OP6oEV!*v?x8Re?>~Gvr)mi&uNMTkYD@3k!75pWn%9qCf`m
z$<Lp^d&L#ibndb-J$FGo26-9I_Y8-)Cppwj?O1rANo9>~0rJTo0eUYgDk>!vGThIQ
zNF#jYkZL@s$6$OV00X#hf_7^H<uS)&L?JnYGS!9%H54Upp0{t`a&mHl3W}4n&H}#^
z(EecS0J8dEr)q9@IA`c^r5nubY=L^!_4W0SA7m?uwLC?Y-B=QU#g{+w(Te!to^t2d
z<68%ju)p3sn7KN25Vh%IK(NyOd9kwIVHJ<4`XTuXEzQDcKCJ4g-Nq^b{r5=yl;M7Y
z!{3wTemh>w6hIEB_y5p^#A%{c_`lHEU|cU=ya0uDo^p`~z#>3V(9oFPUuab>)L?hq
zo!;CuQ97#}!z2}qB8SGG&KmqVf2%&_XxC*!*Rb1udJr@v958eegW@o5KUY9PRE7I|
z36K3UK7pImH~h?M=^NrX_zyX;XQ@V%bY>O|DJj;4y5#T;sljFhE{;%Z*WDn(g-v+p
zX|vsgo_Cf7`mTDsB@;~b0WviI=M~PvpI1`;IFdQ*37her7}YtFm@qmTR{h=*E(bUp
z9;zZZ%iYvlxQ;~;hsdPM0=UN^;c;>I9O!E@vS57H(3)Re9|Pt265R`(H0^1iHd`|n
z$zfTA)+<*lq_Yx{Fo?8J?$d9Uf!yH`%-Y5r24UiGbOJVdx7lk}PP@yY&g+RXL+zEn
zd%32TUECFgwK1=kzkVq;Cwx{Eb$C1h>v2Q}&EwIJ59I_=+Q0HGbj3ZM1=TPi7sIm%
zOE1oLw;0#-mOd6rgoAI4LX3|CDFsX5`U<3iUCBA*JvYLy5!wt!iZ1X3<s{eViReXF
z4xjLk(Gk=he6X`)O6&~q6||~rS!#6S_A&o(6=~R)bcBr6{CB@UHvLsmrFI_0=9rsQ
zeuaGT1ka^K>OLzizbFkDKs<wf*X)}Vjj~(A;)xG^Dav;e7GfR^Kelg5A3tjTRkBwR
zBF(6^6#rpn^~7%BMVxNpr#IeOM<_Z^_&=li%&K!FSxl7bTT~?gEu~$ic4nv#OP^g6
zvZ|VhBEr|VFfx&H-`;B<_i=@3X8*`k$Pk7pkB{iSq!DqLmuH$n#i30l{yN@;ySC>?
zzp1vFl#<eZQ$8Hj7jol^Uz?{y!H=xz%Qq)zp_=U4c${%44y~t+(U{`k_mzw_CQxiP
z-NjUEeAL4q3s+YxdP?d`zn-uf+|GC+DVNpy2pVb?(Eb{_T(4g4HCLZZ$w5_?cvSd^
zAtWRW)8x^2=8(z>6W8*64L_22qs^Vl0Y?N^R~jl?hFH#W3@EW`hSblvX_*=baDeth
zW@vWRf9^>~`%J$kDD;l0&^(>Bl^KHGo8;Q_x*?TrZ+K)_39u?{2_mOm(yuqHM69R^
z{idSKgVFJQyRIFB`)ucA*->@W^Nz-v?MDYo;leAWd@x{IAHKclmtp(wJ#=G+gkY|j
zDHr29_Js>x{oR=o!%6;T{O8>z(M4|1GKp_^pYFQGly1OL3!`D5tFKPrW!VtN2P;;}
zm7LP27FXDi)f|^bpizeWH)K*f@-fT)x*3buZvEQP(&jCbd>JfkcCrb6g?D8sNA&w9
zszQ^0+lk%w`7D$DzNA~sunJ<eoeQ3Y+_=fH@gp<$+T$JH-Rya$sNW*MBS?i>l<9Ro
z-Fz(3Q?IGkSX?cMxNg~hrhYc_{WRutjLM7oQ1YM#R2Pda#M*Mf(G7xEd%xT>F!|&6
zhf)nyEe*?<J_BlJ_lS=4CI#AG>7v`4EY=<VDR&~1YODr(F0oJ()<p@;&lZQ1?@Tss
z*jO{6LGWH#Us#{ZBV^aWxkB%8opGKK4g7rhHZa{-ZNy<e3(*`!mLAnMbFPg>00dQv
zK+K}Xm0?<eDLh<%V>3>ptE45~Yo>K2B?SezCpe#2IkM{OuOpsqr!%dzZ4l?0AU8|A
zlk*G>h4;GJYr4zf6p<3$wX3eS>mS}2bHiSAC^P-=nL0?bE{HAVDd`VFUNo1m_KaDd
z0Du3`tj=2Erht9qbBB@szT2YPA*IKQ0)A@_>gK4VK_4@SP?@Q;LqbAsjFw^m2PyS6
zY;Q2Lo0)3Z{QRq;awp&G_TnctOWL7iV(ApQljvh0;Z!0}!1MJx^r<|Ef2uLeo^(JX
zUFn+^9(k{A>esU6n}XOd$#XvMW7&9-m6f$xcz4A_NtwIr^jYM)RqecQH=&>$aYayr
zo*_lq1eeVZ<+Aw=Z@1HZ56B9=Npp0=U7ZUoY8O~kf_AB7u8Rnt_N2l`7k+D8{7l`2
zM#PSAQ*D1py+zK3)WY>;VlDWymkj{e;_tCZLcCc)=S+;0#`*B&BWH~2RE+?a0E%~i
zdB9?&d3r9TUc)T&Hp?9LS4@5>FJdt`XuEh~k6lxiG%(5j`fO)u>S(%R&lW*(Ky`Os
z3$mJV2{1EqlCc7UrPwT@yRmUjuD#KEHr`g-kY7dm@K~yL>^N;!&MOuOO8<H1j3ycp
z9<{yU=Cfx4&Q;bC%lg&vhHW4B8IUf8kHT)<O{JuE%DrD!QDZQ_Xp%1WW!htO@tC8{
z%~u@VNURF2J)J-%9e!Dlb{Y9bhUaoUgG#w@8x&YaeY~E_9Z!_HO<p+OUeNx>3o!R#
zj7QC?_f>aQd~nL|3TCFg$;a}VzjtR5p+de^y-(p`_rq8{WSKOICwlj>&2_JV)%A2+
zzdHu)1;Y=JlsO%?Wh>Z}Xo~VZ@>4~_SL(c_QvOt|BsB1RwrG`UTY8f5iF?NKSj-Qk
zpJ%g(H)|=LJOJkdRQC7p-wM;QnAli4&D!I%03!Jcwi7pbxs&OMqo>Wj;q!OpLAnV^
zhi1|&wl9$=x|lyQ%`1%|lX@=hAcf(xTDUEL6Zuf6@fO*8#v+>{7qHt!O}DkVzjKy{
z0&^#Xels-Rq^)}X5mxBYG6}Apx&D&^(>#+`H2!)LiTtH+_@2|$837=J{>CwC(kcVm
z18}&2A5?(8vstIdSL7@nb(xT_>>bH<!9i8-&vFXBh4AfuX1$tUz~t}92Ge_Sw6-_b
z#0v-tQqqO>fefJ8D3?)IxM`^t1dmVSKdm1Zc`FkeL!s_b=N}g3oP<I2gw)rqYbrHX
zzeXwnrS26_?5s3~=-v)?;V3cNzrAyq!cF<X1?}T|!iqh8$}!*O>>KWR=n(8N_(Kec
z*o3?uT<z9iH3=9Ef^+pRrddaD<UMSDUYHY~tiX72-{QNkGW9cAT!dgAs)LF4MnGu<
zyb>s3QM~B^anh{}e`LrzJj|o&q!qQ|`nN^GE%s0ltGZ|FVP3GXcN36=kV&<{jw2V_
z1k4)vNvL_p0Rw1uQ<df>*sF}{`IT`9Yw)U|e4=PwEQ>;dEcT>xv(!fMr{NH<w)vj$
zcZ`9V5g~TB+Zrt}>`UGmE1CmDrCK-YrMttLfMK9Ushe&1L90VaPYRuO!0>3Ps{`a^
zx=*hfkkazf_d2$UugPMj*4x{g-F9^vFt{Mq0(L-|9ndzvgK(zIDyyrja}=^EkqSO+
zC0I5ZS4~AmL>T=2J}}ST7`<U7-9=X{F$iVcGHPpFsS!7wC@&s-F;C}u%k6$%3^t(c
zY;Wy?GZ9qluu}_!P#{)JPfzD1*1MkV`uh4hAFW15M*}^M5^1bZGa)9X5>(27u?3(L
zvF{-58SUAR>tAYeo0(%YVP)&?Y<h6-lW26x!eG5j&qeFeu}PQL>URdhUN9N}<_ti_
z43V&2ASr$QTGQ3(?ChMAlLLiEL`1~K8iGn_{`%tR=JEv2OFD^TWpy<fh!Kajpp@Yv
z!Vr?HT}AUxJVJhUgwhQU0Fh@LF3w;_bimud2>lDnI;qV{3rH0^qK@CqHM;T#nJ)#W
zUhdKU?C3m(x`n*j9py+pp;7#3J>tF(NE5jw%tp68OK<9>nycnKI2jok!F~n0@m#5X
zchl`jQC^~1g&G<n9JGGok~fk|0LH%LkA@#CyaY;PBs9F?d9W6L+5bls6Mr3%3%}6#
zs=c?G8$0=`!ce?E)bdb(S(GFGEmks2{OFu>7%5p>6^2#-<}do-K|wtE+lV1dQZhC+
zl_|Vt-bqry3QLx~idu($2X*JW1*Lvhu4KnqXT#BaRfyhcf4;@1=v`-<5!2+317yWU
zAiU(K8S}rEGt5rCgP9W1`1<<#QZDQ|u|KS{QNqRvb(wCIG^R)rDy9w0w|(}x=mI~s
zsy`BsILKzk@~SgTQ6rJnnl0xjddtQ(zG73OK^sjphcBElU{GeP0ztk30m@2B##c+Z
z&Yb;Bz%xWeNy(HCbDc`)l)UELn3eVCs;-}j5Dza`ORT_8*>kLzy3Tg37u0yJ7pXuK
znwXe?!jqDcqN08f{O#=Am~FTM`qrni2@d;BFpE889f!x>={F7{m_qJxI$|b&LcNrK
z84#v-iR*{jo;7u@VT5Axo9sKR|IuSO0TmF~p|2yw*h_6zx~P(-n>_D8HG6z~{PQP8
z(C>)I$fIt$CdmYr9l+AvoK9JSgRr=`I9X|~ewVn~^z_AipV03uQ*#0#p3tD3%(I&)
zk;Q|3&PvGZ&B^O&3~bV2HB8l*26juJ(#I0iTo^o8HZl9v-@_Z&JxfXR2HGnRNhUC{
z=q=MWUiY7?iJmG^MQ51Ex)U>)SmpZ7ywD#48(;wiSkV4TOL%=$DWZMmoa1?!UwT<E
zXajx6e?IXys_E%saoLq4jF;&=W`zO~h<+VYsdtxKMbY?5N=i+)ms?&iaJ$t@=p(r%
z1Oa4CO7iFczw##sqAF~tRwt={=_dCRagulWd5ncj`UBC@=iWh3Sf{C`mMi)B?3qho
zzLZ&2e)^`R+&8URKn1Lp_3`B8=El?O@&oTip)*fTjzJg!JT#W7>*R;U|I^-B+`um&
zz|>>s5Ba3^_Vv2xUXe!<yRAW3K3pVaUtitbEQma&$$fj1!h&+afloLbEIy=xY`O@J
z^_X_(>UhZ~I3#q%?Bp0(PyrSmPwzAYz%^$y)S6G0Z)}>D%Q25^m%M`JiI`B`YIV^0
zkt-leACK$v1N$n&zu&*b#AHcx(~vST9ZT95kac5`s%EZe7BSaE&NaROzgL0$l~65<
z-xu?c6g+Au=dKk;5>V(gXjcM9M}wBCk=B}AuIg(z{CDXOLv>sIHCOa<OA|YLVfF36
z+^UgUwy($1X(xaGZd5fD6cohixO)w}7rAONlR(_OIr%JbH&b>Ot*ljZ88>~}Ln8Lk
zEgd(u|79j9L&Kc$k3s&LYx4Azm6ZiPyMYW5K&KQLDBXHf(&&cW0W`q$ya-(YnN$f}
zT1s7IJS1F0_~c28b)&ZMAB+P|79VPquY^JpiK7)HzOv7pIfZ$R2Y-OrA4<%dyi^@c
zr5yT#QFx^E8b(v??=m40|1>MwKrJ=^MI2y?TD%eF+>UxKPqq;FzJqWB41!8D-NeP5
zRo>JnJCSYcDg~x|01cV8jY?#ISitF!&#>hWe`#lvTOZtWLkL;1fAp0#bxi85t$FRz
zZV;~jF3o4WGU;J3W%Kz7h~Zt2g(t3DA&0dm`~}HX0<0dKTfz}P0FD8&(uqI5h12Cm
z_Lc*=8M;~==Nzq8c^m890B|j;7kG^Jyh(9mcms&8hV!AuhUZ6XK!9OAf)SHqwm+ta
zsu49lnQDLsd46p-8X~>6BkTnL(&ra4P;0YSxy%R8tHLqI`ll|fG9-aIUq5j=4p_N9
ziO*7v895cOjL&NDd^Oy9R8J;`hn@OhIz%40Zs@Nk#PtXKj2tukb@>xIFy#?s_JBGA
zFaVl&s6)aasyClvIq5od;?hc<022Oq1BySYpTe6rZL0N6pL~FfpWuuMalr*J9i_#K
zP4vp%7YPHEkYD@K0LwbFC6d09)q|n$GbaC#U4}`j)fqCh&$CYKemnqZ7Lbi9wVONu
z0;s*LZR}aQAxe|sIuJalcb3q0)8$XpzrDWbnbwG!tAm5*3k-ZR>4-%)#aKz+SxPMy
zQR$l5Rhn-O^Vke2pN~{5VwJgE9^jE$`>n^1AFuw6vz>E4+pwzzk`~yTXKbOafK$;v
z`z-)$Ry|zs(BONG2}DvXw)&eo|21c5eX?S!(fta<J0O&e<cs4^r`@!zMExa|w^6>|
z6r?E!l_I<V0+f7eH_+1y1`0kaE9>jmV6UkWJwZ6Vhu~)t85EEA`viPtQ}-)H?m)1l
zQz<6-mj+qmB=0WAnxJ_==$MR-rhm_GZ8L~Q6DG~fzTZ9Jbk0*5Z+wr}2jC#YdnmN;
z_h_{{mTqr?k>sOXM74VzO!E+@k^x@HEe1dG1#hA&Jk-3Zr=awlp}ofSbo73UAHG9%
zfgp!HX=wj=oo4G;ZOj{!pU0mM<9H*|?tnej*l|zs;Zf#VQ;{t0EFjF<A01gb|2_h6
zOhQ6JsX%~AQHb645+16)Iv%^ftGr(o-(0p#itBETVNX2s2~NDz^+EecPVS1;m4nu}
z4jF^^mKxZ>&%xYxM!vaUS#Sy+cBd~c)>3$FmXV=y%Q>x{NktI?ht(=-FiR~1&c3qE
zhb-&H|2L>#VI6S#gfINpFF#-OhbJ*PU|HwAyFLO7koDt-o`FH3W?f}{z1zlMrb&@*
z4&}?h?FBe!ewP!n8Zhydoj*LK><__b<+)gnZ1qP|i1~x)1$gueqY+Va`X>FS2}+~*
zfCw?wMBzuy>Bvh3rc}*ZyPA_xbvkw)>qWum;wGfqfEk_N9YGr7dXJ32Qsi%;5;Ft#
z)5cJ?5^dHBeYQ-0COLzYtIO#_h>?%2N>YvN90nXB*h%q{uU9&{qN$4PN+|+^aOf&0
z%mDgV$d*<@pWHdc;5lzLDEr;k1~O{_cnAPWKyZigT1$4NWnrmwru$(%4(AH<etl4i
z1+ilJ=%uf0H&`N&Z`5!m#>)QzNkFoK5P^e@twxOvjf0E~A}Ywz4NeD(K(>~{`p0<x
zc#S=as;GouONjw-vE@A_wIkeTSqF$KDj{LZjspf@@B3+0o*>`?W@cvQ=a9U-{NdqY
zRBBjo@HDt906zd$0&dx|>E?r?V$`>9-y$Ly^g1wCLV;q19^j*ktB+$7O&@^aMJ=(j
zn4==$BcRMA^^CuKhBrB#;voiA{$mv~cCh>6tKfv>d@=up1>Ns|D&Z{93uX0ft@1`p
z=y9R<MPup(A69gLV$e?PS#LD(GIZ(JuV1K$0%yB3uE*;odYutK#0KykrWr@uO-@!;
zhaqm(c`a$Rp^(b9t}VY6YMy5DWyZtj68h={!e4F+3X>GD`dTOYGgCB~F;PPhGAVH9
zOr7x`Imp%${-JWKtHkv*LJCaB8$pjC%Tbo0O7B{o2YAh*3eKx_;is$ehkTWH`dv{x
z?w9#$6(&ckJyp{-K{jsV#k%cb#LC_tz?R+B0t|Io4aue-F?NR72}?T_2|s<#%|+~6
zaMr>S;=@$TZNYD8X`zknUa>J&@fk2GpD@G2hM<Lo1>iwW&*J2Ka6l*YtmZxp2oM%)
z&dF=0CMNYwC{ja{r!m*QlnFlneAaO99UL6w<s)o>dwiuTkGk8Ngj&0ii;4;bIy^d3
zw?R_d?SKJCZ5u`HOz8>#PyL^z>`r&LH;fDngF{0Z>FLzi64KJWbWPU`&WFnYue*Y%
zFPw1zj%?A-pH9FW+;q8__vOnMU?bM9k1@@VNlA@Tjh=C>_{#{2+SM<y87ZaIh6V<w
z>jR%a4W?dc=Dgp61Z0?DDv8WA{2NwnhBr2#9!jd3vyUFKS{F{cNZEuiiHLw3%TIvS
zpsXf6oxc^Y8DlcdSb#MxE$!Y$?!;;Ls@ulccq8yRRc{jK7^uKNx$;vj22a#}#7{N4
zVgKb@-+ueHsk8*Z4^)8~O}U6yn2RY6c8*{=%^g4u;5o1u2r9czs307DhC{Nku~FOm
zq4*Mcd!QAXZwb)_#O@1AA5IpQQ&5rT#-vlOo@KNxgr6XizE!=CV?TvJAZAVIA>?!*
zpx1dXDCldmJZ0IWR6sfR31bHdaty=QPgMGfS=;M`e@xeSz8FN;_|xR=W|~Cc#*xtb
zFZ~%+4`@K5&Y_<d&`LqrG?!;hckZ1BU^KZ35ql*_XRuztCh(Z$A@o#~n<Cx7VQ^s*
z5ZnvVdD_90U+&EmSdi6*vv2>&#v~u_$2MfBdLt`g*7NQLWX)1)*KV3B5ICf6Cf$6v
zW5l?K*vF~K${)h@pjXwW<1p@16+XTeaNFk6?d%i}m*aK0=Q!Vw-=22x-R}yt0tXAg
z|1yibVnOQb?pFBVoy=q`59TTl*`;=#N$CSD)6Rx}&=iSpp~9?#4NxNV^)qeia&sv`
zaa9rFCKji(v0>+9*6&1P>5DnoRpEMERDbvwB76NBh@1Ay9p8MzQ#>xgkS*k!Dpy}R
z;3qf|KwR9G!}l3w@Nb0=4+n?Z#T=gx;#(25?l$q$^(UA@Z>jms@U!h{00@r*ladj}
zGYM<|bK%z_3%9FJR$UX=c$KgYFCS}$%S6Y*NV!_9XFXh;>4oOPazp|-aoB%5L3~JO
zG|rq2rGCt|t*(o95z|Z1`!fmCUjhS6G~K0@SM5VgJx5%vMi+=B)Kh<1f4w<&X^6c_
z%@I6u0G%HEOb--ce3mgNZ3)$0@uFQC?5g}BobGm=j$m)Yet{6;N#Qvz9-d`9EshBR
zvs~*!a{)Yi=+Fb6iYkD|SAvroa%s>4s#8!iL9H$W28>rWk_Rea{XXL!4^P0>mg7=%
zMGgu)<V{y#4Nn|Q+8bA8K{UQkM#gmg-pSP(e-A!EBzEn2V8<ep?0YA(djvaim9oR*
zkP9P|3Z)`&$J*mUz{xS+4j6|Y$UjrFDZ6sg`AGzu!@yV8)4d+MteB|0<B?+sFuK3(
zMG*AZMeL(zH!Lu@15WV-LAsyujqTSS$|H{d+WQ@0@Y4NdssSMFM7(6of50WgkHS;m
zIN(1dj~N$}hdaeWk1kOX#DvNL=~hvQK<axfkqUoP^2%$*8Q@b<5_oPUMbYSJSRv=y
zAOC)vkXJLTQSQf2-1hOWib5|6Kl)?x$D;VABE|oC8-35If2Ozd0S!5niW>v}n}|GP
zaX0USVaB2^Dft&zsmR1?uL;09@qp$X50DccJ$eKJGH~ty=d>ca#Z)B?KwK-a+J!ke
zojpC~`~SI?=f7n)YHSopP*@mTUI5}Y@TiiJ=>aw-Hl++G)LmWTNz$w?$41PLkDj*D
zN061|$0(0eUhyeJ#e6bFZ9Q-O(*<<W2r@Jj0HG-YU-}x|-naFjwlX=CS$cOz!+DAG
zS^hV=LsDG)6105a(5SUpYpAHG_^e^!;Katn;Gu%<pb?6{5?y`Km2)qBF$2@f&5`)O
z0OC3dbo9X9v(+o64|nf!^j_WR$IE$o{6A6?Jzhvx3lLu=;}|l5-yPV2ChMJTeb)Yd
ze<x5Q_u)g9u&&nPzx>0LPx>D=0?ibBk0n9szQ1S9xo(CwFx{US44+A=j9Pt+crbAF
zUvCWP@QKf|=SXrPufWa?MkRY_@%eA52g6D=27%A$KXcrFwl{YN;wrE^J$dp3JBa+D
zZ|Bo=wYBMJejKNxg_hPV=sBWg7YPCFSI98XH+|JDJUlM}RyMl~WDJa=+}vMj`2SOj
zPAclm@bIvcCr*5pfPlaVRZc}SN)~ALYQAs5k@W)JZR9j4fG-S_9|qkARHv{6h}`_h
z2yCb>|8Ex${W@ra`v3lgiw$%I^Z<cY!)#-<7=8T!^#Ut`H-YJuoKz*$TL(_}Fan(Y
z?v=sQI>r0IvCp+<$HDwS;~0V90azv0O)U@wGF{Z32yemSWnF~o$6Jz%4l!AfBSRcX
zy$e?JAG`_LjsLCf2k($v(4K_q(wv45#_sg51L=PoyMM#MAAE@z>YD579)qi^xx3lJ
z6Q&pU56v7pw4u@HRx_%^1bhGgH+-1Byf+&%;CKK=FfJ|*BI!N^%tgZY@3_yNG1{(5
zs>Y-^Eq-M*?BxOwIo4Bb>kxl6`v}aI=(Bqi4{CE;8yiY0D&Q~}Ez)iRJUU=80kBk!
zQ2?$z8=L!d1U@{Qb=~Xu_A1sb2ar^1P2Pw)U~T$|S<EMo`vowp32>f2x1Tbvf_WcC
z#0@TO=mTgIFdRZcLML=`ja{+VkI`pHz!xY2(|@A07_lV4=l~uVkea|n5443{z<b?9
z79El|dEc+ICoVvqhAHl=2TovUfQ?6k5?C#Xm`#R(zvS5EW}!78`nN-rK0DrZ1o7dJ
zP!I5&Fu6H+s%*VxW5XS*h4BL55hOB=tv_Zg%8H6cDsbm~oD`=2^FG5rq4p->x;Y*K
zBeeyVCN(uRH@EBW?>-T^ZkOEmOpucU<PKPUBM^AN<j&OEgE7pKYu~eCue3i%Ap54$
zQBiR^?dt%xe`#sS{cs!f|MYkk!}t(E^y_pcaj6)h1s?0GVBS1yZz@Y=c-(GoJHhcC
ztOeofkh6eiJ(d=fBf|w@r<<pqjv*Q~HkqR<piZ1KKH-xYDi?`>-XC_qqX5}dBQHhd
z4uqDzYRkiYUx&KBWLKwKQ?*zSPd!AUQ*r)#>tk07kS|>NlDQ$B3bm_~EyuMa$KH76
zJYal<_W!wZSaxbS0IAa%V}9SI(w`;(Kv0nuEI3!FO5Xn!ahKY|WMpLMsHw|Us3H1$
z?rEtVt{DI&Zky$f!NEbf3}LBwrko2I94xG;`1r}|cEGE6{d3d2?M!%}r?cT3UcPxF
zl09@knV_uz5RRb92n3mqa8i>fz!-##lm|IoyRq>_rvfVsq7MuNGWCc_!CmG^o-zn`
zgwX%7DhMF<P*GC@wFFqMclQxoYN(W2BI1WG8fq&hze>zVrGIKF2^xwlE-IpV(J6@F
z$fW<NM?luk0zC_(qS2|Wcp(mTgb)FGz<^Ocr<S_<mQ79677iefvIz+OV{r)!3|wTB
z&K@#${ftT~7(r(4qje#bVGelR0>Df>)Hr;qE<#85wFV%putt93A(L*73<dstdi;;d
z?d<FXgzmF9()pYo^Ua@$xLqi!0KW#B{LVm)v$D1ZcH3K|z1l6>6-3(4&87KMJENyv
zDuL->V88?8)uIFVyZE*I%ANN^Onpc3O*o*y>O1lc2MXs<IYX^gl7c>wJHY;ZiR_Wd
z$wW_{=$5E})62lX0O^2_0m4@7eZk{)4r*1fTh%o+lK_Dz`~u7g3H@m80lVk=z>*s4
zV~s6DtAd0eHG=~D6gOOTIXO9Xb=S<1Djon^E6t}u@t8N`k=%a+i#@3?(|}GtIQH%*
zBg%+Ax`)f1pzw)Ju@dXI1QiqbP0x#NZRkCgP<)nB?x;k;Z5L>>JY;8igHP+=4HTEL
zq6Usa1*B{WkIP@P;H+0#7(R_#13W<N+&wDn>gl1OqRPw9$AVuVzB_Lhz~KHdwgrkB
z(0+MW55f_4P-cF<8F0)=XGdmjfW6{*z7P;goL^X&3`94IK;Ljb6dYhLDK@4<x*slW
zruvb+X8#tXh3BLjGamvK?<Uj^{rJN2YZ%enW3t@%>TD0=(Ia*s8UQr_C>z?YI~5==
zJ$m+fd2MYh37eSs4m4Hu0-N8}=?)qZ$ahOaGQ8YCe>Ct2|9T&`VxMMw-1MF`C>u$p
zGBJG&>lGP2V$SKH_Z_A4t)IpcoL7QH?ITF?_0TUr6f{HuX=&*jj5|bB+=u#*C@j0h
z2Naqq?6$7~Hon68cpqUiw=txP>ZG8U`qRmSB4t-W<6(t900e#~put6l@v?`)c>%d&
z?dzLlTtFuQUO@9M5wU%Di&#=pk_))g0ksTx)7LV|(``7<pl+;JzTuOry-8OGr<y5y
z6Ri4OSeCt2D%5ixSLPNLg@=9j1Q2j^f|JjRi%RTqo_5y93DSDitcz*emnR%ah3q)+
z=NtL>iCSNWsGfsfE-RN6p#me9%p$Yd`kMOsdcgEC8vIoOk*rhCWO7L-3{3sCA_%V5
z)(7gA4JV*AhlPCwrX6IU4oho-GthIubZ1>G@_)_lYVQ3&t^>^JVassiePb&h-{=1k
zDDQeX_qgB-_x{XX$VjvSC9HxL3H+vX9>yg&aK~XGFkVi!#aBjI|M>=v<cv)>jV1Nn
za?UKbCwp`X+oD53Hy<(riE0d3cd#-uh+|TxEl9+If`W>Qio<g<n_#6!HtZr_OXYfI
zl}e3(ntJd$DI1JSCYj6mdG{^gexp7MXnUONKHLc}tew&Q@|gH<nogBW7^){=B@^zx
zgNdnLWd}rP?o?K8oZ=%FIV>DHEwi!-ptWQH(&tX%jZAWISlB%nSpj#9j*9v&nRTDv
zeZyb#&VUW~Cj@_NI~O2-@1Gg%nU{ASx}k@65->4D+**!os_m9r->oiY?wM9#*DEeA
z1_7EzrFfps^Fm_gRKDv`*J}k}EA#|nx3`ZEA$mIi+Px{f+Mcf{^%HVI&ck@YKt@Be
z0}?m@ggaAJcc5vC=k|Q*5?}6Z5UI8w<)>r7dr6CmerL7R0<@5>Zkmnke_HDmRMpCZ
zrT^mv_~;2xJ1`gmxvvJG8K5DIP{4tp@eZ69V$bW6+FCrwDBfeIVhl7WXo7ory1s=}
zigj8*n|0IGE~kD=Daap}hSI>t4~`nB)Zx9zC1SV$+?cAe1pVJy!1)EWwYT3LcGCgp
zY;F_hNc7La3{dGxNJ$}h0Z#zH`$4~9p@)wCJd1{jInTQpJ%)y(UQX6&D*-#ZavORo
zs_q3Vdeosb0k}Hb3f%atN2EO$uQDzGOfinvS2Z{7uY?d<>Fw3+Oh9bEx5=bm+_%Yt
zuZ%kw9Xj^TO7Co0)XnSyCxVA#yEG7RZ6>rqNUWtRQ5OArf0|CWCm?#=U%pi4bO_KJ
z=|tJe&q}vvn}+uxwJQ#AZh$6fnaL>>e8_)Yco!}WvnCakNGC2gV8PLedGfD^49dV(
zkWAt@nY+7n1Xu-tENC8Z62LDY>jCpq?t5(1mobM-kN7O-<fc_^y<B;Z2_qmvV)COR
z`c&@ut?s1Sae`KPe`YVwazM-h%r6#E=q@j?SmV+p&32k_#!zWFS2(r>n%^5XU@Jiv
zI<WiH?KP^`+HHUng+HzJ7~*i)u6Dlye0P&OFsXu`iW;|9xwU)OZEbBJz1VG!TZ0t`
zq5=&ao#n7=ns`5ch;ZEuI)omuB)%8eq9ZCHVG4R)fg}sa_kX_8L#1gacvd1gAeN2|
z4+94xm<wPd{V&Q0KkWQRRa@yc04MnNu^@QZ14Ls`c9HD5>%0`g3c?<tzH1Kn`C72a
z9+ZI0?CgL5prVl*0wLVKH<29<5f!{c;NENuR3Kh3<AAxC0W>0rBw`tXa^&kk=qxQO
zi;Ro}u6eLHPMNkgwzih_2Y&!4b=VvhyDwoxsp#tWy=^`LU#<@#TJ4}Qu-E6_^-1(j
zml?{;>;U?CbjAamsVIC<Rvu>ID*5w(T;d7F1O(PEUyvUJ*2r()tS{IBwHwc9C=Fn-
znp*t7cTP+*R`P+usQ+5VzM1<I40IOUu+A~fII666^As86=fZzAk4Bz0EX=*?*@vVX
zkLddF`iuXqaQOeNO8Vf9nxU_Ym3gzr%a06r%1Ss=!6{g*d;XV+`u+XM5I?%-`S>oR
zjTF&t_sh01%J1G;U`h?)@zfpKW`cJ>fOj~6iyXj#@>}IAa^|Ek&b)A4zyLebtG+2{
zmAAtKbneXp+Em?(A`rYnLj~$PA_^0ka?rsQK4N`ILD8|}5S0E?7Y!hv2j=AgZb1+r
zbd`Kon+R{<Z#4lL{;v{I!wa5UQ|;GvOr||(2v`ogwhL~QTQ5WK@8gY89UV=J{Co5O
zo^Q!9U5){x$P$1V7QlX+@IZ&mJp!Z!|FmWu9F$-$x+M=xI(zVf{cy-LE-<8aa->RH
z51iDpHZ0kCrcT*7EfF;SNu>NANdkTts#b7;1z*KwYN$p8sYq%bzG3_VwAa1@9LuHe
ze^D-psj)AF*WKWtViD_OaM>*;8y^Oah~@YCC16y2(0XuSo*&ES3k80G7yDQi_K%kd
z0-O)DPeI&C#GP+X3Z9J)mUg|)0Tl7#5<klj9#j+$(td9(7F<7c@X~?SJUu`zL9le|
zKwP>JhIBl5h0ABjz+SrMgAg0S`)_~Y88$76l!6zkeBs0J)i@Ug1Y!Z`$ROQ8_}C9u
zU3UT>m>$$O@O$my37N3J^U)=E1E2;q8!UparEMiqa!P*vZ(RYym)ab_<l*MVB{k$w
z=iV7XZc#O5#=;A7Nqv!{|KV}<H}<%0Jz{bJ%jg%QRrg+nf-jWZmmoWVUiH5TtVQ5;
zBE`kB>=fNLfWijuoQ(q~NC$Xaz!fPV`GBkp@Ti-X4<K358Gzb%OIVcz%&udKk1hcU
ziHgF4M8(890quzns_^`L>VrkRUjT)Jeu4Y9k+hXk9#_BZN*aWNswJ;Qu}SST74+Bt
zm{b6!2I}8B`LN}8@*5PI4-vSrn2isBW|(^*4UT?1r(>aw6?i1@86#dunPJ~4A0ZrK
zOEMwwC`Zm9$_>oUg=HD5#*m90@}Wunrbi|9U1BpgO{ECo{Y~nN8zKxGiurv1cu2|D
zksv4&mxC>hC1gB{UhwUI^4_Co3^-*D#n5V+RH$itT<AAw6%l}X!g#EZu<xjAU)mNS
z?t5G4vR+}iV>a8N^xj_fkNQ<Uy1;Z2WW3S$a#5DEUkv8iq%q;m?_am_Ih`U#|DQEd
zHRgrCu_Br;W{T(S*ge<uzf>t^`-vdo_A}jVm-UVEvVnKhtO%>dSyp=J*PaZf{3RDb
zuO76BY1kz^;wp0PupwAGaMFm^nwA(UAKDl$h9JN^fj7$x+l|pbI5Lx@vmdhNK9`J~
zBG8A8FDc!7wgM<UznuJ^sHCL`Tfo*a00i!`kr}JyVhr%MNfZpk-!5JH_Ive=Q#3FY
z*9w&r-qb(UJ^}p|Qzd?LHA2C`rM%ZaV;v6yh}^BLin~wlfSbh)%l?*k|ICnwkyAZl
z##f4;p96D)TQuq=3CT*a_m^L!E-Z?&T=UY3fsV<60gj2<o|F{W?~9v3tU~#MJm~7{
z8^P}~F>A=v#EZ@5i^-zTD0X^t?Sm9H6W8PVCUVT<lriPPZMv&+9U!I|3*~<g51cj=
z*bR&ApXEV;3K)#%`7rU#)|f(aSVBR+pPti34TA3W`RHMd^1t|5h08k=NEH81TVEa3
zRn%>JkQNC60TBrW6bb2W1q6`}MM^?KI;2B7BqWtq8Uz&S7DWW4yIZ=u@A~=Oci+2X
zJcoZM<8Wf1efC~!t~uw*G-fDS^yxuWi7#n+c5&&vFVdkzZ~_mQQD`hv+B~)h;#_rr
zcJZ@GAscm=k|ADjTD;0g>pSV09o#$NcTvajlttpW(*A_v2K?&&gXo&Ww?vZPNhl=<
zr<O*1l~cIXdun#%P$lEAdul$^+*-MY<cQMEk!%Mhpr+(VGA^PCdsty1=YPyPEL2cy
zkhw4&>8{EOf8~=)zl&|=y#HN#7WZQsR7r~J5LzmMNA8z93H{-Wj3qu3gCDu@@W8qv
z)lqT@RcLwVr_qI;nX+Dsi1Q!j_hiC--^Tr#hP}Bjk1c0rhFcq1<N&pRJ_<>;#Sd(M
z5(lm#e!gz8sHK$h!Ea1>X1B>JNaGTVK|zyyHXJDLZ_xF4D_BLICH&h%9RDCCD2H$E
zFJU8jKTd)_<%gm!fYY6J;JDoC@Z#svoFbJv>M<@C65+c_=a!oYB02jTzm8b0O0qr=
z!h-X=W^$!0++#l6G5L2@pPXUrT6$Ti<w6>Xs`~&|g+BSttfa1E91(ZaSM16I;>tYF
zNtzzSMq=6%to94{*GB9oYFtW3279w<h$K6cKN@FB+#{s(BW-5Z5#MtV)bV~!E$Dzl
zod}CdZF8$Sa~xL9ZN!`H+DN8EhuK=U?{@Nmj^}%oR*GojO0xU?U3W7--QBK<s;8A8
zx^y9h5h0!aB1Bh4`IH|3Rl1N1Hc~Ny7!|qPndXlI=AR@$_k=!H45IB<<<`FM(x=O#
z2{Rb6>(Xf^zZEeqa0}TryyNg*YCI(j`E*w{O0oN0e2hx@=-TV*Yo8^ud;9mDdHGAV
z)*J*GWL3UkHASFjs%5G$omVTJ@&`d8ZBSsI7GutxfSRw|CxwZ3Qk-W5$_)=+;bpB3
zm+F7_8ChR{Y{o1jMIx&*-L8uBkt{E`Y!UqqPUBdm{Ysq}U5e;gpO_U9!P>#tSSr(q
zI?TTBU8~P`_ZC)qe3u%7ess`?ef~EKHrW^7C@-h}l`lOYSK7VA0%#OEmWcPoUnb@D
z4^Jfbt^ZA3{3`&kc?X+Ym!}}KtHZhfezjqlc(;|noPb!-^;c7D_I3x;zwaSK+}9I;
zMp&d@z73-m8PR8V2lbf*YyQ?(o7y5nO+@MJIP~9~esGz4aiKjC3IdBzelsr}fe7%t
z{^H=mM=S2u*x4~U-RTimMupP9Y5g68au@W{zSxB4pjKb6S}6o1Gzl{I9AhR)Ug;y1
zQ6Ty*m}H`eMpz`KT?cEUPOULakV#($u@*$sBqSuhT5&4EXI7Yq>ox_4`hRZsE!-~b
zEWq~!QZhGX7>E|cE`;HvQgV;tW=JIBFfICS`)P%U`%dVy3-9%*0z)3t8`#5uA7OsG
z$Kb+=bqNu1jeKg(QH_F&pYbpMHLojMo3C(=r0@6Cj>MqrBlcU&Nrdec#jKtA84R(0
z?f$dM3rqNxMa5Ukr0?UyxA*jJc-1@OqfA+ni~CG}>qUf4PgA{a3fRpp;|n^(r5gs#
zsop$Pe|H(m&S<5P%6xZp*6YdQFr0r_!B(PWWK^Q@!+uQU<_Ha-63eVOTjQKKs1G~1
zcetVSmY>qBIc~aDo_o*xZ!T^3o@~Tob2AnL04<=NrBeNax$TwW{O<br$%ti!g3kP-
z+gq;wzHT?FbYV48E2VIZPDrRSXbJ(bkcp8IcdjzuJ7Eves%uD%mP*;A?JO35vu#n~
z(>)7`BauMGabClhkS^to|L!dkLvje#8A1vd0--WSYT5<F9eqg8KHUxQ_iqPW2MYW!
zGs;qeISFtFU9P+3rTxA)?yPVG$pq{3vQ=QcLuOiCR327t&-XHjmMwdkb{}3x0tfMw
zS`4JiVy?S68u^&uz19u#E?|LR7JUYeh1SMQG}c1W{jH;QPQUw)EmTueJzZ>VrhlXD
zWo^9ADHA(qQRQ1E{^43nO^%I?q<5Qgez38%@%^s!>{+fpeiuf#ua~XO|K2$I3=#|^
zf2&`ZseE!iR*+=D{xV^4Hl`M;`VniQP{jBFhHbSU`j9FJbqX)(;JE?o8z7t&0C2Ax
z>H!+@GnnMn(9@_4AO(zCmBZwDk}Oy{f(?)W$T?s+nVp%TcA0yJb+k!AmF31bQjzi5
zK;eb0V(E}9>%hS^roAyvrL!O9dFXC6wbm~aq%C0>lzeeYq{P7SWKh?w-H7u;```Mr
zHETc>ieAh@f|!w!fr{%OI7>@Q8^H#x+lQ#A?ViW?pRvTPuC5-}hF%;!e*3jZV=ZPl
z{m6em;3+aNni)9O`e@AR#l`w@JtfTNx*O$-fRTCi-$tRBb?!&;dY&$|I)HQnVvCKy
z*U;Y5BKzo3#s2rj#l>VHCk-Hns;L2v)3@?OTY}c;nZapf($26|r#*Q{{6Nt+SV(5a
z&ag~*3pH;Z_K#-M^e$6=l1FWOd<4e;I2D)OH~%(#8gI!%8P@(XfB!qMLx6;F+Ritr
zmaA5vRl04>Wjpf|9sqW9SUm>!19izTH~qCdn!BSfc%I9Y?Td6XmUY~B>`tRgIde<<
z{L|(XN9?%cxK67<?VdXL)}^0Sn42YtTdS*9ydNM9e*om09tQ6E*|b&T)2!KFV6n1R
zKDaLh@x_m61j+8r?On?AumP5qYL-0M<e@mQ<<^s;7DMdF(=rS2qVbCap83dm7xNy*
z`>W&Twq{MctKaxqRGh-9G^+Ke9qo^<(xulQu4zr)7h)NA<yA<hmKi?;g=eb;?`+PE
zT3tQ$%uPz%5E^V{QuTYW{pR@Y<Z@={uPM0o9G_901_1I8@fku3#aLF>0#HE6Mj2m6
zC=BCot7rEv?HzA!+GtpROAkA9)FL#DnA2fDa^G|3Q4D0WQ{#qDS<f@i%kr+xITyyX
z?PmTDTO8@1JVFw<$3RGi$srX=2H7zKJ$(|4bh6V#<??s<<~AS$a5<Wfhgwwt!Of8=
z9wkZ9M{h%?k9MdxN_-3|Q|hfG$Ib!~@8#cU9Tw)1sCCH}!}W*S?ff73s{JDiHsc<m
zowvUYb0~i^c#F2~XgL^``g&fb<V(X~Wwreu$Bk^uyF-q%fqe346YP$-6YT0H5@y|e
zH$6(1T?*3CrV4tt+Xs$Q($_}=(2!Kew{c{A;<gZu+|_dKLT{M;^&9&ZK4(LT&!-(~
zYY$yHxkuZjmV|F}Jx2zV=IQ*DzLcJ(VSDq<7zpM|MbnEUIemXtKjGSG`NNy&T$`X>
zXz<@rO8yVKUJ0zvW;ml~-qBt+xlh`EXDKs>LT|wep<N%k{A7C@3vwI=dLL>;MZM7t
z*ES|{@8wE;>$0PAn}|M6dJGx)_HaFqnhWG|!XJru5q1`B;4fZAbni2rd{;S8uQ#s!
zKFS5SS!&s(38z3J^KHqyi)d5Biy9=icR3rPF{PdrRz+y<jtSy$YZd0HIGXCA$H8j(
zok&Z95V8K%yx*6zRxHf0AsJf~zGj;>P+@%?2gw^3n_iyA`N+sN=cD`N_Z`_S4q>m3
zS&x#muIGbzc`3Rc^EnhVcEQzUe5P+;&>A4vLrV*5@QUh_rZ>uN=g9w(5nj}2yDsqn
zO`PA(J1(Br$ODc`ljK-Lqt(fG;o}HLK7Z!C$S(A?abL6A&MZNHMpk}4^aVAx=$E@;
zY=!ow?X_gk8uA&8SN@c~KirjD|LBA?ch}gT4r4v1$AdjhV-e!LKW}*_XkO?eTx_H_
zUso+}UM}Wn9j)a<f<}3-Qke%6nMX+qF5)(fZWVBqP-g$3Zmo@6C}@-CUq|!(p!Gvt
zJ!gOV6Xd*EcQYUeQS)<;BArxFi#>KH=^$4d@N8h{H62ICLihf1awjQ|nywGS-?a-}
zoH@nwT@=xe9RaJ(pEZ{PSqAs7)rj<FRyfb>1b;~MQKpl`>Bokf6OW>GPoXNBt?(rn
zG(3{38zCsX^Haovl&I0<B0$sls^k-VtTU-`p5=(B5gb2@;*pmN<O8prn99P+-R^^p
z<hgi8etZ^$h?xGST{NQZ&Jc5uFv<fOKR=Hh7_dav+)V6kjM8D|?TIL@zbWSmi{nzy
z^&Ia{Yl2<5)(>LqC~o(G)s57II?MIbg))m#kP6IaQwMc*THeG&tYc$pHI>!#<Bci9
z+`86K;a8J7Q%%hsn3iNA5_(!%mvMuM$grre%L(`a>5c8|2;vnbzk!bwk;WE&<{Vs(
ztsQrjvm}Q2;i3X9o<RbK=jum_U5L1-O$&WyZrN1pw`F;r9qW{S_c^R!`t=BFQ}RU%
zU);zT_v4^feL8Qqxi4yLR&5@Tg%L?QN<EFXa2E6xKG=ZWd%}4ZKfnLDS?t^vw2_)3
zkD2ybwljK72GkQChM#a2tbM@|K@Q#Ppw!&l)FdS>J+L`rbV63_!9~))x<oEsRP@~c
zUaTC*z`Lt0F(oz!mKck%GBonXt;&l{Ak=<CbSvajn|gd=`He_aYA4489!aGmkJGy`
za#LPr1Y>!)xK#Rx>5xjvMp033WSruC(5Oy`RByr65iV?|cKaOB7N_%!=db@T!1&ec
z=TR9a4osT5%eA@DD}`H*`4UBgpg1kBUe8bOO9ivm(Y4FK90V4K5hv5ho9si|v>3`O
z0j+ZW_fPM7QayxRvfJP*Mjc*e$={dHf^eD&6Omm0Z8MeA2KIu`uYuc_$vk(~LT!I}
zU0J_q5;3fEJ}XVw&**b$emccJ?Lg*5IT$O2!vx+wjOLOWj10SjX33l<pEM`!TP)GM
zZ$F%>NT|ncv78YRIYGQ#SEN7f@>%&Q1^q~?N5hxVpl?3tH?N5i<uaO^%v=3dwzewZ
zd5JFL?@bYOYm)#RgKv4|(-wO;bPO-LCZAF?7bqw!W-326Z+}iGoV0tu2P9+1AGPo0
z=BY%naHbWhroVkf$EM4mxN_{h$aJ7CXP{T`tI4(~2RH?2I#E$7qb_c{Xvki~ji3k9
z`3X25BmE_4?uyLU$;nbsWbG;HUidbV{nlOo{$u{P#?K|Ciwe4-YcQcp*%x0)wAnI8
z&0q(ZiM$m_&^z=%4pcZKY{Kep@P~H_Fc{sep5BM0W(tm!X|v6bA_)js5`HTZrAPXk
z3Rfi&LowA8p|u)*#8R553Ygh4p>N$lpPzdim6!$%Bgg)!|BP3>`j`!QX9Ha^M@WS>
zWfD9{-y-|silo%<o}avg<stfr7c<kvs~5rAhOS(#)xXBjV|sQ>)J_h#H9p|bVR%dE
znZ!g&S!!Jf{K7#X99Go3yWKaW7O&NO6d!lZ$!hlUeQpI1C1X(R?s4B?T;8joAedr~
zHXb`V&7&M7JJ_vNmovkT$~dyRnLGbe9*0iN1diOx;n+^k;H3;-EfbPSYHzpI6Z?*9
z8(x`c=9rYLGPeKV_COHPWiyIheEqkg?jGhUS2|M<oMlqmMlI{tsoLc8?Zc?>{BW8H
z@amII|J=GNNr&fKN7mZbhBBA}m|y2a?S9yC;Omx~eFm|LlwP7<rAt*@xyK{pb8G$N
z$j>K}wv5`gM0BJM6uWN(-S#cAv1wenPga8tL=P9lE?m~If-ha@n9oeHCntV2!3+6I
z7JFQ1?=m3b{Ju{xyh3OB!c*40j+Q)}=&~c6L95o4N0<YDodUB{^x(Mck9j$VQk9^9
zr)$YoL^LO->cHL8#WCLG`h2oem<c65EA!QIiz`;+FjWF!ssKyt9FxHKdSU(4Rff6w
z0p*L}h-<jRmQLZqiEe+?+wUp=A)2OiBqzE>B}7lv+z(5wgr5XW4%j<g(LTXsIYIum
zpViA`4b*I*zYDNdi8jC#9h|-C$mk^H$i;97&n(-CAhx{+B_;fKrEdEXAw>>o?E&4U
z+E5YzCb^-ZVVLwRe)&d$y0vBesk3J{TlMS_UXvSDhm>sr&)7yBvn?T&93I}Hzxy7a
zo)Dv0>qEz`Rv8?BHjC~-A6gusiQv-DpBXrxpV>U=jBbAOl}G)rP)AZ{oq3E%X2;jr
z_ZrQxIq`k|VP*r{wYLNfT)D&Dq_eF5<aH2%TihtuYJwFN8TERN$t9)M)OL+C?K2+8
zsPZDxFaYm>%IpA$Jmc=`>F)RfYh=P%<KQK%g={C~yA=aYnlYb;4;{6Q{U5}o)+?Pp
zn!xP67H^-)i(5!{8xcQsjJH|Nu-m)kr<Da9K@fZZx%_W?qNc8{E=YiW_yREr%+~Ow
z@t>e&_Pu+rV(x@Ad`)j&(2Q+=%c~+;S2`4s)Ka^RrDa`IH?dy(hQoH2ucSt;G73-{
zB_+0Bf9K|QR|W}DQp*IW)kH=rt@(m2H^_wntBrRWt8&aAwjh(*DjT)JlYW6V<C3D%
zx`S_HGXXlFY;v~UB>s_wH-U^3USf1c&iO%N$!*M=&Ksif2M!jy%$5fIRM-gnUn`Ag
z3YTGfIcq#v<d(~&#qo>0app%0%(?=N^W!gfLyB3LpL)Z}Kg%x}G*gcUKuPBg%#}PW
z;|rgJa2Uq+m%ZktNLsfk&=^YzTZ=(LL4&Ff18g1dB<y+WtgQS_A^Qfe#Z&gnctn{o
z90Rn|#E|P>y{bH%`$|&MM=tY{H(kN41OGrVH@1zHkk$**cuU^r@uiAS2z%eqHvDAT
zoT!(D=zLyoJ3E^xvo$4)su|ZOO&?WWnRZEGa{ZvSH9IASPkNyPd&J$$kijl_Oa>G-
z`Tyh&oB2g`lpF>Rot+P3g*x7I{J2PkELESBj8>TxFl(Nla~Qo@Pe!?yUIZx~uGc!<
zzI7{rkcI%c)Zk{@eY<2vFGSAEz6y)x!ctl-|3kO*HJL>b67EzldAm-R`lr*C_DRpX
zhu{7W9;%e9_PSc9>inj8nE2MLI1>-Wl@PslmHz`Sg#+9~+Jpj^Fft=VSy_SgfrmhT
z0a-?#N7uBC1+Yl~5&@RdXvJ4!1$0{39>&C*#L?cfQ{nGGB2l|N?1q1wI+A$>Z}5oX
z#N=7inu+~^KoVe<gF`29)X<A666|SOKT2^WA5q{QL3t7w4nS=I(i})$Jb@ck=(-6o
zjwd7-@B@@J3>3~W)2}os%UKWRe9WMr@V)r~YRpyUzE9ui#U_2Br(^T!nbckvOKNxf
zn)5ShWJ1K|F4C1edKfJedzXr??-P-+n^;kHlf(Gj-@kSHBd;{_9ie=v2s)KLUgoW%
za}~9!?-rQe4`VmAEdHEfd%ie1V3$jtrG~w3Sx@%n-E;Mu2o!8vj2a7Xpn!nf&5Qv^
z$h-!_n(W2}8j7Tt!oX5gSGFB&nRzH-^D_1wK92v?L!3&8{CY?+A^3%PZx@#lr{jb=
zhB*30C57oFuOV-U;VE|G^}7bIPh21MJN-^n$ILpfY)H>AClp<&s8#N@yS@=;)UKIt
zI^mL;{{V+a6YS+e-}DU58i*2`)%34M4*Lw?>I8)AP{-4M)_Jni$*glzGUr)inzbF&
zO~qXFPw(W%D-IvmwleR10TTj>0aTBG7xlD3y;WHURmO+q^{4XN_OXQobbY7#U3>yS
zsavNSUrw!GFZVoZiX84WS#)#_pIdCqa#(VEtbEkm95H&W_6aFn-zw^GsQxA&mfa9L
zbXZ%tI9AR~ym{l>M%L@jhm<Aq9RmUFmp(QJ_u_@f?bPUIGE3p`puCR56|#NNe7$FB
zZ(TCbdR{P~A)HK3<5pof;+|a@)@H^ymmHF}t7_7m(tc9X<KS}Z4`iVL<|N898n4LU
z@bJJX6rkY!Vw7z2D6PaOve(lkIr`BXud0o9nVf^@bhl&mST1i7f(1N(1|3hKo#q<6
zPRofAlk>(cAy0*BuSQTf#CYIAdW6~Bt@$9=b~nBNSTevT1XTzSGv%K=sa<~Ny8ia%
zj}fYHz2x`BoTWcpB)tww`Z?_m3J+2f3+I<lm<kUo%o=QW0k2rR|J`=iF+f5OzX@^>
zV9TMP-0^<V$1E?~BPJoVxK39+;-o#vHJiRuxu3Jxb_2(6w=2hbz})p!-#E8Ho}7B1
z8T+-1A6S5$H>A~L_||pU*lxZIUcwKzCCnvQQ(YV4q!r4)s7$KH72{p`Y}i1nl-pSC
z@Rf6D!)Dz{TYHo~$xi3t*L1BKS<>EzA8X}e(})oip%cVx<QOl&OB6*o+XYi1A57o6
z;afj@PhRTDJ#YVIUD4=sKE?XP(&O*XiRk(H!0pb9=?^BaM{i;$t*5hTmW>+5&cRSp
zH(v~t5aR|f{C*aF?fg`8)1_Xg(%eHuPRQfwmCng8YkC?QP@c~PWDc`VK~VCM#E_$E
zSk|QDj?mk!JK=(Z1y-2@h<8K_P0aH<^>sLGd)-3DuN&tD*X>g6+=|CjsLB@__X1z$
z@e@Bj;=PobMjTa=VCs#L5}}D}i81kr2|?LhOz=(ki7tJyo-8k_0YOwZyLE*i(6sXM
zTA^D*^_O=uI%Fu!K!*?YGQwVx?IOq*{q?aH>c{1acf*Mg5S&=M20&`Ym{T}D#MrhV
zGI2)E_UCw`oZTIR9V?R|;1I(;tlYp%fbAKB8kLxC!U)-2%yPG`gxI!=EkcpnE%iy5
zjKBnPshxmXG~v+u<V{UF&~ta2sRK(Ar&d1{opZ{3?`OD12yAGvE60LA(u$<*uYlRg
z4tS8p&KW7Lb+NUp$c|Yd`sQIp_I%b0Rw~-=rEX`yB~A6%zn;1K!6apAw7f_s4o`SO
zvZ&~3)Meobm0xzr9g?Xr8(V^f7uSgtDhGtEGq|;;(wX{I^A>{MsEA~0h3DX2t(mCS
zJ>(j562lR$0UuPti3rPnIgfO8Vz6tdb>C!vB!?AYGW2Os6r^Vep_H$xFvQts5F1}P
zS$vKYE4R0p9&vnO?Nse|8(SUwfH+@@zd{rKT3wr8=@}pW_>F3KxwTfCEq-tX6NzX>
zM+C!H(#V8z?z7dto~J33joKET5H7mEQ$8sw;!35W^5%BlA_XCxhHRN!N>ai~qQJ_G
z7ujj8wXy)I&M9p~THl4e%cU1o%_PJUxpB9(!i9SH&<zX_1mk1LgJzxAG8may;h|Gz
zLwhpeoWD@p0hpSB-<!7XFXE3xfvm^o+|6VjJoK{s_6cIPF=FxP@d0<B{-$@5=x*7Y
zariUIeqqAXs8z-=z>WUs+;P(5i{**><3rsdOie8<SK!YA*;7hJCjQvnUsvygVqRnh
zaZ$Ns6ibxFJNnQ+f1>7${xGzWrK-(4cn|5Ht;P_c&o>IN`KXA@?^5BXYx6hz+Zr_=
zOwvRdDdkU3y@@dC4B?~T_A?b=OR^ITCn?<Fxg{jz1|t06;9%fZ|1CTs&E4mB7EV#m
z*MM>bc;nq0LXZ0*wE@raZ>r$o9zPE1Wiq-+UL0;KMLyM2l!%*W5L>k<udp#hfxNG`
z?+mK1N2z$3oh%x}Rso|IIsdMLY%l-BrgmPG;4@_VfadLWj+MxALh((m7(+jfK9NPp
z=&!zAT}zTR?_;fGnF3zfDA}l2ixQVH80eE`9jLNzrDdx<2>NaN^W~`LArU>Jgp><m
z>P82hFJ|Ks|EFpGcekgh`g5haSFYi7+a@L4zC|}p#Wy=_GFbA&w`^@HI)b;;jiKRl
zXKSlg_3MIe(<IO}f!e~`xUG0~&-KQOcdx3)Q{v;}lafC7n-_WPyV5uV_p7#f7Th%C
zztl^<fDI6~O|7S0deT&{&n<XGqT5r&9tfwjMQ~x=X=7H|ppYU~%{au!s-_SRDW953
zxRE8y$<<c;Eb7mB5kav<9DOLuDE;ugHpYmB&~dl4S9}PP&m+fr+((HWEn-KcVE3$!
zzp!P2M=%+egX7%#ieUIGS_zf1ZPFw0AIb!@NucY`%hTFDGN)k2k&hKAR``>`@qiu!
zX%vvdB|^|Mkp?tBRRX}5OaU@V5i!*Jfc@3BT)+a%nyuc}3o#?`HibKHTnRwR#T*v9
zf&{$$JCdj8T3ACTz&)l}{f4%i=%XlZP|dT7xcbUb9q#OdUKfgZh?n+F)c%ipeDki0
z2>R|BNl~#u*VCZFPx5Cc9hbBC4h=?s+gkedy$%o37z`gB25=cDq@XB-VC!bgcA?U!
zkRk9z*9Q03vD3E*ROcabTC~Q5_uo?dBueM)u<<Zso7Jz=$s1$Er?cRFl8eZ;G*GOL
z+b?WhrPf#YAYe57<Goark>nrImUbf(c2V|AcOJWDqhyO0Xb5s{4Q7|qL6>om907&)
ztH|MAyq<!(#vA&*P-`y!+q2tY-P19;FkWr8_ts}fsC*A5b!$xiB&T)z{P1i$n5+{t
z>4+86mI_kNwiis+6n&qX?9thr+7KgyAYd5&MGvJ&Lqo7L{(gN5i3ubTi(m@~Opn5x
z97g!TLH!n^(y{x+b3G^(ioZs?a{QQmF~57;_gpUO;p0#<Wjf8L;&?j(I~#ZE=lEMF
zSex%0&&<sKG#V!Q3J#vWk0-0kADrATRv>iVuS8iK<@EQzxa`;1C>h$+^YH<;UHTgl
zPawqsXZKLTKuNdk?|pj!wO#wMG|P<6maJokQ_fDZSkH(W8?l<oQjX_TITbCVRxciI
zalDNGK-j?a*b0l-P(v2WdxwERrkyM(3=_HgyFOP(h|dtwk2SjPjrRUp@{)waExH8C
z?3V^_KUng1nZ7~XPrJztr3rxI8L9az?cf#EjZ3wzoh}J?a{Y=)){{!_K6Dqgo>dgI
zE{CPy(F?RnrG{(QynoovNB<%EdgeZ1d3BnKPWM@g&Gs1hl=^BZJbn89$8@A`{Xj^I
z(Pccm*(38`AA>(R*D7kuMqRRqyo4B9faIBmN`$~_a9QA+Oa!&g*(=VMvBro^Te0d(
zSf`$=*q{kIzDX837u}l%skiI(A!A%5iPOG~6XB=W`OS$`P}sMu0w;6@FEq^qY=8$^
z-sx1E?ve}HutX^agr<7EmfhAq-ib#GDHu=*z1C-LBVf3T6|w9zIVb$Kf9H~{?~&ud
zb72?A#n6Z2h=>nMBgsAoW7QT%=i&L#wiVBin7P#Ic3u-%3R7eaS#QMCet}jSsd#z2
zVq2wR#^T-k({JsLZNAa-8KhZ+CATZefKl0RZt0g+^hT=y#4GrF=5N0u_2%09KoL$B
zapQ#+M$kd#jRjQEfKXUrzqvML7shXA)1Rk~=1Dm}HwTiE9PqjVEpGMx@T&-4)t6R`
zpi%i<Om*|DUSN+Re`yr*oyT!^7#}ou=zqQP?3*35wK}(!<Qe3J&PcWBSiYiE=Q~9n
zkI%<Jf-XLBwb|CSRx}byl#G<SLyo=9I(&mp{Y`u`4%<fytTF@w5!7-$ffsD~+*(Xo
zrKBXxQcb@L-m(;5>pis^`UTYc{q}ltRY@#)mpKRqaBHxVYKfP*YkCa6x<l#7ywnw3
zeD109ILQo3Jh+Js25G?k+_;w?VBQB^V^Hi&S~qnK4K}^HF@vulE)ju!sj2V=L?h<l
zeXFxU_&h43^|a<`woXN);!daiwRxNNXFt{M3EYbEzwxwUqID`?F-~ma+YRK;Xyrmj
zeZ6h(Mziez;!ABqb;iKIoYeuJA2|AwPtow&R#y}gYiTvclGz()Cgp}yB=KxC-o-4?
z(0C=D9$PBdwftV1dBs7V{8wY+yiKiF&;mHCf(RfoA;EP}$4SSHMm1gee}a%~aq+#T
zhov!*EDZE|o__qvNtGd7>Q@tEpR)cjZ<(ksD$vUO6k|t)MjM5p!Cb0^mNwF1D4m;}
zz7`s4-EXdFlybh`*WyO)@mA^~r$kQWdlX3v-rb!1eu0<!Aw*!7zYl_yzWGDspdUI>
zg!bGfGcyyor@@0OXR{fy!OGGzHbn8g7xx#w`2J9!BIvNXtswOEY0ycPrsxNa=3BQI
z9?<vRnA^0;;$!#lqayoTPd~MD#JKxdPi$QQzw^TJq;OQ%Jrg`e_wMU%#j%tvo_&$h
zgNREejE<4<<-q6Aodgaft6)jjWoC;g)TQ5TgTP)(<@Ob7*?W6(TY~uS=x4r=1ST`H
zRG5=XGxhqr^d;~GT<O~sMudkdXOCuODq`rn{5g^fN0t)K+(jpW;?Vl~Y)--I1gJ5(
zZZdZz+i#Y^+hKJTGk$IZwN?Pv7wi>=iKEEbw1;vHWemRA{k><vsT>;o^iNX191EiX
zM>OK8`gS|5|F=1-X-TmLo_W}34PWWDz68r^CwuIJaEn9(Tel16(_NGNLG99_8P;bO
z-6t<X>;8KhWFOM-A|UEXY}eSyxiRBFTpj!ESunKJxhMSfK(l*gsf)3LcJrsz9kzv1
zUmx>^yO^Z`x&88LTw!E==9FaX^DbEft>!$Y+{HO)VOc{4E^BLEP$GUy!C?RzmOamO
zp6_V~5Jq$TXZ|vs2jb&%)~Lh<x5-lNhiYObDa<$!#YaQ;rg?gWz;AhSd7ryN4Op!W
zFJXxzt*Z)bjK7EmQCBReJk*gWctIKVS;ben{UHxYzc>@Z0ze+<$>5^`dhi=Uto!%w
z-D72aA}fpQ9#E{Wbn4#lzpfm{6E4cdWe){|T-+^Hn)f*j;$v*6CQD6~I#YIe^r=Rr
zZ`!_BYTGQ4eA1XCM!a!-0&V{0<xwOL+-VadC*7xUyQBI_oT2sJHPv9+5P4DwH7;eN
zdso$N&AK2@fy_vF;m&nibp3>D;iAsm(Yu^nBH8MD!H1az=vb8eqDvFqcS3PX%)+#h
zPhLdSrDvvW%l1W8{~bk!NzqSE%>+|+cF;*FDQOh*ya>;V+_uvE<lUxNjE@`(?+2}X
zH9z)1O+mT`or%`_Uc&4W`f%ZU&lr5<FnvOTZ>i>blfS2B(JEQ&iqH4pydgAu)ZiA!
zYc;l*8t(vN6EtdUXcOdl=2|r91{e+22_E+U+!J_(7mdtDCMKRhuaRd318C@2D36VR
z@!(NRHu44AQ~?)*(F<0WVkg4rwzCJZjx)6f#N{N~Y%{hb<%!NBKi9A#76Qr>A>iCO
zlA%fFdN$&)*zk(GSPmlvTSDLG^4+}yMh=N4=afxhlBO`R)2B6tE1P|Dj@fnw(vt$~
z9|OIKUN=?g4^m^Nb{>FmS)ph1mf~c7Nx{>vw;D=iP)6XeAAnVt8PuZQc&+{4*ZQLC
zglEcj2&5O_;ta0R;5PH_{d?!yb~eVk<|o4IspVE@Yl)6JpVIB}<PGmAyzN<mhqB_^
zat<0qNA)rb8pP8j#O)z<j-N@%>E|>&@5F7~ktYJoQ9?r_-t;z-#8TAtdUY(zQi#^?
z>ZN;E0Wx^M+|&4UIW+Y;SSVc_?Zg_6W>T{-eREvrHEWFty<rn`=BY*fy6KY^xB<Cu
z)E-bpCE2bND2kmqc$XS>o&0Aaz@RH`%_>2p4XPqw--2^QLOHz^-(k()?d?ID#ACH0
zYU-Bq@2k%1A55H)89QRxOR4b{<Ft>bUX#e)61~6W`k_;1Y&~SVSR*DKf24rsidW0&
zQ5m`3bueL|y#W`R##hDRafIvzm>l><&I}|{L{hbEtGR??I;G~ZS_LVmGe!e>>a=Z?
zG4l(GM?0ydE(=L5mKT+#%90MG?6lN;>>nz|ku)qDf#jbnbj5$@X||3L{e>)=HU%ox
z9d~EDR_Ny@J!YlP`J2;O7!iMeTEB5UUL*S+^S!q*4DS|<f3b+R*tp2LKD#{kxFvG#
zXLS7;8E9R_EV?z(pYM8xlpCQv-$lAAW*0@MJNL49>LzTzXca`!k8h6tG6u&#7_$<(
z3wQsvy=|FPhmDCd*XOz}l?H%S;6-UQ2P~x=aqLuP(!k7MicIx~b@RWM8EP98I>)*j
zz!do7W52m|%{T+_ARyi^Y%;hG?GqZ_preoB`A!g4TM<cK&QWhg7l*e1w1{V;%6z{S
zzY5d=%bSM)rHMA5MhOFQ{Qn83$^OX)wEl^u&8HI<H|6B=bvUcu5lQlgtCj_T2wsk<
zllc@+WuF!i2U(RyzIx3SW4uV?$}eBo8;%-%<c4Rb>?$pI6SWHJwzA@FsLG7d5?1k5
zO#$UW*=O;X$MpE$wSZ15U<P3ZAU6CDEYlAZy^edK1`x~k@C6{=U|cgg(-a!ZmT~kw
zLg+vDn3w}iNl?N^9slj~%|J13e>Oa0@KgiSL+Pb^G0<iXK$njW*V>6+gLYU6FOc=$
z!zBQHO-Vyn_Z&L)fcOKNv2=<}W<9x)$*l4VIAKs9V9Jc5uLT>s*VQ6SOlC0ja)bRj
zY&3q6BFt0-&Gi>&))CEy>=e*7hACy?!M;%rAv9`o&wM3bADx((Kez&{=%cH*rTz4D
zqv>MUe}O;*WLm<&f=*e0z^#4f6h7WEi{U89F9%m**}iaSI}%Cme?ElP3Mk?f=!Zch
z4IPl+3H+v7l)NA&n}dfTB_pc?@e#<EG^@yrE3^wCPSi>96}@vO8%TIa7Z{i(B~eK|
zekH7V6Lqg3xN!y-^-JOqXVc<*bm8oyfi`mK{pO&-I&VNHgeH7@ps|*VPe;O}QASut
z)l9(2|8}&%HfYKX(*N!r%1(7`I-|}IXm<iW2<za<1{xh`gKBS@a%C^jWA{l15Sdml
zIw6QtOcv}fc+O|Mq73c6?D?sXDF?v^uXBv=re1?U9lD6zuicH3PX8YL(dvuT<Q((?
z>6tWY153)#P}M4`c?fKbjg8U7H8eCp+R`(B_cmM>zG7uLFqIPUdO)ohQ1u;PVypVI
zeNfMBgLeaOV*B|-3sF2+4aLWogFR+WPEKSp9_oH!WNwPMI)Gg#jN};@)@&K&+^BhR
z8La8}ExLhghWF18fc2jfK<U3uo_&oky3)x^!QKJHi<(uJ{Su%T<9{Zaa`ZQFOM)2)
zj09UC>LQ!)rWD_^e}wx3O#q<sAT7o8#>6877b_@1SOO97yjB<3=g_zT{y^${$-BiU
zf#22uXlHli-$UNuV~tJMG);<=W7hKLk2Vws!1)D30@dgQ(qS9~ymAC=XZ*TNsem&H
z=9o|r0|Ol=uxLSmD7H9Qlsb;G`acOKxdYmDNUk+MKYa(uCpbPvMfCvZ!g90>{|2x8
zl2bsW0sEqIZ`6kmAd+(d(;jqjPtS93bOcsBbdTK}8hVPn7k1eOgVXqg1VN}WU5IQQ
zjM*!`e3><DLCt5CB$pWy5z*PvAqq$VYx!7ILc(PP#EH2%IZFFa=rUNYxb~HpmX1It
zvGybZ63{|p8(K7lT%&UO8lg;tk=luk_z_*)j!}6Q=OLOZm$@ybqN}_9y84Xf3JzU^
zsB2%}75Ko9w&!1d8=kidqY~&5^X<=ik_gWQ5hEDW2>$co#P$NAFc>3E+qlC&ptsa@
z(D(y=4>sVC(9quA-ZK&^aBck}6_F4Jx+x$`u!x9^gKZV4w!mrvn}CXfUB5xcX<S`f
zTiED#B#`q|NSi=F4emtf2(|0tM=08G`~tz;7<kx~kDd)Yx4s@GojHju`2<J7JRqU!
zVA88TEp4h_Q%}8$$S;9;wF=m|WCYK5b8@*p$cv^zEGD-k35?1AUOo%Kqv8LzmR4L4
YCQ?zcguHJ919JqCl~Q<ED5>xBUwKIkLI3~&

literal 31043
zcmcG$byQW|+cmstR2o4#q#LC>RFIJFMnbx~B}Gb+Zs|rqIt~JY(nxoS#G$+S7T)*!
zd!Kha-}C<Qjqx!C;~eGez1QArt?QcCobx*2N($1L_et(U5QHi7^0^8GA)MX)q9cO;
zVUo3A1TXg-UTQl-&;z`?UxZ(Cepe7g1<5>@Q1eLJn)5WmH@gwr8{|@?|5Wm7R|VZx
z;693u9V6A3fXDCA(9t~}I5EywQA8Xd9kZ0Hy_jl$pXI??|GtnB9>VBE^F#g1uY?jd
zB`?YbF0R4dn78Z?P=3X3<SrdvApQDDd;VMIa^Uw*uEA$SCIL8-j4qBkkHG6oeWD|D
z>W9lNn{&q>X{2LgV;S>$Vq$68w7#@0IndJ6E2MC5G*gbTAW;XGI~9`+b9Fg6I~)36
zyJik*);V6DZ2$cE)A>&y6AKFqGqd2XG*6E>IyEvg`Eb+R&eoQVOzgm1Qf6l6$Bzh*
zrKRQh`FVE?rP!%-YQNjg09M$usP?N)M=}%nnTjG;!F1N%DpqSPq`af%N0l4<!W{3j
zYb!$jY>T50Z@8>c@LEb9x-IfYukb5BBz3{h87?%C=?`I+u;(V}jwWw%-<@~no8ET&
z^fOn_x^8ChQh91^znfCbZ7XTaO25^ofh6QS90w7a4wd6<o^l{7*cQTslw{#vw_gLd
zvxb_<(W!g9Gj92YuR<Q+F`o=)cH8g@r7J)V4SMF)=gpMyz7+mfjxpWOn99n^Y^&PY
zb0^?*GQUn1zuWL$`iNFclo(@5<qWSnS!zL$R+&av$LH<Ll0VyRj(S>JNwxkDagevO
zCg7eK_Cwvi(JLMqniaW!vNr-n>FDSf7#MhXco-PoT%GNEwcN~ti%r+smgq6Euvp(+
z$>+K45JP8xNu;Gg5Nq!=Vb;*qE6qc<thlFL-2QBVpUGVW^@FU2u1u6ZSiE0~Z)j-H
z*4DnhIA&pF{L$AJ86I9aWwUlYiEKs{JTw-Lyp~)!O)>l+FzbBS#lxe%du884m}K0D
zH?`(kgAB^f$tfu==H8VWsU>I3(<rIQ&X(*Tp+cXQz(JP4v9hwdg5AsGe)?3UB{t=O
zu<?EBU`aAm5lQrr`uxkMgw(+#QBP_uN9dd8GCqD(=2O5vzZbKPE2&A#$9KB;EeP|X
zc5vH`QrP25W8?H~YG0aw^Gx%j-UUOa-9>9=78X*2KkArw2S5;eqV>(yxqyHGSULXJ
za6?SgAhEg&xZ$l;&pbX8x*WcZy}9`&5fZd~9N@GWG=mw50IeM}K5B!ru`p@3{7#R1
zX<gnscNt1LQ)7*0fGuz-DluypP`yhmO8F8GI`e5QTHNq{g$ik5w%0BAc~9uOqnw?G
zag({KVw1V%4Ee;NEEc9#)5=&4eb~FbS`%Z@C>`2x;WF=ApQxbRKNrXQwn80=8$)VJ
zEKKpZw{y)bHdn9drfGM2Lc=vOy04;`89QQqDhJI&7;5trh9W&$f}i|3d^`96dVb!2
z<n{7XX77ozTeL@S(u_T9E;HS6Gzu}Om4fK#y%Bmn&V!4ps;XVst*D@&SLZuivNg@_
zjq>doLXqR=vZki$E?W-<Me!|poei)L&)==n*zvs+eJtC#@;Lj)^N)uf;zyoaC!5*r
zgoIq2doQy;Q#+#~Bagt}=DcBN#D7mTpunH=2y1n%X2Q`+mbmRtPm165#ajdHH@ZRh
zq^REPzFQ-9|Fdn|o503!vdvalK5tjWZrG}agY3`zElbmAXaXt7;H^YOVI4V<34FW^
zY<?0^9}n=3!xy`A4W**)YV>DcSia)=Y-P_j(^siit#I~b#~{2Z3O<jP&NUiQj`}(9
zl8xPLuq-fgj4R$}L>2t$*efe;-%FBd1Wvq!^?UVuQ~Eu^!~M;aj{Glgk<gw<KoC8;
zIDgk6>ZhM<@8!FZAfhOUN_B3*d~iokT?*Z1G5Gs#Yi}z1Ezbsca5r7gMt0U`_t#cl
zv`{3uozA&mYI|B*7WJ-HP1*RhN+Y6PYZDzEw`FPSiAeaL?Jc>&$LZ+l=N&pQABFxn
z-=>o3TruFZH*i`S9$o)&Ed5}|WBoIs2FreZ_zb22HhAeq0xupf7u!=1s^$8~2)-jD
zxCpNwR~b+V54s4_6D3CnM!yM!)HF3z5BK58iYMDkA9G>XRmXV*ESSc|;!GnC9QNP2
z2&NzY=@;@kvS@PKzB#Fb>GN-xw7{^dSna2(UOfB`Zv8^6q%Rzw!S8arZgg~1Azko%
z0nwvfEywupMBrXXaP)Sr{1p0dy>#f`#89ScY-?-QJMJDy{7jgA&nrF&wIVF>`OhUP
z3NY$#aO$X=?^K;M?-BE$54y@z`QF&CIRg|1Ju-+f+Tg^4Tn5;vNFy6W6=)JhUe~dw
zVtckzLlx@nLQSU&ho^lKL8#}4N~C;tKD#ZaM_$(^F(L^W8S_5^ZxjE(u!)YMLM05b
zF}y<l_)iY)Wi2!tcfb-_N%d7`;i04qvp)D>;<st-z(Crz|1?uLG!2qDdJ8|dxx(0e
zYC=X0DXpHU>9*_C+M=N%;bTyt%X4zJv>kcnKWej3V-GUeHRklaII3^Y`R>*|ghttV
zdvkg1Q;JE-OD5!2s+cLN-{@lL@=a6WZ~+bDLaHOH0u8lA@=$DRdV0FOz5VR$Y<+#5
zE0u@_OQ@)5Da}nq_gNa&fPq|^!oVtWSUNvh=R{I?`itZZ{`P=Xb+tP9)S-<$JlOzh
z$*Pm`fRYT10--=EI0_ENLaFX)#iLt8_}&-%{jvmh{mok@;R1#9DlJxHW8<5|Fy}c&
z&UI4E?%^Dx=fXQHQ&TK!t(^5W+rfhq)rt$_#^&I%TQ!Rd!!5cGtuea_6L_bLprDV$
zk@7f6RE*to@|*$SYT(wJ7+?ACeM`z-c~NtMX4orVztecQ^PVr&_Yv}JKi`6|&)Hs2
zPY)rR4)=Ny9W5=lg@1548O87fQp`HRTamz$(o)mN7cZXU)J4?mb#6bldsMHNHiN>>
zu;zl7_=%sqbBXFCMea0+E9j#(YoCpA0_T*Cv3Z0I_uHR+tO*;6qQ@S3&(o%kMipVA
zxBERO+cTHi1}|Q`@bK_xDS2e+M@!l#eH2-#-{f}g(;69xQN!ThO#>c1jn_k?=MQCE
z2{ct-Srv!3BsdGcYdJABC|`65zSk_Xw%dmB@d={%J>xcw3|7+1wue)AUF{v(_3`>w
z3I=+6<FRyd%*@Q@ta`uwdyvG{)Z%W1Xi!XSx*~})N&BeQrCAM{ea@YZ6PypcS~7i4
zv~6v--{I0YphSeWXGv=Y*U#z6Jkd0e?Uijfcv{{Z#}5}cdqoSGkNErN#}P;NS(paC
zC4L27q`0_}lZHFx%?SzFQ~NKmDbHatr*kR4+*FssmlSWt636>9y<VG`7<w#)h>JA!
zIyKGtDG+3xttJ}YT<`izzjz^Xv7W=GTdP*_g-$M^d2fsa(h~HYULVf6ZU|@XEzu~O
zZgRJ;FlgzDCNC*X^RWc^Ig<+!TOK8V@}}kWOs#DY8g^@Y4~O~mSIgUtX{6V$Gg5`k
zb`MT<YOL~pA%)OxH5Q?{%`t9X7y4~>hL;N5Rs^*atIqHSP1A0alxz~yDiqn=Gg-oA
zW!2+NRv4dl_ir(m?{<Usj87;lPZfw=B*Z*gz{A8M7c{M&V)vGcE#9;ZGxR$*yK2^?
zX0=gM`<?wF;z~gIu<b3<Gs6opk4;NP0g>M;t6=brodXKmCr?qKvDiMxwBgqR%G&oJ
zgRZnH3`i7JfgUNKFYC|NDWP50Y?YphN|osfJ<-u4BPi7`UGP8(=ZNA2=FWb5`UbUB
z$~+__M0%FuU3BO{m7A1_d~d?et*;rJ5bf3;Q*`0yrWJY}A~d<ZPoE=Z?;TlraNrci
zNGyOyK;Sy{z4cx|a%E6R$k1a1$OxVwFS53lM7PzbjBGiisjhB$wk|x_TUB~Km6XDP
zWyeW0YsbeaRm@yJ^8NRyj92jw=c6zC&7bV#%>{T3qQlK99i3cQBi@-Nd~sIn!+qhn
z+!>C4^jvY{lBeAN_HyA-Mx^r-fP89eYb7FrShmNrl5HNb>(zT6{wXXhT+8&oY3GaQ
z+TMdbwr{?7vrCXAyft~@k!1F}a9BZG#c7<5D~45w_^_uc;t2;*BC976*^i%7FFchl
z-bIsd4}S*1)hANGlo;BIWqnHj)O^B)?#O)ca8R^o-M5#m?yQ=l+i_`W$x8F#RUr3z
zjsBiN!3dMJv$a;xu*Jg{!)w-8;=59@W-973J+d!7i^{811YHB;5=MpQ(-6E%_Ks*O
zS_U4o`*;3!YG}Q=u+ALh1Gy#Q`?9|PVZ`?hmkY|H+lvX9dGo1gaLC+Pv6+U3#@s|u
zP3)GLQyFcO*;Kz)8EtqZ)5@X$we4D%bc{qMMq!!lW%pZa`-T^$rBbn`sOs52&_{pB
z$E}jc96b5+!IZB5+W-FBZ$$NTghAhO*ItpKL0*z^50I&rw<Idx;vjW5%<b&f?gqHo
zO2pzF9EM_3+Kq2utTi8}@Mnt^ya}bDO<U0@*K5deCVBqinYK3UnZJA^oE_;@tc6c_
zkOlTZDjI*F;T+T`&y}}^J_ltgu14|APkLNOW%c=0Z_V*kw0KRWs>%EsFVjuDs>f1D
zs~2LYXJP3b)W$)l#=P)-pe{jjH0e6Rt<QI|xmjl2`ktZmO?Fg8aW28Co|&h}$|C`V
z=y!3;_`icSGHvG!uHY9lf?sccFH(aUd6oS1#n}Bp5o1|!_X#C*4PXP&7HPz8M~=x>
z=_hPg>9NW1h%O|~)IQS@gv4E4OQ&dwG3?Ie#e#H91tM241g)0&k-CY|m4A)@z!kxv
zSw)saMwE(HF`5!`yK-Ty!~e20p+8adid00JF(70GYHN2ClynJZ6ieZcW&rhLj(w0O
z7Xrl0ymNN-DdfSU3sp5cyB#!2A0uuON{>YZF&G9TJG<Y>)EgLEMLiR}7XZ$Vj*e^F
zIEBN~V4tsk-JSThy<$8uK+Jv_!O^<Dh8bVCjY|I3c;YYQ{7iOKCEqJspN;6%Nc3J&
zQs#;O(ns9xpG&w-E*Q2S7qMcDz?P9`&3O||Ol)H4v;Tb{UFh-{&Sy2s=-*m}1SPWC
zUtYT>GP-TfBQa{S{Dc-im{FR@8dhtX%T|0<O(-$@6K57iOI}gP>c;;(N12<Xgl-D=
zU9$#Oa6efAjZvPObeq1xzO**+B;GemMd396kuRTxm)iNGkq8x-&~WT|&xPkXqxXW7
z1QZ(3T3B!QGHVD>Q26E@o(0j{SQZNH)UOo4M9~5f4xA{bs}J)M4ODh3+1Ua*<YCt*
zGf|{`ZkwYU146q;{#jU96*k-$7hb_SLtIjF?_H%2*25)to>Lc9Ech>M{F;PeL~VXx
ze)0W>@~}9Q-f!&7v;7rCvFjragMFQ#o4oVG)%r91Y^^`|a&$${AND>k(nzqT1xv!p
ztU)88#c5F}orV!}<t0&`G1(&p0j};4Rgx}8^XN<Su{Q$4l}1m$g&HP^o_oA@iF`|A
z9$l@ZfM(S4Lwxsi-rFA3Y?YOj{+BZg?7|4LGmb|fIW)NrL<|*=8CsP*V$*p@Nm-#z
zmy)R%AhBQpz$^O4tVG(m%8tg4hQ<!h&YOi@VWK>zhy)eMl4m?8wwZoU%cKP%`HR7r
z!LqBZ=D{;p|H;QD3@`SRy1o10-tOD}Sgs0aK~R-Xco7cATTb(_4M<#|R0Hc%yuT|1
zMyE2+=}0<poisv6j*J8yT}+@=45nM>ai`HddSD$g0l!|3-~eTtbQB3e;eJF4S{iGw
zUEPcY_r>|S3|&ec5~^>`692`JvYwvL)@0?;(Gh22(BOP8<*c+`IoE_TbBzMwr`SA=
za4_ab73k$_o281>y4yGDx({b!NOC%tEtOgK-gmdmBWCWJ%ADgC9M*Tct33SPh!mL7
zGN{LY8E+(K@Tg$q`?|}%AW2S+F&Soo9)I2v*}av*44S;|=qP`8V)+b2Xs-er)_D8}
z0SQ&_WTS6DD9+?w7#8_A;}|Apd9JgXp<siGOn%4LLDOt!`@)p?_&Mjvm4)Z+S=hMN
z<E6<bOVV`(?#Y#IuA#?$!P<Qmanr6iGp;zFW}dX%G^aj{z=O}5n0e54cDZd2AEq|v
zbo+=V>fJI3BMt>l(7`=-D@|aOLRjJ9U(60?v9{mHr#YMl^^Hcmzspc0eDl`8-`Vwr
zlvJqL;Cf{@h0xJn2W<UFN#kPTyyDy$KUk^d3p}5qzXBdwS-PQW2hT{@EJT4&sTgYZ
z^G<Pua>sqcb++)GXEVfvGsNaH#6;$WV@_W$*h+0!JRW2?@bQi)LJ^P6Q2c%VT*Q!%
zYB!A1Z?eVPJyX=T{BXCK;jX{{Q58hapbN@4Wfx2mX-bC_cWvZDJ-%1>n(Rn0K~dVz
zvVQ)|f4$CTv8NYmQ*hmts++u?!@j*bEr`$l+PgXrciMGrIBl3MfcpyCVKeGK7IN>~
zp7B<isr~8Y%P%r=Sbk2)i@dKA`0CzW>9}A4f^noH&sckmwW~BXQoslUJ9~v67j@C{
zAWfO{ydeMWWTxGq=k8LOM{7>jt__X~im!AKj%D3x>!n@N2~Pc7&_sE%+bQ{G|H}}Z
zUpT0~hqS#H3+`ZuRHB)R1v8Ux7P-P6=pn^2=a{_xAPG5mLsV}kj=4#iNHOOkL+wzh
zWlv!%>XD%f^XF^t30RON>TlECIahJ-s}mjdV{57#pLx}&IbB>P5thujXpxR&zkjms
zYVP6s+_wK!t;RLmpBqBe<<+XDSE;<O1SV)CU*Z!TeS7yLAv4pSH<cqTIf7zfuEupl
zdOv)@W!tb!zIPxgWCc~XuY)z;*f?QaxHTXB^X=QuI4;5zgSj%F#*;g;vX|p=I|^Q<
z=G%yEANsc1FTiMH7fiCM=mLL{w)f?fE(P_|ixjLDaif1W3YDOAxl5Vl()RWH^pM9*
z18m-X!8gpbwm#S9Rg^w=FhW4v<jWE>Nwwcb)!`FIYBIq})dAorP)72tJDzNfl#6sx
za7-@|AVBCcC1tHMg)la@>#GWNT*>ui&h;RL)15gn!(*l-`434L?(;PrEKZSgT3(ys
z**Puc<m@%SoXY5em7IgYDPy~%AIRD0&IsZ{E>%AEp8NO$-m0|2JUa?Z2opb_qw^4<
z`r=HjIKi~^8ow_EkB1AtbtG$Uw*FN1H7S{GApG=a<r)X_TO7$7CAxJw8J#aiZ#$M=
z%FCy5TgFxm+ttBtF5n6UTy%71o1;Z$W@cP(2GlevJr8~|Ao1kbaJi1cy;of}CdO(o
z#HD|1kAHcDzpTUIAEuU2?NH);HRojdb)4sMa0-ihpx-~F<w{pnZ(koPGc(`<pgmAV
zVGwaHUcf8-w`(S%DaCeN1f`?N1dQ8}dG4b#6A%ypmX3*u>1%8FD9dxE>fEiIVylWi
zXfc29%cErz75>-fTPB69ZxT4yIxkJFpMTPf!nsdgjO^>@*ZP#eR!2t%yumyA{{6e}
z<q1jvz~R4FS2tW5KQSnpo0;Y1<zW&(ectf7W9b8JI8OF9;)CC%EU*#+77gK>sWlM%
zKg2GB^e|{uGp@gCV&mzlZf>ca$A};vI>pT(T3a!NUS*?IegFOaB!gWF_eT-$MvK|F
z{iy?UBaIldb_{KME!?)VJabU)sRz#6)uqbQL!2a0ZcX}AldZlWi%+EX|LITV!zSS_
zH0w<Oh1~Xd*$kkh$jQmmGcvFs)qMG%y}c2Uk$QD@=_DaMPr4>oDE*KtU?{;hla<@O
z95BG*<vWd?62vQ#i>9o&Ac%YC82dH-o_RgsGVso@EcW0AS@!r;{)x-Wu~IJx;v~YD
zCWIhd+-d);d!cUcqQ_e=%cp9BY7a9|vB4Ps!@RmO?8lG^I2~_}W%^xMT3Ho;f2|z7
zgJ3lI#TZ0ymB5=%ilWNAp<UeHt5}gL&{mV$ZJ2}{n@dYehZsctH;>$T+#vLV*-w?j
zBiWCS+u{mXZDw*8YVV=Ho#_(7)A8iCMe8rfLrKdgf*ewotHH07bze$mVmx5NKYc=i
z0Qm@Jy{Bt-cD7u<N%ZDy1uHD)Sr`^z0)Gq)pa!gT$1u>+7AlAS;g4?0v3<&_jUncT
zz1!jqpR9Z!%ToSR=s-Uz9jTt-S0hc{Hq8@d*Xl=%T)jvEZ-$h9l_e(R(F9ArW0qK`
zFoXpL2KM#!B_$E1_9vvKrpCv|$Hjd$?@!S*^c^#=2E3Gk_jU~^$5KSR-CSL3#8}?(
zsutAQ-RM;Ldl;=<&y7fFj`j)ZZ^ml7=PX^i$=KIg;W`i8EB|_yySJ^`mc-E##%Gch
z$^)*5QO>CzBknr)GWMvg?M99v(v!F55~f4PCHdIZMMJNz<s<IeD+wtwx5Mt<m$p%(
z#WmLLEs?)M+2{(f`o)To+~y$2Z7wZDpD!wuLB8mnikH>Vc6voaZI1<NfN{3FW5K}i
zBaABAY>TS{RTW*DFkSte?&=FE<Hx~DfU+uC{mM8X?C5p+stz$AulA4Q&&$p>>&>Z{
zCreE{N2B-X(aS5{IY-X%hiHO_2->qWzdst|8BAv`dPql3J(5k637`Gu@$r25Sg7v6
zp8`4Bc|lkz`kM?Ej^x`gi3Lvf#9j+-gV$kCmAx)Z_oqpOAX@l2tFC0|M)u{%igd^;
z7!>ek5vS5twyop^I<=DJ=&P4b4SJvGU&%JZGbZ8EHna7W^S<L_f~c#M`no1-5JhzO
zblR^!cua~`#Ze2TsM>@x?nftO6`E+&!H5Vk>(|HM&?rm$>rqj$5Hsz!7Bbk$h0kWK
z=OX*r*nMHmu?Sy>54CH9TOY-EKvc1&de!gi(?CS>{>|8iUxO=I{4MKL)6$aZXdMY^
zJIq=kHgO%XxY{_8=MX#&n?ZtBXi5)tyOJAvebn&$E)obcO`GR=F5KuX{B;B*8G`|Y
zhuO6pYiQLXW0`k6%2eD;(@8w&WhpKf^0~sUV^EP^P_(J}F}_^IG)OtwySsoC>>(B1
z&S8WrX$43cf7jjl>j}*@-`nfcpO)!TQ6wmko3`KP^ZmrJ8pt7P>g!~}P%O;H+sQ1i
zU2y>+pia@>FigQJF$zbL=m>MD!FdhT0xXHx4I--V6|Of<Z~~-Fa^8G@p(=JU6;4Ys
z?kC|R%Jez@&7x8~96WCb<D%=_UA=#V;r(3X_w{w5+F+R0l}NA5*@m}{R|+x4j&3(X
z20{dm6&^VKiiwSz<A(QYJjB^%qw`wMVoBOPDp2eQ4d%~>vrOSwLT1-;g$V>L9Kf|@
z>BZ(hOq<<uKx}JK#`RZxw{lHE_)}dSUCajwn$PfkQI?1=fv&I4xqe+Uv(XCM>txEb
z`28c5O*b+E-M%f#Z0gI2-17d*{d@B;y#k#mbZRv~WWs#Z^)D@knfOe~Jm;ikYPo}k
zvQpu7w%G*=oEh#5PYD?cKz@6IanWl8CDWNzB*t+CSoh_-<1a6hBprb5_r-(@@(7Lk
zK$A1dgijsJeqS-E@gau^_M`M3R;D))eg~U!#m$0q2Wkv_?2b(Ln@8F|$FPPW%izs?
zJ<K~*s?iC?Dc1ZzF>Kv5*{o2I6Q_YNo8j(~*dcSzvq2rP?xOnn6wja9RK0BelS=7O
z^0SW2rffKDg}L{esd-K}DZ9Y->N#I-+?r?h1{r^ximf4f2o?IB))sK5PmxhSKb!#C
z8qhY#x_JswZEphur9Ysy46dmj-^5UeT>MG3A0Hi^bsiJ}63I<nPVfie0&AO_VH%2x
zkd*}b8WLOz-nGtfcuo$~RycA`a8oga`xXcG<`Lv!*Q}0*G4z2UkM&KVn~+|LLcvHa
z6f(Fr!n@96k0#4hcq{Fw4ax-SU=`TS(WXOUYRqqD++2x@O_I-%l?)jkuSbGn#7#%}
zlKmGZvtJY_+;S0o8_?-K>yVc7ech~3_q0e|Ge;se7cc{@XOlF8nCsQ2E;4-q5{YFW
z>h-&JTT#1w^u0Z1^2L!daZThC%xouQ%_1#z0}y8rtd)LLm3m_KkB*(WY3k`U|0KD`
zru%jneL^2*(jDzwab275W@_hGq$tmWzEJmS%S!lApc3eeF|2-The;Z(A9D;rOzstZ
zzm!NB-oYNGE7BoVc)*P#`9n;?>v=_SeLX)TGjah^B>kDKcjAUBy!@gtdox*;4$lOM
z?GLsNgVwD(qbUGcK)l=;e6}9_2adIcBsn{=(C6VBkO@hm^K_089mV~kG`PGBM0o&k
z;w!$n(E>$EvrFAPf<uMGrva79xDht@zE)nhcdk6SI-`X4@=iZYe|?}ehfY1psA)2U
zBtDoSA_g*7BPDTL#P__29%jhM$>n#dOj$B(coe3WafhIaYCo%S2277M$q7Ke`)4!K
zhB`XMR^%jj#NN4uS~BMaE7^u}?^eo<o)fb1cfHv~fKK;5_6-H>$Dvc_;`s-)f~R8A
z6?qN-6c6PJftEoR1*7n>wmJ{m-f3|Co8{N#zvthrbNSrzaMHVNFxT!96+Q-6l073m
z0x&CC)c2xTy+qJ$>ugk2aeJmtajYsORL=aAOl;rw6`SG@F<k~>9CFVP_rit7@RIV^
z^wr*s(O)IdQ6BgQWvB?g?tJ$mf>7kJj|(Ug^B<_6c^z-onDr8>N2tcDChW2^UQF!_
zIsewzZ6~sRW}Z`ut*Mc{P=g80J`1Z3O+${rkt{yoTh}bt6Yx4(|N8ao_2sGmVL#vM
z>MGE`ngH-pXHyVSkL5nm8kTAr`SH=8ZrZLkRDVO{skzpz$jjJKL7A;MigzTql8l^J
z<gSN@gJNQPZAc(l<GldVwG-Jyj&uyj<LYC}YAQ^!b(9>_YfifSb}v*)5X?v77a06C
zedCP&mlwc@l8qlVt}$!@nZE^9k}>Jb`?;9Q`jF*FzNc`;NP(jJ*5rN%wit52{z`X=
zb}BRT3(ML4Y+rVF{#6aS8N}FwMW_3)8)PUW@~zVmfp0pvJ%ly;p}}+^_la_S!U#G6
zfpb9fg0QV#&0M1<_v>us#ijm^LyvEQDcttHyketqhhpby98I>*b@{y?<REE7=cIt)
z2g`ZYNnSqEVBtyuUJ;w?*!}(_<JGwVf~==k_6M<gY-o03?#H^i7a|E5_Bp9MKD|7V
zpv!u_;|h?8zyyQbl4qrjph+iV5>05{B!U}PE?eu$hiai?+ZBAgfMn7do$gqR6s+U`
zro+&4HJ+pO?DbruYc``oDz7czXp0jFv}$67mpnek9%M4iG@>xCBAjit933&`k9a!#
zv=3JL0**A(-uaa$2T&~320zB45cU|!e`Q5T$jP2Or|<B(ZiHN_LGcq}@<At@fcF*=
zYK?r`M}k|}^aA~J8#uZ^F|*Zir3;@y;b?ot9qgTcGvt=k%BK78YX#%>56Zk!5F2n(
z{NNRx#&4E1>=$kjnstM5?z8#_`E5_v0EWBTyua~qKnN4HV{59KqY~)H4+vQu_{qY<
z!vV%n;ho+-nK1O9+i=Ox&6Q6VteN*k?CqUw>5kx?N=KE{phvNwUDei8M}b}PL3<d)
z11>($E-?Tay$i~GfmD!qBD=oZ&TMi@N=jm)dw6=C{X#}sn(&)}G$0peXFoPu;al+G
zGHMGrKU~vPQzH}eYX(;U#ERF_Ps1$*5o$`hDO6PMRRnRcc0bY<sau0=p143t9sG4=
z7x%hQIY%mnB0WCd73_eu)m0!U>AhaNy1D|&4)jt^u6J;-4pdA^dU~^U_J+{<(C23c
z2J=9hWS?aa5}H|c+HetE?G2DJfw&$hl$KShO3uHk)u%MJ9qMPw3;rNiN;ybAFh>3p
zE&7vPeZ1zIT&8ZvIF*cvgKETX$2(`khRZbjeyOLB1DQ1X@Kp^kW&m@;vl)a(Xqq5e
z*tZ;y-&|jQ`t<1$2jFNcU^j=Ex6PdcZcTHhPZ0u!Exyid_jCb`3Lt7wM%d3cdk$qu
z9Pb?dWXOC-NEjLt!lqaMWq<JA;#3X_WxOU{fPbyMv!;*DGyC%A!X~9RC1%>wU$CH;
z6|G&hR+vS(arXnWw$JbPq(+6JKoE7O=~Mlq8VkIXx*(@XW8PGrQiQ-9a{g#*C7~Zt
z`l5`TX9P#0nf6QVA)rD<4)_L?9SY&IUze%<<^?17fTf{iN1a%}>G$SX31AdQMn}~m
zzN_qt0{vH&JuW_;XLAFBOcUZ@+A64!vu@`+{hqqII%W%+1P-kX-iyk4>+>y<(2?(v
z6El|>Mwl>Xv5>GZZ()j<?wnuOmmQ#PBE!Ob00jV%GbM#YCYHNY;?5?bQ#LMjMUR&7
zXPXK=kP01mO>P!~Ri?=XcQE*2q7!?<Fd%({ly~k~0LmRu^gtiL6ON6|w(0Bd2df&s
z5+k<bzz_Da(yplHPSE?}b2>9<&qePjGO18}&X)Y~`}XlDXmNWaGnowm<`4D*zkib*
z5L`hd9s@h-%NH)Li*lWs&DTPw@Cgp!7UJUK5*3{{uU`H02jr$0Pzc|2i{0!4Q4MSh
zU_7d|8rz)pMch+^#>Xc!h13xtfcz^Q$_gWfcc&Gsij#kC!GoSL<4X1;?N^^3f*mDd
z-)net@Q7ejAO}~UPnUdPzx%y#WK?D7?MQ+H9lX_`6dqcy_b2a`ilWZ@?4{x9e3U;V
zW;7A?SWvLBxcGjPU8#okix(lGJmNTCpRF|7J*KH}S*^!e+{N{sJI(vX)V<PDv6vfH
zDM2_QX2VuayUHN}JJwuw?EN_0dPGqy4DxkR+d|gu)!|_L12Ao1f>m#CuI(E)=*Nn!
zi1<wKV+b^4RIqcFRysAjT*t2$i%ZUYVW1e?tI0andKipzkO`B3`J5j#_5w<v2j`5+
znCf>;@R?_UoF?s5UomFtnEgfa%h=iCnxtRKd11h6#lvlWbF{v0(I2LnxfbfsyT{3O
ze5rM_x71<ZydT=Tn!;(^j>$LcFm>JV3-4wy2wJ4@VJm<54#&#i3iHCbAV<JsZ;@vL
zc}_+4rl6U%cQBf=&gQpkW&Y&Gp@AKUy&&4K`{EnJQH|tnMii>(3@kqbVDS|k41(mC
zeukjyW&i@xXi))Eny7E1ujzC69w(GE)UX1I0uFM(_cH0vQ-e50^oyI|-%P?c=kjT0
zdmMgD5hE>-Q*-`4BBB?F)Gpqofqu3(AMcG&G#uSrfHKYGwQTWhmHLR<JCMSD?`kH3
zvfE-LKWbz{*!RK(n7+iW_MWNtsu1M43>?JdlZiU^?xgKt%kNHAujRRkYUc{2U<Z3t
zDDO8T1mmd0z@(2r(wnQXPW=A5Qi~N0i)?)XcI$gOS72pUya`r0&{Gx%Gurbr5!xmy
zs~?h9*u4Vq2FpKYo!A7ZDLt!6Nl7K98B|_Zj37mmt&s<W9io5!)3ZQxaSxv!gIv&M
zw%*aJ#c!-wJx3}^rKXh1(OhP7V8iJvGuIkbuoC{>|CrV=)+=d_OmUUo-<{FC41qyD
z3h*9B>%%>TsdA;Qqeo6oPPH~uzd*IshIR3<))slyVV(rH?JjH4k8erO*#Jsp@4@Y^
zeK6(m{(HF!?=dR9Z%=g8AhNZ7^hVVV9LYinFlmj-e88~>ZaqoJy*x#or>!8<@4mk;
z>eGvF9Wna+iB38Dy1Y=pB-5+tNUTxc$H5=yQ?3hB-@QXuqOWilMB+ih7Et~{E`%8D
zalyNSAAl?Qau{4)7%1QkF`|u6gb@|eJeK1Vk(@GJR=l7+*DcoWPL$tR;B(Jz$~jRW
z*BrZ0;MQ<ley$FSac$7M+3h0|a@|zZ(@RsBNczb?_)H<4YWD^SntZ1CrJhjJFDxqO
zuhGuUj4or%S2PoZ;ec;Odmh3!w+Y1{x={tA2QIob-38(-;8%-NshF6_(j|_Pir=)9
zx_Tw0ozUd6@vB^42-@>%0pWQ5tK}yzGai@kPfNx~3PVmK#e6GF<Yl*)xx?PNJt$C?
zb!3vbBdFLDA0*<9!jDs6gdo^g#Tf-zWyQT73c8gL0|pczC}r?FX4@~cdY^1H{Dp{e
zEMJel?!2VHd5lF=>dFRel|(mw!~lj&gR2fCJy`)H<7}<%%+F5@>TGf>SJ7o{Nweia
zWSLq2ei*fd3LP5ClS>3hEk8g1&+2NCc06zWCiBgZ1oXinTQ*n;eZLNFJ_>TQbj(0c
zPviNY)Wd@VJUq$008N-P>5r<}Dgx_t<P^92G<TrQ`}ys-u6@fv4-1&{h({b)4<4uv
zNPudiBBh3hY1w7H>J$nnSfDDimO%e3esKCO1Ls$nE+0fB41&V9H?4(wm9Jlb@??y;
z%yJ*LOO?$i-Tls1HPCv=IT)w$=?%qj81M4oy-PQwfP!5f9Qnf<>foXHjx022@pK0<
z@l!}hMEb9<<tPBWU<6T!#~wBLBSH3=H#k{SPM~D>Fd9P2bPgC*3mX0n_bO6v$CWHh
z1_(?}PENh-8CT@KP#r=*fwz6vp^jz{Aa?+W1%vVKOL_tfsK*V}BqB#c&o<S@T)^J>
zS{kgp2_M#l>i|<L!42v)s0Z@_lCv5sjwTlpJswpBv@cQsc>KlMx~fCF(^$~!wemij
z;YLCb8bV+aicI8BUUvc)M26>|<U1+u#|P>bujA<|vxwsFMNup)!{lNBD;f~%Oa$Uc
zTJ8Uj^@8<|v(~1&Em~J6?{==1o)I$j!>TJ0t`p!I@H<-y3&SMUsM9k9Wwn9tvHIA3
zRdPo`7k@GcvfuUldurwbmUUlIL*OI=BDvrE$sNj9Qo&zx-=&RZ?Cz40>Ay)u1&<#Y
z)@bLEvC!xWuQcgaW6$5fkr~~+B!`+?E?3gf&_56Tv7dOQrKJUYCl^N>_=JS{Hrh2-
zW8d8pXf3}5qF{y5JbB{yD_5pRs$itS^e0|e$HxyJI6V)39~>McbG_;M{vDwWu&Yy5
zX1y%lk1=zEzGU)~_t|_Cz{ZiJ9$d)E<hE4NH#HmwSPNi<?kLj4<Ya9%HQ+CSi~!&U
zE3R(NZ3a}W;C!?)BmnaS-@_--K8rV!Pd;p$$l>d_JJaWZg)B;L3nc@Cj4G^_3ApcO
zQ2~1;zlWcnpOh3Tl$)2gy0*q5K}ky51PUNva{_03_wL<YJ^TCTC*X1g-q2^E7|M3_
zz`wY;X(fSk2LUEL9!noIRN)P6yj(y)4O(gk7?M0st>CcEaa5dX)ic9lx-55OW79U1
zo*q4rwU>l2>;D9t<lT;##S$~Hti{hg%OJY^S8y2@7q{A*IG8DBSTO<2Kgsg>fEsp#
zk8f|+Eks90D<~?W1t6her3t$B*zg*<&$()zG_!A<l0%T?+}XPyUKItvw)++HktBMa
z+KizpumYDAgir;qy9jnAh4NU;WA#+L{qxuN%rw><YTmPuW!U6qYizt!s$IR)e5erg
zjT6Kixh)&s)N};~s9uk~daBgs>S#o9w`sR^zQr5NxK~FgrmM0jND6?gW|FuC2#bZZ
zx2K2X)Q3Hyq=<Lz4-~-F5~ilByDJ(<^)i<BeYPym8I7>;?41{w0@Io^wIA5CI}jo9
ziV1)zWk%x3@A#UcF{`qc9~IW0hMUQO85sGq#I`t-`gl`oPf-=Whpeouf&v!w`}gnM
z++0RdIvJJdkHjX~jOALDkA;LtA)u?w$FU3@z;+?+1f7}$9qx?@7|5^$>V#;!6BUNQ
zhhlDS?&H(kZ{FMAe+}H*3<_yyAcXmXS_GJ|iwZJ|3k$WHJ@(&Z`V!*f-&Hx{!il{!
z*)ez3r;o@bZ$Ax4S%2PTwq!<Df->G6K5h$?o;&qO<aG8^T3V@rHnZ#8p0_*kxNk43
z@rh-O{_7}Q;3KlQQ6~S_`uqjnWmTi0re>jqge)P%sL%H=UM3;dzSVTpBml^k$OHg1
zAW3W#sTKfK{Kcdj3*eA@ca+yz<7roXE{>!oHuxNHB}9Xg{S<h?>>_xdEmnxnjULY1
z)yF=RTxVXk9|G>hUEl^In}|%L7~P$^2dP+*a3$B=h0K&_*ta>b`2qtG)#a24ii*a`
ze}d>aDEc=_yaJ%Zt7L2%=r^Do>hA1h)NQ>wkQ*z$FYLFlOVz6)$wH<cj?*{|YNkPO
z>wvo}uwr74rM<op$@0MjoPcI>FWNQu3zz@RmHWKsTJ!dVo^zBS9STKeD)2;_bG|D|
z|7Rf6t3fwAO}kjw*uYbJImRKyb~UdLEz0eFbK#dmm0T}&KF^|j@){Y!Amew~U;YlP
zp}#$E8sX)s!k$$ZH7KZ>8nTKH5vfK0j)?huM*4O!2%C_Qa2{YU;X!{86#*d8s(1KY
z9FOweZ<pOj-fBc4I&%CjHvbYwB19V;9Mq&oPv<n21orc4bg$*Z&6`PVXx^^nZ+7{*
zw9D_6+OMIlU8GkIkUTcFW?8QIFY;MMVG3aCtA+o~u(?sal*6#I<p^jf|5Ylxe&Z}K
z`UZahMq$6jwzr!TgwUJT(CumbltY0QbZQqmcFH3*ov%ta<lGh?s`45zN`EU54*$Z-
z8l9=>UOkmU|BT~$o&_{?FZ|KZsMoD?5LGR_jn(+?^rYOI!opm3gkzB-=PjUu-ig=L
zq(_ZnRpEo(?Paf3NbfQ(HQTvRb5-~l8`HeS?>12=z`(cP7;~rYGYAiH1a>vqtX8EX
zmgk83!v3oqo$*{GQGVq?`FQ;^KqdXRGQNX5{WG5h?*GbywG;GO?8~U`%PjIscXN;L
zHQf)*LuSgWkE4Q!Fi;bFL*(vl9((m0KIos3(3bro!J{M_C6(hv$Mfj(lxqizVMpyN
zV@$C$X>ecoff9;7^VNnO%Z0L_l>44FaqYO+H}%DK+FJjCB)TOBa!|$sj1+3#5ZSZQ
z3dRW>df=twltP$^9DSJQK5;i~_wRouJZjtm4~4$QY)RuU!pOqWk>HT=89;z=_N~ET
zf-Jd#8H1Of>h+AcmvTD++s^Pc@TScG+H>3JVI2E=8p6-`N4d436k_+G*E)QEPeX+#
zC{Zyuk5t84W5M*j>+g_IJ}TYw>T*FlMS`M>pQC?141PH@5@@2QPB*gAIm-R_xJA6j
z5`%AcQJ|t{Z_GI@?(PI7Q3)(2M>votA&!$7&>evi=puT7O3lOLq{h8ppEoY^cRD2)
z9tIjjM>)^)Vd^PRJ--7PjO&GXNB?J|jLZ~>;fbu;%MSc_czE=R8LYhrA(wi%IZTi{
zzzo#V@uC-i+yu=YgUMV4K%*=yd^uGQ(i#Yuj|@CM=jYRNa8%6ug6Ty`W0nAIGnokq
zsQ>oO^mI*5P6CbaKPk}b6fB{K4#h5WlXp!o$Pj2gv8!VtRaH?*;4l>Rzwv1?pd=tr
z-xZZhWM^Yz<4Kms7Cp<l>w;0~A^K#d-9ZA9B6aYZr>*rDEmn{MIg{lhpI|=t+X@qm
z^LM4ZRgncrh&+YnzlyTv)!I{M8wHtnZi5!@Q;=<r0i@rYV-OWJsF)zdK$VFdW$aT7
z#?elw!Sq^9mzn_-gq3c9<X@Fxksk{JO)K2?Xp-o6S@!KB1p088*1@<zjiaKf>g?py
z6+>BJ6Gtm^?ratro~^YjS~u@i;`F(+R30=8(V_>^%49QIX(0_Bg6pm9gE-yo$et(%
zvQ?Yu(}p>_erJulO9JNR-1@Vp5PG0Ex}_3koJ!u>B6EQ(r7z6y`u_d<Pr0~EUca{C
z)wQ#;)6f{71Qs2TCZ?+2tgWqKlk#pg`;IaKT>=NfvGo??K#f`N=;#okh^du6GOwOm
za<BkxKF_74@64y(&nrBIlw`~PH+>Lv$Fl&Z{+U0%tfi@G?%!?3iGnDJ{#mXq>u!&Q
zNyLC9ffYpYpY|Dk3@@;69RF#^;shNqjGe|n0abd}@&E0BN4awMtXc`VOysqD^jN%F
z3}R_*6Ngqj=hqJJT9iOg0IborebHTm6)y6(Z8y+*1@fJeEK{CAMV&<5Q<5m=v60C2
zuk2ZVDIBkg&QmJmRP?wAAq>=@-qm&<lVh)zzc?kUlz1kKgfWlkC_tYNtjUA_VerR5
zESZ=G=933?LS)gmm#|yw3ol?U0*`>CYul|tt5OoqOak5g`$q2JcWDYwT0lk7(3k)c
z1!^PmUJhpf2I)@#rP@w&cTGkTbAef;0HFFo#jnPz3zQ9h@Bn{xUhcyDK|lwRJ=iON
zfB-3SX8|V0arYDRBmw-SrZ#!(F9UeMq4U*Zq1AUyf2`6(x-3&d<Zk5aFyNO=^xkdJ
zteEKT?!K!vprJC|Iv_EW=`|dH9wr9G4B^v<t8#h-YG2?Wy5B^s@J9>bM{1N1K)klo
zE&#1~3ugdM^kir5ayFgAQC;cV-5lPMJT?8%P3gaNcn&Oc6Q$Zn5Gk(>5rD>{MXFI!
zD*(I2|8$G96xeKGMn~Dm1=bKV5>YSCK39ASifHFKNZiH6MO~d>z}2Ki2ejO9|E+oB
z8tndeQ&yEk%;WdVpe5r3NCcf-U06h%R7^~kpkxG_-w2Qx0HaAtwr7ZV^V&>)`1tW-
zMusS$jt|ShlT}5^)KEna$pZA;46ug*eg#l~hzILLNB8@0zaRurK%DD?Rw-H7GX1S^
zbMV|^W<7kT(BuqTtd{|wS;G$G@IZxX><eHbVIvjr@rpT$v1>5~Y=}-GsNW<CrqD$4
zMj3h`Dl{|7haiuXPsQZ;uYV59pBLDl{yjdUnAYgO)BDedrQ?CI@&7DTx(3B=eZZ(7
zqq-ST&3OZ3E@&U)VrR#KGJFnxCa~%9?o{51NZ%?_?`VJTCbuU~p1k$C57y*UwR``@
z-WXCob}p{!!onZs)d+1so9K!nMT72mWx$gu>F5YL{r&;i9KG`nZPtXy$Ve6#CD@iS
z|9E*~oB!!zlpSILeUO-_&%m?%{47i%LpT{2HWCteO@H9rwLpFjwVIU?mY7e^k{z1)
zFX1`Udn|>=I$u5+5u*Q8WIa(17&dRv)RU8wGcq!QU<BxD&{;tku^O*wpq#@g=1A5}
zG;)H0l&&Dn#6yHZGA_<Ew!wWv7>0uf3cBsP=!PT7AFZm;J@MfmDK}SQ8z*h;9*>B+
z0dMuxmWv=l+p1G?|DVR~nwpwB!p;9PAT4^jj5|UBU-k0d1$Z;H?bc#Yu{|p;n68ju
zrk%i3`pts-efIj{IpB|RkZlJi5utR2wSAATzISmudWA7BMKLMQQ)o}PVq}A46e}19
z9RE)@X69e#%qO5ueEP)YcXigd4M3xJo!wkqFJSN1r)#Xeye>dZ3&MR|6tKIhvReqD
zAVL;?=A~5Vz_&L8W1%zUVTB-8!iWj@*h!xj06Z#I+$21Ia|X1?Uw#K&##r)+mCeZh
zUqd+|v^8B*;(fZ)BRT<0GeCGUAI#9#*B3ErgZ}AH{rqI&Rojvt-~IkIUW7C_D&$a}
zTAgBEo*LjotgEc7tgGu?IaLRGdZxj_fJrcLGsyyt1~h;T+r+#8T=kvPn*Ng|kfeZ5
z|Iv<v!4S|J{D8bRD0;~Xy^PKE#8#;m;{x4~h|z=nZ>!P1xiwuMCK{5=d`{Pm74^BO
z1JZW3of#fI`fI1A2lh9h4gg{cSVQU>8t=2uPGPs#^WM8WBvJeO`@5}|JAjw~nMzxG
z3M?Pc+mPqdpTYz3dW(aJtLriFC)3G14(?0k+fA`9x11gM8*L7WTW^}#*D;|%Lud0i
zBSlE-^2MB;lYAO~QASp#u;&3NTOU7u?6y1KVl!0*q9N?+pqH4K7_>rOf(e4V$;rxI
z08ai1Fu($L*hJI*9fh$~*$wm|-w;F7*LU`u^W3xG#w>S!vYG;g+WXZ|+sB?wjqRC<
zbVVv@y+02EfVC7+TuW=R+4E4IUKM!O8w_ubcNZw{O07Zim%ck)pg;u%$|xMC5ctD?
zPMLNpU(_TI&4V!fEVE)?=HQ1kB@8@-D{W4t^9xnt?=Xj@c497b44|yk&e#dLXL?=k
z49-mgR4?Lv0<?o2ki@~%MEN|W=6v-8u=2JiTT{J!_PPxwYoO#<8@%C7y>qSg_4caz
zk%4~BtMfxZV7z&n{I`v3Wgt!95Ofz1J$W7c%}6Pq)FO?F+%>3!#nB@VF-#K?t3tSh
z7_$N+4)B<9iHWB`CIqHLp!VK>@W6WZdJz{i!=e-cr!io}FTi;`Hu4fl(hmZB?^><D
z0x1+^uJS9ty?6iD+|P4)_gJw3#VtbYy8|{+d%I*=FCgl&Mz(_dfWUMbxK`p=*K8S)
z45-}kRlqL~S)TmuWAtOghp2v%?1+3Zgx?Wu>)e<4`Q=SJjT0I^dlnTtViY3t=ABV@
zf%d+{-#R%}KEDyAoYeMbEFB1$!0xzK&TzaGMoEGZ3SwbeQ$dydSd7?>$0~~=7#A=~
zFI#2!0~vFqocN;s`qWw<OOkOEG}JPw=KEMjAs0By+8mWsw0f<c3b<~L_U=@_8O&%j
zD=z=Q3@U(MfD|Ibl79Vq8Ms~tS~h=tq>+)4iBsvCyi2+&Dj>9-{Gb8wh~(M<h*IQ2
zZd(MvPbvAPlaik{^YU<{I_@?RfmO(%(9XG7VscdUygd~g1T!EtfxSy39qj|cXNK>|
zRO4!19C_S}{6@F!i#8<e;IObJy>dXmK6>;hvk51@N?zU3aUa+PP%(%cy2;(^=XL-;
z=(qO`tv8WF%$=Iq?CEbgrM#Gb9i#tgb64Sg`oFs1&E8)FL@)jPd0z@o3Ajffb!p^a
z+@TOq@xv~6ngAsQWyOKgycg`EqS8`fcTXnB<8FL_W~^wvkWKzlnw$H3R^$#-xNS`W
zHjUd%BjbO00g&2&4bvC68tmqq1%!otfEEXRh>JS{dfdtS@XG915xZ18&BcF{X8~nZ
zU0n_TlX?xj2p#S1gzS2gKso?Df{di#5Blth3I!nhWQ;YWVC2Q&IMGo)Ehqac#IDtp
zNz5QV6ihF-3s-mr*eJ4YT*-g%db+~IkekJk{eV6B^tk?sSBs*^@#x73dD{}%hGXl|
zFF9Au{YNt4PfWUmffWOC0C~;O2HY;c&)JtMjM9VMywhetAjFFt9v%X$+)|>d9|_v`
z>t^Bw4nQe7XQ#<a64eFROkjx7JRe?i08E=a`2K-(_xbRsDBwvYg+Q8T01|r-X#l8W
zWWqgc-)t5DobNEDXj-f?Z^I4p20AtUCz+W$Ka&!cP@j1<kn<eAf&u~43QaqK1E%U5
zd$iV^zJAU@Xl6U#JY~UcZfbhwj6Mm7RKP8QDX{{SC(wz??q&+0!)suT1zK7cAmyV;
zcsAQZ3OD@@XTDligQW>(3nWutAc6z02&NaC+C8uT<XrtN^(V>lNT?X^A|gnIJsbeT
ziFb6T^Ok=Pk#<<ZeF3oCt5=xp!mn#|?)vTjVmXq!y_e$A2_6}gf-V+8c27ZTCs_Vk
zW#izXHlIveKX}BhmXK%#_|-Yle|-i(NCoHtVX;|+;Z+JZSY?0@nz5<9vrd5+2y%q&
zT;nlY-RuGIC~_APssFbx9C$Rt0Lo6Vc!3jV1GH9=fNu%-ppgOmwyW(~pxGCb#HDf$
zYz()Zn6>Q&aMz8!Vz*b$bE{|+9^wt0&VS_n-z(?At3d)pK|x7QP6iQ#H<gW#kB@}~
z-tHSsAtDGy_5S^P5D^QNQ6<r-+jvaiz&v{Y&P!TO`~a#q-_q7xN~wJNYpXyUh?*{z
z{}e$f*ALM_$P|~Dzc<3~@%;bqg6Y4OWO^Ui>0UC8?F|b=gKu5X1$E#Gf4}umEril-
z>BFCWSMKpt+$l~#`nSMLxjjG*r^58gSmW_=S>2VLF~p@?t+542=_{M~w?m+-{$-dM
zVekJ@lMceBvPjUWH$iy?U`5lM9VVqd4zf(_bLG)5s4WJ&hvz`j4+Su%#+Mpuv*85_
z|9`Z65)9OTNE{sG=oJz*q`XlZ-V#j}=eL>GAk_a`GCl~&TC*y~Tqt|ud+>G~u**uE
z_kRIm!#H^5_6MqVK3zdxy~p2dvWRzI`iPZPUfv1`YTOd}58XilbskYbZEL3ZGQFRl
zf>{Wbq+R#j7J8Z!Ua!~K{Os=sztKGpq+mP-9Yhe|ZOY_8c3Q+^mcRzwAOdtWiL0WY
z_gC9`F3GUII>}%3Le5Cu-vKsW0|tb1FC7R)hF<PUUGtpze?P`5$-Mqu!BW24nP=?l
zV$$D_eL*kEdJ`zIIe8EAAX5TM;7y3y5K@5VM$6A@d_9GKi6tuuj;Yw+KP(=HUx-!x
zWi10G3sFRHmf_Rn&{+#?MY9TgEeSQz5!8B=5vfw19ht%U>o2`%fQ~A{Me_ZG96=oB
zUw3!O4=0kG>p%gnL4$_Gu<`2T{%T4lpNWrDRCEO%zkm1mwj1H@y3|o2=0ix-*-r<Y
z7It+K0mVb2Y8JvI6HCadwFo#Me0;DYU3S#LzLJrZ&5jdH9{|{D27V0e7-1A%FL8p~
z#us1hmM>heu(2#D5dLJdecJ+*H7hAKfou>~yq%m5fUzD_vG3mxVE7tG0<Q$<=Y)%9
zVnd)=XQIY>LcsY?XLq;#^jD@u9?t3)Hi?L5@vMN6Esz}oEJi_I9)kEky$4mp+iq{(
zypfB|y-G?+5d=sW0;G@dV2N^Qg*68>bnW@UW7cX`3|I~y{5MJol%{|kIo%Pd=6nhA
zF@g~Qx}Y%)psm^}$Bkcr3eThev;pD^hB&#U{!)RxjC!8HF3wR?*X@k5EU`%yD_T})
z*RKtiV4V1Q#OsQ0uPZII0@_WVK6y3hJ978UEB|-8>HKvQXs_-AT_Ahy52SLGr|>`*
z+yoqyUvqD736{N%SI2K5p*q<Xt^gb{`xodR&9rB-H0Lc5$FQQFF^vJk>0h1#I9(x&
z8y%3-f^q(Co&C!S(XT~#NB?+Uii(N=-2fc@74Dhfa!Rt99?Rj*eDhxLvYrs&tqM?+
z6m;dW*HY|}ZSnM%E&Fuc98ZNFOB<e@0FOKr-aYmL>`*9=1KFCBrO)HQpQfNuJY}u4
z7APbH$P$0KFC5U}^6QQ&k=<=qR{(S%5UmIvJ~TBq@9ONFw5bK01)v2FPks&z%)|U|
zLGHdYlK>NwcGOdG0lC^|3_w}kQrVyeJN*al9W6z#Bz**lD?UuoU`LCtR`gHPZog`f
z;UpUl`|As`jalzEukKhH`T{}kqN4#h{_EtO!M~QE_qPl4>D_$FfBd0aQLdlz(exYo
zUWqwcS+l21YFGjfm4SzGE=2v|zLutc)1s3H+j=o0MX5BdG9fa-3xVGcKa`+55m>x6
z7-F&g#63ale078VB@g-KOfG)P?&WH)rVRZj`wRt9&x_y5Z;DII{w6prP+yL}t~|TJ
zNy(>q{1{R5SATX$2B&)1ha}d{T0~{jJu6!D&mTq)0NXID?+pbU#4JdR?;ZhFmyjCw
z$ftlEuQygFV!6+<0qnrfe+cm;IGBiAO&&o;4jZJwO6&K+K~!<C%YeimrRB*#_0I{x
zk?dzx3p98aO%SUh9vcUv{LlmlYAcP;ct_CU&M?~l)74i-MfFEr4<Vq^3?U*VNQX*E
zNViCXAR!2XfONM=BZ5dtcZbp;p@4`eozmT1($5+HZ>;sq2N$lHxpU)p&pl_Kz4t*x
z%MuXjEU^PS<^J{TH0AEdeXLn~md8}@|9$Qu9dchxb+beS&G$hKjo&WMU4R4pRj#=s
zw1-oOi_<I$mi@}@QBC3B3>AI`YK6!69}&$|-%3r0B+}_C{NOl5PSJg5t*ABC8C`%D
zFhC{2xg0PeKzH{BgS;Dsatc`I_(xuMuNla1cZ&sRkSI!gYLiSpmx(I(hX^a`D@faP
zihZ&EP3r~(>;%D*4!ytbmq=XLh3b?jHF-^KmafH)e}J}DFjhmB+TO<rgm1?v+rRHV
zp`(NYT^%LINe{cW9D48Hi9=kQNF%>Ze_XYJCUk(49?R(nCtR=L1a@@ZPHoJ$6Ay~J
z-`N3}BExw%b|mZ*dRWmgUq48iJBos}C&xU`*(_A4yD=9lleq(Zx+!y)NrkUX7YC(4
z`{7^zctYT}rNu%(lNA&m8U1#NFF=AWG(#=lYNX^WH0NcY5wk*<vT=aK%qbVWtp;lh
z^P1*_sSS2$1`b`>C8voEc7TL<zvCMVT7S%vltek;qW7=t+jU;KPG<GRlHSB|MAh;m
zzs=<CLg&rHN6?ixz%stO_uzDV!o$P*IfV>TI%Vl*6<d=r1;C2@n_Q$m>C8Wm45fU{
zc_`n_VTwn7#u^=ao%1r*)bvvS$0R;`C$R)PNtOC3!|&KKPOyXMo-wuiIV%@eP&5*O
zNKRRDo1$?glCG^gF!q_q5dPFYn2oKsDy|qcFs*+a;~4UmS4`5I2(4KB@Sce%g-L0;
zHRJ9>k2e-Lfil;z_|GZM^<5^eY;pOiGtG2zxkE7;EK*Z{5dDPIv_Lhb>%hYehmN71
zcBA*y8W=yK{y~?(qfW*mpb&Dlx_wCdnbjq3^Ve&UpAGfn)sC1{1CltE?EGH9>;_=;
z^k{1;TBOGP@VWPtvUY5LfLTCA)dedqKyhD{L>=eIqaH79_S`^>oHzYjA;&kc&ZSEl
zF<ukaZBseTd>KbQ@kDv=EI*wmA2l`^$R;H^{DE3Z#~(_?QM5pELr>MHEXY48a04|9
zwXRgK>(rd!p(9Ku<<-LDK1kzKQZ-_o!*dFZIm&@_9WLVld1u7K6XrD24vAo8B;0k#
zm4^CxR8#Zp`*2!mK>;Q7Ord1}Fn?-fr0wFbkG`>t<l0lO5n{I<NEKE7yPGa%EdS9x
zenzvfX_ug?^zm8;U>acP#_IhY+VGDTn4Sprs$clG*_PonAE=T}4%Vt6q~_%@OZ)&u
zC%SH|6^`8z4K8AT3p;q?jtegi-8^<<3b!dY1&~8FkKH!MYdKt%1YAz>t*of>na%7E
z?Ig?QQ9xz_tCZP9jb6#+#Zi-DEISJaN3jnL-8Wx4MB15`sGWcbGd>&+>O+q}{#oh$
zs`@r|3N?RDQPBnO^R{>TSwGBYomqUQxD?^y&dYL`4TTRbS*mMjX~~eMdI*%aY*$3D
zh}2n4eyP16LoRIc6C&c@oCc4F`2yrmQt7Y5@5jJV#C_N;q&+j1Edd<ZkAs`XwLMlL
zp`p8F-M_3eCdCAp3`_~>9Lp`QmRnn8RQxnyaoD@Bpdd}7Wu}s^@jS)N8km9vCHg)!
zn!CUm`eG&3pYq_~XOc+k#-U3GL?Ta0?zJ4a#!&B)OWOg1j3p&Z@Cvqwl2<|!_n}@d
zU!m9M&lWGBIqfb2`0+H<>q(Du4N9Aa_&k?3`zDjKs<e(zy_J_y!Q#g-^*O*i2A(_8
zv8GUS8n~~3R(=V)q9|}RN9%WJIF>IzYr)}|R|0DbK-y~52!@2k6MS^UVKX)TTIEw<
z|F8r{D5_Rj4sQY^kkYvhOlc?uQw5z^ZB1{4e-C^pg&wcw=-tqkkf&k&Dv>fj?dJJ9
zd*?O3<~?B?zr%6@=JF0+bBbXf+f0`c9QTHlIRQ5={E|V7S9~A4DZ@fT?|po<Ye1!U
z=$!WQ&ogcfZSBr~EssxNJXh3aymL;+qXe})W=U3}G)62-qi2+cZ3>4pVp7RGCQT#B
zr9QPmWid4-`sK-NR4n!(AyZerEY3u<=dFJzHlPUHFZ&qeqlSuf_5g0sGkY#h2#xhy
zAn1#V+8ucw;nn~hIxd~E))(b%?d_K<%09cDL9$tX22n}eGkQDjVFwt`58f{S$l2<j
z$Y(3J;fL_BxbA>Q`DY)+!oR;)DJ$x-IgVnFo7@9T7bFEvu!4t2T>--bU>XpLyOiOe
zR5h@Hd&Nvw_YW}8Imi?Z3=H}W+AjC|^De1xUO|+`+&mgN>&4D}S#n7TT98BHpa}vY
z=$bA%%~7AjHC~;tht009uiG>pFX|F>ba!u!RdXRAO<7!8Vq#>}_j>;5(JzSfmY~Qi
zDEQXWlDJP_vhVn4U<-efNpT}JtlP#ijx5)F^-9n2yW}>n`h1Jq4$lbq=lPnDA@HDF
z{7(0`l|-4xF;}Z_aIdmhx425T*uVz%*38-Y@LRz38-XEjzX*Ka{dK<^i9|x=ft?9_
z_)_0++0hH1Mrh5pi@A7wq!weM*^aJTKy9KlKbt5sAja@s5PqJ`P3dorO8LKKk<u*4
zYsz6A0U6AJMkfEDf#FoDL0s_5V-AfvSCoc_0Q6d4st5%?Oy!;`n-EyGsW1NO1?GKV
zAc)osJ-$laQhtzF+d~k7E~hS(+)(ld5zycxx`p>}+wFLihKKl8>S%bH$yq{fKTe&n
zuKM*;NQmuWl{kSJg$$uT+Y9#!>-O)hYB8kaIjQ5fzw>4D(Y&nGtsdL3uT7Y#Es-wP
z5}G#{`u&W1#EQ>ts-&zMLs}Kkmk^uKoDU7RC<0YkWAGA=B~_!<@^SU#`NMMXEnQ<$
zQc^%83hkT3M8M3B+#7cGSNc<2N*UzOva_#4FK6yJov5E}twV*?ASB{^EMT{~ldV2K
zUbT*Uibk=2zilPP36C=8!a93a1%EiR)O>sC$p`Hy2Dkilw~!2G8#L+zQOCsO^$oFp
z$BF6?Jve2^Ytq1gT=}kCZY@vA-O%R(;63!9+@aY8_ycr`9;ZHRfVP@D|0Ix20JdyF
z>4Kq$9YY#vIz<zX+r&Rki!Q9A<{oHiF&uB>5(c7jzvlJv)wo?Cr@H-h|E6)@(mi7G
z&4(;IvoL(2q>U#p)DtysZS-T0T|SPlBX&xcI(6bhjL@X8wzi#Sf3y5bk|VtB&(chi
z!wG*<a?pyLOn)VgN=_n_?>|nm{C=F1BF%V*VdvG@8-~wg-aE=(kV?672rr}2hNyZ+
zQ7Mz`mS-QH|17w(8D8%spn}xb6C=l*2t9*{t=UQ7j-VjUIhVCUBEA@?R{s8z^&zG8
z2eV6V38LA03RnEWDx{VN{!%>{&HZ;(T~?j^7?ju=Bd033Uevy>R=9RWU#CRaZQO8y
z&R=EPU!jM@mmq4cK40^NxNFVwm)fg_oAQDU^|FSDXD-%^_T8?MIO#X9#X$B-N;leZ
zwfCA}s)G^-=RzX)_Ux+YlfFQr@{o?vwf1Y!7)D{bsAJ$%lsCp1wXXclRcf{G%@8<)
zHF*sIX+p5(;vOk<GEXGfmeIhttdg4!AMx<skf;80>wWL><ofG(KeukE%^a&k!A@o`
zPJkegp|~Ih9{?^r=Bd$Tc>!db=>$F^bWV1;Z_k^Z03LO0NXQTut3zw`X%=I%SlX5x
zlEZ~hHRR;y`tOTHX^ipBXea^%eXTbw_v*0#%R}|Hp=+~}0ZZYYx0z;aTVxb?l#;o$
zQUqtF;0S&gO7JYBi7BeGseK@h4yCX5ur~^kw0ab$$Kzk`v5$suZhtZBd|EF9ocK3A
zc<u=EYz=lr`2m#yx*&(e<31T_k(v{N_Z3F1#xD-fR=3b$lLQiiaIl5C2X<=0N<WSk
z|2!}y4Ig>aTGo|*8<h<!Y95>QD!~RbRZy3<nBXY|&2<Eg_MoRp(aToqzweR*qK9a4
z3Y6BipXz{;=qp?bSB86Mqw#Wxcy+DMKL_GF4!YhyXGq9V42@=rIIb6jGJ+WQB<zBS
zd{3;#0Nr&{;<!rJQj&&KMyl5100ZH?c4yWRD@_bOmqqxmafA{w?ap@q_IfZbfw)K>
zv3L^yol$`xmE~{C<a8KqJr*tzWV6+Gn>MyxfQB^S3YmWfGrqU`;!(ueN*uYFl78h0
z>hU-h<8PA7;H+NA?kBLKrz)v^KNhKAJ;IE5hJP!qlk;zrHT-I{3hoKmXvrxA2Fs~1
zKQkW!K?>JYc6MIOOZgiXn%Rj>xoI^c6Q4ALj}LEnjvdm5t_RYH4|OKd`}df_FGIuF
zwh{9YFD=gY+99=w`#=MKR8Eu4uwaZ4XU(xk7pW$XZJXQDkI%$~8El=S7#?T3yZ|An
zF=L5Ht2S}EA9bxO%dopTb@=Z9N3ytD96eL7pN~;SJk_Cgi;KC|GbU<wb`}M?w!d4$
z`>JRpXbtiKm5Zu3_xQXSP2ggY(_dNmViT14bt>S5NU0wPY(|yg!ghbyaT;1GL?`<;
zJttS1CcK78MCzzGOKlHto~;&^8tm;vNtycKN?(agV!e1NNZ1!lNfAf{nZMJSY`#X_
zg2AwOwwfIk)0Qf$2Xs)M#5(;HNmNAT1qnGX;|h%oQ}PE_5rYmSSNqcVDHnv>qD^(g
z30+gVKMUO<jea8iIYMUQG?6rdLb2}qcSQ5wK~eUpIEQ2rl|jvfY{lSaA=$TfE^C9q
zl>6=??RHedKVo>#C4jDH#RqVwFXX2mIvBRFQ&91>;_I`#l9JfZSU7&p&vc+X2BWT1
zN%Mqvz_~}odljm>9eu8FcTHV;3h9k%A#VzvfxU?$T6t^nFBceX5RfjvBAPGi0-jL%
zxRJm$7sNfexm>Kup{%5#o;5=FaHC4-#roe@-&Sd8QiLu83Fc3535{oQ+VlBm>i}MT
zVAL};B38WE87&nXnK1M!;>*EB{nIKSxNm+Z$b4!2OCO#AUvDhTtcC9ZY$s;ab2>l7
zy-_wGMM`(-w$uHcve5NS`b`2lzOIEE&7+MU3*Ni=k^<?Od)X_7G)MnqY*u!j2w%Iu
zLmO|Sest)XklZ6VhA&SWygB|J?tlmJMS%De0;3{<G+*8b=`=F)rTSBAZc#3C`|?hd
zgm^O73;S&(tA8Xe2)uKev>I4B<dxNfK;3ht&Gcc>ds-QpdMq<EM7gDq=4iZSBJ862
zvmX8@HsS6{y7pOqT~7ATyU0=wX7=&4yBj6!S&1+Qm8Xc)jVxK=qa;Q>{4)HN7W!+^
z3^F?Fi|r{Kg0nTfEMfivG7)hataW96qZf+h10X;lhmWcWcAmw@CUxDDF{mj_bB+7$
zeZIjcDZsyq4|m-H^R6<lVmRQ8;LU`Te`~Lw7`<tsSxh^rty1>K@y-46i4CC|As1|U
zwO4;%Ke0^AmGdY3mS*D%(D-1XHjRMo^e)GHo^$=R`6BLO#i$f^7O&i0tPcydW7g!i
zGecU^4URavsz7TFqI5%`MZ35!zh7x1|8Bzd^wYDi3uBeY11F2w)C_tC-{DTSR8AHr
zLyxZ*^7)RFcGXh-j&^i!t`FVzPq>2BPzt^=SNl`_h%%=;ZrGgHm2c|v&|RN>Zq_bM
z;(M9EPJm3|*Y~KDrmKpv{P7CWy!l5a4teE^wZF=bmb9Z7nM}0q<~nBghDX!)@2V<B
zkvx%be)1}_3%Q_w8dYAB;swEkHgvUeiZGGa77Ql{pY@ztLkcC?Xox|>5jc8zv{R99
zQ2p^)kJqpuqgJ}Wn*D@#m94`DFWJ_e)(5MPdkG(}^BqJ8Jc$5dB^)exBY}9#VyRzw
zf#Y;G#bdK}=tTo>f#IJVXHAHIe^q*qkJt(u@JAF`V;sJD!LlJI=dQ2DF0%M{hH(;d
z?%%ryX=-Vcj-T4+5~ZILptX?S>txF%)8DT9lTAp{ZW@k`aGtVG(t)3(!aea-A=<UG
zKU=Rczc!7n_-e6B#KyB>cJ()4QoFb6U0)2T=OiqalF%WqaW7*8AcRRs2G2X!&2wAG
zNYA}xa%-D5>)h`{Tr_^fZYQ8IRm`v%sDD5~=kNJioJP!#w&KKTZL%TpG`?UQ_f*{E
zmtr>Qcue^^V|Jh25k{1*(oHrEQLhN{TT(d3x3Ihry*wKh6J7ecC!LeU6Fy^C5vkgP
zZ-v!ePkw%k%b1*|a3N5Mrr%<yA66`-fzp+pR-6mx8z?_`0Xl@td2I>@QCZ31HW&v8
z$}e6AmnEwDh&zT^3{uR?B#$?%&ipPl^zYJhWmV1yTMlhamIyfL<`@%6aHxy<dH=7{
zVz|~0px;!_U3ppy9Afs)RY$>WwHwsb4`oWn`tDR#%Pd4^>130thj9%w$ix(OBV2;I
zXypr18*|^r+#7mJXkmnWJ@OLh{Qz*I(L(b|R8*9OrDH7|I%v@7?O8jBqj5c4*LgPw
zAuv1~Ps-?F#>+p3_3kqP<U`8wm`7EU+h0AP#RS}c(cEgc%%a%|`#D{Q4e$ErcJ{`x
z4}X<^=C(%HSC@{_@A0fh&k<EDXAPjIZK9=7MKVHS%cDdhVZts3v7R}Ukp6T_;?Q}U
z>MHOMGi)6xX~o2CDs_1&@ep}~7KMYW2QsQiP{5sRw-A)-*B|a;fCZa|ipn!?idDQV
zp8Lv0a!MBl8if7_FRo`*V<RYB%pO1P0y+lTvvdQ|bj?Cp<)u_R0j3m=Bubhitu-Wv
z5{B>o$lR>#mR~+rtWU{FC$3hm<!abV^wFsi1Q?xLRPGKDGHj%MIC$nuYmN=7sWI`^
z{w_4wvC=_{4QC;Q0M)zBtv+}D65w6V|9F4kZ5Bqxv3mwafEcRr5l>=CF<&p8+x~7c
zVlOYQqoluWv}Ln_wO^QDE5MVA_KAh#n8uWp&GJ`jbgWHbiB9xLL4*t*VnF}sr51ZY
z6uouZy|Iz2w1#F-nEkODe$tj+D<v(h!%jByC#;nh;~#AH=Bw@T^!u>&jpi~aoXI-z
zIL}nbmHLT@h+ea;yda8fvh3^ccW`h(qlF%zh{(hmSPgjr(FqEb$=*y^Ao|k;-}C!w
zS=zwIc`B+?dbYRqxz}afPIjVxpQlpbv!=6xF;Z#Cp+u;Q>^EnzZEzF?$=a7W>ojK8
zBZ_*<M7@~X{<^q*`)HwsSN%S@?ij~)l9H1j0c8LP7w*s-1;%};N2XaP1XfULpfL1<
z!yaSz{l>ic0CKMA;_@eGwBf3p3Rb*gg}tc)^ym8`2!WsoXP2!B;h2Hj9t8M^jEsy&
zcSsJGp~v8KfX8V`Vh)Ab;qpxmTJb4II?{b=(I*y$bt=~LSJ1g1eB0Kl=Q}yDT*2`0
zC0yF>1)MgaoPq!@sV}6~6#H@TK%(?!yv4pbu;!dw-8hyOhiELg>mp#gJk{|^f5PJp
zLq1=rHnpJboIap&{1p4*3{g384DDPdBz|;2)$B#h2nY{X8`MZdT&&odu{s5L5MaV{
zWHe1U>9)&job*IuEU%us=Vd$jE|beOJ7x-mp&7<lQcOy>>`&da!q46PRpy@cU?&4R
z%cp;p<QzMcvF+E^1NvtBVmgQAnz%6+ARm0Qo(R^?g8U5!Hl~`d!F`C|bCZ_$ccq=A
zb98!`?gH)nZ@yc7K@&S=$%Pj?LbKnlgcC_Lf=QkrWOo5mFx!9%KaZJ)g#1)7aUddU
z_UU~Ew&Ewz&WcNXw^GkHs`Yf3?$G&L)58IpLDRf^tST_>LeQf3sc6#$rL-#Yb;!(#
z$V#!O(&J?rbxsM6h<7w<TD*&YJ4!8n@lkbvcMEw}=8;6K+y24wfjdkBcvpW?l5A>9
zll-7snud-T9RV3cuDDB_xTsvA)`?RC=ID|VMpq^_B1zWgYFDNW&++gbDLmd9%|g1~
zUekJK&sSxh@1va!vXAS&pE-q(ioKsqa<EsM-OiKaWn8k40Je>vxQ}`iGPEYWx<SQu
zk>;lNnKbF4lIPEeV&Y$E<3%R?aW_BO&-rq1eB%Dit^Wk8^V$4u<gD%T_b^(Ty8JK!
zs`VkfpX1<cee?EngZ}oEM~`N{edDwt%un+u9Q5KTXw+5Gu2gVZ80i9{Sy_3xChH)H
zvhe$a1YMh@vdesY0Y?mkfTP1z{0SaPtOjy^)gF(YrLeuJQ<u19IfQ^R%VhEA)fd9m
zgPT9>1daup46o3wye%t~nb0i$sqy*!i~i>P{^{eU*DYvU4NrIHbYo4|Hnk4<`9OS!
zz&dCA=hwZ}rbNDvAarT}8<gU1e6f;QEWm`2v#2b5%H?o0`GB~xdvnC5n#NIp<D2H5
zL(*fFd@z98@HA7oe1D_5b*Q$?f8EBa8R{k66GceV@Y<K32Db}3{aqjUXj6~>cQ24;
zu~pif3g5ZAD;~o`X0q?4`BI65MFGxyQRhJr_*Qn@vJ+6Y)#K&Td*p_o;Q9LiA>byX
ztp4go4qj)4QS>b*J$kuU6xlOBE!oJBNR-t#5F4Ov_u5;3*L$3P;NG){$*{!FvfXy$
zD3Th`v7nD8)c#I$k@`9Qne#FITn1fI_xkE)B#VrczPH?!?xv%;I>y>u;;5+o<a^<|
z#qW<M?cQniz%SnFta$$M&s_WD1t{@)(3~&o>=)k_6|vK>@g)AvU_O~_C@3jOfzIo9
z-LYZ?l+!9uH1>C1doy_`lX>h@kH9j+b?37Fo^~V=wfwF8=W|)4TkU+GB~fO^vGa{h
z=jz~{t71REgz>NCVQv)WQMbF>H~*`5#06SvyXwZx1cr6u3Vq3Ule_*DVtaCKh;BZb
z%<?|(qcHuV_C8PJI}5V++Sah9A>@2dPY>TnJg9uN1|+kVxZS1SiPZwPNd!&(+JsN$
z+JJ>?sloR5^*UOR_!i1>QNXd^o`^oHlC<v+@sfF4{@hQf@2@jz#Jtb{)3!dw__9JX
zFgO~c-ly(}Q#I-ir|UUrufpHHUC&oN8psn7+WzvH&GiCz>EWFHf<s9^Rdsz$D>e1Z
zsE=FyVOY3!7J;N(wBNh56}g0WY5BGZLuyFFr>FTth&@AWUuAw93$Nt}{FN2=1Y>3e
z$>30WcDfnIbLHT^J~awne1w?I6T?^6JU!?JjE&%8lJ#XaUHhy<YWA3I_i=B)Qm}1X
zp^%#Ozyu$CmA2ts!NuK$9}S1o_*4SiFD}@6szu{1a~|#o-xjQEUf`TN-s7w;f7wnf
z8622_%afhUETSazHl}GQ_4T|7Gc$#BR?ue6Z8h<ren$qSp#JDt$|ZyYRW-P+!3(&L
zfN}C>r*xWZbx^}ntEs60KG21`bogx#p=S||z1+(U=K112H|*+Hq9lepNPrvkM}}f`
zk}-ck!ZRa2vXzM;|4+`?7bTbc;^N=k-O9?!1cmpFXb_M5R2CO4F+b~0!#|-*92q90
zZMrE_jW&j+kEocDMtqiz7Ocv_nuHJJ9fQM#&nl3lkNe$2{n^Y#q}R+9WK{(dCTmU-
z)j#i~skR)xx^z3q&ssL;m0n?HeVao;EBNPh_OVQ+9kk~`ehFO2{aw8>tZ@*_9r_5l
zNFGstdf5_qy!S<GMEGnBA8eJ8csEs>)BY`2UC$oFSRgY_Xd70Vk<JOa+>0;F-@TWY
zxQ=9$YR8dhF*d%I8c)CCh){X@^z<iNDUy`5(s}~b%mO+?9Fx6ZGJqNjZWrLOJFw#D
zv}GS0rH<stSVl_MGw5J`We+72ufXw>C$v&n^!shlRu5+&f&5X%w8gsFo1nl93JJJQ
zv<z=Eo=Eu49nxHY33}R}5nlH7p%f7o#4Tyzo6Ib%YQ@A4;sWW}rSq?<5@lrMaVH4Y
z`1mN<*$sU%f|O8+W#t6Ll;kL1M5lXJ<hgx12(-}>t_VRe_y$WDo!E1*h=@u`TAy>!
z1kdxuMR@4-uCeWE_Y4mVphl6rX%mcA1s-kpQR(Pi2xM<2!+NfsxZ`Z)C~z@%k*kv{
zEMISHoyqekrJ!cXs?@1ktH#eMK$G7|ydLTust_;g<1MCG=T6<AiL`Y6$`JZOoO{Pl
z8cxF#w&2Q)XZE$)>woa{>(CMLkM0`j%4gdLt34zCi<G`4eV6B!jCx8hQ-bfijH|vW
z83@|9IY@tQ1Z6_k@82RG`+1?N__yw5Dd|xmKKbXpyoMO?-!0Ng&tUGp`YWa3H!xPK
z+s%B{&;qVRte<X7?JZ4w$^bswFWF1X?uP_a*kM!kCmI5!;2h(2<j0?`ZS!Na>5#^U
zhWnXV1Kt_w{@zlzPm`DIk9AzKy$za%OEBU%+06sDN=Vu~5+5x-$h1kl`#dnBJ3xF3
z{J9J@H5up1@8jMMHHKtCgfi%NjE}CW+M{HyjDm(UnTwBx`+e9niVCLF7Qt`dLf~vn
z<*qbt3(bx5Ml-6z{y>asjD*$+XsNRWCozml^|InvBg$!O7(4ESK2|+hi8J&bx<Siw
zgW+nGjmLaSy|k~c_`cP2loS(TF71ePFN*?vOS+d7bBi*T@E}{eolwo?hERtEhrPg8
za8k;<H%WYBboFObhVTH=sN?c`+2Pid;hz$b9wd&0r?&yf_Eujh_g4%X`j2hIGK3h#
zTgI${tMJqmEDEhwCO<PjEjzp6!xbm~5>hF&i|Wss(U|h+2=!MSdAwqXhq`Cy^QYLr
zj4RG9)OLUwI$TeytiWJ=*Lx{bg;`1!X{x`ZpREQnjJAUCTDVI(<I2i(@*T{6)ZSl|
zd@$oQ86?)PCJ_}K$<W3GAp=Q@7aeD}4Va7f*-pEEByd!flLUH?D0wKs?82&N>)VEN
zy&fk`do&`gpuhr3>9IMI^iSI#+5rG>#yH@s$+*RN_2%7c2B+kN=sr~L#H}GmQ#K^h
zam=HP@8hy`4G7WL{Sb)C3}(6J5aX`T&rfl#^xj1va+UGc`vQ#OJL$~HF}ocEFnd`-
z5!2`$-o>@W=~h#dK5QK*>zB?df5LDXlI2i8!$>R<{0U6Sh{?6oRjPIlkE2(E0a0}F
zSCa?W?MBun*;t~DO|mG67-ghFiB#2GYqIWZ)Q;b857&8M^wuwf`rK|V%+NbIHg@qj
z-tTfu^zJ=3H#a~Tp_QRMwN`mct62A7V7HW(&hAO&4#>~%Qs8Qfeo6s7_1oj=+pe2e
zxCe0!_P2N~hX|)iXqrhf23A1)BJ5+~YF#v@<ShS;{Bl^T(yzw!v}(cQ5M56B9pzxA
zta5oZmZL&cc^8wpxcFB{cE!~0MI)6ue%75-EES4w|9Y|Kag*;`4?Dv5+=FIq^7q;o
zT3+dL?Z%!j|6VXQgigexPn$EpIMck~v9g40FAo|5#p}kOe29-bvwir@&@g!_T@t(W
zl_mlH1UMUbycXZGo4czcpAjCTAy21opb!(+rzuk-LqDcx&5?=e`^Dmet71^audWOk
z49emxJd#05`OJe8mu4WM!cz^UQH5o|hVekm(QDWOyL?nnRkgS3)zhC(cMOW5_5%R`
zjEV{%H;_|SZrZ^>q}@=CF93qE_n9k*s2v^m2agOU@L}8*%}IwD_kkV4cWbC<dCCmt
z+@KW{S5)kSK&Ys=I2l|lBIfvf#eJ?qc1f{Mjd#8qj)QJm1jLs71dD>Y(0iIBVIN0|
zY+IhcC6q5F;?-q&#I&*>HA%aSH%4%|bDE$@VCFpHS+e|O?5*3L^%<jy?~jK*!sgv?
z;c1W_ME$3)kc<1*^u|+=B~tg;5hpqIgLAn7oT9%>)0nSEilk*Dtn^R4DC8tPjvfyL
z5!EN#k9^ftT<4p0p0cHBO6J;dI+E%oqzjaln<2`0ymi2ZKnQ?p8H8S7w2UH7gIz@q
z*}qE5^;OW?qof0&f3shX(BDjEx}m?@wl=OLPK+6`YPCqWuncLF8rHR1QgpNpfAb-O
zA=zMHg;BC5t>5rWi1rbadCMJ<laZ&dJ`g+p$>p>1x+u>EO@vY@;hRaxLub^(Qg02b
z<B=M3_BIYq>7{jrZt;CXSGqGjZ_j$?h_|;$q#1r{NPbjm)xDk}_v_9dE5E1=ciHWe
zMt$Ka+eT6Qc)NbVstUP2a|WgOEG<Qg-))x~ta*DJdCZ|GmT?D7qqgI(BwkA`Fb+Y#
zNFcH>7|A?BhqSWnXu5f<=q5c*s}(&NiKZnT+5K01Bu_a$4@K!-->I!-sMJ-<7cVB$
z(KM3I-WVU{{M=i~Ic%2wy*<@U3*N~l(;*wMknhkdyaiqLry3eO?9FE1(GQoozEXRE
zD=TWj(w*MCNh#0}RWl*$Gj_fa*GXT<dTL^dx7rJCS6^Rb!zFPfhABC>h7-sgm3Z)2
zEAzAzixku>8DjqI(e1Rv7JE+SQxv-ohV*xPouy{&9}b`VNh<*#DBvo}$jU-c+YvOQ
z(APxA5<y^jk^Fa?pnesdJ0iult214h3g33*y1jDr#`!B-HNVk?f=Id3Xp>n--xm45
zH+>ZKhO?W9c`_8Pu!iRuGsa+Pu&QQb#VDxdD_UAyJU9<o5fLO$uxQF0f#GvBM=*~>
z4cy6cly17+S3I0Ud-E@MYfd|gJP*hF?euK`rFDS-2W~4U&ByKobt%H;6<YkD1cX@u
zTF@JN!RD1T)E+swY%UYu#6V;)s5T}aX=oRBX1@zzw7|>Lpf5y^jd?=)i8Vws1lM6D
zwJ1x?r^%=#hhTE+L(gjaS@~Ym*`IcjfOY3nxT7fo!Tifal04)K*=WRn1By_KR1)ej
z%$>Hc+7SKrglsCW@>BIodmi=%VP!{+6t%7}JQlJO2<gYi7h0HaewizPPlZ407>qA8
z;a{CdD=#A+`d9%yM#1FCH*Vq<g_O(kqAU}!4mn&5)|fX-sa`wTcX3S@^=`(jg15|x
zy+Rx<4oI<nbabrr=XReTP5B&1p1BGKXFSo;BD-~K<Mb~n;1y{9MFR*35PUv#%s;pv
z+qk;>#NevmcHdriZ~FxyL(N{ddilu_Qxb^)(~Lm3Z(m!gK9!xF|D5opmGMpnLkO;7
z;UIBwBn1`)7V_GAqYw!?79?>Cx{}95)A4Y!qqlX_U#+Lt6aZ5lcboD;(B=;yXg{+f
z%;__&oIM)OQO+faJ`Im?#t-9G0M^;d&g$#y1L03awMJ_GUD2k$EkO1;{;P{BIp0iP
z$8`r_oZzzRP0Nf5YfK|SuGQbov0F6kn{k9k9?}aR$_*ZehG4zBorIysIcrZ8cU_OF
z&8~GLN{`B}HaP5qz`JWBQJ%z|=;>nlk<7XXW>#h^5^Gw_$6A`ztMT~<?4%u^#E594
zmq;Y&F)7FpzJwu~DAU|GSIjav-_9Mog#HB4iH>}Jbe<wjtiP{<Q``MM9_EkWRq`B2
zvNCb?%9j@)nTU=UH16^EZ|S4%IS(NX7;Y3311#E=19!u!IEIzckN0oy*)#h7{VJK7
zbYeCwK)>C(fF5@pGsTu_TX^S;7H4`Er#tnb-4|Zz0fj3}IF(`<6}piO-yUF`uTrjF
zR&CTB&|L0&eKf{(hweDxHE)oSK^SXPE@kj%cfUNJoiz?Lg3g=gS)>MDiRXNFoIi;y
zX>uLAUQJ|pXq{_&cFnL#bju8F5(&fAaqDTf>i*-eQ-+>ta~!Mz_6HsP1P^&7JIfGa
zdQ@_Kmk*hwh=e;06o?mQAKmE>_|o4Ym^`?k<}cgY+DbTy=X9cZfL-LupYO2sX<(j&
zN%^F)<OVvZopo8KN@<sgNJ#dMmr^}y-w<}oiI`W?JG2?{A)Eb=L@&1Yx*xprSg#ED
z>AbPZHMWI^(F}%(j>9D&3W9{L=_2ov7Zf5djdVSu9<GOFGNmLR``Hjkkk9mllM6V`
z(Va$KneM?uOkY(;*uMN|9M*OZjHjmJ3R|FIwcdN%-x?iG42UYMmWyuA&#YY`0Y1n?
z7D@%27w}JZP4-Wrt@}@#Z87uT8`t|*Vjh+dlhXPF$TnY`URMWALmv+AG4owMbOeZ2
zfMz(_8Ci``MRMuYqzzh_7JZeRbhZ^t)+*F=T2K%eUg|7MPN$Vqj+X$JKq?V%9qr9r
z;FNj+m^Kvcpu3BzCwxFkMh1i44j>bLUr+cE1jyFl4Q$hNUNs&t?zue&^fTD46Ix!|
z>E`HYWNl&O6o^^<jDAQM2qs&@tPOi@x+ZvIio8_TX{HBH0;ke;BokD~WSnwl6aM!y
zd;bnMUGVbpks(kG6@P!sTDGWmVBn~$tG`Sj!<+%x4UCNhkOjoV@#9Xsg`MGqWwHSi
z)`k`4?>iUnQQ!j3s?Cg~^Wf&WBI2^CS8FMz&CJdYo>?fz5$I9FNYC+wA7W8ikVHYG
z^=J(yx)xvzmYUZBX2Fkf{EM3d5QrkQb?Qb3E&U@!SiLWfmmt>yO9W8cbeZ!&K^ipu
zwZ~Dwk;L(5XD6IZfO}^_an-3GCY<zm3D9J&(tkz?A3t72fb;|gjF!a?d9LzG{GcX<
z1rPEYgzu+M+|Z&(z?SSc>cPGhF4GTT5G4|aQ!N`o9aB$rBX4jOPC@(qM`#GRM47#=
z*44Vj|9?Z(<MxVDQc?izgKipSri9wo424$@1ki}3Ab^_Ga~}f}OJ~yEB=`TJ^pVLH
zH-5^tfx((Zr7p;8inR)No|#jv_hKhQ!~18yDH}Wc+lYv`daC2>+}x$bMRYN6B5-%F
zgL8J<Bq9`iy83y}*UP)z1$SM!dGG;~t=*^^14>pRtcg)jg~%{T>VcO5z}%Cbd%wZ+
zF;f3J9efkq!KmRe13WY!EP>*doEkU$4bvmwp<E9V0ge3sUC94yg%6-_fC3#Xq3pF^
zt9<%rQ-E6jSCZk{p@v+*`2>a;nlB7F8wcx$S7TL5N=ja!$_GIz0*YqnkT|cbtUx7s
zBy0h=0Q?OvL$3o;D0g!}EtCf0A(+CL${c#Hk;>mBNRc*ex(qgh;#h)DCJ=$ab@dF6
z81W2*Qjoevgof6^^d6K`CfM?#bg;0h5GbuJi2DCnRl!Q20e@2<=WCZ50Eux5@?#*o
z0r3qCZb5S_==s;S*6@Zz83<y(nnK@daBvXHSuO;UoE%I7bHTOvw#-}e!2(i*Fc6kd
zhK+EKfofy^0h2dia`27&iN9f|OepREzQDz`1$L)ExwV<7<3;QPU9F%%VUj!`@(cH}
zs%@`1kMhIgc_8f*AYisaR2K9e)Cbz`%4Dl`><J?XC4{SccsN4oyZ3NgT&;nqN7e53
zkb+Rc_xV2a2rkV-VX#j{4L;~X4FfIr8-#QLTmsxu48ivgeir5()VUv6<o3U;9)qJ5
zK^gwF1fragguCNnVnF=ZS>8-1@dLwSpSfm#b<h%6V8B_M71->RrP&xP90uwKRIEIp
z$%gX<UlrMDC1LLbYlJ2MZV=mG$pca?-KeaVAOg^{hRoG~=>`zI6gG!ns}O#b{;l%q
zX_*NKH9Oc!ztFqT;Y<|SE`#}juE07>I0WsiYf8kscd_+^{?=M3`PRU<{}Xin`_Dfs
hi|aus*9(sEOAKc-x!YXBVYCQ%$xA<$Dv@~R_dkAK&wT&@

diff --git a/docs/articles/examples/vision/mnist_siamese_graph/unnamed-chunk-7-1.png b/docs/articles/examples/vision/mnist_siamese_graph/unnamed-chunk-7-1.png
index 33c831433424ccda8783c897be5d770638d20f39..458fcc97646e3031af5f2138bfca808a643ff2e7 100644
GIT binary patch
delta 7991
zcmch6c|4Twzc<-h2xBRfv5Y+-OJOp0NkmBYEo6yQnz7!N!dS9ab|PD3tc_(XX@+D=
z$-Z^dDBGBPF)_`Y>35#zcb-3<f6n>i-2dI5{d(Q^^|{`k>%DH6ZI}*Y{wG7qm_=Av
zxWONDgf)N8X+BGo<rE;=XKJWtgDTi84A-@_?ds1R3isKXYuC`^`(PjrEh&8-En6a6
z1vj+XR!mAf-j&H3)6`t|<&yd{YYXEEHT4io=SE?wuSNCG6b*9{aqs+Qxm%U&ku6|L
zlY<R@*fX8BNPE+=e-KII(bC@EM;_qaNiCBHZ^WLYXNoPb@c<EG<V<}~>IvTLIQj)Y
zL`e?ja^ni_RXWO4{C`6M{I`>+h|?1KkH&<81-2k|xb+3k3tp(BbW{_$+w9iJ5r~q^
ze=Z<85rZJD8bOrQDhL+?65HWbjw=Voi!3mgJJ*txa+<Mks~-UiM}Yq$A&o8l`8+qc
z&xAfD`~~#HhIem>g5lBl6REyr4t<Yb$9Fh5pnI2f!6$uG+sK`fHzNLz)N5R1$oecH
zP*?2xeE~)$OK?9V=y315gwnB{6g#ize$sqg-s-nDiPO^MI%}7qX2Tp>%d^b>Shtlr
z8lMtd8#@iC3yxBkJE1=M{)pK1%W<OHMM1uwm~!}@{x(>eQ50!(&hVj<gvDwl_!uVH
z7ub6XQaU<U9W$&rAN1y8S!?Gt!AT3<;hl5`wq<m~E~)K0mSZg-Krggs#ylDE>l<4H
zw_7sinhC>V<A^n3Ykn`hHA~<P=dBK3a(>jr8=&&kBFhw5^VxC-e%q#}?TFV_dvr@a
zy=qZtlO7U4{_1J910Ql4n7t>nGS6H8Xa~&qJ_OD>;{4V8iW16`Lq)b8Z*Ux$*eXcY
zwmZ%c%~p~%=8mB;Y=29$zrc{#Wuh2HRgAD%D87irH0(DE%gd5>2H-FcHwr*-e0V(&
zV<toPmUY3;Hn?8EvY#)+=YN|X!<{`WCuQ`MXQ5OJPA+dwL7$llUzH(O<j8@A)K}W-
zHWr{cd1e=<cqmS7jsIwh(=@LA{>GjFYC0T8CQ)_@@Ij`loA!i~ZeO`MInc59cOv4H
z(o9-}@7s#rLWwT9*ff+EU?v>&MTZk!RUKHFi0C(?wq0rv{irlDb`xujzqm()xr9o@
zVAsulyvaNM9m+5nG{<GKsTn{X4?GjDktSFCwhIat{hHlhIB~1R8ejbLy*aM&d>odf
z5f86yxl@@Kv?SXlSDuaCJ~1Re?xcq1Sc<!+qO&mw0^?3i)_9=!>58mNn?YiKQyop~
zIk%P(Rjv*kT^%9boi}$_n*L`q9mTT-+L0$lj(_=Teq1X7|NdSTQr(B-^=@%pE(gzI
z_5SxjL<s3m`&vj6CCjRSpis`&mIQrN+;!g+&Obc;Kr`&R(ii*lXpS<Cn8iyYLjjjh
zJrcbFKU2((A+7+OvOixfc$mV)0Pbbgvp!zn#dd!wayoqWMgP=hsyJy~=@~w*QA%);
zI<wuAT>3Q~bt_ObX0uak%~bM9Cd%z*Dq(j^IHLQdmML6$)yL;$7*w!zKGpRKTx~ei
znuj2>7Ai5xTba13b92$cbK;>INp3LZrYZPMvrvm;ARGwG=)PN`_Klw$;j=`!CsEBr
z2q$g`zE{sgDO8!AH{34Dn!SxPgajR1l;OAEigf6+=M&H}f=}ANjk+Wp6oN^iqP=lh
zs2YlVAi8|PCsotywGJa&TV@}^l~bwV(_z8bF7cxz`8gT0B=9`6_(f^t&&NQ4MA35T
zfJG<*n0l=_dBXT>p5llc`1?&7DHa<SRF6L_r39Hz68Xt<S{@Gv5a)7JS}tu>{r=4G
zbL1|ln}#}^-0FmFDMWuPkm$;_o~+T`;7SP0Xu7b%m)OfNvg>Gy#?4hi#t+wANHOEz
z`N=rpGwj5MuJoF<kNPxbYe6)&fP=l)9O$V%8qQKBFGlVkR~i@_ym<oY%<;Wbeod;X
z^jb2#ZmC@zfp-zyjUh2Z7LC0iDags_yKd^ag2lOqhD6)$YS-d5Mw`M~!<q5n0bEX|
z6$Co<@h`7^<L~3ox;U;xXe)lHIFbkr;d5iCOmr&YXa0%#<^A|?xENK>A>jJ^LD|^a
z56zTK`ozxK@Fc%e%QT}4lCIn>o$lWWwf?8>@fKvqZnytr!3`H5rN1rj#rBf{4viC8
zBD(7_?P-|VZ->Xx-P!M7dQVtlgxzT|b5gqK#mUaiS(zK1{|I~H<XMg?xrEXjmRU3f
zIv3dM`s5^$8a$Y}=Xb_C00lpphbm&=`kyzeVr2Q_sdcZ-T2J5vzknhdhR&kniuWVS
z7`0+bgE#aI79J4aO?o&)ynEHzV%~DNBS;YyBl|rS&dX?0w%xV%68!>(H!pap)x15|
zFG#woH>}#n%+g_Bdu*cKq(1Oka@zdXpT_V+Mm$oQx#Vsba`zCZ5qOd#2#CR?G0MQb
zZTCf%AD~GYQON62kp)=}>u@9Ccg+E@(DCBL#tm+n(&YZL7nQF@#&!+t?cC>7iXlN(
z=lbnxL^+cOz8$^XO`Lp8bu$+l;dNC%sXlrM`*ciP=RhF&^}Te8+Bkf<pt$N!pia%)
zYxx4nt_x??M_Ek(gXWP|muI)%osCd92|<Xek8%Q^r)ZxALO(PL7f42JvW~;8ujOJ)
zBjU%T9|<!8p{qhGE`2r|lt{KkuhuHO(*zYRSndo3+r4Hdv+UX5iiyMfloE5xq`X3P
zNlm&kb_d`Y^7ls&%$coIAHr?7gB4CNq6Cb?o6d1I%K^S?ozIEdCe%FBs!;FWb#{}I
zSTC~l%uT-@J@A3$&b0?--Y>v}YMI(NMwp8h#>1+^xvh15q^obbOoG!Xa#f+k>CA~I
zlo$?03*=KuqtiHir$A&OG`v9SIo);RG<m?w+G>XhRlj3Z@B?r-yLE<KQ~8p}{Hfs6
zbkJ`vV1hn#KOHp(KN__-{p63O0*Ak&IQfjG07!R<Qtv%?VKuQ^9IDR!_$vL-Rtn)2
z*&K(zZZgdBZBQ0is9CN(+q}c5tH!&8_)d9x(VM+ZgeUVXEK1r>loK%Bmh9rb$IaS(
z$%OjRzA99?AkF$Z!9L~!>rXLqlhsaiRbDj!DgLEqT>k)%$%)J3z;~BMc6R8WEAX8>
ziC(>y6lC`5iJ9enM!@raqWoCmK_Zkaf3@xbUI|;`dh|#!*ETQo$V`8|^%Zzfrc&e$
zYur(p2|e(H<xY=V(|KORfo8!i$Y_4tdnI0Il(5W%Cir5QQGA)ytFnEO2Eo`26tFe4
z(OTubwt1!cBm}e8?8CP;xF!}g<#?r<@fSHH=5h4PuxKv~wTP8xqG%6M`dHz+wuy0g
zCBtDB7~qvpf&1sp{Eorztd8ti&GLpf=$pcUlYK4dwCH)@)rl)`Kv?p881h@l(xayo
zh?&ThTOX^En`+Ir&s&<}nE0~+7aoLB3p(E`9mPELFc|vLu-RW5qI5(%ODQq!wb3Aq
zeB|B=c$@R929&WdqY}HC{g%YY98=s;Sc}WVsTZJ8CW<$wrB@+prVy0@b77rm3A|8r
z0C$0*PsNyAz<~)-(Wj0{cD;4^BaEILS(q>%2rEk+XGx?-5MLDc(}9)7Y)k}qI%+dQ
zU|9f~blTS;0#_qfv+=`f7p4cn6h2obN#?=uSH6Il0GnQmb~Dm9M!#F{Q4no0D)S{T
zLv6j`)v`)lf3aCe`bJxZl1qHf9S7>9ih&F}mrz8@Yc?75q+VdIBXwv|OggXoHG=Lp
z+^48Lz8Q8$A|5z-HF+Lvf*M|X{C`{7<h$XbyC?c^k8_SnMY-<34Z?Em-dEp}?$#)B
z*{qQ6SJc@u#~ZGu%uPR%N=>NjZzJ?q_358uHH_%&F03=fox~X+d3|{J%#(wrctS!g
zu^)C{n_K#hy*jCLIthNy$K~Js%LWkyz+5;M_pyZ((Kp<JdA3dQRVuM3^LppOEM-Q3
ze>bePn6d=RKBqDj76z*0fn?d`ATCkrNz8u8iUj4pK*M>&@V{Ycb@)T8ID^YDmH5Mq
z?*)gb@V&;)E0ABR!ZK*A=l+_;XL)-)Desa8_}NC+NI;~`tCf4T0JaeCnANNb2<3-e
z{Fg5Hdfua}g3CJ3LjJ-x{jV0kk7&?>hjbMgE%v@`PMzu(i>$ELPz|$4URBFv3ZG1%
z5O)8=13uDNi1$C{Pwrx|0VaX@Bbj?{?0$z;+?RjS%EX-}bA3gEFH^-|NKv^|E%Pb<
z3jmT%c2BHRElVy=$>SJ;Hcf|+7#@5;`1QS|cGtCaOtNAZTtuBa=PsXpz@&Wi7|NEu
z_F1l63t)3k#(ZFG1L99L!5ww)l8M>2gR-#E6vZgTo0UkjDV6wL?RcRob1HdguynsX
zqHcX?3RL*Q*!R;}_)-fGMixW}u$%Ft;$uVmNQz>Y-x)tq<wHJu){n+<r^cjdYu-{0
zn&IVjs-DRh3nNA%OZZl{b}GoxwI$e%>HaH6mX{EjcSd%vE%sE|H=}`FME~wBR7+QC
zLp?zi$KE5jJ;ozWP(aLeZxt!+7UtsDzkPWpk%Z8;={&6=26N#CT>Fpy){feILlZO&
z;X+ToNB%LQqU|z)FfaO#A9tL6`UDowJnax=&YS7_KwtLbqu*3u6syqHfbAp{r$BDt
z4na>+zZPOMm{9z*dEewuu8w{Z{mzGHabcVXmXo=`1bX5hK9-b2yJv}@$=vuWwV*4X
zLcE>G{fE<z@B@HQtwBcY&yA?pI828etD^*Qh`9<FNLEl@UT>$P92QxEoXA^;ZV(K|
zU9ghX8L@j%(rn+f@F|KBqS!qxLXTmJa5e0g40R$yxmr3<D+$*Q1a0O@mWNuw#m<o}
zFaxTv&gWYDulJOu4vIX!Fp+;X5s=I<I*kG{VJF@FWlD&eMpW0VTPwapx_5fz83$T{
z1Wl>TesMV`=F{V6J}>|3o0e}iZwW0)@V;el+0XFABQ#>TX#A=?!W%6BBjt~2(Gzx5
z1N(rfLxvMf60;ZbF*Yf0;GD}>zflVqrmwJJC7VqENo>eK<zpO~9+_}go&%~VyXQ2*
zWW2@H2L7HRnqGQFyYT0GoD9L#=1f6jiH9xkAnIRPMha%|7sF23RP9OpfH;wpuTHHT
zDzm(Nw?(Ki$00p-r``U3E`-0-U^mO(s<vhw@YjC`jt{?w{Z$k@j}Ye`3mxU!Gd?jB
z1*Sd}dMAJYW(daz89{ihalH2kGAeIvMxz_=xsj@u#mIYG9x0uXdDi%Z3-RzQ=F=O6
z7YZd7nwUG@*x9&|QrCINT#@xyQmk|v{yDf`<Ek$}x8>Z6kNgCCaZZ1j1wvQdHLC&_
zg|~Py-QjVUrYxwxwnSswk^7gU<)iEx9V5om5&`rlhWuwKZxC>}*$o`NAu-#K(^8Si
zQ0g%c3AML;7}-%n#OR+gT3vM@S@L1+2H(@$D`c6(Me^b+B~Y;|tjLktTFJ}zvY`z1
zojM<-acCgWeW%(n!h-9sKR>A=&}0_d&mEeZ;g6G_zx}L(FlwKcb2QbNK=SW?4J_KP
zNU=ZQb<3R9c1#KnWleS_)ad=G+E1Bkysu5NayKLI(u^&X2^Wn}_}Q?biAVnihMj);
zq7{@;_E16sC>86eO=ip5o6z{WHEMm?ypO-T|4_TgXkyt&pP{)djC0Kk|1H|XPq%z-
zN3BPaC0;hne{kM-1dv|L8!%?%B%cSJV82fCsbi^|c<52|fWi>;OszvhqFfa5Dux1&
zbd)%5bl;TMujt=+(C=pFQ#qZ1?*iQ@ITx7P9^ax+4j3NV*>qKeq-$)A;k@@yw+2<w
z-rLNrHvO3i19Rqqh8|Jm6NUsucidM>yk1Y@3kCA*f}(pfPk;~JmU!tKDEM;T`&L@i
zAeB6J;iWkF!1s9fQem61r&p{&vh?oGM93*Zlk)i4Tyawuom1+g$3W~#Ga4E=``kxD
z!&sTUh!UHrLC(79`9xpRTY+D434Q%x3)(e)R_`c}N<LJG*__(oZ?7$xuK&)|<)=?Y
z;}hVLE|qUtD=}?i5@~w|zwy9_RgGM3bQbRePs~1U{)?-X>+%_~uJ;&J-_>BHjHN4G
zdU)wR;~oh8E7L><Pk!Aq!KUyQ-g^9Eek7W*9uOv@<lP-6>4q+DT!WCmTwKEI^WBVZ
zTNz>oMS7LUBl35(oS^bQCSV785W3A;IRM(zl`QYSJSGsPDsfp1UF-}>M*S+3ZTq|H
z%ZSs?)Lcs_sWyVs`1~bzfmSn}&GWS2#p_{x>{E>bPp7oa*O*PHE6?P~zb|^S8c6Of
zG=}N&0=3wW+BH|}mrq;V^3w7v8YV10FB2RE_rzcf6Ah4IQd~cgMk#L{1=`$>)=XgE
z6x(koMqMv}l2VMQZjowg?&s$^c*Bz!!xi7+$81+eD3|%^`;#G#$@FH7E2&oN)}n>4
z1Osu@2h;)NfQ{mFqw(>0|4NN!K2pH&mD%~R4Af?8#Utq=shPlHdk7{MYT#6u)E%5X
zAGKnN^A61WT#f95xYPre&I&^?&pe%}J*scRZYQ^pd(;xLP^q<f3r~=JnW7CEqB#$E
zVJ;%YCJ%oW+Tsga)Sx*{ou)Vq?_rVyTqLNb0?8G51t-17#ko{yn{cM3k;%=%I0L2F
z{HpditZ4Y%ru6Nne3O|d#Q-6`PuiX=Cmn7vE4M_N;@G>6ReuJ=qP~BSUcbsjOrXJB
zSVGx^aUB?vhJ)P^<wY(x_o|q;{PbRB^MzamkbbU)Ij3w4rXv;-{Cx)BMDTqi)P1NG
zkLS3(cP({6NW}Anm%1yb1UUwE(B9KKi`cl^=r~XrXBxrO#kF^_>8<M3tj)1s+sn5g
zi|UGeCqhs@=bC`I%nK#I@5rJ_d8uoJ`_6njr_9!vh=1Jo_*_^2ShpBOtAq53j+)AC
znrt^wMBz9yv2cA;nzl(ppivH1Bd=-ZW_W0t<C`zqo+tWhkXIp3fs@#L%Hu{p%-&ow
ztV`-aQ6MR$9P<Rjns;$|Lz=cKBk(;pYV{tzDL6*`5ODvLwO!7%v(G9RuZ0+Iro<Ha
zSK`WLLDvn;{;3EBe}l)+_RGq#MkP8oE-2C%XRLPJxHE!Zk7^<=5RdC15lyH^ojH{c
zjT;xL*1LvWzcMgDCI9!iW&a3Qj#G3`k7_+q%J2ve^$4lOs`fgIx7c`O#Qe!cyAlHS
z_NWT5QP|LYe9EsRR6W<I5YPD-(v1n9#gZ1*r36}&za$-##;FP(DhFuxY+QApoN#A%
zP`<zKQbSRx2T3A<jK6%VNu*iS%|&$hK(wEemDiO-GG^T?i!!lk(InTx|B?I<8yzb5
zY}aIS+1(-HeSFnmT)R=7opWYv5}+{tgAIt(9)z%NrT?Pd5=_-<4*0L(+U7y7I04{2
zgVG3bBc|za*+G;?Up3@N=EPJG_V08a^S!yb#3f5J^iYXL_Eq?t(B9mueyFi+Hl+Js
z_oqx2)La$sz!j}bD^^=RwY2MQaftVvYKk=7E7x4yX-jTjfj%BLF#mxzDdOdejP;nL
zSnpXw!DOwv0NU%W(A9}l^^S2CPfKuA=^Dq*h?9ZFI~%rP$=m?hHRlA1>b(i+_o2vq
z==SMr_C4@yLVRbMkSva(9Wqq?Ro{!`EEdAN<EGI;J3FL1MnIhL+%OGT%}&^gljY1g
zd@!{Qc(5M_&eNuK9B!JdmDNs$Gd*LrR?Q`JqJ?DNQs&*mh@bquygpPC%~*&<PS3ZI
zL%J^sP|mOIS1h1X<X-i`DfqKg6pO0mZ8C{<(G894OEM8vbv&b-U(t|JH7R-|nNG<Q
z5DfJR(7W0>uom4WS)3YnR@Ksp?Ak9>>?@FC@9P5$J>wW6RW!lI&)Pl~pc_ynPIRh1
zzo=JLwAWquF4M{<DN&x*Z#C0icv{Bi^_3l^pj?763D3!IL;Yz}#c{td%LQhcuud-;
z?yDl!IG}UDQLpcgTZIk$U$gH;&A}7OOEu;MZ$u3K6TnJJk)rldW?8)Foiz4Bhje)f
z#jZehJXqKkAD}{`xdjh<E?XkM@>oT%{fL`7KfJ%_P34NcVcYW=>&bpxmKK=mydhSV
zWY}4{R*=LN0AI^<A>$Dcu0LXL6+VV<UZe>!+gpE-F0tU{wV;}1Y;${bc!bpRte^WR
z*odYl3(4kD444#T{%JTz(_RjOq=+Ql>I2GhVq($sp+?41tg}<vuYE-*oN5?Cc(Nx+
z(NMiN9x^u12e5m@BUEKD?5L`xEZL{u;j%8N7;To)Z7%j)pebOSEBFbdTR9;a*#|}i
zQ6qaT8*{wqjHv`h#-@NPg>45qKGf9M`rF6yA0oU<bV01hhA*3*(b3ghECDdHAGtZC
z8;l1&4JHY-IM|C~)&`P3mO?bz*i|i0lVwcKE&nKN(A0D)9#J~yWxYk<<Ts-J#tAuf
zs%nJAk@x+I^s%UMlP9H#^s_U3Z6o(~m)ojF6m|Bw84F89oOws;-IDOvzqI!uxBJ>v
zawS-sua7B=y%yC?Jn_7#8pu~+A@FpIrC}c`m01GB^X?2hHJ=lsp9rWt$Nswc+Mw-C
zML~zYK6&^3<o3;|7=kZOXJa0$oPQ!D8%rU0M0^TA{X{EZhB*y2&cS{p5%6RRn%}e`
zw>R!5nrsf@U8%oqc<X*BT}b%CH>3Fr4LqJz)$BHW`Bu!&K!@xDfb}xdA;XX*jYHw!
z<WYlprrZw7FmVqWEwdL*-VZqQGYZV-lMl_`d-*WwU+;LtD`d^B^`E25SlmO<#No^2
zM^2-i)NbGRA0BBVQAdM@qka(Fi#h`&k>xv~j(Y}LYw*>Zu<$EiP7l#laCwbt%cL1u
zw!v2rx}QW4Fc9@KuAQae)PS^y&CvJFDJ4%bve;!w5a9L`vm=+?dtvBb-a18@)kt*8
zQd|$;xw!$gD<xy^rk6r;E9{^tt8ZHQdPjwU#lG!xRju5r>Fa`Jzz}%1l`sA!_k!Zv
zd5FBKW6FXo=!{Ds{}`@+e%?Jxw1;!0o$zDsJB|xL_S%f=sd1Ml+waBY#5iG37e+`1
zy)RTg5vlp?kCsQyw1=AQP={upU^YkX+0%!+Jt|FvI~SIsH#hf#9&1mR3NZU~^u;@4
zt_7%QF$Wq^#rJ=@Flo=i?Uwerk}F5gt_esn8h+tx^(uDq7x70=_J3vXN94b>ctO{G
zLlQ{>ez65m8AHy0Qb&(`GG65T6AkzdM86cHu6;~kP(2}DmxhkLADgtban@GDZ<(j~
zbBDJ5#xm1~Jrmqe-@YL`SHm~I&FlIEC~tR}TdkfnnuqnbXLQ`Hs);&Qc<bVQ69#A1
zp24`(QJYAe%Rs$s2wZu};I%>|kqj;js1<3GB<p+5bDm)s{J-3vM<L~G+zqSWKY{6d
zg?sb|&f$1@@)T_6d=Jdy>veF9rzQ%L7Jq9u3#EpCU5qaizxf9y%GI!j&7jyUu;m!m
z9^8d>6(O0Wd26CX&@y8Ge>xGEGN!&0zf3Gs3TE@No>Y}gZ%C#P(<_A=+X9(_GArz&
z_QXlTS{|p!+MfJgLHs27Ys%W~BIt`f6L6#I&H|)B#Sc@EtG?mWA73HZ#DkNC=_twZ
zl@0*xAkQ2ZW`zVjw(}710$>bBJuvlC#PAjow~MP$W<y`ZznpRbcC0##V+_D!%>Q#9
z6uESHflW-JlXC3tVkCVw5mAj?2$uhxOnOAlZ@;Gj^Eo+s!!pA;!rSuUa*^KU%CmMw
zc~b?!9?duIB><&1b)fU}-4Ev%{_p`CNt5|@KJr1VK;}0FQ0bSy`j5+ktvxr|ZVOpM
zJ7^3mUqeJS&~k3jp_MvY)4d(?>*C~(?V@9WPvx)P-I%Ll_u5d!3hbOe&bTt4YqGS;
z+miz>e=2y%2`>q7$ZpL-S{+TNG`Z5i)V|mJ<=zKvKuX_sP<9$-Z4BQb%lhgRLavqE
z7G?J;s;<D|^qKpcTKfu-tH0`wvHT=pzx+z0IE+r>eNc9>%;=!X@9z6h^B|Ytc%=C0
sbQJV0{g=*y6r;cXcgSSnzclkN`hrZ%P?M)Lm5K3~8d(_D>AS`L2L}lGyZ`_I

delta 8077
zcmch6XH-;8vo44P$r;HgN#c-^43bfTA~}gjM(`!hkT$3YLy#azG9pQm<TT_V2}2MB
zK^SBZkj!>OhBTb<`|eryoVCunYn?xL|Jz-=s-LRrU0vNzy3USHiU2W~DyUcQTX*GZ
zYufA8H#bPw0A4;r9j%AZyuEywriCkd`0e;{{(8;4kiLSn8AG}t+&Gw?l=<ORW;S|;
zK~e_Yg@SO-VIQZV%JGJ>vND;cZ`}g9YTRc3Da&yaA1fQwn;9ofO4pgf&W9b4G8}Y0
zoBXBHgU!R9s{<6vZzhjJa8Ssm>ZT1s;En+zQ8G#jAY+8w5^yv#Koq4wwQhjV?_fw{
zkW`d#Hs=2?0^ol=I}OIP+u+&wMnWhYa9(t|+JlTJ%f%gSUrH9M1Q;N)|Lw+=o89XT
zxon22^B$}#9YQ}t>msA8zU9%SKx?PPy9Gi*0>ZH9=SmX*xxwF0G>X1}r33RPMDR@x
z6lS1RxqCPMnYt#XU9S-rp<Ybt8sm&UP~znS?Mp5`bh``>1#t!kw;@b0?PS~P_;H0z
zD}otK_{vPr*|(?bwC~(2>WoPB(P~LK?(Shq)F(EkDvRDDqGVoR(ePr>&vx&VC9P3r
z7QhNngAL3TZ7fud$4<*l?TF}yB_Q;A<+6XjA6`7#X{Zr3y9}|Aa^B{yfCa~YV5iVV
zSSl~x#m2{aemvw!Zno14H(B+XRaId>k}9}<HVF8jA}GFah}B@NJKaEiy5QAtBAf8w
z={<saT2Al7i}NqB0M*>}+}2r_y_`V}98eNT!hM@plT{;<LmL(FWUjCMv3%51l<zO=
z&Ogbg+&A`}^jS4(zhX!4n72`zu?pyhgjG9q;{G`LeoGm2pYYH`HOkg~_891r@_aG8
zC=u|FVsP0e6^F;Zq}<{&h(PyNeehVEX<-+o{N%)+CnYq;{U6PFyjv4-O=%efP(U}2
zbJMEhA=bV#2$?naZbRwtRcGOgCpHC}pC$O!T_-4KRmoYfiNU*>dBbX1zU<(r>)3rP
z7b5jZlS?bL+ID*&V_l4;QweuxiKL<4)*ORI2)mb~aKHw~(*+uv<dMoHQ4G^q68&Xl
z_H^d*cWh1KCxYEkdho$7$`bibz^uW{WR;cCDJf@ukP5CB7VP{}*q>4uTx?S%`&G$t
zwY0Q>vh_8TeX{82dy+?qv5NE$Dw`lXgK!ovM@7%OiIx#Gx_5tM7-O0nG)6q;)he;R
znN|w$`FkkllBdUfe_Ca#guomnUUToJeP2=kAC^zKK^7UHy{}Pi)?>vAs5n*=X!T&E
z)3hj<9_nn{vssXT10|9~LS$^)Upyw&@Mom!BfcanDg9RQI(5cyg%nP{s#ucPGGXn~
z+es-Z1WG+zzrL5R>-*q=6g;+A&E<6;I)GJ7G#g0*8aLHDv^L6|Ilf+G--%PKCMi!y
z4o}=2u1w5Wum~e%q#LpWv=cj<oc1p&`@IZRxlCWN%a3<Y0EM3)O9-H|H$k-%Se`VR
z^F_<8+6<(2gWNL3fw8mcEePSo-!rt97~_Xk<wUbE(tR3)lm0|AS)f>(UqfBU?38CR
zWoZ`9tZhmv9r(cByvgjC?TG%}yC1fjQlQ#G58M0|>X~QG<bZjLm#)s-V1Tz7Hgxlz
zKYPxx$jK*Vuev*!RV9X4`3xD2&F70ZCHT=Po}as(sj|BFy*$76q03G`oKhcCT2jwK
zfi8Cg>bi9Z4n7u1cU0oBtL-@kYdA!k5^)dtSv9uv>w*;PP=w`Gp7l)7@l@2UyB1B=
z1Zz!!lCa(ecHh(u|MZNsZBaOwV<?oS0t{!mmENs|McSS!C4EVSUfgDE)D%95S(EEt
zkq}T1a8vUt;G!MgZbSr4w_$U<8(%|7G9`{jRz1|v*g?+~PY6l|Gt8p+Zew&!Lpd8_
zLVD=S4cg4Wm?04iG-`$ZWXQ8+BMi;gH+r;%w9kQjkNe7ct3;=c9}}Vc!NqBV!Y@7$
zbxp#_7tLm5FiBrSOn5GTMcYx<sp199EHW81>c3x_VVMtKLuP+`AeDB~W+4?oqj+w8
zC768tP%@0H?h5G05=K!igu?WQusejwoOcu@=hN{eOy)oDsr=}Jhf*Qk-pi}=_GP@c
z2?r+9QA&!-3k}Ms6QTy3p=v^wBlVnmP3_!qL~p!Y!sV#V*0W26t)-+1@{fb2wh1}q
zDf;})p>=dn&IUddhQukW06eum)sMmwKLOuh_jOR_M&|Dn4vhP9wRWb(BX<BA1ni<H
zEq6}kZ<UD&03X0wgg}PmzDz<X#RIe50iT_|24~Hu4w~k-(aM`jb2ByFt2K&_{6i7M
z3%KQC#YQkxLKSai<3~eWX6ZU6AZ^t4H0e0Ci_V_qyp3K(-3SZOTjTBI@}{K2h^!C4
z9dpW@`I^JTX95%@a>KyS8W-CSVRi8!!tmn*O=N4^8CO4RVGyrZfvDsCAV9_G;F368
zBxkU-_F4zJ%IW*I!Y0WGraZZaSKgGn$~+^?j}m4aYb2Kd8C_mpLL*uqz5eU!;2Mu6
z%6xde@)Kn5P0&`_tazm6CUlb4>94l?6ZN>9^0a_|jD}suKfUNK3MsM)$5{?%IWSC+
zrwmSVozZ(0*wWHkwgA+a23%vuY_ZL`dg0XbhJLAo<2*vL3Nt_{uL7?E-Wp_>OVk)0
zE7_ja5x0s$?YECJL0Ys=K*22`21tgf1)n-kTX_rT*wS#;`*u}{@9Eg-U*xM9_vWf7
z$KoKy7c5={WN50CFh!>wnw5Z=7*}MLpvLo@@|eyawc#8cfOac~IDe$?=aLGTZCkpK
zU!-wrM>CbH^9?Z6fo!W+3sB<y(_Alcn(>GoT!(X-VVmj|%wOT|OD1;^*C*oQugb#x
zOe_BUjBtY&uvqJ#XD3y_I8MUigb!%cSAN<=xgsZ%t?F%iqSH-|`P7Y9zL}53InldI
zg2Le(XTKeR{38FL$8W9m!d;i%RKRlD6{}3aRh6Tp*4MbN#)JXS1e=A}<4=al!*+V-
z{n_ON09gCSyqH*?DWzo@44bY-QcijPn2Y7_?Ff@f^j0QA53a?jWP|w7|4to-<gzBD
z%@qlOhmy(DMU8=7UF&1+J3NGK#G=Gl*fCTZsAK~N0)YWL{qxV$lkYaPo{218gkGb<
z9!<8q#@PWca>)Bhn3m#uf?Dy@nG8<cCtO}U6cXzDJd?Escj8(SajtI65DQc=f28=(
zm-ry0zG`v~`M@y$a@_HBw?ebKIDfa$d20ns^L+<;IzxPjf}pbQT*lnvnU{e3cmZ7R
zBOt!HOFFB{XG^9MR%fbtjWF?j2mwm!0iqhFK5^4T0R6}@xImgWoI-G32IFlkiVxE$
zHw{h9g^n6w^t)f?kPp50w$c&Rv3;9?<V2az)Amtb(hJYYV60+t9-faD6`*q;rw~dy
zPjHQuDr2Z*>izrC>0aF64G2)O(O2(pNe6COUxphZR(H8(w8QzP9JP4}$yJ_-zf><8
zuEXh6KirAqttXYIOG&R<d-f^oQA`?i&F221ggR3V+b0>!4oVE4Q6*OLlB<tW5F82!
z)M<tCHkmO_5_el6=1nxm1VFBo2v^}xZ>c2+WsUPWO00YTQ_2`MfC%`Qf~%>3rKl=)
z-wn^k%X-vmVw4_T);M4yjvPo!gfkgdZZ9D)-#W<9@~hOX0h$<Ck#~1s5QC!td^5Uw
z;^~mPG{S&!lEh-27F>v9iZoQEQ%ygh=j~yGe3rqW!Yv?Oa|?F7{?w2(B(@iSD;BEZ
z@F&+lIR=^Ax(S$gZE-z&8+ZL0fA<2ae|y%djtJ;H9tuS25b9@MByKF5*FJ0a7vEc?
z7~iyH<QDZ`flJP!u((FBk8UQGn6WM@@Qi*(sW}dwGr00q=mZcp%p$u@=oKlmeGBz1
zqS{qz-N3biH)UPoh5$u?x~9uMv)R*wzR`aBL4BYRS9gcM5plEM7oYntV=8;O_k_0)
z4^HGgdOq}=Z@dah_Vo^d<qv%k`#uU+0Po-OE(#<jl*S>~VSyaOlBjtZAB;X<@KW@@
z+fS4yZMgZYRq2(0gcT+~Cf;Yn($g|(AshJ;-kXo!cMY@3GU$XSbgH{O0mY0Z0DSJF
z8MvPVQ(%`>Pw$tf6XYJc)TB+Md?8=F*__fkJKMWpetle<Bu(2HjOW+AjfSZIR=TRS
zyRmgxV%zU#*A&1i`_Qf<okrZ@#PIjdn|X9a5Kk{J5Z<OOL5w`WUX!>bMvaY}JRy>J
zTzxec7qgxny;!%c66|B&>Rb-&+#~V<%j{baxj%#yLfGAkT=&<E+qDNR8Z3)xYg<yF
z=KF>L8I|%riw@b*gdfng-=QMZtl{N)-7qy_hZnqG_RX_Fm};|e$@s6_uU|qh?15~S
zYcV?0S`Trez|#F{XhD3<Vq8-RLmue=rsrK|eJg|FH#|7s(2`vW_zY90^W%3Hn%O&(
zy9<uu-esl^LgK<0<>|7|C~f)+rAgK!X3rwr&q{(m{*_JgHZl-oN9NXfV;^<ZQiUU@
z^jkD&MeSbF*qkF?4&TQ{!EPom|ACBa!Y~J%BLy87?#Cit>^A31?}yC@LWNk}$VB#A
z*bjKrr_lre{a{X}0jDJLNmA?f+8`*tOEs?9-WnhguOD?Y0b;DXhkL$}Q7`^m?5{o3
zO?$Qm;gh?%68s5;`FJTW9;qHvmp)-Bl6ClGof5}q$2uQYNCWPz3$G4}#7$p@Gn7@<
ztN@p*@}7RHKPlc;k5waL>7oMGi^w0jA{|V30O%FqQh|E-3R883>l09S;kamnxKXiy
z)E-HlE{sQAj$W5K-k4`;P89EcbaQ*n#{&&_#m9+#AL;&}ae~}S=Cb^YUj*j;*Kv&;
zkuc_WEH)U3PKU{>BZ>wbLc}1qVS9l5zG{5d_P!-v`lWc}oj$^e<q@r(zli!45UWTQ
z`|{;2LV&{O<X+{FkC!vp$6vY&|HWFUR6<OMHKf0d*-y;p*qu^iFLkgyqnhB`#-V}a
zAi(&~S_N?lt>|BEBO4k*IP|sKnR$<ay$8cN)_<d9n$yZmFtNtp#(c?iwr3wZ_8>Uo
zxkM!XCIN!U)c`<5{d0HsxR_GB?GUSz5*^{&nE@L{uOe0OP1n0$C5Rzbwo8l1v9B1m
zPI1&Dp$Y1>2j`7eW`_nmcHv*{5scVAZ({~X1({&=gAi>8E25l$X#0dZvniPt+`2g-
z;htSF$f^N9LA1%Z@-t_O{dZP`(?%hj;OQU{V;w+yDYPW`Fna6yJ>H}3Lw$T7aR3}y
zwbn2?SaLaFp-_BhRDjnW{K=+2btqDBd5vX-wloBBvs$M3%EcC|TzLE@aA)wwE`#|0
z5%Vs`cP^>i{?|C^>C3AgIOdg&Y_rt{Hm$<%@%n{fWHf~sZjvepRLqOrO7#?wbMb&O
zYjhV{X{fXlV!+Y^<N6iPICXf-F)f{@WBaPr`J48(v!m#Na;W_Gc)&w6VI_f`f(C9i
zobxzyyJ4a9?cwlp!U$ojNv{XX(CK7n^$ixtEXb2L3XjO{tgWQS=eV<gq+{08%h}OW
zIK`X?h}<1fd+_a1^?O=bdVu3n&vml5>52eezZS9@Z~DZiFMM3n<DPJ&A)w-TaYpDG
z*?Oo$s@bH<n1F6o-%D`qGUB`qOKcBDCdz-u|A>;#hZ=^;{|I>n%sMq44CO^#r+5u$
zlX>wV4{&v|o9q&JYl=VkovG~mh`^Jq!CJ_>_)DvF2gF<6kuyagQ5W^1BUvM$x##!q
zPTDf(3D<80kC`-f<sRRZb7Se*+_t#LZKm}dR82KzdWz8pqOvKHYz8}@pWeSr*iryT
z0iAm<hte^2OBepSEIn=gLXq#@wsOhk<L2bG=EIJt?Du5&=q$p0YmCnmRp^<DrSoxV
zOUtM|R2Y@LWsw%ZQuqiv!J5f0bjL**B(ddoQV!3jD~dmEOsM0GCCs&jjP(l&Utl2|
z<jv1RqJzP*{SMju|9UCW%Y5*&$9epc(NRFIj0qC7`?bw?V1hPlUV6}veTSa+!!stD
z0;xhAx|bOUN!1R=m>>*13=kYfY)VK|Kml}nBj8A5m_!R=?;&1G!>wM_4X9KNM>JfE
z7vUZ2AiaN{nqmTum&*33ra)N`sdIwrUtNi^J*8HssvI?$51<Oe;}_wtkyVepg0gV$
zf;`?5vRqcYVRI+kJ}bmhmR*90Bj-+Op5H4chDppvnZ6s~Czx8ZUHGANwV4Ot(NgX2
zb+=<MIf6lqnIng{F7L;wFSuT|*IB|M%{;>~wU;k1cr)g?HiiW|{IUvTGiCVxAp!p5
z%#1(ug(s>HMCAk=rR$T_kX93VMlr9DV(V3_)xv0@d`*2!#;+>)d>rpZNS-x8<-ri7
zNAEl_^j|M`Cdlm9^5ub{669aozjnsG2OIB`oB0>t04Ya51T$)M5eu?$FxDk9wcdug
zCrOtfYK6g1Q0TixD68uyi)urJ45MXxT-~h!3bI#N3B8P{oA`VKwu8izh=des>djnA
zF=E|tSUV@UkbFacWQIo*!!otIIizCe+HZH#aznt8ct@xmV48e~QiT0Fvok=1FZy{u
z6N`j5Tr@zxhx5U+Cb1&EU9zm<X2(8{x~Xo04eeY`-x6^CGin)^!ME<}(8x97B(8Nq
z-Ng^hy9S~?dGrpT?e-@2=qlXjcI8;@0I%EdtT95@8>+Zshu4vUXBq16#;YT(n749i
zQ6R%OuRJh?z$m^yUd^v(IN0W-8?UElgd_)`k0Roo(ks1H<@B`;5vtol$6NXJ*;zp-
zazfo@gd|LoE5hh|M7I$Iq(lK8%<wE-v??8%J@=#S<dvus)jZ_b>X&JruEw%Hp^nnC
zkgM=4t-BBX@|)Evxx5ReL&*6J5!9@#BZm{#K)=w15z$7k2jjup<-P_)BN^{Zb>c5A
zX87?)do~9QV*|^yQQy6Ual(%cbi3~zBql9XSTXb>*t9Wf^-t4%4yyYOsPaD$ZRA^T
zB%f|QQ3r>+*6pXAoVAQP)m@1iA+KqIm)EuR?+N4meJkCU)kYjk@3K$rvJbp!^I0wj
zgo!YdxACew=M5&9gL_!<GmbmP^&i1Prmqq;+0i+J2XuI?Ua_>mzV*vl{quc3(}>>_
zv!T3SO6VV<?>*L9aVGO(<?TG*%_RwOfotq9pFOKWR~TqrP+JTarY)Fd(HW~vi{ph`
zfbu%po;dz)HRA;c&g55SuhkXQpN9&-xtv>(8_2)cbB9XwA0%YVl8}nH&F^=KH;z4h
zkcxjHT;gNd%S&bNr(VYt!p+w6znpdMz8K}KbNxs^i+3fQ3-t>$gbmr8J_JQ_JD!IT
z@4MfFx!EuuutDu)7e@HAqs@*%_ZFp|E0Xx^<R!^atBq1eqFS<S)Y{hy03uCNj6B#{
zx!8+MRx%j7@06i3AsEUNIHU5$?bk)yMvOQsx|Q-#;LmoIDG_xR%k$FFUj==~dY|`-
z)-|!YT&yS8-1y>ftRV+N-;+;8H;;CiKg(@<PK8FD99<3G(f{kfe;%#9vP>AQK{|a;
z?1c3|N-H1L6*s_KGGQ^9y}~BkaDRW}&qfz%KptIG{ECddr)iB?vpO%4<<;>unD}YL
zxF(9wz^U8*e6}#h^59(kQW=G2uX?T5<!gy7d1FJp{T}jvZ*GZh;d}Yk)qd|=)6Y{|
zGD-D}yZ>`0MIxWDvdLs?W_f*M@3SJAF#g$ZfRX+n5oM$u@w+lAwyZ(AQ<|jN=}XAJ
zYN*NlH2))v>5xZzbWdjgQi7%hae&{AyWyO&DVGelH_h*=Qe_xBf`Zu%QSIIwcNb&W
zN4juSYh__`l~Ho)h10bXZXy-hF;KP@br@wu$Pk8I$6;zo`b8<8ST-d9%o>|jiv|u}
zl-V5+-=fa>=3f7su$ufOyKT3ojFdJ8c*!fMvxgWJ{;-T|b`Q=cM;}jEuYJsF$|Aex
zAi~+oE}P;F9((T>g_vqj?c{iqxaqQUCU=l3Ph#eiKclq{3Oa67w3^6NNV+dne8c#Q
z<16XA5VDp#v5OkpZv|O=`en1D&w=<A#){yV`COT2hw}Iaf8tymvm0M4rgi1>v2w6&
zCAjY8(jRY>8LM2HYXx?aX{*NVB?wNdqKXPS<Bau|jbhEgvvbSs-Te9(X|AMpNr&;W
z>S5|1=I4=qNBouRvKpstr;O2WEe6Zf6WK>fai-)JH=yp-;sv?U?1Oi0fIgD!Xw`TP
zuCD8YDlW30VeHxWn-W=;6WB+Jp(D|31>fBs4b5^W>`9Jm;*kTih{LN85?P_TM>7XV
z>K`Vmzk8`n-(#hv_**kk)-Tvca&dQK3g(Jqg@S8N175oH8!0daH9tiotz7xS+>M=c
zXA1?c+XVTr4p9HNckUql6+p9lQ{Umn{wNp^wfr2%K9Yso)IJL1n^&niHxq`m3zkG@
z%n`C8h(Gv{g_s&@_-4jd@=V{0DYubPHboeWHB<IluIJmzJ7prh(GJh3>81W*aK6wM
zLk4rN_DJh%#Tq7}YWk>u=%1IuWoZZtU+=}KY)r3EB>KU)W+W1rPm-TfadC!JXjl;K
zVydJ_+8KI+-WQ{7x_XMoguWuJ#D}PV=$>n7MCjYX%0ydqFGjtFgd(*B-S~ttkVNVI
z{(Z_BsxO7rZo3$^M`ZptBb^JT7oQ@19sn)2_3*lC0wC@4xo3n6WY5D(>x(Tfq)_Cl
z>bRyPvhZ*&3^1rsWfd&(rJ7;t#QFanWgAzpg|WHJS86yt{fdN$5+bE_E~bo1dT3(a
z8@p|4&7=S(IAl`JGSMOn*MGb~PxJ@Am{V>^fDBUq&<t;G`2u$~l=6_JA|3=n6Q&!&
z9<X_j2Po&_1O>CBhY|{<Ry-EwrLF~W-BSz(8v$t&8nzy*b#>X$=^r6R7fMz7s^CIG
zqU9d)p*H$gKm0QGc}?*@9>3H|@b4}7^Kkt}CAytbqj8R0)5t>1c3StFSGP&_*L6ET
z^n|2M=Pc7FbtDD~zk??$hT>vWSjQg%b6yP9B%4h8l6zdZmMV>>85DC?mqdT44PnT4
z`vA+T=Ew2?8?nC(!&{t7%^qg+F&T$w_rRpb;J&AUC9o*U;LSHQoEJ17yp|BD?LHwL
z;?;UR!f>snxI_9^N;Q@Y8D8Sk>?8dtF;DIbn`a%Yn)^C7scODq@TdJ6`Anxf<M#Hu
z;Bmz_dSk35=1DoMdvMR6%SRizD&?*Tz%;eXoYo=rK888i2QyYh2*>hcIfkh(5vJ8I
zys3{-?!xV(f!H;9AL*QH^X7gl@DrMkb4F8P&mQDj+eo<a%|@KReL7J$LSIPNXxNk_
z@%qo4w!9+uMF_%^O=IydAH}^+dq-9cU)qpjKu@;(wLSmtQ4+Bf4cWQTK1>@(W#Lhp
z-5tx9@7eiIJL&zaAe`-$V!>7(5q8r(n7A40I0*B`R7fxfjbuftg+PjY+ca<K^c1Kt
zPRgGiEu|9Zw7SjL$bck*P@us3_f9)Mfg^L8<R%bl7kb|bblZR96fcl|oEfh^(Ou3j
zEk&KJaV!GImWv!nmp4xWi8)rQA{mp(oQ~c=<!~PgktKar7Xmyq-P^*T$Du_J99hop
z5T%Fvu%d%WS~8-z(!H&i3cbSkKhl)(p2$my$C{p1)4Cp*zHWUzJ{MMyTiwo9xK_G@
zlBVf$OP@TcwpiE=VCdn0m=;XV?hr%f_VPDBDEif!&k!}w=@27CmIh_17Hf>K4F!+#
zv<VFx4JYN(16(B89u*Y{+-jh(U(nO9XxhT#IkKwQWvce|^DlZ<#n%|?;zI@^?mA?y
zO0x(Qd?)%J0sbe!3jDvQ*Kj)8>kdRW`xAIqE|%e)R|<lcVZ$FNAl)k&eiPJg3^3Uw
zaZ;A)!A)pPQ<uEg_t}}xnbog%8$}+BUZg#AdKzeR%HIL~rh=cP0k$eeQ}Npuu$H|Z
zCzGuADa(3Rr5XZ`Kxlv2Y1NMH`&Yb|fkz^h>%*JsoY2vqAAZLo@3zuW)c<oYEoZ=;
zwApB@4sGrnc%LE#x|Q0VHruTq8s?<mBIjTD`#S>1t;ZHa1n+r<D+iu>8)PalJB<5s
zo;UwqFeX_{lB6VF9o!6U?NUE~)dVTU);p_f`?Yq-_+(DY2Rid1aD)pajj{#2Jl>*2
ztS<`bwEkJPMPu>upMhgNw&%p~kjFcdJc=Dxng91BHJ;Z_)o0q{p`0=i;W5-T(W$-j
H==pyDu?zUp

diff --git a/docs/articles/examples/vision/mnist_siamese_graph/unnamed-chunk-7-2.png b/docs/articles/examples/vision/mnist_siamese_graph/unnamed-chunk-7-2.png
index ae74b9af74248351673e248cda0a82299ce1a887..3f11e794022dd7756931883bcb5c8ac8315d45d0 100644
GIT binary patch
delta 9027
zcmZ{pcRZWjAFx}qs`e^s@0n5~_H30Bv-apvF>96}+^s!|5~C$HMHOvrB3go~)~ZsQ
zShdBB{ic2TywC5C_s!>%KXTtEXMFE-pL2b$^BOW~_^#|C;_XFip_-bSy1Kgh`uc{3
zhUVsG6bjYS(t<{#TU%Q{e*F09)2Ft!wszhN2vMRY5+AKo4#Y1aK49qPX_x#589Lfy
zY?AODX;Nx>V+u&9aKJS+6^vH6UUgd&(G{>Bk7?t)q(~jFK#h3fr3Zz#jZ6oAJsoD)
z{`n#@z2zoz!=tn3n@}hJ#|JyF4&kadnyZfwkvpL(F!=t<tjWNQ<HN?RzP^@AVOL+>
z?!WNj9uby`Dn?$0d#WFSr(;8Gaa;yOvCZ;1%ykFiS7nyfhUa3JD-BPf^9I2V+qap8
z_>w;pMOcsIpF_^HzqwsjeuH$Rz1e$E;$RG<h`UPkem?iAmV^ZRnOpFRV$YeEUD=QJ
z7Tlr>TatU{_QE&T3f3z|Hn>_#uX?Qi_#Q2()c{(4;xsj5btPd>Aj$Fl(oQgNz!?-c
zro*M#Q#pFWC5$LZRESkOc}I!QfBMAW1NdFQX7*l@6-&vrCn7)6V&d=v$=)8x9p`z@
zcW!ZJsxnd8JJt4iYKuLt$-ikma&vzUU1YJk0OYvkj~=Dye83;d<~HH>v|3TKJT-?F
zzib{d!ps!9x#N&eXbN;(KNIBx9u{bw%qQMoj5AE^VoT334uj#sW9!7x4hmOKu}tM5
zV-I%cYvpQ^a!+R-s<nscRy79XS1@N*fCBmBSu#*6k1uO7yhCI}*NJYG`Ye(gW8pB2
zgHC9sI4xUknC$9oYN2Ft6TLALkS4qJ#M!XOcdR{ReTQw}OP0HHMve*)yi7Kcv6~qv
zKh_({`W`)>EFPq)-lL|K?S2*ioG4LR2sD+uGYO*RFxF!Jm3C2Q=UXOtq4Z8!rM4Mn
zGZvK)J+6H37<+qZo4sknDnf2H0m((PdKH=*$-BO)_JQj_pe3DVBBUzP{8#Y3Ia0zy
zH7N8?$T2{=C!B26)-wWxcm(3kkT&TXoGlUJ{c*;JPIgG2p)Wtu*daIJecv2Dwr3-y
zi=+kv3mzMVuw+{Wu~jt`_C(3Kue`W>iBNr_{x!=kC!WE237;tCl#Wj}l382S-rG!W
z)rUc(oOdDiC^hKPE>ATinKV-gH2pT-G|{@T$)|mJA_<g7#WR9`bM)N-ygr~`VS3VC
zwb=U8E4tI-NA6jhOOlf-*awQBqY=Kr-@)Zx<wTu+;{Vh%-PGjd221&?L69>s#0mo7
zggSo=4Z)?ojAn*<L=G7M!<oE~Wf=%#dPabI$_PS?DS3;T`({ob>!*l9rcuU_<3ez>
zfTUSov5z4Ax0|uId=*Gv*h*$Yth?2e5M|d&d}kIH&Gh1=vJyJ3+lr^_H=z_raUgvw
zH5YiZa_Q7(mZ97m*RXe2SzqFhCH2>}4|>8^65txA3hg}hwK>mXTMvpe6QYlw!2pL|
z5{4Z8!9e>D$cDWACs8Q94lsvq$>EQ$wPH+$h})Ou#bVU9ca@Ul(4sVN<hM!p7Oup?
zegu&T<`gA}8&-L^xUh;?NvIf4Snp&1xg!=zZk03nA#i8WMxeY{D30{W(pRVM`7YOI
zVyXIiJdV->&iP0jO#Idtw+tXZ0YN$S_C*DAzJX;v1_wUyAjNUqa2o#h(;5$00zZzu
zqHdvFRfrj;y*Try1Ots)l0l_3P-jf}@Qv%Z>bd}~aoK578ANPXh4%-mV!Oh&>QQ7w
zfO>@8JmpGvY?tU@j$W*Pw{6O=aqvA)#9Qs+xPw_g5}LHzx4oH#GJwLBMyP1arhUsh
zm%H_p&M}x^pQzg?ni9T?aRGvFu__8${8Q22li$UGGEW~Q&iwd9{7uz&kcHOoyJdgD
zT|}j)2^RP4hIk7eNj=s6CI#je;<5j>P)WYhb7=GmRs-&!j%s+yZGR!NyU1i%t?At8
zx^C<CPrC;~N)O({u6Y6iGuiy8<4+zUW(9eEQ7n~EEYaBXrB}1r(H)`!s?5Hee5urx
z!Z9G~n0j(dJQ4O(9f!=|`Q%G4S+Uw-0K>M4sNT^W2eTeg>XEk{JE=xKe9ta8mX(#=
zT$M(bB!69x*<nBdEzE5X65?RPxQPkJ1l+>PEqB+}*0%F^Vt|glms-n$PeWSry1I(g
zd*+I=SW<+Xk=<^di{>t;NHDC`*vEz>ATfm>fxpC8;Vm1Ajc0fVGwh{}J*a@HF&OIX
zftABrJ=7lEmai%2&au0@c`T@%F9sDCgt-xVU}4gI<Fb2NO>|bTI$Y!;Zt5a#!FO=?
z2F{Op@26`gfQ?Uj$|(8h?K_zNOiK~s^K%pD0($KGJIkz7{!>M-D)Wd)x$RZBr$F3j
zTS#W8U=BnsCtBy{6L}mMEfUM|GedR8NKuHRA;PZFHqj*@@L`%_N!~lzu&T2X95*S>
zRqF1Ew}_Eks2BDW+|FxXnR79IdSqZg>b(|ODp}ta5JuS)-<~MO<Wy)T3S6=yt1*Kp
zM$lvlNBUA$vXd(+SZ!67%=aQy{FW>mdWB<vjiWA`F6KR&av0F-6_Zf~{fISQB0gqj
zJe7Zo{qugIb2Q?0mdqv7h>Ab$lww5s+~r<jMjT&%uX$P1FmYNg>RZAEjlH3)dZ<ww
z4EPOp>xKi0Uz(1h)B@JpmVvC@SJSsACRvd%wcd9z8Rbwg=KFY1aE^zkCzqI%)4e}a
z(!L<)S-eGa1&bj_;A_EJ!&Ui07t|@(s_jZ{&p`VNH2LxFv|vTUjV?8-hD3`sx#bzm
zu}_pX%3Xz+rOMyN(+eA-pv7xnmje*0X{^YA3<*qbYjxatmi~EkO?tgiD5KD~xF_vZ
zN5YXOqLVavg7cG7J7jyiLy5s0heq<yfou$EtIG4Zry@<mVL=6UmxnonoOIqD6xg6X
zbE^`E&y_M9|0Fx3TJRA;mES#~I>m-aI*3u{3{d7CJ)H{@k%|Fbjsa1o0-wV0(h57v
zI@s>@SH*V2MK#C9ywGYD%bFcxF%R;kpYn6wa6c(TKGy@=-BhfSreA!C8pU9)vkUnH
zp)~#~GBO;uJx8j??Q}o3WG1WapFXijoKq4%J-*7~ry$W%M|>Svjm5SecG7G;t9hqR
z5o?$y0<W0mPXGzWjCAS(MA$thMohC;`jae~meN~C;UCxU*T1Di7w_!PnW?^&Hmh5e
z)17uG^op2Bvet!MTX6jl*rF~1e>etUwFIUjZ}?$VRw%Ht%#0Wdb=0PZsXY!KPM3Bh
zBT_bl_|VON%%=^jmwffrv53-|TIdXX{iZeCfFW!0$%z!Nf)OoHmBD~<lPon+M{x-^
zi?w15?TX(G<}EPRm@M}pxzW#ep%7Y!TSqsbtk`9Rbb7=7e$y+G$C9Uwd8d&Kn2hAJ
zuU4H@O!sNec@B?Z7roNGwMbh?inN*G{e62MI8Nn7%>++hRX=AJZR56I@1&U|)cR<`
zh>i^B3iKHS904~<&OHW~&Kf+%>p-N`mytmVr=Ezj>6P^K^s$c`&rCrj^X8{Jh82g&
z+-kJ4s)8#`i&D!=Re>vk%hp8DGGvO)-mh*J(iTa5MwNq4@}vo#AqGRW`^Y?8y<yBN
z?TZi_MzBErN;o+aIoLzQ0p%+u12gh|`aONS!<XWk86f?!yI2{gG({`3U&-*~^htdl
z?x&#f$gsV}To#bxNKXoGj8S;hp{4405f?><VP~WDbXG^@6Mo#f1wUAeqOS9k;jGMU
zP27y@_dRat{7%U(kF!e>cMi&Ls11<7Mx{`BiF$pBacDU`xk4zbx*gzW0~(1_tlFGm
z1C9VCN2NR3Ho2k8ZSw`40nicN$HJo*N{C9H<0B`X1#J}B4EH$8=9+VYXK>V&-ngSp
z&iUrE;IBha_i$P6|6BI;!_q_vj)Y$wtak8`v}0?fhR#F7Yv`=aqhFnX0al$utD6kW
z1b&368vCIXPACcBJ4rQ0iYfIwA<7%>CsUk$$AOh@bNBtS)-U)H|LwJ&$HX_!v8F;Q
zP-7=(S{6z@&BQa#rpKN%EdO?{8QrvXCO3YMDu!K8?qk_Uie_H~j?Emw%`QavUIjq8
zrr9bQe81x3@EZvwcIfYVstTtVzmbx&8!6{K4Jwpm21iMI__JNH6~n=fnx0NhZinpW
z+PsvEus1!iMs|)uhi`_l&yfKk3G{QfzTGw}6)506^GvR+%Xe@<`-8{WmzC|+e)UR@
zoo&}7sYb*N4BM-O{F071PO*nBQUN&~{nk~JYN5${_-wjn&oe<Q_<5(i)1(%9Z0E2g
zm_}sFEQ8@R6i#So<>ORKz_8m)YM~Rq=n%azotmO_rNjq37o$h_XRYrg1)qA}?h_FC
zlX3s;)uzv(1RQm3IqCD2kQodJ<+JuK+_OC34l0Tg50zfL{k^{1LalWXFqKDFel)e~
z&B@xo>7P<XJLbO-%tioH*4WFn+GYS2w5-9nzW=Pf>e};A+~vbw5*-b-(EaeF(<=mI
zT%3xmvvw}ptm*jG?N-qht#k5ZzN8!btN#w!nxI2V!~D9p>F+o-!uQ^PhHNuw7MeSM
zOfCAS`+rCp_>0mvr@fhX2dkl=$)|+C+}OM|J_;%fOmJQ>7@li(xTaVTCBA5&(md}w
zU!FtLzuhNH2+Iz$+k;jlJkp-k%Z&Inzbv0|emWtuP)V-Gd1aBiJ6v0~4b>V^iSK_s
z@;LxWnov`9{wFBbs$K^z%xufYbN4xbIcAQZu=_oOlZ6>CI*`fD(9m@F4;j~yU!#|m
ze=NYOR`)(LZ!=j-=@A!;%kmIf`2Wx}K8!j`(UGZ*kXZ0BkPpANDlNgqDK(ApLKRDB
zUOkBur#5U@d)@k?ih9atG$P>7?0K<QE}Ii78<0b$XXUC{peZ4b>^;R3jd>+hy-_=j
z{LcApNU<aL#PuwmD!>_j_jDPA+;@H*#TS5(BB1mwlao*Tu8a4&J;}k36a+fdeBWof
zNHpes6zN$8onaa>Gcygz{w!4j0Ctn&8Y6fQp~`s`pukcskl><xf@0K!NHcHC$ag(x
zv@O=?S$M0h+4ZWY*d+ah-&pSVn<tc}!-j0rlEiFpVS&AR{C>!V_}0y~A&6h=73X)@
z1qHF>e(!Y9*;IzB9B2B4Zwr~aCL%X6`hu>-jS6fA{L;OiUs%kdZe%6g$_`((GT!qU
zqhNB&1irmCX~+RjB;|+U`3zfkoaf5V@-*TzX0L?9MuV6y5z`wE-bybSsjI1Z?#Bvp
z#z`aZ(}HaJi0;3@2E14pmB^C!ME`h%vedVNOJ>|Q!W5-xw*|#I4szA_L2T)QDLKtE
zGmLc#b)kj!z1V&t-S4wh6{uaysAB~ax@oD0fYapjipcZkQ`gJZs4LK`n4V#Tz;zte
z65Wc&HaXS*Ui*v9Re3ltJ1F^f@dqoB&t`FY(8-E#Bqdi5!@hWRKIk1WEO{s;GOU@>
zy|~y=Z483)z@oZj(;V-VltnlXEi4&ozz<d;4a%EZG}^iCqiz6Ku#do$6h^2U_nAQL
ztYn1oMMl`dtN8{>BaYmM8+Fz9FRk|tSsB@CFUgVEAHa33w#+YO`+V>9ysM`?=|itS
zjD05|I6}>>%0)|qJtfAji+tTQQw-VF_`0xV>yqnJ!6C4uSNJ~9YRL=S@J_Wz{AEV5
z9(K7eN`@zURt2aD$D^OZXg67CH;W#=!M%K?FLy_5UYTL-()v6SWzuWMl0a$zuIKXN
z?}<y28mxbL`<~Cn6H&e#W|7`?<)#SwAWlV6+|AnU`J#(B?kR1Ykl@JhFtu+w|CdF7
zxyG65tEwb5&rdFjY=G~&)N@gSD?${`u5u_`T5>QC!aoP{JLbYloabsHH@@m}l1r@J
z?Rsl=^4accM?AP)*dF6@r=dfBr#11H27b|Lq}=9L;)UrP-?Ot3bE8j@(SD?RDqu~E
zK|i+5=o$HlDn_j0T}>2LPDLz;hv<;|j5h{)5r=&W;}NFYL}jXG6jme3VL_!DTlTme
zQQj$^wQInYzAaUuN4^TExDT~#tsL%!de~7bH^Vl~4~_?-25r=G-g$rPfvcm$gz5T5
z;^P8Zd`SAT+B_AZb_S#!sS{q6wBq<UU}^CVQ^)rN%E;9--$qSnkYvO-6A8|qR-F5+
zLCLuk)9e$akJ3@$fM1OJho;+^QUMeSq3#+0vGhGj7{nZVbt0=3CTaG-O)*PfE%p7n
zAy(KBaC}r3{3O<K`Nevv^hSEfe>Bb2+aGf<E6%2R4_CVLEQB)djNKfkip;0mY`2nu
z_hSbd)9y@abRS+7l2*}WNO%(uf+il!*9hvv4e1QdLM1fr194xXj32xwp|DHBNhzJw
zNG}nx6tu9tabNVwu7btd;A4d94*S{0@62WXO7icU5n9SE74=J_`}_Ov_bo1Gzyf-`
zVgr`e?#y6A9<MH7s>5&Pj0`u&g68-WXrPiK0qi19J1zXFlPB@b*y`~zGwJRe!9W&Q
zU1UhU8-d&#@wUBa>Lt(Dq7MUlm+Gc|59vw*Y;?Scm}S$~#?zS)7D76^<}vsz{-Cj9
zgh!PVDzJwtq6nbP=9*2syG&sCtVh1etfI3bst8^FEd^NOb>r8t#_^)%-q&r}sP~rM
zpGNJfO#=4P{oks}5CRH{x`|Q~qK+A{1dM2_?35*)W><o8M;W5+gBHZa0_&NYE2rp!
z{EUOBAAQQYewP;HEuXscjZv}MFb9D;<HGciV3vrnVr{IUxRcuKZ}gwB?4q9oDUQdC
z6mWj^-%Aj9m%9uggUD6gB|NsTmhoCT362{LdNifkL7&h$M{kkP-!!gvse{NLc-&=v
zWYGdDiLtq^_G=O2o8Km<G9Cxg#^a)X&pQwarU!EyhaC^9?#QJp*s`kg)8S?_D4FxV
z65FTcGm>Stxc?9-y$G|zs(^82M8!$=;%CZi;*$$d!&P#=2ng!s_hj0bgBcQBCZ+YI
z7^uh`G4&1Bpb|RO7lnQtU;u3yZSSX5GVvzFi(6&8=ww9Z*Xw8Vk4+Si=#dj8#JA2l
z{IJNtAt@Jx7Q{g7aE;cp6fYf~Bs<%hA_$(aAdI(NQvnZdxVQZSux18Mf+^stgoOYN
z;<gv$Mk|&f@hRa+DR-BNsrSe+8Jwn=^0VxkFX3cgtzP4|epCZrTa_$Sek&fU&gtxO
zNnx**1@XDf-n%S>4a(<eS}<fIX}r$k*yNA=`F<|9d|vU`#ifku_Xg&p_xOj}`A{Yo
z#Wu0=+{1!OH%>(8S!PL2Mx}sP`WKPLyGc24<Nnn)?EzmdxM@FrctqRIt&f5YIF5L!
z%Q#bW^BG|3Q?B|iTPxLT{)^no86PzSh>^Bm7+UA3G#+j$8jm;xP_iX?&_D55XOcl5
z3h4?OKSjj9G&NVA*JA#^eEk=;9WeoYkAK~}qcb;qbhwJIyrfc}<fh#6&YNL+?{nB|
zmN=W>f1nxoL(M<N`}%$t<^y!<_k1c!caEqlaV4!<b=BH6d&?P;`jW2j0RR8t@}CJ#
zABqccW}C^1=+#DYP6C#*RGn{VpjTVF^g}x3EdQn9FEO5qQ7b<O2V(4z104Z&ja9Yb
zZpd@YZ1Z7d(&Lj6XMXA-sZ{r;rq4`ooxHJYxj`t3kM^{s^|Ncbw}zSP?c7|~sLx$$
zlH<HpKT3UF{$G3_>YO}zU7<9#mEv}?ZRfkVWk>g+BEJUb9MVqcQaR<OXEoLE(}sLU
zV&rAyJ0&P$fX$zP*jxY?8o9ED+brTm2hX`=Y}TY0Le!)t_sY(n&!0;_?hDgEoIO-Q
ztDVLjAsh#e*UN9M1p;G_9@3lyYzw*Co6Z<7I6f@*B3NK<<b3&9OxjLm72P!``stJ8
zY?Aaqx?49-rg`l=v|7>db*~!v!9Y}Kf3{uC>4FPa$R(!mN}wg^sQ=M#!`b_lT{-?)
zoqaQK!n=z5tETaXQ2$z$^HcWemrwdWxb5p_=6x*u(%or3%b(<^0q(XS-`{AdR(+1F
zdD_awHL82>70Z1yOj6==w|Mb0a#B0#?f%^|=Pl{-(|(YrS4>h(W&BzffdYqn&Xq@q
z1VWYo`2r?+>!g1~H5ScY$$FNN`&?mgcl}$d<}_R3|F*XCZM+f=dX6l5sv^H0zsCRW
zYy9;$YPgJD?#n^PA7?G7Z?(cA;m|Oxlditdt@pXJ=%SH{=6-X^)ZOg5t15vNd-$!G
z*2EJTtFNe|h33a9in}Sm#pa4>4lSDwEwPRSL_$TIE@yX`*J74VMgP;XTm+-n;mX6m
zk-U3ym$~ylB9Cj`T^qe4KSOBbYx98rez}W1pLHN&55K_@omk2fe+<pfwUzf=85PLY
zVka9r_cM?6p8NZ(@{mP`1k0_}tqWX+#n)c+QF!)4Fw}6~Lm9t#ahyd*F=M*_Ngr=b
z?BnMO9XewpfTP;RogfLWhGToeU`}!G620T8-BIrzEpH6m`HUSsU%l#}2A$t{l{?9H
zIP2yqLa_8H@0k?3`K)LmNAzO1T=L#t5E7XQwyV;qqqX$F20uO{;yjs8<pC0sdl{aD
zuQdNsZja}Zd-7TIckZPlNd5MquZiItQ-ELX&0aqUm-H&56@2UCj1>7ls1EP2wy3fY
z&$%qbYLkpmP9dEw0!a~A{mW&qMM~$>H$v~mFvrCg6m93qS7^L=Td;<qvgaL9N;2p-
zXBULR_yRirSsD-r)I7ld3)V$W+mvQf7j{{I2et+V--L+CNg*xB6gAud$scUjK;<KU
zdv97#VWAlLR0a(Dmw(wk45p=^g2|<1Dh)(hrNpx!y7kG_n*8qwW>shxb~`Fcd|T!{
zixgQEO7NrsG}OKV$Y(MCuseXKJi5PlMf;=$^ut|wjUuzB<3YEhWETyhyMcSrr`u7=
zR+1%YjwP8$BF)s)r%_)Jp)W{3;H+%#(yLFF^nF4<WKbugvW#(6ogtn=QRH9iy+X=H
zO*nqYG<gk^?Y+=}s7YQ}u9c9G;3G}D6b=Ibh=GVHiVfQTAH$~wODXH|exeCe&@Lx_
zR^+ud9!qix=>1lUa;fil&$<^(%EBU1abLW#xd~49BPQp((GTOqO5*T|tT$Gj+$BzO
z&wnufZq&W>UPq4)nIw8vS+m8!K5v$Gv#&eqvN=i|I)jCJY^v4)ZfNpS*@AmM=$Q9*
z`y!WLuEoB$93|MjilXmBS(ItuQFq0Jk@N@CvD&<81<jUeGezxV5-A$GB-sS8(?RWN
zuZmf=GhqU%?0?q087G$EC;S%_rG>g8#36SN*Bfyc1zPGLh|bxf_9z>;AfGsYr{vn2
z*ZX*(D&s(0B(QQRzDp?WLm%?JrE;a0_FFRu9G+-STlE^o<U1+hq+23#{~5KX>@ye}
zf$25i#(H$QJyyh9wV$1&KCE7GpfwCwy1cM3l{Xc}Y{R&=`ZJv7HnaZ});mMRbR9X|
zNy3yszI(0ag+3YAK}{9c%S@$w;(x3<!3h(meg*iI$#Z;^myDZywU@Z%)F=)v%QDM!
zy>?Nwlo7pa7W8}|pirH?BvNQhL6YZpVzJpqfxUSVXAaS?tf=5HKT)()uK#{($%KZ#
z&f(@XkCFx-T#o?vzDV$^w#-{4kIN9UyBDk<VRn!We#Z4ushW*qM+{u5IAtLSo^TO3
z)kD>OVo!o@>k2M=dgtoJhW@zYAZ0=%#2W1(Q9XGniCDiQe@C+b^y>M&#QwHevHRcD
zP0T_+t=7%dsw>BAQpT{5V<<1{B@KDuCLcIu4ddPXWg(%7T_e)WITglm)H}td4ef@s
ze%6gw*5Gbt#Kf5JO}kGI_qgqagks16+@q2<thcly7k?&y^2G6dEbiit=KTr-62(Ic
zZ8)0+QI&b*sZN;|cvL)V+}lJX>=!)R8oVQmYtYq`+l{8cAfCclAT+qHmdk9sxEv%8
z{bn1hB<@&IQBlwz#yG?bH-cbeEN3&>XFKMKV-A+YQL3t4dR32;%XnE#%z<NFs01?7
zAjVNKp{luiVa-N4WwxpdH}6MxKCzcnpD3}yWlosMgqiz^Q0;$c|M!wK?$^YZ;v7;5
z>KwHY5EVuQ4Y5mtIY8<bJci&GE|8#fwKdG@1}4Exj|yyuPR9KzWvsz=8?V};sRUkb
zS*HMc@3l(6WoEsHXL`*BM_)aaH#WXn@T=oOk@x8~zp!fJcI&2cpyT7?T|pN7mVX8a
zTD+1$VDcbuXP07soy%RTB%U>?gq#;^*Vu+@f}!TERM#g`1~9Qn5pmR!M|Ny+0Sb0+
zmRluEwmk+TNkj;_?iGTCZ|Aq)uLJCn+#Nk$Q5q;A=u1q`)NnjvW>Iyd|5T*6(YJU2
zIQHh*-K{qiLP_qpNh_#Yi9p~_`x`0d12;MYA67&MWp}N_(-*;*Aer|t$G%~(Z=_Gi
zFyEbNlk7y15AJ0&4_+#HuNPM$_NH$wR-Oc3pDG@bm@(&=u~XeWZi-qx0dme=;(|6g
z!(qniD18kLlw@zHfW2$4U(`UOJDZuga`rWqq^a0mg<_Kl3eHKS`<Q>I<qPhPTtF+g
zZOTY8<K9XOSOfKv26N2B=ovfcBYf>5bW@xbw`=icsmeHLTpb5j52c)Q3l#0KIyoLk
zyu)8oL(_gCOYi<!)kJ~=4hb1I;c!alaXr^TWZZ9M5Xm=l#ij(t1qp`lYyV7UgmZ{M
zq;*9_e4DD3At$;UyQ;#utLKzh28vIHwvrj&%wgq!GMROPIv`Bx5nCguI*J)rId^=Y
zT#$2<D)4RTiNOO+hO}7>SHY^y9p!|Ff@PGs>V%^;<rsj46muNzRE=xEC(#E+d9d9M
zPj@g1-r_74l0|!w2ejonu6|H3qJnT-#1UTmM`2<3FFq@`BOyKJ<YsD68BtjcvR<oD
zb(93RJ#8Ah=o{iUxz(B2b_+u)c{4Ne-(>FEkYAwq`@Gh)or>wl#ot@mS-o!V@m!t9
zI2vFC|K|MNR)Zs;+{W74_3vJ1v)89qyQmX(qCSLwS-P6pRg~MhShiH`Mu9B;&H9Bx
z4vf1^*Nt~4$fp@)(0j7RBV)c{oztp3P6XxcVLX#M@rH)e?AlCCF&x)h(LbZnK~U*m
zSo#FKjJhufWo%<MVOsq^n*qqs-pFKF=ff;*JDXsFeLvGpA*2KIyo;$!G>0!p|5up*
z-nChAlhy2s1Tuy-gq|s)#WefY=hzHE?h}{*l@EE#k=Ws>+P^|sgfDb&f9DTHf1V!j
zXt5PrL7~IlPL?-ctc%lmgd7!9BH{tI>1@0BzicdxW$`b3<-Y8!{3Ez1aWiP+Sr<Ws
zcdWJszt}w5lrqhI{~t$7W3v+T6$wJVfCcJ@v$NQ6Tc2TDQuD??Mz0pKpUcJ)Ke-~n
zK4UhMO4sUYvH4rwpUtwfkl1BNURA<1RO5n!``4EmH7XOL2R!PCzy7PiMQXj}TU}NY
z<xul%t?&_~0^B0P-A&j-*?Rh2;^xsx+wu5+=3Z#6mlbi5<^l5|FmcJ4E8fOZ_yXah
NdlRBvsd+E#{{R+X2|)k=

delta 8830
zcmai(cT`hb*RMrH6a=IndZZTtrS~c#Efi6tMg@!t(o3jY0U>m0A`rTWihzLhP=tgc
zN|#;}AOxgDS_th1&w0=Hz4!icGe*W(d#$<WSaa^R)_&%1Pn*62fBry0nUs{2@`1ue
zu%@P_wzjseuCB4M5s5@LH8nLiH@CF3eEs^ht*xz{{|pL+N)bQXaBj46<9y2USpgvR
zPE)5y)n?@LG4%Gs*G`6?U<B%OC1I7CS`m77a-ZcSK1re?ib`JL(@gw^9<C%O+jLxJ
z^e((*-WYQ;R;g^veu+Pvt}6V}=k|Tsb%giQuPrPB7toub+^5kO*qqB=4jrq4E+KIT
zp?-Z^-U=^3H-#DH&j@Bdhv`yc=;%@+D20Le7~-)>XT%z%QO9?Z{vx5vuCMr5lF(2k
z9UvI{)je19iK31@SO3ZMrZio-_~v`BWNjAZ+1wzvEpr$1kc%Xhz{08qCUeFU+zrSc
zvAo<yld&mvS$#@viKH<x<2yg&@_Kw=1ai&)@aV#xP*Z|5w`y*FMe{<^)zIDX>}jA6
zsf5Gs<sE2N1&v&y;=Kcwf%D`1h1VJ|StDV{b3r^!1J?G@@8P-0Z$`539b3iJJp66z
z4K1wLvzlP9&8L?3&J$`-5ZuKyzqJ}IKK5{-`Z18MmW*CY4}FB;n<b%(_VOf8UN-v{
zolO)M;kc1hg?ICTgja*4zdGo<`v7;X_&;hApbVfGzv<;@T)dtpP(P>vRSw+Np`H*i
z4JJnCl3kCMD(086%IQOX-(uU6f)U=8FlN4R*~bv2$W!iJ0U09pz@(vt*iyY6p+^zh
z%;hEIoUj6+&U3N->o+zKK~WRzHTgf&Rr_ydD_vg^n@Zo#^p~CJ4Fx#<<YN6zVrVd9
z>Ei9eT(eg5r3Ey#cXQBBq5<6qzZ0S1qXZ)>p^@1gGhc(K4hbmm|5+3g`#S#z%<-2#
zv&9gi%B%p2?L1Av=*NLZw~{>TkiBOrCcNF4ZotHT$oOf!Jb|ygIlr~=TX!tOQn24*
znV7^UJvgTN4q)Y`#7dyF3T9;R-|p~y7Cj?i;+T1`{9UHsZ+%;XdLm*RI|3q4S4GL&
zhdJxzt=V;yCpigxGs?T)Lf*di7h;MwVu4vI2N1p@n!G!IoV40Rh&tH%E=CU3^SS4n
z3==<+WG$4k2aHQ^9}bk?J=q%ei7)@CDe!WL<HxWE^+Ip|>2geYK`9`6D`s|97BLRl
znxp_*gAE9#2#((2(0Lw@Z(iqPHDyZk^XG?>!p{~vfV*>vdVg9e`AjT87))20?s9Iq
zU<&@dV3s5cria5pOAXE!lSoXdI9r+~o3-{?DC256tJb$gohw!bJLBkwFvR)`Rh8cX
zVjUJ@*&QBuA1NGRi3O$UkL|D?-R=rsi4WEeSH@m(sLn}>ZaLci<=_*g_I{>|UG9x6
zFXqlFM+R}y;qjQy{c~`DVd1;Xd_8wIPN*lEcEt|bRT9yGH_UG>f0SeY0hZ}{7X;Bh
z{MtDikhjJfVAhl*{TKu_MwN`n$`AynodR4#pirDs_fvc{W~7qu?-3h<YS(X6m2Hh{
zy-lS}V8J~z_g;%=<IqoRbvpG37M+SW#x{%Jod%y+I1~eyo+0cuAH8&~zVNJu$q>=)
zF?;uN?$eCX;mV&Rm9mx<N+m~uKXJILzwE!?_;(o3WflGoV}J61Va@Irh0WDRGHITM
zLPhohtK+WVHdRoY)NiVtMU><C7u;5Xy-cE0lmh+Oq@aX(DTY$X{;RHYl?7rqwFj_N
z-!rBn9J!>|l;ii##3#WZy?;eB%=rlE5Y|z5kkZgN6tzbKlNF(6TzZYhfhV{hAuAF>
zp{*ECRb86t6OzJ|$bfi&-}P33F}S=zS&fX+&#DA^V@bl(KR~=F1}ao6?F9@~9{QmM
zWM6-e?`q06pwVdb%Sm4)yL+w_IEPG$@Yk%9e0hz=5DJ?Q6qd@{ihsp16LRnBRRaFY
z!C(149(vkHb<hmQWoa5X!&$VqL(;iEgSsy{?27Zzw4I@^BWgTlw%JqZBldorrRl`T
z8GrHjsq=@lGbSotd&<VQ!tG8AAMG)SI!~COp}|Zi)tQhYJ32ZpGC5)}0O@<a4|8k1
zLqFEhB5%iY@CR)-v)sC6wZBKnmryV_a#plnGE7y(z)LweB54Gs6pC4kzV~-pM!X}U
ziwjBIykBSia%A3eO|*k@?C<aqg>U3bKvg7RF7xnCi4O}LR5gYw?IuuA^XuoZljlT|
z@{^lyU@EaBS9wPQuqyx>DT=!i3#FcMQHlO8`ck;<a-zLq-)6#=Fpm!vtLPcR3I#!%
zg0SN?7<Y}}Q%O))Yb$H)cG@&C_|@9HO%XMrjBl0y^)0Q(xUrLqb`=rUHgchvl-m)-
zf+|5<5~7N<#)$*b44AlBSbgBltD&aiX_*<x6h&WUk#k{yThN%Nkc+}MTuD^FvzE)^
zTitV?qaSA5ZcZuncXx;Rs|)!rA@Wr_+k9_o9}cCfP09Zp(JaIK*<Q^#jIsJn{_EBc
z4`lxBGLO|4u_{kJTl977JxZ+4LM**~)QOGe%bPECQ%5D@-q6d?cv&@#eeLQ4pTTax
zARJIxX&^;v`v3aM5uCM6fEY|ob7T`iy>;;P5~vz#XflvG=NBHIA%6Xl6DCC2QP80v
zCn?sdv6S6ZTK;>%ue!e!3!fm5l&sn>L8OM-U%j9{*q#+Gt&{E4w605t^2_q^!B8qY
z@;lW0eVd{R;RWJ4qqJ>b7rF$P8Zi=C$#mP##}sL~Ed1Jhvx~efHB>){{bn3gcs%*m
zQ(LfBf3x><7Bcj7Lj<4h{+<zL)T+2Bd~a?Tl#}x~J<K;UXPog@(=wa147{&(u5JaT
zQS$=OUG(DJcskya4+(BvblIKbm?jNVbN3lVYa=B=%~EFPX)&w7!ly^)qD5+<BYxH?
zgX{Y72%g^i1M3W*e7F&gv)`RI2hb0bLg5u_V!Nt3KC#7`7YB16<Ig=5NfRtTf1Utx
z;7NUUYs7~$iPie|m{&X^gFe@(idb%?Bz$%c9h*?6$!=E*J8ZO>*D<P5!I<V%9GA}v
znt(*%qa6lkF*~dP3o6KC#5YT>>HSSocx(29+E292MO(YrVEIonmNhtq?tVz2$IGci
zPGOM*FZpq#n>o$wo84UEz39EUxNKN%U>Gc(ii&Ur&VYROO<DuE#{bDQC7DJ;EUTwN
zQzycI)vNxYc|+cg2PTp7_FMF*T*bJ3C!atvB^4o1SsMu~g^5$4s9#hz{&qPWzC$BH
zXJe|}{_WP%IfAc~pm!KiMJ8E!^|EPS#V{-^GUwsum_J816^|GK)SM9K)T`q59v)6i
z?3(S2eO{HoCB7^KlCPW#56wwmTW&GGg7}XKteQ*H2P)`2;;F00jVd`(SDfbPMtrR`
z0CEqfYo1Lub1RE_tSnw?HX-G@sH&=R2~bmkn-IyiJAG<3>rr8cTEoOw?^he5-sZNf
zS3uvoTtrlR8M^%})X!z#R#CsbJ=F+dmun`-n~!#+gmpkP`5!S+De;)krA~cJ<hA6U
z^La~NKBfK3+Gq-}{tA1ake6Ba8Ig1nfZBkW^efeA5AUQOg>~p=>Df1)r;0B>aY!~`
znOoprN@Zo(hf|@rI4`?D)<Wh}5F*c$FE|KCwO`(i4gQ4X1rW|ROX!$x%yTA6KAZgR
zuU_}|NI7VAE)Yd0#5HF*m&(}UckqF&#z%f;kKJbtG#V2qZQvQNxliYX5Gnvl6I(n;
z=WVf#wQ~G!qT^?Z&lblKi;f=i+BR@%{-rjC@>dVQwTqQ`?ssgg;~JYo7JrFwhgbiP
z?XK?oXi59{<^9(h&8sWw_GD9n0JYmvqsC|aMwOD;s97Zim<r56aPgzB`CL>&sGU`k
zTyF5{9?poGj@O@^j4x9~`#w{BUgpbv-%W4oZ}SiFjp#L7e;=&SC^0RI?dn`3|H3`p
zC)l7LrCh|a@~cgSlh^M_U?P+0Jj6ZP))6UM42CHl44nV`IRI9|QC31gaB*y~7b&Se
z$MndyfRye@{W>mVC-EEhz`O66h9SFmZ$`t$QC6=D-nqQ+%9R3M6G0{;xd#*2m*1~h
zz7i_nKkkceUMD~x)uH@>Vgo^S0pE8*z8s;I`C6#|Uo!T!uiMLXPV3w-p6~_Girtul
z^d<#hpjK*j>t*BE>f>AR_l+%^gGTO8+ETS2{UzQ-MsH1_>!qp{hvh8K&<nLoy*C{<
z%D=Si#2VTEH1>IR>lZr2puEiVXnur-6W>@zrw+4TtT<(0O6x=7xA3*uQ1991*prl|
zgvbausjBaA()pzD;_dSp4}kX|qZ>OS$G<3U43u%H6Oa|lIO&v)?;dxyQbaTkQKp`d
z!PSwh{!_=Qr^MX$4cnA*EVuu#66St54qelE|6o{ZSA~;_e`QNz2H{sIY|P;lnICqQ
z@eToJb-ww+8hE?6esq_|Uta&ev6w^BRks9?E?4`Tf}JWncB5HyfdhM=(L0&ws0fwK
zD>ggUCU0cAF6mNP^NVBrMP^X#&y-ybe&5*mXmr3Pshb56n)S4j9jGEW29YP~@^jg~
z_D=t;Sw53nCbl*1LwKmwdS+$VRW{=p`3-M>6Ve8LNIr&vvHHkJwRoz4oB<%?{(Ddl
zT`_;?T#PhkK4C_wOmZOT?Gs~fzHDpaQ}7#gH@Y@*a>Hfy6)UVC=dDfQL#Cs#e<=9*
zRr>ErxZj$)KmL+myc3Vny)k{Vf%^{AXMT4{=Bt_ZYc4P@6!}uY99t5Z%oy>lfWx|&
zSO_US#(ltZLgZ89K$dJYKe{~re|YTk)6Q%zb@37#;AEq#+g|+7nWIUF<v%@rQnH!;
z`h&;X$b5tb#?<h|qbeRAyiYy1!s8~y13^E;qL&7Q(L(f`cOV>C?})D|!zWS1-$klH
z|BB{;9%0-y)f?HTuTXN9!UB4)F{$>F(6hTb?e8d=@)o_Q4K&K(_pS;{;FoU+SB)1O
zWc{W}o4=ZtY_K;Mc-OKC0oFlhz6K2U)yhQZ_Z;7Yj_0r&YW=&0*B%dJoRzR3^6QlC
z!3~T-L-+47Qg3f>H!oIC(jiio9^<_Fgz=_WD7AGC%&+IQq*YK{I#?oBI=#v|=1qxU
zRw?p^6#FF`Rgp4y6%~WTkDm7_-H1kW>0<RST7y0?-4bzi`-RX1OH{GV5+GmgTHU-$
z`pHra<LFg_r<aNtIbm!~ugHz@UgG@UeK_|G%`e`bX4<kG3JwZced&f4ulCi_dK;`1
zn5P~t(2BQbfEyt#X19uq?v*Zh@$hi?7dn6?V3D2jX-*O>?<1TBu{cxh;FF1>H5by6
zHaS3YEC4yw(i@^P*>y4L^Ks!Y4zR>^_Stzu8L|9kOD}h(SL!-ASzk65_JJn47o$yD
z7HmjXo#Lsm1KS7D#kSzEZX50z^ujm=%8C-B9qgfnlpQ$)7Y%iuL3wfXrS*!{Ru(Qe
zJdtDjvcOAE&93k~m1?W<QVNVzb;aZs6vnp(fI>yd6irQqYQ(>Js`jYjp7;Pwec!o8
z9o2e~a%9k`o}+fv`WiKNcO}L#Zd>wE)f+@mwg+m6-?=VZKhaEQR<{^5b5{Za=Df^+
zF<}7~{*do_vf%hsuP}Qso51tuuah)EXrAJaYrBpVP-jSj=1qP2HmdYAUJb{`Kt|l0
z*n|>KzN&X}r*>)6ijPn}Uwv#Lj1kI>j#c7`t|ihRk20!Xm>rl8@THw*oSW=fpFNqN
z5wF{PRhIXHT7TKlMs)7z@?qp=nvPxZHI#(gP*s5~V;406s-;E?XQxKdYDdHNer_+y
zWM`|Phji(}_F{6`V~8=1d-q{LB!iNetrYf7YUxkn+I_Be!JO>sU7;@){Eb#rOn#rE
z$r$m+4~`O}PWIF6V2Q+~3BYDpLWk5>pNfmVTZr6>xc}Aauu%~bX|6V1XI8h~VppRw
z<b7qT3<Tc`zfVDkYSMNTnn{s;gb7)}V`9w?b1cQ>Wfw0RI?MnjplVf-Ps+zPpOMR^
zq4jaZdK8J<9_9T=MY6%ESc*V_sF}ac2k={~IH`V2XmrkPUYIZYP;>0b%Gb)l^=DXs
zEirIostUF1Y=@@y=(rhtruZ50J-RTZ#40d6dF#5HQ(&@ih9x_sk9(uXI^!BaT~F@k
zLG`(p6=`%p;oBi=rUqWDqaX2!Z!e)4p=*vT$YI#WdzX(@PYZ_}S{+VjUuW9r;Se|-
zjE;f@+HSO_sH>~r&Ms$>Cd?oROdCIxQ@haAQzJdmQ0n;L^3pvfNT)&OnO?7eWd)1x
zY%l}S#(j2wG_-bW-!4Jj<hRstrc#v7r~=*_AS_mT^OVgP-|1YpYkk&K!T2Yv%31?`
zDUG?BnK3xSTwFYCeDbXTJD-PbxlyoiyWvWZ0rNUYMYD~kWH8{s^8;!LCSy5b6<qP=
z2gZAo`#}wu;8kldlx^}-HDKsT(ARPMDhvOe5x-9w3gjgBq4a{UiO|DG@0ndL2kh0P
z-ue25wEl|Lm2UD%PeV@jmzDUx<h4)D(Y4+b{(cRfUdQ$A=+UR%Unw}j@)K%;3<crm
zbE26NRywDprb_p$b+9g#vh=(iR~xgbzbd6lbgqIcrs#u*I-cA8#LnpJ0|WBTh_b**
zT3+pT5*G0Md7-p%E8I-Hb$xB@GXVUg_KK;a+6s3AtFeo_4po)HB-#=c&yiOhlJ&AQ
zvj95QeN}Q?Q{cvYl`B3$OU7E%N#-x6+^B^z#f!LIM}*1}%pMb>MEG#FDAC~yz)Saf
zolsT06VCWh#Nj*M@Dl34BMkO~s`@O(dWHDB{bAC~lL&?dQG4nH>5+VPV7gz-e@sK^
z6>osWu?wfqkd~L?2j1RRm*Z)%G#QTI)zsxfoiifv^o#IXY801vbMKs^39<a`Mf$y$
zA~|mNM5+0x*^Qe?rahQMp!|h2=<kxe=NyW`w2c|!y=h+gfb?AVh=Nev$wK%Dh)4$a
z!<AQA_*w!-_Wf~@00WECj@Jl*++_dn*7;fCkl+53HO~&b7V=s@q{|W&@IcwxuV1cl
z+^3dgIM&=6Yv7~#O=*tREht;%O%6S7=*&qf6Y?1OCRTqZkvGJle|6h<z<VRqzMt4J
zdc&T}l!g;Hg1;e?ug{Gax4lX<fIu_P=R-*(bYrbfAvG(t63+*ST6&kZ5GBHHg}DVQ
zuZ^8MZ$1jyrR8LJ#^$@m$s*T$9AqGD{yZi2&F#Bt3j%Ea#m(dkC)D2TVCF+3E9{FU
zG?^tYs5eEqsMUOVCOf+`m%NMnRT{p^sF>>d{Pv66H-W>6wPKr7&Yp$zSBKk4tTYLt
z<&PH>yF6dRYc!7A@LaC(8#9COx_l=}1Pww4lfGLR;EI2Gcz^Rg^X$$-HbZe;$0Aot
zW9|lNH?1ny<I+gXzqXSM`NCu_FTmN7h-D>vK1FpM=tS<ij15xjyOutTmc6cZ_jx5&
zC1+Imxbj%r>#~m7Q0$ky)o((#p}c;Iq3((e>nx+yYiAlpJfGLIdg}hME>c)$t973q
zh)=h}TDjSLe!da&%8{-penY<2ZLYDoay!AT^L}Z{Dt#{C{@4<==dQ9u$d>xujn`ia
z)Yx&4UV(J*+>?1UqA8xZ(WhR!FKR6(EUqkvLwq>sN~cl#h;Vgf1(Rh&Hj({VvA$9G
zo2m&5bMiZxl>mUZeG^YeA9t7sjG|}Se>j$H6s}44?i1C!=o^m1>aiR8b<%j>;Q6ur
zOJ7#ont;)(C2FD2!7_<v1GWQ)Ks;$_1F}1DsJCErswOMc(1-*G(#ED7O4lHqv{Q*A
zxAhKFQD4?kzgsN&SyN^+Rk`AuS_Y3tq@{eLP5+yhMgK2e27OsWo*WU<s3%+Ok1E$;
zo|P4M0Hton9oi0qV|H6`CCD&%^#h3+*gaE_HyWP<o4L2WmEP_Ds161EMn&Zk#nz1i
zrvsTMY$$|6a98yc9qt7n{ReY}A;xSMLh#z#GsLRCi97#s{Xd?2pBm{!yQ|+p=R&^L
z{Y%ZYL~{`^5En>8*43A$<7%oPCbX0ND?Jj48xHcJ7L?X`>3x9=l8BIpXdFI$r!zJs
zzO>DN7tgMXSO6*(Kh;?NosM->7Hm@z`V5k@Z14MIw!r!ER>^{@8zDL;o^LYtAlaeh
z=0-#5SwqtCW1XiHl_6PR%|@@$R92@KyoXGH4z2J;=AJUi`G|lKbd>N))+eE^fPc4)
zdEL#m|NEK#k(WdNx4f*@9-DhDXo@p}?425#;P%jaP;0V&=%9hQGYFTEbwXjCXUop-
zbH775{Uj;W8bB&_kHsJsk~BQwf1cz|sMYD*$q3C&r9x)t6$TIfCQM>zH7U3FolEeY
zBMR_2{q?w%SCqT}Xy%W?jG>EvOFU9<&p48A;UOb)D$5BfW8T>62Nf0Ay7xXo|DC*?
zT(F9l+1zlC*gkWM=%3a_4Bg^6LZsKJ#C*!opcympWp_~#xMC_Zit8I*;MnPt)(`B}
zB!qHG4{I!OpvqQi)Z9QOqlcAd5W$PdZ3H~ApIiblkp4s&sNg?5&3F<PnyM_o2u#w_
zWziGEUiXmoXDg|#c9vLP_@b8-G)f{}mgA7$BCp!(sbRg{RW|Q>SA~r4PHjt9*$7HJ
zJi{U(+sm1KRrGTBFL^0p#PGzHEv-q3&^*FrzV9Ezd}FMzab{`;iq^cg!0h<Avb9;X
z5}aXaI8_B*;(1f@f4JIPI#R#;(Z2SzsK>sbS1bu3s#!%4+^LA?q<c|<3E`!jTYJ^=
zPDE!kpr8`L=-1~&Ob94Lh+}R`Ze`lk*g&>gGBH;)-lWj6mcp!iyU?{cI*&h3Gsy9h
z<-KvUftYk)G0yJraP26{PimK<G*0vlqVK+IVh9awiu(2JGF!43CiL>`&XTqe_=BbB
zTs3-KHS5RHmJhqcC9uk;A%2e38~ycU*us>G*g(E=mVugqwZUEaBaOW4jC@@I!n|a<
z#(!pv^%p54D|CG*UF0PZ9)%f9tlonw*K4Y)+5xm!4)P`uQ@U~?wo@e6zZZe`5iaz&
z@#!u&I5^?%<??qh)~zYYM`A3Z`Fc^cnObUeaB2c*=Cutfy(f)=Xp?IA*SL3NEox|5
zxdb`IHEe6vsQ0xwL6-RZZS~gcZrHDkr?^k9#%2RBty%3-mRMq4vG-By@YF?~Qz$qK
zV6rI)8hK8_^U9m4Sk=VAYylPOihYARtM?Uo&I%?&Uy}_!6bw4Xz2KsnP?CTZWMetc
zVoE3oya<p3%VkJ+5*H^Im&h1|Iu_?Qv23>zj@m6*R&^429AWx_%-adf#g9v$_^iBE
zvDRhCdnu5ELlqeX#^<wq<5@8cK12aq2qgk@pLc>NA&qXv_}x7dDSaT8+R|{l#DNr(
z#HrM+<m!^Kc9Y0`zcQ+qc08XYHcVNER;28P^ZDcg8XAXMs&Xof_Mw>;QVHZ>W)}E1
z(rAuNcJfQd3DgQ}iGu}87VfylV`N8Gmg|}4@96l~L7&bG+8TSg6teijJAoB#Bp6Ii
z#VWW9;~C?`Czv6SG<EO*L!j_3`J85QPGvuezxlbu3o8ki6Z4hJU^46ZA%b&24YT=d
zy-!Vo-I9~(jvkyP2!dw9hx890w1jos`XinPd2|^4!`ZjZ^f$GYqXiz0$SbzRcDgX~
zeSgxVq0wnr?gk`%;AgkEi!*>qB1VL7l$o`TPN(+tO7eDZj$(;k<g%BP9M$-4g4B5@
z_!?O&J|KHlTWRv{W137S6Zm%y27(6SI&&hp%mHs}7PNWD0eb>UI8yOdC$G2hiJSh|
z<=4?(<|Xb!O@oi;x9gxZd<v=e+guDZ`slMU3tNEm-a*Nl0v_A)yS}l?Cg}p2+OtCs
zDFVHO{#TY>fh0XfA9#`DdL!$Aaduxt=NF|-@Khv>UYvO>Nv^3;jbwmoFn+gW-8?sw
zJb;Q$jEJFsdETBg#$Q?eBL`B_V(Z!9-2xWrb`EMX!+SDG2iZ!4Q%{R*^7)r83;yG@
z+siu_x_r!nn`)YU`~CWvGv}xbZfIK$F1H+w(WoEs^j|FRIm|RpFpWe<GhA*^d=cC?
zUFZI3$Gm*&UX!GGjMe<x=$mA)hL75r`D>}gpC^nJwTYegtCxuP;H15+W^YdU$P{^{
zwWY{z%^S%r(ul}4nE{$H>bSiX)7yi<-e0KLvlwUc2NYjV@^ZBBey1AO>o^3mBlvi2
zs|Sjx;8Od%MPNSOl}{#*9%zS+X`V!Cyll$%QtlZ(@C<`p=Yundi_S0=Et)Bv?VYg}
z#Nf*<XCZ~#H53QITA{R9mw>j#FDJ+3;*M{=lU+x-HAW8y+(`#DpH{AD3PRWFE;iqc
z@KQ&m)Z-lD0hv)-@y4w=7gUqO$_3Efi?@WF!Tuwi(VE@aGt{geM=e4cS*(Ysw4Nf1
zF58{c!Ub;}LH>TAja&+Apu|+e_qa7LsLBvv?p;v5MRz9V%nMP>0Nm}qBtb4A<E>Qi
zS%NM=K~~sVF7sWo5qFlsXc{hX)jR>w*#O$vUxQMchly=GQx}2c($1w;508}#@LDR=
z=tUY#`{HoYD{v1z!~4))CyS?Hafqtp=P-ROBuHG*^3rMV{3&2Rt;>H1(|U1Rx;T<c
zJd*kpn*Z&BZay0n0cwF~lQq)%^fZV-<#6|uW84H;XX)tDKD}q*?vZR_=|Mh7^Rm2#
z;7Y^#6+Esy=obgx<}9@IRJdLqz)>}U`&iWX)<>!0-Gfbrs|j!O2&0Mcj2mPPnng*d
zcI7`vjSP~I`wwY*M{`@sw00M15L6uiW;uGd%f3g>EqR3gN9Lnt+S=IFmUJhwwp|Zb
zc1G)JJp4zH{+F_E-V8pGEEOYTE$?Fo<cp1%{-L0{4o9O&g)MsqsV6}1j7lBvKcq_L
z+Ud}bFUjih-Vv3(YFBl^jFE*M)-*Jn8yfw16)O!On!k<^*CpfOT!sp*A6mr(LZ4`6
zc9KDu;XH!^!QOOc-y_Dwu9?tyuwUj5Q9igxCfk2)n~yF(C3C(&U~^I8Mnj;f71{K#
z>qXXn8G|e7;41yF{6Ca`n3)|ec?#`oieTD?GCLXHr3e1lJ-<SgRj{Z1Bz>L>5A_?{
z{}csJv2LCRBRtcvZu!u~cT}|G5<+>zY=Y?Ey?VZRRl;av*^dnQDF>Xc>`NyUZPg*?
UNQ5UTXUIPTJ@Ab(o%><`2cNdj2LJ#7

diff --git a/docs/articles/examples/vision/oxford_pets_image_segmentation.html b/docs/articles/examples/vision/oxford_pets_image_segmentation.html
index 54f658178..3e73136d9 100644
--- a/docs/articles/examples/vision/oxford_pets_image_segmentation.html
+++ b/docs/articles/examples/vision/oxford_pets_image_segmentation.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../../../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -572,105 +572,105 @@ <h2 id="train-the-model">Train the model<a class="anchor" aria-label="anchor" hr
 <span>    verbose<span class="op">=</span><span class="fl">2</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/50</span></span>
-<span><span class="co">## 32/32 - 27s - 844ms/step - loss: 1.4284 - val_loss: 1.5502</span></span>
+<span><span class="co">## 32/32 - 29s - 914ms/step - loss: 1.4283 - val_loss: 1.5499</span></span>
 <span><span class="co">## Epoch 2/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.9221 - val_loss: 1.9881</span></span>
+<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.9221 - val_loss: 1.9887</span></span>
 <span><span class="co">## Epoch 3/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.7764 - val_loss: 2.5123</span></span>
+<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.7764 - val_loss: 2.5144</span></span>
 <span><span class="co">## Epoch 4/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.7200 - val_loss: 3.0148</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.7200 - val_loss: 3.0185</span></span>
 <span><span class="co">## Epoch 5/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.6848 - val_loss: 3.2898</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.6847 - val_loss: 3.2947</span></span>
 <span><span class="co">## Epoch 6/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.6556 - val_loss: 3.4525</span></span>
+<span><span class="co">## 32/32 - 2s - 68ms/step - loss: 0.6556 - val_loss: 3.4559</span></span>
 <span><span class="co">## Epoch 7/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.6302 - val_loss: 3.5625</span></span>
+<span><span class="co">## 32/32 - 2s - 76ms/step - loss: 0.6303 - val_loss: 3.5671</span></span>
 <span><span class="co">## Epoch 8/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.6082 - val_loss: 3.6537</span></span>
+<span><span class="co">## 32/32 - 2s - 62ms/step - loss: 0.6083 - val_loss: 3.6620</span></span>
 <span><span class="co">## Epoch 9/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.5894 - val_loss: 3.7334</span></span>
+<span><span class="co">## 32/32 - 2s - 63ms/step - loss: 0.5895 - val_loss: 3.7374</span></span>
 <span><span class="co">## Epoch 10/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.5726 - val_loss: 3.7954</span></span>
+<span><span class="co">## 32/32 - 2s - 62ms/step - loss: 0.5726 - val_loss: 3.8015</span></span>
 <span><span class="co">## Epoch 11/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.5566 - val_loss: 3.8266</span></span>
+<span><span class="co">## 32/32 - 2s - 63ms/step - loss: 0.5567 - val_loss: 3.8311</span></span>
 <span><span class="co">## Epoch 12/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.5407 - val_loss: 3.8028</span></span>
+<span><span class="co">## 32/32 - 2s - 63ms/step - loss: 0.5407 - val_loss: 3.8089</span></span>
 <span><span class="co">## Epoch 13/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.5241 - val_loss: 3.7225</span></span>
+<span><span class="co">## 32/32 - 2s - 62ms/step - loss: 0.5242 - val_loss: 3.7293</span></span>
 <span><span class="co">## Epoch 14/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.5063 - val_loss: 3.5891</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.5062 - val_loss: 3.5953</span></span>
 <span><span class="co">## Epoch 15/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.4862 - val_loss: 3.4399</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.4860 - val_loss: 3.4506</span></span>
 <span><span class="co">## Epoch 16/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.4640 - val_loss: 3.2488</span></span>
+<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.4637 - val_loss: 3.2588</span></span>
 <span><span class="co">## Epoch 17/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.4398 - val_loss: 2.9895</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.4396 - val_loss: 3.0063</span></span>
 <span><span class="co">## Epoch 18/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.4141 - val_loss: 2.7040</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.4140 - val_loss: 2.7031</span></span>
 <span><span class="co">## Epoch 19/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.3877 - val_loss: 2.3552</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.3877 - val_loss: 2.3504</span></span>
 <span><span class="co">## Epoch 20/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.3622 - val_loss: 1.9751</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.3623 - val_loss: 1.9645</span></span>
 <span><span class="co">## Epoch 21/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.3391 - val_loss: 1.6415</span></span>
+<span><span class="co">## 32/32 - 2s - 62ms/step - loss: 0.3392 - val_loss: 1.6364</span></span>
 <span><span class="co">## Epoch 22/50</span></span>
-<span><span class="co">## 32/32 - 2s - 65ms/step - loss: 0.3201 - val_loss: 1.3455</span></span>
+<span><span class="co">## 32/32 - 2s - 66ms/step - loss: 0.3203 - val_loss: 1.3376</span></span>
 <span><span class="co">## Epoch 23/50</span></span>
-<span><span class="co">## 32/32 - 2s - 65ms/step - loss: 0.3076 - val_loss: 1.1034</span></span>
+<span><span class="co">## 32/32 - 2s - 67ms/step - loss: 0.3082 - val_loss: 1.0917</span></span>
 <span><span class="co">## Epoch 24/50</span></span>
-<span><span class="co">## 32/32 - 2s - 65ms/step - loss: 0.3076 - val_loss: 1.0204</span></span>
+<span><span class="co">## 32/32 - 2s - 66ms/step - loss: 0.3084 - val_loss: 1.0189</span></span>
 <span><span class="co">## Epoch 25/50</span></span>
-<span><span class="co">## 32/32 - 2s - 65ms/step - loss: 0.3429 - val_loss: 0.9294</span></span>
+<span><span class="co">## 32/32 - 2s - 66ms/step - loss: 0.3461 - val_loss: 0.9181</span></span>
 <span><span class="co">## Epoch 26/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.3633 - val_loss: 1.0385</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.3649 - val_loss: 1.0231</span></span>
 <span><span class="co">## Epoch 27/50</span></span>
-<span><span class="co">## 32/32 - 2s - 65ms/step - loss: 0.3294 - val_loss: 0.8552</span></span>
+<span><span class="co">## 32/32 - 2s - 66ms/step - loss: 0.3294 - val_loss: 0.8574</span></span>
 <span><span class="co">## Epoch 28/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2888 - val_loss: 0.9904</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2891 - val_loss: 1.0065</span></span>
 <span><span class="co">## Epoch 29/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2722 - val_loss: 1.1511</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2731 - val_loss: 1.1668</span></span>
 <span><span class="co">## Epoch 30/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2675 - val_loss: 1.1777</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2681 - val_loss: 1.1974</span></span>
 <span><span class="co">## Epoch 31/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2710 - val_loss: 1.1230</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2723 - val_loss: 1.1259</span></span>
 <span><span class="co">## Epoch 32/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2818 - val_loss: 1.1107</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2872 - val_loss: 1.1112</span></span>
 <span><span class="co">## Epoch 33/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.3111 - val_loss: 1.2046</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.3146 - val_loss: 1.2479</span></span>
 <span><span class="co">## Epoch 34/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.3025 - val_loss: 1.2817</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.3034 - val_loss: 1.1946</span></span>
 <span><span class="co">## Epoch 35/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2902 - val_loss: 1.0870</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2880 - val_loss: 1.0699</span></span>
 <span><span class="co">## Epoch 36/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2812 - val_loss: 0.9639</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2770 - val_loss: 1.0195</span></span>
 <span><span class="co">## Epoch 37/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2769 - val_loss: 1.3830</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2669 - val_loss: 1.2986</span></span>
 <span><span class="co">## Epoch 38/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2629 - val_loss: 1.0656</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2544 - val_loss: 1.0411</span></span>
 <span><span class="co">## Epoch 39/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2439 - val_loss: 1.2142</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2383 - val_loss: 1.3622</span></span>
 <span><span class="co">## Epoch 40/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2417 - val_loss: 1.1791</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2378 - val_loss: 1.3429</span></span>
 <span><span class="co">## Epoch 41/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2437 - val_loss: 1.3024</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2439 - val_loss: 1.2881</span></span>
 <span><span class="co">## Epoch 42/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2340 - val_loss: 1.5025</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2406 - val_loss: 1.5361</span></span>
 <span><span class="co">## Epoch 43/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2330 - val_loss: 1.2029</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2417 - val_loss: 1.2444</span></span>
 <span><span class="co">## Epoch 44/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2228 - val_loss: 1.1434</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2275 - val_loss: 1.1713</span></span>
 <span><span class="co">## Epoch 45/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2169 - val_loss: 1.1099</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2168 - val_loss: 1.0482</span></span>
 <span><span class="co">## Epoch 46/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2116 - val_loss: 1.1009</span></span>
+<span><span class="co">## 32/32 - 2s - 62ms/step - loss: 0.2096 - val_loss: 1.1095</span></span>
 <span><span class="co">## Epoch 47/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2059 - val_loss: 1.0853</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.2026 - val_loss: 1.1017</span></span>
 <span><span class="co">## Epoch 48/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.2004 - val_loss: 1.1272</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.1986 - val_loss: 1.1087</span></span>
 <span><span class="co">## Epoch 49/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.1924 - val_loss: 1.0651</span></span>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.1924 - val_loss: 1.1055</span></span>
 <span><span class="co">## Epoch 50/50</span></span>
-<span><span class="co">## 32/32 - 2s - 60ms/step - loss: 0.1844 - val_loss: 1.1039</span></span></code></pre>
+<span><span class="co">## 32/32 - 2s - 61ms/step - loss: 0.1846 - val_loss: 1.0708</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="visualize-predictions">Visualize predictions<a class="anchor" aria-label="anchor" href="#visualize-predictions"></a>
@@ -682,7 +682,7 @@ <h2 id="visualize-predictions">Visualize predictions<a class="anchor" aria-label
 <span>  <span class="va">batch_size</span>, <span class="va">img_size</span>, <span class="va">val_input_img_paths</span>, <span class="va">val_target_img_paths</span></span>
 <span><span class="op">)</span></span>
 <span><span class="va">val_preds</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">val_dataset</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 32/32 - 3s - 83ms/step</span></span></code></pre>
+<pre><code><span><span class="co">## 32/32 - 3s - 86ms/step</span></span></code></pre>
 <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">display_mask</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span>
 <span>  <span class="co"># Quick utility to display a model's prediction.</span></span>
diff --git a/docs/articles/examples/vision/oxford_pets_image_segmentation/unnamed-chunk-9-1.png b/docs/articles/examples/vision/oxford_pets_image_segmentation/unnamed-chunk-9-1.png
index e10829279310f5614ba7ce8ffc58f43747d3696e..1f21754f9724b18a53c00b87029e64c3f93edc6f 100644
GIT binary patch
literal 36912
zcmeF2<x`vA7wv;fp`|z!io3gOad!z;+}+*X3IvL~YjJmXheB|QyA$;Cy)*ay{R4iP
zc_uSY^6Jb!>#XzHYey<6NTMR+BLe^cRB0(O6#xM0@xKQV`r{3aj0N?_7m|aNwle_m
z3H`qZYHr={1ppuhNQ;T6d1Rihn}Y}rJhz??P?P!G6|+TfO@0BSnW>lq)Ud@zp>33#
zmUtyoY<Whnzta({@8$}0-2;K+vS4?HaourF3WKkt;+{vsk)VcRTsSmh5mGeVkWsev
z`PQ9p=i#mHpBaT!$i;t-?A$yr=*_FX8trdwa;V#@f34ym4ndP)YFmR5MkH~uGd%n%
zO1;N?i%0@;!u9(2g$G4I6tRdP>^n(_%I6>PXreUXd6I~Ep#B-M5T&vI{r!I{^S>ha
z-}d<bQi9>%-T=fr_Icp+#032V^;~XucMjV1Y8TFjtnh||RO0!sN1WnGQ3Wppzc3H!
zgCBOPzx}5`Yx$ChofQ9^S~;wjGQ0jAmE`DG_hy=Z$dl3&%TV3Q>1w03qQT>7{WLO3
zinY`M#)nve{J1uKob!~pjkmowpEq#yJw&Wu*PN#r#!II%Oc6VOJ$0Y@Wq&2*-VKQo
z8>#*xlHVr=Cpw(YLMiROi2t65wVvlwa44QDVx7yWK2azkc<bAJLUi%3LXSFaG(GS!
z@@V!bMH<go&KlJa1qriVPiN{;y27&JA2N$06kY)@3BY##b$N_~!ChEdJPk@Lc!@SL
zshSCAb!&JG5#i=8QMsNlm=#$JLv)86>MK+ucc7FkLqjjMu^g0Pj8%dKbcj<CIe=L-
zn3^3`G=hYKSxLC;7cu~ph{9eZSDp$`jVWhNfJF(mPGg?`Bc~}vA)YVx)8TUn3|n$U
z<6ID{OfR_{-AZh5{M#52>sv9TR72)$rQW<%@=8LIrt@}A*5J58QQ!^}pzC#11lpNx
z)HEE7!h-m`o$h7c$Y*fU6lHs#w?1^gzdo$HZ$gN`kXn?qU%+>;v!C^GfT8DF1vNvU
z(oDJ%47vO&ls%PlK@$JWtjIDo!^<(BV%J%sn_1~Htma6B^JlXRv<+hq+XPP^`ltBd
zf2k%4#U|h|OK=`1{-oI&&jxCz*Dw32xyQqoWveVY=sOngO++C%(#tV~3;cEIP-z~R
zA;f@0X!ft`cI<3)<~q_PmOwUe7?mc<AvaQ?95<Vd1RUBJUUK2LP>^yoTbW2OJOu|y
zv8{DkB6*V@6S7ZngrS2_WC(2d53Z;(L;SU0qG>TQGme-n!4!_a5qoW;aR-R2Z8Jor
z(jx2@Tv5hQ=%jBSALU3*6nm%kWkD=uE`59cPmdS1T+^E-z!MBAg^%jpm~lH_gIqId
zy+GN4o6dY<D6y6*wd-xcO?(#}H)C`)y63g@C8i^=vtH`2!aR!4{Sb~MZ-m5ZYuYN)
z%Bu95<5e52)&qnmeJz7??jQLDZ_hA4-d}YDX}+al#i=@7`>x1nu#>4|v60zg(DgSW
zud)@32Lzi(slybiohw6pX3ndZ2zhLlnGSh;M&7RUA0kd6dlFY(PrLF}{Y@6gtG@@n
zDhsnKt0)bX?zRNKCRmqQGT^@?1iRRlbas(Cc?P4;*-W?UkJizbMT}Fkd6a-D@ae&G
zDudGGuS!bH+z3U~MKNe&t>PjGj-@>}^i#U>(sHndA=IY0VP{%%?7P$e8TxZ(R23Oy
zF}4&(y&_>c$nJr{xqwzQhVS71b*MESGi7Llz`@J=`Aq8#23@><aFtH;+rToKs9pOR
z=g?o<=Jorn;4fi@9^c{JvKf9HqB3+Xyz*H{i7?F9iaBRvNYaQJYYA{wgP38Lfiisa
zw02ha_O?~h-}OdbHa`s>M&KR(F@vX)jZ>b$ZKWqGPRVVt^)BeaY>sv$I2GcbWLrJK
zJ3pB`U|m(d^I;d#Dvyb8dnU?)*dOnw{dsV^*D{#PW~|`~YC?p@l9U;gruMV|7%n(E
z(`i2LXX1X*=Poh>zO2S>3(vYIq7(oCG7-$zgvVNH!?n;w4CAaQOVvzhW0`W<&df-Q
zF;uJxk-u{5?S}qI<QF9-DoMj76ElnxgH$LmFEdYQ#`r{V&@I!~m>IB+$xpuvYI|R=
zl&c5>%rbUV#-pg!=ctZ3vUvkH0yf_6n{zrZ>vLXDYmJAyAd~jl?^hiwv2k(PT?jTe
z`;)Tq-(rX67{xXp;=c=cJ%tCJv{j!l3Lc)E=;%J)&MO)?PjbM(zyO#zIh$I<Xk5?V
z32Xk-yrs6<i$Ye0ewI>92{cOK0I7W3B4Jbv5Fl4Atmuht@Ci!(R=oHDN#G^;eu$|S
z6>74q{1V5fo%%FSge^ytI3kQ(n*7R=^CYc8tYlqq6_IlHYNJm3c(hhVd-5S=5o|}e
z_Aqg~>F>WKwd*Cs+LIfmOld*_FKR5>KpvxPhBmchrN>3Vh8(h*<#y0(o~~Ir_X_!?
zYG@^nm|83kCAv+OH<M}3x<WnIBR}a$O8QlLX5h;Y(TLtO`AADv5;63_YICYUnL<UH
zo7Wyz0o16(nLlH~TDXu*yzE`(a%(+Lm;9@NK%S~V6(wZ)A5bhdW<Wgfoo>%Ui8$kL
zKI749z4jDS@*{^K$GFka(W2oT$b>mIHnzh1v2nbB+dcl%^K(u2+tc81mBz`3{%L2{
z&Y!s%yl-cFkJpC(o(a9v#3y$Vs~0fX<D^TC)0+s)pV+Bef|eEC!bq@C`uR6RDFHT&
z*Q>z!5q`Z}(E#H8udnAYA(mW=8|#iMvlsn@hNVorc~CwYSgcHwAwq(Toxj3UpkudR
z#d#ZgX0njcemb`i2<3H%mbJNSp5*B5Lcw%z1;o-T53OnUGh)L|ufrY{WA01MGOVrY
z^*#>Ook|94NH3#@4cd~)HW;OdBhzA|4JfyRk@`m5kN~Laq%RT$8JM3;`zOc9(M4fJ
zevuoZhlA=XGb|-t0itRXsU~Y2JYRPH?yUW6SVv7Hw`wYYNQknx#BF=J;lk-Gr?6>*
z6+tH^NW&8)^8*6|C4zTld*25${)*%1xeO(ue&h7n!N@7qs9<{Xk~zZZejJ?k*=Nh(
zvKREaJ!E>nW`YT@1@Ad|Ucre*tgNieKU^y+C@9FwcSgqXd~A5*pWRj$4L2P&Ws5L3
zFm>&um9ay{KYAU*fgL3V<j}$uZHjF-<ewwWP)N(DV<S0Bm;w>b<B7cUy=H~37N`F`
zFWMC7eVNFc(Ov1M){HHuzsJRsHK#EaXDlofYc`hWj@a3@^g=K8W?iwau8Y5C?6|ya
zNVPU^s%(_;9Vn#&Mv|*J$Cf7GE*PaSkP#J{foR#LwTo9~H~E;m7ne0-+_0jBmB(HB
zdX|Zkw7i;r&=`X_bz&<|+6JM{L*R|8B)~sc8|0Q7kxbBw8Zc`$Vt9X21Bm%|SE}Kq
z68~<20I@&1S0YG7gNWy1D!-MRd@81_(`7^+L|#E*u}O07k$>8o0fW8!QMo!MKApE7
zWNS9|vE5JQ=tG!%wm;QasoR@YpXmGEEk=KtdQ@rEeOo9IbbY+suJJvyHuo=3IsRnu
zD4IzAe18TLz~p-*+kL0gtxk=ZvP4#-8}&vy>==T^=XRQ!ojqk%Z-{%o+5MjRQlU>Z
zUMpTh!i$cW->)yo@n?<(Gk0^V{3g!C2_cPfIBAAAEhA$`SrWOb5LqsXd@Vr+10aOC
zU@qa(%I3;(2Rz={49$oKikG_YQgeoTrx}m;P^l=77lufsF*7%&s99G<8k+RA_}e%c
z7UyfA;$>{8etWJh74qr<{~05o^?s%EHH*eqcFTuC+y<$j53?Sr<Sw}B8nYj3O@Fsl
zYloaHPkCs_KZP*Q@4$26LG`;<*`byHB@V%`!!3p{Vpk5ypfq;O&#JU!qr2qG2dhP<
zfVf*Lp*zu@s<%jyq(xhd#gU<laCqOus!qJ_kFiYW*^SVmRDSAO7^%pU3{B*i$JBoD
z*@6+eGL7E$+X_VR*?gzjbnFj9723n!;Kel_jw5V;xgMW>n>!GEX#M@J|Ms3!%+Jbt
zJwg`0<~U3!E9>RurQk18rUrabqDhtkFmrI+rHA|a(f>H4nNLnIv#nEH2(4ZsHD1!;
z5=M}DH?sF5*81d`!XSA;$lq1}WXs9NgfC*}qQ$^IaJ36cU9E$RL9wvG8H&2wOo!~$
zq{qMQPAsi2N#N1E{{``BnI-d2w$Mt0h|pz<Vnk@fd6<#WsT*4iA9t&Q6`J^e>N;f!
zv8<$fSkdxuOo!9SSvlyb$}%KJ1s76;-ZsX7Vo>^v;A>?I3OV?=v^%GXd>_PD#<>pQ
zy`J&NCN(52_kdH6rwh8a`eH29a3L*eC~Psg>z2_Y%=8K5%D5`TQ5kE*DTbcevLebt
z3i;G=q5Bd9QFi1s!u1=;kp$G9nHWW`_Di)4wyI`80^IrZ<D2(!Z)<OgWP|H-_ipd2
z-U`|`&F-hgqG5ygr)2{OI1lG;K~=Fc@yj?{H8Lc*#%HhZ6O&Klh3l+1&e>-7rtA73
z8LY9faS72kE&Xwpldv-X)*G6|IwCLjhmuX;I&j4GLEimKRzn!AnWD&GovHTbWYy!$
z%pXSc$19%MQ&Ei61Z7&*>ue{6rYq69ccqtZA}VW1k%XN2cbQK`_V6$%b4JBkE-=HK
zt>Zl|NeW^d0ftE36?~C$(8*Ftu&K!nuc4o7u$%>a@58?FB}uMk73@wlL`)(X^Ts4{
z!^=~%1SXLmOPopfPvF}PR#yhFcHE%9r&Qr^f1mcW<GNf%!P@7OpI^{Vs`-9s&+)B<
z@}SgIiUzMhVTg2<M2%8}I%4N1^AK(kwmGQ+N^YnPrIQ}tB`IDbFi+Zp!AfDOxjuW=
zzzs1kMMPk_IFsFXb1CXtYaEfi_>JE)6nxEORr^@u;6D}oMX=>@D~|7++nDWU@%*dT
zCth#oE{zHb>1&K`M?1Uo+Y-q*0v@M-Bb^T$?G67havlc^UKb6Zdd8CJ>|6Iz{2#;p
zABX*4hKp)GfBu{@zIS)BTw~DfXSqEwF)_)t`EuF+rMt<0{KQ&Yp_!ydfJ~=-&z9|?
z?LI>2!0)a$+5dSOXZ)yzPr&vLXOKCk^2pcm4cb>tQU2>tM1rz_@4!P5D~hFrWW4jT
za_;D(@)D9f_?el{Ve`rMpT#1p^bAse@a_g&1tOJLupG2#Yr-fc`A<v$^e-Sre-mB)
zdeCPz>A;3ba)s2ds>R|J70Hw_D*5b<rTtQ*rP~puSZ?LgYr6i@M*=#mzpLmlNV$zC
z6-p9aLzT3_al~_xv@P({p7qWlU)QxuwegsC_mX80rGcb#35Arn#Z`@;MK>o++Em(9
z*os3x^Pa&5I83!FiS~@W>GJ%>kWncvR0}(^SU(Rd1l{oZA)7yU8FpIi#S`+~L#F-p
zWm+E_N=gp?JLe3nQ2E@?eXd8T6tnp~hpq<&0Oswl_h3c;sAPZ7j}^T*I}>m}F?a%e
z$H(XOxXeY{88>)i1^8Si<@l7@YqvU?RxI+m|L{^zF(v9;YScCz1l11|?DM{!d@CpN
zpWY>c*jp=R0gLcu*iZJ$cMg!be8|$=G~t-fqRl3cD-IJf?IJE`ZZf~laZM1wq_f<%
z7@bD&-p>qi2zfv5V9nNQtB&Lzab<fjb{mqhfKYxgMqbzPA3%9XfJ&)TXrW2clD=jE
z9h`tuA?zCIXt`l&)8xvzJ0Z+dHbeA3BE{hKB;>$Oc56#5F1F9d3>AHLzwsA1QK1V@
zhEqh$_N~R-SQ&F{m%y<SVJD#qstw1EdSR^u&K+G&6ivy=ljSXRBza~-#PD$ETKtI6
z%SM0+RwO0DKxov#%#|b#Bz1^NUMdT<()=IzCrKlD>RJA{-`nmFDK7VAa!kArj(&cA
z%hh^BZ<pbl4_yzPhhEh!J5hAiEk;*Y|JC&)5(_`S&pqA8-D_$-r_1pk&|#=fn@lDd
zN3Y;2^rK4S`Doey_HcF(Aztw5a3{F8w|BkWo#}jd`rzh^;N#Yjx3}Q)%Jf?w(@Wpc
zyc5Cjs0l0Ja-OKw6E(#&TAyuYVF9{7AD_A&PFgCy<2nAb&@I#U@9!Nn5vx`TqOz|)
z=jEHRLaurLL5$Hs<-@|<^>v~yj}s_(`b96eHg<@M6V+fG>FHt{7hwgBMVh{XQg4Te
z5v&H&(AFk-ju8|?xwN%EDCmw#Xx3071oE**62jMb^HUWHdSa1g@a}BFMH-~{Kj?A{
zA@l=%rZ@KOs|3|8KnbK}X-r^~A^t=?o1_8(hH}MxWW|lDA7*P<b~-K-<^&l=6~Q6~
z0*=Q0-(F!obZRv(hUnBa4G_i@ScETWGJ@I)-jGhM_=pu1-q;Y=oTi9?`cEcmoTWo@
zNWQi9p3Kl0Y~T_1CD5KLSie<M3qs+(>H8lL=zM(qw)r?DyH2yS=MKw|RbFpF!+2=g
zv7lBu?f-Ujuyf{iv4INcemm*@49I!AEg2pf!X{$UUR$g;*&9plzLo91?I(Ks1FXt+
z{5pB_Q+t0F!xr4O8@w<uDxF8&9AO1Dx_Mn48oeZVyWQQTPeYn`c#+e%(OfMS-Ni0{
zDAnXgotDYg6xTNqRNbgnao6|MZ2HXKX#D=JhU2MhpE!`|#H#CdW9MPSD7eVnnzkMK
zBuC}YE<wSv#!sf(j?G3WT2$L(waY<efL%xv9Ku?*j-oCDA^ZtKlc!G36{|Phk6IXy
z2jK^=q?WdjsTFL{;?((Nn(*XgXJzp6e3hJZYhSclq-_<gKl!45P1rr?^Qy$*d4TNg
zr4f~;om6Mum$o8iV!MxZI?e?CspHnXa!0ng!&w9zr&dt=d-mjqCN+s`k^XU&Zq?_q
z7|Z40!ZG)qXI-xpXSq38#dBr=Y#sQ&9T-5G{SSW-K?i91S)J^RrkHoUi7Ot6l(3=f
ziq~8(eJpd^N%akLfDj}}$P<F|pEqd)o()7|fSc`!@bI=XKau#o>=GHcE~<M!oD&ie
z2`!W9X8Yc<3(?<vl|~1kiXL(U+Jz%9+f-N3{4TzH{|hP6WS}<zvp&Qf)Vv<tDPq_|
zWBXvynxS*(nf~s46&g4uR`O43ht<u%+3XyBQ>cEGDsXK~RQn{)7FTcgD`jo!Zz02t
z?tXt7?EnXFUu4>%w5YBqG`_rogBk5_G}1IG;owo*{S?o?G5ysFu<AW>va)zf*1qSr
z)v|pt){~2T)n}kF9=mjb*0qEJrqhpWn7?0>77Z)jK0le=k{k_@z``NNohlp49y!#C
zj#>ErpiFpc91A(B=*p;>`}9N3ckNBSx;mi~?)3=fL^RO`G_fdZbj(z1<#1M2aYs!!
z<&mDz7PKMO*#taE{L7!Gt**PVT-k58Z_gL;2Cv`i_1fJ!`<C9<reD`qK2cFp>Ywp-
zEoM6M!vMOjV(kGv?p-%od$?24+%AVRBO@a_pmI4%Hh%uo=QoDtHKz|^mXiZTKVAKe
zp$$`*4LLn1PDGk;Dma-U2IG&E#OBR&{93d3<+T4F@%p}Y#f@9f4gahg2JI|!Yj0w5
zB>`#;JBZA*cnC%LcS%%ZqITZ}zfAAVES;9@0ZM?<RA+LqThWY<;oK7CgOgu79%07z
zH-W?hw4?i*;et`xJA(dy`26!fS*3R*z~r{)W`X#;ajpwcMgb~g6c;P24ymaXq1gwG
z?d!c=*><>Zmy+>rz#D!$hiLVK5^9U+3YcM_Ze(Dk-Lr1u)<F~K2SXa1HHNuEfLM=M
zZe$*tYrTY&LZ~;9f3KtGw1Z;nLIJhdx?PpVau7EE7Ch74KUAs$T$V&`jn&3$(P3q@
zko@Q@e>|>r7u6yasDRMW(D3hGha8pLrzeDrXGxxoDfjm=adB@~MDJJ8UHjq>(=Rtd
z?>E-o!_6%@khC06b${<H>yOISYs*kCndfmiL<9WiWw`uyxw1bFTh$VUtP?2u;YB(q
z(611*{4{tdptU^WHg9Aa2q->>5}0E5zM|R$ewx2nn;<xmJxyO9boF0@G*!pd2a=57
zay5MKE@?1}<B{mqh$QY&fL;EK`B_i9!(H+070uMNPz~2I_<*y96R0P!yq#rL4--3_
z&8c~(Vpk5gIl?_)BrFdPTS%Us;l_B6BR7<4DMeyYjA)3+{+&8JL-Ue=OxT6*7Kv-M
z>fV`&;PYzizy&*aJ1p=mE$t7mra9>GpU>&A5vQ6jPpfYzm&P5_#*B`2LnbmE%H7oB
zx0vCOnM&KefZ?EO`XnG4!rfD(T?Sh)@+^|k)W}$#(a^Y)olBoEk|Dkur!hqoJ1^?j
zp}o3rs-do!!7&)&GPTuK=L751#yX?HsE;F&5hn-l+c$5>U8PR*hbJuSNZdKGxQb%;
zzCY#hxZK)Fyy?K|_CPeZvO1O&dYT>Ys8pg}^Vp5O@Hyanf4g}1bEJ(s@YK|G9IT%f
zJmH=SL;VJifbao`FLK%)89&P1s1-<&OJRX57OWwRlFzOL$alpAWTAJwFS%!1HGFxQ
z9%j-&9+KL-KV@25^!@qs1xfF8wh_a<`j6ns^SyRSC8`YlPnN7`PJ?DQ`V9Z;XDYkx
zbV_6kQV^7*!G67Uy#E1@3d`6udr+&{j2%0YIq0zE=vzu2)@%Z78KOh54!Hs*NeD|W
z$b5|)xw*#TInSO)V(O-?i<8FlHj*m8bkShqIL31B<=?FsDSB{(*{3ju?CCm;Kz#wJ
zC42nqQ=by>gqDkG+26A;oEW=QJ5E{1%W0oYJI2)H6dkjyZ|8|dO))XsiVa^BrzM8~
z51v?}Lp72yXh3AyO?OBwa{v-!Y*L?X{+0e!R;$r!5l>d%_GFrS@3kZAsm1sC{^myF
zh?$R1r%K~ntVE~&X1n_Zgs3N5z*~P`f<=}#d9447kk8>v-qF!f>^CmKs{xE670Zv)
zQg-+lhbhYQoW9@JiX9NhAso)mgrN_xV<sjxmQPbjSCUAT?%6U5V}AZYwDy2-;U{L5
z*T`IS#p3DgYz8O9Q^bdQ-RHl?<2>E;Q${kEg1XoRMR8iUwf53skdTTp4?Y1?jnpNd
z5l;sH`Td+PQfT8Ep}u-Pi3|wMv1X$$jhxOr51Ryoz<kPWNmrh!lE4k$xX}1*2MeaK
zM(vN@@*jraRPTq2W-S#LPu#FicSCEpAoC2#FN%ajHwLLFW7#}yW9uz*9Y1f7k#8SQ
zZE$jbF-Sz7<Kc1=x|?{k)YOzTn_En-qJo<Zn*GiWO@HgPnU04ACetx?I9PY3VmbcZ
z1nZ2MUdJWf*hnNFv{5h(<L)lx0^NMO=k0a`pEUQ>^)6rcGwcOlkH+PGON?7AmVdbD
z$*HOD{2qV6XI%?L6pGz)S0~msHm4t0&l55U5qBi;&Te))Ta_tF95q2CktzGfeqa$2
zopKOnf^3D!s`i_sMfm0?PJkmc07OIKijJOqC^lF4BeiR1sekkN%-d;$fTR|AQS#`l
zPfOCdX~ZKE{5tU{<om}{&7B8H#NDzvV?BL6F@dURDF!MJ1vKTb=t#F*P1L$|$pxJs
zHwB5L<WHIvrBv?2Er?+YhI*0~`hQsf=W%wM@bpZ)>Qc^NiC`T#yA`ggm@sEK7^R_S
zT7g|vjO{)(^e{O#THosAnD#G1ZVvfnz`QkX6YQ~7#@a3s?|e=nPJ?cz`F*2mCF&0s
zj0}feN-nhnQTUB7QK+_-d?KNFr{M)XNVB^r(GFs|d-zNt?4&J8?pL4;H^{zMMIIBR
zbRtGB-L4)_7MSJVI3;KmzR^+LdA~Z&*iN4@85bK{rQi8vs_!{8eDCzpR+|1O;tyTV
zQz%ibS)S%5e8>cb&0qlJSA|31kwTyy$?4Fk6kEgtl#QVCfYzd=Y6-+q0Z<kF>jsa#
z7pIR<=3x^aL~?e!Hf-+lOUQ!*wOLf>uPgh3Apj{}jx(R5Ek3H?aVT~r36!DMS!42q
zeY|$(U8|-%2(@W#fpqoO*Q6Eg<q5Nh-oUS-X{^+^@Q$n#uK5t)Tq=b$`go{Th$C*w
zb@B?(_G#b-#}3$SXHt|wW1*2=(TJT+h<}->lD)uM+*o~naprtO(Kji}v{q;DGvTT`
zCRL=B2vV03a3s)VO|#)-Q>)e*%-qHf?2%G5og67&X)Uv%tidCse$e!KN|pk~y0Pru
z-+@s2Y-%PvG%!l$xKvJQcKM$)$C+ce9%D1#?(N_2Z%fF?$i_Jmxw?ufWIvXYA#3I~
z|80lqZKuSi=D!x_+wJRx|9e&QMw|1&luVJ+^I5Zj_gQ0&k9zboR`lQwk3jMR%6Q#L
zSkM@NJOph9g<UC8lNrzfZ6@OU8+wrxN(R?LN>m=xqFmcyy#2H9X<Ilbf_KbE^A&p?
zFM*BiSBQz-b<^(Vqs^V;jzxN@F_p7u8gZivG6+4B)A$CW?b?4Y+g@+Xx6B#7WB#f^
zb_;`yCaFgOyN$vQC+>oo9%k(IdE9Hwbr&Mx?KKwB&wZ5gDO8~XvP+dsaDI_dh9Hb&
zKJd^^D3qPHBsPvkk<FB9m$T7O%|3hi$<5B?4!OS$*>1||G~w;7dJ~Rhb=^%-(O$MR
z==bP9U6@-XMf+B@(nj9JmB?`}`^(^;rS3{!du`Zs&aY66{i@3w;zEBGTLR%J3fGUw
zyP($!{pIz~?$<Y8*Eh2=0$)rt^;{R7I`1+@dU|?V1>uhV)*1u;_`N<6et^)wwZSZA
z>68FnmsxRH)kpV_7wm9brt}J}Jku%LKW#{*?^?iItBdT44S7AwGsWbegd@z-oh+q)
zMPjo!(}__6DMQfnc_@~`1)XecnO<+3-3Ai7-3_0+KeyM}M_Gu*#p<!UKj1JLz7O}9
z))kbM1%6c$<{=B27@Qe4HSIUUdM&8hHCS`lk}YWp%OejNw9RS5uBCQ%X1_`bp^zSE
zF->inqM<Nd(E7{8={H&|gIOjJo=tcgqd|>~w{aNuiJ{hi(~#~8fg0tDNgSt`V&ooc
zw(3+BK2ShEqcN+}IyF1%tmc*y6Fxk=xELp&yxfGf@>?Tcpxy}nfD?BTE3@g$=Qim?
zCR~>;7Cvt7zgCS1Pw>_u(@biz%9-PPyjjH&7+~U*ZJ_6uFGF3N<^&e6ktq*tY8Sp2
z-j0cTD+=%96A!OA0*1#$m0Go=8I##vCVqbU)&fF8-3=y3x~*pQmfA~v8&`2`8g-8k
z13RWs2nYx`!_M>aTnv792ttp&M|9I>_1&-FZoMiE1bFzj%P;R}fRDa{B7^?h+ly9;
z4pN`w`>^->DcKi2hd}fJVqRD&b_-DyVt8TUQprp}z7z$a5l&#hQjA40(sH7{-^u8E
zlJA9(|Lar8Rr935Yt|$}tOL=sSK9`zz*AM;7@Vqt1}uwv3#>Q*1lpo{6-Qg0{Abbb
z<#4z3O=$A{HD2K@#w6wnH)0{3Tp8SCGu|kM9?4A_#B4Sz8utx#xo}bgbc5d%ksd`O
zQsr|saJx48Lvgs#ZIrD7ADWRsR@d)rDAdc{#pB=Q=;&faShf(a{)*Oj{XJyy4?2{k
zTRaCoc^2uMrNyRO+b3UYRmbWZm{=&wRw>)!mj1gvK}y+-dhUFb&I}`ixM<1IQOOzE
zz?zLML607naZ~emrG&oog*=nZY$oS`C(b-(sp;$na&C_-`x^y#YgGf+V7*+;w)E%2
zC6h~^w_kJ>QRp<=2{>#CGydMqbmz^|<m;PPY;1qn@ctku&*uL3<|j*4I>1FpB|rUq
zuNG;<vyT5wlmDiwT6u*bCOZOpFg!rMhjf6H|5FMQ&^kE)z6xnZ%vv64R!rB=Rxe&f
zssXm^<}tYu!}|6oyo5xHHLpqlLz;*PZMg_d5ysxfB~#E3G#u-Sqd$PjBHnIK-p%;L
zSOn53Ucx8ME1;H3UE1>C+^r^!7W%fDR%1OIYux$1r994QoB$gnH#vzzKd4sc*>jaF
zC8a{mZOwU4Si{NA#g}(J!fnh)C;4PV157v$5#ii*09!!(SdvVe^r-*o*$H~TnD8M7
z)Z0)di8)s3El1|6ft=I43P`YS>R$VU>7K?sr(&dzxj{1JDMYg3(A}}^cKWUL<~0{<
zt@F$=IKEeSY=_g`Y!c7#Le&m$S-zH0nnmC<Mt#>@jf&XVSVAE`0)RpdKL8|CboSv@
ze69wCCciNNJF1lhb2eKXcR!3v(}!Z;ynoV*o3UniWrn{qct5K1_soy)>!AD{urK_!
z*<OzE2g?N0z08#P7qKxkkWTyqXTlsFEeRVg(przyvL{6OEO<WzLNaiFQ7v`t!f{mq
z0x)PA;pNE}lSt?Xw8`kwS}caoD5I^aJjY%9Aghc99tspd<SbadU+H3g1f$&f6I&C1
zr~^w`H2JPMf|KmHaB26Q(q2Y-gf}?p_tn6CbTy^o+dWE6HJf}JQD?f2e@guKq7pPR
zZ3y($Q4^guk;^bk$bU1~uDbN@&nizhHTD9u2!<!)>|2}U#4z0&=kzHxkIHbx!u-N5
zfKF#VLs{9pqm5`$A$fJUlfUbMG*c0lwNgg=23ebstW6%{(X-}FzoET;K*baGAOH8a
z!RO&7@L5_~+RBPH;QvxQ-pRRWF1t04dwB~3nCTn8r{oT^@vmMIPB;(LW%){*o}22^
zLKo<%aDS0kp=SVUFce#s0+po|rKGuyQHY3D7)oL=@8|z$ndPi)#le~70kn7C$B76p
z8Hy&znO4Y{)Mzun;7Z80TCn(J_giyGzS)3GnoZfvQ3JLDA++hsY7~N|m=JTSNEu1L
zlpApMB4j<lq8y2gyjtL<yROxUpuWS_(ayz)5aLL+ASPC16wjn}@l!zsSx(=hmvX#`
z#y#TbE;(w!ISNv@U<5Ayr1|CZ!KXnDTt#)5np6Z)IdEo1#sMX_-Bj+3UGul~bI}TI
z8thP5(b7aD<6p47N(NsVGaJ8gTF9gw@uWx+JIPfi4QyjR%yxqYPkOTreSBU{Tk$@S
zjzJsNLbR8jLWD1B%8BphqeiuES>t)O*@xl%*z>-7)*iRBlK&y0*4Ebl4#d~%-`y|&
zain#-W-WRJMaADTMZ-k?K39sJ#E3z}9|S`wPbnh5^gokq<4!U#XN#miw!`Btg4QwN
z^X_cU!x$+1b60-KV>#2Ki!Avr!`~Sq^tv%$lwYIH6v`X$<rjd3yA}!_lwF_}yaa?v
zsx3}LzUV4L4Wcyy-WMCWR8~V9q~9A<Os-($wO1{oI`J-rSgfdBiOB}7G7I}1jp>Uw
zBI;zKV^<8VHCQzX)I3(d2_VD?+#E(tm*pn0ujcFTx-p^{%e=O}Dq_QXYu?9V(sk|O
ze>#Jw5_4ip$=^~3W(_%vrW%x1;*HY42X}Tx8fuJ%x)Iw%t5c7g5tbd*8xJ$B)_E4M
zvZT_c-S>%xr=bl;s#Z7j|8f|1bLO|hTk+X)2gVjtrmN^rc_w{Y56Z|mYf8V9D9UU^
zp+bhSV+J#z_vuW+!H{P&->GoguHNR43q7<>XZd>nn!ljvble@9@4l_*eyTVy@;EGU
zy#6=JcE0KKWV&gf_R%1$yx*Chot+iUb6D1Mw_T~*TQSc`RyqH$%~y=dY;0`cCR-hp
ztG+&Hz(;<_%+GJ5>CCNNkMZ}X(7u(E`1}Z!WA3T(7{l=t27pqP22M@1`;gD1vn`Ju
z1>*51)i2b~Y47>XFT`5Yt(_;Iz~^tLs$^Jf@EPb5E;`mff=LJRW=f>7h>b)6i()qi
zW?Y9J5u$}WFw?F~x0NeazM_cv_(@^uY3-iF6M-s4e7h9GIG`Q)gT2bM8Z|X^v@-f3
zhU}1Z36Wr2w><xaGyzV2*Z0V=Nez3J>?%r;bv6AGuLj3su;%*p>{*2(J~6)PVqjjj
zMm^YIWP-`XETnp{q1oNVn&rg0%aPgLDzB@2XYu%~H~U(5zS;K2*M3DKA?2j{Pr!1O
zT9<s_2_f#`lOWqpWAa==B+W7#Qb8>9&k(arIZQG564%X(DDK}3F^bbEH{yD<L$wno
znUP1*!2u^cpJK~<g#-J}Aj?0m?upHbGT;JUpC6ds>~*i(*6Md$9X~qkFPGs$y64Ir
zBCr49jFNe6{w}W}1JVC%VY|CMWztHQ?{jna>)BxF>+5T0XJ?q9MU|waBn}RKnB!2E
zad=pmg8vii{D#4(UE1K$&A6-4K;3D9=4>qy#hGeO`}Io2WD;*&*&NYxy|q3!7T_po
z4!}8sOT5GjtCWi@Oh_zaU5<Vb|9Ksed{#{PBluk%w}kqS(*wUnLuFXsz`4llJcQp7
z*JunEVavOGXjYpBNC<s9Vq=~6&EezbBO<m(@4H#0<9CuZ>r2FQ+e6hp5Q935g0DLN
z+%4#}4W4|2g8}-X?XyWtIj}aVS51v@U*T#iPgPR`-Ao-ETFiMlHBfF>70ed5g!?9a
zmgw%ybRtV{Z{y-n<QK}Ti1`II>CD(-sH54Yb44Q*NW<$^fK?kh$FYgIOXb1Vjc{r*
z6w(paJb2ScF6iQ-LD_nxK=>RyAs!BveeJm4cl6W!7{&VaIO{CeZKqKJl73_T@kjSB
zH`Co}!o$PEI8^cs`e&XrC(#JT1=MZtAGo#q{cZTWw_M57=ZSHr-LF<P+IZh<Fcu-f
z4^wYf?%U73UikF}km!@Q6G@_W4`Wp*l2!PKKwNwy08*4JGV$K>XT5cc<N#_JJXuh&
zn6*qJF&P}6YQ&ex8HJZVpAy$P18Vt*2`%;mX>_ZI@|51=;o(5aRv&%Z!=saojEbdw
z1-*xA_rsKOSE>5@mvCgoH`TKrcjjFOJQ-}E+YG^qsw+({m3S456Q(wPE_vtaE5sHk
zIL>!ksQYY9wk_JULDjIwrgeEtHp^}{in9@v%Je~>!ZatFeUWpqCKwv{m-E5|){$7_
z4ovGMn%6&*PCJo`zEWS;c}iO@O>%+Psj&yMjS7vc3J2vS0vw(H)P$$Ik@j~vegBaD
zQnt9_x7C{0RcoE;VZr;_Wn=CdJ=`!^XF4Rm(ww~|9s4`q_YYL3T&tzq7P+O_b)6jV
z|FRoX`(Xi}!JBVQXht7CPf-x^?|88Sr@5@z77YKsIXz!j`Ni)tIC?YnJm>OS_k<D;
z&j=^A%}Yl9;{EnXQt;PlD6`?%>v}um>%*`Bal#U8gg(LdK(Qo1gX_<yAp2O`W#I;L
z85(I}D2uX)VwONTIM(IQmyK9B=%exgV#-Zruj45&rI7d8HtM;S@ykM<ueX{VGrM=P
z@~;W~cDMDV$!|NvJIN~3BUTl4I|4%QQ-?ivMd;(&%@hxRk6BFtl<vXRn>OktB|`@#
zif7($0SLV=RP3P7!NTZswu=}t&aFQ3hg};gC{G@?`pC1$M$u;byl;m;iA?2ai?GzJ
z8(V>>0qH5b;fABG-cj1fTRhoH!0|t|%oqcDW#Wo?f6s<edKu<jSktu1QjhJ{zD7=S
zV;P3b(@neGXQs_h2l1>c@~^g@y1TuNND9_<z<2~de}ov?JveGV0CrK?Ez$eqFw}o7
zcP;+VD75SBhYs^A*$<kx@$sCmxBspdr!A^nJ_-5dqWL|A>NZ3x6pD4Nq=tYru*UUZ
z_xGuGPKGKiDDq~{+wSGQA10)sVILsOjt0mt@Uq@|`0R&<I3KB@lAsl+M*lTeSQb!b
zixFUz&GlJRhZKndOGV;)jU&fW)pu^5gLj8_WrMc4V_U@COAp^okDF%%D)?r*vHOQB
zpERgZOoZT+mtNoIgQpt(`lrxOnx1i{CaEqhx4~}q6!US%te4L0GDe$ofoHgTK@6Bj
zXLL|pMLz}Hl*<MjCxSAvEM2gJ#8|(FZI1%y<H`D@hdBC~8F6F<AVCK$#nv?P6_X07
zQuFL`61&{@a#ZF(5mHi;LQ1(L;=|Rj6&=O{xp-9>l=-c=nWHzI8};_Lr<3XGmz{~$
z-*q2XmjBu;hlI2abK@B!M>@CBU2k_aOag+wz%x81<Cwi0h_gTkSCDDz6=LAro=<}(
zd&(yBGNO^9RXvMV;<Hsp3N@6!>-*vf!rQ}sto#h6aDK%5<JFx3e;r*MgADs@Zjl-g
zBN@$%^<#1c+tk${k~5I(lb-a`5olq%9iDQttF`{4H=uaNr$PJh+xeA~ivSuyLE+0C
zikP$-qsglh%kZBwpVHu%G(7FbqnS4%J<HA4-i&5h@Yt2b^k22X`bZUL3=FvFn8lMs
zXXGp4SuAYAV<H}Y4vGey2`>gXIMN{E^h(G{7#$wCLfojZ8;x9KgVm)^!pmD>a%sFM
z(=b)FaW!nQ;#+KrhRfuoL!;Ru_CA9`GJ4Dvu{3+pTGOjn$$Hv2&-Vif^e1KX11*w_
zQEDuV<fp1%npQu5ZhhE1K5xk5<Kv3+!AI$?L)Bi}Gv0amengY5{vO>e8*ExN0;`rv
zgkFzi8CyeGnp`Xk2G0WKo8b{jm|P$3LE0(b;sZ!sDBVv=8h^<ti#E)Ke0rX9s;kMP
zY*UFccqjVpyZb<dN8OMeFXQj{b9r<Xpx5!uCZC-|jIIi#01HqDsZs<h{FaI*{411d
z*?lqguZ`hF==-B4e)E^__uRP4x^<1&h*&g2aaeiU;dp5;Q9j#x7g`2<g6CCJRE`5@
z$ct+ub-mfiIWN=hJ{Q{;UV{_03bwC@m$Syh^}6YXzvylWaKA8uL6#ippa~OiuWTN-
zKZy)tGI)JNv~6jXJc65!c)!TgY~k<^b*n~86K+m-7gry}1eO9VSD{lQcvCDr^v$Z3
zE1o*iN_DIMS}YOHBV&}&(3m?r$5xxC?advp9Cw)3Gb=gb%1>n%7RMy__QBiD8u&r>
z%r}iqX6@hh?WZOtI{NN11-~HBy%)QE0Q!&czy}DmX+^jms-s_SQ)YhnOX69i7WD0-
z^-&A2AWa{`kfw#6YB{dub9lpbt525H%b`MBZcGuNROgWfD^r#t!&apR3;a41X{1R}
z;<pzUptUYH^RjAiF}0#W9?{F2y_ae@B5hVT|F(6uteBnNVKlBZz4c2yf3a5E(vjKu
z157j11kz@G(*|1-kTtGGLaYpU-UrAO!^Hlf0BQELjGxAo-A^p?Y{@C8I43RKtPJlK
zVa|%tM78x537#;eNDQbkorg=Xm*PGzt#<vWS_#F*GU`%4h;gJfF6v?L6Kg7%aqifV
z<Fp22%-^hyu?(yE=~yV+FCw|Z1+G{nLq(Fd&RC(#EXP#Bl<)6bLte(^SAFT2QFe=H
z-v^oeu97wVzTUsepIvVE7pWY5%xnnm`kh<MUQe{;C6DK01Oe1#tgB)P-e<%)FCw~E
zc6D^F)vQ@%3qfo`F>E^RHc65Z<RMmNy(aW?GE%uAR;m?JG=qrSR8(BC{Fd5rU&Y^-
zHM`M;uXfIKXyX_`Ug&1rVXh8V`|H1T9WQH#C6;5z2kDz95AM&k`8sANJb{#B_J3@I
z@)$`Nk*Izo9I;!`A{7wmHTosnEcTPXZO5B!`tJ6O{JEMiu^ucR8*N>ggk&GJMiRX;
z69DUbnb%e8F&w$h&qme1Wfh22)HGYLAcH=wGMIO0?SE*ownf9x!ME=vV=lVjv{W7H
zJ?GluI}MA~rWmp*Pn48iL^eVLCp76H=hZ5O&UJkH`-!IXfx2&g0F~93D#4?D@e9(O
z=eG|A%#{65n`??4mbc5jtm`~!-NJLJ(_1&uHf!_(Y2M?jK+dh_<r)xj=6M!&dlUF^
z%vDn#-8s-fEbC1uinwcbIiXh9T@6P83&4abYlR=8F<fY9%xcVvNwrrGxac?&<q^1<
z^zO!3v9@nJPh1?NcjvFM-%2yTPZvLF%cxGvNgo9vc;p#Ox`^2dDrR2wOTJps*)_|L
z&rRevk)cDa9Wgw7#8FLuymnT{Je=||gRO9om%=zsA+;-!b|-bRoM=<<Phr{Ij}uL?
z)-Ye=?^6&XMj@WT<7HNK5&d}^8(w@$>85&Y*uX$L_+GK9SvmBtgCXcj(w*y!sqo};
zbrHq2wqS_piYB=MqnlBxB|?*JduydN&!oa|ZA;Do>k)g#WN*jsaUE5RBoFp`ly<i>
z8P&ayXkiWG1KI{b)Zoq>588#-a{CVV`PqM;!2&JW6ys?9h{Ftndb{?g@xb}jKkGD}
zoD}J|%2IX=wat1A7dhkx^4BtOMx=Sg+eEgwpAuTD1`rq1t@0zpDJ+P4Y(L-RQ9&vv
zh&)d>{az;K%`ErMz%n|UH}%_}6dT&5J9+)*!5fT-zApyvwy$A}VMN*doGX^}x}J+z
zRcUE*iYtWY(HK){Whja;wX#@cP7C%mf1R#A4G$f}6Kyuw_4~L=1Q4QSGqw39uPyi#
z)ReFaa=GuNOd_R{{df($eC57flS4#7N#Uvt)iD2)np3*O5d6>9zUjN2J`Z&uolM4E
z&rklju`vhHvwcy9ve4?}MOfY}V)KjyP}3gfwK$v@dEMgKKtgw2$-y>NHa@M=P-4?2
ztpmnbQS&wB>(Mep0O;IfbkAvAAcQMim$BI;9%}kV@MZf8lVH~eIrQJ5u-N#+Y4g|N
zBYeDUZTtSk+x~m;vg@*E_SyME=SA0|K83pF=AQgU#HbN^W=IGM8nFN@C0aUsUkJQt
z@lm!pZeJoo*bywEbO}-x(r(Bv)NpPSN1O2|Q`sfP<igd84d3=>rxeJKd9Rld5*9*m
zjq&0^jrX+oap&eiYk7^r>(s@IUq7=K>}(gbLr{ye#0hKJC48I-02hD=1qJXLkGS2V
z`2#h^V&S1_Tl&@TdB>vgysQT^5Fi5kCq|g~Z}|a36VFTtVq)Q0?gG=>zIPIN+?3!`
zV~yY7YMb>#_1RZcqe{i;7!uM*H8-yY0<Ye^is-@P5Jkw<^!tsi{p;1=FL{x9J#qjA
z<3t(~Vdu6&O(%gG!M2m8dPM;sE`x>e$dQW74E0u9wd=D!OU00u65<eD=)glL+MZKy
ztZ==ybx?NknDPjPG(J;uO=Ttab!#97akdE?9)aWe4X|Eas>FB`idiAU3+-oU&#x<L
zg{Z+7U^^8>!|Yic&Rj_iec-@&SxOO2P+g;$EXU<!rsO;NZ)7}PCR6`eBJOOR*L|#>
zPlwk(wGfJh$4vB9XV4n4b1h(qVYi@P+;@XQ4rBiP@%dx*9d9t`rfZt)Fh>gth7v6t
zjTi|U@5)cqBogf^yoRspfY!eKU7vK~4M*^W$nX6JM*9T;oqVRi)x=TB)W0YiA>;))
zPHCm~`6P1Re_QL&Me?HVlM2GxX39i}!otuZ<g1zy`54kix3*#E*&)T4v6FU5xGFAt
zbIEWFS;>`Zlo_r*@27oxUAA2Kv3GmpZFYs%X<=t9xb`_KDf5kW3155_$crwx7sn7&
zu(Z|709k&-#>CRx)<6)Y2tfs`VIeV+xSEvKnCX1>g}7k{P-k9*8eqe@RM(mh%2DF^
z5^Q2)qpL3GfKhX3Un+P%wi`A^+X%GPlHv4(V<sq|B0w8~gSYXd@!UxwO~>I62|)aq
z&PMtfKunBFuA9w%3#O{7MMgtGS`lic#7_{$B~LX#MEez5`PHW=XqnReY&<S*HQxW!
z|9xRJ2U6soAyK~kQ8_j>`_ZHdnEkiB!TSEOO*Yz!Bs)6{?IS7Ax4z*b-of?@0NATl
zo*q$~xSJ^>Z_ghRP6bDg>sw{^gnS<0GdUJ68+Hx>)iI#5e~&T#oJl2jqDa)b9UF;W
zr<ZsnddWelsNU%UU!=utdrFx)vL}@c+5vGSMGSKvaonWZDpJrd6?Ju7G)64jVQHiC
zQvCR&RA(l+9csiMn<}%$@r3P8uJW>Yx+?8%if8~VBx6W%tgyaN(;M=BnBUUP%}u{b
z-6&lEN!1~i{74jUI|67(yN1a3mFRF;kk)H6jPz@OaiCiX+{uq^qjNxE5;+X)KT*VV
z@{Kb1&b0@ods6QnbRvw)_YTO*Lf5OVilHS~=w*KP{c*#8_hAhZEnAoW_e_H_>&SND
z_cYUGgi{`b-7u0Ur#&Fk8AWCFlW1d1v~%A{#4*aRxsNWD%hmCdS985a14wkJ5-1$9
zA!P~@{<9Gw1lcPvqtukfsVmz#y3uSQd4@J&XD~Kpv}OOKO;<<~p?&%s2|$@CYK&!8
z@R3L;gcC^}GYct3i^$OXEmc=s&H~4yM}u9*GoJ4lL;($el@>iKCioI3;Ob>q)N1~q
zTdSoOId<oQ8A)yut8ucw7VQfLZ@BeSg6Wuq-*Nn}aQajJjM7DIgM@`OzG6#SaQ?7+
zHH1YmO3n>B(i9p5Zle7{8bK82-yz*5-U)zCTpn%0TqX&Mib(UW9m-$~5gXVhB6w6W
z^I-&;LPhkXHNTxs>*;r#qFS}(N#jo1W*rZ5>-@hgfVQ?)5EOM0MSO7UYkdJ<agxM0
zHLARm>p!8a7*3{K)0)4^iAjFd2V!@l77XpI64zub&eoZZ?sdeGfk#p@?i%~+$(#$4
zCY!pKn(12Cs?X}j$5Ex_IZj1}zpKf^KzX_O@kEODiZD62=qaA0B+Gn_@p_HJ2~C~t
zx_~6)u&S$vK@*EQfaAnOL#2*86)1ksao{<YpFGXZ|6W>t0yj-hOif_cr&RtLQ8dv5
zUaSmS?U_Zd5r<^!{M-77A^!M!v%?}N==bCKdJ@zt-=!GVkRL8(5-o);#kfo8z1zsK
zjao`HQ$hr_-GTP9fDEGx7=uLvh?LSLnjO}|scoYf>&e4V6~1k~suHcZkGH6-k~#`U
zN&XU!7}C_h=d)W`wpbP>U0wq3VqlcRKd-kC(}8N$XifQ)TN=odhoOwgi%8MF3OH8#
z@c})fyh||1e)xhx(D|g|i1ekDc%61a*5yYoZ*+<VrP%e}&C{#jFFwPgE_Xg`1?nZ~
zVCEx4mU=2z?V71Ld87SPDa%h*Xt^X~ih-7&RhySOYO5?yNpoC7bHaAsh7F{oNw0Qm
zgYn+7M)T~-@CY{2KawyqaWb<S*P<*KK|XYAkDE7F=+?G3b8fZCW6pHv;@@~$Y8G_A
zgduwDk*)bBz86gNvQ=-NqmyTtB>&ckQetl7<C@ld95fU|mFtCu6V)&)f*IN9+T;@k
z7kB2^%X2^gMI91=E37JBN6sz17HH1po{U5ER#vm&2Id}iqCb%G{LEs%3(k2v`6AFF
zi}8B5l;L-dBf~b>w-z#!J1=uxuxmSgp<9MTdLU8n<vF0E^MiFOanKv*a7Q(a79;uc
z9+^?`{pM;s9ULZo$W1aJ^*dqR-af5V!FJ{9a5kE1DpN~K7sRY{Ho&TqUDiyniL~3I
zi&y3j1O(~QX0S?aHf2;{7y5M0H>m|v(YthZ7`mnt(x;YNqJK;|KfPv?C;a_{XYROz
z5lM|3z-=Z90AOmDo7|0Fu?*i>$jPg#%grm@-Zh3oWTUy%5BFQGA=FX&o+26Fc=G`O
z!FR~?7x7Wb8uGAT`f0+~y>m<*ahz^*!uxdq+|zCY7{wvFMpQ%nPVTo>in2|F07p5*
z0|IDRBPe1iuUZQyX_fqexB6xd-07d{a8>v!g<6v@>+cXgD(kFB(8mBk^UMCn#2a7M
zN5q`Mbk0)K9;{abo=%Pe^45F3skl3K0w+o&$N$<5Pc2L5jHXb;pjdOY+DM_uv&Rq9
zo^tj%F%}&i0WRI@8JYjVc0jrq3_NaSdPaQR*8{&A{Y4M)WT8)%L^u~tnlxK(j5PR8
zBgOp{-I6z0rur~k5EJFcoZB<m;<ZGH>V(OvU8=mc79AYB3xCcal&El$FgijQjX1Pi
z%s!fw(X_EJJTxV{ev2AW8a|hk25lsZ#9{GDtgqc(nZe7T!RejX$5vFS!)sXi2WLp#
zXxB4}E7V^x0jGj91})|o70A`+DCbL?x~2CSPb|G}H$*jz7?U-SL~e;pd@dOC@i?9z
z+6QmktEG#)r0RUv;6s<*6EwR+1zqR#bV1}|QEc87y_;_<;Tu<P2JUN!vR1qXYAC@_
zj4&dY;%Hc*(!T$Prn3x+t82FK;2s9|V1v86y9al-;O?%$-8HyF@Zbb@ch}$$+~Lmq
zRo$O+s^-tGv+H!PUh8SMq6SmlB<^qcySs=dOjWJj54)%zH!q9P6N;s+@;(JMqP3)h
zb6V%axf(X(69exbJLfuFKK@?IW@slegbAXx$0(u@-*Mp}cOnxbnH4ed+WdUv+^Vm5
z_V<hOh)ob+poY^q_~13IFJ?OrUm=cCj8asX#Z(#>AuHA750k_%?*t?`D2L17Y6Xs4
z9xntuALb6{)Ufd@El~@Z$Byg^=h+>+#&z?+M!nXaToqn=7N)OU^haE5cAQ=XxI;lS
zU;64^pCaUuBVXtdVTLj&0*5h;;URjVov1x03IJO0X&|x!TfyLnZ_;jC52iR$t45}R
zk^4)BSbR6u7cJb+gI!@`^@DjU9Jf0JIFU9V(>)(HcN-_REFFT!tKIYNFRY?J9_*0#
zUl&d=_LFsu`;OxCCemc9{n)Q{<8!&kh2FzH#%>-m>x0ynVx$#t3jX<oh@fA~$=Rsx
zlZQhOm6l~alvod)uN1KT8!z41vpo~PUl3hiI84cFZ))P_<JmfWhzcLH3WYfJfP?G<
zDXL9|!p9x3H3z|wm<66y4&cJ2SZB%dG&ZT>Qn+Wp#}RRbvKwrc(*Qa7Y8dv(5qg#P
z=rm-%n|b+NlrjYUi*kKFaz0(CFqV4?D$rvLc-(^G;_0VRrFTLXB<$k@jvB+m`~Vom
z{Q@8Z0KXxBqlN;5RYYho`+B&A>q#2W2V=<3Y__{ozGkkN+-YGFpyM;Cn+HF>{oAy4
zK01iYeEgh(dyFk>?b`K+ghj58Hhd}57kXFC!bq2-%(hfos5pA$dv#bZGhz*J8?<kI
z|1I>ZAL9Ndb74$WHMJgYWQlCbVrV`}%b-pb!@Z(>b<0(w^*s>j-x_iI`>v^_{bp-`
zO|59M0pgF_Xe1i-ZS5L8ZJUfr^rvomI=ul|T7t-*_NZf-gnhWCJ5*@<qLLYi1=0=j
zj?)g~Km_b@_!HJU7@^kRlBTCT{<wD&u0s=tuOT9>^NS#_nbncf8a*dxU(q%zGA=Ii
z|1xhs7<S<z{d<IL5twYkXFZ`I2LlO5{#ArdiLgp=mm~;_K$t^<PfT2z8KOY^7l_AF
z7g}~^03HKH0#MrI>t=O*TH*h`{dr)M30<N`Iz0PxcS-S6>|CNQU_N_ji0(`di3Uc@
z;g{R=)SqOkw2sL)K7TegE~M|=ta)rhjU<IWB8PZAf<ggbq%|Ti!&c|#iwKU2HZica
z3GVPYN>Yq-V30!_{x+Y{eXZ4Cy?-#8e#lLHG&7JoV$@$@P0~Sa_v89nrRZu~vU_K)
za73LtpuEMppW%#9oGI(I;I!$?$@dE`Onz%*3<xQaxW`O=)R605b_ScKh`Fg($<Z38
zk&{smLJ00O@?T3iNTAKa0!f#ZeEU7=5B2MbQh&Nfw2jn${=_xB<Nz*_Ix3uD*L;#$
z;|`L91O#aUc=DCcUEwbNrVD^X!+=MUEclj44>9!ZNgh=UZv@_09<M(%1MzNX`>i&B
zkMvn{?-E^K$57ACVY8=8(?xg$7gM6b!GZhtx1aR@CwUcKJOT##(e01pd`N{YUX_8I
z<vMztp=fsZGFps5z1_DP$wg_2FVc<vR*%2qDyplT85wX3b373!D$f1juDAp4&*L&P
zhx2$mtgTRv$KLkvg`Kw12K5r><1W@8yffg{QYQU7utJqN%_Z0s&$5VqC`+qrqhQEJ
z&;zeSnA2JNXOa=V2r$8SHAzNg+6E_<YRRCFI@3o)iT`ez%cfP|>s<s%C=1i7_jS&k
zKJ<ld4wTq)u9_tWz=%oLVFDasnXyAjf_`h2e3+7^u)q<HK~hU6kx&A@Cm2Rb#DF8-
zk?^D>p*eE~9nX_x`N94IdR=`}hH;QR^$m>j^R$2GG`ubBDf6pJ&F(kYsJeP>ByF27
z{*hG5$HYv>OsO@T%+k&usIY9>tvdCt+Xpz+ptT#g*i~dDMLm=KNs@<G5M6gCg|((^
z=7zuWSrmv0{W4+yh~8~;_OR}8F+pGj0TI87p=9{G;dc_<fzN|*A0nz1V{)Tw8?Q>W
znXAK&r$O-;BF4l$e`bO;3)ScfyPJe+7|;pR)q9iKkzeQ>_9sV#$<VU(nbtSyF89Xl
zQH$5OesrdX>|DjNdgTu96V^tbWYZ$9>7a^f|A)r@w10cf{mvi%C6a!n*xp2$!Aqi&
zj$}j_g)&n?i^YV53rqPWrTvnJhSd8zBkYt+=|?Oq3`{f6MEF92nFRgI_w+#4rlPKt
z&6%@f4fC=4Z*Rc+ZO^{xJXEV3buMqGOM}Wvqlc2enXmi$QX9EPCb=mT-$ZJ3g=kyr
zEVTvwPK2$Z6Gx}HJ(EcJg?Cb6t3W(j+qxS%M2OZ1s}=85a{AKNlc~ehAsf(1Q`~R;
zD1m|A_B)Lji~K-+43;XkWt~ATqozx^HNM%5oq2=C+Kk=wyjt!&w+U$|cX~ZKw&lEq
zB~Th3Ca-@V2=&)>%?5`FMT60>v-7vIL$l5Js$Q)nDhZ0-^8CH6-;P#^@&L4vF;D)K
zl02lgkoO(#Z!1ny2WrkL>akxbow)p59^ISv|ErqR!s`ohdX;cN$fnTT$!gWqBr!(+
zIFzX6>$y3Be**Pm6AHyhaZU9cH<<%>guji>V07<m3cV@WBZr!w|Mq)RCI0-8|Jk+O
znD6iVsPrytI-?u#q833xm;3|zS2aRKEbIPZn-xz@A_x@RCIVf&vVE*sS#<A@bh0#6
zqYH>9&*$yd{L1iR%>M6Pb^P9(FDG|hXG|;dN+ycmpd7PdaQ%VED2tYyriOamvaEF2
znTRW6EVYo@+T918L{AdjvrZ~@>`U5X3Jbd}Ca5o>245%E*J7kMh6jHme1X3S8&ALc
zN$O-MMeodKyn~JaC!#VDSJ~d}a@FG6-D@?#PxTXQW~4TLP6@{B^f@B@t?gm@`JYD@
zYu@O88Iy1Z<77?2ewrYlIDaGwIzUX`6p;lJJ~(2&ZeW)Oz;NlmJ_rdlgeFdZ_kBmI
zU>GC+RdMF>ICYkaIRCR>Y04*ZP1oUL;Pb)9=wj|zU+8bn`%Q~x7v*PBPmW*{U88B0
zbh%0%Mk*9gCbBq5Ezfc;JcjqoO)*HkDV0rAB_bs{EFntQzN3hplW|m%ZjVaSniHm>
zR>S3_i_d~=IT0c=C-)%GyMU`lXBpG{YJ-#O3$J_6(eIkPGjjj8Jqnq5`tcoQH6lC&
zUsUp?Q}+E(3FY4JdbHT4*b+g+KSXi{5TchgHKl&#b6|;qMH|56F>1S&K5eN}QfOu7
zsYi!FPv-c+Pg`EJP1Z(+5`u05Ggj@%k*$eXDFg5~P3UPSq|X1o>IcKY>*2$?L%@R_
zZg}>OVot9=cyY7y(1QpuVv=S63TQNFJ@^P}oYBLXcU{!GpFl|T2#~f>$}ijjnaXeM
zRYleX$gzdiQ~JN#-Fa6HI`1za2~5s5(P&ECdss@v?`%kOAuFJUx;2^nFRDEEZCy{+
z3tI?lYpWKO%|C0xX5aeubdHAyzL|m`Na6^$Iy&{T@vCstny+RUUfr+gl<Y?1r;4MB
z{HOoY8#pW#W|Xmdk>}o{Yt&b}_i!CQa#`DCHW|>GscE<EFX?X1VC4LW>2N9rf9yv#
zCPGbgq|-n$OB4&%uX$JFTEe(eP<&2&t4%>@Jk7hV<dh=d?0^0521Mv@D1;JCtSm%B
zPW5V_tX*$gt6h~2q4Gy>1EBd$TR~@s@io!Kl|ERTrmNV_1MWo|zU}=|x4l6$<Mz5M
zz<{8}fN>rJiW9>3!Wadsl9CLE!h->VkRm~(L6p+yL)tq}TFNO+5@zF_x4BfrA4-rk
zQcNDI&{vs_IHySDs|}?Jj#Y)Mkm8UMZZ8UK*MkAy866H9wVRWomX|~4725T&TojD+
zFj{pt{iNcm_4Kz`wcX!HG^bWWg0iMCsFCS5%iVMq)-z?`7=~>;%q3jj%l9Lkm7}?1
zs0rxemvk8r`gMwOqrG^O$tr^?iqJ(#qxKywn%3O9s3V}p_+x^Z%QH$%#$sbFU<X)A
zlB(C5Ehr5of$4PSTtE3d&dmYP-G*ajuq~>LIMxINVZKh|6B@948dU}~sf5G&y4o0A
z+G~^lO)HNyI_v4r{=gCYKI3$?-7o2a30{x>k39e3S`6@6vNvpz2am&y_+<#EK!vm^
z%JB6OFo6Ixc+%Y%C<GD&s6Yq{APq7`MySjV3n^w`MAROL%lk=F6I+8kq}{FzXG&vU
zGJ79Vv%jRO?b-?gC`AbB=J&cPcjDF4H-7&b!heZs1Q}IAIC4M-v$BTE@Avk!B(Scb
zv`p0+vvfAOf+AT}B&-P@g~wt>g{_9sA=P5EV2DQe`+khoj$X$Ua=eD*q$!?_;YSAv
zKc`@c7Yt4gF5dgCw>~3;C8R|UdRJA9hVKXmd3JHkfPS8Zf^yn(zo_|<Ncabum2BI&
z&xgVllvKu9*41*FgYY?imfoZ$s<f!t@zoZ{<eh0-AkJz9@hh%OVz<kb&412GF}c(Q
zT)Ll8W_PB54~8^}83G_o9SQ)5P~$>Fo08)G0E3Ilfh5u(0O{XQmvE4}R1z(&X{2;;
zAPG;8&2qRFk(|c5@>zitvCnY!wzctoSI;v6p9?<h=+pVs$w|E(Ukj@O+@LmFE~>uN
ztzDs|qwJ6<DCFP9$@2<Q%hh^^>)yhOC&w{d&{<qeTr*AjIdg<*aH1(y>gmAhchj!L
zZwapJ8)lp~&;~{%yTgh_#N>fR8L%>}I<$1wvhMxhFcf_{GTU}bzcZ)mnQP<f9tr$+
zbW5bnh~VL={P$BXVoi=)oXgeRDQrEG=?QB@7gqrkc<CH!O?feho3JF??{Q~daoNo(
zkT;@jm81*DZlTBs2VCn>AI)G+ipTR^a{k-FeeDbrF=ANX523f+BQ_T{_MXw`VPMQx
zauT^XQp;C_9TrR=YXVT7`v6`T5M*W)8VbS~f(h+a6Xy!&M*{$+06RGnwSgHF;LP|J
z=Z1$;pNB;-qa|19^YSn(tSUWT=b>R<8yzv_77l89`DY0FSR%6;&mGNC;pYTJwe~7Q
zvZ-m24DjWB>-=1;W_1x0PbLbdZ^otG*lf-lvmul+W<Le=AfMju7tZWqr^%&qp{O6k
zQfS<kV_3B)IgT|Z!u?CCN}U=2;?C!=wp(`(NgRtHrhMNLPBxNM)zqnB-xYe#>Tq1r
zyLhz$`yA35pBGLu&2!Gy1*BA6O{_E6rf)TD2xxkjPA?K=txT+0BW6lc$JrJu6B+31
zmJMG#9-SqVioxC77k;JmZT^+E(daS9O3llRNyFUMcgc7PmQXE~81XcU$sK4Yh`m4&
zKz{@+tC|~dV1yMfI$|FH4ODp1YQ)5fO`GfaX#DZ<k4fp;wYIi14;!`XWNC4#k0uDp
z%@#8WS!H|xSg%S<pRbPf6R*L<oThJSCB;lOM1%aCeJr)UB(}Y@^4Ye1-33HuEZ*ov
zBpzw36)uq!QOk)RRal;+vT2ZliDBYpY+RBsIk!@k5)J7bDa}yk;4`b{MjrvGzQjr+
zM_HV}$+e~rt88fmSIgJsHlgI%RbMG7IHjeMbif*(DE>8CfgJVm>jFwFNhBThH5q-T
z0WOvtmLx>Y<5X_X2=f52fcshu=T(QdrZ=0T>jp0D3a_3A>=iscJ(VN3NY2hA#TCHW
zSqKZINK*N#4BBha$T%YT`3q!>n5d4T0?oXq_D#fAX$GXs9_xrmlMbIS#uE`(XiR$B
z^D#%!ti$U@vDCOKz&EnHDvZ)TR!dH;EmAp3LE>3mLIo1{S{P=AhlOg@m`6jsh)^y_
zm!ydwuEbMMvZfm^I;2gOz#RgTG0HjNT89y`)>s2aE5Ab7E6N-A-8?Pq>b6sDuDDDT
z9u5~6XRu++e^t1l(U+|N7W#4`v1nCsv6Gu0httvphSVo^mR8gRbvdlJK(ho>d~HkF
z4_mCQ7~!KF!dg!Q!4w9Gek0)#=$Nl-#U)m-Z1C2}&DnCme_kA<%L$CNU_x48C`wKL
z*hER}|AC6C+t(9!)c$7SXptNcDd6*v!{Y)A_}2@CN)p<OwCQL8R-K@Uen&<?3}KQL
zoBx6kCY0!o6-^a|hVLa5BZ~}V!N283AS9r{zt0udFs^oiM=yMv5t!5Dm5?yzq__`1
z2kswjKhK{J1lZ*UnAtu6iqJs9GazHP0y9i<xsa^l_%ep*c80+jGbVACkffhEbBcAm
z9Dm^iIp<;F*wal#BAD-V83sq%x-By;81oIi|FUC^jXL<6I@N!TmJX6$RGYr^Vwo<X
z?_a<tP*`1X6;psN!bA&gICSke<gQJgK8|%aTy|Q%21Likl;GlFOZc=*3<a}|`Q&mY
zItu2>6l!szh^38(FQSUz4w|MaSC<Ep3xtUYVXLVj-(HtZZ@rL<dHVF5fDJk;=pR_N
z?QfLZxtd+4>eGy_K`i0GEihf7veKZ-`yM7>gE{7_=uesmNDvS#uK}R}c!*T22ke)%
zauyKBv?%)cL+moR@mMlP@<2r^%zkc&Z<`+9boJskOL#%RF@Ff}!yN)8{<{JD&D`G?
z4DVZ&QEKIBKP~#;5sQ_-f)wHUDVr*B$7Ecz6?dhT>jBIUK?RgKNh+`qp>h(HMvxd2
z6yFv)B|S_Y<F@8>$Rn{J%|eHzT8IUlXyA9>!p2oybQ$QD^>iRa>1*pW#!V}$Lqi;k
z`9qkQ<^8(se<+uiJJ-xl6F?8+A}+bM7m<7EtMfXg00A@}{JP3&>WP@zx>X$R_S<R9
z^}iZc&TKOf-Jz*d$AtUZ>D6V)?<Fmb&Ao=roZzqQr6;DQ{HJ-0al4^q^H8VVp22V?
z6tLtg1m+c6H?Jit37&9IA$=V7j}}Q~RSM^dF*1h(f06tRHZ63d%~a(pTvJ~=qhvRO
zNL9xh*G~MMpc;<yg$&n4GFU3!;Eyt*aFiNNKmIQddJa6Sl$vmfcBFWG-K)J2IskL<
zd}Pi~5;JPDlH3R>GG_$Yl@V?veH!plK^i6`(hf(|hd$a_ly?fvuu{xc?ZTH14Rw`J
zu;n^$N;AZ|@^yu!E|w_@a&V~+ov?=QjLnHU7_lYFRPmN25>pJ$QEMEk$X@W|S;>;d
z!nVANwGvfwaps6n>4U+bye?K&pAXuhfws8(5n|XUQBf=lDC)`TyY)+JQMGbK`^nco
zb6FduHGr|>7^dt%@)GueIir~_uLk>vtL;yLqt1PzD^G+JUnMYUv4ueLtN+vPA0uW=
zmh4VnZgAQ=5`*gDax;7UFccUjS^zeQS#tn-S5`8tvcSltG;{ODRU4}T8yg#@o%tW8
zcUq!^P+$>&`jDFe3Q<@ZO*Xh03t(1^F0Lk_nP@g3f;38tl`{M_)^=A)b-u#i)W}DM
zHzWr)-~Vr2ne&itz}}?ADCA(&Xva_Yuew&32F8C0Nm=I+BzcbzuIfa!EjNNI=$A0C
z7ZU9u1jL{ST)hEQB#zI$Ly1z#=*AS<y5trM4RAUWWPv5zQ84X}=IY^E^9p#R$ctT#
zd`xw=%#uEFxqs$HBEr1-ARYd+sHRoW@*er~WIIC<NYDKJR{yG%^tY7qtVV~%@Mn$z
zzl*4ncDuO+Qi*pJ)#x7BTD&T#ec%44<%DM#6y5x=6{&1=d}~=(o#WN;%e;O5%BMLt
zySRL{%HY1zSkAM1BLJLv2Z!SG1@_u6OUE4`0nEryfS+gWrB=({{a|)g0NC3I2(;xy
z0)V-AfWYhPYnxlc6|HT@n6FRFutAlh2r%L>38skTkw&d;Xp&G8tqH|wxDc@W<zg{8
zwp!@GV8obW+<#+DCnE%vt#dt3-&Q(gwLZIv{VoziJgW8lP0ZYd?#wJoffGpW@Lm(#
zR3ir)BBIPTbO)r83}2sP;BZyXx$rKG%_5pe^>8$)25+oQN+pngEkk8d%CqL-?>Eqz
zkt2#i^_vdIYtsnik3xQlENnXeBhX4L_%Ko7-H7mW(@C$o%yoMDFCs2!^~k*idC|X*
z-ZR(J{pT2HyjlA7+t!Km@@BqpNU+!Vhc5(mS>n|I0tcqw@}++1#617!#0dr?)sHVk
zE8m+HvSLZU3OU-;tm5V4qehW)$tX997BJf)Oh<>np>FNm00Nl;p2x+|8b`-tk#HEb
z(#*Bftg@CRYYjW0k%y11FcEq~Mna5%jko|(k{B3ZPIefAFwl&JOdGjl0tq94+_nw?
zkw`CB$-<H*=usJ|UFY$^7P=PrEC1C)=&{kkzxM)eI!C|zqLv(aNPL_X?T>-x2uigY
zv<I{b#DbGr2${Ummv91=F-WwC60I3w2$Bb~V_qOUw@xC=fg*8<%7)8#c%vz628Kh2
zwrN9c{)Y)GRf#rN2`YSY6X&Ll(+YxE3!PSRLKQ_;+Qf*^$SB|=+184dWg<DjkT_57
z`1jIG#dCk)OTtg8{I`eNO&`d*BXWL6UnWNq%N*9xIje(*3xisI3LKYLhAP!6o{N{Z
z{cXm$cpSvc)&BM0fw8qAJC3qgb~i-N7uz2f1d^OwMqF)E;8@j1jf3BrkD?Da^;5NY
z3QqMIu9Trtok<RS-GQV=XP3t#ood@b2E>tI(_;t4M^N@*r@GRZBS7=S@&LshGXqn-
zf6^#l?Fq?^l8EdQ`|bqz-6yVpKC2y#@o+uV6!%b9g|^c%BS^c;iSm7`=a%0Gg@XQ)
znCJuza8Ak3d6F21u^=WVn8Jt>|Bcp46h8)OS|c3M$APrNX(rD+8dY6f-5{iz=5^Ta
zVge_|z?@y^acHZ-N(-Opq*4u5yoh>9vvoJ_Q1rCZ=8|>Piy74u?=E|q%5^JG<8L$?
zF5+{zjc{G^B`a2L5|}<AB*`p#U)S8@(Iv)X85eB!D^fPkQYD8IKnb-X49okSULOrv
zljf>JlNdz-g8y+TaZNHl+RT%=^!<|wz!?vwZtS`b#W%2u{(f*^)@4w)d<Ld<`5F@o
z6wjax2{UIvv+pI5xupH$-itx6hLlOZCv#Z}OxcA-0-44{SRf9C?WBZY+Q5dm&+&Ln
zpMB~1-2QN;G!=MrJ$n3%4{PmVcs2YA>)^F`x>BnRq5)LtC6O`({}RhkFo%L=X&7UU
zgvLyH1;VnIlOm`<6!#8Bk)4CZrJYb_Qy|ri-!{U<%|UVX_Yj?VSsaZe0-IQGL^v*U
z?Em)ykZ32tgd<2q$&!iMG2!0%7LGNDqL0A^m5sIoeM?qpi`_uUm%U=Zg*3*qtS@d}
zB?fSir8mzpQ8(Ib5Bj>w?UT(lhdgWu_{}J6i0Fmo-*`R`lEE|@8p}D_62)zq45;6{
zEIb0zg>pwqL|!3)&pn~+>@Vu-Kc4QEdp_=`yEk2*74j5HR1(d-oSd%1o%EL<ueSfA
zYL%3f><`BaJ*=263-N+Ub4b)i+>+Cz0QB&raHt<)%A%(5Fo`G-K|j&%$w(~C*v2X1
zfU@Y}@S#8>m_H;@p7pN-`6*>CSD%X>B@8!I`G$NiPk>Avs*zYby!G$&^5+>MB!2-U
z34jS0S(dQCUy>!k=d}>@5VpO=az)rc7DZ^~gMpUBVzFTD;es<1G8*ad%uy%)_EZsA
z0YkaWNrS7?o1`zEocU)`A}qN?0}HS+6uHJZH7(ib`=yza8F<wr2FDMY>#4Kow_-Vg
ztFtCm{S+FDq?%)?%>S754N@{N(p}P(xXh6^aS7KN`P65SOCLLdB8jYvHGCfV<R<mq
zW7x}0CWGV+$jUlUpLhSn)~UvxWuO1T&wkS3YFITA+|V%Tx8Kd#tQ;H;#S!r_f`5Q3
z<B^8Wrlu4yX<<_mUY`$j(Yo1H9}b|`?Yny0d3b0(>}2rRzT72k5}qu@R7JTj908!A
z1SQ!TRXgH~Rs#pf0q{~8(m5ibXnoKay^$bi{2SiW%;_#vn!D+MPmlSukHoq#!)Gaq
zFBpS_xejEq4qnUoIsDxnG&pqdXU2Q7&iFrZ$q+`1CbZE2CXM3nSi7yDK-1HI-O;$@
zZB75k(Nxqq_{A`yisR&&A={Q=Yscr!|8)N8eO{a0hjUkmRg<Dk@o}2KxUBDOtPn2-
z{Trh7q7~<Yr$X}5PP#}qQq^&@C|0$pu6|JXtIK*N6(QAKx|2B?={wfhIV)dQtz*}v
zLll{7!1tvh&;V@pNTWVpW}LcMU0?t1$!<?y=h8u;UInadCFC6;^02dd|0~`8{HZyN
z%KxvodHt%}nG3o9wliIHDd4D0`?gFe-=7A?_U7iGFANDNI(=)VotKpb2{_;Ec=Uca
z=-7V0e7)>of`{CG)L-5N!*Ql^`3(74#8|uorDoLd`PAI~NCAs#(A59TLB`6k-YAp}
z$ZdeE;KB%NUd9Y+c;hm>M$mJ}_WL4yOWAN$J|B(Xtpu$VAvG3Sum(dhZR1ef+jhR^
zaqISN3-<|Hk@PRJgoy-bG_fVzXdM#!z!&}!7HK@^;ZkT}atWwEIw)EU%G*O$c#<2J
z)p%{e0HAgFPhV#EuLAyiR9~Nr#pX2D-P99(9)8X@k{Xp(x9~#InU|+AW_xqyBG3@k
zWnh4kK|vr4<MIT`nus%6I-SFNZ-rt75y!^mLML?hH1!faS}dGpYs!?|kD(v|J_5U8
zH(ndM@i3VLS@SVT-pC!<?+yylyu%F3TbzPXNSQWrA=#mp9tXGt1ReWveBJv^{{@Qr
zYcw~&bGfHG1p`5&7z&y8G+wV*8~1=%#j@M;r!7@5Mt-Ta`+g9E4Z27QU|X{n7yC{<
zaga){JlC$}K&3mb!YDCS8=73XU>r!LHD6JI9sv-~a4I14d|oK&k-J#sWaFL3B!1^x
zXOARi2_#8s14hxKO|ES2%h4S%rn)Q1I3)-1gLnZE#**d3WckB>U#TJ-6L)Zhe|{$<
z1!VH`p+G@nYMR|P&8lNf++!-5eWlu5jI9&6zToqiR>6Dq^I-nh)G&K15JY0`Tde2z
z^2UWCp-?u*L*A&PyKSJ(Huex$e|kJJCQDwtK+>tkC>L&D!nQIdnd`Rhj#QEq$cQta
z8kxymU_T=M3r)h_w5tG~`s4(bN-Ho_Qf7O-+q`=5%$sapr@h=5SFA#hdZS5Va%pL)
z_C0CC=c)@3s8YdBPXYE4WKaGjkLPEl2Up?MBS*JwdS3vJe<$!{k|-%D|Dz91O-|Zv
zcl%jc$t>S9=(dO}vkM8;Ri3V_tVGjV;7<O5_y(bvU6?XhX#^k{Ink25loX9Z0|Bjs
z`(>7(FXFzh+ICUs8^q)o;IbKX9g-yPs#+F3`j?AQU>cWNB+#1{8~-%54b@AV=7sy%
zX39IL!A@=nWWnY{yXYI)?F%6bb%Gw7?;DxpHd3Zf0!jp-Q(k6P8T;g}`k&vYtol(W
z|4@<qEpW~kpU>anbG8&n5&%T(MSw^=!kLtxVg(Fs`<_~<F5V211tt;9>wa-qcRQZh
zE1^(b=G4GP+c6zfWB#>IBI)FOo*f@Fqh0038dg{>J6O#q(u+}B^|CZ8>*l;Lk#@71
zqwsUajlr-YCKgWSfcHA}Fid~F)Tf@U)pA+?u~SDg*8dp(kKxPmW5Zse)K{;AL@E1~
zXW#k&%i7zGpOfH{+&R~xkw-`f74X~l`KGT=6gS-PsSk%XHR`NAol$>|-44u4UZ&;l
zf~IRR28%dUdG5aH9(o!I`!SMS?M~@KK2l+DMP1~e2u~f=PEh98T^hV96*U?>_MM~d
zr|p(QkPlE<{&K^P8X96`UkgAF2quiS9SQ`l{{Ch8$e@|WukZ7w3}lCi{VfDgHWY{G
zMFc{^*!3mGU^1ji07(CkEvGf&m9R3ZL{V1s+6co}EdRCmJ6p!I<uYDlHLey43B(td
z`7>79m<eV<f&S1%)8XmQGgM_vr^%5QrONZ9hBt6{{QFs(loHt*pT4A_QR2#LVY?Hy
z?tTSd$epY}A+KY{gVb!#X@}jgM&i13Mo=YJRnf%W(~jSf7(qP;F0+sPe$a+PQYcVz
zlgp(g=@{^%W|GT6Z<rC4=211!_dqK>Hyge-gG)`Y|D|o}{QP_{8mA8LePcrp%+j1o
z?}i3E{g-%-_d7SmVKP7t?se0bP<}XHZ3O#g5)%{8e4b(@_hFXVV!ZG85#j>&-umJu
zYRN5=zOP5H3#84$=x#(f!z{+~h$J|<PrKtOm&^AaD)s@S_1yM)mGT|hr6f~SIsI<6
zPVY(}6i5;iK~SVd=yTl!W8pF1b2K+1F&EgJTt7fmS^PW@8SxlHKS2%Vvo@nN92Q>?
zO=)Uarbsoy1mJ1FleJ7<P&*kw{utx)tw!v}7cTtCZXBCXKroi5nb=bE!s%(H{7QWH
zr4)9$hV`U{xYx3iyDiaps>^+BELhqn51V*8KVMtR%3@TOm9n4=+t2IZHT=ao`!m4%
zLzcym_g4UFY6?~SS0U$PBQx1BUvsN2QjwBx)K3LJl0CQo_~qPYgp>@X%4ybJW@h?$
zo7(LUtde_d$9^Za{X2^qa;0Vd0i)!*bpV$o^im|W2F@TK92|hD(>ws-j?G$)DhKcR
z>{_La%uLcyVR%am3-G$8zO&gkoPFvius=dH$LCb(i1?%VI8n&{E8pkB_0!Wc^`G*D
zByWEe4>B&G7t&AYO(8K!EM|)xBM#3!)BFs<a@&f^qv^Iz$$-DxpHu<8c;@uRDUkX>
zZJqSvpqswU3QNp66}1tL7-!a5vU=8dNa_e=nh@m(vk)g?6&k%UsZEIZ%%pJ$MAz_o
z`#oD2k$yXO{39IHEpH#fq1uZughzq=sL+uN3HCVZBy$N4BFmjHWx-?)OsoL`AxL{%
z<v^*@(gI@`k(_Cr^@poS7GT5NJ(P^1NaBFFN@O0Xqp>*_IV&$)w~8S|`2Ag~`MR4a
zFu%tak2T1%!S!BtIy#s{Hv467PO|?JUxr?@v_RhdY7L1?X{ftNUQy9sDqYBFZy=RU
z)4*Fm=qG<ib1xXv2u$t#rbf%f`2)-Y8g8Ck0m&qRjAI}G2bQze%|KCbGAfNihAdR?
zQ|_*Vu=0i20yn!}YwLC2&`#j?q>)NcGXEnR>PJ9@a_9T*OAUGB9D{B#_7MK=58AyJ
zTJ5{z3RoiKUYjeL9KDa#DovVO0C3G!J<^6z7&?*rT4awZ@dpcZC9Jwxk2RP-w}pw`
z+YU*XA0~(@+JOIS3^Q6_?yTPzPguH%9RoSt68a}X$l9eCz$(WJSF6cw>S{eRaqQ>%
z$>W8JU>yZ1j{M_h0tS;=%lb0Q4f9Y&jD~EBon_;TCuvQ#j-4A3i$R42TU+O*O|2_r
z%?z8_s>5;~|BEXrm0iz0Jl4H@38qJ91(6FNDzHjI;Z#b5C%wbj>H1+4jx);+B%{G%
zN-ODN&T@`I=?>m)V(0GWG#Ll`!=1Q+DgXU#w#@%U=nYyVo2s;%YkBkeRet1oU+821
ziu=O^6BuBAdwy#^X_2hLE6%b_1r{{9j(ovLLGYo2>x6~o-sr2>B#?f$FCM@VOv)5`
zEUUek?>0j|>cs?k9*p#4{=V95rV{i&JwZjgyXG@|ysE7wwDtHB6JI2TVHS{>gZHbF
zQhnZ4iS)kh2N*eJ8-^=~)^w&sFCMm6Bb3bU2g2~fWR=Fn5f>F%{y@9^Y-oy6o`g(b
zjsT{S9QEA_v>w`4H#v4|QNz~A5#(+W)pfz0NMy)@IqA4Y2}balXG8bn+{x21e}m(w
zxHGacw^qtyHLHvX+YDs6ojQ6*<<QNmGZYp(pr$g+E2J)YQp9B%mojmQlvTVv{F>M8
zUm&(QImko<yqz1?tBEqQ`Th46B<?p_^j#RX4Ud~Qhn3EI%pbL;eBbI#J+^$L9A@HS
z4i}cOo>%Ir8dn$oFkl?kBTS!T3{ggBx`FJJr8x2Bbj%BrkkHm*W&<3y!0==J9{+!T
z;Fgz{w{P<vk(H+@0fAtuCOqI@OKX?iifFf?bKAz-+Z$L7sB0$l{P3G$SMXtR+Z_4k
zJ#;hR@DDUjO0#iWySI6@^0a0s__6pIb1<>>XF88=tFa|uef99*p0Am=j3d2B-uJv*
ze<i<T18FJm`Q{F_%jzWh)BOB0A0s6M_*8|k6r`PC7UzHZK-HqvNCuC-oQe!1S?{Q?
zVFMC5?#NS8b7A*$^J-3*jI(SU?)i9<CKYP%q3VTq$*N`&A-$bRq{9NfF8ozKHvQvQ
z#zeZa1~p{dwb<zDSC}K0X;HL^$L)?T5h8L`BlHot>|ssNoRlir;CpyCIGTx2`5oUd
zI#`^=1sY>HUJdKA=P)4{p4FAv5ocN;q)=pS4v{U{$6bCppU=)bs+G8n0$wyMgzt(w
z2BmA(x?j@Ndfd~}uW0FHYBJXUP<PmVy?)bFk|uX;Bj!EtKZTELp@Eo)$PawbpLa^1
zV18ttXP%++Fp-Dt$M!!+=Z`lpKri@^9p0B5AOK$ox&N6!(-~-iJBn6tv>8laiHwZ2
zj3)>WViEMX-1v`tPvCC`zDr#4{+BtHw^_@bf5TK>^}>zrfHQ04LY(dIkA3z1S-2wY
zoX%HFu^aDGF8N;DY{MTpJE+8jZ#Qj;#66u;{&yNMgY}UMZz~#)G10q+3?4n1FKWt^
zq!s`XQUs3ZHn@}C%m&4+`dBeLvc?t?cr)m}xXBKv?dTAaUt$(hign@qOTApvIfDGk
zcomALh$p!MGHp2n3p0s%)1Vj$o3oQQ>6P{f^p#)wKaa&#-ZLbO*_CSnBM>v$7isiW
z;?BN*E_t0Dw%t+iPTSrVZ*fW=F18z7vX53lfodZs^$gc*^HFNwj8gJ5qF!Rd+QoSN
z@IdqHl+u+y)9Bi3-SJ&P>6zQ*@qOrrX5$<3bz3HZC9MAi)5on0^2%Yq>2b65m<}tF
z=XNyw_J3Yj)UVsxh=JMRLFxx5@6VQ}YY2Q}A3Ki(TwGiNZn*+(1$y3!h~LY>@c*(t
zh1w7n#S)bYwMsokLt<Z+8~JXf%kJ09vt<z=08pDs@EVugv1#A4-DCV~(OMt1qfDjZ
z2eChQ-2Pd<Y?3P&Yn{*QMk|B9-jc6mlY8}p6t_!!#Pai|_46{?@cB;2-}XH2bEHu>
z%z~z&<(2AF{Fmc(0cwu_`84&@$iw>f{m$5DlTG^lNNa8AyZrkBG1ldWVZ#0xPf^n+
zW3dS|gaSY=F!iv#>xE9J)8J|VTdMsJ37~jYzo%)TQJ*YByaJWv=%!p!3U$Nns!Ci-
zm&e1qNUNe8-fXU&`R3Ft2t)dXsCYyzDdmQg0)c)T1|prksBL4XvUDt1tYp>R{q<d=
zN<TMaql-g{8O4V)5Iv#z`u+9wf+T;50yM?{d^{q)sGE<YJZFEGsnVOn3$fk&$qd+C
z?(sdX^@%4bWSn5Kh&oYW>8Y<TI4*^Z1dXE#JjFkDesnK}!61NSq={elOg*;SEQ?d9
zR;!7^z?t6W$0ew$%D%Q0<XIsP;&a|@r~Nx#2hV<jt$*QMzUk%fQi3;$@|3rJk?RM1
ze|UOsr})n9Uz#ZirfU!GZ+2&;h`anRUc2qqr@wj$($mxP`%F4PrI~}n|KNe5N5>{u
zRr-ua^7BU~cZCvn*m>()-*$Z5ecpcQt)ouOUntuexxs+Mpq@F`73}SMoDA6Ym=^dL
z-m6C~*?RKV>?(5FRefIf-=LN!+4%R_^Ki+}J$2K2`*izt(_!1AwVk4w@hr_3+#47%
z{|E{N#*wP4l0q4CtxjlIQ?43&+`<iNSYTK*@Qp%>ske|MPm1>TLK9MVf`&L(0_$gu
z%~snNRkk-(wugN#i7P#uZy^4_?hf}%8_v-^Uo5AcRY^_19((P3<#vZtAP!@6tbUu6
zX*byoQvcK$@rh2gPM#kjz+=daEJIXI^jNiHQ=P9a#p1b*OM~r5G_KC7tSoJ*MbTJi
zo96epSRyECD5Ze)(?PA*fv(eKu#~B<tS~oV26lDnDOMHIW<W>6M|N`a@!`cO)P1+>
zu`;cj`pZ;YX4jpi!umY>>PH!5*yaVrq^aFyK=;>%Alui#RGS_by6n#hFgeqVpWys7
z>*SS0V+Q<ZOkDNKgHFb(Aaz=6<BKJRumNe7stS5yg{g^Qi{-~^=|NR=QWM{sr)v=j
zX8--|4?AALX6K7`6Z%BDgW9$27=7F8k!bq%O>g@{w#<pPUBOo?uw(;966<)b+qZuH
z2c=5E2|T<Fz%H8g+;m$`l!#aPukua*x9vEY#R10eOioV53w=B}8;+a*qF3b_IJ(Y&
zF?H}*R{GrU!cbKe`b_VsUVp-^Z*h9SugE&|GCeXBRL>+rA@*|KK^0og$k(>EXW?CE
zk3(aN(aPHI6jVRt)>gn7gMfN|IJMNbNG^;}O;pP*<^m&=>7{N|lt~o~vPbP1$4aY*
zbp0%N(&wVEG?;#66)v&!|6AVv$nN<3dwKQY<})4VO1XQzh_!zUE1-11I*VJMz?3>x
z(omRqD(@?pnU}n9KGjUnAuzD;tw^o-c){l~(DG>}2rLI|UH<F^c5MoJc%O%*8(i2r
z+O5<}%xXz8($G_3kF%*_=rhE^8N7<o@czB~?e_=eP=y68l4iAd^0&@miM*#)5=_w~
zKc9fx?c}n2Y@AlLMabe_O+-+3&&%6*&zW_3{?}*jtW1aMDg>%I?0$8jjGlj+oi20%
zi4FiHut(33!FO#i;EUGz@odqq<CCgCtDZJ?9>*tmZKD5QHg*mU27aOhpmQRDsNqE4
zhB@ABvOHsYPP8t}lx?%!@x}Y3QoEtR-J8Q>I{wmjm5wG`8kkaW5d_;WAT*vQ$Qf|@
z)AG=7Q8X%f+(I!$yRjr(jZIvYqa*iX<ee*d(WIUaovm>_=f#y)xdzN$3&%jVG{KXx
zMbY#{pY}bn$v!a47>TktVAobhk<6Q~ve?I%{#+&&d^#qEvh&z_#rmOT9OJ&EV<;U;
z=Q2OP`}Ab1HmAmrfA`Xvy;N<%WAcqIX1gYBobpHV{R-8ze4W-}GA|q1@6F0tSF_LE
zwu*g!s56h%(r{RPlvtP9yC~TG12tPgbi7?xn^xNvb={;Sllx^F!n4Db<m+Mv>=uvv
zaH^r?GO9IBJA@}2=+^$qa{8j;Qc?E~Qr7hD`*1s|_^x^1;jdmsV3>|-s$P?e{<OMa
zp-}4$5=W2p;V4zf8wTAc1)-0DHul3`ecN3eaqxtuL2ueCRhB|w9x+UmHS^8Zk~w?Q
zpziV9p3u9?{~G#Tn|?g+@yUEx;N`f;uy_0|fZfgcI1uclvJkaMUY$6hEG@&g_Itdr
zKPrCQ_P>z9u$j@y9m#+W8T_W}fMPJNqd-6I6s1<J_SSr9)LV@hCAPG2*dI_|VumiC
zx?o#lAU9nm1%CM{b-h}WCJ%3(>m4`FYW;Rv+@cgUuu|)@@3QimGdZ*^v<q*<8$9h-
zo4vNTwl6>5J2+}`Ap&A0BS=;*-1K4dczD)&T=bV8YHSGF+gF_1R2Wkzrph(t4x_fr
zMsX?)(({gBOn?2cZ@rD8j6PKZ?IOT-K^XegY%jNMp8S(cB6ZN8JiX7Z=Ipnnw`E48
z6!^T^@)Ic%ZIU&TnC|@{g`Wi#RJ!y*J}f)S(j`-VKo{qozuKpS`h!=a4i9O%cH_4q
zhqhLm@Og7*-lNwh7>e9Q&^>0rlYEy|ih_!2Slg&ye)Avus82Ox+WY0CES>+K@zqur
z9O~E3i<Wi4_wlahzuA6%en{gIY5JO)%UDfLCrNc3(?`+bWQo$D=Br^pO1r_p8qRFH
z-SDFZBV3F3m-}l^z1R7nZMZLlo@X_!jONvw8yg#|t6mSA!X!Y%Ftz(nJ4JKW%&hEe
zFos1}cm4SIyMx0Ol_1ZNO84b>V_f&t)uFavDh&bF(jIdQG=W+i*RKgE!?xXzv)1zf
zhn*uPe*NX^*d&|QsU8i7c^dS(3QC!IZk0r}TQw~#GYZ3)kDYK98H?59-x=dEjV~J6
zC3}sFTFCWn8=mfhX<&TUkzZ@gB6ngk_mYXlgoQ#iwKq~xWhDq>u^h)%KKAVD^Msh#
z8_YrTy1SZWeBF=#9I)ro;#k$$_WRuY{F|Hn<MkCTH^+Frr6$CI$lC4<Gm8uNU}D7Q
z3Yw4irXqCt3e&u~__P7M4#oEP(&zHqK`_k>1r?<j)s~gsY&8u(ol~3Iz_<B58o7eZ
z6W*TIszw9LhT{uH8G0SxAQSFt%%*zR`-fV^qG>wp%GuF`D$beC(%biL)K=T|<jCAh
z`*J2a6OL5-@AiA_<|ei`mHPT#U~C=(PXlveyYr!J-Es$b^@u~K(@t-Y%XES~B~CYs
ztGl}(c=UwhO&YV!)M3l{-!?UR!3HpudfC9ouC3NmL{3gFAm9_MqaLn}{pY^9KV1w>
zC@3gUA|%dv&wG5<4oB;%q#aFuy}{qsI%9K+Sv=%Cj;uzDULKT98a%=Bd$4P@ySPv{
zVe64W{W_;6MEAsQKvG%CmVqU~tZp9kU1;OUR(l@HrOiU5AtN^VHq*$DIkBd6{zRp_
z7~vr<map4u-eRlI3SZgHJKT~(v95tvBQ5ez)v+J4F^5U=A-w~uy_Lh;ej+(k;<;|X
zhT%~i%1icyh2)W=+v|F}X7%&tF4vd5uWgpg_B7eE3!&v2uFe$r@ho4`C#`pz&>dQ`
zCb9E}l_RCJB(sG$Cg9VAEiGBHfAL+EL|NZ*eew0|ySg8_#Zz=(J%#=;&BlW5oT2Al
zMz_-DOv_2Gi9|$1q%DJnRZ7|0L9letr(8DtefCUEpsu2%V%5yAmozoXT;kl?q4{}P
zPo#rHuNKYE)O3IK)e}ig{T)nu5rOyGi=;~H+>7_WD~isEGR`Q=^FHk)o@2i#NjGRe
zh<?}8Ya@Rd8XBVDnnobSz`z*q=u9%sxV*gF-Q9KN^(%l$$qyRd$0H_IqD;nlF$S0M
z1D{~mQ|r3lHHN|1vLBx4G|<lfQoe$0d4ZFeS1>b1aZ23A+9n4)6r&)fw5G<iVS{&{
zGCFv^C(1$hH@<t)CEPA_j*6Qf5N9<^spAq<NsYFMo~KI}kkpr{PBCXpqd8P%8#Jl^
zMvW05XBXSiJyp$`Bd?XK{7o%(79obLqOz203B6tzGgW!cno_}ewP;_JYW&1Pk_KyJ
z!m($Hxs2MPOu|q`-uZG!p~}6@v$ohAI{A-<$8F9~b_{3L*~~}Cg}%N@b9sdce`3>W
zE=HN)gO^PPeqEn4{xn{W$kZ_b(Xoe?t3}zO+J1Is?lp|>>T~tHJ&h*qwYXZ^+<YTT
z+3MOF@L!W_dDeH#81Y?gNyK3jY$z@Qxko>4+;c6WJ1^1sg4J)GQ@XcX^%(j#4sD2K
z_V(>3rGr=Dq6OgQCL_ne`yJc^b&PoZ$7lafEZ^7%%e&y+c7{pXS-0)ZV)>I69UUF%
z8@jx{PyhCKVwtu?kHJ$PV#6Wbv#iq9iwE#MK~gtJJG{#WioW@O9^t<nw&4*>M^Wa@
zuQc>qGL`Pr>EMr-++<&8Y+h(c_CMMiP+7^c^Y+XhwLC+4*l)97M0R`b5C;TkS?WLA
z8L(`u-ijAipMd5D%H0?{EXolN<!P~b_$&M$+c^!4${G%5y}HXbOTLG;YN^XCu=f$j
zTNe~pCee;(0X1+r1NO)zsLaf?%@rCm&lz+%o3B~~U#oDG6w>lP?>{y>C|_RNyBm%O
z7H7Hi|Jc!SZB(Y1M$Z3eh_WyblNQw0#QA+4r{6hPa&vlDQP9$?tCqLmevgZ<L*8Fx
zU*=(JRBXD>n;Unw_1k!7;QBI;v#Zi}?f(Ah$w|pCT6D~!gPa{pDgB1PqfVnTm}kcC
z<sebG_YAqom*2B`>a@b{`{~u0rDsD)D+4jgp{p&WYPoDll-ggfTXlan_doxU_indD
z1g;>x!i^0)cf*i!5ivLn1HpXIhlhu-Rt^QZ5|wcu0ZTVm*MIy0cpjugxVXRd%GzDW
z*`ykKVRQfh)CL)G5w&e2E;MsN;;d1`;KeJd7;fILDwve(9u~`&Hj#tYZ1BlN7~p|y
z{CI8*4t=oW{#t5>_d#;er%3~~?(&6n+TVcffKyJ7$@6o(_#ig4(%CatDaw3aRt?>K
zq=lN7)3M+r@M@^I-Po-4iWFLLW%$HZ*-RRBEY+wZS7jMYxxzZqe`5tjY=?c@7thho
zl((A!pZ}6S_eZ;TZ?YW}^?G!D9(zB(xa--}n6Apkad_HjZtBE@l@7-Ru&+?n8Xh$;
zOAu9H9`5eq)s0^NssC7Vvvb$JeZFXOH8C@az?~4t=EOQGWgr~?E?%KwiXr8dH=S|>
zlUvLAZQmx5UZ)~K8JSbA4RanLZ8h^dJAO89=H<$7J|3QxwT8Y^c8xu&j!97ckWtnC
zy9es@fJ&s<6Ycq1(#a;$d;7fC(IEn9`C9`4K0Z41pQ}eETaTj3)0;0x2&7SkzPH)i
zDWDVdx`9JyeK2+Z=zV5q#|VV3t<~VQrS#Fs6{H1*JL~ZB5tk$3678+n{@v-;^tkAL
zX#R-*+~mmis$(LE*BJ}TT*&Fa#EhZr8q^xK=9fB!+%ogMTsq@a8ZN@FDiE*6A1-Z>
zV48kEH{AZ1e%wB-&$z0yi%(gwfSa|sO_e3Z99{gqsS!IrPHWnV9<I!hswUp|-MLM6
zAc}8AFH%Bs|3tf_y@E3VA3;63?XqT>|6zal<MfXdd#u;<qvd7Sdb@>~{Z;po!)AR5
zb5xC$O(@ZqEk~c)N!<~34Nk83IC(<v{fpT37I>5(f>WEZ+e{Nm3pZ}8DemcOSu=T>
z796&QMDj-R{?4hr^2dgll>^kchk)Xhd*o3qSMS_m9A2LDFR*IOdp5Sr$M32kyUQQX
zJ%*3X>r#rj8sZl7Yo(R6LsUaC_S0hz*b-<R-fJAT?k*5YLhpBNa7+!Z=&7H5BMrV_
z0RLfkR0(8IFL>&)s?H?`A3QTZe+w1{a$gI=!^0=DxunR@x(osm{SI_>b;0uwY~l)y
zDloJdxOe-E9vM2kukHE$>BvmmeyGx+=Y6AMSGBPE(I;23@z9mldif0MOH~i)&adk*
z(LcQiWJMB1%BjXNQS2-Nbd|ma^e2%A*H0YUIT2-ri->&bPu%DoehPBzOdr!?<N|gK
z!Ev+dM{TlW7OlF-?VglJsGg}NdD2)E$;u7d3M$%lMP$}uMYCz`lCwr%X>nrf7Z?b<
zL)`Kn<L<3g>btw_rl*?!d*i>>`2O3q<@b_w<lA@tJMpjloEAAQcV(u1kjeE|$(s+k
zSS&>An3Y8uTBDZ+l_Pz9)?LW(3*=WhITurQUK$L2KPk59^7`GNrNyo!>h`mC!DGIM
z>&R}ez4iLWvl=n*miK;}$IH_k<S_QsMn2p5{Z)o_pRc}m0d7zCHeC0x`prMKF6q+5
zdY&#Ex%JkZ_Otr0l!m30?0<iJRN2M(J3BXkLG|Yyj~g!2TU|c??Lcm<YV5hMaS#AM
zI{FaFq768lnq|@gZz&{8<bJ(W;M#2JeC20DR@K$@dAU153Nuhn`OfaWg?h2kmVD$@
zGz-3zOg9o>$akbTS#-Q~5@xJq(QNtr3BS``=%RIrU0j}drVUr^a?03>I6GT@`E7?G
zkLS(bq0$vwwUlSxc(LSrv3*PVYV126v+zaS+HO+ZxWNfnIWbj76K{?E(EZVasiAQF
zE&*cXdlY#o0=@8jLC+#RG&sdcSYPj=s=QQn30f^~MgkPJ>TbeVZ97VJ^LxeN504k$
zW`*Q>>D8YE*thpsc8|3T!D;FDYm=?#NwV5v&D>15#Lr6GUJoUjS(@%P)^42G&1njM
z8JGWM7WmY)sZUu|<}E-kFTu96p6q99)cD<dysq08jy9(*WBlppw&A#4`sDe2blIUR
zXY=|4c2Rs}$JycO#gF`o*ux2A!wfy)yawvIS>w~-WaCMGUftfheldEgb=deg3wTj*
ziQ~|s*B*U34tSbc+1ZVYTl(fnNska>dPCG|I9)s}wRPd(&=MMpf}h7MF%r$x{oC^~
z`1a-Fv8U%?1T3Ine-fe?f<%D9e~c)TM~`m*SLMp_8!IVIQ{;ib%NoG#=~)LdY=0Jq
zjbaXWJ$Nm~?T6F<wRh#;Y-dqC)h#u&lqu3^$4<l^TN=lxCADR0B8i~LR1&39du(lu
zIy$wr3`r@8rIe^xg4jx1QY4ldR703f?WG*cOb0FVo%u87^!@hEx##|H-@E6&b3gZe
zKCFY32ZktOSpV%-K)>DqMK7-cXtS^U^@I=Ah<9ARO|K=^>f_TLNM-eNjlP{+KH76U
z5$5MZcx1FNHR&^`UDi!WA8_ELNyIb797$AA!R(luiS2h30^(?bdq?qv_astk{}|Kv
z(f~8}`Xue046?m1><<SdOaFuvfiX5BCYlYO&I%L?lrS0R*eJmxxaGPe+DdhEUdUgS
z#I&uqa1m&DMg7knxS<A2*UT!k$Te3LGe|Zyw^VUU4ro9$HD<aypx>88CyO4_O*5Qg
z@nK^j5#azYI;KeP+x*IFH&m@}J=S|`Y9U##_tH|Gl^xSu{IM{At0*n|QMSAxXoGk(
zoe#^dy?L|u2lmVX{nYQ1wybJ;&PH}d*C?M9IU&Ta72P>s6BJ1|-}PkdE&6xmyOa|U
z$JbjE+Qm5b{9M`Ie%(rH$mhYSeEqn<PvW_VRxQzgf|ThQLW47+e`PBLX!Y#A9RHHG
z2H$0k-O0(xAcQx->CI{fn54D#y{}`jqd6O!RAVHvt>sS|KLMCiICsC7!mIJBPxjsH
zu5)s7VxauVG@4<)o8iuIqC8>d7nXvn_SI+V^2}cA2ucS`Z);?y0>b8UF~%qSUJ-Tk
z^mlwMSp59x>`PHgjozd<pG<KO4;TB4$gtdi*dtz)4lkOWXDwMOlnA(6&CM>6kvnb_
zUC*FxNMgr}*)V~S<H)W8Yun{i<En~rR_P>^%12wXH8aSCP|fqFqPbm^z_7}upRP@H
zEOd2jUzm2YkauouMwp6s3|KSR!#dOjX_ix$Ia9HzLleS|TO7ZDp9YRxV1jIhomiu3
zDTzc|PB%j1BVaA1y!TgTLy)X$Mo+Lf0HKnBFAI3+N*R>0R)F}ggj<Ea%346yc;B2W
zvBBPJ1fXJQ{<0s?QG5fb&Nrn#@T?e8KyCI;@Wj8-4?cu&9@1!`;LQ8SRtR$nZG-&j
zLL&xuNd5~j%rH`h=F_Hk^=uFFsVw5>H2@!G23z-xJJ(K7d1BrTe_>o9hKj{vt)QK`
zZ_di33PRpWXnf3*NGm9y-jkyV{7>A71<KFx;@*H!IJcrgn<>H>GP{!QJ}_1)VC?$h
z(zc+z3aLSmY)xlvhXCs=GFVgxuf7K9uH$gzaAak5eHh6r6X-doajM|eu(ap(@NQ!`
zT3H>h#ZW`pgNIF0D$`u{xB2hA@Z;bS&r}lFUehrz(2)#Q1Jr+@qHu?KD+b9Lhr>DQ
z^}}js6+INU<5{x^9M;^Op_NA>rW!-h%I;Wjw_LsM<f;XQlvb#gBIka_+)1OL0w!(r
z(9>BnrIPcqPDafkH?WkGY<F2PZ(<`&(=4SActruu$wa^l30yl$5R&hV!11Dnr^=bB
z!L0);QSe@48IM4B4@4GKB}*e^Daw^EyOY!Y_Yo1B+|SVaCOvyNq<?IY+>tsTu@+<E
zPf087*c9rg-}kD)+sO?XKHZK*v0M5YfYH&;&RfC3!7(v!)<5x^ngp}qJTdg-T|k>B
zTQom&Ywx401VPydJq!d`R}p%$_oZFdtb|5~<+bJZ#sC=37WYHFcMaQgn+|l6seXk#
zRLSwA>Ymn;gUD$>4iiWrpNQAXQKv5+u>(+OG_t7kb+UJXugemXs%isz{v>K<F#`VC
z>sOcZtaCQIg6p6-NBgBi?_6uz+xH{}K|?e`5?U$|LKt|K0BS*AksUQ<TV?<Tkv+y4
zqIvJN>K0`jPDe+FxvJC^$jdak<0_t#w!C@>q<gpOEC?i2+XjJh0gu4`^<mKElO@MN
z#n6kVK;K)ZoC76AfNemadAaznhF`<;^-}yVQ{jvMs=jep0A}tU2w2XJ7zggffaHGw
DJfU}R

literal 36929
zcmeEtWm8;TwDjOka0%`n+}#2U?(V_e9YSz-8{7i~2<{Rz=-}=I4{pJRcb;2yzrR1=
zo~kpuYGyv2y>_qeUVUQJROB#FKcWHv01O3r84Umc=HGueGA#5PCZH`NbU|^I*Y^Mb
z&~W~{Vdgf2UjYD0fP##qmT%tQO}}h1o1o*T?XCqA8}Z7=rMWdL#eqgP%Wg_bM*>R@
z_!t56?BwKxt3O;{aB!r^og+nZL~z?VvS@Lgkqu!5%qICT6@RWeInuDMHd^UxIN059
zRf0{<x+W<^b(5%98xLfivO)^`PPGaT7H<Moy%r6%WgIY&slprMK0N}kq>x4XIPQ?C
z(%p#s!?DH%&_$(?tw<xuslvH1rIWCvh@*<-kc;C7XK2FJM$C_~r5N}B@9+QjWd5%R
z{_lAFe<8vCgeV|fEz_>n#@aeF08gKTgF^z@&$@mX3Md6~z^X#XUFC#mEUWK?;no0A
z0Q3iA{{PUZT{a2JU*Y%Bt;5m}!Jw(I4`S^`VIBQY>?!_h8Q*irTQ6vNF!({7HC!+A
z+Q<wFUTP0Lr`q*G0S8n3+kwY3RJHdsNh0pMQJz$6(#H`CAzm+~@mRlz8MMlz9$=9*
z@ynU<gl6M875r<K;In=lV-SMq*by-d;99CI=y_4qvEX}lY}<a$gf-^W5i4EflIJ?-
zrgM%oFJaLoOI0m59VjKY8nyy`qSFXd+^RKUXE--qbWLZykutQ>i6wY!Yv*Xs5!@T#
zBunCLzt1p|c&&3XmP+tY(JE+SajM1PMz<_Tk;HS2;9-q?C#a&AVAKV|0%&;RY33=z
zi8SEg5Ykbr01`}N5}M;>KxB^L@39&R{T%I?Oxzd~o)KCaWx#O6BMAWx%@`RLHpWss
zrGe(u9@@a8%QuSnKHJ{v42^(?zxo&I-81Sy_MW>rhL;n#4<Z0SmWe-8xv%HB$cE2$
z5dVhj;Od#+Q7)g822k|oH;qg1ZEoK~OTF>G24kX$WR$9VA99p6%@>S`eG_dvl<;p$
z;U6jO)=>Ny5^^{1rt2^RKKorin)<wsC<;Mb3sLHlIgdyuF{!$(eaUv;14STXFzzy3
zqwMRg=4;+%3Dz|ko=FeX_1FXkO+11k?;ZX=a^JVCF3Ap%6wpl$`YT7h)y*}gzjDGB
zl>wAx>IuP<<E!3lPB}}H4=H2#VL}ShOq$c^x#}?C?w{#ShOw;{_LS^riX;`3qwUrW
zs(F+hm*WP%GFcFb$XinV+MS8Rwa-T6FH@s0#qq=$oiwwXqmgo5r$*$Ac37{#(x@mQ
zd?XFy8U$*Ja&o4j>a(nMNab+RXQ*_%nuZ)n{~f*+&RKe1X|RNrC}Fd4mv{aPiegCU
zdYelhR_Rzcufvk#cJEt3z{RWq`=egwJlB_RwmsDieHuk@v#+2rQ_Kp{Wi5jV8N+)C
z-saWjn<UH!3Io#<p}~?G$V>j<`&$i66%hn=lu0-OV#Lc}z^R|jZRvzNabysYPdrcE
z|BHLLT2iBKL8}<c+rP$3H*qB)&K`-PDdbx5(M|tl)B5D&bDZ*dSgS3{S|L5Bx&)89
zhT6#Y{dQ@%28Sv;HqdJ&((#(JO_25<G(@_x#T<{G|40IMmb&$obG;moUTb+da83^~
zrO3<`6Q~X(rb_pS`-Yh&A9|#p*8xfmM;<^{ALl?{Zz!{)cSBCs+c5uFmW~%~J?Udy
zF2V8?CztqsBO$7ApE#8fx6EaxglZQs9r4~K4KA$wAtGd2AM$p_HUay!p`k&e>h6mw
zO?dZpg3Fh9Cnzu3jiTuN3fJwLNp%-VKye;K0MbyK@MN6@9Sy0+&cN${l^N;Kf3;-J
zJ*<~2*M`=f^PY&tLHgJ-_eQ7G<h-~=OuXY$UH*8)XB&LmYdueZ(cJh%r_g@6!}Hjp
zOLB6_@+av0`)8^G3T9n~k&U3o$yfgS>YVSF1l}$oYdLj4_y-b^r89<rbau3UUjXI{
zZnrGD@P~QX$RG06xSfI*M{IxM2~>d@0bg;bW8tEN2o@DeB<v;{(Zjqx#)U&`+7*+&
z7<8>w!G(|GIeo9}TxP(`gp#49hBDWPF2$rR1q1FXDCs77pv<Tk!<g4dQ7PT}wSWIY
zVWNhL-0!GpWUtRz4n&j@9n@r)L7Q0}MS<A?L;ivjNd<2X*h8KMl8%NiNVh>gn_kOR
z2b9Q|6k^?8XG@w6;|E-T`1p9D$cE;_tKGqT6`RdYFT*zH+U2ua9W(%W;FV<g!Y%Lf
z(J!uW6Nk->0Hd7DT_P=1ns^OL30n1djiN{tAV7_|)P28L!?KUwKlh}^FzNMY({-t%
zwEV5TBz#<`G0wwPvOXgtmW(v0A{Cw|>vmGRWc`HHIWlcb#&no<`R0d#`fU)t*FJ`H
z00jv0`T>!`#RPW<k!XQ~Ih>VV!d5CeGn{iAeL%l)W`=Dnp1Rn(ZT@Xbx|XcwPJ(Jr
zE874Yw!%*Ht313NEzePJVVot##zI~tH9-Ts@;656aH71`^lW-usWP`cfA5dgT8Qb5
zl*nxJ^F@p?=ryq6vTpr3yMmadom**9s(hF0;+4E=sK8JwM#{)3rHM9UFoIH$&Y#;S
z?xnw5JpuZkQ~e5G|1f~{JT7}43mOhbNL*O)P%Ft>*UvA5AG;V-3zSl6mFJIwqQPMY
zD}##1d(SNBID#dMIi@>&ETBZ=@vxG~y~Zs>MJXbz6nizZm2kC`$Vj2?dBJ}`(Vx1e
z*HrFqwhLdlF$h(M;FN8?Pug42w;Z>0*r`wORO55Aj_g{92;1B-*M3_Jg?vv-^z+Cv
zUgH;#rwE9jzC>|92^Vcm&3fiB#uBZTEeepd=O)N$B%jdY7|1?v8d};Bb!9B{Ie5^X
zYY8`ZTmOw+n{Aa+0)!7&*f{YTWNbBtm7vLpEn59sUT!h$>cN!|fn`oqVV6#ieZYG3
zoZk*35e+0ti|?X8GEddZBZ>F+HwTT>s)%eW#UL(oihvXMT+h~G3!B2UI#d!}Cj<HN
zMf`4pE(5mRllEjC*IS)zeylcFj&^OoT|`7gFa(N~-_cM!{Vs%@6gqEpKRut-BYlNH
zf`iw1kOnzAH!n^+t}fg66K7{<31>Mgt&oFY|301@dxz?>w)ndSt3Bw*+&6p)6c9O8
zi(5vNVFonY!<X@^rbgS2P-e!?h1+^O<FwzBjP*RSId&z~2Y=@%vd+!_Y>O#(IR3HJ
zh?nq7mG!^OY4s7Hk-S381ue)Gt2}J}PC#k-b%Vvknc~7w#PhK^`|O|LlEFc+1&>Cp
z%*Uz@>kTCgd(MeWH-H8=FhhT5(#7lk@e%BTahKRoV8O{~pQD0@`0)!!9_-C%6+frb
zmE&RD#`8P>x7m-)zeGy8nJ1lmFyh)k#T47&?{JZ@fPo;eH?IVU9<dVM76+l$niLl&
zvcdYxrYo;54{u5aP=Qfr@@eR&@-z(pNtU#!={Ae$)3}Go?thD4&o1!`u}b}Gl=$oJ
zt$$4x*RofA_sw{D1b47jmDbi{?<1Lr@0Ca4%f>3Z7BJ}FAce}j=Owh3A1@HT_Wg_N
zBO)gkyANl`+xM)#(KT|D#glz`I1fmKkb>h5R!Ww!#K}=hO2x!@+vk`F%=>q$U)DTt
z{JD%3=!=6y#QBH!>+lQ|@Imwn9W)vA07+Wg{`i0HHmAVTmc<DOyouKevt()3P>kT*
zD5+s2OiHW)J)O*WdT3QknSp~c`e0IO==IoePPGBAqococ&?{ky8#vfyklCYs590@V
zHruJpY84`7xF%2`^>?#esK&ZmLPNWQXkdHC42w~d_ej$z7iD-30e|}#GEtqw{eV0j
zB2}L2AO|mFc?|Ui53!=g`5&cRee)Uoo=jj%+0fV#-tjP_D*~aNjK%;Niw+iZ&kBah
zQb3(ey@@)fG7U^IBf>}oJ^dmc&g3h?2vGo(PTr)e8lpbt`)m=z|7v<2!XV)jneg#F
zF^hMb25+Gt0x16VBTc`p&9`l%MM#B&yw6QPGkA=IgjH2l-4dU0=|#?HR@9W%K7Z-6
zMeb2cO3-)Nq$Fc~@9KG)c4foUtHaqA@$_6CSYG|xZ5#R2fZei!CzqsQVLx%mf;2pd
zC&SbOF%ZXjpXod@wQ1dQc1mwRO;qZT#@As}+t#+L{uZsqYy}Hs8V^-ZmZ%!WpvktS
zaCOg++)u3uOG`UX`=C_LaQGyjp@326U|&_Pp^)8Lo*bIlklJA0zCh{i7vF4iL{HdD
z5Wm^X=JRy;YPJoqEL*%_t~Hv-$<7DWfa3&U%D-YQKjxT+=R3;{MViO(B$P(Os&D*3
zY(Ss5HA0O=RQNcmK+VISDt~980Ar9)pmboix}V2b`{JJHWS7e|zt^9=bVny@>L@6Z
zKVIlQuguTJmZy6yeqW`UZvfdN?E7~ojtSGh>cL3iyAxV@YvuQ>O_i1D(skZy`nG5)
zTCcj@>a^Y)^kTGZEQD(j7#Qg8e$&0R`_x3*<edHds47yl_F(+@=l!9kW<Bm{YwKjo
za<q20TA!s_rj_kX{D#GujC>ZqkRl(qmc02Zaj!;$`c=9^Ljqx1P!gRuL{MKCG4{0@
z!pD}`M~Z0xjLT73LnB-N^9gQVxr=iV5J~r~;G%*HLz`I=+e~K-g`lh3WPJJg97DJ(
zEy<@diXqz$I~RAv6R%PM7NlkY%V4(=C!7|U!@W^$Yz%|%yGi$}rp@G!ugGOb4~HJN
zQQl>%l4bx~ufJ>2rhP$nUTG#(`ImU1UfE{p?@EL6Si`@};yHnA(=w?aCE^6dy^V$M
zEQs6IQxtqfot3A3+zU9>+>nbiB}Ks%dClMYmtt>ZCWuVdG{e}-Dm&QT!!u*wc^;1f
zp~!1!@UO-6g%?r?juhS&m11XtqA&>sh4>rtX{vt@#1pe{dTJ`)z?m=i(hnQ}D5|f&
zyy&`|K5&P!e!oX3Qh#{A`T+RpeZJNQIqM64S>J}#zdzSknsD*(+^sG5@?}lbn)X2!
z#-)mXnh!>HUG~1{LuLzbC)_(RU@nL|o@-q4{NA*akUTCbFN~kS+x|IuD*p5HOA-q4
zS7V)9xp<%EqV5;)%;SlAtMAh&lgByAh$+Ozjpah=aijG4xtuPSS0VMa+bWpsdh{q=
z>Yio;UAFkU;kL<k@{IhiMnI|0FqH6Byqu0*%tB7b1F%e~i8=I4+&JUSwvWwo;p)Kj
z3IZ=81q5DF?6%SiDNys0sYdW1!F!_@z7)L8kHvExk2)-7n|011A#K^^9X}WEM2?cK
zIIoGv!1^LBp!^y^Ln0mTFAk?#FCv7x%f~X$a%&|tGH+ofz;2#LaroB^xF?_(dwQ9Y
zLKXV@Z0CHOyx^r*n5`h>+@z%7=Z#z<>C+C9IAghyzh2EE1akQf8QU^nt}}kz4tc-c
z9|i06H5)!Twr#jioP21wIU<PMT^%(mEDw1d4VgS}zrDS6T_bdb!v3f@#Eoj`zfXWK
zbZ5~3`}_Nk8y;ZjmORXxlFO>Mtp_<r-Nmy_i22u^HH`xqZK~v^yq-l#2!f9=#6w0y
z3<LItm>0E57CCe?#MA;0vgYi3w2|jSZ5L}H)#OuW7187Y>D*G!a@m#=ETJHl5ei+o
zi?#McCnVEWYB4ScRN~fehe1hre2%n{sp>;?>L!Utq5yETCO(74FeR-=;B0NwXyt*D
z3~o6JjD0qOc!G3BS-6`uw>2@ajO#C_#ad>QN5Jh|OFm26k63?Ed$n~Q<Ybd%J;a=x
zdfe4}W3hF6Pmprq*|4)>Gl_!q=O+=5ZU~UKAtPg=s*HiEyut-54AzI0JhIq<6JVVK
z|2<JMG*V;h$AOZRcCiub6C#5B$EBTq+Y%Hy5W^VP>v|7uz9`cR%8}SK7!2)tFUuis
z%iDJq{Gz)rf1z@0G?7ek!e_&6bdG=M_wV1U;MXArF}LSI!fEoagg@+Cp8xG9eZa&l
zcq@gTmH6$}`4d()Qn5f^yIrVO(>Eac__6ctVLjxQKSUFG)|RLDY072GcRvAer5kdu
zOYypcw0&Ff=(yHwcQBp-tpoiw=gm1q_YR}VdGq+y*0=bmyGhmwf*$L}@+yQ#Sza}3
zwA574>wqam_t|!7Qt)Lb$HjL+6sP+^<>j6R#`I;nB=VFZEgG1CoDAHO2^;I*znSrA
z%{-*P>o{+2>G|_nS%$g0OXgFGBnBXjBm}A(>ZD(;XocOOSdRaRv`Wqn`zC{ioaSq9
z#4oLJrQB82iGf`SBHp+U1=h;o$msmCi6EsNu)5_pu}jo4X4_9c<4I&)=fF5zh3t<e
zMz?RC)1%#LN|JU(76=Kdvx1Te9gnKS{LI{}rZAC6QY9MJZ1jizw3g(#ZlMSVPZPRr
zpq6@<Da~7bL?0<(98(7hs+swbkk0QgC&+*snYFjiBa2T2u9RH(!wPyU^`A6*uOiq3
zchOAs3?Oq^y?0-HVg()(iy#olJVik)notx&F6#ZS55ZSY|Jv7G`p5ucj~!rR|C4gX
zqwP=USAs3BJN=<>NSJ{BV(F;NB~#B?*(8yh=(A3^|GA*<gh7}0Idt0z2nh?5unU~^
z4`*?m);meQE)0z+7BjX*+ru$+Ki|i#FKykQ6?T7SqMrMA`@zt~K)0eC%FsON48`+Q
zril$l&8*dz*Q+<hnjs@beU)|FkHjrgaW8W`yXk2uv4*atmL6j<6=TYUge2UXGv~yM
z3$AGKuIC1mp2!&^)HSLL`BJjUndneU#yhAw9ksKckqV^7EE%J-KAH7hmxzr&${j^r
zLHE0LsWH>xH`*yTF;QWDuh%a)Z9@6pUB>l?akVBKMr@v8d}w#ZXeVbnEAvenlaruy
zQM{?i3ThkfdRx=rRmAnifLaqQB!V`kMS~HA>Jk%n<N-DN5sCQ)4oC34zqw-Mc}E8=
zVRq)pA!}?-NbjP33-?mq_1RO$rvF(j;Ob7*$yjLhCjO`}Na%wQTu7?o%e{Y(YRL0E
z_)PBo4}<vAm}+n%-g9NMXTl^@Q?i|3?dIW19UdKVfAptp=AcI7RL}%qNFCF{cgaWN
zbsBDB1--LhZrr!An;K1eeSx$jncUajf55q<WkqDxn54D$4>f)O6-F<be}^!IptSR`
zZ#IuXtb1?gtzUq2RRo~t#3twH%*~5`Wo&3h1SL5L@~58ZxZIWV>Fwd+JOv_SIaNkd
z(O(Jx81!v9hP&!4G5$jMS}@#fY*q{3AG_we*kb~hK@OhA>rA>g8$1qAulrFS&V&B!
zngzFNv2hX~h|<_CDl=XB{aCd*MY6^jS3qs7A0W>qWFb2g8u`hCY&B}wBP#Ce>KU|s
zJRX|2e`hfu4@91o!eZLfQ!}fRB^y`5XAs37!G;_4p#pxXw&afC-TAe0v^<?JtQzt!
z!6lGJ@Lklp%jYr}R2wp2M-J7DBHOBgRUHp&4yDK9WR1_z%7+JRzaH|lPXe}}%eMEr
z(~<AM!})s1(}d|Kw#)Ul%lykg27r;*qBcJiZ>oBv!8He~UZt{0u(qEiHD^#5t4jTT
zx`(^L5?tTwLe5nCykBPC-v-%0_%1@hn@CUni_^LX10sfoVbT2qFb(b#@86whShW*q
zoj(SwvROWWJ02-K;+B(#-~r<uZSKG82n?g>ot)g+DLf6XQ;2(KX%H|QE=2r!3XF;h
zEVGTmY(6Sg*v<XTE*sn=Rg-3jN$iq9R|9tZ!7$9l#PzuUecP>4&%a=LCOexH?5)}+
z3QnVMTN!x8LvV6yd6&;+;mn@T|Eh=sIn4x?(KVSlouXr;5@jRvayD;soQ40<zMCkd
za5pj`snQ<jm8H74KfRA6Xg-GF_wCT(AT{N*C{rkDcEAr;+2e?p`5+vExoqrX++u+6
zb$EDqp55i<&e&}j-{yy4i|S!u{@OY>AKwE%6tz|V<uPoC-`2VWo#h1WDUPptY;8ZZ
zw&e?<y2Ipk-2hyxZ){eY?KCUr?f7!RgGG?EgLZ$quQ;h#QatmN5)QQ}R7RC3X&N<p
zAnchNHWy*@uJgXP!<uZ%p!A--hq=zalVD?ZrJujduM%`yLm(M9Ai+Z-S;O*g0AT_X
zUcJD-v2$brr!J8K43_03dL$x!el4>XCmo+IKQ(vGbU#XZXj2?i7@(tXQxU+~b5T;#
z&2BW`D*UwN8c~u)m3+gI!Jhf@6UWKq*KIstHlLAl<G5KkiByQl8)P@*<W+(Z2FJ}k
zD^)s+nYMqckQ}`#e}8n)!qR;mv13K@d^**tX*Imt+wnQ=IvfksR4Z~NGn0X@@geQ!
ztiaNltF;<cr}+S}zM#=f59!foe$))C3iG2t7VWZCr&z`+EfEpU2hvRD4}b%AKPa66
zn=OsU;<KoP{L3>6v<?h>-MI8P?cYr0=F^%bMnpuc>o^3720XByW0*FEyhE<9ua92C
ztgWq8M2(<rac5_z3hK#fYKp(Cf~T)<EE?K9j;3i9=;`S*mqcE#25D$$)bUnZd!Qt<
zxVZSg-U>d?R&5jMa*jT*&-j*E#Ds-m0K*wQCjk?^<c91Cyb2{T@hMqdea|WEQ(_`w
zSTuBhpQsG*!Bp9(ROB@le~7_f3V1Q$KGSNJj6q;6_P9Q*Sx!7ceSF_psp+Vwf(4;k
zNz7foudbDD2Zn1Uj7v|s*5P}D*anE{gzOL^t!!mIStXfaH+BdVG-Q#@(HReVb4O-d
zScD|;^b%7ZyG@FeffpT@y#mf3(%skil?}4<#ByR6C`%S2vEvBc|LT3${g85>8!6f~
zbKVjdSI3&=-*a5jfH7aI)1j7aoOkY7+Og_~VBOK@vU$3QS5`RTwdb3rGoO}b&Wdsb
z;`pq9^WZpYr9@|^aYaUxbt@8Qc2YQ0tz9#%=h0hRuf<#`-fdPXW)g7TLU<U~XzuOp
zZEjw?>d5yoLDO*Rs3qXs$t7UD?`^$L^k6=dUcbQodYG_qS)gX|bkk>h)45I6RFIqd
z778^sc29!u)<T0^8<n5ou=L<O^=?sjWp@zaV7X?~naWYD!gJu!KUpI;K@ZYaU-wC+
z23l<dQ-aT!Zi}_f)xKuuI&D<&O-xR`<A+L5bc1geK!&TGNqmwxAKgt93mkha1<7)$
z%G1={=8nszZKbdnZ;o`2e1ig8lm%B01Ue?$0VP}|Ow#_JY0ahI-N)(nh!!+-o#b`U
zQz7oN-o}Hi=oS$7ZLzp=x-2>M<`vs%WmUgczkfRmv)4N_U-Ygg8?-M*G7e>Pb=8XG
zY-|Dm!$wRhUQVt<o*T0%DG-Q(sgv*28pg6o!2NTJWtOq`NhTW!qHbOLzrETB4Hep1
z2~$(SOF~kH%hh;hu-Al10q01SWXoR!$+r1{pu3}k?oa=MNAiy{6AOw7(|31woz`2~
z-cAW*XEWIh{d-L6mxW)@t2A%Tryo3_=f&m{;dvDt%qJ!m48^I0q$Ke4wAg_#t%63C
zmgBlfmp2<gi#emWxA%@m)rJVXa<h@FB$@wB)k`)Etr_5rO~e3@$kEWK#8$6Tp;PV(
zKY{e;lK*cOfQ+B?74rU&(akR>UqXGa;vdJcG~3M<$nF&oBILGiXc*;{l!gqQp7?jv
zcCht|d}^^qN46?z25T?ZnzRJpm4un1#t4ZJmVsQim!&G)#)(Sfxx+jrcpRfZdBk;k
zwh>eGZLu4T^@hYre{v9u;vaP#a5Wi9hpTvEv#qrdjaYK-E_xsP$1?5Z-1mR#|FG?v
ze2)5?cK>w!G=iVYmvu92(P2=l!}L?F+#EFN6R^NxQJz|0#NS$7Z-9nh8PComd?4jl
z<RXBO3Kpcs08JsNDQRx0lnTT*tH^@9*Ai$6R-SIXM)c*6Wu0yhrxXJ^GwJE`L8p+w
zl}4)xVh7UEle4oXr!J_J{mboMsMceWRAOtFm+*Q2Zr-iXWdh3|wnzafdWJGojWY2I
z-5wrt8!`t_iBii!899TQKn9z`%**p7D&(Ks@_RD3^z)YCJimKuP8Yp{+}@uQ<IxLW
zLx&I`fHoiGE#|-Dm9~2T_{d0uxWDn8*zQM%i;qv8HHpEaTBZQ#PaHXCPQJ%fo+>(K
zJl1%+<S&lseeUI$qcb~?%rZM_LR@S*b*)cYHfQ%ImPsbvc5j`t8Fi)$KSXq0*jqb!
z{`i%PR9Q)#iHgoFUlNRm=VqC7TU`#dpS7o_*jKSK)nqy-q-q6;XWd4zXxF6kZ{T1N
z)oL%t*MahL$Da_Uj?Ju!3`o?do%n_6w$JM`Fa{j!2Ygj<E7!ygs<mCo-=4V8_Ys0=
zDnlSgA!2X4$H&L+34dg1(WL>tjWt-}0Ubup`y&au#$Jnc?=c>|mZO;4kM*6W%-fHJ
zzfjbGOjJX+oNElttX<BGj1-h~;a{zbLQ}telibnfhy!)ueJ5a28<3FxXjdYkIW?f$
z2Wjvz4$8uD)6J=O={E3@JD5Z-!_LVK+@EECTO9z$l-gA%l7i8!zAXTm)PSyQz)3lq
zMV9)WW`5yb2PX#sKLK0*Qpz%b!iJ_4*0hACn#^d5oj}FW&%9A%{u~$YipqFe;G9Xh
z-rrWkzV=H<N+=}fGjh7?=Wj*J7^yjS%FRaVhY=+w4`<3};|#fyv(rhnCgyrNvr~qN
zqCy-qX#SKkmCIF&&udcnqTApu#N%B9({iI9ZCz+h4D8$8RLFz7y;QRBO;bkYhr}bV
z8{g)Hu)pP0*b%2EtF+}JqPnI*hy^X%CN-b`+K9irqW`Jtc|Bk^txNiBBk*!MYSRvR
zK2ueO@dZXm?VgA~tr|v#HB`}}$!$U!3MvpvFc3l`G3f*WP<D~7sQ68(MLye*j%$&N
zE*)!%slhpDG^Hiz?#;AEEamlZ*r88!Zydr@f+EP(>vVxe(Hs6Yykpf^Q&km;ttKHz
z6Fx~d1Ge%Sw8nq0sZHBn`T1VT(E2%rHk>a1YbQYiqsJ`|#$0;2-DJB}cIz|?oz;fX
zhOn61ScN@ljcAgn$RFZ!dAy*lzp`;o4KA0GEEtGP@#xP{xg|Q*e%l_*Puhbu45v-4
zX&xB!O&)NIz3BrpzpobVEb>OQw{3db=P}MLrYSQAbJB~M231xR=cQ(gZl_kEz1zEs
zdqrG}Au!mjn3Uluuy@@lhPDO6g1Aom>dNddx5r56Qe|`Za(U;|VXj;10WovNxL=TA
zq1z{^Vl}<K;J1S;)80EZhqrx-cVB3AibJlG{>SnrleEc`f&mO$T#s#q*xK5j@DHEW
zbvfH9r_xq}v!)K*0cPtDU0e5ceJ@>oa9_or(!}*UAKoGwNDc2kZapROQ*4-W(<1$h
zX^l4N?EMZi1jm7L|9xL=Tzd^+9>Lju0GUI%J@xK}B=NnltM7wGWxq>aAJ;vf1Nm*9
z4efcupedP(^aT;~0^Z8pVO=WDH_sw?sjlDxZvxB(`<iX>2jehx3sAfVrG`WP2$=<u
z4wVam``U~Iie;w$WB~63uU!pUo4>N*P^~;rlC6?wQ!PpS#!TbVH?@Tqt;3bo?MeI0
zesN&s_i-_9T38FDBd7C3fTkugyktJ@{^UugrM_OPnh)dz24^7Ileg&nFp$m8&Jp}o
z#VVxo(M!-0s?2{0X4RoEF;Z!@ZQ{8t(+=E<g26eh!)_iBJK=!O`yD<zd7NPoSg@Oj
zWiuh9laW2}=p}A?xeszbPhkR8)Bkx=-M1l~7oBHfcf}uwBO5%9R3;iTbqf4%=1zVb
zP4G-<UV;Tpg#hc@+wV`{kl+&(!p57QN=TM5lougSl_Y&#m1SD0gG9ENv@jR|&{x@L
zSzk<6+~y@_1PlY*qN*hjPDxDrYjq@fX|g>^;PNcX>FCli6~$+{4hJEPuWUJzlFZeT
zOy!#Rr1=6uZrKb|UW6`|vDqc_;s$zz??~n8h80U6EsFOAfH_M$zI^+2)QTbe&BEI=
zC0Vro+o6M*PW03m2sxl!c7xa|&)Xtg#+1}%J<lb_zv7)U3mbnwce3Dad+Q`VFlaPe
zE+!1*UAnH@B1w*cIoCfCpnc+}5cBWGjq2B*ew-Jm^7$x)pB@X`a^cC<;AH3L;A$@E
z)7Y7eE?1$hDzit5(V|5cYCk(X2^d%^?1Z@^p3asG+0Ex%BmCzUVMQpbUcdZOD*jwr
z`JOsr2iSR7cS+j+H&)Q$DSc)0_q3)S@hj9-g_=~$-G{uwJ{KaR>`36W1LU41u4o*!
z_hqN7x0j$dyhGj&cpjvi_igR`ueJTU8|9h5B)%yj+t%V~gE%mYD%Zn?wQGKukIu<3
z->6(xyxlHyQa;uOom25w0>0uhwIC|fE~a6#(hJfs<g9qVvC+q!o%>CA20vDln+D31
z8=&S#iY9}GBylEaD<*d)e=>%)3+jfvdLT0Nm<Sk+AH5A6@0k7KV?AW`4A<3HPb&Sj
zYxMrrr#XM+X->__>>zg5JJp#~zXhuz1A?!?q@p0EyVZPBGG%F5;N%bn^OIJ=V6)%d
zDJNFo)UI4q+_Y93gSxaxk%z6z`<G8;WugaLIJJ5i>u0o#Je^o>3j@~7?5A4~Z)E3=
zgG}yE6o`6@gvC6{`vQ<-Iq~N^)46l+uFZ?2;Jebw4YTcbxBaF*O(KAA0TKW@*s58y
z|1Yc36f$_*NLp+|``!pjWlp?f=zX}6qwqqRgmpx4g0TfuhvHl{ldCUiYhlkA48w;n
zF)@~ezK{N4=1_8Q(q7N2PG-KR@*2zkRg?H6tHO(biU)n%l1pK>)nj{041Ko}8?pMU
z;EcFU=YZ=W%c6L3z3U$Q>kk;#YWBYwGdEi?JJs1~K~O|dIQ{U}*Ad91p!cg)fWI>)
zF|3Tlj(X3%iU4_uKRqiQHjU7<d92ZCoXo=i?EvRKPotQ+Wu^CIZ&8{i!lH_dP7|$I
zml;4+;RLnuJ#b0+UDS>}g{Dkg8aAslfl{zkT4U*)@`JAJeV^HE&Lc5E8rJ@a2iVbM
zX=glM0>eRfsw0nYO}Z*dN_nDimun97qFyZD8Se7(uwKwvmydd{YK=NH9C~=HdQIvd
z{YBSXqaq{49vYHFr+lLK^^o15{FOI%YB1`<Hq<ccf%;3|7&gAw>(nUfii^ozL}01<
zbwreVTrAa__VL_Q3N9RtyBooV_K%$9#T5A6%+_fwQ9$Mo+JcUMxiigFCrQCk0^~!9
z@>T$0K(KnXq(g1!n2e@E#A8bp#y4gQgNF)@z_Q{23D5(>;^9Wa@6D}?2)}$=sb<Hj
zKVJ;Lt#QZ)zr>2kwnVSx;beY4+-SD<5~;s?4i$Y#67dAbe)#?~mruAv{m5G^76%yC
z5%fqH^{n%dlvy2m@G&l*o<YW)JEi1Udwugzel^?lM;-AL3!-#TP?fn3%diTgW1KeA
z!~tp5$>6?Ro71XD(+qDmbMC{QMOIESZIxz~cS*GCsQ0ai9Z822k>~nUCOt^QWGo;p
zuBRlo@Vvz^!MY{237sKP%9&%G4SQg{Ru+!7faCrPpVQi1_?h@s=!d+ZfaG}_O~;M)
z>pP0K|3(G3oFaFQq~gJ^3vY%Mm6b?HNJ(VE-WNH458MUO2g~)d0W-EdD&nd@V22I3
z0NP!pid_@W)czal1dZIM3M4X>C+_E2qAPM=6A}<)s%#iHSx*TTyyh83viZX;L{(nA
zHorYN+!xdSWwyG2Ts^ElMJi|@@FkPNz%>`Qz`8QtIShm{(&H9^Y$3;g5%bYC0Xyg-
z%RRnjjRS~O;VPiCk-I*y@n;I32@JNmyW>2Kp`GqjZnVW}pft5G!9D?D?7V;@Dn0?y
z2&p)6&U2*2+9`{=n%&Bbc~`(|!(mK)y+IQs!>}JPn+K}<hl%ap{}`dhEyYf|a6z)(
zX^lU2?_|`~%JijnhmB9DC66Rh16Uo6jZ9o-@9wQmtN!cZEdM8;fUO{cb-P2272=LR
zy?HZnMDarJ{;2sRqZ87vm<T^GcjZ|V-9%lcqJmW5c1~fgVyAK1sfxodGazTg`YpJB
z?p$(OuY*U>|LytSCXeve@k0N4!@n`Ewr_~d$>IIM<^ADh`yB$6cAB~<Z<97+w>jXz
zk;We(Pgx;MSrfgNeY+SgfUo(&K9KSH*KxWmqsBuXsMp+i2!aL=^*t`05><8l09PWH
z8yVvFH_${w`3KQH@uQ|z)|X@Lz&|#2M?*+;zW;!??J79ccU@u+vC<4ul%i@M`w>9^
zS_3%%suuQlI2k)-?bFwLOX1Km5F;u8zJKw$^Vch)D%>*gbm+{bRx61#Z4^^Vig+Io
z2>#~gs}t*;1qcdk?O8z+genxuJz!$*NhI6*f!Fo&*7ZvM<vBJtCn>k1it^>l-pX)2
zXPPu(a~KEnzt3B)f$Ii(*(nOAoQ)-^hU$%fPiJQ2orE}b&>vP4`|NEw4y>t;GM?Sf
zwX__9EK3I&&d|;|goJe24+1}@59ipZQOWRU40agxY#p;a4%_Lu@b|X}!B!tM(hjwd
zZhJ`Ok2E9~*$@)4hJlqx3xxzP^b@mgS;2$26$K3p8=N<J+i@3yo(~z<vrM%*XYmfR
zuS+g(OQTTmX>nNI3VPi9FJhRb^QqkX<o9_5#h)94#2H-sdVLf!%KHn`Es>Y;y;&_1
zyMf@#%evry>QNG^(o)UJU$HFgr9y4hj5O2?f*%@Ka?cxKWtl8dk*N&0OAv_J*4B|?
z9Msr8O!=(u!;Lv2l-*$nX@yHFN_U$PyX_yt&+wSK(n-|znbJxIT?|#njV#rh%%3!#
zjFR`X{Nt6k+AqEgn6~Z{XtZK1f{M{Zo$G<UJmvYZ;m8REB0iQ1XIidHKfgX6wHlOj
zU{OV(<)qtYp5+l*uj;%2_O2{V4e5GYM=|~+TWbWO{mT62jE@L=7PnCi?nuFb>pIHb
z%Gd!Nw}*4_;>mE#)myjb@2&zHxA|7mE;ZthA3fatD7*VKMxdyE9A8=$a)lIfHMw<U
zeCfaJ!I@`sac7qg-?vcsTU*x{0Z^`SvY-tA--JEax$`8>^mVWB{i)DpwXuBt92#dT
z7Zxz_UbR}ZU-a56*FR@xn((O2_4Ld({&E_DYwy4PviyF>cEU~t%yEDJGoKawctoD8
z=Kw3ij*%o$t$@6?CLT1aJ6gXe(M$_uQjmbLt%|Ndf$2kqXom$lEk<a$;{u|`>e<Dw
zV)#`ctJ+BX?fX+PxJl=mr8m+ktCjI3Umn(4&RU(flEXRcGaX86YENVNyWhO7A?q1H
zHk-}N*Pa@dbeQZ7i~z}+9u84nPVpd%%hDH7<jSs;Vo$ut)MS&c6b%rV@KoyYF#~r-
z?(9}0+B;isq3{gRQu-~OvnpNZr&>LD@eHugoJScuUT4U1!1(pZSIUm9^l(op`>u?j
z#iD9iku}V);^bd&iI$WX`$;U1SzavT$VrBcaKQ8=@w$Jd$ieJ1cO9?qZqGj1rQQ}y
z_Z6ElsV_91u=6WS(V_PyqkhBhC_jBp(=>s9({ujqar*-xJUQ#{<3Xe2EN+c}{O9L;
z{6Sju<_pL)UBJ@OQF`o`)4*32;lAKUN-C#XK>!v8{MY05S@PGJ&^B0ep+z8bDr0hN
zIV_$n3T2BEtlAXX$ghZ2c^ZX?^oG$hql8C-2Z}<p;O0wLN@Kfw-Uv6>k_MlW`!G0$
zs8;83jJsQ@2$*X{X>&u!l|$Pfq&lOf=b7Oekx{Ndda_SpKj(Gth25khB@0IEaP)+l
z$x1bvBdCMktad8!epqLv1Ib2NJ3Df$eK?iq_SNyo>f652(&zoFBbHSt*Q&#hE!YRu
zmGc<edX;K{D$Kb+v@te9e)FiB*l1L|=AdwMFyCY^kj}+FPs1V`(I<TAqi_hw0qPC^
z9WQ)o{05zi{FhC_-6F$c=;@AzmvptSe&Jm<@)%L=d+fP1zQXR=VVy{jLr!NuvaB79
zc}?y3j1{1fJ5}7cVR&?}h4L@T#i%3rYBZO>RU#KqMg0q$RQLXSzbkF1L<Seb09eke
z&SVbF12S5vFCnKNWBCR)FpL56Q-%EBcK$f<`8<jS?uGWqjc0@cgZmF7rOn>2Pj=VL
z@<uW&K1Jhsps#<D5xP?j@m#DJeR%te`hw;XwAeD&dLy#MEv9|%ShBD&mL?k)&;K#i
z19i-744;6EoJdrFm#*1x+|LO?mU@7r;0My#=jz_`MN3X0HT3zPX2e2Pr9myYO$k6j
z7otP0o?i3&Q|qQ$d}D5|MzyxnxLblsAj*hub_Gbod0nz}{bc%pEXji=;hTW+&5!Kc
zpmLlNlSV@Zn~$WVMnVh+=(xDJsz<b-CU(=rj1adfE#|?&LB)yf=cP91-ls$8+%yPE
zgcWn8>_7!UO+T|hRvp*Kj8Zl?{N1K#V|a8jEXwUa*=x%Ja!<dg0<Q}~y8kM^-&ckW
zKa{#XT|QijnNXA|`h+QG(1+WWXUtUrielZXzpR{NpESI^M7~bv*2&jz4TQKKwb{)H
zy3XvIb?>FtCTM-vLr^F|o)VnALSsQ4UheY_dOHqp>rAHSY^%}opc##{XW4$~dtZCJ
zgnX#~LvgaSoRmN+=FT%(P8{)M;q)%!yGq};>8Q??R<lSh<X!|#Cjg15i9U4%Pv>P{
zcB0FJrdy>kJnObB7Jdgj%tky9jeRbWo^M9r!1iIAzY+<l(>F!-Wzl{LtXj1fsK77R
z(9{?^I0A`+xmXSso6v4Er%Kf5)I|gX@<3ebh;g8H*^sA$Eb&#NXz|xUagn!7i6if4
zC*y`2cKiPfg#Rj4ItufAwaKPq^ai#Oy?EK+*G5m$7)u$>2Ia?QAdf2$$K3H6SHY};
z2G{Kj$#nG*H3zdcMMFKG5hgsfe1cXD*rElpE4P@5<xs&3@blk$3}&O%0e>(oeED<k
zw;qeYskhuj${j0k&Cr$Kw&gzcCuWSyFw#MKP$%b8JQx@kmV?xV9Ik+(b@B>LXWt@M
zEhkN}-|3=0zTH8hqq`+*4n!POOgRso$ymJOoBz^Q5QA0N>U!OO&f`ciPSnt;ON8Q~
z4O=jUc-p(2uc-Cl3M_z(9W|*A*D<DvD!J5Am1uxg42dG6XSW`L>tceU;93z6C4A3t
zf&}j{HC?Q1V?8s8J-tt@hT(I(rDtEOJD&WDukhwOxWF9o3^lKEVmB$3rcZP$kB+{G
zkMV&_dWmfQQg-)^|FS71GUf~%XuvL;*0**4Dn&zE$|YLpn`D53x1~edYnHhpQnXZB
ziWu~k4LVY=bXvIv!$79b$l_uhc6Y%O?(qVU5V3o4DaM|^Yi!zbgcB|4#EIr)*`SV-
zR5GNC9v{akTZ3`8^}s(_0ZWOFJhugzRj43Veu?jj(TXKw^8Yrijmy>ZDr=VNQ_nxA
z(~Cc^{#6!6l9P*%qF-mx(M|$SIe%KHI8uT9m7Bc+VMLx3WQ*q9rS=*G7q5zvw!XXo
z2}&h8ih5!%>A3ow?~g5-L^-$b1hgym*QUohhTW+OZP#q&Dx4;05Aztzh~qitQD~7;
zo(tyfd`R*i)T;Q_ov|mJ5vk<uZnK`tav8eg<gwuUu1WgbevN*cL0fB_?3}gOn<YWp
z)RvtQ%%?aE@fQqGbcSFIo_?rrRSX*HW|Vs&l|tUP{x_r5SznAm2M@r5sp>!+VKQH6
zZpm*VGo5U!>G$cmwh$D(JrM3CUU#VPdQM#|C-o7j54kFi3bxDqQBxl<{JIctO8jRS
zhIbzS{t4EN9+MoCU`?UY(je2CpoJ_2bw|at{BoRyorL_@!>ZBd60+)Dsn^W+!#pVw
zT=2Jrz3U6yuH(;;f4y)YoVMHf@W%8^bG~Nh74o;|8Q4ZC@N6tEABO{*@*L=;SY~QD
zMD$%+=d^}Uis7}^jy+>5V5*u~OYAJxy^Hlw@R+oKoAA~`#ZK$aVn+rlHS1oi<Yh5R
zU9~z6-**nsmKMeoL_P*YZg%<DRNWLlFH~j*AhTosM`dp6jJsN0wzro5yQ1V8RP>>x
zMzzNtGVQ;gLGdB~A<`kg(MGdGK|JCvlB??)_>`b<Q`KCg#i&xT!1HZT*^-m5EsMC=
zKvNm7;X6DAeEXES*bQxAM#Sjl+cZ15{|sw=w^e}O8Oas_S!eycA_>~<^5(yflE>h_
zxBPn&9TG{;*WX{-I(&yGe41^nUk!%IPE<9fbE?o);Tk5it8RFXf2`azO3Tq7CXsJ8
zI}g4N!~=wByVcxZYM!c|tV3#rIQe~sGpCrcO9$bKk-yR27>UCoBC-%IS#$|&G3OcC
z>LD_%Y&p`bb-T;M>ZZ4TEdJ&DBjdnB>U_yUv{tSzV;;V9lfxyqA-(kg4~-5%rmS)E
zjNa{MqfXaLFH&S&dP6gAbKFLF)`Q41_XTXq7#aIhU~os&Rf*^6!o2WB`#ux9<n6Z>
z=!8|%<8xup0kSBary;(Oxkku}e){KodRb)viUgA_;5n0Of4v4G{;(x7U3XUfxG|pt
z&OhqpRn!LA7-nXy(5vjxs^pC++)^el!wX^gen2MMLh`)}dlXZ~g$*n^p}#^26@fjO
zQ6a*S{^pc7mL?+K_(dTTSPnXy-p`2=y`!+d<Mfu#i#k8kyMH^&3-Z}~s}a(!Su%!>
zG9@t5sb@Uv_0(tvZiY&WJL}PL;bGyi`?+9y&7gMg#;yeJK+=9<0*?4o97WtdqsZe-
z*oTd*jb59}f_$obnjhfFL>lTC8S4rIRHOHsi~}jFxzg7$_P2RupFZ{*SX&E54&1Nv
z(PeSSGe16^95`+}X+3UuUQ|WhEOsM?=P}d6V+7Tm8My2aaM_0=M3|N2C5fsct1}<u
zV>KJx6C68KlOh_Wmc>}uxjBgzPGCAwTnnVB>?eC9EgPN(GUNO4eN`V{2%=ztM~shB
zH)8z5Cltg~0kv$X3x&GcVJ-M;=+i*djO(^7+Hj^|zq|TZs+-vx-~-`XZ|U}>c-zFN
zLs-_#c$hj_HY`e72b(0^g2}fMxUX1W(XPOeXgDR@slzwBv3}jL2uE;9Q8U6Vh>=Q^
zm{bxt@LYRg*}rk9_Mp8)2b{V6zO)|Afn*B#6Y`WGp11MRdA;|iLsPXd=rqTXYB!=6
zG(E(5>iLpdmPFPoTa>PFT+pGwIKfH^fI*I?LYB2jVH8S9zv#DMJajVy?*7}rjG>a%
z{lxakt|~rVsf?R={dStom|Rb%X?4S>=f~M+>MUo2Phqput7Q7dEW{oC`!X7ct;S4A
z=K3kx8K^U4|F{rS!?~?f=T?%aGbrQnXp56kQ508|#_u;cqL<D{gplFC)*gPQH%xX<
zV?GL{NJ>)u9*a_EnFR4$95o5RiyJ=LS~|N|XIkCk?YBi2-Tf-@iNmah<=V0kclX;4
z47`j43?Lc?4j$$g<?c;-F-0ygF)P$e{`b}i#w<F*0OiQ5J_U^a?~lLYthzZ{jwC7!
zTF9T~-yfdl#cwnzI1^<07PE6U^~dmK@K_>GyrbTJAvzg07Dx>JT7O7(YCA)62IPON
zUxb059r5djbyT^!aJOlpr|DR2>?wF(Xj38WJt5MZ1VvQXSjXjj5g@vWM&u!9k1tQL
z@Dfsz*R5k^Hg|k<(*8cN`I(<!N|N2q{vdD~({e~Kng)d?o+Fx?JEbv5yhuwMA2D27
zcC>Xrnz?$URA6rdV~_DJtY5MEcsELw)ns)B2Qh1*V381BLUur}*O?|68W(-}%(iQF
zWIx#9z{<)R4;&+?hxz<xmIaiI@8wLvsPYp+`MS0}ha}GSoFt8n*54ozp!p4>$d#iF
zW=<gw5!RgwKEd))Db0l3m++;S_e(<*%D<4UAoJ&b$xl^3+xK1GuV+o)SNa~Grw4}d
z_0@l<2<HPI9TFDN6h+F6Y{?V!|H>W}%0BoZGM&GK8Vp4E?Rv{v5y&%TbJ$k9T6r`r
zZ!*$m1g`y{4MpB)aL<tVxf(C_xF|Q}*_t<QgA>t2NYFtQ09(e(mak-5-Ex!jMFI}1
z;rFct3{epaNegAH=;xHOr{Lt@Wf2uv(T1W?@{JYM*a-T@On<mnzodTV%tQuY4RN*@
zxbuiyFRYfgEcN!>Mo3B0F)wSHGwyxmT|YnbV1IbK40*rhTQv!Qd4~G(;vZYW*-SL!
zE8t;)1ZL*()^mT3Nw7KO8<FvqDE^5!gcwWEQ_|pFC&y)nCBvc0t1t-T7)2p7Q(H_A
z3%Lkl;b0^5`5j-*<KBj{qDjE3Y`>L?d;2{#3ja8Zq&Mg33-WfdLwGLAPBp?j{4lNU
zGc`1Y`CUnJbXSt3oZ;Zcq=1D+TT+U$D>9QJ%G|@xGJAds*pjX^UY?gIJ@Gr;AR&R|
zaOAK?vo^7f^XK^L--3LPno~Clxc|)p)F>+`^VFIzf7DTegYkDZ=f#BkCAr<^WvqHK
z_JvxK<MczPGm+V6q=K^ulL0MlV$`q5zsOkdIPs~S@45~grIx=8Rn?s(74$TBaIQ3X
zcsqNyCM%4iqV(u3l~46*e^0M(`wdO^MaW{R<O}iQaI#ZCwFlV2v^RsKcepPF+wTr5
zMhQj%c8spuA*8v1YJ=)N(ElcvQV;7}p}yr;G>IGlEG4p+>>PT~r=)RGiyt*iW=IPp
zkkh+L%ngBJyfy{W<N-HYtdiM11CQkdBv8Ik^ue>WjfK0$;!Y9afmH-oraUdHFuIOT
z*&MuCJgpYIRLST=#^_*6WqSZ0-`qlz(ij7?MtJ`>`_$P>46X_g@ZduD$+0|nwa;VP
z-Ook9QE1u;ngGmMtH9b!lHJ5OGiJz77M2#zmB&l1%0^@N44YZTQ|3(wubZd_@yxg4
zN=#FKa@<~~m7QO7cCp0}#h_mHu<+6Q&D5h7uRNvVLZVuUuMg8>0&Yc`Byav$tHhW4
zl-SZ_ng)G!aFd<%^&d3AJ<j5%?`NXeO{inLVi9+lF?$!EQ1|1;TY4t?nn(+CKQNT9
zE1J5YVN9pcyw|D2$aPu}Y5-DEBF<=~u%f6~*c=<wik2U|gdB(FI)irf692F=P0<`K
zk-rZVzV3<!@Nlj;_MMFemk1dC2CJfCtep$!<{!74_%wWq&a1V)o6GKM2>sGuPJ1b?
zm{rK7mGeE^XUWy>u&|)}haeddQY)E6yeF}K*mUdI_+6{<2T}j)1@6ks%q+UerLL6t
z^hy$^^`<aJ=8Vygn|xm>bAQvKbN&3G(}lsb{ntCS`j;hvfb+Dxx1lj>cC{T&iY+=~
zSTAkLZww+E^}pCLj6`1<Eu;VdJbh-HyQ&zsbuT*^Wo>Pxd9}N#gz^Z*iDm8M!-(EM
zqR9H5qNx@j`tm<$B$5Y*&agX^NYdX5ee9ATWRI=6Blnpr3!#5!$B_3*rdg*R<#v2H
zrq3`15e`5!8tytxB|(V;_|{GlLy$#F7hk&_5|Vb!qoX}jM<W}%aA*2P(HGdcm9Lb2
ztM9hY9t^qd+t>_QKP&fSJ~$?;IeI``wUAUSUuHKtWpmL%?a*oouT}XHl7OC_r-Vu~
zCB*iL1V=ipmVpNU0VK(kJ9C%*!MAb|i^v^zE9j+_UA+02+x_LtWdxZs4==m}n>Djq
z+UEP1b<L695KF=V9VuE&^Ivm8!z<VisF74B0$~lns-<&+xsycPFE|Q6@Pj_C)QwV8
zP!%aqCI$|sOTp0B=)>>*_99LW{mPk;`H6R=f+60K3tNte#&h%Uh(&xM|4kZnr2Q5g
zLTh27(E5$fu$wl%YzaevY`4-cLQvvVQvWLlkmE{4Ahb#j0k?aUDokF}7tdoHD_n}6
z<LkBcclpjjz=!Vh-2SwNA4s_@?#!zJe|lTWr`mc>dhV*`r*9f*sWh@TO|Reg9^bFR
z`krrFaV->-4^U&xIr^<Aqf3xWN+_LwERT124>e$vbCUl^`Bm+Im-rI$Sjk^+^hHOc
z$!P?G72h3$Ng+YjWWlMeZAztgk)&Y#*y_`diyUT3(Zs-1Fu{g7B>;ok6d)qVM=aAo
zO=Dx`R1^QfD8QoR)F0;m(R7Z%m37?~K1oL%+v(W0jgD>Gwr$(!xMOu}+qP{xx##_=
z?$7gMSDjiLYpyZJcxGxm|36-hh7bL!t+J!U++!!%pJG43nMoSgZ2m}wh_J;TUiGLb
z8(A8fo~ZZh9`}6pAJgg3$(sGiZ(Cx3?bCkZ;*z=lFBaF@{NJ%&ni4N1JuU9I^<FCj
zxMIrDdp)R-y~{k_+GL?3yyy_1MiYiT#E}fa0R2D?RGvL~01a4_5t339qL2th5!bbQ
zLQsFSo>q9_UmuT2=uOX3V%hC>em%ar4Gb^SYmZ|lhl%gkW53v3{wzvkwlJUZ2JR;T
zv6GPiye_L8PA^MCOYZ2`zQR)GL85cu$>uUlE@xlw<xbzm<@n--L1QjjUIy*Y&0D_#
z?wu4h&OC}yDA<r$iPloF<l2R10Pllj$;OfOyYb<K;_$*@aBj=LdM<X>*{eqwvBY`A
zA1o(u;K2^46`4PgaL0?!g)sz10Lv;7jPMkTEGdqu3&?_)T7onjO*w!$^G;PY0&AD`
zb+REt|Na%F#Gm;Ii<2?uO<;Xib7S4{%w|*3ntji3#foLi=ai9q98-TAJFu~2G7KY<
zcD?{x(c<XB6dYgxr(ke>0H6o0hnFB2F&SDiA8~7tFcK^wg~1S)6yN(p(bC#6Yu3!b
zSlqloJ>lop^N`lc!d010L-*Twx0gOq<Nl=`ss0FJ`^)op%kdBXqE!V4>vESX<)F5$
zKVCN_KcLBA)Rrn`hoc~bsWBDV(_|}Y8<nxz{jKSDq&<m>!xtwZSPkML+YYCUoyYH6
z0d5~Xb?<UKP7gi3m#a)M?>}*AXy{3mecXyWT4q^P2A_E}R9nK6MaWQn`>|8!G=dq2
zdZL7fLZTVagc6K1cDj}m2$5+Lk>*TJe){@)1c@y)cJ^FnmHo?@gbVH0Z7!9#tJNAW
zN?h$*7$|bw6tS)T-30_6!~hoyC8*b5(Q~tnZ#zYS<yU|JBLO6nH&w}fyflg|C5gPe
zK!yOO5C{^ov;YMXjl@$cTL3Z(W1omHq5n@>H4Tj}N7I=Xx$i4@GT`@R+L6VY>#@NP
zr*$_6sOP_kcsQ9*=IfupH(##<CCdfNnw0k)`jX>g0NfB+{g4Pmuroge|0OFr`qU3f
zECNx3r3vXqSHcZ$ji(KRZVk0ai=m8Z0Rk)A(z>HPNzTm^*yqa)yf(Q5XXmfEhV>!j
zEML1Fk^PiWpqP6ZP<e_=x<@fVI!RHW4N6&}_{$ZqG<g)Z%78D2nTaV_K%t1@23V*!
z&tG!-Dw0RqRxfC+SD0`c(!~1klc%RKk#hctOnopanl~iKUi$M>{okY1r{<oALzZnb
zHx=o2T(ALDbyl3`_mAklLJR;1Arc};4+fFt?;)fRO;E(}bOCh+=#WU`pA4aarSF(y
z0%A}`W0+(E%73_TuMoZ`iVkSSI?>wdTRXN^eeKP<ZG-$7DWK<i%2zq^N)rGIBFJ1=
zHAiX(!~CCK0!htrXbi_OgNq*`0b*0m9`*5)dg@gw(IVzSs5q$4vM1?dZB%ATfrMib
zI7*2l8@?|`FltkmnK?c$r}t*6Z2r|WG<E<~IBSIwbdF2SToW6JIsx!iT<0}kL{sF^
zcri+yh5`cEzf={1(mxZxiE{CcQA`nAn0f=w-vYAc#0hgE80V6Wal`6ye-;oSRaQQh
zMjej0u}CPO#*3pePB(jf5|}PKlx5L2t?YT}gPo>;0;T{%fruoC80QOK9R5s4{=ymn
za0z6fJ0I?DP+SB`0y#At5mab|^3qLno8uMr0lW8&sNJ6Pq4Q|1gkjQ{FiWz;^w`X4
zRZ>WGn%`&baZ`0<iJR@Y|L$OAz}lrLTSA3eIw>kdxyDA6@0ug%FvfJJr79*xQi$pR
z8cd3bkOiJ*JOK0W>A)RB%O_kj6*3n~^L)%}?j?8J<!TPIA}Hvux?x$q_Lh#+$>X;r
z`95d_bG96rGe?t3dD-);8P`qu6g0B*{Wd1T6d~2bI*YT&5@^DLnd>L70K0bGnR`Jm
zOtEOY1@^i>@ohP(^f{xj)>(tL2aRo;whgM?j;BlwZ%X@%=f^z@+aHtLz%SAG`2EJ~
ze^`h@#ri?3L?ld4D8V%mFydfNLQKO8c5u%HV#=T6A_P4q@NlS&<~%n5Ffap+)QlZ~
z5TOKM+)TEnQ4PY9iq?$joYrAI3*qyb|Am^5kJ;HXc<vO(RW9ZJ{por<MrPu2h0pKf
z@o42`2D-jjh)C~jIWxo=d4(uR1sBdPYg-QSjH9~f1aH|y<m&N4U4S3CURty1bD%iJ
zt?PJmRU4b=D%@V*1Gm1oI5U+2wO<5xT%FOx;c6@;Ly^?#Z;4=Nrz=;IG*;VTTV}Zv
zb_k6>qS^dtx@bo7D&8BiP?yy`MUVgL=G4%jRDa_*20i^U_NA;3YtrOX6=6}=eoI@f
z1?;79U1U0dhT>4Mb(E6eJdV#3RGS@X^cXEm1=U1ZQWst~oqPM#S**Y~YMH9=<^=r4
z5aMS)Cdd>C8Z|VO&<XzubO@zHESv~~zM4?ci9FO0CWbaUECC0iGSfr)_1mi4S0M^W
z0*fnN*2{<4*VC}y<#J_G+{I`9_YCe6yIk(i+GOct0uUM_A<O&+5er{2Sql>QaQ#?(
zP|7>YAjjf0%zp{F8=XElI|?TX>rPUOff&KU)7%Yf>WzV|ua|?)Oi$Vp3FLQ&-X-?)
z0m9NsCM3nECdV5O6YfptFY~L0n4wj=dZ5AM=0F4c;-z!W%ugSvzt!0F<(^&qjBHyC
zg^EM+h>@TOf?dw9iC65TSEu12vg|i_L<ZY&Fd60L=k3qe;)i$Hp%aZhV?5I^MyXO1
z;=NvO(?RDh>`(Mxd-S@@aALl@7`_Gz$W88-%$dkgz9ajHL?8?UECq<d0R&K-o%GOC
zKhh7T_TkYn8Da?x1EhTDVYPpGZ8Ut!s?n?Exf|~`^EK}bS^70R27RyB1jI-!E_V+D
z53zW5?oL;D{5f3p<egUS;bn@M7>B}$5_Lt1BC(}Pq0wzwaIyg+{~FEEQNoj<U;QI-
z?Als6_y|YExDQH~&(Oi^t5xgEKHE)&hf_g-&b2-Nwt)89tr=94?+<wLyY{6&cHEa;
z`-EQygHnklq)8A6{bMWX*R7a!uFYtJG_XEmg@*Zvbqo)k6gPz&ksAM6yZAyoqsuVc
zgiyiFhxAvZP8A75_BrGy2mUNOK~`CF!I;d$pEj-H{oK64<<FTi7d!H_<#9X)X5gKs
zdF?#sv&3i*w#R@$x?C?Du-+xYGFn|nF{2ZU=mJbJ5Mm~O<UlA;Ixvyss29hbj}VwQ
zc_@&dB8fB%ks4qH90~O4Ddm~;f`xxF@27QFIdHCNa6b(K;Oah8<55w(wy_k5ARhf?
zg9Zrqd$LmTy#dQpA!)xb9vAg}l&X|df_~pGZp__@?T8M@6O(`t#BSI6xis?e>arz{
z{`hUnQ~NAn;i*;4l|=z;r44HQTP+Vmk}Gl&quL*;%$9bA_}tZ(udpjp@?^|YH90&g
z{GHQ-mJJ!rt?*a8UV7x?qR5aefkq;+QtI#Kt@d<Moj(I~nCgWyGvyCr#}d<LAu>S*
zUf$M@CU`J;INZ>GJe(*Z3i;{;nY%64CZ~#K4Au|t)%Yb&s|h^q3b!_s+hIU5!?I#G
zFX2Gv^A@)ynpOt{mdr07{V>3oNI*6d7!o9?JxKbFGSmnoAt*q=UqD`8{Ljz5bcdt$
zU#h62DI|__-kV{v+uwTNioZHHq!2sowlpniVoW46*+!F0QjiD?DX5sQGh^t!Y^)kn
z$Bh>piPkm{UFokLl_yw3T3r_`p5bx%yIf6N)|E$}KCCIN;KD4ae*a-eEHmv~FS)vM
z9<y7wEhVSU+z-*wN`}@PF%VyMFIzKjCWSqplx5p1a}N9qQPLy{6+#gixN2E^+>(#~
zn%s){8lcA}Gnky!sERFuRGe6;`KZdJCWM$aMmk*D?sjPgc*#|pAPr5`p*lQo)Z|lJ
zVQbF`_`7MF93{H$%C^Kd;F9Wk|6kxU1kEmS^nmwy<Nl1^)9K-4HqPh5Xxn27$ORZw
z6PrL^#vx{W5MuZ>%w~qHfHp=EtS$m7;13lXZ$A_O79R}K17HqE4gDD%wBnY52rVEY
zs*qi^b<<!ab)GMn6T69UL`7NH?~q&3JC<@W5Df??hBuVUZL6}(hFe-*`7=!r2O*3Z
zB*X|a*XsDI$*Mm|j<3~cJNB_5rp#thtPqXUw^0$i4I50sfktm+nwFN1({(1|C*fNX
zOq5p%*<9ojHf)0?RWUUV4Jha#vIIYD;)b^FHczcn7={v*MVY+$Kdnq_D0tLx_H|(y
z&^B4|iL&`ip~WAHC$HkF5S^;@qAmC&DvPdFkB_O*A`S2hBGo*q6R6a9>x?X;E*nJ|
z0xXIIafYs)A-0NT3*Aq16&p+4_U$fFw7L>vh9`uH3?KjjieLahkOC77%!m*(3?Lc)
z7m+}m1VDHc>=a6(@;i|V)2J@oCy#)$*-TERGHw`Xn)*x$`}MWcel2UbZ`Az))82p$
zaXYYam636a*WSb|0~x}}oC1TZbx%`J>L4^EG|<2Eu^KW01bMA=JDyFvhz#PtbnQp0
z409(-sqXwn>K$p?b2PcrR$sSfM<dx};ZGUG>^jF0cTd>F6@l7eDDcHqiy{@38Ihf_
zW`Qm)FgW5;l^-iQ-_#{j!hGY4DU{~JQ@pteaYFFkM^a(-+gs`^limWkyT~qK&{|gV
z_g{f;2th?9NTVMMHRZ0h;?6J@63C@jVF}Lstd1uP`0%_x`HvQJeRFmU%mpKO@BFAq
zPsfnp%llta)zH*7<fz&G6*C}z7-}TZW+X!p)*TEy7<K>*1)dWWQGfsolaO#U*a3u)
z0c|#lAPyYh&kW!&%O@r<t-H@~>aVQqJYV_|W7hqVkN-V#HaC|t9!FNC(%w#lN^uPT
zUR^4PGnh<l@YiuiVQO&aR7zdSv`Ho=PR)q_2I8}|_0+SXl#(eCmEAw!T)$tu^npR2
zF$Qb+r^H1T<r4&~!IgTfEeY3VC@8T=kRoNXLXDb~$(SI+zw#7lQ~+}JIOb!l3Fnu(
z#{|rtpx<8!hN?2FOV`L7gpSuo4x?&>j+o+HlJN{}!_N$rY&>-zEhT3|%XH>tm*w+D
zFnydDpu3HOllL&}WNz(nRdR6TK#d)z=A*%jl~`i#FR3?<Zc$3NXRQ<Pks=dQ>U3Pz
zz8VWWWPPWJB^XFF9H^(UfFQ?&LVSe<#SB(X$S?oFFC>vv7r(+L0GM_6Z!9V(uw;C^
z=l48l{(6CxGr<|l`DcHi^Lx2n$_(sj@s^6jq9`?-kf@zeo41rofZ3QdBOi)<xRg{0
zyD3YsS%WIBW&u01{D`SccRMva7-{mr8F45yfhCay{?xOfj3SbXG9?FGf{`#aEp_QG
z12$&Ev5bHqEqtK~G8IQwZRm4G&4e~bd@T4t$+T+T2+<`h^_qiueYi87J@ef1WY<|S
za^HH5VQ<DH!Qx=&tG_KDiNpbX&^DZO9<EQQZi3=Z%-1Un239C05$d4sj=r2!F{*oK
ziJEWsjSevzxooz&o}Qn{fVBfJGpO)1ILE#9grNjw3?;I|n$<)jlDs-DbI3?J1SKe@
z1Jz%GRBCfv>P#_?kYp(^@nRYgf!W&l*SwEU(ndv|2F%lpi7uUkP)@x^ud&>H3SHsy
zQBnf;qGBKz$AhD1<~dBbRCgH(p)y_}AEP4q_kPMhDv8I&Ma!~2V<a%7;P7!pR4`4x
zs0rgi1QV4w06TeUHP7wA104I4EO&xcR!9*Bc&NdY>{JWO6sa?1zEHSh2JC=-;p{?m
za6V3XB>;YC*fF!DDQqbKkK1qnD%Jf&J~nO1tAyw}Gh3cUDiJk26k4#$__^xp&#Y-j
zrli?JG8e1U42$FWcvQB(W<?&zS(D{!ik~a-K`4$bW@GSm42I@w)y@R^H}-$%prZ^k
z000OZ!w`b%Sb#GVQ#==&$w!HT(w8O_(!|CAh!~t`P{>n?_yZ;wNfK|c3X$Rrx0~V%
z6sBWM(8RdH2*}0|HmEk2X(^x^C@fe^vM~9-@)2s4zB_$(v-X|BFyRP60*4r_ph&AQ
zvCfuBeTY(|6-%oyGcO=04CRrNmDVoRN``s2`%TAo{7oAs5D-Ik+p91&hMkg1GA0jI
zbNQq~9N*6M^mJ`Sh?EHwSr;4MYzG=5iVQ3#HKo{CH|33bDMZ2Yr70IJ)|-ru=R6yj
zXIwTKbb5%0bCjc5VETR5kBEA)4nJ+f#5vL=hYd)wLJ7x7i8-MJ5XJTnsZ^DF5^{S2
zFn&^vg}#oAI{n^e3oK=)0!CedEf_ET@iDvG94s6hpcQ@DmT?HN59r9zQCXx`tL``$
zkLjBchGGF%@COS7Lh}Lv1j4%_q1j@cGNThebczFngTjXx_Y;Utvw!wc!VzN*0?Hxb
zt;Q=78s_}LgjUCen-Bax>uPwK)ZEDIJg@(BR}-O?i~h}E2nMxCey`gctOZX(gFS3%
zq$0Qf`&S)+>Cq!^BrCxf1LzwBRbdEVJVK^0w{2i=byM>*Da85>4Z_riV#JNl>oSV4
z)7OwzdfZH&w7N8gjcDlM)TxNAW<rVyVN|RU9AI#k1xF3_Lp0@lQ*eraqffpzb+aP&
z&{^eF%s?7YwBbrCs-PmOXlzrswbAMHdtzX2&9PgH8O<6-o+i~l*hPa1V;Fn#YHsbU
zx~vZ>cQZUaE9<@5DUiS&Jh=l?*Zfc}n}hK`3E1-V(Q_IHNoe8xkv!MEujw+)yLI+p
zmRLsR&lDjBrZ7}GzC$mg0!PZksgA)X^yRZ5HbdT6dGuj)INk_lKLjLUGzamQa2(wp
z0|<d=d9ta@3V%X6^!=DCv4p1-0@k|TbXXLCAyBCfmzZClf@E+rJdDU0DspuN3<j6-
z&OaYiF1`+WIJ_<$fyVT&H^ai5=xohCBH0l6MhY5pihZ^`ZR}&)XK~|WsVqM$zfNBn
zawrgxQO5r-UA$B|XMP4|U#Kopr8Qmty~!$5QJ_EH<g-WSH}+&E??nmKzP#A9wkBtb
z6FeaS4#abze@I(_A*_q=qOnR#WoKQ<xuT096}Bs~Da0srfr5o&_M2g4dZ0Qsyw|ea
z(qwNCWcPVCkdF~m<DYmvCd0n@(2?+d-R;ML8!Iw_UL!Pj%3@><^d^41-mCpa6e_WZ
zjf*2s8kHt<VvTV(GTJBAc4i6vB^7Q&P|XHfj9Y3f00Ojz)pgfF_(v-ThL@jEB}R|f
z5o;6&-i;LJlXlp9k};aY)3VDJC*bWi$bFAGFSWcwZrh+I{gG$$Kv<yPF^=Spn#d*>
zG3DwNKjt|Ka-lenCO04odQ5+XD<TibE$ISp^YJ&@;r2G)ohhb(uS2%8Mr5|u5~ejI
z>`_D&1LbXRp%bn=Er~`9J^O0iOk1{$p6#+Ju47?K9pEwTYB$!1C@Q1kI@YC3viF7&
zpV^bIgHTUADlNsn^khsc&DQlkO3oEOlVDDwXIt};K-cqn$l@-gvYp;$WVXvPZrxht
z*8mUgvbS}(%pkv8H*E_F(5lfP7oFU?31tTg4N-^x;Q_*cai`;0j@Sm1!{(MD;K4_F
zzQa?eOu1T@AQ)JaEv`pIL{tRP+f>>C*Uy~+w5xoS5nKd3-UwPaLcdf6rZ2yMJfR2)
z6BMGbRw}yEnuL@93=LBPY3C4YKGmwcovr01<&R%TZSN(pgn8M`TBhS?V{=1nV`4^u
zIs(%P<uS}gF?O^iCRBIiJ^&H3`Q;h?j;VCfh<Ryb9G*;w3#nEzaOP=UA_7*s4w*?N
z&6I$7Tu)_8{P5#PKZ;zgIN5_X*AEVo$hoG_=3ni*1M_CsRp|bz%dWM#GdlE1lSaX{
zMC_WA|N0<>o7Zim59W}KMWi&Vn{8axI_FtY-N$M9cYX?R!truPmgS##JzX)H<nFbW
zPUkvua5^5yS8Xy{kOjK$&CGgMa5M7mVhmL$6_^6uB@4*9s9^~BzPTxo0Pn{NJ-mo?
zTHPFyq|s~ptLsA}meAbLe82{R4(1C|6aW#lPaM$Onvn+vCWua$*jqciLGWZLXa)xr
zZmgjgGkUx5%1k@)_6eg$vH%+Vx#IUxhwt;OGu7kwws9s?!#|}j@(&h;@ZvdmV?a{F
zUW))t058miA`sKmKt2gs0x?iL66_HTK*Sy#3m4$OSWiRH3`vo&Q!VoY&@lQmCV4fq
z-r0DFBA3Uik}Q+rQou-5QeNz;SH-GD$xB(_@0h3>AwnXp9NdC)XKIra(qxU9RIJQa
zm0?QN1mU|Q7?|t6cgySb{A#-`>~8K=Z6`d%VFHY4=QG#7t<L?^9{D9r*8bSGX_4)B
zciT50!k3rZZUBi<yV`(+D^pfL+eO>`Ak__gW2@j+XsaE7d?Jiim4=p<7NB1<&Gm8C
zcr$ziTpJ)77)nVzz=@F!(t|V*DBAaoI4d$!l1N9I2r4K`3=f8+O{QP9YbCkT{UWEx
z6RmKQh5xO$?fkLApL0r=>qbP6g^+5JB5wva#5ZakWr0B@0k6aXPY}TYm61xG5HhdC
zf-R|T5Z%YIqaH95)C#Ifg0ymL$a|D6noly$klz}lYwDR>O}`uOx{ec?iylup55Wl4
z%Ot3oU!ZYSUsQJJjBQwnwCZRLpC4Q-=5x4`pyNrjXwH%tdFGsDyORc!LX*HURB6Dk
zL+8BCizIe6BUcI+2AR58Dl%KY2)u$Qq4w?5&;?fl^b~uc-e*b+gRUaOn#@3(pYaW&
zADF9MMG8IDQ)lz|Zzt8hd;N%E-+I9#gCczU`~pT5U8Y`7=S%h07Js9*^<qLn80Cd(
zD;iH3{tC%f2vi}ksMpnhf{35X)(<d_Mw*rGZ9&X-jMu02TZw2tw(}W>>waVA&ygj+
z=3o7c`Sv*t+s=D!c<=SvuI1}8gO`Brp-M9zfgw0SHdR>l_YF=Y#S8<59^-}bIa^Ku
zD+456>x&{S`R{_&{zzvF$MeiJgH*M!lvMp^(3v8AxXhM?Gbsj2ppgh3$|xEsDI`R@
zjQ_-2Fjg;QI2P7bG1`XcU9~t>>P(WfH6x0+phkC|3Gd=jH46YGb8P(=>4?RAhbyJv
zSrv0x?7{q$^_YqE#;BnDSKECrB4}8Tyi|#*_^Bs01FnUOla)C-B(%2S|GfbHQW#UX
ze(eBv7K9s>@bYqE?awLhYwxSe)RgVL*5q*$jZ_Q_3=<8q13mT4*4EbXH2Rv#$_2~D
z$;nBp{oy!#zn|dILo-fL{=xZ02HU2BnDbCVg2uuE1m@zKh$9xO6ZR3r!F%b1poC!R
zf-?xhx+lC(+{GCeHD4EDN)mV6dTZ_v34~0I)p>Z^Y`Ru-GFO?R1bYAi#2*vjk_;jK
ze`Je0F027GAl5xCzx&w`kz&CW4p!>pi-ZF;mP@i^h$zKFPs@I4Z%z>eRhN=ZsCN6A
zxQIgXB#pxhb;pmv5?cneWJ@wnEpgLEILuEJO|qyOv)e<Q=QuWwbO46+t2NEtS_i1O
z4kXKPU$%<J$B}`u;nUq}YIHtI0P*`1*<EJuLl+JK1#(H7&-2#32o3wv>RqTRMY~8U
zp`^tQ{`L3R)Tm;$b{n72!}gCtDx0Sc?=h(~y3RZ7CiliVtL19o4<KY$M*-Ew)^^C@
z!b7)vn;rHCV8#|`xj4=SE~kjXzqYovuCA}t=8i)XWadmBPYqTqNTC;TM#2Q+A4Vbb
z0K*W1U!VdUeIk3XMA3+3=>S=hgvI!EmAuz>JwfQtpzW9GvZAjt$e{Nx!H7V0#tIl(
zk<%Fbubg?gE1S^}ran(Z?!;_Ria>E8$COnoPyuW7vSzZ5CNO&u8$NqPBa$p+T?)|h
z@=z;tp@fAx2{8gU?j8sCa*}jz+%*Kgw}X7%d83p%rbAoQ+w}GcH(Dwr%BRg+$5`mu
z`=By!*;Hfdoz`I047~HDEUWV$Ngfgz2totGa!NNB3&wN?y48(}5yqWe8fDR<uH7i$
z$^y%AwRBRbuxA*@&zINBnRGFk6<HgWQYp~EXEDFwoz_>?S3CF;ZNL1kU#i=$+C1NL
zc-{ILw3mHIjX#;`Co=hgBtmLR{U|l^X}))w;2D?AUj6v-LuHlCZohA!RO9j3zx|)l
z7+6McI%F%A%C0u~0Aq=lK-$|O3Nx}W!VPAotU=aTEIHVp%t!!uZZrvvq(Du-dMY6X
z5Fvo6C^PJV@(Oa%M1(%v{hH^;7Mzn2F}9t9uGf(!i47^K0rp1+;qO@D{_uCrwC`aW
z-`f|(6-uE%Em8bLJQ$Mjr~`VF-~w0-xFm5r!$Gy^Pk*t%x4_1%TE(K=5G5wq*2PVR
zdJhnS1z5pX`xE|$gEAo9Ehe<6$_=}m=()<E*-o%WTEoUIY&1BZ%w=_fxy)JMRAe=q
z<doxyGy_UFgvB;YTTO#dW6gR&bPN@&`OOA<P|!_9MRL&4z@yMsX!X|u{N?)Q#Y*hv
zrn=UoT!09X4wIq-)H0}*mm~{^E%#Dv1l3e&&99U$S$Estu|V=pmR;BDy6ihJ$Hu(o
zIk;)-7F=O>dj?`eBcVu^G-}MG_WKu*!`ax__}p>Ki`Zw|{J!zBrT6`)xBZ`qB`+P=
zZUO6gg1*vRfeCV!9YrK9j+&J1=p->gIoQx-D3;WLaF1rq$)pN^5(vh^pbAWz)_$uP
z=)!AQTups>(9<Z9=!gdaOPC0eH;ynL+TzpG;PxwPanp>{ggqdb1rdN{7SciY<iWn7
z5DSjN{0#sV-G>4hIcUW?A^yWblvFsGTdrSCZclShaaFba%lqPX`7)=F=?P-W{=|~B
z@VXULaO_3B%<-;|9adC^R8fPtQ+InqLt%bQHm^?7#oDF{OWqPumnMZoSgvG_Bjaa#
zR_ftFlqd)uo#|LogMxI9|IcppP+6yjGAN4EGBR>?|Mbw?p3PR9j?L><qD|e_zXq6s
zWy#Ze&7zZRY;4tE2^&C27r@`Jj)RJH+UZ!UYKgu3by6W>bx%c<E^XYj0pM?z>mim{
zG&(9FGw*t_;&3#P5r*%pzWfMe#e^8N)YkqfKYMw38K*KuU8Dw)2a(O_n_=961z_FT
z^U$*UC@2E^e-;AwMj8p{IspuEMrZ>8f-U-g9QO^#=1ZqSwqH_VNJaHaN|4|P(g*-h
z{r*aa**+)VozD&YN=)i5zZi$iVaBmb{b4X70V0GHli`2HwdI155(!I`_v~Bh35#8R
ziYtq)ZGf0A2PC6^%193qmrh@9c5RYV{DJ`33kjTff-~dQ)dFD<JT7y}jn;e!e-1<*
zhHzY^Q}3fnr|h=7E1H&ILGbBGEUC{kEh!S6_izxdH|l^VzMO3AG?lXWS+CFVVQsa?
z%SEOhhtkbylX_Mw{gbv->a<~Uw#=*vv#71PTs-EiEGuC1E&0x<v5OVTnvkQT%gv}p
zWAWHE7aJen&q5CE%c_IH{C}0W7?4%u%mq{(+Pz=v%gcf2hjG0xCV>9&R8BpORc6!w
z=mhBK=(-?rHeh+5;25Q{Yq|aH@D)Y&nuU$J3x%^^<@N?Ge$g+4au$&|!^}oJghndR
z<Dx-tA0M55t;EzC{qcr(ZJnV)i2gj0U_GD&BXuK1a8L;$z`wZF@-ndcMvMT`O=>>U
zNeBf=^Z=sjk2rFT7;9W)9svsq^%I>dHCuxE@MnTNiv+kWijepwJo`LCPKWz>%x@(L
zB_wD8bIhSvW3m*9>4}K}MqMpksq@TSX>`X7MTbgF*)(KI3i4wF{3Vq{hTEx#y`i<X
zWJ%FzxJ~`xEgfIXV&;f+iE|sTvaRu=32!kRh$qv+rmp&2aS537^8}R0c%n`Br@cTk
zr9t^WMWbM)CX{6)s%i8BrsK#U%Nk*27!fq%H9b`P&rbZ$z_L?1i?*7Po!#wrZ?Iw+
z$Q^z3{Oo$aYWIAPkn=Wlaanx!8FoMwU2V482D0c_fmO3J7v7Xd!>ae_Bd~9OT=#uo
zMo)7odCmDHjeCWeWI8n8ra#`ukfoLrjsk{d)_j$W6f#DleK`c{zoI~n;Ikt%&dFDj
zU3bzxjABL)X8{`#O@ajlvhNR{Ex=|hQkcCbP4*`@whyM1bM~N2qK5~U8G<x{GQw9&
z_AgR{0wPd>bAYd-sx%NNy9k<DAe==5(U-=5FSWztbQEq_#coLugCbBT6k#*gk*chz
z84vE<r?u14)u?FJBs}C@-)i*X(Hdj3Y%CruuJiUZw6!%d5|%-uEH-LT>T{ja(-<M2
z-?g`QHK>cH{>9Z2Cpm@^$LH}evOAM}*LpW*suU>6m-h^cF+3a456N+i1W7$^l~%32
z%FgXq-=x6pa`i&nOuz-u@^~oRSy#>Q8%4>}>&NACJ~@kn5&DmAW|rskG?Bp!36G1}
zSe=$A=e>slq&ysC`+e-==VfJ`J97cyfE`bkjhc9{5N*}MP-bRkH8EyPSWvs;4yfL!
zzd8|*^WHi`-u>u4j_jIcwcXD~Pex%3q80cEA3IwN!eZ$iCHxH^mNXPqdRhWLUB2u1
zKQAT>OteHh?YDMa-iW_XyFG=cD2x~jQBR;Jj*K<P@^LO?c&g%?3WTZ+l2ankBOAdF
zgawVov0#6Y;6(pO6yGtVyMXf;NVZ0o|HB;Zf?_rXg$)d9bkuskoYYZ-<Br1_UbX&0
zV@zO#u|QQM8IQDFdzyjk)8;awpbCG}g~|r<d*WoK1&B}I73h*S9PIUi1L9M1kEI+z
zBRPl?B_CJ<q^!;{6V5IMG>zaeN0Xu|Qujp&+U#GMj6rYR5629*;e7a|Gv4kbt1KR)
zITFX2<;lFBFT%9Qjer|VNl8~#3ct(Q!rtB<b<OcRYBVE2|NHCHZQG~0!~!bcYCWX}
zi=tSqds3QAn9?NrztkoG03^WTxovrvQKbYT5(N_k-|_$WYE{2or1!SK8Z@OzeeS*v
z05Wzt+|Gc`S$=-!emPc{;VU~_ysRYK4YEeYB`AJHbas`IdZ}raQ+ueZ7FIvc$KN&D
z`4iv*=FDiM<=g-yk%E;Ubr@lELx_K|l@lIA>l%_cmBaz((6zki?i&arZPR1(G>#H}
z04Gua;jE%GgLWpG{}8ODR~(?o;m9z)=<<H6M=~xYtn*4=AI*uA7QRKl?HDY(1*9Q6
zAT}Y2ai~a>PBS%S^aSNK+1B#$QD(rsHBrLMNwvFi>9Ns!JKm&5$!~8q>9z9T{fPq=
z_NgP|S=)U35eO=u8cZe&)Tbg_ySu~d%9e1WDx`#BeLNdlVPg~SLs(L~+r<iFb61B!
zNI$VxoO;@{&*^L_-;JJc{GW3!v2UVQ)12>^pPc`p<UVbN^<qKyYE)qRG4b)~(WBI4
zv|6>rpk!oaV>Oq*Ie?hssO$IOcocG3p2r2HWl(wyI_(zkS4Z4oHJK0KcpfOg%TjaK
zV^eeIWr|!O=41e+_|3|#AHT=@@3T0cqlr;|k`v|{uW`SQde@eDUKYlHSn0$d@Vkk4
z6_&EZg7>?K?)Nbb)-*~KBhp~i{kmcXJ&reGhjC%1*@}3<z7_)HJFlhniErkz;uuK?
z|8&9NS};zG>SHdO_NJwwR-vw)X8Ogp&#dbfv{UeO@qiXuO#OI$sEmD)m~oCADYXXQ
zWi(vr>8S<F#|TN%aG_xtDDE>@%X$)pA?OT|LiU4b(xkInt+)Y$jdC~g3L{COYn$q4
zF52m1?vi1Vok?k#N6km!dnLJCE++-vZlh`M_50K}yyFBVNV^U0TvmLKmw|KJ_Evse
zkwC|@(@5{V0hA2sy_=@dxtAg<cqk*KkO#8FrZS_(qe#_U8nYk4O@<HKIeM=tdabTL
z)4b2twzfd_2|iwa?l~@0K8b`J&n-}D*am7=i}S*tzTfZ5Wqv35VRx_IrSl-ur$JBu
z4G954{*IkWJAp%09(d?u_bbSa?7Ban)D#cIJ%{dAtnN7a2}ifzcKc}YD|ugr<9KfT
zh&em`UJ8$4nr(469Bf&;oSK(*{$1H8$I9|p9wM01KYzxQc5~hTxYlfao*uG$WfbIu
z@^AiSgqQ9iadZ+E%iBcDN$$<>iaRQ=kNI=h;L-E~GI!rogtelKy5js*+LS2C<IXBB
z`q0V9%k4)rL+R}iG%6E<b*nZ+!f)DN5Wmnj8X%EDV_A)H+15Ubs^wDaf6iOGwh6CC
z_S<t@Y=3sOZv^PnEJlr@CLRtpV3eAYmlare6F(RYP9RI;(krXD-wY8$6OGLD&S>y`
zvp|QH6{Y6otTkJi?tZKqx7lC3#B||P;C5Uc;ne1{uj8#)%i3j~*e;!IZhd$^cG=~$
zSS-t?VGSx9S}{Mh-~IQ{dNf`D3zGl(*k0YwUU9rnD^cD_z*^hm+^=6M<k$m&=f)p#
z?{*mfyMh0m8PEgd)y!Jqq-?u&oXXgN0)Tsz-`9Pf-<3P6sFjt~^k>l>P<y0?0+qC2
z%J^uqdprUbm51}3puz|sRetxpi#grs(c6C_7>lfonm-p-Xz{te2lYJO2JjI-M*I8o
z`1qf?#<MQ)exsp^k|{dvujiQI!qVHdA8sxbMlyjj{(yp(_=`26BOdnTeD^TP&9Oi>
zk0TW*AVzSZ*}?v>G4kv~t$(yJ;n_nB=e?iNz%HFSOr_K&^mZ((L^q7Y>Rfa@$7r-W
zWJ<-3{*S%@lZ_S<m<Up)H#9($O&tR`0v=RLX~(G&tmSH#@w;vGIYVI|t*^#H%t{e(
zH=4Y{&NT<Zlt;>LZDxlgM8~KZCS+v8f{qmX$u<LE_cHL3ND_cOP#v{)8Lm_r`UPwi
z^`dNRAV`IM4YpQ9)9-1Ox)_ySM%9}l*T)pdQ{NP$0{YK9UMEs`?bg<!!FTkIR6zl$
zsj1aE-L<wiDtvFVaX<?A_bZTd;O2AxAI124P45VOo4QQ-Y_0dmYU;YLFR=Q&5asc|
zQ1A5X{jnX8Fm!)Bt#=6l)UR|oF6qDT=s^ITx^@hhJlDmp?LM?16HPYb_0_uF_GIo3
zRW4t&(LFO#!5@?O-*bAKUfUR7yK=*JUoN?;bpMKoth~NAuc6Rs{0{fc@V|VZ`5Nz6
z!mc&m^PVmweDAfQdI>f;J|jM8zhUa}IIU0-x_*^m3JMtY7{*W7Z)$!c*lk!l7n1yL
z>m>jbEv|R`n`_1;N*5`EBsg|dDO?n;>Ar*``dgF3uSK0aP6c|JOc$Nam2wQ0D2PGH
zltNPMEE4fqbPULo%AD1-aa2(<>CK-z`{x-X*uGgNEql3{OPmhj10(QLd~TpOC@3~h
z>J%Ayn&)<A#dvU28_{6H0zN%)ID3t6+vx@Z5Ln~$IB{;r9UI>EuQr>mC^AI5N>#~O
zDL^Fgux8^Pp?S;W{cSE0Y>(rQUhDI&mTt3MWjb+2G#Z4NT<)sh!%SPbJzbN~!{J@`
z8p-FnZMH7Z`0MxIc!8&D#K!N1Qoa<~(r=l)rk6A*LS+?R&xaEyNPORyU4_$Ecx{{S
zhZ$i69UPCV$0!2}i_=!VVe{Vq1{8cj(~p94JMC?;cU;}f`i#>1?YgDQMQ97v(*gYV
zzOAmOg7#?;c;fi4XRp`Yn%@#Nn~g`?XsofeW8s=^?N>;8`*l*Vnc`O^o9eSNN!wR-
zzDDXBlVF)Ma*P8^OqEcWeiG^%f|4cw92jC_(>F4fXmZh`eJs9G-Cp*(!Y_C6@Cng3
z*bUo0raxb-P^+O`Zr0<yo}ZTLV83*9;mywc1sm7Fu>2i3xhEs>n9%UPLG7kaz0T3^
z&6sfv21))mThL8YRCRGc=xO#U&j~kg8V!3QTHh$$-IFdG8l9zG6iW2S(ZoWR)!A-C
zUgL1@0@r{wsHS9%^y>YR&(Eb{AoBGjJt3Vn15;a8hc2PMJWY~^ytcAVo9?77z?^D#
zG9r+NiI0<o@1p9?ZsT8hQ?W3Mcy1{6kKtTSZW^?DxN&UtPHP8_v0Es+rZ;akiB!>E
z+ei1YVne!=^2-w_1aA($ln1qT9{4CSG>_QkRguPY%T|lVWko|5S=3hq+1$Tnch0nS
z2hIi9>3!N1n31rM1<hOctO~#y*!86KLz!Y@)d|vTKUsJAvA4HZl>Z{f?aMDtoFD&6
ziaJlSm`;cMxa<4at@}NPW*i0w4}O^5_cltd1^4JNk8gW+*5OB$ufy)N&*Rx59v&XX
zr97}Rf3?wi?yH*@-oJZtea-KDf=Eb$9!0yCuxa~zwb_1tcBW0IMuA#hUhdewsrH>{
z_1a25Is<p>!Gk{gub=^#UAzB80g{KmUY9MG_#~{WvI`aIN<Fp~z8cM>8VY-jrXM=*
zjdETS8t>qhL@TBk6(bX%@3WUnEpSWCBeZAjNVe5qnbQU<%jc9Sbcta~{wgXb%d1XR
z6wO+(bJKVXW-obUO&1vNd3Fsz6IE1bntT#(e+}sYAu7+l=j-F`O(cp=zw~7IGFhN`
z6e2~;inN>LDbOR!WyhFjjcFL^txmN(ms~x=Te}o#M|aDfH+`pfc7teXX{ou+MM9zA
zhd*Xkq-r{rbgwlVObW{zq7~&eWXCYfO31h}ap1eYWC?jxcOBl(mQZ0{TjfP$hA&D|
zu<1(!gX)PgFH%=e%hv6-8Cbat8#SWYDw;`N?Y_sNx?gR6`&>SZWY|-!$}3@IOR2}O
zOYiY>W5BItyICC8+=15aydLN2tz4dULu#MA-0ovkQ^WWz+}zmHpr4<fwwik>Da{`o
z93*DXEly5N#YRUX3h@m}DFEAyuVLW2hoLi%8R_jTQUpaz87tlkWRB=rK9iAX-5&<W
zR$+P{k6IybS=Y<f)L2$<(|7QGowr^1#bL4})1Bw1zJIc#oFr%aB%rP8R1+N|PcpCU
zYi%<87}hIQ!<=kQU7bSW!>3cm9jS8mME31TNTq0d6XlLF?6w+YBfujH&d6ldSSHQ)
zI0YNhbA4|iw|ySo{cf6lAHV3ii;;~SYn#?Kv};-TdGVrSr$bG2<{KDmrrT{7B~yvT
zss@x>8x3bKD)o6|M|V$6*j)z0ev33~F=xbex)}SO6;csv9oEa`T)Hi?PlTb2pUEUC
zB^Xn%^kI3jRX3+)8P!`>lq+y9jo1B{P3J{+cCLhyjUEmq(_FTBP*aJtEtOeR7mJYt
z$AvdC=W{rYo%}%_%eLv0HkWqseWOi@Vyn0L7@~7-+FP8W_xDwWO72U2?A!eC_G#H_
zc4^6VRaA#BOxPHQ&DtGk*IZj)N5JK{q3qf)taI<&y7$u%L?n@Db7oI9p>Ow@rR|-8
zn{6CzfRw@IW<;q?r$wJ)VB}TNyY$jOc9i|vnK_?M*C?FACK;l^t{OVi)Y?>CGBwqR
zRXX%8Q&zdGzSm+=aG`2zcR%Qj0dFv;8#Jo`=S`4~o){w0sk^nN%o#OqFzWrgtn)ci
zy;v?ds~qEy4z8nriLRrdo@&mMP?gKwRI}avylmHbt7{_F-d(OpE`o{|n+Z=m4I7=?
zW#TfN#`d`2=3F~ySe8ybmMt*@J(#H9Wa?y6Beml(tE_fy&*843Mq)$7a$Ht%|K`)U
zsM7UBufoxpuim4j)OvU1!q)D<hRl*F($Mp5di}+fB>9l&r=hkzYV;`M>t{56YD5mb
z%h*ekT2E?LHEnoU3i-o^S}g|LtbMt9X+l+X6zMlB{S9A@m$#+iU}lZCL6~i|@+(lw
zGc_36@&K0G8=`7az+qZIUDVC(a_w=ndlNW;!qdx(pPU$${c0wLMB>(68!;H>y6frb
zNvv=d2sRfX7sm`OtFE3hZ8&bu1~SosA^>on+!nd_%XV>QW+w2iP5X^FY=}NPPMSI?
zDJ2m=RaI31HLXJXlTbuOrNcuFzFewI<)t$g*5ySjTsz~8-(4O1`8GJ)p552O^t@{G
zPxify^YV+tD{$o#=b0KyF0skRlT2s}BBGN={c+crNoc8XaZ(uz9Mu0-sz~!X=PaWq
z%!cMi#Z{>_sD7<17CIr5J5?_BsnfJ6D}qxOT4YutPLhPXmdrh}f4@A(@xE>1`+nQe
z&<!ieea`GMtT_*=uD{N7ejlCmB+Qq-Q*-PeO=i7q^KHzn(XmxAQk5k<$1OEBn(RAm
zl|xI+LT3`@#L7)VTy<XCs*GlQ;*D(>199D<CNgbf4NPefVbLj*xdpiC8P#QXuK$*?
zCsccP6N@_*kEbDnEM@VflHJ_?WqIpTs=>=aXfJ-1s9fSd<ZiUQRx2C-ZRl$HD|zT%
z&{dW&zZg+BN`G-N23%zAe}`qIxkSOvZf;(kTQJStK)fs@!mo}8s5*7)-H`mw&d*Wu
zypB2czQ^}H%_i&BVzmceUS6rM>kg0mBVZ2}4grCFNfCX1eje!6S#a^u*WaN+D41oD
z>86WIQIE{3%f!Z6$y<C4wam*fvZiZRAc&N$)Wx74UxKbVaI)TNv>1eDWx5|1`8Lq0
zFci(3iV~TJ6N@U3JgO%`{%p7MQ(b_2l`fG;u0o$TW>QhpVo#$ZM~)C``V$q~)Ts5f
zH@$SPD!UwEiinxJ$SG(9S6P5qsD|bZYv5w2mds+uW~04z6J|90CUn~TeWNO);-aHc
zIM|1oT|HI4G-XoZ>vb^c7AAJJ-lZg4>TDxBCkauE8Y_lbI&RzG=o!$wLW5p0y-y=5
zOjAfc4aZclxV@DtH_b_f8<J>RV|l{uQ9gFAkVq-ZkOf;Xf1H5{!!b$sJEWqz(~F9!
zFxkn*#>PS;MY&{dy8T?uByblxZj=g>C1AO+Z^629={gzawSdZn#;#7Ssx3fOP`#4+
zEi^O~*bReeHqU%Y%KHdLynC3J(|f?n12!`&a3c-U|I^3n^X6PWQ`B@@kCHy6H(Jyl
zTwGkzaw^*XbyG3I&abX=a&V-JlhAR8-Ob;+^Ar{q!U%t)sZgZ=-Bx7e<W8*{W<H|b
zVBb3GsjTi+tT~NdRud^-E8f=TROSwARMJC&I$wXYbD)Q<Oj;#sZBH-U4=R>qA0r0x
zYoz8oB^k#kRvnF*kYyf5zJ1LtOK8e;b<n{JE2&%yA19nic=4r8m`AN;-PF`pVgh3p
zoy7=o$uX5v5OWMrHB^*y8yyv6DX54iPe#MpFs`D8;n){#bxqmJFYI!^Pp`M%O1t9T
z$M0vI&E*-@yFN$!zVFU1nO8YCE!ft1$bQ&8sx7j!<$d&fl}nG2YD`Us5j6x3vY|lV
zSDIT}B#H`Gr?aclVQf_Oo~}AF&CO`jzVJ{MWMwZEkT7SOGW|pem^JqP7zn%8;<!zH
z<lQnE&p`u@>&mBSGUQ68DAJI@!@}qOcrPs9Ik&xr3IQ_wlF$v^uU{l&fVA&8yHOn$
zPI^Qro|m1V1IPYz;MuiDCH8*BC=NITSSrm{4b=pQ>dIRmn?GJu)6+RqB8c+Us4BN)
z%D~0L>zsohG<0xxcek{(MAFl&HngxE5}QxVICgG2dEmT@IH^AhHOzM>z;<ZaJTt%f
zdaN+ffeTBr^0D<w`L57gV?u%M_P)8^<}j>v@e5Lm(Yy5Ytp1B*JU3#_N*88J6E&#J
zgUrQL=5xNJs%u<2H#keO-MCSF7TmnXqA};vhcl^J{l}udgrW^o8H>&9%xICI1Xa{%
zvZ3q&4`-Qn+v0uoo<1*&rt77~?s9|Ap{;p;+>KZJ7w=B_`bOuGY$Ij{4p+9S3l4Q?
zJ@B>uwsEsNuA+-sE}71;xm^8wThvxKuD*?ojq9T3KKv%Owp7iA*)r8=^(O1Hu)0WP
ze%5yB_Y3cPL}|S;v0!i)R$3G#<YFy{da>U8)(svG_x`;T?dyp7liboe_v~$GzOxi7
z*IIiv6AqFIf-PkX2qlrAyX!cF@nv@H-H+q<UFH6pmP0VjFk!Oar9TRZ_!k2TjPIP>
z+M6pVD5$)g?uzmK?G63ef7#Gt=;R#Fw1BK3Ambm!T6sAXaZD_va*D*WtHS&)0D#ah
zAtI;@Z@1l=qHg0_$id0Qh?95j*lIMJt%7QIe!8_)3>*EmVXeYmwS`Pgvd!URo7V2=
z>bhcdFcEBVquL^zH2n*wqWa<0%JxmtO*UuzTr751rmbCwHaWZ;xuCX~NQt82h+`V7
zubu>rM6c6tdv~9CaKo?dyw{NGvC8jRZTh3e%o8#S5?<PRy{y~qCZw&YstG%l)<nIq
z`K^TeKB4Df<v0HGvm|?st42mgK=R$#&QA35#mCLSY3gDXyy&wECsRagLRZ6p`Ke|z
z9Y4R#p}H8kWh-t5$JD)dc{(awsHI<VjYiX1h_)o_TGgP?0D-2NS>LX`v7vFB&E?dJ
zv7gjElU7I>stm`4X$H^$%E9k`Jt9rQhXLaY!rcudF2nG5%&q6pans2`2_B{`g-C&B
zb2^#s2C69}7jqK}K`E#_Lh`=^l$U3^%5)R8YtsoLvRSX#&pv&=KeMy21PkRWmO1ZE
z0jKHme(##q86JkD5<Rr#ij#k``X(u1XUTN7e>YxoH+T8IO>}S8=03m8&DJ)iXGKhN
z<DnId(x}T6|C@3u8oG`VJ-+U6`E+KnZp4BaC~>mp$$7rph|}eL(8F(=H)`Zj?&MLP
z$;=j+Nh(&PLmX3X+hof@L5*=(f$BGJ69Xd1oQu`T6i0QIl_P`23L`WcJF3_4@qZ@Y
zXH3c9<bL4GeH|8V&$}G4+re)8)z~a%PHcVBZ?&4h7$|$~mmqShHygQ?EM|=-w`FCD
z{U(F&_AtLZiZSf;`)y;wm@!sL#AX&UmXU~C6FRi;apx_`=wR$>9M7(v)2N%8fL(DW
zCg)el1r}OV3DLWA3l0IX>EbIx{fd1SBebaOck}rD$#2(b_B_%`sA9PE$dbxR$;D{u
z=+&0SUQxa<+o(Nk$gXw+9CD}Ac1xv1uJC$pWWd$BeBOC8`8y^i1~@N9%2hVFmIjDg
zT?5`W9Sgv3)WPcN>Nx(dmpQpAjfRr4GD=kFpMmzpE(gySt4Xq@zyb2u*w{dpE(Tlb
z_@MxS0MzZPx0938uejk|dzv}D6Q<My(@NKqnVKoM+$7ZeF)f37^{_pIFoKtyrgG2j
z`Ke<^ASzoj`WJi{Ybayc-M~X-(XB=q0pjX@*&Lb$5jH}a)Wyd}hP?4}`H{Wq+v#pX
z^^)mq59XLeHEJ_Au4*A+=9uzprzM}%L1Lz4+37zztIB1K#D|8$-J?v1QFBhMi7cX9
z?xo7V6}KIJ1!`&cjpJI^ZN<j}zwzV3r0{dIV}?RS-9(io;1FV|s<ppY%2;V}DhAk^
z;(h!Mtb?|z9NI!mikwqf!*QjoP4mA+YPOARY%QU}$k7b{+F0#j(K4+p*E@#LNL*#c
zB6!JVb1r6)bkR0Dclfk?1LOK{pX=-7KR|T$ab@2h+&%Zx+J1e0(XW*U=ekgzX)lH$
zJRU^_<b8NDVA5E(+AJm@loS^JuxjFc5Y5u%<m6mkTy**B2X+qafIGx`lMOe&&h@z-
zaB8IQV}(?Ae972$3%e19v#V>hW}~D+a)^qaZRg|CJ%W)Y6<d~#x%r7?zM2XvF1+58
zQWm=%S#Y{^Nl3_Jjq^uFMut2^vTW(JU4jWGE__)3Ze(QSe~u4pwzU$qdYT58%gHuP
z>@29#L?>32E1zrljjb8X3^*xhy?uwXNPAcBm5DPKzS6#0uKvZPWpHAos9DWzb#_MH
z3a7o_bt|-RkLzmAKJHD;c3j%mOkbCU&6K2N*Bh1F<muXtl?7#vQ)3RN_VETGh8%_s
zS*$T=RM~cTmyas!vSLD_g$D9tOQ{bz>+F%9sz?v(*DG`wb3=}Nl$l-6dR?y#YEP^#
zZfBYvY{*S$ld15_J2$KE>>Fjq&N2ikF1(9hvC4aeF6@sTPwUN^mx-&55g6eoovlWs
z_fOZ`o#$I#TF|_u(@$1TAcf<dN7MM2uRFvTzvsjXw+l!n#=4f{)>ak{q|bhT?_O;`
zLO%0!yz716rpaI}nKzc0%s+qcvk$+;{cCIl3uG45xWHGs+Uksvh>q)U^Yln!H7{Sj
z4W<;*^?g13{mY|Bo<iFs57-jm#;{ohx@<zafA_oe-**feVqs#oT&(HlP4$5qN4fk1
z7c0E|P~HrgO$XkEP4D~3|7-6|pV`2}IG%2I-P&l2ES9Bp)O|#hl9USL2-S+jeI>}c
zBSa6hL|l!o4N(-W-VvcuENQE%ip151MhHD3;;5yLrMq3-bpMQ<KJT8H=fyM6{GNH9
zdFDI6Pvy+nNb$QybSCRMf0^Q{r)ZKWm)jym!{zr?X=-Y!e;l%imuD)15%nNV>N>m*
zRl#zwwKofT{$l=I$#li#ku@3St#W=-FmIeSBx)LZ9z7V+^`HkOYAzjy$DFhgM9pK&
zAF^6;XY<kK`jDnz;`E=jP1=9E{%obAFk!Fk7;nKD!Vt2p%TVYnX!w$*j)m&Rrzj|`
z_6tnb_mNY|QixGYR{w~z*($dpBPkGO<xpN-%ZZf~_-yyz<lzDz91}Vt>9Z|iOo9fR
zO-GqZ65E4v(i{(Wr+lWMG1m@Z-6jaL5zT_98Y|nd&k^qY7YPx<rBvl7U%I$UhdhoR
zygKfh)VEY|6k$fkWF$lh=#b$Un;?5)VldKcJLmlSx|~_!y2T$e4vu#T<7ntkRx+Q)
zq;xPQJNvTMd#T*&$<Um3pTkp%&Dt6u(3+r$xEeOyy0WjWYki=m0F@45*^LM}aKa*c
zP(8rn_XCX+T{zwy#f}hgkOH522gfpvwXT9+a9_|f&+f!>Y5B0P<n3Ov^2*AJL}!b5
zo0EgD@}B=}7LG(VONNW|tHK6TPWL2O-?vVkG<p9ruJe{b@1R?KGM3m8r}nt6_`(e|
zCnon2j@|cbQU2&YWkXNg`P+S%j>?dvl9|?0zVc;*p!@_#`uccLPh1hoHrcC$CpPvJ
z@S<&G%62}ubx-$JYX$V2IxtLUlymVu7O4TEOw$YJGKGfKQ!Dcc;IZrt^1Y=fuh*})
z8h00m#@{?nTl-kQyCF-Zep(n(xY;&lXK4ert*@)+K0o4Q;`ORqD=RUM;mgB5vrGt7
znW=S<3>bS*I~=U+ksXZ3bD`q+gu!yfPXE&;TsP|Q5e-T7qPHnT3sjjVR`5%Xr>k(p
zt4<mG@emiYeA$s}twADaO@#5?Vr(A6%C@entn9&=)%u;+;j<f)dCRNpn`V~Kpimrf
zfxN{|8l6`RA2Rt%AP`7JkvZ%CsH-`_k};S$DNVE_>-3rjPRhc5C508MVFi1gfyO2#
ziIC|wonPLCW=dDJivKL2u>#m&<Y}rFoOy3RC=}Xemk=DXcC(x5bcy;)_P(pj8&4P|
z<?9}$BKebq+5r{=sK?Ukw<clHw2AcENHpS1(H$!#KYxlP!$+<kh~(MQCj60eXj&wQ
zc37#5W6cHD>5?QM7clZT%#0Qth%j{JgUGr{Wk^JTKSh^hY0AZ$_N!1tXtEfL;Q6;*
zk9&0KjK0UA@9RVa1fLM<hoM9Uk}b8|L7^!5Uf@v+!A}`6k%iZENx;xDf}3F%^S-e~
zwQZmtDXyB+qNMBkn`#5D!OtH_v0I}vEJ;Si9F1eNNL4sCkQ^zO4D{@4c!??Fz~O!f
zgXm85oU#lp8brQV%%nT?w<`6kxG0fFXN+f2#H~Fv*dSazwaE3o=lL-3W@n+K#UEPG
z*b$yjr+*dtq?8QD1fzc>yrX^i)Nd0CPfOc)#-1zHcB12^{@PmREk5jvl9$N~Zcp$y
zasX8wyJc8aQ<KJ?QTMqh9U4{Mcr8f=Tl8_K&9VKs_LXpOqjc&QHtY;uX_5Y)@9ShA
zi*wlz2nSY@cYV{PH=Wft-Cr6gs;a70-Y=H7%%c`y5~lv9m(3Mzz}tfkXV>R$Yf5i4
zkQ#&mRjQHghAN(z9eYwp+yK0lVM%uqFFTn$XSg3njJE`Z2;Fl}$={H@INCNk5%is%
zk<tv{X>4I6sFIs*<~|_C^D{|}Ja8f&Zw-_S$!y(h=z~D_GqH&#!plDZu4|E#005(c
zvAuv(`)<feeQBO2>`B^yi+DLF06+{p2LNz%Hvxe1lM8#k1%4~fw@2}RY=!NdJ$AcZ
Tt+**G7C>qqa35EuOGNI!tWARD

diff --git a/docs/articles/functional_api.html b/docs/articles/functional_api.html
index 58f9f26e7..b5ef421a0 100644
--- a/docs/articles/functional_api.html
+++ b/docs/articles/functional_api.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -239,17 +239,17 @@ <h2 id="training-evaluation-and-inference">Training, evaluation, and inference<a
 <span>    <span class="va">x_train</span>, <span class="va">y_train</span>, batch_size <span class="op">=</span> <span class="fl">64</span>, epochs <span class="op">=</span> <span class="fl">2</span>, validation_split <span class="op">=</span> <span class="fl">0.2</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/2</span></span>
-<span><span class="co">## 750/750 - 2s - 3ms/step - accuracy: 0.8979 - loss: 0.3540 - val_accuracy: 0.9448 - val_loss: 0.1903</span></span>
+<span><span class="co">## 750/750 - 2s - 2ms/step - accuracy: 0.8979 - loss: 0.3540 - val_accuracy: 0.9448 - val_loss: 0.1903</span></span>
 <span><span class="co">## Epoch 2/2</span></span>
-<span><span class="co">## 750/750 - 1s - 920us/step - accuracy: 0.9509 - loss: 0.1635 - val_accuracy: 0.9597 - val_loss: 0.1397</span></span></code></pre>
+<span><span class="co">## 750/750 - 1s - 773us/step - accuracy: 0.9511 - loss: 0.1634 - val_accuracy: 0.9605 - val_loss: 0.1386</span></span></code></pre>
 <div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">test_scores</span> <span class="op">&lt;-</span> <span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/tensorflow/man/evaluate.html" class="external-link">evaluate</a></span><span class="op">(</span><span class="va">x_test</span>, <span class="va">y_test</span>, verbose<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 313/313 - 1s - 2ms/step - accuracy: 0.9595 - loss: 0.1328</span></span></code></pre>
+<pre><code><span><span class="co">## 313/313 - 0s - 980us/step - accuracy: 0.9593 - loss: 0.1323</span></span></code></pre>
 <div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"Test loss:"</span>, <span class="va">test_scores</span><span class="op">$</span><span class="va">loss</span>, <span class="st">"\n"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"Test accuracy:"</span>, <span class="va">test_scores</span><span class="op">$</span><span class="va">accuracy</span>, <span class="st">"\n"</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Test loss: 0.132778</span></span>
-<span><span class="co">## Test accuracy: 0.9595</span></span></code></pre>
+<pre><code><span><span class="co">## Test loss: 0.1323339</span></span>
+<span><span class="co">## Test accuracy: 0.9593</span></span></code></pre>
 <p>For further reading, see the <a href="training_with_built_in_methods.html">training and evaluation</a>
 guide.</p>
 </div>
@@ -607,9 +607,9 @@ <h3 id="models-with-multiple-inputs-and-outputs">Models with multiple inputs and
 <span>  batch_size <span class="op">=</span> <span class="fl">32</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/2</span></span>
-<span><span class="co">## 40/40 - 3s - 69ms/step - loss: 0.3948</span></span>
+<span><span class="co">## 40/40 - 2s - 57ms/step - loss: 0.3948</span></span>
 <span><span class="co">## Epoch 2/2</span></span>
-<span><span class="co">## 40/40 - 0s - 6ms/step - loss: 0.1971</span></span></code></pre>
+<span><span class="co">## 40/40 - 0s - 5ms/step - loss: 0.1971</span></span></code></pre>
 <p>When calling fit with a <code>Dataset</code> object, it should yield
 either a list of lists like
 <code>list(list(title_data, body_data, tags_data), list(priority_targets, dept_targets))</code>
@@ -722,7 +722,7 @@ <h3 id="a-toy-resnet-model">A toy ResNet model<a class="anchor" aria-label="anch
 <span>  epochs <span class="op">=</span> <span class="fl">1</span>,</span>
 <span>  validation_split <span class="op">=</span> <span class="fl">0.2</span></span>
 <span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 13/13 - 6s - 478ms/step - acc: 0.1250 - loss: 2.2998 - val_acc: 0.1250 - val_loss: 2.2939</span></span></code></pre>
+<pre><code><span><span class="co">## 13/13 - 5s - 373ms/step - acc: 0.1238 - loss: 2.2995 - val_acc: 0.1300 - val_loss: 2.2957</span></span></code></pre>
 </div>
 </div>
 <div class="section level2">
diff --git a/docs/articles/getting_started.html b/docs/articles/getting_started.html
index 3f6149b74..893a2f24c 100644
--- a/docs/articles/getting_started.html
+++ b/docs/articles/getting_started.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -278,12 +278,12 @@ <h3 id="training-and-evaluation">Training and Evaluation<a class="anchor" aria-l
 <p>Evaluate the model’s performance on the test data:</p>
 <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/tensorflow/man/evaluate.html" class="external-link">evaluate</a></span><span class="op">(</span><span class="va">x_test</span>, <span class="va">y_test</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 313/313 - 1s - 2ms/step - accuracy: 0.9808 - loss: 0.0869</span></span></code></pre>
+<pre><code><span><span class="co">## 313/313 - 1s - 2ms/step - accuracy: 0.9814 - loss: 0.0844</span></span></code></pre>
 <pre><code><span><span class="co">## $accuracy</span></span>
-<span><span class="co">## [1] 0.9808</span></span>
+<span><span class="co">## [1] 0.9814</span></span>
 <span><span class="co">##</span></span>
 <span><span class="co">## $loss</span></span>
-<span><span class="co">## [1] 0.08689082</span></span></code></pre>
+<span><span class="co">## [1] 0.08441389</span></span></code></pre>
 <p>Generate predictions on new data:</p>
 <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">probs</span> <span class="op">&lt;-</span> <span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">x_test</span><span class="op">)</span></span></code></pre></div>
diff --git a/docs/articles/getting_started/unnamed-chunk-12-1.png b/docs/articles/getting_started/unnamed-chunk-12-1.png
index b0702848127cc58b38d4783e1df4dd9fbd6a5d78..4e9a631d0c13649596ae1e9b371807d25b84e35e 100644
GIT binary patch
literal 30397
zcmd3Oby$_(x93q5Q9!y8^himEbcqtuAl)6(A$2GL=@O7m>F%y0Al=;{BHf(_X5)9}
zH}}rHcjmeO4bLOU3Eutgz4qGc6YB)Z$%vspA$|gZK+whC3x9w>?w#NLL%9zgc_m>=
z1AaWVd9P*<fuLgF{ku0Z?{N)*Jco!23n@A$ZO=Qo;X2$1?w?gA&u?e@lf9*ReD7^k
zSioo8z=w+YWHe85RR=E-F3=<tnq1s_vO?`0fuEgFOzv%<U|~|c7JrNJ;_KJ)rvARY
z@OHv?pQYovsoJ(KYW-^lJWeb25VbBVQG){>e)71!5Xc+&lpFyV&&=B)B*;3cHyPy3
z8s;$s!qe$r|2c@8G;Cy+w37vG`Rr=k{Yi`wo`^i6!c++J+5^5hZ%0Zk9$D{RPTyNj
zkBpC0u<T`|Hetw`ry**o_|9ix#V&e*BVLMcFqS|4NyhN{3Cu`Mxn{Psl-a-S`=!R5
z1&2AyefnOr;iADc1@{M1RHNturRg-62oDD2iH%SBH|FBOUf_bb?Gg^Xwxr%ErJs->
zX|5<R2qgL4|1nzsv`3JkI${`%67q)I|LL>x*_|Sd8V*9V@5Na$LPJPz#>FFF8+3%c
z^M5KLDk_u0$1NbxY=+pLZ#c_I^)MSwZy);pPVKZj8d*Hyq~o>To>j1r^Sh;3MlG{4
z)#93ClE>a;zdE8LA|g^!7HVymXRYDMdT~!?aq;nIEUKxb6BM2IMzSO|$oQEH6gJD!
zBV(y$Z~jEd_SAe`DfD?1L$6NZ%IEZybZBoikqDl8;L^SIA~pQrqHBxW>9VrY{;I-Z
z>)Z9^(M2B;192yvhv4!7vxlHK_OfkMx)iEGo(d<_%;tQ3rpjC_lsIei{oP8vNqzc^
z=lFEi+O=uHb+^uBs#GtQbE?dslbT83Oe!{P*7JI?a=t;wb*HL#mR_^g=C*urxvec2
z2YWF^K<g{zp)+A>EEvMFUSo=JPS}b0cw|6=h*<Nwoo~^(_ncM7o(UzqeFd5t8<Ck9
zfX{9%Ed0$rB!;}<S`9l8JCOoY-r|==VVz0IVLf5~IRUojLog<P3UQd_HqtE_E7u2H
zYKDZxMwg?oXS}Rd1TIY>lQ`#V8+#M<#A8eyhTFrrEu3N%p<5G?lbz>{A%l`uoXaPX
zlShi;WiF;OSu(LVTZh#3ymN%LEtn4Gs;M$$Oif#de5c*zi3EKS@<A<ckmZ-S+n-0a
z?9aBma|mLh5*2key6|CJ>y%CSx!-7kDUKPih9|G6YBe!eH}GtBQ=@N7TotyKyonKC
z%));3MB(AXhgmg9uJcnC91f{(Gn%aQ4Ge;VgR7<@v$jR8^O=c5y`P)1l&Wt#;NajW
zeE4AJgSOorMZMM+Z&0P)8A3E#q67cYQtmp>ti88c0$+%2W_$HQ@8`TzJRvkZ@xZxj
zOOKDNW6M^fsP4+ck_bmy*s6Q*33A&Be4I!WE7Xvg#ybrKnUxr?B@N~6Y9Nl7Y%+_O
zh(p5)zJZ})nwRNgC!AM}&+X@GD`yGN>ltP!WG?lVr?`)JKds9Z8zlCh{=|_D=wuC^
zONqTb(F4&+>vprfz1`zM*lVl#>C>@8lbP0=p_l7e$1k3#sy!8ETXuY?uYOP~s9w8o
zjsCmOTW>bLV#jrX)QFXpjC1QKqEp(+Nw>egXXO>C7_yH(Cc^v0(I0VmoBZ}Eg~Oh9
zj_v)&$nCo4Y(LRk`)U?Hs@n*37+!NN|8iX{2@ec>e5ls*bbAkedo#ehH??z$^-^@s
zevn@U7BIWRK^&^G!B(0VKW>J8(+*un!aPQSJwRl6G0@XruV;i?aLne)XQ<a$Y3aEp
z_B;Oi;gv0&$YDB^)`}c5sbMEDXkF8;MqfDj&WdyEfSK$pi>RivzjpqD+r%*Er6y)8
zI&#Ne!$7Rf!cu_wgtxFfOMsUrZF(-@&&J5es;vn;o+SRO)2-p0a44~nj~5!$R&&}P
z)%$txVKlOG%*x5uq(;%iJtSmd5s`;F(fE6+58R{mLdib0npMI0Qg|FTHS=1Q+Wc#*
z<`a^VH0udJ#H7=N3tX<>7LaRE({JCB5nrw5-@dk*<FX-GK8u*`uhpvS4EH?f*qSGv
zkagmkorjqxW=nUk=+DVaEK|iM+VT>G`x{;)tRdK+Lx<l8D^jiC$!ES1MfF=e-8ENX
zlF^+;r<Ud1a@A<d?#Gq?)7BTM`}KhkR(qBXLrI1CTEK*OPEj>HwOfbhq^LKQ($94J
zhh*=T%EbvG8JS1)x}lomP@2#rOh|6@#kvs2g6AzlE7cv_=ykoq483aU=c}#E=o_xK
zKY=nkXO^007bOkk^|u}!YesxzeqFjXEZQYS`@jGAm2q}*Qly9P5#|=E8$V?$(`Is+
zJMK(%7Z<*5%(}`V3qH)FxuvD#b{9yU#@5a$wqT}7j}Xq#e@&ZS>vmG}uI&qoJVm<6
zq;-7rAqnMBTHkc&#5ex7)q#@>w~rq`YWatrVq;;^1&xrKA7^yRI`16EdzPrT)mU9!
zZj}_U1*GYy*0+pE2gF**Byq7=FYpzNU;gsJSb%L6shBxtWs!8ORiz3FTK_(ecekcW
zFUqn1&Eq6#HuiOnv13KQDOOps(J|54_voijrcF}+8YgYJ*D`K;=)?9JG-6?@D0*VN
zjKB~oHQeC2T_-Ajj-1;f;mYgx*_K0EFU7*>CkG=bC0jX*NETn5bhWqUJv^n9jCr8;
zS(Jb*Ffg!8y+~ctIrI3L>-G7;bcIQSW-*BP%&aUegqMm}Rw8X%2-#cl$V>wN`KyxI
ztM-YHTnEvyB#a2YSFbh>MMLJPG=eW0?KQ<&3>frwI6Cx9NF6(@Os)k`<)5zv<~qIQ
z)8djO=t&LG;#iq4r}q=1@fN}oVWjUHv<rIvmFC9da4ZI$#8O*!xq8Q+fB&-uCf@LS
zQ4sltuW1{<M#o?DtVg_yOx)??p1caoX*i{WXJd&g#*J+j9rK^Gw#GcWK`e8lGI@3{
zcZ9mSO%|)^6cuaK?EY>KE_uy*cZjMNCG$GrKUk7@=d?Rl2R1=Kx^tF{*?kfD6wjNB
z9a(;{;o;jvc6)>64$Il<qPK#sr`z$oPK;P5yYsHUe+HVx=Gmjbh9-Pb2~M}CqrQDp
z6!OMd`SYhTEchcap13d$Z*~7{bzN0w%S>TNTgy~o$b1L*->6U!6iaL68&4vIxBxt}
zE8e$#=kgQ>3Pn<|Xg8%8&JF0$kG&!<fs?rioSEt!*rVb*?0I~A*kQrZK>8H=0e2-U
z;w5L+{5yI1<CZH<Y3IUEq0NeRc88JZi5wPF`)>YE`x7}!N+Zt}T&>pzk~tE0DzuyW
zjE`2(JuWwLZk^qZI}&rwZmUg)(}~!OH5XRLRdf)~;a~L)t_I>OK5dMYmX_L~A08c%
zdtM(fL=1m(b!_itI@uUq>oLfvd&cW%F;`orR$=^7H00OCVT9dMtM5+4uwo4(*03eQ
zX=g^sH!YUp<NmFyZ#G;+Pfw6BDYf1}Y%qmi>D3LgSK1@=XIwVFLTxdgN{Xw0jheN;
z&dAs!?3=O(srrH{)m=pS%5kfJ(+U0c?qnv57N6Lnr-q&i!S#o*(Us%N`lqD1s;x%$
zT=D%uly9G+DoE*hsNL)~>`6#S?7D5raP!RAPq(!Nu6o{x2nh*+Lt=&QT=-jJV#6!G
zNFJvb(E6=iu$c28U#!d|4Gwy4U#!)Tw5=a82|8b7$t3e&MX1Op8R0|bgIYiBVz(W+
zCdriZ2E149%D$e~-58dFT|X}WqOkm>y;oq*s_Fu-oe?qpW8;&DZeXUoL4fJ#cb}5&
z_wbGE#DIV`7qu9q;Lt@xtw--q>Ri2dk(swOwup5OJmH+lEyPaEA|IKonVFeyFM$XA
zw)6Jd#JZ(jS@a-`cQ<#RFFh$KDb?e$d+*Fi)^fB!rKFdHl+W2_;MOCv*?r^Z0XaS+
z>`#Q){mu*{kpt&|_tjr;+P=2QWHa+PmoCITl5t^je_*Lk^e!G=>Figg+1!hcg{9T#
z96|g#^9NJofEEV1KuB5%ZsD*&s5Zjugg{&I31g{<ZzB96yQa%eOYh)2DLEDaUQrx_
zo%0v{YMLdi^67M8;V?o$z1f%HF!ibz;SB>#PR|4!E+zQi2kA!{g{RjVwKMj5h`frR
z5Py*fZD?G`>V+>T=u3y8z^iT{3ZE)fVv>X2lB#SUV9Q4=!Yyw-BqXSywDg4pcpqOA
zTSn63h-XewL@!d?NbAvST$=y*!8~z|y1S2Z2>W=NsBy!+BC!UK@nOG&^sQUVS=G2+
z9Ii0Nn(zN=IoTX9uoKLYNw!<jF@1*9PoVkh2_y5v3yUR@76LBWFIF|z4i|AoNZgG6
zVl*)}v6OQk%la;L59-IB6128i3f`QbkSt3N52w6Qr3;db<PWlQ`>~Tk6(}s6&f?ia
zvOM?)hS&az&Qa~Ot6Vx}zFhwdmW-C6iCCV{xWbaBlF!!3z7EEFjgrZS)&4Vy8fq<P
zQcI#r6;k&aLkTx4vo-(<4GgfDfEiZ|oBj!Symph6+f(kg+83{%%9Lb8!eL?hniw4o
z=X5fD8<e<qjRM$TOmi|oiQpwDq#DDJ9IIM1;ocdpkX&zAk%gjZ_i@E@-<eO)*ifz)
z7n<(veM;(jQEYg1wkL>3F6-?TK*PH=(c4_QdEh)!5ibAB%hC0v-|yeQd#boSZ`@LZ
zC^8~&+Z8`(a+V6e`$K7mIH@r(^3>rmnpQhq%m88hYqGaP7DnIo&HqE(@L~WPOhlf{
z`Ipy;maCj01;f?vE_kfg4U*>Wy^v06ZKf?dRz_;><ie^iL5yVlc3<?=Qo9u`m~oH%
zu!U$=A3hqK<!&)%jgIScVVqR+={7E_#r;5f%5$<cYpAB&Mb#VGVcqz{jXB9k)i3aO
zZPI8CqiE*`-G>)7O$JvdID{R2<y1bp`&WT9LCXPMedYIi_l1pPD50pmhiGlAQV)8u
zhi}}k$l}>zcN>GBk~-JP^r12}%I%7SlryV$hzr{lfu`@wF0cm!h?J*}@W&u$WMte0
zfM&zLY-OOH+i`npqDUhuGLlwP{=k{<;N9MLwLR>%q1e>ht7-wn)fv;I@qE1l#<eT=
zyrADx!;s$Icy@E;I^=Tt$p-$=n>}(|Jdp}xE7(c&*w~nzWs=OAf-L`uWlOsN@)Ag{
zmW$2qznq!(RAURBapME7J#LRTM&Vt3yBa0Cqq2g}&OiOkqgfF2;A?uqZ6uEQWr2mo
z!ymCZALC2p4)MP6dkhoTf99B-8TaZ{bt-q0{uRe}OV<?K=G@u{Q@Qkv`6qSntL?nQ
z^(^U^l?e%Yr@1C_zD5(wk*@vXrn4@!Z}eF%Y;19y?zEchU1PJ?tTn|X5;Y{m=|#Tz
zXsj07zuE)GG$^Yyo#RR}67gfH2#Gfmi9t6ZG8bxT3l>Zt#=ykdxps+}e!Y7$ymF~s
zVcg$l4$ru{6}>tYh;%yb=UhZ1E!qA-#t-{+wA$a@-R*vL+H|cgA}<<3SmrYIJ}5Bj
z!rlqa$;84^X69pC*OQ`s0p7%SS%T3`6J|XM>j)vj+kJZtrUIm91{RhBuSaCLHc0UX
zoncK!1H3Xh_J6t}d>&ymkJxvYYg*x4NT6@~lY5+FGz23r$bdNUJZ@P__0-m5OVU;|
zLb;qFINsRU*lRjV8C+`sr(j7X-C4bKV)J4HQ?tksReyr^C$0frt&2#-E|MU$x8HSX
zuz$W4sxm)j77m)l+p^30*(qkHnfaVh{IQ7|YA+f8*RsvCjIlbpXM<$loUB^U#ah+E
z3>f>rf!QkW%g3X;-HVd#!aaR2ao88n2CjCgRQ=|wk<0o1k_Nf+SLn-+3k?lxDx@Xm
zlg0D3wg$a14CJu&?;{G>O1Jwj1qE-H!v(9=i`x7#;BIHdTk^g~>v$pu7j7zx_X~*!
zGT_+++RjU(ynb@Y^}@opXZklYL4se!hE#~Wn`0g}&F)yaoyjE};1i)8pK2s(!(L=b
zCn{(!%7;_;F83c^hPm=hE4#9EY8kX#VicweGfQ!05C(!}_I<BQ<E<BNV$|c}LO__E
zQYEYxH=H4w=q%9l{0oZ(Vec1v(ydR&@y8TRJiY<zi#m)u(>vgBO6zTAJ^>2|$^aHh
zN+VDms?3y2L{h*PJx`8WN}4D8>+IKVPD(u0iYDNQ9c!;BJ#WQeftBJWow)jJcJm3o
zYXlE${{@e;?wB>a87vSU$Gg9G)A10YqT^oWur=}R+qXa*>KH16jqrYu5bNP={#5)T
z0HJ5AS@dyNv{S0=lSin0yCid}Y>Ux-TEIlS+}dV9A4B66zIsj*hA`e3NwU7h$?7`o
z2)sjVD>;H^Ta$Y~ru_5l3212xkJev;G|+tg3ZF^)x@VIe7oVfvV;+nf`Np}VDKR1M
zp^$7e`IJ(Da*+<aStJ-god0wxnh_@;t%NnnL7~F!WL!lSQSRRnlA&|<M<!Xl{@ZrU
z%BLK<`K7t`z~iB|SoKa$0U6=)RyHXdINV{_H)bL-=Bc?s4!>A-Q7F!20(8cs(s{!q
zHQF_%wYBv~hhB^Ob!foq-i2kGA9}2{rV_x45;9cKps;T1iS$-8YN<G8<37TgS*<*G
z^m~j>i|%Lh;M@1&w>6l@?7ge~i5uS^8<xqXdXP)sbm}%3<g`__ZOpA5)=J)<qp-1+
zlWKomWM}<-`lUlFjBLBqBxn5yJA#yGWoKkr)1paixB1HAZB%VxUl|?(Z`-aeT{`wg
zIe?v*F3E*|{|6F@fcD#$qQeV1<nWCk^M28cgH6`}q&HnJ*2h1A-3hilyX8!!SU8!A
znS!e7`hjzCWunVb48z7x7PHxuM(xwo5)9sW1(?c%PDhsb`UGjMHYv{d5IqaJ-9IeL
zoI0fw*wD!3P3QWBZR=<W`m65kX;jy_*STu?R{k-AZWk*nYvb%@W9$0*!7dGF?eS8n
z&-e}xTvtdihqavMPqJvNW-6P(a%G4KYD6zVJ|}$lD;|yoVYGlR5@Dnk+ncqkK!}D6
zUo81ei~!kwF9pFdZ_A0OcU5l~%SnN49&+q89t~6$&Ay_yF#NKDI9p`mf4LLaSut$3
zXj)-i#Z77?5wvnawpyyav?{<dEKc8sIJlr)c03S7{CGAc7W6j8M(4KWL&4aHyA?Hn
zb0r>%l>i)mr<-)EI;-iWi=g;8nk_ApQ=tR*P*KM&A|8Ll@f-?mwW=vtC>l2X)G)t!
z`jMH?ImL-5dD2R~+p3CiYJcA*XEg6}#1oxJHO8o|t!<nkgQGP>n;oQ(j5Y1_T$;@Z
z_pK05ZL(tmCheqZOJ3<@QQ=<r<>7893A$E8QN_$;^puQCQ&vAcy{mm~T}}H4@7!F6
zsUcB1{AQ{&M;7H)t7Xbkn4)Uu*IsINoRm?j>5tF%tUYSi_q2Gd_#CKgf9>EY1HMIF
zJpte40fb!|9UYdsH)x%D#5c$+HT^RG7uUerQ686IP(VOh@I!DCG7=z8C?DVw5Ged1
zCs#iuVP>QnaNQou(kIMr65lr9I78=j;vl*ZTwRS^B#1L5LPJg+6bsTnUomMLOcakv
zMjlS(_#!Ux0(!Y+kN%A3n_ir!#mrc}NoqF+|EH|)%UoebY7EHA!BOvUMTXO7C6Nkf
zLy=>^W1^qI*$Uq@nFpj6eJ`?yg^%ZZUy`Z$&t|4WlcL~69@~@UD`STS4XpBAbyV7&
zr5s*1v#X_xh0uS%)hEo?n3TkAvq#Xc-UBOaY>(y{>O`?@OX8{8W<vHNy|4B6j)`52
z^(<l|CsnzYVE5~bcYbI@=@XJjpViTYN)j$lwn{YXMwSl<(fmf*&v3LCH?g;f3;5es
zsGw$qvce~PyWbst`~7TRFvPC=)jaX@&uUz(=fF2yI{Jpg{o$oo;@vFrQXStzNjMiA
zCbbfps31c~58O+`NV!>^b~Q^Tc|oD_<c#|T)K8C?-Aqwa)2(CY!aC4FDRAdQGY*Sz
za;wzGrI;4182sPk&5^}4Jr_r71_lO_P8Gi~$aN=*C{twv1NwHx_dFK`1o$We12jG0
zYA5I>kT*`4I6?=orlwSvm>U*f`qiq@oGfYzMQ5)U1r)bya~HDqM~*YkysA9?a_!>|
zcDn)^p!A{MQ!)HZOnZUUDW=MrHswDrTU$LHwnjwe>RU=(sj<XEakSp~iN#6TB9Nsn
zZ4D~r`A8M4yDe+-u0PxZS3DNtcG$$Fq*Sn=$C6KNc0Jo=AUQ67tFaxnLOF38JvHZ<
zRGeUjyB8c(OU=p`=f+f}+v2SzP2!k4xv+G^f4Fch@NVQhsx#pI#z>osq~LEZXacKI
zPauxLj^kBtUY^R#eKbfKuD1XUn9%HPdOM!?uRmN4jCJ3lkPaSPHq$ezDJw>9HGCR*
zi<7~{$Et5yTbEVl%~0<quoy0QV_GviIx+%<LK&^)K77C?H+ccAc;06f-<8UjZIfj3
zCgHm)6YUXV9PoJgx6pfUa=;C-5Qi={vFtRRt-_iXB3_D7Wo2Funq4)P8jg}nzSg66
zH4#3~%?)V4A=Z$jT^N&+|MnP%3T=NXL%PGZV)5E}Ii{<t%g$0XjP&BCBop7{PBw=v
zOK501wz1<p+Xpkkl9`lNh|RLl(4zv$SjJTMQ#CK0%f{SHh1G+RS8ifawo+1K{1HoD
zTf`MA+7jIMt5R;e8=c6<FQ6q9AUl6tANsyBa}1!;MWQv;?u@Y<E;nrl`?d!+k7$O<
z0|+Myh_=(ZIdX0K%j%!2O1RJ`Yg0?|5eDfm73kE{^S-w0zp~Mu>00t`8&6NqBGFsi
z{WCu+E}wb+U~(v8rTOp)MD8I93<_0?5j_9pvol*$V>Z4yZIa5+a28xR<fJ?cXT(Pw
ziizeBw)-2TiwcXx-NJS%&4U61#fdoVj)O{|!o%aV4sNLeE5rf3)r1}kZ-tw`aimm!
zf6_em2x9hT2nWmku$#JMMJp{<N9$~BH~p2ug3H=7s9kqYtVlb#COyx_2^N;*Yc8mp
zn}s5iy=rvo`rQCv`$Dn3<E46S@VW0azq1|ubUa@`t=D!_1dvd2Mj@XiFpq_Wd7XAM
z#ZoC^vKZ$DG^*^%Dcm(xul0n6MZ}>{w%)L?Am`VNRKqr*IF$DG_MDb8V?ZjwXP|BV
z@CZvpOe_}uCzPIK5+Tb~8XnL%Z*_Xyk#_(|qk6!PC2wZ7x%o_R)Dp2V*8N95gO`lo
zD%J>^-+6>%kkQd0Eb_1{sN899VLDufaGb}bB`>KMha6NbS3WQ+an>TOc9R<!wB=pS
zE+>V_EjI=TWJS>`x-Mo0?s*G_aEHu5HT=C5If_b}c&l<a%u+MsyzeRJv{7B2kaLb+
zQxiiXzZNw|+&AiUSwX}nTlHIbR<(N4#O<s#U2}-LOQhkYBW3CZZ&@LoCCUdfNM+hB
zQB&vg#_jLk<H3)HuZt2{EX9si#l^*qNRm!2R`GD%9nP9EX|UzZGtM=Wr+8n|3t4e0
z_vkWDH3>iuyY1w3RM9itPUU}dO(vgRL@Cgoe1brPMm&d7c^rZwqh9fO2jr^kEqYuP
zy<Q)@LhP-scq%Jma~YH;cb~f@$F*QBDP0kM8p#xwMQd1#ITv+390*T6m|$0bxpFV9
zeqC@q@%6{I@$rTgk=??A)?3W(#8`L|BdtmH!AyS|41EJ;5<{3KL$}DSnp4=c5;!a`
z+fS3%22(vLI8KRGnBMguy<rD~2~Mx|m`DVe<p|b<!S}ja?`U=JkB+9QoN1IH)XWlB
zr<g=zMa_+yDk@JQN2a|U&)Ul-ke=lBj69PMqDX>x#E<2$KC~1iD{#=$V<OV7#TveL
zMG2K)4FVT$pqsOcmPQ@Rq3JkTd@P26(_6pSNbm{+GXBdC1?FoSI?lnsB)C7G<9Gj7
zf4<g#b26IFzF#$U@T?6(kc%{|#pjVuGm+ZYpG73TAx%G-ckel!M`|b);ecQr_8i)C
ztk>ynDDB*Urkkd?@-P~^SyV>oIr|+T&j+T{EO&;(=hs4B2Vv;wEq9w^kJgYlBBX><
zG_I~@916GF?m_ZJ2ZcO)wysPTeA$(Gv4{x4A2?pz`uV`+gBy2_Y^qFGZ~WA)GW75~
z3$TeQrz5j(JG9$QrG@`&*ap`~hSQEgAad-NXs`zQ!G5M|6`}89@`ULxB9@}ICdG66
zqEmV<dOM<IOzcERWVIi9LT77lct7CkbTTez<LOxAL*BfEp~BR@gH+FXc*t~)5FX?1
zI~GOoB)1kjRV+?TO~ssr)_SfS+W550*YAAMutvWrv$%P<oM8Pv2GJiik3iNkp@cx9
zvkKA{*X(W=>yBKLt)ON!`%zotbxgExi|ozrlgQT3?;@iUKe(moutL3BT@2}D7`I+;
z-V(_FGtdEnOv+LVdCEsFboJ^eD?)LIh*rCKSY+GVzvLz7uQ(+);&*JZ7io5=O8)7h
znRL&H-cseg56NXsegKH&HI8Xg;cxg@0!1wj44nm{F`)%2HWzE%xU0lBpRtXfItW2*
zd`85FZZoXrh)S&05%q{RbBUB74bFY)@K>a3^(L95&qy}yAip}YqSCCbS#RdjKUR5k
zp5&jm5&6yY26l~lmX77x=JwwEOp+kuof2Rs(^IiF7$|rHvJ0ofNmawEu@%UV(5KX1
zxl(1bKcjmI;j(69Q==wtz<VHXh6*GjGnoX8EtI*TBMyT!`6D$p_aI}wX(~~*4nA&&
zwNR8@SFu;wTZkBZ$Y<K)3Q^DGr=(n^0CX!CsRN3;!Hj>ulNB9)^HzAl%SpmvGf({)
z<juIAsH|`$sAPS^Ww+zXOs+IW3FuYje97V>^_R~I#N}&?`7^7ZDVAVD(t7Tas*We(
z#MS3=A!f?iI5n~6mCC)A|K$a+UeuoUXNxpcZ}PUS%dD-R(>gPge*t+Dy+N^#ii+w3
z|I)RRo{?c+=m;%$Y0_K~6vE>yS{_JMc;MsO#f*)n#E1lWgIeVgDgermZtB!IXadb`
zYq<45<3@IVQq9dVU=%L?L`i!fb`%45Zry7$KZLKag`-?K;zXG2&)Z39;-!FFQb>5v
zL?Ld!+Goo}`uOo<AQzyLR?hBN!x7b^8&VPy3CwzLg@js;1_gBhCWBsHE#5AGlO&&i
z5~#n`Aw**R8eiM)IB1W9b^@jKOb^5*ol0?|&C**73yUn{<B)!1^)o@}p~L#N`E>mg
zS#(GmyR^3eKR-VUi!G3~D@+Cly1N6z!>Q8&I6mH<wg>W{b<@cN$T~?fChF=lT@f!!
zj_QJff^xOpq0kDE$jq$`+I@{2Y~1$qWnw$-l_@S+9XHM=5HlL-iS>$#3Izp)KpJ*-
zc3xgy8)hb^5|^V@;ctLYj*fP?xxSd2n|p|gZ?VvL0ZTFe8Gv;>KtEFxE8q+_8BCef
za-}5nK==wqNRzI~kmT>P>%yfVpMy+UGiqjc^z`(E4O!#29bJFv2IDgTxj~mBvBrAQ
zqnDxPwZ&AaR)Zrr^;0>mwZW~=R$B&|0&a(!n;Spa=<;|YtEr5>m$@&RvBQ0k|16X9
zW4TdpOtk^|`XbzOU!<I#E=Ye|=Z`O+%PbXC|8mjl<8=Npa~t#0(o#^mV$)iX4Qb~Q
z?QLiM<qvJyno0Ijm0f%!SAG*x&Btxb_`xD2NClT$`B|?Y!z!k?Kk)>1t=Pr)$DX3g
zNJ?r{nT;Q=^a72W9$#{@SPQB1>SW95czyWI5TFGHt!hZnkO8uuNo>*8Rjb~Pumegh
zVEoLX^%9A_kG+4LT@{=Q>|gn=q_TOWMt)u28B6ZrV;Vdn3l}>fe=m>APZnP9{>1x-
zt!5T7Lzfk$_y_j~W|Kb-`AROqH>Te5&6i-J9QaDn+6d9Ouw)X`WF;hc?Ebu$kmvz*
zA|T4JxZ>jDr;D{3fX3FB%*)BmebLJVw*X@W(<qzF6aDQQuIXr#>)DS`Z*T9B1=Sez
z>#qeG6m87fB{gPy*0!TOt8OX!uep&i<lUOfoz&>@OD0{DQ}H!rGO%<BMT4z?hWU3C
z*85(RP=JHme_Xl=fS*=1>S**>W%L?(Rs}ZIm5d+DMHj(!Mss8Vf65D(QUL)0Ha7bf
z?}yR}uhp7hCxGKxUS7t*#<ptMsm7yImP+BP%goG7n4BoVaXk&u+hc?t9+MXe=U%UI
zLq2cDCDi#TIf`1d#yh9fOj~RU_zs&EwuYocWR?yJ7@{76Y_4?{R^KQDLdMp%41bgn
z7-+0gMKk0vAKbw_B6le%DRJ@cP(EOvF=NR9N<5Gz^9R?f2vy~AMdT~hi&_M~EZ6pE
zZ>q(9I_#e^Gni@)Way$+h;toE{$BhL@|*_cQbN~$koYtcC!%2SdAr492X0W0fI2!j
zf?_n>{O^&1D4O-FCxv|0eX;ICxjog2uPGNob@qeztrjZe4dwH7AMgmK(;fv>cF`+{
zk`E+bh~j;30hA#9>y1Z|6nVBdI*E?oi;*E|c9_SLRHWyjy!IcKxWHcs(0i-(6!K=}
z`Fe?$ZQaizL9B=pS0)Qi3`iB0_YH9fiKn3^BR5aS?9Qs(<b8-79?E6G(m>MhaMI_{
zv(LP^#S=QAs1PeDluNoOiskyMOqv^xoh&NpcUtI>H+T<8jxR1Q5+>x4h&-fCP?=~&
zqexW3?|+`5pGw=Ct2=5sTet#R)8$P)owa2|YN{tt9e&NSJ#Bd5&Wq(@)vUzg`%#$y
z@_E%8{Swp-f|u(VbjpRM)8#9`+)(3r>ml9yVX{!IB71@JQ%fxiAi`9?Cf)`nPO_1O
zld4Qkey^BVxd$<&ol0BdvI3$!DgmS0Mpi6v4`c|qUw+yg+W_3`L?xHfpS87A^@hue
z24n`}vAt~XMh1;)&7&8ZR$(@Oo@3QrEy~afbU;4OFihcUR9j2~%MwUqY^<ykg=+Q(
zzuSQ}9rBD<dGxp9(kC7Rsg55zs!<0su7-3$wM&mcX(2(9XPf?aC=62y$edPl=Up$u
z3suWn_s)2eWnzo~dgcO%NIF<S<n7zHGHqWT!gs^*+nKgg1}~56V6J+%C7RmBt*9vr
zHA<3Rz%%y5QFIaHHo!vykHpd1V2#D}AvjSe(?_;iCJfQq=(QVbcYT)Kofgm933R^r
z>sU&%cI>cPElLSDlhx2f$d4;RA)jNslZV0nbpV?QKe%1L42XE8fF{EbI8V!t&I?$(
z0EOP8(UDSnlDZ^2#mX8<jG_WiWstQD01`M)qsHo&qo!t3i|$Zi=2tz*AYb=}z%EyF
z&xZuzk04Dbj-rBgf4Z98ubz@{^yxB#+tu&-`XV6)58JPQi%2|@g#DB<>q|X81Ty}B
z<QVMf+tozti4vW?v#UFf%502Ax;Nirdz$U-#2%<I1&~S!SRm`-r9w=-c8MI837pn4
z!XUu_Yy#5IXnE;o7EMS0mbkoI(ToK8@?8LaP5^NuM(+M;YoZ98A3HNuTVOb5?L@~d
zI39vsE2}i5dv#gXx#ssEa%!dI9U#S>f~5se1qKLApuEQ?CKh~D8xM@Q{#^~q%dL@T
zIg0l`-+<w!1AqYr;kdVu92prI9i3&QG%j&|LXe%Q`k2kN;r9p>lJ>5JaUP9`)et~i
zRDAkcAQx1WmlMA>O#^?@&$a=*!4Weer;{Ow=5B98PQYfdUmNJ}>Uu9FrCDh@9K)ai
zyd&;l5;**{1lbs4NwGGp7hjE4K_I5k+_bX`H?Z))4X_3b3wsM7El$+to3(Onx0W;n
zbj>!9H@|oYf#Ho2H$6wWi!YbM%#L<NPEaR@EK;|RpJZkF-OYW`gN9EyWMm8AKxfpd
z|7P{1TEG2Ce}4W`DZP81zc`_XryJqc@9%vM1H%L52MMQDQg)Uio8u@flH!#>%VMR-
z-SiE~l?qwc%(53{B-pytZAbVB|Ea6;{QHb@sSw<5rRPT|(D;C8oCgBTY0lQ)Sn1<H
zsXNZAAh?L6g);<FPhc_R3Gj1P&C$_OgTq#~H2=6)Z~u}k=}W(Z`4OhSw_QxD(i|2V
zx?*<J9q_w4RG6aXBlTnv!KOL{@>(Sj!$8uSoafW}YNGj;)xEiCwz-X{pXGMeQfv2q
z`C(cNVUTbX9-n@Bi~~E}&XeX(K$ThpiIuj@p=%5MF6kzqa;)#wE<dfAwPYsbBMTqh
z7^|e(v7rvLHIc#Uh1H*pELCCNgV17wkzTCCGBxhMRaRC8Qa*4pECQr2&GgiYBT>>C
zu#e>y(=U}evaMf2v))lFxZNKFkI@C$A?9NYSmPD<lrbMDiKfxxJ*w8cJK@@xInRFp
zPbZ4EC=y+7dHuW@ODSomEoly{hVwpE*O~N(4<mp?Ct)`e7ItxQ;fEb_+5Z0GXoQE0
z`|Tg2=d4Bb)^?O&%*V@By5C%#FJ-$T<V7=vw@&hJZoq^wFfztah|gQ~8&|X~$<@ea
z4AY>ro?vXR%!6}wT<kd&Rd#0PA5k)JXdyh{_(<ke>fq<!air4(rJGi%`*@;s+71s7
zZ^p=zJanB1O{W1$2>M)Da4;L8*=&!0`agt-BWHJQJql#*y|-395x1QoP#68tNsO5Z
zl?zo_47)HvzJBHjP2}~IS>lqdnSnK9g0lU;-2ESt_0~9`hc!F7U&QVb#b+}-iy<6&
zP<4V`iexg+TxHfEcJqgYk<*`RAb5ihGUx2Am2rF6%LKOO<|m%PRKZ11#{phJakP94
zFK*y!rZV;=^xfU5=>QfRX!7+YgJi+&k#TX(K&c0Nz)USVp;mz!D7`<+7mBM+B((t+
zdv|LxAHZ9K9Z}+-!OcT;7m8m#4-D2X+1cOM@jY?e!n=#Wx9`@#%LJ{dpdjCKQ*r!#
zndpC&u9)#k>rCpRIs^3@=dB+*0OI5Yj>XX32?z2jzw7B;d3Clk%jvkS3MS3>yw?$W
zNSW1DX8X5lF!V++JA!-$yg~`DKaMo)H8+6VF*m0v-COUm-G2W@l?v;^W*5xJls7s|
z9yc%-==BXwdorL{>FF6Mr^O3^TTqKcU?)nRklf{g&vH*3H;h4k20mS_Qr(thr-k_F
z=<zfmB=8qyfc@nbfr+JOLIP?K9}i$jA|fIJJ}gjY&wvTO+Jl#TRGSo^tMZ``Hye5{
z@gD8(4dGi1v7wDNySqX+=fTAnnyTnGU@S6^Jax&jTpE_=Tp*v*CfCie+DhQe9&l2U
zHR}}8zcVRoEi~xN*J|w=&^3BuoUf!|aUWtTY%RoOxNS6NMakN;NXvdVy<;esPhjG#
z+w&sA3j_QC%Wv<hj?lD%w6$<j!$QiS?my<U0)JnKTmJ;smhuUD=(t{pQe{kF1Tx<S
z_4)<QwC0VA@3PyohQ9$uEG#HwJyqosaGke<S3&vrt<QNL!*;K^caO#O39WN>@8&<v
zcvyT0)oa5oIA$t>5Ryj9|A60|0gE*p6N~W{9b!|bdsd&<jXS))G49{n8%^Bde>cU%
zV&p<hzh@R95S4WQED{K$_Q?Z&$mf9<>-Qj^o(f?=(tu~{zp_|!|9$Dl@`v94FFfdL
z3C!gV34QCMqocq)n<EwfK~C;#R1^+Zt#N-s3cu?>0((Je>Hb)r0$%k!aQc44KAxVQ
z&dbXa{+5uCkevLCD=sz`07!eNgpABUPfva+Is}p{6O~2MFi(b$FACgy-BvY^o&uMa
zJ`e<rD_pKlcWUPBBz-MK!^x7Pqw$_SYXV5q&OInSS0qn8AS`SmT_dVHQ>2Da70p6X
z@f-fU+3QkU+vwWbuN30yV{%)j*vG~d(;J_#BYIa@6Z^e<fI|`Jo9fMOE)o(!?fQA8
z{lE`M0ajoYnB=mWh(bkE)+9ZNa@$Q`!<w|!V#aqk{w6WulbYCR3r8-}&C|U3I}`!3
z8xPNnS=B_07v6&w<BGi5BAOr%xK6NF)wBipIS;ffkoM4#-(?}kEOUi=gpM;NJa7jY
z7l5A+A3oeE9Qr>4ezf%U^?@B1O30!Q!XG#ZsG>FwIkEzE<mBXvWkux27(QJ1)X&G9
zYSLX&pcNXsPsCW|DWlUX9X=~)w;2~rC>#bm00G<;08@js85|xy*w?37sw)J_4doJT
zegN{%&(FVn`C?~TNrMD|1O(*8!w-4})}^U%29NS@Ulb0HNdLtl2ceCvY2cR~b({hb
zNlq3P7UhDfYWn$re<f;S_ezC^U#JS8o8YfM|DWLbZ^YUEr!UQ*k;paA{oK1k+th!=
z*ITY#qB5WJ%SAhqVqB+r*!0+$FW-p{#T#@r)X1XIkLP=>aDnO?G&O9U^pqF=MU&uI
z?NoBqm8OcSHyRUl%SFwNYg?jevb=+UOs>Ls#7*!}(Tv`e$p3j>;=g>3;*I~$Ir#>+
zoy1L7ME>{+L5yCwxTjL10Z<^odAM}r44t-<ILCk*i5WI^Ta7pAdFI4L3f9yGsPP4J
z_UvsVAT_Pj&hIk8uUW^G06Zgu{E`LcIzSEq5V;L>Ur|wHtXDyY+ZBTxKoJr0LB^q$
zQN%R@R5KtwJ$?sLGs5Y{R#Xc880WzO^s|%P(+wP2ATHC>)2l6KL3FaeeEE{@?n_es
z8?gI;r+_`c$k6a_55f&%W!G%F+$dK^=;2+Xi6>B9MnTsC2z`K@k)M*j7X}20POZ%n
zNa>)#0XP%492+D_bXJOqA4+n9H|VRy7%@FGFf^pV0&bWcU^NFbo*^2#Iag;t-Jw!f
zAPO2jAlG@asRF5<H+6$+bhNbUO)z#W%Y{a61_sO4FOR{lsaf<uO<E-4LgckJm^}w+
z?MEu8D<{E4fg}zPM>rvicL3mKq9P)OzrXVbuK#c%w$-||lwiOz1q2WR__X5WTYnw2
zp%>bmmZl5N6@b<(z%+q+^YcvT+6B1jXx?+cRe$3x2X#N)yb{v*B@yBVag|ESSE{@5
zJ02rMy~<4Kj*QH;HFF<gQY$wUpZP4tTaDP>omDAR#RBXn#4Jgj{Ft=!!Z1GIPr{|r
zL&)c${1fwIAi$k}xe}X?nc_lh*ZF75pxzaa>@r0dm!M>4oG;%2)k#|-XLxY%zn+0X
z%T?(CYlG^52VVM(?5^8~0rWs9IV+;;$mE`ocie~k82`_9U2fq&samDp2>?yttk7b9
zG~PNkWv+7%q6mL7Sl`{>j{uJCw$@hBP+~kREL#^JJCk`pc<BLHcNc6};*mdXlSIdn
zALppthkT<pB>OH%)#BLTzEU~4Cuy}ClPRk+cLJg=Mmt^@OT<(EsF(capp)Fh=&r!T
z#lyQm+;A?s*>6vk0kuK92?him;HyOiL<{PZCm?n0I5u#HX@D34shLSo^}`2{Fj2%K
z3H{<(3_5@rN3$x{2XqP144&`wjHGd5BbHZ-y=FPFhG(oL61#035X-bEm%udT3<fMO
zFAvmKEwCJ-q8%VU>m9Z@+|KvQC3yhD;{tqB+Ab^6AWT)9Z2%epmEAFzY+!zN;b(dT
zPEffN#S)>9L%gI<XY1xfm++xH+KMB&BB&|wZvep_k6&$!7Z70i`uh*YvAm?Gr)Ogu
z-8+k2n9UHwfChzz0&6@T86WI;{bwK!kzd`w7A_Q=%{}&EL^`m<7YRUD8;&k;k4!op
zV*?;rmze+!&FvZ(BLYkTbqVq~D09yE&*l`WrG3rMj4;49{sbyuK-_`@5>)SscsghU
z_4gpP*&wJr%iY(?bsX>1$d=0R@bFuJ<)XmZc|Pg7*W?6<1#q+irdgxW*>->M>iTF9
z4gjPNGCC`9D4;r(UL^;ZZL4t_37-G$^qRfvqiOx$ham??b!{z|&5|x&e82I(C9Nia
zAwu9jI5Q|IDLD^+1f{Q^U4av@U|ijiUkFbftq|1vH0nhYzsOrbBg-+nvPw^$yzBfO
zBMX)nkf_PbdaXftbggY|1rxHsa1q<91WR0Vi7`@wH}VV~0k-Mp5M<EKW%(uzA<UlQ
z-+A|uB&*DHxx=E2)PlXn^*b`!0oduZ)iinpde)Tk-9w+iN+uf&yCXrq0;MDzSkaVr
zbM8Dc$*2#EkJY6bDxEz^LPf!qf~iu}KNYlxX#dT`><Pn<kgM_g*Z-#9X!J|sc3ozv
zgx4FFS1rW<Wo4yVgaPw!I{*zlAsZ7b-~GvW0QK^(Q~)r8C%wxJ?{=}%@^ZFJGV#AM
zL1);rt%cHiSnPoz`(%A6|7?16M62Td<=NitM66`$=9u1mpn~)x^rzs-w0A-2hK7b;
ze0{-8YniB!yX^~UYHEVD0~vE<WaR!M5WET*!(4zQDQ5sHZOJhUqP?fOFae|;@EUbW
z6ejEGNSOVi3OS4haC)B?&fS_1mODKOh@@rK&Uwn%Oa>Zn1c3X>rk?{u7+sUpxI_Ml
zkgV`u`_<Vz!6q%_jo{`pzRTEod2Kh!{aeX^7*alU7*{)um3f5oMH_fmgV}g~TwGjb
zW#z_b4*dGCH!C*w$!io%!+fyV;e3bhg@wQLuE5Th{ErR~d$a>+ZQ}DA8yf+Cb|(h_
zp4zIW0XXh~=#RK-y8|yMB|20ZyJUv}{>2z@AB%v*5Ep-pWlIY7Pvv-l3IQHobc6mm
zD1~%j8(FtEXK*jeS4^6<a@8v!XS;zMS7kcv9RLt7KtEGpvx^A}7iu-c1OTX04!Cd7
z)fUslGakLJCkkrjVy*6eiMvj`wG>yzCJ|Qdq>p54TP1L9SnLmGJb~*4*FAT@`30B)
z%cDwtBQ6%`5}>ysn))GFvTz7i|4C)vz(80?Na6mbqKY>WKje*zG}_yZ+erFVYkbD@
z`d(>sWDHdvd(r6z;wN~6(SQMQ1DW`4*mqqFzYafuUIbg<JndmOA5It16##Y-tIOrL
zibfhSyAtythv|4+_y&xOjv}2YJ3Hjl-JOw|KF?8rFKuU`iPzru)%6N88etlZgrVUd
zB}S%`9=ejoo69Z4<q2wEPj3~yYka;5FjQoO3v2?GOiynKxXP5KFU{9|#cX`@?97IJ
zzuNe|($otAzY!y|{ihJ(2;{LF#Wq3ZBI3R~xKYewd>l#9AJ5jgWJ5tg5kkUQJlDjL
z$gqE+1Az>^b@&hHLE*F*K*;DTWPliQ3&6VpWB>=+%XCm@fW|jay8bu-e3bQcIVBXD
zUTFk_)z4dT5~6|GzOE2XF;N3`l0(67my{m2HdPU1AEA>yJ-~3ZqnN%1%0sTeeYCc*
zbg%JUc<koOH^Sb}6*sSrTZZ%>JP^v==H)nND&N#_c+fO&g@KCt5*pQQSTl?C$Vg4Q
z{?kSrivc)<v}O)LYbdCqrJSAb4iZTW&>hPh(rx&mhBn3sNv(v@PcK9?V-qe2+-hpk
zx7Q<9x}e3?{Q+~SSKo;K1GC;AtY~fJ7FmGSUkt922Nwf4f+tz{k6m4#vHh=KJ<)WJ
zo|39sIe38D#EYZ~hWemj9QM*vL8pNM(`w&q+zRzUVt@8B17>lZ%2j9@&%`>gZ3Fo5
zR#+^I^i>mP?C6Fj(4ej_jxb2M_JN5{<sA_O!kN7pcZUqzfrtT+L9t4Swws%qvGFR%
z1u1Ta;?R1VrB(tRHx_5+;*%$kwCGI~SlDuk%WC|Hr?kx7*VDPX-l4m0KXi5oWUh4I
z_tpO$ME+-G#=mVq{%=DDfw6~*QkY-<X;@Y`4WQ);!+9VSW8~042~Am3Q(jq21Kq|f
z_lX?}Xo5Ch{$TC{{l<KQI1!sKG1ueu2pz7*OdrsaJXA+hvHT(r;;)XFCj;=CphFbi
z(Mu-(u+vr`RW_KYX>l(Wl;r~G$UgWlf0P#j7|C)!D1Gi1QZM)ggpCJ$aqp~PHU}R(
zVDYPy<J{kdG{*|P3fK6qvHHJ#U;kZn{%<DMjhzoP<o<i%mY$Y2fz2fBISS}`BnOuI
zHs*1;$B-ZBxnMT|Q#jyL6tbmW(a>~tcRwc81S(&ekPnq?il(CCCs2EBdE$5M!Ea<B
zkmDR4Zvh~mva?4Ad{9sTnkv#G5Smk2lChDI*#2!;BqXl<2sX<0>~S4`1_VKO`ID9V
zkabB1`gzbA3cA3dtnpHg4g5CD02wbI^ri48u3h|S625EGPD&yTZf6C#eZ;>4>>nwR
zlJHW0Jnzbp`PzvS@9H;GiCPZ!lZbrb3|;*bKRr+efL&Ch5w`5Cy>hEMNj8=5SkV5X
zoIcnqnmw+E8O@+f^_ZtLA6~4U&*Qyd!2$FRs9l|=aRs+cX<FKI4vy1M&V}>Mf)Wov
zsR9-oM;`8h*mi7a(Y0xq&%yv1guXY1{{vZ@-43s9gX1~p?H|Ar87g@{KSy|sFFxWM
zbmb!(_k0denKR$!=_{1`owh0@U=(!Xk()2&8jxewT*e=PZ)!10<o7D33qh|uVTUer
z?p?3^GojRP-|jrZ0C|9}!w^CisEy5dz7oGH^}zjr|K$ZZ1#)rG><%M>q8ikuUL<$D
z%NQnV9Q*G)qH}b49LugRQC)WnZ&hP(Q!%BK(Au1vw8s}0fuyo^A`zKsRWnKIMXO8b
zr$@+84=T+3@Xym0%`J~&?i```NLxo2*JhqSv)X@PWA&CtZvqlx+&nil-yrnPcL<Ol
zU~GWQqWMo&@P3F2jGcg9E}ztPWe^nntBYUYN(g5%)!|_gIqt}17(yV3+%O{kPTN7g
zBdHBI74_X0&M?qMmE7=kY7%`HF%CK_ro7#_i^GEWksxDcmlv(y6mwF>xIpX8zB%lU
zx6c(i#yJ-LvojMFaQ==cq>b#3m@|8#^{@&4(LPUxUo`SdQ^Q~VQTls98t5uo+Z8sP
zx}{q_`b#Gg3t0>8Sx4s!i?T4rmU{lxSaPwB@sEGnF38LWbd{P==ec*VK;BqpHl$G>
zR>8$td|5aWZMa_krA<?w`e-jbT$I)d==$3HY&Pj0WSx0EuxqxgK%cp^T?8mXe`(O4
z$3(|>B%c!w*5i#?l-bF9KSHHbK>_ra4Xj070|e_|wwGpuV?BD`MrX@Wm}2*m`|mX(
zt$b2|84iZsYnLNySI7Miv3g8;Z1-t9t0~yY0{t(EnXz_vci{^^nXdya7@0W#zSnQS
zM?0)wS=LNk*e`kZ?=+aM^WBYbSy`VDu&+)xns>8^au4ySNwFpcu7>?8=xa0o-#5Qe
z^489?)Db#daHC@h`Fg^uV)Nqe3B?+~tpZfT)N~w~P6HS)@Sz95qEeM@lqZ=#`9{LR
zPn;hz7Y8)0R|?sn+0}BY^aVZr_Wn{EXd`F_uNoN5;KTxct_oj*zjs~rgkzmayKxEx
z5x{JEfZatW;ox)JjsvfiF&k5rFqJ3|vERJ+OSJ7l-LD(a-HSa8ptvW{WPvLK{Q0G|
zRQ5UbA9tT8vFegyq*iiP&xG!LNpW>>^^>`0d63p&>xhpDY_wEP!cfuh&J0)$*q3es
z4p)knx=dT(lPT^>ed^wF5a!@RD5^M*C-eajSmr7&M>1!P9|Ml@-#!+co;TOPZP9f8
zJGiW@46W@NxL77C>HXW}VY7y;2YfoG@A29v9!pvbB=fB<?q^)>>ivtQ8EdYMp0-fd
zdvMcZ`ud4?nn(>eDNe93MGlb3|4}nUk+#4`XzVEe`%kp+#<&E2RzRWf*|=_w<)O8i
zDo6V9)f}VqI%szB{q7Z%4Nd%VC%JFhyaP=Y68Vcmi<R83g7i9L{_5b;R^KYDBwSoi
zDE*cy!dRz9upw4xoIPrsS(z~O>;5Ql`Kcn3e}o1Uj`fC58;4+a8FiZZ01X1J2Xr)W
zhtlBvFZ}c>Jb-}oH`wkJDBuOW^ElisDQuvA_-<b^z48tCQbckWW}jDSZ6~CGSs#A>
z8&xd^l#q#EzXCx~{b*rCwo9NVnGZ}7BPGXQ=7AW<#CNdrC@WSQTVz<jtPzy=CGea2
zY@?6TVS&-J4ir$^S<!q;*GfPpNR+Z&4hr7#0S<|tJ|qD2_4tBHyZLm+Tpd4~dt>5W
zUkd`L&|gnne+Bsf!A>#IGDs3mp(F`sP|_Cu0yiLlvRcf)H}e$4hor3n`S-R4j=0lZ
zLGzFbt`SJ98DilRm4%y2J07U1Ww8wPG^BHT=94UUb_G-DR2nc~nt@Xfpk`rV>&637
zVC#=jQi?5Q)>$L-OXUm}v_7qoQF4AU_7J3}y%`}vKn&Q40jc-^aA-hYew!GXA6+Bq
zq_=3*VTE(LbKtn~m&X}%+DY@=F6+$y9wsOMz^6vc+P3Y??|5fn?9}iHs)GmwLfhat
zwCFf%IW8xL;xfBPv5oL$pv85vIyxr7y~BTs%F)3-Tj-rGu8X3=;9~+nGwPk2;*HUr
zqgcm|?v;QSh4{~G6i6B`%B3WqJ2Gb5e!LhEk?wNJ8)@)ZCUU47g9c7StqVT~xFEmK
z&}}!6%6*sX>`g#E_Pvv0$Dgm?Qius(@;Ff5EsN-mIVBS0OAk8MF|}-p+mbIqiu3P4
zAf)ItW!1Pcwc~bt!v)q!P5vriabTDNb?1MmI6m<t|6?HqnFP>QwKPG*{0);^?zU6>
zd+6&Vcw`6b3Q^haO`Eid8+7TzqYbGComH(&f>$4SyRagN0p%rUnQtJPnv#+d7sqAP
z^9}fWl9D82|7gQK+(9jy4YY+4?Iu75emvU-gWQ&@k@+B{n9MvHpv(Z;tsPEIo2#E?
zA%_ye7fW559F;(rC4zd_#KgpDYa;Z2Rxf}^3Z$W=%mUC(*sx6X!P1D8J^`cV{)HQT
zFL7wcKa~htr>&CfDfqk>(k>>OE6Rv9j@#SQU0&uOGjqs!or0sWW0bTk$`{4A>zF8%
zZ+;AYc@*#o%SEE>Iqk2gocm8`2mQYBMaU+|Vqs#+VcmNbNYixo<_YCv%1q0}`1x0h
z<iRZ2WsegLad!7GrY($jllZL$1{Uh<t(8xIDi@JTV?9_ht=nci_3-I(Fkdf-T`45_
zAgrhq{vNaV_S`KFP4UkQ91Zab_K15B8<*VAI9DdUjNuc~*c$JM3Y^R`h7zTyI)f-l
zJLAF6D8RlzY!3f)gOUUE_3(yKgH7TN*jsDsJ@DB%F&6h;C|>KyDJ?84SQl%uS#@Kp
z#fV8IrhNS>cTE?zOgkeM6dEOys_QxXuI7qUaV69jbHuD_9JR`heYoL-Ij~6BH_#mg
z<%O)QY%C}032hns_XWy{+1c5P9ZGRK7qk<3OI%o}D5%UJKg_DkUMKus1E>Qq;Hv}4
z8kz)mjGpt?)Kr~DqE1^}5u(rzvqzXDBw)GKJD0t^?{^}_7+G>8YPLkaq-l;_9dYSu
zGM8E~-<q1fFaU0!jZZwW@TS+qp#VNvCertgyheZ_<*GHz?;fI^UtVsWT#9jnEFX}r
ziWA6Ah?ZX9A3rXvPXx*%P?mAQQN<&HtE(72Lm)qNj~z(PK$wDj{U3e3WmJ^y7dJYD
z(xIdvC4<xuqBKY-EyyU{NQb0!iUJA>(n^Pf(j^@dQX)u7mvl-uXY>5eT4$a2!^;Op
zT?#Yzeck)o`&U<dJPjS)y8AYueMp60AMZ3fmTcOQm;V^bXGP=?p9ST`Rukhi^X47H
zwT7j|#mQ*{80Go-ITI5T?4;mHs)0EQOec^a1F%UVST={GsnyL^2bA~s9@NGtf7dj-
zLzvrUov|8(>;NQ$>f#SbC;@{QZgOOxRM^G}$`Q=Z<W6B7D~pn5UJ+p=Y%W@9jQh;V
z%uiHWVT%>gv@!B_OP!9ed4*MPi9vGduIh|XdXA*ase^u7fHAcOU1hI?f0LuMGy@_v
z{8WXUa==9hdP4Oeg0oj6V=mD&!Hf#e4mJx`wRj1ezY<b;vN9sY?qkbG)<!7k*Q*Zy
zh7MAlh<jHh;uPz75}=52r<^5~hXSPVMrZ;F>Pl_J8KTax8m*1}CeXvhm;V}rd-vB9
z`M%>rRYqKnIS!vaC;i}8#?%Way`@}9yPMBK*9j3nCk-8Nh=Yvxu5j<Fo2(AX?eEKI
za7LZl{P(UG$l;&Lrdj$<lS;l(sgpO;%U^YX_eW1ib<7bJA2cS~f`K?$B_4nI%J}1n
z4P#(E1T>_7nzsq)tw~v%3q_f`4!f#G>xjbit;IiZBAt?uAPV^EM@>Tl8lfe)7v;{X
zfmugs%rB8&Y?gPcp2D9muk4qD!26KZQSq;*X>79*hk;wCEN3W;^U9U6Y8C_{sw9r>
zr{DBGTtAo0Nh)ynNL!MFqvl`E{2o)_A@&z5>g(%kVGM}J&)yk-toeEn=nFtq(cim5
zev}%8Z^FY1T}5F%dqSf#oUf}H*n0QcEDq6AH4eT5V&^5va+3FO!!lNU(l*B`Hj^vP
z&+NX}bvFlH?uCOkrXV+Ji=^y+DDZ6Y@uj(8N3_#mQhG`Xkt*W*raD5qF2jU6XfVgq
za0%mbz5$oD@}oz2t^`1VC&%@2b+)ax{`t1taH^N-v;KTEG=J=6dXcEFq^wD~Nh2>|
zE)!8&uS9KcZ?AKe2f|_4G)t%a%gToL#rKjFi4vWGv4*y!J?fw;T0OvV3?O2vr+c=D
zYmC*fP8inp*NuYSGM6Q`!1T9c!hMta{MVqt&;mGvPV$IUn#6lyhGYhTFfcGM^w}G1
zF3ZBcig$LkxTZo0XCHlz;bdAC+H|U<Y?^vIK2XA0H$2|O;}!okYa;&3GNS(c+ie3)
zpV#CSwZ;9m=Qh?D@mY8Qq}bofgHx?^$YqA>y|>#oe;@TXeraXzZ)_V5<jPg!Na$W@
zx|;UmI#5!mF75`jcD}}r*ygsm&8=c`nF!=tGF`H_nQS489*b^LxL*dXhl9`zRm(T(
z2Ccb{m}StJ$sX<@=@1h&T;G6BX%lzeWUF%P{ZT&jFN!?gRlN&aYT)5j%Xn807*|Kv
zUE`+u8y#=<>~PzEyL}zf(V{&_B*cP@AENW#w%`Y-2BKSkeR<o$NBD~L>yuKX!mKn&
zW%y({d3;u~5B-2OA5YxG5p(iYs3dOw1a+ZIh7vPx(UPQUI$u}*r2SW6VBym+2Fl^#
z`5^p-KT4Zr4PHLR;wv=rX^$yv>+agw3Q|veE^Haftf@1jRVCk#V|a@xHV@^^<;zR*
z<Hwuxh6{lm+7&p4UB?rDDMMf4Vg)NoeV3oE%BS5rM#92LyZU5F!j}9nv9KaUk`*-+
zA?TD`o9y$)*6|2!s{L(jUBxa>walgEX~<A$?`mcK6jQca3ooH_KK#E|R@TOIsm#Bx
zJw9@ez@_8U4btzScNIVRu-XZ5;%dsub-^zd2T<39t3qdItZv*<G1l7Nc)q`wuNrD&
zWK11I0nhLxQ*@SueczI5>DHY|1Eh3tcPwk6g@mIxXX{~+IICx!?`FwoeUp?^#{t##
zPh?<Lc6E{yX8gb)ave9I^N|Vh8c5(Ti&&Zzg=a9E?4FE#M)U6(6Q5eP*I^&?P3#tS
zduoqDzq4r`=sXAc)a$sRe?mWo7eUwmYm-y<;XfZ7O#hX?=)vW=!FLEK>eVzxX>M=S
zzQpody$}+4RvR7{B59F->a1PiWik0D=kA}c2Ag0&<+1Xq$Tk%H=(D;?5a+eGqMkXi
zPB&|-UyX%`I^}dZ@>UNIGp63>rLl$Yruthc;hqRR-M`utLAg}U+AZr}NtL@cUE2gi
zPbfTp;st1xnk#$i9YnXsDvY@uo}6g%Ur*CC^cvGrCAS-P<=`s+S_6-{dP2f4mpL6}
z%j4$h)M2bsP4FM-f37j1nyN{t5GphZC_FGV+*DmO|NL)v_p*ok%786>>i?Z_>xB--
zNf(KR3Ku_7uCEPLG8LO?i(TRbL3?8AE%z?t=ILB@i+DNT0U@^NtoM<%VueLYqkC^h
zD#AbNi=8|<!j-1x)59l{OK(3bdi%5PDJ@Pb?^?w07D1gsVaooxr&cs}3QEWOWTQ2T
zHT%$$!G^wAfFmz7D2m)Hc=E7iFR$et?G)uQ7rz+h6DIc6t?bXT?;{#>8w?*W%nvQi
z!2O#>v+aW1thbC^Xo%s>yUvad3<Q4K%>Bul^rnM{f$w*R`f&CC1`gX#Ja^k0O%y%o
zyF%5Ip&yz4L|g8C92Vwx>luOk)^hshqWGaL0-uKuTTDEv9JYn@st9Y`AeN}~uf2)z
ze%FWTyLTckh<EKDKJ`@4(N-N0KRfQV(N1vjtmoJv99!Q9LjWP2$P0j70|NtNY`-`p
znypF9`%9XZJ0;Y!>};y<l*7suPMe_uO`>Ljp0K8&&|R**mJl8+EUYd&vWMuWmd~^&
zlVp-tuu1qag{+HJx~j$&6c=>eHj;`(%G0|6+ttn19~~9hW+4U7UQ(x^hETcLPi$dE
zQf;pBALW%pCe#|v^l#CE(3$`SUugOYU~hwd3+W2?O80ZWsN22*Mvcd}nt$9n6G!Vk
zRV~X(%@NL^naihJUmyHfz-s~%o|o`#e1wPq{yp2HqnBuQ>-X>j_Oi+!`qEc8=DX~=
zv$Ivw6cWR7cOtrYH+chu&i)nV%^$OkCUHQ&8mjhX5^r0Amh8yIa;{BW-12oF_E^C9
z>7U$Bj}3YZbaOb#yR?7h0}V4}Ab-XG)kwwZte23po?L$18P<ovrsv18wK40nRM2Jx
z5!Ag(XG3IahGt)*$=lPT8v5(~XuvN3YpmoQp+mdv(eV1Xfacw{iJv1mccb$Tf!O2-
z+2r5uE6<;lx|~Z(gNNL;eu%RQ5=&wBJU5Wj?|Ctj+f<~3R#PhjDEsHnV9!-^GqcMS
z03gkr)z!2V{sQd1DiY+Rh4SkbQRu%XMao~CUW-%+Nk>RpS}qN5{<>0%C*ULK)^zuh
zo2*bHd&tUvkXLxXo1aahun_X(B~rh1#O=l-LrQYGa;@%?n~%`R<Rj271C@HP^!q{P
z6Ll7H9ov!kTCW5)RBEAthWDA-h5wU;95s$b3whW5B668XQ)Q$^tXb>e>b%HM5o{Sk
z#bGU-gLGPnkS%FE`#C?_dT*gy>b42U>TdREvKPZFzIkVEPd{yjc7G$Izu-q(TU^Zz
z(0j;$@xt33Tgu_);^{sSh11bV7D^#97Nz}6AmPD-2cTcp+SUXGAJBTt3MB*FM@N47
zoh^<xA$TO?cR9KkWm7s5GBpM385TxpSQs%Lp2u|kV%}-E=UmU#Ipwjl3O-EBwqh(X
z{)`9l4;9i;G5r--2=J=F=nsM`fGod%_gjsCr~iCc+=Yt{H(+z8Qv929$CULwn$nTh
z+_)l?QvVBO<d9v35`}5%sJD;Lo@ZTGLcZKvGiq7btj(t(Wd`0TE*?N<6+F_J4Fe)3
zAZ_BDC1XV0B)>B0&1}C&+_s~xxCX>ZUA3Ep!|8I}p6p~;UtA?&q^-K?+jM4eG54nw
z*TKyN1oqCDpjx@(o&BZX8DNXKBrq!-j8#;^G9o!bB9oKrA-$_Llq!yEx4dU~7*tEY
zJKm2u+j@m14YN((;#upZb)!-KQtR-mGo^H!V3Pv-$$$xc{6msG6M7oImBP5&UE9FQ
zvwHE}NA0Y9;OE?51EDbIdA>Ayh_$V##y9;bPsxevHP58p8V0phdy?ivN9g&|QoQk*
zWt%7e-e?O0wkw<F{RbpoIt=P;s^7IUKC{Vc3n-MRqfsS!amd_sEwffe1#|5gih0{O
zchy`BglX$y;Fnj0X4+;Wj9up1-#SC8S}NZAPUR*4X}Ivm!1{V^B_mG&-$zzf7Vb7i
zDo|vgj0}_R**S&iH9g+|4d=^32Ndhnqu488#+(i15lOaf=6kNSkk5F5s__Z`UE5|U
zV*729thHO@%sh!6qs;shHi^sQ_aX-Q-0i=t(>SSb3=<%nlw+}QhykIVwQplo@ZEA4
zX$u$jmUNlLO&k9ADwuPC`3@g`t`GF4uQanBs4~A<uiVYoo{#k?9jWy_1C888cmy++
z^aHMv^f+WGDq;SFB_U}Gb-IOxi}|rzf3aTl&T1@L<59^^&Jcyj-ALOAvoWk1qV$V3
z6pJd_bih$5@M+|lmvvKpAOi~yP6f>T)rBW$HCmY#2%a6ZMnKkrDW7oiUm-hKoG{RG
zRm}$x(c<Id3x&~2_?{XvJt3DOg>k+d4G~=E=qPc`+fi1hk;ucPr>QwrN>qCrKAB);
zWf8Q*>+Ku!67iSs_uAe*SRp*w`$3r8&`v<*F>|pUD*t-4nB#2j!Syq-n$>MSm+B4W
zgxHgGl@q}}=h6GM5sFHCr0z#w{4le#tVZW=I0UzdkT8M%1y+7ki$RqW3s}BDDQG`c
zPw_m1Erc!P#oHSP@{ymds)ev1IRunUfRz9|$6XN!vMR6;E$tmcQ0*w1QaREzf}#|;
zjO;*lEC{UoFJd4b-dSOICBZSU{ICPrVXj@$UZN8Hvm`MluFY*3gM9BxE3qa=<+zCD
zo4F|ygb}ZLER90nljw?&p%2K=4<s^<*iXn_vnp8Qn%<?x5*NoDuqI~WSMZE@9fTaj
zo{=JzMjN+%vTyw9htb^VF9p|h9aLgwI8EmT0{C^Z8IF2-<vRJ?7a3pazzY)eM&2P&
zui|-1Y^=stpqP7h07Lts8CUoY-|F<Y90Qlt)JhQJXX-~dyFak5niNd<{@{S`nt>sS
z_7*wlzzeO7(Ci&~&>vvdE>UB-548LK)P?7rZ|k=+tRCi44|cD%boRtJ_DDtH7z#Iz
z%dreIK~CHF=7a~YEw=5r*ukG-NMekeFoz07c2Ps9wUD{LPRXUCHtuB?V^)g1@sJ&g
zD|)kzWPP-mWGowbf2cr-s*Ww*to4@CZ3O}ONSVm+WKrEe<*dCg`X;;|uih{j>1hx1
zGr&pD`DqZ&Rj)s3dy_snGkNQl0!d`=htENbwd_xnNy8NLSrqsca(<t&mUG0blk<d>
zP*M=NyWOu9qCial`F$mMJevJ!qT(|=Csk6@2}Bg$JsKg;%89Zs>d$AcY0?JX7ZDNR
z=Pw7<7(fk|Ib5b)mv{VM>RRic_{wJ*+s|z00r<MUF?r0)Om~mI=wLX^kei3L2Lv$<
z`1V)HZa?qnxMtCPDJerVQuB+-c1t@nsv8K|7H?F3avA5?(s>>+w7TkbWH{A@gCGfT
zcyU~@_%X%xw68`XBgTTeZ1=#+W@<Auh(uN<u*mb1kW-@Y-}AB`P)fkp4&@#a9YI7y
z5(NVnr-H4k>pldc>VicC+$<dYy4%mzKsE=lfd>FrT&m|7ze*&=8B^yDuAEbu0HS)_
z7oy(C`*gJIc&nMD%V6ddqKl1>X-TOO`qkWrn~~fH+sY-jk!BglWtI_SmuHQSvH=rH
z5fj^RVe!K0P{<(>t?{k|6@wb;H*`C#p+OW_#E>a!wVdIUX@21-WWghn0-a@@ytFbY
zO+YW{Ye9vtc%Nlo2T2+rJKDEj3p-C|T1k=?xpZlYR}-%f_jHJze$r~qLO&R#_+Z9}
z+x`>xW^aWj-c-$2KX8~3>kM(QW%g5(9kTL~Eg5#HL$VVn-ETk5OM!Vi+ex{2q@*+k
zTsg=jFcFu@IEli}5JXq5v>smVkqYv8AD|M=OOGzY6)LtaX@#-d4Y<}sh(_JgZC6{K
za*UoG<zR6<24BoFeAf+XFeVU{9|x!x#T^?ORbVMd+ZFT_&?PMVdV99V+LMTqY=>d)
zLvI^*$XvCHws20NAjH=>Pp!Go-eP9X{kJ_M_3FW8u(jp6SFayqqWBp)+<md9Ns;mx
zbo}oOYlvmT1N*ZLSY{v;iL0g#)y2-vE}Kzo*x*Y~Pe1?dQ)DDafFj<!dGk9%?&-XK
z8L5op!NvqsNATJL2B;E*@5i(I>C>lBDxPmzDDc5;YD&pOnH~K~5=~5Dkm~nT_{JHc
z9ot=kY7aq2>;<gvUFc#o%ORvccvjiFf4rR&p%BYeVjFAr3PsDJZ4WlCe=ip$L(`Ka
zB|jkW$19Jt5{7gFRLSI(Bk8ec6iCZZ-G=9Wyw>*^F+r|Ssk>OOU-K33FWUx7!uH<Y
zC1f`jV0{DBOa0M2=Y^3y`61Dd*1$V;US-OjqxMwv$7OETAnha;g0L9~U|#CPsVm{t
zjydn84T<cumKZES+1CF!<yG<A^xuCSf`vY(|JpJIfR1@%SW9l;OMzhYn4)@*3H?4Z
zTc-!7-$sv=sAgX6_JITzHYxY0F2zDQ-*N^&{nsDL;XjjO2E>UG^$n2)47{d5lMfP9
zZc9tRQ@r;$4D$k^8eM<!sljpXhm)X^vNC@{H!w`C53=FjK$@4!+#icZv`5#;7FHXM
zsU)doozYbCyG=na5098pBe(}-^<&s3ZQnoj^ZZ)?!5;$g!Y_GrjJ%bD)4u*_;rw>m
z^eTbh%|HA9<aF}Cxnmiiv1Kq2uSUH2Imf1^rn=f-r$#VhZ6VFTQUo_(-rj9JfSB&3
zhSca_r3M#2&3l7Lazqp=G~`)?nN;NabbTiMeesV^$$2Q&9WQ>y?;`Qq=FTK8yv2p6
zsfm4+433+2l+Vs;<@<uov}KGbWR$LhZeSw24im3#rY|t_L2z_!)?P&|mo`4OaGl&K
zY-XGxY1`79MipaLi$ja-8f0@Cj%kGgdQ18mVBymSsvXu`0>90EpAt{ZS^Tq#F<KIO
z{L)Fx$Bz&HO0-e&fZ&#`ygOUrLVt-0m+skj+kO6}=gQKL=4F=bi(gZRz7w>+{bHGN
z__O1ikbPzazmV+=`L^uWY$32C5fc)kXl3tY{I)C|Q6&C{vVvA97rM(^*B^RqewI<v
zD2SxEf>3m2L>bn5>!uNliB*r+h(ZWt+w*6)WOQ3k0)3m^rk>l3lQ%WJtSCF@x|Bi-
zAVQHC{stNd#$n6x7i&Ugn+Sx%rhyWPvZRr}K_o^asw-?$#EnSyrSZ7)XZE)rSfz6C
zK1;WUBDG)haqFYDdNF01=9Qw3_iWPl)W}}r!1gPx#7J^kw{WDi@VA*y&p5v(L#GX^
z^y({P@Sx+AAAqHTQt9%H0O)MPSe4#q@NjTIDz6%Rjkuv>gq|+tVo(A337C;4&A*+U
z{^b`GE-V)2<|5U@tjh5N8FIoqGq?e~fY8|&WRVcyyMvUKM$jtKgsN~$Ityi3n~;Fk
z`tZw&P%Us#11W8Qopv>ojkS#sar<e-)|RA%1i}bUIxy8ea~y<voPmLoxtP@IfNJN5
z*gkE%-9;&Lje@tUL^%>gLH8qR#Fi!`-D)(nc1r5|b?neyn3A@|#iTMZuXq<)?5>d#
zAh0nFgRaS-14%R33~fg+#l0|;zrUO^NAB8=mVJpu;$hPSq#_2ek6Qa!8g{)2y?rnc
zlU|FH2MJxihabBym3aw|vOt_oIcyo1^OiOXL3J#L#l2guvy|KY&*If1S?jSM(skU*
z`DX<Re{PrNKj^>hdf_kX>Q&Ds?p{{H=^lxVhl?=P7SPI{KKx-+k&D;lC|k&cM>FAP
z$b{gDR3pE_^jGFOF6n#8_qd3yTX?TO;7BFjcbv6!75HFkgZ8#tqYi-W|2kseP3koS
zg5?jT-?yy;X?@L*kHXJ&K#g~^Ms(Jg^!qh13!81STwK*z#|<(7JIRRd)}-&r`e5CO
zwEWW#^DS3)1nejE_=>~YfF)N^RGjtj1)(4Wa;c3B`@+8K=Us#dA_^|GSa>M&^W&Y2
zaq__oQ4@^+ecrt<y;zM&x)r_(I`^1+ao9WQB$t`470)F*+L}cF^-9l)`uzC*g;=JW
z;no(yf61reKEJORVk_hpN64Hs7-!{2OR27Vf*i2^@Lh_%cXk*`s@H!2JDqf!5mzo7
zTRN2mK0Fe)<k4aWxSZ=3o=?xmd~}^pu&;L{w9olLj$2h#WnNmfo!d#0h$)rbuZ@Zg
zxf(OIIj}KF#;vb+-+TauF}fbja(XZU+!S)SH2MwT3$V-p?Acp$LvLwSI}OJl*p4Za
zk$K;ERcwTj{fO+6s66|fAQU^h)%SB#>TT>qkrB>|NoYO01THzil=ypBc<lew-F5Cd
zfU@WLGD+C^hvhw(jj7xOYu5DyMUs9w2BV=33{CNFS**qYI)vzO|M-{{0DTzYsP>K}
ze|H*)DP<dcF0dE{{fyh%EZYIFG&l)?i5EVPIsK->ojYlbiAb+s@3UWY4MGNP<tTFh
zxvvWLm-2&rGuRY?Aum{E<IN+UmNw*Z*%-fpSVK6ix~^@c;OR3t9|C-M%_~?)ckvMP
z3_DfYQKJ>yW!t^AG9we9S>KL2*q|0CMAVD<_=0-?xFGJD5pYm|-ACt}t+2<oX^^q?
zu?%nk54M_^_Ywx~{S>p}kl4Z-X`>pk{W=%SZ^)g*K<9cr$HEURJ%PJmX{cE<aIHx?
za@J7}%j!HEtp`Cvu8oHGN;aN^hx=r$2mc8lVEYP(@9NK)6}-lX_lMS(%G8eA8BEW`
z_ZP}<D8Gu{`u@Z{+VqZmMag{XbY!WKfb~H8WLEPDPWW+dRm$2@!uAH{rQ~mAs3Xp-
z?VIg+5}DF8IHJBeq}hX?b27bLJd46{QNU;KdQxl%p(n@P_nnWh5R3{P5~}}5K9m`R
z#8s><m#-yNQT6qAN7mLW#W~fO!ITsBS^wrZ>Q<X&x=uO!!N-vFo8q1WwgRk3<a9k9
zp|h_0fbtow%N)53)5dr@Uw@8h#haQ6G)rzF(`Q~3f-Ou@nKUE>1EC#TYCplZ^5S}S
znDwCI`tp||lmiXG{R1>34{{HW>-cOmOf|9^;!l2G1&O!IBjuHE8Frn4aud^N!Ej7(
z*(pgTq#$uIKXEaJe>&+EkCqA1*9C9xEqy3>!%Nd>E8BM#_>$EA3Zl&~Gy)O9fsc8H
zfhe($_WE#f)x+vR4dk4Sx%Jpx6fd^_R*a$SYURz-n3lf4m6YEni9W+p67POO&ZV>v
z;B8v^GF@mWRWkL|<(Q|-sPrZBRt=rPbuF%TYnD7p-UQrN^mw(gRfa<~0(RKKpSGqR
zo90ttW4|n9m|5pMIqsOO_Ym{j9{94fw3L&RqvPF89dvdD3IeqZMt6r3|H;OOkuu-q
z>pi;z^7i)m6`BNw_R^%db;P?On5pg}^s=!&w)=T!qNU8~=_0O?;^iT6akI;r?(S+<
zH0`tH9k5I|^p=XPpWLH=+~ctb3mBS3h1DVR07gR3TRfU(y__CZIgBZ6C`rn@%A}L1
zxo$qAp=O2-Kbx0xrtcTUr79iHy<f&0SPo`QZCwiq&R+YXJ`g{<%mHW=XpzmPbhQsq
zLlv3@+%?|-4~%IJr1xTDVYQ4dHz*kj<#4>?Iyq*q%-YJ!lb_c<wn;&xvZN4C*lr|g
zPR52`qN9+@Pt7RQRO}v0=lkbH16QQl0FCx4i<h;%UUe;s1DXDmGgLpZ*0iG&)a*fy
zpa2I|i5@x{>^GVwnIeQXe;m@<tb0p)8n#ZCuK6^@Cs#&^TK}p!{cw^i{|d=e`j+<w
zL4l#@QNv)fNj!#R@`$2`RUO`ITY(Qq(|aFevG95mu(yUV{o}LhZKm|JkBevLGu<sI
zNJu)GpZ<3LqrF$S*)taZ=M_`OjRe8OwO%G}?gCE?P@O~QYlyjQbCD-LNuh0OGiQ?O
zgA9ZcQDK=D3Qq9ACm|uh#bv93AQ%W5N+MgMO<g!o^4^MiE)n$lHDe9Y4Z?QQhq72!
zy;I6HUu$069~};s##X*RZD5Cv%`a0VBS)56tLB=;VySRBC(7s6k5>NsQEc+9-MsMZ
zq<dO|YL)HNAD;Td*`LSuKEfJQgWeDkK3|}{MA-JCTOc7n^7*6NPi0nbYhaTUwls6*
z5>a{TE!lkQl~9F*pLl*7IHm^@)u2FWZwNpi0nyM}?(T<;26gfo^f!pwQ{~LbtoUw~
z;=Pk?Ue~?<^=qDs9BE?_U-PYU9gRDD&3_b}dcq~{kBH$TeCt3gu{JE67T4s2-^EUd
z<yTN@T`^XuUo!*|z>gpIIMG7I+Brz^{hXB}TX*6O4Z7K|92W<Nlb%yMzwYFzQ>Mx2
zEsJUCZ~~w5naf}4fUj2fZB~QXlq$t947HFQOteZm@vbO7Vi2E_;Jfa3YbJLa6&;<Y
z@yM?=^l42+_+9u&m*r6x#cAhvSvkpdw?O%zO)R8ht~D(bsIF`3;q<I<JtLUO|8aQo
zyX0e?5t$znr(4b6-R$>mM_Wuw!|Og6{C-wz_TP^$pSkau$7DHLw%nHDyW)<*h|Ul6
z5`11=H<RG!wtDKdpP!Vs=(q6e%A_JLW^Q|cRJc1m-s$%`uLowoqtf<jNpH}rj6Q#}
z_`O&ywvqYeB4V~y979YL`%2@4ZDoS~3L8CQiiBb9koVJgefgcen-+py9J0pRMp}W!
z{>E5@)s*M;i0QO+agIu|ZUv3p7|vp1DQ$rS@Y}1GhvV9~Z`}~TpJ9({vHR-~X78w|
zD3fQ;E}cxXqndohkx1|Xj9K1;h^t;uY5e;<cNg(bqe!P-x71vSsQxAaf?cqL?0A<6
zd(x5N#&!3cNw2qz<Uwe)cTK#h&&Vk<a?~glL>y-#rAT#Je+0F)_=iNOzw7LOiiEU#
zsm#8AO<+Q(T)iPvyEPP|K5yV{0GI%ef>JGY%vX~|n7FS$Jc<*fcmH1w&cVS!VP991
z#jaheGJ#J=1+H>GwiIs6&!Hz=_mo4^m$u^yHF7<Y-HsN66*Iq{&KmF!s6Jy4i8LGe
z4{b{ZW5_Fulux}`^9&;u@$VCO!SoT?AQO<P^|Q8s&1_g@#581+dbRMkf8(n;6G>A4
zihxFDDN^OzeRjF#Jbft-wbO8soQhfMmP1+Zn~L5Tlj~gJpeRZtN~2)#5vp@WA#4?u
z38?R6X*Qg{nJ14AYA`e{5G#gQtMP+YKOe23p|9&-)pI-RB)Ph_;S`P2voj?XTTPB)
zu^wF%rD0q3%HSn{j~43@Z6J`A&)1OnDMfo#kZTvfh1PKG*i5$0c|qBs-Wxtd-F2B7
zxm=W?He-2dEkmlBC4h2&H_F**S@`?+rpOC6=%&CfPw{;2uO^FW#Y;xU1b<tBX%FO<
z5!nYu>YbgQ)7=LbZ&VSL7RB<D^)Dw4a@@Oud;Co%MPd}wETzzd0q-@ObuJJ_)PbML
z<F8U=sFLSU)3S9f_ITZ`zl*S~iu^_F%U5hDVMLv}G^<?*t&4&8zt)`e(^ZkPfgVcM
z+qXrMc|QFqw;A3z7wZT%`zx>>$EB;`y6nn&hcM&Gm#@2Y(a4Tj9*b~3_<)tGS>)p`
zJ4-(etSZLZTvoT|a?)<fbSs|E{W~J4c3sE!^9|?}z_8tq3MSsr-5$C7T-Sh|t?acR
z?HyCx*?jm=6TTNa4@moX^a*c~%AsAgw^eO$J6hv(DT1W1wJnPsBHPi?m<~fm)?>v{
z(vhjp*eIT}T1hfK*<Ijia4&ccO0B(7qVU%(e>TJs2rWv|Czcc+kUSyI3*C1Uh4TsU
zk|3Q6Y~P*q8;Aeq0Qy4oLZA0!DwE@7jonSyH3I2j$WqFilQoR~G-=Zp4S@|J$*4W`
zW$+Tg0R#46QUifd*Sa5sIROqdxcuVr{UcKc2VOS(>+VHk5tecSw75;9{JC<BcT_X3
z`rC7V4olb;d*g~6l_LGXcI$VTt@QpL?fq0<a;qrQ$Ff(-pKy~0U3u+~q?@88k9|tc
z!o25HR!H);SC<{ftH-wjFxg-CKzxHm@AAsnE*r8<Ae<w|I{}1~U|lMXBvRuTgra?Z
ze1j=)-hVzsX^%goN?VOQ2R&xy!OZi1X)xQCSqFXUvG@!6FbM7SPhn{1E5S3Oia;^F
z#8FUeaCbZ@=?Rt%wxGO`^D!3d1Q~#2N|1aSZF$chlW{9sC7Do$?}6%-T7FcuFxMys
zVgy?iG4i^EsyM=-f$IF+jIfRvp>sH~IrW&(#`CC?f|Wpg!Jj}pNId;g;0YJdwKmdM
zxWTA1AiQ%TC_RT2M23Yac+$qgMrnurxA-bMdfUwbumT|#+zamdQ$?$LrzwUp@gy|g
zd`?`GR0JW`LswOG#Ab@!YyYO!X@^acNzeQO!vRtJ+3_P)a#ES1F%17Ayi_&?CCYG;
z-ND`gK`QPZ_jMww`DrxVSchSvjp{E9Z}Z>gj;FIxQc_x%>%^Mr|3RsV7lY3pMDWjt
z@<t%GMQxtT5K=CZ=DXr(34_oXvMflX&<JXy+Og4MmwJCGo$V`NxcrV5w(4*q42*M5
z$6+B?aB%$KXc=`nxo}}IqYyTLr1I*a<AIl#|F;+Ce#c!I8JX5_I+I`}m3NERIbsya
zW#Ym!1V25pF0W2!H_m-;Ho~Diy7<mBPAhEgt2l#(jP)%j=5L`!w)EZOQi$v#vk^!T
z4$I})1SxXvy}v1pxI8>OAaLPfx3dR52FS~DSJ@I{z<w7Kqdtrlx@7k*O+fIM{6>xb
z;D@XZYhnjZ2Ot^^eYu&u%jC^E&+>u{ht5l>F8E8T!_p0!^aVjbK%^&LdG{Mw@hG@j
z)`BkMpMP7fh~Q_#!CEn1g;ie1Z4&2cgVw8;fA#Poo81VrPQgnooT0d1??G$?;>$r7
z^1aA}{of#KFfItFu>%PJjgZ@BHKc=gM6!Tm<KDd&FcG`ZshGhDJQt9nf92{`%tjD#
zfhSIH_?r6VDS6;;L@Er0?o!IF{<;BhB|OEj4|a9gfa;lCrpo!5l8ntgnhAkoxVTEO
zs5Gl;xRKy{i7*>UB3u2|4idd#7s41}r@y!|Xs|DD1I7&@^y5`=`v5!W<hw#d7}ixn
zhp|xnko3>l*`RN)^t(8RZ|&i!Bm+MaM4J{44qFh3=m(eh^z;-`QQTB)zgQ|nz8Gt<
zo2sva?hc$0KcOE5PworQiMXlcjX50@B)og~4#dX@Nl1_3<KtTqN$mW)urLYvC4eC`
zobBuCw>@D>Uc7WFie*<NWLKz_Z6wYB#qnZuk`5szCI&L3otD^;p(HX<xO)K4g6JLc
z{uUP(7v8$)ZWt`l9WY=Z+8n_~UC{jHold?T9J&C<ag@UcfM_xh1kTU>N1ui9EKlz*
zb5Z1rS_s{~4U_fa-;OA7kuCRSh9<CnyZpIY3=9l1Xi9FqH>6K;2knxzD&{wQc7b1e
zR*di;w07As4%M%I;V8D$pWOqVOISm?75{`X=rgIt?>EDdJ>VLKXK7yO*BO~EpAK9g
zU*Gzalqv9+2-uCgul>1m@Ho%Q>EP}$OgRGsY6y8{<@aG>1*;Amr9daPjr0Nr^y<~C
zRgs(70~xykUKWS{pER?6dExEln`d<e;R1xeV<VCmuSx&!JyMxKKJI0pFu3TMd3k$}
z0&lH8G%}h7hj~<D;-kpFa26e@v<n?N5T<d@y%ia>TAjST<K>lx%^VP%BM>sntEiY$
z-@bo;GKox=V-$?e70R~q^6iiw4J?Qonj8)YgbcvhUvSkAgV;e&c=gxBf9ts@_%+l%
zV=b)U|MO)C0TL5SV2oYpt^`#P=q=s3T>bD;QR@D07Qkt^&p#6&7w<C32x>b_ejs&>
zGw_5c6s$(DY+vTk!L>S=@|!9i2IMv4zpvp!LlT5YlHGWvW4=x~ji_s0*RXbL(Q)fU
zwqzqcKNMDYcEliTT)F>}F)uYILac%25U6=yUtb`6_3AubpvxzbICVHb^*}5E3IjxQ
zxA_i4fV#qR04Hu`WhG=WWkSqhc6M-ZFo+?Tg@vntU2r+;<jwm9O?uVkVFU0GfgK+q
z2^~5ln}|TV8ALQgWB^7Zl-5i0)&B?xQM)OI``+%5x?)hsz;7L`m}w2DtEcBN$Yy8f
z=9q6muo=*_&@8!}r1R$4LHk`r1#wXjy3_!sRfJKo7P*Xeg&m#L_@>F{OIaX-tzJ0=
zsh}MzgGL!#2uMg6l47^vU<DcbpDf*RyFAG8;%w8;vo3k@2+EO&dC_R8#bX15WVR4M
z34B1ePw%x>3<(cgV)n&a5WWXtP4IOH2FlQMcL3?(_P1dNg8*pyuIJCXmeyABi_;xP
zN5=q0v4e4E_zlE7t&Ef|LN4mYWL*OMR{*>35IV!*Ai#2BHA1U_LqzkLtrO@=Xd1Y1
zo4{?mu(+r>T!_^?j+;C{=<Eh!9B}0wfrn#7gp@G15caJ9R#6Xpg6#g~hv;4CnpY)Z
uYias_x0e5v<eS{PmzDYd|1y4o#jrKy6LRIK8P0!2D9EThERZ$|_<sOn(*<4t

literal 31998
zcmc$`1yoh<*Dkt{+DIx$r-Fc_fOH6mlz?=1NO!lSfP^S5Al)6C4naVW2I+2)?!GVn
z{^#8Ledqg+^W8JX9e0nxF!o~awchp4IiLAF&s-sIWyLY?6W@m*2vbtxl>!7IoZbDv
z?t%ZImolXTf1ue)s5?Lq`op^)grOPlD+r>5Bwq<DxqkgK<EEvwaslnF{ds3X=q!f$
zKndrO3-T+P-Vrt#MN!Sd%F1%Fg3F2|u99kZMVr+j=iwo1H}<w^YiA}V|NC&I5s4Nu
z<45scd_B97idxK+ShiTKZHL!ag3Wvl4yywF{FvA!yI&8R0vLOIaYVX&J~5F&A}}f|
z{WNhHl~8aXIaR<FDZYCE&VhrYH0oQfq+XTd_;flyN+jX8r+Czo-Q|TBE`u0tFu!l^
za6GY)^jzyyYRPEokdPnk7j_1c(IXp<n?Va<8fIxO+@#8pPP}9?!=su?t%nU$+p*M=
zGuFrMjlCv{*H5fj2`jrG$!HD7t$kOeeqY%fi=~hiZdn{8riT{rDdpRWOfdtVh$Ze~
z;TZ+Iz(8`_O%!l)DNmoc%j2mQX&6^bq^GBw^d@F;XcYa9VNjjiJ{cb$=W$paIg<}#
z#SwwMfBod5u&{7_eVs-+_Df96BRo91@OtNMDM?B7CQmmvxAPxi1Qu7hVuUcNfR`B$
zyu7`=6%-yq)YR0=Yir`KU*CHvFE4Lk@EB_U@ndCWW%R5}I*u7Cby({Q4ZWYTM*AlI
z?I=kbtA=Mrx?{nQ@K*l*0>NXs;mX%U1L>Nf=}P^OFiumnT4HupJaM*JVzwF6!yY@*
zHr7q&DXE9jI01Ff&*)_CH;$T*ZXHA4hBpdT3WmlTJx;wYPlzyre*OCO;K^%nF+ABd
zH8qu$N+n~8c*>(BlF>-QN;dL=Lpk_?I%sM$f-ky#U(c9-LfA`gH$MuYk$hO0r_~*3
zwRy538e)acL4Sne-)ap<^le*UCB%?gI@@0w9v+5UPCTTbpuoXlH6O{{6nfUFB=b8i
zCPw)<TgyrD7*&*sSlg-1?(2;8*y3W;X580&zcsfY{Yr^KeASs!#;Fe6f<a|2DTAt-
z&DY26&s1kP=*sZK_W4V!37aH7^IU*C@%1Yy)b;acNO-t1v4exd<;gam&y8nNFFpZ*
z>XJCq_m{A9O!ZiHHnwV;x#Ekt$m*A$MN+?h_1qZBdc>d-5F8Bd!0PI1u_nA2EIXg`
zAGK;rbt5AqB_*YBiYgc)l~S{=_xZ|7obGJ3m8Rm283XKl0{c5gd}+nU6Q%lXUZ*ps
zi(xD(N=hScExI?ma-DI_8aE4<*qK=Mv(MMvKm4wJW4E1`^wj&K`LOBcQ%1(Z^75fz
z6T-WTROCuaOMCh`kBL_0I4|kr$B%NlYTQ0uzNy7{I-3)xS#dh~UcV+P-l-OAZP;>W
z%cXi>Zo|g}zvbqd&NjFqCjb6gesHPlF2)mNTYt>ImYWL+QzG?;v<16Z&5<kV5c64{
zooROBq(kJ`uKWSi53RW*7FVu3*Rs$B+C+M-U25OF5)*qMiwfZ|6M!FDB?iTfcL&nq
z;q|*smkn-_cw&J80UXRiH<>ZBL}57Q>5Getr?Va<<>d#n9=l|0MiSeI&O6iRaQQYq
zRAO8{r}(QM%x~{E(mf>ST5$HCLMHEjC)cZ)%un|r?9Ave+rD+;IX~%_cgWZ^GXYaF
zDZO)oHSqWPM$p!Nq3>J-uAlhE++Jr$&^?Up2begR(dZ{T#blDI5DXpJmogUfp6}v!
z_u07KZGH9XK{K-?+gFiK+(`%F9qn~m%)xCBNJ+Iu6%1e5uq8xL2$AFBDxUtz89rYx
zRA+Itp3^DuxQ+Xh(0`pLsur)%iWc|TwjM3=@>iZ>^4DsdlsKc>ktdbd7}WgMZ@Jp=
zXcQ`cSYoa6K&-V|CWdc2yZYGLa4?=Ie)(1UQeIpx<?&%ZwbSNX0~b#P>jW;Fl=giH
z;>Sj!av;T7zNGMSbUuxEo5Dv<crqwXVMn?)b8hzv+c%ILT>$?AU$Ss&yZD`2o!wH0
z&8#}XF9)OM_XTsFpC2N9Us*PiELAtIN1QG`C{S&>UhDLl3{KK2W)|=e6RV`1v9_&+
z!O^Fgzi(Jx)JM@vWhX3Jj7^dpQMV+7cJI}kecQ90=T6XnpKTnA7{o-!Jb4j$8tl?U
zUw@S7y}noJu^U6AVnXOYX!^+Kc122B+JlF!!M}Cy7aSGe>&k2tnOOOiyysFlmuw0@
z5h-bd>;9r_GVdsnf|*%f+ro#BAC-zZj#2R4k@4LbkJQ#lqSPuTJP($C#>dBRJ$h0j
zZ%~w#C9QkrFc2fn)*{-^KUJkjJGA{+OV*Yb4UVqGJYik$y69?Y3WH$LuROFzigTtH
zGTXjmQc<zBb(7{OnJ29H8XL)W5*|GkLq|g6O@719)&})-Gon(XK@$@bHcjW*!y8{Y
z%!UL`rxoAbs+Q>Vc6EIS4=3Zc)l)V+Zjb%)rQ!DG5~R_wq*n*g_HJ1dmM1qfYU_{G
z*2@MA({2UI{3t!IkH=zSVwO51u`w}=y!JqVrOG977F)zU3H)J*&(CEw$&r=3SJS#c
znJjBNE*bav0aEw2eUePTFoG=YhP_{#FgIC1kMfeo50p=C<Z7}8Jo7c|1T3@E?ta{|
zDCbLl(HqzT)D@(*Uw@Ui?6Y<DkjA;K4{XBc(pbLAG`Ot#A)^<MS(%#Vf|E<z0w>0G
z&zmT0V0X1InQv0x+Ge0%z3RZ<<|LTtD4<7-J9*v_N0xTMUTeJC>M(Pv&9MRFVB=34
ziZszeDXy$|^5NvHr?%@)%Ub%ToL`4*orvHIb4J!r1t-U+mN4`Z!fEAhjoY#Zu*i&4
zN(9Asd0cjO>T{+CnN|lz(=}7PJJ`QP=pU3OiDT8f8R0tkT_HxS+r8SwesUoij8&x5
zIJva{<}0_|>aT<<Y+IgWmgYUbt#+1<@fx=Z6Q}!_2q2Pb)zlBv7z27XIFryLu7{p5
zB0<yX?L$hU!Lw{V_e%IwmGGn<QcIFIC~%uz1^Dhcv-KmEeC8&1)me2)5RbHWTgjqD
zIqk3U%=+eX3+m{|PWQI+?s!?7C8yk1?lMrSqzuDZ40}xuLvU%+Gcx#GcBa2^TbKqt
zxUO|r`;ui|;u4-6XZvc-lsjFVNe)j;9&fAJL{?oX1FQVcb7k+Fwc9DSRg;wLYHv7P
ztKa$leWc)I$MMz;YEtL#D{OUNEqDW8<_tm*(VWWeOCAQqZ%S^E9({R!cMk?SZ*sd`
z%T{lCyxTk~R6A}G*=X#z*!#pd8Jv8%EUg3o+mX=%)iFWWxw^6psD=K6=CeX}+G*t{
zvtKkjS%q75cjhR#JdX+`KW}=<G4{-Yl5xC>aSsu(06)aM*GS!ar*@4k)g8r8^rmsq
z=JLst%cG6qA>k?>wRL&)sRp;h6E4+FqPn(TIHP8D;mM}PqeqYW1&@{Dh8`yZXp%Jc
zf+L_b9xW-RR70X5uypEW>|C=~rM<02zZ$no_6>XN_#ge}&%3oatyo>@Gz`?|opiV7
zeDvl7!#b_jhmJ>Md}uLI5>~!VKGf*MqG-CT*k-kR_?7#FAk0wjl>CJ|7CJhgs{TXc
zgvTG2e$XJ6IP{!GQHdHD82I`7Z`Hal|Cy)&HB7hJJ6xx2>tLnFVyw8Ru<$Mp6%`dp
z#zMHt%gax8=jMtu;dzQVLyo=C3JMC*M0oeMpFof`nZw!MLdPTbq~4Q-U<wV!SiYT_
z43E<t8SHwQR*}fW<;5RiDg{*_)WbDz2F$Cxt1fy4hG`L75}$PXoNvxa3M`=|1y-uT
z!1c$YRfIgFze(36eMt(v><i(&w3ARs=zH6~Rs0CAUHr9kXgFfBj>762S8~@GA2zm%
z+gh{PZ%uQ$&&CNR^?&$BK4ak^#E?zGgs3Im-%W&mMD_`a!cp6Y2S0me*furF-4n~C
zIiIlh%?l|L!(+SOC;u9gw$ttjMGEdU+?cK9+k?FRX-|jkw>>>Q6dr%dcDc#$2)5Pg
zsA*`jPM()E9CRl-5KwX(9UZKYa9I~Rs2695NA66)S(ur3`uKJWuTe!~SI!UD-tk}2
z1q6B39)y29*<M;&+Ipjndza!S>v!8edMq5};rd)}ZIv|ZEcrTbPqxO~D~)f(ff9(k
zQe7MDXcIwn%r0=X$!P0%ZEj@jHV1EIbHMrrmq@*qa4$CtN{ah>gTKvBJu9&qFBMMk
z$a^l{{H{L*5fuv{&>5fGYf@nx6I^}HPn-;9&mV9mRb<xiuPMc~w2-9qJn4BFTrqAE
zCQU|<N@#5Otqn(y3rWqXE-Yp>goN|E7drwZYa*1r7W|PI4RV%rmVOASQA>VV(e=50
zu{M+yRl4VWeNl_{9TS2$Wa8?zqA{?7my(8?{mPqkjEzafod1l=M;TjG$w}zz>!Xz*
zrl3YS0lZkTHmc3Y=r@2vDZ#4jjB!6-kV5Ppl7iCRK_HVLs&nE$0xR(d@}rCPox4@I
z@3*&9(kwD&4ME1nh_2NRIZ4n<^%dRoKqdx;OL`%S$9(=jN0NPY8r?@>Z5xg|0&e%+
zk06My{&!pefbzV!J>`Xzu;Z)8TJ_E{I01Wmd%U++pOTZk(nM+Jc~*k?g0cOD9%{bW
z)ivTwt#_4=&IDiYv_6p5SD{w)`{2MnS(bpDT*qTze&5xb_cMC8X4i!o53;i6*eLee
zKh=nftqjyD0rDhNA1^dW@3J}^@nY7llS~Ru9xU3V@>(VK_QKF)L5}FtYr`5@0&|Xa
zv~)jxZR!n5diJ&&wC?W%veEW$zl0m`BPrSQVs|DvxpVbQaQAhTTPF(0+pb9YwvrKI
zq<1MP5k7vbRlXdhv9yN&BDi1h+Nix0U=KG3Mo`IRXTEJcL`o|yWqtfOy{|;likT}Q
z2mxY{luv%*K$G`JFZQQ3+?5|+GCz|Lw@t17$JFA5n%U$lcIQz0Xv|dWScrG#=+ZFs
zARt)v_p$gF#3*fM5gb3KhKMzPy8DnLQtKybh>*Vg%zR;?AaJ0TU5g>iWnA%dv}-gH
z6r}Ge|MsaMvv%DVr+L)xOFN?lV*pb_zEqNk=gk??^7g7KNT{e|wKSipCC$Qa?w$4_
zHIi3fZS9`U6<8*pTvo8D8PRiborZ7oQ9Vav)~;cvvmMHmg!J%f@-FJMH#UUaUa_tl
zmf%fa&->%`-19AO{KCe;V>{o1{a$#0{vrR)4iV~RLrmVcZ<Orpim@v@GxfT=oHRMZ
z>N+~0cl7h~GZ)&j<yNtt!-9WAfV%qY*<a3;=r$8~*39fA^SP{!CsWo?G;LSsUv{a*
zI@QPcyb#_CZ!!U;dMZ)?_hpFw6oHS=LC9pM(Ytpxlec77v4;}y_%g%J-*Z&?2;xUp
zj;~k{YY01pqr^-qM5CFa*Q1Q$7?WNNGW9r1KC4o5zI|sS{Fb%mEB*Ba$#)SlKfMR9
z3g{+`eloT>G5hVSK$7LuX>luliw|Mgs1O|kNmqqD*9@M7Jccl#L^Lfd!uOC?&fA_B
z^&u{m&~5clx{$yHopiLj-#pBZ7EM67d^x<C-1TB3=QhTf1t}PUuFh|zTy~WvmF50`
zKIeC#x?#n{(9lpvB!%wUa~>X^ZpYtD@M-%-?+9`MbIv411%-Tkr#$aFq~*OBLfcf$
z(_((^KC$JGf@%IL7vvQgZ%=Xz5`^Xy9Rah&#?x8NB`EC2&~a<<{K^;t)HHeReHu@%
zm_0HPLkWA)6lC?*K($b<j6WS0gA{{#EtgB@^6IdEi~r*icW8RC$F;Jy?W>X&k=@k5
zS(=`>nUo%DxiU&TbdeBB9S832>7Dz+xlt?k6G}PHJKT|JdRoclN;|Fa1Qr-c+F4uO
zlRqtC@>592U*0xe3L&)O5THIioR<@Hw>jQ?8NDp2a4K%b%)+v}e}-G?N)NRZlTkc9
z0@%l3yTWp=Nr0WbLKALX@vi^G{zU=ttmk2`aR&}z;=M;JaG$-MP70sGO89r4vaP(v
zH)X>cmfa_kEKO&Nmp|L6cu|aqQf?JDRGkVLYCX>n9!=y>GFrYdD?61A5k|zdU`rtP
zx!Sj(ChG2SX}nUfx3^CYUT``J4^rf6%K7$f9;{o%geCjCK2mPmZ`7ji=XsVKZ&w{x
z>a^a?>_}Uuoz<N-N4<oUNDvOAa|r?|g$F|Ju}BJ&b1_IF__atsW|4f|+9sJUzqkHM
zk~Ty*z4N7ldm@&Rx}onrs3SYO)r!>n3LUNR)2kfqdgr}n-HyI^&g0s(l*`48V?#tl
z9rukaX3O4<h=G9tTK5xIk5MTY2C<^2)1QjNEev<i1AtYr?tTc16uNP}ap<?2srC9e
zSXxsYIOZjf%k}dKZ1HpoxmajYv&MEoAdW><Mdf5?X6iDlo%k6Yo$29fFMb#eB_#wu
z{CzKCU32Ec4ImH_5)$_J_u;ed7RrXBXA(F8m6eqrCmg7#2_wkl8k~-7*y4Zi&LT7Q
zaPe#>e~o~_D>8-$`<BdUMd^A_o?|=-(Iw@1hDE&i6s^;yd4|A<l*fLAf+gd<KBRYk
zu;LLGs(;oSp({7{#l-{^cD9nww=OT=!KVOeVNj-)Et913f@kUjpW)%b0ciJcZ?3oK
zX^M>G*=^3cJD#ldr^<c3q{ARX;b|^T;xxWE+Au%HsQE3!;Q!>}_X=pBr9IXJ;`xzJ
zuGRwo{CSt^RlS+VA9wj}W?#lmvLjUoT5)gxl!1lz5W<0yiJqoJxyRxiKdZqptZ=p)
zx3-^@=Dz9dQQc|qs3RQH4qd0a-A~T%Ss{j-GffK;so@LC`5gUpGjJRu{`A$MP8E*L
zlTusa;<|niYEMr`Xg;`gtfRA{(mc8KJKY2a7uUgB^8x^iDY(y(yQ;wBTi?JY_3N{x
z$gST+pN-uX+e4go0%KEBu+UTdzfrD_`AnqV+8lhAGtt-<nj{sPB>Ryv$F=i5zo4)G
z>O}3d7}v1=lz|C@=ZM{Dp>Rlxx$X6*GyC=;@%#6Kyp!CfqS9XUeYcyAFCD00&wfiS
z95Cj^(7O$vyXk*n(?`KSUmq4{*zv;K>)}WuQFzCvC*tO1zcI~~O9!|pg4>>-uc*!+
zbNT9@7I|ja%r#Z*B7;6HaOffI{yq75g2wnS9!;C{p_C6@nI%imxm`}?FR;BCpSf=r
z|BMVL?bsa`d8Bm@qJ*V+V2?$mu@q{M9auPzpCmi@f8^=ly_wO_(jt{tE4A1`g!bf5
zHb76gdWXI2eg&Z6az|)Z`S|qP;o|GQqp}Z(l4}u}^DNe?y*E?mUoJj5PX!X7a-hza
zl?<#kIR4iAOLFRIaw3Rsl8ErtJ?ix{eUzCZ{WL_0A&E~AL^jn{nnoEqot7Fevx}P!
z!=q`Zt$C=Jx#tW~_8crBUOJ=Q#{-K$sB0lE5}mo%8HejfeK?jtM8R(IEB?8mQa_?d
zZbH?Nhz+Zf6(a_R%x7x#MzKd6GQ%82i!OtlNmi|X$f>KTN#lZ7;as!1UY%|wI*pV<
z6va9S84pf&>(8%UkW+5uvNq-(W!uQC-d~7hnSiCE_e>L$I!eEt?|NwnlR<#mSX1)z
zNT5Y?gg@g_cmxR0E0>t3vNUbqoqYH&M-=4*c(;rB4C`-P&o_Sw-DWv&VxWkQ=V}so
zdQfV0QeeT($1){l8oV=jM{x@%VIn!hd9zG+bY*4bS-S$>57v*bLzJ)c0pBVu5Bjov
zmnr;tWv+a$rZ^VerpJu;qNCGlOc!&K1h4kmCOg0Kxma&cR?#34IqiDA`#yDT=>YRt
z))jx)hIuiF5H#4~yRH#Ob#qFLqHcnH({+6&AQAm*zc@+U;{q!{Lp3udE{;OTo1c@j
zI^{=qf2z=~Bk$7UB5|XjL9(YyFn!fow=&oLY5jaA(<~6C8z9N36ujW!sXfssgltd#
zsMol0KrdefYttAzk<Q!ia-t-=+qa?xE22OJWDsw~FK)w@aY$6kH>G~zQ9D-sPQmt3
zttW(2*TLH<8x`Kci;V#&g|%X7vHlc+s$6kc+HVx}MtF+bYJwaR(Y<@t>h&i}kwV2@
zVe{*Q8E~%yIf*2!U~GZOW*g;8<H$YZtPSbUD_@3{N5@q==N=HU_7M&wot~(O=C40%
zK13f<qQ`C5iK`UYio2{k&2Kz`iNHpE17{gPcXYk1fz0TM0G;ff+0Qk3RmDA*l$Opg
z<Fy!zo2hs4S^wFrgZqO;Ce|uu^@(#Z8=vSsjG;qZL_}I7B0hG^9)7#Cch_U}FY#y$
z=h=lQyjb~7srs4XFZaq>nk*M(?O{~S!QhK-8`Vi6qjPr`Gr0sd<JC57n_4Z_`clW{
z(be-M8pPu(ZQ=Ky?}d}uD9vdzkAc=L%=;{2mroDSt;xJj_vDwGuS2^8Q6P~w@HuyE
zBbO@=JrPD|I(6W1E!C$(BoZf|0^hyF@z41FLK3gF8l3nZVke#!apPVs*c<kBf<>%Z
zx6ajzk-JrO4wf3W179c&w_oTKug$p3ktynM7;m<NChLcEY@u$22HcRr>HINH0N?W;
zFBW5+u{ukP$M~b$qn7)T16`cAvL};X!OrO!k7h5X=59_}N}4AvtKTD{sx-g(@k~-z
zDN!hoHQAOsCYCj^iJ^;|sf$~pq+a&j*zuH2vj7<xGE|n<`l+)VGwDWH?H5~H%QA<m
zBNsHVb&TD7)3GepuZYC+Cv75JGZmj|BwH`d#+R6+dWBn?gyBu9YW$a(RSDlu2tl3c
zj}_^oqqmyO4+qjjLFatz%CDFs+jzNCZ$ZK8dA0{1hVv(J?<tv0SinD0GMdoSjk}Fu
z2=b!b)Tm+Z8j8SP!0<0naE;s|f1E^?A28O7nuQo8T>AO%prJK{=-;R{mPZiqCigu$
zB%g5~BXbW*xI9UmZS)wM-8^i%+?k?=CCQQd<QgKOQNDi7^HeU*3G*fnK?J7UDl!K>
zmA}gTUYXW#P(Fp|gR$@~5GC?CF&zv3ATu4^`dCx)Cu4;9YU@E-xs*cfbu{1FvgcYG
zG|*oAvEz(goYvTjOBmI%0OMnw+CL9w=^IZ<q$*^hA<C%7yO>z++>DtgOohiELPL4w
z72HS7F&Ar&<_>NeOl`vvVe-q)Hiq^NP)jn$^a}%}ZB3mABDra$1BlT0_KDE#dH*f1
zi?>MqUYxFX?FDjJ&evim;}yO)_{K$kgs};@`0jENdN12tru5ZMM&*Pw&wgvlzS}@7
zVct4vCisOz4&Uiv8u@YfGP>{il7JT~=d~AA0FGXoYM~~4N7oCrWt!HRKqpJb-R%0}
z7_#l!IrI2gb^Uv;`5sx}m;e#Eef^+XzwAgkBLO}>8-_z8`nJc>`u<M+?r&?_uUOaB
z{ap-k#-aAI@sHb&l@_$YXG7q_mseI=zdi?Lfp44cncIN5PsNc&b}tr$)Kc`iO>seA
z6qD=hb!Beu`cK-_kIXEMdTc_svqC`_M2jmEXi3WOW8Ct}S0-wm%k@7@8#u_RGM_Sn
zjy2<T_+*nY`<o;zx7r6j7tQnp%!m*gD}C45o6Efv(k&TUu&K1khs$&obPJ{&`ty~O
zxNJ^Vl>_n=npX@#LfLYRFgpz)<9)G_Od0T&$tr6=x50JL8s6F2ImVM`-!E|b6&*Hh
z`63RBR0-oHZhPf?%lSguOe4eo{m(z~CT3v}0^`!kW@qL^=A|XB8RyTAmmZVX3Z^xu
zIifHsmZ}huX2*>oIE&Uje|^81i%6d<fBl;^g#_I{ePhzySdQVyP$K0H7TXQ|WByRH
zmKBarfbwW{)Xr?118i8di0wKvNyz1tl+RlzFON_&^|W4)P-UXsvK)ztiP@~Jwx$1o
zN<Fq>cbjjUHmirU;x4PwqkWPX*&s0-MZb?e<`lnUtXk-r>PALJoVSE{-H+CpPF-Xq
zCG&3ezFQ9YREKaV8^nmi%zAIp8#|ZY=X)JE^2YTbL#hJbyVHA;N|Zm8zk%MsFkGq9
zwY5Z_ns1H(q`S}-3=r(^W=tIYd=AU=__z62A5pWu%|A^cpbFSrxD9Lq^4&1^YM30S
zu<!orlKAJ8#8Y>>4Ue&Bq|q2q9>N8|lS#~hylTTu%m>a%-dnV4D-T3^J8sbhpoYUf
zzEcyEM62rWwGIj_1hHt3p(Cz!0`Y(xPNVlfhjZknwoix)R5@V_j_ZQA0flsNm<T5u
z>}B0g0uqIj=#G$tVTdS949|EJ?J*&1gLttA`lsTrU@0h;SWUJIBBCcORJ`6X%(hD%
z;g)SV2WYJsQzt~q8jKDNhLep7wx*&UFJWn~2Qml3(re5|p8+lH(W4g@zZtUz&8q+w
z8UlNxqoXf@T|9OLlZ5ljmoJ3j9ki*Q2k!7_&KdQ%%M!6H4<;3eRbhE}AuTd6_y9@1
zAG5gX5kCI?;0l$9=10)%HH`<TQSdo$kG4e|G4D&2&2Hla6H>+92U9Ml<2@kQ%ZMBF
z!tR(u!1l&5sw=3dsJwmqjbmrBih+j47{GV%B4E+^)!ls`3rnZcbg<<;Vpv#M6|C*C
zln>K0qE9~hB|oI@O;bE{WUb!vbJq84yUpz?e{-|{ER=FN-wL_WTcG(Js+XW{HeIK-
z*`iF%IfY2>xhY$Ee}BK6l+-=nfPjFjtE;Xq35f35GpRRkkbHqa+R)I@6URal-T@Zm
zXmhkc6g4Io&erl<YPd(t_=g_^iDK4`@xDG<sD14+o~KBgcl%9j?w0VE11jNl_Fx9d
zkv;Pg-_td`s7#0Ncv=e()yOX(ovIWlmy96-ak@gINXqbZXXbQ!>K!W~8#}xBM?(FI
z2`MQlAVD7k&8-SVGhkYwQupJ{@83oITT5*hzUR-<g+7LTH|215JF!giYCV@0QU`Db
zx2It+>#%Z<*QAX;)Ik0BW!X6@g{SS8c9h!x;RQHamoFH-_*1HXbUgNf7WQ3_^-2!j
zOzP*K06}n>5lpG01IHHw95S4qjlV7ce2dOJ4LNR_LZSIB?L@7D*%YHD84ZL?of4a#
z|Aq+sZEOqB1(R@_)WpQZxVRj=3$VsO^Kc+t+jq@(k&sskkL6XT*%)OI+l~8zR{krx
z{qy#ez#P4ZT=bYn5hV=s!V|0v0c50JOiLJRj4Ygc5fRtbpXHK`==+6Vi}VYUPD2>)
zr!gM_nM^TwI-UD16FknGT7^mQi<){Yjih-`NQ`kp_6CZRG!9bdi<{H~Z_&>p6eJ``
zpFiVLP^5nT{5du@n*-><C)-n8OibHF@Y&F?uqAH53E0fHykF}}2762~g6O*Qi-fwm
zy0T68X(hO8H=lRdG;bBuwALR$dP=_D>YX2~Nztuc1XvvENo+^M<1kSCdsY;z<I|-=
z9XFDMqXTMEA{H{{U)!XS<B83%B8F#(!O0_YZZtcHg-ga%X;j3(8AmK32@gCjvxaNp
zAUV~KJ}x8e{6szR&6++mpV*?>IMI_R>Axpr+uI+2-j9GuLrF)+yA2u9+Ehn);g7WH
zeE|{Fpjn*Jjl=c6nMBVd3)}~s1=dsV0;ZUpLLF!A2zDL?;+WH8Hv49(D^Uz?Ir1iE
zGvP}MbowahyYEO1UZ=H6Kba(=FS&6Cn*5IsH>bU=GuGF!k$DVpSs+~_Iv6E4h0G9R
zpd|HXO9d#qro1D}zG>$y)?|8p^jG<oJht|tD<ckH7ZnxlHL2Kq;{MmX&}nB<F>x99
z7VptRKXl3S9!$m4KqSn;eSt(<?eMxYFOOVMU=y8XWi8x6>Q2iZFxt#Me*hKbbPtPT
zv(b7Z_}R_~N`71)A6lRcnB*0rKPn1#Tp+E#6@uoa$ag|^XYSs>!FN}FfM0Rek4YIS
z0_Q$01vV8d5f}of)t~7$owIs43-E}8;wC5qG_Zxv*{VaRB`@avvAxe0f&p*bn<^wk
zN%`$TvMtdixZd|B)1QZo8pIK)M$G3wkVASPyN<v;n*I=Q<<CzKWl56^B6@Mg2}C19
zBg^$!Fs+1#R3o8!X{gZIMo#ML(XQx&_L^etP<AYiqm_V(%&7`oqlw`hp%>heVK9&n
z2k*cS@QnQ7aRC&2oHjZkca1&^zSvAe(R^vx!nQV?)Akc^)C{Ev;kie4eojg%V9fZ<
z4TzPVKjX$j1blO)_gNZ`6#R91jQ`|>7I!mleRYJtVGoZ$pbFqvJk;&bX>hgM`&v67
zfYp~eQ@tJtY#|)!xUt5%UdPI=$D=__QFV?w35hPVygK(WE!y)Qh)7rn>aLF!X`=RD
zq4A2p?>X-4HV$Vv3K!*0ZkFrqJjQcBwW>aR9Prlv>}n)@#yYr#5;&GP>vJhidz;ZJ
zBso#)I%G}z{&mC$Z=f)yHs26`Y$S^yHQx)Gk!ktL+j`|p4Ez4vJGyZA#gy9}3iNCm
zprxgy(5c{MU?>=?K>tGaEfK*ifB9q^0dg@2C44l@Pb&sX>)1aR!G;F^zIt5*)mKmd
zP)=JvPtQOns1$}(xeE%3&s~ihis$wa0IW7Eh(?#6)g}x}BLOWxYvx4zO(`}sP0uC0
zJqdT@hEX{h0=-)#_WbtGQ-13*FVxehG$t}k5tth?x)&lVZG-|r-#xL~o%oNRIbl?~
z?*nHw1M;Hz_L!iW;0T;HyCD>+Wp<4XWv*96+I;44j%&R8ERkbgmyZ^l!?vGHOSWzK
zDErqPr{Le2dIs1KfuV;<WHeWEFRz`mQj=*mj>SAHFlFsLzZ0V^>Msp|(T0Pxu+m$(
zykU8uB^X8hMs^7^=mL9{TQ-{dg~1gI@B?-E;)i6JC8pDjvcJR$D4~wcHk*@T>?!9c
zvZo)obJD!X)nq&hRnb@{!xBHHFiR{R9C9pYte>&A@wS4+^_04BK{co}wSFSsW+jf?
z8;3VQ17GzL0rI7mWMXS!{kS=Ez`y%1U)5nu`?DB{V{4uWTqNPJJ4c2v&d9b(Si3!`
zIL<5ox-2@WlCc7kAeQeu7bHvG<Kk+J_Igns<GoB|=%9-s#J?c<7Fx_U(G2-I99glo
zRo|`ph@RT|7LDYzkF!5Dn%%z0Df!<;e}RI3aZxIctw1Bj*28O4Ta;x8?%eC|B0kyy
zKfd3%LeO-Z_|J}(SS(bk0OFrhBDxwH`DUa=I4<*=-<R;G#A;A6%r?|;L~{2Vn2!8W
zu~XQLyFnomAdk#OFl#qr1NHznAAYA5w!<lU$TqwQQ{u`6Tcj)4P4hcA>~uXN(t4(@
zN<5OhU-0q|kfb><rL}DukAGLdBfXp^wdI7aD^*XEy4c{iLX<rzDQE_3%#)}ea72g?
zss|MDURY1p++m-E?|~$omV`X^%io0ky|4DgW&9O?fC}$@Y0$IgGE=q?@qAyE8So<5
zT%KqSi*(A4%GpG!o9iYYa6rtiSf$MYad`RXXWWMm%PT5efB*%kljE&1p}iKwM3kKS
zVLu-3ccvqgVWLz_Xx;7yw6z+0qe44rPmGMG?11^sc$WEnsn6|AFom}(AOig;$G4rM
zsZlWqOD!kZz`2OUF>Ci#?ba-`=TSynE<5%GTiDpTFvOjcE!ax5Ql(J+?8T)Lfthtr
zJ-E)5&j9v?E&EzfFF`j{W;Xm3l8}}jX5)0iK`PU4LowMD$ja!j8b9@V`_{|fF~9It
zE%tAx9_|G5EOd~9-uk<0_QW!4yDxWr^8Nb)Z7?Po?@=o%#Rv!6qcTR1F>Mz&2kO&n
zE=oy}H(Y6&Uw_Mk-#grMe#O#be|gfpTm68XyszB5W)s~@FqPK_Ut-AVFg`W{nJPeO
zY5RjWplCt2l*nmC+*1x{{A3QZ=xqHeNK%0_>fng%L%sKtfU6LA;7!$-&J>VuxaK^c
zz`jq{I=uT8pJt}k8_DDLk&)?>`tO+N$)PgJfT%>UccQ6}fn)?e4VrhZ2XZ$snpI*H
zsy`s-%3-wl%%R1JRC%|LGoRX^UuQ%LUbJBgY1P`(Jb%7FUS=q0hunA?U-;&`Nlf_r
zlvc^Vk&yRmip9`nch(D>uG`^i<N0cm(CyVBa2UM6EZ})yg6lzz4t>pocX15g6|typ
za2BMU2rqYM8?h+_Ndbxh$jVr$H}rDfLj<QCD>)#=OM@3b07oGeuE_QIsZ1ij+d-Yv
z*3&ofQE_o&WrpH1ZRVUtZbXM$l8|jy1j;lkIn{C`_*Af=Rh*rj9UYJOb{iZ3{BfD{
zf~WY4$!k@a$Ml+ng@z`+u!tQpt<nJgG&A^=p`qd0uLMPT`7+Qo1nKiSuJ3g{iNs-2
zGO9(qd>P%oZfy`3kB<gX1yrt%j)ZqExgGX$T^?_tLV0<4VFWA@5fQ~}Xa8F-C4k3#
zAb^0YQdLRmtAI!KPn*o=x!)O%CbTGFYoLff1YXk@h$Z(0+E|6IkA?%$2^#)z2nh*M
zQ6WGNA3i)dJdEi`Pk+3!`RgA*55zbuA~q296PDGbrKKJ1vqry8=X@DG7Tu+it<66{
zAWsmv@;MoD5Tjxea~O5uWDMq3TF%rRyQ69rsWj?fLx5m0uX+{^h_*R^Zd>)be*GeS
zZtzM%;zQ4hMwtP!%y4%rbhjFS-(=?I<`xiWbPq-n&TU%vJDM_E7(%9$ybkz@a(pv{
zg9L1Bg(^0q08zQ8YEY{@iB}89L6X0f@(ItDHz46)208ec(DMORPdQg7Fa>;j-P6mZ
z>(V$EPQvw+mKL<_yAu_`Yv=MAuMvFdLN_)x0s{lx8wH;}%?6<k1al-|&y$IFfQ^%q
zkwNy2j(!-~DH?5b90}_`#1jKc(9zJm^~rc9+XqKXHSorN1}>viwlqJlS26KtR8R#o
zZFqweE4a=Jr1jy-?hxd8?!Kk{+?4k~K`j`~zt}CprinFH7si>iH&6$EsW$(!jtMH#
z1^cRI1KA)KU2R!e8DP<Yuye<yOB1dAC$KFoCnqN<xiOp*39PCYPpi#mDmO3drJ`R&
zXVy`?db&m}f0qT<1|F18QrJmGE8SLoGFCqpq>&uT)nsCWpmaI|gDMG2lH2xb<XQX+
zAi#;Zy;WB~2l4|JNGB1%UNYPI^XE^H@HEQ|#9qC61>Bz2);oX`17c+(knvfXuh=*_
zIiEc<2U2o@YKaM9hbc$jS02Sc_@aQMfe$|feV?c1X&Eofc@0Z5S=%0wamvcdl9ZGL
z{lV~KO0nYyjj}lzW|IbGxuZM|o7~Osa(>xGh!cDu%~64ef-TnFEj5ZthT<=LoCl?>
z5e3OVFH@dz=O-U!#~~&*XG;KU_pdUV*lY4X%V?+FG^Pt;QAB!fR1}U=+n(6$ERm+R
zni?Ts)q{uLy!v##feJ-M2leo3ON;yCh$J35Z)R^c(L&rHmjGG4GDO_rR~~L=p2Dgd
z4}I3(nz=e}m6pnBr7^k$EX|pjXC^UtVw?<!5WlDbOb6B4MH)5W?t`ia=72LnHs`ak
zdR&h&eCkEGB;T))jLZon-bN3-^lpbkP^&fE_{47knC)n92P_@1ijoVsTTWI=4wqKG
z{BX}@T^zEd$?g)gXTPiW$?!mLKnjaTkfOn0A0i?Ioi;}RO0N1Sh5M`QNx+ZCTSqAf
z{+f4*%Up04-(5YnV8`S>Huh=ERDA)CUkmvK8Z<2`-t{pOITA-iW?`H38n~v!!--!2
z_g<_q+<J!BY=~N>O@Ry)PW_5*cmJHZZ+~k+V$8qI@tcodeG<6Bg=%mWjV7nyA8|x(
z(h!twuFm#9)5@t7*O}#ci4X_(-2|4Zz05g1>RjL#$?Y{mRGs;d&c?S(9R0E45SGT-
zbz4%WFPlo)`&2#W5JST9FVH|fyHw_i&cs*~@Q>b`bPISpwY;V}!`hre&CqQe;j&~)
zPBo%(ehiLjH!;5F3l_Dowb%skr?~&4%4kl^Ol>>oWM`Wg8JQBr-;bXrEniIhRh}aJ
zPvNK{{I6;ZHvgV)wtk+Uyvf=n=69#H2O?GpaNQ2D-S1pxpgr&aN!aJ+d<|@Lb7Ga@
z!L`G@MUjN`Y5&jvEWRXI{D>v!8z@P^*c1_wk^MbA!)(tU(N!FZ?0VNQFpA`cgN<ea
zVg)Rc4p?}gqa7R^@H+p=HB*y$rPFw)$3;8RUaX?fgGxva&QjB+F(x^gjEw9WC~rW~
zo(J^(C`aMb#V=NwAn8&8XbtvA6{`)pfG`f=U=e)*dp9zo#3XtT(j&~?d@d$`=T6U~
zkk=4N?LmZiXsS|F<?pm6YDpQ4D$c}g$@1s*#bf@|lDq8lG@<Dnn<}x!cPRK3i5#QM
zF!aqG<x7gPZlhEN21hQQFXzUAn0Gw#MGnQw*eq*1g<#o#PM|@h&ZMMsDbKy|il}5(
z0eeX4=h{~!=-uMmvRTlbuI#&35)PvKQHG}1d6hG68KX4hVDMM1tAt^6l}~rzN=Ikn
z6G6b955^kcazFr!&OET#x|XI3Rp3IQSh0EHzS`AKDQS=+@w{AGy3-Fwq{vVAk^A=r
zJ=qipav3W9yI(cXo1Y#1*?$ty$rKoXHnaG*n42?Q<qY7acaid8Ks)Ve^NJ*TQ~^=h
z;6I0RWa*}S&>JtR9T`DThMq~M#TlQN;?t3QoIja^Q7MI|;<@wQVz?E(0bsKE{XbXA
z?5(iwGsjZ83k_`ibKkYE(gqZ>evFLXqm;xhmH@F(b=KxsJ<j~ydCszKFBA`N`VIxX
zEm*=1B!@C(KkdkZ{uvqEw%0eTP9Xi{lB=yh5b5gwFNQw;H*c7gBl*#V7N``JPgsK4
zhzgTl1>z!=t?n3xZD$Wl%RgU1QsoC@8c~}ZM1${)jm5;pS=7rj0dn~E?WNgtm4&L5
zR5vgpf)|TUgWB42FnOVr^Yeos00H#$X#WBMPo6wM08V<k=g%KNAX$Me3&>4{%lLm)
zMC$%S<;G9ck~m0W@|!4<(V0UPbu-Td3TAV_;01Hp=1JaL0i~Aqo570B!RqLC^Doa+
zGbGdC8b$7n<d7q8@~F8oalOlKlg*r<e`{1i0*^ua2i{0^z*GXqDsWDM>4*Xs2^^$0
z*8I-A;SHtT&y*ipT3(LUBSR5OO1s1GhZY>f%DecU&&o9RYTHpzx%6Jx%i}3Wwi1{r
ztYf11&sd{E!EO4igkL)LmpXHDa%?8bX#@oY0V&aE_`5{6gr5#H$eS`b!-E`FWojds
z(Q9t-DJIG2x|yKBKxGw`{F{9?LJZC%nHfPhz|6c3YyWSQps#-o)qe*AazQgUv^@9X
zpMnlW0S=9Xq~Krdnaj=9IcE|RGjnmDA()cMll||HT^Lq^u?wJ?;FL4M9Wk6*@{))>
zO@k0}tif!K?PhjNYf0w+*}nRJzZ3uMZwxY#y_|96)gCnr(wB%XoK3%<rKuh}x{VLH
zro@2(Btf&p-*kxiF4DsspgO<vrG)897Dgj5)n%D!+_l7=tRL%Uz_tO_J8KNV{1|+9
zU^%-MPt3Nc%>BOYTySHNF}9`697h+r>HIg103wjI6AjEmp@6*wTKP3MU}UHJ59Htr
z4$#tX9J}8^gQCiTF>=!a!K2vQZ`N>S0gJ&Re6kA_6i=EmjeyY9@z<UPRj#^Cu(kig
z5dF6c^KahBwYF*4?*N237`n2oR@L>YI$tv5U_1gDid8n}a9B>rYJ-j#LKhH$UB^+v
z<OI)+9335jPuSU+6Y45&&};PrLk1CKe4zbSja>nak#?QqMhI<6?zNJ1ur6Gs?j0jY
zJs+@zuJ%Uq6l-g1fk_&`9?3Uve)aYB-KBz_&&E7dR8+t%nCvq9v#;-NmIj#1<(KjR
zPN_WN%SF7KNV_Njqc1z}QOWhxIUheZ_%mJx4FJF2UAqp7WsY2GllyT#K&HGkDLERC
zz+BaTPE^~@mzTTEHWd?~0CIbr{5d+hBN*=aj+)E7c~i)XK^35Z14h}-FTXm!eTxGD
z@X3F3wsZ*mM9&R7(nN#vDYW_(i$b3|907tEuvgNtjPwi)40LpFi9vG-)DT_IeX)v(
zuW@l;PN5RaFafh%I$$E;GtNo)`XC{ThLiFfq~2cTfWnoV3))On$n*C46pTBhvcHRv
z&EfODs+rnGf#mT@PL&uk|I4%pJYYmbL|_Slc8HFJr4sS0w|B*@$*?n`I})6hN<obA
z1Og(;71yi8r;^c2+)n<PX2ekC*+GD@I+)Je{+v$7#P&_z0L|~M03wp`*;|9E%iSz@
z6v$VfFf@H+TQLlS6=Qfnj!_r~Q=q3%y#m}>d<u%WV(t3MN)9L<fKkIpf#1zOw_wBz
z*bo8BQZeUq>!qhR4-Sq6deBlH7?%o4DI+pfST9m3&?!NMl4ajjX~cAZ-vEuaDUgU5
zdxC<3o`r*<F4yHx;`ur?paBAoPQWgJbyj&1eV5$(56AelB_)3XMbG1S^ARrYl2;V9
zWd6+p3gk!09ie!=iz+4`e%AGSHz>r)5e}NMhXrCWr@QT(i(Z2qclc$*^y*@(#G(?o
z%a+~cszeg;-2Gei;8O@K3$}z9C|P#ETZS!oHb38bm-Cx}Egrz1QG>D?t>UPtsC)uu
zjY<i(w?N+-%^lt_O{`X9bV}K355esrPjGr?372Dj^wDiq4zMA|Y!WGJGuH6UW**SS
z;fINxze^4U$^sa4gv;Hv5w$u7Nkc`QOY#DUfL*uZ|0iRJ8G!Yngl#$({4?p;XUXYt
z`!F<JEmoNeLpSp$sh6dtrK~I#w7k6B-qG<aJe}^DvU05PilWoe`XI1tzu4E>8Y|Hi
z^8S^9c;V@}W(DXDD+3#VO?`lN1zIUU6M-gI%+=NPpH>R63)!;CbwC0IP5Ro}+MIn4
z6J!gBotx`Ru6->$F*h*Illj}d;2sEB#&oCI?v?A2UT~Xl>~}v74klL=<IPqaJ=$fN
zAGF@g1{xq!KdUL1li}aSs4A)Mc~Y-8zmuYueNFGdIY~B<bj$W%c7T>~cjq?~KYznt
z9#^Gv4Tz~oNJw<54vzzH@_Q+O+yTqWx@agn)i`&&SF96)BjSy9mvPQ{?h?`RTIs-(
zfT+|q;6f33#=yeT491_1Q-E<8yXj(+!G?HrNXjN&1eS&`zoyJ}=ePmB)MNMSk3{TX
zmcxSL?NWXc7*rtx+u(dH6&1u+mD*~F@ZNzG`1X*Wz3+ukT2%^;!&G%rh{t;ALz8ZH
z2f!pyzVp*hJhC5lR4!L2ywCJ?baX&~iHQYL!h9~L=Ez;=5rCEa!GnDuoG*bMHsa$)
zMh1qdGQ;5EjZ8Z8&ImFJ&pqMo*PnlWiH&_4`dApJr1|CobbG|7&Ue@IR_3aYCI7wv
z_FkNRNcYlvAeL*6>MOW<=&O(gdY}B6xa+{Mux`MBBft!)$;sE<3?*P*VFpa8jOI9k
zBwG$zVzDrMV-u6#6L%4mlj?ov_oSz%mvQ5}Lvp!KpQjrtr@<#IW4{-05g|hJT7etc
zJ2Np>VbII_O|2rlZNb2soH2L?;$-XiVtRUdv*$iNiA_LYpc{V*FG>Gb9(I=<Ek8;W
zZ1U+ZaR0mcVDs|+tKA?8q?S!p9iGD<Sdnz$KIS+6*n;c5LGQcAQoGI1EPfY)(K{V;
z^G$7+8RhGE-nE?qF3^0E@;Ng;d$tKOAMnwcE+@4XSmTJS2jR2mT*<HYCt;I&@3o>f
z@(8lA9rUm?|LW;c^!^I?UjP_$WRkMSwuH{+5qJA9rycsQuXlYYxU5P054)?q!iS|w
z>h`@87GJj+huS3=0BU}b?MH9L8@mFHtyPfoo1M2c3=9?lI<2;zG3OvA8IxmRI>HLh
z*eoljE#I6f80KIKP2aRhU2}`;$#REdv?-<jP(*%u2l4R40KybAs3P1q{iGI}P8Vu!
zYT(4H?H^&I95}n}3@VTyC|p1Q21UfuQK-l<qUl2ICDEY8^FKq2hrNdlZ3}y8?{n~H
z%%7*PaTkZvs$@K9j8>x{9^}deL&C%w>za>Zjn}}i(2=)o#Q$Ud`hSJuU;7E}hH=_)
z<$k>(J{S<i)^75w{huLq4LCn?XzaucckUyGZk?l<bTGjBMG^f37r+<s)MXO}Y5U!q
z$tl<5B?Ya!lg0+7;%xo#Z_T+g$0bGoi(LVq=O)wJcS>6q_RsRK$;m}yTYx!C8XpBq
zNC9RhfJQki!xc^;q+>Q+X@=W>2pkV3-bQY2wLc6d>RoJ(pOApiz6-k{qSbRm)Q3Ug
z?)z!M8sPh)n)hA#Bl!=yRE4SZ;6g*pmJ{X1GP@3dyy4<s1sg(nO6kQXaD7lbFW?*U
z6g7G82SnX|;C5Q|Q4v3H7VEw(Mpg6Ke535Z-*4;~S$ur}Xtb=HoZz4!Fj9KQ413(Q
z%GGfSSZ_HKHmBgbfY2o+BwV-UHtA2%21CPMPo5bk<%5}V;A+jz%mkZA00_iDwFd(~
zS3oz&w|SMt7^H=rnwoLv4m1Ig1VF;`gg4Mj`4NVahH_A2LqkJ}`GT5HCN0njn5Ub(
z>dP680lRek1Q1jL;Jg9jjMxIO6ou>jdoaJ<($WHmN5q#P4FgX#pI(daX1$_zq|iqY
zn-bc=i*+gj&h;}usjyTz<u0A*tp5WTF8fhM4B?TY7&P##fPbC~aDD(>_>5XLIM5D2
zKwDNU$PoBG<?8p!H$K<mf=2_Q-Jj0S&xM7BopxlM1i+vjEG@CF^v&q)4-~@c#>SmP
zDnyqVL6fEOu>zsdkUWn<z@4vwE4KnxkM1w@-6sln2@W6%3)0_>9xlfG-e-IIXNFJm
zH^o(oq{x6td2e1A>gw)RNGWn~$=?FgUpOLMEv&zQwh;aKbI2CPf&~e?$w$T>a1O_o
zELEn1>A(PX9;<0B|9a&`L}8Q6C`hQBmgB7U%Uz(K1Hud^$BjzeK5U0o*&o{~Ab{`9
z0+ymL-<vA7yVL(qk0SUd6oBg<Ms=iHbY(Hx(fZ)7I@GQb4*o|gOd)Z<1|$B;i7p_=
zG)yBNc!BB-X!2+p>2Bahxqm-j==hG&sPP9}cT`=X;j2{e`tk({LPJ9XOZe&2Cn*m*
zyM16byqf}y04A1ljUvV#EJD`#W~w0$$!OBfJd#H;!*92-41w}daN(tfCua5uA^dKh
zf0Kw3_C5Gv!Hf^fC_4mUv~jF^yW5xoGoe25Mac2ZYn9vIq=B>MRh!4|pqqCD<`T0n
z)Mh}B2IB+MfbSpuba}eVLPrOK!QTGfk_T|-4hC($2JVhc3Txn;_FLi}z4d;JmvteI
zkBi<EtHzYy;6q#OG<!aGm6$O=f`L*$g9Tv)x9PFcg#$SH14Nd^uRa!yqs#Pu!A2y%
z9|mSUE}Ir`BoPw1LT718wIUTEoqG#G7FBTM-sj@~7%l*90|;OeyNT@+uzgF^D^`J&
z1!BQ02s~CH*UB6$D=;nqECCSi1MnguN=Qlq9rY~m8a;%7B}VXk`SX-bXCv?iN-*@w
zuhWMn3i)`CM%w*-buR%AhY-Mxy*P*fW~jTi;r*AD{@+4?{}0}%%R<a0>9kXhGy|}?
z+&%1>Y2s6uNHnz*X!>J~m3L$BVDr3a2+iLcECwn>6m{MY+Sq@A=6XO@Y5Gx)gtv+V
zS2#<k!Kk5RG|Vp-bf1aYV)7%4W3R)k0TKQfv7E7BmPt%r!{}wKF;nz6u~e{qzNj&!
zBU?oefQLdvbDJRyn4>pR!jvOpgqFaQGlYjLDPhEq+((5L%NZ^1c3OcL3iJ{O3AC*M
z4!cOpf4QTw?)V2F=_=r5T}^VX(`<IkXYBQp{r|tNM154Di}}h0mYka4K@6Zp_|gHA
zsB(eIbBtkt^~K5E0w_P2d_bZKnEVE!`G4h%z`m-nTLKV4OJ3d%prY*5KwIv_Su}8I
zsjGBHAZS78LIAHvrKXl}fNsUPD~jsr=bt7OKwWu}<x^fRa$6D}B(v*Jmyg2?ek*P@
zzOL*)kU^p%B3jI2p^q3$6ID`>eF3Bbg>}M|qgcHndvk?a^5LEG<2i%7eyni*V!|4|
z!7+uDb-X86B6``a$!<5797z}n1qD1B#Trg6$%c{Pp2{U*X;>d)tR99@x!?Y5I7!2(
z#gyaX73jJsCCi0FVgL_8=&9uJpZbxq!a_zy#y_K~y4Qay`mxCQ;}a9_L#NXYk!=g#
z%qC4LlpDV;aYs{=9ryhjxJDfmx~3!_U;7Y_x|@Be9{UJM__$wKPw(4|wQ8eg%388)
z&K>tDJzRN^`g+D13|^1kj6He^wk*(Q<O&B(9l1$?=l^cgQTLSJJ+uX65a1tN1<gih
z=i2geW>yw`I8dnoo&^269dEMoT>A-N^6y%rlQ|r-L`qm>r(!>S0D>q6s?THTu$3!z
z5l1eY^mMxJrG3+VR9zgTQ6DR{*digs@R#y<|M#<A7<)hqL@f!PSpoqD7I>&GM;?QJ
zKTBoa)fJKd0-lSKyBeP%Co9_o465M1d~a(5oFI6J2(YwpnGI$3CUM;!m-sL-F#)@%
z*&@ABqwxADDm30ERPcZe+~P>ElYp4-b90qX0fr2~3(^C4<5|j&<oNGjd1bY^#UBxb
zJ|I*6LqrwenRq+RN)s4_rfrDe%ky>gFL%nYC<M6EY0;p2ki#m6e*QmFLDgP?2#fR1
zW2aCgpf>T0{5{lSLG|uX|IY}%KW~t2zKFr70-GPX%UL$yJ(Q}M`Lo~l50QKSMrHjy
z^^B+j<SXF*VXsD$ml(h>H*eK-gn$5Vd*277&QCAZ{*uj1{n+S@Z{)k*2zZ>m(^>`0
z#1$|Z`R3f}4Q!x=CH&<1=Oob&`$bsTKNj5TL3-FGev>^1ps3mG_XBs$j&KNDTlC7<
zdc)j5thA)J=DILYM#v!OPb-BcFCO@A$s5h}n4}!K&}Vg0$*TGVBEWedf1|a&(n2X$
z(gd|!JAV5sMd2^?|6L4mpvbGD16$5NA4%k`v!3-$FYO%#H1FMQdMBpb&dnyHzEZBj
zgnarMT~_<Pm)=i*9O}Dhy;GlVx@w)5m$h-3sGeY)#Z(*<Yqk<SYGGDws-8%*5`E8n
zv56&+@>y7Vd4JQ4NB?Oj9tG8+FZ(aw{@oJ08iyVB$hNiwfZ0&pUNf;AL3-P>=!8MN
zbse)^CwFxEGD21Jfb`A#lk8rEm)^_Ra(ARX`P&!~Ig3dqiE8V{_c%L~k4{!G|GqKW
zJ0SwfJ_nfEliC37|Mk_?oy!9rw4$Mqtmol~0d+;rO9+UpzhAOU(*=WZ;22z7T;*q@
zU|jlm;$3R(3&yw#KHg(hmI}Cy`<)h+uhNHq0XU2%ugg2dvi-vY;P+^@&kCCHqYfH*
z^f>8W)`2K@4ms749+fOsjnMIkA{6!Iiz7&BARE#=eM-)|+>wR+D~t2qGIxYN;s4du
zTSryZMg78CR6sxhX(W{n2?=RL1?lc?>F$(LL_kRq>4ygCPDMmoq#H%LTl&u9`;PH_
z_nyBH_c-iYYt8wqmAv8aflpgk^WBp5g^JZG4yMg_r;kjp@3+jPD=|S*P<L{Crx{d_
zRd)3(WgdJH;ciku>&DY{QifNJ+s4q<Rcm|D0nM9W&YG;1t+Fz~X??e^b6x|wW2k_7
zQ$>WBm|o%So*&IN@UmqMXzgm{t2t9>o+PoT*bokIl~)onJ0*JU6WE{lvJzrdjI-5y
zQ~FlvYy{z)B}-(y?KInbK4M?fAbeH0Uu-UX!OH;e8b7@T-nCJ_wWzPV!u@-`*&occ
ziLpf<8ZN`_P4zp}?wE(E6<=bCIOfi{_x?Nm=CAq<Ukv(#@^Vs9a-f2?t6Xb2A!>|Y
zXNs=Rt@2}LU~~p^!*7Z$4UI_o_Q*RMSN}0~g2GG{_A<||Jd)VcKw38^*Z$|X4|SJ?
z+*bF&7<CT99Dw#fe5~9Sc5|9ML07xs2tAB;JjoW_eHCA#Dy<^a|6p|(z_e-LBo2D&
zome}?e$15LM84mmn~N&P%+M*hU15Ls?p;3nDJE$Zz+pT+YjxY4`E8=L#<Ru7bp^`1
z6Ded<LFQt&U>kh_a?}qGEN}Ji0nA_0bT|P{l#P+nsYq)cm6!Jsn#+4P^hDyJxodn~
zA#&o-a(;MryvGucg&6o948NrEehz-IO`M*MuT*}WNd$7rFV=qxbnyKKPKZ_AB<Xid
z@_*LL(<<iqY7)7dNGtV(;x&@l!bm)cnYMAN^H@dEaY&5<kWxt|0Q|zhIW+JVtr~Al
zMGE<SB;7B6z-X6J?}h66oUtvj&oiG!puhv5KM*Yll7UxynoONHgS)O+*Fn@N@$7kd
zlbcXMD=!xiOFx>h&%yB4CF}wMCeV~a+tw~ZtIn!Bc~~ARMQT^J{uN)h#Q)#()7zQ}
zMM-&v@m_Y67F9_ZF7z4xVT>N1bWd-FBY^7BTb~JS2A~3tz_`J#6OkKLR^z}DbCiq`
zy^Rv%{4r4<r)aFE{Yox`Ahj)h6*bPnC@EhZDX}+#lo^|LNCuzz>#g9>s!bYYTqaj-
zApiyo#mXuk{F?)0w3aU!uA`4nR$?F*;%K$rHGD9o(Xj1b_D^KtE)dBXzgVAQ{x<{M
z4-UP!RL<@i$;LpoU*h6Lj1QtTJ2t|2p%>5{spiNfaT<RC)RRT@=S*3|pC!c}i{p&*
z;oIbJIw=*hG*h?`Lsq0^%tPMR)|M-m^xN~N8F#TxRhLBt)FdKM-U|0OtgLGky6pro
z2JrvJuae_~-UIqiK#I5AIn4!ZW?yt|h;{X+-kxy93w}IBNCf7d<kjT?YCydq1^`;j
zp8oz8|MU9!PI$i6R)ZFxOLJQr5%JtkKkZ9RN;+1Wek&dtWI3KOAm5iNqPw#rF-S^#
z_cmSlY{S)~G_atd4G(02Y-8(ZHa1cT;vW$a5jHj)1R_EtBqWg5jt(h?Sb?ZUK+cD)
z&9z5%?qP%(W71M+wksv`SUE4c+h??ACUp8Y__`gQMMdS1hW&b|z~V59@6?KCf6llb
zX)7wCZaY&@ZfT}HvcM`Jl<8)d9Q9CthOLcr^E-k#QfS`0-Oc$X`J#m>d-EA69Pc{}
z%8HiZ?1{fom{oN{6+m-iWl!?eK#JTE+xvm{Y9MgcR07Usz*0pqLe5+KyLZ2qmTHXK
z!eSB$AgYc$GRN9-^f^y=wQX{lYyPlx%E?SQG!%(EaD`K%4U+fV*68WCmX0=zC*6>`
zspCO>i6LvZVTs~EA%DBg2W3P&*dDU$enzC<c9@;LD&A|sq!wk-L|e9S_=ie-Sb2Ha
zx~kI*4FP3dJtm}&^%-&YB=algpZWOB!Avbc1>tas6gg@v0#So&2kN(IfQ*2_5!C^<
zmnaOZ&@UDh(;F`0g0AXm0yid1=2I!Fsr*vJG?kRzD&!LAix3zVYut&H9Ieh*VghB;
zIo+Xs@5F|Cq9qL{=k^svw*I{!np$Q^L#q8ZgD-idF1ZP&>~R=t^U=_>Rv8aH8Qy7P
z5EK-or@xK>4<mCwgi6r$mHBw0fp;h6bmghkg0sz*KSCqs{(gYi2Q};q>Q|ZeVY2J(
z>e7MaJ5U&X`SJy#10X}d1zstjIQk(mVRNbuAmd>?N%L{t>iE@{_{wufo7<>?rRFxw
z<=R%h9>LM9wc8K=Yd#=2$oqU+Fw2C6)ed?|rm9%Whg#Nqbvy+@y<LFSNT-iqrtoSb
zLjh@J++-k*$VCZw3tA-taH|c<VoIpF$UPf1iD}gy7-zI#bafV;Q=uc{F!S}fDWXuT
z_^{B6nUG~t+5$4Ag0a0jbCr64mj;K<Feh9D=gIXgE_5kU?)^J8$l^(n@H@8^IYkQ;
z_M%o*n_F>&F7<Zl525anjEJ#xTKsDZpFAmAA7KvDz_F6A%zrp`IrT)^9w!l`Mgdv<
zF*LOK_J6H3x<{g@nL3>L{{Fwoh>&eR-gIq9zGyTM)4hGu(5cEhL@5&d*)gQwxSx}n
z!_1@Ve)wN*7F?Q7n}ng2N(!D2d`Ih+q7jx8I>2a?!(pgqP(dMT+P<XvfsT+zA~_FT
zkp9jjpX0&F987pRyv;vmYn@ENRcyD!o+oBD2D5kJN)e7J75D31VV~2Sv+Wnwg3K^8
zNJnz)Q$rNN=S#WZM9a3AY*x)rMb&#U{ufcaj(<&Gj;+737UF;dHQ+#<V&Wk1yu`^Q
zZuw|)`4qYzzE2R(ckbZ$YVxU16~Od8Yu^l%AD7J#t0StlgB5_j!YKs+Nl`eB!;D_O
zh(NJZaK7Z@eL+!Uk9b8`Z0yzNvHehD#^wey=2tBWUe*(pR_ZopknAVD#gSw`s2ebj
zN}m(BlqgX9fDwsEm@$)>Eh%bltizRR441{HH?)66eWnz#;dMJROxtXR5tttMPPeA5
z(W|dT!*=THNcPLK=iF(fx>&`RM-Oj<jysZkHp?R%#97#N$H-e7p0ZwDjl+czD!+C~
z{3nEjlQn3Y522Fb)>^)TSoD3Yldz;b7%YiZ{nVQxRP(dMl63EfRQ|+E=KF-zcJ7Y@
zQLnRsplh4cB{gXqXUN;1{k7M+%PBe_R}lgDY8Kc>Ki;J^%R5|Qvu9PV7jGK<-=AkF
zd<x&Cw(klQ`e07_ZJ^vH86^hLn<+|0sjY=BT98)CaJR~J9fRF)bJF#0G*7z>j`L3F
z@Dc-vDedZaM{}g(#ws{YrEVvdMvT~q-{Eo9?!Hu0B;mtELhwB3M&v_N@qR}c>Y#p1
z(+V@5;(1X^%s4_BKw@*3gB^@WRe#E|T+~j;5@|xlZ23B?bYHoyZhAbBs`m|_Ebn~i
z#BwlDTVTv@9tdA!0dkCNi+sVy#Tt{ge2M(J!MCx2HLJZ>=H8(Hzcm^f_{-z1M<@+7
ztnZ!~%&XwE$$P#=zlI-dQ00gH*&GIu0fOCB$gDmxK)}@hKwy-p<#Ki9IgG7pLhmd(
zKh6J$8ciATnDe3*-r<X59xZttTTQv*(f%>6`tL_TjN#wkk-AMkAN+Rnc3?V`aD7vy
z%Yn=to%dPj9l-F)z|di5-fw0rG){6Y+-T7jc>KwN>VcPkI2Q~<3AG1r>CDeG7&t~z
zt1^`ye4`>R*296y&cR0)&S4)pwZja0$<w^f8^`1M^DQhbTR@BpY7{}2#W$)PG+(*b
zx$^z5npEZaPdwQdBteHMrLT}4nwTK1!)b3fT&btRi(B0Y-EyRpCii7>e5^&)billO
z!+AT2;YS8gygO3MrzNDMZ1fZ|btFlODx9B%8<d<KW0-h6qJtMmyDImG5Km0^1&M}3
zfa_NpLuxF%lEEW|e$T^`-u+Y7Bj9K^*uzFXyOEz*f2*-|2Rj#|;j>}U9H|b~(4Zzi
zRs0qrJs~@4n0=Hcv6>#c7+lzS22aa>@j1<VA_D;i-Z_@S9_Nek1qr7o2Wy5s-8JEN
z5jx9R5ahXER6&!nx6RdBJ~iz6^ozoFR6a~W7*ZQ-`?o07y3)w`-$Y|><!gk;*)xwE
zJnQ#lIJJ14{gZz^e|R;2i~Ww1p!xUc79Ov&a3GM>R~JmOFuPkb#9ysXSzXLEccoI&
zhHSn}y#HcOVtS}6?H)P9+Rbif?5Owbla=*pRrON7ye{M+)d}jqH#*u;A9Vp7pTg>|
z7V`uioze8F*B#cTvbyA<G|nDcr1A>~+RJ<BJ(rZh2LbM#qZj1V$XNd1k$j!-Uzx=%
z@5fb<|BX#}0GD+GIir@Yr6iKbn;5!~FjQ09U#&Kr3y$BODL3S<NNssn$6r36BzE(v
zW6yJM?mbCc?{6a}DNQ<IeRfSJ<CAXJWQcq`x>PO;!XALLWrJfM03fB7i`h#|>=wUM
zcin80$89H!I_+{e`-ud*x$P(G2gxZvKNa_}GTokP#z~^z#P^YteM{FKl~z|t=#h2{
zxUPVV=vS{^v40N^#+cie9&5@1lrw56ndNW1@qq%r&8~^#Tk7ta(#hGnlv%!wuJw<T
z_mjK>G()t|qX{!VykKGpm<+1|)wVr!rw?%UfXi!UG+iVi`FMF!m>%FTqn|jxI9%-V
zNjo<CK;o`fopz=n5%E!@uia49x@StW%Dez^1S;Z`ielId2nis2OO}-qcyX!q3q}kJ
zCUn8bYY8_C7lFP5i0Bjn7mFtEgDw&Ar<eqnvU^9w(V}jRi7Ho+PRYT53g?MjeLpe&
z%lh8U9&Hpz`z!2=p(L&@`DzOw_C6FFc?a+09}&j4oPW*E)!1YQIAM_Ll|+EzEClL&
ztlXbX4R9)KCqF;VeVKJH^#<0q>A0@@ui&YmTUMIX{k0c`eMYe?t8=;8*#}mH=!pE;
zc@at9^l1^B4H=i@^V>Hw^WMy|$kr_v)WCquHU^8;GX;e(sSc$7akPQqMbho|Zjw>#
zx*u%LPlJCPvV>lHYsPuOxF(rA#OmU(nA*^-V+A~}zSmjhi1noRinEqjuEmsULe(3J
zHPSZa^}pKgn{l$U#1g!cKs`=W*+9!KLiubX1O`400|PoryZ61%yYlmFn~+SHFxlLZ
zPpo9X6DeWJKh&*d*E9cmJyM>E?@3)DE8N2>zZCiJ0jSx3I1yMbtPD=L8Pqyv#OCI(
z*-&wPn<=1#x}+jl%(pdr3nzno4padmOO1Tdnvs)DmYp&5skd^8@7hmjUWhJ0wJ5B?
z5>L?q0-(`Ui43JU+gtWkI>7iH4QE$Mt-S=9LEcn`FKc##TNrhOn>2csDJv`559Mr%
zm0YsT!W-vH43p{U>40L%Y|Y8#-*u{4@411Lx~*7k5Mf`=G#Q}k$!{!80}U!ngxpCz
zun=`D&b@f?0&<ZlbxlX(pdH`Z?fne-aYv6|-U@yB62vv{mb^%+_42<qU!1J1or>Wc
ztX@SLFRW)A7PdZj?m_!5s86qPX7R7)Jf62HT-8E*MA#0Z7O)gU<{A)EaTfz%eYnGG
z{bwg-!YkFV8N!oZN7I^xq*;WdF&N_2Sh?xGcC+kFAYj<L5$ko)J9XcB!N{-|&rn_|
zG!oL=?4aD2%yNR)8cNgiOKLomaGxi?y)K@YpJ{QM4kcsUx~`aeeb^ol;MBkK8lEP1
z^%~y_I?OcrU!7utL?UOG3<JN?aT9eJ#Zcu4^^PCC16fi)<~4%x7W~06UN7T*f@2*N
z9<BBs)(R0vd2MBbL1>4Pe1bEToyl&&$$QQ>=3Wg5Us~9o4zW@yrWv}kZZ(cOFOkqU
zOpE&LXSsOR*Za3lXg>gbPZx#J+HgO)5FX~Q`>CpSr>R={bxv3k=Ihf9Bn<Ll^%*|`
zS~Zy$`cJu&<WQoFGL!ZcYE6uWX}G~97`S)1ymtQp<)+RaV>Ou5Zhv`WCV<2s;2@O5
zfXT}sy;6%1`zhC;MT<p68gEi@?GKN5HK}M(a3i21r+)x|4r(Qcf<sER*huc{IUe&n
z)pULT&Pg80u2V@9f{2Ic@v%O1)NnNVFkG!_IhHY)_CQx*?Pl&lSauDq+PC)OJdEc<
zO+(f<yAg2<3yaCOg34De5YB3^L69Hu!+YMtY)jc7)9nA6QmleZQ++nT+}l<7;Gpo3
z|1}jRh_{2{2$zX)+S{+5^DI#9?GGw$t&Cu=jszSftyqqfxSZ`K9Kt;H2{#{CQDOXD
zqNU$flX;r2b#9D79O(@#dC7_=cH%cP>68kH=<)LmGVf-4OP9;WC}+B-Glc8Vcb?7c
zK5v-*#`%DWH~YUyX5IwN!P9^yf9Feoh%0mU69m$X34*L2Wvg5XRLO@d$SB5mnFWv{
zxdVXqQ|gmW)#_qzvUQa=Fv|3b0DEH&Jny=s(ID$CcZMcVmJp?KdtK?ey+dZtXAjzU
zhS>PCykK;}p&K7?-`Z7e43SkYEikSf)U1E`c6a03U3RHwB-CynTO9hsevx2B^aiER
z)%qokrmg(QnBuRhQSxb*xNLLTZPZKKY6-d#o8eE|;e<2)yTp*Kn;U0-=c`~iUZzH&
z{!Ebv1D}Wp3UlL4k^_?uxv;0BbVUqZycrEWJw3?Y7#J*C1Fv}4bXZq?kjrVfsnW9B
z#j2SW;6L~&<$P_#>v;Oe+1$C9<~=iSM2rSu&+R6Cy~bQ0j%5ODgmWo!C26!x%YYS&
zFPxmNlkT;KjEkV#)g%gp=HWx*Mb-_~618Lft4Qm#kAECmcKAFa!+k?<9>Mx@*-){$
zf~+5f!`88#*AR;e?P3j0M>CN2lJnVq-}6n{p-3>F=K69EGblE69vm60_W|7Q!wCFa
zEC%>y!<1-yU6aozdd6t0k@dAQ(@V||9yV?_1kdG@&)?2CK65~j;S+~s18LmvJ){DA
z3-5{9H+@yXTO!>(<C9@JSzNP+&g(#&H~8hG|EqB{_l8iss8$T!BfLM(Uc<9zVpH$5
zlnLoI1KJb17oc!$)J9dro>~hQ;1%a}y4`rl@b~nx+;-)i_km88^>rjp1-MzVhoCqL
z42>}QleIlRqTLZCcC^bD@g(Acc~DTAM}~)4z^nUsFMTI+XK#)CPGKTl1zi$-0nyV~
zx}>MltgjcX`h)4{(dbrt9Tz181|Aj&BSSYu&>f4?OV_XLmM1!rA%+PH+LF?*VvGrK
zvMfl+Xq(yEkewy1jw=*bphgn+)spPvWC{CzBg^E~8$8}N$B^e*30Jlkw9sD(h;dji
z%5t8%z3nz`ZNtE&0w^u}K_^{E?crUQo9dLIqW@ZY0D>7HMgi1~L=@vK0RbJsKPyBC
zB(o5N;tgt#0DFpgI^u{mj3QZ!>8V(Ql9w^j5fUIZ-h9I-Dtghf)AHHWbnZ`h3o*M+
zsnKU~bIuU-T&<$hn>l{yZTMN^Pd*cprI3&{;mi#_AQ8$Z)E_GN#A5D~t<s5y{DtwW
zakN5}K4%()mUeP*#F49L?qF6Sc1g3iq$7CbDAzt-X*GCAM9uP$Pk8eeNXTX_Z?h->
z4XM7oU2i%PDU%Is`F#gKojrUj(|6OfV(C_%NOMSQ+D`{jHKp4#A+29Ku}jSPluya&
zJCP@>V>dI+68^)9K(-n&aIU7*7a>|spBm$p?u0xJc0_8gIWk{Hq3KfvB&c(SFn&!l
zCkkOA{pPs0m_MYJu3{6v`)f>#o|Zfzo*>hh>^iM5JPsck+FwJTgWuh3%aKpj8|`>W
zf9*nKjX_K-05BS;Re@|@`;ie`L6&hF3DieJ6tZ4?ypui}taI~atgc-0su}oMifdht
z1M{L*;xn0m0{muCF<h+c0WJzFD=T2ZlekRLk)AZM0KiMILNj}03%tCuAEX9c98(XI
z|M+_-;Bk`d(CmaG8P5l_SsH`bO;J$<De3P!WpD1e!Y4D&VR-y!`(3izV>jcQ8`%s>
z&LLm7ccWO$6->U2b(3_Q7QB9wlUp+N*P;#m>6iN*zYiqLRJ-3Q>l7Y@zBsQD;ln{@
zq(w)54Ud8>QhfnOjT2j`a2TdN7t6Bh61~F_T?f!#d^xtTS>Epg<sjr?&{-cuNZkrp
zOtS0}F(@1cKY1D8i{O}JItnCUR4CG^k}-Qv1u?T^r?m=%5l@vlWV~hEGV=)Ni<wrR
zaLt;Ol@=*v>L|Mo9i197O-7@h1KID6@{cqhWn_b2F(3%P%SnaY=twaUjxzr5(|+S9
zYXg3oz>4?gF<&ozBpq(Z<O2e-KHBj!90=O=W~mN|6v6GCu6xw}ctk|%p&lsk3AKlV
zZ8ggB3in!}87o&)a8B6U5?dsDfBRP(1Q{!)i7xmrL*__%xiVd)4<;7&OW(S~!jfhd
zmq7S>F*7FAwD7leH#?0(|HHykz?7MQJvYf^gFk)+P$L3)(qG%~BXyVw*;V>%V*D|r
zW6**>xvpP~e;lW7KR^%c4aiP<`G!Ko+f{w$vI*-PR3jREQ9;!k;<<i*2Zy1waBUu!
zAvyu@450AkgA=%k0BB5Ya8~Jnj=w-}nO+*?mhAxH+~Il73D*=;wIQ?muGhkb)0MQb
zhQgCd)S%8~HQ+<FgG0v9UFBjX9VT8`mDR|Pb%p^q<xSITpF1{G<8$>M#QaC{H0#OD
z7_+$lOq6@rSkSAm($6xz6e&<M4t5YTe<D}%ooj#jadAgWi*Vbnb(ymu^noAD!w0r^
zb{BHVh(8%#F1C}LxUOW@&qs`ce#4{E_UemA+ie~sE$2DlkzPpU;^b7y9Rwk8Rp3?*
zT^T5|*)3bpD-1D^ht8g480L#wLbyu<nq<V@iIK;vuJzg#7VWcrYg<{~ODS+|;=)j!
zOiHo-sv|*=nA@T^Zo1=`DF=3IOj^YrSB{qpv7YA0+`PQVj(_l<KwD*V>OgsIdUogE
z(;WhgqYW`?Dtxbn-IABLFfcF@?<hWc^rEFD;QA4hn%*+ldUx7Fr03r6sm_qZVSNCg
zz@own)_E~A3$fdCHPEYoSvF4Ky$UF~$IjcNUX)OuZ4Ev!ENXHwkOKJ*Hb-OWq|tis
zTL{-k^dZ`W<;$k!Z)U3rnTWSsRsuV7pavm5lEU;FL6@FraJaN6rkd~wJZrQd8~u%b
z^QUag(@eBB!f`vkaAF-cX0KyBQcQ$KdM=Z-@&W*hS7GLUWZx3a{j?W%lO8PbkmnJg
zx%(9UO(SXCeH{i5Xsv_08E1|3GwWyyzudy&B04&{v)=L5QoiI3$Wj|wI{;FuK{p=b
zh{XF5-s(*%w5<S4ZnC0N-?hA7FlFTHBx@}f;$SyLaBQkOS{8w3=b;H5n-}#TXRT(}
z%QctH#`OK32mQ9E{X9yi2=ejaOpeEvtj1bOL_YJ=&t2sz)~f`{@20=p;rqStnN3&m
zn?SuAY<B{IQB(c~H|lCU?rTp$-na8(d3pINonojUnQBzlfwcVXk!-xL_0Z3n0UI!3
z5ALhk*yk`FYlk>6>)nIDoYhjd7@iDZN$p%mikVabZR)%#YA&t?zeGdy<f%WY@L5gk
zpqa$c31#!i$Y%m|pUq=vxIGZa^mlOq?3{5uO%GnhU>aq|)O|Tv+>GVu-VVuw>3mJ&
z#RP^YRbW`E*rnsFs6Shw#Vg#lJTl2PZgPp46#lLCQsuea7xApIBt3QjStpGccrC;`
zghhqS;RSY`Uw7+BDVsy}z#yKY-n-y78W-^M20dOwmz_yQ2{+sRFi`As-{0K>St|ZD
z<DoodSOgmO0)a_sdgp>oMR}E^FIvpm5ePwdnL)e5?%N_+!`biI^Cy0<P3cR?s^ft1
zF=w)7&yz~c8ZQ)mssCnV#5`Hp^;Ok^#CoXdV}me7KS;JeeH?~^PxeiY8F^3F$)GI7
zQ41cXQKPr4@#FykZDQfhLnF5{nN(ybRgGj*;{sQo;LeWy?k^7s<Y+4u=V4QgOH4H9
ze*6q``2c^k;$(mM7nH7O{=yt2*HVpn*i2d2CWu<}nOMlVJRK9O?RasrjGnE~zJt&s
zj{tx4{pIT<97Sa3%MfDdzvT7c!GqBv?X1BSFq13Uj~+m#VOzreVCCq~#}yT8=!vP)
zdSxGUm-QhdBi=ZpWCVKr7&_Qd-~<F?py&b$^F!L~tcPs;#oe=C5f4nLtrH8?vh#-%
zS9yC1RFCTQzmq&2V7+ARc9bi=irjomD#3$e`eTK=ygcyd^T-^p!5mSxHvG)HPx?5W
z(PSjRzqPBsOZ$A3s=R_K;|rXPHqt@OP963O^U9-F$%i<N7YwBoMVDtaPNtOn%kd1-
zKcVG8Sr3Sb55L`Um=ciO`VSgl(8<BRD7>~~+x8t(F2%_ZaRsXu53mssz0{XRj@re8
zrA=Z>KlH+0=5hj7KVQ)PW^qr1ErH+}r(9TgXoN=CkkI8immywD^z4O;B(du{W?h}J
z_j&redv<ZJ<5n+M%yo-LX8Tiv7kjTD<=m&}=ue=@U9M-j@s7ag#=t`s=5EIjPC^R)
zBME7{>rD6WQ#IKNoOzHVtuM~8vyvY!UVdM2mL~MO2=_IV_!2I5>xq<<qStR}v8P0q
zJb<}RmA(B2k%HYRn@MqT`+s+F1C1BOgdHJcrLX2Jki!~XYnd8HYuUyigX8nLn?X_~
zMh^pmcZa{seS?|L76(CPkjkv*IxJP=+|qH`ovP_jm>M?%^F2q64X(fci^*y#QQz90
zTM>QVdwcB!)`&b!sgTyb*%96daxRl=Lw{D{KLv$SmHzM{(~E9KC(x<nI$P5A*&7kZ
zOm<SQI=(ZU!%@d-l>vKyBzTn#KBin7&K@x5Ku2qZeFEy4YfY_VjCu@z*6D)?Fr{P6
zwi@ynTuc%bKn>gi_Ma3C$c~c3y7{7CEVy^}K*Xss=*|wlM5;Qa$G5vj87x)i%S^g8
z24`iD^}4R}E!8_uE*_x~-e4I1{b#<DLae2cUCE3)=_%f1*tM&z?Tu*WUKREupY$j9
zG)CsLUS<;!vul;k0TWfb_Z~b&j$iUsy_=d(gX->UM_-}+a|>?VRluaZwlQ<0sE>Er
zrVN+JBFv56&bFqe<f||nG|_!<H96;Gt`t4bCC0dZJ@ghSkDrIcTa_J6qiMGVw{H-H
zl^Xf{8X|x7Dx36z>oFyZla!Fe_s6B++lonRF>f{c=|JPdn<6X!>(hU?E0P*~!y9~W
zfPW@0TJq;#l}lt`bZdham!#u>SC)rP_#+{aQrPu63WTXcfKPa3w0JFXN37{b#&mPf
z+Gmk2Lvvaop;K^K@si>|cUV7Z>lgC2<5&9_xi43>$rHk{bveEzbeBCrpFMQ0<U)6A
z{J?FjFHUOt|6$Rq=jY4F^EL+sw8};#Hfo8{J?j)}v7G&~<qw!8(nuOXVtNJP_~uQv
zJ}{~iw-EB}oy7dJ4ly!2E@l?9eVxqV@_G|aGVSEZv7#Yc^hd}e8j0iX)pEqBDv;>a
zfYOPVK<kp-rNIY_gTUsR!}U3n`Ty!OS`(WlXDljyVTwvng@j%_qCGwiP77F>Nza<M
z`s>jVi-{Je<|}>I{d6|q>a`bIJzs6_O5Wol3s;cc-J=mpG$lozr|(#$vxaPlp&^dk
ze4I)+m@#N*k&U~-94&11AKlq$JTHQ-!;sF;@rb}<xsEMzLek}=WPMwqecl@bm*THi
z+lgd*==uDKhb`&1#c7Mz5#pr}>S~gvCXv>wjd#ZJ?%q*JulZe*r_!eqnYO$p%iBC8
z^_aIgX5wXmpBzJMyU7h#HYN?KM|i-V`uh3B%S;K7XUKrNq~McgnrPF7c3*qh&1?AA
zLX|ldX2Q<&RJ7i@9m<8gF~_78U)|yU4lIzU%iX|X+p=W#d@S?i)FycJy8~zEiLIm2
z&QlQg03&o@z`F6hCa2p<dCas`7wo(R3=WouKdsst81X(m5r`ToX1q3n-%g4=?6*l^
z^IvOMz)fjNVQsG@7c=7=u+dJ5TCVzPL?g(Kzs@F3nVlI0D6Zp)=Qw&iOV#Jv;>WUv
z2KS%+++|P3;EB%g_gBjfQ|Zq00}=eo=Cdz}OIB;{(99PYIa!2IiEAyty93S7@!!8^
zwE+Qg(f;>d;?~TT$@Lb$u@P(h`=v28<;9oKMqfUbdaLRBH_D3pf+jpcbodqdw6cNm
zSCI}*hbMd?@@rQRt%QQ1p;1MZQ>_Kx;CmPp4fb{ytQ*^XluguLA<pGL88hSjciKrX
zZc)IdM7|60nlc&@kCz_>4zk*8?05M%53;p(DU|rG=I4gJPN0eZ9jm(3JeItFdG8?R
z$s0+@l3rnt!!eILK72(E)z}%ZB)@a|>b#Yo85jEk2%|W){7e>gYmJ0M6`7+_Z>!L!
zLf70+inQZ2m9kZcNX^))PM0B7q*BKYB==_A+SemB*&unth!a)~b>oI<ths%ej|ST2
zs6B^#YJY&?@$X;HCIc~ub8Uz$wY0QMgA@>9;VCYMmt%HMa_^yw46Ko6rIJQ=#NN)*
z%1r2fp>or(F8`w%!`09j7Q#{cnQ~$hRT&OhPNOkLjjSu#F)V6o8IDtrjc9wIA;XHI
zGWk%&<k7~4QreW+8%n&A@#^ZhgqG#^u`}_yoT8$b=e7~}DWP0Ri6;2p<tsXLrY&?v
znrN020v!TQH~3Q&_-)5Z#RCFN__!Dd-pjw~#IztV<uVj@`m!|=`5g(Updpm5OiiOM
z(@BrHJSA+s|JpgBO7<sN@NvebNS@5oc$oq<Wdmt-nI?VT)0Io#T}Lrw%<)2Tvux*{
zR219bcM#-@`v0o4+R&lyY0h|chX+z68T+GPUo2F8&>>-X2kTtqM$k4yse*R`yjyHq
zr8GP|gvhJj9Ftesd3bCVrUn-r)c1Yr^;wbHIK%H!Pxy)mzG|@>@exFXYq2SH&JtGW
zmlh0vn8Pbj3X-7R!75NfO2!)fPVu6Kl$nvaB>iye%_(`Wrz_aqD9bX?dF1eDjK73H
zw<3_rrn+1Mnd88}z%C#Fp-%S~sj}_kqM^3Cq6UtFKbt@(;KJAW%u&B;6*p-W!t*tl
z2(zQ<EL?s@&2rcHi{-2D4FzS&MP6=Sae6b319y-wxc#eEk5Tp12KxMK<x~uVM_#F2
zf8sRh(uBt)v*!d3M=J|OzA5Gn(%=wcDHIb6Br(ORrp}h}|N3J|-M=!i^W)$n<%e`I
zHRerOL{o`?{S?w4)ju1;S>d7qYrCj|UEzqb6!%)VY%o8Ja#{CSov<Ln3LUm~^Qr)>
z-o8ZUdOpHHy!mWBE#LywEWnHFZHBENrFN6m;Hwk?bPyy}3z~|b{SUw6Cs)_%SL8I(
zDaVGxd6xyYKVc)b=EL@$jx5wX$K0mK`d#4PnW(ZlmU|>q##cW5Sgr4B2~vj>$MSZM
zCedN4{Hc?OEPX7!nOW2Mfux%ejWScd9Qvid4bhS3y89`(PgL~uguxFFa%$)3I>;mi
zr4Z=0VOJrU>Rb0?wVDhZ_>#9qf?~A*<f$%C(`xLz)mZ(*G`blF7MYx<y>!y`UV9df
zj-`4G_cXA}bi&(lG+4`YTCKA!_7b8sMF+UUOy9fIc^Px{o)cl2)ZiBq*=nd*M=23;
z;l#r^sw!A8el;R7V~PLSkL!7T;o;j(rv9spSs!sc{d+AhZH&3BJ;!mGtz1SvVdLCF
zPkPh!XS+WIGz^rdz9T(kloOPlb=rTGmY@(3NaaWw0}<xsPWvTVyo{-956U1{BbIq&
z1_|way4gA3OrmG5z@URY(+m>F{s$|0%bMsTf4l`s(<RSxvp1<)?C!zF!{z4Ruq}ro
zljX?xKiLP0m5smZ#o|kzFA06ltuN$P;FThdlw#gxOmzBmk*6AphN)`A`4^wXR10~S
zm^@o9rP#$uvm{X0)8{PyD#biNr@>uiZ4r6v$SymO<?+&fnyC%#w7+W%D|^}=8Z0UP
z7+g;I<Q72(f9O=Ugr}iv^j|MLptT+@F8u}#hnsVfFmS#9vvXPrS$Oe!QSuSIEr*+w
z8KMOhA6fyP*cMh~vnZVmH)7<wqg6axWXO+Zd*JKK)LKJwLC$|*1^-18I4p7P#j7AQ
z?gc#sn?Fm~h=%f?!X1CpE~nN`B(Q5i9P@f@!aIDTIb39|&mb<EO3ZqQ$#UYe#8x2G
za%w^#bhH^KU?3zSYMLmBpC4gIjco0o6%BH=dRf(}(a}#<j)<+)ww#IaCz_2P|GX4E
z(A<^)`NK%jFQnD}YI3BK44VbZ>zvF?Ma>ed5u&8^FLR6xl3~1v^j*HfH}kNkOkhyE
zGZ?}+rT++2x7vn3pHM?5y!PVR?XeAzdwpiQi%EltC^@@)%<8%-UaI5OW>mXJpNBS<
z)j=h4xNIY7qAwbK0Sr1v6zN2R@!0re-95uJA$Y@`aUUefSya^h9{vr%XnuZvva>hz
z1VMHMjc`MHx)JOcv`~$T9<AQ27}pp2_`P$QSruAkOk2LY;RvFE{fv<5e0Nas!}9C(
zxK9FJhkGJ7zyap8DDwZkRJ`}fHfxQ$Ov<Keo$hgR{#Y0;Ee}86>7a&OxV!i6!HD;Q
zKOltrmJT%b(j}uu;FJv_D(;3&R01FKV~W%gVm$>?cZw^zx%?Z-+OR%`b<2p<0vrzU
zDN@lCdUeYnkfG%Of)lvve_mO&Zj{eIL+77;cG&r!3p8Qxrxz-enLe7*<z@GD{*pg5
zD39>;M5AYnIP=t4MmK3O5D>08UlwYg>qg2JspPvJf12HVR>brguy^4yptJ~|CNhpZ
z=V6KO`mh^LbS<N2cYw&06#WPT9Z5HE7|f9ewc@@jVc+Y>NKl#Wx)LfJ!=_=d5sSQ{
z)UP7&QyJ7bw+|{mG=~5;up=JAq2#bjrL}cIZkA&gNCX67#Zi<}zW3_~2tJ3MdFT4Z
zpnZ;%oL^!3$m_bS1S&oF={6bJ${(GQ8KdpF&&|!vsZn2;p<Nwfm3q?6`}{}x2ku%(
znwtSlFY2qjR!G4mMyvTb;|_?@78?DQCI4><(^K%{Yf$r{2$v{*F3a8z>Mj0$ic+qc
z;32jf%ERw2=e^rRKj7^WCeJVk4JpN)X_T;&qQlF@r29$X=votB8QfQJ`hy-CjNQP%
zg8)03V6AEll+Ip)VXwh@9x`9Ml<ISzmNo+%i!;J9$w^5oYimL<2H>uEZ#=y&v$D{g
zD0Z@(1-^Ypilv1Wc7kGlOHYWbg7U`)1idBt4GC#!0jGy{4hptO&$SZ{Ccb{_=z!($
z0ussP<m6y$1AuZ47QcG4J5bk4D=In+7ZClg-gA2Jml`B+PJ*Qi{v=QeExD$3tP$<b
zApDEr?QIfnGS7rf?>fGIb={hwM0|aHq5Dc?td^^s`8Wn==)O86T{ui0`3+*kI24OU
zcMm1DYA-QCT9*B};RshXUkyriGJbnK(2harB}}Ps9QTiyTl^by%OGur8+H4Vd2NXC
z@En2ag>u7nb85VsK$k%Wu0~uO$##W6fDk-j^A^GnbY?^)OEe1Q83>T~y%rp4)nkzI
z*AWa}_w1Y;Fz-%O>^#gu&n9UUZG)Jw2c$R<{h-PcKa)206aJ3L-rintP!O1fAbxQX
z{F+_?CQY$5kP4ZV1#KOIV%!7;C*Y2l!5bSjqXbMS$R&g+qg1VxygxTQ@Lm2v3%RP^
zEA&zUga3Pu>yR1gy7T)fih2#f`=477Q3l}o7!cE+gE7E81dIN{iX#gu!tP+ykddCg
zj7`XU|GC7g+<Wzfpjdz;!sPi{@^>>a&zl6=P|`Q3PEh`yxVW!-z7WjRC{SjexiMCj
zHOj!p#s&d)ZXon~NrMTOa1$GQ5A|Rhjj>Q;9G>qZ$UIH^=OAbQw_)@DZ{Sw}uJH2P
z8!{L)c)P^^Gx(veY3!4t_8Wp2C`jsnXN1-Y+6a`C@cZ}gl9A-{X`+x_aJ3b}4<J{9
z2(q6JCmn<gcpYXMq11v8=smB91FQ;ra_Ck*-+|3(8hq2#L#&A%4AHKLJcpO725vXJ
z{GDdyK*570nhKTVa{(@V&@DU)MhD#B6JX`2v_wE;ucX5g1|cC_<dJ~bUGDXZ!9G5K
z<buuX8%o{*re^p_#C!jlj0{Z8d;3C<znjAKZh>UI8L)Rq1w<2fcRvIlFL;G{z*YO>
zhZ6Gsq0?`eB0;i`y8c?f0KoY`nK)=&1v~ixi3ks@78DAQWdO!SVto872&JGa6BE<+
zda}g#3e?wUs^q8oY}SEsl?;(%Ajo!w#TRGqh%^iSaqS8sV?SuAt%0H_We!_<`}-kK
zC`t?mleU%1`;Q%9Q{Zk*AGT&E?uIL-VAa|fuVCchP{a#CZH)>i4p#<`wXod;9K{gR
z5c%&jU2Yyhg9$GHc;d56Upx+Zd-ZVnoyW>p**ri3(_jfL92NxO09;tApj!dBe(y=X
zsRiRDWGz8Rm{+Qp|CuMus8{A7a0L~m!ITZh*a5RRY>kRX06&WGxhUT5-P$~|U3a+c
zO)!!mGje`K9IqhXaT(OF)&lDT_Z&3!^w=<glW`NK#FXGALglR#Ed1~PqaqmopJn9|
ZLy*i{!a<R|6imv9^izc=pCpWe{tsY4l=J`q

diff --git a/docs/articles/index.html b/docs/articles/index.html
index b596e4394..b6d9efe9e 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/intro_to_keras_for_engineers.html b/docs/articles/intro_to_keras_for_engineers.html
index 5d76f3fb7..826c88470 100644
--- a/docs/articles/intro_to_keras_for_engineers.html
+++ b/docs/articles/intro_to_keras_for_engineers.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -241,23 +241,23 @@ <h2 id="a-first-example-a-mnist-convnet">A first example: A MNIST convnet<a clas
 <span>  callbacks <span class="op">=</span> <span class="va">callbacks</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/10</span></span>
-<span><span class="co">## 399/399 - 7s - 18ms/step - acc: 0.7445 - loss: 0.7534 - val_acc: 0.9630 - val_loss: 0.1260</span></span>
+<span><span class="co">## 399/399 - 6s - 16ms/step - acc: 0.7450 - loss: 0.7521 - val_acc: 0.9642 - val_loss: 0.1234</span></span>
 <span><span class="co">## Epoch 2/10</span></span>
-<span><span class="co">## 399/399 - 2s - 5ms/step - acc: 0.9362 - loss: 0.2103 - val_acc: 0.9774 - val_loss: 0.0762</span></span>
+<span><span class="co">## 399/399 - 2s - 5ms/step - acc: 0.9387 - loss: 0.2054 - val_acc: 0.9769 - val_loss: 0.0751</span></span>
 <span><span class="co">## Epoch 3/10</span></span>
-<span><span class="co">## 399/399 - 2s - 5ms/step - acc: 0.9560 - loss: 0.1492 - val_acc: 0.9830 - val_loss: 0.0607</span></span>
+<span><span class="co">## 399/399 - 2s - 5ms/step - acc: 0.9575 - loss: 0.1455 - val_acc: 0.9824 - val_loss: 0.0609</span></span>
 <span><span class="co">## Epoch 4/10</span></span>
-<span><span class="co">## 399/399 - 2s - 5ms/step - acc: 0.9650 - loss: 0.1187 - val_acc: 0.9859 - val_loss: 0.0494</span></span>
+<span><span class="co">## 399/399 - 2s - 5ms/step - acc: 0.9662 - loss: 0.1148 - val_acc: 0.9863 - val_loss: 0.0483</span></span>
 <span><span class="co">## Epoch 5/10</span></span>
-<span><span class="co">## 399/399 - 2s - 5ms/step - acc: 0.9716 - loss: 0.1003 - val_acc: 0.9866 - val_loss: 0.0485</span></span>
+<span><span class="co">## 399/399 - 2s - 5ms/step - acc: 0.9726 - loss: 0.0972 - val_acc: 0.9869 - val_loss: 0.0479</span></span>
 <span><span class="co">## Epoch 6/10</span></span>
-<span><span class="co">## 399/399 - 2s - 5ms/step - acc: 0.9744 - loss: 0.0878 - val_acc: 0.9888 - val_loss: 0.0381</span></span>
+<span><span class="co">## 399/399 - 2s - 5ms/step - acc: 0.9750 - loss: 0.0857 - val_acc: 0.9892 - val_loss: 0.0379</span></span>
 <span><span class="co">## Epoch 7/10</span></span>
-<span><span class="co">## 399/399 - 2s - 5ms/step - acc: 0.9763 - loss: 0.0799 - val_acc: 0.9896 - val_loss: 0.0378</span></span>
+<span><span class="co">## 399/399 - 2s - 5ms/step - acc: 0.9768 - loss: 0.0768 - val_acc: 0.9906 - val_loss: 0.0358</span></span>
 <span><span class="co">## Epoch 8/10</span></span>
-<span><span class="co">## 399/399 - 2s - 5ms/step - acc: 0.9791 - loss: 0.0686 - val_acc: 0.9862 - val_loss: 0.0454</span></span>
+<span><span class="co">## 399/399 - 2s - 5ms/step - acc: 0.9794 - loss: 0.0671 - val_acc: 0.9876 - val_loss: 0.0422</span></span>
 <span><span class="co">## Epoch 9/10</span></span>
-<span><span class="co">## 399/399 - 2s - 5ms/step - acc: 0.9800 - loss: 0.0665 - val_acc: 0.9889 - val_loss: 0.0412</span></span></code></pre>
+<span><span class="co">## 399/399 - 2s - 5ms/step - acc: 0.9808 - loss: 0.0639 - val_acc: 0.9887 - val_loss: 0.0387</span></span></code></pre>
 <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">score</span> <span class="op">&lt;-</span> <span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/tensorflow/man/evaluate.html" class="external-link">evaluate</a></span><span class="op">(</span><span class="va">x_test</span>, <span class="va">y_test</span>, verbose <span class="op">=</span> <span class="fl">0</span><span class="op">)</span></span></code></pre></div>
 <p>During training, we were saving a model at the end of each epoch. You
@@ -271,7 +271,7 @@ <h2 id="a-first-example-a-mnist-convnet">A first example: A MNIST convnet<a clas
 <code><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict()</a></code>:</p>
 <div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">predictions</span> <span class="op">&lt;-</span> <span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">x_test</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 313/313 - 1s - 2ms/step</span></span></code></pre>
+<pre><code><span><span class="co">## 313/313 - 0s - 1ms/step</span></span></code></pre>
 <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/dim.html" class="external-link">dim</a></span><span class="op">(</span><span class="va">predictions</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## [1] 10000    10</span></span></code></pre>
@@ -386,7 +386,7 @@ <h2 id="writing-cross-framework-custom-components">Writing cross-framework custo
 <span>  epochs <span class="op">=</span> <span class="fl">1</span>, <span class="co"># For speed</span></span>
 <span>  validation_split <span class="op">=</span> <span class="fl">0.15</span></span>
 <span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 399/399 - 8s - 19ms/step - acc: 0.7347 - loss: 0.7746 - val_acc: 0.9247 - val_loss: 0.2487</span></span></code></pre>
+<pre><code><span><span class="co">## 399/399 - 6s - 16ms/step - acc: 0.7356 - loss: 0.7726 - val_acc: 0.9301 - val_loss: 0.2358</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="training-models-on-arbitrary-data-sources">Training models on arbitrary data sources<a class="anchor" aria-label="anchor" href="#training-models-on-arbitrary-data-sources"></a>
@@ -454,7 +454,7 @@ <h2 id="training-models-on-arbitrary-data-sources">Training models on arbitrary
 <span><span class="op">)</span></span>
 <span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="va">train_dataset</span>, epochs <span class="op">=</span> <span class="fl">1</span>, validation_data <span class="op">=</span> <span class="va">test_dataset</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 469/469 - 9s - 20ms/step - acc: 0.7534 - loss: 0.7366 - val_acc: 0.8981 - val_loss: 0.3329</span></span></code></pre>
+<pre><code><span><span class="co">## 469/469 - 7s - 16ms/step - acc: 0.7542 - loss: 0.7342 - val_acc: 0.9026 - val_loss: 0.3192</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="further-reading">Further reading<a class="anchor" aria-label="anchor" href="#further-reading"></a>
diff --git a/docs/articles/intro_to_keras_for_researchers.html b/docs/articles/intro_to_keras_for_researchers.html
index 8f4729679..3645c0c41 100644
--- a/docs/articles/intro_to_keras_for_researchers.html
+++ b/docs/articles/intro_to_keras_for_researchers.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -1014,7 +1014,7 @@ <h2 id="the-functional-api-for-model-building">The Functional API for model-buil
 <span>    <span class="va">self</span><span class="op">$</span><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/optimizer_adam.html">optimizer_adam</a></span><span class="op">(</span>learning_rate<span class="op">=</span><span class="fl">1e-3</span><span class="op">)</span></span>
 <span>  <span class="op">}</span>,</span>
 <span>  train_step <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">data</span><span class="op">)</span> <span class="op">{</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, <span class="va">sample_weight</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="fu"><a href="../reference/unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a></span><span class="op">(</span><span class="va">data</span><span class="op">)</span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">x</span>, y <span class="op">=</span> <span class="cn">NULL</span>, sample_weight <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="va">data</span></span>
 <span>    <span class="fu"><a href="https://rdrr.io/r/base/with.html" class="external-link">with</a></span><span class="op">(</span><span class="va">tf</span><span class="op">$</span><span class="fu">GradientTape</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://rstudio.github.io/reticulate/reference/with-as-operator.html" class="external-link">%as%</a></span> <span class="va">tape</span>, <span class="op">{</span></span>
 <span>      <span class="va">y_pred</span> <span class="op">&lt;-</span> <span class="fu">self</span><span class="op">(</span><span class="va">x</span>, training<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span>
 <span>      <span class="va">loss</span> <span class="op">&lt;-</span> <span class="va">self</span><span class="op">$</span><span class="fu">loss_fn</span><span class="op">(</span>y <span class="op">=</span> <span class="va">y</span>, y_pred <span class="op">=</span> <span class="va">y_pred</span>, sample_weight<span class="op">=</span><span class="va">sample_weight</span><span class="op">)</span></span>
diff --git a/docs/articles/making_new_layers_and_models_via_subclassing.html b/docs/articles/making_new_layers_and_models_via_subclassing.html
index 17d1aaf3f..2aad93c56 100644
--- a/docs/articles/making_new_layers_and_models_via_subclassing.html
+++ b/docs/articles/making_new_layers_and_models_via_subclassing.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -483,14 +483,14 @@ <h2 id="the-add_loss-method">The <code>add_loss()</code> method<a class="anchor"
 <span><span class="co"># losses get added to it</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/compile.html" class="external-link">compile</a></span><span class="op">(</span>optimizer <span class="op">=</span> <span class="st">"adam"</span>, loss <span class="op">=</span> <span class="st">"mse"</span><span class="op">)</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="fu"><a href="../reference/random_normal.html">random_normal</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="../reference/random_normal.html">random_normal</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>, epochs <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 1/1 - 0s - 136ms/step - loss: 1.8971</span></span></code></pre>
+<pre><code><span><span class="co">## 1/1 - 0s - 118ms/step - loss: 1.8971</span></span></code></pre>
 <div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># It's also possible not to pass any loss in `compile`,</span></span>
 <span><span class="co"># since the model already has a loss to minimize, via the `add_loss`</span></span>
 <span><span class="co"># call during the forward pass!</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/compile.html" class="external-link">compile</a></span><span class="op">(</span>optimizer <span class="op">=</span> <span class="st">"adam"</span><span class="op">)</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="fu"><a href="../reference/random_normal.html">random_normal</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="../reference/random_normal.html">random_normal</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>, epochs <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 1/1 - 0s - 84ms/step - loss: -3.3344e-03</span></span></code></pre>
+<pre><code><span><span class="co">## 1/1 - 0s - 73ms/step - loss: -3.3344e-03</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="you-can-optionally-enable-serialization-on-your-layers">You can optionally enable serialization on your layers<a class="anchor" aria-label="anchor" href="#you-can-optionally-enable-serialization-on-your-layers"></a>
@@ -816,9 +816,9 @@ <h2 id="putting-it-all-together-an-end-to-end-example">Putting it all together:
 <span></span>
 <span><span class="va">vae</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="va">x_train</span>, <span class="va">x_train</span>, epochs <span class="op">=</span> <span class="fl">2</span>, batch_size <span class="op">=</span> <span class="fl">64</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/2</span></span>
-<span><span class="co">## 938/938 - 5s - 5ms/step - loss: 0.0748</span></span>
+<span><span class="co">## 938/938 - 4s - 5ms/step - loss: 0.0748</span></span>
 <span><span class="co">## Epoch 2/2</span></span>
-<span><span class="co">## 938/938 - 1s - 959us/step - loss: 0.0676</span></span></code></pre>
+<span><span class="co">## 938/938 - 1s - 815us/step - loss: 0.0676</span></span></code></pre>
 </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
     </nav></aside>
diff --git a/docs/articles/sequential_model.html b/docs/articles/sequential_model.html
index f1c27de20..c910925a9 100644
--- a/docs/articles/sequential_model.html
+++ b/docs/articles/sequential_model.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/serialization_and_saving.html b/docs/articles/serialization_and_saving.html
index 4fd7708c2..43b352c4f 100644
--- a/docs/articles/serialization_and_saving.html
+++ b/docs/articles/serialization_and_saving.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/training_with_built_in_methods.html b/docs/articles/training_with_built_in_methods.html
index 7003a2da5..223423fe1 100644
--- a/docs/articles/training_with_built_in_methods.html
+++ b/docs/articles/training_with_built_in_methods.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -226,9 +226,9 @@ <h2 id="api-overview-a-first-end-to-end-example">API overview: a first end-to-en
 <span>  validation_data <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">x_val</span>, <span class="va">y_val</span><span class="op">)</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/2</span></span>
-<span><span class="co">## 782/782 - 2s - 3ms/step - loss: 0.3410 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.1869 - val_sparse_categorical_accuracy: 0.9455</span></span>
+<span><span class="co">## 782/782 - 3s - 3ms/step - loss: 0.3410 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.1869 - val_sparse_categorical_accuracy: 0.9455</span></span>
 <span><span class="co">## Epoch 2/2</span></span>
-<span><span class="co">## 782/782 - 1s - 973us/step - loss: 0.1588 - sparse_categorical_accuracy: 0.9532 - val_loss: 0.1303 - val_sparse_categorical_accuracy: 0.9626</span></span></code></pre>
+<span><span class="co">## 782/782 - 1s - 1ms/step - loss: 0.1588 - sparse_categorical_accuracy: 0.9532 - val_loss: 0.1303 - val_sparse_categorical_accuracy: 0.9626</span></span></code></pre>
 <p>The returned <code>history</code> object holds a record of the loss
 values and metric values during training:</p>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
@@ -246,17 +246,15 @@ <h2 id="api-overview-a-first-end-to-end-example">API overview: a first end-to-en
 <span><span class="va">results</span> <span class="op">&lt;-</span> <span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/tensorflow/man/evaluate.html" class="external-link">evaluate</a></span><span class="op">(</span><span class="va">x_test</span>, <span class="va">y_test</span>, batch_size<span class="op">=</span><span class="fl">128</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## 79/79 - 0s - 3ms/step - loss: 0.1258 - sparse_categorical_accuracy: 0.9625</span></span></code></pre>
 <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">results</span></span></code></pre></div>
-<pre><code><span><span class="co">## $loss</span></span>
-<span><span class="co">## [1] 0.1257554</span></span>
-<span><span class="co">##</span></span>
-<span><span class="co">## $sparse_categorical_accuracy</span></span>
-<span><span class="co">## [1] 0.9625</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/str.html" class="external-link">str</a></span><span class="op">(</span><span class="va">results</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## List of 2</span></span>
+<span><span class="co">##  $ loss                       : num 0.126</span></span>
+<span><span class="co">##  $ sparse_categorical_accuracy: num 0.962</span></span></code></pre>
 <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Generate predictions (probabilities -- the output of the last layer)</span></span>
 <span><span class="co"># on new data using `predict`</span></span>
 <span><span class="va">predictions</span> <span class="op">&lt;-</span> <span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">x_test</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span>,<span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 1/1 - 0s - 152ms/step</span></span></code></pre>
+<pre><code><span><span class="co">## 1/1 - 0s - 136ms/step</span></span></code></pre>
 <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/dim.html" class="external-link">dim</a></span><span class="op">(</span><span class="va">predictions</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## [1]  2 10</span></span></code></pre>
@@ -366,7 +364,7 @@ <h3 id="custom-losses">Custom losses<a class="anchor" aria-label="anchor" href="
 <pre><code><span><span class="co">## Epoch 1/2</span></span>
 <span><span class="co">## 782/782 - 2s - 2ms/step - loss: 0.0161</span></span>
 <span><span class="co">## Epoch 2/2</span></span>
-<span><span class="co">## 782/782 - 1s - 677us/step - loss: 0.0078</span></span></code></pre>
+<span><span class="co">## 782/782 - 0s - 623us/step - loss: 0.0078</span></span></code></pre>
 <p>If you need a loss function that takes in parameters beside
 <code>y_true</code> and <code>y_pred</code>, you can subclass the Keras
 base <code>Loss</code> class using [<code><a href="../reference/Loss.html">Loss()</a></code>] and implement
@@ -477,9 +475,9 @@ <h3 id="custom-metrics">Custom metrics<a class="anchor" aria-label="anchor" href
 <pre><code><span><span class="co">## Epoch 1/3</span></span>
 <span><span class="co">## 782/782 - 1s - 2ms/step - categorical_true_positives: 360502.0000 - loss: 0.3444</span></span>
 <span><span class="co">## Epoch 2/3</span></span>
-<span><span class="co">## 782/782 - 1s - 862us/step - categorical_true_positives: 362616.0000 - loss: 0.1656</span></span>
+<span><span class="co">## 782/782 - 1s - 964us/step - categorical_true_positives: 362616.0000 - loss: 0.1656</span></span>
 <span><span class="co">## Epoch 3/3</span></span>
-<span><span class="co">## 782/782 - 1s - 660us/step - categorical_true_positives: 363187.0000 - loss: 0.1203</span></span></code></pre>
+<span><span class="co">## 782/782 - 1s - 1ms/step - categorical_true_positives: 363187.0000 - loss: 0.1203</span></span></code></pre>
 </div>
 <div class="section level3">
 <h3 id="handling-losses-and-metrics-that-dont-fit-the-standard-signature">Handling losses and metrics that don’t fit the standard
@@ -563,7 +561,7 @@ <h3 id="handling-losses-and-metrics-that-dont-fit-the-standard-signature">Handli
 <span>  targets <span class="op">=</span> <span class="fu"><a href="../reference/random_normal.html">random_normal</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="op">)</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="va">data</span>, epochs <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 1/1 - 1s - 512ms/step - loss: 1.0566</span></span></code></pre>
+<pre><code><span><span class="co">## 1/1 - 1s - 508ms/step - loss: 1.0566</span></span></code></pre>
 <p>For more information about training multi-input models, see the
 section <strong>Passing data to multi-input, multi-output
 models</strong>.</p>
@@ -639,17 +637,17 @@ <h2 id="training-evaluation-using-tf-dataset-objects">Training &amp; evaluation
 <pre><code><span><span class="co">## Epoch 1/3</span></span>
 <span><span class="co">## 782/782 - 2s - 2ms/step - loss: 0.3365 - sparse_categorical_accuracy: 0.9041</span></span>
 <span><span class="co">## Epoch 2/3</span></span>
-<span><span class="co">## 782/782 - 1s - 821us/step - loss: 0.1605 - sparse_categorical_accuracy: 0.9524</span></span>
+<span><span class="co">## 782/782 - 1s - 681us/step - loss: 0.1605 - sparse_categorical_accuracy: 0.9524</span></span>
 <span><span class="co">## Epoch 3/3</span></span>
-<span><span class="co">## 782/782 - 1s - 811us/step - loss: 0.1185 - sparse_categorical_accuracy: 0.9647</span></span></code></pre>
+<span><span class="co">## 782/782 - 1s - 1ms/step - loss: 0.1185 - sparse_categorical_accuracy: 0.9647</span></span></code></pre>
 <div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># You can also evaluate or predict on a dataset.</span></span>
 <span><span class="va">result</span> <span class="op">&lt;-</span> <span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/tensorflow/man/evaluate.html" class="external-link">evaluate</a></span><span class="op">(</span><span class="va">test_dataset</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 157/157 - 1s - 5ms/step - loss: 0.1152 - sparse_categorical_accuracy: 0.9627</span></span></code></pre>
+<pre><code><span><span class="co">## 157/157 - 1s - 4ms/step - loss: 0.1152 - sparse_categorical_accuracy: 0.9627</span></span></code></pre>
 <div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">result</span></span></code></pre></div>
 <pre><code><span><span class="co">## $loss</span></span>
-<span><span class="co">## [1] 0.1151983</span></span>
+<span><span class="co">## [1] 0.1151979</span></span>
 <span><span class="co">##</span></span>
 <span><span class="co">## $sparse_categorical_accuracy</span></span>
 <span><span class="co">## [1] 0.9627</span></span></code></pre>
@@ -671,11 +669,11 @@ <h2 id="training-evaluation-using-tf-dataset-objects">Training &amp; evaluation
 <span><span class="co"># Only use the 100 batches per epoch (that's 64 * 100 samples)</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="va">train_dataset</span>, epochs <span class="op">=</span> <span class="fl">3</span>, steps_per_epoch <span class="op">=</span> <span class="fl">100</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/3</span></span>
-<span><span class="co">## 100/100 - 1s - 8ms/step - loss: 0.8017 - sparse_categorical_accuracy: 0.7806</span></span>
+<span><span class="co">## 100/100 - 1s - 7ms/step - loss: 0.8017 - sparse_categorical_accuracy: 0.7806</span></span>
 <span><span class="co">## Epoch 2/3</span></span>
-<span><span class="co">## 100/100 - 0s - 905us/step - loss: 0.3661 - sparse_categorical_accuracy: 0.9006</span></span>
+<span><span class="co">## 100/100 - 0s - 682us/step - loss: 0.3661 - sparse_categorical_accuracy: 0.9006</span></span>
 <span><span class="co">## Epoch 3/3</span></span>
-<span><span class="co">## 100/100 - 0s - 703us/step - loss: 0.3009 - sparse_categorical_accuracy: 0.9106</span></span></code></pre>
+<span><span class="co">## 100/100 - 0s - 740us/step - loss: 0.3009 - sparse_categorical_accuracy: 0.9106</span></span></code></pre>
 <p>You can also pass a <code>Dataset</code> instance as the
 <code>validation_data</code> argument in <code><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit()</a></code>:</p>
 <div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">
@@ -882,7 +880,7 @@ <h3 id="class-weights">Class weights<a class="anchor" aria-label="anchor" href="
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="va">x_train</span>, <span class="va">y_train</span>,</span>
 <span>             class_weight <span class="op">=</span> <span class="va">class_weight</span>,</span>
 <span>             batch_size <span class="op">=</span> <span class="fl">64</span>, epochs <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 782/782 - 2s - 2ms/step - loss: 0.3713 - sparse_categorical_accuracy: 0.9018</span></span></code></pre>
+<pre><code><span><span class="co">## 782/782 - 1s - 2ms/step - loss: 0.3713 - sparse_categorical_accuracy: 0.9018</span></span></code></pre>
 </div>
 <div class="section level3">
 <h3 id="sample-weights">Sample weights<a class="anchor" aria-label="anchor" href="#sample-weights"></a>
@@ -914,7 +912,7 @@ <h3 id="sample-weights">Sample weights<a class="anchor" aria-label="anchor" href
 <span>  sample_weight <span class="op">=</span> <span class="va">sample_weight</span>,</span>
 <span>  batch_size <span class="op">=</span> <span class="fl">64</span>, epochs <span class="op">=</span> <span class="fl">1</span></span>
 <span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 782/782 - 1s - 2ms/step - loss: 0.3740 - sparse_categorical_accuracy: 0.9015</span></span></code></pre>
+<pre><code><span><span class="co">## 782/782 - 2s - 2ms/step - loss: 0.3740 - sparse_categorical_accuracy: 0.9015</span></span></code></pre>
 <p>Here’s a matching <code>Dataset</code> example:</p>
 <div class="sourceCode" id="cb50"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">sample_weight</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fl">1.0</span>, <span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">y_train</span><span class="op">)</span><span class="op">)</span></span>
@@ -1089,7 +1087,7 @@ <h2 id="passing-data-to-multi-input-multi-output-models">Passing data to multi-i
 <span>    batch_size<span class="op">=</span><span class="fl">32</span>,</span>
 <span>    epochs<span class="op">=</span><span class="fl">1</span></span>
 <span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 4/4 - 2s - 497ms/step - loss: 1.3788</span></span></code></pre>
+<pre><code><span><span class="co">## 4/4 - 2s - 458ms/step - loss: 1.3788</span></span></code></pre>
 <div class="sourceCode" id="cb61"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Alternatively, fit on named lists (names matching)</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span></span>
@@ -1098,7 +1096,7 @@ <h2 id="passing-data-to-multi-input-multi-output-models">Passing data to multi-i
 <span>  batch_size <span class="op">=</span> <span class="fl">32</span>,</span>
 <span>  epochs <span class="op">=</span> <span class="fl">1</span></span>
 <span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 4/4 - 1s - 250ms/step - loss: 0.2857</span></span></code></pre>
+<pre><code><span><span class="co">## 4/4 - 1s - 230ms/step - loss: 0.2857</span></span></code></pre>
 <p>Here’s the <code>Dataset</code> use case: similarly as what we did
 for R arrays, the <code>Dataset</code> should return a tuple of named
 lists (dicts).</p>
@@ -1112,7 +1110,7 @@ <h2 id="passing-data-to-multi-input-multi-output-models">Passing data to multi-i
 <span>  <span class="fu"><a href="https://rdrr.io/pkg/tfdatasets/man/dataset_batch.html" class="external-link">dataset_batch</a></span><span class="op">(</span><span class="fl">64</span><span class="op">)</span></span>
 <span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="va">train_dataset</span>, epochs <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 2/2 - 1s - 665ms/step - loss: 0.5600</span></span></code></pre>
+<pre><code><span><span class="co">## 2/2 - 1s - 629ms/step - loss: 0.5600</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="using-callbacks">Using callbacks<a class="anchor" aria-label="anchor" href="#using-callbacks"></a>
@@ -1159,21 +1157,21 @@ <h2 id="using-callbacks">Using callbacks<a class="anchor" aria-label="anchor" hr
 <span>  validation_split <span class="op">=</span> <span class="fl">0.2</span>,</span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Epoch 1/20</span></span>
-<span><span class="co">## 625/625 - 2s - 3ms/step - loss: 0.3695 - sparse_categorical_accuracy: 0.8961 - val_loss: 0.1873 - val_sparse_categorical_accuracy: 0.9469</span></span>
+<span><span class="co">## 625/625 - 1s - 2ms/step - loss: 0.3695 - sparse_categorical_accuracy: 0.8961 - val_loss: 0.1873 - val_sparse_categorical_accuracy: 0.9469</span></span>
 <span><span class="co">## Epoch 2/20</span></span>
-<span><span class="co">## 625/625 - 1s - 1ms/step - loss: 0.1751 - sparse_categorical_accuracy: 0.9489 - val_loss: 0.1403 - val_sparse_categorical_accuracy: 0.9579</span></span>
+<span><span class="co">## 625/625 - 1s - 974us/step - loss: 0.1751 - sparse_categorical_accuracy: 0.9489 - val_loss: 0.1403 - val_sparse_categorical_accuracy: 0.9579</span></span>
 <span><span class="co">## Epoch 3/20</span></span>
-<span><span class="co">## 625/625 - 1s - 2ms/step - loss: 0.1277 - sparse_categorical_accuracy: 0.9625 - val_loss: 0.1218 - val_sparse_categorical_accuracy: 0.9651</span></span>
+<span><span class="co">## 625/625 - 1s - 903us/step - loss: 0.1277 - sparse_categorical_accuracy: 0.9625 - val_loss: 0.1218 - val_sparse_categorical_accuracy: 0.9651</span></span>
 <span><span class="co">## Epoch 4/20</span></span>
-<span><span class="co">## 625/625 - 1s - 2ms/step - loss: 0.1007 - sparse_categorical_accuracy: 0.9700 - val_loss: 0.1153 - val_sparse_categorical_accuracy: 0.9661</span></span>
+<span><span class="co">## 625/625 - 1s - 889us/step - loss: 0.1007 - sparse_categorical_accuracy: 0.9700 - val_loss: 0.1153 - val_sparse_categorical_accuracy: 0.9661</span></span>
 <span><span class="co">## Epoch 5/20</span></span>
 <span><span class="co">## 625/625 - 1s - 1ms/step - loss: 0.0822 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1104 - val_sparse_categorical_accuracy: 0.9670</span></span>
 <span><span class="co">## Epoch 6/20</span></span>
-<span><span class="co">## 625/625 - 1s - 975us/step - loss: 0.0683 - sparse_categorical_accuracy: 0.9801 - val_loss: 0.1098 - val_sparse_categorical_accuracy: 0.9689</span></span>
+<span><span class="co">## 625/625 - 1s - 899us/step - loss: 0.0683 - sparse_categorical_accuracy: 0.9801 - val_loss: 0.1098 - val_sparse_categorical_accuracy: 0.9689</span></span>
 <span><span class="co">## Epoch 7/20</span></span>
 <span><span class="co">## 625/625 - 1s - 1ms/step - loss: 0.0571 - sparse_categorical_accuracy: 0.9838 - val_loss: 0.1116 - val_sparse_categorical_accuracy: 0.9698</span></span>
 <span><span class="co">## Epoch 8/20</span></span>
-<span><span class="co">## 625/625 - 1s - 1ms/step - loss: 0.0485 - sparse_categorical_accuracy: 0.9864 - val_loss: 0.1126 - val_sparse_categorical_accuracy: 0.9702</span></span>
+<span><span class="co">## 625/625 - 1s - 976us/step - loss: 0.0485 - sparse_categorical_accuracy: 0.9864 - val_loss: 0.1126 - val_sparse_categorical_accuracy: 0.9702</span></span>
 <span><span class="co">## Epoch 8: early stopping</span></span></code></pre>
 <div class="section level3">
 <h3 id="many-built-in-callbacks-are-available">Many built-in callbacks are available<a class="anchor" aria-label="anchor" href="#many-built-in-callbacks-are-available"></a>
@@ -1262,11 +1260,11 @@ <h2 id="checkpointing-models">Checkpointing models<a class="anchor" aria-label="
 <pre><code><span><span class="co">## Epoch 1/2</span></span>
 <span><span class="co">##</span></span>
 <span><span class="co">## Epoch 1: val_loss improved from inf to 0.19344, saving model to mymodel_1.keras</span></span>
-<span><span class="co">## 625/625 - 2s - 3ms/step - loss: 0.3787 - sparse_categorical_accuracy: 0.8940 - val_loss: 0.1934 - val_sparse_categorical_accuracy: 0.9441</span></span>
+<span><span class="co">## 625/625 - 1s - 2ms/step - loss: 0.3787 - sparse_categorical_accuracy: 0.8940 - val_loss: 0.1934 - val_sparse_categorical_accuracy: 0.9441</span></span>
 <span><span class="co">## Epoch 2/2</span></span>
 <span><span class="co">##</span></span>
 <span><span class="co">## Epoch 2: val_loss improved from 0.19344 to 0.14251, saving model to mymodel_2.keras</span></span>
-<span><span class="co">## 625/625 - 1s - 1ms/step - loss: 0.1768 - sparse_categorical_accuracy: 0.9478 - val_loss: 0.1425 - val_sparse_categorical_accuracy: 0.9600</span></span></code></pre>
+<span><span class="co">## 625/625 - 1s - 842us/step - loss: 0.1768 - sparse_categorical_accuracy: 0.9478 - val_loss: 0.1425 - val_sparse_categorical_accuracy: 0.9600</span></span></code></pre>
 <p>The <code>ModelCheckpoint</code> callback can be used to implement
 fault-tolerance: the ability to restart training from the last saved
 state of the model in case training gets randomly interrupted. Here’s a
diff --git a/docs/articles/transfer_learning.html b/docs/articles/transfer_learning.html
index 6e0e3e657..ddbe9581a 100644
--- a/docs/articles/transfer_learning.html
+++ b/docs/articles/transfer_learning.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -244,7 +244,7 @@ <h2 id="freezing-layers-understanding-the-trainable-attribute">Freezing layers:
 <span><span class="co"># Train the model</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/compile.html" class="external-link">compile</a></span><span class="op">(</span>optimizer <span class="op">=</span> <span class="st">"adam"</span>, loss <span class="op">=</span> <span class="st">"mse"</span><span class="op">)</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="fu"><a href="../reference/random_normal.html">random_normal</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>, <span class="fu"><a href="../reference/random_normal.html">random_normal</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>, epochs <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 1/1 - 1s - 567ms/step - loss: 2.1868</span></span></code></pre>
+<pre><code><span><span class="co">## 1/1 - 1s - 544ms/step - loss: 2.1868</span></span></code></pre>
 <div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Check that the weights of layer1 have not changed during training</span></span>
 <span><span class="va">final_layer1_weights_values</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/get_weights.html">get_weights</a></span><span class="op">(</span><span class="va">layer1</span><span class="op">)</span></span>
@@ -654,7 +654,7 @@ <h2 id="train-the-top-layer">Train the top layer<a class="anchor" aria-label="an
 <span></span>
 <span><span class="va">epochs</span> <span class="op">&lt;-</span> <span class="fl">1</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="va">train_ds</span>, epochs <span class="op">=</span> <span class="va">epochs</span>, validation_data <span class="op">=</span> <span class="va">validation_ds</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 146/146 - 45s - 307ms/step - binary_accuracy: 0.9183 - loss: 0.1887 - val_binary_accuracy: 0.9669 - val_loss: 0.0926</span></span></code></pre>
+<pre><code><span><span class="co">## 146/146 - 45s - 306ms/step - binary_accuracy: 0.9183 - loss: 0.1887 - val_binary_accuracy: 0.9669 - val_loss: 0.0926</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="do-a-round-of-fine-tuning-of-the-entire-model">Do a round of fine-tuning of the entire model<a class="anchor" aria-label="anchor" href="#do-a-round-of-fine-tuning-of-the-entire-model"></a>
@@ -705,17 +705,17 @@ <h2 id="do-a-round-of-fine-tuning-of-the-entire-model">Do a round of fine-tuning
 <span></span>
 <span><span class="va">epochs</span> <span class="op">&lt;-</span> <span class="fl">1</span></span>
 <span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="va">train_ds</span>, epochs <span class="op">=</span> <span class="va">epochs</span>, validation_data <span class="op">=</span> <span class="va">validation_ds</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 146/146 - 77s - 524ms/step - binary_accuracy: 0.8660 - loss: 0.3213 - val_binary_accuracy: 0.9652 - val_loss: 0.1022</span></span></code></pre>
+<pre><code><span><span class="co">## 146/146 - 82s - 564ms/step - binary_accuracy: 0.8660 - loss: 0.3213 - val_binary_accuracy: 0.9652 - val_loss: 0.1022</span></span></code></pre>
 <p>After 10 epochs, fine-tuning gains us a nice improvement here. Let’s
 evaluate the model on the test dataset:</p>
 <div class="sourceCode" id="cb48"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">model</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/tensorflow/man/evaluate.html" class="external-link">evaluate</a></span><span class="op">(</span><span class="va">test_ds</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## 37/37 - 2s - 41ms/step - binary_accuracy: 0.9540 - loss: 0.1103</span></span></code></pre>
+<pre><code><span><span class="co">## 37/37 - 2s - 42ms/step - binary_accuracy: 0.9540 - loss: 0.1103</span></span></code></pre>
 <pre><code><span><span class="co">## $binary_accuracy</span></span>
 <span><span class="co">## [1] 0.9539983</span></span>
 <span><span class="co">##</span></span>
 <span><span class="co">## $loss</span></span>
-<span><span class="co">## [1] 0.1102595</span></span></code></pre>
+<span><span class="co">## [1] 0.1102556</span></span></code></pre>
 </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
     </nav></aside>
diff --git a/docs/articles/understanding_masking_and_padding.html b/docs/articles/understanding_masking_and_padding.html
index 3ec7bacfb..2c5926fb7 100644
--- a/docs/articles/understanding_masking_and_padding.html
+++ b/docs/articles/understanding_masking_and_padding.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/writing_a_custom_training_loop_in_tensorflow.html b/docs/articles/writing_a_custom_training_loop_in_tensorflow.html
index 52cb1888a..49feb976b 100644
--- a/docs/articles/writing_a_custom_training_loop_in_tensorflow.html
+++ b/docs/articles/writing_a_custom_training_loop_in_tensorflow.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -356,7 +356,7 @@ <h2 id="low-level-handling-of-metrics">Low-level handling of metrics<a class="an
 <span><span class="co">## Validation acc: 0.9031</span></span></code></pre>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/Sys.time.html" class="external-link">Sys.time</a></span><span class="op">(</span><span class="op">)</span> <span class="op">-</span> <span class="va">time</span></span></code></pre></div>
-<pre><code><span><span class="co">## Time difference of 40.01661 secs</span></span></code></pre>
+<pre><code><span><span class="co">## Time difference of 44.04362 secs</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="speeding-up-your-training-step-with-tf_function">Speeding-up your training step with <code>tf_function()</code><a class="anchor" aria-label="anchor" href="#speeding-up-your-training-step-with-tf_function"></a>
@@ -436,7 +436,7 @@ <h2 id="speeding-up-your-training-step-with-tf_function">Speeding-up your traini
 <span><span class="co">## Validation acc: 0.9031</span></span></code></pre>
 <div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/Sys.time.html" class="external-link">Sys.time</a></span><span class="op">(</span><span class="op">)</span> <span class="op">-</span> <span class="va">time</span></span></code></pre></div>
-<pre><code><span><span class="co">## Time difference of 0.3914127 secs</span></span></code></pre>
+<pre><code><span><span class="co">## Time difference of 0.4195933 secs</span></span></code></pre>
 <p>Much faster, isn’t it?</p>
 </div>
 <div class="section level2">
diff --git a/docs/articles/writing_your_own_callbacks.html b/docs/articles/writing_your_own_callbacks.html
index 5e4ace42f..f368f24d4 100644
--- a/docs/articles/writing_your_own_callbacks.html
+++ b/docs/articles/writing_your_own_callbacks.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/authors.html b/docs/authors.html
index f87d6d7ae..07fc62899 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -119,13 +119,13 @@ <h2 id="citation">Citation</h2>
 
       <p>Kalinowski T, Allaire J, Chollet F (2024).
 <em>keras3: R Interface to 'Keras'</em>.
-R package version 0.2.0.9000, https://github.com/rstudio/keras, <a href="https://keras.posit.co/">https://keras.posit.co/</a>. 
+R package version 1.0.0, https://github.com/rstudio/keras, <a href="https://keras.posit.co/">https://keras.posit.co/</a>. 
 </p>
       <pre>@Manual{,
   title = {keras3: R Interface to 'Keras'},
   author = {Tomasz Kalinowski and JJ Allaire and François Chollet},
   year = {2024},
-  note = {R package version 0.2.0.9000, https://github.com/rstudio/keras},
+  note = {R package version 1.0.0, https://github.com/rstudio/keras},
   url = {https://keras.posit.co/},
 }</pre>
     </div>
diff --git a/docs/index.html b/docs/index.html
index fd871cc7d..c4fcdf90c 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -33,7 +33,7 @@
     
     <a class="navbar-brand me-2" href="index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/news/index.html b/docs/news/index.html
index 3364c6809..02cb4ce31 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -70,7 +70,7 @@ <h6 class="dropdown-header" data-toc-skip>Other topics</h6>
     </div>
 
     <div class="section level2">
-<h2 class="pkg-version" data-toc-text="development version" id="keras3-development-version">keras3 (development version)<a class="anchor" aria-label="anchor" href="#keras3-development-version"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.0.0" id="keras3-100">keras3 1.0.0<a class="anchor" aria-label="anchor" href="#keras3-100"></a></h2>
 <ul><li><p>Chains of <code>layer_*</code> calls with <code>|&gt;</code> now instantiate layers in the same order as <code>%&gt;%</code> pipe chains: left-hand-side first (<a href="https://github.com/rstudio/keras/issues/1440" class="external-link">#1440</a>).</p></li>
 <li><p><code><a href="https://rstudio.github.io/reticulate/reference/iterate.html" class="external-link">iterate()</a></code>, <code><a href="https://rstudio.github.io/reticulate/reference/iterate.html" class="external-link">iter_next()</a></code> and <code><a href="https://rstudio.github.io/reticulate/reference/iterate.html" class="external-link">as_iterator()</a></code> are now reexported from reticulate.</p></li>
 </ul><p>User facing changes with upstream Keras v3.3.2:</p>
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 7e4b2ffcb..573d81e83 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -28,7 +28,7 @@ articles:
   understanding_masking_and_padding: understanding_masking_and_padding.html
   writing_a_custom_training_loop_in_tensorflow: writing_a_custom_training_loop_in_tensorflow.html
   writing_your_own_callbacks: writing_your_own_callbacks.html
-last_built: 2024-05-17T20:33Z
+last_built: 2024-05-21T15:16Z
 urls:
   reference: https://keras.posit.co/reference
   article: https://keras.posit.co/articles
diff --git a/docs/reference/Callback.html b/docs/reference/Callback.html
index 9350e5f5d..2b8628728 100644
--- a/docs/reference/Callback.html
+++ b/docs/reference/Callback.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/Constraint.html b/docs/reference/Constraint.html
index 84ccc4dc6..3eff3ff8a 100644
--- a/docs/reference/Constraint.html
+++ b/docs/reference/Constraint.html
@@ -60,7 +60,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/Layer.html b/docs/reference/Layer.html
index 03046ac6f..f8bfc0f1c 100644
--- a/docs/reference/Layer.html
+++ b/docs/reference/Layer.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/LearningRateSchedule.html b/docs/reference/LearningRateSchedule.html
index 5de61ba9f..36d4e29c0 100644
--- a/docs/reference/LearningRateSchedule.html
+++ b/docs/reference/LearningRateSchedule.html
@@ -52,7 +52,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/Loss.html b/docs/reference/Loss.html
index e5d769f71..477fbe75f 100644
--- a/docs/reference/Loss.html
+++ b/docs/reference/Loss.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/Metric.html b/docs/reference/Metric.html
index ffa2f4467..2702a3e7a 100644
--- a/docs/reference/Metric.html
+++ b/docs/reference/Metric.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/Model.html b/docs/reference/Model.html
index 9fa5529ca..1535158aa 100644
--- a/docs/reference/Model.html
+++ b/docs/reference/Model.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_elu.html b/docs/reference/activation_elu.html
index 584f3261f..2312107f3 100644
--- a/docs/reference/activation_elu.html
+++ b/docs/reference/activation_elu.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_exponential.html b/docs/reference/activation_exponential.html
index a4b2449e2..f67736fa8 100644
--- a/docs/reference/activation_exponential.html
+++ b/docs/reference/activation_exponential.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_gelu.html b/docs/reference/activation_gelu.html
index b9f589aa4..83df47ec1 100644
--- a/docs/reference/activation_gelu.html
+++ b/docs/reference/activation_gelu.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_hard_sigmoid.html b/docs/reference/activation_hard_sigmoid.html
index 01d1623ea..be9f67149 100644
--- a/docs/reference/activation_hard_sigmoid.html
+++ b/docs/reference/activation_hard_sigmoid.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_hard_silu.html b/docs/reference/activation_hard_silu.html
index 9fd7efaaf..75cb4f701 100644
--- a/docs/reference/activation_hard_silu.html
+++ b/docs/reference/activation_hard_silu.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_leaky_relu.html b/docs/reference/activation_leaky_relu.html
index 9d853f5d0..e0e077620 100644
--- a/docs/reference/activation_leaky_relu.html
+++ b/docs/reference/activation_leaky_relu.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_linear.html b/docs/reference/activation_linear.html
index 44840930e..cd016d6e7 100644
--- a/docs/reference/activation_linear.html
+++ b/docs/reference/activation_linear.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_log_softmax.html b/docs/reference/activation_log_softmax.html
index 33bfc2f46..660fc94dd 100644
--- a/docs/reference/activation_log_softmax.html
+++ b/docs/reference/activation_log_softmax.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_mish.html b/docs/reference/activation_mish.html
index 4c92da48e..bacaf61e4 100644
--- a/docs/reference/activation_mish.html
+++ b/docs/reference/activation_mish.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_relu.html b/docs/reference/activation_relu.html
index 558feb488..3f10ae4dc 100644
--- a/docs/reference/activation_relu.html
+++ b/docs/reference/activation_relu.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_relu6.html b/docs/reference/activation_relu6.html
index ca58375ad..f46207f33 100644
--- a/docs/reference/activation_relu6.html
+++ b/docs/reference/activation_relu6.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_selu.html b/docs/reference/activation_selu.html
index 9dea93667..06b8c25c9 100644
--- a/docs/reference/activation_selu.html
+++ b/docs/reference/activation_selu.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_sigmoid.html b/docs/reference/activation_sigmoid.html
index 83cfc6001..c7bd99e0d 100644
--- a/docs/reference/activation_sigmoid.html
+++ b/docs/reference/activation_sigmoid.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_silu.html b/docs/reference/activation_silu.html
index 00fa9231c..92ee5f49f 100644
--- a/docs/reference/activation_silu.html
+++ b/docs/reference/activation_silu.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_softmax.html b/docs/reference/activation_softmax.html
index 63de8d9b7..6e6e0e628 100644
--- a/docs/reference/activation_softmax.html
+++ b/docs/reference/activation_softmax.html
@@ -28,7 +28,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_softplus.html b/docs/reference/activation_softplus.html
index 958b55f1f..31287d4d1 100644
--- a/docs/reference/activation_softplus.html
+++ b/docs/reference/activation_softplus.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_softsign.html b/docs/reference/activation_softsign.html
index 035a8ed0e..707f1169f 100644
--- a/docs/reference/activation_softsign.html
+++ b/docs/reference/activation_softsign.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/activation_tanh.html b/docs/reference/activation_tanh.html
index 748e1938b..641af60db 100644
--- a/docs/reference/activation_tanh.html
+++ b/docs/reference/activation_tanh.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/active_property.html b/docs/reference/active_property.html
index ead2a0b18..1640e9823 100644
--- a/docs/reference/active_property.html
+++ b/docs/reference/active_property.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/adapt.html b/docs/reference/adapt.html
index 2a7d05201..62f37c750 100644
--- a/docs/reference/adapt.html
+++ b/docs/reference/adapt.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_convnext_base.html b/docs/reference/application_convnext_base.html
index 0bf048629..aa47f1bda 100644
--- a/docs/reference/application_convnext_base.html
+++ b/docs/reference/application_convnext_base.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_convnext_large.html b/docs/reference/application_convnext_large.html
index 567484ad3..e1f5e315d 100644
--- a/docs/reference/application_convnext_large.html
+++ b/docs/reference/application_convnext_large.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_convnext_small.html b/docs/reference/application_convnext_small.html
index 9e391cc43..381cc38b8 100644
--- a/docs/reference/application_convnext_small.html
+++ b/docs/reference/application_convnext_small.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_convnext_tiny.html b/docs/reference/application_convnext_tiny.html
index 5caae6856..e9e4daf19 100644
--- a/docs/reference/application_convnext_tiny.html
+++ b/docs/reference/application_convnext_tiny.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_convnext_xlarge.html b/docs/reference/application_convnext_xlarge.html
index fe22c1773..b768c92f4 100644
--- a/docs/reference/application_convnext_xlarge.html
+++ b/docs/reference/application_convnext_xlarge.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_densenet121.html b/docs/reference/application_densenet121.html
index 8513f24fb..89c0fea1f 100644
--- a/docs/reference/application_densenet121.html
+++ b/docs/reference/application_densenet121.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_densenet169.html b/docs/reference/application_densenet169.html
index 763ea08af..a4e4ce604 100644
--- a/docs/reference/application_densenet169.html
+++ b/docs/reference/application_densenet169.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_densenet201.html b/docs/reference/application_densenet201.html
index 89ebe9324..9d279d357 100644
--- a/docs/reference/application_densenet201.html
+++ b/docs/reference/application_densenet201.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_efficientnet_b0.html b/docs/reference/application_efficientnet_b0.html
index 5620f7921..c18bccb4b 100644
--- a/docs/reference/application_efficientnet_b0.html
+++ b/docs/reference/application_efficientnet_b0.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_efficientnet_b1.html b/docs/reference/application_efficientnet_b1.html
index 678afeae6..c4e5ecffd 100644
--- a/docs/reference/application_efficientnet_b1.html
+++ b/docs/reference/application_efficientnet_b1.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_efficientnet_b2.html b/docs/reference/application_efficientnet_b2.html
index 41d385410..8f05f53b3 100644
--- a/docs/reference/application_efficientnet_b2.html
+++ b/docs/reference/application_efficientnet_b2.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_efficientnet_b3.html b/docs/reference/application_efficientnet_b3.html
index 083b36847..c9f8faebb 100644
--- a/docs/reference/application_efficientnet_b3.html
+++ b/docs/reference/application_efficientnet_b3.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_efficientnet_b4.html b/docs/reference/application_efficientnet_b4.html
index 451e5ff6d..8e3b598ae 100644
--- a/docs/reference/application_efficientnet_b4.html
+++ b/docs/reference/application_efficientnet_b4.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_efficientnet_b5.html b/docs/reference/application_efficientnet_b5.html
index 8d28f4d56..a0ab5ef3c 100644
--- a/docs/reference/application_efficientnet_b5.html
+++ b/docs/reference/application_efficientnet_b5.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_efficientnet_b6.html b/docs/reference/application_efficientnet_b6.html
index a531c5168..41a0a20d5 100644
--- a/docs/reference/application_efficientnet_b6.html
+++ b/docs/reference/application_efficientnet_b6.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_efficientnet_b7.html b/docs/reference/application_efficientnet_b7.html
index 70f300c9a..ec7e73c8e 100644
--- a/docs/reference/application_efficientnet_b7.html
+++ b/docs/reference/application_efficientnet_b7.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_efficientnet_v2b0.html b/docs/reference/application_efficientnet_v2b0.html
index 209c73b5d..0c0acda58 100644
--- a/docs/reference/application_efficientnet_v2b0.html
+++ b/docs/reference/application_efficientnet_v2b0.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_efficientnet_v2b1.html b/docs/reference/application_efficientnet_v2b1.html
index 1ccf64c8a..afe5aa876 100644
--- a/docs/reference/application_efficientnet_v2b1.html
+++ b/docs/reference/application_efficientnet_v2b1.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_efficientnet_v2b2.html b/docs/reference/application_efficientnet_v2b2.html
index 08709e6e6..100d50a52 100644
--- a/docs/reference/application_efficientnet_v2b2.html
+++ b/docs/reference/application_efficientnet_v2b2.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_efficientnet_v2b3.html b/docs/reference/application_efficientnet_v2b3.html
index 123f65602..629fa2f54 100644
--- a/docs/reference/application_efficientnet_v2b3.html
+++ b/docs/reference/application_efficientnet_v2b3.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_efficientnet_v2l.html b/docs/reference/application_efficientnet_v2l.html
index 0149700bb..14af8082c 100644
--- a/docs/reference/application_efficientnet_v2l.html
+++ b/docs/reference/application_efficientnet_v2l.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_efficientnet_v2m.html b/docs/reference/application_efficientnet_v2m.html
index 352b0c7d7..b66177af4 100644
--- a/docs/reference/application_efficientnet_v2m.html
+++ b/docs/reference/application_efficientnet_v2m.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_efficientnet_v2s.html b/docs/reference/application_efficientnet_v2s.html
index c854fb3b8..53d4c33ee 100644
--- a/docs/reference/application_efficientnet_v2s.html
+++ b/docs/reference/application_efficientnet_v2s.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_inception_resnet_v2.html b/docs/reference/application_inception_resnet_v2.html
index ee1d35896..1905ea1bc 100644
--- a/docs/reference/application_inception_resnet_v2.html
+++ b/docs/reference/application_inception_resnet_v2.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_inception_v3.html b/docs/reference/application_inception_v3.html
index e394056cc..9f50cd963 100644
--- a/docs/reference/application_inception_v3.html
+++ b/docs/reference/application_inception_v3.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_mobilenet.html b/docs/reference/application_mobilenet.html
index df2cade01..75a98bd39 100644
--- a/docs/reference/application_mobilenet.html
+++ b/docs/reference/application_mobilenet.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_mobilenet_v2.html b/docs/reference/application_mobilenet_v2.html
index f0e86833f..16a5594ca 100644
--- a/docs/reference/application_mobilenet_v2.html
+++ b/docs/reference/application_mobilenet_v2.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_mobilenet_v3_large.html b/docs/reference/application_mobilenet_v3_large.html
index 5a683cc04..61348b489 100644
--- a/docs/reference/application_mobilenet_v3_large.html
+++ b/docs/reference/application_mobilenet_v3_large.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -192,7 +192,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <div class="section level2">
     <h2 id="reference">Reference<a class="anchor" aria-label="anchor" href="#reference"></a></h2>
     
-<ul><li><p><a href="https://arxiv.org/pdf/1905.02244.pdf" class="external-link">Searching for MobileNetV3</a> (ICCV 2019)</p></li>
+<ul><li><p><a href="https://arxiv.org/pdf/1905.02244" class="external-link">Searching for MobileNetV3</a> (ICCV 2019)</p></li>
 </ul><div class="section">
 <h3 id="the-following-table-describes-the-performance-of-mobilenets-v-">The following table describes the performance of MobileNets v3:<a class="anchor" aria-label="anchor" href="#the-following-table-describes-the-performance-of-mobilenets-v-"></a></h3>
 
diff --git a/docs/reference/application_mobilenet_v3_small.html b/docs/reference/application_mobilenet_v3_small.html
index 01322ed9e..aa5ffa0f2 100644
--- a/docs/reference/application_mobilenet_v3_small.html
+++ b/docs/reference/application_mobilenet_v3_small.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -192,7 +192,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <div class="section level2">
     <h2 id="reference">Reference<a class="anchor" aria-label="anchor" href="#reference"></a></h2>
     
-<ul><li><p><a href="https://arxiv.org/pdf/1905.02244.pdf" class="external-link">Searching for MobileNetV3</a> (ICCV 2019)</p></li>
+<ul><li><p><a href="https://arxiv.org/pdf/1905.02244" class="external-link">Searching for MobileNetV3</a> (ICCV 2019)</p></li>
 </ul><div class="section">
 <h3 id="the-following-table-describes-the-performance-of-mobilenets-v-">The following table describes the performance of MobileNets v3:<a class="anchor" aria-label="anchor" href="#the-following-table-describes-the-performance-of-mobilenets-v-"></a></h3>
 
diff --git a/docs/reference/application_nasnetlarge.html b/docs/reference/application_nasnetlarge.html
index 2127aa3a1..5fd6f6cce 100644
--- a/docs/reference/application_nasnetlarge.html
+++ b/docs/reference/application_nasnetlarge.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_nasnetmobile.html b/docs/reference/application_nasnetmobile.html
index ee98975ed..d9ad6d316 100644
--- a/docs/reference/application_nasnetmobile.html
+++ b/docs/reference/application_nasnetmobile.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_resnet101.html b/docs/reference/application_resnet101.html
index 4977a566e..77252d69c 100644
--- a/docs/reference/application_resnet101.html
+++ b/docs/reference/application_resnet101.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_resnet101_v2.html b/docs/reference/application_resnet101_v2.html
index 8c8b28207..ecc7a4258 100644
--- a/docs/reference/application_resnet101_v2.html
+++ b/docs/reference/application_resnet101_v2.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_resnet152.html b/docs/reference/application_resnet152.html
index 0273d0b40..b1c440962 100644
--- a/docs/reference/application_resnet152.html
+++ b/docs/reference/application_resnet152.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_resnet152_v2.html b/docs/reference/application_resnet152_v2.html
index 4457a1ae5..b031112a5 100644
--- a/docs/reference/application_resnet152_v2.html
+++ b/docs/reference/application_resnet152_v2.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_resnet50.html b/docs/reference/application_resnet50.html
index 619e1cd59..36d536615 100644
--- a/docs/reference/application_resnet50.html
+++ b/docs/reference/application_resnet50.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_resnet50_v2.html b/docs/reference/application_resnet50_v2.html
index dfb64d4ef..eb3dccde4 100644
--- a/docs/reference/application_resnet50_v2.html
+++ b/docs/reference/application_resnet50_v2.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_vgg16.html b/docs/reference/application_vgg16.html
index 76673a4aa..774e88923 100644
--- a/docs/reference/application_vgg16.html
+++ b/docs/reference/application_vgg16.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_vgg19.html b/docs/reference/application_vgg19.html
index 2cbf38d44..bebf8a560 100644
--- a/docs/reference/application_vgg19.html
+++ b/docs/reference/application_vgg19.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/application_xception.html b/docs/reference/application_xception.html
index c71e2e58d..7b4303062 100644
--- a/docs/reference/application_xception.html
+++ b/docs/reference/application_xception.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/audio_dataset_from_directory.html b/docs/reference/audio_dataset_from_directory.html
index 537fd3a3a..58247f6e0 100644
--- a/docs/reference/audio_dataset_from_directory.html
+++ b/docs/reference/audio_dataset_from_directory.html
@@ -36,7 +36,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -244,7 +244,7 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <div class="dont-index">
 <ul><li><p><a href="https://keras.io/api/data_loading/audio#audiodatasetfromdirectory-function" class="external-link">https://keras.io/api/data_loading/audio#audiodatasetfromdirectory-function</a></p></li>
 </ul><p>Other dataset utils: <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
+<p>Other utils: <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/docs/reference/bidirectional.html b/docs/reference/bidirectional.html
index ac27d8b1e..1de407bb0 100644
--- a/docs/reference/bidirectional.html
+++ b/docs/reference/bidirectional.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/callback_backup_and_restore.html b/docs/reference/callback_backup_and_restore.html
index a93803097..0033acce3 100644
--- a/docs/reference/callback_backup_and_restore.html
+++ b/docs/reference/callback_backup_and_restore.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/callback_csv_logger.html b/docs/reference/callback_csv_logger.html
index 5836611d8..2bebe371c 100644
--- a/docs/reference/callback_csv_logger.html
+++ b/docs/reference/callback_csv_logger.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/callback_early_stopping.html b/docs/reference/callback_early_stopping.html
index 83071d3e6..18f26d99f 100644
--- a/docs/reference/callback_early_stopping.html
+++ b/docs/reference/callback_early_stopping.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/callback_lambda.html b/docs/reference/callback_lambda.html
index c95bcb9d9..b92a5d1bc 100644
--- a/docs/reference/callback_lambda.html
+++ b/docs/reference/callback_lambda.html
@@ -34,7 +34,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/callback_learning_rate_scheduler.html b/docs/reference/callback_learning_rate_scheduler.html
index 0ebfa0c62..030903e1c 100644
--- a/docs/reference/callback_learning_rate_scheduler.html
+++ b/docs/reference/callback_learning_rate_scheduler.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/callback_model_checkpoint.html b/docs/reference/callback_model_checkpoint.html
index a782d1f6b..31d3b024c 100644
--- a/docs/reference/callback_model_checkpoint.html
+++ b/docs/reference/callback_model_checkpoint.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/callback_reduce_lr_on_plateau.html b/docs/reference/callback_reduce_lr_on_plateau.html
index d04364309..9a1c69040 100644
--- a/docs/reference/callback_reduce_lr_on_plateau.html
+++ b/docs/reference/callback_reduce_lr_on_plateau.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/callback_remote_monitor.html b/docs/reference/callback_remote_monitor.html
index 76f8cd192..647d3bcc3 100644
--- a/docs/reference/callback_remote_monitor.html
+++ b/docs/reference/callback_remote_monitor.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/callback_swap_ema_weights.html b/docs/reference/callback_swap_ema_weights.html
index 4493b971d..a1687fa84 100644
--- a/docs/reference/callback_swap_ema_weights.html
+++ b/docs/reference/callback_swap_ema_weights.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/callback_tensorboard.html b/docs/reference/callback_tensorboard.html
index a8a67fc83..8e3acdb11 100644
--- a/docs/reference/callback_tensorboard.html
+++ b/docs/reference/callback_tensorboard.html
@@ -50,7 +50,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/callback_terminate_on_nan.html b/docs/reference/callback_terminate_on_nan.html
index 22f6e95db..83be73654 100644
--- a/docs/reference/callback_terminate_on_nan.html
+++ b/docs/reference/callback_terminate_on_nan.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/clear_session.html b/docs/reference/clear_session.html
index 47768c7a6..fa76110bf 100644
--- a/docs/reference/clear_session.html
+++ b/docs/reference/clear_session.html
@@ -94,7 +94,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -222,7 +222,7 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <div class="dont-index">
 <ul><li><p><a href="https://keras.io/api/utils/config_utils#clearsession-function" class="external-link">https://keras.io/api/utils/config_utils#clearsession-function</a></p></li>
 </ul><p>Other backend: <br><code><a href="config_backend.html">config_backend</a>()</code> <br><code><a href="config_epsilon.html">config_epsilon</a>()</code> <br><code><a href="config_floatx.html">config_floatx</a>()</code> <br><code><a href="config_image_data_format.html">config_image_data_format</a>()</code> <br><code><a href="config_set_epsilon.html">config_set_epsilon</a>()</code> <br><code><a href="config_set_floatx.html">config_set_floatx</a>()</code> <br><code><a href="config_set_image_data_format.html">config_set_image_data_format</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/docs/reference/clone_model.html b/docs/reference/clone_model.html
index 192b78c29..6cb75cde5 100644
--- a/docs/reference/clone_model.html
+++ b/docs/reference/clone_model.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/compile.keras.src.models.model.Model.html b/docs/reference/compile.keras.src.models.model.Model.html
index 6d6ef66e1..e61fbf3f2 100644
--- a/docs/reference/compile.keras.src.models.model.Model.html
+++ b/docs/reference/compile.keras.src.models.model.Model.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/config_backend.html b/docs/reference/config_backend.html
index 39ca39e92..85eb295f4 100644
--- a/docs/reference/config_backend.html
+++ b/docs/reference/config_backend.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/config_disable_interactive_logging.html b/docs/reference/config_disable_interactive_logging.html
index 187875765..b128d487e 100644
--- a/docs/reference/config_disable_interactive_logging.html
+++ b/docs/reference/config_disable_interactive_logging.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -94,7 +94,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>Other io utils: <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
 <p>Other config: <br><code><a href="config_backend.html">config_backend</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_enable_unsafe_deserialization.html">config_enable_unsafe_deserialization</a>()</code> <br><code><a href="config_epsilon.html">config_epsilon</a>()</code> <br><code><a href="config_floatx.html">config_floatx</a>()</code> <br><code><a href="config_image_data_format.html">config_image_data_format</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="config_set_backend.html">config_set_backend</a>()</code> <br><code><a href="config_set_epsilon.html">config_set_epsilon</a>()</code> <br><code><a href="config_set_floatx.html">config_set_floatx</a>()</code> <br><code><a href="config_set_image_data_format.html">config_set_image_data_format</a>()</code> <br></p></div>
     </div>
 
diff --git a/docs/reference/config_disable_traceback_filtering.html b/docs/reference/config_disable_traceback_filtering.html
index 7b02fc8f5..412dd1eed 100644
--- a/docs/reference/config_disable_traceback_filtering.html
+++ b/docs/reference/config_disable_traceback_filtering.html
@@ -30,7 +30,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -118,7 +118,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>Other traceback utils: <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
 <p>Other config: <br><code><a href="config_backend.html">config_backend</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_enable_unsafe_deserialization.html">config_enable_unsafe_deserialization</a>()</code> <br><code><a href="config_epsilon.html">config_epsilon</a>()</code> <br><code><a href="config_floatx.html">config_floatx</a>()</code> <br><code><a href="config_image_data_format.html">config_image_data_format</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="config_set_backend.html">config_set_backend</a>()</code> <br><code><a href="config_set_epsilon.html">config_set_epsilon</a>()</code> <br><code><a href="config_set_floatx.html">config_set_floatx</a>()</code> <br><code><a href="config_set_image_data_format.html">config_set_image_data_format</a>()</code> <br></p></div>
     </div>
 
diff --git a/docs/reference/config_dtype_policy.html b/docs/reference/config_dtype_policy.html
index 9058e2015..2b9124ff6 100644
--- a/docs/reference/config_dtype_policy.html
+++ b/docs/reference/config_dtype_policy.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/config_enable_interactive_logging.html b/docs/reference/config_enable_interactive_logging.html
index 2c49cea51..f3be4367a 100644
--- a/docs/reference/config_enable_interactive_logging.html
+++ b/docs/reference/config_enable_interactive_logging.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -94,7 +94,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>Other io utils: <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
 <p>Other config: <br><code><a href="config_backend.html">config_backend</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_enable_unsafe_deserialization.html">config_enable_unsafe_deserialization</a>()</code> <br><code><a href="config_epsilon.html">config_epsilon</a>()</code> <br><code><a href="config_floatx.html">config_floatx</a>()</code> <br><code><a href="config_image_data_format.html">config_image_data_format</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="config_set_backend.html">config_set_backend</a>()</code> <br><code><a href="config_set_epsilon.html">config_set_epsilon</a>()</code> <br><code><a href="config_set_floatx.html">config_set_floatx</a>()</code> <br><code><a href="config_set_image_data_format.html">config_set_image_data_format</a>()</code> <br></p></div>
     </div>
 
diff --git a/docs/reference/config_enable_traceback_filtering.html b/docs/reference/config_enable_traceback_filtering.html
index fe6c0c1f6..fd0b03a7e 100644
--- a/docs/reference/config_enable_traceback_filtering.html
+++ b/docs/reference/config_enable_traceback_filtering.html
@@ -30,7 +30,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -118,7 +118,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>Other traceback utils: <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
 <p>Other config: <br><code><a href="config_backend.html">config_backend</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_unsafe_deserialization.html">config_enable_unsafe_deserialization</a>()</code> <br><code><a href="config_epsilon.html">config_epsilon</a>()</code> <br><code><a href="config_floatx.html">config_floatx</a>()</code> <br><code><a href="config_image_data_format.html">config_image_data_format</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="config_set_backend.html">config_set_backend</a>()</code> <br><code><a href="config_set_epsilon.html">config_set_epsilon</a>()</code> <br><code><a href="config_set_floatx.html">config_set_floatx</a>()</code> <br><code><a href="config_set_image_data_format.html">config_set_image_data_format</a>()</code> <br></p></div>
     </div>
 
diff --git a/docs/reference/config_enable_unsafe_deserialization.html b/docs/reference/config_enable_unsafe_deserialization.html
index 341beb4f6..e2ea0e617 100644
--- a/docs/reference/config_enable_unsafe_deserialization.html
+++ b/docs/reference/config_enable_unsafe_deserialization.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/config_epsilon.html b/docs/reference/config_epsilon.html
index a6e4cc7be..14cc621e4 100644
--- a/docs/reference/config_epsilon.html
+++ b/docs/reference/config_epsilon.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/config_floatx.html b/docs/reference/config_floatx.html
index 79e40bbb1..fe0dee113 100644
--- a/docs/reference/config_floatx.html
+++ b/docs/reference/config_floatx.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/config_image_data_format.html b/docs/reference/config_image_data_format.html
index cc1200caf..6dcd301a2 100644
--- a/docs/reference/config_image_data_format.html
+++ b/docs/reference/config_image_data_format.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/config_is_interactive_logging_enabled.html b/docs/reference/config_is_interactive_logging_enabled.html
index df2761fdf..e118197ec 100644
--- a/docs/reference/config_is_interactive_logging_enabled.html
+++ b/docs/reference/config_is_interactive_logging_enabled.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -95,7 +95,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>Other io utils: <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
 <p>Other config: <br><code><a href="config_backend.html">config_backend</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_enable_unsafe_deserialization.html">config_enable_unsafe_deserialization</a>()</code> <br><code><a href="config_epsilon.html">config_epsilon</a>()</code> <br><code><a href="config_floatx.html">config_floatx</a>()</code> <br><code><a href="config_image_data_format.html">config_image_data_format</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="config_set_backend.html">config_set_backend</a>()</code> <br><code><a href="config_set_epsilon.html">config_set_epsilon</a>()</code> <br><code><a href="config_set_floatx.html">config_set_floatx</a>()</code> <br><code><a href="config_set_image_data_format.html">config_set_image_data_format</a>()</code> <br></p></div>
     </div>
 
diff --git a/docs/reference/config_is_traceback_filtering_enabled.html b/docs/reference/config_is_traceback_filtering_enabled.html
index f9d67fad8..0fcdc90cf 100644
--- a/docs/reference/config_is_traceback_filtering_enabled.html
+++ b/docs/reference/config_is_traceback_filtering_enabled.html
@@ -30,7 +30,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -119,7 +119,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>Other traceback utils: <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
 <p>Other config: <br><code><a href="config_backend.html">config_backend</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_enable_unsafe_deserialization.html">config_enable_unsafe_deserialization</a>()</code> <br><code><a href="config_epsilon.html">config_epsilon</a>()</code> <br><code><a href="config_floatx.html">config_floatx</a>()</code> <br><code><a href="config_image_data_format.html">config_image_data_format</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_set_backend.html">config_set_backend</a>()</code> <br><code><a href="config_set_epsilon.html">config_set_epsilon</a>()</code> <br><code><a href="config_set_floatx.html">config_set_floatx</a>()</code> <br><code><a href="config_set_image_data_format.html">config_set_image_data_format</a>()</code> <br></p></div>
     </div>
 
diff --git a/docs/reference/config_set_backend.html b/docs/reference/config_set_backend.html
index 7c81f80c3..86ee391f7 100644
--- a/docs/reference/config_set_backend.html
+++ b/docs/reference/config_set_backend.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/config_set_dtype_policy.html b/docs/reference/config_set_dtype_policy.html
index 6d0240d04..db2350367 100644
--- a/docs/reference/config_set_dtype_policy.html
+++ b/docs/reference/config_set_dtype_policy.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/config_set_epsilon.html b/docs/reference/config_set_epsilon.html
index 3d2f43dcb..1bfc309d1 100644
--- a/docs/reference/config_set_epsilon.html
+++ b/docs/reference/config_set_epsilon.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/config_set_floatx.html b/docs/reference/config_set_floatx.html
index 86e64392d..c03d00c7f 100644
--- a/docs/reference/config_set_floatx.html
+++ b/docs/reference/config_set_floatx.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/config_set_image_data_format.html b/docs/reference/config_set_image_data_format.html
index 6384cd887..48f7d0bf4 100644
--- a/docs/reference/config_set_image_data_format.html
+++ b/docs/reference/config_set_image_data_format.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/constraint_maxnorm.html b/docs/reference/constraint_maxnorm.html
index bb919a67b..e95b793f4 100644
--- a/docs/reference/constraint_maxnorm.html
+++ b/docs/reference/constraint_maxnorm.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/constraint_minmaxnorm.html b/docs/reference/constraint_minmaxnorm.html
index 5726e7d23..289a6c1c8 100644
--- a/docs/reference/constraint_minmaxnorm.html
+++ b/docs/reference/constraint_minmaxnorm.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/constraint_nonneg.html b/docs/reference/constraint_nonneg.html
index 05896186c..394059418 100644
--- a/docs/reference/constraint_nonneg.html
+++ b/docs/reference/constraint_nonneg.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/constraint_unitnorm.html b/docs/reference/constraint_unitnorm.html
index e8b454d96..f31b9f53a 100644
--- a/docs/reference/constraint_unitnorm.html
+++ b/docs/reference/constraint_unitnorm.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/count_params.html b/docs/reference/count_params.html
index 8b824693f..c3b197fa2 100644
--- a/docs/reference/count_params.html
+++ b/docs/reference/count_params.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/custom_metric.html b/docs/reference/custom_metric.html
index 3f4cf03fd..aa18b59e8 100644
--- a/docs/reference/custom_metric.html
+++ b/docs/reference/custom_metric.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/dataset_boston_housing.html b/docs/reference/dataset_boston_housing.html
index 7638544c2..5bb2222b1 100644
--- a/docs/reference/dataset_boston_housing.html
+++ b/docs/reference/dataset_boston_housing.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/dataset_cifar10.html b/docs/reference/dataset_cifar10.html
index ba252777f..1f4a1570a 100644
--- a/docs/reference/dataset_cifar10.html
+++ b/docs/reference/dataset_cifar10.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/dataset_cifar100.html b/docs/reference/dataset_cifar100.html
index 42c4651b9..a940e551a 100644
--- a/docs/reference/dataset_cifar100.html
+++ b/docs/reference/dataset_cifar100.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/dataset_fashion_mnist.html b/docs/reference/dataset_fashion_mnist.html
index 7be2cd0ec..4dce58bc0 100644
--- a/docs/reference/dataset_fashion_mnist.html
+++ b/docs/reference/dataset_fashion_mnist.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/dataset_imdb.html b/docs/reference/dataset_imdb.html
index e3d6be2a6..0249f62f9 100644
--- a/docs/reference/dataset_imdb.html
+++ b/docs/reference/dataset_imdb.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/dataset_mnist.html b/docs/reference/dataset_mnist.html
index 0c90461e8..fb8cd1df9 100644
--- a/docs/reference/dataset_mnist.html
+++ b/docs/reference/dataset_mnist.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/dataset_reuters.html b/docs/reference/dataset_reuters.html
index a1b4dd2f0..b7d2cd539 100644
--- a/docs/reference/dataset_reuters.html
+++ b/docs/reference/dataset_reuters.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/deserialize_keras_object.html b/docs/reference/deserialize_keras_object.html
index d5b8f93de..03da0d2af 100644
--- a/docs/reference/deserialize_keras_object.html
+++ b/docs/reference/deserialize_keras_object.html
@@ -214,7 +214,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/evaluate.keras.src.models.model.Model.html b/docs/reference/evaluate.keras.src.models.model.Model.html
index 9732abd85..b3a7f1178 100644
--- a/docs/reference/evaluate.keras.src.models.model.Model.html
+++ b/docs/reference/evaluate.keras.src.models.model.Model.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/export_savedmodel.keras.src.models.model.Model.html b/docs/reference/export_savedmodel.keras.src.models.model.Model.html
index 1b2e991ed..cc39de3e6 100644
--- a/docs/reference/export_savedmodel.keras.src.models.model.Model.html
+++ b/docs/reference/export_savedmodel.keras.src.models.model.Model.html
@@ -28,7 +28,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/fit.keras.src.models.model.Model.html b/docs/reference/fit.keras.src.models.model.Model.html
index 33bfd47b9..4b69889ab 100644
--- a/docs/reference/fit.keras.src.models.model.Model.html
+++ b/docs/reference/fit.keras.src.models.model.Model.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/freeze_weights.html b/docs/reference/freeze_weights.html
index 80dc773cb..6c20b826d 100644
--- a/docs/reference/freeze_weights.html
+++ b/docs/reference/freeze_weights.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/get_config.html b/docs/reference/get_config.html
index 7d536a13c..66ef29c47 100644
--- a/docs/reference/get_config.html
+++ b/docs/reference/get_config.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/get_custom_objects.html b/docs/reference/get_custom_objects.html
index c8e405766..b5b50ab60 100644
--- a/docs/reference/get_custom_objects.html
+++ b/docs/reference/get_custom_objects.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/get_file.html b/docs/reference/get_file.html
index 8caf269f2..7a8be8ab6 100644
--- a/docs/reference/get_file.html
+++ b/docs/reference/get_file.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -193,7 +193,7 @@ <h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index">
 <ul><li><p><a href="https://keras.io/api/utils/python_utils#getfile-function" class="external-link">https://keras.io/api/utils/python_utils#getfile-function</a></p></li>
-</ul><p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
+</ul><p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/docs/reference/get_layer.html b/docs/reference/get_layer.html
index bca2ea85f..008c66418 100644
--- a/docs/reference/get_layer.html
+++ b/docs/reference/get_layer.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/get_registered_name.html b/docs/reference/get_registered_name.html
index 57635c0f8..f2be23177 100644
--- a/docs/reference/get_registered_name.html
+++ b/docs/reference/get_registered_name.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/get_registered_object.html b/docs/reference/get_registered_object.html
index 0f6f0340e..94dfa904b 100644
--- a/docs/reference/get_registered_object.html
+++ b/docs/reference/get_registered_object.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/get_source_inputs.html b/docs/reference/get_source_inputs.html
index 9728129a1..39d7926e6 100644
--- a/docs/reference/get_source_inputs.html
+++ b/docs/reference/get_source_inputs.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -105,7 +105,7 @@ <h2 id="example">Example<a class="anchor" aria-label="anchor" href="#example"></
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
+    <div class="dont-index"><p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/docs/reference/get_weights.html b/docs/reference/get_weights.html
index cbf3f5090..0bd383b25 100644
--- a/docs/reference/get_weights.html
+++ b/docs/reference/get_weights.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/grapes-py_class-grapes.html b/docs/reference/grapes-py_class-grapes.html
index b5d17cc2a..9d5b33056 100644
--- a/docs/reference/grapes-py_class-grapes.html
+++ b/docs/reference/grapes-py_class-grapes.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/grapes-set-active-grapes.html b/docs/reference/grapes-set-active-grapes.html
index 63474cb32..42f450a9d 100644
--- a/docs/reference/grapes-set-active-grapes.html
+++ b/docs/reference/grapes-set-active-grapes.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/image_array_save.html b/docs/reference/image_array_save.html
index 3d050f99d..65e8265e4 100644
--- a/docs/reference/image_array_save.html
+++ b/docs/reference/image_array_save.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -127,7 +127,7 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <div class="dont-index">
 <ul><li><p><a href="https://keras.io/api/data_loading/image#saveimg-function" class="external-link">https://keras.io/api/data_loading/image#saveimg-function</a></p></li>
 </ul><p>Other image utils: <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="op_image_affine_transform.html">op_image_affine_transform</a>()</code> <br><code><a href="op_image_crop.html">op_image_crop</a>()</code> <br><code><a href="op_image_extract_patches.html">op_image_extract_patches</a>()</code> <br><code><a href="op_image_map_coordinates.html">op_image_map_coordinates</a>()</code> <br><code><a href="op_image_pad.html">op_image_pad</a>()</code> <br><code><a href="op_image_resize.html">op_image_resize</a>()</code> <br><code><a href="op_image_rgb_to_grayscale.html">op_image_rgb_to_grayscale</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/docs/reference/image_dataset_from_directory.html b/docs/reference/image_dataset_from_directory.html
index cec5ae67e..e2b0950bb 100644
--- a/docs/reference/image_dataset_from_directory.html
+++ b/docs/reference/image_dataset_from_directory.html
@@ -36,7 +36,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -286,7 +286,7 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <div class="dont-index">
 <ul><li><p><a href="https://keras.io/api/data_loading/image#imagedatasetfromdirectory-function" class="external-link">https://keras.io/api/data_loading/image#imagedatasetfromdirectory-function</a></p></li>
 </ul><p>Other dataset utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
 <p>Other preprocessing: <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br></p></div>
     </div>
 
diff --git a/docs/reference/image_from_array.html b/docs/reference/image_from_array.html
index 40c4738ba..524ce078e 100644
--- a/docs/reference/image_from_array.html
+++ b/docs/reference/image_from_array.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -120,7 +120,7 @@ <h2 id="example">Example<a class="anchor" aria-label="anchor" href="#example"></
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>Other image utils: <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="op_image_affine_transform.html">op_image_affine_transform</a>()</code> <br><code><a href="op_image_crop.html">op_image_crop</a>()</code> <br><code><a href="op_image_extract_patches.html">op_image_extract_patches</a>()</code> <br><code><a href="op_image_map_coordinates.html">op_image_map_coordinates</a>()</code> <br><code><a href="op_image_pad.html">op_image_pad</a>()</code> <br><code><a href="op_image_resize.html">op_image_resize</a>()</code> <br><code><a href="op_image_rgb_to_grayscale.html">op_image_rgb_to_grayscale</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/docs/reference/image_load.html b/docs/reference/image_load.html
index 2576d60cf..321e9e5b2 100644
--- a/docs/reference/image_load.html
+++ b/docs/reference/image_load.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -141,7 +141,7 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <div class="dont-index">
 <ul><li><p><a href="https://keras.io/api/data_loading/image#loadimg-function" class="external-link">https://keras.io/api/data_loading/image#loadimg-function</a></p></li>
 </ul><p>Other image utils: <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="op_image_affine_transform.html">op_image_affine_transform</a>()</code> <br><code><a href="op_image_crop.html">op_image_crop</a>()</code> <br><code><a href="op_image_extract_patches.html">op_image_extract_patches</a>()</code> <br><code><a href="op_image_map_coordinates.html">op_image_map_coordinates</a>()</code> <br><code><a href="op_image_pad.html">op_image_pad</a>()</code> <br><code><a href="op_image_resize.html">op_image_resize</a>()</code> <br><code><a href="op_image_rgb_to_grayscale.html">op_image_rgb_to_grayscale</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/docs/reference/image_smart_resize.html b/docs/reference/image_smart_resize.html
index 317fa6421..1186ad4c9 100644
--- a/docs/reference/image_smart_resize.html
+++ b/docs/reference/image_smart_resize.html
@@ -70,7 +70,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -210,7 +210,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>Other image utils: <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="op_image_affine_transform.html">op_image_affine_transform</a>()</code> <br><code><a href="op_image_crop.html">op_image_crop</a>()</code> <br><code><a href="op_image_extract_patches.html">op_image_extract_patches</a>()</code> <br><code><a href="op_image_map_coordinates.html">op_image_map_coordinates</a>()</code> <br><code><a href="op_image_pad.html">op_image_pad</a>()</code> <br><code><a href="op_image_resize.html">op_image_resize</a>()</code> <br><code><a href="op_image_rgb_to_grayscale.html">op_image_rgb_to_grayscale</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
 <p>Other preprocessing: <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br></p></div>
     </div>
 
diff --git a/docs/reference/image_to_array.html b/docs/reference/image_to_array.html
index 5f5e69678..40707cb47 100644
--- a/docs/reference/image_to_array.html
+++ b/docs/reference/image_to_array.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -120,7 +120,7 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <div class="dont-index">
 <ul><li><p><a href="https://keras.io/api/data_loading/image#imgtoarray-function" class="external-link">https://keras.io/api/data_loading/image#imgtoarray-function</a></p></li>
 </ul><p>Other image utils: <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="op_image_affine_transform.html">op_image_affine_transform</a>()</code> <br><code><a href="op_image_crop.html">op_image_crop</a>()</code> <br><code><a href="op_image_extract_patches.html">op_image_extract_patches</a>()</code> <br><code><a href="op_image_map_coordinates.html">op_image_map_coordinates</a>()</code> <br><code><a href="op_image_pad.html">op_image_pad</a>()</code> <br><code><a href="op_image_resize.html">op_image_resize</a>()</code> <br><code><a href="op_image_rgb_to_grayscale.html">op_image_rgb_to_grayscale</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/docs/reference/imagenet_decode_predictions.html b/docs/reference/imagenet_decode_predictions.html
index 4e5337254..a8680fc3e 100644
--- a/docs/reference/imagenet_decode_predictions.html
+++ b/docs/reference/imagenet_decode_predictions.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/imagenet_preprocess_input.html b/docs/reference/imagenet_preprocess_input.html
index 0a1ed570a..c88c6f02a 100644
--- a/docs/reference/imagenet_preprocess_input.html
+++ b/docs/reference/imagenet_preprocess_input.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/index.html b/docs/reference/index.html
index d1a52cd33..1746ba29e 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -3605,16 +3605,6 @@ <h3 id="data-utils">Data Utils<a class="anchor" aria-label="anchor" href="#data-
           <code><a href="split_dataset.html">split_dataset()</a></code> 
         </dt>
         <dd>Splits a dataset into a left half and a right half (e.g. train / test).</dd>
-      </dl><dl><dt>
-          
-          <code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight()</a></code> 
-        </dt>
-        <dd>Packs user-provided data into a list.</dd>
-      </dl><dl><dt>
-          
-          <code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight()</a></code> 
-        </dt>
-        <dd>Unpacks user-provided data list.</dd>
       </dl></div><div class="section level2">
       
       <h3 id="serialization-utils">Serialization Utils<a class="anchor" aria-label="anchor" href="#serialization-utils"></a></h3>
diff --git a/docs/reference/initializer_constant.html b/docs/reference/initializer_constant.html
index 4f93aa3f7..ad62ebd87 100644
--- a/docs/reference/initializer_constant.html
+++ b/docs/reference/initializer_constant.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/initializer_glorot_normal.html b/docs/reference/initializer_glorot_normal.html
index 8b875e24d..6b0899924 100644
--- a/docs/reference/initializer_glorot_normal.html
+++ b/docs/reference/initializer_glorot_normal.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/initializer_glorot_uniform.html b/docs/reference/initializer_glorot_uniform.html
index 8aeb43502..eac47397f 100644
--- a/docs/reference/initializer_glorot_uniform.html
+++ b/docs/reference/initializer_glorot_uniform.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/initializer_he_normal.html b/docs/reference/initializer_he_normal.html
index 899fe10f6..ccbfd237d 100644
--- a/docs/reference/initializer_he_normal.html
+++ b/docs/reference/initializer_he_normal.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/initializer_he_uniform.html b/docs/reference/initializer_he_uniform.html
index d95b8dd20..7d574189b 100644
--- a/docs/reference/initializer_he_uniform.html
+++ b/docs/reference/initializer_he_uniform.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/initializer_identity.html b/docs/reference/initializer_identity.html
index 145f36ecf..b09c9bfa4 100644
--- a/docs/reference/initializer_identity.html
+++ b/docs/reference/initializer_identity.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/initializer_lecun_normal.html b/docs/reference/initializer_lecun_normal.html
index 1059abbcd..2b6ed8d25 100644
--- a/docs/reference/initializer_lecun_normal.html
+++ b/docs/reference/initializer_lecun_normal.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/initializer_lecun_uniform.html b/docs/reference/initializer_lecun_uniform.html
index 0a6b77d28..206d4a984 100644
--- a/docs/reference/initializer_lecun_uniform.html
+++ b/docs/reference/initializer_lecun_uniform.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/initializer_ones.html b/docs/reference/initializer_ones.html
index c8884314e..5f310beae 100644
--- a/docs/reference/initializer_ones.html
+++ b/docs/reference/initializer_ones.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/initializer_orthogonal.html b/docs/reference/initializer_orthogonal.html
index d3a71e7eb..fd46d9d92 100644
--- a/docs/reference/initializer_orthogonal.html
+++ b/docs/reference/initializer_orthogonal.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/initializer_random_normal.html b/docs/reference/initializer_random_normal.html
index f4012ea47..ef879f0f7 100644
--- a/docs/reference/initializer_random_normal.html
+++ b/docs/reference/initializer_random_normal.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/initializer_random_uniform.html b/docs/reference/initializer_random_uniform.html
index 4c0a78ecb..8ce7955bb 100644
--- a/docs/reference/initializer_random_uniform.html
+++ b/docs/reference/initializer_random_uniform.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/initializer_truncated_normal.html b/docs/reference/initializer_truncated_normal.html
index 25deb161c..a7c78dd1c 100644
--- a/docs/reference/initializer_truncated_normal.html
+++ b/docs/reference/initializer_truncated_normal.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/initializer_variance_scaling.html b/docs/reference/initializer_variance_scaling.html
index 03393247f..5d35e8f00 100644
--- a/docs/reference/initializer_variance_scaling.html
+++ b/docs/reference/initializer_variance_scaling.html
@@ -28,7 +28,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/initializer_zeros.html b/docs/reference/initializer_zeros.html
index ae2a2ca07..1b01edc4f 100644
--- a/docs/reference/initializer_zeros.html
+++ b/docs/reference/initializer_zeros.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/install_keras.html b/docs/reference/install_keras.html
index f56b3cf73..ce7d7dcf4 100644
--- a/docs/reference/install_keras.html
+++ b/docs/reference/install_keras.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/keras.html b/docs/reference/keras.html
index dc9dda9e4..666cab882 100644
--- a/docs/reference/keras.html
+++ b/docs/reference/keras.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/keras3-package.html b/docs/reference/keras3-package.html
index d608702fe..ff3d79cfe 100644
--- a/docs/reference/keras3-package.html
+++ b/docs/reference/keras3-package.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/keras_input.html b/docs/reference/keras_input.html
index 98a7c30bc..0ecba0fb8 100644
--- a/docs/reference/keras_input.html
+++ b/docs/reference/keras_input.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/keras_model.html b/docs/reference/keras_model.html
index 917d54dbe..e1d179791 100644
--- a/docs/reference/keras_model.html
+++ b/docs/reference/keras_model.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/keras_model_sequential.html b/docs/reference/keras_model_sequential.html
index 79426ac7f..a1027567d 100644
--- a/docs/reference/keras_model_sequential.html
+++ b/docs/reference/keras_model_sequential.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_activation.html b/docs/reference/layer_activation.html
index b508968f9..3bf3b6822 100644
--- a/docs/reference/layer_activation.html
+++ b/docs/reference/layer_activation.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_activation_elu.html b/docs/reference/layer_activation_elu.html
index afced2f08..200bb5cf5 100644
--- a/docs/reference/layer_activation_elu.html
+++ b/docs/reference/layer_activation_elu.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_activation_leaky_relu.html b/docs/reference/layer_activation_leaky_relu.html
index 908007080..ba2628b31 100644
--- a/docs/reference/layer_activation_leaky_relu.html
+++ b/docs/reference/layer_activation_leaky_relu.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_activation_parametric_relu.html b/docs/reference/layer_activation_parametric_relu.html
index 23aa48328..c9af0f287 100644
--- a/docs/reference/layer_activation_parametric_relu.html
+++ b/docs/reference/layer_activation_parametric_relu.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_activation_relu.html b/docs/reference/layer_activation_relu.html
index 27c683631..c296fde41 100644
--- a/docs/reference/layer_activation_relu.html
+++ b/docs/reference/layer_activation_relu.html
@@ -30,7 +30,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_activation_softmax.html b/docs/reference/layer_activation_softmax.html
index 47b6e6947..9f3479856 100644
--- a/docs/reference/layer_activation_softmax.html
+++ b/docs/reference/layer_activation_softmax.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_activity_regularization.html b/docs/reference/layer_activity_regularization.html
index de5018519..f59d6882f 100644
--- a/docs/reference/layer_activity_regularization.html
+++ b/docs/reference/layer_activity_regularization.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_add.html b/docs/reference/layer_add.html
index 465a3afba..0969f9c6d 100644
--- a/docs/reference/layer_add.html
+++ b/docs/reference/layer_add.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_additive_attention.html b/docs/reference/layer_additive_attention.html
index c014fe855..f69a10854 100644
--- a/docs/reference/layer_additive_attention.html
+++ b/docs/reference/layer_additive_attention.html
@@ -42,7 +42,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_alpha_dropout.html b/docs/reference/layer_alpha_dropout.html
index 5943a8785..098ad6b3c 100644
--- a/docs/reference/layer_alpha_dropout.html
+++ b/docs/reference/layer_alpha_dropout.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_attention.html b/docs/reference/layer_attention.html
index 47e403369..948661a69 100644
--- a/docs/reference/layer_attention.html
+++ b/docs/reference/layer_attention.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_average.html b/docs/reference/layer_average.html
index a40121745..1750e559e 100644
--- a/docs/reference/layer_average.html
+++ b/docs/reference/layer_average.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_average_pooling_1d.html b/docs/reference/layer_average_pooling_1d.html
index c8b2af53d..4d553714d 100644
--- a/docs/reference/layer_average_pooling_1d.html
+++ b/docs/reference/layer_average_pooling_1d.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_average_pooling_2d.html b/docs/reference/layer_average_pooling_2d.html
index a2cc9eb7e..218efbf8d 100644
--- a/docs/reference/layer_average_pooling_2d.html
+++ b/docs/reference/layer_average_pooling_2d.html
@@ -28,7 +28,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_average_pooling_3d.html b/docs/reference/layer_average_pooling_3d.html
index 88a1fe142..9c9ad77a7 100644
--- a/docs/reference/layer_average_pooling_3d.html
+++ b/docs/reference/layer_average_pooling_3d.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_batch_normalization.html b/docs/reference/layer_batch_normalization.html
index 5204640d5..9537a96f1 100644
--- a/docs/reference/layer_batch_normalization.html
+++ b/docs/reference/layer_batch_normalization.html
@@ -116,7 +116,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_bidirectional.html b/docs/reference/layer_bidirectional.html
index b9795153d..c9ae0b1af 100644
--- a/docs/reference/layer_bidirectional.html
+++ b/docs/reference/layer_bidirectional.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_category_encoding.html b/docs/reference/layer_category_encoding.html
index 6b1ef5ae2..fba91da20 100644
--- a/docs/reference/layer_category_encoding.html
+++ b/docs/reference/layer_category_encoding.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_center_crop.html b/docs/reference/layer_center_crop.html
index 63a66e07d..d6a937277 100644
--- a/docs/reference/layer_center_crop.html
+++ b/docs/reference/layer_center_crop.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_concatenate.html b/docs/reference/layer_concatenate.html
index afe03500c..021259d90 100644
--- a/docs/reference/layer_concatenate.html
+++ b/docs/reference/layer_concatenate.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_conv_1d.html b/docs/reference/layer_conv_1d.html
index f3518c087..38e9c1d3d 100644
--- a/docs/reference/layer_conv_1d.html
+++ b/docs/reference/layer_conv_1d.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_conv_1d_transpose.html b/docs/reference/layer_conv_1d_transpose.html
index 0a4be70df..7e37a74bd 100644
--- a/docs/reference/layer_conv_1d_transpose.html
+++ b/docs/reference/layer_conv_1d_transpose.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_conv_2d.html b/docs/reference/layer_conv_2d.html
index ded1b0e38..1e4b08c4b 100644
--- a/docs/reference/layer_conv_2d.html
+++ b/docs/reference/layer_conv_2d.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_conv_2d_transpose.html b/docs/reference/layer_conv_2d_transpose.html
index ca9046843..5982a7f52 100644
--- a/docs/reference/layer_conv_2d_transpose.html
+++ b/docs/reference/layer_conv_2d_transpose.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_conv_3d.html b/docs/reference/layer_conv_3d.html
index 1794151f2..568b56f50 100644
--- a/docs/reference/layer_conv_3d.html
+++ b/docs/reference/layer_conv_3d.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_conv_3d_transpose.html b/docs/reference/layer_conv_3d_transpose.html
index 9d40e518a..c27e151f1 100644
--- a/docs/reference/layer_conv_3d_transpose.html
+++ b/docs/reference/layer_conv_3d_transpose.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_conv_lstm_1d.html b/docs/reference/layer_conv_lstm_1d.html
index 7b6b60784..a083a26d6 100644
--- a/docs/reference/layer_conv_lstm_1d.html
+++ b/docs/reference/layer_conv_lstm_1d.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_conv_lstm_2d.html b/docs/reference/layer_conv_lstm_2d.html
index 51f66f91c..b8459afc4 100644
--- a/docs/reference/layer_conv_lstm_2d.html
+++ b/docs/reference/layer_conv_lstm_2d.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_conv_lstm_3d.html b/docs/reference/layer_conv_lstm_3d.html
index 00630ff80..8f5826dcb 100644
--- a/docs/reference/layer_conv_lstm_3d.html
+++ b/docs/reference/layer_conv_lstm_3d.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_cropping_1d.html b/docs/reference/layer_cropping_1d.html
index e124f1704..242d6509d 100644
--- a/docs/reference/layer_cropping_1d.html
+++ b/docs/reference/layer_cropping_1d.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_cropping_2d.html b/docs/reference/layer_cropping_2d.html
index 5de52ebb8..922a33d63 100644
--- a/docs/reference/layer_cropping_2d.html
+++ b/docs/reference/layer_cropping_2d.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_cropping_3d.html b/docs/reference/layer_cropping_3d.html
index 4334a0722..0e9f20c6c 100644
--- a/docs/reference/layer_cropping_3d.html
+++ b/docs/reference/layer_cropping_3d.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_dense.html b/docs/reference/layer_dense.html
index 3d103c9e8..1c5eef9bd 100644
--- a/docs/reference/layer_dense.html
+++ b/docs/reference/layer_dense.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_depthwise_conv_1d.html b/docs/reference/layer_depthwise_conv_1d.html
index bbe01a481..3b5b2f2a5 100644
--- a/docs/reference/layer_depthwise_conv_1d.html
+++ b/docs/reference/layer_depthwise_conv_1d.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_depthwise_conv_2d.html b/docs/reference/layer_depthwise_conv_2d.html
index 5f73da828..d09110690 100644
--- a/docs/reference/layer_depthwise_conv_2d.html
+++ b/docs/reference/layer_depthwise_conv_2d.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_discretization.html b/docs/reference/layer_discretization.html
index a439d8791..5f82d0932 100644
--- a/docs/reference/layer_discretization.html
+++ b/docs/reference/layer_discretization.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_dot.html b/docs/reference/layer_dot.html
index b12c9e40e..86660bde6 100644
--- a/docs/reference/layer_dot.html
+++ b/docs/reference/layer_dot.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_dropout.html b/docs/reference/layer_dropout.html
index 8bb09f822..84a7cbc37 100644
--- a/docs/reference/layer_dropout.html
+++ b/docs/reference/layer_dropout.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_einsum_dense.html b/docs/reference/layer_einsum_dense.html
index 261e813d2..77f220611 100644
--- a/docs/reference/layer_einsum_dense.html
+++ b/docs/reference/layer_einsum_dense.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_embedding.html b/docs/reference/layer_embedding.html
index c400b6def..9cd6c1743 100644
--- a/docs/reference/layer_embedding.html
+++ b/docs/reference/layer_embedding.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_feature_space.html b/docs/reference/layer_feature_space.html
index e3074ce4b..2c2d59e2f 100644
--- a/docs/reference/layer_feature_space.html
+++ b/docs/reference/layer_feature_space.html
@@ -108,7 +108,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -476,7 +476,7 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <ul><li><p><a href="https://keras.io/api/utils/feature_space#featurespace-class" class="external-link">https://keras.io/api/utils/feature_space#featurespace-class</a></p></li>
 </ul><p>Other preprocessing layers: <br><code><a href="layer_category_encoding.html">layer_category_encoding</a>()</code> <br><code><a href="layer_center_crop.html">layer_center_crop</a>()</code> <br><code><a href="layer_discretization.html">layer_discretization</a>()</code> <br><code><a href="layer_hashed_crossing.html">layer_hashed_crossing</a>()</code> <br><code><a href="layer_hashing.html">layer_hashing</a>()</code> <br><code><a href="layer_integer_lookup.html">layer_integer_lookup</a>()</code> <br><code><a href="layer_mel_spectrogram.html">layer_mel_spectrogram</a>()</code> <br><code><a href="layer_normalization.html">layer_normalization</a>()</code> <br><code><a href="layer_random_brightness.html">layer_random_brightness</a>()</code> <br><code><a href="layer_random_contrast.html">layer_random_contrast</a>()</code> <br><code><a href="layer_random_crop.html">layer_random_crop</a>()</code> <br><code><a href="layer_random_flip.html">layer_random_flip</a>()</code> <br><code><a href="layer_random_rotation.html">layer_random_rotation</a>()</code> <br><code><a href="layer_random_translation.html">layer_random_translation</a>()</code> <br><code><a href="layer_random_zoom.html">layer_random_zoom</a>()</code> <br><code><a href="layer_rescaling.html">layer_rescaling</a>()</code> <br><code><a href="layer_resizing.html">layer_resizing</a>()</code> <br><code><a href="layer_string_lookup.html">layer_string_lookup</a>()</code> <br><code><a href="layer_text_vectorization.html">layer_text_vectorization</a>()</code> <br></p>
 <p>Other layers: <br><code><a href="Layer.html">Layer</a>()</code> <br><code><a href="layer_activation.html">layer_activation</a>()</code> <br><code><a href="layer_activation_elu.html">layer_activation_elu</a>()</code> <br><code><a href="layer_activation_leaky_relu.html">layer_activation_leaky_relu</a>()</code> <br><code><a href="layer_activation_parametric_relu.html">layer_activation_parametric_relu</a>()</code> <br><code><a href="layer_activation_relu.html">layer_activation_relu</a>()</code> <br><code><a href="layer_activation_softmax.html">layer_activation_softmax</a>()</code> <br><code><a href="layer_activity_regularization.html">layer_activity_regularization</a>()</code> <br><code><a href="layer_add.html">layer_add</a>()</code> <br><code><a href="layer_additive_attention.html">layer_additive_attention</a>()</code> <br><code><a href="layer_alpha_dropout.html">layer_alpha_dropout</a>()</code> <br><code><a href="layer_attention.html">layer_attention</a>()</code> <br><code><a href="layer_average.html">layer_average</a>()</code> <br><code><a href="layer_average_pooling_1d.html">layer_average_pooling_1d</a>()</code> <br><code><a href="layer_average_pooling_2d.html">layer_average_pooling_2d</a>()</code> <br><code><a href="layer_average_pooling_3d.html">layer_average_pooling_3d</a>()</code> <br><code><a href="layer_batch_normalization.html">layer_batch_normalization</a>()</code> <br><code><a href="layer_bidirectional.html">layer_bidirectional</a>()</code> <br><code><a href="layer_category_encoding.html">layer_category_encoding</a>()</code> <br><code><a href="layer_center_crop.html">layer_center_crop</a>()</code> <br><code><a href="layer_concatenate.html">layer_concatenate</a>()</code> <br><code><a href="layer_conv_1d.html">layer_conv_1d</a>()</code> <br><code><a href="layer_conv_1d_transpose.html">layer_conv_1d_transpose</a>()</code> <br><code><a href="layer_conv_2d.html">layer_conv_2d</a>()</code> <br><code><a href="layer_conv_2d_transpose.html">layer_conv_2d_transpose</a>()</code> <br><code><a href="layer_conv_3d.html">layer_conv_3d</a>()</code> <br><code><a href="layer_conv_3d_transpose.html">layer_conv_3d_transpose</a>()</code> <br><code><a href="layer_conv_lstm_1d.html">layer_conv_lstm_1d</a>()</code> <br><code><a href="layer_conv_lstm_2d.html">layer_conv_lstm_2d</a>()</code> <br><code><a href="layer_conv_lstm_3d.html">layer_conv_lstm_3d</a>()</code> <br><code><a href="layer_cropping_1d.html">layer_cropping_1d</a>()</code> <br><code><a href="layer_cropping_2d.html">layer_cropping_2d</a>()</code> <br><code><a href="layer_cropping_3d.html">layer_cropping_3d</a>()</code> <br><code><a href="layer_dense.html">layer_dense</a>()</code> <br><code><a href="layer_depthwise_conv_1d.html">layer_depthwise_conv_1d</a>()</code> <br><code><a href="layer_depthwise_conv_2d.html">layer_depthwise_conv_2d</a>()</code> <br><code><a href="layer_discretization.html">layer_discretization</a>()</code> <br><code><a href="layer_dot.html">layer_dot</a>()</code> <br><code><a href="layer_dropout.html">layer_dropout</a>()</code> <br><code><a href="layer_einsum_dense.html">layer_einsum_dense</a>()</code> <br><code><a href="layer_embedding.html">layer_embedding</a>()</code> <br><code><a href="layer_flatten.html">layer_flatten</a>()</code> <br><code><a href="layer_flax_module_wrapper.html">layer_flax_module_wrapper</a>()</code> <br><code><a href="layer_gaussian_dropout.html">layer_gaussian_dropout</a>()</code> <br><code><a href="layer_gaussian_noise.html">layer_gaussian_noise</a>()</code> <br><code><a href="layer_global_average_pooling_1d.html">layer_global_average_pooling_1d</a>()</code> <br><code><a href="layer_global_average_pooling_2d.html">layer_global_average_pooling_2d</a>()</code> <br><code><a href="layer_global_average_pooling_3d.html">layer_global_average_pooling_3d</a>()</code> <br><code><a href="layer_global_max_pooling_1d.html">layer_global_max_pooling_1d</a>()</code> <br><code><a href="layer_global_max_pooling_2d.html">layer_global_max_pooling_2d</a>()</code> <br><code><a href="layer_global_max_pooling_3d.html">layer_global_max_pooling_3d</a>()</code> <br><code><a href="layer_group_normalization.html">layer_group_normalization</a>()</code> <br><code><a href="layer_group_query_attention.html">layer_group_query_attention</a>()</code> <br><code><a href="layer_gru.html">layer_gru</a>()</code> <br><code><a href="layer_hashed_crossing.html">layer_hashed_crossing</a>()</code> <br><code><a href="layer_hashing.html">layer_hashing</a>()</code> <br><code><a href="layer_identity.html">layer_identity</a>()</code> <br><code><a href="layer_integer_lookup.html">layer_integer_lookup</a>()</code> <br><code><a href="layer_jax_model_wrapper.html">layer_jax_model_wrapper</a>()</code> <br><code><a href="layer_lambda.html">layer_lambda</a>()</code> <br><code><a href="layer_layer_normalization.html">layer_layer_normalization</a>()</code> <br><code><a href="layer_lstm.html">layer_lstm</a>()</code> <br><code><a href="layer_masking.html">layer_masking</a>()</code> <br><code><a href="layer_max_pooling_1d.html">layer_max_pooling_1d</a>()</code> <br><code><a href="layer_max_pooling_2d.html">layer_max_pooling_2d</a>()</code> <br><code><a href="layer_max_pooling_3d.html">layer_max_pooling_3d</a>()</code> <br><code><a href="layer_maximum.html">layer_maximum</a>()</code> <br><code><a href="layer_mel_spectrogram.html">layer_mel_spectrogram</a>()</code> <br><code><a href="layer_minimum.html">layer_minimum</a>()</code> <br><code><a href="layer_multi_head_attention.html">layer_multi_head_attention</a>()</code> <br><code><a href="layer_multiply.html">layer_multiply</a>()</code> <br><code><a href="layer_normalization.html">layer_normalization</a>()</code> <br><code><a href="layer_permute.html">layer_permute</a>()</code> <br><code><a href="layer_random_brightness.html">layer_random_brightness</a>()</code> <br><code><a href="layer_random_contrast.html">layer_random_contrast</a>()</code> <br><code><a href="layer_random_crop.html">layer_random_crop</a>()</code> <br><code><a href="layer_random_flip.html">layer_random_flip</a>()</code> <br><code><a href="layer_random_rotation.html">layer_random_rotation</a>()</code> <br><code><a href="layer_random_translation.html">layer_random_translation</a>()</code> <br><code><a href="layer_random_zoom.html">layer_random_zoom</a>()</code> <br><code><a href="layer_repeat_vector.html">layer_repeat_vector</a>()</code> <br><code><a href="layer_rescaling.html">layer_rescaling</a>()</code> <br><code><a href="layer_reshape.html">layer_reshape</a>()</code> <br><code><a href="layer_resizing.html">layer_resizing</a>()</code> <br><code><a href="layer_rnn.html">layer_rnn</a>()</code> <br><code><a href="layer_separable_conv_1d.html">layer_separable_conv_1d</a>()</code> <br><code><a href="layer_separable_conv_2d.html">layer_separable_conv_2d</a>()</code> <br><code><a href="layer_simple_rnn.html">layer_simple_rnn</a>()</code> <br><code><a href="layer_spatial_dropout_1d.html">layer_spatial_dropout_1d</a>()</code> <br><code><a href="layer_spatial_dropout_2d.html">layer_spatial_dropout_2d</a>()</code> <br><code><a href="layer_spatial_dropout_3d.html">layer_spatial_dropout_3d</a>()</code> <br><code><a href="layer_spectral_normalization.html">layer_spectral_normalization</a>()</code> <br><code><a href="layer_string_lookup.html">layer_string_lookup</a>()</code> <br><code><a href="layer_subtract.html">layer_subtract</a>()</code> <br><code><a href="layer_text_vectorization.html">layer_text_vectorization</a>()</code> <br><code><a href="layer_tfsm.html">layer_tfsm</a>()</code> <br><code><a href="layer_time_distributed.html">layer_time_distributed</a>()</code> <br><code><a href="layer_torch_module_wrapper.html">layer_torch_module_wrapper</a>()</code> <br><code><a href="layer_unit_normalization.html">layer_unit_normalization</a>()</code> <br><code><a href="layer_upsampling_1d.html">layer_upsampling_1d</a>()</code> <br><code><a href="layer_upsampling_2d.html">layer_upsampling_2d</a>()</code> <br><code><a href="layer_upsampling_3d.html">layer_upsampling_3d</a>()</code> <br><code><a href="layer_zero_padding_1d.html">layer_zero_padding_1d</a>()</code> <br><code><a href="layer_zero_padding_2d.html">layer_zero_padding_2d</a>()</code> <br><code><a href="layer_zero_padding_3d.html">layer_zero_padding_3d</a>()</code> <br><code><a href="rnn_cell_gru.html">rnn_cell_gru</a>()</code> <br><code><a href="rnn_cell_lstm.html">rnn_cell_lstm</a>()</code> <br><code><a href="rnn_cell_simple.html">rnn_cell_simple</a>()</code> <br><code><a href="rnn_cells_stack.html">rnn_cells_stack</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/docs/reference/layer_flatten.html b/docs/reference/layer_flatten.html
index b0259435c..53823744b 100644
--- a/docs/reference/layer_flatten.html
+++ b/docs/reference/layer_flatten.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_flax_module_wrapper.html b/docs/reference/layer_flax_module_wrapper.html
index 4f087b6e9..e57664e79 100644
--- a/docs/reference/layer_flax_module_wrapper.html
+++ b/docs/reference/layer_flax_module_wrapper.html
@@ -180,7 +180,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_gaussian_dropout.html b/docs/reference/layer_gaussian_dropout.html
index 4f7ed7330..ba70e3c4f 100644
--- a/docs/reference/layer_gaussian_dropout.html
+++ b/docs/reference/layer_gaussian_dropout.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_gaussian_noise.html b/docs/reference/layer_gaussian_noise.html
index 01752eb89..eef72d046 100644
--- a/docs/reference/layer_gaussian_noise.html
+++ b/docs/reference/layer_gaussian_noise.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_global_average_pooling_1d.html b/docs/reference/layer_global_average_pooling_1d.html
index 620510768..41d106641 100644
--- a/docs/reference/layer_global_average_pooling_1d.html
+++ b/docs/reference/layer_global_average_pooling_1d.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_global_average_pooling_2d.html b/docs/reference/layer_global_average_pooling_2d.html
index d6e3c7e4f..10e2dd0fd 100644
--- a/docs/reference/layer_global_average_pooling_2d.html
+++ b/docs/reference/layer_global_average_pooling_2d.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_global_average_pooling_3d.html b/docs/reference/layer_global_average_pooling_3d.html
index 2a345c365..453f74455 100644
--- a/docs/reference/layer_global_average_pooling_3d.html
+++ b/docs/reference/layer_global_average_pooling_3d.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_global_max_pooling_1d.html b/docs/reference/layer_global_max_pooling_1d.html
index 22ede6cc0..cc76c2f78 100644
--- a/docs/reference/layer_global_max_pooling_1d.html
+++ b/docs/reference/layer_global_max_pooling_1d.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_global_max_pooling_2d.html b/docs/reference/layer_global_max_pooling_2d.html
index 7ced2ca9e..eb8c0ad80 100644
--- a/docs/reference/layer_global_max_pooling_2d.html
+++ b/docs/reference/layer_global_max_pooling_2d.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_global_max_pooling_3d.html b/docs/reference/layer_global_max_pooling_3d.html
index a69c33021..525f83372 100644
--- a/docs/reference/layer_global_max_pooling_3d.html
+++ b/docs/reference/layer_global_max_pooling_3d.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_group_normalization.html b/docs/reference/layer_group_normalization.html
index 98e33c51a..3f6b6b80a 100644
--- a/docs/reference/layer_group_normalization.html
+++ b/docs/reference/layer_group_normalization.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_group_query_attention.html b/docs/reference/layer_group_query_attention.html
index 0d45260de..df27c6eb7 100644
--- a/docs/reference/layer_group_query_attention.html
+++ b/docs/reference/layer_group_query_attention.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_gru.html b/docs/reference/layer_gru.html
index 490b5f432..e1b0a41a6 100644
--- a/docs/reference/layer_gru.html
+++ b/docs/reference/layer_gru.html
@@ -100,7 +100,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_hashed_crossing.html b/docs/reference/layer_hashed_crossing.html
index 189717b74..a2ab59916 100644
--- a/docs/reference/layer_hashed_crossing.html
+++ b/docs/reference/layer_hashed_crossing.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_hashing.html b/docs/reference/layer_hashing.html
index d6f0ae50a..fef9313d1 100644
--- a/docs/reference/layer_hashing.html
+++ b/docs/reference/layer_hashing.html
@@ -154,7 +154,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_identity.html b/docs/reference/layer_identity.html
index 896541109..054366a09 100644
--- a/docs/reference/layer_identity.html
+++ b/docs/reference/layer_identity.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_input.html b/docs/reference/layer_input.html
index f60a6f30b..03fdede9b 100644
--- a/docs/reference/layer_input.html
+++ b/docs/reference/layer_input.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_integer_lookup.html b/docs/reference/layer_integer_lookup.html
index bba5ee629..986b5dd91 100644
--- a/docs/reference/layer_integer_lookup.html
+++ b/docs/reference/layer_integer_lookup.html
@@ -76,7 +76,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_jax_model_wrapper.html b/docs/reference/layer_jax_model_wrapper.html
index 8edff1945..14ccbb8b0 100644
--- a/docs/reference/layer_jax_model_wrapper.html
+++ b/docs/reference/layer_jax_model_wrapper.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_lambda.html b/docs/reference/layer_lambda.html
index 2f8c7985f..e1e868657 100644
--- a/docs/reference/layer_lambda.html
+++ b/docs/reference/layer_lambda.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_layer_normalization.html b/docs/reference/layer_layer_normalization.html
index e78c51aa6..97452d024 100644
--- a/docs/reference/layer_layer_normalization.html
+++ b/docs/reference/layer_layer_normalization.html
@@ -104,7 +104,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_lstm.html b/docs/reference/layer_lstm.html
index 44ca982d5..3b5079617 100644
--- a/docs/reference/layer_lstm.html
+++ b/docs/reference/layer_lstm.html
@@ -90,7 +90,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_masking.html b/docs/reference/layer_masking.html
index c95e05584..4734c4eff 100644
--- a/docs/reference/layer_masking.html
+++ b/docs/reference/layer_masking.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_max_pooling_1d.html b/docs/reference/layer_max_pooling_1d.html
index 0941bccef..22cd3b304 100644
--- a/docs/reference/layer_max_pooling_1d.html
+++ b/docs/reference/layer_max_pooling_1d.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_max_pooling_2d.html b/docs/reference/layer_max_pooling_2d.html
index e80b0e33a..757cf4a3f 100644
--- a/docs/reference/layer_max_pooling_2d.html
+++ b/docs/reference/layer_max_pooling_2d.html
@@ -28,7 +28,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_max_pooling_3d.html b/docs/reference/layer_max_pooling_3d.html
index 0940a5472..e50063364 100644
--- a/docs/reference/layer_max_pooling_3d.html
+++ b/docs/reference/layer_max_pooling_3d.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_maximum.html b/docs/reference/layer_maximum.html
index 342543653..2540398c0 100644
--- a/docs/reference/layer_maximum.html
+++ b/docs/reference/layer_maximum.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_mel_spectrogram.html b/docs/reference/layer_mel_spectrogram.html
index 243e495e1..326c4b549 100644
--- a/docs/reference/layer_mel_spectrogram.html
+++ b/docs/reference/layer_mel_spectrogram.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_minimum.html b/docs/reference/layer_minimum.html
index ed79e5b30..d01d0c224 100644
--- a/docs/reference/layer_minimum.html
+++ b/docs/reference/layer_minimum.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_multi_head_attention.html b/docs/reference/layer_multi_head_attention.html
index ee2d264c3..eb9e156cb 100644
--- a/docs/reference/layer_multi_head_attention.html
+++ b/docs/reference/layer_multi_head_attention.html
@@ -42,7 +42,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_multiply.html b/docs/reference/layer_multiply.html
index a11fa9b5b..98e3df288 100644
--- a/docs/reference/layer_multiply.html
+++ b/docs/reference/layer_multiply.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_normalization.html b/docs/reference/layer_normalization.html
index b5408c067..7aced5655 100644
--- a/docs/reference/layer_normalization.html
+++ b/docs/reference/layer_normalization.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_permute.html b/docs/reference/layer_permute.html
index 567c6f84e..c42dbc219 100644
--- a/docs/reference/layer_permute.html
+++ b/docs/reference/layer_permute.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_random_brightness.html b/docs/reference/layer_random_brightness.html
index 3a119d0d6..a0cd06e1e 100644
--- a/docs/reference/layer_random_brightness.html
+++ b/docs/reference/layer_random_brightness.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_random_contrast.html b/docs/reference/layer_random_contrast.html
index b28eedb3e..ac559bc27 100644
--- a/docs/reference/layer_random_contrast.html
+++ b/docs/reference/layer_random_contrast.html
@@ -30,7 +30,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_random_crop.html b/docs/reference/layer_random_crop.html
index 50a015b0a..7409bfc16 100644
--- a/docs/reference/layer_random_crop.html
+++ b/docs/reference/layer_random_crop.html
@@ -34,7 +34,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_random_flip.html b/docs/reference/layer_random_flip.html
index 1b3932f30..360a523cd 100644
--- a/docs/reference/layer_random_flip.html
+++ b/docs/reference/layer_random_flip.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_random_rotation.html b/docs/reference/layer_random_rotation.html
index 4de62682c..b96a3eb5e 100644
--- a/docs/reference/layer_random_rotation.html
+++ b/docs/reference/layer_random_rotation.html
@@ -28,7 +28,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_random_translation.html b/docs/reference/layer_random_translation.html
index 010a24010..bf9b325ac 100644
--- a/docs/reference/layer_random_translation.html
+++ b/docs/reference/layer_random_translation.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_random_zoom.html b/docs/reference/layer_random_zoom.html
index fe57bd85a..5c1977e6e 100644
--- a/docs/reference/layer_random_zoom.html
+++ b/docs/reference/layer_random_zoom.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_repeat_vector.html b/docs/reference/layer_repeat_vector.html
index 6c2e6954a..997a7d22f 100644
--- a/docs/reference/layer_repeat_vector.html
+++ b/docs/reference/layer_repeat_vector.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_rescaling.html b/docs/reference/layer_rescaling.html
index 4b07b2810..2615f6d1a 100644
--- a/docs/reference/layer_rescaling.html
+++ b/docs/reference/layer_rescaling.html
@@ -36,7 +36,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_reshape.html b/docs/reference/layer_reshape.html
index c9a82080e..414ead6b7 100644
--- a/docs/reference/layer_reshape.html
+++ b/docs/reference/layer_reshape.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_resizing.html b/docs/reference/layer_resizing.html
index aa86cfdde..cabecaf65 100644
--- a/docs/reference/layer_resizing.html
+++ b/docs/reference/layer_resizing.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_rnn.html b/docs/reference/layer_rnn.html
index a62c1c909..cdd0a48ef 100644
--- a/docs/reference/layer_rnn.html
+++ b/docs/reference/layer_rnn.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_separable_conv_1d.html b/docs/reference/layer_separable_conv_1d.html
index 8ad490c18..cdbf15a63 100644
--- a/docs/reference/layer_separable_conv_1d.html
+++ b/docs/reference/layer_separable_conv_1d.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_separable_conv_2d.html b/docs/reference/layer_separable_conv_2d.html
index 5a8e3be5d..6b560911d 100644
--- a/docs/reference/layer_separable_conv_2d.html
+++ b/docs/reference/layer_separable_conv_2d.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_simple_rnn.html b/docs/reference/layer_simple_rnn.html
index a31305b85..a17a16a8a 100644
--- a/docs/reference/layer_simple_rnn.html
+++ b/docs/reference/layer_simple_rnn.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_spatial_dropout_1d.html b/docs/reference/layer_spatial_dropout_1d.html
index 9bb075d93..555e84642 100644
--- a/docs/reference/layer_spatial_dropout_1d.html
+++ b/docs/reference/layer_spatial_dropout_1d.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_spatial_dropout_2d.html b/docs/reference/layer_spatial_dropout_2d.html
index 75f597801..1119ce390 100644
--- a/docs/reference/layer_spatial_dropout_2d.html
+++ b/docs/reference/layer_spatial_dropout_2d.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_spatial_dropout_3d.html b/docs/reference/layer_spatial_dropout_3d.html
index acddf69f7..6d81ac712 100644
--- a/docs/reference/layer_spatial_dropout_3d.html
+++ b/docs/reference/layer_spatial_dropout_3d.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_spectral_normalization.html b/docs/reference/layer_spectral_normalization.html
index 0959f0cc1..4f9369c72 100644
--- a/docs/reference/layer_spectral_normalization.html
+++ b/docs/reference/layer_spectral_normalization.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_string_lookup.html b/docs/reference/layer_string_lookup.html
index 4fdb332de..cd354fde9 100644
--- a/docs/reference/layer_string_lookup.html
+++ b/docs/reference/layer_string_lookup.html
@@ -74,7 +74,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_subtract.html b/docs/reference/layer_subtract.html
index ab7f4b64a..50bf7a6e5 100644
--- a/docs/reference/layer_subtract.html
+++ b/docs/reference/layer_subtract.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_text_vectorization.html b/docs/reference/layer_text_vectorization.html
index 105c13173..3d751ed3c 100644
--- a/docs/reference/layer_text_vectorization.html
+++ b/docs/reference/layer_text_vectorization.html
@@ -108,7 +108,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_tfsm.html b/docs/reference/layer_tfsm.html
index c4db32ca7..a47c4fb59 100644
--- a/docs/reference/layer_tfsm.html
+++ b/docs/reference/layer_tfsm.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -143,8 +143,8 @@ <h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"
 <span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a><span class="do">## Output Type:</span></span>
 <span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a><span class="do">##   TensorSpec(shape=(None, 10), dtype=tf.float32, name=None)</span></span>
 <span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a><span class="do">## Captures:</span></span>
-<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a><span class="do">##   128937404166592: TensorSpec(shape=(), dtype=tf.resource, name=None)</span></span>
-<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a><span class="do">##   128937404169408: TensorSpec(shape=(), dtype=tf.resource, name=None)</span></span>
+<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a><span class="do">##   128255174032224: TensorSpec(shape=(), dtype=tf.resource, name=None)</span></span>
+<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a><span class="do">##   128255174017264: TensorSpec(shape=(), dtype=tf.resource, name=None)</span></span>
 <span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a></span></code></pre><p></p></div>
 <p></p><div class="sourceCode r"><pre><code><span><span class="va">reloaded_layer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/layer_tfsm.html">layer_tfsm</a></span><span class="op">(</span>filepath <span class="op">=</span> <span class="st">"path/to/artifact"</span><span class="op">)</span></span>
 <span><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/random_normal.html">random_normal</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">784</span><span class="op">)</span><span class="op">)</span></span>
diff --git a/docs/reference/layer_time_distributed.html b/docs/reference/layer_time_distributed.html
index 663490a37..87614a827 100644
--- a/docs/reference/layer_time_distributed.html
+++ b/docs/reference/layer_time_distributed.html
@@ -42,7 +42,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_torch_module_wrapper.html b/docs/reference/layer_torch_module_wrapper.html
index 354e8ac34..926d41213 100644
--- a/docs/reference/layer_torch_module_wrapper.html
+++ b/docs/reference/layer_torch_module_wrapper.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_unit_normalization.html b/docs/reference/layer_unit_normalization.html
index 43a4bfd5b..49e06f1ad 100644
--- a/docs/reference/layer_unit_normalization.html
+++ b/docs/reference/layer_unit_normalization.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_upsampling_1d.html b/docs/reference/layer_upsampling_1d.html
index 5bc34819d..de765a1a2 100644
--- a/docs/reference/layer_upsampling_1d.html
+++ b/docs/reference/layer_upsampling_1d.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_upsampling_2d.html b/docs/reference/layer_upsampling_2d.html
index 7e068dc06..9d49e4f48 100644
--- a/docs/reference/layer_upsampling_2d.html
+++ b/docs/reference/layer_upsampling_2d.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_upsampling_3d.html b/docs/reference/layer_upsampling_3d.html
index b6ceb3159..2941f671e 100644
--- a/docs/reference/layer_upsampling_3d.html
+++ b/docs/reference/layer_upsampling_3d.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_zero_padding_1d.html b/docs/reference/layer_zero_padding_1d.html
index 66d77c6d7..a5fea6861 100644
--- a/docs/reference/layer_zero_padding_1d.html
+++ b/docs/reference/layer_zero_padding_1d.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_zero_padding_2d.html b/docs/reference/layer_zero_padding_2d.html
index 6dc6791aa..592694112 100644
--- a/docs/reference/layer_zero_padding_2d.html
+++ b/docs/reference/layer_zero_padding_2d.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/layer_zero_padding_3d.html b/docs/reference/layer_zero_padding_3d.html
index f32549a8a..ba63edc23 100644
--- a/docs/reference/layer_zero_padding_3d.html
+++ b/docs/reference/layer_zero_padding_3d.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/learning_rate_schedule_cosine_decay.html b/docs/reference/learning_rate_schedule_cosine_decay.html
index f5f90b7ab..066a814ba 100644
--- a/docs/reference/learning_rate_schedule_cosine_decay.html
+++ b/docs/reference/learning_rate_schedule_cosine_decay.html
@@ -126,7 +126,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -190,7 +190,7 @@ <h6 class="dropdown-header" data-toc-skip>Other topics</h6>
     <p>See <a href="https://arxiv.org/abs/1608.03983" class="external-link">Loshchilov &amp; Hutter, ICLR2016</a>,
 SGDR: Stochastic Gradient Descent with Warm Restarts.</p>
 <p>For the idea of a linear warmup of our learning rate,
-see <a href="https://arxiv.org/pdf/1706.02677.pdf" class="external-link">Goyal et al.</a>.</p>
+see <a href="https://arxiv.org/pdf/1706.02677" class="external-link">Goyal et al.</a>.</p>
 <p>When we begin training a model, we often want an initial increase in our
 learning rate followed by a decay. If <code>warmup_target</code> is an int, this
 schedule applies a linear increase per optimizer step to our learning rate
diff --git a/docs/reference/learning_rate_schedule_cosine_decay_restarts.html b/docs/reference/learning_rate_schedule_cosine_decay_restarts.html
index 0b30b221f..f8a2144cd 100644
--- a/docs/reference/learning_rate_schedule_cosine_decay_restarts.html
+++ b/docs/reference/learning_rate_schedule_cosine_decay_restarts.html
@@ -36,7 +36,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/learning_rate_schedule_exponential_decay.html b/docs/reference/learning_rate_schedule_exponential_decay.html
index 55d9c70ae..ede59c0b2 100644
--- a/docs/reference/learning_rate_schedule_exponential_decay.html
+++ b/docs/reference/learning_rate_schedule_exponential_decay.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/learning_rate_schedule_inverse_time_decay.html b/docs/reference/learning_rate_schedule_inverse_time_decay.html
index 365dea21e..c2de57dc0 100644
--- a/docs/reference/learning_rate_schedule_inverse_time_decay.html
+++ b/docs/reference/learning_rate_schedule_inverse_time_decay.html
@@ -50,7 +50,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/learning_rate_schedule_piecewise_constant_decay.html b/docs/reference/learning_rate_schedule_piecewise_constant_decay.html
index 1f371eb6d..de7670d9a 100644
--- a/docs/reference/learning_rate_schedule_piecewise_constant_decay.html
+++ b/docs/reference/learning_rate_schedule_piecewise_constant_decay.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/learning_rate_schedule_polynomial_decay.html b/docs/reference/learning_rate_schedule_polynomial_decay.html
index 857870c55..1f6f62f53 100644
--- a/docs/reference/learning_rate_schedule_polynomial_decay.html
+++ b/docs/reference/learning_rate_schedule_polynomial_decay.html
@@ -68,7 +68,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/load_model.html b/docs/reference/load_model.html
index d081a6b7e..c08c7cd3d 100644
--- a/docs/reference/load_model.html
+++ b/docs/reference/load_model.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/load_model_weights.html b/docs/reference/load_model_weights.html
index 946580e50..ac734125c 100644
--- a/docs/reference/load_model_weights.html
+++ b/docs/reference/load_model_weights.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_binary_crossentropy.html b/docs/reference/loss_binary_crossentropy.html
index 1b3c563cb..7a6f47acc 100644
--- a/docs/reference/loss_binary_crossentropy.html
+++ b/docs/reference/loss_binary_crossentropy.html
@@ -28,7 +28,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_binary_focal_crossentropy.html b/docs/reference/loss_binary_focal_crossentropy.html
index cfeddcb4c..8b59bf0f6 100644
--- a/docs/reference/loss_binary_focal_crossentropy.html
+++ b/docs/reference/loss_binary_focal_crossentropy.html
@@ -66,7 +66,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -127,7 +127,7 @@ <h6 class="dropdown-header" data-toc-skip>Other topics</h6>
     </div>
 
     <div class="ref-description section level2">
-    <p>According to <a href="https://arxiv.org/pdf/1708.02002.pdf" class="external-link">Lin et al., 2018</a>, it
+    <p>According to <a href="https://arxiv.org/pdf/1708.02002" class="external-link">Lin et al., 2018</a>, it
 helps to apply a focal factor to down-weight easy examples and focus more on
 hard examples. By default, the focal tensor is computed as follows:</p>
 <p><code>focal_factor = (1 - output)^gamma</code> for class 1
@@ -146,7 +146,7 @@ <h6 class="dropdown-header" data-toc-skip>Other topics</h6>
 <a href="https://en.wikipedia.org/wiki/Logit" class="external-link">logit</a>, (i.e, value in <code>[-inf, inf]</code>
 when <code>from_logits=TRUE</code>) or a probability (i.e, value in <code>[0., 1.]</code> when
 <code>from_logits=FALSE</code>).</p></li>
-</ul><p>According to <a href="https://arxiv.org/pdf/1708.02002.pdf" class="external-link">Lin et al., 2018</a>, it
+</ul><p>According to <a href="https://arxiv.org/pdf/1708.02002" class="external-link">Lin et al., 2018</a>, it
 helps to apply a "focal factor" to down-weight easy examples and focus more
 on hard examples. By default, the focal tensor is computed as follows:</p>
 <p><code>focal_factor = (1 - output) ** gamma</code> for class 1
@@ -189,14 +189,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 <dt>alpha</dt>
 <dd><p>A weight balancing factor for class 1, default is <code>0.25</code> as
-mentioned in reference <a href="https://arxiv.org/pdf/1708.02002.pdf" class="external-link">Lin et al., 2018</a>.  The weight for class 0 is
+mentioned in reference <a href="https://arxiv.org/pdf/1708.02002" class="external-link">Lin et al., 2018</a>.  The weight for class 0 is
 <code>1.0 - alpha</code>.</p></dd>
 
 
 <dt>gamma</dt>
 <dd><p>A focusing parameter used to compute the focal factor, default is
 <code>2.0</code> as mentioned in the reference
-<a href="https://arxiv.org/pdf/1708.02002.pdf" class="external-link">Lin et al., 2018</a>.</p></dd>
+<a href="https://arxiv.org/pdf/1708.02002" class="external-link">Lin et al., 2018</a>.</p></dd>
 
 
 <dt>from_logits</dt>
diff --git a/docs/reference/loss_categorical_crossentropy.html b/docs/reference/loss_categorical_crossentropy.html
index 51ce38ca7..28a81f294 100644
--- a/docs/reference/loss_categorical_crossentropy.html
+++ b/docs/reference/loss_categorical_crossentropy.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_categorical_focal_crossentropy.html b/docs/reference/loss_categorical_focal_crossentropy.html
index 6739f94fa..cc8b2c747 100644
--- a/docs/reference/loss_categorical_focal_crossentropy.html
+++ b/docs/reference/loss_categorical_focal_crossentropy.html
@@ -66,7 +66,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -131,7 +131,7 @@ <h6 class="dropdown-header" data-toc-skip>Other topics</h6>
 classes and if you want to handle class imbalance without using
 <code>class_weights</code>. We expect labels to be provided in a <code>one_hot</code>
 representation.</p>
-<p>According to <a href="https://arxiv.org/pdf/1708.02002.pdf" class="external-link">Lin et al., 2018</a>, it
+<p>According to <a href="https://arxiv.org/pdf/1708.02002" class="external-link">Lin et al., 2018</a>, it
 helps to apply a focal factor to down-weight easy examples and focus more on
 hard examples. The general formula for the focal loss (FL)
 is as follows:</p>
diff --git a/docs/reference/loss_categorical_hinge.html b/docs/reference/loss_categorical_hinge.html
index 138e3e7ca..29b2f6228 100644
--- a/docs/reference/loss_categorical_hinge.html
+++ b/docs/reference/loss_categorical_hinge.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_cosine_similarity.html b/docs/reference/loss_cosine_similarity.html
index 782e18a72..063032349 100644
--- a/docs/reference/loss_cosine_similarity.html
+++ b/docs/reference/loss_cosine_similarity.html
@@ -28,7 +28,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_ctc.html b/docs/reference/loss_ctc.html
index 16adf4f3f..537823feb 100644
--- a/docs/reference/loss_ctc.html
+++ b/docs/reference/loss_ctc.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_dice.html b/docs/reference/loss_dice.html
index df4ecef80..7b02d1419 100644
--- a/docs/reference/loss_dice.html
+++ b/docs/reference/loss_dice.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_hinge.html b/docs/reference/loss_hinge.html
index 495657b99..ae08e22d3 100644
--- a/docs/reference/loss_hinge.html
+++ b/docs/reference/loss_hinge.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_huber.html b/docs/reference/loss_huber.html
index f497ec767..5d8db5287 100644
--- a/docs/reference/loss_huber.html
+++ b/docs/reference/loss_huber.html
@@ -30,7 +30,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_kl_divergence.html b/docs/reference/loss_kl_divergence.html
index 203d395ac..f505001e3 100644
--- a/docs/reference/loss_kl_divergence.html
+++ b/docs/reference/loss_kl_divergence.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_log_cosh.html b/docs/reference/loss_log_cosh.html
index 9d1ce7047..bf18c2e6d 100644
--- a/docs/reference/loss_log_cosh.html
+++ b/docs/reference/loss_log_cosh.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_mean_absolute_error.html b/docs/reference/loss_mean_absolute_error.html
index fdf7421e9..d032fbd94 100644
--- a/docs/reference/loss_mean_absolute_error.html
+++ b/docs/reference/loss_mean_absolute_error.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_mean_absolute_percentage_error.html b/docs/reference/loss_mean_absolute_percentage_error.html
index 98aaeb966..bbf6ec7d6 100644
--- a/docs/reference/loss_mean_absolute_percentage_error.html
+++ b/docs/reference/loss_mean_absolute_percentage_error.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_mean_squared_error.html b/docs/reference/loss_mean_squared_error.html
index 532d1e363..8f05e62ed 100644
--- a/docs/reference/loss_mean_squared_error.html
+++ b/docs/reference/loss_mean_squared_error.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_mean_squared_logarithmic_error.html b/docs/reference/loss_mean_squared_logarithmic_error.html
index 0b40ad740..20b367876 100644
--- a/docs/reference/loss_mean_squared_logarithmic_error.html
+++ b/docs/reference/loss_mean_squared_logarithmic_error.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_poisson.html b/docs/reference/loss_poisson.html
index 61cd4183e..5272f1f43 100644
--- a/docs/reference/loss_poisson.html
+++ b/docs/reference/loss_poisson.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_sparse_categorical_crossentropy.html b/docs/reference/loss_sparse_categorical_crossentropy.html
index 9f715e856..a2c6de393 100644
--- a/docs/reference/loss_sparse_categorical_crossentropy.html
+++ b/docs/reference/loss_sparse_categorical_crossentropy.html
@@ -28,7 +28,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_squared_hinge.html b/docs/reference/loss_squared_hinge.html
index f3e365c9c..013105d06 100644
--- a/docs/reference/loss_squared_hinge.html
+++ b/docs/reference/loss_squared_hinge.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/loss_tversky.html b/docs/reference/loss_tversky.html
index 8771c782c..440bbc45f 100644
--- a/docs/reference/loss_tversky.html
+++ b/docs/reference/loss_tversky.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/mark_active.html b/docs/reference/mark_active.html
index c14c0e95b..8776e7faf 100644
--- a/docs/reference/mark_active.html
+++ b/docs/reference/mark_active.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_auc.html b/docs/reference/metric_auc.html
index a68e73d1e..dc2293795 100644
--- a/docs/reference/metric_auc.html
+++ b/docs/reference/metric_auc.html
@@ -70,7 +70,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_binary_accuracy.html b/docs/reference/metric_binary_accuracy.html
index f78e088f0..2de72ba21 100644
--- a/docs/reference/metric_binary_accuracy.html
+++ b/docs/reference/metric_binary_accuracy.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_binary_crossentropy.html b/docs/reference/metric_binary_crossentropy.html
index 7a2f3127e..d9d56cd19 100644
--- a/docs/reference/metric_binary_crossentropy.html
+++ b/docs/reference/metric_binary_crossentropy.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_binary_focal_crossentropy.html b/docs/reference/metric_binary_focal_crossentropy.html
index 9f963dfc7..f5035ef08 100644
--- a/docs/reference/metric_binary_focal_crossentropy.html
+++ b/docs/reference/metric_binary_focal_crossentropy.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -93,7 +93,7 @@ <h6 class="dropdown-header" data-toc-skip>Other topics</h6>
     </div>
 
     <div class="ref-description section level2">
-    <p>According to <a href="https://arxiv.org/pdf/1708.02002.pdf" class="external-link">Lin et al., 2018</a>, it
+    <p>According to <a href="https://arxiv.org/pdf/1708.02002" class="external-link">Lin et al., 2018</a>, it
 helps to apply a focal factor to down-weight easy examples and focus more on
 hard examples. By default, the focal tensor is computed as follows:</p>
 <p><code>focal_factor = (1 - output)^gamma</code> for class 1
diff --git a/docs/reference/metric_binary_iou.html b/docs/reference/metric_binary_iou.html
index 224a2e575..5f8a11fad 100644
--- a/docs/reference/metric_binary_iou.html
+++ b/docs/reference/metric_binary_iou.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_categorical_accuracy.html b/docs/reference/metric_categorical_accuracy.html
index f53d51960..742e12495 100644
--- a/docs/reference/metric_categorical_accuracy.html
+++ b/docs/reference/metric_categorical_accuracy.html
@@ -30,7 +30,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_categorical_crossentropy.html b/docs/reference/metric_categorical_crossentropy.html
index 05f151001..8d4bc6e7a 100644
--- a/docs/reference/metric_categorical_crossentropy.html
+++ b/docs/reference/metric_categorical_crossentropy.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_categorical_focal_crossentropy.html b/docs/reference/metric_categorical_focal_crossentropy.html
index 3c7f67e96..3633345c1 100644
--- a/docs/reference/metric_categorical_focal_crossentropy.html
+++ b/docs/reference/metric_categorical_focal_crossentropy.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_categorical_hinge.html b/docs/reference/metric_categorical_hinge.html
index ea50c27f6..b931f33ae 100644
--- a/docs/reference/metric_categorical_hinge.html
+++ b/docs/reference/metric_categorical_hinge.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_cosine_similarity.html b/docs/reference/metric_cosine_similarity.html
index a46e37078..66f8e1721 100644
--- a/docs/reference/metric_cosine_similarity.html
+++ b/docs/reference/metric_cosine_similarity.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_f1_score.html b/docs/reference/metric_f1_score.html
index 7ba35171e..0ce1aac84 100644
--- a/docs/reference/metric_f1_score.html
+++ b/docs/reference/metric_f1_score.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_false_negatives.html b/docs/reference/metric_false_negatives.html
index 4eab27670..2aa6d80d2 100644
--- a/docs/reference/metric_false_negatives.html
+++ b/docs/reference/metric_false_negatives.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_false_positives.html b/docs/reference/metric_false_positives.html
index 74d8a7262..e53f14b9d 100644
--- a/docs/reference/metric_false_positives.html
+++ b/docs/reference/metric_false_positives.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_fbeta_score.html b/docs/reference/metric_fbeta_score.html
index 4e7afac41..8375802b1 100644
--- a/docs/reference/metric_fbeta_score.html
+++ b/docs/reference/metric_fbeta_score.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_hinge.html b/docs/reference/metric_hinge.html
index d62df6ac5..a191e519a 100644
--- a/docs/reference/metric_hinge.html
+++ b/docs/reference/metric_hinge.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_huber.html b/docs/reference/metric_huber.html
index 93d1b16ad..ff024e217 100644
--- a/docs/reference/metric_huber.html
+++ b/docs/reference/metric_huber.html
@@ -30,7 +30,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_iou.html b/docs/reference/metric_iou.html
index 7b8e74089..3771b7b0a 100644
--- a/docs/reference/metric_iou.html
+++ b/docs/reference/metric_iou.html
@@ -34,7 +34,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_kl_divergence.html b/docs/reference/metric_kl_divergence.html
index e2af9477d..6f3b288e2 100644
--- a/docs/reference/metric_kl_divergence.html
+++ b/docs/reference/metric_kl_divergence.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_log_cosh.html b/docs/reference/metric_log_cosh.html
index 0ce23fdef..b3077202f 100644
--- a/docs/reference/metric_log_cosh.html
+++ b/docs/reference/metric_log_cosh.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_log_cosh_error.html b/docs/reference/metric_log_cosh_error.html
index ed363163b..d120bd2fc 100644
--- a/docs/reference/metric_log_cosh_error.html
+++ b/docs/reference/metric_log_cosh_error.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_mean.html b/docs/reference/metric_mean.html
index f2c20dae7..43bf60ae4 100644
--- a/docs/reference/metric_mean.html
+++ b/docs/reference/metric_mean.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_mean_absolute_error.html b/docs/reference/metric_mean_absolute_error.html
index 0758dfb10..083b090af 100644
--- a/docs/reference/metric_mean_absolute_error.html
+++ b/docs/reference/metric_mean_absolute_error.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_mean_absolute_percentage_error.html b/docs/reference/metric_mean_absolute_percentage_error.html
index b8c0650b4..2c0ee63da 100644
--- a/docs/reference/metric_mean_absolute_percentage_error.html
+++ b/docs/reference/metric_mean_absolute_percentage_error.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_mean_iou.html b/docs/reference/metric_mean_iou.html
index 593a93e97..bf72328de 100644
--- a/docs/reference/metric_mean_iou.html
+++ b/docs/reference/metric_mean_iou.html
@@ -30,7 +30,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_mean_squared_error.html b/docs/reference/metric_mean_squared_error.html
index 442758929..1fa028007 100644
--- a/docs/reference/metric_mean_squared_error.html
+++ b/docs/reference/metric_mean_squared_error.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_mean_squared_logarithmic_error.html b/docs/reference/metric_mean_squared_logarithmic_error.html
index f116c82fa..9328c967b 100644
--- a/docs/reference/metric_mean_squared_logarithmic_error.html
+++ b/docs/reference/metric_mean_squared_logarithmic_error.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_mean_wrapper.html b/docs/reference/metric_mean_wrapper.html
index 7efc63789..b49fff0d5 100644
--- a/docs/reference/metric_mean_wrapper.html
+++ b/docs/reference/metric_mean_wrapper.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_one_hot_iou.html b/docs/reference/metric_one_hot_iou.html
index 3ad086138..4b320b659 100644
--- a/docs/reference/metric_one_hot_iou.html
+++ b/docs/reference/metric_one_hot_iou.html
@@ -48,7 +48,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_one_hot_mean_iou.html b/docs/reference/metric_one_hot_mean_iou.html
index 64e92bc96..60b4de878 100644
--- a/docs/reference/metric_one_hot_mean_iou.html
+++ b/docs/reference/metric_one_hot_mean_iou.html
@@ -50,7 +50,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_poisson.html b/docs/reference/metric_poisson.html
index eeda260dc..2bad620ca 100644
--- a/docs/reference/metric_poisson.html
+++ b/docs/reference/metric_poisson.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_precision.html b/docs/reference/metric_precision.html
index 235036e4a..6fb82e92e 100644
--- a/docs/reference/metric_precision.html
+++ b/docs/reference/metric_precision.html
@@ -36,7 +36,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_precision_at_recall.html b/docs/reference/metric_precision_at_recall.html
index 1d86ed645..e18f56c25 100644
--- a/docs/reference/metric_precision_at_recall.html
+++ b/docs/reference/metric_precision_at_recall.html
@@ -28,7 +28,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_r2_score.html b/docs/reference/metric_r2_score.html
index abdd70311..a3ac55b3d 100644
--- a/docs/reference/metric_r2_score.html
+++ b/docs/reference/metric_r2_score.html
@@ -42,7 +42,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_recall.html b/docs/reference/metric_recall.html
index d5982af48..d74fd65de 100644
--- a/docs/reference/metric_recall.html
+++ b/docs/reference/metric_recall.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_recall_at_precision.html b/docs/reference/metric_recall_at_precision.html
index 87a09a3ba..e198dc812 100644
--- a/docs/reference/metric_recall_at_precision.html
+++ b/docs/reference/metric_recall_at_precision.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_root_mean_squared_error.html b/docs/reference/metric_root_mean_squared_error.html
index 600207255..c8c6b992b 100644
--- a/docs/reference/metric_root_mean_squared_error.html
+++ b/docs/reference/metric_root_mean_squared_error.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_sensitivity_at_specificity.html b/docs/reference/metric_sensitivity_at_specificity.html
index 6df2d5eb2..41c0fcd92 100644
--- a/docs/reference/metric_sensitivity_at_specificity.html
+++ b/docs/reference/metric_sensitivity_at_specificity.html
@@ -42,7 +42,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_sparse_categorical_accuracy.html b/docs/reference/metric_sparse_categorical_accuracy.html
index a5d25f416..913f198c6 100644
--- a/docs/reference/metric_sparse_categorical_accuracy.html
+++ b/docs/reference/metric_sparse_categorical_accuracy.html
@@ -28,7 +28,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_sparse_categorical_crossentropy.html b/docs/reference/metric_sparse_categorical_crossentropy.html
index 37ddc9b17..4a34d75a5 100644
--- a/docs/reference/metric_sparse_categorical_crossentropy.html
+++ b/docs/reference/metric_sparse_categorical_crossentropy.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_sparse_top_k_categorical_accuracy.html b/docs/reference/metric_sparse_top_k_categorical_accuracy.html
index 04c3acf15..45f495ef1 100644
--- a/docs/reference/metric_sparse_top_k_categorical_accuracy.html
+++ b/docs/reference/metric_sparse_top_k_categorical_accuracy.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_specificity_at_sensitivity.html b/docs/reference/metric_specificity_at_sensitivity.html
index 9dc494c95..59c5d0645 100644
--- a/docs/reference/metric_specificity_at_sensitivity.html
+++ b/docs/reference/metric_specificity_at_sensitivity.html
@@ -42,7 +42,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_squared_hinge.html b/docs/reference/metric_squared_hinge.html
index 3adbe970a..c5135261f 100644
--- a/docs/reference/metric_squared_hinge.html
+++ b/docs/reference/metric_squared_hinge.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_sum.html b/docs/reference/metric_sum.html
index 7929da59c..16eb9b409 100644
--- a/docs/reference/metric_sum.html
+++ b/docs/reference/metric_sum.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_top_k_categorical_accuracy.html b/docs/reference/metric_top_k_categorical_accuracy.html
index 802f3726d..6813d499e 100644
--- a/docs/reference/metric_top_k_categorical_accuracy.html
+++ b/docs/reference/metric_top_k_categorical_accuracy.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_true_negatives.html b/docs/reference/metric_true_negatives.html
index 68ac62ad8..92bbcda7c 100644
--- a/docs/reference/metric_true_negatives.html
+++ b/docs/reference/metric_true_negatives.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/metric_true_positives.html b/docs/reference/metric_true_positives.html
index 4455b0396..cd9c49b33 100644
--- a/docs/reference/metric_true_positives.html
+++ b/docs/reference/metric_true_positives.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/multi-assign.html b/docs/reference/multi-assign.html
index 9697bfecc..42e6d6346 100644
--- a/docs/reference/multi-assign.html
+++ b/docs/reference/multi-assign.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/new_callback_class.html b/docs/reference/new_callback_class.html
index e3c9058f9..64def534a 100644
--- a/docs/reference/new_callback_class.html
+++ b/docs/reference/new_callback_class.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/new_layer_class.html b/docs/reference/new_layer_class.html
index e425e469f..b27c7797b 100644
--- a/docs/reference/new_layer_class.html
+++ b/docs/reference/new_layer_class.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/new_learning_rate_schedule_class.html b/docs/reference/new_learning_rate_schedule_class.html
index dea4519a2..a4a34b6be 100644
--- a/docs/reference/new_learning_rate_schedule_class.html
+++ b/docs/reference/new_learning_rate_schedule_class.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/new_loss_class.html b/docs/reference/new_loss_class.html
index ed244fd24..7e91c9349 100644
--- a/docs/reference/new_loss_class.html
+++ b/docs/reference/new_loss_class.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/new_metric_class.html b/docs/reference/new_metric_class.html
index 959e2aa76..a0ff3ecdd 100644
--- a/docs/reference/new_metric_class.html
+++ b/docs/reference/new_metric_class.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/new_model_class.html b/docs/reference/new_model_class.html
index 4bc2d0d7a..59e6eca80 100644
--- a/docs/reference/new_model_class.html
+++ b/docs/reference/new_model_class.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/normalize.html b/docs/reference/normalize.html
index d786f7a0d..a58117844 100644
--- a/docs/reference/normalize.html
+++ b/docs/reference/normalize.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -107,7 +107,7 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <div class="dont-index">
 <ul><li><p><a href="https://keras.io/api/utils/python_utils#normalize-function" class="external-link">https://keras.io/api/utils/python_utils#normalize-function</a></p></li>
 </ul><p>Other numerical utils: <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/docs/reference/op_abs.html b/docs/reference/op_abs.html
index eedcbb572..ac1bb3e40 100644
--- a/docs/reference/op_abs.html
+++ b/docs/reference/op_abs.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_add.html b/docs/reference/op_add.html
index 9329079d7..7e50fe92a 100644
--- a/docs/reference/op_add.html
+++ b/docs/reference/op_add.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_all.html b/docs/reference/op_all.html
index ed7a6f227..2c09c9e46 100644
--- a/docs/reference/op_all.html
+++ b/docs/reference/op_all.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_any.html b/docs/reference/op_any.html
index bdff4e3b7..140a99cff 100644
--- a/docs/reference/op_any.html
+++ b/docs/reference/op_any.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_append.html b/docs/reference/op_append.html
index 6aed09551..44872b190 100644
--- a/docs/reference/op_append.html
+++ b/docs/reference/op_append.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_arange.html b/docs/reference/op_arange.html
index 98e389615..b7f7c0670 100644
--- a/docs/reference/op_arange.html
+++ b/docs/reference/op_arange.html
@@ -28,7 +28,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_arccos.html b/docs/reference/op_arccos.html
index 7f9466848..5b92b41b1 100644
--- a/docs/reference/op_arccos.html
+++ b/docs/reference/op_arccos.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_arccosh.html b/docs/reference/op_arccosh.html
index 7d602e184..23244e5ad 100644
--- a/docs/reference/op_arccosh.html
+++ b/docs/reference/op_arccosh.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_arcsin.html b/docs/reference/op_arcsin.html
index c62792e90..9b5d981e1 100644
--- a/docs/reference/op_arcsin.html
+++ b/docs/reference/op_arcsin.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_arcsinh.html b/docs/reference/op_arcsinh.html
index 061ddf543..d46f5d5a1 100644
--- a/docs/reference/op_arcsinh.html
+++ b/docs/reference/op_arcsinh.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_arctan.html b/docs/reference/op_arctan.html
index e7e823464..b291cf566 100644
--- a/docs/reference/op_arctan.html
+++ b/docs/reference/op_arctan.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_arctan2.html b/docs/reference/op_arctan2.html
index b02d47d1b..7e33dc7a2 100644
--- a/docs/reference/op_arctan2.html
+++ b/docs/reference/op_arctan2.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_arctanh.html b/docs/reference/op_arctanh.html
index 9fbd88434..9d1710de1 100644
--- a/docs/reference/op_arctanh.html
+++ b/docs/reference/op_arctanh.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_argmax.html b/docs/reference/op_argmax.html
index c4b0dcc9b..7c21e265f 100644
--- a/docs/reference/op_argmax.html
+++ b/docs/reference/op_argmax.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_argmin.html b/docs/reference/op_argmin.html
index 9bbb70d9c..9cd746705 100644
--- a/docs/reference/op_argmin.html
+++ b/docs/reference/op_argmin.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_argsort.html b/docs/reference/op_argsort.html
index 6b32b38c9..a0fd501bd 100644
--- a/docs/reference/op_argsort.html
+++ b/docs/reference/op_argsort.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_array.html b/docs/reference/op_array.html
index 227207fae..1ce793b2b 100644
--- a/docs/reference/op_array.html
+++ b/docs/reference/op_array.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_average.html b/docs/reference/op_average.html
index 435536881..f989f6092 100644
--- a/docs/reference/op_average.html
+++ b/docs/reference/op_average.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_average_pool.html b/docs/reference/op_average_pool.html
index 66383795f..d1412654b 100644
--- a/docs/reference/op_average_pool.html
+++ b/docs/reference/op_average_pool.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_batch_normalization.html b/docs/reference/op_batch_normalization.html
index 0aa8c8395..b0836ba4a 100644
--- a/docs/reference/op_batch_normalization.html
+++ b/docs/reference/op_batch_normalization.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_binary_crossentropy.html b/docs/reference/op_binary_crossentropy.html
index a71f7c526..5b321c8a0 100644
--- a/docs/reference/op_binary_crossentropy.html
+++ b/docs/reference/op_binary_crossentropy.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_bincount.html b/docs/reference/op_bincount.html
index 816596767..6e42eb025 100644
--- a/docs/reference/op_bincount.html
+++ b/docs/reference/op_bincount.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_broadcast_to.html b/docs/reference/op_broadcast_to.html
index a25b2f0ce..4a44d1e3c 100644
--- a/docs/reference/op_broadcast_to.html
+++ b/docs/reference/op_broadcast_to.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_cast.html b/docs/reference/op_cast.html
index 7154659da..e693423e9 100644
--- a/docs/reference/op_cast.html
+++ b/docs/reference/op_cast.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_categorical_crossentropy.html b/docs/reference/op_categorical_crossentropy.html
index 04a056c9f..156733504 100644
--- a/docs/reference/op_categorical_crossentropy.html
+++ b/docs/reference/op_categorical_crossentropy.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_ceil.html b/docs/reference/op_ceil.html
index 342f4cd28..b4d766546 100644
--- a/docs/reference/op_ceil.html
+++ b/docs/reference/op_ceil.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_cholesky.html b/docs/reference/op_cholesky.html
index 9dca382cc..8e09e0716 100644
--- a/docs/reference/op_cholesky.html
+++ b/docs/reference/op_cholesky.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_clip.html b/docs/reference/op_clip.html
index b11bc4aec..7f6bfe9a6 100644
--- a/docs/reference/op_clip.html
+++ b/docs/reference/op_clip.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_concatenate.html b/docs/reference/op_concatenate.html
index 3b7cf7542..5d5cefe6a 100644
--- a/docs/reference/op_concatenate.html
+++ b/docs/reference/op_concatenate.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_cond.html b/docs/reference/op_cond.html
index a1f25868d..be8676f2f 100644
--- a/docs/reference/op_cond.html
+++ b/docs/reference/op_cond.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_conj.html b/docs/reference/op_conj.html
index 2e19534f8..c09aac431 100644
--- a/docs/reference/op_conj.html
+++ b/docs/reference/op_conj.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_conv.html b/docs/reference/op_conv.html
index e50f147b4..32e6e8d9d 100644
--- a/docs/reference/op_conv.html
+++ b/docs/reference/op_conv.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_conv_transpose.html b/docs/reference/op_conv_transpose.html
index e82c17beb..b95172cc3 100644
--- a/docs/reference/op_conv_transpose.html
+++ b/docs/reference/op_conv_transpose.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_convert_to_numpy.html b/docs/reference/op_convert_to_numpy.html
index cac7dad66..b5b51ff56 100644
--- a/docs/reference/op_convert_to_numpy.html
+++ b/docs/reference/op_convert_to_numpy.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_convert_to_tensor.html b/docs/reference/op_convert_to_tensor.html
index 465f535fb..87ee4571d 100644
--- a/docs/reference/op_convert_to_tensor.html
+++ b/docs/reference/op_convert_to_tensor.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_copy.html b/docs/reference/op_copy.html
index fe1312324..05df673d9 100644
--- a/docs/reference/op_copy.html
+++ b/docs/reference/op_copy.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_correlate.html b/docs/reference/op_correlate.html
index 7a66d5963..fed2f38ca 100644
--- a/docs/reference/op_correlate.html
+++ b/docs/reference/op_correlate.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_cos.html b/docs/reference/op_cos.html
index e964dfd0c..e9348a4b0 100644
--- a/docs/reference/op_cos.html
+++ b/docs/reference/op_cos.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_cosh.html b/docs/reference/op_cosh.html
index af59015dc..5e7c8dc94 100644
--- a/docs/reference/op_cosh.html
+++ b/docs/reference/op_cosh.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_count_nonzero.html b/docs/reference/op_count_nonzero.html
index dedb25a18..35c1cb6bf 100644
--- a/docs/reference/op_count_nonzero.html
+++ b/docs/reference/op_count_nonzero.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_cross.html b/docs/reference/op_cross.html
index 075b9fa88..c9215c6b0 100644
--- a/docs/reference/op_cross.html
+++ b/docs/reference/op_cross.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_ctc_decode.html b/docs/reference/op_ctc_decode.html
index 489715a35..25695e9a0 100644
--- a/docs/reference/op_ctc_decode.html
+++ b/docs/reference/op_ctc_decode.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_ctc_loss.html b/docs/reference/op_ctc_loss.html
index 8634eced7..e61ee7202 100644
--- a/docs/reference/op_ctc_loss.html
+++ b/docs/reference/op_ctc_loss.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_cumprod.html b/docs/reference/op_cumprod.html
index 51e2ae325..a1efc28f9 100644
--- a/docs/reference/op_cumprod.html
+++ b/docs/reference/op_cumprod.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_cumsum.html b/docs/reference/op_cumsum.html
index a6985b3d2..72409af4a 100644
--- a/docs/reference/op_cumsum.html
+++ b/docs/reference/op_cumsum.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_custom_gradient.html b/docs/reference/op_custom_gradient.html
index 59055acf8..391bcd760 100644
--- a/docs/reference/op_custom_gradient.html
+++ b/docs/reference/op_custom_gradient.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_depthwise_conv.html b/docs/reference/op_depthwise_conv.html
index 0e97645b5..dd948a5f7 100644
--- a/docs/reference/op_depthwise_conv.html
+++ b/docs/reference/op_depthwise_conv.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_det.html b/docs/reference/op_det.html
index 6593e5abb..4dcecd4a6 100644
--- a/docs/reference/op_det.html
+++ b/docs/reference/op_det.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_diag.html b/docs/reference/op_diag.html
index e8a262fa5..829a2bc07 100644
--- a/docs/reference/op_diag.html
+++ b/docs/reference/op_diag.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_diagonal.html b/docs/reference/op_diagonal.html
index 4bf59b773..e9005fb40 100644
--- a/docs/reference/op_diagonal.html
+++ b/docs/reference/op_diagonal.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_diff.html b/docs/reference/op_diff.html
index 1dff8973d..dbbaea8ee 100644
--- a/docs/reference/op_diff.html
+++ b/docs/reference/op_diff.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_digitize.html b/docs/reference/op_digitize.html
index 53f6cde33..d76626331 100644
--- a/docs/reference/op_digitize.html
+++ b/docs/reference/op_digitize.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_divide.html b/docs/reference/op_divide.html
index 5b1fe6f8b..6e4a3ac51 100644
--- a/docs/reference/op_divide.html
+++ b/docs/reference/op_divide.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_divide_no_nan.html b/docs/reference/op_divide_no_nan.html
index 3bc29954a..109b3b956 100644
--- a/docs/reference/op_divide_no_nan.html
+++ b/docs/reference/op_divide_no_nan.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_dot.html b/docs/reference/op_dot.html
index 1d4db0072..51b3a082e 100644
--- a/docs/reference/op_dot.html
+++ b/docs/reference/op_dot.html
@@ -34,7 +34,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_eig.html b/docs/reference/op_eig.html
index b2b4b1463..d58f32012 100644
--- a/docs/reference/op_eig.html
+++ b/docs/reference/op_eig.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_eigh.html b/docs/reference/op_eigh.html
index 7c143d128..c4f3645e8 100644
--- a/docs/reference/op_eigh.html
+++ b/docs/reference/op_eigh.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_einsum.html b/docs/reference/op_einsum.html
index 136ff35ae..a894527ad 100644
--- a/docs/reference/op_einsum.html
+++ b/docs/reference/op_einsum.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_elu.html b/docs/reference/op_elu.html
index f21427104..4cf3ec83a 100644
--- a/docs/reference/op_elu.html
+++ b/docs/reference/op_elu.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_empty.html b/docs/reference/op_empty.html
index 91e7ae544..ed9a1ff93 100644
--- a/docs/reference/op_empty.html
+++ b/docs/reference/op_empty.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_equal.html b/docs/reference/op_equal.html
index eed6d02b4..54eb66c56 100644
--- a/docs/reference/op_equal.html
+++ b/docs/reference/op_equal.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_erf.html b/docs/reference/op_erf.html
index 48dc1dd92..e97690b86 100644
--- a/docs/reference/op_erf.html
+++ b/docs/reference/op_erf.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_erfinv.html b/docs/reference/op_erfinv.html
index 24ea51a05..abbf0ad08 100644
--- a/docs/reference/op_erfinv.html
+++ b/docs/reference/op_erfinv.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_exp.html b/docs/reference/op_exp.html
index 6e9db8caf..025d241e5 100644
--- a/docs/reference/op_exp.html
+++ b/docs/reference/op_exp.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_expand_dims.html b/docs/reference/op_expand_dims.html
index 679a2d309..6d012dcc6 100644
--- a/docs/reference/op_expand_dims.html
+++ b/docs/reference/op_expand_dims.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_expm1.html b/docs/reference/op_expm1.html
index 52bb70571..50f44de99 100644
--- a/docs/reference/op_expm1.html
+++ b/docs/reference/op_expm1.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_extract_sequences.html b/docs/reference/op_extract_sequences.html
index 1b68db782..cf84bb158 100644
--- a/docs/reference/op_extract_sequences.html
+++ b/docs/reference/op_extract_sequences.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_eye.html b/docs/reference/op_eye.html
index 1d4fafb23..0834110b0 100644
--- a/docs/reference/op_eye.html
+++ b/docs/reference/op_eye.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_fft.html b/docs/reference/op_fft.html
index 5534b36c2..14272ffc5 100644
--- a/docs/reference/op_fft.html
+++ b/docs/reference/op_fft.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_fft2.html b/docs/reference/op_fft2.html
index 1b9a969e8..b405d3642 100644
--- a/docs/reference/op_fft2.html
+++ b/docs/reference/op_fft2.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_flip.html b/docs/reference/op_flip.html
index 2e0007d30..2fdf5d4f2 100644
--- a/docs/reference/op_flip.html
+++ b/docs/reference/op_flip.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_floor.html b/docs/reference/op_floor.html
index 80e5249b7..13d9ad00e 100644
--- a/docs/reference/op_floor.html
+++ b/docs/reference/op_floor.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_floor_divide.html b/docs/reference/op_floor_divide.html
index 0d884ba47..bcf70a30d 100644
--- a/docs/reference/op_floor_divide.html
+++ b/docs/reference/op_floor_divide.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_fori_loop.html b/docs/reference/op_fori_loop.html
index d54013296..c962fbaf4 100644
--- a/docs/reference/op_fori_loop.html
+++ b/docs/reference/op_fori_loop.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_full.html b/docs/reference/op_full.html
index 1e82a52f3..e29340438 100644
--- a/docs/reference/op_full.html
+++ b/docs/reference/op_full.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_full_like.html b/docs/reference/op_full_like.html
index d943ccf16..4047e0dab 100644
--- a/docs/reference/op_full_like.html
+++ b/docs/reference/op_full_like.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_gelu.html b/docs/reference/op_gelu.html
index bd6230a3e..b2528bf3c 100644
--- a/docs/reference/op_gelu.html
+++ b/docs/reference/op_gelu.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_get_item.html b/docs/reference/op_get_item.html
index 15e10f19b..ed935943d 100644
--- a/docs/reference/op_get_item.html
+++ b/docs/reference/op_get_item.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_greater.html b/docs/reference/op_greater.html
index 7064f1d58..ffde5495c 100644
--- a/docs/reference/op_greater.html
+++ b/docs/reference/op_greater.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_greater_equal.html b/docs/reference/op_greater_equal.html
index 380cf025d..5422efc5d 100644
--- a/docs/reference/op_greater_equal.html
+++ b/docs/reference/op_greater_equal.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_hard_sigmoid.html b/docs/reference/op_hard_sigmoid.html
index 3630b5fc7..68f3b0eb1 100644
--- a/docs/reference/op_hard_sigmoid.html
+++ b/docs/reference/op_hard_sigmoid.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_hard_silu.html b/docs/reference/op_hard_silu.html
index 85caf32fb..cd5eb5014 100644
--- a/docs/reference/op_hard_silu.html
+++ b/docs/reference/op_hard_silu.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_hstack.html b/docs/reference/op_hstack.html
index c7ff43a1c..c0ec7dc38 100644
--- a/docs/reference/op_hstack.html
+++ b/docs/reference/op_hstack.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_identity.html b/docs/reference/op_identity.html
index 99be04730..6a3027c17 100644
--- a/docs/reference/op_identity.html
+++ b/docs/reference/op_identity.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_imag.html b/docs/reference/op_imag.html
index 299c715bf..3038bdc88 100644
--- a/docs/reference/op_imag.html
+++ b/docs/reference/op_imag.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_image_affine_transform.html b/docs/reference/op_image_affine_transform.html
index 4c5e5610e..544019b05 100644
--- a/docs/reference/op_image_affine_transform.html
+++ b/docs/reference/op_image_affine_transform.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_image_crop.html b/docs/reference/op_image_crop.html
index f0b3c7eae..bd77e952c 100644
--- a/docs/reference/op_image_crop.html
+++ b/docs/reference/op_image_crop.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_image_extract_patches.html b/docs/reference/op_image_extract_patches.html
index 6301385e3..f721e2d6c 100644
--- a/docs/reference/op_image_extract_patches.html
+++ b/docs/reference/op_image_extract_patches.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_image_map_coordinates.html b/docs/reference/op_image_map_coordinates.html
index a573e1826..d87575fc1 100644
--- a/docs/reference/op_image_map_coordinates.html
+++ b/docs/reference/op_image_map_coordinates.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_image_pad.html b/docs/reference/op_image_pad.html
index 52beda4c7..3f87b11fb 100644
--- a/docs/reference/op_image_pad.html
+++ b/docs/reference/op_image_pad.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_image_resize.html b/docs/reference/op_image_resize.html
index 47c1b6996..57413abb6 100644
--- a/docs/reference/op_image_resize.html
+++ b/docs/reference/op_image_resize.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_image_rgb_to_grayscale.html b/docs/reference/op_image_rgb_to_grayscale.html
index 16bdc62c1..f1e1065f0 100644
--- a/docs/reference/op_image_rgb_to_grayscale.html
+++ b/docs/reference/op_image_rgb_to_grayscale.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_in_top_k.html b/docs/reference/op_in_top_k.html
index 39b21344e..d98e3523d 100644
--- a/docs/reference/op_in_top_k.html
+++ b/docs/reference/op_in_top_k.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_inv.html b/docs/reference/op_inv.html
index 939ec0dee..7347c9d70 100644
--- a/docs/reference/op_inv.html
+++ b/docs/reference/op_inv.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_irfft.html b/docs/reference/op_irfft.html
index 4e12af1d9..7dec508d7 100644
--- a/docs/reference/op_irfft.html
+++ b/docs/reference/op_irfft.html
@@ -30,7 +30,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_is_tensor.html b/docs/reference/op_is_tensor.html
index 0b6c0fb9c..2d3705465 100644
--- a/docs/reference/op_is_tensor.html
+++ b/docs/reference/op_is_tensor.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_isclose.html b/docs/reference/op_isclose.html
index 0b03bbd7f..84dc8e8f4 100644
--- a/docs/reference/op_isclose.html
+++ b/docs/reference/op_isclose.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_isfinite.html b/docs/reference/op_isfinite.html
index 19da6ae06..a58959ba2 100644
--- a/docs/reference/op_isfinite.html
+++ b/docs/reference/op_isfinite.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_isinf.html b/docs/reference/op_isinf.html
index 36697dcdb..3247f96b3 100644
--- a/docs/reference/op_isinf.html
+++ b/docs/reference/op_isinf.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_isnan.html b/docs/reference/op_isnan.html
index a2d2ceb78..177675031 100644
--- a/docs/reference/op_isnan.html
+++ b/docs/reference/op_isnan.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_istft.html b/docs/reference/op_istft.html
index 95eed8e6b..35ca67cfc 100644
--- a/docs/reference/op_istft.html
+++ b/docs/reference/op_istft.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_leaky_relu.html b/docs/reference/op_leaky_relu.html
index 258238348..ddee8dcc7 100644
--- a/docs/reference/op_leaky_relu.html
+++ b/docs/reference/op_leaky_relu.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_less.html b/docs/reference/op_less.html
index 469e30111..70fecec2e 100644
--- a/docs/reference/op_less.html
+++ b/docs/reference/op_less.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_less_equal.html b/docs/reference/op_less_equal.html
index 45981f66d..c7ad12e28 100644
--- a/docs/reference/op_less_equal.html
+++ b/docs/reference/op_less_equal.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_linspace.html b/docs/reference/op_linspace.html
index 82758bdfb..575c6352f 100644
--- a/docs/reference/op_linspace.html
+++ b/docs/reference/op_linspace.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_log.html b/docs/reference/op_log.html
index bcd576cb1..3531dfe07 100644
--- a/docs/reference/op_log.html
+++ b/docs/reference/op_log.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_log10.html b/docs/reference/op_log10.html
index f120f9c70..908b8ddbc 100644
--- a/docs/reference/op_log10.html
+++ b/docs/reference/op_log10.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_log1p.html b/docs/reference/op_log1p.html
index 71f4d14cd..ea5371b07 100644
--- a/docs/reference/op_log1p.html
+++ b/docs/reference/op_log1p.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_log2.html b/docs/reference/op_log2.html
index 2fec06482..e3dce69c4 100644
--- a/docs/reference/op_log2.html
+++ b/docs/reference/op_log2.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_log_sigmoid.html b/docs/reference/op_log_sigmoid.html
index 2bcdb092f..0cd565794 100644
--- a/docs/reference/op_log_sigmoid.html
+++ b/docs/reference/op_log_sigmoid.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_log_softmax.html b/docs/reference/op_log_softmax.html
index 422b63a84..830ff820e 100644
--- a/docs/reference/op_log_softmax.html
+++ b/docs/reference/op_log_softmax.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_logaddexp.html b/docs/reference/op_logaddexp.html
index 48cffae74..dbc77dc09 100644
--- a/docs/reference/op_logaddexp.html
+++ b/docs/reference/op_logaddexp.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_logical_and.html b/docs/reference/op_logical_and.html
index f6427f066..1f906df3b 100644
--- a/docs/reference/op_logical_and.html
+++ b/docs/reference/op_logical_and.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_logical_not.html b/docs/reference/op_logical_not.html
index 0ccfe6aaf..64c071b2c 100644
--- a/docs/reference/op_logical_not.html
+++ b/docs/reference/op_logical_not.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_logical_or.html b/docs/reference/op_logical_or.html
index a4681fc0e..898995452 100644
--- a/docs/reference/op_logical_or.html
+++ b/docs/reference/op_logical_or.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_logical_xor.html b/docs/reference/op_logical_xor.html
index 1d3fb73e7..44196a9e1 100644
--- a/docs/reference/op_logical_xor.html
+++ b/docs/reference/op_logical_xor.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_logspace.html b/docs/reference/op_logspace.html
index 20a3e186d..9fa0383e2 100644
--- a/docs/reference/op_logspace.html
+++ b/docs/reference/op_logspace.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_logsumexp.html b/docs/reference/op_logsumexp.html
index 7746d2fcc..59f29cdc6 100644
--- a/docs/reference/op_logsumexp.html
+++ b/docs/reference/op_logsumexp.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_lu_factor.html b/docs/reference/op_lu_factor.html
index be2ba210c..53eab575b 100644
--- a/docs/reference/op_lu_factor.html
+++ b/docs/reference/op_lu_factor.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_matmul.html b/docs/reference/op_matmul.html
index 1ffa31300..5cf5e2daf 100644
--- a/docs/reference/op_matmul.html
+++ b/docs/reference/op_matmul.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_max.html b/docs/reference/op_max.html
index 6f5453471..40940818c 100644
--- a/docs/reference/op_max.html
+++ b/docs/reference/op_max.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_max_pool.html b/docs/reference/op_max_pool.html
index e8db48f08..21e925cab 100644
--- a/docs/reference/op_max_pool.html
+++ b/docs/reference/op_max_pool.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_maximum.html b/docs/reference/op_maximum.html
index e189e3690..27805be2d 100644
--- a/docs/reference/op_maximum.html
+++ b/docs/reference/op_maximum.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_mean.html b/docs/reference/op_mean.html
index 4576cc291..17f369abf 100644
--- a/docs/reference/op_mean.html
+++ b/docs/reference/op_mean.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_median.html b/docs/reference/op_median.html
index 159701a3d..5bf4dcb0e 100644
--- a/docs/reference/op_median.html
+++ b/docs/reference/op_median.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_meshgrid.html b/docs/reference/op_meshgrid.html
index 4ff241924..ee4a83305 100644
--- a/docs/reference/op_meshgrid.html
+++ b/docs/reference/op_meshgrid.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_min.html b/docs/reference/op_min.html
index 48ada816e..e1acc8e62 100644
--- a/docs/reference/op_min.html
+++ b/docs/reference/op_min.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_minimum.html b/docs/reference/op_minimum.html
index 395063ccf..8a2a1021e 100644
--- a/docs/reference/op_minimum.html
+++ b/docs/reference/op_minimum.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_mod.html b/docs/reference/op_mod.html
index 7b0df44ab..de1ac2316 100644
--- a/docs/reference/op_mod.html
+++ b/docs/reference/op_mod.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_moments.html b/docs/reference/op_moments.html
index 69e1501e4..0968e69f7 100644
--- a/docs/reference/op_moments.html
+++ b/docs/reference/op_moments.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_moveaxis.html b/docs/reference/op_moveaxis.html
index 7f4c48085..1c92f9988 100644
--- a/docs/reference/op_moveaxis.html
+++ b/docs/reference/op_moveaxis.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_multi_hot.html b/docs/reference/op_multi_hot.html
index 2e7b8b5e9..c5b8e8dbe 100644
--- a/docs/reference/op_multi_hot.html
+++ b/docs/reference/op_multi_hot.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_multiply.html b/docs/reference/op_multiply.html
index 1fc6f4768..75763b745 100644
--- a/docs/reference/op_multiply.html
+++ b/docs/reference/op_multiply.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_nan_to_num.html b/docs/reference/op_nan_to_num.html
index f16e2a7f3..457ac87f1 100644
--- a/docs/reference/op_nan_to_num.html
+++ b/docs/reference/op_nan_to_num.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_ndim.html b/docs/reference/op_ndim.html
index f7aba737d..ece33f9a0 100644
--- a/docs/reference/op_ndim.html
+++ b/docs/reference/op_ndim.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_negative.html b/docs/reference/op_negative.html
index ffc11e150..dbcb32fbf 100644
--- a/docs/reference/op_negative.html
+++ b/docs/reference/op_negative.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_nonzero.html b/docs/reference/op_nonzero.html
index cfbaf4bd1..1f8a34038 100644
--- a/docs/reference/op_nonzero.html
+++ b/docs/reference/op_nonzero.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_norm.html b/docs/reference/op_norm.html
index cc7e2185e..1305b750e 100644
--- a/docs/reference/op_norm.html
+++ b/docs/reference/op_norm.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_normalize.html b/docs/reference/op_normalize.html
index 86fefe917..29ab9af27 100644
--- a/docs/reference/op_normalize.html
+++ b/docs/reference/op_normalize.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_not_equal.html b/docs/reference/op_not_equal.html
index 58ff05f7c..9fad815cc 100644
--- a/docs/reference/op_not_equal.html
+++ b/docs/reference/op_not_equal.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_one_hot.html b/docs/reference/op_one_hot.html
index 242aa4b10..710fe9112 100644
--- a/docs/reference/op_one_hot.html
+++ b/docs/reference/op_one_hot.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_ones.html b/docs/reference/op_ones.html
index 7bea4aede..d332a90e2 100644
--- a/docs/reference/op_ones.html
+++ b/docs/reference/op_ones.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_ones_like.html b/docs/reference/op_ones_like.html
index 9cca9c4dc..015fd53c8 100644
--- a/docs/reference/op_ones_like.html
+++ b/docs/reference/op_ones_like.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_outer.html b/docs/reference/op_outer.html
index af22a0504..498e8499c 100644
--- a/docs/reference/op_outer.html
+++ b/docs/reference/op_outer.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_pad.html b/docs/reference/op_pad.html
index 946bf54c7..8ad37f3b4 100644
--- a/docs/reference/op_pad.html
+++ b/docs/reference/op_pad.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_power.html b/docs/reference/op_power.html
index 545219856..39dd74ac7 100644
--- a/docs/reference/op_power.html
+++ b/docs/reference/op_power.html
@@ -40,7 +40,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_prod.html b/docs/reference/op_prod.html
index da9fc3441..39f3a61e6 100644
--- a/docs/reference/op_prod.html
+++ b/docs/reference/op_prod.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_psnr.html b/docs/reference/op_psnr.html
index e47a5c7b1..c3d9b4c0e 100644
--- a/docs/reference/op_psnr.html
+++ b/docs/reference/op_psnr.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_qr.html b/docs/reference/op_qr.html
index b1437a673..dc60aada7 100644
--- a/docs/reference/op_qr.html
+++ b/docs/reference/op_qr.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_quantile.html b/docs/reference/op_quantile.html
index 39c29b767..00934ba10 100644
--- a/docs/reference/op_quantile.html
+++ b/docs/reference/op_quantile.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_ravel.html b/docs/reference/op_ravel.html
index bbbb84d2c..88e0f2dca 100644
--- a/docs/reference/op_ravel.html
+++ b/docs/reference/op_ravel.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_real.html b/docs/reference/op_real.html
index 50367931c..199f1cf12 100644
--- a/docs/reference/op_real.html
+++ b/docs/reference/op_real.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_reciprocal.html b/docs/reference/op_reciprocal.html
index 541538623..3f44ed7b1 100644
--- a/docs/reference/op_reciprocal.html
+++ b/docs/reference/op_reciprocal.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_relu.html b/docs/reference/op_relu.html
index cb41a8efd..6bf4a2ea3 100644
--- a/docs/reference/op_relu.html
+++ b/docs/reference/op_relu.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_relu6.html b/docs/reference/op_relu6.html
index 6d37706d7..fb3293611 100644
--- a/docs/reference/op_relu6.html
+++ b/docs/reference/op_relu6.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_repeat.html b/docs/reference/op_repeat.html
index 2cb4963aa..d29470c2a 100644
--- a/docs/reference/op_repeat.html
+++ b/docs/reference/op_repeat.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_reshape.html b/docs/reference/op_reshape.html
index 9eeac3756..6ce9f3023 100644
--- a/docs/reference/op_reshape.html
+++ b/docs/reference/op_reshape.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_rfft.html b/docs/reference/op_rfft.html
index f78563c14..27ee58e52 100644
--- a/docs/reference/op_rfft.html
+++ b/docs/reference/op_rfft.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_roll.html b/docs/reference/op_roll.html
index 9bb5854dd..6ce17f4e0 100644
--- a/docs/reference/op_roll.html
+++ b/docs/reference/op_roll.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_round.html b/docs/reference/op_round.html
index 951463f8f..02138ecc7 100644
--- a/docs/reference/op_round.html
+++ b/docs/reference/op_round.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_rsqrt.html b/docs/reference/op_rsqrt.html
index 4fa5e6513..8ba6d5768 100644
--- a/docs/reference/op_rsqrt.html
+++ b/docs/reference/op_rsqrt.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_scatter.html b/docs/reference/op_scatter.html
index 69f0c9281..640fd27b6 100644
--- a/docs/reference/op_scatter.html
+++ b/docs/reference/op_scatter.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_scatter_update.html b/docs/reference/op_scatter_update.html
index 97f90a60f..fa6cd7f3e 100644
--- a/docs/reference/op_scatter_update.html
+++ b/docs/reference/op_scatter_update.html
@@ -160,7 +160,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_segment_max.html b/docs/reference/op_segment_max.html
index ad0073c29..878e2f55f 100644
--- a/docs/reference/op_segment_max.html
+++ b/docs/reference/op_segment_max.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_segment_sum.html b/docs/reference/op_segment_sum.html
index ad2bcc1ee..afe742014 100644
--- a/docs/reference/op_segment_sum.html
+++ b/docs/reference/op_segment_sum.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_select.html b/docs/reference/op_select.html
index 5f3f79fcc..a39550e88 100644
--- a/docs/reference/op_select.html
+++ b/docs/reference/op_select.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_selu.html b/docs/reference/op_selu.html
index e8b35777c..1ed45b4b6 100644
--- a/docs/reference/op_selu.html
+++ b/docs/reference/op_selu.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_separable_conv.html b/docs/reference/op_separable_conv.html
index 4689504ff..8c9739246 100644
--- a/docs/reference/op_separable_conv.html
+++ b/docs/reference/op_separable_conv.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_shape.html b/docs/reference/op_shape.html
index 03a2cf136..eba82546a 100644
--- a/docs/reference/op_shape.html
+++ b/docs/reference/op_shape.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_sigmoid.html b/docs/reference/op_sigmoid.html
index 33fe66248..2c4104840 100644
--- a/docs/reference/op_sigmoid.html
+++ b/docs/reference/op_sigmoid.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_sign.html b/docs/reference/op_sign.html
index 8e3560553..7375bbc55 100644
--- a/docs/reference/op_sign.html
+++ b/docs/reference/op_sign.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_silu.html b/docs/reference/op_silu.html
index 95d204e20..38b5f41ce 100644
--- a/docs/reference/op_silu.html
+++ b/docs/reference/op_silu.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_sin.html b/docs/reference/op_sin.html
index 5f8df2e98..99b763b66 100644
--- a/docs/reference/op_sin.html
+++ b/docs/reference/op_sin.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_sinh.html b/docs/reference/op_sinh.html
index a6a95cd28..36821b6d1 100644
--- a/docs/reference/op_sinh.html
+++ b/docs/reference/op_sinh.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_size.html b/docs/reference/op_size.html
index f1b9cd163..f77c15a4a 100644
--- a/docs/reference/op_size.html
+++ b/docs/reference/op_size.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_slice.html b/docs/reference/op_slice.html
index d5e191426..fa2ba81cf 100644
--- a/docs/reference/op_slice.html
+++ b/docs/reference/op_slice.html
@@ -56,7 +56,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_slice_update.html b/docs/reference/op_slice_update.html
index 19364311d..e79c1e139 100644
--- a/docs/reference/op_slice_update.html
+++ b/docs/reference/op_slice_update.html
@@ -52,7 +52,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_slogdet.html b/docs/reference/op_slogdet.html
index ffbcc32d1..3da1ef8e9 100644
--- a/docs/reference/op_slogdet.html
+++ b/docs/reference/op_slogdet.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_softmax.html b/docs/reference/op_softmax.html
index 6307b0718..e767ff5c6 100644
--- a/docs/reference/op_softmax.html
+++ b/docs/reference/op_softmax.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_softplus.html b/docs/reference/op_softplus.html
index bdb3bbe1e..95aed401a 100644
--- a/docs/reference/op_softplus.html
+++ b/docs/reference/op_softplus.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_softsign.html b/docs/reference/op_softsign.html
index b3ba12934..80e893cb9 100644
--- a/docs/reference/op_softsign.html
+++ b/docs/reference/op_softsign.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_solve.html b/docs/reference/op_solve.html
index 85d91ce77..13e1cede8 100644
--- a/docs/reference/op_solve.html
+++ b/docs/reference/op_solve.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_solve_triangular.html b/docs/reference/op_solve_triangular.html
index 69c88f298..f9b201062 100644
--- a/docs/reference/op_solve_triangular.html
+++ b/docs/reference/op_solve_triangular.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_sort.html b/docs/reference/op_sort.html
index 247de3858..77d9ed311 100644
--- a/docs/reference/op_sort.html
+++ b/docs/reference/op_sort.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_sparse_categorical_crossentropy.html b/docs/reference/op_sparse_categorical_crossentropy.html
index 37c8e69d6..b12251f2d 100644
--- a/docs/reference/op_sparse_categorical_crossentropy.html
+++ b/docs/reference/op_sparse_categorical_crossentropy.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_split.html b/docs/reference/op_split.html
index 79da9fd2e..4dd6636bf 100644
--- a/docs/reference/op_split.html
+++ b/docs/reference/op_split.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_sqrt.html b/docs/reference/op_sqrt.html
index 4c8370f59..ef870388a 100644
--- a/docs/reference/op_sqrt.html
+++ b/docs/reference/op_sqrt.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_square.html b/docs/reference/op_square.html
index 219ffd0da..75b7f4b9f 100644
--- a/docs/reference/op_square.html
+++ b/docs/reference/op_square.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_squeeze.html b/docs/reference/op_squeeze.html
index ea7f3d996..5f3694ec8 100644
--- a/docs/reference/op_squeeze.html
+++ b/docs/reference/op_squeeze.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_stack.html b/docs/reference/op_stack.html
index 860c8d817..9908ad290 100644
--- a/docs/reference/op_stack.html
+++ b/docs/reference/op_stack.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_std.html b/docs/reference/op_std.html
index 71b3d44e3..e06ee43d2 100644
--- a/docs/reference/op_std.html
+++ b/docs/reference/op_std.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_stft.html b/docs/reference/op_stft.html
index 7cc712b5c..438d79b6a 100644
--- a/docs/reference/op_stft.html
+++ b/docs/reference/op_stft.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_stop_gradient.html b/docs/reference/op_stop_gradient.html
index 00e139ae4..ca9ff580c 100644
--- a/docs/reference/op_stop_gradient.html
+++ b/docs/reference/op_stop_gradient.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_subtract.html b/docs/reference/op_subtract.html
index c173d3fdf..681482397 100644
--- a/docs/reference/op_subtract.html
+++ b/docs/reference/op_subtract.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_sum.html b/docs/reference/op_sum.html
index 165916f3b..7e2208f0a 100644
--- a/docs/reference/op_sum.html
+++ b/docs/reference/op_sum.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_svd.html b/docs/reference/op_svd.html
index 03e04f3d8..5ea7a8515 100644
--- a/docs/reference/op_svd.html
+++ b/docs/reference/op_svd.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_swapaxes.html b/docs/reference/op_swapaxes.html
index 9286ec4a1..3f8d9ba45 100644
--- a/docs/reference/op_swapaxes.html
+++ b/docs/reference/op_swapaxes.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_take.html b/docs/reference/op_take.html
index 34a35ff35..e213ffc47 100644
--- a/docs/reference/op_take.html
+++ b/docs/reference/op_take.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_take_along_axis.html b/docs/reference/op_take_along_axis.html
index 9bee4de31..61e116bbe 100644
--- a/docs/reference/op_take_along_axis.html
+++ b/docs/reference/op_take_along_axis.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_tan.html b/docs/reference/op_tan.html
index 54e58ef44..08590d9fa 100644
--- a/docs/reference/op_tan.html
+++ b/docs/reference/op_tan.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_tanh.html b/docs/reference/op_tanh.html
index a8f3e09c1..a4a4a9624 100644
--- a/docs/reference/op_tanh.html
+++ b/docs/reference/op_tanh.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_tensordot.html b/docs/reference/op_tensordot.html
index 7c928017f..74d6a6ce1 100644
--- a/docs/reference/op_tensordot.html
+++ b/docs/reference/op_tensordot.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_tile.html b/docs/reference/op_tile.html
index 9d21757b8..17e262bb8 100644
--- a/docs/reference/op_tile.html
+++ b/docs/reference/op_tile.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_top_k.html b/docs/reference/op_top_k.html
index 9a9074334..af807c7af 100644
--- a/docs/reference/op_top_k.html
+++ b/docs/reference/op_top_k.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_trace.html b/docs/reference/op_trace.html
index 639034076..0337e8332 100644
--- a/docs/reference/op_trace.html
+++ b/docs/reference/op_trace.html
@@ -22,7 +22,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_transpose.html b/docs/reference/op_transpose.html
index ade8344dd..8a0b7cfe2 100644
--- a/docs/reference/op_transpose.html
+++ b/docs/reference/op_transpose.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_tri.html b/docs/reference/op_tri.html
index 38af18a6b..4b0b48670 100644
--- a/docs/reference/op_tri.html
+++ b/docs/reference/op_tri.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_tril.html b/docs/reference/op_tril.html
index 9957033de..7c2c8dce5 100644
--- a/docs/reference/op_tril.html
+++ b/docs/reference/op_tril.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_triu.html b/docs/reference/op_triu.html
index 8705f64ad..87c69a75d 100644
--- a/docs/reference/op_triu.html
+++ b/docs/reference/op_triu.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_unstack.html b/docs/reference/op_unstack.html
index 20ee221df..a9f5e2aea 100644
--- a/docs/reference/op_unstack.html
+++ b/docs/reference/op_unstack.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_var.html b/docs/reference/op_var.html
index abca7b429..d74d970be 100644
--- a/docs/reference/op_var.html
+++ b/docs/reference/op_var.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_vdot.html b/docs/reference/op_vdot.html
index 3c55b9cbf..94937aa4e 100644
--- a/docs/reference/op_vdot.html
+++ b/docs/reference/op_vdot.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_vectorize.html b/docs/reference/op_vectorize.html
index 865e6e2d6..e931dd358 100644
--- a/docs/reference/op_vectorize.html
+++ b/docs/reference/op_vectorize.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_vectorized_map.html b/docs/reference/op_vectorized_map.html
index dd8736fb2..b678b6911 100644
--- a/docs/reference/op_vectorized_map.html
+++ b/docs/reference/op_vectorized_map.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_vstack.html b/docs/reference/op_vstack.html
index d173717f7..b55cc2bbd 100644
--- a/docs/reference/op_vstack.html
+++ b/docs/reference/op_vstack.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_where.html b/docs/reference/op_where.html
index 8be6e0b6b..d95cb3285 100644
--- a/docs/reference/op_where.html
+++ b/docs/reference/op_where.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_while_loop.html b/docs/reference/op_while_loop.html
index 9be0a7f07..99b9557bb 100644
--- a/docs/reference/op_while_loop.html
+++ b/docs/reference/op_while_loop.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_zeros.html b/docs/reference/op_zeros.html
index 86155daaf..29e49dea8 100644
--- a/docs/reference/op_zeros.html
+++ b/docs/reference/op_zeros.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/op_zeros_like.html b/docs/reference/op_zeros_like.html
index 0b571a2cd..66cbad257 100644
--- a/docs/reference/op_zeros_like.html
+++ b/docs/reference/op_zeros_like.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/optimizer_adadelta.html b/docs/reference/optimizer_adadelta.html
index 4b7eff6a4..a14934b6e 100644
--- a/docs/reference/optimizer_adadelta.html
+++ b/docs/reference/optimizer_adadelta.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/optimizer_adafactor.html b/docs/reference/optimizer_adafactor.html
index 01ec5af4b..442b211a7 100644
--- a/docs/reference/optimizer_adafactor.html
+++ b/docs/reference/optimizer_adafactor.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/optimizer_adagrad.html b/docs/reference/optimizer_adagrad.html
index 041434ca6..6e90ff4d0 100644
--- a/docs/reference/optimizer_adagrad.html
+++ b/docs/reference/optimizer_adagrad.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/optimizer_adam.html b/docs/reference/optimizer_adam.html
index ab4396eb4..f55a8983b 100644
--- a/docs/reference/optimizer_adam.html
+++ b/docs/reference/optimizer_adam.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/optimizer_adam_w.html b/docs/reference/optimizer_adam_w.html
index 976d47a5d..af764fab3 100644
--- a/docs/reference/optimizer_adam_w.html
+++ b/docs/reference/optimizer_adam_w.html
@@ -30,7 +30,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/optimizer_adamax.html b/docs/reference/optimizer_adamax.html
index baf2b8d1d..8267f88de 100644
--- a/docs/reference/optimizer_adamax.html
+++ b/docs/reference/optimizer_adamax.html
@@ -46,7 +46,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/optimizer_ftrl.html b/docs/reference/optimizer_ftrl.html
index 000781f72..52b5ed81d 100644
--- a/docs/reference/optimizer_ftrl.html
+++ b/docs/reference/optimizer_ftrl.html
@@ -80,7 +80,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/optimizer_lion.html b/docs/reference/optimizer_lion.html
index 8c2591534..66d6bf0cf 100644
--- a/docs/reference/optimizer_lion.html
+++ b/docs/reference/optimizer_lion.html
@@ -28,7 +28,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/optimizer_loss_scale.html b/docs/reference/optimizer_loss_scale.html
index 5bb8e31fa..5525a2eb7 100644
--- a/docs/reference/optimizer_loss_scale.html
+++ b/docs/reference/optimizer_loss_scale.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/optimizer_nadam.html b/docs/reference/optimizer_nadam.html
index 6c328e18e..2a693f061 100644
--- a/docs/reference/optimizer_nadam.html
+++ b/docs/reference/optimizer_nadam.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/optimizer_rmsprop.html b/docs/reference/optimizer_rmsprop.html
index 22dda54c8..24074b0ed 100644
--- a/docs/reference/optimizer_rmsprop.html
+++ b/docs/reference/optimizer_rmsprop.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/optimizer_sgd.html b/docs/reference/optimizer_sgd.html
index a116d3296..0087e669c 100644
--- a/docs/reference/optimizer_sgd.html
+++ b/docs/reference/optimizer_sgd.html
@@ -30,7 +30,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/pad_sequences.html b/docs/reference/pad_sequences.html
index e39643dc9..e68140331 100644
--- a/docs/reference/pad_sequences.html
+++ b/docs/reference/pad_sequences.html
@@ -100,7 +100,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -257,7 +257,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index">
 <ul><li><p><a href="https://keras.io/api/data_loading/timeseries#padsequences-function" class="external-link">https://keras.io/api/data_loading/timeseries#padsequences-function</a></p></li>
-</ul><p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
+</ul><p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html
index a0d1a74a5..b360acb0e 100644
--- a/docs/reference/pipe.html
+++ b/docs/reference/pipe.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/plot.keras.src.models.model.Model.html b/docs/reference/plot.keras.src.models.model.Model.html
index f6635bbb4..d033b417d 100644
--- a/docs/reference/plot.keras.src.models.model.Model.html
+++ b/docs/reference/plot.keras.src.models.model.Model.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/plot.keras_training_history.html b/docs/reference/plot.keras_training_history.html
index 7db40cb27..fcccc9536 100644
--- a/docs/reference/plot.keras_training_history.html
+++ b/docs/reference/plot.keras_training_history.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/pop_layer.html b/docs/reference/pop_layer.html
index 1ae8401b4..e7c87930a 100644
--- a/docs/reference/pop_layer.html
+++ b/docs/reference/pop_layer.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/predict.keras.src.models.model.Model.html b/docs/reference/predict.keras.src.models.model.Model.html
index e8aedd55b..1cf300895 100644
--- a/docs/reference/predict.keras.src.models.model.Model.html
+++ b/docs/reference/predict.keras.src.models.model.Model.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/predict_on_batch.html b/docs/reference/predict_on_batch.html
index ff9dcf3a8..6e7e2d54f 100644
--- a/docs/reference/predict_on_batch.html
+++ b/docs/reference/predict_on_batch.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/process_utils.html b/docs/reference/process_utils.html
index fad8b4476..46b8aee04 100644
--- a/docs/reference/process_utils.html
+++ b/docs/reference/process_utils.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/quantize_weights.html b/docs/reference/quantize_weights.html
index f0156f78a..19d96862b 100644
--- a/docs/reference/quantize_weights.html
+++ b/docs/reference/quantize_weights.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/random_beta.html b/docs/reference/random_beta.html
index 4cc940032..b9ad18e27 100644
--- a/docs/reference/random_beta.html
+++ b/docs/reference/random_beta.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/random_binomial.html b/docs/reference/random_binomial.html
index a9c28b9ab..b32fc789b 100644
--- a/docs/reference/random_binomial.html
+++ b/docs/reference/random_binomial.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/random_categorical.html b/docs/reference/random_categorical.html
index 79d46eb59..d121a8c37 100644
--- a/docs/reference/random_categorical.html
+++ b/docs/reference/random_categorical.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/random_dropout.html b/docs/reference/random_dropout.html
index 547b4063e..369085211 100644
--- a/docs/reference/random_dropout.html
+++ b/docs/reference/random_dropout.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/random_gamma.html b/docs/reference/random_gamma.html
index 6aa83fb94..ac498e489 100644
--- a/docs/reference/random_gamma.html
+++ b/docs/reference/random_gamma.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/random_integer.html b/docs/reference/random_integer.html
index 06a4a8180..20c06d345 100644
--- a/docs/reference/random_integer.html
+++ b/docs/reference/random_integer.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/random_normal.html b/docs/reference/random_normal.html
index d8177c8f3..bd746c454 100644
--- a/docs/reference/random_normal.html
+++ b/docs/reference/random_normal.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/random_seed_generator.html b/docs/reference/random_seed_generator.html
index d10833d3b..f95799094 100644
--- a/docs/reference/random_seed_generator.html
+++ b/docs/reference/random_seed_generator.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/random_shuffle.html b/docs/reference/random_shuffle.html
index 0c4b136b4..cfcacb1ab 100644
--- a/docs/reference/random_shuffle.html
+++ b/docs/reference/random_shuffle.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/random_truncated_normal.html b/docs/reference/random_truncated_normal.html
index cdcc57a28..d1734d531 100644
--- a/docs/reference/random_truncated_normal.html
+++ b/docs/reference/random_truncated_normal.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/random_uniform.html b/docs/reference/random_uniform.html
index 35700cb2c..76851f153 100644
--- a/docs/reference/random_uniform.html
+++ b/docs/reference/random_uniform.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/reexports.html b/docs/reference/reexports.html
index d179ab692..630c4d6a5 100644
--- a/docs/reference/reexports.html
+++ b/docs/reference/reexports.html
@@ -56,7 +56,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/register_keras_serializable.html b/docs/reference/register_keras_serializable.html
index 54d3c419d..b4e7792b2 100644
--- a/docs/reference/register_keras_serializable.html
+++ b/docs/reference/register_keras_serializable.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/regularizer_l1.html b/docs/reference/regularizer_l1.html
index 52add693b..d633c17dc 100644
--- a/docs/reference/regularizer_l1.html
+++ b/docs/reference/regularizer_l1.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/regularizer_l1_l2.html b/docs/reference/regularizer_l1_l2.html
index 9742dc21b..55d64200b 100644
--- a/docs/reference/regularizer_l1_l2.html
+++ b/docs/reference/regularizer_l1_l2.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/regularizer_l2.html b/docs/reference/regularizer_l2.html
index 1de0d83b6..d8a7bede1 100644
--- a/docs/reference/regularizer_l2.html
+++ b/docs/reference/regularizer_l2.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/regularizer_orthogonal.html b/docs/reference/regularizer_orthogonal.html
index a256fd0b8..0430916b4 100644
--- a/docs/reference/regularizer_orthogonal.html
+++ b/docs/reference/regularizer_orthogonal.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/reset_state.html b/docs/reference/reset_state.html
index 90baac2d0..8bb6eac85 100644
--- a/docs/reference/reset_state.html
+++ b/docs/reference/reset_state.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/rnn_cell_gru.html b/docs/reference/rnn_cell_gru.html
index 03c84689b..dad5c6115 100644
--- a/docs/reference/rnn_cell_gru.html
+++ b/docs/reference/rnn_cell_gru.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/rnn_cell_lstm.html b/docs/reference/rnn_cell_lstm.html
index 367a508b2..dc408a95f 100644
--- a/docs/reference/rnn_cell_lstm.html
+++ b/docs/reference/rnn_cell_lstm.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/rnn_cell_simple.html b/docs/reference/rnn_cell_simple.html
index 4452c78c8..7d0956f36 100644
--- a/docs/reference/rnn_cell_simple.html
+++ b/docs/reference/rnn_cell_simple.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/rnn_cells_stack.html b/docs/reference/rnn_cells_stack.html
index 1367aee50..10e7ea5fd 100644
--- a/docs/reference/rnn_cells_stack.html
+++ b/docs/reference/rnn_cells_stack.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/save_model.html b/docs/reference/save_model.html
index 3ccaf0b04..971eb9d13 100644
--- a/docs/reference/save_model.html
+++ b/docs/reference/save_model.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/save_model_config.html b/docs/reference/save_model_config.html
index 4cc3a561d..fd0888235 100644
--- a/docs/reference/save_model_config.html
+++ b/docs/reference/save_model_config.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/save_model_weights.html b/docs/reference/save_model_weights.html
index 89761480c..0e0dffe30 100644
--- a/docs/reference/save_model_weights.html
+++ b/docs/reference/save_model_weights.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/serialize_keras_object.html b/docs/reference/serialize_keras_object.html
index 12b383527..ace939661 100644
--- a/docs/reference/serialize_keras_object.html
+++ b/docs/reference/serialize_keras_object.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/set_random_seed.html b/docs/reference/set_random_seed.html
index baf69851d..adeb22136 100644
--- a/docs/reference/set_random_seed.html
+++ b/docs/reference/set_random_seed.html
@@ -44,7 +44,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -143,7 +143,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index">
 <ul><li><p><a href="https://keras.io/api/utils/python_utils#setrandomseed-function" class="external-link">https://keras.io/api/utils/python_utils#setrandomseed-function</a></p></li>
-</ul><p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
+</ul><p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/docs/reference/shape.html b/docs/reference/shape.html
index 4a5c96fdf..59dd9e11d 100644
--- a/docs/reference/shape.html
+++ b/docs/reference/shape.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function can be used to create or get the shape of an object."><title>Tensor shape utility — shape • keras3</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Tensor shape utility — shape"><meta property="og:description" content="This function can be used to create or get the shape of an object."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function can be used to get or create a tensor shape."><title>Tensor shape utility — shape • keras3</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Tensor shape utility — shape"><meta property="og:description" content="This function can be used to get or create a tensor shape."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -71,7 +71,7 @@ <h6 class="dropdown-header" data-toc-skip>Other topics</h6>
     </div>
 
     <div class="ref-description section level2">
-    <p>This function can be used to create or get the shape of an object.</p>
+    <p>This function can be used to get or create a tensor shape.</p>
     </div>
 
     <div class="section level2">
@@ -105,7 +105,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>x</dt>
-<dd><p>A 'keras_shape' object</p></dd>
+<dd><p>A <code>keras_shape</code> object.</p></dd>
 
 
 <dt>prefix</dt>
@@ -118,8 +118,8 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A list with a <code>"keras_shape"</code> class attribute. Each element of the
-list will be either a) <code>NULL</code>, b) an integer or c) a scalar integer tensor
-(e.g., when supplied a TF tensor with a unspecified dimension in a function
+list will be either a) <code>NULL</code>, b) an R integer or c) a scalar integer tensor
+(e.g., when supplied a TF tensor with an unspecified dimension in a function
 being traced).</p>
     </div>
     <div class="section level2">
@@ -173,7 +173,16 @@ <h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"
 <p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="do">## shape(1, Tensor("strided_slice:0", shape=(), dtype=int32), 3)</span></span>
 <span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a></span></code></pre><p></p></div>
 <p>A useful pattern is to unpack the <code>shape()</code> with <code>%&lt;-%</code>, like this:</p>
-<p></p><div class="sourceCode r"><pre><code><span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">batch_size</span>, <span class="va">seq_len</span>, <span class="va">channels</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="fu"><a href="../reference/shape.html">shape</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span></code></pre><p></p></div>
+<p></p><div class="sourceCode r"><pre><code><span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">batch_size</span>, <span class="va">seq_len</span>, <span class="va">channels</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="fu"><a href="../reference/shape.html">shape</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># `%&lt;-%` also has support for skipping values</span></span>
+<span><span class="co"># during unpacking with `.` and `...`. For example,</span></span>
+<span><span class="co"># To retrieve just the first and/or last dim:</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">batch_size</span>, <span class="va">...</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="fu"><a href="../reference/shape.html">shape</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">batch_size</span>, <span class="va">.</span>, <span class="va">.</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="fu"><a href="../reference/shape.html">shape</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">...</span>, <span class="va">channels</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="fu"><a href="../reference/shape.html">shape</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">batch_size</span>, <span class="va">...</span>, <span class="va">channels</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="fu"><a href="../reference/shape.html">shape</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">batch_size</span>, <span class="va">.</span>, <span class="va">channels</span><span class="op">)</span> <span class="op"><a href="../reference/multi-assign.html">%&lt;-%</a></span> <span class="fu"><a href="../reference/shape.html">shape</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span></code></pre><p></p></div>
 <p></p><div class="sourceCode r"><pre><code><span><span class="va">echo_print</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/base/message.html" class="external-link">message</a></span><span class="op">(</span><span class="st">"&gt; "</span>, <span class="fu"><a href="https://rdrr.io/r/base/deparse.html" class="external-link">deparse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/substitute.html" class="external-link">substitute</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>;</span>
 <span>  <span class="kw">if</span><span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NULL.html" class="external-link">is.null</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
diff --git a/docs/reference/split_dataset.html b/docs/reference/split_dataset.html
index d99e8c7ef..78c2aefce 100644
--- a/docs/reference/split_dataset.html
+++ b/docs/reference/split_dataset.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -140,7 +140,7 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <div class="dont-index">
 <ul><li><p><a href="https://keras.io/api/utils/python_utils#splitdataset-function" class="external-link">https://keras.io/api/utils/python_utils#splitdataset-function</a></p></li>
 </ul><p>Other dataset utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/docs/reference/summary.keras.src.models.model.Model.html b/docs/reference/summary.keras.src.models.model.Model.html
index cbce5cdc5..7ba4f2f37 100644
--- a/docs/reference/summary.keras.src.models.model.Model.html
+++ b/docs/reference/summary.keras.src.models.model.Model.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/test_on_batch.html b/docs/reference/test_on_batch.html
index 427d87e5d..5ffc7f5a4 100644
--- a/docs/reference/test_on_batch.html
+++ b/docs/reference/test_on_batch.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/text_dataset_from_directory.html b/docs/reference/text_dataset_from_directory.html
index 8293c76e6..bc0e59493 100644
--- a/docs/reference/text_dataset_from_directory.html
+++ b/docs/reference/text_dataset_from_directory.html
@@ -34,7 +34,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -231,7 +231,7 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <div class="dont-index">
 <ul><li><p><a href="https://keras.io/api/data_loading/text#textdatasetfromdirectory-function" class="external-link">https://keras.io/api/data_loading/text#textdatasetfromdirectory-function</a></p></li>
 </ul><p>Other dataset utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
 <p>Other preprocessing: <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br></p></div>
     </div>
 
diff --git a/docs/reference/time_distributed.html b/docs/reference/time_distributed.html
index 4dc2bb55b..2a533b7aa 100644
--- a/docs/reference/time_distributed.html
+++ b/docs/reference/time_distributed.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/timeseries_dataset_from_array.html b/docs/reference/timeseries_dataset_from_array.html
index e0b91185f..47b38bb8d 100644
--- a/docs/reference/timeseries_dataset_from_array.html
+++ b/docs/reference/timeseries_dataset_from_array.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -250,7 +250,7 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <div class="dont-index">
 <ul><li><p><a href="https://keras.io/api/data_loading/timeseries#timeseriesdatasetfromarray-function" class="external-link">https://keras.io/api/data_loading/timeseries#timeseriesdatasetfromarray-function</a></p></li>
 </ul><p>Other dataset utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p>
 <p>Other preprocessing: <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br></p></div>
     </div>
 
diff --git a/docs/reference/to_categorical.html b/docs/reference/to_categorical.html
index 740dcc5a3..66902cfce 100644
--- a/docs/reference/to_categorical.html
+++ b/docs/reference/to_categorical.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -134,7 +134,7 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 matrix.</p></li>
 <li><p><a href="https://keras.io/api/utils/python_utils#tocategorical-function" class="external-link">https://keras.io/api/utils/python_utils#tocategorical-function</a></p></li>
 </ul><p>Other numerical utils: <br><code><a href="normalize.html">normalize</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
+<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="zip_lists.html">zip_lists</a>()</code> <br></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/docs/reference/train_on_batch.html b/docs/reference/train_on_batch.html
index 6915f6d0a..25203fe48 100644
--- a/docs/reference/train_on_batch.html
+++ b/docs/reference/train_on_batch.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/use_backend.html b/docs/reference/use_backend.html
index 1f6235e6e..716731f59 100644
--- a/docs/reference/use_backend.html
+++ b/docs/reference/use_backend.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/with_custom_object_scope.html b/docs/reference/with_custom_object_scope.html
index 7e580d54c..8a31ab25c 100644
--- a/docs/reference/with_custom_object_scope.html
+++ b/docs/reference/with_custom_object_scope.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/zip_lists.html b/docs/reference/zip_lists.html
index 0e63832b0..692c16bea 100644
--- a/docs/reference/zip_lists.html
+++ b/docs/reference/zip_lists.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">keras3</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -112,8 +112,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>Other data utils: <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br></p>
-<p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pack_x_y_sample_weight.html">pack_x_y_sample_weight</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br><code><a href="unpack_x_y_sample_weight.html">unpack_x_y_sample_weight</a>()</code> <br></p></div>
+    <div class="dont-index"><p>Other utils: <br><code><a href="audio_dataset_from_directory.html">audio_dataset_from_directory</a>()</code> <br><code><a href="clear_session.html">clear_session</a>()</code> <br><code><a href="config_disable_interactive_logging.html">config_disable_interactive_logging</a>()</code> <br><code><a href="config_disable_traceback_filtering.html">config_disable_traceback_filtering</a>()</code> <br><code><a href="config_enable_interactive_logging.html">config_enable_interactive_logging</a>()</code> <br><code><a href="config_enable_traceback_filtering.html">config_enable_traceback_filtering</a>()</code> <br><code><a href="config_is_interactive_logging_enabled.html">config_is_interactive_logging_enabled</a>()</code> <br><code><a href="config_is_traceback_filtering_enabled.html">config_is_traceback_filtering_enabled</a>()</code> <br><code><a href="get_file.html">get_file</a>()</code> <br><code><a href="get_source_inputs.html">get_source_inputs</a>()</code> <br><code><a href="image_array_save.html">image_array_save</a>()</code> <br><code><a href="image_dataset_from_directory.html">image_dataset_from_directory</a>()</code> <br><code><a href="image_from_array.html">image_from_array</a>()</code> <br><code><a href="image_load.html">image_load</a>()</code> <br><code><a href="image_smart_resize.html">image_smart_resize</a>()</code> <br><code><a href="image_to_array.html">image_to_array</a>()</code> <br><code><a href="layer_feature_space.html">layer_feature_space</a>()</code> <br><code><a href="normalize.html">normalize</a>()</code> <br><code><a href="pad_sequences.html">pad_sequences</a>()</code> <br><code><a href="set_random_seed.html">set_random_seed</a>()</code> <br><code><a href="split_dataset.html">split_dataset</a>()</code> <br><code><a href="text_dataset_from_directory.html">text_dataset_from_directory</a>()</code> <br><code><a href="timeseries_dataset_from_array.html">timeseries_dataset_from_array</a>()</code> <br><code><a href="to_categorical.html">to_categorical</a>()</code> <br></p></div>
     </div>
 
     <div class="section level2">
diff --git a/docs/search.json b/docs/search.json
index d1f0f47ff..d2cc629f0 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Customizing what happens in `fit()` with JAX","text":"’re supervised learning, can use fit() everything works smoothly. need take control every little detail, can write training loop entirely scratch. need custom training algorithm, still want benefit convenient features fit(), callbacks, built-distribution support, step fusing? core principle Keras progressive disclosure complexity. always able get lower-level workflows gradual way. shouldn’t fall cliff high-level functionality doesn’t exactly match use case. able gain control small details retaining commensurate amount high-level convenience. need customize fit() , override training step function Model class. function called fit() every batch data. able call fit() usual – running learning algorithm. Note pattern prevent building models Functional API. can whether ’re building Sequential models, Functional API models, subclassed models. Let’s see works.","code":""},{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Customizing what happens in `fit()` with JAX","text":"","code":"import os  # This guide can only be run with the JAX backend. os.environ[\"KERAS_BACKEND\"] = \"jax\"  import jax import keras import numpy as np"},{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"a-first-simple-example","dir":"Articles","previous_headings":"","what":"A first simple example","title":"Customizing what happens in `fit()` with JAX","text":"Let’s start simple example: create new class subclasses keras.Model. implement fully-stateless compute_loss_and_updates() method compute loss well updated values non-trainable variables model. Internally, calls stateless_call() built-compute_loss(). implement fully-stateless train_step() method compute current metric values (including loss) well updated values trainable variables, optimizer variables, metric variables. Note can also take account sample_weight argument : Unpacking data x, y, sample_weight = data Passing sample_weight compute_loss() Passing sample_weight alongside y y_pred metrics stateless_update_state() Let’s try :","code":"class CustomModel(keras.Model):     def compute_loss_and_updates(         self,         trainable_variables,         non_trainable_variables,         x,         y,         training=False,     ):         y_pred, non_trainable_variables = self.stateless_call(             trainable_variables,             non_trainable_variables,             x,             training=training,         )         loss = self.compute_loss(x, y, y_pred)         return loss, (y_pred, non_trainable_variables)      def train_step(self, state, data):         (             trainable_variables,             non_trainable_variables,             optimizer_variables,             metrics_variables,         ) = state         x, y = data          # Get the gradient function.         grad_fn = jax.value_and_grad(             self.compute_loss_and_updates, has_aux=True         )          # Compute the gradients.         (loss, (y_pred, non_trainable_variables)), grads = grad_fn(             trainable_variables,             non_trainable_variables,             x,             y,             training=True,         )          # Update trainable variables and optimizer variables.         (             trainable_variables,             optimizer_variables,         ) = self.optimizer.stateless_apply(             optimizer_variables, grads, trainable_variables         )          # Update metrics.         new_metrics_vars = []         for metric in self.metrics:             this_metric_vars = metrics_variables[                 len(new_metrics_vars) : len(new_metrics_vars)                 + len(metric.variables)             ]             if metric.name == \"loss\":                 this_metric_vars = metric.stateless_update_state(                     this_metric_vars, loss                 )             else:                 this_metric_vars = metric.stateless_update_state(                     this_metric_vars, y, y_pred                 )             logs = metric.stateless_result(this_metric_vars)             new_metrics_vars += this_metric_vars          # Return metric logs and updated state variables.         state = (             trainable_variables,             non_trainable_variables,             optimizer_variables,             new_metrics_vars,         )         return logs, state # Construct and compile an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])  # Just use `fit` as usual x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=3)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"going-lower-level","dir":"Articles","previous_headings":"","what":"Going lower-level","title":"Customizing what happens in `fit()` with JAX","text":"Naturally, just skip passing loss function compile(), instead everything manually train_step. Likewise metrics. ’s lower-level example, uses compile() configure optimizer:","code":"class CustomModel(keras.Model):     def __init__(self, *args, **kwargs):         super().__init__(*args, **kwargs)         self.loss_tracker = keras.metrics.Mean(name=\"loss\")         self.mae_metric = keras.metrics.MeanAbsoluteError(name=\"mae\")         self.loss_fn = keras.losses.MeanSquaredError()      def compute_loss_and_updates(         self,         trainable_variables,         non_trainable_variables,         x,         y,         training=False,     ):         y_pred, non_trainable_variables = self.stateless_call(             trainable_variables,             non_trainable_variables,             x,             training=training,         )         loss = self.loss_fn(y, y_pred)         return loss, (y_pred, non_trainable_variables)      def train_step(self, state, data):         (             trainable_variables,             non_trainable_variables,             optimizer_variables,             metrics_variables,         ) = state         x, y = data          # Get the gradient function.         grad_fn = jax.value_and_grad(             self.compute_loss_and_updates, has_aux=True         )          # Compute the gradients.         (loss, (y_pred, non_trainable_variables)), grads = grad_fn(             trainable_variables,             non_trainable_variables,             x,             y,             training=True,         )          # Update trainable variables and optimizer variables.         (             trainable_variables,             optimizer_variables,         ) = self.optimizer.stateless_apply(             optimizer_variables, grads, trainable_variables         )          # Update metrics.         loss_tracker_vars = metrics_variables[             : len(self.loss_tracker.variables)         ]         mae_metric_vars = metrics_variables[len(self.loss_tracker.variables) :]          loss_tracker_vars = self.loss_tracker.stateless_update_state(             loss_tracker_vars, loss         )         mae_metric_vars = self.mae_metric.stateless_update_state(             mae_metric_vars, y, y_pred         )          logs = {}         logs[self.loss_tracker.name] = self.loss_tracker.stateless_result(             loss_tracker_vars         )         logs[self.mae_metric.name] = self.mae_metric.stateless_result(             mae_metric_vars         )          new_metrics_vars = loss_tracker_vars + mae_metric_vars          # Return metric logs and updated state variables.         state = (             trainable_variables,             non_trainable_variables,             optimizer_variables,             new_metrics_vars,         )         return logs, state      @property     def metrics(self):         # We list our `Metric` objects here so that `reset_states()` can be         # called automatically at the start of each epoch         # or at the start of `evaluate()`.         return [self.loss_tracker, self.mae_metric]   # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs)  # We don't passs a loss or metrics here. model.compile(optimizer=\"adam\")  # Just use `fit` as usual -- you can use callbacks, etc. x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=5)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"providing-your-own-evaluation-step","dir":"Articles","previous_headings":"","what":"Providing your own evaluation step","title":"Customizing what happens in `fit()` with JAX","text":"want calls model.evaluate()? override test_step exactly way. ’s looks like: ’s !","code":"class CustomModel(keras.Model):     def test_step(self, state, data):         # Unpack the data.         x, y = data         (             trainable_variables,             non_trainable_variables,             metrics_variables,         ) = state          # Compute predictions and loss.         y_pred, non_trainable_variables = self.stateless_call(             trainable_variables,             non_trainable_variables,             x,             training=False,         )         loss = self.compute_loss(x, y, y_pred)          # Update metrics.         new_metrics_vars = []         for metric in self.metrics:             this_metric_vars = metrics_variables[                 len(new_metrics_vars) : len(new_metrics_vars)                 + len(metric.variables)             ]             if metric.name == \"loss\":                 this_metric_vars = metric.stateless_update_state(                     this_metric_vars, loss                 )             else:                 this_metric_vars = metric.stateless_update_state(                     this_metric_vars, y, y_pred                 )             logs = metric.stateless_result(this_metric_vars)             new_metrics_vars += this_metric_vars          # Return metric logs and updated state variables.         state = (             trainable_variables,             non_trainable_variables,             new_metrics_vars,         )         return logs, state   # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(loss=\"mse\", metrics=[\"mae\"])  # Evaluate with our custom test_step x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.evaluate(x, y)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Customizing what happens in `fit()` with TensorFlow","text":"’re supervised learning, can use fit() everything works smoothly. need take control every little detail, can write training loop entirely scratch. need custom training algorithm, still want benefit convenient features fit(), callbacks, built-distribution support, step fusing? core principle Keras progressive disclosure complexity. always able get lower-level workflows gradual way. shouldn’t fall cliff high-level functionality doesn’t exactly match use case. able gain control small details retaining commensurate amount high-level convenience. need customize fit() , override training step function Model class. function called fit() every batch data. able call fit() usual – running learning algorithm. Note pattern prevent building models Functional API. can whether ’re building Sequential models, Functional API models, subclassed models. Let’s see works.","code":""},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Customizing what happens in `fit()` with TensorFlow","text":"","code":"library(reticulate) library(tensorflow, exclude = c(\"set_random_seed\", \"shape\")) library(keras3)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"a-first-simple-example","dir":"Articles","previous_headings":"","what":"A first simple example","title":"Customizing what happens in `fit()` with TensorFlow","text":"Let’s start simple example: create new class subclasses Model. just override method train_step(self, data). return dictionary mapping metric names (including loss) current value. input argument data gets passed fit training data: pass arrays, calling fit(x, y, ...), data list (x, y) pass tf_dataset, calling fit(dataset, ...), data gets yielded dataset batch. body train_step() method, implement regular training update, similar already familiar . Importantly, compute loss via self.compute_loss(), wraps loss(es) function(s) passed compile(). Similarly, call metric$update_state(y, y_pred) metrics self$metrics, update state metrics passed compile(), query results self$metrics end retrieve current value. Let’s try :","code":"CustomModel <- new_model_class(   \"CustomModel\",   train_step = function(data) {     c(x, y, sample_weight) %<-% unpack_x_y_sample_weight(data)      with(tf$GradientTape() %as% tape, {       y_pred <- self(x, training = TRUE)       loss <- self$compute_loss(y = y, y_pred = y_pred,                                 sample_weight = sample_weight)     })      # Compute gradients     trainable_vars <- self$trainable_variables     gradients <- tape$gradient(loss, trainable_vars)      # Update weights     self$optimizer$apply(gradients, trainable_vars)      # Update metrics (includes the metric that tracks the loss)     for (metric in self$metrics) {       if (metric$name == \"loss\")         metric$update_state(loss)       else         metric$update_state(y, y_pred)     }      # Return a dict mapping metric names to current value     metrics <- lapply(self$metrics, function(m) m$result())     metrics <- setNames(metrics, sapply(self$metrics, function(m) m$name))     metrics   } ) # Construct and compile an instance of CustomModel inputs <- keras_input(shape = 32) outputs <- layer_dense(inputs, 1) model <- CustomModel(inputs, outputs) model |> compile(optimizer = \"adam\", loss = \"mse\", metrics = \"mae\")  # Just use `fit` as usual x <- random_normal(c(1000, 32)) y <- random_normal(c(1000, 1)) model |> fit(x, y, epochs = 3) ## Epoch 1/3 ## 32/32 - 1s - 23ms/step - loss: 2.9118 - mae: 1.3597 ## Epoch 2/3 ## 32/32 - 0s - 1ms/step - loss: 2.6026 - mae: 1.2856 ## Epoch 3/3 ## 32/32 - 0s - 1ms/step - loss: 2.3378 - mae: 1.2193"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"going-lower-level","dir":"Articles","previous_headings":"","what":"Going lower-level","title":"Customizing what happens in `fit()` with TensorFlow","text":"Naturally, just skip passing loss function compile(), instead everything manually train_step. Likewise metrics. ’s lower-level example, uses compile() configure optimizer: start creating Metric instances track loss MAE score (__init__()). implement custom train_step() updates state metrics (calling update_state() ), query (via result()) return current average value, displayed progress bar pass callback. Note need call reset_states() metrics epoch! Otherwise calling result() return average since start training, whereas usually work per-epoch averages. Thankfully, framework can us: just list metric want reset metrics property model. model call reset_states() object listed beginning fit() epoch beginning call evaluate().","code":"CustomModel <- new_model_class(   \"CustomModel\",   initialize = function(...) {     super$initialize(...)     self$loss_tracker <- metric_mean(name = \"loss\")     self$mae_metric <- metric_mean_absolute_error(name = \"mae\")     self$loss_fn <- loss_mean_squared_error()   },   train_step = function(data) {     c(x, y, sample_weight) %<-% unpack_x_y_sample_weight(data)      with(tf$GradientTape() %as% tape, {       y_pred <- self(x, training = TRUE)       loss <- self$loss_fn(y, y_pred, sample_weight = sample_weight)     })      # Compute gradients     trainable_vars <- self$trainable_variables     gradients <- tape$gradient(loss, trainable_vars)      # Update weights     self$optimizer$apply(gradients, trainable_vars)      # Compute our own metrics     self$loss_tracker$update_state(loss)     self$mae_metric$update_state(y, y_pred)      # Return a dict mapping metric names to current value     list(       loss = self$loss_tracker$result(),       mae = self$mae_metric$result()     )   },   metrics = mark_active(function() {     # We list our `Metric` objects here so that `reset_states()` can be     # called automatically at the start of each epoch     # or at the start of `evaluate()`.     list(self$loss_tracker, self$mae_metric)   }) )   # Construct and compile an instance of CustomModel inputs <- keras_input(shape = 32) outputs <- layer_dense(inputs, 1) model <- CustomModel(inputs, outputs)  # We don't pass a loss or metrics here. model |> compile(optimizer = \"adam\")  # Just use `fit` as usual x <- random_normal(c(1000, 32)) y <- random_normal(c(1000, 1)) model |> fit(x, y, epochs = 3) ## Epoch 1/3 ## 32/32 - 1s - 19ms/step - loss: 2.6540 - mae: 1.2901 ## Epoch 2/3 ## 32/32 - 0s - 2ms/step - loss: 2.4139 - mae: 1.2303 ## Epoch 3/3 ## 32/32 - 0s - 2ms/step - loss: 2.2080 - mae: 1.1761"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"supporting-sample_weight-class_weight","dir":"Articles","previous_headings":"","what":"Supporting sample_weight & class_weight","title":"Customizing what happens in `fit()` with TensorFlow","text":"may noticed first basic example didn’t make mention sample weighting. want support fit() arguments sample_weight class_weight, ’d simply following: Unpack sample_weight data argument Pass compute_loss & update_state (course, also just apply manually don’t rely compile() losses & metrics) ’s .","code":"CustomModel <- new_model_class(   \"CustomModel\",   train_step = function(data) {     c(x, y, sample_weight) %<-% unpack_x_y_sample_weight(data)      with(tf$GradientTape() %as% tape, {       y_pred <- self(x, training = TRUE)       loss <- self$compute_loss(y = y, y_pred = y_pred,                                 sample_weight = sample_weight)     })      # Compute gradients     trainable_vars <- self$trainable_variables     gradients <- tape$gradient(loss, trainable_vars)      # Update weights     self$optimizer$apply_gradients(zip_lists(gradients, trainable_vars))      # Update metrics (includes the metric that tracks the loss)     for (metric in self$metrics) {       if (metric$name == \"loss\") {         metric$update_state(loss)       } else {         metric$update_state(y, y_pred, sample_weight = sample_weight)       }     }      # Return a dict mapping metric names to current value     metrics <- lapply(self$metrics, function(m) m$result())     metrics <- setNames(metrics, sapply(self$metrics, function(m) m$name))     metrics   } )   # Construct and compile an instance of CustomModel inputs <- keras_input(shape = 32) outputs <- layer_dense(inputs, units = 1) model <- CustomModel(inputs, outputs) model |> compile(optimizer = \"adam\", loss = \"mse\", metrics = \"mae\")  # You can now use sample_weight argument x <- random_normal(c(1000, 32)) y <- random_normal(c(1000, 1)) sw <- random_normal(c(1000, 1)) model |> fit(x, y, sample_weight = sw, epochs = 3) ## Epoch 1/3 ## 32/32 - 1s - 21ms/step - loss: 0.1607 - mae: 1.3018 ## Epoch 2/3 ## 32/32 - 0s - 1ms/step - loss: 0.1452 - mae: 1.2999 ## Epoch 3/3 ## 32/32 - 0s - 2ms/step - loss: 0.1335 - mae: 1.2986"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"providing-your-own-evaluation-step","dir":"Articles","previous_headings":"","what":"Providing your own evaluation step","title":"Customizing what happens in `fit()` with TensorFlow","text":"want calls model.evaluate()? override test_step exactly way. ’s looks like:","code":"CustomModel <- new_model_class(   \"CustomModel\",   test_step = function(data) {     # Unpack the data     c(x, y, sw) %<-% unpack_x_y_sample_weight(data)     # Compute predictions     y_pred = self(x, training = FALSE)     # Updates the metrics tracking the loss     self$compute_loss(y = y, y_pred = y_pred, sample_weight = sw)     # Update the metrics.     for (metric in self$metrics) {       if (metric$name != \"loss\") {         metric$update_state(y, y_pred, sample_weight = sw)       }     }     # Return a dict mapping metric names to current value.     # Note that it will include the loss (tracked in self.metrics).     metrics <- lapply(self$metrics, function(m) m$result())     metrics <- setNames(metrics, sapply(self$metrics, function(m) m$name))     metrics   } )  # Construct an instance of CustomModel inputs <- keras_input(shape = 32) outputs <- layer_dense(inputs, 1) model <- CustomModel(inputs, outputs) model |> compile(loss = \"mse\", metrics = \"mae\")  # Evaluate with our custom test_step x <- random_normal(c(1000, 32)) y <- random_normal(c(1000, 1)) model |> evaluate(x, y) ## 32/32 - 0s - 9ms/step - loss: 0.0000e+00 - mae: 1.3947 ## $loss ## [1] 0 ## ## $mae ## [1] 1.394695"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"wrapping-up-an-end-to-end-gan-example","dir":"Articles","previous_headings":"","what":"Wrapping up: an end-to-end GAN example","title":"Customizing what happens in `fit()` with TensorFlow","text":"Let’s walk end--end example leverages everything just learned. Let’s consider: generator network meant generate 28x28x1 images. discriminator network meant classify 28x28x1 images two classes (“fake” “real”). One optimizer . loss function train discriminator. ’s feature-complete GAN class, overriding compile() use signature, implementing entire GAN algorithm 17 lines train_step: Let’s test-drive : ideas behind deep learning simple, implementation painful?","code":"# Create the discriminator discriminator <-   keras_model_sequential(name = \"discriminator\", input_shape = c(28, 28, 1)) |>    layer_conv_2d(filters = 64, kernel_size = c(3, 3),                 strides = c(2, 2),  padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_conv_2d(filters = 128, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_global_max_pooling_2d() |>   layer_dense(units = 1)   # Create the generator latent_dim <- 128 generator <-   keras_model_sequential(name = \"generator\", input_shape = latent_dim) |>    layer_dense(7 * 7 * 128) |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_reshape(target_shape = c(7, 7, 128)) |>    layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_conv_2d(filters = 1, kernel_size = c(7, 7), padding = \"same\",                 activation = \"sigmoid\") GAN <- Model(   classname = \"GAN\",    initialize = function(discriminator, generator, latent_dim, ...) {     super$initialize(...)     self$discriminator <- discriminator     self$generator <- generator     self$latent_dim <- as.integer(latent_dim)     self$d_loss_tracker <- metric_mean(name = \"d_loss\")     self$g_loss_tracker <- metric_mean(name = \"g_loss\")   },    compile = function(d_optimizer, g_optimizer, loss_fn, ...) {     super$compile(...)     self$d_optimizer <- d_optimizer     self$g_optimizer <- g_optimizer     self$loss_fn <- loss_fn   },   metrics = active_property(function() {     list(self$d_loss_tracker, self$g_loss_tracker)   }),    train_step = function(real_images) {      # Sample random points in the latent space     batch_size <- shape(real_images)[[1]]     random_latent_vectors <-       tf$random$normal(shape(batch_size, self$latent_dim))      # Decode them to fake images     generated_images <- self$generator(random_latent_vectors)      # Combine them with real images     combined_images <- op_concatenate(list(generated_images,                                            real_images))      # Assemble labels discriminating real from fake images     labels <- op_concatenate(list(op_ones(c(batch_size, 1)),                                   op_zeros(c(batch_size, 1))))      # Add random noise to the labels - important trick!     labels %<>% `+`(tf$random$uniform(shape(.), maxval = 0.05))      # Train the discriminator     with(tf$GradientTape() %as% tape, {       predictions <- self$discriminator(combined_images)       d_loss <- self$loss_fn(labels, predictions)     })     grads <- tape$gradient(d_loss, self$discriminator$trainable_weights)     self$d_optimizer$apply_gradients(       zip_lists(grads, self$discriminator$trainable_weights))      # Sample random points in the latent space     random_latent_vectors <-       tf$random$normal(shape(batch_size, self$latent_dim))      # Assemble labels that say \"all real images\"     misleading_labels <- op_zeros(c(batch_size, 1))      # Train the generator (note that we should *not* update the weights     # of the discriminator)!     with(tf$GradientTape() %as% tape, {       predictions <- self$discriminator(self$generator(random_latent_vectors))       g_loss <- self$loss_fn(misleading_labels, predictions)     })     grads <- tape$gradient(g_loss, self$generator$trainable_weights)     self$g_optimizer$apply_gradients(       zip_lists(grads, self$generator$trainable_weights))      list(d_loss = d_loss, g_loss = g_loss)   } ) batch_size <- 64 c(c(x_train, .), c(x_test, .)) %<-% dataset_mnist() all_digits <- op_concatenate(list(x_train, x_test)) all_digits <- op_reshape(all_digits, c(-1, 28, 28, 1)) dataset <- all_digits |>   tfdatasets::tensor_slices_dataset() |>   tfdatasets::dataset_map(\\(x) op_cast(x, \"float32\") / 255) |>   tfdatasets::dataset_shuffle(buffer_size = 1024) |>   tfdatasets::dataset_batch(batch_size = batch_size)  gan <- GAN(discriminator = discriminator,            generator = generator,            latent_dim = latent_dim)  gan |> compile(   d_optimizer = optimizer_adam(learning_rate = 0.0003),   g_optimizer = optimizer_adam(learning_rate = 0.0003),   loss_fn = loss_binary_crossentropy(from_logits = TRUE) )  # To limit the execution time, we only train on 100 batches. You can train on # the entire dataset. You will need about 20 epochs to get nice results. gan |> fit(   tfdatasets::dataset_take(dataset, 100),   epochs = 1 ) ## 100/100 - 5s - 53ms/step - d_loss: 0.0000e+00 - g_loss: 0.0000e+00"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Customizing what happens in `fit()` with PyTorch","text":"’re supervised learning, can use fit() everything works smoothly. need take control every little detail, can write training loop entirely scratch. need custom training algorithm, still want benefit convenient features fit(), callbacks, built-distribution support, step fusing? core principle Keras progressive disclosure complexity. always able get lower-level workflows gradual way. shouldn’t fall cliff high-level functionality doesn’t exactly match use case. able gain control small details retaining commensurate amount high-level convenience. need customize fit() , override training step function Model class. function called fit() every batch data. able call fit() usual – running learning algorithm. Note pattern prevent building models Functional API. can whether ’re building Sequential models, Functional API models, subclassed models. Let’s see works.","code":""},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Customizing what happens in `fit()` with PyTorch","text":"","code":"import os  # This guide can only be run with the torch backend. os.environ[\"KERAS_BACKEND\"] = \"torch\"  import torch import keras from keras import layers import numpy as np"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"a-first-simple-example","dir":"Articles","previous_headings":"","what":"A first simple example","title":"Customizing what happens in `fit()` with PyTorch","text":"Let’s start simple example: create new class subclasses keras.Model. just override method train_step(self, data). return dictionary mapping metric names (including loss) current value. input argument data gets passed fit training data: pass NumPy arrays, calling fit(x, y, ...), data tuple (x, y) pass torch.utils.data.DataLoader tf.data.Dataset, calling fit(dataset, ...), data gets yielded dataset batch. body train_step() method, implement regular training update, similar already familiar . Importantly, compute loss via self.compute_loss(), wraps loss(es) function(s) passed compile(). Similarly, call metric.update_state(y, y_pred) metrics self.metrics, update state metrics passed compile(), query results self.metrics end retrieve current value. Let’s try :","code":"class CustomModel(keras.Model):     def train_step(self, data):         # Unpack the data. Its structure depends on your model and         # on what you pass to `fit()`.         x, y = data          # Call torch.nn.Module.zero_grad() to clear the leftover gradients         # for the weights from the previous train step.         self.zero_grad()          # Compute loss         y_pred = self(x, training=True)  # Forward pass         loss = self.compute_loss(y=y, y_pred=y_pred)          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()          trainable_weights = [v for v in self.trainable_weights]         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             self.optimizer.apply(gradients, trainable_weights)          # Update metrics (includes the metric that tracks the loss)         for metric in self.metrics:             if metric.name == \"loss\":                 metric.update_state(loss)             else:                 metric.update_state(y, y_pred)          # Return a dict mapping metric names to current value         # Note that it will include the loss (tracked in self.metrics).         return {m.name: m.result() for m in self.metrics} # Construct and compile an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])  # Just use `fit` as usual x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=3)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"going-lower-level","dir":"Articles","previous_headings":"","what":"Going lower-level","title":"Customizing what happens in `fit()` with PyTorch","text":"Naturally, just skip passing loss function compile(), instead everything manually train_step. Likewise metrics. ’s lower-level example, uses compile() configure optimizer: start creating Metric instances track loss MAE score (__init__()). implement custom train_step() updates state metrics (calling update_state() ), query (via result()) return current average value, displayed progress bar pass callback. Note need call reset_states() metrics epoch! Otherwise calling result() return average since start training, whereas usually work per-epoch averages. Thankfully, framework can us: just list metric want reset metrics property model. model call reset_states() object listed beginning fit() epoch beginning call evaluate().","code":"class CustomModel(keras.Model):     def __init__(self, *args, **kwargs):         super().__init__(*args, **kwargs)         self.loss_tracker = keras.metrics.Mean(name=\"loss\")         self.mae_metric = keras.metrics.MeanAbsoluteError(name=\"mae\")         self.loss_fn = keras.losses.MeanSquaredError()      def train_step(self, data):         x, y = data          # Call torch.nn.Module.zero_grad() to clear the leftover gradients         # for the weights from the previous train step.         self.zero_grad()          # Compute loss         y_pred = self(x, training=True)  # Forward pass         loss = self.loss_fn(y, y_pred)          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()          trainable_weights = [v for v in self.trainable_weights]         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             self.optimizer.apply(gradients, trainable_weights)          # Compute our own metrics         self.loss_tracker.update_state(loss)         self.mae_metric.update_state(y, y_pred)         return {             \"loss\": self.loss_tracker.result(),             \"mae\": self.mae_metric.result(),         }      @property     def metrics(self):         # We list our `Metric` objects here so that `reset_states()` can be         # called automatically at the start of each epoch         # or at the start of `evaluate()`.         return [self.loss_tracker, self.mae_metric]   # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs)  # We don't passs a loss or metrics here. model.compile(optimizer=\"adam\")  # Just use `fit` as usual -- you can use callbacks, etc. x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=5)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"supporting-sample_weight-class_weight","dir":"Articles","previous_headings":"","what":"Supporting sample_weight & class_weight","title":"Customizing what happens in `fit()` with PyTorch","text":"may noticed first basic example didn’t make mention sample weighting. want support fit() arguments sample_weight class_weight, ’d simply following: Unpack sample_weight data argument Pass compute_loss & update_state (course, also just apply manually don’t rely compile() losses & metrics) ’s .","code":"class CustomModel(keras.Model):     def train_step(self, data):         # Unpack the data. Its structure depends on your model and         # on what you pass to `fit()`.         if len(data) == 3:             x, y, sample_weight = data         else:             sample_weight = None             x, y = data          # Call torch.nn.Module.zero_grad() to clear the leftover gradients         # for the weights from the previous train step.         self.zero_grad()          # Compute loss         y_pred = self(x, training=True)  # Forward pass         loss = self.compute_loss(             y=y,             y_pred=y_pred,             sample_weight=sample_weight,         )          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()          trainable_weights = [v for v in self.trainable_weights]         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             self.optimizer.apply(gradients, trainable_weights)          # Update metrics (includes the metric that tracks the loss)         for metric in self.metrics:             if metric.name == \"loss\":                 metric.update_state(loss)             else:                 metric.update_state(y, y_pred, sample_weight=sample_weight)          # Return a dict mapping metric names to current value         # Note that it will include the loss (tracked in self.metrics).         return {m.name: m.result() for m in self.metrics}   # Construct and compile an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])  # You can now use sample_weight argument x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) sw = np.random.random((1000, 1)) model.fit(x, y, sample_weight=sw, epochs=3)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"providing-your-own-evaluation-step","dir":"Articles","previous_headings":"","what":"Providing your own evaluation step","title":"Customizing what happens in `fit()` with PyTorch","text":"want calls model.evaluate()? override test_step exactly way. ’s looks like:","code":"class CustomModel(keras.Model):     def test_step(self, data):         # Unpack the data         x, y = data         # Compute predictions         y_pred = self(x, training=False)         # Updates the metrics tracking the loss         loss = self.compute_loss(y=y, y_pred=y_pred)         # Update the metrics.         for metric in self.metrics:             if metric.name == \"loss\":                 metric.update_state(loss)             else:                 metric.update_state(y, y_pred)         # Return a dict mapping metric names to current value.         # Note that it will include the loss (tracked in self.metrics).         return {m.name: m.result() for m in self.metrics}   # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(loss=\"mse\", metrics=[\"mae\"])  # Evaluate with our custom test_step x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.evaluate(x, y)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"wrapping-up-an-end-to-end-gan-example","dir":"Articles","previous_headings":"","what":"Wrapping up: an end-to-end GAN example","title":"Customizing what happens in `fit()` with PyTorch","text":"Let’s walk end--end example leverages everything just learned. Let’s consider: generator network meant generate 28x28x1 images. discriminator network meant classify 28x28x1 images two classes (“fake” “real”). One optimizer . loss function train discriminator. ’s feature-complete GAN class, overriding compile() use signature, implementing entire GAN algorithm 17 lines train_step: Let’s test-drive : ideas behind deep learning simple, implementation painful?","code":"# Create the discriminator discriminator = keras.Sequential(     [         keras.Input(shape=(28, 28, 1)),         layers.Conv2D(64, (3, 3), strides=(2, 2), padding=\"same\"),         layers.LeakyReLU(negative_slope=0.2),         layers.Conv2D(128, (3, 3), strides=(2, 2), padding=\"same\"),         layers.LeakyReLU(negative_slope=0.2),         layers.GlobalMaxPooling2D(),         layers.Dense(1),     ],     name=\"discriminator\", )  # Create the generator latent_dim = 128 generator = keras.Sequential(     [         keras.Input(shape=(latent_dim,)),         # We want to generate 128 coefficients to reshape into a 7x7x128 map         layers.Dense(7 * 7 * 128),         layers.LeakyReLU(negative_slope=0.2),         layers.Reshape((7, 7, 128)),         layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding=\"same\"),         layers.LeakyReLU(negative_slope=0.2),         layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding=\"same\"),         layers.LeakyReLU(negative_slope=0.2),         layers.Conv2D(1, (7, 7), padding=\"same\", activation=\"sigmoid\"),     ],     name=\"generator\", ) class GAN(keras.Model):     def __init__(self, discriminator, generator, latent_dim):         super().__init__()         self.discriminator = discriminator         self.generator = generator         self.latent_dim = latent_dim         self.d_loss_tracker = keras.metrics.Mean(name=\"d_loss\")         self.g_loss_tracker = keras.metrics.Mean(name=\"g_loss\")         self.seed_generator = keras.random.SeedGenerator(1337)         self.built = True      @property     def metrics(self):         return [self.d_loss_tracker, self.g_loss_tracker]      def compile(self, d_optimizer, g_optimizer, loss_fn):         super().compile()         self.d_optimizer = d_optimizer         self.g_optimizer = g_optimizer         self.loss_fn = loss_fn      def train_step(self, real_images):         if isinstance(real_images, tuple):             real_images = real_images[0]         # Sample random points in the latent space         batch_size = real_images.shape[0]         random_latent_vectors = keras.random.normal(             shape=(batch_size, self.latent_dim), seed=self.seed_generator         )          # Decode them to fake images         generated_images = self.generator(random_latent_vectors)          # Combine them with real images         real_images = torch.tensor(real_images)         combined_images = torch.concat([generated_images, real_images], axis=0)          # Assemble labels discriminating real from fake images         labels = torch.concat(             [torch.ones((batch_size, 1)), torch.zeros((batch_size, 1))], axis=0         )         # Add random noise to the labels - important trick!         labels += 0.05 * keras.random.uniform(             labels.shape, seed=self.seed_generator         )          # Train the discriminator         self.zero_grad()         predictions = self.discriminator(combined_images)         d_loss = self.loss_fn(labels, predictions)         d_loss.backward()         grads = [v.value.grad for v in self.discriminator.trainable_weights]         with torch.no_grad():             self.d_optimizer.apply(grads, self.discriminator.trainable_weights)          # Sample random points in the latent space         random_latent_vectors = keras.random.normal(             shape=(batch_size, self.latent_dim), seed=self.seed_generator         )          # Assemble labels that say \"all real images\"         misleading_labels = torch.zeros((batch_size, 1))          # Train the generator (note that we should *not* update the weights         # of the discriminator)!         self.zero_grad()         predictions = self.discriminator(self.generator(random_latent_vectors))         g_loss = self.loss_fn(misleading_labels, predictions)         grads = g_loss.backward()         grads = [v.value.grad for v in self.generator.trainable_weights]         with torch.no_grad():             self.g_optimizer.apply(grads, self.generator.trainable_weights)          # Update metrics and return their value.         self.d_loss_tracker.update_state(d_loss)         self.g_loss_tracker.update_state(g_loss)         return {             \"d_loss\": self.d_loss_tracker.result(),             \"g_loss\": self.g_loss_tracker.result(),         } # Prepare the dataset. We use both the training & test MNIST digits. batch_size = 64 (x_train, _), (x_test, _) = keras.datasets.mnist.load_data() all_digits = np.concatenate([x_train, x_test]) all_digits = all_digits.astype(\"float32\") / 255.0 all_digits = np.reshape(all_digits, (-1, 28, 28, 1))  # Create a TensorDataset dataset = torch.utils.data.TensorDataset(     torch.from_numpy(all_digits), torch.from_numpy(all_digits) ) # Create a DataLoader dataloader = torch.utils.data.DataLoader(     dataset, batch_size=batch_size, shuffle=True )  gan = GAN(     discriminator=discriminator, generator=generator, latent_dim=latent_dim ) gan.compile(     d_optimizer=keras.optimizers.Adam(learning_rate=0.0003),     g_optimizer=keras.optimizers.Adam(learning_rate=0.0003),     loss_fn=keras.losses.BinaryCrossentropy(from_logits=True), )  gan.fit(dataloader, epochs=1)"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Customizing Saving and Serialization","text":"guide covers advanced methods can customized Keras saving. users, methods outlined primary Serialize, save, export guide sufficient.","code":""},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"apis","dir":"Articles","previous_headings":"Introduction","what":"APIs","title":"Customizing Saving and Serialization","text":"cover following APIs: save_assets() load_assets() save_own_variables() load_own_variables() get_build_config() build_from_config() get_compile_config() compile_from_config() restoring model, get executed following order: build_from_config() compile_from_config() load_own_variables() load_assets()","code":""},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Customizing Saving and Serialization","text":"","code":"import os import numpy as np import keras"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"state-saving-customization","dir":"Articles","previous_headings":"","what":"State saving customization","title":"Customizing Saving and Serialization","text":"methods determine state model’s layers saved calling model.save(). can override take full control state saving process.","code":""},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"save_own_variables-and-load_own_variables","dir":"Articles","previous_headings":"State saving customization","what":"save_own_variables() and load_own_variables()","title":"Customizing Saving and Serialization","text":"methods save load state variables layer model.save() keras.models.load_model() called, respectively. default, state variables saved loaded weights layer (trainable non-trainable). default implementation save_own_variables(): store used methods dictionary can populated layer variables. Let’s take look example customizing . Example:","code":"def save_own_variables(self, store):     all_vars = self._trainable_weights + self._non_trainable_weights     for i, v in enumerate(all_vars):         store[f\"{i}\"] = v.numpy() @keras.utils.register_keras_serializable(package=\"my_custom_package\") class LayerWithCustomVariable(keras.layers.Dense):     def __init__(self, units, **kwargs):         super().__init__(units, **kwargs)         self.my_variable = keras.Variable(             np.random.random((units,)), name=\"my_variable\", dtype=\"float32\"         )      def save_own_variables(self, store):         super().save_own_variables(store)         # Stores the value of the variable upon saving         store[\"variables\"] = self.my_variable.numpy()      def load_own_variables(self, store):         # Assigns the value of the variable upon loading         self.my_variable.assign(store[\"variables\"])         # Load the remaining weights         for i, v in enumerate(self.weights):             v.assign(store[f\"{i}\"])         # Note: You must specify how all variables (including layer weights)         # are loaded in `load_own_variables.`      def call(self, inputs):         dense_out = super().call(inputs)         return dense_out + self.my_variable   model = keras.Sequential([LayerWithCustomVariable(1)])  ref_input = np.random.random((8, 10)) ref_output = np.random.random((8, 10)) model.compile(optimizer=\"adam\", loss=\"mean_squared_error\") model.fit(ref_input, ref_output)  model.save(\"custom_vars_model.keras\") restored_model = keras.models.load_model(\"custom_vars_model.keras\")  np.testing.assert_allclose(     model.layers[0].my_variable.numpy(),     restored_model.layers[0].my_variable.numpy(), )"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"save_assets-and-load_assets","dir":"Articles","previous_headings":"State saving customization","what":"save_assets() and load_assets()","title":"Customizing Saving and Serialization","text":"methods can added model class definition store load additional information model needs. example, NLP domain layers TextVectorization layers IndexLookup layers may need store associated vocabulary (lookup table) text file upon saving. Let’s take basics workflow simple file assets.txt. Example:","code":"@keras.saving.register_keras_serializable(package=\"my_custom_package\") class LayerWithCustomAssets(keras.layers.Dense):     def __init__(self, vocab=None, *args, **kwargs):         super().__init__(*args, **kwargs)         self.vocab = vocab      def save_assets(self, inner_path):         # Writes the vocab (sentence) to text file at save time.         with open(os.path.join(inner_path, \"vocabulary.txt\"), \"w\") as f:             f.write(self.vocab)      def load_assets(self, inner_path):         # Reads the vocab (sentence) from text file at load time.         with open(os.path.join(inner_path, \"vocabulary.txt\"), \"r\") as f:             text = f.read()         self.vocab = text.replace(\"<unk>\", \"little\")   model = keras.Sequential(     [LayerWithCustomAssets(vocab=\"Mary had a <unk> lamb.\", units=5)] )  x = np.random.random((10, 10)) y = model(x)  model.save(\"custom_assets_model.keras\") restored_model = keras.models.load_model(\"custom_assets_model.keras\")  np.testing.assert_string_equal(     restored_model.layers[0].vocab, \"Mary had a little lamb.\" )"},{"path":[]},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"get_build_config-and-build_from_config","dir":"Articles","previous_headings":"build and compile saving customization","what":"get_build_config() and build_from_config()","title":"Customizing Saving and Serialization","text":"methods work together save layer’s built states restore upon loading. default, includes build config dictionary layer’s input shape, overriding methods can used include Variables Lookup Tables can useful restore built model. Example:","code":"@keras.saving.register_keras_serializable(package=\"my_custom_package\") class LayerWithCustomBuild(keras.layers.Layer):     def __init__(self, units=32, **kwargs):         super().__init__(**kwargs)         self.units = units      def call(self, inputs):         return keras.ops.matmul(inputs, self.w) + self.b      def get_config(self):         return dict(units=self.units, **super().get_config())      def build(self, input_shape, layer_init):         # Note the overriding of `build()` to add an extra argument.         # Therefore, we will need to manually call build with `layer_init` argument         # before the first execution of `call()`.         super().build(input_shape)         self._input_shape = input_shape         self.w = self.add_weight(             shape=(input_shape[-1], self.units),             initializer=layer_init,             trainable=True,         )         self.b = self.add_weight(             shape=(self.units,),             initializer=layer_init,             trainable=True,         )         self.layer_init = layer_init      def get_build_config(self):         build_config = {             \"layer_init\": self.layer_init,             \"input_shape\": self._input_shape,         }  # Stores our initializer for `build()`         return build_config      def build_from_config(self, config):         # Calls `build()` with the parameters at loading time         self.build(config[\"input_shape\"], config[\"layer_init\"])   custom_layer = LayerWithCustomBuild(units=16) custom_layer.build(input_shape=(8,), layer_init=\"random_normal\")  model = keras.Sequential(     [         custom_layer,         keras.layers.Dense(1, activation=\"sigmoid\"),     ] )  x = np.random.random((16, 8)) y = model(x)  model.save(\"custom_build_model.keras\") restored_model = keras.models.load_model(\"custom_build_model.keras\")  np.testing.assert_equal(restored_model.layers[0].layer_init, \"random_normal\") np.testing.assert_equal(restored_model.built, True)"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"get_compile_config-and-compile_from_config","dir":"Articles","previous_headings":"build and compile saving customization","what":"get_compile_config() and compile_from_config()","title":"Customizing Saving and Serialization","text":"methods work together save information model compiled (optimizers, losses, etc.) restore re-compile model information. Overriding methods can useful compiling restored model custom optimizers, custom losses, etc., need deserialized prior calling model.compile compile_from_config(). Let’s take look example . Example:","code":"@keras.saving.register_keras_serializable(package=\"my_custom_package\") def small_square_sum_loss(y_true, y_pred):     loss = keras.ops.square(y_pred - y_true)     loss = loss / 10.0     loss = keras.ops.sum(loss, axis=1)     return loss   @keras.saving.register_keras_serializable(package=\"my_custom_package\") def mean_pred(y_true, y_pred):     return keras.ops.mean(y_pred)   @keras.saving.register_keras_serializable(package=\"my_custom_package\") class ModelWithCustomCompile(keras.Model):     def __init__(self, **kwargs):         super().__init__(**kwargs)         self.dense1 = keras.layers.Dense(8, activation=\"relu\")         self.dense2 = keras.layers.Dense(4, activation=\"softmax\")      def call(self, inputs):         x = self.dense1(inputs)         return self.dense2(x)      def compile(self, optimizer, loss_fn, metrics):         super().compile(optimizer=optimizer, loss=loss_fn, metrics=metrics)         self.model_optimizer = optimizer         self.loss_fn = loss_fn         self.loss_metrics = metrics      def get_compile_config(self):         # These parameters will be serialized at saving time.         return {             \"model_optimizer\": self.model_optimizer,             \"loss_fn\": self.loss_fn,             \"metric\": self.loss_metrics,         }      def compile_from_config(self, config):         # Deserializes the compile parameters (important, since many are custom)         optimizer = keras.utils.deserialize_keras_object(config[\"model_optimizer\"])         loss_fn = keras.utils.deserialize_keras_object(config[\"loss_fn\"])         metrics = keras.utils.deserialize_keras_object(config[\"metric\"])          # Calls compile with the deserialized parameters         self.compile(optimizer=optimizer, loss_fn=loss_fn, metrics=metrics)   model = ModelWithCustomCompile() model.compile(     optimizer=\"SGD\", loss_fn=small_square_sum_loss, metrics=[\"accuracy\", mean_pred] )  x = np.random.random((4, 8)) y = np.random.random((4,))  model.fit(x, y)  model.save(\"custom_compile_model.keras\") restored_model = keras.models.load_model(\"custom_compile_model.keras\")  np.testing.assert_equal(model.model_optimizer, restored_model.model_optimizer) np.testing.assert_equal(model.loss_fn, restored_model.loss_fn) np.testing.assert_equal(model.loss_metrics, restored_model.loss_metrics)"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"Customizing Saving and Serialization","text":"Using methods learned tutorial allows wide variety use cases, allowing saving loading complex models exotic assets state elements. recap: save_own_variables load_own_variables determine states saved loaded. save_assets load_assets can added store load additional information model needs. get_build_config build_from_config save restore model’s built states. get_compile_config compile_from_config save restore model’s compiled states.","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_jax.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Multi-GPU distributed training with JAX","text":"generally two ways distribute computation across multiple devices: Data parallelism, single model gets replicated multiple devices multiple machines. processes different batches data, merge results. exist many variants setup, differ different model replicas merge results, whether stay sync every batch whether loosely coupled, etc. Model parallelism, different parts single model run different devices, processing single batch data together. works best models naturally-parallel architecture, models feature multiple branches. guide focuses data parallelism, particular synchronous data parallelism, different replicas model stay sync batch process. Synchronicity keeps model convergence behavior identical see single-device training. Specifically, guide teaches use jax.sharding APIs train Keras models, minimal changes code, multiple GPUs TPUS (typically 2 16) installed single machine (single host, multi-device training). common setup researchers small-scale industry workflows.","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_jax.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Multi-GPU distributed training with JAX","text":"Let’s start defining function creates model train, function creates dataset train (MNIST case).","code":"import os  os.environ[\"KERAS_BACKEND\"] = \"jax\"  import jax import numpy as np import tensorflow as tf import keras  from jax.experimental import mesh_utils from jax.sharding import Mesh from jax.sharding import NamedSharding from jax.sharding import PartitionSpec as P   def get_model():     # Make a simple convnet with batch normalization and dropout.     inputs = keras.Input(shape=(28, 28, 1))     x = keras.layers.Rescaling(1.0 / 255.0)(inputs)     x = keras.layers.Conv2D(         filters=12, kernel_size=3, padding=\"same\", use_bias=False     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.Conv2D(         filters=24,         kernel_size=6,         use_bias=False,         strides=2,     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.Conv2D(         filters=32,         kernel_size=6,         padding=\"same\",         strides=2,         name=\"large_k\",     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.GlobalAveragePooling2D()(x)     x = keras.layers.Dense(256, activation=\"relu\")(x)     x = keras.layers.Dropout(0.5)(x)     outputs = keras.layers.Dense(10)(x)     model = keras.Model(inputs, outputs)     return model   def get_datasets():     # Load the data and split it between train and test sets     (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()      # Scale images to the [0, 1] range     x_train = x_train.astype(\"float32\")     x_test = x_test.astype(\"float32\")     # Make sure images have shape (28, 28, 1)     x_train = np.expand_dims(x_train, -1)     x_test = np.expand_dims(x_test, -1)     print(\"x_train shape:\", x_train.shape)     print(x_train.shape[0], \"train samples\")     print(x_test.shape[0], \"test samples\")      # Create TF Datasets     train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))     eval_data = tf.data.Dataset.from_tensor_slices((x_test, y_test))     return train_data, eval_data"},{"path":"https://keras.posit.co/articles/distributed_training_with_jax.html","id":"single-host-multi-device-synchronous-training","dir":"Articles","previous_headings":"","what":"Single-host, multi-device synchronous training","title":"Multi-GPU distributed training with JAX","text":"setup, one machine several GPUs TPUs (typically 2 16). device run copy model (called replica). simplicity, follows, ’ll assume ’re dealing 8 GPUs, loss generality. works step training: current batch data (called global batch) split 8 different sub-batches (called local batches). instance, global batch 512 samples, 8 local batches 64 samples. 8 replicas independently processes local batch: run forward pass, backward pass, outputting gradient weights respect loss model local batch. weight updates originating local gradients efficiently merged across 8 replicas. done end every step, replicas always stay sync. practice, process synchronously updating weights model replicas handled level individual weight variable. done using jax.sharding.NamedSharding configured replicate variables. use single-host, multi-device synchronous training Keras model, use jax.sharding features. ’s works: first create device mesh using mesh_utils.create_device_mesh. specify want replicate model optimizer variables across devices using spec axis. specify want shard data across devices using spec splits along batch dimension. use jax.device_put replicate model optimizer variables across devices. happens beginning. training loop, batch process, use jax.device_put split batch across devices invoking train step. ’s flow, step split utility function: ’s !","code":"# Config num_epochs = 2 batch_size = 64  train_data, eval_data = get_datasets() train_data = train_data.batch(batch_size, drop_remainder=True)  model = get_model() optimizer = keras.optimizers.Adam(1e-3) loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)  # Initialize all state with .build() (one_batch, one_batch_labels) = next(iter(train_data)) model.build(one_batch) optimizer.build(model.trainable_variables)   # This is the loss function that will be differentiated. # Keras provides a pure functional forward pass: model.stateless_call def compute_loss(trainable_variables, non_trainable_variables, x, y):     y_pred, updated_non_trainable_variables = model.stateless_call(         trainable_variables, non_trainable_variables, x     )     loss_value = loss(y, y_pred)     return loss_value, updated_non_trainable_variables   # Function to compute gradients compute_gradients = jax.value_and_grad(compute_loss, has_aux=True)   # Training step, Keras provides a pure functional optimizer.stateless_apply @jax.jit def train_step(train_state, x, y):     (         trainable_variables,         non_trainable_variables,         optimizer_variables,     ) = train_state     (loss_value, non_trainable_variables), grads = compute_gradients(         trainable_variables, non_trainable_variables, x, y     )      trainable_variables, optimizer_variables = optimizer.stateless_apply(         optimizer_variables, grads, trainable_variables     )      return loss_value, (         trainable_variables,         non_trainable_variables,         optimizer_variables,     )   # Replicate the model and optimizer variable on all devices def get_replicated_train_state(devices):     # All variables will be replicated on all devices     var_mesh = Mesh(devices, axis_names=(\"_\"))     # In NamedSharding, axes not mentioned are replicated (all axes here)     var_replication = NamedSharding(var_mesh, P())      # Apply the distribution settings to the model variables     trainable_variables = jax.device_put(         model.trainable_variables, var_replication     )     non_trainable_variables = jax.device_put(         model.non_trainable_variables, var_replication     )     optimizer_variables = jax.device_put(optimizer.variables, var_replication)      # Combine all state in a tuple     return (trainable_variables, non_trainable_variables, optimizer_variables)   num_devices = len(jax.local_devices()) print(f\"Running on {num_devices} devices: {jax.local_devices()}\") devices = mesh_utils.create_device_mesh((num_devices,))  # Data will be split along the batch axis data_mesh = Mesh(devices, axis_names=(\"batch\",))  # naming axes of the mesh data_sharding = NamedSharding(     data_mesh,     P(         \"batch\",     ), )  # naming axes of the sharded partition  # Display data sharding x, y = next(iter(train_data)) sharded_x = jax.device_put(x.numpy(), data_sharding) print(\"Data sharding\") jax.debug.visualize_array_sharding(jax.numpy.reshape(sharded_x, [-1, 28 * 28]))  train_state = get_replicated_train_state(devices)  # Custom training loop for epoch in range(num_epochs):     data_iter = iter(train_data)     for data in data_iter:         x, y = data         sharded_x = jax.device_put(x.numpy(), data_sharding)         loss_value, train_state = train_step(train_state, sharded_x, y.numpy())     print(\"Epoch\", epoch, \"loss:\", loss_value)  # Post-processing model state update to write them back into the model trainable_variables, non_trainable_variables, optimizer_variables = train_state for variable, value in zip(model.trainable_variables, trainable_variables):     variable.assign(value) for variable, value in zip(     model.non_trainable_variables, non_trainable_variables ):     variable.assign(value)"},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Multi-GPU distributed training with TensorFlow","text":"generally two ways distribute computation across multiple devices: Data parallelism, single model gets replicated multiple devices multiple machines. processes different batches data, merge results. exist many variants setup, differ different model replicas merge results, whether stay sync every batch whether loosely coupled, etc. Model parallelism, different parts single model run different devices, processing single batch data together. works best models naturally-parallel architecture, models feature multiple branches. guide focuses data parallelism, particular synchronous data parallelism, different replicas model stay sync batch process. Synchronicity keeps model convergence behavior identical see single-device training. Specifically, guide teaches use tf.distribute API train Keras models multiple GPUs, minimal changes code, multiple GPUs (typically 2 16) installed single machine (single host, multi-device training). common setup researchers small-scale industry workflows.","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Multi-GPU distributed training with TensorFlow","text":"","code":"library(keras3) library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")"},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"single-host-multi-device-synchronous-training","dir":"Articles","previous_headings":"","what":"Single-host, multi-device synchronous training","title":"Multi-GPU distributed training with TensorFlow","text":"setup, one machine several GPUs (typically 2 16). device run copy model (called replica). simplicity, follows, ’ll assume ’re dealing 8 GPUs, loss generality. works step training: current batch data (called global batch) split 8 different sub-batches (called local batches). instance, global batch 512 samples, 8 local batches 64 samples. 8 replicas independently processes local batch: run forward pass, backward pass, outputting gradient weights respect loss model local batch. weight updates originating local gradients efficiently merged across 8 replicas. done end every step, replicas always stay sync. practice, process synchronously updating weights model replicas handled level individual weight variable. done mirrored variable object. use single-host, multi-device synchronous training Keras model, use tf$distribute$MirroredStrategy API. ’s works: Instantiate MirroredStrategy, optionally configuring specific devices want use (default strategy use GPUs available). Use strategy object open scope, within scope, create Keras objects need contain variables. Typically, means creating & compiling model inside distribution scope. cases, first call fit() may also create variables, ’s good idea put fit() call scope well. Train model via fit() usual. Importantly, recommend use tf.data.Dataset objects load data multi-device distributed workflow. Schematically, looks like : ’s simple end--end runnable example:","code":"# Create a MirroredStrategy. strategy <- tf$distribute$MirroredStrategy() cat(sprintf('Number of devices: %d\\n', strategy$num_replicas_in_sync))  # Open a strategy scope. with(startegy$scope(), {   # Everything that creates variables should be under the strategy scope.   # In general this is only model construction & `compile()`.   model <- Model(...)   model |> compile(...)    # Train the model on all available devices.   model |> fit(train_dataset, validation_data=val_dataset, ...)    # Test the model on all available devices.   model |> evaluate(test_dataset) }) get_compiled_model <- function() {   inputs <- keras_input(shape = 784)   outputs <- inputs |>     layer_dense(units = 256, activation = \"relu\") |>     layer_dense(units = 256, activation = \"relu\") |>     layer_dense(units = 10)   model <- keras_model(inputs, outputs)   model |> compile(     optimizer = optimizer_adam(),     loss = loss_sparse_categorical_crossentropy(from_logits = TRUE),     metrics = list(metric_sparse_categorical_accuracy()),      # XLA compilation is temporarily disabled due to a bug     # https://github.com/keras-team/keras/issues/19005     jit_compile = FALSE   )   model }  get_dataset <- function(batch_size = 64) {    c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()   x_train <- array_reshape(x_train, c(-1, 784))   x_test <- array_reshape(x_test, c(-1, 784))    # Reserve 10,000 samples for validation.   val_i <- sample.int(nrow(x_train), 10000)   x_val <- x_train[val_i,]   y_val <- y_train[val_i]   x_train = x_train[-val_i,]   y_train = y_train[-val_i]    # Prepare the training dataset.   train_dataset <- list(x_train, y_train) |>     tensor_slices_dataset() |>     dataset_batch(batch_size)    # Prepare the validation dataset.   val_dataset <- list(x_val, y_val) |>     tensor_slices_dataset() |>     dataset_batch(batch_size)    # Prepare the test dataset.   test_dataset <- list(x_test, y_test) |>     tensor_slices_dataset() |>     dataset_batch(batch_size)    list(train_dataset, val_dataset, test_dataset) }  # Create a MirroredStrategy. strategy <- tf$distribute$MirroredStrategy() cat(sprintf('Number of devices: %d\\n', strategy$num_replicas_in_sync)) ## Number of devices: 2 # Open a strategy scope. with(strategy$scope(), {   # Everything that creates variables should be under the strategy scope.   # In general this is only model construction & `compile()`.   model <- get_compiled_model()    c(train_dataset, val_dataset, test_dataset) %<-% get_dataset()    # Train the model on all available devices.   model |> fit(train_dataset, epochs = 2, validation_data = val_dataset)    # Test the model on all available devices.   model |> evaluate(test_dataset)  }) ## Epoch 1/2 ## 782/782 - 6s - 7ms/step - loss: 3.0622 - sparse_categorical_accuracy: 0.8615 - val_loss: 1.1367 - val_sparse_categorical_accuracy: 0.9006 ## Epoch 2/2 ## 782/782 - 3s - 4ms/step - loss: 0.5774 - sparse_categorical_accuracy: 0.9259 - val_loss: 0.6612 - val_sparse_categorical_accuracy: 0.9210 ## 157/157 - 0s - 3ms/step - loss: 0.6729 - sparse_categorical_accuracy: 0.9150 ## $loss ## [1] 0.6728871 ## ## $sparse_categorical_accuracy ## [1] 0.915"},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"using-callbacks-to-ensure-fault-tolerance","dir":"Articles","previous_headings":"","what":"Using callbacks to ensure fault tolerance","title":"Multi-GPU distributed training with TensorFlow","text":"using distributed training, always make sure strategy recover failure (fault tolerance). simplest way handle pass ModelCheckpoint callback fit(), save model regular intervals (e.g. every 100 batches every epoch). can restart training saved model. ’s simple example:","code":"# Prepare a directory to store all the checkpoints. checkpoint_dir <- \"./ckpt\" if (!dir.exists(checkpoint_dir)) {   dir.create(checkpoint_dir) }  make_or_restore_model <- function() {   # Either restore the latest model, or create a fresh one   # if there is no checkpoint available.   checkpoints <- list.files(checkpoint_dir, #pattern = \"ckpt-.*\\\\.keras\",                             full.names = TRUE)    if (length(checkpoints) > 0) {     checkpoint_epochs <- as.integer(sub(\"ckpt-([0-9]+)\\\\.keras\", \"\\\\1\",                                         basename(checkpoints)))     latest_checkpoint <- checkpoints[which.max(checkpoint_epochs)]     load_model(latest_checkpoint)   } else {     get_compiled_model()   } }    run_training <- function(epochs = 1) {   # Create a MirroredStrategy.   strategy <- tf$distribute$MirroredStrategy()    # Open a strategy scope and create/restore the model   with(strategy$scope(), {     model <- make_or_restore_model()      callbacks <- list(       # This callback saves a SavedModel every epoch       # We include the current epoch in the folder name.       callback_model_checkpoint(         filepath = paste0(checkpoint_dir, \"/ckpt-{epoch}.keras\"),         save_freq = \"epoch\"       ))      model |> fit(       train_dataset,       epochs = epochs,       callbacks = callbacks,       validation_data = val_dataset,       verbose = 2     )   }) }  # Running the first time creates the model run_training(epochs = 1) ## 782/782 - 6s - 7ms/step - loss: 2.9519 - sparse_categorical_accuracy: 0.8655 - val_loss: 1.3110 - val_sparse_categorical_accuracy: 0.8836 # Calling the same function again will resume from where we left off run_training(epochs = 1) ## 782/782 - 3s - 4ms/step - loss: 0.5998 - sparse_categorical_accuracy: 0.9270 - val_loss: 0.8736 - val_sparse_categorical_accuracy: 0.9128"},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"tfdata-performance-tips","dir":"Articles","previous_headings":"","what":"tf$data performance tips","title":"Multi-GPU distributed training with TensorFlow","text":"distributed training, efficiency load data can often become critical. tips make sure tf$data pipelines run fast possible. Note dataset batching creating dataset, make sure batched global batch size. instance, 8 GPUs capable running batch 64 samples, call use global batch size 512. Calling dataset_cache() call dataset_cache() dataset, data cached running first iteration data. Every subsequent iteration use cached data. cache can memory (default) local file specify. can improve performance : data expected change iteration iteration reading data remote distributed filesystem reading data local disk, data fit memory workflow significantly IO-bound (e.g. reading & decoding image files). Calling dataset_prefetch(buffer_size) almost always call dataset_prefetch(buffer_size) creating dataset. means data pipeline run asynchronously model, new samples preprocessed stored buffer current batch samples used train model. next batch prefetched GPU memory time current batch . ’s !","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_torch.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Multi-GPU distributed training with PyTorch","text":"generally two ways distribute computation across multiple devices: Data parallelism, single model gets replicated multiple devices multiple machines. processes different batches data, merge results. exist many variants setup, differ different model replicas merge results, whether stay sync every batch whether loosely coupled, etc. Model parallelism, different parts single model run different devices, processing single batch data together. works best models naturally-parallel architecture, models feature multiple branches. guide focuses data parallelism, particular synchronous data parallelism, different replicas model stay sync batch process. Synchronicity keeps model convergence behavior identical see single-device training. Specifically, guide teaches use PyTorch’s DistributedDataParallel module wrapper train Keras, minimal changes code, multiple GPUs (typically 2 16) installed single machine (single host, multi-device training). common setup researchers small-scale industry workflows.","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_torch.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Multi-GPU distributed training with PyTorch","text":"Let’s start defining function creates model train, function creates dataset train (MNIST case). Next, let’s define simple PyTorch training loop targets GPU (note calls .cuda()).","code":"import os  os.environ[\"KERAS_BACKEND\"] = \"torch\"  import torch import numpy as np import keras   def get_model():     # Make a simple convnet with batch normalization and dropout.     inputs = keras.Input(shape=(28, 28, 1))     x = keras.layers.Rescaling(1.0 / 255.0)(inputs)     x = keras.layers.Conv2D(         filters=12, kernel_size=3, padding=\"same\", use_bias=False     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.Conv2D(         filters=24,         kernel_size=6,         use_bias=False,         strides=2,     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.Conv2D(         filters=32,         kernel_size=6,         padding=\"same\",         strides=2,         name=\"large_k\",     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.GlobalAveragePooling2D()(x)     x = keras.layers.Dense(256, activation=\"relu\")(x)     x = keras.layers.Dropout(0.5)(x)     outputs = keras.layers.Dense(10)(x)     model = keras.Model(inputs, outputs)     return model   def get_dataset():     # Load the data and split it between train and test sets     (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()      # Scale images to the [0, 1] range     x_train = x_train.astype(\"float32\")     x_test = x_test.astype(\"float32\")     # Make sure images have shape (28, 28, 1)     x_train = np.expand_dims(x_train, -1)     x_test = np.expand_dims(x_test, -1)     print(\"x_train shape:\", x_train.shape)      # Create a TensorDataset     dataset = torch.utils.data.TensorDataset(         torch.from_numpy(x_train), torch.from_numpy(y_train)     )     return dataset def train_model(model, dataloader, num_epochs, optimizer, loss_fn):     for epoch in range(num_epochs):         running_loss = 0.0         running_loss_count = 0         for batch_idx, (inputs, targets) in enumerate(dataloader):             inputs = inputs.cuda(non_blocking=True)             targets = targets.cuda(non_blocking=True)              # Forward pass             outputs = model(inputs)             loss = loss_fn(outputs, targets)              # Backward and optimize             optimizer.zero_grad()             loss.backward()             optimizer.step()              running_loss += loss.item()             running_loss_count += 1          # Print loss statistics         print(             f\"Epoch {epoch + 1}/{num_epochs}, \"             f\"Loss: {running_loss / running_loss_count}\"         )"},{"path":"https://keras.posit.co/articles/distributed_training_with_torch.html","id":"single-host-multi-device-synchronous-training","dir":"Articles","previous_headings":"","what":"Single-host, multi-device synchronous training","title":"Multi-GPU distributed training with PyTorch","text":"setup, one machine several GPUs (typically 2 16). device run copy model (called replica). simplicity, follows, ’ll assume ’re dealing 8 GPUs, loss generality. works step training: current batch data (called global batch) split 8 different sub-batches (called local batches). instance, global batch 512 samples, 8 local batches 64 samples. 8 replicas independently processes local batch: run forward pass, backward pass, outputting gradient weights respect loss model local batch. weight updates originating local gradients efficiently merged across 8 replicas. done end every step, replicas always stay sync. practice, process synchronously updating weights model replicas handled level individual weight variable. done mirrored variable object. use single-host, multi-device synchronous training Keras model, use torch.nn.parallel.DistributedDataParallel module wrapper. ’s works: use torch.multiprocessing.start_processes start multiple Python processes, one per device. process run per_device_launch_fn function. uses torch.distributed.init_process_group torch.cuda.set_device configure device used process. uses torch.utils.data.distributed.DistributedSampler torch.utils.data.DataLoader turn data distributed data loader. also uses torch.nn.parallel.DistributedDataParallel turn model distributed PyTorch module. calls train_model function. train_model function run process, model using separate device process. ’s flow, step split utility function: Time start multiple processes: ’s !","code":"# Config num_gpu = torch.cuda.device_count() num_epochs = 2 batch_size = 64 print(f\"Running on {num_gpu} GPUs\")   def setup_device(current_gpu_index, num_gpus):     # Device setup     os.environ[\"MASTER_ADDR\"] = \"localhost\"     os.environ[\"MASTER_PORT\"] = \"56492\"     device = torch.device(\"cuda:{}\".format(current_gpu_index))     torch.distributed.init_process_group(         backend=\"nccl\",         init_method=\"env://\",         world_size=num_gpus,         rank=current_gpu_index,     )     torch.cuda.set_device(device)   def cleanup():     torch.distributed.destroy_process_group()   def prepare_dataloader(dataset, current_gpu_index, num_gpus, batch_size):     sampler = torch.utils.data.distributed.DistributedSampler(         dataset,         num_replicas=num_gpus,         rank=current_gpu_index,         shuffle=False,     )     dataloader = torch.utils.data.DataLoader(         dataset,         sampler=sampler,         batch_size=batch_size,         shuffle=False,     )     return dataloader   def per_device_launch_fn(current_gpu_index, num_gpu):     # Setup the process groups     setup_device(current_gpu_index, num_gpu)      dataset = get_dataset()     model = get_model()      # prepare the dataloader     dataloader = prepare_dataloader(         dataset, current_gpu_index, num_gpu, batch_size     )      # Instantiate the torch optimizer     optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)      # Instantiate the torch loss function     loss_fn = torch.nn.CrossEntropyLoss()      # Put model on device     model = model.to(current_gpu_index)     ddp_model = torch.nn.parallel.DistributedDataParallel(         model, device_ids=[current_gpu_index], output_device=current_gpu_index     )      train_model(ddp_model, dataloader, num_epochs, optimizer, loss_fn)      cleanup() if __name__ == \"__main__\":     # We use the \"fork\" method rather than \"spawn\" to support notebooks     torch.multiprocessing.start_processes(         per_device_launch_fn,         args=(num_gpu,),         nprocs=num_gpu,         join=True,         start_method=\"fork\",     )"},{"path":"https://keras.posit.co/articles/distribution.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Distributed training with Keras 3","text":"Keras distribution API new interface designed facilitate distributed deep learning across variety backends like JAX, TensorFlow PyTorch. powerful API introduces suite tools enabling data model parallelism, allowing efficient scaling deep learning models multiple accelerators hosts. Whether leveraging power GPUs TPUs, API provides streamlined approach initializing distributed environments, defining device meshes, orchestrating layout tensors across computational resources. classes like DataParallel ModelParallel, abstracts complexity involved parallel computation, making easier developers accelerate machine learning workflows.","code":""},{"path":"https://keras.posit.co/articles/distribution.html","id":"how-it-works","dir":"Articles","previous_headings":"","what":"How it works","title":"Distributed training with Keras 3","text":"Keras distribution API provides global programming model allows developers compose applications operate tensors global context (working single device) automatically managing distribution across many devices. API leverages underlying framework (e.g. JAX) distribute program tensors according sharding directives procedure called single program, multiple data (SPMD) expansion. decoupling application sharding directives, API enables running application single device, multiple devices, even multiple clients, preserving global semantics.","code":""},{"path":"https://keras.posit.co/articles/distribution.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Distributed training with Keras 3","text":"","code":"# This guide assumes there are 8 GPUs available for testing. If you don't have # 8 gpus available locally, you can set the following envvar to # make xla initialize the CPU as 8 devices, to enable local testing Sys.setenv(\"CUDA_VISIBLE_DEVICES\" = \"\") Sys.setenv(\"XLA_FLAGS\" = \"--xla_force_host_platform_device_count=8\") library(keras3)  # The distribution API is only implemented for the JAX backend for now. use_backend(\"jax\") jax <- reticulate::import(\"jax\")  library(tfdatasets, exclude = \"shape\") # For dataset input."},{"path":"https://keras.posit.co/articles/distribution.html","id":"devicemesh-and-tensorlayout","dir":"Articles","previous_headings":"","what":"DeviceMesh and TensorLayout","title":"Distributed training with Keras 3","text":"keras$distribution$DeviceMesh class Keras distribution API represents cluster computational devices configured distributed computation. aligns similar concepts jax.sharding.Mesh tf.dtensor.Mesh, ’s used map physical devices logical mesh structure. TensorLayout class specifies tensors distributed across DeviceMesh, detailing sharding tensors along specified axes correspond names axes DeviceMesh. can find detailed concept explainers TensorFlow DTensor guide.","code":"# Retrieve the local available gpu devices. devices <- jax$devices() # \"gpu\" str(devices) ## List of 8 ##  $ :TFRT_CPU_0 ##  $ :TFRT_CPU_1 ##  $ :TFRT_CPU_2 ##  $ :TFRT_CPU_3 ##  $ :TFRT_CPU_4 ##  $ :TFRT_CPU_5 ##  $ :TFRT_CPU_6 ##  $ :TFRT_CPU_7 # Define a 2x4 device mesh with data and model parallel axes mesh <- keras$distribution$DeviceMesh(   shape = shape(2, 4),   axis_names = list(\"data\", \"model\"),   devices = devices )  # A 2D layout, which describes how a tensor is distributed across the # mesh. The layout can be visualized as a 2D grid with \"model\" as rows and # \"data\" as columns, and it is a [4, 2] grid when it mapped to the physical # devices on the mesh. layout_2d <- keras$distribution$TensorLayout(   axes = c(\"model\", \"data\"),   device_mesh = mesh )  # A 4D layout which could be used for data parallelism of an image input. replicated_layout_4d <- keras$distribution$TensorLayout(   axes = list(\"data\", NULL, NULL, NULL),   device_mesh = mesh )"},{"path":"https://keras.posit.co/articles/distribution.html","id":"distribution","dir":"Articles","previous_headings":"","what":"Distribution","title":"Distributed training with Keras 3","text":"Distribution class Keras serves foundational abstract class designed developing custom distribution strategies. encapsulates core logic needed distribute model’s variables, input data, intermediate computations across device mesh. end user, won’t interact directly class, subclasses like DataParallel ModelParallel.","code":""},{"path":"https://keras.posit.co/articles/distribution.html","id":"dataparallel","dir":"Articles","previous_headings":"","what":"DataParallel","title":"Distributed training with Keras 3","text":"DataParallel class Keras distribution API designed data parallelism strategy distributed training, model weights replicated across devices DeviceMesh, device processes portion input data. sample usage class.","code":"# Create DataParallel with list of devices. # As a shortcut, the devices can be skipped, # and Keras will detect all local available devices. # E.g. data_parallel <- DataParallel() data_parallel <- keras$distribution$DataParallel(devices = devices)  # Or you can choose to create DataParallel with a 1D `DeviceMesh`. mesh_1d <- keras$distribution$DeviceMesh(   shape = shape(8),   axis_names = list(\"data\"),   devices = devices ) data_parallel <- keras$distribution$DataParallel(device_mesh = mesh_1d)  inputs <- random_normal(c(128, 28, 28, 1)) labels <- random_normal(c(128, 10)) dataset <- tensor_slices_dataset(c(inputs, labels)) |>   dataset_batch(16)  # Set the global distribution. keras$distribution$set_distribution(data_parallel)  # Note that all the model weights from here on are replicated to # all the devices of the `DeviceMesh`. This includes the RNG # state, optimizer states, metrics, etc. The dataset fed into `model |> fit()` or # `model |> evaluate()` will be split evenly on the batch dimension, and sent to # all the devices. You don't have to do any manual aggregation of losses, # since all the computation happens in a global context. inputs <- keras_input(shape = c(28, 28, 1)) outputs <- inputs |>   layer_flatten() |>   layer_dense(units = 200, use_bias = FALSE, activation = \"relu\") |>   layer_dropout(0.4) |>   layer_dense(units = 10, activation = \"softmax\")  model <- keras_model(inputs = inputs, outputs = outputs)  model |> compile(loss = \"mse\") model |> fit(dataset, epochs = 3) ## Epoch 1/3 ## 8/8 - 0s - 47ms/step - loss: 1.0629 ## Epoch 2/3 ## 8/8 - 0s - 7ms/step - loss: 0.9712 ## Epoch 3/3 ## 8/8 - 0s - 7ms/step - loss: 0.9322 model |> evaluate(dataset) ## 8/8 - 0s - 7ms/step - loss: 0.8859 ## $loss ## [1] 0.8858577"},{"path":"https://keras.posit.co/articles/distribution.html","id":"modelparallel-and-layoutmap","dir":"Articles","previous_headings":"","what":"ModelParallel and LayoutMap","title":"Distributed training with Keras 3","text":"ModelParallel mostly useful model weights large fit single accelerator. setting allows spit model weights activation tensors across devices DeviceMesh, enable horizontal scaling large models. Unlike DataParallel model weights fully replicated, weights layout ModelParallel usually need customization best performances. introduce LayoutMap let specify TensorLayout weights intermediate tensors global perspective. LayoutMap dict-like object maps string TensorLayout instances. behaves differently normal dict string key treated regex retrieving value. class allows define naming schema TensorLayout retrieve corresponding TensorLayout instance. Typically, key used query variable$path attribute, identifier variable. shortcut, list axis names also allowed inserting value, converted TensorLayout. LayoutMap can also optionally contain DeviceMesh populate TensorLayout$device_mesh set. retrieving layout key, isn’t exact match, existing keys layout map treated regex matched input key . multiple matches, ValueError raised. matches found, NULL returned. also easy change mesh structure tune computation data parallel model parallel. can adjusting shape mesh. changes needed code.","code":"mesh_2d <- keras$distribution$DeviceMesh(   shape = shape(2, 4),   axis_names = c(\"data\", \"model\"),   devices = devices ) layout_map  <- keras$distribution$LayoutMap(mesh_2d)  # The rule below means that for any weights that match with d1/kernel, it # will be sharded with model dimensions (4 devices), same for the d1/bias. # All other weights will be fully replicated. layout_map[\"d1/kernel\"] <- tuple(NULL, \"model\") layout_map[\"d1/bias\"] <- tuple(\"model\")  # You can also set the layout for the layer output like layout_map[\"d2/output\"] <- tuple(\"data\", NULL)  model_parallel <- keras$distribution$ModelParallel(   mesh_2d, layout_map, batch_dim_name = \"data\" )  keras$distribution$set_distribution(model_parallel)  inputs <- layer_input(shape = c(28, 28, 1)) outputs <- inputs |>   layer_flatten() |>   layer_dense(units = 200, use_bias = FALSE,               activation = \"relu\", name = \"d1\") |>   layer_dropout(0.4) |>   layer_dense(units = 10,               activation = \"softmax\",               name = \"d2\")  model <- keras_model(inputs = inputs, outputs = outputs)  # The data will be sharded across the \"data\" dimension of the method, which # has 2 devices. model |> compile(loss = \"mse\") model |> fit(dataset, epochs = 3) ## Epoch 1/3 ## 8/8 - 0s - 29ms/step - loss: 1.0714 ## Epoch 2/3 ## 8/8 - 0s - 4ms/step - loss: 0.9744 ## Epoch 3/3 ## 8/8 - 0s - 5ms/step - loss: 0.9280 model |> evaluate(dataset) ## 8/8 - 0s - 9ms/step - loss: 0.8802 ## $loss ## [1] 0.8802156 full_data_parallel_mesh <- keras$distribution$DeviceMesh(   shape = shape(8, 1),   axis_names = list(\"data\", \"model\"),   devices = devices ) more_data_parallel_mesh <- keras$distribution$DeviceMesh(   shape = shape(4, 2),   axis_names = list(\"data\", \"model\"),   devices = devices ) more_model_parallel_mesh <- keras$distribution$DeviceMesh(   shape = shape(2, 4),   axis_names = list(\"data\", \"model\"),   devices = devices ) full_model_parallel_mesh <- keras$distribution$DeviceMesh(   shape = shape(1, 8),   axis_names = list(\"data\", \"model\"),   devices = devices )"},{"path":"https://keras.posit.co/articles/distribution.html","id":"further-reading","dir":"Articles","previous_headings":"ModelParallel and LayoutMap","what":"Further reading","title":"Distributed training with Keras 3","text":"JAX Distributed arrays automatic parallelization JAX sharding module TensorFlow Distributed training DTensors TensorFlow DTensor concepts Using DTensors tf.keras","code":""},{"path":"https://keras.posit.co/articles/examples/autoencoder.html","id":"introduction","dir":"Articles > Examples","previous_headings":"","what":"Introduction","title":"Convolutional autoencoder for image denoising","text":"example demonstrates implement deep convolutional autoencoder image denoising, mapping noisy digits images MNIST dataset clean digits images. implementation based original blog post titled Building Autoencoders Keras François Chollet.","code":""},{"path":"https://keras.posit.co/articles/examples/autoencoder.html","id":"setup","dir":"Articles > Examples","previous_headings":"","what":"Setup","title":"Convolutional autoencoder for image denoising","text":"","code":"library(keras3)  # Normalizes the supplied array and reshapes it. preprocess <- function(array) {   array_reshape(array/255, c(dim(array)[1], 28, 28, 1)) }  # Adds random noise to each image in the supplied array. noise <- function(array) {   noise_factor <- 0.4   noisy_array <- array + noise_factor * random_normal(dim(array))   op_clip(noisy_array, 0.0, 1.0) }  display <- function(array1, array2) {   n <- 2   indices <- sample.int(dim(array1)[1], n)   images1 <- as.array(array1)[indices, , , ]   images2 <- as.array(array2)[indices, , , ]    par(mfrow = c(2, n), mar = c(0, 0, 0, 0))   for (i in seq_len(n)) {     plot(as.raster(images1[i, , ]))     plot(as.raster(images2[i, , ]))   } }"},{"path":"https://keras.posit.co/articles/examples/autoencoder.html","id":"prepare-the-data","dir":"Articles > Examples","previous_headings":"","what":"Prepare the data","title":"Convolutional autoencoder for image denoising","text":"","code":"# Since we only need images from the dataset to encode and decode, we # won't use the labels. c(c(train_data, .), c(test_data, .)) %<-% dataset_mnist()  # Normalize and reshape the data train_data <- preprocess(train_data) test_data <- preprocess(test_data)  # Create a copy of the data with added noise noisy_train_data <- noise(train_data) noisy_test_data <- noise(test_data)  # Display the train data and a version of it with added noise display(train_data, noisy_train_data)"},{"path":"https://keras.posit.co/articles/examples/autoencoder.html","id":"build-the-autoencoder","dir":"Articles > Examples","previous_headings":"","what":"Build the autoencoder","title":"Convolutional autoencoder for image denoising","text":"going use Functional API build convolutional autoencoder. Now can train autoencoder using train_data input data target. Notice setting validation data using format. Let’s predict test dataset display original image together prediction autoencoder. Notice predictions pretty close original images, although quite . Now know autoencoder works, let’s retrain using noisy data input clean data target. want autoencoder learn denoise images. Let’s now predict noisy data display results autoencoder. Notice autoencoder amazing job removing noise input images.","code":"input <- keras_input(shape = c(28, 28, 1))  # Encoder enc <- input |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3),                 activation = \"relu\", padding = \"same\") |>   layer_max_pooling_2d(pool_size = c(2, 2), padding = \"same\") |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3),                 activation = \"relu\", padding = \"same\") |>   layer_max_pooling_2d(pool_size = c(2, 2), padding = \"same\")  # Decoder dec <- enc |>   layer_conv_2d_transpose(filters = 32, kernel_size = c(3, 3), strides = 2,                           activation = \"relu\", padding = \"same\") |>   layer_conv_2d_transpose(filters = 32, kernel_size = c(3, 3), strides = 2,                           activation = \"relu\", padding = \"same\") |>   layer_conv_2d(filters = 1, kernel_size = c(3, 3),                 activation = \"sigmoid\", padding = \"same\")  # Autoencoder autoencoder <- keras_model(input, dec) autoencoder |> compile(optimizer = \"adam\", loss = \"binary_crossentropy\") autoencoder |> summary() ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 28, 28, 32)     │           320 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 14, 14, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 14, 14, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 7, 7, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 14, 14, 32)     │         9,248 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 28, 28, 32)     │         9,248 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 28, 28, 1)      │           289 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 28,353 (110.75 KB) ##  Trainable params: 28,353 (110.75 KB) ##  Non-trainable params: 0 (0.00 B) autoencoder |> fit(   x = train_data,   y = train_data,   epochs = 50,   batch_size = 128,   shuffle = TRUE,   validation_data = list(test_data, test_data), ) ## Epoch 1/50 ## 469/469 - 5s - 10ms/step - loss: 0.1322 - val_loss: 0.0736 ## Epoch 2/50 ## 469/469 - 1s - 3ms/step - loss: 0.0721 - val_loss: 0.0698 ## Epoch 3/50 ## 469/469 - 1s - 3ms/step - loss: 0.0695 - val_loss: 0.0681 ## Epoch 4/50 ## 469/469 - 1s - 3ms/step - loss: 0.0682 - val_loss: 0.0671 ## Epoch 5/50 ## 469/469 - 1s - 3ms/step - loss: 0.0673 - val_loss: 0.0665 ## Epoch 6/50 ## 469/469 - 1s - 3ms/step - loss: 0.0667 - val_loss: 0.0660 ## Epoch 7/50 ## 469/469 - 1s - 3ms/step - loss: 0.0663 - val_loss: 0.0656 ## Epoch 8/50 ## 469/469 - 1s - 3ms/step - loss: 0.0659 - val_loss: 0.0652 ## Epoch 9/50 ## 469/469 - 1s - 3ms/step - loss: 0.0655 - val_loss: 0.0649 ## Epoch 10/50 ## 469/469 - 1s - 3ms/step - loss: 0.0652 - val_loss: 0.0646 ## Epoch 11/50 ## 469/469 - 1s - 3ms/step - loss: 0.0650 - val_loss: 0.0644 ## Epoch 12/50 ## 469/469 - 1s - 3ms/step - loss: 0.0647 - val_loss: 0.0642 ## Epoch 13/50 ## 469/469 - 1s - 3ms/step - loss: 0.0645 - val_loss: 0.0640 ## Epoch 14/50 ## 469/469 - 1s - 3ms/step - loss: 0.0643 - val_loss: 0.0638 ## Epoch 15/50 ## 469/469 - 1s - 3ms/step - loss: 0.0642 - val_loss: 0.0636 ## Epoch 16/50 ## 469/469 - 1s - 3ms/step - loss: 0.0640 - val_loss: 0.0635 ## Epoch 17/50 ## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0634 ## Epoch 18/50 ## 469/469 - 1s - 3ms/step - loss: 0.0638 - val_loss: 0.0633 ## Epoch 19/50 ## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0631 ## Epoch 20/50 ## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631 ## Epoch 21/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0630 ## Epoch 22/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0629 ## Epoch 23/50 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0628 ## Epoch 24/50 ## 469/469 - 1s - 3ms/step - loss: 0.0632 - val_loss: 0.0628 ## Epoch 25/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627 ## Epoch 26/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0626 ## Epoch 27/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0626 ## Epoch 28/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0625 ## Epoch 29/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0625 ## Epoch 30/50 ## 469/469 - 1s - 3ms/step - loss: 0.0628 - val_loss: 0.0624 ## Epoch 31/50 ## 469/469 - 1s - 3ms/step - loss: 0.0628 - val_loss: 0.0624 ## Epoch 32/50 ## 469/469 - 1s - 3ms/step - loss: 0.0627 - val_loss: 0.0623 ## Epoch 33/50 ## 469/469 - 1s - 3ms/step - loss: 0.0627 - val_loss: 0.0623 ## Epoch 34/50 ## 469/469 - 1s - 3ms/step - loss: 0.0627 - val_loss: 0.0623 ## Epoch 35/50 ## 469/469 - 1s - 3ms/step - loss: 0.0626 - val_loss: 0.0622 ## Epoch 36/50 ## 469/469 - 1s - 3ms/step - loss: 0.0626 - val_loss: 0.0622 ## Epoch 37/50 ## 469/469 - 1s - 3ms/step - loss: 0.0626 - val_loss: 0.0622 ## Epoch 38/50 ## 469/469 - 1s - 3ms/step - loss: 0.0625 - val_loss: 0.0622 ## Epoch 39/50 ## 469/469 - 1s - 3ms/step - loss: 0.0625 - val_loss: 0.0621 ## Epoch 40/50 ## 469/469 - 1s - 3ms/step - loss: 0.0625 - val_loss: 0.0621 ## Epoch 41/50 ## 469/469 - 1s - 3ms/step - loss: 0.0624 - val_loss: 0.0621 ## Epoch 42/50 ## 469/469 - 1s - 3ms/step - loss: 0.0624 - val_loss: 0.0621 ## Epoch 43/50 ## 469/469 - 1s - 3ms/step - loss: 0.0624 - val_loss: 0.0620 ## Epoch 44/50 ## 469/469 - 1s - 3ms/step - loss: 0.0624 - val_loss: 0.0620 ## Epoch 45/50 ## 469/469 - 1s - 3ms/step - loss: 0.0623 - val_loss: 0.0620 ## Epoch 46/50 ## 469/469 - 1s - 3ms/step - loss: 0.0623 - val_loss: 0.0620 ## Epoch 47/50 ## 469/469 - 1s - 3ms/step - loss: 0.0623 - val_loss: 0.0619 ## Epoch 48/50 ## 469/469 - 1s - 3ms/step - loss: 0.0623 - val_loss: 0.0619 ## Epoch 49/50 ## 469/469 - 1s - 3ms/step - loss: 0.0622 - val_loss: 0.0619 ## Epoch 50/50 ## 469/469 - 1s - 3ms/step - loss: 0.0622 - val_loss: 0.0619 predictions <- autoencoder |> predict(test_data) ## 313/313 - 1s - 2ms/step display(test_data, predictions) autoencoder |> fit(   x = noisy_train_data,   y = train_data,   epochs = 100,   batch_size = 128,   shuffle = TRUE,   validation_data = list(noisy_test_data, test_data), ) ## Epoch 1/100 ## 469/469 - 1s - 3ms/step - loss: 0.0998 - val_loss: 0.0928 ## Epoch 2/100 ## 469/469 - 1s - 3ms/step - loss: 0.0925 - val_loss: 0.0908 ## Epoch 3/100 ## 469/469 - 1s - 3ms/step - loss: 0.0910 - val_loss: 0.0898 ## Epoch 4/100 ## 469/469 - 1s - 3ms/step - loss: 0.0900 - val_loss: 0.0890 ## Epoch 5/100 ## 469/469 - 1s - 3ms/step - loss: 0.0893 - val_loss: 0.0883 ## Epoch 6/100 ## 469/469 - 1s - 3ms/step - loss: 0.0888 - val_loss: 0.0877 ## Epoch 7/100 ## 469/469 - 1s - 3ms/step - loss: 0.0883 - val_loss: 0.0873 ## Epoch 8/100 ## 469/469 - 1s - 3ms/step - loss: 0.0879 - val_loss: 0.0870 ## Epoch 9/100 ## 469/469 - 1s - 3ms/step - loss: 0.0876 - val_loss: 0.0868 ## Epoch 10/100 ## 469/469 - 1s - 3ms/step - loss: 0.0873 - val_loss: 0.0866 ## Epoch 11/100 ## 469/469 - 1s - 3ms/step - loss: 0.0871 - val_loss: 0.0863 ## Epoch 12/100 ## 469/469 - 1s - 3ms/step - loss: 0.0869 - val_loss: 0.0861 ## Epoch 13/100 ## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0860 ## Epoch 14/100 ## 469/469 - 1s - 3ms/step - loss: 0.0865 - val_loss: 0.0858 ## Epoch 15/100 ## 469/469 - 1s - 3ms/step - loss: 0.0864 - val_loss: 0.0857 ## Epoch 16/100 ## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0855 ## Epoch 17/100 ## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0854 ## Epoch 18/100 ## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0853 ## Epoch 19/100 ## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0852 ## Epoch 20/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0852 ## Epoch 21/100 ## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0851 ## Epoch 22/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0850 ## Epoch 23/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0850 ## Epoch 24/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0849 ## Epoch 25/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0849 ## Epoch 26/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0848 ## Epoch 27/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0848 ## Epoch 28/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0847 ## Epoch 29/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0847 ## Epoch 30/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0847 ## Epoch 31/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847 ## Epoch 32/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0846 ## Epoch 33/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0846 ## Epoch 34/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846 ## Epoch 35/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0845 ## Epoch 36/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0845 ## Epoch 37/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0845 ## Epoch 38/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0845 ## Epoch 39/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0845 ## Epoch 40/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0844 ## Epoch 41/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0844 ## Epoch 42/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0844 ## Epoch 43/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0844 ## Epoch 44/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0844 ## Epoch 45/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 46/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 47/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 48/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 49/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 50/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0843 ## Epoch 51/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 52/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 53/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 54/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 55/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 56/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0842 ## Epoch 57/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0842 ## Epoch 58/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0842 ## Epoch 59/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0842 ## Epoch 60/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0841 ## Epoch 61/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0841 ## Epoch 62/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0841 ## Epoch 63/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0841 ## Epoch 64/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0841 ## Epoch 65/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0841 ## Epoch 66/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0841 ## Epoch 67/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 68/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 69/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 70/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 71/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 72/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 73/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 74/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 75/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 76/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 77/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 78/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 79/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 80/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 81/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 82/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 83/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 84/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 85/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 86/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 87/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 88/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 89/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 90/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 91/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 92/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 93/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 94/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 95/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 96/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 97/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 98/100 ## 469/469 - 1s - 3ms/step - loss: 0.0841 - val_loss: 0.0839 ## Epoch 99/100 ## 469/469 - 1s - 3ms/step - loss: 0.0841 - val_loss: 0.0839 ## Epoch 100/100 ## 469/469 - 1s - 3ms/step - loss: 0.0841 - val_loss: 0.0839 predictions <- autoencoder |> predict(noisy_test_data) ## 313/313 - 0s - 581us/step display(noisy_test_data, predictions)"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"setup","dir":"Articles > Examples","previous_headings":"","what":"Setup","title":"Simple MNIST convnet","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"prepare-the-data","dir":"Articles > Examples","previous_headings":"","what":"Prepare the data","title":"Simple MNIST convnet","text":"","code":"# Model / data parameters num_classes <- 10 input_shape <- c(28, 28, 1)  # Load the data and split it between train and test sets c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()  # Scale images to the [0, 1] range x_train <- x_train / 255 x_test <- x_test / 255 # Make sure images have shape (28, 28, 1) x_train <- op_expand_dims(x_train, -1) x_test <- op_expand_dims(x_test, -1)   dim(x_train) ## [1] 60000    28    28     1 dim(x_test) ## [1] 10000    28    28     1 # convert class vectors to binary class matrices y_train <- to_categorical(y_train, num_classes) y_test <- to_categorical(y_test, num_classes)"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"build-the-model","dir":"Articles > Examples","previous_headings":"","what":"Build the model","title":"Simple MNIST convnet","text":"","code":"model <- keras_model_sequential(input_shape = input_shape) model |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_flatten() |>   layer_dropout(rate = 0.5) |>   layer_dense(units = num_classes, activation = \"softmax\")  summary(model) ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d_1 (Conv2D)               │ (None, 26, 26, 32)     │           320 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 13, 13, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 11, 11, 64)     │        18,496 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 5, 5, 64)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ flatten (Flatten)               │ (None, 1600)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 1600)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 10)             │        16,010 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 34,826 (136.04 KB) ##  Trainable params: 34,826 (136.04 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"train-the-model","dir":"Articles > Examples","previous_headings":"","what":"Train the model","title":"Simple MNIST convnet","text":"","code":"batch_size <- 128 epochs <- 15  model |> compile(   loss = \"categorical_crossentropy\",   optimizer = \"adam\",   metrics = \"accuracy\" )  model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = epochs,   validation_split = 0.1 ) ## Epoch 1/15 ## 422/422 - 4s - 11ms/step - accuracy: 0.8845 - loss: 0.3815 - val_accuracy: 0.9783 - val_loss: 0.0810 ## Epoch 2/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9644 - loss: 0.1151 - val_accuracy: 0.9863 - val_loss: 0.0548 ## Epoch 3/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9735 - loss: 0.0838 - val_accuracy: 0.9880 - val_loss: 0.0455 ## Epoch 4/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9781 - loss: 0.0689 - val_accuracy: 0.9893 - val_loss: 0.0413 ## Epoch 5/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9811 - loss: 0.0608 - val_accuracy: 0.9907 - val_loss: 0.0373 ## Epoch 6/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9824 - loss: 0.0561 - val_accuracy: 0.9908 - val_loss: 0.0359 ## Epoch 7/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9848 - loss: 0.0487 - val_accuracy: 0.9917 - val_loss: 0.0328 ## Epoch 8/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9845 - loss: 0.0484 - val_accuracy: 0.9915 - val_loss: 0.0334 ## Epoch 9/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9859 - loss: 0.0439 - val_accuracy: 0.9923 - val_loss: 0.0320 ## Epoch 10/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9868 - loss: 0.0405 - val_accuracy: 0.9925 - val_loss: 0.0319 ## Epoch 11/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9874 - loss: 0.0387 - val_accuracy: 0.9920 - val_loss: 0.0317 ## Epoch 12/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9882 - loss: 0.0367 - val_accuracy: 0.9925 - val_loss: 0.0301 ## Epoch 13/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9879 - loss: 0.0361 - val_accuracy: 0.9928 - val_loss: 0.0281 ## Epoch 14/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9889 - loss: 0.0332 - val_accuracy: 0.9930 - val_loss: 0.0284 ## Epoch 15/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9897 - loss: 0.0311 - val_accuracy: 0.9925 - val_loss: 0.0293"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"evaluate-the-trained-model","dir":"Articles > Examples","previous_headings":"","what":"Evaluate the trained model","title":"Simple MNIST convnet","text":"","code":"score <- model |> evaluate(x_test, y_test, verbose = 0) score ## $accuracy ## [1] 0.9911 ## ## $loss ## [1] 0.02564374"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"introduction","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Introduction","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"example, ’ll build sequence--sequence Transformer model, ’ll train English--Spanish machine translation task. ’ll learn : Vectorize text using layer_text_vectorization(). Implement layer_transformer_encoder(), layer_transformer_decoder(), layer_positional_embedding(). Prepare data training sequence--sequence model. Use trained model generate translations never-seen-input sentences (sequence--sequence inference). code featured adapted book Deep Learning R, Second Edition (chapter 11: Deep learning text). present example fairly barebones, detailed explanations building block works, well theory behind Transformers, recommend reading book.","code":""},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"setup","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Setup","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"","code":"library(glue)  library(tensorflow, exclude = c(\"set_random_seed\", \"shape\")) library(keras3)"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"downloading-the-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Downloading the data","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"’ll working English--Spanish translation dataset provided Anki. Let’s download :","code":"zipfile <- get_file(\"spa-eng.zip\", origin =   \"http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip\")  zip::zip_list(zipfile) # See what's in the zipfile ##             filename compressed_size uncompressed_size           timestamp ## 1           spa-eng/               0                 0 2018-06-13 12:21:20 ## 2 spa-eng/_about.txt             685              1441 2018-05-13 19:50:34 ## 3    spa-eng/spa.txt         2637619           8042772 2018-05-13 19:50:34 ##   permissions    crc32 offset ## 1         755 00000000      0 ## 2         644 4b18349d     38 ## 3         644 63c5c4a2    771 zip::unzip(zipfile, exdir = \".\") # unzip into the current directory  text_file <- fs::path(\"./spa-eng/spa.txt\")"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"parsing-the-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Parsing the data","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"line contains English sentence corresponding Spanish sentence. English sentence source sequence Spanish one target sequence. prepend token \"[start]\" append token \"[end]\" Spanish sentence. ’s sentence pairs look like: Now, let’s split sentence pairs training set, validation set, test set.","code":"text_file <- \"spa-eng/spa.txt\" text_pairs <- text_file %>%   readr::read_tsv(col_names = c(\"english\", \"spanish\"),                   col_types = c(\"cc\")) %>%   within(spanish %<>% paste(\"[start]\", ., \"[end]\")) df <- text_pairs[sample(nrow(text_pairs), 5), ] glue::glue_data(df, r\"(   english: {english}   spanish: {spanish} )\") |> cat(sep = \"\\n\\n\") ## english: I'm staying in Italy. ## spanish: [start] Me estoy quedando en Italia. [end] ## ## english: What's so strange about that? ## spanish: [start] ¿Qué es tan extraño acerca de eso? [end] ## ## english: All of the buses are full. ## spanish: [start] Todos los bondis están llenos. [end] ## ## english: Is this where your mother works? ## spanish: [start] ¿Es aquí donde trabaja tu madre? [end] ## ## english: Take precautions. ## spanish: [start] Ten cuidado. [end] random.shuffle(text_pairs) num_val_samples = int(0.15 * len(text_pairs)) num_train_samples = len(text_pairs) - 2 * num_val_samples train_pairs = text_pairs[:num_train_samples] val_pairs = text_pairs[num_train_samples : num_train_samples + num_val_samples] test_pairs = text_pairs[num_train_samples + num_val_samples :]  print(f\"{len(text_pairs)} total pairs\") print(f\"{len(train_pairs)} training pairs\") print(f\"{len(val_pairs)} validation pairs\") print(f\"{len(test_pairs)} test pairs\") num_test_samples <- num_val_samples <-   round(0.15 * nrow(text_pairs)) num_train_samples <- nrow(text_pairs) - num_val_samples - num_test_samples  pair_group <- sample(c(   rep(\"train\", num_train_samples),   rep(\"test\", num_test_samples),   rep(\"val\", num_val_samples) ))  train_pairs <- text_pairs[pair_group == \"train\", ] test_pairs <- text_pairs[pair_group == \"test\", ] val_pairs <- text_pairs[pair_group == \"val\", ] glue(r\"(   {nrow(text_pairs)} total pairs   {nrow(train_pairs)} training pairs   {nrow(val_pairs)} validation pairs   {nrow(test_pairs)} test pairs )\", .transformer = function(text, envir) {   val <- eval(str2lang(text), envir)   prettyNum(val, big.mark = \",\") }) ## 118,493 total pairs ## 82,945 training pairs ## 17,774 validation pairs ## 17,774 test pairs"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"vectorizing-the-text-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Vectorizing the text data","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"’ll use two instances layer_text_vectorization() vectorize text data (one English one Spanish), say, turn original strings integer sequences integer represents index word vocabulary. English layer use default string standardization (strip punctuation characters) splitting scheme (split whitespace), Spanish layer use custom standardization, add character \"¿\" set punctuation characters stripped. Note: production-grade machine translation model, recommend stripping punctuation characters either language. Instead, recommend turning punctuation character token, achieve providing custom split function layer_text_vectorization(). Next, ’ll format datasets. training step, model seek predict target words N+1 (beyond) using source sentence target words 1 N. , training dataset yield tuple (inputs, targets), : inputs dictionary (named list) keys (names) encoder_inputs decoder_inputs. encoder_inputs vectorized source sentence encoder_inputs target sentence “far”, say, words 0 N used predict word N+1 (beyond) target sentence. target target sentence offset one step: provides next words target sentence – model try predict. Let’s take quick look sequence shapes (batches 64 pairs, sequences 20 steps long):","code":"punctuation_regex <- \"[¡¿]|[^[:^punct:][\\\\]]\" # the regex explained: Match ¡, or ¿, or any punctuation character except ] # # [:^punct:]: is a negated POSIX character class. # [:punct:] matches any punctuation character, so [:^punct:] matches any # character that is not a punctuation character. # [^...] negates the whole character class # So [^[:^punct:]] would matche any character that is a punctuation character. # Putting this all together, [^[:^punct:][\\\\]] matches any # punctuation character except the ] character.  custom_standardization <- function(input_string) {   input_string %>%     tf$strings$lower() %>%     tf$strings$regex_replace(punctuation_regex, \"\") }  input_string <- as_tensor(\"[start] ¡corre! [end]\") custom_standardization(input_string) ## tf.Tensor(b'[start] corre [end]', shape=(), dtype=string) vocab_size <- 15000 sequence_length <- 20  # rename to eng_vectorization eng_vectorization <- layer_text_vectorization(   max_tokens = vocab_size,   output_mode = \"int\",   output_sequence_length = sequence_length )  spa_vectorization <- layer_text_vectorization(   max_tokens = vocab_size,   output_mode = \"int\",   output_sequence_length = sequence_length + 1,   standardize = custom_standardization )  adapt(eng_vectorization, train_pairs$english) adapt(spa_vectorization, train_pairs$spanish) format_pair <- function(pair) {   # the vectorization layers requrie batched inputs,   # reshape scalar string tensor to add a batch dim   pair %<>% lapply(op_expand_dims, 1)    # vectorize   eng <- eng_vectorization(pair$english)   spa <- spa_vectorization(pair$spanish)    # drop the batch dim   eng %<>% tf$ensure_shape(shape(1, sequence_length)) %>% op_squeeze(1)   spa %<>% tf$ensure_shape(shape(1, sequence_length+1)) %>% op_squeeze(1)    inputs <- list(encoder_inputs = eng,                  decoder_inputs = spa[NA:-2])   targets <- spa[2:NA]   list(inputs, targets) }   batch_size <- 64  library(tfdatasets, exclude = \"shape\") make_dataset <- function(pairs) {   tensor_slices_dataset(pairs) %>%     dataset_map(format_pair, num_parallel_calls = 4) %>%     dataset_cache() %>%     dataset_shuffle(2048) %>%     dataset_batch(batch_size) %>%     dataset_prefetch(2) } train_ds <- make_dataset(train_pairs) ## Warning: Negative numbers are interpreted python-style when subsetting tensorflow tensors. ## See: ?`[.tensorflow.tensor` for details. ## To turn off this warning, set `options(tensorflow.extract.warn_negatives_pythonic = FALSE)` val_ds <- make_dataset(val_pairs) c(inputs, targets) %<-% iter_next(as_iterator(train_ds)) str(inputs) ## List of 2 ##  $ encoder_inputs:<tf.Tensor: shape=(64, 20), dtype=int64, numpy=…> ##  $ decoder_inputs:<tf.Tensor: shape=(64, 20), dtype=int64, numpy=…> str(targets) ## <tf.Tensor: shape=(64, 20), dtype=int64, numpy=…>"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"building-the-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Building the model","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"sequence--sequence Transformer consists TransformerEncoder TransformerDecoder chained together. make model aware word order, also use PositionalEmbedding layer. source sequence pass TransformerEncoder, produce new representation . new representation passed TransformerDecoder, together target sequence far (target words 1 N). TransformerDecoder seek predict next words target sequence (N+1 beyond). key detail makes possible causal masking (see method get_causal_attention_mask() TransformerDecoder). TransformerDecoder sees entire sequences , thus must make sure uses information target tokens 0 N predicting token N+1 (otherwise, use information future, result model used inference time). Next, assemble end--end model.","code":"layer_transformer_encoder <- Layer(   classname = \"TransformerEncoder\",   initialize = function(embed_dim, dense_dim, num_heads, ...) {     super$initialize(...)     self$embed_dim <- embed_dim     self$dense_dim <- dense_dim     self$num_heads <- num_heads     self$attention <-       layer_multi_head_attention(num_heads = num_heads,                                  key_dim = embed_dim)      self$dense_proj <- keras_model_sequential() %>%       layer_dense(dense_dim, activation = \"relu\") %>%       layer_dense(embed_dim)      self$layernorm_1 <- layer_layer_normalization()     self$layernorm_2 <- layer_layer_normalization()     self$supports_masking <- TRUE   },    call = function(inputs, mask = NULL) {     if (!is.null(mask))       mask <- mask[, NULL, ] |> op_cast(\"int32\")      inputs %>%       { self$attention(., ., attention_mask = mask) + . } %>%       self$layernorm_1() %>%       { self$dense_proj(.) + . } %>%       self$layernorm_2()   },    get_config = function() {     config <- super$get_config()     for(name in c(\"embed_dim\", \"num_heads\", \"dense_dim\"))       config[[name]] <- self[[name]]     config   } )  layer_transformer_decoder <- Layer(   classname = \"TransformerDecoder\",    initialize = function(embed_dim, latent_dim, num_heads, ...) {     super$initialize(...)     self$embed_dim <- embed_dim     self$latent_dim <- latent_dim     self$num_heads <- num_heads     self$attention_1 <- layer_multi_head_attention(num_heads = num_heads,                                                    key_dim = embed_dim)     self$attention_2 <- layer_multi_head_attention(num_heads = num_heads,                                                    key_dim = embed_dim)     self$dense_proj <- keras_model_sequential() %>%       layer_dense(latent_dim, activation = \"relu\") %>%       layer_dense(embed_dim)      self$layernorm_1 <- layer_layer_normalization()     self$layernorm_2 <- layer_layer_normalization()     self$layernorm_3 <- layer_layer_normalization()     self$supports_masking <- TRUE   },    get_config = function() {     config <- super$get_config()     for (name in c(\"embed_dim\", \"num_heads\", \"latent_dim\"))       config[[name]] <- self[[name]]     config   },     get_causal_attention_mask = function(inputs) {     c(batch_size, sequence_length, encoding_length) %<-% op_shape(inputs)      x <- op_arange(sequence_length)     i <- x[, NULL]     j <- x[NULL, ]     mask <- op_cast(i >= j, \"int32\")      repeats <- op_stack(c(batch_size, 1L, 1L))     op_tile(mask[NULL, , ], repeats)   },   call = function(inputs, encoder_outputs, mask = NULL) {     causal_mask <- self$get_causal_attention_mask(inputs)      if (is.null(mask))       mask <- causal_mask     else       mask %<>% { op_minimum(op_cast(.[, NULL, ], \"int32\"),                              causal_mask) }      inputs %>%       { self$attention_1(query = ., value = ., key = .,                          attention_mask = causal_mask) + . } %>%       self$layernorm_1() %>%        { self$attention_2(query = .,                          value = encoder_outputs,                          key = encoder_outputs,                          attention_mask = mask) + . } %>%       self$layernorm_2() %>%        { self$dense_proj(.) + . } %>%       self$layernorm_3()    } )  layer_positional_embedding <- Layer(   classname = \"PositionalEmbedding\",    initialize = function(sequence_length, vocab_size, embed_dim, ...) {     super$initialize(...)     self$token_embeddings <- layer_embedding(       input_dim = vocab_size, output_dim = embed_dim     )     self$position_embeddings <- layer_embedding(       input_dim = sequence_length, output_dim = embed_dim     )     self$sequence_length <- sequence_length     self$vocab_size <- vocab_size     self$embed_dim <- embed_dim   },    call = function(inputs) {     c(., len) %<-% op_shape(inputs) # (batch_size, seq_len)     positions <- op_arange(0, len, dtype = \"int32\")     embedded_tokens <- self$token_embeddings(inputs)     embedded_positions <- self$position_embeddings(positions)     embedded_tokens + embedded_positions   },    compute_mask = function(inputs, mask = NULL) {     if (is.null(mask)) return (NULL)     inputs != 0L   },    get_config = function() {     config <- super$get_config()     for(name in c(\"sequence_length\", \"vocab_size\", \"embed_dim\"))       config[[name]] <- self[[name]]     config   } ) embed_dim <- 256 latent_dim <- 2048 num_heads <- 8  encoder_inputs <- layer_input(shape(NA), dtype = \"int64\",                               name = \"encoder_inputs\") encoder_outputs <- encoder_inputs %>%   layer_positional_embedding(sequence_length, vocab_size, embed_dim) %>%   layer_transformer_encoder(embed_dim, latent_dim, num_heads)  encoder <- keras_model(encoder_inputs, encoder_outputs)  decoder_inputs <-  layer_input(shape(NA), dtype = \"int64\",                                name = \"decoder_inputs\") encoded_seq_inputs <- layer_input(shape(NA, embed_dim),                                   name = \"decoder_state_inputs\")  transformer_decoder <- layer_transformer_decoder(NULL,   embed_dim, latent_dim, num_heads)  decoder_outputs <- decoder_inputs %>%   layer_positional_embedding(sequence_length, vocab_size, embed_dim) %>%   transformer_decoder(., encoded_seq_inputs) %>%   layer_dropout(0.5) %>%   layer_dense(vocab_size, activation=\"softmax\")  decoder <- keras_model(inputs = list(decoder_inputs, encoded_seq_inputs),                        outputs = decoder_outputs)  decoder_outputs = decoder(list(decoder_inputs, encoder_outputs))  transformer <- keras_model(list(encoder_inputs, decoder_inputs),                            decoder_outputs,                            name = \"transformer\")"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"training-our-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Training our model","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"’ll use accuracy quick way monitor training progress validation data. Note machine translation typically uses BLEU scores well metrics, rather accuracy. train 1 epoch, get model actually converge train least 30 epochs.","code":"epochs <- 1  # This should be at least 30 for convergence  transformer ## Model: \"transformer\" ## ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ ## │ encoder_inputs      │ (None, None)      │          0 │ -                 │ ## │ (InputLayer)        │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ positional_embeddi… │ (None, None, 256) │  3,845,120 │ encoder_inputs[0… │ ## │ (PositionalEmbeddi… │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ decoder_inputs      │ (None, None)      │          0 │ -                 │ ## │ (InputLayer)        │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ transformer_encoder │ (None, None, 256) │  3,155,456 │ positional_embed… │ ## │ (TransformerEncode… │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ functional_5        │ (None, None,      │ 12,959,640 │ decoder_inputs[0… │ ## │ (Functional)        │ 15000)            │            │ transformer_enco… │ ## └─────────────────────┴───────────────────┴────────────┴───────────────────┘ ##  Total params: 19,960,216 (76.14 MB) ##  Trainable params: 19,960,216 (76.14 MB) ##  Non-trainable params: 0 (0.00 B) transformer |> compile(   \"rmsprop\",   loss = \"sparse_categorical_crossentropy\",   metrics = \"accuracy\" ) transformer |> fit(train_ds, epochs = epochs,                    validation_data = val_ds) ## 1297/1297 - 56s - 43ms/step - accuracy: 0.7708 - loss: 1.5755 - val_accuracy: 0.7784 - val_loss: 1.3806"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"decoding-test-sentences","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Decoding test sentences","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"Finally, let’s demonstrate translate brand new English sentences. simply feed model vectorized English sentence well target token \"[start]\", repeatedly generated next token, hit token \"[end]\". 30 epochs, get results :","code":"spa_vocab <- spa_vectorization |> get_vocabulary() max_decoded_sentence_length <- 20 tf_decode_sequence <- tf_function(function(input_sentence) {   withr::local_options(tensorflow.extract.style = \"python\")    tokenized_input_sentence <- input_sentence %>%     as_tensor(shape = c(1, 1)) %>%     eng_vectorization()   spa_vocab <- as_tensor(spa_vocab)   decoded_sentence <- as_tensor(\"[start]\", shape = c(1, 1))    for (i in tf$range(as.integer(max_decoded_sentence_length))) {      tokenized_target_sentence <-       spa_vectorization(decoded_sentence)[,NA:-1]      next_token_predictions <-       transformer(list(tokenized_input_sentence,                        tokenized_target_sentence))      sampled_token_index <- tf$argmax(next_token_predictions[0, i, ])     sampled_token <- spa_vocab[sampled_token_index]     decoded_sentence <-       tf$strings$join(c(decoded_sentence, sampled_token),                       separator = \" \")      if (sampled_token == \"[end]\")       break   }    decoded_sentence  })  for (i in seq(20)) {      c(input_sentence, correct_translation) %<-%       test_pairs[sample.int(nrow(test_pairs), 1), ]     cat(\"-\\n\")     cat(\"English:\", input_sentence, \"\\n\")     cat(\"Correct Translation:\", tolower(correct_translation), \"\\n\")     cat(\"  Model Translation:\", input_sentence %>% as_tensor() %>%           tf_decode_sequence() %>% as.character(), \"\\n\") } English: I'm sure everything will be fine. Correct Translation: [start] estoy segura de que todo irá bien. [end]   Model Translation: [start] estoy seguro de que todo va bien [end]"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"introduction","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Introduction","title":"Text classification from scratch","text":"example shows text classification starting raw text (set text files disk). demonstrate workflow IMDB sentiment classification dataset (unprocessed version). use layer_text_vectorization() word splitting & indexing.","code":""},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"setup","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Setup","title":"Text classification from scratch","text":"","code":"options(conflicts.policy = \"strict\") library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\") library(keras3) use_virtualenv(\"r-keras\")"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"load-the-data-imdb-movie-review-sentiment-classification","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Load the data: IMDB movie review sentiment classification","title":"Text classification from scratch","text":"Let’s download data inspect structure. aclImdb folder contains train test subfolder: aclImdb/train/pos aclImdb/train/neg folders contain text files, represents one review (either positive negative): interested pos neg subfolders, let’s delete subfolder text files : can use utility text_dataset_from_directory() generate labeled tf_dataset object set text files disk filed class-specific folders. Let’s use generate training, validation, test datasets. validation training datasets generated two subsets train directory, 20% samples going validation dataset 80% going training dataset. validation dataset addition test dataset useful tuning hyperparameters, model architecture, test dataset used. putting model real world however, retrained using available training data (without creating validation dataset), performance maximized. using validation_split subset arguments, make sure either specify random seed, pass shuffle=FALSE, validation & training splits get overlap. Let’s preview samples:","code":"if (!dir.exists(\"datasets/aclImdb\")) {   dir.create(\"datasets\")   download.file(     \"https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\",     \"datasets/aclImdb_v1.tar.gz\"   )   untar(\"datasets/aclImdb_v1.tar.gz\", exdir = \"datasets\")   unlink(\"datasets/aclImdb/train/unsup\", recursive = TRUE) } head(list.files(\"datasets/aclImdb/test\")) ## [1] \"labeledBow.feat\" \"neg\"             \"pos\"             \"urls_neg.txt\" ## [5] \"urls_pos.txt\" head(list.files(\"datasets/aclImdb/train\")) ## [1] \"labeledBow.feat\" \"neg\"             \"pos\"             \"unsupBow.feat\" ## [5] \"urls_neg.txt\"    \"urls_pos.txt\" cat(readLines(\"datasets/aclImdb/train/pos/6248_7.txt\")) ## Being an Austrian myself this has been a straight knock in my face. Fortunately I don't live nowhere near the place where this movie takes place but unfortunately it portrays everything that the rest of Austria hates about Viennese people (or people close to that region). And it is very easy to read that this is exactly the directors intention: to let your head sink into your hands and say \"Oh my god, how can THAT be possible!\". No, not with me, the (in my opinion) totally exaggerated uncensored swinger club scene is not necessary, I watch porn, sure, but in this context I was rather disgusted than put in the right context.<br /><br />This movie tells a story about how misled people who suffer from lack of education or bad company try to survive and live in a world of redundancy and boring horizons. A girl who is treated like a whore by her super-jealous boyfriend (and still keeps coming back), a female teacher who discovers her masochism by putting the life of her super-cruel \"lover\" on the line, an old couple who has an almost mathematical daily cycle (she is the \"official replacement\" of his ex wife), a couple that has just divorced and has the ex husband suffer under the acts of his former wife obviously having a relationship with her masseuse and finally a crazy hitchhiker who asks her drivers the most unusual questions and stretches their nerves by just being super-annoying.<br /><br />After having seen it you feel almost nothing. You're not even shocked, sad, depressed or feel like doing anything... Maybe that's why I gave it 7 points, it made me react in a way I never reacted before. If that's good or bad is up to you! unlink(\"datasets/aclImdb/train/unsup\", recursive = TRUE) batch_size <- 32  raw_train_ds <- text_dataset_from_directory(   \"datasets/aclImdb/train\",   batch_size = batch_size,   validation_split = 0.2,   subset = \"training\",   seed = 1337 ) ## Found 25000 files belonging to 2 classes. ## Using 20000 files for training. raw_val_ds <- text_dataset_from_directory(   \"datasets/aclImdb/train\",   batch_size = batch_size,   validation_split = 0.2,   subset = \"validation\",   seed = 1337 ) ## Found 25000 files belonging to 2 classes. ## Using 5000 files for validation. raw_test_ds <- text_dataset_from_directory(   \"datasets/aclImdb/test\",   batch_size = batch_size ) ## Found 25000 files belonging to 2 classes. cat(\"Number of batches in raw_train_ds:\", length(raw_train_ds), \"\\n\") ## Number of batches in raw_train_ds: 625 cat(\"Number of batches in raw_val_ds:\", length(raw_val_ds), \"\\n\") ## Number of batches in raw_val_ds: 157 cat(\"Number of batches in raw_test_ds:\", length(raw_test_ds), \"\\n\") ## Number of batches in raw_test_ds: 782 # It's important to take a look at your raw data to ensure your normalization # and tokenization will work as expected. We can do that by taking a few # examples from the training set and looking at them. # This is one of the places where eager execution shines: # we can just evaluate these tensors using .numpy() # instead of needing to evaluate them in a Session/Graph context. batch <- iter_next(as_iterator(raw_train_ds)) str(batch) ## List of 2 ##  $ :<tf.Tensor: shape=(32), dtype=string, numpy=…> ##  $ :<tf.Tensor: shape=(32), dtype=int32, numpy=…> c(text_batch, label_batch) %<-% batch for (i in 1:3) {   print(text_batch[i])   print(label_batch[i]) } ## tf.Tensor(b\"I have read the novel Reaper of Ben Mezrich a fews years ago and last night I accidentally came to see this adaption.<br /><br />Although it's been years since I read the story the first time, the differences between the novel and the movie are humongous. Very important elements, which made the whole thing plausible are just written out or changed to bad.<br /><br />If the plot sounds interesting to you: go and get the novel. Its much, much, much better.<br /><br />Still 4 out of 10 since it was hard to stop watching because of the great basic plot by Ben Mezrich.\", shape=(), dtype=string) ## tf.Tensor(0, shape=(), dtype=int32) ## tf.Tensor(b'After seeing all the Jesse James, Quantrill, jayhawkers,etc films in the fifties, it is quite a thrill to see this film with a new perspective by director Ang Lee. The scene of the attack of Lawrence, Kansas is awesome. The romantic relationship between Jewel and Toby Mcguire turns out to be one of the best parts and Jonathan Rhys-Meyers is outstanding as the bad guy. All the time this film makes you feel the horror of war, and the desperate situation of the main characters who do not know if they are going to survive the next hours. Definitely worth seeing.', shape=(), dtype=string) ## tf.Tensor(1, shape=(), dtype=int32) ## tf.Tensor(b'AG was an excellent presentation of drama, suspense and thriller that is so rare to American TV. Sheriff Lucas gave many a viewer the willies. We rooted for Caleb as he strove to resist the overtures of Sheriff Lucas. We became engrossed and fearful upon learning of the unthinkable connection between these two characters. The manipulations which weekly gave cause to fear what Lucas would do next were truly surprising. This show lived up to the \"Gothic\" moniker in ways American entertainment has so seldom attempted, much less mastered. The suits definitely made a big mistake in not supporting this show. This show puts shame to the current glut of \"reality\" shows- which are so less than satisfying viewing.The call for a DVD box set is well based. This show is quality viewing for a discerning market hungry for quality viewing. A public that is tiring of over-saturation of mind-numbing reality fare will welcome this gem of real storytelling. Bring on the DVD box set!!', shape=(), dtype=string) ## tf.Tensor(1, shape=(), dtype=int32)"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"prepare-the-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Prepare the data","title":"Text classification from scratch","text":"particular, remove <br /> tags.","code":"# Having looked at our data above, we see that the raw text contains HTML break # tags of the form '<br />'. These tags will not be removed by the default # standardizer (which doesn't strip HTML). Because of this, we will need to # create a custom standardization function. custom_standardization_fn <- function(string_tensor) {   string_tensor |>     tf$strings$lower() |> # convert to all lowercase     tf$strings$regex_replace(\"<br />\", \" \") |> # remove '<br />' HTML tag     tf$strings$regex_replace(\"[[:punct:]]\", \"\") # remove punctuation }   # Model constants. max_features <- 20000 embedding_dim <- 128 sequence_length <- 500  # Now that we have our custom standardization, we can instantiate our text # vectorization layer. We are using this layer to normalize, split, and map # strings to integers, so we set our 'output_mode' to 'int'. # Note that we're using the default split function, # and the custom standardization defined above. # We also set an explicit maximum sequence length, since the CNNs later in our # model won't support ragged sequences. vectorize_layer <- layer_text_vectorization(   standardize = custom_standardization_fn,   max_tokens = max_features,   output_mode = \"int\",   output_sequence_length = sequence_length, )  # Now that the vectorize_layer has been created, call `adapt` on a text-only # dataset to create the vocabulary. You don't have to batch, but for very large # datasets this means you're not keeping spare copies of the dataset in memory.  # Let's make a text-only dataset (no labels): text_ds <- raw_train_ds |>   dataset_map(\\(x, y) x) # Let's call `adapt`: vectorize_layer |> adapt(text_ds)"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"two-options-to-vectorize-the-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Two options to vectorize the data","title":"Text classification from scratch","text":"2 ways can use text vectorization layer: Option 1: Make part model, obtain model processes raw strings, like : Option 2: Apply text dataset obtain dataset word indices, feed model expects integer sequences inputs. important difference two option 2 enables asynchronous CPU processing buffering data training GPU. ’re training model GPU, probably want go option get best performance. . export model production, ’d ship model accepts raw strings input, like code snippet option 1 . can done training. last section.","code":"text_input <- keras_input(shape = c(1L), dtype = \"string\", name = 'text') x <- text_input |>   vectorize_layer() |>   layer_embedding(max_features + 1, embedding_dim) vectorize_text <- function(text, label) {   text <- text |>     op_expand_dims(-1) |>     vectorize_layer()   list(text, label) }  # Vectorize the data. train_ds <- raw_train_ds |> dataset_map(vectorize_text) val_ds   <- raw_val_ds   |> dataset_map(vectorize_text) test_ds  <- raw_test_ds  |> dataset_map(vectorize_text)  # Do async prefetching / buffering of the data for best performance on GPU. train_ds <- train_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10) val_ds <- val_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10) test_ds <- test_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10)"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"build-a-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Build a model","title":"Text classification from scratch","text":"choose simple 1D convnet starting Embedding layer.","code":"# A integer input for vocab indices. inputs <- keras_input(shape = c(NA), dtype = \"int64\")  predictions <- inputs |>   # Next, we add a layer to map those vocab indices into a space of dimensionality   # 'embedding_dim'.   layer_embedding(max_features, embedding_dim) |>   layer_dropout(0.5) |>   # Conv1D + global max pooling   layer_conv_1d(128, 7, padding = \"valid\", activation = \"relu\", strides = 3) |>   layer_conv_1d(128, 7, padding = \"valid\", activation = \"relu\", strides = 3) |>   layer_global_max_pooling_1d() |>   # We add a vanilla hidden layer:   layer_dense(128, activation = \"relu\") |>   layer_dropout(0.5) |>   # We project onto a single unit output layer, and squash it with a sigmoid:   layer_dense(1, activation = \"sigmoid\", name = \"predictions\")  model <- keras_model(inputs, predictions)  summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, None)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ embedding_1 (Embedding)         │ (None, None, 128)      │     2,560,000 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, None, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d (Conv1D)                 │ (None, None, 128)      │       114,816 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_1 (Conv1D)               │ (None, None, 128)      │       114,816 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling1d            │ (None, 128)            │             0 │ ## │ (GlobalMaxPooling1D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 128)            │        16,512 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, 128)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ predictions (Dense)             │ (None, 1)              │           129 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 2,806,273 (10.71 MB) ##  Trainable params: 2,806,273 (10.71 MB) ##  Non-trainable params: 0 (0.00 B) # Compile the model with binary crossentropy loss and an adam optimizer. model |> compile(loss = \"binary_crossentropy\",                  optimizer = \"adam\",                  metrics = \"accuracy\")"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"train-the-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Train the model","title":"Text classification from scratch","text":"","code":"epochs <- 3  # Fit the model using the train and test datasets. model |> fit(train_ds, validation_data = val_ds, epochs = epochs) ## Epoch 1/3 ## 625/625 - 5s - 8ms/step - accuracy: 0.6944 - loss: 0.5248 - val_accuracy: 0.8624 - val_loss: 0.3150 ## Epoch 2/3 ## 625/625 - 2s - 2ms/step - accuracy: 0.9046 - loss: 0.2403 - val_accuracy: 0.8730 - val_loss: 0.3135 ## Epoch 3/3 ## 625/625 - 2s - 2ms/step - accuracy: 0.9524 - loss: 0.1275 - val_accuracy: 0.8716 - val_loss: 0.3424"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"evaluate-the-model-on-the-test-set","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Evaluate the model on the test set","title":"Text classification from scratch","text":"","code":"model |> evaluate(test_ds) ## 782/782 - 1s - 2ms/step - accuracy: 0.8608 - loss: 0.3672 ## $accuracy ## [1] 0.86084 ## ## $loss ## [1] 0.3671538"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"make-an-end-to-end-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Make an end-to-end model","title":"Text classification from scratch","text":"want obtain model capable processing raw strings, can simply create new model (using weights just trained):","code":"# A string input inputs <- keras_input(shape = c(1), dtype = \"string\") # Turn strings into vocab indices indices <- vectorize_layer(inputs) # Turn vocab indices into predictions outputs <- model(indices)  # Our end to end model end_to_end_model <- keras_model(inputs, outputs) end_to_end_model |> compile(   loss = \"binary_crossentropy\",   optimizer = \"adam\",   metrics = c(\"accuracy\") )  # Test it with `raw_test_ds`, which yields raw strings end_to_end_model |> evaluate(raw_test_ds) ## 782/782 - 3s - 4ms/step - accuracy: 0.8608 - loss: 0.0000e+00 ## $accuracy ## [1] 0.86084 ## ## $loss ## [1] 0"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"download-the-data","dir":"Articles > Examples","previous_headings":"","what":"Download the data","title":"Image segmentation with a U-Net-like architecture","text":"","code":"options(timeout = 5000) download.file(   \"https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\",   \"datasets/images.tar.gz\" ) download.file(   \"https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\",   \"datasets/annotations.tar.gz\" )  untar(\"datasets/images.tar.gz\", exdir = \"datasets\") untar(\"datasets/annotations.tar.gz\", exdir = \"datasets\")"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"prepare-paths-of-input-images-and-target-segmentation-masks","dir":"Articles > Examples","previous_headings":"","what":"Prepare paths of input images and target segmentation masks","title":"Image segmentation with a U-Net-like architecture","text":"","code":"library(keras3) input_dir <- \"datasets/images/\" target_dir <- \"datasets/annotations/trimaps/\" img_size <- c(160, 160) num_classes <- 3 batch_size <- 32  input_img_paths <- fs::dir_ls(input_dir, glob = \"*.jpg\") |> sort() target_img_paths <- fs::dir_ls(target_dir, glob = \"*.png\") |> sort()  cat(\"Number of samples:\", length(input_img_paths), \"\\n\") ## Number of samples: 7390 for (i in 1:10) {   cat(input_img_paths[i], \"|\", target_img_paths[i], \"\\n\") } ## datasets/images/Abyssinian_1.jpg | datasets/annotations/trimaps/Abyssinian_1.png ## datasets/images/Abyssinian_10.jpg | datasets/annotations/trimaps/Abyssinian_10.png ## datasets/images/Abyssinian_100.jpg | datasets/annotations/trimaps/Abyssinian_100.png ## datasets/images/Abyssinian_101.jpg | datasets/annotations/trimaps/Abyssinian_101.png ## datasets/images/Abyssinian_102.jpg | datasets/annotations/trimaps/Abyssinian_102.png ## datasets/images/Abyssinian_103.jpg | datasets/annotations/trimaps/Abyssinian_103.png ## datasets/images/Abyssinian_104.jpg | datasets/annotations/trimaps/Abyssinian_104.png ## datasets/images/Abyssinian_105.jpg | datasets/annotations/trimaps/Abyssinian_105.png ## datasets/images/Abyssinian_106.jpg | datasets/annotations/trimaps/Abyssinian_106.png ## datasets/images/Abyssinian_107.jpg | datasets/annotations/trimaps/Abyssinian_107.png"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"what-does-one-input-image-and-corresponding-segmentation-mask-look-like","dir":"Articles > Examples","previous_headings":"","what":"What does one input image and corresponding segmentation mask look like?","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Display input image #10 input_img_paths[10] |>   jpeg::readJPEG() |>   as.raster() |>   plot() target_img_paths[10] |>   png::readPNG() |>   magrittr::multiply_by(255)|>   as.raster(max = 3) |>   plot()"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"prepare-dataset-to-load-vectorize-batches-of-data","dir":"Articles > Examples","previous_headings":"","what":"Prepare dataset to load & vectorize batches of data","title":"Image segmentation with a U-Net-like architecture","text":"","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")   # Returns a tf_dataset get_dataset <- function(batch_size, img_size, input_img_paths, target_img_paths,                         max_dataset_len = NULL) {    img_size <- as.integer(img_size)    load_img_masks <- function(input_img_path, target_img_path) {     input_img <- input_img_path |>       tf$io$read_file() |>       tf$io$decode_jpeg(channels = 3) |>       tf$image$resize(img_size) |>       tf$image$convert_image_dtype(\"float32\")      target_img <- target_img_path |>       tf$io$read_file() |>       tf$io$decode_png(channels = 1) |>       tf$image$resize(img_size, method = \"nearest\") |>       tf$image$convert_image_dtype(\"uint8\")      # Ground truth labels are 1, 2, 3. Subtract one to make them 0, 1, 2:     target_img <- target_img - 1L      list(input_img, target_img)   }    if (!is.null(max_dataset_len)) {     input_img_paths <- input_img_paths[1:max_dataset_len]     target_img_paths <- target_img_paths[1:max_dataset_len]   }    list(input_img_paths, target_img_paths) |>     tensor_slices_dataset() |>     dataset_map(load_img_masks, num_parallel_calls = tf$data$AUTOTUNE)|>     dataset_batch(batch_size) }"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"prepare-u-net-xception-style-model","dir":"Articles > Examples","previous_headings":"","what":"Prepare U-Net Xception-style model","title":"Image segmentation with a U-Net-like architecture","text":"","code":"get_model <- function(img_size, num_classes) {    inputs <- keras_input(shape = c(img_size, 3))    ### [First half of the network: downsampling inputs] ###    # Entry block   x <- inputs |>     layer_conv_2d(filters = 32, kernel_size = 3, strides = 2, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation(\"relu\")    previous_block_activation <- x  # Set aside residual    for (filters in c(64, 128, 256)) {     x <- x |>       layer_activation(\"relu\") |>       layer_separable_conv_2d(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_activation(\"relu\") |>       layer_separable_conv_2d(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_max_pooling_2d(pool_size = 3, strides = 2, padding = \"same\")      residual <- previous_block_activation |>       layer_conv_2d(filters = filters, kernel_size = 1, strides = 2, padding = \"same\")      x <- layer_add(x, residual)  # Add back residual     previous_block_activation <- x  # Set aside next residual   }    ### [Second half of the network: upsampling inputs] ###    for (filters in c(256, 128, 64, 32)) {     x <- x |>       layer_activation(\"relu\") |>       layer_conv_2d_transpose(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_activation(\"relu\") |>       layer_conv_2d_transpose(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_upsampling_2d(size = 2)      # Project residual     residual <- previous_block_activation |>       layer_upsampling_2d(size = 2) |>       layer_conv_2d(filters = filters, kernel_size = 1, padding = \"same\")      x <- layer_add(x, residual)     # Add back residual     previous_block_activation <- x  # Set aside next residual   }    # Add a per-pixel classification layer   outputs <- x |>     layer_conv_2d(num_classes, 3, activation = \"softmax\", padding = \"same\")    # Define the model   keras_model(inputs, outputs) }  # Build model model <- get_model(img_size, num_classes) summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)      ┃ Output Shape    ┃   Param # ┃ Connected to   ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer       │ (None, 160,     │         0 │ -              │   -   │ ## │ (InputLayer)      │ 160, 3)         │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d (Conv2D)   │ (None, 80, 80,  │       896 │ input_layer[0… │   Y   │ ## │                   │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d[0][0]   │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation        │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_2      │ (None, 80, 80,  │         0 │ activation[0]… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 80, 80,  │     2,400 │ activation_2[… │   Y   │ ## │ (SeparableConv2D) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       256 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_1      │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d  │ (None, 80, 80,  │     4,736 │ activation_1[… │   Y   │ ## │ (SeparableConv2D) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       256 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d     │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_1 (Conv2D) │ (None, 40, 40,  │     2,112 │ activation[0]… │   Y   │ ## │                   │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add (Add)         │ (None, 40, 40,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 64)             │           │ conv2d_1[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_4      │ (None, 40, 40,  │         0 │ add[0][0]      │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 40, 40,  │     8,896 │ activation_4[… │   Y   │ ## │ (SeparableConv2D) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       512 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_3      │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 40, 40,  │    17,664 │ activation_3[… │   Y   │ ## │ (SeparableConv2D) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       512 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d_1   │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_2 (Conv2D) │ (None, 20, 20,  │     8,320 │ add[0][0]      │   Y   │ ## │                   │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_1 (Add)       │ (None, 20, 20,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 128)            │           │ conv2d_2[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_6      │ (None, 20, 20,  │         0 │ add_1[0][0]    │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 20, 20,  │    34,176 │ activation_6[… │   Y   │ ## │ (SeparableConv2D) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │     1,024 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_5      │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 20, 20,  │    68,096 │ activation_5[… │   Y   │ ## │ (SeparableConv2D) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │     1,024 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d_2   │ (None, 10, 10,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_3 (Conv2D) │ (None, 10, 10,  │    33,024 │ add_1[0][0]    │   Y   │ ## │                   │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_2 (Add)       │ (None, 10, 10,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 256)            │           │ conv2d_3[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_8      │ (None, 10, 10,  │         0 │ add_2[0][0]    │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 10, 10,  │   590,080 │ activation_8[… │   Y   │ ## │ (Conv2DTranspose) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 10, 10,  │     1,024 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_7      │ (None, 10, 10,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose  │ (None, 10, 10,  │   590,080 │ activation_7[… │   Y   │ ## │ (Conv2DTranspose) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 10, 10,  │     1,024 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_1   │ (None, 20, 20,  │         0 │ add_2[0][0]    │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d     │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_4 (Conv2D) │ (None, 20, 20,  │    65,792 │ up_sampling2d… │   Y   │ ## │                   │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_3 (Add)       │ (None, 20, 20,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 256)            │           │ conv2d_4[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_10     │ (None, 20, 20,  │         0 │ add_3[0][0]    │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 20, 20,  │   295,040 │ activation_10… │   Y   │ ## │ (Conv2DTranspose) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │       512 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_9      │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 20, 20,  │   147,584 │ activation_9[… │   Y   │ ## │ (Conv2DTranspose) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │       512 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_3   │ (None, 40, 40,  │         0 │ add_3[0][0]    │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_2   │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_5 (Conv2D) │ (None, 40, 40,  │    32,896 │ up_sampling2d… │   Y   │ ## │                   │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_4 (Add)       │ (None, 40, 40,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 128)            │           │ conv2d_5[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_12     │ (None, 40, 40,  │         0 │ add_4[0][0]    │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 40, 40,  │    73,792 │ activation_12… │   Y   │ ## │ (Conv2DTranspose) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       256 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_11     │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 40, 40,  │    36,928 │ activation_11… │   Y   │ ## │ (Conv2DTranspose) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       256 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_5   │ (None, 80, 80,  │         0 │ add_4[0][0]    │   -   │ ## │ (UpSampling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_4   │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_6 (Conv2D) │ (None, 80, 80,  │     8,256 │ up_sampling2d… │   Y   │ ## │                   │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_5 (Add)       │ (None, 80, 80,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 64)             │           │ conv2d_6[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_14     │ (None, 80, 80,  │         0 │ add_5[0][0]    │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 80, 80,  │    18,464 │ activation_14… │   Y   │ ## │ (Conv2DTranspose) │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_13     │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 80, 80,  │     9,248 │ activation_13… │   Y   │ ## │ (Conv2DTranspose) │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_7   │ (None, 160,     │         0 │ add_5[0][0]    │   -   │ ## │ (UpSampling2D)    │ 160, 64)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_6   │ (None, 160,     │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 160, 32)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_7 (Conv2D) │ (None, 160,     │     2,080 │ up_sampling2d… │   Y   │ ## │                   │ 160, 32)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_6 (Add)       │ (None, 160,     │         0 │ up_sampling2d… │   -   │ ## │                   │ 160, 32)        │           │ conv2d_7[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_8 (Conv2D) │ (None, 160,     │       867 │ add_6[0][0]    │   Y   │ ## │                   │ 160, 3)         │           │                │       │ ## └───────────────────┴─────────────────┴───────────┴────────────────┴───────┘ ##  Total params: 2,058,979 (7.85 MB) ##  Trainable params: 2,055,203 (7.84 MB) ##  Non-trainable params: 3,776 (14.75 KB)"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"set-aside-a-validation-split","dir":"Articles > Examples","previous_headings":"","what":"Set aside a validation split","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Split our img paths into a training and a validation set val_samples <- 1000 val_samples <- sample.int(length(input_img_paths), val_samples)  train_input_img_paths <- input_img_paths[-val_samples] train_target_img_paths <- target_img_paths[-val_samples]  val_input_img_paths <- input_img_paths[val_samples] val_target_img_paths <- target_img_paths[val_samples]  # Instantiate dataset for each split # Limit input files in `max_dataset_len` for faster epoch training time. # Remove the `max_dataset_len` arg when running with full dataset. train_dataset <- get_dataset(   batch_size,   img_size,   train_input_img_paths,   train_target_img_paths,   max_dataset_len = 1000 ) valid_dataset <- get_dataset(   batch_size, img_size, val_input_img_paths, val_target_img_paths )"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"train-the-model","dir":"Articles > Examples","previous_headings":"","what":"Train the model","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Configure the model for training. # We use the \"sparse\" version of categorical_crossentropy # because our target data is integers. model |> compile(   optimizer = optimizer_adam(1e-4),   loss = \"sparse_categorical_crossentropy\" )  callbacks <- list(   callback_model_checkpoint(     \"models/oxford_segmentation.keras\", save_best_only = TRUE   ) )  # Train the model, doing validation at the end of each epoch. epochs <- 50 model |> fit(     train_dataset,     epochs=epochs,     validation_data=valid_dataset,     callbacks=callbacks,     verbose=2 ) ## Epoch 1/50 ## 32/32 - 32s - 986ms/step - loss: 1.3044 - val_loss: 1.5502 ## Epoch 2/50 ## 32/32 - 2s - 60ms/step - loss: 0.8717 - val_loss: 1.9567 ## Epoch 3/50 ## 32/32 - 2s - 60ms/step - loss: 0.7695 - val_loss: 2.1948 ## Epoch 4/50 ## 32/32 - 2s - 60ms/step - loss: 0.7080 - val_loss: 2.4837 ## Epoch 5/50 ## 32/32 - 2s - 64ms/step - loss: 0.6689 - val_loss: 2.7751 ## Epoch 6/50 ## 32/32 - 2s - 61ms/step - loss: 0.6378 - val_loss: 3.1156 ## Epoch 7/50 ## 32/32 - 2s - 60ms/step - loss: 0.6129 - val_loss: 3.3611 ## Epoch 8/50 ## 32/32 - 2s - 60ms/step - loss: 0.5917 - val_loss: 3.5193 ## Epoch 9/50 ## 32/32 - 2s - 62ms/step - loss: 0.5723 - val_loss: 3.6237 ## Epoch 10/50 ## 32/32 - 2s - 62ms/step - loss: 0.5538 - val_loss: 3.7209 ## Epoch 11/50 ## 32/32 - 2s - 60ms/step - loss: 0.5352 - val_loss: 3.8216 ## Epoch 12/50 ## 32/32 - 2s - 61ms/step - loss: 0.5161 - val_loss: 3.9090 ## Epoch 13/50 ## 32/32 - 2s - 60ms/step - loss: 0.4958 - val_loss: 4.0158 ## Epoch 14/50 ## 32/32 - 2s - 60ms/step - loss: 0.4739 - val_loss: 4.0756 ## Epoch 15/50 ## 32/32 - 2s - 60ms/step - loss: 0.4501 - val_loss: 4.0083 ## Epoch 16/50 ## 32/32 - 2s - 60ms/step - loss: 0.4243 - val_loss: 3.7728 ## Epoch 17/50 ## 32/32 - 2s - 60ms/step - loss: 0.3971 - val_loss: 3.3484 ## Epoch 18/50 ## 32/32 - 2s - 61ms/step - loss: 0.3696 - val_loss: 2.6909 ## Epoch 19/50 ## 32/32 - 2s - 60ms/step - loss: 0.3428 - val_loss: 1.9726 ## Epoch 20/50 ## 32/32 - 2s - 65ms/step - loss: 0.3178 - val_loss: 1.4081 ## Epoch 21/50 ## 32/32 - 2s - 65ms/step - loss: 0.2954 - val_loss: 1.1136 ## Epoch 22/50 ## 32/32 - 2s - 65ms/step - loss: 0.2764 - val_loss: 1.0246 ## Epoch 23/50 ## 32/32 - 2s - 61ms/step - loss: 0.2610 - val_loss: 1.0390 ## Epoch 24/50 ## 32/32 - 2s - 60ms/step - loss: 0.2507 - val_loss: 1.1001 ## Epoch 25/50 ## 32/32 - 2s - 60ms/step - loss: 0.2525 - val_loss: 1.1915 ## Epoch 26/50 ## 32/32 - 2s - 61ms/step - loss: 0.2961 - val_loss: 1.2296 ## Epoch 27/50 ## 32/32 - 2s - 61ms/step - loss: 0.3545 - val_loss: 1.2625 ## Epoch 28/50 ## 32/32 - 2s - 65ms/step - loss: 0.3326 - val_loss: 0.9711 ## Epoch 29/50 ## 32/32 - 2s - 60ms/step - loss: 0.3302 - val_loss: 1.0251 ## Epoch 30/50 ## 32/32 - 2s - 60ms/step - loss: 0.3163 - val_loss: 1.0867 ## Epoch 31/50 ## 32/32 - 2s - 60ms/step - loss: 0.3131 - val_loss: 1.4197 ## Epoch 32/50 ## 32/32 - 2s - 60ms/step - loss: 0.3093 - val_loss: 1.0851 ## Epoch 33/50 ## 32/32 - 2s - 61ms/step - loss: 0.2939 - val_loss: 1.4807 ## Epoch 34/50 ## 32/32 - 2s - 60ms/step - loss: 0.2763 - val_loss: 1.1851 ## Epoch 35/50 ## 32/32 - 2s - 60ms/step - loss: 0.2702 - val_loss: 1.1337 ## Epoch 36/50 ## 32/32 - 2s - 61ms/step - loss: 0.2584 - val_loss: 1.0315 ## Epoch 37/50 ## 32/32 - 2s - 61ms/step - loss: 0.2440 - val_loss: 1.0631 ## Epoch 38/50 ## 32/32 - 2s - 60ms/step - loss: 0.2353 - val_loss: 1.1609 ## Epoch 39/50 ## 32/32 - 2s - 61ms/step - loss: 0.2285 - val_loss: 1.1839 ## Epoch 40/50 ## 32/32 - 2s - 60ms/step - loss: 0.2307 - val_loss: 1.1293 ## Epoch 41/50 ## 32/32 - 2s - 61ms/step - loss: 0.2357 - val_loss: 0.9948 ## Epoch 42/50 ## 32/32 - 2s - 60ms/step - loss: 0.2279 - val_loss: 1.1329 ## Epoch 43/50 ## 32/32 - 2s - 61ms/step - loss: 0.2174 - val_loss: 1.0418 ## Epoch 44/50 ## 32/32 - 2s - 61ms/step - loss: 0.2076 - val_loss: 1.1141 ## Epoch 45/50 ## 32/32 - 2s - 61ms/step - loss: 0.2004 - val_loss: 1.1162 ## Epoch 46/50 ## 32/32 - 2s - 60ms/step - loss: 0.1930 - val_loss: 1.0939 ## Epoch 47/50 ## 32/32 - 2s - 61ms/step - loss: 0.1852 - val_loss: 1.0499 ## Epoch 48/50 ## 32/32 - 2s - 60ms/step - loss: 0.1796 - val_loss: 1.0651 ## Epoch 49/50 ## 32/32 - 2s - 61ms/step - loss: 0.1762 - val_loss: 1.0679 ## Epoch 50/50 ## 32/32 - 2s - 60ms/step - loss: 0.1757 - val_loss: 1.1762"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"visualize-predictions","dir":"Articles > Examples","previous_headings":"","what":"Visualize predictions","title":"Image segmentation with a U-Net-like architecture","text":"","code":"model <- load_model(\"models/oxford_segmentation.keras\") # Generate predictions for all images in the validation set val_dataset <- get_dataset(   batch_size, img_size, val_input_img_paths, val_target_img_paths ) val_preds <- predict(model, val_dataset) ## 32/32 - 4s - 111ms/step display_mask <- function(i) {   # Quick utility to display a model's prediction.   mask <- val_preds[i,,,] %>%     apply(c(1,2), which.max) %>%     array_reshape(dim = c(img_size, 1))   mask <- abind::abind(mask, mask, mask, along = 3)   plot(as.raster(mask, max = 3)) }  # Display results for validation image #10 i <- 10  par(mfrow = c(1, 3)) # Display input image input_img_paths[i] |>   jpeg::readJPEG() |>   as.raster() |>   plot()  # Display ground-truth target mask target_img_paths[i] |>   png::readPNG() |>   magrittr::multiply_by(255)|>   as.raster(max = 3) |>   plot()  # Display mask predicted by our model display_mask(i)  # Note that the model only sees inputs at 150x150."},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"introduction","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Introduction","title":"Imbalanced classification: credit card fraud detection","text":"example looks Kaggle Credit Card Fraud Detection dataset demonstrate train classification model data highly imbalanced classes. can download data clicking “Download” link, ’re setup kaggle API key \"~/.kaggle/kagle.json\", can run following:","code":"reticulate::py_install(\"kaggle\", pip = TRUE) reticulate::py_available(TRUE) # ensure 'kaggle' is on the PATH system(\"kaggle datasets download -d mlg-ulb/creditcardfraud\") zip::unzip(\"creditcardfraud.zip\", files = \"creditcard.csv\")"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"first-load-the-data","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"First, load the data","title":"Imbalanced classification: credit card fraud detection","text":"","code":"library(readr) df <- read_csv(\"creditcard.csv\", col_types = cols(   Class = col_integer(),   .default = col_double() )) tibble::glimpse(df) ## Rows: 284,807 ## Columns: 31 ## $ Time   <dbl> 0, 0, 1, 1, 2, 2, 4, 7, 7, 9, 10, 10, 10, 11, 12, 12, 12, 1… ## $ V1     <dbl> -1.3598071, 1.1918571, -1.3583541, -0.9662717, -1.1582331, … ## $ V2     <dbl> -0.07278117, 0.26615071, -1.34016307, -0.18522601, 0.877736… ## $ V3     <dbl> 2.53634674, 0.16648011, 1.77320934, 1.79299334, 1.54871785,… ## $ V4     <dbl> 1.37815522, 0.44815408, 0.37977959, -0.86329128, 0.40303393… ## $ V5     <dbl> -0.33832077, 0.06001765, -0.50319813, -0.01030888, -0.40719… ## $ V6     <dbl> 0.46238778, -0.08236081, 1.80049938, 1.24720317, 0.09592146… ## $ V7     <dbl> 0.239598554, -0.078802983, 0.791460956, 0.237608940, 0.5929… ## $ V8     <dbl> 0.098697901, 0.085101655, 0.247675787, 0.377435875, -0.2705… ## $ V9     <dbl> 0.3637870, -0.2554251, -1.5146543, -1.3870241, 0.8177393, -… ## $ V10    <dbl> 0.09079417, -0.16697441, 0.20764287, -0.05495192, 0.7530744… ## $ V11    <dbl> -0.55159953, 1.61272666, 0.62450146, -0.22648726, -0.822842… ## $ V12    <dbl> -0.61780086, 1.06523531, 0.06608369, 0.17822823, 0.53819555… ## $ V13    <dbl> -0.99138985, 0.48909502, 0.71729273, 0.50775687, 1.34585159… ## $ V14    <dbl> -0.31116935, -0.14377230, -0.16594592, -0.28792375, -1.1196… ## $ V15    <dbl> 1.468176972, 0.635558093, 2.345864949, -0.631418118, 0.1751… ## $ V16    <dbl> -0.47040053, 0.46391704, -2.89008319, -1.05964725, -0.45144… ## $ V17    <dbl> 0.207971242, -0.114804663, 1.109969379, -0.684092786, -0.23… ## $ V18    <dbl> 0.02579058, -0.18336127, -0.12135931, 1.96577500, -0.038194… ## $ V19    <dbl> 0.40399296, -0.14578304, -2.26185710, -1.23262197, 0.803486… ## $ V20    <dbl> 0.25141210, -0.06908314, 0.52497973, -0.20803778, 0.4085423… ## $ V21    <dbl> -0.018306778, -0.225775248, 0.247998153, -0.108300452, -0.0… ## $ V22    <dbl> 0.277837576, -0.638671953, 0.771679402, 0.005273597, 0.7982… ## $ V23    <dbl> -0.110473910, 0.101288021, 0.909412262, -0.190320519, -0.13… ## $ V24    <dbl> 0.06692807, -0.33984648, -0.68928096, -1.17557533, 0.141266… ## $ V25    <dbl> 0.12853936, 0.16717040, -0.32764183, 0.64737603, -0.2060095… ## $ V26    <dbl> -0.18911484, 0.12589453, -0.13909657, -0.22192884, 0.502292… ## $ V27    <dbl> 0.133558377, -0.008983099, -0.055352794, 0.062722849, 0.219… ## $ V28    <dbl> -0.021053053, 0.014724169, -0.059751841, 0.061457629, 0.215… ## $ Amount <dbl> 149.62, 2.69, 378.66, 123.50, 69.99, 3.67, 4.99, 40.80, 93.… ## $ Class  <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"prepare-a-validation-set","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Prepare a validation set","title":"Imbalanced classification: credit card fraud detection","text":"","code":"val_idx <- nrow(df) %>% sample.int(., round( . * 0.2)) val_df <- df[val_idx, ] train_df <- df[-val_idx, ]  cat(\"Number of training samples:\", nrow(train_df), \"\\n\") ## Number of training samples: 227846 cat(\"Number of validation samples:\", nrow(val_df), \"\\n\") ## Number of validation samples: 56961"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"analyze-class-imbalance-in-the-targets","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Analyze class imbalance in the targets","title":"Imbalanced classification: credit card fraud detection","text":"","code":"counts <- table(train_df$Class) counts ## ##      0      1 ## 227463    383 cat(sprintf(\"Number of positive samples in training data: %i (%.2f%% of total)\",             counts[\"1\"], 100 * counts[\"1\"] / sum(counts))) ## Number of positive samples in training data: 383 (0.17% of total) weight_for_0 = 1 / counts[\"0\"] weight_for_1 = 1 / counts[\"1\"]"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"normalize-the-data-using-training-set-statistics","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Normalize the data using training set statistics","title":"Imbalanced classification: credit card fraud detection","text":"","code":"feature_names <- colnames(train_df) %>% setdiff(\"Class\")  train_features <- as.matrix(train_df[feature_names]) train_targets <- as.matrix(train_df$Class)  val_features <- as.matrix(val_df[feature_names]) val_targets <- as.matrix(val_df$Class)  train_features %<>% scale() val_features %<>% scale(center = attr(train_features, \"scaled:center\"),                         scale = attr(train_features, \"scaled:scale\"))"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"build-a-binary-classification-model","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Build a binary classification model","title":"Imbalanced classification: credit card fraud detection","text":"","code":"model <-   keras_model_sequential(input_shape = ncol(train_features)) |>   layer_dense(256, activation = \"relu\") |>   layer_dense(256, activation = \"relu\") |>   layer_dropout(0.3) |>   layer_dense(256, activation = \"relu\") |>   layer_dropout(0.3) |>   layer_dense(1, activation = \"sigmoid\")  model ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense (Dense)                   │ (None, 256)            │         7,936 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_1 (Dense)                 │ (None, 256)            │        65,792 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 256)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 256)            │        65,792 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, 256)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_3 (Dense)                 │ (None, 1)              │           257 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 139,777 (546.00 KB) ##  Trainable params: 139,777 (546.00 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"train-the-model-with-class_weight-argument","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Train the model with class_weight argument","title":"Imbalanced classification: credit card fraud detection","text":"","code":"metrics <- list(   metric_false_negatives(name = \"fn\"),   metric_false_positives(name = \"fp\"),   metric_true_negatives(name = \"tn\"),   metric_true_positives(name = \"tp\"),   metric_precision(name = \"precision\"),   metric_recall(name = \"recall\") ) model |> compile(   optimizer = optimizer_adam(1e-2),   loss = \"binary_crossentropy\",   metrics = metrics ) callbacks <- list(   callback_model_checkpoint(\"fraud_model_at_epoch_{epoch}.keras\") )  class_weight <- list(\"0\" = weight_for_0,                      \"1\" = weight_for_1)  model |> fit(   train_features, train_targets,   validation_data = list(val_features, val_targets),   class_weight = class_weight,   batch_size = 2048,   epochs = 30,   callbacks = callbacks,   verbose = 2 ) ## Epoch 1/30 ## 112/112 - 2s - 22ms/step - fn: 42.0000 - fp: 22983.0000 - loss: 2.3302e-06 - precision: 0.0146 - recall: 0.8903 - tn: 204480.0000 - tp: 341.0000 - val_fn: 12.0000 - val_fp: 536.0000 - val_loss: 0.0540 - val_precision: 0.1532 - val_recall: 0.8899 - val_tn: 56316.0000 - val_tp: 97.0000 ## Epoch 2/30 ## 112/112 - 0s - 4ms/step - fn: 36.0000 - fp: 6311.0000 - loss: 1.4468e-06 - precision: 0.0521 - recall: 0.9060 - tn: 221152.0000 - tp: 347.0000 - val_fn: 9.0000 - val_fp: 1081.0000 - val_loss: 0.0956 - val_precision: 0.0847 - val_recall: 0.9174 - val_tn: 55771.0000 - val_tp: 100.0000 ## Epoch 3/30 ## 112/112 - 0s - 2ms/step - fn: 27.0000 - fp: 7704.0000 - loss: 1.2139e-06 - precision: 0.0442 - recall: 0.9295 - tn: 219759.0000 - tp: 356.0000 - val_fn: 11.0000 - val_fp: 631.0000 - val_loss: 0.0443 - val_precision: 0.1344 - val_recall: 0.8991 - val_tn: 56221.0000 - val_tp: 98.0000 ## Epoch 4/30 ## 112/112 - 0s - 1ms/step - fn: 26.0000 - fp: 8933.0000 - loss: 1.1396e-06 - precision: 0.0384 - recall: 0.9321 - tn: 218530.0000 - tp: 357.0000 - val_fn: 4.0000 - val_fp: 3813.0000 - val_loss: 0.1850 - val_precision: 0.0268 - val_recall: 0.9633 - val_tn: 53039.0000 - val_tp: 105.0000 ## Epoch 5/30 ## 112/112 - 0s - 1ms/step - fn: 21.0000 - fp: 6451.0000 - loss: 9.1104e-07 - precision: 0.0531 - recall: 0.9452 - tn: 221012.0000 - tp: 362.0000 - val_fn: 9.0000 - val_fp: 1398.0000 - val_loss: 0.0724 - val_precision: 0.0668 - val_recall: 0.9174 - val_tn: 55454.0000 - val_tp: 100.0000 ## Epoch 6/30 ## 112/112 - 0s - 1ms/step - fn: 16.0000 - fp: 5997.0000 - loss: 7.4602e-07 - precision: 0.0577 - recall: 0.9582 - tn: 221466.0000 - tp: 367.0000 - val_fn: 8.0000 - val_fp: 2379.0000 - val_loss: 0.1421 - val_precision: 0.0407 - val_recall: 0.9266 - val_tn: 54473.0000 - val_tp: 101.0000 ## Epoch 7/30 ## 112/112 - 0s - 1ms/step - fn: 14.0000 - fp: 7075.0000 - loss: 7.6051e-07 - precision: 0.0496 - recall: 0.9634 - tn: 220388.0000 - tp: 369.0000 - val_fn: 10.0000 - val_fp: 888.0000 - val_loss: 0.0525 - val_precision: 0.1003 - val_recall: 0.9083 - val_tn: 55964.0000 - val_tp: 99.0000 ## Epoch 8/30 ## 112/112 - 0s - 2ms/step - fn: 20.0000 - fp: 6333.0000 - loss: 8.2217e-07 - precision: 0.0542 - recall: 0.9478 - tn: 221130.0000 - tp: 363.0000 - val_fn: 6.0000 - val_fp: 3549.0000 - val_loss: 0.1391 - val_precision: 0.0282 - val_recall: 0.9450 - val_tn: 53303.0000 - val_tp: 103.0000 ## Epoch 9/30 ## 112/112 - 0s - 2ms/step - fn: 11.0000 - fp: 7070.0000 - loss: 8.2005e-07 - precision: 0.0500 - recall: 0.9713 - tn: 220393.0000 - tp: 372.0000 - val_fn: 11.0000 - val_fp: 1307.0000 - val_loss: 0.0807 - val_precision: 0.0698 - val_recall: 0.8991 - val_tn: 55545.0000 - val_tp: 98.0000 ## Epoch 10/30 ## 112/112 - 0s - 2ms/step - fn: 13.0000 - fp: 6641.0000 - loss: 7.9420e-07 - precision: 0.0528 - recall: 0.9661 - tn: 220822.0000 - tp: 370.0000 - val_fn: 8.0000 - val_fp: 1721.0000 - val_loss: 0.0840 - val_precision: 0.0554 - val_recall: 0.9266 - val_tn: 55131.0000 - val_tp: 101.0000 ## Epoch 11/30 ## 112/112 - 0s - 2ms/step - fn: 13.0000 - fp: 7348.0000 - loss: 7.5710e-07 - precision: 0.0479 - recall: 0.9661 - tn: 220115.0000 - tp: 370.0000 - val_fn: 9.0000 - val_fp: 1006.0000 - val_loss: 0.0516 - val_precision: 0.0904 - val_recall: 0.9174 - val_tn: 55846.0000 - val_tp: 100.0000 ## Epoch 12/30 ## 112/112 - 0s - 2ms/step - fn: 11.0000 - fp: 5095.0000 - loss: 5.2767e-07 - precision: 0.0680 - recall: 0.9713 - tn: 222368.0000 - tp: 372.0000 - val_fn: 10.0000 - val_fp: 932.0000 - val_loss: 0.0462 - val_precision: 0.0960 - val_recall: 0.9083 - val_tn: 55920.0000 - val_tp: 99.0000 ## Epoch 13/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 4173.0000 - loss: 4.2331e-07 - precision: 0.0833 - recall: 0.9896 - tn: 223290.0000 - tp: 379.0000 - val_fn: 11.0000 - val_fp: 442.0000 - val_loss: 0.0245 - val_precision: 0.1815 - val_recall: 0.8991 - val_tn: 56410.0000 - val_tp: 98.0000 ## Epoch 14/30 ## 112/112 - 0s - 2ms/step - fn: 9.0000 - fp: 5914.0000 - loss: 6.4355e-07 - precision: 0.0595 - recall: 0.9765 - tn: 221549.0000 - tp: 374.0000 - val_fn: 8.0000 - val_fp: 1851.0000 - val_loss: 0.0791 - val_precision: 0.0517 - val_recall: 0.9266 - val_tn: 55001.0000 - val_tp: 101.0000 ## Epoch 15/30 ## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 6205.0000 - loss: 5.7546e-07 - precision: 0.0571 - recall: 0.9817 - tn: 221258.0000 - tp: 376.0000 - val_fn: 8.0000 - val_fp: 1595.0000 - val_loss: 0.0678 - val_precision: 0.0596 - val_recall: 0.9266 - val_tn: 55257.0000 - val_tp: 101.0000 ## Epoch 16/30 ## 112/112 - 0s - 2ms/step - fn: 8.0000 - fp: 6424.0000 - loss: 7.3225e-07 - precision: 0.0552 - recall: 0.9791 - tn: 221039.0000 - tp: 375.0000 - val_fn: 11.0000 - val_fp: 904.0000 - val_loss: 0.0412 - val_precision: 0.0978 - val_recall: 0.8991 - val_tn: 55948.0000 - val_tp: 98.0000 ## Epoch 17/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 4041.0000 - loss: 3.7349e-07 - precision: 0.0857 - recall: 0.9896 - tn: 223422.0000 - tp: 379.0000 - val_fn: 9.0000 - val_fp: 1348.0000 - val_loss: 0.0906 - val_precision: 0.0691 - val_recall: 0.9174 - val_tn: 55504.0000 - val_tp: 100.0000 ## Epoch 18/30 ## 112/112 - 0s - 2ms/step - fn: 6.0000 - fp: 5602.0000 - loss: 6.0889e-07 - precision: 0.0631 - recall: 0.9843 - tn: 221861.0000 - tp: 377.0000 - val_fn: 8.0000 - val_fp: 935.0000 - val_loss: 0.0487 - val_precision: 0.0975 - val_recall: 0.9266 - val_tn: 55917.0000 - val_tp: 101.0000 ## Epoch 19/30 ## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 5741.0000 - loss: 5.3650e-07 - precision: 0.0615 - recall: 0.9817 - tn: 221722.0000 - tp: 376.0000 - val_fn: 8.0000 - val_fp: 1279.0000 - val_loss: 0.0567 - val_precision: 0.0732 - val_recall: 0.9266 - val_tn: 55573.0000 - val_tp: 101.0000 ## Epoch 20/30 ## 112/112 - 0s - 2ms/step - fn: 1.0000 - fp: 3224.0000 - loss: 2.7791e-07 - precision: 0.1059 - recall: 0.9974 - tn: 224239.0000 - tp: 382.0000 - val_fn: 11.0000 - val_fp: 705.0000 - val_loss: 0.0347 - val_precision: 0.1220 - val_recall: 0.8991 - val_tn: 56147.0000 - val_tp: 98.0000 ## Epoch 21/30 ## 112/112 - 0s - 2ms/step - fn: 2.0000 - fp: 3403.0000 - loss: 2.7057e-07 - precision: 0.1007 - recall: 0.9948 - tn: 224060.0000 - tp: 381.0000 - val_fn: 11.0000 - val_fp: 330.0000 - val_loss: 0.0178 - val_precision: 0.2290 - val_recall: 0.8991 - val_tn: 56522.0000 - val_tp: 98.0000 ## Epoch 22/30 ## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 1904.0000 - loss: 2.0272e-07 - precision: 0.1664 - recall: 0.9922 - tn: 225559.0000 - tp: 380.0000 - val_fn: 10.0000 - val_fp: 1666.0000 - val_loss: 0.1536 - val_precision: 0.0561 - val_recall: 0.9083 - val_tn: 55186.0000 - val_tp: 99.0000 ## Epoch 23/30 ## 112/112 - 0s - 2ms/step - fn: 11.0000 - fp: 6652.0000 - loss: 7.3955e-07 - precision: 0.0530 - recall: 0.9713 - tn: 220811.0000 - tp: 372.0000 - val_fn: 9.0000 - val_fp: 1633.0000 - val_loss: 0.0681 - val_precision: 0.0577 - val_recall: 0.9174 - val_tn: 55219.0000 - val_tp: 100.0000 ## Epoch 24/30 ## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 3458.0000 - loss: 2.8492e-07 - precision: 0.0990 - recall: 0.9922 - tn: 224005.0000 - tp: 380.0000 - val_fn: 12.0000 - val_fp: 635.0000 - val_loss: 0.0299 - val_precision: 0.1325 - val_recall: 0.8899 - val_tn: 56217.0000 - val_tp: 97.0000 ## Epoch 25/30 ## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 5581.0000 - loss: 6.4007e-07 - precision: 0.0631 - recall: 0.9817 - tn: 221882.0000 - tp: 376.0000 - val_fn: 9.0000 - val_fp: 1782.0000 - val_loss: 0.0677 - val_precision: 0.0531 - val_recall: 0.9174 - val_tn: 55070.0000 - val_tp: 100.0000 ## Epoch 26/30 ## 112/112 - 0s - 2ms/step - fn: 1.0000 - fp: 3655.0000 - loss: 2.9078e-07 - precision: 0.0946 - recall: 0.9974 - tn: 223808.0000 - tp: 382.0000 - val_fn: 13.0000 - val_fp: 677.0000 - val_loss: 0.0310 - val_precision: 0.1242 - val_recall: 0.8807 - val_tn: 56175.0000 - val_tp: 96.0000 ## Epoch 27/30 ## 112/112 - 0s - 2ms/step - fn: 2.0000 - fp: 3187.0000 - loss: 2.7425e-07 - precision: 0.1068 - recall: 0.9948 - tn: 224276.0000 - tp: 381.0000 - val_fn: 12.0000 - val_fp: 701.0000 - val_loss: 0.0271 - val_precision: 0.1216 - val_recall: 0.8899 - val_tn: 56151.0000 - val_tp: 97.0000 ## Epoch 28/30 ## 112/112 - 0s - 2ms/step - fn: 10.0000 - fp: 4659.0000 - loss: 6.8191e-07 - precision: 0.0741 - recall: 0.9739 - tn: 222804.0000 - tp: 373.0000 - val_fn: 11.0000 - val_fp: 1022.0000 - val_loss: 0.0797 - val_precision: 0.0875 - val_recall: 0.8991 - val_tn: 55830.0000 - val_tp: 98.0000 ## Epoch 29/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 4183.0000 - loss: 3.9729e-07 - precision: 0.0831 - recall: 0.9896 - tn: 223280.0000 - tp: 379.0000 - val_fn: 12.0000 - val_fp: 406.0000 - val_loss: 0.0232 - val_precision: 0.1928 - val_recall: 0.8899 - val_tn: 56446.0000 - val_tp: 97.0000 ## Epoch 30/30 ## 112/112 - 0s - 2ms/step - fn: 2.0000 - fp: 2432.0000 - loss: 2.3592e-07 - precision: 0.1354 - recall: 0.9948 - tn: 225031.0000 - tp: 381.0000 - val_fn: 13.0000 - val_fp: 371.0000 - val_loss: 0.0200 - val_precision: 0.2056 - val_recall: 0.8807 - val_tn: 56481.0000 - val_tp: 96.0000 val_pred <- model %>%   predict(val_features) %>%   { as.integer(. > 0.5) } ## 1781/1781 - 1s - 286us/step pred_correct <- val_df$Class == val_pred cat(sprintf(\"Validation accuracy: %.2f\", mean(pred_correct))) ## Validation accuracy: 0.99 fraudulent <- val_df$Class == 1  n_fraudulent_detected <- sum(fraudulent & pred_correct) n_fraudulent_missed <- sum(fraudulent & !pred_correct) n_legitimate_flagged <- sum(!fraudulent & !pred_correct)"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"conclusions","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Conclusions","title":"Imbalanced classification: credit card fraud detection","text":"end training, 56,961 validation transactions, : Correctly identifying 96 fraudulent Missing 13 fraudulent transactions cost incorrectly flagging 371 legitimate transactions real world, one put even higher weight class 1, reflect False Negatives costly False Positives. Next time credit card gets declined online purchase – .","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"introduction","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Introduction","title":"Structured data classification with FeatureSpace","text":"example demonstrates structured data classification (also known tabular data classification), starting raw CSV file. data includes numerical features, integer categorical features, string categorical features. use utility layer_feature_space() index, preprocess, encode features. code adapted example Structured data classification scratch. previous example managed low-level feature preprocessing encoding Keras preprocessing layers, example delegate everything layer_feature_space(), making workflow extremely quick easy.","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"the-dataset","dir":"Articles > Examples > Structured_data","previous_headings":"Introduction","what":"The dataset","title":"Structured data classification with FeatureSpace","text":"dataset provided Cleveland Clinic Foundation Heart Disease. ’s CSV file 303 rows. row contains information patient (sample), column describes attribute patient (feature). use features predict whether patient heart disease (binary classification). ’s description feature:","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"setup","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Setup","title":"Structured data classification with FeatureSpace","text":"","code":"library(readr) library(dplyr, warn.conflicts = FALSE) library(keras3) library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")  conflicted::conflicts_prefer(   keras3::shape(),   keras3::set_random_seed(),   dplyr::filter(),   .quiet = TRUE )  use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"preparing-the-data","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Preparing the data","title":"Structured data classification with FeatureSpace","text":"Let’s download data load Pandas dataframe: dataset includes 303 samples 14 columns per sample (13 features, plus target label) ’s preview samples: last column, “target”, indicates whether patient heart disease (1) (0). Let’s split data training validation set: Let’s generate tf_dataset objects dataframe: tf_dataset yields tuple (input, target) input dictionary (named list) features target value 0 1: Let’s batch datasets:","code":"file_url <-   \"http://storage.googleapis.com/download.tensorflow.org/data/heart.csv\" df <- read_csv(file_url, col_types = cols(   oldpeak = col_double(),   thal = col_character(),   .default = col_integer() ))  # the dataset has two malformed rows, filter them out df <- df |> filter(!thal %in% c(\"1\", \"2\")) glimpse(df) ## Rows: 301 ## Columns: 14 ## $ age      <int> 63, 67, 67, 37, 41, 56, 62, 57, 63, 53, 57, 56, 56, 44, 5… ## $ sex      <int> 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, … ## $ cp       <int> 1, 4, 4, 3, 2, 2, 4, 4, 4, 4, 4, 2, 3, 2, 3, 3, 2, 4, 3, … ## $ trestbps <int> 145, 160, 120, 130, 130, 120, 140, 120, 130, 140, 140, 14… ## $ chol     <int> 233, 286, 229, 250, 204, 236, 268, 354, 254, 203, 192, 29… ## $ fbs      <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, … ## $ restecg  <int> 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, … ## $ thalach  <int> 150, 108, 129, 187, 172, 178, 160, 163, 147, 155, 148, 15… ## $ exang    <int> 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, … ## $ oldpeak  <dbl> 2.3, 1.5, 2.6, 3.5, 1.4, 0.8, 3.6, 0.6, 1.4, 3.1, 0.4, 1.… ## $ slope    <int> 3, 2, 2, 3, 1, 1, 3, 1, 2, 3, 2, 2, 2, 1, 1, 1, 3, 1, 1, … ## $ ca       <int> 0, 3, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, … ## $ thal     <chr> \"fixed\", \"normal\", \"reversible\", \"normal\", \"normal\", \"nor… ## $ target   <int> 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, … df ## # A tibble: 301 × 14 ##      age   sex    cp trestbps  chol   fbs restecg thalach exang oldpeak ##    <int> <int> <int>    <int> <int> <int>   <int>   <int> <int>   <dbl> ##  1    63     1     1      145   233     1       2     150     0     2.3 ##  2    67     1     4      160   286     0       2     108     1     1.5 ##  3    67     1     4      120   229     0       2     129     1     2.6 ##  4    37     1     3      130   250     0       0     187     0     3.5 ##  5    41     0     2      130   204     0       2     172     0     1.4 ##  6    56     1     2      120   236     0       0     178     0     0.8 ##  7    62     0     4      140   268     0       2     160     0     3.6 ##  8    57     0     4      120   354     0       0     163     1     0.6 ##  9    63     1     4      130   254     0       2     147     0     1.4 ## 10    53     1     4      140   203     1       2     155     1     3.1 ## # ℹ 291 more rows ## # ℹ 4 more variables: slope <int>, ca <int>, thal <chr>, target <int> val_idx <- nrow(df) %>% sample.int(., . * 0.2) val_df <- df[val_idx, ] train_df <- df[-val_idx, ]  cat(sprintf(   \"Using %d samples for training and %d for validation\",   nrow(train_df), nrow(val_df) )) ## Using 241 samples for training and 60 for validation dataframe_to_dataset <- function(df) {   labels <- df |> pull(target) |> as.integer()   inputs <- df |> select(-target) |> as.list()    ds <- tensor_slices_dataset(list(inputs, labels)) |>     dataset_shuffle(nrow(df))    ds }  train_ds <- dataframe_to_dataset(train_df) val_ds <- dataframe_to_dataset(val_df) c(x, y) %<-% iter_next(as_iterator(train_ds)) cat(\"Input: \"); str(x) cat(\"Target: \"); str(y) ## Input: List of 13 ##  $ age     :<tf.Tensor: shape=(), dtype=int32, numpy=41> ##  $ sex     :<tf.Tensor: shape=(), dtype=int32, numpy=1> ##  $ cp      :<tf.Tensor: shape=(), dtype=int32, numpy=2> ##  $ trestbps:<tf.Tensor: shape=(), dtype=int32, numpy=120> ##  $ chol    :<tf.Tensor: shape=(), dtype=int32, numpy=157> ##  $ fbs     :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ restecg :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ thalach :<tf.Tensor: shape=(), dtype=int32, numpy=182> ##  $ exang   :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ oldpeak :<tf.Tensor: shape=(), dtype=float32, numpy=0.0> ##  $ slope   :<tf.Tensor: shape=(), dtype=int32, numpy=1> ##  $ ca      :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ thal    :<tf.Tensor: shape=(), dtype=string, numpy=b'normal'> ## Target: <tf.Tensor: shape=(), dtype=int32, numpy=0> train_ds <- train_ds |> dataset_batch(32) val_ds <- val_ds |> dataset_batch(32)"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"configuring-a-featurespace","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Configuring a FeatureSpace","title":"Structured data classification with FeatureSpace","text":"configure feature preprocessed, instantiate layer_feature_space, pass dictionary (named list unique names) maps name features string describes feature type. “integer categorical” features \"FBS\", one “string categorical” feature (\"thal\"), numerical features, ’d like normalize – except \"age\", ’d like discretize number bins. also use crosses argument capture feature interactions categorical features, say, create additional features represent value co-occurrences categorical features. can compute feature crosses like arbitrary sets categorical features – just tuples two features. resulting co-occurences hashed fixed-sized vector, don’t need worry whether co-occurence space large.","code":"feature_space <- layer_feature_space(   features = list(     # Categorical features encoded as integers     sex = \"integer_categorical\",     cp = \"integer_categorical\",     fbs = \"integer_categorical\",     restecg = \"integer_categorical\",     exang = \"integer_categorical\",     ca = \"integer_categorical\",     # Categorical feature encoded as string     thal = \"string_categorical\",     # Numerical features to discretize     age = \"float_discretized\",     # Numerical features to normalize     trestbps = \"float_normalized\",     chol = \"float_normalized\",     thalach = \"float_normalized\",     oldpeak = \"float_normalized\",     slope = \"float_normalized\"   ),   # We create additional features by hashing   # value co-occurrences for the   # following groups of categorical features.   crosses = list(c(\"sex\", \"age\"), c(\"thal\", \"ca\")),   # The hashing space for these co-occurrences   # wil be 32-dimensional.   crossing_dim = 32,   # Our utility will one-hot encode all categorical   # features and concat all features into a single   # vector (one vector per sample).   output_mode = \"concat\" )"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"further-customizing-a-featurespace","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Further customizing a FeatureSpace","title":"Structured data classification with FeatureSpace","text":"Specifying feature type via string name quick easy, sometimes may want configure preprocessing feature. instance, case, categorical features don’t large set possible values – ’s handful values per feature (e.g. 1 0 feature \"FBS\"), possible values represented training set. result, don’t need reserve index represent “vocabulary” values features – default behavior. , just specify num_oov_indices=0 features tell feature preprocessor skip “vocabulary” indexing. customizations access include specifying number bins discretizing features type \"float_discretized\", dimensionality hashing space feature crossing.","code":"feature_space <- layer_feature_space(   features = list(     # Categorical features encoded as integers     sex       = feature_integer_categorical(num_oov_indices = 0),     cp        = feature_integer_categorical(num_oov_indices = 0),     fbs       = feature_integer_categorical(num_oov_indices = 0),     restecg   = feature_integer_categorical(num_oov_indices = 0),     exang     = feature_integer_categorical(num_oov_indices = 0),     ca        = feature_integer_categorical(num_oov_indices = 0),     # Categorical feature encoded as string     thal      = feature_string_categorical(num_oov_indices = 0),     # Numerical features to discretize     age       = feature_float_discretized(num_bins = 30),     # Numerical features to normalize     trestbps  = feature_float_normalized(),     chol      = feature_float_normalized(),     thalach   = feature_float_normalized(),     oldpeak   = feature_float_normalized(),     slope     = feature_float_normalized()   ),   # Specify feature cross with a custom crossing dim.   crosses = list(     feature_cross(       feature_names = c(\"sex\", \"age\"),       crossing_dim = 64     ),     feature_cross(       feature_names = c(\"thal\", \"ca\"),       crossing_dim = 16     )   ),   output_mode = \"concat\" )"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"adapt-the-featurespace-to-the-training-data","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Adapt the FeatureSpace to the training data","title":"Structured data classification with FeatureSpace","text":"start using FeatureSpace build model, adapt training data. adapt(), FeatureSpace : Index set possible values categorical features. Compute mean variance numerical features normalize. Compute value boundaries different bins numerical features discretize. Note adapt() called tf_dataset yields dicts (named lists) feature values – labels. point, FeatureSpace can called dict raw feature values, return single concatenate vector sample, combining encoded features feature crosses.","code":"train_ds_with_no_labels <- train_ds |> dataset_map(\\(x, y) x) feature_space |> adapt(train_ds_with_no_labels) c(x, y) %<-% iter_next(as_iterator(train_ds)) preprocessed_x <- feature_space(x) preprocessed_x ## tf.Tensor( ## [[0. 0. 0. ... 0. 1. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  ... ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.]], shape=(32, 136), dtype=float32)"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"two-ways-to-manage-preprocessing-as-part-of-the-tf-data-pipeline-or-in-the-model-itself","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","title":"Structured data classification with FeatureSpace","text":"two ways can leverage FeatureSpace:","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"asynchronous-preprocessing-in-tf-data","dir":"Articles > Examples > Structured_data","previous_headings":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","what":"Asynchronous preprocessing in tf.data","title":"Structured data classification with FeatureSpace","text":"can make part data pipeline, model. enables asynchronous parallel preprocessing data CPU hits model. ’re training GPU TPU, want speed preprocessing. Usually, always right thing training.","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"synchronous-preprocessing-in-the-model","dir":"Articles > Examples > Structured_data","previous_headings":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","what":"Synchronous preprocessing in the model","title":"Structured data classification with FeatureSpace","text":"can make part model. means model expect dicts raw feature values, preprocessing batch done synchronously (blocking manner) rest forward pass. want end--end model can process raw feature values – keep mind model able run CPU, since types feature preprocessing (e.g. string preprocessing) GPU TPU compatible. GPU / TPU performance-sensitive settings. general, want -model preprocessing inference CPU. case, apply FeatureSpace tf.data pipeline training, inference end--end model includes FeatureSpace. Let’s create training validation dataset preprocessed batches:","code":"preprocessed_train_ds <- train_ds |>   dataset_map(\\(x, y) list(feature_space(x), y),               num_parallel_calls = tf$data$AUTOTUNE) |>   dataset_prefetch(tf$data$AUTOTUNE)  preprocessed_val_ds <- val_ds |>   dataset_map(\\(x, y) list(feature_space(x), y),               num_parallel_calls = tf$data$AUTOTUNE) |>   dataset_prefetch(tf$data$AUTOTUNE)"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"build-a-model","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Build a model","title":"Structured data classification with FeatureSpace","text":"Time build model – rather two models: training model expects preprocessed features (one sample = one vector) inference model expects raw features (one sample = dict raw feature values)","code":"dict_inputs <- feature_space$get_inputs() encoded_features <- feature_space$get_encoded_features()  predictions <- encoded_features |>   layer_dense(32, activation=\"relu\") |>   layer_dropout(0.5) |>   layer_dense(1, activation=\"sigmoid\")  training_model <- keras_model(inputs = encoded_features,                               outputs = predictions) training_model |> compile(optimizer = \"adam\",                           loss = \"binary_crossentropy\",                           metrics = \"accuracy\")  inference_model <- keras_model(inputs = dict_inputs,                                outputs = predictions)"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"train-the-model","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Train the model","title":"Structured data classification with FeatureSpace","text":"Let’s train model 20 epochs. Note feature preprocessing happening part tf.data pipeline, part model. quickly get 80% validation accuracy.","code":"training_model |> fit(   preprocessed_train_ds,   epochs = 20,   validation_data = preprocessed_val_ds,   verbose = 2 ) ## Epoch 1/20 ## 8/8 - 3s - 320ms/step - accuracy: 0.4564 - loss: 0.7517 - val_accuracy: 0.4833 - val_loss: 0.7192 ## Epoch 2/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.5436 - loss: 0.6978 - val_accuracy: 0.6167 - val_loss: 0.6730 ## Epoch 3/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.6473 - loss: 0.6429 - val_accuracy: 0.6500 - val_loss: 0.6319 ## Epoch 4/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.6639 - loss: 0.6126 - val_accuracy: 0.6833 - val_loss: 0.5965 ## Epoch 5/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7137 - loss: 0.5854 - val_accuracy: 0.7167 - val_loss: 0.5653 ## Epoch 6/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.7386 - loss: 0.5565 - val_accuracy: 0.7667 - val_loss: 0.5353 ## Epoch 7/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7718 - loss: 0.5237 - val_accuracy: 0.7667 - val_loss: 0.5086 ## Epoch 8/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.7718 - loss: 0.5098 - val_accuracy: 0.8167 - val_loss: 0.4835 ## Epoch 9/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8091 - loss: 0.4921 - val_accuracy: 0.8500 - val_loss: 0.4621 ## Epoch 10/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8050 - loss: 0.4562 - val_accuracy: 0.8333 - val_loss: 0.4435 ## Epoch 11/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8091 - loss: 0.4398 - val_accuracy: 0.8333 - val_loss: 0.4261 ## Epoch 12/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8257 - loss: 0.4272 - val_accuracy: 0.8167 - val_loss: 0.4095 ## Epoch 13/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8050 - loss: 0.4381 - val_accuracy: 0.8333 - val_loss: 0.3949 ## Epoch 14/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8091 - loss: 0.4118 - val_accuracy: 0.8333 - val_loss: 0.3828 ## Epoch 15/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8382 - loss: 0.3797 - val_accuracy: 0.8500 - val_loss: 0.3712 ## Epoch 16/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8423 - loss: 0.3884 - val_accuracy: 0.8500 - val_loss: 0.3610 ## Epoch 17/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8548 - loss: 0.3654 - val_accuracy: 0.8500 - val_loss: 0.3511 ## Epoch 18/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8299 - loss: 0.3726 - val_accuracy: 0.8667 - val_loss: 0.3434 ## Epoch 19/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8672 - loss: 0.3472 - val_accuracy: 0.8667 - val_loss: 0.3365 ## Epoch 20/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8714 - loss: 0.3380 - val_accuracy: 0.8667 - val_loss: 0.3301"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"inference-on-new-data-with-the-end-to-end-model","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Inference on new data with the end-to-end model","title":"Structured data classification with FeatureSpace","text":"Now, can use inference model (includes FeatureSpace) make predictions based dicts raw features values, follows:","code":"sample <- list(   age = 60,   sex = 1,   cp = 1,   trestbps = 145,   chol = 233,   fbs = 1,   restecg = 2,   thalach = 150,   exang = 0,   oldpeak = 2.3,   slope = 3,   ca = 0,   thal = \"fixed\" )  input_dict <- lapply(sample, \\(x) op_convert_to_tensor(array(x))) predictions <- inference_model |> predict(input_dict) ## 1/1 - 0s - 257ms/step glue::glue(r\"---(   This particular patient had a {(100 * predictions) |> signif(3)}% probability   of having a heart disease, as evaluated by our model. )---\") ## This particular patient had a 48.9% probability ## of having a heart disease, as evaluated by our model."},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"introduction","dir":"Articles > Examples","previous_headings":"","what":"Introduction","title":"Structured data classification with FeatureSpace","text":"example demonstrates structured data classification (also known tabular data classification), starting raw CSV file. data includes numerical features, integer categorical features, string categorical features. use utility layer_feature_space() index, preprocess, encode features. code adapted example Structured data classification scratch. previous example managed low-level feature preprocessing encoding Keras preprocessing layers, example delegate everything layer_feature_space(), making workflow extremely quick easy.","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"the-dataset","dir":"Articles > Examples","previous_headings":"Introduction","what":"The dataset","title":"Structured data classification with FeatureSpace","text":"dataset provided Cleveland Clinic Foundation Heart Disease. ’s CSV file 303 rows. row contains information patient (sample), column describes attribute patient (feature). use features predict whether patient heart disease (binary classification). ’s description feature:","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"setup","dir":"Articles > Examples","previous_headings":"","what":"Setup","title":"Structured data classification with FeatureSpace","text":"","code":"library(readr) library(dplyr, warn.conflicts = FALSE) library(keras3) library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")  conflicted::conflicts_prefer(   keras3::shape(),   keras3::set_random_seed(),   dplyr::filter(),   .quiet = TRUE )  use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"preparing-the-data","dir":"Articles > Examples","previous_headings":"","what":"Preparing the data","title":"Structured data classification with FeatureSpace","text":"Let’s download data load Pandas dataframe: dataset includes 303 samples 14 columns per sample (13 features, plus target label) ’s preview samples: last column, “target”, indicates whether patient heart disease (1) (0). Let’s split data training validation set: Let’s generate tf_dataset objects dataframe: tf_dataset yields tuple (input, target) input dictionary (named list) features target value 0 1: Let’s batch datasets:","code":"file_url <-   \"http://storage.googleapis.com/download.tensorflow.org/data/heart.csv\" df <- read_csv(file_url, col_types = cols(   oldpeak = col_double(),   thal = col_character(),   .default = col_integer() ))  # the dataset has two malformed rows, filter them out df <- df |> filter(!thal %in% c(\"1\", \"2\")) glimpse(df) ## Rows: 301 ## Columns: 14 ## $ age      <int> 63, 67, 67, 37, 41, 56, 62, 57, 63, 53, 57, 56, 56, 44, 5… ## $ sex      <int> 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, … ## $ cp       <int> 1, 4, 4, 3, 2, 2, 4, 4, 4, 4, 4, 2, 3, 2, 3, 3, 2, 4, 3, … ## $ trestbps <int> 145, 160, 120, 130, 130, 120, 140, 120, 130, 140, 140, 14… ## $ chol     <int> 233, 286, 229, 250, 204, 236, 268, 354, 254, 203, 192, 29… ## $ fbs      <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, … ## $ restecg  <int> 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, … ## $ thalach  <int> 150, 108, 129, 187, 172, 178, 160, 163, 147, 155, 148, 15… ## $ exang    <int> 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, … ## $ oldpeak  <dbl> 2.3, 1.5, 2.6, 3.5, 1.4, 0.8, 3.6, 0.6, 1.4, 3.1, 0.4, 1.… ## $ slope    <int> 3, 2, 2, 3, 1, 1, 3, 1, 2, 3, 2, 2, 2, 1, 1, 1, 3, 1, 1, … ## $ ca       <int> 0, 3, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, … ## $ thal     <chr> \"fixed\", \"normal\", \"reversible\", \"normal\", \"normal\", \"nor… ## $ target   <int> 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, … df ## # A tibble: 301 × 14 ##      age   sex    cp trestbps  chol   fbs restecg thalach exang oldpeak ##    <int> <int> <int>    <int> <int> <int>   <int>   <int> <int>   <dbl> ##  1    63     1     1      145   233     1       2     150     0     2.3 ##  2    67     1     4      160   286     0       2     108     1     1.5 ##  3    67     1     4      120   229     0       2     129     1     2.6 ##  4    37     1     3      130   250     0       0     187     0     3.5 ##  5    41     0     2      130   204     0       2     172     0     1.4 ##  6    56     1     2      120   236     0       0     178     0     0.8 ##  7    62     0     4      140   268     0       2     160     0     3.6 ##  8    57     0     4      120   354     0       0     163     1     0.6 ##  9    63     1     4      130   254     0       2     147     0     1.4 ## 10    53     1     4      140   203     1       2     155     1     3.1 ## # ℹ 291 more rows ## # ℹ 4 more variables: slope <int>, ca <int>, thal <chr>, target <int> val_idx <- nrow(df) %>% sample.int(., . * 0.2) val_df <- df[val_idx, ] train_df <- df[-val_idx, ]  cat(sprintf(   \"Using %d samples for training and %d for validation\",   nrow(train_df), nrow(val_df) )) ## Using 241 samples for training and 60 for validation dataframe_to_dataset <- function(df) {   labels <- df |> pull(target) |> as.integer()   inputs <- df |> select(-target) |> as.list()    ds <- tensor_slices_dataset(list(inputs, labels)) |>     dataset_shuffle(nrow(df))    ds }  train_ds <- dataframe_to_dataset(train_df) val_ds <- dataframe_to_dataset(val_df) c(x, y) %<-% iter_next(as_iterator(train_ds)) cat(\"Input: \"); str(x) cat(\"Target: \"); str(y) ## Input: List of 13 ##  $ age     :<tf.Tensor: shape=(), dtype=int32, numpy=57> ##  $ sex     :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ cp      :<tf.Tensor: shape=(), dtype=int32, numpy=4> ##  $ trestbps:<tf.Tensor: shape=(), dtype=int32, numpy=140> ##  $ chol    :<tf.Tensor: shape=(), dtype=int32, numpy=241> ##  $ fbs     :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ restecg :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ thalach :<tf.Tensor: shape=(), dtype=int32, numpy=123> ##  $ exang   :<tf.Tensor: shape=(), dtype=int32, numpy=1> ##  $ oldpeak :<tf.Tensor: shape=(), dtype=float32, numpy=0.2> ##  $ slope   :<tf.Tensor: shape=(), dtype=int32, numpy=2> ##  $ ca      :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ thal    :<tf.Tensor: shape=(), dtype=string, numpy=b'reversible'> ## Target: <tf.Tensor: shape=(), dtype=int32, numpy=0> train_ds <- train_ds |> dataset_batch(32) val_ds <- val_ds |> dataset_batch(32)"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"configuring-a-featurespace","dir":"Articles > Examples","previous_headings":"","what":"Configuring a FeatureSpace","title":"Structured data classification with FeatureSpace","text":"configure feature preprocessed, instantiate layer_feature_space, pass dictionary (named list unique names) maps name features string describes feature type. “integer categorical” features \"FBS\", one “string categorical” feature (\"thal\"), numerical features, ’d like normalize – except \"age\", ’d like discretize number bins. also use crosses argument capture feature interactions categorical features, say, create additional features represent value co-occurrences categorical features. can compute feature crosses like arbitrary sets categorical features – just tuples two features. resulting co-occurences hashed fixed-sized vector, don’t need worry whether co-occurence space large.","code":"feature_space <- layer_feature_space(   features = list(     # Categorical features encoded as integers     sex = \"integer_categorical\",     cp = \"integer_categorical\",     fbs = \"integer_categorical\",     restecg = \"integer_categorical\",     exang = \"integer_categorical\",     ca = \"integer_categorical\",     # Categorical feature encoded as string     thal = \"string_categorical\",     # Numerical features to discretize     age = \"float_discretized\",     # Numerical features to normalize     trestbps = \"float_normalized\",     chol = \"float_normalized\",     thalach = \"float_normalized\",     oldpeak = \"float_normalized\",     slope = \"float_normalized\"   ),   # We create additional features by hashing   # value co-occurrences for the   # following groups of categorical features.   crosses = list(c(\"sex\", \"age\"), c(\"thal\", \"ca\")),   # The hashing space for these co-occurrences   # wil be 32-dimensional.   crossing_dim = 32,   # Our utility will one-hot encode all categorical   # features and concat all features into a single   # vector (one vector per sample).   output_mode = \"concat\" )"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"further-customizing-a-featurespace","dir":"Articles > Examples","previous_headings":"","what":"Further customizing a FeatureSpace","title":"Structured data classification with FeatureSpace","text":"Specifying feature type via string name quick easy, sometimes may want configure preprocessing feature. instance, case, categorical features don’t large set possible values – ’s handful values per feature (e.g. 1 0 feature \"FBS\"), possible values represented training set. result, don’t need reserve index represent “vocabulary” values features – default behavior. , just specify num_oov_indices=0 features tell feature preprocessor skip “vocabulary” indexing. customizations access include specifying number bins discretizing features type \"float_discretized\", dimensionality hashing space feature crossing.","code":"feature_space <- layer_feature_space(   features = list(     # Categorical features encoded as integers     sex       = feature_integer_categorical(num_oov_indices = 0),     cp        = feature_integer_categorical(num_oov_indices = 0),     fbs       = feature_integer_categorical(num_oov_indices = 0),     restecg   = feature_integer_categorical(num_oov_indices = 0),     exang     = feature_integer_categorical(num_oov_indices = 0),     ca        = feature_integer_categorical(num_oov_indices = 0),     # Categorical feature encoded as string     thal      = feature_string_categorical(num_oov_indices = 0),     # Numerical features to discretize     age       = feature_float_discretized(num_bins = 30),     # Numerical features to normalize     trestbps  = feature_float_normalized(),     chol      = feature_float_normalized(),     thalach   = feature_float_normalized(),     oldpeak   = feature_float_normalized(),     slope     = feature_float_normalized()   ),   # Specify feature cross with a custom crossing dim.   crosses = list(     feature_cross(       feature_names = c(\"sex\", \"age\"),       crossing_dim = 64     ),     feature_cross(       feature_names = c(\"thal\", \"ca\"),       crossing_dim = 16     )   ),   output_mode = \"concat\" )"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"adapt-the-featurespace-to-the-training-data","dir":"Articles > Examples","previous_headings":"","what":"Adapt the FeatureSpace to the training data","title":"Structured data classification with FeatureSpace","text":"start using FeatureSpace build model, adapt training data. adapt(), FeatureSpace : Index set possible values categorical features. Compute mean variance numerical features normalize. Compute value boundaries different bins numerical features discretize. Note adapt() called tf_dataset yields dicts (named lists) feature values – labels. point, FeatureSpace can called dict raw feature values, return single concatenate vector sample, combining encoded features feature crosses.","code":"train_ds_with_no_labels <- train_ds |> dataset_map(\\(x, y) x) feature_space |> adapt(train_ds_with_no_labels) c(x, y) %<-% iter_next(as_iterator(train_ds)) preprocessed_x <- feature_space(x) preprocessed_x ## tf.Tensor( ## [[0. 0. 0. ... 1. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  ... ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.]], shape=(32, 136), dtype=float32)"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"two-ways-to-manage-preprocessing-as-part-of-the-tf-data-pipeline-or-in-the-model-itself","dir":"Articles > Examples","previous_headings":"","what":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","title":"Structured data classification with FeatureSpace","text":"two ways can leverage FeatureSpace:","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"asynchronous-preprocessing-in-tf-data","dir":"Articles > Examples","previous_headings":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","what":"Asynchronous preprocessing in tf.data","title":"Structured data classification with FeatureSpace","text":"can make part data pipeline, model. enables asynchronous parallel preprocessing data CPU hits model. ’re training GPU TPU, want speed preprocessing. Usually, always right thing training.","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"synchronous-preprocessing-in-the-model","dir":"Articles > Examples","previous_headings":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","what":"Synchronous preprocessing in the model","title":"Structured data classification with FeatureSpace","text":"can make part model. means model expect dicts raw feature values, preprocessing batch done synchronously (blocking manner) rest forward pass. want end--end model can process raw feature values – keep mind model able run CPU, since types feature preprocessing (e.g. string preprocessing) GPU TPU compatible. GPU / TPU performance-sensitive settings. general, want -model preprocessing inference CPU. case, apply FeatureSpace tf.data pipeline training, inference end--end model includes FeatureSpace. Let’s create training validation dataset preprocessed batches:","code":"preprocessed_train_ds <- train_ds |>   dataset_map(\\(x, y) list(feature_space(x), y),               num_parallel_calls = tf$data$AUTOTUNE) |>   dataset_prefetch(tf$data$AUTOTUNE)  preprocessed_val_ds <- val_ds |>   dataset_map(\\(x, y) list(feature_space(x), y),               num_parallel_calls = tf$data$AUTOTUNE) |>   dataset_prefetch(tf$data$AUTOTUNE)"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"build-a-model","dir":"Articles > Examples","previous_headings":"","what":"Build a model","title":"Structured data classification with FeatureSpace","text":"Time build model – rather two models: training model expects preprocessed features (one sample = one vector) inference model expects raw features (one sample = dict raw feature values)","code":"dict_inputs <- feature_space$get_inputs() encoded_features <- feature_space$get_encoded_features()  predictions <- encoded_features |>   layer_dense(32, activation=\"relu\") |>   layer_dropout(0.5) |>   layer_dense(1, activation=\"sigmoid\")  training_model <- keras_model(inputs = encoded_features,                               outputs = predictions) training_model |> compile(optimizer = \"adam\",                           loss = \"binary_crossentropy\",                           metrics = \"accuracy\")  inference_model <- keras_model(inputs = dict_inputs,                                outputs = predictions)"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"train-the-model","dir":"Articles > Examples","previous_headings":"","what":"Train the model","title":"Structured data classification with FeatureSpace","text":"Let’s train model 20 epochs. Note feature preprocessing happening part tf.data pipeline, part model. quickly get 80% validation accuracy.","code":"training_model |> fit(   preprocessed_train_ds,   epochs = 20,   validation_data = preprocessed_val_ds,   verbose = 2 ) ## Epoch 1/20 ## 8/8 - 3s - 343ms/step - accuracy: 0.5685 - loss: 0.7120 - val_accuracy: 0.6500 - val_loss: 0.6607 ## Epoch 2/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.6722 - loss: 0.6131 - val_accuracy: 0.6500 - val_loss: 0.6212 ## Epoch 3/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7137 - loss: 0.5685 - val_accuracy: 0.6500 - val_loss: 0.5961 ## Epoch 4/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.7718 - loss: 0.4981 - val_accuracy: 0.6667 - val_loss: 0.5758 ## Epoch 5/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7635 - loss: 0.4804 - val_accuracy: 0.6500 - val_loss: 0.5575 ## Epoch 6/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7676 - loss: 0.4739 - val_accuracy: 0.6667 - val_loss: 0.5309 ## Epoch 7/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.7842 - loss: 0.4141 - val_accuracy: 0.6667 - val_loss: 0.5161 ## Epoch 8/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8133 - loss: 0.3841 - val_accuracy: 0.6667 - val_loss: 0.5080 ## Epoch 9/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8133 - loss: 0.3997 - val_accuracy: 0.6667 - val_loss: 0.4980 ## Epoch 10/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8340 - loss: 0.3618 - val_accuracy: 0.6833 - val_loss: 0.4882 ## Epoch 11/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8548 - loss: 0.3335 - val_accuracy: 0.6833 - val_loss: 0.4814 ## Epoch 12/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8465 - loss: 0.3535 - val_accuracy: 0.6833 - val_loss: 0.4818 ## Epoch 13/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8340 - loss: 0.3363 - val_accuracy: 0.7000 - val_loss: 0.4755 ## Epoch 14/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8548 - loss: 0.3224 - val_accuracy: 0.7333 - val_loss: 0.4745 ## Epoch 15/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8714 - loss: 0.3083 - val_accuracy: 0.7500 - val_loss: 0.4813 ## Epoch 16/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8797 - loss: 0.3270 - val_accuracy: 0.7500 - val_loss: 0.4690 ## Epoch 17/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8631 - loss: 0.2955 - val_accuracy: 0.7500 - val_loss: 0.4782 ## Epoch 18/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8797 - loss: 0.2831 - val_accuracy: 0.7667 - val_loss: 0.4818 ## Epoch 19/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8797 - loss: 0.2872 - val_accuracy: 0.7667 - val_loss: 0.4767 ## Epoch 20/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8631 - loss: 0.2877 - val_accuracy: 0.7833 - val_loss: 0.4781"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"inference-on-new-data-with-the-end-to-end-model","dir":"Articles > Examples","previous_headings":"","what":"Inference on new data with the end-to-end model","title":"Structured data classification with FeatureSpace","text":"Now, can use inference model (includes FeatureSpace) make predictions based dicts raw features values, follows:","code":"sample <- list(   age = 60,   sex = 1,   cp = 1,   trestbps = 145,   chol = 233,   fbs = 1,   restecg = 2,   thalach = 150,   exang = 0,   oldpeak = 2.3,   slope = 3,   ca = 0,   thal = \"fixed\" )  input_dict <- lapply(sample, \\(x) op_convert_to_tensor(array(x))) predictions <- inference_model |> predict(input_dict) ## 1/1 - 1s - 548ms/step glue::glue(r\"---(   This particular patient had a {(100 * predictions) |> signif(3)}% probability   of having a heart disease, as evaluated by our model. )---\") ## This particular patient had a 55% probability ## of having a heart disease, as evaluated by our model."},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"introduction","dir":"Articles > Examples","previous_headings":"","what":"Introduction","title":"Text classification from scratch","text":"example shows text classification starting raw text (set text files disk). demonstrate workflow IMDB sentiment classification dataset (unprocessed version). use [layer_text_vectorization()] word splitting & indexing.","code":""},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"setup","dir":"Articles > Examples","previous_headings":"","what":"Setup","title":"Text classification from scratch","text":"","code":"options(conflicts.policy = \"strict\") library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\") library(keras3) use_virtualenv(\"r-keras\")"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"load-the-data-imdb-movie-review-sentiment-classification","dir":"Articles > Examples","previous_headings":"","what":"Load the data: IMDB movie review sentiment classification","title":"Text classification from scratch","text":"Let’s download data inspect structure. aclImdb folder contains train test subfolder: aclImdb/train/pos aclImdb/train/neg folders contain text files, represents one review (either positive negative): interested pos neg subfolders, let’s delete subfolder text files : can use utility text_dataset_from_directory() generate labeled tf_dataset object set text files disk filed class-specific folders. Let’s use generate training, validation, test datasets. validation training datasets generated two subsets train directory, 20% samples going validation dataset 80% going training dataset. validation dataset addition test dataset useful tuning hyperparameters, model architecture, test dataset used. putting model real world however, retrained using available training data (without creating validation dataset), performance maximized. using validation_split subset arguments, make sure either specify random seed, pass shuffle=FALSE, validation & training splits get overlap. Let’s preview samples:","code":"if (!dir.exists(\"datasets/aclImdb\")) {   dir.create(\"datasets\")   download.file(     \"https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\",     \"datasets/aclImdb_v1.tar.gz\"   )   untar(\"datasets/aclImdb_v1.tar.gz\", exdir = \"datasets\")   unlink(\"datasets/aclImdb/train/unsup\", recursive = TRUE) } head(list.files(\"datasets/aclImdb/test\")) ## [1] \"labeledBow.feat\" \"neg\"             \"pos\"             \"urls_neg.txt\" ## [5] \"urls_pos.txt\" head(list.files(\"datasets/aclImdb/train\")) ## [1] \"labeledBow.feat\" \"neg\"             \"pos\"             \"unsupBow.feat\" ## [5] \"urls_neg.txt\"    \"urls_pos.txt\" cat(readLines(\"datasets/aclImdb/train/pos/6248_7.txt\")) ## Being an Austrian myself this has been a straight knock in my face. Fortunately I don't live nowhere near the place where this movie takes place but unfortunately it portrays everything that the rest of Austria hates about Viennese people (or people close to that region). And it is very easy to read that this is exactly the directors intention: to let your head sink into your hands and say \"Oh my god, how can THAT be possible!\". No, not with me, the (in my opinion) totally exaggerated uncensored swinger club scene is not necessary, I watch porn, sure, but in this context I was rather disgusted than put in the right context.<br /><br />This movie tells a story about how misled people who suffer from lack of education or bad company try to survive and live in a world of redundancy and boring horizons. A girl who is treated like a whore by her super-jealous boyfriend (and still keeps coming back), a female teacher who discovers her masochism by putting the life of her super-cruel \"lover\" on the line, an old couple who has an almost mathematical daily cycle (she is the \"official replacement\" of his ex wife), a couple that has just divorced and has the ex husband suffer under the acts of his former wife obviously having a relationship with her masseuse and finally a crazy hitchhiker who asks her drivers the most unusual questions and stretches their nerves by just being super-annoying.<br /><br />After having seen it you feel almost nothing. You're not even shocked, sad, depressed or feel like doing anything... Maybe that's why I gave it 7 points, it made me react in a way I never reacted before. If that's good or bad is up to you! unlink(\"datasets/aclImdb/train/unsup\", recursive = TRUE) batch_size <- 32  raw_train_ds <- text_dataset_from_directory(   \"datasets/aclImdb/train\",   batch_size = batch_size,   validation_split = 0.2,   subset = \"training\",   seed = 1337 ) ## Found 25000 files belonging to 2 classes. ## Using 20000 files for training. raw_val_ds <- text_dataset_from_directory(   \"datasets/aclImdb/train\",   batch_size = batch_size,   validation_split = 0.2,   subset = \"validation\",   seed = 1337 ) ## Found 25000 files belonging to 2 classes. ## Using 5000 files for validation. raw_test_ds <- text_dataset_from_directory(   \"datasets/aclImdb/test\",   batch_size = batch_size ) ## Found 25000 files belonging to 2 classes. cat(\"Number of batches in raw_train_ds:\", length(raw_train_ds), \"\\n\") ## Number of batches in raw_train_ds: 625 cat(\"Number of batches in raw_val_ds:\", length(raw_val_ds), \"\\n\") ## Number of batches in raw_val_ds: 157 cat(\"Number of batches in raw_test_ds:\", length(raw_test_ds), \"\\n\") ## Number of batches in raw_test_ds: 782 # It's important to take a look at your raw data to ensure your normalization # and tokenization will work as expected. We can do that by taking a few # examples from the training set and looking at them. # This is one of the places where eager execution shines: # we can just evaluate these tensors using .numpy() # instead of needing to evaluate them in a Session/Graph context. batch <- iter_next(as_iterator(raw_train_ds)) str(batch) ## List of 2 ##  $ :<tf.Tensor: shape=(32), dtype=string, numpy=…> ##  $ :<tf.Tensor: shape=(32), dtype=int32, numpy=…> c(text_batch, label_batch) %<-% batch for (i in 1:3) {   print(text_batch[i])   print(label_batch[i]) } ## tf.Tensor(b\"I have read the novel Reaper of Ben Mezrich a fews years ago and last night I accidentally came to see this adaption.<br /><br />Although it's been years since I read the story the first time, the differences between the novel and the movie are humongous. Very important elements, which made the whole thing plausible are just written out or changed to bad.<br /><br />If the plot sounds interesting to you: go and get the novel. Its much, much, much better.<br /><br />Still 4 out of 10 since it was hard to stop watching because of the great basic plot by Ben Mezrich.\", shape=(), dtype=string) ## tf.Tensor(0, shape=(), dtype=int32) ## tf.Tensor(b'After seeing all the Jesse James, Quantrill, jayhawkers,etc films in the fifties, it is quite a thrill to see this film with a new perspective by director Ang Lee. The scene of the attack of Lawrence, Kansas is awesome. The romantic relationship between Jewel and Toby Mcguire turns out to be one of the best parts and Jonathan Rhys-Meyers is outstanding as the bad guy. All the time this film makes you feel the horror of war, and the desperate situation of the main characters who do not know if they are going to survive the next hours. Definitely worth seeing.', shape=(), dtype=string) ## tf.Tensor(1, shape=(), dtype=int32) ## tf.Tensor(b'AG was an excellent presentation of drama, suspense and thriller that is so rare to American TV. Sheriff Lucas gave many a viewer the willies. We rooted for Caleb as he strove to resist the overtures of Sheriff Lucas. We became engrossed and fearful upon learning of the unthinkable connection between these two characters. The manipulations which weekly gave cause to fear what Lucas would do next were truly surprising. This show lived up to the \"Gothic\" moniker in ways American entertainment has so seldom attempted, much less mastered. The suits definitely made a big mistake in not supporting this show. This show puts shame to the current glut of \"reality\" shows- which are so less than satisfying viewing.The call for a DVD box set is well based. This show is quality viewing for a discerning market hungry for quality viewing. A public that is tiring of over-saturation of mind-numbing reality fare will welcome this gem of real storytelling. Bring on the DVD box set!!', shape=(), dtype=string) ## tf.Tensor(1, shape=(), dtype=int32)"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"prepare-the-data","dir":"Articles > Examples","previous_headings":"","what":"Prepare the data","title":"Text classification from scratch","text":"particular, remove <br /> tags.","code":"# Having looked at our data above, we see that the raw text contains HTML break # tags of the form '<br />'. These tags will not be removed by the default # standardizer (which doesn't strip HTML). Because of this, we will need to # create a custom standardization function. custom_standardization_fn <- function(string_tensor) {   string_tensor |>     tf$strings$lower() |> # convert to all lowercase     tf$strings$regex_replace(\"<br />\", \" \") |> # remove '<br />' HTML tag     tf$strings$regex_replace(\"[[:punct:]]\", \"\") # remove punctuation }   # Model constants. max_features <- 20000 embedding_dim <- 128 sequence_length <- 500  # Now that we have our custom standardization, we can instantiate our text # vectorization layer. We are using this layer to normalize, split, and map # strings to integers, so we set our 'output_mode' to 'int'. # Note that we're using the default split function, # and the custom standardization defined above. # We also set an explicit maximum sequence length, since the CNNs later in our # model won't support ragged sequences. vectorize_layer <- layer_text_vectorization(   standardize = custom_standardization_fn,   max_tokens = max_features,   output_mode = \"int\",   output_sequence_length = sequence_length, )  # Now that the vectorize_layer has been created, call `adapt` on a text-only # dataset to create the vocabulary. You don't have to batch, but for very large # datasets this means you're not keeping spare copies of the dataset in memory.  # Let's make a text-only dataset (no labels): text_ds <- raw_train_ds |>   dataset_map(\\(x, y) x) # Let's call `adapt`: vectorize_layer |> adapt(text_ds)"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"two-options-to-vectorize-the-data","dir":"Articles > Examples","previous_headings":"","what":"Two options to vectorize the data","title":"Text classification from scratch","text":"2 ways can use text vectorization layer: Option 1: Make part model, obtain model processes raw strings, like : Option 2: Apply text dataset obtain dataset word indices, feed model expects integer sequences inputs. important difference two option 2 enables asynchronous CPU processing buffering data training GPU. ’re training model GPU, probably want go option get best performance. . export model production, ’d ship model accepts raw strings input, like code snippet option 1 . can done training. last section.","code":"text_input <- keras_input(shape = c(1L), dtype = \"string\", name = 'text') x <- text_input |>   vectorize_layer() |>   layer_embedding(max_features + 1, embedding_dim) vectorize_text <- function(text, label) {   text <- text |>     op_expand_dims(-1) |>     vectorize_layer()   list(text, label) }  # Vectorize the data. train_ds <- raw_train_ds |> dataset_map(vectorize_text) val_ds   <- raw_val_ds   |> dataset_map(vectorize_text) test_ds  <- raw_test_ds  |> dataset_map(vectorize_text)  # Do async prefetching / buffering of the data for best performance on GPU. train_ds <- train_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10) val_ds <- val_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10) test_ds <- test_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10)"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"build-a-model","dir":"Articles > Examples","previous_headings":"","what":"Build a model","title":"Text classification from scratch","text":"choose simple 1D convnet starting Embedding layer.","code":"# A integer input for vocab indices. inputs <- keras_input(shape = c(NA), dtype = \"int64\")  predictions <- inputs |>   # Next, we add a layer to map those vocab indices into a space of dimensionality   # 'embedding_dim'.   layer_embedding(max_features, embedding_dim) |>   layer_dropout(0.5) |>   # Conv1D + global max pooling   layer_conv_1d(128, 7, padding = \"valid\", activation = \"relu\", strides = 3) |>   layer_conv_1d(128, 7, padding = \"valid\", activation = \"relu\", strides = 3) |>   layer_global_max_pooling_1d() |>   # We add a vanilla hidden layer:   layer_dense(128, activation = \"relu\") |>   layer_dropout(0.5) |>   # We project onto a single unit output layer, and squash it with a sigmoid:   layer_dense(1, activation = \"sigmoid\", name = \"predictions\")  model <- keras_model(inputs, predictions)  summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, None)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ embedding_1 (Embedding)         │ (None, None, 128)      │     2,560,000 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, None, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_1 (Conv1D)               │ (None, None, 128)      │       114,816 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d (Conv1D)                 │ (None, None, 128)      │       114,816 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling1d            │ (None, 128)            │             0 │ ## │ (GlobalMaxPooling1D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 128)            │        16,512 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 128)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ predictions (Dense)             │ (None, 1)              │           129 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 2,806,273 (10.71 MB) ##  Trainable params: 2,806,273 (10.71 MB) ##  Non-trainable params: 0 (0.00 B) # Compile the model with binary crossentropy loss and an adam optimizer. model |> compile(loss = \"binary_crossentropy\",                  optimizer = \"adam\",                  metrics = \"accuracy\")"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"train-the-model","dir":"Articles > Examples","previous_headings":"","what":"Train the model","title":"Text classification from scratch","text":"","code":"epochs <- 3  # Fit the model using the train and test datasets. model |> fit(train_ds, validation_data = val_ds, epochs = epochs) ## Epoch 1/3 ## 625/625 - 6s - 9ms/step - accuracy: 0.7327 - loss: 0.4894 - val_accuracy: 0.8638 - val_loss: 0.3162 ## Epoch 2/3 ## 625/625 - 2s - 2ms/step - accuracy: 0.9116 - loss: 0.2230 - val_accuracy: 0.8780 - val_loss: 0.3131 ## Epoch 3/3 ## 625/625 - 2s - 2ms/step - accuracy: 0.9603 - loss: 0.1118 - val_accuracy: 0.8646 - val_loss: 0.3735"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"evaluate-the-model-on-the-test-set","dir":"Articles > Examples","previous_headings":"","what":"Evaluate the model on the test set","title":"Text classification from scratch","text":"","code":"model |> evaluate(test_ds) ## 782/782 - 1s - 2ms/step - accuracy: 0.8532 - loss: 0.4071 ## $accuracy ## [1] 0.8532 ## ## $loss ## [1] 0.407085"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"make-an-end-to-end-model","dir":"Articles > Examples","previous_headings":"","what":"Make an end-to-end model","title":"Text classification from scratch","text":"want obtain model capable processing raw strings, can simply create new model (using weights just trained):","code":"# A string input inputs <- keras_input(shape = c(1), dtype = \"string\") # Turn strings into vocab indices indices <- vectorize_layer(inputs) # Turn vocab indices into predictions outputs <- model(indices)  # Our end to end model end_to_end_model <- keras_model(inputs, outputs) end_to_end_model |> compile(   loss = \"binary_crossentropy\",   optimizer = \"adam\",   metrics = c(\"accuracy\") )  # Test it with `raw_test_ds`, which yields raw strings end_to_end_model |> evaluate(raw_test_ds) ## 782/782 - 3s - 4ms/step - accuracy: 0.8532 - loss: 0.4063 ## $accuracy ## [1] 0.8532 ## ## $loss ## [1] 0.4062699"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"introduction","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Introduction","title":"Timeseries anomaly detection using an Autoencoder","text":"script demonstrates can use reconstruction convolutional autoencoder model detect anomalies timeseries data.","code":""},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"setup","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Setup","title":"Timeseries anomaly detection using an Autoencoder","text":"","code":"library(dplyr, warn.conflicts = FALSE) library(ggplot2) theme_set(theme_minimal())  library(listarrays) library(tfdatasets, exclude = c(\"shape\")) library(keras3)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"load-the-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Load the data","title":"Timeseries anomaly detection using an Autoencoder","text":"use Numenta Anomaly Benchmark(NAB) dataset. provides artificial timeseries data containing labeled anomalous periods behavior. Data ordered, timestamped, single-valued metrics. use art_daily_small_noise.csv file training art_daily_jumpsup.csv file testing. simplicity dataset allows us demonstrate anomaly detection.","code":"get_data <- function(url_suffix) {   url_root <- \"https://raw.githubusercontent.com/numenta/NAB/master/data/\"   url <- paste0(url_root, url_suffix)   file <- get_file(origin = url) # cache file locally   # parse csv; 2 columns with types: datetime (T), double (d)   readr::read_csv(file, col_types = \"Td\") }  df_small_noise   <- get_data(\"artificialNoAnomaly/art_daily_small_noise.csv\") df_daily_jumpsup <- get_data(\"artificialWithAnomaly/art_daily_jumpsup.csv\")"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"quick-look-at-the-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Quick look at the data","title":"Timeseries anomaly detection using an Autoencoder","text":"","code":"df_small_noise ## # A tibble: 4,032 × 2 ##    timestamp           value ##    <dttm>              <dbl> ##  1 2014-04-01 00:00:00  18.3 ##  2 2014-04-01 00:05:00  22.0 ##  3 2014-04-01 00:10:00  18.6 ##  4 2014-04-01 00:15:00  22.0 ##  5 2014-04-01 00:20:00  21.9 ##  6 2014-04-01 00:25:00  21.2 ##  7 2014-04-01 00:30:00  20.6 ##  8 2014-04-01 00:35:00  20.3 ##  9 2014-04-01 00:40:00  21.5 ## 10 2014-04-01 00:45:00  19.2 ## # ℹ 4,022 more rows df_daily_jumpsup ## # A tibble: 4,032 × 2 ##    timestamp           value ##    <dttm>              <dbl> ##  1 2014-04-01 00:00:00  19.8 ##  2 2014-04-01 00:05:00  20.5 ##  3 2014-04-01 00:10:00  20.0 ##  4 2014-04-01 00:15:00  21.5 ##  5 2014-04-01 00:20:00  20.2 ##  6 2014-04-01 00:25:00  19.9 ##  7 2014-04-01 00:30:00  21.7 ##  8 2014-04-01 00:35:00  20.9 ##  9 2014-04-01 00:40:00  18.4 ## 10 2014-04-01 00:45:00  18.7 ## # ℹ 4,022 more rows"},{"path":[]},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"timeseries-data-without-anomalies","dir":"Articles > Examples > Timeseries","previous_headings":"Visualize the data","what":"Timeseries data without anomalies","title":"Timeseries anomaly detection using an Autoencoder","text":"use following data training.","code":"plot_ts <- function(df) {   ggplot(df, aes(x = timestamp, y = value)) + geom_line() +     scale_x_datetime(date_breaks = \"1 day\", date_labels = \"%b-%d\") }  plot_ts(df_small_noise) + ggtitle(\"Without Anomaly\")"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"timeseries-data-with-anomalies","dir":"Articles > Examples > Timeseries","previous_headings":"Visualize the data","what":"Timeseries data with anomalies","title":"Timeseries anomaly detection using an Autoencoder","text":"use following data testing see sudden jump data detected anomaly.","code":"plot_ts(df_daily_jumpsup) + ggtitle(\"With Anomaly\")"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"prepare-training-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Prepare training data","title":"Timeseries anomaly detection using an Autoencoder","text":"Get data values training timeseries data file normalize value data. value every 5 mins 14 days. 24 * 60 / 5 = 288 timesteps per day 288 * 14 = 4032 data points total","code":"df_train <- df_small_noise |>   mutate(value = (value - mean(value)) / sd(value))  cat(\"Number of training samples:\", nrow(df_train), \"\\n\") ## Number of training samples: 4032"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"create-sequences","dir":"Articles > Examples > Timeseries","previous_headings":"Prepare training data","what":"Create sequences","title":"Timeseries anomaly detection using an Autoencoder","text":"Create sequences combining TIME_STEPS contiguous data values training data.","code":"TIME_STEPS <- 288  as_dataset <- function(df) {   x <- as.matrix(df$value)   ds <- timeseries_dataset_from_array(x, NULL, sequence_length = TIME_STEPS)   # Because the dataset is small, cast TF Dataset to an R array for convenience.   ds |> as_array_iterator() |> iterate() |> bind_on_rows() }  x_train <- as_dataset(df_train) writeLines(sprintf(\"Training input shape: (%s)\", toString(dim(x_train)))) ## Training input shape: (3745, 288, 1)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"build-a-model","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Build a model","title":"Timeseries anomaly detection using an Autoencoder","text":"build convolutional reconstruction autoencoder model. model take input shape (batch_size, sequence_length, num_features) return output shape. case, sequence_length 288 num_features 1.","code":"model <- keras_model_sequential(input_shape = c(TIME_STEPS, 1)) |>   layer_conv_1d(     filters = 32, kernel_size = 7, padding = \"same\",     strides = 2, activation = \"relu\"   ) |>   layer_dropout(rate = 0.2) |>   layer_conv_1d(     filters = 16, kernel_size = 7, padding = \"same\",     strides = 2, activation = \"relu\"   ) |>   layer_conv_1d_transpose(     filters = 16, kernel_size = 7, padding = \"same\",     strides = 2, activation = \"relu\"   ) |>   layer_dropout(rate = 0.2) |>   layer_conv_1d_transpose(     filters = 32, kernel_size = 7, padding = \"same\",     strides = 2, activation = \"relu\"   ) |>   layer_conv_1d_transpose(filters = 1, kernel_size = 7, padding = \"same\")  model |> compile(optimizer=optimizer_adam(learning_rate=0.001), loss=\"mse\") model ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv1d (Conv1D)                 │ (None, 144, 32)        │           256 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 144, 32)        │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_1 (Conv1D)               │ (None, 72, 16)         │         3,600 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_transpose                │ (None, 144, 16)        │         1,808 │ ## │ (Conv1DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, 144, 16)        │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_transpose_1              │ (None, 288, 32)        │         3,616 │ ## │ (Conv1DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_transpose_2              │ (None, 288, 1)         │           225 │ ## │ (Conv1DTranspose)               │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 9,505 (37.13 KB) ##  Trainable params: 9,505 (37.13 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"train-the-model","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Train the model","title":"Timeseries anomaly detection using an Autoencoder","text":"Please note using x_train input target since reconstruction model. Let’s plot training validation loss see training went.","code":"history = model |> fit(   x_train, x_train,   epochs = 50,   validation_split = 0.1,   callbacks = c(     callback_early_stopping(       monitor = \"val_loss\", patience = 5, mode = \"min\"     )   ) ) ## Epoch 1/50 ## 106/106 - 5s - 51ms/step - loss: 0.1863 - val_loss: 0.0316 ## Epoch 2/50 ## 106/106 - 0s - 2ms/step - loss: 0.0333 - val_loss: 0.0233 ## Epoch 3/50 ## 106/106 - 0s - 2ms/step - loss: 0.0248 - val_loss: 0.0201 ## Epoch 4/50 ## 106/106 - 0s - 2ms/step - loss: 0.0209 - val_loss: 0.0187 ## Epoch 5/50 ## 106/106 - 0s - 2ms/step - loss: 0.0179 - val_loss: 0.0146 ## Epoch 6/50 ## 106/106 - 0s - 2ms/step - loss: 0.0150 - val_loss: 0.0113 ## Epoch 7/50 ## 106/106 - 0s - 2ms/step - loss: 0.0127 - val_loss: 0.0094 ## Epoch 8/50 ## 106/106 - 0s - 2ms/step - loss: 0.0109 - val_loss: 0.0088 ## Epoch 9/50 ## 106/106 - 0s - 2ms/step - loss: 0.0096 - val_loss: 0.0084 ## Epoch 10/50 ## 106/106 - 0s - 2ms/step - loss: 0.0086 - val_loss: 0.0069 ## Epoch 11/50 ## 106/106 - 0s - 2ms/step - loss: 0.0078 - val_loss: 0.0061 ## Epoch 12/50 ## 106/106 - 0s - 2ms/step - loss: 0.0073 - val_loss: 0.0057 ## Epoch 13/50 ## 106/106 - 0s - 2ms/step - loss: 0.0068 - val_loss: 0.0053 ## Epoch 14/50 ## 106/106 - 0s - 2ms/step - loss: 0.0064 - val_loss: 0.0050 ## Epoch 15/50 ## 106/106 - 0s - 2ms/step - loss: 0.0061 - val_loss: 0.0045 ## Epoch 16/50 ## 106/106 - 0s - 2ms/step - loss: 0.0058 - val_loss: 0.0039 ## Epoch 17/50 ## 106/106 - 0s - 2ms/step - loss: 0.0056 - val_loss: 0.0040 ## Epoch 18/50 ## 106/106 - 0s - 2ms/step - loss: 0.0053 - val_loss: 0.0035 ## Epoch 19/50 ## 106/106 - 0s - 2ms/step - loss: 0.0050 - val_loss: 0.0037 ## Epoch 20/50 ## 106/106 - 0s - 2ms/step - loss: 0.0048 - val_loss: 0.0032 ## Epoch 21/50 ## 106/106 - 0s - 2ms/step - loss: 0.0046 - val_loss: 0.0031 ## Epoch 22/50 ## 106/106 - 0s - 2ms/step - loss: 0.0044 - val_loss: 0.0032 ## Epoch 23/50 ## 106/106 - 0s - 2ms/step - loss: 0.0042 - val_loss: 0.0033 ## Epoch 24/50 ## 106/106 - 0s - 2ms/step - loss: 0.0041 - val_loss: 0.0031 ## Epoch 25/50 ## 106/106 - 0s - 2ms/step - loss: 0.0039 - val_loss: 0.0033 ## Epoch 26/50 ## 106/106 - 0s - 2ms/step - loss: 0.0037 - val_loss: 0.0031 ## Epoch 27/50 ## 106/106 - 0s - 2ms/step - loss: 0.0035 - val_loss: 0.0032 ## Epoch 28/50 ## 106/106 - 0s - 2ms/step - loss: 0.0034 - val_loss: 0.0025 ## Epoch 29/50 ## 106/106 - 0s - 2ms/step - loss: 0.0033 - val_loss: 0.0026 ## Epoch 30/50 ## 106/106 - 0s - 2ms/step - loss: 0.0032 - val_loss: 0.0028 ## Epoch 31/50 ## 106/106 - 0s - 2ms/step - loss: 0.0030 - val_loss: 0.0024 ## Epoch 32/50 ## 106/106 - 0s - 2ms/step - loss: 0.0029 - val_loss: 0.0026 ## Epoch 33/50 ## 106/106 - 0s - 2ms/step - loss: 0.0028 - val_loss: 0.0026 ## Epoch 34/50 ## 106/106 - 0s - 2ms/step - loss: 0.0027 - val_loss: 0.0022 ## Epoch 35/50 ## 106/106 - 0s - 2ms/step - loss: 0.0027 - val_loss: 0.0024 ## Epoch 36/50 ## 106/106 - 0s - 2ms/step - loss: 0.0026 - val_loss: 0.0026 ## Epoch 37/50 ## 106/106 - 0s - 2ms/step - loss: 0.0025 - val_loss: 0.0026 ## Epoch 38/50 ## 106/106 - 0s - 2ms/step - loss: 0.0025 - val_loss: 0.0023 ## Epoch 39/50 ## 106/106 - 0s - 2ms/step - loss: 0.0024 - val_loss: 0.0023 plot(history)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"detecting-anomalies","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Detecting anomalies","title":"Timeseries anomaly detection using an Autoencoder","text":"detect anomalies determining well model can reconstruct input data. Find MAE loss training samples. Find max MAE loss value. worst model performed trying reconstruct sample. make threshold anomaly detection. reconstruction loss sample greater threshold value can infer model seeing pattern isn’t familiar . label sample anomaly.","code":"# Get train MAE loss. x_train_pred <- model |> predict(x_train) ## 118/118 - 0s - 3ms/step train_mae_loss <- apply(abs(x_train_pred - x_train), 1, mean)  hist(train_mae_loss, breaks = 50) # Get reconstruction loss threshold. threshold <- max(train_mae_loss) cat(\"Reconstruction error threshold: \", threshold, \"\\n\") ## Reconstruction error threshold:  0.039214"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"compare-recontruction","dir":"Articles > Examples > Timeseries","previous_headings":"Detecting anomalies","what":"Compare recontruction","title":"Timeseries anomaly detection using an Autoencoder","text":"Just fun, let’s see model recontructed first sample. 288 timesteps day 1 training dataset.","code":"# Checking how the first sequence is learnt plot(NULL, NULL, ylab = 'Value',      xlim = c(0, TIME_STEPS),      ylim = range(c(x_train[1,,], x_train_pred[1,,]))) lines(x_train[1,,]) lines(x_train_pred[1,,], col = 'red') legend(\"topleft\", lty = 1,        legend = c(\"actual\", \"predicted\"),        col = c(\"black\", \"red\"))"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"prepare-test-data","dir":"Articles > Examples > Timeseries","previous_headings":"Detecting anomalies","what":"Prepare test data","title":"Timeseries anomaly detection using an Autoencoder","text":"","code":"df_test <- df_daily_jumpsup |>   mutate(value =            (value - mean(df_small_noise$value)) /              sd(df_small_noise$value))  df_test |> head() plot_ts(df_test) # Create sequences from test values. x_test <- as_dataset(df_test)  # Get test MAE loss. x_test_pred <- model |> predict(x_test) test_mae_loss <- apply(abs(x_test_pred - x_test), 1, mean)  hist(test_mae_loss, breaks = 50, xlab = \"test MAE loss\", ylab = \"No of samples\") # Detect all the samples which are anomalies. anomalies <- test_mae_loss > threshold cat(\"Number of anomaly samples:\", sum(anomalies), \"\\n\") cat(\"Indices of anomaly samples:\", which(anomalies), \"\\n\", fill = TRUE) ## # A tibble: 6 × 2 ##   timestamp            value ##   <dttm>               <dbl> ## 1 2014-04-01 00:00:00 -0.808 ## 2 2014-04-01 00:05:00 -0.781 ## 3 2014-04-01 00:10:00 -0.801 ## 4 2014-04-01 00:15:00 -0.746 ## 5 2014-04-01 00:20:00 -0.792 ## 6 2014-04-01 00:25:00 -0.802 ## 118/118 - 0s - 755us/step ## Number of anomaly samples: 509 ## Indices of anomaly samples: 216 218 219 220 221 396 398 507 793 794 795 797 ## 798 799 861 974 975 1658 1659 1944 1945 1946 1947 1950 2004 2008 2012 2016 ## 2020 2022 2024 2026 2028 2036 2038 2040 2042 2044 2045 2046 2048 2050 2052 ## 2053 2054 2056 2057 2058 2060 2061 2062 2064 2065 2066 2068 2069 2070 2072 ## 2073 2074 2076 2077 2078 2080 2081 2082 2083 2084 2085 2086 2088 2089 2090 ## 2092 2093 2094 2096 2097 2098 2100 2101 2102 2104 2105 2106 2108 2109 2110 ## 2112 2113 2114 2116 2117 2118 2120 2121 2122 2124 2126 2129 2141 2521 2522 ## 2523 2525 2546 2698 2700 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 ## 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 ## 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 ## 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 ## 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 ## 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 ## 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 ## 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 ## 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 ## 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 ## 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 ## 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 ## 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 ## 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 ## 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 ## 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 ## 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 ## 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 ## 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 ## 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 ## 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 ## 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 ## 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 ## 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 ## 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 ## 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 ## 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101 ## 3102"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"plot-anomalies","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Plot anomalies","title":"Timeseries anomaly detection using an Autoencoder","text":"now know samples data anomalies. , find corresponding timestamps original test data. using following method : Let’s say time_steps = 3 10 training values. x_train look like : 0, 1, 2 1, 2, 3 2, 3, 4 3, 4, 5 4, 5, 6 5, 6, 7 6, 7, 8 7, 8, 9 except initial final time_steps-1 data values, appear time_steps number samples. , know samples [(3, 4, 5), (4, 5, 6), (5, 6, 7)] anomalies, can say data point 5 anomaly. Let’s overlay anomalies original test data plot.","code":"is_anomaly <- test_mae_loss > threshold is_anomaly <- is_anomaly &   zoo::rollsum(is_anomaly, TIME_STEPS,                align = \"right\", na.pad = TRUE) >= TIME_STEPS  with(df_test, {   plot(value ~ timestamp, type = 'l', xaxt = 'n', las = 2)   axis.POSIXct(1, at = seq(timestamp[1], tail(timestamp, 1), by = \"days\"),                format = \"%b-%d\") })  with(df_test[which(is_anomaly),], {   points(value ~ timestamp, col = \"red\") })"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"introduction","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Introduction","title":"Timeseries classification from scratch","text":"example shows timeseries classification scratch, starting raw CSV timeseries files disk. demonstrate workflow FordA dataset UCR/UEA archive.","code":""},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"setup","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Setup","title":"Timeseries classification from scratch","text":"","code":"library(keras3) use_backend(\"jax\")"},{"path":[]},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"dataset-description","dir":"Articles > Examples > Timeseries","previous_headings":"Load the data: the FordA dataset","what":"Dataset description","title":"Timeseries classification from scratch","text":"dataset using called FordA. data comes UCR archive. dataset contains 3601 training instances another 1320 testing instances. timeseries corresponds measurement engine noise captured motor sensor. task, goal automatically detect presence specific issue engine. problem balanced binary classification task. full description dataset can found .","code":""},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"read-the-tsv-data","dir":"Articles > Examples > Timeseries","previous_headings":"Load the data: the FordA dataset","what":"Read the TSV data","title":"Timeseries classification from scratch","text":"use FordA_TRAIN file training FordA_TEST file testing. simplicity dataset allows us demonstrate effectively use ConvNets timeseries classification. file, first column corresponds label.","code":"get_data <- function(path) {   if(path |> startsWith(\"https://\"))     path <- get_file(origin = path)  # cache file locally    data <- readr::read_tsv(     path, col_names = FALSE,     # Each row is: one integer (the label),     # followed by 500 doubles (the timeseries)     col_types = paste0(\"i\", strrep(\"d\", 500))   )    y <- as.matrix(data[[1]])   x <- as.matrix(data[,-1])   dimnames(x) <- dimnames(y) <- NULL    list(x, y) }  root_url <- \"https://raw.githubusercontent.com/hfawaz/cd-diagram/master/FordA/\" c(x_train, y_train) %<-% get_data(paste0(root_url, \"FordA_TRAIN.tsv\")) c(x_test, y_test) %<-% get_data(paste0(root_url, \"FordA_TEST.tsv\"))  str(keras3:::named_list(   x_train, y_train,   x_test, y_test )) ## List of 4 ##  $ x_train: num [1:3601, 1:500] -0.797 0.805 0.728 -0.234 -0.171 ... ##  $ y_train: int [1:3601, 1] -1 1 -1 -1 -1 1 1 1 1 1 ... ##  $ x_test : num [1:1320, 1:500] -0.14 0.334 0.717 1.24 -1.159 ... ##  $ y_test : int [1:1320, 1] -1 -1 -1 1 -1 1 -1 -1 1 1 ..."},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"visualize-the-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Visualize the data","title":"Timeseries classification from scratch","text":"visualize one timeseries example class dataset.","code":"plot(NULL, main = \"Timeseries Data\",      xlab = \"Timepoints\",  ylab = \"Values\",      xlim = c(1, ncol(x_test)),      ylim = range(x_test)) grid() lines(x_test[match(-1, y_test), ], col = \"blue\") lines(x_test[match( 1, y_test), ], col = \"red\") legend(\"topright\", legend=c(\"label -1\", \"label 1\"), col=c(\"blue\", \"red\"), lty=1)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"standardize-the-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Standardize the data","title":"Timeseries classification from scratch","text":"timeseries already single length (500). However, values usually various ranges. ideal neural network; general seek make input values normalized. specific dataset, data already z-normalized: timeseries sample mean equal zero standard deviation equal one. type normalization common timeseries classification problems, see Bagnall et al. (2016). Note timeseries data used univariate, meaning one channel per timeseries example. therefore transform timeseries multivariate one one channel using simple reshaping via numpy. allow us construct model easily applicable multivariate time series. Finally, order use sparse_categorical_crossentropy, count number classes beforehand. Now shuffle training set using validation_split option later training. Standardize labels positive integers. expected labels 0 1.","code":"dim(x_train) <- c(dim(x_train), 1) dim(x_test) <- c(dim(x_test), 1) num_classes <- length(unique(y_train)) c(x_train, y_train) %<-% listarrays::shuffle_rows(x_train, y_train) # idx <- sample.int(nrow(x_train)) # x_train %<>% .[idx,, ,drop = FALSE] # y_train %<>% .[idx,  ,drop = FALSE] y_train[y_train == -1L] <- 0L y_test[y_test == -1L] <- 0L"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"build-a-model","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Build a model","title":"Timeseries classification from scratch","text":"build Fully Convolutional Neural Network originally proposed paper. implementation based TF 2 version provided . following hyperparameters (kernel_size, filters, usage BatchNorm) found via random search using KerasTuner. plot chunk unnamed-chunk-9","code":"make_model <- function(input_shape) {   inputs <- keras_input(input_shape)    outputs <- inputs |>     # conv1     layer_conv_1d(filters = 64, kernel_size = 3, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation_relu() |>     # conv2     layer_conv_1d(filters = 64, kernel_size = 3, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation_relu() |>     # conv3     layer_conv_1d(filters = 64, kernel_size = 3, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation_relu() |>     # pooling     layer_global_average_pooling_1d() |>     # final output     layer_dense(num_classes, activation = \"softmax\")    keras_model(inputs, outputs) }  model <- make_model(input_shape = dim(x_train)[-1]) model ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)                ┃ Output Shape          ┃    Param # ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer (InputLayer)    │ (None, 500, 1)        │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ conv1d (Conv1D)             │ (None, 500, 64)       │        256 │   Y   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ batch_normalization         │ (None, 500, 64)       │        256 │   Y   │ ## │ (BatchNormalization)        │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ re_lu (ReLU)                │ (None, 500, 64)       │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ conv1d_1 (Conv1D)           │ (None, 500, 64)       │     12,352 │   Y   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ batch_normalization_1       │ (None, 500, 64)       │        256 │   Y   │ ## │ (BatchNormalization)        │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ re_lu_1 (ReLU)              │ (None, 500, 64)       │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ conv1d_2 (Conv1D)           │ (None, 500, 64)       │     12,352 │   Y   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ batch_normalization_2       │ (None, 500, 64)       │        256 │   Y   │ ## │ (BatchNormalization)        │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ re_lu_2 (ReLU)              │ (None, 500, 64)       │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ global_average_pooling1d    │ (None, 64)            │          0 │   -   │ ## │ (GlobalAveragePooling1D)    │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense (Dense)               │ (None, 2)             │        130 │   Y   │ ## └─────────────────────────────┴───────────────────────┴────────────┴───────┘ ##  Total params: 25,858 (101.01 KB) ##  Trainable params: 25,474 (99.51 KB) ##  Non-trainable params: 384 (1.50 KB) plot(model, show_shapes = TRUE)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"train-the-model","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Train the model","title":"Timeseries classification from scratch","text":"","code":"epochs <- 500 batch_size <- 32  callbacks <- c(   callback_model_checkpoint(     \"best_model.keras\", save_best_only = TRUE,     monitor = \"val_loss\"   ),   callback_reduce_lr_on_plateau(     monitor = \"val_loss\", factor = 0.5,     patience = 20, min_lr = 0.0001   ),   callback_early_stopping(     monitor = \"val_loss\", patience = 50,     verbose = 1   ) )   model |> compile(   optimizer = \"adam\",   loss = \"sparse_categorical_crossentropy\",   metrics = \"sparse_categorical_accuracy\" )  history <- model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = epochs,   callbacks = callbacks,   validation_split = 0.2 ) ## Epoch 1/500 ## 90/90 - 2s - 26ms/step - loss: 0.5577 - sparse_categorical_accuracy: 0.7066 - val_loss: 0.8512 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010 ## Epoch 2/500 ## 90/90 - 1s - 7ms/step - loss: 0.4871 - sparse_categorical_accuracy: 0.7649 - val_loss: 0.9151 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010 ## Epoch 3/500 ## 90/90 - 0s - 2ms/step - loss: 0.4678 - sparse_categorical_accuracy: 0.7663 - val_loss: 0.7265 - val_sparse_categorical_accuracy: 0.4910 - learning_rate: 0.0010 ## Epoch 4/500 ## 90/90 - 0s - 2ms/step - loss: 0.4174 - sparse_categorical_accuracy: 0.7941 - val_loss: 0.6591 - val_sparse_categorical_accuracy: 0.5534 - learning_rate: 0.0010 ## Epoch 5/500 ## 90/90 - 0s - 2ms/step - loss: 0.4289 - sparse_categorical_accuracy: 0.7826 - val_loss: 0.5279 - val_sparse_categorical_accuracy: 0.6741 - learning_rate: 0.0010 ## Epoch 6/500 ## 90/90 - 0s - 2ms/step - loss: 0.4058 - sparse_categorical_accuracy: 0.8035 - val_loss: 0.4469 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010 ## Epoch 7/500 ## 90/90 - 0s - 2ms/step - loss: 0.4005 - sparse_categorical_accuracy: 0.8042 - val_loss: 0.4278 - val_sparse_categorical_accuracy: 0.7448 - learning_rate: 0.0010 ## Epoch 8/500 ## 90/90 - 0s - 2ms/step - loss: 0.3904 - sparse_categorical_accuracy: 0.8049 - val_loss: 0.3824 - val_sparse_categorical_accuracy: 0.8128 - learning_rate: 0.0010 ## Epoch 9/500 ## 90/90 - 0s - 1ms/step - loss: 0.3830 - sparse_categorical_accuracy: 0.8188 - val_loss: 0.4006 - val_sparse_categorical_accuracy: 0.8100 - learning_rate: 0.0010 ## Epoch 10/500 ## 90/90 - 0s - 1ms/step - loss: 0.3845 - sparse_categorical_accuracy: 0.8080 - val_loss: 0.4014 - val_sparse_categorical_accuracy: 0.8114 - learning_rate: 0.0010 ## Epoch 11/500 ## 90/90 - 0s - 2ms/step - loss: 0.3804 - sparse_categorical_accuracy: 0.8167 - val_loss: 0.3738 - val_sparse_categorical_accuracy: 0.8017 - learning_rate: 0.0010 ## Epoch 12/500 ## 90/90 - 0s - 1ms/step - loss: 0.3695 - sparse_categorical_accuracy: 0.8226 - val_loss: 0.4010 - val_sparse_categorical_accuracy: 0.7989 - learning_rate: 0.0010 ## Epoch 13/500 ## 90/90 - 0s - 1ms/step - loss: 0.3598 - sparse_categorical_accuracy: 0.8313 - val_loss: 0.3742 - val_sparse_categorical_accuracy: 0.8128 - learning_rate: 0.0010 ## Epoch 14/500 ## 90/90 - 0s - 1ms/step - loss: 0.3604 - sparse_categorical_accuracy: 0.8250 - val_loss: 0.3839 - val_sparse_categorical_accuracy: 0.8225 - learning_rate: 0.0010 ## Epoch 15/500 ## 90/90 - 0s - 1ms/step - loss: 0.3436 - sparse_categorical_accuracy: 0.8417 - val_loss: 0.3815 - val_sparse_categorical_accuracy: 0.8225 - learning_rate: 0.0010 ## Epoch 16/500 ## 90/90 - 0s - 2ms/step - loss: 0.3441 - sparse_categorical_accuracy: 0.8444 - val_loss: 0.3387 - val_sparse_categorical_accuracy: 0.8433 - learning_rate: 0.0010 ## Epoch 17/500 ## 90/90 - 0s - 2ms/step - loss: 0.3327 - sparse_categorical_accuracy: 0.8427 - val_loss: 0.3360 - val_sparse_categorical_accuracy: 0.8433 - learning_rate: 0.0010 ## Epoch 18/500 ## 90/90 - 0s - 1ms/step - loss: 0.3296 - sparse_categorical_accuracy: 0.8493 - val_loss: 0.5619 - val_sparse_categorical_accuracy: 0.7074 - learning_rate: 0.0010 ## Epoch 19/500 ## 90/90 - 0s - 1ms/step - loss: 0.3222 - sparse_categorical_accuracy: 0.8608 - val_loss: 0.3951 - val_sparse_categorical_accuracy: 0.8128 - learning_rate: 0.0010 ## Epoch 20/500 ## 90/90 - 0s - 1ms/step - loss: 0.3195 - sparse_categorical_accuracy: 0.8590 - val_loss: 0.5172 - val_sparse_categorical_accuracy: 0.7226 - learning_rate: 0.0010 ## Epoch 21/500 ## 90/90 - 0s - 2ms/step - loss: 0.3003 - sparse_categorical_accuracy: 0.8698 - val_loss: 0.3359 - val_sparse_categorical_accuracy: 0.8405 - learning_rate: 0.0010 ## Epoch 22/500 ## 90/90 - 0s - 2ms/step - loss: 0.3012 - sparse_categorical_accuracy: 0.8740 - val_loss: 0.3339 - val_sparse_categorical_accuracy: 0.8447 - learning_rate: 0.0010 ## Epoch 23/500 ## 90/90 - 0s - 1ms/step - loss: 0.2901 - sparse_categorical_accuracy: 0.8799 - val_loss: 0.3859 - val_sparse_categorical_accuracy: 0.7920 - learning_rate: 0.0010 ## Epoch 24/500 ## 90/90 - 0s - 2ms/step - loss: 0.2898 - sparse_categorical_accuracy: 0.8788 - val_loss: 0.2806 - val_sparse_categorical_accuracy: 0.8863 - learning_rate: 0.0010 ## Epoch 25/500 ## 90/90 - 0s - 1ms/step - loss: 0.2831 - sparse_categorical_accuracy: 0.8785 - val_loss: 0.3124 - val_sparse_categorical_accuracy: 0.8585 - learning_rate: 0.0010 ## Epoch 26/500 ## 90/90 - 0s - 1ms/step - loss: 0.3130 - sparse_categorical_accuracy: 0.8580 - val_loss: 0.3423 - val_sparse_categorical_accuracy: 0.8377 - learning_rate: 0.0010 ## Epoch 27/500 ## 90/90 - 0s - 1ms/step - loss: 0.2741 - sparse_categorical_accuracy: 0.8910 - val_loss: 0.2908 - val_sparse_categorical_accuracy: 0.8835 - learning_rate: 0.0010 ## Epoch 28/500 ## 90/90 - 0s - 1ms/step - loss: 0.2855 - sparse_categorical_accuracy: 0.8837 - val_loss: 0.5778 - val_sparse_categorical_accuracy: 0.7323 - learning_rate: 0.0010 ## Epoch 29/500 ## 90/90 - 0s - 1ms/step - loss: 0.2671 - sparse_categorical_accuracy: 0.8809 - val_loss: 0.4736 - val_sparse_categorical_accuracy: 0.7628 - learning_rate: 0.0010 ## Epoch 30/500 ## 90/90 - 0s - 1ms/step - loss: 0.2720 - sparse_categorical_accuracy: 0.8833 - val_loss: 0.4564 - val_sparse_categorical_accuracy: 0.7767 - learning_rate: 0.0010 ## Epoch 31/500 ## 90/90 - 0s - 1ms/step - loss: 0.2724 - sparse_categorical_accuracy: 0.8882 - val_loss: 0.2784 - val_sparse_categorical_accuracy: 0.8918 - learning_rate: 0.0010 ## Epoch 32/500 ## 90/90 - 0s - 1ms/step - loss: 0.2709 - sparse_categorical_accuracy: 0.8830 - val_loss: 0.3442 - val_sparse_categorical_accuracy: 0.8280 - learning_rate: 0.0010 ## Epoch 33/500 ## 90/90 - 0s - 1ms/step - loss: 0.2571 - sparse_categorical_accuracy: 0.8986 - val_loss: 0.3730 - val_sparse_categorical_accuracy: 0.7864 - learning_rate: 0.0010 ## Epoch 34/500 ## 90/90 - 0s - 1ms/step - loss: 0.2449 - sparse_categorical_accuracy: 0.9003 - val_loss: 0.2799 - val_sparse_categorical_accuracy: 0.8877 - learning_rate: 0.0010 ## Epoch 35/500 ## 90/90 - 0s - 1ms/step - loss: 0.2698 - sparse_categorical_accuracy: 0.8861 - val_loss: 0.4860 - val_sparse_categorical_accuracy: 0.7420 - learning_rate: 0.0010 ## Epoch 36/500 ## 90/90 - 0s - 1ms/step - loss: 0.2539 - sparse_categorical_accuracy: 0.8944 - val_loss: 0.2572 - val_sparse_categorical_accuracy: 0.8974 - learning_rate: 0.0010 ## Epoch 37/500 ## 90/90 - 0s - 1ms/step - loss: 0.2495 - sparse_categorical_accuracy: 0.8962 - val_loss: 0.2735 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 0.0010 ## Epoch 38/500 ## 90/90 - 0s - 1ms/step - loss: 0.2474 - sparse_categorical_accuracy: 0.9000 - val_loss: 0.3009 - val_sparse_categorical_accuracy: 0.8641 - learning_rate: 0.0010 ## Epoch 39/500 ## 90/90 - 0s - 1ms/step - loss: 0.2492 - sparse_categorical_accuracy: 0.8976 - val_loss: 1.0276 - val_sparse_categorical_accuracy: 0.6283 - learning_rate: 0.0010 ## Epoch 40/500 ## 90/90 - 0s - 1ms/step - loss: 0.2477 - sparse_categorical_accuracy: 0.8896 - val_loss: 0.4444 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010 ## Epoch 41/500 ## 90/90 - 0s - 1ms/step - loss: 0.2315 - sparse_categorical_accuracy: 0.9069 - val_loss: 0.4963 - val_sparse_categorical_accuracy: 0.7573 - learning_rate: 0.0010 ## Epoch 42/500 ## 90/90 - 0s - 1ms/step - loss: 0.2279 - sparse_categorical_accuracy: 0.9101 - val_loss: 0.2943 - val_sparse_categorical_accuracy: 0.8849 - learning_rate: 0.0010 ## Epoch 43/500 ## 90/90 - 0s - 1ms/step - loss: 0.2365 - sparse_categorical_accuracy: 0.8983 - val_loss: 0.6205 - val_sparse_categorical_accuracy: 0.7226 - learning_rate: 0.0010 ## Epoch 44/500 ## 90/90 - 0s - 1ms/step - loss: 0.2314 - sparse_categorical_accuracy: 0.9080 - val_loss: 0.3348 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010 ## Epoch 45/500 ## 90/90 - 0s - 1ms/step - loss: 0.2301 - sparse_categorical_accuracy: 0.9094 - val_loss: 0.4539 - val_sparse_categorical_accuracy: 0.7753 - learning_rate: 0.0010 ## Epoch 46/500 ## 90/90 - 0s - 1ms/step - loss: 0.2265 - sparse_categorical_accuracy: 0.9090 - val_loss: 0.2660 - val_sparse_categorical_accuracy: 0.8974 - learning_rate: 0.0010 ## Epoch 47/500 ## 90/90 - 0s - 1ms/step - loss: 0.2065 - sparse_categorical_accuracy: 0.9188 - val_loss: 0.2680 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010 ## Epoch 48/500 ## 90/90 - 0s - 2ms/step - loss: 0.2107 - sparse_categorical_accuracy: 0.9139 - val_loss: 0.2204 - val_sparse_categorical_accuracy: 0.9126 - learning_rate: 0.0010 ## Epoch 49/500 ## 90/90 - 0s - 1ms/step - loss: 0.2009 - sparse_categorical_accuracy: 0.9174 - val_loss: 0.3135 - val_sparse_categorical_accuracy: 0.8336 - learning_rate: 0.0010 ## Epoch 50/500 ## 90/90 - 0s - 1ms/step - loss: 0.2138 - sparse_categorical_accuracy: 0.9160 - val_loss: 0.4732 - val_sparse_categorical_accuracy: 0.7587 - learning_rate: 0.0010 ## Epoch 51/500 ## 90/90 - 0s - 1ms/step - loss: 0.1992 - sparse_categorical_accuracy: 0.9201 - val_loss: 0.6327 - val_sparse_categorical_accuracy: 0.7365 - learning_rate: 0.0010 ## Epoch 52/500 ## 90/90 - 0s - 1ms/step - loss: 0.1888 - sparse_categorical_accuracy: 0.9267 - val_loss: 0.2356 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010 ## Epoch 53/500 ## 90/90 - 0s - 1ms/step - loss: 0.1837 - sparse_categorical_accuracy: 0.9278 - val_loss: 0.2637 - val_sparse_categorical_accuracy: 0.8877 - learning_rate: 0.0010 ## Epoch 54/500 ## 90/90 - 0s - 2ms/step - loss: 0.1758 - sparse_categorical_accuracy: 0.9354 - val_loss: 0.2062 - val_sparse_categorical_accuracy: 0.9126 - learning_rate: 0.0010 ## Epoch 55/500 ## 90/90 - 0s - 1ms/step - loss: 0.1668 - sparse_categorical_accuracy: 0.9438 - val_loss: 0.6011 - val_sparse_categorical_accuracy: 0.7559 - learning_rate: 0.0010 ## Epoch 56/500 ## 90/90 - 0s - 1ms/step - loss: 0.1580 - sparse_categorical_accuracy: 0.9476 - val_loss: 0.4532 - val_sparse_categorical_accuracy: 0.7587 - learning_rate: 0.0010 ## Epoch 57/500 ## 90/90 - 0s - 1ms/step - loss: 0.1518 - sparse_categorical_accuracy: 0.9521 - val_loss: 1.1827 - val_sparse_categorical_accuracy: 0.5492 - learning_rate: 0.0010 ## Epoch 58/500 ## 90/90 - 0s - 2ms/step - loss: 0.1449 - sparse_categorical_accuracy: 0.9528 - val_loss: 0.1922 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 0.0010 ## Epoch 59/500 ## 90/90 - 0s - 1ms/step - loss: 0.1520 - sparse_categorical_accuracy: 0.9476 - val_loss: 2.0179 - val_sparse_categorical_accuracy: 0.6089 - learning_rate: 0.0010 ## Epoch 60/500 ## 90/90 - 0s - 1ms/step - loss: 0.1457 - sparse_categorical_accuracy: 0.9503 - val_loss: 0.2761 - val_sparse_categorical_accuracy: 0.8946 - learning_rate: 0.0010 ## Epoch 61/500 ## 90/90 - 0s - 1ms/step - loss: 0.1280 - sparse_categorical_accuracy: 0.9580 - val_loss: 1.6068 - val_sparse_categorical_accuracy: 0.6796 - learning_rate: 0.0010 ## Epoch 62/500 ## 90/90 - 0s - 1ms/step - loss: 0.1408 - sparse_categorical_accuracy: 0.9510 - val_loss: 0.9512 - val_sparse_categorical_accuracy: 0.7129 - learning_rate: 0.0010 ## Epoch 63/500 ## 90/90 - 0s - 1ms/step - loss: 0.1381 - sparse_categorical_accuracy: 0.9514 - val_loss: 1.2871 - val_sparse_categorical_accuracy: 0.6976 - learning_rate: 0.0010 ## Epoch 64/500 ## 90/90 - 0s - 1ms/step - loss: 0.1280 - sparse_categorical_accuracy: 0.9569 - val_loss: 0.3003 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 0.0010 ## Epoch 65/500 ## 90/90 - 0s - 1ms/step - loss: 0.1206 - sparse_categorical_accuracy: 0.9618 - val_loss: 0.4581 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010 ## Epoch 66/500 ## 90/90 - 0s - 1ms/step - loss: 0.1148 - sparse_categorical_accuracy: 0.9628 - val_loss: 0.3863 - val_sparse_categorical_accuracy: 0.8266 - learning_rate: 0.0010 ## Epoch 67/500 ## 90/90 - 0s - 1ms/step - loss: 0.1068 - sparse_categorical_accuracy: 0.9663 - val_loss: 1.2923 - val_sparse_categorical_accuracy: 0.6852 - learning_rate: 0.0010 ## Epoch 68/500 ## 90/90 - 0s - 1ms/step - loss: 0.1159 - sparse_categorical_accuracy: 0.9615 - val_loss: 0.2969 - val_sparse_categorical_accuracy: 0.8558 - learning_rate: 0.0010 ## Epoch 69/500 ## 90/90 - 0s - 1ms/step - loss: 0.1228 - sparse_categorical_accuracy: 0.9580 - val_loss: 0.2251 - val_sparse_categorical_accuracy: 0.9071 - learning_rate: 0.0010 ## Epoch 70/500 ## 90/90 - 0s - 2ms/step - loss: 0.1138 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.1805 - val_sparse_categorical_accuracy: 0.9251 - learning_rate: 0.0010 ## Epoch 71/500 ## 90/90 - 0s - 2ms/step - loss: 0.1101 - sparse_categorical_accuracy: 0.9618 - val_loss: 0.1439 - val_sparse_categorical_accuracy: 0.9487 - learning_rate: 0.0010 ## Epoch 72/500 ## 90/90 - 0s - 2ms/step - loss: 0.1133 - sparse_categorical_accuracy: 0.9628 - val_loss: 0.1390 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 0.0010 ## Epoch 73/500 ## 90/90 - 0s - 1ms/step - loss: 0.1042 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.1421 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 0.0010 ## Epoch 74/500 ## 90/90 - 0s - 1ms/step - loss: 0.1098 - sparse_categorical_accuracy: 0.9604 - val_loss: 2.0644 - val_sparse_categorical_accuracy: 0.6685 - learning_rate: 0.0010 ## Epoch 75/500 ## 90/90 - 0s - 1ms/step - loss: 0.1419 - sparse_categorical_accuracy: 0.9486 - val_loss: 0.4101 - val_sparse_categorical_accuracy: 0.8336 - learning_rate: 0.0010 ## Epoch 76/500 ## 90/90 - 0s - 1ms/step - loss: 0.1230 - sparse_categorical_accuracy: 0.9569 - val_loss: 0.3055 - val_sparse_categorical_accuracy: 0.8946 - learning_rate: 0.0010 ## Epoch 77/500 ## 90/90 - 0s - 1ms/step - loss: 0.1028 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.1704 - val_sparse_categorical_accuracy: 0.9265 - learning_rate: 0.0010 ## Epoch 78/500 ## 90/90 - 0s - 1ms/step - loss: 0.1003 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.3304 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 0.0010 ## Epoch 79/500 ## 90/90 - 0s - 1ms/step - loss: 0.1019 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.5429 - val_sparse_categorical_accuracy: 0.7822 - learning_rate: 0.0010 ## Epoch 80/500 ## 90/90 - 0s - 1ms/step - loss: 0.1037 - sparse_categorical_accuracy: 0.9670 - val_loss: 2.0288 - val_sparse_categorical_accuracy: 0.6976 - learning_rate: 0.0010 ## Epoch 81/500 ## 90/90 - 0s - 1ms/step - loss: 0.1030 - sparse_categorical_accuracy: 0.9667 - val_loss: 3.3225 - val_sparse_categorical_accuracy: 0.5492 - learning_rate: 0.0010 ## Epoch 82/500 ## 90/90 - 0s - 2ms/step - loss: 0.0947 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.1449 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 0.0010 ## Epoch 83/500 ## 90/90 - 0s - 2ms/step - loss: 0.1060 - sparse_categorical_accuracy: 0.9639 - val_loss: 0.2491 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010 ## Epoch 84/500 ## 90/90 - 0s - 2ms/step - loss: 0.0958 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.1281 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 0.0010 ## Epoch 85/500 ## 90/90 - 0s - 1ms/step - loss: 0.0993 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.1956 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 0.0010 ## Epoch 86/500 ## 90/90 - 0s - 1ms/step - loss: 0.1003 - sparse_categorical_accuracy: 0.9604 - val_loss: 0.4167 - val_sparse_categorical_accuracy: 0.8239 - learning_rate: 0.0010 ## Epoch 87/500 ## 90/90 - 0s - 1ms/step - loss: 0.1099 - sparse_categorical_accuracy: 0.9604 - val_loss: 0.1557 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 0.0010 ## Epoch 88/500 ## 90/90 - 0s - 1ms/step - loss: 0.1002 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.1204 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010 ## Epoch 89/500 ## 90/90 - 0s - 2ms/step - loss: 0.0969 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1194 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010 ## Epoch 90/500 ## 90/90 - 0s - 1ms/step - loss: 0.0937 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.1397 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010 ## Epoch 91/500 ## 90/90 - 0s - 1ms/step - loss: 0.1024 - sparse_categorical_accuracy: 0.9660 - val_loss: 2.1705 - val_sparse_categorical_accuracy: 0.6671 - learning_rate: 0.0010 ## Epoch 92/500 ## 90/90 - 0s - 2ms/step - loss: 0.0928 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.7758 - val_sparse_categorical_accuracy: 0.7559 - learning_rate: 0.0010 ## Epoch 93/500 ## 90/90 - 0s - 1ms/step - loss: 0.0905 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.2143 - val_sparse_categorical_accuracy: 0.9140 - learning_rate: 0.0010 ## Epoch 94/500 ## 90/90 - 0s - 1ms/step - loss: 0.0919 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.1413 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 0.0010 ## Epoch 95/500 ## 90/90 - 0s - 1ms/step - loss: 0.0884 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.1715 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 0.0010 ## Epoch 96/500 ## 90/90 - 0s - 1ms/step - loss: 0.1015 - sparse_categorical_accuracy: 0.9653 - val_loss: 0.3827 - val_sparse_categorical_accuracy: 0.8669 - learning_rate: 0.0010 ## Epoch 97/500 ## 90/90 - 0s - 1ms/step - loss: 0.0945 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1286 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010 ## Epoch 98/500 ## 90/90 - 0s - 1ms/step - loss: 0.0924 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.2279 - val_sparse_categorical_accuracy: 0.9126 - learning_rate: 0.0010 ## Epoch 99/500 ## 90/90 - 0s - 1ms/step - loss: 0.0951 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.1800 - val_sparse_categorical_accuracy: 0.9348 - learning_rate: 0.0010 ## Epoch 100/500 ## 90/90 - 0s - 1ms/step - loss: 0.0864 - sparse_categorical_accuracy: 0.9729 - val_loss: 0.2424 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010 ## Epoch 101/500 ## 90/90 - 0s - 1ms/step - loss: 0.0830 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.1904 - val_sparse_categorical_accuracy: 0.9251 - learning_rate: 0.0010 ## Epoch 102/500 ## 90/90 - 0s - 1ms/step - loss: 0.0973 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.1916 - val_sparse_categorical_accuracy: 0.9182 - learning_rate: 0.0010 ## Epoch 103/500 ## 90/90 - 0s - 1ms/step - loss: 0.0883 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.1190 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 0.0010 ## Epoch 104/500 ## 90/90 - 0s - 1ms/step - loss: 0.0936 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.2695 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010 ## Epoch 105/500 ## 90/90 - 0s - 1ms/step - loss: 0.0905 - sparse_categorical_accuracy: 0.9684 - val_loss: 0.1941 - val_sparse_categorical_accuracy: 0.9182 - learning_rate: 0.0010 ## Epoch 106/500 ## 90/90 - 0s - 1ms/step - loss: 0.0885 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.5663 - val_sparse_categorical_accuracy: 0.8322 - learning_rate: 0.0010 ## Epoch 107/500 ## 90/90 - 0s - 1ms/step - loss: 0.0891 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.6871 - val_sparse_categorical_accuracy: 0.7490 - learning_rate: 0.0010 ## Epoch 108/500 ## 90/90 - 0s - 1ms/step - loss: 0.0890 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.7344 - val_sparse_categorical_accuracy: 0.8031 - learning_rate: 0.0010 ## Epoch 109/500 ## 90/90 - 0s - 1ms/step - loss: 0.0894 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.3726 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 0.0010 ## Epoch 110/500 ## 90/90 - 0s - 1ms/step - loss: 0.0963 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.6424 - val_sparse_categorical_accuracy: 0.7961 - learning_rate: 0.0010 ## Epoch 111/500 ## 90/90 - 0s - 1ms/step - loss: 0.0819 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.8653 - val_sparse_categorical_accuracy: 0.7642 - learning_rate: 0.0010 ## Epoch 112/500 ## 90/90 - 0s - 1ms/step - loss: 0.0852 - sparse_categorical_accuracy: 0.9691 - val_loss: 1.0738 - val_sparse_categorical_accuracy: 0.7393 - learning_rate: 0.0010 ## Epoch 113/500 ## 90/90 - 0s - 1ms/step - loss: 0.0828 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.1299 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 0.0010 ## Epoch 114/500 ## 90/90 - 0s - 1ms/step - loss: 0.1042 - sparse_categorical_accuracy: 0.9649 - val_loss: 0.2819 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010 ## Epoch 115/500 ## 90/90 - 0s - 1ms/step - loss: 0.0780 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.2349 - val_sparse_categorical_accuracy: 0.9001 - learning_rate: 0.0010 ## Epoch 116/500 ## 90/90 - 0s - 1ms/step - loss: 0.0850 - sparse_categorical_accuracy: 0.9743 - val_loss: 0.9277 - val_sparse_categorical_accuracy: 0.7406 - learning_rate: 0.0010 ## Epoch 117/500 ## 90/90 - 0s - 1ms/step - loss: 0.0813 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.7052 - val_sparse_categorical_accuracy: 0.7836 - learning_rate: 0.0010 ## Epoch 118/500 ## 90/90 - 0s - 1ms/step - loss: 0.0838 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1543 - val_sparse_categorical_accuracy: 0.9307 - learning_rate: 0.0010 ## Epoch 119/500 ## 90/90 - 0s - 2ms/step - loss: 0.0817 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.1127 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010 ## Epoch 120/500 ## 90/90 - 0s - 1ms/step - loss: 0.0846 - sparse_categorical_accuracy: 0.9715 - val_loss: 0.1209 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010 ## Epoch 121/500 ## 90/90 - 0s - 1ms/step - loss: 0.0817 - sparse_categorical_accuracy: 0.9740 - val_loss: 0.1134 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 0.0010 ## Epoch 122/500 ## 90/90 - 0s - 1ms/step - loss: 0.0872 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.6854 - val_sparse_categorical_accuracy: 0.7892 - learning_rate: 0.0010 ## Epoch 123/500 ## 90/90 - 0s - 1ms/step - loss: 0.0795 - sparse_categorical_accuracy: 0.9743 - val_loss: 0.8494 - val_sparse_categorical_accuracy: 0.7448 - learning_rate: 0.0010 ## Epoch 124/500 ## 90/90 - 0s - 1ms/step - loss: 0.0965 - sparse_categorical_accuracy: 0.9632 - val_loss: 0.2033 - val_sparse_categorical_accuracy: 0.9140 - learning_rate: 0.0010 ## Epoch 125/500 ## 90/90 - 0s - 1ms/step - loss: 0.0866 - sparse_categorical_accuracy: 0.9715 - val_loss: 0.3366 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 0.0010 ## Epoch 126/500 ## 90/90 - 0s - 1ms/step - loss: 0.0792 - sparse_categorical_accuracy: 0.9747 - val_loss: 1.1318 - val_sparse_categorical_accuracy: 0.7462 - learning_rate: 0.0010 ## Epoch 127/500 ## 90/90 - 0s - 1ms/step - loss: 0.0810 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1230 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 0.0010 ## Epoch 128/500 ## 90/90 - 0s - 1ms/step - loss: 0.0839 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.1734 - val_sparse_categorical_accuracy: 0.9279 - learning_rate: 0.0010 ## Epoch 129/500 ## 90/90 - 0s - 1ms/step - loss: 0.0845 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.2233 - val_sparse_categorical_accuracy: 0.9154 - learning_rate: 0.0010 ## Epoch 130/500 ## 90/90 - 0s - 1ms/step - loss: 0.0906 - sparse_categorical_accuracy: 0.9694 - val_loss: 1.4223 - val_sparse_categorical_accuracy: 0.6963 - learning_rate: 0.0010 ## Epoch 131/500 ## 90/90 - 0s - 1ms/step - loss: 0.0774 - sparse_categorical_accuracy: 0.9743 - val_loss: 2.8725 - val_sparse_categorical_accuracy: 0.7018 - learning_rate: 0.0010 ## Epoch 132/500 ## 90/90 - 0s - 1ms/step - loss: 0.0748 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.8038 - val_sparse_categorical_accuracy: 0.7781 - learning_rate: 0.0010 ## Epoch 133/500 ## 90/90 - 0s - 1ms/step - loss: 0.0739 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.5711 - val_sparse_categorical_accuracy: 0.7809 - learning_rate: 0.0010 ## Epoch 134/500 ## 90/90 - 0s - 1ms/step - loss: 0.0780 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.2510 - val_sparse_categorical_accuracy: 0.8918 - learning_rate: 0.0010 ## Epoch 135/500 ## 90/90 - 0s - 1ms/step - loss: 0.0801 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.4907 - val_sparse_categorical_accuracy: 0.8585 - learning_rate: 0.0010 ## Epoch 136/500 ## 90/90 - 0s - 1ms/step - loss: 0.0819 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.1151 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 0.0010 ## Epoch 137/500 ## 90/90 - 0s - 1ms/step - loss: 0.0840 - sparse_categorical_accuracy: 0.9715 - val_loss: 1.2419 - val_sparse_categorical_accuracy: 0.7240 - learning_rate: 0.0010 ## Epoch 138/500 ## 90/90 - 0s - 1ms/step - loss: 0.0774 - sparse_categorical_accuracy: 0.9743 - val_loss: 0.6905 - val_sparse_categorical_accuracy: 0.7864 - learning_rate: 0.0010 ## Epoch 139/500 ## 90/90 - 0s - 1ms/step - loss: 0.0852 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.1253 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 0.0010 ## Epoch 140/500 ## 90/90 - 1s - 6ms/step - loss: 0.0666 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.2353 - val_sparse_categorical_accuracy: 0.9140 - learning_rate: 5.0000e-04 ## Epoch 141/500 ## 90/90 - 0s - 1ms/step - loss: 0.0657 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1275 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 5.0000e-04 ## Epoch 142/500 ## 90/90 - 0s - 1ms/step - loss: 0.0635 - sparse_categorical_accuracy: 0.9788 - val_loss: 0.2696 - val_sparse_categorical_accuracy: 0.9029 - learning_rate: 5.0000e-04 ## Epoch 143/500 ## 90/90 - 0s - 1ms/step - loss: 0.0688 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.1737 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 5.0000e-04 ## Epoch 144/500 ## 90/90 - 0s - 1ms/step - loss: 0.0615 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.2506 - val_sparse_categorical_accuracy: 0.8946 - learning_rate: 5.0000e-04 ## Epoch 145/500 ## 90/90 - 0s - 1ms/step - loss: 0.0629 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.1486 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 5.0000e-04 ## Epoch 146/500 ## 90/90 - 0s - 1ms/step - loss: 0.0656 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1651 - val_sparse_categorical_accuracy: 0.9307 - learning_rate: 5.0000e-04 ## Epoch 147/500 ## 90/90 - 0s - 1ms/step - loss: 0.0677 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1034 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 5.0000e-04 ## Epoch 148/500 ## 90/90 - 0s - 1ms/step - loss: 0.0762 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.1455 - val_sparse_categorical_accuracy: 0.9417 - learning_rate: 5.0000e-04 ## Epoch 149/500 ## 90/90 - 0s - 1ms/step - loss: 0.0657 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1037 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 5.0000e-04 ## Epoch 150/500 ## 90/90 - 0s - 1ms/step - loss: 0.0686 - sparse_categorical_accuracy: 0.9753 - val_loss: 0.1306 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 5.0000e-04 ## Epoch 151/500 ## 90/90 - 0s - 1ms/step - loss: 0.0667 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1227 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 5.0000e-04 ## Epoch 152/500 ## 90/90 - 0s - 1ms/step - loss: 0.0677 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.1041 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 5.0000e-04 ## Epoch 153/500 ## 90/90 - 0s - 1ms/step - loss: 0.0638 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.1837 - val_sparse_categorical_accuracy: 0.9307 - learning_rate: 5.0000e-04 ## Epoch 154/500 ## 90/90 - 0s - 1ms/step - loss: 0.0608 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1237 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 5.0000e-04 ## Epoch 155/500 ## 90/90 - 0s - 1ms/step - loss: 0.0662 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1192 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 5.0000e-04 ## Epoch 156/500 ## 90/90 - 0s - 1ms/step - loss: 0.0672 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1262 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 5.0000e-04 ## Epoch 157/500 ## 90/90 - 0s - 1ms/step - loss: 0.0634 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1035 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 5.0000e-04 ## Epoch 158/500 ## 90/90 - 0s - 1ms/step - loss: 0.0657 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.2735 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 5.0000e-04 ## Epoch 159/500 ## 90/90 - 0s - 1ms/step - loss: 0.0685 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.2094 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 5.0000e-04 ## Epoch 160/500 ## 90/90 - 0s - 1ms/step - loss: 0.0712 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.1137 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 5.0000e-04 ## Epoch 161/500 ## 90/90 - 0s - 1ms/step - loss: 0.0675 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1544 - val_sparse_categorical_accuracy: 0.9320 - learning_rate: 5.0000e-04 ## Epoch 162/500 ## 90/90 - 0s - 1ms/step - loss: 0.0610 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.1070 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04 ## Epoch 163/500 ## 90/90 - 0s - 1ms/step - loss: 0.0572 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1382 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 5.0000e-04 ## Epoch 164/500 ## 90/90 - 0s - 1ms/step - loss: 0.0617 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.1737 - val_sparse_categorical_accuracy: 0.9265 - learning_rate: 5.0000e-04 ## Epoch 165/500 ## 90/90 - 0s - 1ms/step - loss: 0.0660 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.1737 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 5.0000e-04 ## Epoch 166/500 ## 90/90 - 0s - 1ms/step - loss: 0.0632 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1408 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 5.0000e-04 ## Epoch 167/500 ## 90/90 - 0s - 1ms/step - loss: 0.0620 - sparse_categorical_accuracy: 0.9788 - val_loss: 0.2021 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 5.0000e-04 ## Epoch 168/500 ## 90/90 - 0s - 1ms/step - loss: 0.0669 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1003 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04 ## Epoch 169/500 ## 90/90 - 0s - 2ms/step - loss: 0.0578 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04 ## Epoch 170/500 ## 90/90 - 0s - 1ms/step - loss: 0.0562 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0970 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04 ## Epoch 171/500 ## 90/90 - 0s - 1ms/step - loss: 0.0535 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1347 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04 ## Epoch 172/500 ## 90/90 - 0s - 1ms/step - loss: 0.0558 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.0975 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 2.5000e-04 ## Epoch 173/500 ## 90/90 - 0s - 1ms/step - loss: 0.0536 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1328 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04 ## Epoch 174/500 ## 90/90 - 0s - 1ms/step - loss: 0.0550 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1243 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 2.5000e-04 ## Epoch 175/500 ## 90/90 - 0s - 1ms/step - loss: 0.0583 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.1018 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 2.5000e-04 ## Epoch 176/500 ## 90/90 - 0s - 1ms/step - loss: 0.0548 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.1016 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04 ## Epoch 177/500 ## 90/90 - 0s - 1ms/step - loss: 0.0541 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1646 - val_sparse_categorical_accuracy: 0.9320 - learning_rate: 2.5000e-04 ## Epoch 178/500 ## 90/90 - 0s - 1ms/step - loss: 0.0522 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1151 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 2.5000e-04 ## Epoch 179/500 ## 90/90 - 0s - 1ms/step - loss: 0.0538 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1049 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 2.5000e-04 ## Epoch 180/500 ## 90/90 - 0s - 1ms/step - loss: 0.0531 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1178 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 2.5000e-04 ## Epoch 181/500 ## 90/90 - 0s - 1ms/step - loss: 0.0562 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1024 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04 ## Epoch 182/500 ## 90/90 - 0s - 1ms/step - loss: 0.0525 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1600 - val_sparse_categorical_accuracy: 0.9376 - learning_rate: 2.5000e-04 ## Epoch 183/500 ## 90/90 - 0s - 1ms/step - loss: 0.0554 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1395 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 2.5000e-04 ## Epoch 184/500 ## 90/90 - 0s - 1ms/step - loss: 0.0514 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1340 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 2.5000e-04 ## Epoch 185/500 ## 90/90 - 0s - 1ms/step - loss: 0.0531 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1383 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 2.5000e-04 ## Epoch 186/500 ## 90/90 - 0s - 1ms/step - loss: 0.0574 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1325 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 2.5000e-04 ## Epoch 187/500 ## 90/90 - 0s - 1ms/step - loss: 0.0532 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1442 - val_sparse_categorical_accuracy: 0.9487 - learning_rate: 2.5000e-04 ## Epoch 188/500 ## 90/90 - 0s - 1ms/step - loss: 0.0533 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1016 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04 ## Epoch 189/500 ## 90/90 - 0s - 1ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1639 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 2.5000e-04 ## Epoch 190/500 ## 90/90 - 0s - 1ms/step - loss: 0.0583 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.3468 - val_sparse_categorical_accuracy: 0.8863 - learning_rate: 2.5000e-04 ## Epoch 191/500 ## 90/90 - 0s - 1ms/step - loss: 0.0496 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1010 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04 ## Epoch 192/500 ## 90/90 - 0s - 1ms/step - loss: 0.0536 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1100 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 193/500 ## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1388 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 1.2500e-04 ## Epoch 194/500 ## 90/90 - 0s - 1ms/step - loss: 0.0490 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0983 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 195/500 ## 90/90 - 0s - 1ms/step - loss: 0.0499 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1001 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04 ## Epoch 196/500 ## 90/90 - 0s - 1ms/step - loss: 0.0517 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1001 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 197/500 ## 90/90 - 0s - 1ms/step - loss: 0.0489 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 198/500 ## 90/90 - 0s - 1ms/step - loss: 0.0472 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1672 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 1.2500e-04 ## Epoch 199/500 ## 90/90 - 0s - 1ms/step - loss: 0.0508 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0990 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04 ## Epoch 200/500 ## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1290 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04 ## Epoch 201/500 ## 90/90 - 0s - 1ms/step - loss: 0.0480 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1390 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04 ## Epoch 202/500 ## 90/90 - 0s - 1ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1194 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 203/500 ## 90/90 - 0s - 1ms/step - loss: 0.0508 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 204/500 ## 90/90 - 0s - 1ms/step - loss: 0.0449 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1227 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 205/500 ## 90/90 - 0s - 1ms/step - loss: 0.0487 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1194 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04 ## Epoch 206/500 ## 90/90 - 0s - 1ms/step - loss: 0.0491 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1540 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 1.2500e-04 ## Epoch 207/500 ## 90/90 - 0s - 1ms/step - loss: 0.0529 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1023 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04 ## Epoch 208/500 ## 90/90 - 0s - 1ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1295 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 209/500 ## 90/90 - 0s - 1ms/step - loss: 0.0455 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1169 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04 ## Epoch 210/500 ## 90/90 - 0s - 1ms/step - loss: 0.0489 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04 ## Epoch 211/500 ## 90/90 - 0s - 1ms/step - loss: 0.0475 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1001 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 212/500 ## 90/90 - 0s - 1ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.0986 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 213/500 ## 90/90 - 0s - 1ms/step - loss: 0.0483 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0997 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04 ## Epoch 214/500 ## 90/90 - 0s - 1ms/step - loss: 0.0490 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1029 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 215/500 ## 90/90 - 0s - 1ms/step - loss: 0.0464 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1445 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 1.0000e-04 ## Epoch 216/500 ## 90/90 - 0s - 1ms/step - loss: 0.0456 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1137 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04 ## Epoch 217/500 ## 90/90 - 0s - 1ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1345 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 218/500 ## 90/90 - 0s - 1ms/step - loss: 0.0501 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 219/500 ## 90/90 - 0s - 1ms/step - loss: 0.0445 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1149 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 ## Epoch 220/500 ## 90/90 - 0s - 1ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1003 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 220: early stopping"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"evaluate-model-on-test-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Evaluate model on test data","title":"Timeseries classification from scratch","text":"","code":"model <- load_model(\"best_model.keras\")  results <- model |> evaluate(x_test, y_test) ## 42/42 - 0s - 10ms/step - loss: 0.0917 - sparse_categorical_accuracy: 0.9712 str(results) ## List of 2 ##  $ loss                       : num 0.0917 ##  $ sparse_categorical_accuracy: num 0.971 cat(   \"Test accuracy: \", results$sparse_categorical_accuracy, \"\\n\",   \"Test loss: \", results$loss, \"\\n\",   sep = \"\" ) ## Test accuracy: 0.9712121 ## Test loss: 0.09173314"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"plot-the-models-training-history","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Plot the model’s training history","title":"Timeseries classification from scratch","text":"Plot just training validation accuracy: can see training accuracy reaches almost 0.95 100 epochs. However, observing validation accuracy can see network still needs training reaches almost 0.97 validation training accuracy 200 epochs. Beyond 200th epoch, continue training, validation accuracy start decreasing training accuracy continue increasing: model starts overfitting.","code":"plot(history) plot(history, metric = \"sparse_categorical_accuracy\") +   # scale x axis to actual number of epochs run before early stopping   ggplot2::xlim(0, length(history$metrics$loss))"},{"path":"https://keras.posit.co/articles/examples/vision/autoencoder.html","id":"introduction","dir":"Articles > Examples > Vision","previous_headings":"","what":"Introduction","title":"Convolutional autoencoder for image denoising","text":"example demonstrates implement deep convolutional autoencoder image denoising, mapping noisy digits images MNIST dataset clean digits images. implementation based original blog post titled Building Autoencoders Keras François Chollet.","code":""},{"path":"https://keras.posit.co/articles/examples/vision/autoencoder.html","id":"setup","dir":"Articles > Examples > Vision","previous_headings":"","what":"Setup","title":"Convolutional autoencoder for image denoising","text":"","code":"library(keras3)  # Normalizes the supplied array and reshapes it. preprocess <- function(array) {   array_reshape(array/255, c(dim(array)[1], 28, 28, 1)) }  # Adds random noise to each image in the supplied array. noise <- function(array) {   noise_factor <- 0.4   noisy_array <- array + noise_factor * random_normal(dim(array))   op_clip(noisy_array, 0.0, 1.0) }  display <- function(array1, array2) {   n <- 2   indices <- sample.int(dim(array1)[1], n)   images1 <- as.array(array1)[indices, , , ]   images2 <- as.array(array2)[indices, , , ]    par(mfrow = c(2, n), mar = c(0, 0, 0, 0))   for (i in seq_len(n)) {     plot(as.raster(images1[i, , ]))     plot(as.raster(images2[i, , ]))   } }"},{"path":"https://keras.posit.co/articles/examples/vision/autoencoder.html","id":"prepare-the-data","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare the data","title":"Convolutional autoencoder for image denoising","text":"","code":"# Since we only need images from the dataset to encode and decode, we # won't use the labels. c(c(train_data, .), c(test_data, .)) %<-% dataset_mnist()  # Normalize and reshape the data train_data <- preprocess(train_data) test_data <- preprocess(test_data)  # Create a copy of the data with added noise noisy_train_data <- noise(train_data) noisy_test_data <- noise(test_data)  # Display the train data and a version of it with added noise display(train_data, noisy_train_data)"},{"path":"https://keras.posit.co/articles/examples/vision/autoencoder.html","id":"build-the-autoencoder","dir":"Articles > Examples > Vision","previous_headings":"","what":"Build the autoencoder","title":"Convolutional autoencoder for image denoising","text":"going use Functional API build convolutional autoencoder. Now can train autoencoder using train_data input data target. Notice setting validation data using format. Let’s predict test dataset display original image together prediction autoencoder. Notice predictions pretty close original images, although quite . Now know autoencoder works, let’s retrain using noisy data input clean data target. want autoencoder learn denoise images. Let’s now predict noisy data display results autoencoder. Notice autoencoder amazing job removing noise input images.","code":"input <- keras_input(shape = c(28, 28, 1))  # Encoder enc <- input |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3),                 activation = \"relu\", padding = \"same\") |>   layer_max_pooling_2d(pool_size = c(2, 2), padding = \"same\") |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3),                 activation = \"relu\", padding = \"same\") |>   layer_max_pooling_2d(pool_size = c(2, 2), padding = \"same\")  # Decoder dec <- enc |>   layer_conv_2d_transpose(filters = 32, kernel_size = c(3, 3), strides = 2,                           activation = \"relu\", padding = \"same\") |>   layer_conv_2d_transpose(filters = 32, kernel_size = c(3, 3), strides = 2,                           activation = \"relu\", padding = \"same\") |>   layer_conv_2d(filters = 1, kernel_size = c(3, 3),                 activation = \"sigmoid\", padding = \"same\")  # Autoencoder autoencoder <- keras_model(input, dec) autoencoder |> compile(optimizer = \"adam\", loss = \"binary_crossentropy\") autoencoder |> summary() ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 28, 28, 32)     │           320 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 14, 14, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 14, 14, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 7, 7, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 14, 14, 32)     │         9,248 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 28, 28, 32)     │         9,248 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 28, 28, 1)      │           289 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 28,353 (110.75 KB) ##  Trainable params: 28,353 (110.75 KB) ##  Non-trainable params: 0 (0.00 B) autoencoder |> fit(   x = train_data,   y = train_data,   epochs = 50,   batch_size = 128,   shuffle = TRUE,   validation_data = list(test_data, test_data), ) ## Epoch 1/50 ## 469/469 - 4s - 9ms/step - loss: 0.1371 - val_loss: 0.0727 ## Epoch 2/50 ## 469/469 - 1s - 3ms/step - loss: 0.0712 - val_loss: 0.0692 ## Epoch 3/50 ## 469/469 - 1s - 3ms/step - loss: 0.0690 - val_loss: 0.0679 ## Epoch 4/50 ## 469/469 - 1s - 3ms/step - loss: 0.0679 - val_loss: 0.0671 ## Epoch 5/50 ## 469/469 - 1s - 3ms/step - loss: 0.0672 - val_loss: 0.0665 ## Epoch 6/50 ## 469/469 - 1s - 3ms/step - loss: 0.0667 - val_loss: 0.0660 ## Epoch 7/50 ## 469/469 - 1s - 3ms/step - loss: 0.0662 - val_loss: 0.0656 ## Epoch 8/50 ## 469/469 - 1s - 3ms/step - loss: 0.0659 - val_loss: 0.0653 ## Epoch 9/50 ## 469/469 - 1s - 3ms/step - loss: 0.0656 - val_loss: 0.0650 ## Epoch 10/50 ## 469/469 - 1s - 3ms/step - loss: 0.0653 - val_loss: 0.0648 ## Epoch 11/50 ## 469/469 - 1s - 3ms/step - loss: 0.0651 - val_loss: 0.0646 ## Epoch 12/50 ## 469/469 - 1s - 3ms/step - loss: 0.0649 - val_loss: 0.0644 ## Epoch 13/50 ## 469/469 - 1s - 3ms/step - loss: 0.0648 - val_loss: 0.0643 ## Epoch 14/50 ## 469/469 - 1s - 3ms/step - loss: 0.0646 - val_loss: 0.0641 ## Epoch 15/50 ## 469/469 - 1s - 3ms/step - loss: 0.0645 - val_loss: 0.0640 ## Epoch 16/50 ## 469/469 - 1s - 3ms/step - loss: 0.0644 - val_loss: 0.0639 ## Epoch 17/50 ## 469/469 - 1s - 3ms/step - loss: 0.0643 - val_loss: 0.0638 ## Epoch 18/50 ## 469/469 - 1s - 3ms/step - loss: 0.0642 - val_loss: 0.0637 ## Epoch 19/50 ## 469/469 - 1s - 3ms/step - loss: 0.0641 - val_loss: 0.0636 ## Epoch 20/50 ## 469/469 - 1s - 3ms/step - loss: 0.0640 - val_loss: 0.0636 ## Epoch 21/50 ## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0635 ## Epoch 22/50 ## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0635 ## Epoch 23/50 ## 469/469 - 1s - 3ms/step - loss: 0.0638 - val_loss: 0.0634 ## Epoch 24/50 ## 469/469 - 1s - 3ms/step - loss: 0.0637 - val_loss: 0.0634 ## Epoch 25/50 ## 469/469 - 1s - 3ms/step - loss: 0.0637 - val_loss: 0.0633 ## Epoch 26/50 ## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0633 ## Epoch 27/50 ## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0632 ## Epoch 28/50 ## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0632 ## Epoch 29/50 ## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631 ## Epoch 30/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0631 ## Epoch 31/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0631 ## Epoch 32/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0630 ## Epoch 33/50 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0630 ## Epoch 34/50 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0629 ## Epoch 35/50 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0629 ## Epoch 36/50 ## 469/469 - 1s - 3ms/step - loss: 0.0632 - val_loss: 0.0629 ## Epoch 37/50 ## 469/469 - 1s - 3ms/step - loss: 0.0632 - val_loss: 0.0628 ## Epoch 38/50 ## 469/469 - 1s - 3ms/step - loss: 0.0632 - val_loss: 0.0628 ## Epoch 39/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0628 ## Epoch 40/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0628 ## Epoch 41/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0628 ## Epoch 42/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627 ## Epoch 43/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627 ## Epoch 44/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627 ## Epoch 45/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627 ## Epoch 46/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0627 ## Epoch 47/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0627 ## Epoch 48/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626 ## Epoch 49/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626 ## Epoch 50/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626 predictions <- autoencoder |> predict(test_data) ## 313/313 - 0s - 2ms/step display(test_data, predictions) autoencoder |> fit(   x = noisy_train_data,   y = train_data,   epochs = 100,   batch_size = 128,   shuffle = TRUE,   validation_data = list(noisy_test_data, test_data), ) ## Epoch 1/100 ## 469/469 - 1s - 3ms/step - loss: 0.1004 - val_loss: 0.0939 ## Epoch 2/100 ## 469/469 - 1s - 3ms/step - loss: 0.0936 - val_loss: 0.0919 ## Epoch 3/100 ## 469/469 - 1s - 3ms/step - loss: 0.0921 - val_loss: 0.0908 ## Epoch 4/100 ## 469/469 - 1s - 3ms/step - loss: 0.0911 - val_loss: 0.0901 ## Epoch 5/100 ## 469/469 - 1s - 3ms/step - loss: 0.0904 - val_loss: 0.0896 ## Epoch 6/100 ## 469/469 - 1s - 3ms/step - loss: 0.0899 - val_loss: 0.0891 ## Epoch 7/100 ## 469/469 - 1s - 3ms/step - loss: 0.0894 - val_loss: 0.0887 ## Epoch 8/100 ## 469/469 - 1s - 3ms/step - loss: 0.0891 - val_loss: 0.0883 ## Epoch 9/100 ## 469/469 - 1s - 3ms/step - loss: 0.0887 - val_loss: 0.0880 ## Epoch 10/100 ## 469/469 - 1s - 3ms/step - loss: 0.0884 - val_loss: 0.0877 ## Epoch 11/100 ## 469/469 - 1s - 3ms/step - loss: 0.0881 - val_loss: 0.0875 ## Epoch 12/100 ## 469/469 - 1s - 3ms/step - loss: 0.0879 - val_loss: 0.0873 ## Epoch 13/100 ## 469/469 - 1s - 3ms/step - loss: 0.0877 - val_loss: 0.0871 ## Epoch 14/100 ## 469/469 - 1s - 3ms/step - loss: 0.0875 - val_loss: 0.0869 ## Epoch 15/100 ## 469/469 - 1s - 3ms/step - loss: 0.0873 - val_loss: 0.0867 ## Epoch 16/100 ## 469/469 - 1s - 3ms/step - loss: 0.0871 - val_loss: 0.0866 ## Epoch 17/100 ## 469/469 - 1s - 3ms/step - loss: 0.0870 - val_loss: 0.0865 ## Epoch 18/100 ## 469/469 - 1s - 3ms/step - loss: 0.0869 - val_loss: 0.0864 ## Epoch 19/100 ## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0863 ## Epoch 20/100 ## 469/469 - 1s - 3ms/step - loss: 0.0866 - val_loss: 0.0862 ## Epoch 21/100 ## 469/469 - 1s - 3ms/step - loss: 0.0865 - val_loss: 0.0861 ## Epoch 22/100 ## 469/469 - 1s - 3ms/step - loss: 0.0864 - val_loss: 0.0860 ## Epoch 23/100 ## 469/469 - 1s - 3ms/step - loss: 0.0864 - val_loss: 0.0859 ## Epoch 24/100 ## 469/469 - 1s - 3ms/step - loss: 0.0863 - val_loss: 0.0858 ## Epoch 25/100 ## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0858 ## Epoch 26/100 ## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0857 ## Epoch 27/100 ## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0856 ## Epoch 28/100 ## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0856 ## Epoch 29/100 ## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0855 ## Epoch 30/100 ## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0855 ## Epoch 31/100 ## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0854 ## Epoch 32/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854 ## Epoch 33/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854 ## Epoch 34/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0853 ## Epoch 35/100 ## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853 ## Epoch 36/100 ## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853 ## Epoch 37/100 ## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0852 ## Epoch 38/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852 ## Epoch 39/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852 ## Epoch 40/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852 ## Epoch 41/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851 ## Epoch 42/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851 ## Epoch 43/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851 ## Epoch 44/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851 ## Epoch 45/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850 ## Epoch 46/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850 ## Epoch 47/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850 ## Epoch 48/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850 ## Epoch 49/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0850 ## Epoch 50/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849 ## Epoch 51/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849 ## Epoch 52/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849 ## Epoch 53/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849 ## Epoch 54/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849 ## Epoch 55/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849 ## Epoch 56/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848 ## Epoch 57/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848 ## Epoch 58/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848 ## Epoch 59/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848 ## Epoch 60/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 61/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 62/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 63/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 64/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847 ## Epoch 65/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847 ## Epoch 66/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 67/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 68/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 69/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 70/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 71/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846 ## Epoch 72/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846 ## Epoch 73/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846 ## Epoch 74/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 75/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 76/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 77/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 78/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 79/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 80/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 81/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 82/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 83/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 84/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0846 ## Epoch 85/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845 ## Epoch 86/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845 ## Epoch 87/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845 ## Epoch 88/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845 ## Epoch 89/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845 ## Epoch 90/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845 ## Epoch 91/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845 ## Epoch 92/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845 ## Epoch 93/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845 ## Epoch 94/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845 ## Epoch 95/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0845 ## Epoch 96/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0845 ## Epoch 97/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0845 ## Epoch 98/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0845 ## Epoch 99/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0845 ## Epoch 100/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0845 predictions <- autoencoder |> predict(noisy_test_data) ## 313/313 - 0s - 689us/step display(noisy_test_data, predictions)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"setup","dir":"Articles > Examples > Vision","previous_headings":"","what":"Setup","title":"Simple MNIST convnet","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"prepare-the-data","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare the data","title":"Simple MNIST convnet","text":"","code":"# Model / data parameters num_classes <- 10 input_shape <- c(28, 28, 1)  # Load the data and split it between train and test sets c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()  # Scale images to the [0, 1] range x_train <- x_train / 255 x_test <- x_test / 255 # Make sure images have shape (28, 28, 1) x_train <- op_expand_dims(x_train, -1) x_test <- op_expand_dims(x_test, -1)   dim(x_train) ## [1] 60000    28    28     1 dim(x_test) ## [1] 10000    28    28     1 # convert class vectors to binary class matrices y_train <- to_categorical(y_train, num_classes) y_test <- to_categorical(y_test, num_classes)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"build-the-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Build the model","title":"Simple MNIST convnet","text":"","code":"model <- keras_model_sequential(input_shape = input_shape) model |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_flatten() |>   layer_dropout(rate = 0.5) |>   layer_dense(units = num_classes, activation = \"softmax\")  summary(model) ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 26, 26, 32)     │           320 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 13, 13, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 11, 11, 64)     │        18,496 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 5, 5, 64)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ flatten (Flatten)               │ (None, 1600)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 1600)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 10)             │        16,010 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 34,826 (136.04 KB) ##  Trainable params: 34,826 (136.04 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"train-the-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Train the model","title":"Simple MNIST convnet","text":"","code":"batch_size <- 128 epochs <- 15  model |> compile(   loss = \"categorical_crossentropy\",   optimizer = \"adam\",   metrics = \"accuracy\" )  model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = epochs,   validation_split = 0.1 ) ## Epoch 1/15 ## 422/422 - 4s - 10ms/step - accuracy: 0.8895 - loss: 0.3636 - val_accuracy: 0.9787 - val_loss: 0.0793 ## Epoch 2/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9666 - loss: 0.1112 - val_accuracy: 0.9852 - val_loss: 0.0550 ## Epoch 3/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9743 - loss: 0.0824 - val_accuracy: 0.9882 - val_loss: 0.0439 ## Epoch 4/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9786 - loss: 0.0695 - val_accuracy: 0.9897 - val_loss: 0.0399 ## Epoch 5/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9803 - loss: 0.0626 - val_accuracy: 0.9900 - val_loss: 0.0354 ## Epoch 6/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9823 - loss: 0.0557 - val_accuracy: 0.9912 - val_loss: 0.0332 ## Epoch 7/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9837 - loss: 0.0499 - val_accuracy: 0.9918 - val_loss: 0.0310 ## Epoch 8/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9849 - loss: 0.0481 - val_accuracy: 0.9920 - val_loss: 0.0310 ## Epoch 9/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9861 - loss: 0.0444 - val_accuracy: 0.9917 - val_loss: 0.0302 ## Epoch 10/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9863 - loss: 0.0439 - val_accuracy: 0.9913 - val_loss: 0.0297 ## Epoch 11/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9872 - loss: 0.0393 - val_accuracy: 0.9917 - val_loss: 0.0306 ## Epoch 12/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9874 - loss: 0.0372 - val_accuracy: 0.9922 - val_loss: 0.0289 ## Epoch 13/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9888 - loss: 0.0344 - val_accuracy: 0.9918 - val_loss: 0.0293 ## Epoch 14/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9890 - loss: 0.0343 - val_accuracy: 0.9918 - val_loss: 0.0282 ## Epoch 15/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9894 - loss: 0.0322 - val_accuracy: 0.9915 - val_loss: 0.0284"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"evaluate-the-trained-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Evaluate the trained model","title":"Simple MNIST convnet","text":"","code":"score <- model |> evaluate(x_test, y_test, verbose = 0) score ## $accuracy ## [1] 0.9912 ## ## $loss ## [1] 0.02448307"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"introduction","dir":"Articles > Examples > Vision","previous_headings":"","what":"Introduction","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"Siamese Networks neural networks share weights two sister networks, producing embedding vectors respective inputs. supervised similarity learning, networks trained maximize contrast (distance) embeddings inputs different classes, minimizing distance embeddings similar classes, resulting embedding spaces reflect class segmentation training inputs. implementation loosely follows Hadsell-et-al.’06 [1] (see paper mode details) euclidean distance replaced subtraction layer one fully-connect (FC) layer. [1] “Dimensionality Reduction Learning Invariant Mapping” https://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf Gets 98.11% test accuracy 20 epochs. 3 seconds per epoch AMD Ryzen 7 PRO 4750U (CPU)","code":"library(keras3) contrastive_loss <- function(y_true, y_pred) {     # Contrastive loss from Hadsell-et-al.'06     # https://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf     margin = 1     margin_square = op_square(op_maximum(margin - (y_pred), 0))     op_mean((1 - y_true) * op_square(y_pred) + (y_true) * margin_square) }"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"create-pairs-of-images","dir":"Articles > Examples > Vision","previous_headings":"","what":"Create pairs of images","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"train model differentiate digits different classes. example, digit 0 needs differentiated rest digits (1 9), digit 1 - 0 2 9, . carry , select N random images class (example, digit 0) pair N random images another class B (example, digit 1). , can repeat process classes digits (digit 9). paired digit 0 digits, can repeat process remaining classes rest digits (1 9).","code":"create_pairs <- function(x, y) {     # Positive and negative pair creation.     # Alternates between positive and negative pairs.     digit_indices <- tapply(1:length(y), y, list)     y1 <- y     y2 <- sapply(y, function(a) sample(0:9,1,prob=0.1+0.8*(0:9==a)))     idx1 <- 1:nrow(x)     idx2 <- sapply(as.character(y2), function(a) sample(digit_indices[[a]],1))     is_same  <- 1*(y1==y2)     list(pair1 = x[idx1,], pair2 = x[idx2,], y = is_same) }  compute_accuracy <- function(predictions, labels) {     # Compute classification accuracy with a fixed threshold on distances.     mean(labels[predictions > 0.5]) } # the data, shuffled and split between train and test sets mnist   <- dataset_mnist() x_train <- mnist$train$x y_train <- mnist$train$y x_test  <- mnist$test$x y_test  <- mnist$test$y x_train <- array_reshape(x_train, c(nrow(x_train), 784)) x_test  <- array_reshape(x_test, c(nrow(x_test), 784)) x_train <- x_train / 255 x_test  <- x_test / 255  # create training+test positive and negative pairs tr <- create_pairs(x_train, y_train) te <- create_pairs(x_test,  y_test)  names(tr) ## [1] \"pair1\" \"pair2\" \"y\""},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"network-definition","dir":"Articles > Examples > Vision","previous_headings":"","what":"Network definition","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"","code":"# input layers input_dim = 784 input_1 <- layer_input(shape = c(input_dim)) input_2 <- layer_input(shape = c(input_dim))  # definition of the base network that will be shared base_network <- keras_model_sequential() %>%     layer_dense(units = 128, activation = 'relu') %>%     layer_dropout(rate = 0.1) %>%     layer_dense(units = 128, activation = 'relu') %>%     layer_dropout(rate = 0.1) %>%     layer_dense(units = 128, activation = 'relu')  # because we re-use the same instance `base_network`, the weights of # the network will be shared across the two branches branch_1 <- base_network(input_1) branch_2 <- base_network(input_2)  # merging layer out <- layer_subtract(list(branch_1, branch_2)) %>%     layer_dropout(rate = 0.1) %>%     layer_dense(units = 16, activation = 'relu') %>%     layer_dense(1, activation = \"sigmoid\")  # create and compile model model <- keras_model(list(input_1, input_2), out)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"train","dir":"Articles > Examples > Vision","previous_headings":"","what":"Train","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"","code":"model %>% compile(     optimizer = \"rmsprop\",     #loss = \"binary_crossentropy\",     loss = contrastive_loss,     metrics = metric_binary_accuracy )  history <- model %>% fit(     list(tr$pair1, tr$pair2), tr$y,     batch_size = 128,     epochs = 20,     validation_data = list(         list(te$pair1, te$pair2),         te$y     ) ) ## Epoch 1/20 ## 469/469 - 22s - 47ms/step - binary_accuracy: 0.7565 - loss: 0.1644 - val_binary_accuracy: 0.8782 - val_loss: 0.0989 ## Epoch 2/20 ## 469/469 - 1s - 2ms/step - binary_accuracy: 0.8886 - loss: 0.0873 - val_binary_accuracy: 0.9220 - val_loss: 0.0622 ## Epoch 3/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9246 - loss: 0.0590 - val_binary_accuracy: 0.9380 - val_loss: 0.0485 ## Epoch 4/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9456 - loss: 0.0436 - val_binary_accuracy: 0.9493 - val_loss: 0.0398 ## Epoch 5/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9572 - loss: 0.0343 - val_binary_accuracy: 0.9582 - val_loss: 0.0331 ## Epoch 6/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9644 - loss: 0.0283 - val_binary_accuracy: 0.9661 - val_loss: 0.0273 ## Epoch 7/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9707 - loss: 0.0236 - val_binary_accuracy: 0.9658 - val_loss: 0.0271 ## Epoch 8/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9747 - loss: 0.0206 - val_binary_accuracy: 0.9692 - val_loss: 0.0247 ## Epoch 9/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9771 - loss: 0.0184 - val_binary_accuracy: 0.9695 - val_loss: 0.0238 ## Epoch 10/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9795 - loss: 0.0167 - val_binary_accuracy: 0.9697 - val_loss: 0.0244 ## Epoch 11/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9814 - loss: 0.0152 - val_binary_accuracy: 0.9726 - val_loss: 0.0218 ## Epoch 12/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9831 - loss: 0.0139 - val_binary_accuracy: 0.9734 - val_loss: 0.0210 ## Epoch 13/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9841 - loss: 0.0128 - val_binary_accuracy: 0.9748 - val_loss: 0.0202 ## Epoch 14/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9852 - loss: 0.0121 - val_binary_accuracy: 0.9755 - val_loss: 0.0198 ## Epoch 15/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9860 - loss: 0.0112 - val_binary_accuracy: 0.9730 - val_loss: 0.0217 ## Epoch 16/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9868 - loss: 0.0106 - val_binary_accuracy: 0.9758 - val_loss: 0.0204 ## Epoch 17/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9879 - loss: 0.0100 - val_binary_accuracy: 0.9754 - val_loss: 0.0202 ## Epoch 18/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9884 - loss: 0.0095 - val_binary_accuracy: 0.9773 - val_loss: 0.0185 ## Epoch 19/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9883 - loss: 0.0096 - val_binary_accuracy: 0.9747 - val_loss: 0.0207 ## Epoch 20/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9896 - loss: 0.0087 - val_binary_accuracy: 0.9764 - val_loss: 0.0199 plot(history)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"evaluate","dir":"Articles > Examples > Vision","previous_headings":"","what":"Evaluate","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"","code":"# compute final accuracy on training and test sets  tr_pred <- predict(model, list(tr$pair1, tr$pair2))[,1] ## 1875/1875 - 2s - 856us/step tr_acc  <- compute_accuracy(tr_pred, tr$y) te_pred <- predict(model, list(te$pair1, te$pair2))[,1] ## 313/313 - 0s - 1ms/step te_acc  <- compute_accuracy(te_pred, te$y)  sprintf('* Accuracy on training set: %0.2f%%', (100 * tr_acc)) ## [1] \"* Accuracy on training set: 99.71%\" sprintf('* Accuracy on test set: %0.2f%%', (100 * te_acc)) ## [1] \"* Accuracy on test set: 98.54%\""},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"plots","dir":"Articles > Examples > Vision","previous_headings":"","what":"Plots","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"","code":"par(mfrow=c(1,1)) vioplot::vioplot( te_pred ~ te$y ) i=3 visualizePair <- function(i) {     image(rbind(matrix( te$pair1[i,],28,28)[,28:1], matrix( te$pair2[i,],28,28)[,28:1]))     title(paste(\"true:\", te$y[i],\"|  pred:\", round(te_pred[i],5))) } par(mfrow=c(3,3)) lapply(1:9, visualizePair) ## [[1]] ## NULL ## ## [[2]] ## NULL ## ## [[3]] ## NULL ## ## [[4]] ## NULL ## ## [[5]] ## NULL ## ## [[6]] ## NULL ## ## [[7]] ## NULL ## ## [[8]] ## NULL ## ## [[9]] ## NULL"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"download-the-data","dir":"Articles > Examples > Vision","previous_headings":"","what":"Download the data","title":"Image segmentation with a U-Net-like architecture","text":"","code":"options(timeout = 5000) download.file(   \"https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\",   \"datasets/images.tar.gz\" ) download.file(   \"https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\",   \"datasets/annotations.tar.gz\" )  untar(\"datasets/images.tar.gz\", exdir = \"datasets\") untar(\"datasets/annotations.tar.gz\", exdir = \"datasets\")"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"prepare-paths-of-input-images-and-target-segmentation-masks","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare paths of input images and target segmentation masks","title":"Image segmentation with a U-Net-like architecture","text":"","code":"library(keras3) input_dir <- \"datasets/images/\" target_dir <- \"datasets/annotations/trimaps/\" img_size <- c(160, 160) num_classes <- 3 batch_size <- 32  input_img_paths <- fs::dir_ls(input_dir, glob = \"*.jpg\") |> sort() target_img_paths <- fs::dir_ls(target_dir, glob = \"*.png\") |> sort()  cat(\"Number of samples:\", length(input_img_paths), \"\\n\") ## Number of samples: 7390 for (i in 1:10) {   cat(input_img_paths[i], \"|\", target_img_paths[i], \"\\n\") } ## datasets/images/Abyssinian_1.jpg | datasets/annotations/trimaps/Abyssinian_1.png ## datasets/images/Abyssinian_10.jpg | datasets/annotations/trimaps/Abyssinian_10.png ## datasets/images/Abyssinian_100.jpg | datasets/annotations/trimaps/Abyssinian_100.png ## datasets/images/Abyssinian_101.jpg | datasets/annotations/trimaps/Abyssinian_101.png ## datasets/images/Abyssinian_102.jpg | datasets/annotations/trimaps/Abyssinian_102.png ## datasets/images/Abyssinian_103.jpg | datasets/annotations/trimaps/Abyssinian_103.png ## datasets/images/Abyssinian_104.jpg | datasets/annotations/trimaps/Abyssinian_104.png ## datasets/images/Abyssinian_105.jpg | datasets/annotations/trimaps/Abyssinian_105.png ## datasets/images/Abyssinian_106.jpg | datasets/annotations/trimaps/Abyssinian_106.png ## datasets/images/Abyssinian_107.jpg | datasets/annotations/trimaps/Abyssinian_107.png"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"what-does-one-input-image-and-corresponding-segmentation-mask-look-like","dir":"Articles > Examples > Vision","previous_headings":"","what":"What does one input image and corresponding segmentation mask look like?","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Display input image #10 input_img_paths[10] |>   jpeg::readJPEG() |>   as.raster() |>   plot() target_img_paths[10] |>   png::readPNG() |>   magrittr::multiply_by(255)|>   as.raster(max = 3) |>   plot()"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"prepare-dataset-to-load-vectorize-batches-of-data","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare dataset to load & vectorize batches of data","title":"Image segmentation with a U-Net-like architecture","text":"","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")   # Returns a tf_dataset get_dataset <- function(batch_size, img_size, input_img_paths, target_img_paths,                         max_dataset_len = NULL) {    img_size <- as.integer(img_size)    load_img_masks <- function(input_img_path, target_img_path) {     input_img <- input_img_path |>       tf$io$read_file() |>       tf$io$decode_jpeg(channels = 3) |>       tf$image$resize(img_size) |>       tf$image$convert_image_dtype(\"float32\")      target_img <- target_img_path |>       tf$io$read_file() |>       tf$io$decode_png(channels = 1) |>       tf$image$resize(img_size, method = \"nearest\") |>       tf$image$convert_image_dtype(\"uint8\")      # Ground truth labels are 1, 2, 3. Subtract one to make them 0, 1, 2:     target_img <- target_img - 1L      list(input_img, target_img)   }    if (!is.null(max_dataset_len)) {     input_img_paths <- input_img_paths[1:max_dataset_len]     target_img_paths <- target_img_paths[1:max_dataset_len]   }    list(input_img_paths, target_img_paths) |>     tensor_slices_dataset() |>     dataset_map(load_img_masks, num_parallel_calls = tf$data$AUTOTUNE)|>     dataset_batch(batch_size) }"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"prepare-u-net-xception-style-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare U-Net Xception-style model","title":"Image segmentation with a U-Net-like architecture","text":"","code":"get_model <- function(img_size, num_classes) {    inputs <- keras_input(shape = c(img_size, 3))    ### [First half of the network: downsampling inputs] ###    # Entry block   x <- inputs |>     layer_conv_2d(filters = 32, kernel_size = 3, strides = 2, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation(\"relu\")    previous_block_activation <- x  # Set aside residual    for (filters in c(64, 128, 256)) {     x <- x |>       layer_activation(\"relu\") |>       layer_separable_conv_2d(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_activation(\"relu\") |>       layer_separable_conv_2d(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_max_pooling_2d(pool_size = 3, strides = 2, padding = \"same\")      residual <- previous_block_activation |>       layer_conv_2d(filters = filters, kernel_size = 1, strides = 2, padding = \"same\")      x <- layer_add(x, residual)  # Add back residual     previous_block_activation <- x  # Set aside next residual   }    ### [Second half of the network: upsampling inputs] ###    for (filters in c(256, 128, 64, 32)) {     x <- x |>       layer_activation(\"relu\") |>       layer_conv_2d_transpose(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_activation(\"relu\") |>       layer_conv_2d_transpose(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_upsampling_2d(size = 2)      # Project residual     residual <- previous_block_activation |>       layer_upsampling_2d(size = 2) |>       layer_conv_2d(filters = filters, kernel_size = 1, padding = \"same\")      x <- layer_add(x, residual)     # Add back residual     previous_block_activation <- x  # Set aside next residual   }    # Add a per-pixel classification layer   outputs <- x |>     layer_conv_2d(num_classes, 3, activation = \"softmax\", padding = \"same\")    # Define the model   keras_model(inputs, outputs) }  # Build model model <- get_model(img_size, num_classes) summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)      ┃ Output Shape    ┃   Param # ┃ Connected to   ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer       │ (None, 160,     │         0 │ -              │   -   │ ## │ (InputLayer)      │ 160, 3)         │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d (Conv2D)   │ (None, 80, 80,  │       896 │ input_layer[0… │   Y   │ ## │                   │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d[0][0]   │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation        │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_1      │ (None, 80, 80,  │         0 │ activation[0]… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d  │ (None, 80, 80,  │     2,400 │ activation_1[… │   Y   │ ## │ (SeparableConv2D) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       256 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_2      │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 80, 80,  │     4,736 │ activation_2[… │   Y   │ ## │ (SeparableConv2D) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       256 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d     │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_1 (Conv2D) │ (None, 40, 40,  │     2,112 │ activation[0]… │   Y   │ ## │                   │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add (Add)         │ (None, 40, 40,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 64)             │           │ conv2d_1[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_3      │ (None, 40, 40,  │         0 │ add[0][0]      │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 40, 40,  │     8,896 │ activation_3[… │   Y   │ ## │ (SeparableConv2D) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       512 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_4      │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 40, 40,  │    17,664 │ activation_4[… │   Y   │ ## │ (SeparableConv2D) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       512 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d_1   │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_2 (Conv2D) │ (None, 20, 20,  │     8,320 │ add[0][0]      │   Y   │ ## │                   │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_1 (Add)       │ (None, 20, 20,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 128)            │           │ conv2d_2[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_5      │ (None, 20, 20,  │         0 │ add_1[0][0]    │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 20, 20,  │    34,176 │ activation_5[… │   Y   │ ## │ (SeparableConv2D) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │     1,024 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_6      │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 20, 20,  │    68,096 │ activation_6[… │   Y   │ ## │ (SeparableConv2D) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │     1,024 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d_2   │ (None, 10, 10,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_3 (Conv2D) │ (None, 10, 10,  │    33,024 │ add_1[0][0]    │   Y   │ ## │                   │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_2 (Add)       │ (None, 10, 10,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 256)            │           │ conv2d_3[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_7      │ (None, 10, 10,  │         0 │ add_2[0][0]    │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose  │ (None, 10, 10,  │   590,080 │ activation_7[… │   Y   │ ## │ (Conv2DTranspose) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 10, 10,  │     1,024 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_8      │ (None, 10, 10,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 10, 10,  │   590,080 │ activation_8[… │   Y   │ ## │ (Conv2DTranspose) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 10, 10,  │     1,024 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_1   │ (None, 20, 20,  │         0 │ add_2[0][0]    │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d     │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_4 (Conv2D) │ (None, 20, 20,  │    65,792 │ up_sampling2d… │   Y   │ ## │                   │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_3 (Add)       │ (None, 20, 20,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 256)            │           │ conv2d_4[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_9      │ (None, 20, 20,  │         0 │ add_3[0][0]    │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 20, 20,  │   295,040 │ activation_9[… │   Y   │ ## │ (Conv2DTranspose) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │       512 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_10     │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 20, 20,  │   147,584 │ activation_10… │   Y   │ ## │ (Conv2DTranspose) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │       512 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_3   │ (None, 40, 40,  │         0 │ add_3[0][0]    │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_2   │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_5 (Conv2D) │ (None, 40, 40,  │    32,896 │ up_sampling2d… │   Y   │ ## │                   │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_4 (Add)       │ (None, 40, 40,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 128)            │           │ conv2d_5[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_11     │ (None, 40, 40,  │         0 │ add_4[0][0]    │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 40, 40,  │    73,792 │ activation_11… │   Y   │ ## │ (Conv2DTranspose) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       256 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_12     │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 40, 40,  │    36,928 │ activation_12… │   Y   │ ## │ (Conv2DTranspose) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       256 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_5   │ (None, 80, 80,  │         0 │ add_4[0][0]    │   -   │ ## │ (UpSampling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_4   │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_6 (Conv2D) │ (None, 80, 80,  │     8,256 │ up_sampling2d… │   Y   │ ## │                   │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_5 (Add)       │ (None, 80, 80,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 64)             │           │ conv2d_6[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_13     │ (None, 80, 80,  │         0 │ add_5[0][0]    │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 80, 80,  │    18,464 │ activation_13… │   Y   │ ## │ (Conv2DTranspose) │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_14     │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 80, 80,  │     9,248 │ activation_14… │   Y   │ ## │ (Conv2DTranspose) │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_7   │ (None, 160,     │         0 │ add_5[0][0]    │   -   │ ## │ (UpSampling2D)    │ 160, 64)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_6   │ (None, 160,     │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 160, 32)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_7 (Conv2D) │ (None, 160,     │     2,080 │ up_sampling2d… │   Y   │ ## │                   │ 160, 32)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_6 (Add)       │ (None, 160,     │         0 │ up_sampling2d… │   -   │ ## │                   │ 160, 32)        │           │ conv2d_7[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_8 (Conv2D) │ (None, 160,     │       867 │ add_6[0][0]    │   Y   │ ## │                   │ 160, 3)         │           │                │       │ ## └───────────────────┴─────────────────┴───────────┴────────────────┴───────┘ ##  Total params: 2,058,979 (7.85 MB) ##  Trainable params: 2,055,203 (7.84 MB) ##  Non-trainable params: 3,776 (14.75 KB)"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"set-aside-a-validation-split","dir":"Articles > Examples > Vision","previous_headings":"","what":"Set aside a validation split","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Split our img paths into a training and a validation set val_samples <- 1000 val_samples <- sample.int(length(input_img_paths), val_samples)  train_input_img_paths <- input_img_paths[-val_samples] train_target_img_paths <- target_img_paths[-val_samples]  val_input_img_paths <- input_img_paths[val_samples] val_target_img_paths <- target_img_paths[val_samples]  # Instantiate dataset for each split # Limit input files in `max_dataset_len` for faster epoch training time. # Remove the `max_dataset_len` arg when running with full dataset. train_dataset <- get_dataset(   batch_size,   img_size,   train_input_img_paths,   train_target_img_paths,   max_dataset_len = 1000 ) valid_dataset <- get_dataset(   batch_size, img_size, val_input_img_paths, val_target_img_paths )"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"train-the-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Train the model","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Configure the model for training. # We use the \"sparse\" version of categorical_crossentropy # because our target data is integers. model |> compile(   optimizer = optimizer_adam(1e-4),   loss = \"sparse_categorical_crossentropy\" )  callbacks <- list(   callback_model_checkpoint(     \"models/oxford_segmentation.keras\", save_best_only = TRUE   ) )  # Train the model, doing validation at the end of each epoch. epochs <- 50 model |> fit(     train_dataset,     epochs=epochs,     validation_data=valid_dataset,     callbacks=callbacks,     verbose=2 ) ## Epoch 1/50 ## 32/32 - 27s - 844ms/step - loss: 1.4284 - val_loss: 1.5502 ## Epoch 2/50 ## 32/32 - 2s - 60ms/step - loss: 0.9221 - val_loss: 1.9881 ## Epoch 3/50 ## 32/32 - 2s - 60ms/step - loss: 0.7764 - val_loss: 2.5123 ## Epoch 4/50 ## 32/32 - 2s - 60ms/step - loss: 0.7200 - val_loss: 3.0148 ## Epoch 5/50 ## 32/32 - 2s - 60ms/step - loss: 0.6848 - val_loss: 3.2898 ## Epoch 6/50 ## 32/32 - 2s - 60ms/step - loss: 0.6556 - val_loss: 3.4525 ## Epoch 7/50 ## 32/32 - 2s - 60ms/step - loss: 0.6302 - val_loss: 3.5625 ## Epoch 8/50 ## 32/32 - 2s - 60ms/step - loss: 0.6082 - val_loss: 3.6537 ## Epoch 9/50 ## 32/32 - 2s - 60ms/step - loss: 0.5894 - val_loss: 3.7334 ## Epoch 10/50 ## 32/32 - 2s - 60ms/step - loss: 0.5726 - val_loss: 3.7954 ## Epoch 11/50 ## 32/32 - 2s - 60ms/step - loss: 0.5566 - val_loss: 3.8266 ## Epoch 12/50 ## 32/32 - 2s - 60ms/step - loss: 0.5407 - val_loss: 3.8028 ## Epoch 13/50 ## 32/32 - 2s - 60ms/step - loss: 0.5241 - val_loss: 3.7225 ## Epoch 14/50 ## 32/32 - 2s - 60ms/step - loss: 0.5063 - val_loss: 3.5891 ## Epoch 15/50 ## 32/32 - 2s - 60ms/step - loss: 0.4862 - val_loss: 3.4399 ## Epoch 16/50 ## 32/32 - 2s - 60ms/step - loss: 0.4640 - val_loss: 3.2488 ## Epoch 17/50 ## 32/32 - 2s - 60ms/step - loss: 0.4398 - val_loss: 2.9895 ## Epoch 18/50 ## 32/32 - 2s - 60ms/step - loss: 0.4141 - val_loss: 2.7040 ## Epoch 19/50 ## 32/32 - 2s - 60ms/step - loss: 0.3877 - val_loss: 2.3552 ## Epoch 20/50 ## 32/32 - 2s - 60ms/step - loss: 0.3622 - val_loss: 1.9751 ## Epoch 21/50 ## 32/32 - 2s - 60ms/step - loss: 0.3391 - val_loss: 1.6415 ## Epoch 22/50 ## 32/32 - 2s - 65ms/step - loss: 0.3201 - val_loss: 1.3455 ## Epoch 23/50 ## 32/32 - 2s - 65ms/step - loss: 0.3076 - val_loss: 1.1034 ## Epoch 24/50 ## 32/32 - 2s - 65ms/step - loss: 0.3076 - val_loss: 1.0204 ## Epoch 25/50 ## 32/32 - 2s - 65ms/step - loss: 0.3429 - val_loss: 0.9294 ## Epoch 26/50 ## 32/32 - 2s - 60ms/step - loss: 0.3633 - val_loss: 1.0385 ## Epoch 27/50 ## 32/32 - 2s - 65ms/step - loss: 0.3294 - val_loss: 0.8552 ## Epoch 28/50 ## 32/32 - 2s - 60ms/step - loss: 0.2888 - val_loss: 0.9904 ## Epoch 29/50 ## 32/32 - 2s - 60ms/step - loss: 0.2722 - val_loss: 1.1511 ## Epoch 30/50 ## 32/32 - 2s - 60ms/step - loss: 0.2675 - val_loss: 1.1777 ## Epoch 31/50 ## 32/32 - 2s - 60ms/step - loss: 0.2710 - val_loss: 1.1230 ## Epoch 32/50 ## 32/32 - 2s - 60ms/step - loss: 0.2818 - val_loss: 1.1107 ## Epoch 33/50 ## 32/32 - 2s - 60ms/step - loss: 0.3111 - val_loss: 1.2046 ## Epoch 34/50 ## 32/32 - 2s - 60ms/step - loss: 0.3025 - val_loss: 1.2817 ## Epoch 35/50 ## 32/32 - 2s - 60ms/step - loss: 0.2902 - val_loss: 1.0870 ## Epoch 36/50 ## 32/32 - 2s - 60ms/step - loss: 0.2812 - val_loss: 0.9639 ## Epoch 37/50 ## 32/32 - 2s - 60ms/step - loss: 0.2769 - val_loss: 1.3830 ## Epoch 38/50 ## 32/32 - 2s - 60ms/step - loss: 0.2629 - val_loss: 1.0656 ## Epoch 39/50 ## 32/32 - 2s - 60ms/step - loss: 0.2439 - val_loss: 1.2142 ## Epoch 40/50 ## 32/32 - 2s - 60ms/step - loss: 0.2417 - val_loss: 1.1791 ## Epoch 41/50 ## 32/32 - 2s - 60ms/step - loss: 0.2437 - val_loss: 1.3024 ## Epoch 42/50 ## 32/32 - 2s - 60ms/step - loss: 0.2340 - val_loss: 1.5025 ## Epoch 43/50 ## 32/32 - 2s - 60ms/step - loss: 0.2330 - val_loss: 1.2029 ## Epoch 44/50 ## 32/32 - 2s - 60ms/step - loss: 0.2228 - val_loss: 1.1434 ## Epoch 45/50 ## 32/32 - 2s - 60ms/step - loss: 0.2169 - val_loss: 1.1099 ## Epoch 46/50 ## 32/32 - 2s - 60ms/step - loss: 0.2116 - val_loss: 1.1009 ## Epoch 47/50 ## 32/32 - 2s - 60ms/step - loss: 0.2059 - val_loss: 1.0853 ## Epoch 48/50 ## 32/32 - 2s - 60ms/step - loss: 0.2004 - val_loss: 1.1272 ## Epoch 49/50 ## 32/32 - 2s - 60ms/step - loss: 0.1924 - val_loss: 1.0651 ## Epoch 50/50 ## 32/32 - 2s - 60ms/step - loss: 0.1844 - val_loss: 1.1039"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"visualize-predictions","dir":"Articles > Examples > Vision","previous_headings":"","what":"Visualize predictions","title":"Image segmentation with a U-Net-like architecture","text":"","code":"model <- load_model(\"models/oxford_segmentation.keras\") # Generate predictions for all images in the validation set val_dataset <- get_dataset(   batch_size, img_size, val_input_img_paths, val_target_img_paths ) val_preds <- predict(model, val_dataset) ## 32/32 - 3s - 83ms/step display_mask <- function(i) {   # Quick utility to display a model's prediction.   mask <- val_preds[i,,,] %>%     apply(c(1,2), which.max) %>%     array_reshape(dim = c(img_size, 1))   mask <- abind::abind(mask, mask, mask, along = 3)   plot(as.raster(mask, max = 3)) }  # Display results for validation image #10 i <- 10  par(mfrow = c(1, 3)) # Display input image input_img_paths[i] |>   jpeg::readJPEG() |>   as.raster() |>   plot()  # Display ground-truth target mask target_img_paths[i] |>   png::readPNG() |>   magrittr::multiply_by(255)|>   as.raster(max = 3) |>   plot()  # Display mask predicted by our model display_mask(i)  # Note that the model only sees inputs at 150x150."},{"path":"https://keras.posit.co/articles/functional_api.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"The Functional API","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"The Functional API","text":"Keras functional API way create models flexible sequential API. functional API can handle models non-linear topology, shared layers, even multiple inputs outputs. main idea deep learning model usually directed acyclic graph (DAG) layers. functional API way build graphs layers. Consider following model: basic graph three layers. build model using functional API, start creating input node: shape data set 784-dimensional vector. batch size always omitted since shape sample specified. , example, image input shape (32, 32, 3), use: inputs returned contains information shape dtype input data feed model. ’s shape: ’s dtype: create new node graph layers calling layer inputs object: “layer call” action like drawing arrow “inputs” layer created. ’re “passing” inputs dense layer, get x output. Let’s add layers graph layers: point, can create Model specifying inputs outputs graph layers: Let’s check model summary looks like: can also plot model graph:  , optionally, display input output shapes layer plotted graph:  figure code almost identical. code version, connection arrows replaced call operation. “graph layers” intuitive mental image deep learning model, functional API way create models closely mirrors .","code":"(input: 784-dimensional vectors)        ↧ [Dense (64 units, relu activation)]        ↧ [Dense (64 units, relu activation)]        ↧ [Dense (10 units, softmax activation)]        ↧ (output: logits of a probability distribution over 10 classes) inputs <- keras_input(shape = c(784)) # Just for demonstration purposes. img_inputs <- keras_input(shape = c(32, 32, 3)) shape(inputs) ## shape(NA, 784) inputs$dtype ## [1] \"float32\" dense <- layer_dense(units = 64, activation=\"relu\") x <- dense(inputs) outputs <- x |>   layer_dense(units = 64, activation = \"relu\") |>   layer_dense(units = 10) model <- keras_model(inputs = inputs, outputs = outputs, name = \"mnist_model\") summary(model) ## Model: \"mnist_model\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, 784)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 64)             │        50,240 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_1 (Dense)                 │ (None, 64)             │         4,160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 10)             │           650 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 55,050 (215.04 KB) ##  Trainable params: 55,050 (215.04 KB) ##  Non-trainable params: 0 (0.00 B) plot(model) plot(model, show_shapes = TRUE)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"training-evaluation-and-inference","dir":"Articles","previous_headings":"","what":"Training, evaluation, and inference","title":"The Functional API","text":"Training, evaluation, inference work exactly way models built using functional API Sequential models. Model class offers built-training loop (fit() method) built-evaluation loop (evaluate() method). Note can easily customize loops implement training routines beyond supervised learning (e.g. GANs). , load MNIST image data, reshape vectors, fit model data (monitoring performance validation split), evaluate model test data: reading, see training evaluation guide.","code":"c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()  x_train <- array_reshape(x_train, c(60000, 784)) / 255 x_test <- array_reshape(x_test, c(10000, 784)) / 255  model |> compile(   loss = loss_sparse_categorical_crossentropy(from_logits = TRUE),   optimizer = optimizer_rmsprop(),   metrics = \"accuracy\" )  history <- model |> fit(     x_train, y_train, batch_size = 64, epochs = 2, validation_split = 0.2 ) ## Epoch 1/2 ## 750/750 - 2s - 3ms/step - accuracy: 0.8979 - loss: 0.3540 - val_accuracy: 0.9448 - val_loss: 0.1903 ## Epoch 2/2 ## 750/750 - 1s - 920us/step - accuracy: 0.9509 - loss: 0.1635 - val_accuracy: 0.9597 - val_loss: 0.1397 test_scores <- model |> evaluate(x_test, y_test, verbose=2) ## 313/313 - 1s - 2ms/step - accuracy: 0.9595 - loss: 0.1328 cat(\"Test loss:\", test_scores$loss, \"\\n\") cat(\"Test accuracy:\", test_scores$accuracy, \"\\n\") ## Test loss: 0.132778 ## Test accuracy: 0.9595"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"save-and-serialize","dir":"Articles","previous_headings":"","what":"Save and serialize","title":"The Functional API","text":"Saving model serialization work way models built using functional API Sequential models. standard way save functional model call model.save() save entire model single file. can later recreate model file, even code built model longer available. saved file includes : - model architecture - model weight values (learned training) - model training config, (passed compile()) - optimizer state, (restart training left ) details, read model serialization & saving guide.","code":"model |> save_model(\"my_model.keras\") rm(model) # Recreate the exact same model purely from the file: model <- load_model(\"my_model.keras\")"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"use-the-same-graph-of-layers-to-define-multiple-models","dir":"Articles","previous_headings":"","what":"Use the same graph of layers to define multiple models","title":"The Functional API","text":"functional API, models created specifying inputs outputs graph layers. means single graph layers can used generate multiple models. example , use stack layers instantiate two models: encoder model turns image inputs 16-dimensional vectors, end--end autoencoder model training. , decoding architecture strictly symmetrical encoding architecture, output shape input shape (28, 28, 1). reverse conv_2d layer conv_2d_transpose layer, reverse max_pooling_2d layer upsampling_2d layer.","code":"encoder_input <- keras_input(shape = c(28, 28, 1), name=\"img\") encoder_output <- encoder_input |>   layer_conv_2d(16, 3, activation = \"relu\") |>   layer_conv_2d(32, 3, activation = \"relu\") |>   layer_max_pooling_2d(3) |>   layer_conv_2d(32, 3, activation = \"relu\") |>   layer_conv_2d(16, 3, activation = \"relu\") |>   layer_global_max_pooling_2d()  encoder <- keras_model(encoder_input, encoder_output, name=\"encoder\") summary(encoder) ## Model: \"encoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)                │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 26, 26, 16)     │           160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 24, 24, 32)     │         4,640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 8, 8, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 6, 6, 32)       │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_3 (Conv2D)               │ (None, 4, 4, 16)       │         4,624 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d            │ (None, 16)             │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 18,672 (72.94 KB) ##  Trainable params: 18,672 (72.94 KB) ##  Non-trainable params: 0 (0.00 B) decoder_output <- encoder_output |>   layer_reshape(c(4, 4, 1)) |>   layer_conv_2d_transpose(16, 3, activation = \"relu\") |>   layer_conv_2d_transpose(32, 3, activation = \"relu\") |>   layer_upsampling_2d(3) |>   layer_conv_2d_transpose(16, 3, activation = \"relu\") |>   layer_conv_2d_transpose(1, 3, activation = \"relu\")  autoencoder <- keras_model(encoder_input, decoder_output, name=\"autoencoder\") summary(autoencoder) ## Model: \"autoencoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)                │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 26, 26, 16)     │           160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 24, 24, 32)     │         4,640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 8, 8, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 6, 6, 32)       │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_3 (Conv2D)               │ (None, 4, 4, 16)       │         4,624 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d            │ (None, 16)             │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ reshape (Reshape)               │ (None, 4, 4, 1)        │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 6, 6, 16)       │           160 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 8, 8, 32)       │         4,640 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ up_sampling2d (UpSampling2D)    │ (None, 24, 24, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_2              │ (None, 26, 26, 16)     │         4,624 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_3              │ (None, 28, 28, 1)      │           145 │ ## │ (Conv2DTranspose)               │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 28,241 (110.32 KB) ##  Trainable params: 28,241 (110.32 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"all-models-are-callable-just-like-layers","dir":"Articles","previous_headings":"","what":"All models are callable, just like layers","title":"The Functional API","text":"can treat model layer invoking Input output another layer. calling model aren’t just reusing architecture model, ’re also reusing weights. see action, ’s different take autoencoder example creates encoder model, decoder model, chains two calls obtain autoencoder model: can see, model can nested: model can contain sub-models (since model just like layer). common use case model nesting ensembling. example, ’s ensemble set models single model averages predictions:","code":"encoder_input <- keras_input(shape = c(28, 28, 1), name=\"img\") encoder_output <- encoder_input |>   layer_conv_2d(16, 3, activation = \"relu\") |>   layer_conv_2d(32, 3, activation = \"relu\") |>   layer_max_pooling_2d(3) |>   layer_conv_2d(32, 3, activation = \"relu\") |>   layer_conv_2d(16, 3, activation = \"relu\") |>   layer_global_max_pooling_2d()  encoder <- keras_model(encoder_input, encoder_output, name=\"encoder\") summary(encoder) ## Model: \"encoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)                │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_4 (Conv2D)               │ (None, 26, 26, 16)     │           160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_5 (Conv2D)               │ (None, 24, 24, 32)     │         4,640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 8, 8, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_6 (Conv2D)               │ (None, 6, 6, 32)       │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_7 (Conv2D)               │ (None, 4, 4, 16)       │         4,624 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d_1          │ (None, 16)             │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 18,672 (72.94 KB) ##  Trainable params: 18,672 (72.94 KB) ##  Non-trainable params: 0 (0.00 B) decoder_input <- keras_input(shape = c(16), name = \"encoded_img\") decoder_output <- decoder_input |>   layer_reshape(c(4, 4, 1)) |>   layer_conv_2d_transpose(16, 3, activation = \"relu\") |>   layer_conv_2d_transpose(32, 3, activation = \"relu\") |>   layer_upsampling_2d(3) |>   layer_conv_2d_transpose(16, 3, activation = \"relu\") |>   layer_conv_2d_transpose(1, 3, activation = \"relu\")  decoder <- keras_model(decoder_input, decoder_output, name = \"decoder\") summary(decoder) ## Model: \"decoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ encoded_img (InputLayer)        │ (None, 16)             │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ reshape_1 (Reshape)             │ (None, 4, 4, 1)        │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_4              │ (None, 6, 6, 16)       │           160 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_5              │ (None, 8, 8, 32)       │         4,640 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ up_sampling2d_1 (UpSampling2D)  │ (None, 24, 24, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_6              │ (None, 26, 26, 16)     │         4,624 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_7              │ (None, 28, 28, 1)      │           145 │ ## │ (Conv2DTranspose)               │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 9,569 (37.38 KB) ##  Trainable params: 9,569 (37.38 KB) ##  Non-trainable params: 0 (0.00 B) autoencoder_input <- keras_input(shape = c(28, 28, 1), name = \"img\") encoded_img <- encoder(autoencoder_input) decoded_img <- decoder(encoded_img) autoencoder <- keras_model(autoencoder_input, decoded_img,                            name = \"autoencoder\") summary(autoencoder) ## Model: \"autoencoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)                │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ encoder (Functional)            │ (None, 16)             │        18,672 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ decoder (Functional)            │ (None, 28, 28, 1)      │         9,569 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 28,241 (110.32 KB) ##  Trainable params: 28,241 (110.32 KB) ##  Non-trainable params: 0 (0.00 B) get_model <- function() {   inputs <- keras_input(shape = 128)   outputs <- inputs |> layer_dense(1)   keras_model(inputs, outputs) }  model1 <- get_model() model2 <- get_model() model3 <- get_model()  inputs <- keras_input(shape = 128) y1 <- model1(inputs) y2 <- model2(inputs) y3 <- model3(inputs) outputs <- layer_average(list(y1, y2, y3)) ensemble_model <- keras_model(inputs = inputs, outputs = outputs)"},{"path":[]},{"path":"https://keras.posit.co/articles/functional_api.html","id":"models-with-multiple-inputs-and-outputs","dir":"Articles","previous_headings":"Manipulate complex graph topologies","what":"Models with multiple inputs and outputs","title":"The Functional API","text":"functional API makes easy manipulate multiple inputs outputs. handled Sequential API. example, ’re building system ranking customer issue tickets priority routing correct department, model three inputs: title ticket (text input), text body ticket (text input), tags added user (categorical input) model two outputs: priority score 0 1 (scalar sigmoid output), department handle ticket (softmax output set departments). can build model lines functional API: Now plot model:  compiling model, can assign different losses output. can even assign different weights loss – modulate contribution total training loss. Since output layers different names, also specify losses loss weights corresponding layer names: Train model passing lists NumPy arrays inputs targets: calling fit Dataset object, yield either list lists like list(list(title_data, body_data, tags_data), list(priority_targets, dept_targets)) list named lists like list(list(title = title_data, body = body_data, tags = tags_data), list(priority = priority_targets, department = dept_targets)). detailed explanation, refer training evaluation guide.","code":"num_tags <- 12  # Number of unique issue tags num_words <- 10000  # Size of vocabulary obtained when preprocessing text data num_departments <- 4  # Number of departments for predictions  title_input <- # Variable-length sequence of ints   keras_input(shape(NA), name = \"title\") body_input <-  # Variable-length sequence of ints   keras_input(shape(NA), name = \"body\") tags_input <-  # Binary vectors of size `num_tags`   keras_input(shape = num_tags, name = \"tags\")  # Embed each word in the title into a 64-dimensional vector title_features <- layer_embedding(title_input, num_words, 64) # Embed each word in the text into a 64-dimensional vector body_features <- layer_embedding(body_input, num_words, 64)  # Reduce sequence of embedded words in the title # into a single 128-dimensional vector title_features <- layer_lstm(title_features, 128) # Reduce sequence of embedded words in the body # into a single 32-dimensional vector body_features <- layer_lstm(body_features, 32)  # Merge all available features into a single large vector via concatenation x <- layer_concatenate(title_features, body_features, tags_input)  # Stick a logistic regression for priority prediction on top of the features priority_pred <- layer_dense(x, 1, name = \"priority\")  # Stick a department classifier on top of the features department_pred <- layer_dense(x, num_departments, name = \"department\")  # Instantiate an end-to-end model predicting both priority and department model <- keras_model(   inputs = list(title_input, body_input, tags_input),   outputs = list(priority = priority_pred, department = department_pred) ) plot(model, show_shapes = TRUE) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     loss_binary_crossentropy(from_logits = TRUE),     loss_categorical_crossentropy(from_logits = TRUE)   ),   loss_weights = c(1.0, 0.2) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     priority = loss_binary_crossentropy(from_logits = TRUE),     department = loss_categorical_crossentropy(from_logits = TRUE)   ),   loss_weights = list(priority = 1.0, department = 0.2) ) # Dummy input data title_data <- random_integer(c(1280, 10), 0, num_words) body_data <- random_integer(c(1280, 100), 0, num_words) tags_data <- random_integer(c(1280, num_tags), 0, 2)  # Dummy target data priority_targets <- random_normal(c(1280, 1)) dept_targets <- random_integer(c(1280, num_departments), 0, 2)  model |> fit(   list(title = title_data, body = body_data, tags = tags_data),   list(priority = priority_targets, department = dept_targets),   epochs = 2,   batch_size = 32 ) ## Epoch 1/2 ## 40/40 - 3s - 69ms/step - loss: 0.3948 ## Epoch 2/2 ## 40/40 - 0s - 6ms/step - loss: 0.1971"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"a-toy-resnet-model","dir":"Articles","previous_headings":"Manipulate complex graph topologies","what":"A toy ResNet model","title":"The Functional API","text":"addition models multiple inputs outputs, functional API makes easy manipulate non-linear connectivity topologies – models layers connected sequentially, Sequential API handle. common use case residual connections. Let’s build toy ResNet model CIFAR10 demonstrate : Plot model:  Now train model:","code":"inputs <- keras_input(shape = c(32, 32, 3), name = \"img\") block_1_output <- inputs |>   layer_conv_2d(32, kernel_size = 3, activation = \"relu\") |>   layer_conv_2d(64, kernel_size = 3, activation = \"relu\") |>   layer_max_pooling_2d(pool_size = 3)  block_2_output <- block_1_output |>   layer_conv_2d(32, kernel_size = 3, activation = \"relu\", padding = \"same\") |>   layer_conv_2d(64, kernel_size = 3, activation = \"relu\", padding = \"same\") |>   layer_add(block_1_output)  block_3_output <- block_2_output |>   layer_conv_2d(64, kernel_size = 3, activation = \"relu\", padding = \"same\") |>   layer_conv_2d(64, kernel_size = 3, activation = \"relu\", padding = \"same\") |>   layer_add(block_2_output)  outputs <- block_3_output |>   layer_conv_2d(64, 3, activation = \"relu\") |>   layer_global_average_pooling_2d() |>   layer_dense(256, activation = \"relu\") |>   layer_dropout(0.5) |>   layer_dense(10)  model <- keras_model(inputs, outputs, name = \"toy_resnet\") summary(model) ## Model: \"toy_resnet\" ## ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)    │ (None, 32, 32, 3) │          0 │ -                 │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_8 (Conv2D)   │ (None, 30, 30,    │        896 │ img[0][0]         │ ## │                     │ 32)               │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_9 (Conv2D)   │ (None, 28, 28,    │     18,496 │ conv2d_8[0][0]    │ ## │                     │ 64)               │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ max_pooling2d_2     │ (None, 9, 9, 64)  │          0 │ conv2d_9[0][0]    │ ## │ (MaxPooling2D)      │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_10 (Conv2D)  │ (None, 9, 9, 32)  │     18,464 │ max_pooling2d_2[… │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_11 (Conv2D)  │ (None, 9, 9, 64)  │     18,496 │ conv2d_10[0][0]   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ add (Add)           │ (None, 9, 9, 64)  │          0 │ conv2d_11[0][0],  │ ## │                     │                   │            │ max_pooling2d_2[… │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_12 (Conv2D)  │ (None, 9, 9, 64)  │     36,928 │ add[0][0]         │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_13 (Conv2D)  │ (None, 9, 9, 64)  │     36,928 │ conv2d_12[0][0]   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ add_1 (Add)         │ (None, 9, 9, 64)  │          0 │ conv2d_13[0][0],  │ ## │                     │                   │            │ add[0][0]         │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_14 (Conv2D)  │ (None, 7, 7, 64)  │     36,928 │ add_1[0][0]       │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ global_average_poo… │ (None, 64)        │          0 │ conv2d_14[0][0]   │ ## │ (GlobalAveragePool… │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ dense_6 (Dense)     │ (None, 256)       │     16,640 │ global_average_p… │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ dropout (Dropout)   │ (None, 256)       │          0 │ dense_6[0][0]     │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ dense_7 (Dense)     │ (None, 10)        │      2,570 │ dropout[0][0]     │ ## └─────────────────────┴───────────────────┴────────────┴───────────────────┘ ##  Total params: 186,346 (727.91 KB) ##  Trainable params: 186,346 (727.91 KB) ##  Non-trainable params: 0 (0.00 B) plot(model, show_shapes = TRUE) c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_cifar10()  x_train <- x_train / 255.0 x_test <- x_test / 255.0  model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = loss_sparse_categorical_crossentropy(from_logits = TRUE),   metrics = \"acc\" ) # We restrict the data to the first 1000 samples so as to limit the # guide render time. # Try to train on the entire dataset until convergence! model |> fit(   x_train[1:1000, , , ],   y_train[1:1000, ],   batch_size = 64,   epochs = 1,   validation_split = 0.2 ) ## 13/13 - 6s - 478ms/step - acc: 0.1250 - loss: 2.2998 - val_acc: 0.1250 - val_loss: 2.2939"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"shared-layers","dir":"Articles","previous_headings":"","what":"Shared layers","title":"The Functional API","text":"Another good use functional API models use shared layers. Shared layers layer instances reused multiple times model – learn features correspond multiple paths graph--layers. Shared layers often used encode inputs similar spaces (say, two different pieces text feature similar vocabulary). enable sharing information across different inputs, make possible train model less data. given word seen one inputs, benefit processing inputs pass shared layer. share layer functional API, call layer instance multiple times. instance, ’s Embedding layer shared across two different text inputs:","code":"# Embedding for 1000 unique words mapped to 128-dimensional vectors shared_embedding <- layer_embedding(input_dim = 1000, output_dim = 128)  # Variable-length sequence of integers text_input_a <- keras_input(shape = shape(NA), dtype=\"int32\")  # Variable-length sequence of integers text_input_b <- keras_input(shape = shape(NA), dtype=\"int32\")  # Reuse the same layer to encode both inputs encoded_input_a <- shared_embedding(text_input_a) encoded_input_b <- shared_embedding(text_input_b)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"extract-and-reuse-nodes-in-the-graph-of-layers","dir":"Articles","previous_headings":"","what":"Extract and reuse nodes in the graph of layers","title":"The Functional API","text":"graph layers manipulating static data structure, can accessed inspected. able plot functional models images. also means can access activations intermediate layers (“nodes” graph) reuse elsewhere – useful something like feature extraction. Let’s look example. VGG19 model weights pretrained ImageNet: intermediate activations model, obtained querying graph data structure: Use features create new feature-extraction model returns values intermediate layer activations: comes handy tasks like neural style transfer, among things.","code":"vgg19 <- application_vgg19() features_list <- lapply(vgg19$layers, function(x) x$output) feat_extraction_model <- keras_model(inputs = vgg19$input,                                      outputs = features_list)  img <- random_normal(c(1, 224, 224, 3)) extracted_features <- feat_extraction_model(img)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"extend-the-api-using-custom-layers","dir":"Articles","previous_headings":"","what":"Extend the API using custom layers","title":"The Functional API","text":"keras includes wide range built-layers, example: Convolutional layers: conv_1d, conv_2d, conv_3d, conv_2d_transpose Pooling layers: max_pooling_1d, max_pooling_2d, max_pooling_3d, average_pooling_3d RNN layers: gru, lstm, conv_lstm_2d batch_normalization, dropout, embedding, etc. don’t find need, ’s easy extend API creating layers. layers subclass Layer class implement: call method, specifies computation done layer. build method, creates weights layer (just style convention since can create weights initialize, well). learn creating layers scratch, read custom layers models guide. following basic implementation layer_dense(): serialization support custom layer, define get_config() method returns constructor arguments layer instance: Optionally, implement class method from_config(cls, config) used recreating layer instance given config dictionary. default implementation from_config :","code":"custom_dense <- Layer(   classname = \"CustomDense\",   initialize = function(units = 32) {     super$initialize()     self$units <- as.integer(units)   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(input_shape[[2]], self$units),       initializer = \"random_normal\",       trainable = TRUE,     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer=\"random_normal\",       trainable = TRUE     )   },   call = function(inputs) {     op_matmul(inputs, self$w) + self$b   } )  inputs <- keras_input(c(4)) outputs <- custom_dense(inputs, 10)  model <- keras_model(inputs, outputs) custom_dense <- Layer(   classname = \"CustomDense\",    initialize = function(units = 32, ...) {     super$initialize()     self$units <- as.integer(units)   },    build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(input_shape[[2]], self$units),       initializer = \"random_normal\",       trainable = TRUE,     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer=\"random_normal\",       trainable = TRUE     )   },    call = function(inputs) {     op_matmul(inputs, self$w) + self$b   },    get_config = function() {     list(units = self$units)   } )  inputs <- keras_input(c(4)) outputs <- custom_dense(inputs, 10)  model <- keras_model(inputs, outputs) config <- get_config(model)  new_model <-   from_config(config, custom_objects = list(CustomDense = custom_dense)) from_config <- function(cls, config) {   do.call(cls, config) }"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"when-to-use-the-functional-api","dir":"Articles","previous_headings":"","what":"When to use the functional API","title":"The Functional API","text":"use Keras functional API create new model, just subclass Model class directly? general, functional API higher-level, easier safer, number features subclassed models support. However, model subclassing provides greater flexibility building models easily expressible directed acyclic graphs layers. example, implement Tree-RNN functional API subclass Model directly. -depth look differences functional API model subclassing, read Symbolic Imperative APIs TensorFlow 2.0?.","code":""},{"path":"https://keras.posit.co/articles/functional_api.html","id":"functional-api-strengths","dir":"Articles","previous_headings":"When to use the functional API","what":"Functional API strengths:","title":"The Functional API","text":"following properties also true Sequential models (also data structures), true subclassed models (R Python (byte)code, data structures).","code":""},{"path":"https://keras.posit.co/articles/functional_api.html","id":"less-verbose","dir":"Articles","previous_headings":"When to use the functional API > Functional API strengths:","what":"Less verbose","title":"The Functional API","text":"super$initialize(...), call = function(...), self$..., etc. Compare: subclassed version:","code":"inputs <- keras_input(shape = shape(32)) outputs <- inputs |>   layer_dense(64, activation = \"relu\") |>   layer_dense(10) mlp <- keras_model(inputs, outputs) MLP <- Model(   classname = \"MLP\",   initialize = function(...) {     super$initialize(...)     self$dense_1 <- layer_dense(units = 64, activation = \"relu\")     self$dense_2 <- layer_dense(units = 10)   },   call = function(inputs) {     inputs |>       self$dense_1() |>       self$dense_2()   } )  # Instantiate the model. mlp <- MLP() # Necessary to create the model's state. # The model doesn't have a state until it's called at least once. out <- mlp(op_zeros(c(1, 32)))"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"model-validation-while-defining-its-connectivity-graph","dir":"Articles","previous_headings":"When to use the functional API > Functional API strengths:","what":"Model validation while defining its connectivity graph","title":"The Functional API","text":"functional API, input specification (shape dtype) created advance (using Input). Every time call layer, layer checks specification passed matches assumptions, raise helpful error message . guarantees model can build functional API run. debugging – convergence-related debugging – happens statically model construction execution time. similar type checking compiler.","code":""},{"path":"https://keras.posit.co/articles/functional_api.html","id":"a-functional-model-is-plottable-and-inspectable","dir":"Articles","previous_headings":"When to use the functional API > Functional API strengths:","what":"A functional model is plottable and inspectable","title":"The Functional API","text":"can plot model graph, can easily access intermediate nodes graph. example, extract reuse activations intermediate layers (seen previous example):","code":"features_list <- lapply(vgg19$layers, function(x) x$output) feat_extraction_model <- keras_model(inputs = vgg19$input,                                      outputs = features_list)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"a-functional-model-can-be-serialized-or-cloned","dir":"Articles","previous_headings":"When to use the functional API > Functional API strengths:","what":"A functional model can be serialized or cloned","title":"The Functional API","text":"functional model data structure rather piece code, safely serializable can saved single file allows recreate exact model without access original code. See serialization & saving guide. serialize subclassed model, necessary implementer specify get_config() from_config() method model level.","code":""},{"path":[]},{"path":"https://keras.posit.co/articles/functional_api.html","id":"it-does-not-support-dynamic-architectures","dir":"Articles","previous_headings":"When to use the functional API > Functional API weakness:","what":"It does not support dynamic architectures","title":"The Functional API","text":"functional API treats models DAGs layers. true deep learning architectures, – example, recursive networks Tree RNNs follow assumption implemented functional API.","code":""},{"path":"https://keras.posit.co/articles/functional_api.html","id":"mix-and-match-api-styles","dir":"Articles","previous_headings":"","what":"Mix-and-match API styles","title":"The Functional API","text":"Choosing functional API Model subclassing isn’t binary decision restricts one category models. models keras API can interact , whether ’re Sequential models, functional models, subclassed models written scratch. can always use functional model Sequential model part subclassed model layer: can use subclassed layer model functional API long implements call method follows one following patterns: call(inputs, ...) – inputs tensor nested structure tensors (e.g. list tensors), ... non-tensor arguments (non-inputs). call(inputs, training = NULL, ...) – training boolean indicating whether layer behave training mode inference mode. call(inputs, mask = NULL, ...) – mask boolean mask tensor (useful RNNs, instance). call(inputs, training = NULL, mask = NULL, ...) – course, can masking training-specific behavior time. Additionally, implement get_config() method custom Layer model, functional models create still serializable cloneable. ’s quick example custom RNN, written scratch, used functional model:","code":"units <- 32 timesteps <- 10 input_dim <- 5  # Define a Functional model inputs <- keras_input(shape(NA, units)) outputs <- inputs |>   layer_global_average_pooling_1d() |>   layer_dense(units = 1)  model <- keras_model(inputs, outputs)  layer_custom_rnn <- Layer(   classname = \"CustomRNN\",   initialize = function(...) {     super$initialize(...)     self$units <- units     self$projection_1 <- layer_dense(units = units, activation = \"tanh\")     self$projection_2 <- layer_dense(units = units, activation = \"tanh\")     self$classifier <- model   },   call = function(inputs, ...) {     outputs <- list()     state <- op_zeros(c(shape(inputs)[[1]], self$units))     for (t in 1:(shape(inputs)[[2]])) {       x <- inputs[, t, ]       h <- self$projection_1(x)       y <- h + self$projection_2(state)       state <- y       outputs[[t]] <- y     }     features <- op_stack(outputs, axis = 2)     self$classifier(features)   } )  rnn <- layer_custom_rnn() out <- rnn(op_zeros(c(1, timesteps, input_dim))) units <- 32 timesteps <- 10 input_dim <- 5 batch_size <- 16  layer_custom_rnn <- Layer(   \"custom_rnn\",   initialize = function(...) {     super$initialize(...)     self$units <- units     self$projection_1 <- layer_dense(units = units, activation = \"tanh\")     self$projection_2 <- layer_dense(units = units, activation = \"tanh\")     self$classifier <- layer_dense(units = 1)   },   call = function(inputs, ...) {     outputs <- list()     state <- op_zeros(c(shape(inputs)[[1]], self$units))     for (t in 1:(shape(inputs)[[2]])) {       x <- inputs[, t, ]       h <- self$projection_1(x)       y <- h + self$projection_2(state)       state <- y       outputs[[t]] <- y     }     features <- op_stack(outputs, axis = 2)     self$classifier(features)   } )  # Note that you specify a static batch size for the inputs with the `batch_shape` # arg, because the inner computation of `layer_custom_rnn()` requires a static batch size # (when you create the `state` zeros tensor). inputs <- keras_input(batch_shape = shape(batch_size, timesteps, input_dim)) outputs <- inputs |>   layer_conv_1d(filters = 32, kernel_size = 3) |>   layer_custom_rnn()  model <- keras_model(inputs, outputs) out <- model(op_zeros(c(1, 10, 5)))"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Getting Started with Keras","text":"Keras high-level neural networks API developed focus enabling fast experimentation. able go idea result least possible delay key good research. Keras following key features: Allows code run CPU GPU, seamlessly. User-friendly API makes easy quickly prototype deep learning models. Built-support convolutional networks (computer vision), recurrent networks (sequence processing), combination . Supports arbitrary network architectures: multi-input multi-output models, layer sharing, model sharing, etc. means Keras appropriate building essentially deep learning model, memory network neural Turing machine. website provides documentation R interface Keras. See main Keras website https://keras.io additional information project.","code":""},{"path":"https://keras.posit.co/articles/getting_started.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Getting Started with Keras","text":"First, install keras R package: install development version : Keras R interface requires backend engine installed. TensorFlow default. provide default installation Keras TensorFlow GPU capable, GPU available. want customized installation, e.g. see documentation install_keras() installation section.","code":"install.packages(\"keras3\") remotes::install_github(\"rstudio/keras\") keras3::install_keras(backend = \"tensorflow\")"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"mnist-example","dir":"Articles","previous_headings":"","what":"MNIST Example","title":"Getting Started with Keras","text":"can learn basics Keras walking simple example: recognizing handwritten digits MNIST dataset. MNIST consists 28 x 28 grayscale images handwritten digits like :  dataset also includes labels image, telling us digit . example, labels images 5, 0, 4, 1.","code":""},{"path":"https://keras.posit.co/articles/getting_started.html","id":"preparing-the-data","dir":"Articles","previous_headings":"MNIST Example","what":"Preparing the Data","title":"Getting Started with Keras","text":"MNIST dataset included Keras can accessed using dataset_mnist() function. load dataset create variables test training data: x data 3-d array (images, width, height) grayscale values. prepare data training convert 3-d arrays matrices reshaping width height single dimension (28x28 images flattened length 784 vectors). , convert grayscale values integers ranging 0 255 floating point values ranging 0 1: Note use array_reshape() function rather dim<-() function reshape array. data re-interpreted using row-major semantics (opposed R’s default column-major semantics), turn compatible way numerical libraries called Keras interpret array dimensions. y data integer vector values ranging 0 9. prepare data training one-hot encode vectors binary class matrices using Keras to_categorical() function:","code":"library(keras3) mnist <- dataset_mnist() x_train <- mnist$train$x y_train <- mnist$train$y x_test <- mnist$test$x y_test <- mnist$test$y # reshape x_train <- array_reshape(x_train, c(nrow(x_train), 784)) x_test <- array_reshape(x_test, c(nrow(x_test), 784)) # rescale x_train <- x_train / 255 x_test <- x_test / 255 y_train <- to_categorical(y_train, 10) y_test <- to_categorical(y_test, 10)"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"defining-the-model","dir":"Articles","previous_headings":"MNIST Example","what":"Defining the Model","title":"Getting Started with Keras","text":"core data structure Keras model, way organize layers. simplest type model Sequential model, linear stack layers. begin creating sequential model adding layers using pipe (|>) operator: input_shape argument first layer specifies shape input data (length 784 numeric vector representing grayscale image). final layer outputs length 10 numeric vector (probabilities digit) using softmax activation function. Use summary() function print details model: Next, compile model appropriate loss function, optimizer, metrics:","code":"model <- keras_model_sequential(input_shape = c(784)) model |>   layer_dense(units = 256, activation = 'relu') |>   layer_dropout(rate = 0.4) |>   layer_dense(units = 128, activation = 'relu') |>   layer_dropout(rate = 0.3) |>   layer_dense(units = 10, activation = 'softmax') summary(model) ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense (Dense)                   │ (None, 256)            │       200,960 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 256)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_1 (Dense)                 │ (None, 128)            │        32,896 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, 128)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 10)             │         1,290 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 235,146 (918.54 KB) ##  Trainable params: 235,146 (918.54 KB) ##  Non-trainable params: 0 (0.00 B) plot(model) model |> compile(   loss = 'categorical_crossentropy',   optimizer = optimizer_rmsprop(),   metrics = c('accuracy') )"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"training-and-evaluation","dir":"Articles","previous_headings":"MNIST Example","what":"Training and Evaluation","title":"Getting Started with Keras","text":"Use fit() function train model 30 epochs using batches 128 images: history object returned fit() includes loss accuracy metrics can plot: Evaluate model’s performance test data: Generate predictions new data: Keras provides vocabulary building deep learning models simple, elegant, intuitive. Building question answering system, image classification model, neural Turing machine, model just straightforward.","code":"history <- model |> fit(   x_train, y_train,   epochs = 30, batch_size = 128,   validation_split = 0.2 ) plot(history) model |> evaluate(x_test, y_test) ## 313/313 - 1s - 2ms/step - accuracy: 0.9808 - loss: 0.0869 ## $accuracy ## [1] 0.9808 ## ## $loss ## [1] 0.08689082 probs <- model |> predict(x_test) ## 313/313 - 0s - 1ms/step max.col(probs) - 1L ##   [1] 7 2 1 0 4 1 4 9 6 9 0 6 9 0 1 5 9 7 3 4 9 6 6 5 4 0 7 4 0 1 3 1 3 4 7 ##  [36] 2 7 1 2 1 1 7 4 2 3 5 1 2 4 4 6 3 5 5 6 0 4 1 9 5 7 8 9 3 7 4 6 4 3 0 ##  [71] 7 0 2 9 1 7 3 2 9 7 7 6 2 7 8 4 7 3 6 1 3 6 9 3 1 4 1 7 6 9 ##  [ reached getOption(\"max.print\") -- omitted 9900 entries ]"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"deep-learning-with-r-book","dir":"Articles","previous_headings":"MNIST Example","what":"Deep Learning with R Book","title":"Getting Started with Keras","text":"want comprehensive introduction Keras concepts practice deep learning, recommend Deep Learning R, 2nd Edition book Manning. book collaboration François Chollet, creator (Python) Keras, J.J. Allaire, wrote original R interface Keras, Tomasz Kalinowski, maintainer R interface Keras. book presumes significant knowledge machine learning deep learning, goes way basic theory advanced practical applications, using R interface Keras.","code":""},{"path":"https://keras.posit.co/articles/getting_started.html","id":"why-this-name-keras","dir":"Articles","previous_headings":"","what":"Why this name, Keras?","title":"Getting Started with Keras","text":"Keras (κέρας) means horn Greek. reference literary image ancient Greek Latin literature, first found Odyssey, dream spirits (Oneiroi, singular Oneiros) divided deceive men false visions, arrive Earth gate ivory, announce future come pass, arrive gate horn. ’s play words κέρας (horn) / κραίνω (fulfill), ἐλέφας (ivory) / ἐλεφαίρομαι (deceive). Keras initially developed part research effort project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System). “Oneiroi beyond unravelling –can sure tale tell? men look comes pass. Two gates give passage fleeting Oneiroi; one made horn, one ivory. Oneiroi pass sawn ivory deceitful, bearing message fulfilled; come polished horn truth behind , accomplished men see .” Homer, Odyssey 19. 562 ff (Shewring translation).","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Introduction to Keras for engineers","text":"Keras 3 deep learning framework works TensorFlow, JAX, PyTorch interchangeably. notebook walk key Keras 3 workflows. Let’s start installing Keras 3: pip install keras –upgrade –quiet","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Introduction to Keras for engineers","text":"’re going using tensorflow backend – can edit string \"jax\" \"torch\" hit “Restart runtime”, whole notebook run just ! entire guide backend-agnostic.","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(keras3)  # Note that you must configure the backend # before calling any other keras functions. # The backend cannot be changed once the # package is imported. use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"a-first-example-a-mnist-convnet","dir":"Articles","previous_headings":"","what":"A first example: A MNIST convnet","title":"Introduction to Keras for engineers","text":"Let’s start Hello World ML: training convnet classify MNIST digits. ’s data: ’s model. Different model-building options Keras offers include: Sequential API (use ) Functional API (typical) Writing models via subclassing (advanced use cases) ’s model summary: use compile() method specify optimizer, loss function, metrics monitor. Note JAX TensorFlow backends, XLA compilation turned default. Let’s train evaluate model. ’ll set aside validation split 15% data training monitor generalization unseen data. training, saving model end epoch. can also save model latest state like : reload like : Next, can query predictions class probabilities predict(): ’s basics!","code":"# Load the data and split it between train and test sets c(c(x_train, y_train), c(x_test, y_test)) %<-% keras3::dataset_mnist()  # Scale images to the [0, 1] range x_train <- x_train / 255 x_test <- x_test / 255 # Make sure images have shape (28, 28, 1) x_train <- op_expand_dims(x_train, -1) x_test <- op_expand_dims(x_test, -1)  dim(x_train) ## [1] 60000    28    28     1 dim(x_test) ## [1] 10000    28    28     1 # Model parameters num_classes <- 10 input_shape <- c(28, 28, 1)  model <- keras_model_sequential(input_shape = input_shape) model |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = \"relu\") |>   layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = \"relu\") |>   layer_global_average_pooling_2d() |>   layer_dropout(rate = 0.5) |>   layer_dense(units = num_classes, activation = \"softmax\") summary(model) ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 26, 26, 64)     │           640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 24, 24, 64)     │        36,928 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 12, 12, 64)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 10, 10, 128)    │        73,856 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_3 (Conv2D)               │ (None, 8, 8, 128)      │       147,584 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_average_pooling2d        │ (None, 128)            │             0 │ ## │ (GlobalAveragePooling2D)        │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 128)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 10)             │         1,290 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 260,298 (1016.79 KB) ##  Trainable params: 260,298 (1016.79 KB) ##  Non-trainable params: 0 (0.00 B) model |> compile(   optimizer = \"adam\",   loss = \"sparse_categorical_crossentropy\",   metrics = list(     metric_sparse_categorical_accuracy(name = \"acc\")   ) ) batch_size <- 128 epochs <- 10  callbacks <- list(   callback_model_checkpoint(filepath=\"model_at_epoch_{epoch}.keras\"),   callback_early_stopping(monitor=\"val_loss\", patience=2) )  model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = epochs,   validation_split = 0.15,   callbacks = callbacks ) ## Epoch 1/10 ## 399/399 - 7s - 18ms/step - acc: 0.7445 - loss: 0.7534 - val_acc: 0.9630 - val_loss: 0.1260 ## Epoch 2/10 ## 399/399 - 2s - 5ms/step - acc: 0.9362 - loss: 0.2103 - val_acc: 0.9774 - val_loss: 0.0762 ## Epoch 3/10 ## 399/399 - 2s - 5ms/step - acc: 0.9560 - loss: 0.1492 - val_acc: 0.9830 - val_loss: 0.0607 ## Epoch 4/10 ## 399/399 - 2s - 5ms/step - acc: 0.9650 - loss: 0.1187 - val_acc: 0.9859 - val_loss: 0.0494 ## Epoch 5/10 ## 399/399 - 2s - 5ms/step - acc: 0.9716 - loss: 0.1003 - val_acc: 0.9866 - val_loss: 0.0485 ## Epoch 6/10 ## 399/399 - 2s - 5ms/step - acc: 0.9744 - loss: 0.0878 - val_acc: 0.9888 - val_loss: 0.0381 ## Epoch 7/10 ## 399/399 - 2s - 5ms/step - acc: 0.9763 - loss: 0.0799 - val_acc: 0.9896 - val_loss: 0.0378 ## Epoch 8/10 ## 399/399 - 2s - 5ms/step - acc: 0.9791 - loss: 0.0686 - val_acc: 0.9862 - val_loss: 0.0454 ## Epoch 9/10 ## 399/399 - 2s - 5ms/step - acc: 0.9800 - loss: 0.0665 - val_acc: 0.9889 - val_loss: 0.0412 score <- model |> evaluate(x_test, y_test, verbose = 0) save_model(model, \"final_model.keras\", overwrite=TRUE) model <- load_model(\"final_model.keras\") predictions <- model |> predict(x_test) ## 313/313 - 1s - 2ms/step dim(predictions) ## [1] 10000    10"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"writing-cross-framework-custom-components","dir":"Articles","previous_headings":"","what":"Writing cross-framework custom components","title":"Introduction to Keras for engineers","text":"Keras enables write custom Layers, Models, Metrics, Losses, Optimizers work across TensorFlow, JAX, PyTorch codebase. Let’s take look custom layers first. op_ namespace contains: implementation NumPy API, e.g. op_stack op_matmul. set neural network specific ops absent NumPy, op_conv op_binary_crossentropy. Let’s make custom Dense layer works backends: Next, let’s make custom Dropout layer relies random_* namespace: Next, let’s write custom subclassed model uses two custom layers: Let’s compile fit :","code":"layer_my_dense <- Layer(   classname = \"MyDense\",   initialize = function(units, activation = NULL, name = NULL, ...) {     super$initialize(name = name, ...)     self$units <- units     self$activation <- activation   },   build = function(input_shape) {     input_dim <- tail(input_shape, 1)     self$w <- self$add_weight(       shape = shape(input_dim, self$units),       initializer = initializer_glorot_normal(),       name = \"kernel\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = initializer_zeros(),       name = \"bias\",       trainable = TRUE     )   },   call = function(inputs) {     # Use Keras ops to create backend-agnostic layers/metrics/etc.     x <- op_matmul(inputs, self$w) + self$b     if (!is.null(self$activation))       x <- self$activation(x)     x   } ) layer_my_dropout <- Layer(   \"MyDropout\",   initialize = function(rate, name = NULL, seed = NULL, ...) {     super$initialize(name = name)     self$rate <- rate     # Use seed_generator for managing RNG state.     # It is a state element and its seed variable is     # tracked as part of `layer$variables`.     self$seed_generator <- random_seed_generator(seed)   },   call = function(inputs) {     # Use `keras3::random_*` for random ops.     random_dropout(inputs, self$rate, seed = self$seed_generator)   } ) MyModel <- Model(   \"MyModel\",   initialize = function(num_classes, ...) {     super$initialize(...)     self$conv_base <-       keras_model_sequential() |>       layer_conv_2d(64, kernel_size = c(3, 3), activation = \"relu\") |>       layer_conv_2d(64, kernel_size = c(3, 3), activation = \"relu\") |>       layer_max_pooling_2d(pool_size = c(2, 2)) |>       layer_conv_2d(128, kernel_size = c(3, 3), activation = \"relu\") |>       layer_conv_2d(128, kernel_size = c(3, 3), activation = \"relu\") |>       layer_global_average_pooling_2d()      self$dp <- layer_my_dropout(rate = 0.5)     self$dense <- layer_my_dense(units = num_classes,                                  activation = activation_softmax)   },   call = function(inputs) {     inputs |>       self$conv_base() |>       self$dp() |>       self$dense()   } ) model <- MyModel(num_classes = 10) model |> compile(   loss = loss_sparse_categorical_crossentropy(),   optimizer = optimizer_adam(learning_rate = 1e-3),   metrics = list(     metric_sparse_categorical_accuracy(name = \"acc\")   ) )  model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = 1, # For speed   validation_split = 0.15 ) ## 399/399 - 8s - 19ms/step - acc: 0.7347 - loss: 0.7746 - val_acc: 0.9247 - val_loss: 0.2487"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"training-models-on-arbitrary-data-sources","dir":"Articles","previous_headings":"","what":"Training models on arbitrary data sources","title":"Introduction to Keras for engineers","text":"Keras models can trained evaluated wide variety data sources, independently backend ’re using. includes: Arrays Dataframes TensorFlow tf_dataset objects PyTorch DataLoader objects Keras PyDataset objects work whether ’re using TensorFlow, JAX, PyTorch Keras backend. Let’s try tf_dataset:","code":"library(tfdatasets, exclude = \"shape\")  train_dataset <- list(x_train, y_train) |>   tensor_slices_dataset() |>   dataset_batch(batch_size) |>   dataset_prefetch(buffer_size = tf$data$AUTOTUNE)  test_dataset <- list(x_test, y_test) |>   tensor_slices_dataset() |>   dataset_batch(batch_size) |>   dataset_prefetch(buffer_size = tf$data$AUTOTUNE)  model <- MyModel(num_classes = 10) model |> compile(   loss = loss_sparse_categorical_crossentropy(),   optimizer = optimizer_adam(learning_rate = 1e-3),   metrics = list(     metric_sparse_categorical_accuracy(name = \"acc\")   ) )  model |> fit(train_dataset, epochs = 1, validation_data = test_dataset) ## 469/469 - 9s - 20ms/step - acc: 0.7534 - loss: 0.7366 - val_acc: 0.8981 - val_loss: 0.3329"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"further-reading","dir":"Articles","previous_headings":"","what":"Further reading","title":"Introduction to Keras for engineers","text":"concludes short overview new multi-backend capabilities Keras 3. Next, can learn :","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"how-to-customize-what-happens-in-fit","dir":"Articles","previous_headings":"Further reading","what":"How to customize what happens in fit()","title":"Introduction to Keras for engineers","text":"Want implement non-standard training algorithm still want benefit power usability fit()? ’s easy customize fit() support arbitrary use cases: Customizing happens fit() TensorFlow","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"how-to-write-custom-training-loops","dir":"Articles","previous_headings":"","what":"How to write custom training loops","title":"Introduction to Keras for engineers","text":"Writing training loop scratch TensorFlow","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"how-to-distribute-training","dir":"Articles","previous_headings":"","what":"How to distribute training","title":"Introduction to Keras for engineers","text":"Guide distributed training TensorFlow Enjoy library! 🚀","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Introduction to Keras for Researchers","text":"","code":"library(keras3) library(tensorflow)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Introduction to Keras for Researchers","text":"machine learning researcher? publish NeurIPS push state---art CV NLP? guide serve first introduction core Keras & TensorFlow API concepts. guide, learn : Tensors, variables, gradients TensorFlow Creating layers subclassing [Layer] class Writing low-level training loops Tracking losses created layers via add_loss() method Tracking metrics low-level training loop Speeding execution compiled [tensorflow::tf_function()] Executing layers training inference mode Keras Functional API also see Keras API action two end--end research examples: Variational Autoencoder, Hypernetwork.","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"tensors","dir":"Articles","previous_headings":"","what":"Tensors","title":"Introduction to Keras for Researchers","text":"TensorFlow infrastructure layer differentiable programming. heart, ’s framework manipulating N-dimensional arrays (tensors), much like NumPy. However, three key differences NumPy TensorFlow: TensorFlow can leverage hardware accelerators GPUs TPUs. TensorFlow can automatically compute gradient arbitrary differentiable tensor expressions. TensorFlow computation can distributed large numbers devices single machine, large number machines (potentially multiple devices ). Let’s take look object core TensorFlow: Tensor. ’s constant tensor: can get value R array calling .array(): features attributes dtype shape: common way create constant tensors via tf$ones tf$zeros: can also create random constant tensors:","code":"x <- tf$constant(rbind(c(5, 2), c(1, 3))) print(x) ## tf.Tensor( ## [[5. 2.] ##  [1. 3.]], shape=(2, 2), dtype=float64) as.array(x) ##      [,1] [,2] ## [1,]    5    2 ## [2,]    1    3 x$dtype ## tf.float64 x$shape ## TensorShape([2, 2]) tf$ones(shape = shape(2, 1)) ## tf.Tensor( ## [[1.] ##  [1.]], shape=(2, 1), dtype=float32) tf$zeros(shape = shape(2, 1)) ## tf.Tensor( ## [[0.] ##  [0.]], shape=(2, 1), dtype=float32) x <- random_normal(shape = c(2, 2), mean = 0.0, stddev = 1.0) x <- random_uniform(shape = c(2, 2), minval = 0, maxval = 10)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"variables","dir":"Articles","previous_headings":"","what":"Variables","title":"Introduction to Keras for Researchers","text":"Variables special tensors used store mutable state (weights neural network). create Variable using initial value: update value Variable using methods $assign(value), $assign_add(increment), $assign_sub(decrement):","code":"initial_value <- random_normal(shape=c(2, 2)) a <- tf$Variable(initial_value) print(a) ## <tf.Variable 'Variable:0' shape=(2, 2) dtype=float32, numpy= ## array([[ 0.9057419 ,  0.7916686 ], ##        [ 0.28754202, -0.5408822 ]], dtype=float32)> new_value <- random_normal(shape=c(2, 2)) a$assign(new_value) ## <tf.Variable 'UnreadVariable' shape=(2, 2) dtype=float32, numpy= ## array([[-0.3405368 , -2.1463926 ], ##        [ 1.2602988 ,  0.12241419]], dtype=float32)> added_value <- random_normal(shape=c(2, 2)) a$assign_add(added_value) ## <tf.Variable 'UnreadVariable' shape=(2, 2) dtype=float32, numpy= ## array([[ 0.04820395, -2.6854615 ], ##        [ 0.23246336,  1.4535258 ]], dtype=float32)>"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"doing-math-in-tensorflow","dir":"Articles","previous_headings":"","what":"Doing math in TensorFlow","title":"Introduction to Keras for Researchers","text":"’ve used NumPy, math TensorFlow look familiar. main difference TensorFlow code can run GPU TPU.","code":"a <- random_normal(shape=c(2, 2)) b <- random_normal(shape=c(2, 2))  c <- a + b d <- tf$square(c) e <- tf$exp(d)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"gradients","dir":"Articles","previous_headings":"","what":"Gradients","title":"Introduction to Keras for Researchers","text":"’s another big difference R: can automatically retrieve gradient differentiable expression. Just open GradientTape, start “watching” tensor via tape$watch(), compose differentiable expression using tensor input: default, variables watched automatically, don’t need manually watch : Note can compute higher-order derivatives nesting tapes:","code":"a <- random_normal(shape=c(2, 2)) b <- random_normal(shape=c(2, 2))  with(tf$GradientTape() %as% tape, {   tape$watch(a)  # Start recording the history of operations applied to `a`   c <- tf$sqrt(tf$square(a) + tf$square(b))  # Do some math using `a`   # What's the gradient of `c` with respect to `a`?   dc_da <- tape$gradient(c, a)   print(dc_da) }) ## tf.Tensor( ## [[ 0.9969011  -0.7707146 ] ##  [ 0.23378514  0.96255165]], shape=(2, 2), dtype=float32) a <- tf$Variable(a)  with(tf$GradientTape() %as% tape, {   c <- tf$sqrt(tf$square(a) + tf$square(b))   dc_da <- tape$gradient(c, a)   print(dc_da) }) ## tf.Tensor( ## [[ 0.9969011  -0.7707146 ] ##  [ 0.23378514  0.96255165]], shape=(2, 2), dtype=float32) with(tf$GradientTape() %as% outer_tape, {   with(tf$GradientTape() %as% tape, {     c <- tf$sqrt(tf$square(a) + tf$square(b))     dc_da <- tape$gradient(c, a)   })   d2c_da2 <- outer_tape$gradient(dc_da, a)   print(d2c_da2) }) ## tf.Tensor( ## [[3.3447742e-03 7.1282005e-01] ##  [5.7464113e+00 5.5013180e-02]], shape=(2, 2), dtype=float32)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"keras-layers","dir":"Articles","previous_headings":"","what":"Keras layers","title":"Introduction to Keras for Researchers","text":"TensorFlow infrastructure layer differentiable programming, dealing tensors, variables, gradients, Keras user interface deep learning, dealing layers, models, optimizers, loss functions, metrics, . Keras serves high-level API TensorFlow: Keras makes TensorFlow simple productive. Layer class fundamental abstraction Keras. Layer encapsulates state (weights) computation (defined call method). simple layer looks like . self$add_weight() method gives shortcut creating weights: use Layer instance much like R function: weight variables (created initialize) automatically tracked weights property: many built-layers available, Dense Conv2D LSTM fancier ones like Conv3DTranspose ConvLSTM2D. smart reusing built-functionality.","code":"Linear <- new_layer_class(   \"Linear\",   initialize = function(units = 32, input_dim = 32) {     super$initialize()     self$w <- self$add_weight(       shape = shape(input_dim, units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     tf$matmul(inputs, self$w) + self$b   } ) # Instantiate our layer. linear_layer <- Linear(units=4, input_dim=2)  # The layer can be treated as a function. # Here we call it on some data. y <- linear_layer(tf$ones(shape(2, 2))) linear_layer$weights ## [[1]] ## <KerasVariable shape=(2, 4), dtype=float32, path=linear/variable> ## ## [[2]] ## <KerasVariable shape=(4), dtype=float32, path=linear/variable_1>"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"layer-weight-creation-in-buildinput_shape","dir":"Articles","previous_headings":"","what":"Layer weight creation in build(input_shape)","title":"Introduction to Keras for Researchers","text":"’s often good idea defer weight creation build() method, don’t need specify input dim/shape layer construction time:","code":"Linear <- new_layer_class(   \"Linear\",   initialize = function(units = 32) {     super$initialize()     self$units <- units   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(input_shape[-1], self$units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     tf$matmul(inputs, self$w) + self$b   } )  # Instantiate our layer. linear_layer <- Linear(units = 4)  # This will also call `build(input_shape)` and create the weights. y <- linear_layer(tf$ones(shape(2, 2)))"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"layer-gradients","dir":"Articles","previous_headings":"","what":"Layer gradients","title":"Introduction to Keras for Researchers","text":"can automatically retrieve gradients weights layer calling inside GradientTape. Using gradients, can update weights layer, either manually, using optimizer object. course, can modify gradients using , need .","code":"# Prepare a dataset. c(c(x_train, y_train), .) %<-% dataset_mnist()  x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(list(x_train, y_train)) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   tfdatasets::dataset_batch(64)  # Instantiate our linear layer (defined above) with 10 units. linear_layer <- Linear(units = 10)  # Instantiate a logistic loss function that expects integer targets. loss_fn <- loss_sparse_categorical_crossentropy(from_logits=TRUE)  # Instantiate an optimizer. optimizer <- optimizer_sgd(learning_rate=1e-3)  # Iterate over the batches of the dataset. coro::loop(for(data in dataset) {   # Open a GradientTape.   with(tf$GradientTape() %as% tape, {     # Forward pass.     logits <- linear_layer(data[[1]])      # Loss value for this batch.     loss_value <- loss_fn(data[[2]], logits)   })    # Get gradients of the loss wrt the weights.   gradients <- tape$gradient(loss_value, linear_layer$trainable_weights)    # Update the weights of our linear layer.   optimizer$apply_gradients(zip_lists(gradients, linear_layer$trainable_weights)) }) loss_value ## tf.Tensor(1.2819729, shape=(), dtype=float32)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"trainable-and-non-trainable-weights","dir":"Articles","previous_headings":"","what":"Trainable and non-trainable weights","title":"Introduction to Keras for Researchers","text":"Weights created layers can either trainable non-trainable. ’re exposed trainable_weights non_trainable_weights respectively. ’s layer non-trainable weight:","code":"ComputeSum <- new_layer_class(   \"ComputeSum\",   initialize = function(input_dim) {     super$initialize()     # Create a non-trainable weight.     self$total <- self$add_weight(       initializer = \"zeros\",       shape = shape(input_dim),       trainable = FALSE     )   },   call = function(inputs) {     self$total$assign_add(tf$reduce_sum(inputs, axis=0L))     self$total   } )  my_sum <- ComputeSum(input_dim = 2) x <- tf$ones(shape(2, 2))  as.array(my_sum(x)) ## [1] 2 2 as.array(my_sum(x)) ## [1] 4 4 my_sum$trainable_weights ## list()"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"layers-that-own-layers","dir":"Articles","previous_headings":"","what":"Layers that own layers","title":"Introduction to Keras for Researchers","text":"Layers can recursively nested create bigger computation blocks. layer track weights sublayers (trainable non-trainable). Note manually-created MLP equivalent following built-option:","code":"# Let's reuse the Linear class # with a `build` method that we defined above.  MLP <- new_layer_class(   \"MLP\",   initialize = function() {     super$initialize()     self$linear_1 <- Linear(units = 32)     self$linear_2 <- Linear(units = 32)     self$linear_3 <- Linear(units = 10)   },   call = function(inputs) {     x <- self$linear_1(inputs)     x <- tf$nn$relu(x)     x <- self$linear_2(x)     x <- tf$nn$relu(x)     return(self$linear_3(x))   } )  mlp <- MLP()  # The first call to the `mlp` object will create the weights. y <- mlp(tf$ones(shape=shape(3, 64)))  # Weights are recursively tracked. length(mlp$weights) ## [1] 6 mlp <- keras_model_sequential() %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"tracking-losses-created-by-layers","dir":"Articles","previous_headings":"","what":"Tracking losses created by layers","title":"Introduction to Keras for Researchers","text":"Layers can create losses forward pass via add_loss() method. especially useful regularization losses. losses created sublayers recursively tracked parent layers. ’s layer creates activity regularization loss: model incorporating layer track regularization loss: losses cleared top-level layer start forward pass – don’t accumulate. layer.losses always contains losses created last forward pass. typically use losses summing computing gradients writing training loop.","code":"# A layer that creates an activity sparsity regularization loss ActivityRegularization <- new_layer_class(   \"ActivityRegularization\",   initialize = function(rate=1e-2) {     super$initialize()     self$rate <- rate   },   call = function(inputs) {     self$add_loss(self$rate * tf$reduce_sum(tf$abs(inputs)))     inputs   } ) # Let's use the loss layer in a MLP block. SparseMLP <- new_layer_class(   \"SparseMLP\",   initialize = function() {     super$initialize()     self$linear_1 <- Linear(units = 32)     self$reg <- ActivityRegularization(rate = 1e-2)     self$linear_3 <- Linear(units = 10)   },   call = function(inputs) {     x <- self$linear_1(inputs)     x <- tf$nn$relu(x)     x <- self$reg(x)     return(self$linear_3(x))   } )  mlp <- SparseMLP() y <- mlp(tf$ones(shape(10, 10)))  mlp$losses  # List containing one float32 scalar ## [[1]] ## tf.Tensor(0.18065463, shape=(), dtype=float32) # Losses correspond to the *last* forward pass. mlp <- SparseMLP() mlp(tf$ones(shape(10, 10))) ## tf.Tensor( ## [[ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621]], shape=(10, 10), dtype=float32) length(mlp$losses) ## [1] 1 mlp(tf$ones(shape(10, 10))) ## tf.Tensor( ## [[ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621]], shape=(10, 10), dtype=float32) length(mlp$losses)  # No accumulation. ## [1] 1 # Let's demonstrate how to use these losses in a training loop.  # Prepare a dataset. c(c(x_train, y_train), .) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(list(x_train, y_train)) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   tfdatasets::dataset_batch(64)  # A new MLP. mlp <- SparseMLP()  # Loss and optimizer. loss_fn <- loss_sparse_categorical_crossentropy(from_logits=TRUE) optimizer <- optimizer_sgd(learning_rate=1e-3)  coro::loop(for(data in dataset) {   x <- data[[1]]   y <- data[[2]]   with(tf$GradientTape() %as% tape, {     # Forward pass.     logits <- mlp(x)      # External loss value for this batch.     loss <- loss_fn(y, logits)      # Add the losses created during the forward pass.     loss <- loss + Reduce(`+`, mlp$losses)      # Get gradients of the loss wrt the weights.     gradients <- tape$gradient(loss, mlp$trainable_weights)      # Update the weights of our linear layer.     optimizer$apply_gradients(zip_lists(gradients, mlp$trainable_weights))   }) })"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"keeping-track-of-training-metrics","dir":"Articles","previous_headings":"","what":"Keeping track of training metrics","title":"Introduction to Keras for Researchers","text":"Keras offers broad range built-metrics, like metric_auc metric_precision_at_recall. ’s also easy create metrics lines code. use metric custom training loop, : Instantiate metric object, e.g. metric = metric_auc() Call metric$udpate_state(targets, predictions) method batch data Query result via metric$result() Reset metric’s state end epoch start evaluation via metric$reset_state() ’s simple example: can also define metrics subclassing keras.metrics.Metric. need override three functions called : Override update_state() update statistic values. Override result() return metric value. Override reset_state() reset metric initial state. example implement F1-score metric (support sample weighting). Let’s test-drive :","code":"# Instantiate a metric object accuracy <- metric_sparse_categorical_accuracy()  # Prepare our layer, loss, and optimizer. model <- keras_model_sequential() %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10) loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE) optimizer <- optimizer_adam(learning_rate=1e-3)  for (epoch in seq_len(2)) {   coro::loop(for (data in dataset) {     x <- data[[1]]     y <- data[[2]]     with(tf$GradientTape() %as% tape, {       # Forward pass.       logits <- model(x)        # External loss value for this batch.       loss_value <- loss_fn(y, logits)     })      # Update the state of the `accuracy` metric.     accuracy$update_state(y, logits)      # Update the weights of the model to minimize the loss value.     gradients <- tape$gradient(loss_value, model$trainable_weights)     optimizer$apply_gradients(zip_lists(gradients, model$trainable_weights))    })   cat(\"Epoch:\", epoch, \"Accuracy:\", as.numeric(accuracy$result()), \"\\n\")   accuracy$reset_state() } ## Epoch: 1 Accuracy: 0.8757833 ## Epoch: 2 Accuracy: 0.93915 F1Score <- new_metric_class(   \"F1Score\",   initialize = function(self, name=\"f1_score\", dtype=\"float32\", threshold=0.5, ...) {     super$initialize(name=name, dtype=dtype, ...)     self$threshold <- threshold     self$true_positives <- self$add_weight(       name=\"tp\", dtype=dtype, initializer=\"zeros\"     )     self$false_positives <- self$add_weight(       name=\"fp\", dtype=dtype, initializer=\"zeros\"     )     self$false_negatives <- self$add_weight(       name=\"fn\", dtype=dtype, initializer=\"zeros\"     )   },   update_state = function(y_true, y_pred, sample_weight=NULL) {     y_pred <- tf$math$greater_equal(y_pred, self$threshold)     y_true <- tf$cast(y_true, tf$bool)     y_pred <- tf$cast(y_pred, tf$bool)      true_positives <- tf$cast(y_true & y_pred, self$dtype)     false_positives <- tf$cast((!y_true) & y_pred, self$dtype)     false_negatives <- tf$cast(y_true & (!y_pred), self$dtype)      if (!is.null(sample_weight)) {       sample_weight <- tf$cast(sample_weight, self$dtype)       true_positives <- true_positives * sample_weight       false_positives <- false_positives * sample_weight       false_negatives <- false_negatives * sample_weight     }      self$true_positives$assign_add(tf$reduce_sum(true_positives))     self$false_positives$assign_add(tf$reduce_sum(false_positives))     self$false_negatives$assign_add(tf$reduce_sum(false_negatives))   },    result = function() {     precision <- self$true_positives / (self$true_positives + self$false_positives)     recall <- self$true_positives / (self$true_positives + self$false_negatives)     f1_score <- 2 * precision * recall / (precision + recall)     f1_score   },    reset_state = function() {     self$true_positives$assign(0)     self$false_positives$assign(0)     self$false_negatives$assign(0)   } ) m <- F1Score() m$update_state(c(0, 1, 0, 0), c(0.3, 0.5, 0.8, 0.9)) cat(\"Intermediate result:\", as.numeric(m$result()), \"\\n\") ## Intermediate result: 0.5 m$update_state(c(1, 1, 1, 1), c(0.1, 0.7, 0.6, 0.0)) cat(\"Final result:\", as.numeric(m$result()), \"\\n\") ## Final result: 0.6"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"compiled-functions","dir":"Articles","previous_headings":"","what":"Compiled functions","title":"Introduction to Keras for Researchers","text":"Running eagerly great debugging, get better performance compiling computation static graphs. Static graphs researcher’s best friends. can compile function wrapping tf.function decorator.","code":"# Prepare our layer, loss, and optimizer. model <- keras_model_sequential() %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10) loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE) optimizer <- optimizer_adam(learning_rate=1e-3)  # Create a training step function. train_on_batch <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     # Forward pass.     logits <- model(x)     # External loss value for this batch.     loss_value <- loss_fn(y, logits)   })   # Update the weights of the model to minimize the loss value.   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply_gradients(zip_lists(gradients, model$trainable_weights))   loss_value })   # Prepare a dataset. c(c(x_train, y_train), .) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(list(x_train, y_train)) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   tfdatasets::dataset_batch(64)  i <- 0 coro::loop(for (data in dataset) {   i <- i + 1   x <- data[[1]]   y <- data[[2]]   loss <- train_on_batch(x, y)   if (i %% 100 == 0)     cat(\"Loss:\", as.numeric(loss), \"\\n\") }) ## Loss: 0.551749 ## Loss: 0.2131135 ## Loss: 0.2765952 ## Loss: 0.1296219 ## Loss: 0.2657076 ## Loss: 0.2683381 ## Loss: 0.1570166 ## Loss: 0.3139241 ## Loss: 0.08981849"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"training-mode-inference-mode","dir":"Articles","previous_headings":"","what":"Training mode & inference mode","title":"Introduction to Keras for Researchers","text":"layers, particular BatchNormalization layer Dropout layer, different behaviors training inference. layers, standard practice expose training (boolean) argument call method. exposing argument call, enable built-training evaluation loops (e.g. fit) correctly use layer training inference modes.","code":"Dropout <- new_layer_class(   \"Dropout\",   initialize = function(rate) {     super$initialize()     self$rate <- rate   },   call = function(inputs, training = NULL) {     if (!is.null(training) && training) {       return(tf$nn$dropout(inputs, rate = self$rate))     }     inputs   } )  MLPWithDropout <- new_layer_class(   \"MLPWithDropout\",   initialize = function() {     super$initialize()     self$linear_1 <- Linear(units = 32)     self$dropout <- Dropout(rate = 0.5)     self$linear_3 <- Linear(units = 10)   },   call = function(inputs, training = NULL) {     x <- self$linear_1(inputs)     x <- tf$nn$relu(x)     x <- self$dropout(x, training = training)     self$linear_3(x)   } )  mlp <- MLPWithDropout() y_train <- mlp(tf$ones(shape(2, 2)), training=TRUE) y_test <- mlp(tf$ones(shape(2, 2)), training=FALSE)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"the-functional-api-for-model-building","dir":"Articles","previous_headings":"","what":"The Functional API for model-building","title":"Introduction to Keras for Researchers","text":"build deep learning models, don’t use object-oriented programming time. layers ’ve seen far can also composed functionally, like (call “Functional API”): Functional API tends concise subclassing, provides advantages (generally advantages functional, typed languages provide untyped OO development). However, can used define DAGs layers – recursive networks defined Layer subclasses instead. Learn Functional API . research workflows, may often find mix--matching OO models Functional models. Note Model class also features built-training & evaluation loops: fit(), predict() evaluate() (configured via compile() method). built-functions give access following built-training infrastructure features: Callbacks. can leverage built-callbacks early-stopping, model checkpointing, monitoring training TensorBoard. can also implement custom callbacks needed. Distributed training. can easily scale training multiple GPUs, TPU, even multiple machines tf.distribute API – changes code. Step fusing. steps_per_execution argument Model.compile(), can process multiple batches single tf.function call, greatly improves device utilization TPUs. won’t go details, provide simple code example . leverages built-training infrastructure implement MNIST example . can always subclass Model class (works exactly like subclassing Layer) want leverage built-training loops OO models. Just override Model$train_step() customize happens fit() retaining support built-infrastructure features outlined – callbacks, zero-code distribution support, step fusing support. may also override test_step() customize happens evaluate(), override predict_step() customize happens predict(). information, please refer guide.","code":"# We use an `Input` object to describe the shape and dtype of the inputs. # This is the deep learning equivalent of *declaring a type*. # The shape argument is per-sample; it does not include the batch size. # The functional API focused on defining per-sample transformations. # The model we create will automatically batch the per-sample transformations, # so that it can be called on batches of data. inputs <- layer_input(shape = 16, dtype = \"float32\")  # We call layers on these \"type\" objects # and they return updated types (new shapes/dtypes). outputs <- inputs %>%   Linear(units = 32) %>% # We are reusing the Linear layer we defined earlier.   Dropout(rate = 0.5) %>% # We are reusing the Dropout layer we defined earlier.   Linear(units = 10)  # A functional `Model` can be defined by specifying inputs and outputs. # A model is itself a layer like any other. model <- keras_model(inputs, outputs)  # A functional model already has weights, before being called on any data. # That's because we defined its input shape in advance (in `Input`). length(model$weights) ## [1] 4 # Let's call our model on some data, for fun. y <- model(tf$ones(shape(2, 16))) y$shape ## TensorShape([2, 10]) # You can pass a `training` argument in `__call__` # (it will get passed down to the Dropout layer). y <- model(tf$ones(shape(2, 16)), training=TRUE) inputs <- layer_input(shape = 784, dtype=\"float32\") outputs <- inputs %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10) model <- keras_model(inputs, outputs)  # Specify the loss, optimizer, and metrics with `compile()`. model %>% compile(     loss = loss_sparse_categorical_crossentropy(from_logits=TRUE),     optimizer=optimizer_adam(learning_rate=1e-3),     metrics=list(metric_sparse_categorical_accuracy()), )  # Train the model with the dataset for 2 epochs. model %>% fit(dataset, epochs=2) ## Epoch 1/2 ## 938/938 - 4s - 4ms/step - loss: 0.3958 - sparse_categorical_accuracy: 0.8866 ## Epoch 2/2 ## 938/938 - 1s - 960us/step - loss: 0.1888 - sparse_categorical_accuracy: 0.9443 predictions <- model %>% predict(dataset) ## 938/938 - 1s - 1ms/step model %>% evaluate(dataset) ## 938/938 - 1s - 1ms/step - loss: 0.1763 - sparse_categorical_accuracy: 0.9454 ## $loss ## [1] 0.1763445 ## ## $sparse_categorical_accuracy ## [1] 0.9454167 CustomModel <- new_model_class(   \"CustomModel\",   initialize = function(...) {     super$initialize(...)     self$loss_tracker <- metric_mean(name=\"loss\")     self$accuracy <- metric_sparse_categorical_accuracy()     self$loss_fn <- loss_sparse_categorical_crossentropy(from_logits=TRUE)     self$optimizer <- optimizer_adam(learning_rate=1e-3)   },   train_step = function(data) {     c(x, y, sample_weight) %<-% unpack_x_y_sample_weight(data)     with(tf$GradientTape() %as% tape, {       y_pred <- self(x, training=TRUE)       loss <- self$loss_fn(y = y, y_pred = y_pred, sample_weight=sample_weight)     })     gradients <- tape$gradient(loss, self$trainable_variables)     self$optimizer$apply_gradients(       zip_lists(gradients, self$trainable_variables)     )      # Update metrics (includes the metric that tracks the loss)     self$loss_tracker$update_state(loss)     self$accuracy$update_state(y, y_pred, sample_weight=sample_weight)     # Return a list mapping metric names to current value     list(       loss = self$loss_tracker$result(),       accuracy = self$accuracy$result()     )   },   metrics = mark_active(function() {     list(self$loss_tracker, self$accuracy)   }) )  inputs <- layer_input(shape = 784, dtype=\"float32\") outputs <- inputs %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10) model <- CustomModel(inputs, outputs) model %>% compile() model %>% fit(dataset, epochs=2) ## Epoch 1/2 ## 938/938 - 2s - 2ms/step - loss: 0.3869 - sparse_categorical_accuracy: 0.8924 ## Epoch 2/2 ## 938/938 - 1s - 1ms/step - loss: 0.2163 - sparse_categorical_accuracy: 0.9370"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"end-to-end-experiment-example-1-variational-autoencoders-","dir":"Articles","previous_headings":"","what":"End-to-end experiment example 1: variational autoencoders.","title":"Introduction to Keras for Researchers","text":"things ’ve learned far: Layer encapsulates state (created __init__ build) computation (defined call). Layers can recursively nested create new, bigger computation blocks. can easily write highly hackable training loops opening GradientTape, calling model inside tape’s scope, retrieving gradients applying via optimizer. can speed training loops using @tf.function decorator. Layers can create track losses (typically regularization losses) via self.add_loss(). Let’s put things together end--end example: ’re going implement Variational AutoEncoder (VAE). ’ll train MNIST digits. VAE subclass Layer, built nested composition layers subclass Layer. feature regularization loss (KL divergence). model definition. First, Encoder class, uses Sampling layer map MNIST digit latent-space triplet (z_mean, z_log_var, z). Next, Decoder class, maps probabilistic latent space coordinates back MNIST digit. Finally, VariationalAutoEncoder composes together encoder decoder, creates KL divergence regularization loss via add_loss(). Now, let’s write training loop. training step decorated @tf.function compile super fast graph function. can see, building training type model Keras quick painless.","code":"Sampling <- new_layer_class(   \"Sampling\",   call = function(inputs) {     c(z_mean, z_log_var) %<-% inputs     batch <- op_shape(z_mean)[[1]]     dim <- op_shape(z_mean)[[2]]     epsilon <- random_normal(shape = c(batch, dim))     z_mean + op_exp(0.5 * z_log_var) * epsilon   } )  Encoder <- new_layer_class(   \"Encoder\",   initialize = function(latent_dim = 32, intermediate_dim = 64, ...) {     super$initialize(...)     self$dense_proj <- layer_dense(units = intermediate_dim, activation = \"relu\")     self$dense_mean <- layer_dense(units = latent_dim)     self$dense_log_var <- layer_dense(units = latent_dim)     self$sampling <- Sampling()   },   call = function(inputs) {     x <- self$dense_proj(inputs)     z_mean <- self$dense_mean(x)     z_log_var <- self$dense_log_var(x)     z <- self$sampling(list(z_mean, z_log_var))     list(z_mean, z_log_var, z)   } ) Decoder <- new_layer_class(   \"Decoder\",   initialize = function(original_dim, intermediate_dim = 64, ...) {     super$initialize(...)     self$dense_proj <- layer_dense(units = intermediate_dim, activation = \"relu\")     self$dense_output <- layer_dense(units = original_dim, activation = \"sigmoid\")   },   call = function(inputs) {     x <- self$dense_proj(inputs)     self$dense_output(x)   } ) VariationalAutoEncoder <- new_model_class(   \"VariationalAutoEncoder\",   initialize = function(original_dim,         intermediate_dim=64,         latent_dim=32,         name=\"autoencoder\", ...) {     super$initialize(name = name, ...)     self$original_dim <- original_dim     self$encoder <- Encoder(       latent_dim = latent_dim,       intermediate_dim = intermediate_dim     )     self$decoder <- Decoder(       original_dim = original_dim,       intermediate_dim = intermediate_dim     )   },   call = function(inputs) {     c(z_mean, z_log_var, z) %<-% self$encoder(inputs)     reconstructed <- self$decoder(z)     # Add KL divergence regularization loss.     kl_loss <- -0.5 * op_mean(       z_log_var - op_square(z_mean) - op_exp(z_log_var) + 1     )     self$add_loss(kl_loss)     reconstructed   } ) # Our model. vae <- VariationalAutoEncoder(   original_dim = 784,   intermediate_dim = 64,   latent_dim = 32 )  # Loss and optimizer. loss_fn <- loss_mean_squared_error() optimizer = optimizer_adam(learning_rate=1e-3)  # Prepare a dataset. c(c(x_train, .), .) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(x_train) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   tfdatasets::dataset_batch(32)   training_step <- tf_function(function(x) {   with(tf$GradientTape() %as% tape, {     reconstructed <- vae(x)  # Compute input reconstruction.     # Compute loss.     loss <- loss_fn(x, reconstructed)     loss <- loss + op_sum(vae$losses)  # Add KLD term.   })   # Update the weights of the VAE.   grads <- tape$gradient(loss, vae$trainable_weights)   optimizer$apply_gradients(zip_lists(grads, vae$trainable_weights))   loss })  losses <- c()  # Keep track of the losses over time. coro::loop(for(data in dataset) {   loss <- training_step(data)    # Logging.   losses[length(losses) + 1] <- as.numeric(loss)   if (length(losses) %% 100 == 0) {     cat(\"Step:\", length(losses), \"Loss:\", mean(losses), \"\\n\")   }   # Stop after 1000 steps.   # Training the model to convergence is left   # as an exercise to the reader.   if (length(losses) >= 1000) {     break   } }) ## Step: 100 Loss: 0.1270978 ## Step: 200 Loss: 0.1003238 ## Step: 300 Loss: 0.09001128 ## Step: 400 Loss: 0.08493649 ## Step: 500 Loss: 0.08171404 ## Step: 600 Loss: 0.07926706 ## Step: 700 Loss: 0.07790599 ## Step: 800 Loss: 0.07670419 ## Step: 900 Loss: 0.07570736 ## Step: 1000 Loss: 0.07476593"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"end-to-end-experiment-example-2-hypernetworks-","dir":"Articles","previous_headings":"","what":"End-to-end experiment example 2: hypernetworks.","title":"Introduction to Keras for Researchers","text":"Let’s take look another kind research experiment: hypernetworks. idea use small deep neural network (hypernetwork) generate weights larger network (main network). Let’s implement really trivial hypernetwork: ’ll use small 2-layer network generate weights larger 3-layer network. training loop. batch data: use hypernetwork generate array weight coefficients, weights_pred reshape coefficients kernel & bias tensors main_network run forward pass main_network compute actual MNIST predictions run backprop weights hypernetwork minimize final classification loss Implementing arbitrary research ideas Keras straightforward highly productive. Imagine trying 25 ideas per day (20 minutes per experiment average)! Keras designed go idea results fast possible, believe key great research. hope enjoyed quick introduction. Let us know build Keras!","code":"input_dim <- 784 classes <- 10  # This is the main network we'll actually use to predict labels. inputs <- layer_input(shape = input_dim) dense1 <- layer_dense(units = 64, activation = \"relu\") dense1$built <- TRUE  dense2 <- layer_dense(units = classes) dense2$built <- TRUE  outputs <- inputs %>% dense1() %>% dense2() main_network <- keras_model(inputs, outputs)  # This is the number of weight coefficients to generate. Each layer in the # main network requires output_dim * input_dim + output_dim coefficients. num_weights_to_generate <- (classes * 64 + classes) + (64 * input_dim + 64)  # This is the hypernetwork that generates the weights of the `main_network` above. hypernetwork <- keras_model_sequential() %>%   layer_dense(units=16, activation=\"relu\") %>%   layer_dense(units=num_weights_to_generate, activation=\"sigmoid\") # Loss and optimizer. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE) optimizer <- optimizer_adam(learning_rate=1e-4)  # Prepare a dataset. c(c(x_train, y_train), .) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(list(x_train, y_train)) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   # We'll use a batch size of 1 for this experiment.   tfdatasets::dataset_batch(1)  train_step <- function(x, y) {   with(tf$GradientTape() %as% tape, {     weights_pred <- hypernetwork(x)      # Reshape them to the expected shapes for w and b for the outer model.     # Layer 1 kernel.     start_index <- 1     w1_shape <- c(input_dim, 64)     w1_coeffs <- weights_pred[, start_index:(start_index + prod(w1_shape) - 1)]     w1 <- tf$reshape(w1_coeffs, as.integer(w1_shape))     start_index <- start_index + prod(w1_shape)      # Layer 1 bias.     b1_shape <- c(64)     b1_coeffs <- weights_pred[, start_index:(start_index + prod(b1_shape) - 1)]     b1 <- tf$reshape(b1_coeffs, as.integer(b1_shape))     start_index <- start_index + prod(b1_shape)      # Layer 2 kernel.     w2_shape <- c(64, classes)     w2_coeffs <- weights_pred[, start_index:(start_index + prod(w2_shape) - 1)]     w2 <- tf$reshape(w2_coeffs, as.integer(w2_shape))     start_index <- start_index + prod(w2_shape)      # Layer 2 bias.     b2_shape <- c(classes)     b2_coeffs <- weights_pred[, start_index:(start_index + prod(b2_shape) - 1)]     b2 <- tf$reshape(b2_coeffs, as.integer(b2_shape))     start_index <- start_index + prod(b2_shape)      # Set the weight predictions as the weight variables on the outer model.     dense1$kernel <- w1     dense1$bias <- b1     dense2$kernel <- w2     dense2$bias <- b2      # Inference on the outer model.     preds <- main_network(x)     loss <- loss_fn(y, preds)   })    grads <- tape$gradient(loss, hypernetwork$trainable_weights)   optimizer$apply_gradients(zip_lists(grads, hypernetwork$trainable_weights))   loss }  losses <- c()  # Keep track of the losses over time. coro::loop(for (data in dataset) {   x <- data[[1]]   y <- data[[2]]   loss <- train_step(x, y)    # Logging.   losses[length(losses) + 1] <- as.numeric(loss)   if (length(losses) %% 100 == 0) {     cat(\"Step:\", length(losses), \"Loss:\", mean(losses), \"\\n\")   }   # Stop after 1000 steps.   # Training the model to convergence is left   # as an exercise to the reader.   if (length(losses) >= 1000) {     break   } }) ## Step: 100 Loss: 2.536778 ## Step: 200 Loss: 2.236472 ## Step: 300 Loss: 2.119417 ## Step: 400 Loss: 2.040341 ## Step: 500 Loss: 1.949125 ## Step: 600 Loss: 1.859384 ## Step: 700 Loss: 1.845726 ## Step: 800 Loss: 1.820594 ## Step: 900 Loss: 1.771334 ## Step: 1000 Loss: 1.730648"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Making new layers and models via subclassing","text":"guide cover everything need know build subclassed layers models. particular, ’ll learn following features: Layer class add_weight() method Trainable non-trainable weights build() method Making sure layers can used backend add_loss() method training argument call() mask argument call() Making sure layers can serialized Let’s dive .","code":""},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Making new layers and models via subclassing","text":"","code":"library(keras3) library(tensorflow, exclude = c(\"set_random_seed\", \"shape\")) library(tfdatasets, exclude = \"shape\")"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"the-layer-class-the-combination-of-state-weights-and-some-computation","dir":"Articles","previous_headings":"","what":"The Layer class: the combination of state (weights) and some computation","title":"Making new layers and models via subclassing","text":"One central abstractions Keras Layer class. layer encapsulates state (layer’s “weights”) transformation inputs outputs (“call”, layer’s forward pass). ’s densely-connected layer. two state variables: variables w b. use layer calling tensor input(s), much like R function. Note weights w b automatically tracked layer upon set layer attributes:","code":"layer_linear <- Layer(\"Linear\",    initialize = function(units = 32, input_dim = 32, ...) {     super$initialize(...)     self$w <- self$add_weight(       shape = shape(input_dim, units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(units),       initializer = \"zeros\",       trainable = TRUE     )   },    call = function(inputs) {     op_matmul(inputs, self$w) + self$b   } ) x <- op_ones(c(2, 2)) linear_layer <- layer_linear(units = 4, input_dim = 2) y <- linear_layer(x) print(y) ## tf.Tensor( ## [[0.02153057 0.15450525 0.0205495  0.04493225] ##  [0.02153057 0.15450525 0.0205495  0.04493225]], shape=(2, 4), dtype=float32) linear_layer$weights ## [[1]] ## <KerasVariable shape=(2, 4), dtype=float32, path=linear/variable> ## ## [[2]] ## <KerasVariable shape=(4), dtype=float32, path=linear/variable_1>"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"layers-can-have-non-trainable-weights","dir":"Articles","previous_headings":"","what":"Layers can have non-trainable weights","title":"Making new layers and models via subclassing","text":"Besides trainable weights, can add non-trainable weights layer well. weights meant taken account backpropagation, training layer. ’s add use non-trainable weight: ’s part layer$weights, gets categorized non-trainable weight:","code":"layer_compute_sum <- Layer(   \"ComputeSum\",   initialize = function(input_dim) {     super$initialize()     self$total <- self$add_weight(       initializer = \"zeros\",       shape = shape(input_dim),       trainable = FALSE     )   },   call = function(inputs) {     self$total$assign_add(op_sum(inputs, axis = 1))     self$total   } )  x <- op_ones(c(2, 2)) my_sum <- layer_compute_sum(input_dim = 2) y <- my_sum(x) print(as.array(y)) ## [1] 2 2 y <- my_sum(x) print(as.array(y)) ## [1] 4 4 cat(\"weights:\", length(my_sum$weights)) ## weights: 1 cat(\"non-trainable weights:\", length(my_sum$non_trainable_weights)) ## non-trainable weights: 1 # It's not included in the trainable weights: cat(\"trainable_weights:\", length(my_sum$trainable_weights)) ## trainable_weights: 0"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"best-practice-deferring-weight-creation-until-the-shape-of-the-inputs-is-known","dir":"Articles","previous_headings":"","what":"Best practice: deferring weight creation until the shape of the inputs is known","title":"Making new layers and models via subclassing","text":"Linear layer took input_dim argument used compute shape weights w b initialize(): many cases, may know advance size inputs, like lazily create weights value becomes known, time instantiating layer. Keras API, recommend creating layer weights build(self, inputs_shape) method layer. Like : call() method layer automatically run build first time called. now layer ’s lazy thus easier use: Implementing build() separately shown nicely separates creating weights using weights every call.","code":"layer_linear <- Layer(\"Linear\",    initialize = function(units = 32, input_dim = 32, ...) {     super$initialize(...)     self$w <- self$add_weight(       shape = shape(input_dim, units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(units),       initializer = \"zeros\",       trainable = TRUE     )   },    call = function(inputs) {     op_matmul(inputs, self$w) + self$b   } ) layer_linear <- Layer(   \"Linear\",   initialize = function(units = 32, ...) {     self$units <- as.integer(units)     super$initialize(...)   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     op_matmul(inputs, self$w) + self$b   } ) # At instantiation, we don't know on what inputs this is going to get called linear_layer <- layer_linear(units = 32)  # The layer's weights are created dynamically the first time the layer is called y <- linear_layer(x)"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"layers-are-recursively-composable","dir":"Articles","previous_headings":"","what":"Layers are recursively composable","title":"Making new layers and models via subclassing","text":"assign Layer instance attribute another Layer, outer layer start tracking weights created inner layer. recommend creating sublayers initialize() method leave first call() trigger building weights.","code":"MLPBlock <- Layer(   \"MLPBlock\",   initialize = function() {     super$initialize()     self$linear_1 <- layer_linear(units = 32)     self$linear_2 <- layer_linear(units = 32)     self$linear_3 <- layer_linear(units = 1)   },   call = function(inputs) {     inputs |>       self$linear_1() |>       activation_relu() |>       self$linear_2() |>       activation_relu() |>       self$linear_3()   } )  mlp <- MLPBlock() # The first call to the `mlp` will create the weights y <- mlp(op_ones(shape = c(3, 64)))  cat(\"weights:\", length(mlp$weights), \"\\n\") ## weights: 6 cat(\"trainable weights:\", length(mlp$trainable_weights), \"\\n\") ## trainable weights: 6"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"backend-agnostic-layers-and-backend-specific-layers","dir":"Articles","previous_headings":"","what":"Backend-agnostic layers and backend-specific layers","title":"Making new layers and models via subclassing","text":"long layer uses APIs ops namespace (ie. using functions starting op_), (Keras namespaces activations_*, random_*, layer_*), can used backend – TensorFlow, JAX, PyTorch. layers ’ve seen far guide work Keras backends. ops namespace gives access : NumPy API, e.g. op_matmul, op_sum, op_reshape, op_stack, etc. Neural networks-specific APIs op_softmax, op_conv, op_binary_crossentropy, op_relu, etc. can also use backend-native APIs layers (tf$nn functions), , layer usable backend question. instance, write following JAX-specific layer using jax$numpy: equivalent TensorFlow-specific layer: equivalent PyTorch-specific layer: cross-backend compatibility tremendously useful property, strongly recommend seek always make layers backend-agnostic leveraging Keras APIs.","code":"# keras3::install_keras(backend = c(\"jax\")) jax <- reticulate::import(\"jax\")  Linear <- new_layer_class(   ...   call = function(inputs) {     jax$numpy$matmul(inputs, self$w) + self$b   } ) library(tensorflow)  Linear <- new_layer_class(   ...   call = function(inputs) {     tf$matmul(inputs, self$w) + self$b   } ) torch <- reticulate::import(\"torch\")  Linear <- new_layer_class(   ...   call = function(inputs) {     torch$matmul(inputs, self$w) + self$b   } )"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"the-add_loss-method","dir":"Articles","previous_headings":"","what":"The add_loss() method","title":"Making new layers and models via subclassing","text":"writing call() method layer, can create loss tensors want use later, writing training loop. doable calling self$add_loss(value): losses (including created inner layer) can retrieved via layer$losses. property reset start every call top-level layer, layer$losses always contains loss values created last forward pass. addition, loss property also contains regularization losses created weights inner layer: losses meant taken account writing custom training loops. also work seamlessly fit() (get automatically summed added main loss, ):","code":"# A layer that creates an activity regularization loss layer_activity_regularization <- Layer(   \"ActivityRegularizationLayer\",   initialize = function(rate = 1e-2) {     self$rate <- as.numeric(rate)     super$initialize()   },   call = function(inputs) {     self$add_loss(self$rate * op_mean(inputs))     inputs   } ) layer_outer <- Layer(   \"OuterLayer\",   initialize = function() {     super$initialize()     self$activity_reg <- layer_activity_regularization(rate = 1e-2)   },   call = function(inputs) {     self$activity_reg(inputs)     inputs   } )  layer <- layer_outer() # No losses yet since the layer has never been called cat(\"losses:\", length(layer$losses), \"\\n\") ## losses: 0 x <- layer(op_zeros(c(1, 1))) # We created one loss value cat(\"losses:\", length(layer$losses), \"\\n\") ## losses: 1 # `layer$losses` gets reset at the start of each call x <- layer(op_zeros(c(1, 1))) # This is the loss created during the call above cat(\"losses:\", length(layer$losses), \"\\n\") ## losses: 1 layer_outer_with_kernel_regularizer <- Layer(   \"OuterLayerWithKernelRegularizer\",   initialize = function() {     super$initialize()     self$dense <- layer_dense(units = 32,                               kernel_regularizer = regularizer_l2(1e-3))   },   call = function(inputs) {     self$dense(inputs)   } )  layer <- layer_outer_with_kernel_regularizer() x <- layer(op_zeros(c(1, 1)))  # This is `1e-3 * sum(layer$dense$kernel ** 2)`, # created by the `kernel_regularizer` above. print(layer$losses) ## [[1]] ## tf.Tensor(0.002025157, shape=(), dtype=float32) inputs <- keras_input(shape = 3) outputs <- inputs |> layer_activity_regularization() model <- keras_model(inputs, outputs)  # If there is a loss passed in `compile`, the regularization # losses get added to it model |> compile(optimizer = \"adam\", loss = \"mse\") model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1) ## 1/1 - 0s - 136ms/step - loss: 1.8971 # It's also possible not to pass any loss in `compile`, # since the model already has a loss to minimize, via the `add_loss` # call during the forward pass! model |> compile(optimizer = \"adam\") model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1) ## 1/1 - 0s - 84ms/step - loss: -3.3344e-03"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"you-can-optionally-enable-serialization-on-your-layers","dir":"Articles","previous_headings":"","what":"You can optionally enable serialization on your layers","title":"Making new layers and models via subclassing","text":"need custom layers serializable part Functional model, can optionally implement get_config() method: Note initialize() method base Layer class takes keyword arguments, particular name dtype. ’s good practice pass arguments parent class initialize() include layer config: need flexibility deserializing layer config, can also override from_config() class method. base implementation from_config(): learn serialization saving, see complete guide saving serializing models.","code":"layer_linear <- Layer(   \"Linear\",   initialize = function(units = 32) {     self$units <- as.integer(units)     super$initialize()   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     op_matmul(inputs, self$w) + self$b   },   get_config = function() {     list(units = self$units)   } )  # Now you can recreate the layer from its config: layer <- layer_linear(units = 64) config <- get_config(layer) str(config) ## List of 1 ##  $ units: int 64 ##  - attr(*, \"__class__\")=<class '<r-globalenv>.Linear'> new_layer <- from_config(config) Linear <- new_layer_class(   \"Linear\",   initialize = function(units = 32, ...) {     self$units <- as.integer(units)     super$initialize(...)   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     op_matmul(inputs, self$w) + self$b   },   get_config = function() {     list(units = self$units)   } )  layer <- Linear(units = 64) config <- get_config(layer) str(config) ## List of 1 ##  $ units: int 64 ##  - attr(*, \"__class__\")=<class '<r-globalenv>.Linear'> new_layer <- from_config(config) Layer(   ...,   from_config = function(config) {     # calling `__class__`() creates a new instance and calls initialize()     do.call(`__class__`, config)   } )"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"privileged-training-argument-in-the-call-method","dir":"Articles","previous_headings":"","what":"Privileged training argument in the call() method","title":"Making new layers and models via subclassing","text":"layers, particular BatchNormalization layer Dropout layer, different behaviors training inference. layers, standard practice expose training (boolean) argument call() method. exposing argument call(), enable built-training evaluation loops (e.g. fit()) correctly use layer training inference.","code":"layer_custom_dropout <- Layer(   \"CustomDropout\",   initialize = function(rate, ...) {     super$initialize(...)     self$rate <- rate     self$seed_generator <- random_seed_generator(1337)   },   call = function(inputs, training = NULL) {     if (isTRUE(training))       return(random_dropout(inputs, rate = self$rate,                             seed = self.seed_generator))     inputs   } )"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"privileged-mask-argument-in-the-call-method","dir":"Articles","previous_headings":"","what":"Privileged mask argument in the call() method","title":"Making new layers and models via subclassing","text":"privileged argument supported call() mask argument. find Keras RNN layers. mask boolean tensor (one boolean value per timestep input) used skip certain input timesteps processing timeseries data. Keras automatically pass correct mask argument call() layers support , mask generated prior layer. Mask-generating layers Embedding layer configured mask_zero = TRUE, Masking layer.","code":""},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"the-model-class","dir":"Articles","previous_headings":"","what":"The Model class","title":"Making new layers and models via subclassing","text":"general, use Layer class define inner computation blocks, use Model class define outer model – object train. instance, ResNet50 model, several ResNet blocks subclassing Layer, single Model encompassing entire ResNet50 network. Model class API Layer, following differences: exposes built-training, evaluation, prediction loops (fit(), evaluate(), predict()). exposes list inner layers, via model$layers property. exposes saving serialization APIs (save(), save_weights()…) Effectively, Layer class corresponds refer literature “layer” (“convolution layer” “recurrent layer”) “block” (“ResNet block” “Inception block”). Meanwhile, Model class corresponds referred literature “model” (“deep learning model”) “network” (“deep neural network”). ’re wondering, “use Layer class Model class?”, ask : need call fit() ? need call save() ? , go Model. (either class just block bigger system, writing training & saving code ), use Layer. instance, take mini-resnet example , use build Model train fit(), save save_weights():","code":"ResNet <- Model(   \"ResNet\",   initialize = function(num_classes = 1000, ...) {     super$initialize(...)     self$block_1 <- layer_resnet_block()     self$block_2 <- layer_resnet_block()     self$global_pool <- layer_global_average_pooling_2d()     self$classifier <- layer_dense(num_classes)   },   call = function(inputs) {     inputs |>       self$block_1() |>       self$block_2() |>       self$global_pool() |>       self$classifier()   } )  resnet <- ResNet() dataset <- ... resnet |> fit(dataset, epochs=10) resnet |> save_model(\"filepath.keras\")"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"putting-it-all-together-an-end-to-end-example","dir":"Articles","previous_headings":"","what":"Putting it all together: an end-to-end example","title":"Making new layers and models via subclassing","text":"’s ’ve learned far: Layer encapsulate state (created initialize() build()) computation (defined call()). Layers can recursively nested create new, bigger computation blocks. Layers backend-agnostic long use Keras APIs. can use backend-native APIs (jax$numpy, torch$nn tf$nn), layer usable specific backend. Layers can create track losses (typically regularization losses) via add_loss(). outer container, thing want train, Model. Model just like Layer, added training serialization utilities. Let’s put things together end--end example: ’re going implement Variational AutoEncoder (VAE) backend-agnostic fashion – runs TensorFlow, JAX, PyTorch. ’ll train MNIST digits. VAE subclass Model, built nested composition layers subclass Layer. feature regularization loss (KL divergence). Let’s train MNIST using fit() API:","code":"layer_sampling <- Layer(   \"Sampling\",   initialize = function(...) {     super$initialize(...)     self$seed_generator <- random_seed_generator(1337)   },   call = function(inputs) {     c(z_mean, z_log_var) %<-% inputs     batch <- op_shape(z_mean)[[1]]     dim <- op_shape(z_mean)[[2]]     epsilon <- random_normal(shape = c(batch, dim),                              seed=self$seed_generator)     z_mean + op_exp(0.5 * z_log_var) * epsilon   } )  # Maps MNIST digits to a triplet (z_mean, z_log_var, z). layer_encoder <- Layer(   \"Encoder\",   initialize = function(latent_dim = 32, intermediate_dim = 64, ...) {     super$initialize(...)     self$dense_proj <-       layer_dense(units = intermediate_dim,  activation = \"relu\")     self$dense_mean <- layer_dense(units = latent_dim)     self$dense_log_var <- layer_dense(units = latent_dim)     self$sampling <- layer_sampling()   },   call = function(inputs) {     x <- self$dense_proj(inputs)     z_mean <- self$dense_mean(x)     z_log_var <- self$dense_log_var(x)     z <- self$sampling(list(z_mean, z_log_var))     list(z_mean, z_log_var, z)   } )  # Converts z, the encoded digit vector, back into a readable digit. layer_decoder <- Layer(   \"Decoder\",   initialize = function(original_dim, intermediate_dim = 64, ...) {     super$initialize(...)     self$dense_proj <-       layer_dense(units = intermediate_dim, activation = \"relu\")     self$dense_output <-       layer_dense(units = original_dim, activation = \"sigmoid\")   },   call = function(inputs) {     x <- self$dense_proj(inputs)     self$dense_output(x)   } )  # Combines the encoder and decoder into an end-to-end model for training. VariationalAutoEncoder <- Model(   \"VariationalAutoEncoder\",    initialize = function(original_dim, intermediate_dim = 64, latent_dim = 32,                         name = \"autoencoder\", ...) {     super$initialize(name = name, ...)     self$original_dim <- original_dim     self$encoder <- layer_encoder(latent_dim = latent_dim,                             intermediate_dim = intermediate_dim)     self$decoder <- layer_decoder(original_dim = original_dim,                             intermediate_dim = intermediate_dim)   },    call = function(inputs) {     c(z_mean, z_log_var, z) %<-% self$encoder(inputs)     reconstructed <- self$decoder(z)     # Add KL divergence regularization loss.     kl_loss <- -0.5 * op_mean(z_log_var - op_square(z_mean) - op_exp(z_log_var) + 1)     self$add_loss(kl_loss)     reconstructed   } ) c(c(x_train, .), .) %<-% dataset_mnist() x_train <- x_train |>   op_reshape(c(60000, 784)) |>   op_cast(\"float32\") |>   op_divide(255)  original_dim <- 784 vae <- VariationalAutoEncoder(   original_dim = 784,   intermediate_dim = 64,   latent_dim = 32 )  optimizer <- optimizer_adam(learning_rate = 1e-3) vae |> compile(optimizer, loss = loss_mean_squared_error())  vae |> fit(x_train, x_train, epochs = 2, batch_size = 64) ## Epoch 1/2 ## 938/938 - 5s - 5ms/step - loss: 0.0748 ## Epoch 2/2 ## 938/938 - 1s - 959us/step - loss: 0.0676"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"The Sequential model","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"when-to-use-a-sequential-model","dir":"Articles","previous_headings":"","what":"When to use a Sequential model","title":"The Sequential model","text":"Sequential model appropriate plain stack layers layer exactly one input tensor one output tensor. Schematically, following Sequential model: equivalent function: Sequential model appropriate : model multiple inputs multiple outputs layers multiple inputs multiple outputs need layer sharing want non-linear topology (e.g. residual connection, multi-branch model)","code":"model <- keras_model_sequential() |>   layer_dense(units = 2, activation = \"relu\", name = \"layer1\") |>   layer_dense(units = 3, activation = \"relu\", name = \"layer2\") |>   layer_dense(units = 4, name = \"layer3\")  # Call model on a test input x <- op_ones(c(3, 3)) y <- model(x) # Create 3 layers layer1 <- layer_dense(units = 2, activation=\"relu\", name=\"layer1\") layer2 <- layer_dense(units = 3, activation=\"relu\", name=\"layer2\") layer3 <- layer_dense(units = 4, name=\"layer3\")  # Call layers on a test input x <- op_ones(c(3, 3)) y <- x |> layer1() |> layer2() |> layer3()"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"creating-a-sequential-model","dir":"Articles","previous_headings":"","what":"Creating a Sequential model","title":"The Sequential model","text":"can create Sequential model piping layers keras_model_sequential() object: passing list layers keras_model_sequential(): layers accessible via layers attribute: can also create Sequential model incrementally: Note ’s also corresponding pop_layer() method remove layers: Sequential model behaves much like stack layers. Also note Sequential constructor accepts name argument, just like layer model Keras. useful annotate TensorBoard graphs semantically meaningful names.","code":"model <- keras_model_sequential() |>   layer_dense(units = 2, activation = \"relu\") |>   layer_dense(units = 3, activation = \"relu\") |>   layer_dense(units = 4) model <- keras_model_sequential(layers = list(   layer_dense(units = 2, activation = \"relu\"),   layer_dense(units = 3, activation = \"relu\"),   layer_dense(units = 4) )) model$layers ## [[1]] ## <Dense name=dense_3, built=False> ##  signature: (*args, **kwargs) ## ## [[2]] ## <Dense name=dense_4, built=False> ##  signature: (*args, **kwargs) ## ## [[3]] ## <Dense name=dense_5, built=False> ##  signature: (*args, **kwargs) model <- keras_model_sequential() model |> layer_dense(units = 2, activation=\"relu\") model |> layer_dense(units = 3, activation=\"relu\") model |> layer_dense(units = 4) model |> pop_layer() length(model$layers)  # 2 ## [1] 2 model <- keras_model_sequential(name = \"my_sequential\") model |> layer_dense(units = 2, activation=\"relu\", name = \"layer1\") model |> layer_dense(units = 3, activation=\"relu\", name = \"layer2\") model |> layer_dense(units = 4, name = \"layer3\")"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"specifying-the-input-shape-in-advance","dir":"Articles","previous_headings":"","what":"Specifying the input shape in advance","title":"The Sequential model","text":"Generally, layers Keras need know shape inputs order able create weights. create layer like , initially, weights: creates weights first time called input, since shape weights depends shape inputs: Naturally, also applies Sequential models. instantiate Sequential model without input shape, isn’t “built”: weights (calling model$weights results error stating just ). weights created model first sees input data: model “built”, can call summary() method display contents: However, can useful building Sequential model incrementally able display summary model far, including current output shape. case, start model passing input_shape argument model, knows input shape start: Models built predefined input shape like always weights (even seeing data) always defined output shape. general, ’s recommended best practice always specify input shape Sequential model advance know .","code":"layer <- layer_dense(units = 3) layer$weights  # Empty ## list() # Call layer on a test input x <- op_ones(c(1, 4)) y <- layer(x) layer$weights  # Now it has weights, of shape (4, 3) and (3,) ## [[1]] ## <KerasVariable shape=(4, 3), dtype=float32, path=dense_9/kernel> ## ## [[2]] ## <KerasVariable shape=(3), dtype=float32, path=dense_9/bias> model <- keras_model_sequential() |>   layer_dense(units = 2, activation = \"relu\") |>   layer_dense(units = 3, activation = \"relu\") |>   layer_dense(units = 4) # No weights at this stage!  # At this point, you can't do this: # model$weights   # Call the model on a test input x <- op_ones(c(1, 4)) y <- model(x) length(model$weights) ## [1] 6 summary(model) ## Model: \"sequential_4\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_10 (Dense)                │ (1, 2)                 │            10 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_11 (Dense)                │ (1, 3)                 │             9 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_12 (Dense)                │ (1, 4)                 │            16 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 35 (140.00 B) ##  Trainable params: 35 (140.00 B) ##  Non-trainable params: 0 (0.00 B) model <- keras_model_sequential(input_shape = 4) |>   layer_dense(units = 2, activation = \"relu\") summary(model) ## Model: \"sequential_5\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_13 (Dense)                │ (None, 2)              │            10 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 10 (40.00 B) ##  Trainable params: 10 (40.00 B) ##  Non-trainable params: 0 (0.00 B) model$layers ## [[1]] ## <Dense name=dense_13, built=True> ##  signature: (*args, **kwargs)"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"a-common-debugging-workflow-add-layers-summary","dir":"Articles","previous_headings":"","what":"A common debugging workflow: add layers + summary()","title":"The Sequential model","text":"building new Sequential architecture, ’s useful incrementally stack layers |> frequently print model summaries. instance, enables monitor stack Conv2D MaxPooling2D layers downsampling image feature maps: practical, right? Note |> equivalent calling model$add(), modifies model -place, don’t need reassign model symbol step.","code":"model <- keras_model_sequential(input_shape = c(250, 250, 3)) |>   layer_conv_2d(filters = 32, kernel_size = 5, strides = 2, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(3, 3))  # Can you guess what the current output shape is at this point? Probably not. # Let's just print it: summary(model) ## Model: \"sequential_6\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 123, 123, 32)   │         2,432 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 121, 121, 32)   │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 40, 40, 32)     │             0 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 11,680 (45.62 KB) ##  Trainable params: 11,680 (45.62 KB) ##  Non-trainable params: 0 (0.00 B) # The answer was: (40, 40, 32), so we can keep downsampling...  model |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_max_pooling_2d(pool_size = 3) |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_max_pooling_2d(pool_size = 2)  # And now? summary(model) ## Model: \"sequential_6\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 123, 123, 32)   │         2,432 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 121, 121, 32)   │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 40, 40, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 38, 38, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_3 (Conv2D)               │ (None, 36, 36, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 12, 12, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_4 (Conv2D)               │ (None, 10, 10, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_5 (Conv2D)               │ (None, 8, 8, 32)       │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_2 (MaxPooling2D)  │ (None, 4, 4, 32)       │             0 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 48,672 (190.12 KB) ##  Trainable params: 48,672 (190.12 KB) ##  Non-trainable params: 0 (0.00 B) # Now that we have 4x4 feature maps, time to apply global max pooling. model |>   layer_global_max_pooling_2d()  # Finally, we add a classification layer. model |>   layer_dense(units = 10, activation = \"softmax\")"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"what-to-do-once-you-have-a-model","dir":"Articles","previous_headings":"","what":"What to do once you have a model","title":"The Sequential model","text":"model architecture ready, want : Train model, evaluate , run inference. See guide training & evaluation built-loops Save model disk restore . See guide serialization & saving.","code":""},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"feature-extraction-with-a-sequential-model","dir":"Articles","previous_headings":"","what":"Feature extraction with a Sequential model","title":"The Sequential model","text":"Sequential model built, behaves like Functional API model. means every layer input output attribute. attributes can used neat things, like quickly creating model extracts outputs intermediate layers Sequential model: ’s similar example extract features one layer:","code":"initial_model <- keras_model_sequential(input_shape = c(250, 250, 3)) |>   layer_conv_2d(filters = 32, kernel_size = 5, strides = 2, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\")   feature_extractor <- keras_model(     inputs = initial_model$inputs,     outputs = lapply(initial_model$layers, function(x) x$output), )  # Call feature extractor on test input. x <- op_ones(c(1, 250, 250, 3)) features <- feature_extractor(x) initial_model <-   keras_model_sequential(input_shape = c(250, 250, 3)) |>   layer_conv_2d(filters = 32, kernel_size = 5, strides = 2,                 activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\",                 name = \"my_intermediate_layer\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\")  feature_extractor <- keras_model(   inputs = initial_model$inputs,   outputs = get_layer(initial_model, \"my_intermediate_layer\")$output, )  # Call feature extractor on test input. x <- op_ones(c(1, 250, 250, 3)) features <- feature_extractor(x)"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"transfer-learning-with-a-sequential-model","dir":"Articles","previous_headings":"","what":"Transfer learning with a Sequential model","title":"The Sequential model","text":"Transfer learning consists freezing bottom layers model training top layers. aren’t familiar , make sure read guide transfer learning. two common transfer learning blueprint involving Sequential models. First, let’s say Sequential model, want freeze layers except last one. case, can call freeze_weights(). Alternatively, can iterate model$layers set layer$trainable <- FALSE layer, except last one. Like : Another common blueprint use Sequential model stack pre-trained model freshly initialized classification layers. Like : transfer learning, probably find frequently using two patterns. ’s need know Sequential models! find building models Keras, see: Guide Functional API Guide making new Layers & Models via subclassing","code":"model <- keras_model_sequential(input_shape = 784) |>   layer_dense(units = 32, activation = \"relu\") |>   layer_dense(units = 32, activation = \"relu\") |>   layer_dense(units = 32, activation = \"relu\") |>   layer_dense(units = 10) # Presumably you would want to first load pre-trained weights. model |> load_model_weights(...) # Freeze all layers except the last one. model |> freeze_weights(from = 1, to = -2) model # note the \"Trainable\" column now visible in the summary table ## Model: \"sequential_9\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)                ┃ Output Shape          ┃    Param # ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩ ## │ dense_15 (Dense)            │ (None, 32)            │     25,120 │   N   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_16 (Dense)            │ (None, 32)            │      1,056 │   N   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_17 (Dense)            │ (None, 32)            │      1,056 │   N   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_18 (Dense)            │ (None, 10)            │        330 │   Y   │ ## └─────────────────────────────┴───────────────────────┴────────────┴───────┘ ##  Total params: 27,562 (107.66 KB) ##  Trainable params: 330 (1.29 KB) ##  Non-trainable params: 27,232 (106.38 KB) # Another way to freeze all layers except the last one. for (layer in model$layers[-length(model$layers)]) {   layer$trainable <- FALSE }  # Recompile and train (this will only update the weights of the last layer). model |> compile(...) model |> fit(...) # Load a convolutional base with pre-trained weights base_model <- application_xception(weights = 'imagenet',                                    include_top = FALSE,                                    pooling = 'avg')  # Freeze the base model freeze_weights(base_model)  # Use a Sequential model to add a trainable classifier on top model <- keras_model_sequential() |>   base_model() |>   layer_dense(1000) # Compile & train model |> compile(...) model |> fit(...)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Save, serialize, and export models","text":"Keras model consists multiple components: architecture, configuration, specifies layers model contain, ’re connected. set weights values (“state model”). optimizer (defined compiling model). set losses metrics (defined compiling model). Keras API saves pieces together unified format, marked .keras extension. zip archive consisting following: JSON-based configuration file (config.json): Records model, layer, trackables’ configuration. H5-based state file, model.weights.h5 (whole model), directory keys layers weights. metadata file JSON, storing things current Keras version. Let’s take look works.","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"how-to-save-and-load-a-model","dir":"Articles","previous_headings":"","what":"How to save and load a model","title":"Save, serialize, and export models","text":"10 seconds read guide, ’s need know. Saving Keras model: Loading model back: Now, let’s look details.","code":"# Get model (Sequential, Functional Model, or Model subclass) model <- ...  # The filename needs to end with the .keras extension model |> save_model('path/to/location.keras') model <- load_model('path/to/location.keras')"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Save, serialize, and export models","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"saving","dir":"Articles","previous_headings":"","what":"Saving","title":"Save, serialize, and export models","text":"section saving entire model single file. file include: model’s architecture/config model’s weight values (learned training) model’s compilation information (compile() called) optimizer state, (enables restart training left)","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"apis","dir":"Articles","previous_headings":"Saving","what":"APIs","title":"Save, serialize, and export models","text":"can save model save_model(). can load back load_model(). supported format Keras 3 “Keras v3” format, uses .keras extension. Example:","code":"get_model <- function() {   # Create a simple model.   inputs <- keras_input(shape(32))   outputs <- inputs |> layer_dense(1)   model <-  keras_model(inputs, outputs)   model |> compile(optimizer = optimizer_adam(), loss = \"mean_squared_error\")   model }  model <- get_model()  # Train the model. test_input <- random_uniform(c(128, 32)) test_target <- random_uniform(c(128, 1)) model |> fit(test_input, test_target)  # Calling `save('my_model.keras')` creates a zip archive `my_model.keras`. model |> save_model(\"my_model.keras\")  # It can be used to reconstruct the model identically. reconstructed_model <- load_model(\"my_model.keras\")  # Let's check: stopifnot(all.equal(   model |> predict(test_input),   reconstructed_model |> predict(test_input) ))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"custom-objects","dir":"Articles","previous_headings":"Saving","what":"Custom objects","title":"Save, serialize, and export models","text":"section covers basic workflows handling custom layers, functions, models Keras saving reloading. saving model includes custom objects, subclassed Layer, must define get_config() method object class. arguments passed constructor (initialize() method) custom object aren’t simple objects (anything types like ints, strings, etc.), must also explicitly deserialize arguments from_config() class method. Like : Please see Defining config methods section details examples. saved .keras file lightweight store Python code custom objects. Therefore, reload model, load_model requires access definition custom objects used one following methods: Registering custom objects (preferred), Passing custom objects directly loading, Using custom object scope examples workflow:","code":"layer_custom <- Layer(   \"CustomLayer\",   initialize = function(sublayer, ...) {     super$initialize(...)     self$sublayer <- sublayer   },   call = function(x) {     self$sublayer(x)   },   get_config = function() {     base_config <- super$get_config()     config <- list(       sublayer = serialize_keras_object(self$sublayer)     )     c(base_config, config)   },   from_config = function(cls, config) {     sublayer_config <- config$sublayer     sublayer <- deserialize_keras_object(sublayer_config)     cls(sublayer, !!!config)   } )"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"registering-custom-objects-preferred","dir":"Articles","previous_headings":"Saving > Custom objects","what":"Registering custom objects (preferred)","title":"Save, serialize, and export models","text":"preferred method, custom object registration greatly simplifies saving loading code. Calling register_keras_serializable() custom object registers object globally master list, allowing Keras recognize object loading model. Let’s create custom model involving custom layer custom activation function demonstrate . Example:","code":"# Clear all previously registered custom objects set_custom_objects(clear = TRUE) ## named list() layer_custom <- Layer(   \"CustomLayer\",   initialize = function(self, factor) {     super$initialize()     self$factor = factor   },    call = function(self, x) {     x * self$factor   },    get_config = function(self) {     list(factor = self$factor)   } )  # Upon registration, you can optionally specify a package or a name. # If left blank, the package defaults to \"Custom\" and the name defaults to # the class name. register_keras_serializable(layer_custom, package = \"MyLayers\")  custom_fn <- keras3:::py_func2(function(x) x^2, name = \"custom_fn\", convert = TRUE)  register_keras_serializable(custom_fn, name=\"custom_fn\", package=\"my_package\")   # Create the model. get_model <- function() {   inputs <- keras_input(shape(4))   mid <- inputs |> layer_custom(0.5)   outputs <- mid |> layer_dense(1, activation = custom_fn)   model <- keras_model(inputs, outputs)   model |> compile(optimizer = \"rmsprop\", loss = \"mean_squared_error\")   model }   # Train the model. train_model <- function(model) {   input <- random_uniform(c(4, 4))   target <- random_uniform(c(4, 1))   model |> fit(input, target, verbose = FALSE, epochs = 1)   model }  test_input <- random_uniform(c(4, 4)) test_target <- random_uniform(c(4, 1))  model <- get_model() |> train_model() model |> save_model(\"custom_model.keras\", overwrite = TRUE)  # Now, we can simply load without worrying about our custom objects. reconstructed_model <- load_model(\"custom_model.keras\")  # Let's check: stopifnot(all.equal(   model |> predict(test_input, verbose = FALSE),   reconstructed_model |> predict(test_input, verbose = FALSE) ))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"passing-custom-objects-to-load_model","dir":"Articles","previous_headings":"Saving > Custom objects","what":"Passing custom objects to load_model()","title":"Save, serialize, and export models","text":"","code":"model <- get_model() |> train_model()  # Calling `save_model('my_model.keras')` creates a zip archive `my_model.keras`. model |> save_model(\"custom_model.keras\", overwrite = TRUE)  # Upon loading, pass a named list containing the custom objects used in the # `custom_objects` argument of `load_model()`. reconstructed_model <-  load_model(  \"custom_model.keras\",   custom_objects = list(CustomLayer = layer_custom,                         custom_fn = custom_fn), )  # Let's check: stopifnot(all.equal(   model |> predict(test_input, verbose = FALSE),   reconstructed_model |> predict(test_input, verbose = FALSE) ))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"using-a-custom-object-scope","dir":"Articles","previous_headings":"Saving > Custom objects","what":"Using a custom object scope","title":"Save, serialize, and export models","text":"code within custom object scope able recognize custom objects passed scope argument. Therefore, loading model within scope allow loading custom objects. Example:","code":"model <- get_model() |> train_model() model |> save_model(\"custom_model.keras\", overwrite = TRUE)  # Pass the custom objects dictionary to a custom object scope and place # the `keras.models.load_model()` call within the scope. custom_objects <- list(CustomLayer = layer_custom, custom_fn = custom_fn)  with_custom_object_scope(custom_objects, {   reconstructed_model <- load_model(\"custom_model.keras\") })  # Let's check: stopifnot(all.equal(   model |> predict(test_input, verbose = FALSE),   reconstructed_model |> predict(test_input, verbose = FALSE) ))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"model-serialization","dir":"Articles","previous_headings":"Saving","what":"Model serialization","title":"Save, serialize, and export models","text":"section saving model’s configuration, without state. model’s configuration (architecture) specifies layers model contains, layers connected. configuration model, model can created freshly initialized state (weights compilation information).","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"apis-1","dir":"Articles","previous_headings":"Saving > Model serialization","what":"APIs","title":"Save, serialize, and export models","text":"following serialization APIs available: clone_model(model): make (randomly initialized) copy model. get_config() cls.from_config(): retrieve configuration layer model, recreate model instance config, respectively. keras.models.model_to_json() keras.models.model_from_json(): similar, JSON strings. keras.saving.serialize_keras_object(): retrieve configuration arbitrary Keras object. keras.saving.deserialize_keras_object(): recreate object instance configuration.","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"in-memory-model-cloning","dir":"Articles","previous_headings":"Saving > Model serialization","what":"In-memory model cloning","title":"Save, serialize, and export models","text":"can -memory cloning model via clone_model(). equivalent getting config recreating model config (preserve compilation information layer weights values). Example:","code":"new_model <- clone_model(model)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"get_config-and-from_config","dir":"Articles","previous_headings":"Saving > Model serialization","what":"get_config() and from_config()","title":"Save, serialize, and export models","text":"Calling get_config(model) get_config(layer) return named list containing configuration model layer, respectively. define get_config() contain arguments needed initialize() method model layer. loading time, from_config(config) method call initialize() arguments reconstruct model layer. Layer example: Now let’s reconstruct layer using from_config() method: Sequential model example: Functional model example:","code":"layer <- layer_dense(, 3, activation=\"relu\") layer_config <- get_config(layer) str(layer_config) ## List of 12 ##  $ name              : chr \"dense_4\" ##  $ trainable         : logi TRUE ##  $ dtype             : chr \"float32\" ##  $ units             : int 3 ##  $ activation        : chr \"relu\" ##  $ use_bias          : logi TRUE ##  $ kernel_initializer:List of 4 ##   ..$ module         : chr \"keras.initializers\" ##   ..$ class_name     : chr \"GlorotUniform\" ##   ..$ config         :List of 1 ##   .. ..$ seed: NULL ##   ..$ registered_name: NULL ##  $ bias_initializer  :List of 4 ##   ..$ module         : chr \"keras.initializers\" ##   ..$ class_name     : chr \"Zeros\" ##   ..$ config         : Named list() ##   ..$ registered_name: NULL ##  $ kernel_regularizer: NULL ##  $ bias_regularizer  : NULL ##  $ kernel_constraint : NULL ##  $ bias_constraint   : NULL ##  - attr(*, \"__class__\")=<class 'keras.src.layers.core.dense.Dense'> new_layer <- from_config(layer_config) model <- keras_model_sequential(input_shape = c(32)) |>   layer_dense(1) config <- get_config(model) new_model <- from_config(config) inputs <- keras_input(c(32)) outputs <- inputs |> layer_dense(1) model <- keras_model(inputs, outputs) config <- get_config(model) new_model <- from_config(config)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"save_model_config-and-load_model_config","dir":"Articles","previous_headings":"Saving > Model serialization","what":"save_model_config() and load_model_config()","title":"Save, serialize, and export models","text":"similar get_config / from_config, except turns model JSON file, can loaded without original model class. also specific models, isn’t meant layers. Example:","code":"model <- keras_model_sequential(input_shape = c(32)) |>   layer_dense(1) save_model_config(model, \"model_config.json\") new_model <- load_model_config(\"model_config.json\") unlink(\"model_config.json\")"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"arbitrary-object-serialization-and-deserialization","dir":"Articles","previous_headings":"Saving > Model serialization","what":"Arbitrary object serialization and deserialization","title":"Save, serialize, and export models","text":"serialize_keras_object() deserialize_keras_object() APIs general-purpose APIs can used serialize deserialize Keras object custom object. foundation saving model architecture behind serialize()/deserialize() calls keras. Example: Note serialization format containing necessary information proper reconstruction: module containing name Keras module identifying module object comes class_name containing name object’s class. config information needed reconstruct object registered_name custom objects. See . Now can reconstruct regularizer.","code":"my_reg <- regularizer_l1(0.005) config <- serialize_keras_object(my_reg) str(config) ## List of 4 ##  $ module         : chr \"keras.regularizers\" ##  $ class_name     : chr \"L1\" ##  $ config         :List of 1 ##   ..$ l1: num 0.005 ##  $ registered_name: NULL new_reg <- deserialize_keras_object(config) new_reg ## <keras.src.regularizers.regularizers.L1 object> ##  signature: (x)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"model-weights-saving","dir":"Articles","previous_headings":"Saving","what":"Model weights saving","title":"Save, serialize, and export models","text":"can choose save & load model’s weights. can useful : need model inference: case won’t need restart training, don’t need compilation information optimizer state. transfer learning: case training new model reusing state prior model, don’t need compilation information prior model.","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"apis-for-in-memory-weight-transfer","dir":"Articles","previous_headings":"Saving > Model weights saving","what":"APIs for in-memory weight transfer","title":"Save, serialize, and export models","text":"Weights can copied different objects using get_weights() set_weights(): get_weights(<layer>): Returns list arrays weight values. set_weights(<layer>weights): Sets model/layer weights values provided (arrays). Examples: Transferring weights one layer another, memory Transferring weights one model another model compatible architecture, memory case stateless layers stateless layers change order number weights, models can compatible architectures even extra/missing stateless layers.","code":"create_layer <- function() {   layer <- layer_dense(, 64, activation = \"relu\", name = \"dense_2\")   layer$build(shape(NA, 784))   layer }  layer_1 <- create_layer() layer_2 <- create_layer()  # Copy weights from layer 1 to layer 2 layer_2 |> set_weights(get_weights(layer_1)) # Create a simple functional model inputs <- keras_input(shape=c(784), name=\"digits\") outputs <- inputs |>   layer_dense(64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(10, name = \"predictions\") functional_model <- keras_model(inputs = inputs, outputs = outputs,                                name = \"3_layer_mlp\")  # Define a subclassed model with the same architecture SubclassedModel <- new_model_class(   \"SubclassedModel\",   initialize = function(output_dim, name = NULL) {     super$initialize(name = name)     self$output_dim <- output_dim |> as.integer()     self$dense_1 <- layer_dense(, 64, activation = \"relu\",                                 name = \"dense_1\")     self$dense_2 <- layer_dense(, 64, activation = \"relu\",                                 name = \"dense_2\")     self$dense_3 <- layer_dense(, self$output_dim,                                 name = \"predictions\")   },    call = function(inputs) {     inputs |>       self$dense_1() |>       self$dense_2() |>       self$dense_3()   },    get_config = function(self) {     list(output_dim = self$output_dim,          name = self$name)   } )   subclassed_model <- SubclassedModel(10) # Call the subclassed model once to create the weights. subclassed_model(op_ones(c(1, 784))) |> invisible()  # Copy weights from functional_model to subclassed_model. set_weights(subclassed_model, get_weights(functional_model))  stopifnot(all.equal(   get_weights(functional_model),   get_weights(subclassed_model) )) input <- keras_input(shape = c(784), name = \"digits\") output <- input |>   layer_dense(64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(10, name = \"predictions\") functional_model <- keras_model(inputs, outputs,                                 name = \"3_layer_mlp\")  input <- keras_input(shape = c(784), name = \"digits\") output <- input |>   layer_dense(64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(64, activation = \"relu\", name = \"dense_2\") |>   # Add a dropout layer, which does not contain any weights.   layer_dropout(0.5) |>   layer_dense(10, name = \"predictions\")  functional_model_with_dropout <-   keras_model(input, output, name = \"3_layer_mlp\")  set_weights(functional_model_with_dropout,             get_weights(functional_model))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"apis-for-saving-weights-to-disk-loading-them-back","dir":"Articles","previous_headings":"Saving > Model weights saving","what":"APIs for saving weights to disk & loading them back","title":"Save, serialize, and export models","text":"Weights can saved disk calling save_model_weights(filepath). filename end .weights.h5. Example: Note using freeze_weights() may result different output get_weights(layer) ordering model contains nested layers.","code":"sequential_model = keras_model_sequential(input_shape = c(784),                                           input_name = \"digits\") |>   layer_dense(64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(10, name = \"predictions\") sequential_model |> save_model_weights(\"my_model.weights.h5\") sequential_model |> load_model_weights(\"my_model.weights.h5\")"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"transfer-learning-example","dir":"Articles","previous_headings":"Saving > Model weights saving > APIs for saving weights to disk & loading them back","what":"Transfer learning example","title":"Save, serialize, and export models","text":"loading pretrained weights weights file, recommended load weights original checkpointed model, extract desired weights/layers new model. Example:","code":"create_functional_model <- function() {   inputs <- keras_input(shape = c(784), name = \"digits\")   outputs <- inputs |>     layer_dense(64, activation = \"relu\", name = \"dense_1\") |>     layer_dense(64, activation = \"relu\", name = \"dense_2\") |>     layer_dense(10, name = \"predictions\")   keras_model(inputs, outputs, name = \"3_layer_mlp\") }  functional_model <- create_functional_model() functional_model |> save_model_weights(\"pretrained.weights.h5\")  # In a separate program: pretrained_model <- create_functional_model() pretrained_model |> load_model_weights(\"pretrained.weights.h5\")  # Create a new model by extracting layers from the original model: extracted_layers <- pretrained_model$layers |> head(-1) model <- keras_model_sequential(layers = extracted_layers) |>   layer_dense(5, name = \"dense_3\") summary(model) ## Model: \"sequential_4\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_1 (Dense)                 │ (None, 64)             │        50,240 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 64)             │         4,160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_3 (Dense)                 │ (None, 5)              │           325 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 54,725 (213.77 KB) ##  Trainable params: 54,725 (213.77 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":[]},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"defining-the-config-methods","dir":"Articles","previous_headings":"Saving > Appendix: Handling custom objects","what":"Defining the config methods","title":"Save, serialize, and export models","text":"Specifications: get_config() return JSON-serializable named list order compatible Keras architecture model-saving APIs. from_config(config) (class method) return new layer model object created config. default implementation returns .call(cls, config). NOTE: constructor arguments already serializable, e.g. strings ints, non-custom Keras objects, overriding from_config() necessary. However, complex objects layers models passed initialize(), deserialization must handled explicitly either initialize overriding from_config() method. Example: Note overriding from_config unnecessary MyDense hidden_units, kernel_initializer, kernel_regularizer ints, strings, built-Keras object, respectively. means default from_config implementation cls(!!!config) work intended. complex objects, layers models passed initialize(), example, must explicitly deserialize objects. Let’s take look example model from_config override necessary. Example:","code":"layer_my_dense <- register_keras_serializable(   package = \"MyLayers\", name = \"KernelMult\",   object = Layer(     \"MyDense\",     initialize = function(units,                           ...,                           kernel_regularizer = NULL,                           kernel_initializer = NULL,                           nested_model = NULL) {       super$initialize(...)       self$hidden_units <- units       self$kernel_regularizer <- kernel_regularizer       self$kernel_initializer <- kernel_initializer       self$nested_model <- nested_model     },     get_config = function() {       config <- super$get_config()       # Update the config with the custom layer's parameters       config <- modifyList(config, list(         units = self$hidden_units,         kernel_regularizer = self$kernel_regularizer,         kernel_initializer = self$kernel_initializer,         nested_model = self$nested_model       ))       config     },     build = function(input_shape) {       input_units <- tail(input_shape, 1)       self$kernel <- self$add_weight(         name = \"kernel\",         shape = shape(input_units, self$hidden_units),         regularizer = self$kernel_regularizer,         initializer = self$kernel_initializer,       )     },     call = function(inputs) {       op_matmul(inputs, self$kernel)     }   ) )   layer <- layer_my_dense(units = 16,                         kernel_regularizer = \"l1\",                         kernel_initializer = \"ones\") layer3 <- layer_my_dense(units = 64, nested_model = layer)  config <- serialize_keras_object(layer3) str(config) ## List of 4 ##  $ module         : chr \"<r-globalenv>\" ##  $ class_name     : chr \"MyDense\" ##  $ config         :List of 5 ##   ..$ name        : chr \"my_dense_1\" ##   ..$ trainable   : logi TRUE ##   ..$ dtype       : chr \"float32\" ##   ..$ units       : num 64 ##   ..$ nested_model:List of 4 ##   .. ..$ module         : chr \"<r-globalenv>\" ##   .. ..$ class_name     : chr \"MyDense\" ##   .. ..$ config         :List of 6 ##   .. .. ..$ name              : chr \"my_dense\" ##   .. .. ..$ trainable         : logi TRUE ##   .. .. ..$ dtype             : chr \"float32\" ##   .. .. ..$ units             : num 16 ##   .. .. ..$ kernel_regularizer: chr \"l1\" ##   .. .. ..$ kernel_initializer: chr \"ones\" ##   .. ..$ registered_name: chr \"MyLayers>KernelMult\" ##  $ registered_name: chr \"MyLayers>KernelMult\" new_layer <- deserialize_keras_object(config) new_layer ## <MyDense name=my_dense_1, built=False> ##  signature: (*args, **kwargs) `%||%` <- \\(x, y) if(is.null(x)) y else x layer_custom_model <- register_keras_serializable(   package = \"ComplexModels\",   object = Layer(     \"CustomModel\",     initialize = function(first_layer, second_layer = NULL, ...) {       super$initialize(...)       self$first_layer <- first_layer       self$second_layer <- second_layer %||% layer_dense(, 8)     },      get_config = function() {       config <- super$get_config()       config <- modifyList(config, list(         first_layer = self$first_layer,         second_layer = self$second_layer       ))       config     },      from_config = function(config) {       config$first_layer %<>% deserialize_keras_object()       config$second_layer %<>% deserialize_keras_object()       # note that the class is available in methods under the classname symbol,       # (`CustomModel` for this class), and also under the symbol `__class__`       cls(!!!config)       # CustomModel(!!!config)     },     call = function(self, inputs) {       inputs |>         self$first_layer() |>         self$second_layer()     }   ) )  # Let's make our first layer the custom layer from the previous example (MyDense) inputs <- keras_input(c(32)) outputs <-  inputs |> layer_custom_model(first_layer=layer) model <- keras_model(inputs, outputs)  config <- get_config(model) new_model <- from_config(config)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"how-custom-objects-are-serialized","dir":"Articles","previous_headings":"Saving > Appendix: Handling custom objects","what":"How custom objects are serialized","title":"Save, serialize, and export models","text":"serialization format special key custom objects registered via register_keras_serializable(). registered_name key allows easy retrieval loading/deserialization time also allowing users add custom naming. Let’s take look config serializing custom layer MyDense defined . Example: shown, registered_name key contains lookup information Keras master list, including package MyLayers custom name KernelMult gave calling register_keras_serializables(). Take look custom class definition/registration . Note class_name key contains original name class, allowing proper re-initialization from_config. Additionally, note module key NULL since custom object.","code":"layer <- layer_my_dense(   units = 16,   kernel_regularizer = regularizer_l1_l2(l1 = 1e-5, l2 = 1e-4),   kernel_initializer = \"ones\", ) config <- serialize_keras_object(layer) str(config) ## List of 4 ##  $ module         : chr \"<r-globalenv>\" ##  $ class_name     : chr \"MyDense\" ##  $ config         :List of 6 ##   ..$ name              : chr \"my_dense_2\" ##   ..$ trainable         : logi TRUE ##   ..$ dtype             : chr \"float32\" ##   ..$ units             : num 16 ##   ..$ kernel_regularizer:List of 4 ##   .. ..$ module         : chr \"keras.regularizers\" ##   .. ..$ class_name     : chr \"L1L2\" ##   .. ..$ config         :List of 2 ##   .. .. ..$ l1: num 1e-05 ##   .. .. ..$ l2: num 1e-04 ##   .. ..$ registered_name: NULL ##   ..$ kernel_initializer: chr \"ones\" ##  $ registered_name: chr \"MyLayers>KernelMult\""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Training & evaluation with the built-in methods","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Training & evaluation with the built-in methods","text":"guide covers training, evaluation, prediction (inference) models using built-APIs training & validation (fit(), evaluate() predict()). interested leveraging fit() specifying training step function, see Customizing happens fit() guide. interested writing training & evaluation loops scratch, see guide Writing training loop scratch. general, whether using built-loops writing , model training & evaluation works strictly way across every kind Keras model – Sequential models, models built Functional API, models written scratch via model subclassing.","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"api-overview-a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"API overview: a first end-to-end example","title":"Training & evaluation with the built-in methods","text":"passing data built-training loops model, either use: Arrays (data small fits memory) tf_dataset objects PyTorch DataLoader instances next paragraphs, ’ll use MNIST dataset NumPy arrays, order demonstrate use optimizers, losses, metrics. Afterwards, ’ll take close look options. Let’s consider following model (, build Functional API, Sequential model subclassed model well): ’s typical end--end workflow looks like, consisting : Training Validation holdout set generated original training data Evaluation test data ’ll use MNIST data example. specify training configuration (optimizer, loss, metrics): call fit(), train model slicing data “batches” size batch_size, repeatedly iterating entire dataset given number epochs. returned history object holds record loss values metric values training: evaluate model test data via evaluate(): Now, let’s review piece workflow detail.","code":"inputs <- keras_input(shape = 784, name=\"digits\") outputs <- inputs |>   layer_dense(units = 64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(units = 64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(units = 10, activation = \"softmax\", name = \"predictions\") model <- keras_model(inputs = inputs, outputs = outputs) summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ digits (InputLayer)             │ (None, 784)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_1 (Dense)                 │ (None, 64)             │        50,240 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 64)             │         4,160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ predictions (Dense)             │ (None, 10)             │           650 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 55,050 (215.04 KB) ##  Trainable params: 55,050 (215.04 KB) ##  Non-trainable params: 0 (0.00 B) c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()  # Preprocess the data (these are NumPy arrays) x_train <- array_reshape(x_train, c(60000, 784)) / 255 x_test <- array_reshape(x_test, c(10000, 784)) / 255  # Reserve 10,000 samples for validation x_val <- x_train[1:10000,] y_val <- y_train[1:10000] x_train <- x_train[-c(1:10000),] y_train <- y_train[-c(1:10000)] model |> compile(   # Optimizer   optimizer = optimizer_rmsprop(),   # Loss function to minimize   loss = loss_sparse_categorical_crossentropy(),   # List of metrics to monitor   metrics = list(metric_sparse_categorical_accuracy()) ) history <- model |> fit(   x_train, y_train,   batch_size = 64,   epochs = 2,   # We pass some validation for   # monitoring validation loss and metrics   # at the end of each epoch   validation_data = list(x_val, y_val) ) ## Epoch 1/2 ## 782/782 - 2s - 3ms/step - loss: 0.3410 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.1869 - val_sparse_categorical_accuracy: 0.9455 ## Epoch 2/2 ## 782/782 - 1s - 973us/step - loss: 0.1588 - sparse_categorical_accuracy: 0.9532 - val_loss: 0.1303 - val_sparse_categorical_accuracy: 0.9626 history ## ## Final epoch (plot to see history): ##                            loss: 0.1588 ##     sparse_categorical_accuracy: 0.9532 ##                        val_loss: 0.1303 ## val_sparse_categorical_accuracy: 0.9626 # Evaluate the model on the test data using `evaluate` results <- model |> evaluate(x_test, y_test, batch_size=128) ## 79/79 - 0s - 3ms/step - loss: 0.1258 - sparse_categorical_accuracy: 0.9625 results ## $loss ## [1] 0.1257554 ## ## $sparse_categorical_accuracy ## [1] 0.9625 # Generate predictions (probabilities -- the output of the last layer) # on new data using `predict` predictions <- model |> predict(x_test[1:2,]) ## 1/1 - 0s - 152ms/step dim(predictions) ## [1]  2 10"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"the-compile-method-specifying-a-loss-metrics-and-an-optimizer","dir":"Articles","previous_headings":"","what":"The compile() method: specifying a loss, metrics, and an optimizer","title":"Training & evaluation with the built-in methods","text":"train model fit(), need specify loss function, optimizer, optionally, metrics monitor. pass model arguments compile() method: metrics argument list – model can number metrics. model multiple outputs, can specify different losses metrics output, can modulate contribution output total loss model. find details Passing data multi-input, multi-output models section. Note ’re satisfied default settings, many cases optimizer, loss, metrics can specified via string identifiers shortcut: later reuse, let’s put model definition compile step functions; call several times across different examples guide.","code":"model |> compile(   optimizer = optimizer_rmsprop(learning_rate = 1e-3),   loss = loss_sparse_categorical_crossentropy(),   metrics = list(metric_sparse_categorical_accuracy()) ) model |> compile(   optimizer = \"rmsprop\",   loss = \"sparse_categorical_crossentropy\",   metrics = c(\"sparse_categorical_accuracy\") ) get_uncompiled_model <- function() {   inputs <- keras_input(shape = 784, name = \"digits\")   outputs <- inputs |>     layer_dense(units = 64, activation = \"relu\", name = \"dense_1\") |>     layer_dense(units = 64, activation = \"relu\", name = \"dense_2\") |>     layer_dense(units = 10, activation = \"softmax\", name = \"predictions\")   keras_model(inputs = inputs, outputs = outputs) }  get_compiled_model <- function() {   model <- get_uncompiled_model()   model |> compile(     optimizer = \"rmsprop\",     loss = \"sparse_categorical_crossentropy\",     metrics = c(\"sparse_categorical_accuracy\")   )   model }"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"many-built-in-optimizers-losses-and-metrics-are-available","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Many built-in optimizers, losses, and metrics are available","title":"Training & evaluation with the built-in methods","text":"general, won’t create losses, metrics, optimizers scratch, need likely already part Keras API: Optimizers: [optimizer_sgd()] (without momentum) [optimizer_rmsprop()] [optimizer_adam()] etc. Losses: [loss_mean_squared_error()] [loss_kl_divergence()] [loss_cosine_similarity()] etc. Metrics: [metric_auc()] [metric_precision()] [metric_recall()] etc.","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"custom-losses","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Custom losses","title":"Training & evaluation with the built-in methods","text":"need create custom loss, Keras provides three ways . first method involves creating function accepts inputs y_true y_pred. following example shows loss function computes mean squared error real data predictions: need loss function takes parameters beside y_true y_pred, can subclass Keras base Loss class using [Loss()] implement following two methods: initialize(): accept parameters pass call loss function call(y_true, y_pred): use targets (y_true) model predictions (y_pred) compute model’s loss Let’s say want use mean squared error, added term de-incentivize prediction values far 0.5 (assume categorical targets one-hot encoded take values 0 1). creates incentive model confident, may help reduce overfitting (won’t know works try!). ’s :","code":"custom_mean_squared_error <- function(y_true, y_pred) {   op_mean(op_square(y_true - y_pred), axis = -1) }  model <- get_uncompiled_model() model |> compile(optimizer = \"adam\",                  loss = custom_mean_squared_error)  # We need to one-hot encode the labels to use MSE y_train_one_hot <- op_one_hot(y_train, num_classes = 10) model |> fit(x_train, y_train_one_hot, batch_size = 64, epochs = 2) ## Epoch 1/2 ## 782/782 - 2s - 2ms/step - loss: 0.0161 ## Epoch 2/2 ## 782/782 - 1s - 677us/step - loss: 0.0078 loss_custom_mse <- Loss(   classname = \"CustomMSE\",   initialize = function(regularization_factor = 0.1, name = \"custom_mse\") {     super$initialize(name = name)     self$regularization_factor <- regularization_factor   },   call = function(y_true, y_pred) {     mse <- op_mean(op_square(y_true - y_pred), axis = -1)     reg <- op_mean(op_square(0.5 - y_pred), axis = -1)     mse + reg * self$regularization_factor   } )  model <- get_uncompiled_model() model |> compile(optimizer=\"adam\", loss = loss_custom_mse())  y_train_one_hot <- op_one_hot(y_train, num_classes=10) model |> fit(x_train, y_train_one_hot, batch_size=64, epochs=1) ## 782/782 - 2s - 2ms/step - loss: 0.0390"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"custom-metrics","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Custom metrics","title":"Training & evaluation with the built-in methods","text":"need metric isn’t part API, can easily create custom metrics subclassing Keras base Metric class using [Metric()]. need implement 4 methods: initialize(), create state variables metric. update_state(y_true, y_pred, sample_weight = NULL), uses targets y_true model predictions y_pred update state variables. result(), uses state variables compute final results. reset_state(), reinitializes state metric. State update results computation kept separate (update_state() result(), respectively) cases, results computation might expensive done periodically. ’s simple example showing implement CategoricalTruePositives metric counts many samples correctly classified belonging given class:","code":"metric_categorical_true_positives <- Metric(   \"CategoricalTruePositives\",    initialize = function(name = \"categorical_true_positives\", ...) {     super$initialize(name = name, ...)     self$true_positives <- self$add_variable(shape = shape(),                                              name = \"ctp\",                                              initializer = \"zeros\")   },    update_state = function(y_true, y_pred, sample_weight = NULL) {     y_pred <- op_argmax(y_pred, axis = 2) |> op_reshape(c(-1, 1))     values <- op_cast(y_true, \"int32\") == op_cast(y_pred, \"int32\")     values <- op_cast(values, \"float32\")     if (!is.null(sample_weight)) {       sample_weight <- op_cast(sample_weight, \"float32\")       values <- op_multiply(values, sample_weight)     }     self$true_positives$assign_add(op_sum(values))   },    result = function() {     self$true_positives$value   },    reset_state = function() {     self$true_positives$assign(0.0)   } )  model <- get_uncompiled_model() model |> compile(   optimizer = optimizer_rmsprop(learning_rate = 1e-3),   loss = loss_sparse_categorical_crossentropy(),   metrics = c(metric_categorical_true_positives()) ) history <- model |> fit(x_train, y_train, batch_size = 64, epochs = 3) ## Epoch 1/3 ## 782/782 - 1s - 2ms/step - categorical_true_positives: 360502.0000 - loss: 0.3444 ## Epoch 2/3 ## 782/782 - 1s - 862us/step - categorical_true_positives: 362616.0000 - loss: 0.1656 ## Epoch 3/3 ## 782/782 - 1s - 660us/step - categorical_true_positives: 363187.0000 - loss: 0.1203"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"handling-losses-and-metrics-that-dont-fit-the-standard-signature","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Handling losses and metrics that don’t fit the standard signature","title":"Training & evaluation with the built-in methods","text":"overwhelming majority losses metrics can computed y_true y_pred, y_pred output model – . instance, regularization loss may require activation layer (targets case), activation may model output. cases, can call self$add_loss(loss_value) inside call method custom layer. Losses added way get added “main” loss training (one passed compile()). ’s simple example adds activity regularization (note activity regularization built-Keras layers – layer just sake providing concrete example): Note pass losses via add_loss(), becomes possible call compile() without loss function, since model already loss minimize. Consider following LogisticEndpoint layer: takes inputs targets & logits, tracks crossentropy loss via add_loss(). can use model two inputs (input data & targets), compiled without loss argument, like : information training multi-input models, see section Passing data multi-input, multi-output models.","code":"layer_custom_activity_regularizer <- Layer(   \"ActivityRegularization\",   call = function(inputs) {     self$add_loss(op_sum(inputs) * 0.1)     inputs  # Pass-through layer.   } )  inputs <- keras_input(shape = 784, name = \"digits\") outputs <- inputs |>   layer_dense(units = 32, activation = \"relu\", name = \"dense_1\") |>   layer_custom_activity_regularizer() |>   layer_dense(units = 64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(units = 10, name = \"predictions\")  model <- keras_model(inputs = inputs, outputs = outputs) model |> compile(optimizer = optimizer_rmsprop(learning_rate = 1e-3),                  loss = loss_sparse_categorical_crossentropy(from_logits = TRUE))  # The displayed loss will be much higher than before # due to the regularization component. model |> fit(x_train, y_train, batch_size = 64, epochs = 1) ## 782/782 - 2s - 2ms/step - loss: 2.3721 layer_logistic_endpoint <- Layer(   \"LogisticEndpoint\",   initialize = function(name = NULL) {     super$initialize(name = name)     self$loss_fn <- loss_binary_crossentropy(from_logits = TRUE)   },   call = function(targets, logits, sample_weights = NULL) {     # Compute the training-time loss value and add it     # to the layer using `self.add_loss()`.     loss <- self$loss_fn(targets, logits, sample_weights)     self$add_loss(loss)      # Return the inference-time prediction tensor (for `predict()`).     op_softmax(logits)   } ) inputs <- keras_input(shape = 3, name = \"inputs\") targets <- keras_input(shape = 10, name = \"targets\")  logits <- inputs |> layer_dense(10) predictions <- layer_logistic_endpoint(name = \"predictions\")(targets, logits)  model <- keras_model(inputs = list(inputs, targets),                      outputs = predictions) model |> compile(optimizer = \"adam\")  # No loss argument!  data <- list(   inputs = random_normal(c(3, 3)),   targets = random_normal(c(3, 10)) ) model |> fit(data, epochs = 1) ## 1/1 - 1s - 512ms/step - loss: 1.0566"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"automatically-setting-apart-a-validation-holdout-set","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Automatically setting apart a validation holdout set","title":"Training & evaluation with the built-in methods","text":"first end--end example saw, used validation_data argument pass list arrays list(x_val, y_val) model evaluating validation loss validation metrics end epoch. ’s another option: argument validation_split allows automatically reserve part training data validation. argument value represents fraction data reserved validation, set number higher 0 lower 1. instance, validation_split = 0.2 means “use 20% data validation”, validation_split = 0.6 means “use 60% data validation”. way validation computed taking last x% samples arrays received fit() call, shuffling. Note can use validation_split training NumPy data.","code":"model <- get_compiled_model() model |> fit(x_train, y_train,              batch_size = 64,              validation_split = 0.2, epochs = 1) ## 625/625 - 2s - 3ms/step - loss: 0.3817 - sparse_categorical_accuracy: 0.8919 - val_loss: 0.1953 - val_sparse_categorical_accuracy: 0.9431"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"training-evaluation-using-tf-dataset-objects","dir":"Articles","previous_headings":"","what":"Training & evaluation using TF Dataset objects","title":"Training & evaluation with the built-in methods","text":"past paragraphs, ’ve seen handle losses, metrics, optimizers, ’ve seen use validation_data validation_split arguments fit(), data passed arrays. Another option use iterator-like, tf.data.Dataset, PyTorch DataLoader, R generator function. Let’s take look former. tfdatasets R package containes set utilities loading preprocessing data way ’s fast scalable. complete guide creating Datasets, see tf.data documentation. can use tf.data train Keras models regardless backend ’re using – whether ’s JAX, PyTorch, TensorFlow. can pass Dataset instance directly methods fit(), evaluate(), predict(): Note Dataset reset end epoch, can reused next epoch. want run training specific number batches Dataset, can pass steps_per_epoch argument, specifies many training steps model run using Dataset moving next epoch. can also pass Dataset instance validation_data argument fit(): end epoch, model iterate validation dataset compute validation loss validation metrics. want run validation specific number batches dataset, can pass validation_steps argument, specifies many validation steps model run validation dataset interrupting validation moving next epoch: Note validation dataset reset use (always evaluating samples epoch epoch). argument validation_split (generating holdout set training data) supported training Dataset objects, since feature requires ability index samples datasets, possible general Dataset API.","code":"library(tfdatasets, exclude = \"shape\") model <- get_compiled_model()  # First, let's create a training Dataset instance. # For the sake of our example, we'll use the same MNIST data as before. train_dataset <- tensor_slices_dataset(list(x_train, y_train))  # Shuffle and slice the dataset. train_dataset <- train_dataset |>   dataset_shuffle(buffer_size=1024) |>   dataset_batch(64)  # Now we get a test dataset. test_dataset <-   tensor_slices_dataset(list(x_test, y_test)) |>   dataset_batch(64)  # Since the dataset already takes care of batching, # we don't pass a `batch_size` argument. model |> fit(train_dataset, epochs = 3) ## Epoch 1/3 ## 782/782 - 2s - 2ms/step - loss: 0.3365 - sparse_categorical_accuracy: 0.9041 ## Epoch 2/3 ## 782/782 - 1s - 821us/step - loss: 0.1605 - sparse_categorical_accuracy: 0.9524 ## Epoch 3/3 ## 782/782 - 1s - 811us/step - loss: 0.1185 - sparse_categorical_accuracy: 0.9647 # You can also evaluate or predict on a dataset. result <- model |> evaluate(test_dataset) ## 157/157 - 1s - 5ms/step - loss: 0.1152 - sparse_categorical_accuracy: 0.9627 result ## $loss ## [1] 0.1151983 ## ## $sparse_categorical_accuracy ## [1] 0.9627 model <- get_compiled_model()  # Prepare the training dataset train_dataset <- tensor_slices_dataset(list(x_train, y_train)) train_dataset <- train_dataset |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(64)  # Only use the 100 batches per epoch (that's 64 * 100 samples) model |> fit(train_dataset, epochs = 3, steps_per_epoch = 100) ## Epoch 1/3 ## 100/100 - 1s - 8ms/step - loss: 0.8017 - sparse_categorical_accuracy: 0.7806 ## Epoch 2/3 ## 100/100 - 0s - 905us/step - loss: 0.3661 - sparse_categorical_accuracy: 0.9006 ## Epoch 3/3 ## 100/100 - 0s - 703us/step - loss: 0.3009 - sparse_categorical_accuracy: 0.9106 model <- get_compiled_model()  # Prepare the training dataset train_dataset <- tensor_slices_dataset(list(x_train, y_train)) train_dataset <- train_dataset |>   dataset_shuffle(buffer_size=1024) |>   dataset_batch(64)  # Prepare the validation dataset val_dataset <- tensor_slices_dataset(list(x_val, y_val)) val_dataset <- val_dataset |> dataset_batch(64)  model |> fit(train_dataset, epochs = 1, validation_data = val_dataset) ## 782/782 - 2s - 3ms/step - loss: 0.3428 - sparse_categorical_accuracy: 0.9022 - val_loss: 0.2337 - val_sparse_categorical_accuracy: 0.9291 model  <- get_compiled_model()  # Prepare the training dataset train_dataset <- tensor_slices_dataset(list(x_train, y_train)) train_dataset <- train_dataset |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(64)  # Prepare the validation dataset val_dataset <- tensor_slices_dataset(list(x_val, y_val)) val_dataset <- val_dataset |> dataset_batch(64)  model %>% fit(   train_dataset,   epochs = 1,   # Only run validation using the first 10 batches of the dataset   # using the `validation_steps` argument   validation_data = val_dataset,   validation_steps = 10, ) ## 782/782 - 2s - 2ms/step - loss: 0.3391 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.1997 - val_sparse_categorical_accuracy: 0.9391"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-sample-weighting-and-class-weighting","dir":"Articles","previous_headings":"","what":"Using sample weighting and class weighting","title":"Training & evaluation with the built-in methods","text":"default settings weight sample decided frequency dataset. two methods weight data, independent sample frequency: Class weights Sample weights","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"class-weights","dir":"Articles","previous_headings":"Using sample weighting and class weighting","what":"Class weights","title":"Training & evaluation with the built-in methods","text":"set passing named list class_weight argument fit(). list maps class indices weight used samples belonging class. can used balance classes without resampling, train model gives importance particular class. instance, class “0” half represented class “1” data, use model |> fit(..., class_weight = c(\"0\" = 1, \"1\" = 0.5)). ’s R example use class weights sample weights give importance correct classification class #5 (digit “5” MNIST dataset).","code":"class_weight <- c(     \"0\" = 1.0,     \"1\" = 1.0,     \"2\" = 1.0,     \"3\" = 1.0,     \"4\" = 1.0,     # Set weight \"2\" for class \"5\",     # making this class 2x more important     \"5\" = 2.0,     \"6\" = 1.0,     \"7\" = 1.0,     \"8\" = 1.0,     \"9\" = 1.0 )  model <- get_compiled_model() model |> fit(x_train, y_train,              class_weight = class_weight,              batch_size = 64, epochs = 1) ## 782/782 - 2s - 2ms/step - loss: 0.3713 - sparse_categorical_accuracy: 0.9018"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"sample-weights","dir":"Articles","previous_headings":"Using sample weighting and class weighting","what":"Sample weights","title":"Training & evaluation with the built-in methods","text":"fine grained control, building classifier, can use sample_weights. training R arrays: Pass sample_weight argument fit(). training tf_dataset sort iterator: yield (input_batch, label_batch, sample_weight_batch) tuples. “sample weights” array array numbers specify much weight sample batch computing total loss. commonly used imbalanced classification problems (idea give weight rarely-seen classes). weights used ones zeros, array can used mask loss function (entirely discarding contribution certain samples total loss). ’s matching Dataset example:","code":"sample_weight <- rep(1.0, length(y_train)) sample_weight[y_train == 5] <- 2.0  model <- get_compiled_model() model |> fit(   x_train, y_train,   sample_weight = sample_weight,   batch_size = 64, epochs = 1 ) ## 782/782 - 1s - 2ms/step - loss: 0.3740 - sparse_categorical_accuracy: 0.9015 sample_weight <- rep(1.0, length(y_train)) sample_weight[y_train == 5] <- 2.0  # Create a Dataset that includes sample weights # (3rd element in the return tuple). train_dataset <- tensor_slices_dataset(list(     x_train, y_train, sample_weight ))  # Shuffle and slice the dataset. train_dataset <- train_dataset |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(64)  model <- get_compiled_model() model |> fit(train_dataset, epochs = 1) ## 782/782 - 2s - 2ms/step - loss: 0.3654 - sparse_categorical_accuracy: 0.9057"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"passing-data-to-multi-input-multi-output-models","dir":"Articles","previous_headings":"","what":"Passing data to multi-input, multi-output models","title":"Training & evaluation with the built-in methods","text":"previous examples, considering model single input (tensor shape (764)) single output (prediction tensor shape (10)). models multiple inputs outputs? Consider following model, image input shape (32, 32, 3) (’s (height, width, channels)) time series input shape (NA, 10) (’s (timesteps, features)). model two outputs computed combination inputs: “score” (shape (1)) probability distribution five classes (shape (5)). Let’s plot model, can clearly see ’re (note shapes shown plot batch shapes, rather per-sample shapes). plot chunk unnamed-chunk-26 compilation time, can specify different losses different outputs, passing loss functions list: passed single loss function model, loss function applied every output (appropriate ). Likewise metrics: Since gave names output layers, also specify per-output losses metrics via named list: recommend use names 2 outputs. ’s possible give different weights different output-specific losses (instance, one might wish privilege “score” loss example, giving 2x importance class loss), using loss_weights argument: also choose compute loss certain outputs, outputs meant prediction training: Passing data multi-input multi-output model fit() works similar way specifying loss function compile: can pass lists arrays (1:1 mapping outputs received loss function) dicts mapping output names arrays. ’s Dataset use case: similarly R arrays, Dataset return tuple named lists (dicts).","code":"image_input <- keras_input(c(32, 32, 3), name = \"img_input\") timeseries_input <- keras_input(c(NA, 10), name = \"ts_input\")  x1 <- image_input |>   layer_conv_2d(filters = 3, kernel_size = c(3, 3)) |>   layer_global_max_pooling_2d()  x2 <- timeseries_input |>   layer_conv_1d(filters = 3, kernel_size = 3) |>   layer_global_max_pooling_1d()  x <- layer_concatenate(x1, x2)  score_output <- layer_dense(x, 1, name = \"score_output\") class_output <- layer_dense(x, 5, name = \"class_output\")  model <- keras_model(   inputs = list(image_input, timeseries_input),   outputs = list(score_output, class_output) ) plot(model, show_shapes = TRUE) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     loss_mean_squared_error(),     loss_categorical_crossentropy()   ) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     loss_mean_squared_error(),     loss_categorical_crossentropy()   ),   metrics = list(     list(       metric_mean_absolute_error(),       metric_mean_absolute_percentage_error()     ),     list(metric_categorical_accuracy())   ) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     score_output = loss_mean_squared_error(),     class_output = loss_categorical_crossentropy()   ),   metrics = list(     score_output = list(       metric_mean_absolute_error(),       metric_mean_absolute_percentage_error()     ),     class_output = list(metric_categorical_accuracy())   ) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     score_output = loss_mean_squared_error(),     class_output = loss_categorical_crossentropy()   ),   metrics = list(     score_output = list(       metric_mean_absolute_error(),       metric_mean_absolute_percentage_error()     ),     class_output = list(metric_categorical_accuracy())   ),   loss_weights = list(score_output = 2.0, class_output = 1.0) ) # loss list, positional version model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(NULL, loss_categorical_crossentropy()) )  # Or loss list, named version model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(class_output = loss_categorical_crossentropy()) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     loss_mean_squared_error(),     loss_categorical_crossentropy()   ) )  # Generate dummy data img_data <- random_normal(c(100, 32, 32, 3)) ts_data <- random_normal(c(100, 20, 10)) score_targets <- random_normal(c(100, 1)) class_targets <- random_normal(c(100, 5))  # Fit on unnamed lists (positional matching) model |> fit(     list(img_data, ts_data),     list(score_targets, class_targets),     batch_size=32,     epochs=1 ) ## 4/4 - 2s - 497ms/step - loss: 1.3788 # Alternatively, fit on named lists (names matching) model |> fit(   list(img_input = img_data, ts_input = ts_data),   list(score_output = score_targets, class_output = class_targets),   batch_size = 32,   epochs = 1 ) ## 4/4 - 1s - 250ms/step - loss: 0.2857 train_dataset <- tensor_slices_dataset(list(   list(img_input = img_data, ts_input = ts_data),   list(score_output = score_targets, class_output = class_targets) )) train_dataset <- train_dataset |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(64)  model |> fit(train_dataset, epochs = 1) ## 2/2 - 1s - 665ms/step - loss: 0.5600"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-callbacks","dir":"Articles","previous_headings":"","what":"Using callbacks","title":"Training & evaluation with the built-in methods","text":"Callbacks Keras objects called different points training (start epoch, end batch, end epoch, etc.). can used implement certain behaviors, : validation different points training (beyond built-per-epoch validation) Checkpointing model regular intervals exceeds certain accuracy threshold Changing learning rate model training seems plateauing fine-tuning top layers training seems plateauing Sending email instant message notifications training ends certain performance threshold exceeded Etc. Callbacks can passed list call fit():","code":"model <- get_compiled_model()  callbacks <- list(   callback_early_stopping(     # Stop training when `val_loss` is no longer improving     monitor = \"val_loss\",     # \"no longer improving\" being defined as \"no better than 1e-2 less\"     min_delta = 1e-2,     # \"no longer improving\" being further defined as \"for at least 2 epochs\"     patience = 2,     verbose = 1   ) ) model |> fit(   x_train,   y_train,   epochs = 20,   batch_size = 64,   callbacks = callbacks,   validation_split = 0.2, ) ## Epoch 1/20 ## 625/625 - 2s - 3ms/step - loss: 0.3695 - sparse_categorical_accuracy: 0.8961 - val_loss: 0.1873 - val_sparse_categorical_accuracy: 0.9469 ## Epoch 2/20 ## 625/625 - 1s - 1ms/step - loss: 0.1751 - sparse_categorical_accuracy: 0.9489 - val_loss: 0.1403 - val_sparse_categorical_accuracy: 0.9579 ## Epoch 3/20 ## 625/625 - 1s - 2ms/step - loss: 0.1277 - sparse_categorical_accuracy: 0.9625 - val_loss: 0.1218 - val_sparse_categorical_accuracy: 0.9651 ## Epoch 4/20 ## 625/625 - 1s - 2ms/step - loss: 0.1007 - sparse_categorical_accuracy: 0.9700 - val_loss: 0.1153 - val_sparse_categorical_accuracy: 0.9661 ## Epoch 5/20 ## 625/625 - 1s - 1ms/step - loss: 0.0822 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1104 - val_sparse_categorical_accuracy: 0.9670 ## Epoch 6/20 ## 625/625 - 1s - 975us/step - loss: 0.0683 - sparse_categorical_accuracy: 0.9801 - val_loss: 0.1098 - val_sparse_categorical_accuracy: 0.9689 ## Epoch 7/20 ## 625/625 - 1s - 1ms/step - loss: 0.0571 - sparse_categorical_accuracy: 0.9838 - val_loss: 0.1116 - val_sparse_categorical_accuracy: 0.9698 ## Epoch 8/20 ## 625/625 - 1s - 1ms/step - loss: 0.0485 - sparse_categorical_accuracy: 0.9864 - val_loss: 0.1126 - val_sparse_categorical_accuracy: 0.9702 ## Epoch 8: early stopping"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"many-built-in-callbacks-are-available","dir":"Articles","previous_headings":"Using callbacks","what":"Many built-in callbacks are available","title":"Training & evaluation with the built-in methods","text":"many built-callbacks already available Keras, : callback_model_checkpoint(): Periodically save model. callback_early_stopping(): Stop training training longer improving validation metrics. callback_tensorboard(): periodically write model logs can visualized TensorBoard (details section “Visualization”). callback_csv_logger(): streams loss metrics data CSV file. etc. See callbacks documentation complete list.","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"writing-your-own-callback","dir":"Articles","previous_headings":"Using callbacks","what":"Writing your own callback","title":"Training & evaluation with the built-in methods","text":"can create custom callback subclassing base [Callback()] class. callback access associated model class property self$model. Make sure read complete guide writing custom callbacks. ’s simple example saving list per-batch loss values training:","code":"callback_loss_history <- Callback(   classname = \"LossHistory\",   initialize = function(file = \"per_training_batch_losses.txt\", ...) {     super$initialize(...)     private$file <- file   },   on_train_begin = function(logs = NULL) {     private$per_batch_losses <- fastmap::faststack()   },   on_train_batch_begin = function(batch, logs = NULL) {     private$per_batch_losses$push(logs$loss)   },   on_train_end = function(logs = NULL) {     per_batch_losses <- private$per_batch_losses$as_list() |> as.numeric()     write(per_batch_losses, private$file)   } )"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"checkpointing-models","dir":"Articles","previous_headings":"","what":"Checkpointing models","title":"Training & evaluation with the built-in methods","text":"’re training model relatively large datasets, ’s crucial save checkpoints model frequent intervals. easiest way achieve [callback_model_checkpoint()]: ModelCheckpoint callback can used implement fault-tolerance: ability restart training last saved state model case training gets randomly interrupted. ’s basic example: call also write callback saving restoring models. complete guide serialization saving, see guide saving serializing Models.","code":"model <- get_compiled_model()  callbacks <- list(   callback_model_checkpoint(     # Path where to save the model     # The two parameters below mean that we will overwrite     # the current checkpoint if and only if     # the `val_loss` score has improved.     # The saved model name will include the current epoch.     filepath = \"mymodel_{epoch}.keras\",     save_best_only = TRUE,     # Only save a model if `val_loss` has improved.     monitor = \"val_loss\",     verbose = 1   ) ) model |> fit(   x_train, y_train,   epochs = 2, batch_size = 64,   callbacks = callbacks,   validation_split = 0.2 ) ## Epoch 1/2 ## ## Epoch 1: val_loss improved from inf to 0.19344, saving model to mymodel_1.keras ## 625/625 - 2s - 3ms/step - loss: 0.3787 - sparse_categorical_accuracy: 0.8940 - val_loss: 0.1934 - val_sparse_categorical_accuracy: 0.9441 ## Epoch 2/2 ## ## Epoch 2: val_loss improved from 0.19344 to 0.14251, saving model to mymodel_2.keras ## 625/625 - 1s - 1ms/step - loss: 0.1768 - sparse_categorical_accuracy: 0.9478 - val_loss: 0.1425 - val_sparse_categorical_accuracy: 0.9600 # Prepare a directory to store all the checkpoints. checkpoint_dir <- \"./ckpt\" fs::dir_create(checkpoint_dir)  make_or_restore_model <- function() {   # Either restore the latest (best) model, or create a fresh one   # if there is no checkpoint available.   checkpoints <- Sys.glob(file.path(checkpoint_dir, \"model-loss=*.keras\"))    if (length(checkpoints) > 0) {     checkpoint_losses <- sub(\"^model-loss=([0-9.]+)\\\\.keras$\", \"\\\\1\",                              basename(checkpoints)) |> as.numeric()     best_checkpoint <- checkpoints[which.min(checkpoint_losses)]     load_model(best_checkpoint)   } else {     get_compiled_model()   } }  model <- make_or_restore_model() callbacks <- list(   # This callback saves the model every 100 batches.   # We include the training loss in the saved model name.   callback_model_checkpoint(     filepath = file.path(checkpoint_dir, \"model-loss={loss:.2f}.keras\"),     save_freq = 100   ) ) model |> fit(x_train, y_train, epochs = 1, callbacks = callbacks) ## 1563/1563 - 2s - 2ms/step - loss: 0.2932 - sparse_categorical_accuracy: 0.9145"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-learning-rate-schedules","dir":"Articles","previous_headings":"","what":"Using learning rate schedules","title":"Training & evaluation with the built-in methods","text":"common pattern training deep learning models gradually reduce learning training progresses. generally known “learning rate decay”. learning decay schedule static (fixed advance, function current epoch current batch index), dynamic (responding current behavior model, particular validation loss).","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"passing-a-schedule-to-an-optimizer","dir":"Articles","previous_headings":"Using learning rate schedules","what":"Passing a schedule to an optimizer","title":"Training & evaluation with the built-in methods","text":"can easily use static learning rate decay schedule passing schedule object learning_rate argument optimizer: Several built-schedules available: ExponentialDecay, PiecewiseConstantDecay, PolynomialDecay, InverseTimeDecay.","code":"initial_learning_rate <- 0.1 lr_schedule <- learning_rate_schedule_exponential_decay(     initial_learning_rate, decay_steps=100000, decay_rate=0.96,     staircase=TRUE )  optimizer <- optimizer_rmsprop(learning_rate = lr_schedule)"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-callbacks-to-implement-a-dynamic-learning-rate-schedule","dir":"Articles","previous_headings":"Using learning rate schedules","what":"Using callbacks to implement a dynamic learning rate schedule","title":"Training & evaluation with the built-in methods","text":"dynamic learning rate schedule (instance, decreasing learning rate validation loss longer improving) achieved schedule objects, since optimizer access validation metrics. However, callbacks access metrics, including validation metrics! can thus achieve pattern using callback modifies current learning rate optimizer. fact, even built-[callback_reduce_lr_on_plateau()].","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"visualizing-loss-and-metrics-during-training-with-tensorboard","dir":"Articles","previous_headings":"","what":"Visualizing loss and metrics during training with TensorBoard","title":"Training & evaluation with the built-in methods","text":"best way keep eye model training use TensorBoard – browser-based application can run locally provides : Live plots loss metrics training evaluation (optionally) Visualizations histograms layer activations (optionally) 3D visualizations embedding spaces learned layer_embedding() installed TensorFlow pip, able launch TensorBoard command line: R using:","code":"tensorboard --logdir=/full_path_to_your_logs tensorflow::tensorboard(logdir = \"/full_path_to_your_logs\")"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-the-tensorboard-callback","dir":"Articles","previous_headings":"Visualizing loss and metrics during training with TensorBoard","what":"Using the TensorBoard callback","title":"Training & evaluation with the built-in methods","text":"easiest way use TensorBoard Keras model fit() method [callback_tensorboard()]. simplest case, just specify want callback write logs, ’re good go: information, see callback_tensorboard().","code":"tb_callback <- callback_tensorboard(   log_dir = \"/full_path_to_your_logs\",   histogram_freq = 0, # How often to log histogram visualizations   embeddings_freq = 0, # How often to log embedding visualizations   update_freq = \"epoch\", # How often to write logs (default: once per epoch) )"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Transfer learning & fine-tuning","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Transfer learning & fine-tuning","text":"Transfer learning consists taking features learned one problem, leveraging new, similar problem. instance, features model learned identify raccoon may useful kick-start model meant identify tanukis. Transfer learning usually done tasks dataset little data train full-scale model scratch. common incarnation transfer learning context deep learning following workflow: Take layers previously trained model. Freeze , avoid destroying information contain future training rounds. Add new, trainable layers top frozen layers. learn turn old features predictions new dataset. Train new layers dataset. last, optional step, fine-tuning, consists unfreezing entire model obtained (part ), re-training new data low learning rate. can potentially achieve meaningful improvements, incrementally adapting pretrained features new data. First, go Keras trainable API detail, underlies transfer learning & fine-tuning workflows. , ’ll demonstrate typical workflow taking model pretrained ImageNet dataset, retraining Kaggle “cats vs dogs” classification dataset. adapted Deep Learning Python 2016 blog post “building powerful image classification models using little data”.","code":""},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"freezing-layers-understanding-the-trainable-attribute","dir":"Articles","previous_headings":"","what":"Freezing layers: understanding the trainable attribute","title":"Transfer learning & fine-tuning","text":"Layers & models three weight attributes: weights list weights variables layer. trainable_weights list meant updated (via gradient descent) minimize loss training. non_trainable_weights list aren’t meant trained. Typically updated model forward pass. Example: Dense layer 2 trainable weights (kernel & bias) general, weights trainable weights. built-layer non-trainable weights [layer_batch_normalization()]. uses non-trainable weights keep track mean variance inputs training. learn use non-trainable weights custom layers, see guide writing new layers scratch. Example: BatchNormalization layer 2 trainable weights 2 non-trainable weights Layers & models also feature boolean attribute trainable. value can changed. Setting layer$trainable FALSE moves layer’s weights trainable non-trainable. called “freezing” layer: state frozen layer won’t updated training (either training fit() training custom loop relies trainable_weights apply gradient updates). Example: setting trainable False trainable weight becomes non-trainable, value longer updated training. confuse layer$trainable attribute argument training layer$call() (controls whether layer run forward pass inference mode training mode). information, see Keras FAQ.","code":"layer <- layer_dense(units = 3) layer$build(shape(NULL, 4))  # Create the weights  length(layer$weights) ## [1] 2 length(layer$trainable_weights) ## [1] 2 length(layer$non_trainable_weights) ## [1] 0 layer <- layer_batch_normalization() layer$build(shape(NA, 4))  # Create the weights  length(layer$weights) ## [1] 4 length(layer$trainable_weights) ## [1] 2 length(layer$non_trainable_weights) ## [1] 2 layer <- layer_dense(units = 3) layer$build(shape(NULL, 4))  # Create the weights layer$trainable <- FALSE  # Freeze the layer  length(layer$weights) ## [1] 2 length(layer$trainable_weights) ## [1] 0 length(layer$non_trainable_weights) ## [1] 2 # Make a model with 2 layers layer1 <- layer_dense(units = 3, activation = \"relu\") layer2 <- layer_dense(units = 3, activation = \"sigmoid\") model <- keras_model_sequential(input_shape = 3) |>   layer1() |>   layer2()  # Freeze the first layer layer1$trainable <- FALSE  # Keep a copy of the weights of layer1 for later reference # (get_weights() returns a list of R arrays, #  layer$weights returns a list of KerasVariables) initial_layer1_weights_values <- get_weights(layer1)  # Train the model model |> compile(optimizer = \"adam\", loss = \"mse\") model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1) ## 1/1 - 1s - 567ms/step - loss: 2.1868 # Check that the weights of layer1 have not changed during training final_layer1_weights_values <- get_weights(layer1)  all.equal(initial_layer1_weights_values,           final_layer1_weights_values) ## [1] TRUE"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"recursive-setting-of-the-trainable-attribute","dir":"Articles","previous_headings":"","what":"Recursive setting of the trainable attribute","title":"Transfer learning & fine-tuning","text":"set $trainable <- FALSE model layer sublayers, children layers become non-trainable well. Example:","code":"inner_model <- keras_model_sequential(input_shape = 3) |>   layer_dense(units = 3, activation = \"relu\") |>   layer_dense(units = 3, activation = \"relu\")  model <- keras_model_sequential(input_shape = 3) |>   inner_model() |>   layer_dense(units = 3, activation = \"sigmoid\")  model$trainable <- FALSE  # Freeze the outer model  inner_model$trainable  # All layers in `model` are now frozen ## [1] FALSE inner_model$layers[[1]]$trainable  # `trainable` is propagated recursively ## [1] FALSE"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"the-typical-transfer-learning-workflow","dir":"Articles","previous_headings":"","what":"The typical transfer-learning workflow","title":"Transfer learning & fine-tuning","text":"leads us typical transfer learning workflow can implemented Keras: Instantiate base model load pre-trained weights . Freeze layers base model setting trainable <- FALSE. Create new model top output one (several) layers base model. Train new model new dataset. Note alternative, lightweight workflow also : Instantiate base model load pre-trained weights . Run new dataset record output one (several) layers base model. called feature extraction. Use output input data new, smaller model. key advantage second workflow run base model data, rather per epoch training. ’s lot faster & cheaper. issue second workflow, though, doesn’t allow dynamically modify input data new model training, required data augmentation, instance. Transfer learning typically used tasks new dataset little data train full-scale model scratch, scenarios data augmentation important. follows, focus first workflow. ’s first workflow looks like Keras: First, instantiate base model pre-trained weights. , freeze base model. Create new model top. Train model new data.","code":"base_model <- application_xception(   weights = 'imagenet', # Load weights pre-trained on ImageNet.   input_shape = c(150, 150, 3),   include_top = FALSE  # Do not include the ImageNet classifier at the top. ) base_model$trainable <- FALSE inputs <- keras_input(shape = c(150, 150, 3)) # We make sure that the base_model is running in inference mode here, # by passing `training <- FALSE`. This is important for fine-tuning, as you will # learn in a few paragraphs. outputs <- inputs |>   base_model(training = FALSE) |>   # Convert features of shape `base_model$output_shape[-1]` to vectors   layer_global_average_pooling_2d() |>   # A Dense classifier with a single unit (binary classification)   layer_dense(1)  model <- keras_model(inputs, outputs) model |> compile(   optimizer = optimizer_adam(),   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_binary_accuracy()) ) model |> fit(new_dataset, epochs = 20,              callbacks = ..., validation_data = ...)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"fine-tuning","dir":"Articles","previous_headings":"","what":"Fine-tuning","title":"Transfer learning & fine-tuning","text":"model converged new data, can try unfreeze part base model retrain whole model end--end low learning rate. optional last step can potentially give incremental improvements. also potentially lead quick overfitting – keep mind. critical step model frozen layers trained convergence. mix randomly-initialized trainable layers trainable layers hold pre-trained features, randomly-initialized layers cause large gradient updates training, destroy pre-trained features. ’s also critical use low learning rate stage, training much larger model first round training, dataset typically small. result, risk overfitting quickly apply large weight updates. , want readapt pretrained weights incremental way. implement fine-tuning whole base model: Important note compile() trainable Calling compile() model meant “freeze” behavior model. implies trainable attribute values time model compiled preserved throughout lifetime model, compile called . Hence, change trainable value, make sure call compile() model changes taken account. Important notes BatchNormalization layer Many image models contain BatchNormalization layers. layer special case every imaginable count. things keep mind. BatchNormalization contains 2 non-trainable weights get updated training. variables tracking mean variance inputs. set bn_layer$trainable <- FALSE, BatchNormalization layer run inference mode, update mean & variance statistics. case layers general, weight trainability & inference/training modes two orthogonal concepts. two tied case BatchNormalization layer. unfreeze model contains BatchNormalization layers order fine-tuning, keep BatchNormalization layers inference mode passing training = FALSE calling base model. Otherwise updates applied non-trainable weights suddenly destroy model learned. ’ll see pattern action end--end example end guide.","code":"# Unfreeze the base model base_model$trainable <- TRUE  # It's important to recompile your model after you make any changes # to the `trainable` attribute of any inner layer, so that your changes # are take into account model |> compile(   optimizer = optimizer_adam(1e-5), # Very low learning rate   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = c(metric_binary_accuracy()) )  # Train end-to-end. Be careful to stop before you overfit! model |> fit(new_dataset, epochs = 10,              callbacks = ..., validation_data = ...)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"an-end-to-end-example-fine-tuning-an-image-classification-model-on-a-cats-vs--dogs-dataset","dir":"Articles","previous_headings":"","what":"An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset","title":"Transfer learning & fine-tuning","text":"solidify concepts, let’s walk concrete end--end transfer learning & fine-tuning example. load Xception model, pre-trained ImageNet, use Kaggle “cats vs. dogs” classification dataset.","code":""},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"getting-the-data","dir":"Articles","previous_headings":"An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset","what":"Getting the data","title":"Transfer learning & fine-tuning","text":"First, let’s fetch cats vs. dogs dataset using TFDS. dataset, ’ll probably want use utility [image_dataset_from_directory()] generate similar labeled dataset objects set images disk filed class-specific folders. Transfer learning useful working small datasets. keep dataset small, use 40% original training data (25,000 images) training, 10% validation, 10% testing. first 9 images training dataset – can see, ’re different sizes. can also see label 1 “dog” label 0 “cat”.","code":"# reticulate::py_install(\"tensorflow-datasets\") tfds <- reticulate::import(\"tensorflow_datasets\")  c(train_ds, validation_ds, test_ds) %<-% tfds$load(   \"cats_vs_dogs\",   # Reserve 10% for validation and 10% for test   split = c(\"train[:40%]\", \"train[40%:50%]\", \"train[50%:60%]\"),   as_supervised = TRUE  # Include labels )  length(train_ds) ## [1] 9305 library(tfdatasets, exclude = \"shape\")  par(mfrow = c(3, 3), mar = c(1,0,1.5,0)) train_ds |>   dataset_take(9) |>   as_array_iterator() |>   iterate(function(batch) {     c(image, label) %<-% batch     plot(as.raster(image, max = 255L))     title(sprintf(       \"label: %s   size: %s\",       label, paste(dim(image), collapse = \" x \")))   })"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"standardizing-the-data","dir":"Articles","previous_headings":"An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset","what":"Standardizing the data","title":"Transfer learning & fine-tuning","text":"raw images variety sizes. addition, pixel consists 3 integer values 0 255 (RGB level values). isn’t great fit feeding neural network. need 2 things: Standardize fixed image size. pick 150x150. Normalize pixel values -1 1. ’ll using Normalization layer part model . general, ’s good practice develop models take raw data input, opposed models take already-preprocessed data. reason , model expects preprocessed data, time export model use elsewhere (web browser, mobile app), ’ll need reimplement exact preprocessing pipeline. gets tricky quickly. least possible amount preprocessing hitting model. , ’ll image resizing data pipeline (deep neural network can process contiguous batches data), ’ll input value scaling part model, create . Let’s resize images 150x150:","code":"resize_fn <- layer_resizing(width = 150, height = 150) resize_pair <- function(x, y) list(resize_fn(x), y)  train_ds <- train_ds |> dataset_map(resize_pair) validation_ds <- validation_ds |> dataset_map(resize_pair) test_ds <- test_ds |> dataset_map(resize_pair)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"using-random-data-augmentation","dir":"Articles","previous_headings":"An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset","what":"Using random data augmentation","title":"Transfer learning & fine-tuning","text":"don’t large image dataset, ’s good practice artificially introduce sample diversity applying random yet realistic transformations training images, random horizontal flipping small random rotations. helps expose model different aspects training data slowing overfitting. Let’s batch data use prefetching optimize loading speed. Let’s visualize first image first batch looks like various random transformations:","code":"data_augmentation <- keras_model_sequential() |>   layer_random_flip(\"horizontal\") |>   layer_random_rotation(.1)  train_ds <- train_ds %>%   dataset_map(function(x, y) list(data_augmentation(x), y)) library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) batch_size <- 64  train_ds <- train_ds |>   dataset_batch(batch_size) |>   dataset_prefetch()  validation_ds <- validation_ds |>   dataset_batch(batch_size) |>   dataset_prefetch()  test_ds <- test_ds |>   dataset_batch(batch_size) |>   dataset_prefetch() batch <- train_ds |>   dataset_take(1) |>   as_iterator() |>   iter_next()  c(images, labels) %<-% batch first_image <- images[1, all_dims(), drop = TRUE] augmented_image <- data_augmentation(first_image, training = TRUE)  plot_image <- function(image, main = deparse1(substitute(image))) {   image |>     as.array() |> # convert from tensor to R array     as.raster(max = 255) |>     plot()    if(!is.null(main))     title(main) }  par(mfrow = c(2, 2), mar = c(1, 1, 1.5, 1)) plot_image(first_image) plot_image(augmented_image) plot_image(data_augmentation(first_image, training = TRUE), \"augmented 2\") plot_image(data_augmentation(first_image, training = TRUE), \"augmented 3\")"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"build-a-model","dir":"Articles","previous_headings":"","what":"Build a model","title":"Transfer learning & fine-tuning","text":"Now let’s built model follows blueprint ’ve explained earlier. Note : add Rescaling layer scale input values (initially [0, 255] range) [-1, 1] range. add Dropout layer classification layer, regularization. make sure pass training=FALSE calling base model, runs inference mode, batchnorm statistics don’t get updated even unfreeze base model fine-tuning.","code":"base_model <- application_xception(   weights = \"imagenet\", # Load weights pre-trained on ImageNet.   input_shape = c(150, 150, 3),   include_top = FALSE, # Do not include the ImageNet classifier at the top. )  # Freeze the base_model base_model$trainable <- FALSE  # Create new model on top inputs <- keras_input(shape = c(150, 150, 3))  # Pre-trained Xception weights requires that input be scaled # from (0, 255) to a range of (-1., +1.), the rescaling layer # outputs: `(inputs * scale) + offset` scale_layer <- layer_rescaling(scale = 1 / 127.5, offset = -1) x <- scale_layer(inputs)  # The base model contains batchnorm layers. We want to keep them in inference mode # when we unfreeze the base model for fine-tuning, so we make sure that the # base_model is running in inference mode here. outputs <- x |>   base_model(training = FALSE) |>   layer_global_average_pooling_2d() |>   layer_dropout(0.2) |>   layer_dense(1)  model <- keras_model(inputs, outputs)  summary(model, show_trainable = TRUE) ## Model: \"functional_10\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)                ┃ Output Shape          ┃    Param # ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer_7 (InputLayer)  │ (None, 150, 150, 3)   │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ rescaling (Rescaling)       │ (None, 150, 150, 3)   │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ xception (Functional)       │ (None, 5, 5, 2048)    │ 20,861,480 │   N   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ global_average_pooling2d_1  │ (None, 2048)          │          0 │   -   │ ## │ (GlobalAveragePooling2D)    │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dropout (Dropout)           │ (None, 2048)          │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_8 (Dense)             │ (None, 1)             │      2,049 │   Y   │ ## └─────────────────────────────┴───────────────────────┴────────────┴───────┘ ##  Total params: 20,863,529 (79.59 MB) ##  Trainable params: 2,049 (8.00 KB) ##  Non-trainable params: 20,861,480 (79.58 MB)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"train-the-top-layer","dir":"Articles","previous_headings":"","what":"Train the top layer","title":"Transfer learning & fine-tuning","text":"","code":"model |> compile(   optimizer = optimizer_adam(),   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_binary_accuracy()) )  epochs <- 1 model |> fit(train_ds, epochs = epochs, validation_data = validation_ds) ## 146/146 - 45s - 307ms/step - binary_accuracy: 0.9183 - loss: 0.1887 - val_binary_accuracy: 0.9669 - val_loss: 0.0926"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"do-a-round-of-fine-tuning-of-the-entire-model","dir":"Articles","previous_headings":"","what":"Do a round of fine-tuning of the entire model","title":"Transfer learning & fine-tuning","text":"Finally, let’s unfreeze base model train entire model end--end low learning rate. Importantly, although base model becomes trainable, still running inference mode since passed training=FALSE calling built model. means batch normalization layers inside won’t update batch statistics. , wreck havoc representations learned model far. 10 epochs, fine-tuning gains us nice improvement . Let’s evaluate model test dataset:","code":"# Unfreeze the base_model. Note that it keeps running in inference mode # since we passed `training=FALSE` when calling it. This means that # the batchnorm layers will not update their batch statistics. # This prevents the batchnorm layers from undoing all the training # we've done so far. base_model$trainable <- TRUE summary(model, show_trainable = TRUE) ## Model: \"functional_10\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)                ┃ Output Shape          ┃    Param # ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer_7 (InputLayer)  │ (None, 150, 150, 3)   │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ rescaling (Rescaling)       │ (None, 150, 150, 3)   │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ xception (Functional)       │ (None, 5, 5, 2048)    │ 20,861,480 │   Y   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ global_average_pooling2d_1  │ (None, 2048)          │          0 │   -   │ ## │ (GlobalAveragePooling2D)    │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dropout (Dropout)           │ (None, 2048)          │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_8 (Dense)             │ (None, 1)             │      2,049 │   Y   │ ## └─────────────────────────────┴───────────────────────┴────────────┴───────┘ ##  Total params: 20,867,629 (79.60 MB) ##  Trainable params: 20,809,001 (79.38 MB) ##  Non-trainable params: 54,528 (213.00 KB) ##  Optimizer params: 4,100 (16.02 KB) model |> compile(   optimizer = optimizer_adam(1e-5), # Low learning rate   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_binary_accuracy()) )  epochs <- 1 model |> fit(train_ds, epochs = epochs, validation_data = validation_ds) ## 146/146 - 77s - 524ms/step - binary_accuracy: 0.8660 - loss: 0.3213 - val_binary_accuracy: 0.9652 - val_loss: 0.1022 model |> evaluate(test_ds) ## 37/37 - 2s - 41ms/step - binary_accuracy: 0.9540 - loss: 0.1103 ## $binary_accuracy ## [1] 0.9539983 ## ## $loss ## [1] 0.1102595"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Understanding masking & padding","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Understanding masking & padding","text":"Masking way tell sequence-processing layers certain timesteps input missing, thus skipped processing data. Padding special form masking masked steps start end sequence. Padding comes need encode sequence data contiguous batches: order make sequences batch fit given standard length, necessary pad truncate sequences. Let’s take close look.","code":""},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"padding-sequence-data","dir":"Articles","previous_headings":"","what":"Padding sequence data","title":"Understanding masking & padding","text":"processing sequence data, common individual samples different lengths. Consider following example (text tokenized words): vocabulary lookup, data might vectorized integers, e.g.: data nested list individual samples length 3, 5, 6, respectively. Since input data deep learning model must single tensor (shape e.g. (batch_size, 6, vocab_size) case), samples shorter longest item need padded placeholder value (alternatively, one might also truncate long samples padding short samples). Keras provides utility function truncate pad Python lists common length: pad_sequences.","code":"data <- list(   c(\"Hello\", \"world\", \"!\"),   c(\"How\", \"are\", \"you\", \"doing\", \"today\"),   c(\"The\", \"weather\", \"will\", \"be\", \"nice\", \"tomorrow\") ) data <- list(   c(71, 1331, 4231),   c(73, 8, 3215, 55, 927),   c(83, 91, 1, 645, 1253, 927) ) raw_inputs <- list(   c(711, 632, 71),   c(73, 8, 3215, 55, 927),   c(83, 91, 1, 645, 1253, 927) )  # By default, this will pad using 0s; it is configurable via the # \"value\" parameter. # Note that you could use \"pre\" padding (at the beginning) or # \"post\" padding (at the end). # We recommend using \"post\" padding when working with RNN layers # (in order to be able to use the # CuDNN implementation of the layers). padded_inputs <- pad_sequences(raw_inputs, padding=\"post\") padded_inputs ##      [,1] [,2] [,3] [,4] [,5] [,6] ## [1,]  711  632   71    0    0    0 ## [2,]   73    8 3215   55  927    0 ## [3,]   83   91    1  645 1253  927"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"masking","dir":"Articles","previous_headings":"","what":"Masking","title":"Understanding masking & padding","text":"Now samples uniform length, model must informed part data actually padding ignored. mechanism masking. three ways introduce input masks Keras models: Add layer_masking layer. Configure layer_embedding layer mask_zero=TRUE. Pass mask argument manually calling layers support argument (e.g. RNN layers).","code":""},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"mask-generating-layers-embedding-and-masking","dir":"Articles","previous_headings":"","what":"Mask-generating layers: Embedding and Masking","title":"Understanding masking & padding","text":"hood, layers create mask tensor (2D tensor shape (batch, sequence_length)), attach tensor output returned Masking Embedding layer. can see printed result, mask 2D boolean tensor shape (batch_size, sequence_length), individual FALSE entry indicates corresponding timestep ignored processing.","code":"embedding <- layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) masked_output <- embedding(padded_inputs)  masked_output$`_keras_mask` ## tf.Tensor( ## [[ True  True  True False False False] ##  [ True  True  True  True  True False] ##  [ True  True  True  True  True  True]], shape=(3, 6), dtype=bool) masking_layer <- layer_masking() # Simulate the embedding lookup by expanding the 2D input to 3D, # with embedding dimension of 10. unmasked_embedding <- op_cast(     op_tile(op_expand_dims(padded_inputs, axis=-1), c(1L, 1L, 10L)),     dtype=\"float32\" )  masked_embedding <- masking_layer(unmasked_embedding) masked_embedding$`_keras_mask` ## tf.Tensor( ## [[ True  True  True False False False] ##  [ True  True  True  True  True False] ##  [ True  True  True  True  True  True]], shape=(3, 6), dtype=bool)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"mask-propagation-in-the-functional-api-and-sequential-api","dir":"Articles","previous_headings":"","what":"Mask propagation in the Functional API and Sequential API","title":"Understanding masking & padding","text":"using Functional API Sequential API, mask generated Embedding Masking layer propagated network layer capable using (example, RNN layers). Keras automatically fetch mask corresponding input pass layer knows use . instance, following Sequential model, LSTM layer automatically receive mask, means ignore padded values: also case following Functional API model:","code":"model <- keras_model_sequential() %>%   layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%   layer_lstm(units=32) inputs <- keras_input(shape = shape(NULL), dtype=\"int32\") outputs <- inputs %>%   layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%   layer_lstm(units=32)  model <- keras_model(inputs, outputs)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"passing-mask-tensors-directly-to-layers","dir":"Articles","previous_headings":"","what":"Passing mask tensors directly to layers","title":"Understanding masking & padding","text":"Layers can handle masks (LSTM layer) mask argument call method. Meanwhile, layers produce mask (e.g. Embedding) expose compute_mask(input, previous_mask) method can call. Thus, can pass output compute_mask() method mask-producing layer call method mask-consuming layer, like :","code":"MyLayer <- new_layer_class(   \"MyLayer\",   initialize = function(...) {     super$initialize(...)     self$embedding <- layer_embedding(       input_dim=5000, output_dim=16, mask_zero=TRUE     )     self$lstm <- layer_lstm(units=32)   },   call = function(inputs) {     inputs %>%       self$embedding() %>%       # Note that you could also prepare a `mask` tensor manually.       # It only needs to be a boolean tensor       # with the right shape, i.e. (batch_size, timesteps).       self$lstm(mask=self$embedding$compute_mask(inputs))   } )  layer <- MyLayer() x <- random_integer(c(32, 10), 0, 100) layer(x) ## tf.Tensor( ## [[ 0.00130048 -0.00113367 -0.00715671 ... -0.00107615 -0.00162071 ##    0.00135018] ##  [-0.004185    0.00726349  0.00520932 ...  0.00119117  0.00230441 ##    0.00174123] ##  [-0.00537032 -0.00164898 -0.00238435 ... -0.00154158 -0.0038603 ##   -0.00105811] ##  ... ##  [ 0.00622133 -0.00905907 -0.00599518 ...  0.00025823 -0.00142478 ##   -0.00125036] ##  [-0.00523904  0.00336683 -0.00299453 ...  0.00876719  0.00172074 ##    0.00903089] ##  [-0.00393721  0.00058538  0.00503809 ... -0.00203075  0.00325885 ##   -0.00299755]], shape=(32, 32), dtype=float32)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"supporting-masking-in-your-custom-layers","dir":"Articles","previous_headings":"","what":"Supporting masking in your custom layers","title":"Understanding masking & padding","text":"Sometimes, may need write layers generate mask (like Embedding), layers need modify current mask. instance, layer produces tensor different time dimension input, Concatenate layer concatenates time dimension, need modify current mask downstream layers able properly take masked timesteps account. , layer implement layer.compute_mask() method, produces new mask given input current mask. example TemporalSplit layer needs modify current mask. another example CustomEmbedding layer capable generating mask input values: Note: details format limitations related masking, see serialization guide.","code":"TemporalSplit <- new_layer_class(   \"TemporalSplit\",   call = function(inputs) {     # Expect the input to be 3D and mask to be 2D, split the input tensor into 2     # subtensors along the time axis (axis 1).     op_split(inputs, 2, axis=2)   },   compute_mask = function(inputs, mask = NULL) {     # Also split the mask into 2 if it presents.     if (!is.null(mask)) {       op_split(mask, 2, axis=2)     } else {       NULL     }   } )  c(first_half, second_half) %<-% TemporalSplit(masked_embedding) first_half$`_keras_mask` ## tf.Tensor( ## [[ True  True  True] ##  [ True  True  True] ##  [ True  True  True]], shape=(3, 3), dtype=bool) second_half$`_keras_mask` ## tf.Tensor( ## [[False False False] ##  [ True  True False] ##  [ True  True  True]], shape=(3, 3), dtype=bool) CustomEmbedding <- new_layer_class(   \"CustomEmbedding\",   initialize = function(input_dim, output_dim, mask_zero=FALSE, ...) {     super$initialize(...)     self$input_dim <- as.integer(input_dim)     self$output_dim <- as.integer(output_dim)     self$mask_zero <- mask_zero   },   build = function(input_shape) {     self$embeddings <- self$add_weight(       shape=c(self$input_dim, self$output_dim),       initializer=\"random_normal\",       dtype=\"float32\"     )   },   call = function(inputs) {     inputs <- op_cast(inputs, \"int32\")     op_take(self$embeddings, inputs)   },   compute_mask = function(inputs, mask=NULL) {     if (!self$mask_zero) {       NULL     } else {       op_not_equal(inputs, 0)     }   } )  layer <- CustomEmbedding(input_dim = 10, output_dim = 32, mask_zero=TRUE) x <- random_integer(c(3, 10), 0, 9)  y <- layer(x) mask <- layer$compute_mask(x)  mask ## tf.Tensor( ## [[ True  True  True  True  True  True  True  True  True  True] ##  [ True  True  True  True  True  True  True  True  True  True] ##  [False False  True  True  True  True  True  True  True  True]], shape=(3, 10), dtype=bool)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"opting-in-to-mask-propagation-on-compatible-layers","dir":"Articles","previous_headings":"","what":"Opting-in to mask propagation on compatible layers","title":"Understanding masking & padding","text":"layers don’t modify time dimension, don’t need modify current mask. However, may still want able propagate current mask, unchanged, next layer. opt-behavior. default, custom layer destroy current mask (since framework way tell whether propagating mask safe ). custom layer modify time dimension, want able propagate current input mask, set self.supports_masking = True layer constructor. case, default behavior compute_mask() just pass current mask . ’s example layer whitelisted mask propagation: can now use custom layer -mask-generating layer (like Embedding) mask-consuming layer (like LSTM), pass mask along reaches mask-consuming layer.","code":"MyActivation <- new_layer_class(   \"MyActivation\",   initialize = function(...) {     super$initialize(...)     self$supports_masking <- TRUE   },   call = function(inputs) {     op_relu(inputs)   } ) inputs <- keras_input(shape = shape(NULL), dtype=\"int32\") outputs <- inputs %>%   layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%   MyActivation() %>%   layer_lstm(units=32)  model <- keras_model(inputs, outputs) y <- model(random_integer(c(32, 100), 0, 5000))"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"writing-layers-that-need-mask-information","dir":"Articles","previous_headings":"","what":"Writing layers that need mask information","title":"Understanding masking & padding","text":"layers mask consumers: accept mask argument call use determine whether skip certain time steps. write layer, can simply add mask=None argument call signature. mask associated inputs passed layer whenever available. ’s simple example : layer computes softmax time dimension (axis 1) input sequence, discarding masked timesteps.","code":"TemporalSoftmax <- new_layer_class(   \"TemporalSoftmax\",   initialize = function(...) {     super$initialize(...)     self$supports_masking <- TRUE   },   call = function(inputs, mask=NULL) {     if (is.null(mask)) {       stop(\"`TemporalSoftmax` layer requires a previous layer to support masking.\")     }     broadcast_float_mask <- op_expand_dims(op_cast(mask, \"float32\"), -1)     inputs_exp <- op_exp(inputs) * broadcast_float_mask     inputs_sum <- op_sum(inputs_exp * broadcast_float_mask, axis=-1, keepdims=TRUE)     inputs_exp / inputs_sum   } )  inputs <- keras_input(shape = shape(NULL), dtype=\"int32\") outputs <- inputs %>%   layer_embedding(input_dim=10, output_dim=32, mask_zero=TRUE) %>%   layer_dense(1) %>%   TemporalSoftmax()  model <- keras_model(inputs, outputs) y <- model(random_integer(c(32, 100), 0, 10))"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Understanding masking & padding","text":"need know padding & masking Keras. recap: “Masking” layers able know skip / ignore certain timesteps sequence inputs. layers mask-generators: Embedding can generate mask input values (mask_zero=TRUE), can Masking layer. layers mask-consumers: expose mask argument call method. case RNN layers. Functional API Sequential API, mask information propagated automatically. using layers standalone way, can pass mask arguments layers manually. can easily write layers modify current mask, generate new mask, consume mask associated inputs.","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing a training loop from scratch in JAX","text":"","code":"import os  # This guide can only be run with the jax backend. os.environ[\"KERAS_BACKEND\"] = \"jax\"  import jax  # We import TF so we can use tf.data. import tensorflow as tf import keras import numpy as np"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing a training loop from scratch in JAX","text":"Keras provides default training evaluation loops, fit() evaluate(). usage covered guide Training & evaluation built-methods. want customize learning algorithm model still leveraging convenience fit() (instance, train GAN using fit()), can subclass Model class implement train_step() method, called repeatedly fit(). Now, want low-level control training & evaluation, write training & evaluation loops scratch. guide .","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"A first end-to-end example","title":"Writing a training loop from scratch in JAX","text":"write custom training loop, need following ingredients: model train, course. optimizer. either use optimizer keras.optimizers, one optax package. loss function. dataset. standard JAX ecosystem load data via tf.data, ’s ’ll use. Let’s line . First, let’s get model MNIST dataset: Next, ’s loss function optimizer. ’ll use Keras optimizer case.","code":"def get_model():     inputs = keras.Input(shape=(784,), name=\"digits\")     x1 = keras.layers.Dense(64, activation=\"relu\")(inputs)     x2 = keras.layers.Dense(64, activation=\"relu\")(x1)     outputs = keras.layers.Dense(10, name=\"predictions\")(x2)     model = keras.Model(inputs=inputs, outputs=outputs)     return model   model = get_model()  # Prepare the training dataset. batch_size = 32 (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() x_train = np.reshape(x_train, (-1, 784)).astype(\"float32\") x_test = np.reshape(x_test, (-1, 784)).astype(\"float32\") y_train = keras.utils.to_categorical(y_train) y_test = keras.utils.to_categorical(y_test)  # Reserve 10,000 samples for validation. x_val = x_train[-10000:] y_val = y_train[-10000:] x_train = x_train[:-10000] y_train = y_train[:-10000]  # Prepare the training dataset. train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)) train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size)  # Prepare the validation dataset. val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val)) val_dataset = val_dataset.batch(batch_size) # Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  # Instantiate an optimizer. optimizer = keras.optimizers.Adam(learning_rate=1e-3)"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"getting-gradients-in-jax","dir":"Articles","previous_headings":"A first end-to-end example","what":"Getting gradients in JAX","title":"Writing a training loop from scratch in JAX","text":"Let’s train model using mini-batch gradient custom training loop. JAX, gradients computed via metaprogramming: call jax.grad (jax.value_and_grad function order create gradient-computing function first function. first thing need function returns loss value. ’s function ’ll use generate gradient function. Something like : function, can compute gradients via metaprogramming : Typically, don’t just want get gradient values, also want get loss value. can using jax.value_and_grad instead jax.grad:","code":"def compute_loss(x, y):     ...     return loss grad_fn = jax.grad(compute_loss) grads = grad_fn(x, y) grad_fn = jax.value_and_grad(compute_loss) loss, grads = grad_fn(x, y)"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"jax-computation-is-purely-stateless","dir":"Articles","previous_headings":"A first end-to-end example","what":"JAX computation is purely stateless","title":"Writing a training loop from scratch in JAX","text":"JAX, everything must stateless function – loss computation function must stateless well. means Keras variables (e.g. weight tensors) must passed function inputs, variable updated forward pass must returned function output. function side effect. forward pass, non-trainable variables Keras model might get updated. variables , instance, RNG seed state variables BatchNormalization statistics. ’re going need return . need something like : function, can get gradient function specifying hax_aux value_and_grad: tells JAX loss computation function returns outputs just loss. Note loss always first output. Now established basics, let’s implement compute_loss_and_updates function. Keras models stateless_call method come handy . works just like model.__call__, requires explicitly pass value variables model, returns just __call__ outputs also (potentially updated) non-trainable variables. Let’s get gradient function:","code":"def compute_loss_and_updates(trainable_variables, non_trainable_variables, x, y):     ...     return loss, non_trainable_variables grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True) (loss, non_trainable_variables), grads = grad_fn(     trainable_variables, non_trainable_variables, x, y ) def compute_loss_and_updates(     trainable_variables, non_trainable_variables, x, y ):     y_pred, non_trainable_variables = model.stateless_call(         trainable_variables, non_trainable_variables, x     )     loss = loss_fn(y, y_pred)     return loss, non_trainable_variables grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True)"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"the-training-step-function","dir":"Articles","previous_headings":"A first end-to-end example","what":"The training step function","title":"Writing a training loop from scratch in JAX","text":"Next, let’s implement end--end training step, function run forward pass, compute loss, compute gradients, also use optimizer update trainable variables. function also needs stateless, get input state tuple includes every state element ’re going use: trainable_variables non_trainable_variables: model’s variables. optimizer_variables: optimizer’s state variables, momentum accumulators. update trainable variables, use optimizer’s stateless method stateless_apply. ’s equivalent optimizer.apply(), requires always passing trainable_variables optimizer_variables. returns updated trainable variables updated optimizer_variables.","code":"def train_step(state, data):     trainable_variables, non_trainable_variables, optimizer_variables = state     x, y = data     (loss, non_trainable_variables), grads = grad_fn(         trainable_variables, non_trainable_variables, x, y     )     trainable_variables, optimizer_variables = optimizer.stateless_apply(         optimizer_variables, grads, trainable_variables     )     # Return updated state     return loss, (         trainable_variables,         non_trainable_variables,         optimizer_variables,     )"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"make-it-fast-with-jax-jit","dir":"Articles","previous_headings":"A first end-to-end example","what":"Make it fast with jax.jit","title":"Writing a training loop from scratch in JAX","text":"default, JAX operations run eagerly, just like TensorFlow eager mode PyTorch eager mode. just like TensorFlow eager mode PyTorch eager mode, ’s pretty slow – eager mode better used debugging environment, way actual work. let’s make train_step fast compiling . stateless JAX function, can compile XLA via @jax.jit decorator. get traced first execution, subsequent executions executing traced graph (just like @tf.function(jit_compile=True). Let’s try : ’re now ready train model. training loop trivial: just repeatedly call loss, state = train_step(state, data). Note: convert TF tensors yielded tf.data.Dataset NumPy passing JAX function. variables must built beforehand: model must built optimizer must built. Since ’re using Functional API model, ’s already built, subclassed model ’d need call batch data build . key thing notice loop entirely stateless – variables attached model (model.weights) never getting updated loop. new values stored state tuple. means point, saving model, attaching new variable values back model. Just call variable.assign(new_value) model variable want update:","code":"@jax.jit def train_step(state, data):     trainable_variables, non_trainable_variables, optimizer_variables = state     x, y = data     (loss, non_trainable_variables), grads = grad_fn(         trainable_variables, non_trainable_variables, x, y     )     trainable_variables, optimizer_variables = optimizer.stateless_apply(         optimizer_variables, grads, trainable_variables     )     # Return updated state     return loss, (         trainable_variables,         non_trainable_variables,         optimizer_variables,     ) # Build optimizer variables. optimizer.build(model.trainable_variables)  trainable_variables = model.trainable_variables non_trainable_variables = model.non_trainable_variables optimizer_variables = optimizer.variables state = trainable_variables, non_trainable_variables, optimizer_variables  # Training loop for step, data in enumerate(train_dataset):     data = (data[0].numpy(), data[1].numpy())     loss, state = train_step(state, data)     # Log every 100 batches.     if step % 100 == 0:         print(f\"Training loss (for 1 batch) at step {step}: {float(loss):.4f}\")         print(f\"Seen so far: {(step + 1) * batch_size} samples\") trainable_variables, non_trainable_variables, optimizer_variables = state for variable, value in zip(model.trainable_variables, trainable_variables):     variable.assign(value) for variable, value in zip(     model.non_trainable_variables, non_trainable_variables ):     variable.assign(value)"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"low-level-handling-of-metrics","dir":"Articles","previous_headings":"","what":"Low-level handling of metrics","title":"Writing a training loop from scratch in JAX","text":"Let’s add metrics monitoring basic training loop. can readily reuse built-Keras metrics (custom ones wrote) training loops written scratch. ’s flow: Instantiate metric start loop Include metric_variables train_step arguments compute_loss_and_updates arguments. Call metric.stateless_update_state() compute_loss_and_updates function. ’s equivalent update_state() – stateless. need display current value metric, outside train_step (eager scope), attach new metric variable values metric object vall metric.result(). Call metric.reset_state() need clear state metric (typically end epoch) Let’s use knowledge compute CategoricalAccuracy training validation data end training: ’ll also prepare evaluation step function: loops:","code":"# Get a fresh model model = get_model()  # Instantiate an optimizer to train the model. optimizer = keras.optimizers.Adam(learning_rate=1e-3) # Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  # Prepare the metrics. train_acc_metric = keras.metrics.CategoricalAccuracy() val_acc_metric = keras.metrics.CategoricalAccuracy()   def compute_loss_and_updates(     trainable_variables, non_trainable_variables, metric_variables, x, y ):     y_pred, non_trainable_variables = model.stateless_call(         trainable_variables, non_trainable_variables, x     )     loss = loss_fn(y, y_pred)     metric_variables = train_acc_metric.stateless_update_state(         metric_variables, y, y_pred     )     return loss, (non_trainable_variables, metric_variables)   grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True)   @jax.jit def train_step(state, data):     (         trainable_variables,         non_trainable_variables,         optimizer_variables,         metric_variables,     ) = state     x, y = data     (loss, (non_trainable_variables, metric_variables)), grads = grad_fn(         trainable_variables, non_trainable_variables, metric_variables, x, y     )     trainable_variables, optimizer_variables = optimizer.stateless_apply(         optimizer_variables, grads, trainable_variables     )     # Return updated state     return loss, (         trainable_variables,         non_trainable_variables,         optimizer_variables,         metric_variables,     ) @jax.jit def eval_step(state, data):     trainable_variables, non_trainable_variables, metric_variables = state     x, y = data     y_pred, non_trainable_variables = model.stateless_call(         trainable_variables, non_trainable_variables, x     )     loss = loss_fn(y, y_pred)     metric_variables = val_acc_metric.stateless_update_state(         metric_variables, y, y_pred     )     return loss, (         trainable_variables,         non_trainable_variables,         metric_variables,     ) # Build optimizer variables. optimizer.build(model.trainable_variables)  trainable_variables = model.trainable_variables non_trainable_variables = model.non_trainable_variables optimizer_variables = optimizer.variables metric_variables = train_acc_metric.variables state = (     trainable_variables,     non_trainable_variables,     optimizer_variables,     metric_variables, )  # Training loop for step, data in enumerate(train_dataset):     data = (data[0].numpy(), data[1].numpy())     loss, state = train_step(state, data)     # Log every 100 batches.     if step % 100 == 0:         print(f\"Training loss (for 1 batch) at step {step}: {float(loss):.4f}\")         _, _, _, metric_variables = state         for variable, value in zip(             train_acc_metric.variables, metric_variables         ):             variable.assign(value)         print(f\"Training accuracy: {train_acc_metric.result()}\")         print(f\"Seen so far: {(step + 1) * batch_size} samples\")  metric_variables = val_acc_metric.variables (     trainable_variables,     non_trainable_variables,     optimizer_variables,     metric_variables, ) = state state = trainable_variables, non_trainable_variables, metric_variables  # Eval loop for step, data in enumerate(val_dataset):     data = (data[0].numpy(), data[1].numpy())     loss, state = eval_step(state, data)     # Log every 100 batches.     if step % 100 == 0:         print(             f\"Validation loss (for 1 batch) at step {step}: {float(loss):.4f}\"         )         _, _, metric_variables = state         for variable, value in zip(val_acc_metric.variables, metric_variables):             variable.assign(value)         print(f\"Validation accuracy: {val_acc_metric.result()}\")         print(f\"Seen so far: {(step + 1) * batch_size} samples\")"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"low-level-handling-of-losses-tracked-by-the-model","dir":"Articles","previous_headings":"","what":"Low-level handling of losses tracked by the model","title":"Writing a training loop from scratch in JAX","text":"Layers & models recursively track losses created forward pass layers call self.add_loss(value). resulting list scalar loss values available via property model.losses end forward pass. want using loss components, sum add main loss training step. Consider layer, creates activity regularization loss: Let’s build really simple model uses : ’s compute_loss_and_updates function look like now: Pass return_losses=True model.stateless_call(). Sum resulting losses add main loss. ’s !","code":"class ActivityRegularizationLayer(keras.layers.Layer):     def call(self, inputs):         self.add_loss(1e-2 * jax.numpy.sum(inputs))         return inputs inputs = keras.Input(shape=(784,), name=\"digits\") x = keras.layers.Dense(64, activation=\"relu\")(inputs) # Insert activity regularization as a layer x = ActivityRegularizationLayer()(x) x = keras.layers.Dense(64, activation=\"relu\")(x) outputs = keras.layers.Dense(10, name=\"predictions\")(x)  model = keras.Model(inputs=inputs, outputs=outputs) def compute_loss_and_updates(     trainable_variables, non_trainable_variables, metric_variables, x, y ):     y_pred, non_trainable_variables, losses = model.stateless_call(         trainable_variables, non_trainable_variables, x, return_losses=True     )     loss = loss_fn(y, y_pred)     if losses:         loss += jax.numpy.sum(losses)     metric_variables = train_acc_metric.stateless_update_state(         metric_variables, y, y_pred     )     return loss, non_trainable_variables, metric_variables"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing a training loop from scratch in TensorFlow","text":"","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\") library(keras3)  # This guide can only be run with the TensorFlow backend. use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing a training loop from scratch in TensorFlow","text":"Keras provides default training evaluation loops, fit() evaluate(). usage covered guide Training & evaluation built-methods. want customize learning algorithm model still leveraging convenience fit() (instance, train GAN using fit()), can subclass Model class implement train_step() method, called repeatedly fit(). covered guide Customizing happens fit(). Now, want low-level control training & evaluation, write training & evaluation loops scratch. guide .","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"using-the-gradienttape-a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"Using the GradientTape: a first end-to-end example","title":"Writing a training loop from scratch in TensorFlow","text":"Calling model inside GradientTape scope enables retrieve gradients trainable weights layer respect loss value. Using optimizer instance, can use gradients update variables (can retrieve using model$trainable_weights). Let’s consider simple MNIST model: Let’s train using mini-batch gradient custom training loop. First, ’re going need optimizer, loss function, dataset: ’s training loop: open loop iterates epochs epoch, iterate dataset, batches batch, open GradientTape() scope Inside scope, call model (forward pass) compute loss Outside scope, retrieve gradients weights model regard loss Finally, use optimizer update weights model based gradients","code":"get_model <- function() {   inputs <- keras_input(shape = 784, name = \"digits\")   outputs <- inputs |>     layer_dense(units = 64, activation = \"relu\") |>     layer_dense(units = 64, activation = \"relu\") |>     layer_dense(units = 10, name = \"predictions\")   keras_model(inputs = inputs, outputs = outputs) }  model <- get_model() # Instantiate an optimizer. optimizer <- optimizer_adam(learning_rate = 1e-3) # Instantiate a loss function. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE)  # Prepare the training dataset. batch_size <- 64 c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(-1, 784)) x_test <- array_reshape(x_test, c(-1, 784))  # Reserve 10,000 samples for validation. val_i <- sample.int(nrow(x_train), 10000) x_val <- x_train[val_i,] y_val <- y_train[val_i] x_train %<>% .[-val_i,] y_train %<>% .[-val_i]  # Prepare the training dataset. train_dataset <- list(x_train, y_train) |>   tensor_slices_dataset() |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(batch_size)  # Prepare the validation dataset. val_dataset <- list(x_val, y_val) |>   tensor_slices_dataset() |>   dataset_batch(batch_size) epochs <- 3 for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   iterator <- as_iterator(train_dataset)   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1     # Open a GradientTape to record the operations run     # during the forward pass, which enables auto-differentiation.     with(tf$GradientTape() %as% tape, {       # Run the forward pass of the layer.       # The operations that the layer applies       # to its inputs are going to be recorded       # on the GradientTape.       logits <- model(x_batch_train, training = TRUE) # Logits for this minibatch        # Compute the loss value for this minibatch.       loss_value <- loss_fn(y_batch_train, logits)     })      # Use the gradient tape to automatically retrieve     # the gradients of the trainable variables with respect to the loss.     gradients <- tape$gradient(loss_value, model$trainable_weights)      # Run one step of gradient descent by updating     # the value of the variables to minimize the loss.     optimizer$apply(gradients, model$trainable_weights)      # Log every 200 batches.     if (step %% 200 == 0) {       cat(sprintf(\"Training loss (for one batch) at step %d: %.4f\\n\",                   step, loss_value))       cat(sprintf(\"Seen so far: %d samples\\n\", (step * batch_size)))     }   } } ## Start of epoch  1 ## Training loss (for one batch) at step 200: 1.1675 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 1.6450 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.6477 ## Seen so far: 38400 samples ## Start of epoch  2 ## Training loss (for one batch) at step 200: 0.6421 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.1827 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.4249 ## Seen so far: 38400 samples ## Start of epoch  3 ## Training loss (for one batch) at step 200: 0.2715 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.3106 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.2905 ## Seen so far: 38400 samples"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"low-level-handling-of-metrics","dir":"Articles","previous_headings":"","what":"Low-level handling of metrics","title":"Writing a training loop from scratch in TensorFlow","text":"Let’s add metrics monitoring basic loop. can readily reuse built-metrics (custom ones wrote) training loops written scratch. ’s flow: Instantiate metric start loop Call metric$update_state() batch Call metric$result() need display current value metric Call metric$reset_state() need clear state metric (typically end epoch) Let’s use knowledge compute SparseCategoricalAccuracy validation data end epoch: ’s training & evaluation loop:","code":"# Get a fresh model model <- get_model()  # Instantiate an optimizer to train the model. optimizer <- optimizer_adam(learning_rate = 1e-3) # Instantiate a loss function. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE)  # Prepare the metrics. train_acc_metric <- metric_sparse_categorical_accuracy() val_acc_metric <- metric_sparse_categorical_accuracy() epochs <- 2 time <- Sys.time() for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   train_dataset_iterator <- as_iterator(train_dataset)   while (!is.null(train_batch <- iter_next(train_dataset_iterator))) {     c(x_batch_train, y_batch_train) %<-% train_batch     step <- step + 1      with(tf$GradientTape() %as% tape, {       logits <- model(x_batch_train, training = TRUE)       loss_value <- loss_fn(y_batch_train, logits)     })     gradients <- tape$gradient(loss_value, model$trainable_weights)     optimizer$apply(gradients, model$trainable_weights)      # Update training metric.     train_acc_metric$update_state(y_batch_train, logits)     if (step %% 200 == 0) {       cat(sprintf(         \"Training loss (for one batch) at step %d: %.4f\\n\", step, loss_value))       cat(sprintf(\"Seen so far: %d samples \\n\", (step * batch_size)))     }   }    # Display metrics at the end of each epoch.   train_acc <- train_acc_metric$result()   cat(sprintf(\"Training acc over epoch: %.4f\\n\", train_acc))    # Reset training metrics at the end of each epoch   train_acc_metric$reset_state()    # Run a validation loop at the end of each epoch.   iterate(val_dataset, function(val_batch) {     c(x_batch_val, y_batch_val) %<-% val_batch     val_logits <- model(x_batch_val, training = FALSE)     val_acc_metric$update_state(y_batch_val, val_logits)   })   val_acc <- val_acc_metric$result()   val_acc_metric$reset_state()   cat(sprintf(\"Validation acc: %.4f\\n\", val_acc)) } ## Start of epoch  1 ## Training loss (for one batch) at step 200: 1.6268 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 1.2241 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.4987 ## Seen so far: 38400 samples ## Training acc over epoch: 0.7844 ## Validation acc: 0.8680 ## Start of epoch  2 ## Training loss (for one batch) at step 200: 0.4626 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.4654 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.5022 ## Seen so far: 38400 samples ## Training acc over epoch: 0.8837 ## Validation acc: 0.9031 Sys.time() - time ## Time difference of 40.01661 secs"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"speeding-up-your-training-step-with-tf_function","dir":"Articles","previous_headings":"","what":"Speeding-up your training step with tf_function()","title":"Writing a training loop from scratch in TensorFlow","text":"default runtime TensorFlow 2 eager execution. , training loop executes eagerly. great debugging, graph compilation definite performance advantage. Describing computation static graph enables framework apply global performance optimizations. impossible framework constrained greedily execute one operation another, knowledge comes next. can compile static graph function takes tensors input. Just add @tf.function decorator , like : Let’s evaluation step: Now, let’s re-run training loop compiled training step: Much faster, isn’t ?","code":"train_step <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     logits <- model(x, training = TRUE)     loss_value <- loss_fn(y, logits)   })   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply(gradients, model$trainable_weights)   train_acc_metric$update_state(y, logits)   invisible(NULL) # return nothing }) test_step <- tf_function(function(x, y) {   val_logits <- model(x, training=FALSE)   val_acc_metric$update_state(y, val_logits)   invisible(NULL) # return nothing }) epochs <- 2 time <- Sys.time() for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1     train_step(x_batch_train, y_batch_train)      if (step %% 200 == 0) {       cat(sprintf(         \"Training loss (for one batch) at step %d: %.4f\\n\", step, loss_value       ))       cat(sprintf(\"Seen so far: %d samples \\n\", (step * batch_size)))     }   }    # Display metrics at the end of each epoch.   train_acc <- train_acc_metric$result()   cat(sprintf(\"Training acc over epoch: %.4f\\n\", train_acc))     # Reset training metrics at the end of each epoch   train_acc_metric$reset_state()    # Run a validation loop at the end of each epoch.    iterate(val_dataset, function(val_batch) {     c(x_batch_val, y_batch_val) %<-% val_batch     test_step(x_batch_val, y_batch_val)   })    val_acc <- val_acc_metric$result()   val_acc_metric$reset_state()   cat(sprintf(\"Validation acc: %.4f\\n\", val_acc)) } ## Start of epoch  1 ## Training acc over epoch: 0.0000 ## Validation acc: 0.9031 ## Start of epoch  2 ## Training acc over epoch: 0.0000 ## Validation acc: 0.9031 Sys.time() - time ## Time difference of 0.3914127 secs"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"low-level-handling-of-losses-tracked-by-the-model","dir":"Articles","previous_headings":"","what":"Low-level handling of losses tracked by the model","title":"Writing a training loop from scratch in TensorFlow","text":"Layers models recursively track losses created forward pass layers call self$add_loss(value). resulting list scalar loss values available via property model$losses end forward pass. want using loss components, sum add main loss training step. Consider layer, creates activity regularization loss: Let’s build really simple model uses : ’s training step look like now:","code":"layer_activity_regularization <- Layer(   \"ActivityRegularizationLayer\",   call = function(inputs) {     self$add_loss(0.1 * op_mean(inputs))     inputs   } ) inputs <- keras_input(shape = 784, name=\"digits\") outputs <- inputs |>   layer_dense(units = 64, activation = \"relu\") |>   layer_activity_regularization() |>   layer_dense(units = 64, activation = \"relu\") |>   layer_dense(units = 10, name = \"predictions\") model <- keras_model(inputs = inputs, outputs = outputs) train_step <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     logits <- model(x, training = TRUE)     loss_value <- Reduce(`+`, c(loss_fn(y, logits),                                 model$losses))   })   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply(gradients, model$trainable_weights)   train_acc_metric$update_state(y, logits)   invisible(NULL) })"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Writing a training loop from scratch in TensorFlow","text":"Now know everything know using built-training loops writing scratch. conclude, ’s simple end--end example ties together everything ’ve learned guide: DCGAN trained MNIST digits.","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"end-to-end-example-a-gan-training-loop-from-scratch","dir":"Articles","previous_headings":"","what":"End-to-end example: a GAN training loop from scratch","title":"Writing a training loop from scratch in TensorFlow","text":"may familiar Generative Adversarial Networks (GANs). GANs can generate new images look almost real, learning latent distribution training dataset images (“latent space” images). GAN made two parts: “generator” model maps points latent space points image space, “discriminator” model, classifier can tell difference real images (training dataset) fake images (output generator network). GAN training loop looks like : Train discriminator. Sample batch random points latent space. Turn points fake images via “generator” model. Get batch real images combine generated images. Train “discriminator” model classify generated vs. real images. Train generator. Sample random points latent space. Turn points fake images via “generator” network. Get batch real images combine generated images. Train “generator” model “fool” discriminator classify fake images real. much detailed overview GANs works, see Deep Learning Python. Let’s implement training loop. First, create discriminator meant classify fake vs real digits: let’s create generator network, turns latent vectors outputs shape (28, 28, 1) (representing MNIST digits): ’s key bit: training loop. can see quite straightforward. training step function takes 17 lines. Let’s train GAN, repeatedly calling train_step batches images. Since discriminator generator convnets, ’re going want run code GPU. ’s ! ’ll get nice-looking fake MNIST digits just ~30s training GPU.","code":"# Create the discriminator discriminator <-   keras_model_sequential(name = \"discriminator\",                          input_shape = c(28, 28, 1)) |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d(filters = 128, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_global_max_pooling_2d() |>   layer_dense(units = 1)  summary(discriminator) ## Model: \"discriminator\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 14, 14, 64)     │           640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu (LeakyReLU)         │ (None, 14, 14, 64)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 7, 7, 128)      │        73,856 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_1 (LeakyReLU)       │ (None, 7, 7, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d            │ (None, 128)            │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_6 (Dense)                 │ (None, 1)              │           129 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 74,625 (291.50 KB) ##  Trainable params: 74,625 (291.50 KB) ##  Non-trainable params: 0 (0.00 B) latent_dim <- 128L  generator <-   keras_model_sequential(name = \"generator\",                          input_shape = latent_dim) |>   layer_dense(7 * 7 * 128) |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_reshape(target_shape = c(7, 7, 128)) |>   layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d(filters = 1, kernel_size = c(7, 7), padding = \"same\",                 activation = \"sigmoid\")  summary(generator) ## Model: \"generator\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_7 (Dense)                 │ (None, 6272)           │       809,088 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_2 (LeakyReLU)       │ (None, 6272)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ reshape (Reshape)               │ (None, 7, 7, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 14, 14, 128)    │       262,272 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_3 (LeakyReLU)       │ (None, 14, 14, 128)    │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 28, 28, 128)    │       262,272 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_4 (LeakyReLU)       │ (None, 28, 28, 128)    │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 28, 28, 1)      │         6,273 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 1,339,905 (5.11 MB) ##  Trainable params: 1,339,905 (5.11 MB) ##  Non-trainable params: 0 (0.00 B) # Instantiate one optimizer for the discriminator and another for the generator. d_optimizer <- optimizer_adam(learning_rate = 0.0003) g_optimizer <- optimizer_adam(learning_rate = 0.0004)  # Instantiate a loss function. loss_fn <- loss_binary_crossentropy(from_logits = TRUE)  train_step <- tf_function(function(real_images) {   # Sample random points in the latent space   c(batch_size, ...) %<-% op_shape(real_images)   random_latent_vectors <-     tf$random$normal(shape(batch_size, latent_dim))    # Decode them to fake images   generated_images <- generator(random_latent_vectors)    # Combine them with real images   combined_images <- tf$concat(list(generated_images, real_images),                                axis = 0L)    # Assemble labels discriminating real from fake images   labels <- tf$concat(list(tf$ones(shape(batch_size, 1)),                            tf$zeros(shape(batch_size, 1))),                       axis = 0L)    # Add random noise to the labels - important trick!   labels %<>% `+`(tf$random$uniform(tf$shape(.), maxval = 0.05))    # Train the discriminator   with(tf$GradientTape() %as% tape, {     predictions <- discriminator(combined_images)     d_loss <- loss_fn(labels, predictions)   })   grads <- tape$gradient(d_loss, discriminator$trainable_weights)   d_optimizer$apply(grads, discriminator$trainable_weights)    # Sample random points in the latent space   random_latent_vectors <-     tf$random$normal(shape(batch_size, latent_dim))    # Assemble labels that say \"all real images\"   misleading_labels <- tf$zeros(shape(batch_size, 1))    # Train the generator (note that we should *not* update the weights   # of the discriminator)!   with(tf$GradientTape() %as% tape, {     predictions <- discriminator(generator(random_latent_vectors))     g_loss <- loss_fn(misleading_labels, predictions)   })    grads <- tape$gradient(g_loss, generator$trainable_weights)   g_optimizer$apply(grads, generator$trainable_weights)    list(d_loss, g_loss, generated_images) }) # Prepare the dataset. We use both the training & test MNIST digits. batch_size <- 64 c(c(x_train, .), c(x_test, .)) %<-% dataset_mnist() all_digits <- op_concatenate(list(x_train, x_test)) all_digits <- op_reshape(all_digits, c(-1, 28, 28, 1)) dataset <- all_digits |>   tensor_slices_dataset() |>   dataset_map(\\(x) op_cast(x, \"float32\") / 255) |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(batch_size = batch_size)  epochs <- 1 # In practice you need at least 20 epochs to generate nice digits. save_dir <- \"./\"  for (epoch in seq_len(epochs)) {   cat(\"Start epoch: \", epoch, \"\\n\")   step <- 0   train_iterator <- as_iterator(dataset)   while (!is.null(real_images <- iter_next(train_iterator))) {     step <- step + 1     # Train the discriminator & generator on one batch of real images.     c(d_loss, g_loss, generated_images) %<-% train_step(real_images)      # Logging.     if (step %% 200 == 0) {       # Print metrics       cat(sprintf(\"discriminator loss at step %d: %.2f\\n\", step, d_loss))       cat(sprintf(\"adversarial loss at step %d: %.2f\\n\", step, g_loss))     }      # To limit execution time we stop after 10 steps.     # Remove the lines below to actually train the model!     if (step > 10)       break   } } ## Start epoch:  1"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing a training loop from scratch in PyTorch","text":"","code":"import os  # This guide can only be run with the torch backend. os.environ[\"KERAS_BACKEND\"] = \"torch\"  import torch import keras import numpy as np"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing a training loop from scratch in PyTorch","text":"Keras provides default training evaluation loops, fit() evaluate(). usage covered guide Training & evaluation built-methods. want customize learning algorithm model still leveraging convenience fit() (instance, train GAN using fit()), can subclass Model class implement train_step() method, called repeatedly fit(). Now, want low-level control training & evaluation, write training & evaluation loops scratch. guide .","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"A first end-to-end example","title":"Writing a training loop from scratch in PyTorch","text":"write custom training loop, need following ingredients: model train, course. optimizer. either use keras.optimizers optimizer, native PyTorch optimizer torch.optim. loss function. either use keras.losses loss, native PyTorch loss torch.nn. dataset. use format: tf.data.Dataset, PyTorch DataLoader, Python generator, etc. Let’s line . ’ll use torch-native objects case – except, course, Keras model. First, let’s get model MNIST dataset: Next, ’s PyTorch optimizer PyTorch loss function: Let’s train model using mini-batch gradient custom training loop. Calling loss.backward() loss tensor triggers backpropagation. ’s done, optimizer magically aware gradients variable can update variables, done via optimizer.step(). Tensors, variables, optimizers interconnected one another via hidden global state. Also, don’t forget call model.zero_grad() loss.backward(), won’t get right gradients variables. ’s training loop, step step: open loop iterates epochs epoch, open loop iterates dataset, batches batch, call model input data retrive predictions, use compute loss value call loss.backward() Outside scope, retrieve gradients weights model regard loss Finally, use optimizer update weights model based gradients alternative, let’s look loop looks like using Keras optimizer Keras loss function. Important differences: retrieve gradients variables via v.value.grad, called trainable variable. update variables via optimizer.apply(), must called torch.no_grad() scope. Also, big gotcha: NumPy/TensorFlow/JAX/Keras APIs well Python unittest APIs use argument order convention fn(y_true, y_pred) (reference values first, predicted values second), PyTorch actually uses fn(y_pred, y_true) losses. make sure invert order logits targets.","code":"# Let's consider a simple MNIST model def get_model():     inputs = keras.Input(shape=(784,), name=\"digits\")     x1 = keras.layers.Dense(64, activation=\"relu\")(inputs)     x2 = keras.layers.Dense(64, activation=\"relu\")(x1)     outputs = keras.layers.Dense(10, name=\"predictions\")(x2)     model = keras.Model(inputs=inputs, outputs=outputs)     return model   # Create load up the MNIST dataset and put it in a torch DataLoader # Prepare the training dataset. batch_size = 32 (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() x_train = np.reshape(x_train, (-1, 784)).astype(\"float32\") x_test = np.reshape(x_test, (-1, 784)).astype(\"float32\") y_train = keras.utils.to_categorical(y_train) y_test = keras.utils.to_categorical(y_test)  # Reserve 10,000 samples for validation. x_val = x_train[-10000:] y_val = y_train[-10000:] x_train = x_train[:-10000] y_train = y_train[:-10000]  # Create torch Datasets train_dataset = torch.utils.data.TensorDataset(     torch.from_numpy(x_train), torch.from_numpy(y_train) ) val_dataset = torch.utils.data.TensorDataset(     torch.from_numpy(x_val), torch.from_numpy(y_val) )  # Create DataLoaders for the Datasets train_dataloader = torch.utils.data.DataLoader(     train_dataset, batch_size=batch_size, shuffle=True ) val_dataloader = torch.utils.data.DataLoader(     val_dataset, batch_size=batch_size, shuffle=False ) # Instantiate a torch optimizer model = get_model() optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)  # Instantiate a torch loss function loss_fn = torch.nn.CrossEntropyLoss() epochs = 3 for epoch in range(epochs):     for step, (inputs, targets) in enumerate(train_dataloader):         # Forward pass         logits = model(inputs)         loss = loss_fn(logits, targets)          # Backward pass         model.zero_grad()         loss.backward()          # Optimizer variable updates         optimizer.step()          # Log every 100 batches.         if step % 100 == 0:             print(                 f\"Training loss (for 1 batch) at step {step}: {loss.detach().numpy():.4f}\"             )             print(f\"Seen so far: {(step + 1) * batch_size} samples\") model = get_model() optimizer = keras.optimizers.Adam(learning_rate=1e-3) loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  for epoch in range(epochs):     print(f\"\\nStart of epoch {epoch}\")     for step, (inputs, targets) in enumerate(train_dataloader):         # Forward pass         logits = model(inputs)         loss = loss_fn(targets, logits)          # Backward pass         model.zero_grad()         trainable_weights = [v for v in model.trainable_weights]          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             optimizer.apply(gradients, trainable_weights)          # Log every 100 batches.         if step % 100 == 0:             print(                 f\"Training loss (for 1 batch) at step {step}: {loss.detach().numpy():.4f}\"             )             print(f\"Seen so far: {(step + 1) * batch_size} samples\")"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"low-level-handling-of-metrics","dir":"Articles","previous_headings":"","what":"Low-level handling of metrics","title":"Writing a training loop from scratch in PyTorch","text":"Let’s add metrics monitoring basic training loop. can readily reuse built-Keras metrics (custom ones wrote) training loops written scratch. ’s flow: Instantiate metric start loop Call metric.update_state() batch Call metric.result() need display current value metric Call metric.reset_state() need clear state metric (typically end epoch) Let’s use knowledge compute CategoricalAccuracy training validation data end epoch: ’s training & evaluation loop:","code":"# Get a fresh model model = get_model()  # Instantiate an optimizer to train the model. optimizer = keras.optimizers.Adam(learning_rate=1e-3) # Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  # Prepare the metrics. train_acc_metric = keras.metrics.CategoricalAccuracy() val_acc_metric = keras.metrics.CategoricalAccuracy() for epoch in range(epochs):     print(f\"\\nStart of epoch {epoch}\")     for step, (inputs, targets) in enumerate(train_dataloader):         # Forward pass         logits = model(inputs)         loss = loss_fn(targets, logits)          # Backward pass         model.zero_grad()         trainable_weights = [v for v in model.trainable_weights]          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             optimizer.apply(gradients, trainable_weights)          # Update training metric.         train_acc_metric.update_state(targets, logits)          # Log every 100 batches.         if step % 100 == 0:             print(                 f\"Training loss (for 1 batch) at step {step}: {loss.detach().numpy():.4f}\"             )             print(f\"Seen so far: {(step + 1) * batch_size} samples\")      # Display metrics at the end of each epoch.     train_acc = train_acc_metric.result()     print(f\"Training acc over epoch: {float(train_acc):.4f}\")      # Reset training metrics at the end of each epoch     train_acc_metric.reset_state()      # Run a validation loop at the end of each epoch.     for x_batch_val, y_batch_val in val_dataloader:         val_logits = model(x_batch_val, training=False)         # Update val metrics         val_acc_metric.update_state(y_batch_val, val_logits)     val_acc = val_acc_metric.result()     val_acc_metric.reset_state()     print(f\"Validation acc: {float(val_acc):.4f}\")"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"low-level-handling-of-losses-tracked-by-the-model","dir":"Articles","previous_headings":"","what":"Low-level handling of losses tracked by the model","title":"Writing a training loop from scratch in PyTorch","text":"Layers & models recursively track losses created forward pass layers call self.add_loss(value). resulting list scalar loss values available via property model.losses end forward pass. want using loss components, sum add main loss training step. Consider layer, creates activity regularization loss: Let’s build really simple model uses : ’s training loop look like now: ’s !","code":"class ActivityRegularizationLayer(keras.layers.Layer):     def call(self, inputs):         self.add_loss(1e-2 * torch.sum(inputs))         return inputs inputs = keras.Input(shape=(784,), name=\"digits\") x = keras.layers.Dense(64, activation=\"relu\")(inputs) # Insert activity regularization as a layer x = ActivityRegularizationLayer()(x) x = keras.layers.Dense(64, activation=\"relu\")(x) outputs = keras.layers.Dense(10, name=\"predictions\")(x)  model = keras.Model(inputs=inputs, outputs=outputs) # Get a fresh model model = get_model()  # Instantiate an optimizer to train the model. optimizer = keras.optimizers.Adam(learning_rate=1e-3) # Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  # Prepare the metrics. train_acc_metric = keras.metrics.CategoricalAccuracy() val_acc_metric = keras.metrics.CategoricalAccuracy()  for epoch in range(epochs):     print(f\"\\nStart of epoch {epoch}\")     for step, (inputs, targets) in enumerate(train_dataloader):         # Forward pass         logits = model(inputs)         loss = loss_fn(targets, logits)         if model.losses:             loss = loss + torch.sum(*model.losses)          # Backward pass         model.zero_grad()         trainable_weights = [v for v in model.trainable_weights]          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             optimizer.apply(gradients, trainable_weights)          # Update training metric.         train_acc_metric.update_state(targets, logits)          # Log every 100 batches.         if step % 100 == 0:             print(                 f\"Training loss (for 1 batch) at step {step}: {loss.detach().numpy():.4f}\"             )             print(f\"Seen so far: {(step + 1) * batch_size} samples\")      # Display metrics at the end of each epoch.     train_acc = train_acc_metric.result()     print(f\"Training acc over epoch: {float(train_acc):.4f}\")      # Reset training metrics at the end of each epoch     train_acc_metric.reset_state()      # Run a validation loop at the end of each epoch.     for x_batch_val, y_batch_val in val_dataloader:         val_logits = model(x_batch_val, training=False)         # Update val metrics         val_acc_metric.update_state(y_batch_val, val_logits)     val_acc = val_acc_metric.result()     val_acc_metric.reset_state()     print(f\"Validation acc: {float(val_acc):.4f}\")"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing a training loop from scratch in TensorFlow","text":"","code":"library(tensorflow) library(tfdatasets) library(keras3, warn.conflicts = FALSE)  # This guide can only be run with the TensorFlow backend. use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing a training loop from scratch in TensorFlow","text":"Keras provides default training evaluation loops, fit() evaluate(). usage covered guide Training & evaluation built-methods. want customize learning algorithm model still leveraging convenience fit() (instance, train GAN using fit()), can subclass Model class implement train_step() method, called repeatedly fit(). covered guide Customizing happens fit(). Now, want low-level control training & evaluation, write training & evaluation loops scratch. guide .","code":""},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"using-the-gradienttape-a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"Using the GradientTape: a first end-to-end example","title":"Writing a training loop from scratch in TensorFlow","text":"Calling model inside GradientTape scope enables retrieve gradients trainable weights layer respect loss value. Using optimizer instance, can use gradients update variables (can retrieve using model$trainable_weights). Let’s consider simple MNIST model: Let’s train using mini-batch gradient custom training loop. First, ’re going need optimizer, loss function, dataset: ’s training loop: open loop iterates epochs epoch, iterate dataset, batches batch, open GradientTape() scope Inside scope, call model (forward pass) compute loss Outside scope, retrieve gradients weights model regard loss Finally, use optimizer update weights model based gradients","code":"get_model <- function() {   inputs <- keras_input(shape = 784, name = \"digits\")   outputs <- inputs |>     layer_dense(units = 64, activation = \"relu\") |>     layer_dense(units = 64, activation = \"relu\") |>     layer_dense(units = 10, name = \"predictions\")   keras_model(inputs = inputs, outputs = outputs) }  model <- get_model() # Instantiate an optimizer. optimizer <- optimizer_adam(learning_rate = 1e-3) # Instantiate a loss function. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE)  # Prepare the training dataset. batch_size <- 64 c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(-1, 784)) x_test <- array_reshape(x_test, c(-1, 784))  # Reserve 10,000 samples for validation. x_val <- x_train[1:10000, ] y_val <- y_train[1:10000] x_train = x_train[-c(1:10000), ] y_train = y_train[-c(1:10000)]  # Prepare the training dataset. train_dataset <- list(x_train, y_train) |>   tensor_slices_dataset() |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(batch_size)  # Prepare the validation dataset. val_dataset <- list(x_val, y_val) |>   tensor_slices_dataset() |>   dataset_batch(batch_size) epochs <- 3 for (epoch in seq_len(epochs)) {   cat(\"\\nStart of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   iterator <- as_iterator(train_dataset)   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1     # Open a GradientTape to record the operations run     # during the forward pass, which enables auto-differentiation.     with(tf$GradientTape() %as% tape, {       # Run the forward pass of the layer.       # The operations that the layer applies       # to its inputs are going to be recorded       # on the GradientTape.       logits <- model(x_batch_train, training = TRUE) # Logits for this minibatch        # Compute the loss value for this minibatch.       loss_value <- loss_fn(y_batch_train, logits)     })      # Use the gradient tape to automatically retrieve     # the gradients of the trainable variables with respect to the loss.     gradients <- tape$gradient(loss_value, model$trainable_weights)      # Run one step of gradient descent by updating     # the value of the variables to minimize the loss.     optimizer$apply(gradients, model$trainable_weights)      # Log every 200 batches.     if (step %% 200 == 0) {       cat(sprintf(\"Training loss (for one batch) at step %d: %.4f\\n\",                   step, loss_value))       cat(sprintf(\"Seen so far: %d samples\\n\", (step * batch_size)))     }   } } ## ## Start of epoch  1 ## Training loss (for one batch) at step 200: 1.9342 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.8244 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.8011 ## Seen so far: 38400 samples ## ## Start of epoch  2 ## Training loss (for one batch) at step 200: 0.3505 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.6878 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.3928 ## Seen so far: 38400 samples ## ## Start of epoch  3 ## Training loss (for one batch) at step 200: 0.3366 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.1102 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.3629 ## Seen so far: 38400 samples"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"low-level-handling-of-metrics","dir":"Articles","previous_headings":"","what":"Low-level handling of metrics","title":"Writing a training loop from scratch in TensorFlow","text":"Let’s add metrics monitoring basic loop. can readily reuse built-metrics (custom ones wrote) training loops written scratch. ’s flow: Instantiate metric start loop Call metric$update_state() batch Call metric$result() need display current value metric Call metric$reset_state() need clear state metric (typically end epoch) Let’s use knowledge compute SparseCategoricalAccuracy validation data end epoch: ’s training & evaluation loop:","code":"# Get model model <- get_model()  # Instantiate an optimizer to train the model. optimizer <- optimizer_adam(learning_rate = 1e-3) # Instantiate a loss function. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE)  # Prepare the metrics. train_acc_metric <- metric_sparse_categorical_accuracy() val_acc_metric <- metric_sparse_categorical_accuracy() epochs <- 2 time <- Sys.time() for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   iterator <- as_iterator(train_dataset)   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1      with(tf$GradientTape() %as% tape, {       logits <- model(x_batch_train, training = TRUE)       loss_value <- loss_fn(y_batch_train, logits)     })     gradients <- tape$gradient(loss_value, model$trainable_weights)     optimizer$apply(gradients, model$trainable_weights)      # Update training metric.     train_acc_metric$update_state(y_batch_train, logits)     if (step %% 200 == 0) {       cat(sprintf(         \"Training loss (for one batch) at step %d: %.4f\\n\", step, loss_value))       cat(sprintf(\"Seen so far: %d samples \\n\", (step * batch_size)))     }   }    # Display metrics at the end of each epoch.   train_acc <- train_acc_metric$result()   cat(sprintf(\"Training acc over epoch: %.4f\\n\", train_acc))    # Reset training metrics at the end of each epoch   train_acc_metric$reset_state()    # Run a validation loop at the end of each epoch.   iterate(val_dataset, function(val_batch) {     c(x_batch_val, y_batch_val) %<-% val_batch     val_logits <- model(x_batch_val, training = FALSE)     val_acc_metric$update_state(y_batch_val, val_logits)   })   val_acc <- val_acc_metric$result()   val_acc_metric$reset_state()   cat(sprintf(\"Validation acc: %.4f\\n\", val_acc)) } ## Start of epoch  1 ## Training loss (for one batch) at step 200: 2.4070 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.8216 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 1.2056 ## Seen so far: 38400 samples ## Training acc over epoch: 0.8094 ## Validation acc: 0.8687 ## Start of epoch  2 ## Training loss (for one batch) at step 200: 0.1857 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.3803 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.3399 ## Seen so far: 38400 samples ## Training acc over epoch: 0.8859 ## Validation acc: 0.9002 Sys.time() - time ## Time difference of 44.61786 secs"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"speeding-up-your-training-step-with-tf_function","dir":"Articles","previous_headings":"","what":"Speeding-up your training step with tf_function()","title":"Writing a training loop from scratch in TensorFlow","text":"default runtime TensorFlow 2 eager execution. , training loop executes eagerly. great debugging, graph compilation definite performance advantage. Describing computation static graph enables framework apply global performance optimizations. impossible framework constrained greedily execute one operation another, knowledge comes next. can compile static graph function takes tensors input. Just add @tf.function decorator , like : Let’s evaluation step: Now, let’s re-run training loop compiled training step: Much faster, isn’t ?","code":"train_step <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     logits <- model(x, training = TRUE)     loss_value <- loss_fn(y, logits)   })   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply(gradients, model$trainable_weights)   train_acc_metric$update_state(y, logits)   invisible(NULL) # return nothing }) test_step <- tf_function(function(x, y) {   val_logits <- model(x, training=FALSE)   val_acc_metric$update_state(y, val_logits)   invisible(NULL) # return nothing }) epochs <- 2 time <- Sys.time() for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1     train_step(x_batch_train, y_batch_train)      if (step %% 200 == 0) {       cat(sprintf(         \"Training loss (for one batch) at step %d: %.4f\\n\", step, loss_value       ))       cat(sprintf(\"Seen so far: %d samples \\n\", (step * batch_size)))     }   }    # Display metrics at the end of each epoch.   train_acc <- train_acc_metric$result()   cat(sprintf(\"Training acc over epoch: %.4f\\n\", train_acc))     # Reset training metrics at the end of each epoch   train_acc_metric$reset_state()    # Run a validation loop at the end of each epoch.    iterate(val_dataset, function(val_batch) {     c(x_batch_val, y_batch_val) %<-% val_batch     test_step(x_batch_val, y_batch_val)   })    val_acc <- val_acc_metric$result()   val_acc_metric$reset_state()   cat(sprintf(\"Validation acc: %.4f\\n\", val_acc)) } ## Start of epoch  1 ## Training acc over epoch: 0.0000 ## Validation acc: 0.9002 ## Start of epoch  2 ## Training acc over epoch: 0.0000 ## Validation acc: 0.9002 Sys.time() - time ## Time difference of 0.5360565 secs"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"low-level-handling-of-losses-tracked-by-the-model","dir":"Articles","previous_headings":"","what":"Low-level handling of losses tracked by the model","title":"Writing a training loop from scratch in TensorFlow","text":"Layers models recursively track losses created forward pass layers call self$add_loss(value). resulting list scalar loss values available via property model$losses end forward pass. want using loss components, sum add main loss training step. Consider layer, creates activity regularization loss: Let’s build really simple model uses : ’s training step look like now:","code":"layer_activity_regularization <- Layer(   \"ActivityRegularizationLayer\",   call = function(inputs) {     self$add_loss(0.1 * op_mean(inputs))     inputs   } ) inputs <- keras_input(shape = 784, name=\"digits\") outputs <- inputs |>   layer_dense(units = 64, activation = \"relu\") |>   layer_activity_regularization() |>   layer_dense(units = 64, activation = \"relu\") |>   layer_dense(units = 10, name = \"predictions\") model <- keras_model(inputs = inputs, outputs = outputs) train_step <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     logits <- model(x, training = TRUE)     loss_value <- Reduce(`+`, c(loss_fn(y, logits),                                 model$losses))   })   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply(gradients, model$trainable_weights)   train_acc_metric$update_state(y, logits)   invisible(NULL) })"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Writing a training loop from scratch in TensorFlow","text":"Now know everything know using built-training loops writing scratch. conclude, ’s simple end--end example ties together everything ’ve learned guide: DCGAN trained MNIST digits.","code":""},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"end-to-end-example-a-gan-training-loop-from-scratch","dir":"Articles","previous_headings":"","what":"End-to-end example: a GAN training loop from scratch","title":"Writing a training loop from scratch in TensorFlow","text":"may familiar Generative Adversarial Networks (GANs). GANs can generate new images look almost real, learning latent distribution training dataset images (“latent space” images). GAN made two parts: “generator” model maps points latent space points image space, “discriminator” model, classifier can tell difference real images (training dataset) fake images (output generator network). GAN training loop looks like : Train discriminator. Sample batch random points latent space. Turn points fake images via “generator” model. Get batch real images combine generated images. Train “discriminator” model classify generated vs. real images. Train generator. Sample random points latent space. Turn points fake images via “generator” network. Get batch real images combine generated images. Train “generator” model “fool” discriminator classify fake images real. much detailed overview GANs works, see Deep Learning Python. Let’s implement training loop. First, create discriminator meant classify fake vs real digits: let’s create generator network, turns latent vectors outputs shape (28, 28, 1) (representing MNIST digits): ’s key bit: training loop. can see quite straightforward. training step function takes 17 lines. Let’s train GAN, repeatedly calling train_step batches images. Since discriminator generator convnets, ’re going want run code GPU. ’s ! ’ll get nice-looking fake MNIST digits just ~30s training Colab GPU.","code":"# Create the discriminator discriminator <-   keras_model_sequential(name = \"discriminator\",                          input_shape = c(28, 28, 1)) |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d(filters = 128, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_global_max_pooling_2d() |>   layer_dense(units = 1)  summary(discriminator) ## Model: \"discriminator\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d_1 (Conv2D)               │ (None, 14, 14, 64)     │           640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_1 (LeakyReLU)       │ (None, 14, 14, 64)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 7, 7, 128)      │        73,856 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu (LeakyReLU)         │ (None, 7, 7, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d            │ (None, 128)            │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_6 (Dense)                 │ (None, 1)              │           129 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 74,625 (291.50 KB) ##  Trainable params: 74,625 (291.50 KB) ##  Non-trainable params: 0 (0.00 B) latent_dim <- 128L  generator <-   keras_model_sequential(name = \"generator\",                          input_shape = latent_dim) |>   layer_dense(7 * 7 * 128) |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_reshape(target_shape = c(7, 7, 128)) |>   layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d(filters = 1, kernel_size = c(7, 7), padding = \"same\",                 activation = \"sigmoid\")  summary(generator) ## Model: \"generator\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_7 (Dense)                 │ (None, 6272)           │       809,088 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_4 (LeakyReLU)       │ (None, 6272)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ reshape (Reshape)               │ (None, 7, 7, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 14, 14, 128)    │       262,272 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_3 (LeakyReLU)       │ (None, 14, 14, 128)    │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 28, 28, 128)    │       262,272 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_2 (LeakyReLU)       │ (None, 28, 28, 128)    │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 28, 28, 1)      │         6,273 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 1,339,905 (5.11 MB) ##  Trainable params: 1,339,905 (5.11 MB) ##  Non-trainable params: 0 (0.00 B) # Instantiate one optimizer for the discriminator and another for the generator. d_optimizer <- optimizer_adam(learning_rate = 0.0003) g_optimizer <- optimizer_adam(learning_rate = 0.0004)  # Instantiate a loss function. loss_fn <- loss_binary_crossentropy(from_logits = TRUE)  train_step <- tf_function(function(real_images) {   # Sample random points in the latent space   c(batch_size, ...) %<-% op_shape(real_images)   random_latent_vectors <-     tf$random$normal(shape(batch_size, latent_dim))    # Decode them to fake images   generated_images <- generator(random_latent_vectors)    # Combine them with real images   combined_images <- tf$concat(list(generated_images, real_images),                                axis = 0L)    # Assemble labels discriminating real from fake images   labels <- tf$concat(list(tf$ones(shape(batch_size, 1)),                            tf$zeros(shape(batch_size, 1))),                       axis = 0L)    # Add random noise to the labels - important trick!   labels %<>% `+`(tf$random$uniform(tf$shape(.), maxval = 0.05))    # Train the discriminator   with(tf$GradientTape() %as% tape, {     predictions <- discriminator(combined_images)     d_loss <- loss_fn(labels, predictions)   })   grads <- tape$gradient(d_loss, discriminator$trainable_weights)   d_optimizer$apply(grads, discriminator$trainable_weights)    # Sample random points in the latent space   random_latent_vectors <-     tf$random$normal(shape(batch_size, latent_dim))    # Assemble labels that say \"all real images\"   misleading_labels <- tf$zeros(shape(batch_size, 1))    # Train the generator (note that we should *not* update the weights   # of the discriminator)!   with(tf$GradientTape() %as% tape, {     predictions <- discriminator(generator(random_latent_vectors))     g_loss <- loss_fn(misleading_labels, predictions)   })    grads <- tape$gradient(g_loss, generator$trainable_weights)   g_optimizer$apply(grads, generator$trainable_weights)    list(d_loss, g_loss, generated_images) }) # Prepare the dataset. We use both the training & test MNIST digits. batch_size <- 64 c(c(x_train, .), c(x_test, .)) %<-% dataset_mnist() all_digits <- op_concatenate(list(x_train, x_test)) all_digits <- op_reshape(all_digits, c(-1, 28, 28, 1)) dataset <- all_digits |>   tfdatasets::tensor_slices_dataset() |>   tfdatasets::dataset_map(function(x) tf$cast(x, \"float\") / 255) |>   tfdatasets::dataset_shuffle(buffer_size = 1024) |>   tfdatasets::dataset_batch(batch_size = batch_size)  epochs <- 1 # In practice you need at least 20 epochs to generate nice digits. save_dir <- \"./\"  for (epoch in seq_len(epochs)) {   cat(\"Start epoch: \", epoch, \"\\n\")   step <- 0   train_iterator <- as_iterator(dataset)   while (!is.null(real_images <- iter_next(train_iterator))) {     step <- step + 1     # Train the discriminator & generator on one batch of real images.     c(d_loss, g_loss, generated_images) %<-% train_step(real_images)      # Logging.     if (step %% 200 == 0) {       # Print metrics       cat(sprintf(\"discriminator loss at step %d: %.2f\\n\", step, d_loss))       cat(sprintf(\"adversarial loss at step %d: %.2f\\n\", step, g_loss))     }      # To limit execution time we stop after 10 steps.     # Remove the lines below to actually train the model!     if (step > 10)       break   } } ## Start epoch:  1"},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing your own callbacks","text":"callback powerful tool customize behavior Keras model training, evaluation, inference. Examples include keras.callbacks.TensorBoard visualize training progress results TensorBoard, keras.callbacks.ModelCheckpoint periodically save model training. guide, learn Keras callback , can , can build . provide demos simple callback applications get started.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing your own callbacks","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"keras-callbacks-overview","dir":"Articles","previous_headings":"","what":"Keras callbacks overview","title":"Writing your own callbacks","text":"callbacks subclass keras.callbacks.Callback class, override set methods called various stages training, testing, predicting. Callbacks useful get view internal states statistics model training. can pass list callbacks (keyword argument callbacks) following model methods: fit() evaluate() predict()","code":""},{"path":[]},{"path":[]},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_traintestpredict_beginlogs-null","dir":"Articles","previous_headings":"An overview of callback methods > Global methods","what":"on_(train|test|predict)_begin(logs = NULL)","title":"Writing your own callbacks","text":"Called beginning fit/evaluate/predict.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_traintestpredict_endlogs-null","dir":"Articles","previous_headings":"An overview of callback methods > Global methods","what":"on_(train|test|predict)_end(logs = NULL)","title":"Writing your own callbacks","text":"Called end fit/evaluate/predict.","code":""},{"path":[]},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_traintestpredict_batch_beginbatch-logs-null","dir":"Articles","previous_headings":"An overview of callback methods > Batch-level methods for training/testing/predicting","what":"on_(train|test|predict)_batch_begin(batch, logs = NULL)","title":"Writing your own callbacks","text":"Called right processing batch training/testing/predicting.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_traintestpredict_batch_endbatch-logs-null","dir":"Articles","previous_headings":"An overview of callback methods > Batch-level methods for training/testing/predicting","what":"on_(train|test|predict)_batch_end(batch, logs = NULL)","title":"Writing your own callbacks","text":"Called end training/testing/predicting batch. Within method, logs named list containing metrics results.","code":""},{"path":[]},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_epoch_beginepoch-logs-null","dir":"Articles","previous_headings":"An overview of callback methods > Epoch-level methods (training only)","what":"on_epoch_begin(epoch, logs = NULL)","title":"Writing your own callbacks","text":"Called beginning epoch training.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_epoch_endepoch-logs-null","dir":"Articles","previous_headings":"An overview of callback methods > Epoch-level methods (training only)","what":"on_epoch_end(epoch, logs = NULL)","title":"Writing your own callbacks","text":"Called end epoch training.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"a-basic-example","dir":"Articles","previous_headings":"","what":"A basic example","title":"Writing your own callbacks","text":"Let’s take look concrete example. get started, let’s import tensorflow define simple Sequential Keras model: , load MNIST data training testing Keras datasets API: Now, define simple custom callback logs: fit/evaluate/predict starts & ends epoch starts & ends training batch starts & ends evaluation (test) batch starts & ends inference (prediction) batch starts & ends Let’s try :","code":"# Define the Keras model to add callbacks to get_model <- function() {   model <- keras_model_sequential()   model |> layer_dense(units = 1)   model |> compile(     optimizer = optimizer_rmsprop(learning_rate = 0.1),     loss = \"mean_squared_error\",     metrics = \"mean_absolute_error\"   )   model } # Load example MNIST data and pre-process it mnist <- dataset_mnist()  flatten_and_rescale <- function(x) {   x <- array_reshape(x, c(-1, 784))   x <- x / 255   x }  mnist$train$x <- flatten_and_rescale(mnist$train$x) mnist$test$x  <- flatten_and_rescale(mnist$test$x)  # limit to 1000 samples n <- 1000 mnist$train$x <- mnist$train$x[1:n,] mnist$train$y <- mnist$train$y[1:n] mnist$test$x  <- mnist$test$x[1:n,] mnist$test$y  <- mnist$test$y[1:n] show <- function(msg, logs) {   cat(glue::glue(msg, .envir = parent.frame()),       \"got logs: \", sep = \"; \")   str(logs); cat(\"\\n\") }  callback_custom <- Callback(   \"CustomCallback\",   on_train_begin         = \\(logs = NULL)        show(\"Starting training\", logs),   on_epoch_begin         = \\(epoch, logs = NULL) show(\"Start epoch {epoch} of training\", logs),   on_train_batch_begin   = \\(batch, logs = NULL) show(\"...Training: start of batch {batch}\", logs),   on_train_batch_end     = \\(batch, logs = NULL) show(\"...Training: end of batch {batch}\",  logs),   on_epoch_end           = \\(epoch, logs = NULL) show(\"End epoch {epoch} of training\", logs),   on_train_end           = \\(logs = NULL)        show(\"Stop training\", logs),     on_test_begin          = \\(logs = NULL)        show(\"Start testing\", logs),   on_test_batch_begin    = \\(batch, logs = NULL) show(\"...Evaluating: start of batch {batch}\", logs),   on_test_batch_end      = \\(batch, logs = NULL) show(\"...Evaluating: end of batch {batch}\", logs),   on_test_end            = \\(logs = NULL)        show(\"Stop testing\", logs),    on_predict_begin       = \\(logs = NULL)        show(\"Start predicting\", logs),   on_predict_end         = \\(logs = NULL)        show(\"Stop predicting\", logs),   on_predict_batch_begin = \\(batch, logs = NULL) show(\"...Predicting: start of batch {batch}\", logs),   on_predict_batch_end   = \\(batch, logs = NULL) show(\"...Predicting: end of batch {batch}\", logs), ) model <- get_model() model |> fit(   mnist$train$x, mnist$train$y,   batch_size = 128,   epochs = 2,   verbose = 0,   validation_split = 0.5,   callbacks = list(callback_custom()) ) ## Starting training; got logs:  Named list() ## ## Start epoch 1 of training; got logs:  Named list() ## ## ...Training: start of batch 1; got logs:  Named list() ## ## ...Training: end of batch 1; got logs: List of 2 ##  $ loss               : num 25.9 ##  $ mean_absolute_error: num 4.19 ## ## ...Training: start of batch 2; got logs:  Named list() ## ## ...Training: end of batch 2; got logs: List of 2 ##  $ loss               : num 433 ##  $ mean_absolute_error: num 15.5 ## ## ...Training: start of batch 3; got logs:  Named list() ## ## ...Training: end of batch 3; got logs: List of 2 ##  $ loss               : num 297 ##  $ mean_absolute_error: num 11.8 ## ## ...Training: start of batch 4; got logs:  Named list() ## ## ...Training: end of batch 4; got logs: List of 2 ##  $ loss               : num 231 ##  $ mean_absolute_error: num 9.68 ## ## Start testing; got logs:  Named list() ## ## ...Evaluating: start of batch 1; got logs:  Named list() ## ## ...Evaluating: end of batch 1; got logs: List of 2 ##  $ loss               : num 8.1 ##  $ mean_absolute_error: num 2.3 ## ## ...Evaluating: start of batch 2; got logs:  Named list() ## ## ...Evaluating: end of batch 2; got logs: List of 2 ##  $ loss               : num 7.58 ##  $ mean_absolute_error: num 2.23 ## ## ...Evaluating: start of batch 3; got logs:  Named list() ## ## ...Evaluating: end of batch 3; got logs: List of 2 ##  $ loss               : num 7.38 ##  $ mean_absolute_error: num 2.21 ## ## ...Evaluating: start of batch 4; got logs:  Named list() ## ## ...Evaluating: end of batch 4; got logs: List of 2 ##  $ loss               : num 7.3 ##  $ mean_absolute_error: num 2.21 ## ## Stop testing; got logs: List of 2 ##  $ loss               : num 7.3 ##  $ mean_absolute_error: num 2.21 ## ## End epoch 1 of training; got logs: List of 4 ##  $ loss                   : num 231 ##  $ mean_absolute_error    : num 9.68 ##  $ val_loss               : num 7.3 ##  $ val_mean_absolute_error: num 2.21 ## ## Start epoch 2 of training; got logs:  Named list() ## ## ...Training: start of batch 1; got logs:  Named list() ## ## ...Training: end of batch 1; got logs: List of 2 ##  $ loss               : num 7.44 ##  $ mean_absolute_error: num 2.27 ## ## ...Training: start of batch 2; got logs:  Named list() ## ## ...Training: end of batch 2; got logs: List of 2 ##  $ loss               : num 6.81 ##  $ mean_absolute_error: num 2.16 ## ## ...Training: start of batch 3; got logs:  Named list() ## ## ...Training: end of batch 3; got logs: List of 2 ##  $ loss               : num 6.12 ##  $ mean_absolute_error: num 2.06 ## ## ...Training: start of batch 4; got logs:  Named list() ## ## ...Training: end of batch 4; got logs: List of 2 ##  $ loss               : num 6.08 ##  $ mean_absolute_error: num 2.04 ## ## Start testing; got logs:  Named list() ## ## ...Evaluating: start of batch 1; got logs:  Named list() ## ## ...Evaluating: end of batch 1; got logs: List of 2 ##  $ loss               : num 5.54 ##  $ mean_absolute_error: num 1.92 ## ## ...Evaluating: start of batch 2; got logs:  Named list() ## ## ...Evaluating: end of batch 2; got logs: List of 2 ##  $ loss               : num 5.31 ##  $ mean_absolute_error: num 1.87 ## ## ...Evaluating: start of batch 3; got logs:  Named list() ## ## ...Evaluating: end of batch 3; got logs: List of 2 ##  $ loss               : num 5.11 ##  $ mean_absolute_error: num 1.8 ## ## ...Evaluating: start of batch 4; got logs:  Named list() ## ## ...Evaluating: end of batch 4; got logs: List of 2 ##  $ loss               : num 5.15 ##  $ mean_absolute_error: num 1.82 ## ## Stop testing; got logs: List of 2 ##  $ loss               : num 5.15 ##  $ mean_absolute_error: num 1.82 ## ## End epoch 2 of training; got logs: List of 4 ##  $ loss                   : num 6.08 ##  $ mean_absolute_error    : num 2.04 ##  $ val_loss               : num 5.15 ##  $ val_mean_absolute_error: num 1.82 ## ## Stop training; got logs: List of 4 ##  $ loss                   : num 6.08 ##  $ mean_absolute_error    : num 2.04 ##  $ val_loss               : num 5.15 ##  $ val_mean_absolute_error: num 1.82 res <- model |> evaluate(   mnist$test$x, mnist$test$y,   batch_size = 128, verbose = 0,   callbacks = list(callback_custom()) ) ## Start testing; got logs:  Named list() ## ## ...Evaluating: start of batch 1; got logs:  Named list() ## ## ...Evaluating: end of batch 1; got logs: List of 2 ##  $ loss               : num 5.2 ##  $ mean_absolute_error: num 1.84 ## ## ...Evaluating: start of batch 2; got logs:  Named list() ## ## ...Evaluating: end of batch 2; got logs: List of 2 ##  $ loss               : num 4.62 ##  $ mean_absolute_error: num 1.73 ## ## ...Evaluating: start of batch 3; got logs:  Named list() ## ## ...Evaluating: end of batch 3; got logs: List of 2 ##  $ loss               : num 4.61 ##  $ mean_absolute_error: num 1.74 ## ## ...Evaluating: start of batch 4; got logs:  Named list() ## ## ...Evaluating: end of batch 4; got logs: List of 2 ##  $ loss               : num 4.65 ##  $ mean_absolute_error: num 1.75 ## ## ...Evaluating: start of batch 5; got logs:  Named list() ## ## ...Evaluating: end of batch 5; got logs: List of 2 ##  $ loss               : num 4.84 ##  $ mean_absolute_error: num 1.77 ## ## ...Evaluating: start of batch 6; got logs:  Named list() ## ## ...Evaluating: end of batch 6; got logs: List of 2 ##  $ loss               : num 4.76 ##  $ mean_absolute_error: num 1.76 ## ## ...Evaluating: start of batch 7; got logs:  Named list() ## ## ...Evaluating: end of batch 7; got logs: List of 2 ##  $ loss               : num 4.74 ##  $ mean_absolute_error: num 1.76 ## ## ...Evaluating: start of batch 8; got logs:  Named list() ## ## ...Evaluating: end of batch 8; got logs: List of 2 ##  $ loss               : num 4.67 ##  $ mean_absolute_error: num 1.75 ## ## Stop testing; got logs: List of 2 ##  $ loss               : num 4.67 ##  $ mean_absolute_error: num 1.75 res <- model |> predict(   mnist$test$x,   batch_size = 128, verbose = 0,   callbacks = list(callback_custom()) ) ## Start predicting; got logs:  Named list() ## ## ...Predicting: start of batch 1; got logs:  Named list() ## ## ...Predicting: end of batch 1; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 2; got logs:  Named list() ## ## ...Predicting: end of batch 2; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 3; got logs:  Named list() ## ## ...Predicting: end of batch 3; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 4; got logs:  Named list() ## ## ...Predicting: end of batch 4; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 5; got logs:  Named list() ## ## ...Predicting: end of batch 5; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 6; got logs:  Named list() ## ## ...Predicting: end of batch 6; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 7; got logs:  Named list() ## ## ...Predicting: end of batch 7; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 8; got logs:  Named list() ## ## ...Predicting: end of batch 8; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(104, 1), dtype=float32, numpy=…> ## ## Stop predicting; got logs:  Named list()"},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"usage-of-logs-list","dir":"Articles","previous_headings":"A basic example","what":"Usage of logs list","title":"Writing your own callbacks","text":"logs named list contains loss value, metrics end batch epoch. Example includes loss mean absolute error. information callbacks, can check Keras callback API documentation.","code":"callback_print_loss_and_mae <- Callback(   \"LossAndErrorPrintingCallback\",    on_train_batch_end = function(batch, logs = NULL)     cat(sprintf(\"Up to batch %i, the average loss is %7.2f.\\n\",                 batch,  logs$loss)),    on_test_batch_end = function(batch, logs = NULL)     cat(sprintf(\"Up to batch %i, the average loss is %7.2f.\\n\",                 batch, logs$loss)),    on_epoch_end = function(epoch, logs = NULL)     cat(sprintf(       \"The average loss for epoch %2i is %9.2f and mean absolute error is %7.2f.\\n\",       epoch, logs$loss, logs$mean_absolute_error     )) )   model <- get_model() model |> fit(   mnist$train$x, mnist$train$y,   epochs = 2, verbose = 0, batch_size = 128,   callbacks = list(callback_print_loss_and_mae()) ) ## Up to batch 1, the average loss is   25.12. ## Up to batch 2, the average loss is  398.92. ## Up to batch 3, the average loss is  274.04. ## Up to batch 4, the average loss is  208.32. ## Up to batch 5, the average loss is  168.15. ## Up to batch 6, the average loss is  141.31. ## Up to batch 7, the average loss is  122.19. ## Up to batch 8, the average loss is  110.05. ## The average loss for epoch  1 is    110.05 and mean absolute error is    5.79. ## Up to batch 1, the average loss is    4.71. ## Up to batch 2, the average loss is    4.74. ## Up to batch 3, the average loss is    4.81. ## Up to batch 4, the average loss is    5.07. ## Up to batch 5, the average loss is    5.08. ## Up to batch 6, the average loss is    5.09. ## Up to batch 7, the average loss is    5.19. ## Up to batch 8, the average loss is    5.51. ## The average loss for epoch  2 is      5.51 and mean absolute error is    1.90. res = model |> evaluate(   mnist$test$x, mnist$test$y,   verbose = 0, batch_size = 128,   callbacks = list(callback_print_loss_and_mae()) ) ## Up to batch 1, the average loss is   15.86. ## Up to batch 2, the average loss is   16.13. ## Up to batch 3, the average loss is   16.02. ## Up to batch 4, the average loss is   16.11. ## Up to batch 5, the average loss is   16.23. ## Up to batch 6, the average loss is   16.68. ## Up to batch 7, the average loss is   16.61. ## Up to batch 8, the average loss is   16.54."},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"usage-of-selfmodel-attribute","dir":"Articles","previous_headings":"","what":"Usage of self$model attribute","title":"Writing your own callbacks","text":"addition receiving log information one methods called, callbacks access model associated current round training/evaluation/inference: self$model. things can self$model callback: Set self$model$stop_training <- TRUE immediately interrupt training. Mutate hyperparameters optimizer (available self$model$optimizer), self$model$optimizer$learning_rate. Save model period intervals. Record output model |> predict() test samples end epoch, use sanity check training. Extract visualizations intermediate features end epoch, monitor model learning time. etc. Let’s see action couple examples.","code":""},{"path":[]},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"early-stopping-at-minimum-loss","dir":"Articles","previous_headings":"Examples of Keras callback applications","what":"Early stopping at minimum loss","title":"Writing your own callbacks","text":"first example shows creation Callback stops training minimum loss reached, setting attribute self$model$stop_training (boolean). Optionally, can provide argument patience specify many epochs wait stopping reached local minimum. callback_early_stopping() provides complete general implementation.","code":"callback_early_stopping_at_min_loss <- Callback(   \"EarlyStoppingAtMinLoss\",   `__doc__` =     \"Stop training when the loss is at its min, i.e. the loss stops decreasing.      Arguments:         patience: Number of epochs to wait after min has been hit. After this         number of no improvement, training stops.     \",    initialize = function(patience = 0) {     super$initialize()     self$patience <- patience     # best_weights to store the weights at which the minimum loss occurs.     self$best_weights <- NULL   },    on_train_begin = function(logs = NULL) {     # The number of epoch it has waited when loss is no longer minimum.     self$wait <- 0     # The epoch the training stops at.     self$stopped_epoch <- 0     # Initialize the best as infinity.     self$best <- Inf   },    on_epoch_end = function(epoch, logs = NULL) {     current <- logs$loss     if (current < self$best) {       self$best <- current       self$wait <- 0L       # Record the best weights if current results is better (less).       self$best_weights <- get_weights(self$model)     } else {       add(self$wait) <- 1L       if (self$wait >= self$patience) {         self$stopped_epoch <- epoch         self$model$stop_training <- TRUE         cat(\"Restoring model weights from the end of the best epoch.\\n\")         model$set_weights(self$best_weights)       }     }   },    on_train_end = function(logs = NULL)     if (self$stopped_epoch > 0)       cat(sprintf(\"Epoch %05d: early stopping\\n\", self$stopped_epoch + 1)) ) `add<-` <- `+`   model <- get_model() model |> fit(   mnist$train$x,   mnist$train$y,   epochs = 30,   batch_size = 64,   verbose = 0,   callbacks = list(callback_print_loss_and_mae(),                    callback_early_stopping_at_min_loss()) ) ## Up to batch 1, the average loss is   30.54. ## Up to batch 2, the average loss is  513.27. ## Up to batch 3, the average loss is  352.60. ## Up to batch 4, the average loss is  266.37. ## Up to batch 5, the average loss is  214.68. ## Up to batch 6, the average loss is  179.97. ## Up to batch 7, the average loss is  155.06. ## Up to batch 8, the average loss is  136.59. ## Up to batch 9, the average loss is  121.96. ## Up to batch 10, the average loss is  110.28. ## Up to batch 11, the average loss is  100.72. ## Up to batch 12, the average loss is   92.71. ## Up to batch 13, the average loss is   85.95. ## Up to batch 14, the average loss is   80.21. ## Up to batch 15, the average loss is   75.17. ## Up to batch 16, the average loss is   72.48. ## The average loss for epoch  1 is     72.48 and mean absolute error is    4.08. ## Up to batch 1, the average loss is    7.98. ## Up to batch 2, the average loss is    9.92. ## Up to batch 3, the average loss is   12.88. ## Up to batch 4, the average loss is   16.61. ## Up to batch 5, the average loss is   20.49. ## Up to batch 6, the average loss is   26.14. ## Up to batch 7, the average loss is   30.44. ## Up to batch 8, the average loss is   33.76. ## Up to batch 9, the average loss is   36.32. ## Up to batch 10, the average loss is   35.26. ## Up to batch 11, the average loss is   34.22. ## Up to batch 12, the average loss is   33.53. ## Up to batch 13, the average loss is   32.84. ## Up to batch 14, the average loss is   31.80. ## Up to batch 15, the average loss is   31.39. ## Up to batch 16, the average loss is   31.45. ## The average loss for epoch  2 is     31.45 and mean absolute error is    4.82. ## Up to batch 1, the average loss is   39.60. ## Up to batch 2, the average loss is   41.95. ## Up to batch 3, the average loss is   41.29. ## Up to batch 4, the average loss is   36.77. ## Up to batch 5, the average loss is   32.08. ## Up to batch 6, the average loss is   28.17. ## Up to batch 7, the average loss is   25.33. ## Up to batch 8, the average loss is   23.56. ## Up to batch 9, the average loss is   22.28. ## Up to batch 10, the average loss is   21.22. ## Up to batch 11, the average loss is   20.87. ## Up to batch 12, the average loss is   22.25. ## Up to batch 13, the average loss is   25.08. ## Up to batch 14, the average loss is   27.87. ## Up to batch 15, the average loss is   31.72. ## Up to batch 16, the average loss is   33.21. ## The average loss for epoch  3 is     33.21 and mean absolute error is    4.79. ## Restoring model weights from the end of the best epoch. ## Epoch 00004: early stopping"},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"learning-rate-scheduling","dir":"Articles","previous_headings":"Examples of Keras callback applications","what":"Learning rate scheduling","title":"Writing your own callbacks","text":"example, show custom Callback can used dynamically change learning rate optimizer course training. See keras$callbacks$LearningRateScheduler general implementations (RStudio, press F1 cursor LearningRateScheduler browser open page).","code":"callback_custom_learning_rate_scheduler <- Callback(   \"CustomLearningRateScheduler\",   `__doc__` =   \"Learning rate scheduler which sets the learning rate according to schedule.      Arguments:         schedule: a function that takes an epoch index             (integer, indexed from 0) and current learning rate             as inputs and returns a new learning rate as output (float).     \",    initialize = function(schedule) {     super$initialize()     self$schedule <- schedule   },    on_epoch_begin = function(epoch, logs = NULL) {     ## When in doubt about what types of objects are in scope (e.g., self$model)     ## use a debugger to interact with the actual objects at the console!     # browser()      if (!\"learning_rate\" %in% names(self$model$optimizer))       stop('Optimizer must have a \"learning_rate\" attribute.')      # # Get the current learning rate from model's optimizer.     # use as.numeric() to convert the keras variablea to an R numeric     lr <- as.numeric(self$model$optimizer$learning_rate)     # # Call schedule function to get the scheduled learning rate.     scheduled_lr <- self$schedule(epoch, lr)     # # Set the value back to the optimizer before this epoch starts     optimizer <- self$model$optimizer     optimizer$learning_rate <- scheduled_lr     cat(sprintf(\"\\nEpoch %03d: Learning rate is %6.4f.\\n\", epoch, scheduled_lr))   } )  LR_SCHEDULE <- tibble::tribble(   ~start_epoch, ~learning_rate,              0,            0.1,              3,           0.05,              6,           0.01,              9,          0.005,             12,          0.001,   )  last <- function(x) x[length(x)] lr_schedule <- function(epoch, learning_rate) {   \"Helper function to retrieve the scheduled learning rate based on epoch.\"   with(LR_SCHEDULE, learning_rate[last(which(epoch >= start_epoch))]) }  model <- get_model() model |> fit(   mnist$train$x,   mnist$train$y,   epochs = 14,   batch_size = 64,   verbose = 0,   callbacks = list(     callback_print_loss_and_mae(),     callback_custom_learning_rate_scheduler(lr_schedule)   ) ) ## ## Epoch 001: Learning rate is 0.1000. ## Up to batch 1, the average loss is   29.36. ## Up to batch 2, the average loss is  513.95. ## Up to batch 3, the average loss is  352.70. ## Up to batch 4, the average loss is  266.46. ## Up to batch 5, the average loss is  214.73. ## Up to batch 6, the average loss is  180.00. ## Up to batch 7, the average loss is  155.05. ## Up to batch 8, the average loss is  136.64. ## Up to batch 9, the average loss is  121.97. ## Up to batch 10, the average loss is  110.30. ## Up to batch 11, the average loss is  100.76. ## Up to batch 12, the average loss is   92.74. ## Up to batch 13, the average loss is   85.95. ## Up to batch 14, the average loss is   80.18. ## Up to batch 15, the average loss is   75.11. ## Up to batch 16, the average loss is   72.38. ## The average loss for epoch  1 is     72.38 and mean absolute error is    4.04. ## ## Epoch 002: Learning rate is 0.1000. ## Up to batch 1, the average loss is    6.95. ## Up to batch 2, the average loss is    8.71. ## Up to batch 3, the average loss is   11.42. ## Up to batch 4, the average loss is   15.15. ## Up to batch 5, the average loss is   19.28. ## Up to batch 6, the average loss is   25.54. ## Up to batch 7, the average loss is   30.38. ## Up to batch 8, the average loss is   33.95. ## Up to batch 9, the average loss is   36.58. ## Up to batch 10, the average loss is   35.46. ## Up to batch 11, the average loss is   34.34. ## Up to batch 12, the average loss is   33.51. ## Up to batch 13, the average loss is   32.67. ## Up to batch 14, the average loss is   31.54. ## Up to batch 15, the average loss is   31.05. ## Up to batch 16, the average loss is   31.09. ## The average loss for epoch  2 is     31.09 and mean absolute error is    4.77. ## ## Epoch 003: Learning rate is 0.0500. ## Up to batch 1, the average loss is   40.40. ## Up to batch 2, the average loss is   22.33. ## Up to batch 3, the average loss is   16.18. ## Up to batch 4, the average loss is   13.09. ## Up to batch 5, the average loss is   11.48. ## Up to batch 6, the average loss is   10.21. ## Up to batch 7, the average loss is    9.22. ## Up to batch 8, the average loss is    8.70. ## Up to batch 9, the average loss is    8.16. ## Up to batch 10, the average loss is    7.80. ## Up to batch 11, the average loss is    7.50. ## Up to batch 12, the average loss is    7.17. ## Up to batch 13, the average loss is    6.89. ## Up to batch 14, the average loss is    6.70. ## Up to batch 15, the average loss is    6.52. ## Up to batch 16, the average loss is    6.54. ## The average loss for epoch  3 is      6.54 and mean absolute error is    1.93. ## ## Epoch 004: Learning rate is 0.0500. ## Up to batch 1, the average loss is    8.74. ## Up to batch 2, the average loss is    8.34. ## Up to batch 3, the average loss is    9.09. ## Up to batch 4, the average loss is    9.72. ## Up to batch 5, the average loss is   10.48. ## Up to batch 6, the average loss is   11.69. ## Up to batch 7, the average loss is   11.83. ## Up to batch 8, the average loss is   11.56. ## Up to batch 9, the average loss is   11.24. ## Up to batch 10, the average loss is   10.84. ## Up to batch 11, the average loss is   10.66. ## Up to batch 12, the average loss is   10.44. ## Up to batch 13, the average loss is   10.21. ## Up to batch 14, the average loss is   10.06. ## Up to batch 15, the average loss is   10.00. ## Up to batch 16, the average loss is   10.20. ## The average loss for epoch  4 is     10.20 and mean absolute error is    2.71. ## ## Epoch 005: Learning rate is 0.0500. ## Up to batch 1, the average loss is   17.26. ## Up to batch 2, the average loss is   14.09. ## Up to batch 3, the average loss is   12.67. ## Up to batch 4, the average loss is   11.44. ## Up to batch 5, the average loss is   10.54. ## Up to batch 6, the average loss is   10.10. ## Up to batch 7, the average loss is    9.53. ## Up to batch 8, the average loss is    9.17. ## Up to batch 9, the average loss is    8.78. ## Up to batch 10, the average loss is    8.49. ## Up to batch 11, the average loss is    8.50. ## Up to batch 12, the average loss is    8.59. ## Up to batch 13, the average loss is    8.68. ## Up to batch 14, the average loss is    8.86. ## Up to batch 15, the average loss is    9.17. ## Up to batch 16, the average loss is    9.53. ## The average loss for epoch  5 is      9.53 and mean absolute error is    2.58. ## ## Epoch 006: Learning rate is 0.0100. ## Up to batch 1, the average loss is   17.04. ## Up to batch 2, the average loss is   14.85. ## Up to batch 3, the average loss is   11.53. ## Up to batch 4, the average loss is    9.65. ## Up to batch 5, the average loss is    8.44. ## Up to batch 6, the average loss is    7.50. ## Up to batch 7, the average loss is    6.74. ## Up to batch 8, the average loss is    6.56. ## Up to batch 9, the average loss is    6.18. ## Up to batch 10, the average loss is    5.87. ## Up to batch 11, the average loss is    5.63. ## Up to batch 12, the average loss is    5.45. ## Up to batch 13, the average loss is    5.23. ## Up to batch 14, the average loss is    5.12. ## Up to batch 15, the average loss is    4.96. ## Up to batch 16, the average loss is    4.91. ## The average loss for epoch  6 is      4.91 and mean absolute error is    1.67. ## ## Epoch 007: Learning rate is 0.0100. ## Up to batch 1, the average loss is    3.65. ## Up to batch 2, the average loss is    3.04. ## Up to batch 3, the average loss is    2.88. ## Up to batch 4, the average loss is    2.85. ## Up to batch 5, the average loss is    2.88. ## Up to batch 6, the average loss is    2.81. ## Up to batch 7, the average loss is    2.70. ## Up to batch 8, the average loss is    2.96. ## Up to batch 9, the average loss is    2.96. ## Up to batch 10, the average loss is    2.93. ## Up to batch 11, the average loss is    2.95. ## Up to batch 12, the average loss is    2.98. ## Up to batch 13, the average loss is    2.97. ## Up to batch 14, the average loss is    3.01. ## Up to batch 15, the average loss is    3.00. ## Up to batch 16, the average loss is    3.05. ## The average loss for epoch  7 is      3.05 and mean absolute error is    1.34. ## ## Epoch 008: Learning rate is 0.0100. ## Up to batch 1, the average loss is    3.69. ## Up to batch 2, the average loss is    3.21. ## Up to batch 3, the average loss is    3.00. ## Up to batch 4, the average loss is    2.91. ## Up to batch 5, the average loss is    2.94. ## Up to batch 6, the average loss is    2.85. ## Up to batch 7, the average loss is    2.72. ## Up to batch 8, the average loss is    2.95. ## Up to batch 9, the average loss is    2.97. ## Up to batch 10, the average loss is    2.93. ## Up to batch 11, the average loss is    2.96. ## Up to batch 12, the average loss is    2.98. ## Up to batch 13, the average loss is    2.99. ## Up to batch 14, the average loss is    3.05. ## Up to batch 15, the average loss is    3.08. ## Up to batch 16, the average loss is    3.14. ## The average loss for epoch  8 is      3.14 and mean absolute error is    1.36. ## ## Epoch 009: Learning rate is 0.0050. ## Up to batch 1, the average loss is    3.71. ## Up to batch 2, the average loss is    2.93. ## Up to batch 3, the average loss is    2.76. ## Up to batch 4, the average loss is    2.70. ## Up to batch 5, the average loss is    2.76. ## Up to batch 6, the average loss is    2.69. ## Up to batch 7, the average loss is    2.57. ## Up to batch 8, the average loss is    2.79. ## Up to batch 9, the average loss is    2.80. ## Up to batch 10, the average loss is    2.77. ## Up to batch 11, the average loss is    2.79. ## Up to batch 12, the average loss is    2.80. ## Up to batch 13, the average loss is    2.78. ## Up to batch 14, the average loss is    2.81. ## Up to batch 15, the average loss is    2.80. ## Up to batch 16, the average loss is    2.83. ## The average loss for epoch  9 is      2.83 and mean absolute error is    1.28. ## ## Epoch 010: Learning rate is 0.0050. ## Up to batch 1, the average loss is    3.02. ## Up to batch 2, the average loss is    2.69. ## Up to batch 3, the average loss is    2.58. ## Up to batch 4, the average loss is    2.57. ## Up to batch 5, the average loss is    2.65. ## Up to batch 6, the average loss is    2.60. ## Up to batch 7, the average loss is    2.48. ## Up to batch 8, the average loss is    2.72. ## Up to batch 9, the average loss is    2.74. ## Up to batch 10, the average loss is    2.71. ## Up to batch 11, the average loss is    2.74. ## Up to batch 12, the average loss is    2.75. ## Up to batch 13, the average loss is    2.74. ## Up to batch 14, the average loss is    2.77. ## Up to batch 15, the average loss is    2.77. ## Up to batch 16, the average loss is    2.80. ## The average loss for epoch 10 is      2.80 and mean absolute error is    1.27. ## ## Epoch 011: Learning rate is 0.0050. ## Up to batch 1, the average loss is    3.01. ## Up to batch 2, the average loss is    2.69. ## Up to batch 3, the average loss is    2.58. ## Up to batch 4, the average loss is    2.56. ## Up to batch 5, the average loss is    2.63. ## Up to batch 6, the average loss is    2.58. ## Up to batch 7, the average loss is    2.47. ## Up to batch 8, the average loss is    2.70. ## Up to batch 9, the average loss is    2.72. ## Up to batch 10, the average loss is    2.69. ## Up to batch 11, the average loss is    2.71. ## Up to batch 12, the average loss is    2.72. ## Up to batch 13, the average loss is    2.71. ## Up to batch 14, the average loss is    2.75. ## Up to batch 15, the average loss is    2.74. ## Up to batch 16, the average loss is    2.77. ## The average loss for epoch 11 is      2.77 and mean absolute error is    1.27. ## ## Epoch 012: Learning rate is 0.0010. ## Up to batch 1, the average loss is    2.96. ## Up to batch 2, the average loss is    2.53. ## Up to batch 3, the average loss is    2.47. ## Up to batch 4, the average loss is    2.46. ## Up to batch 5, the average loss is    2.54. ## Up to batch 6, the average loss is    2.48. ## Up to batch 7, the average loss is    2.39. ## Up to batch 8, the average loss is    2.60. ## Up to batch 9, the average loss is    2.62. ## Up to batch 10, the average loss is    2.59. ## Up to batch 11, the average loss is    2.61. ## Up to batch 12, the average loss is    2.62. ## Up to batch 13, the average loss is    2.60. ## Up to batch 14, the average loss is    2.64. ## Up to batch 15, the average loss is    2.62. ## Up to batch 16, the average loss is    2.64. ## The average loss for epoch 12 is      2.64 and mean absolute error is    1.24. ## ## Epoch 013: Learning rate is 0.0010. ## Up to batch 1, the average loss is    2.82. ## Up to batch 2, the average loss is    2.46. ## Up to batch 3, the average loss is    2.42. ## Up to batch 4, the average loss is    2.42. ## Up to batch 5, the average loss is    2.50. ## Up to batch 6, the average loss is    2.45. ## Up to batch 7, the average loss is    2.36. ## Up to batch 8, the average loss is    2.57. ## Up to batch 9, the average loss is    2.59. ## Up to batch 10, the average loss is    2.57. ## Up to batch 11, the average loss is    2.59. ## Up to batch 12, the average loss is    2.60. ## Up to batch 13, the average loss is    2.59. ## Up to batch 14, the average loss is    2.62. ## Up to batch 15, the average loss is    2.61. ## Up to batch 16, the average loss is    2.63. ## The average loss for epoch 13 is      2.63 and mean absolute error is    1.23. ## ## Epoch 014: Learning rate is 0.0010. ## Up to batch 1, the average loss is    2.79. ## Up to batch 2, the average loss is    2.44. ## Up to batch 3, the average loss is    2.40. ## Up to batch 4, the average loss is    2.41. ## Up to batch 5, the average loss is    2.49. ## Up to batch 6, the average loss is    2.44. ## Up to batch 7, the average loss is    2.34. ## Up to batch 8, the average loss is    2.56. ## Up to batch 9, the average loss is    2.58. ## Up to batch 10, the average loss is    2.56. ## Up to batch 11, the average loss is    2.58. ## Up to batch 12, the average loss is    2.59. ## Up to batch 13, the average loss is    2.58. ## Up to batch 14, the average loss is    2.61. ## Up to batch 15, the average loss is    2.60. ## Up to batch 16, the average loss is    2.62. ## The average loss for epoch 14 is      2.62 and mean absolute error is    1.23."},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"built-in-keras-callbacks","dir":"Articles","previous_headings":"Examples of Keras callback applications","what":"Built-in Keras callbacks","title":"Writing your own callbacks","text":"sure check existing Keras callbacks reading API docs. Applications include logging CSV, saving model, visualizing metrics TensorBoard, lot !","code":""},{"path":"https://keras.posit.co/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Tomasz Kalinowski. Author, copyright holder, maintainer. Daniel Falbel. Contributor, copyright holder. JJ Allaire. Author, copyright holder. François Chollet. Author, copyright holder. Posit Software, PBC. Copyright holder, funder. Google. Copyright holder, funder. Yuan Tang. Contributor, copyright holder. Wouter Van Der Bijl. Contributor, copyright holder. Martin Studer. Contributor, copyright holder. Sigrid Keydana. Contributor.","code":""},{"path":"https://keras.posit.co/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Kalinowski T, Allaire J, Chollet F (2024). keras3: R Interface 'Keras'. R package version 0.2.0.9000, https://github.com/rstudio/keras, https://keras.posit.co/.","code":"@Manual{,   title = {keras3: R Interface to 'Keras'},   author = {Tomasz Kalinowski and JJ Allaire and François Chollet},   year = {2024},   note = {R package version 0.2.0.9000, https://github.com/rstudio/keras},   url = {https://keras.posit.co/}, }"},{"path":"https://keras.posit.co/index.html","id":"r-interface-to-keras","dir":"","previous_headings":"","what":"R Interface to Keras","title":"R Interface to Keras","text":"Keras high-level neural networks API developed focus enabling fast experimentation. able go idea result least possible delay key good research. Keras following key features: Allows code run CPU GPU, seamlessly. User-friendly API makes easy quickly prototype deep learning models. Built-support convolutional networks (computer vision), recurrent networks (sequence processing), combination . Supports arbitrary network architectures: multi-input multi-output models, layer sharing, model sharing, etc. means Keras appropriate building essentially deep learning model, memory network neural Turing machine. See package website https://keras.posit.co complete documentation.","code":""},{"path":"https://keras.posit.co/reference/Callback.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a custom Callback class — Callback","title":"Define a custom Callback class — Callback","text":"Callbacks can passed keras methods fit(), evaluate(), predict() order hook various stages model training, evaluation, inference lifecycle. create custom callback, call Callback() override method associated stage interest.","code":""},{"path":"https://keras.posit.co/reference/Callback.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a custom Callback class — Callback","text":"","code":"Callback(   classname,   on_epoch_begin = NULL,   on_epoch_end = NULL,   on_train_begin = NULL,   on_train_end = NULL,   on_train_batch_begin = NULL,   on_train_batch_end = NULL,   on_test_begin = NULL,   on_test_end = NULL,   on_test_batch_begin = NULL,   on_test_batch_end = NULL,   on_predict_begin = NULL,   on_predict_end = NULL,   on_predict_batch_begin = NULL,   on_predict_batch_end = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Callback.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a custom Callback class — Callback","text":"classname String, name custom class. (Conventionally, CamelCase). on_epoch_begin   Called start epoch. Subclasses override actions run. function called TRAIN mode. Args: epoch: Integer, index epoch. logs: Named List. Currently data passed argument method may change future. on_epoch_end   Called end epoch. Subclasses override actions run. function called TRAIN mode. Args: epoch: Integer, index epoch. logs: Named List, metric results training epoch, validation epoch validation performed. Validation result keys prefixed val_. training epoch, values Model's metrics returned. Example: list(loss = 0.2, accuracy = 0.7). on_train_begin   Called beginning training. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_train_end   Called end training. Subclasses override actions run. Args: logs: Named list. Currently output last call on_epoch_end() passed argument method may change future. on_train_batch_begin   Called beginning training batch fit() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_train_batch_end   Called end training batch fit() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. on_test_begin   Called beginning evaluation validation. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_test_end   Called end evaluation validation. Subclasses override actions run. Args: logs: Named list. Currently output last call on_test_batch_end() passed argument method may change future. on_test_batch_begin   Called beginning batch evaluate() methods. Also called beginning validation batch fit() methods, validation data provided. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_test_batch_end   Called end batch evaluate() methods. Also called end validation batch fit() methods, validation data provided. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. on_predict_begin   Called beginning prediction. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_predict_end   Called end prediction. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_predict_batch_begin   Called beginning batch predict() methods. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_predict_batch_end   Called end batch predict() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"\\(epoch, logs = NULL) \\(epoch, logs = NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs=NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs = NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs = NULL)"},{"path":"https://keras.posit.co/reference/Callback.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a custom Callback class — Callback","text":"function returns custom Callback instances, similar builtin callback functions.","code":""},{"path":"https://keras.posit.co/reference/Callback.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define a custom Callback class — Callback","text":"R function custom methods (public private) following symbols scope: self: Layer instance. super: Layer superclass. private: R environment specific class instance. objects defined invisible Keras framework. __class__ current class type object. also available alias symbol, value supplied Layer(classname = )","code":"training_finished <- FALSE callback_mark_finished <- Callback(\"MarkFinished\",   on_train_end = function(logs = NULL) {     training_finished <<- TRUE   } )  model <- keras_model_sequential(input_shape = c(1)) |>   layer_dense(1) model |> compile(loss = 'mean_squared_error') model |> fit(op_ones(c(1, 1)), op_ones(c(1, 1)),              callbacks = callback_mark_finished()) stopifnot(isTRUE(training_finished))"},{"path":"https://keras.posit.co/reference/Callback.html","id":"attributes-accessible-via-self-","dir":"Reference","previous_headings":"","what":"Attributes (accessible via self$)","title":"Define a custom Callback class — Callback","text":"params: Named list, Training parameters (e.g. verbosity, batch size, number epochs, ...). model: Instance Model. Reference model trained. logs named list callback methods take argument contain keys quantities relevant current batch epoch (see method-specific docstrings).","code":""},{"path":"https://keras.posit.co/reference/Callback.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Define a custom Callback class — Callback","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Constraint.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a custom Constraint class — Constraint","title":"Define a custom Constraint class — Constraint","text":"Base class weight constraints. Constraint() instance works like stateless function. Users subclass Constraint class override call() method, takes single weight parameter return projected version parameter (e.g. normalized clipped). Constraints can used various Keras layers via kernel_constraint bias_constraint arguments. simple example non-negative weight constraint:     Usage layer:","code":"constraint_nonnegative <- Constraint(\"NonNegative\",   call = function(w) {     w * op_cast(w >= 0, dtype = w$dtype)   } ) weight <- op_convert_to_tensor(c(-1, 1)) constraint_nonnegative()(weight) ## tf.Tensor([-0.  1.], shape=(2), dtype=float32) layer_dense(units = 4, kernel_constraint = constraint_nonnegative()) ## <Dense name=dense, built=False> ##  signature: (*args, **kwargs)"},{"path":"https://keras.posit.co/reference/Constraint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a custom Constraint class — Constraint","text":"","code":"Constraint(   classname,   call = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Constraint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a custom Constraint class — Constraint","text":"classname String, name custom class. (Conventionally, CamelCase). call   Applies constraint input weight variable. default, inputs weight variable modified. Users override method implement projection function. Args: w: Input weight variable. Returns: Projected variable (default, returns unmodified inputs). get_config   Function returns named list object config. constraint config named list (JSON-serializable) can used reinstantiate object (via .call(<constraint_class>, <config>)). ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"\\(w) \\()"},{"path":"https://keras.posit.co/reference/Constraint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a custom Constraint class — Constraint","text":"function returns Constraint instances, similar builtin constraint functions like constraint_maxnorm().","code":""},{"path":"https://keras.posit.co/reference/Constraint.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Define a custom Constraint class — Constraint","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Layer.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a custom Layer class. — Layer","title":"Define a custom Layer class. — Layer","text":"layer callable object takes input one tensors outputs one tensors. involves computation, defined call() method, state (weight variables). State can created: initialize(), instance via self$add_weight(); optional build() method, invoked first call() layer, supplies shape(s) input(s), may known initialization time. Layers recursively composable: assign Layer instance attribute another Layer, outer layer start tracking weights created inner layer. Nested layers instantiated initialize() method build() method. Users just instantiate layer treat callable.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a custom Layer class. — Layer","text":"","code":"Layer(   classname,   initialize = NULL,   call = NULL,   build = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Layer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a custom Layer class. — Layer","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, call, build, get_config Recommended methods implement. See description details sections. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a custom Layer class. — Layer","text":"composing layer constructor, similar behavior layer functions like layer_dense(). first argument returned function object, enabling initialize()ing call() layer one step composing layer pipe, like   initialize() layer instance call() , pass missing NULL value object, pass arguments initialize() name.","code":"layer_foo <- Layer(\"Foo\", ....) output <- inputs |> layer_foo() layer <- layer_dense(units = 2, activation = \"relu\") layer <- layer_dense(NULL, 2, activation = \"relu\") layer <- layer_dense(, 2, activation = \"relu\")  # then you can call() the layer in a separate step outputs <- inputs |> layer()"},{"path":"https://keras.posit.co/reference/Layer.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Define a custom Layer class. — Layer","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Define a custom Layer class. — Layer","text":"name: name layer (string). dtype: Dtype layer's weights. Alias layer$variable_dtype. variable_dtype: Dtype layer's weights. compute_dtype: dtype layer's computations. Layers automatically cast inputs dtype, causes computations output also dtype. mixed precision used keras$mixed_precision$DTypePolicy, different variable_dtype. trainable_weights: List variables included backprop. non_trainable_weights: List variables included backprop. weights: concatenation lists trainable_weights non_trainable_weights (order). trainable: Whether layer trained (boolean), .e. whether potentially-trainable weights returned part layer$trainable_weights. input_spec: Optional (list ) InputSpec object(s) specifying constraints inputs can accepted layer. recommend custom Layers implement following methods: initialize(): Defines custom layer attributes, creates layer weights depend input shapes, using add_weight(), state. build(input_shape): method can used create weights depend shape(s) input(s), using add_weight(), state. Calling call() automatically build layer (built yet) calling build(). call(...): Method called making sure build() called. call() performs logic applying layer input arguments. Two reserved arguments can optionally use call() : training (boolean, whether call inference mode training mode). mask (boolean tensor encoding masked timesteps input, used e.g. RNN layers). typical signature method call(inputs), user optionally add training mask layer need . get_config(): Returns named list containing configuration used initialize layer. list names differ arguments initialize(), override from_config() well. method used saving layer model contains layer.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define a custom Layer class. — Layer","text":"basic example: layer two variables, w b, returns y <- (w %*% x) + b. shows implement build() call(). Variables set attributes layer tracked weights layers (layer$weights).   Besides trainable weights, updated via backpropagation training, layers can also non-trainable weights. weights meant updated manually call(). example layer computes running sum inputs:","code":"layer_simple_dense <- Layer(   \"SimpleDense\",   initialize = function(units = 32) {     super$initialize()     self$units <- units   },    # Create the state of the layer (weights)   build = function(input_shape) {     self$kernel <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = \"glorot_uniform\",       trainable = TRUE,       name = \"kernel\"     )     self$bias = self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE,       name = \"bias\"     )   },    # Defines the computation   call = function(self, inputs) {     op_matmul(inputs, self$kernel) + self$bias   } )  # Instantiates the layer. # Supply missing `object` arg to skip invoking `call()` and instead return # the Layer instance linear_layer <- layer_simple_dense(, 4)  # This will call `build(input_shape)` and create the weights, # and then invoke `call()`. y <- linear_layer(op_ones(c(2, 2))) stopifnot(length(linear_layer$weights) == 2)  # These weights are trainable, so they're listed in `trainable_weights`: stopifnot(length(linear_layer$trainable_weights) == 2) layer_compute_sum <- Layer(   classname = \"ComputeSum\",    initialize = function(input_dim) {     super$initialize()      # Create a non-trainable weight.     self$total <- self$add_weight(       shape = shape(),       initializer = \"zeros\",       trainable = FALSE,       name = \"total\"     )   },    call = function(inputs) {     self$total$assign(self$total + op_sum(inputs))     self$total   } )  my_sum <- layer_compute_sum(, 2) x <- op_ones(c(2, 2)) y <- my_sum(x)  stopifnot(exprs = {   all.equal(my_sum$weights,               list(my_sum$total))   all.equal(my_sum$non_trainable_weights, list(my_sum$total))   all.equal(my_sum$trainable_weights,     list()) })"},{"path":"https://keras.posit.co/reference/Layer.html","id":"methods-available","dir":"Reference","previous_headings":"","what":"Methods available","title":"Define a custom Layer class. — Layer","text":"Initialize self. method typically called custom initialize() method. Example:   Args: trainable: Boolean, whether layer's variables trainable. name: String name layer. dtype: dtype layer's computations weights. Can also keras$DTypePolicy, allows computation weight dtype differ. Defaults NULL. NULL means use config_dtype_policy(), \"float32\" policy unless set different value (via config_set_dtype_policy()).    Can called inside call() method add scalar loss. Example:         Add weight variable layer. Alias add_weight().    Add weight variable layer. Args: shape: shape variable (defined shape()) Must fully-defined (NA/NULL/-1 entries). Defaults () (scalar) unspecified. initializer: Initializer object use populate initial variable value, string name built-initializer (e.g. \"random_normal\"). unspecified, defaults \"glorot_uniform\" floating-point variables \"zeros\" types (e.g. int, bool). dtype: Dtype variable create, e.g. \"float32\". unspecified, defaults layer's variable dtype (defaults \"float32\" unspecified). trainable: Boolean, whether variable trainable via backprop whether updates managed manually. Defaults TRUE. autocast: Boolean, whether autocast layers variables accessing . Defaults TRUE. regularizer: Regularizer object call apply penalty weight. penalties summed loss function optimization. Defaults NULL. constraint: Constraint object call variable optimizer update, string name built-constraint. Defaults NULL. aggregation: String, one 'mean', 'sum', 'only_first_replica'. Annotates variable type multi-replica aggregation used variable writing custom data parallel training loops. name: String name variable. Useful debugging purposes. Returns: backend tensor, wrapped KerasVariable class. KerasVariable class Methods: assign(value) assign_add(value) assign_sub(value) numpy() (calling .array(<variable>) preferred) Properties/Attributes: value dtype ndim shape (calling shape(<variable>) preferred) trainable       Builds layer's states supplied config (named list args). default, method calls .call(build, config$input_shape) method, creates weights based layer's input shape supplied config. config contains information needed load layer's state, override method. Args: config: Named list containing input shape associated layer.    See description             Count total number scalars composing weights. Returns: integer count.    Returns named list layer's input shape. method returns config (named list) can used build_from_config(config) create states (e.g. Variables Lookup tables) needed layer. default, config contains input shape layer built . writing custom layer creates state unusual way, override method make sure state already created Keras attempts load value upon model loading. Returns: named list containing input shape associated layer.    Returns config object. object config named list (serializable) containing information needed re-instantiate . config expected serializable JSON, expected consist (potentially complex, nested) structure names lists consisting simple objects like strings, ints.    Return values layer$weights list R NumPy arrays.    Currently, Dense EinsumDense layers support -place quantization via quantize() method. Example:         Loads state layer. can override method take full control state layer loaded upon calling load_model(). Args: store: Named list state model loaded.    Saves state layer. can override method take full control state layer saved upon calling save_model(). Args: store: Named list state model saved.    Sets values weights list R NumPy arrays.    Call layer without side effects. Args: trainable_variables: List trainable variables model. non_trainable_variables: List non-trainable variables model. ...: Positional named arguments passed call(). return_losses: TRUE, stateless_call() return list losses created call() part return values. Returns: unnamed list. default, returns list(outputs, non_trainable_variables). return_losses = TRUE, returns list(outputs, non_trainable_variables, losses). Note: non_trainable_variables include non-trainable weights BatchNormalization statistics, also RNG seed state (random operations part layer, dropout), Metric state (metrics attached layer). elements state layer. Example:         Creates layer config. class method, meaning, R function self symbol (class instance) scope. Use __class__ classname symbol provided Layer() constructed) resolve class definition. default implementation :   method reverse get_config(), capable instantiating layer config named list. handle layer connectivity (handled Network), weights (handled set_weights()). Args: config: named list, typically output get_config(). Returns: layer instance.","code":"initialize(...,            activity_regularizer = NULL,            trainable = TRUE,            dtype = NULL,            autocast = TRUE,            name = NULL) layer_my_layer <- Layer(\"MyLayer\",   initialize = function(units, ..., dtype = NULL, name = NULL) {     super$initialize(..., dtype = dtype, name = name)     # .... finish initializing `self` instance   } ) add_loss(loss) Layer(\"MyLayer\",   ...   call = function(x) {     self$add_loss(op_sum(x))     x   } add_metric() add_variable(...) add_weight(shape = NULL,            initializer = NULL,            dtype = NULL,            trainable = TRUE,            autocast = TRUE,            regularizer = NULL,            constraint = NULL,            aggregation = 'mean',            name = NULL) build(input_shape) build_from_config(config) call(...) compute_mask(inputs, previous_mask) compute_output_shape(...) compute_output_spec(...) count_params() get_build_config() get_config() get_weights() quantize(mode) model$quantize(\"int8\") # quantize model in-place model |> predict(data) # faster inference quantized_call(...) load_own_variables(store) save_own_variables(store) set_weights(weights) stateless_call(trainable_variables, non_trainable_variables,                ..., return_losses = FALSE) model <- ... data <- ... trainable_variables <- model$trainable_variables non_trainable_variables <- model$non_trainable_variables # Call the model with zero side effects c(outputs, non_trainable_variables) %<-% model$stateless_call(     trainable_variables,     non_trainable_variables,     data ) # Attach the updated state to the model # (until you do this, the model is still in its pre-call state). purrr::walk2(   model$non_trainable_variables, non_trainable_variables,   \\(variable, value) variable$assign(value)) symbolic_call(...) from_config(config) from_config = function(config) {   do.call(`__class__`, config) }"},{"path":"https://keras.posit.co/reference/Layer.html","id":"readonly-properties-","dir":"Reference","previous_headings":"","what":"Readonly properties:","title":"Define a custom Layer class. — Layer","text":"compute_dtype dtype computations performed layer. dtype Alias layer$variable_dtype. input_dtype dtype layer inputs converted . losses List scalar losses add_loss(), regularizers sublayers. metrics List metrics. metrics_variables List metric variables. non_trainable_variables List non-trainable layer state. extends layer$non_trainable_weights include state used layer including state metrics SeedGenerators. non_trainable_weights List non-trainable weight variables layer. weights updated optimizer training. Unlike, layer$non_trainable_variables excludes metric state random seeds. trainable_variables List trainable layer state. equivalent layer$trainable_weights. trainable_weights List trainable weight variables layer. weights get updated optimizer training. variable_dtype dtype state (weights) layer. variables List layer state, including random seeds. extends layer$weights include state used layer including SeedGenerators. Note metrics variables included , use metrics_variables visit metric variables. weights List weight variables layer. Unlike, layer$variables excludes metric state random seeds. input Retrieves input tensor(s) symbolic operation. returns tensor(s) corresponding first time operation called. Returns: Input tensor list input tensors. output Retrieves output tensor(s) layer. returns tensor(s) corresponding first time operation called. Returns: Output tensor list output tensors.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"data-descriptors-attributes-","dir":"Reference","previous_headings":"","what":"Data descriptors (Attributes):","title":"Define a custom Layer class. — Layer","text":"dtype_policy input_spec supports_masking Whether layer supports computing mask using compute_mask. trainable Settable boolean, whether layer trainable .","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a custom LearningRateSchedule class — LearningRateSchedule","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"Subclass Keras LearningRateSchedule base class. can use learning rate schedule modulate learning rate optimizer changes time. Several built-learning rate schedules available, learning_rate_schedule_exponential_decay() learning_rate_schedule_piecewise_constant_decay():   LearningRateSchedule() instance can passed learning_rate argument optimizer. implement schedule object, implement call method, takes step argument (scalar integer backend tensor, current training step count). Note step 0-based (.e., first step 0). Like Keras object, can also optionally make object serializable implementing get_config() from_config() methods.","code":"lr_schedule <- learning_rate_schedule_exponential_decay(   initial_learning_rate = 1e-2,   decay_steps = 10000,   decay_rate = 0.9 ) optimizer <- optimizer_sgd(learning_rate = lr_schedule)"},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"","code":"LearningRateSchedule(   classname,   call = NULL,   initialize = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"classname String, name custom class. (Conventionally, CamelCase). call, initialize, get_config Recommended methods implement. See description details sections. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"function returns LearningRateSchedule instances, similar built-learning_rate_schedule_* family functions.","code":""},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"","code":"my_custom_learning_rate_schedule <- LearningRateSchedule(   classname = \"MyLRSchedule\",    initialize = function(initial_learning_rate) {     self$initial_learning_rate <- initial_learning_rate   },    call = function(step) {     # note that `step` is a tensor     # and call() will be traced via tf_function() or similar.      str(step) # <KerasVariable shape=(), dtype=int64, path=SGD/iteration>      # print 'step' every 1000 steps     op_cond((step %% 1000) == 0,             \\() {tensorflow::tf$print(step); NULL},             \\() {NULL})     self$initial_learning_rate / (step + 1)   } )  optimizer <- optimizer_sgd(   learning_rate = my_custom_learning_rate_schedule(0.1) )  # You can also call schedule instances directly # (e.g., for interactive testing, or if implementing a custom optimizer) schedule <- my_custom_learning_rate_schedule(0.1) step <- keras$Variable(initializer = op_ones,                        shape = shape(),                        dtype = \"int64\") schedule(step) ## <KerasVariable shape=(), dtype=int64, path=variable> ## tf.Tensor(0.0, shape=(), dtype=float64)"},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"methods-available-","dir":"Reference","previous_headings":"","what":"Methods available:","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"","code":"get_config()"},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Subclass the base Loss class — Loss","title":"Subclass the base Loss class — Loss","text":"Use define custom loss class. Note, cases need subclass Loss define custom loss: can also pass bare R function, named R function defined custom_metric(), loss function compile().","code":""},{"path":"https://keras.posit.co/reference/Loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subclass the base Loss class — Loss","text":"","code":"Loss(   classname,   call = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subclass the base Loss class — Loss","text":"classname String, name custom class. (Conventionally, CamelCase). call   Method implemented subclasses: Function contains logic loss calculation using y_true, y_pred. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"function(y_true, y_pred)"},{"path":"https://keras.posit.co/reference/Loss.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subclass the base Loss class — Loss","text":"function returns Loss instances, similar builtin loss functions.","code":""},{"path":"https://keras.posit.co/reference/Loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Subclass the base Loss class — Loss","text":"Example subclass implementation:","code":"loss_custom_mse <- Loss(   classname = \"CustomMeanSquaredError\",   call = function(y_true, y_pred) {     op_mean(op_square(y_pred - y_true), axis = -1)   } )  # Usage in compile() model <- keras_model_sequential(input_shape = 10) |> layer_dense(10) model |> compile(loss = loss_custom_mse())  # Standalone usage mse <- loss_custom_mse(name = \"my_custom_mse_instance\")  y_true <- op_arange(20) |> op_reshape(c(4, 5)) y_pred <- op_arange(20) |> op_reshape(c(4, 5)) * 2 (loss <- mse(y_true, y_pred)) ## tf.Tensor(123.5, shape=(), dtype=float32) loss2 <- (y_pred - y_true)^2 |>   op_mean(axis = -1) |>   op_mean()  stopifnot(all.equal(as.array(loss), as.array(loss2)))  sample_weight <-array(c(.25, .25, 1, 1)) (weighted_loss <- mse(y_true, y_pred, sample_weight = sample_weight)) ## tf.Tensor(112.8125, shape=(), dtype=float32) weighted_loss2 <- (y_true - y_pred)^2 |>   op_mean(axis = -1) |>   op_multiply(sample_weight) |>   op_mean()  stopifnot(all.equal(as.array(weighted_loss),                     as.array(weighted_loss2)))"},{"path":"https://keras.posit.co/reference/Loss.html","id":"methods-defined-by-base-loss-class-","dir":"Reference","previous_headings":"","what":"Methods defined by base Loss class:","title":"Subclass the base Loss class — Loss","text":"Args: name reduction: Valid values one {\"sum_over_batch_size\", \"sum\", NULL, \"none\"} dtype    Call loss instance function, optionally sample_weight.","code":"initialize(name=NULL, reduction=\"sum_over_batch_size\", dtype=NULL) __call__(y_true, y_pred, sample_weight=NULL) get_config()"},{"path":"https://keras.posit.co/reference/Loss.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Subclass the base Loss class — Loss","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Metric.html","id":null,"dir":"Reference","previous_headings":"","what":"Subclass the base Metric class — Metric","title":"Subclass the base Metric class — Metric","text":"Metric object encapsulates metric logic state can used track model performance training. returned family metric functions start prefix metric_*, well returned custom metrics defined Metric().","code":""},{"path":"https://keras.posit.co/reference/Metric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subclass the base Metric class — Metric","text":"","code":"Metric(   classname,   initialize = NULL,   update_state = NULL,   result = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Metric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subclass the base Metric class — Metric","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, update_state, result Recommended methods implement. See description section. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/Metric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subclass the base Metric class — Metric","text":"function returns Metric instances, similar builtin metric functions.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Metric.html","id":"usage-with-compile-","dir":"Reference","previous_headings":"","what":"Usage with compile():","title":"Subclass the base Metric class — Metric","text":"","code":"model |> compile(   optimizer = 'sgd',   loss = 'mse',   metrics = c(metric_SOME_METRIC(), metric_SOME_OTHER_METRIC()) )"},{"path":"https://keras.posit.co/reference/Metric.html","id":"standalone-usage-","dir":"Reference","previous_headings":"","what":"Standalone usage:","title":"Subclass the base Metric class — Metric","text":"","code":"m <- metric_SOME_METRIC() for (e in seq(epochs)) {   for (i in seq(train_steps)) {     c(y_true, y_pred, sample_weight = NULL) %<-% ...     m$update_state(y_true, y_pred, sample_weight)   }   cat('Final epoch result: ', as.numeric(m$result()), \"\\n\")   m$reset_state() }"},{"path":[]},{"path":"https://keras.posit.co/reference/Metric.html","id":"usage-with-compile--1","dir":"Reference","previous_headings":"","what":"Usage with compile():","title":"Subclass the base Metric class — Metric","text":"implemented subclasses (custom metrics): initialize(): state variables created method calling self$add_variable() like: self$var <- self$add_variable(...). update_state(): Updates state variables like: self$var$assign(...). result(): Computes returns scalar value named list scalar values metric state variables. Example subclass implementation:","code":"model <- keras_model_sequential() model |>   layer_dense(64, activation = \"relu\") |>   layer_dense(64, activation = \"relu\") |>   layer_dense(10, activation = \"softmax\") model |>   compile(optimizer = optimizer_rmsprop(0.01),           loss = loss_categorical_crossentropy(),           metrics = metric_categorical_accuracy())  data <- random_uniform(c(1000, 32)) labels <- random_uniform(c(1000, 10))  model |> fit(data, labels, verbose = 0) metric_binary_true_positives <- Metric(   classname = \"BinaryTruePositives\",    initialize = function(name = 'binary_true_positives', ...) {     super$initialize(name = name, ...)     self$true_positives <-       self$add_weight(shape = shape(),                       initializer = 'zeros',                       name = 'true_positives')   },    update_state = function(y_true, y_pred, sample_weight = NULL) {     y_true <- op_cast(y_true, \"bool\")     y_pred <- op_cast(y_pred, \"bool\")      values <- y_true & y_pred # `&` calls op_logical_and()     values <- op_cast(values, self$dtype)     if (!is.null(sample_weight)) {       sample_weight <- op_cast(sample_weight, self$dtype)       sample_weight <- op_broadcast_to(sample_weight, shape(values))       values <- values * sample_weight # `*` calls op_multiply()     }     self$true_positives$assign(self$true_positives + op_sum(values))   },    result = function() {     self$true_positives   } ) model <- keras_model_sequential(input_shape = 32) |> layer_dense(10) model |> compile(loss = loss_binary_crossentropy(),                  metrics = list(metric_binary_true_positives())) model |> fit(data, labels, verbose = 0)"},{"path":"https://keras.posit.co/reference/Metric.html","id":"methods-defined-by-the-base-metric-class-","dir":"Reference","previous_headings":"","what":"Methods defined by the base Metric class:","title":"Subclass the base Metric class — Metric","text":"Calling metric instance self like m(...) equivalent calling:      Initialize self. Args: name: (Optional) string name metric instance. dtype: (Optional) data type metric result.          Return serializable config metric.    Reset metric state variables. function called epochs/steps, metric evaluated training.    Compute current metric value. Returns: scalar tensor, named list scalar tensors.             Accumulate statistics metric.","code":"__call__(...) function(...) {   m$update_state(...)   m$result() } initialize(dtype=NULL, name=NULL) add_variable(shape, initializer, dtype=NULL, aggregation = 'sum', name=NULL) add_weight(shape=shape(), initializer=NULL, dtype=NULL, name=NULL) get_config() reset_state() result() stateless_result(metric_variables) stateless_reset_state() stateless_update_state(metric_variables, ...) update_state(...)"},{"path":"https://keras.posit.co/reference/Metric.html","id":"readonly-properties","dir":"Reference","previous_headings":"","what":"Readonly properties","title":"Subclass the base Metric class — Metric","text":"dtype variables","code":""},{"path":"https://keras.posit.co/reference/Metric.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Subclass the base Metric class — Metric","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Subclass the base Keras Model Class — Model","title":"Subclass the base Keras Model Class — Model","text":"advanced use cases need subclass base Model type, e.g., want override train_step() method. just want create define keras model, prefer keras_model() keras_model_sequential(). just want encapsulate custom logic state, need customize training behavior (besides calling self$add_loss() call() method), prefer Layer().","code":""},{"path":"https://keras.posit.co/reference/Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subclass the base Keras Model Class — Model","text":"","code":"Model(   classname,   initialize = NULL,   call = NULL,   train_step = NULL,   predict_step = NULL,   test_step = NULL,   compute_loss = NULL,   compute_metrics = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subclass the base Keras Model Class — Model","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, call, train_step, predict_step, test_step, compute_loss, compute_metrics Optional methods can overridden. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subclass the base Keras Model Class — Model","text":"model constructor function, can call create instance new model type.","code":""},{"path":"https://keras.posit.co/reference/Model.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Subclass the base Keras Model Class — Model","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"Exponential Linear Unit. — activation_elu","title":"Exponential Linear Unit. — activation_elu","text":"exponential linear unit (ELU) alpha > 0 defined : x x > 0 alpha * exp(x) - 1 x < 0 ELUs negative values pushes mean activations closer zero. Mean activations closer zero enable faster learning bring gradient closer natural gradient. ELUs saturate negative value argument gets smaller. Saturation means small derivative decreases variation information propagated next layer.","code":""},{"path":"https://keras.posit.co/reference/activation_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exponential Linear Unit. — activation_elu","text":"","code":"activation_elu(x, alpha = 1)"},{"path":"https://keras.posit.co/reference/activation_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exponential Linear Unit. — activation_elu","text":"x Input tensor. alpha Numeric. See description details.","code":""},{"path":"https://keras.posit.co/reference/activation_elu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exponential Linear Unit. — activation_elu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_elu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Exponential Linear Unit. — activation_elu","text":"Clevert et al., 2016","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_exponential.html","id":null,"dir":"Reference","previous_headings":"","what":"Exponential activation function. — activation_exponential","title":"Exponential activation function. — activation_exponential","text":"Exponential activation function.","code":""},{"path":"https://keras.posit.co/reference/activation_exponential.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exponential activation function. — activation_exponential","text":"","code":"activation_exponential(x)"},{"path":"https://keras.posit.co/reference/activation_exponential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exponential activation function. — activation_exponential","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_exponential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exponential activation function. — activation_exponential","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian error linear unit (GELU) activation function. — activation_gelu","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"Gaussian error linear unit (GELU) defined : gelu(x) = x * P(X <= x) P(X) ~ N(0, 1), .e. gelu(x) = 0.5 * x * (1 + erf(x / sqrt(2))). GELU weights inputs value, rather gating inputs sign ReLU.","code":""},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"","code":"activation_gelu(x, approximate = FALSE)"},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"x Input tensor. approximate bool, whether enable approximation.","code":""},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"Hendrycks et al., 2016","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Hard sigmoid activation function. — activation_hard_sigmoid","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"hard sigmoid activation defined : 0 x <= -3 1 x >= 3 (x/6) + 0.5 -3 < x < 3 faster, piecewise linear approximation sigmoid activation.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"","code":"activation_hard_sigmoid(x)"},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"Wikipedia \"Hard sigmoid\"","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"defined : 0 x < -3 x x > 3 x * (x + 3) / 6 -3 <= x <= 3 faster, piecewise linear approximation silu activation.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"","code":"activation_hard_silu(x)  activation_hard_swish(x)"},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"Howard, 2019","code":""},{"path":"https://keras.posit.co/reference/activation_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaky relu activation function. — activation_leaky_relu","title":"Leaky relu activation function. — activation_leaky_relu","text":"Leaky relu activation function.","code":""},{"path":"https://keras.posit.co/reference/activation_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leaky relu activation function. — activation_leaky_relu","text":"","code":"activation_leaky_relu(x, negative_slope = 0.2)"},{"path":"https://keras.posit.co/reference/activation_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leaky relu activation function. — activation_leaky_relu","text":"x Input tensor. negative_slope float controls slope values lower threshold.","code":""},{"path":"https://keras.posit.co/reference/activation_leaky_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Leaky relu activation function. — activation_leaky_relu","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_linear.html","id":null,"dir":"Reference","previous_headings":"","what":"Linear activation function (pass-through). — activation_linear","title":"Linear activation function (pass-through). — activation_linear","text":"\"linear\" activation identity function: returns input, unmodified.","code":""},{"path":"https://keras.posit.co/reference/activation_linear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linear activation function (pass-through). — activation_linear","text":"","code":"activation_linear(x)"},{"path":"https://keras.posit.co/reference/activation_linear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linear activation function (pass-through). — activation_linear","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_linear.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Linear activation function (pass-through). — activation_linear","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_log_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Log-Softmax activation function. — activation_log_softmax","title":"Log-Softmax activation function. — activation_log_softmax","text":"input vector handled independently. axis argument sets axis input function applied along.","code":""},{"path":"https://keras.posit.co/reference/activation_log_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log-Softmax activation function. — activation_log_softmax","text":"","code":"activation_log_softmax(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/activation_log_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log-Softmax activation function. — activation_log_softmax","text":"x Input tensor. axis Integer, axis along softmax applied.","code":""},{"path":"https://keras.posit.co/reference/activation_log_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Log-Softmax activation function. — activation_log_softmax","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_mish.html","id":null,"dir":"Reference","previous_headings":"","what":"Mish activation function. — activation_mish","title":"Mish activation function. — activation_mish","text":"defined : mish(x) = x * tanh(softplus(x)) softplus defined : softplus(x) = log(exp(x) + 1)","code":""},{"path":"https://keras.posit.co/reference/activation_mish.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mish activation function. — activation_mish","text":"","code":"activation_mish(x)"},{"path":"https://keras.posit.co/reference/activation_mish.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mish activation function. — activation_mish","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_mish.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mish activation function. — activation_mish","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_mish.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Mish activation function. — activation_mish","text":"Misra, 2019","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies the rectified linear unit activation function. — activation_relu","title":"Applies the rectified linear unit activation function. — activation_relu","text":"default values, returns standard ReLU activation: max(x, 0), element-wise maximum 0 input tensor. Modifying default parameters allows use non-zero thresholds, change max value activation, use non-zero multiple input values threshold.","code":""},{"path":"https://keras.posit.co/reference/activation_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies the rectified linear unit activation function. — activation_relu","text":"","code":"activation_relu(x, negative_slope = 0, max_value = NULL, threshold = 0)"},{"path":"https://keras.posit.co/reference/activation_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies the rectified linear unit activation function. — activation_relu","text":"x Input tensor. negative_slope numeric controls slope values lower threshold. max_value numeric sets saturation threshold (largest value function return). threshold numeric giving threshold value activation function values damped set zero.","code":""},{"path":"https://keras.posit.co/reference/activation_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies the rectified linear unit activation function. — activation_relu","text":"tensor shape dtype input x.","code":""},{"path":"https://keras.posit.co/reference/activation_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies the rectified linear unit activation function. — activation_relu","text":"","code":"x <- c(-10, -5, 0, 5, 10) activation_relu(x) ## tf.Tensor([ 0.  0.  0.  5. 10.], shape=(5), dtype=float32) activation_relu(x, negative_slope = 0.5) ## tf.Tensor([-5.  -2.5  0.   5.  10. ], shape=(5), dtype=float32) activation_relu(x, max_value = 5) ## tf.Tensor([0. 0. 0. 5. 5.], shape=(5), dtype=float32) activation_relu(x, threshold = 5) ## tf.Tensor([-0. -0.  0.  0. 10.], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/activation_relu6.html","id":null,"dir":"Reference","previous_headings":"","what":"Relu6 activation function. — activation_relu6","title":"Relu6 activation function. — activation_relu6","text":"ReLU function, truncated maximum value 6.","code":""},{"path":"https://keras.posit.co/reference/activation_relu6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relu6 activation function. — activation_relu6","text":"","code":"activation_relu6(x)"},{"path":"https://keras.posit.co/reference/activation_relu6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relu6 activation function. — activation_relu6","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_relu6.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Relu6 activation function. — activation_relu6","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"Scaled Exponential Linear Unit (SELU). — activation_selu","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"Scaled Exponential Linear Unit (SELU) activation function defined : scale * x x > 0 scale * alpha * (exp(x) - 1) x < 0 alpha scale pre-defined constants (alpha = 1.67326324 scale = 1.05070098). Basically, SELU activation function multiplies scale (> 1) output activation_elu function ensure slope larger one positive inputs. values alpha scale chosen mean variance inputs preserved two consecutive layers long weights initialized correctly (see initializer_lecun_normal()) number input units \"large enough\" (see reference paper information).","code":""},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"","code":"activation_selu(x)"},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"notes","dir":"Reference","previous_headings":"","what":"Notes","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"used together initializer_lecun_normal(). used together dropout variant layer_alpha_dropout() (legacy, depracated).","code":""},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"Klambauer et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid activation function. — activation_sigmoid","title":"Sigmoid activation function. — activation_sigmoid","text":"defined : sigmoid(x) = 1 / (1 + exp(-x)). small values (<-5), sigmoid returns value close zero, large values (>5) result function gets close 1. Sigmoid equivalent 2-element softmax, second element assumed zero. sigmoid function always returns value 0 1.","code":""},{"path":"https://keras.posit.co/reference/activation_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid activation function. — activation_sigmoid","text":"","code":"activation_sigmoid(x)"},{"path":"https://keras.posit.co/reference/activation_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid activation function. — activation_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sigmoid activation function. — activation_sigmoid","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Swish (or Silu) activation function. — activation_silu","title":"Swish (or Silu) activation function. — activation_silu","text":"defined : swish(x) = x * sigmoid(x). Swish (Silu) activation function smooth, non-monotonic function unbounded bounded .","code":""},{"path":"https://keras.posit.co/reference/activation_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Swish (or Silu) activation function. — activation_silu","text":"","code":"activation_silu(x)"},{"path":"https://keras.posit.co/reference/activation_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Swish (or Silu) activation function. — activation_silu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_silu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Swish (or Silu) activation function. — activation_silu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_silu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Swish (or Silu) activation function. — activation_silu","text":"Ramachandran et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax converts a vector of values to a probability distribution. — activation_softmax","title":"Softmax converts a vector of values to a probability distribution. — activation_softmax","text":"elements output vector range [0, 1] sum 1. input vector handled independently. axis argument sets axis input function applied along. Softmax often used activation last layer classification network result interpreted probability distribution. softmax vector x computed exp(x) / sum(exp(x)). input values log-odds resulting probability.","code":""},{"path":"https://keras.posit.co/reference/activation_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax converts a vector of values to a probability distribution. — activation_softmax","text":"","code":"activation_softmax(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/activation_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax converts a vector of values to a probability distribution. — activation_softmax","text":"x Input tensor. axis Integer, axis along softmax applied.","code":""},{"path":"https://keras.posit.co/reference/activation_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax converts a vector of values to a probability distribution. — activation_softmax","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_softplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Softplus activation function. — activation_softplus","title":"Softplus activation function. — activation_softplus","text":"defined : softplus(x) = log(exp(x) + 1).","code":""},{"path":"https://keras.posit.co/reference/activation_softplus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softplus activation function. — activation_softplus","text":"","code":"activation_softplus(x)"},{"path":"https://keras.posit.co/reference/activation_softplus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softplus activation function. — activation_softplus","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_softplus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softplus activation function. — activation_softplus","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_softsign.html","id":null,"dir":"Reference","previous_headings":"","what":"Softsign activation function. — activation_softsign","title":"Softsign activation function. — activation_softsign","text":"Softsign defined : softsign(x) = x / (abs(x) + 1).","code":""},{"path":"https://keras.posit.co/reference/activation_softsign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softsign activation function. — activation_softsign","text":"","code":"activation_softsign(x)"},{"path":"https://keras.posit.co/reference/activation_softsign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softsign activation function. — activation_softsign","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_softsign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softsign activation function. — activation_softsign","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_tanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hyperbolic tangent activation function. — activation_tanh","title":"Hyperbolic tangent activation function. — activation_tanh","text":"defined : tanh(x) = sinh(x) / cosh(x), .e. tanh(x) = ((exp(x) - exp(-x)) / (exp(x) + exp(-x))).","code":""},{"path":"https://keras.posit.co/reference/activation_tanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hyperbolic tangent activation function. — activation_tanh","text":"","code":"activation_tanh(x)"},{"path":"https://keras.posit.co/reference/activation_tanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hyperbolic tangent activation function. — activation_tanh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_tanh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hyperbolic tangent activation function. — activation_tanh","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/active_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an active property class method — active_property","title":"Create an active property class method — active_property","text":"Create active property class method","code":""},{"path":"https://keras.posit.co/reference/active_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an active property class method — active_property","text":"","code":"active_property(fn)"},{"path":"https://keras.posit.co/reference/active_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an active property class method — active_property","text":"fn R function","code":""},{"path":"https://keras.posit.co/reference/active_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an active property class method — active_property","text":"fn, additional R attribute cause fn converted active property converted method custom subclass.","code":""},{"path":"https://keras.posit.co/reference/active_property.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Create an active property class method — active_property","text":"","code":"layer_foo <- Model(\"Foo\", ...,   metrics = active_property(function() {     list(self$d_loss_metric,          self$g_loss_metric)   }))"},{"path":"https://keras.posit.co/reference/adapt.html","id":null,"dir":"Reference","previous_headings":"","what":"Fits the state of the preprocessing layer to the data being passed — adapt","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"Fits state preprocessing layer data passed","code":""},{"path":"https://keras.posit.co/reference/adapt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"","code":"adapt(object, data, ..., batch_size = NULL, steps = NULL)"},{"path":"https://keras.posit.co/reference/adapt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"object Preprocessing layer object data data train . can passed either tf.data.Dataset R array. ... Used forwards backwards compatibility. Passed underlying method. batch_size Integer NULL. Number asamples per state update. unspecified, batch_size default 32. specify batch_size data form TF Dataset generator (since generate batches). steps Integer NULL. Total number steps (batches samples) training input tensors TensorFlow data tensors, default NULL equal number samples dataset divided batch size, 1 determined. x tf.data.Dataset, steps NULL, epoch run input dataset exhausted. passing infinitely repeating dataset, must specify steps argument. argument supported array inputs.","code":""},{"path":"https://keras.posit.co/reference/adapt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"Returns object, invisibly.","code":""},{"path":"https://keras.posit.co/reference/adapt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"calling adapt layer, preprocessing layer's state update training. order make preprocessing layers efficient distribution context, kept constant respect compiled tf.Graphs call layer. affect layer use adapting layer , adapt layer multiple times need take care re-compile compiled functions follows: adding preprocessing layer keras model, need call compile(model) subsequent call adapt(). calling preprocessing layer inside tfdatasets::dataset_map(), call dataset_map() input Dataset adapt(). using tensorflow::tf_function() directly calls preprocessing layer, need call tf_function() callable subsequent call adapt(). keras_model() example multiple adapts:         tfdatasets example multiple adapts:","code":"layer <- layer_normalization(axis = NULL) adapt(layer, c(0, 2)) model <- keras_model_sequential() |> layer() predict(model, c(0, 1, 2), verbose = FALSE) # [1] -1  0  1 ## [1] -1  0  1 adapt(layer, c(-1, 1)) compile(model)  # This is needed to re-compile model.predict! predict(model, c(0, 1, 2), verbose = FALSE) # [1] 0 1 2 ## [1] 0 1 2 layer <- layer_normalization(axis = NULL) adapt(layer, c(0, 2)) input_ds <- tfdatasets::range_dataset(0, 3) normalized_ds <- input_ds |>   tfdatasets::dataset_map(layer) str(tfdatasets::iterate(normalized_ds)) ## List of 3 ##  $ :<tf.Tensor: shape=(1), dtype=float32, numpy=array([-1.], dtype=float32)> ##  $ :<tf.Tensor: shape=(1), dtype=float32, numpy=array([0.], dtype=float32)> ##  $ :<tf.Tensor: shape=(1), dtype=float32, numpy=array([1.], dtype=float32)> adapt(layer, c(-1, 1)) normalized_ds <- input_ds |>   tfdatasets::dataset_map(layer) # Re-map over the input dataset.  normalized_ds |>   tfdatasets::as_array_iterator() |>   tfdatasets::iterate(simplify = FALSE) |>   str() ## List of 3 ##  $ : num [1(1d)] 0 ##  $ : num [1(1d)] 1 ##  $ : num [1(1d)] 2"},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"Instantiates ConvNeXtBase architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"","code":"application_convnext_base(   model_name = \"convnext_base\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"Instantiates ConvNeXtLarge architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"","code":"application_convnext_large(   model_name = \"convnext_large\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"Instantiates ConvNeXtSmall architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"","code":"application_convnext_small(   model_name = \"convnext_small\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"Instantiates ConvNeXtTiny architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"","code":"application_convnext_tiny(   model_name = \"convnext_tiny\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"Instantiates ConvNeXtXLarge architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"","code":"application_convnext_xlarge(   model_name = \"convnext_xlarge\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Densenet121 architecture. — application_densenet121","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"Instantiates Densenet121 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"","code":"application_densenet121(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) ('channels_last' data format) (3, 224, 224) ('channels_first' data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"Densely Connected Convolutional Networks (CVPR 2017) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"Keras Application expects specific kind input preprocessing. DenseNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Densenet169 architecture. — application_densenet169","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"Instantiates Densenet169 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"","code":"application_densenet169(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) ('channels_last' data format) (3, 224, 224) ('channels_first' data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"Densely Connected Convolutional Networks (CVPR 2017) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"Keras Application expects specific kind input preprocessing. DenseNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Densenet201 architecture. — application_densenet201","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"Instantiates Densenet201 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"","code":"application_densenet201(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) ('channels_last' data format) (3, 224, 224) ('channels_first' data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"Densely Connected Convolutional Networks (CVPR 2017) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"Keras Application expects specific kind input preprocessing. DenseNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"Instantiates EfficientNetB0 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"","code":"application_efficientnet_b0(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"Instantiates EfficientNetB1 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"","code":"application_efficientnet_b1(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"Instantiates EfficientNetB2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"","code":"application_efficientnet_b2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"Instantiates EfficientNetB3 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"","code":"application_efficientnet_b3(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"Instantiates EfficientNetB4 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"","code":"application_efficientnet_b4(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"Instantiates EfficientNetB5 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"","code":"application_efficientnet_b5(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"Instantiates EfficientNetB6 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"","code":"application_efficientnet_b6(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"Instantiates EfficientNetB7 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"","code":"application_efficientnet_b7(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"Instantiates EfficientNetV2B0 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"","code":"application_efficientnet_v2b0(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"Instantiates EfficientNetV2B1 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"","code":"application_efficientnet_v2b1(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"Instantiates EfficientNetV2B2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"","code":"application_efficientnet_v2b2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"Instantiates EfficientNetV2B3 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"","code":"application_efficientnet_v2b3(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"Instantiates EfficientNetV2L architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"","code":"application_efficientnet_v2l(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"Instantiates EfficientNetV2M architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"","code":"application_efficientnet_v2m(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"Instantiates EfficientNetV2S architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"","code":"application_efficientnet_v2s(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"Instantiates Inception-ResNet v2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"","code":"application_inception_resnet_v2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (299, 299, 3) ('channels_last' data format) (3, 299, 299) ('channels_first' data format). exactly 3 inputs channels, width height smaller 75. E.g. (150, 150, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. 'avg' means global average pooling applied output last convolutional block, thus output model 2D tensor. 'max' means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"Inception-v4, Inception-ResNet Impact Residual Connections Learning (AAAI 2017) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"Keras Application expects specific kind input preprocessing. InceptionResNetV2, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Inception v3 architecture. — application_inception_v3","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"Instantiates Inception v3 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"","code":"application_inception_v3(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"include_top Boolean, whether include fully-connected layer top, last layer network. Defaults TRUE. weights One NULL (random initialization), imagenet (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_tensor useful sharing inputs multiple different networks. Defaults NULL. input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (299, 299, 3) (channels_last data format) (3, 299, 299) (channels_first data format). exactly 3 inputs channels, width height smaller 75. E.g. (150, 150, 3) one valid value. input_shape ignored input_tensor provided. pooling Optional pooling mode feature extraction include_top FALSE. NULL (default) means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"Rethinking Inception Architecture Computer Vision (CVPR 2016) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"Keras Application expects specific kind input preprocessing. InceptionV3, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the MobileNet architecture. — application_mobilenet","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"Instantiates MobileNet architecture.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"","code":"application_mobilenet(   input_shape = NULL,   alpha = 1,   depth_multiplier = 1L,   dropout = 0.001,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. Defaults NULL. input_shape ignored input_tensor provided. alpha Controls width network. known width multiplier MobileNet paper. alpha < 1.0, proportionally decreases number filters layer. alpha > 1.0, proportionally increases number filters layer. alpha == 1, default number filters paper used layer. Defaults 1.0. depth_multiplier Depth multiplier depthwise convolution. called resolution multiplier MobileNet paper. Defaults 1.0. dropout Dropout rate. Defaults 0.001. include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_tensor useful sharing inputs multiple different networks. Defaults NULL. pooling Optional pooling mode feature extraction include_top FALSE. NULL (default) means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"MobileNets: Efficient Convolutional Neural Networks Mobile Vision Applications function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"Keras Application expects specific kind input preprocessing. MobileNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"MobileNetV2 similar original MobileNet, except uses inverted residual blocks bottlenecking features. drastically lower parameter count original MobileNet. MobileNets support input size greater 32 x 32, larger image sizes offering better performance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"","code":"application_mobilenet_v2(   input_shape = NULL,   alpha = 1,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. Defaults NULL. input_shape ignored input_tensor provided. alpha Controls width network. known width multiplier MobileNet paper. alpha < 1.0, proportionally decreases number filters layer. alpha > 1.0, proportionally increases number filters layer. alpha == 1, default number filters paper used layer. Defaults 1.0. include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_tensor useful sharing inputs multiple different networks. Defaults NULL. pooling Optional pooling mode feature extraction include_top FALSE. NULL (default) means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"MobileNetV2: Inverted Residuals Linear Bottlenecks (CVPR 2018) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"Keras Application expects specific kind input preprocessing. MobileNetV2, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"Instantiates MobileNetV3Large architecture.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"","code":"application_mobilenet_v3_large(   input_shape = NULL,   alpha = 1,   minimalistic = FALSE,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   classes = 1000L,   pooling = NULL,   dropout_rate = 0.2,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"input_shape Optional shape tuple, specified like use model input image resolution (224, 224, 3). exactly 3 inputs channels. can also omit option like infer input_shape input_tensor. choose include input_tensor input_shape input_shape used match, shapes match throw error. E.g. (160, 160, 3) one valid value. alpha controls width network. known depth multiplier MobileNetV3 paper, name kept consistency MobileNetV1 Keras. alpha < 1.0, proportionally decreases number filters layer. alpha > 1.0, proportionally increases number filters layer. alpha == 1, default number filters paper used layer. minimalistic addition large small models module also contains -called minimalistic models, models per-layer dimensions characteristic MobilenetV3 however, utilize advanced blocks (squeeze--excite units, hard-swish, 5x5 convolutions). models less efficient CPU, much performant GPU/DSP. include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights String, one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. classes Integer, optional number classes classify images , specified include_top TRUE, weights argument specified. pooling String, optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. dropout_rate fraction input units drop last layer. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer (Rescaling) bottom network. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"Searching MobileNetV3 (ICCV 2019)","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"the-following-table-describes-the-performance-of-mobilenets-v-","dir":"Reference","previous_headings":"","what":"The following table describes the performance of MobileNets v3:","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"MACs stands Multiply Adds image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"Keras Application expects specific kind input preprocessing. MobileNetV3, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, MobileNetV3 models expect inputs float tensors pixels values [0-255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled MobileNetV3 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"inputs: floating point numpy.array backend-native tensor, 4D 3 color channels, values range [0, 255] include_preprocessing TRUE range [-1, 1] otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"Instantiates MobileNetV3Small architecture.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"","code":"application_mobilenet_v3_small(   input_shape = NULL,   alpha = 1,   minimalistic = FALSE,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   classes = 1000L,   pooling = NULL,   dropout_rate = 0.2,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"input_shape Optional shape tuple, specified like use model input image resolution (224, 224, 3). exactly 3 inputs channels. can also omit option like infer input_shape input_tensor. choose include input_tensor input_shape input_shape used match, shapes match throw error. E.g. (160, 160, 3) one valid value. alpha controls width network. known depth multiplier MobileNetV3 paper, name kept consistency MobileNetV1 Keras. alpha < 1.0, proportionally decreases number filters layer. alpha > 1.0, proportionally increases number filters layer. alpha == 1, default number filters paper used layer. minimalistic addition large small models module also contains -called minimalistic models, models per-layer dimensions characteristic MobilenetV3 however, utilize advanced blocks (squeeze--excite units, hard-swish, 5x5 convolutions). models less efficient CPU, much performant GPU/DSP. include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights String, one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. classes Integer, optional number classes classify images , specified include_top TRUE, weights argument specified. pooling String, optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. dropout_rate fraction input units drop last layer. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer (Rescaling) bottom network. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"Searching MobileNetV3 (ICCV 2019)","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"the-following-table-describes-the-performance-of-mobilenets-v-","dir":"Reference","previous_headings":"","what":"The following table describes the performance of MobileNets v3:","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"MACs stands Multiply Adds image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"Keras Application expects specific kind input preprocessing. MobileNetV3, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, MobileNetV3 models expect inputs float tensors pixels values [0-255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled MobileNetV3 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"inputs: floating point numpy.array backend-native tensor, 4D 3 color channels, values range [0, 255] include_preprocessing TRUE range [-1, 1] otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"Instantiates NASNet model ImageNet mode.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"","code":"application_nasnetlarge(   input_shape = NULL,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (331, 331, 3) NASNetLarge. exactly 3 inputs channels, width height smaller 32. E.g. (224, 224, 3) one valid value. include_top Whether include fully-connected layer top network. weights NULL (random initialization) imagenet (ImageNet weights).  loading imagenet weights, input_shape (331, 331, 3) input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"Learning Transferable Architectures Scalable Image Recognition (CVPR 2018) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"Keras Application expects specific kind input preprocessing. NASNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"Instantiates Mobile NASNet model ImageNet mode.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"","code":"application_nasnetmobile(   input_shape = NULL,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) NASNetMobile exactly 3 inputs channels, width height smaller 32. E.g. (224, 224, 3) one valid value. include_top Whether include fully-connected layer top network. weights NULL (random initialization) imagenet (ImageNet weights). loading imagenet weights, input_shape (224, 224, 3) input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"Learning Transferable Architectures Scalable Image Recognition (CVPR 2018) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"Keras Application expects specific kind input preprocessing. NASNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet101 architecture. — application_resnet101","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"Instantiates ResNet101 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"","code":"application_resnet101(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"Deep Residual Learning Image Recognition (CVPR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"Instantiates ResNet101V2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"","code":"application_resnet101_v2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"Identity Mappings Deep Residual Networks (CVPR 2016) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet152 architecture. — application_resnet152","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"Instantiates ResNet152 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"","code":"application_resnet152(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"Deep Residual Learning Image Recognition (CVPR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"Instantiates ResNet152V2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"","code":"application_resnet152_v2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"Identity Mappings Deep Residual Networks (CVPR 2016) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet50 architecture. — application_resnet50","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"Instantiates ResNet50 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"","code":"application_resnet50(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"Deep Residual Learning Image Recognition (CVPR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"Instantiates ResNet50V2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"","code":"application_resnet50_v2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"Identity Mappings Deep Residual Networks (CVPR 2016) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the VGG16 model. — application_vgg16","title":"Instantiates the VGG16 model. — application_vgg16","text":"Instantiates VGG16 model.","code":""},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the VGG16 model. — application_vgg16","text":"","code":"application_vgg16(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the VGG16 model. — application_vgg16","text":"include_top whether include 3 fully-connected layers top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (channels_last data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 input channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the VGG16 model. — application_vgg16","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the VGG16 model. — application_vgg16","text":"Deep Convolutional Networks Large-Scale Image Recognition (ICLR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. default input size model 224x224.","code":""},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the VGG16 model. — application_vgg16","text":"Keras Application expects specific kind input preprocessing. VGG16, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the VGG19 model. — application_vgg19","title":"Instantiates the VGG19 model. — application_vgg19","text":"Instantiates VGG19 model.","code":""},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the VGG19 model. — application_vgg19","text":"","code":"application_vgg19(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the VGG19 model. — application_vgg19","text":"include_top whether include 3 fully-connected layers top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (channels_last data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 input channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the VGG19 model. — application_vgg19","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the VGG19 model. — application_vgg19","text":"Deep Convolutional Networks Large-Scale Image Recognition (ICLR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. default input size model 224x224.","code":""},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the VGG19 model. — application_vgg19","text":"Keras Application expects specific kind input preprocessing. VGG19, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_xception.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Xception architecture. — application_xception","title":"Instantiates the Xception architecture. — application_xception","text":"Instantiates Xception architecture.","code":""},{"path":"https://keras.posit.co/reference/application_xception.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Xception architecture. — application_xception","text":"","code":"application_xception(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_xception.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Xception architecture. — application_xception","text":"include_top whether include 3 fully-connected layers top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (299, 299, 3). exactly 3 inputs channels, width height smaller 71. E.g. (150, 150, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_xception.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Xception architecture. — application_xception","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_xception.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Xception architecture. — application_xception","text":"Xception: Deep Learning Depthwise Separable Convolutions (CVPR 2017) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. default input image size model 299x299.","code":""},{"path":"https://keras.posit.co/reference/application_xception.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Xception architecture. — application_xception","text":"Keras Application expects specific kind input preprocessing. Xception, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/audio_dataset_from_directory.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","title":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","text":"directory structure :   calling audio_dataset_from_directory(main_directory, labels = 'inferred') return tf.data.Dataset yields batches audio files subdirectories class_a class_b, together labels 0 1 (0 corresponding class_a 1 corresponding class_b). .wav files supported time.","code":"main_directory/ ...class_a/ ......a_audio_1.wav ......a_audio_2.wav ...class_b/ ......b_audio_1.wav ......b_audio_2.wav"},{"path":"https://keras.posit.co/reference/audio_dataset_from_directory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","text":"","code":"audio_dataset_from_directory(   directory,   labels = \"inferred\",   label_mode = \"int\",   class_names = NULL,   batch_size = 32L,   sampling_rate = NULL,   output_sequence_length = NULL,   ragged = FALSE,   shuffle = TRUE,   seed = NULL,   validation_split = NULL,   subset = NULL,   follow_links = FALSE,   verbose = TRUE )"},{"path":"https://keras.posit.co/reference/audio_dataset_from_directory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","text":"directory Directory data located. labels \"inferred\", contain subdirectories, containing audio files class. Otherwise, directory structure ignored. labels Either \"inferred\" (labels generated directory structure), NULL (labels), list/tuple integer labels size number audio files found directory. Labels sorted according alphanumeric order audio file paths (obtained via os.walk(directory) Python). label_mode String describing encoding labels. Options : \"int\": means labels encoded integers (e.g. sparse_categorical_crossentropy loss). \"categorical\" means labels encoded categorical vector (e.g. categorical_crossentropy loss) \"binary\" means labels (can 2) encoded float32 scalars values 0 1 (e.g. binary_crossentropy). NULL (labels). class_names valid \"labels\" \"inferred\". explicit list class names (must match names subdirectories). Used control order classes (otherwise alphanumerical order used). batch_size Size batches data. Default: 32. NULL, data batched (dataset yield individual samples). sampling_rate Audio sampling rate (samples per second). output_sequence_length Maximum length audio sequence. Audio files longer truncated output_sequence_length. set NULL, sequences batch padded length longest sequence batch. ragged Whether return Ragged dataset (sequence length). Defaults FALSE. shuffle Whether shuffle data. Defaults TRUE. set FALSE, sorts data alphanumeric order. seed Optional random seed shuffling transformations. validation_split Optional float 0 1, fraction data reserve validation. subset Subset data return. One \"training\", \"validation\" \"\". used validation_split set. follow_links Whether visits subdirectories pointed symlinks. Defaults FALSE. verbose Whether display number information classes number files found. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/audio_dataset_from_directory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","text":"tf.data.Dataset object. label_mode NULL, yields string tensors shape (batch_size,), containing contents batch audio files. Otherwise, yields tuple (audio, labels), audio shape (batch_size, sequence_length, num_channels) labels follows format described . Rules regarding labels format: label_mode int, labels int32 tensor shape (batch_size,). label_mode binary, labels float32 tensor 1s 0s shape (batch_size, 1). label_mode categorical, labels float32 tensor shape (batch_size, num_classes), representing one-hot encoding class index.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/bidirectional.html","id":null,"dir":"Reference","previous_headings":"","what":"layer_bidirectional — bidirectional","title":"layer_bidirectional — bidirectional","text":"bidirectional() alias layer_bidirectional(). See ?layer_bidirectional() full documentation.","code":""},{"path":"https://keras.posit.co/reference/bidirectional.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"layer_bidirectional — bidirectional","text":"","code":"bidirectional(   object,   layer,   merge_mode = \"concat\",   weights = NULL,   backward_layer = NULL,   ... )"},{"path":"https://keras.posit.co/reference/bidirectional.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"layer_bidirectional — bidirectional","text":"object Object compose layer . tensor, array, sequential model. layer RNN instance, layer_lstm() layer_gru(). also Layer() instance meets following criteria: sequence-processing layer (accepts 3D+ inputs). go_backwards, return_sequences return_state attribute (semantics RNN class). input_spec attribute. Implement serialization via get_config() from_config(). Note recommended way create new RNN layers write custom RNN cell use layer_rnn(), instead subclassing Layer() directly. return_sequences TRUE, output masked timestep zero regardless layer's original zero_output_for_mask value. merge_mode Mode outputs forward backward RNNs combined. One {\"sum\", \"mul\", \"concat\", \"ave\", NULL}. NULL, outputs combined, returned list. Defaults \"concat\". weights see description backward_layer Optional RNN, Layer() instance used handle backwards input processing. backward_layer provided, layer instance passed layer argument used generate backward layer automatically. Note provided backward_layer layer properties matching layer argument, particular values stateful, return_states, return_sequences, etc. addition, backward_layer layer different go_backwards argument values. ValueError raised requirements met. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/bidirectional.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"layer_bidirectional — bidirectional","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback to back up and restore the training state. — callback_backup_and_restore","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"callback_backup_and_restore() callback intended recover training interruption happened middle fit execution, backing training states temporary checkpoint file, end epoch. backup overwrites previously written checkpoint file, given time one checkpoint file backup/restoring purpose. training restarts completion, training state (includes model weights epoch number) restored recently saved state beginning new fit run. completion fit run, temporary checkpoint file deleted. Note user responsible bring jobs back interruption. callback important backup restore mechanism fault tolerance purpose, model restored previous checkpoint expected one used back . user changes arguments passed compile fit, checkpoint saved fault tolerance can become invalid.","code":""},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"","code":"callback_backup_and_restore(   backup_dir,   save_freq = \"epoch\",   delete_checkpoint = TRUE )"},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"backup_dir String, path directory store data needed restore model. directory reused elsewhere store files, e.g. backup_and_restore callback another training run, another callback (e.g. callback_model_checkpoint) training run. save_freq \"epoch\", integer, FALSE. set \"epoch\", callback saves checkpoint end epoch. set integer, callback saves checkpoint every save_freq batches. Set save_freq = FALSE using preemption checkpointing (.e. save_before_preemption = TRUE). delete_checkpoint Boolean, defaults TRUE. backup_and_restore callback works saving checkpoint back training state. delete_checkpoint = TRUE, checkpoint deleted training finished. Use FALSE like keep checkpoint future usage.","code":""},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"","code":"callback_interrupting <- new_callback_class(   \"InterruptingCallback\",   on_epoch_begin = function(epoch, logs = NULL) {     if (epoch == 4) {       stop('Interrupting!')     }   } )  backup_dir <- tempfile() callback <- callback_backup_and_restore(backup_dir = backup_dir) model <- keras_model_sequential() %>%   layer_dense(10) model %>% compile(optimizer = optimizer_sgd(), loss = 'mse')  # ensure model is built (i.e., weights are initialized) for # callback_backup_and_restore() model(op_ones(c(5, 20))) |> invisible()  tryCatch({   model %>% fit(x = op_ones(c(5, 20)),                 y = op_zeros(5),                 epochs = 10, batch_size = 1,                 callbacks = list(callback, callback_interrupting()),                 verbose = 0) }, python.builtin.RuntimeError = function(e) message(\"Interrupted!\")) ## Interrupted! model$history$epoch ## [1] 0 1 2 # model$history %>% keras3:::to_keras_training_history() %>% as.data.frame() %>% print()  history <- model %>% fit(x = op_ones(c(5, 20)),                          y = op_zeros(5),                          epochs = 10, batch_size = 1,                          callbacks = list(callback),                          verbose = 0)  # Only 6 more epochs are run, since first training got interrupted at # zero-indexed epoch 4, second training will continue from 4 to 9. nrow(as.data.frame(history)) ## [1] 10"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback that streams epoch results to a CSV file. — callback_csv_logger","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"Supports values can represented string, including 1D iterables atomic vectors.","code":""},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"","code":"callback_csv_logger(filename, separator = \",\", append = FALSE)"},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"filename Filename CSV file, e.g. 'run/log.csv'. separator String used separate elements CSV file. append Boolean. TRUE: append file exists (useful continuing training). FALSE: overwrite existing file.","code":""},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"","code":"csv_logger <- callback_csv_logger('training.log') model %>% fit(X_train, Y_train, callbacks = list(csv_logger))"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":null,"dir":"Reference","previous_headings":"","what":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"Assuming goal training minimize loss. , metric monitored 'loss', mode 'min'. model$fit() training loop check end every epoch whether loss longer decreasing, considering min_delta patience applicable. found longer decreasing, model$stop_training marked TRUE training terminates. quantity monitored needs available logs list. make , pass loss metrics model$compile().","code":""},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"","code":"callback_early_stopping(   monitor = \"val_loss\",   min_delta = 0L,   patience = 0L,   verbose = 0L,   mode = \"auto\",   baseline = NULL,   restore_best_weights = FALSE,   start_from_epoch = 0L )"},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"monitor Quantity monitored. Defaults \"val_loss\". min_delta Minimum change monitored quantity qualify improvement, .e. absolute change less min_delta, count improvement. Defaults 0. patience Number epochs improvement training stopped. Defaults 0. verbose Verbosity mode, 0 1. Mode 0 silent, mode 1 displays messages callback takes action. Defaults 0. mode One {\"auto\", \"min\", \"max\"}. min mode, training stop quantity monitored stopped decreasing; \"max\" mode stop quantity monitored stopped increasing; \"auto\" mode, direction automatically inferred name monitored quantity. Defaults \"auto\". baseline Baseline value monitored quantity. NULL, training stop model show improvement baseline. Defaults NULL. restore_best_weights Whether restore model weights epoch best value monitored quantity. FALSE, model weights obtained last step training used. epoch restored regardless performance relative baseline. epoch improves baseline, training run patience epochs restore weights best epoch set. Defaults FALSE. start_from_epoch Number epochs wait starting monitor improvement. allows warm-period improvement expected thus training stopped. Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"","code":"callback <- callback_early_stopping(monitor = 'loss',                                    patience = 3) # This callback will stop the training when there is no improvement in # the loss for three consecutive epochs. model <- keras_model_sequential() %>%   layer_dense(10) model %>% compile(optimizer = optimizer_sgd(), loss = 'mse') history <- model %>% fit(x = op_ones(c(5, 20)),                          y = op_zeros(5),                          epochs = 10, batch_size = 1,                          callbacks = list(callback),                          verbose = 0) nrow(as.data.frame(history))  # Only 4 epochs are run. ## [1] 10"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"callback constructed anonymous functions called appropriate time (Model.{fit | evaluate | predict}). Note callbacks expects positional arguments, : on_epoch_begin on_epoch_end expect two positional arguments: epoch, logs on_train_begin on_train_end expect one positional argument: logs on_train_batch_begin on_train_batch_end expect two positional arguments: batch, logs See Callback class definition full list functions expected arguments.","code":""},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"","code":"callback_lambda(   on_epoch_begin = NULL,   on_epoch_end = NULL,   on_train_begin = NULL,   on_train_end = NULL,   on_train_batch_begin = NULL,   on_train_batch_end = NULL,   ... )"},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"on_epoch_begin called beginning every epoch. on_epoch_end called end every epoch. on_train_begin called beginning model training. on_train_end called end model training. on_train_batch_begin called beginning every train batch. on_train_batch_end called end every train batch. ... function Callback() want override passing function_name = function. example, callback_lambda(.., on_train_end = train_end_fn). custom function needs arguments ones defined Callback().","code":""},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"","code":"# Print the batch number at the beginning of every batch. batch_print_callback <- callback_lambda(   on_train_batch_begin = function(batch, logs) {     print(batch)   } )  # Stream the epoch loss to a file in new-line delimited JSON format # (one valid JSON object per line) json_log <- file('loss_log.json', open = 'wt') json_logging_callback <- callback_lambda(   on_epoch_end = function(epoch, logs) {     jsonlite::write_json(       list(epoch = epoch, loss = logs$loss),       json_log,       append = TRUE     )   },   on_train_end = function(logs) {     close(json_log)   } )  # Terminate some processes after having finished model training. processes <- ... cleanup_callback <- callback_lambda(   on_train_end = function(logs) {     for (p in processes) {       if (is_alive(p)) {         terminate(p)       }     }   } )  model %>% fit(   ...,   callbacks = list(     batch_print_callback,     json_logging_callback,     cleanup_callback   ) )"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":null,"dir":"Reference","previous_headings":"","what":"Learning rate scheduler. — callback_learning_rate_scheduler","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"beginning every epoch, callback gets updated learning rate value schedule function provided, current epoch current learning rate, applies updated learning rate optimizer.","code":""},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"","code":"callback_learning_rate_scheduler(schedule, verbose = 0L)"},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"schedule function takes epoch index (integer, indexed 0) current learning rate (float) inputs returns new learning rate output (float). verbose Integer. 0: quiet, 1: log update messages.","code":""},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"","code":"# This function keeps the initial learning rate steady for the first ten epochs # and decreases it exponentially after that. scheduler <- function(epoch, lr) {   if (epoch < 10)     return(lr)   else     return(lr * exp(-0.1)) }  model <- keras_model_sequential() |> layer_dense(units = 10) model |> compile(optimizer = optimizer_sgd(), loss = 'mse') model$optimizer$learning_rate |> as.array() |> round(5) ## [1] 0.01 callback <- callback_learning_rate_scheduler(schedule = scheduler) history <- model |> fit(x = array(runif(100), c(5, 20)),                         y = array(0, c(5, 1)),                         epochs = 15, callbacks = list(callback), verbose = 0) model$optimizer$learning_rate |> as.array() |> round(5) ## [1] 0.00607"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"callback_model_checkpoint() used conjunction training using model |> fit() save model weights (checkpoint file) interval, model weights can loaded later continue training state saved. options callback provides include: Whether keep model achieved \"best performance\" far, whether save model end every epoch regardless performance. Definition \"best\"; quantity monitor whether maximized minimized. frequency save . Currently, callback supports saving end every epoch, fixed number training batches. Whether weights saved, whole model saved.","code":""},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"","code":"callback_model_checkpoint(   filepath,   monitor = \"val_loss\",   verbose = 0L,   save_best_only = FALSE,   save_weights_only = FALSE,   mode = \"auto\",   save_freq = \"epoch\",   initial_value_threshold = NULL )"},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"filepath string, path save model file. filepath can contain named formatting options, filled value epoch keys logs (passed on_epoch_end). filepath name needs end \".weights.h5\" save_weights_only = TRUE end \".keras\" checkpoint saving whole model (default). example: filepath \"{epoch:02d}-{val_loss:.2f}.keras\", model checkpoints saved epoch number validation loss filename. directory filepath reused callbacks avoid conflicts. monitor metric name monitor. Typically metrics set model |> compile() method. Note: Prefix name \"val_\" monitor validation metrics. Use \"loss\" \"val_loss\" monitor model's total loss. specify metrics strings, like \"accuracy\", pass string (without \"val_\" prefix). pass Metric objects (created one metric_*()), monitor set metric$name. sure metric names can check contents history$metrics list returned history <- model |> fit() Multi-output models set additional prefixes metric names. verbose Verbosity mode, 0 1. Mode 0 silent, mode 1 displays messages callback takes action. save_best_only save_best_only = TRUE, saves model considered \"best\" latest best model according quantity monitored overwritten. filepath contain formatting options like {epoch} filepath overwritten new better model. save_weights_only TRUE, model's weights saved (model |> save_model_weights(filepath)), else full model saved (model |> save_model(filepath)). mode one {\"auto\", \"min\", \"max\"}. save_best_only = TRUE, decision overwrite current save file made based either maximization minimization monitored quantity. val_acc, \"max\", val_loss \"min\", etc. \"auto\" mode, mode set \"max\" quantities monitored \"acc\" start \"fmeasure\" set \"min\" rest quantities. save_freq \"epoch\" integer. using \"epoch\", callback saves model epoch. using integer, callback saves model end many batches. Model compiled steps_per_execution = N, saving criteria checked every Nth batch. Note saving aligned epochs, monitored metric may potentially less reliable (reflect little 1 batch, since metrics get reset every epoch). Defaults \"epoch\". initial_value_threshold Floating point initial \"best\" value metric monitored. applies save_best_value = TRUE. overwrites model weights already saved performance current model better value.","code":""},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"","code":"model <- keras_model_sequential(input_shape = c(10)) |>   layer_dense(1, activation = \"sigmoid\") |>   compile(loss = \"binary_crossentropy\", optimizer = \"adam\",           metrics = c('accuracy'))  EPOCHS <- 10 checkpoint_filepath <- tempfile('checkpoint-model-', fileext = \".keras\") model_checkpoint_callback <- callback_model_checkpoint(   filepath = checkpoint_filepath,   monitor = 'val_accuracy',   mode = 'max',   save_best_only = TRUE )  # Model is saved at the end of every epoch, if it's the best seen so far. model |> fit(x = random_uniform(c(2, 10)), y = op_ones(2, 1),              epochs = EPOCHS, validation_split = .5, verbose = 0,              callbacks = list(model_checkpoint_callback))  # The model (that are considered the best) can be loaded as - load_model(checkpoint_filepath) ## Model: \"sequential\" ## +---------------------------------+------------------------+---------------+ ## | Layer (type)                    | Output Shape           |       Param # | ## +=================================+========================+===============+ ## | dense (Dense)                   | (None, 1)              |            11 | ## +---------------------------------+------------------------+---------------+ ##  Total params: 35 (144.00 B) ##  Trainable params: 11 (44.00 B) ##  Non-trainable params: 0 (0.00 B) ##  Optimizer params: 24 (100.00 B) # Alternatively, one could checkpoint just the model weights as - checkpoint_filepath <- tempfile('checkpoint-', fileext = \".weights.h5\") model_checkpoint_callback <- callback_model_checkpoint(   filepath = checkpoint_filepath,   save_weights_only = TRUE,   monitor = 'val_accuracy',   mode = 'max',   save_best_only = TRUE )  # Model weights are saved at the end of every epoch, if it's the best seen # so far. # same as above model |> fit(x = random_uniform(c(2, 10)), y = op_ones(2, 1),              epochs = EPOCHS, validation_split = .5, verbose = 0,              callbacks = list(model_checkpoint_callback))  # The model weights (that are considered the best) can be loaded model |> load_model_weights(checkpoint_filepath)"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":null,"dir":"Reference","previous_headings":"","what":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"Models often benefit reducing learning rate factor 2-10 learning stagnates. callback monitors quantity improvement seen 'patience' number epochs, learning rate reduced.","code":""},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"","code":"callback_reduce_lr_on_plateau(   monitor = \"val_loss\",   factor = 0.1,   patience = 10L,   verbose = 0L,   mode = \"auto\",   min_delta = 1e-04,   cooldown = 0L,   min_lr = 0,   ... )"},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"monitor String. Quantity monitored. factor Float. Factor learning rate reduced. new_lr = lr * factor. patience Integer. Number epochs improvement learning rate reduced. verbose Integer. 0: quiet, 1: update messages. mode String. One {'auto', 'min', 'max'}. 'min' mode, learning rate reduced quantity monitored stopped decreasing; 'max' mode reduced quantity monitored stopped increasing; 'auto' mode, direction automatically inferred name monitored quantity. min_delta Float. Threshold measuring new optimum, focus significant changes. cooldown Integer. Number epochs wait resuming normal operation learning rate reduced. min_lr Float. Lower bound learning rate. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"","code":"reduce_lr <- callback_reduce_lr_on_plateau(monitor = 'val_loss', factor = 0.2,                                            patience = 5, min_lr = 0.001) model %>% fit(x_train, y_train, callbacks = list(reduce_lr))"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_remote_monitor.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback used to stream events to a server. — callback_remote_monitor","title":"Callback used to stream events to a server. — callback_remote_monitor","text":"Requires requests library. Events sent root + '/publish/epoch/end/' default. Calls HTTP POST, data argument JSON-encoded named list event data. send_as_json = TRUE, content type request \"application/json\". Otherwise serialized JSON sent within form.","code":""},{"path":"https://keras.posit.co/reference/callback_remote_monitor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback used to stream events to a server. — callback_remote_monitor","text":"","code":"callback_remote_monitor(   root = \"http://localhost:9000\",   path = \"/publish/epoch/end/\",   field = \"data\",   headers = NULL,   send_as_json = FALSE )"},{"path":"https://keras.posit.co/reference/callback_remote_monitor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback used to stream events to a server. — callback_remote_monitor","text":"root String; root url target server. path String; path relative root events sent. field String; JSON field data stored. field used payload sent within form (.e. send_as_json = FALSE). headers Named list; optional custom HTTP headers. send_as_json Boolean; whether request sent \"application/json\".","code":""},{"path":"https://keras.posit.co/reference/callback_remote_monitor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback used to stream events to a server. — callback_remote_monitor","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"callbacks replaces model's weight values values optimizer's EMA weights (exponential moving average past model weights values, implementing \"Polyak averaging\") model evaluation, restores previous weights evaluation. SwapEMAWeights callback used conjunction optimizer sets use_ema = TRUE. Note weights swapped -place order save memory. behavior undefined modify EMA weights model weights callbacks.","code":""},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"","code":"callback_swap_ema_weights(swap_on_epoch = FALSE)"},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"swap_on_epoch Whether perform swapping on_epoch_begin() on_epoch_end(). useful want use EMA weights callbacks callback_model_checkpoint(). Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"","code":"# Remember to set `use_ema=TRUE` in the optimizer optimizer <- optimizer_sgd(use_ema = TRUE) model |> compile(optimizer = optimizer, loss = ..., metrics = ...)  # Metrics will be computed with EMA weights model |> fit(X_train, Y_train,              callbacks = c(callback_swap_ema_weights()))  # If you want to save model checkpoint with EMA weights, you can set # `swap_on_epoch=TRUE` and place ModelCheckpoint after SwapEMAWeights. model |> fit(   X_train, Y_train,   callbacks = c(     callback_swap_ema_weights(swap_on_epoch = TRUE),     callback_model_checkpoint(...)   ) )"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":null,"dir":"Reference","previous_headings":"","what":"Enable visualizations for TensorBoard. — callback_tensorboard","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"TensorBoard visualization tool provided TensorFlow. TensorFlow installation required use callback. callback logs events TensorBoard, including: Metrics summary plots Training graph visualization Weight histograms Sampled profiling used model |> evaluate() regular validation addition epoch summaries, summary records evaluation metrics vs model$optimizer$iterations written. metric names prepended evaluation, model$optimizer$iterations step visualized TensorBoard. installed TensorFlow pip reticulate::py_install(), able launch TensorBoard command line:   R tensorflow::tensorboard(). can find information TensorBoard .","code":"tensorboard --logdir=path_to_your_logs"},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"","code":"callback_tensorboard(   log_dir = \"logs\",   histogram_freq = 0L,   write_graph = TRUE,   write_images = FALSE,   write_steps_per_second = FALSE,   update_freq = \"epoch\",   profile_batch = 0L,   embeddings_freq = 0L,   embeddings_metadata = NULL )"},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"log_dir path directory save log files parsed TensorBoard. e.g., log_dir = file.path(working_dir, 'logs'). directory reused callbacks. histogram_freq frequency (epochs) compute weight histograms layers model. set 0, histograms computed. Validation data (split) must specified histogram visualizations. write_graph (supported time) Whether visualize graph TensorBoard. Note log file can become quite large write_graph set TRUE. write_images whether write model weights visualize image TensorBoard. write_steps_per_second whether log training steps per second TensorBoard. supports epoch batch frequency logging. update_freq \"batch\" \"epoch\" integer. using \"epoch\", writes losses metrics TensorBoard every epoch. using integer, say 1000, metrics losses (including custom ones added Model.compile) logged TensorBoard every 1000 batches. \"batch\" synonym 1, meaning written every batch. Note however writing frequently TensorBoard can slow training, especially used distribution strategies incur additional synchronization overhead. Batch-level summary writing also available via train_step override. Please see TensorBoard Scalars tutorial  # noqa: E501 details. profile_batch (supported time) Profile batch(es) sample compute characteristics. profile_batch must non-negative integer tuple integers. pair positive integers signify range batches profile. default, profiling disabled. embeddings_freq frequency (epochs) embedding layers visualized. set 0, embeddings visualized. embeddings_metadata Named list maps embedding layer names filename file save metadata embedding layer. case metadata file used embedding layers, single filename can passed.","code":""},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"Custom batch-level summaries subclassed Model:   Custom batch-level summaries Functional API Model:   Profiling:","code":"tensorboard_callback <- callback_tensorboard(log_dir = \"./logs\") model %>% fit(x_train, y_train, epochs = 2, callbacks = list(tensorboard_callback)) # Then run the tensorboard command to view the visualizations. MyModel <- new_model_class(\"MyModel\",   initialize = function() {     self$dense <- layer_dense(units = 10)   },   call = function(x) {     outputs <- x |> self$dense()     tf$summary$histogram('outputs', outputs)     outputs   } )  model <- MyModel() model |> compile(optimizer = 'sgd', loss = 'mse')  # Make sure to set `update_freq = N` to log a batch-level summary every N # batches. In addition to any `tf$summary` contained in `model$call()`, # metrics added in `model |>compile` will be logged every N batches. tb_callback <- callback_tensorboard(log_dir = './logs', update_freq = 1) model |> fit(x_train, y_train, callbacks = list(tb_callback)) my_summary <- function(x) {   tf$summary$histogram('x', x)   x }  inputs <- layer_input(10) outputs <- inputs |>   layer_dense(10) |>   layer_lambda(my_summary)  model <- keras_model(inputs, outputs) model |> compile(optimizer = 'sgd', loss = 'mse')  # Make sure to set `update_freq = N` to log a batch-level summary every N # batches. In addition to any `tf.summary` contained in `Model.call`, # metrics added in `Model.compile` will be logged every N batches. tb_callback <- callback_tensorboard(log_dir = './logs', update_freq = 1) model |> fit(x_train, y_train, callbacks = list(tb_callback)) # Profile a single batch, e.g. the 5th batch. tensorboard_callback <- callback_tensorboard(   log_dir = './logs', profile_batch = 5) model |> fit(x_train, y_train, epochs = 2,              callbacks = list(tensorboard_callback))  # Profile a range of batches, e.g. from 10 to 20. tensorboard_callback <- callback_tensorboard(   log_dir = './logs', profile_batch = c(10, 20)) model |> fit(x_train, y_train, epochs = 2,              callbacks = list(tensorboard_callback))"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_terminate_on_nan.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback that terminates training when a NaN loss is encountered. — callback_terminate_on_nan","title":"Callback that terminates training when a NaN loss is encountered. — callback_terminate_on_nan","text":"Callback terminates training NaN loss encountered.","code":""},{"path":"https://keras.posit.co/reference/callback_terminate_on_nan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback that terminates training when a NaN loss is encountered. — callback_terminate_on_nan","text":"","code":"callback_terminate_on_nan()"},{"path":"https://keras.posit.co/reference/callback_terminate_on_nan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback that terminates training when a NaN loss is encountered. — callback_terminate_on_nan","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/clear_session.html","id":null,"dir":"Reference","previous_headings":"","what":"Resets all state generated by Keras. — clear_session","title":"Resets all state generated by Keras. — clear_session","text":"Keras manages global state, uses implement Functional model-building API uniquify autogenerated layer names. creating many models loop, global state consume increasing amount memory time, may want clear . Calling clear_session() releases global state: helps avoid clutter old models layers, especially memory limited. Example 1: calling clear_session() creating models loop   Example 2: resetting layer name generation counter","code":"for (i in 1:100) {   # Without `clear_session()`, each iteration of this loop will   # slightly increase the size of the global state managed by Keras   model <- keras_model_sequential()   for (j in 1:10) {     model <- model |> layer_dense(units = 10)   } }  for (i in 1:100) {   # With `clear_session()` called at the beginning,   # Keras starts with a blank state at each iteration   # and memory consumption is constant over time.   clear_session()   model <- keras_model_sequential()   for (j in 1:10) {     model <- model |> layer_dense(units = 10)   } } layers <- lapply(1:10, \\(i) layer_dense(units = 10))  new_layer <- layer_dense(units = 10) print(new_layer$name) ## [1] \"dense_10\" clear_session() new_layer <- layer_dense(units = 10) print(new_layer$name) ## [1] \"dense\""},{"path":"https://keras.posit.co/reference/clear_session.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resets all state generated by Keras. — clear_session","text":"","code":"clear_session(free_memory = TRUE)"},{"path":"https://keras.posit.co/reference/clear_session.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resets all state generated by Keras. — clear_session","text":"free_memory Whether call Python garbage collection. usually good practice call make sure memory used deleted objects immediately freed. However, may take seconds execute, using clear_session() short loop, may want skip .","code":""},{"path":"https://keras.posit.co/reference/clear_session.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resets all state generated by Keras. — clear_session","text":"NULL, invisibly, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/clone_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Clone a Functional or Sequential Model instance. — clone_model","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"Model cloning similar calling model new inputs, except creates new layers (thus new weights) instead sharing weights existing layers. Note clone_model() preserve uniqueness shared objects within model (e.g. single variable attached two distinct layers restored two separate variables).","code":""},{"path":"https://keras.posit.co/reference/clone_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"","code":"clone_model(   model,   input_tensors = NULL,   clone_function = NULL,   call_function = NULL,   recursive = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/clone_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"model Instance Model (Functional model Sequential model). input_tensors Optional list input tensors build model upon. provided, new keras_input() objects created. clone_function Callable signature function(layer) used clone layer target model (except Input instances). takes argument layer instance cloned, returns corresponding layer instance used model copy. unspecified, callable defaults following serialization/deserialization function: function(layer) layer$`__class__`$from_config(layer$get_config()). passing custom callable, can customize copy model, e.g. wrapping certain layers interest (might want replace LSTM instances equivalent Bidirectional(LSTM(...)) instances, example). Defaults NULL. call_function Callable signature function(layer, ...) used call cloned layer set inputs. takes layer instance, call arguments, returns call outputs. unspecified, callable defaults regular call() method: function(layer, ...) .call(layer, list(...)). passing custom callable, can insert new layers given layer. recursive Note, argument can used Functional models. Boolean. Whether recursively clone Sequential Functional models encountered original Sequential/Functional model. FALSE, inner models cloned calling clone_function(). TRUE, inner models cloned calling clone_model() clone_function, call_function, recursive arguments. Note case, call_function propagated Sequential model (since applicable Sequential models). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/clone_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"instance Model reproducing behavior original model, top new inputs tensors, using newly instantiated weights. cloned model may behave differently original model custom clone_function call_function modifies layer layer call.","code":""},{"path":"https://keras.posit.co/reference/clone_model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"Using clone_function make model deterministic setting random seed everywhere:   Using call_function add Dropout layer Dense layer (without recreating new layers):     Note subclassed models cloned default, since internal layer structure known. achieve equivalent functionality clone_model case subclassed model, simply make sure model class implements get_config() (optionally from_config()), call:   case subclassed model, using custom clone_function.","code":"# Create a test Sequential model. model <- keras_model_sequential(input_shape = c(728)) |>   layer_dense(32, activation = 'relu') |>   layer_dense(1, activation = 'sigmoid')  # Create a copy of the test model (with freshly initialized weights). new_model <- clone_model(model) clone_function <- function(layer) {   config <- layer$get_config()   if (\"seed\" %in% names(config))     config$seed <- 1337L   layer$`__class__`$from_config(config) }  new_model <- clone_model(model, clone_function = clone_function) call_function <- function(layer, ...) {   out <- layer(...)   if (inherits(layer, keras$layers$Dense))     out <- out |> layer_dropout(0.5)   out }  inputs <- keras_input(c(728)) outputs <- inputs |>   layer_dense(32, activation = 'relu') |>   layer_dense(1, activation = 'sigmoid') model <- keras_model(inputs, outputs)  new_model <- clone_model(   model,   clone_function = function(x) x, # Reuse the same layers.   call_function = call_function, ) new_model ## Model: \"functional_5\" ## +-----------------------------------+--------------------------+---------------+ ## | Layer (type)                      | Output Shape             |       Param # | ## +===================================+==========================+===============+ ## | keras_tensor_8 (InputLayer)       | (None, 728)              |             0 | ## +-----------------------------------+--------------------------+---------------+ ## | dense_2 (Dense)                   | (None, 32)               |        23,328 | ## +-----------------------------------+--------------------------+---------------+ ## | dropout (Dropout)                 | (None, 32)               |             0 | ## +-----------------------------------+--------------------------+---------------+ ## | dense_3 (Dense)                   | (None, 1)                |            33 | ## +-----------------------------------+--------------------------+---------------+ ## | dropout_1 (Dropout)               | (None, 1)                |             0 | ## +-----------------------------------+--------------------------+---------------+ ##  Total params: 23,361 (91.25 KB) ##  Trainable params: 23,361 (91.25 KB) ##  Non-trainable params: 0 (0.00 B) new_model <- model$`__class__`$from_config(model$get_config())"},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Configure a model for training. — compile.keras.src.models.model.Model","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"Configure model training.","code":""},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model compile(   object,   optimizer = \"rmsprop\",   loss = NULL,   metrics = NULL,   ...,   loss_weights = NULL,   weighted_metrics = NULL,   run_eagerly = FALSE,   steps_per_execution = 1L,   jit_compile = \"auto\",   auto_scale_loss = TRUE )"},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"object Keras model object optimizer String (name optimizer) optimizer instance. See optimizer_* family. loss Loss function. May : string (name builtin loss function), custom function, Loss instance (returned loss_* family functions). loss function callable signature loss = fn(y_true, y_pred), y_true ground truth values, y_pred model's predictions. y_true shape (batch_size, d1, .. dN) (except case sparse loss functions sparse categorical crossentropy expects integer arrays shape (batch_size, d1, .. dN-1)). y_pred shape (batch_size, d1, .. dN). loss function return float tensor. metrics List metrics evaluated model training testing. can : string (name built-function), function, optionally \"name\" attribute Metric() instance. See metric_* family functions. Typically use metrics = c('accuracy'). function callable signature result = fn(y_true, y_pred). specify different metrics different outputs multi-output model, also pass named list, metrics = list(= 'accuracy', b = c('accuracy', 'mse')). can also pass list specify metric list metrics output, metrics = list(c('accuracy'), c('accuracy', 'mse')) metrics = list('accuracy', c('accuracy', 'mse')). pass strings 'accuracy' 'acc', convert one metric_binary_accuracy(), metric_categorical_accuracy(), metric_sparse_categorical_accuracy() based shapes targets model output. similar conversion done strings \"crossentropy\" \"ce\" well. metrics passed evaluated without sample weighting; like sample weighting apply, can specify metrics via weighted_metrics argument instead. providing anonymous R function, can customize printed name training assigning attr(<fn>, \"name\") <- \"my_custom_metric_name\", calling custom_metric(\"my_custom_metric_name\", <fn>) ... Additional arguments passed compile() model method. loss_weights Optional list (named unnamed) specifying scalar coefficients (R numerics) weight loss contributions different model outputs. loss value minimized model weighted sum individual losses, weighted loss_weights coefficients.  unnamed list, expected 1:1 mapping model's outputs. named list, expected map output names (strings) scalar coefficients. weighted_metrics List metrics evaluated weighted sample_weight class_weight training testing. run_eagerly Bool. TRUE, model's forward pass never compiled. recommended leave FALSE training (best performance), set TRUE debugging. steps_per_execution Int. number batches run single compiled function call. Running multiple batches inside single compiled function call can greatly improve performance TPUs small models large R/Python overhead. , one full epoch run execution. number larger size epoch passed, execution truncated size epoch. Note steps_per_execution set N, Callback$on_batch_begin Callback$on_batch_end methods called every N batches (.e. /compiled function execution). supported PyTorch backend. jit_compile Bool \"auto\". Whether use XLA compilation compiling model. jax tensorflow backends, jit_compile=\"auto\" enables XLA compilation model supports , disabled otherwise. torch backend, \"auto\" default eager execution jit_compile=True run torch.compile \"inductor\" backend. auto_scale_loss Bool. TRUE model dtype policy \"mixed_float16\", passed optimizer automatically wrapped LossScaleOptimizer, dynamically scale loss prevent underflow.","code":""},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"called primarily side effect modifying object -place. first argument object also returned, invisibly, enable usage pipe.","code":""},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"","code":"model |> compile(   optimizer = optimizer_adam(learning_rate = 1e-3),   loss = loss_binary_crossentropy(),   metrics = c(metric_binary_accuracy(),               metric_false_negatives()) )"},{"path":[]},{"path":"https://keras.posit.co/reference/config_backend.html","id":null,"dir":"Reference","previous_headings":"","what":"Publicly accessible method for determining the current backend. — config_backend","title":"Publicly accessible method for determining the current backend. — config_backend","text":"Publicly accessible method determining current backend.","code":""},{"path":"https://keras.posit.co/reference/config_backend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Publicly accessible method for determining the current backend. — config_backend","text":"","code":"config_backend()"},{"path":"https://keras.posit.co/reference/config_backend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Publicly accessible method for determining the current backend. — config_backend","text":"String, name backend Keras currently using. One \"tensorflow\", \"torch\", \"jax\".","code":""},{"path":"https://keras.posit.co/reference/config_backend.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Publicly accessible method for determining the current backend. — config_backend","text":"","code":"config_backend() ## [1] \"tensorflow\""},{"path":[]},{"path":"https://keras.posit.co/reference/config_disable_interactive_logging.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn off interactive logging. — config_disable_interactive_logging","title":"Turn off interactive logging. — config_disable_interactive_logging","text":"interactive logging disabled, Keras sends logs absl.logging. best option using Keras non-interactive way, running training inference job server.","code":""},{"path":"https://keras.posit.co/reference/config_disable_interactive_logging.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn off interactive logging. — config_disable_interactive_logging","text":"","code":"config_disable_interactive_logging()"},{"path":"https://keras.posit.co/reference/config_disable_interactive_logging.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn off interactive logging. — config_disable_interactive_logging","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_disable_traceback_filtering.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn off traceback filtering. — config_disable_traceback_filtering","title":"Turn off traceback filtering. — config_disable_traceback_filtering","text":"Raw Keras tracebacks (also known stack traces) involve many internal frames, can challenging read , actionable end users. default, Keras filters internal frames exceptions raises, keep traceback short, readable, focused actionable (code). See also config_enable_traceback_filtering() config_is_traceback_filtering_enabled(). previously disabled traceback filtering via config_disable_traceback_filtering(), can re-enable via config_enable_traceback_filtering().","code":""},{"path":"https://keras.posit.co/reference/config_disable_traceback_filtering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn off traceback filtering. — config_disable_traceback_filtering","text":"","code":"config_disable_traceback_filtering()"},{"path":"https://keras.posit.co/reference/config_disable_traceback_filtering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn off traceback filtering. — config_disable_traceback_filtering","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_dtype_policy.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the current default dtype policy object. — config_dtype_policy","title":"Returns the current default dtype policy object. — config_dtype_policy","text":"Returns current default dtype policy object.","code":""},{"path":"https://keras.posit.co/reference/config_dtype_policy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the current default dtype policy object. — config_dtype_policy","text":"","code":"config_dtype_policy()"},{"path":"https://keras.posit.co/reference/config_dtype_policy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the current default dtype policy object. — config_dtype_policy","text":"DTypePolicy object.","code":""},{"path":"https://keras.posit.co/reference/config_enable_interactive_logging.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn on interactive logging. — config_enable_interactive_logging","title":"Turn on interactive logging. — config_enable_interactive_logging","text":"interactive logging enabled, Keras displays logs via stdout. provides best experience using Keras interactive environment shell notebook.","code":""},{"path":"https://keras.posit.co/reference/config_enable_interactive_logging.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn on interactive logging. — config_enable_interactive_logging","text":"","code":"config_enable_interactive_logging()"},{"path":"https://keras.posit.co/reference/config_enable_interactive_logging.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn on interactive logging. — config_enable_interactive_logging","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_enable_traceback_filtering.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn on traceback filtering. — config_enable_traceback_filtering","title":"Turn on traceback filtering. — config_enable_traceback_filtering","text":"Raw Keras tracebacks (also known stack traces) involve many internal frames, can challenging read , actionable end users. default, Keras filters internal frames exceptions raises, keep traceback short, readable, focused actionable (code). See also config_disable_traceback_filtering() config_is_traceback_filtering_enabled(). previously disabled traceback filtering via config_disable_traceback_filtering(), can re-enable via config_enable_traceback_filtering().","code":""},{"path":"https://keras.posit.co/reference/config_enable_traceback_filtering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn on traceback filtering. — config_enable_traceback_filtering","text":"","code":"config_enable_traceback_filtering()"},{"path":"https://keras.posit.co/reference/config_enable_traceback_filtering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn on traceback filtering. — config_enable_traceback_filtering","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_enable_unsafe_deserialization.html","id":null,"dir":"Reference","previous_headings":"","what":"Disables safe mode globally, allowing deserialization of lambdas. — config_enable_unsafe_deserialization","title":"Disables safe mode globally, allowing deserialization of lambdas. — config_enable_unsafe_deserialization","text":"Disables safe mode globally, allowing deserialization lambdas.","code":""},{"path":"https://keras.posit.co/reference/config_enable_unsafe_deserialization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Disables safe mode globally, allowing deserialization of lambdas. — config_enable_unsafe_deserialization","text":"","code":"config_enable_unsafe_deserialization()"},{"path":"https://keras.posit.co/reference/config_enable_unsafe_deserialization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Disables safe mode globally, allowing deserialization of lambdas. — config_enable_unsafe_deserialization","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_epsilon.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","title":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","text":"Return value fuzz factor used numeric expressions.","code":""},{"path":"https://keras.posit.co/reference/config_epsilon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","text":"","code":"config_epsilon()"},{"path":"https://keras.posit.co/reference/config_epsilon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","text":"float.","code":""},{"path":"https://keras.posit.co/reference/config_epsilon.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","text":"","code":"config_epsilon() ## [1] 1e-07"},{"path":[]},{"path":"https://keras.posit.co/reference/config_floatx.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the default float type, as a string. — config_floatx","title":"Return the default float type, as a string. — config_floatx","text":"E.g. 'bfloat16' 'float16', 'float32', 'float64'.","code":""},{"path":"https://keras.posit.co/reference/config_floatx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the default float type, as a string. — config_floatx","text":"","code":"config_floatx()"},{"path":"https://keras.posit.co/reference/config_floatx.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the default float type, as a string. — config_floatx","text":"String, current default float type.","code":""},{"path":"https://keras.posit.co/reference/config_floatx.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the default float type, as a string. — config_floatx","text":"","code":"keras3::config_floatx() ## [1] \"float32\""},{"path":[]},{"path":"https://keras.posit.co/reference/config_image_data_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the default image data format convention. — config_image_data_format","title":"Return the default image data format convention. — config_image_data_format","text":"Return default image data format convention.","code":""},{"path":"https://keras.posit.co/reference/config_image_data_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the default image data format convention. — config_image_data_format","text":"","code":"config_image_data_format()"},{"path":"https://keras.posit.co/reference/config_image_data_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the default image data format convention. — config_image_data_format","text":"string, either 'channels_first' 'channels_last'.","code":""},{"path":"https://keras.posit.co/reference/config_image_data_format.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the default image data format convention. — config_image_data_format","text":"","code":"config_image_data_format() ## [1] \"channels_last\""},{"path":[]},{"path":"https://keras.posit.co/reference/config_is_interactive_logging_enabled.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if interactive logging is enabled. — config_is_interactive_logging_enabled","title":"Check if interactive logging is enabled. — config_is_interactive_logging_enabled","text":"switch writing logs stdout absl.logging, may use config_enable_interactive_logging() config_disable_interactive_logging().","code":""},{"path":"https://keras.posit.co/reference/config_is_interactive_logging_enabled.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if interactive logging is enabled. — config_is_interactive_logging_enabled","text":"","code":"config_is_interactive_logging_enabled()"},{"path":"https://keras.posit.co/reference/config_is_interactive_logging_enabled.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if interactive logging is enabled. — config_is_interactive_logging_enabled","text":"Boolean, TRUE interactive logging enabled, FALSE otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_is_traceback_filtering_enabled.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if traceback filtering is enabled. — config_is_traceback_filtering_enabled","title":"Check if traceback filtering is enabled. — config_is_traceback_filtering_enabled","text":"Raw Keras tracebacks (also known stack traces) involve many internal frames, can challenging read , actionable end users. default, Keras filters internal frames exceptions raises, keep traceback short, readable, focused actionable (code). See also config_enable_traceback_filtering() config_disable_traceback_filtering(). previously disabled traceback filtering via config_disable_traceback_filtering(), can re-enable via config_enable_traceback_filtering().","code":""},{"path":"https://keras.posit.co/reference/config_is_traceback_filtering_enabled.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if traceback filtering is enabled. — config_is_traceback_filtering_enabled","text":"","code":"config_is_traceback_filtering_enabled()"},{"path":"https://keras.posit.co/reference/config_is_traceback_filtering_enabled.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if traceback filtering is enabled. — config_is_traceback_filtering_enabled","text":"Boolean, TRUE traceback filtering enabled, FALSE otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":null,"dir":"Reference","previous_headings":"","what":"Reload the backend (and the Keras package). — config_set_backend","title":"Reload the backend (and the Keras package). — config_set_backend","text":"Reload backend (Keras package).","code":""},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reload the backend (and the Keras package). — config_set_backend","text":"","code":"config_set_backend(backend)"},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reload the backend (and the Keras package). — config_set_backend","text":"backend String","code":""},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reload the backend (and the Keras package). — config_set_backend","text":"Nothing, function called side effect.","code":""},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reload the backend (and the Keras package). — config_set_backend","text":"","code":"config_set_backend(\"jax\")"},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"warning","dir":"Reference","previous_headings":"","what":"WARNING","title":"Reload the backend (and the Keras package). — config_set_backend","text":"Using function dangerous done carefully. Changing backend convert type already-instantiated objects. Thus, layers / tensors / etc. already created longer usable without errors. strongly recommended keep around Keras-originated objects instances created calling config_set_backend(). includes function class instance uses Keras functionality. code needs re-executed calling config_set_backend().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets the default dtype policy globally. — config_set_dtype_policy","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"Sets default dtype policy globally.","code":""},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"","code":"config_set_dtype_policy(policy)"},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"policy string DTypePolicy object.","code":""},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"return value, called side effects.","code":""},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"","code":"config_set_dtype_policy(\"mixed_float16\")"},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"Set value fuzz factor used numeric expressions.","code":""},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"","code":"config_set_epsilon(value)"},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"value float. New value epsilon.","code":""},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"return value, called side effects.","code":""},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"","code":"config_epsilon() ## [1] 1e-07 config_set_epsilon(1e-5) config_epsilon() ## [1] 1e-05 # Set it back to the default value. config_set_epsilon(1e-7)"},{"path":[]},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the default float dtype. — config_set_floatx","title":"Set the default float dtype. — config_set_floatx","text":"Set default float dtype.","code":""},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the default float dtype. — config_set_floatx","text":"","code":"config_set_floatx(value)"},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the default float dtype. — config_set_floatx","text":"value String; 'bfloat16', 'float16', 'float32', 'float64'.","code":""},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the default float dtype. — config_set_floatx","text":"return value, called side effects.","code":""},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Set the default float dtype. — config_set_floatx","text":"recommended set \"float16\" training, likely cause numeric stability issues. Instead, mixed precision, leverages mix float16 float32. can configured calling keras3::keras$mixed_precision$set_dtype_policy('mixed_float16').","code":""},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set the default float dtype. — config_set_floatx","text":"","code":"config_floatx() ## [1] \"float32\" config_set_floatx('float64') config_floatx() ## [1] \"float64\" # Set it back to float32 config_set_floatx('float32')"},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"Set the default float dtype. — config_set_floatx","text":"ValueError: case invalid value.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the value of the image data format convention. — config_set_image_data_format","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"Set value image data format convention.","code":""},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"","code":"config_set_image_data_format(data_format)"},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"data_format string. 'channels_first' 'channels_last'.","code":""},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"return value, called side effects.","code":""},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"","code":"config_image_data_format() ## [1] \"channels_last\" # 'channels_last' keras3::config_set_image_data_format('channels_first') config_image_data_format() ## [1] \"channels_first\" # Set it back to `'channels_last'` keras3::config_set_image_data_format('channels_last')"},{"path":[]},{"path":"https://keras.posit.co/reference/constraint_maxnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"MaxNorm weight constraint. — constraint_maxnorm","title":"MaxNorm weight constraint. — constraint_maxnorm","text":"Constrains weights incident hidden unit norm less equal desired value.","code":""},{"path":"https://keras.posit.co/reference/constraint_maxnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MaxNorm weight constraint. — constraint_maxnorm","text":"","code":"constraint_maxnorm(max_value = 2L, axis = 1L)"},{"path":"https://keras.posit.co/reference/constraint_maxnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MaxNorm weight constraint. — constraint_maxnorm","text":"max_value maximum norm value incoming weights. axis integer, axis along calculate weight norms. instance, Dense layer weight matrix shape (input_dim, output_dim), set axis 0 constrain weight vector length (input_dim,). Conv2D layer data_format = \"channels_last\", weight tensor shape (rows, cols, input_depth, output_depth), set axis [0, 1, 2] constrain weights filter tensor size (rows, cols, input_depth).","code":""},{"path":"https://keras.posit.co/reference/constraint_maxnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MaxNorm weight constraint. — constraint_maxnorm","text":"Constraint instance, callable can passed layer constructors used directly calling tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/constraint_minmaxnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"MinMaxNorm weight constraint. — constraint_minmaxnorm","title":"MinMaxNorm weight constraint. — constraint_minmaxnorm","text":"Constrains weights incident hidden unit norm lower bound upper bound.","code":""},{"path":"https://keras.posit.co/reference/constraint_minmaxnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MinMaxNorm weight constraint. — constraint_minmaxnorm","text":"","code":"constraint_minmaxnorm(min_value = 0, max_value = 1, rate = 1, axis = 1L)"},{"path":"https://keras.posit.co/reference/constraint_minmaxnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MinMaxNorm weight constraint. — constraint_minmaxnorm","text":"min_value minimum norm incoming weights. max_value maximum norm incoming weights. rate rate enforcing constraint: weights rescaled yield op_clip? (1 - rate) * norm + rate * op_clip(norm, min_value, max_value). Effectively, means rate = 1.0 stands strict enforcement constraint, rate<1.0 means weights rescaled step slowly move towards value inside desired interval. axis integer, axis along calculate weight norms. instance, Dense layer weight matrix shape (input_dim, output_dim), set axis 0 constrain weight vector length (input_dim,). Conv2D layer data_format = \"channels_last\", weight tensor shape (rows, cols, input_depth, output_depth), set axis [0, 1, 2] constrain weights filter tensor size (rows, cols, input_depth).","code":""},{"path":"https://keras.posit.co/reference/constraint_minmaxnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MinMaxNorm weight constraint. — constraint_minmaxnorm","text":"Constraint instance, callable can passed layer constructors used directly calling tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/constraint_nonneg.html","id":null,"dir":"Reference","previous_headings":"","what":"Constrains the weights to be non-negative. — constraint_nonneg","title":"Constrains the weights to be non-negative. — constraint_nonneg","text":"Constrains weights non-negative.","code":""},{"path":"https://keras.posit.co/reference/constraint_nonneg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constrains the weights to be non-negative. — constraint_nonneg","text":"","code":"constraint_nonneg()"},{"path":"https://keras.posit.co/reference/constraint_nonneg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constrains the weights to be non-negative. — constraint_nonneg","text":"Constraint instance, callable can passed layer constructors used directly calling tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/constraint_unitnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","title":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","text":"Constrains weights incident hidden unit unit norm.","code":""},{"path":"https://keras.posit.co/reference/constraint_unitnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","text":"","code":"constraint_unitnorm(axis = 1L)"},{"path":"https://keras.posit.co/reference/constraint_unitnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","text":"axis integer, axis along calculate weight norms. instance, Dense layer weight matrix shape (input_dim, output_dim), set axis 0 constrain weight vector length (input_dim,). Conv2D layer data_format = \"channels_last\", weight tensor shape (rows, cols, input_depth, output_depth), set axis [0, 1, 2] constrain weights filter tensor size (rows, cols, input_depth).","code":""},{"path":"https://keras.posit.co/reference/constraint_unitnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","text":"Constraint instance, callable can passed layer constructors used directly calling tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/count_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Count the total number of scalars composing the weights. — count_params","title":"Count the total number of scalars composing the weights. — count_params","text":"Count total number scalars composing weights.","code":""},{"path":"https://keras.posit.co/reference/count_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count the total number of scalars composing the weights. — count_params","text":"","code":"count_params(object)"},{"path":"https://keras.posit.co/reference/count_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count the total number of scalars composing the weights. — count_params","text":"object Layer model object","code":""},{"path":"https://keras.posit.co/reference/count_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count the total number of scalars composing the weights. — count_params","text":"integer count","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/custom_metric.html","id":null,"dir":"Reference","previous_headings":"","what":"Custom metric function — custom_metric","title":"Custom metric function — custom_metric","text":"Custom metric function","code":""},{"path":"https://keras.posit.co/reference/custom_metric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Custom metric function — custom_metric","text":"","code":"custom_metric(name, metric_fn)"},{"path":"https://keras.posit.co/reference/custom_metric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Custom metric function — custom_metric","text":"name name used show training progress output metric_fn R function signature function(y_true, y_pred) accepts tensors.","code":""},{"path":"https://keras.posit.co/reference/custom_metric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Custom metric function — custom_metric","text":"callable function __name__ attribute.","code":""},{"path":"https://keras.posit.co/reference/custom_metric.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Custom metric function — custom_metric","text":"can provide arbitrary R function custom metric. Note y_true y_pred parameters tensors, computations use op_* tensor functions. Use custom_metric() function define custom metric. Note name ('mean_pred') provided custom metric function: name used within training progress output. want save load model custom metrics, also call register_keras_serializable(), specify metric call load_model(). example: load_model(\"my_model.keras\", c('mean_pred' = metric_mean_pred)). Alternatively, can wrap code call with_custom_object_scope() allow refer metric name just like built keras metrics. Alternative ways supplying custom metrics: custom_metric(): Arbitrary R function. metric_mean_wrapper(): Wrap arbitrary R function Metric instance. Create custom Metric() subclass.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_boston_housing.html","id":null,"dir":"Reference","previous_headings":"","what":"Boston housing price regression dataset — dataset_boston_housing","title":"Boston housing price regression dataset — dataset_boston_housing","text":"Dataset taken StatLib library maintained Carnegie Mellon University.","code":""},{"path":"https://keras.posit.co/reference/dataset_boston_housing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Boston housing price regression dataset — dataset_boston_housing","text":"","code":"dataset_boston_housing(   path = \"boston_housing.npz\",   test_split = 0.2,   seed = 113L )"},{"path":"https://keras.posit.co/reference/dataset_boston_housing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Boston housing price regression dataset — dataset_boston_housing","text":"path Path cache dataset locally (relative ~/.keras/datasets). test_split fraction data reserve test set. seed Random seed shuffling data computing test split.","code":""},{"path":"https://keras.posit.co/reference/dataset_boston_housing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Boston housing price regression dataset — dataset_boston_housing","text":"Lists training test data: train$x, train$y, test$x, test$y. Samples contain 13 attributes houses different locations around Boston suburbs late 1970s. Targets median values houses location (k$).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_cifar10.html","id":null,"dir":"Reference","previous_headings":"","what":"CIFAR10 small image classification — dataset_cifar10","title":"CIFAR10 small image classification — dataset_cifar10","text":"Dataset 50,000 32x32 color training images, labeled 10 categories, 10,000 test images.","code":""},{"path":"https://keras.posit.co/reference/dataset_cifar10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIFAR10 small image classification — dataset_cifar10","text":"","code":"dataset_cifar10()"},{"path":"https://keras.posit.co/reference/dataset_cifar10.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIFAR10 small image classification — dataset_cifar10","text":"Lists training test data: train$x, train$y, test$x, test$y. x data array RGB image data shape (num_samples, 3, 32, 32). y data array category labels (integers range 0-9) shape (num_samples).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_cifar100.html","id":null,"dir":"Reference","previous_headings":"","what":"CIFAR100 small image classification — dataset_cifar100","title":"CIFAR100 small image classification — dataset_cifar100","text":"Dataset 50,000 32x32 color training images, labeled 100 categories, 10,000 test images.","code":""},{"path":"https://keras.posit.co/reference/dataset_cifar100.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIFAR100 small image classification — dataset_cifar100","text":"","code":"dataset_cifar100(label_mode = c(\"fine\", \"coarse\"))"},{"path":"https://keras.posit.co/reference/dataset_cifar100.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIFAR100 small image classification — dataset_cifar100","text":"label_mode one \"fine\", \"coarse\".","code":""},{"path":"https://keras.posit.co/reference/dataset_cifar100.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIFAR100 small image classification — dataset_cifar100","text":"Lists training test data: train$x, train$y, test$x, test$y. x data array RGB image data shape (num_samples, 3, 32, 32). y data array category labels shape (num_samples).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_fashion_mnist.html","id":null,"dir":"Reference","previous_headings":"","what":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","title":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","text":"Dataset 60,000 28x28 grayscale images 10 fashion article classes, along test set 10,000 images. dataset can used drop-replacement MNIST. class labels encoded integers 0-9 correspond T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt,","code":""},{"path":"https://keras.posit.co/reference/dataset_fashion_mnist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","text":"","code":"dataset_fashion_mnist()"},{"path":"https://keras.posit.co/reference/dataset_fashion_mnist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","text":"Lists training test data: train$x, train$y, test$x, test$y, x array grayscale image data shape (num_samples, 28, 28) y array article labels (integers range 0-9) shape (num_samples).","code":""},{"path":"https://keras.posit.co/reference/dataset_fashion_mnist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","text":"Dataset 60,000 28x28 grayscale images 10 fashion categories, along test set 10,000 images. dataset can used drop-replacement MNIST. class labels : 0 - T-shirt/top 1 - Trouser 2 - Pullover 3 - Dress 4 - Coat 5 - Sandal 6 - Shirt 7 - Sneaker 8 - Bag 9 - Ankle boot","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":null,"dir":"Reference","previous_headings":"","what":"IMDB Movie reviews sentiment classification — dataset_imdb","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"Dataset 25,000 movies reviews IMDB, labeled sentiment (positive/negative). Reviews preprocessed, review encoded sequence word indexes (integers). convenience, words indexed overall frequency dataset, instance integer \"3\" encodes 3rd frequent word data. allows quick filtering operations : \"consider top 10,000 common words, eliminate top 20 common words\".","code":""},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"","code":"dataset_imdb(   path = \"imdb.npz\",   num_words = NULL,   skip_top = 0L,   maxlen = NULL,   seed = 113L,   start_char = 1L,   oov_char = 2L,   index_from = 3L )  dataset_imdb_word_index(path = \"imdb_word_index.json\")"},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"path cache data (relative ~/.keras/dataset). num_words Max number words include. Words ranked often occur (training set) frequent words kept skip_top Skip top N frequently occuring words (may informative). maxlen sequences longer filtered . seed random seed sample shuffling. start_char start sequence marked character. Set 1 0 usually padding character. oov_char Words cut num_words skip_top limit replaced character. index_from Index actual words index higher.","code":""},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"Lists training test data: train$x, train$y, test$x, test$y. x data includes integer sequences. num_words argument specific, maximum possible index value num_words-1. maxlen argument specified, largest possible sequence length maxlen. y data includes set integer labels (0 1). dataset_imdb_word_index() function returns list names words values integer.","code":""},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"convention, \"0\" stand specific word, instead used encode unknown word.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_mnist.html","id":null,"dir":"Reference","previous_headings":"","what":"MNIST database of handwritten digits — dataset_mnist","title":"MNIST database of handwritten digits — dataset_mnist","text":"Dataset 60,000 28x28 grayscale images 10 digits, along test set 10,000 images.","code":""},{"path":"https://keras.posit.co/reference/dataset_mnist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MNIST database of handwritten digits — dataset_mnist","text":"","code":"dataset_mnist(path = \"mnist.npz\")"},{"path":"https://keras.posit.co/reference/dataset_mnist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MNIST database of handwritten digits — dataset_mnist","text":"path Path cache dataset locally (relative ~/.keras/datasets).","code":""},{"path":"https://keras.posit.co/reference/dataset_mnist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MNIST database of handwritten digits — dataset_mnist","text":"Lists training test data: train$x, train$y, test$x, test$y, x array grayscale image data shape (num_samples, 28, 28) y array digit labels (integers range 0-9) shape (num_samples).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_reuters.html","id":null,"dir":"Reference","previous_headings":"","what":"Reuters newswire topics classification — dataset_reuters","title":"Reuters newswire topics classification — dataset_reuters","text":"Dataset 11,228 newswires Reuters, labeled 46 topics. dataset_imdb() , wire encoded sequence word indexes (conventions).","code":""},{"path":"https://keras.posit.co/reference/dataset_reuters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reuters newswire topics classification — dataset_reuters","text":"","code":"dataset_reuters(   path = \"reuters.npz\",   num_words = NULL,   skip_top = 0L,   maxlen = NULL,   test_split = 0.2,   seed = 113L,   start_char = 1L,   oov_char = 2L,   index_from = 3L )  dataset_reuters_word_index(path = \"reuters_word_index.pkl\")"},{"path":"https://keras.posit.co/reference/dataset_reuters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reuters newswire topics classification — dataset_reuters","text":"path cache data (relative ~/.keras/dataset). num_words Max number words include. Words ranked often occur (training set) frequent words kept skip_top Skip top N frequently occuring words (may informative). maxlen Truncate sequences length. test_split Fraction dataset used test data. seed Random seed sample shuffling. start_char start sequence marked character. Set 1 0 usually padding character. oov_char words cut num_words skip_top limit replaced character. index_from index actual words index higher.","code":""},{"path":"https://keras.posit.co/reference/dataset_reuters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reuters newswire topics classification — dataset_reuters","text":"Lists training test data: train$x, train$y, test$x, test$y format dataset_imdb(). dataset_reuters_word_index() function returns list names words values integer. e.g. word_index[[\"giraffe\"]] might return 1234.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/deserialize_keras_object.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","title":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","text":"config dict Python dictionary consists set key-value pairs, represents Keras object, Optimizer, Layer, Metrics, etc. saving loading library uses following keys record information Keras object: class_name: String. name class, exactly defined source code, \"LossesContainer\". config: Named List. Library-defined user-defined key-value pairs store configuration object, obtained object$get_config(). module: String. path python module. Built-Keras classes expect prefix keras. registered_name: String. key class registered via register_keras_serializable(package, name) API. key format '{package}>{name}', package name arguments passed register_keras_serializable(). name provided, uses class name. registered_name successfully resolves class (registered), class_name config values config dict used. registered_name used non-built-classes. example, following config list represents built-Adam optimizer relevant config:     class exported Keras namespace, library tracks module class_name. example:   following config represents user-customized MeanSquaredError loss:","code":"config <- list(   class_name = \"Adam\",   config = list(     amsgrad = FALSE,     beta_1 = 0.8999999761581421,     beta_2 = 0.9990000128746033,     epsilon = 1e-07,     learning_rate = 0.0010000000474974513,     name = \"Adam\"   ),   module = \"keras.optimizers\",   registered_name = NULL ) # Returns an `Adam` instance identical to the original one. deserialize_keras_object(config) ## <keras.src.optimizers.adam.Adam object> config <- list(   class_name = \"MetricsList\",   config =  list(     ...   ),   module = \"keras.trainers.compile_utils\",   registered_name = \"MetricsList\" )  # Returns a `MetricsList` instance identical to the original one. deserialize_keras_object(config) # define a custom object loss_modified_mse <- Loss(   \"ModifiedMeanSquaredError\",   inherit = loss_mean_squared_error)  # register the custom object register_keras_serializable(loss_modified_mse)  # confirm object is registered get_custom_objects() ## $`keras3>ModifiedMeanSquaredError` ## <class '<r-namespace:keras3>.ModifiedMeanSquaredError'> ##  signature: (reduction='sum_over_batch_size', name='mean_squared_error') get_registered_name(loss_modified_mse) ## [1] \"keras3>ModifiedMeanSquaredError\" # now custom object instances can be serialized full_config <- serialize_keras_object(loss_modified_mse())  # the `config` arguments will be passed to loss_modified_mse() str(full_config) ## List of 4 ##  $ module         : chr \"<r-namespace:keras3>\" ##  $ class_name     : chr \"ModifiedMeanSquaredError\" ##  $ config         :List of 2 ##   ..$ name     : chr \"mean_squared_error\" ##   ..$ reduction: chr \"sum_over_batch_size\" ##  $ registered_name: chr \"keras3>ModifiedMeanSquaredError\" # and custom object instances can be deserialized deserialize_keras_object(full_config) ## <<r-namespace:keras3>.ModifiedMeanSquaredError object> ##  signature: (y_true, y_pred, sample_weight=None) # Returns the `ModifiedMeanSquaredError` object"},{"path":"https://keras.posit.co/reference/deserialize_keras_object.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","text":"","code":"deserialize_keras_object(config, custom_objects = NULL, safe_mode = TRUE, ...)"},{"path":"https://keras.posit.co/reference/deserialize_keras_object.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","text":"config Named list describing object. custom_objects Named list containing mapping custom object names corresponding classes functions. safe_mode Boolean, whether disallow unsafe lambda deserialization. safe_mode=FALSE, loading object potential trigger arbitrary code execution. argument applicable Keras v3 model format. Defaults TRUE. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/deserialize_keras_object.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","text":"object described config dictionary.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/evaluate.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","title":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","text":"functions returns loss value metrics values model test mode. Computation done batches (see batch_size arg.)","code":""},{"path":"https://keras.posit.co/reference/evaluate.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model evaluate(   object,   x = NULL,   y = NULL,   ...,   batch_size = NULL,   verbose = getOption(\"keras.verbose\", default = \"auto\"),   sample_weight = NULL,   steps = NULL,   callbacks = NULL )"},{"path":"https://keras.posit.co/reference/evaluate.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","text":"object Keras model object x Input data. : R array (array-like), list arrays (case model multiple inputs). tensor, list tensors (case model multiple inputs). named list mapping input names corresponding array/tensors, model named inputs. tf.data.Dataset. return tuple either (inputs, targets) (inputs, targets, sample_weights). generator returning (inputs, targets) (inputs, targets, sample_weights). y Target data. Like input data x, either R array(s) backend-native tensor(s). x tf.data.Dataset generator function, y specified (since targets obtained iterator/dataset). ... forward/backward compatability. batch_size Integer NULL. Number samples per batch computation. unspecified, batch_size default 32. specify batch_size data form tf dataset generator (since generate batches). verbose \"auto\", 0, 1, 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. \"auto\" becomes 1 cases, 2 knitr render running distributed training server. Note progress bar particularly useful logged file, verbose=2 recommended running interactively (e.g. production environment). Defaults \"auto\". sample_weight Optional array weights test samples, used weighting loss function. can either pass flat (1D) R array length input samples (1:1 mapping weights samples), case temporal data, can pass 2D array shape (samples, sequence_length), apply different weight every timestep every sample. argument supported x tfdataset, instead pass sample weights third element x. steps Integer NULL. Total number steps (batches samples) declaring evaluation round finished. Ignored default value NULL. x tf.data.Dataset steps NULL, evaluation run dataset exhausted. callbacks List Callback instances. List callbacks apply evaluation.","code":""},{"path":"https://keras.posit.co/reference/evaluate.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","text":"Scalar test loss (model single output metrics) list scalars (model multiple outputs /metrics). attribute model$metrics_names give display labels scalar outputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"(e.g. via TF-Serving). Note: can currently used TensorFlow JAX backends. method lets export model lightweight SavedModel artifact contains model's forward pass (call() method) can served via e.g. TF-Serving. forward pass registered name serve() (see example ). original code model (including custom layers may used) longer necessary reload artifact -- entirely standalone.","code":""},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model export_savedmodel(object, export_dir_base, ...)"},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"object keras model. export_dir_base string, file path save artifact. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"called primarily side effect exporting object. first argument, object also returned, invisibly, enable usage pipe.","code":""},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"","code":"# Create the artifact model |> tensorflow::export_savedmodel(\"path/to/location\")  # Later, in a different process / environment... library(tensorflow) reloaded_artifact <- tf$saved_model$load(\"path/to/location\") predictions <- reloaded_artifact$serve(input_data)  # see tfdeploy::serve_savedmodel() for serving a model over a local web api."},{"path":[]},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"Train model fixed number epochs (dataset iterations).","code":""},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model fit(   object,   x = NULL,   y = NULL,   ...,   batch_size = NULL,   epochs = 1L,   callbacks = NULL,   validation_split = 0,   validation_data = NULL,   shuffle = TRUE,   class_weight = NULL,   sample_weight = NULL,   initial_epoch = 1L,   steps_per_epoch = NULL,   validation_steps = NULL,   validation_batch_size = NULL,   validation_freq = 1L,   verbose = getOption(\"keras.verbose\", default = \"auto\"),   view_metrics = getOption(\"keras.view_metrics\", default = \"auto\") )"},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"object Keras model object x Input data. : array (array-like), list arrays (case model multiple inputs). tensor, list tensors (case model multiple inputs). named list mapping input names corresponding array/tensors, model named inputs. tf.data.Dataset. return tuple either (inputs, targets) (inputs, targets, sample_weights). generator returning (inputs, targets) (inputs, targets, sample_weights). y Target data. Like input data x, either array(s) backend-native tensor(s). x TF Dataset generator, y specified (since targets obtained x). ... Additional arguments passed model fit() method. batch_size Integer NULL. Number samples per gradient update. unspecified, batch_size default 32. specify batch_size data form TF Datasets generators, (since generate batches). epochs Integer. Number epochs train model. epoch iteration entire x y data provided (unless steps_per_epoch flag set something NULL). Note conjunction initial_epoch, epochs understood \"final epoch\". model trained number iterations given epochs, merely epoch index epochs reached. callbacks List Callback() instances. List callbacks apply training. See callback_*. validation_split Float 0 1. Fraction training data used validation data. model set apart fraction training data, train , evaluate loss model metrics data end epoch. validation data selected last samples x y data provided, shuffling. argument supported x TF Dataset generator. validation_data validation_split provided, validation_data override validation_split. validation_data Data evaluate loss model metrics end epoch. model trained data. Thus, note fact validation loss data provided using validation_split validation_data affected regularization layers like noise dropout. validation_data override validation_split. : tuple (x_val, y_val) arrays tensors. tuple (x_val, y_val, val_sample_weights) arrays. generator returning (inputs, targets) (inputs, targets, sample_weights). shuffle Boolean, whether shuffle training data epoch. argument ignored x generator TF Dataset. class_weight Optional named list mapping class indices (integers, 0-based) weight (float) value, used weighting loss function (training ). can useful tell model \"pay attention\" samples -represented class. class_weight specified targets rank 2 greater, either y must one-hot encoded, explicit final dimension 1 must included sparse class labels. sample_weight Optional array weights training samples, used weighting loss function (training ). can either pass flat (1D) array/vector length input samples (1:1 mapping weights samples), case temporal data, can pass 2D array (matrix) shape (samples, sequence_length), apply different weight every timestep every sample. argument supported x TF Dataset generator, instead provide sample_weights third element x. Note sample weighting apply metrics specified via metrics argument compile(). apply sample weighting metrics, can specify via weighted_metrics compile() instead. initial_epoch Integer. Epoch start training (useful resuming previous training run). steps_per_epoch Integer NULL. Total number steps (batches samples) declaring one epoch finished starting next epoch. training input tensors backend-native tensors, default NULL equal number samples dataset divided batch size, 1 determined. x TF Dataset, steps_per_epoch NULL, epoch run input dataset exhausted.  passing infinitely repeating dataset, must specify steps_per_epoch argument. steps_per_epoch = -1 training run indefinitely infinitely repeating dataset. validation_steps relevant validation_data provided. Total number steps (batches samples) draw stopping performing validation end every epoch. validation_steps NULL, validation run validation_data dataset exhausted. case infinitely repeated dataset, run infinite loop. validation_steps specified part dataset consumed, evaluation start beginning dataset epoch. ensures validation samples used every time. validation_batch_size Integer NULL. Number samples per validation batch. unspecified, default batch_size. specify validation_batch_size data form TF Datasets generator instances (since generate batches). validation_freq relevant validation data provided. Specifies many training epochs run new validation run performed, e.g. validation_freq=2 runs validation every 2 epochs. verbose \"auto\", 0, 1, 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. \"auto\" becomes 1 cases, 2 knitr render running distributed training server. Note progress bar particularly useful logged file, verbose=2 recommended running interactively (e.g., production environment). Defaults \"auto\". view_metrics View realtime plot training metrics (epoch). default (\"auto\") display plot running within RStudio, metrics specified model compile(), epochs > 1 verbose > 0. Set global options(keras.view_metrics = ) option establish different default.","code":""},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"keras_training_history object, named list: list(params = <params>, metrics = <metrics>\"), S3 methods print(), plot(), .data.frame(). metrics field record training loss values metrics values successive epochs, well validation loss values validation metrics values (applicable).","code":""},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"Unpacking behavior iterator-like inputs: common pattern pass iterator like object tf.data.Dataset generator fit(), fact yield features (x) optionally targets (y) sample weights (sample_weight). Keras requires output iterator-likes unambiguous. iterator return tuple() length 1, 2, 3, optional second third elements used y sample_weight respectively. type provided wrapped length-one tuple(), effectively treating everything x. yielding named lists, still adhere top-level tuple structure, e.g. tuple(list(x0 = x0, x = x1), y). Keras attempt separate features, targets, weights keys single dict.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Freeze and unfreeze weights — freeze_weights","title":"Freeze and unfreeze weights — freeze_weights","text":"Freeze weights model layer longer trainable.","code":""},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Freeze and unfreeze weights — freeze_weights","text":"","code":"freeze_weights(object, from = NULL, to = NULL, which = NULL)  unfreeze_weights(object, from = NULL, to = NULL, which = NULL)"},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Freeze and unfreeze weights — freeze_weights","text":"object Keras model layer object Layer instance, layer name, layer index within model Layer instance, layer name, layer index within model layer names, integer positions, layers, logical vector (length(object$layers)), function returning logical vector.","code":""},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Freeze and unfreeze weights — freeze_weights","text":"input object frozen weights returned, invisibly. Note, object modified place, return value provided make usage pipe convenient.","code":""},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Freeze and unfreeze weights — freeze_weights","text":"layer arguments inclusive. applied model, freeze unfreeze global operation layers model (.e. layers within specified range set opposite value, e.g. unfrozen call freeze). Models must compiled weights frozen unfrozen.","code":""},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Freeze and unfreeze weights — freeze_weights","text":"","code":"# instantiate a VGG16 model conv_base <- application_vgg16(   weights = \"imagenet\",   include_top = FALSE,   input_shape = c(150, 150, 3) )  # freeze it's weights freeze_weights(conv_base)  # Note the \"Trainable\" column conv_base ## Model: \"vgg16\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | input_layer (InputLayer)    | (None, 150, 150, 3)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv1 (Conv2D)       | (None, 150, 150, 64)  |      1,792 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv2 (Conv2D)       | (None, 150, 150, 64)  |     36,928 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_pool (MaxPooling2D)  | (None, 75, 75, 64)    |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv1 (Conv2D)       | (None, 75, 75, 128)   |     73,856 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv2 (Conv2D)       | (None, 75, 75, 128)   |    147,584 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_pool (MaxPooling2D)  | (None, 37, 37, 128)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv1 (Conv2D)       | (None, 37, 37, 256)   |    295,168 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv2 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv3 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_pool (MaxPooling2D)  | (None, 18, 18, 256)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv1 (Conv2D)       | (None, 18, 18, 512)   |  1,180,160 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv2 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv3 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_pool (MaxPooling2D)  | (None, 9, 9, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv1 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv2 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv3 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_pool (MaxPooling2D)  | (None, 4, 4, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 14,714,688 (56.13 MB) ##  Trainable params: 0 (0.00 B) ##  Non-trainable params: 14,714,688 (56.13 MB) # create a composite model that includes the base + more layers model <- keras_model_sequential(input_batch_shape = shape(conv_base$input)) |>   conv_base() |>   layer_flatten() |>   layer_dense(units = 256, activation = \"relu\") |>   layer_dense(units = 1, activation = \"sigmoid\")  # compile model |> compile(   loss = \"binary_crossentropy\",   optimizer = optimizer_rmsprop(learning_rate = 2e-5),   metrics = c(\"accuracy\") )  model ## Model: \"sequential\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | vgg16 (Functional)          | (None, 4, 4, 512)     | 14,714,688 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | flatten (Flatten)           | (None, 8192)          |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense (Dense)               | (None, 256)           |  2,097,408 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense_1 (Dense)             | (None, 1)             |        257 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 16,812,353 (64.13 MB) ##  Trainable params: 2,097,665 (8.00 MB) ##  Non-trainable params: 14,714,688 (56.13 MB) print(model, expand_nested = TRUE) ## Model: \"sequential\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | vgg16 (Functional)          | (None, 4, 4, 512)     | 14,714,688 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > input_layer            | (None, 150, 150, 3)   |          0 |   -   | ## | (InputLayer)                |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_conv1 (Conv2D)  | (None, 150, 150, 64)  |      1,792 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_conv2 (Conv2D)  | (None, 150, 150, 64)  |     36,928 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_pool            | (None, 75, 75, 64)    |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_conv1 (Conv2D)  | (None, 75, 75, 128)   |     73,856 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_conv2 (Conv2D)  | (None, 75, 75, 128)   |    147,584 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_pool            | (None, 37, 37, 128)   |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv1 (Conv2D)  | (None, 37, 37, 256)   |    295,168 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv2 (Conv2D)  | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv3 (Conv2D)  | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_pool            | (None, 18, 18, 256)   |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv1 (Conv2D)  | (None, 18, 18, 512)   |  1,180,160 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv2 (Conv2D)  | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv3 (Conv2D)  | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_pool            | (None, 9, 9, 512)     |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv1 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv2 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv3 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_pool            | (None, 4, 4, 512)     |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## | flatten (Flatten)           | (None, 8192)          |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense (Dense)               | (None, 256)           |  2,097,408 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense_1 (Dense)             | (None, 1)             |        257 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 16,812,353 (64.13 MB) ##  Trainable params: 2,097,665 (8.00 MB) ##  Non-trainable params: 14,714,688 (56.13 MB) # unfreeze weights from \"block5_conv1\" on unfreeze_weights(conv_base, from = \"block5_conv1\")  # compile again since we froze or unfroze weights model |> compile(   loss = \"binary_crossentropy\",   optimizer = optimizer_rmsprop(learning_rate = 2e-5),   metrics = c(\"accuracy\") )  conv_base ## Model: \"vgg16\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | input_layer (InputLayer)    | (None, 150, 150, 3)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv1 (Conv2D)       | (None, 150, 150, 64)  |      1,792 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv2 (Conv2D)       | (None, 150, 150, 64)  |     36,928 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_pool (MaxPooling2D)  | (None, 75, 75, 64)    |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv1 (Conv2D)       | (None, 75, 75, 128)   |     73,856 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv2 (Conv2D)       | (None, 75, 75, 128)   |    147,584 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_pool (MaxPooling2D)  | (None, 37, 37, 128)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv1 (Conv2D)       | (None, 37, 37, 256)   |    295,168 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv2 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv3 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_pool (MaxPooling2D)  | (None, 18, 18, 256)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv1 (Conv2D)       | (None, 18, 18, 512)   |  1,180,160 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv2 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv3 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_pool (MaxPooling2D)  | (None, 9, 9, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv1 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv2 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv3 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_pool (MaxPooling2D)  | (None, 4, 4, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 14,714,688 (56.13 MB) ##  Trainable params: 7,079,424 (27.01 MB) ##  Non-trainable params: 7,635,264 (29.13 MB) print(model, expand_nested = TRUE) ## Model: \"sequential\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | vgg16 (Functional)          | (None, 4, 4, 512)     | 14,714,688 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > input_layer            | (None, 150, 150, 3)   |          0 |   -   | ## | (InputLayer)                |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_conv1 (Conv2D)  | (None, 150, 150, 64)  |      1,792 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_conv2 (Conv2D)  | (None, 150, 150, 64)  |     36,928 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_pool            | (None, 75, 75, 64)    |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_conv1 (Conv2D)  | (None, 75, 75, 128)   |     73,856 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_conv2 (Conv2D)  | (None, 75, 75, 128)   |    147,584 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_pool            | (None, 37, 37, 128)   |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv1 (Conv2D)  | (None, 37, 37, 256)   |    295,168 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv2 (Conv2D)  | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv3 (Conv2D)  | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_pool            | (None, 18, 18, 256)   |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv1 (Conv2D)  | (None, 18, 18, 512)   |  1,180,160 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv2 (Conv2D)  | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv3 (Conv2D)  | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_pool            | (None, 9, 9, 512)     |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv1 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv2 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv3 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_pool            | (None, 4, 4, 512)     |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## | flatten (Flatten)           | (None, 8192)          |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense (Dense)               | (None, 256)           |  2,097,408 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense_1 (Dense)             | (None, 1)             |        257 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 16,812,353 (64.13 MB) ##  Trainable params: 9,177,089 (35.01 MB) ##  Non-trainable params: 7,635,264 (29.13 MB) # freeze only the last 5 layers freeze_weights(conv_base, from = -5) conv_base ## Model: \"vgg16\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | input_layer (InputLayer)    | (None, 150, 150, 3)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv1 (Conv2D)       | (None, 150, 150, 64)  |      1,792 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv2 (Conv2D)       | (None, 150, 150, 64)  |     36,928 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_pool (MaxPooling2D)  | (None, 75, 75, 64)    |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv1 (Conv2D)       | (None, 75, 75, 128)   |     73,856 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv2 (Conv2D)       | (None, 75, 75, 128)   |    147,584 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_pool (MaxPooling2D)  | (None, 37, 37, 128)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv1 (Conv2D)       | (None, 37, 37, 256)   |    295,168 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv2 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv3 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_pool (MaxPooling2D)  | (None, 18, 18, 256)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv1 (Conv2D)       | (None, 18, 18, 512)   |  1,180,160 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv2 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv3 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_pool (MaxPooling2D)  | (None, 9, 9, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv1 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv2 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv3 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_pool (MaxPooling2D)  | (None, 4, 4, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 14,714,688 (56.13 MB) ##  Trainable params: 7,635,264 (29.13 MB) ##  Non-trainable params: 7,079,424 (27.01 MB) # freeze only the last 5 layers, a different way unfreeze_weights(conv_base, to = -6) conv_base ## Model: \"vgg16\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | input_layer (InputLayer)    | (None, 150, 150, 3)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv1 (Conv2D)       | (None, 150, 150, 64)  |      1,792 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv2 (Conv2D)       | (None, 150, 150, 64)  |     36,928 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_pool (MaxPooling2D)  | (None, 75, 75, 64)    |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv1 (Conv2D)       | (None, 75, 75, 128)   |     73,856 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv2 (Conv2D)       | (None, 75, 75, 128)   |    147,584 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_pool (MaxPooling2D)  | (None, 37, 37, 128)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv1 (Conv2D)       | (None, 37, 37, 256)   |    295,168 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv2 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv3 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_pool (MaxPooling2D)  | (None, 18, 18, 256)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv1 (Conv2D)       | (None, 18, 18, 512)   |  1,180,160 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv2 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv3 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_pool (MaxPooling2D)  | (None, 9, 9, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv1 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv2 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv3 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_pool (MaxPooling2D)  | (None, 4, 4, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 14,714,688 (56.13 MB) ##  Trainable params: 7,635,264 (29.13 MB) ##  Non-trainable params: 7,079,424 (27.01 MB) # Freeze only layers of a certain type, e.g, BatchNorm layers batch_norm_layer_class_name <- class(layer_batch_normalization())[1] is_batch_norm_layer <- function(x) inherits(x, batch_norm_layer_class_name)  model <- application_efficientnet_b0() freeze_weights(model, which = is_batch_norm_layer) # print(model)  # equivalent to: for(layer in model$layers) {   if(is_batch_norm_layer(layer))     layer$trainable <- FALSE   else     layer$trainable <- TRUE }"},{"path":"https://keras.posit.co/reference/get_config.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer/Model configuration — get_config","title":"Layer/Model configuration — get_config","text":"layer config object returned get_config() contains configuration layer model. layer model can reinstantiated later (without trained weights) configuration using from_config(). config include connectivity information, class name (handled externally).","code":""},{"path":"https://keras.posit.co/reference/get_config.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer/Model configuration — get_config","text":"","code":"get_config(object)  from_config(config, custom_objects = NULL)"},{"path":"https://keras.posit.co/reference/get_config.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer/Model configuration — get_config","text":"object Layer model object config Object layer model configuration custom_objects list custom objects needed instantiate layer, e.g., custom layers defined new_layer_class() similar.","code":""},{"path":"https://keras.posit.co/reference/get_config.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer/Model configuration — get_config","text":"get_config() returns object configuration, from_config() returns re-instantiation object.","code":""},{"path":"https://keras.posit.co/reference/get_config.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layer/Model configuration — get_config","text":"Objects returned get_config() serializable via RDS. want save restore model across sessions, can use save_model_config() (model configuration , weights) save_model() save model configuration weights filesystem.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":null,"dir":"Reference","previous_headings":"","what":"Get/set the currently registered custom objects. — get_custom_objects","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"Custom objects set using custom_object_scope() added global list custom objects, appear returned list.","code":""},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"","code":"get_custom_objects()  set_custom_objects(objects = named_list(), clear = TRUE)"},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"objects named list custom objects, returned get_custom_objects() set_custom_objects(). clear bool, whether clear custom object registry populating objects.","code":""},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"R named list mapping registered names registered objects. set_custom_objects() returns registry values updating, invisibly.","code":""},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"register_keras_serializable() preferred set_custom_objects() registering new objects.","code":""},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"can use set_custom_objects() restore previous registry state.","code":"get_custom_objects() # within a function, if you want to temporarily modify the registry, function() {   orig_objects <- set_custom_objects(clear = TRUE)   on.exit(set_custom_objects(orig_objects))    ## temporarily modify the global registry   # register_keras_serializable(....)   # ....  <do work>   # on.exit(), the previous registry state is restored. }"},{"path":[]},{"path":"https://keras.posit.co/reference/get_file.html","id":null,"dir":"Reference","previous_headings":"","what":"Downloads a file from a URL if it not already in the cache. — get_file","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"default file url origin downloaded cache_dir ~/.keras, placed cache_subdir datasets, given filename fname. final location file example.txt therefore ~/.keras/datasets/example.txt. Files .tar, .tar.gz, .tar.bz, .zip formats can also extracted. Passing hash verify file download. command line programs shasum sha256sum can compute hash.","code":""},{"path":"https://keras.posit.co/reference/get_file.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"","code":"get_file(   fname = NULL,   origin = NULL,   ...,   file_hash = NULL,   cache_subdir = \"datasets\",   hash_algorithm = \"auto\",   extract = FALSE,   archive_format = \"auto\",   cache_dir = NULL,   force_download = FALSE )"},{"path":"https://keras.posit.co/reference/get_file.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"fname Name file. absolute path, e.g. \"/path//file.txt\" specified, file saved location. NULL, name file origin used. origin Original URL file. ... forward/backward compatability. file_hash expected hash string file download. sha256 md5 hash algorithms supported. cache_subdir Subdirectory Keras cache dir file saved. absolute path, e.g. \"/path//folder\" specified, file saved location. hash_algorithm Select hash algorithm verify file. options \"md5', \"sha256', \"auto'. default 'auto' detects hash algorithm use. extract TRUE tries extracting file Archive, like tar zip. archive_format Archive format try extracting file. Options \"auto', \"tar', \"zip', NULL. \"tar\" includes tar, tar.gz, tar.bz files. default \"auto\" corresponds c(\"tar\", \"zip\"). NULL empty list return matches found. cache_dir Location store cached files, NULL defaults Sys.getenv(\"KERAS_HOME\", \"~/.keras/\"). force_download TRUE, file always re-downloaded regardless cache state.","code":""},{"path":"https://keras.posit.co/reference/get_file.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"Path downloaded file. ** Warning malicious downloads ** Downloading something Internet carries risk. NEVER download file/archive trust source. recommend specify file_hash argument (hash source file known) make sure file getting one expect.","code":""},{"path":"https://keras.posit.co/reference/get_file.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"","code":"path_to_downloaded_file <- get_file(     origin = \"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\",     extract = TRUE )"},{"path":[]},{"path":"https://keras.posit.co/reference/get_layer.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieves a layer based on either its name (unique) or index. — get_layer","title":"Retrieves a layer based on either its name (unique) or index. — get_layer","text":"Indices based order horizontal graph traversal (bottom-) 1-based. name index provided, index take precedence.","code":""},{"path":"https://keras.posit.co/reference/get_layer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieves a layer based on either its name (unique) or index. — get_layer","text":"","code":"get_layer(object, name = NULL, index = NULL)"},{"path":"https://keras.posit.co/reference/get_layer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieves a layer based on either its name (unique) or index. — get_layer","text":"object Keras model object name String, name layer. index Integer, index layer (1-based). Also valid negative values, count end model.","code":""},{"path":"https://keras.posit.co/reference/get_layer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieves a layer based on either its name (unique) or index. — get_layer","text":"layer instance.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/get_registered_name.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the name registered to an object within the Keras framework. — get_registered_name","title":"Returns the name registered to an object within the Keras framework. — get_registered_name","text":"function part Keras serialization deserialization framework. maps objects string names associated objects serialization/deserialization.","code":""},{"path":"https://keras.posit.co/reference/get_registered_name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the name registered to an object within the Keras framework. — get_registered_name","text":"","code":"get_registered_name(obj)"},{"path":"https://keras.posit.co/reference/get_registered_name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the name registered to an object within the Keras framework. — get_registered_name","text":"obj object look .","code":""},{"path":"https://keras.posit.co/reference/get_registered_name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the name registered to an object within the Keras framework. — get_registered_name","text":"name associated object, default name object registered.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the class associated with name if it is registered with Keras. — get_registered_object","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"function part Keras serialization deserialization framework. maps strings objects associated serialization/deserialization.","code":""},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"","code":"get_registered_object(name, custom_objects = NULL, module_objects = NULL)"},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"name name look . custom_objects named list custom objects look name . Generally, custom_objects provided user. module_objects named list custom objects look name . Generally, module_objects provided midlevel library implementers.","code":""},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"instantiable class associated name, NULL class exists.","code":""},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"","code":"from_config <- function(cls, config, custom_objects = NULL) {   if ('my_custom_object_name' \\%in\\% names(config)) {     config$hidden_cls <- get_registered_object(       config$my_custom_object_name,       custom_objects = custom_objects)   } }"},{"path":[]},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"Output always list tensors (potentially 1 element).","code":""},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"","code":"get_source_inputs(tensor)"},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"tensor tensor start .","code":""},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"List input tensors.","code":""},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"","code":"input <- keras_input(c(3)) output <- input |> layer_dense(4) |> op_multiply(5) reticulate::py_id(get_source_inputs(output)[[1]]) == reticulate::py_id(input) ## [1] TRUE"},{"path":[]},{"path":"https://keras.posit.co/reference/get_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer/Model weights as R arrays — get_weights","title":"Layer/Model weights as R arrays — get_weights","text":"Layer/Model weights R arrays","code":""},{"path":"https://keras.posit.co/reference/get_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer/Model weights as R arrays — get_weights","text":"","code":"get_weights(object, trainable = NA)  set_weights(object, weights)"},{"path":"https://keras.posit.co/reference/get_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer/Model weights as R arrays — get_weights","text":"object Layer model object trainable NA (default), weights returned. TRUE, weights trainable variables returned. FALSE, weights non-trainable variables returned. weights Weights R array","code":""},{"path":"https://keras.posit.co/reference/get_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer/Model weights as R arrays — get_weights","text":"list R arrays.","code":""},{"path":"https://keras.posit.co/reference/get_weights.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layer/Model weights as R arrays — get_weights","text":"can access Layer/Model KerasVariables (also backend-native tensors like tf.Variable) object$weights, object$trainable_weights, object$non_trainable_weights","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":null,"dir":"Reference","previous_headings":"","what":"Make a python class constructor — %py_class%","title":"Make a python class constructor — %py_class%","text":"Make python class constructor","code":""},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make a python class constructor — %py_class%","text":"","code":"spec %py_class% body"},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make a python class constructor — %py_class%","text":"spec bare symbol MyClassName, call MyClassName(SuperClass) body expression can evaluated construct class methods.","code":""},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make a python class constructor — %py_class%","text":"python class constructor, invisibly. Note, constructor also assigned parent frame.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make a python class constructor — %py_class%","text":"","code":"if (FALSE) { MyClass %py_class% {   initialize <- function(x) {     print(\"Hi from MyClass$initialize()!\")     self$x <- x   }   my_method <- function() {     self$x   } }  my_class_instance <- MyClass(42) my_class_instance$my_method()  MyClass2(MyClass) %py_class% {   \"This will be a __doc__ string for MyClass2\"    initialize <- function(...) {     \"This will be the __doc__ string for the MyClass2.__init__() method\"     print(\"Hi from MyClass2$initialize()!\")     super$initialize(...)   } }  my_class_instance2 <- MyClass2(42) my_class_instance2$my_method()  reticulate::py_help(MyClass2) # see the __doc__ strings and more!  # In addition to `self`, there is also `private` available. # This is an R environment unique to each class instance, where you can # store objects that you don't want converted to Python, but still want # available from methods. You can also assign methods to private, and # `self` and `private` will be available in private methods.  MyClass %py_class% {    initialize <- function(x) {     print(\"Hi from MyClass$initialize()!\")     private$y <- paste(\"A Private field:\", x)   }    get_private_field <- function() {     private$y   }    private$a_private_method <- function() {     cat(\"a_private_method() was called.\\n\")     cat(\"private$y is \", sQuote(private$y), \"\\n\")   }    call_private_method <- function()     private$a_private_method()    # equivalent of @property decorator in python   an_active_property %<-active% function(x = NULL) {     if(!is.null(x)) {       cat(\"`an_active_property` was assigned\", x, \"\\n\")       return(x)     } else {       cat(\"`an_active_property` was accessed\\n\")       return(42)     }   } }  inst1 <- MyClass(1) inst2 <- MyClass(2) inst1$get_private_field() inst2$get_private_field() inst1$call_private_method() inst2$call_private_method() inst1$an_active_property inst1$an_active_property <- 11 }"},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":null,"dir":"Reference","previous_headings":"","what":"Make an Active Binding — %<-active%","title":"Make an Active Binding — %<-active%","text":"Make Active Binding","code":""},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make an Active Binding — %<-active%","text":"","code":"sym %<-active% value"},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make an Active Binding — %<-active%","text":"sym symbol bind value function call value sym accessed.","code":""},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make an Active Binding — %<-active%","text":"value, invisibly","code":""},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make an Active Binding — %<-active%","text":"Active bindings defined %py_class% converted @property decorated methods.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make an Active Binding — %<-active%","text":"","code":"set.seed(1234) x %<-active% function(value) {   message(\"Evaluating function of active binding\")   if(missing(value))     runif(1)   else    message(\"Received: \", value) } x #> Evaluating function of active binding #> [1] 0.1137034 x #> Evaluating function of active binding #> [1] 0.6222994 x <- \"foo\" #> Evaluating function of active binding #> Received: foo x <- \"foo\" #> Evaluating function of active binding #> Received: foo x #> Evaluating function of active binding #> [1] 0.6092747 rm(x) # cleanup"},{"path":"https://keras.posit.co/reference/image_array_save.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves an image stored as an array to a path or file object. — image_array_save","title":"Saves an image stored as an array to a path or file object. — image_array_save","text":"Saves image stored array path file object.","code":""},{"path":"https://keras.posit.co/reference/image_array_save.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves an image stored as an array to a path or file object. — image_array_save","text":"","code":"image_array_save(   x,   path,   data_format = NULL,   file_format = NULL,   scale = TRUE,   ... )"},{"path":"https://keras.posit.co/reference/image_array_save.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves an image stored as an array to a path or file object. — image_array_save","text":"x array. path Path file object. data_format Image data format, either \"channels_first\" \"channels_last\". file_format Optional file format override. omitted, format use determined filename extension. file object used instead filename, parameter always used. scale Whether rescale image values within [0, 255]. ... Additional keyword arguments passed PIL.Image.save().","code":""},{"path":"https://keras.posit.co/reference/image_array_save.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Saves an image stored as an array to a path or file object. — image_array_save","text":"Called primarily side effects. input x returned, invisibly, enable usage pipe.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/image_dataset_from_directory.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","title":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","text":"directory structure :   calling image_dataset_from_directory(main_directory, labels = 'inferred') return tf.data.Dataset yields batches images subdirectories class_a class_b, together labels 0 1 (0 corresponding class_a 1 corresponding class_b). Supported image formats: .jpeg, .jpg, .png, .bmp, .gif. Animated gifs truncated first frame.","code":"main_directory/ ...class_a/ ......a_image_1.jpg ......a_image_2.jpg ...class_b/ ......b_image_1.jpg ......b_image_2.jpg"},{"path":"https://keras.posit.co/reference/image_dataset_from_directory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","text":"","code":"image_dataset_from_directory(   directory,   labels = \"inferred\",   label_mode = \"int\",   class_names = NULL,   color_mode = \"rgb\",   batch_size = 32L,   image_size = c(256L, 256L),   shuffle = TRUE,   seed = NULL,   validation_split = NULL,   subset = NULL,   interpolation = \"bilinear\",   follow_links = FALSE,   crop_to_aspect_ratio = FALSE,   pad_to_aspect_ratio = FALSE,   data_format = NULL,   verbose = TRUE )"},{"path":"https://keras.posit.co/reference/image_dataset_from_directory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","text":"directory Directory data located. labels \"inferred\", contain subdirectories, containing images class. Otherwise, directory structure ignored. labels Either \"inferred\" (labels generated directory structure), NULL (labels), list/tuple integer labels size number image files found directory. Labels sorted according alphanumeric order image file paths (obtained via os.walk(directory) Python). label_mode String describing encoding labels. Options : \"int\": means labels encoded integers (e.g. sparse_categorical_crossentropy loss). \"categorical\" means labels encoded categorical vector (e.g. categorical_crossentropy loss). \"binary\" means labels (can 2) encoded float32 scalars values 0 1 (e.g. binary_crossentropy). NULL (labels). class_names valid labels \"inferred\". explicit list class names (must match names subdirectories). Used control order classes (otherwise alphanumerical order used). color_mode One \"grayscale\", \"rgb\", \"rgba\". Defaults \"rgb\". Whether images converted 1, 3, 4 channels. batch_size Size batches data. Defaults 32. NULL, data batched (dataset yield individual samples). image_size Size resize images read disk, specified (height, width). Defaults (256, 256). Since pipeline processes batches images must size, must provided. shuffle Whether shuffle data. Defaults TRUE. set FALSE, sorts data alphanumeric order. seed Optional random seed shuffling transformations. validation_split Optional float 0 1, fraction data reserve validation. subset Subset data return. One \"training\", \"validation\", \"\". used validation_split set. subset = \"\", utility returns tuple two datasets (training validation datasets respectively). interpolation String, interpolation method used resizing images. Defaults \"bilinear\". Supports \"bilinear\", \"nearest\", \"bicubic\", \"area\", \"lanczos3\", \"lanczos5\", \"gaussian\", \"mitchellcubic\". follow_links Whether visit subdirectories pointed symlinks. Defaults FALSE. crop_to_aspect_ratio TRUE, resize images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image cropped return largest possible window image (size image_size) matches target aspect ratio. default (crop_to_aspect_ratio = FALSE), aspect ratio may preserved. pad_to_aspect_ratio TRUE, resize images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image padded return largest possible window image (size image_size) matches target aspect ratio. default (pad_to_aspect_ratio=FALSE), aspect ratio may preserved. data_format NULL uses config_image_data_format() otherwise either 'channel_last' 'channel_first'. verbose Whether display number information classes number files found. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/image_dataset_from_directory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","text":"tf.data.Dataset object. label_mode NULL, yields float32 tensors shape (batch_size, image_size[1], image_size[2], num_channels), encoding images (see rules regarding num_channels). Otherwise, yields tuple (images, labels), images shape (batch_size, image_size[1], image_size[2], num_channels), labels follows format described . Rules regarding labels format: label_mode \"int\", labels int32 tensor shape (batch_size,). label_mode \"binary\", labels float32 tensor 1s 0s shape (batch_size, 1). label_mode \"categorical\", labels float32 tensor shape (batch_size, num_classes), representing one-hot encoding class index. Rules regarding number channels yielded images: color_mode \"grayscale\", 1 channel image tensors. color_mode \"rgb\", 3 channels image tensors. color_mode \"rgba\", 4 channels image tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/image_from_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts a 3D array to a PIL Image instance. — image_from_array","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"Converts 3D array PIL Image instance.","code":""},{"path":"https://keras.posit.co/reference/image_from_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"","code":"image_from_array(x, data_format = NULL, scale = TRUE, dtype = NULL)"},{"path":"https://keras.posit.co/reference/image_from_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"x Input data, form can converted array. data_format Image data format, can either \"channels_first\" \"channels_last\". Defaults NULL, case global setting config_image_data_format() used (unless changed , defaults \"channels_last\"). scale Whether rescale image minimum maximum values 0 255 respectively. Defaults TRUE. dtype Dtype use. NULL means global setting config_floatx() used (unless changed , defaults \"float32\"). Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/image_from_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"PIL Image instance.","code":""},{"path":"https://keras.posit.co/reference/image_from_array.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"","code":"img <- array(runif(30000), dim = c(100, 100, 3)) pil_img <- image_from_array(img) pil_img ## <PIL.Image.Image image mode=RGB size=100x100>"},{"path":[]},{"path":"https://keras.posit.co/reference/image_load.html","id":null,"dir":"Reference","previous_headings":"","what":"Loads an image into PIL format. — image_load","title":"Loads an image into PIL format. — image_load","text":"Loads image PIL format.","code":""},{"path":"https://keras.posit.co/reference/image_load.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loads an image into PIL format. — image_load","text":"","code":"image_load(   path,   color_mode = \"rgb\",   target_size = NULL,   interpolation = \"nearest\",   keep_aspect_ratio = FALSE )"},{"path":"https://keras.posit.co/reference/image_load.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loads an image into PIL format. — image_load","text":"path Path image file. color_mode One \"grayscale\", \"rgb\", \"rgba\". Default: \"rgb\". desired image format. target_size Either NULL (default original size) tuple ints (img_height, img_width). interpolation Interpolation method used resample image target size different loaded image. Supported methods \"nearest\", \"bilinear\", \"bicubic\". PIL version 1.1.3 newer installed, \"lanczos\" also supported. PIL version 3.4.0 newer installed, \"box\" \"hamming\" also supported. default, \"nearest\" used. keep_aspect_ratio Boolean, whether resize images target size without aspect ratio distortion. image cropped center target aspect ratio resizing.","code":""},{"path":"https://keras.posit.co/reference/image_load.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loads an image into PIL format. — image_load","text":"PIL Image instance.","code":""},{"path":"https://keras.posit.co/reference/image_load.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Loads an image into PIL format. — image_load","text":"","code":"image_path <- get_file(origin = \"https://www.r-project.org/logo/Rlogo.png\") (image <- image_load(image_path)) ## <PIL.Image.Image image mode=RGB size=724x561> input_arr <- image_to_array(image) str(input_arr) ##  num [1:561, 1:724, 1:3] 0 0 0 0 0 0 0 0 0 0 ... input_arr %<>% array_reshape(dim = c(1, dim(input_arr))) # Convert single image to a batch. model |> predict(input_arr)"},{"path":[]},{"path":"https://keras.posit.co/reference/image_smart_resize.html","id":null,"dir":"Reference","previous_headings":"","what":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","title":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","text":"Image datasets typically yield images different size. However, images need batched can processed Keras layers. batched, images need share height width. simply , TF (JAX equivalent):   However, , distort aspect ratio images, since general aspect ratio size. fine many cases, always (e.g. image generation models can problem). Note passing argument preserve_aspect_ratio = TRUE tf$image$resize() preserve aspect ratio, cost longer respecting provided target size. calls :   output images actually (200, 200), distorted. Instead, parts image fit within target size get cropped . resizing process : Take largest centered crop image aspect ratio target size. instance, size = c(200, 200) input image size (340, 500), take crop (340, 340) centered along width. Resize cropped image target size. example , resize (340, 340) crop (200, 200).","code":"size <- c(200, 200) ds <- ds$map(\\(img) tf$image$resize(img, size)) size <- c(200, 200) ds <- ds$map(\\(img) image_smart_resize(img, size))"},{"path":"https://keras.posit.co/reference/image_smart_resize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","text":"","code":"image_smart_resize(   x,   size,   interpolation = \"bilinear\",   data_format = \"channels_last\",   backend_module = NULL )"},{"path":"https://keras.posit.co/reference/image_smart_resize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","text":"x Input image batch images (tensor array). Must format (height, width, channels) (batch_size, height, width, channels). size Tuple (height, width) integer. Target size. interpolation String, interpolation use resizing. Defaults 'bilinear'. Supports bilinear, nearest, bicubic, lanczos3, lanczos5. data_format \"channels_last\" \"channels_first\". backend_module Backend module use (different default backend).","code":""},{"path":"https://keras.posit.co/reference/image_smart_resize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","text":"Array shape (size[1], size[2], channels). input image array, output array, backend-native tensor, output backend-native tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/image_to_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts a PIL Image instance to a matrix. — image_to_array","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"Converts PIL Image instance matrix.","code":""},{"path":"https://keras.posit.co/reference/image_to_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"","code":"image_to_array(img, data_format = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/image_to_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"img Input PIL Image instance. data_format Image data format, can either \"channels_first\" \"channels_last\". Defaults NULL, case global setting config_image_data_format() used (unless changed , defaults \"channels_last\"). dtype Dtype use. NULL means global setting config_floatx() used (unless changed , defaults \"float32\").","code":""},{"path":"https://keras.posit.co/reference/image_to_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"3D array.","code":""},{"path":"https://keras.posit.co/reference/image_to_array.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"","code":"image_path <- get_file(origin = \"https://www.r-project.org/logo/Rlogo.png\") (img <- image_load(image_path)) ## <PIL.Image.Image image mode=RGB size=724x561> array <- image_to_array(img) str(array) ##  num [1:561, 1:724, 1:3] 0 0 0 0 0 0 0 0 0 0 ..."},{"path":[]},{"path":"https://keras.posit.co/reference/imagenet_decode_predictions.html","id":null,"dir":"Reference","previous_headings":"","what":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","title":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","text":"Decodes prediction ImageNet model.","code":""},{"path":"https://keras.posit.co/reference/imagenet_decode_predictions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","text":"","code":"imagenet_decode_predictions(preds, top = 5)"},{"path":"https://keras.posit.co/reference/imagenet_decode_predictions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","text":"preds Tensor encoding batch predictions. top integer, many top-guesses return.","code":""},{"path":"https://keras.posit.co/reference/imagenet_decode_predictions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","text":"List data frames variables class_name, class_description, score (one data frame per sample batch input).","code":""},{"path":"https://keras.posit.co/reference/imagenet_preprocess_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","title":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","text":"Preprocesses tensor array encoding batch images.","code":""},{"path":"https://keras.posit.co/reference/imagenet_preprocess_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","text":"","code":"imagenet_preprocess_input(x, data_format = NULL, mode = \"caffe\")"},{"path":"https://keras.posit.co/reference/imagenet_preprocess_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","text":"x Input Numpy symbolic tensor, 3D 4D. data_format Data format image tensor/array. mode One \"caffe\", \"tf\", \"torch\" caffe: convert images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling. tf: scale pixels -1 1, sample-wise. torch: scale pixels 0 1 normalize channel respect ImageNet dataset.","code":""},{"path":"https://keras.posit.co/reference/imagenet_preprocess_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","text":"Preprocessed tensor array.","code":""},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates tensors with constant values. — initializer_constant","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"scalar values allowed. constant value provided must convertible dtype requested calling initializer.","code":""},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"","code":"initializer_constant(value = 0)"},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"value numeric scalar.","code":""},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"","code":"# Standalone usage: initializer <- initializer_constant(10) values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_constant(10) layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"Draws samples truncated normal distribution centered 0 stddev = sqrt(2 / (fan_in + fan_out)) fan_in number input units weight tensor fan_out number output units weight tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"","code":"initializer_glorot_normal(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"","code":"# Standalone usage: initializer <- initializer_glorot_normal() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_glorot_normal() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"Glorot et al., 2010","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"Draws samples uniform distribution within [-limit, limit], limit = sqrt(6 / (fan_in + fan_out)) (fan_in number input units weight tensor fan_out number output units).","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"","code":"initializer_glorot_uniform(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"","code":"# Standalone usage: initializer <- initializer_glorot_uniform() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_glorot_uniform() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"Glorot et al., 2010","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"He normal initializer. — initializer_he_normal","title":"He normal initializer. — initializer_he_normal","text":"draws samples truncated normal distribution centered 0 stddev = sqrt(2 / fan_in) fan_in number input units weight tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"He normal initializer. — initializer_he_normal","text":"","code":"initializer_he_normal(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"He normal initializer. — initializer_he_normal","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"He normal initializer. — initializer_he_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"He normal initializer. — initializer_he_normal","text":"","code":"# Standalone usage: initializer <- initializer_he_normal() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_he_normal() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"He normal initializer. — initializer_he_normal","text":"et al., 2015","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"He uniform variance scaling initializer. — initializer_he_uniform","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"Draws samples uniform distribution within [-limit, limit], limit = sqrt(6 / fan_in) (fan_in number input units weight tensor).","code":""},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"","code":"initializer_he_uniform(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"","code":"# Standalone usage: initializer <- initializer_he_uniform() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_he_uniform() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"et al., 2015","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates the identity matrix. — initializer_identity","title":"Initializer that generates the identity matrix. — initializer_identity","text":"usable generating 2D matrices.","code":""},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates the identity matrix. — initializer_identity","text":"","code":"initializer_identity(gain = 1)"},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that generates the identity matrix. — initializer_identity","text":"gain Multiplicative factor apply identity matrix.","code":""},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates the identity matrix. — initializer_identity","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates the identity matrix. — initializer_identity","text":"","code":"# Standalone usage: initializer <- initializer_identity() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_identity() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Lecun normal initializer. — initializer_lecun_normal","title":"Lecun normal initializer. — initializer_lecun_normal","text":"Initializers allow pre-specify initialization strategy, encoded Initializer object, without knowing shape dtype variable initialized. Draws samples truncated normal distribution centered 0 stddev = sqrt(1 / fan_in) fan_in number input units weight tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lecun normal initializer. — initializer_lecun_normal","text":"","code":"initializer_lecun_normal(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lecun normal initializer. — initializer_lecun_normal","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Lecun normal initializer. — initializer_lecun_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lecun normal initializer. — initializer_lecun_normal","text":"","code":"# Standalone usage: initializer <- initializer_lecun_normal() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_lecun_normal() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Lecun normal initializer. — initializer_lecun_normal","text":"Klambauer et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"Lecun uniform initializer. — initializer_lecun_uniform","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"Draws samples uniform distribution within [-limit, limit], limit = sqrt(3 / fan_in) (fan_in number input units weight tensor).","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"","code":"initializer_lecun_uniform(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"","code":"# Standalone usage: initializer <- initializer_lecun_uniform() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_lecun_uniform() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"Klambauer et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_ones.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates tensors initialized to 1. — initializer_ones","title":"Initializer that generates tensors initialized to 1. — initializer_ones","text":"Also available via shortcut function ones.","code":""},{"path":"https://keras.posit.co/reference/initializer_ones.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates tensors initialized to 1. — initializer_ones","text":"","code":"initializer_ones()"},{"path":"https://keras.posit.co/reference/initializer_ones.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates tensors initialized to 1. — initializer_ones","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_ones.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates tensors initialized to 1. — initializer_ones","text":"","code":"# Standalone usage: initializer <- initializer_ones() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_ones() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"shape tensor initialize two-dimensional, initialized orthogonal matrix obtained QR decomposition matrix random numbers drawn normal distribution. matrix fewer rows columns output orthogonal rows. Otherwise, output orthogonal columns. shape tensor initialize two-dimensional, matrix shape (shape[1] * ... * shape[n - 1], shape[n]) initialized, n length shape vector. matrix subsequently reshaped give tensor desired shape.","code":""},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"","code":"initializer_orthogonal(gain = 1, seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"gain Multiplicative factor apply orthogonal matrix. seed integer. Used make behavior initializer deterministic.","code":""},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"","code":"# Standalone usage: initializer <- initializer_orthogonal() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_orthogonal() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"Saxe et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Random normal initializer. — initializer_random_normal","title":"Random normal initializer. — initializer_random_normal","text":"Draws samples normal distribution given parameters.","code":""},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random normal initializer. — initializer_random_normal","text":"","code":"initializer_random_normal(mean = 0, stddev = 0.05, seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random normal initializer. — initializer_random_normal","text":"mean numeric scalar. Mean random values generate. stddev numeric scalar. Standard deviation random values generate. seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random normal initializer. — initializer_random_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random normal initializer. — initializer_random_normal","text":"","code":"# Standalone usage: initializer <- initializer_random_normal(mean = 0.0, stddev = 1.0) values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_random_normal(mean = 0.0, stddev = 1.0) layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"Random uniform initializer. — initializer_random_uniform","title":"Random uniform initializer. — initializer_random_uniform","text":"Draws samples uniform distribution given parameters.","code":""},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random uniform initializer. — initializer_random_uniform","text":"","code":"initializer_random_uniform(minval = -0.05, maxval = 0.05, seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random uniform initializer. — initializer_random_uniform","text":"minval numeric scalar scalar keras tensor. Lower bound range random values generate (inclusive). maxval numeric scalar scalar keras tensor. Upper bound range random values generate (exclusive). seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random uniform initializer. — initializer_random_uniform","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random uniform initializer. — initializer_random_uniform","text":"","code":"# Standalone usage: initializer <- initializer_random_uniform(minval = 0.0, maxval = 1.0) values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_random_uniform(minval = 0.0, maxval = 1.0) layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"values generated similar values RandomNormal initializer, except values two standard deviations mean discarded re-drawn.","code":""},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"","code":"initializer_truncated_normal(mean = 0, stddev = 0.05, seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"mean numeric scalar. Mean random values generate. stddev numeric scalar. Standard deviation random values generate. seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"","code":"# Standalone usage: initializer <- initializer_truncated_normal(mean = 0, stddev = 1) values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_truncated_normal(mean = 0, stddev = 1) layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"distribution = \"truncated_normal\" \"untruncated_normal\", samples drawn truncated/untruncated normal distribution mean zero standard deviation (truncation, used) stddev = sqrt(scale / n), n : number input units weight tensor, mode = \"fan_in\" number output units, mode = \"fan_out\" average numbers input output units, mode = \"fan_avg\" distribution = \"uniform\", samples drawn uniform distribution within [-limit, limit], limit = sqrt(3 * scale / n).","code":""},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"","code":"initializer_variance_scaling(   scale = 1,   mode = \"fan_in\",   distribution = \"truncated_normal\",   seed = NULL )"},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"scale Scaling factor (positive float). mode One \"fan_in\", \"fan_out\", \"fan_avg\". distribution Random distribution use. One \"truncated_normal\", \"untruncated_normal\", \"uniform\". seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"","code":"# Standalone usage: initializer <- initializer_variance_scaling(scale = 0.1, mode = 'fan_in',                                             distribution = 'uniform') values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_variance_scaling(scale = 0.1, mode = 'fan_in',                                             distribution = 'uniform') layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_zeros.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates tensors initialized to 0. — initializer_zeros","title":"Initializer that generates tensors initialized to 0. — initializer_zeros","text":"Initializer generates tensors initialized 0.","code":""},{"path":"https://keras.posit.co/reference/initializer_zeros.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates tensors initialized to 0. — initializer_zeros","text":"","code":"initializer_zeros()"},{"path":"https://keras.posit.co/reference/initializer_zeros.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates tensors initialized to 0. — initializer_zeros","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_zeros.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates tensors initialized to 0. — initializer_zeros","text":"","code":"# Standalone usage: initializer <- initializer_zeros() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_zeros() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/install_keras.html","id":null,"dir":"Reference","previous_headings":"","what":"Install Keras — install_keras","title":"Install Keras — install_keras","text":"function install Keras along selected backend, including Python dependencies.","code":""},{"path":"https://keras.posit.co/reference/install_keras.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Install Keras — install_keras","text":"","code":"install_keras(   envname = \"r-keras\",   ...,   extra_packages = c(\"scipy\", \"pandas\", \"Pillow\", \"pydot\", \"ipython\",     \"tensorflow_datasets\"),   python_version = \">=3.9,<=3.11\",   backend = c(\"tensorflow\", \"jax\"),   gpu = NA,   restart_session = TRUE )"},{"path":"https://keras.posit.co/reference/install_keras.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Install Keras — install_keras","text":"envname Name path Python virtual environment ... reserved future compatibility. extra_packages Additional Python packages install alongside Keras python_version Passed reticulate::virtualenv_starter() backend backend(s) install. Accepted values include \"tensorflow\", \"jax\" \"torch\" gpu whether install GPU capable version backend. restart_session Whether restart R session installing (note occur within RStudio).","code":""},{"path":"https://keras.posit.co/reference/install_keras.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Install Keras — install_keras","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/keras.html","id":null,"dir":"Reference","previous_headings":"","what":"Main Keras module — keras","title":"Main Keras module — keras","text":"keras module object equivalent reticulate::import(\"keras\") provided mainly convenience.","code":""},{"path":"https://keras.posit.co/reference/keras.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Main Keras module — keras","text":"object class python.builtin.module","code":""},{"path":"https://keras.posit.co/reference/keras.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Main Keras module — keras","text":"keras Python module","code":""},{"path":"https://keras.posit.co/reference/keras3-package.html","id":null,"dir":"Reference","previous_headings":"","what":"keras3: R Interface to 'Keras' — keras3-package","title":"keras3: R Interface to 'Keras' — keras3-package","text":"Interface 'Keras' https://keras.io, high-level neural networks API. 'Keras' developed focus enabling fast experimentation, supports convolution based networks recurrent networks (well combinations two), runs seamlessly CPU GPU devices. Keras high-level neural networks API, developed focus enabling fast experimentation. Keras following key features:","code":""},{"path":"https://keras.posit.co/reference/keras3-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"keras3: R Interface to 'Keras' — keras3-package","text":"Allows code run CPU GPU, seamlessly. User-friendly API makes easy quickly prototype deep learning models. Built-support convolutional networks (computer vision), recurrent networks (sequence processing), combination . Supports arbitrary network architectures: multi-input multi-output models, layer sharing, model sharing, etc. means Keras appropriate building essentially deep learning model, memory network neural Turing machine. capable running top multiple back-ends including TensorFlow, Jax, PyTorch. See package website https://keras.posit.co complete documentation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/keras3-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"keras3: R Interface to 'Keras' — keras3-package","text":"Maintainer: Tomasz Kalinowski tomasz@posit.co [copyright holder] Authors: JJ Allaire [copyright holder] François Chollet [copyright holder] contributors: Daniel Falbel daniel@posit.co [contributor, copyright holder] Posit Software, PBC [copyright holder, funder] Google [copyright holder, funder] Yuan Tang terrytangyuan@gmail.com (ORCID) [contributor, copyright holder] Wouter Van Der Bijl [contributor, copyright holder] Martin Studer [contributor, copyright holder] Sigrid Keydana [contributor]","code":""},{"path":"https://keras.posit.co/reference/keras_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Keras tensor (Functional API input). — keras_input","title":"Create a Keras tensor (Functional API input). — keras_input","text":"Keras tensor symbolic tensor-like object, augment certain attributes allow us build Keras model just knowing inputs outputs model. instance, , b c Keras tensors, becomes possible : model <- keras_model(input = c(, b), output = c)","code":""},{"path":"https://keras.posit.co/reference/keras_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Keras tensor (Functional API input). — keras_input","text":"","code":"keras_input(   shape = NULL,   batch_size = NULL,   dtype = NULL,   sparse = NULL,   batch_shape = NULL,   name = NULL,   tensor = NULL )"},{"path":"https://keras.posit.co/reference/keras_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Keras tensor (Functional API input). — keras_input","text":"shape shape list (list integers NULL objects), including batch size. instance, shape = c(32) indicates expected input batches 32-dimensional vectors. Elements list can NULL NA; NULL/NA elements represent dimensions shape known may vary (e.g. sequence length). batch_size Optional static batch size (integer). dtype data type expected input, string (e.g. \"float32\", \"int32\"...) sparse boolean specifying whether expected input sparse tensors. Note , sparse FALSE, sparse tensors can still passed input - densified default value 0. feature supported TensorFlow backend. Defaults FALSE. batch_shape Shape, including batch dim. name Optional name string layer. unique model (reuse name twice). autogenerated provided. tensor Optional existing tensor wrap Input layer. set, layer use tensor rather creating new placeholder tensor.","code":""},{"path":"https://keras.posit.co/reference/keras_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Keras tensor (Functional API input). — keras_input","text":"Keras tensor, can passed inputs argument (keras_model()).","code":""},{"path":"https://keras.posit.co/reference/keras_input.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Keras tensor (Functional API input). — keras_input","text":"","code":"# This is a logistic regression in Keras input <- layer_input(shape=c(32)) output <- input |> layer_dense(16, activation='softmax') model <- keras_model(input, output)"},{"path":[]},{"path":"https://keras.posit.co/reference/keras_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Keras Model (Functional API) — keras_model","title":"Keras Model (Functional API) — keras_model","text":"model directed acyclic graph layers.","code":""},{"path":"https://keras.posit.co/reference/keras_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keras Model (Functional API) — keras_model","text":"","code":"keras_model(inputs = NULL, outputs = NULL, ...)"},{"path":"https://keras.posit.co/reference/keras_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keras Model (Functional API) — keras_model","text":"inputs Input tensor(s) (keras_input()) outputs Output tensors (calling layers inputs) ... additional arguments","code":""},{"path":"https://keras.posit.co/reference/keras_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keras Model (Functional API) — keras_model","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/keras_model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Keras Model (Functional API) — keras_model","text":"","code":"library(keras3)  # input tensor inputs <- keras_input(shape = c(784))  # outputs compose input + dense layers predictions <- inputs |>   layer_dense(units = 64, activation = 'relu') |>   layer_dense(units = 64, activation = 'relu') |>   layer_dense(units = 10, activation = 'softmax')  # create and compile model model <- keras_model(inputs = inputs, outputs = predictions) model |> compile(   optimizer = 'rmsprop',   loss = 'categorical_crossentropy',   metrics = c('accuracy') )"},{"path":[]},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":null,"dir":"Reference","previous_headings":"","what":"Keras Model composed of a linear stack of layers — keras_model_sequential","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"Keras Model composed linear stack layers","code":""},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"","code":"keras_model_sequential(   input_shape = NULL,   name = NULL,   ...,   input_dtype = NULL,   input_batch_size = NULL,   input_sparse = NULL,   input_batch_shape = NULL,   input_name = NULL,   input_tensor = NULL,   trainable = TRUE,   layers = list() )"},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"input_shape shape integer vector, including batch size. instance, shape=c(32) indicates expected input batches 32-dimensional vectors. Elements shape can NA; NA elements represent dimensions shape known may vary (e.g. sequence length). name Name model ... additional arguments passed keras.layers.InputLayer. input_dtype data type expected input, string (e.g. \"float32\", \"int32\"...) input_batch_size Optional static batch size (integer). input_sparse boolean specifying whether expected input sparse tensors. Note , sparse FALSE, sparse tensors can still passed input - densified default value 0. feature supported TensorFlow backend. Defaults FALSE. input_batch_shape optional way specify batch_size input_shape one argument. input_name Optional name string input layer. unique model (reuse name twice). autogenerated provided. input_tensor Optional existing tensor wrap InputLayer. set, layer use tensor rather creating new placeholder tensor. trainable Boolean, whether model's variables trainable. can also change trainable status model/layer freeze_weights() unfreeze_weights(). layers List layers add model.","code":""},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"Sequential model instance.","code":""},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"input_shape omitted, model layer shapes, including final model output shape, known model built, either calling model input tensor/array like model(input), (possibly via fit()/evaluate()/predict()), explicitly calling model$build(input_shape).","code":""},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"","code":"model <- keras_model_sequential(input_shape = c(784)) model |>   layer_dense(units = 32) |>   layer_activation('relu') |>   layer_dense(units = 10) |>   layer_activation('softmax')  model |> compile(   optimizer = 'rmsprop',   loss = 'categorical_crossentropy',   metrics = c('accuracy') )  model ## Model: \"sequential\" ## +---------------------------------+------------------------+---------------+ ## | Layer (type)                    | Output Shape           |       Param # | ## +=================================+========================+===============+ ## | dense (Dense)                   | (None, 32)             |        25,120 | ## +---------------------------------+------------------------+---------------+ ## | activation (Activation)         | (None, 32)             |             0 | ## +---------------------------------+------------------------+---------------+ ## | dense_1 (Dense)                 | (None, 10)             |           330 | ## +---------------------------------+------------------------+---------------+ ## | activation_1 (Activation)       | (None, 10)             |             0 | ## +---------------------------------+------------------------+---------------+ ##  Total params: 25,450 (99.41 KB) ##  Trainable params: 25,450 (99.41 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies an activation function to an output. — layer_activation","title":"Applies an activation function to an output. — layer_activation","text":"Applies activation function output.","code":""},{"path":"https://keras.posit.co/reference/layer_activation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies an activation function to an output. — layer_activation","text":"","code":"layer_activation(object, activation, ...)"},{"path":"https://keras.posit.co/reference/layer_activation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies an activation function to an output. — layer_activation","text":"object Object compose layer . tensor, array, sequential model. activation Activation function. callable, name activation keras3::activation_* namespace. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies an activation function to an output. — layer_activation","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_activation.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies an activation function to an output. — layer_activation","text":"","code":"x <- array(c(-3, -1, 0, 2)) layer <- layer_activation(activation = 'relu') layer(x) ## tf.Tensor([0. 0. 0. 2.], shape=(4), dtype=float32) layer <- layer_activation(activation = activation_relu) layer(x) ## tf.Tensor([0. 0. 0. 2.], shape=(4), dtype=float32) layer <- layer_activation(activation = op_relu) layer(x) ## tf.Tensor([0. 0. 0. 2.], shape=(4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","title":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","text":"Formula:","code":"f(x) = alpha * (exp(x) - 1.) for x < 0 f(x) = x for x >= 0"},{"path":"https://keras.posit.co/reference/layer_activation_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","text":"","code":"layer_activation_elu(object, alpha = 1, ...)"},{"path":"https://keras.posit.co/reference/layer_activation_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","text":"object Object compose layer . tensor, array, sequential model. alpha float, slope negative section. Defaults 1.0. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_elu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"layer allows small gradient unit active. Formula:","code":"f <- function(x) ifelse(x >= 0, x, alpha * x)"},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"","code":"layer_activation_leaky_relu(object, negative_slope = 0.3, ...)"},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"object Object compose layer . tensor, array, sequential model. negative_slope Float >= 0.0. Negative slope coefficient. Defaults 0.3. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"","code":"leaky_relu_layer <- layer_activation_leaky_relu(negative_slope=0.5) input <- array(c(-10, -5, 0.0, 5, 10)) result <- leaky_relu_layer(input) as.array(result) ## [1] -5.0 -2.5  0.0  5.0 10.0"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_parametric_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","title":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","text":"Formula:   alpha learned array shape x.","code":"f <- function(x) ifelse(x >= 0, x, alpha * x)"},{"path":"https://keras.posit.co/reference/layer_activation_parametric_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","text":"","code":"layer_activation_parametric_relu(   object,   alpha_initializer = \"Zeros\",   alpha_regularizer = NULL,   alpha_constraint = NULL,   shared_axes = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_activation_parametric_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","text":"object Object compose layer . tensor, array, sequential model. alpha_initializer Initializer function weights. alpha_regularizer Regularizer weights. alpha_constraint Constraint weights. shared_axes axes along share learnable parameters activation function. example, incoming feature maps 2D convolution output shape (batch, height, width, channels), wish share parameters across space filter one set parameters, set shared_axes=[1, 2]. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_parametric_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Rectified Linear Unit activation function layer. — layer_activation_relu","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"Formula:","code":"f <- function(x, max_value = Inf, negative_slope = 0, threshold = 0) {  x <- max(x,0)  if (x >= max_value)    max_value  else if (threshold <= x && x < max_value)    x  else    negative_slope * (x - threshold) }"},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"","code":"layer_activation_relu(   object,   max_value = NULL,   negative_slope = 0,   threshold = 0,   ... )"},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"object Object compose layer . tensor, array, sequential model. max_value Float >= 0. Maximum activation value. NULL means unlimited. Defaults NULL. negative_slope Float >= 0. Negative slope coefficient. Defaults 0.0. threshold Float >= 0. Threshold value thresholded activation. Defaults 0.0. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"","code":"relu_layer <- layer_activation_relu(max_value = 10,                                     negative_slope = 0.5,                                     threshold = 0) input <- array(c(-10, -5, 0.0, 5, 10)) result <- relu_layer(input) as.array(result) ## [1] -5.0 -2.5  0.0  5.0 10.0"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax activation layer. — layer_activation_softmax","title":"Softmax activation layer. — layer_activation_softmax","text":"Formula:","code":"exp_x = exp(x - max(x)) f(x) = exp_x / sum(exp_x)"},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax activation layer. — layer_activation_softmax","text":"","code":"layer_activation_softmax(object, axis = -1L, ...)"},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax activation layer. — layer_activation_softmax","text":"object Object compose layer . tensor, array, sequential model. axis Integer, list Integers, axis along softmax normalization applied. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax activation layer. — layer_activation_softmax","text":"Softmaxed output shape inputs.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmax activation layer. — layer_activation_softmax","text":"","code":"softmax_layer <- layer_activation_softmax() input <- op_array(c(1, 2, 1)) softmax_layer(input) ## tf.Tensor([0.21194157 0.5761169  0.21194157], shape=(3), dtype=float32)"},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Softmax activation layer. — layer_activation_softmax","text":"inputs: inputs (logits) softmax layer. mask: boolean mask shape inputs. mask specifies 1 keep 0 mask. Defaults NULL.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"Layer applies update cost function based input activity.","code":""},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"","code":"layer_activity_regularization(object, l1 = 0, l2 = 0, ...)"},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"object Object compose layer . tensor, array, sequential model. l1 L1 regularization factor (positive float). l2 L2 regularization factor (positive float). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"Arbitrary. Use keyword argument input_shape (tuple integers, include samples axis) using layer first layer model.","code":""},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"shape input.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_add.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs elementwise addition operation. — layer_add","title":"Performs elementwise addition operation. — layer_add","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_add.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs elementwise addition operation. — layer_add","text":"","code":"layer_add(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_add.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs elementwise addition operation. — layer_add","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_add.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs elementwise addition operation. — layer_add","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_add.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs elementwise addition operation. — layer_add","text":"Usage Keras model:","code":"input_shape <- c(1, 2, 3) x1 <- op_ones(input_shape) x2 <- op_ones(input_shape) layer_add(x1, x2) ## tf.Tensor( ## [[[2. 2. 2.] ##   [2. 2. 2.]]], shape=(1, 2, 3), dtype=float32) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu')  input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu')  # equivalent to `added = layer_add([x1, x2))` added <- layer_add(x1, x2) output <- added |> layer_dense(4)  model <- keras_model(inputs = c(input1, input2), outputs = output)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"Inputs list 2 3 elements: query tensor shape (batch_size, Tq, dim). value tensor shape (batch_size, Tv, dim). optional key tensor shape (batch_size, Tv, dim). none supplied, value used key. calculation follows steps: Calculate attention scores using query key shape (batch_size, Tq, Tv) non-linear sum scores = reduce_sum(tanh(query + key), axis=-1). Use scores calculate softmax distribution shape (batch_size, Tq, Tv). Use softmax distribution create linear combination value shape (batch_size, Tq, dim).","code":""},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"","code":"layer_additive_attention(object, use_scale = TRUE, dropout = 0, ...)"},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"object Object compose layer . tensor, array, sequential model. use_scale TRUE, create scalar variable scale attention scores. dropout Float 0 1. Fraction units drop attention scores. Defaults 0.0. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"inputs: List following tensors: query: Query tensor shape (batch_size, Tq, dim). value: Value tensor shape (batch_size, Tv, dim). key: Optional key tensor shape (batch_size, Tv, dim). given, use value key value, common case. mask: List following tensors: query_mask: boolean mask tensor shape (batch_size, Tq). given, output zero positions mask==FALSE. value_mask: boolean mask tensor shape (batch_size, Tv). given, apply mask values positions mask==FALSE contribute result. return_attention_scores: bool, TRUE, returns attention scores (masking softmax) additional output argument. training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (dropout). use_causal_mask: Boolean. Set TRUE decoder self-attention. Adds mask position attend positions j > . prevents flow information future towards past. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"output","dir":"Reference","previous_headings":"","what":"Output","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"Attention outputs shape (batch_size, Tq, dim). (Optional) Attention scores masking softmax shape (batch_size, Tq, Tv).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies Alpha Dropout to the input. — layer_alpha_dropout","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"Alpha Dropout Dropout keeps mean variance inputs original values, order ensure self-normalizing property even dropout. Alpha Dropout fits well Scaled Exponential Linear Units (SELU) randomly setting activations negative saturation value.","code":""},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"","code":"layer_alpha_dropout(object, rate, noise_shape = NULL, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. multiplicative noise standard deviation sqrt(rate / (1 - rate)). noise_shape 1D integer tensor representing shape binary alpha dropout mask multiplied input. instance, inputs shape (batch_size, timesteps, features) want alpha dropout mask timesteps, can use noise_shape = (batch_size, 1, features). seed integer use random seed. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"inputs: Input tensor (rank). training: R boolean indicating whether layer behave training mode (adding alpha dropout) inference mode (nothing).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"Inputs list 2 3 elements: query tensor shape (batch_size, Tq, dim). value tensor shape (batch_size, Tv, dim). optional key tensor shape (batch_size, Tv, dim). none supplied, value used key. calculation follows steps: Calculate attention scores using query key shape (batch_size, Tq, Tv). Use scores calculate softmax distribution shape (batch_size, Tq, Tv). Use softmax distribution create linear combination value shape (batch_size, Tq, dim).","code":""},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"","code":"layer_attention(   object,   use_scale = FALSE,   score_mode = \"dot\",   dropout = 0,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"object Object compose layer . tensor, array, sequential model. use_scale TRUE, create scalar variable scale attention scores. score_mode Function use compute attention scores, one {\"dot\", \"concat\"}. \"dot\" refers dot product query key vectors. \"concat\" refers hyperbolic tangent concatenation query key vectors. dropout Float 0 1. Fraction units drop attention scores. Defaults 0.0. seed integer use random seed incase dropout. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"inputs: List following tensors: query: Query tensor shape (batch_size, Tq, dim). value: Value tensor shape (batch_size, Tv, dim). key: Optional key tensor shape (batch_size, Tv, dim). given, use value key value, common case. mask: List following tensors: query_mask: boolean mask tensor shape (batch_size, Tq). given, output zero positions mask==FALSE. value_mask: boolean mask tensor shape (batch_size, Tv). given, apply mask values positions mask==FALSE contribute result. return_attention_scores: bool, TRUE, returns attention scores (masking softmax) additional output argument. training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (dropout). use_causal_mask: Boolean. Set TRUE decoder self-attention. Adds mask position attend positions j > . prevents flow information future towards past. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"output","dir":"Reference","previous_headings":"","what":"Output","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"Attention outputs shape (batch_size, Tq, dim). (Optional) Attention scores masking softmax shape (batch_size, Tq, Tv).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_average.html","id":null,"dir":"Reference","previous_headings":"","what":"Averages a list of inputs element-wise.. — layer_average","title":"Averages a list of inputs element-wise.. — layer_average","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_average.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Averages a list of inputs element-wise.. — layer_average","text":"","code":"layer_average(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_average.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Averages a list of inputs element-wise.. — layer_average","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_average.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Averages a list of inputs element-wise.. — layer_average","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_average.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Averages a list of inputs element-wise.. — layer_average","text":"Usage Keras model:","code":"input_shape <- c(1, 2, 3) x1 <- op_ones(input_shape) x2 <- op_zeros(input_shape) layer_average(x1, x2) ## tf.Tensor( ## [[[0.5 0.5 0.5] ##   [0.5 0.5 0.5]]], shape=(1, 2, 3), dtype=float32) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu')  input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu')  added <- layer_average(x1, x2) output <- added |> layer_dense(4)  model <- keras_model(inputs = c(input1, input2), outputs = output)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Average pooling for temporal data. — layer_average_pooling_1d","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"Downsamples input representation taking average value window defined pool_size. window shifted strides.  resulting output using \"valid\" padding option shape : output_shape = (input_shape - pool_size + 1) / strides) resulting output shape using \"\" padding option : output_shape = input_shape / strides","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"","code":"layer_average_pooling_1d(   object,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"object Object compose layer . tensor, array, sequential model. pool_size int, size max pooling window. strides int NULL. Specifies much pooling window moves pooling step. NULL, default pool_size. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"data_format=\"channels_last\": 3D tensor shape (batch_size, steps, features). data_format=\"channels_first\": 3D tensor shape (batch_size, features, steps).","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"data_format=\"channels_last\": 3D tensor shape (batch_size, downsampled_steps, features). data_format=\"channels_first\": 3D tensor shape (batch_size, features, downsampled_steps).","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"strides=1 padding=\"valid\":     strides=2 padding=\"valid\":     strides=1 padding=\"\":","code":"x <- op_array(c(1., 2., 3., 4., 5.)) |> op_reshape(c(1, 5, 1)) output <- x |>   layer_average_pooling_1d(pool_size = 2,                            strides = 1,                            padding = \"valid\") output ## tf.Tensor( ## [[[1.5] ##   [2.5] ##   [3.5] ##   [4.5]]], shape=(1, 4, 1), dtype=float32) x <- op_array(c(1., 2., 3., 4., 5.)) |> op_reshape(c(1, 5, 1)) output <- x |>   layer_average_pooling_1d(pool_size = 2,                            strides = 2,                            padding = \"valid\") output ## tf.Tensor( ## [[[1.5] ##   [3.5]]], shape=(1, 2, 1), dtype=float32) x <- op_array(c(1., 2., 3., 4., 5.)) |> op_reshape(c(1, 5, 1)) output <- x |>   layer_average_pooling_1d(pool_size = 2,                            strides = 1,                            padding = \"same\") output ## tf.Tensor( ## [[[1.5] ##   [2.5] ##   [3.5] ##   [4.5] ##   [5. ]]], shape=(1, 5, 1), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"Downsamples input along spatial dimensions (height width) taking average value input window (size defined pool_size) channel input. window shifted strides along dimension. resulting output using \"valid\" padding option spatial shape (number rows columns) : output_shape = math.floor((input_shape - pool_size) / strides) + 1 (input_shape >= pool_size) resulting output shape using \"\" padding option : output_shape = math.floor((input_shape - 1) / strides) + 1","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"","code":"layer_average_pooling_2d(   object,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"object Object compose layer . tensor, array, sequential model. pool_size int list 2 integers, factors downscale (dim1, dim2). one integer specified, window length used dimensions. strides int list 2 integers, NULL. Strides values. NULL, default pool_size. one int specified, stride size used dimensions. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"data_format=\"channels_last\": 4D tensor shape (batch_size, height, width, channels). data_format=\"channels_first\": 4D tensor shape (batch_size, channels, height, width).","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"data_format=\"channels_last\": 4D tensor shape (batch_size, pooled_height, pooled_width, channels). data_format=\"channels_first\": 4D tensor shape (batch_size, channels, pooled_height, pooled_width).","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"strides=(1, 1) padding=\"valid\":     strides=(2, 2) padding=\"valid\":     stride=(1, 1) padding=\"\":","code":"x <- op_array(1:9, \"float32\") |> op_reshape(c(1, 3, 3, 1)) output <- x |>   layer_average_pooling_2d(pool_size = c(2, 2),                            strides = c(1, 1),                            padding = \"valid\") output ## tf.Tensor( ## [[[[3.] ##    [4.]] ## ##   [[6.] ##    [7.]]]], shape=(1, 2, 2, 1), dtype=float32) x <- op_array(1:12, \"float32\") |> op_reshape(c(1, 3, 4, 1)) output <- x |>   layer_average_pooling_2d(pool_size = c(2, 2),                            strides = c(2, 2),                            padding = \"valid\") output ## tf.Tensor( ## [[[[3.5] ##    [5.5]]]], shape=(1, 1, 2, 1), dtype=float32) x <- op_array(1:9, \"float32\") |> op_reshape(c(1, 3, 3, 1)) output <- x |>   layer_average_pooling_2d(pool_size = c(2, 2),                            strides = c(1, 1),                            padding = \"same\") output ## tf.Tensor( ## [[[[3. ] ##    [4. ] ##    [4.5]] ## ##   [[6. ] ##    [7. ] ##    [7.5]] ## ##   [[7.5] ##    [8.5] ##    [9. ]]]], shape=(1, 3, 3, 1), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"Downsamples input along spatial dimensions (depth, height, width) taking average value input window (size defined pool_size) channel input. window shifted strides along dimension.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"","code":"layer_average_pooling_3d(   object,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"object Object compose layer . tensor, array, sequential model. pool_size int list 3 integers, factors downscale (dim1, dim2, dim3). one integer specified, window length used dimensions. strides int list 3 integers, NULL. Strides values. NULL, default pool_size. one int specified, stride size used dimensions. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"","code":"depth <- height <- width <- 30 channels <- 3  inputs <- layer_input(shape = c(depth, height, width, channels)) outputs <- inputs |> layer_average_pooling_3d(pool_size = 3) outputs # Shape: (batch_size, 10, 10, 10, 3) ## <KerasTensor shape=(None, 10, 10, 10, 3), dtype=float32, sparse=False, name=keras_tensor_1>"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer that normalizes its inputs. — layer_batch_normalization","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"Batch normalization applies transformation maintains mean output close 0 output standard deviation close 1. Importantly, batch normalization works differently training inference. training (.e. using fit() calling layer/model argument training = TRUE), layer normalizes output using mean standard deviation current batch inputs. say, channel normalized, layer returns gamma * (batch - mean(batch)) / sqrt(var(batch) + epsilon) + beta, : epsilon small constant (configurable part constructor arguments) gamma learned scaling factor (initialized 1), can disabled passing scale = FALSE constructor. beta learned offset factor (initialized 0), can disabled passing center = FALSE constructor. inference (.e. using evaluate() predict() calling layer/model argument training = FALSE (default), layer normalizes output using moving average mean standard deviation batches seen training. say, returns gamma * (batch - self$moving_mean) / sqrt(self$moving_var+epsilon) + beta. self$moving_mean self$moving_var non-trainable variables updated time layer called training mode, : moving_mean = moving_mean * momentum + mean(batch) * (1 - momentum) moving_var = moving_var * momentum + var(batch) * (1 - momentum) , layer normalize inputs inference trained data similar statistics inference data. setting layer$trainable <- FALSE BatchNormalization layer: meaning setting layer$trainable <- FALSE freeze layer, .e. internal state change training: trainable weights updated fit() train_on_batch(), state updates run. Usually, necessarily mean layer run inference mode (normally controlled training argument can passed calling layer). \"Frozen state\" \"inference mode\" two separate concepts. However, case BatchNormalization layer, setting trainable <- FALSE layer means layer subsequently run inference mode (meaning use moving mean moving variance normalize current batch, rather using mean variance current batch). Note : Setting trainable model containing layers recursively set trainable value inner layers. value trainable attribute changed calling compile() model, new value take effect model compile() called .","code":""},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"","code":"layer_batch_normalization(   object,   axis = -1L,   momentum = 0.99,   epsilon = 0.001,   center = TRUE,   scale = TRUE,   beta_initializer = \"zeros\",   gamma_initializer = \"ones\",   moving_mean_initializer = \"zeros\",   moving_variance_initializer = \"ones\",   beta_regularizer = NULL,   gamma_regularizer = NULL,   beta_constraint = NULL,   gamma_constraint = NULL,   synchronized = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"object Object compose layer . tensor, array, sequential model. axis Integer, axis normalized (typically features axis). instance, Conv2D layer data_format = \"channels_first\", use axis = 2. momentum Momentum moving average. epsilon Small float added variance avoid dividing zero. center TRUE, add offset beta normalized tensor. FALSE, beta ignored. scale TRUE, multiply gamma. FALSE, gamma used. next layer linear can disabled since scaling done next layer. beta_initializer Initializer beta weight. gamma_initializer Initializer gamma weight. moving_mean_initializer Initializer moving mean. moving_variance_initializer Initializer moving variance. beta_regularizer Optional regularizer beta weight. gamma_regularizer Optional regularizer gamma weight. beta_constraint Optional constraint beta weight. gamma_constraint Optional constraint gamma weight. synchronized applicable TensorFlow backend. TRUE, synchronizes global batch statistics (mean variance) layer across devices training step distributed training strategy. FALSE, replica uses local batch statistics. ... Base layer keyword arguments (e.g. name dtype).","code":""},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"inputs: Input tensor (rank). training: R boolean indicating whether layer behave training mode inference mode. training = TRUE: layer normalize inputs using mean variance current batch inputs. training = FALSE: layer normalize inputs using mean variance moving statistics, learned training. mask: Binary tensor shape broadcastable inputs tensor, TRUE values indicating positions mean variance computed. Masked elements current inputs taken account mean variance computation training. prior unmasked element values taken account momentum expires.","code":""},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"Ioffe Szegedy, 2015.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":null,"dir":"Reference","previous_headings":"","what":"Bidirectional wrapper for RNNs. — layer_bidirectional","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"Bidirectional wrapper RNNs.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"","code":"layer_bidirectional(   object,   layer,   merge_mode = \"concat\",   weights = NULL,   backward_layer = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"object Object compose layer . tensor, array, sequential model. layer RNN instance, layer_lstm() layer_gru(). also Layer() instance meets following criteria: sequence-processing layer (accepts 3D+ inputs). go_backwards, return_sequences return_state attribute (semantics RNN class). input_spec attribute. Implement serialization via get_config() from_config(). Note recommended way create new RNN layers write custom RNN cell use layer_rnn(), instead subclassing Layer() directly. return_sequences TRUE, output masked timestep zero regardless layer's original zero_output_for_mask value. merge_mode Mode outputs forward backward RNNs combined. One {\"sum\", \"mul\", \"concat\", \"ave\", NULL}. NULL, outputs combined, returned list. Defaults \"concat\". weights see description backward_layer Optional RNN, Layer() instance used handle backwards input processing. backward_layer provided, layer instance passed layer argument used generate backward layer automatically. Note provided backward_layer layer properties matching layer argument, particular values stateful, return_states, return_sequences, etc. addition, backward_layer layer different go_backwards argument values. ValueError raised requirements met. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"call arguments layer wrapped RNN layer. Beware passing initial_state argument call layer, first half list elements initial_state list passed forward RNN call last half list elements passed backward RNN call.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"instantiating Bidirectional layer existing RNN layer instance reuse weights state RNN layer instance -- Bidirectional layer freshly initialized weights.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"","code":"model <- keras_model_sequential(input_shape = c(5, 10)) %>%   layer_bidirectional(layer_lstm(units = 10, return_sequences = TRUE)) %>%   layer_bidirectional(layer_lstm(units = 10)) %>%   layer_dense(5, activation = \"softmax\")  model %>% compile(loss = \"categorical_crossentropy\",                   optimizer = \"rmsprop\")  # With custom backward layer forward_layer <- layer_lstm(units = 10, return_sequences = TRUE) backward_layer <- layer_lstm(units = 10, activation = \"relu\",                              return_sequences = TRUE, go_backwards = TRUE)  model <- keras_model_sequential(input_shape = c(5, 10)) %>%   bidirectional(forward_layer, backward_layer = backward_layer) %>%   layer_dense(5, activation = \"softmax\")  model %>% compile(loss = \"categorical_crossentropy\",                   optimizer = \"rmsprop\")"},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"states","dir":"Reference","previous_headings":"","what":"States","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"Bidirectional layer instance property states, can access layer$states. can also reset states using reset_state()","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which encodes integer features. — layer_category_encoding","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"layer provides options condensing data categorical encoding total number tokens known advance. accepts integer values inputs, outputs dense sparse representation inputs. integer inputs total number tokens known, use layer_integer_lookup() instead. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"","code":"layer_category_encoding(   object,   num_tokens = NULL,   output_mode = \"multi_hot\",   sparse = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"object Object compose layer . tensor, array, sequential model. num_tokens total number tokens layer support. inputs layer must integers range 0 <= value < num_tokens, error thrown. output_mode Specification output layer. Values can \"one_hot\", \"multi_hot\" \"count\", configuring layer follows: - \"one_hot\": Encodes individual element input array num_tokens size, containing 1 element index. last dimension size 1, encode dimension. last dimension size 1, append new dimension encoded output. - \"multi_hot\": Encodes sample input single array num_tokens size, containing 1 vocabulary term present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). - \"count\": Like \"multi_hot\", int array contains count number times token index appeared sample. output modes, currently output rank 2 supported. Defaults \"multi_hot\". sparse Whether return sparse tensor; backends support sparse tensors. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"One-hot encoding data     Multi-hot encoding data     Using weighted inputs \"count\" mode","code":"layer <- layer_category_encoding(num_tokens = 4, output_mode = \"one_hot\") x <- op_array(c(3, 2, 0, 1), \"int32\") layer(x) ## tf.Tensor( ## [[0. 0. 0. 1.] ##  [0. 0. 1. 0.] ##  [1. 0. 0. 0.] ##  [0. 1. 0. 0.]], shape=(4, 4), dtype=float32) layer <- layer_category_encoding(num_tokens = 4, output_mode = \"multi_hot\") x <- op_array(rbind(c(0, 1),                    c(0, 0),                    c(1, 2),                    c(3, 1)), \"int32\") layer(x) ## tf.Tensor( ## [[1. 1. 0. 0.] ##  [1. 0. 0. 0.] ##  [0. 1. 1. 0.] ##  [0. 1. 0. 1.]], shape=(4, 4), dtype=float32) layer <- layer_category_encoding(num_tokens = 4, output_mode = \"count\") count_weights <- op_array(rbind(c(.1, .2),                                c(.1, .1),                                c(.2, .3),                                c(.4, .2))) x <- op_array(rbind(c(0, 1),                    c(0, 0),                    c(1, 2),                    c(3, 1)), \"int32\") layer(x, count_weights = count_weights) #   array([[01, 02, 0. , 0. ], #          [02, 0. , 0. , 0. ], #          [0. , 02, 03, 0. ], #          [0. , 02, 0. , 04]]>"},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"inputs: 1D 2D tensor integer inputs. count_weights: tensor shape inputs indicating weight sample value summing count mode. used \"multi_hot\" \"one_hot\" modes.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which crops images. — layer_center_crop","title":"A preprocessing layer which crops images. — layer_center_crop","text":"layers crops central portion images target size. image smaller target size, resized cropped return largest possible window image matches target aspect ratio. Input pixel values can range (e.g. [0., 1.) [0, 255]).","code":""},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which crops images. — layer_center_crop","text":"","code":"layer_center_crop(object, height, width, data_format = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which crops images. — layer_center_crop","text":"object Object compose layer . tensor, array, sequential model. height Integer, height output shape. width Integer, width output shape. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which crops images. — layer_center_crop","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which crops images. — layer_center_crop","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format, (..., channels, height, width), \"channels_first\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which crops images. — layer_center_crop","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels), (..., channels, target_height, target_width), \"channels_first\" format. input height/width even target height/width odd (inversely), input image left-padded 1 pixel. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":null,"dir":"Reference","previous_headings":"","what":"Concatenates a list of inputs. — layer_concatenate","title":"Concatenates a list of inputs. — layer_concatenate","text":"takes input list tensors, shape except concatenation axis, returns single tensor concatenation inputs.","code":""},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Concatenates a list of inputs. — layer_concatenate","text":"","code":"layer_concatenate(inputs, ..., axis = -1L)"},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Concatenates a list of inputs. — layer_concatenate","text":"inputs layers combine ... Standard layer keyword arguments. axis Axis along concatenate.","code":""},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Concatenates a list of inputs. — layer_concatenate","text":"tensor, concatenation inputs alongside axis axis.","code":""},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Concatenates a list of inputs. — layer_concatenate","text":"Usage Keras model:","code":"x <- op_arange(20) |> op_reshape(c(2, 2, 5)) y <- op_arange(20, 40) |> op_reshape(c(2, 2, 5)) layer_concatenate(x, y, axis = 2) ## tf.Tensor( ## [[[ 0.  1.  2.  3.  4.] ##   [ 5.  6.  7.  8.  9.] ##   [20. 21. 22. 23. 24.] ##   [25. 26. 27. 28. 29.]] ## ##  [[10. 11. 12. 13. 14.] ##   [15. 16. 17. 18. 19.] ##   [30. 31. 32. 33. 34.] ##   [35. 36. 37. 38. 39.]]], shape=(2, 4, 5), dtype=float32) x1 <- op_arange(10)     |> op_reshape(c(5, 2)) |> layer_dense(8) x2 <- op_arange(10, 20) |> op_reshape(c(5, 2)) |> layer_dense(8) y <- layer_concatenate(x1, x2)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"layer creates convolution kernel convolved layer input single spatial (temporal) dimension produce tensor outputs. use_bias TRUE, bias vector created added outputs. Finally, activation NULL, applied outputs well.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"","code":"layer_conv_1d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   groups = 1L,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int list 1 integer, specifying size convolution window. strides int list 1 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, \"valid\", \"\" \"causal\"(case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. \"causal\" results causal (dilated) convolutions, e.g. output[t] depend ontail(input, t+1). Useful modeling temporal data model violate temporal order. See WaveNet: Generative Model Raw Audio, section2.1. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated convolution. groups positive int specifying number groups input split along channel axis. group convolved separately filters // groups filters. output concatenation groups results along channel axis. Input channels filters must divisible groups. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"3D tensor representing activation(conv1d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, steps, channels) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, new_steps, filters) data_format=\"channels_first\": 3D tensor shape: (batch_shape, filters, new_steps)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"","code":"# The inputs are 128-length vectors with 10 timesteps, and the # batch size is 4. x <- random_uniform(c(4, 10, 128)) y <- x |> layer_conv_1d(32, 3, activation='relu') shape(y) ## shape(4, 8, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"1D transposed convolution layer. — layer_conv_1d_transpose","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"need transposed convolutions generally arise desire use transformation going opposite direction normal convolution, .e., something shape output convolution something shape input maintaining connectivity pattern compatible said convolution.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"","code":"layer_conv_1d_transpose(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters transpose convolution). kernel_size int list 1 integer, specifying size transposed convolution window. strides int list 1 integer, specifying stride length transposed convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated transposed convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"3D tensor representing activation(conv1d_transpose(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, steps, channels) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, new_steps, filters) data_format=\"channels_first\": 3D tensor shape: (batch_shape, filters, new_steps)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"guide convolution arithmetic deep learning Deconvolutional Networks","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"","code":"x <- random_uniform(c(4, 10, 128)) y <- x |> layer_conv_1d_transpose(32, 3, 2, activation='relu') shape(y) ## shape(4, 21, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"2D convolution layer. — layer_conv_2d","title":"2D convolution layer. — layer_conv_2d","text":"layer creates convolution kernel convolved layer input single spatial (temporal) dimension produce tensor outputs. use_bias TRUE, bias vector created added outputs. Finally, activation NULL, applied outputs well.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D convolution layer. — layer_conv_2d","text":"","code":"layer_conv_2d(   object,   filters,   kernel_size,   strides = list(1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L),   groups = 1L,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D convolution layer. — layer_conv_2d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int list 2 integer, specifying size convolution window. strides int list 2 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 2 integers, specifying dilation rate use dilated convolution. groups positive int specifying number groups input split along channel axis. group convolved separately filters // groups filters. output concatenation groups results along channel axis. Input channels filters must divisible groups. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D convolution layer. — layer_conv_2d","text":"4D tensor representing activation(conv2d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D convolution layer. — layer_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, height, width, channels) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D convolution layer. — layer_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, new_height, new_width, filters) data_format=\"channels_first\": 4D tensor shape: (batch_size, filters, new_height, new_width)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"2D convolution layer. — layer_conv_2d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"2D convolution layer. — layer_conv_2d","text":"","code":"x <- random_uniform(c(4, 10, 10, 128)) y <- x |> layer_conv_2d(32, 3, activation='relu') shape(y) ## shape(4, 8, 8, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"2D transposed convolution layer. — layer_conv_2d_transpose","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"need transposed convolutions generally arise desire use transformation going opposite direction normal convolution, .e., something shape output convolution something shape input maintaining connectivity pattern compatible said convolution.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"","code":"layer_conv_2d_transpose(   object,   filters,   kernel_size,   strides = list(1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L),   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters transposed convolution). kernel_size int list 1 integer, specifying size transposed convolution window. strides int list 1 integer, specifying stride length transposed convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated transposed convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"4D tensor representing activation(conv2d_transpose(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, height, width, channels) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, new_height, new_width, filters) data_format=\"channels_first\": 4D tensor shape: (batch_size, filters, new_height, new_width)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"guide convolution arithmetic deep learning Deconvolutional Networks","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"","code":"x <- random_uniform(c(4, 10, 8, 128)) y <- x |> layer_conv_2d_transpose(32, 2, 2, activation='relu') shape(y) ## shape(4, 20, 16, 32) # (4, 20, 16, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"3D convolution layer. — layer_conv_3d","title":"3D convolution layer. — layer_conv_3d","text":"layer creates convolution kernel convolved layer input single spatial (temporal) dimension produce tensor outputs. use_bias TRUE, bias vector created added outputs. Finally, activation NULL, applied outputs well.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"3D convolution layer. — layer_conv_3d","text":"","code":"layer_conv_3d(   object,   filters,   kernel_size,   strides = list(1L, 1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L, 1L),   groups = 1L,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"3D convolution layer. — layer_conv_3d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int list 3 integer, specifying size convolution window. strides int list 3 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 3 integers, specifying dilation rate use dilated convolution. groups positive int specifying number groups input split along channel axis. group convolved separately filters %/% groups filters. output concatenation groups results along channel axis. Input channels filters must divisible groups. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"3D convolution layer. — layer_conv_3d","text":"5D tensor representing activation(conv3d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"3D convolution layer. — layer_conv_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"3D convolution layer. — layer_conv_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3, filters) data_format=\"channels_first\": 5D tensor shape: (batch_size, filters, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"3D convolution layer. — layer_conv_3d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"3D convolution layer. — layer_conv_3d","text":"","code":"x <- random_uniform(c(4, 10, 10, 10, 128)) y <- x |> layer_conv_3d(32, 3, activation = 'relu') shape(y) ## shape(4, 8, 8, 8, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"3D transposed convolution layer. — layer_conv_3d_transpose","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"need transposed convolutions generally arise desire use transformation going opposite direction normal convolution, .e., something shape output convolution something shape input maintaining connectivity pattern compatible said convolution.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"","code":"layer_conv_3d_transpose(   object,   filters,   kernel_size,   strides = list(1L, 1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L, 1L),   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters transposed convolution). kernel_size int list 1 integer, specifying size transposed convolution window. strides int list 1 integer, specifying stride length transposed convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated transposed convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"5D tensor representing activation(conv3d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3, filters) data_format=\"channels_first\": 5D tensor shape: (batch_size, filters, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"guide convolution arithmetic deep learning Deconvolutional Networks","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"","code":"x <- random_uniform(c(4, 10, 8, 12, 128)) y <- x |> layer_conv_3d_transpose(32, 2, 2, activation = 'relu') shape(y) ## shape(4, 20, 16, 24, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"1D Convolutional LSTM. — layer_conv_lstm_1d","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"Similar LSTM layer, input transformations recurrent transformations convolutional.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"","code":"layer_conv_lstm_1d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   ...,   unroll = NULL )"},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int tuple/list 1 integer, specifying size convolution window. strides int tuple/list 1 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int tuple/list 1 integers, specifying dilation rate use dilated convolution. activation Activation function use. default hyperbolic tangent activation function applied (tanh(x)). recurrent_activation Activation function use recurrent step. use_bias Boolean, whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. bias_initializer Initializer bias vector. unit_forget_bias Boolean. TRUE, add 1 bias forget gate initialization. Use combination bias_initializer=\"zeros\". recommended Jozefowicz et al., 2015 kernel_regularizer Regularizer function applied kernel weights matrix. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. bias_regularizer Regularizer function applied bias vector. activity_regularizer Regularizer function applied . kernel_constraint Constraint function applied kernel weights matrix. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. bias_constraint Constraint function applied bias vector. dropout Float 0 1. Fraction units drop linear transformation inputs. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default FALSE). TRUE, last state sample index batch used initial state sample index following batch. ... forward/backward compatability. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"inputs: 4D tensor. initial_state: List initial state tensors passed first call cell. mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout set.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"data_format=\"channels_first\": 4D tensor shape: (samples, time, channels, rows) data_format=\"channels_last\": 4D tensor shape: (samples, time, rows, channels)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"return_state: list tensors. first tensor output. remaining tensors last states, 3D tensor shape: (samples, filters, new_rows) data_format='channels_first' shape: (samples, new_rows, filters) data_format='channels_last'. rows values might changed due padding. return_sequences: 4D tensor shape: (samples, timesteps, filters, new_rows) data_format='channels_first' shape: (samples, timesteps, new_rows, filters) data_format='channels_last'. Else, 3D tensor shape: (samples, filters, new_rows) data_format='channels_first' shape: (samples, new_rows, filters) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"Shi et al., 2015 (current implementation include feedback loop cells output).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"2D Convolutional LSTM. — layer_conv_lstm_2d","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"Similar LSTM layer, input transformations recurrent transformations convolutional.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"","code":"layer_conv_lstm_2d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   ...,   unroll = NULL )"},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int tuple/list 2 integers, specifying size convolution window. strides int tuple/list 2 integers, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int tuple/list 2 integers, specifying dilation rate use dilated convolution. activation Activation function use. default hyperbolic tangent activation function applied (tanh(x)). recurrent_activation Activation function use recurrent step. use_bias Boolean, whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. bias_initializer Initializer bias vector. unit_forget_bias Boolean. TRUE, add 1 bias forget gate initialization. Use combination bias_initializer=\"zeros\". recommended Jozefowicz et al., 2015 kernel_regularizer Regularizer function applied kernel weights matrix. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. bias_regularizer Regularizer function applied bias vector. activity_regularizer Regularizer function applied . kernel_constraint Constraint function applied kernel weights matrix. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. bias_constraint Constraint function applied bias vector. dropout Float 0 1. Fraction units drop linear transformation inputs. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default FALSE). TRUE, last state sample index batch used initial state sample index following batch. ... forward/backward compatability. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"inputs: 5D tensor. mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout set. initial_state: List initial state tensors passed first call cell.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"data_format='channels_first': 5D tensor shape: (samples, time, channels, rows, cols) data_format='channels_last': 5D tensor shape: (samples, time, rows, cols, channels)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"return_state: list tensors. first tensor output. remaining tensors last states, 4D tensor shape: (samples, filters, new_rows, new_cols) data_format='channels_first' shape: (samples, new_rows, new_cols, filters) data_format='channels_last'. rows cols values might changed due padding. return_sequences: 5D tensor shape: (samples, timesteps, filters, new_rows, new_cols) data_format='channels_first' shape: (samples, timesteps, new_rows, new_cols, filters) data_format='channels_last'. Else, 4D tensor shape: (samples, filters, new_rows, new_cols) data_format='channels_first' shape: (samples, new_rows, new_cols, filters) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"Shi et al., 2015 (current implementation include feedback loop cells output).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"3D Convolutional LSTM. — layer_conv_lstm_3d","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"Similar LSTM layer, input transformations recurrent transformations convolutional.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"","code":"layer_conv_lstm_3d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   ...,   unroll = NULL )"},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int tuple/list 3 integers, specifying size convolution window. strides int tuple/list 3 integers, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int tuple/list 3 integers, specifying dilation rate use dilated convolution. activation Activation function use. default hyperbolic tangent activation function applied (tanh(x)). recurrent_activation Activation function use recurrent step. use_bias Boolean, whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. bias_initializer Initializer bias vector. unit_forget_bias Boolean. TRUE, add 1 bias forget gate initialization. Use combination bias_initializer=\"zeros\". recommended Jozefowicz et al., 2015 kernel_regularizer Regularizer function applied kernel weights matrix. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. bias_regularizer Regularizer function applied bias vector. activity_regularizer Regularizer function applied . kernel_constraint Constraint function applied kernel weights matrix. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. bias_constraint Constraint function applied bias vector. dropout Float 0 1. Fraction units drop linear transformation inputs. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default FALSE). TRUE, last state sample index batch used initial state sample index following batch. ... forward/backward compatability. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"inputs: 6D tensor. mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout set. initial_state: List initial state tensors passed first call cell.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"data_format='channels_first': 5D tensor shape: (samples, time, channels, *spatial_dims) data_format='channels_last': 5D tensor shape: (samples, time, *spatial_dims, channels)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"return_state: list tensors. first tensor output. remaining tensors last states, 4D tensor shape: (samples, filters, *spatial_dims) data_format='channels_first' shape: (samples, *spatial_dims, filters) data_format='channels_last'. return_sequences: 5D tensor shape: (samples, timesteps, filters, *spatial_dims) data_format='channels_first' shape: (samples, timesteps, *spatial_dims, filters) data_format='channels_last'. Else, 4D tensor shape: (samples, filters, *spatial_dims) data_format='channels_first' shape: (samples, *spatial_dims, filters) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"Shi et al., 2015 (current implementation include feedback loop cells output).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"crops along time dimension (axis 2).","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"","code":"layer_cropping_1d(object, cropping = list(1L, 1L), ...)"},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"object Object compose layer . tensor, array, sequential model. cropping Int, list int (length 2). int: many units trimmed beginning end cropping dimension (axis 1). list 2 ints: many units trimmed beginning end cropping dimension ((left_crop, right_crop)). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"","code":"input_shape <- c(2, 3, 2) x <- op_arange(prod(input_shape)) |> op_reshape(input_shape) x ## tf.Tensor( ## [[[ 0.  1.] ##   [ 2.  3.] ##   [ 4.  5.]] ## ##  [[ 6.  7.] ##   [ 8.  9.] ##   [10. 11.]]], shape=(2, 3, 2), dtype=float32) y <- x |> layer_cropping_1d(cropping = 1) y ## tf.Tensor( ## [[[2. 3.]] ## ##  [[8. 9.]]], shape=(2, 1, 2), dtype=float32)"},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"3D tensor shape (batch_size, axis_to_crop, features)","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"3D tensor shape (batch_size, cropped_axis, features)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"crops along spatial dimensions, .e. height width.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"","code":"layer_cropping_2d(   object,   cropping = list(list(0L, 0L), list(0L, 0L)),   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"object Object compose layer . tensor, array, sequential model. cropping Int, list 2 ints, list 2 lists 2 ints. int: symmetric cropping applied height width. list 2 ints: interpreted two different symmetric cropping values height width: (symmetric_height_crop, symmetric_width_crop). list 2 lists 2 ints: interpreted ((top_crop, bottom_crop), (left_crop, right_crop)). data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"","code":"input_shape <- c(2, 28, 28, 3) x <- op_arange(prod(input_shape), dtype ='int32') |> op_reshape(input_shape) y <- x |> layer_cropping_2d(cropping=list(c(2, 2), c(4, 4))) shape(y) ## shape(2, 24, 20, 3)"},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, height, width, channels) data_format \"channels_first\": (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, cropped_height, cropped_width, channels) data_format \"channels_first\": (batch_size, channels, cropped_height, cropped_width)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"Cropping layer 3D data (e.g. spatial spatio-temporal).","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"","code":"layer_cropping_3d(   object,   cropping = list(list(1L, 1L), list(1L, 1L), list(1L, 1L)),   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"object Object compose layer . tensor, array, sequential model. cropping Int, list 3 ints, list 3 lists 2 ints. int: symmetric cropping applied depth, height, width. list 3 ints: interpreted three different symmetric cropping values depth, height, width: (symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop). list 3 lists 2 ints: interpreted ((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_crop)). data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"","code":"input_shape <- c(2, 28, 28, 10, 3) x <- input_shape %>% { op_reshape(seq(prod(.)), .) } y <- x |> layer_cropping_3d(cropping = c(2, 4, 2)) shape(y) ## shape(2, 24, 20, 6, 3)"},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop, channels) data_format \"channels_first\": (batch_size, channels, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, first_cropped_axis, second_cropped_axis, third_cropped_axis, channels) data_format \"channels_first\": (batch_size, channels, first_cropped_axis, second_cropped_axis, third_cropped_axis)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_dense.html","id":null,"dir":"Reference","previous_headings":"","what":"Just your regular densely-connected NN layer. — layer_dense","title":"Just your regular densely-connected NN layer. — layer_dense","text":"Dense implements operation: output = activation(dot(input, kernel) + bias) activation element-wise activation function passed activation argument, kernel weights matrix created layer, bias bias vector created layer (applicable use_bias TRUE).","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Just your regular densely-connected NN layer. — layer_dense","text":"","code":"layer_dense(   object,   units,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   lora_rank = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Just your regular densely-connected NN layer. — layer_dense","text":"object Object compose layer . tensor, array, sequential model. units Positive integer, dimensionality output space. activation Activation function use. specify anything, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix. bias_initializer Initializer bias vector. kernel_regularizer Regularizer function applied kernel weights matrix. bias_regularizer Regularizer function applied bias vector. activity_regularizer Regularizer function applied output layer (\"activation\"). kernel_constraint Constraint function applied kernel weights matrix. bias_constraint Constraint function applied bias vector. lora_rank Optional integer. set, layer's forward pass implement LoRA (Low-Rank Adaptation) provided rank. LoRA sets layer's kernel non-trainable replaces delta original kernel, obtained via multiplying two lower-rank trainable matrices. can useful reduce computation cost fine-tuning large dense layers. can also enable LoRA existing Dense layer calling layer$enable_lora(rank). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Just your regular densely-connected NN layer. — layer_dense","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Just your regular densely-connected NN layer. — layer_dense","text":"input layer rank greater 2, Dense computes dot product inputs kernel along last axis inputs axis 0 kernel (using tf.tensordot). example, input dimensions (batch_size, d0, d1), create kernel shape (d1, units), kernel operates along axis 2 input, every sub-tensor shape (1, 1, d1) (batch_size * d0 sub-tensors). output case shape (batch_size, d0, units).","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Just your regular densely-connected NN layer. — layer_dense","text":"N-D tensor shape: (batch_size, ..., input_dim). common situation 2D input shape (batch_size, input_dim).","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Just your regular densely-connected NN layer. — layer_dense","text":"N-D tensor shape: (batch_size, ..., units). instance, 2D input shape (batch_size, input_dim), output shape (batch_size, units).","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Just your regular densely-connected NN layer. — layer_dense","text":"","code":"enable_lora(   rank,   a_initializer = 'he_uniform',   b_initializer = 'zeros' )"},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"readonly-properties-","dir":"Reference","previous_headings":"","what":"Readonly properties:","title":"Just your regular densely-connected NN layer. — layer_dense","text":"kernel","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"1D depthwise convolution layer. — layer_depthwise_conv_1d","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"Depthwise convolution type convolution input channel convolved different kernel (called depthwise kernel). can understand depthwise convolution first step depthwise separable convolution. implemented via following steps: Split input individual channels. Convolve channel individual depthwise kernel depth_multiplier output channels. Concatenate convolved outputs along channels axis. Unlike regular 1D convolution, depthwise convolution mix information across different input channels. depth_multiplier argument determines many filters applied one input channel. , controls amount output channels generated per input channel depthwise step.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"","code":"layer_depthwise_conv_1d(   object,   kernel_size,   strides = 1L,   padding = \"valid\",   depth_multiplier = 1L,   data_format = NULL,   dilation_rate = 1L,   activation = NULL,   use_bias = TRUE,   depthwise_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   depthwise_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   depthwise_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"object Object compose layer . tensor, array, sequential model. kernel_size int list 1 integer, specifying size depthwise convolution window. strides int list 1 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. depth_multiplier number depthwise convolution output channels input channel. total number depthwise convolution output channels equal input_channel * depth_multiplier. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. depthwise_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. depthwise_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. depthwise_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"3D tensor representing activation(depthwise_conv1d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, steps, channels) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, new_steps, channels * depth_multiplier) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels * depth_multiplier, new_steps)","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"","code":"x <- random_uniform(c(4, 10, 12)) y <- x |> layer_depthwise_conv_1d(   kernel_size = 3,   depth_multiplier = 3,   activation = 'relu' ) shape(y) ## shape(4, 8, 36)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"2D depthwise convolution layer. — layer_depthwise_conv_2d","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"Depthwise convolution type convolution input channel convolved different kernel (called depthwise kernel). can understand depthwise convolution first step depthwise separable convolution. implemented via following steps: Split input individual channels. Convolve channel individual depthwise kernel depth_multiplier output channels. Concatenate convolved outputs along channels axis. Unlike regular 2D convolution, depthwise convolution mix information across different input channels. depth_multiplier argument determines many filters applied one input channel. , controls amount output channels generated per input channel depthwise step.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"","code":"layer_depthwise_conv_2d(   object,   kernel_size,   strides = list(1L, 1L),   padding = \"valid\",   depth_multiplier = 1L,   data_format = NULL,   dilation_rate = list(1L, 1L),   activation = NULL,   use_bias = TRUE,   depthwise_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   depthwise_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   depthwise_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"object Object compose layer . tensor, array, sequential model. kernel_size int list 2 integer, specifying size depthwise convolution window. strides int list 2 integer, specifying stride length depthwise convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. depth_multiplier number depthwise convolution output channels input channel. total number depthwise convolution output channels equal input_channel * depth_multiplier. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 2 integers, specifying dilation rate use dilated convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. depthwise_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. depthwise_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. depthwise_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"4D tensor representing activation(depthwise_conv2d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, height, width, channels) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, new_height, new_width, channels * depth_multiplier) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels * depth_multiplier, new_height, new_width)","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"","code":"x <- random_uniform(c(4, 10, 10, 12)) y <- x |> layer_depthwise_conv_2d(3, 3, activation = 'relu') shape(y) ## shape(4, 3, 3, 12)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"layer place element input data one several contiguous ranges output integer index indicating range element placed . Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"","code":"layer_discretization(   object,   bin_boundaries = NULL,   num_bins = NULL,   epsilon = 0.01,   output_mode = \"int\",   sparse = FALSE,   dtype = NULL,   name = NULL )"},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"object Object compose layer . tensor, array, sequential model. bin_boundaries list bin boundaries. leftmost rightmost bins always extend -Inf Inf, bin_boundaries = c(0, 1, 2) generates bins (-Inf, 0), [0, 1), [1, 2), [2, +Inf). option set, adapt() called. num_bins integer number bins compute. option set, adapt() called learn bin boundaries. epsilon Error tolerance, typically small fraction close zero (e.g. 0.01). Higher values epsilon increase quantile approximation, hence result unequal buckets, improve performance resource consumption. output_mode Specification output layer. Values can \"int\", \"one_hot\", \"multi_hot\", \"count\" configuring layer follows: \"int\": Return discretized bin indices directly. \"one_hot\": Encodes individual element input array size num_bins, containing 1 input's bin index. last dimension size 1, encode dimension.  last dimension size 1, append new dimension encoded output. \"multi_hot\": Encodes sample input single array size num_bins, containing 1 bin index index present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). \"count\": \"multi_hot\", int array contains count number times bin index appeared sample. Defaults \"int\". sparse Boolean. applicable \"one_hot\", \"multi_hot\", \"count\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. dtype datatype (e.g., \"float32\"). name String, name object","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"array dimension 2 higher.","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"input shape.","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"Discretize float values based provided buckets.     Discretize float values based number buckets compute.","code":"input <- op_array(rbind(c(-1.5, 1, 3.4, 0.5),                        c(0, 3, 1.3, 0),                        c(-.5, 0, .5, 1),                        c(1.5, 2, 2.5, 3))) output <- input |> layer_discretization(bin_boundaries = c(0, 1, 2)) output ## tf.Tensor( ## [[0 2 3 1] ##  [1 3 2 1] ##  [0 1 1 2] ##  [2 3 3 3]], shape=(4, 4), dtype=int64) layer <- layer_discretization(num_bins = 4, epsilon = 0.01) layer |> adapt(input) layer(input) ## tf.Tensor( ## [[0 2 3 1] ##  [1 3 2 1] ##  [0 1 1 2] ##  [2 3 3 3]], shape=(4, 4), dtype=int64)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_dot.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes element-wise dot product of two tensors. — layer_dot","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"takes list inputs size 2, axes corresponding input along dot product performed. say x y two input tensors shapes (2, 3, 5) (2, 10, 3). batch dimension size inputs, axes correspond dimensions size corresponding inputs. e.g. axes = c(1, 2), dot product x, y result tensor shape (2, 5, 10)","code":""},{"path":"https://keras.posit.co/reference/layer_dot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"","code":"layer_dot(inputs, ..., axes, normalize = FALSE)"},{"path":"https://keras.posit.co/reference/layer_dot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"inputs layers combine ... Standard layer keyword arguments. axes Integer list integers, axis axes along take dot product. list, two integers corresponding desired axis first input desired axis second input, respectively. Note size two selected axes must match. normalize Whether L2-normalize samples along dot product axis taking dot product. set TRUE, output dot product cosine proximity two samples.","code":""},{"path":"https://keras.posit.co/reference/layer_dot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"tensor, dot product samples inputs.","code":""},{"path":"https://keras.posit.co/reference/layer_dot.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"Usage Keras model:","code":"x <- op_reshape(0:9,   c(1, 5, 2)) y <- op_reshape(10:19, c(1, 2, 5)) layer_dot(x, y, axes=c(2, 3)) ## tf.Tensor( ## [[[260 360] ##   [320 445]]], shape=(1, 2, 2), dtype=int32) x1 <- op_reshape(0:9, c(5, 2)) |> layer_dense(8) x2 <- op_reshape(10:19, c(5, 2)) |> layer_dense(8) shape(x1) ## shape(5, 8) shape(x2) ## shape(5, 8) y <- layer_dot(x1, x2, axes=2) shape(y) ## shape(5, 1)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies dropout to the input. — layer_dropout","title":"Applies dropout to the input. — layer_dropout","text":"Dropout layer randomly sets input units 0 frequency rate step training time, helps prevent overfitting. Inputs set 0 scaled 1 / (1 - rate) sum inputs unchanged. Note Dropout layer applies training set TRUE call(), values dropped inference. using model.fit, training appropriately set TRUE automatically. contexts, can set argument explicitly TRUE calling layer. (contrast setting trainable=FALSE Dropout layer. trainable affect layer's behavior, Dropout variables/weights can frozen training.)","code":""},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies dropout to the input. — layer_dropout","text":"","code":"layer_dropout(object, rate, noise_shape = NULL, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies dropout to the input. — layer_dropout","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. Fraction input units drop. noise_shape 1D integer tensor representing shape binary dropout mask multiplied input. instance, inputs shape (batch_size, timesteps, features) want dropout mask timesteps, can use noise_shape=(batch_size, 1, features). seed R integer use random seed. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies dropout to the input. — layer_dropout","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Applies dropout to the input. — layer_dropout","text":"inputs: Input tensor (rank). training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (nothing).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":null,"dir":"Reference","previous_headings":"","what":"A layer that uses einsum as the backing computation. — layer_einsum_dense","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"layer can perform einsum calculations arbitrary dimensionality.","code":""},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"","code":"layer_einsum_dense(   object,   equation,   output_shape,   activation = NULL,   bias_axes = NULL,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   lora_rank = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"object Object compose layer . tensor, array, sequential model. equation equation describing einsum perform. equation must valid einsum string form ab,bc->ac, ...ab,bc->...ac, ab...,bc->ac... 'ab', 'bc', 'ac' can valid einsum axis expression sequence. output_shape expected shape output tensor (excluding batch dimension dimensions represented ellipses). can specify NA NULL dimension unknown can inferred input shape. activation Activation function use. specify anything, activation applied (, \"linear\" activation: (x) = x). bias_axes string containing output dimension(s) apply bias . character bias_axes string correspond character output portion equation string. kernel_initializer Initializer kernel weights matrix. bias_initializer Initializer bias vector. kernel_regularizer Regularizer function applied kernel weights matrix. bias_regularizer Regularizer function applied bias vector. kernel_constraint Constraint function applied kernel weights matrix. bias_constraint Constraint function applied bias vector. lora_rank Optional integer. set, layer's forward pass implement LoRA (Low-Rank Adaptation) provided rank. LoRA sets layer's kernel non-trainable replaces delta original kernel, obtained via multiplying two lower-rank trainable matrices (factorization happens last dimension). can useful reduce computation cost fine-tuning large dense layers. can also enable LoRA existing EinsumDense layer calling layer$enable_lora(rank). ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"Biased dense layer einsums example shows instantiate standard Keras dense layer using einsum operations. example equivalent layer_Dense(64, use_bias=TRUE).     Applying dense layer sequence example shows instantiate layer applies dense operation every element sequence. , output_shape two values (since two non-batch dimensions output); first dimension output_shape NA, sequence dimension b unknown shape.     Applying dense layer sequence using ellipses example shows instantiate layer applies dense operation every element sequence, uses ellipsis notation instead specifying batch sequence dimensions. using ellipsis notation specified one axis, output_shape arg single value. instantiated way, layer can handle number sequence dimensions - including case sequence dimension exists.","code":"input <- layer_input(shape = c(32)) output <- input |>   layer_einsum_dense(\"ab,bc->ac\",                      output_shape = 64,                      bias_axes = \"c\") output # shape(NA, 64) ## <KerasTensor shape=(None, 64), dtype=float32, sparse=False, name=keras_tensor_1> input <- layer_input(shape = c(32, 128)) output <- input |>   layer_einsum_dense(\"abc,cd->abd\",                      output_shape = c(NA, 64),                      bias_axes = \"d\") output  # shape(NA, 32, 64) ## <KerasTensor shape=(None, None, 64), dtype=float32, sparse=False, name=keras_tensor_3> input <- layer_input(shape = c(32, 128)) output <- input |>   layer_einsum_dense(\"...x,xy->...y\",                      output_shape = 64,                      bias_axes = \"y\")  output  # shape(NA, 32, 64) ## <KerasTensor shape=(None, 32, 64), dtype=float32, sparse=False, name=keras_tensor_5>"},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"","code":"enable_lora(   rank,   a_initializer = 'he_uniform',   b_initializer = 'zeros' ) quantize(mode)"},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"readonly-properties-","dir":"Reference","previous_headings":"","what":"Readonly properties:","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"kernel","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":null,"dir":"Reference","previous_headings":"","what":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"e.g. rbind(4L, 20L) \\(\\rightarrow\\) rbind(c(0.25, 0.1), c(0.6, -0.2)) layer can used positive integer inputs fixed range.","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"","code":"layer_embedding(   object,   input_dim,   output_dim,   embeddings_initializer = \"uniform\",   embeddings_regularizer = NULL,   embeddings_constraint = NULL,   mask_zero = FALSE,   weights = NULL,   lora_rank = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"object Object compose layer . tensor, array, sequential model. input_dim Integer. Size vocabulary, .e. maximum integer index + 1. output_dim Integer. Dimension dense embedding. embeddings_initializer Initializer embeddings matrix (see keras3::initializer_*). embeddings_regularizer Regularizer function applied embeddings matrix (see keras3::regularizer_*). embeddings_constraint Constraint function applied embeddings matrix (see keras3::constraint_*). mask_zero Boolean, whether input value 0 special \"padding\" value masked . useful using recurrent layers may take variable length input. TRUE, subsequent layers model need support masking exception raised. mask_zero set TRUE, consequence, index 0 used vocabulary (input_dim equal size vocabulary + 1). weights Optional floating-point matrix size (input_dim, output_dim). initial embeddings values use. lora_rank Optional integer. set, layer's forward pass implement LoRA (Low-Rank Adaptation) provided rank. LoRA sets layer's embeddings matrix non-trainable replaces delta original matrix, obtained via multiplying two lower-rank trainable matrices. can useful reduce computation cost fine-tuning large embedding layers. can also enable LoRA existing Embedding layer instance calling layer$enable_lora(rank). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"","code":"model <- keras_model_sequential() |>   layer_embedding(1000, 64)  # The model will take as input an integer matrix of size (batch,input_length), # and the largest integer (i.e. word index) in the input # should be no larger than 999 (vocabulary size). # Now model$output_shape is (NA, 10, 64), where `NA` is the batch # dimension.  input_array <- random_integer(shape = c(32, 10), minval = 0, maxval = 1000) model |> compile('rmsprop', 'mse') output_array <- model |> predict(input_array, verbose = 0) dim(output_array)    # (32, 10, 64) ## [1] 32 10 64"},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"2D tensor shape: (batch_size, input_length).","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"3D tensor shape: (batch_size, input_length, output_dim).","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"","code":"enable_lora(   rank,   a_initializer = 'he_uniform',   b_initializer = 'zeros' ) quantize(mode) quantized_build(input_shape, mode) quantized_call(inputs)"},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"readonly-properties-","dir":"Reference","previous_headings":"","what":"Readonly properties:","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"embeddings","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":null,"dir":"Reference","previous_headings":"","what":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"Available feature types: Note features can referred string name, e.g. \"integer_categorical\". using string name, default argument values used.","code":"# Plain float values. feature_float(name = NULL)  # Float values to be preprocessed via featurewise standardization # (i.e. via a `layer_normalization()` layer). feature_float_normalized(name = NULL)  # Float values to be preprocessed via linear rescaling # (i.e. via a `layer_rescaling` layer). feature_float_rescaled(scale = 1., offset = 0., name = NULL)  # Float values to be discretized. By default, the discrete # representation will then be one-hot encoded. feature_float_discretized(   num_bins,   bin_boundaries = NULL,   output_mode = \"one_hot\",   name = NULL )  # Integer values to be indexed. By default, the discrete # representation will then be one-hot encoded. feature_integer_categorical(   max_tokens = NULL,   num_oov_indices = 1,   output_mode = \"one_hot\",   name = NULL )  # String values to be indexed. By default, the discrete # representation will then be one-hot encoded. feature_string_categorical(   max_tokens = NULL,   num_oov_indices = 1,   output_mode = \"one_hot\",   name = NULL )  # Integer values to be hashed into a fixed number of bins. # By default, the discrete representation will then be one-hot encoded. feature_integer_hashed(num_bins, output_mode = \"one_hot\", name = NULL)  # String values to be hashed into a fixed number of bins. # By default, the discrete representation will then be one-hot encoded. feature_string_hashed(num_bins, output_mode = \"one_hot\", name = NULL)"},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"","code":"layer_feature_space(   object,   features,   output_mode = \"concat\",   crosses = NULL,   crossing_dim = 32L,   hashing_dim = 32L,   num_discretization_bins = 32L,   name = NULL,   feature_names = NULL )  feature_cross(feature_names, crossing_dim, output_mode = \"one_hot\")  feature_custom(dtype, preprocessor, output_mode)  feature_float(name = NULL)  feature_float_rescaled(scale = 1, offset = 0, name = NULL)  feature_float_normalized(name = NULL)  feature_float_discretized(   num_bins,   bin_boundaries = NULL,   output_mode = \"one_hot\",   name = NULL )  feature_integer_categorical(   max_tokens = NULL,   num_oov_indices = 1,   output_mode = \"one_hot\",   name = NULL )  feature_string_categorical(   max_tokens = NULL,   num_oov_indices = 1,   output_mode = \"one_hot\",   name = NULL )  feature_string_hashed(num_bins, output_mode = \"one_hot\", name = NULL)  feature_integer_hashed(num_bins, output_mode = \"one_hot\", name = NULL)"},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"object see description features see description output_mode string. layer_feature_space(), one \"concat\" \"dict\". concat mode, features get concatenated together single vector. dict mode, FeatureSpace returns named list individually encoded features (names input list names). feature_* functions, one : \"int\" \"one_hot\" \"float\". crosses List features crossed together, e.g. crosses=list(c(\"feature_1\", \"feature_2\")). features \"crossed\" hashing combined value fixed-length vector. crossing_dim Default vector size hashing crossed features. Defaults 32. hashing_dim Default vector size hashing features type \"integer_hashed\" \"string_hashed\". Defaults 32. num_discretization_bins Default number bins used discretizing features type \"float_discretized\". Defaults 32. name String, name object feature_names Named list mapping names features type specification, e.g. list(my_feature = \"integer_categorical\") list(my_feature = feature_integer_categorical()). complete list supported types, see \"Available feature types\" paragraph . dtype string, output dtype feature. E.g., \"float32\". preprocessor callable. scale, offset Passed layer_rescaling() num_bins, bin_boundaries Passed layer_discretization() max_tokens, num_oov_indices Passed layer_integer_lookup() feature_integer_categorical() layer_string_lookup() feature_string_categorical().","code":""},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"Basic usage named list input data:   Basic usage tf.data:   Basic usage Keras Functional API:           Customizing feature feature cross:   Returning dict (named list) integer-encoded features:   Specifying Keras preprocessing layer:   Retrieving underlying Keras preprocessing layers:   Saving reloading FeatureSpace:","code":"raw_data <- list(   float_values = c(0.0, 0.1, 0.2, 0.3),   string_values = c(\"zero\", \"one\", \"two\", \"three\"),   int_values = as.integer(c(0, 1, 2, 3)) )  dataset <- tfdatasets::tensor_slices_dataset(raw_data)  feature_space <- layer_feature_space(   features = list(     float_values = \"float_normalized\",     string_values = \"string_categorical\",     int_values = \"integer_categorical\"   ),   crosses = list(c(\"string_values\", \"int_values\")),   output_mode = \"concat\" )  # Before you start using the feature_space(), # you must `adapt()` it on some data. feature_space |> adapt(dataset)  # You can call the feature_space() on a named list of # data (batched or unbatched). output_vector <- feature_space(raw_data) library(tfdatasets) # Unlabeled data preprocessed_ds <- unlabeled_dataset |>   dataset_map(feature_space)  # Labeled data preprocessed_ds <- labeled_dataset |>   dataset_map(function(x, y) tuple(feature_space(x), y)) # Retrieve a named list of Keras layer_input() objects (inputs <- feature_space$get_inputs()) ## $float_values ## <KerasTensor shape=(None, 1), dtype=float32, sparse=None, name=float_values> ## ## $string_values ## <KerasTensor shape=(None, 1), dtype=string, sparse=None, name=string_values> ## ## $int_values ## <KerasTensor shape=(None, 1), dtype=int32, sparse=None, name=int_values> # Retrieve the corresponding encoded Keras tensors (encoded_features <- feature_space$get_encoded_features()) ## <KerasTensor shape=(None, 43), dtype=float32, sparse=False, name=keras_tensor_7> # Build a Functional model outputs <- encoded_features |> layer_dense(1, activation = \"sigmoid\") model <- keras_model(inputs, outputs) feature_space <- layer_feature_space(   features = list(     float_values = feature_float_normalized(),     string_values = feature_string_categorical(max_tokens = 10),     int_values = feature_integer_categorical(max_tokens = 10)   ),   crosses = list(     feature_cross(c(\"string_values\", \"int_values\"), crossing_dim = 32)   ),   output_mode = \"concat\" ) feature_space <- layer_feature_space(   features = list(     \"string_values\" = feature_string_categorical(output_mode = \"int\"),     \"int_values\" = feature_integer_categorical(output_mode = \"int\")   ),   crosses = list(     feature_cross(       feature_names = c(\"string_values\", \"int_values\"),       crossing_dim = 32,       output_mode = \"int\"     )   ),   output_mode = \"dict\" ) # Let's say that one of the features is a short text paragraph that # we want to encode as a vector (one vector per paragraph) via TF-IDF. data <- list(text = c(\"1st string\", \"2nd string\", \"3rd string\"))  # There's a Keras layer for this: layer_text_vectorization() custom_layer <- layer_text_vectorization(output_mode = \"tf_idf\")  # We can use feature_custom() to create a custom feature # that will use our preprocessing layer. feature_space <- layer_feature_space(   features = list(     text = feature_custom(preprocessor = custom_layer,                           dtype = \"string\",                           output_mode = \"float\"     )   ),   output_mode = \"concat\" ) feature_space |> adapt(tfdatasets::tensor_slices_dataset(data)) output_vector <- feature_space(data) # The preprocessing layer of each feature is available in `$preprocessors`. preprocessing_layer <- feature_space$preprocessors$feature1  # The crossing layer of each feature cross is available in `$crossers`. # It's an instance of layer_hashed_crossing() crossing_layer <- feature_space$crossers[[\"feature1_X_feature2\"]] feature_space$save(\"featurespace.keras\") reloaded_feature_space <- keras$models$load_model(\"featurespace.keras\")"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":null,"dir":"Reference","previous_headings":"","what":"Flattens the input. Does not affect the batch size. — layer_flatten","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"Flattens input. affect batch size.","code":""},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"","code":"layer_flatten(object, data_format = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"object Object compose layer . tensor, array, sequential model. data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, ..., channels) \"channels_first\" corresponds inputs shape (batch, channels, ...). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"inputs shaped (batch) without feature axis, flattening adds extra channel dimension output shape (batch, 1).","code":""},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"","code":"x <- layer_input(shape=c(10, 64)) y <- x |> layer_flatten() shape(y) ## shape(NA, 640)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_flax_module_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","title":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","text":"layer enables use Flax components form flax.linen.Module instances within Keras using JAX backend Keras. module method use forward pass can specified via method argument __call__ default. method must take following arguments exact names: self method bound module, case default __call__, module otherwise pass module. inputs: inputs model, JAX array PyTree arrays. training (optional): argument specifying training mode inference mode, TRUE passed training mode. FlaxLayer handles non-trainable state model required RNGs automatically. Note mutable parameter flax.linen.Module.apply() set DenyList([\"params\"]), therefore making assumption variables outside \"params\" collection non-trainable weights. example shows create FlaxLayer Flax Module default __call__ method training argument:   example shows wrap module method conform required signature. allows multiple input arguments training argument different name values. additionally shows use function bound module.","code":"# keras3::use_backend(\"jax\") # py_install(\"flax\", \"r-keras\")  if(config_backend() == \"jax\" &&    reticulate::py_module_available(\"flax\")) {  flax <- import(\"flax\")  MyFlaxModule(flax$linen$Module) %py_class% {   `__call__` <- flax$linen$compact(\\(self, inputs) {     inputs |>       (flax$linen$Conv(features = 32L, kernel_size = tuple(3L, 3L)))() |>       flax$linen$relu() |>       flax$linen$avg_pool(window_shape = tuple(2L, 2L),                           strides = tuple(2L, 2L)) |>       # flatten all except batch_size axis       (\\(x) x$reshape(tuple(x$shape[[1]], -1L)))() |>       (flax$linen$Dense(features = 200L))() |>       flax$linen$relu() |>       (flax$linen$Dense(features = 10L))() |>       flax$linen$softmax()   }) }  # typical usage: input <- keras_input(c(28, 28, 3)) output <- input |>   layer_flax_module_wrapper(MyFlaxModule())  model <- keras_model(input, output)  # to instantiate the layer before composing: flax_module <- MyFlaxModule() keras_layer <- layer_flax_module_wrapper(module = flax_module)  input <- keras_input(c(28, 28, 3)) output <- input |>   keras_layer()  model <- keras_model(input, output)  } flax <- import(\"flax\")  MyFlaxModule(flax$linen$Module) \\%py_class\\% {   forward <-     flax$linen$compact(\\(self, inputs1, input2, deterministic) {       # do work ....       outputs # return     }) }  my_flax_module_wrapper <- function(module, inputs, training) {   c(input1, input2) \\%<-\\% inputs   module$forward(input1, input2,!training) }  flax_module <- MyFlaxModule() keras_layer <- layer_flax_module_wrapper(module = flax_module,                                          method = my_flax_module_wrapper)"},{"path":"https://keras.posit.co/reference/layer_flax_module_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","text":"","code":"layer_flax_module_wrapper(object, module, method = NULL, variables = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_flax_module_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","text":"object Object compose layer . tensor, array, sequential model. module instance flax.linen.Module subclass. method method call model. generally method Module. provided, __call__ method used. method can also function defined Module, case must take Module first argument. used Module.init Module.apply. Details documented method argument flax.linen.Module.apply(). variables dict (named R list) containing variables module format returned flax.linen.Module.init(). contain \"params\" key , applicable, keys collections variables non-trainable state. allows passing trained parameters learned non-trainable state controlling initialization. NULL passed, module's init function called build time initialize variables model. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_flax_module_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"regularization layer, active training time.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"","code":"layer_gaussian_dropout(object, rate, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"object Object compose layer . tensor, array, sequential model. rate Float, drop probability (Dropout). multiplicative noise standard deviation sqrt(rate / (1 - rate)). seed Integer, optional random seed enable deterministic behavior. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"inputs: Input tensor (rank). training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (nothing).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"useful mitigate overfitting (see form random data augmentation). Gaussian Noise (GS) natural choice corruption process real valued inputs. regularization layer, active training time.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"","code":"layer_gaussian_noise(object, stddev, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"object Object compose layer . tensor, array, sequential model. stddev Float, standard deviation noise distribution. seed Integer, optional random seed enable deterministic behavior. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"inputs: Input tensor (rank). training: Python boolean indicating whether layer behave training mode (adding noise) inference mode (nothing).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"Global average pooling operation temporal data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"","code":"layer_global_average_pooling_1d(   object,   data_format = NULL,   keepdims = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, temporal dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"inputs: 3D tensor. mask: Binary tensor shape (batch_size, steps) indicating whether given step masked (excluded average).","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"data_format='channels_last': 3D tensor shape: (batch_size, steps, features) data_format='channels_first': 3D tensor shape: (batch_size, features, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"keepdims=FALSE: 2D tensor shape (batch_size, features). keepdims=TRUE: data_format=\"channels_last\": 3D tensor shape (batch_size, 1, features) data_format=\"channels_first\": 3D tensor shape (batch_size, features, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"","code":"x <- random_uniform(c(2, 3, 4)) y <- x |> layer_global_average_pooling_1d() shape(y) ## shape(2, 4)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"Global average pooling operation 2D data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"","code":"layer_global_average_pooling_2d(   object,   data_format = NULL,   keepdims = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, features, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, spatial dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"data_format='channels_last': 4D tensor shape: (batch_size, height, width, channels) data_format='channels_first': 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"keepdims=FALSE: 2D tensor shape (batch_size, channels). keepdims=TRUE: data_format=\"channels_last\": 4D tensor shape (batch_size, 1, 1, channels) data_format=\"channels_first\": 4D tensor shape (batch_size, channels, 1, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"","code":"x <- random_uniform(c(2, 4, 5, 3)) y <- x |> layer_global_average_pooling_2d() shape(y) ## shape(2, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"Global average pooling operation 3D data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"","code":"layer_global_average_pooling_3d(   object,   data_format = NULL,   keepdims = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, spatial dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"data_format='channels_last': 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format='channels_first': 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"keepdims=FALSE: 2D tensor shape (batch_size, channels). keepdims=TRUE: data_format=\"channels_last\": 5D tensor shape (batch_size, 1, 1, 1, channels) data_format=\"channels_first\": 5D tensor shape (batch_size, channels, 1, 1, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"","code":"x <- random_uniform(c(2, 4, 5, 4, 3)) y <- x |> layer_global_average_pooling_3d() shape(y) ## shape(2, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"Global max pooling operation temporal data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"","code":"layer_global_max_pooling_1d(object, data_format = NULL, keepdims = FALSE, ...)"},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, temporal dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"data_format='channels_last': 3D tensor shape: (batch_size, steps, features) data_format='channels_first': 3D tensor shape: (batch_size, features, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"keepdims=FALSE: 2D tensor shape (batch_size, features). keepdims=TRUE: data_format=\"channels_last\": 3D tensor shape (batch_size, 1, features) data_format=\"channels_first\": 3D tensor shape (batch_size, features, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"","code":"x <- random_uniform(c(2, 3, 4)) y <- x |> layer_global_max_pooling_1d() shape(y) ## shape(2, 4)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"Global max pooling operation 2D data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"","code":"layer_global_max_pooling_2d(object, data_format = NULL, keepdims = FALSE, ...)"},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, features, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, spatial dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"data_format='channels_last': 4D tensor shape: (batch_size, height, width, channels) data_format='channels_first': 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"keepdims=FALSE: 2D tensor shape (batch_size, channels). keepdims=TRUE: data_format=\"channels_last\": 4D tensor shape (batch_size, 1, 1, channels) data_format=\"channels_first\": 4D tensor shape (batch_size, channels, 1, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"","code":"x <- random_uniform(c(2, 4, 5, 3)) y <- x |> layer_global_max_pooling_2d() shape(y) ## shape(2, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"Global max pooling operation 3D data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"","code":"layer_global_max_pooling_3d(object, data_format = NULL, keepdims = FALSE, ...)"},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, spatial dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"data_format='channels_last': 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format='channels_first': 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"keepdims=FALSE: 2D tensor shape (batch_size, channels). keepdims=TRUE: data_format=\"channels_last\": 5D tensor shape (batch_size, 1, 1, 1, channels) data_format=\"channels_first\": 5D tensor shape (batch_size, channels, 1, 1, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"","code":"x <- random_uniform(c(2, 4, 5, 4, 3)) y <- x |> layer_global_max_pooling_3d() shape(y) ## shape(2, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Group normalization layer. — layer_group_normalization","title":"Group normalization layer. — layer_group_normalization","text":"Group Normalization divides channels groups computes within group mean variance normalization. Empirically, accuracy stable batch norm wide range small batch sizes, learning rate adjusted linearly batch sizes. Relation Layer Normalization: number groups set 1, operation becomes nearly identical Layer Normalization (see Layer Normalization docs details). Relation Instance Normalization: number groups set input dimension (number groups equal number channels), operation becomes identical Instance Normalization. can achieve via groups=-1.","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Group normalization layer. — layer_group_normalization","text":"","code":"layer_group_normalization(   object,   groups = 32L,   axis = -1L,   epsilon = 0.001,   center = TRUE,   scale = TRUE,   beta_initializer = \"zeros\",   gamma_initializer = \"ones\",   beta_regularizer = NULL,   gamma_regularizer = NULL,   beta_constraint = NULL,   gamma_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Group normalization layer. — layer_group_normalization","text":"object Object compose layer . tensor, array, sequential model. groups Integer, number groups Group Normalization. Can range [1, N] N input dimension. input dimension must divisible number groups. Defaults 32. axis Integer List/Tuple. axis axes normalize across. Typically, features axis/axes. left-axes typically batch axis/axes. -1 last dimension input. Defaults -1. epsilon Small float added variance avoid dividing zero. Defaults 1e-3. center TRUE, add offset beta normalized tensor. FALSE, beta ignored. Defaults TRUE. scale TRUE, multiply gamma. FALSE, gamma used. next layer linear (also e.g. relu), can disabled since scaling done next layer. Defaults TRUE. beta_initializer Initializer beta weight. Defaults zeros. gamma_initializer Initializer gamma weight. Defaults ones. beta_regularizer Optional regularizer beta weight. NULL default. gamma_regularizer Optional regularizer gamma weight. NULL default. beta_constraint Optional constraint beta weight. NULL default. gamma_constraint Optional constraint gamma weight. NULL default. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Group normalization layer. — layer_group_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Group normalization layer. — layer_group_normalization","text":"Arbitrary. Use keyword argument input_shape (tuple integers, include samples axis) using layer first layer model.","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Group normalization layer. — layer_group_normalization","text":"shape input. **kwargs: Base layer keyword arguments (e.g. name dtype).","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Group normalization layer. — layer_group_normalization","text":"Yuxin Wu & Kaiming , 2018","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"Grouped Query Attention layer. — layer_group_query_attention","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"implementation grouped-query attention introduced Ainslie et al., 2023. num_key_value_heads denotes number groups, setting num_key_value_heads 1 equivalent multi-query attention, num_key_value_heads equal num_query_heads equivalent multi-head attention. layer first projects query, key, value tensors. , key value repeated match number heads query. , query scaled dot-producted key tensors. softmaxed obtain attention probabilities. value tensors interpolated probabilities concatenated back single tensor.","code":""},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"","code":"layer_group_query_attention(   object,   head_dim,   num_query_heads,   num_key_value_heads,   dropout = 0,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"object Object compose layer . tensor, array, sequential model. head_dim Size attention head. num_query_heads Number query attention heads. num_key_value_heads Number key value attention heads. dropout Dropout probability. use_bias Boolean, whether dense layers use bias vectors/matrices. kernel_initializer Initializer dense layer kernels. bias_initializer Initializer dense layer biases. kernel_regularizer Regularizer dense layer kernels. bias_regularizer Regularizer dense layer biases. activity_regularizer Regularizer dense layer activity. kernel_constraint Constraint dense layer kernels. bias_constraint Constraint dense layer kernels. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"attention_output: Result computation, shape (batch_dim, target_seq_len, feature_dim), target_seq_len target sequence length feature_dim query input last dim. attention_scores: (Optional) attention coefficients shape (batch_dim, num_query_heads, target_seq_len, source_seq_len).","code":""},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"query: Query tensor shape (batch_dim, target_seq_len, feature_dim), batch_dim batch size, target_seq_len length target sequence, feature_dim dimension feature. value: Value tensor shape (batch_dim, source_seq_len, feature_dim), batch_dim batch size, source_seq_len length source sequence, feature_dim dimension feature. key: Optional key tensor shape (batch_dim, source_seq_len, feature_dim). given, use value key value, common case. attention_mask: boolean mask shape (batch_dim, target_seq_len, source_seq_len), prevents attention certain positions. boolean mask specifies query elements can attend key elements, 1 indicates attention 0 indicates attention. Broadcasting can happen missing batch dimensions head dimension. return_attention_scores: boolean indicate whether output (attention_output, attention_scores) TRUE, attention_output FALSE. Defaults FALSE. training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (dropout). go either using training mode parent layer/model FALSE (inference) parent layer. use_causal_mask: boolean indicate whether apply causal mask prevent tokens attending future tokens (e.g., used decoder Transformer).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_gru.html","id":null,"dir":"Reference","previous_headings":"","what":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"Based available runtime hardware constraints, layer choose different implementations (cuDNN-based backend-native) maximize performance. GPU available arguments layer meet requirement cuDNN kernel (see details), layer use fast cuDNN implementation using TensorFlow backend. requirements use cuDNN implementation : activation == tanh recurrent_activation == sigmoid dropout == 0 recurrent_dropout == 0 unroll FALSE use_bias TRUE reset_after TRUE Inputs, use masking, strictly right-padded. Eager execution enabled outermost context. two variants GRU implementation. default one based v3 reset gate applied hidden state matrix multiplication. one based original order reversed. second variant compatible CuDNNGRU (GPU-) allows inference CPU. Thus separate biases kernel recurrent_kernel. use variant, set reset_after=TRUE recurrent_activation='sigmoid'. example:","code":"inputs <- random_uniform(c(32, 10, 8)) outputs <- inputs |> layer_gru(4) shape(outputs) ## shape(32, 4) # (32, 4) gru <- layer_gru(, 4, return_sequences = TRUE, return_state = TRUE) c(whole_sequence_output, final_state) %<-% gru(inputs) shape(whole_sequence_output) ## shape(32, 10, 4) shape(final_state) ## shape(32, 4)"},{"path":"https://keras.posit.co/reference/layer_gru.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"","code":"layer_gru(   object,   units,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   unroll = FALSE,   reset_after = TRUE,   use_cudnn = \"auto\",   ... )"},{"path":"https://keras.posit.co/reference/layer_gru.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"object Object compose layer . tensor, array, sequential model. units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). recurrent_activation Activation function use recurrent step. Default: sigmoid (sigmoid). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. activity_regularizer Regularizer function applied output layer (\"activation\"). Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default: FALSE). TRUE, last state sample index batch used initial state sample index following batch. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences. reset_after GRU convention (whether apply reset gate matrix multiplication). FALSE \"\", TRUE \"\" (default cuDNN compatible). use_cudnn Whether use cuDNN-backed implementation. \"auto\" attempt use cuDNN feasible, fallback default implementation . ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_gru.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_gru.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"inputs: 3D tensor, shape (batch, timesteps, feature). mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked  (optional). individual TRUE entry indicates corresponding timestep utilized, FALSE entry indicates corresponding timestep ignored. Defaults NULL. training: Python boolean indicating whether layer behave training mode inference mode. argument passed cell calling . relevant dropout recurrent_dropout used  (optional). Defaults NULL. initial_state: List initial state tensors passed first call cell (optional, NULL causes creation zero-filled initial state tensors). Defaults NULL.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which crosses features using the ","title":"A preprocessing layer which crosses features using the ","text":"layer performs crosses categorical features using \"hashing trick\". Conceptually, transformation can thought : hash(concatenate(features)) %% num_bins. layer currently performs crosses scalar inputs batches scalar inputs. Valid input shapes (batch_size, 1), (batch_size) (). Note: layer wraps tf.keras.layers.HashedCrossing. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tfdatasets pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which crosses features using the ","text":"","code":"layer_hashed_crossing(   object,   num_bins,   output_mode = \"int\",   sparse = FALSE,   name = NULL,   dtype = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which crosses features using the ","text":"object Object compose layer . tensor, array, sequential model. num_bins Number hash bins. output_mode Specification output layer. Values can \"int\", \"one_hot\" configuring layer follows: \"int\": Return integer bin indices directly. \"one_hot\": Encodes individual element input array size num_bins, containing 1 input's bin index. Defaults \"int\". sparse Boolean. applicable \"one_hot\" mode valid using TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. name String, name object dtype datatype (e.g., \"float32\"). ... Keyword arguments construct layer.","code":""},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which crosses features using the ","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which crosses features using the ","text":"Crossing two scalar features.     Crossing one-hotting two scalar features.","code":"feat1 <- c('A', 'B', 'A', 'B', 'A') |> as.array() feat2 <- c(101, 101, 101, 102, 102) |> as.integer() |> as.array() layer <- layer_hashed_crossing(num_bins = 5) layer(list(feat1, feat2)) ## tf.Tensor([1 4 1 1 3], shape=(5), dtype=int64) layer <- layer_hashed_crossing(num_bins = 5, output_mode = 'one_hot') layer(list(feat1, feat2)) ## tf.Tensor( ## [[0. 1. 0. 0. 0.] ##  [0. 0. 0. 0. 1.] ##  [0. 1. 0. 0. 0.] ##  [0. 1. 0. 0. 0.] ##  [0. 0. 0. 1. 0.]], shape=(5, 5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"layer transforms categorical inputs hashed output. element-wise converts ints strings ints fixed range. stable hash function uses tensorflow::ops::Fingerprint produce output consistently across platforms. layer uses FarmHash64 default, provides consistent hashed output across different platforms stable across invocations, regardless device context, mixing input bits thoroughly. want obfuscate hashed output, can also pass random salt argument constructor. case, layer use SipHash64 hash function, salt value serving additional input hash function. Note: layer internally uses TensorFlow. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tf.data pipeline (independently backend using). Example (FarmHash64)     Example (FarmHash64) mask value     Example (SipHash64)     Example (Siphash64 single integer, salt=[133, 133])","code":"layer <- layer_hashing(num_bins = 3) inp <- c('A', 'B', 'C', 'D', 'E') |> array(dim = c(5, 1)) layer(inp) ## tf.Tensor( ## [[1] ##  [0] ##  [1] ##  [1] ##  [2]], shape=(5, 1), dtype=int64) layer <- layer_hashing(num_bins=3, mask_value='') inp <- c('A', 'B', '', 'C', 'D') |> array(dim = c(5, 1)) layer(inp) ## tf.Tensor( ## [[1] ##  [1] ##  [0] ##  [2] ##  [2]], shape=(5, 1), dtype=int64) layer <- layer_hashing(num_bins=3, salt=c(133, 137)) inp <- c('A', 'B', 'C', 'D', 'E') |> array(dim = c(5, 1)) layer(inp) ## tf.Tensor( ## [[1] ##  [2] ##  [1] ##  [0] ##  [2]], shape=(5, 1), dtype=int64) layer <- layer_hashing(num_bins=3, salt=133) inp <- c('A', 'B', 'C', 'D', 'E') |> array(dim = c(5, 1)) layer(inp) ## tf.Tensor( ## [[0] ##  [0] ##  [2] ##  [1] ##  [0]], shape=(5, 1), dtype=int64)"},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"","code":"layer_hashing(   object,   num_bins,   mask_value = NULL,   salt = NULL,   output_mode = \"int\",   sparse = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"object Object compose layer . tensor, array, sequential model. num_bins Number hash bins. Note includes mask_value bin, effective number bins (num_bins - 1) mask_value set. mask_value value represents masked inputs, mapped index 0. NULL means mask term added hashing start index 0. Defaults NULL. salt single unsigned integer NULL. passed, hash function used SipHash64, values used additional input (known \"salt\" cryptography). non-zero. NULL, uses FarmHash64 hash function. also supports list 2 unsigned integer numbers, see reference paper details. Defaults NULL. output_mode Specification output layer. Values can \"int\", \"one_hot\", \"multi_hot\", \"count\" configuring layer follows: \"int\": Return integer bin indices directly. \"one_hot\": Encodes individual element input array size num_bins, containing 1 input's bin index. last dimension size 1, encode dimension. last dimension size 1, append new dimension encoded output. \"multi_hot\": Encodes sample input single array size num_bins, containing 1 bin index index present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). \"count\": \"multi_hot\", int array contains count number times bin index appeared sample. Defaults \"int\". sparse Boolean. applicable \"one_hot\", \"multi_hot\", \"count\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. ... Keyword arguments construct layer.","code":""},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"single string, list strings, int32 int64 tensor shape (batch_size, ...,).","code":""},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"int32 tensor shape (batch_size, ...).","code":""},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"SipHash salt","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Identity layer. — layer_identity","title":"Identity layer. — layer_identity","text":"layer used placeholder operation performed. layer just returns inputs argument output.","code":""},{"path":"https://keras.posit.co/reference/layer_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identity layer. — layer_identity","text":"","code":"layer_identity(object, ...)"},{"path":"https://keras.posit.co/reference/layer_identity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identity layer. — layer_identity","text":"object Object compose layer . tensor, array, sequential model. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_identity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identity layer. — layer_identity","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_input.html","id":null,"dir":"Reference","previous_headings":"","what":"keras_input — layer_input","title":"keras_input — layer_input","text":"layer_input() alias keras_input(). See ?keras_input() full documentation.","code":""},{"path":"https://keras.posit.co/reference/layer_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"keras_input — layer_input","text":"","code":"layer_input(   shape = NULL,   batch_size = NULL,   dtype = NULL,   sparse = NULL,   batch_shape = NULL,   name = NULL,   tensor = NULL )"},{"path":"https://keras.posit.co/reference/layer_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"keras_input — layer_input","text":"shape shape list (list integers NULL objects), including batch size. instance, shape = c(32) indicates expected input batches 32-dimensional vectors. Elements list can NULL NA; NULL/NA elements represent dimensions shape known may vary (e.g. sequence length). batch_size Optional static batch size (integer). dtype data type expected input, string (e.g. \"float32\", \"int32\"...) sparse boolean specifying whether expected input sparse tensors. Note , sparse FALSE, sparse tensors can still passed input - densified default value 0. feature supported TensorFlow backend. Defaults FALSE. batch_shape Shape, including batch dim. name Optional name string layer. unique model (reuse name twice). autogenerated provided. tensor Optional existing tensor wrap Input layer. set, layer use tensor rather creating new placeholder tensor.","code":""},{"path":"https://keras.posit.co/reference/layer_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"keras_input — layer_input","text":"Keras tensor, can passed inputs argument (keras_model()).","code":""},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"layer maps set arbitrary integer input tokens indexed integer output via table-based vocabulary lookup. layer's output indices contiguously arranged maximum vocab size, even input tokens non-continguous unbounded. layer supports multiple options encoding output via output_mode, optional support --vocabulary (OOV) tokens masking. vocabulary layer must either supplied construction learned via adapt(). adapt(), layer analyze data set, determine frequency individual integer tokens, create vocabulary . vocabulary capped size, frequent tokens used create vocabulary others treated OOV. two possible output modes layer.  output_mode \"int\", input integers converted index vocabulary (integer).  output_mode \"multi_hot\", \"count\", \"tf_idf\", input integers encoded array dimension corresponds element vocabulary. vocabulary can optionally contain mask token well OOV token (can optionally occupy multiple indices vocabulary, set num_oov_indices). position tokens vocabulary fixed. output_mode \"int\", vocabulary begin mask token index 0, followed OOV indices, followed rest vocabulary. output_mode \"multi_hot\", \"count\", \"tf_idf\" vocabulary begin OOV indices instances mask token dropped. Note: layer uses TensorFlow internally. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"","code":"layer_integer_lookup(   object,   max_tokens = NULL,   num_oov_indices = 1L,   mask_token = NULL,   oov_token = -1L,   vocabulary = NULL,   vocabulary_dtype = \"int64\",   idf_weights = NULL,   invert = FALSE,   output_mode = \"int\",   sparse = FALSE,   pad_to_max_tokens = FALSE,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"object Object compose layer . tensor, array, sequential model. max_tokens Maximum size vocabulary layer. specified adapting vocabulary setting pad_to_max_tokens=TRUE. NULL, cap size vocabulary. Note size includes OOV mask tokens. Defaults NULL. num_oov_indices number --vocabulary tokens use. value 1, OOV inputs modulated determine OOV value. value 0, OOV inputs cause error calling layer. Defaults 1. mask_token integer token represents masked inputs. output_mode \"int\", token included vocabulary mapped index 0. output modes, token appear vocabulary instances mask token input dropped. set NULL, mask term added. Defaults NULL. oov_token used invert TRUE. token return OOV indices. Defaults -1. vocabulary Optional. Either array integers string path text file. passing array, can pass list, list, 1D NumPy array, 1D tensor containing integer vocbulary terms. passing file path, file contain one line per term vocabulary. argument set, need adapt() layer. vocabulary_dtype dtype vocabulary terms, example \"int64\" \"int32\". Defaults \"int64\". idf_weights valid output_mode \"tf_idf\". list, list, 1D NumPy array, 1D tensor length vocabulary, containing floating point inverse document frequency weights, multiplied per sample term counts final TF-IDF weight. vocabulary argument set, output_mode \"tf_idf\", argument must supplied. invert valid output_mode \"int\". TRUE, layer map indices vocabulary items instead mapping vocabulary items indices. Defaults FALSE. output_mode Specification output layer. Values can \"int\", \"one_hot\", \"multi_hot\", \"count\", \"tf_idf\" configuring layer follows: \"int\": Return vocabulary indices input tokens. \"one_hot\": Encodes individual element input array size vocabulary, containing 1 element index. last dimension size 1, encode dimension. last dimension size 1, append new dimension encoded output. \"multi_hot\": Encodes sample input single array size vocabulary, containing 1 vocabulary term present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). \"count\": \"multi_hot\", int array contains count number times token index appeared sample. \"tf_idf\": \"multi_hot\", TF-IDF algorithm applied find value token slot. \"int\" output, shape input output supported. output modes, currently output rank 2 supported. Defaults \"int\". sparse Boolean. applicable \"multi_hot\", \"count\", \"tf_idf\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. pad_to_max_tokens applicable output_mode \"multi_hot\", \"count\", \"tf_idf\". TRUE, output feature axis padded max_tokens even number unique tokens vocabulary less max_tokens, resulting tensor shape (batch_size, max_tokens) regardless vocabulary size. Defaults FALSE. name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"Creating lookup layer known vocabulary example creates lookup layer pre-existing vocabulary.     Creating lookup layer adapted vocabulary example creates lookup layer generates vocabulary analyzing dataset.     Note OOV token -1 added vocabulary. remaining tokens sorted frequency (42, 2 occurrences, first) inverse sort order.     Lookups multiple OOV indices example demonstrates use lookup layer multiple OOV indices.  layer created one OOV index, OOV tokens hashed number OOV buckets, distributing OOV tokens deterministic fashion across set.     Note output OOV token 37 1, output OOV token 1000 0. -vocab terms output index increased 1 earlier examples (12 maps 2, etc) order make space extra OOV token. One-hot output Configure layer output_mode='one_hot'. Note first num_oov_indices dimensions ont_hot encoding represent OOV values.     Multi-hot output Configure layer output_mode = 'multi_hot'. Note first num_oov_indices dimensions multi_hot encoding represent OOV tokens     Token count output Configure layer output_mode='count'. multi_hot output, first num_oov_indices dimensions output represent OOV tokens.     TF-IDF output Configure layer output_mode='tf_idf'. multi_hot output, first num_oov_indices dimensions output represent OOV tokens. token bin output token_count * idf_weight, idf weights inverse document frequency weights per token. provided along vocabulary. Note idf_weight OOV tokens default average idf weights passed .     specify idf weights oov tokens, need pass entire vocabulary including leading oov token.     adapting layer \"tf_idf\" mode, input sample considered document, IDF weight per token calculated : log(1 + num_documents / (1 + token_document_count)). Inverse lookup example demonstrates map indices tokens using layer. (can also use adapt() inverse = TRUE, simplicity pass vocab example.)     Note first index correspond oov token default. Forward inverse lookup pairs example demonstrates use vocabulary standard lookup layer create inverse lookup layer.     example, input token 1000 resulted output -1, since 1000 vocabulary - got represented OOV, OOV tokens returned -1 inverse layer. Also, note inverse work, must already set forward layer vocabulary either directly via adapt() calling get_vocabulary().","code":"vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(rbind(c(12, 1138, 42),                       c(42, 1000, 36)))  # Note OOV tokens out <- data |> layer_integer_lookup(vocabulary = vocab) out ## tf.Tensor( ## [[1 3 4] ##  [4 0 2]], shape=(2, 3), dtype=int64) data <- op_array(rbind(c(12, 1138, 42),                       c(42, 1000, 36)))  # Note OOV tokens layer <- layer_integer_lookup() layer |> adapt(data) layer |> get_vocabulary() |> str() ## List of 6 ##  $ : int -1 ##  $ : num 42 ##  $ : num 1138 ##  $ : num 1000 ##  $ : num 36 ##  $ : num 12 layer(data) ## tf.Tensor( ## [[5 2 1] ##  [1 3 4]], shape=(2, 3), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(rbind(c(12, 1138, 42),                       c(37, 1000, 36)))  # Note OOV tokens out <- data |>   layer_integer_lookup(vocabulary = vocab,                        num_oov_indices = 2) out ## tf.Tensor( ## [[2 4 5] ##  [1 0 3]], shape=(2, 3), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(c(12, 36, 1138, 42, 7), 'int32')  # Note OOV tokens layer <- layer_integer_lookup(vocabulary = vocab,                               output_mode = 'one_hot') layer(data) ## tf.Tensor( ## [[0 1 0 0 0] ##  [0 0 1 0 0] ##  [0 0 0 1 0] ##  [0 0 0 0 1] ##  [1 0 0 0 0]], shape=(5, 5), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(rbind(c(12, 1138, 42, 42),                       c(42,    7, 36,  7)), \"int64\")  # Note OOV tokens layer <- layer_integer_lookup(vocabulary = vocab,                               output_mode = 'multi_hot') layer(data) ## tf.Tensor( ## [[0 1 0 1 1] ##  [1 0 1 0 1]], shape=(2, 5), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- rbind(c(12, 1138, 42, 42),               c(42,    7, 36,  7)) |> op_array(\"int64\") layer <- layer_integer_lookup(vocabulary = vocab,                               output_mode = 'count') layer(data) ## tf.Tensor( ## [[0 1 0 1 2] ##  [2 0 1 0 1]], shape=(2, 5), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() idf_weights <- c(0.25, 0.75, 0.6, 0.4) data <- rbind(c(12, 1138, 42, 42),               c(42,    7, 36,  7)) |> op_array(\"int64\") layer <- layer_integer_lookup(output_mode = 'tf_idf',                               vocabulary = vocab,                               idf_weights = idf_weights) layer(data) ## tf.Tensor( ## [[0.   0.25 0.   0.6  0.8 ] ##  [1.   0.   0.75 0.   0.4 ]], shape=(2, 5), dtype=float32) vocab <- c(-1, 12, 36, 1138, 42) |> as.integer() idf_weights <- c(0.9, 0.25, 0.75, 0.6, 0.4) data <- rbind(c(12, 1138, 42, 42),               c(42,    7, 36,  7)) |> op_array(\"int64\") layer <- layer_integer_lookup(output_mode = 'tf_idf',                               vocabulary = vocab,                               idf_weights = idf_weights) layer(data) ## tf.Tensor( ## [[0.   0.25 0.   0.6  0.8 ] ##  [1.8  0.   0.75 0.   0.4 ]], shape=(2, 5), dtype=float32) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(c(1, 3, 4,                   4, 0, 2)) |> op_reshape(c(2,-1)) |> op_cast(\"int32\") layer <- layer_integer_lookup(vocabulary = vocab, invert = TRUE) layer(data) ## tf.Tensor( ## [[  12 1138   42] ##  [  42   -1   36]], shape=(2, 3), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(rbind(c(12, 1138, 42), c(42, 1000, 36)), \"int32\") layer <- layer_integer_lookup(vocabulary = vocab) i_layer <- layer_integer_lookup(vocabulary = get_vocabulary(layer),                                 invert = TRUE) int_data <- layer(data) i_layer(int_data) ## tf.Tensor( ## [[  12 1138   42] ##  [  42   -1   36]], shape=(2, 3), dtype=int64)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"layer enables use JAX components within Keras using JAX backend Keras.","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"","code":"layer_jax_model_wrapper(   object,   call_fn,   init_fn = NULL,   params = NULL,   state = NULL,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"object Object compose layer . tensor, array, sequential model. call_fn function call model. See description list arguments takes outputs returns. init_fn function call initialize model. See description list arguments takes ouputs returns. NULL, params /state must provided. params PyTree containing model trainable parameters. allows passing trained parameters controlling initialization. params state NULL, init_fn() called build time initialize trainable parameters model. state PyTree containing model non-trainable state. allows passing learned state controlling initialization. params state NULL, call_fn() takes state argument, init_fn() called build time initialize non-trainable state model. seed Seed random number generator. Optional. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"model-function","dir":"Reference","previous_headings":"","what":"Model function","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"layer accepts JAX models form function, call_fn(), must take following arguments exact names: params: trainable parameters model. state (optional): non-trainable state model. Can omitted model non-trainable state. rng (optional): jax.random.PRNGKey instance. Can omitted model need RNGs, neither training inference. inputs: inputs model, JAX array PyTree arrays. training (optional): argument specifying training mode inference mode, TRUE passed training mode. Can omitted model behaves training mode inference mode. inputs argument mandatory. Inputs model must provided via single argument. JAX model takes multiple inputs separate arguments, must combined single structure, instance tuple() dict().","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"model-weights-initialization","dir":"Reference","previous_headings":"","what":"Model weights initialization","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"initialization params state model can handled layer, case init_fn() argument must provided. allows model initialized dynamically right shape. Alternatively, shape known, params argument optionally state argument can used create already initialized model. init_fn() function, provided, must take following arguments exact names: rng: jax.random.PRNGKey instance. inputs: JAX array PyTree arrays placeholder values provide shape inputs. training (optional): argument specifying training mode inference mode. True always passed init_fn. Can omitted regardless whether call_fn training argument.","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"models-with-non-trainable-state","dir":"Reference","previous_headings":"","what":"Models with non-trainable state","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"JAX models non-trainable state: call_fn() must state argument call_fn() must return tuple() containing outputs model new non-trainable state model init_fn() must return tuple() containing initial trainable params model initial non-trainable state model. code shows possible combination call_fn() init_fn() signatures model non-trainable state. example, model training argument rng argument call_fn().","code":"stateful_call <- function(params, state, rng, inputs, training) {   outputs <- ....   new_state <- ....   tuple(outputs, new_state) }  stateful_init <- function(rng, inputs) {   initial_params <- ....   initial_state <- ....   tuple(initial_params, initial_state) }"},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"models-without-non-trainable-state","dir":"Reference","previous_headings":"","what":"Models without non-trainable state","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"JAX models non-trainable state: call_fn() must state argument call_fn() must return outputs model init_fn() must return initial trainable params model. code shows possible combination call_fn() init_fn() signatures model without non-trainable state. example, model training argument rng argument call_fn().","code":"stateful_call <- function(pparams, inputs) {   outputs <- ....   outputs }  stateful_init <- function(rng, inputs) {   initial_params <- ....   initial_params }"},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"conforming-to-the-required-signature","dir":"Reference","previous_headings":"","what":"Conforming to the required signature","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"model different signature one required JaxLayer, one can easily write wrapper method adapt arguments. example shows model multiple inputs separate arguments, expects multiple RNGs dict, deterministic argument opposite meaning training. conform, inputs combined single structure using tuple, RNG split used populate expected dict, Boolean flag negated:","code":"jax <- import(\"jax\") my_model_fn <- function(params, rngs, input1, input2, deterministic) {   ....   if (!deterministic) {     dropout_rng <- rngs$dropout     keep <- jax$random$bernoulli(dropout_rng, dropout_rate, x$shape)     x <- jax$numpy$where(keep, x / dropout_rate, 0)     ....   }   ....   return(outputs) }  my_model_wrapper_fn <- function(params, rng, inputs, training) {   c(input1, input2) %<-% inputs   c(rng1, rng2) %<-% jax$random$split(rng)   rngs <-  list(dropout = rng1, preprocessing = rng2)   deterministic <-  !training   my_model_fn(params, rngs, input1, input2, deterministic) }  keras_layer <- layer_jax_model_wrapper(call_fn = my_model_wrapper_fn,                                        params = initial_params)"},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"usage-with-haiku-modules","dir":"Reference","previous_headings":"","what":"Usage with Haiku modules","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"JaxLayer enables use Haiku components form haiku.Module. achieved transforming module per Haiku pattern passing module.apply call_fn parameter module.init init_fn parameter needed. model non-trainable state, transformed haiku.transform_with_state. model non-trainable state, transformed haiku.transform. Additionally, optionally, module use RNGs \"apply\", can transformed haiku.without_apply_rng. following example shows create JaxLayer Haiku module uses random number generators via hk.next_rng_key() takes training positional argument:","code":"# reticulate::py_install(\"haiku\", \"r-keras\") hk <- import(\"haiku\") MyHaikuModule(hk$Module) \\%py_class\\% {    `__call__` <- \\(self, x, training) {     x <- hk$Conv2D(32L, tuple(3L, 3L))(x)     x <- jax$nn$relu(x)     x <- hk$AvgPool(tuple(1L, 2L, 2L, 1L),                     tuple(1L, 2L, 2L, 1L), \"VALID\")(x)     x <- hk$Flatten()(x)     x <- hk$Linear(200L)(x)     if (training)       x <- hk$dropout(rng = hk$next_rng_key(), rate = 0.3, x = x)     x <- jax$nn$relu(x)     x <- hk$Linear(10L)(x)     x <- jax$nn$softmax(x)     x   }  }  my_haiku_module_fn <- function(inputs, training) {   module <- MyHaikuModule()   module(inputs, training) }  transformed_module <- hk$transform(my_haiku_module_fn)  keras_layer <-   layer_jax_model_wrapper(call_fn = transformed_module$apply,                           init_fn = transformed_module$init)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":null,"dir":"Reference","previous_headings":"","what":"Wraps arbitrary expressions as a Layer object. — layer_lambda","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"layer_lambda() layer exists arbitrary expressions can used Layer constructing Sequential Functional API models. Lambda layers best suited simple operations quick experimentation. advanced use cases, prefer writing new subclasses Layer using new_layer_class().","code":""},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"","code":"layer_lambda(   object,   f,   output_shape = NULL,   mask = NULL,   arguments = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"object Object compose layer . tensor, array, sequential model. f function evaluated. Takes input tensor first argument. output_shape Expected output shape function. argument can usually inferred explicitly provided. Can list function. list, specifies first dimension onward; sample dimension assumed either input: output_shape = c(input_shape[1], output_shape) , input NULL sample dimension also NULL: output_shape = c(NA, output_shape). function, specifies entire shape function input shape: output_shape = f(input_shape). mask Either NULL (indicating masking) callable signature compute_mask layer method, tensor returned output mask regardless input . arguments Optional named list arguments passed function. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"","code":"# add a x -> x^2 layer model <- keras_model_sequential() model |> layer_lambda(\\(x) x^2)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"Normalize activations previous layer given example batch independently, rather across batch like Batch Normalization. .e. applies transformation maintains mean activation within example close 0 activation standard deviation close 1. scale center enabled, layer scale normalized outputs broadcasting trainable variable gamma, center outputs broadcasting trainable variable beta. gamma default ones tensor beta default zeros tensor, centering scaling -ops training begun. , scaling centering enabled normalization equations follows: Let intermediate activations mini-batch inputs. sample x batch inputs, compute mean variance sample, normalize value sample (including small factor epsilon numerical stability), finally, transform normalized output gamma beta, learned parameters:   gamma beta span axes inputs specified axis, part inputs' shape must fully defined. example:         Note implementations layer normalization may choose define gamma beta separate set axes axes normalized across. example, Group Normalization (Wu et al. 2018) group size 1 corresponds layer_layer_normalization() normalizes across height, width, channel gamma beta span channel dimension. , layer_layer_normalization() implementation match layer_group_normalization() layer group size set 1.","code":"outputs <- inputs |> apply(1, function(x) {   x_normalized <- (x - mean(x)) /                   sqrt(var(x) + epsilon)   x_normalized * gamma + beta }) layer <- layer_layer_normalization(axis = c(2, 3, 4))  layer(op_ones(c(5, 20, 30, 40))) |> invisible() # build() shape(layer$beta) ## shape(20, 30, 40) shape(layer$gamma) ## shape(20, 30, 40)"},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"","code":"layer_layer_normalization(   object,   axis = -1L,   epsilon = 0.001,   center = TRUE,   scale = TRUE,   rms_scaling = FALSE,   beta_initializer = \"zeros\",   gamma_initializer = \"ones\",   beta_regularizer = NULL,   gamma_regularizer = NULL,   beta_constraint = NULL,   gamma_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"object Object compose layer . tensor, array, sequential model. axis Integer list. axis axes normalize across. Typically, features axis/axes. left-axes typically batch axis/axes. -1 last dimension input. Defaults -1. epsilon Small float added variance avoid dividing zero. Defaults 1e-3. center TRUE, add offset beta normalized tensor. FALSE, beta ignored. Defaults TRUE. scale TRUE, multiply gamma. FALSE, gamma used. next layer linear (also e.g. layer_activation_relu()), can disabled since scaling done next layer. Defaults TRUE. rms_scaling TRUE, center scale ignored, inputs scaled gamma inverse square root square inputs. approximate faster approach avoids ever computing mean input. beta_initializer Initializer beta weight. Defaults zeros. gamma_initializer Initializer gamma weight. Defaults ones. beta_regularizer Optional regularizer beta weight. NULL default. gamma_regularizer Optional regularizer gamma weight. NULL default. beta_constraint Optional constraint beta weight. NULL default. gamma_constraint Optional constraint gamma weight. NULL default. ... Base layer keyword arguments (e.g. name dtype).","code":""},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"Lei Ba et al., 2016.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":null,"dir":"Reference","previous_headings":"","what":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"Based available runtime hardware constraints, layer choose different implementations (cuDNN-based backend-native) maximize performance. GPU available arguments layer meet requirement cuDNN kernel (see details), layer use fast cuDNN implementation using TensorFlow backend. requirements use cuDNN implementation : activation == tanh recurrent_activation == sigmoid dropout == 0 recurrent_dropout == 0 unroll FALSE use_bias TRUE Inputs, use masking, strictly right-padded. Eager execution enabled outermost context. example:","code":"input <- random_uniform(c(32, 10, 8)) output <- input |> layer_lstm(4) shape(output) ## shape(32, 4) lstm <- layer_lstm(units = 4, return_sequences = TRUE, return_state = TRUE) c(whole_seq_output, final_memory_state, final_carry_state) %<-% lstm(input) shape(whole_seq_output) ## shape(32, 10, 4) shape(final_memory_state) ## shape(32, 4) shape(final_carry_state) ## shape(32, 4)"},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"","code":"layer_lstm(   object,   units,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   unroll = FALSE,   use_cudnn = \"auto\",   ... )"},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"object Object compose layer . tensor, array, sequential model. units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). recurrent_activation Activation function use recurrent step. Default: sigmoid (sigmoid). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". unit_forget_bias Boolean (default TRUE). TRUE, add 1 bias forget gate initialization. Setting TRUE also force bias_initializer=\"zeros\". recommended Jozefowicz et al. kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. activity_regularizer Regularizer function applied output layer (\"activation\"). Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default: FALSE). TRUE, last state sample index batch used initial state sample index following batch. unroll Boolean (default FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences. use_cudnn Whether use cuDNN-backed implementation. \"auto\" attempt use cuDNN feasible, fallback default implementation . ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"inputs: 3D tensor, shape (batch, timesteps, feature). mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked  (optional). individual TRUE entry indicates corresponding timestep utilized, FALSE entry indicates corresponding timestep ignored. Defaults NULL. training: Boolean indicating whether layer behave training mode inference mode. argument passed cell calling . relevant dropout recurrent_dropout used  (optional). Defaults NULL. initial_state: List initial state tensors passed first call cell (optional, NULL causes creation zero-filled initial state tensors). Defaults NULL.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_masking.html","id":null,"dir":"Reference","previous_headings":"","what":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"timestep input tensor (dimension #1 tensor), values input tensor timestep equal mask_value, timestep masked (skipped) downstream layers (long support masking). downstream layer support masking yet receives input mask, exception raised.","code":""},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"","code":"layer_masking(object, mask_value = 0, ...)"},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"object Object compose layer . tensor, array, sequential model. mask_value see description ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"Consider array x shape c(samples, timesteps, features), fed LSTM layer. want mask timestep #3 #5 lack data timesteps. can: Set x[, 3, ] <- 0. x[, 5, ] <- 0. Insert layer_masking() layer mask_value = 0. LSTM layer:","code":"c(samples, timesteps, features) %<-% c(32, 10, 8) inputs <- c(samples, timesteps, features) %>% { array(runif(prod(.)), dim = .) } inputs[, 3, ] <- 0 inputs[, 5, ] <- 0  model <- keras_model_sequential() %>%   layer_masking(mask_value = 0) %>%   layer_lstm(32)  output <- model(inputs) # The time step 3 and 5 will be skipped from LSTM calculation."},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"Keras masking convention, masked timestep denoted mask value FALSE, non-masked (.e. usable) timestep denoted mask value TRUE.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"Downsamples input representation taking maximum value spatial window size pool_size. window shifted strides. resulting output using \"valid\" padding option shape : output_shape = (input_shape - pool_size + 1) / strides). resulting output shape using \"\" padding option : output_shape = input_shape / strides","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"","code":"layer_max_pooling_1d(   object,   pool_size = 2L,   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"object Object compose layer . tensor, array, sequential model. pool_size int, size max pooling window. strides int NULL. Specifies much pooling window moves pooling step. NULL, default pool_size. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"data_format=\"channels_last\": 3D tensor shape (batch_size, steps, features). data_format=\"channels_first\": 3D tensor shape (batch_size, features, steps).","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"data_format=\"channels_last\": 3D tensor shape (batch_size, downsampled_steps, features). data_format=\"channels_first\": 3D tensor shape (batch_size, features, downsampled_steps).","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"strides=1 padding=\"valid\":     strides=2 padding=\"valid\":     strides=1 padding=\"\":","code":"x <- op_reshape(c(1, 2, 3, 4, 5),                c(1, 5, 1)) max_pool_1d <- layer_max_pooling_1d(pool_size = 2,                                     strides = 1,                                     padding = \"valid\") max_pool_1d(x) ## tf.Tensor( ## [[[2.] ##   [3.] ##   [4.] ##   [5.]]], shape=(1, 4, 1), dtype=float32) x <- op_reshape(c(1, 2, 3, 4, 5),                c(1, 5, 1)) max_pool_1d <- layer_max_pooling_1d(pool_size = 2,                                     strides = 2,                                     padding = \"valid\") max_pool_1d(x) ## tf.Tensor( ## [[[2.] ##   [4.]]], shape=(1, 2, 1), dtype=float32) x <- op_reshape(c(1, 2, 3, 4, 5),                c(1, 5, 1)) max_pool_1d <- layer_max_pooling_1d(pool_size = 2,                                     strides = 1,                                     padding = \"same\") max_pool_1d(x) ## tf.Tensor( ## [[[2.] ##   [3.] ##   [4.] ##   [5.] ##   [5.]]], shape=(1, 5, 1), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"Downsamples input along spatial dimensions (height width) taking maximum value input window (size defined pool_size) channel input. window shifted strides along dimension. resulting output using \"valid\" padding option spatial shape (number rows columns) : output_shape = floor((input_shape - pool_size) / strides) + 1 (input_shape >= pool_size) resulting output shape using \"\" padding option : output_shape = floor((input_shape - 1) / strides) + 1","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"","code":"layer_max_pooling_2d(   object,   pool_size = list(2L, 2L),   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"object Object compose layer . tensor, array, sequential model. pool_size int list 2 integers, factors downscale (dim1, dim2). one integer specified, window length used dimensions. strides int list 2 integers, NULL. Strides values. NULL, default pool_size. one int specified, stride size used dimensions. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"data_format=\"channels_last\": 4D tensor shape (batch_size, height, width, channels). data_format=\"channels_first\": 4D tensor shape (batch_size, channels, height, width).","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"data_format=\"channels_last\": 4D tensor shape (batch_size, pooled_height, pooled_width, channels). data_format=\"channels_first\": 4D tensor shape (batch_size, channels, pooled_height, pooled_width).","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"strides = (1, 1) padding = \"valid\":     strides = c(2, 2) padding = \"valid\":     stride = (1, 1) padding = \"\":","code":"x <- rbind(c(1., 2., 3.),            c(4., 5., 6.),            c(7., 8., 9.)) |> op_reshape(c(1, 3, 3, 1)) max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),                                     strides = c(1, 1),                                     padding = \"valid\") max_pool_2d(x) ## tf.Tensor( ## [[[[5.] ##    [6.]] ## ##   [[8.] ##    [9.]]]], shape=(1, 2, 2, 1), dtype=float32) x <- rbind(c(1., 2., 3., 4.),            c(5., 6., 7., 8.),            c(9., 10., 11., 12.)) |> op_reshape(c(1, 3, 4, 1)) max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),                                     strides = c(2, 2),                                     padding = \"valid\") max_pool_2d(x) ## tf.Tensor( ## [[[[6.] ##    [8.]]]], shape=(1, 1, 2, 1), dtype=float32) x <- rbind(c(1., 2., 3.),            c(4., 5., 6.),            c(7., 8., 9.)) |> op_reshape(c(1, 3, 3, 1)) max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),                                     strides = c(1, 1),                                     padding = \"same\") max_pool_2d(x) ## tf.Tensor( ## [[[[5.] ##    [6.] ##    [6.]] ## ##   [[8.] ##    [9.] ##    [9.]] ## ##   [[8.] ##    [9.] ##    [9.]]]], shape=(1, 3, 3, 1), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"Downsamples input along spatial dimensions (depth, height, width) taking maximum value input window (size defined pool_size) channel input. window shifted strides along dimension.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"","code":"layer_max_pooling_3d(   object,   pool_size = list(2L, 2L, 2L),   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"object Object compose layer . tensor, array, sequential model. pool_size int list 3 integers, factors downscale (dim1, dim2, dim3). one integer specified, window length used dimensions. strides int list 3 integers, NULL. Strides values. NULL, default pool_size. one int specified, stride size used dimensions. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"","code":"depth <- 30 height <- 30 width <- 30 channels <- 3  inputs <- layer_input(shape=c(depth, height, width, channels)) layer <- layer_max_pooling_3d(pool_size=3) outputs <- inputs |> layer() outputs ## <KerasTensor shape=(None, 10, 10, 10, 3), dtype=float32, sparse=False, name=keras_tensor_1>"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes element-wise maximum on a list of inputs. — layer_maximum","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"","code":"layer_maximum(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"Usage Keras model:","code":"input_shape <- c(2, 3, 4) x1 <- random_uniform(input_shape) x2 <- random_uniform(input_shape) y <- layer_maximum(x1, x2) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu') input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu') # equivalent to `y <- layer_maximum(x1, x2)` y <- layer_maximum(x1, x2) out <- y |> layer_dense(4) model <- keras_model(inputs = c(input1, input2), outputs = out)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"layer takes float32/float64 single batched audio signal inputs computes Mel spectrogram using Short-Time Fourier Transform Mel scaling. input 1D (unbatched) 2D (batched) tensor representing audio signals. output 2D 3D tensor representing Mel spectrograms. spectrogram image-like representation shows frequency spectrum signal time. uses x-axis represent time, y-axis represent frequency, pixel represent intensity. Mel spectrograms special type spectrogram use mel scale, approximates humans perceive sound. commonly used speech music processing tasks like speech recognition, speaker identification, music genre classification.","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"","code":"layer_mel_spectrogram(   object,   fft_length = 2048L,   sequence_stride = 512L,   sequence_length = NULL,   window = \"hann\",   sampling_rate = 16000L,   num_mel_bins = 128L,   min_freq = 20,   max_freq = NULL,   power_to_db = TRUE,   top_db = 80,   mag_exp = 2,   min_power = 1e-10,   ref_power = 1,   ... )"},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"object Object compose layer . tensor, array, sequential model. fft_length Integer, size FFT window. sequence_stride Integer, number samples successive STFT columns. sequence_length Integer, size window used applying window audio frame. NULL, defaults fft_length. window String, name window function use. Available values \"hann\" \"hamming\". window tensor, used directly window length must sequence_length. window NULL, windowing used. Defaults \"hann\". sampling_rate Integer, sample rate input signal. num_mel_bins Integer, number mel bins generate. min_freq Float, minimum frequency mel bins. max_freq Float, maximum frequency mel bins. NULL, defaults sampling_rate / 2. power_to_db TRUE, convert power spectrogram decibels. top_db Float, minimum negative cut-max(10 * log10(S)) - top_db. mag_exp Float, exponent magnitude spectrogram. 1 magnitude, 2 power, etc. Default 2. min_power Float, minimum value power ref_power. ref_power Float, power scaled relative 10 * log10(S / ref_power). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"Spectrogram, Mel scale.","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"Unbatched audio signal   Batched audio signal","code":"layer <- layer_mel_spectrogram(   num_mel_bins = 64,   sampling_rate = 8000,   sequence_stride = 256,   fft_length = 2048 ) layer(random_uniform(shape = c(16000))) |> shape() layer <- layer_mel_spectrogram(   num_mel_bins = 80,   sampling_rate = 8000,   sequence_stride = 128,   fft_length = 2048 ) layer(random_uniform(shape = c(2, 16000))) |> shape()"},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"1D (unbatched) 2D (batched) tensor shape:(..., samples).","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"2D (unbatched) 3D (batched) tensor shape:(..., num_mel_bins, time).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes elementwise minimum on a list of inputs. — layer_minimum","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"","code":"layer_minimum(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"Usage Keras model:","code":"input_shape <- c(2, 3, 4) x1 <- random_uniform(input_shape) x2 <- random_uniform(input_shape) y <- layer_minimum(x1, x2) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu') input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu') # equivalent to `y <- layer_minimum(x1, x2)` y <- layer_minimum(x1, x2) out <- y |> layer_dense(4) model <- keras_model(inputs = c(input1, input2), outputs = out)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi Head Attention layer. — layer_multi_head_attention","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"implementation multi-headed attention described paper \"Attention Need\" Vaswani et al., 2017. query, key, value , self-attention. timestep query attends corresponding sequence key, returns fixed-width vector. layer first projects query, key value. (effectively) list tensors length num_attention_heads, corresponding shapes (batch_size, <query dimensions>, key_dim), (batch_size, <key/value dimensions>, key_dim), (batch_size, <key/value dimensions>, value_dim). , query key tensors dot-producted scaled. softmaxed obtain attention probabilities. value tensors interpolated probabilities, concatenated back single tensor. Finally, result tensor last dimension value_dim can take linear projection return.","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"","code":"layer_multi_head_attention(   inputs,   num_heads,   key_dim,   value_dim = NULL,   dropout = 0,   use_bias = TRUE,   output_shape = NULL,   attention_axes = NULL,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"inputs see description num_heads Number attention heads. key_dim Size attention head query key. value_dim Size attention head value. dropout Dropout probability. use_bias Boolean, whether dense layers use bias vectors/matrices. output_shape expected shape output tensor, besides batch sequence dims. specified, projects back query feature dim (query input's last dimension). attention_axes axes attention applied. NULL means attention axes, batch, heads, features. kernel_initializer Initializer dense layer kernels. bias_initializer Initializer dense layer biases. kernel_regularizer Regularizer dense layer kernels. bias_regularizer Regularizer dense layer biases. activity_regularizer Regularizer dense layer activity. kernel_constraint Constraint dense layer kernels. bias_constraint Constraint dense layer kernels. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"query: Query tensor shape (B, T, dim), B batch size, T target sequence length, dim feature dimension. value: Value tensor shape (B, S, dim), B batch size, S source sequence length, dim feature dimension. key: Optional key tensor shape (B, S, dim). given, use value key value, common case. attention_mask: boolean mask shape (B, T, S), prevents attention certain positions. boolean mask specifies query elements can attend key elements, 1 indicates attention 0 indicates attention. Broadcasting can happen missing batch dimensions head dimension. return_attention_scores: boolean indicate whether output (attention_output, attention_scores) TRUE, attention_output FALSE. Defaults FALSE. training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (dropout). go either using training mode parent layer/model, FALSE (inference) parent layer. use_causal_mask: boolean indicate whether apply causal mask prevent tokens attending future tokens (e.g., used decoder Transformer).","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"call-return","dir":"Reference","previous_headings":"","what":"Call return","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"attention_output: result computation, shape (B, T, E), T target sequence shapes E query input last dimension output_shape NULL. Otherwise, multi-head outputs projected shape specified output_shape. attention_scores: (Optional) multi-head attention coefficients attention axes.","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"properties","dir":"Reference","previous_headings":"","what":"Properties","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"MultiHeadAttention Layer instance following additional read-properties: attention_axes dropout key_dense key_dim num_heads output_dense output_shape query_dense use_bias value_dense value_dim","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs elementwise multiplication. — layer_multiply","title":"Performs elementwise multiplication. — layer_multiply","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs elementwise multiplication. — layer_multiply","text":"","code":"layer_multiply(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs elementwise multiplication. — layer_multiply","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs elementwise multiplication. — layer_multiply","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs elementwise multiplication. — layer_multiply","text":"Usage Keras model:","code":"input_shape <- c(2, 3, 4) x1 <- random_uniform(input_shape) x2 <- random_uniform(input_shape) y <- layer_multiply(x1, x2) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu') input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu') # equivalent to `y <- layer_multiply(x1, x2)` y <- layer_multiply(x1, x2) out <- y |> layer_dense(4) model <- keras_model(inputs = c(input1, input2), outputs = out)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer that normalizes continuous features. — layer_normalization","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"layer shift scale inputs distribution centered around 0 standard deviation 1. accomplishes precomputing mean variance data, calling (input - mean) / sqrt(var) runtime. mean variance values layer must either supplied construction learned via adapt(). adapt() compute mean variance data store layer's weights. adapt() called fit(), evaluate(), predict().","code":""},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"","code":"layer_normalization(   object,   axis = -1L,   mean = NULL,   variance = NULL,   invert = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"object Object compose layer . tensor, array, sequential model. axis Integer, list integers, NULL. axis axes separate mean variance index shape. example, shape (NULL, 5) axis=1, layer track 5 separate mean variance values last axis. axis set NULL, layer normalize elements input scalar mean variance. -1, last axis input assumed feature dimension normalized per index. Note specific case batched scalar inputs axis batch axis, default normalize index batch separately. case, consider passing axis=NULL. Defaults -1. mean mean value(s) use normalization. passed value(s) broadcast shape kept axes ; value(s) broadcast, error raised layer's build() method called. variance variance value(s) use normalization. passed value(s) broadcast shape kept axes ; value(s) broadcast, error raised layer's build() method called. invert TRUE, layer apply inverse transformation inputs: turn normalized input back original form. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"Calculate global mean variance analyzing dataset adapt().     Calculate mean variance index last axis.     Pass mean variance directly.     Use layer de-normalize inputs (adapting layer).","code":"adapt_data <- op_array(c(1., 2., 3., 4., 5.), dtype='float32') input_data <- op_array(c(1., 2., 3.), dtype='float32') layer <- layer_normalization(axis = NULL) layer %>% adapt(adapt_data) layer(input_data) ## tf.Tensor([-1.4142135  -0.70710677  0.        ], shape=(3), dtype=float32) adapt_data <- op_array(rbind(c(0., 7., 4.),                        c(2., 9., 6.),                        c(0., 7., 4.),                        c(2., 9., 6.)), dtype='float32') input_data <- op_array(matrix(c(0., 7., 4.), nrow = 1), dtype='float32') layer <- layer_normalization(axis=-1) layer %>% adapt(adapt_data) layer(input_data) ## tf.Tensor([[-1. -1. -1.]], shape=(1, 3), dtype=float32) input_data <- op_array(rbind(1, 2, 3), dtype='float32') layer <- layer_normalization(mean=3., variance=2.) layer(input_data) ## tf.Tensor( ## [[-1.4142135 ] ##  [-0.70710677] ##  [ 0.        ]], shape=(3, 1), dtype=float32) adapt_data <- op_array(rbind(c(0., 7., 4.),                        c(2., 9., 6.),                        c(0., 7., 4.),                        c(2., 9., 6.)), dtype='float32') input_data <- op_array(c(1., 2., 3.), dtype='float32') layer <- layer_normalization(axis=-1, invert=TRUE) layer %>% adapt(adapt_data) layer(input_data) ## tf.Tensor([[ 2. 10.  8.]], shape=(1, 3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_permute.html","id":null,"dir":"Reference","previous_headings":"","what":"Permutes the dimensions of the input according to a given pattern. — layer_permute","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"Useful e.g. connecting RNNs convnets.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"","code":"layer_permute(object, dims, ...)"},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"object Object compose layer . tensor, array, sequential model. dims List integers. Permutation pattern include batch dimension. Indexing starts 1. instance, c(2, 1) permutes first second dimensions input. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"Arbitrary.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"input shape, dimensions re-ordered according specified pattern.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"","code":"x <- layer_input(shape=c(10, 64)) y <- layer_permute(x, c(2, 1)) shape(y) ## shape(NA, 64, 10)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"layer randomly increase/reduce brightness input RGB images. inference time, output identical input. Call layer training=TRUE adjust brightness input. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"","code":"layer_random_brightness(   object,   factor,   value_range = list(0L, 255L),   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"object Object compose layer . tensor, array, sequential model. factor Float list 2 floats -1.0 1.0. factor used determine lower bound upper bound brightness adjustment. float value chosen randomly limits. -1.0 chosen, output image black, 1.0 chosen, image fully white. one float provided, eg, 0.2, -0.2 used lower bound 0.2 used upper bound. value_range Optional list 2 floats lower upper limit values input data. make change, use c(0.0, 1.0), e.g., image input scaled layer. Defaults c(0.0, 255.0). brightness adjustment scaled range, output values clipped range. seed optional integer, fixed RNG behavior. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"3D (HWC) 4D (NHWC) tensor, float int dtype. Input pixel values can range (e.g. [0., 1.) [0, 255])","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"output","dir":"Reference","previous_headings":"","what":"Output","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"3D (HWC) 4D (NHWC) tensor brightness adjusted based factor. default, layer output floats. output value clipped range [0, 255], valid range RGB colors, rescaled based value_range needed.","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"","code":"random_bright <- layer_random_brightness(factor=0.2, seed = 1)  # An image with shape [2, 2, 3] image <- array(1:12, dim=c(2, 2, 3))  # Assume we randomly select the factor to be 0.1, then it will apply # 0.1 * 255 to all the channel output <- random_bright(image, training=TRUE) output ## tf.Tensor( ## [[[39.605797 43.605797 47.605797] ##   [41.605797 45.605797 49.605797]] ## ##  [[40.605797 44.605797 48.605797] ##   [42.605797 46.605797 50.605797]]], shape=(2, 2, 3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"layer randomly adjust contrast image images random factor. Contrast adjusted independently channel image training. channel, layer computes mean image pixels channel adjusts component x pixel (x - mean) * contrast_factor + mean. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"","code":"layer_random_contrast(object, factor, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"object Object compose layer . tensor, array, sequential model. factor positive float represented fraction value, tuple size 2 representing lower upper bound. represented single float, lower = upper. contrast factor randomly picked [1.0 - lower, 1.0 + upper]. pixel x channel, output (x - mean) * factor + mean mean mean value channel. seed Integer. Used create random seed. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly crops images during training. — layer_random_crop","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"training, layer randomly choose location crop images target size. layer crop images batch cropping location. inference time, training input image smaller target size, input resized cropped return largest possible window image matches target aspect ratio. need apply random cropping inference time, set training TRUE calling layer. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"","code":"layer_random_crop(   object,   height,   width,   seed = NULL,   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"object Object compose layer . tensor, array, sequential model. height Integer, height output shape. width Integer, width output shape. seed Integer. Used create random seed. data_format see description name String, name object ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly flips images during training. — layer_random_flip","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"layer flip images horizontally vertically based mode attribute. inference time, output identical input. Call layer training=TRUE flip input. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"","code":"layer_random_flip(object, mode = \"horizontal_and_vertical\", seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"object Object compose layer . tensor, array, sequential model. mode String indicating flip mode use. Can \"horizontal\", \"vertical\", \"horizontal_and_vertical\". \"horizontal\" left-right flip \"vertical\" top-bottom flip. Defaults \"horizontal_and_vertical\" seed Integer. Used create random seed. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"layer apply random rotations image, filling empty space according fill_mode. default, random rotations applied training. inference time, layer nothing. need apply random rotations inference time, pass training = TRUE calling layer. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"","code":"layer_random_rotation(   object,   factor,   fill_mode = \"reflect\",   interpolation = \"bilinear\",   seed = NULL,   fill_value = 0,   value_range = list(0L, 255L),   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"object Object compose layer . tensor, array, sequential model. factor float represented fraction 2 Pi, tuple size 2 representing lower upper bound rotating clockwise counter-clockwise. positive values means rotating counter clock-wise, negative value means clock-wise. represented single float, value used upper lower bound. instance, factor=(-0.2, 0.3) results output rotation random amount range [-20% * 2pi, 30% * 2pi]. factor=0.2 results output rotating random amount range [-20% * 2pi, 20% * 2pi]. fill_mode Points outside boundaries input filled according given mode (one {\"constant\", \"reflect\", \"wrap\", \"nearest\"}). reflect: (d c b | b c d | d c b ) input extended reflecting edge last pixel. constant: (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k = 0. wrap: (b c d | b c d | b c d) input extended wrapping around opposite edge. nearest: (| b c d | d d d d) input extended nearest pixel. interpolation Interpolation mode. Supported values: \"nearest\", \"bilinear\". seed Integer. Used create random seed. fill_value float represents value filled outside boundaries fill_mode=\"constant\". value_range see description data_format see description ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format","code":""},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly translates images during training. — layer_random_translation","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"layer apply random translations image training, filling empty space according fill_mode. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats.","code":""},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"","code":"layer_random_translation(   object,   height_factor,   width_factor,   fill_mode = \"reflect\",   interpolation = \"bilinear\",   seed = NULL,   fill_value = 0,   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"object Object compose layer . tensor, array, sequential model. height_factor float represented fraction value, tuple size 2 representing lower upper bound shifting vertically. negative value means shifting image , positive value means shifting image . represented single positive float, value used upper lower bound. instance, height_factor=(-0.2, 0.3) results output shifted random amount range [-20%, +30%]. height_factor=0.2 results output height shifted random amount range [-20%, +20%]. width_factor float represented fraction value, tuple size 2 representing lower upper bound shifting horizontally. negative value means shifting image left, positive value means shifting image right. represented single positive float, value used upper lower bound. instance, width_factor=(-0.2, 0.3) results output shifted left 20%, shifted right 30%. width_factor=0.2 results output height shifted left right 20%. fill_mode Points outside boundaries input filled according given mode. Available methods \"constant\", \"nearest\", \"wrap\" \"reflect\". Defaults \"constant\". \"reflect\": (d c b | b c d | d c b ) input extended reflecting edge last pixel. \"constant\": (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k specified fill_value. \"wrap\": (b c d | b c d | b c d) input extended wrapping around opposite edge. \"nearest\": (| b c d | d d d d) input extended nearest pixel. Note using torch backend, \"reflect\" redirected \"mirror\" (c d c b | b c d | c b b) torch support \"reflect\". Note torch backend support \"wrap\". interpolation Interpolation mode. Supported values: \"nearest\", \"bilinear\". seed Integer. Used create random seed. fill_value float represents value filled outside boundaries fill_mode=\"constant\". data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format, (..., channels, height, width), \"channels_first\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels), (..., channels, target_height, target_width), \"channels_first\" format. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"layer randomly zoom axis image independently, filling empty space according fill_mode. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats.","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"","code":"layer_random_zoom(   object,   height_factor,   width_factor = NULL,   fill_mode = \"reflect\",   interpolation = \"bilinear\",   seed = NULL,   fill_value = 0,   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"object Object compose layer . tensor, array, sequential model. height_factor float represented fraction value, list size 2 representing lower upper bound zooming vertically. represented single float, value used upper lower bound. positive value means zooming , negative value means zooming . instance, height_factor=c(0.2, 0.3) result output zoomed random amount range [+20%, +30%]. height_factor=c(-0.3, -0.2) result output zoomed random amount range [+20%, +30%]. width_factor float represented fraction value, list size 2 representing lower upper bound zooming horizontally. represented single float, value used upper lower bound. instance, width_factor=c(0.2, 0.3) result output zooming 20% 30%. width_factor=c(-0.3, -0.2) result output zooming 20% 30%. NULL means .e., zooming vertical horizontal directions preserving aspect ratio. Defaults NULL. fill_mode Points outside boundaries input filled according given mode. Available methods \"constant\", \"nearest\", \"wrap\" \"reflect\". Defaults \"constant\". \"reflect\": (d c b | b c d | d c b ) input extended reflecting edge last pixel. \"constant\": (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k specified fill_value. \"wrap\": (b c d | b c d | b c d) input extended wrapping around opposite edge. \"nearest\": (| b c d | d d d d) input extended nearest pixel. Note using torch backend, \"reflect\" redirected \"mirror\" (c d c b | b c d | c b b) torch support \"reflect\". Note torch backend support \"wrap\". interpolation Interpolation mode. Supported values: \"nearest\", \"bilinear\". seed Integer. Used create random seed. fill_value float represents value filled outside boundaries fill_mode=\"constant\". data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format, (..., channels, height, width), \"channels_first\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels), (..., channels, target_height, target_width), \"channels_first\" format. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"","code":"input_img <- random_uniform(c(32, 224, 224, 3)) layer <- layer_random_zoom(height_factor = .5, width_factor = .2) out_img <- layer(input_img)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeats the input n times. — layer_repeat_vector","title":"Repeats the input n times. — layer_repeat_vector","text":"Repeats input n times.","code":""},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeats the input n times. — layer_repeat_vector","text":"","code":"layer_repeat_vector(object, n, ...)"},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeats the input n times. — layer_repeat_vector","text":"object Object compose layer . tensor, array, sequential model. n Integer, repetition factor. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Repeats the input n times. — layer_repeat_vector","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Repeats the input n times. — layer_repeat_vector","text":"","code":"x <- layer_input(shape = 32) y <- layer_repeat_vector(x, n = 3) shape(y) ## shape(NA, 3, 32)"},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Repeats the input n times. — layer_repeat_vector","text":"2D tensor shape (batch_size, features).","code":""},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Repeats the input n times. — layer_repeat_vector","text":"3D tensor shape (batch_size, n, features).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_rescaling.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","title":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","text":"layer rescales every value input (often image) multiplying scale adding offset. instance: rescale input [0, 255] range [0, 1] range, pass scale=1./255. rescale input [0, 255] range [-1, 1] range, pass scale=1./127.5, offset=-1. rescaling applied training inference. Inputs can integer floating point dtype, default layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_rescaling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","text":"","code":"layer_rescaling(object, scale, offset = 0, ...)"},{"path":"https://keras.posit.co/reference/layer_rescaling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","text":"object Object compose layer . tensor, array, sequential model. scale Float, scale apply inputs. offset Float, offset apply inputs. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_rescaling.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer that reshapes inputs into the given shape. — layer_reshape","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"Layer reshapes inputs given shape.","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"","code":"layer_reshape(object, target_shape, ...)"},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"object Object compose layer . tensor, array, sequential model. target_shape Target shape. List integers, include samples dimension (batch size). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"Arbitrary, although dimensions input shape must known/fixed. Use keyword argument input_shape (list integers, include samples/batch size axis) using layer first layer model.","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"(batch_size, *target_shape)","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"","code":"x <- layer_input(shape = 12) y <- layer_reshape(x, c(3, 4)) shape(y) ## shape(NA, 3, 4) # also supports shape inference using `-1` as dimension y <- layer_reshape(x, c(-1, 2, 2)) shape(y) ## shape(NA, 3, 2, 2)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which resizes images. — layer_resizing","title":"A preprocessing layer which resizes images. — layer_resizing","text":"layer resizes image input target height width. input 4D (batched) 3D (unbatched) tensor \"channels_last\" format. Input pixel values can range (e.g. [0., 1.) [0, 255]).","code":""},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which resizes images. — layer_resizing","text":"","code":"layer_resizing(   object,   height,   width,   interpolation = \"bilinear\",   crop_to_aspect_ratio = FALSE,   pad_to_aspect_ratio = FALSE,   fill_mode = \"constant\",   fill_value = 0,   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which resizes images. — layer_resizing","text":"object Object compose layer . tensor, array, sequential model. height Integer, height output shape. width Integer, width output shape. interpolation String, interpolation method. Supports \"bilinear\", \"nearest\", \"bicubic\", \"lanczos3\", \"lanczos5\". Defaults \"bilinear\". crop_to_aspect_ratio TRUE, resize images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image cropped return largest possible window image (size (height, width)) matches target aspect ratio. default (crop_to_aspect_ratio=FALSE), aspect ratio may preserved. pad_to_aspect_ratio TRUE, pad images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image evenly padded short side. fill_mode using pad_to_aspect_ratio=TRUE, padded areas filled according given mode. \"constant\" supported time (fill constant value, equal fill_value). fill_value Float. Padding value use pad_to_aspect_ratio=TRUE. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which resizes images. — layer_resizing","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which resizes images. — layer_resizing","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format, (..., channels, height, width), \"channels_first\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which resizes images. — layer_resizing","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels), (..., channels, target_height, target_width), \"channels_first\" format. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":null,"dir":"Reference","previous_headings":"","what":"Base class for recurrent layers — layer_rnn","title":"Base class for recurrent layers — layer_rnn","text":"Base class recurrent layers","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Base class for recurrent layers — layer_rnn","text":"","code":"layer_rnn(   object,   cell,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   unroll = FALSE,   zero_output_for_mask = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Base class for recurrent layers — layer_rnn","text":"object Object compose layer . tensor, array, sequential model. cell RNN cell instance list RNN cell instances. RNN cell class : call(input_at_t, states_at_t) method, returning (output_at_t, states_at_t_plus_1). call method cell can also take optional argument constants, see section \"Note passing external constants\" . state_size attribute. can single integer (single state) case size recurrent state. can also list integers (one size per state). output_size attribute, single integer. get_initial_state(batch_size=NULL) method creates tensor meant fed call() initial state, user specify initial state via means. returned initial state shape (batch_size, cell.state_size). cell might choose create tensor full zeros, values based cell's implementation. inputs input tensor RNN layer, shape (batch_size, timesteps, features). method implemented cell, RNN layer create zero filled tensor shape (batch_size, cell$state_size). case cell list RNN cell instances, cells stacked top RNN, resulting efficient stacked RNN. return_sequences Boolean (default FALSE). Whether return last output output sequence, full sequence. return_state Boolean (default FALSE). Whether return last state addition output. go_backwards Boolean (default FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default FALSE). TRUE, last state sample index batch used initial state sample index following batch. unroll Boolean (default FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences. zero_output_for_mask Boolean (default FALSE). Whether output use zeros masked timesteps. Note field used return_sequences TRUE mask provided. can useful want reuse raw output sequence RNN without interference masked timesteps, e.g., merging bidirectional RNNs. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Base class for recurrent layers — layer_rnn","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Base class for recurrent layers — layer_rnn","text":"inputs: Input tensor. initial_state: List initial state tensors passed first call cell. mask: Binary tensor shape [batch_size, timesteps] indicating whether given timestep masked. individual TRUE entry indicates corresponding timestep utilized, FALSE entry indicates corresponding timestep ignored. training: Python boolean indicating whether layer behave training mode inference mode. argument passed cell calling . use cells use dropout.","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Base class for recurrent layers — layer_rnn","text":"3-D tensor shape (batch_size, timesteps, features).","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Base class for recurrent layers — layer_rnn","text":"return_state: list tensors. first tensor output. remaining tensors last states, shape (batch_size, state_size), state_size high dimension tensor shape. return_sequences: 3D tensor shape (batch_size, timesteps, output_size).","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"masking-","dir":"Reference","previous_headings":"","what":"Masking:","title":"Base class for recurrent layers — layer_rnn","text":"layer supports masking input data variable number timesteps. introduce masks data, use layer_embedding() layer mask_zero parameter set TRUE. Note using statefulness RNNs: can set RNN layers 'stateful', means states computed samples one batch reused initial states samples next batch. assumes one--one mapping samples different successive batches. enable statefulness: Specify stateful = TRUE layer constructor. Specify fixed batch size model, passing sequential model: input_batch_shape = c(...) keras_model_sequential() call. Else functional model 1 input layers: batch_shape = c(...) layer_input() call(s). expected shape inputs including batch size. list integers, e.g. c(32, 10, 100). Specify shuffle = FALSE calling fit(). reset states model, call reset_state() either specific layer, entire model. Note specifying initial state RNNs: can specify initial state RNN layers symbolically calling keyword argument initial_state. value initial_state tensor list tensors representing initial state RNN layer.","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Base class for recurrent layers — layer_rnn","text":"First, define RNN Cell, layer subclass.   use cell RNN layer:","code":"rnn_cell_minimal <- Layer(   \"MinimalRNNCell\",    initialize = function(units, ...) {     super$initialize(...)     self$units <- as.integer(units)     self$state_size <- as.integer(units)   },    build = function(input_shape) {     self$kernel <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = 'uniform',       name = 'kernel'     )     self$recurrent_kernel <- self$add_weight(       shape = shape(self$units, self$units),       initializer = 'uniform',       name = 'recurrent_kernel'     )     self$built <- TRUE   },    call = function(inputs, states) {     prev_output <- states[[1]]     h <- op_matmul(inputs, self$kernel)     output <- h + op_matmul(prev_output, self$recurrent_kernel)     list(output, list(output))   } ) cell <- rnn_cell_minimal(units = 32) x <- layer_input(shape = shape(NULL, 5)) layer <- layer_rnn(cell = cell) y <- layer(x) cells <- list(rnn_cell_minimal(units = 32), rnn_cell_minimal(units = 4)) x <- layer_input(shape = shape(NULL, 5)) layer <- layer_rnn(cell = cells) y <- layer(x)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"1D separable convolution layer. — layer_separable_conv_1d","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"layer performs depthwise convolution acts separately channels, followed pointwise convolution mixes channels. use_bias TRUE bias initializer provided, adds bias vector output. optionally applies activation function produce final output.","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"","code":"layer_separable_conv_1d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   depth_multiplier = 1L,   activation = NULL,   use_bias = TRUE,   depthwise_initializer = \"glorot_uniform\",   pointwise_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   depthwise_regularizer = NULL,   pointwise_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   depthwise_constraint = NULL,   pointwise_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimensionality output space (.e. number filters pointwise convolution). kernel_size int list 1 integers, specifying size depthwise convolution window. strides int list 1 integers, specifying stride length depthwise convolution. one int specified, stride size used dimensions. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated convolution. one int specified, dilation rate used dimensions. depth_multiplier number depthwise convolution output channels input channel. total number depthwise convolution output channels equal input_channel * depth_multiplier. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. depthwise_initializer initializer depthwise convolution kernel. NULL, default initializer (\"glorot_uniform\") used. pointwise_initializer initializer pointwise convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer initializer bias vector. NULL, default initializer ('\"zeros\"') used. depthwise_regularizer Optional regularizer depthwise convolution kernel. pointwise_regularizer Optional regularizer pointwise convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. depthwise_constraint Optional projection function applied depthwise kernel updated Optimizer (e.g. used norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). pointwise_constraint Optional projection function applied pointwise kernel updated Optimizer. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"3D tensor representing activation(separable_conv1d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, steps, channels) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, new_steps, filters) data_format=\"channels_first\": 3D tensor shape: (batch_shape, filters, new_steps)","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"","code":"x <- random_uniform(c(4, 10, 12)) y <- layer_separable_conv_1d(x, 3, 2, 2, activation='relu') shape(y) ## shape(4, 5, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"2D separable convolution layer. — layer_separable_conv_2d","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"layer performs depthwise convolution acts separately channels, followed pointwise convolution mixes channels. use_bias TRUE bias initializer provided, adds bias vector output. optionally applies activation function produce final output.","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"","code":"layer_separable_conv_2d(   object,   filters,   kernel_size,   strides = list(1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L),   depth_multiplier = 1L,   activation = NULL,   use_bias = TRUE,   depthwise_initializer = \"glorot_uniform\",   pointwise_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   depthwise_regularizer = NULL,   pointwise_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   depthwise_constraint = NULL,   pointwise_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimensionality output space (.e. number filters pointwise convolution). kernel_size int list 2 integers, specifying size depthwise convolution window. strides int list 2 integers, specifying stride length depthwise convolution. one int specified, stride size used dimensions. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 2 integers, specifying dilation rate use dilated convolution. one int specified, dilation rate used dimensions. depth_multiplier number depthwise convolution output channels input channel. total number depthwise convolution output channels equal input_channel * depth_multiplier. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. depthwise_initializer initializer depthwise convolution kernel. NULL, default initializer (\"glorot_uniform\") used. pointwise_initializer initializer pointwise convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer initializer bias vector. NULL, default initializer ('\"zeros\"') used. depthwise_regularizer Optional regularizer depthwise convolution kernel. pointwise_regularizer Optional regularizer pointwise convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. depthwise_constraint Optional projection function applied depthwise kernel updated Optimizer (e.g. used norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). pointwise_constraint Optional projection function applied pointwise kernel updated Optimizer. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"4D tensor representing activation(separable_conv2d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, height, width, channels) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, new_height, new_width, filters) data_format=\"channels_first\": 4D tensor shape: (batch_size, filters, new_height, new_width)","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"","code":"x <- random_uniform(c(4, 10, 10, 12)) y <- layer_separable_conv_2d(x, 3, c(4, 3), 2, activation='relu') shape(y) ## shape(4, 4, 4, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":null,"dir":"Reference","previous_headings":"","what":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"Fully-connected RNN output fed back new input.","code":""},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"","code":"layer_simple_rnn(   object,   units,   activation = \"tanh\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   unroll = FALSE,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"object Object compose layer . tensor, array, sequential model. units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state.  Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. activity_regularizer Regularizer function applied output layer (\"activation\"). Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix.  Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default: FALSE). TRUE, last state sample index batch used initial state sample index following batch. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences. seed Initial seed random number generator ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"sequence: 3D tensor, shape [batch, timesteps, feature]. mask: Binary tensor shape [batch, timesteps] indicating whether given timestep masked. individual TRUE entry indicates corresponding timestep utilized, FALSE entry indicates corresponding timestep ignored. training: Python boolean indicating whether layer behave training mode inference mode. argument passed cell calling . relevant dropout recurrent_dropout used. initial_state: List initial state tensors passed first call cell.","code":""},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"","code":"inputs <- random_uniform(c(32, 10, 8)) simple_rnn <- layer_simple_rnn(units = 4) output <- simple_rnn(inputs)  # The output has shape `(32, 4)`. simple_rnn <- layer_simple_rnn(     units = 4, return_sequences=TRUE, return_state=TRUE ) # whole_sequence_output has shape `(32, 10, 4)`. # final_state has shape `(32, 4)`. c(whole_sequence_output, final_state) %<-% simple_rnn(inputs)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"layer performs function Dropout, however, drops entire 1D feature maps instead individual elements. adjacent frames within feature maps strongly correlated (normally case early convolution layers) regular dropout regularize activations otherwise just result effective learning rate decrease. case, SpatialDropout1D help promote independence feature maps used instead.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"","code":"layer_spatial_dropout_1d(object, rate, seed = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. Fraction input units drop. seed Initial seed random number generator name String, name object dtype datatype (e.g., \"float32\").","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"inputs: 3D tensor. training: Python boolean indicating whether layer behave training mode (applying dropout) inference mode (pass-).","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"3D tensor shape: (samples, timesteps, channels)","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"input.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"Tompson et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"version performs function Dropout, however, drops entire 2D feature maps instead individual elements. adjacent pixels within feature maps strongly correlated (normally case early convolution layers) regular dropout regularize activations otherwise just result effective learning rate decrease. case, SpatialDropout2D help promote independence feature maps used instead.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"","code":"layer_spatial_dropout_2d(   object,   rate,   data_format = NULL,   seed = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. Fraction input units drop. data_format \"channels_first\" \"channels_last\". \"channels_first\" mode, channels dimension (depth) index 1, \"channels_last\" mode index 3. defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". seed Initial seed random number generator name String, name object dtype datatype (e.g., \"float32\").","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"inputs: 4D tensor. training: Python boolean indicating whether layer behave training mode (applying dropout) inference mode (pass-).","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"4D tensor shape: (samples, channels, rows, cols) data_format='channels_first' 4D tensor shape: (samples, rows, cols, channels) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"input.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"Tompson et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"version performs function Dropout, however, drops entire 3D feature maps instead individual elements. adjacent voxels within feature maps strongly correlated (normally case early convolution layers) regular dropout regularize activations otherwise just result effective learning rate decrease. case, SpatialDropout3D help promote independence feature maps used instead.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"","code":"layer_spatial_dropout_3d(   object,   rate,   data_format = NULL,   seed = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. Fraction input units drop. data_format \"channels_first\" \"channels_last\". \"channels_first\" mode, channels dimension (depth) index 1, \"channels_last\" mode index 4. defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". seed Initial seed random number generator name String, name object dtype datatype (e.g., \"float32\").","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"inputs: 5D tensor. training: Python boolean indicating whether layer behave training mode (applying dropout) inference mode (pass-).","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"5D tensor shape: (samples, channels, dim1, dim2, dim3) data_format='channels_first' 5D tensor shape: (samples, dim1, dim2, dim3, channels) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"input.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"Tompson et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"wrapper controls Lipschitz constant weights layer constraining spectral norm, can stabilize training GANs.","code":""},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"","code":"layer_spectral_normalization(object, layer, power_iterations = 1L, ...)"},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"object Object compose layer . tensor, array, sequential model. layer Layer instance either kernel (e.g. layer_conv_2d, layer_dense...) embeddings attribute (layer_embedding layer). power_iterations int, number iterations normalization. ... Base wrapper keyword arguments.","code":""},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"Wrap layer_conv_2d:     Wrap layer_dense:","code":"x <- random_uniform(c(1, 10, 10, 1)) conv2d <- layer_spectral_normalization(   layer = layer_conv_2d(filters = 2, kernel_size = 2) ) y <- conv2d(x) shape(y) ## shape(1, 9, 9, 2) x <- random_uniform(c(1, 10, 10, 1)) dense <- layer_spectral_normalization(layer = layer_dense(units = 10)) y <- dense(x) shape(y) ## shape(1, 10, 10, 10)"},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"Spectral Normalization GAN.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"layer translates set arbitrary strings integer output via table-based vocabulary lookup. layer perform splitting transformation input strings. layer can split tokenize natural language, see layer_text_vectorization layer. vocabulary layer must either supplied construction learned via adapt(). adapt(), layer analyze data set, determine frequency individual strings tokens, create vocabulary . vocabulary capped size, frequent tokens used create vocabulary others treated --vocabulary (OOV). two possible output modes layer. output_mode \"int\", input strings converted index vocabulary (integer). output_mode \"multi_hot\", \"count\", \"tf_idf\", input strings encoded array dimension corresponds element vocabulary. vocabulary can optionally contain mask token well OOV token (can optionally occupy multiple indices vocabulary, set num_oov_indices). position tokens vocabulary fixed. output_mode \"int\", vocabulary begin mask token (set), followed OOV indices, followed rest vocabulary. output_mode \"multi_hot\", \"count\", \"tf_idf\" vocabulary begin OOV indices instances mask token dropped. Note: layer uses TensorFlow internally. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"","code":"layer_string_lookup(   object,   max_tokens = NULL,   num_oov_indices = 1L,   mask_token = NULL,   oov_token = \"[UNK]\",   vocabulary = NULL,   idf_weights = NULL,   invert = FALSE,   output_mode = \"int\",   pad_to_max_tokens = FALSE,   sparse = FALSE,   encoding = \"utf-8\",   name = NULL,   ...,   vocabulary_dtype = NULL )"},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"object Object compose layer . tensor, array, sequential model. max_tokens Maximum size vocabulary layer. specified adapting vocabulary setting pad_to_max_tokens=TRUE. NULL, cap size vocabulary. Note size includes OOV mask tokens. Defaults NULL. num_oov_indices number --vocabulary tokens use. value 1, OOV inputs modulated determine OOV value. value 0, OOV inputs cause error calling layer. Defaults 1. mask_token token represents masked inputs. output_mode \"int\", token included vocabulary mapped index 0. output modes, token appear vocabulary instances mask token input dropped. set NULL, mask term added. Defaults NULL. oov_token used invert TRUE. token return OOV indices. Defaults \"[UNK]\". vocabulary Optional. Either array integers string path text file. passing array, can pass list, list, 1D NumPy array, 1D tensor containing integer vocbulary terms. passing file path, file contain one line per term vocabulary. argument set, need adapt() layer. idf_weights valid output_mode \"tf_idf\". list, list, 1D NumPy array, 1D tensor length vocabulary, containing floating point inverse document frequency weights, multiplied per sample term counts final TF-IDF weight. vocabulary argument set, output_mode \"tf_idf\", argument must supplied. invert valid output_mode \"int\". TRUE, layer map indices vocabulary items instead mapping vocabulary items indices. Defaults FALSE. output_mode Specification output layer. Values can \"int\", \"one_hot\", \"multi_hot\", \"count\", \"tf_idf\" configuring layer follows: \"int\": Return vocabulary indices input tokens. \"one_hot\": Encodes individual element input array size vocabulary, containing 1 element index. last dimension size 1, encode dimension. last dimension size 1, append new dimension encoded output. \"multi_hot\": Encodes sample input single array size vocabulary, containing 1 vocabulary term present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). \"count\": \"multi_hot\", int array contains count number times token index appeared sample. \"tf_idf\": \"multi_hot\", TF-IDF algorithm applied find value token slot. \"int\" output, shape input output supported. output modes, currently output rank 2 supported. Defaults \"int\". pad_to_max_tokens applicable output_mode \"multi_hot\", \"count\", \"tf_idf\". TRUE, output feature axis padded max_tokens even number unique tokens vocabulary less max_tokens, resulting tensor shape (batch_size, max_tokens) regardless vocabulary size. Defaults FALSE. sparse Boolean. applicable \"multi_hot\", \"count\", \"tf_idf\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. encoding Optional. text encoding use interpret input strings. Defaults \"utf-8\". name String, name object ... forward/backward compatability. vocabulary_dtype dtype vocabulary terms, example \"int64\" \"int32\". Defaults \"int64\".","code":""},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"Creating lookup layer known vocabulary example creates lookup layer pre-existing vocabulary.     Creating lookup layer adapted vocabulary example creates lookup layer generates vocabulary analyzing dataset.     Note OOV token \"[UNK]\" added vocabulary. remaining tokens sorted frequency (\"d\", 2 occurrences, first) inverse sort order.     Lookups multiple OOV indices example demonstrates use lookup layer multiple OOV indices.  layer created one OOV index, OOV values hashed number OOV buckets, distributing OOV values deterministic fashion across set.     Note output OOV value 'm' 0, output OOV value \"z\" 1. -vocab terms output index increased 1 earlier examples (maps 2, etc) order make space extra OOV value. One-hot output Configure layer output_mode='one_hot'. Note first num_oov_indices dimensions ont_hot encoding represent OOV values.     Multi-hot output Configure layer output_mode='multi_hot'. Note first num_oov_indices dimensions multi_hot encoding represent OOV values.     Token count output Configure layer output_mode='count'. multi_hot output, first num_oov_indices dimensions output represent OOV values.     TF-IDF output Configure layer output_mode=\"tf_idf\". multi_hot output, first num_oov_indices dimensions output represent OOV values. token bin output token_count * idf_weight, idf weights inverse document frequency weights per token. provided along vocabulary. Note idf_weight OOV values default average idf weights passed .     specify idf weights oov values, need pass entire vocabulary including leading oov token.     adapting layer \"tf_idf\" mode, input sample considered document, IDF weight per token calculated log(1 + num_documents / (1 + token_document_count)). Inverse lookup example demonstrates map indices strings using layer. (can also use adapt() inverse=TRUE, simplicity pass vocab example.)     Note first index correspond oov token default. Forward inverse lookup pairs example demonstrates use vocabulary standard lookup layer create inverse lookup layer.     example, input value \"z\" resulted output \"[UNK]\", since 1000 vocabulary - got represented OOV, OOV values returned \"[UNK]\" inverse layer. Also, note inverse work, must already set forward layer vocabulary either directly via adapt() calling get_vocabulary().","code":"vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\"), c(\"d\", \"z\", \"b\")) layer <- layer_string_lookup(vocabulary=vocab) layer(data) ## tf.Tensor( ## [[1 3 4] ##  [4 0 2]], shape=(2, 3), dtype=int64) data <- rbind(c(\"a\", \"c\", \"d\"), c(\"d\", \"z\", \"b\")) layer <- layer_string_lookup() layer %>% adapt(data) get_vocabulary(layer) ## [1] \"[UNK]\" \"d\"     \"z\"     \"c\"     \"b\"     \"a\" data <- rbind(c(\"a\", \"c\", \"d\"), c(\"d\", \"z\", \"b\")) layer <- layer_string_lookup() layer %>% adapt(data) layer(data) ## tf.Tensor( ## [[5 3 1] ##  [1 2 4]], shape=(2, 3), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\"), c(\"m\", \"z\", \"b\")) layer <- layer_string_lookup(vocabulary = vocab, num_oov_indices = 2) layer(data) ## tf.Tensor( ## [[2 4 5] ##  [0 1 3]], shape=(2, 3), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- c(\"a\", \"b\", \"c\", \"d\", \"z\") layer <- layer_string_lookup(vocabulary = vocab, output_mode = 'one_hot') layer(data) ## tf.Tensor( ## [[0 1 0 0 0] ##  [0 0 1 0 0] ##  [0 0 0 1 0] ##  [0 0 0 0 1] ##  [1 0 0 0 0]], shape=(5, 5), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\", \"d\"), c(\"d\", \"z\", \"b\", \"z\")) layer <- layer_string_lookup(vocabulary = vocab, output_mode = 'multi_hot') layer(data) ## tf.Tensor( ## [[0 1 0 1 1] ##  [1 0 1 0 1]], shape=(2, 5), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\", \"d\"), c(\"d\", \"z\", \"b\", \"z\")) layer <- layer_string_lookup(vocabulary = vocab, output_mode = 'count') layer(data) ## tf.Tensor( ## [[0 1 0 1 2] ##  [2 0 1 0 1]], shape=(2, 5), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") idf_weights <- c(0.25, 0.75, 0.6, 0.4) data <- rbind(c(\"a\", \"c\", \"d\", \"d\"), c(\"d\", \"z\", \"b\", \"z\")) layer <- layer_string_lookup(output_mode = \"tf_idf\") layer %>% set_vocabulary(vocab, idf_weights=idf_weights) layer(data) ## tf.Tensor( ## [[0.   0.25 0.   0.6  0.8 ] ##  [1.   0.   0.75 0.   0.4 ]], shape=(2, 5), dtype=float32) vocab <- c(\"[UNK]\", \"a\", \"b\", \"c\", \"d\") idf_weights <- c(0.9, 0.25, 0.75, 0.6, 0.4) data <- rbind(c(\"a\", \"c\", \"d\", \"d\"), c(\"d\", \"z\", \"b\", \"z\")) layer <- layer_string_lookup(output_mode = \"tf_idf\") layer %>% set_vocabulary(vocab, idf_weights=idf_weights) layer(data) ## tf.Tensor( ## [[0.   0.25 0.   0.6  0.8 ] ##  [1.8  0.   0.75 0.   0.4 ]], shape=(2, 5), dtype=float32) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(1, 3, 4), c(4, 0, 2)) layer <- layer_string_lookup(vocabulary = vocab, invert = TRUE) layer(data) ## tf.Tensor( ## [[b'a' b'c' b'd'] ##  [b'd' b'[UNK]' b'b']], shape=(2, 3), dtype=string) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\"), c(\"d\", \"z\", \"b\")) layer <- layer_string_lookup(vocabulary = vocab) i_layer <- layer_string_lookup(vocabulary = vocab, invert = TRUE) int_data <- layer(data) i_layer(int_data) ## tf.Tensor( ## [[b'a' b'c' b'd'] ##  [b'd' b'[UNK]' b'b']], shape=(2, 3), dtype=string)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs elementwise subtraction. — layer_subtract","title":"Performs elementwise subtraction. — layer_subtract","text":"takes input list tensors size 2 shape, returns single tensor (inputs[0] - inputs[1)) shape.","code":""},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs elementwise subtraction. — layer_subtract","text":"","code":"layer_subtract(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs elementwise subtraction. — layer_subtract","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs elementwise subtraction. — layer_subtract","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs elementwise subtraction. — layer_subtract","text":"Usage Keras model:","code":"input_shape <- c(2, 3, 4) x1 <- random_uniform(input_shape) x2 <- random_uniform(input_shape) y <- layer_subtract(list(x1, x2)) input1 <- layer_input(shape = 16) x1 <- layer_dense(input1, units = 8, activation = 'relu') input2 <- layer_input(shape = 32) x2 <- layer_dense(input2, units = 8, activation = 'relu') subtracted <- layer_subtract(list(x1, x2)) out <- layer_dense(subtracted, units = 4) model <- keras_model(inputs = list(input1, input2), outputs = out)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"layer basic options managing text Keras model. transforms batch strings (one example = one string) either list token indices (one example = 1D tensor integer token indices) dense representation (one example = 1D tensor float values representing data example's tokens). layer meant handle natural language inputs. handle simple string inputs (categorical strings pre-tokenized strings) see layer_string_lookup(). vocabulary layer must either supplied construction learned via adapt(). layer adapted, analyze dataset, determine frequency individual string values, create vocabulary . vocabulary can unlimited size capped, depending configuration options layer; unique values input maximum vocabulary size, frequent terms used create vocabulary. processing example contains following steps: Standardize example (usually lowercasing + punctuation stripping) Split example substrings (usually words) Recombine substrings tokens (usually ngrams) Index tokens (associate unique int value token) Transform example using index, either vector ints dense float vector. notes passing callables customize splitting normalization layer: callable can passed Layer, want serialize object pass functions registered Keras serializables (see register_keras_serializable() details). using custom callable standardize, data received callable exactly passed layer. callable return tensor shape input. using custom callable split, data received callable 1st dimension squeezed - instead list(\"string split\", \"another string split\"), Callable see c(\"string split\", \"another string split\"). callable return tf.Tensor dtype string first dimension containing split tokens - example, see something like list(c(\"string\", \"\", \"split\"), c(\"another\", \"string\", \"\", \"split\")). Note: layer uses TensorFlow internally. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"","code":"layer_text_vectorization(   object,   max_tokens = NULL,   standardize = \"lower_and_strip_punctuation\",   split = \"whitespace\",   ngrams = NULL,   output_mode = \"int\",   output_sequence_length = NULL,   pad_to_max_tokens = FALSE,   vocabulary = NULL,   idf_weights = NULL,   sparse = FALSE,   ragged = FALSE,   encoding = \"utf-8\",   name = NULL,   ... )  get_vocabulary(object, include_special_tokens = TRUE)  set_vocabulary(object, vocabulary, idf_weights = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"object Object compose layer . tensor, array, sequential model. max_tokens Maximum size vocabulary layer. specified adapting vocabulary setting pad_to_max_tokens=TRUE. Note vocabulary contains 1 OOV token, effective number tokens (max_tokens - 1 - (1 output_mode == \"int\" else 0)). standardize Optional specification standardization apply input text. Values can : NULL: standardization. \"lower_and_strip_punctuation\": Text lowercased punctuation removed. \"lower\": Text lowercased. \"strip_punctuation\": punctuation removed. Callable: Inputs passed callable function, standardized returned. split Optional specification splitting input text. Values can : NULL: splitting. \"whitespace\": Split whitespace. \"character\": Split unicode character. Callable: Standardized inputs passed callable function, split returned. ngrams Optional specification ngrams create possibly-split input text. Values can NULL, integer list integers; passing integer create ngrams integer, passing list integers create ngrams specified values list. Passing NULL means ngrams created. output_mode Optional specification output layer. Values can \"int\", \"multi_hot\", \"count\" \"tf_idf\", configuring layer follows: \"int\": Outputs integer indices, one integer index per split string token. output_mode == \"int\", 0 reserved masked locations; reduces vocab size max_tokens - 2 instead max_tokens - 1. \"multi_hot\": Outputs single int array per batch, either vocab_size max_tokens size, containing 1s elements token mapped index exists least batch item. \"count\": Like \"multi_hot\", int array contains count number times token index appeared batch item. \"tf_idf\": Like \"multi_hot\", TF-IDF algorithm applied find value token slot. \"int\" output, shape input output supported. output modes, currently rank 1 inputs (rank 2 outputs splitting) supported. output_sequence_length valid INT mode. set, output time dimension padded truncated exactly output_sequence_length values, resulting tensor shape (batch_size, output_sequence_length) regardless many tokens resulted splitting step. Defaults NULL. ragged TRUE output_sequence_length may still truncate output. pad_to_max_tokens valid  \"multi_hot\", \"count\", \"tf_idf\" modes. TRUE, output feature axis padded max_tokens even number unique tokens vocabulary less max_tokens, resulting tensor shape (batch_size, max_tokens) regardless vocabulary size. Defaults FALSE. vocabulary Optional. Either array strings string path text file. passing array, can pass list, list, 1D NumPy array, 1D tensor containing string vocabulary terms. passing file path, file contain one line per term vocabulary. argument set, need adapt() layer. idf_weights R vector, 1D numpy array, 1D tensor inverse document frequency weights equal length vocabulary. Must set output_mode \"tf_idf\". set otherwise. sparse Boolean. applicable \"multi_hot\", \"count\", \"tf_idf\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. ragged Boolean. applicable \"int\" output mode. supported TensorFlow backend. TRUE, returns RaggedTensor instead dense Tensor, sequence may different length string splitting. Defaults FALSE. encoding Optional. text encoding use interpret input strings. Defaults \"utf-8\". name String, name object ... forward/backward compatability. include_special_tokens TRUE, returned vocabulary include padding OOV tokens, term's index vocabulary equal term's index calling layer. FALSE, returned vocabulary include padding OOV tokens.","code":""},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"example instantiates TextVectorization layer lowercases text, splits whitespace, strips punctuation, outputs integer vocab indices.         example instantiates TextVectorization layer passing list vocabulary terms layer's initialize() method.","code":"max_tokens <- 5000  # Maximum vocab size. max_len <- 4  # Sequence length to pad the outputs to. # Create the layer. vectorize_layer <- layer_text_vectorization(     max_tokens = max_tokens,     output_mode = 'int',     output_sequence_length = max_len) # Now that the vocab layer has been created, call `adapt` on the # list of strings to create the vocabulary. vectorize_layer %>% adapt(c(\"foo bar\", \"bar baz\", \"baz bada boom\")) # Now, the layer can map strings to integers -- you can use an # embedding layer to map these integers to learned embeddings. input_data <- rbind(\"foo qux bar\", \"qux baz\") vectorize_layer(input_data) ## tf.Tensor( ## [[4 1 3 0] ##  [1 2 0 0]], shape=(2, 4), dtype=int64) vocab_data <- c(\"earth\", \"wind\", \"and\", \"fire\") max_len <- 4  # Sequence length to pad the outputs to. # Create the layer, passing the vocab directly. You can also pass the # vocabulary arg a path to a file containing one vocabulary word per # line. vectorize_layer <- layer_text_vectorization(     max_tokens = max_tokens,     output_mode = 'int',     output_sequence_length = max_len,     vocabulary = vocab_data) # Because we've passed the vocabulary directly, we don't need to adapt # the layer - the vocabulary is already set. The vocabulary contains the # padding token ('') and OOV token ('[UNK]') # as well as the passed tokens. vectorize_layer %>% get_vocabulary() ## [1] \"\"      \"[UNK]\" \"earth\" \"wind\"  \"and\"   \"fire\" # ['', '[UNK]', 'earth', 'wind', 'and', 'fire']"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"Reload Keras model/layer saved via export_savedmodel().","code":""},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"","code":"layer_tfsm(   object,   filepath,   call_endpoint = \"serve\",   call_training_endpoint = NULL,   trainable = TRUE,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"object Object compose layer . tensor, array, sequential model. filepath string, path SavedModel. call_endpoint Name endpoint use call() method reloaded layer. SavedModel created via export_savedmodel(), default endpoint name 'serve'. cases may named 'serving_default'. call_training_endpoint see description trainable see description name String, name object dtype datatype (e.g., \"float32\").","code":""},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"reloaded object can used like regular Keras layer, supports training/fine-tuning trainable weights. Note reloaded object retains none internal structure custom methods original object -- brand new layer created around saved function. Limitations: call endpoints single inputs tensor argument (may optionally named list/list tensors) supported. endpoints multiple separate input tensor arguments, consider subclassing layer_tfsm implementing call() method custom signature. need training-time behavior differ inference-time behavior (.e. need reloaded object support training=TRUE argument __call__()), make sure training-time call function saved standalone endpoint artifact, provide name layer_tfsm via call_training_endpoint argument.","code":"model <- keras_model_sequential(input_shape = c(784)) |> layer_dense(10) model |> export_savedmodel(\"path/to/artifact\") ## Saved artifact at 'path/to/artifact'. The following endpoints are available: ## ## * Endpoint 'serve' ##   args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 784), dtype=tf.float32, name='keras_tensor') ## Output Type: ##   TensorSpec(shape=(None, 10), dtype=tf.float32, name=None) ## Captures: ##   128937404166592: TensorSpec(shape=(), dtype=tf.resource, name=None) ##   128937404169408: TensorSpec(shape=(), dtype=tf.resource, name=None) reloaded_layer <- layer_tfsm(filepath = \"path/to/artifact\") input <- random_normal(c(2, 784)) output <- reloaded_layer(input) stopifnot(all.equal(as.array(output), as.array(model(input))))"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":null,"dir":"Reference","previous_headings":"","what":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"Every input least 3D, dimension index one first input considered temporal dimension. Consider batch 32 video samples, sample 128x128 RGB image channels_last data format, across 10 timesteps. batch input shape (32, 10, 128, 128, 3). can use TimeDistributed apply Conv2D layer 10 timesteps, independently:     layer_time_distributed applies instance layer_conv2d timestamps, set weights used timestamp.","code":"inputs <- keras_input(shape = c(10, 128, 128, 3), batch_size = 32) conv_2d_layer <- layer_conv_2d(filters = 64, kernel_size = c(3, 3)) outputs <- layer_time_distributed(inputs, layer = conv_2d_layer) shape(outputs) ## shape(32, 10, 126, 126, 64)"},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"","code":"layer_time_distributed(object, layer, ...)"},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"object Object compose layer . tensor, array, sequential model. layer Layer instance. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"inputs: Input tensor shape (batch, time, ...) nested tensors, shape (batch, time, ...). training: Boolean indicating whether layer behave training mode inference mode. argument passed wrapped layer (layer supports argument). mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked. argument passed wrapped layer (layer supports argument).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Torch module wrapper layer. — layer_torch_module_wrapper","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"layer_torch_module_wrapper wrapper class can turn torch.nn.Module Keras layer, particular making parameters trackable Keras.","code":""},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"","code":"layer_torch_module_wrapper(object, module, name = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"object Object compose layer . tensor, array, sequential model. module torch.nn.Module instance. LazyModule instance, parameters must initialized passing instance layer_torch_module_wrapper (e.g. calling ). name name layer (string). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"example layer_torch_module_wrapper() can used vanilla PyTorch modules.","code":"# reticulate::py_install( #   packages = c(\"torch\", \"torchvision\", \"torchaudio\"), #   envname = \"r-keras\", #   pip_options = c(\"--index-url https://download.pytorch.org/whl/cpu\") # ) library(keras3) use_backend(\"torch\") torch <- reticulate::import(\"torch\") nn <- reticulate::import(\"torch.nn\") nnf <- reticulate::import(\"torch.nn.functional\")  Classifier(keras$Model) \\%py_class\\% {   initialize <- function(...) {     super$initialize(...)      self$conv1 <- layer_torch_module_wrapper(module = nn$Conv2d(       in_channels = 1L,       out_channels = 32L,       kernel_size = tuple(3L, 3L)     ))     self$conv2 <- layer_torch_module_wrapper(module = nn$Conv2d(       in_channels = 32L,       out_channels = 64L,       kernel_size = tuple(3L, 3L)     ))     self$pool <- nn$MaxPool2d(kernel_size = tuple(2L, 2L))     self$flatten <- nn$Flatten()     self$dropout <- nn$Dropout(p = 0.5)     self$fc <-       layer_torch_module_wrapper(module = nn$Linear(1600L, 10L))   }    call <- function(inputs) {     x <- nnf$relu(self$conv1(inputs))     x <- self$pool(x)     x <- nnf$relu(self$conv2(x))     x <- self$pool(x)     x <- self$flatten(x)     x <- self$dropout(x)     x <- self$fc(x)     nnf$softmax(x, dim = 1L)   } } model <- Classifier() model$build(shape(1, 28, 28)) cat(\"Output shape:\", format(shape(model(torch$ones(1L, 1L, 28L, 28L)))))  model |> compile(loss = \"sparse_categorical_crossentropy\",                  optimizer = \"adam\",                  metrics = \"accuracy\") model |> fit(train_loader, epochs = 5)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Unit normalization layer. — layer_unit_normalization","title":"Unit normalization layer. — layer_unit_normalization","text":"Normalize batch inputs input batch L2 norm equal 1 (across axes specified axis).","code":""},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unit normalization layer. — layer_unit_normalization","text":"","code":"layer_unit_normalization(object, axis = -1L, ...)"},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unit normalization layer. — layer_unit_normalization","text":"object Object compose layer . tensor, array, sequential model. axis Integer list. axis axes normalize across. Typically, features axis axes. left-axes typically batch axis axes. -1 last dimension input. Defaults -1. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unit normalization layer. — layer_unit_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unit normalization layer. — layer_unit_normalization","text":"","code":"data <- op_reshape(1:6, newshape = c(2, 3)) normalized_data <- layer_unit_normalization(data) op_sum(normalized_data[1,]^2) ## tf.Tensor(0.9999999, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsampling layer for 1D inputs. — layer_upsampling_1d","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"Repeats temporal step size times along time axis.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"","code":"layer_upsampling_1d(object, size = 2L, ...)"},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"object Object compose layer . tensor, array, sequential model. size Integer. Upsampling factor. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"","code":"input_shape <- c(2, 2, 3) x <- seq_len(prod(input_shape)) %>% op_reshape(input_shape) x ## tf.Tensor( ## [[[ 1  2  3] ##   [ 4  5  6]] ## ##  [[ 7  8  9] ##   [10 11 12]]], shape=(2, 2, 3), dtype=int32) y <- layer_upsampling_1d(x, size = 2) y ## tf.Tensor( ## [[[ 1  2  3] ##   [ 1  2  3] ##   [ 4  5  6] ##   [ 4  5  6]] ## ##  [[ 7  8  9] ##   [ 7  8  9] ##   [10 11 12] ##   [10 11 12]]], shape=(2, 4, 3), dtype=int32)"},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"3D tensor shape: (batch_size, steps, features).","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"3D tensor shape: (batch_size, upsampled_steps, features).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsampling layer for 2D inputs. — layer_upsampling_2d","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"implementation uses interpolative resizing, given resize method (specified interpolation argument). Use interpolation=nearest repeat rows columns data.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"","code":"layer_upsampling_2d(   object,   size = list(2L, 2L),   data_format = NULL,   interpolation = \"nearest\",   ... )"},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"object Object compose layer . tensor, array, sequential model. size Int, list 2 integers. upsampling factors rows columns. data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists) else \"channels_last\". Defaults \"channels_last\". interpolation string, one \"bicubic\", \"bilinear\", \"lanczos3\", \"lanczos5\", \"nearest\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"","code":"input_shape <- c(2, 2, 1, 3) x <- op_reshape(seq_len(prod(input_shape)), input_shape) print(x) ## tf.Tensor( ## [[[[ 1  2  3]] ## ##   [[ 4  5  6]]] ## ## ##  [[[ 7  8  9]] ## ##   [[10 11 12]]]], shape=(2, 2, 1, 3), dtype=int32) y <- layer_upsampling_2d(x, size = c(1, 2)) print(y) ## tf.Tensor( ## [[[[ 1  2  3] ##    [ 1  2  3]] ## ##   [[ 4  5  6] ##    [ 4  5  6]]] ## ## ##  [[[ 7  8  9] ##    [ 7  8  9]] ## ##   [[10 11 12] ##    [10 11 12]]]], shape=(2, 2, 2, 3), dtype=int32)"},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, rows, cols, channels) data_format \"channels_first\": (batch_size, channels, rows, cols)","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, upsampled_rows, upsampled_cols, channels) data_format \"channels_first\": (batch_size, channels, upsampled_rows, upsampled_cols)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsampling layer for 3D inputs. — layer_upsampling_3d","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"Repeats 1st, 2nd 3rd dimensions data size[0], size[1] size[2] respectively.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"","code":"layer_upsampling_3d(object, size = list(2L, 2L, 2L), data_format = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"object Object compose layer . tensor, array, sequential model. size Int, list 3 integers. upsampling factors dim1, dim2 dim3. data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists) else \"channels_last\". Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"","code":"input_shape <- c(2, 1, 2, 1, 3) x <- array(1, dim = input_shape) y <- layer_upsampling_3d(x, size = c(2, 2, 2)) shape(y) ## shape(2, 2, 4, 2, 3)"},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, dim1, dim2, dim3, channels) data_format \"channels_first\": (batch_size, channels, dim1, dim2, dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels) data_format \"channels_first\": (batch_size, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"Zero-padding layer 1D input (e.g. temporal sequence).","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"","code":"layer_zero_padding_1d(object, padding = 1L, ...)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"object Object compose layer . tensor, array, sequential model. padding Int, list int (length 2), named listionary. int: many zeros add beginning end padding dimension (axis 1). list 2 ints: many zeros add beginning end padding dimension ((left_pad, right_pad)). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"","code":"input_shape <- c(2, 2, 3) x <- op_reshape(seq_len(prod(input_shape)), input_shape) x ## tf.Tensor( ## [[[ 1  2  3] ##   [ 4  5  6]] ## ##  [[ 7  8  9] ##   [10 11 12]]], shape=(2, 2, 3), dtype=int32) y <- layer_zero_padding_1d(x, padding = 2) y ## tf.Tensor( ## [[[ 0  0  0] ##   [ 0  0  0] ##   [ 1  2  3] ##   [ 4  5  6] ##   [ 0  0  0] ##   [ 0  0  0]] ## ##  [[ 0  0  0] ##   [ 0  0  0] ##   [ 7  8  9] ##   [10 11 12] ##   [ 0  0  0] ##   [ 0  0  0]]], shape=(2, 6, 3), dtype=int32)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"3D tensor shape (batch_size, axis_to_pad, features)","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"3D tensor shape (batch_size, padded_axis, features)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"layer can add rows columns zeros top, bottom, left right side image tensor.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"","code":"layer_zero_padding_2d(object, padding = list(1L, 1L), data_format = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"object Object compose layer . tensor, array, sequential model. padding Int, list 2 ints, list 2 lists 2 ints. int: symmetric padding applied height width. list 2 ints: interpreted two different symmetric padding values height width: (symmetric_height_pad, symmetric_width_pad). list 2 lists 2 ints: interpreted ((top_pad, bottom_pad), (left_pad, right_pad)). data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"","code":"input_shape <- c(1, 1, 2, 2) x <- op_reshape(seq_len(prod(input_shape)), input_shape) x ## tf.Tensor( ## [[[[1 2] ##    [3 4]]]], shape=(1, 1, 2, 2), dtype=int32) y <- layer_zero_padding_2d(x, padding = 1) y ## tf.Tensor( ## [[[[0 0] ##    [0 0] ##    [0 0] ##    [0 0]] ## ##   [[0 0] ##    [1 2] ##    [3 4] ##    [0 0]] ## ##   [[0 0] ##    [0 0] ##    [0 0] ##    [0 0]]]], shape=(1, 3, 4, 2), dtype=int32)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, height, width, channels) data_format \"channels_first\": (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, padded_height, padded_width, channels) data_format \"channels_first\": (batch_size, channels, padded_height, padded_width)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"Zero-padding layer 3D data (spatial spatio-temporal).","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"","code":"layer_zero_padding_3d(   object,   padding = list(list(1L, 1L), list(1L, 1L), list(1L, 1L)),   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"object Object compose layer . tensor, array, sequential model. padding Int, list 3 ints, list 3 lists 2 ints. int: symmetric padding applied depth, height, width. list 3 ints: interpreted three different symmetric padding values depth, height, width: (symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad). list 3 lists 2 ints: interpreted ((left_dim1_pad, right_dim1_pad), (left_dim2_pad, right_dim2_pad), (left_dim3_pad, right_dim3_pad)). data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"","code":"input_shape <- c(1, 1, 2, 2, 3) x <- op_reshape(seq_len(prod(input_shape)), input_shape) x ## tf.Tensor( ## [[[[[ 1  2  3] ##     [ 4  5  6]] ## ##    [[ 7  8  9] ##     [10 11 12]]]]], shape=(1, 1, 2, 2, 3), dtype=int32) y <- layer_zero_padding_3d(x, padding = 2) shape(y) ## shape(1, 5, 6, 6, 3)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad, depth) data_format \"channels_first\": (batch_size, depth, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad)","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, first_padded_axis, second_padded_axis, third_axis_to_pad, depth) data_format \"channels_first\": (batch_size, depth, first_padded_axis, second_padded_axis, third_axis_to_pad)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","title":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","text":"See Loshchilov & Hutter, ICLR2016, SGDR: Stochastic Gradient Descent Warm Restarts. idea linear warmup learning rate, see Goyal et al.. begin training model, often want initial increase learning rate followed decay. warmup_target int, schedule applies linear increase per optimizer step learning rate initial_learning_rate warmup_target duration warmup_steps. Afterwards, applies cosine decay function taking learning rate warmup_target alpha duration decay_steps. warmup_target NULL skip warmup decay take learning rate initial_learning_rate alpha. requires step value  compute learning rate. can just pass backend variable increment training step. schedule 1-arg callable produces warmup followed decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. warmup computed :   decay computed :   Example usage without warmup:   Example usage warmup:   can pass schedule directly optimizer learning rate. learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"warmup_learning_rate <- function(step) {   completed_fraction <- step / warmup_steps   total_delta <- target_warmup - initial_learning_rate   completed_fraction * total_delta } if (is.null(warmup_target)) {   initial_decay_lr <- initial_learning_rate } else {   initial_decay_lr <- warmup_target }  decayed_learning_rate <- function(step) {   step <- min(step, decay_steps)   cosine_decay <- 0.5 * (1 + cos(pi * step / decay_steps))   decayed <- (1 - alpha) * cosine_decay + alpha   initial_decay_lr * decayed } decay_steps <- 1000 initial_learning_rate <- 0.1 lr_decayed_fn <- learning_rate_schedule_cosine_decay(     initial_learning_rate, decay_steps) decay_steps <- 1000 initial_learning_rate <- 0 warmup_steps <- 1000 target_learning_rate <- 0.1 lr_warmup_decayed_fn <- learning_rate_schedule_cosine_decay(     initial_learning_rate, decay_steps, warmup_target = target_learning_rate,     warmup_steps = warmup_steps )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","text":"","code":"learning_rate_schedule_cosine_decay(   initial_learning_rate,   decay_steps,   alpha = 0,   name = \"CosineDecay\",   warmup_target = NULL,   warmup_steps = 0L )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","text":"initial_learning_rate float. initial learning rate. decay_steps int. Number steps decay . alpha float. Minimum learning rate value decay fraction initial_learning_rate. name String. Optional name operation.  Defaults \"CosineDecay\". warmup_target float. target learning rate warmup phase. cast initial_learning_rate datatype. Setting NULL skip warmup begins decay phase initial_learning_rate. Otherwise scheduler warmup initial_learning_rate warmup_target. warmup_steps int. Number steps warmup .","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"See Loshchilov & Hutter, ICLR2016, SGDR: Stochastic Gradient Descent Warm Restarts. training model, often useful lower learning rate training progresses. schedule applies cosine decay function restarts optimizer step, given provided initial learning rate. requires step value compute decayed learning rate. can just pass backend variable increment training step. schedule 1-arg callable produces decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. learning rate multiplier first decays 1 alpha first_decay_steps steps. , warm restart performed. new warm restart runs t_mul times steps m_mul times initial learning rate new learning rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"","code":"learning_rate_schedule_cosine_decay_restarts(   initial_learning_rate,   first_decay_steps,   t_mul = 2,   m_mul = 1,   alpha = 0,   name = \"SGDRDecay\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"initial_learning_rate float. initial learning rate. first_decay_steps integer. Number steps decay . t_mul float. Used derive number iterations -th period. m_mul float. Used derive initial learning rate -th period. alpha float. Minimum learning rate value fraction initial_learning_rate. name String. Optional name operation. Defaults \"SGDRDecay\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"can pass schedule directly optimizer learning rate. learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"first_decay_steps <- 1000 lr_decayed_fn <- learning_rate_schedule_cosine_decay_restarts(         0.001,         first_decay_steps)"},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"training model, often useful lower learning rate training progresses. schedule applies exponential decay function optimizer step, given provided initial learning rate. schedule 1-arg callable produces decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. computed :   argument staircase TRUE, step / decay_steps integer division decayed learning rate follows staircase function. can pass schedule directly optimizer learning rate.","code":"decayed_learning_rate <- function(step) {   initial_learning_rate * decay_rate ^ (step / decay_steps) }"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"","code":"learning_rate_schedule_exponential_decay(   initial_learning_rate,   decay_steps,   decay_rate,   staircase = FALSE,   name = \"ExponentialDecay\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"initial_learning_rate float. initial learning rate. decay_steps integer. Must positive. See decay computation . decay_rate float. decay rate. staircase Boolean.  TRUE decay learning rate discrete intervals. name String.  Optional name operation.  Defaults \"ExponentialDecay\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"fitting Keras model, decay every 100000 steps base 0.96:   learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"initial_learning_rate <- 0.1 lr_schedule <- learning_rate_schedule_exponential_decay(     initial_learning_rate,     decay_steps=100000,     decay_rate=0.96,     staircase=TRUE)  model %>% compile(   optimizer = optimizer_sgd(learning_rate = lr_schedule),   loss = 'sparse_categorical_crossentropy',   metrics = c('accuracy'))  model %>% fit(data, labels, epochs=5)"},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"training model, often useful lower learning rate training progresses. schedule applies inverse decay function optimizer step, given provided initial learning rate. requires step value compute decayed learning rate. can just pass backend variable increment training step. schedule 1-arg callable produces decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. computed :   , staircase TRUE, :   can pass schedule directly optimizer_* learning rate.","code":"decayed_learning_rate <- function(step) {   initial_learning_rate / (1 + decay_rate * step / decay_step) } decayed_learning_rate <- function(step) {   initial_learning_rate /            (1 + decay_rate * floor(step / decay_step)) }"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"","code":"learning_rate_schedule_inverse_time_decay(   initial_learning_rate,   decay_steps,   decay_rate,   staircase = FALSE,   name = \"InverseTimeDecay\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"initial_learning_rate float. initial learning rate. decay_steps often apply decay. decay_rate number.  decay rate. staircase Whether apply decay discrete staircase, o pposed continuous, fashion. name String.  Optional name operation.  Defaults \"InverseTimeDecay\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"Fit Keras model decaying 1/t rate 0.5:","code":"... initial_learning_rate <- 0.1 decay_steps <- 1.0 decay_rate <- 0.5 learning_rate_fn <- learning_rate_schedule_inverse_time_decay(     initial_learning_rate, decay_steps, decay_rate)  model %>% compile(   optimizer = optimizer_sgd(learning_rate=learning_rate_fn),   loss = 'sparse_categorical_crossentropy',   metrics = 'accuracy') )  model %>% fit(data, labels, epochs=5)"},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"function returns 1-arg callable compute piecewise constant passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"","code":"learning_rate_schedule_piecewise_constant_decay(   boundaries,   values,   name = \"PiecewiseConstant\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"boundaries list Python numbers strictly increasing entries, elements type optimizer step. values list Python numbers specifies values intervals defined boundaries. one element boundaries, elements type. name string. Optional name operation. Defaults \"PiecewiseConstant\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type boundary tensors. output 1-arg function takes step values[0] step <= boundaries[0], values[1] step > boundaries[0] step <= boundaries[1], ..., values[-1] step > boundaries[-1].","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"use learning rate 1.0 first 100001 steps, 0.5 next 10000 steps, 0.1 additional steps.   can pass schedule directly optimizer learning rate. learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"step <- 0 boundaries <- c(100000, 110000) values <- c(1.0, 0.5, 0.1) learning_rate_fn <- learning_rate_schedule_piecewise_constant_decay(   boundaries, values)  # Later, whenever we perform an optimization step, we pass in the step. learning_rate <- learning_rate_fn(step)"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"ValueError: number elements boundaries values lists match.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"commonly observed monotonically decreasing learning rate, whose degree change carefully chosen, results better performing model. schedule applies polynomial decay function optimizer step, given provided initial_learning_rate, reach end_learning_rate given decay_steps. requires step value compute decayed learning rate. can just pass backend variable increment training step. schedule 1-arg callable produces decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. computed :   cycle TRUE multiple decay_steps used, first one bigger step.   can pass schedule directly Optimizer learning rate.","code":"decayed_learning_rate <- function(step) {   step = min(step, decay_steps)   ((initial_learning_rate - end_learning_rate) *     (1 - step / decay_steps) ^ (power)) +     end_learning_rate } decayed_learning_rate <- function(step) {   decay_steps = decay_steps * ceil(step / decay_steps)   ((initial_learning_rate - end_learning_rate) *       (1 - step / decay_steps) ^ (power)) +     end_learning_rate }"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"","code":"learning_rate_schedule_polynomial_decay(   initial_learning_rate,   decay_steps,   end_learning_rate = 1e-04,   power = 1,   cycle = FALSE,   name = \"PolynomialDecay\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"initial_learning_rate float. initial learning rate. decay_steps integer. Must positive. See decay computation . end_learning_rate float. minimal end learning rate. power float. power polynomial. Defaults 1.0. cycle boolean, whether cycle beyond decay_steps. name String.  Optional name operation. Defaults \"PolynomialDecay\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"Fit model decaying 0.1 0.01 10000 steps using sqrt (.e. power=0.5):   learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"... starter_learning_rate <- 0.1 end_learning_rate <- 0.01 decay_steps <- 10000 learning_rate_fn <- learning_rate_schedule_polynomial_decay(     starter_learning_rate,     decay_steps,     end_learning_rate,     power=0.5)  model %>% compile(   optimizer = optimizer_sgd(learning_rate=learning_rate_fn),   loss = 'sparse_categorical_crossentropy',   metrics = 'accuracy' )  model %>% fit(data, labels, epochs=5)"},{"path":[]},{"path":"https://keras.posit.co/reference/load_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Loads a model saved via save_model(). — load_model","title":"Loads a model saved via save_model(). — load_model","text":"Loads model saved via save_model().","code":""},{"path":"https://keras.posit.co/reference/load_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loads a model saved via save_model(). — load_model","text":"","code":"load_model(model, custom_objects = NULL, compile = TRUE, safe_mode = TRUE)"},{"path":"https://keras.posit.co/reference/load_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loads a model saved via save_model(). — load_model","text":"model string, path saved model file, raw vector, returned save_model(filepath = NULL) custom_objects Optional named list mapping names custom classes functions considered deserialization. compile Boolean, whether compile model loading. safe_mode Boolean, whether disallow unsafe lambda deserialization. safe_mode=FALSE, loading object potential trigger arbitrary code execution. argument applicable Keras v3 model format. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/load_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loads a model saved via save_model(). — load_model","text":"Keras model instance. original model compiled, argument compile = TRUE set, returned model compiled. Otherwise, model left uncompiled.","code":""},{"path":"https://keras.posit.co/reference/load_model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Loads a model saved via save_model(). — load_model","text":"Note model variables may different name values (var$name property, e.g. \"dense_1/kernel:0\") reloaded. recommended use layer attributes access specific variables, e.g. model |> get_layer(\"dense_1\") |> _$kernel.","code":"model <- keras_model_sequential(input_shape = c(3)) |>   layer_dense(5) |>   layer_activation_softmax()  model |> save_model(\"model.keras\") loaded_model <- load_model(\"model.keras\") x <- random_uniform(c(10, 3)) stopifnot(all.equal(   model |> predict(x),   loaded_model |> predict(x) ))"},{"path":[]},{"path":"https://keras.posit.co/reference/load_model_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Load weights from a file saved via save_model_weights(). — load_model_weights","title":"Load weights from a file saved via save_model_weights(). — load_model_weights","text":"Weights loaded based network's topology. means architecture weights saved. Note layers weights taken account topological ordering, adding removing layers fine long weights. Partial weight loading modified model, instance adding new layer (weights) changing shape weights layer, can choose ignore errors continue loading setting skip_mismatch=TRUE. case layer mismatching weights skipped. warning displayed skipped layer.","code":""},{"path":"https://keras.posit.co/reference/load_model_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load weights from a file saved via save_model_weights(). — load_model_weights","text":"","code":"load_model_weights(model, filepath, skip_mismatch = FALSE, ...)"},{"path":"https://keras.posit.co/reference/load_model_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load weights from a file saved via save_model_weights(). — load_model_weights","text":"model keras model. filepath String, path weights file load. can either .weights.h5 file legacy .h5 weights file. skip_mismatch Boolean, whether skip loading layers mismatch number weights, mismatch shape weights. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/load_model_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load weights from a file saved via save_model_weights(). — load_model_weights","text":"called primarily side effects. model returned, invisibly, enable usage pipe.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"Use cross-entropy loss binary (0 1) classification applications. loss function requires following inputs: y_true (true label): either 0 1. y_pred (predicted value): model's prediction, .e, single floating-point value either represents logit, (.e, value [-inf, inf] from_logits=TRUE) probability (.e, value [0., 1.] from_logits=FALSE).","code":""},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"","code":"loss_binary_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"binary_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"y_true Ground truth values. shape = [batch_size, d0, .. dN]. y_pred predicted values. shape = [batch_size, d0, .. dN]. from_logits Whether interpret y_pred tensor logit values. default, assume y_pred probabilities (.e., values [0, 1)). label_smoothing Float range [0, 1]. 0, smoothing occurs. > 0, compute loss predicted labels smoothed version true labels, smoothing squeezes labels towards 0.5. Larger values label_smoothing correspond heavier smoothing. axis axis along compute crossentropy (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"Binary crossentropy loss value. shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"Recommended Usage: (set from_logits=TRUE) compile() API:   standalone function:                     Default Usage: (set from_logits=FALSE)","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_binary_crossentropy(y_true, y_pred) loss ## tf.Tensor([0.91629073 0.71355818], shape=(2), dtype=float64) model %>% compile(     loss = loss_binary_crossentropy(from_logits=TRUE),     ... ) # Example 1: (batch_size = 1, number of samples = 4) y_true <- op_array(c(0, 1, 0, 0)) y_pred <- op_array(c(-18.6, 0.51, 2.94, -12.8)) bce <- loss_binary_crossentropy(from_logits = TRUE) bce(y_true, y_pred) ## tf.Tensor(0.865458, shape=(), dtype=float32) # Example 2: (batch_size = 2, number of samples = 4) y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(-18.6, 0.51), c(2.94, -12.8)) # Using default 'auto'/'sum_over_batch_size' reduction type. bce <- loss_binary_crossentropy(from_logits = TRUE) bce(y_true, y_pred) ## tf.Tensor(0.865458, shape=(), dtype=float32) # Using 'sample_weight' attribute bce(y_true, y_pred, sample_weight = c(0.8, 0.2)) ## tf.Tensor(0.2436386, shape=(), dtype=float32) # 0.243 # Using 'sum' reduction` type. bce <- loss_binary_crossentropy(from_logits = TRUE, reduction = \"sum\") bce(y_true, y_pred) ## tf.Tensor(1.730916, shape=(), dtype=float32) # Using 'none' reduction type. bce <- loss_binary_crossentropy(from_logits = TRUE, reduction = NULL) bce(y_true, y_pred) ## tf.Tensor([0.23515666 1.4957594 ], shape=(2), dtype=float32) # Make the following updates to the above \"Recommended Usage\" section # 1. Set `from_logits=FALSE` loss_binary_crossentropy() # OR ...('from_logits=FALSE') ## <keras.src.losses.losses.BinaryCrossentropy object> ##  signature: (y_true, y_pred, sample_weight=None) # 2. Update `y_pred` to use probabilities instead of logits y_pred <- c(0.6, 0.3, 0.2, 0.8) # OR [[0.6, 0.3], [0.2, 0.8]]"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"According Lin et al., 2018, helps apply focal factor -weight easy examples focus hard examples. default, focal tensor computed follows: focal_factor = (1 - output)^gamma class 1 focal_factor = output^gamma class 0 gamma focusing parameter. gamma = 0, focal effect binary crossentropy loss. apply_class_balancing == TRUE, function also takes account weight balancing factor binary classes 0 1 follows: weight = alpha class 1 (target == 1) weight = 1 - alpha class 0 alpha float range [0, 1]. Binary cross-entropy loss often used binary (0 1) classification tasks. loss function requires following inputs: y_true (true label): either 0 1. y_pred (predicted value): model's prediction, .e, single floating-point value either represents logit, (.e, value [-inf, inf] from_logits=TRUE) probability (.e, value [0., 1.] from_logits=FALSE). According Lin et al., 2018, helps apply \"focal factor\" -weight easy examples focus hard examples. default, focal tensor computed follows: focal_factor = (1 - output) ** gamma class 1 focal_factor = output ** gamma class 0 gamma focusing parameter. gamma=0, function equivalent binary crossentropy loss.","code":""},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"","code":"loss_binary_focal_crossentropy(   y_true,   y_pred,   apply_class_balancing = FALSE,   alpha = 0.25,   gamma = 2,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"binary_focal_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"y_true Ground truth values, shape (batch_size, d0, .. dN). y_pred predicted values, shape (batch_size, d0, .. dN). apply_class_balancing bool, whether apply weight balancing binary classes 0 1. alpha weight balancing factor class 1, default 0.25 mentioned reference Lin et al., 2018.  weight class 0 1.0 - alpha. gamma focusing parameter used compute focal factor, default 2.0 mentioned reference Lin et al., 2018. from_logits Whether interpret y_pred tensor logit values. default, assume y_pred probabilities (.e., values [0, 1]). label_smoothing Float [0, 1]. 0, smoothing occurs. > 0, compute loss predicted labels smoothed version true labels, smoothing squeezes labels towards 0.5. Larger values label_smoothing correspond heavier smoothing. axis axis along compute crossentropy (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"Binary focal crossentropy loss value shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"compile() API:   standalone function:","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_binary_focal_crossentropy(y_true, y_pred, gamma = 2) loss ## tf.Tensor([0.32986466 0.20579838], shape=(2), dtype=float64) model %>% compile(     loss = loss_binary_focal_crossentropy(         gamma = 2.0, from_logits = TRUE),     ... ) # Example 1: (batch_size = 1, number of samples = 4) y_true <- op_array(c(0, 1, 0, 0)) y_pred <- op_array(c(-18.6, 0.51, 2.94, -12.8)) loss <- loss_binary_focal_crossentropy(gamma = 2, from_logits = TRUE) loss(y_true, y_pred) ## tf.Tensor(0.6912122, shape=(), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(   apply_class_balancing = TRUE, gamma = 2, from_logits = TRUE) loss(y_true, y_pred) ## tf.Tensor(0.5101333, shape=(), dtype=float32) # Example 2: (batch_size = 2, number of samples = 4) y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(-18.6, 0.51), c(2.94, -12.8)) # Using default 'auto'/'sum_over_batch_size' reduction type. loss <- loss_binary_focal_crossentropy(     gamma = 3, from_logits = TRUE) loss(y_true, y_pred) ## tf.Tensor(0.6469951, shape=(), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(      apply_class_balancing = TRUE, gamma = 3, from_logits = TRUE) loss(y_true, y_pred) ## tf.Tensor(0.48214132, shape=(), dtype=float32) # Using 'sample_weight' attribute with focal effect loss <- loss_binary_focal_crossentropy(     gamma = 3, from_logits = TRUE) loss(y_true, y_pred, sample_weight = c(0.8, 0.2)) ## tf.Tensor(0.13312504, shape=(), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(      apply_class_balancing = TRUE, gamma = 3, from_logits = TRUE) loss(y_true, y_pred, sample_weight = c(0.8, 0.2)) ## tf.Tensor(0.09735977, shape=(), dtype=float32) # Using 'sum' reduction` type. loss <- loss_binary_focal_crossentropy(     gamma = 4, from_logits = TRUE,     reduction = \"sum\") loss(y_true, y_pred) ## tf.Tensor(1.2218808, shape=(), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(     apply_class_balancing = TRUE, gamma = 4, from_logits = TRUE,     reduction = \"sum\") loss(y_true, y_pred) ## tf.Tensor(0.9140807, shape=(), dtype=float32) # Using 'none' reduction type. loss <- loss_binary_focal_crossentropy(     gamma = 5, from_logits = TRUE,     reduction = NULL) loss(y_true, y_pred) ## tf.Tensor([0.00174837 1.1561027 ], shape=(2), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(     apply_class_balancing = TRUE, gamma = 5, from_logits = TRUE,     reduction = NULL) loss(y_true, y_pred) ## tf.Tensor([4.3709317e-04 8.6707699e-01], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"Use crossentropy loss function two label classes. expect labels provided one_hot representation. want provide labels integers, please use SparseCategoricalCrossentropy loss. num_classes floating point values per feature, .e., shape y_pred y_true [batch_size, num_classes].","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"","code":"loss_categorical_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"categorical_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"y_true Tensor one-hot true targets. y_pred Tensor predicted targets. from_logits Whether y_pred expected logits tensor. default, assume y_pred encodes probability distribution. label_smoothing Float [0, 1]. > 0, label values smoothed, meaning confidence label values relaxed. example, 0.1, use 0.1 / num_classes non-target labels 0.9 + 0.1 / num_classes target labels. axis axis along compute crossentropy (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"Categorical crossentropy loss value.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"Standalone usage:                 Usage compile() API:","code":"y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) loss <- loss_categorical_crossentropy(y_true, y_pred) loss ## tf.Tensor([0.05129329 2.30258509], shape=(2), dtype=float64) y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) # Using 'auto'/'sum_over_batch_size' reduction type. cce <- loss_categorical_crossentropy() cce(y_true, y_pred) ## tf.Tensor(1.1769392, shape=(), dtype=float32) # Calling with 'sample_weight'. cce(y_true, y_pred, sample_weight = op_array(c(0.3, 0.7))) ## tf.Tensor(0.8135988, shape=(), dtype=float32) # Using 'sum' reduction type. cce <- loss_categorical_crossentropy(reduction = \"sum\") cce(y_true, y_pred) ## tf.Tensor(2.3538785, shape=(), dtype=float32) # Using 'none' reduction type. cce <- loss_categorical_crossentropy(reduction = NULL) cce(y_true, y_pred) ## tf.Tensor([0.05129331 2.3025851 ], shape=(2), dtype=float32) model %>% compile(optimizer = 'sgd',               loss=loss_categorical_crossentropy())"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"Use crossentropy loss function two label classes want handle class imbalance without using class_weights. expect labels provided one_hot representation. According Lin et al., 2018, helps apply focal factor -weight easy examples focus hard examples. general formula focal loss (FL) follows: FL(p_t) = (1 - p_t)^gamma * log(p_t) p_t defined follows: p_t = output y_true == 1, else 1 - output (1 - p_t)^gamma modulating_factor, gamma focusing parameter. gamma = 0, focal effect cross entropy. gamma reduces importance given simple examples smooth manner. authors use alpha-balanced variant focal loss (FL) paper: FL(p_t) = -alpha * (1 - p_t)^gamma * log(p_t) alpha weight factor classes. alpha = 1, loss able handle class imbalance properly classes weight. can constant list constants. alpha list, must length number classes. formula can generalized : FL(p_t) = alpha * (1 - p_t)^gamma * CrossEntropy(y_true, y_pred) minus comes CrossEntropy(y_true, y_pred) (CE). Extending multi-class case straightforward: FL(p_t) = alpha * (1 - p_t) ** gamma * CategoricalCE(y_true, y_pred) snippet , num_classes floating pointing values per example. shape y_pred y_true (batch_size, num_classes).","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"","code":"loss_categorical_focal_crossentropy(   y_true,   y_pred,   alpha = 0.25,   gamma = 2,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"categorical_focal_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"y_true Tensor one-hot true targets. y_pred Tensor predicted targets. alpha weight balancing factor classes, default 0.25 mentioned reference. can list floats scalar. multi-class case, alpha may set inverse class frequency using compute_class_weight sklearn.utils. gamma focusing parameter, default 2.0 mentioned reference. helps gradually reduce importance given simple examples smooth manner. gamma = 0, focal effect categorical crossentropy. from_logits Whether output expected logits tensor. default, consider output encodes probability distribution. label_smoothing Float [0, 1]. > 0, label values smoothed, meaning confidence label values relaxed. example, 0.1, use 0.1 / num_classes non-target labels 0.9 + 0.1 / num_classes target labels. axis axis along compute crossentropy (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"Categorical focal crossentropy loss value.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"Standalone usage:                 Usage compile() API:","code":"y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) loss <- loss_categorical_focal_crossentropy(y_true, y_pred) loss ## tf.Tensor([3.20583090e-05 4.66273481e-01], shape=(2), dtype=float64) y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) # Using 'auto'/'sum_over_batch_size' reduction type. cce <- loss_categorical_focal_crossentropy() cce(y_true, y_pred) ## tf.Tensor(0.23315276, shape=(), dtype=float32) # Calling with 'sample_weight'. cce(y_true, y_pred, sample_weight = op_array(c(0.3, 0.7))) ## tf.Tensor(0.16320053, shape=(), dtype=float32) # Using 'sum' reduction type. cce <- loss_categorical_focal_crossentropy(reduction = \"sum\") cce(y_true, y_pred) ## tf.Tensor(0.46630552, shape=(), dtype=float32) # Using 'none' reduction type. cce <- loss_categorical_focal_crossentropy(reduction = NULL) cce(y_true, y_pred) ## tf.Tensor([3.2058331e-05 4.6627346e-01], shape=(2), dtype=float32) model %>% compile(   optimizer = 'adam',   loss = loss_categorical_focal_crossentropy())"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"Formula:   neg=maximum((1-y_true)*y_pred) pos=sum(y_true*y_pred)","code":"loss <- maximum(neg - pos + 1, 0)"},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"","code":"loss_categorical_hinge(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"categorical_hinge\" )"},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"y_true ground truth values. y_true values expected either {-1, +1} {0, 1} (.e. one-hot-encoded tensor) shape <- [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"Categorical hinge loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_categorical_hinge(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"Formula:   Note number -1 1. negative number -1 0, 0 indicates orthogonality values closer -1 indicate greater similarity. makes usable loss function setting try maximize proximity predictions targets. either y_true y_pred zero vector, cosine similarity 0 regardless proximity predictions targets.","code":"loss <- -sum(l2_norm(y_true) * l2_norm(y_pred))"},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"","code":"loss_cosine_similarity(   y_true,   y_pred,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"cosine_similarity\" )"},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"y_true Tensor true targets. y_pred Tensor predicted targets. axis axis along cosine similarity computed (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"Cosine similarity tensor.","code":""},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"","code":"y_true <- rbind(c(0., 1.), c(1., 1.), c(1., 1.)) y_pred <- rbind(c(1., 0.), c(1., 1.), c(-1., -1.)) loss <- loss_cosine_similarity(y_true, y_pred, axis=-1) loss ## tf.Tensor([-0. -1.  1.], shape=(3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_ctc.html","id":null,"dir":"Reference","previous_headings":"","what":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","title":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","text":"CTC (Connectionist Temporal Classification) loss.","code":""},{"path":"https://keras.posit.co/reference/loss_ctc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","text":"","code":"loss_ctc(y_true, y_pred, ..., reduction = \"sum_over_batch_size\", name = \"ctc\")"},{"path":"https://keras.posit.co/reference/loss_ctc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","text":"y_true tensor shape (batch_size, target_max_length) containing true labels integer format. 0 always represents blank/mask index used classes. y_pred tensor shape (batch_size, output_max_length, num_classes) containing logits (output model). normalized via softmax. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name String, name object","code":""},{"path":"https://keras.posit.co/reference/loss_ctc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","text":"CTC loss value.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/loss_dice.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Dice loss value between y_true and y_pred. — loss_dice","title":"Computes the Dice loss value between y_true and y_pred. — loss_dice","text":"Formula:   Formula:","code":"loss = 1 - (2 * sum(y_true * y_pred)) / (sum(y_true) + sum(y_pred)) loss = 1 - (2 * sum(y_true * y_pred)) / (sum(y_true) + sum(y_pred))"},{"path":"https://keras.posit.co/reference/loss_dice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Dice loss value between y_true and y_pred. — loss_dice","text":"","code":"loss_dice(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"dice\" )"},{"path":"https://keras.posit.co/reference/loss_dice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Dice loss value between y_true and y_pred. — loss_dice","text":"y_true tensor true targets. y_pred tensor predicted targets. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name String, name object","code":""},{"path":"https://keras.posit.co/reference/loss_dice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Dice loss value between y_true and y_pred. — loss_dice","text":"y_true y_pred provided, Dice loss value. Otherwise, Loss() instance.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the hinge loss between y_true & y_pred. — loss_hinge","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"Formula:   y_true values expected -1 1. binary (0 1) labels provided convert -1 1.","code":"loss <- mean(maximum(1 - y_true * y_pred, 0), axis=-1)"},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"","code":"loss_hinge(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"hinge\" )"},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"y_true ground truth values. y_true values expected -1 1. binary (0 1) labels provided converted -1 1 shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"Hinge loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"","code":"y_true <- array(sample(c(-1,1), 6, replace = TRUE), dim = c(2, 3)) y_pred <- random_uniform(c(2, 3)) loss <- loss_hinge(y_true, y_pred) loss ## tf.Tensor([1.0610152  0.93285507], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_huber.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Huber loss between y_true & y_pred. — loss_huber","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"Formula:   See: Huber loss.","code":"for (x in error) {   if (abs(x) <= delta){     loss <- c(loss, (0.5 * x^2))   } else if (abs(x) > delta) {     loss <- c(loss, (delta * abs(x) - 0.5 * delta^2))   } } loss <- mean(loss)"},{"path":"https://keras.posit.co/reference/loss_huber.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"","code":"loss_huber(   y_true,   y_pred,   delta = 1,   ...,   reduction = \"sum_over_batch_size\",   name = \"huber_loss\" )"},{"path":"https://keras.posit.co/reference/loss_huber.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"y_true tensor true targets. y_pred tensor predicted targets. delta float, point Huber loss function changes quadratic linear. Defaults 1.0. ... forward/backward compatability. reduction Type reduction apply loss. Options \"sum\", \"sum_over_batch_size\" NULL. Defaults \"sum_over_batch_size\". name Optional name instance.","code":""},{"path":"https://keras.posit.co/reference/loss_huber.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"Tensor one scalar loss entry per sample.","code":""},{"path":"https://keras.posit.co/reference/loss_huber.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_huber(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"Formula:   y_true y_pred expected probability distributions, values 0 1. get clipped [0, 1] range.","code":"loss <- y_true * log(y_true / y_pred)"},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"","code":"loss_kl_divergence(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"kl_divergence\" )"},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"y_true Tensor true targets. y_pred Tensor predicted targets. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"KL Divergence loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2,3)) loss <- loss_kl_divergence(y_true, y_pred) loss ## tf.Tensor([3.5312676 0.2128672], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"Formula:   Note log(cosh(x)) approximately equal (x ** 2) / 2 small x abs(x) - log(2) large x. means 'logcosh' works mostly like mean squared error, strongly affected occasional wildly incorrect prediction.","code":"loss <- mean(log(cosh(y_pred - y_true)), axis=-1)"},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"","code":"loss_log_cosh(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"log_cosh\" )"},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. Options \"sum\", \"sum_over_batch_size\" NULL. Defaults \"sum_over_batch_size\". name Optional name instance.","code":""},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"Logcosh error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"","code":"y_true <- rbind(c(0., 1.), c(0., 0.)) y_pred <- rbind(c(1., 1.), c(0., 0.)) loss <- loss_log_cosh(y_true, y_pred) # 0.108"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"Formula:","code":"loss <- mean(abs(y_true - y_pred))"},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"","code":"loss_mean_absolute_error(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"mean_absolute_error\" )"},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"Mean absolute error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2, 3)) loss <- loss_mean_absolute_error(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"Formula:   Division zero prevented dividing max(y_true, epsilon) epsilon = config_epsilon() (default 1e-7).","code":"loss <- 100 * op_mean(op_abs((y_true - y_pred) / y_true),                       axis=-1)"},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"","code":"loss_mean_absolute_percentage_error(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"mean_absolute_percentage_error\" )"},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"Mean absolute percentage error values shape = [batch_size, d0, ..dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"","code":"y_true <- random_uniform(c(2, 3)) y_pred <- random_uniform(c(2, 3)) loss <- loss_mean_absolute_percentage_error(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"Formula:","code":"loss <- mean(square(y_true - y_pred))"},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"","code":"loss_mean_squared_error(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"mean_squared_error\" )"},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"Mean squared error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2, 3)) loss <- loss_mean_squared_error(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"Note y_pred y_true less equal 0. Negative values 0 values replaced config_epsilon() (default 1e-7). Formula:","code":"loss <- mean(square(log(y_true + 1) - log(y_pred + 1)))"},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"","code":"loss_mean_squared_logarithmic_error(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"mean_squared_logarithmic_error\" )"},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"Mean squared logarithmic error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2, 3)) loss <- loss_mean_squared_logarithmic_error(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"Formula:","code":"loss <- y_pred - y_true * log(y_pred)"},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"","code":"loss_poisson(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"poisson\" )"},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"y_true Ground truth values. shape = [batch_size, d0, .. dN]. y_pred predicted values. shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"Poisson loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2, 3)) loss <- loss_poisson(y_true, y_pred) loss ## tf.Tensor([2.5907533  0.66836613], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"Use crossentropy loss function two label classes.  expect labels provided integers. want provide labels using one-hot representation, please use CategoricalCrossentropy loss.  # classes floating point values per feature y_pred single floating point value per feature y_true. snippet , single floating point value per example y_true num_classes floating pointing values per example y_pred. shape y_true [batch_size] shape y_pred [batch_size, num_classes].","code":""},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"","code":"loss_sparse_categorical_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   ignore_class = NULL,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"sparse_categorical_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"y_true Ground truth values. y_pred predicted values. from_logits Whether y_pred expected logits tensor. default, assume y_pred encodes probability distribution. ignore_class Optional integer. ID class ignored loss computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. axis Defaults -1. dimension along entropy computed. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"Sparse categorical crossentropy loss value.","code":""},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"Usage compile() API:","code":"y_true <- c(1, 2) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) loss <- loss_sparse_categorical_crossentropy(y_true, y_pred) loss ## tf.Tensor([0.05129339 2.30258509], shape=(2), dtype=float64) y_true <- c(1, 2) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) # Using 'auto'/'sum_over_batch_size' reduction type. scce <- loss_sparse_categorical_crossentropy() scce(op_array(y_true), op_array(y_pred)) ## tf.Tensor(1.1769392, shape=(), dtype=float32) # 1.177 # Calling with 'sample_weight'. scce(op_array(y_true), op_array(y_pred), sample_weight = op_array(c(0.3, 0.7))) ## tf.Tensor(0.8135988, shape=(), dtype=float32) # Using 'sum' reduction type. scce <- loss_sparse_categorical_crossentropy(reduction=\"sum\") scce(op_array(y_true), op_array(y_pred)) ## tf.Tensor(2.3538785, shape=(), dtype=float32) # 2.354 # Using 'none' reduction type. scce <- loss_sparse_categorical_crossentropy(reduction=NULL) scce(op_array(y_true), op_array(y_pred)) ## tf.Tensor([0.05129344 2.3025851 ], shape=(2), dtype=float32) # array([0.0513, 2.303], dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = loss_sparse_categorical_crossentropy())"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"Formula:   y_true values expected -1 1. binary (0 1) labels provided convert -1 1.","code":"loss <- square(maximum(1 - y_true * y_pred, 0))"},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"","code":"loss_squared_hinge(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"squared_hinge\" )"},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"y_true ground truth values. y_true values expected -1 1. binary (0 1) labels provided convert -1 1 shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"Squared hinge loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"","code":"y_true <- array(sample(c(-1,1), 6, replace = TRUE), dim = c(2, 3)) y_pred <- random_uniform(c(2, 3)) loss <- loss_squared_hinge(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"loss function weighted alpha beta coefficients penalize false positives false negatives. alpha=0.5 beta=0.5, loss value becomes equivalent Dice Loss. loss function weighted alpha beta coefficients penalize false positives false negatives. alpha=0.5 beta=0.5, loss value becomes equivalent Dice Loss.","code":""},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"","code":"loss_tversky(   y_true,   y_pred,   ...,   alpha = 0.5,   beta = 0.5,   reduction = \"sum_over_batch_size\",   name = \"tversky\" )"},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"y_true tensor true targets. y_pred tensor predicted targets. ... forward/backward compatability. alpha coefficient controlling incidence false positives. beta coefficient controlling incidence false negatives. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name String, name object","code":""},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"Tversky loss value.","code":""},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"Salehi et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/mark_active.html","id":null,"dir":"Reference","previous_headings":"","what":"active_property — mark_active","title":"active_property — mark_active","text":"mark_active() alias active_property(). See ?active_property() full documentation.","code":""},{"path":"https://keras.posit.co/reference/mark_active.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"active_property — mark_active","text":"","code":"mark_active(fn)"},{"path":"https://keras.posit.co/reference/mark_active.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"active_property — mark_active","text":"fn R function","code":""},{"path":"https://keras.posit.co/reference/mark_active.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"active_property — mark_active","text":"fn, additional R attribute cause fn converted active property converted method custom subclass.","code":""},{"path":"https://keras.posit.co/reference/metric_auc.html","id":null,"dir":"Reference","previous_headings":"","what":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"AUC (Area curve) ROC (Receiver operating characteristic; default) PR (Precision Recall) curves quality measures binary classifiers. Unlike accuracy, like cross-entropy losses, ROC-AUC PR-AUC evaluate operational points model. class approximates AUCs using Riemann sum. metric accumulation phrase, predictions accumulated within predefined buckets value. AUC computed interpolating per-bucket averages. buckets define evaluated operational points. metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute AUC.  discretize AUC curve, linearly spaced set thresholds used compute pairs recall precision values. area ROC-curve therefore computed using height recall values false positive rate, area PR-curve computed using height precision values recall. value ultimately returned auc, idempotent operation computes area discretized curve precision versus recall values (computed using aforementioned variables). num_thresholds variable controls degree discretization larger numbers thresholds closely approximating true AUC. quality approximation may vary dramatically depending num_thresholds. thresholds parameter can used manually specify thresholds split predictions evenly. best approximation real AUC, predictions distributed approximately uniformly range [0, 1] (from_logits=FALSE). quality AUC approximation may poor case. Setting summation_method 'minoring' 'majoring' can help quantify error approximation providing lower upper bound estimate AUC. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_auc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"","code":"metric_auc(   ...,   num_thresholds = 200L,   curve = \"ROC\",   summation_method = \"interpolation\",   name = NULL,   dtype = NULL,   thresholds = NULL,   multi_label = FALSE,   num_labels = NULL,   label_weights = NULL,   from_logits = FALSE )"},{"path":"https://keras.posit.co/reference/metric_auc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"... forward/backward compatability. num_thresholds (Optional) number thresholds use discretizing roc curve. Values must > 1. Defaults 200. curve (Optional) Specifies name curve computed, 'ROC' (default) 'PR' Precision-Recall-curve. summation_method (Optional) Specifies Riemann summation method used. 'interpolation' (default) applies mid-point summation scheme ROC.  PR-AUC, interpolates (true/false) positives ratio precision (see Davis & Goadrich 2006 details); 'minoring' applies left summation increasing intervals right summation decreasing intervals; 'majoring' opposite. name (Optional) string name metric instance. dtype (Optional) data type metric result. thresholds (Optional) list floating point values use thresholds discretizing curve. set, num_thresholds parameter ignored. Values [0, 1]. Endpoint thresholds equal {-epsilon, 1+epsilon} small positive epsilon value automatically included correctly handle predictions equal exactly 0 1. multi_label boolean indicating whether multilabel data treated , wherein AUC computed separately label averaged across labels, (FALSE) data flattened single label AUC computation. latter case, multilabel data passed AUC, label-prediction pair treated individual data point. set `FALSE`` multi-class data. num_labels (Optional) number labels, used multi_label TRUE. num_labels specified, state variables get created first call update_state. label_weights (Optional) list, array, tensor non-negative weights used compute AUCs multilabel data. multi_label TRUE, weights applied individual label AUCs averaged produce multi-label AUC. FALSE, used weight individual label predictions computing confusion matrix flattened data. Note unlike class_weights class_weights weights example depending value label, whereas label_weights depends index label flattening; therefore label_weights used multi-class data. from_logits boolean indicating whether predictions (y_pred update_state) probabilities sigmoid logits. rule thumb, using keras loss, from_logits constructor argument loss match AUC from_logits constructor argument.","code":""},{"path":"https://keras.posit.co/reference/metric_auc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_auc.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_auc(num_thresholds = 3) m$update_state(c(0,   0,   1,   1),                c(0, 0.5, 0.3, 0.9)) # threshold values are [0 - 1e-7, 0.5, 1 + 1e-7] # tp = [2, 1, 0], fp = [2, 0, 0], fn = [0, 1, 2], tn = [0, 2, 2] # tp_rate = recall = [1, 0.5, 0], fp_rate = [1, 0, 0] # auc = ((((1 + 0.5) / 2) * (1 - 0)) + (((0.5 + 0) / 2) * (0 - 0))) #     = 0.75 m$result() ## tf.Tensor(0.75, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   1,   1),                c(0, 0.5, 0.3, 0.9),                sample_weight=c(1, 0, 0, 1)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32) # Reports the AUC of a model outputting a probability. model |> compile(   optimizer = 'sgd',   loss = loss_binary_crossentropy(),   metrics = list(metric_auc()) )  # Reports the AUC of a model outputting a logit. model |> compile(   optimizer = 'sgd',   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_auc(from_logits = TRUE)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates how often predictions match binary labels. — metric_binary_accuracy","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"metric creates two local variables, total count used compute frequency y_pred matches y_true. frequency ultimately returned binary accuracy: idempotent operation simply divides total count. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"","code":"metric_binary_accuracy(   y_true,   y_pred,   threshold = 0.5,   ...,   name = \"binary_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. threshold (Optional) Float representing threshold deciding whether prediction values 1 0. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"Standalone usage:             Usage compile() API:","code":"m <- metric_binary_accuracy() m$update_state(rbind(1, 1, 0, 0), rbind(0.98, 1, 0, 0.6)) m$result() ## tf.Tensor(0.75, shape=(), dtype=float32) # 0.75 m$reset_state() m$update_state(rbind(1, 1, 0, 0), rbind(0.98, 1, 0, 0.6),                sample_weight = c(1, 0, 0, 1)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) # 0.5 model %>% compile(optimizer='sgd',                   loss='binary_crossentropy',                   metrics=list(metric_binary_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"crossentropy metric class used two label classes (0 1).","code":""},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"","code":"metric_binary_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   name = \"binary_crossentropy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"y_true Ground truth values. shape = [batch_size, d0, .. dN]. y_pred predicted values. shape = [batch_size, d0, .. dN]. from_logits (Optional) Whether output expected logits tensor. default, consider output encodes probability distribution. label_smoothing (Optional) Float [0, 1]. > 0, label values smoothed, meaning confidence label values relaxed. e.g. label_smoothing=0.2 means use value 0.1 label \"0\" 0.9 label \"1\". axis axis along mean computed. Defaults -1. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_binary_crossentropy() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(0.8149245, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight=c(1, 0)) m$result() ## tf.Tensor(0.91629076, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_binary_crossentropy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"According Lin et al., 2018, helps apply focal factor -weight easy examples focus hard examples. default, focal tensor computed follows: focal_factor = (1 - output)^gamma class 1 focal_factor = output^gamma class 0 gamma focusing parameter. gamma = 0, focal effect binary crossentropy loss. apply_class_balancing == TRUE, function also takes account weight balancing factor binary classes 0 1 follows: weight = alpha class 1 (target == 1) weight = 1 - alpha class 0 alpha float range [0, 1].","code":""},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"","code":"metric_binary_focal_crossentropy(   y_true,   y_pred,   apply_class_balancing = FALSE,   alpha = 0.25,   gamma = 2,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"y_true Ground truth values, shape (batch_size, d0, .. dN). y_pred predicted values, shape (batch_size, d0, .. dN). apply_class_balancing bool, whether apply weight balancing binary classes 0 1. alpha weight balancing factor class 1, default 0.25 mentioned reference. weight class 0 1.0 - alpha. gamma focusing parameter, default 2.0 mentioned reference. from_logits Whether y_pred expected logits tensor. default, assume y_pred encodes probability distribution. label_smoothing Float [0, 1]. > 0 smooth labels squeezing towards 0.5, , using 1. - 0.5 * label_smoothing target class 0.5 * label_smoothing non-target class. axis axis along mean computed. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"Binary focal crossentropy loss value shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_binary_focal_crossentropy(y_true, y_pred, gamma=2) loss ## tf.Tensor([0.32986466 0.20579838], shape=(2), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class can used compute IoUs binary classification task predictions provided logits. First threshold applied predicted values threshold converted class 0 threshold converted class 1. IoUs classes 0 1 computed, mean IoUs classes specified target_class_ids returned.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"","code":"metric_binary_iou(   ...,   target_class_ids = list(0L, 1L),   threshold = 0.5,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"... forward/backward compatability. target_class_ids list list target class ids metric returned. Options 0, 1, c(0, 1). 0 (1), IoU metric class 0 (class 1, respectively) returned. c(0, 1), mean IoUs two classes returned. threshold threshold applies prediction logits convert either predicted class 0 logit threshold predicted class 1 logit threshold. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"threshold=0, metric behavior IoU.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_binary_iou(target_class_ids=c(0L, 1L), threshold = 0.3) m$update_state(c(0, 1, 0, 1), c(0.1, 0.2, 0.4, 0.7)) m$result() ## tf.Tensor(0.33333334, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 0, 1), c(0.1, 0.2, 0.4, 0.7),                sample_weight = c(0.2, 0.3, 0.4, 0.1)) m$result() ## tf.Tensor(0.17361109, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_binary_iou(         target_class_ids = 0L,         threshold = 0.5     )) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"can provide logits classes y_pred, since argmax logits probabilities . metric creates two local variables, total count used compute frequency y_pred matches y_true. frequency ultimately returned categorical accuracy: idempotent operation simply divides total count. y_pred y_true passed vectors probabilities, rather labels. necessary, use op_one_hot expand y_true vector. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"","code":"metric_categorical_accuracy(   y_true,   y_pred,   ...,   name = \"categorical_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"Standalone usage:           Usage compile() API:","code":"m <- metric_categorical_accuracy() m$update_state(rbind(c(0, 0, 1), c(0, 1, 0)), rbind(c(0.1, 0.9, 0.8),                 c(0.05, 0.95, 0))) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 0, 1), c(0, 1, 0)), rbind(c(0.1, 0.9, 0.8),                c(0.05, 0.95, 0)),                sample_weight = c(0.7, 0.3)) m$result() ## tf.Tensor(0.3, shape=(), dtype=float32) # 0.3 model %>% compile(optimizer = 'sgd',                   loss = 'categorical_crossentropy',                   metrics = list(metric_categorical_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"crossentropy metric class used multiple label classes (2 ). assumes labels one-hot encoded, e.g., labels values c(2, 0, 1), y_true rbind(c([0, 0, 1), c(1, 0, 0), c(0, 1, 0)).","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"","code":"metric_categorical_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   name = \"categorical_crossentropy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"y_true Tensor one-hot true targets. y_pred Tensor predicted targets. from_logits (Optional) Whether output expected logits tensor. default, consider output encodes probability distribution. label_smoothing (Optional) Float [0, 1]. > 0, label values smoothed, meaning confidence label values relaxed. e.g. label_smoothing=0.2 means use value 0.1 label \"0\" 0.9 label \"1\". axis (Optional) Defaults -1. dimension along entropy computed. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"Standalone usage:           Usage compile() API:","code":"# EPSILON = 1e-7, y = y_true, y` = y_pred # y` = clip_op_clip_by_value(output, EPSILON, 1. - EPSILON) # y` = rbind(c(0.05, 0.95, EPSILON), c(0.1, 0.8, 0.1)) # xent = -sum(y * log(y'), axis = -1) #      = -((log 0.95), (log 0.1)) #      = [0.051, 2.302] # Reduced xent = (0.051 + 2.302) / 2 m <- metric_categorical_crossentropy() m$update_state(rbind(c(0, 1, 0), c(0, 0, 1)),                rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1))) m$result() ## tf.Tensor(1.1769392, shape=(), dtype=float32) # 1.1769392 m$reset_state() m$update_state(rbind(c(0, 1, 0), c(0, 0, 1)),                rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)),                sample_weight = c(0.3, 0.7)) m$result() ## tf.Tensor(1.6271976, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_categorical_crossentropy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"Computes categorical focal crossentropy loss.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"","code":"metric_categorical_focal_crossentropy(   y_true,   y_pred,   alpha = 0.25,   gamma = 2,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"y_true Tensor one-hot true targets. y_pred Tensor predicted targets. alpha weight balancing factor classes, default 0.25 mentioned reference. can list floats scalar. multi-class case, alpha may set inverse class frequency using compute_class_weight sklearn.utils. gamma focusing parameter, default 2.0 mentioned reference. helps gradually reduce importance given simple examples smooth manner. gamma = 0, focal effect categorical crossentropy. from_logits Whether y_pred expected logits tensor. default, assume y_pred encodes probability distribution. label_smoothing Float [0, 1]. > 0 smooth labels. example, 0.1, use 0.1 / num_classes non-target labels 0.9 + 0.1 / num_classes target labels. axis Defaults -1. dimension along entropy computed.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"Categorical focal crossentropy loss value.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"","code":"y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.9, 0.05), c(0.1, 0.85, 0.05)) loss <- loss_categorical_focal_crossentropy(y_true, y_pred) loss ## tf.Tensor([2.63401289e-04 6.75912094e-01], shape=(2), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"Formula:   neg=maximum((1-y_true)*y_pred) pos=sum(y_true*y_pred)","code":"loss <- maximum(neg - pos + 1, 0)"},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"","code":"metric_categorical_hinge(   y_true,   y_pred,   ...,   name = \"categorical_hinge\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"y_true ground truth values. y_true values expected either {-1, +1} {0, 1} (.e. one-hot-encoded tensor) shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"Categorical hinge loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"Standalone usage:","code":"m <- metric_categorical_hinge() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(1.4000001, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(1.2, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"Formula:   See: Cosine Similarity. metric keeps average cosine similarity predictions labels stream data.","code":"loss <- sum(l2_norm(y_true) * l2_norm(y_pred))"},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"","code":"metric_cosine_similarity(   ...,   name = \"cosine_similarity\",   dtype = NULL,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result. axis (Optional) Defaults -1. dimension along cosine similarity computed.","code":""},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_cosine_similarity(axis=2) m$update_state(rbind(c(0., 1.), c(1., 1.)), rbind(c(1., 0.), c(1., 1.))) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0., 1.), c(1., 1.)), rbind(c(1., 0.), c(1., 1.)),                sample_weight = c(0.3, 0.7)) m$result() ## tf.Tensor(0.7, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_cosine_similarity(axis=2)))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes F-1 Score. — metric_f1_score","title":"Computes F-1 Score. — metric_f1_score","text":"Formula:   harmonic mean precision recall. output range [0, 1]. works multi-class multi-label classification.","code":"f1_score <- 2 * (precision * recall) / (precision + recall)"},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes F-1 Score. — metric_f1_score","text":"","code":"metric_f1_score(   ...,   average = NULL,   threshold = NULL,   name = \"f1_score\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes F-1 Score. — metric_f1_score","text":"... forward/backward compatability. average Type averaging performed data. Acceptable values NULL, \"micro\", \"macro\" \"weighted\". Defaults NULL. NULL, averaging performed result() return score class. \"micro\", compute metrics globally counting total true positives, false negatives false positives. \"macro\", compute metrics label, return unweighted mean. take label imbalance account. \"weighted\", compute metrics label, return average weighted support (number true instances label). alters \"macro\" account label imbalance. can result score precision recall. threshold Elements y_pred greater threshold converted 1, rest 0. threshold NULL, argmax y_pred converted 1, rest 0. name Optional. String name metric instance. dtype Optional. Data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes F-1 Score. — metric_f1_score","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes F-1 Score. — metric_f1_score","text":"","code":"metric <- metric_f1_score(threshold = 0.5) y_true <- rbind(c(1, 1, 1),                 c(1, 0, 0),                 c(1, 1, 0)) y_pred <- rbind(c(0.2, 0.6, 0.7),                 c(0.2, 0.6, 0.6),                 c(0.6, 0.8, 0.0)) metric$update_state(y_true, y_pred) result <- metric$result() result ## tf.Tensor([0.49999997 0.79999995 0.66666657], shape=(3), dtype=float32)"},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Computes F-1 Score. — metric_f1_score","text":"F-1 Score: float.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the number of false negatives. — metric_false_negatives","title":"Calculates the number of false negatives. — metric_false_negatives","text":"sample_weight given, calculates sum weights false negatives. metric creates one local variable, accumulator used keep track number false negatives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of false negatives. — metric_false_negatives","text":"","code":"metric_false_negatives(..., thresholds = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the number of false negatives. — metric_false_negatives","text":"... forward/backward compatability. thresholds (Optional) Defaults 0.5. float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the number of false negatives. — metric_false_negatives","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of false negatives. — metric_false_negatives","text":"Standalone usage:","code":"m <- metric_false_negatives() m$update_state(c(0, 1, 1, 1), c(0, 1, 0, 0)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 1, 1), c(0, 1, 0, 0), sample_weight=c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32) # 1.0"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the number of false positives. — metric_false_positives","title":"Calculates the number of false positives. — metric_false_positives","text":"sample_weight given, calculates sum weights false positives. metric creates one local variable, accumulator used keep track number false positives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of false positives. — metric_false_positives","text":"","code":"metric_false_positives(..., thresholds = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the number of false positives. — metric_false_positives","text":"... forward/backward compatability. thresholds (Optional) Defaults 0.5. float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the number of false positives. — metric_false_positives","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of false positives. — metric_false_positives","text":"Standalone usage:","code":"m <- metric_false_positives() m$update_state(c(0, 1, 0, 0), c(0, 0, 1, 1)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 0, 0), c(0, 0, 1, 1), sample_weight = c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes F-Beta score. — metric_fbeta_score","title":"Computes F-Beta score. — metric_fbeta_score","text":"Formula:   weighted harmonic mean precision recall. output range [0, 1]. works multi-class multi-label classification.","code":"b2 <- beta^2 f_beta_score <- (1 + b2) * (precision * recall) / (precision * b2 + recall)"},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes F-Beta score. — metric_fbeta_score","text":"","code":"metric_fbeta_score(   ...,   average = NULL,   beta = 1,   threshold = NULL,   name = \"fbeta_score\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes F-Beta score. — metric_fbeta_score","text":"... forward/backward compatability. average Type averaging performed across per-class results multi-class case. Acceptable values NULL, \"micro\", \"macro\" \"weighted\". Defaults NULL. NULL, averaging performed result() return score class. \"micro\", compute metrics globally counting total true positives, false negatives false positives. \"macro\", compute metrics label, return unweighted mean. take label imbalance account. \"weighted\", compute metrics label, return average weighted support (number true instances label). alters \"macro\" account label imbalance. can result score precision recall. beta Determines weight given recall harmonic mean precision recall (see pseudocode equation ). Defaults 1. threshold Elements y_pred greater threshold converted 1, rest 0. threshold NULL, argmax y_pred converted 1, rest 0. name Optional. String name metric instance. dtype Optional. Data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes F-Beta score. — metric_fbeta_score","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes F-Beta score. — metric_fbeta_score","text":"","code":"metric <- metric_fbeta_score(beta = 2.0, threshold = 0.5) y_true <- rbind(c(1, 1, 1),                 c(1, 0, 0),                 c(1, 1, 0)) y_pred <- rbind(c(0.2, 0.6, 0.7),                 c(0.2, 0.6, 0.6),                 c(0.6, 0.8, 0.0)) metric$update_state(y_true, y_pred) metric$result() ## tf.Tensor([0.3846154  0.90909094 0.8333332 ], shape=(3), dtype=float32)"},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Computes F-Beta score. — metric_fbeta_score","text":"F-Beta Score: float.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the hinge metric between y_true and y_pred. — metric_hinge","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"Formula:   y_true values expected -1 1. binary (0 1) labels provided convert -1 1.","code":"loss <- mean(maximum(1 - y_true * y_pred, 0), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"","code":"metric_hinge(y_true, y_pred, ..., name = \"hinge\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"y_true ground truth values. y_true values expected -1 1. binary (0 1) labels provided converted -1 1 shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"Standalone usage:","code":"m <- metric_hinge() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(1.3, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(1.1, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_huber.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes Huber loss value. — metric_huber","title":"Computes Huber loss value. — metric_huber","text":"Formula:   See: Huber loss.","code":"for (x in error) {   if (abs(x) <= delta){     loss <- c(loss, (0.5 * x^2))   } else if (abs(x) > delta) {     loss <- c(loss, (delta * abs(x) - 0.5 * delta^2))   } } loss <- mean(loss)"},{"path":"https://keras.posit.co/reference/metric_huber.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes Huber loss value. — metric_huber","text":"","code":"metric_huber(y_true, y_pred, delta = 1)"},{"path":"https://keras.posit.co/reference/metric_huber.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes Huber loss value. — metric_huber","text":"y_true tensor true targets. y_pred tensor predicted targets. delta float, point Huber loss function changes quadratic linear. Defaults 1.0.","code":""},{"path":"https://keras.posit.co/reference/metric_huber.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes Huber loss value. — metric_huber","text":"Tensor one scalar loss entry per sample.","code":""},{"path":"https://keras.posit.co/reference/metric_huber.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes Huber loss value. — metric_huber","text":"","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_huber(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. Note, class first computes IoUs individual classes, returns mean IoUs classes specified target_class_ids. target_class_ids one id value, IoU specific class returned.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"","code":"metric_iou(   ...,   num_classes,   target_class_ids,   name = NULL,   dtype = NULL,   ignore_class = NULL,   sparse_y_true = TRUE,   sparse_y_pred = TRUE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"... forward/backward compatability. num_classes possible number labels prediction task can . target_class_ids list target class ids metric returned. compute IoU specific class, list single id value provided. name (Optional) string name metric instance. dtype (Optional) data type metric result. ignore_class Optional integer. ID class ignored metric computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. sparse_y_true Whether labels encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. sparse_y_pred Whether predictions encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. axis (Optional) -1 dimension containing logits. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_iou(num_classes = 2L, target_class_ids = list(0L)) m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1)) m$result() ## tf.Tensor(0.3333333, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1),                sample_weight = c(0.3, 0.3, 0.3, 0.1)) m$result() ## tf.Tensor(0.33333325, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_iou(num_classes = 2L, target_class_ids = list(0L))))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"Formula:   y_true y_pred expected probability distributions, values 0 1. get clipped [0, 1] range.","code":"loss <- y_true * log(y_true / y_pred)"},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"","code":"metric_kl_divergence(y_true, y_pred, ..., name = \"kl_divergence\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"y_true Tensor true targets. y_pred Tensor predicted targets. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_kl_divergence() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(0.45814303, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.91628915, shape=(), dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = 'mse',                   metrics = list(metric_kl_divergence()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"Formula:   Note log(cosh(x)) approximately equal (x ** 2) / 2 small x abs(x) - log(2) large x. means 'logcosh' works mostly like mean squared error, strongly affected occasional wildly incorrect prediction.","code":"loss <- mean(log(cosh(y_pred - y_true)), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"","code":"metric_log_cosh(y_true, y_pred)"},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN].","code":""},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"Logcosh error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"","code":"y_true <- rbind(c(0., 1.), c(0., 0.)) y_pred <- rbind(c(1., 1.), c(0., 0.)) loss <- metric_log_cosh(y_true, y_pred) loss ## tf.Tensor([ 2.16890413e-01 -1.90465432e-09], shape=(2), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"Formula:","code":"error <- y_pred - y_true logcosh <- mean(log((exp(error) + exp(-error))/2), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"","code":"metric_log_cosh_error(..., name = \"logcosh\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_log_cosh_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.108445205, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.21689041, shape=(), dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = 'mse',                   metrics = list(metric_log_cosh_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the (weighted) mean of the given values. — metric_mean","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"example, values c(1, 3, 5, 7) mean 4. sample_weight specified c(1, 1, 0, 0) mean 2. metric creates two variables, total count. mean value returned simply total divided count.","code":""},{"path":"https://keras.posit.co/reference/metric_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"","code":"metric_mean(..., name = \"mean\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_mean.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"","code":"m <- metric_mean() m$update_state(c(1, 3, 5, 7)) m$result() ## tf.Tensor(4.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(1, 3, 5, 7), sample_weight = c(1, 1, 0, 0)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"Formula:","code":"loss <- mean(abs(y_true - y_pred))"},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"","code":"metric_mean_absolute_error(   y_true,   y_pred,   ...,   name = \"mean_absolute_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_mean_absolute_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.25, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_mean_absolute_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"Formula:   Division zero prevented dividing maximum(y_true, epsilon) epsilon = keras$backend$epsilon() (default 1e-7).","code":"loss <- 100 * mean(abs((y_true - y_pred) / y_true), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"","code":"metric_mean_absolute_percentage_error(   y_true,   y_pred,   ...,   name = \"mean_absolute_percentage_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_mean_absolute_percentage_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(250000000.0, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(500000000.0, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_mean_absolute_percentage_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. Note class first computes IoUs individual classes, returns mean values.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"","code":"metric_mean_iou(   ...,   num_classes,   name = NULL,   dtype = NULL,   ignore_class = NULL,   sparse_y_true = TRUE,   sparse_y_pred = TRUE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"... forward/backward compatability. num_classes possible number labels prediction task can . value must provided, since confusion matrix dimension = [num_classes, num_classes] allocated. name (Optional) string name metric instance. dtype (Optional) data type metric result. ignore_class Optional integer. ID class ignored metric computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. sparse_y_true Whether labels encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. sparse_y_pred Whether predictions encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. axis (Optional) dimension containing logits. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"Standalone usage:         Usage compile() API:","code":"# cm = [[1, 1], #        [1, 1]] # sum_row = [2, 2], sum_col = [2, 2], true_positives = [1, 1] # iou = true_positives / (sum_row + sum_col - true_positives)) # result = (1 / (2 + 2 - 1) + 1 / (2 + 2 - 1)) / 2 = 0.33 m <- metric_mean_iou(num_classes = 2) m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1)) m$result() ## tf.Tensor(0.33333334, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1),                sample_weight=c(0.3, 0.3, 0.3, 0.1)) m$result() ## tf.Tensor(0.2380952, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_mean_iou(num_classes=2)))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"Formula:","code":"loss <- mean(square(y_true - y_pred))"},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"","code":"metric_mean_squared_error(   y_true,   y_pred,   ...,   name = \"mean_squared_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"","code":"m <- metric_mean_squared_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.25, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"Formula:   Note y_pred y_true less equal 0. Negative values 0 values replaced keras$backend$epsilon() (default 1e-7).","code":"loss <- mean(square(log(y_true + 1) - log(y_pred + 1)), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"","code":"metric_mean_squared_logarithmic_error(   y_true,   y_pred,   ...,   name = \"mean_squared_logarithmic_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_mean_squared_logarithmic_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.12011322, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.24022643, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_mean_squared_logarithmic_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","title":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","text":"use class quickly build mean metric function. function needs signature fn(y_true, y_pred) return per-sample loss array. metric_mean_wrapper$result() return average metric value across samples seen far. example:","code":"mse <- function(y_true, y_pred) {   (y_true - y_pred)^2 }  mse_metric <- metric_mean_wrapper(fn = mse) mse_metric$update_state(c(0, 1), c(1, 1)) mse_metric$result() ## tf.Tensor(0.5, shape=(), dtype=float32)"},{"path":"https://keras.posit.co/reference/metric_mean_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","text":"","code":"metric_mean_wrapper(..., fn, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_mean_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","text":"... Keyword arguments pass fn. fn metric function wrap, signature fn(y_true, y_pred). name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class can used compute IoU multi-class classification tasks labels one-hot encoded (last axis one dimension per class). Note predictions also shape. compute IoU, first labels predictions converted back integer format taking argmax class axis. computation steps base IoU class apply. Note, one channel labels predictions, class class IoU. case, use IoU instead. Also, make sure num_classes equal number classes data, avoid \"labels bound\" error confusion matrix computed.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"","code":"metric_one_hot_iou(   ...,   num_classes,   target_class_ids,   name = NULL,   dtype = NULL,   ignore_class = NULL,   sparse_y_pred = FALSE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"... forward/backward compatability. num_classes possible number labels prediction task can . target_class_ids list list target class ids metric returned. compute IoU specific class, list (list) single id value provided. name (Optional) string name metric instance. dtype (Optional) data type metric result. ignore_class Optional integer. ID class ignored metric computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. sparse_y_pred Whether predictions encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. axis (Optional) dimension containing logits. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"Standalone usage:     Usage compile() API:","code":"y_true <- rbind(c(0, 0, 1), c(1, 0, 0), c(0, 1, 0), c(1, 0, 0)) y_pred <- rbind(c(0.2, 0.3, 0.5), c(0.1, 0.2, 0.7), c(0.5, 0.3, 0.1),                 c(0.1, 0.4, 0.5)) sample_weight <- c(0.1, 0.2, 0.3, 0.4) m <- metric_one_hot_iou(num_classes = 3, target_class_ids = c(0, 2)) m$update_state(     y_true = y_true, y_pred = y_pred, sample_weight = sample_weight) m$result() ## tf.Tensor(0.07142855, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_one_hot_iou(     num_classes = 3L,     target_class_id = list(1L)   )) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class can used compute mean IoU multi-class classification tasks labels one-hot encoded (last axis one dimension per class). Note predictions also shape. compute mean IoU, first labels predictions converted back integer format taking argmax class axis. computation steps base MeanIoU class apply. Note, one channel labels predictions, class class metric_mean_iou. case, use metric_mean_iou instead. Also, make sure num_classes equal number classes data, avoid \"labels bound\" error confusion matrix computed.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"","code":"metric_one_hot_mean_iou(   ...,   num_classes,   name = NULL,   dtype = NULL,   ignore_class = NULL,   sparse_y_pred = FALSE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"... forward/backward compatability. num_classes possible number labels prediction task can . name (Optional) string name metric instance. dtype (Optional) data type metric result. ignore_class Optional integer. ID class ignored metric computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. sparse_y_pred Whether predictions encoded using natural numbers probability distribution vectors. FALSE, argmax function used determine sample's likely associated label. axis (Optional) dimension containing logits. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"Standalone usage:     Usage compile() API:","code":"y_true <- rbind(c(0, 0, 1), c(1, 0, 0), c(0, 1, 0), c(1, 0, 0)) y_pred <- rbind(c(0.2, 0.3, 0.5), c(0.1, 0.2, 0.7), c(0.5, 0.3, 0.1),                 c(0.1, 0.4, 0.5)) sample_weight <- c(0.1, 0.2, 0.3, 0.4) m <- metric_one_hot_mean_iou(num_classes = 3L) m$update_state(     y_true = y_true, y_pred = y_pred, sample_weight = sample_weight) m$result() ## tf.Tensor(0.047619034, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_one_hot_mean_iou(num_classes = 3L)))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"Formula:","code":"metric <- y_pred - y_true * log(y_pred)"},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"","code":"metric_poisson(y_true, y_pred, ..., name = \"poisson\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"y_true Ground truth values. shape = [batch_size, d0, .. dN]. y_pred predicted values. shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_poisson() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.49999997, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.99999994, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_poisson()) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_precision.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the precision of the predictions with respect to the labels. — metric_precision","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"metric creates two local variables, true_positives false_positives used compute precision. value ultimately returned precision, idempotent operation simply divides true_positives sum true_positives false_positives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. top_k set, calculate precision often average class among top-k classes highest predicted values batch entry correct can found label entry. class_id specified, calculate precision considering entries batch class_id threshold /top-k highest predictions, computing fraction class_id indeed correct label.","code":""},{"path":"https://keras.posit.co/reference/metric_precision.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"","code":"metric_precision(   ...,   thresholds = NULL,   top_k = NULL,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_precision.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"... forward/backward compatability. thresholds (Optional) float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. neither thresholds top_k set, default calculate precision thresholds=0.5. top_k (Optional) Unset default. int value specifying top-k predictions consider calculating precision. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_precision.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_precision.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"Standalone usage:                 Usage compile() API:   Usage loss from_logits=TRUE:","code":"m <- metric_precision() m$update_state(c(0, 1, 1, 1),                c(1, 0, 1, 1)) m$result() |> as.double() |> signif() ## [1] 0.666667 m$reset_state() m$update_state(c(0, 1, 1, 1),                c(1, 0, 1, 1),                sample_weight = c(0, 0, 1, 0)) m$result() |> as.double() |> signif() ## [1] 1 # With top_k=2, it will calculate precision over y_true[1:2] # and y_pred[1:2] m <- metric_precision(top_k = 2) m$update_state(c(0, 0, 1, 1), c(1, 1, 1, 1)) m$result() ## tf.Tensor(0.0, shape=(), dtype=float32) # With top_k=4, it will calculate precision over y_true[1:4] # and y_pred[1:4] m <- metric_precision(top_k = 4) m$update_state(c(0, 0, 1, 1), c(1, 1, 1, 1)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_precision()) ) model |> compile(   optimizer = 'adam',   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_precision(thresholds = 0)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute precision given recall. threshold given recall value computed used evaluate corresponding precision. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class_id specified, calculate precision considering entries batch class_id threshold predictions, computing fraction class_id indeed correct label.","code":""},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"","code":"metric_precision_at_recall(   ...,   recall,   num_thresholds = 200L,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"... forward/backward compatability. recall scalar value range [0, 1]. num_thresholds (Optional) Defaults 200. number thresholds use matching given recall. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_precision_at_recall(recall = 0.5) m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8),                sample_weight = c(2, 2, 2, 1, 1)) m$result() ## tf.Tensor(0.33333334, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_precision_at_recall(recall = 0.8)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes R2 score. — metric_r2_score","title":"Computes R2 score. — metric_r2_score","text":"Formula:   also called coefficient determination. indicates close fitted regression line ground-truth data. highest score possible 1.0. indicates predictors perfectly accounts variation target. score 0.0 indicates predictors account variation target. can also negative model worse random. metric can also compute \"Adjusted R2\" score.","code":"sum_squares_residuals <- sum((y_true - y_pred) ** 2) sum_squares <- sum((y_true - mean(y_true)) ** 2) R2 <- 1 - sum_squares_residuals / sum_squares"},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes R2 score. — metric_r2_score","text":"","code":"metric_r2_score(   ...,   class_aggregation = \"uniform_average\",   num_regressors = 0L,   name = \"r2_score\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes R2 score. — metric_r2_score","text":"... forward/backward compatability. class_aggregation Specifies aggregate scores corresponding different output classes (target dimensions), .e. different dimensions last axis predictions. Equivalent multioutput argument Scikit-Learn. one NULL (aggregation), \"uniform_average\", \"variance_weighted_average\". num_regressors Number independent regressors used (\"Adjusted R2\" score). 0 standard R2 score. Defaults 0. name Optional. string name metric instance. dtype Optional. data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes R2 score. — metric_r2_score","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes R2 score. — metric_r2_score","text":"","code":"y_true <- rbind(1, 4, 3) y_pred <- rbind(2, 4, 4) metric <- metric_r2_score() metric$update_state(y_true, y_pred) metric$result() ## tf.Tensor(0.57142854, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_recall.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the recall of the predictions with respect to the labels. — metric_recall","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"metric creates two local variables, true_positives false_negatives, used compute recall. value ultimately returned recall, idempotent operation simply divides true_positives sum true_positives false_negatives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. top_k set, recall computed often average class among labels batch entry top-k predictions. class_id specified, calculate recall considering entries batch class_id label, computing fraction class_id threshold /top-k predictions.","code":""},{"path":"https://keras.posit.co/reference/metric_recall.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"","code":"metric_recall(   ...,   thresholds = NULL,   top_k = NULL,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_recall.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"... forward/backward compatability. thresholds (Optional) float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. neither thresholds top_k set, default calculate recall thresholds=0.5. top_k (Optional) Unset default. int value specifying top-k predictions consider calculating recall. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_recall.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_recall.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"Standalone usage:         Usage compile() API:   Usage loss from_logits=TRUE:","code":"m <- metric_recall() m$update_state(c(0, 1, 1, 1),                c(1, 0, 1, 1)) m$result() ## tf.Tensor(0.6666667, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 1, 1),                c(1, 0, 1, 1),                sample_weight = c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_recall()) ) model |> compile(   optimizer = 'adam',   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_recall(thresholds = 0)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"given score-label-distribution required precision might achievable, case 0.0 returned recall. metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute recall given precision. threshold given precision value computed used evaluate corresponding recall. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class_id specified, calculate precision considering entries batch class_id threshold predictions, computing fraction class_id indeed correct label.","code":""},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"","code":"metric_recall_at_precision(   ...,   precision,   num_thresholds = 200L,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"... forward/backward compatability. precision scalar value range [0, 1]. num_thresholds (Optional) Defaults 200. number thresholds use matching given precision. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_recall_at_precision(precision = 0.8) m$update_state(c(0,   0,   1,   1),                c(0, 0.5, 0.3, 0.9)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   1,   1),                c(0, 0.5, 0.3, 0.9),                sample_weight = c(1, 0, 0, 1)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_recall_at_precision(precision = 0.8)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"Formula:","code":"loss <- sqrt(mean((y_pred - y_true) ^ 2))"},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"","code":"metric_root_mean_squared_error(   ...,   name = \"root_mean_squared_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_root_mean_squared_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.70710677, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_root_mean_squared_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"Sensitivity measures proportion actual positives correctly identified (tp / (tp + fn)). Specificity measures proportion actual negatives correctly identified (tn / (tn + fp)). metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute sensitivity given specificity. threshold given specificity value computed used evaluate corresponding sensitivity. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class_id specified, calculate precision considering entries batch class_id threshold predictions, computing fraction class_id indeed correct label. additional information specificity sensitivity, see following.","code":""},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"","code":"metric_sensitivity_at_specificity(   ...,   specificity,   num_thresholds = 200L,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"... forward/backward compatability. specificity scalar value range [0, 1]. num_thresholds (Optional) Defaults 200. number thresholds use matching given specificity. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_sensitivity_at_specificity(specificity = 0.5) m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8),                sample_weight = c(1, 1, 2, 2, 1)) m$result() ## tf.Tensor(0.33333334, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_sensitivity_at_specificity()) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"can provide logits classes y_pred, since argmax logits probabilities . metric creates two local variables, total count used compute frequency y_pred matches y_true. frequency ultimately returned sparse categorical accuracy: idempotent operation simply divides total count. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":"acc <- sample_weight %*% (y_true == which.max(y_pred))"},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"","code":"metric_sparse_categorical_accuracy(   y_true,   y_pred,   ...,   name = \"sparse_categorical_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_sparse_categorical_accuracy() m$update_state(rbind(2L, 1L), rbind(c(0.1, 0.6, 0.3), c(0.05, 0.95, 0))) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(2L, 1L), rbind(c(0.1, 0.6, 0.3), c(0.05, 0.95, 0)),                sample_weight = c(0.7, 0.3)) m$result() ## tf.Tensor(0.3, shape=(), dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = 'sparse_categorical_crossentropy',                   metrics = list(metric_sparse_categorical_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"Use crossentropy metric two label classes. expects labels provided integers. want provide labels one-hot encoded, please use metric_categorical_crossentropy() metric instead. num_classes floating point values per feature y_pred single floating point value per feature y_true.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"","code":"metric_sparse_categorical_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   ignore_class = NULL,   axis = -1L,   ...,   name = \"sparse_categorical_crossentropy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"y_true Ground truth values. y_pred predicted values. from_logits (Optional) Whether output expected logits tensor. default, consider output encodes probability distribution. ignore_class Optional integer. ID class ignored loss computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. axis (Optional) Defaults -1. dimension along entropy computed. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"Standalone usage:           Usage compile() API:","code":"m <- metric_sparse_categorical_crossentropy() m$update_state(c(1, 2),                rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1))) m$result() ## tf.Tensor(1.1769392, shape=(), dtype=float32) m$reset_state() m$update_state(c(1, 2),                rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)),                sample_weight = c(0.3, 0.7)) m$result() ## tf.Tensor(1.6271976, shape=(), dtype=float32) # 1.6271976 model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_sparse_categorical_crossentropy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"Computes often integer targets top K predictions.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"","code":"metric_sparse_top_k_categorical_accuracy(   y_true,   y_pred,   k = 5L,   ...,   name = \"sparse_top_k_categorical_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. k (Optional) Number top elements look computing accuracy. Defaults 5. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_sparse_top_k_categorical_accuracy(k = 1L) m$update_state(   rbind(2, 1),   op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = \"float32\") ) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(   rbind(2, 1),   op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = \"float32\"),   sample_weight = c(0.7, 0.3) ) m$result() ## tf.Tensor(0.3, shape=(), dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = 'sparse_categorical_crossentropy',                   metrics = list(metric_sparse_top_k_categorical_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"Sensitivity measures proportion actual positives correctly identified (tp / (tp + fn)). Specificity measures proportion actual negatives correctly identified (tn / (tn + fp)). metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute specificity given sensitivity. threshold given sensitivity value computed used evaluate corresponding specificity. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class_id specified, calculate precision considering entries batch class_id threshold predictions, computing fraction class_id indeed correct label. additional information specificity sensitivity, see following.","code":""},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"","code":"metric_specificity_at_sensitivity(   ...,   sensitivity,   num_thresholds = 200L,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"... forward/backward compatability. sensitivity scalar value range [0, 1]. num_thresholds (Optional) Defaults 200. number thresholds use matching given sensitivity. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_specificity_at_sensitivity(sensitivity = 0.5) m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8)) m$result() ## tf.Tensor(0.6666667, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8),                sample_weight = c(1, 1, 2, 2, 2)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_sensitivity_at_specificity()) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"Formula:   y_true values expected -1 1. binary (0 1) labels provided convert -1 1.","code":"loss <- mean(square(maximum(1 - y_true * y_pred, 0)))"},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"","code":"metric_squared_hinge(y_true, y_pred, ..., name = \"squared_hinge\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"y_true ground truth values. y_true values expected -1 1. binary (0 1) labels provided convert -1 1 shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"Standalone usage:","code":"m <- metric_squared_hinge() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(1.86, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(1.46, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the (weighted) sum of the given values. — metric_sum","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"example, values [1, 3, 5, 7] sum 16. sample_weight specified [1, 1, 0, 0] sum 4. metric creates one variable, total. ultimately returned sum value.","code":""},{"path":"https://keras.posit.co/reference/metric_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"","code":"metric_sum(..., name = \"sum\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_sum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"","code":"m <- metric_sum() m$update_state(c(1, 3, 5, 7)) m$result() ## tf.Tensor(16.0, shape=(), dtype=float32) m <- metric_sum() m$update_state(c(1, 3, 5, 7), sample_weight = c(1, 1, 0, 0)) m$result() ## tf.Tensor(4.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"Computes often targets top K predictions.","code":""},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"","code":"metric_top_k_categorical_accuracy(   y_true,   y_pred,   k = 5L,   ...,   name = \"top_k_categorical_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. k (Optional) Number top elements look computing accuracy. Defaults 5. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_top_k_categorical_accuracy(k = 1) m$update_state(   rbind(c(0, 0, 1), c(0, 1, 0)),   op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = \"float32\") ) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(   rbind(c(0, 0, 1), c(0, 1, 0)),   op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = \"float32\"),   sample_weight = c(0.7, 0.3)) m$result() ## tf.Tensor(0.3, shape=(), dtype=float32) model.compile(optimizer = 'sgd',               loss = 'categorical_crossentropy',               metrics = list(metric_top_k_categorical_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the number of true negatives. — metric_true_negatives","title":"Calculates the number of true negatives. — metric_true_negatives","text":"sample_weight given, calculates sum weights true negatives. metric creates one local variable, accumulator used keep track number true negatives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of true negatives. — metric_true_negatives","text":"","code":"metric_true_negatives(..., thresholds = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the number of true negatives. — metric_true_negatives","text":"... forward/backward compatability. thresholds (Optional) Defaults 0.5. float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the number of true negatives. — metric_true_negatives","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of true negatives. — metric_true_negatives","text":"Standalone usage:","code":"m <- metric_true_negatives() m$update_state(c(0, 1, 0, 0), c(1, 1, 0, 0)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 0, 0), c(1, 1, 0, 0), sample_weight = c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the number of true positives. — metric_true_positives","title":"Calculates the number of true positives. — metric_true_positives","text":"sample_weight given, calculates sum weights true positives. metric creates one local variable, true_positives used keep track number true positives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of true positives. — metric_true_positives","text":"","code":"metric_true_positives(..., thresholds = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the number of true positives. — metric_true_positives","text":"... forward/backward compatability. thresholds (Optional) Defaults 0.5. float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the number of true positives. — metric_true_positives","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of true positives. — metric_true_positives","text":"Standalone usage:","code":"m <- metric_true_positives() m$update_state(c(0, 1, 1, 1), c(1, 0, 1, 1)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 1, 1), c(1, 0, 1, 1), sample_weight = c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/multi-assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Assign values to names — %<-%","title":"Assign values to names — %<-%","text":"See %<-% details.","code":""},{"path":"https://keras.posit.co/reference/multi-assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assign values to names — %<-%","text":"","code":"x %<-% value"},{"path":"https://keras.posit.co/reference/multi-assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assign values to names — %<-%","text":"right-hand-side argument, value, invisibly. called primarily side-effect assigning symbols current frame.","code":""},{"path":"https://keras.posit.co/reference/new_callback_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback — new_callback_class","title":"Callback — new_callback_class","text":"new_callback_class() alias Callback(). See ?Callback() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_callback_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback — new_callback_class","text":"","code":"new_callback_class(   classname,   on_epoch_begin = NULL,   on_epoch_end = NULL,   on_train_begin = NULL,   on_train_end = NULL,   on_train_batch_begin = NULL,   on_train_batch_end = NULL,   on_test_begin = NULL,   on_test_end = NULL,   on_test_batch_begin = NULL,   on_test_batch_end = NULL,   on_predict_begin = NULL,   on_predict_end = NULL,   on_predict_batch_begin = NULL,   on_predict_batch_end = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_callback_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback — new_callback_class","text":"classname String, name custom class. (Conventionally, CamelCase). on_epoch_begin   Called start epoch. Subclasses override actions run. function called TRAIN mode. Args: epoch: Integer, index epoch. logs: Named List. Currently data passed argument method may change future. on_epoch_end   Called end epoch. Subclasses override actions run. function called TRAIN mode. Args: epoch: Integer, index epoch. logs: Named List, metric results training epoch, validation epoch validation performed. Validation result keys prefixed val_. training epoch, values Model's metrics returned. Example: list(loss = 0.2, accuracy = 0.7). on_train_begin   Called beginning training. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_train_end   Called end training. Subclasses override actions run. Args: logs: Named list. Currently output last call on_epoch_end() passed argument method may change future. on_train_batch_begin   Called beginning training batch fit() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_train_batch_end   Called end training batch fit() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. on_test_begin   Called beginning evaluation validation. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_test_end   Called end evaluation validation. Subclasses override actions run. Args: logs: Named list. Currently output last call on_test_batch_end() passed argument method may change future. on_test_batch_begin   Called beginning batch evaluate() methods. Also called beginning validation batch fit() methods, validation data provided. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_test_batch_end   Called end batch evaluate() methods. Also called end validation batch fit() methods, validation data provided. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. on_predict_begin   Called beginning prediction. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_predict_end   Called end prediction. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_predict_batch_begin   Called beginning batch predict() methods. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_predict_batch_end   Called end batch predict() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"\\(epoch, logs = NULL) \\(epoch, logs = NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs=NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs = NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs = NULL)"},{"path":"https://keras.posit.co/reference/new_callback_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback — new_callback_class","text":"function returns custom Callback instances, similar builtin callback functions.","code":""},{"path":"https://keras.posit.co/reference/new_layer_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer — new_layer_class","title":"Layer — new_layer_class","text":"new_layer_class() alias Layer(). See ?Layer() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_layer_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer — new_layer_class","text":"","code":"new_layer_class(   classname,   initialize = NULL,   call = NULL,   build = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_layer_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer — new_layer_class","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, call, build, get_config Recommended methods implement. See description details sections. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/new_layer_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer — new_layer_class","text":"composing layer constructor, similar behavior layer functions like layer_dense(). first argument returned function object, enabling initialize()ing call() layer one step composing layer pipe, like   initialize() layer instance call() , pass missing NULL value object, pass arguments initialize() name.","code":"layer_foo <- Layer(\"Foo\", ....) output <- inputs |> layer_foo() layer <- layer_dense(units = 2, activation = \"relu\") layer <- layer_dense(NULL, 2, activation = \"relu\") layer <- layer_dense(, 2, activation = \"relu\")  # then you can call() the layer in a separate step outputs <- inputs |> layer()"},{"path":"https://keras.posit.co/reference/new_learning_rate_schedule_class.html","id":null,"dir":"Reference","previous_headings":"","what":"LearningRateSchedule — new_learning_rate_schedule_class","title":"LearningRateSchedule — new_learning_rate_schedule_class","text":"new_learning_rate_schedule_class() alias LearningRateSchedule(). See ?LearningRateSchedule() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_learning_rate_schedule_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LearningRateSchedule — new_learning_rate_schedule_class","text":"","code":"new_learning_rate_schedule_class(   classname,   call = NULL,   initialize = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_learning_rate_schedule_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LearningRateSchedule — new_learning_rate_schedule_class","text":"classname String, name custom class. (Conventionally, CamelCase). call, initialize, get_config Recommended methods implement. See description details sections. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/new_learning_rate_schedule_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"LearningRateSchedule — new_learning_rate_schedule_class","text":"function returns LearningRateSchedule instances, similar built-learning_rate_schedule_* family functions.","code":""},{"path":"https://keras.posit.co/reference/new_loss_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Loss — new_loss_class","title":"Loss — new_loss_class","text":"new_loss_class() alias Loss(). See ?Loss() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_loss_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loss — new_loss_class","text":"","code":"new_loss_class(   classname,   call = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_loss_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loss — new_loss_class","text":"classname String, name custom class. (Conventionally, CamelCase). call   Method implemented subclasses: Function contains logic loss calculation using y_true, y_pred. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"function(y_true, y_pred)"},{"path":"https://keras.posit.co/reference/new_loss_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loss — new_loss_class","text":"function returns Loss instances, similar builtin loss functions.","code":""},{"path":"https://keras.posit.co/reference/new_metric_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Metric — new_metric_class","title":"Metric — new_metric_class","text":"new_metric_class() alias Metric(). See ?Metric() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_metric_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metric — new_metric_class","text":"","code":"new_metric_class(   classname,   initialize = NULL,   update_state = NULL,   result = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_metric_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metric — new_metric_class","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, update_state, result Recommended methods implement. See description section. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/new_metric_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metric — new_metric_class","text":"function returns Metric instances, similar builtin metric functions.","code":""},{"path":"https://keras.posit.co/reference/new_model_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Model — new_model_class","title":"Model — new_model_class","text":"new_model_class() alias Model(). See ?Model() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_model_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model — new_model_class","text":"","code":"new_model_class(   classname,   initialize = NULL,   call = NULL,   train_step = NULL,   predict_step = NULL,   test_step = NULL,   compute_loss = NULL,   compute_metrics = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_model_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model — new_model_class","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, call, train_step, predict_step, test_step, compute_loss, compute_metrics Optional methods can overridden. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/new_model_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model — new_model_class","text":"model constructor function, can call create instance new model type.","code":""},{"path":"https://keras.posit.co/reference/normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalizes an array. — normalize","title":"Normalizes an array. — normalize","text":"input R array, R array returned. backend tensor, backend tensor returned.","code":""},{"path":"https://keras.posit.co/reference/normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalizes an array. — normalize","text":"","code":"normalize(x, axis = -1L, order = 2L)"},{"path":"https://keras.posit.co/reference/normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalizes an array. — normalize","text":"x Array normalize. axis axis along normalize. order Normalization order (e.g. order=2 L2 norm).","code":""},{"path":"https://keras.posit.co/reference/normalize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalizes an array. — normalize","text":"normalized copy array.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_abs.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the absolute value element-wise. — op_abs","title":"Compute the absolute value element-wise. — op_abs","text":"Compute absolute value element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_abs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the absolute value element-wise. — op_abs","text":"","code":"op_abs(x)"},{"path":"https://keras.posit.co/reference/op_abs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the absolute value element-wise. — op_abs","text":"x Input tensor","code":""},{"path":"https://keras.posit.co/reference/op_abs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the absolute value element-wise. — op_abs","text":"array containing absolute value element x.","code":""},{"path":"https://keras.posit.co/reference/op_abs.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Compute the absolute value element-wise. — op_abs","text":"","code":"x <- op_convert_to_tensor(c(-1.2, 1.2)) op_abs(x) ## tf.Tensor([1.2 1.2], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_add.html","id":null,"dir":"Reference","previous_headings":"","what":"Add arguments element-wise. — op_add","title":"Add arguments element-wise. — op_add","text":"Add arguments element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_add.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add arguments element-wise. — op_add","text":"","code":"op_add(x1, x2)"},{"path":"https://keras.posit.co/reference/op_add.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add arguments element-wise. — op_add","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_add.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add arguments element-wise. — op_add","text":"tensor containing element-wise sum x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_add.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add arguments element-wise. — op_add","text":"op_add also broadcasts shapes:     Note function automatically called using R operator + tensors.","code":"x1 <- op_convert_to_tensor(c(1, 4)) x2 <- op_convert_to_tensor(c(5, 6)) op_add(x1, x2) ## tf.Tensor([ 6. 10.], shape=(2), dtype=float32) # alias for x1 + x2 x1 + x2 ## tf.Tensor([ 6. 10.], shape=(2), dtype=float32) x1 <- op_convert_to_tensor(array(c(5, 5, 4, 6), dim =c(2, 2))) x2 <- op_convert_to_tensor(c(5, 6)) op_add(x1, x2) ## tf.Tensor( ## [[10. 10.] ##  [10. 12.]], shape=(2, 2), dtype=float64) x <- op_ones(c(3)) op_add(x, x) ## tf.Tensor([2. 2. 2.], shape=(3), dtype=float32) x + x ## tf.Tensor([2. 2. 2.], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_all.html","id":null,"dir":"Reference","previous_headings":"","what":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"Test whether array elements along given axis evaluate TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_all.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"","code":"op_all(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_all.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"x Input tensor. axis integer tuple integers represent axis along logical reduction performed. default (axis = NULL) perform logical dimensions input array. axis may negative, case counts last first axis. keepdims TRUE, axes reduced left result dimensions size one. option, result broadcast correctly input array. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_all.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"tensor containing logical reduction axis.","code":""},{"path":"https://keras.posit.co/reference/op_all.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"keepdims = TRUE outputs tensor dimensions reduced one.","code":"x <- op_convert_to_tensor(c(TRUE, FALSE)) op_all(x) ## tf.Tensor(False, shape=(), dtype=bool) (x <- op_convert_to_tensor(array(c(TRUE, FALSE, TRUE, TRUE, TRUE, TRUE), dim = c(3, 2)))) ## tf.Tensor( ## [[ True  True] ##  [False  True] ##  [ True  True]], shape=(3, 2), dtype=bool) op_all(x, axis = 1) ## tf.Tensor([False  True], shape=(2), dtype=bool) op_all(x, keepdims = TRUE) ## tf.Tensor([[False]], shape=(1, 1), dtype=bool)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_any.html","id":null,"dir":"Reference","previous_headings":"","what":"Test whether any array element along a given axis evaluates to TRUE. — op_any","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"Test whether array element along given axis evaluates TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_any.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"","code":"op_any(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_any.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"x Input tensor. axis integer tuple integers represent axis along logical reduction performed. default (axis = NULL) perform logical dimensions input array. axis may negative, case counts last first axis. keepdims TRUE, axes reduced left result dimensions size one. option, result broadcast correctly input array. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_any.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"tensor containing logical reduction axis.","code":""},{"path":"https://keras.posit.co/reference/op_any.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"keepdims = TRUE outputs tensor dimensions reduced one.","code":"x <- op_array(c(TRUE, FALSE)) op_any(x) ## tf.Tensor(True, shape=(), dtype=bool) (x <- op_reshape(c(FALSE, FALSE, FALSE,                    TRUE, FALSE, FALSE),                  c(2, 3))) ## tf.Tensor( ## [[False False False] ##  [ True False False]], shape=(2, 3), dtype=bool) op_any(x, axis = 1) ## tf.Tensor([ True False False], shape=(3), dtype=bool) op_any(x, axis = 2) ## tf.Tensor([False  True], shape=(2), dtype=bool) op_any(x, axis = -1) ## tf.Tensor([False  True], shape=(2), dtype=bool) op_any(x, keepdims = TRUE) ## tf.Tensor([[ True]], shape=(1, 1), dtype=bool) op_any(x, 1, keepdims = TRUE) ## tf.Tensor([[ True False False]], shape=(1, 3), dtype=bool)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_append.html","id":null,"dir":"Reference","previous_headings":"","what":"Append tensor x2 to the end of tensor x1. — op_append","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"Append tensor x2 end tensor x1.","code":""},{"path":"https://keras.posit.co/reference/op_append.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"","code":"op_append(x1, x2, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_append.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"x1 First input tensor. x2 Second input tensor. axis Axis along tensor x2 appended tensor x1. NULL, tensors flattened use.","code":""},{"path":"https://keras.posit.co/reference/op_append.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"tensor values x2 appended x1.","code":""},{"path":"https://keras.posit.co/reference/op_append.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"axis specified, x1 x2 must compatible shapes.","code":"x1 <- op_convert_to_tensor(c(1, 2, 3)) x2 <- op_convert_to_tensor(rbind(c(4, 5, 6), c(7, 8, 9))) op_append(x1, x2) ## tf.Tensor([1. 2. 3. 4. 5. 6. 7. 8. 9.], shape=(9), dtype=float64) x1 <- op_convert_to_tensor(rbind(c(1, 2, 3), c(4, 5, 6))) x2 <- op_convert_to_tensor(rbind(c(7, 8, 9))) op_append(x1, x2, axis = 1) ## tf.Tensor( ## [[1. 2. 3.] ##  [4. 5. 6.] ##  [7. 8. 9.]], shape=(3, 3), dtype=float64) x3 <- op_convert_to_tensor(c(7, 8, 9)) try(op_append(x1, x3, axis = 1)) ## Error in py_call_impl(callable, call_args$unnamed, call_args$named) : ##   tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__ConcatV2_N_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} ConcatOp : Ranks of all input tensors should match: shape[0] = [2,3] vs. shape[1] = [3] [Op:ConcatV2] name: concat"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arange.html","id":null,"dir":"Reference","previous_headings":"","what":"Return evenly spaced values within a given interval. — op_arange","title":"Return evenly spaced values within a given interval. — op_arange","text":"arange can called varying number positional arguments: arange(stop): Values generated within half-open interval [0, stop) (words, interval including start excluding stop). arange(start, stop): Values generated within half-open interval [start, stop). arange(start, stop, step): Values generated within half-open interval [start, stop), spacing values given step.","code":""},{"path":"https://keras.posit.co/reference/op_arange.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return evenly spaced values within a given interval. — op_arange","text":"","code":"op_arange(start, stop = NULL, step = 1L, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_arange.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return evenly spaced values within a given interval. — op_arange","text":"start Integer real, representing start interval. interval includes value. stop Integer real, representing end interval. interval include value, except cases step integer floating point round-affects length . Defaults NULL. step Integer real, represent spacing values. output , distance two adjacent values, [+1] - []. default step size 1. step specified position argument, start must also given. dtype type output array. dtype given, infer data type input arguments.","code":""},{"path":"https://keras.posit.co/reference/op_arange.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return evenly spaced values within a given interval. — op_arange","text":"Tensor evenly spaced values. floating point arguments, length result ceiling((stop - start)/step). floating point overflow, rule may result last element greater stop.","code":""},{"path":"https://keras.posit.co/reference/op_arange.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return evenly spaced values within a given interval. — op_arange","text":"","code":"op_arange(3L) ## tf.Tensor([0 1 2], shape=(3), dtype=int32) op_arange(3) # float ## tf.Tensor([0. 1. 2.], shape=(3), dtype=float32) op_arange(3, dtype = 'int32') #int ## tf.Tensor([0 1 2], shape=(3), dtype=int32) op_arange(3L, 7L) ## tf.Tensor([3 4 5 6], shape=(4), dtype=int32) op_arange(3L, 7L, 2L) ## tf.Tensor([3 5], shape=(2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arccos.html","id":null,"dir":"Reference","previous_headings":"","what":"Trigonometric inverse cosine, element-wise. — op_arccos","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"inverse cos , y = cos(x), x = arccos(y).","code":""},{"path":"https://keras.posit.co/reference/op_arccos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"","code":"op_arccos(x)"},{"path":"https://keras.posit.co/reference/op_arccos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arccos.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"Tensor angle ray intersecting unit circle given x-coordinate radians [0, pi].","code":""},{"path":"https://keras.posit.co/reference/op_arccos.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"","code":"x <- op_convert_to_tensor(c(1, -1)) op_arccos(x) ## tf.Tensor([0.        3.1415927], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse hyperbolic cosine, element-wise. — op_arccosh","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"Inverse hyperbolic cosine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"","code":"op_arccosh(x)"},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"Output tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"","code":"x <- op_convert_to_tensor(c(10, 100)) op_arccosh(x) ## tf.Tensor([2.993223 5.298292], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse sine, element-wise. — op_arcsin","title":"Inverse sine, element-wise. — op_arcsin","text":"Inverse sine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse sine, element-wise. — op_arcsin","text":"","code":"op_arcsin(x)"},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse sine, element-wise. — op_arcsin","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse sine, element-wise. — op_arcsin","text":"Tensor inverse sine element x, radians closed interval [-pi/2, pi/2].","code":""},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse sine, element-wise. — op_arcsin","text":"","code":"x <- op_convert_to_tensor(c(1, -1, 0)) op_arcsin(x) ## tf.Tensor([ 1.5707964 -1.5707964  0.       ], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse hyperbolic sine, element-wise. — op_arcsinh","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"Inverse hyperbolic sine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"","code":"op_arcsinh(x)"},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"Output tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"","code":"x <- op_convert_to_tensor(c(1, -1, 0)) op_arcsinh(x) ## tf.Tensor([ 0.8813736 -0.8813736  0.       ], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arctan.html","id":null,"dir":"Reference","previous_headings":"","what":"Trigonometric inverse tangent, element-wise. — op_arctan","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"Trigonometric inverse tangent, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arctan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"","code":"op_arctan(x)"},{"path":"https://keras.posit.co/reference/op_arctan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arctan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"Tensor inverse tangent element x, interval [-pi/2, pi/2].","code":""},{"path":"https://keras.posit.co/reference/op_arctan.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"","code":"x <- op_convert_to_tensor(c(0, 1)) op_arctan(x) ## tf.Tensor([0.        0.7853982], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":null,"dir":"Reference","previous_headings":"","what":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"quadrant (.e., branch) chosen arctan2(x1, x2) signed angle radians ray ending origin passing point (1, 0), ray ending origin passing point (x2, x1). (Note role reversal: \"y-coordinate\" first function parameter, \"x-coordinate\" second.) IEEE convention, function defined x2 = +/-0 either x1 x2 = +/-inf.","code":""},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"","code":"op_arctan2(x1, x2)"},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"Tensor angles radians, range [-pi, pi].","code":""},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"Consider four points different quadrants:     Note order parameters. arctan2 defined also x2 = 0 several points, obtaining values range [-pi, pi]:","code":"x <- op_array(c(-1, 1, 1, -1)) y <- op_array(c(-1, -1, 1, 1)) op_arctan2(y, x) * 180 / pi ## tf.Tensor([-135.  -45.   45.  135.], shape=(4), dtype=float32) op_arctan2(     op_array(c(1, -1)),     op_array(c(0, 0)) ) ## tf.Tensor([ 1.5707964 -1.5707964], shape=(2), dtype=float32) op_arctan2(     op_array(c(0, 0, Inf)),     op_array(c(+0, -0, Inf)) ) ## tf.Tensor([0.        3.1415927 0.7853982], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arctanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse hyperbolic tangent, element-wise. — op_arctanh","title":"Inverse hyperbolic tangent, element-wise. — op_arctanh","text":"Inverse hyperbolic tangent, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arctanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse hyperbolic tangent, element-wise. — op_arctanh","text":"","code":"op_arctanh(x)"},{"path":"https://keras.posit.co/reference/op_arctanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse hyperbolic tangent, element-wise. — op_arctanh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arctanh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse hyperbolic tangent, element-wise. — op_arctanh","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_argmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the indices of the maximum values along an axis. — op_argmax","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"Returns indices maximum values along axis.","code":""},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"","code":"op_argmax(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"x Input tensor. axis default, index flattened tensor, otherwise along specified axis. keepdims set TRUE, axes reduced left result dimensions size one. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"Tensor indices. shape x, dimension along axis removed. Note returned integer 0-based (.e., argmax first index position, returned value 0)","code":""},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"similar R max.col(x) - 1 case 2-d array (matrix), nd-array, apply(x, axis, .max) - 1","code":""},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"","code":"x <- op_arange(6L) |> op_reshape(c(2, 3)) |> op_add(10) x ## tf.Tensor( ## [[10. 11. 12.] ##  [13. 14. 15.]], shape=(2, 3), dtype=float32) op_argmax(x) ## tf.Tensor(5, shape=(), dtype=int32) op_argmax(x, axis = 1) ## tf.Tensor([1 1 1], shape=(3), dtype=int32) op_argmax(x, axis = 2) ## tf.Tensor([2 2], shape=(2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_argmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the indices of the minimum values along an axis. — op_argmin","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"Returns indices minimum values along axis.","code":""},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"","code":"op_argmin(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"x Input tensor. axis default, index flattened tensor, otherwise along specified axis. keepdims set TRUE, axes reduced left result dimensions size one. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"Tensor indices. shape x, dimension along axis removed.","code":""},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"similar R expression apply(x, axis, .min) - 1, x R array.","code":""},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"","code":"x <- op_arange(6L) |> op_reshape(c(2, 3)) |> op_add(10) x ## tf.Tensor( ## [[10. 11. 12.] ##  [13. 14. 15.]], shape=(2, 3), dtype=float32) op_argmin(x) ## tf.Tensor(0, shape=(), dtype=int32) op_argmin(x, axis = 1) ## tf.Tensor([0 0 0], shape=(3), dtype=int32) op_argmin(x, axis = 2) ## tf.Tensor([0 0], shape=(2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_argsort.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the indices that would sort a tensor. — op_argsort","title":"Returns the indices that would sort a tensor. — op_argsort","text":"Returns indices sort tensor.","code":""},{"path":"https://keras.posit.co/reference/op_argsort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the indices that would sort a tensor. — op_argsort","text":"","code":"op_argsort(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_argsort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the indices that would sort a tensor. — op_argsort","text":"x Input tensor. axis Axis along sort. Defaults -1 (last axis). NULL, flattened tensor used.","code":""},{"path":"https://keras.posit.co/reference/op_argsort.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the indices that would sort a tensor. — op_argsort","text":"Tensor indices sort x along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_argsort.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the indices that would sort a tensor. — op_argsort","text":"One dimensional array:     Two-dimensional array:","code":"x <- op_array(c(3, 1, 2)) op_argsort(x) ## tf.Tensor([1 2 0], shape=(3), dtype=int32) x <- op_array(rbind(c(0, 3),                    c(3, 2),                    c(4, 5)), dtype = \"int32\") op_argsort(x, axis = 1) ## tf.Tensor( ## [[0 1] ##  [1 0] ##  [2 2]], shape=(3, 2), dtype=int32) op_argsort(x, axis = 2) ## tf.Tensor( ## [[0 1] ##  [1 0] ##  [0 1]], shape=(3, 2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a tensor. — op_array","title":"Create a tensor. — op_array","text":"Create tensor.","code":""},{"path":"https://keras.posit.co/reference/op_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a tensor. — op_array","text":"","code":"op_array(x, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a tensor. — op_array","text":"x Input tensor. dtype desired data-type tensor.","code":""},{"path":"https://keras.posit.co/reference/op_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a tensor. — op_array","text":"tensor.","code":""},{"path":"https://keras.posit.co/reference/op_array.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a tensor. — op_array","text":"","code":"op_array(c(1, 2, 3)) ## tf.Tensor([1. 2. 3.], shape=(3), dtype=float32) op_array(c(1, 2, 3), dtype = \"float32\") ## tf.Tensor([1. 2. 3.], shape=(3), dtype=float32) op_array(c(1, 2, 3), dtype = \"int32\") ## tf.Tensor([1 2 3], shape=(3), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_average.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the weighted average along the specified axis. — op_average","title":"Compute the weighted average along the specified axis. — op_average","text":"Compute weighted average along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_average.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the weighted average along the specified axis. — op_average","text":"","code":"op_average(x, axis = NULL, weights = NULL)"},{"path":"https://keras.posit.co/reference/op_average.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the weighted average along the specified axis. — op_average","text":"x Input tensor. axis Integer along average x. default, axis = NULL, average elements input tensor. axis negative counts last first axis. weights Tensor wieghts associated values x. value x contributes average according associated weight. weights array can either 1-D (case length must size along given axis) shape x. weights = NULL (default), data x assumed weight equal one. 1-D calculation : avg = sum(* weights) / sum(weights). constraint weights sum(weights) must 0.","code":""},{"path":"https://keras.posit.co/reference/op_average.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the weighted average along the specified axis. — op_average","text":"Return average along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_average.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the weighted average along the specified axis. — op_average","text":"","code":"data <- op_arange(1, 5, dtype = \"int32\") data ## tf.Tensor([1 2 3 4], shape=(4), dtype=int32) op_average(data) ## tf.Tensor(2.5, shape=(), dtype=float32) op_average(   op_arange(1, 11),   weights = op_arange(10, 0, -1) ) ## tf.Tensor(4.0, shape=(), dtype=float32) data <- op_arange(6) |> op_reshape(c(3, 2)) data ## tf.Tensor( ## [[0. 1.] ##  [2. 3.] ##  [4. 5.]], shape=(3, 2), dtype=float32) op_average(   data,   axis = 2,   weights = op_array(c(1/4, 3/4)) ) ## tf.Tensor([0.75 2.75 4.75], shape=(3), dtype=float32) # Error: Axis must be specified when shapes of x and weights differ. try(op_average(   data,   weights = op_array(c(1/4, 3/4)) )) ## Error in op_average(data, weights = op_array(c(1/4, 3/4))) : ##   Axis must be specified when shapes of x and weights differ."},{"path":[]},{"path":"https://keras.posit.co/reference/op_average_pool.html","id":null,"dir":"Reference","previous_headings":"","what":"Average pooling operation. — op_average_pool","title":"Average pooling operation. — op_average_pool","text":"Average pooling operation.","code":""},{"path":"https://keras.posit.co/reference/op_average_pool.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average pooling operation. — op_average_pool","text":"","code":"op_average_pool(   inputs,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL )"},{"path":"https://keras.posit.co/reference/op_average_pool.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average pooling operation. — op_average_pool","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". Pooling happens spatial dimensions . pool_size int tuple/list integers size len(inputs_spatial_shape), specifying size pooling window spatial dimension input tensor. pool_size int, every spatial dimension shares pool_size. strides int tuple/list integers size len(inputs_spatial_shape). stride sliding window spatial dimension input tensor. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...).","code":""},{"path":"https://keras.posit.co/reference/op_average_pool.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average pooling operation. — op_average_pool","text":"tensor rank N+2, result average pooling operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalizes x by mean and variance. — op_batch_normalization","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"op typically used batch normalization step neural network. normalizes input tensor along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"","code":"op_batch_normalization(   x,   mean,   variance,   axis,   offset = NULL,   scale = NULL,   epsilon = 0.001 )"},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"x Input tensor. mean mean vector length axis dimension input thensor. variance variance vector length axis dimension input tensor. axis Integer, axis normalized. offset offset vector length axis dimension input tensor. NULL, offset added normalized tensor. Defaults NULL. scale scale vector length axis dimension input tensor. NULL, normalized tensor multiplied scale. Defaults NULL. epsilon Small float added variance avoid dividing zero. Defaults 1e-3.","code":""},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"normalized tensor.","code":""},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"","code":"x <- op_convert_to_tensor(rbind(c(0.1, 0.2, 0.3),                                 c(0.4, 0.5, 0.6),                                 c(0.7, 0.8, 0.9))) op_batch_normalization(   x,   mean = c(0.4, 0.5, 0.6),   variance = c(0.67, 0.67, 0.67),   axis = -1 ) ## tf.Tensor( ## [[-0.36623513 -0.36623513 -0.36623513] ##  [ 0.          0.          0.        ] ##  [ 0.36623513  0.36623513  0.36623513]], shape=(3, 3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"binary cross-entropy loss commonly used binary classification tasks input sample belongs one two classes. measures dissimilarity target output probabilities logits.","code":""},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"","code":"op_binary_crossentropy(target, output, from_logits = FALSE)"},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"target target tensor representing true binary labels. shape match shape output tensor. output output tensor representing predicted probabilities logits. shape match shape target tensor. from_logits (optional) Whether output tensor logits probabilities. Set TRUE output represents logits; otherwise, set FALSE output represents probabilities. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"Integer tensor: computed binary cross-entropy loss target output.","code":""},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"","code":"target <- op_array(c(0, 1, 1, 0)) output <- op_array(c(0.1, 0.9, 0.8, 0.2)) op_binary_crossentropy(target, output) ## tf.Tensor([0.10536055 0.10536055 0.22314353 0.22314353], shape=(4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_bincount.html","id":null,"dir":"Reference","previous_headings":"","what":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"Count number occurrences value tensor integers.","code":""},{"path":"https://keras.posit.co/reference/op_bincount.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"","code":"op_bincount(x, weights = NULL, minlength = 0L, sparse = FALSE)"},{"path":"https://keras.posit.co/reference/op_bincount.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"x Input tensor. must dimension 1, must contain non-negative integer(s). weights Weight tensor. must length x. default value NULL. specified, x weighted , .e. n = x[], [n] += weight[] instead default behavior [n] += 1. minlength integer. default value 0. specified, least number bins output tensor. greater max(x) + 1, value output index higher max(x) set 0. sparse Whether return sparse tensor; backends support sparse tensors.","code":""},{"path":"https://keras.posit.co/reference/op_bincount.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"1D tensor element gives number occurrence(s) index value x. length maximum max(x) + 1 minlength.","code":""},{"path":"https://keras.posit.co/reference/op_bincount.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"","code":"(x <- op_array(c(1, 2, 2, 3), dtype = \"uint8\")) ## tf.Tensor([1 2 2 3], shape=(4), dtype=uint8) op_bincount(x) ## tf.Tensor([0 1 2 1], shape=(4), dtype=int32) (weights <- x / 2) ## tf.Tensor([0.5 1.  1.  1.5], shape=(4), dtype=float32) op_bincount(x, weights = weights) ## tf.Tensor([0.  0.5 2.  1.5], shape=(4), dtype=float32) minlength <- as.integer(op_max(x) + 1 + 2) # 6 op_bincount(x, minlength = minlength) ## tf.Tensor([0 1 2 1 0 0], shape=(6), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":null,"dir":"Reference","previous_headings":"","what":"Broadcast a tensor to a new shape. — op_broadcast_to","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"Broadcast tensor new shape.","code":""},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"","code":"op_broadcast_to(x, shape)"},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"x tensor broadcast. shape shape desired tensor.","code":""},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"tensor desired shape.","code":""},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"","code":"x <- op_array(c(1, 2, 3)) op_broadcast_to(x, shape = c(3, 3)) ## tf.Tensor( ## [[1. 2. 3.] ##  [1. 2. 3.] ##  [1. 2. 3.]], shape=(3, 3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_cast.html","id":null,"dir":"Reference","previous_headings":"","what":"Cast a tensor to the desired dtype. — op_cast","title":"Cast a tensor to the desired dtype. — op_cast","text":"Cast tensor desired dtype.","code":""},{"path":"https://keras.posit.co/reference/op_cast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cast a tensor to the desired dtype. — op_cast","text":"","code":"op_cast(x, dtype)"},{"path":"https://keras.posit.co/reference/op_cast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cast a tensor to the desired dtype. — op_cast","text":"x tensor variable. dtype target type.","code":""},{"path":"https://keras.posit.co/reference/op_cast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cast a tensor to the desired dtype. — op_cast","text":"tensor specified dtype.","code":""},{"path":"https://keras.posit.co/reference/op_cast.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cast a tensor to the desired dtype. — op_cast","text":"","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_cast(x, dtype = \"float16\") ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float16)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"categorical cross-entropy loss commonly used multi-class classification tasks input sample can belong one multiple classes. measures dissimilarity target output probabilities logits.","code":""},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"","code":"op_categorical_crossentropy(target, output, from_logits = FALSE, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"target target tensor representing true categorical labels. shape match shape output tensor except last dimension. output output tensor representing predicted probabilities logits. shape match shape target tensor except last dimension. from_logits (optional) Whether output tensor logits probabilities. Set TRUE output represents logits; otherwise, set FALSE output represents probabilities. Defaults FALSE. axis (optional) axis along categorical cross-entropy computed. Defaults -1, corresponds last dimension tensors.","code":""},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"Integer tensor: computed categorical cross-entropy loss target output.","code":""},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"","code":"target <- op_array(rbind(c(1, 0, 0),                         c(0, 1, 0),                         c(0, 0, 1))) output <- op_array(rbind(c(0.9, 0.05, 0.05),                         c(0.1, 0.8, 0.1),                         c(0.2, 0.3, 0.5))) op_categorical_crossentropy(target, output) ## tf.Tensor([0.10536052 0.22314355 0.69314718], shape=(3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_ceil.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the ceiling of the input, element-wise. — op_ceil","title":"Return the ceiling of the input, element-wise. — op_ceil","text":"ceil scalar x smallest integer , >= x.","code":""},{"path":"https://keras.posit.co/reference/op_ceil.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the ceiling of the input, element-wise. — op_ceil","text":"","code":"op_ceil(x)"},{"path":"https://keras.posit.co/reference/op_ceil.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the ceiling of the input, element-wise. — op_ceil","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ceil.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the ceiling of the input, element-wise. — op_ceil","text":"ceiling element x, float dtype.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cholesky.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","title":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","text":"Computes Cholesky decomposition positive semi-definite matrix.","code":""},{"path":"https://keras.posit.co/reference/op_cholesky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","text":"","code":"op_cholesky(x)"},{"path":"https://keras.posit.co/reference/op_cholesky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_cholesky.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","text":"tensor shape (..., M, M) representing lower triangular Cholesky factor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_clip.html","id":null,"dir":"Reference","previous_headings":"","what":"Clip (limit) the values in a tensor. — op_clip","title":"Clip (limit) the values in a tensor. — op_clip","text":"Given interval, values outside interval clipped interval edges. example, interval [0, 1] specified, values smaller 0 become 0, values larger 1 become 1.","code":""},{"path":"https://keras.posit.co/reference/op_clip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clip (limit) the values in a tensor. — op_clip","text":"","code":"op_clip(x, x_min, x_max)"},{"path":"https://keras.posit.co/reference/op_clip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clip (limit) the values in a tensor. — op_clip","text":"x Input tensor. x_min Minimum value. x_max Maximum value.","code":""},{"path":"https://keras.posit.co/reference/op_clip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clip (limit) the values in a tensor. — op_clip","text":"clipped tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_concatenate.html","id":null,"dir":"Reference","previous_headings":"","what":"Join a sequence of tensors along an existing axis. — op_concatenate","title":"Join a sequence of tensors along an existing axis. — op_concatenate","text":"Join sequence tensors along existing axis.","code":""},{"path":"https://keras.posit.co/reference/op_concatenate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join a sequence of tensors along an existing axis. — op_concatenate","text":"","code":"op_concatenate(xs, axis = 1L)"},{"path":"https://keras.posit.co/reference/op_concatenate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join a sequence of tensors along an existing axis. — op_concatenate","text":"xs sequence tensors concatenate. axis axis along tensors joined. Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/op_concatenate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join a sequence of tensors along an existing axis. — op_concatenate","text":"concatenated tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cond.html","id":null,"dir":"Reference","previous_headings":"","what":"Conditionally applies true_fn or false_fn. — op_cond","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"Conditionally applies true_fn false_fn.","code":""},{"path":"https://keras.posit.co/reference/op_cond.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"","code":"op_cond(pred, true_fn, false_fn)"},{"path":"https://keras.posit.co/reference/op_cond.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"pred Boolean scalar type true_fn Callable returning output pred == TRUE case. false_fn Callable returning output pred == FALSE case.","code":""},{"path":"https://keras.posit.co/reference/op_cond.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"output either true_fn false_fn depending pred.","code":""},{"path":"https://keras.posit.co/reference/op_cond.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"","code":"fn <- tensorflow::tf_function(function(x) {   op_cond(x > 0,     true_fn = \\() x + 1,     false_fn = \\() x - 1) })  fn(tensorflow::as_tensor(1)) ## tf.Tensor(2.0, shape=(), dtype=float64) fn(tensorflow::as_tensor(-1)) ## tf.Tensor(-2.0, shape=(), dtype=float64) # # Conditional side-effect (print only, no return value). file <- tempfile(fileext = \".txt\") fn <- tensorflow::tf_function(function(epochs) {   op_fori_loop(     0, epochs,     body_fun = \\(epoch, state) {       op_cond(epoch %% 20 == 0,               \\() {                 tensorflow::tf$print(                   \"epoch:\", epoch,                   output_stream = paste0(\"file://\", file))                 NULL               },               \\() {NULL})       state     },     init_val = tensorflow::as_tensor(0)) })  fn(tensorflow::as_tensor(100)) ## tf.Tensor(0.0, shape=(), dtype=float64) readLines(file) ## [1] \"epoch: 0\"  \"epoch: 20\" \"epoch: 40\" \"epoch: 60\" \"epoch: 80\" # cleanup unlink(file)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_conj.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the complex conjugate, element-wise. — op_conj","title":"Returns the complex conjugate, element-wise. — op_conj","text":"complex conjugate complex number obtained changing sign imaginary part.","code":""},{"path":"https://keras.posit.co/reference/op_conj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the complex conjugate, element-wise. — op_conj","text":"","code":"op_conj(x)"},{"path":"https://keras.posit.co/reference/op_conj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the complex conjugate, element-wise. — op_conj","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_conj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the complex conjugate, element-wise. — op_conj","text":"complex conjugate element x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_conv.html","id":null,"dir":"Reference","previous_headings":"","what":"General N-D convolution. — op_conv","title":"General N-D convolution. — op_conv","text":"ops supports 1D, 2D 3D convolution.","code":""},{"path":"https://keras.posit.co/reference/op_conv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General N-D convolution. — op_conv","text":"","code":"op_conv(   inputs,   kernel,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L )"},{"path":"https://keras.posit.co/reference/op_conv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General N-D convolution. — op_conv","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". kernel Tensor rank N+2. kernel shape (kernel_spatial_shape, num_input_channels, num_output_channels). num_input_channels match number channels inputs. strides int int tuple/list len(inputs_spatial_shape), specifying strides convolution along spatial dimension. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...). dilation_rate int int tuple/list len(inputs_spatial_shape), specifying dilation rate use dilated convolution. dilation_rate int, every spatial dimension shares dilation_rate.","code":""},{"path":"https://keras.posit.co/reference/op_conv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General N-D convolution. — op_conv","text":"tensor rank N+2, result conv operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_conv_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"General N-D convolution transpose. — op_conv_transpose","title":"General N-D convolution transpose. — op_conv_transpose","text":"Also known de-convolution. ops supports 1D, 2D 3D convolution.","code":""},{"path":"https://keras.posit.co/reference/op_conv_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General N-D convolution transpose. — op_conv_transpose","text":"","code":"op_conv_transpose(   inputs,   kernel,   strides,   padding = \"valid\",   output_padding = NULL,   data_format = NULL,   dilation_rate = 1L )"},{"path":"https://keras.posit.co/reference/op_conv_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General N-D convolution transpose. — op_conv_transpose","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". kernel Tensor rank N+2. kernel shape [kernel_spatial_shape, num_output_channels, num_input_channels], num_input_channels match number channels inputs. strides int int tuple/list len(inputs_spatial_shape), specifying strides convolution along spatial dimension. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. output_padding int int tuple/list len(inputs_spatial_shape), specifying amount padding along height width output tensor. Can single integer specify value spatial dimensions. amount output padding along given dimension must lower stride along dimension. set NULL (default), output shape inferred. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...). dilation_rate int int tuple/list len(inputs_spatial_shape), specifying dilation rate use dilated convolution. dilation_rate int, every spatial dimension shares dilation_rate.","code":""},{"path":"https://keras.posit.co/reference/op_conv_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General N-D convolution transpose. — op_conv_transpose","text":"tensor rank N+2, result conv operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_convert_to_numpy.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a tensor to a NumPy array. — op_convert_to_numpy","title":"Convert a tensor to a NumPy array. — op_convert_to_numpy","text":"Convert tensor NumPy array.","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_numpy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a tensor to a NumPy array. — op_convert_to_numpy","text":"","code":"op_convert_to_numpy(x)"},{"path":"https://keras.posit.co/reference/op_convert_to_numpy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a tensor to a NumPy array. — op_convert_to_numpy","text":"x tensor.","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_numpy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a tensor to a NumPy array. — op_convert_to_numpy","text":"NumPy array.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert an array to a tensor. — op_convert_to_tensor","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"Convert array tensor.","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"","code":"op_convert_to_tensor(x, dtype = NULL, sparse = NULL)"},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"x array. dtype target type. sparse Whether keep sparse tensors. FALSE cause sparse tensors densified. default value NULL means sparse tensors kept backend supports .","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"tensor specified dtype.","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"","code":"x <- array(c(1, 2, 3)) y <- op_convert_to_tensor(x) y ## tf.Tensor([1. 2. 3.], shape=(3), dtype=float64) op_convert_to_tensor(c(1, 3, 2, 0), \"int32\") ## tf.Tensor([1 3 2 0], shape=(4), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_copy.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a copy of x. — op_copy","title":"Returns a copy of x. — op_copy","text":"Returns copy x.","code":""},{"path":"https://keras.posit.co/reference/op_copy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a copy of x. — op_copy","text":"","code":"op_copy(x)"},{"path":"https://keras.posit.co/reference/op_copy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a copy of x. — op_copy","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_copy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a copy of x. — op_copy","text":"copy x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_correlate.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","title":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","text":"Compute cross-correlation two 1-dimensional tensors.","code":""},{"path":"https://keras.posit.co/reference/op_correlate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","text":"","code":"op_correlate(x1, x2, mode = \"valid\")"},{"path":"https://keras.posit.co/reference/op_correlate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","text":"x1 First 1-dimensional input tensor length M. x2 Second 1-dimensional input tensor length N. mode Either \"valid\", \"\" \"full\". default mode set \"valid\", returns output length max(M, N) - min(M, N) + 1. \"\" returns output length max(M, N). \"full\" mode returns convolution point overlap, output length N+M-1.","code":""},{"path":"https://keras.posit.co/reference/op_correlate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","text":"Output tensor, cross-correlation x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cos.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosine, element-wise. — op_cos","title":"Cosine, element-wise. — op_cos","text":"Cosine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_cos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosine, element-wise. — op_cos","text":"","code":"op_cos(x)"},{"path":"https://keras.posit.co/reference/op_cos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosine, element-wise. — op_cos","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_cos.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cosine, element-wise. — op_cos","text":"corresponding cosine values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hyperbolic cosine, element-wise. — op_cosh","title":"Hyperbolic cosine, element-wise. — op_cosh","text":"Hyperbolic cosine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_cosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hyperbolic cosine, element-wise. — op_cosh","text":"","code":"op_cosh(x)"},{"path":"https://keras.posit.co/reference/op_cosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hyperbolic cosine, element-wise. — op_cosh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_cosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hyperbolic cosine, element-wise. — op_cosh","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"axis specified non-zeros tensor counted.","code":""},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"","code":"op_count_nonzero(x, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"x Input tensor. axis Axis tuple axes along count number non-zeros. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"integer tensor integers.","code":""},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"","code":"x <- op_array(rbind(c(0, 1, 7, 0),                    c(3, 0, 2, 19))) op_count_nonzero(x) ## tf.Tensor(5, shape=(), dtype=int32) op_count_nonzero(x, axis = 1) ## tf.Tensor([1 1 2 1], shape=(4), dtype=int32) op_count_nonzero(x, axis = 2) ## tf.Tensor([2 3], shape=(2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_cross.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the cross product of two (arrays of) vectors. — op_cross","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"cross product x1 x2 R^3 vector perpendicular x1 x2. x1 x2 arrays vectors, vectors defined last axis x1 x2 default, axes can dimensions 2 3. dimension either x1 x2 2, third component input vector assumed zero cross product calculated accordingly. cases input vectors dimension 2, z-component cross product returned.","code":""},{"path":"https://keras.posit.co/reference/op_cross.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"","code":"op_cross(x1, x2, axisa = -1L, axisb = -1L, axisc = -1L, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_cross.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"x1 Components first vector(s). x2 Components second vector(s). axisa Axis x1 defines vector(s). Defaults -1. axisb Axis x2 defines vector(s). Defaults -1. axisc Axis result containing cross product vector(s). Ignored input vectors dimension 2, return scalar. default, last axis. axis defined, axis x1, x2 result defines vector(s) cross product(s). Overrides axisa, axisb axisc.","code":""},{"path":"https://keras.posit.co/reference/op_cross.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"Vector cross product(s).","code":""},{"path":"https://keras.posit.co/reference/op_cross.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"Torch backend support two dimensional vectors, arguments axisa, axisb axisc. Use axis instead.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_ctc_decode.html","id":null,"dir":"Reference","previous_headings":"","what":"Decodes the output of a CTC model. — op_ctc_decode","title":"Decodes the output of a CTC model. — op_ctc_decode","text":"Decodes output CTC model.","code":""},{"path":"https://keras.posit.co/reference/op_ctc_decode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decodes the output of a CTC model. — op_ctc_decode","text":"","code":"op_ctc_decode(   inputs,   sequence_lengths,   strategy = \"greedy\",   beam_width = 100L,   top_paths = 1L,   merge_repeated = TRUE,   mask_index = NULL )"},{"path":"https://keras.posit.co/reference/op_ctc_decode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decodes the output of a CTC model. — op_ctc_decode","text":"inputs tensor shape (batch_size, max_length, num_classes) containing logits (output model). normalized via softmax. sequence_lengths tensor shape (batch_size) containing sequence lengths batch. strategy string decoding strategy. Supported values \"greedy\" \"beam_search\". beam_width integer scalar beam width used beam search. Defaults 100. top_paths integer scalar, number top paths return. Defaults 1. merge_repeated boolean scalar, whether merge repeated labels output. Defaults TRUE. mask_index integer scalar, index mask character vocabulary. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_ctc_decode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decodes the output of a CTC model. — op_ctc_decode","text":"list containing: tensor representing list decoded sequences. strategy=\"greedy\", shape (1, batch_size, max_length). strategy=\"beam_seatch\", shape (top_paths, batch_size, max_length). Note : -1 indicates blank label. strategy=\"greedy\", tensor shape (batch_size, 1) representing negative sum probability logits sequence. strategy=\"beam_seatch\", tensor shape (batch_size, top_paths) representing log probability sequence.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_ctc_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","title":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","text":"CTC (Connectionist Temporal Classification) loss.","code":""},{"path":"https://keras.posit.co/reference/op_ctc_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","text":"","code":"op_ctc_loss(target, output, target_length, output_length, mask_index = 0L)"},{"path":"https://keras.posit.co/reference/op_ctc_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","text":"target tensor shape (batch_size, max_length) containing true labels integer format. output tensor shape (batch_size, max_length, num_classes) containing logits (output model). target_length tensor shape (batch_size) containing true label lengths. output_length tensor shape (batch_size) containing output lengths. mask_index index mask character vocabulary. Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/op_ctc_loss.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","text":"tensor, shape (batch_size), loss values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cumprod.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the cumulative product of elements along a given axis. — op_cumprod","title":"Return the cumulative product of elements along a given axis. — op_cumprod","text":"Return cumulative product elements along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_cumprod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the cumulative product of elements along a given axis. — op_cumprod","text":"","code":"op_cumprod(x, axis = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_cumprod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the cumulative product of elements along a given axis. — op_cumprod","text":"x Input tensor. axis Axis along cumulative product computed. default input flattened. dtype dtype returned tensor. Defaults x$dtype.","code":""},{"path":"https://keras.posit.co/reference/op_cumprod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the cumulative product of elements along a given axis. — op_cumprod","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cumsum.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the cumulative sum of elements along a given axis. — op_cumsum","title":"Returns the cumulative sum of elements along a given axis. — op_cumsum","text":"Returns cumulative sum elements along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_cumsum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the cumulative sum of elements along a given axis. — op_cumsum","text":"","code":"op_cumsum(x, axis = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_cumsum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the cumulative sum of elements along a given axis. — op_cumsum","text":"x Input tensor. axis Axis along cumulative sum computed. default input flattened. dtype dtype returned tensor. Defaults x$dtype.","code":""},{"path":"https://keras.posit.co/reference/op_cumsum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the cumulative sum of elements along a given axis. — op_cumsum","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":null,"dir":"Reference","previous_headings":"","what":"Decorator to define a function with a custom gradient. — op_custom_gradient","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"decorator allows fine grained control gradients sequence operations. may useful multiple reasons, including providing efficient numerically stable gradient sequence operations.","code":""},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"","code":"op_custom_gradient(f)"},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"f Function f(...) returns tuple (output, grad_fn) : ... sequence unnamed arguments, tensor input nested structure tensor inputs function. output (potentially nested structure ) tensor outputs applying operations forward_fn f() .... grad_fn function signature grad_fn(..., upstream) returns list tensors size (flattened) ...: derivatives tensors output respect tensors .... upstream tensor sequence tensors holding initial value gradients tensor output.","code":""},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"function h(...) returns value f(...)[[1]] whose gradient determined f(...)[[2]].","code":""},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"Note grad function returns gradient computation requires ... well upstream named argument, depending backend set. JAX TensorFlow backends, requires one argument, whereas might use upstream argument case PyTorch backend. working TensorFlow/JAX backend, grad(upstream) sufficient. PyTorch, grad function requires ... well upstream, e.g. grad <- \\(..., upstream). Follow example use op_custom_gradient() way compatible backends.","code":""},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"Backend-agnostic example.","code":"log1pexp <- op_custom_gradient(\\(x) {      e <- op_exp(x)      grad <- function(..., upstream = NULL) {       upstream <- upstream %||% ..1       op_multiply(upstream, 1.0 - 1.0 / op_add(1, e))     }      tuple(op_log(1 + e), grad)  })  if(config_backend() == \"tensorflow\") {   tf <- tensorflow::tf   x <- op_convert_to_tensor(100.0)   with(tf$GradientTape() %as% tape, {     tape$watch(x)     y <- log1pexp(x)   })   dy_dx <- tape$gradient(y, x)   stopifnot(as.numeric(dy_dx) == 1) }"},{"path":[]},{"path":"https://keras.posit.co/reference/op_depthwise_conv.html","id":null,"dir":"Reference","previous_headings":"","what":"General N-D depthwise convolution. — op_depthwise_conv","title":"General N-D depthwise convolution. — op_depthwise_conv","text":"ops supports 1D 2D depthwise convolution.","code":""},{"path":"https://keras.posit.co/reference/op_depthwise_conv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General N-D depthwise convolution. — op_depthwise_conv","text":"","code":"op_depthwise_conv(   inputs,   kernel,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L )"},{"path":"https://keras.posit.co/reference/op_depthwise_conv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General N-D depthwise convolution. — op_depthwise_conv","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". kernel Tensor rank N+2. kernel shape [kernel_spatial_shape, num_input_channels, num_channels_multiplier], num_input_channels match number channels inputs. strides int int tuple/list len(inputs_spatial_shape), specifying strides convolution along spatial dimension. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...). dilation_rate int int tuple/list len(inputs_spatial_shape), specifying dilation rate use dilated convolution. dilation_rate int, every spatial dimension shares dilation_rate.","code":""},{"path":"https://keras.posit.co/reference/op_depthwise_conv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General N-D depthwise convolution. — op_depthwise_conv","text":"tensor rank N+2, result depthwise conv operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_det.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the determinant of a square tensor. — op_det","title":"Computes the determinant of a square tensor. — op_det","text":"Computes determinant square tensor.","code":""},{"path":"https://keras.posit.co/reference/op_det.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the determinant of a square tensor. — op_det","text":"","code":"op_det(x)"},{"path":"https://keras.posit.co/reference/op_det.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the determinant of a square tensor. — op_det","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_det.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the determinant of a square tensor. — op_det","text":"tensor shape (...) represeting determinant x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_diag.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a diagonal or construct a diagonal array. — op_diag","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"Extract diagonal construct diagonal array.","code":""},{"path":"https://keras.posit.co/reference/op_diag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"","code":"op_diag(x, k = 0L)"},{"path":"https://keras.posit.co/reference/op_diag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"x Input tensor. x 2-D, returns k-th diagonal x. x 1-D, return 2-D tensor x k-th diagonal. k diagonal consider. Defaults 0. Use k > 0 diagonals main diagonal, k < 0 diagonals main diagonal.","code":""},{"path":"https://keras.posit.co/reference/op_diag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"extracted diagonal constructed diagonal tensor.","code":""},{"path":"https://keras.posit.co/reference/op_diag.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"","code":"x <- op_arange(9L) |> op_reshape(c(3, 3)) x ## tf.Tensor( ## [[0 1 2] ##  [3 4 5] ##  [6 7 8]], shape=(3, 3), dtype=int32) op_diag(x) ## tf.Tensor([0 4 8], shape=(3), dtype=int32) op_diag(x, k = 1) ## tf.Tensor([1 5], shape=(2), dtype=int32) op_diag(x, k = -1) ## tf.Tensor([3 7], shape=(2), dtype=int32) op_diag(op_diag(x)) ## tf.Tensor( ## [[0 0 0] ##  [0 4 0] ##  [0 0 8]], shape=(3, 3), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return specified diagonals. — op_diagonal","title":"Return specified diagonals. — op_diagonal","text":"x 2-D, returns diagonal x given offset, .e., collection elements form x[, +offset]. x two dimensions, axes specified axis1 axis2 used determine 2-D sub-array whose diagonal returned. shape resulting array can determined removing axis1 axis2 appending index right equal size resulting diagonals.","code":""},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return specified diagonals. — op_diagonal","text":"","code":"op_diagonal(x, offset = 0L, axis1 = 1L, axis2 = 2L)"},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return specified diagonals. — op_diagonal","text":"x Input tensor. offset Offset diagonal main diagonal. Can positive negative. Defaults 0 (main diagonal). axis1 Axis used first axis 2-D sub-arrays. Defaults 1 (first axis). axis2 Axis used second axis 2-D sub-arrays. Defaults 2 (second axis).","code":""},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return specified diagonals. — op_diagonal","text":"Tensor diagonals.","code":""},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return specified diagonals. — op_diagonal","text":"","code":"x <- op_arange(4L) |> op_reshape(c(2, 2)) x ## tf.Tensor( ## [[0 1] ##  [2 3]], shape=(2, 2), dtype=int32) op_diagonal(x) ## tf.Tensor([0 3], shape=(2), dtype=int32) op_diagonal(x, offset = 1) ## tf.Tensor([1], shape=(1), dtype=int32) x <- op_array(1:8) |> op_reshape(c(2, 2, 2)) x ## tf.Tensor( ## [[[1 2] ##   [3 4]] ## ##  [[5 6] ##   [7 8]]], shape=(2, 2, 2), dtype=int32) x |> op_diagonal(0) ## tf.Tensor( ## [[1 7] ##  [2 8]], shape=(2, 2), dtype=int32) x |> op_diagonal(0, 1, 2) # same as above, the default ## tf.Tensor( ## [[1 7] ##  [2 8]], shape=(2, 2), dtype=int32) x |> op_diagonal(0, 2, 3) ## tf.Tensor( ## [[1 4] ##  [5 8]], shape=(2, 2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the n-th discrete difference along the given axis. — op_diff","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"first difference given [] = [+1] - [] along given axis, higher differences calculated using diff recursively.","code":""},{"path":"https://keras.posit.co/reference/op_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"","code":"op_diff(a, n = 1L, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"Input tensor. n number times values differenced. Defaults 1. axis Axis compute discrete difference(s) along. Defaults -1 (last axis).","code":""},{"path":"https://keras.posit.co/reference/op_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"Tensor diagonals.","code":""},{"path":"https://keras.posit.co/reference/op_diff.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"","code":"x <- op_array(c(1, 2, 4, 7, 0)) op_diff(x) ## tf.Tensor([ 1.  2.  3. -7.], shape=(4), dtype=float32) op_diff(x, n = 2) ## tf.Tensor([  1.   1. -10.], shape=(3), dtype=float32) x <- op_array(rbind(c(1, 3, 6, 10),                   c(0, 5, 6, 8))) op_diff(x) ## tf.Tensor( ## [[2. 3. 4.] ##  [5. 1. 2.]], shape=(2, 3), dtype=float64) op_diff(x, axis = 1) ## tf.Tensor([[-1.  2.  0. -2.]], shape=(1, 4), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_digitize.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the indices of the bins to which each value in x belongs. — op_digitize","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"Returns indices bins value x belongs.","code":""},{"path":"https://keras.posit.co/reference/op_digitize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"","code":"op_digitize(x, bins)"},{"path":"https://keras.posit.co/reference/op_digitize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"x Input array binned. bins Array bins. one-dimensional monotonically increasing.","code":""},{"path":"https://keras.posit.co/reference/op_digitize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"Output array indices, shape x.","code":""},{"path":"https://keras.posit.co/reference/op_digitize.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"","code":"x <- op_array(c(0.0, 1.0, 3.0, 1.6)) bins <- array(c(0.0, 3.0, 4.5, 7.0)) op_digitize(x, bins) ## tf.Tensor([1 1 2 1], shape=(4), dtype=int32) # array([1, 1, 2, 1])"},{"path":[]},{"path":"https://keras.posit.co/reference/op_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"Divide arguments element-wise. — op_divide","title":"Divide arguments element-wise. — op_divide","text":"Note function automatically called using R operator * tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_divide(x, 2) ## tf.Tensor([0.  0.5 1.  1.5], shape=(4), dtype=float32) x / 2 ## tf.Tensor([0.  0.5 1.  1.5], shape=(4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Divide arguments element-wise. — op_divide","text":"","code":"op_divide(x1, x2)"},{"path":"https://keras.posit.co/reference/op_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Divide arguments element-wise. — op_divide","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_divide.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Divide arguments element-wise. — op_divide","text":"Output tensor, quotient x1/x2, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_divide.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Divide arguments element-wise. — op_divide","text":"","code":"op_divide(3, 2) ## tf.Tensor(1.5, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_divide_no_nan.html","id":null,"dir":"Reference","previous_headings":"","what":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","title":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","text":"Safe element-wise division returns 0 denominator 0.","code":""},{"path":"https://keras.posit.co/reference/op_divide_no_nan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","text":"","code":"op_divide_no_nan(x1, x2)"},{"path":"https://keras.posit.co/reference/op_divide_no_nan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_divide_no_nan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","text":"quotient x1/x2, element-wise, zero x2 zero.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_dot.html","id":null,"dir":"Reference","previous_headings":"","what":"Dot product of two tensors. — op_dot","title":"Dot product of two tensors. — op_dot","text":"x1 x2 1-D tensors, inner product vectors (without complex conjugation). x1 x2 2-D tensors, matrix multiplication. either x1 x2 0-D (scalar), equivalent x1 * x2. x1 N-D tensor x2 1-D tensor, sum product last axis x1 x2. x1 N-D tensor x2 M-D tensor (M >= 2), sum product last axis x1 second--last axis x2: dot(x1, x2)[,j,k,m] = sum([,j,:] * b[k,:,m]).","code":""},{"path":"https://keras.posit.co/reference/op_dot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dot product of two tensors. — op_dot","text":"","code":"op_dot(x1, x2)"},{"path":"https://keras.posit.co/reference/op_dot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dot product of two tensors. — op_dot","text":"x1 First argument. x2 Second argument.","code":""},{"path":"https://keras.posit.co/reference/op_dot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dot product of two tensors. — op_dot","text":"Dot product x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_dot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Dot product of two tensors. — op_dot","text":"Torch backend accept 0-D tensors arguments.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_eig.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","title":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","text":"Computes eigenvalues eigenvectors square matrix.","code":""},{"path":"https://keras.posit.co/reference/op_eig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","text":"","code":"op_eig(x)"},{"path":"https://keras.posit.co/reference/op_eig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_eig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","text":"list two tensors: tensor shape (..., M) containing eigenvalues tensor shape (..., M, M) containing eigenvectors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_eigh.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","title":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","text":"Computes eigenvalues eigenvectors complex Hermitian.","code":""},{"path":"https://keras.posit.co/reference/op_eigh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","text":"","code":"op_eigh(x)"},{"path":"https://keras.posit.co/reference/op_eigh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_eigh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","text":"list two tensors: tensor shape (..., M) containing eigenvalues tensor shape (..., M, M) containing eigenvectors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_einsum.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluates the Einstein summation convention on the operands. — op_einsum","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"Evaluates Einstein summation convention operands.","code":""},{"path":"https://keras.posit.co/reference/op_einsum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"","code":"op_einsum(subscripts, ...)"},{"path":"https://keras.posit.co/reference/op_einsum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"subscripts Specifies subscripts summation comma separated list subscript labels. implicit (classical Einstein summation) calculation performed unless explicit indicator -> included well subscript labels precise output form. ... operands compute Einstein sum .","code":""},{"path":"https://keras.posit.co/reference/op_einsum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"calculation based Einstein summation convention.","code":""},{"path":"https://keras.posit.co/reference/op_einsum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"Trace matrix:     Extract diagonal:     Sum axis:     higher dimensional tensors summing single axis can done ellipsis:     Compute matrix transpose reorder number axes:         Matrix vector multiplication:","code":"a <- op_arange(25) |> op_reshape(c(5, 5)) b <- op_arange(5) c <- op_arange(6) |> op_reshape(c(2, 3)) op_einsum(\"ii\", a) op_trace(a) ## tf.Tensor(60.0, shape=(), dtype=float32) ## tf.Tensor(60.0, shape=(), dtype=float32) op_einsum(\"ii -> i\", a) op_diag(a) ## tf.Tensor([ 0.  6. 12. 18. 24.], shape=(5), dtype=float32) ## tf.Tensor([ 0.  6. 12. 18. 24.], shape=(5), dtype=float32) op_einsum(\"ij -> i\", a) op_sum(a, axis = 2) ## tf.Tensor([ 10.  35.  60.  85. 110.], shape=(5), dtype=float32) ## tf.Tensor([ 10.  35.  60.  85. 110.], shape=(5), dtype=float32) op_einsum(\"...j -> ...\", a) op_sum(a, axis = -1) ## tf.Tensor([ 10.  35.  60.  85. 110.], shape=(5), dtype=float32) ## tf.Tensor([ 10.  35.  60.  85. 110.], shape=(5), dtype=float32) op_einsum(\"ji\", c) # return c unchanged ## tf.Tensor( ## [[0. 1. 2.] ##  [3. 4. 5.]], shape=(2, 3), dtype=float32) op_einsum(\"ij -> ji\", c) # transpose op_transpose(c)          # same as above ## tf.Tensor( ## [[0. 3.] ##  [1. 4.] ##  [2. 5.]], shape=(3, 2), dtype=float32) ## tf.Tensor( ## [[0. 3.] ##  [1. 4.] ##  [2. 5.]], shape=(3, 2), dtype=float32) op_einsum(\"ij, j\", a, b) op_einsum(\"...j, j\", a, b) a %*% b op_matmul(a, b) ## tf.Tensor([ 30.  80. 130. 180. 230.], shape=(5), dtype=float32) ## tf.Tensor([ 30.  80. 130. 180. 230.], shape=(5), dtype=float32) ## tf.Tensor([ 30.  80. 130. 180. 230.], shape=(5), dtype=float32) ## tf.Tensor([ 30.  80. 130. 180. 230.], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"Exponential Linear Unit activation function. — op_elu","title":"Exponential Linear Unit activation function. — op_elu","text":"defined : f(x) =  alpha * (exp(x) - 1.) x < 0, f(x) = x x >= 0.","code":""},{"path":"https://keras.posit.co/reference/op_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exponential Linear Unit activation function. — op_elu","text":"","code":"op_elu(x, alpha = 1)"},{"path":"https://keras.posit.co/reference/op_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exponential Linear Unit activation function. — op_elu","text":"x Input tensor. alpha scalar, slope positive section. Defaults 1.0.","code":""},{"path":"https://keras.posit.co/reference/op_elu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exponential Linear Unit activation function. — op_elu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_elu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Exponential Linear Unit activation function. — op_elu","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_elu(x) ## tf.Tensor([-0.63212055  0.          1.        ], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_empty.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","title":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","text":"Return tensor given shape type filled uninitialized data.","code":""},{"path":"https://keras.posit.co/reference/op_empty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","text":"","code":"op_empty(shape, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_empty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","text":"shape Shape empty tensor. dtype Desired data type empty tensor.","code":""},{"path":"https://keras.posit.co/reference/op_empty.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","text":"empty tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns (x1 == x2) element-wise. — op_equal","title":"Returns (x1 == x2) element-wise. — op_equal","text":"Note function automatically called using R operator == tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_equal(x, 2) ## tf.Tensor([False False  True False], shape=(4), dtype=bool) x == 2 ## tf.Tensor([False False  True False], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns (x1 == x2) element-wise. — op_equal","text":"","code":"op_equal(x1, x2)"},{"path":"https://keras.posit.co/reference/op_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns (x1 == x2) element-wise. — op_equal","text":"x1 Tensor compare. x2 Tensor compare.","code":""},{"path":"https://keras.posit.co/reference/op_equal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns (x1 == x2) element-wise. — op_equal","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_erf.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the error function of x, element-wise. — op_erf","title":"Computes the error function of x, element-wise. — op_erf","text":"Computes error function x, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_erf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the error function of x, element-wise. — op_erf","text":"","code":"op_erf(x)"},{"path":"https://keras.posit.co/reference/op_erf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the error function of x, element-wise. — op_erf","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_erf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the error function of x, element-wise. — op_erf","text":"tensor dtype x.","code":""},{"path":"https://keras.posit.co/reference/op_erf.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the error function of x, element-wise. — op_erf","text":"","code":"x <- op_array(c(-3, -2, -1, 0, 1)) op_erf(x) ## tf.Tensor([-0.99997777 -0.9953222  -0.84270084  0.          0.84270084], shape=(5), dtype=float32) # array([-0.99998 , -0.99532, -0.842701,  0.,  0.842701], dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the inverse error function of x, element-wise. — op_erfinv","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"Computes inverse error function x, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"","code":"op_erfinv(x)"},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"tensor dtype x.","code":""},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"","code":"x <- op_array(c(-0.5, -0.2, -0.1, 0.0, 0.3)) op_erfinv(x) ## tf.Tensor([-0.4769363  -0.17914344 -0.088856    0.          0.27246267], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the exponential of all elements in the input tensor. — op_exp","title":"Calculate the exponential of all elements in the input tensor. — op_exp","text":"Calculate exponential elements input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the exponential of all elements in the input tensor. — op_exp","text":"","code":"op_exp(x)"},{"path":"https://keras.posit.co/reference/op_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the exponential of all elements in the input tensor. — op_exp","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_exp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the exponential of all elements in the input tensor. — op_exp","text":"Output tensor, element-wise exponential x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_expand_dims.html","id":null,"dir":"Reference","previous_headings":"","what":"Expand the shape of a tensor. — op_expand_dims","title":"Expand the shape of a tensor. — op_expand_dims","text":"Insert new axis axis position expanded tensor shape.","code":""},{"path":"https://keras.posit.co/reference/op_expand_dims.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expand the shape of a tensor. — op_expand_dims","text":"","code":"op_expand_dims(x, axis)"},{"path":"https://keras.posit.co/reference/op_expand_dims.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expand the shape of a tensor. — op_expand_dims","text":"x Input tensor. axis Position expanded axes new axis (axes) placed.","code":""},{"path":"https://keras.posit.co/reference/op_expand_dims.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expand the shape of a tensor. — op_expand_dims","text":"Output tensor number dimensions increased.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_expm1.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","title":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","text":"Calculate exp(x) - 1 elements tensor.","code":""},{"path":"https://keras.posit.co/reference/op_expm1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","text":"","code":"op_expm1(x)"},{"path":"https://keras.posit.co/reference/op_expm1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","text":"x Input values.","code":""},{"path":"https://keras.posit.co/reference/op_expm1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","text":"Output tensor, element-wise exponential minus one.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":null,"dir":"Reference","previous_headings":"","what":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"Slides window size sequence_length last axis input stride sequence_stride, replacing last axis [num_sequences, sequence_length] sequences. dimension along last axis N, number sequences can computed : num_sequences = 1 + (N - sequence_length) // sequence_stride","code":""},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"","code":"op_extract_sequences(x, sequence_length, sequence_stride)"},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"x Input tensor. sequence_length integer representing sequences length. sequence_stride integer representing sequences hop size.","code":""},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"tensor sequences shape [..., num_sequences, sequence_length].","code":""},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"","code":"x <- op_convert_to_tensor(1:6) op_extract_sequences(x, 3, 2) ## tf.Tensor( ## [[1 2 3] ##  [3 4 5]], shape=(2, 3), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_eye.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","title":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","text":"Return 2-D tensor ones diagonal zeros elsewhere.","code":""},{"path":"https://keras.posit.co/reference/op_eye.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","text":"","code":"op_eye(N, M = NULL, k = 0L, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_eye.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","text":"N Number rows output. M Number columns output. NULL, defaults N. k Index diagonal: 0 (default) refers main diagonal, positive value refers upper diagonal, negative value lower diagonal. dtype Data type returned tensor.","code":""},{"path":"https://keras.posit.co/reference/op_eye.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","text":"Tensor ones k-th diagonal zeros elsewhere.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_fft.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Fast Fourier Transform along last axis of input. — op_fft","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"Computes Fast Fourier Transform along last axis input.","code":""},{"path":"https://keras.posit.co/reference/op_fft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"","code":"op_fft(x)"},{"path":"https://keras.posit.co/reference/op_fft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"x list real imaginary parts input tensor. tensors provided floating type.","code":""},{"path":"https://keras.posit.co/reference/op_fft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"list containing two tensors - real imaginary parts output tensor.","code":""},{"path":"https://keras.posit.co/reference/op_fft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"","code":"x = c(op_array(c(1., 2.)),       op_array(c(0., 1.))) op_fft(x) ## [[1]] ## tf.Tensor([ 3. -1.], shape=(2), dtype=float32) ## ## [[2]] ## tf.Tensor([ 1. -1.], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_fft2.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"Computes 2D Fast Fourier Transform along last two axes input.","code":""},{"path":"https://keras.posit.co/reference/op_fft2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"","code":"op_fft2(x)"},{"path":"https://keras.posit.co/reference/op_fft2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"x list real imaginary parts input tensor. tensors provided floating type.","code":""},{"path":"https://keras.posit.co/reference/op_fft2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"list containing two tensors - real imaginary parts output.","code":""},{"path":"https://keras.posit.co/reference/op_fft2.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"","code":"x <- c(op_array(rbind(c(1, 2),                      c(2, 1))),        op_array(rbind(c(0, 1),                      c(1, 0)))) op_fft2(x) ## [[1]] ## tf.Tensor( ## [[ 6.  0.] ##  [ 0. -2.]], shape=(2, 2), dtype=float64) ## ## [[2]] ## tf.Tensor( ## [[ 2.  0.] ##  [ 0. -2.]], shape=(2, 2), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_flip.html","id":null,"dir":"Reference","previous_headings":"","what":"Reverse the order of elements in the tensor along the given axis. — op_flip","title":"Reverse the order of elements in the tensor along the given axis. — op_flip","text":"shape tensor preserved, elements reordered.","code":""},{"path":"https://keras.posit.co/reference/op_flip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reverse the order of elements in the tensor along the given axis. — op_flip","text":"","code":"op_flip(x, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_flip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reverse the order of elements in the tensor along the given axis. — op_flip","text":"x Input tensor. axis Axis axes along flip tensor. default, axis = NULL, flip axes input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_flip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reverse the order of elements in the tensor along the given axis. — op_flip","text":"Output tensor entries axis reversed.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_floor.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the floor of the input, element-wise. — op_floor","title":"Return the floor of the input, element-wise. — op_floor","text":"floor scalar x largest integer , <= x.","code":""},{"path":"https://keras.posit.co/reference/op_floor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the floor of the input, element-wise. — op_floor","text":"","code":"op_floor(x)"},{"path":"https://keras.posit.co/reference/op_floor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the floor of the input, element-wise. — op_floor","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_floor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the floor of the input, element-wise. — op_floor","text":"Output tensor, element-wise floor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_floor_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","title":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","text":"Note function automatically called using R operator %/% tensor.","code":"(x <- op_arange(10)) ## tf.Tensor([0. 1. 2. 3. 4. 5. 6. 7. 8. 9.], shape=(10), dtype=float32) op_floor_divide(x, 2) ## tf.Tensor([0. 0. 1. 1. 2. 2. 3. 3. 4. 4.], shape=(10), dtype=float32) x %/% 2 ## tf.Tensor([0. 0. 1. 1. 2. 2. 3. 3. 4. 4.], shape=(10), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_floor_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","text":"","code":"op_floor_divide(x1, x2)"},{"path":"https://keras.posit.co/reference/op_floor_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","text":"x1 Numerator. x2 Denominator.","code":""},{"path":"https://keras.posit.co/reference/op_floor_divide.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","text":"Output tensor, y <- floor(x1/x2)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":null,"dir":"Reference","previous_headings":"","what":"For loop implementation. — op_fori_loop","title":"For loop implementation. — op_fori_loop","text":"loop implementation.","code":""},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"For loop implementation. — op_fori_loop","text":"","code":"op_fori_loop(lower, upper, body_fun, init_val)"},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"For loop implementation. — op_fori_loop","text":"lower initial value loop variable. upper upper bound loop variable. body_fun callable represents loop body. Must take two arguments: loop variable loop state. loop state updated returned function. init_val initial value loop state.","code":""},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"For loop implementation. — op_fori_loop","text":"final state loop.","code":""},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"For loop implementation. — op_fori_loop","text":"","code":"lower <- 0L upper <- 10L body_fun <- function(i, state) state + i init_state <- 0L final_state <- op_fori_loop(lower, upper, body_fun, init_state) final_state ## tf.Tensor(45, shape=(), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_full.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a new tensor of given shape and type, filled with fill_value. — op_full","title":"Return a new tensor of given shape and type, filled with fill_value. — op_full","text":"Return new tensor given shape type, filled fill_value.","code":""},{"path":"https://keras.posit.co/reference/op_full.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a new tensor of given shape and type, filled with fill_value. — op_full","text":"","code":"op_full(shape, fill_value, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_full.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a new tensor of given shape and type, filled with fill_value. — op_full","text":"shape Shape new tensor. fill_value Fill value. dtype Desired data type tensor.","code":""},{"path":"https://keras.posit.co/reference/op_full.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a new tensor of given shape and type, filled with fill_value. — op_full","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_full_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","title":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","text":"Return full tensor shape type given tensor.","code":""},{"path":"https://keras.posit.co/reference/op_full_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","text":"","code":"op_full_like(x, fill_value, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_full_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","text":"x Input tensor. fill_value Fill value. dtype Overrides data type result.","code":""},{"path":"https://keras.posit.co/reference/op_full_like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","text":"Tensor fill_value shape type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_gelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"approximate TRUE, defined : f(x) = 0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * x^3))) approximate FALSE, defined : f(x) = x * P(X <= x) = 0.5 * x * (1 + erf(x / sqrt(2))), P(X) ~ N(0, 1).","code":""},{"path":"https://keras.posit.co/reference/op_gelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"","code":"op_gelu(x, approximate = TRUE)"},{"path":"https://keras.posit.co/reference/op_gelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"x Input tensor. approximate Approximate version GELU activation. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_gelu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_gelu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_gelu(x) ## tf.Tensor([-0.15880796  0.          0.841192  ], shape=(3), dtype=float32) op_gelu(x, FALSE) ## tf.Tensor([-0.15865526  0.          0.8413447 ], shape=(3), dtype=float32) x <- seq(-5, 5, .1) plot(x, op_gelu(x),      type = \"l\", #, frame.plot = FALSE,      panel.first = grid())"},{"path":[]},{"path":"https://keras.posit.co/reference/op_get_item.html","id":null,"dir":"Reference","previous_headings":"","what":"Return x[key]. — op_get_item","title":"Return x[key]. — op_get_item","text":"Return x[key].","code":""},{"path":"https://keras.posit.co/reference/op_get_item.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return x[key]. — op_get_item","text":"","code":"op_get_item(x, key)"},{"path":"https://keras.posit.co/reference/op_get_item.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return x[key]. — op_get_item","text":"x dictionary-like object key Generally, string, object __hash__ method acceptable.","code":""},{"path":"https://keras.posit.co/reference/op_get_item.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return x[key]. — op_get_item","text":"key.","code":""},{"path":"https://keras.posit.co/reference/op_get_item.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Return x[key]. — op_get_item","text":"Generally, calling x[[key]] x$key preferable.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_greater.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the truth value of x1 > x2 element-wise. — op_greater","title":"Return the truth value of x1 > x2 element-wise. — op_greater","text":"Note function automatically called using R operator > tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_greater(x, 2) ## tf.Tensor([False False False  True], shape=(4), dtype=bool) x > 2 ## tf.Tensor([False False False  True], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_greater.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the truth value of x1 > x2 element-wise. — op_greater","text":"","code":"op_greater(x1, x2)"},{"path":"https://keras.posit.co/reference/op_greater.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the truth value of x1 > x2 element-wise. — op_greater","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_greater.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the truth value of x1 > x2 element-wise. — op_greater","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_greater_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","title":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","text":"Note function automatically called using R operator >= tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_greater_equal(x, 2) ## tf.Tensor([False False  True  True], shape=(4), dtype=bool) x >= 2 ## tf.Tensor([False False  True  True], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_greater_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","text":"","code":"op_greater_equal(x1, x2)"},{"path":"https://keras.posit.co/reference/op_greater_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_greater_equal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Hard sigmoid activation function. — op_hard_sigmoid","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"defined : 0 x < -2.5, 1 x > 2.5, (0.2 * x) + 0.5 -2.5 <= x <= 2.5.","code":""},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"","code":"op_hard_sigmoid(x)"},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_hard_sigmoid(x) ## tf.Tensor([0.33333334 0.5        0.6666667 ], shape=(3), dtype=float32) x <- as.array(seq(-5, 5, .1)) plot(x, op_hard_sigmoid(x),      type = 'l', panel.first = grid(), frame.plot = FALSE)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"defined : 0 x < -3 x x > 3 x * (x + 3) / 6 -3 <= x <= 3 faster, piecewise linear approximation silu activation.","code":""},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"","code":"op_hard_silu(x)  op_hard_swish(x)"},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"","code":"x <- op_convert_to_tensor(c(-3.0, -1.0, 0.0, 1.0, 3.0)) op_hard_silu(x) ## tf.Tensor([-0.         -0.33333334  0.          0.6666667   3.        ], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_hstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Stack tensors in sequence horizontally (column wise). — op_hstack","title":"Stack tensors in sequence horizontally (column wise). — op_hstack","text":"equivalent concatenation along first axis 1-D tensors, along second axis tensors.","code":""},{"path":"https://keras.posit.co/reference/op_hstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stack tensors in sequence horizontally (column wise). — op_hstack","text":"","code":"op_hstack(xs)"},{"path":"https://keras.posit.co/reference/op_hstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stack tensors in sequence horizontally (column wise). — op_hstack","text":"xs Sequence tensors.","code":""},{"path":"https://keras.posit.co/reference/op_hstack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stack tensors in sequence horizontally (column wise). — op_hstack","text":"tensor formed stacking given tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the identity tensor. — op_identity","title":"Return the identity tensor. — op_identity","text":"identity tensor square tensor ones main diagonal zeros elsewhere.","code":""},{"path":"https://keras.posit.co/reference/op_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the identity tensor. — op_identity","text":"","code":"op_identity(n, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_identity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the identity tensor. — op_identity","text":"n Number rows (columns) n x n output tensor. dtype Data type output tensor.","code":""},{"path":"https://keras.posit.co/reference/op_identity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the identity tensor. — op_identity","text":"identity tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_imag.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the imaginary part of the complex argument. — op_imag","title":"Return the imaginary part of the complex argument. — op_imag","text":"Return imaginary part complex argument.","code":""},{"path":"https://keras.posit.co/reference/op_imag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the imaginary part of the complex argument. — op_imag","text":"","code":"op_imag(x)"},{"path":"https://keras.posit.co/reference/op_imag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the imaginary part of the complex argument. — op_imag","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_imag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the imaginary part of the complex argument. — op_imag","text":"imaginary component complex argument.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies the given transform(s) to the image(s). — op_image_affine_transform","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"Applies given transform(s) image(s).","code":""},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"","code":"op_image_affine_transform(   image,   transform,   interpolation = \"bilinear\",   fill_mode = \"constant\",   fill_value = 0L,   data_format = \"channels_last\" )"},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"image Input image batch images. Must 3D 4D. transform Projective transform matrix/matrices. vector length 8 tensor size N x 8. one row transform [a0, a1, a2, b0, b1, b2, c0, c1], maps output point (x, y) transformed input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k), k = c0 x + c1 y + 1. transform inverted compared transform mapping input points output points. Note gradients backpropagated transformation parameters. Note c0 c1 effective using TensorFlow backend considered 0 using backends. interpolation Interpolation method. Available methods \"nearest\", \"bilinear\". Defaults \"bilinear\". fill_mode Points outside boundaries input filled according given mode. Available methods \"constant\", \"nearest\", \"wrap\" \"reflect\". Defaults \"constant\". \"reflect\": (d c b | b c d | d c b ) input extended reflecting edge last pixel. \"constant\": (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k specified fill_value. \"wrap\": (b c d | b c d | b c d) input extended wrapping around opposite edge. \"nearest\": (| b c d | d d d d) input extended nearest pixel. fill_value Value used points outside boundaries input fill_mode = \"constant\". Defaults 0. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\".","code":""},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"Applied affine transform image batch images.","code":""},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"","code":"x <- random_uniform(c(2, 64, 80, 3)) # batch of 2 RGB images transform <- op_array(rbind(c(1.5, 0, -20, 0, 1.5, -16, 0, 0),  # zoom                            c(1, 0, -20, 0, 1, -16, 0, 0)))  # translation)) y <- op_image_affine_transform(x, transform) shape(y) ## shape(2, 64, 80, 3) # (2, 64, 80, 3) x <- random_uniform(c(64, 80, 3)) # single RGB image transform <- op_array(c(1.0, 0.5, -20, 0.5, 1.0, -16, 0, 0))  # shear y <- op_image_affine_transform(x, transform) shape(y) ## shape(64, 80, 3) # (64, 80, 3) x <- random_uniform(c(2, 3, 64, 80)) # batch of 2 RGB images transform <- op_array(rbind(   c(1.5, 0,-20, 0, 1.5,-16, 0, 0),  # zoom   c(1, 0,-20, 0, 1,-16, 0, 0)  # translation )) y <- op_image_affine_transform(x, transform, data_format = \"channels_first\") shape(y) ## shape(2, 3, 64, 80) # (2, 3, 64, 80)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":null,"dir":"Reference","previous_headings":"","what":"Crop images to a specified height and width. — op_image_crop","title":"Crop images to a specified height and width. — op_image_crop","text":"Crop images specified height width.","code":""},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Crop images to a specified height and width. — op_image_crop","text":"","code":"op_image_crop(   images,   top_cropping = NULL,   left_cropping = NULL,   target_height = NULL,   target_width = NULL,   bottom_cropping = NULL,   right_cropping = NULL )"},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Crop images to a specified height and width. — op_image_crop","text":"images 4-D batch images shape (batch, height, width, channels) 3-D single image shape (height, width, channels). top_cropping Number columns crop top. left_cropping Number columns crop left. target_height Height output images. target_width Width output images. bottom_cropping Number columns crop bottom. right_cropping Number columns crop right.","code":""},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Crop images to a specified height and width. — op_image_crop","text":"images 4D, 4D float Tensor shape (batch, target_height, target_width, channels) images 3D, 3D float Tensor shape (target_height, target_width, channels)","code":""},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Crop images to a specified height and width. — op_image_crop","text":"","code":"images <- op_reshape(op_arange(1, 28, dtype=\"float32\"), c(3, 3, 3)) images[, , 1] # print the first channel of the images  cropped_images <- op_image_crop(images, 0, 0, 2, 2) cropped_images[, , 1] # print the first channel of the cropped images"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":null,"dir":"Reference","previous_headings":"","what":"Extracts patches from the image(s). — op_image_extract_patches","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"Extracts patches image(s).","code":""},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"","code":"op_image_extract_patches(   image,   size,   strides = NULL,   dilation_rate = 1L,   padding = \"valid\",   data_format = \"channels_last\" )"},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"image Input image batch images. Must 3D 4D. size Patch size int list (patch_height, patch_width) strides strides along height width. specified, NULL, defaults value size. dilation_rate input stride, specifying far two consecutive patch samples input. value 1, strides must 1. NOTE: strides > 1 supported conjunction dilation_rate > 1 padding type padding algorithm use: \"\" \"valid\". data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\".","code":""},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"Extracted patches 3D (batched) 4D (batched)","code":""},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"","code":"image <- random_uniform(c(2, 20, 20, 3), dtype = \"float32\") # batch of 2 RGB images patches <- op_image_extract_patches(image, c(5, 5)) shape(patches) ## shape(2, 4, 4, 75) # (2, 4, 4, 75) image <- random_uniform(c(20, 20, 3), dtype = \"float32\") # 1 RGB image patches <- op_image_extract_patches(image, c(3, 3), c(1, 1)) shape(patches) ## shape(18, 18, 27) # (18, 18, 27)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_map_coordinates.html","id":null,"dir":"Reference","previous_headings":"","what":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","title":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","text":"Note interpolation near boundaries differs scipy function, fixed outstanding bug scipy/issues/2640.","code":""},{"path":"https://keras.posit.co/reference/op_image_map_coordinates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","text":"","code":"op_image_map_coordinates(   input,   coordinates,   order,   fill_mode = \"constant\",   fill_value = 0L )"},{"path":"https://keras.posit.co/reference/op_image_map_coordinates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","text":"input input array. coordinates coordinates input evaluated. order order spline interpolation. order must 0 1. 0 indicates nearest neighbor 1 indicates linear interpolation. fill_mode Points outside boundaries input filled according given mode. Available methods \"constant\", \"nearest\", \"wrap\" \"mirror\" \"reflect\". Defaults \"constant\". \"constant\": (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k specified fill_value. \"nearest\": (| b c d | d d d d) input extended nearest pixel. \"wrap\": (b c d | b c d | b c d) input extended wrapping around opposite edge. \"mirror\": (c d c b | b c d | c b b) input extended mirroring edge. \"reflect\": (d c b | b c d | d c b ) input extended reflecting edge last pixel. fill_value Value used points outside boundaries input fill_mode = \"constant\". Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/op_image_map_coordinates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","text":"Output image batch images.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":null,"dir":"Reference","previous_headings":"","what":"Pad images with zeros to the specified height and width. — op_image_pad","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"Pad images zeros specified height width.","code":""},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"","code":"op_image_pad(   images,   top_padding = NULL,   left_padding = NULL,   target_height = NULL,   target_width = NULL,   bottom_padding = NULL,   right_padding = NULL )"},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"images 4D Tensor shape (batch, height, width, channels) 3D Tensor shape (height, width, channels). top_padding Number rows zeros add top. left_padding Number columns zeros add left. target_height Height output images. target_width Width output images. bottom_padding Number rows zeros add bottom. right_padding Number columns zeros add right.","code":""},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"images 4D, 4D float Tensor shape (batch, target_height, target_width, channels) images 3D, 3D float Tensor shape (target_height, target_width, channels)","code":""},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"","code":"images <- random_uniform(c(15, 25, 3)) padded_images <- op_image_pad(     images, 2, 3, target_height = 20, target_width = 30 ) shape(padded_images) ## shape(20, 30, 3) batch_images <- random_uniform(c(2, 15, 25, 3)) padded_batch <- op_image_pad(batch_images, 2, 3,                              target_height = 20,                              target_width = 30) shape(padded_batch) ## shape(2, 20, 30, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":null,"dir":"Reference","previous_headings":"","what":"Resize images to size using the specified interpolation method. — op_image_resize","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"Resize images size using specified interpolation method.","code":""},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"","code":"op_image_resize(   image,   size,   interpolation = \"bilinear\",   antialias = FALSE,   crop_to_aspect_ratio = FALSE,   pad_to_aspect_ratio = FALSE,   fill_mode = \"constant\",   fill_value = 0,   data_format = \"channels_last\" )"},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"image Input image batch images. Must 3D 4D. size Size output image (height, width) format. interpolation Interpolation method. Available methods \"nearest\", \"bilinear\", \"bicubic\". Defaults \"bilinear\". antialias Whether use antialiasing filter downsampling image. Defaults FALSE. crop_to_aspect_ratio TRUE, resize images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image cropped return largest possible window image (size (height, width)) matches target aspect ratio. default (crop_to_aspect_ratio=FALSE), aspect ratio may preserved. pad_to_aspect_ratio TRUE, pad images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image evenly padded short side. fill_mode using pad_to_aspect_ratio=TRUE, padded areas filled according given mode. \"constant\" supported time (fill constant value, equal fill_value). fill_value Float. Padding value use pad_to_aspect_ratio=TRUE. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\".","code":""},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"Resized image batch images.","code":""},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"","code":"x <- random_uniform(c(2, 4, 4, 3)) # batch of 2 RGB images y <- op_image_resize(x, c(2, 2)) shape(y) ## shape(2, 2, 2, 3) x <- random_uniform(c(4, 4, 3)) # single RGB image y <- op_image_resize(x, c(2, 2)) shape(y) ## shape(2, 2, 3) x <- random_uniform(c(2, 3, 4, 4)) # batch of 2 RGB images y <- op_image_resize(x, c(2, 2), data_format = \"channels_first\") shape(y) ## shape(2, 3, 2, 2)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"function converts RGB images grayscale images. supports 3D 4D tensors, last dimension represents channels.","code":""},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"","code":"op_image_rgb_to_grayscale(image, data_format = \"channels_last\")"},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"image Input RGB image batch RGB images. Must 3D tensor shape (height, width, channels) 4D tensor shape (batch, height, width, channels). data_format string specifying data format input tensor. can either \"channels_last\" \"channels_first\". \"channels_last\" corresponds inputs shape (batch, height, width, channels), \"channels_first\" corresponds inputs shape (batch, channels, height, width). Defaults \"channels_last\".","code":""},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"Grayscale image batch grayscale images.","code":""},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"","code":"x <- random_uniform(c(2, 4, 4, 3)) y <- op_image_rgb_to_grayscale(x) shape(y) ## shape(2, 4, 4, 1) x <- random_uniform(c(4, 4, 3)) # Single RGB image y = op_image_rgb_to_grayscale(x) shape(y) ## shape(4, 4, 1) x <- random_uniform(c(2, 3, 4, 4)) y <- op_image_rgb_to_grayscale(x, data_format=\"channels_first\") shape(y) ## shape(2, 1, 4, 4)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if the targets are in the top-k predictions. — op_in_top_k","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"Checks targets top-k predictions.","code":""},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"","code":"op_in_top_k(targets, predictions, k)"},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"targets tensor true labels. predictions tensor predicted labels. k integer representing number predictions consider.","code":""},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"boolean tensor shape targets, element indicates whether corresponding target top-k predictions.","code":""},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"","code":"targets <- op_array(c(2, 5, 3), \"int32\") predictions <- op_array(dtype = \"float32\", rbind(   c(0.1, 0.4, 0.6, 0.9, 0.5),   c(0.1, 0.7, 0.9, 0.8, 0.3),   c(0.1, 0.6, 0.9, 0.9, 0.5) )) op_in_top_k(targets, predictions, k = 3L) ## tf.Tensor([ True False  True], shape=(3), dtype=bool)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_inv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the inverse of a square tensor. — op_inv","title":"Computes the inverse of a square tensor. — op_inv","text":"Computes inverse square tensor.","code":""},{"path":"https://keras.posit.co/reference/op_inv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the inverse of a square tensor. — op_inv","text":"","code":"op_inv(x)"},{"path":"https://keras.posit.co/reference/op_inv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the inverse of a square tensor. — op_inv","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_inv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the inverse of a square tensor. — op_inv","text":"tensor shape (..., M, M) representing inverse x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_irfft.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"Computes inverse 1D Discrete Fourier Transform real-valued signal inner-dimension input. inner-dimension input assumed result RFFT: fft_length / 2 + 1 unique components DFT real-valued signal. fft_length provided, computed size inner-dimension input (fft_length = 2 * (inner - 1)). FFT length used compute odd, provided since inferred properly. Along axis IRFFT computed , fft_length / 2 + 1 smaller corresponding dimension input, dimension cropped. larger, dimension padded zeros.","code":""},{"path":"https://keras.posit.co/reference/op_irfft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"","code":"op_irfft(x, fft_length = NULL)"},{"path":"https://keras.posit.co/reference/op_irfft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"x List real imaginary parts input tensor. tensors list floating type. fft_length integer representing number fft length. specified, inferred length last axis x. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_irfft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"tensor containing inverse real-valued Fast Fourier Transform along last axis x.","code":""},{"path":"https://keras.posit.co/reference/op_irfft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"","code":"real <- op_array(c(0, 1, 2, 3, 4)) imag <- op_array(c(0, 1, 2, 3, 4)) op_irfft(c(real, imag)) #> tf.Tensor( #> [ 2.         -2.0606601   0.5        -0.35355338  0.          0.06066012 #>  -0.5         0.35355338], shape=(8), dtype=float32) all.equal(op_irfft(op_rfft(real, 5), 5), real) #> [1] TRUE"},{"path":[]},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check whether the given object is a tensor. — op_is_tensor","title":"Check whether the given object is a tensor. — op_is_tensor","text":"Check whether given object tensor.","code":""},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check whether the given object is a tensor. — op_is_tensor","text":"","code":"op_is_tensor(x)"},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check whether the given object is a tensor. — op_is_tensor","text":"x variable.","code":""},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check whether the given object is a tensor. — op_is_tensor","text":"TRUE x tensor, otherwise FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Check whether the given object is a tensor. — op_is_tensor","text":"checks backend specific tensors passing TensorFlow tensor return FALSE backend PyTorch JAX.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_isclose.html","id":null,"dir":"Reference","previous_headings":"","what":"Return whether two tensors are element-wise almost equal. — op_isclose","title":"Return whether two tensors are element-wise almost equal. — op_isclose","text":"Return whether two tensors element-wise almost equal.","code":""},{"path":"https://keras.posit.co/reference/op_isclose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return whether two tensors are element-wise almost equal. — op_isclose","text":"","code":"op_isclose(x1, x2)"},{"path":"https://keras.posit.co/reference/op_isclose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return whether two tensors are element-wise almost equal. — op_isclose","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isclose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return whether two tensors are element-wise almost equal. — op_isclose","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_isfinite.html","id":null,"dir":"Reference","previous_headings":"","what":"Return whether a tensor is finite, element-wise. — op_isfinite","title":"Return whether a tensor is finite, element-wise. — op_isfinite","text":"Real values finite NaN, positive infinity, negative infinity. Complex values finite real imaginary parts finite.","code":""},{"path":"https://keras.posit.co/reference/op_isfinite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return whether a tensor is finite, element-wise. — op_isfinite","text":"","code":"op_isfinite(x)"},{"path":"https://keras.posit.co/reference/op_isfinite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return whether a tensor is finite, element-wise. — op_isfinite","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isfinite.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return whether a tensor is finite, element-wise. — op_isfinite","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_isinf.html","id":null,"dir":"Reference","previous_headings":"","what":"Test element-wise for positive or negative infinity. — op_isinf","title":"Test element-wise for positive or negative infinity. — op_isinf","text":"Test element-wise positive negative infinity.","code":""},{"path":"https://keras.posit.co/reference/op_isinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test element-wise for positive or negative infinity. — op_isinf","text":"","code":"op_isinf(x)"},{"path":"https://keras.posit.co/reference/op_isinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test element-wise for positive or negative infinity. — op_isinf","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isinf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test element-wise for positive or negative infinity. — op_isinf","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_isnan.html","id":null,"dir":"Reference","previous_headings":"","what":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","title":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","text":"Test element-wise NaN return result boolean tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isnan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","text":"","code":"op_isnan(x)"},{"path":"https://keras.posit.co/reference/op_isnan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isnan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_istft.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"reconstruct original waveform, parameters stft.","code":""},{"path":"https://keras.posit.co/reference/op_istft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"","code":"op_istft(   x,   sequence_length,   sequence_stride,   fft_length,   length = NULL,   window = \"hann\",   center = TRUE )"},{"path":"https://keras.posit.co/reference/op_istft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"x Tuple real imaginary parts input tensor. tensors list floating type. sequence_length integer representing sequence length. sequence_stride integer representing sequence hop size. fft_length integer representing size FFT produced stft. length integer representing output clipped exactly length. specified, padding clipping take place. Defaults NULL. window string, tensor window NULL. window string, available values \"hann\" \"hamming\". window tensor, used directly window length must sequence_length. window NULL, windowing used. Defaults \"hann\". center Whether x padded sides t-th sequence centered time t * sequence_stride. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_istft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"tensor containing inverse Short-Time Fourier Transform along last axis x.","code":""},{"path":"https://keras.posit.co/reference/op_istft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"","code":"x <- op_convert_to_tensor(c(0, 1, 2, 3, 4)) op_istft(op_stft(x, 1, 1, 1), 1, 1, 1) ## tf.Tensor([], shape=(0), dtype=float32) # array([0.0, 1.0, 2.0, 3.0, 4.0])"},{"path":[]},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"allows small gradient unit active, defined : f(x) = alpha * x x < 0 f(x) = x x >= 0.","code":""},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"","code":"op_leaky_relu(x, negative_slope = 0.2)"},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"x Input tensor. negative_slope Slope activation function x < 0. Defaults 0.2.","code":""},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_leaky_relu(x) ## tf.Tensor([-0.2  0.   1. ], shape=(3), dtype=float32) # array([-0.2,  0. ,  1. ], shape=(3,), dtype=float64) x <- seq(-5, 5, .1) plot(x, op_leaky_relu(x),      type = 'l', panel.first = grid())"},{"path":[]},{"path":"https://keras.posit.co/reference/op_less.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the truth value of x1 < x2 element-wise. — op_less","title":"Return the truth value of x1 < x2 element-wise. — op_less","text":"Note function automatically called using R operator < tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_less(x, 2) ## tf.Tensor([ True  True False False], shape=(4), dtype=bool) x < 2 ## tf.Tensor([ True  True False False], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_less.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the truth value of x1 < x2 element-wise. — op_less","text":"","code":"op_less(x1, x2)"},{"path":"https://keras.posit.co/reference/op_less.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the truth value of x1 < x2 element-wise. — op_less","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_less.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the truth value of x1 < x2 element-wise. — op_less","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_less_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","title":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","text":"Note function automatically called using R operator <= tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_less_equal(x, 2) ## tf.Tensor([ True  True  True False], shape=(4), dtype=bool) x <= 2 ## tf.Tensor([ True  True  True False], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_less_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","text":"","code":"op_less_equal(x1, x2)"},{"path":"https://keras.posit.co/reference/op_less_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_less_equal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_linspace.html","id":null,"dir":"Reference","previous_headings":"","what":"Return evenly spaced numbers over a specified interval. — op_linspace","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"Returns num evenly spaced samples, calculated interval [start, stop]. endpoint interval can optionally excluded.","code":""},{"path":"https://keras.posit.co/reference/op_linspace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"","code":"op_linspace(   start,   stop,   num = 50L,   endpoint = TRUE,   retstep = FALSE,   dtype = NULL,   axis = 1L )"},{"path":"https://keras.posit.co/reference/op_linspace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"start starting value sequence. stop end value sequence, unless endpoint set FALSE. case, sequence consists last num + 1 evenly spaced samples, stop excluded. Note step size changes endpoint FALSE. num Number samples generate. Defaults 50. Must non-negative. endpoint TRUE, stop last sample. Otherwise, included. Defaults TRUE. retstep TRUE, return (samples, step), step spacing samples. dtype type output tensor. axis axis result store samples. Relevant start stop array-like. Defaults 1, first axis.","code":""},{"path":"https://keras.posit.co/reference/op_linspace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"tensor evenly spaced numbers. retstep TRUE, returns (samples, step)","code":""},{"path":"https://keras.posit.co/reference/op_linspace.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"Torch backend support axis argument.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log.html","id":null,"dir":"Reference","previous_headings":"","what":"Natural logarithm, element-wise. — op_log","title":"Natural logarithm, element-wise. — op_log","text":"Natural logarithm, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_log.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Natural logarithm, element-wise. — op_log","text":"","code":"op_log(x)"},{"path":"https://keras.posit.co/reference/op_log.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Natural logarithm, element-wise. — op_log","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Natural logarithm, element-wise. — op_log","text":"Output tensor, element-wise natural logarithm x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log10.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","title":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","text":"Return base 10 logarithm input tensor, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_log10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","text":"","code":"op_log10(x)"},{"path":"https://keras.posit.co/reference/op_log10.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log10.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","text":"Output tensor, element-wise base 10 logarithm x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log1p.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","title":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","text":"Calculates log(1 + x).","code":""},{"path":"https://keras.posit.co/reference/op_log1p.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","text":"","code":"op_log1p(x)"},{"path":"https://keras.posit.co/reference/op_log1p.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log1p.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","text":"Output tensor, element-wise natural logarithm 1 + x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log2.html","id":null,"dir":"Reference","previous_headings":"","what":"Base-2 logarithm of x, element-wise. — op_log2","title":"Base-2 logarithm of x, element-wise. — op_log2","text":"Base-2 logarithm x, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_log2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Base-2 logarithm of x, element-wise. — op_log2","text":"","code":"op_log2(x)"},{"path":"https://keras.posit.co/reference/op_log2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Base-2 logarithm of x, element-wise. — op_log2","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Base-2 logarithm of x, element-wise. — op_log2","text":"Output tensor, element-wise base-2 logarithm x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Logarithm of the sigmoid activation function. — op_log_sigmoid","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"defined f(x) = log(1 / (1 + exp(-x))).","code":""},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"","code":"op_log_sigmoid(x)"},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"","code":"x <- op_convert_to_tensor(c(-0.541391, 0.0, 0.50, 5.0)) op_log_sigmoid(x) ## tf.Tensor([-1.0000418  -0.6931472  -0.474077   -0.00671535], shape=(4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Log-softmax activation function. — op_log_softmax","title":"Log-softmax activation function. — op_log_softmax","text":"defined : f(x) = x - max(x) - log(sum(exp(x - max(x))))","code":""},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log-softmax activation function. — op_log_softmax","text":"","code":"op_log_softmax(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log-softmax activation function. — op_log_softmax","text":"x Input tensor. axis Integer, axis along log-softmax applied. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Log-softmax activation function. — op_log_softmax","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Log-softmax activation function. — op_log_softmax","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_log_softmax(x) ## tf.Tensor([-2.407606   -1.4076059  -0.40760595], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_logaddexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","title":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","text":"Calculates log(exp(x1) + exp(x2)).","code":""},{"path":"https://keras.posit.co/reference/op_logaddexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","text":"","code":"op_logaddexp(x1, x2)"},{"path":"https://keras.posit.co/reference/op_logaddexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","text":"x1 Input tensor. x2 Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logaddexp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","text":"Output tensor, element-wise logarithm sum exponentiations inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"Zeros treated FALSE non-zeros treated TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"","code":"op_logical_and(x1, x2)"},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"x1 Input tensor. x2 Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"Output tensor, element-wise logical inputs.","code":""},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"Note function automatically called using R operator & tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logical_not.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the element-wise NOT of the given input tensor. — op_logical_not","title":"Computes the element-wise NOT of the given input tensor. — op_logical_not","text":"Zeros treated FALSE non-zeros treated TRUE. Note function automatically called using R operator ! tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_not.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the element-wise NOT of the given input tensor. — op_logical_not","text":"","code":"op_logical_not(x)"},{"path":"https://keras.posit.co/reference/op_logical_not.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the element-wise NOT of the given input tensor. — op_logical_not","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_not.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the element-wise NOT of the given input tensor. — op_logical_not","text":"Output tensor, element-wise logical input.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logical_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","title":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","text":"Zeros treated FALSE non-zeros treated TRUE. Note function automatically called using R operator | tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","text":"","code":"op_logical_or(x1, x2)"},{"path":"https://keras.posit.co/reference/op_logical_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","text":"x1 Input tensor. x2 Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_or.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","text":"Output tensor, element-wise logical inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logical_xor.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","title":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","text":"Compute truth value x1 XOR x2, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_logical_xor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","text":"","code":"op_logical_xor(x1, x2)"},{"path":"https://keras.posit.co/reference/op_logical_xor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_xor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logspace.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns numbers spaced evenly on a log scale. — op_logspace","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"linear space, sequence starts base ** start ends base ** stop (see endpoint ).","code":""},{"path":"https://keras.posit.co/reference/op_logspace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"","code":"op_logspace(   start,   stop,   num = 50L,   endpoint = TRUE,   base = 10L,   dtype = NULL,   axis = 1L )"},{"path":"https://keras.posit.co/reference/op_logspace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"start starting value sequence. stop final value sequence, unless endpoint FALSE. case, num + 1 values spaced interval log-space, last (sequence length num) returned. num Number samples generate. Defaults 50. endpoint TRUE, stop last sample. Otherwise, included. Defaults TRUE. base base log space. Defaults 10. dtype type output tensor. axis axis result store samples. Relevant start stop array-like.","code":""},{"path":"https://keras.posit.co/reference/op_logspace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"tensor evenly spaced samples log scale.","code":""},{"path":"https://keras.posit.co/reference/op_logspace.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"Torch backend support axis argument.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"Computes logarithm sum exponentials elements tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"","code":"op_logsumexp(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"x Input tensor. axis integer list integers specifying axis/axes along compute sum. NULL, sum computed elements. Defaults toNULL. keepdims boolean indicating whether keep dimensions input tensor computing sum. Defaults toFALSE.","code":""},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"tensor containing logarithm sum exponentials elements x.","code":""},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"","code":"x <- op_convert_to_tensor(c(1, 2, 3)) op_logsumexp(x) ## tf.Tensor(3.407606, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_lu_factor.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","title":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","text":"Computes lower-upper decomposition square matrix.","code":""},{"path":"https://keras.posit.co/reference/op_lu_factor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","text":"","code":"op_lu_factor(x)"},{"path":"https://keras.posit.co/reference/op_lu_factor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","text":"x tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_lu_factor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","text":"tuple two tensors: tensor shape (..., M, M) containing lower upper triangular matrices tensor shape (..., M) containing pivots.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_matmul.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix product of two tensors. — op_matmul","title":"Matrix product of two tensors. — op_matmul","text":"tensors 1-dimensional, dot product (scalar) returned. either tensor N-D, N > 2, treated stack matrices residing last two indexes broadcast accordingly. first tensor 1-D, promoted matrix prepending 1 dimensions. matrix multiplication prepended 1 removed. second tensor 1-D, promoted matrix appending 1 dimensions. matrix multiplication appended 1 removed.","code":""},{"path":"https://keras.posit.co/reference/op_matmul.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix product of two tensors. — op_matmul","text":"","code":"op_matmul(x1, x2)"},{"path":"https://keras.posit.co/reference/op_matmul.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix product of two tensors. — op_matmul","text":"x1 First tensor. x2 Second tensor.","code":""},{"path":"https://keras.posit.co/reference/op_matmul.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matrix product of two tensors. — op_matmul","text":"Output tensor, matrix product inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_max.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the maximum of a tensor or maximum along an axis. — op_max","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"Return maximum tensor maximum along axis.","code":""},{"path":"https://keras.posit.co/reference/op_max.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"","code":"op_max(x, axis = NULL, keepdims = FALSE, initial = NULL)"},{"path":"https://keras.posit.co/reference/op_max.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"x Input tensor. axis Axis axes along operate. default, flattened input used. keepdims set TRUE, axes reduced left result dimensions size one. Defaults FALSE. initial minimum value output element. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_max.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"Maximum x.","code":""},{"path":"https://keras.posit.co/reference/op_max.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"","code":"(x <- op_convert_to_tensor(rbind(c(1, 3, 5), c(1, 5, 2)))) ## tf.Tensor( ## [[1. 3. 5.] ##  [1. 5. 2.]], shape=(2, 3), dtype=float64) op_max(x) ## tf.Tensor(5.0, shape=(), dtype=float64) op_max(x, axis = 1) ## tf.Tensor([1. 5. 5.], shape=(3), dtype=float64) op_max(x, axis = 1, keepdims = TRUE) ## tf.Tensor([[1. 5. 5.]], shape=(1, 3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_max_pool.html","id":null,"dir":"Reference","previous_headings":"","what":"Max pooling operation. — op_max_pool","title":"Max pooling operation. — op_max_pool","text":"Max pooling operation.","code":""},{"path":"https://keras.posit.co/reference/op_max_pool.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max pooling operation. — op_max_pool","text":"","code":"op_max_pool(   inputs,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL )"},{"path":"https://keras.posit.co/reference/op_max_pool.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max pooling operation. — op_max_pool","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". Pooling happens spatial dimensions . pool_size int tuple/list integers size len(inputs_spatial_shape), specifying size pooling window spatial dimension input tensor. pool_size int, every spatial dimension shares pool_size. strides int tuple/list integers size len(inputs_spatial_shape). stride sliding window spatial dimension input tensor. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...).","code":""},{"path":"https://keras.posit.co/reference/op_max_pool.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Max pooling operation. — op_max_pool","text":"tensor rank N+2, result max pooling operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_maximum.html","id":null,"dir":"Reference","previous_headings":"","what":"Element-wise maximum of x1 and x2. — op_maximum","title":"Element-wise maximum of x1 and x2. — op_maximum","text":"Element-wise maximum x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_maximum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Element-wise maximum of x1 and x2. — op_maximum","text":"","code":"op_maximum(x1, x2)  op_pmax(x1, x2)"},{"path":"https://keras.posit.co/reference/op_maximum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Element-wise maximum of x1 and x2. — op_maximum","text":"x1 First tensor. x2 Second tensor.","code":""},{"path":"https://keras.posit.co/reference/op_maximum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Element-wise maximum of x1 and x2. — op_maximum","text":"Output tensor, element-wise maximum x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the arithmetic mean along the specified axes. — op_mean","title":"Compute the arithmetic mean along the specified axes. — op_mean","text":"Compute arithmetic mean along specified axes.","code":""},{"path":"https://keras.posit.co/reference/op_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the arithmetic mean along the specified axes. — op_mean","text":"","code":"op_mean(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the arithmetic mean along the specified axes. — op_mean","text":"x Input tensor. axis Axis axes along means computed. default compute mean flattened tensor. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the arithmetic mean along the specified axes. — op_mean","text":"Output tensor containing mean values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_median.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the median along the specified axis. — op_median","title":"Compute the median along the specified axis. — op_median","text":"Compute median along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_median.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the median along the specified axis. — op_median","text":"","code":"op_median(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_median.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the median along the specified axis. — op_median","text":"x Input tensor. axis Axis axes along medians computed. Defaults axis = NULL compute median(s) along flattened version array. keepdims set TRUE, axes reduce left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_median.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the median along the specified axis. — op_median","text":"output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"Given N 1-D tensors T0, T1, ..., TN-1 inputs corresponding lengths S0, S1, ..., SN-1, creates N N-dimensional tensors G0, G1, ..., GN-1 shape (S0, ..., SN-1) output Gi constructed expanding Ti result shape.","code":""},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"","code":"op_meshgrid(..., indexing = \"xy\")"},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"... 1-D tensors representing coordinates grid. indexing \"xy\" \"ij\". \"xy\" cartesian; \"ij\" matrix indexing output. Defaults \"xy\".","code":""},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"Sequence N tensors.","code":""},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"","code":"x <- op_array(c(1, 2, 3), \"int32\") y <- op_array(c(4, 5, 6), \"int32\") c(grid_x, grid_y) %<-% op_meshgrid(x, y, indexing = \"ij\") grid_x ## tf.Tensor( ## [[1 1 1] ##  [2 2 2] ##  [3 3 3]], shape=(3, 3), dtype=int32) # array([[1, 1, 1], #        [2, 2, 2], #        [3, 3, 3])) grid_y ## tf.Tensor( ## [[4 5 6] ##  [4 5 6] ##  [4 5 6]], shape=(3, 3), dtype=int32) # array([[4, 5, 6], #        [4, 5, 6], #        [4, 5, 6]))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_min.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the minimum of a tensor or minimum along an axis. — op_min","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"Return minimum tensor minimum along axis.","code":""},{"path":"https://keras.posit.co/reference/op_min.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"","code":"op_min(x, axis = NULL, keepdims = FALSE, initial = NULL)"},{"path":"https://keras.posit.co/reference/op_min.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"x Input tensor. axis Axis axes along operate. default, flattened input used. keepdims set TRUE, axes reduced left result dimensions size one. Defaults FALSE. initial maximum value output element. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_min.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"Minimum x.","code":""},{"path":"https://keras.posit.co/reference/op_min.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"","code":"(x <- op_convert_to_tensor(rbind(c(1, 3, 5), c(1, 5, 2)))) ## tf.Tensor( ## [[1. 3. 5.] ##  [1. 5. 2.]], shape=(2, 3), dtype=float64) op_min(x) ## tf.Tensor(1.0, shape=(), dtype=float64) op_min(x, axis = 1) ## tf.Tensor([1. 3. 2.], shape=(3), dtype=float64) op_min(x, axis = 1, keepdims = TRUE) ## tf.Tensor([[1. 3. 2.]], shape=(1, 3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_minimum.html","id":null,"dir":"Reference","previous_headings":"","what":"Element-wise minimum of x1 and x2. — op_minimum","title":"Element-wise minimum of x1 and x2. — op_minimum","text":"Element-wise minimum x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_minimum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Element-wise minimum of x1 and x2. — op_minimum","text":"","code":"op_minimum(x1, x2)  op_pmin(x1, x2)"},{"path":"https://keras.posit.co/reference/op_minimum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Element-wise minimum of x1 and x2. — op_minimum","text":"x1 First tensor. x2 Second tensor.","code":""},{"path":"https://keras.posit.co/reference/op_minimum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Element-wise minimum of x1 and x2. — op_minimum","text":"Output tensor, element-wise minimum x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_mod.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the element-wise remainder of division. — op_mod","title":"Returns the element-wise remainder of division. — op_mod","text":"Note function automatically called using R operator %% tensor.","code":"(x <- op_arange(10)) ## tf.Tensor([0. 1. 2. 3. 4. 5. 6. 7. 8. 9.], shape=(10), dtype=float32) op_mod(x, 3) ## tf.Tensor([0. 1. 2. 0. 1. 2. 0. 1. 2. 0.], shape=(10), dtype=float32) x %% 3 ## tf.Tensor([0. 1. 2. 0. 1. 2. 0. 1. 2. 0.], shape=(10), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_mod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the element-wise remainder of division. — op_mod","text":"","code":"op_mod(x1, x2)"},{"path":"https://keras.posit.co/reference/op_mod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the element-wise remainder of division. — op_mod","text":"x1 First tensor. x2 Second tensor.","code":""},{"path":"https://keras.posit.co/reference/op_mod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the element-wise remainder of division. — op_mod","text":"Output tensor, element-wise remainder division.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_moments.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the mean and variance of x. — op_moments","title":"Calculates the mean and variance of x. — op_moments","text":"mean variance calculated aggregating contents x across axes. x 1-D axes = c(1) just mean variance vector.","code":""},{"path":"https://keras.posit.co/reference/op_moments.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the mean and variance of x. — op_moments","text":"","code":"op_moments(x, axes, keepdims = FALSE, synchronized = FALSE)"},{"path":"https://keras.posit.co/reference/op_moments.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the mean and variance of x. — op_moments","text":"x Input tensor. axes list axes compute mean variance. keepdims set TRUE, axes reduced left result dimensions size one. synchronized applicable TensorFlow backend. TRUE, synchronizes global batch statistics (mean variance) across devices training step distributed training strategy. FALSE, replica uses local batch statistics.","code":""},{"path":"https://keras.posit.co/reference/op_moments.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the mean and variance of x. — op_moments","text":"list containing two tensors - mean variance.","code":""},{"path":"https://keras.posit.co/reference/op_moments.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculates the mean and variance of x. — op_moments","text":"","code":"x <- op_convert_to_tensor(c(0, 1, 2, 3, 100), dtype = \"float32\") op_moments(x, axes = c(1)) ## [[1]] ## tf.Tensor(21.2, shape=(), dtype=float32) ## ## [[2]] ## tf.Tensor(1553.3601, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_moveaxis.html","id":null,"dir":"Reference","previous_headings":"","what":"Move axes of a tensor to new positions. — op_moveaxis","title":"Move axes of a tensor to new positions. — op_moveaxis","text":"axes remain original order.","code":""},{"path":"https://keras.posit.co/reference/op_moveaxis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Move axes of a tensor to new positions. — op_moveaxis","text":"","code":"op_moveaxis(x, source, destination)"},{"path":"https://keras.posit.co/reference/op_moveaxis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Move axes of a tensor to new positions. — op_moveaxis","text":"x Tensor whose axes reordered. source Original positions axes move. must unique. destination Destinations positions original axes. must also unique.","code":""},{"path":"https://keras.posit.co/reference/op_moveaxis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Move axes of a tensor to new positions. — op_moveaxis","text":"Tensor moved axes.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":null,"dir":"Reference","previous_headings":"","what":"Encodes integer labels as multi-hot vectors. — op_multi_hot","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"function encodes integer labels multi-hot vectors, label mapped binary value resulting vector.","code":""},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"","code":"op_multi_hot(   inputs,   num_classes,   axis = -1L,   dtype = NULL,   sparse = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"inputs Tensor integer labels converted multi-hot vectors. num_classes Integer, total number unique classes. axis (optional) Axis along multi-hot encoding added. Defaults -1, corresponds last dimension. dtype (optional) data type resulting tensor. Default backend's float type. sparse Whether return sparse tensor; backends support sparse tensors. ... forward/backwards compatability","code":""},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"Tensor: multi-hot encoded tensor.","code":""},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"","code":"data <- op_convert_to_tensor(c(0, 4)) op_multi_hot(data, num_classes = 5) ## tf.Tensor([1. 0. 0. 0. 1.], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_multiply.html","id":null,"dir":"Reference","previous_headings":"","what":"Multiply arguments element-wise. — op_multiply","title":"Multiply arguments element-wise. — op_multiply","text":"Note function automatically called using R operator * tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_multiply(x, x) ## tf.Tensor([0. 1. 4. 9.], shape=(4), dtype=float32) x * x ## tf.Tensor([0. 1. 4. 9.], shape=(4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_multiply.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multiply arguments element-wise. — op_multiply","text":"","code":"op_multiply(x1, x2)"},{"path":"https://keras.posit.co/reference/op_multiply.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multiply arguments element-wise. — op_multiply","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_multiply.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multiply arguments element-wise. — op_multiply","text":"Output tensor, element-wise product x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"Replace NaN zero infinity large finite numbers.","code":""},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"","code":"op_nan_to_num(x, nan = 0, posinf = NULL, neginf = NULL)"},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"x Input data. nan Optional float int. Value replace NaN entries . posinf Optional float int. Value replace positive infinity . neginf Optional float int. Value replace negative infinity .","code":""},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"x, non-finite values replaced.","code":""},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"","code":"(x <- op_convert_to_tensor(c(1, NaN, -Inf, Inf))) ## tf.Tensor([  1.  nan -inf  inf], shape=(4), dtype=float32) op_nan_to_num(x) ## tf.Tensor([ 1.0000000e+00  0.0000000e+00 -3.4028235e+38  3.4028235e+38], shape=(4), dtype=float32) op_nan_to_num(x, nan = -1, posinf = 2, neginf = -2) ## tf.Tensor([ 1. -1. -2.  2.], shape=(4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_ndim.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the number of dimensions of a tensor. — op_ndim","title":"Return the number of dimensions of a tensor. — op_ndim","text":"Return number dimensions tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ndim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the number of dimensions of a tensor. — op_ndim","text":"","code":"op_ndim(x)"},{"path":"https://keras.posit.co/reference/op_ndim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the number of dimensions of a tensor. — op_ndim","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ndim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the number of dimensions of a tensor. — op_ndim","text":"number dimensions x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_negative.html","id":null,"dir":"Reference","previous_headings":"","what":"Numerical negative, element-wise. — op_negative","title":"Numerical negative, element-wise. — op_negative","text":"Note function automatically called using unary R operator - tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_negative(x) ## tf.Tensor([-0. -1. -2. -3.], shape=(4), dtype=float32) -x ## tf.Tensor([-0. -1. -2. -3.], shape=(4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_negative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Numerical negative, element-wise. — op_negative","text":"","code":"op_negative(x)"},{"path":"https://keras.posit.co/reference/op_negative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Numerical negative, element-wise. — op_negative","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_negative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Numerical negative, element-wise. — op_negative","text":"Output tensor, y = -x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the indices of the elements that are non-zero. — op_nonzero","title":"Return the indices of the elements that are non-zero. — op_nonzero","text":"Return indices elements non-zero.","code":""},{"path":"https://keras.posit.co/reference/op_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the indices of the elements that are non-zero. — op_nonzero","text":"","code":"op_nonzero(x)"},{"path":"https://keras.posit.co/reference/op_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the indices of the elements that are non-zero. — op_nonzero","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_nonzero.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the indices of the elements that are non-zero. — op_nonzero","text":"Indices elements non-zero.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix or vector norm. — op_norm","title":"Matrix or vector norm. — op_norm","text":"function able return one eight different matrix norms, one infinite number vector norms (described ), depending value ord parameter.","code":""},{"path":"https://keras.posit.co/reference/op_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix or vector norm. — op_norm","text":"","code":"op_norm(x, ord = NULL, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix or vector norm. — op_norm","text":"x Input tensor. ord Order norm (see table Notes). default NULL. axis axis integer, specifies axis x along compute vector norms. axis length 2 vector, specifies axes hold 2-D matrices, matrix norms matrices computed. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matrix or vector norm. — op_norm","text":"Norm matrix vector(s).","code":""},{"path":"https://keras.posit.co/reference/op_norm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Matrix or vector norm. — op_norm","text":"values ord < 1, result , strictly speaking, mathematical 'norm', may still useful various numerical purposes. following norms can calculated: matrices: ord=NULL: Frobenius norm ord=\"fro\": Frobenius norm ord=\"nuc\": nuclear norm ord=Inf: max(sum(abs(x), axis=2)) ord=-Inf: min(sum(abs(x), axis=2)) ord=0: supported ord=1: max(sum(abs(x), axis=1)) ord=-1: min(sum(abs(x), axis=1)) ord=2: 2-norm (largest sing. value) ord=-2: smallest singular value : supported vectors: ord=NULL: 2-norm ord=\"fro\": supported ord=\"nuc\": supported ord=Inf: max(abs(x)) ord=-Inf: min(abs(x)) ord=0: sum(x != 0) ord=1: ord=-1: ord=2: ord=-2: : sum(abs(x)^ord)^(1/ord)","code":""},{"path":"https://keras.posit.co/reference/op_norm.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrix or vector norm. — op_norm","text":"","code":"x <- op_reshape(op_arange(9, dtype=\"float32\") - 4, c(3, 3)) op_norm(x) ## tf.Tensor(7.745967, shape=(), dtype=float32) # 7.7459664"},{"path":[]},{"path":"https://keras.posit.co/reference/op_normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalizes x over the specified axis. — op_normalize","title":"Normalizes x over the specified axis. — op_normalize","text":"defined : normalize(x) = x / max(norm(x), epsilon).","code":""},{"path":"https://keras.posit.co/reference/op_normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalizes x over the specified axis. — op_normalize","text":"","code":"op_normalize(x, axis = -1L, order = 2L)"},{"path":"https://keras.posit.co/reference/op_normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalizes x over the specified axis. — op_normalize","text":"x Input tensor. axis axis axes along perform normalization. Default -1. order exponent value norm formulation. Defaults 2.","code":""},{"path":"https://keras.posit.co/reference/op_normalize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalizes x over the specified axis. — op_normalize","text":"normalized array.","code":""},{"path":"https://keras.posit.co/reference/op_normalize.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalizes x over the specified axis. — op_normalize","text":"","code":"x <- op_convert_to_tensor(rbind(c(1, 2, 3), c(4, 5, 6))) x_norm <- op_normalize(x) x_norm ## tf.Tensor( ## [[0.26726124 0.53452248 0.80178373] ##  [0.45584231 0.56980288 0.68376346]], shape=(2, 3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_not_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return (x1 != x2) element-wise. — op_not_equal","title":"Return (x1 != x2) element-wise. — op_not_equal","text":"Note function automatically called using R operator != tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_not_equal(x, 2) ## tf.Tensor([ True  True False  True], shape=(4), dtype=bool) x != 2 ## tf.Tensor([ True  True False  True], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_not_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return (x1 != x2) element-wise. — op_not_equal","text":"","code":"op_not_equal(x1, x2)"},{"path":"https://keras.posit.co/reference/op_not_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return (x1 != x2) element-wise. — op_not_equal","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_not_equal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return (x1 != x2) element-wise. — op_not_equal","text":"Output tensor, element-wise comparsion x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts integer tensor x into a one-hot tensor. — op_one_hot","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"one-hot encoding representation integer value converted binary vector length equal num_classes, index corresponding integer value marked 1, indices marked 0.","code":""},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"","code":"op_one_hot(x, num_classes, axis = -1L, dtype = NULL, sparse = FALSE)"},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"x Integer tensor encoded. shape can arbitrary, dtype integer. R factors coerced integer offset 0-based, .e., .integer(x) - 1L. num_classes Number classes one-hot encoding. axis Axis along encoding performed. Defaults -1, represents last axis. dtype (Optional) Data type output tensor. provided, defaults default data type backend. sparse Whether return sparse tensor; backends support sparse tensors.","code":""},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"Integer tensor: One-hot encoded tensor shape x except specified axis dimension, length num_classes. dtype output tensor determined dtype default data type backend.","code":""},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"","code":"x <- op_array(c(1, 3, 2, 0), \"int32\") op_one_hot(x, num_classes = 4) ## tf.Tensor( ## [[0. 1. 0. 0.] ##  [0. 0. 0. 1.] ##  [0. 0. 1. 0.] ##  [1. 0. 0. 0.]], shape=(4, 4), dtype=float32) # array([[0. 1. 0. 0.] #        [0. 0. 0. 1.] #        [0. 0. 1. 0.] #        [1. 0. 0. 0.]], shape=(4, 4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_ones.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a new tensor of given shape and type, filled with ones. — op_ones","title":"Return a new tensor of given shape and type, filled with ones. — op_ones","text":"Return new tensor given shape type, filled ones.","code":""},{"path":"https://keras.posit.co/reference/op_ones.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a new tensor of given shape and type, filled with ones. — op_ones","text":"","code":"op_ones(shape, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_ones.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a new tensor of given shape and type, filled with ones. — op_ones","text":"shape Shape new tensor. dtype Desired data type tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ones.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a new tensor of given shape and type, filled with ones. — op_ones","text":"Tensor ones given shape dtype.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_ones_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a tensor of ones with the same shape and type of x. — op_ones_like","title":"Return a tensor of ones with the same shape and type of x. — op_ones_like","text":"Return tensor ones shape type x.","code":""},{"path":"https://keras.posit.co/reference/op_ones_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a tensor of ones with the same shape and type of x. — op_ones_like","text":"","code":"op_ones_like(x, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_ones_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a tensor of ones with the same shape and type of x. — op_ones_like","text":"x Input tensor. dtype Overrides data type result.","code":""},{"path":"https://keras.posit.co/reference/op_ones_like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a tensor of ones with the same shape and type of x. — op_ones_like","text":"tensor ones shape type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_outer.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the outer product of two vectors. — op_outer","title":"Compute the outer product of two vectors. — op_outer","text":"Given two vectors x1 x2, outer product :","code":"out[i, j] = x1[i] * x2[j]"},{"path":"https://keras.posit.co/reference/op_outer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the outer product of two vectors. — op_outer","text":"","code":"op_outer(x1, x2)"},{"path":"https://keras.posit.co/reference/op_outer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the outer product of two vectors. — op_outer","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_outer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the outer product of two vectors. — op_outer","text":"Outer product x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_pad.html","id":null,"dir":"Reference","previous_headings":"","what":"Pad a tensor. — op_pad","title":"Pad a tensor. — op_pad","text":"Pad tensor.","code":""},{"path":"https://keras.posit.co/reference/op_pad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pad a tensor. — op_pad","text":"","code":"op_pad(x, pad_width, mode = \"constant\", constant_values = NULL)"},{"path":"https://keras.posit.co/reference/op_pad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pad a tensor. — op_pad","text":"x Tensor pad. pad_width Number values padded edges axis. ((before_1, after_1), ...(before_N, after_N)) unique pad widths axis. ((, ),) yields pad axis. (pad,) int shortcut = = pad width axes. mode One \"constant\", \"edge\", \"linear_ramp\", \"maximum\", \"mean\", \"median\", \"minimum\", \"reflect\", \"symmetric\", \"wrap\", \"empty\", \"circular\". Defaults \"constant\". constant_values Value pad mode == \"constant\". Defaults 0. ValueError raised NULL mode != \"constant\".","code":""},{"path":"https://keras.posit.co/reference/op_pad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pad a tensor. — op_pad","text":"Padded tensor.","code":""},{"path":"https://keras.posit.co/reference/op_pad.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Pad a tensor. — op_pad","text":"Torch backend supports modes \"constant\", \"reflect\", \"symmetric\" \"circular\". Torch backend supports \"circular\" mode. Note: Tensorflow backend supports modes \"constant\", \"reflect\" \"symmetric\".","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_power.html","id":null,"dir":"Reference","previous_headings":"","what":"First tensor elements raised to powers from second tensor, element-wise. — op_power","title":"First tensor elements raised to powers from second tensor, element-wise. — op_power","text":"Note function automatically called using R operator ^ tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_power(2, x) ## tf.Tensor([1. 2. 4. 8.], shape=(4), dtype=float32) 2 ^ x ## tf.Tensor([1. 2. 4. 8.], shape=(4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_power.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"First tensor elements raised to powers from second tensor, element-wise. — op_power","text":"","code":"op_power(x1, x2)"},{"path":"https://keras.posit.co/reference/op_power.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"First tensor elements raised to powers from second tensor, element-wise. — op_power","text":"x1 bases. x2 exponents.","code":""},{"path":"https://keras.posit.co/reference/op_power.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"First tensor elements raised to powers from second tensor, element-wise. — op_power","text":"Output tensor, bases x1 raised exponents x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_prod.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the product of tensor elements over a given axis. — op_prod","title":"Return the product of tensor elements over a given axis. — op_prod","text":"Return product tensor elements given axis.","code":""},{"path":"https://keras.posit.co/reference/op_prod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the product of tensor elements over a given axis. — op_prod","text":"","code":"op_prod(x, axis = NULL, keepdims = FALSE, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_prod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the product of tensor elements over a given axis. — op_prod","text":"x Input tensor. axis Axis axes along product performed. default, axis = NULL, compute product elements input tensor. keepdims set TRUE, axes reduce left result dimensions size one. dtype Data type returned tensor.","code":""},{"path":"https://keras.posit.co/reference/op_prod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the product of tensor elements over a given axis. — op_prod","text":"Product elements x given axis axes.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_psnr.html","id":null,"dir":"Reference","previous_headings":"","what":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"function computes Peak Signal--Noise Ratio two signals, x1 x2. PSNR measure quality reconstructed signal. higher PSNR, closer reconstructed signal original signal. Note can become negative signal power smaller noise power.","code":""},{"path":"https://keras.posit.co/reference/op_psnr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"","code":"op_psnr(x1, x2, max_val)"},{"path":"https://keras.posit.co/reference/op_psnr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"x1 first input signal. x2 second input signal. Must shape x1. max_val maximum possible value signals.","code":""},{"path":"https://keras.posit.co/reference/op_psnr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"float: PSNR value x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_psnr.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"","code":"x1 <- random_normal(c(2, 4, 4, 3)) x2 <- random_normal(c(2, 4, 4, 3)) max_val <- 1.0 op_psnr(x1, x2, max_val) ## tf.Tensor(-3.433579, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_qr.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the QR decomposition of a tensor. — op_qr","title":"Computes the QR decomposition of a tensor. — op_qr","text":"Computes QR decomposition tensor.","code":""},{"path":"https://keras.posit.co/reference/op_qr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the QR decomposition of a tensor. — op_qr","text":"","code":"op_qr(x, mode = \"reduced\")"},{"path":"https://keras.posit.co/reference/op_qr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the QR decomposition of a tensor. — op_qr","text":"x Input tensor shape (..., M, N). mode string specifying mode QR decomposition. 'reduced': Returns reduced QR decomposition. (default) 'complete': Returns complete QR decomposition.","code":""},{"path":"https://keras.posit.co/reference/op_qr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the QR decomposition of a tensor. — op_qr","text":"list containing two tensors. first tensor shape (..., M, K) orthogonal matrix q second tensor shape (..., K, N)upper triangular matrixr, K = min(M, N)`.","code":""},{"path":"https://keras.posit.co/reference/op_qr.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the QR decomposition of a tensor. — op_qr","text":"","code":"x <- op_convert_to_tensor(rbind(c(1, 2), c(3, 4), c(5, 6))) op_qr(x) ## $q ## tf.Tensor( ## [[-0.16903085  0.89708523] ##  [-0.50709255  0.27602622] ##  [-0.84515425 -0.34503278]], shape=(3, 2), dtype=float64) ## ## $r ## tf.Tensor( ## [[-5.91607978 -7.43735744] ##  [ 0.          0.82807867]], shape=(2, 2), dtype=float64) c(q, r) %<-% op_qr(x)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","title":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","text":"Compute q-th quantile(s) data along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","text":"","code":"op_quantile(x, q, axis = NULL, method = \"linear\", keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","text":"x Input tensor. q Probability sequence probabilities quantiles compute. Values must 0 1 inclusive. axis Axis axes along quantiles computed. Defaults axis=NULL compute quantile(s) along flattened version array. method string specifies method use estimating quantile. Available methods \"linear\", \"lower\", \"higher\", \"midpoint\", \"nearest\". Defaults \"linear\". desired quantile lies two data points < j: \"linear\": + (j - ) * fraction, fraction fractional part index surrounded j. \"lower\": . \"higher\": j. \"midpoint\": (+ j) / 2 \"nearest\": j, whichever nearest. keepdims set TRUE, axes reduce left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","text":"quantile(s). q single probability axis=NULL, result scalar. multiple probabilies levels given, first axis result corresponds quantiles. axes axes remain reduction x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_ravel.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a contiguous flattened tensor. — op_ravel","title":"Return a contiguous flattened tensor. — op_ravel","text":"1-D tensor, containing elements input, returned.","code":""},{"path":"https://keras.posit.co/reference/op_ravel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a contiguous flattened tensor. — op_ravel","text":"","code":"op_ravel(x)"},{"path":"https://keras.posit.co/reference/op_ravel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a contiguous flattened tensor. — op_ravel","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ravel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a contiguous flattened tensor. — op_ravel","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_real.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the real part of the complex argument. — op_real","title":"Return the real part of the complex argument. — op_real","text":"Return real part complex argument.","code":""},{"path":"https://keras.posit.co/reference/op_real.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the real part of the complex argument. — op_real","text":"","code":"op_real(x)"},{"path":"https://keras.posit.co/reference/op_real.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the real part of the complex argument. — op_real","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_real.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the real part of the complex argument. — op_real","text":"real component complex argument.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_reciprocal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the reciprocal of the argument, element-wise. — op_reciprocal","title":"Return the reciprocal of the argument, element-wise. — op_reciprocal","text":"Calculates 1/x.","code":""},{"path":"https://keras.posit.co/reference/op_reciprocal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the reciprocal of the argument, element-wise. — op_reciprocal","text":"","code":"op_reciprocal(x)"},{"path":"https://keras.posit.co/reference/op_reciprocal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the reciprocal of the argument, element-wise. — op_reciprocal","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_reciprocal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the reciprocal of the argument, element-wise. — op_reciprocal","text":"Output tensor, element-wise reciprocal x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Rectified linear unit activation function. — op_relu","title":"Rectified linear unit activation function. — op_relu","text":"defined f(x) = max(0, x).","code":""},{"path":"https://keras.posit.co/reference/op_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rectified linear unit activation function. — op_relu","text":"","code":"op_relu(x)"},{"path":"https://keras.posit.co/reference/op_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rectified linear unit activation function. — op_relu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rectified linear unit activation function. — op_relu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rectified linear unit activation function. — op_relu","text":"","code":"x1 <- op_convert_to_tensor(c(-1, 0, 1, 0.2)) op_relu(x1) ## tf.Tensor([0.  0.  1.  0.2], shape=(4), dtype=float32) x <- seq(-10, 10, .1) plot(x, op_relu(x))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_relu6.html","id":null,"dir":"Reference","previous_headings":"","what":"Rectified linear unit activation function with upper bound of 6. — op_relu6","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"defined f(x) = op_clip(x, 0, 6).","code":""},{"path":"https://keras.posit.co/reference/op_relu6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"","code":"op_relu6(x)"},{"path":"https://keras.posit.co/reference/op_relu6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_relu6.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_relu6.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"","code":"x <- op_convert_to_tensor(c(-3, -2, 0.1, 0.2, 6, 8)) op_relu6(x) ## tf.Tensor([0.  0.  0.1 0.2 6.  6. ], shape=(6), dtype=float32) x <- seq(-10, 10, .1) plot(x, op_relu6(x))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_repeat.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeat each element of a tensor after themselves. — op_repeat","title":"Repeat each element of a tensor after themselves. — op_repeat","text":"Repeat element tensor .","code":""},{"path":"https://keras.posit.co/reference/op_repeat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeat each element of a tensor after themselves. — op_repeat","text":"","code":"op_repeat(x, repeats, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_repeat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeat each element of a tensor after themselves. — op_repeat","text":"x Input tensor. repeats number repetitions element. axis axis along repeat values. default, use flattened input array, return flat output array.","code":""},{"path":"https://keras.posit.co/reference/op_repeat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Repeat each element of a tensor after themselves. — op_repeat","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_reshape.html","id":null,"dir":"Reference","previous_headings":"","what":"Gives a new shape to a tensor without changing its data. — op_reshape","title":"Gives a new shape to a tensor without changing its data. — op_reshape","text":"Gives new shape tensor without changing data.","code":""},{"path":"https://keras.posit.co/reference/op_reshape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gives a new shape to a tensor without changing its data. — op_reshape","text":"","code":"op_reshape(x, newshape)"},{"path":"https://keras.posit.co/reference/op_reshape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gives a new shape to a tensor without changing its data. — op_reshape","text":"x Input tensor. newshape new shape compatible original shape. One shape dimension can -1 case value inferred length array remaining dimensions.","code":""},{"path":"https://keras.posit.co/reference/op_reshape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gives a new shape to a tensor without changing its data. — op_reshape","text":"reshaped tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_rfft.html","id":null,"dir":"Reference","previous_headings":"","what":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"Computes 1D Discrete Fourier Transform real-valued signal inner-dimension input. Since Discrete Fourier Transform real-valued signal Hermitian-symmetric, RFFT returns fft_length / 2 + 1 unique components FFT: zero-frequency term, followed fft_length / 2 positive-frequency terms. Along axis RFFT computed , fft_length smaller corresponding dimension input, dimension cropped. larger, dimension padded zeros.","code":""},{"path":"https://keras.posit.co/reference/op_rfft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"","code":"op_rfft(x, fft_length = NULL)"},{"path":"https://keras.posit.co/reference/op_rfft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"x Input tensor. fft_length integer representing number fft length. specified, inferred length last axis x. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_rfft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"list containing two tensors - real imaginary parts output.","code":""},{"path":"https://keras.posit.co/reference/op_rfft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"","code":"x <- op_convert_to_tensor(c(0, 1, 2, 3, 4)) op_rfft(x) ## [[1]] ## tf.Tensor([10.  -2.5 -2.5], shape=(3), dtype=float32) ## ## [[2]] ## tf.Tensor([0.        3.440955  0.8122992], shape=(3), dtype=float32) op_rfft(x, 3) ## [[1]] ## tf.Tensor([ 3.  -1.5], shape=(2), dtype=float32) ## ## [[2]] ## tf.Tensor([0.        0.8660254], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_roll.html","id":null,"dir":"Reference","previous_headings":"","what":"Roll tensor elements along a given axis. — op_roll","title":"Roll tensor elements along a given axis. — op_roll","text":"Elements roll beyond last position re-introduced first.","code":""},{"path":"https://keras.posit.co/reference/op_roll.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Roll tensor elements along a given axis. — op_roll","text":"","code":"op_roll(x, shift, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_roll.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Roll tensor elements along a given axis. — op_roll","text":"x Input tensor. shift number places elements shifted. axis axis along elements shifted. default, array flattened shifting, original shape restored.","code":""},{"path":"https://keras.posit.co/reference/op_roll.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Roll tensor elements along a given axis. — op_roll","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_round.html","id":null,"dir":"Reference","previous_headings":"","what":"Evenly round to the given number of decimals. — op_round","title":"Evenly round to the given number of decimals. — op_round","text":"Evenly round given number decimals.","code":""},{"path":"https://keras.posit.co/reference/op_round.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evenly round to the given number of decimals. — op_round","text":"","code":"op_round(x, decimals = 0L)"},{"path":"https://keras.posit.co/reference/op_round.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evenly round to the given number of decimals. — op_round","text":"x Input tensor. decimals Number decimal places round . Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/op_round.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evenly round to the given number of decimals. — op_round","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes reciprocal of square root of x element-wise. — op_rsqrt","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"Computes reciprocal square root x element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"","code":"op_rsqrt(x)"},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"x input tensor","code":""},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"tensor dtype x.","code":""},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"","code":"x <- op_convert_to_tensor(c(1, 10, 100)) op_rsqrt(x) ## tf.Tensor([1.         0.31622776 0.1       ], shape=(3), dtype=float32) # array([1, 0.31622776, 0.1], dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_scatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a tensor of shape shape where indices are set to values. — op_scatter","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"high level, operation zeros[indices] = updates returns output. equivalent :","code":"output <- op_scatter_update(op_zeros(shape), indices, values)"},{"path":"https://keras.posit.co/reference/op_scatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"","code":"op_scatter(indices, values, shape)"},{"path":"https://keras.posit.co/reference/op_scatter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"indices tensor list specifying indices values values. values tensor, values set indices. shape Shape output tensor.","code":""},{"path":"https://keras.posit.co/reference/op_scatter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"tensor shape shape indices set values.","code":""},{"path":"https://keras.posit.co/reference/op_scatter.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"","code":"indices <- rbind(c(1, 2), c(2, 2)) values <- op_array(c(1, 1)) op_scatter(indices, values, shape= c(2, 2)) ## tf.Tensor( ## [[0. 1.] ##  [0. 1.]], shape=(2, 2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_scatter_update.html","id":null,"dir":"Reference","previous_headings":"","what":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","title":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","text":"high level, operation inputs[indices] <- updates. Assume inputs tensor shape (D1, D2, ..., Dn), 2 main usages scatter_update. indices 2D tensor shape (num_updates, n), num_updates number updates perform, updates 1D tensor shape (num_updates). example, inputs op_zeros(c(4, 4, 4)), want update inputs[2, 3, 4] inputs[1, 2, 4] 1, can use:     2 indices 2D tensor shape (num_updates, k), num_updates number updates perform, k (k <= n) size index indices. updates n - k-D tensor shape (num_updates, shape(inputs)[-(1:k)]). example, inputs <- op_zeros(c(4, 4, 4)), want update inputs[1, 2, ] inputs[2, 3, ] [1, 1, 1, 1], indices shape (num_updates, 2) (k = 2), updates shape (num_updates, 4) (shape(inputs)[3:4] == 4). See code :","code":"inputs <- op_zeros(c(4, 4, 4)) indices <- rbind(c(2, 3, 4),                  c(1, 2, 4)) updates <- op_array(c(1, 1), \"float32\") op_scatter_update(inputs, indices, updates) ## tf.Tensor( ## [[[0. 0. 0. 0.] ##   [0. 0. 0. 1.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 1.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]]], shape=(4, 4, 4), dtype=float32) inputs <- op_zeros(c(4, 4, 4)) indices <- rbind(c(2, 3),                  c(3, 4)) updates <- op_array(rbind(c(1, 1, 1, 1),                           c(1, 1, 1, 1)),                     \"float32\") op_scatter_update(inputs, indices, updates) ## tf.Tensor( ## [[[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [1. 1. 1. 1.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [1. 1. 1. 1.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]]], shape=(4, 4, 4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_scatter_update.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","text":"","code":"op_scatter_update(inputs, indices, updates)"},{"path":"https://keras.posit.co/reference/op_scatter_update.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","text":"inputs tensor, tensor updated. indices tensor list shape (N, inputs$ndim), specifying indices update. N number indices update, must equal first dimension updates. updates tensor, new values put inputs indices.","code":""},{"path":"https://keras.posit.co/reference/op_scatter_update.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","text":"tensor, shape dtype inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the max of segments in a tensor. — op_segment_max","title":"Computes the max of segments in a tensor. — op_segment_max","text":"Computes max segments tensor.","code":""},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the max of segments in a tensor. — op_segment_max","text":"","code":"op_segment_max(data, segment_ids, num_segments = NULL, sorted = FALSE)"},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the max of segments in a tensor. — op_segment_max","text":"data Input tensor. segment_ids 1-D tensor containing segment indices element data. num_segments integer representing total number segments. specified, inferred maximum value segment_ids. sorted boolean indicating whether segment_ids sorted. Defaults toFALSE.","code":""},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the max of segments in a tensor. — op_segment_max","text":"tensor containing max segments, element represents max corresponding segment data.","code":""},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the max of segments in a tensor. — op_segment_max","text":"","code":"data <- op_convert_to_tensor(c(1, 2, 10, 20, 100, 200)) segment_ids <- op_array(c(1, 1, 2, 2, 3, 3), \"int32\") num_segments <- 3 op_segment_max(data, segment_ids, num_segments) ## tf.Tensor([  2.  20. 200.], shape=(3), dtype=float32) # array([2, 20, 200], dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the sum of segments in a tensor. — op_segment_sum","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"Computes sum segments tensor.","code":""},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"","code":"op_segment_sum(data, segment_ids, num_segments = NULL, sorted = FALSE)"},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"data Input tensor. segment_ids 1-D tensor containing segment indices element data. num_segments integer representing total number segments. specified, inferred maximum value segment_ids. sorted boolean indicating whether segment_ids sorted. Defaults toFALSE.","code":""},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"tensor containing sum segments, element represents sum corresponding segment data.","code":""},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"","code":"data <- op_array(c(1, 2, 10, 20, 100, 200)) segment_ids <- op_array(c(1, 1, 2, 2, 3, 3), \"int32\") num_segments <- 3 op_segment_sum(data, segment_ids, num_segments) ## tf.Tensor([  3.  30. 300.], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Return elements from choicelist, based on conditions in condlist. — op_select","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"Return elements choicelist, based conditions condlist.","code":""},{"path":"https://keras.posit.co/reference/op_select.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"","code":"op_select(condlist, choicelist, default = 0L)"},{"path":"https://keras.posit.co/reference/op_select.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"condlist List boolean tensors. list conditions determine array choicelist output elements taken. multiple conditions satisfied, first one encountered condlist used. choicelist List tensors. list tensors output elements taken. list length condlist. default Optional scalar value. element inserted output conditions evaluate FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_select.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"Tensor output position m m-th element tensor choicelist m-th element corresponding tensor condlist TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_select.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"","code":"x <- op_arange(6L) condlist <- list(x < 3, x > 3) choicelist <- list(x, x^2) op_select(condlist, choicelist, 42) ## tf.Tensor([ 0  1  2 42 16 25], shape=(6), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"defined : f(x) =  scale * alpha * (exp(x) - 1.) x < 0, f(x) = scale * x x >= 0.","code":""},{"path":"https://keras.posit.co/reference/op_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"","code":"op_selu(x)"},{"path":"https://keras.posit.co/reference/op_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_selu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_selu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"","code":"x <- op_array(c(-1, 0, 1)) op_selu(x) ## tf.Tensor([-1.1113307  0.         1.050701 ], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_separable_conv.html","id":null,"dir":"Reference","previous_headings":"","what":"General N-D separable convolution. — op_separable_conv","title":"General N-D separable convolution. — op_separable_conv","text":"ops supports 1D 2D separable convolution. separable_conv depthwise conv followed pointwise conv.","code":""},{"path":"https://keras.posit.co/reference/op_separable_conv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General N-D separable convolution. — op_separable_conv","text":"","code":"op_separable_conv(   inputs,   depthwise_kernel,   pointwise_kernel,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L )"},{"path":"https://keras.posit.co/reference/op_separable_conv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General N-D separable convolution. — op_separable_conv","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format=\"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format=\"channels_first\". depthwise_kernel Tensor rank N+2. depthwise_kernel shape [kernel_spatial_shape, num_input_channels, num_channels_multiplier], num_input_channels match number channels inputs. pointwise_kernel Tensor rank N+2. pointwise_kernel shape (*ones_like(kernel_spatial_shape), num_input_channels * num_channels_multiplier, num_output_channels). strides int int tuple/list len(inputs_spatial_shape), specifying strides convolution along spatial dimension. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides=1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format=\"channels_last\", inputs shape (batch_size, ..., channels) data_format=\"channels_first\", inputs shape (batch_size, channels, ...). dilation_rate int int tuple/list len(inputs_spatial_shape), specifying dilation rate use dilated convolution. dilation_rate int, every spatial dimension shares dilation_rate.","code":""},{"path":"https://keras.posit.co/reference/op_separable_conv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General N-D separable convolution. — op_separable_conv","text":"tensor rank N+2, result depthwise conv operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_shape.html","id":null,"dir":"Reference","previous_headings":"","what":"Gets the shape of the tensor input. — op_shape","title":"Gets the shape of the tensor input. — op_shape","text":"Gets shape tensor input.","code":""},{"path":"https://keras.posit.co/reference/op_shape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gets the shape of the tensor input. — op_shape","text":"","code":"op_shape(x)"},{"path":"https://keras.posit.co/reference/op_shape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gets the shape of the tensor input. — op_shape","text":"x tensor. function try access shape attribute input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_shape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gets the shape of the tensor input. — op_shape","text":"list integers NULL values, indicating shape input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_shape.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Gets the shape of the tensor input. — op_shape","text":"TensorFlow backend, x tf.Tensor dynamic shape, dimensions dynamic context compiled function tf.Tensor value instead static integer value.","code":""},{"path":"https://keras.posit.co/reference/op_shape.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gets the shape of the tensor input. — op_shape","text":"","code":"x <- op_zeros(c(8, 12)) op_shape(x) ## shape(8, 12)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid activation function. — op_sigmoid","title":"Sigmoid activation function. — op_sigmoid","text":"defined f(x) = 1 / (1 + exp(-x)).","code":""},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid activation function. — op_sigmoid","text":"","code":"op_sigmoid(x)"},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid activation function. — op_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sigmoid activation function. — op_sigmoid","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sigmoid activation function. — op_sigmoid","text":"","code":"x <- op_convert_to_tensor(c(-6, 1, 0, 1, 6)) op_sigmoid(x) ## tf.Tensor([0.00247262 0.7310586  0.5        0.7310586  0.99752736], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_sign.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a tensor with the signs of the elements of x. — op_sign","title":"Returns a tensor with the signs of the elements of x. — op_sign","text":"Returns tensor signs elements x.","code":""},{"path":"https://keras.posit.co/reference/op_sign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a tensor with the signs of the elements of x. — op_sign","text":"","code":"op_sign(x)"},{"path":"https://keras.posit.co/reference/op_sign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a tensor with the signs of the elements of x. — op_sign","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a tensor with the signs of the elements of x. — op_sign","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"SiLU activation function computed sigmoid function multiplied input. defined f(x) = x * sigmoid(x).","code":""},{"path":"https://keras.posit.co/reference/op_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"","code":"op_silu(x)"},{"path":"https://keras.posit.co/reference/op_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_silu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_silu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"","code":"x <- op_convert_to_tensor(c(-6, 1, 0, 1, 6)) op_sigmoid(x) ## tf.Tensor([0.00247262 0.7310586  0.5        0.7310586  0.99752736], shape=(5), dtype=float32) op_silu(x) ## tf.Tensor([-0.01483574  0.7310586   0.          0.7310586   5.985164  ], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_sin.html","id":null,"dir":"Reference","previous_headings":"","what":"Trigonometric sine, element-wise. — op_sin","title":"Trigonometric sine, element-wise. — op_sin","text":"Trigonometric sine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_sin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trigonometric sine, element-wise. — op_sin","text":"","code":"op_sin(x)"},{"path":"https://keras.posit.co/reference/op_sin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trigonometric sine, element-wise. — op_sin","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trigonometric sine, element-wise. — op_sin","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hyperbolic sine, element-wise. — op_sinh","title":"Hyperbolic sine, element-wise. — op_sinh","text":"Hyperbolic sine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_sinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hyperbolic sine, element-wise. — op_sinh","text":"","code":"op_sinh(x)"},{"path":"https://keras.posit.co/reference/op_sinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hyperbolic sine, element-wise. — op_sinh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sinh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hyperbolic sine, element-wise. — op_sinh","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_size.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the number of elements in a tensor. — op_size","title":"Return the number of elements in a tensor. — op_size","text":"Return number elements tensor.","code":""},{"path":"https://keras.posit.co/reference/op_size.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the number of elements in a tensor. — op_size","text":"","code":"op_size(x)"},{"path":"https://keras.posit.co/reference/op_size.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the number of elements in a tensor. — op_size","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_size.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the number of elements in a tensor. — op_size","text":"Number elements x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a slice of an input tensor. — op_slice","title":"Return a slice of an input tensor. — op_slice","text":"high level, operation explicit replacement array slicing e.g. inputs[start_indices:(start_indices + shape)]. Unlike slicing via brackets, operation accept tensor start indices backends, useful indices dynamically computed via tensor operations.","code":"(inputs <- op_arange(5*5) |> op_reshape(c(5, 5))) ## tf.Tensor( ## [[ 0.  1.  2.  3.  4.] ##  [ 5.  6.  7.  8.  9.] ##  [10. 11. 12. 13. 14.] ##  [15. 16. 17. 18. 19.] ##  [20. 21. 22. 23. 24.]], shape=(5, 5), dtype=float32) start_indices <- c(3, 3) shape <- c(2, 2) op_slice(inputs, start_indices, shape) ## tf.Tensor( ## [[12. 13.] ##  [17. 18.]], shape=(2, 2), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a slice of an input tensor. — op_slice","text":"","code":"op_slice(inputs, start_indices, shape)"},{"path":"https://keras.posit.co/reference/op_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a slice of an input tensor. — op_slice","text":"inputs tensor, tensor sliced. start_indices list length inputs$ndim, specifying starting indices updating. shape full shape returned slice.","code":""},{"path":"https://keras.posit.co/reference/op_slice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a slice of an input tensor. — op_slice","text":"tensor, shape dtype inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_slice_update.html","id":null,"dir":"Reference","previous_headings":"","what":"Update an input by slicing in a tensor of updated values. — op_slice_update","title":"Update an input by slicing in a tensor of updated values. — op_slice_update","text":"high level, operation inputs[start_indices: start_indices + updates.shape] = updates. Assume inputs tensor shape (D1, D2, ..., Dn), start_indices must list n integers, specifying starting indices. updates must rank inputs, size dim must exceed Di - start_indices[]. example, 2D inputs inputs = op_zeros(c(5, 5)), want update intersection last 2 rows last 2 columns 1, .e., inputs[4:5, 4:5] = op_ones(c(2, 2)), can use code :","code":"inputs <- op_zeros(c(5, 5)) start_indices <- c(3, 3) updates <- op_ones(c(2, 2)) op_slice_update(inputs, start_indices, updates) ## tf.Tensor( ## [[0. 0. 0. 0. 0.] ##  [0. 0. 0. 0. 0.] ##  [0. 0. 1. 1. 0.] ##  [0. 0. 1. 1. 0.] ##  [0. 0. 0. 0. 0.]], shape=(5, 5), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_slice_update.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update an input by slicing in a tensor of updated values. — op_slice_update","text":"","code":"op_slice_update(inputs, start_indices, updates)"},{"path":"https://keras.posit.co/reference/op_slice_update.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update an input by slicing in a tensor of updated values. — op_slice_update","text":"inputs tensor, tensor updated. start_indices list length inputs$ndim, specifying starting indices updating. updates tensor, new values put inputs indices. updates must rank inputs.","code":""},{"path":"https://keras.posit.co/reference/op_slice_update.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update an input by slicing in a tensor of updated values. — op_slice_update","text":"tensor, shape dtype inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_slogdet.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","title":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","text":"Compute sign natural logarithm determinant matrix.","code":""},{"path":"https://keras.posit.co/reference/op_slogdet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","text":"","code":"op_slogdet(x)"},{"path":"https://keras.posit.co/reference/op_slogdet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","text":"x Input matrix. must 2D square.","code":""},{"path":"https://keras.posit.co/reference/op_slogdet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","text":"list: (sign, logabsdet). sign number representing sign determinant. real matrix, 1, 0, -1. complex matrix, complex number absolute value 1 (.e., unit circle), else 0. logabsdet natural log absolute value determinant.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax activation function. — op_softmax","title":"Softmax activation function. — op_softmax","text":"elements output vector lie within range (0, 1), total sum exactly 1 (excluding floating point rounding error). vector processed independently. axis argument specifies axis along function applied within input. defined : f(x) = exp(x) / sum(exp(x))","code":""},{"path":"https://keras.posit.co/reference/op_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax activation function. — op_softmax","text":"","code":"op_softmax(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax activation function. — op_softmax","text":"x Input tensor. axis Integer, axis along softmax applied.","code":""},{"path":"https://keras.posit.co/reference/op_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax activation function. — op_softmax","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_softmax.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmax activation function. — op_softmax","text":"","code":"x <- op_array(c(-1, 0, 1)) op_softmax(x) ## tf.Tensor([0.09003057 0.24472848 0.66524094], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_softplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Softplus activation function. — op_softplus","title":"Softplus activation function. — op_softplus","text":"defined f(x) = log(exp(x) + 1), log natural logarithm exp exponential function.","code":""},{"path":"https://keras.posit.co/reference/op_softplus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softplus activation function. — op_softplus","text":"","code":"op_softplus(x)"},{"path":"https://keras.posit.co/reference/op_softplus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softplus activation function. — op_softplus","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_softplus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softplus activation function. — op_softplus","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_softplus.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softplus activation function. — op_softplus","text":"","code":"x <- op_convert_to_tensor(c(-0.555, 0, 0.555)) op_softplus(x) ## tf.Tensor([0.45366603 0.6931472  1.008666  ], shape=(3), dtype=float32) x <- seq(-10, 10, .1) plot(x, op_softplus(x))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_softsign.html","id":null,"dir":"Reference","previous_headings":"","what":"Softsign activation function. — op_softsign","title":"Softsign activation function. — op_softsign","text":"defined f(x) = x / (abs(x) + 1).","code":""},{"path":"https://keras.posit.co/reference/op_softsign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softsign activation function. — op_softsign","text":"","code":"op_softsign(x)"},{"path":"https://keras.posit.co/reference/op_softsign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softsign activation function. — op_softsign","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_softsign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softsign activation function. — op_softsign","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_softsign.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softsign activation function. — op_softsign","text":"","code":"x <- op_convert_to_tensor(c(-0.100, -10.0, 1.0, 0.0, 100.0)) op_softsign(x) ## tf.Tensor([-0.09090909 -0.90909094  0.5         0.          0.990099  ], shape=(5), dtype=float32) x <- seq(-10, 10, .1) plot(x, op_softsign(x), ylim = c(-1, 1))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Solves a linear system of equations given by a x = b. — op_solve","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"Solves x equation %*% x == b.","code":""},{"path":"https://keras.posit.co/reference/op_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"","code":"op_solve(a, b)"},{"path":"https://keras.posit.co/reference/op_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"tensor shape (..., M, M) representing coefficients matrix. b tensor shape (..., M) (..., M, N) represeting right-hand side \"dependent variable\" matrix.","code":""},{"path":"https://keras.posit.co/reference/op_solve.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"tensor shape (..., M) (..., M, N) representing solution linear system. Returned shape identical b.","code":""},{"path":"https://keras.posit.co/reference/op_solve.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"","code":"a <- op_array(c(1, 2, 4, 5), dtype=\"float32\") |> op_reshape(c(2, 2)) b <- op_array(c(2, 4, 8, 10), dtype=\"float32\") |> op_reshape(c(2, 2)) op_solve(a, b) ## tf.Tensor( ## [[2. 0.] ##  [0. 2.]], shape=(2, 2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_solve_triangular.html","id":null,"dir":"Reference","previous_headings":"","what":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","title":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","text":"Solves linear system equations given %*% x = b.","code":""},{"path":"https://keras.posit.co/reference/op_solve_triangular.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","text":"","code":"op_solve_triangular(a, b, lower = FALSE)"},{"path":"https://keras.posit.co/reference/op_solve_triangular.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","text":"tensor shape (..., M, M) representing coefficients matrix. b tensor shape (..., M) (..., M, N) represeting right-hand side \"dependent variable\" matrix. lower logical. Use data contained lower triangle . Default use upper triangle.","code":""},{"path":"https://keras.posit.co/reference/op_solve_triangular.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","text":"tensor shape (..., M) (..., M, N) representing solution linear system. Returned shape identical b.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sort.html","id":null,"dir":"Reference","previous_headings":"","what":"Sorts the elements of x along a given axis in ascending order. — op_sort","title":"Sorts the elements of x along a given axis in ascending order. — op_sort","text":"Sorts elements x along given axis ascending order.","code":""},{"path":"https://keras.posit.co/reference/op_sort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sorts the elements of x along a given axis in ascending order. — op_sort","text":"","code":"op_sort(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_sort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sorts the elements of x along a given axis in ascending order. — op_sort","text":"x Input tensor. axis Axis along sort. NULL, tensor flattened sorting. Defaults -1; last axis.","code":""},{"path":"https://keras.posit.co/reference/op_sort.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sorts the elements of x along a given axis in ascending order. — op_sort","text":"Sorted tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"sparse categorical cross-entropy loss similar categorical cross-entropy, used target tensor contains integer class labels instead one-hot encoded vectors. measures dissimilarity target output probabilities logits.","code":""},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"","code":"op_sparse_categorical_crossentropy(   target,   output,   from_logits = FALSE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"target target tensor representing true class labels integers. shape match shape output tensor except last dimension. output output tensor representing predicted probabilities logits. shape match shape target tensor except last dimension. from_logits (optional) Whether output tensor logits probabilities. Set TRUE output represents logits; otherwise, set FALSE output represents probabilities. Defaults toFALSE. axis (optional) axis along sparse categorical cross-entropy computed. Defaults -1, corresponds last dimension tensors.","code":""},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"Integer tensor: computed sparse categorical cross-entropy loss target output.","code":""},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"","code":"target <- op_array(c(0, 1, 2), dtype=\"int32\") output <- op_array(rbind(c(0.9, 0.05, 0.05),                         c(0.1, 0.8,  0.1),                         c(0.2, 0.3,  0.5))) op_sparse_categorical_crossentropy(target, output) ## tf.Tensor([0.10536052 0.22314355 0.69314718], shape=(3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Split a tensor into chunks. — op_split","title":"Split a tensor into chunks. — op_split","text":"Split tensor chunks.","code":""},{"path":"https://keras.posit.co/reference/op_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split a tensor into chunks. — op_split","text":"","code":"op_split(x, indices_or_sections, axis = 1L)"},{"path":"https://keras.posit.co/reference/op_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split a tensor into chunks. — op_split","text":"x Input tensor. indices_or_sections integer, N, tensor split N equal sections along axis. 1-D array sorted integers, entries indicate indices tensor split along axis. axis Axis along split. Defaults 1, first axis.","code":""},{"path":"https://keras.posit.co/reference/op_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split a tensor into chunks. — op_split","text":"list tensors.","code":""},{"path":"https://keras.posit.co/reference/op_split.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Split a tensor into chunks. — op_split","text":"split result equal division using Torch backend.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sqrt.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","title":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","text":"Return non-negative square root tensor, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_sqrt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","text":"","code":"op_sqrt(x)"},{"path":"https://keras.posit.co/reference/op_sqrt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sqrt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","text":"Output tensor, non-negative square root x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_square.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the element-wise square of the input. — op_square","title":"Return the element-wise square of the input. — op_square","text":"Return element-wise square input.","code":""},{"path":"https://keras.posit.co/reference/op_square.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the element-wise square of the input. — op_square","text":"","code":"op_square(x)"},{"path":"https://keras.posit.co/reference/op_square.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the element-wise square of the input. — op_square","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_square.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the element-wise square of the input. — op_square","text":"Output tensor, square x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_squeeze.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove axes of length one from x. — op_squeeze","title":"Remove axes of length one from x. — op_squeeze","text":"Remove axes length one x.","code":""},{"path":"https://keras.posit.co/reference/op_squeeze.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove axes of length one from x. — op_squeeze","text":"","code":"op_squeeze(x, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_squeeze.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove axes of length one from x. — op_squeeze","text":"x Input tensor. axis Select subset entries length one shape.","code":""},{"path":"https://keras.posit.co/reference/op_squeeze.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove axes of length one from x. — op_squeeze","text":"input tensor subset dimensions length 1 removed.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Join a sequence of tensors along a new axis. — op_stack","title":"Join a sequence of tensors along a new axis. — op_stack","text":"axis parameter specifies index new axis dimensions result.","code":""},{"path":"https://keras.posit.co/reference/op_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join a sequence of tensors along a new axis. — op_stack","text":"","code":"op_stack(x, axis = 1L)"},{"path":"https://keras.posit.co/reference/op_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join a sequence of tensors along a new axis. — op_stack","text":"x sequence tensors. axis Axis along stack. Defaults 1, first axis.","code":""},{"path":"https://keras.posit.co/reference/op_stack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join a sequence of tensors along a new axis. — op_stack","text":"stacked tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_std.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the standard deviation along the specified axis. — op_std","title":"Compute the standard deviation along the specified axis. — op_std","text":"Compute standard deviation along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_std.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the standard deviation along the specified axis. — op_std","text":"","code":"op_std(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_std.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the standard deviation along the specified axis. — op_std","text":"x Input tensor. axis Axis along compute standard deviation. Default compute standard deviation flattened tensor. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_std.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the standard deviation along the specified axis. — op_std","text":"Output tensor containing standard deviation values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_stft.html","id":null,"dir":"Reference","previous_headings":"","what":"Short-Time Fourier Transform along the last axis of the input. — op_stft","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"STFT computes Fourier transform short overlapping windows input. giving frequency components signal change time.","code":""},{"path":"https://keras.posit.co/reference/op_stft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"","code":"op_stft(   x,   sequence_length,   sequence_stride,   fft_length,   window = \"hann\",   center = TRUE )"},{"path":"https://keras.posit.co/reference/op_stft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"x Input tensor. sequence_length integer representing sequence length. sequence_stride integer representing sequence hop size. fft_length integer representing size FFT apply. specified, uses smallest power 2 enclosing sequence_length. window string, tensor window NULL. window string, available values \"hann\" \"hamming\". window tensor, used directly window length must sequence_length. window NULL, windowing used. Defaults \"hann\". center Whether pad x sides t-th sequence centered time t * sequence_stride. Otherwise, t-th sequence begins time t * sequence_stride. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_stft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"list containing two tensors - real imaginary parts STFT output.","code":""},{"path":"https://keras.posit.co/reference/op_stft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"","code":"x <- op_array(c(0, 1, 2, 3, 4)) op_stft(x, 3, 2, 3) ## [[1]] ## tf.Tensor( ## [[ 0.  0.] ##  [ 2. -1.] ##  [ 4. -2.]], shape=(3, 2), dtype=float32) ## ## [[2]] ## tf.Tensor( ## [[ 0.         0.       ] ##  [ 0.        -1.7320508] ##  [ 0.        -3.4641016]], shape=(3, 2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":null,"dir":"Reference","previous_headings":"","what":"Stops gradient computation. — op_stop_gradient","title":"Stops gradient computation. — op_stop_gradient","text":"Stops gradient computation.","code":""},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stops gradient computation. — op_stop_gradient","text":"","code":"op_stop_gradient(variable)"},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stops gradient computation. — op_stop_gradient","text":"variable tensor variable gradient computation disabled.","code":""},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stops gradient computation. — op_stop_gradient","text":"variable gradient computation disabled.","code":""},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stops gradient computation. — op_stop_gradient","text":"","code":"var <- op_convert_to_tensor(c(1, 2, 3), dtype=\"float32\") var <- op_stop_gradient(var)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_subtract.html","id":null,"dir":"Reference","previous_headings":"","what":"Subtract arguments element-wise. — op_subtract","title":"Subtract arguments element-wise. — op_subtract","text":"Note function automatically called using R operator - tensor.","code":"x <- op_ones(c(3)) op_subtract(x, x) ## tf.Tensor([0. 0. 0.], shape=(3), dtype=float32) x - x ## tf.Tensor([0. 0. 0.], shape=(3), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_subtract.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subtract arguments element-wise. — op_subtract","text":"","code":"op_subtract(x1, x2)"},{"path":"https://keras.posit.co/reference/op_subtract.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subtract arguments element-wise. — op_subtract","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_subtract.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subtract arguments element-wise. — op_subtract","text":"Output tensor, element-wise difference x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Sum of a tensor over the given axes. — op_sum","title":"Sum of a tensor over the given axes. — op_sum","text":"Sum tensor given axes.","code":""},{"path":"https://keras.posit.co/reference/op_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sum of a tensor over the given axes. — op_sum","text":"","code":"op_sum(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sum of a tensor over the given axes. — op_sum","text":"x Input tensor. axis Axis axes along sum computed. default compute sum flattened tensor. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_sum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sum of a tensor over the given axes. — op_sum","text":"Output tensor containing sum.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_svd.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the singular value decomposition of a matrix. — op_svd","title":"Computes the singular value decomposition of a matrix. — op_svd","text":"Computes singular value decomposition matrix.","code":""},{"path":"https://keras.posit.co/reference/op_svd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the singular value decomposition of a matrix. — op_svd","text":"","code":"op_svd(x, full_matrices = TRUE, compute_uv = TRUE)"},{"path":"https://keras.posit.co/reference/op_svd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the singular value decomposition of a matrix. — op_svd","text":"x Input tensor shape (..., M, N). full_matrices Logical compute_uv Logical","code":""},{"path":"https://keras.posit.co/reference/op_svd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the singular value decomposition of a matrix. — op_svd","text":"list three tensors: tensor shape (..., M, M) containing left singular vectors, tensor shape (..., M, N) containing singular values tensor shape (..., N, N) containing right singular vectors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_swapaxes.html","id":null,"dir":"Reference","previous_headings":"","what":"Interchange two axes of a tensor. — op_swapaxes","title":"Interchange two axes of a tensor. — op_swapaxes","text":"Interchange two axes tensor.","code":""},{"path":"https://keras.posit.co/reference/op_swapaxes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interchange two axes of a tensor. — op_swapaxes","text":"","code":"op_swapaxes(x, axis1, axis2)"},{"path":"https://keras.posit.co/reference/op_swapaxes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interchange two axes of a tensor. — op_swapaxes","text":"x Input tensor. axis1 First axis. axis2 Second axis.","code":""},{"path":"https://keras.posit.co/reference/op_swapaxes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interchange two axes of a tensor. — op_swapaxes","text":"tensor axes swapped.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_take.html","id":null,"dir":"Reference","previous_headings":"","what":"Take elements from a tensor along an axis. — op_take","title":"Take elements from a tensor along an axis. — op_take","text":"Take elements tensor along axis.","code":""},{"path":"https://keras.posit.co/reference/op_take.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Take elements from a tensor along an axis. — op_take","text":"","code":"op_take(x, indices, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_take.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Take elements from a tensor along an axis. — op_take","text":"x Source tensor. indices indices values extract. axis axis select values. default, flattened input tensor used.","code":""},{"path":"https://keras.posit.co/reference/op_take.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Take elements from a tensor along an axis. — op_take","text":"corresponding tensor values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_take_along_axis.html","id":null,"dir":"Reference","previous_headings":"","what":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","title":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","text":"Select values x 1-D indices along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_take_along_axis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","text":"","code":"op_take_along_axis(x, indices, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_take_along_axis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","text":"x Source tensor. indices indices values extract. axis axis select values. default, flattened input tensor used.","code":""},{"path":"https://keras.posit.co/reference/op_take_along_axis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","text":"corresponding tensor values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tan.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute tangent, element-wise. — op_tan","title":"Compute tangent, element-wise. — op_tan","text":"Compute tangent, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_tan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute tangent, element-wise. — op_tan","text":"","code":"op_tan(x)"},{"path":"https://keras.posit.co/reference/op_tan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute tangent, element-wise. — op_tan","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_tan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute tangent, element-wise. — op_tan","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hyperbolic tangent, element-wise. — op_tanh","title":"Hyperbolic tangent, element-wise. — op_tanh","text":"Hyperbolic tangent, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_tanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hyperbolic tangent, element-wise. — op_tanh","text":"","code":"op_tanh(x)"},{"path":"https://keras.posit.co/reference/op_tanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hyperbolic tangent, element-wise. — op_tanh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_tanh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hyperbolic tangent, element-wise. — op_tanh","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tensordot.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the tensor dot product along specified axes. — op_tensordot","title":"Compute the tensor dot product along specified axes. — op_tensordot","text":"Compute tensor dot product along specified axes.","code":""},{"path":"https://keras.posit.co/reference/op_tensordot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the tensor dot product along specified axes. — op_tensordot","text":"","code":"op_tensordot(x1, x2, axes = 3L)"},{"path":"https://keras.posit.co/reference/op_tensordot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the tensor dot product along specified axes. — op_tensordot","text":"x1 First tensor. x2 Second tensor. axes integer, N, sum last N axes x1 first N axes x2 order. sizes corresponding axes must match. , list axes summed , first sequence applying x1, second x2. sequences must length.","code":""},{"path":"https://keras.posit.co/reference/op_tensordot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the tensor dot product along specified axes. — op_tensordot","text":"tensor dot product inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tile.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeat x the number of times given by repeats. — op_tile","title":"Repeat x the number of times given by repeats. — op_tile","text":"repeats length d, result dimension max(d, x.ndim). x.ndim < d, x promoted d-dimensional prepending new axes. x.ndim > d, repeats promoted x.ndim prepending 1's .","code":""},{"path":"https://keras.posit.co/reference/op_tile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeat x the number of times given by repeats. — op_tile","text":"","code":"op_tile(x, repeats)"},{"path":"https://keras.posit.co/reference/op_tile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeat x the number of times given by repeats. — op_tile","text":"x Input tensor. repeats number repetitions x along axis.","code":""},{"path":"https://keras.posit.co/reference/op_tile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Repeat x the number of times given by repeats. — op_tile","text":"tiled output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_top_k.html","id":null,"dir":"Reference","previous_headings":"","what":"Finds the top-k values and their indices in a tensor. — op_top_k","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"Finds top-k values indices tensor.","code":""},{"path":"https://keras.posit.co/reference/op_top_k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"","code":"op_top_k(x, k, sorted = TRUE)"},{"path":"https://keras.posit.co/reference/op_top_k.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"x Input tensor. k integer representing number top elements retrieve. sorted boolean indicating whether sort output descending order. Defaults toTRUE.","code":""},{"path":"https://keras.posit.co/reference/op_top_k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"list containing two tensors. first tensor contains top-k values, second tensor contains indices top-k values input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_top_k.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"","code":"x <- op_array(c(5, 2, 7, 1, 9, 3), \"int32\") op_top_k(x, k = 3) ## $values ## tf.Tensor([9 7 5], shape=(3), dtype=int32) ## ## $indices ## tf.Tensor([4 2 0], shape=(3), dtype=int32) c(values, indices) %<-% op_top_k(x, k = 3) values ## tf.Tensor([9 7 5], shape=(3), dtype=int32) indices ## tf.Tensor([4 2 0], shape=(3), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_trace.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the sum along diagonals of the tensor. — op_trace","title":"Return the sum along diagonals of the tensor. — op_trace","text":"x 2-D, sum along diagonal given offset returned, .e., sum elements x[, +offset] . two dimensions, axes specified axis1 axis2 used determine 2-D sub-arrays whose traces returned. shape resulting tensor x axis1 axis2 removed.","code":""},{"path":"https://keras.posit.co/reference/op_trace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the sum along diagonals of the tensor. — op_trace","text":"","code":"op_trace(x, offset = 0L, axis1 = 1L, axis2 = 2L)"},{"path":"https://keras.posit.co/reference/op_trace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the sum along diagonals of the tensor. — op_trace","text":"x Input tensor. offset Offset diagonal main diagonal. Can positive negative. Defaults 0. axis1 Axis used first axis 2-D sub-arrays. Defaults 1. (first axis). axis2 Axis used second axis 2-D sub-arrays. Defaults 2. (second axis).","code":""},{"path":"https://keras.posit.co/reference/op_trace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the sum along diagonals of the tensor. — op_trace","text":"x 2-D, sum diagonal returned. x larger dimensions, tensor sums along diagonals returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a tensor with axes transposed. — op_transpose","title":"Returns a tensor with axes transposed. — op_transpose","text":"Returns tensor axes transposed.","code":""},{"path":"https://keras.posit.co/reference/op_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a tensor with axes transposed. — op_transpose","text":"","code":"op_transpose(x, axes = NULL)"},{"path":"https://keras.posit.co/reference/op_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a tensor with axes transposed. — op_transpose","text":"x Input tensor. axes Sequence integers. Permutation dimensions x. default, order axes reversed.","code":""},{"path":"https://keras.posit.co/reference/op_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a tensor with axes transposed. — op_transpose","text":"x axes permuted.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tri.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","title":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","text":"Return tensor ones diagonal zeros elsewhere.","code":""},{"path":"https://keras.posit.co/reference/op_tri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","text":"","code":"op_tri(N, M = NULL, k = 0L, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_tri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","text":"N Number rows tensor. M Number columns tensor. k sub-diagonal array filled. k = 0 main diagonal, k < 0 , k > 0 . default 0. dtype Data type returned tensor. default \"float32\".","code":""},{"path":"https://keras.posit.co/reference/op_tri.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","text":"Tensor lower triangle filled ones zeros elsewhere. T[, j] == 1 j <= + k, 0 otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tril.html","id":null,"dir":"Reference","previous_headings":"","what":"Return lower triangle of a tensor. — op_tril","title":"Return lower triangle of a tensor. — op_tril","text":"tensors ndim exceeding 2, tril apply final two axes.","code":""},{"path":"https://keras.posit.co/reference/op_tril.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return lower triangle of a tensor. — op_tril","text":"","code":"op_tril(x, k = 0L)"},{"path":"https://keras.posit.co/reference/op_tril.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return lower triangle of a tensor. — op_tril","text":"x Input tensor. k Diagonal zero elements. Defaults 0. main diagonal. k < 0 , k > 0 .","code":""},{"path":"https://keras.posit.co/reference/op_tril.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return lower triangle of a tensor. — op_tril","text":"Lower triangle x, shape data type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_triu.html","id":null,"dir":"Reference","previous_headings":"","what":"Return upper triangle of a tensor. — op_triu","title":"Return upper triangle of a tensor. — op_triu","text":"tensors ndim exceeding 2, triu apply final two axes.","code":""},{"path":"https://keras.posit.co/reference/op_triu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return upper triangle of a tensor. — op_triu","text":"","code":"op_triu(x, k = 0L)"},{"path":"https://keras.posit.co/reference/op_triu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return upper triangle of a tensor. — op_triu","text":"x Input tensor. k Diagonal zero elements. Defaults 0. main diagonal. k < 0 , k > 0 .","code":""},{"path":"https://keras.posit.co/reference/op_triu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return upper triangle of a tensor. — op_triu","text":"Upper triangle x, shape data type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_unstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"Unpacks given dimension rank-R tensor rank-(R-1) tensors.","code":""},{"path":"https://keras.posit.co/reference/op_unstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"","code":"op_unstack(x, num = NULL, axis = 1L)"},{"path":"https://keras.posit.co/reference/op_unstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"x input tensor. num length dimension axis. Automatically inferred NULL. axis axis along unpack.","code":""},{"path":"https://keras.posit.co/reference/op_unstack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"list tensors unpacked along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_unstack.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"[3, 4))]: R:3,%204))","code":"x <- op_array(rbind(c(1, 2),                    c(3, 4))) op_unstack(x, axis=1) ## [[1]] ## tf.Tensor([1. 2.], shape=(2), dtype=float64) ## ## [[2]] ## tf.Tensor([3. 4.], shape=(2), dtype=float64) op_unstack(x, axis=2) ## [[1]] ## tf.Tensor([1. 3.], shape=(2), dtype=float64) ## ## [[2]] ## tf.Tensor([2. 4.], shape=(2), dtype=float64) all.equal(op_unstack(x),           op_unstack(x, axis = 1)) ## [1] TRUE all.equal(op_unstack(x, axis = -1),           op_unstack(x, axis = 2)) ## [1] TRUE # [array([1, 2)), array([3, 4))]"},{"path":[]},{"path":"https://keras.posit.co/reference/op_var.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the variance along the specified axes. — op_var","title":"Compute the variance along the specified axes. — op_var","text":"Compute variance along specified axes.","code":""},{"path":"https://keras.posit.co/reference/op_var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the variance along the specified axes. — op_var","text":"","code":"op_var(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the variance along the specified axes. — op_var","text":"x Input tensor. axis Axis axes along variance computed. default compute variance flattened tensor. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the variance along the specified axes. — op_var","text":"Output tensor containing variance.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_vdot.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the dot product of two vectors. — op_vdot","title":"Return the dot product of two vectors. — op_vdot","text":"first argument complex, complex conjugate first argument used calculation dot product. Multidimensional tensors flattened dot product taken.","code":""},{"path":"https://keras.posit.co/reference/op_vdot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the dot product of two vectors. — op_vdot","text":"","code":"op_vdot(x1, x2)"},{"path":"https://keras.posit.co/reference/op_vdot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the dot product of two vectors. — op_vdot","text":"x1 First input tensor. complex, complex conjugate taken calculation dot product. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_vdot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the dot product of two vectors. — op_vdot","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn a function into a vectorized function. — op_vectorize","title":"Turn a function into a vectorized function. — op_vectorize","text":"Turn function vectorized function.","code":""},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn a function into a vectorized function. — op_vectorize","text":"","code":"op_vectorize(func, ..., excluded = NULL, signature = NULL)"},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Turn a function into a vectorized function. — op_vectorize","text":"func Callable single tensor argument. ... forward/backward compatability. excluded Optional set integers representing positional arguments function vectorized. passed directly func unmodified. signature Optional generalized universal function signature, e.g., \"(m,n),(n)->(m)\" vectorized matrix-vector multiplication. provided, func called (expected return) arrays shapes given size corresponding core dimensions. default, func assumed take scalar tensors input output.","code":""},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn a function into a vectorized function. — op_vectorize","text":"new function applies func every element input along axis 1 (batch axis, first axis).","code":""},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Turn a function into a vectorized function. — op_vectorize","text":"","code":"# currently does not work w/ tensorflow backend if(config_backend() != \"tensorflow\") {    myfunc <- function(a, b) a + b    vfunc <- op_vectorize(myfunc)   y <- vfunc(c(1, 2, 3, 4), 2)   print(y)   # with Jax backend, y is:   # Array([3., 4., 5., 6.], dtype=float32) }"},{"path":[]},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":null,"dir":"Reference","previous_headings":"","what":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"Schematically, op_vectorized_map() maps first dimension provided tensors. elements list tensors, tensors required size first dimension, iterated together.","code":""},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"","code":"op_vectorized_map(elements, f)"},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"elements see description f function taking either tensor, list tensors.","code":""},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"tensor list tensors, result mapping f across elements.","code":""},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"Note f may traced compiled. Meaning, R function may evaluated symbolic tensors using Jax TensorFlow backends, eager tensors. See output str() examples:","code":"(x <- op_arange(12L) |> op_reshape(c(3, 4))) ## tf.Tensor( ## [[ 0  1  2  3] ##  [ 4  5  6  7] ##  [ 8  9 10 11]], shape=(3, 4), dtype=int32) x |> op_vectorized_map(\\(row) {row + 10}) ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) list(x, x, x) |> op_vectorized_map(\\(rows) Reduce(`+`, rows)) ## tf.Tensor( ## [[ 0  3  6  9] ##  [12 15 18 21] ##  [24 27 30 33]], shape=(3, 4), dtype=int32) # simplest case, map f over rows of x, # where .x is 1 row of x input <- x output <- op_vectorized_map(input, function(.x) {   str(.x)   .x + 10 }) ## <tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> output ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) # map f over two tensors simultaneously. Here, # `.x` is a list of two # tensors. The return values from each call of `f(row)` are stacked to form the # final output input <- list(x, x) output <- op_vectorized_map(input, function(.x) {   str(.x)   .x[[1]] + 10 }) ## List of 2 ##  $ :<tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> ##  $ :<tf.Tensor 'loop_body/GatherV2_1:0' shape=(4) dtype=int32> output ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) # same as above, but now returning two tensors in the final output output <- op_vectorized_map(input, function(.x) {   str(.x)   c(.x1, .x2) %<-% .x   list(.x1+10, .x2+20) }) ## List of 2 ##  $ :<tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> ##  $ :<tf.Tensor 'loop_body/GatherV2_1:0' shape=(4) dtype=int32> output ## [[1]] ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) ## ## [[2]] ## tf.Tensor( ## [[20 21 22 23] ##  [24 25 26 27] ##  [28 29 30 31]], shape=(3, 4), dtype=int32) # passing named lists. # WARNING: if passing a named list, the order of elements of `.x` supplied # to `f` is not stable. Only retrieve elements by name. input <- list(name1 = x, name2 = x) output <- op_vectorized_map(input, function(.x) {   str(.x)   list(outname1 = .x$name1 + 10,        outname2 = .x$name2 + 20) }) ## List of 2 ##  $ name1:<tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> ##  $ name2:<tf.Tensor 'loop_body/GatherV2_1:0' shape=(4) dtype=int32> output ## $outname1 ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) ## ## $outname2 ## tf.Tensor( ## [[20 21 22 23] ##  [24 25 26 27] ##  [28 29 30 31]], shape=(3, 4), dtype=int32) # passing a tuple() is equivalent to passing an unnamed list() input <- tuple(x, x) output <- op_vectorized_map(input, function(.x) {   str(.x)   list(.x[[1]] + 10) }) ## List of 2 ##  $ :<tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> ##  $ :<tf.Tensor 'loop_body/GatherV2_1:0' shape=(4) dtype=int32> output ## [[1]] ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32)"},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"debugging-f","dir":"Reference","previous_headings":"","what":"Debugging f","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"Even eager contexts, op_vectorized_map() may trace f. case, want eagerly debug f (e.g., browser()), can swap manual (slow) implementation op_vectorized_map(). Note example debug implementation handle edge cases op_vectorized_map(), particular, f returns structure multiple tensors.","code":"op_vectorized_map_debug <- function(elements, fn) {    if (!is.list(elements)) {     # `elements` is a single tensor     batch_size <- op_shape(elements)[[1]]     out <- elements |>       op_split(batch_size) |>       lapply(fn) |>       op_stack()     return(out)   }    # `elements` is a list of tensors   batch_size <- elements[[1]] |> op_shape() |> _[[1]]   elements |>     lapply(\\(e) op_split(e, batch_size)) |>     zip_lists() |>     lapply(fn) |>     op_stack()  }"},{"path":[]},{"path":"https://keras.posit.co/reference/op_vstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Stack tensors in sequence vertically (row wise). — op_vstack","title":"Stack tensors in sequence vertically (row wise). — op_vstack","text":"Stack tensors sequence vertically (row wise).","code":""},{"path":"https://keras.posit.co/reference/op_vstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stack tensors in sequence vertically (row wise). — op_vstack","text":"","code":"op_vstack(xs)"},{"path":"https://keras.posit.co/reference/op_vstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stack tensors in sequence vertically (row wise). — op_vstack","text":"xs Sequence tensors.","code":""},{"path":"https://keras.posit.co/reference/op_vstack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stack tensors in sequence vertically (row wise). — op_vstack","text":"Tensor formed stacking given tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_where.html","id":null,"dir":"Reference","previous_headings":"","what":"Return elements chosen from x1 or x2 depending on condition. — op_where","title":"Return elements chosen from x1 or x2 depending on condition. — op_where","text":"Return elements chosen x1 x2 depending condition.","code":""},{"path":"https://keras.posit.co/reference/op_where.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return elements chosen from x1 or x2 depending on condition. — op_where","text":"","code":"op_where(condition, x1 = NULL, x2 = NULL)"},{"path":"https://keras.posit.co/reference/op_where.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return elements chosen from x1 or x2 depending on condition. — op_where","text":"condition TRUE, yield x1, otherwise yield x2. x1 Values choose condition TRUE. x2 Values choose condition FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_where.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return elements chosen from x1 or x2 depending on condition. — op_where","text":"tensor elements x1 condition TRUE, elements x2 condition FALSE.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":null,"dir":"Reference","previous_headings":"","what":"While loop implementation. — op_while_loop","title":"While loop implementation. — op_while_loop","text":"loop implementation.","code":""},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"While loop implementation. — op_while_loop","text":"","code":"op_while_loop(cond, body, loop_vars, maximum_iterations = NULL)"},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"While loop implementation. — op_while_loop","text":"cond callable represents termination condition loop. Must accept loop_vars like structure argument. loop_vars tuple unnamed list, element loop_vars passed positionally callable. body callable represents loop body. Must accept loop_vars like structure argument, return update value structure. loop_vars tuple unnamed list, element loop_vars passed positionally callable. loop_vars arbitrary nested structure tensor state persist across loop iterations. maximum_iterations Optional maximum number iterations loop run. provided, cond output -ed additional condition ensuring number iterations executed greater maximum_iterations.","code":""},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"While loop implementation. — op_while_loop","text":"list tensors, shape dtype loop_vars.","code":""},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"While loop implementation. — op_while_loop","text":"","code":"i <- 0 loop_vars <- list(i)  # cond() must return a scalar bool cond <- function(i) i < 10L  # body must return same shape as loop_vars body <- function(i) list(i + 1L)  op_while_loop(cond, body, loop_vars) ## [[1]] ## tf.Tensor(10.0, shape=(), dtype=float32) x <- 0; y <- 1 cond <- \\(x, y) x < 10 body <- \\(x, y) list(x+1, y+1) op_while_loop(cond, body, list(x, y)) ## [[1]] ## tf.Tensor(10.0, shape=(), dtype=float32) ## ## [[2]] ## tf.Tensor(11.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_zeros.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","title":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","text":"Return new tensor given shape type, filled zeros.","code":""},{"path":"https://keras.posit.co/reference/op_zeros.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","text":"","code":"op_zeros(shape, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_zeros.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","text":"shape Shape new tensor. dtype Desired data type tensor.","code":""},{"path":"https://keras.posit.co/reference/op_zeros.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","text":"Tensor zeros given shape dtype.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_zeros_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","title":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","text":"Return tensor zeros shape type x.","code":""},{"path":"https://keras.posit.co/reference/op_zeros_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","text":"","code":"op_zeros_like(x, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_zeros_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","text":"x Input tensor. dtype Overrides data type result.","code":""},{"path":"https://keras.posit.co/reference/op_zeros_like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","text":"tensor zeros shape type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"Adadelta optimization stochastic gradient descent method based adaptive learning rate per dimension address two drawbacks: continual decay learning rates throughout training. need manually selected global learning rate. Adadelta robust extension Adagrad adapts learning rates based moving window gradient updates, instead accumulating past gradients. way, Adadelta continues learning even many updates done. Compared Adagrad, original version Adadelta set initial learning rate. version, initial learning rate can set, Keras optimizers.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"","code":"optimizer_adadelta(   learning_rate = 0.001,   rho = 0.95,   epsilon = 1e-07,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adadelta\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"learning_rate float, [LearningRateSchedule()] instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. Note Adadelta tends benefit higher initial learning rate values compared optimizers. match exact form original paper, use 1.0. rho floating point value. decay rate. Defaults 0.95. epsilon Small floating point value maintaining numerical stability. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema = TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema = TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"Zeiler, 2012","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"Adafactor commonly used NLP tasks, advantage taking less memory saves partial information previous gradients. default argument setup based original paper (see reference). gradients dimension > 2, Adafactor optimizer delete last 2 dimensions separately accumulator variables.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"","code":"optimizer_adafactor(   learning_rate = 0.001,   beta_2_decay = -0.8,   epsilon_1 = 1e-30,   epsilon_2 = 0.001,   clip_threshold = 1,   relative_step = TRUE,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adafactor\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_2_decay float, defaults -0.8. decay rate beta_2. epsilon_1 float, defaults 1e-30. small offset keep denominator away 0. epsilon_2 float, defaults 1e-3. small offset avoid learning rate becoming small time. clip_threshold float, defaults 1.0. Clipping threshold. part Adafactor algorithm, independent clipnorm, clipvalue, global_clipnorm. relative_step bool, defaults TRUE. learning_rate constant relative_step=TRUE, learning rate adjusted based current iterations. default learning rate decay Adafactor. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema = TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"Shazeer, Noam et al., 2018.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"Adagrad optimizer parameter-specific learning rates, adapted relative frequently parameter gets updated training. updates parameter receives, smaller updates.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"","code":"optimizer_adagrad(   learning_rate = 0.001,   initial_accumulator_value = 0.1,   epsilon = 1e-07,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adagrad\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. Note Adagrad tends benefit higher initial learning rate values compared optimizers. match exact form original paper, use 1.0. initial_accumulator_value Floating point value. Starting value accumulators (per-parameter momentum values). Must non-negative. epsilon Small floating point value maintaining numerical stability. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"Duchi et al., 2011.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adam.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adam algorithm. — optimizer_adam","title":"Optimizer that implements the Adam algorithm. — optimizer_adam","text":"Adam optimization stochastic gradient descent method based adaptive estimation first-order second-order moments. According Kingma et al., 2014, method \"computationally efficient, little memory requirement, invariant diagonal rescaling gradients, well suited problems large terms data/parameters\".","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adam algorithm. — optimizer_adam","text":"","code":"optimizer_adam(   learning_rate = 0.001,   beta_1 = 0.9,   beta_2 = 0.999,   epsilon = 1e-07,   amsgrad = FALSE,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adam\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adam algorithm. — optimizer_adam","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_1 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 1st moment estimates. Defaults 0.9. beta_2 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 2nd moment estimates. Defaults 0.999. epsilon small constant numerical stability. epsilon \"epsilon hat\" Kingma Ba paper (formula just Section 2.1), epsilon Algorithm 1 paper. Defaults 1e-7. amsgrad Boolean. Whether apply AMSGrad variant algorithm paper \"Convergence Adam beyond\". Defaults FALSE. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adam algorithm. — optimizer_adam","text":"Optimizer instance","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"AdamW optimization stochastic gradient descent method based adaptive estimation first-order second-order moments added method decay weights per techniques discussed paper, 'Decoupled Weight Decay Regularization' Loshchilov, Hutter et al., 2019. According Kingma et al., 2014, underying Adam method \"computationally efficient, little memory requirement, invariant diagonal rescaling gradients, well suited problems large terms data/parameters\".","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"","code":"optimizer_adam_w(   learning_rate = 0.001,   weight_decay = 0.004,   beta_1 = 0.9,   beta_2 = 0.999,   epsilon = 1e-07,   amsgrad = FALSE,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adamw\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. weight_decay Float. set, weight decay applied. beta_1 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 1st moment estimates. Defaults 0.9. beta_2 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 2nd moment estimates. Defaults 0.999. epsilon small constant numerical stability. epsilon \"epsilon hat\" Kingma Ba paper (formula just Section 2.1), epsilon Algorithm 1 paper. Defaults 1e-7. amsgrad Boolean. Whether apply AMSGrad variant algorithm paper \"Convergence Adam beyond\". Defaults FALSE. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"Loshchilov et al., 2019 Kingma et al., 2014 adam Reddi et al., 2018 amsgrad.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"Adamax, variant Adam based infinity norm, first-order gradient-based optimization method. Due capability adjusting learning rate based data characteristics, suited learn time-variant process, e.g., speech data dynamically changed noise conditions. Default parameters follow provided paper (see references ). Initialization:   update rule parameter w gradient g described end section 7.1 paper (see referenece section):","code":"m <- 0  # Initialize initial 1st moment vector u <- 0  # Initialize the exponentially weighted infinity norm t <- 0  # Initialize timestep t <-  t + 1 m <- beta1 * m + (1 - beta) * g u <- max(beta2 * u, abs(g)) current_lr <- learning_rate / (1 - beta1 ** t) w <- w - current_lr * m / (u + epsilon)"},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"","code":"optimizer_adamax(   learning_rate = 0.001,   beta_1 = 0.9,   beta_2 = 0.999,   epsilon = 1e-07,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adamax\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_1 float value constant float tensor. exponential decay rate 1st moment estimates. beta_2 float value constant float tensor. exponential decay rate exponentially weighted infinity norm. epsilon small constant numerical stability. name: String. name use momentum accumulator weights created optimizer. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String, name object ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"Kingma et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_ftrl.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","title":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","text":"\"Follow Regularized Leader\" (FTRL) optimization algorithm developed Google click-rate prediction early 2010s. suitable shallow models large sparse feature spaces. algorithm described McMahan et al., 2013. Keras version support online L2 regularization (L2 regularization described paper ) shrinkage-type L2 regularization (addition L2 penalty loss function). Initialization:   Update rule one variable w:   Notation: lr learning rate g gradient variable lambda_1 L1 regularization strength lambda_2 L2 regularization strength lr_power power scale n. Check documentation l2_shrinkage_regularization_strength parameter details shrinkage enabled, case gradient replaced gradient shrinkage.","code":"n <- 0 sigma <- 0 z <- 0 prev_n <- n n <- n + g^2 sigma <- (n^(-lr_power) - prev_n^(-lr_power)) / lr z <- z + g - sigma * w if (abs(z) < lambda_1) {   w <- 0 } else {   w <- (sgn(z) * lambda_1 - z) / ((beta + sqrt(n)) / alpha + lambda_2) }"},{"path":"https://keras.posit.co/reference/optimizer_ftrl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","text":"","code":"optimizer_ftrl(   learning_rate = 0.001,   learning_rate_power = -0.5,   initial_accumulator_value = 0.1,   l1_regularization_strength = 0,   l2_regularization_strength = 0,   l2_shrinkage_regularization_strength = 0,   beta = 0,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"ftrl\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_ftrl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. learning_rate_power float value, must less equal zero. Controls learning rate decreases training. Use zero fixed learning rate. initial_accumulator_value starting value accumulators. zero positive values allowed. l1_regularization_strength float value, must greater equal zero. Defaults 0.0. l2_regularization_strength float value, must greater equal zero. Defaults 0.0. l2_shrinkage_regularization_strength float value, must greater equal zero. differs L2 L2 stabilization penalty, whereas L2 shrinkage magnitude penalty. input sparse shrinkage happen active weights. beta float value, representing beta value paper. Defaults 0.0. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_ftrl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","text":"Optimizer instance","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Lion algorithm. — optimizer_lion","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"Lion optimizer stochastic-gradient-descent method uses sign operator control magnitude update, unlike adaptive optimizers Adam rely second-order moments. make Lion memory-efficient keeps track momentum. According authors (see reference), performance gain Adam grows batch size. update Lion produced sign operation, resulting larger norm, suitable learning rate Lion typically 3-10x smaller AdamW. weight decay Lion turn 3-10x larger AdamW maintain similar strength (lr * wd).","code":""},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"","code":"optimizer_lion(   learning_rate = 0.001,   beta_1 = 0.9,   beta_2 = 0.99,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"lion\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_1 float value constant float tensor, callable takes arguments returns actual value use. rate combine current gradient 1st moment estimate. Defaults 0.9. beta_2 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 1st moment estimate. Defaults 0.99. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"Chen et al., 2023 Authors' implementation","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_loss_scale.html","id":null,"dir":"Reference","previous_headings":"","what":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","title":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","text":"Loss scaling technique prevent numeric underflow intermediate gradients float16 used. prevent underflow, loss multiplied (\"scaled\") certain factor called \"loss scale\", causes intermediate gradients scaled loss scale well. final gradients divided (\"unscaled\") loss scale bring back original value. LossScaleOptimizer wraps another optimizer applies dynamic loss scaling . loss scale dynamically updated time follows: train step, nonfinite gradient encountered, loss scale halved, train step skipped. dynamic_growth_steps ocurred since last time loss scale updated, nonfinite gradients occurred, loss scale doubled.","code":""},{"path":"https://keras.posit.co/reference/optimizer_loss_scale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","text":"","code":"optimizer_loss_scale(   inner_optimizer,   initial_scale = 32768,   dynamic_growth_steps = 2000L,   ...,   name = NULL,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = NULL,   ema_momentum = NULL,   ema_overwrite_frequency = NULL,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_loss_scale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","text":"inner_optimizer keras Optimizer instance wrap. initial_scale Float. initial loss scale. scale updated training. recommended high number, loss scale high gets lowered far quickly loss scale low gets raised. dynamic_growth_steps Int. often update scale upwards. every dynamic_growth_steps steps finite gradients, loss scale doubled. ... forward/backward compatability. name String. name use momentum accumulator weights created optimizer. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_loss_scale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","text":"Optimizer instance","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"Much like Adam essentially RMSprop momentum, Nadam Adam Nesterov momentum.","code":""},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"","code":"optimizer_nadam(   learning_rate = 0.001,   beta_1 = 0.9,   beta_2 = 0.999,   epsilon = 1e-07,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"nadam\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_1 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 1st moment estimates. Defaults 0.9. beta_2 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 2nd moment estimates. Defaults 0.999. epsilon small constant numerical stability. epsilon \"epsilon hat\" Kingma Ba paper (formula just Section 2.1), epsilon Algorithm 1 paper. Defaults 1e-7. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"Dozat, 2015.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"gist RMSprop : Maintain moving (discounted) average square gradients Divide gradient root average implementation RMSprop uses plain momentum, Nesterov momentum. centered version additionally maintains moving average gradients, uses average estimate variance.","code":""},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"","code":"optimizer_rmsprop(   learning_rate = 0.001,   rho = 0.9,   momentum = 0,   epsilon = 1e-07,   centered = FALSE,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"rmsprop\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"learning_rate float, learning_rate_schedule_* instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. rho float, defaults 0.9. Discounting factor old gradients. momentum float, defaults 0.0. 0.0., optimizer tracks momentum value, decay rate equals 1 - momentum. epsilon small constant numerical stability. epsilon \"epsilon hat\" Kingma Ba paper (formula just Section 2.1), epsilon Algorithm 1 paper. Defaults 1e-7. centered Boolean. TRUE, gradients normalized estimated variance gradient; FALSE, uncentered second moment. Setting TRUE may help training, slightly expensive terms computation memory. Defaults FALSE. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"","code":"opt <- optimizer_rmsprop(learning_rate=0.1)"},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"Hinton, 2012","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_sgd.html","id":null,"dir":"Reference","previous_headings":"","what":"Gradient descent (with momentum) optimizer. — optimizer_sgd","title":"Gradient descent (with momentum) optimizer. — optimizer_sgd","text":"Update rule parameter w gradient g momentum 0:   Update rule momentum larger 0:   nesterov=TRUE, rule becomes:","code":"w <- w - learning_rate * g velocity <- momentum * velocity - learning_rate * g w <- w + velocity velocity <- momentum * velocity - learning_rate * g w <- w + momentum * velocity - learning_rate * g"},{"path":"https://keras.posit.co/reference/optimizer_sgd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gradient descent (with momentum) optimizer. — optimizer_sgd","text":"","code":"optimizer_sgd(   learning_rate = 0.01,   momentum = 0,   nesterov = FALSE,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"SGD\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_sgd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gradient descent (with momentum) optimizer. — optimizer_sgd","text":"learning_rate float, learning_rate_schedule_* instance, callable takes arguments returns actual value use. learning rate. Defaults 0.01. momentum float hyperparameter >= 0 accelerates gradient descent relevant direction dampens oscillations. 0 vanilla gradient descent. Defaults 0.0. nesterov boolean. Whether apply Nesterov momentum. Defaults FALSE. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_sgd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gradient descent (with momentum) optimizer. — optimizer_sgd","text":"Optimizer instance","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":null,"dir":"Reference","previous_headings":"","what":"Packs user-provided data into a list. — pack_x_y_sample_weight","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"convenience utility packing data list formats fit() uses.","code":""},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"","code":"pack_x_y_sample_weight(x, y = NULL, sample_weight = NULL)"},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"x Features pass Model. y Ground-truth targets pass Model. sample_weight Sample weight element.","code":""},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"List format used fit().","code":""},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"","code":"x <- op_ones(c(10, 1)) data <- pack_x_y_sample_weight(x)   y <- op_ones(c(10, 1)) data <- pack_x_y_sample_weight(x, y)"},{"path":[]},{"path":"https://keras.posit.co/reference/pad_sequences.html","id":null,"dir":"Reference","previous_headings":"","what":"Pads sequences to the same length. — pad_sequences","title":"Pads sequences to the same length. — pad_sequences","text":"function transforms list (length num_samples) sequences (lists integers) 2D NumPy array shape (num_samples, num_timesteps). num_timesteps either maxlen argument provided, length longest sequence list. Sequences shorter num_timesteps padded value num_timesteps long. Sequences longer num_timesteps truncated fit desired length. position padding truncation happens determined arguments padding truncating, respectively. Pre-padding removing values beginning sequence default.","code":"sequence <- list(c(1), c(2, 3), c(4, 5, 6)) pad_sequences(sequence) ##      [,1] [,2] [,3] ## [1,]    0    0    1 ## [2,]    0    2    3 ## [3,]    4    5    6 pad_sequences(sequence, value=-1) ##      [,1] [,2] [,3] ## [1,]   -1   -1    1 ## [2,]   -1    2    3 ## [3,]    4    5    6 pad_sequences(sequence, padding='post') ##      [,1] [,2] [,3] ## [1,]    1    0    0 ## [2,]    2    3    0 ## [3,]    4    5    6 pad_sequences(sequence, maxlen=2) ##      [,1] [,2] ## [1,]    0    1 ## [2,]    2    3 ## [3,]    5    6"},{"path":"https://keras.posit.co/reference/pad_sequences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pads sequences to the same length. — pad_sequences","text":"","code":"pad_sequences(   sequences,   maxlen = NULL,   dtype = \"int32\",   padding = \"pre\",   truncating = \"pre\",   value = 0 )"},{"path":"https://keras.posit.co/reference/pad_sequences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pads sequences to the same length. — pad_sequences","text":"sequences List sequences (sequence list integers). maxlen Optional Int, maximum length sequences. provided, sequences padded length longest individual sequence. dtype (Optional, defaults \"int32\"). Type output sequences. pad sequences variable length strings, can use object. padding String, \"pre\" \"post\" (optional, defaults \"pre\"): pad either sequence. truncating String, \"pre\" \"post\" (optional, defaults \"pre\"): remove values sequences larger maxlen, either beginning end sequences. value Float String, padding value. (Optional, defaults 0.)","code":""},{"path":"https://keras.posit.co/reference/pad_sequences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pads sequences to the same length. — pad_sequences","text":"Array shape (len(sequences), maxlen)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See %>% details.","code":""},{"path":"https://keras.posit.co/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://keras.posit.co/reference/pipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pipe operator — %>%","text":"commonly, result calling right hand side left hand side argument: rhs(lhs). See magritter vignette , advanced, usages.","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a Keras model — plot.keras.src.models.model.Model","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"Plot Keras model","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model plot(   x,   show_shapes = FALSE,   show_dtype = FALSE,   show_layer_names = FALSE,   ...,   rankdir = \"TB\",   expand_nested = FALSE,   dpi = 200,   layer_range = NULL,   show_layer_activations = FALSE,   show_trainable = NA,   to_file = NULL )"},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"x Keras model instance show_shapes whether display shape information. show_dtype whether display layer dtypes. show_layer_names whether display layer names. ... passed Python keras.utils.model_to_dot(). Used forward backward compatibility. rankdir string specifying format plot: 'TB' creates vertical plot; 'LR' creates horizontal plot. (argument passed PyDot) expand_nested Whether expand nested models clusters. dpi Dots per inch. Increase value image text appears excessively pixelated. layer_range list containing two character strings, starting layer name ending layer name (inclusive) indicating range layers plot generated. also accepts regex patterns instead exact name. case, start predicate first element matches layer_range[1] end predicate last element matches layer_range[2]. default NULL considers layers model. Note must pass range resultant subgraph must complete. show_layer_activations Display layer activations (layers activation property). show_trainable whether display layer trainable. to_file File name plot image. NULL (default), model drawn default graphics device. Otherwise, file saved.","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"Nothing, called side effects.","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"ValueError: plot(model) called model built, unless input_shape =  argument supplied keras_model_sequential().","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"requirements","dir":"Reference","previous_headings":"","what":"Requirements","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"function requires pydot graphviz. pydot default installed install_keras(), installed keras means, can install pydot directly :   can install graphviz directly : https://graphviz.gitlab.io/download/ Linux platforms, can install graphviz via package manager. example, Ubuntu/Debian can install   conda environment, can install graphviz :","code":"reticulate::py_install(\"pydot\", pip = TRUE) sudo apt install graphviz reticulate::conda_install(packages = \"graphviz\") # Restart the R session after install."},{"path":"https://keras.posit.co/reference/plot.keras_training_history.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot training history — plot.keras_training_history","title":"Plot training history — plot.keras_training_history","text":"Plots metrics recorded training.","code":""},{"path":"https://keras.posit.co/reference/plot.keras_training_history.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot training history — plot.keras_training_history","text":"","code":"# S3 method for keras_training_history plot(   x,   y,   metrics = NULL,   method = c(\"auto\", \"ggplot2\", \"base\"),   smooth = getOption(\"keras.plot.history.smooth\", TRUE),   theme_bw = getOption(\"keras.plot.history.theme_bw\", FALSE),   ... )"},{"path":"https://keras.posit.co/reference/plot.keras_training_history.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot training history — plot.keras_training_history","text":"x Training history object returned fit.keras.src.models.model.Model(). y Unused. metrics One metrics plot (e.g. c('loss', 'accuracy')). Defaults plotting captured metrics. method Method use plotting. default \"auto\" use ggplot2 available, otherwise use base graphics. smooth Whether loess smooth added plot, available ggplot2 method. number epochs smaller ten, forced false. theme_bw Use ggplot2::theme_bw() plot history black white. ... Additional parameters pass plot() method.","code":""},{"path":"https://keras.posit.co/reference/plot.keras_training_history.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot training history — plot.keras_training_history","text":"method == \"ggplot2\", ggplot object returned. method == \"base\", function draw graphics device return NULL, invisibly.","code":""},{"path":"https://keras.posit.co/reference/pop_layer.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove the last layer in a Sequential model — pop_layer","title":"Remove the last layer in a Sequential model — pop_layer","text":"Remove last layer Sequential model","code":""},{"path":"https://keras.posit.co/reference/pop_layer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove the last layer in a Sequential model — pop_layer","text":"","code":"pop_layer(object)"},{"path":"https://keras.posit.co/reference/pop_layer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove the last layer in a Sequential model — pop_layer","text":"object Sequential keras model object","code":""},{"path":"https://keras.posit.co/reference/pop_layer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove the last layer in a Sequential model — pop_layer","text":"input object, invisibly.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"Generates output predictions input samples.","code":""},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model predict(   object,   x,   ...,   batch_size = NULL,   verbose = getOption(\"keras.verbose\", default = \"auto\"),   steps = NULL,   callbacks = NULL )"},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"object Keras model object x Input samples. : array (array-like), list arrays (case model multiple inputs). tensor, list tensors (case model multiple inputs). TF Dataset. ... forward/backward compatability. batch_size Integer NULL. Number samples per batch. unspecified, batch_size default 32. specify batch_size data form TF Dataset generator (since generate batches). verbose \"auto\", 0, 1, 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. \"auto\" becomes 1 cases, 2 knitr render running distributed training server. Note progress bar particularly useful logged file, verbose=2 recommended running interactively (e.g., production environment). Defaults \"auto\". steps Total number steps (batches samples) declaring prediction round finished. Ignored default value NULL. x TF Dataset steps NULL, predict() run input dataset exhausted. callbacks List Callback instances. List callbacks apply prediction.","code":""},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"R array(s) predictions.","code":""},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"Computation done batches. method designed batch processing large numbers inputs. intended use inside loops iterate data process small numbers inputs time. small numbers inputs fit one batch, directly call model model$call faster execution, e.g., model(x), model(x, training = FALSE) layers BatchNormalization behave differently inference.","code":""},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"See FAQ entry details difference Model methods predict() call().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/predict_on_batch.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns predictions for a single batch of samples. — predict_on_batch","title":"Returns predictions for a single batch of samples. — predict_on_batch","text":"Returns predictions single batch samples.","code":""},{"path":"https://keras.posit.co/reference/predict_on_batch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns predictions for a single batch of samples. — predict_on_batch","text":"","code":"predict_on_batch(object, x)"},{"path":"https://keras.posit.co/reference/predict_on_batch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns predictions for a single batch of samples. — predict_on_batch","text":"object Keras model object x Input data. must array-like.","code":""},{"path":"https://keras.posit.co/reference/predict_on_batch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns predictions for a single batch of samples. — predict_on_batch","text":"Array(s) predictions.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/process_utils.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocessing and postprocessing utilities — process_utils","title":"Preprocessing and postprocessing utilities — process_utils","text":"functions used preprocess postprocess inputs outputs Keras applications.","code":""},{"path":"https://keras.posit.co/reference/process_utils.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocessing and postprocessing utilities — process_utils","text":"","code":"application_preprocess_inputs(model, x, ..., data_format = NULL)  application_decode_predictions(model, preds, top = 5L, ...)"},{"path":"https://keras.posit.co/reference/process_utils.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocessing and postprocessing utilities — process_utils","text":"model Keras model initialized using application_ function. x batch inputs model. ... Additional arguments passed preprocessing decoding function. data_format Optional data format image tensor/array. NULL means global setting config_image_data_format() used (unless changed , uses \"channels_last\"). Defaults NULL. preds batch outputs model. top number top predictions return.","code":""},{"path":"https://keras.posit.co/reference/process_utils.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocessing and postprocessing utilities — process_utils","text":"list decoded predictions case application_decode_predictions(). batch preprocessed inputs case application_preprocess_inputs().","code":""},{"path":"https://keras.posit.co/reference/process_utils.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Preprocessing and postprocessing utilities — process_utils","text":"application_preprocess_inputs(): Pre-process inputs used model application_decode_predictions(): Decode predictions model","code":""},{"path":"https://keras.posit.co/reference/process_utils.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preprocessing and postprocessing utilities — process_utils","text":"","code":"if (FALSE) { model <- application_convnext_tiny()  inputs <- random_normal(c(32, 224, 224, 3)) processed_inputs <- application_preprocess_inputs(model, inputs)  preds <- random_normal(c(32, 1000)) decoded_preds <- application_decode_predictions(model, preds)  }"},{"path":"https://keras.posit.co/reference/quantize_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantize the weights of a model. — quantize_weights","title":"Quantize the weights of a model. — quantize_weights","text":"Note model must built first calling method. quantize_weights() recursively call layer$quantize(mode) layers skipped layer implement function. Currently Dense EinsumDense layers support quantization.","code":""},{"path":"https://keras.posit.co/reference/quantize_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantize the weights of a model. — quantize_weights","text":"","code":"quantize_weights(object, mode)"},{"path":"https://keras.posit.co/reference/quantize_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantize the weights of a model. — quantize_weights","text":"object Keras Model Layer. mode mode quantization. 'int8' supported time.","code":""},{"path":"https://keras.posit.co/reference/quantize_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantize the weights of a model. — quantize_weights","text":"model, invisibly. Note just convenience usage |>, model modified -place.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_beta.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from a Beta distribution. — random_beta","title":"Draw samples from a Beta distribution. — random_beta","text":"values drawm Beta distribution parametrized alpha beta.","code":""},{"path":"https://keras.posit.co/reference/random_beta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from a Beta distribution. — random_beta","text":"","code":"random_beta(shape, alpha, beta, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_beta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from a Beta distribution. — random_beta","text":"shape shape random values generate. alpha Float array floats representing first parameter alpha. Must broadcastable beta shape. beta Float array floats representing second parameter beta. Must broadcastable alpha shape. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults \"float32\" unless configured otherwise (via config_set_floatx(float_dtype)). seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_beta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from a Beta distribution. — random_beta","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_binomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from a Binomial distribution. — random_binomial","title":"Draw samples from a Binomial distribution. — random_binomial","text":"values drawn Binomial distribution specified trial count probability success.","code":""},{"path":"https://keras.posit.co/reference/random_binomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from a Binomial distribution. — random_binomial","text":"","code":"random_binomial(shape, counts, probabilities, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_binomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from a Binomial distribution. — random_binomial","text":"shape shape random values generate. counts number array numbers representing number trials. must broadcastable probabilities. probabilities float array floats representing probability success individual event. must broadcastable counts. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults \"float32\" unless configured otherwise (via config_set_floatx(float_dtype)). seed Python integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer None (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_binomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from a Binomial distribution. — random_binomial","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_categorical.html","id":null,"dir":"Reference","previous_headings":"","what":"Draws samples from a categorical distribution. — random_categorical","title":"Draws samples from a categorical distribution. — random_categorical","text":"function takes input logits, 2-D input tensor shape (batch_size, num_classes). row input represents categorical distribution, column index containing log-probability given class. function output 2-D tensor shape (batch_size, num_samples), row contains samples corresponding row logits. column index contains independent samples drawn input distribution.","code":""},{"path":"https://keras.posit.co/reference/random_categorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draws samples from a categorical distribution. — random_categorical","text":"","code":"random_categorical(logits, num_samples, dtype = \"int32\", seed = NULL)"},{"path":"https://keras.posit.co/reference/random_categorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draws samples from a categorical distribution. — random_categorical","text":"logits 2-D Tensor shape (batch_size, num_classes). row define categorical distibution unnormalized log-probabilities classes. num_samples Int, number independent samples draw row input. second dimension output tensor's shape. dtype Optional dtype output tensor. seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_categorical.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draws samples from a categorical distribution. — random_categorical","text":"2-D tensor (batch_size, num_samples).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomly set some values in a tensor to 0. — random_dropout","title":"Randomly set some values in a tensor to 0. — random_dropout","text":"Randomly set portion values tensor 0.","code":""},{"path":"https://keras.posit.co/reference/random_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomly set some values in a tensor to 0. — random_dropout","text":"","code":"random_dropout(inputs, rate, noise_shape = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomly set some values in a tensor to 0. — random_dropout","text":"inputs tensor rate numeric noise_shape shape() value seed Initial seed random number generator","code":""},{"path":"https://keras.posit.co/reference/random_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Randomly set some values in a tensor to 0. — random_dropout","text":"tensor copy inputs values set 0.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_gamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw random samples from the Gamma distribution. — random_gamma","title":"Draw random samples from the Gamma distribution. — random_gamma","text":"Draw random samples Gamma distribution.","code":""},{"path":"https://keras.posit.co/reference/random_gamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw random samples from the Gamma distribution. — random_gamma","text":"","code":"random_gamma(shape, alpha, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_gamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw random samples from the Gamma distribution. — random_gamma","text":"shape shape random values generate. alpha Float, parameter distribution. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults float32 unless configured otherwise (via config_set_floatx(float_dtype)). seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_gamma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw random samples from the Gamma distribution. — random_gamma","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_integer.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw random integers from a uniform distribution. — random_integer","title":"Draw random integers from a uniform distribution. — random_integer","text":"generated values follow uniform distribution range [minval, maxval). lower bound minval included range, upper bound maxval excluded. dtype must integer type.","code":""},{"path":"https://keras.posit.co/reference/random_integer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw random integers from a uniform distribution. — random_integer","text":"","code":"random_integer(shape, minval, maxval, dtype = \"int32\", seed = NULL)"},{"path":"https://keras.posit.co/reference/random_integer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw random integers from a uniform distribution. — random_integer","text":"shape shape random values generate. minval integer, lower bound range random values generate (inclusive). maxval integer, upper bound range random values generate (exclusive). dtype Optional dtype tensor. integer types supported. specified, \"int32\" used. seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_integer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw random integers from a uniform distribution. — random_integer","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw random samples from a normal (Gaussian) distribution. — random_normal","title":"Draw random samples from a normal (Gaussian) distribution. — random_normal","text":"Draw random samples normal (Gaussian) distribution.","code":""},{"path":"https://keras.posit.co/reference/random_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw random samples from a normal (Gaussian) distribution. — random_normal","text":"","code":"random_normal(shape, mean = 0, stddev = 1, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw random samples from a normal (Gaussian) distribution. — random_normal","text":"shape shape random values generate. mean Float, defaults 0. Mean random values generate. stddev Float, defaults 1. Standard deviation random values generate. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults float32 unless configured otherwise (via config_set_floatx(float_dtype)). seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw random samples from a normal (Gaussian) distribution. — random_normal","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"Keras, RNG-using methods (random_normal()) stateless, meaning pass integer seed (seed = 42), return values call. order get different values call, must use SeedGenerator instead seed argument. SeedGenerator object stateful.","code":""},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"","code":"random_seed_generator(seed = NULL, name = NULL, ...)"},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"seed Initial seed random number generator name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"SeedGenerator instance, can passed seed = argument random tensor generators.","code":""},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"Usage layer:","code":"seed_gen <- random_seed_generator(seed = 42) values <- random_normal(shape = c(2, 3), seed = seed_gen) new_values <- random_normal(shape = c(2, 3), seed = seed_gen) layer_dropout2 <- new_layer_class(   \"dropout2\",   initialize = function(...) {     super$initialize(...)     self$seed_generator <- random_seed_generator(seed = 1337)   },   call = function(x, training = FALSE) {     if (training) {       return(random_dropout(x, rate = 0.5, seed = self$seed_generator))     }     return(x)   } )  out <- layer_dropout(rate = 0.8) out(op_ones(10), training = TRUE) ## tf.Tensor([0. 5. 5. 0. 0. 0. 0. 0. 0. 0.], shape=(10), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/random_shuffle.html","id":null,"dir":"Reference","previous_headings":"","what":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","title":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","text":"Shuffle elements tensor uniformly random along axis.","code":""},{"path":"https://keras.posit.co/reference/random_shuffle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","text":"","code":"random_shuffle(x, axis = 1L, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_shuffle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","text":"x tensor shuffled. axis integer specifying axis along shuffle. Defaults 0. seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_shuffle.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","text":"tensor, copy x axis axis shuffled.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_truncated_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from a truncated normal distribution. — random_truncated_normal","title":"Draw samples from a truncated normal distribution. — random_truncated_normal","text":"values drawn normal distribution specified mean standard deviation, discarding re-drawing samples two standard deviations mean.","code":""},{"path":"https://keras.posit.co/reference/random_truncated_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from a truncated normal distribution. — random_truncated_normal","text":"","code":"random_truncated_normal(shape, mean = 0, stddev = 1, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_truncated_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from a truncated normal distribution. — random_truncated_normal","text":"shape shape random values generate. mean Float, defaults 0. Mean random values generate. stddev Float, defaults 1. Standard deviation random values generate. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults float32 unless configured otherwise (via config_set_floatx(float_dtype)) seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_truncated_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from a truncated normal distribution. — random_truncated_normal","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from a uniform distribution. — random_uniform","title":"Draw samples from a uniform distribution. — random_uniform","text":"generated values follow uniform distribution range [minval, maxval). lower bound minval included range, upper bound maxval excluded. dtype must floating point type, default range [0, 1).","code":""},{"path":"https://keras.posit.co/reference/random_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from a uniform distribution. — random_uniform","text":"","code":"random_uniform(shape, minval = 0, maxval = 1, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from a uniform distribution. — random_uniform","text":"shape shape random values generate. minval Float, defaults 0. Lower bound range random values generate (inclusive). maxval Float, defaults 1. Upper bound range random values generate (exclusive). dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults float32 unless configured otherwise (via config_set_floatx(float_dtype)) seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from a uniform distribution. — random_uniform","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. generics compile, fit magrittr %<>% reticulate array_reshape, as_iterator, iter_next, iterate, np_array, tuple, use_python, use_virtualenv tensorflow all_dims, as_tensor, evaluate, export_savedmodel, tensorboard tfruns flag_boolean, flag_integer, flag_numeric, flag_string, flags, run_dir","code":""},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":null,"dir":"Reference","previous_headings":"","what":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"function registers custom class function Keras custom object registry, can serialized deserialized without needing entry user-provided custom_objects argument. also injects function Keras call get object's serializable string key. Note serialized deserialized, classes must implement get_config() method. Functions requirement. object registered key 'package>name' name, defaults object name passed.","code":""},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"","code":"register_keras_serializable(object, name = NULL, package = NULL)"},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"object keras object. name name serialize class package. package package class belongs . used key (\"package>name\") identify class. Defaults current package name, \"Custom\" outside package.","code":""},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"object returned invisibly, convenient piping. primarily called side effects.","code":""},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"","code":"# Note that `'my_package'` is used as the `package` argument here, and since # the `name` argument is not provided, `'MyDense'` is used as the `name`. layer_my_dense <- Layer(\"MyDense\") register_keras_serializable(layer_my_dense, package = \"my_package\")  MyDense <- environment(layer_my_dense)$`__class__` # the python class obj stopifnot(exprs = {   get_registered_object('my_package>MyDense') == MyDense   get_registered_name(MyDense) == 'my_package>MyDense' })"},{"path":[]},{"path":"https://keras.posit.co/reference/regularizer_l1.html","id":null,"dir":"Reference","previous_headings":"","what":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","title":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","text":"L1 regularization penalty computed : loss = l1 * reduce_sum(abs(x)) L1 may passed layer string identifier:   case, default value used l1=0.01.","code":"dense <- layer_dense(units = 3, kernel_regularizer = 'l1')"},{"path":"https://keras.posit.co/reference/regularizer_l1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","text":"","code":"regularizer_l1(l1 = 0.01)"},{"path":"https://keras.posit.co/reference/regularizer_l1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","text":"l1 float, L1 regularization factor.","code":""},{"path":"https://keras.posit.co/reference/regularizer_l1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","text":"Regularizer instance can passed layer constructors used standalone object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/regularizer_l1_l2.html","id":null,"dir":"Reference","previous_headings":"","what":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","title":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","text":"L1 regularization penalty computed : loss = l1 * reduce_sum(abs(x)) L2 regularization penalty computed loss = l2 * reduce_sum(square(x)) L1L2 may passed layer string identifier:   case, default values used l1=0.01 l2=0.01.","code":"dense <- layer_dense(units = 3, kernel_regularizer = 'L1L2')"},{"path":"https://keras.posit.co/reference/regularizer_l1_l2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","text":"","code":"regularizer_l1_l2(l1 = 0, l2 = 0)"},{"path":"https://keras.posit.co/reference/regularizer_l1_l2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","text":"l1 float, L1 regularization factor. l2 float, L2 regularization factor.","code":""},{"path":"https://keras.posit.co/reference/regularizer_l1_l2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","text":"Regularizer instance can passed layer constructors used standalone object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/regularizer_l2.html","id":null,"dir":"Reference","previous_headings":"","what":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","title":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","text":"L2 regularization penalty computed : loss = l2 * reduce_sum(square(x)) L2 may passed layer string identifier:   case, default value used l2=0.01.","code":"dense <- layer_dense(units = 3, kernel_regularizer='l2')"},{"path":"https://keras.posit.co/reference/regularizer_l2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","text":"","code":"regularizer_l2(l2 = 0.01)"},{"path":"https://keras.posit.co/reference/regularizer_l2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","text":"l2 float, L2 regularization factor.","code":""},{"path":"https://keras.posit.co/reference/regularizer_l2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","text":"Regularizer instance can passed layer constructors used standalone object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":null,"dir":"Reference","previous_headings":"","what":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"can applied either rows matrix (mode=\"rows\") columns (mode=\"columns\"). applied Dense kernel shape (input_dim, units), rows mode seek make feature vectors (.e. basis output space) orthogonal .","code":""},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"","code":"regularizer_orthogonal(factor = 0.01, mode = \"rows\")"},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"factor Float. regularization factor. regularization penalty proportional factor times mean dot products L2-normalized rows (mode=\"rows\", columns mode=\"columns\") inputs, excluding product row/column .  Defaults 0.01. mode String, one {\"rows\", \"columns\"}. Defaults \"rows\". rows mode, regularization effect seeks make rows input orthogonal . columns mode, seeks make columns input orthogonal .","code":""},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"Regularizer instance can passed layer constructors used standalone object.","code":""},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"","code":"regularizer <- regularizer_orthogonal(factor=0.01) layer <- layer_dense(units=4, kernel_regularizer=regularizer)"},{"path":[]},{"path":"https://keras.posit.co/reference/reset_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Reset the state for a model, layer or metric. — reset_state","title":"Reset the state for a model, layer or metric. — reset_state","text":"Reset state model, layer metric.","code":""},{"path":"https://keras.posit.co/reference/reset_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reset the state for a model, layer or metric. — reset_state","text":"","code":"reset_state(object)"},{"path":"https://keras.posit.co/reference/reset_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reset the state for a model, layer or metric. — reset_state","text":"object Model, Layer, Metric instance Layers resettable state (E.g., adapt()-able preprocessing layers rnn layers resettable state, layer_dense() ). Calling Layer instance without resettable-state error.","code":""},{"path":"https://keras.posit.co/reference/reset_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reset the state for a model, layer or metric. — reset_state","text":"object, invisibly.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell class for the GRU layer. — rnn_cell_gru","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"class processes one step within whole time sequence input, whereas layer_gru() processes whole sequence.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"","code":"rnn_cell_gru(   units,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   reset_after = TRUE,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). recurrent_activation Activation function use recurrent step. Default: sigmoid (sigmoid). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. reset_after GRU convention (whether apply reset gate matrix multiplication). FALSE = \"\", TRUE = \"\" (default cuDNN compatible). seed Random seed dropout. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"Layer instance, intended used layer_rnn().","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"inputs: 2D tensor, shape (batch, features). states: 2D tensor shape (batch, units), state previous time step. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout used.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"","code":"inputs <- random_uniform(c(32, 10, 8)) outputs <- inputs |> layer_rnn(rnn_cell_gru(4)) shape(outputs) ## shape(32, 4) rnn <- layer_rnn(    cell = rnn_cell_gru(4),    return_sequences=TRUE,    return_state=TRUE) c(whole_sequence_output, final_state) %<-% rnn(inputs) shape(whole_sequence_output) ## shape(32, 10, 4) shape(final_state) ## shape(32, 4)"},{"path":[]},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell class for the LSTM layer. — rnn_cell_lstm","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"class processes one step within whole time sequence input, whereas layer_lstm() processes whole sequence.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"","code":"rnn_cell_lstm(   units,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). recurrent_activation Activation function use recurrent step. Default: sigmoid (sigmoid). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". unit_forget_bias Boolean (default TRUE). TRUE, add 1 bias forget gate initialization. Setting TRUE also force bias_initializer=\"zeros\". recommended Jozefowicz et al. kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. seed Random seed dropout. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"Layer instance, intended used layer_rnn().","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"inputs: 2D tensor, shape (batch, features). states: 2D tensor shape (batch, units), state previous time step. training: Boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout used.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"","code":"inputs <- random_uniform(c(32, 10, 8)) output <- inputs |>   layer_rnn(cell = rnn_cell_lstm(4)) shape(output) ## shape(32, 4) rnn <- layer_rnn(cell = rnn_cell_lstm(4),                  return_sequences = T,                  return_state = T) c(whole_sequence_output, ...final_state) %<-% rnn(inputs) str(whole_sequence_output) ## <tf.Tensor: shape=(32, 10, 4), dtype=float32, numpy=…> str(final_state) ## List of 2 ##  $ :<tf.Tensor: shape=(32, 4), dtype=float32, numpy=…> ##  $ :<tf.Tensor: shape=(32, 4), dtype=float32, numpy=…>"},{"path":[]},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell class for SimpleRNN. — rnn_cell_simple","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"class processes one step within whole time sequence input, whereas layer_simple_rnn() processes whole sequence.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"","code":"rnn_cell_simple(   units,   activation = \"tanh\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. seed Random seed dropout. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"Layer instance, intended used layer_rnn().","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"sequence: 2D tensor, shape (batch, features). states: 2D tensor shape (batch, units), state previous time step. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout used.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"","code":"inputs <- random_uniform(c(32, 10, 8)) rnn <- layer_rnn(cell = rnn_cell_simple(units = 4)) output <- rnn(inputs)  # The output has shape `(32, 4)`. rnn <- layer_rnn(     cell = rnn_cell_simple(units = 4),     return_sequences=TRUE,     return_state=TRUE ) # whole_sequence_output has shape `(32, 10, 4)`. # final_state has shape `(32, 4)`. c(whole_sequence_output, final_state) %<-% rnn(inputs)"},{"path":[]},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"Used implement efficient stacked RNNs.","code":""},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"","code":"rnn_cells_stack(cells, ...)"},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"cells List RNN cell instances. ... Unnamed arguments treated additional cells. Named arguments passed underlying layer.","code":""},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"Layer instance, intended used layer_rnn().","code":""},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"","code":"batch_size <- 3 sentence_length <- 5 num_features <- 2 new_shape <- c(batch_size, sentence_length, num_features) x <- array(1:30, dim = new_shape)  rnn_cells <- lapply(1:2, function(x) rnn_cell_lstm(units = 128)) stacked_lstm <- rnn_cells_stack(rnn_cells) lstm_layer <- layer_rnn(cell = stacked_lstm)  result <- lstm_layer(x) str(result) ## <tf.Tensor: shape=(3, 128), dtype=float32, numpy=…>"},{"path":[]},{"path":"https://keras.posit.co/reference/save_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves a model as a .keras file. — save_model","title":"Saves a model as a .keras file. — save_model","text":"Saves model .keras file.","code":""},{"path":"https://keras.posit.co/reference/save_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves a model as a .keras file. — save_model","text":"","code":"save_model(model, filepath = NULL, overwrite = FALSE, ...)"},{"path":"https://keras.posit.co/reference/save_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves a model as a .keras file. — save_model","text":"model keras model. filepath string, Path save model. Must end .keras. overwrite Whether overwrite existing model target location, instead ask user via interactive prompt. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/save_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Saves a model as a .keras file. — save_model","text":"filepath provided, function called primarily side effects, model returned invisibly. filepath provided NULL, serialized model returned R raw vector.","code":""},{"path":"https://keras.posit.co/reference/save_model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Saves a model as a .keras file. — save_model","text":"saved .keras file contains: model's configuration (architecture) model's weights model's optimizer's state () Thus models can reinstantiated exact state.","code":"model <- keras_model_sequential(input_shape = c(3)) |>   layer_dense(5) |>   layer_activation_softmax()  model |> save_model(\"model.keras\") loaded_model <- load_model(\"model.keras\") x <- random_uniform(c(10, 3)) stopifnot(all.equal(   model |> predict(x),   loaded_model |> predict(x) )) zip::zip_list(\"model.keras\")[, \"filename\"] ## [1] \"metadata.json\"    \"config.json\"      \"model.weights.h5\""},{"path":[]},{"path":"https://keras.posit.co/reference/save_model_config.html","id":null,"dir":"Reference","previous_headings":"","what":"Save and load model configuration as JSON — save_model_config","title":"Save and load model configuration as JSON — save_model_config","text":"Save re-load models configurations JSON. Note representation include weights, architecture.","code":""},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Save and load model configuration as JSON — save_model_config","text":"","code":"save_model_config(model, filepath = NULL, overwrite = FALSE)  load_model_config(filepath, custom_objects = NULL)"},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Save and load model configuration as JSON — save_model_config","text":"model Model object save filepath path json file model config. overwrite Whether overwrite existing model configuration json filepath, instead ask user via interactive prompt. custom_objects Optional named list mapping names custom classes functions considered deserialization.","code":""},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Save and load model configuration as JSON — save_model_config","text":"called primarily side effects. model returned, invisibly, enable usage pipe.","code":""},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Save and load model configuration as JSON — save_model_config","text":"Note: save_model_config() serializes model JSON using serialize_keras_object(), get_config(). serialize_keras_object() returns superset get_config(), additional information needed create class object needed restore model. See example extract get_config() value saved model.","code":""},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Save and load model configuration as JSON — save_model_config","text":"","code":"model <- keras_model_sequential(input_shape = 10) |> layer_dense(10) file <- tempfile(\"model-config-\", fileext = \".json\") save_model_config(model, file)  # load a new model instance with the same architecture but different weights model2 <- load_model_config(file)  stopifnot(exprs = {   all.equal(get_config(model), get_config(model2))    # To extract the `get_config()` value from a saved model config:   all.equal(       get_config(model),       structure(jsonlite::read_json(file)$config,                 \"__class__\" = keras_model_sequential()$`__class__`)   ) })"},{"path":[]},{"path":"https://keras.posit.co/reference/save_model_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves all layer weights to a .weights.h5 file. — save_model_weights","title":"Saves all layer weights to a .weights.h5 file. — save_model_weights","text":"Saves layer weights .weights.h5 file.","code":""},{"path":"https://keras.posit.co/reference/save_model_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves all layer weights to a .weights.h5 file. — save_model_weights","text":"","code":"save_model_weights(model, filepath, overwrite = FALSE)"},{"path":"https://keras.posit.co/reference/save_model_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves all layer weights to a .weights.h5 file. — save_model_weights","text":"model keras Model object filepath string. Path save model. Must end .weights.h5. overwrite Whether overwrite existing model target location, instead ask user via interactive prompt.","code":""},{"path":"https://keras.posit.co/reference/save_model_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Saves all layer weights to a .weights.h5 file. — save_model_weights","text":"called primarily side effects. model returned, invisibly, enable usage pipe.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/serialize_keras_object.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","title":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","text":"serialize_keras_object() serializes Keras object named list represents object, reciprocal function deserialize_keras_object(). See deserialize_keras_object() information full config format.","code":""},{"path":"https://keras.posit.co/reference/serialize_keras_object.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","text":"","code":"serialize_keras_object(obj)"},{"path":"https://keras.posit.co/reference/serialize_keras_object.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","text":"obj Keras object serialize.","code":""},{"path":"https://keras.posit.co/reference/serialize_keras_object.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","text":"named list represents object config. config expected contain simple types , can saved json. object can deserialized config via deserialize_keras_object().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/set_random_seed.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","title":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","text":"can use utility make almost Keras program fully deterministic. limitations apply cases network communications involved (e.g. parameter server distribution), creates additional sources randomness, certain non-deterministic cuDNN ops involved. sets: R session seed: set.seed() Python session seed: import random; random.seed(seed) Python NumPy seed: import numpy; numpy.random.seed(seed) TensorFlow seed: tf$random$set_seed(seed) (TF installed) Torch seed: import(\"torch\")$manual_seed(seed) (backend torch) disables Python hash randomization. Note TensorFlow seed set even using TensorFlow backend framework, since many workflows leverage tf$data pipelines (feature random shuffling). Likewise many workflows might leverage NumPy APIs.","code":""},{"path":"https://keras.posit.co/reference/set_random_seed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","text":"","code":"set_random_seed(seed)"},{"path":"https://keras.posit.co/reference/set_random_seed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","text":"seed Integer, random seed use.","code":""},{"path":"https://keras.posit.co/reference/set_random_seed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/shape.html","id":null,"dir":"Reference","previous_headings":"","what":"Tensor shape utility — shape","title":"Tensor shape utility — shape","text":"function can used create get shape object.","code":""},{"path":"https://keras.posit.co/reference/shape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tensor shape utility — shape","text":"","code":"shape(...)  # S3 method for keras_shape format(x, ..., prefix = TRUE)  # S3 method for keras_shape print(x, ...)  # S3 method for keras_shape [(x, ...)  # S3 method for keras_shape as.integer(x, ...)  # S3 method for keras_shape as.list(x, ...)"},{"path":"https://keras.posit.co/reference/shape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tensor shape utility — shape","text":"... shape specification. Numerics, NULL tensors valid. NULL, NA, -1L can used specify unspecified dim size. Tensors dispatched op_shape() extract tensor shape. Values wrapped () used asis (see examples). objects coerced via .integer(). x 'keras_shape' object prefix Whether format shape object prefix. Defaults \"shape\".","code":""},{"path":"https://keras.posit.co/reference/shape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tensor shape utility — shape","text":"list \"keras_shape\" class attribute. element list either ) NULL, b) integer c) scalar integer tensor (e.g., supplied TF tensor unspecified dimension function traced).","code":""},{"path":"https://keras.posit.co/reference/shape.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tensor shape utility — shape","text":"3 ways specify unknown dimension     functions take 'shape' argument also coerce shape()     can also use shape() get shape tensor (excepting scalar integer tensors).             Combine expand shapes         Scalar integer tensors treated axis values. commonly encountered tracing function graph mode, axis size might unknown.     useful pattern unpack shape() %<-%, like :                     want resolve shape tensor can potentially scalar integer, can wrap tensor (), use op_shape().","code":"shape(1, 2, 3) ## shape(1, 2, 3) shape(NA,   2, 3) shape(NULL, 2, 3) shape(-1,   2, 3) ## shape(NA, 2, 3) ## shape(NA, 2, 3) ## shape(NA, 2, 3) layer_input(c(1, 2, 3)) layer_input(shape(1, 2, 3)) ## <KerasTensor shape=(None, 1, 2, 3), dtype=float32, sparse=None, name=keras_tensor> ## <KerasTensor shape=(None, 1, 2, 3), dtype=float32, sparse=None, name=keras_tensor_1> symbolic_tensor <- layer_input(shape(1, 2, 3)) shape(symbolic_tensor) ## shape(NA, 1, 2, 3) eager_tensor <- op_ones(c(1,2,3)) shape(eager_tensor) ## shape(1, 2, 3) op_shape(eager_tensor) ## shape(1, 2, 3) shape(symbolic_tensor, 4) ## shape(NA, 1, 2, 3, 4) shape(5, symbolic_tensor, 4) ## shape(5, NA, 1, 2, 3, 4) tfn <- tensorflow::tf_function(function(x) {   print(op_shape(x))   x }, input_signature = list(tensorflow::tf$TensorSpec(shape(1, NA, 3)))) invisible(tfn(op_ones(shape(1, 2, 3)))) ## shape(1, Tensor(\"strided_slice:0\", shape=(), dtype=int32), 3) c(batch_size, seq_len, channels) %<-% shape(x) echo_print <- function(x) {   message(\"> \", deparse(substitute(x)));   if(!is.null(x)) print(x) } tfn <- tensorflow::tf_function(function(x) {   c(axis1, axis2, axis3) %<-% shape(x)   echo_print(str(list(axis1 = axis1, axis2 = axis2, axis3 = axis3)))    echo_print(shape(axis1))               # use axis1 tensor as axis value   echo_print(shape(axis1, axis2, axis3)) # use axis1 tensor as axis value    # use shape() to compose a new shape, e.g., in multihead attention   n_heads <- 4   echo_print(shape(axis1, axis2, n_heads, axis3/n_heads))    x }, input_signature = list(tensorflow::tf$TensorSpec(shape(NA, 4, 16)))) invisible(tfn(op_ones(shape(2, 4, 16)))) ## > str(list(axis1 = axis1, axis2 = axis2, axis3 = axis3)) ## List of 3 ##  $ axis1:<tf.Tensor 'strided_slice:0' shape=() dtype=int32> ##  $ axis2: int 4 ##  $ axis3: int 16 ## > shape(axis1) ## shape(Tensor(\"strided_slice:0\", shape=(), dtype=int32)) ## > shape(axis1, axis2, axis3) ## shape(Tensor(\"strided_slice:0\", shape=(), dtype=int32), 4, 16) ## > shape(axis1, axis2, n_heads, axis3/n_heads) ## shape(Tensor(\"strided_slice:0\", shape=(), dtype=int32), 4, 4, 4) (x <- op_convert_to_tensor(2L)) ## tf.Tensor(2, shape=(), dtype=int32) # by default, shape() treats scalar integer tensors as axis values shape(x) ## shape(tf.Tensor(2, shape=(), dtype=int32)) # to access the shape of a scalar integer, # call `op_shape()`, or protect with `I()` op_shape(x) ## shape() shape(I(x)) ## shape()"},{"path":[]},{"path":"https://keras.posit.co/reference/split_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"Splits dataset left half right half (e.g. train / test).","code":""},{"path":"https://keras.posit.co/reference/split_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"","code":"split_dataset(   dataset,   left_size = NULL,   right_size = NULL,   shuffle = FALSE,   seed = NULL )"},{"path":"https://keras.posit.co/reference/split_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"dataset tf$data$Dataset, torch$utils$data$Dataset object, list arrays length. left_size float (range [0, 1]), signifies fraction data pack left dataset. integer, signifies number samples pack left dataset. NULL, defaults complement right_size. Defaults NULL. right_size float (range [0, 1]), signifies fraction data pack right dataset. integer, signifies number samples pack right dataset. NULL, defaults complement left_size. Defaults NULL. shuffle Boolean, whether shuffle data splitting . seed random seed shuffling.","code":""},{"path":"https://keras.posit.co/reference/split_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"list two tf$data$Dataset objects: left right splits.","code":""},{"path":"https://keras.posit.co/reference/split_dataset.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"","code":"data <- random_uniform(c(1000, 4)) c(left_ds, right_ds) %<-% split_dataset(list(data$numpy()), left_size = 0.8) left_ds$cardinality() ## tf.Tensor(800, shape=(), dtype=int64) right_ds$cardinality() ## tf.Tensor(200, shape=(), dtype=int64)"},{"path":[]},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"Print summary Keras Model","code":""},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model summary(object, ...)  # S3 method for keras.src.models.model.Model format(   x,   line_length = getOption(\"width\"),   positions = NULL,   expand_nested = FALSE,   show_trainable = NA,   ...,   layer_range = NULL,   compact = TRUE )  # S3 method for keras.src.models.model.Model print(x, ...)"},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"object, x Keras model instance ... summary() print(), passed format(). format(), passed model$summary(). line_length Total length printed lines positions Relative absolute positions log elements line. provided, defaults c(0.33, 0.55, 0.67, 1.0). expand_nested Whether expand nested models. provided, defaults FALSE. show_trainable Whether show layer trainable. provided, defaults FALSE. layer_range list, tuple, vector 2 strings, starting layer name ending layer name (inclusive) indicating range layers printed summary. also accepts regex patterns instead exact name. case, start predicate first element matches layer_range[[1]] end predicate last element matches layer_range[[1]]. default NULL considers layers model. compact Whether remove white-space lines model summary. (Default TRUE)","code":""},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"format() returns length 1 character vector. print() returns model object invisibly. summary() returns output format() invisibly printing .","code":""},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":"enabling-color-output-in-knitr-rmarkdown-quarto-","dir":"Reference","previous_headings":"","what":"Enabling color output in Knitr (RMarkdown, Quarto)","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"order enable color output quarto rmarkdown document html output format (include revealjs presentations), need following setup chunk:","code":"```{r setup, include = FALSE}  options(cli.num_colors = 256)  fansi::set_knit_hooks(knitr::knit_hooks)  options(width = 75) # adjust as needed for format  ```"},{"path":[]},{"path":"https://keras.posit.co/reference/test_on_batch.html","id":null,"dir":"Reference","previous_headings":"","what":"Test the model on a single batch of samples. — test_on_batch","title":"Test the model on a single batch of samples. — test_on_batch","text":"Test model single batch samples.","code":""},{"path":"https://keras.posit.co/reference/test_on_batch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test the model on a single batch of samples. — test_on_batch","text":"","code":"test_on_batch(object, x, y = NULL, sample_weight = NULL, ...)"},{"path":"https://keras.posit.co/reference/test_on_batch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test the model on a single batch of samples. — test_on_batch","text":"object Keras model object x Input data. Must array-like. y Target data. Must array-like. sample_weight Optional array length x, containing weights apply model's loss sample. case temporal data, can pass 2D array shape (samples, sequence_length), apply different weight every timestep every sample. ... forward/backward compatability","code":""},{"path":"https://keras.posit.co/reference/test_on_batch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test the model on a single batch of samples. — test_on_batch","text":"scalar loss value (metrics), named list loss metric values (metrics).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/text_dataset_from_directory.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","title":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","text":"directory structure :   calling text_dataset_from_directory(main_directory, labels='inferred') return tf.data.Dataset yields batches texts subdirectories class_a class_b, together labels 0 1 (0 corresponding class_a 1 corresponding class_b). .txt files supported time.","code":"main_directory/ ...class_a/ ......a_text_1.txt ......a_text_2.txt ...class_b/ ......b_text_1.txt ......b_text_2.txt"},{"path":"https://keras.posit.co/reference/text_dataset_from_directory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","text":"","code":"text_dataset_from_directory(   directory,   labels = \"inferred\",   label_mode = \"int\",   class_names = NULL,   batch_size = 32L,   max_length = NULL,   shuffle = TRUE,   seed = NULL,   validation_split = NULL,   subset = NULL,   follow_links = FALSE,   verbose = TRUE )"},{"path":"https://keras.posit.co/reference/text_dataset_from_directory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","text":"directory Directory data located. labels \"inferred\", contain subdirectories, containing text files class. Otherwise, directory structure ignored. labels Either \"inferred\" (labels generated directory structure), NULL (labels), list/tuple integer labels size number text files found directory. Labels sorted according alphanumeric order text file paths (obtained via os.walk(directory) Python). label_mode String describing encoding labels. Options : \"int\": means labels encoded integers (e.g. sparse_categorical_crossentropy loss). \"categorical\" means labels encoded categorical vector (e.g. categorical_crossentropy loss). \"binary\" means labels (can 2) encoded float32 scalars values 0 1 (e.g. binary_crossentropy). NULL (labels). class_names valid \"labels\" \"inferred\". explicit list class names (must match names subdirectories). Used control order classes (otherwise alphanumerical order used). batch_size Size batches data. Defaults 32. NULL, data batched (dataset yield individual samples). max_length Maximum size text string. Texts longer truncated max_length. shuffle Whether shuffle data. Defaults TRUE. set FALSE, sorts data alphanumeric order. seed Optional random seed shuffling transformations. validation_split Optional float 0 1, fraction data reserve validation. subset Subset data return. One \"training\", \"validation\" \"\". used validation_split set. subset=\"\", utility returns tuple two datasets (training validation datasets respectively). follow_links Whether visits subdirectories pointed symlinks. Defaults FALSE. verbose Whether display number information classes number files found. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/text_dataset_from_directory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","text":"tf.data.Dataset object. label_mode NULL, yields string tensors shape (batch_size,), containing contents batch text files. Otherwise, yields tuple (texts, labels), texts shape (batch_size,) labels follows format described . Rules regarding labels format: label_mode int, labels int32 tensor shape (batch_size,). label_mode binary, labels float32 tensor 1s 0s shape (batch_size, 1). label_mode categorical, labels float32 tensor shape (batch_size, num_classes), representing one-hot encoding class index.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/time_distributed.html","id":null,"dir":"Reference","previous_headings":"","what":"layer_time_distributed — time_distributed","title":"layer_time_distributed — time_distributed","text":"time_distributed() alias layer_time_distributed(). See ?layer_time_distributed() full documentation.","code":""},{"path":"https://keras.posit.co/reference/time_distributed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"layer_time_distributed — time_distributed","text":"","code":"time_distributed(object, layer, ...)"},{"path":"https://keras.posit.co/reference/time_distributed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"layer_time_distributed — time_distributed","text":"object Object compose layer . tensor, array, sequential model. layer Layer instance. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/time_distributed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"layer_time_distributed — time_distributed","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/timeseries_dataset_from_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","title":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","text":"function takes sequence data-points gathered equal intervals, along time series parameters length sequences/windows, spacing two sequence/windows, etc., produce batches timeseries inputs targets.","code":""},{"path":"https://keras.posit.co/reference/timeseries_dataset_from_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","text":"","code":"timeseries_dataset_from_array(   data,   targets,   sequence_length,   sequence_stride = 1L,   sampling_rate = 1L,   batch_size = 128L,   shuffle = FALSE,   seed = NULL,   start_index = NULL,   end_index = NULL )"},{"path":"https://keras.posit.co/reference/timeseries_dataset_from_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","text":"data array eager tensor containing consecutive data points (timesteps). first dimension expected time dimension. targets Targets corresponding timesteps data. targets[] target corresponding window starts index (see example 2 ). Pass NULL target data (case dataset yield input data). sequence_length Length output sequences (number timesteps). sequence_stride Period successive output sequences. stride s, output samples start index data[], data[+ s], data[+ 2 * s], etc. sampling_rate Period successive individual timesteps within sequences. rate r, timesteps data[], data[+ r], ... data[+ sequence_length] used creating sample sequence. batch_size Number timeseries samples batch (except maybe last one). NULL, data batched (dataset yield individual samples). shuffle Whether shuffle output samples, instead draw chronological order. seed Optional int; random seed shuffling. start_index Optional int; data points earlier (exclusive) start_index used output sequences. useful reserve part data test validation. end_index Optional int; data points later (exclusive) end_index used output sequences. useful reserve part data test validation.","code":""},{"path":"https://keras.posit.co/reference/timeseries_dataset_from_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","text":"tf$data$Dataset instance. targets passed, dataset yields list (batch_of_sequences, batch_of_targets). , dataset yields batch_of_sequences. Example 1: Consider indices [0, 1, ... 98]. sequence_length=10,  sampling_rate=2, sequence_stride=3, shuffle=FALSE, dataset yield batches sequences composed following indices:   case last 2 data points discarded since full sequence can generated include (next sequence started index 81, thus last step gone 98). Example 2: Temporal regression. Consider array data scalar values, shape (steps,). generate dataset uses past 10 timesteps predict next timestep, use:     Example 3: Temporal regression many--many architectures. Consider two arrays scalar values X Y, shape (100,). resulting dataset consist samples 20 timestamps . samples overlap. generate dataset uses current timestamp predict corresponding target timestep, use:","code":"First sequence:  [0  2  4  6  8 10 12 14 16 18] Second sequence: [3  5  7  9 11 13 15 17 19 21] Third sequence:  [6  8 10 12 14 16 18 20 22 24] ... Last sequence:   [78 80 82 84 86 88 90 92 94 96] data <- op_array(1:20) input_data <- data[1:10] targets <- data[11:20] dataset <- timeseries_dataset_from_array(   input_data, targets, sequence_length=10) iter <- reticulate::as_iterator(dataset) reticulate::iter_next(iter) ## [[1]] ## tf.Tensor([[ 1  2  3  4  5  6  7  8  9 10]], shape=(1, 10), dtype=int32) ## ## [[2]] ## tf.Tensor([11], shape=(1), dtype=int32) X <- op_array(1:100) Y <- X*2  sample_length <- 20 input_dataset <- timeseries_dataset_from_array(     X, NULL, sequence_length=sample_length, sequence_stride=sample_length) target_dataset <- timeseries_dataset_from_array(     Y, NULL, sequence_length=sample_length, sequence_stride=sample_length)   inputs <- reticulate::as_iterator(input_dataset) %>% reticulate::iter_next() targets <- reticulate::as_iterator(target_dataset) %>% reticulate::iter_next()"},{"path":[]},{"path":"https://keras.posit.co/reference/to_categorical.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts a class vector (integers) to binary class matrix. — to_categorical","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"E.g. use loss_categorical_crossentropy().","code":""},{"path":"https://keras.posit.co/reference/to_categorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"","code":"to_categorical(x, num_classes = NULL)"},{"path":"https://keras.posit.co/reference/to_categorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"x Array-like class values converted matrix (integers 0 num_classes - 1). R factors coerced integer offset 0-based, .e., .integer(x) - 1L. num_classes Total number classes. NULL, inferred max(x) + 1. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/to_categorical.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"binary matrix representation input R array. class axis placed last.","code":""},{"path":"https://keras.posit.co/reference/to_categorical.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"","code":"a <- to_categorical(c(0, 1, 2, 3), num_classes=4) print(a) ##      [,1] [,2] [,3] [,4] ## [1,]    1    0    0    0 ## [2,]    0    1    0    0 ## [3,]    0    0    1    0 ## [4,]    0    0    0    1 b <- array(c(.9, .04, .03, .03,               .3, .45, .15, .13,               .04, .01, .94, .05,               .12, .21, .5, .17),               dim = c(4, 4)) loss <- op_categorical_crossentropy(a, b) loss ## tf.Tensor([0.41284522 0.45601739 0.54430155 0.80437282], shape=(4), dtype=float64) loss <- op_categorical_crossentropy(a, a) loss ## tf.Tensor([1.00000005e-07 1.00000005e-07 1.00000005e-07 1.00000005e-07], shape=(4), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/train_on_batch.html","id":null,"dir":"Reference","previous_headings":"","what":"Runs a single gradient update on a single batch of data. — train_on_batch","title":"Runs a single gradient update on a single batch of data. — train_on_batch","text":"Runs single gradient update single batch data.","code":""},{"path":"https://keras.posit.co/reference/train_on_batch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Runs a single gradient update on a single batch of data. — train_on_batch","text":"","code":"train_on_batch(object, x, y = NULL, sample_weight = NULL, class_weight = NULL)"},{"path":"https://keras.posit.co/reference/train_on_batch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Runs a single gradient update on a single batch of data. — train_on_batch","text":"object Keras model object x Input data. Must array-like. y Target data. Must array-like. sample_weight Optional array length x, containing weights apply model's loss sample. case temporal data, can pass 2D array shape (samples, sequence_length), apply different weight every timestep every sample. class_weight Optional named list mapping class indices (integers, 0-based) weight (float) apply model's loss samples class training. can useful tell model \"pay attention\" samples -represented class. class_weight specified targets rank 2 greater, either y must one-hot encoded, explicit final dimension 1 must included sparse class labels.","code":""},{"path":"https://keras.posit.co/reference/train_on_batch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Runs a single gradient update on a single batch of data. — train_on_batch","text":"scalar loss value (metrics), named list loss metric values (metrics). property model$metrics_names give display labels scalar outputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":null,"dir":"Reference","previous_headings":"","what":"Unpacks user-provided data list. — unpack_x_y_sample_weight","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"convenience utility used overriding $train_step, $test_step, $predict_step. utility makes easy support data form (x,), (x, y), (x, y, sample_weight).","code":""},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"","code":"unpack_x_y_sample_weight(data)"},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"data list form (x), (x, y), (x, y, sample_weight).","code":""},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"unpacked list, NULLs y sample_weight provided.","code":""},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":"example-","dir":"Reference","previous_headings":"","what":"Example:","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"can also equivalent providing default values %<-%","code":"features_batch <- op_ones(c(10, 5)) labels_batch <- op_zeros(c(10, 5)) data <- list(features_batch, labels_batch) # `y` and `sample_weight` will default to `NULL` if not provided. c(x, y, sample_weight) %<-% unpack_x_y_sample_weight(data) c(x, y = NULL, sample_weight = NULL) %<-% data"},{"path":[]},{"path":"https://keras.posit.co/reference/use_backend.html","id":null,"dir":"Reference","previous_headings":"","what":"Configure a Keras backend — use_backend","title":"Configure a Keras backend — use_backend","text":"Configure Keras backend","code":""},{"path":"https://keras.posit.co/reference/use_backend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Configure a Keras backend — use_backend","text":"","code":"use_backend(backend)"},{"path":"https://keras.posit.co/reference/use_backend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Configure a Keras backend — use_backend","text":"backend string, can \"tensorflow\", \"jax\", \"numpy\", \"torch\".","code":""},{"path":"https://keras.posit.co/reference/use_backend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Configure a Keras backend — use_backend","text":"Called primarily side effects. Returns provided backend, invisibly.","code":""},{"path":"https://keras.posit.co/reference/use_backend.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Configure a Keras backend — use_backend","text":"functions allow configuring backend keras use. Note one backend can configured time. function called library(keras3) calling functions within package (see example). experimental support changing backend keras initialized. using config_set_backend().","code":"library(keras3) use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":null,"dir":"Reference","previous_headings":"","what":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"Provide scope mappings names custom objects","code":""},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"","code":"with_custom_object_scope(objects, expr)"},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"objects Named list objects expr Expression evaluate","code":""},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"result evaluating expr within custom object scope.","code":""},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"many elements Keras models can customized user objects (e.g. losses, metrics, regularizers, etc.). loading saved models use functions typically need explicitly map names user objects via custom_objects parameter. with_custom_object_scope() function provides alternative lets create named alias user object applies entire block code, automatically recognized loading saved models.","code":""},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"","code":"# define custom metric metric_top_3_categorical_accuracy <-   custom_metric(\"top_3_categorical_accuracy\", function(y_true, y_pred) {     metric_top_k_categorical_accuracy(y_true, y_pred, k = 3)   })  with_custom_object_scope(c(top_k_acc = sparse_top_k_cat_acc), {    # ...define model...    # compile model (refer to \"top_k_acc\" by name)   model |> compile(     loss = \"binary_crossentropy\",     optimizer = optimizer_nadam(),     metrics = c(\"top_k_acc\")   )    # save the model   model |> save_model(\"my_model.keras\")    # loading the model within the custom object scope doesn't   # require explicitly providing the custom_object   reloaded_model <- load_model(\"my_model.keras\") })"},{"path":[]},{"path":"https://keras.posit.co/reference/zip_lists.html","id":null,"dir":"Reference","previous_headings":"","what":"Zip lists — zip_lists","title":"Zip lists — zip_lists","text":"conceptually similar zip() Python, R functions purrr::transpose() data.table::transpose() (albeit, accepting elements ... instead single list), one crucial difference: provided objects named, matching done names, positions.","code":""},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zip lists — zip_lists","text":"","code":"zip_lists(...)"},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zip lists — zip_lists","text":"... R lists atomic vectors, optionally named.","code":""},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zip lists — zip_lists","text":"inverted list","code":""},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Zip lists — zip_lists","text":"arguments supplied must length. positional matching required, arguments provided must unnamed. matching names, arguments must set names, can different orders.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Zip lists — zip_lists","text":"","code":"gradients <- list(\"grad_for_wt_1\", \"grad_for_wt_2\", \"grad_for_wt_3\") weights <- list(\"weight_1\", \"weight_2\", \"weight_3\") str(zip_lists(gradients, weights)) #> List of 3 #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_1\" #>   ..$ : chr \"weight_1\" #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_2\" #>   ..$ : chr \"weight_2\" #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_3\" #>   ..$ : chr \"weight_3\" str(zip_lists(gradient = gradients, weight = weights)) #> List of 3 #>  $ :List of 2 #>   ..$ gradient: chr \"grad_for_wt_1\" #>   ..$ weight  : chr \"weight_1\" #>  $ :List of 2 #>   ..$ gradient: chr \"grad_for_wt_2\" #>   ..$ weight  : chr \"weight_2\" #>  $ :List of 2 #>   ..$ gradient: chr \"grad_for_wt_3\" #>   ..$ weight  : chr \"weight_3\"  names(gradients) <- names(weights) <- paste0(\"layer_\", 1:3) str(zip_lists(gradients, weights[c(3, 1, 2)])) #> List of 3 #>  $ layer_1:List of 2 #>   ..$ : chr \"grad_for_wt_1\" #>   ..$ : chr \"weight_1\" #>  $ layer_2:List of 2 #>   ..$ : chr \"grad_for_wt_2\" #>   ..$ : chr \"weight_2\" #>  $ layer_3:List of 2 #>   ..$ : chr \"grad_for_wt_3\" #>   ..$ : chr \"weight_3\"  names(gradients) <- paste0(\"gradient_\", 1:3) try(zip_lists(gradients, weights)) # error, names don't match #> Error in zip_lists(gradients, weights) :  #>   All names of arguments provided to `zip_lists()` must match. Call `unname()` on each argument if you want positional matching # call unname directly for positional matching str(zip_lists(unname(gradients), unname(weights))) #> List of 3 #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_1\" #>   ..$ : chr \"weight_1\" #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_2\" #>   ..$ : chr \"weight_2\" #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_3\" #>   ..$ : chr \"weight_3\""},{"path":"https://keras.posit.co/news/index.html","id":"keras3-development-version","dir":"Changelog","previous_headings":"","what":"keras3 (development version)","title":"keras3 (development version)","text":"Chains layer_* calls |> now instantiate layers order %>% pipe chains: left-hand-side first (#1440). iterate(), iter_next() as_iterator() now reexported reticulate. User facing changes upstream Keras v3.3.2: new function: op_ctc_decode() new function: op_eigh() new function: op_select() new function: op_vectorize() new function: op_image_rgb_to_grayscale() new function: loss_tversky() new args: layer_resizing(pad_to_aspect_ratio, fill_mode, fill_value) new arg: layer_embedding(weights) providing initial weights matrix new args: op_nan_to_num(nan, posinf, neginf) new args: op_image_resize(crop_to_aspect_ratio, pad_to_aspect_ratio, fill_mode, fill_value) new args: op_argmax(keepdims) op_argmin(keepdims) new arg: clear_session(free_memory) clearing without invoking garbage collector. metric_kl_divergence() loss_kl_divergence() clip inputs (y_true y_pred) [0, 1] range. new Layer() attributes: metrics, dtype_policy Added initial support float8 training layer_conv_*d() layers now support LoRa op_digitize() now supports sparse tensors. Models layers now return owned metrics recursively. Add pickling support Keras models. (e.g., via reticulate::py_save_object()) Note pickling recommended, prefer using Keras saving APIs.","code":""},{"path":"https://keras.posit.co/news/index.html","id":"keras3-020","dir":"Changelog","previous_headings":"","what":"keras3 0.2.0","title":"keras3 0.2.0","text":"CRAN release: 2024-04-18 New functions: quantize_weights(): quantize model layer weights -place. Currently, Dense, EinsumDense, Embedding layers supported (enough cover majority transformers today) layer_mel_spectrogram() layer_flax_module_wrapper() layer_jax_model_wrapper() loss_dice() random_beta() random_binomial() config_set_backend(): change backend Keras initialized. config_dtype_policy() config_set_dtype_policy() New Ops op_custom_gradient() op_batch_normalization() op_image_crop() op_divide_no_nan() op_normalize() op_correlate() ` New family linear algebra ops op_cholesky() op_det() op_eig() op_inv() op_lu_factor() op_norm() op_erfinv() op_solve_triangular() op_svd() audio_dataset_from_directory(), image_dataset_from_directory() text_dataset_from_directory() gain verbose argument (default TRUE) image_dataset_from_directory() gains pad_to_aspect_ratio argument (default FALSE) to_categorical(), op_one_hot(), fit() can now accept R factors, offset 0-based (reported #1055). op_convert_to_numpy() now returns unconverted NumPy arrays. op_array() op_convert_to_tensor() longer error casting R doubles integer types. export_savedmodel() now works Jax backend. Metric()$add_variable() method gains arg: aggregration. Layer()$add_weight() method gains args: autocast, regularizer, aggregation. op_bincount(), op_multi_hot(), op_one_hot(), layer_category_encoding() now support sparse tensors. op_custom_gradient() now supports PyTorch backend layer_lstm() layer_gru() gain arg use_cudnn, default 'auto'. Fixed issue application_preprocess_inputs() error supplied R array input. Doc improvements.","code":""},{"path":"https://keras.posit.co/news/index.html","id":"keras3-010","dir":"Changelog","previous_headings":"","what":"keras3 0.1.0","title":"keras3 0.1.0","text":"CRAN release: 2024-02-17 package rebuilt Keras 3.0. Refer  overview https://keras.posit.co current --date documentation.","code":""}]
+[{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Customizing what happens in `fit()` with JAX","text":"’re supervised learning, can use fit() everything works smoothly. need take control every little detail, can write training loop entirely scratch. need custom training algorithm, still want benefit convenient features fit(), callbacks, built-distribution support, step fusing? core principle Keras progressive disclosure complexity. always able get lower-level workflows gradual way. shouldn’t fall cliff high-level functionality doesn’t exactly match use case. able gain control small details retaining commensurate amount high-level convenience. need customize fit() , override training step function Model class. function called fit() every batch data. able call fit() usual – running learning algorithm. Note pattern prevent building models Functional API. can whether ’re building Sequential models, Functional API models, subclassed models. Let’s see works.","code":""},{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Customizing what happens in `fit()` with JAX","text":"","code":"import os  # This guide can only be run with the JAX backend. os.environ[\"KERAS_BACKEND\"] = \"jax\"  import jax import keras import numpy as np"},{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"a-first-simple-example","dir":"Articles","previous_headings":"","what":"A first simple example","title":"Customizing what happens in `fit()` with JAX","text":"Let’s start simple example: create new class subclasses keras.Model. implement fully-stateless compute_loss_and_updates() method compute loss well updated values non-trainable variables model. Internally, calls stateless_call() built-compute_loss(). implement fully-stateless train_step() method compute current metric values (including loss) well updated values trainable variables, optimizer variables, metric variables. Note can also take account sample_weight argument : Unpacking data x, y, sample_weight = data Passing sample_weight compute_loss() Passing sample_weight alongside y y_pred metrics stateless_update_state() Let’s try :","code":"class CustomModel(keras.Model):     def compute_loss_and_updates(         self,         trainable_variables,         non_trainable_variables,         x,         y,         training=False,     ):         y_pred, non_trainable_variables = self.stateless_call(             trainable_variables,             non_trainable_variables,             x,             training=training,         )         loss = self.compute_loss(x, y, y_pred)         return loss, (y_pred, non_trainable_variables)      def train_step(self, state, data):         (             trainable_variables,             non_trainable_variables,             optimizer_variables,             metrics_variables,         ) = state         x, y = data          # Get the gradient function.         grad_fn = jax.value_and_grad(             self.compute_loss_and_updates, has_aux=True         )          # Compute the gradients.         (loss, (y_pred, non_trainable_variables)), grads = grad_fn(             trainable_variables,             non_trainable_variables,             x,             y,             training=True,         )          # Update trainable variables and optimizer variables.         (             trainable_variables,             optimizer_variables,         ) = self.optimizer.stateless_apply(             optimizer_variables, grads, trainable_variables         )          # Update metrics.         new_metrics_vars = []         for metric in self.metrics:             this_metric_vars = metrics_variables[                 len(new_metrics_vars) : len(new_metrics_vars)                 + len(metric.variables)             ]             if metric.name == \"loss\":                 this_metric_vars = metric.stateless_update_state(                     this_metric_vars, loss                 )             else:                 this_metric_vars = metric.stateless_update_state(                     this_metric_vars, y, y_pred                 )             logs = metric.stateless_result(this_metric_vars)             new_metrics_vars += this_metric_vars          # Return metric logs and updated state variables.         state = (             trainable_variables,             non_trainable_variables,             optimizer_variables,             new_metrics_vars,         )         return logs, state # Construct and compile an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])  # Just use `fit` as usual x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=3)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"going-lower-level","dir":"Articles","previous_headings":"","what":"Going lower-level","title":"Customizing what happens in `fit()` with JAX","text":"Naturally, just skip passing loss function compile(), instead everything manually train_step. Likewise metrics. ’s lower-level example, uses compile() configure optimizer:","code":"class CustomModel(keras.Model):     def __init__(self, *args, **kwargs):         super().__init__(*args, **kwargs)         self.loss_tracker = keras.metrics.Mean(name=\"loss\")         self.mae_metric = keras.metrics.MeanAbsoluteError(name=\"mae\")         self.loss_fn = keras.losses.MeanSquaredError()      def compute_loss_and_updates(         self,         trainable_variables,         non_trainable_variables,         x,         y,         training=False,     ):         y_pred, non_trainable_variables = self.stateless_call(             trainable_variables,             non_trainable_variables,             x,             training=training,         )         loss = self.loss_fn(y, y_pred)         return loss, (y_pred, non_trainable_variables)      def train_step(self, state, data):         (             trainable_variables,             non_trainable_variables,             optimizer_variables,             metrics_variables,         ) = state         x, y = data          # Get the gradient function.         grad_fn = jax.value_and_grad(             self.compute_loss_and_updates, has_aux=True         )          # Compute the gradients.         (loss, (y_pred, non_trainable_variables)), grads = grad_fn(             trainable_variables,             non_trainable_variables,             x,             y,             training=True,         )          # Update trainable variables and optimizer variables.         (             trainable_variables,             optimizer_variables,         ) = self.optimizer.stateless_apply(             optimizer_variables, grads, trainable_variables         )          # Update metrics.         loss_tracker_vars = metrics_variables[             : len(self.loss_tracker.variables)         ]         mae_metric_vars = metrics_variables[len(self.loss_tracker.variables) :]          loss_tracker_vars = self.loss_tracker.stateless_update_state(             loss_tracker_vars, loss         )         mae_metric_vars = self.mae_metric.stateless_update_state(             mae_metric_vars, y, y_pred         )          logs = {}         logs[self.loss_tracker.name] = self.loss_tracker.stateless_result(             loss_tracker_vars         )         logs[self.mae_metric.name] = self.mae_metric.stateless_result(             mae_metric_vars         )          new_metrics_vars = loss_tracker_vars + mae_metric_vars          # Return metric logs and updated state variables.         state = (             trainable_variables,             non_trainable_variables,             optimizer_variables,             new_metrics_vars,         )         return logs, state      @property     def metrics(self):         # We list our `Metric` objects here so that `reset_states()` can be         # called automatically at the start of each epoch         # or at the start of `evaluate()`.         return [self.loss_tracker, self.mae_metric]   # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs)  # We don't passs a loss or metrics here. model.compile(optimizer=\"adam\")  # Just use `fit` as usual -- you can use callbacks, etc. x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=5)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"providing-your-own-evaluation-step","dir":"Articles","previous_headings":"","what":"Providing your own evaluation step","title":"Customizing what happens in `fit()` with JAX","text":"want calls model.evaluate()? override test_step exactly way. ’s looks like: ’s !","code":"class CustomModel(keras.Model):     def test_step(self, state, data):         # Unpack the data.         x, y = data         (             trainable_variables,             non_trainable_variables,             metrics_variables,         ) = state          # Compute predictions and loss.         y_pred, non_trainable_variables = self.stateless_call(             trainable_variables,             non_trainable_variables,             x,             training=False,         )         loss = self.compute_loss(x, y, y_pred)          # Update metrics.         new_metrics_vars = []         for metric in self.metrics:             this_metric_vars = metrics_variables[                 len(new_metrics_vars) : len(new_metrics_vars)                 + len(metric.variables)             ]             if metric.name == \"loss\":                 this_metric_vars = metric.stateless_update_state(                     this_metric_vars, loss                 )             else:                 this_metric_vars = metric.stateless_update_state(                     this_metric_vars, y, y_pred                 )             logs = metric.stateless_result(this_metric_vars)             new_metrics_vars += this_metric_vars          # Return metric logs and updated state variables.         state = (             trainable_variables,             non_trainable_variables,             new_metrics_vars,         )         return logs, state   # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(loss=\"mse\", metrics=[\"mae\"])  # Evaluate with our custom test_step x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.evaluate(x, y)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Customizing what happens in `fit()` with TensorFlow","text":"’re supervised learning, can use fit() everything works smoothly. need take control every little detail, can write training loop entirely scratch. need custom training algorithm, still want benefit convenient features fit(), callbacks, built-distribution support, step fusing? core principle Keras progressive disclosure complexity. always able get lower-level workflows gradual way. shouldn’t fall cliff high-level functionality doesn’t exactly match use case. able gain control small details retaining commensurate amount high-level convenience. need customize fit() , override training step function Model class. function called fit() every batch data. able call fit() usual – running learning algorithm. Note pattern prevent building models Functional API. can whether ’re building Sequential models, Functional API models, subclassed models. Let’s see works.","code":""},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Customizing what happens in `fit()` with TensorFlow","text":"","code":"library(reticulate) library(tensorflow, exclude = c(\"set_random_seed\", \"shape\")) library(keras3)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"a-first-simple-example","dir":"Articles","previous_headings":"","what":"A first simple example","title":"Customizing what happens in `fit()` with TensorFlow","text":"Let’s start simple example: create new class subclasses Model. just override method train_step(self, data). return dictionary mapping metric names (including loss) current value. input argument data gets passed fit training data: pass arrays, calling fit(x, y, ...), data list (x, y) pass tf_dataset, calling fit(dataset, ...), data gets yielded dataset batch. body train_step() method, implement regular training update, similar already familiar . Importantly, compute loss via self.compute_loss(), wraps loss(es) function(s) passed compile(). Similarly, call metric$update_state(y, y_pred) metrics self$metrics, update state metrics passed compile(), query results self$metrics end retrieve current value. Let’s try :","code":"CustomModel <- new_model_class(   \"CustomModel\",   train_step = function(data) {     c(x, y = NULL, sample_weight = NULL) %<-% data      with(tf$GradientTape() %as% tape, {       y_pred <- self(x, training = TRUE)       loss <- self$compute_loss(y = y, y_pred = y_pred,                                 sample_weight = sample_weight)     })      # Compute gradients     trainable_vars <- self$trainable_variables     gradients <- tape$gradient(loss, trainable_vars)      # Update weights     self$optimizer$apply(gradients, trainable_vars)      # Update metrics (includes the metric that tracks the loss)     for (metric in self$metrics) {       if (metric$name == \"loss\")         metric$update_state(loss)       else         metric$update_state(y, y_pred)     }      # Return a dict mapping metric names to current value     metrics <- lapply(self$metrics, function(m) m$result())     metrics <- setNames(metrics, sapply(self$metrics, function(m) m$name))     metrics   } ) # Construct and compile an instance of CustomModel inputs <- keras_input(shape = 32) outputs <- layer_dense(inputs, 1) model <- CustomModel(inputs, outputs) model |> compile(optimizer = \"adam\", loss = \"mse\", metrics = \"mae\")  # Just use `fit` as usual x <- random_normal(c(1000, 32)) y <- random_normal(c(1000, 1)) model |> fit(x, y, epochs = 3) ## Epoch 1/3 ## 32/32 - 1s - 19ms/step - loss: 2.9118 - mae: 1.3597 ## Epoch 2/3 ## 32/32 - 0s - 1ms/step - loss: 2.6026 - mae: 1.2856 ## Epoch 3/3 ## 32/32 - 0s - 1ms/step - loss: 2.3378 - mae: 1.2193"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"going-lower-level","dir":"Articles","previous_headings":"","what":"Going lower-level","title":"Customizing what happens in `fit()` with TensorFlow","text":"Naturally, just skip passing loss function compile(), instead everything manually train_step. Likewise metrics. ’s lower-level example, uses compile() configure optimizer: start creating Metric instances track loss MAE score (__init__()). implement custom train_step() updates state metrics (calling update_state() ), query (via result()) return current average value, displayed progress bar pass callback. Note need call reset_states() metrics epoch! Otherwise calling result() return average since start training, whereas usually work per-epoch averages. Thankfully, framework can us: just list metric want reset metrics property model. model call reset_states() object listed beginning fit() epoch beginning call evaluate().","code":"CustomModel <- new_model_class(   \"CustomModel\",   initialize = function(...) {     super$initialize(...)     self$loss_tracker <- metric_mean(name = \"loss\")     self$mae_metric <- metric_mean_absolute_error(name = \"mae\")     self$loss_fn <- loss_mean_squared_error()   },   train_step = function(data) {     c(x, y = NULL, sample_weight = NULL) %<-% data      with(tf$GradientTape() %as% tape, {       y_pred <- self(x, training = TRUE)       loss <- self$loss_fn(y, y_pred, sample_weight = sample_weight)     })      # Compute gradients     trainable_vars <- self$trainable_variables     gradients <- tape$gradient(loss, trainable_vars)      # Update weights     self$optimizer$apply(gradients, trainable_vars)      # Compute our own metrics     self$loss_tracker$update_state(loss)     self$mae_metric$update_state(y, y_pred)      # Return a dict mapping metric names to current value     list(       loss = self$loss_tracker$result(),       mae = self$mae_metric$result()     )   },   metrics = mark_active(function() {     # We list our `Metric` objects here so that `reset_states()` can be     # called automatically at the start of each epoch     # or at the start of `evaluate()`.     list(self$loss_tracker, self$mae_metric)   }) )   # Construct and compile an instance of CustomModel inputs <- keras_input(shape = 32) outputs <- layer_dense(inputs, 1) model <- CustomModel(inputs, outputs)  # We don't pass a loss or metrics here. model |> compile(optimizer = \"adam\")  # Just use `fit` as usual x <- random_normal(c(1000, 32)) y <- random_normal(c(1000, 1)) model |> fit(x, y, epochs = 3) ## Epoch 1/3 ## 32/32 - 1s - 16ms/step - loss: 2.6540 - mae: 1.2901 ## Epoch 2/3 ## 32/32 - 0s - 1ms/step - loss: 2.4139 - mae: 1.2303 ## Epoch 3/3 ## 32/32 - 0s - 1ms/step - loss: 2.2080 - mae: 1.1761"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"supporting-sample_weight-class_weight","dir":"Articles","previous_headings":"","what":"Supporting sample_weight & class_weight","title":"Customizing what happens in `fit()` with TensorFlow","text":"may noticed first basic example didn’t make mention sample weighting. want support fit() arguments sample_weight class_weight, ’d simply following: Unpack sample_weight data argument Pass compute_loss & update_state (course, also just apply manually don’t rely compile() losses & metrics) ’s .","code":"CustomModel <- new_model_class(   \"CustomModel\",   train_step = function(data) {     c(x, y = NULL, sample_weight = NULL) %<-% data      with(tf$GradientTape() %as% tape, {       y_pred <- self(x, training = TRUE)       loss <- self$compute_loss(y = y, y_pred = y_pred,                                 sample_weight = sample_weight)     })      # Compute gradients     trainable_vars <- self$trainable_variables     gradients <- tape$gradient(loss, trainable_vars)      # Update weights     self$optimizer$apply_gradients(zip_lists(gradients, trainable_vars))      # Update metrics (includes the metric that tracks the loss)     for (metric in self$metrics) {       if (metric$name == \"loss\") {         metric$update_state(loss)       } else {         metric$update_state(y, y_pred, sample_weight = sample_weight)       }     }      # Return a dict mapping metric names to current value     metrics <- lapply(self$metrics, function(m) m$result())     metrics <- setNames(metrics, sapply(self$metrics, function(m) m$name))     metrics   } )   # Construct and compile an instance of CustomModel inputs <- keras_input(shape = 32) outputs <- layer_dense(inputs, units = 1) model <- CustomModel(inputs, outputs) model |> compile(optimizer = \"adam\", loss = \"mse\", metrics = \"mae\")  # You can now use sample_weight argument x <- random_normal(c(1000, 32)) y <- random_normal(c(1000, 1)) sw <- random_normal(c(1000, 1)) model |> fit(x, y, sample_weight = sw, epochs = 3) ## Epoch 1/3 ## 32/32 - 1s - 18ms/step - loss: 0.1607 - mae: 1.3018 ## Epoch 2/3 ## 32/32 - 0s - 1ms/step - loss: 0.1452 - mae: 1.2999 ## Epoch 3/3 ## 32/32 - 0s - 1ms/step - loss: 0.1335 - mae: 1.2986"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"providing-your-own-evaluation-step","dir":"Articles","previous_headings":"","what":"Providing your own evaluation step","title":"Customizing what happens in `fit()` with TensorFlow","text":"want calls model.evaluate()? override test_step exactly way. ’s looks like:","code":"CustomModel <- new_model_class(   \"CustomModel\",   test_step = function(data) {     # Unpack the data     c(x, y = NULL, sw = NULL) %<-% data     # Compute predictions     y_pred = self(x, training = FALSE)     # Updates the metrics tracking the loss     self$compute_loss(y = y, y_pred = y_pred, sample_weight = sw)     # Update the metrics.     for (metric in self$metrics) {       if (metric$name != \"loss\") {         metric$update_state(y, y_pred, sample_weight = sw)       }     }     # Return a dict mapping metric names to current value.     # Note that it will include the loss (tracked in self.metrics).     metrics <- lapply(self$metrics, function(m) m$result())     metrics <- setNames(metrics, sapply(self$metrics, function(m) m$name))     metrics   } )  # Construct an instance of CustomModel inputs <- keras_input(shape = 32) outputs <- layer_dense(inputs, 1) model <- CustomModel(inputs, outputs) model |> compile(loss = \"mse\", metrics = \"mae\")  # Evaluate with our custom test_step x <- random_normal(c(1000, 32)) y <- random_normal(c(1000, 1)) model |> evaluate(x, y) ## 32/32 - 0s - 8ms/step - loss: 0.0000e+00 - mae: 1.3947 ## $loss ## [1] 0 ## ## $mae ## [1] 1.394695"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"wrapping-up-an-end-to-end-gan-example","dir":"Articles","previous_headings":"","what":"Wrapping up: an end-to-end GAN example","title":"Customizing what happens in `fit()` with TensorFlow","text":"Let’s walk end--end example leverages everything just learned. Let’s consider: generator network meant generate 28x28x1 images. discriminator network meant classify 28x28x1 images two classes (“fake” “real”). One optimizer . loss function train discriminator. ’s feature-complete GAN class, overriding compile() use signature, implementing entire GAN algorithm 17 lines train_step: Let’s test-drive : ideas behind deep learning simple, implementation painful?","code":"# Create the discriminator discriminator <-   keras_model_sequential(name = \"discriminator\", input_shape = c(28, 28, 1)) |>    layer_conv_2d(filters = 64, kernel_size = c(3, 3),                 strides = c(2, 2),  padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_conv_2d(filters = 128, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_global_max_pooling_2d() |>   layer_dense(units = 1)   # Create the generator latent_dim <- 128 generator <-   keras_model_sequential(name = \"generator\", input_shape = latent_dim) |>    layer_dense(7 * 7 * 128) |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_reshape(target_shape = c(7, 7, 128)) |>    layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_conv_2d(filters = 1, kernel_size = c(7, 7), padding = \"same\",                 activation = \"sigmoid\") GAN <- Model(   classname = \"GAN\",    initialize = function(discriminator, generator, latent_dim, ...) {     super$initialize(...)     self$discriminator <- discriminator     self$generator <- generator     self$latent_dim <- as.integer(latent_dim)     self$d_loss_tracker <- metric_mean(name = \"d_loss\")     self$g_loss_tracker <- metric_mean(name = \"g_loss\")   },    compile = function(d_optimizer, g_optimizer, loss_fn, ...) {     super$compile(...)     self$d_optimizer <- d_optimizer     self$g_optimizer <- g_optimizer     self$loss_fn <- loss_fn   },   metrics = active_property(function() {     list(self$d_loss_tracker, self$g_loss_tracker)   }),    train_step = function(real_images) {      # Sample random points in the latent space     batch_size <- shape(real_images)[[1]]     random_latent_vectors <-       tf$random$normal(shape(batch_size, self$latent_dim))      # Decode them to fake images     generated_images <- self$generator(random_latent_vectors)      # Combine them with real images     combined_images <- op_concatenate(list(generated_images,                                            real_images))      # Assemble labels discriminating real from fake images     labels <- op_concatenate(list(op_ones(c(batch_size, 1)),                                   op_zeros(c(batch_size, 1))))      # Add random noise to the labels - important trick!     labels %<>% `+`(tf$random$uniform(shape(.), maxval = 0.05))      # Train the discriminator     with(tf$GradientTape() %as% tape, {       predictions <- self$discriminator(combined_images)       d_loss <- self$loss_fn(labels, predictions)     })     grads <- tape$gradient(d_loss, self$discriminator$trainable_weights)     self$d_optimizer$apply_gradients(       zip_lists(grads, self$discriminator$trainable_weights))      # Sample random points in the latent space     random_latent_vectors <-       tf$random$normal(shape(batch_size, self$latent_dim))      # Assemble labels that say \"all real images\"     misleading_labels <- op_zeros(c(batch_size, 1))      # Train the generator (note that we should *not* update the weights     # of the discriminator)!     with(tf$GradientTape() %as% tape, {       predictions <- self$discriminator(self$generator(random_latent_vectors))       g_loss <- self$loss_fn(misleading_labels, predictions)     })     grads <- tape$gradient(g_loss, self$generator$trainable_weights)     self$g_optimizer$apply_gradients(       zip_lists(grads, self$generator$trainable_weights))      list(d_loss = d_loss, g_loss = g_loss)   } ) batch_size <- 64 c(c(x_train, .), c(x_test, .)) %<-% dataset_mnist() all_digits <- op_concatenate(list(x_train, x_test)) all_digits <- op_reshape(all_digits, c(-1, 28, 28, 1)) dataset <- all_digits |>   tfdatasets::tensor_slices_dataset() |>   tfdatasets::dataset_map(\\(x) op_cast(x, \"float32\") / 255) |>   tfdatasets::dataset_shuffle(buffer_size = 1024) |>   tfdatasets::dataset_batch(batch_size = batch_size)  gan <- GAN(discriminator = discriminator,            generator = generator,            latent_dim = latent_dim)  gan |> compile(   d_optimizer = optimizer_adam(learning_rate = 0.0003),   g_optimizer = optimizer_adam(learning_rate = 0.0003),   loss_fn = loss_binary_crossentropy(from_logits = TRUE) )  # To limit the execution time, we only train on 100 batches. You can train on # the entire dataset. You will need about 20 epochs to get nice results. gan |> fit(   tfdatasets::dataset_take(dataset, 100),   epochs = 1 ) ## 100/100 - 5s - 47ms/step - d_loss: 0.0000e+00 - g_loss: 0.0000e+00"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Customizing what happens in `fit()` with PyTorch","text":"’re supervised learning, can use fit() everything works smoothly. need take control every little detail, can write training loop entirely scratch. need custom training algorithm, still want benefit convenient features fit(), callbacks, built-distribution support, step fusing? core principle Keras progressive disclosure complexity. always able get lower-level workflows gradual way. shouldn’t fall cliff high-level functionality doesn’t exactly match use case. able gain control small details retaining commensurate amount high-level convenience. need customize fit() , override training step function Model class. function called fit() every batch data. able call fit() usual – running learning algorithm. Note pattern prevent building models Functional API. can whether ’re building Sequential models, Functional API models, subclassed models. Let’s see works.","code":""},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Customizing what happens in `fit()` with PyTorch","text":"","code":"import os  # This guide can only be run with the torch backend. os.environ[\"KERAS_BACKEND\"] = \"torch\"  import torch import keras from keras import layers import numpy as np"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"a-first-simple-example","dir":"Articles","previous_headings":"","what":"A first simple example","title":"Customizing what happens in `fit()` with PyTorch","text":"Let’s start simple example: create new class subclasses keras.Model. just override method train_step(self, data). return dictionary mapping metric names (including loss) current value. input argument data gets passed fit training data: pass NumPy arrays, calling fit(x, y, ...), data tuple (x, y) pass torch.utils.data.DataLoader tf.data.Dataset, calling fit(dataset, ...), data gets yielded dataset batch. body train_step() method, implement regular training update, similar already familiar . Importantly, compute loss via self.compute_loss(), wraps loss(es) function(s) passed compile(). Similarly, call metric.update_state(y, y_pred) metrics self.metrics, update state metrics passed compile(), query results self.metrics end retrieve current value. Let’s try :","code":"class CustomModel(keras.Model):     def train_step(self, data):         # Unpack the data. Its structure depends on your model and         # on what you pass to `fit()`.         x, y = data          # Call torch.nn.Module.zero_grad() to clear the leftover gradients         # for the weights from the previous train step.         self.zero_grad()          # Compute loss         y_pred = self(x, training=True)  # Forward pass         loss = self.compute_loss(y=y, y_pred=y_pred)          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()          trainable_weights = [v for v in self.trainable_weights]         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             self.optimizer.apply(gradients, trainable_weights)          # Update metrics (includes the metric that tracks the loss)         for metric in self.metrics:             if metric.name == \"loss\":                 metric.update_state(loss)             else:                 metric.update_state(y, y_pred)          # Return a dict mapping metric names to current value         # Note that it will include the loss (tracked in self.metrics).         return {m.name: m.result() for m in self.metrics} # Construct and compile an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])  # Just use `fit` as usual x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=3)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"going-lower-level","dir":"Articles","previous_headings":"","what":"Going lower-level","title":"Customizing what happens in `fit()` with PyTorch","text":"Naturally, just skip passing loss function compile(), instead everything manually train_step. Likewise metrics. ’s lower-level example, uses compile() configure optimizer: start creating Metric instances track loss MAE score (__init__()). implement custom train_step() updates state metrics (calling update_state() ), query (via result()) return current average value, displayed progress bar pass callback. Note need call reset_states() metrics epoch! Otherwise calling result() return average since start training, whereas usually work per-epoch averages. Thankfully, framework can us: just list metric want reset metrics property model. model call reset_states() object listed beginning fit() epoch beginning call evaluate().","code":"class CustomModel(keras.Model):     def __init__(self, *args, **kwargs):         super().__init__(*args, **kwargs)         self.loss_tracker = keras.metrics.Mean(name=\"loss\")         self.mae_metric = keras.metrics.MeanAbsoluteError(name=\"mae\")         self.loss_fn = keras.losses.MeanSquaredError()      def train_step(self, data):         x, y = data          # Call torch.nn.Module.zero_grad() to clear the leftover gradients         # for the weights from the previous train step.         self.zero_grad()          # Compute loss         y_pred = self(x, training=True)  # Forward pass         loss = self.loss_fn(y, y_pred)          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()          trainable_weights = [v for v in self.trainable_weights]         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             self.optimizer.apply(gradients, trainable_weights)          # Compute our own metrics         self.loss_tracker.update_state(loss)         self.mae_metric.update_state(y, y_pred)         return {             \"loss\": self.loss_tracker.result(),             \"mae\": self.mae_metric.result(),         }      @property     def metrics(self):         # We list our `Metric` objects here so that `reset_states()` can be         # called automatically at the start of each epoch         # or at the start of `evaluate()`.         return [self.loss_tracker, self.mae_metric]   # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs)  # We don't passs a loss or metrics here. model.compile(optimizer=\"adam\")  # Just use `fit` as usual -- you can use callbacks, etc. x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=5)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"supporting-sample_weight-class_weight","dir":"Articles","previous_headings":"","what":"Supporting sample_weight & class_weight","title":"Customizing what happens in `fit()` with PyTorch","text":"may noticed first basic example didn’t make mention sample weighting. want support fit() arguments sample_weight class_weight, ’d simply following: Unpack sample_weight data argument Pass compute_loss & update_state (course, also just apply manually don’t rely compile() losses & metrics) ’s .","code":"class CustomModel(keras.Model):     def train_step(self, data):         # Unpack the data. Its structure depends on your model and         # on what you pass to `fit()`.         if len(data) == 3:             x, y, sample_weight = data         else:             sample_weight = None             x, y = data          # Call torch.nn.Module.zero_grad() to clear the leftover gradients         # for the weights from the previous train step.         self.zero_grad()          # Compute loss         y_pred = self(x, training=True)  # Forward pass         loss = self.compute_loss(             y=y,             y_pred=y_pred,             sample_weight=sample_weight,         )          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()          trainable_weights = [v for v in self.trainable_weights]         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             self.optimizer.apply(gradients, trainable_weights)          # Update metrics (includes the metric that tracks the loss)         for metric in self.metrics:             if metric.name == \"loss\":                 metric.update_state(loss)             else:                 metric.update_state(y, y_pred, sample_weight=sample_weight)          # Return a dict mapping metric names to current value         # Note that it will include the loss (tracked in self.metrics).         return {m.name: m.result() for m in self.metrics}   # Construct and compile an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])  # You can now use sample_weight argument x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) sw = np.random.random((1000, 1)) model.fit(x, y, sample_weight=sw, epochs=3)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"providing-your-own-evaluation-step","dir":"Articles","previous_headings":"","what":"Providing your own evaluation step","title":"Customizing what happens in `fit()` with PyTorch","text":"want calls model.evaluate()? override test_step exactly way. ’s looks like:","code":"class CustomModel(keras.Model):     def test_step(self, data):         # Unpack the data         x, y = data         # Compute predictions         y_pred = self(x, training=False)         # Updates the metrics tracking the loss         loss = self.compute_loss(y=y, y_pred=y_pred)         # Update the metrics.         for metric in self.metrics:             if metric.name == \"loss\":                 metric.update_state(loss)             else:                 metric.update_state(y, y_pred)         # Return a dict mapping metric names to current value.         # Note that it will include the loss (tracked in self.metrics).         return {m.name: m.result() for m in self.metrics}   # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(loss=\"mse\", metrics=[\"mae\"])  # Evaluate with our custom test_step x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.evaluate(x, y)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"wrapping-up-an-end-to-end-gan-example","dir":"Articles","previous_headings":"","what":"Wrapping up: an end-to-end GAN example","title":"Customizing what happens in `fit()` with PyTorch","text":"Let’s walk end--end example leverages everything just learned. Let’s consider: generator network meant generate 28x28x1 images. discriminator network meant classify 28x28x1 images two classes (“fake” “real”). One optimizer . loss function train discriminator. ’s feature-complete GAN class, overriding compile() use signature, implementing entire GAN algorithm 17 lines train_step: Let’s test-drive : ideas behind deep learning simple, implementation painful?","code":"# Create the discriminator discriminator = keras.Sequential(     [         keras.Input(shape=(28, 28, 1)),         layers.Conv2D(64, (3, 3), strides=(2, 2), padding=\"same\"),         layers.LeakyReLU(negative_slope=0.2),         layers.Conv2D(128, (3, 3), strides=(2, 2), padding=\"same\"),         layers.LeakyReLU(negative_slope=0.2),         layers.GlobalMaxPooling2D(),         layers.Dense(1),     ],     name=\"discriminator\", )  # Create the generator latent_dim = 128 generator = keras.Sequential(     [         keras.Input(shape=(latent_dim,)),         # We want to generate 128 coefficients to reshape into a 7x7x128 map         layers.Dense(7 * 7 * 128),         layers.LeakyReLU(negative_slope=0.2),         layers.Reshape((7, 7, 128)),         layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding=\"same\"),         layers.LeakyReLU(negative_slope=0.2),         layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding=\"same\"),         layers.LeakyReLU(negative_slope=0.2),         layers.Conv2D(1, (7, 7), padding=\"same\", activation=\"sigmoid\"),     ],     name=\"generator\", ) class GAN(keras.Model):     def __init__(self, discriminator, generator, latent_dim):         super().__init__()         self.discriminator = discriminator         self.generator = generator         self.latent_dim = latent_dim         self.d_loss_tracker = keras.metrics.Mean(name=\"d_loss\")         self.g_loss_tracker = keras.metrics.Mean(name=\"g_loss\")         self.seed_generator = keras.random.SeedGenerator(1337)         self.built = True      @property     def metrics(self):         return [self.d_loss_tracker, self.g_loss_tracker]      def compile(self, d_optimizer, g_optimizer, loss_fn):         super().compile()         self.d_optimizer = d_optimizer         self.g_optimizer = g_optimizer         self.loss_fn = loss_fn      def train_step(self, real_images):         if isinstance(real_images, tuple):             real_images = real_images[0]         # Sample random points in the latent space         batch_size = real_images.shape[0]         random_latent_vectors = keras.random.normal(             shape=(batch_size, self.latent_dim), seed=self.seed_generator         )          # Decode them to fake images         generated_images = self.generator(random_latent_vectors)          # Combine them with real images         real_images = torch.tensor(real_images)         combined_images = torch.concat([generated_images, real_images], axis=0)          # Assemble labels discriminating real from fake images         labels = torch.concat(             [torch.ones((batch_size, 1)), torch.zeros((batch_size, 1))], axis=0         )         # Add random noise to the labels - important trick!         labels += 0.05 * keras.random.uniform(             labels.shape, seed=self.seed_generator         )          # Train the discriminator         self.zero_grad()         predictions = self.discriminator(combined_images)         d_loss = self.loss_fn(labels, predictions)         d_loss.backward()         grads = [v.value.grad for v in self.discriminator.trainable_weights]         with torch.no_grad():             self.d_optimizer.apply(grads, self.discriminator.trainable_weights)          # Sample random points in the latent space         random_latent_vectors = keras.random.normal(             shape=(batch_size, self.latent_dim), seed=self.seed_generator         )          # Assemble labels that say \"all real images\"         misleading_labels = torch.zeros((batch_size, 1))          # Train the generator (note that we should *not* update the weights         # of the discriminator)!         self.zero_grad()         predictions = self.discriminator(self.generator(random_latent_vectors))         g_loss = self.loss_fn(misleading_labels, predictions)         grads = g_loss.backward()         grads = [v.value.grad for v in self.generator.trainable_weights]         with torch.no_grad():             self.g_optimizer.apply(grads, self.generator.trainable_weights)          # Update metrics and return their value.         self.d_loss_tracker.update_state(d_loss)         self.g_loss_tracker.update_state(g_loss)         return {             \"d_loss\": self.d_loss_tracker.result(),             \"g_loss\": self.g_loss_tracker.result(),         } # Prepare the dataset. We use both the training & test MNIST digits. batch_size = 64 (x_train, _), (x_test, _) = keras.datasets.mnist.load_data() all_digits = np.concatenate([x_train, x_test]) all_digits = all_digits.astype(\"float32\") / 255.0 all_digits = np.reshape(all_digits, (-1, 28, 28, 1))  # Create a TensorDataset dataset = torch.utils.data.TensorDataset(     torch.from_numpy(all_digits), torch.from_numpy(all_digits) ) # Create a DataLoader dataloader = torch.utils.data.DataLoader(     dataset, batch_size=batch_size, shuffle=True )  gan = GAN(     discriminator=discriminator, generator=generator, latent_dim=latent_dim ) gan.compile(     d_optimizer=keras.optimizers.Adam(learning_rate=0.0003),     g_optimizer=keras.optimizers.Adam(learning_rate=0.0003),     loss_fn=keras.losses.BinaryCrossentropy(from_logits=True), )  gan.fit(dataloader, epochs=1)"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Customizing Saving and Serialization","text":"guide covers advanced methods can customized Keras saving. users, methods outlined primary Serialize, save, export guide sufficient.","code":""},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"apis","dir":"Articles","previous_headings":"Introduction","what":"APIs","title":"Customizing Saving and Serialization","text":"cover following APIs: save_assets() load_assets() save_own_variables() load_own_variables() get_build_config() build_from_config() get_compile_config() compile_from_config() restoring model, get executed following order: build_from_config() compile_from_config() load_own_variables() load_assets()","code":""},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Customizing Saving and Serialization","text":"","code":"import os import numpy as np import keras"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"state-saving-customization","dir":"Articles","previous_headings":"","what":"State saving customization","title":"Customizing Saving and Serialization","text":"methods determine state model’s layers saved calling model.save(). can override take full control state saving process.","code":""},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"save_own_variables-and-load_own_variables","dir":"Articles","previous_headings":"State saving customization","what":"save_own_variables() and load_own_variables()","title":"Customizing Saving and Serialization","text":"methods save load state variables layer model.save() keras.models.load_model() called, respectively. default, state variables saved loaded weights layer (trainable non-trainable). default implementation save_own_variables(): store used methods dictionary can populated layer variables. Let’s take look example customizing . Example:","code":"def save_own_variables(self, store):     all_vars = self._trainable_weights + self._non_trainable_weights     for i, v in enumerate(all_vars):         store[f\"{i}\"] = v.numpy() @keras.utils.register_keras_serializable(package=\"my_custom_package\") class LayerWithCustomVariable(keras.layers.Dense):     def __init__(self, units, **kwargs):         super().__init__(units, **kwargs)         self.my_variable = keras.Variable(             np.random.random((units,)), name=\"my_variable\", dtype=\"float32\"         )      def save_own_variables(self, store):         super().save_own_variables(store)         # Stores the value of the variable upon saving         store[\"variables\"] = self.my_variable.numpy()      def load_own_variables(self, store):         # Assigns the value of the variable upon loading         self.my_variable.assign(store[\"variables\"])         # Load the remaining weights         for i, v in enumerate(self.weights):             v.assign(store[f\"{i}\"])         # Note: You must specify how all variables (including layer weights)         # are loaded in `load_own_variables.`      def call(self, inputs):         dense_out = super().call(inputs)         return dense_out + self.my_variable   model = keras.Sequential([LayerWithCustomVariable(1)])  ref_input = np.random.random((8, 10)) ref_output = np.random.random((8, 10)) model.compile(optimizer=\"adam\", loss=\"mean_squared_error\") model.fit(ref_input, ref_output)  model.save(\"custom_vars_model.keras\") restored_model = keras.models.load_model(\"custom_vars_model.keras\")  np.testing.assert_allclose(     model.layers[0].my_variable.numpy(),     restored_model.layers[0].my_variable.numpy(), )"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"save_assets-and-load_assets","dir":"Articles","previous_headings":"State saving customization","what":"save_assets() and load_assets()","title":"Customizing Saving and Serialization","text":"methods can added model class definition store load additional information model needs. example, NLP domain layers TextVectorization layers IndexLookup layers may need store associated vocabulary (lookup table) text file upon saving. Let’s take basics workflow simple file assets.txt. Example:","code":"@keras.saving.register_keras_serializable(package=\"my_custom_package\") class LayerWithCustomAssets(keras.layers.Dense):     def __init__(self, vocab=None, *args, **kwargs):         super().__init__(*args, **kwargs)         self.vocab = vocab      def save_assets(self, inner_path):         # Writes the vocab (sentence) to text file at save time.         with open(os.path.join(inner_path, \"vocabulary.txt\"), \"w\") as f:             f.write(self.vocab)      def load_assets(self, inner_path):         # Reads the vocab (sentence) from text file at load time.         with open(os.path.join(inner_path, \"vocabulary.txt\"), \"r\") as f:             text = f.read()         self.vocab = text.replace(\"<unk>\", \"little\")   model = keras.Sequential(     [LayerWithCustomAssets(vocab=\"Mary had a <unk> lamb.\", units=5)] )  x = np.random.random((10, 10)) y = model(x)  model.save(\"custom_assets_model.keras\") restored_model = keras.models.load_model(\"custom_assets_model.keras\")  np.testing.assert_string_equal(     restored_model.layers[0].vocab, \"Mary had a little lamb.\" )"},{"path":[]},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"get_build_config-and-build_from_config","dir":"Articles","previous_headings":"build and compile saving customization","what":"get_build_config() and build_from_config()","title":"Customizing Saving and Serialization","text":"methods work together save layer’s built states restore upon loading. default, includes build config dictionary layer’s input shape, overriding methods can used include Variables Lookup Tables can useful restore built model. Example:","code":"@keras.saving.register_keras_serializable(package=\"my_custom_package\") class LayerWithCustomBuild(keras.layers.Layer):     def __init__(self, units=32, **kwargs):         super().__init__(**kwargs)         self.units = units      def call(self, inputs):         return keras.ops.matmul(inputs, self.w) + self.b      def get_config(self):         return dict(units=self.units, **super().get_config())      def build(self, input_shape, layer_init):         # Note the overriding of `build()` to add an extra argument.         # Therefore, we will need to manually call build with `layer_init` argument         # before the first execution of `call()`.         super().build(input_shape)         self._input_shape = input_shape         self.w = self.add_weight(             shape=(input_shape[-1], self.units),             initializer=layer_init,             trainable=True,         )         self.b = self.add_weight(             shape=(self.units,),             initializer=layer_init,             trainable=True,         )         self.layer_init = layer_init      def get_build_config(self):         build_config = {             \"layer_init\": self.layer_init,             \"input_shape\": self._input_shape,         }  # Stores our initializer for `build()`         return build_config      def build_from_config(self, config):         # Calls `build()` with the parameters at loading time         self.build(config[\"input_shape\"], config[\"layer_init\"])   custom_layer = LayerWithCustomBuild(units=16) custom_layer.build(input_shape=(8,), layer_init=\"random_normal\")  model = keras.Sequential(     [         custom_layer,         keras.layers.Dense(1, activation=\"sigmoid\"),     ] )  x = np.random.random((16, 8)) y = model(x)  model.save(\"custom_build_model.keras\") restored_model = keras.models.load_model(\"custom_build_model.keras\")  np.testing.assert_equal(restored_model.layers[0].layer_init, \"random_normal\") np.testing.assert_equal(restored_model.built, True)"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"get_compile_config-and-compile_from_config","dir":"Articles","previous_headings":"build and compile saving customization","what":"get_compile_config() and compile_from_config()","title":"Customizing Saving and Serialization","text":"methods work together save information model compiled (optimizers, losses, etc.) restore re-compile model information. Overriding methods can useful compiling restored model custom optimizers, custom losses, etc., need deserialized prior calling model.compile compile_from_config(). Let’s take look example . Example:","code":"@keras.saving.register_keras_serializable(package=\"my_custom_package\") def small_square_sum_loss(y_true, y_pred):     loss = keras.ops.square(y_pred - y_true)     loss = loss / 10.0     loss = keras.ops.sum(loss, axis=1)     return loss   @keras.saving.register_keras_serializable(package=\"my_custom_package\") def mean_pred(y_true, y_pred):     return keras.ops.mean(y_pred)   @keras.saving.register_keras_serializable(package=\"my_custom_package\") class ModelWithCustomCompile(keras.Model):     def __init__(self, **kwargs):         super().__init__(**kwargs)         self.dense1 = keras.layers.Dense(8, activation=\"relu\")         self.dense2 = keras.layers.Dense(4, activation=\"softmax\")      def call(self, inputs):         x = self.dense1(inputs)         return self.dense2(x)      def compile(self, optimizer, loss_fn, metrics):         super().compile(optimizer=optimizer, loss=loss_fn, metrics=metrics)         self.model_optimizer = optimizer         self.loss_fn = loss_fn         self.loss_metrics = metrics      def get_compile_config(self):         # These parameters will be serialized at saving time.         return {             \"model_optimizer\": self.model_optimizer,             \"loss_fn\": self.loss_fn,             \"metric\": self.loss_metrics,         }      def compile_from_config(self, config):         # Deserializes the compile parameters (important, since many are custom)         optimizer = keras.utils.deserialize_keras_object(config[\"model_optimizer\"])         loss_fn = keras.utils.deserialize_keras_object(config[\"loss_fn\"])         metrics = keras.utils.deserialize_keras_object(config[\"metric\"])          # Calls compile with the deserialized parameters         self.compile(optimizer=optimizer, loss_fn=loss_fn, metrics=metrics)   model = ModelWithCustomCompile() model.compile(     optimizer=\"SGD\", loss_fn=small_square_sum_loss, metrics=[\"accuracy\", mean_pred] )  x = np.random.random((4, 8)) y = np.random.random((4,))  model.fit(x, y)  model.save(\"custom_compile_model.keras\") restored_model = keras.models.load_model(\"custom_compile_model.keras\")  np.testing.assert_equal(model.model_optimizer, restored_model.model_optimizer) np.testing.assert_equal(model.loss_fn, restored_model.loss_fn) np.testing.assert_equal(model.loss_metrics, restored_model.loss_metrics)"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"Customizing Saving and Serialization","text":"Using methods learned tutorial allows wide variety use cases, allowing saving loading complex models exotic assets state elements. recap: save_own_variables load_own_variables determine states saved loaded. save_assets load_assets can added store load additional information model needs. get_build_config build_from_config save restore model’s built states. get_compile_config compile_from_config save restore model’s compiled states.","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_jax.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Multi-GPU distributed training with JAX","text":"generally two ways distribute computation across multiple devices: Data parallelism, single model gets replicated multiple devices multiple machines. processes different batches data, merge results. exist many variants setup, differ different model replicas merge results, whether stay sync every batch whether loosely coupled, etc. Model parallelism, different parts single model run different devices, processing single batch data together. works best models naturally-parallel architecture, models feature multiple branches. guide focuses data parallelism, particular synchronous data parallelism, different replicas model stay sync batch process. Synchronicity keeps model convergence behavior identical see single-device training. Specifically, guide teaches use jax.sharding APIs train Keras models, minimal changes code, multiple GPUs TPUS (typically 2 16) installed single machine (single host, multi-device training). common setup researchers small-scale industry workflows.","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_jax.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Multi-GPU distributed training with JAX","text":"Let’s start defining function creates model train, function creates dataset train (MNIST case).","code":"import os  os.environ[\"KERAS_BACKEND\"] = \"jax\"  import jax import numpy as np import tensorflow as tf import keras  from jax.experimental import mesh_utils from jax.sharding import Mesh from jax.sharding import NamedSharding from jax.sharding import PartitionSpec as P   def get_model():     # Make a simple convnet with batch normalization and dropout.     inputs = keras.Input(shape=(28, 28, 1))     x = keras.layers.Rescaling(1.0 / 255.0)(inputs)     x = keras.layers.Conv2D(         filters=12, kernel_size=3, padding=\"same\", use_bias=False     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.Conv2D(         filters=24,         kernel_size=6,         use_bias=False,         strides=2,     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.Conv2D(         filters=32,         kernel_size=6,         padding=\"same\",         strides=2,         name=\"large_k\",     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.GlobalAveragePooling2D()(x)     x = keras.layers.Dense(256, activation=\"relu\")(x)     x = keras.layers.Dropout(0.5)(x)     outputs = keras.layers.Dense(10)(x)     model = keras.Model(inputs, outputs)     return model   def get_datasets():     # Load the data and split it between train and test sets     (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()      # Scale images to the [0, 1] range     x_train = x_train.astype(\"float32\")     x_test = x_test.astype(\"float32\")     # Make sure images have shape (28, 28, 1)     x_train = np.expand_dims(x_train, -1)     x_test = np.expand_dims(x_test, -1)     print(\"x_train shape:\", x_train.shape)     print(x_train.shape[0], \"train samples\")     print(x_test.shape[0], \"test samples\")      # Create TF Datasets     train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))     eval_data = tf.data.Dataset.from_tensor_slices((x_test, y_test))     return train_data, eval_data"},{"path":"https://keras.posit.co/articles/distributed_training_with_jax.html","id":"single-host-multi-device-synchronous-training","dir":"Articles","previous_headings":"","what":"Single-host, multi-device synchronous training","title":"Multi-GPU distributed training with JAX","text":"setup, one machine several GPUs TPUs (typically 2 16). device run copy model (called replica). simplicity, follows, ’ll assume ’re dealing 8 GPUs, loss generality. works step training: current batch data (called global batch) split 8 different sub-batches (called local batches). instance, global batch 512 samples, 8 local batches 64 samples. 8 replicas independently processes local batch: run forward pass, backward pass, outputting gradient weights respect loss model local batch. weight updates originating local gradients efficiently merged across 8 replicas. done end every step, replicas always stay sync. practice, process synchronously updating weights model replicas handled level individual weight variable. done using jax.sharding.NamedSharding configured replicate variables. use single-host, multi-device synchronous training Keras model, use jax.sharding features. ’s works: first create device mesh using mesh_utils.create_device_mesh. specify want replicate model optimizer variables across devices using spec axis. specify want shard data across devices using spec splits along batch dimension. use jax.device_put replicate model optimizer variables across devices. happens beginning. training loop, batch process, use jax.device_put split batch across devices invoking train step. ’s flow, step split utility function: ’s !","code":"# Config num_epochs = 2 batch_size = 64  train_data, eval_data = get_datasets() train_data = train_data.batch(batch_size, drop_remainder=True)  model = get_model() optimizer = keras.optimizers.Adam(1e-3) loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)  # Initialize all state with .build() (one_batch, one_batch_labels) = next(iter(train_data)) model.build(one_batch) optimizer.build(model.trainable_variables)   # This is the loss function that will be differentiated. # Keras provides a pure functional forward pass: model.stateless_call def compute_loss(trainable_variables, non_trainable_variables, x, y):     y_pred, updated_non_trainable_variables = model.stateless_call(         trainable_variables, non_trainable_variables, x     )     loss_value = loss(y, y_pred)     return loss_value, updated_non_trainable_variables   # Function to compute gradients compute_gradients = jax.value_and_grad(compute_loss, has_aux=True)   # Training step, Keras provides a pure functional optimizer.stateless_apply @jax.jit def train_step(train_state, x, y):     (         trainable_variables,         non_trainable_variables,         optimizer_variables,     ) = train_state     (loss_value, non_trainable_variables), grads = compute_gradients(         trainable_variables, non_trainable_variables, x, y     )      trainable_variables, optimizer_variables = optimizer.stateless_apply(         optimizer_variables, grads, trainable_variables     )      return loss_value, (         trainable_variables,         non_trainable_variables,         optimizer_variables,     )   # Replicate the model and optimizer variable on all devices def get_replicated_train_state(devices):     # All variables will be replicated on all devices     var_mesh = Mesh(devices, axis_names=(\"_\"))     # In NamedSharding, axes not mentioned are replicated (all axes here)     var_replication = NamedSharding(var_mesh, P())      # Apply the distribution settings to the model variables     trainable_variables = jax.device_put(         model.trainable_variables, var_replication     )     non_trainable_variables = jax.device_put(         model.non_trainable_variables, var_replication     )     optimizer_variables = jax.device_put(optimizer.variables, var_replication)      # Combine all state in a tuple     return (trainable_variables, non_trainable_variables, optimizer_variables)   num_devices = len(jax.local_devices()) print(f\"Running on {num_devices} devices: {jax.local_devices()}\") devices = mesh_utils.create_device_mesh((num_devices,))  # Data will be split along the batch axis data_mesh = Mesh(devices, axis_names=(\"batch\",))  # naming axes of the mesh data_sharding = NamedSharding(     data_mesh,     P(         \"batch\",     ), )  # naming axes of the sharded partition  # Display data sharding x, y = next(iter(train_data)) sharded_x = jax.device_put(x.numpy(), data_sharding) print(\"Data sharding\") jax.debug.visualize_array_sharding(jax.numpy.reshape(sharded_x, [-1, 28 * 28]))  train_state = get_replicated_train_state(devices)  # Custom training loop for epoch in range(num_epochs):     data_iter = iter(train_data)     for data in data_iter:         x, y = data         sharded_x = jax.device_put(x.numpy(), data_sharding)         loss_value, train_state = train_step(train_state, sharded_x, y.numpy())     print(\"Epoch\", epoch, \"loss:\", loss_value)  # Post-processing model state update to write them back into the model trainable_variables, non_trainable_variables, optimizer_variables = train_state for variable, value in zip(model.trainable_variables, trainable_variables):     variable.assign(value) for variable, value in zip(     model.non_trainable_variables, non_trainable_variables ):     variable.assign(value)"},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Multi-GPU distributed training with TensorFlow","text":"generally two ways distribute computation across multiple devices: Data parallelism, single model gets replicated multiple devices multiple machines. processes different batches data, merge results. exist many variants setup, differ different model replicas merge results, whether stay sync every batch whether loosely coupled, etc. Model parallelism, different parts single model run different devices, processing single batch data together. works best models naturally-parallel architecture, models feature multiple branches. guide focuses data parallelism, particular synchronous data parallelism, different replicas model stay sync batch process. Synchronicity keeps model convergence behavior identical see single-device training. Specifically, guide teaches use tf.distribute API train Keras models multiple GPUs, minimal changes code, multiple GPUs (typically 2 16) installed single machine (single host, multi-device training). common setup researchers small-scale industry workflows.","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Multi-GPU distributed training with TensorFlow","text":"","code":"library(keras3) library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")"},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"single-host-multi-device-synchronous-training","dir":"Articles","previous_headings":"","what":"Single-host, multi-device synchronous training","title":"Multi-GPU distributed training with TensorFlow","text":"setup, one machine several GPUs (typically 2 16). device run copy model (called replica). simplicity, follows, ’ll assume ’re dealing 8 GPUs, loss generality. works step training: current batch data (called global batch) split 8 different sub-batches (called local batches). instance, global batch 512 samples, 8 local batches 64 samples. 8 replicas independently processes local batch: run forward pass, backward pass, outputting gradient weights respect loss model local batch. weight updates originating local gradients efficiently merged across 8 replicas. done end every step, replicas always stay sync. practice, process synchronously updating weights model replicas handled level individual weight variable. done mirrored variable object. use single-host, multi-device synchronous training Keras model, use tf$distribute$MirroredStrategy API. ’s works: Instantiate MirroredStrategy, optionally configuring specific devices want use (default strategy use GPUs available). Use strategy object open scope, within scope, create Keras objects need contain variables. Typically, means creating & compiling model inside distribution scope. cases, first call fit() may also create variables, ’s good idea put fit() call scope well. Train model via fit() usual. Importantly, recommend use tf.data.Dataset objects load data multi-device distributed workflow. Schematically, looks like : ’s simple end--end runnable example:","code":"# Create a MirroredStrategy. strategy <- tf$distribute$MirroredStrategy() cat(sprintf('Number of devices: %d\\n', strategy$num_replicas_in_sync))  # Open a strategy scope. with(startegy$scope(), {   # Everything that creates variables should be under the strategy scope.   # In general this is only model construction & `compile()`.   model <- Model(...)   model |> compile(...)    # Train the model on all available devices.   model |> fit(train_dataset, validation_data=val_dataset, ...)    # Test the model on all available devices.   model |> evaluate(test_dataset) }) get_compiled_model <- function() {   inputs <- keras_input(shape = 784)   outputs <- inputs |>     layer_dense(units = 256, activation = \"relu\") |>     layer_dense(units = 256, activation = \"relu\") |>     layer_dense(units = 10)   model <- keras_model(inputs, outputs)   model |> compile(     optimizer = optimizer_adam(),     loss = loss_sparse_categorical_crossentropy(from_logits = TRUE),     metrics = list(metric_sparse_categorical_accuracy()),      # XLA compilation is temporarily disabled due to a bug     # https://github.com/keras-team/keras/issues/19005     jit_compile = FALSE   )   model }  get_dataset <- function(batch_size = 64) {    c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()   x_train <- array_reshape(x_train, c(-1, 784))   x_test <- array_reshape(x_test, c(-1, 784))    # Reserve 10,000 samples for validation.   val_i <- sample.int(nrow(x_train), 10000)   x_val <- x_train[val_i,]   y_val <- y_train[val_i]   x_train = x_train[-val_i,]   y_train = y_train[-val_i]    # Prepare the training dataset.   train_dataset <- list(x_train, y_train) |>     tensor_slices_dataset() |>     dataset_batch(batch_size)    # Prepare the validation dataset.   val_dataset <- list(x_val, y_val) |>     tensor_slices_dataset() |>     dataset_batch(batch_size)    # Prepare the test dataset.   test_dataset <- list(x_test, y_test) |>     tensor_slices_dataset() |>     dataset_batch(batch_size)    list(train_dataset, val_dataset, test_dataset) }  # Create a MirroredStrategy. strategy <- tf$distribute$MirroredStrategy() cat(sprintf('Number of devices: %d\\n', strategy$num_replicas_in_sync)) ## Number of devices: 2 # Open a strategy scope. with(strategy$scope(), {   # Everything that creates variables should be under the strategy scope.   # In general this is only model construction & `compile()`.   model <- get_compiled_model()    c(train_dataset, val_dataset, test_dataset) %<-% get_dataset()    # Train the model on all available devices.   model |> fit(train_dataset, epochs = 2, validation_data = val_dataset)    # Test the model on all available devices.   model |> evaluate(test_dataset)  }) ## Epoch 1/2 ## 782/782 - 5s - 7ms/step - loss: 3.0622 - sparse_categorical_accuracy: 0.8615 - val_loss: 1.1367 - val_sparse_categorical_accuracy: 0.9006 ## Epoch 2/2 ## 782/782 - 3s - 4ms/step - loss: 0.5774 - sparse_categorical_accuracy: 0.9259 - val_loss: 0.6612 - val_sparse_categorical_accuracy: 0.9210 ## 157/157 - 0s - 2ms/step - loss: 0.6729 - sparse_categorical_accuracy: 0.9150 ## $loss ## [1] 0.6728871 ## ## $sparse_categorical_accuracy ## [1] 0.915"},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"using-callbacks-to-ensure-fault-tolerance","dir":"Articles","previous_headings":"","what":"Using callbacks to ensure fault tolerance","title":"Multi-GPU distributed training with TensorFlow","text":"using distributed training, always make sure strategy recover failure (fault tolerance). simplest way handle pass ModelCheckpoint callback fit(), save model regular intervals (e.g. every 100 batches every epoch). can restart training saved model. ’s simple example:","code":"# Prepare a directory to store all the checkpoints. checkpoint_dir <- \"./ckpt\" if (!dir.exists(checkpoint_dir)) {   dir.create(checkpoint_dir) }  make_or_restore_model <- function() {   # Either restore the latest model, or create a fresh one   # if there is no checkpoint available.   checkpoints <- list.files(checkpoint_dir, #pattern = \"ckpt-.*\\\\.keras\",                             full.names = TRUE)    if (length(checkpoints) > 0) {     checkpoint_epochs <- as.integer(sub(\"ckpt-([0-9]+)\\\\.keras\", \"\\\\1\",                                         basename(checkpoints)))     latest_checkpoint <- checkpoints[which.max(checkpoint_epochs)]     load_model(latest_checkpoint)   } else {     get_compiled_model()   } }    run_training <- function(epochs = 1) {   # Create a MirroredStrategy.   strategy <- tf$distribute$MirroredStrategy()    # Open a strategy scope and create/restore the model   with(strategy$scope(), {     model <- make_or_restore_model()      callbacks <- list(       # This callback saves a SavedModel every epoch       # We include the current epoch in the folder name.       callback_model_checkpoint(         filepath = paste0(checkpoint_dir, \"/ckpt-{epoch}.keras\"),         save_freq = \"epoch\"       ))      model |> fit(       train_dataset,       epochs = epochs,       callbacks = callbacks,       validation_data = val_dataset,       verbose = 2     )   }) }  # Running the first time creates the model run_training(epochs = 1) ## 782/782 - 4s - 6ms/step - loss: 2.9519 - sparse_categorical_accuracy: 0.8655 - val_loss: 1.3110 - val_sparse_categorical_accuracy: 0.8836 # Calling the same function again will resume from where we left off run_training(epochs = 1) ## 782/782 - 3s - 4ms/step - loss: 0.5998 - sparse_categorical_accuracy: 0.9270 - val_loss: 0.8736 - val_sparse_categorical_accuracy: 0.9128"},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"tfdata-performance-tips","dir":"Articles","previous_headings":"","what":"tf$data performance tips","title":"Multi-GPU distributed training with TensorFlow","text":"distributed training, efficiency load data can often become critical. tips make sure tf$data pipelines run fast possible. Note dataset batching creating dataset, make sure batched global batch size. instance, 8 GPUs capable running batch 64 samples, call use global batch size 512. Calling dataset_cache() call dataset_cache() dataset, data cached running first iteration data. Every subsequent iteration use cached data. cache can memory (default) local file specify. can improve performance : data expected change iteration iteration reading data remote distributed filesystem reading data local disk, data fit memory workflow significantly IO-bound (e.g. reading & decoding image files). Calling dataset_prefetch(buffer_size) almost always call dataset_prefetch(buffer_size) creating dataset. means data pipeline run asynchronously model, new samples preprocessed stored buffer current batch samples used train model. next batch prefetched GPU memory time current batch . ’s !","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_torch.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Multi-GPU distributed training with PyTorch","text":"generally two ways distribute computation across multiple devices: Data parallelism, single model gets replicated multiple devices multiple machines. processes different batches data, merge results. exist many variants setup, differ different model replicas merge results, whether stay sync every batch whether loosely coupled, etc. Model parallelism, different parts single model run different devices, processing single batch data together. works best models naturally-parallel architecture, models feature multiple branches. guide focuses data parallelism, particular synchronous data parallelism, different replicas model stay sync batch process. Synchronicity keeps model convergence behavior identical see single-device training. Specifically, guide teaches use PyTorch’s DistributedDataParallel module wrapper train Keras, minimal changes code, multiple GPUs (typically 2 16) installed single machine (single host, multi-device training). common setup researchers small-scale industry workflows.","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_torch.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Multi-GPU distributed training with PyTorch","text":"Let’s start defining function creates model train, function creates dataset train (MNIST case). Next, let’s define simple PyTorch training loop targets GPU (note calls .cuda()).","code":"import os  os.environ[\"KERAS_BACKEND\"] = \"torch\"  import torch import numpy as np import keras   def get_model():     # Make a simple convnet with batch normalization and dropout.     inputs = keras.Input(shape=(28, 28, 1))     x = keras.layers.Rescaling(1.0 / 255.0)(inputs)     x = keras.layers.Conv2D(         filters=12, kernel_size=3, padding=\"same\", use_bias=False     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.Conv2D(         filters=24,         kernel_size=6,         use_bias=False,         strides=2,     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.Conv2D(         filters=32,         kernel_size=6,         padding=\"same\",         strides=2,         name=\"large_k\",     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.GlobalAveragePooling2D()(x)     x = keras.layers.Dense(256, activation=\"relu\")(x)     x = keras.layers.Dropout(0.5)(x)     outputs = keras.layers.Dense(10)(x)     model = keras.Model(inputs, outputs)     return model   def get_dataset():     # Load the data and split it between train and test sets     (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()      # Scale images to the [0, 1] range     x_train = x_train.astype(\"float32\")     x_test = x_test.astype(\"float32\")     # Make sure images have shape (28, 28, 1)     x_train = np.expand_dims(x_train, -1)     x_test = np.expand_dims(x_test, -1)     print(\"x_train shape:\", x_train.shape)      # Create a TensorDataset     dataset = torch.utils.data.TensorDataset(         torch.from_numpy(x_train), torch.from_numpy(y_train)     )     return dataset def train_model(model, dataloader, num_epochs, optimizer, loss_fn):     for epoch in range(num_epochs):         running_loss = 0.0         running_loss_count = 0         for batch_idx, (inputs, targets) in enumerate(dataloader):             inputs = inputs.cuda(non_blocking=True)             targets = targets.cuda(non_blocking=True)              # Forward pass             outputs = model(inputs)             loss = loss_fn(outputs, targets)              # Backward and optimize             optimizer.zero_grad()             loss.backward()             optimizer.step()              running_loss += loss.item()             running_loss_count += 1          # Print loss statistics         print(             f\"Epoch {epoch + 1}/{num_epochs}, \"             f\"Loss: {running_loss / running_loss_count}\"         )"},{"path":"https://keras.posit.co/articles/distributed_training_with_torch.html","id":"single-host-multi-device-synchronous-training","dir":"Articles","previous_headings":"","what":"Single-host, multi-device synchronous training","title":"Multi-GPU distributed training with PyTorch","text":"setup, one machine several GPUs (typically 2 16). device run copy model (called replica). simplicity, follows, ’ll assume ’re dealing 8 GPUs, loss generality. works step training: current batch data (called global batch) split 8 different sub-batches (called local batches). instance, global batch 512 samples, 8 local batches 64 samples. 8 replicas independently processes local batch: run forward pass, backward pass, outputting gradient weights respect loss model local batch. weight updates originating local gradients efficiently merged across 8 replicas. done end every step, replicas always stay sync. practice, process synchronously updating weights model replicas handled level individual weight variable. done mirrored variable object. use single-host, multi-device synchronous training Keras model, use torch.nn.parallel.DistributedDataParallel module wrapper. ’s works: use torch.multiprocessing.start_processes start multiple Python processes, one per device. process run per_device_launch_fn function. uses torch.distributed.init_process_group torch.cuda.set_device configure device used process. uses torch.utils.data.distributed.DistributedSampler torch.utils.data.DataLoader turn data distributed data loader. also uses torch.nn.parallel.DistributedDataParallel turn model distributed PyTorch module. calls train_model function. train_model function run process, model using separate device process. ’s flow, step split utility function: Time start multiple processes: ’s !","code":"# Config num_gpu = torch.cuda.device_count() num_epochs = 2 batch_size = 64 print(f\"Running on {num_gpu} GPUs\")   def setup_device(current_gpu_index, num_gpus):     # Device setup     os.environ[\"MASTER_ADDR\"] = \"localhost\"     os.environ[\"MASTER_PORT\"] = \"56492\"     device = torch.device(\"cuda:{}\".format(current_gpu_index))     torch.distributed.init_process_group(         backend=\"nccl\",         init_method=\"env://\",         world_size=num_gpus,         rank=current_gpu_index,     )     torch.cuda.set_device(device)   def cleanup():     torch.distributed.destroy_process_group()   def prepare_dataloader(dataset, current_gpu_index, num_gpus, batch_size):     sampler = torch.utils.data.distributed.DistributedSampler(         dataset,         num_replicas=num_gpus,         rank=current_gpu_index,         shuffle=False,     )     dataloader = torch.utils.data.DataLoader(         dataset,         sampler=sampler,         batch_size=batch_size,         shuffle=False,     )     return dataloader   def per_device_launch_fn(current_gpu_index, num_gpu):     # Setup the process groups     setup_device(current_gpu_index, num_gpu)      dataset = get_dataset()     model = get_model()      # prepare the dataloader     dataloader = prepare_dataloader(         dataset, current_gpu_index, num_gpu, batch_size     )      # Instantiate the torch optimizer     optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)      # Instantiate the torch loss function     loss_fn = torch.nn.CrossEntropyLoss()      # Put model on device     model = model.to(current_gpu_index)     ddp_model = torch.nn.parallel.DistributedDataParallel(         model, device_ids=[current_gpu_index], output_device=current_gpu_index     )      train_model(ddp_model, dataloader, num_epochs, optimizer, loss_fn)      cleanup() if __name__ == \"__main__\":     # We use the \"fork\" method rather than \"spawn\" to support notebooks     torch.multiprocessing.start_processes(         per_device_launch_fn,         args=(num_gpu,),         nprocs=num_gpu,         join=True,         start_method=\"fork\",     )"},{"path":"https://keras.posit.co/articles/distribution.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Distributed training with Keras 3","text":"Keras distribution API new interface designed facilitate distributed deep learning across variety backends like JAX, TensorFlow PyTorch. powerful API introduces suite tools enabling data model parallelism, allowing efficient scaling deep learning models multiple accelerators hosts. Whether leveraging power GPUs TPUs, API provides streamlined approach initializing distributed environments, defining device meshes, orchestrating layout tensors across computational resources. classes like DataParallel ModelParallel, abstracts complexity involved parallel computation, making easier developers accelerate machine learning workflows.","code":""},{"path":"https://keras.posit.co/articles/distribution.html","id":"how-it-works","dir":"Articles","previous_headings":"","what":"How it works","title":"Distributed training with Keras 3","text":"Keras distribution API provides global programming model allows developers compose applications operate tensors global context (working single device) automatically managing distribution across many devices. API leverages underlying framework (e.g. JAX) distribute program tensors according sharding directives procedure called single program, multiple data (SPMD) expansion. decoupling application sharding directives, API enables running application single device, multiple devices, even multiple clients, preserving global semantics.","code":""},{"path":"https://keras.posit.co/articles/distribution.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Distributed training with Keras 3","text":"","code":"# This guide assumes there are 8 GPUs available for testing. If you don't have # 8 gpus available locally, you can set the following envvar to # make xla initialize the CPU as 8 devices, to enable local testing Sys.setenv(\"CUDA_VISIBLE_DEVICES\" = \"\") Sys.setenv(\"XLA_FLAGS\" = \"--xla_force_host_platform_device_count=8\") library(keras3)  # The distribution API is only implemented for the JAX backend for now. use_backend(\"jax\") jax <- reticulate::import(\"jax\")  library(tfdatasets, exclude = \"shape\") # For dataset input."},{"path":"https://keras.posit.co/articles/distribution.html","id":"devicemesh-and-tensorlayout","dir":"Articles","previous_headings":"","what":"DeviceMesh and TensorLayout","title":"Distributed training with Keras 3","text":"keras$distribution$DeviceMesh class Keras distribution API represents cluster computational devices configured distributed computation. aligns similar concepts jax.sharding.Mesh tf.dtensor.Mesh, ’s used map physical devices logical mesh structure. TensorLayout class specifies tensors distributed across DeviceMesh, detailing sharding tensors along specified axes correspond names axes DeviceMesh. can find detailed concept explainers TensorFlow DTensor guide.","code":"# Retrieve the local available gpu devices. devices <- jax$devices() # \"gpu\" str(devices) ## List of 8 ##  $ :TFRT_CPU_0 ##  $ :TFRT_CPU_1 ##  $ :TFRT_CPU_2 ##  $ :TFRT_CPU_3 ##  $ :TFRT_CPU_4 ##  $ :TFRT_CPU_5 ##  $ :TFRT_CPU_6 ##  $ :TFRT_CPU_7 # Define a 2x4 device mesh with data and model parallel axes mesh <- keras$distribution$DeviceMesh(   shape = shape(2, 4),   axis_names = list(\"data\", \"model\"),   devices = devices )  # A 2D layout, which describes how a tensor is distributed across the # mesh. The layout can be visualized as a 2D grid with \"model\" as rows and # \"data\" as columns, and it is a [4, 2] grid when it mapped to the physical # devices on the mesh. layout_2d <- keras$distribution$TensorLayout(   axes = c(\"model\", \"data\"),   device_mesh = mesh )  # A 4D layout which could be used for data parallelism of an image input. replicated_layout_4d <- keras$distribution$TensorLayout(   axes = list(\"data\", NULL, NULL, NULL),   device_mesh = mesh )"},{"path":"https://keras.posit.co/articles/distribution.html","id":"distribution","dir":"Articles","previous_headings":"","what":"Distribution","title":"Distributed training with Keras 3","text":"Distribution class Keras serves foundational abstract class designed developing custom distribution strategies. encapsulates core logic needed distribute model’s variables, input data, intermediate computations across device mesh. end user, won’t interact directly class, subclasses like DataParallel ModelParallel.","code":""},{"path":"https://keras.posit.co/articles/distribution.html","id":"dataparallel","dir":"Articles","previous_headings":"","what":"DataParallel","title":"Distributed training with Keras 3","text":"DataParallel class Keras distribution API designed data parallelism strategy distributed training, model weights replicated across devices DeviceMesh, device processes portion input data. sample usage class.","code":"# Create DataParallel with list of devices. # As a shortcut, the devices can be skipped, # and Keras will detect all local available devices. # E.g. data_parallel <- DataParallel() data_parallel <- keras$distribution$DataParallel(devices = devices)  # Or you can choose to create DataParallel with a 1D `DeviceMesh`. mesh_1d <- keras$distribution$DeviceMesh(   shape = shape(8),   axis_names = list(\"data\"),   devices = devices ) data_parallel <- keras$distribution$DataParallel(device_mesh = mesh_1d)  inputs <- random_normal(c(128, 28, 28, 1)) labels <- random_normal(c(128, 10)) dataset <- tensor_slices_dataset(c(inputs, labels)) |>   dataset_batch(16)  # Set the global distribution. keras$distribution$set_distribution(data_parallel)  # Note that all the model weights from here on are replicated to # all the devices of the `DeviceMesh`. This includes the RNG # state, optimizer states, metrics, etc. The dataset fed into `model |> fit()` or # `model |> evaluate()` will be split evenly on the batch dimension, and sent to # all the devices. You don't have to do any manual aggregation of losses, # since all the computation happens in a global context. inputs <- keras_input(shape = c(28, 28, 1)) outputs <- inputs |>   layer_flatten() |>   layer_dense(units = 200, use_bias = FALSE, activation = \"relu\") |>   layer_dropout(0.4) |>   layer_dense(units = 10, activation = \"softmax\")  model <- keras_model(inputs = inputs, outputs = outputs)  model |> compile(loss = \"mse\") model |> fit(dataset, epochs = 3) ## Epoch 1/3 ## 8/8 - 0s - 37ms/step - loss: 1.0629 ## Epoch 2/3 ## 8/8 - 0s - 5ms/step - loss: 0.9712 ## Epoch 3/3 ## 8/8 - 0s - 5ms/step - loss: 0.9322 model |> evaluate(dataset) ## 8/8 - 0s - 7ms/step - loss: 0.8859 ## $loss ## [1] 0.8858577"},{"path":"https://keras.posit.co/articles/distribution.html","id":"modelparallel-and-layoutmap","dir":"Articles","previous_headings":"","what":"ModelParallel and LayoutMap","title":"Distributed training with Keras 3","text":"ModelParallel mostly useful model weights large fit single accelerator. setting allows spit model weights activation tensors across devices DeviceMesh, enable horizontal scaling large models. Unlike DataParallel model weights fully replicated, weights layout ModelParallel usually need customization best performances. introduce LayoutMap let specify TensorLayout weights intermediate tensors global perspective. LayoutMap dict-like object maps string TensorLayout instances. behaves differently normal dict string key treated regex retrieving value. class allows define naming schema TensorLayout retrieve corresponding TensorLayout instance. Typically, key used query variable$path attribute, identifier variable. shortcut, list axis names also allowed inserting value, converted TensorLayout. LayoutMap can also optionally contain DeviceMesh populate TensorLayout$device_mesh set. retrieving layout key, isn’t exact match, existing keys layout map treated regex matched input key . multiple matches, ValueError raised. matches found, NULL returned. also easy change mesh structure tune computation data parallel model parallel. can adjusting shape mesh. changes needed code.","code":"mesh_2d <- keras$distribution$DeviceMesh(   shape = shape(2, 4),   axis_names = c(\"data\", \"model\"),   devices = devices ) layout_map  <- keras$distribution$LayoutMap(mesh_2d)  # The rule below means that for any weights that match with d1/kernel, it # will be sharded with model dimensions (4 devices), same for the d1/bias. # All other weights will be fully replicated. layout_map[\"d1/kernel\"] <- tuple(NULL, \"model\") layout_map[\"d1/bias\"] <- tuple(\"model\")  # You can also set the layout for the layer output like layout_map[\"d2/output\"] <- tuple(\"data\", NULL)  model_parallel <- keras$distribution$ModelParallel(   mesh_2d, layout_map, batch_dim_name = \"data\" )  keras$distribution$set_distribution(model_parallel)  inputs <- layer_input(shape = c(28, 28, 1)) outputs <- inputs |>   layer_flatten() |>   layer_dense(units = 200, use_bias = FALSE,               activation = \"relu\", name = \"d1\") |>   layer_dropout(0.4) |>   layer_dense(units = 10,               activation = \"softmax\",               name = \"d2\")  model <- keras_model(inputs = inputs, outputs = outputs)  # The data will be sharded across the \"data\" dimension of the method, which # has 2 devices. model |> compile(loss = \"mse\") model |> fit(dataset, epochs = 3) ## Epoch 1/3 ## 8/8 - 0s - 29ms/step - loss: 1.0714 ## Epoch 2/3 ## 8/8 - 0s - 3ms/step - loss: 0.9744 ## Epoch 3/3 ## 8/8 - 0s - 4ms/step - loss: 0.9280 model |> evaluate(dataset) ## 8/8 - 0s - 7ms/step - loss: 0.8802 ## $loss ## [1] 0.8802156 full_data_parallel_mesh <- keras$distribution$DeviceMesh(   shape = shape(8, 1),   axis_names = list(\"data\", \"model\"),   devices = devices ) more_data_parallel_mesh <- keras$distribution$DeviceMesh(   shape = shape(4, 2),   axis_names = list(\"data\", \"model\"),   devices = devices ) more_model_parallel_mesh <- keras$distribution$DeviceMesh(   shape = shape(2, 4),   axis_names = list(\"data\", \"model\"),   devices = devices ) full_model_parallel_mesh <- keras$distribution$DeviceMesh(   shape = shape(1, 8),   axis_names = list(\"data\", \"model\"),   devices = devices )"},{"path":"https://keras.posit.co/articles/distribution.html","id":"further-reading","dir":"Articles","previous_headings":"ModelParallel and LayoutMap","what":"Further reading","title":"Distributed training with Keras 3","text":"JAX Distributed arrays automatic parallelization JAX sharding module TensorFlow Distributed training DTensors TensorFlow DTensor concepts Using DTensors tf.keras","code":""},{"path":"https://keras.posit.co/articles/examples/autoencoder.html","id":"introduction","dir":"Articles > Examples","previous_headings":"","what":"Introduction","title":"Convolutional autoencoder for image denoising","text":"example demonstrates implement deep convolutional autoencoder image denoising, mapping noisy digits images MNIST dataset clean digits images. implementation based original blog post titled Building Autoencoders Keras François Chollet.","code":""},{"path":"https://keras.posit.co/articles/examples/autoencoder.html","id":"setup","dir":"Articles > Examples","previous_headings":"","what":"Setup","title":"Convolutional autoencoder for image denoising","text":"","code":"library(keras3)  # Normalizes the supplied array and reshapes it. preprocess <- function(array) {   array_reshape(array/255, c(dim(array)[1], 28, 28, 1)) }  # Adds random noise to each image in the supplied array. noise <- function(array) {   noise_factor <- 0.4   noisy_array <- array + noise_factor * random_normal(dim(array))   op_clip(noisy_array, 0.0, 1.0) }  display <- function(array1, array2) {   n <- 2   indices <- sample.int(dim(array1)[1], n)   images1 <- as.array(array1)[indices, , , ]   images2 <- as.array(array2)[indices, , , ]    par(mfrow = c(2, n), mar = c(0, 0, 0, 0))   for (i in seq_len(n)) {     plot(as.raster(images1[i, , ]))     plot(as.raster(images2[i, , ]))   } }"},{"path":"https://keras.posit.co/articles/examples/autoencoder.html","id":"prepare-the-data","dir":"Articles > Examples","previous_headings":"","what":"Prepare the data","title":"Convolutional autoencoder for image denoising","text":"","code":"# Since we only need images from the dataset to encode and decode, we # won't use the labels. c(c(train_data, .), c(test_data, .)) %<-% dataset_mnist()  # Normalize and reshape the data train_data <- preprocess(train_data) test_data <- preprocess(test_data)  # Create a copy of the data with added noise noisy_train_data <- noise(train_data) noisy_test_data <- noise(test_data)  # Display the train data and a version of it with added noise display(train_data, noisy_train_data)"},{"path":"https://keras.posit.co/articles/examples/autoencoder.html","id":"build-the-autoencoder","dir":"Articles > Examples","previous_headings":"","what":"Build the autoencoder","title":"Convolutional autoencoder for image denoising","text":"going use Functional API build convolutional autoencoder. Now can train autoencoder using train_data input data target. Notice setting validation data using format. Let’s predict test dataset display original image together prediction autoencoder. Notice predictions pretty close original images, although quite . Now know autoencoder works, let’s retrain using noisy data input clean data target. want autoencoder learn denoise images. Let’s now predict noisy data display results autoencoder. Notice autoencoder amazing job removing noise input images.","code":"input <- keras_input(shape = c(28, 28, 1))  # Encoder enc <- input |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3),                 activation = \"relu\", padding = \"same\") |>   layer_max_pooling_2d(pool_size = c(2, 2), padding = \"same\") |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3),                 activation = \"relu\", padding = \"same\") |>   layer_max_pooling_2d(pool_size = c(2, 2), padding = \"same\")  # Decoder dec <- enc |>   layer_conv_2d_transpose(filters = 32, kernel_size = c(3, 3), strides = 2,                           activation = \"relu\", padding = \"same\") |>   layer_conv_2d_transpose(filters = 32, kernel_size = c(3, 3), strides = 2,                           activation = \"relu\", padding = \"same\") |>   layer_conv_2d(filters = 1, kernel_size = c(3, 3),                 activation = \"sigmoid\", padding = \"same\")  # Autoencoder autoencoder <- keras_model(input, dec) autoencoder |> compile(optimizer = \"adam\", loss = \"binary_crossentropy\") autoencoder |> summary() ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 28, 28, 32)     │           320 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 14, 14, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 14, 14, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 7, 7, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 14, 14, 32)     │         9,248 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 28, 28, 32)     │         9,248 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 28, 28, 1)      │           289 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 28,353 (110.75 KB) ##  Trainable params: 28,353 (110.75 KB) ##  Non-trainable params: 0 (0.00 B) autoencoder |> fit(   x = train_data,   y = train_data,   epochs = 50,   batch_size = 128,   shuffle = TRUE,   validation_data = list(test_data, test_data), ) ## Epoch 1/50 ## 469/469 - 5s - 10ms/step - loss: 0.1322 - val_loss: 0.0736 ## Epoch 2/50 ## 469/469 - 1s - 3ms/step - loss: 0.0721 - val_loss: 0.0698 ## Epoch 3/50 ## 469/469 - 1s - 3ms/step - loss: 0.0695 - val_loss: 0.0681 ## Epoch 4/50 ## 469/469 - 1s - 3ms/step - loss: 0.0682 - val_loss: 0.0671 ## Epoch 5/50 ## 469/469 - 1s - 3ms/step - loss: 0.0673 - val_loss: 0.0665 ## Epoch 6/50 ## 469/469 - 1s - 3ms/step - loss: 0.0667 - val_loss: 0.0660 ## Epoch 7/50 ## 469/469 - 1s - 3ms/step - loss: 0.0663 - val_loss: 0.0656 ## Epoch 8/50 ## 469/469 - 1s - 3ms/step - loss: 0.0659 - val_loss: 0.0652 ## Epoch 9/50 ## 469/469 - 1s - 3ms/step - loss: 0.0655 - val_loss: 0.0649 ## Epoch 10/50 ## 469/469 - 1s - 3ms/step - loss: 0.0652 - val_loss: 0.0646 ## Epoch 11/50 ## 469/469 - 1s - 3ms/step - loss: 0.0650 - val_loss: 0.0644 ## Epoch 12/50 ## 469/469 - 1s - 3ms/step - loss: 0.0647 - val_loss: 0.0642 ## Epoch 13/50 ## 469/469 - 1s - 3ms/step - loss: 0.0645 - val_loss: 0.0640 ## Epoch 14/50 ## 469/469 - 1s - 3ms/step - loss: 0.0643 - val_loss: 0.0638 ## Epoch 15/50 ## 469/469 - 1s - 3ms/step - loss: 0.0642 - val_loss: 0.0636 ## Epoch 16/50 ## 469/469 - 1s - 3ms/step - loss: 0.0640 - val_loss: 0.0635 ## Epoch 17/50 ## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0634 ## Epoch 18/50 ## 469/469 - 1s - 3ms/step - loss: 0.0638 - val_loss: 0.0633 ## Epoch 19/50 ## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0631 ## Epoch 20/50 ## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631 ## Epoch 21/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0630 ## Epoch 22/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0629 ## Epoch 23/50 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0628 ## Epoch 24/50 ## 469/469 - 1s - 3ms/step - loss: 0.0632 - val_loss: 0.0628 ## Epoch 25/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627 ## Epoch 26/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0626 ## Epoch 27/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0626 ## Epoch 28/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0625 ## Epoch 29/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0625 ## Epoch 30/50 ## 469/469 - 1s - 3ms/step - loss: 0.0628 - val_loss: 0.0624 ## Epoch 31/50 ## 469/469 - 1s - 3ms/step - loss: 0.0628 - val_loss: 0.0624 ## Epoch 32/50 ## 469/469 - 1s - 3ms/step - loss: 0.0627 - val_loss: 0.0623 ## Epoch 33/50 ## 469/469 - 1s - 3ms/step - loss: 0.0627 - val_loss: 0.0623 ## Epoch 34/50 ## 469/469 - 1s - 3ms/step - loss: 0.0627 - val_loss: 0.0623 ## Epoch 35/50 ## 469/469 - 1s - 3ms/step - loss: 0.0626 - val_loss: 0.0622 ## Epoch 36/50 ## 469/469 - 1s - 3ms/step - loss: 0.0626 - val_loss: 0.0622 ## Epoch 37/50 ## 469/469 - 1s - 3ms/step - loss: 0.0626 - val_loss: 0.0622 ## Epoch 38/50 ## 469/469 - 1s - 3ms/step - loss: 0.0625 - val_loss: 0.0622 ## Epoch 39/50 ## 469/469 - 1s - 3ms/step - loss: 0.0625 - val_loss: 0.0621 ## Epoch 40/50 ## 469/469 - 1s - 3ms/step - loss: 0.0625 - val_loss: 0.0621 ## Epoch 41/50 ## 469/469 - 1s - 3ms/step - loss: 0.0624 - val_loss: 0.0621 ## Epoch 42/50 ## 469/469 - 1s - 3ms/step - loss: 0.0624 - val_loss: 0.0621 ## Epoch 43/50 ## 469/469 - 1s - 3ms/step - loss: 0.0624 - val_loss: 0.0620 ## Epoch 44/50 ## 469/469 - 1s - 3ms/step - loss: 0.0624 - val_loss: 0.0620 ## Epoch 45/50 ## 469/469 - 1s - 3ms/step - loss: 0.0623 - val_loss: 0.0620 ## Epoch 46/50 ## 469/469 - 1s - 3ms/step - loss: 0.0623 - val_loss: 0.0620 ## Epoch 47/50 ## 469/469 - 1s - 3ms/step - loss: 0.0623 - val_loss: 0.0619 ## Epoch 48/50 ## 469/469 - 1s - 3ms/step - loss: 0.0623 - val_loss: 0.0619 ## Epoch 49/50 ## 469/469 - 1s - 3ms/step - loss: 0.0622 - val_loss: 0.0619 ## Epoch 50/50 ## 469/469 - 1s - 3ms/step - loss: 0.0622 - val_loss: 0.0619 predictions <- autoencoder |> predict(test_data) ## 313/313 - 1s - 2ms/step display(test_data, predictions) autoencoder |> fit(   x = noisy_train_data,   y = train_data,   epochs = 100,   batch_size = 128,   shuffle = TRUE,   validation_data = list(noisy_test_data, test_data), ) ## Epoch 1/100 ## 469/469 - 1s - 3ms/step - loss: 0.0998 - val_loss: 0.0928 ## Epoch 2/100 ## 469/469 - 1s - 3ms/step - loss: 0.0925 - val_loss: 0.0908 ## Epoch 3/100 ## 469/469 - 1s - 3ms/step - loss: 0.0910 - val_loss: 0.0898 ## Epoch 4/100 ## 469/469 - 1s - 3ms/step - loss: 0.0900 - val_loss: 0.0890 ## Epoch 5/100 ## 469/469 - 1s - 3ms/step - loss: 0.0893 - val_loss: 0.0883 ## Epoch 6/100 ## 469/469 - 1s - 3ms/step - loss: 0.0888 - val_loss: 0.0877 ## Epoch 7/100 ## 469/469 - 1s - 3ms/step - loss: 0.0883 - val_loss: 0.0873 ## Epoch 8/100 ## 469/469 - 1s - 3ms/step - loss: 0.0879 - val_loss: 0.0870 ## Epoch 9/100 ## 469/469 - 1s - 3ms/step - loss: 0.0876 - val_loss: 0.0868 ## Epoch 10/100 ## 469/469 - 1s - 3ms/step - loss: 0.0873 - val_loss: 0.0866 ## Epoch 11/100 ## 469/469 - 1s - 3ms/step - loss: 0.0871 - val_loss: 0.0863 ## Epoch 12/100 ## 469/469 - 1s - 3ms/step - loss: 0.0869 - val_loss: 0.0861 ## Epoch 13/100 ## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0860 ## Epoch 14/100 ## 469/469 - 1s - 3ms/step - loss: 0.0865 - val_loss: 0.0858 ## Epoch 15/100 ## 469/469 - 1s - 3ms/step - loss: 0.0864 - val_loss: 0.0857 ## Epoch 16/100 ## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0855 ## Epoch 17/100 ## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0854 ## Epoch 18/100 ## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0853 ## Epoch 19/100 ## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0852 ## Epoch 20/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0852 ## Epoch 21/100 ## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0851 ## Epoch 22/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0850 ## Epoch 23/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0850 ## Epoch 24/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0849 ## Epoch 25/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0849 ## Epoch 26/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0848 ## Epoch 27/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0848 ## Epoch 28/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0847 ## Epoch 29/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0847 ## Epoch 30/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0847 ## Epoch 31/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847 ## Epoch 32/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0846 ## Epoch 33/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0846 ## Epoch 34/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846 ## Epoch 35/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0845 ## Epoch 36/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0845 ## Epoch 37/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0845 ## Epoch 38/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0845 ## Epoch 39/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0845 ## Epoch 40/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0844 ## Epoch 41/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0844 ## Epoch 42/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0844 ## Epoch 43/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0844 ## Epoch 44/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0844 ## Epoch 45/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 46/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 47/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 48/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 49/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 50/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0843 ## Epoch 51/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 52/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 53/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 54/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 55/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 56/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0842 ## Epoch 57/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0842 ## Epoch 58/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0842 ## Epoch 59/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0842 ## Epoch 60/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0841 ## Epoch 61/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0841 ## Epoch 62/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0841 ## Epoch 63/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0841 ## Epoch 64/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0841 ## Epoch 65/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0841 ## Epoch 66/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0841 ## Epoch 67/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 68/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 69/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 70/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 71/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 72/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 73/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 74/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 75/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 76/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 77/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 78/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 79/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 80/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 81/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 82/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 83/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 84/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 85/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 86/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 87/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 88/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 89/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 90/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 91/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 92/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 93/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 94/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 95/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 96/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 97/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 98/100 ## 469/469 - 1s - 3ms/step - loss: 0.0841 - val_loss: 0.0839 ## Epoch 99/100 ## 469/469 - 1s - 3ms/step - loss: 0.0841 - val_loss: 0.0839 ## Epoch 100/100 ## 469/469 - 1s - 3ms/step - loss: 0.0841 - val_loss: 0.0839 predictions <- autoencoder |> predict(noisy_test_data) ## 313/313 - 0s - 581us/step display(noisy_test_data, predictions)"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"setup","dir":"Articles > Examples","previous_headings":"","what":"Setup","title":"Simple MNIST convnet","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"prepare-the-data","dir":"Articles > Examples","previous_headings":"","what":"Prepare the data","title":"Simple MNIST convnet","text":"","code":"# Model / data parameters num_classes <- 10 input_shape <- c(28, 28, 1)  # Load the data and split it between train and test sets c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()  # Scale images to the [0, 1] range x_train <- x_train / 255 x_test <- x_test / 255 # Make sure images have shape (28, 28, 1) x_train <- op_expand_dims(x_train, -1) x_test <- op_expand_dims(x_test, -1)   dim(x_train) ## [1] 60000    28    28     1 dim(x_test) ## [1] 10000    28    28     1 # convert class vectors to binary class matrices y_train <- to_categorical(y_train, num_classes) y_test <- to_categorical(y_test, num_classes)"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"build-the-model","dir":"Articles > Examples","previous_headings":"","what":"Build the model","title":"Simple MNIST convnet","text":"","code":"model <- keras_model_sequential(input_shape = input_shape) model |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_flatten() |>   layer_dropout(rate = 0.5) |>   layer_dense(units = num_classes, activation = \"softmax\")  summary(model) ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d_1 (Conv2D)               │ (None, 26, 26, 32)     │           320 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 13, 13, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 11, 11, 64)     │        18,496 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 5, 5, 64)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ flatten (Flatten)               │ (None, 1600)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 1600)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 10)             │        16,010 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 34,826 (136.04 KB) ##  Trainable params: 34,826 (136.04 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"train-the-model","dir":"Articles > Examples","previous_headings":"","what":"Train the model","title":"Simple MNIST convnet","text":"","code":"batch_size <- 128 epochs <- 15  model |> compile(   loss = \"categorical_crossentropy\",   optimizer = \"adam\",   metrics = \"accuracy\" )  model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = epochs,   validation_split = 0.1 ) ## Epoch 1/15 ## 422/422 - 4s - 11ms/step - accuracy: 0.8845 - loss: 0.3815 - val_accuracy: 0.9783 - val_loss: 0.0810 ## Epoch 2/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9644 - loss: 0.1151 - val_accuracy: 0.9863 - val_loss: 0.0548 ## Epoch 3/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9735 - loss: 0.0838 - val_accuracy: 0.9880 - val_loss: 0.0455 ## Epoch 4/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9781 - loss: 0.0689 - val_accuracy: 0.9893 - val_loss: 0.0413 ## Epoch 5/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9811 - loss: 0.0608 - val_accuracy: 0.9907 - val_loss: 0.0373 ## Epoch 6/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9824 - loss: 0.0561 - val_accuracy: 0.9908 - val_loss: 0.0359 ## Epoch 7/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9848 - loss: 0.0487 - val_accuracy: 0.9917 - val_loss: 0.0328 ## Epoch 8/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9845 - loss: 0.0484 - val_accuracy: 0.9915 - val_loss: 0.0334 ## Epoch 9/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9859 - loss: 0.0439 - val_accuracy: 0.9923 - val_loss: 0.0320 ## Epoch 10/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9868 - loss: 0.0405 - val_accuracy: 0.9925 - val_loss: 0.0319 ## Epoch 11/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9874 - loss: 0.0387 - val_accuracy: 0.9920 - val_loss: 0.0317 ## Epoch 12/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9882 - loss: 0.0367 - val_accuracy: 0.9925 - val_loss: 0.0301 ## Epoch 13/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9879 - loss: 0.0361 - val_accuracy: 0.9928 - val_loss: 0.0281 ## Epoch 14/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9889 - loss: 0.0332 - val_accuracy: 0.9930 - val_loss: 0.0284 ## Epoch 15/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9897 - loss: 0.0311 - val_accuracy: 0.9925 - val_loss: 0.0293"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"evaluate-the-trained-model","dir":"Articles > Examples","previous_headings":"","what":"Evaluate the trained model","title":"Simple MNIST convnet","text":"","code":"score <- model |> evaluate(x_test, y_test, verbose = 0) score ## $accuracy ## [1] 0.9911 ## ## $loss ## [1] 0.02564374"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"introduction","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Introduction","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"example, ’ll build sequence--sequence Transformer model, ’ll train English--Spanish machine translation task. ’ll learn : Vectorize text using layer_text_vectorization(). Implement layer_transformer_encoder(), layer_transformer_decoder(), layer_positional_embedding(). Prepare data training sequence--sequence model. Use trained model generate translations never-seen-input sentences (sequence--sequence inference). code featured adapted book Deep Learning R, Second Edition (chapter 11: Deep learning text). present example fairly barebones, detailed explanations building block works, well theory behind Transformers, recommend reading book.","code":""},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"setup","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Setup","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"","code":"library(glue)  library(tensorflow, exclude = c(\"set_random_seed\", \"shape\")) library(keras3)"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"downloading-the-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Downloading the data","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"’ll working English--Spanish translation dataset provided Anki. Let’s download :","code":"zipfile <- get_file(\"spa-eng.zip\", origin =   \"http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip\")  zip::zip_list(zipfile) # See what's in the zipfile ##             filename compressed_size uncompressed_size           timestamp ## 1           spa-eng/               0                 0 2018-06-13 12:21:20 ## 2 spa-eng/_about.txt             685              1441 2018-05-13 19:50:34 ## 3    spa-eng/spa.txt         2637619           8042772 2018-05-13 19:50:34 ##   permissions    crc32 offset ## 1         755 00000000      0 ## 2         644 4b18349d     38 ## 3         644 63c5c4a2    771 zip::unzip(zipfile, exdir = \".\") # unzip into the current directory  text_file <- fs::path(\"./spa-eng/spa.txt\")"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"parsing-the-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Parsing the data","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"line contains English sentence corresponding Spanish sentence. English sentence source sequence Spanish one target sequence. prepend token \"[start]\" append token \"[end]\" Spanish sentence. ’s sentence pairs look like: Now, let’s split sentence pairs training set, validation set, test set.","code":"text_file <- \"spa-eng/spa.txt\" text_pairs <- text_file %>%   readr::read_tsv(col_names = c(\"english\", \"spanish\"),                   col_types = c(\"cc\")) %>%   within(spanish %<>% paste(\"[start]\", ., \"[end]\")) df <- text_pairs[sample(nrow(text_pairs), 5), ] glue::glue_data(df, r\"(   english: {english}   spanish: {spanish} )\") |> cat(sep = \"\\n\\n\") ## english: I'm staying in Italy. ## spanish: [start] Me estoy quedando en Italia. [end] ## ## english: What's so strange about that? ## spanish: [start] ¿Qué es tan extraño acerca de eso? [end] ## ## english: All of the buses are full. ## spanish: [start] Todos los bondis están llenos. [end] ## ## english: Is this where your mother works? ## spanish: [start] ¿Es aquí donde trabaja tu madre? [end] ## ## english: Take precautions. ## spanish: [start] Ten cuidado. [end] random.shuffle(text_pairs) num_val_samples = int(0.15 * len(text_pairs)) num_train_samples = len(text_pairs) - 2 * num_val_samples train_pairs = text_pairs[:num_train_samples] val_pairs = text_pairs[num_train_samples : num_train_samples + num_val_samples] test_pairs = text_pairs[num_train_samples + num_val_samples :]  print(f\"{len(text_pairs)} total pairs\") print(f\"{len(train_pairs)} training pairs\") print(f\"{len(val_pairs)} validation pairs\") print(f\"{len(test_pairs)} test pairs\") num_test_samples <- num_val_samples <-   round(0.15 * nrow(text_pairs)) num_train_samples <- nrow(text_pairs) - num_val_samples - num_test_samples  pair_group <- sample(c(   rep(\"train\", num_train_samples),   rep(\"test\", num_test_samples),   rep(\"val\", num_val_samples) ))  train_pairs <- text_pairs[pair_group == \"train\", ] test_pairs <- text_pairs[pair_group == \"test\", ] val_pairs <- text_pairs[pair_group == \"val\", ] glue(r\"(   {nrow(text_pairs)} total pairs   {nrow(train_pairs)} training pairs   {nrow(val_pairs)} validation pairs   {nrow(test_pairs)} test pairs )\", .transformer = function(text, envir) {   val <- eval(str2lang(text), envir)   prettyNum(val, big.mark = \",\") }) ## 118,493 total pairs ## 82,945 training pairs ## 17,774 validation pairs ## 17,774 test pairs"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"vectorizing-the-text-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Vectorizing the text data","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"’ll use two instances layer_text_vectorization() vectorize text data (one English one Spanish), say, turn original strings integer sequences integer represents index word vocabulary. English layer use default string standardization (strip punctuation characters) splitting scheme (split whitespace), Spanish layer use custom standardization, add character \"¿\" set punctuation characters stripped. Note: production-grade machine translation model, recommend stripping punctuation characters either language. Instead, recommend turning punctuation character token, achieve providing custom split function layer_text_vectorization(). Next, ’ll format datasets. training step, model seek predict target words N+1 (beyond) using source sentence target words 1 N. , training dataset yield tuple (inputs, targets), : inputs dictionary (named list) keys (names) encoder_inputs decoder_inputs. encoder_inputs vectorized source sentence encoder_inputs target sentence “far”, say, words 0 N used predict word N+1 (beyond) target sentence. target target sentence offset one step: provides next words target sentence – model try predict. Let’s take quick look sequence shapes (batches 64 pairs, sequences 20 steps long):","code":"punctuation_regex <- \"[¡¿]|[^[:^punct:][\\\\]]\" # the regex explained: Match ¡, or ¿, or any punctuation character except ] # # [:^punct:]: is a negated POSIX character class. # [:punct:] matches any punctuation character, so [:^punct:] matches any # character that is not a punctuation character. # [^...] negates the whole character class # So [^[:^punct:]] would matche any character that is a punctuation character. # Putting this all together, [^[:^punct:][\\\\]] matches any # punctuation character except the ] character.  custom_standardization <- function(input_string) {   input_string %>%     tf$strings$lower() %>%     tf$strings$regex_replace(punctuation_regex, \"\") }  input_string <- as_tensor(\"[start] ¡corre! [end]\") custom_standardization(input_string) ## tf.Tensor(b'[start] corre [end]', shape=(), dtype=string) vocab_size <- 15000 sequence_length <- 20  # rename to eng_vectorization eng_vectorization <- layer_text_vectorization(   max_tokens = vocab_size,   output_mode = \"int\",   output_sequence_length = sequence_length )  spa_vectorization <- layer_text_vectorization(   max_tokens = vocab_size,   output_mode = \"int\",   output_sequence_length = sequence_length + 1,   standardize = custom_standardization )  adapt(eng_vectorization, train_pairs$english) adapt(spa_vectorization, train_pairs$spanish) format_pair <- function(pair) {   # the vectorization layers requrie batched inputs,   # reshape scalar string tensor to add a batch dim   pair %<>% lapply(op_expand_dims, 1)    # vectorize   eng <- eng_vectorization(pair$english)   spa <- spa_vectorization(pair$spanish)    # drop the batch dim   eng %<>% tf$ensure_shape(shape(1, sequence_length)) %>% op_squeeze(1)   spa %<>% tf$ensure_shape(shape(1, sequence_length+1)) %>% op_squeeze(1)    inputs <- list(encoder_inputs = eng,                  decoder_inputs = spa[NA:-2])   targets <- spa[2:NA]   list(inputs, targets) }   batch_size <- 64  library(tfdatasets, exclude = \"shape\") make_dataset <- function(pairs) {   tensor_slices_dataset(pairs) %>%     dataset_map(format_pair, num_parallel_calls = 4) %>%     dataset_cache() %>%     dataset_shuffle(2048) %>%     dataset_batch(batch_size) %>%     dataset_prefetch(2) } train_ds <- make_dataset(train_pairs) ## Warning: Negative numbers are interpreted python-style when subsetting tensorflow tensors. ## See: ?`[.tensorflow.tensor` for details. ## To turn off this warning, set `options(tensorflow.extract.warn_negatives_pythonic = FALSE)` val_ds <- make_dataset(val_pairs) c(inputs, targets) %<-% iter_next(as_iterator(train_ds)) str(inputs) ## List of 2 ##  $ encoder_inputs:<tf.Tensor: shape=(64, 20), dtype=int64, numpy=…> ##  $ decoder_inputs:<tf.Tensor: shape=(64, 20), dtype=int64, numpy=…> str(targets) ## <tf.Tensor: shape=(64, 20), dtype=int64, numpy=…>"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"building-the-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Building the model","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"sequence--sequence Transformer consists TransformerEncoder TransformerDecoder chained together. make model aware word order, also use PositionalEmbedding layer. source sequence pass TransformerEncoder, produce new representation . new representation passed TransformerDecoder, together target sequence far (target words 1 N). TransformerDecoder seek predict next words target sequence (N+1 beyond). key detail makes possible causal masking (see method get_causal_attention_mask() TransformerDecoder). TransformerDecoder sees entire sequences , thus must make sure uses information target tokens 0 N predicting token N+1 (otherwise, use information future, result model used inference time). Next, assemble end--end model.","code":"layer_transformer_encoder <- Layer(   classname = \"TransformerEncoder\",   initialize = function(embed_dim, dense_dim, num_heads, ...) {     super$initialize(...)     self$embed_dim <- embed_dim     self$dense_dim <- dense_dim     self$num_heads <- num_heads     self$attention <-       layer_multi_head_attention(num_heads = num_heads,                                  key_dim = embed_dim)      self$dense_proj <- keras_model_sequential() %>%       layer_dense(dense_dim, activation = \"relu\") %>%       layer_dense(embed_dim)      self$layernorm_1 <- layer_layer_normalization()     self$layernorm_2 <- layer_layer_normalization()     self$supports_masking <- TRUE   },    call = function(inputs, mask = NULL) {     if (!is.null(mask))       mask <- mask[, NULL, ] |> op_cast(\"int32\")      inputs %>%       { self$attention(., ., attention_mask = mask) + . } %>%       self$layernorm_1() %>%       { self$dense_proj(.) + . } %>%       self$layernorm_2()   },    get_config = function() {     config <- super$get_config()     for(name in c(\"embed_dim\", \"num_heads\", \"dense_dim\"))       config[[name]] <- self[[name]]     config   } )  layer_transformer_decoder <- Layer(   classname = \"TransformerDecoder\",    initialize = function(embed_dim, latent_dim, num_heads, ...) {     super$initialize(...)     self$embed_dim <- embed_dim     self$latent_dim <- latent_dim     self$num_heads <- num_heads     self$attention_1 <- layer_multi_head_attention(num_heads = num_heads,                                                    key_dim = embed_dim)     self$attention_2 <- layer_multi_head_attention(num_heads = num_heads,                                                    key_dim = embed_dim)     self$dense_proj <- keras_model_sequential() %>%       layer_dense(latent_dim, activation = \"relu\") %>%       layer_dense(embed_dim)      self$layernorm_1 <- layer_layer_normalization()     self$layernorm_2 <- layer_layer_normalization()     self$layernorm_3 <- layer_layer_normalization()     self$supports_masking <- TRUE   },    get_config = function() {     config <- super$get_config()     for (name in c(\"embed_dim\", \"num_heads\", \"latent_dim\"))       config[[name]] <- self[[name]]     config   },     get_causal_attention_mask = function(inputs) {     c(batch_size, sequence_length, encoding_length) %<-% op_shape(inputs)      x <- op_arange(sequence_length)     i <- x[, NULL]     j <- x[NULL, ]     mask <- op_cast(i >= j, \"int32\")      repeats <- op_stack(c(batch_size, 1L, 1L))     op_tile(mask[NULL, , ], repeats)   },   call = function(inputs, encoder_outputs, mask = NULL) {     causal_mask <- self$get_causal_attention_mask(inputs)      if (is.null(mask))       mask <- causal_mask     else       mask %<>% { op_minimum(op_cast(.[, NULL, ], \"int32\"),                              causal_mask) }      inputs %>%       { self$attention_1(query = ., value = ., key = .,                          attention_mask = causal_mask) + . } %>%       self$layernorm_1() %>%        { self$attention_2(query = .,                          value = encoder_outputs,                          key = encoder_outputs,                          attention_mask = mask) + . } %>%       self$layernorm_2() %>%        { self$dense_proj(.) + . } %>%       self$layernorm_3()    } )  layer_positional_embedding <- Layer(   classname = \"PositionalEmbedding\",    initialize = function(sequence_length, vocab_size, embed_dim, ...) {     super$initialize(...)     self$token_embeddings <- layer_embedding(       input_dim = vocab_size, output_dim = embed_dim     )     self$position_embeddings <- layer_embedding(       input_dim = sequence_length, output_dim = embed_dim     )     self$sequence_length <- sequence_length     self$vocab_size <- vocab_size     self$embed_dim <- embed_dim   },    call = function(inputs) {     c(., len) %<-% op_shape(inputs) # (batch_size, seq_len)     positions <- op_arange(0, len, dtype = \"int32\")     embedded_tokens <- self$token_embeddings(inputs)     embedded_positions <- self$position_embeddings(positions)     embedded_tokens + embedded_positions   },    compute_mask = function(inputs, mask = NULL) {     if (is.null(mask)) return (NULL)     inputs != 0L   },    get_config = function() {     config <- super$get_config()     for(name in c(\"sequence_length\", \"vocab_size\", \"embed_dim\"))       config[[name]] <- self[[name]]     config   } ) embed_dim <- 256 latent_dim <- 2048 num_heads <- 8  encoder_inputs <- layer_input(shape(NA), dtype = \"int64\",                               name = \"encoder_inputs\") encoder_outputs <- encoder_inputs %>%   layer_positional_embedding(sequence_length, vocab_size, embed_dim) %>%   layer_transformer_encoder(embed_dim, latent_dim, num_heads)  encoder <- keras_model(encoder_inputs, encoder_outputs)  decoder_inputs <-  layer_input(shape(NA), dtype = \"int64\",                                name = \"decoder_inputs\") encoded_seq_inputs <- layer_input(shape(NA, embed_dim),                                   name = \"decoder_state_inputs\")  transformer_decoder <- layer_transformer_decoder(NULL,   embed_dim, latent_dim, num_heads)  decoder_outputs <- decoder_inputs %>%   layer_positional_embedding(sequence_length, vocab_size, embed_dim) %>%   transformer_decoder(., encoded_seq_inputs) %>%   layer_dropout(0.5) %>%   layer_dense(vocab_size, activation=\"softmax\")  decoder <- keras_model(inputs = list(decoder_inputs, encoded_seq_inputs),                        outputs = decoder_outputs)  decoder_outputs = decoder(list(decoder_inputs, encoder_outputs))  transformer <- keras_model(list(encoder_inputs, decoder_inputs),                            decoder_outputs,                            name = \"transformer\")"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"training-our-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Training our model","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"’ll use accuracy quick way monitor training progress validation data. Note machine translation typically uses BLEU scores well metrics, rather accuracy. train 1 epoch, get model actually converge train least 30 epochs.","code":"epochs <- 1  # This should be at least 30 for convergence  transformer ## Model: \"transformer\" ## ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ ## │ encoder_inputs      │ (None, None)      │          0 │ -                 │ ## │ (InputLayer)        │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ positional_embeddi… │ (None, None, 256) │  3,845,120 │ encoder_inputs[0… │ ## │ (PositionalEmbeddi… │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ decoder_inputs      │ (None, None)      │          0 │ -                 │ ## │ (InputLayer)        │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ transformer_encoder │ (None, None, 256) │  3,155,456 │ positional_embed… │ ## │ (TransformerEncode… │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ functional_5        │ (None, None,      │ 12,959,640 │ decoder_inputs[0… │ ## │ (Functional)        │ 15000)            │            │ transformer_enco… │ ## └─────────────────────┴───────────────────┴────────────┴───────────────────┘ ##  Total params: 19,960,216 (76.14 MB) ##  Trainable params: 19,960,216 (76.14 MB) ##  Non-trainable params: 0 (0.00 B) transformer |> compile(   \"rmsprop\",   loss = \"sparse_categorical_crossentropy\",   metrics = \"accuracy\" ) transformer |> fit(train_ds, epochs = epochs,                    validation_data = val_ds) ## 1297/1297 - 58s - 44ms/step - accuracy: 0.7709 - loss: 1.5752 - val_accuracy: 0.7731 - val_loss: 1.4209"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"decoding-test-sentences","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Decoding test sentences","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"Finally, let’s demonstrate translate brand new English sentences. simply feed model vectorized English sentence well target token \"[start]\", repeatedly generated next token, hit token \"[end]\". 30 epochs, get results :","code":"spa_vocab <- spa_vectorization |> get_vocabulary() max_decoded_sentence_length <- 20 tf_decode_sequence <- tf_function(function(input_sentence) {   withr::local_options(tensorflow.extract.style = \"python\")    tokenized_input_sentence <- input_sentence %>%     as_tensor(shape = c(1, 1)) %>%     eng_vectorization()   spa_vocab <- as_tensor(spa_vocab)   decoded_sentence <- as_tensor(\"[start]\", shape = c(1, 1))    for (i in tf$range(as.integer(max_decoded_sentence_length))) {      tokenized_target_sentence <-       spa_vectorization(decoded_sentence)[,NA:-1]      next_token_predictions <-       transformer(list(tokenized_input_sentence,                        tokenized_target_sentence))      sampled_token_index <- tf$argmax(next_token_predictions[0, i, ])     sampled_token <- spa_vocab[sampled_token_index]     decoded_sentence <-       tf$strings$join(c(decoded_sentence, sampled_token),                       separator = \" \")      if (sampled_token == \"[end]\")       break   }    decoded_sentence  })  for (i in seq(20)) {      c(input_sentence, correct_translation) %<-%       test_pairs[sample.int(nrow(test_pairs), 1), ]     cat(\"-\\n\")     cat(\"English:\", input_sentence, \"\\n\")     cat(\"Correct Translation:\", tolower(correct_translation), \"\\n\")     cat(\"  Model Translation:\", input_sentence %>% as_tensor() %>%           tf_decode_sequence() %>% as.character(), \"\\n\") } English: I'm sure everything will be fine. Correct Translation: [start] estoy segura de que todo irá bien. [end]   Model Translation: [start] estoy seguro de que todo va bien [end]"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"introduction","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Introduction","title":"Text classification from scratch","text":"example shows text classification starting raw text (set text files disk). demonstrate workflow IMDB sentiment classification dataset (unprocessed version). use layer_text_vectorization() word splitting & indexing.","code":""},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"setup","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Setup","title":"Text classification from scratch","text":"","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\") library(keras3) use_virtualenv(\"r-keras\")"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"load-the-data-imdb-movie-review-sentiment-classification","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Load the data: IMDB movie review sentiment classification","title":"Text classification from scratch","text":"Let’s download data inspect structure. aclImdb folder contains train test subfolder: aclImdb/train/pos aclImdb/train/neg folders contain text files, represents one review (either positive negative): interested pos neg subfolders, let’s delete subfolder text files : can use utility text_dataset_from_directory() generate labeled tf_dataset object set text files disk filed class-specific folders. Let’s use generate training, validation, test datasets. validation training datasets generated two subsets train directory, 20% samples going validation dataset 80% going training dataset. validation dataset addition test dataset useful tuning hyperparameters, model architecture, test dataset used. putting model real world however, retrained using available training data (without creating validation dataset), performance maximized. using validation_split subset arguments, make sure either specify random seed, pass shuffle=FALSE, validation & training splits get overlap. Let’s preview samples:","code":"if (!dir.exists(\"datasets/aclImdb\")) {   dir.create(\"datasets\")   download.file(     \"https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\",     \"datasets/aclImdb_v1.tar.gz\"   )   untar(\"datasets/aclImdb_v1.tar.gz\", exdir = \"datasets\")   unlink(\"datasets/aclImdb/train/unsup\", recursive = TRUE) } head(list.files(\"datasets/aclImdb/test\")) ## [1] \"labeledBow.feat\" \"neg\"             \"pos\"             \"urls_neg.txt\" ## [5] \"urls_pos.txt\" head(list.files(\"datasets/aclImdb/train\")) ## [1] \"labeledBow.feat\" \"neg\"             \"pos\"             \"unsupBow.feat\" ## [5] \"urls_neg.txt\"    \"urls_pos.txt\" writeLines(strwrap(readLines(\"datasets/aclImdb/train/pos/4229_10.txt\"))) ## Don't waste time reading my review. Go out and see this ## astonishingly good episode, which may very well be the best Columbo ## ever written! Ruth Gordon is perfectly cast as the scheming yet ## charming mystery writer who murders her son-in-law to avenge his ## murder of her daughter. Columbo is his usual rumpled, befuddled and ## far-cleverer-than-he-seems self, and this particular installment ## features fantastic chemistry between Gordon and Falk. Ironically, ## this was not written by heralded creators Levinson or Link yet is ## possibly the densest, most thoroughly original and twist-laden ## Columbo plot ever. Utterly satisfying in nearly every department ## and overflowing with droll and witty dialogue and thinking. Truly ## unexpected and inventive climax tops all. 10/10...seek this one out ## on Netflix! unlink(\"datasets/aclImdb/train/unsup\", recursive = TRUE) batch_size <- 32  raw_train_ds <- text_dataset_from_directory(   \"datasets/aclImdb/train\",   batch_size = batch_size,   validation_split = 0.2,   subset = \"training\",   seed = 1337 ) ## Found 25000 files belonging to 2 classes. ## Using 20000 files for training. raw_val_ds <- text_dataset_from_directory(   \"datasets/aclImdb/train\",   batch_size = batch_size,   validation_split = 0.2,   subset = \"validation\",   seed = 1337 ) ## Found 25000 files belonging to 2 classes. ## Using 5000 files for validation. raw_test_ds <- text_dataset_from_directory(   \"datasets/aclImdb/test\",   batch_size = batch_size ) ## Found 25000 files belonging to 2 classes. cat(\"Number of batches in raw_train_ds:\", length(raw_train_ds), \"\\n\") ## Number of batches in raw_train_ds: 625 cat(\"Number of batches in raw_val_ds:\", length(raw_val_ds), \"\\n\") ## Number of batches in raw_val_ds: 157 cat(\"Number of batches in raw_test_ds:\", length(raw_test_ds), \"\\n\") ## Number of batches in raw_test_ds: 782 # It's important to take a look at your raw data to ensure your normalization # and tokenization will work as expected. We can do that by taking a few # examples from the training set and looking at them. # This is one of the places where eager execution shines: # we can just evaluate these tensors using .numpy() # instead of needing to evaluate them in a Session/Graph context. batch <- iter_next(as_iterator(raw_train_ds)) str(batch) ## List of 2 ##  $ :<tf.Tensor: shape=(32), dtype=string, numpy=…> ##  $ :<tf.Tensor: shape=(32), dtype=int32, numpy=…> c(text_batch, label_batch) %<-% batch for (i in 1:3) {   print(text_batch[i])   print(label_batch[i]) } ## tf.Tensor(b\"I have read the novel Reaper of Ben Mezrich a fews years ago and last night I accidentally came to see this adaption.<br /><br />Although it's been years since I read the story the first time, the differences between the novel and the movie are humongous. Very important elements, which made the whole thing plausible are just written out or changed to bad.<br /><br />If the plot sounds interesting to you: go and get the novel. Its much, much, much better.<br /><br />Still 4 out of 10 since it was hard to stop watching because of the great basic plot by Ben Mezrich.\", shape=(), dtype=string) ## tf.Tensor(0, shape=(), dtype=int32) ## tf.Tensor(b'After seeing all the Jesse James, Quantrill, jayhawkers,etc films in the fifties, it is quite a thrill to see this film with a new perspective by director Ang Lee. The scene of the attack of Lawrence, Kansas is awesome. The romantic relationship between Jewel and Toby Mcguire turns out to be one of the best parts and Jonathan Rhys-Meyers is outstanding as the bad guy. All the time this film makes you feel the horror of war, and the desperate situation of the main characters who do not know if they are going to survive the next hours. Definitely worth seeing.', shape=(), dtype=string) ## tf.Tensor(1, shape=(), dtype=int32) ## tf.Tensor(b'AG was an excellent presentation of drama, suspense and thriller that is so rare to American TV. Sheriff Lucas gave many a viewer the willies. We rooted for Caleb as he strove to resist the overtures of Sheriff Lucas. We became engrossed and fearful upon learning of the unthinkable connection between these two characters. The manipulations which weekly gave cause to fear what Lucas would do next were truly surprising. This show lived up to the \"Gothic\" moniker in ways American entertainment has so seldom attempted, much less mastered. The suits definitely made a big mistake in not supporting this show. This show puts shame to the current glut of \"reality\" shows- which are so less than satisfying viewing.The call for a DVD box set is well based. This show is quality viewing for a discerning market hungry for quality viewing. A public that is tiring of over-saturation of mind-numbing reality fare will welcome this gem of real storytelling. Bring on the DVD box set!!', shape=(), dtype=string) ## tf.Tensor(1, shape=(), dtype=int32)"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"prepare-the-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Prepare the data","title":"Text classification from scratch","text":"particular, remove <br /> tags.","code":"# Having looked at our data above, we see that the raw text contains HTML break # tags of the form '<br />'. These tags will not be removed by the default # standardizer (which doesn't strip HTML). Because of this, we will need to # create a custom standardization function. custom_standardization_fn <- function(string_tensor) {   string_tensor |>     tf$strings$lower() |> # convert to all lowercase     tf$strings$regex_replace(\"<br />\", \" \") |> # remove '<br />' HTML tag     tf$strings$regex_replace(\"[[:punct:]]\", \"\") # remove punctuation }   # Model constants. max_features <- 20000 embedding_dim <- 128 sequence_length <- 500  # Now that we have our custom standardization, we can instantiate our text # vectorization layer. We are using this layer to normalize, split, and map # strings to integers, so we set our 'output_mode' to 'int'. # Note that we're using the default split function, # and the custom standardization defined above. # We also set an explicit maximum sequence length, since the CNNs later in our # model won't support ragged sequences. vectorize_layer <- layer_text_vectorization(   standardize = custom_standardization_fn,   max_tokens = max_features,   output_mode = \"int\",   output_sequence_length = sequence_length, )  # Now that the vectorize_layer has been created, call `adapt` on a text-only # dataset to create the vocabulary. You don't have to batch, but for very large # datasets this means you're not keeping spare copies of the dataset in memory.  # Let's make a text-only dataset (no labels): text_ds <- raw_train_ds |>   dataset_map(\\(x, y) x) # Let's call `adapt`: vectorize_layer |> adapt(text_ds)"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"two-options-to-vectorize-the-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Two options to vectorize the data","title":"Text classification from scratch","text":"2 ways can use text vectorization layer: Option 1: Make part model, obtain model processes raw strings, like : Option 2: Apply text dataset obtain dataset word indices, feed model expects integer sequences inputs. important difference two option 2 enables asynchronous CPU processing buffering data training GPU. ’re training model GPU, probably want go option get best performance. . export model production, ’d ship model accepts raw strings input, like code snippet option 1 . can done training. last section.","code":"text_input <- keras_input(shape = c(1L), dtype = \"string\", name = 'text') x <- text_input |>   vectorize_layer() |>   layer_embedding(max_features + 1, embedding_dim) vectorize_text <- function(text, label) {   text <- text |>     op_expand_dims(-1) |>     vectorize_layer()   list(text, label) }  # Vectorize the data. train_ds <- raw_train_ds |> dataset_map(vectorize_text) val_ds   <- raw_val_ds   |> dataset_map(vectorize_text) test_ds  <- raw_test_ds  |> dataset_map(vectorize_text)  # Do async prefetching / buffering of the data for best performance on GPU. train_ds <- train_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10) val_ds <- val_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10) test_ds <- test_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10)"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"build-a-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Build a model","title":"Text classification from scratch","text":"choose simple 1D convnet starting Embedding layer.","code":"# A integer input for vocab indices. inputs <- keras_input(shape = c(NA), dtype = \"int64\")  predictions <- inputs |>   # Next, we add a layer to map those vocab indices into a space of dimensionality   # 'embedding_dim'.   layer_embedding(max_features, embedding_dim) |>   layer_dropout(0.5) |>   # Conv1D + global max pooling   layer_conv_1d(128, 7, padding = \"valid\", activation = \"relu\", strides = 3) |>   layer_conv_1d(128, 7, padding = \"valid\", activation = \"relu\", strides = 3) |>   layer_global_max_pooling_1d() |>   # We add a vanilla hidden layer:   layer_dense(128, activation = \"relu\") |>   layer_dropout(0.5) |>   # We project onto a single unit output layer, and squash it with a sigmoid:   layer_dense(1, activation = \"sigmoid\", name = \"predictions\")  model <- keras_model(inputs, predictions)  summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, None)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ embedding_1 (Embedding)         │ (None, None, 128)      │     2,560,000 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, None, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d (Conv1D)                 │ (None, None, 128)      │       114,816 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_1 (Conv1D)               │ (None, None, 128)      │       114,816 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling1d            │ (None, 128)            │             0 │ ## │ (GlobalMaxPooling1D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 128)            │        16,512 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, 128)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ predictions (Dense)             │ (None, 1)              │           129 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 2,806,273 (10.71 MB) ##  Trainable params: 2,806,273 (10.71 MB) ##  Non-trainable params: 0 (0.00 B) # Compile the model with binary crossentropy loss and an adam optimizer. model |> compile(loss = \"binary_crossentropy\",                  optimizer = \"adam\",                  metrics = \"accuracy\")"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"train-the-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Train the model","title":"Text classification from scratch","text":"","code":"epochs <- 3  # Fit the model using the train and test datasets. model |> fit(train_ds, validation_data = val_ds, epochs = epochs) ## Epoch 1/3 ## 625/625 - 6s - 10ms/step - accuracy: 0.6909 - loss: 0.5300 - val_accuracy: 0.8658 - val_loss: 0.3229 ## Epoch 2/3 ## 625/625 - 2s - 3ms/step - accuracy: 0.9047 - loss: 0.2412 - val_accuracy: 0.8742 - val_loss: 0.3202 ## Epoch 3/3 ## 625/625 - 2s - 3ms/step - accuracy: 0.9573 - loss: 0.1237 - val_accuracy: 0.8704 - val_loss: 0.3551"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"evaluate-the-model-on-the-test-set","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Evaluate the model on the test set","title":"Text classification from scratch","text":"","code":"model |> evaluate(test_ds) ## 782/782 - 1s - 2ms/step - accuracy: 0.8594 - loss: 0.3818 ## $accuracy ## [1] 0.85936 ## ## $loss ## [1] 0.381799"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"make-an-end-to-end-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Make an end-to-end model","title":"Text classification from scratch","text":"want obtain model capable processing raw strings, can simply create new model (using weights just trained):","code":"# A string input inputs <- keras_input(shape = c(1), dtype = \"string\") # Turn strings into vocab indices indices <- vectorize_layer(inputs) # Turn vocab indices into predictions outputs <- model(indices)  # Our end to end model end_to_end_model <- keras_model(inputs, outputs) end_to_end_model |> compile(   loss = \"binary_crossentropy\",   optimizer = \"adam\",   metrics = c(\"accuracy\") )  # Test it with `raw_test_ds`, which yields raw strings end_to_end_model |> evaluate(raw_test_ds) ## 782/782 - 3s - 4ms/step - accuracy: 0.8594 - loss: 0.0000e+00 ## $accuracy ## [1] 0.85936 ## ## $loss ## [1] 0"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"download-the-data","dir":"Articles > Examples","previous_headings":"","what":"Download the data","title":"Image segmentation with a U-Net-like architecture","text":"","code":"options(timeout = 5000) download.file(   \"https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\",   \"datasets/images.tar.gz\" ) download.file(   \"https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\",   \"datasets/annotations.tar.gz\" )  untar(\"datasets/images.tar.gz\", exdir = \"datasets\") untar(\"datasets/annotations.tar.gz\", exdir = \"datasets\")"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"prepare-paths-of-input-images-and-target-segmentation-masks","dir":"Articles > Examples","previous_headings":"","what":"Prepare paths of input images and target segmentation masks","title":"Image segmentation with a U-Net-like architecture","text":"","code":"library(keras3) input_dir <- \"datasets/images/\" target_dir <- \"datasets/annotations/trimaps/\" img_size <- c(160, 160) num_classes <- 3 batch_size <- 32  input_img_paths <- fs::dir_ls(input_dir, glob = \"*.jpg\") |> sort() target_img_paths <- fs::dir_ls(target_dir, glob = \"*.png\") |> sort()  cat(\"Number of samples:\", length(input_img_paths), \"\\n\") ## Number of samples: 7390 for (i in 1:10) {   cat(input_img_paths[i], \"|\", target_img_paths[i], \"\\n\") } ## datasets/images/Abyssinian_1.jpg | datasets/annotations/trimaps/Abyssinian_1.png ## datasets/images/Abyssinian_10.jpg | datasets/annotations/trimaps/Abyssinian_10.png ## datasets/images/Abyssinian_100.jpg | datasets/annotations/trimaps/Abyssinian_100.png ## datasets/images/Abyssinian_101.jpg | datasets/annotations/trimaps/Abyssinian_101.png ## datasets/images/Abyssinian_102.jpg | datasets/annotations/trimaps/Abyssinian_102.png ## datasets/images/Abyssinian_103.jpg | datasets/annotations/trimaps/Abyssinian_103.png ## datasets/images/Abyssinian_104.jpg | datasets/annotations/trimaps/Abyssinian_104.png ## datasets/images/Abyssinian_105.jpg | datasets/annotations/trimaps/Abyssinian_105.png ## datasets/images/Abyssinian_106.jpg | datasets/annotations/trimaps/Abyssinian_106.png ## datasets/images/Abyssinian_107.jpg | datasets/annotations/trimaps/Abyssinian_107.png"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"what-does-one-input-image-and-corresponding-segmentation-mask-look-like","dir":"Articles > Examples","previous_headings":"","what":"What does one input image and corresponding segmentation mask look like?","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Display input image #10 input_img_paths[10] |>   jpeg::readJPEG() |>   as.raster() |>   plot() target_img_paths[10] |>   png::readPNG() |>   magrittr::multiply_by(255)|>   as.raster(max = 3) |>   plot()"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"prepare-dataset-to-load-vectorize-batches-of-data","dir":"Articles > Examples","previous_headings":"","what":"Prepare dataset to load & vectorize batches of data","title":"Image segmentation with a U-Net-like architecture","text":"","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")   # Returns a tf_dataset get_dataset <- function(batch_size, img_size, input_img_paths, target_img_paths,                         max_dataset_len = NULL) {    img_size <- as.integer(img_size)    load_img_masks <- function(input_img_path, target_img_path) {     input_img <- input_img_path |>       tf$io$read_file() |>       tf$io$decode_jpeg(channels = 3) |>       tf$image$resize(img_size) |>       tf$image$convert_image_dtype(\"float32\")      target_img <- target_img_path |>       tf$io$read_file() |>       tf$io$decode_png(channels = 1) |>       tf$image$resize(img_size, method = \"nearest\") |>       tf$image$convert_image_dtype(\"uint8\")      # Ground truth labels are 1, 2, 3. Subtract one to make them 0, 1, 2:     target_img <- target_img - 1L      list(input_img, target_img)   }    if (!is.null(max_dataset_len)) {     input_img_paths <- input_img_paths[1:max_dataset_len]     target_img_paths <- target_img_paths[1:max_dataset_len]   }    list(input_img_paths, target_img_paths) |>     tensor_slices_dataset() |>     dataset_map(load_img_masks, num_parallel_calls = tf$data$AUTOTUNE)|>     dataset_batch(batch_size) }"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"prepare-u-net-xception-style-model","dir":"Articles > Examples","previous_headings":"","what":"Prepare U-Net Xception-style model","title":"Image segmentation with a U-Net-like architecture","text":"","code":"get_model <- function(img_size, num_classes) {    inputs <- keras_input(shape = c(img_size, 3))    ### [First half of the network: downsampling inputs] ###    # Entry block   x <- inputs |>     layer_conv_2d(filters = 32, kernel_size = 3, strides = 2, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation(\"relu\")    previous_block_activation <- x  # Set aside residual    for (filters in c(64, 128, 256)) {     x <- x |>       layer_activation(\"relu\") |>       layer_separable_conv_2d(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_activation(\"relu\") |>       layer_separable_conv_2d(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_max_pooling_2d(pool_size = 3, strides = 2, padding = \"same\")      residual <- previous_block_activation |>       layer_conv_2d(filters = filters, kernel_size = 1, strides = 2, padding = \"same\")      x <- layer_add(x, residual)  # Add back residual     previous_block_activation <- x  # Set aside next residual   }    ### [Second half of the network: upsampling inputs] ###    for (filters in c(256, 128, 64, 32)) {     x <- x |>       layer_activation(\"relu\") |>       layer_conv_2d_transpose(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_activation(\"relu\") |>       layer_conv_2d_transpose(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_upsampling_2d(size = 2)      # Project residual     residual <- previous_block_activation |>       layer_upsampling_2d(size = 2) |>       layer_conv_2d(filters = filters, kernel_size = 1, padding = \"same\")      x <- layer_add(x, residual)     # Add back residual     previous_block_activation <- x  # Set aside next residual   }    # Add a per-pixel classification layer   outputs <- x |>     layer_conv_2d(num_classes, 3, activation = \"softmax\", padding = \"same\")    # Define the model   keras_model(inputs, outputs) }  # Build model model <- get_model(img_size, num_classes) summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)      ┃ Output Shape    ┃   Param # ┃ Connected to   ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer       │ (None, 160,     │         0 │ -              │   -   │ ## │ (InputLayer)      │ 160, 3)         │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d (Conv2D)   │ (None, 80, 80,  │       896 │ input_layer[0… │   Y   │ ## │                   │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d[0][0]   │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation        │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_2      │ (None, 80, 80,  │         0 │ activation[0]… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 80, 80,  │     2,400 │ activation_2[… │   Y   │ ## │ (SeparableConv2D) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       256 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_1      │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d  │ (None, 80, 80,  │     4,736 │ activation_1[… │   Y   │ ## │ (SeparableConv2D) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       256 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d     │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_1 (Conv2D) │ (None, 40, 40,  │     2,112 │ activation[0]… │   Y   │ ## │                   │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add (Add)         │ (None, 40, 40,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 64)             │           │ conv2d_1[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_4      │ (None, 40, 40,  │         0 │ add[0][0]      │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 40, 40,  │     8,896 │ activation_4[… │   Y   │ ## │ (SeparableConv2D) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       512 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_3      │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 40, 40,  │    17,664 │ activation_3[… │   Y   │ ## │ (SeparableConv2D) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       512 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d_1   │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_2 (Conv2D) │ (None, 20, 20,  │     8,320 │ add[0][0]      │   Y   │ ## │                   │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_1 (Add)       │ (None, 20, 20,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 128)            │           │ conv2d_2[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_6      │ (None, 20, 20,  │         0 │ add_1[0][0]    │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 20, 20,  │    34,176 │ activation_6[… │   Y   │ ## │ (SeparableConv2D) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │     1,024 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_5      │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 20, 20,  │    68,096 │ activation_5[… │   Y   │ ## │ (SeparableConv2D) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │     1,024 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d_2   │ (None, 10, 10,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_3 (Conv2D) │ (None, 10, 10,  │    33,024 │ add_1[0][0]    │   Y   │ ## │                   │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_2 (Add)       │ (None, 10, 10,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 256)            │           │ conv2d_3[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_8      │ (None, 10, 10,  │         0 │ add_2[0][0]    │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 10, 10,  │   590,080 │ activation_8[… │   Y   │ ## │ (Conv2DTranspose) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 10, 10,  │     1,024 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_7      │ (None, 10, 10,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose  │ (None, 10, 10,  │   590,080 │ activation_7[… │   Y   │ ## │ (Conv2DTranspose) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 10, 10,  │     1,024 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_1   │ (None, 20, 20,  │         0 │ add_2[0][0]    │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d     │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_4 (Conv2D) │ (None, 20, 20,  │    65,792 │ up_sampling2d… │   Y   │ ## │                   │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_3 (Add)       │ (None, 20, 20,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 256)            │           │ conv2d_4[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_10     │ (None, 20, 20,  │         0 │ add_3[0][0]    │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 20, 20,  │   295,040 │ activation_10… │   Y   │ ## │ (Conv2DTranspose) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │       512 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_9      │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 20, 20,  │   147,584 │ activation_9[… │   Y   │ ## │ (Conv2DTranspose) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │       512 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_3   │ (None, 40, 40,  │         0 │ add_3[0][0]    │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_2   │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_5 (Conv2D) │ (None, 40, 40,  │    32,896 │ up_sampling2d… │   Y   │ ## │                   │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_4 (Add)       │ (None, 40, 40,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 128)            │           │ conv2d_5[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_12     │ (None, 40, 40,  │         0 │ add_4[0][0]    │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 40, 40,  │    73,792 │ activation_12… │   Y   │ ## │ (Conv2DTranspose) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       256 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_11     │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 40, 40,  │    36,928 │ activation_11… │   Y   │ ## │ (Conv2DTranspose) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       256 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_5   │ (None, 80, 80,  │         0 │ add_4[0][0]    │   -   │ ## │ (UpSampling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_4   │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_6 (Conv2D) │ (None, 80, 80,  │     8,256 │ up_sampling2d… │   Y   │ ## │                   │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_5 (Add)       │ (None, 80, 80,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 64)             │           │ conv2d_6[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_14     │ (None, 80, 80,  │         0 │ add_5[0][0]    │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 80, 80,  │    18,464 │ activation_14… │   Y   │ ## │ (Conv2DTranspose) │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_13     │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 80, 80,  │     9,248 │ activation_13… │   Y   │ ## │ (Conv2DTranspose) │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_7   │ (None, 160,     │         0 │ add_5[0][0]    │   -   │ ## │ (UpSampling2D)    │ 160, 64)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_6   │ (None, 160,     │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 160, 32)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_7 (Conv2D) │ (None, 160,     │     2,080 │ up_sampling2d… │   Y   │ ## │                   │ 160, 32)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_6 (Add)       │ (None, 160,     │         0 │ up_sampling2d… │   -   │ ## │                   │ 160, 32)        │           │ conv2d_7[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_8 (Conv2D) │ (None, 160,     │       867 │ add_6[0][0]    │   Y   │ ## │                   │ 160, 3)         │           │                │       │ ## └───────────────────┴─────────────────┴───────────┴────────────────┴───────┘ ##  Total params: 2,058,979 (7.85 MB) ##  Trainable params: 2,055,203 (7.84 MB) ##  Non-trainable params: 3,776 (14.75 KB)"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"set-aside-a-validation-split","dir":"Articles > Examples","previous_headings":"","what":"Set aside a validation split","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Split our img paths into a training and a validation set val_samples <- 1000 val_samples <- sample.int(length(input_img_paths), val_samples)  train_input_img_paths <- input_img_paths[-val_samples] train_target_img_paths <- target_img_paths[-val_samples]  val_input_img_paths <- input_img_paths[val_samples] val_target_img_paths <- target_img_paths[val_samples]  # Instantiate dataset for each split # Limit input files in `max_dataset_len` for faster epoch training time. # Remove the `max_dataset_len` arg when running with full dataset. train_dataset <- get_dataset(   batch_size,   img_size,   train_input_img_paths,   train_target_img_paths,   max_dataset_len = 1000 ) valid_dataset <- get_dataset(   batch_size, img_size, val_input_img_paths, val_target_img_paths )"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"train-the-model","dir":"Articles > Examples","previous_headings":"","what":"Train the model","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Configure the model for training. # We use the \"sparse\" version of categorical_crossentropy # because our target data is integers. model |> compile(   optimizer = optimizer_adam(1e-4),   loss = \"sparse_categorical_crossentropy\" )  callbacks <- list(   callback_model_checkpoint(     \"models/oxford_segmentation.keras\", save_best_only = TRUE   ) )  # Train the model, doing validation at the end of each epoch. epochs <- 50 model |> fit(     train_dataset,     epochs=epochs,     validation_data=valid_dataset,     callbacks=callbacks,     verbose=2 ) ## Epoch 1/50 ## 32/32 - 32s - 986ms/step - loss: 1.3044 - val_loss: 1.5502 ## Epoch 2/50 ## 32/32 - 2s - 60ms/step - loss: 0.8717 - val_loss: 1.9567 ## Epoch 3/50 ## 32/32 - 2s - 60ms/step - loss: 0.7695 - val_loss: 2.1948 ## Epoch 4/50 ## 32/32 - 2s - 60ms/step - loss: 0.7080 - val_loss: 2.4837 ## Epoch 5/50 ## 32/32 - 2s - 64ms/step - loss: 0.6689 - val_loss: 2.7751 ## Epoch 6/50 ## 32/32 - 2s - 61ms/step - loss: 0.6378 - val_loss: 3.1156 ## Epoch 7/50 ## 32/32 - 2s - 60ms/step - loss: 0.6129 - val_loss: 3.3611 ## Epoch 8/50 ## 32/32 - 2s - 60ms/step - loss: 0.5917 - val_loss: 3.5193 ## Epoch 9/50 ## 32/32 - 2s - 62ms/step - loss: 0.5723 - val_loss: 3.6237 ## Epoch 10/50 ## 32/32 - 2s - 62ms/step - loss: 0.5538 - val_loss: 3.7209 ## Epoch 11/50 ## 32/32 - 2s - 60ms/step - loss: 0.5352 - val_loss: 3.8216 ## Epoch 12/50 ## 32/32 - 2s - 61ms/step - loss: 0.5161 - val_loss: 3.9090 ## Epoch 13/50 ## 32/32 - 2s - 60ms/step - loss: 0.4958 - val_loss: 4.0158 ## Epoch 14/50 ## 32/32 - 2s - 60ms/step - loss: 0.4739 - val_loss: 4.0756 ## Epoch 15/50 ## 32/32 - 2s - 60ms/step - loss: 0.4501 - val_loss: 4.0083 ## Epoch 16/50 ## 32/32 - 2s - 60ms/step - loss: 0.4243 - val_loss: 3.7728 ## Epoch 17/50 ## 32/32 - 2s - 60ms/step - loss: 0.3971 - val_loss: 3.3484 ## Epoch 18/50 ## 32/32 - 2s - 61ms/step - loss: 0.3696 - val_loss: 2.6909 ## Epoch 19/50 ## 32/32 - 2s - 60ms/step - loss: 0.3428 - val_loss: 1.9726 ## Epoch 20/50 ## 32/32 - 2s - 65ms/step - loss: 0.3178 - val_loss: 1.4081 ## Epoch 21/50 ## 32/32 - 2s - 65ms/step - loss: 0.2954 - val_loss: 1.1136 ## Epoch 22/50 ## 32/32 - 2s - 65ms/step - loss: 0.2764 - val_loss: 1.0246 ## Epoch 23/50 ## 32/32 - 2s - 61ms/step - loss: 0.2610 - val_loss: 1.0390 ## Epoch 24/50 ## 32/32 - 2s - 60ms/step - loss: 0.2507 - val_loss: 1.1001 ## Epoch 25/50 ## 32/32 - 2s - 60ms/step - loss: 0.2525 - val_loss: 1.1915 ## Epoch 26/50 ## 32/32 - 2s - 61ms/step - loss: 0.2961 - val_loss: 1.2296 ## Epoch 27/50 ## 32/32 - 2s - 61ms/step - loss: 0.3545 - val_loss: 1.2625 ## Epoch 28/50 ## 32/32 - 2s - 65ms/step - loss: 0.3326 - val_loss: 0.9711 ## Epoch 29/50 ## 32/32 - 2s - 60ms/step - loss: 0.3302 - val_loss: 1.0251 ## Epoch 30/50 ## 32/32 - 2s - 60ms/step - loss: 0.3163 - val_loss: 1.0867 ## Epoch 31/50 ## 32/32 - 2s - 60ms/step - loss: 0.3131 - val_loss: 1.4197 ## Epoch 32/50 ## 32/32 - 2s - 60ms/step - loss: 0.3093 - val_loss: 1.0851 ## Epoch 33/50 ## 32/32 - 2s - 61ms/step - loss: 0.2939 - val_loss: 1.4807 ## Epoch 34/50 ## 32/32 - 2s - 60ms/step - loss: 0.2763 - val_loss: 1.1851 ## Epoch 35/50 ## 32/32 - 2s - 60ms/step - loss: 0.2702 - val_loss: 1.1337 ## Epoch 36/50 ## 32/32 - 2s - 61ms/step - loss: 0.2584 - val_loss: 1.0315 ## Epoch 37/50 ## 32/32 - 2s - 61ms/step - loss: 0.2440 - val_loss: 1.0631 ## Epoch 38/50 ## 32/32 - 2s - 60ms/step - loss: 0.2353 - val_loss: 1.1609 ## Epoch 39/50 ## 32/32 - 2s - 61ms/step - loss: 0.2285 - val_loss: 1.1839 ## Epoch 40/50 ## 32/32 - 2s - 60ms/step - loss: 0.2307 - val_loss: 1.1293 ## Epoch 41/50 ## 32/32 - 2s - 61ms/step - loss: 0.2357 - val_loss: 0.9948 ## Epoch 42/50 ## 32/32 - 2s - 60ms/step - loss: 0.2279 - val_loss: 1.1329 ## Epoch 43/50 ## 32/32 - 2s - 61ms/step - loss: 0.2174 - val_loss: 1.0418 ## Epoch 44/50 ## 32/32 - 2s - 61ms/step - loss: 0.2076 - val_loss: 1.1141 ## Epoch 45/50 ## 32/32 - 2s - 61ms/step - loss: 0.2004 - val_loss: 1.1162 ## Epoch 46/50 ## 32/32 - 2s - 60ms/step - loss: 0.1930 - val_loss: 1.0939 ## Epoch 47/50 ## 32/32 - 2s - 61ms/step - loss: 0.1852 - val_loss: 1.0499 ## Epoch 48/50 ## 32/32 - 2s - 60ms/step - loss: 0.1796 - val_loss: 1.0651 ## Epoch 49/50 ## 32/32 - 2s - 61ms/step - loss: 0.1762 - val_loss: 1.0679 ## Epoch 50/50 ## 32/32 - 2s - 60ms/step - loss: 0.1757 - val_loss: 1.1762"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"visualize-predictions","dir":"Articles > Examples","previous_headings":"","what":"Visualize predictions","title":"Image segmentation with a U-Net-like architecture","text":"","code":"model <- load_model(\"models/oxford_segmentation.keras\") # Generate predictions for all images in the validation set val_dataset <- get_dataset(   batch_size, img_size, val_input_img_paths, val_target_img_paths ) val_preds <- predict(model, val_dataset) ## 32/32 - 4s - 111ms/step display_mask <- function(i) {   # Quick utility to display a model's prediction.   mask <- val_preds[i,,,] %>%     apply(c(1,2), which.max) %>%     array_reshape(dim = c(img_size, 1))   mask <- abind::abind(mask, mask, mask, along = 3)   plot(as.raster(mask, max = 3)) }  # Display results for validation image #10 i <- 10  par(mfrow = c(1, 3)) # Display input image input_img_paths[i] |>   jpeg::readJPEG() |>   as.raster() |>   plot()  # Display ground-truth target mask target_img_paths[i] |>   png::readPNG() |>   magrittr::multiply_by(255)|>   as.raster(max = 3) |>   plot()  # Display mask predicted by our model display_mask(i)  # Note that the model only sees inputs at 150x150."},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"introduction","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Introduction","title":"Imbalanced classification: credit card fraud detection","text":"example looks Kaggle Credit Card Fraud Detection dataset demonstrate train classification model data highly imbalanced classes. can download data clicking “Download” link, ’re setup kaggle API key \"~/.kaggle/kagle.json\", can run following:","code":"reticulate::py_install(\"kaggle\", pip = TRUE) reticulate::py_available(TRUE) # ensure 'kaggle' is on the PATH system(\"kaggle datasets download -d mlg-ulb/creditcardfraud\") zip::unzip(\"creditcardfraud.zip\", files = \"creditcard.csv\")"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"first-load-the-data","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"First, load the data","title":"Imbalanced classification: credit card fraud detection","text":"","code":"library(readr) df <- read_csv(\"creditcard.csv\", col_types = cols(   Class = col_integer(),   .default = col_double() )) tibble::glimpse(df) ## Rows: 284,807 ## Columns: 31 ## $ Time   <dbl> 0, 0, 1, 1, 2, 2, 4, 7, 7, 9, 10, 10, 10, 11, 12, 12, 12, 1… ## $ V1     <dbl> -1.3598071, 1.1918571, -1.3583541, -0.9662717, -1.1582331, … ## $ V2     <dbl> -0.07278117, 0.26615071, -1.34016307, -0.18522601, 0.877736… ## $ V3     <dbl> 2.53634674, 0.16648011, 1.77320934, 1.79299334, 1.54871785,… ## $ V4     <dbl> 1.37815522, 0.44815408, 0.37977959, -0.86329128, 0.40303393… ## $ V5     <dbl> -0.33832077, 0.06001765, -0.50319813, -0.01030888, -0.40719… ## $ V6     <dbl> 0.46238778, -0.08236081, 1.80049938, 1.24720317, 0.09592146… ## $ V7     <dbl> 0.239598554, -0.078802983, 0.791460956, 0.237608940, 0.5929… ## $ V8     <dbl> 0.098697901, 0.085101655, 0.247675787, 0.377435875, -0.2705… ## $ V9     <dbl> 0.3637870, -0.2554251, -1.5146543, -1.3870241, 0.8177393, -… ## $ V10    <dbl> 0.09079417, -0.16697441, 0.20764287, -0.05495192, 0.7530744… ## $ V11    <dbl> -0.55159953, 1.61272666, 0.62450146, -0.22648726, -0.822842… ## $ V12    <dbl> -0.61780086, 1.06523531, 0.06608369, 0.17822823, 0.53819555… ## $ V13    <dbl> -0.99138985, 0.48909502, 0.71729273, 0.50775687, 1.34585159… ## $ V14    <dbl> -0.31116935, -0.14377230, -0.16594592, -0.28792375, -1.1196… ## $ V15    <dbl> 1.468176972, 0.635558093, 2.345864949, -0.631418118, 0.1751… ## $ V16    <dbl> -0.47040053, 0.46391704, -2.89008319, -1.05964725, -0.45144… ## $ V17    <dbl> 0.207971242, -0.114804663, 1.109969379, -0.684092786, -0.23… ## $ V18    <dbl> 0.02579058, -0.18336127, -0.12135931, 1.96577500, -0.038194… ## $ V19    <dbl> 0.40399296, -0.14578304, -2.26185710, -1.23262197, 0.803486… ## $ V20    <dbl> 0.25141210, -0.06908314, 0.52497973, -0.20803778, 0.4085423… ## $ V21    <dbl> -0.018306778, -0.225775248, 0.247998153, -0.108300452, -0.0… ## $ V22    <dbl> 0.277837576, -0.638671953, 0.771679402, 0.005273597, 0.7982… ## $ V23    <dbl> -0.110473910, 0.101288021, 0.909412262, -0.190320519, -0.13… ## $ V24    <dbl> 0.06692807, -0.33984648, -0.68928096, -1.17557533, 0.141266… ## $ V25    <dbl> 0.12853936, 0.16717040, -0.32764183, 0.64737603, -0.2060095… ## $ V26    <dbl> -0.18911484, 0.12589453, -0.13909657, -0.22192884, 0.502292… ## $ V27    <dbl> 0.133558377, -0.008983099, -0.055352794, 0.062722849, 0.219… ## $ V28    <dbl> -0.021053053, 0.014724169, -0.059751841, 0.061457629, 0.215… ## $ Amount <dbl> 149.62, 2.69, 378.66, 123.50, 69.99, 3.67, 4.99, 40.80, 93.… ## $ Class  <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"prepare-a-validation-set","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Prepare a validation set","title":"Imbalanced classification: credit card fraud detection","text":"","code":"val_idx <- nrow(df) %>% sample.int(., round( . * 0.2)) val_df <- df[val_idx, ] train_df <- df[-val_idx, ]  cat(\"Number of training samples:\", nrow(train_df), \"\\n\") ## Number of training samples: 227846 cat(\"Number of validation samples:\", nrow(val_df), \"\\n\") ## Number of validation samples: 56961"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"analyze-class-imbalance-in-the-targets","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Analyze class imbalance in the targets","title":"Imbalanced classification: credit card fraud detection","text":"","code":"counts <- table(train_df$Class) counts ## ##      0      1 ## 227450    396 cat(sprintf(\"Number of positive samples in training data: %i (%.2f%% of total)\",             counts[\"1\"], 100 * counts[\"1\"] / sum(counts))) ## Number of positive samples in training data: 396 (0.17% of total) weight_for_0 = 1 / counts[\"0\"] weight_for_1 = 1 / counts[\"1\"]"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"normalize-the-data-using-training-set-statistics","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Normalize the data using training set statistics","title":"Imbalanced classification: credit card fraud detection","text":"","code":"feature_names <- colnames(train_df) %>% setdiff(\"Class\")  train_features <- as.matrix(train_df[feature_names]) train_targets <- as.matrix(train_df$Class)  val_features <- as.matrix(val_df[feature_names]) val_targets <- as.matrix(val_df$Class)  train_features %<>% scale() val_features %<>% scale(center = attr(train_features, \"scaled:center\"),                         scale = attr(train_features, \"scaled:scale\"))"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"build-a-binary-classification-model","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Build a binary classification model","title":"Imbalanced classification: credit card fraud detection","text":"","code":"model <-   keras_model_sequential(input_shape = ncol(train_features)) |>   layer_dense(256, activation = \"relu\") |>   layer_dense(256, activation = \"relu\") |>   layer_dropout(0.3) |>   layer_dense(256, activation = \"relu\") |>   layer_dropout(0.3) |>   layer_dense(1, activation = \"sigmoid\")  model ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense (Dense)                   │ (None, 256)            │         7,936 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_1 (Dense)                 │ (None, 256)            │        65,792 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 256)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 256)            │        65,792 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, 256)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_3 (Dense)                 │ (None, 1)              │           257 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 139,777 (546.00 KB) ##  Trainable params: 139,777 (546.00 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"train-the-model-with-class_weight-argument","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Train the model with class_weight argument","title":"Imbalanced classification: credit card fraud detection","text":"","code":"metrics <- list(   metric_false_negatives(name = \"fn\"),   metric_false_positives(name = \"fp\"),   metric_true_negatives(name = \"tn\"),   metric_true_positives(name = \"tp\"),   metric_precision(name = \"precision\"),   metric_recall(name = \"recall\") ) model |> compile(   optimizer = optimizer_adam(1e-2),   loss = \"binary_crossentropy\",   metrics = metrics ) callbacks <- list(   callback_model_checkpoint(\"fraud_model_at_epoch_{epoch}.keras\") )  class_weight <- list(\"0\" = weight_for_0,                      \"1\" = weight_for_1)  model |> fit(   train_features, train_targets,   validation_data = list(val_features, val_targets),   class_weight = class_weight,   batch_size = 2048,   epochs = 30,   callbacks = callbacks,   verbose = 2 ) ## Epoch 1/30 ## 112/112 - 3s - 27ms/step - fn: 40.0000 - fp: 23705.0000 - loss: 2.2728e-06 - precision: 0.0148 - recall: 0.8990 - tn: 203745.0000 - tp: 356.0000 - val_fn: 8.0000 - val_fp: 1502.0000 - val_loss: 0.1089 - val_precision: 0.0553 - val_recall: 0.9167 - val_tn: 55363.0000 - val_tp: 88.0000 ## Epoch 2/30 ## 112/112 - 1s - 6ms/step - fn: 33.0000 - fp: 8009.0000 - loss: 1.4949e-06 - precision: 0.0434 - recall: 0.9167 - tn: 219441.0000 - tp: 363.0000 - val_fn: 10.0000 - val_fp: 1176.0000 - val_loss: 0.0912 - val_precision: 0.0681 - val_recall: 0.8958 - val_tn: 55689.0000 - val_tp: 86.0000 ## Epoch 3/30 ## 112/112 - 0s - 2ms/step - fn: 32.0000 - fp: 7704.0000 - loss: 1.3369e-06 - precision: 0.0451 - recall: 0.9192 - tn: 219746.0000 - tp: 364.0000 - val_fn: 9.0000 - val_fp: 1202.0000 - val_loss: 0.0870 - val_precision: 0.0675 - val_recall: 0.9062 - val_tn: 55663.0000 - val_tp: 87.0000 ## Epoch 4/30 ## 112/112 - 0s - 2ms/step - fn: 28.0000 - fp: 8366.0000 - loss: 1.2269e-06 - precision: 0.0421 - recall: 0.9293 - tn: 219084.0000 - tp: 368.0000 - val_fn: 10.0000 - val_fp: 1560.0000 - val_loss: 0.0967 - val_precision: 0.0522 - val_recall: 0.8958 - val_tn: 55305.0000 - val_tp: 86.0000 ## Epoch 5/30 ## 112/112 - 0s - 2ms/step - fn: 19.0000 - fp: 6217.0000 - loss: 8.5492e-07 - precision: 0.0572 - recall: 0.9520 - tn: 221233.0000 - tp: 377.0000 - val_fn: 9.0000 - val_fp: 2626.0000 - val_loss: 0.1236 - val_precision: 0.0321 - val_recall: 0.9062 - val_tn: 54239.0000 - val_tp: 87.0000 ## Epoch 6/30 ## 112/112 - 0s - 2ms/step - fn: 18.0000 - fp: 6566.0000 - loss: 7.4300e-07 - precision: 0.0544 - recall: 0.9545 - tn: 220884.0000 - tp: 378.0000 - val_fn: 8.0000 - val_fp: 2302.0000 - val_loss: 0.1081 - val_precision: 0.0368 - val_recall: 0.9167 - val_tn: 54563.0000 - val_tp: 88.0000 ## Epoch 7/30 ## 112/112 - 0s - 2ms/step - fn: 14.0000 - fp: 6819.0000 - loss: 7.1838e-07 - precision: 0.0530 - recall: 0.9646 - tn: 220631.0000 - tp: 382.0000 - val_fn: 7.0000 - val_fp: 1376.0000 - val_loss: 0.0800 - val_precision: 0.0608 - val_recall: 0.9271 - val_tn: 55489.0000 - val_tp: 89.0000 ## Epoch 8/30 ## 112/112 - 0s - 2ms/step - fn: 19.0000 - fp: 7345.0000 - loss: 8.3921e-07 - precision: 0.0488 - recall: 0.9520 - tn: 220105.0000 - tp: 377.0000 - val_fn: 10.0000 - val_fp: 416.0000 - val_loss: 0.0406 - val_precision: 0.1713 - val_recall: 0.8958 - val_tn: 56449.0000 - val_tp: 86.0000 ## Epoch 9/30 ## 112/112 - 0s - 2ms/step - fn: 13.0000 - fp: 6282.0000 - loss: 8.3488e-07 - precision: 0.0575 - recall: 0.9672 - tn: 221168.0000 - tp: 383.0000 - val_fn: 10.0000 - val_fp: 933.0000 - val_loss: 0.0450 - val_precision: 0.0844 - val_recall: 0.8958 - val_tn: 55932.0000 - val_tp: 86.0000 ## Epoch 10/30 ## 112/112 - 0s - 2ms/step - fn: 15.0000 - fp: 7597.0000 - loss: 1.1098e-06 - precision: 0.0478 - recall: 0.9621 - tn: 219853.0000 - tp: 381.0000 - val_fn: 10.0000 - val_fp: 2417.0000 - val_loss: 0.4261 - val_precision: 0.0344 - val_recall: 0.8958 - val_tn: 54448.0000 - val_tp: 86.0000 ## Epoch 11/30 ## 112/112 - 0s - 2ms/step - fn: 24.0000 - fp: 10269.0000 - loss: 1.9062e-06 - precision: 0.0350 - recall: 0.9394 - tn: 217181.0000 - tp: 372.0000 - val_fn: 9.0000 - val_fp: 1434.0000 - val_loss: 0.1261 - val_precision: 0.0572 - val_recall: 0.9062 - val_tn: 55431.0000 - val_tp: 87.0000 ## Epoch 12/30 ## 112/112 - 0s - 2ms/step - fn: 17.0000 - fp: 6634.0000 - loss: 1.0711e-06 - precision: 0.0540 - recall: 0.9571 - tn: 220816.0000 - tp: 379.0000 - val_fn: 8.0000 - val_fp: 2263.0000 - val_loss: 0.1654 - val_precision: 0.0374 - val_recall: 0.9167 - val_tn: 54602.0000 - val_tp: 88.0000 ## Epoch 13/30 ## 112/112 - 0s - 2ms/step - fn: 10.0000 - fp: 5620.0000 - loss: 5.3600e-07 - precision: 0.0643 - recall: 0.9747 - tn: 221830.0000 - tp: 386.0000 - val_fn: 12.0000 - val_fp: 914.0000 - val_loss: 0.0414 - val_precision: 0.0842 - val_recall: 0.8750 - val_tn: 55951.0000 - val_tp: 84.0000 ## Epoch 14/30 ## 112/112 - 0s - 2ms/step - fn: 8.0000 - fp: 5103.0000 - loss: 5.1168e-07 - precision: 0.0707 - recall: 0.9798 - tn: 222347.0000 - tp: 388.0000 - val_fn: 11.0000 - val_fp: 1382.0000 - val_loss: 0.0660 - val_precision: 0.0579 - val_recall: 0.8854 - val_tn: 55483.0000 - val_tp: 85.0000 ## Epoch 15/30 ## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 4497.0000 - loss: 4.4632e-07 - precision: 0.0796 - recall: 0.9823 - tn: 222953.0000 - tp: 389.0000 - val_fn: 10.0000 - val_fp: 1805.0000 - val_loss: 0.0786 - val_precision: 0.0455 - val_recall: 0.8958 - val_tn: 55060.0000 - val_tp: 86.0000 ## Epoch 16/30 ## 112/112 - 0s - 2ms/step - fn: 5.0000 - fp: 5056.0000 - loss: 6.1038e-07 - precision: 0.0718 - recall: 0.9874 - tn: 222394.0000 - tp: 391.0000 - val_fn: 13.0000 - val_fp: 889.0000 - val_loss: 0.0676 - val_precision: 0.0854 - val_recall: 0.8646 - val_tn: 55976.0000 - val_tp: 83.0000 ## Epoch 17/30 ## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 4639.0000 - loss: 4.5763e-07 - precision: 0.0781 - recall: 0.9924 - tn: 222811.0000 - tp: 393.0000 - val_fn: 11.0000 - val_fp: 1185.0000 - val_loss: 0.0669 - val_precision: 0.0669 - val_recall: 0.8854 - val_tn: 55680.0000 - val_tp: 85.0000 ## Epoch 18/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 4355.0000 - loss: 4.5299e-07 - precision: 0.0826 - recall: 0.9899 - tn: 223095.0000 - tp: 392.0000 - val_fn: 9.0000 - val_fp: 1405.0000 - val_loss: 0.0772 - val_precision: 0.0583 - val_recall: 0.9062 - val_tn: 55460.0000 - val_tp: 87.0000 ## Epoch 19/30 ## 112/112 - 0s - 2ms/step - fn: 6.0000 - fp: 5779.0000 - loss: 4.8324e-07 - precision: 0.0632 - recall: 0.9848 - tn: 221671.0000 - tp: 390.0000 - val_fn: 10.0000 - val_fp: 1135.0000 - val_loss: 0.0546 - val_precision: 0.0704 - val_recall: 0.8958 - val_tn: 55730.0000 - val_tp: 86.0000 ## Epoch 20/30 ## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 4748.0000 - loss: 3.7424e-07 - precision: 0.0764 - recall: 0.9924 - tn: 222702.0000 - tp: 393.0000 - val_fn: 9.0000 - val_fp: 895.0000 - val_loss: 0.0396 - val_precision: 0.0886 - val_recall: 0.9062 - val_tn: 55970.0000 - val_tp: 87.0000 ## Epoch 21/30 ## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 3798.0000 - loss: 3.1417e-07 - precision: 0.0938 - recall: 0.9924 - tn: 223652.0000 - tp: 393.0000 - val_fn: 10.0000 - val_fp: 652.0000 - val_loss: 0.0309 - val_precision: 0.1165 - val_recall: 0.8958 - val_tn: 56213.0000 - val_tp: 86.0000 ## Epoch 22/30 ## 112/112 - 0s - 2ms/step - fn: 0.0000e+00 - fp: 2188.0000 - loss: 1.8112e-07 - precision: 0.1533 - recall: 1.0000 - tn: 225262.0000 - tp: 396.0000 - val_fn: 10.0000 - val_fp: 427.0000 - val_loss: 0.0250 - val_precision: 0.1676 - val_recall: 0.8958 - val_tn: 56438.0000 - val_tp: 86.0000 ## Epoch 23/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 3066.0000 - loss: 3.4932e-07 - precision: 0.1134 - recall: 0.9899 - tn: 224384.0000 - tp: 392.0000 - val_fn: 11.0000 - val_fp: 1053.0000 - val_loss: 0.0494 - val_precision: 0.0747 - val_recall: 0.8854 - val_tn: 55812.0000 - val_tp: 85.0000 ## Epoch 24/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 3175.0000 - loss: 5.1994e-07 - precision: 0.1099 - recall: 0.9899 - tn: 224275.0000 - tp: 392.0000 - val_fn: 11.0000 - val_fp: 1012.0000 - val_loss: 0.0997 - val_precision: 0.0775 - val_recall: 0.8854 - val_tn: 55853.0000 - val_tp: 85.0000 ## Epoch 25/30 ## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 5874.0000 - loss: 6.5862e-07 - precision: 0.0621 - recall: 0.9823 - tn: 221576.0000 - tp: 389.0000 - val_fn: 9.0000 - val_fp: 3090.0000 - val_loss: 0.1296 - val_precision: 0.0274 - val_recall: 0.9062 - val_tn: 53775.0000 - val_tp: 87.0000 ## Epoch 26/30 ## 112/112 - 0s - 2ms/step - fn: 6.0000 - fp: 5395.0000 - loss: 5.9253e-07 - precision: 0.0674 - recall: 0.9848 - tn: 222055.0000 - tp: 390.0000 - val_fn: 9.0000 - val_fp: 1320.0000 - val_loss: 0.0928 - val_precision: 0.0618 - val_recall: 0.9062 - val_tn: 55545.0000 - val_tp: 87.0000 ## Epoch 27/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 3887.0000 - loss: 7.1278e-07 - precision: 0.0916 - recall: 0.9899 - tn: 223563.0000 - tp: 392.0000 - val_fn: 12.0000 - val_fp: 572.0000 - val_loss: 0.0352 - val_precision: 0.1280 - val_recall: 0.8750 - val_tn: 56293.0000 - val_tp: 84.0000 ## Epoch 28/30 ## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 2209.0000 - loss: 5.8153e-07 - precision: 0.1510 - recall: 0.9924 - tn: 225241.0000 - tp: 393.0000 - val_fn: 11.0000 - val_fp: 1373.0000 - val_loss: 0.0808 - val_precision: 0.0583 - val_recall: 0.8854 - val_tn: 55492.0000 - val_tp: 85.0000 ## Epoch 29/30 ## 112/112 - 0s - 2ms/step - fn: 1.0000 - fp: 2506.0000 - loss: 2.9031e-07 - precision: 0.1362 - recall: 0.9975 - tn: 224944.0000 - tp: 395.0000 - val_fn: 11.0000 - val_fp: 690.0000 - val_loss: 0.0401 - val_precision: 0.1097 - val_recall: 0.8854 - val_tn: 56175.0000 - val_tp: 85.0000 ## Epoch 30/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 2719.0000 - loss: 6.1097e-07 - precision: 0.1260 - recall: 0.9899 - tn: 224731.0000 - tp: 392.0000 - val_fn: 13.0000 - val_fp: 861.0000 - val_loss: 0.0362 - val_precision: 0.0879 - val_recall: 0.8646 - val_tn: 56004.0000 - val_tp: 83.0000 val_pred <- model %>%   predict(val_features) %>%   { as.integer(. > 0.5) } ## 1781/1781 - 1s - 311us/step pred_correct <- val_df$Class == val_pred cat(sprintf(\"Validation accuracy: %.2f\", mean(pred_correct))) ## Validation accuracy: 0.98 fraudulent <- val_df$Class == 1  n_fraudulent_detected <- sum(fraudulent & pred_correct) n_fraudulent_missed <- sum(fraudulent & !pred_correct) n_legitimate_flagged <- sum(!fraudulent & !pred_correct)"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"conclusions","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Conclusions","title":"Imbalanced classification: credit card fraud detection","text":"end training, 56,961 validation transactions, : Correctly identifying 83 fraudulent Missing 13 fraudulent transactions cost incorrectly flagging 861 legitimate transactions real world, one put even higher weight class 1, reflect False Negatives costly False Positives. Next time credit card gets declined online purchase – .","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"introduction","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Introduction","title":"Structured data classification with FeatureSpace","text":"example demonstrates structured data classification (also known tabular data classification), starting raw CSV file. data includes numerical features, integer categorical features, string categorical features. use utility layer_feature_space() index, preprocess, encode features. code adapted example Structured data classification scratch. previous example managed low-level feature preprocessing encoding Keras preprocessing layers, example delegate everything layer_feature_space(), making workflow extremely quick easy.","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"the-dataset","dir":"Articles > Examples > Structured_data","previous_headings":"Introduction","what":"The dataset","title":"Structured data classification with FeatureSpace","text":"dataset provided Cleveland Clinic Foundation Heart Disease. ’s CSV file 303 rows. row contains information patient (sample), column describes attribute patient (feature). use features predict whether patient heart disease (binary classification). ’s description feature:","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"setup","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Setup","title":"Structured data classification with FeatureSpace","text":"","code":"library(readr) library(dplyr, warn.conflicts = FALSE) library(keras3) library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")  conflicted::conflicts_prefer(   keras3::shape(),   keras3::set_random_seed(),   dplyr::filter(),   .quiet = TRUE )  use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"preparing-the-data","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Preparing the data","title":"Structured data classification with FeatureSpace","text":"Let’s download data load Pandas dataframe: dataset includes 303 samples 14 columns per sample (13 features, plus target label) ’s preview samples: last column, “target”, indicates whether patient heart disease (1) (0). Let’s split data training validation set: Let’s generate tf_dataset objects dataframe: tf_dataset yields tuple (input, target) input dictionary (named list) features target value 0 1: Let’s batch datasets:","code":"file_url <-   \"http://storage.googleapis.com/download.tensorflow.org/data/heart.csv\" df <- read_csv(file_url, col_types = cols(   oldpeak = col_double(),   thal = col_character(),   .default = col_integer() ))  # the dataset has two malformed rows, filter them out df <- df |> filter(!thal %in% c(\"1\", \"2\")) glimpse(df) ## Rows: 301 ## Columns: 14 ## $ age      <int> 63, 67, 67, 37, 41, 56, 62, 57, 63, 53, 57, 56, 56, 44, 5… ## $ sex      <int> 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, … ## $ cp       <int> 1, 4, 4, 3, 2, 2, 4, 4, 4, 4, 4, 2, 3, 2, 3, 3, 2, 4, 3, … ## $ trestbps <int> 145, 160, 120, 130, 130, 120, 140, 120, 130, 140, 140, 14… ## $ chol     <int> 233, 286, 229, 250, 204, 236, 268, 354, 254, 203, 192, 29… ## $ fbs      <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, … ## $ restecg  <int> 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, … ## $ thalach  <int> 150, 108, 129, 187, 172, 178, 160, 163, 147, 155, 148, 15… ## $ exang    <int> 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, … ## $ oldpeak  <dbl> 2.3, 1.5, 2.6, 3.5, 1.4, 0.8, 3.6, 0.6, 1.4, 3.1, 0.4, 1.… ## $ slope    <int> 3, 2, 2, 3, 1, 1, 3, 1, 2, 3, 2, 2, 2, 1, 1, 1, 3, 1, 1, … ## $ ca       <int> 0, 3, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, … ## $ thal     <chr> \"fixed\", \"normal\", \"reversible\", \"normal\", \"normal\", \"nor… ## $ target   <int> 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, … df ## # A tibble: 301 × 14 ##      age   sex    cp trestbps  chol   fbs restecg thalach exang oldpeak ##    <int> <int> <int>    <int> <int> <int>   <int>   <int> <int>   <dbl> ##  1    63     1     1      145   233     1       2     150     0     2.3 ##  2    67     1     4      160   286     0       2     108     1     1.5 ##  3    67     1     4      120   229     0       2     129     1     2.6 ##  4    37     1     3      130   250     0       0     187     0     3.5 ##  5    41     0     2      130   204     0       2     172     0     1.4 ##  6    56     1     2      120   236     0       0     178     0     0.8 ##  7    62     0     4      140   268     0       2     160     0     3.6 ##  8    57     0     4      120   354     0       0     163     1     0.6 ##  9    63     1     4      130   254     0       2     147     0     1.4 ## 10    53     1     4      140   203     1       2     155     1     3.1 ## # ℹ 291 more rows ## # ℹ 4 more variables: slope <int>, ca <int>, thal <chr>, target <int> val_idx <- nrow(df) %>% sample.int(., . * 0.2) val_df <- df[val_idx, ] train_df <- df[-val_idx, ]  cat(sprintf(   \"Using %d samples for training and %d for validation\",   nrow(train_df), nrow(val_df) )) ## Using 241 samples for training and 60 for validation dataframe_to_dataset <- function(df) {   labels <- df |> pull(target) |> as.integer()   inputs <- df |> select(-target) |> as.list()    ds <- tensor_slices_dataset(list(inputs, labels)) |>     dataset_shuffle(nrow(df))    ds }  train_ds <- dataframe_to_dataset(train_df) val_ds <- dataframe_to_dataset(val_df) c(x, y) %<-% iter_next(as_iterator(train_ds)) cat(\"Input: \"); str(x) cat(\"Target: \"); str(y) ## Input: List of 13 ##  $ age     :<tf.Tensor: shape=(), dtype=int32, numpy=59> ##  $ sex     :<tf.Tensor: shape=(), dtype=int32, numpy=1> ##  $ cp      :<tf.Tensor: shape=(), dtype=int32, numpy=4> ##  $ trestbps:<tf.Tensor: shape=(), dtype=int32, numpy=164> ##  $ chol    :<tf.Tensor: shape=(), dtype=int32, numpy=176> ##  $ fbs     :<tf.Tensor: shape=(), dtype=int32, numpy=1> ##  $ restecg :<tf.Tensor: shape=(), dtype=int32, numpy=2> ##  $ thalach :<tf.Tensor: shape=(), dtype=int32, numpy=90> ##  $ exang   :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ oldpeak :<tf.Tensor: shape=(), dtype=float32, numpy=1.0> ##  $ slope   :<tf.Tensor: shape=(), dtype=int32, numpy=2> ##  $ ca      :<tf.Tensor: shape=(), dtype=int32, numpy=2> ##  $ thal    :<tf.Tensor: shape=(), dtype=string, numpy=b'fixed'> ## Target: <tf.Tensor: shape=(), dtype=int32, numpy=1> train_ds <- train_ds |> dataset_batch(32) val_ds <- val_ds |> dataset_batch(32)"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"configuring-a-featurespace","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Configuring a FeatureSpace","title":"Structured data classification with FeatureSpace","text":"configure feature preprocessed, instantiate layer_feature_space(), pass dictionary (named list unique names) maps name features string describes feature type. “integer categorical” features \"FBS\", one “string categorical” feature (\"thal\"), numerical features, ’d like normalize – except \"age\", ’d like discretize number bins. also use crosses argument capture feature interactions categorical features, say, create additional features represent value co-occurrences categorical features. can compute feature crosses like arbitrary sets categorical features – just tuples two features. resulting co-occurences hashed fixed-sized vector, don’t need worry whether co-occurence space large.","code":"feature_space <- layer_feature_space(   features = list(     # Categorical features encoded as integers     sex = \"integer_categorical\",     cp = \"integer_categorical\",     fbs = \"integer_categorical\",     restecg = \"integer_categorical\",     exang = \"integer_categorical\",     ca = \"integer_categorical\",     # Categorical feature encoded as string     thal = \"string_categorical\",     # Numerical features to discretize     age = \"float_discretized\",     # Numerical features to normalize     trestbps = \"float_normalized\",     chol = \"float_normalized\",     thalach = \"float_normalized\",     oldpeak = \"float_normalized\",     slope = \"float_normalized\"   ),   # We create additional features by hashing   # value co-occurrences for the   # following groups of categorical features.   crosses = list(c(\"sex\", \"age\"), c(\"thal\", \"ca\")),   # The hashing space for these co-occurrences   # wil be 32-dimensional.   crossing_dim = 32,   # Our utility will one-hot encode all categorical   # features and concat all features into a single   # vector (one vector per sample).   output_mode = \"concat\" )"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"further-customizing-a-featurespace","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Further customizing a FeatureSpace","title":"Structured data classification with FeatureSpace","text":"Specifying feature type via string name quick easy, sometimes may want configure preprocessing feature. instance, case, categorical features don’t large set possible values – ’s handful values per feature (e.g. 1 0 feature \"FBS\"), possible values represented training set. result, don’t need reserve index represent “vocabulary” values features – default behavior. , just specify num_oov_indices=0 features tell feature preprocessor skip “vocabulary” indexing. customizations access include specifying number bins discretizing features type \"float_discretized\", dimensionality hashing space feature crossing.","code":"feature_space <- layer_feature_space(   features = list(     # Categorical features encoded as integers     sex       = feature_integer_categorical(num_oov_indices = 0),     cp        = feature_integer_categorical(num_oov_indices = 0),     fbs       = feature_integer_categorical(num_oov_indices = 0),     restecg   = feature_integer_categorical(num_oov_indices = 0),     exang     = feature_integer_categorical(num_oov_indices = 0),     ca        = feature_integer_categorical(num_oov_indices = 0),     # Categorical feature encoded as string     thal      = feature_string_categorical(num_oov_indices = 0),     # Numerical features to discretize     age       = feature_float_discretized(num_bins = 30),     # Numerical features to normalize     trestbps  = feature_float_normalized(),     chol      = feature_float_normalized(),     thalach   = feature_float_normalized(),     oldpeak   = feature_float_normalized(),     slope     = feature_float_normalized()   ),   # Specify feature cross with a custom crossing dim.   crosses = list(     feature_cross(       feature_names = c(\"sex\", \"age\"),       crossing_dim = 64     ),     feature_cross(       feature_names = c(\"thal\", \"ca\"),       crossing_dim = 16     )   ),   output_mode = \"concat\" )"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"adapt-the-featurespace-to-the-training-data","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Adapt the FeatureSpace to the training data","title":"Structured data classification with FeatureSpace","text":"start using FeatureSpace build model, adapt training data. adapt(), FeatureSpace : Index set possible values categorical features. Compute mean variance numerical features normalize. Compute value boundaries different bins numerical features discretize. Note adapt() called tf_dataset yields dicts (named lists) feature values – labels. point, FeatureSpace can called dict raw feature values, return single concatenate vector sample, combining encoded features feature crosses.","code":"train_ds_with_no_labels <- train_ds |> dataset_map(\\(x, y) x) feature_space |> adapt(train_ds_with_no_labels) c(x, y) %<-% iter_next(as_iterator(train_ds)) preprocessed_x <- feature_space(x) preprocessed_x ## tf.Tensor( ## [[0. 0. 1. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  ... ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.]], shape=(32, 136), dtype=float32)"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"two-ways-to-manage-preprocessing-as-part-of-the-tf-data-pipeline-or-in-the-model-itself","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","title":"Structured data classification with FeatureSpace","text":"two ways can leverage FeatureSpace:","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"asynchronous-preprocessing-in-tf-data","dir":"Articles > Examples > Structured_data","previous_headings":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","what":"Asynchronous preprocessing in tf.data","title":"Structured data classification with FeatureSpace","text":"can make part data pipeline, model. enables asynchronous parallel preprocessing data CPU hits model. ’re training GPU TPU, want speed preprocessing. Usually, always right thing training.","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"synchronous-preprocessing-in-the-model","dir":"Articles > Examples > Structured_data","previous_headings":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","what":"Synchronous preprocessing in the model","title":"Structured data classification with FeatureSpace","text":"can make part model. means model expect dicts raw feature values, preprocessing batch done synchronously (blocking manner) rest forward pass. want end--end model can process raw feature values – keep mind model able run CPU, since types feature preprocessing (e.g. string preprocessing) GPU TPU compatible. GPU / TPU performance-sensitive settings. general, want -model preprocessing inference CPU. case, apply FeatureSpace tf.data pipeline training, inference end--end model includes FeatureSpace. Let’s create training validation dataset preprocessed batches:","code":"preprocessed_train_ds <- train_ds |>   dataset_map(\\(x, y) list(feature_space(x), y),               num_parallel_calls = tf$data$AUTOTUNE) |>   dataset_prefetch(tf$data$AUTOTUNE)  preprocessed_val_ds <- val_ds |>   dataset_map(\\(x, y) list(feature_space(x), y),               num_parallel_calls = tf$data$AUTOTUNE) |>   dataset_prefetch(tf$data$AUTOTUNE)"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"build-a-model","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Build a model","title":"Structured data classification with FeatureSpace","text":"Time build model – rather two models: training model expects preprocessed features (one sample = one vector) inference model expects raw features (one sample = dict raw feature values)","code":"dict_inputs <- feature_space$get_inputs() encoded_features <- feature_space$get_encoded_features()  predictions <- encoded_features |>   layer_dense(32, activation=\"relu\") |>   layer_dropout(0.5) |>   layer_dense(1, activation=\"sigmoid\")  training_model <- keras_model(inputs = encoded_features,                               outputs = predictions) training_model |> compile(optimizer = \"adam\",                           loss = \"binary_crossentropy\",                           metrics = \"accuracy\")  inference_model <- keras_model(inputs = dict_inputs,                                outputs = predictions)"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"train-the-model","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Train the model","title":"Structured data classification with FeatureSpace","text":"Let’s train model 20 epochs. Note feature preprocessing happening part tf.data pipeline, part model. quickly get 80% validation accuracy.","code":"training_model |> fit(   preprocessed_train_ds,   epochs = 20,   validation_data = preprocessed_val_ds,   verbose = 2 ) ## Epoch 1/20 ## 8/8 - 3s - 326ms/step - accuracy: 0.4315 - loss: 0.7427 - val_accuracy: 0.5333 - val_loss: 0.7048 ## Epoch 2/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.5311 - loss: 0.6984 - val_accuracy: 0.6500 - val_loss: 0.6438 ## Epoch 3/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.6473 - loss: 0.6316 - val_accuracy: 0.6833 - val_loss: 0.5937 ## Epoch 4/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.6639 - loss: 0.6134 - val_accuracy: 0.7500 - val_loss: 0.5524 ## Epoch 5/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7178 - loss: 0.5820 - val_accuracy: 0.7667 - val_loss: 0.5176 ## Epoch 6/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.7718 - loss: 0.5573 - val_accuracy: 0.7500 - val_loss: 0.4897 ## Epoch 7/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7718 - loss: 0.5200 - val_accuracy: 0.8167 - val_loss: 0.4640 ## Epoch 8/20 ## 8/8 - 0s - 14ms/step - accuracy: 0.7759 - loss: 0.5068 - val_accuracy: 0.8167 - val_loss: 0.4388 ## Epoch 9/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8174 - loss: 0.4724 - val_accuracy: 0.8333 - val_loss: 0.4162 ## Epoch 10/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8050 - loss: 0.4545 - val_accuracy: 0.8167 - val_loss: 0.3960 ## Epoch 11/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8091 - loss: 0.4514 - val_accuracy: 0.8500 - val_loss: 0.3786 ## Epoch 12/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8423 - loss: 0.4291 - val_accuracy: 0.8500 - val_loss: 0.3647 ## Epoch 13/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8465 - loss: 0.4028 - val_accuracy: 0.8667 - val_loss: 0.3499 ## Epoch 14/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8340 - loss: 0.4037 - val_accuracy: 0.8667 - val_loss: 0.3369 ## Epoch 15/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8548 - loss: 0.3928 - val_accuracy: 0.8667 - val_loss: 0.3289 ## Epoch 16/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8589 - loss: 0.3745 - val_accuracy: 0.8667 - val_loss: 0.3206 ## Epoch 17/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8257 - loss: 0.3820 - val_accuracy: 0.8667 - val_loss: 0.3129 ## Epoch 18/20 ## 8/8 - 0s - 14ms/step - accuracy: 0.8631 - loss: 0.3650 - val_accuracy: 0.8667 - val_loss: 0.3079 ## Epoch 19/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8382 - loss: 0.3635 - val_accuracy: 0.8667 - val_loss: 0.3024 ## Epoch 20/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8631 - loss: 0.3524 - val_accuracy: 0.8833 - val_loss: 0.2970"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"inference-on-new-data-with-the-end-to-end-model","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Inference on new data with the end-to-end model","title":"Structured data classification with FeatureSpace","text":"Now, can use inference model (includes FeatureSpace) make predictions based dicts raw features values, follows:","code":"sample <- list(   age = 60,   sex = 1,   cp = 1,   trestbps = 145,   chol = 233,   fbs = 1,   restecg = 2,   thalach = 150,   exang = 0,   oldpeak = 2.3,   slope = 3,   ca = 0,   thal = \"fixed\" )  input_dict <- lapply(sample, \\(x) op_convert_to_tensor(array(x))) predictions <- inference_model |> predict(input_dict) ## 1/1 - 0s - 257ms/step glue::glue(r\"---(   This particular patient had a {(100 * predictions) |> signif(3)}% probability   of having a heart disease, as evaluated by our model. )---\") ## This particular patient had a 49.7% probability ## of having a heart disease, as evaluated by our model."},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"introduction","dir":"Articles > Examples","previous_headings":"","what":"Introduction","title":"Structured data classification with FeatureSpace","text":"example demonstrates structured data classification (also known tabular data classification), starting raw CSV file. data includes numerical features, integer categorical features, string categorical features. use utility layer_feature_space() index, preprocess, encode features. code adapted example Structured data classification scratch. previous example managed low-level feature preprocessing encoding Keras preprocessing layers, example delegate everything layer_feature_space(), making workflow extremely quick easy.","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"the-dataset","dir":"Articles > Examples","previous_headings":"Introduction","what":"The dataset","title":"Structured data classification with FeatureSpace","text":"dataset provided Cleveland Clinic Foundation Heart Disease. ’s CSV file 303 rows. row contains information patient (sample), column describes attribute patient (feature). use features predict whether patient heart disease (binary classification). ’s description feature:","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"setup","dir":"Articles > Examples","previous_headings":"","what":"Setup","title":"Structured data classification with FeatureSpace","text":"","code":"library(readr) library(dplyr, warn.conflicts = FALSE) library(keras3) library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")  conflicted::conflicts_prefer(   keras3::shape(),   keras3::set_random_seed(),   dplyr::filter(),   .quiet = TRUE )  use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"preparing-the-data","dir":"Articles > Examples","previous_headings":"","what":"Preparing the data","title":"Structured data classification with FeatureSpace","text":"Let’s download data load Pandas dataframe: dataset includes 303 samples 14 columns per sample (13 features, plus target label) ’s preview samples: last column, “target”, indicates whether patient heart disease (1) (0). Let’s split data training validation set: Let’s generate tf_dataset objects dataframe: tf_dataset yields tuple (input, target) input dictionary (named list) features target value 0 1: Let’s batch datasets:","code":"file_url <-   \"http://storage.googleapis.com/download.tensorflow.org/data/heart.csv\" df <- read_csv(file_url, col_types = cols(   oldpeak = col_double(),   thal = col_character(),   .default = col_integer() ))  # the dataset has two malformed rows, filter them out df <- df |> filter(!thal %in% c(\"1\", \"2\")) glimpse(df) ## Rows: 301 ## Columns: 14 ## $ age      <int> 63, 67, 67, 37, 41, 56, 62, 57, 63, 53, 57, 56, 56, 44, 5… ## $ sex      <int> 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, … ## $ cp       <int> 1, 4, 4, 3, 2, 2, 4, 4, 4, 4, 4, 2, 3, 2, 3, 3, 2, 4, 3, … ## $ trestbps <int> 145, 160, 120, 130, 130, 120, 140, 120, 130, 140, 140, 14… ## $ chol     <int> 233, 286, 229, 250, 204, 236, 268, 354, 254, 203, 192, 29… ## $ fbs      <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, … ## $ restecg  <int> 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, … ## $ thalach  <int> 150, 108, 129, 187, 172, 178, 160, 163, 147, 155, 148, 15… ## $ exang    <int> 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, … ## $ oldpeak  <dbl> 2.3, 1.5, 2.6, 3.5, 1.4, 0.8, 3.6, 0.6, 1.4, 3.1, 0.4, 1.… ## $ slope    <int> 3, 2, 2, 3, 1, 1, 3, 1, 2, 3, 2, 2, 2, 1, 1, 1, 3, 1, 1, … ## $ ca       <int> 0, 3, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, … ## $ thal     <chr> \"fixed\", \"normal\", \"reversible\", \"normal\", \"normal\", \"nor… ## $ target   <int> 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, … df ## # A tibble: 301 × 14 ##      age   sex    cp trestbps  chol   fbs restecg thalach exang oldpeak ##    <int> <int> <int>    <int> <int> <int>   <int>   <int> <int>   <dbl> ##  1    63     1     1      145   233     1       2     150     0     2.3 ##  2    67     1     4      160   286     0       2     108     1     1.5 ##  3    67     1     4      120   229     0       2     129     1     2.6 ##  4    37     1     3      130   250     0       0     187     0     3.5 ##  5    41     0     2      130   204     0       2     172     0     1.4 ##  6    56     1     2      120   236     0       0     178     0     0.8 ##  7    62     0     4      140   268     0       2     160     0     3.6 ##  8    57     0     4      120   354     0       0     163     1     0.6 ##  9    63     1     4      130   254     0       2     147     0     1.4 ## 10    53     1     4      140   203     1       2     155     1     3.1 ## # ℹ 291 more rows ## # ℹ 4 more variables: slope <int>, ca <int>, thal <chr>, target <int> val_idx <- nrow(df) %>% sample.int(., . * 0.2) val_df <- df[val_idx, ] train_df <- df[-val_idx, ]  cat(sprintf(   \"Using %d samples for training and %d for validation\",   nrow(train_df), nrow(val_df) )) ## Using 241 samples for training and 60 for validation dataframe_to_dataset <- function(df) {   labels <- df |> pull(target) |> as.integer()   inputs <- df |> select(-target) |> as.list()    ds <- tensor_slices_dataset(list(inputs, labels)) |>     dataset_shuffle(nrow(df))    ds }  train_ds <- dataframe_to_dataset(train_df) val_ds <- dataframe_to_dataset(val_df) c(x, y) %<-% iter_next(as_iterator(train_ds)) cat(\"Input: \"); str(x) cat(\"Target: \"); str(y) ## Input: List of 13 ##  $ age     :<tf.Tensor: shape=(), dtype=int32, numpy=57> ##  $ sex     :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ cp      :<tf.Tensor: shape=(), dtype=int32, numpy=4> ##  $ trestbps:<tf.Tensor: shape=(), dtype=int32, numpy=140> ##  $ chol    :<tf.Tensor: shape=(), dtype=int32, numpy=241> ##  $ fbs     :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ restecg :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ thalach :<tf.Tensor: shape=(), dtype=int32, numpy=123> ##  $ exang   :<tf.Tensor: shape=(), dtype=int32, numpy=1> ##  $ oldpeak :<tf.Tensor: shape=(), dtype=float32, numpy=0.2> ##  $ slope   :<tf.Tensor: shape=(), dtype=int32, numpy=2> ##  $ ca      :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ thal    :<tf.Tensor: shape=(), dtype=string, numpy=b'reversible'> ## Target: <tf.Tensor: shape=(), dtype=int32, numpy=0> train_ds <- train_ds |> dataset_batch(32) val_ds <- val_ds |> dataset_batch(32)"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"configuring-a-featurespace","dir":"Articles > Examples","previous_headings":"","what":"Configuring a FeatureSpace","title":"Structured data classification with FeatureSpace","text":"configure feature preprocessed, instantiate layer_feature_space, pass dictionary (named list unique names) maps name features string describes feature type. “integer categorical” features \"FBS\", one “string categorical” feature (\"thal\"), numerical features, ’d like normalize – except \"age\", ’d like discretize number bins. also use crosses argument capture feature interactions categorical features, say, create additional features represent value co-occurrences categorical features. can compute feature crosses like arbitrary sets categorical features – just tuples two features. resulting co-occurences hashed fixed-sized vector, don’t need worry whether co-occurence space large.","code":"feature_space <- layer_feature_space(   features = list(     # Categorical features encoded as integers     sex = \"integer_categorical\",     cp = \"integer_categorical\",     fbs = \"integer_categorical\",     restecg = \"integer_categorical\",     exang = \"integer_categorical\",     ca = \"integer_categorical\",     # Categorical feature encoded as string     thal = \"string_categorical\",     # Numerical features to discretize     age = \"float_discretized\",     # Numerical features to normalize     trestbps = \"float_normalized\",     chol = \"float_normalized\",     thalach = \"float_normalized\",     oldpeak = \"float_normalized\",     slope = \"float_normalized\"   ),   # We create additional features by hashing   # value co-occurrences for the   # following groups of categorical features.   crosses = list(c(\"sex\", \"age\"), c(\"thal\", \"ca\")),   # The hashing space for these co-occurrences   # wil be 32-dimensional.   crossing_dim = 32,   # Our utility will one-hot encode all categorical   # features and concat all features into a single   # vector (one vector per sample).   output_mode = \"concat\" )"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"further-customizing-a-featurespace","dir":"Articles > Examples","previous_headings":"","what":"Further customizing a FeatureSpace","title":"Structured data classification with FeatureSpace","text":"Specifying feature type via string name quick easy, sometimes may want configure preprocessing feature. instance, case, categorical features don’t large set possible values – ’s handful values per feature (e.g. 1 0 feature \"FBS\"), possible values represented training set. result, don’t need reserve index represent “vocabulary” values features – default behavior. , just specify num_oov_indices=0 features tell feature preprocessor skip “vocabulary” indexing. customizations access include specifying number bins discretizing features type \"float_discretized\", dimensionality hashing space feature crossing.","code":"feature_space <- layer_feature_space(   features = list(     # Categorical features encoded as integers     sex       = feature_integer_categorical(num_oov_indices = 0),     cp        = feature_integer_categorical(num_oov_indices = 0),     fbs       = feature_integer_categorical(num_oov_indices = 0),     restecg   = feature_integer_categorical(num_oov_indices = 0),     exang     = feature_integer_categorical(num_oov_indices = 0),     ca        = feature_integer_categorical(num_oov_indices = 0),     # Categorical feature encoded as string     thal      = feature_string_categorical(num_oov_indices = 0),     # Numerical features to discretize     age       = feature_float_discretized(num_bins = 30),     # Numerical features to normalize     trestbps  = feature_float_normalized(),     chol      = feature_float_normalized(),     thalach   = feature_float_normalized(),     oldpeak   = feature_float_normalized(),     slope     = feature_float_normalized()   ),   # Specify feature cross with a custom crossing dim.   crosses = list(     feature_cross(       feature_names = c(\"sex\", \"age\"),       crossing_dim = 64     ),     feature_cross(       feature_names = c(\"thal\", \"ca\"),       crossing_dim = 16     )   ),   output_mode = \"concat\" )"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"adapt-the-featurespace-to-the-training-data","dir":"Articles > Examples","previous_headings":"","what":"Adapt the FeatureSpace to the training data","title":"Structured data classification with FeatureSpace","text":"start using FeatureSpace build model, adapt training data. adapt(), FeatureSpace : Index set possible values categorical features. Compute mean variance numerical features normalize. Compute value boundaries different bins numerical features discretize. Note adapt() called tf_dataset yields dicts (named lists) feature values – labels. point, FeatureSpace can called dict raw feature values, return single concatenate vector sample, combining encoded features feature crosses.","code":"train_ds_with_no_labels <- train_ds |> dataset_map(\\(x, y) x) feature_space |> adapt(train_ds_with_no_labels) c(x, y) %<-% iter_next(as_iterator(train_ds)) preprocessed_x <- feature_space(x) preprocessed_x ## tf.Tensor( ## [[0. 0. 0. ... 1. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  ... ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.]], shape=(32, 136), dtype=float32)"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"two-ways-to-manage-preprocessing-as-part-of-the-tf-data-pipeline-or-in-the-model-itself","dir":"Articles > Examples","previous_headings":"","what":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","title":"Structured data classification with FeatureSpace","text":"two ways can leverage FeatureSpace:","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"asynchronous-preprocessing-in-tf-data","dir":"Articles > Examples","previous_headings":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","what":"Asynchronous preprocessing in tf.data","title":"Structured data classification with FeatureSpace","text":"can make part data pipeline, model. enables asynchronous parallel preprocessing data CPU hits model. ’re training GPU TPU, want speed preprocessing. Usually, always right thing training.","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"synchronous-preprocessing-in-the-model","dir":"Articles > Examples","previous_headings":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","what":"Synchronous preprocessing in the model","title":"Structured data classification with FeatureSpace","text":"can make part model. means model expect dicts raw feature values, preprocessing batch done synchronously (blocking manner) rest forward pass. want end--end model can process raw feature values – keep mind model able run CPU, since types feature preprocessing (e.g. string preprocessing) GPU TPU compatible. GPU / TPU performance-sensitive settings. general, want -model preprocessing inference CPU. case, apply FeatureSpace tf.data pipeline training, inference end--end model includes FeatureSpace. Let’s create training validation dataset preprocessed batches:","code":"preprocessed_train_ds <- train_ds |>   dataset_map(\\(x, y) list(feature_space(x), y),               num_parallel_calls = tf$data$AUTOTUNE) |>   dataset_prefetch(tf$data$AUTOTUNE)  preprocessed_val_ds <- val_ds |>   dataset_map(\\(x, y) list(feature_space(x), y),               num_parallel_calls = tf$data$AUTOTUNE) |>   dataset_prefetch(tf$data$AUTOTUNE)"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"build-a-model","dir":"Articles > Examples","previous_headings":"","what":"Build a model","title":"Structured data classification with FeatureSpace","text":"Time build model – rather two models: training model expects preprocessed features (one sample = one vector) inference model expects raw features (one sample = dict raw feature values)","code":"dict_inputs <- feature_space$get_inputs() encoded_features <- feature_space$get_encoded_features()  predictions <- encoded_features |>   layer_dense(32, activation=\"relu\") |>   layer_dropout(0.5) |>   layer_dense(1, activation=\"sigmoid\")  training_model <- keras_model(inputs = encoded_features,                               outputs = predictions) training_model |> compile(optimizer = \"adam\",                           loss = \"binary_crossentropy\",                           metrics = \"accuracy\")  inference_model <- keras_model(inputs = dict_inputs,                                outputs = predictions)"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"train-the-model","dir":"Articles > Examples","previous_headings":"","what":"Train the model","title":"Structured data classification with FeatureSpace","text":"Let’s train model 20 epochs. Note feature preprocessing happening part tf.data pipeline, part model. quickly get 80% validation accuracy.","code":"training_model |> fit(   preprocessed_train_ds,   epochs = 20,   validation_data = preprocessed_val_ds,   verbose = 2 ) ## Epoch 1/20 ## 8/8 - 3s - 343ms/step - accuracy: 0.5685 - loss: 0.7120 - val_accuracy: 0.6500 - val_loss: 0.6607 ## Epoch 2/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.6722 - loss: 0.6131 - val_accuracy: 0.6500 - val_loss: 0.6212 ## Epoch 3/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7137 - loss: 0.5685 - val_accuracy: 0.6500 - val_loss: 0.5961 ## Epoch 4/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.7718 - loss: 0.4981 - val_accuracy: 0.6667 - val_loss: 0.5758 ## Epoch 5/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7635 - loss: 0.4804 - val_accuracy: 0.6500 - val_loss: 0.5575 ## Epoch 6/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7676 - loss: 0.4739 - val_accuracy: 0.6667 - val_loss: 0.5309 ## Epoch 7/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.7842 - loss: 0.4141 - val_accuracy: 0.6667 - val_loss: 0.5161 ## Epoch 8/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8133 - loss: 0.3841 - val_accuracy: 0.6667 - val_loss: 0.5080 ## Epoch 9/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8133 - loss: 0.3997 - val_accuracy: 0.6667 - val_loss: 0.4980 ## Epoch 10/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8340 - loss: 0.3618 - val_accuracy: 0.6833 - val_loss: 0.4882 ## Epoch 11/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8548 - loss: 0.3335 - val_accuracy: 0.6833 - val_loss: 0.4814 ## Epoch 12/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8465 - loss: 0.3535 - val_accuracy: 0.6833 - val_loss: 0.4818 ## Epoch 13/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8340 - loss: 0.3363 - val_accuracy: 0.7000 - val_loss: 0.4755 ## Epoch 14/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8548 - loss: 0.3224 - val_accuracy: 0.7333 - val_loss: 0.4745 ## Epoch 15/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8714 - loss: 0.3083 - val_accuracy: 0.7500 - val_loss: 0.4813 ## Epoch 16/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8797 - loss: 0.3270 - val_accuracy: 0.7500 - val_loss: 0.4690 ## Epoch 17/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8631 - loss: 0.2955 - val_accuracy: 0.7500 - val_loss: 0.4782 ## Epoch 18/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8797 - loss: 0.2831 - val_accuracy: 0.7667 - val_loss: 0.4818 ## Epoch 19/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8797 - loss: 0.2872 - val_accuracy: 0.7667 - val_loss: 0.4767 ## Epoch 20/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8631 - loss: 0.2877 - val_accuracy: 0.7833 - val_loss: 0.4781"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"inference-on-new-data-with-the-end-to-end-model","dir":"Articles > Examples","previous_headings":"","what":"Inference on new data with the end-to-end model","title":"Structured data classification with FeatureSpace","text":"Now, can use inference model (includes FeatureSpace) make predictions based dicts raw features values, follows:","code":"sample <- list(   age = 60,   sex = 1,   cp = 1,   trestbps = 145,   chol = 233,   fbs = 1,   restecg = 2,   thalach = 150,   exang = 0,   oldpeak = 2.3,   slope = 3,   ca = 0,   thal = \"fixed\" )  input_dict <- lapply(sample, \\(x) op_convert_to_tensor(array(x))) predictions <- inference_model |> predict(input_dict) ## 1/1 - 1s - 548ms/step glue::glue(r\"---(   This particular patient had a {(100 * predictions) |> signif(3)}% probability   of having a heart disease, as evaluated by our model. )---\") ## This particular patient had a 55% probability ## of having a heart disease, as evaluated by our model."},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"introduction","dir":"Articles > Examples","previous_headings":"","what":"Introduction","title":"Text classification from scratch","text":"example shows text classification starting raw text (set text files disk). demonstrate workflow IMDB sentiment classification dataset (unprocessed version). use [layer_text_vectorization()] word splitting & indexing.","code":""},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"setup","dir":"Articles > Examples","previous_headings":"","what":"Setup","title":"Text classification from scratch","text":"","code":"options(conflicts.policy = \"strict\") library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\") library(keras3) use_virtualenv(\"r-keras\")"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"load-the-data-imdb-movie-review-sentiment-classification","dir":"Articles > Examples","previous_headings":"","what":"Load the data: IMDB movie review sentiment classification","title":"Text classification from scratch","text":"Let’s download data inspect structure. aclImdb folder contains train test subfolder: aclImdb/train/pos aclImdb/train/neg folders contain text files, represents one review (either positive negative): interested pos neg subfolders, let’s delete subfolder text files : can use utility text_dataset_from_directory() generate labeled tf_dataset object set text files disk filed class-specific folders. Let’s use generate training, validation, test datasets. validation training datasets generated two subsets train directory, 20% samples going validation dataset 80% going training dataset. validation dataset addition test dataset useful tuning hyperparameters, model architecture, test dataset used. putting model real world however, retrained using available training data (without creating validation dataset), performance maximized. using validation_split subset arguments, make sure either specify random seed, pass shuffle=FALSE, validation & training splits get overlap. Let’s preview samples:","code":"if (!dir.exists(\"datasets/aclImdb\")) {   dir.create(\"datasets\")   download.file(     \"https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\",     \"datasets/aclImdb_v1.tar.gz\"   )   untar(\"datasets/aclImdb_v1.tar.gz\", exdir = \"datasets\")   unlink(\"datasets/aclImdb/train/unsup\", recursive = TRUE) } head(list.files(\"datasets/aclImdb/test\")) ## [1] \"labeledBow.feat\" \"neg\"             \"pos\"             \"urls_neg.txt\" ## [5] \"urls_pos.txt\" head(list.files(\"datasets/aclImdb/train\")) ## [1] \"labeledBow.feat\" \"neg\"             \"pos\"             \"unsupBow.feat\" ## [5] \"urls_neg.txt\"    \"urls_pos.txt\" cat(readLines(\"datasets/aclImdb/train/pos/6248_7.txt\")) ## Being an Austrian myself this has been a straight knock in my face. Fortunately I don't live nowhere near the place where this movie takes place but unfortunately it portrays everything that the rest of Austria hates about Viennese people (or people close to that region). And it is very easy to read that this is exactly the directors intention: to let your head sink into your hands and say \"Oh my god, how can THAT be possible!\". No, not with me, the (in my opinion) totally exaggerated uncensored swinger club scene is not necessary, I watch porn, sure, but in this context I was rather disgusted than put in the right context.<br /><br />This movie tells a story about how misled people who suffer from lack of education or bad company try to survive and live in a world of redundancy and boring horizons. A girl who is treated like a whore by her super-jealous boyfriend (and still keeps coming back), a female teacher who discovers her masochism by putting the life of her super-cruel \"lover\" on the line, an old couple who has an almost mathematical daily cycle (she is the \"official replacement\" of his ex wife), a couple that has just divorced and has the ex husband suffer under the acts of his former wife obviously having a relationship with her masseuse and finally a crazy hitchhiker who asks her drivers the most unusual questions and stretches their nerves by just being super-annoying.<br /><br />After having seen it you feel almost nothing. You're not even shocked, sad, depressed or feel like doing anything... Maybe that's why I gave it 7 points, it made me react in a way I never reacted before. If that's good or bad is up to you! unlink(\"datasets/aclImdb/train/unsup\", recursive = TRUE) batch_size <- 32  raw_train_ds <- text_dataset_from_directory(   \"datasets/aclImdb/train\",   batch_size = batch_size,   validation_split = 0.2,   subset = \"training\",   seed = 1337 ) ## Found 25000 files belonging to 2 classes. ## Using 20000 files for training. raw_val_ds <- text_dataset_from_directory(   \"datasets/aclImdb/train\",   batch_size = batch_size,   validation_split = 0.2,   subset = \"validation\",   seed = 1337 ) ## Found 25000 files belonging to 2 classes. ## Using 5000 files for validation. raw_test_ds <- text_dataset_from_directory(   \"datasets/aclImdb/test\",   batch_size = batch_size ) ## Found 25000 files belonging to 2 classes. cat(\"Number of batches in raw_train_ds:\", length(raw_train_ds), \"\\n\") ## Number of batches in raw_train_ds: 625 cat(\"Number of batches in raw_val_ds:\", length(raw_val_ds), \"\\n\") ## Number of batches in raw_val_ds: 157 cat(\"Number of batches in raw_test_ds:\", length(raw_test_ds), \"\\n\") ## Number of batches in raw_test_ds: 782 # It's important to take a look at your raw data to ensure your normalization # and tokenization will work as expected. We can do that by taking a few # examples from the training set and looking at them. # This is one of the places where eager execution shines: # we can just evaluate these tensors using .numpy() # instead of needing to evaluate them in a Session/Graph context. batch <- iter_next(as_iterator(raw_train_ds)) str(batch) ## List of 2 ##  $ :<tf.Tensor: shape=(32), dtype=string, numpy=…> ##  $ :<tf.Tensor: shape=(32), dtype=int32, numpy=…> c(text_batch, label_batch) %<-% batch for (i in 1:3) {   print(text_batch[i])   print(label_batch[i]) } ## tf.Tensor(b\"I have read the novel Reaper of Ben Mezrich a fews years ago and last night I accidentally came to see this adaption.<br /><br />Although it's been years since I read the story the first time, the differences between the novel and the movie are humongous. Very important elements, which made the whole thing plausible are just written out or changed to bad.<br /><br />If the plot sounds interesting to you: go and get the novel. Its much, much, much better.<br /><br />Still 4 out of 10 since it was hard to stop watching because of the great basic plot by Ben Mezrich.\", shape=(), dtype=string) ## tf.Tensor(0, shape=(), dtype=int32) ## tf.Tensor(b'After seeing all the Jesse James, Quantrill, jayhawkers,etc films in the fifties, it is quite a thrill to see this film with a new perspective by director Ang Lee. The scene of the attack of Lawrence, Kansas is awesome. The romantic relationship between Jewel and Toby Mcguire turns out to be one of the best parts and Jonathan Rhys-Meyers is outstanding as the bad guy. All the time this film makes you feel the horror of war, and the desperate situation of the main characters who do not know if they are going to survive the next hours. Definitely worth seeing.', shape=(), dtype=string) ## tf.Tensor(1, shape=(), dtype=int32) ## tf.Tensor(b'AG was an excellent presentation of drama, suspense and thriller that is so rare to American TV. Sheriff Lucas gave many a viewer the willies. We rooted for Caleb as he strove to resist the overtures of Sheriff Lucas. We became engrossed and fearful upon learning of the unthinkable connection between these two characters. The manipulations which weekly gave cause to fear what Lucas would do next were truly surprising. This show lived up to the \"Gothic\" moniker in ways American entertainment has so seldom attempted, much less mastered. The suits definitely made a big mistake in not supporting this show. This show puts shame to the current glut of \"reality\" shows- which are so less than satisfying viewing.The call for a DVD box set is well based. This show is quality viewing for a discerning market hungry for quality viewing. A public that is tiring of over-saturation of mind-numbing reality fare will welcome this gem of real storytelling. Bring on the DVD box set!!', shape=(), dtype=string) ## tf.Tensor(1, shape=(), dtype=int32)"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"prepare-the-data","dir":"Articles > Examples","previous_headings":"","what":"Prepare the data","title":"Text classification from scratch","text":"particular, remove <br /> tags.","code":"# Having looked at our data above, we see that the raw text contains HTML break # tags of the form '<br />'. These tags will not be removed by the default # standardizer (which doesn't strip HTML). Because of this, we will need to # create a custom standardization function. custom_standardization_fn <- function(string_tensor) {   string_tensor |>     tf$strings$lower() |> # convert to all lowercase     tf$strings$regex_replace(\"<br />\", \" \") |> # remove '<br />' HTML tag     tf$strings$regex_replace(\"[[:punct:]]\", \"\") # remove punctuation }   # Model constants. max_features <- 20000 embedding_dim <- 128 sequence_length <- 500  # Now that we have our custom standardization, we can instantiate our text # vectorization layer. We are using this layer to normalize, split, and map # strings to integers, so we set our 'output_mode' to 'int'. # Note that we're using the default split function, # and the custom standardization defined above. # We also set an explicit maximum sequence length, since the CNNs later in our # model won't support ragged sequences. vectorize_layer <- layer_text_vectorization(   standardize = custom_standardization_fn,   max_tokens = max_features,   output_mode = \"int\",   output_sequence_length = sequence_length, )  # Now that the vectorize_layer has been created, call `adapt` on a text-only # dataset to create the vocabulary. You don't have to batch, but for very large # datasets this means you're not keeping spare copies of the dataset in memory.  # Let's make a text-only dataset (no labels): text_ds <- raw_train_ds |>   dataset_map(\\(x, y) x) # Let's call `adapt`: vectorize_layer |> adapt(text_ds)"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"two-options-to-vectorize-the-data","dir":"Articles > Examples","previous_headings":"","what":"Two options to vectorize the data","title":"Text classification from scratch","text":"2 ways can use text vectorization layer: Option 1: Make part model, obtain model processes raw strings, like : Option 2: Apply text dataset obtain dataset word indices, feed model expects integer sequences inputs. important difference two option 2 enables asynchronous CPU processing buffering data training GPU. ’re training model GPU, probably want go option get best performance. . export model production, ’d ship model accepts raw strings input, like code snippet option 1 . can done training. last section.","code":"text_input <- keras_input(shape = c(1L), dtype = \"string\", name = 'text') x <- text_input |>   vectorize_layer() |>   layer_embedding(max_features + 1, embedding_dim) vectorize_text <- function(text, label) {   text <- text |>     op_expand_dims(-1) |>     vectorize_layer()   list(text, label) }  # Vectorize the data. train_ds <- raw_train_ds |> dataset_map(vectorize_text) val_ds   <- raw_val_ds   |> dataset_map(vectorize_text) test_ds  <- raw_test_ds  |> dataset_map(vectorize_text)  # Do async prefetching / buffering of the data for best performance on GPU. train_ds <- train_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10) val_ds <- val_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10) test_ds <- test_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10)"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"build-a-model","dir":"Articles > Examples","previous_headings":"","what":"Build a model","title":"Text classification from scratch","text":"choose simple 1D convnet starting Embedding layer.","code":"# A integer input for vocab indices. inputs <- keras_input(shape = c(NA), dtype = \"int64\")  predictions <- inputs |>   # Next, we add a layer to map those vocab indices into a space of dimensionality   # 'embedding_dim'.   layer_embedding(max_features, embedding_dim) |>   layer_dropout(0.5) |>   # Conv1D + global max pooling   layer_conv_1d(128, 7, padding = \"valid\", activation = \"relu\", strides = 3) |>   layer_conv_1d(128, 7, padding = \"valid\", activation = \"relu\", strides = 3) |>   layer_global_max_pooling_1d() |>   # We add a vanilla hidden layer:   layer_dense(128, activation = \"relu\") |>   layer_dropout(0.5) |>   # We project onto a single unit output layer, and squash it with a sigmoid:   layer_dense(1, activation = \"sigmoid\", name = \"predictions\")  model <- keras_model(inputs, predictions)  summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, None)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ embedding_1 (Embedding)         │ (None, None, 128)      │     2,560,000 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, None, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_1 (Conv1D)               │ (None, None, 128)      │       114,816 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d (Conv1D)                 │ (None, None, 128)      │       114,816 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling1d            │ (None, 128)            │             0 │ ## │ (GlobalMaxPooling1D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 128)            │        16,512 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 128)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ predictions (Dense)             │ (None, 1)              │           129 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 2,806,273 (10.71 MB) ##  Trainable params: 2,806,273 (10.71 MB) ##  Non-trainable params: 0 (0.00 B) # Compile the model with binary crossentropy loss and an adam optimizer. model |> compile(loss = \"binary_crossentropy\",                  optimizer = \"adam\",                  metrics = \"accuracy\")"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"train-the-model","dir":"Articles > Examples","previous_headings":"","what":"Train the model","title":"Text classification from scratch","text":"","code":"epochs <- 3  # Fit the model using the train and test datasets. model |> fit(train_ds, validation_data = val_ds, epochs = epochs) ## Epoch 1/3 ## 625/625 - 6s - 9ms/step - accuracy: 0.7327 - loss: 0.4894 - val_accuracy: 0.8638 - val_loss: 0.3162 ## Epoch 2/3 ## 625/625 - 2s - 2ms/step - accuracy: 0.9116 - loss: 0.2230 - val_accuracy: 0.8780 - val_loss: 0.3131 ## Epoch 3/3 ## 625/625 - 2s - 2ms/step - accuracy: 0.9603 - loss: 0.1118 - val_accuracy: 0.8646 - val_loss: 0.3735"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"evaluate-the-model-on-the-test-set","dir":"Articles > Examples","previous_headings":"","what":"Evaluate the model on the test set","title":"Text classification from scratch","text":"","code":"model |> evaluate(test_ds) ## 782/782 - 1s - 2ms/step - accuracy: 0.8532 - loss: 0.4071 ## $accuracy ## [1] 0.8532 ## ## $loss ## [1] 0.407085"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"make-an-end-to-end-model","dir":"Articles > Examples","previous_headings":"","what":"Make an end-to-end model","title":"Text classification from scratch","text":"want obtain model capable processing raw strings, can simply create new model (using weights just trained):","code":"# A string input inputs <- keras_input(shape = c(1), dtype = \"string\") # Turn strings into vocab indices indices <- vectorize_layer(inputs) # Turn vocab indices into predictions outputs <- model(indices)  # Our end to end model end_to_end_model <- keras_model(inputs, outputs) end_to_end_model |> compile(   loss = \"binary_crossentropy\",   optimizer = \"adam\",   metrics = c(\"accuracy\") )  # Test it with `raw_test_ds`, which yields raw strings end_to_end_model |> evaluate(raw_test_ds) ## 782/782 - 3s - 4ms/step - accuracy: 0.8532 - loss: 0.4063 ## $accuracy ## [1] 0.8532 ## ## $loss ## [1] 0.4062699"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"introduction","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Introduction","title":"Timeseries anomaly detection using an Autoencoder","text":"script demonstrates can use reconstruction convolutional autoencoder model detect anomalies timeseries data.","code":""},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"setup","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Setup","title":"Timeseries anomaly detection using an Autoencoder","text":"","code":"library(dplyr, warn.conflicts = FALSE) library(ggplot2) theme_set(theme_minimal())  library(listarrays) library(tfdatasets, exclude = c(\"shape\")) library(keras3)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"load-the-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Load the data","title":"Timeseries anomaly detection using an Autoencoder","text":"use Numenta Anomaly Benchmark(NAB) dataset. provides artificial timeseries data containing labeled anomalous periods behavior. Data ordered, timestamped, single-valued metrics. use art_daily_small_noise.csv file training art_daily_jumpsup.csv file testing. simplicity dataset allows us demonstrate anomaly detection.","code":"get_data <- function(url_suffix) {   url_root <- \"https://raw.githubusercontent.com/numenta/NAB/master/data/\"   url <- paste0(url_root, url_suffix)   file <- get_file(origin = url) # cache file locally   # parse csv; 2 columns with types: datetime (T), double (d)   readr::read_csv(file, col_types = \"Td\") }  df_small_noise   <- get_data(\"artificialNoAnomaly/art_daily_small_noise.csv\") df_daily_jumpsup <- get_data(\"artificialWithAnomaly/art_daily_jumpsup.csv\")"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"quick-look-at-the-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Quick look at the data","title":"Timeseries anomaly detection using an Autoencoder","text":"","code":"df_small_noise ## # A tibble: 4,032 × 2 ##    timestamp           value ##    <dttm>              <dbl> ##  1 2014-04-01 00:00:00  18.3 ##  2 2014-04-01 00:05:00  22.0 ##  3 2014-04-01 00:10:00  18.6 ##  4 2014-04-01 00:15:00  22.0 ##  5 2014-04-01 00:20:00  21.9 ##  6 2014-04-01 00:25:00  21.2 ##  7 2014-04-01 00:30:00  20.6 ##  8 2014-04-01 00:35:00  20.3 ##  9 2014-04-01 00:40:00  21.5 ## 10 2014-04-01 00:45:00  19.2 ## # ℹ 4,022 more rows df_daily_jumpsup ## # A tibble: 4,032 × 2 ##    timestamp           value ##    <dttm>              <dbl> ##  1 2014-04-01 00:00:00  19.8 ##  2 2014-04-01 00:05:00  20.5 ##  3 2014-04-01 00:10:00  20.0 ##  4 2014-04-01 00:15:00  21.5 ##  5 2014-04-01 00:20:00  20.2 ##  6 2014-04-01 00:25:00  19.9 ##  7 2014-04-01 00:30:00  21.7 ##  8 2014-04-01 00:35:00  20.9 ##  9 2014-04-01 00:40:00  18.4 ## 10 2014-04-01 00:45:00  18.7 ## # ℹ 4,022 more rows"},{"path":[]},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"timeseries-data-without-anomalies","dir":"Articles > Examples > Timeseries","previous_headings":"Visualize the data","what":"Timeseries data without anomalies","title":"Timeseries anomaly detection using an Autoencoder","text":"use following data training.","code":"plot_ts <- function(df) {   ggplot(df, aes(x = timestamp, y = value)) + geom_line() +     scale_x_datetime(date_breaks = \"1 day\", date_labels = \"%b-%d\") }  plot_ts(df_small_noise) + ggtitle(\"Without Anomaly\")"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"timeseries-data-with-anomalies","dir":"Articles > Examples > Timeseries","previous_headings":"Visualize the data","what":"Timeseries data with anomalies","title":"Timeseries anomaly detection using an Autoencoder","text":"use following data testing see sudden jump data detected anomaly.","code":"plot_ts(df_daily_jumpsup) + ggtitle(\"With Anomaly\")"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"prepare-training-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Prepare training data","title":"Timeseries anomaly detection using an Autoencoder","text":"Get data values training timeseries data file normalize value data. value every 5 mins 14 days. 24 * 60 / 5 = 288 timesteps per day 288 * 14 = 4032 data points total","code":"df_train <- df_small_noise |>   mutate(value = (value - mean(value)) / sd(value))  cat(\"Number of training samples:\", nrow(df_train), \"\\n\") ## Number of training samples: 4032"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"create-sequences","dir":"Articles > Examples > Timeseries","previous_headings":"Prepare training data","what":"Create sequences","title":"Timeseries anomaly detection using an Autoencoder","text":"Create sequences combining TIME_STEPS contiguous data values training data.","code":"TIME_STEPS <- 288  as_dataset <- function(df) {   x <- as.matrix(df$value)   ds <- timeseries_dataset_from_array(x, NULL, sequence_length = TIME_STEPS)   # Because the dataset is small, cast TF Dataset to an R array for convenience.   ds |> as_array_iterator() |> iterate() |> bind_on_rows() }  x_train <- as_dataset(df_train) writeLines(sprintf(\"Training input shape: (%s)\", toString(dim(x_train)))) ## Training input shape: (3745, 288, 1)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"build-a-model","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Build a model","title":"Timeseries anomaly detection using an Autoencoder","text":"build convolutional reconstruction autoencoder model. model take input shape (batch_size, sequence_length, num_features) return output shape. case, sequence_length 288 num_features 1.","code":"model <- keras_model_sequential(input_shape = c(TIME_STEPS, 1)) |>   layer_conv_1d(     filters = 32, kernel_size = 7, padding = \"same\",     strides = 2, activation = \"relu\"   ) |>   layer_dropout(rate = 0.2) |>   layer_conv_1d(     filters = 16, kernel_size = 7, padding = \"same\",     strides = 2, activation = \"relu\"   ) |>   layer_conv_1d_transpose(     filters = 16, kernel_size = 7, padding = \"same\",     strides = 2, activation = \"relu\"   ) |>   layer_dropout(rate = 0.2) |>   layer_conv_1d_transpose(     filters = 32, kernel_size = 7, padding = \"same\",     strides = 2, activation = \"relu\"   ) |>   layer_conv_1d_transpose(filters = 1, kernel_size = 7, padding = \"same\")  model |> compile(optimizer=optimizer_adam(learning_rate=0.001), loss=\"mse\") model ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv1d (Conv1D)                 │ (None, 144, 32)        │           256 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 144, 32)        │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_1 (Conv1D)               │ (None, 72, 16)         │         3,600 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_transpose                │ (None, 144, 16)        │         1,808 │ ## │ (Conv1DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, 144, 16)        │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_transpose_1              │ (None, 288, 32)        │         3,616 │ ## │ (Conv1DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_transpose_2              │ (None, 288, 1)         │           225 │ ## │ (Conv1DTranspose)               │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 9,505 (37.13 KB) ##  Trainable params: 9,505 (37.13 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"train-the-model","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Train the model","title":"Timeseries anomaly detection using an Autoencoder","text":"Please note using x_train input target since reconstruction model. Let’s plot training validation loss see training went.","code":"history = model |> fit(   x_train, x_train,   epochs = 50,   validation_split = 0.1,   callbacks = c(     callback_early_stopping(       monitor = \"val_loss\", patience = 5, mode = \"min\"     )   ) ) ## Epoch 1/50 ## 106/106 - 7s - 62ms/step - loss: 0.1863 - val_loss: 0.0316 ## Epoch 2/50 ## 106/106 - 0s - 2ms/step - loss: 0.0333 - val_loss: 0.0233 ## Epoch 3/50 ## 106/106 - 0s - 2ms/step - loss: 0.0248 - val_loss: 0.0201 ## Epoch 4/50 ## 106/106 - 0s - 2ms/step - loss: 0.0209 - val_loss: 0.0186 ## Epoch 5/50 ## 106/106 - 0s - 2ms/step - loss: 0.0179 - val_loss: 0.0146 ## Epoch 6/50 ## 106/106 - 0s - 2ms/step - loss: 0.0150 - val_loss: 0.0113 ## Epoch 7/50 ## 106/106 - 0s - 2ms/step - loss: 0.0127 - val_loss: 0.0094 ## Epoch 8/50 ## 106/106 - 0s - 2ms/step - loss: 0.0109 - val_loss: 0.0088 ## Epoch 9/50 ## 106/106 - 0s - 3ms/step - loss: 0.0096 - val_loss: 0.0084 ## Epoch 10/50 ## 106/106 - 0s - 2ms/step - loss: 0.0086 - val_loss: 0.0069 ## Epoch 11/50 ## 106/106 - 0s - 2ms/step - loss: 0.0078 - val_loss: 0.0062 ## Epoch 12/50 ## 106/106 - 0s - 2ms/step - loss: 0.0073 - val_loss: 0.0058 ## Epoch 13/50 ## 106/106 - 0s - 2ms/step - loss: 0.0067 - val_loss: 0.0054 ## Epoch 14/50 ## 106/106 - 0s - 2ms/step - loss: 0.0063 - val_loss: 0.0051 ## Epoch 15/50 ## 106/106 - 0s - 2ms/step - loss: 0.0060 - val_loss: 0.0045 ## Epoch 16/50 ## 106/106 - 0s - 2ms/step - loss: 0.0058 - val_loss: 0.0039 ## Epoch 17/50 ## 106/106 - 0s - 2ms/step - loss: 0.0055 - val_loss: 0.0041 ## Epoch 18/50 ## 106/106 - 0s - 2ms/step - loss: 0.0052 - val_loss: 0.0036 ## Epoch 19/50 ## 106/106 - 0s - 2ms/step - loss: 0.0050 - val_loss: 0.0037 ## Epoch 20/50 ## 106/106 - 0s - 2ms/step - loss: 0.0048 - val_loss: 0.0033 ## Epoch 21/50 ## 106/106 - 0s - 2ms/step - loss: 0.0046 - val_loss: 0.0031 ## Epoch 22/50 ## 106/106 - 0s - 2ms/step - loss: 0.0044 - val_loss: 0.0032 ## Epoch 23/50 ## 106/106 - 0s - 2ms/step - loss: 0.0042 - val_loss: 0.0033 ## Epoch 24/50 ## 106/106 - 0s - 2ms/step - loss: 0.0040 - val_loss: 0.0031 ## Epoch 25/50 ## 106/106 - 0s - 2ms/step - loss: 0.0038 - val_loss: 0.0032 ## Epoch 26/50 ## 106/106 - 0s - 2ms/step - loss: 0.0036 - val_loss: 0.0031 ## Epoch 27/50 ## 106/106 - 0s - 2ms/step - loss: 0.0035 - val_loss: 0.0031 ## Epoch 28/50 ## 106/106 - 0s - 2ms/step - loss: 0.0033 - val_loss: 0.0025 ## Epoch 29/50 ## 106/106 - 0s - 2ms/step - loss: 0.0032 - val_loss: 0.0026 ## Epoch 30/50 ## 106/106 - 0s - 2ms/step - loss: 0.0031 - val_loss: 0.0029 ## Epoch 31/50 ## 106/106 - 0s - 2ms/step - loss: 0.0030 - val_loss: 0.0024 ## Epoch 32/50 ## 106/106 - 0s - 2ms/step - loss: 0.0029 - val_loss: 0.0025 ## Epoch 33/50 ## 106/106 - 0s - 2ms/step - loss: 0.0028 - val_loss: 0.0025 ## Epoch 34/50 ## 106/106 - 0s - 2ms/step - loss: 0.0027 - val_loss: 0.0022 ## Epoch 35/50 ## 106/106 - 0s - 2ms/step - loss: 0.0026 - val_loss: 0.0023 ## Epoch 36/50 ## 106/106 - 0s - 2ms/step - loss: 0.0026 - val_loss: 0.0025 ## Epoch 37/50 ## 106/106 - 0s - 2ms/step - loss: 0.0025 - val_loss: 0.0025 ## Epoch 38/50 ## 106/106 - 0s - 2ms/step - loss: 0.0025 - val_loss: 0.0023 ## Epoch 39/50 ## 106/106 - 0s - 2ms/step - loss: 0.0024 - val_loss: 0.0023 plot(history)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"detecting-anomalies","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Detecting anomalies","title":"Timeseries anomaly detection using an Autoencoder","text":"detect anomalies determining well model can reconstruct input data. Find MAE loss training samples. Find max MAE loss value. worst model performed trying reconstruct sample. make threshold anomaly detection. reconstruction loss sample greater threshold value can infer model seeing pattern isn’t familiar . label sample anomaly.","code":"# Get train MAE loss. x_train_pred <- model |> predict(x_train) ## 118/118 - 0s - 3ms/step train_mae_loss <- apply(abs(x_train_pred - x_train), 1, mean)  hist(train_mae_loss, breaks = 50) # Get reconstruction loss threshold. threshold <- max(train_mae_loss) cat(\"Reconstruction error threshold: \", threshold, \"\\n\") ## Reconstruction error threshold:  0.03930207"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"compare-recontruction","dir":"Articles > Examples > Timeseries","previous_headings":"Detecting anomalies","what":"Compare recontruction","title":"Timeseries anomaly detection using an Autoencoder","text":"Just fun, let’s see model recontructed first sample. 288 timesteps day 1 training dataset.","code":"# Checking how the first sequence is learnt plot(NULL, NULL, ylab = 'Value',      xlim = c(0, TIME_STEPS),      ylim = range(c(x_train[1,,], x_train_pred[1,,]))) lines(x_train[1,,]) lines(x_train_pred[1,,], col = 'red') legend(\"topleft\", lty = 1,        legend = c(\"actual\", \"predicted\"),        col = c(\"black\", \"red\"))"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"prepare-test-data","dir":"Articles > Examples > Timeseries","previous_headings":"Detecting anomalies","what":"Prepare test data","title":"Timeseries anomaly detection using an Autoencoder","text":"","code":"df_test <- df_daily_jumpsup |>   mutate(value =            (value - mean(df_small_noise$value)) /              sd(df_small_noise$value))  df_test |> head() plot_ts(df_test) # Create sequences from test values. x_test <- as_dataset(df_test)  # Get test MAE loss. x_test_pred <- model |> predict(x_test) test_mae_loss <- apply(abs(x_test_pred - x_test), 1, mean)  hist(test_mae_loss, breaks = 50, xlab = \"test MAE loss\", ylab = \"No of samples\") # Detect all the samples which are anomalies. anomalies <- test_mae_loss > threshold cat(\"Number of anomaly samples:\", sum(anomalies), \"\\n\") cat(\"Indices of anomaly samples:\", which(anomalies), \"\\n\", fill = TRUE) ## # A tibble: 6 × 2 ##   timestamp            value ##   <dttm>               <dbl> ## 1 2014-04-01 00:00:00 -0.808 ## 2 2014-04-01 00:05:00 -0.781 ## 3 2014-04-01 00:10:00 -0.801 ## 4 2014-04-01 00:15:00 -0.746 ## 5 2014-04-01 00:20:00 -0.792 ## 6 2014-04-01 00:25:00 -0.802 ## 118/118 - 0s - 763us/step ## Number of anomaly samples: 486 ## Indices of anomaly samples: 216 218 220 396 507 793 795 1659 1945 1946 1947 ## 1949 2013 2016 2017 2021 2025 2029 2033 2037 2041 2045 2046 2049 2052 2053 ## 2054 2056 2057 2058 2060 2061 2062 2064 2065 2066 2068 2069 2070 2072 2073 ## 2074 2076 2077 2078 2080 2081 2082 2084 2085 2086 2088 2089 2090 2092 2093 ## 2094 2096 2097 2098 2100 2101 2102 2105 2108 2109 2112 2113 2117 2121 2124 ## 2126 2129 2141 2145 2153 2521 2522 2523 2525 2538 2542 2546 2550 2698 2700 ## 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 ## 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 ## 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 ## 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 ## 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 ## 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 ## 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 ## 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 ## 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 ## 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 ## 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 ## 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 ## 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 ## 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 ## 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 ## 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 ## 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 ## 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 ## 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 ## 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 ## 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 ## 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 ## 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 ## 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 ## 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 ## 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 ## 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"plot-anomalies","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Plot anomalies","title":"Timeseries anomaly detection using an Autoencoder","text":"now know samples data anomalies. , find corresponding timestamps original test data. using following method : Let’s say time_steps = 3 10 training values. x_train look like : 0, 1, 2 1, 2, 3 2, 3, 4 3, 4, 5 4, 5, 6 5, 6, 7 6, 7, 8 7, 8, 9 except initial final time_steps-1 data values, appear time_steps number samples. , know samples [(3, 4, 5), (4, 5, 6), (5, 6, 7)] anomalies, can say data point 5 anomaly. Let’s overlay anomalies original test data plot.","code":"is_anomaly <- test_mae_loss > threshold is_anomaly <- is_anomaly &   zoo::rollsum(is_anomaly, TIME_STEPS,                align = \"right\", na.pad = TRUE) >= TIME_STEPS  with(df_test, {   plot(value ~ timestamp, type = 'l', xaxt = 'n', las = 2)   axis.POSIXct(1, at = seq(timestamp[1], tail(timestamp, 1), by = \"days\"),                format = \"%b-%d\") })  with(df_test[which(is_anomaly),], {   points(value ~ timestamp, col = \"red\") })"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"introduction","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Introduction","title":"Timeseries classification from scratch","text":"example shows timeseries classification scratch, starting raw CSV timeseries files disk. demonstrate workflow FordA dataset UCR/UEA archive.","code":""},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"setup","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Setup","title":"Timeseries classification from scratch","text":"","code":"library(keras3) use_backend(\"jax\")"},{"path":[]},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"dataset-description","dir":"Articles > Examples > Timeseries","previous_headings":"Load the data: the FordA dataset","what":"Dataset description","title":"Timeseries classification from scratch","text":"dataset using called FordA. data comes UCR archive. dataset contains 3601 training instances another 1320 testing instances. timeseries corresponds measurement engine noise captured motor sensor. task, goal automatically detect presence specific issue engine. problem balanced binary classification task. full description dataset can found .","code":""},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"read-the-tsv-data","dir":"Articles > Examples > Timeseries","previous_headings":"Load the data: the FordA dataset","what":"Read the TSV data","title":"Timeseries classification from scratch","text":"use FordA_TRAIN file training FordA_TEST file testing. simplicity dataset allows us demonstrate effectively use ConvNets timeseries classification. file, first column corresponds label.","code":"get_data <- function(path) {   if(path |> startsWith(\"https://\"))     path <- get_file(origin = path)  # cache file locally    data <- readr::read_tsv(     path, col_names = FALSE,     # Each row is: one integer (the label),     # followed by 500 doubles (the timeseries)     col_types = paste0(\"i\", strrep(\"d\", 500))   )    y <- as.matrix(data[[1]])   x <- as.matrix(data[,-1])   dimnames(x) <- dimnames(y) <- NULL    list(x, y) }  root_url <- \"https://raw.githubusercontent.com/hfawaz/cd-diagram/master/FordA/\" c(x_train, y_train) %<-% get_data(paste0(root_url, \"FordA_TRAIN.tsv\")) c(x_test, y_test) %<-% get_data(paste0(root_url, \"FordA_TEST.tsv\"))  str(keras3:::named_list(   x_train, y_train,   x_test, y_test )) ## List of 4 ##  $ x_train: num [1:3601, 1:500] -0.797 0.805 0.728 -0.234 -0.171 ... ##  $ y_train: int [1:3601, 1] -1 1 -1 -1 -1 1 1 1 1 1 ... ##  $ x_test : num [1:1320, 1:500] -0.14 0.334 0.717 1.24 -1.159 ... ##  $ y_test : int [1:1320, 1] -1 -1 -1 1 -1 1 -1 -1 1 1 ..."},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"visualize-the-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Visualize the data","title":"Timeseries classification from scratch","text":"visualize one timeseries example class dataset.","code":"plot(NULL, main = \"Timeseries Data\",      xlab = \"Timepoints\",  ylab = \"Values\",      xlim = c(1, ncol(x_test)),      ylim = range(x_test)) grid() lines(x_test[match(-1, y_test), ], col = \"blue\") lines(x_test[match( 1, y_test), ], col = \"red\") legend(\"topright\", legend=c(\"label -1\", \"label 1\"), col=c(\"blue\", \"red\"), lty=1)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"standardize-the-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Standardize the data","title":"Timeseries classification from scratch","text":"timeseries already single length (500). However, values usually various ranges. ideal neural network; general seek make input values normalized. specific dataset, data already z-normalized: timeseries sample mean equal zero standard deviation equal one. type normalization common timeseries classification problems, see Bagnall et al. (2016). Note timeseries data used univariate, meaning one channel per timeseries example. therefore transform timeseries multivariate one one channel using simple reshaping via numpy. allow us construct model easily applicable multivariate time series. Finally, order use sparse_categorical_crossentropy, count number classes beforehand. Now shuffle training set using validation_split option later training. Standardize labels positive integers. expected labels 0 1.","code":"dim(x_train) <- c(dim(x_train), 1) dim(x_test) <- c(dim(x_test), 1) num_classes <- length(unique(y_train)) c(x_train, y_train) %<-% listarrays::shuffle_rows(x_train, y_train) # idx <- sample.int(nrow(x_train)) # x_train %<>% .[idx,, ,drop = FALSE] # y_train %<>% .[idx,  ,drop = FALSE] y_train[y_train == -1L] <- 0L y_test[y_test == -1L] <- 0L"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"build-a-model","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Build a model","title":"Timeseries classification from scratch","text":"build Fully Convolutional Neural Network originally proposed paper. implementation based TF 2 version provided . following hyperparameters (kernel_size, filters, usage BatchNorm) found via random search using KerasTuner. plot chunk unnamed-chunk-9","code":"make_model <- function(input_shape) {   inputs <- keras_input(input_shape)    outputs <- inputs |>     # conv1     layer_conv_1d(filters = 64, kernel_size = 3, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation_relu() |>     # conv2     layer_conv_1d(filters = 64, kernel_size = 3, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation_relu() |>     # conv3     layer_conv_1d(filters = 64, kernel_size = 3, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation_relu() |>     # pooling     layer_global_average_pooling_1d() |>     # final output     layer_dense(num_classes, activation = \"softmax\")    keras_model(inputs, outputs) }  model <- make_model(input_shape = dim(x_train)[-1]) model ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)                ┃ Output Shape          ┃    Param # ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer (InputLayer)    │ (None, 500, 1)        │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ conv1d (Conv1D)             │ (None, 500, 64)       │        256 │   Y   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ batch_normalization         │ (None, 500, 64)       │        256 │   Y   │ ## │ (BatchNormalization)        │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ re_lu (ReLU)                │ (None, 500, 64)       │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ conv1d_1 (Conv1D)           │ (None, 500, 64)       │     12,352 │   Y   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ batch_normalization_1       │ (None, 500, 64)       │        256 │   Y   │ ## │ (BatchNormalization)        │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ re_lu_1 (ReLU)              │ (None, 500, 64)       │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ conv1d_2 (Conv1D)           │ (None, 500, 64)       │     12,352 │   Y   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ batch_normalization_2       │ (None, 500, 64)       │        256 │   Y   │ ## │ (BatchNormalization)        │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ re_lu_2 (ReLU)              │ (None, 500, 64)       │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ global_average_pooling1d    │ (None, 64)            │          0 │   -   │ ## │ (GlobalAveragePooling1D)    │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense (Dense)               │ (None, 2)             │        130 │   Y   │ ## └─────────────────────────────┴───────────────────────┴────────────┴───────┘ ##  Total params: 25,858 (101.01 KB) ##  Trainable params: 25,474 (99.51 KB) ##  Non-trainable params: 384 (1.50 KB) plot(model, show_shapes = TRUE)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"train-the-model","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Train the model","title":"Timeseries classification from scratch","text":"","code":"epochs <- 500 batch_size <- 32  callbacks <- c(   callback_model_checkpoint(     \"best_model.keras\", save_best_only = TRUE,     monitor = \"val_loss\"   ),   callback_reduce_lr_on_plateau(     monitor = \"val_loss\", factor = 0.5,     patience = 20, min_lr = 0.0001   ),   callback_early_stopping(     monitor = \"val_loss\", patience = 50,     verbose = 1   ) )   model |> compile(   optimizer = \"adam\",   loss = \"sparse_categorical_crossentropy\",   metrics = \"sparse_categorical_accuracy\" )  history <- model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = epochs,   callbacks = callbacks,   validation_split = 0.2 ) ## Epoch 1/500 ## 90/90 - 2s - 24ms/step - loss: 0.5557 - sparse_categorical_accuracy: 0.7073 - val_loss: 0.8517 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010 ## Epoch 2/500 ## 90/90 - 1s - 6ms/step - loss: 0.4850 - sparse_categorical_accuracy: 0.7611 - val_loss: 0.9464 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010 ## Epoch 3/500 ## 90/90 - 0s - 2ms/step - loss: 0.4713 - sparse_categorical_accuracy: 0.7705 - val_loss: 0.7593 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010 ## Epoch 4/500 ## 90/90 - 0s - 2ms/step - loss: 0.4239 - sparse_categorical_accuracy: 0.7885 - val_loss: 0.6721 - val_sparse_categorical_accuracy: 0.5021 - learning_rate: 0.0010 ## Epoch 5/500 ## 90/90 - 0s - 2ms/step - loss: 0.4218 - sparse_categorical_accuracy: 0.7885 - val_loss: 0.5647 - val_sparse_categorical_accuracy: 0.7198 - learning_rate: 0.0010 ## Epoch 6/500 ## 90/90 - 0s - 2ms/step - loss: 0.4060 - sparse_categorical_accuracy: 0.8028 - val_loss: 0.4563 - val_sparse_categorical_accuracy: 0.8141 - learning_rate: 0.0010 ## Epoch 7/500 ## 90/90 - 0s - 2ms/step - loss: 0.4032 - sparse_categorical_accuracy: 0.8017 - val_loss: 0.4209 - val_sparse_categorical_accuracy: 0.7892 - learning_rate: 0.0010 ## Epoch 8/500 ## 90/90 - 0s - 2ms/step - loss: 0.3920 - sparse_categorical_accuracy: 0.8003 - val_loss: 0.3938 - val_sparse_categorical_accuracy: 0.8239 - learning_rate: 0.0010 ## Epoch 9/500 ## 90/90 - 0s - 2ms/step - loss: 0.3892 - sparse_categorical_accuracy: 0.8135 - val_loss: 0.3849 - val_sparse_categorical_accuracy: 0.8225 - learning_rate: 0.0010 ## Epoch 10/500 ## 90/90 - 0s - 2ms/step - loss: 0.3874 - sparse_categorical_accuracy: 0.8021 - val_loss: 0.4934 - val_sparse_categorical_accuracy: 0.7503 - learning_rate: 0.0010 ## Epoch 11/500 ## 90/90 - 0s - 1ms/step - loss: 0.3798 - sparse_categorical_accuracy: 0.8149 - val_loss: 0.5693 - val_sparse_categorical_accuracy: 0.6990 - learning_rate: 0.0010 ## Epoch 12/500 ## 90/90 - 0s - 1ms/step - loss: 0.3671 - sparse_categorical_accuracy: 0.8215 - val_loss: 0.3998 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010 ## Epoch 13/500 ## 90/90 - 0s - 1ms/step - loss: 0.3555 - sparse_categorical_accuracy: 0.8389 - val_loss: 0.4145 - val_sparse_categorical_accuracy: 0.8031 - learning_rate: 0.0010 ## Epoch 14/500 ## 90/90 - 0s - 1ms/step - loss: 0.3628 - sparse_categorical_accuracy: 0.8253 - val_loss: 0.3886 - val_sparse_categorical_accuracy: 0.8211 - learning_rate: 0.0010 ## Epoch 15/500 ## 90/90 - 0s - 1ms/step - loss: 0.3433 - sparse_categorical_accuracy: 0.8406 - val_loss: 0.7763 - val_sparse_categorical_accuracy: 0.6019 - learning_rate: 0.0010 ## Epoch 16/500 ## 90/90 - 0s - 2ms/step - loss: 0.3455 - sparse_categorical_accuracy: 0.8434 - val_loss: 0.3537 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010 ## Epoch 17/500 ## 90/90 - 0s - 1ms/step - loss: 0.3336 - sparse_categorical_accuracy: 0.8462 - val_loss: 0.5375 - val_sparse_categorical_accuracy: 0.7074 - learning_rate: 0.0010 ## Epoch 18/500 ## 90/90 - 0s - 1ms/step - loss: 0.3263 - sparse_categorical_accuracy: 0.8503 - val_loss: 0.4134 - val_sparse_categorical_accuracy: 0.7892 - learning_rate: 0.0010 ## Epoch 19/500 ## 90/90 - 0s - 2ms/step - loss: 0.3195 - sparse_categorical_accuracy: 0.8601 - val_loss: 0.3432 - val_sparse_categorical_accuracy: 0.8433 - learning_rate: 0.0010 ## Epoch 20/500 ## 90/90 - 0s - 1ms/step - loss: 0.3136 - sparse_categorical_accuracy: 0.8674 - val_loss: 0.9287 - val_sparse_categorical_accuracy: 0.5950 - learning_rate: 0.0010 ## Epoch 21/500 ## 90/90 - 0s - 1ms/step - loss: 0.3001 - sparse_categorical_accuracy: 0.8691 - val_loss: 0.3926 - val_sparse_categorical_accuracy: 0.7795 - learning_rate: 0.0010 ## Epoch 22/500 ## 90/90 - 0s - 2ms/step - loss: 0.2984 - sparse_categorical_accuracy: 0.8719 - val_loss: 0.3023 - val_sparse_categorical_accuracy: 0.8655 - learning_rate: 0.0010 ## Epoch 23/500 ## 90/90 - 0s - 1ms/step - loss: 0.2930 - sparse_categorical_accuracy: 0.8767 - val_loss: 0.3501 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010 ## Epoch 24/500 ## 90/90 - 0s - 1ms/step - loss: 0.2861 - sparse_categorical_accuracy: 0.8799 - val_loss: 0.9187 - val_sparse_categorical_accuracy: 0.5992 - learning_rate: 0.0010 ## Epoch 25/500 ## 90/90 - 0s - 1ms/step - loss: 0.2843 - sparse_categorical_accuracy: 0.8816 - val_loss: 0.3731 - val_sparse_categorical_accuracy: 0.8363 - learning_rate: 0.0010 ## Epoch 26/500 ## 90/90 - 0s - 1ms/step - loss: 0.3182 - sparse_categorical_accuracy: 0.8569 - val_loss: 0.5553 - val_sparse_categorical_accuracy: 0.7115 - learning_rate: 0.0010 ## Epoch 27/500 ## 90/90 - 0s - 1ms/step - loss: 0.2732 - sparse_categorical_accuracy: 0.8875 - val_loss: 0.3950 - val_sparse_categorical_accuracy: 0.7628 - learning_rate: 0.0010 ## Epoch 28/500 ## 90/90 - 0s - 1ms/step - loss: 0.2943 - sparse_categorical_accuracy: 0.8705 - val_loss: 0.8138 - val_sparse_categorical_accuracy: 0.7254 - learning_rate: 0.0010 ## Epoch 29/500 ## 90/90 - 0s - 1ms/step - loss: 0.2772 - sparse_categorical_accuracy: 0.8781 - val_loss: 0.3712 - val_sparse_categorical_accuracy: 0.8405 - learning_rate: 0.0010 ## Epoch 30/500 ## 90/90 - 0s - 1ms/step - loss: 0.2714 - sparse_categorical_accuracy: 0.8833 - val_loss: 0.4209 - val_sparse_categorical_accuracy: 0.7975 - learning_rate: 0.0010 ## Epoch 31/500 ## 90/90 - 0s - 2ms/step - loss: 0.2720 - sparse_categorical_accuracy: 0.8854 - val_loss: 1.9298 - val_sparse_categorical_accuracy: 0.5104 - learning_rate: 0.0010 ## Epoch 32/500 ## 90/90 - 0s - 2ms/step - loss: 0.2699 - sparse_categorical_accuracy: 0.8833 - val_loss: 0.3155 - val_sparse_categorical_accuracy: 0.8516 - learning_rate: 0.0010 ## Epoch 33/500 ## 90/90 - 0s - 2ms/step - loss: 0.2586 - sparse_categorical_accuracy: 0.8951 - val_loss: 0.3628 - val_sparse_categorical_accuracy: 0.7947 - learning_rate: 0.0010 ## Epoch 34/500 ## 90/90 - 0s - 1ms/step - loss: 0.2490 - sparse_categorical_accuracy: 0.9003 - val_loss: 0.5844 - val_sparse_categorical_accuracy: 0.7309 - learning_rate: 0.0010 ## Epoch 35/500 ## 90/90 - 0s - 1ms/step - loss: 0.2627 - sparse_categorical_accuracy: 0.8938 - val_loss: 0.3480 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010 ## Epoch 36/500 ## 90/90 - 0s - 1ms/step - loss: 0.2558 - sparse_categorical_accuracy: 0.8948 - val_loss: 0.3793 - val_sparse_categorical_accuracy: 0.8128 - learning_rate: 0.0010 ## Epoch 37/500 ## 90/90 - 0s - 1ms/step - loss: 0.2515 - sparse_categorical_accuracy: 0.8958 - val_loss: 0.4828 - val_sparse_categorical_accuracy: 0.7503 - learning_rate: 0.0010 ## Epoch 38/500 ## 90/90 - 0s - 2ms/step - loss: 0.2541 - sparse_categorical_accuracy: 0.8986 - val_loss: 0.2950 - val_sparse_categorical_accuracy: 0.8669 - learning_rate: 0.0010 ## Epoch 39/500 ## 90/90 - 0s - 1ms/step - loss: 0.2505 - sparse_categorical_accuracy: 0.8976 - val_loss: 0.7151 - val_sparse_categorical_accuracy: 0.7087 - learning_rate: 0.0010 ## Epoch 40/500 ## 90/90 - 0s - 1ms/step - loss: 0.2490 - sparse_categorical_accuracy: 0.8979 - val_loss: 0.3170 - val_sparse_categorical_accuracy: 0.8627 - learning_rate: 0.0010 ## Epoch 41/500 ## 90/90 - 0s - 2ms/step - loss: 0.2325 - sparse_categorical_accuracy: 0.9062 - val_loss: 0.2560 - val_sparse_categorical_accuracy: 0.8904 - learning_rate: 0.0010 ## Epoch 42/500 ## 90/90 - 0s - 1ms/step - loss: 0.2312 - sparse_categorical_accuracy: 0.9076 - val_loss: 0.3396 - val_sparse_categorical_accuracy: 0.8405 - learning_rate: 0.0010 ## Epoch 43/500 ## 90/90 - 0s - 1ms/step - loss: 0.2402 - sparse_categorical_accuracy: 0.8976 - val_loss: 0.3650 - val_sparse_categorical_accuracy: 0.8294 - learning_rate: 0.0010 ## Epoch 44/500 ## 90/90 - 0s - 1ms/step - loss: 0.2342 - sparse_categorical_accuracy: 0.9038 - val_loss: 1.0563 - val_sparse_categorical_accuracy: 0.6172 - learning_rate: 0.0010 ## Epoch 45/500 ## 90/90 - 0s - 1ms/step - loss: 0.2320 - sparse_categorical_accuracy: 0.9080 - val_loss: 0.2506 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010 ## Epoch 46/500 ## 90/90 - 0s - 1ms/step - loss: 0.2317 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.4473 - val_sparse_categorical_accuracy: 0.7920 - learning_rate: 0.0010 ## Epoch 47/500 ## 90/90 - 0s - 1ms/step - loss: 0.2164 - sparse_categorical_accuracy: 0.9125 - val_loss: 0.6324 - val_sparse_categorical_accuracy: 0.7240 - learning_rate: 0.0010 ## Epoch 48/500 ## 90/90 - 0s - 2ms/step - loss: 0.2186 - sparse_categorical_accuracy: 0.9122 - val_loss: 0.2525 - val_sparse_categorical_accuracy: 0.9001 - learning_rate: 0.0010 ## Epoch 49/500 ## 90/90 - 0s - 2ms/step - loss: 0.2119 - sparse_categorical_accuracy: 0.9118 - val_loss: 0.2401 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010 ## Epoch 50/500 ## 90/90 - 0s - 1ms/step - loss: 0.2134 - sparse_categorical_accuracy: 0.9191 - val_loss: 0.2410 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010 ## Epoch 51/500 ## 90/90 - 0s - 2ms/step - loss: 0.2120 - sparse_categorical_accuracy: 0.9174 - val_loss: 0.6803 - val_sparse_categorical_accuracy: 0.6935 - learning_rate: 0.0010 ## Epoch 52/500 ## 90/90 - 0s - 2ms/step - loss: 0.2023 - sparse_categorical_accuracy: 0.9205 - val_loss: 0.2938 - val_sparse_categorical_accuracy: 0.8779 - learning_rate: 0.0010 ## Epoch 53/500 ## 90/90 - 0s - 2ms/step - loss: 0.1980 - sparse_categorical_accuracy: 0.9243 - val_loss: 0.5380 - val_sparse_categorical_accuracy: 0.7531 - learning_rate: 0.0010 ## Epoch 54/500 ## 90/90 - 0s - 2ms/step - loss: 0.1883 - sparse_categorical_accuracy: 0.9271 - val_loss: 0.2272 - val_sparse_categorical_accuracy: 0.9071 - learning_rate: 0.0010 ## Epoch 55/500 ## 90/90 - 0s - 2ms/step - loss: 0.1761 - sparse_categorical_accuracy: 0.9385 - val_loss: 0.2707 - val_sparse_categorical_accuracy: 0.8821 - learning_rate: 0.0010 ## Epoch 56/500 ## 90/90 - 0s - 1ms/step - loss: 0.1683 - sparse_categorical_accuracy: 0.9417 - val_loss: 0.2584 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010 ## Epoch 57/500 ## 90/90 - 0s - 1ms/step - loss: 0.1581 - sparse_categorical_accuracy: 0.9490 - val_loss: 1.4305 - val_sparse_categorical_accuracy: 0.7184 - learning_rate: 0.0010 ## Epoch 58/500 ## 90/90 - 0s - 1ms/step - loss: 0.1518 - sparse_categorical_accuracy: 0.9497 - val_loss: 0.7991 - val_sparse_categorical_accuracy: 0.7531 - learning_rate: 0.0010 ## Epoch 59/500 ## 90/90 - 0s - 1ms/step - loss: 0.1529 - sparse_categorical_accuracy: 0.9479 - val_loss: 0.3389 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 0.0010 ## Epoch 60/500 ## 90/90 - 0s - 1ms/step - loss: 0.1784 - sparse_categorical_accuracy: 0.9368 - val_loss: 0.2534 - val_sparse_categorical_accuracy: 0.8974 - learning_rate: 0.0010 ## Epoch 61/500 ## 90/90 - 0s - 1ms/step - loss: 0.1335 - sparse_categorical_accuracy: 0.9566 - val_loss: 0.2323 - val_sparse_categorical_accuracy: 0.9098 - learning_rate: 0.0010 ## Epoch 62/500 ## 90/90 - 0s - 1ms/step - loss: 0.1401 - sparse_categorical_accuracy: 0.9531 - val_loss: 0.3259 - val_sparse_categorical_accuracy: 0.8696 - learning_rate: 0.0010 ## Epoch 63/500 ## 90/90 - 0s - 1ms/step - loss: 0.1404 - sparse_categorical_accuracy: 0.9521 - val_loss: 0.2970 - val_sparse_categorical_accuracy: 0.8627 - learning_rate: 0.0010 ## Epoch 64/500 ## 90/90 - 0s - 1ms/step - loss: 0.1375 - sparse_categorical_accuracy: 0.9559 - val_loss: 0.3416 - val_sparse_categorical_accuracy: 0.8183 - learning_rate: 0.0010 ## Epoch 65/500 ## 90/90 - 0s - 1ms/step - loss: 0.1303 - sparse_categorical_accuracy: 0.9583 - val_loss: 0.2586 - val_sparse_categorical_accuracy: 0.8766 - learning_rate: 0.0010 ## Epoch 66/500 ## 90/90 - 0s - 1ms/step - loss: 0.1199 - sparse_categorical_accuracy: 0.9608 - val_loss: 1.2218 - val_sparse_categorical_accuracy: 0.7337 - learning_rate: 0.0010 ## Epoch 67/500 ## 90/90 - 0s - 2ms/step - loss: 0.1186 - sparse_categorical_accuracy: 0.9615 - val_loss: 0.1464 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010 ## Epoch 68/500 ## 90/90 - 0s - 2ms/step - loss: 0.1176 - sparse_categorical_accuracy: 0.9632 - val_loss: 0.1451 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010 ## Epoch 69/500 ## 90/90 - 0s - 2ms/step - loss: 0.1295 - sparse_categorical_accuracy: 0.9583 - val_loss: 1.7723 - val_sparse_categorical_accuracy: 0.5395 - learning_rate: 0.0010 ## Epoch 70/500 ## 90/90 - 0s - 2ms/step - loss: 0.1232 - sparse_categorical_accuracy: 0.9587 - val_loss: 0.1802 - val_sparse_categorical_accuracy: 0.9237 - learning_rate: 0.0010 ## Epoch 71/500 ## 90/90 - 0s - 1ms/step - loss: 0.1202 - sparse_categorical_accuracy: 0.9573 - val_loss: 0.1932 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010 ## Epoch 72/500 ## 90/90 - 0s - 1ms/step - loss: 0.1124 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.4178 - val_sparse_categorical_accuracy: 0.8003 - learning_rate: 0.0010 ## Epoch 73/500 ## 90/90 - 0s - 1ms/step - loss: 0.1068 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1517 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 0.0010 ## Epoch 74/500 ## 90/90 - 0s - 1ms/step - loss: 0.1085 - sparse_categorical_accuracy: 0.9639 - val_loss: 0.2126 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 0.0010 ## Epoch 75/500 ## 90/90 - 0s - 1ms/step - loss: 0.1263 - sparse_categorical_accuracy: 0.9580 - val_loss: 0.1689 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 0.0010 ## Epoch 76/500 ## 90/90 - 0s - 1ms/step - loss: 0.1169 - sparse_categorical_accuracy: 0.9597 - val_loss: 0.2955 - val_sparse_categorical_accuracy: 0.8460 - learning_rate: 0.0010 ## Epoch 77/500 ## 90/90 - 0s - 1ms/step - loss: 0.1089 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.6333 - val_sparse_categorical_accuracy: 0.6241 - learning_rate: 0.0010 ## Epoch 78/500 ## 90/90 - 0s - 2ms/step - loss: 0.1017 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.1416 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 0.0010 ## Epoch 79/500 ## 90/90 - 0s - 2ms/step - loss: 0.1099 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.3434 - val_sparse_categorical_accuracy: 0.8544 - learning_rate: 0.0010 ## Epoch 80/500 ## 90/90 - 0s - 2ms/step - loss: 0.1046 - sparse_categorical_accuracy: 0.9653 - val_loss: 0.4430 - val_sparse_categorical_accuracy: 0.8280 - learning_rate: 0.0010 ## Epoch 81/500 ## 90/90 - 0s - 1ms/step - loss: 0.1098 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.1632 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 0.0010 ## Epoch 82/500 ## 90/90 - 0s - 1ms/step - loss: 0.1021 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.3984 - val_sparse_categorical_accuracy: 0.7906 - learning_rate: 0.0010 ## Epoch 83/500 ## 90/90 - 0s - 1ms/step - loss: 0.1086 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.1976 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010 ## Epoch 84/500 ## 90/90 - 0s - 1ms/step - loss: 0.1026 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.2123 - val_sparse_categorical_accuracy: 0.9251 - learning_rate: 0.0010 ## Epoch 85/500 ## 90/90 - 0s - 1ms/step - loss: 0.1032 - sparse_categorical_accuracy: 0.9632 - val_loss: 0.3491 - val_sparse_categorical_accuracy: 0.8613 - learning_rate: 0.0010 ## Epoch 86/500 ## 90/90 - 0s - 1ms/step - loss: 0.1136 - sparse_categorical_accuracy: 0.9576 - val_loss: 0.1498 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 0.0010 ## Epoch 87/500 ## 90/90 - 0s - 2ms/step - loss: 0.1107 - sparse_categorical_accuracy: 0.9608 - val_loss: 0.1406 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 0.0010 ## Epoch 88/500 ## 90/90 - 0s - 1ms/step - loss: 0.0995 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.6864 - val_sparse_categorical_accuracy: 0.7656 - learning_rate: 0.0010 ## Epoch 89/500 ## 90/90 - 0s - 1ms/step - loss: 0.1013 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.8218 - val_sparse_categorical_accuracy: 0.7476 - learning_rate: 0.0010 ## Epoch 90/500 ## 90/90 - 0s - 1ms/step - loss: 0.1002 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.2009 - val_sparse_categorical_accuracy: 0.9209 - learning_rate: 0.0010 ## Epoch 91/500 ## 90/90 - 0s - 1ms/step - loss: 0.1010 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.2869 - val_sparse_categorical_accuracy: 0.8849 - learning_rate: 0.0010 ## Epoch 92/500 ## 90/90 - 0s - 1ms/step - loss: 0.1012 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.5128 - val_sparse_categorical_accuracy: 0.7434 - learning_rate: 0.0010 ## Epoch 93/500 ## 90/90 - 0s - 1ms/step - loss: 0.1009 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.5212 - val_sparse_categorical_accuracy: 0.7753 - learning_rate: 0.0010 ## Epoch 94/500 ## 90/90 - 0s - 1ms/step - loss: 0.1016 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.3146 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 0.0010 ## Epoch 95/500 ## 90/90 - 0s - 1ms/step - loss: 0.0975 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1750 - val_sparse_categorical_accuracy: 0.9376 - learning_rate: 0.0010 ## Epoch 96/500 ## 90/90 - 0s - 1ms/step - loss: 0.1121 - sparse_categorical_accuracy: 0.9583 - val_loss: 0.2399 - val_sparse_categorical_accuracy: 0.9112 - learning_rate: 0.0010 ## Epoch 97/500 ## 90/90 - 0s - 1ms/step - loss: 0.1000 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.5611 - val_sparse_categorical_accuracy: 0.7101 - learning_rate: 0.0010 ## Epoch 98/500 ## 90/90 - 0s - 1ms/step - loss: 0.0949 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1864 - val_sparse_categorical_accuracy: 0.9279 - learning_rate: 0.0010 ## Epoch 99/500 ## 90/90 - 0s - 2ms/step - loss: 0.0925 - sparse_categorical_accuracy: 0.9667 - val_loss: 0.1228 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010 ## Epoch 100/500 ## 90/90 - 0s - 1ms/step - loss: 0.0922 - sparse_categorical_accuracy: 0.9691 - val_loss: 1.9448 - val_sparse_categorical_accuracy: 0.7115 - learning_rate: 0.0010 ## Epoch 101/500 ## 90/90 - 0s - 1ms/step - loss: 0.0887 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.2127 - val_sparse_categorical_accuracy: 0.9071 - learning_rate: 0.0010 ## Epoch 102/500 ## 90/90 - 0s - 1ms/step - loss: 0.0999 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.1903 - val_sparse_categorical_accuracy: 0.9154 - learning_rate: 0.0010 ## Epoch 103/500 ## 90/90 - 0s - 1ms/step - loss: 0.0929 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.2647 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010 ## Epoch 104/500 ## 90/90 - 0s - 1ms/step - loss: 0.1008 - sparse_categorical_accuracy: 0.9642 - val_loss: 1.1019 - val_sparse_categorical_accuracy: 0.6019 - learning_rate: 0.0010 ## Epoch 105/500 ## 90/90 - 0s - 1ms/step - loss: 0.0969 - sparse_categorical_accuracy: 0.9681 - val_loss: 1.1464 - val_sparse_categorical_accuracy: 0.5645 - learning_rate: 0.0010 ## Epoch 106/500 ## 90/90 - 0s - 2ms/step - loss: 0.0924 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.2535 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010 ## Epoch 107/500 ## 90/90 - 0s - 2ms/step - loss: 0.0930 - sparse_categorical_accuracy: 0.9677 - val_loss: 1.5288 - val_sparse_categorical_accuracy: 0.5118 - learning_rate: 0.0010 ## Epoch 108/500 ## 90/90 - 0s - 1ms/step - loss: 0.0893 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1496 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 0.0010 ## Epoch 109/500 ## 90/90 - 0s - 1ms/step - loss: 0.0891 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.2799 - val_sparse_categorical_accuracy: 0.8835 - learning_rate: 0.0010 ## Epoch 110/500 ## 90/90 - 0s - 1ms/step - loss: 0.0963 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.4827 - val_sparse_categorical_accuracy: 0.8155 - learning_rate: 0.0010 ## Epoch 111/500 ## 90/90 - 0s - 2ms/step - loss: 0.0841 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1203 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 0.0010 ## Epoch 112/500 ## 90/90 - 0s - 1ms/step - loss: 0.0861 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.5081 - val_sparse_categorical_accuracy: 0.7559 - learning_rate: 0.0010 ## Epoch 113/500 ## 90/90 - 0s - 1ms/step - loss: 0.0831 - sparse_categorical_accuracy: 0.9729 - val_loss: 1.2423 - val_sparse_categorical_accuracy: 0.5687 - learning_rate: 0.0010 ## Epoch 114/500 ## 90/90 - 0s - 1ms/step - loss: 0.1094 - sparse_categorical_accuracy: 0.9653 - val_loss: 0.3459 - val_sparse_categorical_accuracy: 0.8252 - learning_rate: 0.0010 ## Epoch 115/500 ## 90/90 - 0s - 1ms/step - loss: 0.0886 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.7117 - val_sparse_categorical_accuracy: 0.7226 - learning_rate: 0.0010 ## Epoch 116/500 ## 90/90 - 0s - 1ms/step - loss: 0.0989 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.1669 - val_sparse_categorical_accuracy: 0.9334 - learning_rate: 0.0010 ## Epoch 117/500 ## 90/90 - 0s - 1ms/step - loss: 0.0891 - sparse_categorical_accuracy: 0.9705 - val_loss: 0.2413 - val_sparse_categorical_accuracy: 0.9154 - learning_rate: 0.0010 ## Epoch 118/500 ## 90/90 - 0s - 1ms/step - loss: 0.0902 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.7796 - val_sparse_categorical_accuracy: 0.6741 - learning_rate: 0.0010 ## Epoch 119/500 ## 90/90 - 0s - 1ms/step - loss: 0.0877 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.1806 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 0.0010 ## Epoch 120/500 ## 90/90 - 0s - 2ms/step - loss: 0.0880 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.1146 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 0.0010 ## Epoch 121/500 ## 90/90 - 0s - 1ms/step - loss: 0.0896 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.1279 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 0.0010 ## Epoch 122/500 ## 90/90 - 0s - 1ms/step - loss: 0.0883 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.3067 - val_sparse_categorical_accuracy: 0.8918 - learning_rate: 0.0010 ## Epoch 123/500 ## 90/90 - 0s - 2ms/step - loss: 0.0855 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.1060 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 0.0010 ## Epoch 124/500 ## 90/90 - 0s - 1ms/step - loss: 0.0917 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.2169 - val_sparse_categorical_accuracy: 0.9112 - learning_rate: 0.0010 ## Epoch 125/500 ## 90/90 - 0s - 1ms/step - loss: 0.0976 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.1407 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 0.0010 ## Epoch 126/500 ## 90/90 - 0s - 1ms/step - loss: 0.0866 - sparse_categorical_accuracy: 0.9722 - val_loss: 1.4019 - val_sparse_categorical_accuracy: 0.7295 - learning_rate: 0.0010 ## Epoch 127/500 ## 90/90 - 0s - 1ms/step - loss: 0.0877 - sparse_categorical_accuracy: 0.9747 - val_loss: 0.2518 - val_sparse_categorical_accuracy: 0.8918 - learning_rate: 0.0010 ## Epoch 128/500 ## 90/90 - 0s - 1ms/step - loss: 0.0942 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.1465 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 0.0010 ## Epoch 129/500 ## 90/90 - 0s - 1ms/step - loss: 0.0904 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.1546 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 0.0010 ## Epoch 130/500 ## 90/90 - 0s - 1ms/step - loss: 0.0953 - sparse_categorical_accuracy: 0.9705 - val_loss: 0.1201 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 0.0010 ## Epoch 131/500 ## 90/90 - 0s - 1ms/step - loss: 0.0846 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.1980 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 0.0010 ## Epoch 132/500 ## 90/90 - 0s - 1ms/step - loss: 0.0812 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.4080 - val_sparse_categorical_accuracy: 0.8599 - learning_rate: 0.0010 ## Epoch 133/500 ## 90/90 - 0s - 1ms/step - loss: 0.0785 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.3284 - val_sparse_categorical_accuracy: 0.8835 - learning_rate: 0.0010 ## Epoch 134/500 ## 90/90 - 0s - 1ms/step - loss: 0.0885 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.5050 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010 ## Epoch 135/500 ## 90/90 - 0s - 1ms/step - loss: 0.0859 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.4296 - val_sparse_categorical_accuracy: 0.8141 - learning_rate: 0.0010 ## Epoch 136/500 ## 90/90 - 0s - 1ms/step - loss: 0.0885 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.1295 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 0.0010 ## Epoch 137/500 ## 90/90 - 0s - 1ms/step - loss: 0.0892 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.4052 - val_sparse_categorical_accuracy: 0.8031 - learning_rate: 0.0010 ## Epoch 138/500 ## 90/90 - 0s - 1ms/step - loss: 0.0835 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.1160 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 0.0010 ## Epoch 139/500 ## 90/90 - 0s - 1ms/step - loss: 0.0877 - sparse_categorical_accuracy: 0.9708 - val_loss: 3.0631 - val_sparse_categorical_accuracy: 0.7032 - learning_rate: 0.0010 ## Epoch 140/500 ## 90/90 - 0s - 1ms/step - loss: 0.0867 - sparse_categorical_accuracy: 0.9719 - val_loss: 1.3638 - val_sparse_categorical_accuracy: 0.7143 - learning_rate: 0.0010 ## Epoch 141/500 ## 90/90 - 0s - 1ms/step - loss: 0.0857 - sparse_categorical_accuracy: 0.9715 - val_loss: 0.2599 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010 ## Epoch 142/500 ## 90/90 - 0s - 1ms/step - loss: 0.0771 - sparse_categorical_accuracy: 0.9764 - val_loss: 0.2860 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010 ## Epoch 143/500 ## 90/90 - 0s - 1ms/step - loss: 0.0835 - sparse_categorical_accuracy: 0.9694 - val_loss: 0.1252 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 0.0010 ## Epoch 144/500 ## 90/90 - 1s - 7ms/step - loss: 0.0674 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.1234 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 5.0000e-04 ## Epoch 145/500 ## 90/90 - 0s - 1ms/step - loss: 0.0680 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1147 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 5.0000e-04 ## Epoch 146/500 ## 90/90 - 0s - 2ms/step - loss: 0.0702 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1042 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 5.0000e-04 ## Epoch 147/500 ## 90/90 - 0s - 1ms/step - loss: 0.0742 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.1511 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 5.0000e-04 ## Epoch 148/500 ## 90/90 - 0s - 1ms/step - loss: 0.0801 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.1219 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 5.0000e-04 ## Epoch 149/500 ## 90/90 - 0s - 2ms/step - loss: 0.0725 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04 ## Epoch 150/500 ## 90/90 - 0s - 1ms/step - loss: 0.0738 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.2021 - val_sparse_categorical_accuracy: 0.9334 - learning_rate: 5.0000e-04 ## Epoch 151/500 ## 90/90 - 0s - 1ms/step - loss: 0.0707 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.1358 - val_sparse_categorical_accuracy: 0.9376 - learning_rate: 5.0000e-04 ## Epoch 152/500 ## 90/90 - 0s - 1ms/step - loss: 0.0727 - sparse_categorical_accuracy: 0.9740 - val_loss: 0.1989 - val_sparse_categorical_accuracy: 0.9098 - learning_rate: 5.0000e-04 ## Epoch 153/500 ## 90/90 - 0s - 1ms/step - loss: 0.0671 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.1250 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04 ## Epoch 154/500 ## 90/90 - 0s - 1ms/step - loss: 0.0687 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1973 - val_sparse_categorical_accuracy: 0.9237 - learning_rate: 5.0000e-04 ## Epoch 155/500 ## 90/90 - 0s - 1ms/step - loss: 0.0697 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1190 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 5.0000e-04 ## Epoch 156/500 ## 90/90 - 0s - 1ms/step - loss: 0.0731 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.1409 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 5.0000e-04 ## Epoch 157/500 ## 90/90 - 0s - 1ms/step - loss: 0.0719 - sparse_categorical_accuracy: 0.9740 - val_loss: 0.4024 - val_sparse_categorical_accuracy: 0.8655 - learning_rate: 5.0000e-04 ## Epoch 158/500 ## 90/90 - 0s - 1ms/step - loss: 0.0722 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.3254 - val_sparse_categorical_accuracy: 0.8821 - learning_rate: 5.0000e-04 ## Epoch 159/500 ## 90/90 - 0s - 1ms/step - loss: 0.0706 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.2014 - val_sparse_categorical_accuracy: 0.9265 - learning_rate: 5.0000e-04 ## Epoch 160/500 ## 90/90 - 0s - 1ms/step - loss: 0.0707 - sparse_categorical_accuracy: 0.9753 - val_loss: 0.1128 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 5.0000e-04 ## Epoch 161/500 ## 90/90 - 0s - 1ms/step - loss: 0.0727 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.1118 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 5.0000e-04 ## Epoch 162/500 ## 90/90 - 0s - 1ms/step - loss: 0.0660 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.2972 - val_sparse_categorical_accuracy: 0.8960 - learning_rate: 5.0000e-04 ## Epoch 163/500 ## 90/90 - 0s - 1ms/step - loss: 0.0619 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.1079 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 5.0000e-04 ## Epoch 164/500 ## 90/90 - 0s - 1ms/step - loss: 0.0670 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.2169 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 5.0000e-04 ## Epoch 165/500 ## 90/90 - 0s - 1ms/step - loss: 0.0690 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.2980 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 5.0000e-04 ## Epoch 166/500 ## 90/90 - 0s - 1ms/step - loss: 0.0650 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.1257 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 5.0000e-04 ## Epoch 167/500 ## 90/90 - 0s - 1ms/step - loss: 0.0656 - sparse_categorical_accuracy: 0.9788 - val_loss: 0.1451 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 5.0000e-04 ## Epoch 168/500 ## 90/90 - 0s - 1ms/step - loss: 0.0750 - sparse_categorical_accuracy: 0.9736 - val_loss: 0.3402 - val_sparse_categorical_accuracy: 0.8350 - learning_rate: 5.0000e-04 ## Epoch 169/500 ## 90/90 - 0s - 1ms/step - loss: 0.0664 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.1295 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 5.0000e-04 ## Epoch 170/500 ## 90/90 - 0s - 2ms/step - loss: 0.0635 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04 ## Epoch 171/500 ## 90/90 - 0s - 1ms/step - loss: 0.0593 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1230 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 2.5000e-04 ## Epoch 172/500 ## 90/90 - 0s - 1ms/step - loss: 0.0591 - sparse_categorical_accuracy: 0.9799 - val_loss: 0.1079 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04 ## Epoch 173/500 ## 90/90 - 0s - 1ms/step - loss: 0.0593 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1073 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04 ## Epoch 174/500 ## 90/90 - 0s - 2ms/step - loss: 0.0605 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 2.5000e-04 ## Epoch 175/500 ## 90/90 - 0s - 1ms/step - loss: 0.0616 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.2084 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 2.5000e-04 ## Epoch 176/500 ## 90/90 - 0s - 1ms/step - loss: 0.0584 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.0993 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04 ## Epoch 177/500 ## 90/90 - 0s - 1ms/step - loss: 0.0582 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1498 - val_sparse_categorical_accuracy: 0.9487 - learning_rate: 2.5000e-04 ## Epoch 178/500 ## 90/90 - 0s - 1ms/step - loss: 0.0566 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1165 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 2.5000e-04 ## Epoch 179/500 ## 90/90 - 0s - 2ms/step - loss: 0.0566 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1714 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 2.5000e-04 ## Epoch 180/500 ## 90/90 - 0s - 2ms/step - loss: 0.0563 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1042 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04 ## Epoch 181/500 ## 90/90 - 0s - 2ms/step - loss: 0.0609 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.2017 - val_sparse_categorical_accuracy: 0.9209 - learning_rate: 2.5000e-04 ## Epoch 182/500 ## 90/90 - 0s - 1ms/step - loss: 0.0559 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1059 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 2.5000e-04 ## Epoch 183/500 ## 90/90 - 0s - 1ms/step - loss: 0.0597 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.1279 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 2.5000e-04 ## Epoch 184/500 ## 90/90 - 0s - 1ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04 ## Epoch 185/500 ## 90/90 - 0s - 1ms/step - loss: 0.0570 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.1022 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04 ## Epoch 186/500 ## 90/90 - 0s - 1ms/step - loss: 0.0633 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04 ## Epoch 187/500 ## 90/90 - 0s - 2ms/step - loss: 0.0576 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0969 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04 ## Epoch 188/500 ## 90/90 - 0s - 1ms/step - loss: 0.0567 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1043 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04 ## Epoch 189/500 ## 90/90 - 0s - 1ms/step - loss: 0.0546 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1734 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 2.5000e-04 ## Epoch 190/500 ## 90/90 - 0s - 1ms/step - loss: 0.0639 - sparse_categorical_accuracy: 0.9799 - val_loss: 0.2847 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 2.5000e-04 ## Epoch 191/500 ## 90/90 - 0s - 1ms/step - loss: 0.0551 - sparse_categorical_accuracy: 0.9799 - val_loss: 0.4059 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 2.5000e-04 ## Epoch 192/500 ## 90/90 - 0s - 1ms/step - loss: 0.0613 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.3821 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 2.5000e-04 ## Epoch 193/500 ## 90/90 - 0s - 1ms/step - loss: 0.0559 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04 ## Epoch 194/500 ## 90/90 - 0s - 1ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1008 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04 ## Epoch 195/500 ## 90/90 - 0s - 1ms/step - loss: 0.0557 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0996 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 2.5000e-04 ## Epoch 196/500 ## 90/90 - 0s - 1ms/step - loss: 0.0581 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.1143 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 2.5000e-04 ## Epoch 197/500 ## 90/90 - 0s - 1ms/step - loss: 0.0544 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1027 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 2.5000e-04 ## Epoch 198/500 ## 90/90 - 0s - 1ms/step - loss: 0.0533 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1310 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04 ## Epoch 199/500 ## 90/90 - 0s - 1ms/step - loss: 0.0578 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.1236 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 2.5000e-04 ## Epoch 200/500 ## 90/90 - 0s - 1ms/step - loss: 0.0582 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 2.5000e-04 ## Epoch 201/500 ## 90/90 - 0s - 1ms/step - loss: 0.0551 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1327 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04 ## Epoch 202/500 ## 90/90 - 0s - 1ms/step - loss: 0.0543 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.2115 - val_sparse_categorical_accuracy: 0.9140 - learning_rate: 2.5000e-04 ## Epoch 203/500 ## 90/90 - 0s - 1ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.2015 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 2.5000e-04 ## Epoch 204/500 ## 90/90 - 0s - 1ms/step - loss: 0.0508 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04 ## Epoch 205/500 ## 90/90 - 0s - 1ms/step - loss: 0.0542 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0996 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04 ## Epoch 206/500 ## 90/90 - 0s - 1ms/step - loss: 0.0559 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1172 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 2.5000e-04 ## Epoch 207/500 ## 90/90 - 0s - 2ms/step - loss: 0.0570 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 2.5000e-04 ## Epoch 208/500 ## 90/90 - 0s - 2ms/step - loss: 0.0521 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1000 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 209/500 ## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1286 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04 ## Epoch 210/500 ## 90/90 - 0s - 1ms/step - loss: 0.0541 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 211/500 ## 90/90 - 0s - 1ms/step - loss: 0.0509 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0993 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 212/500 ## 90/90 - 0s - 1ms/step - loss: 0.0503 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04 ## Epoch 213/500 ## 90/90 - 0s - 2ms/step - loss: 0.0534 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0968 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 214/500 ## 90/90 - 0s - 1ms/step - loss: 0.0532 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04 ## Epoch 215/500 ## 90/90 - 0s - 1ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1013 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04 ## Epoch 216/500 ## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1244 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04 ## Epoch 217/500 ## 90/90 - 0s - 2ms/step - loss: 0.0495 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.1141 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.2500e-04 ## Epoch 218/500 ## 90/90 - 0s - 2ms/step - loss: 0.0547 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04 ## Epoch 219/500 ## 90/90 - 0s - 2ms/step - loss: 0.0480 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1387 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.2500e-04 ## Epoch 220/500 ## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1161 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 221/500 ## 90/90 - 0s - 1ms/step - loss: 0.0516 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.1338 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04 ## Epoch 222/500 ## 90/90 - 0s - 1ms/step - loss: 0.0525 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.0995 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 223/500 ## 90/90 - 0s - 1ms/step - loss: 0.0504 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04 ## Epoch 224/500 ## 90/90 - 0s - 1ms/step - loss: 0.0471 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0994 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04 ## Epoch 225/500 ## 90/90 - 0s - 1ms/step - loss: 0.0536 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1145 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04 ## Epoch 226/500 ## 90/90 - 0s - 1ms/step - loss: 0.0487 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1047 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.2500e-04 ## Epoch 227/500 ## 90/90 - 0s - 1ms/step - loss: 0.0529 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.1087 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04 ## Epoch 228/500 ## 90/90 - 0s - 1ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 229/500 ## 90/90 - 0s - 1ms/step - loss: 0.0499 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1064 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04 ## Epoch 230/500 ## 90/90 - 0s - 2ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0967 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04 ## Epoch 231/500 ## 90/90 - 0s - 1ms/step - loss: 0.0527 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1730 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 1.2500e-04 ## Epoch 232/500 ## 90/90 - 0s - 1ms/step - loss: 0.0496 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1109 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.2500e-04 ## Epoch 233/500 ## 90/90 - 0s - 2ms/step - loss: 0.0483 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0963 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 234/500 ## 90/90 - 0s - 1ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1158 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 235/500 ## 90/90 - 0s - 1ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1057 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04 ## Epoch 236/500 ## 90/90 - 0s - 1ms/step - loss: 0.0502 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0982 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04 ## Epoch 237/500 ## 90/90 - 0s - 1ms/step - loss: 0.0501 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 238/500 ## 90/90 - 0s - 1ms/step - loss: 0.0487 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1031 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.2500e-04 ## Epoch 239/500 ## 90/90 - 0s - 1ms/step - loss: 0.0491 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1139 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 1.2500e-04 ## Epoch 240/500 ## 90/90 - 0s - 2ms/step - loss: 0.0478 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0990 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04 ## Epoch 241/500 ## 90/90 - 0s - 2ms/step - loss: 0.0498 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0994 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04 ## Epoch 242/500 ## 90/90 - 0s - 2ms/step - loss: 0.0490 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1118 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04 ## Epoch 243/500 ## 90/90 - 0s - 2ms/step - loss: 0.0523 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1052 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 244/500 ## 90/90 - 0s - 2ms/step - loss: 0.0527 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1055 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 245/500 ## 90/90 - 0s - 2ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1045 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04 ## Epoch 246/500 ## 90/90 - 0s - 1ms/step - loss: 0.0518 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1214 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 1.2500e-04 ## Epoch 247/500 ## 90/90 - 0s - 1ms/step - loss: 0.0457 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0964 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 248/500 ## 90/90 - 0s - 1ms/step - loss: 0.0450 - sparse_categorical_accuracy: 0.9885 - val_loss: 0.1161 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04 ## Epoch 249/500 ## 90/90 - 0s - 1ms/step - loss: 0.0492 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1282 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04 ## Epoch 250/500 ## 90/90 - 0s - 2ms/step - loss: 0.0510 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0972 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 251/500 ## 90/90 - 0s - 1ms/step - loss: 0.0506 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0986 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 252/500 ## 90/90 - 0s - 2ms/step - loss: 0.0503 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.1075 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 253/500 ## 90/90 - 0s - 2ms/step - loss: 0.0507 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0999 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 254/500 ## 90/90 - 0s - 2ms/step - loss: 0.0478 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0971 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.0000e-04 ## Epoch 255/500 ## 90/90 - 0s - 2ms/step - loss: 0.0464 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0954 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 256/500 ## 90/90 - 0s - 2ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0954 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 257/500 ## 90/90 - 0s - 1ms/step - loss: 0.0472 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1047 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.0000e-04 ## Epoch 258/500 ## 90/90 - 0s - 1ms/step - loss: 0.0448 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 259/500 ## 90/90 - 0s - 1ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1245 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04 ## Epoch 260/500 ## 90/90 - 0s - 1ms/step - loss: 0.0454 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1113 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04 ## Epoch 261/500 ## 90/90 - 0s - 1ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1697 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 1.0000e-04 ## Epoch 262/500 ## 90/90 - 0s - 1ms/step - loss: 0.0450 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 263/500 ## 90/90 - 0s - 1ms/step - loss: 0.0439 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0975 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 264/500 ## 90/90 - 0s - 1ms/step - loss: 0.0498 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1111 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 265/500 ## 90/90 - 0s - 1ms/step - loss: 0.0523 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.0983 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 266/500 ## 90/90 - 0s - 1ms/step - loss: 0.0459 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0997 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 267/500 ## 90/90 - 0s - 1ms/step - loss: 0.0462 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1002 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 268/500 ## 90/90 - 0s - 1ms/step - loss: 0.0438 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.1058 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04 ## Epoch 269/500 ## 90/90 - 0s - 2ms/step - loss: 0.0491 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1289 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.0000e-04 ## Epoch 270/500 ## 90/90 - 0s - 2ms/step - loss: 0.0430 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.0000e-04 ## Epoch 271/500 ## 90/90 - 0s - 1ms/step - loss: 0.0454 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1096 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04 ## Epoch 272/500 ## 90/90 - 0s - 1ms/step - loss: 0.0455 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1262 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04 ## Epoch 273/500 ## 90/90 - 0s - 1ms/step - loss: 0.0469 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1129 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04 ## Epoch 274/500 ## 90/90 - 0s - 1ms/step - loss: 0.0429 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.1119 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 275/500 ## 90/90 - 0s - 1ms/step - loss: 0.0430 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1392 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04 ## Epoch 276/500 ## 90/90 - 0s - 1ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 277/500 ## 90/90 - 0s - 1ms/step - loss: 0.0457 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 278/500 ## 90/90 - 0s - 1ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1205 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 279/500 ## 90/90 - 0s - 1ms/step - loss: 0.0473 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0958 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 280/500 ## 90/90 - 0s - 2ms/step - loss: 0.0459 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0947 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 281/500 ## 90/90 - 0s - 1ms/step - loss: 0.0438 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1216 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04 ## Epoch 282/500 ## 90/90 - 0s - 1ms/step - loss: 0.0419 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.1008 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 283/500 ## 90/90 - 0s - 1ms/step - loss: 0.0481 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1129 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 284/500 ## 90/90 - 0s - 1ms/step - loss: 0.0480 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.1418 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04 ## Epoch 285/500 ## 90/90 - 0s - 1ms/step - loss: 0.0467 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1058 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 286/500 ## 90/90 - 0s - 1ms/step - loss: 0.0449 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1039 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 1.0000e-04 ## Epoch 287/500 ## 90/90 - 0s - 1ms/step - loss: 0.0450 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1015 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 288/500 ## 90/90 - 0s - 2ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1156 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 289/500 ## 90/90 - 0s - 1ms/step - loss: 0.0460 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1000 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04 ## Epoch 290/500 ## 90/90 - 0s - 1ms/step - loss: 0.0482 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0967 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 291/500 ## 90/90 - 0s - 1ms/step - loss: 0.0433 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.1002 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04 ## Epoch 292/500 ## 90/90 - 0s - 1ms/step - loss: 0.0506 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1102 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04 ## Epoch 293/500 ## 90/90 - 0s - 1ms/step - loss: 0.0473 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1139 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04 ## Epoch 294/500 ## 90/90 - 0s - 1ms/step - loss: 0.0459 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1005 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04 ## Epoch 295/500 ## 90/90 - 0s - 1ms/step - loss: 0.0502 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.0978 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 296/500 ## 90/90 - 0s - 1ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1006 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 ## Epoch 297/500 ## 90/90 - 0s - 1ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0978 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 298/500 ## 90/90 - 0s - 1ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0953 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 ## Epoch 299/500 ## 90/90 - 0s - 1ms/step - loss: 0.0410 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.1107 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 300/500 ## 90/90 - 0s - 1ms/step - loss: 0.0423 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.0000e-04 ## Epoch 301/500 ## 90/90 - 0s - 1ms/step - loss: 0.0457 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0983 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04 ## Epoch 302/500 ## 90/90 - 0s - 1ms/step - loss: 0.0422 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1233 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04 ## Epoch 303/500 ## 90/90 - 0s - 2ms/step - loss: 0.0417 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.1175 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04 ## Epoch 304/500 ## 90/90 - 0s - 2ms/step - loss: 0.0467 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1007 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 1.0000e-04 ## Epoch 305/500 ## 90/90 - 0s - 2ms/step - loss: 0.0466 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1058 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 306/500 ## 90/90 - 0s - 1ms/step - loss: 0.0402 - sparse_categorical_accuracy: 0.9899 - val_loss: 0.0999 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 ## Epoch 307/500 ## 90/90 - 0s - 2ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1130 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 1.0000e-04 ## Epoch 308/500 ## 90/90 - 0s - 1ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04 ## Epoch 309/500 ## 90/90 - 0s - 1ms/step - loss: 0.0472 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1179 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 310/500 ## 90/90 - 0s - 1ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 311/500 ## 90/90 - 0s - 1ms/step - loss: 0.0448 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1012 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04 ## Epoch 312/500 ## 90/90 - 0s - 1ms/step - loss: 0.0442 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1172 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04 ## Epoch 313/500 ## 90/90 - 0s - 2ms/step - loss: 0.0438 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0990 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 314/500 ## 90/90 - 0s - 2ms/step - loss: 0.0432 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 315/500 ## 90/90 - 0s - 1ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0980 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04 ## Epoch 316/500 ## 90/90 - 0s - 1ms/step - loss: 0.0429 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1030 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04 ## Epoch 317/500 ## 90/90 - 0s - 3ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1018 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04 ## Epoch 318/500 ## 90/90 - 0s - 2ms/step - loss: 0.0423 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1024 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 1.0000e-04 ## Epoch 319/500 ## 90/90 - 0s - 2ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1197 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 ## Epoch 320/500 ## 90/90 - 0s - 2ms/step - loss: 0.0405 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0994 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.0000e-04 ## Epoch 321/500 ## 90/90 - 0s - 2ms/step - loss: 0.0442 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.1499 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 1.0000e-04 ## Epoch 322/500 ## 90/90 - 0s - 2ms/step - loss: 0.0404 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1253 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04 ## Epoch 323/500 ## 90/90 - 0s - 1ms/step - loss: 0.0468 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1081 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 324/500 ## 90/90 - 0s - 1ms/step - loss: 0.0418 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1016 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04 ## Epoch 325/500 ## 90/90 - 0s - 1ms/step - loss: 0.0420 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1290 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 1.0000e-04 ## Epoch 326/500 ## 90/90 - 0s - 2ms/step - loss: 0.0436 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1270 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 1.0000e-04 ## Epoch 327/500 ## 90/90 - 0s - 1ms/step - loss: 0.0417 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1009 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 328/500 ## 90/90 - 0s - 2ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1141 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 ## Epoch 329/500 ## 90/90 - 0s - 2ms/step - loss: 0.0422 - sparse_categorical_accuracy: 0.9885 - val_loss: 0.1198 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04 ## Epoch 330/500 ## 90/90 - 0s - 3ms/step - loss: 0.0435 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0988 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 330: early stopping"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"evaluate-model-on-test-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Evaluate model on test data","title":"Timeseries classification from scratch","text":"","code":"model <- load_model(\"best_model.keras\")  results <- model |> evaluate(x_test, y_test) ## 42/42 - 1s - 15ms/step - loss: 0.0980 - sparse_categorical_accuracy: 0.9682 str(results) ## List of 2 ##  $ loss                       : num 0.098 ##  $ sparse_categorical_accuracy: num 0.968 cat(   \"Test accuracy: \", results$sparse_categorical_accuracy, \"\\n\",   \"Test loss: \", results$loss, \"\\n\",   sep = \"\" ) ## Test accuracy: 0.9681818 ## Test loss: 0.09795157"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"plot-the-models-training-history","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Plot the model’s training history","title":"Timeseries classification from scratch","text":"Plot just training validation accuracy: can see training accuracy reaches almost 0.95 100 epochs. However, observing validation accuracy can see network still needs training reaches almost 0.97 validation training accuracy 200 epochs. Beyond 200th epoch, continue training, validation accuracy start decreasing training accuracy continue increasing: model starts overfitting.","code":"plot(history) plot(history, metric = \"sparse_categorical_accuracy\") +   # scale x axis to actual number of epochs run before early stopping   ggplot2::xlim(0, length(history$metrics$loss))"},{"path":"https://keras.posit.co/articles/examples/vision/autoencoder.html","id":"introduction","dir":"Articles > Examples > Vision","previous_headings":"","what":"Introduction","title":"Convolutional autoencoder for image denoising","text":"example demonstrates implement deep convolutional autoencoder image denoising, mapping noisy digits images MNIST dataset clean digits images. implementation based original blog post titled Building Autoencoders Keras François Chollet.","code":""},{"path":"https://keras.posit.co/articles/examples/vision/autoencoder.html","id":"setup","dir":"Articles > Examples > Vision","previous_headings":"","what":"Setup","title":"Convolutional autoencoder for image denoising","text":"","code":"library(keras3)  # Normalizes the supplied array and reshapes it. preprocess <- function(array) {   array_reshape(array/255, c(dim(array)[1], 28, 28, 1)) }  # Adds random noise to each image in the supplied array. noise <- function(array) {   noise_factor <- 0.4   noisy_array <- array + noise_factor * random_normal(dim(array))   op_clip(noisy_array, 0.0, 1.0) }  display <- function(array1, array2) {   n <- 2   indices <- sample.int(dim(array1)[1], n)   images1 <- as.array(array1)[indices, , , ]   images2 <- as.array(array2)[indices, , , ]    par(mfrow = c(2, n), mar = c(0, 0, 0, 0))   for (i in seq_len(n)) {     plot(as.raster(images1[i, , ]))     plot(as.raster(images2[i, , ]))   } }"},{"path":"https://keras.posit.co/articles/examples/vision/autoencoder.html","id":"prepare-the-data","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare the data","title":"Convolutional autoencoder for image denoising","text":"","code":"# Since we only need images from the dataset to encode and decode, we # won't use the labels. c(c(train_data, .), c(test_data, .)) %<-% dataset_mnist()  # Normalize and reshape the data train_data <- preprocess(train_data) test_data <- preprocess(test_data)  # Create a copy of the data with added noise noisy_train_data <- noise(train_data) noisy_test_data <- noise(test_data)  # Display the train data and a version of it with added noise display(train_data, noisy_train_data)"},{"path":"https://keras.posit.co/articles/examples/vision/autoencoder.html","id":"build-the-autoencoder","dir":"Articles > Examples > Vision","previous_headings":"","what":"Build the autoencoder","title":"Convolutional autoencoder for image denoising","text":"going use Functional API build convolutional autoencoder. Now can train autoencoder using train_data input data target. Notice setting validation data using format. Let’s predict test dataset display original image together prediction autoencoder. Notice predictions pretty close original images, although quite . Now know autoencoder works, let’s retrain using noisy data input clean data target. want autoencoder learn denoise images. Let’s now predict noisy data display results autoencoder. Notice autoencoder amazing job removing noise input images.","code":"input <- keras_input(shape = c(28, 28, 1))  # Encoder enc <- input |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3),                 activation = \"relu\", padding = \"same\") |>   layer_max_pooling_2d(pool_size = c(2, 2), padding = \"same\") |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3),                 activation = \"relu\", padding = \"same\") |>   layer_max_pooling_2d(pool_size = c(2, 2), padding = \"same\")  # Decoder dec <- enc |>   layer_conv_2d_transpose(filters = 32, kernel_size = c(3, 3), strides = 2,                           activation = \"relu\", padding = \"same\") |>   layer_conv_2d_transpose(filters = 32, kernel_size = c(3, 3), strides = 2,                           activation = \"relu\", padding = \"same\") |>   layer_conv_2d(filters = 1, kernel_size = c(3, 3),                 activation = \"sigmoid\", padding = \"same\")  # Autoencoder autoencoder <- keras_model(input, dec) autoencoder |> compile(optimizer = \"adam\", loss = \"binary_crossentropy\") autoencoder |> summary() ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 28, 28, 32)     │           320 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 14, 14, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 14, 14, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 7, 7, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 14, 14, 32)     │         9,248 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 28, 28, 32)     │         9,248 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 28, 28, 1)      │           289 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 28,353 (110.75 KB) ##  Trainable params: 28,353 (110.75 KB) ##  Non-trainable params: 0 (0.00 B) autoencoder |> fit(   x = train_data,   y = train_data,   epochs = 50,   batch_size = 128,   shuffle = TRUE,   validation_data = list(test_data, test_data), ) ## Epoch 1/50 ## 469/469 - 5s - 10ms/step - loss: 0.1371 - val_loss: 0.0727 ## Epoch 2/50 ## 469/469 - 1s - 3ms/step - loss: 0.0712 - val_loss: 0.0692 ## Epoch 3/50 ## 469/469 - 1s - 3ms/step - loss: 0.0690 - val_loss: 0.0679 ## Epoch 4/50 ## 469/469 - 1s - 3ms/step - loss: 0.0679 - val_loss: 0.0671 ## Epoch 5/50 ## 469/469 - 1s - 3ms/step - loss: 0.0672 - val_loss: 0.0665 ## Epoch 6/50 ## 469/469 - 1s - 3ms/step - loss: 0.0667 - val_loss: 0.0660 ## Epoch 7/50 ## 469/469 - 1s - 3ms/step - loss: 0.0662 - val_loss: 0.0656 ## Epoch 8/50 ## 469/469 - 1s - 3ms/step - loss: 0.0659 - val_loss: 0.0653 ## Epoch 9/50 ## 469/469 - 2s - 3ms/step - loss: 0.0656 - val_loss: 0.0650 ## Epoch 10/50 ## 469/469 - 2s - 3ms/step - loss: 0.0653 - val_loss: 0.0648 ## Epoch 11/50 ## 469/469 - 1s - 3ms/step - loss: 0.0651 - val_loss: 0.0646 ## Epoch 12/50 ## 469/469 - 1s - 3ms/step - loss: 0.0650 - val_loss: 0.0644 ## Epoch 13/50 ## 469/469 - 1s - 3ms/step - loss: 0.0648 - val_loss: 0.0643 ## Epoch 14/50 ## 469/469 - 2s - 3ms/step - loss: 0.0646 - val_loss: 0.0642 ## Epoch 15/50 ## 469/469 - 1s - 3ms/step - loss: 0.0645 - val_loss: 0.0640 ## Epoch 16/50 ## 469/469 - 1s - 3ms/step - loss: 0.0644 - val_loss: 0.0639 ## Epoch 17/50 ## 469/469 - 1s - 3ms/step - loss: 0.0643 - val_loss: 0.0638 ## Epoch 18/50 ## 469/469 - 1s - 3ms/step - loss: 0.0642 - val_loss: 0.0637 ## Epoch 19/50 ## 469/469 - 1s - 3ms/step - loss: 0.0641 - val_loss: 0.0637 ## Epoch 20/50 ## 469/469 - 1s - 3ms/step - loss: 0.0640 - val_loss: 0.0636 ## Epoch 21/50 ## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0635 ## Epoch 22/50 ## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0634 ## Epoch 23/50 ## 469/469 - 1s - 3ms/step - loss: 0.0638 - val_loss: 0.0634 ## Epoch 24/50 ## 469/469 - 1s - 3ms/step - loss: 0.0637 - val_loss: 0.0633 ## Epoch 25/50 ## 469/469 - 1s - 3ms/step - loss: 0.0637 - val_loss: 0.0633 ## Epoch 26/50 ## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0632 ## Epoch 27/50 ## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0632 ## Epoch 28/50 ## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631 ## Epoch 29/50 ## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631 ## Epoch 30/50 ## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631 ## Epoch 31/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0630 ## Epoch 32/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0630 ## Epoch 33/50 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0629 ## Epoch 34/50 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0629 ## Epoch 35/50 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0629 ## Epoch 36/50 ## 469/469 - 1s - 3ms/step - loss: 0.0632 - val_loss: 0.0629 ## Epoch 37/50 ## 469/469 - 1s - 3ms/step - loss: 0.0632 - val_loss: 0.0628 ## Epoch 38/50 ## 469/469 - 1s - 3ms/step - loss: 0.0632 - val_loss: 0.0628 ## Epoch 39/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0628 ## Epoch 40/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627 ## Epoch 41/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627 ## Epoch 42/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627 ## Epoch 43/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627 ## Epoch 44/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627 ## Epoch 45/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0626 ## Epoch 46/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0626 ## Epoch 47/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626 ## Epoch 48/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626 ## Epoch 49/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626 ## Epoch 50/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0625 predictions <- autoencoder |> predict(test_data) ## 313/313 - 1s - 2ms/step display(test_data, predictions) autoencoder |> fit(   x = noisy_train_data,   y = train_data,   epochs = 100,   batch_size = 128,   shuffle = TRUE,   validation_data = list(noisy_test_data, test_data), ) ## Epoch 1/100 ## 469/469 - 1s - 3ms/step - loss: 0.1006 - val_loss: 0.0941 ## Epoch 2/100 ## 469/469 - 1s - 3ms/step - loss: 0.0938 - val_loss: 0.0922 ## Epoch 3/100 ## 469/469 - 1s - 3ms/step - loss: 0.0922 - val_loss: 0.0910 ## Epoch 4/100 ## 469/469 - 1s - 3ms/step - loss: 0.0912 - val_loss: 0.0901 ## Epoch 5/100 ## 469/469 - 1s - 3ms/step - loss: 0.0905 - val_loss: 0.0895 ## Epoch 6/100 ## 469/469 - 1s - 3ms/step - loss: 0.0899 - val_loss: 0.0891 ## Epoch 7/100 ## 469/469 - 1s - 3ms/step - loss: 0.0895 - val_loss: 0.0887 ## Epoch 8/100 ## 469/469 - 1s - 3ms/step - loss: 0.0891 - val_loss: 0.0883 ## Epoch 9/100 ## 469/469 - 1s - 3ms/step - loss: 0.0888 - val_loss: 0.0881 ## Epoch 10/100 ## 469/469 - 1s - 3ms/step - loss: 0.0885 - val_loss: 0.0878 ## Epoch 11/100 ## 469/469 - 1s - 3ms/step - loss: 0.0882 - val_loss: 0.0876 ## Epoch 12/100 ## 469/469 - 1s - 3ms/step - loss: 0.0880 - val_loss: 0.0874 ## Epoch 13/100 ## 469/469 - 1s - 3ms/step - loss: 0.0878 - val_loss: 0.0873 ## Epoch 14/100 ## 469/469 - 1s - 3ms/step - loss: 0.0877 - val_loss: 0.0871 ## Epoch 15/100 ## 469/469 - 1s - 3ms/step - loss: 0.0875 - val_loss: 0.0869 ## Epoch 16/100 ## 469/469 - 1s - 3ms/step - loss: 0.0874 - val_loss: 0.0868 ## Epoch 17/100 ## 469/469 - 1s - 3ms/step - loss: 0.0872 - val_loss: 0.0867 ## Epoch 18/100 ## 469/469 - 1s - 3ms/step - loss: 0.0871 - val_loss: 0.0866 ## Epoch 19/100 ## 469/469 - 1s - 3ms/step - loss: 0.0870 - val_loss: 0.0865 ## Epoch 20/100 ## 469/469 - 1s - 3ms/step - loss: 0.0869 - val_loss: 0.0864 ## Epoch 21/100 ## 469/469 - 1s - 3ms/step - loss: 0.0868 - val_loss: 0.0863 ## Epoch 22/100 ## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0862 ## Epoch 23/100 ## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0861 ## Epoch 24/100 ## 469/469 - 1s - 3ms/step - loss: 0.0866 - val_loss: 0.0861 ## Epoch 25/100 ## 469/469 - 2s - 4ms/step - loss: 0.0865 - val_loss: 0.0860 ## Epoch 26/100 ## 469/469 - 2s - 4ms/step - loss: 0.0865 - val_loss: 0.0860 ## Epoch 27/100 ## 469/469 - 2s - 4ms/step - loss: 0.0864 - val_loss: 0.0859 ## Epoch 28/100 ## 469/469 - 2s - 4ms/step - loss: 0.0863 - val_loss: 0.0858 ## Epoch 29/100 ## 469/469 - 1s - 3ms/step - loss: 0.0863 - val_loss: 0.0858 ## Epoch 30/100 ## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0858 ## Epoch 31/100 ## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0857 ## Epoch 32/100 ## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0857 ## Epoch 33/100 ## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0856 ## Epoch 34/100 ## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0856 ## Epoch 35/100 ## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0855 ## Epoch 36/100 ## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0855 ## Epoch 37/100 ## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0855 ## Epoch 38/100 ## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0855 ## Epoch 39/100 ## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0854 ## Epoch 40/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854 ## Epoch 41/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854 ## Epoch 42/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854 ## Epoch 43/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0853 ## Epoch 44/100 ## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853 ## Epoch 45/100 ## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853 ## Epoch 46/100 ## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853 ## Epoch 47/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852 ## Epoch 48/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852 ## Epoch 49/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852 ## Epoch 50/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852 ## Epoch 51/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851 ## Epoch 52/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851 ## Epoch 53/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851 ## Epoch 54/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851 ## Epoch 55/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0851 ## Epoch 56/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850 ## Epoch 57/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850 ## Epoch 58/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850 ## Epoch 59/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850 ## Epoch 60/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0850 ## Epoch 61/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0850 ## Epoch 62/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849 ## Epoch 63/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849 ## Epoch 64/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849 ## Epoch 65/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849 ## Epoch 66/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849 ## Epoch 67/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849 ## Epoch 68/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849 ## Epoch 69/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849 ## Epoch 70/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848 ## Epoch 71/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848 ## Epoch 72/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 73/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 74/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 75/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 76/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 77/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 78/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847 ## Epoch 79/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847 ## Epoch 80/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 81/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 82/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 83/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 84/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 85/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 86/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 87/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 88/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846 ## Epoch 89/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 90/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 91/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 92/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 93/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 94/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 95/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 96/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 97/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 98/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 99/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 100/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 predictions <- autoencoder |> predict(noisy_test_data) ## 313/313 - 0s - 673us/step display(noisy_test_data, predictions)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"setup","dir":"Articles > Examples > Vision","previous_headings":"","what":"Setup","title":"Simple MNIST convnet","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"prepare-the-data","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare the data","title":"Simple MNIST convnet","text":"","code":"# Model / data parameters num_classes <- 10 input_shape <- c(28, 28, 1)  # Load the data and split it between train and test sets c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()  # Scale images to the [0, 1] range x_train <- x_train / 255 x_test <- x_test / 255 # Make sure images have shape (28, 28, 1) x_train <- op_expand_dims(x_train, -1) x_test <- op_expand_dims(x_test, -1)   dim(x_train) ## [1] 60000    28    28     1 dim(x_test) ## [1] 10000    28    28     1 # convert class vectors to binary class matrices y_train <- to_categorical(y_train, num_classes) y_test <- to_categorical(y_test, num_classes)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"build-the-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Build the model","title":"Simple MNIST convnet","text":"","code":"model <- keras_model_sequential(input_shape = input_shape) model |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_flatten() |>   layer_dropout(rate = 0.5) |>   layer_dense(units = num_classes, activation = \"softmax\")  summary(model) ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 26, 26, 32)     │           320 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 13, 13, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 11, 11, 64)     │        18,496 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 5, 5, 64)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ flatten (Flatten)               │ (None, 1600)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 1600)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 10)             │        16,010 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 34,826 (136.04 KB) ##  Trainable params: 34,826 (136.04 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"train-the-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Train the model","title":"Simple MNIST convnet","text":"","code":"batch_size <- 128 epochs <- 15  model |> compile(   loss = \"categorical_crossentropy\",   optimizer = \"adam\",   metrics = \"accuracy\" )  model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = epochs,   validation_split = 0.1 ) ## Epoch 1/15 ## 422/422 - 5s - 11ms/step - accuracy: 0.8895 - loss: 0.3636 - val_accuracy: 0.9787 - val_loss: 0.0792 ## Epoch 2/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9664 - loss: 0.1111 - val_accuracy: 0.9850 - val_loss: 0.0550 ## Epoch 3/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9743 - loss: 0.0824 - val_accuracy: 0.9882 - val_loss: 0.0441 ## Epoch 4/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9786 - loss: 0.0695 - val_accuracy: 0.9895 - val_loss: 0.0400 ## Epoch 5/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9804 - loss: 0.0626 - val_accuracy: 0.9900 - val_loss: 0.0355 ## Epoch 6/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9824 - loss: 0.0558 - val_accuracy: 0.9912 - val_loss: 0.0333 ## Epoch 7/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9835 - loss: 0.0501 - val_accuracy: 0.9918 - val_loss: 0.0310 ## Epoch 8/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9851 - loss: 0.0479 - val_accuracy: 0.9922 - val_loss: 0.0310 ## Epoch 9/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9862 - loss: 0.0444 - val_accuracy: 0.9920 - val_loss: 0.0300 ## Epoch 10/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9863 - loss: 0.0438 - val_accuracy: 0.9912 - val_loss: 0.0294 ## Epoch 11/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9873 - loss: 0.0394 - val_accuracy: 0.9913 - val_loss: 0.0304 ## Epoch 12/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9875 - loss: 0.0371 - val_accuracy: 0.9927 - val_loss: 0.0287 ## Epoch 13/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9891 - loss: 0.0346 - val_accuracy: 0.9920 - val_loss: 0.0292 ## Epoch 14/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9891 - loss: 0.0343 - val_accuracy: 0.9922 - val_loss: 0.0284 ## Epoch 15/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9895 - loss: 0.0320 - val_accuracy: 0.9920 - val_loss: 0.0282"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"evaluate-the-trained-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Evaluate the trained model","title":"Simple MNIST convnet","text":"","code":"score <- model |> evaluate(x_test, y_test, verbose = 0) score ## $accuracy ## [1] 0.9914 ## ## $loss ## [1] 0.02402576"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"introduction","dir":"Articles > Examples > Vision","previous_headings":"","what":"Introduction","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"Siamese Networks neural networks share weights two sister networks, producing embedding vectors respective inputs. supervised similarity learning, networks trained maximize contrast (distance) embeddings inputs different classes, minimizing distance embeddings similar classes, resulting embedding spaces reflect class segmentation training inputs. implementation loosely follows Hadsell-et-al.’06 [1] (see paper mode details) euclidean distance replaced subtraction layer one fully-connect (FC) layer. [1] “Dimensionality Reduction Learning Invariant Mapping” https://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf Gets 98.11% test accuracy 20 epochs. 3 seconds per epoch AMD Ryzen 7 PRO 4750U (CPU)","code":"library(keras3) contrastive_loss <- function(y_true, y_pred) {     # Contrastive loss from Hadsell-et-al.'06     # https://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf     margin = 1     margin_square = op_square(op_maximum(margin - (y_pred), 0))     op_mean((1 - y_true) * op_square(y_pred) + (y_true) * margin_square) }"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"create-pairs-of-images","dir":"Articles > Examples > Vision","previous_headings":"","what":"Create pairs of images","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"train model differentiate digits different classes. example, digit 0 needs differentiated rest digits (1 9), digit 1 - 0 2 9, . carry , select N random images class (example, digit 0) pair N random images another class B (example, digit 1). , can repeat process classes digits (digit 9). paired digit 0 digits, can repeat process remaining classes rest digits (1 9).","code":"create_pairs <- function(x, y) {     # Positive and negative pair creation.     # Alternates between positive and negative pairs.     digit_indices <- tapply(1:length(y), y, list)     y1 <- y     y2 <- sapply(y, function(a) sample(0:9,1,prob=0.1+0.8*(0:9==a)))     idx1 <- 1:nrow(x)     idx2 <- sapply(as.character(y2), function(a) sample(digit_indices[[a]],1))     is_same  <- 1*(y1==y2)     list(pair1 = x[idx1,], pair2 = x[idx2,], y = is_same) }  compute_accuracy <- function(predictions, labels) {     # Compute classification accuracy with a fixed threshold on distances.     mean(labels[predictions > 0.5]) } # the data, shuffled and split between train and test sets mnist   <- dataset_mnist() x_train <- mnist$train$x y_train <- mnist$train$y x_test  <- mnist$test$x y_test  <- mnist$test$y x_train <- array_reshape(x_train, c(nrow(x_train), 784)) x_test  <- array_reshape(x_test, c(nrow(x_test), 784)) x_train <- x_train / 255 x_test  <- x_test / 255  # create training+test positive and negative pairs tr <- create_pairs(x_train, y_train) te <- create_pairs(x_test,  y_test)  names(tr) ## [1] \"pair1\" \"pair2\" \"y\""},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"network-definition","dir":"Articles > Examples > Vision","previous_headings":"","what":"Network definition","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"","code":"# input layers input_dim = 784 input_1 <- layer_input(shape = c(input_dim)) input_2 <- layer_input(shape = c(input_dim))  # definition of the base network that will be shared base_network <- keras_model_sequential() %>%     layer_dense(units = 128, activation = 'relu') %>%     layer_dropout(rate = 0.1) %>%     layer_dense(units = 128, activation = 'relu') %>%     layer_dropout(rate = 0.1) %>%     layer_dense(units = 128, activation = 'relu')  # because we re-use the same instance `base_network`, the weights of # the network will be shared across the two branches branch_1 <- base_network(input_1) branch_2 <- base_network(input_2)  # merging layer out <- layer_subtract(list(branch_1, branch_2)) %>%     layer_dropout(rate = 0.1) %>%     layer_dense(units = 16, activation = 'relu') %>%     layer_dense(1, activation = \"sigmoid\")  # create and compile model model <- keras_model(list(input_1, input_2), out)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"train","dir":"Articles > Examples > Vision","previous_headings":"","what":"Train","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"","code":"model %>% compile(     optimizer = \"rmsprop\",     #loss = \"binary_crossentropy\",     loss = contrastive_loss,     metrics = metric_binary_accuracy )  history <- model %>% fit(     list(tr$pair1, tr$pair2), tr$y,     batch_size = 128,     epochs = 20,     validation_data = list(         list(te$pair1, te$pair2),         te$y     ) ) ## Epoch 1/20 ## 469/469 - 17s - 36ms/step - binary_accuracy: 0.7566 - loss: 0.1644 - val_binary_accuracy: 0.8753 - val_loss: 0.1001 ## Epoch 2/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.8896 - loss: 0.0867 - val_binary_accuracy: 0.9248 - val_loss: 0.0614 ## Epoch 3/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9261 - loss: 0.0579 - val_binary_accuracy: 0.9409 - val_loss: 0.0461 ## Epoch 4/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9474 - loss: 0.0419 - val_binary_accuracy: 0.9529 - val_loss: 0.0382 ## Epoch 5/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9589 - loss: 0.0328 - val_binary_accuracy: 0.9603 - val_loss: 0.0316 ## Epoch 6/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9660 - loss: 0.0272 - val_binary_accuracy: 0.9617 - val_loss: 0.0297 ## Epoch 7/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9708 - loss: 0.0234 - val_binary_accuracy: 0.9665 - val_loss: 0.0270 ## Epoch 8/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9747 - loss: 0.0201 - val_binary_accuracy: 0.9674 - val_loss: 0.0259 ## Epoch 9/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9787 - loss: 0.0175 - val_binary_accuracy: 0.9697 - val_loss: 0.0247 ## Epoch 10/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9807 - loss: 0.0157 - val_binary_accuracy: 0.9684 - val_loss: 0.0251 ## Epoch 11/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9810 - loss: 0.0152 - val_binary_accuracy: 0.9710 - val_loss: 0.0230 ## Epoch 12/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9831 - loss: 0.0138 - val_binary_accuracy: 0.9726 - val_loss: 0.0224 ## Epoch 13/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9854 - loss: 0.0121 - val_binary_accuracy: 0.9724 - val_loss: 0.0224 ## Epoch 14/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9861 - loss: 0.0114 - val_binary_accuracy: 0.9738 - val_loss: 0.0217 ## Epoch 15/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9864 - loss: 0.0110 - val_binary_accuracy: 0.9738 - val_loss: 0.0213 ## Epoch 16/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9881 - loss: 0.0101 - val_binary_accuracy: 0.9754 - val_loss: 0.0206 ## Epoch 17/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9883 - loss: 0.0097 - val_binary_accuracy: 0.9733 - val_loss: 0.0214 ## Epoch 18/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9881 - loss: 0.0097 - val_binary_accuracy: 0.9723 - val_loss: 0.0216 ## Epoch 19/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9891 - loss: 0.0090 - val_binary_accuracy: 0.9749 - val_loss: 0.0202 ## Epoch 20/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9898 - loss: 0.0085 - val_binary_accuracy: 0.9752 - val_loss: 0.0205 plot(history)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"evaluate","dir":"Articles > Examples > Vision","previous_headings":"","what":"Evaluate","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"","code":"# compute final accuracy on training and test sets  tr_pred <- predict(model, list(tr$pair1, tr$pair2))[,1] ## 1875/1875 - 1s - 793us/step tr_acc  <- compute_accuracy(tr_pred, tr$y) te_pred <- predict(model, list(te$pair1, te$pair2))[,1] ## 313/313 - 0s - 1ms/step te_acc  <- compute_accuracy(te_pred, te$y)  sprintf('* Accuracy on training set: %0.2f%%', (100 * tr_acc)) ## [1] \"* Accuracy on training set: 99.63%\" sprintf('* Accuracy on test set: %0.2f%%', (100 * te_acc)) ## [1] \"* Accuracy on test set: 97.93%\""},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"plots","dir":"Articles > Examples > Vision","previous_headings":"","what":"Plots","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"","code":"par(mfrow=c(1,1)) vioplot::vioplot( te_pred ~ te$y ) i=3 visualizePair <- function(i) {     image(rbind(matrix( te$pair1[i,],28,28)[,28:1], matrix( te$pair2[i,],28,28)[,28:1]))     title(paste(\"true:\", te$y[i],\"|  pred:\", round(te_pred[i],5))) } par(mfrow=c(3,3)) lapply(1:9, visualizePair) ## [[1]] ## NULL ## ## [[2]] ## NULL ## ## [[3]] ## NULL ## ## [[4]] ## NULL ## ## [[5]] ## NULL ## ## [[6]] ## NULL ## ## [[7]] ## NULL ## ## [[8]] ## NULL ## ## [[9]] ## NULL"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"download-the-data","dir":"Articles > Examples > Vision","previous_headings":"","what":"Download the data","title":"Image segmentation with a U-Net-like architecture","text":"","code":"options(timeout = 5000) download.file(   \"https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\",   \"datasets/images.tar.gz\" ) download.file(   \"https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\",   \"datasets/annotations.tar.gz\" )  untar(\"datasets/images.tar.gz\", exdir = \"datasets\") untar(\"datasets/annotations.tar.gz\", exdir = \"datasets\")"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"prepare-paths-of-input-images-and-target-segmentation-masks","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare paths of input images and target segmentation masks","title":"Image segmentation with a U-Net-like architecture","text":"","code":"library(keras3) input_dir <- \"datasets/images/\" target_dir <- \"datasets/annotations/trimaps/\" img_size <- c(160, 160) num_classes <- 3 batch_size <- 32  input_img_paths <- fs::dir_ls(input_dir, glob = \"*.jpg\") |> sort() target_img_paths <- fs::dir_ls(target_dir, glob = \"*.png\") |> sort()  cat(\"Number of samples:\", length(input_img_paths), \"\\n\") ## Number of samples: 7390 for (i in 1:10) {   cat(input_img_paths[i], \"|\", target_img_paths[i], \"\\n\") } ## datasets/images/Abyssinian_1.jpg | datasets/annotations/trimaps/Abyssinian_1.png ## datasets/images/Abyssinian_10.jpg | datasets/annotations/trimaps/Abyssinian_10.png ## datasets/images/Abyssinian_100.jpg | datasets/annotations/trimaps/Abyssinian_100.png ## datasets/images/Abyssinian_101.jpg | datasets/annotations/trimaps/Abyssinian_101.png ## datasets/images/Abyssinian_102.jpg | datasets/annotations/trimaps/Abyssinian_102.png ## datasets/images/Abyssinian_103.jpg | datasets/annotations/trimaps/Abyssinian_103.png ## datasets/images/Abyssinian_104.jpg | datasets/annotations/trimaps/Abyssinian_104.png ## datasets/images/Abyssinian_105.jpg | datasets/annotations/trimaps/Abyssinian_105.png ## datasets/images/Abyssinian_106.jpg | datasets/annotations/trimaps/Abyssinian_106.png ## datasets/images/Abyssinian_107.jpg | datasets/annotations/trimaps/Abyssinian_107.png"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"what-does-one-input-image-and-corresponding-segmentation-mask-look-like","dir":"Articles > Examples > Vision","previous_headings":"","what":"What does one input image and corresponding segmentation mask look like?","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Display input image #10 input_img_paths[10] |>   jpeg::readJPEG() |>   as.raster() |>   plot() target_img_paths[10] |>   png::readPNG() |>   magrittr::multiply_by(255)|>   as.raster(max = 3) |>   plot()"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"prepare-dataset-to-load-vectorize-batches-of-data","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare dataset to load & vectorize batches of data","title":"Image segmentation with a U-Net-like architecture","text":"","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")   # Returns a tf_dataset get_dataset <- function(batch_size, img_size, input_img_paths, target_img_paths,                         max_dataset_len = NULL) {    img_size <- as.integer(img_size)    load_img_masks <- function(input_img_path, target_img_path) {     input_img <- input_img_path |>       tf$io$read_file() |>       tf$io$decode_jpeg(channels = 3) |>       tf$image$resize(img_size) |>       tf$image$convert_image_dtype(\"float32\")      target_img <- target_img_path |>       tf$io$read_file() |>       tf$io$decode_png(channels = 1) |>       tf$image$resize(img_size, method = \"nearest\") |>       tf$image$convert_image_dtype(\"uint8\")      # Ground truth labels are 1, 2, 3. Subtract one to make them 0, 1, 2:     target_img <- target_img - 1L      list(input_img, target_img)   }    if (!is.null(max_dataset_len)) {     input_img_paths <- input_img_paths[1:max_dataset_len]     target_img_paths <- target_img_paths[1:max_dataset_len]   }    list(input_img_paths, target_img_paths) |>     tensor_slices_dataset() |>     dataset_map(load_img_masks, num_parallel_calls = tf$data$AUTOTUNE)|>     dataset_batch(batch_size) }"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"prepare-u-net-xception-style-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare U-Net Xception-style model","title":"Image segmentation with a U-Net-like architecture","text":"","code":"get_model <- function(img_size, num_classes) {    inputs <- keras_input(shape = c(img_size, 3))    ### [First half of the network: downsampling inputs] ###    # Entry block   x <- inputs |>     layer_conv_2d(filters = 32, kernel_size = 3, strides = 2, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation(\"relu\")    previous_block_activation <- x  # Set aside residual    for (filters in c(64, 128, 256)) {     x <- x |>       layer_activation(\"relu\") |>       layer_separable_conv_2d(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_activation(\"relu\") |>       layer_separable_conv_2d(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_max_pooling_2d(pool_size = 3, strides = 2, padding = \"same\")      residual <- previous_block_activation |>       layer_conv_2d(filters = filters, kernel_size = 1, strides = 2, padding = \"same\")      x <- layer_add(x, residual)  # Add back residual     previous_block_activation <- x  # Set aside next residual   }    ### [Second half of the network: upsampling inputs] ###    for (filters in c(256, 128, 64, 32)) {     x <- x |>       layer_activation(\"relu\") |>       layer_conv_2d_transpose(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_activation(\"relu\") |>       layer_conv_2d_transpose(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_upsampling_2d(size = 2)      # Project residual     residual <- previous_block_activation |>       layer_upsampling_2d(size = 2) |>       layer_conv_2d(filters = filters, kernel_size = 1, padding = \"same\")      x <- layer_add(x, residual)     # Add back residual     previous_block_activation <- x  # Set aside next residual   }    # Add a per-pixel classification layer   outputs <- x |>     layer_conv_2d(num_classes, 3, activation = \"softmax\", padding = \"same\")    # Define the model   keras_model(inputs, outputs) }  # Build model model <- get_model(img_size, num_classes) summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)      ┃ Output Shape    ┃   Param # ┃ Connected to   ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer       │ (None, 160,     │         0 │ -              │   -   │ ## │ (InputLayer)      │ 160, 3)         │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d (Conv2D)   │ (None, 80, 80,  │       896 │ input_layer[0… │   Y   │ ## │                   │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d[0][0]   │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation        │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_1      │ (None, 80, 80,  │         0 │ activation[0]… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d  │ (None, 80, 80,  │     2,400 │ activation_1[… │   Y   │ ## │ (SeparableConv2D) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       256 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_2      │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 80, 80,  │     4,736 │ activation_2[… │   Y   │ ## │ (SeparableConv2D) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       256 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d     │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_1 (Conv2D) │ (None, 40, 40,  │     2,112 │ activation[0]… │   Y   │ ## │                   │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add (Add)         │ (None, 40, 40,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 64)             │           │ conv2d_1[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_3      │ (None, 40, 40,  │         0 │ add[0][0]      │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 40, 40,  │     8,896 │ activation_3[… │   Y   │ ## │ (SeparableConv2D) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       512 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_4      │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 40, 40,  │    17,664 │ activation_4[… │   Y   │ ## │ (SeparableConv2D) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       512 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d_1   │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_2 (Conv2D) │ (None, 20, 20,  │     8,320 │ add[0][0]      │   Y   │ ## │                   │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_1 (Add)       │ (None, 20, 20,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 128)            │           │ conv2d_2[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_5      │ (None, 20, 20,  │         0 │ add_1[0][0]    │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 20, 20,  │    34,176 │ activation_5[… │   Y   │ ## │ (SeparableConv2D) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │     1,024 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_6      │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 20, 20,  │    68,096 │ activation_6[… │   Y   │ ## │ (SeparableConv2D) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │     1,024 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d_2   │ (None, 10, 10,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_3 (Conv2D) │ (None, 10, 10,  │    33,024 │ add_1[0][0]    │   Y   │ ## │                   │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_2 (Add)       │ (None, 10, 10,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 256)            │           │ conv2d_3[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_7      │ (None, 10, 10,  │         0 │ add_2[0][0]    │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose  │ (None, 10, 10,  │   590,080 │ activation_7[… │   Y   │ ## │ (Conv2DTranspose) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 10, 10,  │     1,024 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_8      │ (None, 10, 10,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 10, 10,  │   590,080 │ activation_8[… │   Y   │ ## │ (Conv2DTranspose) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 10, 10,  │     1,024 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_1   │ (None, 20, 20,  │         0 │ add_2[0][0]    │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d     │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_4 (Conv2D) │ (None, 20, 20,  │    65,792 │ up_sampling2d… │   Y   │ ## │                   │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_3 (Add)       │ (None, 20, 20,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 256)            │           │ conv2d_4[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_9      │ (None, 20, 20,  │         0 │ add_3[0][0]    │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 20, 20,  │   295,040 │ activation_9[… │   Y   │ ## │ (Conv2DTranspose) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │       512 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_10     │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 20, 20,  │   147,584 │ activation_10… │   Y   │ ## │ (Conv2DTranspose) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │       512 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_3   │ (None, 40, 40,  │         0 │ add_3[0][0]    │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_2   │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_5 (Conv2D) │ (None, 40, 40,  │    32,896 │ up_sampling2d… │   Y   │ ## │                   │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_4 (Add)       │ (None, 40, 40,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 128)            │           │ conv2d_5[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_11     │ (None, 40, 40,  │         0 │ add_4[0][0]    │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 40, 40,  │    73,792 │ activation_11… │   Y   │ ## │ (Conv2DTranspose) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       256 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_12     │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 40, 40,  │    36,928 │ activation_12… │   Y   │ ## │ (Conv2DTranspose) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       256 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_5   │ (None, 80, 80,  │         0 │ add_4[0][0]    │   -   │ ## │ (UpSampling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_4   │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_6 (Conv2D) │ (None, 80, 80,  │     8,256 │ up_sampling2d… │   Y   │ ## │                   │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_5 (Add)       │ (None, 80, 80,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 64)             │           │ conv2d_6[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_13     │ (None, 80, 80,  │         0 │ add_5[0][0]    │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 80, 80,  │    18,464 │ activation_13… │   Y   │ ## │ (Conv2DTranspose) │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_14     │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 80, 80,  │     9,248 │ activation_14… │   Y   │ ## │ (Conv2DTranspose) │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_7   │ (None, 160,     │         0 │ add_5[0][0]    │   -   │ ## │ (UpSampling2D)    │ 160, 64)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_6   │ (None, 160,     │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 160, 32)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_7 (Conv2D) │ (None, 160,     │     2,080 │ up_sampling2d… │   Y   │ ## │                   │ 160, 32)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_6 (Add)       │ (None, 160,     │         0 │ up_sampling2d… │   -   │ ## │                   │ 160, 32)        │           │ conv2d_7[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_8 (Conv2D) │ (None, 160,     │       867 │ add_6[0][0]    │   Y   │ ## │                   │ 160, 3)         │           │                │       │ ## └───────────────────┴─────────────────┴───────────┴────────────────┴───────┘ ##  Total params: 2,058,979 (7.85 MB) ##  Trainable params: 2,055,203 (7.84 MB) ##  Non-trainable params: 3,776 (14.75 KB)"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"set-aside-a-validation-split","dir":"Articles > Examples > Vision","previous_headings":"","what":"Set aside a validation split","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Split our img paths into a training and a validation set val_samples <- 1000 val_samples <- sample.int(length(input_img_paths), val_samples)  train_input_img_paths <- input_img_paths[-val_samples] train_target_img_paths <- target_img_paths[-val_samples]  val_input_img_paths <- input_img_paths[val_samples] val_target_img_paths <- target_img_paths[val_samples]  # Instantiate dataset for each split # Limit input files in `max_dataset_len` for faster epoch training time. # Remove the `max_dataset_len` arg when running with full dataset. train_dataset <- get_dataset(   batch_size,   img_size,   train_input_img_paths,   train_target_img_paths,   max_dataset_len = 1000 ) valid_dataset <- get_dataset(   batch_size, img_size, val_input_img_paths, val_target_img_paths )"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"train-the-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Train the model","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Configure the model for training. # We use the \"sparse\" version of categorical_crossentropy # because our target data is integers. model |> compile(   optimizer = optimizer_adam(1e-4),   loss = \"sparse_categorical_crossentropy\" )  callbacks <- list(   callback_model_checkpoint(     \"models/oxford_segmentation.keras\", save_best_only = TRUE   ) )  # Train the model, doing validation at the end of each epoch. epochs <- 50 model |> fit(     train_dataset,     epochs=epochs,     validation_data=valid_dataset,     callbacks=callbacks,     verbose=2 ) ## Epoch 1/50 ## 32/32 - 29s - 914ms/step - loss: 1.4283 - val_loss: 1.5499 ## Epoch 2/50 ## 32/32 - 2s - 60ms/step - loss: 0.9221 - val_loss: 1.9887 ## Epoch 3/50 ## 32/32 - 2s - 60ms/step - loss: 0.7764 - val_loss: 2.5144 ## Epoch 4/50 ## 32/32 - 2s - 61ms/step - loss: 0.7200 - val_loss: 3.0185 ## Epoch 5/50 ## 32/32 - 2s - 61ms/step - loss: 0.6847 - val_loss: 3.2947 ## Epoch 6/50 ## 32/32 - 2s - 68ms/step - loss: 0.6556 - val_loss: 3.4559 ## Epoch 7/50 ## 32/32 - 2s - 76ms/step - loss: 0.6303 - val_loss: 3.5671 ## Epoch 8/50 ## 32/32 - 2s - 62ms/step - loss: 0.6083 - val_loss: 3.6620 ## Epoch 9/50 ## 32/32 - 2s - 63ms/step - loss: 0.5895 - val_loss: 3.7374 ## Epoch 10/50 ## 32/32 - 2s - 62ms/step - loss: 0.5726 - val_loss: 3.8015 ## Epoch 11/50 ## 32/32 - 2s - 63ms/step - loss: 0.5567 - val_loss: 3.8311 ## Epoch 12/50 ## 32/32 - 2s - 63ms/step - loss: 0.5407 - val_loss: 3.8089 ## Epoch 13/50 ## 32/32 - 2s - 62ms/step - loss: 0.5242 - val_loss: 3.7293 ## Epoch 14/50 ## 32/32 - 2s - 61ms/step - loss: 0.5062 - val_loss: 3.5953 ## Epoch 15/50 ## 32/32 - 2s - 61ms/step - loss: 0.4860 - val_loss: 3.4506 ## Epoch 16/50 ## 32/32 - 2s - 60ms/step - loss: 0.4637 - val_loss: 3.2588 ## Epoch 17/50 ## 32/32 - 2s - 61ms/step - loss: 0.4396 - val_loss: 3.0063 ## Epoch 18/50 ## 32/32 - 2s - 61ms/step - loss: 0.4140 - val_loss: 2.7031 ## Epoch 19/50 ## 32/32 - 2s - 61ms/step - loss: 0.3877 - val_loss: 2.3504 ## Epoch 20/50 ## 32/32 - 2s - 61ms/step - loss: 0.3623 - val_loss: 1.9645 ## Epoch 21/50 ## 32/32 - 2s - 62ms/step - loss: 0.3392 - val_loss: 1.6364 ## Epoch 22/50 ## 32/32 - 2s - 66ms/step - loss: 0.3203 - val_loss: 1.3376 ## Epoch 23/50 ## 32/32 - 2s - 67ms/step - loss: 0.3082 - val_loss: 1.0917 ## Epoch 24/50 ## 32/32 - 2s - 66ms/step - loss: 0.3084 - val_loss: 1.0189 ## Epoch 25/50 ## 32/32 - 2s - 66ms/step - loss: 0.3461 - val_loss: 0.9181 ## Epoch 26/50 ## 32/32 - 2s - 61ms/step - loss: 0.3649 - val_loss: 1.0231 ## Epoch 27/50 ## 32/32 - 2s - 66ms/step - loss: 0.3294 - val_loss: 0.8574 ## Epoch 28/50 ## 32/32 - 2s - 61ms/step - loss: 0.2891 - val_loss: 1.0065 ## Epoch 29/50 ## 32/32 - 2s - 61ms/step - loss: 0.2731 - val_loss: 1.1668 ## Epoch 30/50 ## 32/32 - 2s - 61ms/step - loss: 0.2681 - val_loss: 1.1974 ## Epoch 31/50 ## 32/32 - 2s - 61ms/step - loss: 0.2723 - val_loss: 1.1259 ## Epoch 32/50 ## 32/32 - 2s - 61ms/step - loss: 0.2872 - val_loss: 1.1112 ## Epoch 33/50 ## 32/32 - 2s - 61ms/step - loss: 0.3146 - val_loss: 1.2479 ## Epoch 34/50 ## 32/32 - 2s - 61ms/step - loss: 0.3034 - val_loss: 1.1946 ## Epoch 35/50 ## 32/32 - 2s - 61ms/step - loss: 0.2880 - val_loss: 1.0699 ## Epoch 36/50 ## 32/32 - 2s - 61ms/step - loss: 0.2770 - val_loss: 1.0195 ## Epoch 37/50 ## 32/32 - 2s - 61ms/step - loss: 0.2669 - val_loss: 1.2986 ## Epoch 38/50 ## 32/32 - 2s - 61ms/step - loss: 0.2544 - val_loss: 1.0411 ## Epoch 39/50 ## 32/32 - 2s - 61ms/step - loss: 0.2383 - val_loss: 1.3622 ## Epoch 40/50 ## 32/32 - 2s - 61ms/step - loss: 0.2378 - val_loss: 1.3429 ## Epoch 41/50 ## 32/32 - 2s - 61ms/step - loss: 0.2439 - val_loss: 1.2881 ## Epoch 42/50 ## 32/32 - 2s - 61ms/step - loss: 0.2406 - val_loss: 1.5361 ## Epoch 43/50 ## 32/32 - 2s - 61ms/step - loss: 0.2417 - val_loss: 1.2444 ## Epoch 44/50 ## 32/32 - 2s - 61ms/step - loss: 0.2275 - val_loss: 1.1713 ## Epoch 45/50 ## 32/32 - 2s - 61ms/step - loss: 0.2168 - val_loss: 1.0482 ## Epoch 46/50 ## 32/32 - 2s - 62ms/step - loss: 0.2096 - val_loss: 1.1095 ## Epoch 47/50 ## 32/32 - 2s - 61ms/step - loss: 0.2026 - val_loss: 1.1017 ## Epoch 48/50 ## 32/32 - 2s - 61ms/step - loss: 0.1986 - val_loss: 1.1087 ## Epoch 49/50 ## 32/32 - 2s - 61ms/step - loss: 0.1924 - val_loss: 1.1055 ## Epoch 50/50 ## 32/32 - 2s - 61ms/step - loss: 0.1846 - val_loss: 1.0708"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"visualize-predictions","dir":"Articles > Examples > Vision","previous_headings":"","what":"Visualize predictions","title":"Image segmentation with a U-Net-like architecture","text":"","code":"model <- load_model(\"models/oxford_segmentation.keras\") # Generate predictions for all images in the validation set val_dataset <- get_dataset(   batch_size, img_size, val_input_img_paths, val_target_img_paths ) val_preds <- predict(model, val_dataset) ## 32/32 - 3s - 86ms/step display_mask <- function(i) {   # Quick utility to display a model's prediction.   mask <- val_preds[i,,,] %>%     apply(c(1,2), which.max) %>%     array_reshape(dim = c(img_size, 1))   mask <- abind::abind(mask, mask, mask, along = 3)   plot(as.raster(mask, max = 3)) }  # Display results for validation image #10 i <- 10  par(mfrow = c(1, 3)) # Display input image input_img_paths[i] |>   jpeg::readJPEG() |>   as.raster() |>   plot()  # Display ground-truth target mask target_img_paths[i] |>   png::readPNG() |>   magrittr::multiply_by(255)|>   as.raster(max = 3) |>   plot()  # Display mask predicted by our model display_mask(i)  # Note that the model only sees inputs at 150x150."},{"path":"https://keras.posit.co/articles/functional_api.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"The Functional API","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"The Functional API","text":"Keras functional API way create models flexible sequential API. functional API can handle models non-linear topology, shared layers, even multiple inputs outputs. main idea deep learning model usually directed acyclic graph (DAG) layers. functional API way build graphs layers. Consider following model: basic graph three layers. build model using functional API, start creating input node: shape data set 784-dimensional vector. batch size always omitted since shape sample specified. , example, image input shape (32, 32, 3), use: inputs returned contains information shape dtype input data feed model. ’s shape: ’s dtype: create new node graph layers calling layer inputs object: “layer call” action like drawing arrow “inputs” layer created. ’re “passing” inputs dense layer, get x output. Let’s add layers graph layers: point, can create Model specifying inputs outputs graph layers: Let’s check model summary looks like: can also plot model graph:  , optionally, display input output shapes layer plotted graph:  figure code almost identical. code version, connection arrows replaced call operation. “graph layers” intuitive mental image deep learning model, functional API way create models closely mirrors .","code":"(input: 784-dimensional vectors)        ↧ [Dense (64 units, relu activation)]        ↧ [Dense (64 units, relu activation)]        ↧ [Dense (10 units, softmax activation)]        ↧ (output: logits of a probability distribution over 10 classes) inputs <- keras_input(shape = c(784)) # Just for demonstration purposes. img_inputs <- keras_input(shape = c(32, 32, 3)) shape(inputs) ## shape(NA, 784) inputs$dtype ## [1] \"float32\" dense <- layer_dense(units = 64, activation=\"relu\") x <- dense(inputs) outputs <- x |>   layer_dense(units = 64, activation = \"relu\") |>   layer_dense(units = 10) model <- keras_model(inputs = inputs, outputs = outputs, name = \"mnist_model\") summary(model) ## Model: \"mnist_model\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, 784)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 64)             │        50,240 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_1 (Dense)                 │ (None, 64)             │         4,160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 10)             │           650 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 55,050 (215.04 KB) ##  Trainable params: 55,050 (215.04 KB) ##  Non-trainable params: 0 (0.00 B) plot(model) plot(model, show_shapes = TRUE)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"training-evaluation-and-inference","dir":"Articles","previous_headings":"","what":"Training, evaluation, and inference","title":"The Functional API","text":"Training, evaluation, inference work exactly way models built using functional API Sequential models. Model class offers built-training loop (fit() method) built-evaluation loop (evaluate() method). Note can easily customize loops implement training routines beyond supervised learning (e.g. GANs). , load MNIST image data, reshape vectors, fit model data (monitoring performance validation split), evaluate model test data: reading, see training evaluation guide.","code":"c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()  x_train <- array_reshape(x_train, c(60000, 784)) / 255 x_test <- array_reshape(x_test, c(10000, 784)) / 255  model |> compile(   loss = loss_sparse_categorical_crossentropy(from_logits = TRUE),   optimizer = optimizer_rmsprop(),   metrics = \"accuracy\" )  history <- model |> fit(     x_train, y_train, batch_size = 64, epochs = 2, validation_split = 0.2 ) ## Epoch 1/2 ## 750/750 - 2s - 2ms/step - accuracy: 0.8979 - loss: 0.3540 - val_accuracy: 0.9448 - val_loss: 0.1903 ## Epoch 2/2 ## 750/750 - 1s - 773us/step - accuracy: 0.9511 - loss: 0.1634 - val_accuracy: 0.9605 - val_loss: 0.1386 test_scores <- model |> evaluate(x_test, y_test, verbose=2) ## 313/313 - 0s - 980us/step - accuracy: 0.9593 - loss: 0.1323 cat(\"Test loss:\", test_scores$loss, \"\\n\") cat(\"Test accuracy:\", test_scores$accuracy, \"\\n\") ## Test loss: 0.1323339 ## Test accuracy: 0.9593"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"save-and-serialize","dir":"Articles","previous_headings":"","what":"Save and serialize","title":"The Functional API","text":"Saving model serialization work way models built using functional API Sequential models. standard way save functional model call model.save() save entire model single file. can later recreate model file, even code built model longer available. saved file includes : - model architecture - model weight values (learned training) - model training config, (passed compile()) - optimizer state, (restart training left ) details, read model serialization & saving guide.","code":"model |> save_model(\"my_model.keras\") rm(model) # Recreate the exact same model purely from the file: model <- load_model(\"my_model.keras\")"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"use-the-same-graph-of-layers-to-define-multiple-models","dir":"Articles","previous_headings":"","what":"Use the same graph of layers to define multiple models","title":"The Functional API","text":"functional API, models created specifying inputs outputs graph layers. means single graph layers can used generate multiple models. example , use stack layers instantiate two models: encoder model turns image inputs 16-dimensional vectors, end--end autoencoder model training. , decoding architecture strictly symmetrical encoding architecture, output shape input shape (28, 28, 1). reverse conv_2d layer conv_2d_transpose layer, reverse max_pooling_2d layer upsampling_2d layer.","code":"encoder_input <- keras_input(shape = c(28, 28, 1), name=\"img\") encoder_output <- encoder_input |>   layer_conv_2d(16, 3, activation = \"relu\") |>   layer_conv_2d(32, 3, activation = \"relu\") |>   layer_max_pooling_2d(3) |>   layer_conv_2d(32, 3, activation = \"relu\") |>   layer_conv_2d(16, 3, activation = \"relu\") |>   layer_global_max_pooling_2d()  encoder <- keras_model(encoder_input, encoder_output, name=\"encoder\") summary(encoder) ## Model: \"encoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)                │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 26, 26, 16)     │           160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 24, 24, 32)     │         4,640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 8, 8, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 6, 6, 32)       │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_3 (Conv2D)               │ (None, 4, 4, 16)       │         4,624 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d            │ (None, 16)             │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 18,672 (72.94 KB) ##  Trainable params: 18,672 (72.94 KB) ##  Non-trainable params: 0 (0.00 B) decoder_output <- encoder_output |>   layer_reshape(c(4, 4, 1)) |>   layer_conv_2d_transpose(16, 3, activation = \"relu\") |>   layer_conv_2d_transpose(32, 3, activation = \"relu\") |>   layer_upsampling_2d(3) |>   layer_conv_2d_transpose(16, 3, activation = \"relu\") |>   layer_conv_2d_transpose(1, 3, activation = \"relu\")  autoencoder <- keras_model(encoder_input, decoder_output, name=\"autoencoder\") summary(autoencoder) ## Model: \"autoencoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)                │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 26, 26, 16)     │           160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 24, 24, 32)     │         4,640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 8, 8, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 6, 6, 32)       │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_3 (Conv2D)               │ (None, 4, 4, 16)       │         4,624 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d            │ (None, 16)             │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ reshape (Reshape)               │ (None, 4, 4, 1)        │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 6, 6, 16)       │           160 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 8, 8, 32)       │         4,640 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ up_sampling2d (UpSampling2D)    │ (None, 24, 24, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_2              │ (None, 26, 26, 16)     │         4,624 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_3              │ (None, 28, 28, 1)      │           145 │ ## │ (Conv2DTranspose)               │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 28,241 (110.32 KB) ##  Trainable params: 28,241 (110.32 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"all-models-are-callable-just-like-layers","dir":"Articles","previous_headings":"","what":"All models are callable, just like layers","title":"The Functional API","text":"can treat model layer invoking Input output another layer. calling model aren’t just reusing architecture model, ’re also reusing weights. see action, ’s different take autoencoder example creates encoder model, decoder model, chains two calls obtain autoencoder model: can see, model can nested: model can contain sub-models (since model just like layer). common use case model nesting ensembling. example, ’s ensemble set models single model averages predictions:","code":"encoder_input <- keras_input(shape = c(28, 28, 1), name=\"img\") encoder_output <- encoder_input |>   layer_conv_2d(16, 3, activation = \"relu\") |>   layer_conv_2d(32, 3, activation = \"relu\") |>   layer_max_pooling_2d(3) |>   layer_conv_2d(32, 3, activation = \"relu\") |>   layer_conv_2d(16, 3, activation = \"relu\") |>   layer_global_max_pooling_2d()  encoder <- keras_model(encoder_input, encoder_output, name=\"encoder\") summary(encoder) ## Model: \"encoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)                │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_4 (Conv2D)               │ (None, 26, 26, 16)     │           160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_5 (Conv2D)               │ (None, 24, 24, 32)     │         4,640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 8, 8, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_6 (Conv2D)               │ (None, 6, 6, 32)       │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_7 (Conv2D)               │ (None, 4, 4, 16)       │         4,624 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d_1          │ (None, 16)             │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 18,672 (72.94 KB) ##  Trainable params: 18,672 (72.94 KB) ##  Non-trainable params: 0 (0.00 B) decoder_input <- keras_input(shape = c(16), name = \"encoded_img\") decoder_output <- decoder_input |>   layer_reshape(c(4, 4, 1)) |>   layer_conv_2d_transpose(16, 3, activation = \"relu\") |>   layer_conv_2d_transpose(32, 3, activation = \"relu\") |>   layer_upsampling_2d(3) |>   layer_conv_2d_transpose(16, 3, activation = \"relu\") |>   layer_conv_2d_transpose(1, 3, activation = \"relu\")  decoder <- keras_model(decoder_input, decoder_output, name = \"decoder\") summary(decoder) ## Model: \"decoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ encoded_img (InputLayer)        │ (None, 16)             │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ reshape_1 (Reshape)             │ (None, 4, 4, 1)        │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_4              │ (None, 6, 6, 16)       │           160 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_5              │ (None, 8, 8, 32)       │         4,640 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ up_sampling2d_1 (UpSampling2D)  │ (None, 24, 24, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_6              │ (None, 26, 26, 16)     │         4,624 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_7              │ (None, 28, 28, 1)      │           145 │ ## │ (Conv2DTranspose)               │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 9,569 (37.38 KB) ##  Trainable params: 9,569 (37.38 KB) ##  Non-trainable params: 0 (0.00 B) autoencoder_input <- keras_input(shape = c(28, 28, 1), name = \"img\") encoded_img <- encoder(autoencoder_input) decoded_img <- decoder(encoded_img) autoencoder <- keras_model(autoencoder_input, decoded_img,                            name = \"autoencoder\") summary(autoencoder) ## Model: \"autoencoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)                │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ encoder (Functional)            │ (None, 16)             │        18,672 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ decoder (Functional)            │ (None, 28, 28, 1)      │         9,569 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 28,241 (110.32 KB) ##  Trainable params: 28,241 (110.32 KB) ##  Non-trainable params: 0 (0.00 B) get_model <- function() {   inputs <- keras_input(shape = 128)   outputs <- inputs |> layer_dense(1)   keras_model(inputs, outputs) }  model1 <- get_model() model2 <- get_model() model3 <- get_model()  inputs <- keras_input(shape = 128) y1 <- model1(inputs) y2 <- model2(inputs) y3 <- model3(inputs) outputs <- layer_average(list(y1, y2, y3)) ensemble_model <- keras_model(inputs = inputs, outputs = outputs)"},{"path":[]},{"path":"https://keras.posit.co/articles/functional_api.html","id":"models-with-multiple-inputs-and-outputs","dir":"Articles","previous_headings":"Manipulate complex graph topologies","what":"Models with multiple inputs and outputs","title":"The Functional API","text":"functional API makes easy manipulate multiple inputs outputs. handled Sequential API. example, ’re building system ranking customer issue tickets priority routing correct department, model three inputs: title ticket (text input), text body ticket (text input), tags added user (categorical input) model two outputs: priority score 0 1 (scalar sigmoid output), department handle ticket (softmax output set departments). can build model lines functional API: Now plot model:  compiling model, can assign different losses output. can even assign different weights loss – modulate contribution total training loss. Since output layers different names, also specify losses loss weights corresponding layer names: Train model passing lists NumPy arrays inputs targets: calling fit Dataset object, yield either list lists like list(list(title_data, body_data, tags_data), list(priority_targets, dept_targets)) list named lists like list(list(title = title_data, body = body_data, tags = tags_data), list(priority = priority_targets, department = dept_targets)). detailed explanation, refer training evaluation guide.","code":"num_tags <- 12  # Number of unique issue tags num_words <- 10000  # Size of vocabulary obtained when preprocessing text data num_departments <- 4  # Number of departments for predictions  title_input <- # Variable-length sequence of ints   keras_input(shape(NA), name = \"title\") body_input <-  # Variable-length sequence of ints   keras_input(shape(NA), name = \"body\") tags_input <-  # Binary vectors of size `num_tags`   keras_input(shape = num_tags, name = \"tags\")  # Embed each word in the title into a 64-dimensional vector title_features <- layer_embedding(title_input, num_words, 64) # Embed each word in the text into a 64-dimensional vector body_features <- layer_embedding(body_input, num_words, 64)  # Reduce sequence of embedded words in the title # into a single 128-dimensional vector title_features <- layer_lstm(title_features, 128) # Reduce sequence of embedded words in the body # into a single 32-dimensional vector body_features <- layer_lstm(body_features, 32)  # Merge all available features into a single large vector via concatenation x <- layer_concatenate(title_features, body_features, tags_input)  # Stick a logistic regression for priority prediction on top of the features priority_pred <- layer_dense(x, 1, name = \"priority\")  # Stick a department classifier on top of the features department_pred <- layer_dense(x, num_departments, name = \"department\")  # Instantiate an end-to-end model predicting both priority and department model <- keras_model(   inputs = list(title_input, body_input, tags_input),   outputs = list(priority = priority_pred, department = department_pred) ) plot(model, show_shapes = TRUE) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     loss_binary_crossentropy(from_logits = TRUE),     loss_categorical_crossentropy(from_logits = TRUE)   ),   loss_weights = c(1.0, 0.2) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     priority = loss_binary_crossentropy(from_logits = TRUE),     department = loss_categorical_crossentropy(from_logits = TRUE)   ),   loss_weights = list(priority = 1.0, department = 0.2) ) # Dummy input data title_data <- random_integer(c(1280, 10), 0, num_words) body_data <- random_integer(c(1280, 100), 0, num_words) tags_data <- random_integer(c(1280, num_tags), 0, 2)  # Dummy target data priority_targets <- random_normal(c(1280, 1)) dept_targets <- random_integer(c(1280, num_departments), 0, 2)  model |> fit(   list(title = title_data, body = body_data, tags = tags_data),   list(priority = priority_targets, department = dept_targets),   epochs = 2,   batch_size = 32 ) ## Epoch 1/2 ## 40/40 - 2s - 57ms/step - loss: 0.3948 ## Epoch 2/2 ## 40/40 - 0s - 5ms/step - loss: 0.1971"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"a-toy-resnet-model","dir":"Articles","previous_headings":"Manipulate complex graph topologies","what":"A toy ResNet model","title":"The Functional API","text":"addition models multiple inputs outputs, functional API makes easy manipulate non-linear connectivity topologies – models layers connected sequentially, Sequential API handle. common use case residual connections. Let’s build toy ResNet model CIFAR10 demonstrate : Plot model:  Now train model:","code":"inputs <- keras_input(shape = c(32, 32, 3), name = \"img\") block_1_output <- inputs |>   layer_conv_2d(32, kernel_size = 3, activation = \"relu\") |>   layer_conv_2d(64, kernel_size = 3, activation = \"relu\") |>   layer_max_pooling_2d(pool_size = 3)  block_2_output <- block_1_output |>   layer_conv_2d(32, kernel_size = 3, activation = \"relu\", padding = \"same\") |>   layer_conv_2d(64, kernel_size = 3, activation = \"relu\", padding = \"same\") |>   layer_add(block_1_output)  block_3_output <- block_2_output |>   layer_conv_2d(64, kernel_size = 3, activation = \"relu\", padding = \"same\") |>   layer_conv_2d(64, kernel_size = 3, activation = \"relu\", padding = \"same\") |>   layer_add(block_2_output)  outputs <- block_3_output |>   layer_conv_2d(64, 3, activation = \"relu\") |>   layer_global_average_pooling_2d() |>   layer_dense(256, activation = \"relu\") |>   layer_dropout(0.5) |>   layer_dense(10)  model <- keras_model(inputs, outputs, name = \"toy_resnet\") summary(model) ## Model: \"toy_resnet\" ## ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)    │ (None, 32, 32, 3) │          0 │ -                 │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_8 (Conv2D)   │ (None, 30, 30,    │        896 │ img[0][0]         │ ## │                     │ 32)               │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_9 (Conv2D)   │ (None, 28, 28,    │     18,496 │ conv2d_8[0][0]    │ ## │                     │ 64)               │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ max_pooling2d_2     │ (None, 9, 9, 64)  │          0 │ conv2d_9[0][0]    │ ## │ (MaxPooling2D)      │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_10 (Conv2D)  │ (None, 9, 9, 32)  │     18,464 │ max_pooling2d_2[… │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_11 (Conv2D)  │ (None, 9, 9, 64)  │     18,496 │ conv2d_10[0][0]   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ add (Add)           │ (None, 9, 9, 64)  │          0 │ conv2d_11[0][0],  │ ## │                     │                   │            │ max_pooling2d_2[… │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_12 (Conv2D)  │ (None, 9, 9, 64)  │     36,928 │ add[0][0]         │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_13 (Conv2D)  │ (None, 9, 9, 64)  │     36,928 │ conv2d_12[0][0]   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ add_1 (Add)         │ (None, 9, 9, 64)  │          0 │ conv2d_13[0][0],  │ ## │                     │                   │            │ add[0][0]         │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_14 (Conv2D)  │ (None, 7, 7, 64)  │     36,928 │ add_1[0][0]       │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ global_average_poo… │ (None, 64)        │          0 │ conv2d_14[0][0]   │ ## │ (GlobalAveragePool… │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ dense_6 (Dense)     │ (None, 256)       │     16,640 │ global_average_p… │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ dropout (Dropout)   │ (None, 256)       │          0 │ dense_6[0][0]     │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ dense_7 (Dense)     │ (None, 10)        │      2,570 │ dropout[0][0]     │ ## └─────────────────────┴───────────────────┴────────────┴───────────────────┘ ##  Total params: 186,346 (727.91 KB) ##  Trainable params: 186,346 (727.91 KB) ##  Non-trainable params: 0 (0.00 B) plot(model, show_shapes = TRUE) c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_cifar10()  x_train <- x_train / 255.0 x_test <- x_test / 255.0  model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = loss_sparse_categorical_crossentropy(from_logits = TRUE),   metrics = \"acc\" ) # We restrict the data to the first 1000 samples so as to limit the # guide render time. # Try to train on the entire dataset until convergence! model |> fit(   x_train[1:1000, , , ],   y_train[1:1000, ],   batch_size = 64,   epochs = 1,   validation_split = 0.2 ) ## 13/13 - 5s - 373ms/step - acc: 0.1238 - loss: 2.2995 - val_acc: 0.1300 - val_loss: 2.2957"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"shared-layers","dir":"Articles","previous_headings":"","what":"Shared layers","title":"The Functional API","text":"Another good use functional API models use shared layers. Shared layers layer instances reused multiple times model – learn features correspond multiple paths graph--layers. Shared layers often used encode inputs similar spaces (say, two different pieces text feature similar vocabulary). enable sharing information across different inputs, make possible train model less data. given word seen one inputs, benefit processing inputs pass shared layer. share layer functional API, call layer instance multiple times. instance, ’s Embedding layer shared across two different text inputs:","code":"# Embedding for 1000 unique words mapped to 128-dimensional vectors shared_embedding <- layer_embedding(input_dim = 1000, output_dim = 128)  # Variable-length sequence of integers text_input_a <- keras_input(shape = shape(NA), dtype=\"int32\")  # Variable-length sequence of integers text_input_b <- keras_input(shape = shape(NA), dtype=\"int32\")  # Reuse the same layer to encode both inputs encoded_input_a <- shared_embedding(text_input_a) encoded_input_b <- shared_embedding(text_input_b)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"extract-and-reuse-nodes-in-the-graph-of-layers","dir":"Articles","previous_headings":"","what":"Extract and reuse nodes in the graph of layers","title":"The Functional API","text":"graph layers manipulating static data structure, can accessed inspected. able plot functional models images. also means can access activations intermediate layers (“nodes” graph) reuse elsewhere – useful something like feature extraction. Let’s look example. VGG19 model weights pretrained ImageNet: intermediate activations model, obtained querying graph data structure: Use features create new feature-extraction model returns values intermediate layer activations: comes handy tasks like neural style transfer, among things.","code":"vgg19 <- application_vgg19() features_list <- lapply(vgg19$layers, function(x) x$output) feat_extraction_model <- keras_model(inputs = vgg19$input,                                      outputs = features_list)  img <- random_normal(c(1, 224, 224, 3)) extracted_features <- feat_extraction_model(img)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"extend-the-api-using-custom-layers","dir":"Articles","previous_headings":"","what":"Extend the API using custom layers","title":"The Functional API","text":"keras includes wide range built-layers, example: Convolutional layers: conv_1d, conv_2d, conv_3d, conv_2d_transpose Pooling layers: max_pooling_1d, max_pooling_2d, max_pooling_3d, average_pooling_3d RNN layers: gru, lstm, conv_lstm_2d batch_normalization, dropout, embedding, etc. don’t find need, ’s easy extend API creating layers. layers subclass Layer class implement: call method, specifies computation done layer. build method, creates weights layer (just style convention since can create weights initialize, well). learn creating layers scratch, read custom layers models guide. following basic implementation layer_dense(): serialization support custom layer, define get_config() method returns constructor arguments layer instance: Optionally, implement class method from_config(cls, config) used recreating layer instance given config dictionary. default implementation from_config :","code":"custom_dense <- Layer(   classname = \"CustomDense\",   initialize = function(units = 32) {     super$initialize()     self$units <- as.integer(units)   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(input_shape[[2]], self$units),       initializer = \"random_normal\",       trainable = TRUE,     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer=\"random_normal\",       trainable = TRUE     )   },   call = function(inputs) {     op_matmul(inputs, self$w) + self$b   } )  inputs <- keras_input(c(4)) outputs <- custom_dense(inputs, 10)  model <- keras_model(inputs, outputs) custom_dense <- Layer(   classname = \"CustomDense\",    initialize = function(units = 32, ...) {     super$initialize()     self$units <- as.integer(units)   },    build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(input_shape[[2]], self$units),       initializer = \"random_normal\",       trainable = TRUE,     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer=\"random_normal\",       trainable = TRUE     )   },    call = function(inputs) {     op_matmul(inputs, self$w) + self$b   },    get_config = function() {     list(units = self$units)   } )  inputs <- keras_input(c(4)) outputs <- custom_dense(inputs, 10)  model <- keras_model(inputs, outputs) config <- get_config(model)  new_model <-   from_config(config, custom_objects = list(CustomDense = custom_dense)) from_config <- function(cls, config) {   do.call(cls, config) }"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"when-to-use-the-functional-api","dir":"Articles","previous_headings":"","what":"When to use the functional API","title":"The Functional API","text":"use Keras functional API create new model, just subclass Model class directly? general, functional API higher-level, easier safer, number features subclassed models support. However, model subclassing provides greater flexibility building models easily expressible directed acyclic graphs layers. example, implement Tree-RNN functional API subclass Model directly. -depth look differences functional API model subclassing, read Symbolic Imperative APIs TensorFlow 2.0?.","code":""},{"path":"https://keras.posit.co/articles/functional_api.html","id":"functional-api-strengths","dir":"Articles","previous_headings":"When to use the functional API","what":"Functional API strengths:","title":"The Functional API","text":"following properties also true Sequential models (also data structures), true subclassed models (R Python (byte)code, data structures).","code":""},{"path":"https://keras.posit.co/articles/functional_api.html","id":"less-verbose","dir":"Articles","previous_headings":"When to use the functional API > Functional API strengths:","what":"Less verbose","title":"The Functional API","text":"super$initialize(...), call = function(...), self$..., etc. Compare: subclassed version:","code":"inputs <- keras_input(shape = shape(32)) outputs <- inputs |>   layer_dense(64, activation = \"relu\") |>   layer_dense(10) mlp <- keras_model(inputs, outputs) MLP <- Model(   classname = \"MLP\",   initialize = function(...) {     super$initialize(...)     self$dense_1 <- layer_dense(units = 64, activation = \"relu\")     self$dense_2 <- layer_dense(units = 10)   },   call = function(inputs) {     inputs |>       self$dense_1() |>       self$dense_2()   } )  # Instantiate the model. mlp <- MLP() # Necessary to create the model's state. # The model doesn't have a state until it's called at least once. out <- mlp(op_zeros(c(1, 32)))"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"model-validation-while-defining-its-connectivity-graph","dir":"Articles","previous_headings":"When to use the functional API > Functional API strengths:","what":"Model validation while defining its connectivity graph","title":"The Functional API","text":"functional API, input specification (shape dtype) created advance (using Input). Every time call layer, layer checks specification passed matches assumptions, raise helpful error message . guarantees model can build functional API run. debugging – convergence-related debugging – happens statically model construction execution time. similar type checking compiler.","code":""},{"path":"https://keras.posit.co/articles/functional_api.html","id":"a-functional-model-is-plottable-and-inspectable","dir":"Articles","previous_headings":"When to use the functional API > Functional API strengths:","what":"A functional model is plottable and inspectable","title":"The Functional API","text":"can plot model graph, can easily access intermediate nodes graph. example, extract reuse activations intermediate layers (seen previous example):","code":"features_list <- lapply(vgg19$layers, function(x) x$output) feat_extraction_model <- keras_model(inputs = vgg19$input,                                      outputs = features_list)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"a-functional-model-can-be-serialized-or-cloned","dir":"Articles","previous_headings":"When to use the functional API > Functional API strengths:","what":"A functional model can be serialized or cloned","title":"The Functional API","text":"functional model data structure rather piece code, safely serializable can saved single file allows recreate exact model without access original code. See serialization & saving guide. serialize subclassed model, necessary implementer specify get_config() from_config() method model level.","code":""},{"path":[]},{"path":"https://keras.posit.co/articles/functional_api.html","id":"it-does-not-support-dynamic-architectures","dir":"Articles","previous_headings":"When to use the functional API > Functional API weakness:","what":"It does not support dynamic architectures","title":"The Functional API","text":"functional API treats models DAGs layers. true deep learning architectures, – example, recursive networks Tree RNNs follow assumption implemented functional API.","code":""},{"path":"https://keras.posit.co/articles/functional_api.html","id":"mix-and-match-api-styles","dir":"Articles","previous_headings":"","what":"Mix-and-match API styles","title":"The Functional API","text":"Choosing functional API Model subclassing isn’t binary decision restricts one category models. models keras API can interact , whether ’re Sequential models, functional models, subclassed models written scratch. can always use functional model Sequential model part subclassed model layer: can use subclassed layer model functional API long implements call method follows one following patterns: call(inputs, ...) – inputs tensor nested structure tensors (e.g. list tensors), ... non-tensor arguments (non-inputs). call(inputs, training = NULL, ...) – training boolean indicating whether layer behave training mode inference mode. call(inputs, mask = NULL, ...) – mask boolean mask tensor (useful RNNs, instance). call(inputs, training = NULL, mask = NULL, ...) – course, can masking training-specific behavior time. Additionally, implement get_config() method custom Layer model, functional models create still serializable cloneable. ’s quick example custom RNN, written scratch, used functional model:","code":"units <- 32 timesteps <- 10 input_dim <- 5  # Define a Functional model inputs <- keras_input(shape(NA, units)) outputs <- inputs |>   layer_global_average_pooling_1d() |>   layer_dense(units = 1)  model <- keras_model(inputs, outputs)  layer_custom_rnn <- Layer(   classname = \"CustomRNN\",   initialize = function(...) {     super$initialize(...)     self$units <- units     self$projection_1 <- layer_dense(units = units, activation = \"tanh\")     self$projection_2 <- layer_dense(units = units, activation = \"tanh\")     self$classifier <- model   },   call = function(inputs, ...) {     outputs <- list()     state <- op_zeros(c(shape(inputs)[[1]], self$units))     for (t in 1:(shape(inputs)[[2]])) {       x <- inputs[, t, ]       h <- self$projection_1(x)       y <- h + self$projection_2(state)       state <- y       outputs[[t]] <- y     }     features <- op_stack(outputs, axis = 2)     self$classifier(features)   } )  rnn <- layer_custom_rnn() out <- rnn(op_zeros(c(1, timesteps, input_dim))) units <- 32 timesteps <- 10 input_dim <- 5 batch_size <- 16  layer_custom_rnn <- Layer(   \"custom_rnn\",   initialize = function(...) {     super$initialize(...)     self$units <- units     self$projection_1 <- layer_dense(units = units, activation = \"tanh\")     self$projection_2 <- layer_dense(units = units, activation = \"tanh\")     self$classifier <- layer_dense(units = 1)   },   call = function(inputs, ...) {     outputs <- list()     state <- op_zeros(c(shape(inputs)[[1]], self$units))     for (t in 1:(shape(inputs)[[2]])) {       x <- inputs[, t, ]       h <- self$projection_1(x)       y <- h + self$projection_2(state)       state <- y       outputs[[t]] <- y     }     features <- op_stack(outputs, axis = 2)     self$classifier(features)   } )  # Note that you specify a static batch size for the inputs with the `batch_shape` # arg, because the inner computation of `layer_custom_rnn()` requires a static batch size # (when you create the `state` zeros tensor). inputs <- keras_input(batch_shape = shape(batch_size, timesteps, input_dim)) outputs <- inputs |>   layer_conv_1d(filters = 32, kernel_size = 3) |>   layer_custom_rnn()  model <- keras_model(inputs, outputs) out <- model(op_zeros(c(1, 10, 5)))"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Getting Started with Keras","text":"Keras high-level neural networks API developed focus enabling fast experimentation. able go idea result least possible delay key good research. Keras following key features: Allows code run CPU GPU, seamlessly. User-friendly API makes easy quickly prototype deep learning models. Built-support convolutional networks (computer vision), recurrent networks (sequence processing), combination . Supports arbitrary network architectures: multi-input multi-output models, layer sharing, model sharing, etc. means Keras appropriate building essentially deep learning model, memory network neural Turing machine. website provides documentation R interface Keras. See main Keras website https://keras.io additional information project.","code":""},{"path":"https://keras.posit.co/articles/getting_started.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Getting Started with Keras","text":"First, install keras R package: install development version : Keras R interface requires backend engine installed. TensorFlow default. provide default installation Keras TensorFlow GPU capable, GPU available. want customized installation, e.g. see documentation install_keras() installation section.","code":"install.packages(\"keras3\") remotes::install_github(\"rstudio/keras\") keras3::install_keras(backend = \"tensorflow\")"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"mnist-example","dir":"Articles","previous_headings":"","what":"MNIST Example","title":"Getting Started with Keras","text":"can learn basics Keras walking simple example: recognizing handwritten digits MNIST dataset. MNIST consists 28 x 28 grayscale images handwritten digits like :  dataset also includes labels image, telling us digit . example, labels images 5, 0, 4, 1.","code":""},{"path":"https://keras.posit.co/articles/getting_started.html","id":"preparing-the-data","dir":"Articles","previous_headings":"MNIST Example","what":"Preparing the Data","title":"Getting Started with Keras","text":"MNIST dataset included Keras can accessed using dataset_mnist() function. load dataset create variables test training data: x data 3-d array (images, width, height) grayscale values. prepare data training convert 3-d arrays matrices reshaping width height single dimension (28x28 images flattened length 784 vectors). , convert grayscale values integers ranging 0 255 floating point values ranging 0 1: Note use array_reshape() function rather dim<-() function reshape array. data re-interpreted using row-major semantics (opposed R’s default column-major semantics), turn compatible way numerical libraries called Keras interpret array dimensions. y data integer vector values ranging 0 9. prepare data training one-hot encode vectors binary class matrices using Keras to_categorical() function:","code":"library(keras3) mnist <- dataset_mnist() x_train <- mnist$train$x y_train <- mnist$train$y x_test <- mnist$test$x y_test <- mnist$test$y # reshape x_train <- array_reshape(x_train, c(nrow(x_train), 784)) x_test <- array_reshape(x_test, c(nrow(x_test), 784)) # rescale x_train <- x_train / 255 x_test <- x_test / 255 y_train <- to_categorical(y_train, 10) y_test <- to_categorical(y_test, 10)"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"defining-the-model","dir":"Articles","previous_headings":"MNIST Example","what":"Defining the Model","title":"Getting Started with Keras","text":"core data structure Keras model, way organize layers. simplest type model Sequential model, linear stack layers. begin creating sequential model adding layers using pipe (|>) operator: input_shape argument first layer specifies shape input data (length 784 numeric vector representing grayscale image). final layer outputs length 10 numeric vector (probabilities digit) using softmax activation function. Use summary() function print details model: Next, compile model appropriate loss function, optimizer, metrics:","code":"model <- keras_model_sequential(input_shape = c(784)) model |>   layer_dense(units = 256, activation = 'relu') |>   layer_dropout(rate = 0.4) |>   layer_dense(units = 128, activation = 'relu') |>   layer_dropout(rate = 0.3) |>   layer_dense(units = 10, activation = 'softmax') summary(model) ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense (Dense)                   │ (None, 256)            │       200,960 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 256)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_1 (Dense)                 │ (None, 128)            │        32,896 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, 128)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 10)             │         1,290 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 235,146 (918.54 KB) ##  Trainable params: 235,146 (918.54 KB) ##  Non-trainable params: 0 (0.00 B) plot(model) model |> compile(   loss = 'categorical_crossentropy',   optimizer = optimizer_rmsprop(),   metrics = c('accuracy') )"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"training-and-evaluation","dir":"Articles","previous_headings":"MNIST Example","what":"Training and Evaluation","title":"Getting Started with Keras","text":"Use fit() function train model 30 epochs using batches 128 images: history object returned fit() includes loss accuracy metrics can plot: Evaluate model’s performance test data: Generate predictions new data: Keras provides vocabulary building deep learning models simple, elegant, intuitive. Building question answering system, image classification model, neural Turing machine, model just straightforward.","code":"history <- model |> fit(   x_train, y_train,   epochs = 30, batch_size = 128,   validation_split = 0.2 ) plot(history) model |> evaluate(x_test, y_test) ## 313/313 - 1s - 2ms/step - accuracy: 0.9814 - loss: 0.0844 ## $accuracy ## [1] 0.9814 ## ## $loss ## [1] 0.08441389 probs <- model |> predict(x_test) ## 313/313 - 0s - 1ms/step max.col(probs) - 1L ##   [1] 7 2 1 0 4 1 4 9 6 9 0 6 9 0 1 5 9 7 3 4 9 6 6 5 4 0 7 4 0 1 3 1 3 4 7 ##  [36] 2 7 1 2 1 1 7 4 2 3 5 1 2 4 4 6 3 5 5 6 0 4 1 9 5 7 8 9 3 7 4 6 4 3 0 ##  [71] 7 0 2 9 1 7 3 2 9 7 7 6 2 7 8 4 7 3 6 1 3 6 9 3 1 4 1 7 6 9 ##  [ reached getOption(\"max.print\") -- omitted 9900 entries ]"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"deep-learning-with-r-book","dir":"Articles","previous_headings":"MNIST Example","what":"Deep Learning with R Book","title":"Getting Started with Keras","text":"want comprehensive introduction Keras concepts practice deep learning, recommend Deep Learning R, 2nd Edition book Manning. book collaboration François Chollet, creator (Python) Keras, J.J. Allaire, wrote original R interface Keras, Tomasz Kalinowski, maintainer R interface Keras. book presumes significant knowledge machine learning deep learning, goes way basic theory advanced practical applications, using R interface Keras.","code":""},{"path":"https://keras.posit.co/articles/getting_started.html","id":"why-this-name-keras","dir":"Articles","previous_headings":"","what":"Why this name, Keras?","title":"Getting Started with Keras","text":"Keras (κέρας) means horn Greek. reference literary image ancient Greek Latin literature, first found Odyssey, dream spirits (Oneiroi, singular Oneiros) divided deceive men false visions, arrive Earth gate ivory, announce future come pass, arrive gate horn. ’s play words κέρας (horn) / κραίνω (fulfill), ἐλέφας (ivory) / ἐλεφαίρομαι (deceive). Keras initially developed part research effort project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System). “Oneiroi beyond unravelling –can sure tale tell? men look comes pass. Two gates give passage fleeting Oneiroi; one made horn, one ivory. Oneiroi pass sawn ivory deceitful, bearing message fulfilled; come polished horn truth behind , accomplished men see .” Homer, Odyssey 19. 562 ff (Shewring translation).","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Introduction to Keras for engineers","text":"Keras 3 deep learning framework works TensorFlow, JAX, PyTorch interchangeably. notebook walk key Keras 3 workflows. Let’s start installing Keras 3: pip install keras –upgrade –quiet","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Introduction to Keras for engineers","text":"’re going using tensorflow backend – can edit string \"jax\" \"torch\" hit “Restart runtime”, whole notebook run just ! entire guide backend-agnostic.","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(keras3)  # Note that you must configure the backend # before calling any other keras functions. # The backend cannot be changed once the # package is imported. use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"a-first-example-a-mnist-convnet","dir":"Articles","previous_headings":"","what":"A first example: A MNIST convnet","title":"Introduction to Keras for engineers","text":"Let’s start Hello World ML: training convnet classify MNIST digits. ’s data: ’s model. Different model-building options Keras offers include: Sequential API (use ) Functional API (typical) Writing models via subclassing (advanced use cases) ’s model summary: use compile() method specify optimizer, loss function, metrics monitor. Note JAX TensorFlow backends, XLA compilation turned default. Let’s train evaluate model. ’ll set aside validation split 15% data training monitor generalization unseen data. training, saving model end epoch. can also save model latest state like : reload like : Next, can query predictions class probabilities predict(): ’s basics!","code":"# Load the data and split it between train and test sets c(c(x_train, y_train), c(x_test, y_test)) %<-% keras3::dataset_mnist()  # Scale images to the [0, 1] range x_train <- x_train / 255 x_test <- x_test / 255 # Make sure images have shape (28, 28, 1) x_train <- op_expand_dims(x_train, -1) x_test <- op_expand_dims(x_test, -1)  dim(x_train) ## [1] 60000    28    28     1 dim(x_test) ## [1] 10000    28    28     1 # Model parameters num_classes <- 10 input_shape <- c(28, 28, 1)  model <- keras_model_sequential(input_shape = input_shape) model |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = \"relu\") |>   layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = \"relu\") |>   layer_global_average_pooling_2d() |>   layer_dropout(rate = 0.5) |>   layer_dense(units = num_classes, activation = \"softmax\") summary(model) ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 26, 26, 64)     │           640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 24, 24, 64)     │        36,928 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 12, 12, 64)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 10, 10, 128)    │        73,856 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_3 (Conv2D)               │ (None, 8, 8, 128)      │       147,584 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_average_pooling2d        │ (None, 128)            │             0 │ ## │ (GlobalAveragePooling2D)        │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 128)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 10)             │         1,290 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 260,298 (1016.79 KB) ##  Trainable params: 260,298 (1016.79 KB) ##  Non-trainable params: 0 (0.00 B) model |> compile(   optimizer = \"adam\",   loss = \"sparse_categorical_crossentropy\",   metrics = list(     metric_sparse_categorical_accuracy(name = \"acc\")   ) ) batch_size <- 128 epochs <- 10  callbacks <- list(   callback_model_checkpoint(filepath=\"model_at_epoch_{epoch}.keras\"),   callback_early_stopping(monitor=\"val_loss\", patience=2) )  model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = epochs,   validation_split = 0.15,   callbacks = callbacks ) ## Epoch 1/10 ## 399/399 - 6s - 16ms/step - acc: 0.7450 - loss: 0.7521 - val_acc: 0.9642 - val_loss: 0.1234 ## Epoch 2/10 ## 399/399 - 2s - 5ms/step - acc: 0.9387 - loss: 0.2054 - val_acc: 0.9769 - val_loss: 0.0751 ## Epoch 3/10 ## 399/399 - 2s - 5ms/step - acc: 0.9575 - loss: 0.1455 - val_acc: 0.9824 - val_loss: 0.0609 ## Epoch 4/10 ## 399/399 - 2s - 5ms/step - acc: 0.9662 - loss: 0.1148 - val_acc: 0.9863 - val_loss: 0.0483 ## Epoch 5/10 ## 399/399 - 2s - 5ms/step - acc: 0.9726 - loss: 0.0972 - val_acc: 0.9869 - val_loss: 0.0479 ## Epoch 6/10 ## 399/399 - 2s - 5ms/step - acc: 0.9750 - loss: 0.0857 - val_acc: 0.9892 - val_loss: 0.0379 ## Epoch 7/10 ## 399/399 - 2s - 5ms/step - acc: 0.9768 - loss: 0.0768 - val_acc: 0.9906 - val_loss: 0.0358 ## Epoch 8/10 ## 399/399 - 2s - 5ms/step - acc: 0.9794 - loss: 0.0671 - val_acc: 0.9876 - val_loss: 0.0422 ## Epoch 9/10 ## 399/399 - 2s - 5ms/step - acc: 0.9808 - loss: 0.0639 - val_acc: 0.9887 - val_loss: 0.0387 score <- model |> evaluate(x_test, y_test, verbose = 0) save_model(model, \"final_model.keras\", overwrite=TRUE) model <- load_model(\"final_model.keras\") predictions <- model |> predict(x_test) ## 313/313 - 0s - 1ms/step dim(predictions) ## [1] 10000    10"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"writing-cross-framework-custom-components","dir":"Articles","previous_headings":"","what":"Writing cross-framework custom components","title":"Introduction to Keras for engineers","text":"Keras enables write custom Layers, Models, Metrics, Losses, Optimizers work across TensorFlow, JAX, PyTorch codebase. Let’s take look custom layers first. op_ namespace contains: implementation NumPy API, e.g. op_stack op_matmul. set neural network specific ops absent NumPy, op_conv op_binary_crossentropy. Let’s make custom Dense layer works backends: Next, let’s make custom Dropout layer relies random_* namespace: Next, let’s write custom subclassed model uses two custom layers: Let’s compile fit :","code":"layer_my_dense <- Layer(   classname = \"MyDense\",   initialize = function(units, activation = NULL, name = NULL, ...) {     super$initialize(name = name, ...)     self$units <- units     self$activation <- activation   },   build = function(input_shape) {     input_dim <- tail(input_shape, 1)     self$w <- self$add_weight(       shape = shape(input_dim, self$units),       initializer = initializer_glorot_normal(),       name = \"kernel\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = initializer_zeros(),       name = \"bias\",       trainable = TRUE     )   },   call = function(inputs) {     # Use Keras ops to create backend-agnostic layers/metrics/etc.     x <- op_matmul(inputs, self$w) + self$b     if (!is.null(self$activation))       x <- self$activation(x)     x   } ) layer_my_dropout <- Layer(   \"MyDropout\",   initialize = function(rate, name = NULL, seed = NULL, ...) {     super$initialize(name = name)     self$rate <- rate     # Use seed_generator for managing RNG state.     # It is a state element and its seed variable is     # tracked as part of `layer$variables`.     self$seed_generator <- random_seed_generator(seed)   },   call = function(inputs) {     # Use `keras3::random_*` for random ops.     random_dropout(inputs, self$rate, seed = self$seed_generator)   } ) MyModel <- Model(   \"MyModel\",   initialize = function(num_classes, ...) {     super$initialize(...)     self$conv_base <-       keras_model_sequential() |>       layer_conv_2d(64, kernel_size = c(3, 3), activation = \"relu\") |>       layer_conv_2d(64, kernel_size = c(3, 3), activation = \"relu\") |>       layer_max_pooling_2d(pool_size = c(2, 2)) |>       layer_conv_2d(128, kernel_size = c(3, 3), activation = \"relu\") |>       layer_conv_2d(128, kernel_size = c(3, 3), activation = \"relu\") |>       layer_global_average_pooling_2d()      self$dp <- layer_my_dropout(rate = 0.5)     self$dense <- layer_my_dense(units = num_classes,                                  activation = activation_softmax)   },   call = function(inputs) {     inputs |>       self$conv_base() |>       self$dp() |>       self$dense()   } ) model <- MyModel(num_classes = 10) model |> compile(   loss = loss_sparse_categorical_crossentropy(),   optimizer = optimizer_adam(learning_rate = 1e-3),   metrics = list(     metric_sparse_categorical_accuracy(name = \"acc\")   ) )  model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = 1, # For speed   validation_split = 0.15 ) ## 399/399 - 6s - 16ms/step - acc: 0.7356 - loss: 0.7726 - val_acc: 0.9301 - val_loss: 0.2358"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"training-models-on-arbitrary-data-sources","dir":"Articles","previous_headings":"","what":"Training models on arbitrary data sources","title":"Introduction to Keras for engineers","text":"Keras models can trained evaluated wide variety data sources, independently backend ’re using. includes: Arrays Dataframes TensorFlow tf_dataset objects PyTorch DataLoader objects Keras PyDataset objects work whether ’re using TensorFlow, JAX, PyTorch Keras backend. Let’s try tf_dataset:","code":"library(tfdatasets, exclude = \"shape\")  train_dataset <- list(x_train, y_train) |>   tensor_slices_dataset() |>   dataset_batch(batch_size) |>   dataset_prefetch(buffer_size = tf$data$AUTOTUNE)  test_dataset <- list(x_test, y_test) |>   tensor_slices_dataset() |>   dataset_batch(batch_size) |>   dataset_prefetch(buffer_size = tf$data$AUTOTUNE)  model <- MyModel(num_classes = 10) model |> compile(   loss = loss_sparse_categorical_crossentropy(),   optimizer = optimizer_adam(learning_rate = 1e-3),   metrics = list(     metric_sparse_categorical_accuracy(name = \"acc\")   ) )  model |> fit(train_dataset, epochs = 1, validation_data = test_dataset) ## 469/469 - 7s - 16ms/step - acc: 0.7542 - loss: 0.7342 - val_acc: 0.9026 - val_loss: 0.3192"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"further-reading","dir":"Articles","previous_headings":"","what":"Further reading","title":"Introduction to Keras for engineers","text":"concludes short overview new multi-backend capabilities Keras 3. Next, can learn :","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"how-to-customize-what-happens-in-fit","dir":"Articles","previous_headings":"Further reading","what":"How to customize what happens in fit()","title":"Introduction to Keras for engineers","text":"Want implement non-standard training algorithm still want benefit power usability fit()? ’s easy customize fit() support arbitrary use cases: Customizing happens fit() TensorFlow","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"how-to-write-custom-training-loops","dir":"Articles","previous_headings":"","what":"How to write custom training loops","title":"Introduction to Keras for engineers","text":"Writing training loop scratch TensorFlow","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"how-to-distribute-training","dir":"Articles","previous_headings":"","what":"How to distribute training","title":"Introduction to Keras for engineers","text":"Guide distributed training TensorFlow Enjoy library! 🚀","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Introduction to Keras for Researchers","text":"","code":"library(keras3) library(tensorflow)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Introduction to Keras for Researchers","text":"machine learning researcher? publish NeurIPS push state---art CV NLP? guide serve first introduction core Keras & TensorFlow API concepts. guide, learn : Tensors, variables, gradients TensorFlow Creating layers subclassing [Layer] class Writing low-level training loops Tracking losses created layers via add_loss() method Tracking metrics low-level training loop Speeding execution compiled [tensorflow::tf_function()] Executing layers training inference mode Keras Functional API also see Keras API action two end--end research examples: Variational Autoencoder, Hypernetwork.","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"tensors","dir":"Articles","previous_headings":"","what":"Tensors","title":"Introduction to Keras for Researchers","text":"TensorFlow infrastructure layer differentiable programming. heart, ’s framework manipulating N-dimensional arrays (tensors), much like NumPy. However, three key differences NumPy TensorFlow: TensorFlow can leverage hardware accelerators GPUs TPUs. TensorFlow can automatically compute gradient arbitrary differentiable tensor expressions. TensorFlow computation can distributed large numbers devices single machine, large number machines (potentially multiple devices ). Let’s take look object core TensorFlow: Tensor. ’s constant tensor: can get value R array calling .array(): features attributes dtype shape: common way create constant tensors via tf$ones tf$zeros: can also create random constant tensors:","code":"x <- tf$constant(rbind(c(5, 2), c(1, 3))) print(x) ## tf.Tensor( ## [[5. 2.] ##  [1. 3.]], shape=(2, 2), dtype=float64) as.array(x) ##      [,1] [,2] ## [1,]    5    2 ## [2,]    1    3 x$dtype ## tf.float64 x$shape ## TensorShape([2, 2]) tf$ones(shape = shape(2, 1)) ## tf.Tensor( ## [[1.] ##  [1.]], shape=(2, 1), dtype=float32) tf$zeros(shape = shape(2, 1)) ## tf.Tensor( ## [[0.] ##  [0.]], shape=(2, 1), dtype=float32) x <- random_normal(shape = c(2, 2), mean = 0.0, stddev = 1.0) x <- random_uniform(shape = c(2, 2), minval = 0, maxval = 10)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"variables","dir":"Articles","previous_headings":"","what":"Variables","title":"Introduction to Keras for Researchers","text":"Variables special tensors used store mutable state (weights neural network). create Variable using initial value: update value Variable using methods $assign(value), $assign_add(increment), $assign_sub(decrement):","code":"initial_value <- random_normal(shape=c(2, 2)) a <- tf$Variable(initial_value) print(a) ## <tf.Variable 'Variable:0' shape=(2, 2) dtype=float32, numpy= ## array([[ 0.9057419 ,  0.7916686 ], ##        [ 0.28754202, -0.5408822 ]], dtype=float32)> new_value <- random_normal(shape=c(2, 2)) a$assign(new_value) ## <tf.Variable 'UnreadVariable' shape=(2, 2) dtype=float32, numpy= ## array([[-0.3405368 , -2.1463926 ], ##        [ 1.2602988 ,  0.12241419]], dtype=float32)> added_value <- random_normal(shape=c(2, 2)) a$assign_add(added_value) ## <tf.Variable 'UnreadVariable' shape=(2, 2) dtype=float32, numpy= ## array([[ 0.04820395, -2.6854615 ], ##        [ 0.23246336,  1.4535258 ]], dtype=float32)>"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"doing-math-in-tensorflow","dir":"Articles","previous_headings":"","what":"Doing math in TensorFlow","title":"Introduction to Keras for Researchers","text":"’ve used NumPy, math TensorFlow look familiar. main difference TensorFlow code can run GPU TPU.","code":"a <- random_normal(shape=c(2, 2)) b <- random_normal(shape=c(2, 2))  c <- a + b d <- tf$square(c) e <- tf$exp(d)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"gradients","dir":"Articles","previous_headings":"","what":"Gradients","title":"Introduction to Keras for Researchers","text":"’s another big difference R: can automatically retrieve gradient differentiable expression. Just open GradientTape, start “watching” tensor via tape$watch(), compose differentiable expression using tensor input: default, variables watched automatically, don’t need manually watch : Note can compute higher-order derivatives nesting tapes:","code":"a <- random_normal(shape=c(2, 2)) b <- random_normal(shape=c(2, 2))  with(tf$GradientTape() %as% tape, {   tape$watch(a)  # Start recording the history of operations applied to `a`   c <- tf$sqrt(tf$square(a) + tf$square(b))  # Do some math using `a`   # What's the gradient of `c` with respect to `a`?   dc_da <- tape$gradient(c, a)   print(dc_da) }) ## tf.Tensor( ## [[ 0.9969011  -0.7707146 ] ##  [ 0.23378514  0.96255165]], shape=(2, 2), dtype=float32) a <- tf$Variable(a)  with(tf$GradientTape() %as% tape, {   c <- tf$sqrt(tf$square(a) + tf$square(b))   dc_da <- tape$gradient(c, a)   print(dc_da) }) ## tf.Tensor( ## [[ 0.9969011  -0.7707146 ] ##  [ 0.23378514  0.96255165]], shape=(2, 2), dtype=float32) with(tf$GradientTape() %as% outer_tape, {   with(tf$GradientTape() %as% tape, {     c <- tf$sqrt(tf$square(a) + tf$square(b))     dc_da <- tape$gradient(c, a)   })   d2c_da2 <- outer_tape$gradient(dc_da, a)   print(d2c_da2) }) ## tf.Tensor( ## [[3.3447742e-03 7.1282005e-01] ##  [5.7464113e+00 5.5013180e-02]], shape=(2, 2), dtype=float32)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"keras-layers","dir":"Articles","previous_headings":"","what":"Keras layers","title":"Introduction to Keras for Researchers","text":"TensorFlow infrastructure layer differentiable programming, dealing tensors, variables, gradients, Keras user interface deep learning, dealing layers, models, optimizers, loss functions, metrics, . Keras serves high-level API TensorFlow: Keras makes TensorFlow simple productive. Layer class fundamental abstraction Keras. Layer encapsulates state (weights) computation (defined call method). simple layer looks like . self$add_weight() method gives shortcut creating weights: use Layer instance much like R function: weight variables (created initialize) automatically tracked weights property: many built-layers available, Dense Conv2D LSTM fancier ones like Conv3DTranspose ConvLSTM2D. smart reusing built-functionality.","code":"Linear <- new_layer_class(   \"Linear\",   initialize = function(units = 32, input_dim = 32) {     super$initialize()     self$w <- self$add_weight(       shape = shape(input_dim, units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     tf$matmul(inputs, self$w) + self$b   } ) # Instantiate our layer. linear_layer <- Linear(units=4, input_dim=2)  # The layer can be treated as a function. # Here we call it on some data. y <- linear_layer(tf$ones(shape(2, 2))) linear_layer$weights ## [[1]] ## <KerasVariable shape=(2, 4), dtype=float32, path=linear/variable> ## ## [[2]] ## <KerasVariable shape=(4), dtype=float32, path=linear/variable_1>"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"layer-weight-creation-in-buildinput_shape","dir":"Articles","previous_headings":"","what":"Layer weight creation in build(input_shape)","title":"Introduction to Keras for Researchers","text":"’s often good idea defer weight creation build() method, don’t need specify input dim/shape layer construction time:","code":"Linear <- new_layer_class(   \"Linear\",   initialize = function(units = 32) {     super$initialize()     self$units <- units   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(input_shape[-1], self$units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     tf$matmul(inputs, self$w) + self$b   } )  # Instantiate our layer. linear_layer <- Linear(units = 4)  # This will also call `build(input_shape)` and create the weights. y <- linear_layer(tf$ones(shape(2, 2)))"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"layer-gradients","dir":"Articles","previous_headings":"","what":"Layer gradients","title":"Introduction to Keras for Researchers","text":"can automatically retrieve gradients weights layer calling inside GradientTape. Using gradients, can update weights layer, either manually, using optimizer object. course, can modify gradients using , need .","code":"# Prepare a dataset. c(c(x_train, y_train), .) %<-% dataset_mnist()  x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(list(x_train, y_train)) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   tfdatasets::dataset_batch(64)  # Instantiate our linear layer (defined above) with 10 units. linear_layer <- Linear(units = 10)  # Instantiate a logistic loss function that expects integer targets. loss_fn <- loss_sparse_categorical_crossentropy(from_logits=TRUE)  # Instantiate an optimizer. optimizer <- optimizer_sgd(learning_rate=1e-3)  # Iterate over the batches of the dataset. coro::loop(for(data in dataset) {   # Open a GradientTape.   with(tf$GradientTape() %as% tape, {     # Forward pass.     logits <- linear_layer(data[[1]])      # Loss value for this batch.     loss_value <- loss_fn(data[[2]], logits)   })    # Get gradients of the loss wrt the weights.   gradients <- tape$gradient(loss_value, linear_layer$trainable_weights)    # Update the weights of our linear layer.   optimizer$apply_gradients(zip_lists(gradients, linear_layer$trainable_weights)) }) loss_value ## tf.Tensor(1.2819729, shape=(), dtype=float32)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"trainable-and-non-trainable-weights","dir":"Articles","previous_headings":"","what":"Trainable and non-trainable weights","title":"Introduction to Keras for Researchers","text":"Weights created layers can either trainable non-trainable. ’re exposed trainable_weights non_trainable_weights respectively. ’s layer non-trainable weight:","code":"ComputeSum <- new_layer_class(   \"ComputeSum\",   initialize = function(input_dim) {     super$initialize()     # Create a non-trainable weight.     self$total <- self$add_weight(       initializer = \"zeros\",       shape = shape(input_dim),       trainable = FALSE     )   },   call = function(inputs) {     self$total$assign_add(tf$reduce_sum(inputs, axis=0L))     self$total   } )  my_sum <- ComputeSum(input_dim = 2) x <- tf$ones(shape(2, 2))  as.array(my_sum(x)) ## [1] 2 2 as.array(my_sum(x)) ## [1] 4 4 my_sum$trainable_weights ## list()"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"layers-that-own-layers","dir":"Articles","previous_headings":"","what":"Layers that own layers","title":"Introduction to Keras for Researchers","text":"Layers can recursively nested create bigger computation blocks. layer track weights sublayers (trainable non-trainable). Note manually-created MLP equivalent following built-option:","code":"# Let's reuse the Linear class # with a `build` method that we defined above.  MLP <- new_layer_class(   \"MLP\",   initialize = function() {     super$initialize()     self$linear_1 <- Linear(units = 32)     self$linear_2 <- Linear(units = 32)     self$linear_3 <- Linear(units = 10)   },   call = function(inputs) {     x <- self$linear_1(inputs)     x <- tf$nn$relu(x)     x <- self$linear_2(x)     x <- tf$nn$relu(x)     return(self$linear_3(x))   } )  mlp <- MLP()  # The first call to the `mlp` object will create the weights. y <- mlp(tf$ones(shape=shape(3, 64)))  # Weights are recursively tracked. length(mlp$weights) ## [1] 6 mlp <- keras_model_sequential() %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"tracking-losses-created-by-layers","dir":"Articles","previous_headings":"","what":"Tracking losses created by layers","title":"Introduction to Keras for Researchers","text":"Layers can create losses forward pass via add_loss() method. especially useful regularization losses. losses created sublayers recursively tracked parent layers. ’s layer creates activity regularization loss: model incorporating layer track regularization loss: losses cleared top-level layer start forward pass – don’t accumulate. layer.losses always contains losses created last forward pass. typically use losses summing computing gradients writing training loop.","code":"# A layer that creates an activity sparsity regularization loss ActivityRegularization <- new_layer_class(   \"ActivityRegularization\",   initialize = function(rate=1e-2) {     super$initialize()     self$rate <- rate   },   call = function(inputs) {     self$add_loss(self$rate * tf$reduce_sum(tf$abs(inputs)))     inputs   } ) # Let's use the loss layer in a MLP block. SparseMLP <- new_layer_class(   \"SparseMLP\",   initialize = function() {     super$initialize()     self$linear_1 <- Linear(units = 32)     self$reg <- ActivityRegularization(rate = 1e-2)     self$linear_3 <- Linear(units = 10)   },   call = function(inputs) {     x <- self$linear_1(inputs)     x <- tf$nn$relu(x)     x <- self$reg(x)     return(self$linear_3(x))   } )  mlp <- SparseMLP() y <- mlp(tf$ones(shape(10, 10)))  mlp$losses  # List containing one float32 scalar ## [[1]] ## tf.Tensor(0.18065463, shape=(), dtype=float32) # Losses correspond to the *last* forward pass. mlp <- SparseMLP() mlp(tf$ones(shape(10, 10))) ## tf.Tensor( ## [[ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621]], shape=(10, 10), dtype=float32) length(mlp$losses) ## [1] 1 mlp(tf$ones(shape(10, 10))) ## tf.Tensor( ## [[ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621]], shape=(10, 10), dtype=float32) length(mlp$losses)  # No accumulation. ## [1] 1 # Let's demonstrate how to use these losses in a training loop.  # Prepare a dataset. c(c(x_train, y_train), .) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(list(x_train, y_train)) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   tfdatasets::dataset_batch(64)  # A new MLP. mlp <- SparseMLP()  # Loss and optimizer. loss_fn <- loss_sparse_categorical_crossentropy(from_logits=TRUE) optimizer <- optimizer_sgd(learning_rate=1e-3)  coro::loop(for(data in dataset) {   x <- data[[1]]   y <- data[[2]]   with(tf$GradientTape() %as% tape, {     # Forward pass.     logits <- mlp(x)      # External loss value for this batch.     loss <- loss_fn(y, logits)      # Add the losses created during the forward pass.     loss <- loss + Reduce(`+`, mlp$losses)      # Get gradients of the loss wrt the weights.     gradients <- tape$gradient(loss, mlp$trainable_weights)      # Update the weights of our linear layer.     optimizer$apply_gradients(zip_lists(gradients, mlp$trainable_weights))   }) })"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"keeping-track-of-training-metrics","dir":"Articles","previous_headings":"","what":"Keeping track of training metrics","title":"Introduction to Keras for Researchers","text":"Keras offers broad range built-metrics, like metric_auc metric_precision_at_recall. ’s also easy create metrics lines code. use metric custom training loop, : Instantiate metric object, e.g. metric = metric_auc() Call metric$udpate_state(targets, predictions) method batch data Query result via metric$result() Reset metric’s state end epoch start evaluation via metric$reset_state() ’s simple example: can also define metrics subclassing keras.metrics.Metric. need override three functions called : Override update_state() update statistic values. Override result() return metric value. Override reset_state() reset metric initial state. example implement F1-score metric (support sample weighting). Let’s test-drive :","code":"# Instantiate a metric object accuracy <- metric_sparse_categorical_accuracy()  # Prepare our layer, loss, and optimizer. model <- keras_model_sequential() %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10) loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE) optimizer <- optimizer_adam(learning_rate=1e-3)  for (epoch in seq_len(2)) {   coro::loop(for (data in dataset) {     x <- data[[1]]     y <- data[[2]]     with(tf$GradientTape() %as% tape, {       # Forward pass.       logits <- model(x)        # External loss value for this batch.       loss_value <- loss_fn(y, logits)     })      # Update the state of the `accuracy` metric.     accuracy$update_state(y, logits)      # Update the weights of the model to minimize the loss value.     gradients <- tape$gradient(loss_value, model$trainable_weights)     optimizer$apply_gradients(zip_lists(gradients, model$trainable_weights))    })   cat(\"Epoch:\", epoch, \"Accuracy:\", as.numeric(accuracy$result()), \"\\n\")   accuracy$reset_state() } ## Epoch: 1 Accuracy: 0.8757833 ## Epoch: 2 Accuracy: 0.93915 F1Score <- new_metric_class(   \"F1Score\",   initialize = function(self, name=\"f1_score\", dtype=\"float32\", threshold=0.5, ...) {     super$initialize(name=name, dtype=dtype, ...)     self$threshold <- threshold     self$true_positives <- self$add_weight(       name=\"tp\", dtype=dtype, initializer=\"zeros\"     )     self$false_positives <- self$add_weight(       name=\"fp\", dtype=dtype, initializer=\"zeros\"     )     self$false_negatives <- self$add_weight(       name=\"fn\", dtype=dtype, initializer=\"zeros\"     )   },   update_state = function(y_true, y_pred, sample_weight=NULL) {     y_pred <- tf$math$greater_equal(y_pred, self$threshold)     y_true <- tf$cast(y_true, tf$bool)     y_pred <- tf$cast(y_pred, tf$bool)      true_positives <- tf$cast(y_true & y_pred, self$dtype)     false_positives <- tf$cast((!y_true) & y_pred, self$dtype)     false_negatives <- tf$cast(y_true & (!y_pred), self$dtype)      if (!is.null(sample_weight)) {       sample_weight <- tf$cast(sample_weight, self$dtype)       true_positives <- true_positives * sample_weight       false_positives <- false_positives * sample_weight       false_negatives <- false_negatives * sample_weight     }      self$true_positives$assign_add(tf$reduce_sum(true_positives))     self$false_positives$assign_add(tf$reduce_sum(false_positives))     self$false_negatives$assign_add(tf$reduce_sum(false_negatives))   },    result = function() {     precision <- self$true_positives / (self$true_positives + self$false_positives)     recall <- self$true_positives / (self$true_positives + self$false_negatives)     f1_score <- 2 * precision * recall / (precision + recall)     f1_score   },    reset_state = function() {     self$true_positives$assign(0)     self$false_positives$assign(0)     self$false_negatives$assign(0)   } ) m <- F1Score() m$update_state(c(0, 1, 0, 0), c(0.3, 0.5, 0.8, 0.9)) cat(\"Intermediate result:\", as.numeric(m$result()), \"\\n\") ## Intermediate result: 0.5 m$update_state(c(1, 1, 1, 1), c(0.1, 0.7, 0.6, 0.0)) cat(\"Final result:\", as.numeric(m$result()), \"\\n\") ## Final result: 0.6"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"compiled-functions","dir":"Articles","previous_headings":"","what":"Compiled functions","title":"Introduction to Keras for Researchers","text":"Running eagerly great debugging, get better performance compiling computation static graphs. Static graphs researcher’s best friends. can compile function wrapping tf.function decorator.","code":"# Prepare our layer, loss, and optimizer. model <- keras_model_sequential() %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10) loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE) optimizer <- optimizer_adam(learning_rate=1e-3)  # Create a training step function. train_on_batch <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     # Forward pass.     logits <- model(x)     # External loss value for this batch.     loss_value <- loss_fn(y, logits)   })   # Update the weights of the model to minimize the loss value.   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply_gradients(zip_lists(gradients, model$trainable_weights))   loss_value })   # Prepare a dataset. c(c(x_train, y_train), .) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(list(x_train, y_train)) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   tfdatasets::dataset_batch(64)  i <- 0 coro::loop(for (data in dataset) {   i <- i + 1   x <- data[[1]]   y <- data[[2]]   loss <- train_on_batch(x, y)   if (i %% 100 == 0)     cat(\"Loss:\", as.numeric(loss), \"\\n\") }) ## Loss: 0.551749 ## Loss: 0.2131135 ## Loss: 0.2765952 ## Loss: 0.1296219 ## Loss: 0.2657076 ## Loss: 0.2683381 ## Loss: 0.1570166 ## Loss: 0.3139241 ## Loss: 0.08981849"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"training-mode-inference-mode","dir":"Articles","previous_headings":"","what":"Training mode & inference mode","title":"Introduction to Keras for Researchers","text":"layers, particular BatchNormalization layer Dropout layer, different behaviors training inference. layers, standard practice expose training (boolean) argument call method. exposing argument call, enable built-training evaluation loops (e.g. fit) correctly use layer training inference modes.","code":"Dropout <- new_layer_class(   \"Dropout\",   initialize = function(rate) {     super$initialize()     self$rate <- rate   },   call = function(inputs, training = NULL) {     if (!is.null(training) && training) {       return(tf$nn$dropout(inputs, rate = self$rate))     }     inputs   } )  MLPWithDropout <- new_layer_class(   \"MLPWithDropout\",   initialize = function() {     super$initialize()     self$linear_1 <- Linear(units = 32)     self$dropout <- Dropout(rate = 0.5)     self$linear_3 <- Linear(units = 10)   },   call = function(inputs, training = NULL) {     x <- self$linear_1(inputs)     x <- tf$nn$relu(x)     x <- self$dropout(x, training = training)     self$linear_3(x)   } )  mlp <- MLPWithDropout() y_train <- mlp(tf$ones(shape(2, 2)), training=TRUE) y_test <- mlp(tf$ones(shape(2, 2)), training=FALSE)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"the-functional-api-for-model-building","dir":"Articles","previous_headings":"","what":"The Functional API for model-building","title":"Introduction to Keras for Researchers","text":"build deep learning models, don’t use object-oriented programming time. layers ’ve seen far can also composed functionally, like (call “Functional API”): Functional API tends concise subclassing, provides advantages (generally advantages functional, typed languages provide untyped OO development). However, can used define DAGs layers – recursive networks defined Layer subclasses instead. Learn Functional API . research workflows, may often find mix--matching OO models Functional models. Note Model class also features built-training & evaluation loops: fit(), predict() evaluate() (configured via compile() method). built-functions give access following built-training infrastructure features: Callbacks. can leverage built-callbacks early-stopping, model checkpointing, monitoring training TensorBoard. can also implement custom callbacks needed. Distributed training. can easily scale training multiple GPUs, TPU, even multiple machines tf.distribute API – changes code. Step fusing. steps_per_execution argument Model.compile(), can process multiple batches single tf.function call, greatly improves device utilization TPUs. won’t go details, provide simple code example . leverages built-training infrastructure implement MNIST example . can always subclass Model class (works exactly like subclassing Layer) want leverage built-training loops OO models. Just override Model$train_step() customize happens fit() retaining support built-infrastructure features outlined – callbacks, zero-code distribution support, step fusing support. may also override test_step() customize happens evaluate(), override predict_step() customize happens predict(). information, please refer guide.","code":"# We use an `Input` object to describe the shape and dtype of the inputs. # This is the deep learning equivalent of *declaring a type*. # The shape argument is per-sample; it does not include the batch size. # The functional API focused on defining per-sample transformations. # The model we create will automatically batch the per-sample transformations, # so that it can be called on batches of data. inputs <- layer_input(shape = 16, dtype = \"float32\")  # We call layers on these \"type\" objects # and they return updated types (new shapes/dtypes). outputs <- inputs %>%   Linear(units = 32) %>% # We are reusing the Linear layer we defined earlier.   Dropout(rate = 0.5) %>% # We are reusing the Dropout layer we defined earlier.   Linear(units = 10)  # A functional `Model` can be defined by specifying inputs and outputs. # A model is itself a layer like any other. model <- keras_model(inputs, outputs)  # A functional model already has weights, before being called on any data. # That's because we defined its input shape in advance (in `Input`). length(model$weights) ## [1] 4 # Let's call our model on some data, for fun. y <- model(tf$ones(shape(2, 16))) y$shape ## TensorShape([2, 10]) # You can pass a `training` argument in `__call__` # (it will get passed down to the Dropout layer). y <- model(tf$ones(shape(2, 16)), training=TRUE) inputs <- layer_input(shape = 784, dtype=\"float32\") outputs <- inputs %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10) model <- keras_model(inputs, outputs)  # Specify the loss, optimizer, and metrics with `compile()`. model %>% compile(     loss = loss_sparse_categorical_crossentropy(from_logits=TRUE),     optimizer=optimizer_adam(learning_rate=1e-3),     metrics=list(metric_sparse_categorical_accuracy()), )  # Train the model with the dataset for 2 epochs. model %>% fit(dataset, epochs=2) ## Epoch 1/2 ## 938/938 - 4s - 4ms/step - loss: 0.3958 - sparse_categorical_accuracy: 0.8866 ## Epoch 2/2 ## 938/938 - 1s - 960us/step - loss: 0.1888 - sparse_categorical_accuracy: 0.9443 predictions <- model %>% predict(dataset) ## 938/938 - 1s - 1ms/step model %>% evaluate(dataset) ## 938/938 - 1s - 1ms/step - loss: 0.1763 - sparse_categorical_accuracy: 0.9454 ## $loss ## [1] 0.1763445 ## ## $sparse_categorical_accuracy ## [1] 0.9454167 CustomModel <- new_model_class(   \"CustomModel\",   initialize = function(...) {     super$initialize(...)     self$loss_tracker <- metric_mean(name=\"loss\")     self$accuracy <- metric_sparse_categorical_accuracy()     self$loss_fn <- loss_sparse_categorical_crossentropy(from_logits=TRUE)     self$optimizer <- optimizer_adam(learning_rate=1e-3)   },   train_step = function(data) {     c(x, y = NULL, sample_weight = NULL) %<-% data     with(tf$GradientTape() %as% tape, {       y_pred <- self(x, training=TRUE)       loss <- self$loss_fn(y = y, y_pred = y_pred, sample_weight=sample_weight)     })     gradients <- tape$gradient(loss, self$trainable_variables)     self$optimizer$apply_gradients(       zip_lists(gradients, self$trainable_variables)     )      # Update metrics (includes the metric that tracks the loss)     self$loss_tracker$update_state(loss)     self$accuracy$update_state(y, y_pred, sample_weight=sample_weight)     # Return a list mapping metric names to current value     list(       loss = self$loss_tracker$result(),       accuracy = self$accuracy$result()     )   },   metrics = mark_active(function() {     list(self$loss_tracker, self$accuracy)   }) )  inputs <- layer_input(shape = 784, dtype=\"float32\") outputs <- inputs %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10) model <- CustomModel(inputs, outputs) model %>% compile() model %>% fit(dataset, epochs=2) ## Epoch 1/2 ## 938/938 - 2s - 2ms/step - loss: 0.3869 - sparse_categorical_accuracy: 0.8924 ## Epoch 2/2 ## 938/938 - 1s - 1ms/step - loss: 0.2163 - sparse_categorical_accuracy: 0.9370"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"end-to-end-experiment-example-1-variational-autoencoders-","dir":"Articles","previous_headings":"","what":"End-to-end experiment example 1: variational autoencoders.","title":"Introduction to Keras for Researchers","text":"things ’ve learned far: Layer encapsulates state (created __init__ build) computation (defined call). Layers can recursively nested create new, bigger computation blocks. can easily write highly hackable training loops opening GradientTape, calling model inside tape’s scope, retrieving gradients applying via optimizer. can speed training loops using @tf.function decorator. Layers can create track losses (typically regularization losses) via self.add_loss(). Let’s put things together end--end example: ’re going implement Variational AutoEncoder (VAE). ’ll train MNIST digits. VAE subclass Layer, built nested composition layers subclass Layer. feature regularization loss (KL divergence). model definition. First, Encoder class, uses Sampling layer map MNIST digit latent-space triplet (z_mean, z_log_var, z). Next, Decoder class, maps probabilistic latent space coordinates back MNIST digit. Finally, VariationalAutoEncoder composes together encoder decoder, creates KL divergence regularization loss via add_loss(). Now, let’s write training loop. training step decorated @tf.function compile super fast graph function. can see, building training type model Keras quick painless.","code":"Sampling <- new_layer_class(   \"Sampling\",   call = function(inputs) {     c(z_mean, z_log_var) %<-% inputs     batch <- op_shape(z_mean)[[1]]     dim <- op_shape(z_mean)[[2]]     epsilon <- random_normal(shape = c(batch, dim))     z_mean + op_exp(0.5 * z_log_var) * epsilon   } )  Encoder <- new_layer_class(   \"Encoder\",   initialize = function(latent_dim = 32, intermediate_dim = 64, ...) {     super$initialize(...)     self$dense_proj <- layer_dense(units = intermediate_dim, activation = \"relu\")     self$dense_mean <- layer_dense(units = latent_dim)     self$dense_log_var <- layer_dense(units = latent_dim)     self$sampling <- Sampling()   },   call = function(inputs) {     x <- self$dense_proj(inputs)     z_mean <- self$dense_mean(x)     z_log_var <- self$dense_log_var(x)     z <- self$sampling(list(z_mean, z_log_var))     list(z_mean, z_log_var, z)   } ) Decoder <- new_layer_class(   \"Decoder\",   initialize = function(original_dim, intermediate_dim = 64, ...) {     super$initialize(...)     self$dense_proj <- layer_dense(units = intermediate_dim, activation = \"relu\")     self$dense_output <- layer_dense(units = original_dim, activation = \"sigmoid\")   },   call = function(inputs) {     x <- self$dense_proj(inputs)     self$dense_output(x)   } ) VariationalAutoEncoder <- new_model_class(   \"VariationalAutoEncoder\",   initialize = function(original_dim,         intermediate_dim=64,         latent_dim=32,         name=\"autoencoder\", ...) {     super$initialize(name = name, ...)     self$original_dim <- original_dim     self$encoder <- Encoder(       latent_dim = latent_dim,       intermediate_dim = intermediate_dim     )     self$decoder <- Decoder(       original_dim = original_dim,       intermediate_dim = intermediate_dim     )   },   call = function(inputs) {     c(z_mean, z_log_var, z) %<-% self$encoder(inputs)     reconstructed <- self$decoder(z)     # Add KL divergence regularization loss.     kl_loss <- -0.5 * op_mean(       z_log_var - op_square(z_mean) - op_exp(z_log_var) + 1     )     self$add_loss(kl_loss)     reconstructed   } ) # Our model. vae <- VariationalAutoEncoder(   original_dim = 784,   intermediate_dim = 64,   latent_dim = 32 )  # Loss and optimizer. loss_fn <- loss_mean_squared_error() optimizer = optimizer_adam(learning_rate=1e-3)  # Prepare a dataset. c(c(x_train, .), .) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(x_train) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   tfdatasets::dataset_batch(32)   training_step <- tf_function(function(x) {   with(tf$GradientTape() %as% tape, {     reconstructed <- vae(x)  # Compute input reconstruction.     # Compute loss.     loss <- loss_fn(x, reconstructed)     loss <- loss + op_sum(vae$losses)  # Add KLD term.   })   # Update the weights of the VAE.   grads <- tape$gradient(loss, vae$trainable_weights)   optimizer$apply_gradients(zip_lists(grads, vae$trainable_weights))   loss })  losses <- c()  # Keep track of the losses over time. coro::loop(for(data in dataset) {   loss <- training_step(data)    # Logging.   losses[length(losses) + 1] <- as.numeric(loss)   if (length(losses) %% 100 == 0) {     cat(\"Step:\", length(losses), \"Loss:\", mean(losses), \"\\n\")   }   # Stop after 1000 steps.   # Training the model to convergence is left   # as an exercise to the reader.   if (length(losses) >= 1000) {     break   } }) ## Step: 100 Loss: 0.1270978 ## Step: 200 Loss: 0.1003238 ## Step: 300 Loss: 0.09001128 ## Step: 400 Loss: 0.08493649 ## Step: 500 Loss: 0.08171404 ## Step: 600 Loss: 0.07926706 ## Step: 700 Loss: 0.07790599 ## Step: 800 Loss: 0.07670419 ## Step: 900 Loss: 0.07570736 ## Step: 1000 Loss: 0.07476593"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"end-to-end-experiment-example-2-hypernetworks-","dir":"Articles","previous_headings":"","what":"End-to-end experiment example 2: hypernetworks.","title":"Introduction to Keras for Researchers","text":"Let’s take look another kind research experiment: hypernetworks. idea use small deep neural network (hypernetwork) generate weights larger network (main network). Let’s implement really trivial hypernetwork: ’ll use small 2-layer network generate weights larger 3-layer network. training loop. batch data: use hypernetwork generate array weight coefficients, weights_pred reshape coefficients kernel & bias tensors main_network run forward pass main_network compute actual MNIST predictions run backprop weights hypernetwork minimize final classification loss Implementing arbitrary research ideas Keras straightforward highly productive. Imagine trying 25 ideas per day (20 minutes per experiment average)! Keras designed go idea results fast possible, believe key great research. hope enjoyed quick introduction. Let us know build Keras!","code":"input_dim <- 784 classes <- 10  # This is the main network we'll actually use to predict labels. inputs <- layer_input(shape = input_dim) dense1 <- layer_dense(units = 64, activation = \"relu\") dense1$built <- TRUE  dense2 <- layer_dense(units = classes) dense2$built <- TRUE  outputs <- inputs %>% dense1() %>% dense2() main_network <- keras_model(inputs, outputs)  # This is the number of weight coefficients to generate. Each layer in the # main network requires output_dim * input_dim + output_dim coefficients. num_weights_to_generate <- (classes * 64 + classes) + (64 * input_dim + 64)  # This is the hypernetwork that generates the weights of the `main_network` above. hypernetwork <- keras_model_sequential() %>%   layer_dense(units=16, activation=\"relu\") %>%   layer_dense(units=num_weights_to_generate, activation=\"sigmoid\") # Loss and optimizer. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE) optimizer <- optimizer_adam(learning_rate=1e-4)  # Prepare a dataset. c(c(x_train, y_train), .) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(list(x_train, y_train)) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   # We'll use a batch size of 1 for this experiment.   tfdatasets::dataset_batch(1)  train_step <- function(x, y) {   with(tf$GradientTape() %as% tape, {     weights_pred <- hypernetwork(x)      # Reshape them to the expected shapes for w and b for the outer model.     # Layer 1 kernel.     start_index <- 1     w1_shape <- c(input_dim, 64)     w1_coeffs <- weights_pred[, start_index:(start_index + prod(w1_shape) - 1)]     w1 <- tf$reshape(w1_coeffs, as.integer(w1_shape))     start_index <- start_index + prod(w1_shape)      # Layer 1 bias.     b1_shape <- c(64)     b1_coeffs <- weights_pred[, start_index:(start_index + prod(b1_shape) - 1)]     b1 <- tf$reshape(b1_coeffs, as.integer(b1_shape))     start_index <- start_index + prod(b1_shape)      # Layer 2 kernel.     w2_shape <- c(64, classes)     w2_coeffs <- weights_pred[, start_index:(start_index + prod(w2_shape) - 1)]     w2 <- tf$reshape(w2_coeffs, as.integer(w2_shape))     start_index <- start_index + prod(w2_shape)      # Layer 2 bias.     b2_shape <- c(classes)     b2_coeffs <- weights_pred[, start_index:(start_index + prod(b2_shape) - 1)]     b2 <- tf$reshape(b2_coeffs, as.integer(b2_shape))     start_index <- start_index + prod(b2_shape)      # Set the weight predictions as the weight variables on the outer model.     dense1$kernel <- w1     dense1$bias <- b1     dense2$kernel <- w2     dense2$bias <- b2      # Inference on the outer model.     preds <- main_network(x)     loss <- loss_fn(y, preds)   })    grads <- tape$gradient(loss, hypernetwork$trainable_weights)   optimizer$apply_gradients(zip_lists(grads, hypernetwork$trainable_weights))   loss }  losses <- c()  # Keep track of the losses over time. coro::loop(for (data in dataset) {   x <- data[[1]]   y <- data[[2]]   loss <- train_step(x, y)    # Logging.   losses[length(losses) + 1] <- as.numeric(loss)   if (length(losses) %% 100 == 0) {     cat(\"Step:\", length(losses), \"Loss:\", mean(losses), \"\\n\")   }   # Stop after 1000 steps.   # Training the model to convergence is left   # as an exercise to the reader.   if (length(losses) >= 1000) {     break   } }) ## Step: 100 Loss: 2.536778 ## Step: 200 Loss: 2.236472 ## Step: 300 Loss: 2.119417 ## Step: 400 Loss: 2.040341 ## Step: 500 Loss: 1.949125 ## Step: 600 Loss: 1.859384 ## Step: 700 Loss: 1.845726 ## Step: 800 Loss: 1.820594 ## Step: 900 Loss: 1.771334 ## Step: 1000 Loss: 1.730648"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Making new layers and models via subclassing","text":"guide cover everything need know build subclassed layers models. particular, ’ll learn following features: Layer class add_weight() method Trainable non-trainable weights build() method Making sure layers can used backend add_loss() method training argument call() mask argument call() Making sure layers can serialized Let’s dive .","code":""},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Making new layers and models via subclassing","text":"","code":"library(keras3) library(tensorflow, exclude = c(\"set_random_seed\", \"shape\")) library(tfdatasets, exclude = \"shape\")"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"the-layer-class-the-combination-of-state-weights-and-some-computation","dir":"Articles","previous_headings":"","what":"The Layer class: the combination of state (weights) and some computation","title":"Making new layers and models via subclassing","text":"One central abstractions Keras Layer class. layer encapsulates state (layer’s “weights”) transformation inputs outputs (“call”, layer’s forward pass). ’s densely-connected layer. two state variables: variables w b. use layer calling tensor input(s), much like R function. Note weights w b automatically tracked layer upon set layer attributes:","code":"layer_linear <- Layer(\"Linear\",    initialize = function(units = 32, input_dim = 32, ...) {     super$initialize(...)     self$w <- self$add_weight(       shape = shape(input_dim, units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(units),       initializer = \"zeros\",       trainable = TRUE     )   },    call = function(inputs) {     op_matmul(inputs, self$w) + self$b   } ) x <- op_ones(c(2, 2)) linear_layer <- layer_linear(units = 4, input_dim = 2) y <- linear_layer(x) print(y) ## tf.Tensor( ## [[0.02153057 0.15450525 0.0205495  0.04493225] ##  [0.02153057 0.15450525 0.0205495  0.04493225]], shape=(2, 4), dtype=float32) linear_layer$weights ## [[1]] ## <KerasVariable shape=(2, 4), dtype=float32, path=linear/variable> ## ## [[2]] ## <KerasVariable shape=(4), dtype=float32, path=linear/variable_1>"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"layers-can-have-non-trainable-weights","dir":"Articles","previous_headings":"","what":"Layers can have non-trainable weights","title":"Making new layers and models via subclassing","text":"Besides trainable weights, can add non-trainable weights layer well. weights meant taken account backpropagation, training layer. ’s add use non-trainable weight: ’s part layer$weights, gets categorized non-trainable weight:","code":"layer_compute_sum <- Layer(   \"ComputeSum\",   initialize = function(input_dim) {     super$initialize()     self$total <- self$add_weight(       initializer = \"zeros\",       shape = shape(input_dim),       trainable = FALSE     )   },   call = function(inputs) {     self$total$assign_add(op_sum(inputs, axis = 1))     self$total   } )  x <- op_ones(c(2, 2)) my_sum <- layer_compute_sum(input_dim = 2) y <- my_sum(x) print(as.array(y)) ## [1] 2 2 y <- my_sum(x) print(as.array(y)) ## [1] 4 4 cat(\"weights:\", length(my_sum$weights)) ## weights: 1 cat(\"non-trainable weights:\", length(my_sum$non_trainable_weights)) ## non-trainable weights: 1 # It's not included in the trainable weights: cat(\"trainable_weights:\", length(my_sum$trainable_weights)) ## trainable_weights: 0"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"best-practice-deferring-weight-creation-until-the-shape-of-the-inputs-is-known","dir":"Articles","previous_headings":"","what":"Best practice: deferring weight creation until the shape of the inputs is known","title":"Making new layers and models via subclassing","text":"Linear layer took input_dim argument used compute shape weights w b initialize(): many cases, may know advance size inputs, like lazily create weights value becomes known, time instantiating layer. Keras API, recommend creating layer weights build(self, inputs_shape) method layer. Like : call() method layer automatically run build first time called. now layer ’s lazy thus easier use: Implementing build() separately shown nicely separates creating weights using weights every call.","code":"layer_linear <- Layer(\"Linear\",    initialize = function(units = 32, input_dim = 32, ...) {     super$initialize(...)     self$w <- self$add_weight(       shape = shape(input_dim, units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(units),       initializer = \"zeros\",       trainable = TRUE     )   },    call = function(inputs) {     op_matmul(inputs, self$w) + self$b   } ) layer_linear <- Layer(   \"Linear\",   initialize = function(units = 32, ...) {     self$units <- as.integer(units)     super$initialize(...)   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     op_matmul(inputs, self$w) + self$b   } ) # At instantiation, we don't know on what inputs this is going to get called linear_layer <- layer_linear(units = 32)  # The layer's weights are created dynamically the first time the layer is called y <- linear_layer(x)"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"layers-are-recursively-composable","dir":"Articles","previous_headings":"","what":"Layers are recursively composable","title":"Making new layers and models via subclassing","text":"assign Layer instance attribute another Layer, outer layer start tracking weights created inner layer. recommend creating sublayers initialize() method leave first call() trigger building weights.","code":"MLPBlock <- Layer(   \"MLPBlock\",   initialize = function() {     super$initialize()     self$linear_1 <- layer_linear(units = 32)     self$linear_2 <- layer_linear(units = 32)     self$linear_3 <- layer_linear(units = 1)   },   call = function(inputs) {     inputs |>       self$linear_1() |>       activation_relu() |>       self$linear_2() |>       activation_relu() |>       self$linear_3()   } )  mlp <- MLPBlock() # The first call to the `mlp` will create the weights y <- mlp(op_ones(shape = c(3, 64)))  cat(\"weights:\", length(mlp$weights), \"\\n\") ## weights: 6 cat(\"trainable weights:\", length(mlp$trainable_weights), \"\\n\") ## trainable weights: 6"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"backend-agnostic-layers-and-backend-specific-layers","dir":"Articles","previous_headings":"","what":"Backend-agnostic layers and backend-specific layers","title":"Making new layers and models via subclassing","text":"long layer uses APIs ops namespace (ie. using functions starting op_), (Keras namespaces activations_*, random_*, layer_*), can used backend – TensorFlow, JAX, PyTorch. layers ’ve seen far guide work Keras backends. ops namespace gives access : NumPy API, e.g. op_matmul, op_sum, op_reshape, op_stack, etc. Neural networks-specific APIs op_softmax, op_conv, op_binary_crossentropy, op_relu, etc. can also use backend-native APIs layers (tf$nn functions), , layer usable backend question. instance, write following JAX-specific layer using jax$numpy: equivalent TensorFlow-specific layer: equivalent PyTorch-specific layer: cross-backend compatibility tremendously useful property, strongly recommend seek always make layers backend-agnostic leveraging Keras APIs.","code":"# keras3::install_keras(backend = c(\"jax\")) jax <- reticulate::import(\"jax\")  Linear <- new_layer_class(   ...   call = function(inputs) {     jax$numpy$matmul(inputs, self$w) + self$b   } ) library(tensorflow)  Linear <- new_layer_class(   ...   call = function(inputs) {     tf$matmul(inputs, self$w) + self$b   } ) torch <- reticulate::import(\"torch\")  Linear <- new_layer_class(   ...   call = function(inputs) {     torch$matmul(inputs, self$w) + self$b   } )"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"the-add_loss-method","dir":"Articles","previous_headings":"","what":"The add_loss() method","title":"Making new layers and models via subclassing","text":"writing call() method layer, can create loss tensors want use later, writing training loop. doable calling self$add_loss(value): losses (including created inner layer) can retrieved via layer$losses. property reset start every call top-level layer, layer$losses always contains loss values created last forward pass. addition, loss property also contains regularization losses created weights inner layer: losses meant taken account writing custom training loops. also work seamlessly fit() (get automatically summed added main loss, ):","code":"# A layer that creates an activity regularization loss layer_activity_regularization <- Layer(   \"ActivityRegularizationLayer\",   initialize = function(rate = 1e-2) {     self$rate <- as.numeric(rate)     super$initialize()   },   call = function(inputs) {     self$add_loss(self$rate * op_mean(inputs))     inputs   } ) layer_outer <- Layer(   \"OuterLayer\",   initialize = function() {     super$initialize()     self$activity_reg <- layer_activity_regularization(rate = 1e-2)   },   call = function(inputs) {     self$activity_reg(inputs)     inputs   } )  layer <- layer_outer() # No losses yet since the layer has never been called cat(\"losses:\", length(layer$losses), \"\\n\") ## losses: 0 x <- layer(op_zeros(c(1, 1))) # We created one loss value cat(\"losses:\", length(layer$losses), \"\\n\") ## losses: 1 # `layer$losses` gets reset at the start of each call x <- layer(op_zeros(c(1, 1))) # This is the loss created during the call above cat(\"losses:\", length(layer$losses), \"\\n\") ## losses: 1 layer_outer_with_kernel_regularizer <- Layer(   \"OuterLayerWithKernelRegularizer\",   initialize = function() {     super$initialize()     self$dense <- layer_dense(units = 32,                               kernel_regularizer = regularizer_l2(1e-3))   },   call = function(inputs) {     self$dense(inputs)   } )  layer <- layer_outer_with_kernel_regularizer() x <- layer(op_zeros(c(1, 1)))  # This is `1e-3 * sum(layer$dense$kernel ** 2)`, # created by the `kernel_regularizer` above. print(layer$losses) ## [[1]] ## tf.Tensor(0.002025157, shape=(), dtype=float32) inputs <- keras_input(shape = 3) outputs <- inputs |> layer_activity_regularization() model <- keras_model(inputs, outputs)  # If there is a loss passed in `compile`, the regularization # losses get added to it model |> compile(optimizer = \"adam\", loss = \"mse\") model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1) ## 1/1 - 0s - 118ms/step - loss: 1.8971 # It's also possible not to pass any loss in `compile`, # since the model already has a loss to minimize, via the `add_loss` # call during the forward pass! model |> compile(optimizer = \"adam\") model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1) ## 1/1 - 0s - 73ms/step - loss: -3.3344e-03"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"you-can-optionally-enable-serialization-on-your-layers","dir":"Articles","previous_headings":"","what":"You can optionally enable serialization on your layers","title":"Making new layers and models via subclassing","text":"need custom layers serializable part Functional model, can optionally implement get_config() method: Note initialize() method base Layer class takes keyword arguments, particular name dtype. ’s good practice pass arguments parent class initialize() include layer config: need flexibility deserializing layer config, can also override from_config() class method. base implementation from_config(): learn serialization saving, see complete guide saving serializing models.","code":"layer_linear <- Layer(   \"Linear\",   initialize = function(units = 32) {     self$units <- as.integer(units)     super$initialize()   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     op_matmul(inputs, self$w) + self$b   },   get_config = function() {     list(units = self$units)   } )  # Now you can recreate the layer from its config: layer <- layer_linear(units = 64) config <- get_config(layer) str(config) ## List of 1 ##  $ units: int 64 ##  - attr(*, \"__class__\")=<class '<r-globalenv>.Linear'> new_layer <- from_config(config) Linear <- new_layer_class(   \"Linear\",   initialize = function(units = 32, ...) {     self$units <- as.integer(units)     super$initialize(...)   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     op_matmul(inputs, self$w) + self$b   },   get_config = function() {     list(units = self$units)   } )  layer <- Linear(units = 64) config <- get_config(layer) str(config) ## List of 1 ##  $ units: int 64 ##  - attr(*, \"__class__\")=<class '<r-globalenv>.Linear'> new_layer <- from_config(config) Layer(   ...,   from_config = function(config) {     # calling `__class__`() creates a new instance and calls initialize()     do.call(`__class__`, config)   } )"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"privileged-training-argument-in-the-call-method","dir":"Articles","previous_headings":"","what":"Privileged training argument in the call() method","title":"Making new layers and models via subclassing","text":"layers, particular BatchNormalization layer Dropout layer, different behaviors training inference. layers, standard practice expose training (boolean) argument call() method. exposing argument call(), enable built-training evaluation loops (e.g. fit()) correctly use layer training inference.","code":"layer_custom_dropout <- Layer(   \"CustomDropout\",   initialize = function(rate, ...) {     super$initialize(...)     self$rate <- rate     self$seed_generator <- random_seed_generator(1337)   },   call = function(inputs, training = NULL) {     if (isTRUE(training))       return(random_dropout(inputs, rate = self$rate,                             seed = self.seed_generator))     inputs   } )"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"privileged-mask-argument-in-the-call-method","dir":"Articles","previous_headings":"","what":"Privileged mask argument in the call() method","title":"Making new layers and models via subclassing","text":"privileged argument supported call() mask argument. find Keras RNN layers. mask boolean tensor (one boolean value per timestep input) used skip certain input timesteps processing timeseries data. Keras automatically pass correct mask argument call() layers support , mask generated prior layer. Mask-generating layers Embedding layer configured mask_zero = TRUE, Masking layer.","code":""},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"the-model-class","dir":"Articles","previous_headings":"","what":"The Model class","title":"Making new layers and models via subclassing","text":"general, use Layer class define inner computation blocks, use Model class define outer model – object train. instance, ResNet50 model, several ResNet blocks subclassing Layer, single Model encompassing entire ResNet50 network. Model class API Layer, following differences: exposes built-training, evaluation, prediction loops (fit(), evaluate(), predict()). exposes list inner layers, via model$layers property. exposes saving serialization APIs (save(), save_weights()…) Effectively, Layer class corresponds refer literature “layer” (“convolution layer” “recurrent layer”) “block” (“ResNet block” “Inception block”). Meanwhile, Model class corresponds referred literature “model” (“deep learning model”) “network” (“deep neural network”). ’re wondering, “use Layer class Model class?”, ask : need call fit() ? need call save() ? , go Model. (either class just block bigger system, writing training & saving code ), use Layer. instance, take mini-resnet example , use build Model train fit(), save save_weights():","code":"ResNet <- Model(   \"ResNet\",   initialize = function(num_classes = 1000, ...) {     super$initialize(...)     self$block_1 <- layer_resnet_block()     self$block_2 <- layer_resnet_block()     self$global_pool <- layer_global_average_pooling_2d()     self$classifier <- layer_dense(num_classes)   },   call = function(inputs) {     inputs |>       self$block_1() |>       self$block_2() |>       self$global_pool() |>       self$classifier()   } )  resnet <- ResNet() dataset <- ... resnet |> fit(dataset, epochs=10) resnet |> save_model(\"filepath.keras\")"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"putting-it-all-together-an-end-to-end-example","dir":"Articles","previous_headings":"","what":"Putting it all together: an end-to-end example","title":"Making new layers and models via subclassing","text":"’s ’ve learned far: Layer encapsulate state (created initialize() build()) computation (defined call()). Layers can recursively nested create new, bigger computation blocks. Layers backend-agnostic long use Keras APIs. can use backend-native APIs (jax$numpy, torch$nn tf$nn), layer usable specific backend. Layers can create track losses (typically regularization losses) via add_loss(). outer container, thing want train, Model. Model just like Layer, added training serialization utilities. Let’s put things together end--end example: ’re going implement Variational AutoEncoder (VAE) backend-agnostic fashion – runs TensorFlow, JAX, PyTorch. ’ll train MNIST digits. VAE subclass Model, built nested composition layers subclass Layer. feature regularization loss (KL divergence). Let’s train MNIST using fit() API:","code":"layer_sampling <- Layer(   \"Sampling\",   initialize = function(...) {     super$initialize(...)     self$seed_generator <- random_seed_generator(1337)   },   call = function(inputs) {     c(z_mean, z_log_var) %<-% inputs     batch <- op_shape(z_mean)[[1]]     dim <- op_shape(z_mean)[[2]]     epsilon <- random_normal(shape = c(batch, dim),                              seed=self$seed_generator)     z_mean + op_exp(0.5 * z_log_var) * epsilon   } )  # Maps MNIST digits to a triplet (z_mean, z_log_var, z). layer_encoder <- Layer(   \"Encoder\",   initialize = function(latent_dim = 32, intermediate_dim = 64, ...) {     super$initialize(...)     self$dense_proj <-       layer_dense(units = intermediate_dim,  activation = \"relu\")     self$dense_mean <- layer_dense(units = latent_dim)     self$dense_log_var <- layer_dense(units = latent_dim)     self$sampling <- layer_sampling()   },   call = function(inputs) {     x <- self$dense_proj(inputs)     z_mean <- self$dense_mean(x)     z_log_var <- self$dense_log_var(x)     z <- self$sampling(list(z_mean, z_log_var))     list(z_mean, z_log_var, z)   } )  # Converts z, the encoded digit vector, back into a readable digit. layer_decoder <- Layer(   \"Decoder\",   initialize = function(original_dim, intermediate_dim = 64, ...) {     super$initialize(...)     self$dense_proj <-       layer_dense(units = intermediate_dim, activation = \"relu\")     self$dense_output <-       layer_dense(units = original_dim, activation = \"sigmoid\")   },   call = function(inputs) {     x <- self$dense_proj(inputs)     self$dense_output(x)   } )  # Combines the encoder and decoder into an end-to-end model for training. VariationalAutoEncoder <- Model(   \"VariationalAutoEncoder\",    initialize = function(original_dim, intermediate_dim = 64, latent_dim = 32,                         name = \"autoencoder\", ...) {     super$initialize(name = name, ...)     self$original_dim <- original_dim     self$encoder <- layer_encoder(latent_dim = latent_dim,                             intermediate_dim = intermediate_dim)     self$decoder <- layer_decoder(original_dim = original_dim,                             intermediate_dim = intermediate_dim)   },    call = function(inputs) {     c(z_mean, z_log_var, z) %<-% self$encoder(inputs)     reconstructed <- self$decoder(z)     # Add KL divergence regularization loss.     kl_loss <- -0.5 * op_mean(z_log_var - op_square(z_mean) - op_exp(z_log_var) + 1)     self$add_loss(kl_loss)     reconstructed   } ) c(c(x_train, .), .) %<-% dataset_mnist() x_train <- x_train |>   op_reshape(c(60000, 784)) |>   op_cast(\"float32\") |>   op_divide(255)  original_dim <- 784 vae <- VariationalAutoEncoder(   original_dim = 784,   intermediate_dim = 64,   latent_dim = 32 )  optimizer <- optimizer_adam(learning_rate = 1e-3) vae |> compile(optimizer, loss = loss_mean_squared_error())  vae |> fit(x_train, x_train, epochs = 2, batch_size = 64) ## Epoch 1/2 ## 938/938 - 4s - 5ms/step - loss: 0.0748 ## Epoch 2/2 ## 938/938 - 1s - 815us/step - loss: 0.0676"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"The Sequential model","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"when-to-use-a-sequential-model","dir":"Articles","previous_headings":"","what":"When to use a Sequential model","title":"The Sequential model","text":"Sequential model appropriate plain stack layers layer exactly one input tensor one output tensor. Schematically, following Sequential model: equivalent function: Sequential model appropriate : model multiple inputs multiple outputs layers multiple inputs multiple outputs need layer sharing want non-linear topology (e.g. residual connection, multi-branch model)","code":"model <- keras_model_sequential() |>   layer_dense(units = 2, activation = \"relu\", name = \"layer1\") |>   layer_dense(units = 3, activation = \"relu\", name = \"layer2\") |>   layer_dense(units = 4, name = \"layer3\")  # Call model on a test input x <- op_ones(c(3, 3)) y <- model(x) # Create 3 layers layer1 <- layer_dense(units = 2, activation=\"relu\", name=\"layer1\") layer2 <- layer_dense(units = 3, activation=\"relu\", name=\"layer2\") layer3 <- layer_dense(units = 4, name=\"layer3\")  # Call layers on a test input x <- op_ones(c(3, 3)) y <- x |> layer1() |> layer2() |> layer3()"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"creating-a-sequential-model","dir":"Articles","previous_headings":"","what":"Creating a Sequential model","title":"The Sequential model","text":"can create Sequential model piping layers keras_model_sequential() object: passing list layers keras_model_sequential(): layers accessible via layers attribute: can also create Sequential model incrementally: Note ’s also corresponding pop_layer() method remove layers: Sequential model behaves much like stack layers. Also note Sequential constructor accepts name argument, just like layer model Keras. useful annotate TensorBoard graphs semantically meaningful names.","code":"model <- keras_model_sequential() |>   layer_dense(units = 2, activation = \"relu\") |>   layer_dense(units = 3, activation = \"relu\") |>   layer_dense(units = 4) model <- keras_model_sequential(layers = list(   layer_dense(units = 2, activation = \"relu\"),   layer_dense(units = 3, activation = \"relu\"),   layer_dense(units = 4) )) model$layers ## [[1]] ## <Dense name=dense_3, built=False> ##  signature: (*args, **kwargs) ## ## [[2]] ## <Dense name=dense_4, built=False> ##  signature: (*args, **kwargs) ## ## [[3]] ## <Dense name=dense_5, built=False> ##  signature: (*args, **kwargs) model <- keras_model_sequential() model |> layer_dense(units = 2, activation=\"relu\") model |> layer_dense(units = 3, activation=\"relu\") model |> layer_dense(units = 4) model |> pop_layer() length(model$layers)  # 2 ## [1] 2 model <- keras_model_sequential(name = \"my_sequential\") model |> layer_dense(units = 2, activation=\"relu\", name = \"layer1\") model |> layer_dense(units = 3, activation=\"relu\", name = \"layer2\") model |> layer_dense(units = 4, name = \"layer3\")"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"specifying-the-input-shape-in-advance","dir":"Articles","previous_headings":"","what":"Specifying the input shape in advance","title":"The Sequential model","text":"Generally, layers Keras need know shape inputs order able create weights. create layer like , initially, weights: creates weights first time called input, since shape weights depends shape inputs: Naturally, also applies Sequential models. instantiate Sequential model without input shape, isn’t “built”: weights (calling model$weights results error stating just ). weights created model first sees input data: model “built”, can call summary() method display contents: However, can useful building Sequential model incrementally able display summary model far, including current output shape. case, start model passing input_shape argument model, knows input shape start: Models built predefined input shape like always weights (even seeing data) always defined output shape. general, ’s recommended best practice always specify input shape Sequential model advance know .","code":"layer <- layer_dense(units = 3) layer$weights  # Empty ## list() # Call layer on a test input x <- op_ones(c(1, 4)) y <- layer(x) layer$weights  # Now it has weights, of shape (4, 3) and (3,) ## [[1]] ## <KerasVariable shape=(4, 3), dtype=float32, path=dense_9/kernel> ## ## [[2]] ## <KerasVariable shape=(3), dtype=float32, path=dense_9/bias> model <- keras_model_sequential() |>   layer_dense(units = 2, activation = \"relu\") |>   layer_dense(units = 3, activation = \"relu\") |>   layer_dense(units = 4) # No weights at this stage!  # At this point, you can't do this: # model$weights   # Call the model on a test input x <- op_ones(c(1, 4)) y <- model(x) length(model$weights) ## [1] 6 summary(model) ## Model: \"sequential_4\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_10 (Dense)                │ (1, 2)                 │            10 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_11 (Dense)                │ (1, 3)                 │             9 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_12 (Dense)                │ (1, 4)                 │            16 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 35 (140.00 B) ##  Trainable params: 35 (140.00 B) ##  Non-trainable params: 0 (0.00 B) model <- keras_model_sequential(input_shape = 4) |>   layer_dense(units = 2, activation = \"relu\") summary(model) ## Model: \"sequential_5\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_13 (Dense)                │ (None, 2)              │            10 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 10 (40.00 B) ##  Trainable params: 10 (40.00 B) ##  Non-trainable params: 0 (0.00 B) model$layers ## [[1]] ## <Dense name=dense_13, built=True> ##  signature: (*args, **kwargs)"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"a-common-debugging-workflow-add-layers-summary","dir":"Articles","previous_headings":"","what":"A common debugging workflow: add layers + summary()","title":"The Sequential model","text":"building new Sequential architecture, ’s useful incrementally stack layers |> frequently print model summaries. instance, enables monitor stack Conv2D MaxPooling2D layers downsampling image feature maps: practical, right? Note |> equivalent calling model$add(), modifies model -place, don’t need reassign model symbol step.","code":"model <- keras_model_sequential(input_shape = c(250, 250, 3)) |>   layer_conv_2d(filters = 32, kernel_size = 5, strides = 2, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(3, 3))  # Can you guess what the current output shape is at this point? Probably not. # Let's just print it: summary(model) ## Model: \"sequential_6\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 123, 123, 32)   │         2,432 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 121, 121, 32)   │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 40, 40, 32)     │             0 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 11,680 (45.62 KB) ##  Trainable params: 11,680 (45.62 KB) ##  Non-trainable params: 0 (0.00 B) # The answer was: (40, 40, 32), so we can keep downsampling...  model |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_max_pooling_2d(pool_size = 3) |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_max_pooling_2d(pool_size = 2)  # And now? summary(model) ## Model: \"sequential_6\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 123, 123, 32)   │         2,432 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 121, 121, 32)   │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 40, 40, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 38, 38, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_3 (Conv2D)               │ (None, 36, 36, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 12, 12, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_4 (Conv2D)               │ (None, 10, 10, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_5 (Conv2D)               │ (None, 8, 8, 32)       │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_2 (MaxPooling2D)  │ (None, 4, 4, 32)       │             0 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 48,672 (190.12 KB) ##  Trainable params: 48,672 (190.12 KB) ##  Non-trainable params: 0 (0.00 B) # Now that we have 4x4 feature maps, time to apply global max pooling. model |>   layer_global_max_pooling_2d()  # Finally, we add a classification layer. model |>   layer_dense(units = 10, activation = \"softmax\")"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"what-to-do-once-you-have-a-model","dir":"Articles","previous_headings":"","what":"What to do once you have a model","title":"The Sequential model","text":"model architecture ready, want : Train model, evaluate , run inference. See guide training & evaluation built-loops Save model disk restore . See guide serialization & saving.","code":""},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"feature-extraction-with-a-sequential-model","dir":"Articles","previous_headings":"","what":"Feature extraction with a Sequential model","title":"The Sequential model","text":"Sequential model built, behaves like Functional API model. means every layer input output attribute. attributes can used neat things, like quickly creating model extracts outputs intermediate layers Sequential model: ’s similar example extract features one layer:","code":"initial_model <- keras_model_sequential(input_shape = c(250, 250, 3)) |>   layer_conv_2d(filters = 32, kernel_size = 5, strides = 2, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\")   feature_extractor <- keras_model(     inputs = initial_model$inputs,     outputs = lapply(initial_model$layers, function(x) x$output), )  # Call feature extractor on test input. x <- op_ones(c(1, 250, 250, 3)) features <- feature_extractor(x) initial_model <-   keras_model_sequential(input_shape = c(250, 250, 3)) |>   layer_conv_2d(filters = 32, kernel_size = 5, strides = 2,                 activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\",                 name = \"my_intermediate_layer\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\")  feature_extractor <- keras_model(   inputs = initial_model$inputs,   outputs = get_layer(initial_model, \"my_intermediate_layer\")$output, )  # Call feature extractor on test input. x <- op_ones(c(1, 250, 250, 3)) features <- feature_extractor(x)"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"transfer-learning-with-a-sequential-model","dir":"Articles","previous_headings":"","what":"Transfer learning with a Sequential model","title":"The Sequential model","text":"Transfer learning consists freezing bottom layers model training top layers. aren’t familiar , make sure read guide transfer learning. two common transfer learning blueprint involving Sequential models. First, let’s say Sequential model, want freeze layers except last one. case, can call freeze_weights(). Alternatively, can iterate model$layers set layer$trainable <- FALSE layer, except last one. Like : Another common blueprint use Sequential model stack pre-trained model freshly initialized classification layers. Like : transfer learning, probably find frequently using two patterns. ’s need know Sequential models! find building models Keras, see: Guide Functional API Guide making new Layers & Models via subclassing","code":"model <- keras_model_sequential(input_shape = 784) |>   layer_dense(units = 32, activation = \"relu\") |>   layer_dense(units = 32, activation = \"relu\") |>   layer_dense(units = 32, activation = \"relu\") |>   layer_dense(units = 10) # Presumably you would want to first load pre-trained weights. model |> load_model_weights(...) # Freeze all layers except the last one. model |> freeze_weights(from = 1, to = -2) model # note the \"Trainable\" column now visible in the summary table ## Model: \"sequential_9\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)                ┃ Output Shape          ┃    Param # ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩ ## │ dense_15 (Dense)            │ (None, 32)            │     25,120 │   N   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_16 (Dense)            │ (None, 32)            │      1,056 │   N   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_17 (Dense)            │ (None, 32)            │      1,056 │   N   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_18 (Dense)            │ (None, 10)            │        330 │   Y   │ ## └─────────────────────────────┴───────────────────────┴────────────┴───────┘ ##  Total params: 27,562 (107.66 KB) ##  Trainable params: 330 (1.29 KB) ##  Non-trainable params: 27,232 (106.38 KB) # Another way to freeze all layers except the last one. for (layer in model$layers[-length(model$layers)]) {   layer$trainable <- FALSE }  # Recompile and train (this will only update the weights of the last layer). model |> compile(...) model |> fit(...) # Load a convolutional base with pre-trained weights base_model <- application_xception(weights = 'imagenet',                                    include_top = FALSE,                                    pooling = 'avg')  # Freeze the base model freeze_weights(base_model)  # Use a Sequential model to add a trainable classifier on top model <- keras_model_sequential() |>   base_model() |>   layer_dense(1000) # Compile & train model |> compile(...) model |> fit(...)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Save, serialize, and export models","text":"Keras model consists multiple components: architecture, configuration, specifies layers model contain, ’re connected. set weights values (“state model”). optimizer (defined compiling model). set losses metrics (defined compiling model). Keras API saves pieces together unified format, marked .keras extension. zip archive consisting following: JSON-based configuration file (config.json): Records model, layer, trackables’ configuration. H5-based state file, model.weights.h5 (whole model), directory keys layers weights. metadata file JSON, storing things current Keras version. Let’s take look works.","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"how-to-save-and-load-a-model","dir":"Articles","previous_headings":"","what":"How to save and load a model","title":"Save, serialize, and export models","text":"10 seconds read guide, ’s need know. Saving Keras model: Loading model back: Now, let’s look details.","code":"# Get model (Sequential, Functional Model, or Model subclass) model <- ...  # The filename needs to end with the .keras extension model |> save_model('path/to/location.keras') model <- load_model('path/to/location.keras')"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Save, serialize, and export models","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"saving","dir":"Articles","previous_headings":"","what":"Saving","title":"Save, serialize, and export models","text":"section saving entire model single file. file include: model’s architecture/config model’s weight values (learned training) model’s compilation information (compile() called) optimizer state, (enables restart training left)","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"apis","dir":"Articles","previous_headings":"Saving","what":"APIs","title":"Save, serialize, and export models","text":"can save model save_model(). can load back load_model(). supported format Keras 3 “Keras v3” format, uses .keras extension. Example:","code":"get_model <- function() {   # Create a simple model.   inputs <- keras_input(shape(32))   outputs <- inputs |> layer_dense(1)   model <-  keras_model(inputs, outputs)   model |> compile(optimizer = optimizer_adam(), loss = \"mean_squared_error\")   model }  model <- get_model()  # Train the model. test_input <- random_uniform(c(128, 32)) test_target <- random_uniform(c(128, 1)) model |> fit(test_input, test_target)  # Calling `save('my_model.keras')` creates a zip archive `my_model.keras`. model |> save_model(\"my_model.keras\")  # It can be used to reconstruct the model identically. reconstructed_model <- load_model(\"my_model.keras\")  # Let's check: stopifnot(all.equal(   model |> predict(test_input),   reconstructed_model |> predict(test_input) ))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"custom-objects","dir":"Articles","previous_headings":"Saving","what":"Custom objects","title":"Save, serialize, and export models","text":"section covers basic workflows handling custom layers, functions, models Keras saving reloading. saving model includes custom objects, subclassed Layer, must define get_config() method object class. arguments passed constructor (initialize() method) custom object aren’t simple objects (anything types like ints, strings, etc.), must also explicitly deserialize arguments from_config() class method. Like : Please see Defining config methods section details examples. saved .keras file lightweight store Python code custom objects. Therefore, reload model, load_model requires access definition custom objects used one following methods: Registering custom objects (preferred), Passing custom objects directly loading, Using custom object scope examples workflow:","code":"layer_custom <- Layer(   \"CustomLayer\",   initialize = function(sublayer, ...) {     super$initialize(...)     self$sublayer <- sublayer   },   call = function(x) {     self$sublayer(x)   },   get_config = function() {     base_config <- super$get_config()     config <- list(       sublayer = serialize_keras_object(self$sublayer)     )     c(base_config, config)   },   from_config = function(cls, config) {     sublayer_config <- config$sublayer     sublayer <- deserialize_keras_object(sublayer_config)     cls(sublayer, !!!config)   } )"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"registering-custom-objects-preferred","dir":"Articles","previous_headings":"Saving > Custom objects","what":"Registering custom objects (preferred)","title":"Save, serialize, and export models","text":"preferred method, custom object registration greatly simplifies saving loading code. Calling register_keras_serializable() custom object registers object globally master list, allowing Keras recognize object loading model. Let’s create custom model involving custom layer custom activation function demonstrate . Example:","code":"# Clear all previously registered custom objects set_custom_objects(clear = TRUE) ## named list() layer_custom <- Layer(   \"CustomLayer\",   initialize = function(self, factor) {     super$initialize()     self$factor = factor   },    call = function(self, x) {     x * self$factor   },    get_config = function(self) {     list(factor = self$factor)   } )  # Upon registration, you can optionally specify a package or a name. # If left blank, the package defaults to \"Custom\" and the name defaults to # the class name. register_keras_serializable(layer_custom, package = \"MyLayers\")  custom_fn <- keras3:::py_func2(function(x) x^2, name = \"custom_fn\", convert = TRUE)  register_keras_serializable(custom_fn, name=\"custom_fn\", package=\"my_package\")   # Create the model. get_model <- function() {   inputs <- keras_input(shape(4))   mid <- inputs |> layer_custom(0.5)   outputs <- mid |> layer_dense(1, activation = custom_fn)   model <- keras_model(inputs, outputs)   model |> compile(optimizer = \"rmsprop\", loss = \"mean_squared_error\")   model }   # Train the model. train_model <- function(model) {   input <- random_uniform(c(4, 4))   target <- random_uniform(c(4, 1))   model |> fit(input, target, verbose = FALSE, epochs = 1)   model }  test_input <- random_uniform(c(4, 4)) test_target <- random_uniform(c(4, 1))  model <- get_model() |> train_model() model |> save_model(\"custom_model.keras\", overwrite = TRUE)  # Now, we can simply load without worrying about our custom objects. reconstructed_model <- load_model(\"custom_model.keras\")  # Let's check: stopifnot(all.equal(   model |> predict(test_input, verbose = FALSE),   reconstructed_model |> predict(test_input, verbose = FALSE) ))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"passing-custom-objects-to-load_model","dir":"Articles","previous_headings":"Saving > Custom objects","what":"Passing custom objects to load_model()","title":"Save, serialize, and export models","text":"","code":"model <- get_model() |> train_model()  # Calling `save_model('my_model.keras')` creates a zip archive `my_model.keras`. model |> save_model(\"custom_model.keras\", overwrite = TRUE)  # Upon loading, pass a named list containing the custom objects used in the # `custom_objects` argument of `load_model()`. reconstructed_model <-  load_model(  \"custom_model.keras\",   custom_objects = list(CustomLayer = layer_custom,                         custom_fn = custom_fn), )  # Let's check: stopifnot(all.equal(   model |> predict(test_input, verbose = FALSE),   reconstructed_model |> predict(test_input, verbose = FALSE) ))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"using-a-custom-object-scope","dir":"Articles","previous_headings":"Saving > Custom objects","what":"Using a custom object scope","title":"Save, serialize, and export models","text":"code within custom object scope able recognize custom objects passed scope argument. Therefore, loading model within scope allow loading custom objects. Example:","code":"model <- get_model() |> train_model() model |> save_model(\"custom_model.keras\", overwrite = TRUE)  # Pass the custom objects dictionary to a custom object scope and place # the `keras.models.load_model()` call within the scope. custom_objects <- list(CustomLayer = layer_custom, custom_fn = custom_fn)  with_custom_object_scope(custom_objects, {   reconstructed_model <- load_model(\"custom_model.keras\") })  # Let's check: stopifnot(all.equal(   model |> predict(test_input, verbose = FALSE),   reconstructed_model |> predict(test_input, verbose = FALSE) ))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"model-serialization","dir":"Articles","previous_headings":"Saving","what":"Model serialization","title":"Save, serialize, and export models","text":"section saving model’s configuration, without state. model’s configuration (architecture) specifies layers model contains, layers connected. configuration model, model can created freshly initialized state (weights compilation information).","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"apis-1","dir":"Articles","previous_headings":"Saving > Model serialization","what":"APIs","title":"Save, serialize, and export models","text":"following serialization APIs available: clone_model(model): make (randomly initialized) copy model. get_config() cls.from_config(): retrieve configuration layer model, recreate model instance config, respectively. keras.models.model_to_json() keras.models.model_from_json(): similar, JSON strings. keras.saving.serialize_keras_object(): retrieve configuration arbitrary Keras object. keras.saving.deserialize_keras_object(): recreate object instance configuration.","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"in-memory-model-cloning","dir":"Articles","previous_headings":"Saving > Model serialization","what":"In-memory model cloning","title":"Save, serialize, and export models","text":"can -memory cloning model via clone_model(). equivalent getting config recreating model config (preserve compilation information layer weights values). Example:","code":"new_model <- clone_model(model)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"get_config-and-from_config","dir":"Articles","previous_headings":"Saving > Model serialization","what":"get_config() and from_config()","title":"Save, serialize, and export models","text":"Calling get_config(model) get_config(layer) return named list containing configuration model layer, respectively. define get_config() contain arguments needed initialize() method model layer. loading time, from_config(config) method call initialize() arguments reconstruct model layer. Layer example: Now let’s reconstruct layer using from_config() method: Sequential model example: Functional model example:","code":"layer <- layer_dense(, 3, activation=\"relu\") layer_config <- get_config(layer) str(layer_config) ## List of 12 ##  $ name              : chr \"dense_4\" ##  $ trainable         : logi TRUE ##  $ dtype             : chr \"float32\" ##  $ units             : int 3 ##  $ activation        : chr \"relu\" ##  $ use_bias          : logi TRUE ##  $ kernel_initializer:List of 4 ##   ..$ module         : chr \"keras.initializers\" ##   ..$ class_name     : chr \"GlorotUniform\" ##   ..$ config         :List of 1 ##   .. ..$ seed: NULL ##   ..$ registered_name: NULL ##  $ bias_initializer  :List of 4 ##   ..$ module         : chr \"keras.initializers\" ##   ..$ class_name     : chr \"Zeros\" ##   ..$ config         : Named list() ##   ..$ registered_name: NULL ##  $ kernel_regularizer: NULL ##  $ bias_regularizer  : NULL ##  $ kernel_constraint : NULL ##  $ bias_constraint   : NULL ##  - attr(*, \"__class__\")=<class 'keras.src.layers.core.dense.Dense'> new_layer <- from_config(layer_config) model <- keras_model_sequential(input_shape = c(32)) |>   layer_dense(1) config <- get_config(model) new_model <- from_config(config) inputs <- keras_input(c(32)) outputs <- inputs |> layer_dense(1) model <- keras_model(inputs, outputs) config <- get_config(model) new_model <- from_config(config)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"save_model_config-and-load_model_config","dir":"Articles","previous_headings":"Saving > Model serialization","what":"save_model_config() and load_model_config()","title":"Save, serialize, and export models","text":"similar get_config / from_config, except turns model JSON file, can loaded without original model class. also specific models, isn’t meant layers. Example:","code":"model <- keras_model_sequential(input_shape = c(32)) |>   layer_dense(1) save_model_config(model, \"model_config.json\") new_model <- load_model_config(\"model_config.json\") unlink(\"model_config.json\")"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"arbitrary-object-serialization-and-deserialization","dir":"Articles","previous_headings":"Saving > Model serialization","what":"Arbitrary object serialization and deserialization","title":"Save, serialize, and export models","text":"serialize_keras_object() deserialize_keras_object() APIs general-purpose APIs can used serialize deserialize Keras object custom object. foundation saving model architecture behind serialize()/deserialize() calls keras. Example: Note serialization format containing necessary information proper reconstruction: module containing name Keras module identifying module object comes class_name containing name object’s class. config information needed reconstruct object registered_name custom objects. See . Now can reconstruct regularizer.","code":"my_reg <- regularizer_l1(0.005) config <- serialize_keras_object(my_reg) str(config) ## List of 4 ##  $ module         : chr \"keras.regularizers\" ##  $ class_name     : chr \"L1\" ##  $ config         :List of 1 ##   ..$ l1: num 0.005 ##  $ registered_name: NULL new_reg <- deserialize_keras_object(config) new_reg ## <keras.src.regularizers.regularizers.L1 object> ##  signature: (x)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"model-weights-saving","dir":"Articles","previous_headings":"Saving","what":"Model weights saving","title":"Save, serialize, and export models","text":"can choose save & load model’s weights. can useful : need model inference: case won’t need restart training, don’t need compilation information optimizer state. transfer learning: case training new model reusing state prior model, don’t need compilation information prior model.","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"apis-for-in-memory-weight-transfer","dir":"Articles","previous_headings":"Saving > Model weights saving","what":"APIs for in-memory weight transfer","title":"Save, serialize, and export models","text":"Weights can copied different objects using get_weights() set_weights(): get_weights(<layer>): Returns list arrays weight values. set_weights(<layer>weights): Sets model/layer weights values provided (arrays). Examples: Transferring weights one layer another, memory Transferring weights one model another model compatible architecture, memory case stateless layers stateless layers change order number weights, models can compatible architectures even extra/missing stateless layers.","code":"create_layer <- function() {   layer <- layer_dense(, 64, activation = \"relu\", name = \"dense_2\")   layer$build(shape(NA, 784))   layer }  layer_1 <- create_layer() layer_2 <- create_layer()  # Copy weights from layer 1 to layer 2 layer_2 |> set_weights(get_weights(layer_1)) # Create a simple functional model inputs <- keras_input(shape=c(784), name=\"digits\") outputs <- inputs |>   layer_dense(64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(10, name = \"predictions\") functional_model <- keras_model(inputs = inputs, outputs = outputs,                                name = \"3_layer_mlp\")  # Define a subclassed model with the same architecture SubclassedModel <- new_model_class(   \"SubclassedModel\",   initialize = function(output_dim, name = NULL) {     super$initialize(name = name)     self$output_dim <- output_dim |> as.integer()     self$dense_1 <- layer_dense(, 64, activation = \"relu\",                                 name = \"dense_1\")     self$dense_2 <- layer_dense(, 64, activation = \"relu\",                                 name = \"dense_2\")     self$dense_3 <- layer_dense(, self$output_dim,                                 name = \"predictions\")   },    call = function(inputs) {     inputs |>       self$dense_1() |>       self$dense_2() |>       self$dense_3()   },    get_config = function(self) {     list(output_dim = self$output_dim,          name = self$name)   } )   subclassed_model <- SubclassedModel(10) # Call the subclassed model once to create the weights. subclassed_model(op_ones(c(1, 784))) |> invisible()  # Copy weights from functional_model to subclassed_model. set_weights(subclassed_model, get_weights(functional_model))  stopifnot(all.equal(   get_weights(functional_model),   get_weights(subclassed_model) )) input <- keras_input(shape = c(784), name = \"digits\") output <- input |>   layer_dense(64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(10, name = \"predictions\") functional_model <- keras_model(inputs, outputs,                                 name = \"3_layer_mlp\")  input <- keras_input(shape = c(784), name = \"digits\") output <- input |>   layer_dense(64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(64, activation = \"relu\", name = \"dense_2\") |>   # Add a dropout layer, which does not contain any weights.   layer_dropout(0.5) |>   layer_dense(10, name = \"predictions\")  functional_model_with_dropout <-   keras_model(input, output, name = \"3_layer_mlp\")  set_weights(functional_model_with_dropout,             get_weights(functional_model))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"apis-for-saving-weights-to-disk-loading-them-back","dir":"Articles","previous_headings":"Saving > Model weights saving","what":"APIs for saving weights to disk & loading them back","title":"Save, serialize, and export models","text":"Weights can saved disk calling save_model_weights(filepath). filename end .weights.h5. Example: Note using freeze_weights() may result different output get_weights(layer) ordering model contains nested layers.","code":"sequential_model = keras_model_sequential(input_shape = c(784),                                           input_name = \"digits\") |>   layer_dense(64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(10, name = \"predictions\") sequential_model |> save_model_weights(\"my_model.weights.h5\") sequential_model |> load_model_weights(\"my_model.weights.h5\")"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"transfer-learning-example","dir":"Articles","previous_headings":"Saving > Model weights saving > APIs for saving weights to disk & loading them back","what":"Transfer learning example","title":"Save, serialize, and export models","text":"loading pretrained weights weights file, recommended load weights original checkpointed model, extract desired weights/layers new model. Example:","code":"create_functional_model <- function() {   inputs <- keras_input(shape = c(784), name = \"digits\")   outputs <- inputs |>     layer_dense(64, activation = \"relu\", name = \"dense_1\") |>     layer_dense(64, activation = \"relu\", name = \"dense_2\") |>     layer_dense(10, name = \"predictions\")   keras_model(inputs, outputs, name = \"3_layer_mlp\") }  functional_model <- create_functional_model() functional_model |> save_model_weights(\"pretrained.weights.h5\")  # In a separate program: pretrained_model <- create_functional_model() pretrained_model |> load_model_weights(\"pretrained.weights.h5\")  # Create a new model by extracting layers from the original model: extracted_layers <- pretrained_model$layers |> head(-1) model <- keras_model_sequential(layers = extracted_layers) |>   layer_dense(5, name = \"dense_3\") summary(model) ## Model: \"sequential_4\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_1 (Dense)                 │ (None, 64)             │        50,240 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 64)             │         4,160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_3 (Dense)                 │ (None, 5)              │           325 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 54,725 (213.77 KB) ##  Trainable params: 54,725 (213.77 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":[]},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"defining-the-config-methods","dir":"Articles","previous_headings":"Saving > Appendix: Handling custom objects","what":"Defining the config methods","title":"Save, serialize, and export models","text":"Specifications: get_config() return JSON-serializable named list order compatible Keras architecture model-saving APIs. from_config(config) (class method) return new layer model object created config. default implementation returns .call(cls, config). NOTE: constructor arguments already serializable, e.g. strings ints, non-custom Keras objects, overriding from_config() necessary. However, complex objects layers models passed initialize(), deserialization must handled explicitly either initialize overriding from_config() method. Example: Note overriding from_config unnecessary MyDense hidden_units, kernel_initializer, kernel_regularizer ints, strings, built-Keras object, respectively. means default from_config implementation cls(!!!config) work intended. complex objects, layers models passed initialize(), example, must explicitly deserialize objects. Let’s take look example model from_config override necessary. Example:","code":"layer_my_dense <- register_keras_serializable(   package = \"MyLayers\", name = \"KernelMult\",   object = Layer(     \"MyDense\",     initialize = function(units,                           ...,                           kernel_regularizer = NULL,                           kernel_initializer = NULL,                           nested_model = NULL) {       super$initialize(...)       self$hidden_units <- units       self$kernel_regularizer <- kernel_regularizer       self$kernel_initializer <- kernel_initializer       self$nested_model <- nested_model     },     get_config = function() {       config <- super$get_config()       # Update the config with the custom layer's parameters       config <- modifyList(config, list(         units = self$hidden_units,         kernel_regularizer = self$kernel_regularizer,         kernel_initializer = self$kernel_initializer,         nested_model = self$nested_model       ))       config     },     build = function(input_shape) {       input_units <- tail(input_shape, 1)       self$kernel <- self$add_weight(         name = \"kernel\",         shape = shape(input_units, self$hidden_units),         regularizer = self$kernel_regularizer,         initializer = self$kernel_initializer,       )     },     call = function(inputs) {       op_matmul(inputs, self$kernel)     }   ) )   layer <- layer_my_dense(units = 16,                         kernel_regularizer = \"l1\",                         kernel_initializer = \"ones\") layer3 <- layer_my_dense(units = 64, nested_model = layer)  config <- serialize_keras_object(layer3) str(config) ## List of 4 ##  $ module         : chr \"<r-globalenv>\" ##  $ class_name     : chr \"MyDense\" ##  $ config         :List of 5 ##   ..$ name        : chr \"my_dense_1\" ##   ..$ trainable   : logi TRUE ##   ..$ dtype       : chr \"float32\" ##   ..$ units       : num 64 ##   ..$ nested_model:List of 4 ##   .. ..$ module         : chr \"<r-globalenv>\" ##   .. ..$ class_name     : chr \"MyDense\" ##   .. ..$ config         :List of 6 ##   .. .. ..$ name              : chr \"my_dense\" ##   .. .. ..$ trainable         : logi TRUE ##   .. .. ..$ dtype             : chr \"float32\" ##   .. .. ..$ units             : num 16 ##   .. .. ..$ kernel_regularizer: chr \"l1\" ##   .. .. ..$ kernel_initializer: chr \"ones\" ##   .. ..$ registered_name: chr \"MyLayers>KernelMult\" ##  $ registered_name: chr \"MyLayers>KernelMult\" new_layer <- deserialize_keras_object(config) new_layer ## <MyDense name=my_dense_1, built=False> ##  signature: (*args, **kwargs) `%||%` <- \\(x, y) if(is.null(x)) y else x layer_custom_model <- register_keras_serializable(   package = \"ComplexModels\",   object = Layer(     \"CustomModel\",     initialize = function(first_layer, second_layer = NULL, ...) {       super$initialize(...)       self$first_layer <- first_layer       self$second_layer <- second_layer %||% layer_dense(, 8)     },      get_config = function() {       config <- super$get_config()       config <- modifyList(config, list(         first_layer = self$first_layer,         second_layer = self$second_layer       ))       config     },      from_config = function(config) {       config$first_layer %<>% deserialize_keras_object()       config$second_layer %<>% deserialize_keras_object()       # note that the class is available in methods under the classname symbol,       # (`CustomModel` for this class), and also under the symbol `__class__`       cls(!!!config)       # CustomModel(!!!config)     },     call = function(self, inputs) {       inputs |>         self$first_layer() |>         self$second_layer()     }   ) )  # Let's make our first layer the custom layer from the previous example (MyDense) inputs <- keras_input(c(32)) outputs <-  inputs |> layer_custom_model(first_layer=layer) model <- keras_model(inputs, outputs)  config <- get_config(model) new_model <- from_config(config)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"how-custom-objects-are-serialized","dir":"Articles","previous_headings":"Saving > Appendix: Handling custom objects","what":"How custom objects are serialized","title":"Save, serialize, and export models","text":"serialization format special key custom objects registered via register_keras_serializable(). registered_name key allows easy retrieval loading/deserialization time also allowing users add custom naming. Let’s take look config serializing custom layer MyDense defined . Example: shown, registered_name key contains lookup information Keras master list, including package MyLayers custom name KernelMult gave calling register_keras_serializables(). Take look custom class definition/registration . Note class_name key contains original name class, allowing proper re-initialization from_config. Additionally, note module key NULL since custom object.","code":"layer <- layer_my_dense(   units = 16,   kernel_regularizer = regularizer_l1_l2(l1 = 1e-5, l2 = 1e-4),   kernel_initializer = \"ones\", ) config <- serialize_keras_object(layer) str(config) ## List of 4 ##  $ module         : chr \"<r-globalenv>\" ##  $ class_name     : chr \"MyDense\" ##  $ config         :List of 6 ##   ..$ name              : chr \"my_dense_2\" ##   ..$ trainable         : logi TRUE ##   ..$ dtype             : chr \"float32\" ##   ..$ units             : num 16 ##   ..$ kernel_regularizer:List of 4 ##   .. ..$ module         : chr \"keras.regularizers\" ##   .. ..$ class_name     : chr \"L1L2\" ##   .. ..$ config         :List of 2 ##   .. .. ..$ l1: num 1e-05 ##   .. .. ..$ l2: num 1e-04 ##   .. ..$ registered_name: NULL ##   ..$ kernel_initializer: chr \"ones\" ##  $ registered_name: chr \"MyLayers>KernelMult\""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Training & evaluation with the built-in methods","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Training & evaluation with the built-in methods","text":"guide covers training, evaluation, prediction (inference) models using built-APIs training & validation (fit(), evaluate() predict()). interested leveraging fit() specifying training step function, see Customizing happens fit() guide. interested writing training & evaluation loops scratch, see guide Writing training loop scratch. general, whether using built-loops writing , model training & evaluation works strictly way across every kind Keras model – Sequential models, models built Functional API, models written scratch via model subclassing.","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"api-overview-a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"API overview: a first end-to-end example","title":"Training & evaluation with the built-in methods","text":"passing data built-training loops model, either use: Arrays (data small fits memory) tf_dataset objects PyTorch DataLoader instances next paragraphs, ’ll use MNIST dataset NumPy arrays, order demonstrate use optimizers, losses, metrics. Afterwards, ’ll take close look options. Let’s consider following model (, build Functional API, Sequential model subclassed model well): ’s typical end--end workflow looks like, consisting : Training Validation holdout set generated original training data Evaluation test data ’ll use MNIST data example. specify training configuration (optimizer, loss, metrics): call fit(), train model slicing data “batches” size batch_size, repeatedly iterating entire dataset given number epochs. returned history object holds record loss values metric values training: evaluate model test data via evaluate(): Now, let’s review piece workflow detail.","code":"inputs <- keras_input(shape = 784, name=\"digits\") outputs <- inputs |>   layer_dense(units = 64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(units = 64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(units = 10, activation = \"softmax\", name = \"predictions\") model <- keras_model(inputs = inputs, outputs = outputs) summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ digits (InputLayer)             │ (None, 784)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_1 (Dense)                 │ (None, 64)             │        50,240 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 64)             │         4,160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ predictions (Dense)             │ (None, 10)             │           650 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 55,050 (215.04 KB) ##  Trainable params: 55,050 (215.04 KB) ##  Non-trainable params: 0 (0.00 B) c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()  # Preprocess the data (these are NumPy arrays) x_train <- array_reshape(x_train, c(60000, 784)) / 255 x_test <- array_reshape(x_test, c(10000, 784)) / 255  # Reserve 10,000 samples for validation x_val <- x_train[1:10000,] y_val <- y_train[1:10000] x_train <- x_train[-c(1:10000),] y_train <- y_train[-c(1:10000)] model |> compile(   # Optimizer   optimizer = optimizer_rmsprop(),   # Loss function to minimize   loss = loss_sparse_categorical_crossentropy(),   # List of metrics to monitor   metrics = list(metric_sparse_categorical_accuracy()) ) history <- model |> fit(   x_train, y_train,   batch_size = 64,   epochs = 2,   # We pass some validation for   # monitoring validation loss and metrics   # at the end of each epoch   validation_data = list(x_val, y_val) ) ## Epoch 1/2 ## 782/782 - 3s - 3ms/step - loss: 0.3410 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.1869 - val_sparse_categorical_accuracy: 0.9455 ## Epoch 2/2 ## 782/782 - 1s - 1ms/step - loss: 0.1588 - sparse_categorical_accuracy: 0.9532 - val_loss: 0.1303 - val_sparse_categorical_accuracy: 0.9626 history ## ## Final epoch (plot to see history): ##                            loss: 0.1588 ##     sparse_categorical_accuracy: 0.9532 ##                        val_loss: 0.1303 ## val_sparse_categorical_accuracy: 0.9626 # Evaluate the model on the test data using `evaluate` results <- model |> evaluate(x_test, y_test, batch_size=128) ## 79/79 - 0s - 3ms/step - loss: 0.1258 - sparse_categorical_accuracy: 0.9625 str(results) ## List of 2 ##  $ loss                       : num 0.126 ##  $ sparse_categorical_accuracy: num 0.962 # Generate predictions (probabilities -- the output of the last layer) # on new data using `predict` predictions <- model |> predict(x_test[1:2,]) ## 1/1 - 0s - 136ms/step dim(predictions) ## [1]  2 10"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"the-compile-method-specifying-a-loss-metrics-and-an-optimizer","dir":"Articles","previous_headings":"","what":"The compile() method: specifying a loss, metrics, and an optimizer","title":"Training & evaluation with the built-in methods","text":"train model fit(), need specify loss function, optimizer, optionally, metrics monitor. pass model arguments compile() method: metrics argument list – model can number metrics. model multiple outputs, can specify different losses metrics output, can modulate contribution output total loss model. find details Passing data multi-input, multi-output models section. Note ’re satisfied default settings, many cases optimizer, loss, metrics can specified via string identifiers shortcut: later reuse, let’s put model definition compile step functions; call several times across different examples guide.","code":"model |> compile(   optimizer = optimizer_rmsprop(learning_rate = 1e-3),   loss = loss_sparse_categorical_crossentropy(),   metrics = list(metric_sparse_categorical_accuracy()) ) model |> compile(   optimizer = \"rmsprop\",   loss = \"sparse_categorical_crossentropy\",   metrics = c(\"sparse_categorical_accuracy\") ) get_uncompiled_model <- function() {   inputs <- keras_input(shape = 784, name = \"digits\")   outputs <- inputs |>     layer_dense(units = 64, activation = \"relu\", name = \"dense_1\") |>     layer_dense(units = 64, activation = \"relu\", name = \"dense_2\") |>     layer_dense(units = 10, activation = \"softmax\", name = \"predictions\")   keras_model(inputs = inputs, outputs = outputs) }  get_compiled_model <- function() {   model <- get_uncompiled_model()   model |> compile(     optimizer = \"rmsprop\",     loss = \"sparse_categorical_crossentropy\",     metrics = c(\"sparse_categorical_accuracy\")   )   model }"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"many-built-in-optimizers-losses-and-metrics-are-available","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Many built-in optimizers, losses, and metrics are available","title":"Training & evaluation with the built-in methods","text":"general, won’t create losses, metrics, optimizers scratch, need likely already part Keras API: Optimizers: [optimizer_sgd()] (without momentum) [optimizer_rmsprop()] [optimizer_adam()] etc. Losses: [loss_mean_squared_error()] [loss_kl_divergence()] [loss_cosine_similarity()] etc. Metrics: [metric_auc()] [metric_precision()] [metric_recall()] etc.","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"custom-losses","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Custom losses","title":"Training & evaluation with the built-in methods","text":"need create custom loss, Keras provides three ways . first method involves creating function accepts inputs y_true y_pred. following example shows loss function computes mean squared error real data predictions: need loss function takes parameters beside y_true y_pred, can subclass Keras base Loss class using [Loss()] implement following two methods: initialize(): accept parameters pass call loss function call(y_true, y_pred): use targets (y_true) model predictions (y_pred) compute model’s loss Let’s say want use mean squared error, added term de-incentivize prediction values far 0.5 (assume categorical targets one-hot encoded take values 0 1). creates incentive model confident, may help reduce overfitting (won’t know works try!). ’s :","code":"custom_mean_squared_error <- function(y_true, y_pred) {   op_mean(op_square(y_true - y_pred), axis = -1) }  model <- get_uncompiled_model() model |> compile(optimizer = \"adam\",                  loss = custom_mean_squared_error)  # We need to one-hot encode the labels to use MSE y_train_one_hot <- op_one_hot(y_train, num_classes = 10) model |> fit(x_train, y_train_one_hot, batch_size = 64, epochs = 2) ## Epoch 1/2 ## 782/782 - 2s - 2ms/step - loss: 0.0161 ## Epoch 2/2 ## 782/782 - 0s - 623us/step - loss: 0.0078 loss_custom_mse <- Loss(   classname = \"CustomMSE\",   initialize = function(regularization_factor = 0.1, name = \"custom_mse\") {     super$initialize(name = name)     self$regularization_factor <- regularization_factor   },   call = function(y_true, y_pred) {     mse <- op_mean(op_square(y_true - y_pred), axis = -1)     reg <- op_mean(op_square(0.5 - y_pred), axis = -1)     mse + reg * self$regularization_factor   } )  model <- get_uncompiled_model() model |> compile(optimizer=\"adam\", loss = loss_custom_mse())  y_train_one_hot <- op_one_hot(y_train, num_classes=10) model |> fit(x_train, y_train_one_hot, batch_size=64, epochs=1) ## 782/782 - 2s - 2ms/step - loss: 0.0390"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"custom-metrics","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Custom metrics","title":"Training & evaluation with the built-in methods","text":"need metric isn’t part API, can easily create custom metrics subclassing Keras base Metric class using [Metric()]. need implement 4 methods: initialize(), create state variables metric. update_state(y_true, y_pred, sample_weight = NULL), uses targets y_true model predictions y_pred update state variables. result(), uses state variables compute final results. reset_state(), reinitializes state metric. State update results computation kept separate (update_state() result(), respectively) cases, results computation might expensive done periodically. ’s simple example showing implement CategoricalTruePositives metric counts many samples correctly classified belonging given class:","code":"metric_categorical_true_positives <- Metric(   \"CategoricalTruePositives\",    initialize = function(name = \"categorical_true_positives\", ...) {     super$initialize(name = name, ...)     self$true_positives <- self$add_variable(shape = shape(),                                              name = \"ctp\",                                              initializer = \"zeros\")   },    update_state = function(y_true, y_pred, sample_weight = NULL) {     y_pred <- op_argmax(y_pred, axis = 2) |> op_reshape(c(-1, 1))     values <- op_cast(y_true, \"int32\") == op_cast(y_pred, \"int32\")     values <- op_cast(values, \"float32\")     if (!is.null(sample_weight)) {       sample_weight <- op_cast(sample_weight, \"float32\")       values <- op_multiply(values, sample_weight)     }     self$true_positives$assign_add(op_sum(values))   },    result = function() {     self$true_positives$value   },    reset_state = function() {     self$true_positives$assign(0.0)   } )  model <- get_uncompiled_model() model |> compile(   optimizer = optimizer_rmsprop(learning_rate = 1e-3),   loss = loss_sparse_categorical_crossentropy(),   metrics = c(metric_categorical_true_positives()) ) history <- model |> fit(x_train, y_train, batch_size = 64, epochs = 3) ## Epoch 1/3 ## 782/782 - 1s - 2ms/step - categorical_true_positives: 360502.0000 - loss: 0.3444 ## Epoch 2/3 ## 782/782 - 1s - 964us/step - categorical_true_positives: 362616.0000 - loss: 0.1656 ## Epoch 3/3 ## 782/782 - 1s - 1ms/step - categorical_true_positives: 363187.0000 - loss: 0.1203"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"handling-losses-and-metrics-that-dont-fit-the-standard-signature","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Handling losses and metrics that don’t fit the standard signature","title":"Training & evaluation with the built-in methods","text":"overwhelming majority losses metrics can computed y_true y_pred, y_pred output model – . instance, regularization loss may require activation layer (targets case), activation may model output. cases, can call self$add_loss(loss_value) inside call method custom layer. Losses added way get added “main” loss training (one passed compile()). ’s simple example adds activity regularization (note activity regularization built-Keras layers – layer just sake providing concrete example): Note pass losses via add_loss(), becomes possible call compile() without loss function, since model already loss minimize. Consider following LogisticEndpoint layer: takes inputs targets & logits, tracks crossentropy loss via add_loss(). can use model two inputs (input data & targets), compiled without loss argument, like : information training multi-input models, see section Passing data multi-input, multi-output models.","code":"layer_custom_activity_regularizer <- Layer(   \"ActivityRegularization\",   call = function(inputs) {     self$add_loss(op_sum(inputs) * 0.1)     inputs  # Pass-through layer.   } )  inputs <- keras_input(shape = 784, name = \"digits\") outputs <- inputs |>   layer_dense(units = 32, activation = \"relu\", name = \"dense_1\") |>   layer_custom_activity_regularizer() |>   layer_dense(units = 64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(units = 10, name = \"predictions\")  model <- keras_model(inputs = inputs, outputs = outputs) model |> compile(optimizer = optimizer_rmsprop(learning_rate = 1e-3),                  loss = loss_sparse_categorical_crossentropy(from_logits = TRUE))  # The displayed loss will be much higher than before # due to the regularization component. model |> fit(x_train, y_train, batch_size = 64, epochs = 1) ## 782/782 - 2s - 2ms/step - loss: 2.3721 layer_logistic_endpoint <- Layer(   \"LogisticEndpoint\",   initialize = function(name = NULL) {     super$initialize(name = name)     self$loss_fn <- loss_binary_crossentropy(from_logits = TRUE)   },   call = function(targets, logits, sample_weights = NULL) {     # Compute the training-time loss value and add it     # to the layer using `self.add_loss()`.     loss <- self$loss_fn(targets, logits, sample_weights)     self$add_loss(loss)      # Return the inference-time prediction tensor (for `predict()`).     op_softmax(logits)   } ) inputs <- keras_input(shape = 3, name = \"inputs\") targets <- keras_input(shape = 10, name = \"targets\")  logits <- inputs |> layer_dense(10) predictions <- layer_logistic_endpoint(name = \"predictions\")(targets, logits)  model <- keras_model(inputs = list(inputs, targets),                      outputs = predictions) model |> compile(optimizer = \"adam\")  # No loss argument!  data <- list(   inputs = random_normal(c(3, 3)),   targets = random_normal(c(3, 10)) ) model |> fit(data, epochs = 1) ## 1/1 - 1s - 508ms/step - loss: 1.0566"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"automatically-setting-apart-a-validation-holdout-set","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Automatically setting apart a validation holdout set","title":"Training & evaluation with the built-in methods","text":"first end--end example saw, used validation_data argument pass list arrays list(x_val, y_val) model evaluating validation loss validation metrics end epoch. ’s another option: argument validation_split allows automatically reserve part training data validation. argument value represents fraction data reserved validation, set number higher 0 lower 1. instance, validation_split = 0.2 means “use 20% data validation”, validation_split = 0.6 means “use 60% data validation”. way validation computed taking last x% samples arrays received fit() call, shuffling. Note can use validation_split training NumPy data.","code":"model <- get_compiled_model() model |> fit(x_train, y_train,              batch_size = 64,              validation_split = 0.2, epochs = 1) ## 625/625 - 2s - 3ms/step - loss: 0.3817 - sparse_categorical_accuracy: 0.8919 - val_loss: 0.1953 - val_sparse_categorical_accuracy: 0.9431"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"training-evaluation-using-tf-dataset-objects","dir":"Articles","previous_headings":"","what":"Training & evaluation using TF Dataset objects","title":"Training & evaluation with the built-in methods","text":"past paragraphs, ’ve seen handle losses, metrics, optimizers, ’ve seen use validation_data validation_split arguments fit(), data passed arrays. Another option use iterator-like, tf.data.Dataset, PyTorch DataLoader, R generator function. Let’s take look former. tfdatasets R package containes set utilities loading preprocessing data way ’s fast scalable. complete guide creating Datasets, see tf.data documentation. can use tf.data train Keras models regardless backend ’re using – whether ’s JAX, PyTorch, TensorFlow. can pass Dataset instance directly methods fit(), evaluate(), predict(): Note Dataset reset end epoch, can reused next epoch. want run training specific number batches Dataset, can pass steps_per_epoch argument, specifies many training steps model run using Dataset moving next epoch. can also pass Dataset instance validation_data argument fit(): end epoch, model iterate validation dataset compute validation loss validation metrics. want run validation specific number batches dataset, can pass validation_steps argument, specifies many validation steps model run validation dataset interrupting validation moving next epoch: Note validation dataset reset use (always evaluating samples epoch epoch). argument validation_split (generating holdout set training data) supported training Dataset objects, since feature requires ability index samples datasets, possible general Dataset API.","code":"library(tfdatasets, exclude = \"shape\") model <- get_compiled_model()  # First, let's create a training Dataset instance. # For the sake of our example, we'll use the same MNIST data as before. train_dataset <- tensor_slices_dataset(list(x_train, y_train))  # Shuffle and slice the dataset. train_dataset <- train_dataset |>   dataset_shuffle(buffer_size=1024) |>   dataset_batch(64)  # Now we get a test dataset. test_dataset <-   tensor_slices_dataset(list(x_test, y_test)) |>   dataset_batch(64)  # Since the dataset already takes care of batching, # we don't pass a `batch_size` argument. model |> fit(train_dataset, epochs = 3) ## Epoch 1/3 ## 782/782 - 2s - 2ms/step - loss: 0.3365 - sparse_categorical_accuracy: 0.9041 ## Epoch 2/3 ## 782/782 - 1s - 681us/step - loss: 0.1605 - sparse_categorical_accuracy: 0.9524 ## Epoch 3/3 ## 782/782 - 1s - 1ms/step - loss: 0.1185 - sparse_categorical_accuracy: 0.9647 # You can also evaluate or predict on a dataset. result <- model |> evaluate(test_dataset) ## 157/157 - 1s - 4ms/step - loss: 0.1152 - sparse_categorical_accuracy: 0.9627 result ## $loss ## [1] 0.1151979 ## ## $sparse_categorical_accuracy ## [1] 0.9627 model <- get_compiled_model()  # Prepare the training dataset train_dataset <- tensor_slices_dataset(list(x_train, y_train)) train_dataset <- train_dataset |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(64)  # Only use the 100 batches per epoch (that's 64 * 100 samples) model |> fit(train_dataset, epochs = 3, steps_per_epoch = 100) ## Epoch 1/3 ## 100/100 - 1s - 7ms/step - loss: 0.8017 - sparse_categorical_accuracy: 0.7806 ## Epoch 2/3 ## 100/100 - 0s - 682us/step - loss: 0.3661 - sparse_categorical_accuracy: 0.9006 ## Epoch 3/3 ## 100/100 - 0s - 740us/step - loss: 0.3009 - sparse_categorical_accuracy: 0.9106 model <- get_compiled_model()  # Prepare the training dataset train_dataset <- tensor_slices_dataset(list(x_train, y_train)) train_dataset <- train_dataset |>   dataset_shuffle(buffer_size=1024) |>   dataset_batch(64)  # Prepare the validation dataset val_dataset <- tensor_slices_dataset(list(x_val, y_val)) val_dataset <- val_dataset |> dataset_batch(64)  model |> fit(train_dataset, epochs = 1, validation_data = val_dataset) ## 782/782 - 2s - 3ms/step - loss: 0.3428 - sparse_categorical_accuracy: 0.9022 - val_loss: 0.2337 - val_sparse_categorical_accuracy: 0.9291 model  <- get_compiled_model()  # Prepare the training dataset train_dataset <- tensor_slices_dataset(list(x_train, y_train)) train_dataset <- train_dataset |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(64)  # Prepare the validation dataset val_dataset <- tensor_slices_dataset(list(x_val, y_val)) val_dataset <- val_dataset |> dataset_batch(64)  model %>% fit(   train_dataset,   epochs = 1,   # Only run validation using the first 10 batches of the dataset   # using the `validation_steps` argument   validation_data = val_dataset,   validation_steps = 10, ) ## 782/782 - 2s - 2ms/step - loss: 0.3391 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.1997 - val_sparse_categorical_accuracy: 0.9391"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-sample-weighting-and-class-weighting","dir":"Articles","previous_headings":"","what":"Using sample weighting and class weighting","title":"Training & evaluation with the built-in methods","text":"default settings weight sample decided frequency dataset. two methods weight data, independent sample frequency: Class weights Sample weights","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"class-weights","dir":"Articles","previous_headings":"Using sample weighting and class weighting","what":"Class weights","title":"Training & evaluation with the built-in methods","text":"set passing named list class_weight argument fit(). list maps class indices weight used samples belonging class. can used balance classes without resampling, train model gives importance particular class. instance, class “0” half represented class “1” data, use model |> fit(..., class_weight = c(\"0\" = 1, \"1\" = 0.5)). ’s R example use class weights sample weights give importance correct classification class #5 (digit “5” MNIST dataset).","code":"class_weight <- c(     \"0\" = 1.0,     \"1\" = 1.0,     \"2\" = 1.0,     \"3\" = 1.0,     \"4\" = 1.0,     # Set weight \"2\" for class \"5\",     # making this class 2x more important     \"5\" = 2.0,     \"6\" = 1.0,     \"7\" = 1.0,     \"8\" = 1.0,     \"9\" = 1.0 )  model <- get_compiled_model() model |> fit(x_train, y_train,              class_weight = class_weight,              batch_size = 64, epochs = 1) ## 782/782 - 1s - 2ms/step - loss: 0.3713 - sparse_categorical_accuracy: 0.9018"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"sample-weights","dir":"Articles","previous_headings":"Using sample weighting and class weighting","what":"Sample weights","title":"Training & evaluation with the built-in methods","text":"fine grained control, building classifier, can use sample_weights. training R arrays: Pass sample_weight argument fit(). training tf_dataset sort iterator: yield (input_batch, label_batch, sample_weight_batch) tuples. “sample weights” array array numbers specify much weight sample batch computing total loss. commonly used imbalanced classification problems (idea give weight rarely-seen classes). weights used ones zeros, array can used mask loss function (entirely discarding contribution certain samples total loss). ’s matching Dataset example:","code":"sample_weight <- rep(1.0, length(y_train)) sample_weight[y_train == 5] <- 2.0  model <- get_compiled_model() model |> fit(   x_train, y_train,   sample_weight = sample_weight,   batch_size = 64, epochs = 1 ) ## 782/782 - 2s - 2ms/step - loss: 0.3740 - sparse_categorical_accuracy: 0.9015 sample_weight <- rep(1.0, length(y_train)) sample_weight[y_train == 5] <- 2.0  # Create a Dataset that includes sample weights # (3rd element in the return tuple). train_dataset <- tensor_slices_dataset(list(     x_train, y_train, sample_weight ))  # Shuffle and slice the dataset. train_dataset <- train_dataset |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(64)  model <- get_compiled_model() model |> fit(train_dataset, epochs = 1) ## 782/782 - 2s - 2ms/step - loss: 0.3654 - sparse_categorical_accuracy: 0.9057"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"passing-data-to-multi-input-multi-output-models","dir":"Articles","previous_headings":"","what":"Passing data to multi-input, multi-output models","title":"Training & evaluation with the built-in methods","text":"previous examples, considering model single input (tensor shape (764)) single output (prediction tensor shape (10)). models multiple inputs outputs? Consider following model, image input shape (32, 32, 3) (’s (height, width, channels)) time series input shape (NA, 10) (’s (timesteps, features)). model two outputs computed combination inputs: “score” (shape (1)) probability distribution five classes (shape (5)). Let’s plot model, can clearly see ’re (note shapes shown plot batch shapes, rather per-sample shapes). plot chunk unnamed-chunk-26 compilation time, can specify different losses different outputs, passing loss functions list: passed single loss function model, loss function applied every output (appropriate ). Likewise metrics: Since gave names output layers, also specify per-output losses metrics via named list: recommend use names 2 outputs. ’s possible give different weights different output-specific losses (instance, one might wish privilege “score” loss example, giving 2x importance class loss), using loss_weights argument: also choose compute loss certain outputs, outputs meant prediction training: Passing data multi-input multi-output model fit() works similar way specifying loss function compile: can pass lists arrays (1:1 mapping outputs received loss function) dicts mapping output names arrays. ’s Dataset use case: similarly R arrays, Dataset return tuple named lists (dicts).","code":"image_input <- keras_input(c(32, 32, 3), name = \"img_input\") timeseries_input <- keras_input(c(NA, 10), name = \"ts_input\")  x1 <- image_input |>   layer_conv_2d(filters = 3, kernel_size = c(3, 3)) |>   layer_global_max_pooling_2d()  x2 <- timeseries_input |>   layer_conv_1d(filters = 3, kernel_size = 3) |>   layer_global_max_pooling_1d()  x <- layer_concatenate(x1, x2)  score_output <- layer_dense(x, 1, name = \"score_output\") class_output <- layer_dense(x, 5, name = \"class_output\")  model <- keras_model(   inputs = list(image_input, timeseries_input),   outputs = list(score_output, class_output) ) plot(model, show_shapes = TRUE) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     loss_mean_squared_error(),     loss_categorical_crossentropy()   ) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     loss_mean_squared_error(),     loss_categorical_crossentropy()   ),   metrics = list(     list(       metric_mean_absolute_error(),       metric_mean_absolute_percentage_error()     ),     list(metric_categorical_accuracy())   ) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     score_output = loss_mean_squared_error(),     class_output = loss_categorical_crossentropy()   ),   metrics = list(     score_output = list(       metric_mean_absolute_error(),       metric_mean_absolute_percentage_error()     ),     class_output = list(metric_categorical_accuracy())   ) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     score_output = loss_mean_squared_error(),     class_output = loss_categorical_crossentropy()   ),   metrics = list(     score_output = list(       metric_mean_absolute_error(),       metric_mean_absolute_percentage_error()     ),     class_output = list(metric_categorical_accuracy())   ),   loss_weights = list(score_output = 2.0, class_output = 1.0) ) # loss list, positional version model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(NULL, loss_categorical_crossentropy()) )  # Or loss list, named version model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(class_output = loss_categorical_crossentropy()) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     loss_mean_squared_error(),     loss_categorical_crossentropy()   ) )  # Generate dummy data img_data <- random_normal(c(100, 32, 32, 3)) ts_data <- random_normal(c(100, 20, 10)) score_targets <- random_normal(c(100, 1)) class_targets <- random_normal(c(100, 5))  # Fit on unnamed lists (positional matching) model |> fit(     list(img_data, ts_data),     list(score_targets, class_targets),     batch_size=32,     epochs=1 ) ## 4/4 - 2s - 458ms/step - loss: 1.3788 # Alternatively, fit on named lists (names matching) model |> fit(   list(img_input = img_data, ts_input = ts_data),   list(score_output = score_targets, class_output = class_targets),   batch_size = 32,   epochs = 1 ) ## 4/4 - 1s - 230ms/step - loss: 0.2857 train_dataset <- tensor_slices_dataset(list(   list(img_input = img_data, ts_input = ts_data),   list(score_output = score_targets, class_output = class_targets) )) train_dataset <- train_dataset |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(64)  model |> fit(train_dataset, epochs = 1) ## 2/2 - 1s - 629ms/step - loss: 0.5600"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-callbacks","dir":"Articles","previous_headings":"","what":"Using callbacks","title":"Training & evaluation with the built-in methods","text":"Callbacks Keras objects called different points training (start epoch, end batch, end epoch, etc.). can used implement certain behaviors, : validation different points training (beyond built-per-epoch validation) Checkpointing model regular intervals exceeds certain accuracy threshold Changing learning rate model training seems plateauing fine-tuning top layers training seems plateauing Sending email instant message notifications training ends certain performance threshold exceeded Etc. Callbacks can passed list call fit():","code":"model <- get_compiled_model()  callbacks <- list(   callback_early_stopping(     # Stop training when `val_loss` is no longer improving     monitor = \"val_loss\",     # \"no longer improving\" being defined as \"no better than 1e-2 less\"     min_delta = 1e-2,     # \"no longer improving\" being further defined as \"for at least 2 epochs\"     patience = 2,     verbose = 1   ) ) model |> fit(   x_train,   y_train,   epochs = 20,   batch_size = 64,   callbacks = callbacks,   validation_split = 0.2, ) ## Epoch 1/20 ## 625/625 - 1s - 2ms/step - loss: 0.3695 - sparse_categorical_accuracy: 0.8961 - val_loss: 0.1873 - val_sparse_categorical_accuracy: 0.9469 ## Epoch 2/20 ## 625/625 - 1s - 974us/step - loss: 0.1751 - sparse_categorical_accuracy: 0.9489 - val_loss: 0.1403 - val_sparse_categorical_accuracy: 0.9579 ## Epoch 3/20 ## 625/625 - 1s - 903us/step - loss: 0.1277 - sparse_categorical_accuracy: 0.9625 - val_loss: 0.1218 - val_sparse_categorical_accuracy: 0.9651 ## Epoch 4/20 ## 625/625 - 1s - 889us/step - loss: 0.1007 - sparse_categorical_accuracy: 0.9700 - val_loss: 0.1153 - val_sparse_categorical_accuracy: 0.9661 ## Epoch 5/20 ## 625/625 - 1s - 1ms/step - loss: 0.0822 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1104 - val_sparse_categorical_accuracy: 0.9670 ## Epoch 6/20 ## 625/625 - 1s - 899us/step - loss: 0.0683 - sparse_categorical_accuracy: 0.9801 - val_loss: 0.1098 - val_sparse_categorical_accuracy: 0.9689 ## Epoch 7/20 ## 625/625 - 1s - 1ms/step - loss: 0.0571 - sparse_categorical_accuracy: 0.9838 - val_loss: 0.1116 - val_sparse_categorical_accuracy: 0.9698 ## Epoch 8/20 ## 625/625 - 1s - 976us/step - loss: 0.0485 - sparse_categorical_accuracy: 0.9864 - val_loss: 0.1126 - val_sparse_categorical_accuracy: 0.9702 ## Epoch 8: early stopping"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"many-built-in-callbacks-are-available","dir":"Articles","previous_headings":"Using callbacks","what":"Many built-in callbacks are available","title":"Training & evaluation with the built-in methods","text":"many built-callbacks already available Keras, : callback_model_checkpoint(): Periodically save model. callback_early_stopping(): Stop training training longer improving validation metrics. callback_tensorboard(): periodically write model logs can visualized TensorBoard (details section “Visualization”). callback_csv_logger(): streams loss metrics data CSV file. etc. See callbacks documentation complete list.","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"writing-your-own-callback","dir":"Articles","previous_headings":"Using callbacks","what":"Writing your own callback","title":"Training & evaluation with the built-in methods","text":"can create custom callback subclassing base [Callback()] class. callback access associated model class property self$model. Make sure read complete guide writing custom callbacks. ’s simple example saving list per-batch loss values training:","code":"callback_loss_history <- Callback(   classname = \"LossHistory\",   initialize = function(file = \"per_training_batch_losses.txt\", ...) {     super$initialize(...)     private$file <- file   },   on_train_begin = function(logs = NULL) {     private$per_batch_losses <- fastmap::faststack()   },   on_train_batch_begin = function(batch, logs = NULL) {     private$per_batch_losses$push(logs$loss)   },   on_train_end = function(logs = NULL) {     per_batch_losses <- private$per_batch_losses$as_list() |> as.numeric()     write(per_batch_losses, private$file)   } )"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"checkpointing-models","dir":"Articles","previous_headings":"","what":"Checkpointing models","title":"Training & evaluation with the built-in methods","text":"’re training model relatively large datasets, ’s crucial save checkpoints model frequent intervals. easiest way achieve [callback_model_checkpoint()]: ModelCheckpoint callback can used implement fault-tolerance: ability restart training last saved state model case training gets randomly interrupted. ’s basic example: call also write callback saving restoring models. complete guide serialization saving, see guide saving serializing Models.","code":"model <- get_compiled_model()  callbacks <- list(   callback_model_checkpoint(     # Path where to save the model     # The two parameters below mean that we will overwrite     # the current checkpoint if and only if     # the `val_loss` score has improved.     # The saved model name will include the current epoch.     filepath = \"mymodel_{epoch}.keras\",     save_best_only = TRUE,     # Only save a model if `val_loss` has improved.     monitor = \"val_loss\",     verbose = 1   ) ) model |> fit(   x_train, y_train,   epochs = 2, batch_size = 64,   callbacks = callbacks,   validation_split = 0.2 ) ## Epoch 1/2 ## ## Epoch 1: val_loss improved from inf to 0.19344, saving model to mymodel_1.keras ## 625/625 - 1s - 2ms/step - loss: 0.3787 - sparse_categorical_accuracy: 0.8940 - val_loss: 0.1934 - val_sparse_categorical_accuracy: 0.9441 ## Epoch 2/2 ## ## Epoch 2: val_loss improved from 0.19344 to 0.14251, saving model to mymodel_2.keras ## 625/625 - 1s - 842us/step - loss: 0.1768 - sparse_categorical_accuracy: 0.9478 - val_loss: 0.1425 - val_sparse_categorical_accuracy: 0.9600 # Prepare a directory to store all the checkpoints. checkpoint_dir <- \"./ckpt\" fs::dir_create(checkpoint_dir)  make_or_restore_model <- function() {   # Either restore the latest (best) model, or create a fresh one   # if there is no checkpoint available.   checkpoints <- Sys.glob(file.path(checkpoint_dir, \"model-loss=*.keras\"))    if (length(checkpoints) > 0) {     checkpoint_losses <- sub(\"^model-loss=([0-9.]+)\\\\.keras$\", \"\\\\1\",                              basename(checkpoints)) |> as.numeric()     best_checkpoint <- checkpoints[which.min(checkpoint_losses)]     load_model(best_checkpoint)   } else {     get_compiled_model()   } }  model <- make_or_restore_model() callbacks <- list(   # This callback saves the model every 100 batches.   # We include the training loss in the saved model name.   callback_model_checkpoint(     filepath = file.path(checkpoint_dir, \"model-loss={loss:.2f}.keras\"),     save_freq = 100   ) ) model |> fit(x_train, y_train, epochs = 1, callbacks = callbacks) ## 1563/1563 - 2s - 2ms/step - loss: 0.2932 - sparse_categorical_accuracy: 0.9145"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-learning-rate-schedules","dir":"Articles","previous_headings":"","what":"Using learning rate schedules","title":"Training & evaluation with the built-in methods","text":"common pattern training deep learning models gradually reduce learning training progresses. generally known “learning rate decay”. learning decay schedule static (fixed advance, function current epoch current batch index), dynamic (responding current behavior model, particular validation loss).","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"passing-a-schedule-to-an-optimizer","dir":"Articles","previous_headings":"Using learning rate schedules","what":"Passing a schedule to an optimizer","title":"Training & evaluation with the built-in methods","text":"can easily use static learning rate decay schedule passing schedule object learning_rate argument optimizer: Several built-schedules available: ExponentialDecay, PiecewiseConstantDecay, PolynomialDecay, InverseTimeDecay.","code":"initial_learning_rate <- 0.1 lr_schedule <- learning_rate_schedule_exponential_decay(     initial_learning_rate, decay_steps=100000, decay_rate=0.96,     staircase=TRUE )  optimizer <- optimizer_rmsprop(learning_rate = lr_schedule)"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-callbacks-to-implement-a-dynamic-learning-rate-schedule","dir":"Articles","previous_headings":"Using learning rate schedules","what":"Using callbacks to implement a dynamic learning rate schedule","title":"Training & evaluation with the built-in methods","text":"dynamic learning rate schedule (instance, decreasing learning rate validation loss longer improving) achieved schedule objects, since optimizer access validation metrics. However, callbacks access metrics, including validation metrics! can thus achieve pattern using callback modifies current learning rate optimizer. fact, even built-[callback_reduce_lr_on_plateau()].","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"visualizing-loss-and-metrics-during-training-with-tensorboard","dir":"Articles","previous_headings":"","what":"Visualizing loss and metrics during training with TensorBoard","title":"Training & evaluation with the built-in methods","text":"best way keep eye model training use TensorBoard – browser-based application can run locally provides : Live plots loss metrics training evaluation (optionally) Visualizations histograms layer activations (optionally) 3D visualizations embedding spaces learned layer_embedding() installed TensorFlow pip, able launch TensorBoard command line: R using:","code":"tensorboard --logdir=/full_path_to_your_logs tensorflow::tensorboard(logdir = \"/full_path_to_your_logs\")"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-the-tensorboard-callback","dir":"Articles","previous_headings":"Visualizing loss and metrics during training with TensorBoard","what":"Using the TensorBoard callback","title":"Training & evaluation with the built-in methods","text":"easiest way use TensorBoard Keras model fit() method [callback_tensorboard()]. simplest case, just specify want callback write logs, ’re good go: information, see callback_tensorboard().","code":"tb_callback <- callback_tensorboard(   log_dir = \"/full_path_to_your_logs\",   histogram_freq = 0, # How often to log histogram visualizations   embeddings_freq = 0, # How often to log embedding visualizations   update_freq = \"epoch\", # How often to write logs (default: once per epoch) )"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Transfer learning & fine-tuning","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Transfer learning & fine-tuning","text":"Transfer learning consists taking features learned one problem, leveraging new, similar problem. instance, features model learned identify raccoon may useful kick-start model meant identify tanukis. Transfer learning usually done tasks dataset little data train full-scale model scratch. common incarnation transfer learning context deep learning following workflow: Take layers previously trained model. Freeze , avoid destroying information contain future training rounds. Add new, trainable layers top frozen layers. learn turn old features predictions new dataset. Train new layers dataset. last, optional step, fine-tuning, consists unfreezing entire model obtained (part ), re-training new data low learning rate. can potentially achieve meaningful improvements, incrementally adapting pretrained features new data. First, go Keras trainable API detail, underlies transfer learning & fine-tuning workflows. , ’ll demonstrate typical workflow taking model pretrained ImageNet dataset, retraining Kaggle “cats vs dogs” classification dataset. adapted Deep Learning Python 2016 blog post “building powerful image classification models using little data”.","code":""},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"freezing-layers-understanding-the-trainable-attribute","dir":"Articles","previous_headings":"","what":"Freezing layers: understanding the trainable attribute","title":"Transfer learning & fine-tuning","text":"Layers & models three weight attributes: weights list weights variables layer. trainable_weights list meant updated (via gradient descent) minimize loss training. non_trainable_weights list aren’t meant trained. Typically updated model forward pass. Example: Dense layer 2 trainable weights (kernel & bias) general, weights trainable weights. built-layer non-trainable weights [layer_batch_normalization()]. uses non-trainable weights keep track mean variance inputs training. learn use non-trainable weights custom layers, see guide writing new layers scratch. Example: BatchNormalization layer 2 trainable weights 2 non-trainable weights Layers & models also feature boolean attribute trainable. value can changed. Setting layer$trainable FALSE moves layer’s weights trainable non-trainable. called “freezing” layer: state frozen layer won’t updated training (either training fit() training custom loop relies trainable_weights apply gradient updates). Example: setting trainable False trainable weight becomes non-trainable, value longer updated training. confuse layer$trainable attribute argument training layer$call() (controls whether layer run forward pass inference mode training mode). information, see Keras FAQ.","code":"layer <- layer_dense(units = 3) layer$build(shape(NULL, 4))  # Create the weights  length(layer$weights) ## [1] 2 length(layer$trainable_weights) ## [1] 2 length(layer$non_trainable_weights) ## [1] 0 layer <- layer_batch_normalization() layer$build(shape(NA, 4))  # Create the weights  length(layer$weights) ## [1] 4 length(layer$trainable_weights) ## [1] 2 length(layer$non_trainable_weights) ## [1] 2 layer <- layer_dense(units = 3) layer$build(shape(NULL, 4))  # Create the weights layer$trainable <- FALSE  # Freeze the layer  length(layer$weights) ## [1] 2 length(layer$trainable_weights) ## [1] 0 length(layer$non_trainable_weights) ## [1] 2 # Make a model with 2 layers layer1 <- layer_dense(units = 3, activation = \"relu\") layer2 <- layer_dense(units = 3, activation = \"sigmoid\") model <- keras_model_sequential(input_shape = 3) |>   layer1() |>   layer2()  # Freeze the first layer layer1$trainable <- FALSE  # Keep a copy of the weights of layer1 for later reference # (get_weights() returns a list of R arrays, #  layer$weights returns a list of KerasVariables) initial_layer1_weights_values <- get_weights(layer1)  # Train the model model |> compile(optimizer = \"adam\", loss = \"mse\") model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1) ## 1/1 - 1s - 544ms/step - loss: 2.1868 # Check that the weights of layer1 have not changed during training final_layer1_weights_values <- get_weights(layer1)  all.equal(initial_layer1_weights_values,           final_layer1_weights_values) ## [1] TRUE"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"recursive-setting-of-the-trainable-attribute","dir":"Articles","previous_headings":"","what":"Recursive setting of the trainable attribute","title":"Transfer learning & fine-tuning","text":"set $trainable <- FALSE model layer sublayers, children layers become non-trainable well. Example:","code":"inner_model <- keras_model_sequential(input_shape = 3) |>   layer_dense(units = 3, activation = \"relu\") |>   layer_dense(units = 3, activation = \"relu\")  model <- keras_model_sequential(input_shape = 3) |>   inner_model() |>   layer_dense(units = 3, activation = \"sigmoid\")  model$trainable <- FALSE  # Freeze the outer model  inner_model$trainable  # All layers in `model` are now frozen ## [1] FALSE inner_model$layers[[1]]$trainable  # `trainable` is propagated recursively ## [1] FALSE"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"the-typical-transfer-learning-workflow","dir":"Articles","previous_headings":"","what":"The typical transfer-learning workflow","title":"Transfer learning & fine-tuning","text":"leads us typical transfer learning workflow can implemented Keras: Instantiate base model load pre-trained weights . Freeze layers base model setting trainable <- FALSE. Create new model top output one (several) layers base model. Train new model new dataset. Note alternative, lightweight workflow also : Instantiate base model load pre-trained weights . Run new dataset record output one (several) layers base model. called feature extraction. Use output input data new, smaller model. key advantage second workflow run base model data, rather per epoch training. ’s lot faster & cheaper. issue second workflow, though, doesn’t allow dynamically modify input data new model training, required data augmentation, instance. Transfer learning typically used tasks new dataset little data train full-scale model scratch, scenarios data augmentation important. follows, focus first workflow. ’s first workflow looks like Keras: First, instantiate base model pre-trained weights. , freeze base model. Create new model top. Train model new data.","code":"base_model <- application_xception(   weights = 'imagenet', # Load weights pre-trained on ImageNet.   input_shape = c(150, 150, 3),   include_top = FALSE  # Do not include the ImageNet classifier at the top. ) base_model$trainable <- FALSE inputs <- keras_input(shape = c(150, 150, 3)) # We make sure that the base_model is running in inference mode here, # by passing `training <- FALSE`. This is important for fine-tuning, as you will # learn in a few paragraphs. outputs <- inputs |>   base_model(training = FALSE) |>   # Convert features of shape `base_model$output_shape[-1]` to vectors   layer_global_average_pooling_2d() |>   # A Dense classifier with a single unit (binary classification)   layer_dense(1)  model <- keras_model(inputs, outputs) model |> compile(   optimizer = optimizer_adam(),   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_binary_accuracy()) ) model |> fit(new_dataset, epochs = 20,              callbacks = ..., validation_data = ...)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"fine-tuning","dir":"Articles","previous_headings":"","what":"Fine-tuning","title":"Transfer learning & fine-tuning","text":"model converged new data, can try unfreeze part base model retrain whole model end--end low learning rate. optional last step can potentially give incremental improvements. also potentially lead quick overfitting – keep mind. critical step model frozen layers trained convergence. mix randomly-initialized trainable layers trainable layers hold pre-trained features, randomly-initialized layers cause large gradient updates training, destroy pre-trained features. ’s also critical use low learning rate stage, training much larger model first round training, dataset typically small. result, risk overfitting quickly apply large weight updates. , want readapt pretrained weights incremental way. implement fine-tuning whole base model: Important note compile() trainable Calling compile() model meant “freeze” behavior model. implies trainable attribute values time model compiled preserved throughout lifetime model, compile called . Hence, change trainable value, make sure call compile() model changes taken account. Important notes BatchNormalization layer Many image models contain BatchNormalization layers. layer special case every imaginable count. things keep mind. BatchNormalization contains 2 non-trainable weights get updated training. variables tracking mean variance inputs. set bn_layer$trainable <- FALSE, BatchNormalization layer run inference mode, update mean & variance statistics. case layers general, weight trainability & inference/training modes two orthogonal concepts. two tied case BatchNormalization layer. unfreeze model contains BatchNormalization layers order fine-tuning, keep BatchNormalization layers inference mode passing training = FALSE calling base model. Otherwise updates applied non-trainable weights suddenly destroy model learned. ’ll see pattern action end--end example end guide.","code":"# Unfreeze the base model base_model$trainable <- TRUE  # It's important to recompile your model after you make any changes # to the `trainable` attribute of any inner layer, so that your changes # are take into account model |> compile(   optimizer = optimizer_adam(1e-5), # Very low learning rate   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = c(metric_binary_accuracy()) )  # Train end-to-end. Be careful to stop before you overfit! model |> fit(new_dataset, epochs = 10,              callbacks = ..., validation_data = ...)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"an-end-to-end-example-fine-tuning-an-image-classification-model-on-a-cats-vs--dogs-dataset","dir":"Articles","previous_headings":"","what":"An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset","title":"Transfer learning & fine-tuning","text":"solidify concepts, let’s walk concrete end--end transfer learning & fine-tuning example. load Xception model, pre-trained ImageNet, use Kaggle “cats vs. dogs” classification dataset.","code":""},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"getting-the-data","dir":"Articles","previous_headings":"An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset","what":"Getting the data","title":"Transfer learning & fine-tuning","text":"First, let’s fetch cats vs. dogs dataset using TFDS. dataset, ’ll probably want use utility [image_dataset_from_directory()] generate similar labeled dataset objects set images disk filed class-specific folders. Transfer learning useful working small datasets. keep dataset small, use 40% original training data (25,000 images) training, 10% validation, 10% testing. first 9 images training dataset – can see, ’re different sizes. can also see label 1 “dog” label 0 “cat”.","code":"# reticulate::py_install(\"tensorflow-datasets\") tfds <- reticulate::import(\"tensorflow_datasets\")  c(train_ds, validation_ds, test_ds) %<-% tfds$load(   \"cats_vs_dogs\",   # Reserve 10% for validation and 10% for test   split = c(\"train[:40%]\", \"train[40%:50%]\", \"train[50%:60%]\"),   as_supervised = TRUE  # Include labels )  length(train_ds) ## [1] 9305 library(tfdatasets, exclude = \"shape\")  par(mfrow = c(3, 3), mar = c(1,0,1.5,0)) train_ds |>   dataset_take(9) |>   as_array_iterator() |>   iterate(function(batch) {     c(image, label) %<-% batch     plot(as.raster(image, max = 255L))     title(sprintf(       \"label: %s   size: %s\",       label, paste(dim(image), collapse = \" x \")))   })"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"standardizing-the-data","dir":"Articles","previous_headings":"An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset","what":"Standardizing the data","title":"Transfer learning & fine-tuning","text":"raw images variety sizes. addition, pixel consists 3 integer values 0 255 (RGB level values). isn’t great fit feeding neural network. need 2 things: Standardize fixed image size. pick 150x150. Normalize pixel values -1 1. ’ll using Normalization layer part model . general, ’s good practice develop models take raw data input, opposed models take already-preprocessed data. reason , model expects preprocessed data, time export model use elsewhere (web browser, mobile app), ’ll need reimplement exact preprocessing pipeline. gets tricky quickly. least possible amount preprocessing hitting model. , ’ll image resizing data pipeline (deep neural network can process contiguous batches data), ’ll input value scaling part model, create . Let’s resize images 150x150:","code":"resize_fn <- layer_resizing(width = 150, height = 150) resize_pair <- function(x, y) list(resize_fn(x), y)  train_ds <- train_ds |> dataset_map(resize_pair) validation_ds <- validation_ds |> dataset_map(resize_pair) test_ds <- test_ds |> dataset_map(resize_pair)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"using-random-data-augmentation","dir":"Articles","previous_headings":"An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset","what":"Using random data augmentation","title":"Transfer learning & fine-tuning","text":"don’t large image dataset, ’s good practice artificially introduce sample diversity applying random yet realistic transformations training images, random horizontal flipping small random rotations. helps expose model different aspects training data slowing overfitting. Let’s batch data use prefetching optimize loading speed. Let’s visualize first image first batch looks like various random transformations:","code":"data_augmentation <- keras_model_sequential() |>   layer_random_flip(\"horizontal\") |>   layer_random_rotation(.1)  train_ds <- train_ds %>%   dataset_map(function(x, y) list(data_augmentation(x), y)) library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) batch_size <- 64  train_ds <- train_ds |>   dataset_batch(batch_size) |>   dataset_prefetch()  validation_ds <- validation_ds |>   dataset_batch(batch_size) |>   dataset_prefetch()  test_ds <- test_ds |>   dataset_batch(batch_size) |>   dataset_prefetch() batch <- train_ds |>   dataset_take(1) |>   as_iterator() |>   iter_next()  c(images, labels) %<-% batch first_image <- images[1, all_dims(), drop = TRUE] augmented_image <- data_augmentation(first_image, training = TRUE)  plot_image <- function(image, main = deparse1(substitute(image))) {   image |>     as.array() |> # convert from tensor to R array     as.raster(max = 255) |>     plot()    if(!is.null(main))     title(main) }  par(mfrow = c(2, 2), mar = c(1, 1, 1.5, 1)) plot_image(first_image) plot_image(augmented_image) plot_image(data_augmentation(first_image, training = TRUE), \"augmented 2\") plot_image(data_augmentation(first_image, training = TRUE), \"augmented 3\")"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"build-a-model","dir":"Articles","previous_headings":"","what":"Build a model","title":"Transfer learning & fine-tuning","text":"Now let’s built model follows blueprint ’ve explained earlier. Note : add Rescaling layer scale input values (initially [0, 255] range) [-1, 1] range. add Dropout layer classification layer, regularization. make sure pass training=FALSE calling base model, runs inference mode, batchnorm statistics don’t get updated even unfreeze base model fine-tuning.","code":"base_model <- application_xception(   weights = \"imagenet\", # Load weights pre-trained on ImageNet.   input_shape = c(150, 150, 3),   include_top = FALSE, # Do not include the ImageNet classifier at the top. )  # Freeze the base_model base_model$trainable <- FALSE  # Create new model on top inputs <- keras_input(shape = c(150, 150, 3))  # Pre-trained Xception weights requires that input be scaled # from (0, 255) to a range of (-1., +1.), the rescaling layer # outputs: `(inputs * scale) + offset` scale_layer <- layer_rescaling(scale = 1 / 127.5, offset = -1) x <- scale_layer(inputs)  # The base model contains batchnorm layers. We want to keep them in inference mode # when we unfreeze the base model for fine-tuning, so we make sure that the # base_model is running in inference mode here. outputs <- x |>   base_model(training = FALSE) |>   layer_global_average_pooling_2d() |>   layer_dropout(0.2) |>   layer_dense(1)  model <- keras_model(inputs, outputs)  summary(model, show_trainable = TRUE) ## Model: \"functional_10\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)                ┃ Output Shape          ┃    Param # ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer_7 (InputLayer)  │ (None, 150, 150, 3)   │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ rescaling (Rescaling)       │ (None, 150, 150, 3)   │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ xception (Functional)       │ (None, 5, 5, 2048)    │ 20,861,480 │   N   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ global_average_pooling2d_1  │ (None, 2048)          │          0 │   -   │ ## │ (GlobalAveragePooling2D)    │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dropout (Dropout)           │ (None, 2048)          │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_8 (Dense)             │ (None, 1)             │      2,049 │   Y   │ ## └─────────────────────────────┴───────────────────────┴────────────┴───────┘ ##  Total params: 20,863,529 (79.59 MB) ##  Trainable params: 2,049 (8.00 KB) ##  Non-trainable params: 20,861,480 (79.58 MB)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"train-the-top-layer","dir":"Articles","previous_headings":"","what":"Train the top layer","title":"Transfer learning & fine-tuning","text":"","code":"model |> compile(   optimizer = optimizer_adam(),   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_binary_accuracy()) )  epochs <- 1 model |> fit(train_ds, epochs = epochs, validation_data = validation_ds) ## 146/146 - 45s - 306ms/step - binary_accuracy: 0.9183 - loss: 0.1887 - val_binary_accuracy: 0.9669 - val_loss: 0.0926"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"do-a-round-of-fine-tuning-of-the-entire-model","dir":"Articles","previous_headings":"","what":"Do a round of fine-tuning of the entire model","title":"Transfer learning & fine-tuning","text":"Finally, let’s unfreeze base model train entire model end--end low learning rate. Importantly, although base model becomes trainable, still running inference mode since passed training=FALSE calling built model. means batch normalization layers inside won’t update batch statistics. , wreck havoc representations learned model far. 10 epochs, fine-tuning gains us nice improvement . Let’s evaluate model test dataset:","code":"# Unfreeze the base_model. Note that it keeps running in inference mode # since we passed `training=FALSE` when calling it. This means that # the batchnorm layers will not update their batch statistics. # This prevents the batchnorm layers from undoing all the training # we've done so far. base_model$trainable <- TRUE summary(model, show_trainable = TRUE) ## Model: \"functional_10\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)                ┃ Output Shape          ┃    Param # ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer_7 (InputLayer)  │ (None, 150, 150, 3)   │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ rescaling (Rescaling)       │ (None, 150, 150, 3)   │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ xception (Functional)       │ (None, 5, 5, 2048)    │ 20,861,480 │   Y   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ global_average_pooling2d_1  │ (None, 2048)          │          0 │   -   │ ## │ (GlobalAveragePooling2D)    │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dropout (Dropout)           │ (None, 2048)          │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_8 (Dense)             │ (None, 1)             │      2,049 │   Y   │ ## └─────────────────────────────┴───────────────────────┴────────────┴───────┘ ##  Total params: 20,867,629 (79.60 MB) ##  Trainable params: 20,809,001 (79.38 MB) ##  Non-trainable params: 54,528 (213.00 KB) ##  Optimizer params: 4,100 (16.02 KB) model |> compile(   optimizer = optimizer_adam(1e-5), # Low learning rate   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_binary_accuracy()) )  epochs <- 1 model |> fit(train_ds, epochs = epochs, validation_data = validation_ds) ## 146/146 - 82s - 564ms/step - binary_accuracy: 0.8660 - loss: 0.3213 - val_binary_accuracy: 0.9652 - val_loss: 0.1022 model |> evaluate(test_ds) ## 37/37 - 2s - 42ms/step - binary_accuracy: 0.9540 - loss: 0.1103 ## $binary_accuracy ## [1] 0.9539983 ## ## $loss ## [1] 0.1102556"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Understanding masking & padding","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Understanding masking & padding","text":"Masking way tell sequence-processing layers certain timesteps input missing, thus skipped processing data. Padding special form masking masked steps start end sequence. Padding comes need encode sequence data contiguous batches: order make sequences batch fit given standard length, necessary pad truncate sequences. Let’s take close look.","code":""},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"padding-sequence-data","dir":"Articles","previous_headings":"","what":"Padding sequence data","title":"Understanding masking & padding","text":"processing sequence data, common individual samples different lengths. Consider following example (text tokenized words): vocabulary lookup, data might vectorized integers, e.g.: data nested list individual samples length 3, 5, 6, respectively. Since input data deep learning model must single tensor (shape e.g. (batch_size, 6, vocab_size) case), samples shorter longest item need padded placeholder value (alternatively, one might also truncate long samples padding short samples). Keras provides utility function truncate pad Python lists common length: pad_sequences.","code":"data <- list(   c(\"Hello\", \"world\", \"!\"),   c(\"How\", \"are\", \"you\", \"doing\", \"today\"),   c(\"The\", \"weather\", \"will\", \"be\", \"nice\", \"tomorrow\") ) data <- list(   c(71, 1331, 4231),   c(73, 8, 3215, 55, 927),   c(83, 91, 1, 645, 1253, 927) ) raw_inputs <- list(   c(711, 632, 71),   c(73, 8, 3215, 55, 927),   c(83, 91, 1, 645, 1253, 927) )  # By default, this will pad using 0s; it is configurable via the # \"value\" parameter. # Note that you could use \"pre\" padding (at the beginning) or # \"post\" padding (at the end). # We recommend using \"post\" padding when working with RNN layers # (in order to be able to use the # CuDNN implementation of the layers). padded_inputs <- pad_sequences(raw_inputs, padding=\"post\") padded_inputs ##      [,1] [,2] [,3] [,4] [,5] [,6] ## [1,]  711  632   71    0    0    0 ## [2,]   73    8 3215   55  927    0 ## [3,]   83   91    1  645 1253  927"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"masking","dir":"Articles","previous_headings":"","what":"Masking","title":"Understanding masking & padding","text":"Now samples uniform length, model must informed part data actually padding ignored. mechanism masking. three ways introduce input masks Keras models: Add layer_masking layer. Configure layer_embedding layer mask_zero=TRUE. Pass mask argument manually calling layers support argument (e.g. RNN layers).","code":""},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"mask-generating-layers-embedding-and-masking","dir":"Articles","previous_headings":"","what":"Mask-generating layers: Embedding and Masking","title":"Understanding masking & padding","text":"hood, layers create mask tensor (2D tensor shape (batch, sequence_length)), attach tensor output returned Masking Embedding layer. can see printed result, mask 2D boolean tensor shape (batch_size, sequence_length), individual FALSE entry indicates corresponding timestep ignored processing.","code":"embedding <- layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) masked_output <- embedding(padded_inputs)  masked_output$`_keras_mask` ## tf.Tensor( ## [[ True  True  True False False False] ##  [ True  True  True  True  True False] ##  [ True  True  True  True  True  True]], shape=(3, 6), dtype=bool) masking_layer <- layer_masking() # Simulate the embedding lookup by expanding the 2D input to 3D, # with embedding dimension of 10. unmasked_embedding <- op_cast(     op_tile(op_expand_dims(padded_inputs, axis=-1), c(1L, 1L, 10L)),     dtype=\"float32\" )  masked_embedding <- masking_layer(unmasked_embedding) masked_embedding$`_keras_mask` ## tf.Tensor( ## [[ True  True  True False False False] ##  [ True  True  True  True  True False] ##  [ True  True  True  True  True  True]], shape=(3, 6), dtype=bool)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"mask-propagation-in-the-functional-api-and-sequential-api","dir":"Articles","previous_headings":"","what":"Mask propagation in the Functional API and Sequential API","title":"Understanding masking & padding","text":"using Functional API Sequential API, mask generated Embedding Masking layer propagated network layer capable using (example, RNN layers). Keras automatically fetch mask corresponding input pass layer knows use . instance, following Sequential model, LSTM layer automatically receive mask, means ignore padded values: also case following Functional API model:","code":"model <- keras_model_sequential() %>%   layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%   layer_lstm(units=32) inputs <- keras_input(shape = shape(NULL), dtype=\"int32\") outputs <- inputs %>%   layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%   layer_lstm(units=32)  model <- keras_model(inputs, outputs)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"passing-mask-tensors-directly-to-layers","dir":"Articles","previous_headings":"","what":"Passing mask tensors directly to layers","title":"Understanding masking & padding","text":"Layers can handle masks (LSTM layer) mask argument call method. Meanwhile, layers produce mask (e.g. Embedding) expose compute_mask(input, previous_mask) method can call. Thus, can pass output compute_mask() method mask-producing layer call method mask-consuming layer, like :","code":"MyLayer <- new_layer_class(   \"MyLayer\",   initialize = function(...) {     super$initialize(...)     self$embedding <- layer_embedding(       input_dim=5000, output_dim=16, mask_zero=TRUE     )     self$lstm <- layer_lstm(units=32)   },   call = function(inputs) {     inputs %>%       self$embedding() %>%       # Note that you could also prepare a `mask` tensor manually.       # It only needs to be a boolean tensor       # with the right shape, i.e. (batch_size, timesteps).       self$lstm(mask=self$embedding$compute_mask(inputs))   } )  layer <- MyLayer() x <- random_integer(c(32, 10), 0, 100) layer(x) ## tf.Tensor( ## [[ 0.00130048 -0.00113367 -0.00715671 ... -0.00107615 -0.00162071 ##    0.00135018] ##  [-0.004185    0.00726349  0.00520932 ...  0.00119117  0.00230441 ##    0.00174123] ##  [-0.00537032 -0.00164898 -0.00238435 ... -0.00154158 -0.0038603 ##   -0.00105811] ##  ... ##  [ 0.00622133 -0.00905907 -0.00599518 ...  0.00025823 -0.00142478 ##   -0.00125036] ##  [-0.00523904  0.00336683 -0.00299453 ...  0.00876719  0.00172074 ##    0.00903089] ##  [-0.00393721  0.00058538  0.00503809 ... -0.00203075  0.00325885 ##   -0.00299755]], shape=(32, 32), dtype=float32)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"supporting-masking-in-your-custom-layers","dir":"Articles","previous_headings":"","what":"Supporting masking in your custom layers","title":"Understanding masking & padding","text":"Sometimes, may need write layers generate mask (like Embedding), layers need modify current mask. instance, layer produces tensor different time dimension input, Concatenate layer concatenates time dimension, need modify current mask downstream layers able properly take masked timesteps account. , layer implement layer.compute_mask() method, produces new mask given input current mask. example TemporalSplit layer needs modify current mask. another example CustomEmbedding layer capable generating mask input values: Note: details format limitations related masking, see serialization guide.","code":"TemporalSplit <- new_layer_class(   \"TemporalSplit\",   call = function(inputs) {     # Expect the input to be 3D and mask to be 2D, split the input tensor into 2     # subtensors along the time axis (axis 1).     op_split(inputs, 2, axis=2)   },   compute_mask = function(inputs, mask = NULL) {     # Also split the mask into 2 if it presents.     if (!is.null(mask)) {       op_split(mask, 2, axis=2)     } else {       NULL     }   } )  c(first_half, second_half) %<-% TemporalSplit(masked_embedding) first_half$`_keras_mask` ## tf.Tensor( ## [[ True  True  True] ##  [ True  True  True] ##  [ True  True  True]], shape=(3, 3), dtype=bool) second_half$`_keras_mask` ## tf.Tensor( ## [[False False False] ##  [ True  True False] ##  [ True  True  True]], shape=(3, 3), dtype=bool) CustomEmbedding <- new_layer_class(   \"CustomEmbedding\",   initialize = function(input_dim, output_dim, mask_zero=FALSE, ...) {     super$initialize(...)     self$input_dim <- as.integer(input_dim)     self$output_dim <- as.integer(output_dim)     self$mask_zero <- mask_zero   },   build = function(input_shape) {     self$embeddings <- self$add_weight(       shape=c(self$input_dim, self$output_dim),       initializer=\"random_normal\",       dtype=\"float32\"     )   },   call = function(inputs) {     inputs <- op_cast(inputs, \"int32\")     op_take(self$embeddings, inputs)   },   compute_mask = function(inputs, mask=NULL) {     if (!self$mask_zero) {       NULL     } else {       op_not_equal(inputs, 0)     }   } )  layer <- CustomEmbedding(input_dim = 10, output_dim = 32, mask_zero=TRUE) x <- random_integer(c(3, 10), 0, 9)  y <- layer(x) mask <- layer$compute_mask(x)  mask ## tf.Tensor( ## [[ True  True  True  True  True  True  True  True  True  True] ##  [ True  True  True  True  True  True  True  True  True  True] ##  [False False  True  True  True  True  True  True  True  True]], shape=(3, 10), dtype=bool)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"opting-in-to-mask-propagation-on-compatible-layers","dir":"Articles","previous_headings":"","what":"Opting-in to mask propagation on compatible layers","title":"Understanding masking & padding","text":"layers don’t modify time dimension, don’t need modify current mask. However, may still want able propagate current mask, unchanged, next layer. opt-behavior. default, custom layer destroy current mask (since framework way tell whether propagating mask safe ). custom layer modify time dimension, want able propagate current input mask, set self.supports_masking = True layer constructor. case, default behavior compute_mask() just pass current mask . ’s example layer whitelisted mask propagation: can now use custom layer -mask-generating layer (like Embedding) mask-consuming layer (like LSTM), pass mask along reaches mask-consuming layer.","code":"MyActivation <- new_layer_class(   \"MyActivation\",   initialize = function(...) {     super$initialize(...)     self$supports_masking <- TRUE   },   call = function(inputs) {     op_relu(inputs)   } ) inputs <- keras_input(shape = shape(NULL), dtype=\"int32\") outputs <- inputs %>%   layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%   MyActivation() %>%   layer_lstm(units=32)  model <- keras_model(inputs, outputs) y <- model(random_integer(c(32, 100), 0, 5000))"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"writing-layers-that-need-mask-information","dir":"Articles","previous_headings":"","what":"Writing layers that need mask information","title":"Understanding masking & padding","text":"layers mask consumers: accept mask argument call use determine whether skip certain time steps. write layer, can simply add mask=None argument call signature. mask associated inputs passed layer whenever available. ’s simple example : layer computes softmax time dimension (axis 1) input sequence, discarding masked timesteps.","code":"TemporalSoftmax <- new_layer_class(   \"TemporalSoftmax\",   initialize = function(...) {     super$initialize(...)     self$supports_masking <- TRUE   },   call = function(inputs, mask=NULL) {     if (is.null(mask)) {       stop(\"`TemporalSoftmax` layer requires a previous layer to support masking.\")     }     broadcast_float_mask <- op_expand_dims(op_cast(mask, \"float32\"), -1)     inputs_exp <- op_exp(inputs) * broadcast_float_mask     inputs_sum <- op_sum(inputs_exp * broadcast_float_mask, axis=-1, keepdims=TRUE)     inputs_exp / inputs_sum   } )  inputs <- keras_input(shape = shape(NULL), dtype=\"int32\") outputs <- inputs %>%   layer_embedding(input_dim=10, output_dim=32, mask_zero=TRUE) %>%   layer_dense(1) %>%   TemporalSoftmax()  model <- keras_model(inputs, outputs) y <- model(random_integer(c(32, 100), 0, 10))"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Understanding masking & padding","text":"need know padding & masking Keras. recap: “Masking” layers able know skip / ignore certain timesteps sequence inputs. layers mask-generators: Embedding can generate mask input values (mask_zero=TRUE), can Masking layer. layers mask-consumers: expose mask argument call method. case RNN layers. Functional API Sequential API, mask information propagated automatically. using layers standalone way, can pass mask arguments layers manually. can easily write layers modify current mask, generate new mask, consume mask associated inputs.","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing a training loop from scratch in JAX","text":"","code":"import os  # This guide can only be run with the jax backend. os.environ[\"KERAS_BACKEND\"] = \"jax\"  import jax  # We import TF so we can use tf.data. import tensorflow as tf import keras import numpy as np"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing a training loop from scratch in JAX","text":"Keras provides default training evaluation loops, fit() evaluate(). usage covered guide Training & evaluation built-methods. want customize learning algorithm model still leveraging convenience fit() (instance, train GAN using fit()), can subclass Model class implement train_step() method, called repeatedly fit(). Now, want low-level control training & evaluation, write training & evaluation loops scratch. guide .","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"A first end-to-end example","title":"Writing a training loop from scratch in JAX","text":"write custom training loop, need following ingredients: model train, course. optimizer. either use optimizer keras.optimizers, one optax package. loss function. dataset. standard JAX ecosystem load data via tf.data, ’s ’ll use. Let’s line . First, let’s get model MNIST dataset: Next, ’s loss function optimizer. ’ll use Keras optimizer case.","code":"def get_model():     inputs = keras.Input(shape=(784,), name=\"digits\")     x1 = keras.layers.Dense(64, activation=\"relu\")(inputs)     x2 = keras.layers.Dense(64, activation=\"relu\")(x1)     outputs = keras.layers.Dense(10, name=\"predictions\")(x2)     model = keras.Model(inputs=inputs, outputs=outputs)     return model   model = get_model()  # Prepare the training dataset. batch_size = 32 (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() x_train = np.reshape(x_train, (-1, 784)).astype(\"float32\") x_test = np.reshape(x_test, (-1, 784)).astype(\"float32\") y_train = keras.utils.to_categorical(y_train) y_test = keras.utils.to_categorical(y_test)  # Reserve 10,000 samples for validation. x_val = x_train[-10000:] y_val = y_train[-10000:] x_train = x_train[:-10000] y_train = y_train[:-10000]  # Prepare the training dataset. train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)) train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size)  # Prepare the validation dataset. val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val)) val_dataset = val_dataset.batch(batch_size) # Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  # Instantiate an optimizer. optimizer = keras.optimizers.Adam(learning_rate=1e-3)"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"getting-gradients-in-jax","dir":"Articles","previous_headings":"A first end-to-end example","what":"Getting gradients in JAX","title":"Writing a training loop from scratch in JAX","text":"Let’s train model using mini-batch gradient custom training loop. JAX, gradients computed via metaprogramming: call jax.grad (jax.value_and_grad function order create gradient-computing function first function. first thing need function returns loss value. ’s function ’ll use generate gradient function. Something like : function, can compute gradients via metaprogramming : Typically, don’t just want get gradient values, also want get loss value. can using jax.value_and_grad instead jax.grad:","code":"def compute_loss(x, y):     ...     return loss grad_fn = jax.grad(compute_loss) grads = grad_fn(x, y) grad_fn = jax.value_and_grad(compute_loss) loss, grads = grad_fn(x, y)"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"jax-computation-is-purely-stateless","dir":"Articles","previous_headings":"A first end-to-end example","what":"JAX computation is purely stateless","title":"Writing a training loop from scratch in JAX","text":"JAX, everything must stateless function – loss computation function must stateless well. means Keras variables (e.g. weight tensors) must passed function inputs, variable updated forward pass must returned function output. function side effect. forward pass, non-trainable variables Keras model might get updated. variables , instance, RNG seed state variables BatchNormalization statistics. ’re going need return . need something like : function, can get gradient function specifying hax_aux value_and_grad: tells JAX loss computation function returns outputs just loss. Note loss always first output. Now established basics, let’s implement compute_loss_and_updates function. Keras models stateless_call method come handy . works just like model.__call__, requires explicitly pass value variables model, returns just __call__ outputs also (potentially updated) non-trainable variables. Let’s get gradient function:","code":"def compute_loss_and_updates(trainable_variables, non_trainable_variables, x, y):     ...     return loss, non_trainable_variables grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True) (loss, non_trainable_variables), grads = grad_fn(     trainable_variables, non_trainable_variables, x, y ) def compute_loss_and_updates(     trainable_variables, non_trainable_variables, x, y ):     y_pred, non_trainable_variables = model.stateless_call(         trainable_variables, non_trainable_variables, x     )     loss = loss_fn(y, y_pred)     return loss, non_trainable_variables grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True)"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"the-training-step-function","dir":"Articles","previous_headings":"A first end-to-end example","what":"The training step function","title":"Writing a training loop from scratch in JAX","text":"Next, let’s implement end--end training step, function run forward pass, compute loss, compute gradients, also use optimizer update trainable variables. function also needs stateless, get input state tuple includes every state element ’re going use: trainable_variables non_trainable_variables: model’s variables. optimizer_variables: optimizer’s state variables, momentum accumulators. update trainable variables, use optimizer’s stateless method stateless_apply. ’s equivalent optimizer.apply(), requires always passing trainable_variables optimizer_variables. returns updated trainable variables updated optimizer_variables.","code":"def train_step(state, data):     trainable_variables, non_trainable_variables, optimizer_variables = state     x, y = data     (loss, non_trainable_variables), grads = grad_fn(         trainable_variables, non_trainable_variables, x, y     )     trainable_variables, optimizer_variables = optimizer.stateless_apply(         optimizer_variables, grads, trainable_variables     )     # Return updated state     return loss, (         trainable_variables,         non_trainable_variables,         optimizer_variables,     )"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"make-it-fast-with-jax-jit","dir":"Articles","previous_headings":"A first end-to-end example","what":"Make it fast with jax.jit","title":"Writing a training loop from scratch in JAX","text":"default, JAX operations run eagerly, just like TensorFlow eager mode PyTorch eager mode. just like TensorFlow eager mode PyTorch eager mode, ’s pretty slow – eager mode better used debugging environment, way actual work. let’s make train_step fast compiling . stateless JAX function, can compile XLA via @jax.jit decorator. get traced first execution, subsequent executions executing traced graph (just like @tf.function(jit_compile=True). Let’s try : ’re now ready train model. training loop trivial: just repeatedly call loss, state = train_step(state, data). Note: convert TF tensors yielded tf.data.Dataset NumPy passing JAX function. variables must built beforehand: model must built optimizer must built. Since ’re using Functional API model, ’s already built, subclassed model ’d need call batch data build . key thing notice loop entirely stateless – variables attached model (model.weights) never getting updated loop. new values stored state tuple. means point, saving model, attaching new variable values back model. Just call variable.assign(new_value) model variable want update:","code":"@jax.jit def train_step(state, data):     trainable_variables, non_trainable_variables, optimizer_variables = state     x, y = data     (loss, non_trainable_variables), grads = grad_fn(         trainable_variables, non_trainable_variables, x, y     )     trainable_variables, optimizer_variables = optimizer.stateless_apply(         optimizer_variables, grads, trainable_variables     )     # Return updated state     return loss, (         trainable_variables,         non_trainable_variables,         optimizer_variables,     ) # Build optimizer variables. optimizer.build(model.trainable_variables)  trainable_variables = model.trainable_variables non_trainable_variables = model.non_trainable_variables optimizer_variables = optimizer.variables state = trainable_variables, non_trainable_variables, optimizer_variables  # Training loop for step, data in enumerate(train_dataset):     data = (data[0].numpy(), data[1].numpy())     loss, state = train_step(state, data)     # Log every 100 batches.     if step % 100 == 0:         print(f\"Training loss (for 1 batch) at step {step}: {float(loss):.4f}\")         print(f\"Seen so far: {(step + 1) * batch_size} samples\") trainable_variables, non_trainable_variables, optimizer_variables = state for variable, value in zip(model.trainable_variables, trainable_variables):     variable.assign(value) for variable, value in zip(     model.non_trainable_variables, non_trainable_variables ):     variable.assign(value)"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"low-level-handling-of-metrics","dir":"Articles","previous_headings":"","what":"Low-level handling of metrics","title":"Writing a training loop from scratch in JAX","text":"Let’s add metrics monitoring basic training loop. can readily reuse built-Keras metrics (custom ones wrote) training loops written scratch. ’s flow: Instantiate metric start loop Include metric_variables train_step arguments compute_loss_and_updates arguments. Call metric.stateless_update_state() compute_loss_and_updates function. ’s equivalent update_state() – stateless. need display current value metric, outside train_step (eager scope), attach new metric variable values metric object vall metric.result(). Call metric.reset_state() need clear state metric (typically end epoch) Let’s use knowledge compute CategoricalAccuracy training validation data end training: ’ll also prepare evaluation step function: loops:","code":"# Get a fresh model model = get_model()  # Instantiate an optimizer to train the model. optimizer = keras.optimizers.Adam(learning_rate=1e-3) # Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  # Prepare the metrics. train_acc_metric = keras.metrics.CategoricalAccuracy() val_acc_metric = keras.metrics.CategoricalAccuracy()   def compute_loss_and_updates(     trainable_variables, non_trainable_variables, metric_variables, x, y ):     y_pred, non_trainable_variables = model.stateless_call(         trainable_variables, non_trainable_variables, x     )     loss = loss_fn(y, y_pred)     metric_variables = train_acc_metric.stateless_update_state(         metric_variables, y, y_pred     )     return loss, (non_trainable_variables, metric_variables)   grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True)   @jax.jit def train_step(state, data):     (         trainable_variables,         non_trainable_variables,         optimizer_variables,         metric_variables,     ) = state     x, y = data     (loss, (non_trainable_variables, metric_variables)), grads = grad_fn(         trainable_variables, non_trainable_variables, metric_variables, x, y     )     trainable_variables, optimizer_variables = optimizer.stateless_apply(         optimizer_variables, grads, trainable_variables     )     # Return updated state     return loss, (         trainable_variables,         non_trainable_variables,         optimizer_variables,         metric_variables,     ) @jax.jit def eval_step(state, data):     trainable_variables, non_trainable_variables, metric_variables = state     x, y = data     y_pred, non_trainable_variables = model.stateless_call(         trainable_variables, non_trainable_variables, x     )     loss = loss_fn(y, y_pred)     metric_variables = val_acc_metric.stateless_update_state(         metric_variables, y, y_pred     )     return loss, (         trainable_variables,         non_trainable_variables,         metric_variables,     ) # Build optimizer variables. optimizer.build(model.trainable_variables)  trainable_variables = model.trainable_variables non_trainable_variables = model.non_trainable_variables optimizer_variables = optimizer.variables metric_variables = train_acc_metric.variables state = (     trainable_variables,     non_trainable_variables,     optimizer_variables,     metric_variables, )  # Training loop for step, data in enumerate(train_dataset):     data = (data[0].numpy(), data[1].numpy())     loss, state = train_step(state, data)     # Log every 100 batches.     if step % 100 == 0:         print(f\"Training loss (for 1 batch) at step {step}: {float(loss):.4f}\")         _, _, _, metric_variables = state         for variable, value in zip(             train_acc_metric.variables, metric_variables         ):             variable.assign(value)         print(f\"Training accuracy: {train_acc_metric.result()}\")         print(f\"Seen so far: {(step + 1) * batch_size} samples\")  metric_variables = val_acc_metric.variables (     trainable_variables,     non_trainable_variables,     optimizer_variables,     metric_variables, ) = state state = trainable_variables, non_trainable_variables, metric_variables  # Eval loop for step, data in enumerate(val_dataset):     data = (data[0].numpy(), data[1].numpy())     loss, state = eval_step(state, data)     # Log every 100 batches.     if step % 100 == 0:         print(             f\"Validation loss (for 1 batch) at step {step}: {float(loss):.4f}\"         )         _, _, metric_variables = state         for variable, value in zip(val_acc_metric.variables, metric_variables):             variable.assign(value)         print(f\"Validation accuracy: {val_acc_metric.result()}\")         print(f\"Seen so far: {(step + 1) * batch_size} samples\")"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"low-level-handling-of-losses-tracked-by-the-model","dir":"Articles","previous_headings":"","what":"Low-level handling of losses tracked by the model","title":"Writing a training loop from scratch in JAX","text":"Layers & models recursively track losses created forward pass layers call self.add_loss(value). resulting list scalar loss values available via property model.losses end forward pass. want using loss components, sum add main loss training step. Consider layer, creates activity regularization loss: Let’s build really simple model uses : ’s compute_loss_and_updates function look like now: Pass return_losses=True model.stateless_call(). Sum resulting losses add main loss. ’s !","code":"class ActivityRegularizationLayer(keras.layers.Layer):     def call(self, inputs):         self.add_loss(1e-2 * jax.numpy.sum(inputs))         return inputs inputs = keras.Input(shape=(784,), name=\"digits\") x = keras.layers.Dense(64, activation=\"relu\")(inputs) # Insert activity regularization as a layer x = ActivityRegularizationLayer()(x) x = keras.layers.Dense(64, activation=\"relu\")(x) outputs = keras.layers.Dense(10, name=\"predictions\")(x)  model = keras.Model(inputs=inputs, outputs=outputs) def compute_loss_and_updates(     trainable_variables, non_trainable_variables, metric_variables, x, y ):     y_pred, non_trainable_variables, losses = model.stateless_call(         trainable_variables, non_trainable_variables, x, return_losses=True     )     loss = loss_fn(y, y_pred)     if losses:         loss += jax.numpy.sum(losses)     metric_variables = train_acc_metric.stateless_update_state(         metric_variables, y, y_pred     )     return loss, non_trainable_variables, metric_variables"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing a training loop from scratch in TensorFlow","text":"","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\") library(keras3)  # This guide can only be run with the TensorFlow backend. use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing a training loop from scratch in TensorFlow","text":"Keras provides default training evaluation loops, fit() evaluate(). usage covered guide Training & evaluation built-methods. want customize learning algorithm model still leveraging convenience fit() (instance, train GAN using fit()), can subclass Model class implement train_step() method, called repeatedly fit(). covered guide Customizing happens fit(). Now, want low-level control training & evaluation, write training & evaluation loops scratch. guide .","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"using-the-gradienttape-a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"Using the GradientTape: a first end-to-end example","title":"Writing a training loop from scratch in TensorFlow","text":"Calling model inside GradientTape scope enables retrieve gradients trainable weights layer respect loss value. Using optimizer instance, can use gradients update variables (can retrieve using model$trainable_weights). Let’s consider simple MNIST model: Let’s train using mini-batch gradient custom training loop. First, ’re going need optimizer, loss function, dataset: ’s training loop: open loop iterates epochs epoch, iterate dataset, batches batch, open GradientTape() scope Inside scope, call model (forward pass) compute loss Outside scope, retrieve gradients weights model regard loss Finally, use optimizer update weights model based gradients","code":"get_model <- function() {   inputs <- keras_input(shape = 784, name = \"digits\")   outputs <- inputs |>     layer_dense(units = 64, activation = \"relu\") |>     layer_dense(units = 64, activation = \"relu\") |>     layer_dense(units = 10, name = \"predictions\")   keras_model(inputs = inputs, outputs = outputs) }  model <- get_model() # Instantiate an optimizer. optimizer <- optimizer_adam(learning_rate = 1e-3) # Instantiate a loss function. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE)  # Prepare the training dataset. batch_size <- 64 c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(-1, 784)) x_test <- array_reshape(x_test, c(-1, 784))  # Reserve 10,000 samples for validation. val_i <- sample.int(nrow(x_train), 10000) x_val <- x_train[val_i,] y_val <- y_train[val_i] x_train %<>% .[-val_i,] y_train %<>% .[-val_i]  # Prepare the training dataset. train_dataset <- list(x_train, y_train) |>   tensor_slices_dataset() |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(batch_size)  # Prepare the validation dataset. val_dataset <- list(x_val, y_val) |>   tensor_slices_dataset() |>   dataset_batch(batch_size) epochs <- 3 for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   iterator <- as_iterator(train_dataset)   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1     # Open a GradientTape to record the operations run     # during the forward pass, which enables auto-differentiation.     with(tf$GradientTape() %as% tape, {       # Run the forward pass of the layer.       # The operations that the layer applies       # to its inputs are going to be recorded       # on the GradientTape.       logits <- model(x_batch_train, training = TRUE) # Logits for this minibatch        # Compute the loss value for this minibatch.       loss_value <- loss_fn(y_batch_train, logits)     })      # Use the gradient tape to automatically retrieve     # the gradients of the trainable variables with respect to the loss.     gradients <- tape$gradient(loss_value, model$trainable_weights)      # Run one step of gradient descent by updating     # the value of the variables to minimize the loss.     optimizer$apply(gradients, model$trainable_weights)      # Log every 200 batches.     if (step %% 200 == 0) {       cat(sprintf(\"Training loss (for one batch) at step %d: %.4f\\n\",                   step, loss_value))       cat(sprintf(\"Seen so far: %d samples\\n\", (step * batch_size)))     }   } } ## Start of epoch  1 ## Training loss (for one batch) at step 200: 1.1675 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 1.6450 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.6477 ## Seen so far: 38400 samples ## Start of epoch  2 ## Training loss (for one batch) at step 200: 0.6421 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.1827 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.4249 ## Seen so far: 38400 samples ## Start of epoch  3 ## Training loss (for one batch) at step 200: 0.2715 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.3106 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.2905 ## Seen so far: 38400 samples"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"low-level-handling-of-metrics","dir":"Articles","previous_headings":"","what":"Low-level handling of metrics","title":"Writing a training loop from scratch in TensorFlow","text":"Let’s add metrics monitoring basic loop. can readily reuse built-metrics (custom ones wrote) training loops written scratch. ’s flow: Instantiate metric start loop Call metric$update_state() batch Call metric$result() need display current value metric Call metric$reset_state() need clear state metric (typically end epoch) Let’s use knowledge compute SparseCategoricalAccuracy validation data end epoch: ’s training & evaluation loop:","code":"# Get a fresh model model <- get_model()  # Instantiate an optimizer to train the model. optimizer <- optimizer_adam(learning_rate = 1e-3) # Instantiate a loss function. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE)  # Prepare the metrics. train_acc_metric <- metric_sparse_categorical_accuracy() val_acc_metric <- metric_sparse_categorical_accuracy() epochs <- 2 time <- Sys.time() for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   train_dataset_iterator <- as_iterator(train_dataset)   while (!is.null(train_batch <- iter_next(train_dataset_iterator))) {     c(x_batch_train, y_batch_train) %<-% train_batch     step <- step + 1      with(tf$GradientTape() %as% tape, {       logits <- model(x_batch_train, training = TRUE)       loss_value <- loss_fn(y_batch_train, logits)     })     gradients <- tape$gradient(loss_value, model$trainable_weights)     optimizer$apply(gradients, model$trainable_weights)      # Update training metric.     train_acc_metric$update_state(y_batch_train, logits)     if (step %% 200 == 0) {       cat(sprintf(         \"Training loss (for one batch) at step %d: %.4f\\n\", step, loss_value))       cat(sprintf(\"Seen so far: %d samples \\n\", (step * batch_size)))     }   }    # Display metrics at the end of each epoch.   train_acc <- train_acc_metric$result()   cat(sprintf(\"Training acc over epoch: %.4f\\n\", train_acc))    # Reset training metrics at the end of each epoch   train_acc_metric$reset_state()    # Run a validation loop at the end of each epoch.   iterate(val_dataset, function(val_batch) {     c(x_batch_val, y_batch_val) %<-% val_batch     val_logits <- model(x_batch_val, training = FALSE)     val_acc_metric$update_state(y_batch_val, val_logits)   })   val_acc <- val_acc_metric$result()   val_acc_metric$reset_state()   cat(sprintf(\"Validation acc: %.4f\\n\", val_acc)) } ## Start of epoch  1 ## Training loss (for one batch) at step 200: 1.6268 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 1.2241 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.4987 ## Seen so far: 38400 samples ## Training acc over epoch: 0.7844 ## Validation acc: 0.8680 ## Start of epoch  2 ## Training loss (for one batch) at step 200: 0.4626 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.4654 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.5022 ## Seen so far: 38400 samples ## Training acc over epoch: 0.8837 ## Validation acc: 0.9031 Sys.time() - time ## Time difference of 44.04362 secs"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"speeding-up-your-training-step-with-tf_function","dir":"Articles","previous_headings":"","what":"Speeding-up your training step with tf_function()","title":"Writing a training loop from scratch in TensorFlow","text":"default runtime TensorFlow 2 eager execution. , training loop executes eagerly. great debugging, graph compilation definite performance advantage. Describing computation static graph enables framework apply global performance optimizations. impossible framework constrained greedily execute one operation another, knowledge comes next. can compile static graph function takes tensors input. Just add @tf.function decorator , like : Let’s evaluation step: Now, let’s re-run training loop compiled training step: Much faster, isn’t ?","code":"train_step <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     logits <- model(x, training = TRUE)     loss_value <- loss_fn(y, logits)   })   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply(gradients, model$trainable_weights)   train_acc_metric$update_state(y, logits)   invisible(NULL) # return nothing }) test_step <- tf_function(function(x, y) {   val_logits <- model(x, training=FALSE)   val_acc_metric$update_state(y, val_logits)   invisible(NULL) # return nothing }) epochs <- 2 time <- Sys.time() for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1     train_step(x_batch_train, y_batch_train)      if (step %% 200 == 0) {       cat(sprintf(         \"Training loss (for one batch) at step %d: %.4f\\n\", step, loss_value       ))       cat(sprintf(\"Seen so far: %d samples \\n\", (step * batch_size)))     }   }    # Display metrics at the end of each epoch.   train_acc <- train_acc_metric$result()   cat(sprintf(\"Training acc over epoch: %.4f\\n\", train_acc))     # Reset training metrics at the end of each epoch   train_acc_metric$reset_state()    # Run a validation loop at the end of each epoch.    iterate(val_dataset, function(val_batch) {     c(x_batch_val, y_batch_val) %<-% val_batch     test_step(x_batch_val, y_batch_val)   })    val_acc <- val_acc_metric$result()   val_acc_metric$reset_state()   cat(sprintf(\"Validation acc: %.4f\\n\", val_acc)) } ## Start of epoch  1 ## Training acc over epoch: 0.0000 ## Validation acc: 0.9031 ## Start of epoch  2 ## Training acc over epoch: 0.0000 ## Validation acc: 0.9031 Sys.time() - time ## Time difference of 0.4195933 secs"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"low-level-handling-of-losses-tracked-by-the-model","dir":"Articles","previous_headings":"","what":"Low-level handling of losses tracked by the model","title":"Writing a training loop from scratch in TensorFlow","text":"Layers models recursively track losses created forward pass layers call self$add_loss(value). resulting list scalar loss values available via property model$losses end forward pass. want using loss components, sum add main loss training step. Consider layer, creates activity regularization loss: Let’s build really simple model uses : ’s training step look like now:","code":"layer_activity_regularization <- Layer(   \"ActivityRegularizationLayer\",   call = function(inputs) {     self$add_loss(0.1 * op_mean(inputs))     inputs   } ) inputs <- keras_input(shape = 784, name=\"digits\") outputs <- inputs |>   layer_dense(units = 64, activation = \"relu\") |>   layer_activity_regularization() |>   layer_dense(units = 64, activation = \"relu\") |>   layer_dense(units = 10, name = \"predictions\") model <- keras_model(inputs = inputs, outputs = outputs) train_step <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     logits <- model(x, training = TRUE)     loss_value <- Reduce(`+`, c(loss_fn(y, logits),                                 model$losses))   })   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply(gradients, model$trainable_weights)   train_acc_metric$update_state(y, logits)   invisible(NULL) })"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Writing a training loop from scratch in TensorFlow","text":"Now know everything know using built-training loops writing scratch. conclude, ’s simple end--end example ties together everything ’ve learned guide: DCGAN trained MNIST digits.","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"end-to-end-example-a-gan-training-loop-from-scratch","dir":"Articles","previous_headings":"","what":"End-to-end example: a GAN training loop from scratch","title":"Writing a training loop from scratch in TensorFlow","text":"may familiar Generative Adversarial Networks (GANs). GANs can generate new images look almost real, learning latent distribution training dataset images (“latent space” images). GAN made two parts: “generator” model maps points latent space points image space, “discriminator” model, classifier can tell difference real images (training dataset) fake images (output generator network). GAN training loop looks like : Train discriminator. Sample batch random points latent space. Turn points fake images via “generator” model. Get batch real images combine generated images. Train “discriminator” model classify generated vs. real images. Train generator. Sample random points latent space. Turn points fake images via “generator” network. Get batch real images combine generated images. Train “generator” model “fool” discriminator classify fake images real. much detailed overview GANs works, see Deep Learning Python. Let’s implement training loop. First, create discriminator meant classify fake vs real digits: let’s create generator network, turns latent vectors outputs shape (28, 28, 1) (representing MNIST digits): ’s key bit: training loop. can see quite straightforward. training step function takes 17 lines. Let’s train GAN, repeatedly calling train_step batches images. Since discriminator generator convnets, ’re going want run code GPU. ’s ! ’ll get nice-looking fake MNIST digits just ~30s training GPU.","code":"# Create the discriminator discriminator <-   keras_model_sequential(name = \"discriminator\",                          input_shape = c(28, 28, 1)) |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d(filters = 128, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_global_max_pooling_2d() |>   layer_dense(units = 1)  summary(discriminator) ## Model: \"discriminator\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 14, 14, 64)     │           640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu (LeakyReLU)         │ (None, 14, 14, 64)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 7, 7, 128)      │        73,856 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_1 (LeakyReLU)       │ (None, 7, 7, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d            │ (None, 128)            │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_6 (Dense)                 │ (None, 1)              │           129 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 74,625 (291.50 KB) ##  Trainable params: 74,625 (291.50 KB) ##  Non-trainable params: 0 (0.00 B) latent_dim <- 128L  generator <-   keras_model_sequential(name = \"generator\",                          input_shape = latent_dim) |>   layer_dense(7 * 7 * 128) |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_reshape(target_shape = c(7, 7, 128)) |>   layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d(filters = 1, kernel_size = c(7, 7), padding = \"same\",                 activation = \"sigmoid\")  summary(generator) ## Model: \"generator\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_7 (Dense)                 │ (None, 6272)           │       809,088 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_2 (LeakyReLU)       │ (None, 6272)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ reshape (Reshape)               │ (None, 7, 7, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 14, 14, 128)    │       262,272 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_3 (LeakyReLU)       │ (None, 14, 14, 128)    │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 28, 28, 128)    │       262,272 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_4 (LeakyReLU)       │ (None, 28, 28, 128)    │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 28, 28, 1)      │         6,273 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 1,339,905 (5.11 MB) ##  Trainable params: 1,339,905 (5.11 MB) ##  Non-trainable params: 0 (0.00 B) # Instantiate one optimizer for the discriminator and another for the generator. d_optimizer <- optimizer_adam(learning_rate = 0.0003) g_optimizer <- optimizer_adam(learning_rate = 0.0004)  # Instantiate a loss function. loss_fn <- loss_binary_crossentropy(from_logits = TRUE)  train_step <- tf_function(function(real_images) {   # Sample random points in the latent space   c(batch_size, ...) %<-% op_shape(real_images)   random_latent_vectors <-     tf$random$normal(shape(batch_size, latent_dim))    # Decode them to fake images   generated_images <- generator(random_latent_vectors)    # Combine them with real images   combined_images <- tf$concat(list(generated_images, real_images),                                axis = 0L)    # Assemble labels discriminating real from fake images   labels <- tf$concat(list(tf$ones(shape(batch_size, 1)),                            tf$zeros(shape(batch_size, 1))),                       axis = 0L)    # Add random noise to the labels - important trick!   labels %<>% `+`(tf$random$uniform(tf$shape(.), maxval = 0.05))    # Train the discriminator   with(tf$GradientTape() %as% tape, {     predictions <- discriminator(combined_images)     d_loss <- loss_fn(labels, predictions)   })   grads <- tape$gradient(d_loss, discriminator$trainable_weights)   d_optimizer$apply(grads, discriminator$trainable_weights)    # Sample random points in the latent space   random_latent_vectors <-     tf$random$normal(shape(batch_size, latent_dim))    # Assemble labels that say \"all real images\"   misleading_labels <- tf$zeros(shape(batch_size, 1))    # Train the generator (note that we should *not* update the weights   # of the discriminator)!   with(tf$GradientTape() %as% tape, {     predictions <- discriminator(generator(random_latent_vectors))     g_loss <- loss_fn(misleading_labels, predictions)   })    grads <- tape$gradient(g_loss, generator$trainable_weights)   g_optimizer$apply(grads, generator$trainable_weights)    list(d_loss, g_loss, generated_images) }) # Prepare the dataset. We use both the training & test MNIST digits. batch_size <- 64 c(c(x_train, .), c(x_test, .)) %<-% dataset_mnist() all_digits <- op_concatenate(list(x_train, x_test)) all_digits <- op_reshape(all_digits, c(-1, 28, 28, 1)) dataset <- all_digits |>   tensor_slices_dataset() |>   dataset_map(\\(x) op_cast(x, \"float32\") / 255) |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(batch_size = batch_size)  epochs <- 1 # In practice you need at least 20 epochs to generate nice digits. save_dir <- \"./\"  for (epoch in seq_len(epochs)) {   cat(\"Start epoch: \", epoch, \"\\n\")   step <- 0   train_iterator <- as_iterator(dataset)   while (!is.null(real_images <- iter_next(train_iterator))) {     step <- step + 1     # Train the discriminator & generator on one batch of real images.     c(d_loss, g_loss, generated_images) %<-% train_step(real_images)      # Logging.     if (step %% 200 == 0) {       # Print metrics       cat(sprintf(\"discriminator loss at step %d: %.2f\\n\", step, d_loss))       cat(sprintf(\"adversarial loss at step %d: %.2f\\n\", step, g_loss))     }      # To limit execution time we stop after 10 steps.     # Remove the lines below to actually train the model!     if (step > 10)       break   } } ## Start epoch:  1"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing a training loop from scratch in PyTorch","text":"","code":"import os  # This guide can only be run with the torch backend. os.environ[\"KERAS_BACKEND\"] = \"torch\"  import torch import keras import numpy as np"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing a training loop from scratch in PyTorch","text":"Keras provides default training evaluation loops, fit() evaluate(). usage covered guide Training & evaluation built-methods. want customize learning algorithm model still leveraging convenience fit() (instance, train GAN using fit()), can subclass Model class implement train_step() method, called repeatedly fit(). Now, want low-level control training & evaluation, write training & evaluation loops scratch. guide .","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"A first end-to-end example","title":"Writing a training loop from scratch in PyTorch","text":"write custom training loop, need following ingredients: model train, course. optimizer. either use keras.optimizers optimizer, native PyTorch optimizer torch.optim. loss function. either use keras.losses loss, native PyTorch loss torch.nn. dataset. use format: tf.data.Dataset, PyTorch DataLoader, Python generator, etc. Let’s line . ’ll use torch-native objects case – except, course, Keras model. First, let’s get model MNIST dataset: Next, ’s PyTorch optimizer PyTorch loss function: Let’s train model using mini-batch gradient custom training loop. Calling loss.backward() loss tensor triggers backpropagation. ’s done, optimizer magically aware gradients variable can update variables, done via optimizer.step(). Tensors, variables, optimizers interconnected one another via hidden global state. Also, don’t forget call model.zero_grad() loss.backward(), won’t get right gradients variables. ’s training loop, step step: open loop iterates epochs epoch, open loop iterates dataset, batches batch, call model input data retrive predictions, use compute loss value call loss.backward() Outside scope, retrieve gradients weights model regard loss Finally, use optimizer update weights model based gradients alternative, let’s look loop looks like using Keras optimizer Keras loss function. Important differences: retrieve gradients variables via v.value.grad, called trainable variable. update variables via optimizer.apply(), must called torch.no_grad() scope. Also, big gotcha: NumPy/TensorFlow/JAX/Keras APIs well Python unittest APIs use argument order convention fn(y_true, y_pred) (reference values first, predicted values second), PyTorch actually uses fn(y_pred, y_true) losses. make sure invert order logits targets.","code":"# Let's consider a simple MNIST model def get_model():     inputs = keras.Input(shape=(784,), name=\"digits\")     x1 = keras.layers.Dense(64, activation=\"relu\")(inputs)     x2 = keras.layers.Dense(64, activation=\"relu\")(x1)     outputs = keras.layers.Dense(10, name=\"predictions\")(x2)     model = keras.Model(inputs=inputs, outputs=outputs)     return model   # Create load up the MNIST dataset and put it in a torch DataLoader # Prepare the training dataset. batch_size = 32 (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() x_train = np.reshape(x_train, (-1, 784)).astype(\"float32\") x_test = np.reshape(x_test, (-1, 784)).astype(\"float32\") y_train = keras.utils.to_categorical(y_train) y_test = keras.utils.to_categorical(y_test)  # Reserve 10,000 samples for validation. x_val = x_train[-10000:] y_val = y_train[-10000:] x_train = x_train[:-10000] y_train = y_train[:-10000]  # Create torch Datasets train_dataset = torch.utils.data.TensorDataset(     torch.from_numpy(x_train), torch.from_numpy(y_train) ) val_dataset = torch.utils.data.TensorDataset(     torch.from_numpy(x_val), torch.from_numpy(y_val) )  # Create DataLoaders for the Datasets train_dataloader = torch.utils.data.DataLoader(     train_dataset, batch_size=batch_size, shuffle=True ) val_dataloader = torch.utils.data.DataLoader(     val_dataset, batch_size=batch_size, shuffle=False ) # Instantiate a torch optimizer model = get_model() optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)  # Instantiate a torch loss function loss_fn = torch.nn.CrossEntropyLoss() epochs = 3 for epoch in range(epochs):     for step, (inputs, targets) in enumerate(train_dataloader):         # Forward pass         logits = model(inputs)         loss = loss_fn(logits, targets)          # Backward pass         model.zero_grad()         loss.backward()          # Optimizer variable updates         optimizer.step()          # Log every 100 batches.         if step % 100 == 0:             print(                 f\"Training loss (for 1 batch) at step {step}: {loss.detach().numpy():.4f}\"             )             print(f\"Seen so far: {(step + 1) * batch_size} samples\") model = get_model() optimizer = keras.optimizers.Adam(learning_rate=1e-3) loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  for epoch in range(epochs):     print(f\"\\nStart of epoch {epoch}\")     for step, (inputs, targets) in enumerate(train_dataloader):         # Forward pass         logits = model(inputs)         loss = loss_fn(targets, logits)          # Backward pass         model.zero_grad()         trainable_weights = [v for v in model.trainable_weights]          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             optimizer.apply(gradients, trainable_weights)          # Log every 100 batches.         if step % 100 == 0:             print(                 f\"Training loss (for 1 batch) at step {step}: {loss.detach().numpy():.4f}\"             )             print(f\"Seen so far: {(step + 1) * batch_size} samples\")"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"low-level-handling-of-metrics","dir":"Articles","previous_headings":"","what":"Low-level handling of metrics","title":"Writing a training loop from scratch in PyTorch","text":"Let’s add metrics monitoring basic training loop. can readily reuse built-Keras metrics (custom ones wrote) training loops written scratch. ’s flow: Instantiate metric start loop Call metric.update_state() batch Call metric.result() need display current value metric Call metric.reset_state() need clear state metric (typically end epoch) Let’s use knowledge compute CategoricalAccuracy training validation data end epoch: ’s training & evaluation loop:","code":"# Get a fresh model model = get_model()  # Instantiate an optimizer to train the model. optimizer = keras.optimizers.Adam(learning_rate=1e-3) # Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  # Prepare the metrics. train_acc_metric = keras.metrics.CategoricalAccuracy() val_acc_metric = keras.metrics.CategoricalAccuracy() for epoch in range(epochs):     print(f\"\\nStart of epoch {epoch}\")     for step, (inputs, targets) in enumerate(train_dataloader):         # Forward pass         logits = model(inputs)         loss = loss_fn(targets, logits)          # Backward pass         model.zero_grad()         trainable_weights = [v for v in model.trainable_weights]          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             optimizer.apply(gradients, trainable_weights)          # Update training metric.         train_acc_metric.update_state(targets, logits)          # Log every 100 batches.         if step % 100 == 0:             print(                 f\"Training loss (for 1 batch) at step {step}: {loss.detach().numpy():.4f}\"             )             print(f\"Seen so far: {(step + 1) * batch_size} samples\")      # Display metrics at the end of each epoch.     train_acc = train_acc_metric.result()     print(f\"Training acc over epoch: {float(train_acc):.4f}\")      # Reset training metrics at the end of each epoch     train_acc_metric.reset_state()      # Run a validation loop at the end of each epoch.     for x_batch_val, y_batch_val in val_dataloader:         val_logits = model(x_batch_val, training=False)         # Update val metrics         val_acc_metric.update_state(y_batch_val, val_logits)     val_acc = val_acc_metric.result()     val_acc_metric.reset_state()     print(f\"Validation acc: {float(val_acc):.4f}\")"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"low-level-handling-of-losses-tracked-by-the-model","dir":"Articles","previous_headings":"","what":"Low-level handling of losses tracked by the model","title":"Writing a training loop from scratch in PyTorch","text":"Layers & models recursively track losses created forward pass layers call self.add_loss(value). resulting list scalar loss values available via property model.losses end forward pass. want using loss components, sum add main loss training step. Consider layer, creates activity regularization loss: Let’s build really simple model uses : ’s training loop look like now: ’s !","code":"class ActivityRegularizationLayer(keras.layers.Layer):     def call(self, inputs):         self.add_loss(1e-2 * torch.sum(inputs))         return inputs inputs = keras.Input(shape=(784,), name=\"digits\") x = keras.layers.Dense(64, activation=\"relu\")(inputs) # Insert activity regularization as a layer x = ActivityRegularizationLayer()(x) x = keras.layers.Dense(64, activation=\"relu\")(x) outputs = keras.layers.Dense(10, name=\"predictions\")(x)  model = keras.Model(inputs=inputs, outputs=outputs) # Get a fresh model model = get_model()  # Instantiate an optimizer to train the model. optimizer = keras.optimizers.Adam(learning_rate=1e-3) # Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  # Prepare the metrics. train_acc_metric = keras.metrics.CategoricalAccuracy() val_acc_metric = keras.metrics.CategoricalAccuracy()  for epoch in range(epochs):     print(f\"\\nStart of epoch {epoch}\")     for step, (inputs, targets) in enumerate(train_dataloader):         # Forward pass         logits = model(inputs)         loss = loss_fn(targets, logits)         if model.losses:             loss = loss + torch.sum(*model.losses)          # Backward pass         model.zero_grad()         trainable_weights = [v for v in model.trainable_weights]          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             optimizer.apply(gradients, trainable_weights)          # Update training metric.         train_acc_metric.update_state(targets, logits)          # Log every 100 batches.         if step % 100 == 0:             print(                 f\"Training loss (for 1 batch) at step {step}: {loss.detach().numpy():.4f}\"             )             print(f\"Seen so far: {(step + 1) * batch_size} samples\")      # Display metrics at the end of each epoch.     train_acc = train_acc_metric.result()     print(f\"Training acc over epoch: {float(train_acc):.4f}\")      # Reset training metrics at the end of each epoch     train_acc_metric.reset_state()      # Run a validation loop at the end of each epoch.     for x_batch_val, y_batch_val in val_dataloader:         val_logits = model(x_batch_val, training=False)         # Update val metrics         val_acc_metric.update_state(y_batch_val, val_logits)     val_acc = val_acc_metric.result()     val_acc_metric.reset_state()     print(f\"Validation acc: {float(val_acc):.4f}\")"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing a training loop from scratch in TensorFlow","text":"","code":"library(tensorflow) library(tfdatasets) library(keras3, warn.conflicts = FALSE)  # This guide can only be run with the TensorFlow backend. use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing a training loop from scratch in TensorFlow","text":"Keras provides default training evaluation loops, fit() evaluate(). usage covered guide Training & evaluation built-methods. want customize learning algorithm model still leveraging convenience fit() (instance, train GAN using fit()), can subclass Model class implement train_step() method, called repeatedly fit(). covered guide Customizing happens fit(). Now, want low-level control training & evaluation, write training & evaluation loops scratch. guide .","code":""},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"using-the-gradienttape-a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"Using the GradientTape: a first end-to-end example","title":"Writing a training loop from scratch in TensorFlow","text":"Calling model inside GradientTape scope enables retrieve gradients trainable weights layer respect loss value. Using optimizer instance, can use gradients update variables (can retrieve using model$trainable_weights). Let’s consider simple MNIST model: Let’s train using mini-batch gradient custom training loop. First, ’re going need optimizer, loss function, dataset: ’s training loop: open loop iterates epochs epoch, iterate dataset, batches batch, open GradientTape() scope Inside scope, call model (forward pass) compute loss Outside scope, retrieve gradients weights model regard loss Finally, use optimizer update weights model based gradients","code":"get_model <- function() {   inputs <- keras_input(shape = 784, name = \"digits\")   outputs <- inputs |>     layer_dense(units = 64, activation = \"relu\") |>     layer_dense(units = 64, activation = \"relu\") |>     layer_dense(units = 10, name = \"predictions\")   keras_model(inputs = inputs, outputs = outputs) }  model <- get_model() # Instantiate an optimizer. optimizer <- optimizer_adam(learning_rate = 1e-3) # Instantiate a loss function. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE)  # Prepare the training dataset. batch_size <- 64 c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(-1, 784)) x_test <- array_reshape(x_test, c(-1, 784))  # Reserve 10,000 samples for validation. x_val <- x_train[1:10000, ] y_val <- y_train[1:10000] x_train = x_train[-c(1:10000), ] y_train = y_train[-c(1:10000)]  # Prepare the training dataset. train_dataset <- list(x_train, y_train) |>   tensor_slices_dataset() |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(batch_size)  # Prepare the validation dataset. val_dataset <- list(x_val, y_val) |>   tensor_slices_dataset() |>   dataset_batch(batch_size) epochs <- 3 for (epoch in seq_len(epochs)) {   cat(\"\\nStart of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   iterator <- as_iterator(train_dataset)   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1     # Open a GradientTape to record the operations run     # during the forward pass, which enables auto-differentiation.     with(tf$GradientTape() %as% tape, {       # Run the forward pass of the layer.       # The operations that the layer applies       # to its inputs are going to be recorded       # on the GradientTape.       logits <- model(x_batch_train, training = TRUE) # Logits for this minibatch        # Compute the loss value for this minibatch.       loss_value <- loss_fn(y_batch_train, logits)     })      # Use the gradient tape to automatically retrieve     # the gradients of the trainable variables with respect to the loss.     gradients <- tape$gradient(loss_value, model$trainable_weights)      # Run one step of gradient descent by updating     # the value of the variables to minimize the loss.     optimizer$apply(gradients, model$trainable_weights)      # Log every 200 batches.     if (step %% 200 == 0) {       cat(sprintf(\"Training loss (for one batch) at step %d: %.4f\\n\",                   step, loss_value))       cat(sprintf(\"Seen so far: %d samples\\n\", (step * batch_size)))     }   } } ## ## Start of epoch  1 ## Training loss (for one batch) at step 200: 1.9342 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.8244 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.8011 ## Seen so far: 38400 samples ## ## Start of epoch  2 ## Training loss (for one batch) at step 200: 0.3505 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.6878 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.3928 ## Seen so far: 38400 samples ## ## Start of epoch  3 ## Training loss (for one batch) at step 200: 0.3366 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.1102 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.3629 ## Seen so far: 38400 samples"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"low-level-handling-of-metrics","dir":"Articles","previous_headings":"","what":"Low-level handling of metrics","title":"Writing a training loop from scratch in TensorFlow","text":"Let’s add metrics monitoring basic loop. can readily reuse built-metrics (custom ones wrote) training loops written scratch. ’s flow: Instantiate metric start loop Call metric$update_state() batch Call metric$result() need display current value metric Call metric$reset_state() need clear state metric (typically end epoch) Let’s use knowledge compute SparseCategoricalAccuracy validation data end epoch: ’s training & evaluation loop:","code":"# Get model model <- get_model()  # Instantiate an optimizer to train the model. optimizer <- optimizer_adam(learning_rate = 1e-3) # Instantiate a loss function. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE)  # Prepare the metrics. train_acc_metric <- metric_sparse_categorical_accuracy() val_acc_metric <- metric_sparse_categorical_accuracy() epochs <- 2 time <- Sys.time() for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   iterator <- as_iterator(train_dataset)   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1      with(tf$GradientTape() %as% tape, {       logits <- model(x_batch_train, training = TRUE)       loss_value <- loss_fn(y_batch_train, logits)     })     gradients <- tape$gradient(loss_value, model$trainable_weights)     optimizer$apply(gradients, model$trainable_weights)      # Update training metric.     train_acc_metric$update_state(y_batch_train, logits)     if (step %% 200 == 0) {       cat(sprintf(         \"Training loss (for one batch) at step %d: %.4f\\n\", step, loss_value))       cat(sprintf(\"Seen so far: %d samples \\n\", (step * batch_size)))     }   }    # Display metrics at the end of each epoch.   train_acc <- train_acc_metric$result()   cat(sprintf(\"Training acc over epoch: %.4f\\n\", train_acc))    # Reset training metrics at the end of each epoch   train_acc_metric$reset_state()    # Run a validation loop at the end of each epoch.   iterate(val_dataset, function(val_batch) {     c(x_batch_val, y_batch_val) %<-% val_batch     val_logits <- model(x_batch_val, training = FALSE)     val_acc_metric$update_state(y_batch_val, val_logits)   })   val_acc <- val_acc_metric$result()   val_acc_metric$reset_state()   cat(sprintf(\"Validation acc: %.4f\\n\", val_acc)) } ## Start of epoch  1 ## Training loss (for one batch) at step 200: 2.4070 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.8216 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 1.2056 ## Seen so far: 38400 samples ## Training acc over epoch: 0.8094 ## Validation acc: 0.8687 ## Start of epoch  2 ## Training loss (for one batch) at step 200: 0.1857 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.3803 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.3399 ## Seen so far: 38400 samples ## Training acc over epoch: 0.8859 ## Validation acc: 0.9002 Sys.time() - time ## Time difference of 44.61786 secs"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"speeding-up-your-training-step-with-tf_function","dir":"Articles","previous_headings":"","what":"Speeding-up your training step with tf_function()","title":"Writing a training loop from scratch in TensorFlow","text":"default runtime TensorFlow 2 eager execution. , training loop executes eagerly. great debugging, graph compilation definite performance advantage. Describing computation static graph enables framework apply global performance optimizations. impossible framework constrained greedily execute one operation another, knowledge comes next. can compile static graph function takes tensors input. Just add @tf.function decorator , like : Let’s evaluation step: Now, let’s re-run training loop compiled training step: Much faster, isn’t ?","code":"train_step <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     logits <- model(x, training = TRUE)     loss_value <- loss_fn(y, logits)   })   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply(gradients, model$trainable_weights)   train_acc_metric$update_state(y, logits)   invisible(NULL) # return nothing }) test_step <- tf_function(function(x, y) {   val_logits <- model(x, training=FALSE)   val_acc_metric$update_state(y, val_logits)   invisible(NULL) # return nothing }) epochs <- 2 time <- Sys.time() for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1     train_step(x_batch_train, y_batch_train)      if (step %% 200 == 0) {       cat(sprintf(         \"Training loss (for one batch) at step %d: %.4f\\n\", step, loss_value       ))       cat(sprintf(\"Seen so far: %d samples \\n\", (step * batch_size)))     }   }    # Display metrics at the end of each epoch.   train_acc <- train_acc_metric$result()   cat(sprintf(\"Training acc over epoch: %.4f\\n\", train_acc))     # Reset training metrics at the end of each epoch   train_acc_metric$reset_state()    # Run a validation loop at the end of each epoch.    iterate(val_dataset, function(val_batch) {     c(x_batch_val, y_batch_val) %<-% val_batch     test_step(x_batch_val, y_batch_val)   })    val_acc <- val_acc_metric$result()   val_acc_metric$reset_state()   cat(sprintf(\"Validation acc: %.4f\\n\", val_acc)) } ## Start of epoch  1 ## Training acc over epoch: 0.0000 ## Validation acc: 0.9002 ## Start of epoch  2 ## Training acc over epoch: 0.0000 ## Validation acc: 0.9002 Sys.time() - time ## Time difference of 0.5360565 secs"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"low-level-handling-of-losses-tracked-by-the-model","dir":"Articles","previous_headings":"","what":"Low-level handling of losses tracked by the model","title":"Writing a training loop from scratch in TensorFlow","text":"Layers models recursively track losses created forward pass layers call self$add_loss(value). resulting list scalar loss values available via property model$losses end forward pass. want using loss components, sum add main loss training step. Consider layer, creates activity regularization loss: Let’s build really simple model uses : ’s training step look like now:","code":"layer_activity_regularization <- Layer(   \"ActivityRegularizationLayer\",   call = function(inputs) {     self$add_loss(0.1 * op_mean(inputs))     inputs   } ) inputs <- keras_input(shape = 784, name=\"digits\") outputs <- inputs |>   layer_dense(units = 64, activation = \"relu\") |>   layer_activity_regularization() |>   layer_dense(units = 64, activation = \"relu\") |>   layer_dense(units = 10, name = \"predictions\") model <- keras_model(inputs = inputs, outputs = outputs) train_step <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     logits <- model(x, training = TRUE)     loss_value <- Reduce(`+`, c(loss_fn(y, logits),                                 model$losses))   })   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply(gradients, model$trainable_weights)   train_acc_metric$update_state(y, logits)   invisible(NULL) })"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Writing a training loop from scratch in TensorFlow","text":"Now know everything know using built-training loops writing scratch. conclude, ’s simple end--end example ties together everything ’ve learned guide: DCGAN trained MNIST digits.","code":""},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"end-to-end-example-a-gan-training-loop-from-scratch","dir":"Articles","previous_headings":"","what":"End-to-end example: a GAN training loop from scratch","title":"Writing a training loop from scratch in TensorFlow","text":"may familiar Generative Adversarial Networks (GANs). GANs can generate new images look almost real, learning latent distribution training dataset images (“latent space” images). GAN made two parts: “generator” model maps points latent space points image space, “discriminator” model, classifier can tell difference real images (training dataset) fake images (output generator network). GAN training loop looks like : Train discriminator. Sample batch random points latent space. Turn points fake images via “generator” model. Get batch real images combine generated images. Train “discriminator” model classify generated vs. real images. Train generator. Sample random points latent space. Turn points fake images via “generator” network. Get batch real images combine generated images. Train “generator” model “fool” discriminator classify fake images real. much detailed overview GANs works, see Deep Learning Python. Let’s implement training loop. First, create discriminator meant classify fake vs real digits: let’s create generator network, turns latent vectors outputs shape (28, 28, 1) (representing MNIST digits): ’s key bit: training loop. can see quite straightforward. training step function takes 17 lines. Let’s train GAN, repeatedly calling train_step batches images. Since discriminator generator convnets, ’re going want run code GPU. ’s ! ’ll get nice-looking fake MNIST digits just ~30s training Colab GPU.","code":"# Create the discriminator discriminator <-   keras_model_sequential(name = \"discriminator\",                          input_shape = c(28, 28, 1)) |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d(filters = 128, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_global_max_pooling_2d() |>   layer_dense(units = 1)  summary(discriminator) ## Model: \"discriminator\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d_1 (Conv2D)               │ (None, 14, 14, 64)     │           640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_1 (LeakyReLU)       │ (None, 14, 14, 64)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 7, 7, 128)      │        73,856 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu (LeakyReLU)         │ (None, 7, 7, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d            │ (None, 128)            │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_6 (Dense)                 │ (None, 1)              │           129 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 74,625 (291.50 KB) ##  Trainable params: 74,625 (291.50 KB) ##  Non-trainable params: 0 (0.00 B) latent_dim <- 128L  generator <-   keras_model_sequential(name = \"generator\",                          input_shape = latent_dim) |>   layer_dense(7 * 7 * 128) |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_reshape(target_shape = c(7, 7, 128)) |>   layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d(filters = 1, kernel_size = c(7, 7), padding = \"same\",                 activation = \"sigmoid\")  summary(generator) ## Model: \"generator\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_7 (Dense)                 │ (None, 6272)           │       809,088 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_4 (LeakyReLU)       │ (None, 6272)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ reshape (Reshape)               │ (None, 7, 7, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 14, 14, 128)    │       262,272 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_3 (LeakyReLU)       │ (None, 14, 14, 128)    │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 28, 28, 128)    │       262,272 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_2 (LeakyReLU)       │ (None, 28, 28, 128)    │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 28, 28, 1)      │         6,273 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 1,339,905 (5.11 MB) ##  Trainable params: 1,339,905 (5.11 MB) ##  Non-trainable params: 0 (0.00 B) # Instantiate one optimizer for the discriminator and another for the generator. d_optimizer <- optimizer_adam(learning_rate = 0.0003) g_optimizer <- optimizer_adam(learning_rate = 0.0004)  # Instantiate a loss function. loss_fn <- loss_binary_crossentropy(from_logits = TRUE)  train_step <- tf_function(function(real_images) {   # Sample random points in the latent space   c(batch_size, ...) %<-% op_shape(real_images)   random_latent_vectors <-     tf$random$normal(shape(batch_size, latent_dim))    # Decode them to fake images   generated_images <- generator(random_latent_vectors)    # Combine them with real images   combined_images <- tf$concat(list(generated_images, real_images),                                axis = 0L)    # Assemble labels discriminating real from fake images   labels <- tf$concat(list(tf$ones(shape(batch_size, 1)),                            tf$zeros(shape(batch_size, 1))),                       axis = 0L)    # Add random noise to the labels - important trick!   labels %<>% `+`(tf$random$uniform(tf$shape(.), maxval = 0.05))    # Train the discriminator   with(tf$GradientTape() %as% tape, {     predictions <- discriminator(combined_images)     d_loss <- loss_fn(labels, predictions)   })   grads <- tape$gradient(d_loss, discriminator$trainable_weights)   d_optimizer$apply(grads, discriminator$trainable_weights)    # Sample random points in the latent space   random_latent_vectors <-     tf$random$normal(shape(batch_size, latent_dim))    # Assemble labels that say \"all real images\"   misleading_labels <- tf$zeros(shape(batch_size, 1))    # Train the generator (note that we should *not* update the weights   # of the discriminator)!   with(tf$GradientTape() %as% tape, {     predictions <- discriminator(generator(random_latent_vectors))     g_loss <- loss_fn(misleading_labels, predictions)   })    grads <- tape$gradient(g_loss, generator$trainable_weights)   g_optimizer$apply(grads, generator$trainable_weights)    list(d_loss, g_loss, generated_images) }) # Prepare the dataset. We use both the training & test MNIST digits. batch_size <- 64 c(c(x_train, .), c(x_test, .)) %<-% dataset_mnist() all_digits <- op_concatenate(list(x_train, x_test)) all_digits <- op_reshape(all_digits, c(-1, 28, 28, 1)) dataset <- all_digits |>   tfdatasets::tensor_slices_dataset() |>   tfdatasets::dataset_map(function(x) tf$cast(x, \"float\") / 255) |>   tfdatasets::dataset_shuffle(buffer_size = 1024) |>   tfdatasets::dataset_batch(batch_size = batch_size)  epochs <- 1 # In practice you need at least 20 epochs to generate nice digits. save_dir <- \"./\"  for (epoch in seq_len(epochs)) {   cat(\"Start epoch: \", epoch, \"\\n\")   step <- 0   train_iterator <- as_iterator(dataset)   while (!is.null(real_images <- iter_next(train_iterator))) {     step <- step + 1     # Train the discriminator & generator on one batch of real images.     c(d_loss, g_loss, generated_images) %<-% train_step(real_images)      # Logging.     if (step %% 200 == 0) {       # Print metrics       cat(sprintf(\"discriminator loss at step %d: %.2f\\n\", step, d_loss))       cat(sprintf(\"adversarial loss at step %d: %.2f\\n\", step, g_loss))     }      # To limit execution time we stop after 10 steps.     # Remove the lines below to actually train the model!     if (step > 10)       break   } } ## Start epoch:  1"},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing your own callbacks","text":"callback powerful tool customize behavior Keras model training, evaluation, inference. Examples include keras.callbacks.TensorBoard visualize training progress results TensorBoard, keras.callbacks.ModelCheckpoint periodically save model training. guide, learn Keras callback , can , can build . provide demos simple callback applications get started.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing your own callbacks","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"keras-callbacks-overview","dir":"Articles","previous_headings":"","what":"Keras callbacks overview","title":"Writing your own callbacks","text":"callbacks subclass keras.callbacks.Callback class, override set methods called various stages training, testing, predicting. Callbacks useful get view internal states statistics model training. can pass list callbacks (keyword argument callbacks) following model methods: fit() evaluate() predict()","code":""},{"path":[]},{"path":[]},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_traintestpredict_beginlogs-null","dir":"Articles","previous_headings":"An overview of callback methods > Global methods","what":"on_(train|test|predict)_begin(logs = NULL)","title":"Writing your own callbacks","text":"Called beginning fit/evaluate/predict.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_traintestpredict_endlogs-null","dir":"Articles","previous_headings":"An overview of callback methods > Global methods","what":"on_(train|test|predict)_end(logs = NULL)","title":"Writing your own callbacks","text":"Called end fit/evaluate/predict.","code":""},{"path":[]},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_traintestpredict_batch_beginbatch-logs-null","dir":"Articles","previous_headings":"An overview of callback methods > Batch-level methods for training/testing/predicting","what":"on_(train|test|predict)_batch_begin(batch, logs = NULL)","title":"Writing your own callbacks","text":"Called right processing batch training/testing/predicting.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_traintestpredict_batch_endbatch-logs-null","dir":"Articles","previous_headings":"An overview of callback methods > Batch-level methods for training/testing/predicting","what":"on_(train|test|predict)_batch_end(batch, logs = NULL)","title":"Writing your own callbacks","text":"Called end training/testing/predicting batch. Within method, logs named list containing metrics results.","code":""},{"path":[]},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_epoch_beginepoch-logs-null","dir":"Articles","previous_headings":"An overview of callback methods > Epoch-level methods (training only)","what":"on_epoch_begin(epoch, logs = NULL)","title":"Writing your own callbacks","text":"Called beginning epoch training.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_epoch_endepoch-logs-null","dir":"Articles","previous_headings":"An overview of callback methods > Epoch-level methods (training only)","what":"on_epoch_end(epoch, logs = NULL)","title":"Writing your own callbacks","text":"Called end epoch training.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"a-basic-example","dir":"Articles","previous_headings":"","what":"A basic example","title":"Writing your own callbacks","text":"Let’s take look concrete example. get started, let’s import tensorflow define simple Sequential Keras model: , load MNIST data training testing Keras datasets API: Now, define simple custom callback logs: fit/evaluate/predict starts & ends epoch starts & ends training batch starts & ends evaluation (test) batch starts & ends inference (prediction) batch starts & ends Let’s try :","code":"# Define the Keras model to add callbacks to get_model <- function() {   model <- keras_model_sequential()   model |> layer_dense(units = 1)   model |> compile(     optimizer = optimizer_rmsprop(learning_rate = 0.1),     loss = \"mean_squared_error\",     metrics = \"mean_absolute_error\"   )   model } # Load example MNIST data and pre-process it mnist <- dataset_mnist()  flatten_and_rescale <- function(x) {   x <- array_reshape(x, c(-1, 784))   x <- x / 255   x }  mnist$train$x <- flatten_and_rescale(mnist$train$x) mnist$test$x  <- flatten_and_rescale(mnist$test$x)  # limit to 1000 samples n <- 1000 mnist$train$x <- mnist$train$x[1:n,] mnist$train$y <- mnist$train$y[1:n] mnist$test$x  <- mnist$test$x[1:n,] mnist$test$y  <- mnist$test$y[1:n] show <- function(msg, logs) {   cat(glue::glue(msg, .envir = parent.frame()),       \"got logs: \", sep = \"; \")   str(logs); cat(\"\\n\") }  callback_custom <- Callback(   \"CustomCallback\",   on_train_begin         = \\(logs = NULL)        show(\"Starting training\", logs),   on_epoch_begin         = \\(epoch, logs = NULL) show(\"Start epoch {epoch} of training\", logs),   on_train_batch_begin   = \\(batch, logs = NULL) show(\"...Training: start of batch {batch}\", logs),   on_train_batch_end     = \\(batch, logs = NULL) show(\"...Training: end of batch {batch}\",  logs),   on_epoch_end           = \\(epoch, logs = NULL) show(\"End epoch {epoch} of training\", logs),   on_train_end           = \\(logs = NULL)        show(\"Stop training\", logs),     on_test_begin          = \\(logs = NULL)        show(\"Start testing\", logs),   on_test_batch_begin    = \\(batch, logs = NULL) show(\"...Evaluating: start of batch {batch}\", logs),   on_test_batch_end      = \\(batch, logs = NULL) show(\"...Evaluating: end of batch {batch}\", logs),   on_test_end            = \\(logs = NULL)        show(\"Stop testing\", logs),    on_predict_begin       = \\(logs = NULL)        show(\"Start predicting\", logs),   on_predict_end         = \\(logs = NULL)        show(\"Stop predicting\", logs),   on_predict_batch_begin = \\(batch, logs = NULL) show(\"...Predicting: start of batch {batch}\", logs),   on_predict_batch_end   = \\(batch, logs = NULL) show(\"...Predicting: end of batch {batch}\", logs), ) model <- get_model() model |> fit(   mnist$train$x, mnist$train$y,   batch_size = 128,   epochs = 2,   verbose = 0,   validation_split = 0.5,   callbacks = list(callback_custom()) ) ## Starting training; got logs:  Named list() ## ## Start epoch 1 of training; got logs:  Named list() ## ## ...Training: start of batch 1; got logs:  Named list() ## ## ...Training: end of batch 1; got logs: List of 2 ##  $ loss               : num 25.9 ##  $ mean_absolute_error: num 4.19 ## ## ...Training: start of batch 2; got logs:  Named list() ## ## ...Training: end of batch 2; got logs: List of 2 ##  $ loss               : num 433 ##  $ mean_absolute_error: num 15.5 ## ## ...Training: start of batch 3; got logs:  Named list() ## ## ...Training: end of batch 3; got logs: List of 2 ##  $ loss               : num 297 ##  $ mean_absolute_error: num 11.8 ## ## ...Training: start of batch 4; got logs:  Named list() ## ## ...Training: end of batch 4; got logs: List of 2 ##  $ loss               : num 231 ##  $ mean_absolute_error: num 9.68 ## ## Start testing; got logs:  Named list() ## ## ...Evaluating: start of batch 1; got logs:  Named list() ## ## ...Evaluating: end of batch 1; got logs: List of 2 ##  $ loss               : num 8.1 ##  $ mean_absolute_error: num 2.3 ## ## ...Evaluating: start of batch 2; got logs:  Named list() ## ## ...Evaluating: end of batch 2; got logs: List of 2 ##  $ loss               : num 7.58 ##  $ mean_absolute_error: num 2.23 ## ## ...Evaluating: start of batch 3; got logs:  Named list() ## ## ...Evaluating: end of batch 3; got logs: List of 2 ##  $ loss               : num 7.38 ##  $ mean_absolute_error: num 2.21 ## ## ...Evaluating: start of batch 4; got logs:  Named list() ## ## ...Evaluating: end of batch 4; got logs: List of 2 ##  $ loss               : num 7.3 ##  $ mean_absolute_error: num 2.21 ## ## Stop testing; got logs: List of 2 ##  $ loss               : num 7.3 ##  $ mean_absolute_error: num 2.21 ## ## End epoch 1 of training; got logs: List of 4 ##  $ loss                   : num 231 ##  $ mean_absolute_error    : num 9.68 ##  $ val_loss               : num 7.3 ##  $ val_mean_absolute_error: num 2.21 ## ## Start epoch 2 of training; got logs:  Named list() ## ## ...Training: start of batch 1; got logs:  Named list() ## ## ...Training: end of batch 1; got logs: List of 2 ##  $ loss               : num 7.44 ##  $ mean_absolute_error: num 2.27 ## ## ...Training: start of batch 2; got logs:  Named list() ## ## ...Training: end of batch 2; got logs: List of 2 ##  $ loss               : num 6.81 ##  $ mean_absolute_error: num 2.16 ## ## ...Training: start of batch 3; got logs:  Named list() ## ## ...Training: end of batch 3; got logs: List of 2 ##  $ loss               : num 6.12 ##  $ mean_absolute_error: num 2.06 ## ## ...Training: start of batch 4; got logs:  Named list() ## ## ...Training: end of batch 4; got logs: List of 2 ##  $ loss               : num 6.08 ##  $ mean_absolute_error: num 2.04 ## ## Start testing; got logs:  Named list() ## ## ...Evaluating: start of batch 1; got logs:  Named list() ## ## ...Evaluating: end of batch 1; got logs: List of 2 ##  $ loss               : num 5.54 ##  $ mean_absolute_error: num 1.92 ## ## ...Evaluating: start of batch 2; got logs:  Named list() ## ## ...Evaluating: end of batch 2; got logs: List of 2 ##  $ loss               : num 5.31 ##  $ mean_absolute_error: num 1.87 ## ## ...Evaluating: start of batch 3; got logs:  Named list() ## ## ...Evaluating: end of batch 3; got logs: List of 2 ##  $ loss               : num 5.11 ##  $ mean_absolute_error: num 1.8 ## ## ...Evaluating: start of batch 4; got logs:  Named list() ## ## ...Evaluating: end of batch 4; got logs: List of 2 ##  $ loss               : num 5.15 ##  $ mean_absolute_error: num 1.82 ## ## Stop testing; got logs: List of 2 ##  $ loss               : num 5.15 ##  $ mean_absolute_error: num 1.82 ## ## End epoch 2 of training; got logs: List of 4 ##  $ loss                   : num 6.08 ##  $ mean_absolute_error    : num 2.04 ##  $ val_loss               : num 5.15 ##  $ val_mean_absolute_error: num 1.82 ## ## Stop training; got logs: List of 4 ##  $ loss                   : num 6.08 ##  $ mean_absolute_error    : num 2.04 ##  $ val_loss               : num 5.15 ##  $ val_mean_absolute_error: num 1.82 res <- model |> evaluate(   mnist$test$x, mnist$test$y,   batch_size = 128, verbose = 0,   callbacks = list(callback_custom()) ) ## Start testing; got logs:  Named list() ## ## ...Evaluating: start of batch 1; got logs:  Named list() ## ## ...Evaluating: end of batch 1; got logs: List of 2 ##  $ loss               : num 5.2 ##  $ mean_absolute_error: num 1.84 ## ## ...Evaluating: start of batch 2; got logs:  Named list() ## ## ...Evaluating: end of batch 2; got logs: List of 2 ##  $ loss               : num 4.62 ##  $ mean_absolute_error: num 1.73 ## ## ...Evaluating: start of batch 3; got logs:  Named list() ## ## ...Evaluating: end of batch 3; got logs: List of 2 ##  $ loss               : num 4.61 ##  $ mean_absolute_error: num 1.74 ## ## ...Evaluating: start of batch 4; got logs:  Named list() ## ## ...Evaluating: end of batch 4; got logs: List of 2 ##  $ loss               : num 4.65 ##  $ mean_absolute_error: num 1.75 ## ## ...Evaluating: start of batch 5; got logs:  Named list() ## ## ...Evaluating: end of batch 5; got logs: List of 2 ##  $ loss               : num 4.84 ##  $ mean_absolute_error: num 1.77 ## ## ...Evaluating: start of batch 6; got logs:  Named list() ## ## ...Evaluating: end of batch 6; got logs: List of 2 ##  $ loss               : num 4.76 ##  $ mean_absolute_error: num 1.76 ## ## ...Evaluating: start of batch 7; got logs:  Named list() ## ## ...Evaluating: end of batch 7; got logs: List of 2 ##  $ loss               : num 4.74 ##  $ mean_absolute_error: num 1.76 ## ## ...Evaluating: start of batch 8; got logs:  Named list() ## ## ...Evaluating: end of batch 8; got logs: List of 2 ##  $ loss               : num 4.67 ##  $ mean_absolute_error: num 1.75 ## ## Stop testing; got logs: List of 2 ##  $ loss               : num 4.67 ##  $ mean_absolute_error: num 1.75 res <- model |> predict(   mnist$test$x,   batch_size = 128, verbose = 0,   callbacks = list(callback_custom()) ) ## Start predicting; got logs:  Named list() ## ## ...Predicting: start of batch 1; got logs:  Named list() ## ## ...Predicting: end of batch 1; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 2; got logs:  Named list() ## ## ...Predicting: end of batch 2; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 3; got logs:  Named list() ## ## ...Predicting: end of batch 3; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 4; got logs:  Named list() ## ## ...Predicting: end of batch 4; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 5; got logs:  Named list() ## ## ...Predicting: end of batch 5; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 6; got logs:  Named list() ## ## ...Predicting: end of batch 6; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 7; got logs:  Named list() ## ## ...Predicting: end of batch 7; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 8; got logs:  Named list() ## ## ...Predicting: end of batch 8; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(104, 1), dtype=float32, numpy=…> ## ## Stop predicting; got logs:  Named list()"},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"usage-of-logs-list","dir":"Articles","previous_headings":"A basic example","what":"Usage of logs list","title":"Writing your own callbacks","text":"logs named list contains loss value, metrics end batch epoch. Example includes loss mean absolute error. information callbacks, can check Keras callback API documentation.","code":"callback_print_loss_and_mae <- Callback(   \"LossAndErrorPrintingCallback\",    on_train_batch_end = function(batch, logs = NULL)     cat(sprintf(\"Up to batch %i, the average loss is %7.2f.\\n\",                 batch,  logs$loss)),    on_test_batch_end = function(batch, logs = NULL)     cat(sprintf(\"Up to batch %i, the average loss is %7.2f.\\n\",                 batch, logs$loss)),    on_epoch_end = function(epoch, logs = NULL)     cat(sprintf(       \"The average loss for epoch %2i is %9.2f and mean absolute error is %7.2f.\\n\",       epoch, logs$loss, logs$mean_absolute_error     )) )   model <- get_model() model |> fit(   mnist$train$x, mnist$train$y,   epochs = 2, verbose = 0, batch_size = 128,   callbacks = list(callback_print_loss_and_mae()) ) ## Up to batch 1, the average loss is   25.12. ## Up to batch 2, the average loss is  398.92. ## Up to batch 3, the average loss is  274.04. ## Up to batch 4, the average loss is  208.32. ## Up to batch 5, the average loss is  168.15. ## Up to batch 6, the average loss is  141.31. ## Up to batch 7, the average loss is  122.19. ## Up to batch 8, the average loss is  110.05. ## The average loss for epoch  1 is    110.05 and mean absolute error is    5.79. ## Up to batch 1, the average loss is    4.71. ## Up to batch 2, the average loss is    4.74. ## Up to batch 3, the average loss is    4.81. ## Up to batch 4, the average loss is    5.07. ## Up to batch 5, the average loss is    5.08. ## Up to batch 6, the average loss is    5.09. ## Up to batch 7, the average loss is    5.19. ## Up to batch 8, the average loss is    5.51. ## The average loss for epoch  2 is      5.51 and mean absolute error is    1.90. res = model |> evaluate(   mnist$test$x, mnist$test$y,   verbose = 0, batch_size = 128,   callbacks = list(callback_print_loss_and_mae()) ) ## Up to batch 1, the average loss is   15.86. ## Up to batch 2, the average loss is   16.13. ## Up to batch 3, the average loss is   16.02. ## Up to batch 4, the average loss is   16.11. ## Up to batch 5, the average loss is   16.23. ## Up to batch 6, the average loss is   16.68. ## Up to batch 7, the average loss is   16.61. ## Up to batch 8, the average loss is   16.54."},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"usage-of-selfmodel-attribute","dir":"Articles","previous_headings":"","what":"Usage of self$model attribute","title":"Writing your own callbacks","text":"addition receiving log information one methods called, callbacks access model associated current round training/evaluation/inference: self$model. things can self$model callback: Set self$model$stop_training <- TRUE immediately interrupt training. Mutate hyperparameters optimizer (available self$model$optimizer), self$model$optimizer$learning_rate. Save model period intervals. Record output model |> predict() test samples end epoch, use sanity check training. Extract visualizations intermediate features end epoch, monitor model learning time. etc. Let’s see action couple examples.","code":""},{"path":[]},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"early-stopping-at-minimum-loss","dir":"Articles","previous_headings":"Examples of Keras callback applications","what":"Early stopping at minimum loss","title":"Writing your own callbacks","text":"first example shows creation Callback stops training minimum loss reached, setting attribute self$model$stop_training (boolean). Optionally, can provide argument patience specify many epochs wait stopping reached local minimum. callback_early_stopping() provides complete general implementation.","code":"callback_early_stopping_at_min_loss <- Callback(   \"EarlyStoppingAtMinLoss\",   `__doc__` =     \"Stop training when the loss is at its min, i.e. the loss stops decreasing.      Arguments:         patience: Number of epochs to wait after min has been hit. After this         number of no improvement, training stops.     \",    initialize = function(patience = 0) {     super$initialize()     self$patience <- patience     # best_weights to store the weights at which the minimum loss occurs.     self$best_weights <- NULL   },    on_train_begin = function(logs = NULL) {     # The number of epoch it has waited when loss is no longer minimum.     self$wait <- 0     # The epoch the training stops at.     self$stopped_epoch <- 0     # Initialize the best as infinity.     self$best <- Inf   },    on_epoch_end = function(epoch, logs = NULL) {     current <- logs$loss     if (current < self$best) {       self$best <- current       self$wait <- 0L       # Record the best weights if current results is better (less).       self$best_weights <- get_weights(self$model)     } else {       add(self$wait) <- 1L       if (self$wait >= self$patience) {         self$stopped_epoch <- epoch         self$model$stop_training <- TRUE         cat(\"Restoring model weights from the end of the best epoch.\\n\")         model$set_weights(self$best_weights)       }     }   },    on_train_end = function(logs = NULL)     if (self$stopped_epoch > 0)       cat(sprintf(\"Epoch %05d: early stopping\\n\", self$stopped_epoch + 1)) ) `add<-` <- `+`   model <- get_model() model |> fit(   mnist$train$x,   mnist$train$y,   epochs = 30,   batch_size = 64,   verbose = 0,   callbacks = list(callback_print_loss_and_mae(),                    callback_early_stopping_at_min_loss()) ) ## Up to batch 1, the average loss is   30.54. ## Up to batch 2, the average loss is  513.27. ## Up to batch 3, the average loss is  352.60. ## Up to batch 4, the average loss is  266.37. ## Up to batch 5, the average loss is  214.68. ## Up to batch 6, the average loss is  179.97. ## Up to batch 7, the average loss is  155.06. ## Up to batch 8, the average loss is  136.59. ## Up to batch 9, the average loss is  121.96. ## Up to batch 10, the average loss is  110.28. ## Up to batch 11, the average loss is  100.72. ## Up to batch 12, the average loss is   92.71. ## Up to batch 13, the average loss is   85.95. ## Up to batch 14, the average loss is   80.21. ## Up to batch 15, the average loss is   75.17. ## Up to batch 16, the average loss is   72.48. ## The average loss for epoch  1 is     72.48 and mean absolute error is    4.08. ## Up to batch 1, the average loss is    7.98. ## Up to batch 2, the average loss is    9.92. ## Up to batch 3, the average loss is   12.88. ## Up to batch 4, the average loss is   16.61. ## Up to batch 5, the average loss is   20.49. ## Up to batch 6, the average loss is   26.14. ## Up to batch 7, the average loss is   30.44. ## Up to batch 8, the average loss is   33.76. ## Up to batch 9, the average loss is   36.32. ## Up to batch 10, the average loss is   35.26. ## Up to batch 11, the average loss is   34.22. ## Up to batch 12, the average loss is   33.53. ## Up to batch 13, the average loss is   32.84. ## Up to batch 14, the average loss is   31.80. ## Up to batch 15, the average loss is   31.39. ## Up to batch 16, the average loss is   31.45. ## The average loss for epoch  2 is     31.45 and mean absolute error is    4.82. ## Up to batch 1, the average loss is   39.60. ## Up to batch 2, the average loss is   41.95. ## Up to batch 3, the average loss is   41.29. ## Up to batch 4, the average loss is   36.77. ## Up to batch 5, the average loss is   32.08. ## Up to batch 6, the average loss is   28.17. ## Up to batch 7, the average loss is   25.33. ## Up to batch 8, the average loss is   23.56. ## Up to batch 9, the average loss is   22.28. ## Up to batch 10, the average loss is   21.22. ## Up to batch 11, the average loss is   20.87. ## Up to batch 12, the average loss is   22.25. ## Up to batch 13, the average loss is   25.08. ## Up to batch 14, the average loss is   27.87. ## Up to batch 15, the average loss is   31.72. ## Up to batch 16, the average loss is   33.21. ## The average loss for epoch  3 is     33.21 and mean absolute error is    4.79. ## Restoring model weights from the end of the best epoch. ## Epoch 00004: early stopping"},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"learning-rate-scheduling","dir":"Articles","previous_headings":"Examples of Keras callback applications","what":"Learning rate scheduling","title":"Writing your own callbacks","text":"example, show custom Callback can used dynamically change learning rate optimizer course training. See keras$callbacks$LearningRateScheduler general implementations (RStudio, press F1 cursor LearningRateScheduler browser open page).","code":"callback_custom_learning_rate_scheduler <- Callback(   \"CustomLearningRateScheduler\",   `__doc__` =   \"Learning rate scheduler which sets the learning rate according to schedule.      Arguments:         schedule: a function that takes an epoch index             (integer, indexed from 0) and current learning rate             as inputs and returns a new learning rate as output (float).     \",    initialize = function(schedule) {     super$initialize()     self$schedule <- schedule   },    on_epoch_begin = function(epoch, logs = NULL) {     ## When in doubt about what types of objects are in scope (e.g., self$model)     ## use a debugger to interact with the actual objects at the console!     # browser()      if (!\"learning_rate\" %in% names(self$model$optimizer))       stop('Optimizer must have a \"learning_rate\" attribute.')      # # Get the current learning rate from model's optimizer.     # use as.numeric() to convert the keras variablea to an R numeric     lr <- as.numeric(self$model$optimizer$learning_rate)     # # Call schedule function to get the scheduled learning rate.     scheduled_lr <- self$schedule(epoch, lr)     # # Set the value back to the optimizer before this epoch starts     optimizer <- self$model$optimizer     optimizer$learning_rate <- scheduled_lr     cat(sprintf(\"\\nEpoch %03d: Learning rate is %6.4f.\\n\", epoch, scheduled_lr))   } )  LR_SCHEDULE <- tibble::tribble(   ~start_epoch, ~learning_rate,              0,            0.1,              3,           0.05,              6,           0.01,              9,          0.005,             12,          0.001,   )  last <- function(x) x[length(x)] lr_schedule <- function(epoch, learning_rate) {   \"Helper function to retrieve the scheduled learning rate based on epoch.\"   with(LR_SCHEDULE, learning_rate[last(which(epoch >= start_epoch))]) }  model <- get_model() model |> fit(   mnist$train$x,   mnist$train$y,   epochs = 14,   batch_size = 64,   verbose = 0,   callbacks = list(     callback_print_loss_and_mae(),     callback_custom_learning_rate_scheduler(lr_schedule)   ) ) ## ## Epoch 001: Learning rate is 0.1000. ## Up to batch 1, the average loss is   29.36. ## Up to batch 2, the average loss is  513.95. ## Up to batch 3, the average loss is  352.70. ## Up to batch 4, the average loss is  266.46. ## Up to batch 5, the average loss is  214.73. ## Up to batch 6, the average loss is  180.00. ## Up to batch 7, the average loss is  155.05. ## Up to batch 8, the average loss is  136.64. ## Up to batch 9, the average loss is  121.97. ## Up to batch 10, the average loss is  110.30. ## Up to batch 11, the average loss is  100.76. ## Up to batch 12, the average loss is   92.74. ## Up to batch 13, the average loss is   85.95. ## Up to batch 14, the average loss is   80.18. ## Up to batch 15, the average loss is   75.11. ## Up to batch 16, the average loss is   72.38. ## The average loss for epoch  1 is     72.38 and mean absolute error is    4.04. ## ## Epoch 002: Learning rate is 0.1000. ## Up to batch 1, the average loss is    6.95. ## Up to batch 2, the average loss is    8.71. ## Up to batch 3, the average loss is   11.42. ## Up to batch 4, the average loss is   15.15. ## Up to batch 5, the average loss is   19.28. ## Up to batch 6, the average loss is   25.54. ## Up to batch 7, the average loss is   30.38. ## Up to batch 8, the average loss is   33.95. ## Up to batch 9, the average loss is   36.58. ## Up to batch 10, the average loss is   35.46. ## Up to batch 11, the average loss is   34.34. ## Up to batch 12, the average loss is   33.51. ## Up to batch 13, the average loss is   32.67. ## Up to batch 14, the average loss is   31.54. ## Up to batch 15, the average loss is   31.05. ## Up to batch 16, the average loss is   31.09. ## The average loss for epoch  2 is     31.09 and mean absolute error is    4.77. ## ## Epoch 003: Learning rate is 0.0500. ## Up to batch 1, the average loss is   40.40. ## Up to batch 2, the average loss is   22.33. ## Up to batch 3, the average loss is   16.18. ## Up to batch 4, the average loss is   13.09. ## Up to batch 5, the average loss is   11.48. ## Up to batch 6, the average loss is   10.21. ## Up to batch 7, the average loss is    9.22. ## Up to batch 8, the average loss is    8.70. ## Up to batch 9, the average loss is    8.16. ## Up to batch 10, the average loss is    7.80. ## Up to batch 11, the average loss is    7.50. ## Up to batch 12, the average loss is    7.17. ## Up to batch 13, the average loss is    6.89. ## Up to batch 14, the average loss is    6.70. ## Up to batch 15, the average loss is    6.52. ## Up to batch 16, the average loss is    6.54. ## The average loss for epoch  3 is      6.54 and mean absolute error is    1.93. ## ## Epoch 004: Learning rate is 0.0500. ## Up to batch 1, the average loss is    8.74. ## Up to batch 2, the average loss is    8.34. ## Up to batch 3, the average loss is    9.09. ## Up to batch 4, the average loss is    9.72. ## Up to batch 5, the average loss is   10.48. ## Up to batch 6, the average loss is   11.69. ## Up to batch 7, the average loss is   11.83. ## Up to batch 8, the average loss is   11.56. ## Up to batch 9, the average loss is   11.24. ## Up to batch 10, the average loss is   10.84. ## Up to batch 11, the average loss is   10.66. ## Up to batch 12, the average loss is   10.44. ## Up to batch 13, the average loss is   10.21. ## Up to batch 14, the average loss is   10.06. ## Up to batch 15, the average loss is   10.00. ## Up to batch 16, the average loss is   10.20. ## The average loss for epoch  4 is     10.20 and mean absolute error is    2.71. ## ## Epoch 005: Learning rate is 0.0500. ## Up to batch 1, the average loss is   17.26. ## Up to batch 2, the average loss is   14.09. ## Up to batch 3, the average loss is   12.67. ## Up to batch 4, the average loss is   11.44. ## Up to batch 5, the average loss is   10.54. ## Up to batch 6, the average loss is   10.10. ## Up to batch 7, the average loss is    9.53. ## Up to batch 8, the average loss is    9.17. ## Up to batch 9, the average loss is    8.78. ## Up to batch 10, the average loss is    8.49. ## Up to batch 11, the average loss is    8.50. ## Up to batch 12, the average loss is    8.59. ## Up to batch 13, the average loss is    8.68. ## Up to batch 14, the average loss is    8.86. ## Up to batch 15, the average loss is    9.17. ## Up to batch 16, the average loss is    9.53. ## The average loss for epoch  5 is      9.53 and mean absolute error is    2.58. ## ## Epoch 006: Learning rate is 0.0100. ## Up to batch 1, the average loss is   17.04. ## Up to batch 2, the average loss is   14.85. ## Up to batch 3, the average loss is   11.53. ## Up to batch 4, the average loss is    9.65. ## Up to batch 5, the average loss is    8.44. ## Up to batch 6, the average loss is    7.50. ## Up to batch 7, the average loss is    6.74. ## Up to batch 8, the average loss is    6.56. ## Up to batch 9, the average loss is    6.18. ## Up to batch 10, the average loss is    5.87. ## Up to batch 11, the average loss is    5.63. ## Up to batch 12, the average loss is    5.45. ## Up to batch 13, the average loss is    5.23. ## Up to batch 14, the average loss is    5.12. ## Up to batch 15, the average loss is    4.96. ## Up to batch 16, the average loss is    4.91. ## The average loss for epoch  6 is      4.91 and mean absolute error is    1.67. ## ## Epoch 007: Learning rate is 0.0100. ## Up to batch 1, the average loss is    3.65. ## Up to batch 2, the average loss is    3.04. ## Up to batch 3, the average loss is    2.88. ## Up to batch 4, the average loss is    2.85. ## Up to batch 5, the average loss is    2.88. ## Up to batch 6, the average loss is    2.81. ## Up to batch 7, the average loss is    2.70. ## Up to batch 8, the average loss is    2.96. ## Up to batch 9, the average loss is    2.96. ## Up to batch 10, the average loss is    2.93. ## Up to batch 11, the average loss is    2.95. ## Up to batch 12, the average loss is    2.98. ## Up to batch 13, the average loss is    2.97. ## Up to batch 14, the average loss is    3.01. ## Up to batch 15, the average loss is    3.00. ## Up to batch 16, the average loss is    3.05. ## The average loss for epoch  7 is      3.05 and mean absolute error is    1.34. ## ## Epoch 008: Learning rate is 0.0100. ## Up to batch 1, the average loss is    3.69. ## Up to batch 2, the average loss is    3.21. ## Up to batch 3, the average loss is    3.00. ## Up to batch 4, the average loss is    2.91. ## Up to batch 5, the average loss is    2.94. ## Up to batch 6, the average loss is    2.85. ## Up to batch 7, the average loss is    2.72. ## Up to batch 8, the average loss is    2.95. ## Up to batch 9, the average loss is    2.97. ## Up to batch 10, the average loss is    2.93. ## Up to batch 11, the average loss is    2.96. ## Up to batch 12, the average loss is    2.98. ## Up to batch 13, the average loss is    2.99. ## Up to batch 14, the average loss is    3.05. ## Up to batch 15, the average loss is    3.08. ## Up to batch 16, the average loss is    3.14. ## The average loss for epoch  8 is      3.14 and mean absolute error is    1.36. ## ## Epoch 009: Learning rate is 0.0050. ## Up to batch 1, the average loss is    3.71. ## Up to batch 2, the average loss is    2.93. ## Up to batch 3, the average loss is    2.76. ## Up to batch 4, the average loss is    2.70. ## Up to batch 5, the average loss is    2.76. ## Up to batch 6, the average loss is    2.69. ## Up to batch 7, the average loss is    2.57. ## Up to batch 8, the average loss is    2.79. ## Up to batch 9, the average loss is    2.80. ## Up to batch 10, the average loss is    2.77. ## Up to batch 11, the average loss is    2.79. ## Up to batch 12, the average loss is    2.80. ## Up to batch 13, the average loss is    2.78. ## Up to batch 14, the average loss is    2.81. ## Up to batch 15, the average loss is    2.80. ## Up to batch 16, the average loss is    2.83. ## The average loss for epoch  9 is      2.83 and mean absolute error is    1.28. ## ## Epoch 010: Learning rate is 0.0050. ## Up to batch 1, the average loss is    3.02. ## Up to batch 2, the average loss is    2.69. ## Up to batch 3, the average loss is    2.58. ## Up to batch 4, the average loss is    2.57. ## Up to batch 5, the average loss is    2.65. ## Up to batch 6, the average loss is    2.60. ## Up to batch 7, the average loss is    2.48. ## Up to batch 8, the average loss is    2.72. ## Up to batch 9, the average loss is    2.74. ## Up to batch 10, the average loss is    2.71. ## Up to batch 11, the average loss is    2.74. ## Up to batch 12, the average loss is    2.75. ## Up to batch 13, the average loss is    2.74. ## Up to batch 14, the average loss is    2.77. ## Up to batch 15, the average loss is    2.77. ## Up to batch 16, the average loss is    2.80. ## The average loss for epoch 10 is      2.80 and mean absolute error is    1.27. ## ## Epoch 011: Learning rate is 0.0050. ## Up to batch 1, the average loss is    3.01. ## Up to batch 2, the average loss is    2.69. ## Up to batch 3, the average loss is    2.58. ## Up to batch 4, the average loss is    2.56. ## Up to batch 5, the average loss is    2.63. ## Up to batch 6, the average loss is    2.58. ## Up to batch 7, the average loss is    2.47. ## Up to batch 8, the average loss is    2.70. ## Up to batch 9, the average loss is    2.72. ## Up to batch 10, the average loss is    2.69. ## Up to batch 11, the average loss is    2.71. ## Up to batch 12, the average loss is    2.72. ## Up to batch 13, the average loss is    2.71. ## Up to batch 14, the average loss is    2.75. ## Up to batch 15, the average loss is    2.74. ## Up to batch 16, the average loss is    2.77. ## The average loss for epoch 11 is      2.77 and mean absolute error is    1.27. ## ## Epoch 012: Learning rate is 0.0010. ## Up to batch 1, the average loss is    2.96. ## Up to batch 2, the average loss is    2.53. ## Up to batch 3, the average loss is    2.47. ## Up to batch 4, the average loss is    2.46. ## Up to batch 5, the average loss is    2.54. ## Up to batch 6, the average loss is    2.48. ## Up to batch 7, the average loss is    2.39. ## Up to batch 8, the average loss is    2.60. ## Up to batch 9, the average loss is    2.62. ## Up to batch 10, the average loss is    2.59. ## Up to batch 11, the average loss is    2.61. ## Up to batch 12, the average loss is    2.62. ## Up to batch 13, the average loss is    2.60. ## Up to batch 14, the average loss is    2.64. ## Up to batch 15, the average loss is    2.62. ## Up to batch 16, the average loss is    2.64. ## The average loss for epoch 12 is      2.64 and mean absolute error is    1.24. ## ## Epoch 013: Learning rate is 0.0010. ## Up to batch 1, the average loss is    2.82. ## Up to batch 2, the average loss is    2.46. ## Up to batch 3, the average loss is    2.42. ## Up to batch 4, the average loss is    2.42. ## Up to batch 5, the average loss is    2.50. ## Up to batch 6, the average loss is    2.45. ## Up to batch 7, the average loss is    2.36. ## Up to batch 8, the average loss is    2.57. ## Up to batch 9, the average loss is    2.59. ## Up to batch 10, the average loss is    2.57. ## Up to batch 11, the average loss is    2.59. ## Up to batch 12, the average loss is    2.60. ## Up to batch 13, the average loss is    2.59. ## Up to batch 14, the average loss is    2.62. ## Up to batch 15, the average loss is    2.61. ## Up to batch 16, the average loss is    2.63. ## The average loss for epoch 13 is      2.63 and mean absolute error is    1.23. ## ## Epoch 014: Learning rate is 0.0010. ## Up to batch 1, the average loss is    2.79. ## Up to batch 2, the average loss is    2.44. ## Up to batch 3, the average loss is    2.40. ## Up to batch 4, the average loss is    2.41. ## Up to batch 5, the average loss is    2.49. ## Up to batch 6, the average loss is    2.44. ## Up to batch 7, the average loss is    2.34. ## Up to batch 8, the average loss is    2.56. ## Up to batch 9, the average loss is    2.58. ## Up to batch 10, the average loss is    2.56. ## Up to batch 11, the average loss is    2.58. ## Up to batch 12, the average loss is    2.59. ## Up to batch 13, the average loss is    2.58. ## Up to batch 14, the average loss is    2.61. ## Up to batch 15, the average loss is    2.60. ## Up to batch 16, the average loss is    2.62. ## The average loss for epoch 14 is      2.62 and mean absolute error is    1.23."},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"built-in-keras-callbacks","dir":"Articles","previous_headings":"Examples of Keras callback applications","what":"Built-in Keras callbacks","title":"Writing your own callbacks","text":"sure check existing Keras callbacks reading API docs. Applications include logging CSV, saving model, visualizing metrics TensorBoard, lot !","code":""},{"path":"https://keras.posit.co/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Tomasz Kalinowski. Author, copyright holder, maintainer. Daniel Falbel. Contributor, copyright holder. JJ Allaire. Author, copyright holder. François Chollet. Author, copyright holder. Posit Software, PBC. Copyright holder, funder. Google. Copyright holder, funder. Yuan Tang. Contributor, copyright holder. Wouter Van Der Bijl. Contributor, copyright holder. Martin Studer. Contributor, copyright holder. Sigrid Keydana. Contributor.","code":""},{"path":"https://keras.posit.co/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Kalinowski T, Allaire J, Chollet F (2024). keras3: R Interface 'Keras'. R package version 1.0.0, https://github.com/rstudio/keras, https://keras.posit.co/.","code":"@Manual{,   title = {keras3: R Interface to 'Keras'},   author = {Tomasz Kalinowski and JJ Allaire and François Chollet},   year = {2024},   note = {R package version 1.0.0, https://github.com/rstudio/keras},   url = {https://keras.posit.co/}, }"},{"path":"https://keras.posit.co/index.html","id":"r-interface-to-keras","dir":"","previous_headings":"","what":"R Interface to Keras","title":"R Interface to Keras","text":"Keras high-level neural networks API developed focus enabling fast experimentation. able go idea result least possible delay key good research. Keras following key features: Allows code run CPU GPU, seamlessly. User-friendly API makes easy quickly prototype deep learning models. Built-support convolutional networks (computer vision), recurrent networks (sequence processing), combination . Supports arbitrary network architectures: multi-input multi-output models, layer sharing, model sharing, etc. means Keras appropriate building essentially deep learning model, memory network neural Turing machine. See package website https://keras.posit.co complete documentation.","code":""},{"path":"https://keras.posit.co/reference/Callback.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a custom Callback class — Callback","title":"Define a custom Callback class — Callback","text":"Callbacks can passed keras methods fit(), evaluate(), predict() order hook various stages model training, evaluation, inference lifecycle. create custom callback, call Callback() override method associated stage interest.","code":""},{"path":"https://keras.posit.co/reference/Callback.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a custom Callback class — Callback","text":"","code":"Callback(   classname,   on_epoch_begin = NULL,   on_epoch_end = NULL,   on_train_begin = NULL,   on_train_end = NULL,   on_train_batch_begin = NULL,   on_train_batch_end = NULL,   on_test_begin = NULL,   on_test_end = NULL,   on_test_batch_begin = NULL,   on_test_batch_end = NULL,   on_predict_begin = NULL,   on_predict_end = NULL,   on_predict_batch_begin = NULL,   on_predict_batch_end = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Callback.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a custom Callback class — Callback","text":"classname String, name custom class. (Conventionally, CamelCase). on_epoch_begin   Called start epoch. Subclasses override actions run. function called TRAIN mode. Args: epoch: Integer, index epoch. logs: Named List. Currently data passed argument method may change future. on_epoch_end   Called end epoch. Subclasses override actions run. function called TRAIN mode. Args: epoch: Integer, index epoch. logs: Named List, metric results training epoch, validation epoch validation performed. Validation result keys prefixed val_. training epoch, values Model's metrics returned. Example: list(loss = 0.2, accuracy = 0.7). on_train_begin   Called beginning training. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_train_end   Called end training. Subclasses override actions run. Args: logs: Named list. Currently output last call on_epoch_end() passed argument method may change future. on_train_batch_begin   Called beginning training batch fit() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_train_batch_end   Called end training batch fit() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. on_test_begin   Called beginning evaluation validation. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_test_end   Called end evaluation validation. Subclasses override actions run. Args: logs: Named list. Currently output last call on_test_batch_end() passed argument method may change future. on_test_batch_begin   Called beginning batch evaluate() methods. Also called beginning validation batch fit() methods, validation data provided. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_test_batch_end   Called end batch evaluate() methods. Also called end validation batch fit() methods, validation data provided. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. on_predict_begin   Called beginning prediction. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_predict_end   Called end prediction. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_predict_batch_begin   Called beginning batch predict() methods. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_predict_batch_end   Called end batch predict() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"\\(epoch, logs = NULL) \\(epoch, logs = NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs=NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs = NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs = NULL)"},{"path":"https://keras.posit.co/reference/Callback.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a custom Callback class — Callback","text":"function returns custom Callback instances, similar builtin callback functions.","code":""},{"path":"https://keras.posit.co/reference/Callback.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define a custom Callback class — Callback","text":"R function custom methods (public private) following symbols scope: self: Layer instance. super: Layer superclass. private: R environment specific class instance. objects defined invisible Keras framework. __class__ current class type object. also available alias symbol, value supplied Layer(classname = )","code":"training_finished <- FALSE callback_mark_finished <- Callback(\"MarkFinished\",   on_train_end = function(logs = NULL) {     training_finished <<- TRUE   } )  model <- keras_model_sequential(input_shape = c(1)) |>   layer_dense(1) model |> compile(loss = 'mean_squared_error') model |> fit(op_ones(c(1, 1)), op_ones(c(1, 1)),              callbacks = callback_mark_finished()) stopifnot(isTRUE(training_finished))"},{"path":"https://keras.posit.co/reference/Callback.html","id":"attributes-accessible-via-self-","dir":"Reference","previous_headings":"","what":"Attributes (accessible via self$)","title":"Define a custom Callback class — Callback","text":"params: Named list, Training parameters (e.g. verbosity, batch size, number epochs, ...). model: Instance Model. Reference model trained. logs named list callback methods take argument contain keys quantities relevant current batch epoch (see method-specific docstrings).","code":""},{"path":"https://keras.posit.co/reference/Callback.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Define a custom Callback class — Callback","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Constraint.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a custom Constraint class — Constraint","title":"Define a custom Constraint class — Constraint","text":"Base class weight constraints. Constraint() instance works like stateless function. Users subclass Constraint class override call() method, takes single weight parameter return projected version parameter (e.g. normalized clipped). Constraints can used various Keras layers via kernel_constraint bias_constraint arguments. simple example non-negative weight constraint:     Usage layer:","code":"constraint_nonnegative <- Constraint(\"NonNegative\",   call = function(w) {     w * op_cast(w >= 0, dtype = w$dtype)   } ) weight <- op_convert_to_tensor(c(-1, 1)) constraint_nonnegative()(weight) ## tf.Tensor([-0.  1.], shape=(2), dtype=float32) layer_dense(units = 4, kernel_constraint = constraint_nonnegative()) ## <Dense name=dense, built=False> ##  signature: (*args, **kwargs)"},{"path":"https://keras.posit.co/reference/Constraint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a custom Constraint class — Constraint","text":"","code":"Constraint(   classname,   call = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Constraint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a custom Constraint class — Constraint","text":"classname String, name custom class. (Conventionally, CamelCase). call   Applies constraint input weight variable. default, inputs weight variable modified. Users override method implement projection function. Args: w: Input weight variable. Returns: Projected variable (default, returns unmodified inputs). get_config   Function returns named list object config. constraint config named list (JSON-serializable) can used reinstantiate object (via .call(<constraint_class>, <config>)). ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"\\(w) \\()"},{"path":"https://keras.posit.co/reference/Constraint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a custom Constraint class — Constraint","text":"function returns Constraint instances, similar builtin constraint functions like constraint_maxnorm().","code":""},{"path":"https://keras.posit.co/reference/Constraint.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Define a custom Constraint class — Constraint","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Layer.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a custom Layer class. — Layer","title":"Define a custom Layer class. — Layer","text":"layer callable object takes input one tensors outputs one tensors. involves computation, defined call() method, state (weight variables). State can created: initialize(), instance via self$add_weight(); optional build() method, invoked first call() layer, supplies shape(s) input(s), may known initialization time. Layers recursively composable: assign Layer instance attribute another Layer, outer layer start tracking weights created inner layer. Nested layers instantiated initialize() method build() method. Users just instantiate layer treat callable.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a custom Layer class. — Layer","text":"","code":"Layer(   classname,   initialize = NULL,   call = NULL,   build = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Layer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a custom Layer class. — Layer","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, call, build, get_config Recommended methods implement. See description details sections. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a custom Layer class. — Layer","text":"composing layer constructor, similar behavior layer functions like layer_dense(). first argument returned function object, enabling initialize()ing call() layer one step composing layer pipe, like   initialize() layer instance call() , pass missing NULL value object, pass arguments initialize() name.","code":"layer_foo <- Layer(\"Foo\", ....) output <- inputs |> layer_foo() layer <- layer_dense(units = 2, activation = \"relu\") layer <- layer_dense(NULL, 2, activation = \"relu\") layer <- layer_dense(, 2, activation = \"relu\")  # then you can call() the layer in a separate step outputs <- inputs |> layer()"},{"path":"https://keras.posit.co/reference/Layer.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Define a custom Layer class. — Layer","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Define a custom Layer class. — Layer","text":"name: name layer (string). dtype: Dtype layer's weights. Alias layer$variable_dtype. variable_dtype: Dtype layer's weights. compute_dtype: dtype layer's computations. Layers automatically cast inputs dtype, causes computations output also dtype. mixed precision used keras$mixed_precision$DTypePolicy, different variable_dtype. trainable_weights: List variables included backprop. non_trainable_weights: List variables included backprop. weights: concatenation lists trainable_weights non_trainable_weights (order). trainable: Whether layer trained (boolean), .e. whether potentially-trainable weights returned part layer$trainable_weights. input_spec: Optional (list ) InputSpec object(s) specifying constraints inputs can accepted layer. recommend custom Layers implement following methods: initialize(): Defines custom layer attributes, creates layer weights depend input shapes, using add_weight(), state. build(input_shape): method can used create weights depend shape(s) input(s), using add_weight(), state. Calling call() automatically build layer (built yet) calling build(). call(...): Method called making sure build() called. call() performs logic applying layer input arguments. Two reserved arguments can optionally use call() : training (boolean, whether call inference mode training mode). mask (boolean tensor encoding masked timesteps input, used e.g. RNN layers). typical signature method call(inputs), user optionally add training mask layer need . get_config(): Returns named list containing configuration used initialize layer. list names differ arguments initialize(), override from_config() well. method used saving layer model contains layer.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define a custom Layer class. — Layer","text":"basic example: layer two variables, w b, returns y <- (w %*% x) + b. shows implement build() call(). Variables set attributes layer tracked weights layers (layer$weights).   Besides trainable weights, updated via backpropagation training, layers can also non-trainable weights. weights meant updated manually call(). example layer computes running sum inputs:","code":"layer_simple_dense <- Layer(   \"SimpleDense\",   initialize = function(units = 32) {     super$initialize()     self$units <- units   },    # Create the state of the layer (weights)   build = function(input_shape) {     self$kernel <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = \"glorot_uniform\",       trainable = TRUE,       name = \"kernel\"     )     self$bias = self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE,       name = \"bias\"     )   },    # Defines the computation   call = function(self, inputs) {     op_matmul(inputs, self$kernel) + self$bias   } )  # Instantiates the layer. # Supply missing `object` arg to skip invoking `call()` and instead return # the Layer instance linear_layer <- layer_simple_dense(, 4)  # This will call `build(input_shape)` and create the weights, # and then invoke `call()`. y <- linear_layer(op_ones(c(2, 2))) stopifnot(length(linear_layer$weights) == 2)  # These weights are trainable, so they're listed in `trainable_weights`: stopifnot(length(linear_layer$trainable_weights) == 2) layer_compute_sum <- Layer(   classname = \"ComputeSum\",    initialize = function(input_dim) {     super$initialize()      # Create a non-trainable weight.     self$total <- self$add_weight(       shape = shape(),       initializer = \"zeros\",       trainable = FALSE,       name = \"total\"     )   },    call = function(inputs) {     self$total$assign(self$total + op_sum(inputs))     self$total   } )  my_sum <- layer_compute_sum(, 2) x <- op_ones(c(2, 2)) y <- my_sum(x)  stopifnot(exprs = {   all.equal(my_sum$weights,               list(my_sum$total))   all.equal(my_sum$non_trainable_weights, list(my_sum$total))   all.equal(my_sum$trainable_weights,     list()) })"},{"path":"https://keras.posit.co/reference/Layer.html","id":"methods-available","dir":"Reference","previous_headings":"","what":"Methods available","title":"Define a custom Layer class. — Layer","text":"Initialize self. method typically called custom initialize() method. Example:   Args: trainable: Boolean, whether layer's variables trainable. name: String name layer. dtype: dtype layer's computations weights. Can also keras$DTypePolicy, allows computation weight dtype differ. Defaults NULL. NULL means use config_dtype_policy(), \"float32\" policy unless set different value (via config_set_dtype_policy()).    Can called inside call() method add scalar loss. Example:         Add weight variable layer. Alias add_weight().    Add weight variable layer. Args: shape: shape variable (defined shape()) Must fully-defined (NA/NULL/-1 entries). Defaults () (scalar) unspecified. initializer: Initializer object use populate initial variable value, string name built-initializer (e.g. \"random_normal\"). unspecified, defaults \"glorot_uniform\" floating-point variables \"zeros\" types (e.g. int, bool). dtype: Dtype variable create, e.g. \"float32\". unspecified, defaults layer's variable dtype (defaults \"float32\" unspecified). trainable: Boolean, whether variable trainable via backprop whether updates managed manually. Defaults TRUE. autocast: Boolean, whether autocast layers variables accessing . Defaults TRUE. regularizer: Regularizer object call apply penalty weight. penalties summed loss function optimization. Defaults NULL. constraint: Constraint object call variable optimizer update, string name built-constraint. Defaults NULL. aggregation: String, one 'mean', 'sum', 'only_first_replica'. Annotates variable type multi-replica aggregation used variable writing custom data parallel training loops. name: String name variable. Useful debugging purposes. Returns: backend tensor, wrapped KerasVariable class. KerasVariable class Methods: assign(value) assign_add(value) assign_sub(value) numpy() (calling .array(<variable>) preferred) Properties/Attributes: value dtype ndim shape (calling shape(<variable>) preferred) trainable       Builds layer's states supplied config (named list args). default, method calls .call(build, config$input_shape) method, creates weights based layer's input shape supplied config. config contains information needed load layer's state, override method. Args: config: Named list containing input shape associated layer.    See description             Count total number scalars composing weights. Returns: integer count.    Returns named list layer's input shape. method returns config (named list) can used build_from_config(config) create states (e.g. Variables Lookup tables) needed layer. default, config contains input shape layer built . writing custom layer creates state unusual way, override method make sure state already created Keras attempts load value upon model loading. Returns: named list containing input shape associated layer.    Returns config object. object config named list (serializable) containing information needed re-instantiate . config expected serializable JSON, expected consist (potentially complex, nested) structure names lists consisting simple objects like strings, ints.    Return values layer$weights list R NumPy arrays.    Currently, Dense EinsumDense layers support -place quantization via quantize() method. Example:         Loads state layer. can override method take full control state layer loaded upon calling load_model(). Args: store: Named list state model loaded.    Saves state layer. can override method take full control state layer saved upon calling save_model(). Args: store: Named list state model saved.    Sets values weights list R NumPy arrays.    Call layer without side effects. Args: trainable_variables: List trainable variables model. non_trainable_variables: List non-trainable variables model. ...: Positional named arguments passed call(). return_losses: TRUE, stateless_call() return list losses created call() part return values. Returns: unnamed list. default, returns list(outputs, non_trainable_variables). return_losses = TRUE, returns list(outputs, non_trainable_variables, losses). Note: non_trainable_variables include non-trainable weights BatchNormalization statistics, also RNG seed state (random operations part layer, dropout), Metric state (metrics attached layer). elements state layer. Example:         Creates layer config. class method, meaning, R function self symbol (class instance) scope. Use __class__ classname symbol provided Layer() constructed) resolve class definition. default implementation :   method reverse get_config(), capable instantiating layer config named list. handle layer connectivity (handled Network), weights (handled set_weights()). Args: config: named list, typically output get_config(). Returns: layer instance.","code":"initialize(...,            activity_regularizer = NULL,            trainable = TRUE,            dtype = NULL,            autocast = TRUE,            name = NULL) layer_my_layer <- Layer(\"MyLayer\",   initialize = function(units, ..., dtype = NULL, name = NULL) {     super$initialize(..., dtype = dtype, name = name)     # .... finish initializing `self` instance   } ) add_loss(loss) Layer(\"MyLayer\",   ...   call = function(x) {     self$add_loss(op_sum(x))     x   } add_metric() add_variable(...) add_weight(shape = NULL,            initializer = NULL,            dtype = NULL,            trainable = TRUE,            autocast = TRUE,            regularizer = NULL,            constraint = NULL,            aggregation = 'mean',            name = NULL) build(input_shape) build_from_config(config) call(...) compute_mask(inputs, previous_mask) compute_output_shape(...) compute_output_spec(...) count_params() get_build_config() get_config() get_weights() quantize(mode) model$quantize(\"int8\") # quantize model in-place model |> predict(data) # faster inference quantized_call(...) load_own_variables(store) save_own_variables(store) set_weights(weights) stateless_call(trainable_variables, non_trainable_variables,                ..., return_losses = FALSE) model <- ... data <- ... trainable_variables <- model$trainable_variables non_trainable_variables <- model$non_trainable_variables # Call the model with zero side effects c(outputs, non_trainable_variables) %<-% model$stateless_call(     trainable_variables,     non_trainable_variables,     data ) # Attach the updated state to the model # (until you do this, the model is still in its pre-call state). purrr::walk2(   model$non_trainable_variables, non_trainable_variables,   \\(variable, value) variable$assign(value)) symbolic_call(...) from_config(config) from_config = function(config) {   do.call(`__class__`, config) }"},{"path":"https://keras.posit.co/reference/Layer.html","id":"readonly-properties-","dir":"Reference","previous_headings":"","what":"Readonly properties:","title":"Define a custom Layer class. — Layer","text":"compute_dtype dtype computations performed layer. dtype Alias layer$variable_dtype. input_dtype dtype layer inputs converted . losses List scalar losses add_loss(), regularizers sublayers. metrics List metrics. metrics_variables List metric variables. non_trainable_variables List non-trainable layer state. extends layer$non_trainable_weights include state used layer including state metrics SeedGenerators. non_trainable_weights List non-trainable weight variables layer. weights updated optimizer training. Unlike, layer$non_trainable_variables excludes metric state random seeds. trainable_variables List trainable layer state. equivalent layer$trainable_weights. trainable_weights List trainable weight variables layer. weights get updated optimizer training. variable_dtype dtype state (weights) layer. variables List layer state, including random seeds. extends layer$weights include state used layer including SeedGenerators. Note metrics variables included , use metrics_variables visit metric variables. weights List weight variables layer. Unlike, layer$variables excludes metric state random seeds. input Retrieves input tensor(s) symbolic operation. returns tensor(s) corresponding first time operation called. Returns: Input tensor list input tensors. output Retrieves output tensor(s) layer. returns tensor(s) corresponding first time operation called. Returns: Output tensor list output tensors.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"data-descriptors-attributes-","dir":"Reference","previous_headings":"","what":"Data descriptors (Attributes):","title":"Define a custom Layer class. — Layer","text":"dtype_policy input_spec supports_masking Whether layer supports computing mask using compute_mask. trainable Settable boolean, whether layer trainable .","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a custom LearningRateSchedule class — LearningRateSchedule","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"Subclass Keras LearningRateSchedule base class. can use learning rate schedule modulate learning rate optimizer changes time. Several built-learning rate schedules available, learning_rate_schedule_exponential_decay() learning_rate_schedule_piecewise_constant_decay():   LearningRateSchedule() instance can passed learning_rate argument optimizer. implement schedule object, implement call method, takes step argument (scalar integer backend tensor, current training step count). Note step 0-based (.e., first step 0). Like Keras object, can also optionally make object serializable implementing get_config() from_config() methods.","code":"lr_schedule <- learning_rate_schedule_exponential_decay(   initial_learning_rate = 1e-2,   decay_steps = 10000,   decay_rate = 0.9 ) optimizer <- optimizer_sgd(learning_rate = lr_schedule)"},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"","code":"LearningRateSchedule(   classname,   call = NULL,   initialize = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"classname String, name custom class. (Conventionally, CamelCase). call, initialize, get_config Recommended methods implement. See description details sections. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"function returns LearningRateSchedule instances, similar built-learning_rate_schedule_* family functions.","code":""},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"","code":"my_custom_learning_rate_schedule <- LearningRateSchedule(   classname = \"MyLRSchedule\",    initialize = function(initial_learning_rate) {     self$initial_learning_rate <- initial_learning_rate   },    call = function(step) {     # note that `step` is a tensor     # and call() will be traced via tf_function() or similar.      str(step) # <KerasVariable shape=(), dtype=int64, path=SGD/iteration>      # print 'step' every 1000 steps     op_cond((step %% 1000) == 0,             \\() {tensorflow::tf$print(step); NULL},             \\() {NULL})     self$initial_learning_rate / (step + 1)   } )  optimizer <- optimizer_sgd(   learning_rate = my_custom_learning_rate_schedule(0.1) )  # You can also call schedule instances directly # (e.g., for interactive testing, or if implementing a custom optimizer) schedule <- my_custom_learning_rate_schedule(0.1) step <- keras$Variable(initializer = op_ones,                        shape = shape(),                        dtype = \"int64\") schedule(step) ## <KerasVariable shape=(), dtype=int64, path=variable> ## tf.Tensor(0.0, shape=(), dtype=float64)"},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"methods-available-","dir":"Reference","previous_headings":"","what":"Methods available:","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"","code":"get_config()"},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Subclass the base Loss class — Loss","title":"Subclass the base Loss class — Loss","text":"Use define custom loss class. Note, cases need subclass Loss define custom loss: can also pass bare R function, named R function defined custom_metric(), loss function compile().","code":""},{"path":"https://keras.posit.co/reference/Loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subclass the base Loss class — Loss","text":"","code":"Loss(   classname,   call = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subclass the base Loss class — Loss","text":"classname String, name custom class. (Conventionally, CamelCase). call   Method implemented subclasses: Function contains logic loss calculation using y_true, y_pred. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"function(y_true, y_pred)"},{"path":"https://keras.posit.co/reference/Loss.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subclass the base Loss class — Loss","text":"function returns Loss instances, similar builtin loss functions.","code":""},{"path":"https://keras.posit.co/reference/Loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Subclass the base Loss class — Loss","text":"Example subclass implementation:","code":"loss_custom_mse <- Loss(   classname = \"CustomMeanSquaredError\",   call = function(y_true, y_pred) {     op_mean(op_square(y_pred - y_true), axis = -1)   } )  # Usage in compile() model <- keras_model_sequential(input_shape = 10) |> layer_dense(10) model |> compile(loss = loss_custom_mse())  # Standalone usage mse <- loss_custom_mse(name = \"my_custom_mse_instance\")  y_true <- op_arange(20) |> op_reshape(c(4, 5)) y_pred <- op_arange(20) |> op_reshape(c(4, 5)) * 2 (loss <- mse(y_true, y_pred)) ## tf.Tensor(123.5, shape=(), dtype=float32) loss2 <- (y_pred - y_true)^2 |>   op_mean(axis = -1) |>   op_mean()  stopifnot(all.equal(as.array(loss), as.array(loss2)))  sample_weight <-array(c(.25, .25, 1, 1)) (weighted_loss <- mse(y_true, y_pred, sample_weight = sample_weight)) ## tf.Tensor(112.8125, shape=(), dtype=float32) weighted_loss2 <- (y_true - y_pred)^2 |>   op_mean(axis = -1) |>   op_multiply(sample_weight) |>   op_mean()  stopifnot(all.equal(as.array(weighted_loss),                     as.array(weighted_loss2)))"},{"path":"https://keras.posit.co/reference/Loss.html","id":"methods-defined-by-base-loss-class-","dir":"Reference","previous_headings":"","what":"Methods defined by base Loss class:","title":"Subclass the base Loss class — Loss","text":"Args: name reduction: Valid values one {\"sum_over_batch_size\", \"sum\", NULL, \"none\"} dtype    Call loss instance function, optionally sample_weight.","code":"initialize(name=NULL, reduction=\"sum_over_batch_size\", dtype=NULL) __call__(y_true, y_pred, sample_weight=NULL) get_config()"},{"path":"https://keras.posit.co/reference/Loss.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Subclass the base Loss class — Loss","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Metric.html","id":null,"dir":"Reference","previous_headings":"","what":"Subclass the base Metric class — Metric","title":"Subclass the base Metric class — Metric","text":"Metric object encapsulates metric logic state can used track model performance training. returned family metric functions start prefix metric_*, well returned custom metrics defined Metric().","code":""},{"path":"https://keras.posit.co/reference/Metric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subclass the base Metric class — Metric","text":"","code":"Metric(   classname,   initialize = NULL,   update_state = NULL,   result = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Metric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subclass the base Metric class — Metric","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, update_state, result Recommended methods implement. See description section. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/Metric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subclass the base Metric class — Metric","text":"function returns Metric instances, similar builtin metric functions.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Metric.html","id":"usage-with-compile-","dir":"Reference","previous_headings":"","what":"Usage with compile():","title":"Subclass the base Metric class — Metric","text":"","code":"model |> compile(   optimizer = 'sgd',   loss = 'mse',   metrics = c(metric_SOME_METRIC(), metric_SOME_OTHER_METRIC()) )"},{"path":"https://keras.posit.co/reference/Metric.html","id":"standalone-usage-","dir":"Reference","previous_headings":"","what":"Standalone usage:","title":"Subclass the base Metric class — Metric","text":"","code":"m <- metric_SOME_METRIC() for (e in seq(epochs)) {   for (i in seq(train_steps)) {     c(y_true, y_pred, sample_weight = NULL) %<-% ...     m$update_state(y_true, y_pred, sample_weight)   }   cat('Final epoch result: ', as.numeric(m$result()), \"\\n\")   m$reset_state() }"},{"path":[]},{"path":"https://keras.posit.co/reference/Metric.html","id":"usage-with-compile--1","dir":"Reference","previous_headings":"","what":"Usage with compile():","title":"Subclass the base Metric class — Metric","text":"implemented subclasses (custom metrics): initialize(): state variables created method calling self$add_variable() like: self$var <- self$add_variable(...). update_state(): Updates state variables like: self$var$assign(...). result(): Computes returns scalar value named list scalar values metric state variables. Example subclass implementation:","code":"model <- keras_model_sequential() model |>   layer_dense(64, activation = \"relu\") |>   layer_dense(64, activation = \"relu\") |>   layer_dense(10, activation = \"softmax\") model |>   compile(optimizer = optimizer_rmsprop(0.01),           loss = loss_categorical_crossentropy(),           metrics = metric_categorical_accuracy())  data <- random_uniform(c(1000, 32)) labels <- random_uniform(c(1000, 10))  model |> fit(data, labels, verbose = 0) metric_binary_true_positives <- Metric(   classname = \"BinaryTruePositives\",    initialize = function(name = 'binary_true_positives', ...) {     super$initialize(name = name, ...)     self$true_positives <-       self$add_weight(shape = shape(),                       initializer = 'zeros',                       name = 'true_positives')   },    update_state = function(y_true, y_pred, sample_weight = NULL) {     y_true <- op_cast(y_true, \"bool\")     y_pred <- op_cast(y_pred, \"bool\")      values <- y_true & y_pred # `&` calls op_logical_and()     values <- op_cast(values, self$dtype)     if (!is.null(sample_weight)) {       sample_weight <- op_cast(sample_weight, self$dtype)       sample_weight <- op_broadcast_to(sample_weight, shape(values))       values <- values * sample_weight # `*` calls op_multiply()     }     self$true_positives$assign(self$true_positives + op_sum(values))   },    result = function() {     self$true_positives   } ) model <- keras_model_sequential(input_shape = 32) |> layer_dense(10) model |> compile(loss = loss_binary_crossentropy(),                  metrics = list(metric_binary_true_positives())) model |> fit(data, labels, verbose = 0)"},{"path":"https://keras.posit.co/reference/Metric.html","id":"methods-defined-by-the-base-metric-class-","dir":"Reference","previous_headings":"","what":"Methods defined by the base Metric class:","title":"Subclass the base Metric class — Metric","text":"Calling metric instance self like m(...) equivalent calling:      Initialize self. Args: name: (Optional) string name metric instance. dtype: (Optional) data type metric result.          Return serializable config metric.    Reset metric state variables. function called epochs/steps, metric evaluated training.    Compute current metric value. Returns: scalar tensor, named list scalar tensors.             Accumulate statistics metric.","code":"__call__(...) function(...) {   m$update_state(...)   m$result() } initialize(dtype=NULL, name=NULL) add_variable(shape, initializer, dtype=NULL, aggregation = 'sum', name=NULL) add_weight(shape=shape(), initializer=NULL, dtype=NULL, name=NULL) get_config() reset_state() result() stateless_result(metric_variables) stateless_reset_state() stateless_update_state(metric_variables, ...) update_state(...)"},{"path":"https://keras.posit.co/reference/Metric.html","id":"readonly-properties","dir":"Reference","previous_headings":"","what":"Readonly properties","title":"Subclass the base Metric class — Metric","text":"dtype variables","code":""},{"path":"https://keras.posit.co/reference/Metric.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Subclass the base Metric class — Metric","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Subclass the base Keras Model Class — Model","title":"Subclass the base Keras Model Class — Model","text":"advanced use cases need subclass base Model type, e.g., want override train_step() method. just want create define keras model, prefer keras_model() keras_model_sequential(). just want encapsulate custom logic state, need customize training behavior (besides calling self$add_loss() call() method), prefer Layer().","code":""},{"path":"https://keras.posit.co/reference/Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subclass the base Keras Model Class — Model","text":"","code":"Model(   classname,   initialize = NULL,   call = NULL,   train_step = NULL,   predict_step = NULL,   test_step = NULL,   compute_loss = NULL,   compute_metrics = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subclass the base Keras Model Class — Model","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, call, train_step, predict_step, test_step, compute_loss, compute_metrics Optional methods can overridden. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subclass the base Keras Model Class — Model","text":"model constructor function, can call create instance new model type.","code":""},{"path":"https://keras.posit.co/reference/Model.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Subclass the base Keras Model Class — Model","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"Exponential Linear Unit. — activation_elu","title":"Exponential Linear Unit. — activation_elu","text":"exponential linear unit (ELU) alpha > 0 defined : x x > 0 alpha * exp(x) - 1 x < 0 ELUs negative values pushes mean activations closer zero. Mean activations closer zero enable faster learning bring gradient closer natural gradient. ELUs saturate negative value argument gets smaller. Saturation means small derivative decreases variation information propagated next layer.","code":""},{"path":"https://keras.posit.co/reference/activation_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exponential Linear Unit. — activation_elu","text":"","code":"activation_elu(x, alpha = 1)"},{"path":"https://keras.posit.co/reference/activation_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exponential Linear Unit. — activation_elu","text":"x Input tensor. alpha Numeric. See description details.","code":""},{"path":"https://keras.posit.co/reference/activation_elu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exponential Linear Unit. — activation_elu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_elu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Exponential Linear Unit. — activation_elu","text":"Clevert et al., 2016","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_exponential.html","id":null,"dir":"Reference","previous_headings":"","what":"Exponential activation function. — activation_exponential","title":"Exponential activation function. — activation_exponential","text":"Exponential activation function.","code":""},{"path":"https://keras.posit.co/reference/activation_exponential.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exponential activation function. — activation_exponential","text":"","code":"activation_exponential(x)"},{"path":"https://keras.posit.co/reference/activation_exponential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exponential activation function. — activation_exponential","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_exponential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exponential activation function. — activation_exponential","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian error linear unit (GELU) activation function. — activation_gelu","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"Gaussian error linear unit (GELU) defined : gelu(x) = x * P(X <= x) P(X) ~ N(0, 1), .e. gelu(x) = 0.5 * x * (1 + erf(x / sqrt(2))). GELU weights inputs value, rather gating inputs sign ReLU.","code":""},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"","code":"activation_gelu(x, approximate = FALSE)"},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"x Input tensor. approximate bool, whether enable approximation.","code":""},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"Hendrycks et al., 2016","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Hard sigmoid activation function. — activation_hard_sigmoid","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"hard sigmoid activation defined : 0 x <= -3 1 x >= 3 (x/6) + 0.5 -3 < x < 3 faster, piecewise linear approximation sigmoid activation.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"","code":"activation_hard_sigmoid(x)"},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"Wikipedia \"Hard sigmoid\"","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"defined : 0 x < -3 x x > 3 x * (x + 3) / 6 -3 <= x <= 3 faster, piecewise linear approximation silu activation.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"","code":"activation_hard_silu(x)  activation_hard_swish(x)"},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"Howard, 2019","code":""},{"path":"https://keras.posit.co/reference/activation_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaky relu activation function. — activation_leaky_relu","title":"Leaky relu activation function. — activation_leaky_relu","text":"Leaky relu activation function.","code":""},{"path":"https://keras.posit.co/reference/activation_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leaky relu activation function. — activation_leaky_relu","text":"","code":"activation_leaky_relu(x, negative_slope = 0.2)"},{"path":"https://keras.posit.co/reference/activation_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leaky relu activation function. — activation_leaky_relu","text":"x Input tensor. negative_slope float controls slope values lower threshold.","code":""},{"path":"https://keras.posit.co/reference/activation_leaky_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Leaky relu activation function. — activation_leaky_relu","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_linear.html","id":null,"dir":"Reference","previous_headings":"","what":"Linear activation function (pass-through). — activation_linear","title":"Linear activation function (pass-through). — activation_linear","text":"\"linear\" activation identity function: returns input, unmodified.","code":""},{"path":"https://keras.posit.co/reference/activation_linear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linear activation function (pass-through). — activation_linear","text":"","code":"activation_linear(x)"},{"path":"https://keras.posit.co/reference/activation_linear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linear activation function (pass-through). — activation_linear","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_linear.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Linear activation function (pass-through). — activation_linear","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_log_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Log-Softmax activation function. — activation_log_softmax","title":"Log-Softmax activation function. — activation_log_softmax","text":"input vector handled independently. axis argument sets axis input function applied along.","code":""},{"path":"https://keras.posit.co/reference/activation_log_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log-Softmax activation function. — activation_log_softmax","text":"","code":"activation_log_softmax(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/activation_log_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log-Softmax activation function. — activation_log_softmax","text":"x Input tensor. axis Integer, axis along softmax applied.","code":""},{"path":"https://keras.posit.co/reference/activation_log_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Log-Softmax activation function. — activation_log_softmax","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_mish.html","id":null,"dir":"Reference","previous_headings":"","what":"Mish activation function. — activation_mish","title":"Mish activation function. — activation_mish","text":"defined : mish(x) = x * tanh(softplus(x)) softplus defined : softplus(x) = log(exp(x) + 1)","code":""},{"path":"https://keras.posit.co/reference/activation_mish.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mish activation function. — activation_mish","text":"","code":"activation_mish(x)"},{"path":"https://keras.posit.co/reference/activation_mish.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mish activation function. — activation_mish","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_mish.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mish activation function. — activation_mish","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_mish.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Mish activation function. — activation_mish","text":"Misra, 2019","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies the rectified linear unit activation function. — activation_relu","title":"Applies the rectified linear unit activation function. — activation_relu","text":"default values, returns standard ReLU activation: max(x, 0), element-wise maximum 0 input tensor. Modifying default parameters allows use non-zero thresholds, change max value activation, use non-zero multiple input values threshold.","code":""},{"path":"https://keras.posit.co/reference/activation_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies the rectified linear unit activation function. — activation_relu","text":"","code":"activation_relu(x, negative_slope = 0, max_value = NULL, threshold = 0)"},{"path":"https://keras.posit.co/reference/activation_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies the rectified linear unit activation function. — activation_relu","text":"x Input tensor. negative_slope numeric controls slope values lower threshold. max_value numeric sets saturation threshold (largest value function return). threshold numeric giving threshold value activation function values damped set zero.","code":""},{"path":"https://keras.posit.co/reference/activation_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies the rectified linear unit activation function. — activation_relu","text":"tensor shape dtype input x.","code":""},{"path":"https://keras.posit.co/reference/activation_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies the rectified linear unit activation function. — activation_relu","text":"","code":"x <- c(-10, -5, 0, 5, 10) activation_relu(x) ## tf.Tensor([ 0.  0.  0.  5. 10.], shape=(5), dtype=float32) activation_relu(x, negative_slope = 0.5) ## tf.Tensor([-5.  -2.5  0.   5.  10. ], shape=(5), dtype=float32) activation_relu(x, max_value = 5) ## tf.Tensor([0. 0. 0. 5. 5.], shape=(5), dtype=float32) activation_relu(x, threshold = 5) ## tf.Tensor([-0. -0.  0.  0. 10.], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/activation_relu6.html","id":null,"dir":"Reference","previous_headings":"","what":"Relu6 activation function. — activation_relu6","title":"Relu6 activation function. — activation_relu6","text":"ReLU function, truncated maximum value 6.","code":""},{"path":"https://keras.posit.co/reference/activation_relu6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relu6 activation function. — activation_relu6","text":"","code":"activation_relu6(x)"},{"path":"https://keras.posit.co/reference/activation_relu6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relu6 activation function. — activation_relu6","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_relu6.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Relu6 activation function. — activation_relu6","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"Scaled Exponential Linear Unit (SELU). — activation_selu","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"Scaled Exponential Linear Unit (SELU) activation function defined : scale * x x > 0 scale * alpha * (exp(x) - 1) x < 0 alpha scale pre-defined constants (alpha = 1.67326324 scale = 1.05070098). Basically, SELU activation function multiplies scale (> 1) output activation_elu function ensure slope larger one positive inputs. values alpha scale chosen mean variance inputs preserved two consecutive layers long weights initialized correctly (see initializer_lecun_normal()) number input units \"large enough\" (see reference paper information).","code":""},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"","code":"activation_selu(x)"},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"notes","dir":"Reference","previous_headings":"","what":"Notes","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"used together initializer_lecun_normal(). used together dropout variant layer_alpha_dropout() (legacy, depracated).","code":""},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"Klambauer et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid activation function. — activation_sigmoid","title":"Sigmoid activation function. — activation_sigmoid","text":"defined : sigmoid(x) = 1 / (1 + exp(-x)). small values (<-5), sigmoid returns value close zero, large values (>5) result function gets close 1. Sigmoid equivalent 2-element softmax, second element assumed zero. sigmoid function always returns value 0 1.","code":""},{"path":"https://keras.posit.co/reference/activation_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid activation function. — activation_sigmoid","text":"","code":"activation_sigmoid(x)"},{"path":"https://keras.posit.co/reference/activation_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid activation function. — activation_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sigmoid activation function. — activation_sigmoid","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Swish (or Silu) activation function. — activation_silu","title":"Swish (or Silu) activation function. — activation_silu","text":"defined : swish(x) = x * sigmoid(x). Swish (Silu) activation function smooth, non-monotonic function unbounded bounded .","code":""},{"path":"https://keras.posit.co/reference/activation_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Swish (or Silu) activation function. — activation_silu","text":"","code":"activation_silu(x)"},{"path":"https://keras.posit.co/reference/activation_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Swish (or Silu) activation function. — activation_silu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_silu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Swish (or Silu) activation function. — activation_silu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_silu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Swish (or Silu) activation function. — activation_silu","text":"Ramachandran et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax converts a vector of values to a probability distribution. — activation_softmax","title":"Softmax converts a vector of values to a probability distribution. — activation_softmax","text":"elements output vector range [0, 1] sum 1. input vector handled independently. axis argument sets axis input function applied along. Softmax often used activation last layer classification network result interpreted probability distribution. softmax vector x computed exp(x) / sum(exp(x)). input values log-odds resulting probability.","code":""},{"path":"https://keras.posit.co/reference/activation_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax converts a vector of values to a probability distribution. — activation_softmax","text":"","code":"activation_softmax(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/activation_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax converts a vector of values to a probability distribution. — activation_softmax","text":"x Input tensor. axis Integer, axis along softmax applied.","code":""},{"path":"https://keras.posit.co/reference/activation_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax converts a vector of values to a probability distribution. — activation_softmax","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_softplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Softplus activation function. — activation_softplus","title":"Softplus activation function. — activation_softplus","text":"defined : softplus(x) = log(exp(x) + 1).","code":""},{"path":"https://keras.posit.co/reference/activation_softplus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softplus activation function. — activation_softplus","text":"","code":"activation_softplus(x)"},{"path":"https://keras.posit.co/reference/activation_softplus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softplus activation function. — activation_softplus","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_softplus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softplus activation function. — activation_softplus","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_softsign.html","id":null,"dir":"Reference","previous_headings":"","what":"Softsign activation function. — activation_softsign","title":"Softsign activation function. — activation_softsign","text":"Softsign defined : softsign(x) = x / (abs(x) + 1).","code":""},{"path":"https://keras.posit.co/reference/activation_softsign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softsign activation function. — activation_softsign","text":"","code":"activation_softsign(x)"},{"path":"https://keras.posit.co/reference/activation_softsign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softsign activation function. — activation_softsign","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_softsign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softsign activation function. — activation_softsign","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_tanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hyperbolic tangent activation function. — activation_tanh","title":"Hyperbolic tangent activation function. — activation_tanh","text":"defined : tanh(x) = sinh(x) / cosh(x), .e. tanh(x) = ((exp(x) - exp(-x)) / (exp(x) + exp(-x))).","code":""},{"path":"https://keras.posit.co/reference/activation_tanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hyperbolic tangent activation function. — activation_tanh","text":"","code":"activation_tanh(x)"},{"path":"https://keras.posit.co/reference/activation_tanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hyperbolic tangent activation function. — activation_tanh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_tanh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hyperbolic tangent activation function. — activation_tanh","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/active_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an active property class method — active_property","title":"Create an active property class method — active_property","text":"Create active property class method","code":""},{"path":"https://keras.posit.co/reference/active_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an active property class method — active_property","text":"","code":"active_property(fn)"},{"path":"https://keras.posit.co/reference/active_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an active property class method — active_property","text":"fn R function","code":""},{"path":"https://keras.posit.co/reference/active_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an active property class method — active_property","text":"fn, additional R attribute cause fn converted active property converted method custom subclass.","code":""},{"path":"https://keras.posit.co/reference/active_property.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Create an active property class method — active_property","text":"","code":"layer_foo <- Model(\"Foo\", ...,   metrics = active_property(function() {     list(self$d_loss_metric,          self$g_loss_metric)   }))"},{"path":"https://keras.posit.co/reference/adapt.html","id":null,"dir":"Reference","previous_headings":"","what":"Fits the state of the preprocessing layer to the data being passed — adapt","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"Fits state preprocessing layer data passed","code":""},{"path":"https://keras.posit.co/reference/adapt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"","code":"adapt(object, data, ..., batch_size = NULL, steps = NULL)"},{"path":"https://keras.posit.co/reference/adapt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"object Preprocessing layer object data data train . can passed either tf.data.Dataset R array. ... Used forwards backwards compatibility. Passed underlying method. batch_size Integer NULL. Number asamples per state update. unspecified, batch_size default 32. specify batch_size data form TF Dataset generator (since generate batches). steps Integer NULL. Total number steps (batches samples) training input tensors TensorFlow data tensors, default NULL equal number samples dataset divided batch size, 1 determined. x tf.data.Dataset, steps NULL, epoch run input dataset exhausted. passing infinitely repeating dataset, must specify steps argument. argument supported array inputs.","code":""},{"path":"https://keras.posit.co/reference/adapt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"Returns object, invisibly.","code":""},{"path":"https://keras.posit.co/reference/adapt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"calling adapt layer, preprocessing layer's state update training. order make preprocessing layers efficient distribution context, kept constant respect compiled tf.Graphs call layer. affect layer use adapting layer , adapt layer multiple times need take care re-compile compiled functions follows: adding preprocessing layer keras model, need call compile(model) subsequent call adapt(). calling preprocessing layer inside tfdatasets::dataset_map(), call dataset_map() input Dataset adapt(). using tensorflow::tf_function() directly calls preprocessing layer, need call tf_function() callable subsequent call adapt(). keras_model() example multiple adapts:         tfdatasets example multiple adapts:","code":"layer <- layer_normalization(axis = NULL) adapt(layer, c(0, 2)) model <- keras_model_sequential() |> layer() predict(model, c(0, 1, 2), verbose = FALSE) # [1] -1  0  1 ## [1] -1  0  1 adapt(layer, c(-1, 1)) compile(model)  # This is needed to re-compile model.predict! predict(model, c(0, 1, 2), verbose = FALSE) # [1] 0 1 2 ## [1] 0 1 2 layer <- layer_normalization(axis = NULL) adapt(layer, c(0, 2)) input_ds <- tfdatasets::range_dataset(0, 3) normalized_ds <- input_ds |>   tfdatasets::dataset_map(layer) str(tfdatasets::iterate(normalized_ds)) ## List of 3 ##  $ :<tf.Tensor: shape=(1), dtype=float32, numpy=array([-1.], dtype=float32)> ##  $ :<tf.Tensor: shape=(1), dtype=float32, numpy=array([0.], dtype=float32)> ##  $ :<tf.Tensor: shape=(1), dtype=float32, numpy=array([1.], dtype=float32)> adapt(layer, c(-1, 1)) normalized_ds <- input_ds |>   tfdatasets::dataset_map(layer) # Re-map over the input dataset.  normalized_ds |>   tfdatasets::as_array_iterator() |>   tfdatasets::iterate(simplify = FALSE) |>   str() ## List of 3 ##  $ : num [1(1d)] 0 ##  $ : num [1(1d)] 1 ##  $ : num [1(1d)] 2"},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"Instantiates ConvNeXtBase architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"","code":"application_convnext_base(   model_name = \"convnext_base\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"Instantiates ConvNeXtLarge architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"","code":"application_convnext_large(   model_name = \"convnext_large\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"Instantiates ConvNeXtSmall architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"","code":"application_convnext_small(   model_name = \"convnext_small\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"Instantiates ConvNeXtTiny architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"","code":"application_convnext_tiny(   model_name = \"convnext_tiny\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"Instantiates ConvNeXtXLarge architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"","code":"application_convnext_xlarge(   model_name = \"convnext_xlarge\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Densenet121 architecture. — application_densenet121","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"Instantiates Densenet121 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"","code":"application_densenet121(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) ('channels_last' data format) (3, 224, 224) ('channels_first' data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"Densely Connected Convolutional Networks (CVPR 2017) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"Keras Application expects specific kind input preprocessing. DenseNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Densenet169 architecture. — application_densenet169","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"Instantiates Densenet169 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"","code":"application_densenet169(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) ('channels_last' data format) (3, 224, 224) ('channels_first' data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"Densely Connected Convolutional Networks (CVPR 2017) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"Keras Application expects specific kind input preprocessing. DenseNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Densenet201 architecture. — application_densenet201","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"Instantiates Densenet201 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"","code":"application_densenet201(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) ('channels_last' data format) (3, 224, 224) ('channels_first' data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"Densely Connected Convolutional Networks (CVPR 2017) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"Keras Application expects specific kind input preprocessing. DenseNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"Instantiates EfficientNetB0 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"","code":"application_efficientnet_b0(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"Instantiates EfficientNetB1 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"","code":"application_efficientnet_b1(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"Instantiates EfficientNetB2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"","code":"application_efficientnet_b2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"Instantiates EfficientNetB3 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"","code":"application_efficientnet_b3(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"Instantiates EfficientNetB4 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"","code":"application_efficientnet_b4(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"Instantiates EfficientNetB5 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"","code":"application_efficientnet_b5(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"Instantiates EfficientNetB6 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"","code":"application_efficientnet_b6(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"Instantiates EfficientNetB7 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"","code":"application_efficientnet_b7(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"Instantiates EfficientNetV2B0 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"","code":"application_efficientnet_v2b0(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"Instantiates EfficientNetV2B1 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"","code":"application_efficientnet_v2b1(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"Instantiates EfficientNetV2B2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"","code":"application_efficientnet_v2b2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"Instantiates EfficientNetV2B3 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"","code":"application_efficientnet_v2b3(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"Instantiates EfficientNetV2L architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"","code":"application_efficientnet_v2l(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"Instantiates EfficientNetV2M architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"","code":"application_efficientnet_v2m(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"Instantiates EfficientNetV2S architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"","code":"application_efficientnet_v2s(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"Instantiates Inception-ResNet v2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"","code":"application_inception_resnet_v2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (299, 299, 3) ('channels_last' data format) (3, 299, 299) ('channels_first' data format). exactly 3 inputs channels, width height smaller 75. E.g. (150, 150, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. 'avg' means global average pooling applied output last convolutional block, thus output model 2D tensor. 'max' means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"Inception-v4, Inception-ResNet Impact Residual Connections Learning (AAAI 2017) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"Keras Application expects specific kind input preprocessing. InceptionResNetV2, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Inception v3 architecture. — application_inception_v3","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"Instantiates Inception v3 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"","code":"application_inception_v3(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"include_top Boolean, whether include fully-connected layer top, last layer network. Defaults TRUE. weights One NULL (random initialization), imagenet (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_tensor useful sharing inputs multiple different networks. Defaults NULL. input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (299, 299, 3) (channels_last data format) (3, 299, 299) (channels_first data format). exactly 3 inputs channels, width height smaller 75. E.g. (150, 150, 3) one valid value. input_shape ignored input_tensor provided. pooling Optional pooling mode feature extraction include_top FALSE. NULL (default) means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"Rethinking Inception Architecture Computer Vision (CVPR 2016) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"Keras Application expects specific kind input preprocessing. InceptionV3, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the MobileNet architecture. — application_mobilenet","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"Instantiates MobileNet architecture.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"","code":"application_mobilenet(   input_shape = NULL,   alpha = 1,   depth_multiplier = 1L,   dropout = 0.001,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. Defaults NULL. input_shape ignored input_tensor provided. alpha Controls width network. known width multiplier MobileNet paper. alpha < 1.0, proportionally decreases number filters layer. alpha > 1.0, proportionally increases number filters layer. alpha == 1, default number filters paper used layer. Defaults 1.0. depth_multiplier Depth multiplier depthwise convolution. called resolution multiplier MobileNet paper. Defaults 1.0. dropout Dropout rate. Defaults 0.001. include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_tensor useful sharing inputs multiple different networks. Defaults NULL. pooling Optional pooling mode feature extraction include_top FALSE. NULL (default) means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"MobileNets: Efficient Convolutional Neural Networks Mobile Vision Applications function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"Keras Application expects specific kind input preprocessing. MobileNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"MobileNetV2 similar original MobileNet, except uses inverted residual blocks bottlenecking features. drastically lower parameter count original MobileNet. MobileNets support input size greater 32 x 32, larger image sizes offering better performance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"","code":"application_mobilenet_v2(   input_shape = NULL,   alpha = 1,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. Defaults NULL. input_shape ignored input_tensor provided. alpha Controls width network. known width multiplier MobileNet paper. alpha < 1.0, proportionally decreases number filters layer. alpha > 1.0, proportionally increases number filters layer. alpha == 1, default number filters paper used layer. Defaults 1.0. include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_tensor useful sharing inputs multiple different networks. Defaults NULL. pooling Optional pooling mode feature extraction include_top FALSE. NULL (default) means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"MobileNetV2: Inverted Residuals Linear Bottlenecks (CVPR 2018) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"Keras Application expects specific kind input preprocessing. MobileNetV2, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"Instantiates MobileNetV3Large architecture.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"","code":"application_mobilenet_v3_large(   input_shape = NULL,   alpha = 1,   minimalistic = FALSE,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   classes = 1000L,   pooling = NULL,   dropout_rate = 0.2,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"input_shape Optional shape tuple, specified like use model input image resolution (224, 224, 3). exactly 3 inputs channels. can also omit option like infer input_shape input_tensor. choose include input_tensor input_shape input_shape used match, shapes match throw error. E.g. (160, 160, 3) one valid value. alpha controls width network. known depth multiplier MobileNetV3 paper, name kept consistency MobileNetV1 Keras. alpha < 1.0, proportionally decreases number filters layer. alpha > 1.0, proportionally increases number filters layer. alpha == 1, default number filters paper used layer. minimalistic addition large small models module also contains -called minimalistic models, models per-layer dimensions characteristic MobilenetV3 however, utilize advanced blocks (squeeze--excite units, hard-swish, 5x5 convolutions). models less efficient CPU, much performant GPU/DSP. include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights String, one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. classes Integer, optional number classes classify images , specified include_top TRUE, weights argument specified. pooling String, optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. dropout_rate fraction input units drop last layer. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer (Rescaling) bottom network. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"Searching MobileNetV3 (ICCV 2019)","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"the-following-table-describes-the-performance-of-mobilenets-v-","dir":"Reference","previous_headings":"","what":"The following table describes the performance of MobileNets v3:","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"MACs stands Multiply Adds image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"Keras Application expects specific kind input preprocessing. MobileNetV3, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, MobileNetV3 models expect inputs float tensors pixels values [0-255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled MobileNetV3 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"inputs: floating point numpy.array backend-native tensor, 4D 3 color channels, values range [0, 255] include_preprocessing TRUE range [-1, 1] otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"Instantiates MobileNetV3Small architecture.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"","code":"application_mobilenet_v3_small(   input_shape = NULL,   alpha = 1,   minimalistic = FALSE,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   classes = 1000L,   pooling = NULL,   dropout_rate = 0.2,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"input_shape Optional shape tuple, specified like use model input image resolution (224, 224, 3). exactly 3 inputs channels. can also omit option like infer input_shape input_tensor. choose include input_tensor input_shape input_shape used match, shapes match throw error. E.g. (160, 160, 3) one valid value. alpha controls width network. known depth multiplier MobileNetV3 paper, name kept consistency MobileNetV1 Keras. alpha < 1.0, proportionally decreases number filters layer. alpha > 1.0, proportionally increases number filters layer. alpha == 1, default number filters paper used layer. minimalistic addition large small models module also contains -called minimalistic models, models per-layer dimensions characteristic MobilenetV3 however, utilize advanced blocks (squeeze--excite units, hard-swish, 5x5 convolutions). models less efficient CPU, much performant GPU/DSP. include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights String, one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. classes Integer, optional number classes classify images , specified include_top TRUE, weights argument specified. pooling String, optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. dropout_rate fraction input units drop last layer. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer (Rescaling) bottom network. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"Searching MobileNetV3 (ICCV 2019)","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"the-following-table-describes-the-performance-of-mobilenets-v-","dir":"Reference","previous_headings":"","what":"The following table describes the performance of MobileNets v3:","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"MACs stands Multiply Adds image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"Keras Application expects specific kind input preprocessing. MobileNetV3, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, MobileNetV3 models expect inputs float tensors pixels values [0-255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled MobileNetV3 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"inputs: floating point numpy.array backend-native tensor, 4D 3 color channels, values range [0, 255] include_preprocessing TRUE range [-1, 1] otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"Instantiates NASNet model ImageNet mode.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"","code":"application_nasnetlarge(   input_shape = NULL,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (331, 331, 3) NASNetLarge. exactly 3 inputs channels, width height smaller 32. E.g. (224, 224, 3) one valid value. include_top Whether include fully-connected layer top network. weights NULL (random initialization) imagenet (ImageNet weights).  loading imagenet weights, input_shape (331, 331, 3) input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"Learning Transferable Architectures Scalable Image Recognition (CVPR 2018) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"Keras Application expects specific kind input preprocessing. NASNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"Instantiates Mobile NASNet model ImageNet mode.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"","code":"application_nasnetmobile(   input_shape = NULL,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) NASNetMobile exactly 3 inputs channels, width height smaller 32. E.g. (224, 224, 3) one valid value. include_top Whether include fully-connected layer top network. weights NULL (random initialization) imagenet (ImageNet weights). loading imagenet weights, input_shape (224, 224, 3) input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"Learning Transferable Architectures Scalable Image Recognition (CVPR 2018) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"Keras Application expects specific kind input preprocessing. NASNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet101 architecture. — application_resnet101","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"Instantiates ResNet101 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"","code":"application_resnet101(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"Deep Residual Learning Image Recognition (CVPR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"Instantiates ResNet101V2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"","code":"application_resnet101_v2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"Identity Mappings Deep Residual Networks (CVPR 2016) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet152 architecture. — application_resnet152","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"Instantiates ResNet152 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"","code":"application_resnet152(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"Deep Residual Learning Image Recognition (CVPR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"Instantiates ResNet152V2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"","code":"application_resnet152_v2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"Identity Mappings Deep Residual Networks (CVPR 2016) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet50 architecture. — application_resnet50","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"Instantiates ResNet50 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"","code":"application_resnet50(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"Deep Residual Learning Image Recognition (CVPR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"Instantiates ResNet50V2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"","code":"application_resnet50_v2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"Identity Mappings Deep Residual Networks (CVPR 2016) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the VGG16 model. — application_vgg16","title":"Instantiates the VGG16 model. — application_vgg16","text":"Instantiates VGG16 model.","code":""},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the VGG16 model. — application_vgg16","text":"","code":"application_vgg16(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the VGG16 model. — application_vgg16","text":"include_top whether include 3 fully-connected layers top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (channels_last data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 input channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the VGG16 model. — application_vgg16","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the VGG16 model. — application_vgg16","text":"Deep Convolutional Networks Large-Scale Image Recognition (ICLR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. default input size model 224x224.","code":""},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the VGG16 model. — application_vgg16","text":"Keras Application expects specific kind input preprocessing. VGG16, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the VGG19 model. — application_vgg19","title":"Instantiates the VGG19 model. — application_vgg19","text":"Instantiates VGG19 model.","code":""},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the VGG19 model. — application_vgg19","text":"","code":"application_vgg19(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the VGG19 model. — application_vgg19","text":"include_top whether include 3 fully-connected layers top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (channels_last data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 input channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the VGG19 model. — application_vgg19","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the VGG19 model. — application_vgg19","text":"Deep Convolutional Networks Large-Scale Image Recognition (ICLR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. default input size model 224x224.","code":""},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the VGG19 model. — application_vgg19","text":"Keras Application expects specific kind input preprocessing. VGG19, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_xception.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Xception architecture. — application_xception","title":"Instantiates the Xception architecture. — application_xception","text":"Instantiates Xception architecture.","code":""},{"path":"https://keras.posit.co/reference/application_xception.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Xception architecture. — application_xception","text":"","code":"application_xception(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_xception.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Xception architecture. — application_xception","text":"include_top whether include 3 fully-connected layers top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (299, 299, 3). exactly 3 inputs channels, width height smaller 71. E.g. (150, 150, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_xception.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Xception architecture. — application_xception","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_xception.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Xception architecture. — application_xception","text":"Xception: Deep Learning Depthwise Separable Convolutions (CVPR 2017) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. default input image size model 299x299.","code":""},{"path":"https://keras.posit.co/reference/application_xception.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Xception architecture. — application_xception","text":"Keras Application expects specific kind input preprocessing. Xception, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/audio_dataset_from_directory.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","title":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","text":"directory structure :   calling audio_dataset_from_directory(main_directory, labels = 'inferred') return tf.data.Dataset yields batches audio files subdirectories class_a class_b, together labels 0 1 (0 corresponding class_a 1 corresponding class_b). .wav files supported time.","code":"main_directory/ ...class_a/ ......a_audio_1.wav ......a_audio_2.wav ...class_b/ ......b_audio_1.wav ......b_audio_2.wav"},{"path":"https://keras.posit.co/reference/audio_dataset_from_directory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","text":"","code":"audio_dataset_from_directory(   directory,   labels = \"inferred\",   label_mode = \"int\",   class_names = NULL,   batch_size = 32L,   sampling_rate = NULL,   output_sequence_length = NULL,   ragged = FALSE,   shuffle = TRUE,   seed = NULL,   validation_split = NULL,   subset = NULL,   follow_links = FALSE,   verbose = TRUE )"},{"path":"https://keras.posit.co/reference/audio_dataset_from_directory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","text":"directory Directory data located. labels \"inferred\", contain subdirectories, containing audio files class. Otherwise, directory structure ignored. labels Either \"inferred\" (labels generated directory structure), NULL (labels), list/tuple integer labels size number audio files found directory. Labels sorted according alphanumeric order audio file paths (obtained via os.walk(directory) Python). label_mode String describing encoding labels. Options : \"int\": means labels encoded integers (e.g. sparse_categorical_crossentropy loss). \"categorical\" means labels encoded categorical vector (e.g. categorical_crossentropy loss) \"binary\" means labels (can 2) encoded float32 scalars values 0 1 (e.g. binary_crossentropy). NULL (labels). class_names valid \"labels\" \"inferred\". explicit list class names (must match names subdirectories). Used control order classes (otherwise alphanumerical order used). batch_size Size batches data. Default: 32. NULL, data batched (dataset yield individual samples). sampling_rate Audio sampling rate (samples per second). output_sequence_length Maximum length audio sequence. Audio files longer truncated output_sequence_length. set NULL, sequences batch padded length longest sequence batch. ragged Whether return Ragged dataset (sequence length). Defaults FALSE. shuffle Whether shuffle data. Defaults TRUE. set FALSE, sorts data alphanumeric order. seed Optional random seed shuffling transformations. validation_split Optional float 0 1, fraction data reserve validation. subset Subset data return. One \"training\", \"validation\" \"\". used validation_split set. follow_links Whether visits subdirectories pointed symlinks. Defaults FALSE. verbose Whether display number information classes number files found. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/audio_dataset_from_directory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","text":"tf.data.Dataset object. label_mode NULL, yields string tensors shape (batch_size,), containing contents batch audio files. Otherwise, yields tuple (audio, labels), audio shape (batch_size, sequence_length, num_channels) labels follows format described . Rules regarding labels format: label_mode int, labels int32 tensor shape (batch_size,). label_mode binary, labels float32 tensor 1s 0s shape (batch_size, 1). label_mode categorical, labels float32 tensor shape (batch_size, num_classes), representing one-hot encoding class index.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/bidirectional.html","id":null,"dir":"Reference","previous_headings":"","what":"layer_bidirectional — bidirectional","title":"layer_bidirectional — bidirectional","text":"bidirectional() alias layer_bidirectional(). See ?layer_bidirectional() full documentation.","code":""},{"path":"https://keras.posit.co/reference/bidirectional.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"layer_bidirectional — bidirectional","text":"","code":"bidirectional(   object,   layer,   merge_mode = \"concat\",   weights = NULL,   backward_layer = NULL,   ... )"},{"path":"https://keras.posit.co/reference/bidirectional.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"layer_bidirectional — bidirectional","text":"object Object compose layer . tensor, array, sequential model. layer RNN instance, layer_lstm() layer_gru(). also Layer() instance meets following criteria: sequence-processing layer (accepts 3D+ inputs). go_backwards, return_sequences return_state attribute (semantics RNN class). input_spec attribute. Implement serialization via get_config() from_config(). Note recommended way create new RNN layers write custom RNN cell use layer_rnn(), instead subclassing Layer() directly. return_sequences TRUE, output masked timestep zero regardless layer's original zero_output_for_mask value. merge_mode Mode outputs forward backward RNNs combined. One {\"sum\", \"mul\", \"concat\", \"ave\", NULL}. NULL, outputs combined, returned list. Defaults \"concat\". weights see description backward_layer Optional RNN, Layer() instance used handle backwards input processing. backward_layer provided, layer instance passed layer argument used generate backward layer automatically. Note provided backward_layer layer properties matching layer argument, particular values stateful, return_states, return_sequences, etc. addition, backward_layer layer different go_backwards argument values. ValueError raised requirements met. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/bidirectional.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"layer_bidirectional — bidirectional","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback to back up and restore the training state. — callback_backup_and_restore","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"callback_backup_and_restore() callback intended recover training interruption happened middle fit execution, backing training states temporary checkpoint file, end epoch. backup overwrites previously written checkpoint file, given time one checkpoint file backup/restoring purpose. training restarts completion, training state (includes model weights epoch number) restored recently saved state beginning new fit run. completion fit run, temporary checkpoint file deleted. Note user responsible bring jobs back interruption. callback important backup restore mechanism fault tolerance purpose, model restored previous checkpoint expected one used back . user changes arguments passed compile fit, checkpoint saved fault tolerance can become invalid.","code":""},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"","code":"callback_backup_and_restore(   backup_dir,   save_freq = \"epoch\",   delete_checkpoint = TRUE )"},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"backup_dir String, path directory store data needed restore model. directory reused elsewhere store files, e.g. backup_and_restore callback another training run, another callback (e.g. callback_model_checkpoint) training run. save_freq \"epoch\", integer, FALSE. set \"epoch\", callback saves checkpoint end epoch. set integer, callback saves checkpoint every save_freq batches. Set save_freq = FALSE using preemption checkpointing (.e. save_before_preemption = TRUE). delete_checkpoint Boolean, defaults TRUE. backup_and_restore callback works saving checkpoint back training state. delete_checkpoint = TRUE, checkpoint deleted training finished. Use FALSE like keep checkpoint future usage.","code":""},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"","code":"callback_interrupting <- new_callback_class(   \"InterruptingCallback\",   on_epoch_begin = function(epoch, logs = NULL) {     if (epoch == 4) {       stop('Interrupting!')     }   } )  backup_dir <- tempfile() callback <- callback_backup_and_restore(backup_dir = backup_dir) model <- keras_model_sequential() %>%   layer_dense(10) model %>% compile(optimizer = optimizer_sgd(), loss = 'mse')  # ensure model is built (i.e., weights are initialized) for # callback_backup_and_restore() model(op_ones(c(5, 20))) |> invisible()  tryCatch({   model %>% fit(x = op_ones(c(5, 20)),                 y = op_zeros(5),                 epochs = 10, batch_size = 1,                 callbacks = list(callback, callback_interrupting()),                 verbose = 0) }, python.builtin.RuntimeError = function(e) message(\"Interrupted!\")) ## Interrupted! model$history$epoch ## [1] 0 1 2 # model$history %>% keras3:::to_keras_training_history() %>% as.data.frame() %>% print()  history <- model %>% fit(x = op_ones(c(5, 20)),                          y = op_zeros(5),                          epochs = 10, batch_size = 1,                          callbacks = list(callback),                          verbose = 0)  # Only 6 more epochs are run, since first training got interrupted at # zero-indexed epoch 4, second training will continue from 4 to 9. nrow(as.data.frame(history)) ## [1] 10"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback that streams epoch results to a CSV file. — callback_csv_logger","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"Supports values can represented string, including 1D iterables atomic vectors.","code":""},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"","code":"callback_csv_logger(filename, separator = \",\", append = FALSE)"},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"filename Filename CSV file, e.g. 'run/log.csv'. separator String used separate elements CSV file. append Boolean. TRUE: append file exists (useful continuing training). FALSE: overwrite existing file.","code":""},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"","code":"csv_logger <- callback_csv_logger('training.log') model %>% fit(X_train, Y_train, callbacks = list(csv_logger))"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":null,"dir":"Reference","previous_headings":"","what":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"Assuming goal training minimize loss. , metric monitored 'loss', mode 'min'. model$fit() training loop check end every epoch whether loss longer decreasing, considering min_delta patience applicable. found longer decreasing, model$stop_training marked TRUE training terminates. quantity monitored needs available logs list. make , pass loss metrics model$compile().","code":""},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"","code":"callback_early_stopping(   monitor = \"val_loss\",   min_delta = 0L,   patience = 0L,   verbose = 0L,   mode = \"auto\",   baseline = NULL,   restore_best_weights = FALSE,   start_from_epoch = 0L )"},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"monitor Quantity monitored. Defaults \"val_loss\". min_delta Minimum change monitored quantity qualify improvement, .e. absolute change less min_delta, count improvement. Defaults 0. patience Number epochs improvement training stopped. Defaults 0. verbose Verbosity mode, 0 1. Mode 0 silent, mode 1 displays messages callback takes action. Defaults 0. mode One {\"auto\", \"min\", \"max\"}. min mode, training stop quantity monitored stopped decreasing; \"max\" mode stop quantity monitored stopped increasing; \"auto\" mode, direction automatically inferred name monitored quantity. Defaults \"auto\". baseline Baseline value monitored quantity. NULL, training stop model show improvement baseline. Defaults NULL. restore_best_weights Whether restore model weights epoch best value monitored quantity. FALSE, model weights obtained last step training used. epoch restored regardless performance relative baseline. epoch improves baseline, training run patience epochs restore weights best epoch set. Defaults FALSE. start_from_epoch Number epochs wait starting monitor improvement. allows warm-period improvement expected thus training stopped. Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"","code":"callback <- callback_early_stopping(monitor = 'loss',                                    patience = 3) # This callback will stop the training when there is no improvement in # the loss for three consecutive epochs. model <- keras_model_sequential() %>%   layer_dense(10) model %>% compile(optimizer = optimizer_sgd(), loss = 'mse') history <- model %>% fit(x = op_ones(c(5, 20)),                          y = op_zeros(5),                          epochs = 10, batch_size = 1,                          callbacks = list(callback),                          verbose = 0) nrow(as.data.frame(history))  # Only 4 epochs are run. ## [1] 10"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"callback constructed anonymous functions called appropriate time (Model.{fit | evaluate | predict}). Note callbacks expects positional arguments, : on_epoch_begin on_epoch_end expect two positional arguments: epoch, logs on_train_begin on_train_end expect one positional argument: logs on_train_batch_begin on_train_batch_end expect two positional arguments: batch, logs See Callback class definition full list functions expected arguments.","code":""},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"","code":"callback_lambda(   on_epoch_begin = NULL,   on_epoch_end = NULL,   on_train_begin = NULL,   on_train_end = NULL,   on_train_batch_begin = NULL,   on_train_batch_end = NULL,   ... )"},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"on_epoch_begin called beginning every epoch. on_epoch_end called end every epoch. on_train_begin called beginning model training. on_train_end called end model training. on_train_batch_begin called beginning every train batch. on_train_batch_end called end every train batch. ... function Callback() want override passing function_name = function. example, callback_lambda(.., on_train_end = train_end_fn). custom function needs arguments ones defined Callback().","code":""},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"","code":"# Print the batch number at the beginning of every batch. batch_print_callback <- callback_lambda(   on_train_batch_begin = function(batch, logs) {     print(batch)   } )  # Stream the epoch loss to a file in new-line delimited JSON format # (one valid JSON object per line) json_log <- file('loss_log.json', open = 'wt') json_logging_callback <- callback_lambda(   on_epoch_end = function(epoch, logs) {     jsonlite::write_json(       list(epoch = epoch, loss = logs$loss),       json_log,       append = TRUE     )   },   on_train_end = function(logs) {     close(json_log)   } )  # Terminate some processes after having finished model training. processes <- ... cleanup_callback <- callback_lambda(   on_train_end = function(logs) {     for (p in processes) {       if (is_alive(p)) {         terminate(p)       }     }   } )  model %>% fit(   ...,   callbacks = list(     batch_print_callback,     json_logging_callback,     cleanup_callback   ) )"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":null,"dir":"Reference","previous_headings":"","what":"Learning rate scheduler. — callback_learning_rate_scheduler","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"beginning every epoch, callback gets updated learning rate value schedule function provided, current epoch current learning rate, applies updated learning rate optimizer.","code":""},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"","code":"callback_learning_rate_scheduler(schedule, verbose = 0L)"},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"schedule function takes epoch index (integer, indexed 0) current learning rate (float) inputs returns new learning rate output (float). verbose Integer. 0: quiet, 1: log update messages.","code":""},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"","code":"# This function keeps the initial learning rate steady for the first ten epochs # and decreases it exponentially after that. scheduler <- function(epoch, lr) {   if (epoch < 10)     return(lr)   else     return(lr * exp(-0.1)) }  model <- keras_model_sequential() |> layer_dense(units = 10) model |> compile(optimizer = optimizer_sgd(), loss = 'mse') model$optimizer$learning_rate |> as.array() |> round(5) ## [1] 0.01 callback <- callback_learning_rate_scheduler(schedule = scheduler) history <- model |> fit(x = array(runif(100), c(5, 20)),                         y = array(0, c(5, 1)),                         epochs = 15, callbacks = list(callback), verbose = 0) model$optimizer$learning_rate |> as.array() |> round(5) ## [1] 0.00607"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"callback_model_checkpoint() used conjunction training using model |> fit() save model weights (checkpoint file) interval, model weights can loaded later continue training state saved. options callback provides include: Whether keep model achieved \"best performance\" far, whether save model end every epoch regardless performance. Definition \"best\"; quantity monitor whether maximized minimized. frequency save . Currently, callback supports saving end every epoch, fixed number training batches. Whether weights saved, whole model saved.","code":""},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"","code":"callback_model_checkpoint(   filepath,   monitor = \"val_loss\",   verbose = 0L,   save_best_only = FALSE,   save_weights_only = FALSE,   mode = \"auto\",   save_freq = \"epoch\",   initial_value_threshold = NULL )"},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"filepath string, path save model file. filepath can contain named formatting options, filled value epoch keys logs (passed on_epoch_end). filepath name needs end \".weights.h5\" save_weights_only = TRUE end \".keras\" checkpoint saving whole model (default). example: filepath \"{epoch:02d}-{val_loss:.2f}.keras\", model checkpoints saved epoch number validation loss filename. directory filepath reused callbacks avoid conflicts. monitor metric name monitor. Typically metrics set model |> compile() method. Note: Prefix name \"val_\" monitor validation metrics. Use \"loss\" \"val_loss\" monitor model's total loss. specify metrics strings, like \"accuracy\", pass string (without \"val_\" prefix). pass Metric objects (created one metric_*()), monitor set metric$name. sure metric names can check contents history$metrics list returned history <- model |> fit() Multi-output models set additional prefixes metric names. verbose Verbosity mode, 0 1. Mode 0 silent, mode 1 displays messages callback takes action. save_best_only save_best_only = TRUE, saves model considered \"best\" latest best model according quantity monitored overwritten. filepath contain formatting options like {epoch} filepath overwritten new better model. save_weights_only TRUE, model's weights saved (model |> save_model_weights(filepath)), else full model saved (model |> save_model(filepath)). mode one {\"auto\", \"min\", \"max\"}. save_best_only = TRUE, decision overwrite current save file made based either maximization minimization monitored quantity. val_acc, \"max\", val_loss \"min\", etc. \"auto\" mode, mode set \"max\" quantities monitored \"acc\" start \"fmeasure\" set \"min\" rest quantities. save_freq \"epoch\" integer. using \"epoch\", callback saves model epoch. using integer, callback saves model end many batches. Model compiled steps_per_execution = N, saving criteria checked every Nth batch. Note saving aligned epochs, monitored metric may potentially less reliable (reflect little 1 batch, since metrics get reset every epoch). Defaults \"epoch\". initial_value_threshold Floating point initial \"best\" value metric monitored. applies save_best_value = TRUE. overwrites model weights already saved performance current model better value.","code":""},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"","code":"model <- keras_model_sequential(input_shape = c(10)) |>   layer_dense(1, activation = \"sigmoid\") |>   compile(loss = \"binary_crossentropy\", optimizer = \"adam\",           metrics = c('accuracy'))  EPOCHS <- 10 checkpoint_filepath <- tempfile('checkpoint-model-', fileext = \".keras\") model_checkpoint_callback <- callback_model_checkpoint(   filepath = checkpoint_filepath,   monitor = 'val_accuracy',   mode = 'max',   save_best_only = TRUE )  # Model is saved at the end of every epoch, if it's the best seen so far. model |> fit(x = random_uniform(c(2, 10)), y = op_ones(2, 1),              epochs = EPOCHS, validation_split = .5, verbose = 0,              callbacks = list(model_checkpoint_callback))  # The model (that are considered the best) can be loaded as - load_model(checkpoint_filepath) ## Model: \"sequential\" ## +---------------------------------+------------------------+---------------+ ## | Layer (type)                    | Output Shape           |       Param # | ## +=================================+========================+===============+ ## | dense (Dense)                   | (None, 1)              |            11 | ## +---------------------------------+------------------------+---------------+ ##  Total params: 35 (144.00 B) ##  Trainable params: 11 (44.00 B) ##  Non-trainable params: 0 (0.00 B) ##  Optimizer params: 24 (100.00 B) # Alternatively, one could checkpoint just the model weights as - checkpoint_filepath <- tempfile('checkpoint-', fileext = \".weights.h5\") model_checkpoint_callback <- callback_model_checkpoint(   filepath = checkpoint_filepath,   save_weights_only = TRUE,   monitor = 'val_accuracy',   mode = 'max',   save_best_only = TRUE )  # Model weights are saved at the end of every epoch, if it's the best seen # so far. # same as above model |> fit(x = random_uniform(c(2, 10)), y = op_ones(2, 1),              epochs = EPOCHS, validation_split = .5, verbose = 0,              callbacks = list(model_checkpoint_callback))  # The model weights (that are considered the best) can be loaded model |> load_model_weights(checkpoint_filepath)"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":null,"dir":"Reference","previous_headings":"","what":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"Models often benefit reducing learning rate factor 2-10 learning stagnates. callback monitors quantity improvement seen 'patience' number epochs, learning rate reduced.","code":""},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"","code":"callback_reduce_lr_on_plateau(   monitor = \"val_loss\",   factor = 0.1,   patience = 10L,   verbose = 0L,   mode = \"auto\",   min_delta = 1e-04,   cooldown = 0L,   min_lr = 0,   ... )"},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"monitor String. Quantity monitored. factor Float. Factor learning rate reduced. new_lr = lr * factor. patience Integer. Number epochs improvement learning rate reduced. verbose Integer. 0: quiet, 1: update messages. mode String. One {'auto', 'min', 'max'}. 'min' mode, learning rate reduced quantity monitored stopped decreasing; 'max' mode reduced quantity monitored stopped increasing; 'auto' mode, direction automatically inferred name monitored quantity. min_delta Float. Threshold measuring new optimum, focus significant changes. cooldown Integer. Number epochs wait resuming normal operation learning rate reduced. min_lr Float. Lower bound learning rate. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"","code":"reduce_lr <- callback_reduce_lr_on_plateau(monitor = 'val_loss', factor = 0.2,                                            patience = 5, min_lr = 0.001) model %>% fit(x_train, y_train, callbacks = list(reduce_lr))"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_remote_monitor.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback used to stream events to a server. — callback_remote_monitor","title":"Callback used to stream events to a server. — callback_remote_monitor","text":"Requires requests library. Events sent root + '/publish/epoch/end/' default. Calls HTTP POST, data argument JSON-encoded named list event data. send_as_json = TRUE, content type request \"application/json\". Otherwise serialized JSON sent within form.","code":""},{"path":"https://keras.posit.co/reference/callback_remote_monitor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback used to stream events to a server. — callback_remote_monitor","text":"","code":"callback_remote_monitor(   root = \"http://localhost:9000\",   path = \"/publish/epoch/end/\",   field = \"data\",   headers = NULL,   send_as_json = FALSE )"},{"path":"https://keras.posit.co/reference/callback_remote_monitor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback used to stream events to a server. — callback_remote_monitor","text":"root String; root url target server. path String; path relative root events sent. field String; JSON field data stored. field used payload sent within form (.e. send_as_json = FALSE). headers Named list; optional custom HTTP headers. send_as_json Boolean; whether request sent \"application/json\".","code":""},{"path":"https://keras.posit.co/reference/callback_remote_monitor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback used to stream events to a server. — callback_remote_monitor","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"callbacks replaces model's weight values values optimizer's EMA weights (exponential moving average past model weights values, implementing \"Polyak averaging\") model evaluation, restores previous weights evaluation. SwapEMAWeights callback used conjunction optimizer sets use_ema = TRUE. Note weights swapped -place order save memory. behavior undefined modify EMA weights model weights callbacks.","code":""},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"","code":"callback_swap_ema_weights(swap_on_epoch = FALSE)"},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"swap_on_epoch Whether perform swapping on_epoch_begin() on_epoch_end(). useful want use EMA weights callbacks callback_model_checkpoint(). Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"","code":"# Remember to set `use_ema=TRUE` in the optimizer optimizer <- optimizer_sgd(use_ema = TRUE) model |> compile(optimizer = optimizer, loss = ..., metrics = ...)  # Metrics will be computed with EMA weights model |> fit(X_train, Y_train,              callbacks = c(callback_swap_ema_weights()))  # If you want to save model checkpoint with EMA weights, you can set # `swap_on_epoch=TRUE` and place ModelCheckpoint after SwapEMAWeights. model |> fit(   X_train, Y_train,   callbacks = c(     callback_swap_ema_weights(swap_on_epoch = TRUE),     callback_model_checkpoint(...)   ) )"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":null,"dir":"Reference","previous_headings":"","what":"Enable visualizations for TensorBoard. — callback_tensorboard","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"TensorBoard visualization tool provided TensorFlow. TensorFlow installation required use callback. callback logs events TensorBoard, including: Metrics summary plots Training graph visualization Weight histograms Sampled profiling used model |> evaluate() regular validation addition epoch summaries, summary records evaluation metrics vs model$optimizer$iterations written. metric names prepended evaluation, model$optimizer$iterations step visualized TensorBoard. installed TensorFlow pip reticulate::py_install(), able launch TensorBoard command line:   R tensorflow::tensorboard(). can find information TensorBoard .","code":"tensorboard --logdir=path_to_your_logs"},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"","code":"callback_tensorboard(   log_dir = \"logs\",   histogram_freq = 0L,   write_graph = TRUE,   write_images = FALSE,   write_steps_per_second = FALSE,   update_freq = \"epoch\",   profile_batch = 0L,   embeddings_freq = 0L,   embeddings_metadata = NULL )"},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"log_dir path directory save log files parsed TensorBoard. e.g., log_dir = file.path(working_dir, 'logs'). directory reused callbacks. histogram_freq frequency (epochs) compute weight histograms layers model. set 0, histograms computed. Validation data (split) must specified histogram visualizations. write_graph (supported time) Whether visualize graph TensorBoard. Note log file can become quite large write_graph set TRUE. write_images whether write model weights visualize image TensorBoard. write_steps_per_second whether log training steps per second TensorBoard. supports epoch batch frequency logging. update_freq \"batch\" \"epoch\" integer. using \"epoch\", writes losses metrics TensorBoard every epoch. using integer, say 1000, metrics losses (including custom ones added Model.compile) logged TensorBoard every 1000 batches. \"batch\" synonym 1, meaning written every batch. Note however writing frequently TensorBoard can slow training, especially used distribution strategies incur additional synchronization overhead. Batch-level summary writing also available via train_step override. Please see TensorBoard Scalars tutorial  # noqa: E501 details. profile_batch (supported time) Profile batch(es) sample compute characteristics. profile_batch must non-negative integer tuple integers. pair positive integers signify range batches profile. default, profiling disabled. embeddings_freq frequency (epochs) embedding layers visualized. set 0, embeddings visualized. embeddings_metadata Named list maps embedding layer names filename file save metadata embedding layer. case metadata file used embedding layers, single filename can passed.","code":""},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"Custom batch-level summaries subclassed Model:   Custom batch-level summaries Functional API Model:   Profiling:","code":"tensorboard_callback <- callback_tensorboard(log_dir = \"./logs\") model %>% fit(x_train, y_train, epochs = 2, callbacks = list(tensorboard_callback)) # Then run the tensorboard command to view the visualizations. MyModel <- new_model_class(\"MyModel\",   initialize = function() {     self$dense <- layer_dense(units = 10)   },   call = function(x) {     outputs <- x |> self$dense()     tf$summary$histogram('outputs', outputs)     outputs   } )  model <- MyModel() model |> compile(optimizer = 'sgd', loss = 'mse')  # Make sure to set `update_freq = N` to log a batch-level summary every N # batches. In addition to any `tf$summary` contained in `model$call()`, # metrics added in `model |>compile` will be logged every N batches. tb_callback <- callback_tensorboard(log_dir = './logs', update_freq = 1) model |> fit(x_train, y_train, callbacks = list(tb_callback)) my_summary <- function(x) {   tf$summary$histogram('x', x)   x }  inputs <- layer_input(10) outputs <- inputs |>   layer_dense(10) |>   layer_lambda(my_summary)  model <- keras_model(inputs, outputs) model |> compile(optimizer = 'sgd', loss = 'mse')  # Make sure to set `update_freq = N` to log a batch-level summary every N # batches. In addition to any `tf.summary` contained in `Model.call`, # metrics added in `Model.compile` will be logged every N batches. tb_callback <- callback_tensorboard(log_dir = './logs', update_freq = 1) model |> fit(x_train, y_train, callbacks = list(tb_callback)) # Profile a single batch, e.g. the 5th batch. tensorboard_callback <- callback_tensorboard(   log_dir = './logs', profile_batch = 5) model |> fit(x_train, y_train, epochs = 2,              callbacks = list(tensorboard_callback))  # Profile a range of batches, e.g. from 10 to 20. tensorboard_callback <- callback_tensorboard(   log_dir = './logs', profile_batch = c(10, 20)) model |> fit(x_train, y_train, epochs = 2,              callbacks = list(tensorboard_callback))"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_terminate_on_nan.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback that terminates training when a NaN loss is encountered. — callback_terminate_on_nan","title":"Callback that terminates training when a NaN loss is encountered. — callback_terminate_on_nan","text":"Callback terminates training NaN loss encountered.","code":""},{"path":"https://keras.posit.co/reference/callback_terminate_on_nan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback that terminates training when a NaN loss is encountered. — callback_terminate_on_nan","text":"","code":"callback_terminate_on_nan()"},{"path":"https://keras.posit.co/reference/callback_terminate_on_nan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback that terminates training when a NaN loss is encountered. — callback_terminate_on_nan","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/clear_session.html","id":null,"dir":"Reference","previous_headings":"","what":"Resets all state generated by Keras. — clear_session","title":"Resets all state generated by Keras. — clear_session","text":"Keras manages global state, uses implement Functional model-building API uniquify autogenerated layer names. creating many models loop, global state consume increasing amount memory time, may want clear . Calling clear_session() releases global state: helps avoid clutter old models layers, especially memory limited. Example 1: calling clear_session() creating models loop   Example 2: resetting layer name generation counter","code":"for (i in 1:100) {   # Without `clear_session()`, each iteration of this loop will   # slightly increase the size of the global state managed by Keras   model <- keras_model_sequential()   for (j in 1:10) {     model <- model |> layer_dense(units = 10)   } }  for (i in 1:100) {   # With `clear_session()` called at the beginning,   # Keras starts with a blank state at each iteration   # and memory consumption is constant over time.   clear_session()   model <- keras_model_sequential()   for (j in 1:10) {     model <- model |> layer_dense(units = 10)   } } layers <- lapply(1:10, \\(i) layer_dense(units = 10))  new_layer <- layer_dense(units = 10) print(new_layer$name) ## [1] \"dense_10\" clear_session() new_layer <- layer_dense(units = 10) print(new_layer$name) ## [1] \"dense\""},{"path":"https://keras.posit.co/reference/clear_session.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resets all state generated by Keras. — clear_session","text":"","code":"clear_session(free_memory = TRUE)"},{"path":"https://keras.posit.co/reference/clear_session.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resets all state generated by Keras. — clear_session","text":"free_memory Whether call Python garbage collection. usually good practice call make sure memory used deleted objects immediately freed. However, may take seconds execute, using clear_session() short loop, may want skip .","code":""},{"path":"https://keras.posit.co/reference/clear_session.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resets all state generated by Keras. — clear_session","text":"NULL, invisibly, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/clone_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Clone a Functional or Sequential Model instance. — clone_model","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"Model cloning similar calling model new inputs, except creates new layers (thus new weights) instead sharing weights existing layers. Note clone_model() preserve uniqueness shared objects within model (e.g. single variable attached two distinct layers restored two separate variables).","code":""},{"path":"https://keras.posit.co/reference/clone_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"","code":"clone_model(   model,   input_tensors = NULL,   clone_function = NULL,   call_function = NULL,   recursive = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/clone_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"model Instance Model (Functional model Sequential model). input_tensors Optional list input tensors build model upon. provided, new keras_input() objects created. clone_function Callable signature function(layer) used clone layer target model (except Input instances). takes argument layer instance cloned, returns corresponding layer instance used model copy. unspecified, callable defaults following serialization/deserialization function: function(layer) layer$`__class__`$from_config(layer$get_config()). passing custom callable, can customize copy model, e.g. wrapping certain layers interest (might want replace LSTM instances equivalent Bidirectional(LSTM(...)) instances, example). Defaults NULL. call_function Callable signature function(layer, ...) used call cloned layer set inputs. takes layer instance, call arguments, returns call outputs. unspecified, callable defaults regular call() method: function(layer, ...) .call(layer, list(...)). passing custom callable, can insert new layers given layer. recursive Note, argument can used Functional models. Boolean. Whether recursively clone Sequential Functional models encountered original Sequential/Functional model. FALSE, inner models cloned calling clone_function(). TRUE, inner models cloned calling clone_model() clone_function, call_function, recursive arguments. Note case, call_function propagated Sequential model (since applicable Sequential models). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/clone_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"instance Model reproducing behavior original model, top new inputs tensors, using newly instantiated weights. cloned model may behave differently original model custom clone_function call_function modifies layer layer call.","code":""},{"path":"https://keras.posit.co/reference/clone_model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"Using clone_function make model deterministic setting random seed everywhere:   Using call_function add Dropout layer Dense layer (without recreating new layers):     Note subclassed models cloned default, since internal layer structure known. achieve equivalent functionality clone_model case subclassed model, simply make sure model class implements get_config() (optionally from_config()), call:   case subclassed model, using custom clone_function.","code":"# Create a test Sequential model. model <- keras_model_sequential(input_shape = c(728)) |>   layer_dense(32, activation = 'relu') |>   layer_dense(1, activation = 'sigmoid')  # Create a copy of the test model (with freshly initialized weights). new_model <- clone_model(model) clone_function <- function(layer) {   config <- layer$get_config()   if (\"seed\" %in% names(config))     config$seed <- 1337L   layer$`__class__`$from_config(config) }  new_model <- clone_model(model, clone_function = clone_function) call_function <- function(layer, ...) {   out <- layer(...)   if (inherits(layer, keras$layers$Dense))     out <- out |> layer_dropout(0.5)   out }  inputs <- keras_input(c(728)) outputs <- inputs |>   layer_dense(32, activation = 'relu') |>   layer_dense(1, activation = 'sigmoid') model <- keras_model(inputs, outputs)  new_model <- clone_model(   model,   clone_function = function(x) x, # Reuse the same layers.   call_function = call_function, ) new_model ## Model: \"functional_5\" ## +-----------------------------------+--------------------------+---------------+ ## | Layer (type)                      | Output Shape             |       Param # | ## +===================================+==========================+===============+ ## | keras_tensor_8 (InputLayer)       | (None, 728)              |             0 | ## +-----------------------------------+--------------------------+---------------+ ## | dense_2 (Dense)                   | (None, 32)               |        23,328 | ## +-----------------------------------+--------------------------+---------------+ ## | dropout (Dropout)                 | (None, 32)               |             0 | ## +-----------------------------------+--------------------------+---------------+ ## | dense_3 (Dense)                   | (None, 1)                |            33 | ## +-----------------------------------+--------------------------+---------------+ ## | dropout_1 (Dropout)               | (None, 1)                |             0 | ## +-----------------------------------+--------------------------+---------------+ ##  Total params: 23,361 (91.25 KB) ##  Trainable params: 23,361 (91.25 KB) ##  Non-trainable params: 0 (0.00 B) new_model <- model$`__class__`$from_config(model$get_config())"},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Configure a model for training. — compile.keras.src.models.model.Model","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"Configure model training.","code":""},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model compile(   object,   optimizer = \"rmsprop\",   loss = NULL,   metrics = NULL,   ...,   loss_weights = NULL,   weighted_metrics = NULL,   run_eagerly = FALSE,   steps_per_execution = 1L,   jit_compile = \"auto\",   auto_scale_loss = TRUE )"},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"object Keras model object optimizer String (name optimizer) optimizer instance. See optimizer_* family. loss Loss function. May : string (name builtin loss function), custom function, Loss instance (returned loss_* family functions). loss function callable signature loss = fn(y_true, y_pred), y_true ground truth values, y_pred model's predictions. y_true shape (batch_size, d1, .. dN) (except case sparse loss functions sparse categorical crossentropy expects integer arrays shape (batch_size, d1, .. dN-1)). y_pred shape (batch_size, d1, .. dN). loss function return float tensor. metrics List metrics evaluated model training testing. can : string (name built-function), function, optionally \"name\" attribute Metric() instance. See metric_* family functions. Typically use metrics = c('accuracy'). function callable signature result = fn(y_true, y_pred). specify different metrics different outputs multi-output model, also pass named list, metrics = list(= 'accuracy', b = c('accuracy', 'mse')). can also pass list specify metric list metrics output, metrics = list(c('accuracy'), c('accuracy', 'mse')) metrics = list('accuracy', c('accuracy', 'mse')). pass strings 'accuracy' 'acc', convert one metric_binary_accuracy(), metric_categorical_accuracy(), metric_sparse_categorical_accuracy() based shapes targets model output. similar conversion done strings \"crossentropy\" \"ce\" well. metrics passed evaluated without sample weighting; like sample weighting apply, can specify metrics via weighted_metrics argument instead. providing anonymous R function, can customize printed name training assigning attr(<fn>, \"name\") <- \"my_custom_metric_name\", calling custom_metric(\"my_custom_metric_name\", <fn>) ... Additional arguments passed compile() model method. loss_weights Optional list (named unnamed) specifying scalar coefficients (R numerics) weight loss contributions different model outputs. loss value minimized model weighted sum individual losses, weighted loss_weights coefficients.  unnamed list, expected 1:1 mapping model's outputs. named list, expected map output names (strings) scalar coefficients. weighted_metrics List metrics evaluated weighted sample_weight class_weight training testing. run_eagerly Bool. TRUE, model's forward pass never compiled. recommended leave FALSE training (best performance), set TRUE debugging. steps_per_execution Int. number batches run single compiled function call. Running multiple batches inside single compiled function call can greatly improve performance TPUs small models large R/Python overhead. , one full epoch run execution. number larger size epoch passed, execution truncated size epoch. Note steps_per_execution set N, Callback$on_batch_begin Callback$on_batch_end methods called every N batches (.e. /compiled function execution). supported PyTorch backend. jit_compile Bool \"auto\". Whether use XLA compilation compiling model. jax tensorflow backends, jit_compile=\"auto\" enables XLA compilation model supports , disabled otherwise. torch backend, \"auto\" default eager execution jit_compile=True run torch.compile \"inductor\" backend. auto_scale_loss Bool. TRUE model dtype policy \"mixed_float16\", passed optimizer automatically wrapped LossScaleOptimizer, dynamically scale loss prevent underflow.","code":""},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"called primarily side effect modifying object -place. first argument object also returned, invisibly, enable usage pipe.","code":""},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"","code":"model |> compile(   optimizer = optimizer_adam(learning_rate = 1e-3),   loss = loss_binary_crossentropy(),   metrics = c(metric_binary_accuracy(),               metric_false_negatives()) )"},{"path":[]},{"path":"https://keras.posit.co/reference/config_backend.html","id":null,"dir":"Reference","previous_headings":"","what":"Publicly accessible method for determining the current backend. — config_backend","title":"Publicly accessible method for determining the current backend. — config_backend","text":"Publicly accessible method determining current backend.","code":""},{"path":"https://keras.posit.co/reference/config_backend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Publicly accessible method for determining the current backend. — config_backend","text":"","code":"config_backend()"},{"path":"https://keras.posit.co/reference/config_backend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Publicly accessible method for determining the current backend. — config_backend","text":"String, name backend Keras currently using. One \"tensorflow\", \"torch\", \"jax\".","code":""},{"path":"https://keras.posit.co/reference/config_backend.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Publicly accessible method for determining the current backend. — config_backend","text":"","code":"config_backend() ## [1] \"tensorflow\""},{"path":[]},{"path":"https://keras.posit.co/reference/config_disable_interactive_logging.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn off interactive logging. — config_disable_interactive_logging","title":"Turn off interactive logging. — config_disable_interactive_logging","text":"interactive logging disabled, Keras sends logs absl.logging. best option using Keras non-interactive way, running training inference job server.","code":""},{"path":"https://keras.posit.co/reference/config_disable_interactive_logging.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn off interactive logging. — config_disable_interactive_logging","text":"","code":"config_disable_interactive_logging()"},{"path":"https://keras.posit.co/reference/config_disable_interactive_logging.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn off interactive logging. — config_disable_interactive_logging","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_disable_traceback_filtering.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn off traceback filtering. — config_disable_traceback_filtering","title":"Turn off traceback filtering. — config_disable_traceback_filtering","text":"Raw Keras tracebacks (also known stack traces) involve many internal frames, can challenging read , actionable end users. default, Keras filters internal frames exceptions raises, keep traceback short, readable, focused actionable (code). See also config_enable_traceback_filtering() config_is_traceback_filtering_enabled(). previously disabled traceback filtering via config_disable_traceback_filtering(), can re-enable via config_enable_traceback_filtering().","code":""},{"path":"https://keras.posit.co/reference/config_disable_traceback_filtering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn off traceback filtering. — config_disable_traceback_filtering","text":"","code":"config_disable_traceback_filtering()"},{"path":"https://keras.posit.co/reference/config_disable_traceback_filtering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn off traceback filtering. — config_disable_traceback_filtering","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_dtype_policy.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the current default dtype policy object. — config_dtype_policy","title":"Returns the current default dtype policy object. — config_dtype_policy","text":"Returns current default dtype policy object.","code":""},{"path":"https://keras.posit.co/reference/config_dtype_policy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the current default dtype policy object. — config_dtype_policy","text":"","code":"config_dtype_policy()"},{"path":"https://keras.posit.co/reference/config_dtype_policy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the current default dtype policy object. — config_dtype_policy","text":"DTypePolicy object.","code":""},{"path":"https://keras.posit.co/reference/config_enable_interactive_logging.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn on interactive logging. — config_enable_interactive_logging","title":"Turn on interactive logging. — config_enable_interactive_logging","text":"interactive logging enabled, Keras displays logs via stdout. provides best experience using Keras interactive environment shell notebook.","code":""},{"path":"https://keras.posit.co/reference/config_enable_interactive_logging.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn on interactive logging. — config_enable_interactive_logging","text":"","code":"config_enable_interactive_logging()"},{"path":"https://keras.posit.co/reference/config_enable_interactive_logging.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn on interactive logging. — config_enable_interactive_logging","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_enable_traceback_filtering.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn on traceback filtering. — config_enable_traceback_filtering","title":"Turn on traceback filtering. — config_enable_traceback_filtering","text":"Raw Keras tracebacks (also known stack traces) involve many internal frames, can challenging read , actionable end users. default, Keras filters internal frames exceptions raises, keep traceback short, readable, focused actionable (code). See also config_disable_traceback_filtering() config_is_traceback_filtering_enabled(). previously disabled traceback filtering via config_disable_traceback_filtering(), can re-enable via config_enable_traceback_filtering().","code":""},{"path":"https://keras.posit.co/reference/config_enable_traceback_filtering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn on traceback filtering. — config_enable_traceback_filtering","text":"","code":"config_enable_traceback_filtering()"},{"path":"https://keras.posit.co/reference/config_enable_traceback_filtering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn on traceback filtering. — config_enable_traceback_filtering","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_enable_unsafe_deserialization.html","id":null,"dir":"Reference","previous_headings":"","what":"Disables safe mode globally, allowing deserialization of lambdas. — config_enable_unsafe_deserialization","title":"Disables safe mode globally, allowing deserialization of lambdas. — config_enable_unsafe_deserialization","text":"Disables safe mode globally, allowing deserialization lambdas.","code":""},{"path":"https://keras.posit.co/reference/config_enable_unsafe_deserialization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Disables safe mode globally, allowing deserialization of lambdas. — config_enable_unsafe_deserialization","text":"","code":"config_enable_unsafe_deserialization()"},{"path":"https://keras.posit.co/reference/config_enable_unsafe_deserialization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Disables safe mode globally, allowing deserialization of lambdas. — config_enable_unsafe_deserialization","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_epsilon.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","title":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","text":"Return value fuzz factor used numeric expressions.","code":""},{"path":"https://keras.posit.co/reference/config_epsilon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","text":"","code":"config_epsilon()"},{"path":"https://keras.posit.co/reference/config_epsilon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","text":"float.","code":""},{"path":"https://keras.posit.co/reference/config_epsilon.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","text":"","code":"config_epsilon() ## [1] 1e-07"},{"path":[]},{"path":"https://keras.posit.co/reference/config_floatx.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the default float type, as a string. — config_floatx","title":"Return the default float type, as a string. — config_floatx","text":"E.g. 'bfloat16' 'float16', 'float32', 'float64'.","code":""},{"path":"https://keras.posit.co/reference/config_floatx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the default float type, as a string. — config_floatx","text":"","code":"config_floatx()"},{"path":"https://keras.posit.co/reference/config_floatx.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the default float type, as a string. — config_floatx","text":"String, current default float type.","code":""},{"path":"https://keras.posit.co/reference/config_floatx.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the default float type, as a string. — config_floatx","text":"","code":"keras3::config_floatx() ## [1] \"float32\""},{"path":[]},{"path":"https://keras.posit.co/reference/config_image_data_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the default image data format convention. — config_image_data_format","title":"Return the default image data format convention. — config_image_data_format","text":"Return default image data format convention.","code":""},{"path":"https://keras.posit.co/reference/config_image_data_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the default image data format convention. — config_image_data_format","text":"","code":"config_image_data_format()"},{"path":"https://keras.posit.co/reference/config_image_data_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the default image data format convention. — config_image_data_format","text":"string, either 'channels_first' 'channels_last'.","code":""},{"path":"https://keras.posit.co/reference/config_image_data_format.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the default image data format convention. — config_image_data_format","text":"","code":"config_image_data_format() ## [1] \"channels_last\""},{"path":[]},{"path":"https://keras.posit.co/reference/config_is_interactive_logging_enabled.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if interactive logging is enabled. — config_is_interactive_logging_enabled","title":"Check if interactive logging is enabled. — config_is_interactive_logging_enabled","text":"switch writing logs stdout absl.logging, may use config_enable_interactive_logging() config_disable_interactive_logging().","code":""},{"path":"https://keras.posit.co/reference/config_is_interactive_logging_enabled.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if interactive logging is enabled. — config_is_interactive_logging_enabled","text":"","code":"config_is_interactive_logging_enabled()"},{"path":"https://keras.posit.co/reference/config_is_interactive_logging_enabled.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if interactive logging is enabled. — config_is_interactive_logging_enabled","text":"Boolean, TRUE interactive logging enabled, FALSE otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_is_traceback_filtering_enabled.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if traceback filtering is enabled. — config_is_traceback_filtering_enabled","title":"Check if traceback filtering is enabled. — config_is_traceback_filtering_enabled","text":"Raw Keras tracebacks (also known stack traces) involve many internal frames, can challenging read , actionable end users. default, Keras filters internal frames exceptions raises, keep traceback short, readable, focused actionable (code). See also config_enable_traceback_filtering() config_disable_traceback_filtering(). previously disabled traceback filtering via config_disable_traceback_filtering(), can re-enable via config_enable_traceback_filtering().","code":""},{"path":"https://keras.posit.co/reference/config_is_traceback_filtering_enabled.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if traceback filtering is enabled. — config_is_traceback_filtering_enabled","text":"","code":"config_is_traceback_filtering_enabled()"},{"path":"https://keras.posit.co/reference/config_is_traceback_filtering_enabled.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if traceback filtering is enabled. — config_is_traceback_filtering_enabled","text":"Boolean, TRUE traceback filtering enabled, FALSE otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":null,"dir":"Reference","previous_headings":"","what":"Reload the backend (and the Keras package). — config_set_backend","title":"Reload the backend (and the Keras package). — config_set_backend","text":"Reload backend (Keras package).","code":""},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reload the backend (and the Keras package). — config_set_backend","text":"","code":"config_set_backend(backend)"},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reload the backend (and the Keras package). — config_set_backend","text":"backend String","code":""},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reload the backend (and the Keras package). — config_set_backend","text":"Nothing, function called side effect.","code":""},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reload the backend (and the Keras package). — config_set_backend","text":"","code":"config_set_backend(\"jax\")"},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"warning","dir":"Reference","previous_headings":"","what":"WARNING","title":"Reload the backend (and the Keras package). — config_set_backend","text":"Using function dangerous done carefully. Changing backend convert type already-instantiated objects. Thus, layers / tensors / etc. already created longer usable without errors. strongly recommended keep around Keras-originated objects instances created calling config_set_backend(). includes function class instance uses Keras functionality. code needs re-executed calling config_set_backend().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets the default dtype policy globally. — config_set_dtype_policy","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"Sets default dtype policy globally.","code":""},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"","code":"config_set_dtype_policy(policy)"},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"policy string DTypePolicy object.","code":""},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"return value, called side effects.","code":""},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"","code":"config_set_dtype_policy(\"mixed_float16\")"},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"Set value fuzz factor used numeric expressions.","code":""},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"","code":"config_set_epsilon(value)"},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"value float. New value epsilon.","code":""},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"return value, called side effects.","code":""},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"","code":"config_epsilon() ## [1] 1e-07 config_set_epsilon(1e-5) config_epsilon() ## [1] 1e-05 # Set it back to the default value. config_set_epsilon(1e-7)"},{"path":[]},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the default float dtype. — config_set_floatx","title":"Set the default float dtype. — config_set_floatx","text":"Set default float dtype.","code":""},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the default float dtype. — config_set_floatx","text":"","code":"config_set_floatx(value)"},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the default float dtype. — config_set_floatx","text":"value String; 'bfloat16', 'float16', 'float32', 'float64'.","code":""},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the default float dtype. — config_set_floatx","text":"return value, called side effects.","code":""},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Set the default float dtype. — config_set_floatx","text":"recommended set \"float16\" training, likely cause numeric stability issues. Instead, mixed precision, leverages mix float16 float32. can configured calling keras3::keras$mixed_precision$set_dtype_policy('mixed_float16').","code":""},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set the default float dtype. — config_set_floatx","text":"","code":"config_floatx() ## [1] \"float32\" config_set_floatx('float64') config_floatx() ## [1] \"float64\" # Set it back to float32 config_set_floatx('float32')"},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"Set the default float dtype. — config_set_floatx","text":"ValueError: case invalid value.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the value of the image data format convention. — config_set_image_data_format","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"Set value image data format convention.","code":""},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"","code":"config_set_image_data_format(data_format)"},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"data_format string. 'channels_first' 'channels_last'.","code":""},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"return value, called side effects.","code":""},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"","code":"config_image_data_format() ## [1] \"channels_last\" # 'channels_last' keras3::config_set_image_data_format('channels_first') config_image_data_format() ## [1] \"channels_first\" # Set it back to `'channels_last'` keras3::config_set_image_data_format('channels_last')"},{"path":[]},{"path":"https://keras.posit.co/reference/constraint_maxnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"MaxNorm weight constraint. — constraint_maxnorm","title":"MaxNorm weight constraint. — constraint_maxnorm","text":"Constrains weights incident hidden unit norm less equal desired value.","code":""},{"path":"https://keras.posit.co/reference/constraint_maxnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MaxNorm weight constraint. — constraint_maxnorm","text":"","code":"constraint_maxnorm(max_value = 2L, axis = 1L)"},{"path":"https://keras.posit.co/reference/constraint_maxnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MaxNorm weight constraint. — constraint_maxnorm","text":"max_value maximum norm value incoming weights. axis integer, axis along calculate weight norms. instance, Dense layer weight matrix shape (input_dim, output_dim), set axis 0 constrain weight vector length (input_dim,). Conv2D layer data_format = \"channels_last\", weight tensor shape (rows, cols, input_depth, output_depth), set axis [0, 1, 2] constrain weights filter tensor size (rows, cols, input_depth).","code":""},{"path":"https://keras.posit.co/reference/constraint_maxnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MaxNorm weight constraint. — constraint_maxnorm","text":"Constraint instance, callable can passed layer constructors used directly calling tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/constraint_minmaxnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"MinMaxNorm weight constraint. — constraint_minmaxnorm","title":"MinMaxNorm weight constraint. — constraint_minmaxnorm","text":"Constrains weights incident hidden unit norm lower bound upper bound.","code":""},{"path":"https://keras.posit.co/reference/constraint_minmaxnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MinMaxNorm weight constraint. — constraint_minmaxnorm","text":"","code":"constraint_minmaxnorm(min_value = 0, max_value = 1, rate = 1, axis = 1L)"},{"path":"https://keras.posit.co/reference/constraint_minmaxnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MinMaxNorm weight constraint. — constraint_minmaxnorm","text":"min_value minimum norm incoming weights. max_value maximum norm incoming weights. rate rate enforcing constraint: weights rescaled yield op_clip? (1 - rate) * norm + rate * op_clip(norm, min_value, max_value). Effectively, means rate = 1.0 stands strict enforcement constraint, rate<1.0 means weights rescaled step slowly move towards value inside desired interval. axis integer, axis along calculate weight norms. instance, Dense layer weight matrix shape (input_dim, output_dim), set axis 0 constrain weight vector length (input_dim,). Conv2D layer data_format = \"channels_last\", weight tensor shape (rows, cols, input_depth, output_depth), set axis [0, 1, 2] constrain weights filter tensor size (rows, cols, input_depth).","code":""},{"path":"https://keras.posit.co/reference/constraint_minmaxnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MinMaxNorm weight constraint. — constraint_minmaxnorm","text":"Constraint instance, callable can passed layer constructors used directly calling tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/constraint_nonneg.html","id":null,"dir":"Reference","previous_headings":"","what":"Constrains the weights to be non-negative. — constraint_nonneg","title":"Constrains the weights to be non-negative. — constraint_nonneg","text":"Constrains weights non-negative.","code":""},{"path":"https://keras.posit.co/reference/constraint_nonneg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constrains the weights to be non-negative. — constraint_nonneg","text":"","code":"constraint_nonneg()"},{"path":"https://keras.posit.co/reference/constraint_nonneg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constrains the weights to be non-negative. — constraint_nonneg","text":"Constraint instance, callable can passed layer constructors used directly calling tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/constraint_unitnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","title":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","text":"Constrains weights incident hidden unit unit norm.","code":""},{"path":"https://keras.posit.co/reference/constraint_unitnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","text":"","code":"constraint_unitnorm(axis = 1L)"},{"path":"https://keras.posit.co/reference/constraint_unitnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","text":"axis integer, axis along calculate weight norms. instance, Dense layer weight matrix shape (input_dim, output_dim), set axis 0 constrain weight vector length (input_dim,). Conv2D layer data_format = \"channels_last\", weight tensor shape (rows, cols, input_depth, output_depth), set axis [0, 1, 2] constrain weights filter tensor size (rows, cols, input_depth).","code":""},{"path":"https://keras.posit.co/reference/constraint_unitnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","text":"Constraint instance, callable can passed layer constructors used directly calling tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/count_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Count the total number of scalars composing the weights. — count_params","title":"Count the total number of scalars composing the weights. — count_params","text":"Count total number scalars composing weights.","code":""},{"path":"https://keras.posit.co/reference/count_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count the total number of scalars composing the weights. — count_params","text":"","code":"count_params(object)"},{"path":"https://keras.posit.co/reference/count_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count the total number of scalars composing the weights. — count_params","text":"object Layer model object","code":""},{"path":"https://keras.posit.co/reference/count_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count the total number of scalars composing the weights. — count_params","text":"integer count","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/custom_metric.html","id":null,"dir":"Reference","previous_headings":"","what":"Custom metric function — custom_metric","title":"Custom metric function — custom_metric","text":"Custom metric function","code":""},{"path":"https://keras.posit.co/reference/custom_metric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Custom metric function — custom_metric","text":"","code":"custom_metric(name, metric_fn)"},{"path":"https://keras.posit.co/reference/custom_metric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Custom metric function — custom_metric","text":"name name used show training progress output metric_fn R function signature function(y_true, y_pred) accepts tensors.","code":""},{"path":"https://keras.posit.co/reference/custom_metric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Custom metric function — custom_metric","text":"callable function __name__ attribute.","code":""},{"path":"https://keras.posit.co/reference/custom_metric.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Custom metric function — custom_metric","text":"can provide arbitrary R function custom metric. Note y_true y_pred parameters tensors, computations use op_* tensor functions. Use custom_metric() function define custom metric. Note name ('mean_pred') provided custom metric function: name used within training progress output. want save load model custom metrics, also call register_keras_serializable(), specify metric call load_model(). example: load_model(\"my_model.keras\", c('mean_pred' = metric_mean_pred)). Alternatively, can wrap code call with_custom_object_scope() allow refer metric name just like built keras metrics. Alternative ways supplying custom metrics: custom_metric(): Arbitrary R function. metric_mean_wrapper(): Wrap arbitrary R function Metric instance. Create custom Metric() subclass.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_boston_housing.html","id":null,"dir":"Reference","previous_headings":"","what":"Boston housing price regression dataset — dataset_boston_housing","title":"Boston housing price regression dataset — dataset_boston_housing","text":"Dataset taken StatLib library maintained Carnegie Mellon University.","code":""},{"path":"https://keras.posit.co/reference/dataset_boston_housing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Boston housing price regression dataset — dataset_boston_housing","text":"","code":"dataset_boston_housing(   path = \"boston_housing.npz\",   test_split = 0.2,   seed = 113L )"},{"path":"https://keras.posit.co/reference/dataset_boston_housing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Boston housing price regression dataset — dataset_boston_housing","text":"path Path cache dataset locally (relative ~/.keras/datasets). test_split fraction data reserve test set. seed Random seed shuffling data computing test split.","code":""},{"path":"https://keras.posit.co/reference/dataset_boston_housing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Boston housing price regression dataset — dataset_boston_housing","text":"Lists training test data: train$x, train$y, test$x, test$y. Samples contain 13 attributes houses different locations around Boston suburbs late 1970s. Targets median values houses location (k$).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_cifar10.html","id":null,"dir":"Reference","previous_headings":"","what":"CIFAR10 small image classification — dataset_cifar10","title":"CIFAR10 small image classification — dataset_cifar10","text":"Dataset 50,000 32x32 color training images, labeled 10 categories, 10,000 test images.","code":""},{"path":"https://keras.posit.co/reference/dataset_cifar10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIFAR10 small image classification — dataset_cifar10","text":"","code":"dataset_cifar10()"},{"path":"https://keras.posit.co/reference/dataset_cifar10.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIFAR10 small image classification — dataset_cifar10","text":"Lists training test data: train$x, train$y, test$x, test$y. x data array RGB image data shape (num_samples, 3, 32, 32). y data array category labels (integers range 0-9) shape (num_samples).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_cifar100.html","id":null,"dir":"Reference","previous_headings":"","what":"CIFAR100 small image classification — dataset_cifar100","title":"CIFAR100 small image classification — dataset_cifar100","text":"Dataset 50,000 32x32 color training images, labeled 100 categories, 10,000 test images.","code":""},{"path":"https://keras.posit.co/reference/dataset_cifar100.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIFAR100 small image classification — dataset_cifar100","text":"","code":"dataset_cifar100(label_mode = c(\"fine\", \"coarse\"))"},{"path":"https://keras.posit.co/reference/dataset_cifar100.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIFAR100 small image classification — dataset_cifar100","text":"label_mode one \"fine\", \"coarse\".","code":""},{"path":"https://keras.posit.co/reference/dataset_cifar100.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIFAR100 small image classification — dataset_cifar100","text":"Lists training test data: train$x, train$y, test$x, test$y. x data array RGB image data shape (num_samples, 3, 32, 32). y data array category labels shape (num_samples).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_fashion_mnist.html","id":null,"dir":"Reference","previous_headings":"","what":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","title":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","text":"Dataset 60,000 28x28 grayscale images 10 fashion article classes, along test set 10,000 images. dataset can used drop-replacement MNIST. class labels encoded integers 0-9 correspond T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt,","code":""},{"path":"https://keras.posit.co/reference/dataset_fashion_mnist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","text":"","code":"dataset_fashion_mnist()"},{"path":"https://keras.posit.co/reference/dataset_fashion_mnist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","text":"Lists training test data: train$x, train$y, test$x, test$y, x array grayscale image data shape (num_samples, 28, 28) y array article labels (integers range 0-9) shape (num_samples).","code":""},{"path":"https://keras.posit.co/reference/dataset_fashion_mnist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","text":"Dataset 60,000 28x28 grayscale images 10 fashion categories, along test set 10,000 images. dataset can used drop-replacement MNIST. class labels : 0 - T-shirt/top 1 - Trouser 2 - Pullover 3 - Dress 4 - Coat 5 - Sandal 6 - Shirt 7 - Sneaker 8 - Bag 9 - Ankle boot","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":null,"dir":"Reference","previous_headings":"","what":"IMDB Movie reviews sentiment classification — dataset_imdb","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"Dataset 25,000 movies reviews IMDB, labeled sentiment (positive/negative). Reviews preprocessed, review encoded sequence word indexes (integers). convenience, words indexed overall frequency dataset, instance integer \"3\" encodes 3rd frequent word data. allows quick filtering operations : \"consider top 10,000 common words, eliminate top 20 common words\".","code":""},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"","code":"dataset_imdb(   path = \"imdb.npz\",   num_words = NULL,   skip_top = 0L,   maxlen = NULL,   seed = 113L,   start_char = 1L,   oov_char = 2L,   index_from = 3L )  dataset_imdb_word_index(path = \"imdb_word_index.json\")"},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"path cache data (relative ~/.keras/dataset). num_words Max number words include. Words ranked often occur (training set) frequent words kept skip_top Skip top N frequently occuring words (may informative). maxlen sequences longer filtered . seed random seed sample shuffling. start_char start sequence marked character. Set 1 0 usually padding character. oov_char Words cut num_words skip_top limit replaced character. index_from Index actual words index higher.","code":""},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"Lists training test data: train$x, train$y, test$x, test$y. x data includes integer sequences. num_words argument specific, maximum possible index value num_words-1. maxlen argument specified, largest possible sequence length maxlen. y data includes set integer labels (0 1). dataset_imdb_word_index() function returns list names words values integer.","code":""},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"convention, \"0\" stand specific word, instead used encode unknown word.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_mnist.html","id":null,"dir":"Reference","previous_headings":"","what":"MNIST database of handwritten digits — dataset_mnist","title":"MNIST database of handwritten digits — dataset_mnist","text":"Dataset 60,000 28x28 grayscale images 10 digits, along test set 10,000 images.","code":""},{"path":"https://keras.posit.co/reference/dataset_mnist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MNIST database of handwritten digits — dataset_mnist","text":"","code":"dataset_mnist(path = \"mnist.npz\")"},{"path":"https://keras.posit.co/reference/dataset_mnist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MNIST database of handwritten digits — dataset_mnist","text":"path Path cache dataset locally (relative ~/.keras/datasets).","code":""},{"path":"https://keras.posit.co/reference/dataset_mnist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MNIST database of handwritten digits — dataset_mnist","text":"Lists training test data: train$x, train$y, test$x, test$y, x array grayscale image data shape (num_samples, 28, 28) y array digit labels (integers range 0-9) shape (num_samples).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_reuters.html","id":null,"dir":"Reference","previous_headings":"","what":"Reuters newswire topics classification — dataset_reuters","title":"Reuters newswire topics classification — dataset_reuters","text":"Dataset 11,228 newswires Reuters, labeled 46 topics. dataset_imdb() , wire encoded sequence word indexes (conventions).","code":""},{"path":"https://keras.posit.co/reference/dataset_reuters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reuters newswire topics classification — dataset_reuters","text":"","code":"dataset_reuters(   path = \"reuters.npz\",   num_words = NULL,   skip_top = 0L,   maxlen = NULL,   test_split = 0.2,   seed = 113L,   start_char = 1L,   oov_char = 2L,   index_from = 3L )  dataset_reuters_word_index(path = \"reuters_word_index.pkl\")"},{"path":"https://keras.posit.co/reference/dataset_reuters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reuters newswire topics classification — dataset_reuters","text":"path cache data (relative ~/.keras/dataset). num_words Max number words include. Words ranked often occur (training set) frequent words kept skip_top Skip top N frequently occuring words (may informative). maxlen Truncate sequences length. test_split Fraction dataset used test data. seed Random seed sample shuffling. start_char start sequence marked character. Set 1 0 usually padding character. oov_char words cut num_words skip_top limit replaced character. index_from index actual words index higher.","code":""},{"path":"https://keras.posit.co/reference/dataset_reuters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reuters newswire topics classification — dataset_reuters","text":"Lists training test data: train$x, train$y, test$x, test$y format dataset_imdb(). dataset_reuters_word_index() function returns list names words values integer. e.g. word_index[[\"giraffe\"]] might return 1234.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/deserialize_keras_object.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","title":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","text":"config dict Python dictionary consists set key-value pairs, represents Keras object, Optimizer, Layer, Metrics, etc. saving loading library uses following keys record information Keras object: class_name: String. name class, exactly defined source code, \"LossesContainer\". config: Named List. Library-defined user-defined key-value pairs store configuration object, obtained object$get_config(). module: String. path python module. Built-Keras classes expect prefix keras. registered_name: String. key class registered via register_keras_serializable(package, name) API. key format '{package}>{name}', package name arguments passed register_keras_serializable(). name provided, uses class name. registered_name successfully resolves class (registered), class_name config values config dict used. registered_name used non-built-classes. example, following config list represents built-Adam optimizer relevant config:     class exported Keras namespace, library tracks module class_name. example:   following config represents user-customized MeanSquaredError loss:","code":"config <- list(   class_name = \"Adam\",   config = list(     amsgrad = FALSE,     beta_1 = 0.8999999761581421,     beta_2 = 0.9990000128746033,     epsilon = 1e-07,     learning_rate = 0.0010000000474974513,     name = \"Adam\"   ),   module = \"keras.optimizers\",   registered_name = NULL ) # Returns an `Adam` instance identical to the original one. deserialize_keras_object(config) ## <keras.src.optimizers.adam.Adam object> config <- list(   class_name = \"MetricsList\",   config =  list(     ...   ),   module = \"keras.trainers.compile_utils\",   registered_name = \"MetricsList\" )  # Returns a `MetricsList` instance identical to the original one. deserialize_keras_object(config) # define a custom object loss_modified_mse <- Loss(   \"ModifiedMeanSquaredError\",   inherit = loss_mean_squared_error)  # register the custom object register_keras_serializable(loss_modified_mse)  # confirm object is registered get_custom_objects() ## $`keras3>ModifiedMeanSquaredError` ## <class '<r-namespace:keras3>.ModifiedMeanSquaredError'> ##  signature: (reduction='sum_over_batch_size', name='mean_squared_error') get_registered_name(loss_modified_mse) ## [1] \"keras3>ModifiedMeanSquaredError\" # now custom object instances can be serialized full_config <- serialize_keras_object(loss_modified_mse())  # the `config` arguments will be passed to loss_modified_mse() str(full_config) ## List of 4 ##  $ module         : chr \"<r-namespace:keras3>\" ##  $ class_name     : chr \"ModifiedMeanSquaredError\" ##  $ config         :List of 2 ##   ..$ name     : chr \"mean_squared_error\" ##   ..$ reduction: chr \"sum_over_batch_size\" ##  $ registered_name: chr \"keras3>ModifiedMeanSquaredError\" # and custom object instances can be deserialized deserialize_keras_object(full_config) ## <<r-namespace:keras3>.ModifiedMeanSquaredError object> ##  signature: (y_true, y_pred, sample_weight=None) # Returns the `ModifiedMeanSquaredError` object"},{"path":"https://keras.posit.co/reference/deserialize_keras_object.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","text":"","code":"deserialize_keras_object(config, custom_objects = NULL, safe_mode = TRUE, ...)"},{"path":"https://keras.posit.co/reference/deserialize_keras_object.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","text":"config Named list describing object. custom_objects Named list containing mapping custom object names corresponding classes functions. safe_mode Boolean, whether disallow unsafe lambda deserialization. safe_mode=FALSE, loading object potential trigger arbitrary code execution. argument applicable Keras v3 model format. Defaults TRUE. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/deserialize_keras_object.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","text":"object described config dictionary.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/evaluate.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","title":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","text":"functions returns loss value metrics values model test mode. Computation done batches (see batch_size arg.)","code":""},{"path":"https://keras.posit.co/reference/evaluate.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model evaluate(   object,   x = NULL,   y = NULL,   ...,   batch_size = NULL,   verbose = getOption(\"keras.verbose\", default = \"auto\"),   sample_weight = NULL,   steps = NULL,   callbacks = NULL )"},{"path":"https://keras.posit.co/reference/evaluate.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","text":"object Keras model object x Input data. : R array (array-like), list arrays (case model multiple inputs). tensor, list tensors (case model multiple inputs). named list mapping input names corresponding array/tensors, model named inputs. tf.data.Dataset. return tuple either (inputs, targets) (inputs, targets, sample_weights). generator returning (inputs, targets) (inputs, targets, sample_weights). y Target data. Like input data x, either R array(s) backend-native tensor(s). x tf.data.Dataset generator function, y specified (since targets obtained iterator/dataset). ... forward/backward compatability. batch_size Integer NULL. Number samples per batch computation. unspecified, batch_size default 32. specify batch_size data form tf dataset generator (since generate batches). verbose \"auto\", 0, 1, 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. \"auto\" becomes 1 cases, 2 knitr render running distributed training server. Note progress bar particularly useful logged file, verbose=2 recommended running interactively (e.g. production environment). Defaults \"auto\". sample_weight Optional array weights test samples, used weighting loss function. can either pass flat (1D) R array length input samples (1:1 mapping weights samples), case temporal data, can pass 2D array shape (samples, sequence_length), apply different weight every timestep every sample. argument supported x tfdataset, instead pass sample weights third element x. steps Integer NULL. Total number steps (batches samples) declaring evaluation round finished. Ignored default value NULL. x tf.data.Dataset steps NULL, evaluation run dataset exhausted. callbacks List Callback instances. List callbacks apply evaluation.","code":""},{"path":"https://keras.posit.co/reference/evaluate.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","text":"Scalar test loss (model single output metrics) list scalars (model multiple outputs /metrics). attribute model$metrics_names give display labels scalar outputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"(e.g. via TF-Serving). Note: can currently used TensorFlow JAX backends. method lets export model lightweight SavedModel artifact contains model's forward pass (call() method) can served via e.g. TF-Serving. forward pass registered name serve() (see example ). original code model (including custom layers may used) longer necessary reload artifact -- entirely standalone.","code":""},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model export_savedmodel(object, export_dir_base, ...)"},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"object keras model. export_dir_base string, file path save artifact. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"called primarily side effect exporting object. first argument, object also returned, invisibly, enable usage pipe.","code":""},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"","code":"# Create the artifact model |> tensorflow::export_savedmodel(\"path/to/location\")  # Later, in a different process / environment... library(tensorflow) reloaded_artifact <- tf$saved_model$load(\"path/to/location\") predictions <- reloaded_artifact$serve(input_data)  # see tfdeploy::serve_savedmodel() for serving a model over a local web api."},{"path":[]},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"Train model fixed number epochs (dataset iterations).","code":""},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model fit(   object,   x = NULL,   y = NULL,   ...,   batch_size = NULL,   epochs = 1L,   callbacks = NULL,   validation_split = 0,   validation_data = NULL,   shuffle = TRUE,   class_weight = NULL,   sample_weight = NULL,   initial_epoch = 1L,   steps_per_epoch = NULL,   validation_steps = NULL,   validation_batch_size = NULL,   validation_freq = 1L,   verbose = getOption(\"keras.verbose\", default = \"auto\"),   view_metrics = getOption(\"keras.view_metrics\", default = \"auto\") )"},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"object Keras model object x Input data. : array (array-like), list arrays (case model multiple inputs). tensor, list tensors (case model multiple inputs). named list mapping input names corresponding array/tensors, model named inputs. tf.data.Dataset. return tuple either (inputs, targets) (inputs, targets, sample_weights). generator returning (inputs, targets) (inputs, targets, sample_weights). y Target data. Like input data x, either array(s) backend-native tensor(s). x TF Dataset generator, y specified (since targets obtained x). ... Additional arguments passed model fit() method. batch_size Integer NULL. Number samples per gradient update. unspecified, batch_size default 32. specify batch_size data form TF Datasets generators, (since generate batches). epochs Integer. Number epochs train model. epoch iteration entire x y data provided (unless steps_per_epoch flag set something NULL). Note conjunction initial_epoch, epochs understood \"final epoch\". model trained number iterations given epochs, merely epoch index epochs reached. callbacks List Callback() instances. List callbacks apply training. See callback_*. validation_split Float 0 1. Fraction training data used validation data. model set apart fraction training data, train , evaluate loss model metrics data end epoch. validation data selected last samples x y data provided, shuffling. argument supported x TF Dataset generator. validation_data validation_split provided, validation_data override validation_split. validation_data Data evaluate loss model metrics end epoch. model trained data. Thus, note fact validation loss data provided using validation_split validation_data affected regularization layers like noise dropout. validation_data override validation_split. : tuple (x_val, y_val) arrays tensors. tuple (x_val, y_val, val_sample_weights) arrays. generator returning (inputs, targets) (inputs, targets, sample_weights). shuffle Boolean, whether shuffle training data epoch. argument ignored x generator TF Dataset. class_weight Optional named list mapping class indices (integers, 0-based) weight (float) value, used weighting loss function (training ). can useful tell model \"pay attention\" samples -represented class. class_weight specified targets rank 2 greater, either y must one-hot encoded, explicit final dimension 1 must included sparse class labels. sample_weight Optional array weights training samples, used weighting loss function (training ). can either pass flat (1D) array/vector length input samples (1:1 mapping weights samples), case temporal data, can pass 2D array (matrix) shape (samples, sequence_length), apply different weight every timestep every sample. argument supported x TF Dataset generator, instead provide sample_weights third element x. Note sample weighting apply metrics specified via metrics argument compile(). apply sample weighting metrics, can specify via weighted_metrics compile() instead. initial_epoch Integer. Epoch start training (useful resuming previous training run). steps_per_epoch Integer NULL. Total number steps (batches samples) declaring one epoch finished starting next epoch. training input tensors backend-native tensors, default NULL equal number samples dataset divided batch size, 1 determined. x TF Dataset, steps_per_epoch NULL, epoch run input dataset exhausted.  passing infinitely repeating dataset, must specify steps_per_epoch argument. steps_per_epoch = -1 training run indefinitely infinitely repeating dataset. validation_steps relevant validation_data provided. Total number steps (batches samples) draw stopping performing validation end every epoch. validation_steps NULL, validation run validation_data dataset exhausted. case infinitely repeated dataset, run infinite loop. validation_steps specified part dataset consumed, evaluation start beginning dataset epoch. ensures validation samples used every time. validation_batch_size Integer NULL. Number samples per validation batch. unspecified, default batch_size. specify validation_batch_size data form TF Datasets generator instances (since generate batches). validation_freq relevant validation data provided. Specifies many training epochs run new validation run performed, e.g. validation_freq=2 runs validation every 2 epochs. verbose \"auto\", 0, 1, 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. \"auto\" becomes 1 cases, 2 knitr render running distributed training server. Note progress bar particularly useful logged file, verbose=2 recommended running interactively (e.g., production environment). Defaults \"auto\". view_metrics View realtime plot training metrics (epoch). default (\"auto\") display plot running within RStudio, metrics specified model compile(), epochs > 1 verbose > 0. Set global options(keras.view_metrics = ) option establish different default.","code":""},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"keras_training_history object, named list: list(params = <params>, metrics = <metrics>\"), S3 methods print(), plot(), .data.frame(). metrics field record training loss values metrics values successive epochs, well validation loss values validation metrics values (applicable).","code":""},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"Unpacking behavior iterator-like inputs: common pattern pass iterator like object tf.data.Dataset generator fit(), fact yield features (x) optionally targets (y) sample weights (sample_weight). Keras requires output iterator-likes unambiguous. iterator return tuple() length 1, 2, 3, optional second third elements used y sample_weight respectively. type provided wrapped length-one tuple(), effectively treating everything x. yielding named lists, still adhere top-level tuple structure, e.g. tuple(list(x0 = x0, x = x1), y). Keras attempt separate features, targets, weights keys single dict.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Freeze and unfreeze weights — freeze_weights","title":"Freeze and unfreeze weights — freeze_weights","text":"Freeze weights model layer longer trainable.","code":""},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Freeze and unfreeze weights — freeze_weights","text":"","code":"freeze_weights(object, from = NULL, to = NULL, which = NULL)  unfreeze_weights(object, from = NULL, to = NULL, which = NULL)"},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Freeze and unfreeze weights — freeze_weights","text":"object Keras model layer object Layer instance, layer name, layer index within model Layer instance, layer name, layer index within model layer names, integer positions, layers, logical vector (length(object$layers)), function returning logical vector.","code":""},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Freeze and unfreeze weights — freeze_weights","text":"input object frozen weights returned, invisibly. Note, object modified place, return value provided make usage pipe convenient.","code":""},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Freeze and unfreeze weights — freeze_weights","text":"layer arguments inclusive. applied model, freeze unfreeze global operation layers model (.e. layers within specified range set opposite value, e.g. unfrozen call freeze). Models must compiled weights frozen unfrozen.","code":""},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Freeze and unfreeze weights — freeze_weights","text":"","code":"# instantiate a VGG16 model conv_base <- application_vgg16(   weights = \"imagenet\",   include_top = FALSE,   input_shape = c(150, 150, 3) )  # freeze it's weights freeze_weights(conv_base)  # Note the \"Trainable\" column conv_base ## Model: \"vgg16\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | input_layer (InputLayer)    | (None, 150, 150, 3)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv1 (Conv2D)       | (None, 150, 150, 64)  |      1,792 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv2 (Conv2D)       | (None, 150, 150, 64)  |     36,928 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_pool (MaxPooling2D)  | (None, 75, 75, 64)    |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv1 (Conv2D)       | (None, 75, 75, 128)   |     73,856 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv2 (Conv2D)       | (None, 75, 75, 128)   |    147,584 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_pool (MaxPooling2D)  | (None, 37, 37, 128)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv1 (Conv2D)       | (None, 37, 37, 256)   |    295,168 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv2 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv3 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_pool (MaxPooling2D)  | (None, 18, 18, 256)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv1 (Conv2D)       | (None, 18, 18, 512)   |  1,180,160 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv2 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv3 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_pool (MaxPooling2D)  | (None, 9, 9, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv1 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv2 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv3 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_pool (MaxPooling2D)  | (None, 4, 4, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 14,714,688 (56.13 MB) ##  Trainable params: 0 (0.00 B) ##  Non-trainable params: 14,714,688 (56.13 MB) # create a composite model that includes the base + more layers model <- keras_model_sequential(input_batch_shape = shape(conv_base$input)) |>   conv_base() |>   layer_flatten() |>   layer_dense(units = 256, activation = \"relu\") |>   layer_dense(units = 1, activation = \"sigmoid\")  # compile model |> compile(   loss = \"binary_crossentropy\",   optimizer = optimizer_rmsprop(learning_rate = 2e-5),   metrics = c(\"accuracy\") )  model ## Model: \"sequential\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | vgg16 (Functional)          | (None, 4, 4, 512)     | 14,714,688 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | flatten (Flatten)           | (None, 8192)          |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense (Dense)               | (None, 256)           |  2,097,408 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense_1 (Dense)             | (None, 1)             |        257 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 16,812,353 (64.13 MB) ##  Trainable params: 2,097,665 (8.00 MB) ##  Non-trainable params: 14,714,688 (56.13 MB) print(model, expand_nested = TRUE) ## Model: \"sequential\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | vgg16 (Functional)          | (None, 4, 4, 512)     | 14,714,688 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > input_layer            | (None, 150, 150, 3)   |          0 |   -   | ## | (InputLayer)                |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_conv1 (Conv2D)  | (None, 150, 150, 64)  |      1,792 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_conv2 (Conv2D)  | (None, 150, 150, 64)  |     36,928 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_pool            | (None, 75, 75, 64)    |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_conv1 (Conv2D)  | (None, 75, 75, 128)   |     73,856 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_conv2 (Conv2D)  | (None, 75, 75, 128)   |    147,584 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_pool            | (None, 37, 37, 128)   |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv1 (Conv2D)  | (None, 37, 37, 256)   |    295,168 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv2 (Conv2D)  | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv3 (Conv2D)  | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_pool            | (None, 18, 18, 256)   |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv1 (Conv2D)  | (None, 18, 18, 512)   |  1,180,160 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv2 (Conv2D)  | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv3 (Conv2D)  | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_pool            | (None, 9, 9, 512)     |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv1 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv2 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv3 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_pool            | (None, 4, 4, 512)     |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## | flatten (Flatten)           | (None, 8192)          |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense (Dense)               | (None, 256)           |  2,097,408 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense_1 (Dense)             | (None, 1)             |        257 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 16,812,353 (64.13 MB) ##  Trainable params: 2,097,665 (8.00 MB) ##  Non-trainable params: 14,714,688 (56.13 MB) # unfreeze weights from \"block5_conv1\" on unfreeze_weights(conv_base, from = \"block5_conv1\")  # compile again since we froze or unfroze weights model |> compile(   loss = \"binary_crossentropy\",   optimizer = optimizer_rmsprop(learning_rate = 2e-5),   metrics = c(\"accuracy\") )  conv_base ## Model: \"vgg16\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | input_layer (InputLayer)    | (None, 150, 150, 3)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv1 (Conv2D)       | (None, 150, 150, 64)  |      1,792 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv2 (Conv2D)       | (None, 150, 150, 64)  |     36,928 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_pool (MaxPooling2D)  | (None, 75, 75, 64)    |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv1 (Conv2D)       | (None, 75, 75, 128)   |     73,856 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv2 (Conv2D)       | (None, 75, 75, 128)   |    147,584 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_pool (MaxPooling2D)  | (None, 37, 37, 128)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv1 (Conv2D)       | (None, 37, 37, 256)   |    295,168 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv2 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv3 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_pool (MaxPooling2D)  | (None, 18, 18, 256)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv1 (Conv2D)       | (None, 18, 18, 512)   |  1,180,160 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv2 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv3 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_pool (MaxPooling2D)  | (None, 9, 9, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv1 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv2 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv3 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_pool (MaxPooling2D)  | (None, 4, 4, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 14,714,688 (56.13 MB) ##  Trainable params: 7,079,424 (27.01 MB) ##  Non-trainable params: 7,635,264 (29.13 MB) print(model, expand_nested = TRUE) ## Model: \"sequential\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | vgg16 (Functional)          | (None, 4, 4, 512)     | 14,714,688 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > input_layer            | (None, 150, 150, 3)   |          0 |   -   | ## | (InputLayer)                |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_conv1 (Conv2D)  | (None, 150, 150, 64)  |      1,792 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_conv2 (Conv2D)  | (None, 150, 150, 64)  |     36,928 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_pool            | (None, 75, 75, 64)    |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_conv1 (Conv2D)  | (None, 75, 75, 128)   |     73,856 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_conv2 (Conv2D)  | (None, 75, 75, 128)   |    147,584 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_pool            | (None, 37, 37, 128)   |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv1 (Conv2D)  | (None, 37, 37, 256)   |    295,168 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv2 (Conv2D)  | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv3 (Conv2D)  | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_pool            | (None, 18, 18, 256)   |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv1 (Conv2D)  | (None, 18, 18, 512)   |  1,180,160 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv2 (Conv2D)  | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv3 (Conv2D)  | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_pool            | (None, 9, 9, 512)     |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv1 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv2 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv3 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_pool            | (None, 4, 4, 512)     |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## | flatten (Flatten)           | (None, 8192)          |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense (Dense)               | (None, 256)           |  2,097,408 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense_1 (Dense)             | (None, 1)             |        257 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 16,812,353 (64.13 MB) ##  Trainable params: 9,177,089 (35.01 MB) ##  Non-trainable params: 7,635,264 (29.13 MB) # freeze only the last 5 layers freeze_weights(conv_base, from = -5) conv_base ## Model: \"vgg16\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | input_layer (InputLayer)    | (None, 150, 150, 3)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv1 (Conv2D)       | (None, 150, 150, 64)  |      1,792 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv2 (Conv2D)       | (None, 150, 150, 64)  |     36,928 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_pool (MaxPooling2D)  | (None, 75, 75, 64)    |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv1 (Conv2D)       | (None, 75, 75, 128)   |     73,856 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv2 (Conv2D)       | (None, 75, 75, 128)   |    147,584 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_pool (MaxPooling2D)  | (None, 37, 37, 128)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv1 (Conv2D)       | (None, 37, 37, 256)   |    295,168 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv2 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv3 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_pool (MaxPooling2D)  | (None, 18, 18, 256)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv1 (Conv2D)       | (None, 18, 18, 512)   |  1,180,160 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv2 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv3 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_pool (MaxPooling2D)  | (None, 9, 9, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv1 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv2 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv3 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_pool (MaxPooling2D)  | (None, 4, 4, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 14,714,688 (56.13 MB) ##  Trainable params: 7,635,264 (29.13 MB) ##  Non-trainable params: 7,079,424 (27.01 MB) # freeze only the last 5 layers, a different way unfreeze_weights(conv_base, to = -6) conv_base ## Model: \"vgg16\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | input_layer (InputLayer)    | (None, 150, 150, 3)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv1 (Conv2D)       | (None, 150, 150, 64)  |      1,792 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv2 (Conv2D)       | (None, 150, 150, 64)  |     36,928 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_pool (MaxPooling2D)  | (None, 75, 75, 64)    |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv1 (Conv2D)       | (None, 75, 75, 128)   |     73,856 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv2 (Conv2D)       | (None, 75, 75, 128)   |    147,584 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_pool (MaxPooling2D)  | (None, 37, 37, 128)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv1 (Conv2D)       | (None, 37, 37, 256)   |    295,168 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv2 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv3 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_pool (MaxPooling2D)  | (None, 18, 18, 256)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv1 (Conv2D)       | (None, 18, 18, 512)   |  1,180,160 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv2 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv3 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_pool (MaxPooling2D)  | (None, 9, 9, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv1 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv2 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv3 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_pool (MaxPooling2D)  | (None, 4, 4, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 14,714,688 (56.13 MB) ##  Trainable params: 7,635,264 (29.13 MB) ##  Non-trainable params: 7,079,424 (27.01 MB) # Freeze only layers of a certain type, e.g, BatchNorm layers batch_norm_layer_class_name <- class(layer_batch_normalization())[1] is_batch_norm_layer <- function(x) inherits(x, batch_norm_layer_class_name)  model <- application_efficientnet_b0() freeze_weights(model, which = is_batch_norm_layer) # print(model)  # equivalent to: for(layer in model$layers) {   if(is_batch_norm_layer(layer))     layer$trainable <- FALSE   else     layer$trainable <- TRUE }"},{"path":"https://keras.posit.co/reference/get_config.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer/Model configuration — get_config","title":"Layer/Model configuration — get_config","text":"layer config object returned get_config() contains configuration layer model. layer model can reinstantiated later (without trained weights) configuration using from_config(). config include connectivity information, class name (handled externally).","code":""},{"path":"https://keras.posit.co/reference/get_config.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer/Model configuration — get_config","text":"","code":"get_config(object)  from_config(config, custom_objects = NULL)"},{"path":"https://keras.posit.co/reference/get_config.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer/Model configuration — get_config","text":"object Layer model object config Object layer model configuration custom_objects list custom objects needed instantiate layer, e.g., custom layers defined new_layer_class() similar.","code":""},{"path":"https://keras.posit.co/reference/get_config.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer/Model configuration — get_config","text":"get_config() returns object configuration, from_config() returns re-instantiation object.","code":""},{"path":"https://keras.posit.co/reference/get_config.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layer/Model configuration — get_config","text":"Objects returned get_config() serializable via RDS. want save restore model across sessions, can use save_model_config() (model configuration , weights) save_model() save model configuration weights filesystem.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":null,"dir":"Reference","previous_headings":"","what":"Get/set the currently registered custom objects. — get_custom_objects","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"Custom objects set using custom_object_scope() added global list custom objects, appear returned list.","code":""},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"","code":"get_custom_objects()  set_custom_objects(objects = named_list(), clear = TRUE)"},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"objects named list custom objects, returned get_custom_objects() set_custom_objects(). clear bool, whether clear custom object registry populating objects.","code":""},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"R named list mapping registered names registered objects. set_custom_objects() returns registry values updating, invisibly.","code":""},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"register_keras_serializable() preferred set_custom_objects() registering new objects.","code":""},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"can use set_custom_objects() restore previous registry state.","code":"get_custom_objects() # within a function, if you want to temporarily modify the registry, function() {   orig_objects <- set_custom_objects(clear = TRUE)   on.exit(set_custom_objects(orig_objects))    ## temporarily modify the global registry   # register_keras_serializable(....)   # ....  <do work>   # on.exit(), the previous registry state is restored. }"},{"path":[]},{"path":"https://keras.posit.co/reference/get_file.html","id":null,"dir":"Reference","previous_headings":"","what":"Downloads a file from a URL if it not already in the cache. — get_file","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"default file url origin downloaded cache_dir ~/.keras, placed cache_subdir datasets, given filename fname. final location file example.txt therefore ~/.keras/datasets/example.txt. Files .tar, .tar.gz, .tar.bz, .zip formats can also extracted. Passing hash verify file download. command line programs shasum sha256sum can compute hash.","code":""},{"path":"https://keras.posit.co/reference/get_file.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"","code":"get_file(   fname = NULL,   origin = NULL,   ...,   file_hash = NULL,   cache_subdir = \"datasets\",   hash_algorithm = \"auto\",   extract = FALSE,   archive_format = \"auto\",   cache_dir = NULL,   force_download = FALSE )"},{"path":"https://keras.posit.co/reference/get_file.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"fname Name file. absolute path, e.g. \"/path//file.txt\" specified, file saved location. NULL, name file origin used. origin Original URL file. ... forward/backward compatability. file_hash expected hash string file download. sha256 md5 hash algorithms supported. cache_subdir Subdirectory Keras cache dir file saved. absolute path, e.g. \"/path//folder\" specified, file saved location. hash_algorithm Select hash algorithm verify file. options \"md5', \"sha256', \"auto'. default 'auto' detects hash algorithm use. extract TRUE tries extracting file Archive, like tar zip. archive_format Archive format try extracting file. Options \"auto', \"tar', \"zip', NULL. \"tar\" includes tar, tar.gz, tar.bz files. default \"auto\" corresponds c(\"tar\", \"zip\"). NULL empty list return matches found. cache_dir Location store cached files, NULL defaults Sys.getenv(\"KERAS_HOME\", \"~/.keras/\"). force_download TRUE, file always re-downloaded regardless cache state.","code":""},{"path":"https://keras.posit.co/reference/get_file.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"Path downloaded file. ** Warning malicious downloads ** Downloading something Internet carries risk. NEVER download file/archive trust source. recommend specify file_hash argument (hash source file known) make sure file getting one expect.","code":""},{"path":"https://keras.posit.co/reference/get_file.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"","code":"path_to_downloaded_file <- get_file(     origin = \"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\",     extract = TRUE )"},{"path":[]},{"path":"https://keras.posit.co/reference/get_layer.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieves a layer based on either its name (unique) or index. — get_layer","title":"Retrieves a layer based on either its name (unique) or index. — get_layer","text":"Indices based order horizontal graph traversal (bottom-) 1-based. name index provided, index take precedence.","code":""},{"path":"https://keras.posit.co/reference/get_layer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieves a layer based on either its name (unique) or index. — get_layer","text":"","code":"get_layer(object, name = NULL, index = NULL)"},{"path":"https://keras.posit.co/reference/get_layer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieves a layer based on either its name (unique) or index. — get_layer","text":"object Keras model object name String, name layer. index Integer, index layer (1-based). Also valid negative values, count end model.","code":""},{"path":"https://keras.posit.co/reference/get_layer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieves a layer based on either its name (unique) or index. — get_layer","text":"layer instance.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/get_registered_name.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the name registered to an object within the Keras framework. — get_registered_name","title":"Returns the name registered to an object within the Keras framework. — get_registered_name","text":"function part Keras serialization deserialization framework. maps objects string names associated objects serialization/deserialization.","code":""},{"path":"https://keras.posit.co/reference/get_registered_name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the name registered to an object within the Keras framework. — get_registered_name","text":"","code":"get_registered_name(obj)"},{"path":"https://keras.posit.co/reference/get_registered_name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the name registered to an object within the Keras framework. — get_registered_name","text":"obj object look .","code":""},{"path":"https://keras.posit.co/reference/get_registered_name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the name registered to an object within the Keras framework. — get_registered_name","text":"name associated object, default name object registered.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the class associated with name if it is registered with Keras. — get_registered_object","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"function part Keras serialization deserialization framework. maps strings objects associated serialization/deserialization.","code":""},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"","code":"get_registered_object(name, custom_objects = NULL, module_objects = NULL)"},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"name name look . custom_objects named list custom objects look name . Generally, custom_objects provided user. module_objects named list custom objects look name . Generally, module_objects provided midlevel library implementers.","code":""},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"instantiable class associated name, NULL class exists.","code":""},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"","code":"from_config <- function(cls, config, custom_objects = NULL) {   if ('my_custom_object_name' \\%in\\% names(config)) {     config$hidden_cls <- get_registered_object(       config$my_custom_object_name,       custom_objects = custom_objects)   } }"},{"path":[]},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"Output always list tensors (potentially 1 element).","code":""},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"","code":"get_source_inputs(tensor)"},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"tensor tensor start .","code":""},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"List input tensors.","code":""},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"","code":"input <- keras_input(c(3)) output <- input |> layer_dense(4) |> op_multiply(5) reticulate::py_id(get_source_inputs(output)[[1]]) == reticulate::py_id(input) ## [1] TRUE"},{"path":[]},{"path":"https://keras.posit.co/reference/get_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer/Model weights as R arrays — get_weights","title":"Layer/Model weights as R arrays — get_weights","text":"Layer/Model weights R arrays","code":""},{"path":"https://keras.posit.co/reference/get_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer/Model weights as R arrays — get_weights","text":"","code":"get_weights(object, trainable = NA)  set_weights(object, weights)"},{"path":"https://keras.posit.co/reference/get_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer/Model weights as R arrays — get_weights","text":"object Layer model object trainable NA (default), weights returned. TRUE, weights trainable variables returned. FALSE, weights non-trainable variables returned. weights Weights R array","code":""},{"path":"https://keras.posit.co/reference/get_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer/Model weights as R arrays — get_weights","text":"list R arrays.","code":""},{"path":"https://keras.posit.co/reference/get_weights.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layer/Model weights as R arrays — get_weights","text":"can access Layer/Model KerasVariables (also backend-native tensors like tf.Variable) object$weights, object$trainable_weights, object$non_trainable_weights","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":null,"dir":"Reference","previous_headings":"","what":"Make a python class constructor — %py_class%","title":"Make a python class constructor — %py_class%","text":"Make python class constructor","code":""},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make a python class constructor — %py_class%","text":"","code":"spec %py_class% body"},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make a python class constructor — %py_class%","text":"spec bare symbol MyClassName, call MyClassName(SuperClass) body expression can evaluated construct class methods.","code":""},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make a python class constructor — %py_class%","text":"python class constructor, invisibly. Note, constructor also assigned parent frame.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make a python class constructor — %py_class%","text":"","code":"if (FALSE) { MyClass %py_class% {   initialize <- function(x) {     print(\"Hi from MyClass$initialize()!\")     self$x <- x   }   my_method <- function() {     self$x   } }  my_class_instance <- MyClass(42) my_class_instance$my_method()  MyClass2(MyClass) %py_class% {   \"This will be a __doc__ string for MyClass2\"    initialize <- function(...) {     \"This will be the __doc__ string for the MyClass2.__init__() method\"     print(\"Hi from MyClass2$initialize()!\")     super$initialize(...)   } }  my_class_instance2 <- MyClass2(42) my_class_instance2$my_method()  reticulate::py_help(MyClass2) # see the __doc__ strings and more!  # In addition to `self`, there is also `private` available. # This is an R environment unique to each class instance, where you can # store objects that you don't want converted to Python, but still want # available from methods. You can also assign methods to private, and # `self` and `private` will be available in private methods.  MyClass %py_class% {    initialize <- function(x) {     print(\"Hi from MyClass$initialize()!\")     private$y <- paste(\"A Private field:\", x)   }    get_private_field <- function() {     private$y   }    private$a_private_method <- function() {     cat(\"a_private_method() was called.\\n\")     cat(\"private$y is \", sQuote(private$y), \"\\n\")   }    call_private_method <- function()     private$a_private_method()    # equivalent of @property decorator in python   an_active_property %<-active% function(x = NULL) {     if(!is.null(x)) {       cat(\"`an_active_property` was assigned\", x, \"\\n\")       return(x)     } else {       cat(\"`an_active_property` was accessed\\n\")       return(42)     }   } }  inst1 <- MyClass(1) inst2 <- MyClass(2) inst1$get_private_field() inst2$get_private_field() inst1$call_private_method() inst2$call_private_method() inst1$an_active_property inst1$an_active_property <- 11 }"},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":null,"dir":"Reference","previous_headings":"","what":"Make an Active Binding — %<-active%","title":"Make an Active Binding — %<-active%","text":"Make Active Binding","code":""},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make an Active Binding — %<-active%","text":"","code":"sym %<-active% value"},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make an Active Binding — %<-active%","text":"sym symbol bind value function call value sym accessed.","code":""},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make an Active Binding — %<-active%","text":"value, invisibly","code":""},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make an Active Binding — %<-active%","text":"Active bindings defined %py_class% converted @property decorated methods.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make an Active Binding — %<-active%","text":"","code":"set.seed(1234) x %<-active% function(value) {   message(\"Evaluating function of active binding\")   if(missing(value))     runif(1)   else    message(\"Received: \", value) } x #> Evaluating function of active binding #> [1] 0.1137034 x #> Evaluating function of active binding #> [1] 0.6222994 x <- \"foo\" #> Evaluating function of active binding #> Received: foo x <- \"foo\" #> Evaluating function of active binding #> Received: foo x #> Evaluating function of active binding #> [1] 0.6092747 rm(x) # cleanup"},{"path":"https://keras.posit.co/reference/image_array_save.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves an image stored as an array to a path or file object. — image_array_save","title":"Saves an image stored as an array to a path or file object. — image_array_save","text":"Saves image stored array path file object.","code":""},{"path":"https://keras.posit.co/reference/image_array_save.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves an image stored as an array to a path or file object. — image_array_save","text":"","code":"image_array_save(   x,   path,   data_format = NULL,   file_format = NULL,   scale = TRUE,   ... )"},{"path":"https://keras.posit.co/reference/image_array_save.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves an image stored as an array to a path or file object. — image_array_save","text":"x array. path Path file object. data_format Image data format, either \"channels_first\" \"channels_last\". file_format Optional file format override. omitted, format use determined filename extension. file object used instead filename, parameter always used. scale Whether rescale image values within [0, 255]. ... Additional keyword arguments passed PIL.Image.save().","code":""},{"path":"https://keras.posit.co/reference/image_array_save.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Saves an image stored as an array to a path or file object. — image_array_save","text":"Called primarily side effects. input x returned, invisibly, enable usage pipe.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/image_dataset_from_directory.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","title":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","text":"directory structure :   calling image_dataset_from_directory(main_directory, labels = 'inferred') return tf.data.Dataset yields batches images subdirectories class_a class_b, together labels 0 1 (0 corresponding class_a 1 corresponding class_b). Supported image formats: .jpeg, .jpg, .png, .bmp, .gif. Animated gifs truncated first frame.","code":"main_directory/ ...class_a/ ......a_image_1.jpg ......a_image_2.jpg ...class_b/ ......b_image_1.jpg ......b_image_2.jpg"},{"path":"https://keras.posit.co/reference/image_dataset_from_directory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","text":"","code":"image_dataset_from_directory(   directory,   labels = \"inferred\",   label_mode = \"int\",   class_names = NULL,   color_mode = \"rgb\",   batch_size = 32L,   image_size = c(256L, 256L),   shuffle = TRUE,   seed = NULL,   validation_split = NULL,   subset = NULL,   interpolation = \"bilinear\",   follow_links = FALSE,   crop_to_aspect_ratio = FALSE,   pad_to_aspect_ratio = FALSE,   data_format = NULL,   verbose = TRUE )"},{"path":"https://keras.posit.co/reference/image_dataset_from_directory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","text":"directory Directory data located. labels \"inferred\", contain subdirectories, containing images class. Otherwise, directory structure ignored. labels Either \"inferred\" (labels generated directory structure), NULL (labels), list/tuple integer labels size number image files found directory. Labels sorted according alphanumeric order image file paths (obtained via os.walk(directory) Python). label_mode String describing encoding labels. Options : \"int\": means labels encoded integers (e.g. sparse_categorical_crossentropy loss). \"categorical\" means labels encoded categorical vector (e.g. categorical_crossentropy loss). \"binary\" means labels (can 2) encoded float32 scalars values 0 1 (e.g. binary_crossentropy). NULL (labels). class_names valid labels \"inferred\". explicit list class names (must match names subdirectories). Used control order classes (otherwise alphanumerical order used). color_mode One \"grayscale\", \"rgb\", \"rgba\". Defaults \"rgb\". Whether images converted 1, 3, 4 channels. batch_size Size batches data. Defaults 32. NULL, data batched (dataset yield individual samples). image_size Size resize images read disk, specified (height, width). Defaults (256, 256). Since pipeline processes batches images must size, must provided. shuffle Whether shuffle data. Defaults TRUE. set FALSE, sorts data alphanumeric order. seed Optional random seed shuffling transformations. validation_split Optional float 0 1, fraction data reserve validation. subset Subset data return. One \"training\", \"validation\", \"\". used validation_split set. subset = \"\", utility returns tuple two datasets (training validation datasets respectively). interpolation String, interpolation method used resizing images. Defaults \"bilinear\". Supports \"bilinear\", \"nearest\", \"bicubic\", \"area\", \"lanczos3\", \"lanczos5\", \"gaussian\", \"mitchellcubic\". follow_links Whether visit subdirectories pointed symlinks. Defaults FALSE. crop_to_aspect_ratio TRUE, resize images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image cropped return largest possible window image (size image_size) matches target aspect ratio. default (crop_to_aspect_ratio = FALSE), aspect ratio may preserved. pad_to_aspect_ratio TRUE, resize images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image padded return largest possible window image (size image_size) matches target aspect ratio. default (pad_to_aspect_ratio=FALSE), aspect ratio may preserved. data_format NULL uses config_image_data_format() otherwise either 'channel_last' 'channel_first'. verbose Whether display number information classes number files found. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/image_dataset_from_directory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","text":"tf.data.Dataset object. label_mode NULL, yields float32 tensors shape (batch_size, image_size[1], image_size[2], num_channels), encoding images (see rules regarding num_channels). Otherwise, yields tuple (images, labels), images shape (batch_size, image_size[1], image_size[2], num_channels), labels follows format described . Rules regarding labels format: label_mode \"int\", labels int32 tensor shape (batch_size,). label_mode \"binary\", labels float32 tensor 1s 0s shape (batch_size, 1). label_mode \"categorical\", labels float32 tensor shape (batch_size, num_classes), representing one-hot encoding class index. Rules regarding number channels yielded images: color_mode \"grayscale\", 1 channel image tensors. color_mode \"rgb\", 3 channels image tensors. color_mode \"rgba\", 4 channels image tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/image_from_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts a 3D array to a PIL Image instance. — image_from_array","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"Converts 3D array PIL Image instance.","code":""},{"path":"https://keras.posit.co/reference/image_from_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"","code":"image_from_array(x, data_format = NULL, scale = TRUE, dtype = NULL)"},{"path":"https://keras.posit.co/reference/image_from_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"x Input data, form can converted array. data_format Image data format, can either \"channels_first\" \"channels_last\". Defaults NULL, case global setting config_image_data_format() used (unless changed , defaults \"channels_last\"). scale Whether rescale image minimum maximum values 0 255 respectively. Defaults TRUE. dtype Dtype use. NULL means global setting config_floatx() used (unless changed , defaults \"float32\"). Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/image_from_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"PIL Image instance.","code":""},{"path":"https://keras.posit.co/reference/image_from_array.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"","code":"img <- array(runif(30000), dim = c(100, 100, 3)) pil_img <- image_from_array(img) pil_img ## <PIL.Image.Image image mode=RGB size=100x100>"},{"path":[]},{"path":"https://keras.posit.co/reference/image_load.html","id":null,"dir":"Reference","previous_headings":"","what":"Loads an image into PIL format. — image_load","title":"Loads an image into PIL format. — image_load","text":"Loads image PIL format.","code":""},{"path":"https://keras.posit.co/reference/image_load.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loads an image into PIL format. — image_load","text":"","code":"image_load(   path,   color_mode = \"rgb\",   target_size = NULL,   interpolation = \"nearest\",   keep_aspect_ratio = FALSE )"},{"path":"https://keras.posit.co/reference/image_load.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loads an image into PIL format. — image_load","text":"path Path image file. color_mode One \"grayscale\", \"rgb\", \"rgba\". Default: \"rgb\". desired image format. target_size Either NULL (default original size) tuple ints (img_height, img_width). interpolation Interpolation method used resample image target size different loaded image. Supported methods \"nearest\", \"bilinear\", \"bicubic\". PIL version 1.1.3 newer installed, \"lanczos\" also supported. PIL version 3.4.0 newer installed, \"box\" \"hamming\" also supported. default, \"nearest\" used. keep_aspect_ratio Boolean, whether resize images target size without aspect ratio distortion. image cropped center target aspect ratio resizing.","code":""},{"path":"https://keras.posit.co/reference/image_load.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loads an image into PIL format. — image_load","text":"PIL Image instance.","code":""},{"path":"https://keras.posit.co/reference/image_load.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Loads an image into PIL format. — image_load","text":"","code":"image_path <- get_file(origin = \"https://www.r-project.org/logo/Rlogo.png\") (image <- image_load(image_path)) ## <PIL.Image.Image image mode=RGB size=724x561> input_arr <- image_to_array(image) str(input_arr) ##  num [1:561, 1:724, 1:3] 0 0 0 0 0 0 0 0 0 0 ... input_arr %<>% array_reshape(dim = c(1, dim(input_arr))) # Convert single image to a batch. model |> predict(input_arr)"},{"path":[]},{"path":"https://keras.posit.co/reference/image_smart_resize.html","id":null,"dir":"Reference","previous_headings":"","what":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","title":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","text":"Image datasets typically yield images different size. However, images need batched can processed Keras layers. batched, images need share height width. simply , TF (JAX equivalent):   However, , distort aspect ratio images, since general aspect ratio size. fine many cases, always (e.g. image generation models can problem). Note passing argument preserve_aspect_ratio = TRUE tf$image$resize() preserve aspect ratio, cost longer respecting provided target size. calls :   output images actually (200, 200), distorted. Instead, parts image fit within target size get cropped . resizing process : Take largest centered crop image aspect ratio target size. instance, size = c(200, 200) input image size (340, 500), take crop (340, 340) centered along width. Resize cropped image target size. example , resize (340, 340) crop (200, 200).","code":"size <- c(200, 200) ds <- ds$map(\\(img) tf$image$resize(img, size)) size <- c(200, 200) ds <- ds$map(\\(img) image_smart_resize(img, size))"},{"path":"https://keras.posit.co/reference/image_smart_resize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","text":"","code":"image_smart_resize(   x,   size,   interpolation = \"bilinear\",   data_format = \"channels_last\",   backend_module = NULL )"},{"path":"https://keras.posit.co/reference/image_smart_resize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","text":"x Input image batch images (tensor array). Must format (height, width, channels) (batch_size, height, width, channels). size Tuple (height, width) integer. Target size. interpolation String, interpolation use resizing. Defaults 'bilinear'. Supports bilinear, nearest, bicubic, lanczos3, lanczos5. data_format \"channels_last\" \"channels_first\". backend_module Backend module use (different default backend).","code":""},{"path":"https://keras.posit.co/reference/image_smart_resize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","text":"Array shape (size[1], size[2], channels). input image array, output array, backend-native tensor, output backend-native tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/image_to_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts a PIL Image instance to a matrix. — image_to_array","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"Converts PIL Image instance matrix.","code":""},{"path":"https://keras.posit.co/reference/image_to_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"","code":"image_to_array(img, data_format = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/image_to_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"img Input PIL Image instance. data_format Image data format, can either \"channels_first\" \"channels_last\". Defaults NULL, case global setting config_image_data_format() used (unless changed , defaults \"channels_last\"). dtype Dtype use. NULL means global setting config_floatx() used (unless changed , defaults \"float32\").","code":""},{"path":"https://keras.posit.co/reference/image_to_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"3D array.","code":""},{"path":"https://keras.posit.co/reference/image_to_array.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"","code":"image_path <- get_file(origin = \"https://www.r-project.org/logo/Rlogo.png\") (img <- image_load(image_path)) ## <PIL.Image.Image image mode=RGB size=724x561> array <- image_to_array(img) str(array) ##  num [1:561, 1:724, 1:3] 0 0 0 0 0 0 0 0 0 0 ..."},{"path":[]},{"path":"https://keras.posit.co/reference/imagenet_decode_predictions.html","id":null,"dir":"Reference","previous_headings":"","what":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","title":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","text":"Decodes prediction ImageNet model.","code":""},{"path":"https://keras.posit.co/reference/imagenet_decode_predictions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","text":"","code":"imagenet_decode_predictions(preds, top = 5)"},{"path":"https://keras.posit.co/reference/imagenet_decode_predictions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","text":"preds Tensor encoding batch predictions. top integer, many top-guesses return.","code":""},{"path":"https://keras.posit.co/reference/imagenet_decode_predictions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","text":"List data frames variables class_name, class_description, score (one data frame per sample batch input).","code":""},{"path":"https://keras.posit.co/reference/imagenet_preprocess_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","title":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","text":"Preprocesses tensor array encoding batch images.","code":""},{"path":"https://keras.posit.co/reference/imagenet_preprocess_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","text":"","code":"imagenet_preprocess_input(x, data_format = NULL, mode = \"caffe\")"},{"path":"https://keras.posit.co/reference/imagenet_preprocess_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","text":"x Input Numpy symbolic tensor, 3D 4D. data_format Data format image tensor/array. mode One \"caffe\", \"tf\", \"torch\" caffe: convert images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling. tf: scale pixels -1 1, sample-wise. torch: scale pixels 0 1 normalize channel respect ImageNet dataset.","code":""},{"path":"https://keras.posit.co/reference/imagenet_preprocess_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","text":"Preprocessed tensor array.","code":""},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates tensors with constant values. — initializer_constant","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"scalar values allowed. constant value provided must convertible dtype requested calling initializer.","code":""},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"","code":"initializer_constant(value = 0)"},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"value numeric scalar.","code":""},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"","code":"# Standalone usage: initializer <- initializer_constant(10) values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_constant(10) layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"Draws samples truncated normal distribution centered 0 stddev = sqrt(2 / (fan_in + fan_out)) fan_in number input units weight tensor fan_out number output units weight tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"","code":"initializer_glorot_normal(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"","code":"# Standalone usage: initializer <- initializer_glorot_normal() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_glorot_normal() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"Glorot et al., 2010","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"Draws samples uniform distribution within [-limit, limit], limit = sqrt(6 / (fan_in + fan_out)) (fan_in number input units weight tensor fan_out number output units).","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"","code":"initializer_glorot_uniform(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"","code":"# Standalone usage: initializer <- initializer_glorot_uniform() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_glorot_uniform() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"Glorot et al., 2010","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"He normal initializer. — initializer_he_normal","title":"He normal initializer. — initializer_he_normal","text":"draws samples truncated normal distribution centered 0 stddev = sqrt(2 / fan_in) fan_in number input units weight tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"He normal initializer. — initializer_he_normal","text":"","code":"initializer_he_normal(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"He normal initializer. — initializer_he_normal","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"He normal initializer. — initializer_he_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"He normal initializer. — initializer_he_normal","text":"","code":"# Standalone usage: initializer <- initializer_he_normal() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_he_normal() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"He normal initializer. — initializer_he_normal","text":"et al., 2015","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"He uniform variance scaling initializer. — initializer_he_uniform","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"Draws samples uniform distribution within [-limit, limit], limit = sqrt(6 / fan_in) (fan_in number input units weight tensor).","code":""},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"","code":"initializer_he_uniform(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"","code":"# Standalone usage: initializer <- initializer_he_uniform() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_he_uniform() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"et al., 2015","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates the identity matrix. — initializer_identity","title":"Initializer that generates the identity matrix. — initializer_identity","text":"usable generating 2D matrices.","code":""},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates the identity matrix. — initializer_identity","text":"","code":"initializer_identity(gain = 1)"},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that generates the identity matrix. — initializer_identity","text":"gain Multiplicative factor apply identity matrix.","code":""},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates the identity matrix. — initializer_identity","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates the identity matrix. — initializer_identity","text":"","code":"# Standalone usage: initializer <- initializer_identity() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_identity() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Lecun normal initializer. — initializer_lecun_normal","title":"Lecun normal initializer. — initializer_lecun_normal","text":"Initializers allow pre-specify initialization strategy, encoded Initializer object, without knowing shape dtype variable initialized. Draws samples truncated normal distribution centered 0 stddev = sqrt(1 / fan_in) fan_in number input units weight tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lecun normal initializer. — initializer_lecun_normal","text":"","code":"initializer_lecun_normal(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lecun normal initializer. — initializer_lecun_normal","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Lecun normal initializer. — initializer_lecun_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lecun normal initializer. — initializer_lecun_normal","text":"","code":"# Standalone usage: initializer <- initializer_lecun_normal() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_lecun_normal() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Lecun normal initializer. — initializer_lecun_normal","text":"Klambauer et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"Lecun uniform initializer. — initializer_lecun_uniform","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"Draws samples uniform distribution within [-limit, limit], limit = sqrt(3 / fan_in) (fan_in number input units weight tensor).","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"","code":"initializer_lecun_uniform(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"","code":"# Standalone usage: initializer <- initializer_lecun_uniform() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_lecun_uniform() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"Klambauer et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_ones.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates tensors initialized to 1. — initializer_ones","title":"Initializer that generates tensors initialized to 1. — initializer_ones","text":"Also available via shortcut function ones.","code":""},{"path":"https://keras.posit.co/reference/initializer_ones.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates tensors initialized to 1. — initializer_ones","text":"","code":"initializer_ones()"},{"path":"https://keras.posit.co/reference/initializer_ones.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates tensors initialized to 1. — initializer_ones","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_ones.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates tensors initialized to 1. — initializer_ones","text":"","code":"# Standalone usage: initializer <- initializer_ones() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_ones() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"shape tensor initialize two-dimensional, initialized orthogonal matrix obtained QR decomposition matrix random numbers drawn normal distribution. matrix fewer rows columns output orthogonal rows. Otherwise, output orthogonal columns. shape tensor initialize two-dimensional, matrix shape (shape[1] * ... * shape[n - 1], shape[n]) initialized, n length shape vector. matrix subsequently reshaped give tensor desired shape.","code":""},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"","code":"initializer_orthogonal(gain = 1, seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"gain Multiplicative factor apply orthogonal matrix. seed integer. Used make behavior initializer deterministic.","code":""},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"","code":"# Standalone usage: initializer <- initializer_orthogonal() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_orthogonal() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"Saxe et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Random normal initializer. — initializer_random_normal","title":"Random normal initializer. — initializer_random_normal","text":"Draws samples normal distribution given parameters.","code":""},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random normal initializer. — initializer_random_normal","text":"","code":"initializer_random_normal(mean = 0, stddev = 0.05, seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random normal initializer. — initializer_random_normal","text":"mean numeric scalar. Mean random values generate. stddev numeric scalar. Standard deviation random values generate. seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random normal initializer. — initializer_random_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random normal initializer. — initializer_random_normal","text":"","code":"# Standalone usage: initializer <- initializer_random_normal(mean = 0.0, stddev = 1.0) values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_random_normal(mean = 0.0, stddev = 1.0) layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"Random uniform initializer. — initializer_random_uniform","title":"Random uniform initializer. — initializer_random_uniform","text":"Draws samples uniform distribution given parameters.","code":""},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random uniform initializer. — initializer_random_uniform","text":"","code":"initializer_random_uniform(minval = -0.05, maxval = 0.05, seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random uniform initializer. — initializer_random_uniform","text":"minval numeric scalar scalar keras tensor. Lower bound range random values generate (inclusive). maxval numeric scalar scalar keras tensor. Upper bound range random values generate (exclusive). seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random uniform initializer. — initializer_random_uniform","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random uniform initializer. — initializer_random_uniform","text":"","code":"# Standalone usage: initializer <- initializer_random_uniform(minval = 0.0, maxval = 1.0) values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_random_uniform(minval = 0.0, maxval = 1.0) layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"values generated similar values RandomNormal initializer, except values two standard deviations mean discarded re-drawn.","code":""},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"","code":"initializer_truncated_normal(mean = 0, stddev = 0.05, seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"mean numeric scalar. Mean random values generate. stddev numeric scalar. Standard deviation random values generate. seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"","code":"# Standalone usage: initializer <- initializer_truncated_normal(mean = 0, stddev = 1) values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_truncated_normal(mean = 0, stddev = 1) layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"distribution = \"truncated_normal\" \"untruncated_normal\", samples drawn truncated/untruncated normal distribution mean zero standard deviation (truncation, used) stddev = sqrt(scale / n), n : number input units weight tensor, mode = \"fan_in\" number output units, mode = \"fan_out\" average numbers input output units, mode = \"fan_avg\" distribution = \"uniform\", samples drawn uniform distribution within [-limit, limit], limit = sqrt(3 * scale / n).","code":""},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"","code":"initializer_variance_scaling(   scale = 1,   mode = \"fan_in\",   distribution = \"truncated_normal\",   seed = NULL )"},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"scale Scaling factor (positive float). mode One \"fan_in\", \"fan_out\", \"fan_avg\". distribution Random distribution use. One \"truncated_normal\", \"untruncated_normal\", \"uniform\". seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"","code":"# Standalone usage: initializer <- initializer_variance_scaling(scale = 0.1, mode = 'fan_in',                                             distribution = 'uniform') values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_variance_scaling(scale = 0.1, mode = 'fan_in',                                             distribution = 'uniform') layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_zeros.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates tensors initialized to 0. — initializer_zeros","title":"Initializer that generates tensors initialized to 0. — initializer_zeros","text":"Initializer generates tensors initialized 0.","code":""},{"path":"https://keras.posit.co/reference/initializer_zeros.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates tensors initialized to 0. — initializer_zeros","text":"","code":"initializer_zeros()"},{"path":"https://keras.posit.co/reference/initializer_zeros.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates tensors initialized to 0. — initializer_zeros","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_zeros.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates tensors initialized to 0. — initializer_zeros","text":"","code":"# Standalone usage: initializer <- initializer_zeros() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_zeros() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/install_keras.html","id":null,"dir":"Reference","previous_headings":"","what":"Install Keras — install_keras","title":"Install Keras — install_keras","text":"function install Keras along selected backend, including Python dependencies.","code":""},{"path":"https://keras.posit.co/reference/install_keras.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Install Keras — install_keras","text":"","code":"install_keras(   envname = \"r-keras\",   ...,   extra_packages = c(\"scipy\", \"pandas\", \"Pillow\", \"pydot\", \"ipython\",     \"tensorflow_datasets\"),   python_version = \">=3.9,<=3.11\",   backend = c(\"tensorflow\", \"jax\"),   gpu = NA,   restart_session = TRUE )"},{"path":"https://keras.posit.co/reference/install_keras.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Install Keras — install_keras","text":"envname Name path Python virtual environment ... reserved future compatibility. extra_packages Additional Python packages install alongside Keras python_version Passed reticulate::virtualenv_starter() backend backend(s) install. Accepted values include \"tensorflow\", \"jax\" \"torch\" gpu whether install GPU capable version backend. restart_session Whether restart R session installing (note occur within RStudio).","code":""},{"path":"https://keras.posit.co/reference/install_keras.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Install Keras — install_keras","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/keras.html","id":null,"dir":"Reference","previous_headings":"","what":"Main Keras module — keras","title":"Main Keras module — keras","text":"keras module object equivalent reticulate::import(\"keras\") provided mainly convenience.","code":""},{"path":"https://keras.posit.co/reference/keras.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Main Keras module — keras","text":"object class python.builtin.module","code":""},{"path":"https://keras.posit.co/reference/keras.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Main Keras module — keras","text":"keras Python module","code":""},{"path":"https://keras.posit.co/reference/keras3-package.html","id":null,"dir":"Reference","previous_headings":"","what":"keras3: R Interface to 'Keras' — keras3-package","title":"keras3: R Interface to 'Keras' — keras3-package","text":"Interface 'Keras' https://keras.io, high-level neural networks API. 'Keras' developed focus enabling fast experimentation, supports convolution based networks recurrent networks (well combinations two), runs seamlessly CPU GPU devices. Keras high-level neural networks API, developed focus enabling fast experimentation. Keras following key features:","code":""},{"path":"https://keras.posit.co/reference/keras3-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"keras3: R Interface to 'Keras' — keras3-package","text":"Allows code run CPU GPU, seamlessly. User-friendly API makes easy quickly prototype deep learning models. Built-support convolutional networks (computer vision), recurrent networks (sequence processing), combination . Supports arbitrary network architectures: multi-input multi-output models, layer sharing, model sharing, etc. means Keras appropriate building essentially deep learning model, memory network neural Turing machine. capable running top multiple back-ends including TensorFlow, Jax, PyTorch. See package website https://keras.posit.co complete documentation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/keras3-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"keras3: R Interface to 'Keras' — keras3-package","text":"Maintainer: Tomasz Kalinowski tomasz@posit.co [copyright holder] Authors: JJ Allaire [copyright holder] François Chollet [copyright holder] contributors: Daniel Falbel daniel@posit.co [contributor, copyright holder] Posit Software, PBC [copyright holder, funder] Google [copyright holder, funder] Yuan Tang terrytangyuan@gmail.com (ORCID) [contributor, copyright holder] Wouter Van Der Bijl [contributor, copyright holder] Martin Studer [contributor, copyright holder] Sigrid Keydana [contributor]","code":""},{"path":"https://keras.posit.co/reference/keras_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Keras tensor (Functional API input). — keras_input","title":"Create a Keras tensor (Functional API input). — keras_input","text":"Keras tensor symbolic tensor-like object, augment certain attributes allow us build Keras model just knowing inputs outputs model. instance, , b c Keras tensors, becomes possible : model <- keras_model(input = c(, b), output = c)","code":""},{"path":"https://keras.posit.co/reference/keras_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Keras tensor (Functional API input). — keras_input","text":"","code":"keras_input(   shape = NULL,   batch_size = NULL,   dtype = NULL,   sparse = NULL,   batch_shape = NULL,   name = NULL,   tensor = NULL )"},{"path":"https://keras.posit.co/reference/keras_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Keras tensor (Functional API input). — keras_input","text":"shape shape list (list integers NULL objects), including batch size. instance, shape = c(32) indicates expected input batches 32-dimensional vectors. Elements list can NULL NA; NULL/NA elements represent dimensions shape known may vary (e.g. sequence length). batch_size Optional static batch size (integer). dtype data type expected input, string (e.g. \"float32\", \"int32\"...) sparse boolean specifying whether expected input sparse tensors. Note , sparse FALSE, sparse tensors can still passed input - densified default value 0. feature supported TensorFlow backend. Defaults FALSE. batch_shape Shape, including batch dim. name Optional name string layer. unique model (reuse name twice). autogenerated provided. tensor Optional existing tensor wrap Input layer. set, layer use tensor rather creating new placeholder tensor.","code":""},{"path":"https://keras.posit.co/reference/keras_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Keras tensor (Functional API input). — keras_input","text":"Keras tensor, can passed inputs argument (keras_model()).","code":""},{"path":"https://keras.posit.co/reference/keras_input.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Keras tensor (Functional API input). — keras_input","text":"","code":"# This is a logistic regression in Keras input <- layer_input(shape=c(32)) output <- input |> layer_dense(16, activation='softmax') model <- keras_model(input, output)"},{"path":[]},{"path":"https://keras.posit.co/reference/keras_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Keras Model (Functional API) — keras_model","title":"Keras Model (Functional API) — keras_model","text":"model directed acyclic graph layers.","code":""},{"path":"https://keras.posit.co/reference/keras_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keras Model (Functional API) — keras_model","text":"","code":"keras_model(inputs = NULL, outputs = NULL, ...)"},{"path":"https://keras.posit.co/reference/keras_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keras Model (Functional API) — keras_model","text":"inputs Input tensor(s) (keras_input()) outputs Output tensors (calling layers inputs) ... additional arguments","code":""},{"path":"https://keras.posit.co/reference/keras_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keras Model (Functional API) — keras_model","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/keras_model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Keras Model (Functional API) — keras_model","text":"","code":"library(keras3)  # input tensor inputs <- keras_input(shape = c(784))  # outputs compose input + dense layers predictions <- inputs |>   layer_dense(units = 64, activation = 'relu') |>   layer_dense(units = 64, activation = 'relu') |>   layer_dense(units = 10, activation = 'softmax')  # create and compile model model <- keras_model(inputs = inputs, outputs = predictions) model |> compile(   optimizer = 'rmsprop',   loss = 'categorical_crossentropy',   metrics = c('accuracy') )"},{"path":[]},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":null,"dir":"Reference","previous_headings":"","what":"Keras Model composed of a linear stack of layers — keras_model_sequential","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"Keras Model composed linear stack layers","code":""},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"","code":"keras_model_sequential(   input_shape = NULL,   name = NULL,   ...,   input_dtype = NULL,   input_batch_size = NULL,   input_sparse = NULL,   input_batch_shape = NULL,   input_name = NULL,   input_tensor = NULL,   trainable = TRUE,   layers = list() )"},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"input_shape shape integer vector, including batch size. instance, shape=c(32) indicates expected input batches 32-dimensional vectors. Elements shape can NA; NA elements represent dimensions shape known may vary (e.g. sequence length). name Name model ... additional arguments passed keras.layers.InputLayer. input_dtype data type expected input, string (e.g. \"float32\", \"int32\"...) input_batch_size Optional static batch size (integer). input_sparse boolean specifying whether expected input sparse tensors. Note , sparse FALSE, sparse tensors can still passed input - densified default value 0. feature supported TensorFlow backend. Defaults FALSE. input_batch_shape optional way specify batch_size input_shape one argument. input_name Optional name string input layer. unique model (reuse name twice). autogenerated provided. input_tensor Optional existing tensor wrap InputLayer. set, layer use tensor rather creating new placeholder tensor. trainable Boolean, whether model's variables trainable. can also change trainable status model/layer freeze_weights() unfreeze_weights(). layers List layers add model.","code":""},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"Sequential model instance.","code":""},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"input_shape omitted, model layer shapes, including final model output shape, known model built, either calling model input tensor/array like model(input), (possibly via fit()/evaluate()/predict()), explicitly calling model$build(input_shape).","code":""},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"","code":"model <- keras_model_sequential(input_shape = c(784)) model |>   layer_dense(units = 32) |>   layer_activation('relu') |>   layer_dense(units = 10) |>   layer_activation('softmax')  model |> compile(   optimizer = 'rmsprop',   loss = 'categorical_crossentropy',   metrics = c('accuracy') )  model ## Model: \"sequential\" ## +---------------------------------+------------------------+---------------+ ## | Layer (type)                    | Output Shape           |       Param # | ## +=================================+========================+===============+ ## | dense (Dense)                   | (None, 32)             |        25,120 | ## +---------------------------------+------------------------+---------------+ ## | activation (Activation)         | (None, 32)             |             0 | ## +---------------------------------+------------------------+---------------+ ## | dense_1 (Dense)                 | (None, 10)             |           330 | ## +---------------------------------+------------------------+---------------+ ## | activation_1 (Activation)       | (None, 10)             |             0 | ## +---------------------------------+------------------------+---------------+ ##  Total params: 25,450 (99.41 KB) ##  Trainable params: 25,450 (99.41 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies an activation function to an output. — layer_activation","title":"Applies an activation function to an output. — layer_activation","text":"Applies activation function output.","code":""},{"path":"https://keras.posit.co/reference/layer_activation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies an activation function to an output. — layer_activation","text":"","code":"layer_activation(object, activation, ...)"},{"path":"https://keras.posit.co/reference/layer_activation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies an activation function to an output. — layer_activation","text":"object Object compose layer . tensor, array, sequential model. activation Activation function. callable, name activation keras3::activation_* namespace. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies an activation function to an output. — layer_activation","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_activation.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies an activation function to an output. — layer_activation","text":"","code":"x <- array(c(-3, -1, 0, 2)) layer <- layer_activation(activation = 'relu') layer(x) ## tf.Tensor([0. 0. 0. 2.], shape=(4), dtype=float32) layer <- layer_activation(activation = activation_relu) layer(x) ## tf.Tensor([0. 0. 0. 2.], shape=(4), dtype=float32) layer <- layer_activation(activation = op_relu) layer(x) ## tf.Tensor([0. 0. 0. 2.], shape=(4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","title":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","text":"Formula:","code":"f(x) = alpha * (exp(x) - 1.) for x < 0 f(x) = x for x >= 0"},{"path":"https://keras.posit.co/reference/layer_activation_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","text":"","code":"layer_activation_elu(object, alpha = 1, ...)"},{"path":"https://keras.posit.co/reference/layer_activation_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","text":"object Object compose layer . tensor, array, sequential model. alpha float, slope negative section. Defaults 1.0. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_elu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"layer allows small gradient unit active. Formula:","code":"f <- function(x) ifelse(x >= 0, x, alpha * x)"},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"","code":"layer_activation_leaky_relu(object, negative_slope = 0.3, ...)"},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"object Object compose layer . tensor, array, sequential model. negative_slope Float >= 0.0. Negative slope coefficient. Defaults 0.3. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"","code":"leaky_relu_layer <- layer_activation_leaky_relu(negative_slope=0.5) input <- array(c(-10, -5, 0.0, 5, 10)) result <- leaky_relu_layer(input) as.array(result) ## [1] -5.0 -2.5  0.0  5.0 10.0"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_parametric_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","title":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","text":"Formula:   alpha learned array shape x.","code":"f <- function(x) ifelse(x >= 0, x, alpha * x)"},{"path":"https://keras.posit.co/reference/layer_activation_parametric_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","text":"","code":"layer_activation_parametric_relu(   object,   alpha_initializer = \"Zeros\",   alpha_regularizer = NULL,   alpha_constraint = NULL,   shared_axes = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_activation_parametric_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","text":"object Object compose layer . tensor, array, sequential model. alpha_initializer Initializer function weights. alpha_regularizer Regularizer weights. alpha_constraint Constraint weights. shared_axes axes along share learnable parameters activation function. example, incoming feature maps 2D convolution output shape (batch, height, width, channels), wish share parameters across space filter one set parameters, set shared_axes=[1, 2]. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_parametric_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Rectified Linear Unit activation function layer. — layer_activation_relu","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"Formula:","code":"f <- function(x, max_value = Inf, negative_slope = 0, threshold = 0) {  x <- max(x,0)  if (x >= max_value)    max_value  else if (threshold <= x && x < max_value)    x  else    negative_slope * (x - threshold) }"},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"","code":"layer_activation_relu(   object,   max_value = NULL,   negative_slope = 0,   threshold = 0,   ... )"},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"object Object compose layer . tensor, array, sequential model. max_value Float >= 0. Maximum activation value. NULL means unlimited. Defaults NULL. negative_slope Float >= 0. Negative slope coefficient. Defaults 0.0. threshold Float >= 0. Threshold value thresholded activation. Defaults 0.0. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"","code":"relu_layer <- layer_activation_relu(max_value = 10,                                     negative_slope = 0.5,                                     threshold = 0) input <- array(c(-10, -5, 0.0, 5, 10)) result <- relu_layer(input) as.array(result) ## [1] -5.0 -2.5  0.0  5.0 10.0"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax activation layer. — layer_activation_softmax","title":"Softmax activation layer. — layer_activation_softmax","text":"Formula:","code":"exp_x = exp(x - max(x)) f(x) = exp_x / sum(exp_x)"},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax activation layer. — layer_activation_softmax","text":"","code":"layer_activation_softmax(object, axis = -1L, ...)"},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax activation layer. — layer_activation_softmax","text":"object Object compose layer . tensor, array, sequential model. axis Integer, list Integers, axis along softmax normalization applied. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax activation layer. — layer_activation_softmax","text":"Softmaxed output shape inputs.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmax activation layer. — layer_activation_softmax","text":"","code":"softmax_layer <- layer_activation_softmax() input <- op_array(c(1, 2, 1)) softmax_layer(input) ## tf.Tensor([0.21194157 0.5761169  0.21194157], shape=(3), dtype=float32)"},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Softmax activation layer. — layer_activation_softmax","text":"inputs: inputs (logits) softmax layer. mask: boolean mask shape inputs. mask specifies 1 keep 0 mask. Defaults NULL.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"Layer applies update cost function based input activity.","code":""},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"","code":"layer_activity_regularization(object, l1 = 0, l2 = 0, ...)"},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"object Object compose layer . tensor, array, sequential model. l1 L1 regularization factor (positive float). l2 L2 regularization factor (positive float). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"Arbitrary. Use keyword argument input_shape (tuple integers, include samples axis) using layer first layer model.","code":""},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"shape input.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_add.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs elementwise addition operation. — layer_add","title":"Performs elementwise addition operation. — layer_add","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_add.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs elementwise addition operation. — layer_add","text":"","code":"layer_add(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_add.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs elementwise addition operation. — layer_add","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_add.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs elementwise addition operation. — layer_add","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_add.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs elementwise addition operation. — layer_add","text":"Usage Keras model:","code":"input_shape <- c(1, 2, 3) x1 <- op_ones(input_shape) x2 <- op_ones(input_shape) layer_add(x1, x2) ## tf.Tensor( ## [[[2. 2. 2.] ##   [2. 2. 2.]]], shape=(1, 2, 3), dtype=float32) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu')  input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu')  # equivalent to `added = layer_add([x1, x2))` added <- layer_add(x1, x2) output <- added |> layer_dense(4)  model <- keras_model(inputs = c(input1, input2), outputs = output)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"Inputs list 2 3 elements: query tensor shape (batch_size, Tq, dim). value tensor shape (batch_size, Tv, dim). optional key tensor shape (batch_size, Tv, dim). none supplied, value used key. calculation follows steps: Calculate attention scores using query key shape (batch_size, Tq, Tv) non-linear sum scores = reduce_sum(tanh(query + key), axis=-1). Use scores calculate softmax distribution shape (batch_size, Tq, Tv). Use softmax distribution create linear combination value shape (batch_size, Tq, dim).","code":""},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"","code":"layer_additive_attention(object, use_scale = TRUE, dropout = 0, ...)"},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"object Object compose layer . tensor, array, sequential model. use_scale TRUE, create scalar variable scale attention scores. dropout Float 0 1. Fraction units drop attention scores. Defaults 0.0. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"inputs: List following tensors: query: Query tensor shape (batch_size, Tq, dim). value: Value tensor shape (batch_size, Tv, dim). key: Optional key tensor shape (batch_size, Tv, dim). given, use value key value, common case. mask: List following tensors: query_mask: boolean mask tensor shape (batch_size, Tq). given, output zero positions mask==FALSE. value_mask: boolean mask tensor shape (batch_size, Tv). given, apply mask values positions mask==FALSE contribute result. return_attention_scores: bool, TRUE, returns attention scores (masking softmax) additional output argument. training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (dropout). use_causal_mask: Boolean. Set TRUE decoder self-attention. Adds mask position attend positions j > . prevents flow information future towards past. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"output","dir":"Reference","previous_headings":"","what":"Output","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"Attention outputs shape (batch_size, Tq, dim). (Optional) Attention scores masking softmax shape (batch_size, Tq, Tv).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies Alpha Dropout to the input. — layer_alpha_dropout","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"Alpha Dropout Dropout keeps mean variance inputs original values, order ensure self-normalizing property even dropout. Alpha Dropout fits well Scaled Exponential Linear Units (SELU) randomly setting activations negative saturation value.","code":""},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"","code":"layer_alpha_dropout(object, rate, noise_shape = NULL, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. multiplicative noise standard deviation sqrt(rate / (1 - rate)). noise_shape 1D integer tensor representing shape binary alpha dropout mask multiplied input. instance, inputs shape (batch_size, timesteps, features) want alpha dropout mask timesteps, can use noise_shape = (batch_size, 1, features). seed integer use random seed. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"inputs: Input tensor (rank). training: R boolean indicating whether layer behave training mode (adding alpha dropout) inference mode (nothing).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"Inputs list 2 3 elements: query tensor shape (batch_size, Tq, dim). value tensor shape (batch_size, Tv, dim). optional key tensor shape (batch_size, Tv, dim). none supplied, value used key. calculation follows steps: Calculate attention scores using query key shape (batch_size, Tq, Tv). Use scores calculate softmax distribution shape (batch_size, Tq, Tv). Use softmax distribution create linear combination value shape (batch_size, Tq, dim).","code":""},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"","code":"layer_attention(   object,   use_scale = FALSE,   score_mode = \"dot\",   dropout = 0,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"object Object compose layer . tensor, array, sequential model. use_scale TRUE, create scalar variable scale attention scores. score_mode Function use compute attention scores, one {\"dot\", \"concat\"}. \"dot\" refers dot product query key vectors. \"concat\" refers hyperbolic tangent concatenation query key vectors. dropout Float 0 1. Fraction units drop attention scores. Defaults 0.0. seed integer use random seed incase dropout. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"inputs: List following tensors: query: Query tensor shape (batch_size, Tq, dim). value: Value tensor shape (batch_size, Tv, dim). key: Optional key tensor shape (batch_size, Tv, dim). given, use value key value, common case. mask: List following tensors: query_mask: boolean mask tensor shape (batch_size, Tq). given, output zero positions mask==FALSE. value_mask: boolean mask tensor shape (batch_size, Tv). given, apply mask values positions mask==FALSE contribute result. return_attention_scores: bool, TRUE, returns attention scores (masking softmax) additional output argument. training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (dropout). use_causal_mask: Boolean. Set TRUE decoder self-attention. Adds mask position attend positions j > . prevents flow information future towards past. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"output","dir":"Reference","previous_headings":"","what":"Output","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"Attention outputs shape (batch_size, Tq, dim). (Optional) Attention scores masking softmax shape (batch_size, Tq, Tv).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_average.html","id":null,"dir":"Reference","previous_headings":"","what":"Averages a list of inputs element-wise.. — layer_average","title":"Averages a list of inputs element-wise.. — layer_average","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_average.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Averages a list of inputs element-wise.. — layer_average","text":"","code":"layer_average(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_average.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Averages a list of inputs element-wise.. — layer_average","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_average.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Averages a list of inputs element-wise.. — layer_average","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_average.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Averages a list of inputs element-wise.. — layer_average","text":"Usage Keras model:","code":"input_shape <- c(1, 2, 3) x1 <- op_ones(input_shape) x2 <- op_zeros(input_shape) layer_average(x1, x2) ## tf.Tensor( ## [[[0.5 0.5 0.5] ##   [0.5 0.5 0.5]]], shape=(1, 2, 3), dtype=float32) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu')  input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu')  added <- layer_average(x1, x2) output <- added |> layer_dense(4)  model <- keras_model(inputs = c(input1, input2), outputs = output)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Average pooling for temporal data. — layer_average_pooling_1d","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"Downsamples input representation taking average value window defined pool_size. window shifted strides.  resulting output using \"valid\" padding option shape : output_shape = (input_shape - pool_size + 1) / strides) resulting output shape using \"\" padding option : output_shape = input_shape / strides","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"","code":"layer_average_pooling_1d(   object,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"object Object compose layer . tensor, array, sequential model. pool_size int, size max pooling window. strides int NULL. Specifies much pooling window moves pooling step. NULL, default pool_size. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"data_format=\"channels_last\": 3D tensor shape (batch_size, steps, features). data_format=\"channels_first\": 3D tensor shape (batch_size, features, steps).","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"data_format=\"channels_last\": 3D tensor shape (batch_size, downsampled_steps, features). data_format=\"channels_first\": 3D tensor shape (batch_size, features, downsampled_steps).","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"strides=1 padding=\"valid\":     strides=2 padding=\"valid\":     strides=1 padding=\"\":","code":"x <- op_array(c(1., 2., 3., 4., 5.)) |> op_reshape(c(1, 5, 1)) output <- x |>   layer_average_pooling_1d(pool_size = 2,                            strides = 1,                            padding = \"valid\") output ## tf.Tensor( ## [[[1.5] ##   [2.5] ##   [3.5] ##   [4.5]]], shape=(1, 4, 1), dtype=float32) x <- op_array(c(1., 2., 3., 4., 5.)) |> op_reshape(c(1, 5, 1)) output <- x |>   layer_average_pooling_1d(pool_size = 2,                            strides = 2,                            padding = \"valid\") output ## tf.Tensor( ## [[[1.5] ##   [3.5]]], shape=(1, 2, 1), dtype=float32) x <- op_array(c(1., 2., 3., 4., 5.)) |> op_reshape(c(1, 5, 1)) output <- x |>   layer_average_pooling_1d(pool_size = 2,                            strides = 1,                            padding = \"same\") output ## tf.Tensor( ## [[[1.5] ##   [2.5] ##   [3.5] ##   [4.5] ##   [5. ]]], shape=(1, 5, 1), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"Downsamples input along spatial dimensions (height width) taking average value input window (size defined pool_size) channel input. window shifted strides along dimension. resulting output using \"valid\" padding option spatial shape (number rows columns) : output_shape = math.floor((input_shape - pool_size) / strides) + 1 (input_shape >= pool_size) resulting output shape using \"\" padding option : output_shape = math.floor((input_shape - 1) / strides) + 1","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"","code":"layer_average_pooling_2d(   object,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"object Object compose layer . tensor, array, sequential model. pool_size int list 2 integers, factors downscale (dim1, dim2). one integer specified, window length used dimensions. strides int list 2 integers, NULL. Strides values. NULL, default pool_size. one int specified, stride size used dimensions. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"data_format=\"channels_last\": 4D tensor shape (batch_size, height, width, channels). data_format=\"channels_first\": 4D tensor shape (batch_size, channels, height, width).","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"data_format=\"channels_last\": 4D tensor shape (batch_size, pooled_height, pooled_width, channels). data_format=\"channels_first\": 4D tensor shape (batch_size, channels, pooled_height, pooled_width).","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"strides=(1, 1) padding=\"valid\":     strides=(2, 2) padding=\"valid\":     stride=(1, 1) padding=\"\":","code":"x <- op_array(1:9, \"float32\") |> op_reshape(c(1, 3, 3, 1)) output <- x |>   layer_average_pooling_2d(pool_size = c(2, 2),                            strides = c(1, 1),                            padding = \"valid\") output ## tf.Tensor( ## [[[[3.] ##    [4.]] ## ##   [[6.] ##    [7.]]]], shape=(1, 2, 2, 1), dtype=float32) x <- op_array(1:12, \"float32\") |> op_reshape(c(1, 3, 4, 1)) output <- x |>   layer_average_pooling_2d(pool_size = c(2, 2),                            strides = c(2, 2),                            padding = \"valid\") output ## tf.Tensor( ## [[[[3.5] ##    [5.5]]]], shape=(1, 1, 2, 1), dtype=float32) x <- op_array(1:9, \"float32\") |> op_reshape(c(1, 3, 3, 1)) output <- x |>   layer_average_pooling_2d(pool_size = c(2, 2),                            strides = c(1, 1),                            padding = \"same\") output ## tf.Tensor( ## [[[[3. ] ##    [4. ] ##    [4.5]] ## ##   [[6. ] ##    [7. ] ##    [7.5]] ## ##   [[7.5] ##    [8.5] ##    [9. ]]]], shape=(1, 3, 3, 1), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"Downsamples input along spatial dimensions (depth, height, width) taking average value input window (size defined pool_size) channel input. window shifted strides along dimension.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"","code":"layer_average_pooling_3d(   object,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"object Object compose layer . tensor, array, sequential model. pool_size int list 3 integers, factors downscale (dim1, dim2, dim3). one integer specified, window length used dimensions. strides int list 3 integers, NULL. Strides values. NULL, default pool_size. one int specified, stride size used dimensions. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"","code":"depth <- height <- width <- 30 channels <- 3  inputs <- layer_input(shape = c(depth, height, width, channels)) outputs <- inputs |> layer_average_pooling_3d(pool_size = 3) outputs # Shape: (batch_size, 10, 10, 10, 3) ## <KerasTensor shape=(None, 10, 10, 10, 3), dtype=float32, sparse=False, name=keras_tensor_1>"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer that normalizes its inputs. — layer_batch_normalization","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"Batch normalization applies transformation maintains mean output close 0 output standard deviation close 1. Importantly, batch normalization works differently training inference. training (.e. using fit() calling layer/model argument training = TRUE), layer normalizes output using mean standard deviation current batch inputs. say, channel normalized, layer returns gamma * (batch - mean(batch)) / sqrt(var(batch) + epsilon) + beta, : epsilon small constant (configurable part constructor arguments) gamma learned scaling factor (initialized 1), can disabled passing scale = FALSE constructor. beta learned offset factor (initialized 0), can disabled passing center = FALSE constructor. inference (.e. using evaluate() predict() calling layer/model argument training = FALSE (default), layer normalizes output using moving average mean standard deviation batches seen training. say, returns gamma * (batch - self$moving_mean) / sqrt(self$moving_var+epsilon) + beta. self$moving_mean self$moving_var non-trainable variables updated time layer called training mode, : moving_mean = moving_mean * momentum + mean(batch) * (1 - momentum) moving_var = moving_var * momentum + var(batch) * (1 - momentum) , layer normalize inputs inference trained data similar statistics inference data. setting layer$trainable <- FALSE BatchNormalization layer: meaning setting layer$trainable <- FALSE freeze layer, .e. internal state change training: trainable weights updated fit() train_on_batch(), state updates run. Usually, necessarily mean layer run inference mode (normally controlled training argument can passed calling layer). \"Frozen state\" \"inference mode\" two separate concepts. However, case BatchNormalization layer, setting trainable <- FALSE layer means layer subsequently run inference mode (meaning use moving mean moving variance normalize current batch, rather using mean variance current batch). Note : Setting trainable model containing layers recursively set trainable value inner layers. value trainable attribute changed calling compile() model, new value take effect model compile() called .","code":""},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"","code":"layer_batch_normalization(   object,   axis = -1L,   momentum = 0.99,   epsilon = 0.001,   center = TRUE,   scale = TRUE,   beta_initializer = \"zeros\",   gamma_initializer = \"ones\",   moving_mean_initializer = \"zeros\",   moving_variance_initializer = \"ones\",   beta_regularizer = NULL,   gamma_regularizer = NULL,   beta_constraint = NULL,   gamma_constraint = NULL,   synchronized = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"object Object compose layer . tensor, array, sequential model. axis Integer, axis normalized (typically features axis). instance, Conv2D layer data_format = \"channels_first\", use axis = 2. momentum Momentum moving average. epsilon Small float added variance avoid dividing zero. center TRUE, add offset beta normalized tensor. FALSE, beta ignored. scale TRUE, multiply gamma. FALSE, gamma used. next layer linear can disabled since scaling done next layer. beta_initializer Initializer beta weight. gamma_initializer Initializer gamma weight. moving_mean_initializer Initializer moving mean. moving_variance_initializer Initializer moving variance. beta_regularizer Optional regularizer beta weight. gamma_regularizer Optional regularizer gamma weight. beta_constraint Optional constraint beta weight. gamma_constraint Optional constraint gamma weight. synchronized applicable TensorFlow backend. TRUE, synchronizes global batch statistics (mean variance) layer across devices training step distributed training strategy. FALSE, replica uses local batch statistics. ... Base layer keyword arguments (e.g. name dtype).","code":""},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"inputs: Input tensor (rank). training: R boolean indicating whether layer behave training mode inference mode. training = TRUE: layer normalize inputs using mean variance current batch inputs. training = FALSE: layer normalize inputs using mean variance moving statistics, learned training. mask: Binary tensor shape broadcastable inputs tensor, TRUE values indicating positions mean variance computed. Masked elements current inputs taken account mean variance computation training. prior unmasked element values taken account momentum expires.","code":""},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"Ioffe Szegedy, 2015.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":null,"dir":"Reference","previous_headings":"","what":"Bidirectional wrapper for RNNs. — layer_bidirectional","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"Bidirectional wrapper RNNs.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"","code":"layer_bidirectional(   object,   layer,   merge_mode = \"concat\",   weights = NULL,   backward_layer = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"object Object compose layer . tensor, array, sequential model. layer RNN instance, layer_lstm() layer_gru(). also Layer() instance meets following criteria: sequence-processing layer (accepts 3D+ inputs). go_backwards, return_sequences return_state attribute (semantics RNN class). input_spec attribute. Implement serialization via get_config() from_config(). Note recommended way create new RNN layers write custom RNN cell use layer_rnn(), instead subclassing Layer() directly. return_sequences TRUE, output masked timestep zero regardless layer's original zero_output_for_mask value. merge_mode Mode outputs forward backward RNNs combined. One {\"sum\", \"mul\", \"concat\", \"ave\", NULL}. NULL, outputs combined, returned list. Defaults \"concat\". weights see description backward_layer Optional RNN, Layer() instance used handle backwards input processing. backward_layer provided, layer instance passed layer argument used generate backward layer automatically. Note provided backward_layer layer properties matching layer argument, particular values stateful, return_states, return_sequences, etc. addition, backward_layer layer different go_backwards argument values. ValueError raised requirements met. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"call arguments layer wrapped RNN layer. Beware passing initial_state argument call layer, first half list elements initial_state list passed forward RNN call last half list elements passed backward RNN call.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"instantiating Bidirectional layer existing RNN layer instance reuse weights state RNN layer instance -- Bidirectional layer freshly initialized weights.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"","code":"model <- keras_model_sequential(input_shape = c(5, 10)) %>%   layer_bidirectional(layer_lstm(units = 10, return_sequences = TRUE)) %>%   layer_bidirectional(layer_lstm(units = 10)) %>%   layer_dense(5, activation = \"softmax\")  model %>% compile(loss = \"categorical_crossentropy\",                   optimizer = \"rmsprop\")  # With custom backward layer forward_layer <- layer_lstm(units = 10, return_sequences = TRUE) backward_layer <- layer_lstm(units = 10, activation = \"relu\",                              return_sequences = TRUE, go_backwards = TRUE)  model <- keras_model_sequential(input_shape = c(5, 10)) %>%   bidirectional(forward_layer, backward_layer = backward_layer) %>%   layer_dense(5, activation = \"softmax\")  model %>% compile(loss = \"categorical_crossentropy\",                   optimizer = \"rmsprop\")"},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"states","dir":"Reference","previous_headings":"","what":"States","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"Bidirectional layer instance property states, can access layer$states. can also reset states using reset_state()","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which encodes integer features. — layer_category_encoding","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"layer provides options condensing data categorical encoding total number tokens known advance. accepts integer values inputs, outputs dense sparse representation inputs. integer inputs total number tokens known, use layer_integer_lookup() instead. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"","code":"layer_category_encoding(   object,   num_tokens = NULL,   output_mode = \"multi_hot\",   sparse = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"object Object compose layer . tensor, array, sequential model. num_tokens total number tokens layer support. inputs layer must integers range 0 <= value < num_tokens, error thrown. output_mode Specification output layer. Values can \"one_hot\", \"multi_hot\" \"count\", configuring layer follows: - \"one_hot\": Encodes individual element input array num_tokens size, containing 1 element index. last dimension size 1, encode dimension. last dimension size 1, append new dimension encoded output. - \"multi_hot\": Encodes sample input single array num_tokens size, containing 1 vocabulary term present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). - \"count\": Like \"multi_hot\", int array contains count number times token index appeared sample. output modes, currently output rank 2 supported. Defaults \"multi_hot\". sparse Whether return sparse tensor; backends support sparse tensors. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"One-hot encoding data     Multi-hot encoding data     Using weighted inputs \"count\" mode","code":"layer <- layer_category_encoding(num_tokens = 4, output_mode = \"one_hot\") x <- op_array(c(3, 2, 0, 1), \"int32\") layer(x) ## tf.Tensor( ## [[0. 0. 0. 1.] ##  [0. 0. 1. 0.] ##  [1. 0. 0. 0.] ##  [0. 1. 0. 0.]], shape=(4, 4), dtype=float32) layer <- layer_category_encoding(num_tokens = 4, output_mode = \"multi_hot\") x <- op_array(rbind(c(0, 1),                    c(0, 0),                    c(1, 2),                    c(3, 1)), \"int32\") layer(x) ## tf.Tensor( ## [[1. 1. 0. 0.] ##  [1. 0. 0. 0.] ##  [0. 1. 1. 0.] ##  [0. 1. 0. 1.]], shape=(4, 4), dtype=float32) layer <- layer_category_encoding(num_tokens = 4, output_mode = \"count\") count_weights <- op_array(rbind(c(.1, .2),                                c(.1, .1),                                c(.2, .3),                                c(.4, .2))) x <- op_array(rbind(c(0, 1),                    c(0, 0),                    c(1, 2),                    c(3, 1)), \"int32\") layer(x, count_weights = count_weights) #   array([[01, 02, 0. , 0. ], #          [02, 0. , 0. , 0. ], #          [0. , 02, 03, 0. ], #          [0. , 02, 0. , 04]]>"},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"inputs: 1D 2D tensor integer inputs. count_weights: tensor shape inputs indicating weight sample value summing count mode. used \"multi_hot\" \"one_hot\" modes.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which crops images. — layer_center_crop","title":"A preprocessing layer which crops images. — layer_center_crop","text":"layers crops central portion images target size. image smaller target size, resized cropped return largest possible window image matches target aspect ratio. Input pixel values can range (e.g. [0., 1.) [0, 255]).","code":""},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which crops images. — layer_center_crop","text":"","code":"layer_center_crop(object, height, width, data_format = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which crops images. — layer_center_crop","text":"object Object compose layer . tensor, array, sequential model. height Integer, height output shape. width Integer, width output shape. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which crops images. — layer_center_crop","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which crops images. — layer_center_crop","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format, (..., channels, height, width), \"channels_first\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which crops images. — layer_center_crop","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels), (..., channels, target_height, target_width), \"channels_first\" format. input height/width even target height/width odd (inversely), input image left-padded 1 pixel. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":null,"dir":"Reference","previous_headings":"","what":"Concatenates a list of inputs. — layer_concatenate","title":"Concatenates a list of inputs. — layer_concatenate","text":"takes input list tensors, shape except concatenation axis, returns single tensor concatenation inputs.","code":""},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Concatenates a list of inputs. — layer_concatenate","text":"","code":"layer_concatenate(inputs, ..., axis = -1L)"},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Concatenates a list of inputs. — layer_concatenate","text":"inputs layers combine ... Standard layer keyword arguments. axis Axis along concatenate.","code":""},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Concatenates a list of inputs. — layer_concatenate","text":"tensor, concatenation inputs alongside axis axis.","code":""},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Concatenates a list of inputs. — layer_concatenate","text":"Usage Keras model:","code":"x <- op_arange(20) |> op_reshape(c(2, 2, 5)) y <- op_arange(20, 40) |> op_reshape(c(2, 2, 5)) layer_concatenate(x, y, axis = 2) ## tf.Tensor( ## [[[ 0.  1.  2.  3.  4.] ##   [ 5.  6.  7.  8.  9.] ##   [20. 21. 22. 23. 24.] ##   [25. 26. 27. 28. 29.]] ## ##  [[10. 11. 12. 13. 14.] ##   [15. 16. 17. 18. 19.] ##   [30. 31. 32. 33. 34.] ##   [35. 36. 37. 38. 39.]]], shape=(2, 4, 5), dtype=float32) x1 <- op_arange(10)     |> op_reshape(c(5, 2)) |> layer_dense(8) x2 <- op_arange(10, 20) |> op_reshape(c(5, 2)) |> layer_dense(8) y <- layer_concatenate(x1, x2)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"layer creates convolution kernel convolved layer input single spatial (temporal) dimension produce tensor outputs. use_bias TRUE, bias vector created added outputs. Finally, activation NULL, applied outputs well.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"","code":"layer_conv_1d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   groups = 1L,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int list 1 integer, specifying size convolution window. strides int list 1 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, \"valid\", \"\" \"causal\"(case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. \"causal\" results causal (dilated) convolutions, e.g. output[t] depend ontail(input, t+1). Useful modeling temporal data model violate temporal order. See WaveNet: Generative Model Raw Audio, section2.1. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated convolution. groups positive int specifying number groups input split along channel axis. group convolved separately filters // groups filters. output concatenation groups results along channel axis. Input channels filters must divisible groups. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"3D tensor representing activation(conv1d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, steps, channels) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, new_steps, filters) data_format=\"channels_first\": 3D tensor shape: (batch_shape, filters, new_steps)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"","code":"# The inputs are 128-length vectors with 10 timesteps, and the # batch size is 4. x <- random_uniform(c(4, 10, 128)) y <- x |> layer_conv_1d(32, 3, activation='relu') shape(y) ## shape(4, 8, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"1D transposed convolution layer. — layer_conv_1d_transpose","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"need transposed convolutions generally arise desire use transformation going opposite direction normal convolution, .e., something shape output convolution something shape input maintaining connectivity pattern compatible said convolution.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"","code":"layer_conv_1d_transpose(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters transpose convolution). kernel_size int list 1 integer, specifying size transposed convolution window. strides int list 1 integer, specifying stride length transposed convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated transposed convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"3D tensor representing activation(conv1d_transpose(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, steps, channels) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, new_steps, filters) data_format=\"channels_first\": 3D tensor shape: (batch_shape, filters, new_steps)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"guide convolution arithmetic deep learning Deconvolutional Networks","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"","code":"x <- random_uniform(c(4, 10, 128)) y <- x |> layer_conv_1d_transpose(32, 3, 2, activation='relu') shape(y) ## shape(4, 21, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"2D convolution layer. — layer_conv_2d","title":"2D convolution layer. — layer_conv_2d","text":"layer creates convolution kernel convolved layer input single spatial (temporal) dimension produce tensor outputs. use_bias TRUE, bias vector created added outputs. Finally, activation NULL, applied outputs well.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D convolution layer. — layer_conv_2d","text":"","code":"layer_conv_2d(   object,   filters,   kernel_size,   strides = list(1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L),   groups = 1L,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D convolution layer. — layer_conv_2d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int list 2 integer, specifying size convolution window. strides int list 2 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 2 integers, specifying dilation rate use dilated convolution. groups positive int specifying number groups input split along channel axis. group convolved separately filters // groups filters. output concatenation groups results along channel axis. Input channels filters must divisible groups. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D convolution layer. — layer_conv_2d","text":"4D tensor representing activation(conv2d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D convolution layer. — layer_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, height, width, channels) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D convolution layer. — layer_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, new_height, new_width, filters) data_format=\"channels_first\": 4D tensor shape: (batch_size, filters, new_height, new_width)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"2D convolution layer. — layer_conv_2d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"2D convolution layer. — layer_conv_2d","text":"","code":"x <- random_uniform(c(4, 10, 10, 128)) y <- x |> layer_conv_2d(32, 3, activation='relu') shape(y) ## shape(4, 8, 8, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"2D transposed convolution layer. — layer_conv_2d_transpose","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"need transposed convolutions generally arise desire use transformation going opposite direction normal convolution, .e., something shape output convolution something shape input maintaining connectivity pattern compatible said convolution.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"","code":"layer_conv_2d_transpose(   object,   filters,   kernel_size,   strides = list(1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L),   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters transposed convolution). kernel_size int list 1 integer, specifying size transposed convolution window. strides int list 1 integer, specifying stride length transposed convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated transposed convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"4D tensor representing activation(conv2d_transpose(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, height, width, channels) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, new_height, new_width, filters) data_format=\"channels_first\": 4D tensor shape: (batch_size, filters, new_height, new_width)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"guide convolution arithmetic deep learning Deconvolutional Networks","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"","code":"x <- random_uniform(c(4, 10, 8, 128)) y <- x |> layer_conv_2d_transpose(32, 2, 2, activation='relu') shape(y) ## shape(4, 20, 16, 32) # (4, 20, 16, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"3D convolution layer. — layer_conv_3d","title":"3D convolution layer. — layer_conv_3d","text":"layer creates convolution kernel convolved layer input single spatial (temporal) dimension produce tensor outputs. use_bias TRUE, bias vector created added outputs. Finally, activation NULL, applied outputs well.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"3D convolution layer. — layer_conv_3d","text":"","code":"layer_conv_3d(   object,   filters,   kernel_size,   strides = list(1L, 1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L, 1L),   groups = 1L,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"3D convolution layer. — layer_conv_3d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int list 3 integer, specifying size convolution window. strides int list 3 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 3 integers, specifying dilation rate use dilated convolution. groups positive int specifying number groups input split along channel axis. group convolved separately filters %/% groups filters. output concatenation groups results along channel axis. Input channels filters must divisible groups. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"3D convolution layer. — layer_conv_3d","text":"5D tensor representing activation(conv3d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"3D convolution layer. — layer_conv_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"3D convolution layer. — layer_conv_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3, filters) data_format=\"channels_first\": 5D tensor shape: (batch_size, filters, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"3D convolution layer. — layer_conv_3d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"3D convolution layer. — layer_conv_3d","text":"","code":"x <- random_uniform(c(4, 10, 10, 10, 128)) y <- x |> layer_conv_3d(32, 3, activation = 'relu') shape(y) ## shape(4, 8, 8, 8, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"3D transposed convolution layer. — layer_conv_3d_transpose","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"need transposed convolutions generally arise desire use transformation going opposite direction normal convolution, .e., something shape output convolution something shape input maintaining connectivity pattern compatible said convolution.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"","code":"layer_conv_3d_transpose(   object,   filters,   kernel_size,   strides = list(1L, 1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L, 1L),   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters transposed convolution). kernel_size int list 1 integer, specifying size transposed convolution window. strides int list 1 integer, specifying stride length transposed convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated transposed convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"5D tensor representing activation(conv3d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3, filters) data_format=\"channels_first\": 5D tensor shape: (batch_size, filters, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"guide convolution arithmetic deep learning Deconvolutional Networks","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"","code":"x <- random_uniform(c(4, 10, 8, 12, 128)) y <- x |> layer_conv_3d_transpose(32, 2, 2, activation = 'relu') shape(y) ## shape(4, 20, 16, 24, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"1D Convolutional LSTM. — layer_conv_lstm_1d","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"Similar LSTM layer, input transformations recurrent transformations convolutional.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"","code":"layer_conv_lstm_1d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   ...,   unroll = NULL )"},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int tuple/list 1 integer, specifying size convolution window. strides int tuple/list 1 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int tuple/list 1 integers, specifying dilation rate use dilated convolution. activation Activation function use. default hyperbolic tangent activation function applied (tanh(x)). recurrent_activation Activation function use recurrent step. use_bias Boolean, whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. bias_initializer Initializer bias vector. unit_forget_bias Boolean. TRUE, add 1 bias forget gate initialization. Use combination bias_initializer=\"zeros\". recommended Jozefowicz et al., 2015 kernel_regularizer Regularizer function applied kernel weights matrix. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. bias_regularizer Regularizer function applied bias vector. activity_regularizer Regularizer function applied . kernel_constraint Constraint function applied kernel weights matrix. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. bias_constraint Constraint function applied bias vector. dropout Float 0 1. Fraction units drop linear transformation inputs. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default FALSE). TRUE, last state sample index batch used initial state sample index following batch. ... forward/backward compatability. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"inputs: 4D tensor. initial_state: List initial state tensors passed first call cell. mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout set.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"data_format=\"channels_first\": 4D tensor shape: (samples, time, channels, rows) data_format=\"channels_last\": 4D tensor shape: (samples, time, rows, channels)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"return_state: list tensors. first tensor output. remaining tensors last states, 3D tensor shape: (samples, filters, new_rows) data_format='channels_first' shape: (samples, new_rows, filters) data_format='channels_last'. rows values might changed due padding. return_sequences: 4D tensor shape: (samples, timesteps, filters, new_rows) data_format='channels_first' shape: (samples, timesteps, new_rows, filters) data_format='channels_last'. Else, 3D tensor shape: (samples, filters, new_rows) data_format='channels_first' shape: (samples, new_rows, filters) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"Shi et al., 2015 (current implementation include feedback loop cells output).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"2D Convolutional LSTM. — layer_conv_lstm_2d","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"Similar LSTM layer, input transformations recurrent transformations convolutional.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"","code":"layer_conv_lstm_2d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   ...,   unroll = NULL )"},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int tuple/list 2 integers, specifying size convolution window. strides int tuple/list 2 integers, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int tuple/list 2 integers, specifying dilation rate use dilated convolution. activation Activation function use. default hyperbolic tangent activation function applied (tanh(x)). recurrent_activation Activation function use recurrent step. use_bias Boolean, whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. bias_initializer Initializer bias vector. unit_forget_bias Boolean. TRUE, add 1 bias forget gate initialization. Use combination bias_initializer=\"zeros\". recommended Jozefowicz et al., 2015 kernel_regularizer Regularizer function applied kernel weights matrix. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. bias_regularizer Regularizer function applied bias vector. activity_regularizer Regularizer function applied . kernel_constraint Constraint function applied kernel weights matrix. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. bias_constraint Constraint function applied bias vector. dropout Float 0 1. Fraction units drop linear transformation inputs. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default FALSE). TRUE, last state sample index batch used initial state sample index following batch. ... forward/backward compatability. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"inputs: 5D tensor. mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout set. initial_state: List initial state tensors passed first call cell.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"data_format='channels_first': 5D tensor shape: (samples, time, channels, rows, cols) data_format='channels_last': 5D tensor shape: (samples, time, rows, cols, channels)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"return_state: list tensors. first tensor output. remaining tensors last states, 4D tensor shape: (samples, filters, new_rows, new_cols) data_format='channels_first' shape: (samples, new_rows, new_cols, filters) data_format='channels_last'. rows cols values might changed due padding. return_sequences: 5D tensor shape: (samples, timesteps, filters, new_rows, new_cols) data_format='channels_first' shape: (samples, timesteps, new_rows, new_cols, filters) data_format='channels_last'. Else, 4D tensor shape: (samples, filters, new_rows, new_cols) data_format='channels_first' shape: (samples, new_rows, new_cols, filters) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"Shi et al., 2015 (current implementation include feedback loop cells output).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"3D Convolutional LSTM. — layer_conv_lstm_3d","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"Similar LSTM layer, input transformations recurrent transformations convolutional.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"","code":"layer_conv_lstm_3d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   ...,   unroll = NULL )"},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int tuple/list 3 integers, specifying size convolution window. strides int tuple/list 3 integers, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int tuple/list 3 integers, specifying dilation rate use dilated convolution. activation Activation function use. default hyperbolic tangent activation function applied (tanh(x)). recurrent_activation Activation function use recurrent step. use_bias Boolean, whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. bias_initializer Initializer bias vector. unit_forget_bias Boolean. TRUE, add 1 bias forget gate initialization. Use combination bias_initializer=\"zeros\". recommended Jozefowicz et al., 2015 kernel_regularizer Regularizer function applied kernel weights matrix. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. bias_regularizer Regularizer function applied bias vector. activity_regularizer Regularizer function applied . kernel_constraint Constraint function applied kernel weights matrix. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. bias_constraint Constraint function applied bias vector. dropout Float 0 1. Fraction units drop linear transformation inputs. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default FALSE). TRUE, last state sample index batch used initial state sample index following batch. ... forward/backward compatability. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"inputs: 6D tensor. mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout set. initial_state: List initial state tensors passed first call cell.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"data_format='channels_first': 5D tensor shape: (samples, time, channels, *spatial_dims) data_format='channels_last': 5D tensor shape: (samples, time, *spatial_dims, channels)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"return_state: list tensors. first tensor output. remaining tensors last states, 4D tensor shape: (samples, filters, *spatial_dims) data_format='channels_first' shape: (samples, *spatial_dims, filters) data_format='channels_last'. return_sequences: 5D tensor shape: (samples, timesteps, filters, *spatial_dims) data_format='channels_first' shape: (samples, timesteps, *spatial_dims, filters) data_format='channels_last'. Else, 4D tensor shape: (samples, filters, *spatial_dims) data_format='channels_first' shape: (samples, *spatial_dims, filters) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"Shi et al., 2015 (current implementation include feedback loop cells output).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"crops along time dimension (axis 2).","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"","code":"layer_cropping_1d(object, cropping = list(1L, 1L), ...)"},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"object Object compose layer . tensor, array, sequential model. cropping Int, list int (length 2). int: many units trimmed beginning end cropping dimension (axis 1). list 2 ints: many units trimmed beginning end cropping dimension ((left_crop, right_crop)). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"","code":"input_shape <- c(2, 3, 2) x <- op_arange(prod(input_shape)) |> op_reshape(input_shape) x ## tf.Tensor( ## [[[ 0.  1.] ##   [ 2.  3.] ##   [ 4.  5.]] ## ##  [[ 6.  7.] ##   [ 8.  9.] ##   [10. 11.]]], shape=(2, 3, 2), dtype=float32) y <- x |> layer_cropping_1d(cropping = 1) y ## tf.Tensor( ## [[[2. 3.]] ## ##  [[8. 9.]]], shape=(2, 1, 2), dtype=float32)"},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"3D tensor shape (batch_size, axis_to_crop, features)","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"3D tensor shape (batch_size, cropped_axis, features)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"crops along spatial dimensions, .e. height width.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"","code":"layer_cropping_2d(   object,   cropping = list(list(0L, 0L), list(0L, 0L)),   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"object Object compose layer . tensor, array, sequential model. cropping Int, list 2 ints, list 2 lists 2 ints. int: symmetric cropping applied height width. list 2 ints: interpreted two different symmetric cropping values height width: (symmetric_height_crop, symmetric_width_crop). list 2 lists 2 ints: interpreted ((top_crop, bottom_crop), (left_crop, right_crop)). data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"","code":"input_shape <- c(2, 28, 28, 3) x <- op_arange(prod(input_shape), dtype ='int32') |> op_reshape(input_shape) y <- x |> layer_cropping_2d(cropping=list(c(2, 2), c(4, 4))) shape(y) ## shape(2, 24, 20, 3)"},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, height, width, channels) data_format \"channels_first\": (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, cropped_height, cropped_width, channels) data_format \"channels_first\": (batch_size, channels, cropped_height, cropped_width)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"Cropping layer 3D data (e.g. spatial spatio-temporal).","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"","code":"layer_cropping_3d(   object,   cropping = list(list(1L, 1L), list(1L, 1L), list(1L, 1L)),   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"object Object compose layer . tensor, array, sequential model. cropping Int, list 3 ints, list 3 lists 2 ints. int: symmetric cropping applied depth, height, width. list 3 ints: interpreted three different symmetric cropping values depth, height, width: (symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop). list 3 lists 2 ints: interpreted ((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_crop)). data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"","code":"input_shape <- c(2, 28, 28, 10, 3) x <- input_shape %>% { op_reshape(seq(prod(.)), .) } y <- x |> layer_cropping_3d(cropping = c(2, 4, 2)) shape(y) ## shape(2, 24, 20, 6, 3)"},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop, channels) data_format \"channels_first\": (batch_size, channels, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, first_cropped_axis, second_cropped_axis, third_cropped_axis, channels) data_format \"channels_first\": (batch_size, channels, first_cropped_axis, second_cropped_axis, third_cropped_axis)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_dense.html","id":null,"dir":"Reference","previous_headings":"","what":"Just your regular densely-connected NN layer. — layer_dense","title":"Just your regular densely-connected NN layer. — layer_dense","text":"Dense implements operation: output = activation(dot(input, kernel) + bias) activation element-wise activation function passed activation argument, kernel weights matrix created layer, bias bias vector created layer (applicable use_bias TRUE).","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Just your regular densely-connected NN layer. — layer_dense","text":"","code":"layer_dense(   object,   units,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   lora_rank = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Just your regular densely-connected NN layer. — layer_dense","text":"object Object compose layer . tensor, array, sequential model. units Positive integer, dimensionality output space. activation Activation function use. specify anything, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix. bias_initializer Initializer bias vector. kernel_regularizer Regularizer function applied kernel weights matrix. bias_regularizer Regularizer function applied bias vector. activity_regularizer Regularizer function applied output layer (\"activation\"). kernel_constraint Constraint function applied kernel weights matrix. bias_constraint Constraint function applied bias vector. lora_rank Optional integer. set, layer's forward pass implement LoRA (Low-Rank Adaptation) provided rank. LoRA sets layer's kernel non-trainable replaces delta original kernel, obtained via multiplying two lower-rank trainable matrices. can useful reduce computation cost fine-tuning large dense layers. can also enable LoRA existing Dense layer calling layer$enable_lora(rank). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Just your regular densely-connected NN layer. — layer_dense","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Just your regular densely-connected NN layer. — layer_dense","text":"input layer rank greater 2, Dense computes dot product inputs kernel along last axis inputs axis 0 kernel (using tf.tensordot). example, input dimensions (batch_size, d0, d1), create kernel shape (d1, units), kernel operates along axis 2 input, every sub-tensor shape (1, 1, d1) (batch_size * d0 sub-tensors). output case shape (batch_size, d0, units).","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Just your regular densely-connected NN layer. — layer_dense","text":"N-D tensor shape: (batch_size, ..., input_dim). common situation 2D input shape (batch_size, input_dim).","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Just your regular densely-connected NN layer. — layer_dense","text":"N-D tensor shape: (batch_size, ..., units). instance, 2D input shape (batch_size, input_dim), output shape (batch_size, units).","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Just your regular densely-connected NN layer. — layer_dense","text":"","code":"enable_lora(   rank,   a_initializer = 'he_uniform',   b_initializer = 'zeros' )"},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"readonly-properties-","dir":"Reference","previous_headings":"","what":"Readonly properties:","title":"Just your regular densely-connected NN layer. — layer_dense","text":"kernel","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"1D depthwise convolution layer. — layer_depthwise_conv_1d","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"Depthwise convolution type convolution input channel convolved different kernel (called depthwise kernel). can understand depthwise convolution first step depthwise separable convolution. implemented via following steps: Split input individual channels. Convolve channel individual depthwise kernel depth_multiplier output channels. Concatenate convolved outputs along channels axis. Unlike regular 1D convolution, depthwise convolution mix information across different input channels. depth_multiplier argument determines many filters applied one input channel. , controls amount output channels generated per input channel depthwise step.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"","code":"layer_depthwise_conv_1d(   object,   kernel_size,   strides = 1L,   padding = \"valid\",   depth_multiplier = 1L,   data_format = NULL,   dilation_rate = 1L,   activation = NULL,   use_bias = TRUE,   depthwise_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   depthwise_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   depthwise_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"object Object compose layer . tensor, array, sequential model. kernel_size int list 1 integer, specifying size depthwise convolution window. strides int list 1 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. depth_multiplier number depthwise convolution output channels input channel. total number depthwise convolution output channels equal input_channel * depth_multiplier. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. depthwise_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. depthwise_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. depthwise_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"3D tensor representing activation(depthwise_conv1d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, steps, channels) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, new_steps, channels * depth_multiplier) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels * depth_multiplier, new_steps)","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"","code":"x <- random_uniform(c(4, 10, 12)) y <- x |> layer_depthwise_conv_1d(   kernel_size = 3,   depth_multiplier = 3,   activation = 'relu' ) shape(y) ## shape(4, 8, 36)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"2D depthwise convolution layer. — layer_depthwise_conv_2d","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"Depthwise convolution type convolution input channel convolved different kernel (called depthwise kernel). can understand depthwise convolution first step depthwise separable convolution. implemented via following steps: Split input individual channels. Convolve channel individual depthwise kernel depth_multiplier output channels. Concatenate convolved outputs along channels axis. Unlike regular 2D convolution, depthwise convolution mix information across different input channels. depth_multiplier argument determines many filters applied one input channel. , controls amount output channels generated per input channel depthwise step.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"","code":"layer_depthwise_conv_2d(   object,   kernel_size,   strides = list(1L, 1L),   padding = \"valid\",   depth_multiplier = 1L,   data_format = NULL,   dilation_rate = list(1L, 1L),   activation = NULL,   use_bias = TRUE,   depthwise_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   depthwise_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   depthwise_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"object Object compose layer . tensor, array, sequential model. kernel_size int list 2 integer, specifying size depthwise convolution window. strides int list 2 integer, specifying stride length depthwise convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. depth_multiplier number depthwise convolution output channels input channel. total number depthwise convolution output channels equal input_channel * depth_multiplier. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 2 integers, specifying dilation rate use dilated convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. depthwise_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. depthwise_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. depthwise_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"4D tensor representing activation(depthwise_conv2d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, height, width, channels) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, new_height, new_width, channels * depth_multiplier) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels * depth_multiplier, new_height, new_width)","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"","code":"x <- random_uniform(c(4, 10, 10, 12)) y <- x |> layer_depthwise_conv_2d(3, 3, activation = 'relu') shape(y) ## shape(4, 3, 3, 12)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"layer place element input data one several contiguous ranges output integer index indicating range element placed . Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"","code":"layer_discretization(   object,   bin_boundaries = NULL,   num_bins = NULL,   epsilon = 0.01,   output_mode = \"int\",   sparse = FALSE,   dtype = NULL,   name = NULL )"},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"object Object compose layer . tensor, array, sequential model. bin_boundaries list bin boundaries. leftmost rightmost bins always extend -Inf Inf, bin_boundaries = c(0, 1, 2) generates bins (-Inf, 0), [0, 1), [1, 2), [2, +Inf). option set, adapt() called. num_bins integer number bins compute. option set, adapt() called learn bin boundaries. epsilon Error tolerance, typically small fraction close zero (e.g. 0.01). Higher values epsilon increase quantile approximation, hence result unequal buckets, improve performance resource consumption. output_mode Specification output layer. Values can \"int\", \"one_hot\", \"multi_hot\", \"count\" configuring layer follows: \"int\": Return discretized bin indices directly. \"one_hot\": Encodes individual element input array size num_bins, containing 1 input's bin index. last dimension size 1, encode dimension.  last dimension size 1, append new dimension encoded output. \"multi_hot\": Encodes sample input single array size num_bins, containing 1 bin index index present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). \"count\": \"multi_hot\", int array contains count number times bin index appeared sample. Defaults \"int\". sparse Boolean. applicable \"one_hot\", \"multi_hot\", \"count\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. dtype datatype (e.g., \"float32\"). name String, name object","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"array dimension 2 higher.","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"input shape.","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"Discretize float values based provided buckets.     Discretize float values based number buckets compute.","code":"input <- op_array(rbind(c(-1.5, 1, 3.4, 0.5),                        c(0, 3, 1.3, 0),                        c(-.5, 0, .5, 1),                        c(1.5, 2, 2.5, 3))) output <- input |> layer_discretization(bin_boundaries = c(0, 1, 2)) output ## tf.Tensor( ## [[0 2 3 1] ##  [1 3 2 1] ##  [0 1 1 2] ##  [2 3 3 3]], shape=(4, 4), dtype=int64) layer <- layer_discretization(num_bins = 4, epsilon = 0.01) layer |> adapt(input) layer(input) ## tf.Tensor( ## [[0 2 3 1] ##  [1 3 2 1] ##  [0 1 1 2] ##  [2 3 3 3]], shape=(4, 4), dtype=int64)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_dot.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes element-wise dot product of two tensors. — layer_dot","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"takes list inputs size 2, axes corresponding input along dot product performed. say x y two input tensors shapes (2, 3, 5) (2, 10, 3). batch dimension size inputs, axes correspond dimensions size corresponding inputs. e.g. axes = c(1, 2), dot product x, y result tensor shape (2, 5, 10)","code":""},{"path":"https://keras.posit.co/reference/layer_dot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"","code":"layer_dot(inputs, ..., axes, normalize = FALSE)"},{"path":"https://keras.posit.co/reference/layer_dot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"inputs layers combine ... Standard layer keyword arguments. axes Integer list integers, axis axes along take dot product. list, two integers corresponding desired axis first input desired axis second input, respectively. Note size two selected axes must match. normalize Whether L2-normalize samples along dot product axis taking dot product. set TRUE, output dot product cosine proximity two samples.","code":""},{"path":"https://keras.posit.co/reference/layer_dot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"tensor, dot product samples inputs.","code":""},{"path":"https://keras.posit.co/reference/layer_dot.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"Usage Keras model:","code":"x <- op_reshape(0:9,   c(1, 5, 2)) y <- op_reshape(10:19, c(1, 2, 5)) layer_dot(x, y, axes=c(2, 3)) ## tf.Tensor( ## [[[260 360] ##   [320 445]]], shape=(1, 2, 2), dtype=int32) x1 <- op_reshape(0:9, c(5, 2)) |> layer_dense(8) x2 <- op_reshape(10:19, c(5, 2)) |> layer_dense(8) shape(x1) ## shape(5, 8) shape(x2) ## shape(5, 8) y <- layer_dot(x1, x2, axes=2) shape(y) ## shape(5, 1)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies dropout to the input. — layer_dropout","title":"Applies dropout to the input. — layer_dropout","text":"Dropout layer randomly sets input units 0 frequency rate step training time, helps prevent overfitting. Inputs set 0 scaled 1 / (1 - rate) sum inputs unchanged. Note Dropout layer applies training set TRUE call(), values dropped inference. using model.fit, training appropriately set TRUE automatically. contexts, can set argument explicitly TRUE calling layer. (contrast setting trainable=FALSE Dropout layer. trainable affect layer's behavior, Dropout variables/weights can frozen training.)","code":""},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies dropout to the input. — layer_dropout","text":"","code":"layer_dropout(object, rate, noise_shape = NULL, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies dropout to the input. — layer_dropout","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. Fraction input units drop. noise_shape 1D integer tensor representing shape binary dropout mask multiplied input. instance, inputs shape (batch_size, timesteps, features) want dropout mask timesteps, can use noise_shape=(batch_size, 1, features). seed R integer use random seed. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies dropout to the input. — layer_dropout","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Applies dropout to the input. — layer_dropout","text":"inputs: Input tensor (rank). training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (nothing).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":null,"dir":"Reference","previous_headings":"","what":"A layer that uses einsum as the backing computation. — layer_einsum_dense","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"layer can perform einsum calculations arbitrary dimensionality.","code":""},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"","code":"layer_einsum_dense(   object,   equation,   output_shape,   activation = NULL,   bias_axes = NULL,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   lora_rank = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"object Object compose layer . tensor, array, sequential model. equation equation describing einsum perform. equation must valid einsum string form ab,bc->ac, ...ab,bc->...ac, ab...,bc->ac... 'ab', 'bc', 'ac' can valid einsum axis expression sequence. output_shape expected shape output tensor (excluding batch dimension dimensions represented ellipses). can specify NA NULL dimension unknown can inferred input shape. activation Activation function use. specify anything, activation applied (, \"linear\" activation: (x) = x). bias_axes string containing output dimension(s) apply bias . character bias_axes string correspond character output portion equation string. kernel_initializer Initializer kernel weights matrix. bias_initializer Initializer bias vector. kernel_regularizer Regularizer function applied kernel weights matrix. bias_regularizer Regularizer function applied bias vector. kernel_constraint Constraint function applied kernel weights matrix. bias_constraint Constraint function applied bias vector. lora_rank Optional integer. set, layer's forward pass implement LoRA (Low-Rank Adaptation) provided rank. LoRA sets layer's kernel non-trainable replaces delta original kernel, obtained via multiplying two lower-rank trainable matrices (factorization happens last dimension). can useful reduce computation cost fine-tuning large dense layers. can also enable LoRA existing EinsumDense layer calling layer$enable_lora(rank). ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"Biased dense layer einsums example shows instantiate standard Keras dense layer using einsum operations. example equivalent layer_Dense(64, use_bias=TRUE).     Applying dense layer sequence example shows instantiate layer applies dense operation every element sequence. , output_shape two values (since two non-batch dimensions output); first dimension output_shape NA, sequence dimension b unknown shape.     Applying dense layer sequence using ellipses example shows instantiate layer applies dense operation every element sequence, uses ellipsis notation instead specifying batch sequence dimensions. using ellipsis notation specified one axis, output_shape arg single value. instantiated way, layer can handle number sequence dimensions - including case sequence dimension exists.","code":"input <- layer_input(shape = c(32)) output <- input |>   layer_einsum_dense(\"ab,bc->ac\",                      output_shape = 64,                      bias_axes = \"c\") output # shape(NA, 64) ## <KerasTensor shape=(None, 64), dtype=float32, sparse=False, name=keras_tensor_1> input <- layer_input(shape = c(32, 128)) output <- input |>   layer_einsum_dense(\"abc,cd->abd\",                      output_shape = c(NA, 64),                      bias_axes = \"d\") output  # shape(NA, 32, 64) ## <KerasTensor shape=(None, None, 64), dtype=float32, sparse=False, name=keras_tensor_3> input <- layer_input(shape = c(32, 128)) output <- input |>   layer_einsum_dense(\"...x,xy->...y\",                      output_shape = 64,                      bias_axes = \"y\")  output  # shape(NA, 32, 64) ## <KerasTensor shape=(None, 32, 64), dtype=float32, sparse=False, name=keras_tensor_5>"},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"","code":"enable_lora(   rank,   a_initializer = 'he_uniform',   b_initializer = 'zeros' ) quantize(mode)"},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"readonly-properties-","dir":"Reference","previous_headings":"","what":"Readonly properties:","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"kernel","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":null,"dir":"Reference","previous_headings":"","what":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"e.g. rbind(4L, 20L) \\(\\rightarrow\\) rbind(c(0.25, 0.1), c(0.6, -0.2)) layer can used positive integer inputs fixed range.","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"","code":"layer_embedding(   object,   input_dim,   output_dim,   embeddings_initializer = \"uniform\",   embeddings_regularizer = NULL,   embeddings_constraint = NULL,   mask_zero = FALSE,   weights = NULL,   lora_rank = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"object Object compose layer . tensor, array, sequential model. input_dim Integer. Size vocabulary, .e. maximum integer index + 1. output_dim Integer. Dimension dense embedding. embeddings_initializer Initializer embeddings matrix (see keras3::initializer_*). embeddings_regularizer Regularizer function applied embeddings matrix (see keras3::regularizer_*). embeddings_constraint Constraint function applied embeddings matrix (see keras3::constraint_*). mask_zero Boolean, whether input value 0 special \"padding\" value masked . useful using recurrent layers may take variable length input. TRUE, subsequent layers model need support masking exception raised. mask_zero set TRUE, consequence, index 0 used vocabulary (input_dim equal size vocabulary + 1). weights Optional floating-point matrix size (input_dim, output_dim). initial embeddings values use. lora_rank Optional integer. set, layer's forward pass implement LoRA (Low-Rank Adaptation) provided rank. LoRA sets layer's embeddings matrix non-trainable replaces delta original matrix, obtained via multiplying two lower-rank trainable matrices. can useful reduce computation cost fine-tuning large embedding layers. can also enable LoRA existing Embedding layer instance calling layer$enable_lora(rank). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"","code":"model <- keras_model_sequential() |>   layer_embedding(1000, 64)  # The model will take as input an integer matrix of size (batch,input_length), # and the largest integer (i.e. word index) in the input # should be no larger than 999 (vocabulary size). # Now model$output_shape is (NA, 10, 64), where `NA` is the batch # dimension.  input_array <- random_integer(shape = c(32, 10), minval = 0, maxval = 1000) model |> compile('rmsprop', 'mse') output_array <- model |> predict(input_array, verbose = 0) dim(output_array)    # (32, 10, 64) ## [1] 32 10 64"},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"2D tensor shape: (batch_size, input_length).","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"3D tensor shape: (batch_size, input_length, output_dim).","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"","code":"enable_lora(   rank,   a_initializer = 'he_uniform',   b_initializer = 'zeros' ) quantize(mode) quantized_build(input_shape, mode) quantized_call(inputs)"},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"readonly-properties-","dir":"Reference","previous_headings":"","what":"Readonly properties:","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"embeddings","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":null,"dir":"Reference","previous_headings":"","what":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"Available feature types: Note features can referred string name, e.g. \"integer_categorical\". using string name, default argument values used.","code":"# Plain float values. feature_float(name = NULL)  # Float values to be preprocessed via featurewise standardization # (i.e. via a `layer_normalization()` layer). feature_float_normalized(name = NULL)  # Float values to be preprocessed via linear rescaling # (i.e. via a `layer_rescaling` layer). feature_float_rescaled(scale = 1., offset = 0., name = NULL)  # Float values to be discretized. By default, the discrete # representation will then be one-hot encoded. feature_float_discretized(   num_bins,   bin_boundaries = NULL,   output_mode = \"one_hot\",   name = NULL )  # Integer values to be indexed. By default, the discrete # representation will then be one-hot encoded. feature_integer_categorical(   max_tokens = NULL,   num_oov_indices = 1,   output_mode = \"one_hot\",   name = NULL )  # String values to be indexed. By default, the discrete # representation will then be one-hot encoded. feature_string_categorical(   max_tokens = NULL,   num_oov_indices = 1,   output_mode = \"one_hot\",   name = NULL )  # Integer values to be hashed into a fixed number of bins. # By default, the discrete representation will then be one-hot encoded. feature_integer_hashed(num_bins, output_mode = \"one_hot\", name = NULL)  # String values to be hashed into a fixed number of bins. # By default, the discrete representation will then be one-hot encoded. feature_string_hashed(num_bins, output_mode = \"one_hot\", name = NULL)"},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"","code":"layer_feature_space(   object,   features,   output_mode = \"concat\",   crosses = NULL,   crossing_dim = 32L,   hashing_dim = 32L,   num_discretization_bins = 32L,   name = NULL,   feature_names = NULL )  feature_cross(feature_names, crossing_dim, output_mode = \"one_hot\")  feature_custom(dtype, preprocessor, output_mode)  feature_float(name = NULL)  feature_float_rescaled(scale = 1, offset = 0, name = NULL)  feature_float_normalized(name = NULL)  feature_float_discretized(   num_bins,   bin_boundaries = NULL,   output_mode = \"one_hot\",   name = NULL )  feature_integer_categorical(   max_tokens = NULL,   num_oov_indices = 1,   output_mode = \"one_hot\",   name = NULL )  feature_string_categorical(   max_tokens = NULL,   num_oov_indices = 1,   output_mode = \"one_hot\",   name = NULL )  feature_string_hashed(num_bins, output_mode = \"one_hot\", name = NULL)  feature_integer_hashed(num_bins, output_mode = \"one_hot\", name = NULL)"},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"object see description features see description output_mode string. layer_feature_space(), one \"concat\" \"dict\". concat mode, features get concatenated together single vector. dict mode, FeatureSpace returns named list individually encoded features (names input list names). feature_* functions, one : \"int\" \"one_hot\" \"float\". crosses List features crossed together, e.g. crosses=list(c(\"feature_1\", \"feature_2\")). features \"crossed\" hashing combined value fixed-length vector. crossing_dim Default vector size hashing crossed features. Defaults 32. hashing_dim Default vector size hashing features type \"integer_hashed\" \"string_hashed\". Defaults 32. num_discretization_bins Default number bins used discretizing features type \"float_discretized\". Defaults 32. name String, name object feature_names Named list mapping names features type specification, e.g. list(my_feature = \"integer_categorical\") list(my_feature = feature_integer_categorical()). complete list supported types, see \"Available feature types\" paragraph . dtype string, output dtype feature. E.g., \"float32\". preprocessor callable. scale, offset Passed layer_rescaling() num_bins, bin_boundaries Passed layer_discretization() max_tokens, num_oov_indices Passed layer_integer_lookup() feature_integer_categorical() layer_string_lookup() feature_string_categorical().","code":""},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"Basic usage named list input data:   Basic usage tf.data:   Basic usage Keras Functional API:           Customizing feature feature cross:   Returning dict (named list) integer-encoded features:   Specifying Keras preprocessing layer:   Retrieving underlying Keras preprocessing layers:   Saving reloading FeatureSpace:","code":"raw_data <- list(   float_values = c(0.0, 0.1, 0.2, 0.3),   string_values = c(\"zero\", \"one\", \"two\", \"three\"),   int_values = as.integer(c(0, 1, 2, 3)) )  dataset <- tfdatasets::tensor_slices_dataset(raw_data)  feature_space <- layer_feature_space(   features = list(     float_values = \"float_normalized\",     string_values = \"string_categorical\",     int_values = \"integer_categorical\"   ),   crosses = list(c(\"string_values\", \"int_values\")),   output_mode = \"concat\" )  # Before you start using the feature_space(), # you must `adapt()` it on some data. feature_space |> adapt(dataset)  # You can call the feature_space() on a named list of # data (batched or unbatched). output_vector <- feature_space(raw_data) library(tfdatasets) # Unlabeled data preprocessed_ds <- unlabeled_dataset |>   dataset_map(feature_space)  # Labeled data preprocessed_ds <- labeled_dataset |>   dataset_map(function(x, y) tuple(feature_space(x), y)) # Retrieve a named list of Keras layer_input() objects (inputs <- feature_space$get_inputs()) ## $float_values ## <KerasTensor shape=(None, 1), dtype=float32, sparse=None, name=float_values> ## ## $string_values ## <KerasTensor shape=(None, 1), dtype=string, sparse=None, name=string_values> ## ## $int_values ## <KerasTensor shape=(None, 1), dtype=int32, sparse=None, name=int_values> # Retrieve the corresponding encoded Keras tensors (encoded_features <- feature_space$get_encoded_features()) ## <KerasTensor shape=(None, 43), dtype=float32, sparse=False, name=keras_tensor_7> # Build a Functional model outputs <- encoded_features |> layer_dense(1, activation = \"sigmoid\") model <- keras_model(inputs, outputs) feature_space <- layer_feature_space(   features = list(     float_values = feature_float_normalized(),     string_values = feature_string_categorical(max_tokens = 10),     int_values = feature_integer_categorical(max_tokens = 10)   ),   crosses = list(     feature_cross(c(\"string_values\", \"int_values\"), crossing_dim = 32)   ),   output_mode = \"concat\" ) feature_space <- layer_feature_space(   features = list(     \"string_values\" = feature_string_categorical(output_mode = \"int\"),     \"int_values\" = feature_integer_categorical(output_mode = \"int\")   ),   crosses = list(     feature_cross(       feature_names = c(\"string_values\", \"int_values\"),       crossing_dim = 32,       output_mode = \"int\"     )   ),   output_mode = \"dict\" ) # Let's say that one of the features is a short text paragraph that # we want to encode as a vector (one vector per paragraph) via TF-IDF. data <- list(text = c(\"1st string\", \"2nd string\", \"3rd string\"))  # There's a Keras layer for this: layer_text_vectorization() custom_layer <- layer_text_vectorization(output_mode = \"tf_idf\")  # We can use feature_custom() to create a custom feature # that will use our preprocessing layer. feature_space <- layer_feature_space(   features = list(     text = feature_custom(preprocessor = custom_layer,                           dtype = \"string\",                           output_mode = \"float\"     )   ),   output_mode = \"concat\" ) feature_space |> adapt(tfdatasets::tensor_slices_dataset(data)) output_vector <- feature_space(data) # The preprocessing layer of each feature is available in `$preprocessors`. preprocessing_layer <- feature_space$preprocessors$feature1  # The crossing layer of each feature cross is available in `$crossers`. # It's an instance of layer_hashed_crossing() crossing_layer <- feature_space$crossers[[\"feature1_X_feature2\"]] feature_space$save(\"featurespace.keras\") reloaded_feature_space <- keras$models$load_model(\"featurespace.keras\")"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":null,"dir":"Reference","previous_headings":"","what":"Flattens the input. Does not affect the batch size. — layer_flatten","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"Flattens input. affect batch size.","code":""},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"","code":"layer_flatten(object, data_format = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"object Object compose layer . tensor, array, sequential model. data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, ..., channels) \"channels_first\" corresponds inputs shape (batch, channels, ...). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"inputs shaped (batch) without feature axis, flattening adds extra channel dimension output shape (batch, 1).","code":""},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"","code":"x <- layer_input(shape=c(10, 64)) y <- x |> layer_flatten() shape(y) ## shape(NA, 640)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_flax_module_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","title":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","text":"layer enables use Flax components form flax.linen.Module instances within Keras using JAX backend Keras. module method use forward pass can specified via method argument __call__ default. method must take following arguments exact names: self method bound module, case default __call__, module otherwise pass module. inputs: inputs model, JAX array PyTree arrays. training (optional): argument specifying training mode inference mode, TRUE passed training mode. FlaxLayer handles non-trainable state model required RNGs automatically. Note mutable parameter flax.linen.Module.apply() set DenyList([\"params\"]), therefore making assumption variables outside \"params\" collection non-trainable weights. example shows create FlaxLayer Flax Module default __call__ method training argument:   example shows wrap module method conform required signature. allows multiple input arguments training argument different name values. additionally shows use function bound module.","code":"# keras3::use_backend(\"jax\") # py_install(\"flax\", \"r-keras\")  if(config_backend() == \"jax\" &&    reticulate::py_module_available(\"flax\")) {  flax <- import(\"flax\")  MyFlaxModule(flax$linen$Module) %py_class% {   `__call__` <- flax$linen$compact(\\(self, inputs) {     inputs |>       (flax$linen$Conv(features = 32L, kernel_size = tuple(3L, 3L)))() |>       flax$linen$relu() |>       flax$linen$avg_pool(window_shape = tuple(2L, 2L),                           strides = tuple(2L, 2L)) |>       # flatten all except batch_size axis       (\\(x) x$reshape(tuple(x$shape[[1]], -1L)))() |>       (flax$linen$Dense(features = 200L))() |>       flax$linen$relu() |>       (flax$linen$Dense(features = 10L))() |>       flax$linen$softmax()   }) }  # typical usage: input <- keras_input(c(28, 28, 3)) output <- input |>   layer_flax_module_wrapper(MyFlaxModule())  model <- keras_model(input, output)  # to instantiate the layer before composing: flax_module <- MyFlaxModule() keras_layer <- layer_flax_module_wrapper(module = flax_module)  input <- keras_input(c(28, 28, 3)) output <- input |>   keras_layer()  model <- keras_model(input, output)  } flax <- import(\"flax\")  MyFlaxModule(flax$linen$Module) \\%py_class\\% {   forward <-     flax$linen$compact(\\(self, inputs1, input2, deterministic) {       # do work ....       outputs # return     }) }  my_flax_module_wrapper <- function(module, inputs, training) {   c(input1, input2) \\%<-\\% inputs   module$forward(input1, input2,!training) }  flax_module <- MyFlaxModule() keras_layer <- layer_flax_module_wrapper(module = flax_module,                                          method = my_flax_module_wrapper)"},{"path":"https://keras.posit.co/reference/layer_flax_module_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","text":"","code":"layer_flax_module_wrapper(object, module, method = NULL, variables = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_flax_module_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","text":"object Object compose layer . tensor, array, sequential model. module instance flax.linen.Module subclass. method method call model. generally method Module. provided, __call__ method used. method can also function defined Module, case must take Module first argument. used Module.init Module.apply. Details documented method argument flax.linen.Module.apply(). variables dict (named R list) containing variables module format returned flax.linen.Module.init(). contain \"params\" key , applicable, keys collections variables non-trainable state. allows passing trained parameters learned non-trainable state controlling initialization. NULL passed, module's init function called build time initialize variables model. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_flax_module_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"regularization layer, active training time.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"","code":"layer_gaussian_dropout(object, rate, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"object Object compose layer . tensor, array, sequential model. rate Float, drop probability (Dropout). multiplicative noise standard deviation sqrt(rate / (1 - rate)). seed Integer, optional random seed enable deterministic behavior. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"inputs: Input tensor (rank). training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (nothing).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"useful mitigate overfitting (see form random data augmentation). Gaussian Noise (GS) natural choice corruption process real valued inputs. regularization layer, active training time.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"","code":"layer_gaussian_noise(object, stddev, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"object Object compose layer . tensor, array, sequential model. stddev Float, standard deviation noise distribution. seed Integer, optional random seed enable deterministic behavior. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"inputs: Input tensor (rank). training: Python boolean indicating whether layer behave training mode (adding noise) inference mode (nothing).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"Global average pooling operation temporal data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"","code":"layer_global_average_pooling_1d(   object,   data_format = NULL,   keepdims = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, temporal dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"inputs: 3D tensor. mask: Binary tensor shape (batch_size, steps) indicating whether given step masked (excluded average).","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"data_format='channels_last': 3D tensor shape: (batch_size, steps, features) data_format='channels_first': 3D tensor shape: (batch_size, features, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"keepdims=FALSE: 2D tensor shape (batch_size, features). keepdims=TRUE: data_format=\"channels_last\": 3D tensor shape (batch_size, 1, features) data_format=\"channels_first\": 3D tensor shape (batch_size, features, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"","code":"x <- random_uniform(c(2, 3, 4)) y <- x |> layer_global_average_pooling_1d() shape(y) ## shape(2, 4)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"Global average pooling operation 2D data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"","code":"layer_global_average_pooling_2d(   object,   data_format = NULL,   keepdims = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, features, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, spatial dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"data_format='channels_last': 4D tensor shape: (batch_size, height, width, channels) data_format='channels_first': 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"keepdims=FALSE: 2D tensor shape (batch_size, channels). keepdims=TRUE: data_format=\"channels_last\": 4D tensor shape (batch_size, 1, 1, channels) data_format=\"channels_first\": 4D tensor shape (batch_size, channels, 1, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"","code":"x <- random_uniform(c(2, 4, 5, 3)) y <- x |> layer_global_average_pooling_2d() shape(y) ## shape(2, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"Global average pooling operation 3D data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"","code":"layer_global_average_pooling_3d(   object,   data_format = NULL,   keepdims = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, spatial dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"data_format='channels_last': 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format='channels_first': 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"keepdims=FALSE: 2D tensor shape (batch_size, channels). keepdims=TRUE: data_format=\"channels_last\": 5D tensor shape (batch_size, 1, 1, 1, channels) data_format=\"channels_first\": 5D tensor shape (batch_size, channels, 1, 1, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"","code":"x <- random_uniform(c(2, 4, 5, 4, 3)) y <- x |> layer_global_average_pooling_3d() shape(y) ## shape(2, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"Global max pooling operation temporal data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"","code":"layer_global_max_pooling_1d(object, data_format = NULL, keepdims = FALSE, ...)"},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, temporal dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"data_format='channels_last': 3D tensor shape: (batch_size, steps, features) data_format='channels_first': 3D tensor shape: (batch_size, features, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"keepdims=FALSE: 2D tensor shape (batch_size, features). keepdims=TRUE: data_format=\"channels_last\": 3D tensor shape (batch_size, 1, features) data_format=\"channels_first\": 3D tensor shape (batch_size, features, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"","code":"x <- random_uniform(c(2, 3, 4)) y <- x |> layer_global_max_pooling_1d() shape(y) ## shape(2, 4)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"Global max pooling operation 2D data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"","code":"layer_global_max_pooling_2d(object, data_format = NULL, keepdims = FALSE, ...)"},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, features, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, spatial dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"data_format='channels_last': 4D tensor shape: (batch_size, height, width, channels) data_format='channels_first': 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"keepdims=FALSE: 2D tensor shape (batch_size, channels). keepdims=TRUE: data_format=\"channels_last\": 4D tensor shape (batch_size, 1, 1, channels) data_format=\"channels_first\": 4D tensor shape (batch_size, channels, 1, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"","code":"x <- random_uniform(c(2, 4, 5, 3)) y <- x |> layer_global_max_pooling_2d() shape(y) ## shape(2, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"Global max pooling operation 3D data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"","code":"layer_global_max_pooling_3d(object, data_format = NULL, keepdims = FALSE, ...)"},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, spatial dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"data_format='channels_last': 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format='channels_first': 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"keepdims=FALSE: 2D tensor shape (batch_size, channels). keepdims=TRUE: data_format=\"channels_last\": 5D tensor shape (batch_size, 1, 1, 1, channels) data_format=\"channels_first\": 5D tensor shape (batch_size, channels, 1, 1, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"","code":"x <- random_uniform(c(2, 4, 5, 4, 3)) y <- x |> layer_global_max_pooling_3d() shape(y) ## shape(2, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Group normalization layer. — layer_group_normalization","title":"Group normalization layer. — layer_group_normalization","text":"Group Normalization divides channels groups computes within group mean variance normalization. Empirically, accuracy stable batch norm wide range small batch sizes, learning rate adjusted linearly batch sizes. Relation Layer Normalization: number groups set 1, operation becomes nearly identical Layer Normalization (see Layer Normalization docs details). Relation Instance Normalization: number groups set input dimension (number groups equal number channels), operation becomes identical Instance Normalization. can achieve via groups=-1.","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Group normalization layer. — layer_group_normalization","text":"","code":"layer_group_normalization(   object,   groups = 32L,   axis = -1L,   epsilon = 0.001,   center = TRUE,   scale = TRUE,   beta_initializer = \"zeros\",   gamma_initializer = \"ones\",   beta_regularizer = NULL,   gamma_regularizer = NULL,   beta_constraint = NULL,   gamma_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Group normalization layer. — layer_group_normalization","text":"object Object compose layer . tensor, array, sequential model. groups Integer, number groups Group Normalization. Can range [1, N] N input dimension. input dimension must divisible number groups. Defaults 32. axis Integer List/Tuple. axis axes normalize across. Typically, features axis/axes. left-axes typically batch axis/axes. -1 last dimension input. Defaults -1. epsilon Small float added variance avoid dividing zero. Defaults 1e-3. center TRUE, add offset beta normalized tensor. FALSE, beta ignored. Defaults TRUE. scale TRUE, multiply gamma. FALSE, gamma used. next layer linear (also e.g. relu), can disabled since scaling done next layer. Defaults TRUE. beta_initializer Initializer beta weight. Defaults zeros. gamma_initializer Initializer gamma weight. Defaults ones. beta_regularizer Optional regularizer beta weight. NULL default. gamma_regularizer Optional regularizer gamma weight. NULL default. beta_constraint Optional constraint beta weight. NULL default. gamma_constraint Optional constraint gamma weight. NULL default. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Group normalization layer. — layer_group_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Group normalization layer. — layer_group_normalization","text":"Arbitrary. Use keyword argument input_shape (tuple integers, include samples axis) using layer first layer model.","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Group normalization layer. — layer_group_normalization","text":"shape input. **kwargs: Base layer keyword arguments (e.g. name dtype).","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Group normalization layer. — layer_group_normalization","text":"Yuxin Wu & Kaiming , 2018","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"Grouped Query Attention layer. — layer_group_query_attention","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"implementation grouped-query attention introduced Ainslie et al., 2023. num_key_value_heads denotes number groups, setting num_key_value_heads 1 equivalent multi-query attention, num_key_value_heads equal num_query_heads equivalent multi-head attention. layer first projects query, key, value tensors. , key value repeated match number heads query. , query scaled dot-producted key tensors. softmaxed obtain attention probabilities. value tensors interpolated probabilities concatenated back single tensor.","code":""},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"","code":"layer_group_query_attention(   object,   head_dim,   num_query_heads,   num_key_value_heads,   dropout = 0,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"object Object compose layer . tensor, array, sequential model. head_dim Size attention head. num_query_heads Number query attention heads. num_key_value_heads Number key value attention heads. dropout Dropout probability. use_bias Boolean, whether dense layers use bias vectors/matrices. kernel_initializer Initializer dense layer kernels. bias_initializer Initializer dense layer biases. kernel_regularizer Regularizer dense layer kernels. bias_regularizer Regularizer dense layer biases. activity_regularizer Regularizer dense layer activity. kernel_constraint Constraint dense layer kernels. bias_constraint Constraint dense layer kernels. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"attention_output: Result computation, shape (batch_dim, target_seq_len, feature_dim), target_seq_len target sequence length feature_dim query input last dim. attention_scores: (Optional) attention coefficients shape (batch_dim, num_query_heads, target_seq_len, source_seq_len).","code":""},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"query: Query tensor shape (batch_dim, target_seq_len, feature_dim), batch_dim batch size, target_seq_len length target sequence, feature_dim dimension feature. value: Value tensor shape (batch_dim, source_seq_len, feature_dim), batch_dim batch size, source_seq_len length source sequence, feature_dim dimension feature. key: Optional key tensor shape (batch_dim, source_seq_len, feature_dim). given, use value key value, common case. attention_mask: boolean mask shape (batch_dim, target_seq_len, source_seq_len), prevents attention certain positions. boolean mask specifies query elements can attend key elements, 1 indicates attention 0 indicates attention. Broadcasting can happen missing batch dimensions head dimension. return_attention_scores: boolean indicate whether output (attention_output, attention_scores) TRUE, attention_output FALSE. Defaults FALSE. training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (dropout). go either using training mode parent layer/model FALSE (inference) parent layer. use_causal_mask: boolean indicate whether apply causal mask prevent tokens attending future tokens (e.g., used decoder Transformer).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_gru.html","id":null,"dir":"Reference","previous_headings":"","what":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"Based available runtime hardware constraints, layer choose different implementations (cuDNN-based backend-native) maximize performance. GPU available arguments layer meet requirement cuDNN kernel (see details), layer use fast cuDNN implementation using TensorFlow backend. requirements use cuDNN implementation : activation == tanh recurrent_activation == sigmoid dropout == 0 recurrent_dropout == 0 unroll FALSE use_bias TRUE reset_after TRUE Inputs, use masking, strictly right-padded. Eager execution enabled outermost context. two variants GRU implementation. default one based v3 reset gate applied hidden state matrix multiplication. one based original order reversed. second variant compatible CuDNNGRU (GPU-) allows inference CPU. Thus separate biases kernel recurrent_kernel. use variant, set reset_after=TRUE recurrent_activation='sigmoid'. example:","code":"inputs <- random_uniform(c(32, 10, 8)) outputs <- inputs |> layer_gru(4) shape(outputs) ## shape(32, 4) # (32, 4) gru <- layer_gru(, 4, return_sequences = TRUE, return_state = TRUE) c(whole_sequence_output, final_state) %<-% gru(inputs) shape(whole_sequence_output) ## shape(32, 10, 4) shape(final_state) ## shape(32, 4)"},{"path":"https://keras.posit.co/reference/layer_gru.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"","code":"layer_gru(   object,   units,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   unroll = FALSE,   reset_after = TRUE,   use_cudnn = \"auto\",   ... )"},{"path":"https://keras.posit.co/reference/layer_gru.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"object Object compose layer . tensor, array, sequential model. units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). recurrent_activation Activation function use recurrent step. Default: sigmoid (sigmoid). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. activity_regularizer Regularizer function applied output layer (\"activation\"). Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default: FALSE). TRUE, last state sample index batch used initial state sample index following batch. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences. reset_after GRU convention (whether apply reset gate matrix multiplication). FALSE \"\", TRUE \"\" (default cuDNN compatible). use_cudnn Whether use cuDNN-backed implementation. \"auto\" attempt use cuDNN feasible, fallback default implementation . ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_gru.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_gru.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"inputs: 3D tensor, shape (batch, timesteps, feature). mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked  (optional). individual TRUE entry indicates corresponding timestep utilized, FALSE entry indicates corresponding timestep ignored. Defaults NULL. training: Python boolean indicating whether layer behave training mode inference mode. argument passed cell calling . relevant dropout recurrent_dropout used  (optional). Defaults NULL. initial_state: List initial state tensors passed first call cell (optional, NULL causes creation zero-filled initial state tensors). Defaults NULL.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which crosses features using the ","title":"A preprocessing layer which crosses features using the ","text":"layer performs crosses categorical features using \"hashing trick\". Conceptually, transformation can thought : hash(concatenate(features)) %% num_bins. layer currently performs crosses scalar inputs batches scalar inputs. Valid input shapes (batch_size, 1), (batch_size) (). Note: layer wraps tf.keras.layers.HashedCrossing. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tfdatasets pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which crosses features using the ","text":"","code":"layer_hashed_crossing(   object,   num_bins,   output_mode = \"int\",   sparse = FALSE,   name = NULL,   dtype = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which crosses features using the ","text":"object Object compose layer . tensor, array, sequential model. num_bins Number hash bins. output_mode Specification output layer. Values can \"int\", \"one_hot\" configuring layer follows: \"int\": Return integer bin indices directly. \"one_hot\": Encodes individual element input array size num_bins, containing 1 input's bin index. Defaults \"int\". sparse Boolean. applicable \"one_hot\" mode valid using TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. name String, name object dtype datatype (e.g., \"float32\"). ... Keyword arguments construct layer.","code":""},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which crosses features using the ","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which crosses features using the ","text":"Crossing two scalar features.     Crossing one-hotting two scalar features.","code":"feat1 <- c('A', 'B', 'A', 'B', 'A') |> as.array() feat2 <- c(101, 101, 101, 102, 102) |> as.integer() |> as.array() layer <- layer_hashed_crossing(num_bins = 5) layer(list(feat1, feat2)) ## tf.Tensor([1 4 1 1 3], shape=(5), dtype=int64) layer <- layer_hashed_crossing(num_bins = 5, output_mode = 'one_hot') layer(list(feat1, feat2)) ## tf.Tensor( ## [[0. 1. 0. 0. 0.] ##  [0. 0. 0. 0. 1.] ##  [0. 1. 0. 0. 0.] ##  [0. 1. 0. 0. 0.] ##  [0. 0. 0. 1. 0.]], shape=(5, 5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"layer transforms categorical inputs hashed output. element-wise converts ints strings ints fixed range. stable hash function uses tensorflow::ops::Fingerprint produce output consistently across platforms. layer uses FarmHash64 default, provides consistent hashed output across different platforms stable across invocations, regardless device context, mixing input bits thoroughly. want obfuscate hashed output, can also pass random salt argument constructor. case, layer use SipHash64 hash function, salt value serving additional input hash function. Note: layer internally uses TensorFlow. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tf.data pipeline (independently backend using). Example (FarmHash64)     Example (FarmHash64) mask value     Example (SipHash64)     Example (Siphash64 single integer, salt=[133, 133])","code":"layer <- layer_hashing(num_bins = 3) inp <- c('A', 'B', 'C', 'D', 'E') |> array(dim = c(5, 1)) layer(inp) ## tf.Tensor( ## [[1] ##  [0] ##  [1] ##  [1] ##  [2]], shape=(5, 1), dtype=int64) layer <- layer_hashing(num_bins=3, mask_value='') inp <- c('A', 'B', '', 'C', 'D') |> array(dim = c(5, 1)) layer(inp) ## tf.Tensor( ## [[1] ##  [1] ##  [0] ##  [2] ##  [2]], shape=(5, 1), dtype=int64) layer <- layer_hashing(num_bins=3, salt=c(133, 137)) inp <- c('A', 'B', 'C', 'D', 'E') |> array(dim = c(5, 1)) layer(inp) ## tf.Tensor( ## [[1] ##  [2] ##  [1] ##  [0] ##  [2]], shape=(5, 1), dtype=int64) layer <- layer_hashing(num_bins=3, salt=133) inp <- c('A', 'B', 'C', 'D', 'E') |> array(dim = c(5, 1)) layer(inp) ## tf.Tensor( ## [[0] ##  [0] ##  [2] ##  [1] ##  [0]], shape=(5, 1), dtype=int64)"},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"","code":"layer_hashing(   object,   num_bins,   mask_value = NULL,   salt = NULL,   output_mode = \"int\",   sparse = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"object Object compose layer . tensor, array, sequential model. num_bins Number hash bins. Note includes mask_value bin, effective number bins (num_bins - 1) mask_value set. mask_value value represents masked inputs, mapped index 0. NULL means mask term added hashing start index 0. Defaults NULL. salt single unsigned integer NULL. passed, hash function used SipHash64, values used additional input (known \"salt\" cryptography). non-zero. NULL, uses FarmHash64 hash function. also supports list 2 unsigned integer numbers, see reference paper details. Defaults NULL. output_mode Specification output layer. Values can \"int\", \"one_hot\", \"multi_hot\", \"count\" configuring layer follows: \"int\": Return integer bin indices directly. \"one_hot\": Encodes individual element input array size num_bins, containing 1 input's bin index. last dimension size 1, encode dimension. last dimension size 1, append new dimension encoded output. \"multi_hot\": Encodes sample input single array size num_bins, containing 1 bin index index present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). \"count\": \"multi_hot\", int array contains count number times bin index appeared sample. Defaults \"int\". sparse Boolean. applicable \"one_hot\", \"multi_hot\", \"count\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. ... Keyword arguments construct layer.","code":""},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"single string, list strings, int32 int64 tensor shape (batch_size, ...,).","code":""},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"int32 tensor shape (batch_size, ...).","code":""},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"SipHash salt","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Identity layer. — layer_identity","title":"Identity layer. — layer_identity","text":"layer used placeholder operation performed. layer just returns inputs argument output.","code":""},{"path":"https://keras.posit.co/reference/layer_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identity layer. — layer_identity","text":"","code":"layer_identity(object, ...)"},{"path":"https://keras.posit.co/reference/layer_identity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identity layer. — layer_identity","text":"object Object compose layer . tensor, array, sequential model. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_identity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identity layer. — layer_identity","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_input.html","id":null,"dir":"Reference","previous_headings":"","what":"keras_input — layer_input","title":"keras_input — layer_input","text":"layer_input() alias keras_input(). See ?keras_input() full documentation.","code":""},{"path":"https://keras.posit.co/reference/layer_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"keras_input — layer_input","text":"","code":"layer_input(   shape = NULL,   batch_size = NULL,   dtype = NULL,   sparse = NULL,   batch_shape = NULL,   name = NULL,   tensor = NULL )"},{"path":"https://keras.posit.co/reference/layer_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"keras_input — layer_input","text":"shape shape list (list integers NULL objects), including batch size. instance, shape = c(32) indicates expected input batches 32-dimensional vectors. Elements list can NULL NA; NULL/NA elements represent dimensions shape known may vary (e.g. sequence length). batch_size Optional static batch size (integer). dtype data type expected input, string (e.g. \"float32\", \"int32\"...) sparse boolean specifying whether expected input sparse tensors. Note , sparse FALSE, sparse tensors can still passed input - densified default value 0. feature supported TensorFlow backend. Defaults FALSE. batch_shape Shape, including batch dim. name Optional name string layer. unique model (reuse name twice). autogenerated provided. tensor Optional existing tensor wrap Input layer. set, layer use tensor rather creating new placeholder tensor.","code":""},{"path":"https://keras.posit.co/reference/layer_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"keras_input — layer_input","text":"Keras tensor, can passed inputs argument (keras_model()).","code":""},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"layer maps set arbitrary integer input tokens indexed integer output via table-based vocabulary lookup. layer's output indices contiguously arranged maximum vocab size, even input tokens non-continguous unbounded. layer supports multiple options encoding output via output_mode, optional support --vocabulary (OOV) tokens masking. vocabulary layer must either supplied construction learned via adapt(). adapt(), layer analyze data set, determine frequency individual integer tokens, create vocabulary . vocabulary capped size, frequent tokens used create vocabulary others treated OOV. two possible output modes layer.  output_mode \"int\", input integers converted index vocabulary (integer).  output_mode \"multi_hot\", \"count\", \"tf_idf\", input integers encoded array dimension corresponds element vocabulary. vocabulary can optionally contain mask token well OOV token (can optionally occupy multiple indices vocabulary, set num_oov_indices). position tokens vocabulary fixed. output_mode \"int\", vocabulary begin mask token index 0, followed OOV indices, followed rest vocabulary. output_mode \"multi_hot\", \"count\", \"tf_idf\" vocabulary begin OOV indices instances mask token dropped. Note: layer uses TensorFlow internally. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"","code":"layer_integer_lookup(   object,   max_tokens = NULL,   num_oov_indices = 1L,   mask_token = NULL,   oov_token = -1L,   vocabulary = NULL,   vocabulary_dtype = \"int64\",   idf_weights = NULL,   invert = FALSE,   output_mode = \"int\",   sparse = FALSE,   pad_to_max_tokens = FALSE,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"object Object compose layer . tensor, array, sequential model. max_tokens Maximum size vocabulary layer. specified adapting vocabulary setting pad_to_max_tokens=TRUE. NULL, cap size vocabulary. Note size includes OOV mask tokens. Defaults NULL. num_oov_indices number --vocabulary tokens use. value 1, OOV inputs modulated determine OOV value. value 0, OOV inputs cause error calling layer. Defaults 1. mask_token integer token represents masked inputs. output_mode \"int\", token included vocabulary mapped index 0. output modes, token appear vocabulary instances mask token input dropped. set NULL, mask term added. Defaults NULL. oov_token used invert TRUE. token return OOV indices. Defaults -1. vocabulary Optional. Either array integers string path text file. passing array, can pass list, list, 1D NumPy array, 1D tensor containing integer vocbulary terms. passing file path, file contain one line per term vocabulary. argument set, need adapt() layer. vocabulary_dtype dtype vocabulary terms, example \"int64\" \"int32\". Defaults \"int64\". idf_weights valid output_mode \"tf_idf\". list, list, 1D NumPy array, 1D tensor length vocabulary, containing floating point inverse document frequency weights, multiplied per sample term counts final TF-IDF weight. vocabulary argument set, output_mode \"tf_idf\", argument must supplied. invert valid output_mode \"int\". TRUE, layer map indices vocabulary items instead mapping vocabulary items indices. Defaults FALSE. output_mode Specification output layer. Values can \"int\", \"one_hot\", \"multi_hot\", \"count\", \"tf_idf\" configuring layer follows: \"int\": Return vocabulary indices input tokens. \"one_hot\": Encodes individual element input array size vocabulary, containing 1 element index. last dimension size 1, encode dimension. last dimension size 1, append new dimension encoded output. \"multi_hot\": Encodes sample input single array size vocabulary, containing 1 vocabulary term present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). \"count\": \"multi_hot\", int array contains count number times token index appeared sample. \"tf_idf\": \"multi_hot\", TF-IDF algorithm applied find value token slot. \"int\" output, shape input output supported. output modes, currently output rank 2 supported. Defaults \"int\". sparse Boolean. applicable \"multi_hot\", \"count\", \"tf_idf\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. pad_to_max_tokens applicable output_mode \"multi_hot\", \"count\", \"tf_idf\". TRUE, output feature axis padded max_tokens even number unique tokens vocabulary less max_tokens, resulting tensor shape (batch_size, max_tokens) regardless vocabulary size. Defaults FALSE. name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"Creating lookup layer known vocabulary example creates lookup layer pre-existing vocabulary.     Creating lookup layer adapted vocabulary example creates lookup layer generates vocabulary analyzing dataset.     Note OOV token -1 added vocabulary. remaining tokens sorted frequency (42, 2 occurrences, first) inverse sort order.     Lookups multiple OOV indices example demonstrates use lookup layer multiple OOV indices.  layer created one OOV index, OOV tokens hashed number OOV buckets, distributing OOV tokens deterministic fashion across set.     Note output OOV token 37 1, output OOV token 1000 0. -vocab terms output index increased 1 earlier examples (12 maps 2, etc) order make space extra OOV token. One-hot output Configure layer output_mode='one_hot'. Note first num_oov_indices dimensions ont_hot encoding represent OOV values.     Multi-hot output Configure layer output_mode = 'multi_hot'. Note first num_oov_indices dimensions multi_hot encoding represent OOV tokens     Token count output Configure layer output_mode='count'. multi_hot output, first num_oov_indices dimensions output represent OOV tokens.     TF-IDF output Configure layer output_mode='tf_idf'. multi_hot output, first num_oov_indices dimensions output represent OOV tokens. token bin output token_count * idf_weight, idf weights inverse document frequency weights per token. provided along vocabulary. Note idf_weight OOV tokens default average idf weights passed .     specify idf weights oov tokens, need pass entire vocabulary including leading oov token.     adapting layer \"tf_idf\" mode, input sample considered document, IDF weight per token calculated : log(1 + num_documents / (1 + token_document_count)). Inverse lookup example demonstrates map indices tokens using layer. (can also use adapt() inverse = TRUE, simplicity pass vocab example.)     Note first index correspond oov token default. Forward inverse lookup pairs example demonstrates use vocabulary standard lookup layer create inverse lookup layer.     example, input token 1000 resulted output -1, since 1000 vocabulary - got represented OOV, OOV tokens returned -1 inverse layer. Also, note inverse work, must already set forward layer vocabulary either directly via adapt() calling get_vocabulary().","code":"vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(rbind(c(12, 1138, 42),                       c(42, 1000, 36)))  # Note OOV tokens out <- data |> layer_integer_lookup(vocabulary = vocab) out ## tf.Tensor( ## [[1 3 4] ##  [4 0 2]], shape=(2, 3), dtype=int64) data <- op_array(rbind(c(12, 1138, 42),                       c(42, 1000, 36)))  # Note OOV tokens layer <- layer_integer_lookup() layer |> adapt(data) layer |> get_vocabulary() |> str() ## List of 6 ##  $ : int -1 ##  $ : num 42 ##  $ : num 1138 ##  $ : num 1000 ##  $ : num 36 ##  $ : num 12 layer(data) ## tf.Tensor( ## [[5 2 1] ##  [1 3 4]], shape=(2, 3), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(rbind(c(12, 1138, 42),                       c(37, 1000, 36)))  # Note OOV tokens out <- data |>   layer_integer_lookup(vocabulary = vocab,                        num_oov_indices = 2) out ## tf.Tensor( ## [[2 4 5] ##  [1 0 3]], shape=(2, 3), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(c(12, 36, 1138, 42, 7), 'int32')  # Note OOV tokens layer <- layer_integer_lookup(vocabulary = vocab,                               output_mode = 'one_hot') layer(data) ## tf.Tensor( ## [[0 1 0 0 0] ##  [0 0 1 0 0] ##  [0 0 0 1 0] ##  [0 0 0 0 1] ##  [1 0 0 0 0]], shape=(5, 5), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(rbind(c(12, 1138, 42, 42),                       c(42,    7, 36,  7)), \"int64\")  # Note OOV tokens layer <- layer_integer_lookup(vocabulary = vocab,                               output_mode = 'multi_hot') layer(data) ## tf.Tensor( ## [[0 1 0 1 1] ##  [1 0 1 0 1]], shape=(2, 5), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- rbind(c(12, 1138, 42, 42),               c(42,    7, 36,  7)) |> op_array(\"int64\") layer <- layer_integer_lookup(vocabulary = vocab,                               output_mode = 'count') layer(data) ## tf.Tensor( ## [[0 1 0 1 2] ##  [2 0 1 0 1]], shape=(2, 5), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() idf_weights <- c(0.25, 0.75, 0.6, 0.4) data <- rbind(c(12, 1138, 42, 42),               c(42,    7, 36,  7)) |> op_array(\"int64\") layer <- layer_integer_lookup(output_mode = 'tf_idf',                               vocabulary = vocab,                               idf_weights = idf_weights) layer(data) ## tf.Tensor( ## [[0.   0.25 0.   0.6  0.8 ] ##  [1.   0.   0.75 0.   0.4 ]], shape=(2, 5), dtype=float32) vocab <- c(-1, 12, 36, 1138, 42) |> as.integer() idf_weights <- c(0.9, 0.25, 0.75, 0.6, 0.4) data <- rbind(c(12, 1138, 42, 42),               c(42,    7, 36,  7)) |> op_array(\"int64\") layer <- layer_integer_lookup(output_mode = 'tf_idf',                               vocabulary = vocab,                               idf_weights = idf_weights) layer(data) ## tf.Tensor( ## [[0.   0.25 0.   0.6  0.8 ] ##  [1.8  0.   0.75 0.   0.4 ]], shape=(2, 5), dtype=float32) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(c(1, 3, 4,                   4, 0, 2)) |> op_reshape(c(2,-1)) |> op_cast(\"int32\") layer <- layer_integer_lookup(vocabulary = vocab, invert = TRUE) layer(data) ## tf.Tensor( ## [[  12 1138   42] ##  [  42   -1   36]], shape=(2, 3), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(rbind(c(12, 1138, 42), c(42, 1000, 36)), \"int32\") layer <- layer_integer_lookup(vocabulary = vocab) i_layer <- layer_integer_lookup(vocabulary = get_vocabulary(layer),                                 invert = TRUE) int_data <- layer(data) i_layer(int_data) ## tf.Tensor( ## [[  12 1138   42] ##  [  42   -1   36]], shape=(2, 3), dtype=int64)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"layer enables use JAX components within Keras using JAX backend Keras.","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"","code":"layer_jax_model_wrapper(   object,   call_fn,   init_fn = NULL,   params = NULL,   state = NULL,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"object Object compose layer . tensor, array, sequential model. call_fn function call model. See description list arguments takes outputs returns. init_fn function call initialize model. See description list arguments takes ouputs returns. NULL, params /state must provided. params PyTree containing model trainable parameters. allows passing trained parameters controlling initialization. params state NULL, init_fn() called build time initialize trainable parameters model. state PyTree containing model non-trainable state. allows passing learned state controlling initialization. params state NULL, call_fn() takes state argument, init_fn() called build time initialize non-trainable state model. seed Seed random number generator. Optional. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"model-function","dir":"Reference","previous_headings":"","what":"Model function","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"layer accepts JAX models form function, call_fn(), must take following arguments exact names: params: trainable parameters model. state (optional): non-trainable state model. Can omitted model non-trainable state. rng (optional): jax.random.PRNGKey instance. Can omitted model need RNGs, neither training inference. inputs: inputs model, JAX array PyTree arrays. training (optional): argument specifying training mode inference mode, TRUE passed training mode. Can omitted model behaves training mode inference mode. inputs argument mandatory. Inputs model must provided via single argument. JAX model takes multiple inputs separate arguments, must combined single structure, instance tuple() dict().","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"model-weights-initialization","dir":"Reference","previous_headings":"","what":"Model weights initialization","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"initialization params state model can handled layer, case init_fn() argument must provided. allows model initialized dynamically right shape. Alternatively, shape known, params argument optionally state argument can used create already initialized model. init_fn() function, provided, must take following arguments exact names: rng: jax.random.PRNGKey instance. inputs: JAX array PyTree arrays placeholder values provide shape inputs. training (optional): argument specifying training mode inference mode. True always passed init_fn. Can omitted regardless whether call_fn training argument.","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"models-with-non-trainable-state","dir":"Reference","previous_headings":"","what":"Models with non-trainable state","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"JAX models non-trainable state: call_fn() must state argument call_fn() must return tuple() containing outputs model new non-trainable state model init_fn() must return tuple() containing initial trainable params model initial non-trainable state model. code shows possible combination call_fn() init_fn() signatures model non-trainable state. example, model training argument rng argument call_fn().","code":"stateful_call <- function(params, state, rng, inputs, training) {   outputs <- ....   new_state <- ....   tuple(outputs, new_state) }  stateful_init <- function(rng, inputs) {   initial_params <- ....   initial_state <- ....   tuple(initial_params, initial_state) }"},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"models-without-non-trainable-state","dir":"Reference","previous_headings":"","what":"Models without non-trainable state","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"JAX models non-trainable state: call_fn() must state argument call_fn() must return outputs model init_fn() must return initial trainable params model. code shows possible combination call_fn() init_fn() signatures model without non-trainable state. example, model training argument rng argument call_fn().","code":"stateful_call <- function(pparams, inputs) {   outputs <- ....   outputs }  stateful_init <- function(rng, inputs) {   initial_params <- ....   initial_params }"},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"conforming-to-the-required-signature","dir":"Reference","previous_headings":"","what":"Conforming to the required signature","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"model different signature one required JaxLayer, one can easily write wrapper method adapt arguments. example shows model multiple inputs separate arguments, expects multiple RNGs dict, deterministic argument opposite meaning training. conform, inputs combined single structure using tuple, RNG split used populate expected dict, Boolean flag negated:","code":"jax <- import(\"jax\") my_model_fn <- function(params, rngs, input1, input2, deterministic) {   ....   if (!deterministic) {     dropout_rng <- rngs$dropout     keep <- jax$random$bernoulli(dropout_rng, dropout_rate, x$shape)     x <- jax$numpy$where(keep, x / dropout_rate, 0)     ....   }   ....   return(outputs) }  my_model_wrapper_fn <- function(params, rng, inputs, training) {   c(input1, input2) %<-% inputs   c(rng1, rng2) %<-% jax$random$split(rng)   rngs <-  list(dropout = rng1, preprocessing = rng2)   deterministic <-  !training   my_model_fn(params, rngs, input1, input2, deterministic) }  keras_layer <- layer_jax_model_wrapper(call_fn = my_model_wrapper_fn,                                        params = initial_params)"},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"usage-with-haiku-modules","dir":"Reference","previous_headings":"","what":"Usage with Haiku modules","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"JaxLayer enables use Haiku components form haiku.Module. achieved transforming module per Haiku pattern passing module.apply call_fn parameter module.init init_fn parameter needed. model non-trainable state, transformed haiku.transform_with_state. model non-trainable state, transformed haiku.transform. Additionally, optionally, module use RNGs \"apply\", can transformed haiku.without_apply_rng. following example shows create JaxLayer Haiku module uses random number generators via hk.next_rng_key() takes training positional argument:","code":"# reticulate::py_install(\"haiku\", \"r-keras\") hk <- import(\"haiku\") MyHaikuModule(hk$Module) \\%py_class\\% {    `__call__` <- \\(self, x, training) {     x <- hk$Conv2D(32L, tuple(3L, 3L))(x)     x <- jax$nn$relu(x)     x <- hk$AvgPool(tuple(1L, 2L, 2L, 1L),                     tuple(1L, 2L, 2L, 1L), \"VALID\")(x)     x <- hk$Flatten()(x)     x <- hk$Linear(200L)(x)     if (training)       x <- hk$dropout(rng = hk$next_rng_key(), rate = 0.3, x = x)     x <- jax$nn$relu(x)     x <- hk$Linear(10L)(x)     x <- jax$nn$softmax(x)     x   }  }  my_haiku_module_fn <- function(inputs, training) {   module <- MyHaikuModule()   module(inputs, training) }  transformed_module <- hk$transform(my_haiku_module_fn)  keras_layer <-   layer_jax_model_wrapper(call_fn = transformed_module$apply,                           init_fn = transformed_module$init)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":null,"dir":"Reference","previous_headings":"","what":"Wraps arbitrary expressions as a Layer object. — layer_lambda","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"layer_lambda() layer exists arbitrary expressions can used Layer constructing Sequential Functional API models. Lambda layers best suited simple operations quick experimentation. advanced use cases, prefer writing new subclasses Layer using new_layer_class().","code":""},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"","code":"layer_lambda(   object,   f,   output_shape = NULL,   mask = NULL,   arguments = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"object Object compose layer . tensor, array, sequential model. f function evaluated. Takes input tensor first argument. output_shape Expected output shape function. argument can usually inferred explicitly provided. Can list function. list, specifies first dimension onward; sample dimension assumed either input: output_shape = c(input_shape[1], output_shape) , input NULL sample dimension also NULL: output_shape = c(NA, output_shape). function, specifies entire shape function input shape: output_shape = f(input_shape). mask Either NULL (indicating masking) callable signature compute_mask layer method, tensor returned output mask regardless input . arguments Optional named list arguments passed function. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"","code":"# add a x -> x^2 layer model <- keras_model_sequential() model |> layer_lambda(\\(x) x^2)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"Normalize activations previous layer given example batch independently, rather across batch like Batch Normalization. .e. applies transformation maintains mean activation within example close 0 activation standard deviation close 1. scale center enabled, layer scale normalized outputs broadcasting trainable variable gamma, center outputs broadcasting trainable variable beta. gamma default ones tensor beta default zeros tensor, centering scaling -ops training begun. , scaling centering enabled normalization equations follows: Let intermediate activations mini-batch inputs. sample x batch inputs, compute mean variance sample, normalize value sample (including small factor epsilon numerical stability), finally, transform normalized output gamma beta, learned parameters:   gamma beta span axes inputs specified axis, part inputs' shape must fully defined. example:         Note implementations layer normalization may choose define gamma beta separate set axes axes normalized across. example, Group Normalization (Wu et al. 2018) group size 1 corresponds layer_layer_normalization() normalizes across height, width, channel gamma beta span channel dimension. , layer_layer_normalization() implementation match layer_group_normalization() layer group size set 1.","code":"outputs <- inputs |> apply(1, function(x) {   x_normalized <- (x - mean(x)) /                   sqrt(var(x) + epsilon)   x_normalized * gamma + beta }) layer <- layer_layer_normalization(axis = c(2, 3, 4))  layer(op_ones(c(5, 20, 30, 40))) |> invisible() # build() shape(layer$beta) ## shape(20, 30, 40) shape(layer$gamma) ## shape(20, 30, 40)"},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"","code":"layer_layer_normalization(   object,   axis = -1L,   epsilon = 0.001,   center = TRUE,   scale = TRUE,   rms_scaling = FALSE,   beta_initializer = \"zeros\",   gamma_initializer = \"ones\",   beta_regularizer = NULL,   gamma_regularizer = NULL,   beta_constraint = NULL,   gamma_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"object Object compose layer . tensor, array, sequential model. axis Integer list. axis axes normalize across. Typically, features axis/axes. left-axes typically batch axis/axes. -1 last dimension input. Defaults -1. epsilon Small float added variance avoid dividing zero. Defaults 1e-3. center TRUE, add offset beta normalized tensor. FALSE, beta ignored. Defaults TRUE. scale TRUE, multiply gamma. FALSE, gamma used. next layer linear (also e.g. layer_activation_relu()), can disabled since scaling done next layer. Defaults TRUE. rms_scaling TRUE, center scale ignored, inputs scaled gamma inverse square root square inputs. approximate faster approach avoids ever computing mean input. beta_initializer Initializer beta weight. Defaults zeros. gamma_initializer Initializer gamma weight. Defaults ones. beta_regularizer Optional regularizer beta weight. NULL default. gamma_regularizer Optional regularizer gamma weight. NULL default. beta_constraint Optional constraint beta weight. NULL default. gamma_constraint Optional constraint gamma weight. NULL default. ... Base layer keyword arguments (e.g. name dtype).","code":""},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"Lei Ba et al., 2016.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":null,"dir":"Reference","previous_headings":"","what":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"Based available runtime hardware constraints, layer choose different implementations (cuDNN-based backend-native) maximize performance. GPU available arguments layer meet requirement cuDNN kernel (see details), layer use fast cuDNN implementation using TensorFlow backend. requirements use cuDNN implementation : activation == tanh recurrent_activation == sigmoid dropout == 0 recurrent_dropout == 0 unroll FALSE use_bias TRUE Inputs, use masking, strictly right-padded. Eager execution enabled outermost context. example:","code":"input <- random_uniform(c(32, 10, 8)) output <- input |> layer_lstm(4) shape(output) ## shape(32, 4) lstm <- layer_lstm(units = 4, return_sequences = TRUE, return_state = TRUE) c(whole_seq_output, final_memory_state, final_carry_state) %<-% lstm(input) shape(whole_seq_output) ## shape(32, 10, 4) shape(final_memory_state) ## shape(32, 4) shape(final_carry_state) ## shape(32, 4)"},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"","code":"layer_lstm(   object,   units,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   unroll = FALSE,   use_cudnn = \"auto\",   ... )"},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"object Object compose layer . tensor, array, sequential model. units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). recurrent_activation Activation function use recurrent step. Default: sigmoid (sigmoid). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". unit_forget_bias Boolean (default TRUE). TRUE, add 1 bias forget gate initialization. Setting TRUE also force bias_initializer=\"zeros\". recommended Jozefowicz et al. kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. activity_regularizer Regularizer function applied output layer (\"activation\"). Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default: FALSE). TRUE, last state sample index batch used initial state sample index following batch. unroll Boolean (default FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences. use_cudnn Whether use cuDNN-backed implementation. \"auto\" attempt use cuDNN feasible, fallback default implementation . ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"inputs: 3D tensor, shape (batch, timesteps, feature). mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked  (optional). individual TRUE entry indicates corresponding timestep utilized, FALSE entry indicates corresponding timestep ignored. Defaults NULL. training: Boolean indicating whether layer behave training mode inference mode. argument passed cell calling . relevant dropout recurrent_dropout used  (optional). Defaults NULL. initial_state: List initial state tensors passed first call cell (optional, NULL causes creation zero-filled initial state tensors). Defaults NULL.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_masking.html","id":null,"dir":"Reference","previous_headings":"","what":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"timestep input tensor (dimension #1 tensor), values input tensor timestep equal mask_value, timestep masked (skipped) downstream layers (long support masking). downstream layer support masking yet receives input mask, exception raised.","code":""},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"","code":"layer_masking(object, mask_value = 0, ...)"},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"object Object compose layer . tensor, array, sequential model. mask_value see description ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"Consider array x shape c(samples, timesteps, features), fed LSTM layer. want mask timestep #3 #5 lack data timesteps. can: Set x[, 3, ] <- 0. x[, 5, ] <- 0. Insert layer_masking() layer mask_value = 0. LSTM layer:","code":"c(samples, timesteps, features) %<-% c(32, 10, 8) inputs <- c(samples, timesteps, features) %>% { array(runif(prod(.)), dim = .) } inputs[, 3, ] <- 0 inputs[, 5, ] <- 0  model <- keras_model_sequential() %>%   layer_masking(mask_value = 0) %>%   layer_lstm(32)  output <- model(inputs) # The time step 3 and 5 will be skipped from LSTM calculation."},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"Keras masking convention, masked timestep denoted mask value FALSE, non-masked (.e. usable) timestep denoted mask value TRUE.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"Downsamples input representation taking maximum value spatial window size pool_size. window shifted strides. resulting output using \"valid\" padding option shape : output_shape = (input_shape - pool_size + 1) / strides). resulting output shape using \"\" padding option : output_shape = input_shape / strides","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"","code":"layer_max_pooling_1d(   object,   pool_size = 2L,   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"object Object compose layer . tensor, array, sequential model. pool_size int, size max pooling window. strides int NULL. Specifies much pooling window moves pooling step. NULL, default pool_size. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"data_format=\"channels_last\": 3D tensor shape (batch_size, steps, features). data_format=\"channels_first\": 3D tensor shape (batch_size, features, steps).","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"data_format=\"channels_last\": 3D tensor shape (batch_size, downsampled_steps, features). data_format=\"channels_first\": 3D tensor shape (batch_size, features, downsampled_steps).","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"strides=1 padding=\"valid\":     strides=2 padding=\"valid\":     strides=1 padding=\"\":","code":"x <- op_reshape(c(1, 2, 3, 4, 5),                c(1, 5, 1)) max_pool_1d <- layer_max_pooling_1d(pool_size = 2,                                     strides = 1,                                     padding = \"valid\") max_pool_1d(x) ## tf.Tensor( ## [[[2.] ##   [3.] ##   [4.] ##   [5.]]], shape=(1, 4, 1), dtype=float32) x <- op_reshape(c(1, 2, 3, 4, 5),                c(1, 5, 1)) max_pool_1d <- layer_max_pooling_1d(pool_size = 2,                                     strides = 2,                                     padding = \"valid\") max_pool_1d(x) ## tf.Tensor( ## [[[2.] ##   [4.]]], shape=(1, 2, 1), dtype=float32) x <- op_reshape(c(1, 2, 3, 4, 5),                c(1, 5, 1)) max_pool_1d <- layer_max_pooling_1d(pool_size = 2,                                     strides = 1,                                     padding = \"same\") max_pool_1d(x) ## tf.Tensor( ## [[[2.] ##   [3.] ##   [4.] ##   [5.] ##   [5.]]], shape=(1, 5, 1), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"Downsamples input along spatial dimensions (height width) taking maximum value input window (size defined pool_size) channel input. window shifted strides along dimension. resulting output using \"valid\" padding option spatial shape (number rows columns) : output_shape = floor((input_shape - pool_size) / strides) + 1 (input_shape >= pool_size) resulting output shape using \"\" padding option : output_shape = floor((input_shape - 1) / strides) + 1","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"","code":"layer_max_pooling_2d(   object,   pool_size = list(2L, 2L),   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"object Object compose layer . tensor, array, sequential model. pool_size int list 2 integers, factors downscale (dim1, dim2). one integer specified, window length used dimensions. strides int list 2 integers, NULL. Strides values. NULL, default pool_size. one int specified, stride size used dimensions. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"data_format=\"channels_last\": 4D tensor shape (batch_size, height, width, channels). data_format=\"channels_first\": 4D tensor shape (batch_size, channels, height, width).","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"data_format=\"channels_last\": 4D tensor shape (batch_size, pooled_height, pooled_width, channels). data_format=\"channels_first\": 4D tensor shape (batch_size, channels, pooled_height, pooled_width).","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"strides = (1, 1) padding = \"valid\":     strides = c(2, 2) padding = \"valid\":     stride = (1, 1) padding = \"\":","code":"x <- rbind(c(1., 2., 3.),            c(4., 5., 6.),            c(7., 8., 9.)) |> op_reshape(c(1, 3, 3, 1)) max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),                                     strides = c(1, 1),                                     padding = \"valid\") max_pool_2d(x) ## tf.Tensor( ## [[[[5.] ##    [6.]] ## ##   [[8.] ##    [9.]]]], shape=(1, 2, 2, 1), dtype=float32) x <- rbind(c(1., 2., 3., 4.),            c(5., 6., 7., 8.),            c(9., 10., 11., 12.)) |> op_reshape(c(1, 3, 4, 1)) max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),                                     strides = c(2, 2),                                     padding = \"valid\") max_pool_2d(x) ## tf.Tensor( ## [[[[6.] ##    [8.]]]], shape=(1, 1, 2, 1), dtype=float32) x <- rbind(c(1., 2., 3.),            c(4., 5., 6.),            c(7., 8., 9.)) |> op_reshape(c(1, 3, 3, 1)) max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),                                     strides = c(1, 1),                                     padding = \"same\") max_pool_2d(x) ## tf.Tensor( ## [[[[5.] ##    [6.] ##    [6.]] ## ##   [[8.] ##    [9.] ##    [9.]] ## ##   [[8.] ##    [9.] ##    [9.]]]], shape=(1, 3, 3, 1), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"Downsamples input along spatial dimensions (depth, height, width) taking maximum value input window (size defined pool_size) channel input. window shifted strides along dimension.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"","code":"layer_max_pooling_3d(   object,   pool_size = list(2L, 2L, 2L),   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"object Object compose layer . tensor, array, sequential model. pool_size int list 3 integers, factors downscale (dim1, dim2, dim3). one integer specified, window length used dimensions. strides int list 3 integers, NULL. Strides values. NULL, default pool_size. one int specified, stride size used dimensions. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"","code":"depth <- 30 height <- 30 width <- 30 channels <- 3  inputs <- layer_input(shape=c(depth, height, width, channels)) layer <- layer_max_pooling_3d(pool_size=3) outputs <- inputs |> layer() outputs ## <KerasTensor shape=(None, 10, 10, 10, 3), dtype=float32, sparse=False, name=keras_tensor_1>"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes element-wise maximum on a list of inputs. — layer_maximum","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"","code":"layer_maximum(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"Usage Keras model:","code":"input_shape <- c(2, 3, 4) x1 <- random_uniform(input_shape) x2 <- random_uniform(input_shape) y <- layer_maximum(x1, x2) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu') input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu') # equivalent to `y <- layer_maximum(x1, x2)` y <- layer_maximum(x1, x2) out <- y |> layer_dense(4) model <- keras_model(inputs = c(input1, input2), outputs = out)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"layer takes float32/float64 single batched audio signal inputs computes Mel spectrogram using Short-Time Fourier Transform Mel scaling. input 1D (unbatched) 2D (batched) tensor representing audio signals. output 2D 3D tensor representing Mel spectrograms. spectrogram image-like representation shows frequency spectrum signal time. uses x-axis represent time, y-axis represent frequency, pixel represent intensity. Mel spectrograms special type spectrogram use mel scale, approximates humans perceive sound. commonly used speech music processing tasks like speech recognition, speaker identification, music genre classification.","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"","code":"layer_mel_spectrogram(   object,   fft_length = 2048L,   sequence_stride = 512L,   sequence_length = NULL,   window = \"hann\",   sampling_rate = 16000L,   num_mel_bins = 128L,   min_freq = 20,   max_freq = NULL,   power_to_db = TRUE,   top_db = 80,   mag_exp = 2,   min_power = 1e-10,   ref_power = 1,   ... )"},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"object Object compose layer . tensor, array, sequential model. fft_length Integer, size FFT window. sequence_stride Integer, number samples successive STFT columns. sequence_length Integer, size window used applying window audio frame. NULL, defaults fft_length. window String, name window function use. Available values \"hann\" \"hamming\". window tensor, used directly window length must sequence_length. window NULL, windowing used. Defaults \"hann\". sampling_rate Integer, sample rate input signal. num_mel_bins Integer, number mel bins generate. min_freq Float, minimum frequency mel bins. max_freq Float, maximum frequency mel bins. NULL, defaults sampling_rate / 2. power_to_db TRUE, convert power spectrogram decibels. top_db Float, minimum negative cut-max(10 * log10(S)) - top_db. mag_exp Float, exponent magnitude spectrogram. 1 magnitude, 2 power, etc. Default 2. min_power Float, minimum value power ref_power. ref_power Float, power scaled relative 10 * log10(S / ref_power). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"Spectrogram, Mel scale.","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"Unbatched audio signal   Batched audio signal","code":"layer <- layer_mel_spectrogram(   num_mel_bins = 64,   sampling_rate = 8000,   sequence_stride = 256,   fft_length = 2048 ) layer(random_uniform(shape = c(16000))) |> shape() layer <- layer_mel_spectrogram(   num_mel_bins = 80,   sampling_rate = 8000,   sequence_stride = 128,   fft_length = 2048 ) layer(random_uniform(shape = c(2, 16000))) |> shape()"},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"1D (unbatched) 2D (batched) tensor shape:(..., samples).","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"2D (unbatched) 3D (batched) tensor shape:(..., num_mel_bins, time).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes elementwise minimum on a list of inputs. — layer_minimum","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"","code":"layer_minimum(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"Usage Keras model:","code":"input_shape <- c(2, 3, 4) x1 <- random_uniform(input_shape) x2 <- random_uniform(input_shape) y <- layer_minimum(x1, x2) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu') input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu') # equivalent to `y <- layer_minimum(x1, x2)` y <- layer_minimum(x1, x2) out <- y |> layer_dense(4) model <- keras_model(inputs = c(input1, input2), outputs = out)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi Head Attention layer. — layer_multi_head_attention","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"implementation multi-headed attention described paper \"Attention Need\" Vaswani et al., 2017. query, key, value , self-attention. timestep query attends corresponding sequence key, returns fixed-width vector. layer first projects query, key value. (effectively) list tensors length num_attention_heads, corresponding shapes (batch_size, <query dimensions>, key_dim), (batch_size, <key/value dimensions>, key_dim), (batch_size, <key/value dimensions>, value_dim). , query key tensors dot-producted scaled. softmaxed obtain attention probabilities. value tensors interpolated probabilities, concatenated back single tensor. Finally, result tensor last dimension value_dim can take linear projection return.","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"","code":"layer_multi_head_attention(   inputs,   num_heads,   key_dim,   value_dim = NULL,   dropout = 0,   use_bias = TRUE,   output_shape = NULL,   attention_axes = NULL,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"inputs see description num_heads Number attention heads. key_dim Size attention head query key. value_dim Size attention head value. dropout Dropout probability. use_bias Boolean, whether dense layers use bias vectors/matrices. output_shape expected shape output tensor, besides batch sequence dims. specified, projects back query feature dim (query input's last dimension). attention_axes axes attention applied. NULL means attention axes, batch, heads, features. kernel_initializer Initializer dense layer kernels. bias_initializer Initializer dense layer biases. kernel_regularizer Regularizer dense layer kernels. bias_regularizer Regularizer dense layer biases. activity_regularizer Regularizer dense layer activity. kernel_constraint Constraint dense layer kernels. bias_constraint Constraint dense layer kernels. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"query: Query tensor shape (B, T, dim), B batch size, T target sequence length, dim feature dimension. value: Value tensor shape (B, S, dim), B batch size, S source sequence length, dim feature dimension. key: Optional key tensor shape (B, S, dim). given, use value key value, common case. attention_mask: boolean mask shape (B, T, S), prevents attention certain positions. boolean mask specifies query elements can attend key elements, 1 indicates attention 0 indicates attention. Broadcasting can happen missing batch dimensions head dimension. return_attention_scores: boolean indicate whether output (attention_output, attention_scores) TRUE, attention_output FALSE. Defaults FALSE. training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (dropout). go either using training mode parent layer/model, FALSE (inference) parent layer. use_causal_mask: boolean indicate whether apply causal mask prevent tokens attending future tokens (e.g., used decoder Transformer).","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"call-return","dir":"Reference","previous_headings":"","what":"Call return","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"attention_output: result computation, shape (B, T, E), T target sequence shapes E query input last dimension output_shape NULL. Otherwise, multi-head outputs projected shape specified output_shape. attention_scores: (Optional) multi-head attention coefficients attention axes.","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"properties","dir":"Reference","previous_headings":"","what":"Properties","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"MultiHeadAttention Layer instance following additional read-properties: attention_axes dropout key_dense key_dim num_heads output_dense output_shape query_dense use_bias value_dense value_dim","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs elementwise multiplication. — layer_multiply","title":"Performs elementwise multiplication. — layer_multiply","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs elementwise multiplication. — layer_multiply","text":"","code":"layer_multiply(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs elementwise multiplication. — layer_multiply","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs elementwise multiplication. — layer_multiply","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs elementwise multiplication. — layer_multiply","text":"Usage Keras model:","code":"input_shape <- c(2, 3, 4) x1 <- random_uniform(input_shape) x2 <- random_uniform(input_shape) y <- layer_multiply(x1, x2) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu') input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu') # equivalent to `y <- layer_multiply(x1, x2)` y <- layer_multiply(x1, x2) out <- y |> layer_dense(4) model <- keras_model(inputs = c(input1, input2), outputs = out)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer that normalizes continuous features. — layer_normalization","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"layer shift scale inputs distribution centered around 0 standard deviation 1. accomplishes precomputing mean variance data, calling (input - mean) / sqrt(var) runtime. mean variance values layer must either supplied construction learned via adapt(). adapt() compute mean variance data store layer's weights. adapt() called fit(), evaluate(), predict().","code":""},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"","code":"layer_normalization(   object,   axis = -1L,   mean = NULL,   variance = NULL,   invert = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"object Object compose layer . tensor, array, sequential model. axis Integer, list integers, NULL. axis axes separate mean variance index shape. example, shape (NULL, 5) axis=1, layer track 5 separate mean variance values last axis. axis set NULL, layer normalize elements input scalar mean variance. -1, last axis input assumed feature dimension normalized per index. Note specific case batched scalar inputs axis batch axis, default normalize index batch separately. case, consider passing axis=NULL. Defaults -1. mean mean value(s) use normalization. passed value(s) broadcast shape kept axes ; value(s) broadcast, error raised layer's build() method called. variance variance value(s) use normalization. passed value(s) broadcast shape kept axes ; value(s) broadcast, error raised layer's build() method called. invert TRUE, layer apply inverse transformation inputs: turn normalized input back original form. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"Calculate global mean variance analyzing dataset adapt().     Calculate mean variance index last axis.     Pass mean variance directly.     Use layer de-normalize inputs (adapting layer).","code":"adapt_data <- op_array(c(1., 2., 3., 4., 5.), dtype='float32') input_data <- op_array(c(1., 2., 3.), dtype='float32') layer <- layer_normalization(axis = NULL) layer %>% adapt(adapt_data) layer(input_data) ## tf.Tensor([-1.4142135  -0.70710677  0.        ], shape=(3), dtype=float32) adapt_data <- op_array(rbind(c(0., 7., 4.),                        c(2., 9., 6.),                        c(0., 7., 4.),                        c(2., 9., 6.)), dtype='float32') input_data <- op_array(matrix(c(0., 7., 4.), nrow = 1), dtype='float32') layer <- layer_normalization(axis=-1) layer %>% adapt(adapt_data) layer(input_data) ## tf.Tensor([[-1. -1. -1.]], shape=(1, 3), dtype=float32) input_data <- op_array(rbind(1, 2, 3), dtype='float32') layer <- layer_normalization(mean=3., variance=2.) layer(input_data) ## tf.Tensor( ## [[-1.4142135 ] ##  [-0.70710677] ##  [ 0.        ]], shape=(3, 1), dtype=float32) adapt_data <- op_array(rbind(c(0., 7., 4.),                        c(2., 9., 6.),                        c(0., 7., 4.),                        c(2., 9., 6.)), dtype='float32') input_data <- op_array(c(1., 2., 3.), dtype='float32') layer <- layer_normalization(axis=-1, invert=TRUE) layer %>% adapt(adapt_data) layer(input_data) ## tf.Tensor([[ 2. 10.  8.]], shape=(1, 3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_permute.html","id":null,"dir":"Reference","previous_headings":"","what":"Permutes the dimensions of the input according to a given pattern. — layer_permute","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"Useful e.g. connecting RNNs convnets.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"","code":"layer_permute(object, dims, ...)"},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"object Object compose layer . tensor, array, sequential model. dims List integers. Permutation pattern include batch dimension. Indexing starts 1. instance, c(2, 1) permutes first second dimensions input. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"Arbitrary.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"input shape, dimensions re-ordered according specified pattern.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"","code":"x <- layer_input(shape=c(10, 64)) y <- layer_permute(x, c(2, 1)) shape(y) ## shape(NA, 64, 10)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"layer randomly increase/reduce brightness input RGB images. inference time, output identical input. Call layer training=TRUE adjust brightness input. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"","code":"layer_random_brightness(   object,   factor,   value_range = list(0L, 255L),   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"object Object compose layer . tensor, array, sequential model. factor Float list 2 floats -1.0 1.0. factor used determine lower bound upper bound brightness adjustment. float value chosen randomly limits. -1.0 chosen, output image black, 1.0 chosen, image fully white. one float provided, eg, 0.2, -0.2 used lower bound 0.2 used upper bound. value_range Optional list 2 floats lower upper limit values input data. make change, use c(0.0, 1.0), e.g., image input scaled layer. Defaults c(0.0, 255.0). brightness adjustment scaled range, output values clipped range. seed optional integer, fixed RNG behavior. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"3D (HWC) 4D (NHWC) tensor, float int dtype. Input pixel values can range (e.g. [0., 1.) [0, 255])","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"output","dir":"Reference","previous_headings":"","what":"Output","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"3D (HWC) 4D (NHWC) tensor brightness adjusted based factor. default, layer output floats. output value clipped range [0, 255], valid range RGB colors, rescaled based value_range needed.","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"","code":"random_bright <- layer_random_brightness(factor=0.2, seed = 1)  # An image with shape [2, 2, 3] image <- array(1:12, dim=c(2, 2, 3))  # Assume we randomly select the factor to be 0.1, then it will apply # 0.1 * 255 to all the channel output <- random_bright(image, training=TRUE) output ## tf.Tensor( ## [[[39.605797 43.605797 47.605797] ##   [41.605797 45.605797 49.605797]] ## ##  [[40.605797 44.605797 48.605797] ##   [42.605797 46.605797 50.605797]]], shape=(2, 2, 3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"layer randomly adjust contrast image images random factor. Contrast adjusted independently channel image training. channel, layer computes mean image pixels channel adjusts component x pixel (x - mean) * contrast_factor + mean. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"","code":"layer_random_contrast(object, factor, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"object Object compose layer . tensor, array, sequential model. factor positive float represented fraction value, tuple size 2 representing lower upper bound. represented single float, lower = upper. contrast factor randomly picked [1.0 - lower, 1.0 + upper]. pixel x channel, output (x - mean) * factor + mean mean mean value channel. seed Integer. Used create random seed. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly crops images during training. — layer_random_crop","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"training, layer randomly choose location crop images target size. layer crop images batch cropping location. inference time, training input image smaller target size, input resized cropped return largest possible window image matches target aspect ratio. need apply random cropping inference time, set training TRUE calling layer. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"","code":"layer_random_crop(   object,   height,   width,   seed = NULL,   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"object Object compose layer . tensor, array, sequential model. height Integer, height output shape. width Integer, width output shape. seed Integer. Used create random seed. data_format see description name String, name object ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly flips images during training. — layer_random_flip","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"layer flip images horizontally vertically based mode attribute. inference time, output identical input. Call layer training=TRUE flip input. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"","code":"layer_random_flip(object, mode = \"horizontal_and_vertical\", seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"object Object compose layer . tensor, array, sequential model. mode String indicating flip mode use. Can \"horizontal\", \"vertical\", \"horizontal_and_vertical\". \"horizontal\" left-right flip \"vertical\" top-bottom flip. Defaults \"horizontal_and_vertical\" seed Integer. Used create random seed. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"layer apply random rotations image, filling empty space according fill_mode. default, random rotations applied training. inference time, layer nothing. need apply random rotations inference time, pass training = TRUE calling layer. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"","code":"layer_random_rotation(   object,   factor,   fill_mode = \"reflect\",   interpolation = \"bilinear\",   seed = NULL,   fill_value = 0,   value_range = list(0L, 255L),   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"object Object compose layer . tensor, array, sequential model. factor float represented fraction 2 Pi, tuple size 2 representing lower upper bound rotating clockwise counter-clockwise. positive values means rotating counter clock-wise, negative value means clock-wise. represented single float, value used upper lower bound. instance, factor=(-0.2, 0.3) results output rotation random amount range [-20% * 2pi, 30% * 2pi]. factor=0.2 results output rotating random amount range [-20% * 2pi, 20% * 2pi]. fill_mode Points outside boundaries input filled according given mode (one {\"constant\", \"reflect\", \"wrap\", \"nearest\"}). reflect: (d c b | b c d | d c b ) input extended reflecting edge last pixel. constant: (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k = 0. wrap: (b c d | b c d | b c d) input extended wrapping around opposite edge. nearest: (| b c d | d d d d) input extended nearest pixel. interpolation Interpolation mode. Supported values: \"nearest\", \"bilinear\". seed Integer. Used create random seed. fill_value float represents value filled outside boundaries fill_mode=\"constant\". value_range see description data_format see description ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format","code":""},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly translates images during training. — layer_random_translation","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"layer apply random translations image training, filling empty space according fill_mode. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats.","code":""},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"","code":"layer_random_translation(   object,   height_factor,   width_factor,   fill_mode = \"reflect\",   interpolation = \"bilinear\",   seed = NULL,   fill_value = 0,   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"object Object compose layer . tensor, array, sequential model. height_factor float represented fraction value, tuple size 2 representing lower upper bound shifting vertically. negative value means shifting image , positive value means shifting image . represented single positive float, value used upper lower bound. instance, height_factor=(-0.2, 0.3) results output shifted random amount range [-20%, +30%]. height_factor=0.2 results output height shifted random amount range [-20%, +20%]. width_factor float represented fraction value, tuple size 2 representing lower upper bound shifting horizontally. negative value means shifting image left, positive value means shifting image right. represented single positive float, value used upper lower bound. instance, width_factor=(-0.2, 0.3) results output shifted left 20%, shifted right 30%. width_factor=0.2 results output height shifted left right 20%. fill_mode Points outside boundaries input filled according given mode. Available methods \"constant\", \"nearest\", \"wrap\" \"reflect\". Defaults \"constant\". \"reflect\": (d c b | b c d | d c b ) input extended reflecting edge last pixel. \"constant\": (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k specified fill_value. \"wrap\": (b c d | b c d | b c d) input extended wrapping around opposite edge. \"nearest\": (| b c d | d d d d) input extended nearest pixel. Note using torch backend, \"reflect\" redirected \"mirror\" (c d c b | b c d | c b b) torch support \"reflect\". Note torch backend support \"wrap\". interpolation Interpolation mode. Supported values: \"nearest\", \"bilinear\". seed Integer. Used create random seed. fill_value float represents value filled outside boundaries fill_mode=\"constant\". data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format, (..., channels, height, width), \"channels_first\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels), (..., channels, target_height, target_width), \"channels_first\" format. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"layer randomly zoom axis image independently, filling empty space according fill_mode. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats.","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"","code":"layer_random_zoom(   object,   height_factor,   width_factor = NULL,   fill_mode = \"reflect\",   interpolation = \"bilinear\",   seed = NULL,   fill_value = 0,   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"object Object compose layer . tensor, array, sequential model. height_factor float represented fraction value, list size 2 representing lower upper bound zooming vertically. represented single float, value used upper lower bound. positive value means zooming , negative value means zooming . instance, height_factor=c(0.2, 0.3) result output zoomed random amount range [+20%, +30%]. height_factor=c(-0.3, -0.2) result output zoomed random amount range [+20%, +30%]. width_factor float represented fraction value, list size 2 representing lower upper bound zooming horizontally. represented single float, value used upper lower bound. instance, width_factor=c(0.2, 0.3) result output zooming 20% 30%. width_factor=c(-0.3, -0.2) result output zooming 20% 30%. NULL means .e., zooming vertical horizontal directions preserving aspect ratio. Defaults NULL. fill_mode Points outside boundaries input filled according given mode. Available methods \"constant\", \"nearest\", \"wrap\" \"reflect\". Defaults \"constant\". \"reflect\": (d c b | b c d | d c b ) input extended reflecting edge last pixel. \"constant\": (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k specified fill_value. \"wrap\": (b c d | b c d | b c d) input extended wrapping around opposite edge. \"nearest\": (| b c d | d d d d) input extended nearest pixel. Note using torch backend, \"reflect\" redirected \"mirror\" (c d c b | b c d | c b b) torch support \"reflect\". Note torch backend support \"wrap\". interpolation Interpolation mode. Supported values: \"nearest\", \"bilinear\". seed Integer. Used create random seed. fill_value float represents value filled outside boundaries fill_mode=\"constant\". data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format, (..., channels, height, width), \"channels_first\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels), (..., channels, target_height, target_width), \"channels_first\" format. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"","code":"input_img <- random_uniform(c(32, 224, 224, 3)) layer <- layer_random_zoom(height_factor = .5, width_factor = .2) out_img <- layer(input_img)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeats the input n times. — layer_repeat_vector","title":"Repeats the input n times. — layer_repeat_vector","text":"Repeats input n times.","code":""},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeats the input n times. — layer_repeat_vector","text":"","code":"layer_repeat_vector(object, n, ...)"},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeats the input n times. — layer_repeat_vector","text":"object Object compose layer . tensor, array, sequential model. n Integer, repetition factor. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Repeats the input n times. — layer_repeat_vector","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Repeats the input n times. — layer_repeat_vector","text":"","code":"x <- layer_input(shape = 32) y <- layer_repeat_vector(x, n = 3) shape(y) ## shape(NA, 3, 32)"},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Repeats the input n times. — layer_repeat_vector","text":"2D tensor shape (batch_size, features).","code":""},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Repeats the input n times. — layer_repeat_vector","text":"3D tensor shape (batch_size, n, features).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_rescaling.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","title":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","text":"layer rescales every value input (often image) multiplying scale adding offset. instance: rescale input [0, 255] range [0, 1] range, pass scale=1./255. rescale input [0, 255] range [-1, 1] range, pass scale=1./127.5, offset=-1. rescaling applied training inference. Inputs can integer floating point dtype, default layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_rescaling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","text":"","code":"layer_rescaling(object, scale, offset = 0, ...)"},{"path":"https://keras.posit.co/reference/layer_rescaling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","text":"object Object compose layer . tensor, array, sequential model. scale Float, scale apply inputs. offset Float, offset apply inputs. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_rescaling.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer that reshapes inputs into the given shape. — layer_reshape","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"Layer reshapes inputs given shape.","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"","code":"layer_reshape(object, target_shape, ...)"},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"object Object compose layer . tensor, array, sequential model. target_shape Target shape. List integers, include samples dimension (batch size). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"Arbitrary, although dimensions input shape must known/fixed. Use keyword argument input_shape (list integers, include samples/batch size axis) using layer first layer model.","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"(batch_size, *target_shape)","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"","code":"x <- layer_input(shape = 12) y <- layer_reshape(x, c(3, 4)) shape(y) ## shape(NA, 3, 4) # also supports shape inference using `-1` as dimension y <- layer_reshape(x, c(-1, 2, 2)) shape(y) ## shape(NA, 3, 2, 2)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which resizes images. — layer_resizing","title":"A preprocessing layer which resizes images. — layer_resizing","text":"layer resizes image input target height width. input 4D (batched) 3D (unbatched) tensor \"channels_last\" format. Input pixel values can range (e.g. [0., 1.) [0, 255]).","code":""},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which resizes images. — layer_resizing","text":"","code":"layer_resizing(   object,   height,   width,   interpolation = \"bilinear\",   crop_to_aspect_ratio = FALSE,   pad_to_aspect_ratio = FALSE,   fill_mode = \"constant\",   fill_value = 0,   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which resizes images. — layer_resizing","text":"object Object compose layer . tensor, array, sequential model. height Integer, height output shape. width Integer, width output shape. interpolation String, interpolation method. Supports \"bilinear\", \"nearest\", \"bicubic\", \"lanczos3\", \"lanczos5\". Defaults \"bilinear\". crop_to_aspect_ratio TRUE, resize images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image cropped return largest possible window image (size (height, width)) matches target aspect ratio. default (crop_to_aspect_ratio=FALSE), aspect ratio may preserved. pad_to_aspect_ratio TRUE, pad images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image evenly padded short side. fill_mode using pad_to_aspect_ratio=TRUE, padded areas filled according given mode. \"constant\" supported time (fill constant value, equal fill_value). fill_value Float. Padding value use pad_to_aspect_ratio=TRUE. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which resizes images. — layer_resizing","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which resizes images. — layer_resizing","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format, (..., channels, height, width), \"channels_first\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which resizes images. — layer_resizing","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels), (..., channels, target_height, target_width), \"channels_first\" format. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":null,"dir":"Reference","previous_headings":"","what":"Base class for recurrent layers — layer_rnn","title":"Base class for recurrent layers — layer_rnn","text":"Base class recurrent layers","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Base class for recurrent layers — layer_rnn","text":"","code":"layer_rnn(   object,   cell,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   unroll = FALSE,   zero_output_for_mask = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Base class for recurrent layers — layer_rnn","text":"object Object compose layer . tensor, array, sequential model. cell RNN cell instance list RNN cell instances. RNN cell class : call(input_at_t, states_at_t) method, returning (output_at_t, states_at_t_plus_1). call method cell can also take optional argument constants, see section \"Note passing external constants\" . state_size attribute. can single integer (single state) case size recurrent state. can also list integers (one size per state). output_size attribute, single integer. get_initial_state(batch_size=NULL) method creates tensor meant fed call() initial state, user specify initial state via means. returned initial state shape (batch_size, cell.state_size). cell might choose create tensor full zeros, values based cell's implementation. inputs input tensor RNN layer, shape (batch_size, timesteps, features). method implemented cell, RNN layer create zero filled tensor shape (batch_size, cell$state_size). case cell list RNN cell instances, cells stacked top RNN, resulting efficient stacked RNN. return_sequences Boolean (default FALSE). Whether return last output output sequence, full sequence. return_state Boolean (default FALSE). Whether return last state addition output. go_backwards Boolean (default FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default FALSE). TRUE, last state sample index batch used initial state sample index following batch. unroll Boolean (default FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences. zero_output_for_mask Boolean (default FALSE). Whether output use zeros masked timesteps. Note field used return_sequences TRUE mask provided. can useful want reuse raw output sequence RNN without interference masked timesteps, e.g., merging bidirectional RNNs. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Base class for recurrent layers — layer_rnn","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Base class for recurrent layers — layer_rnn","text":"inputs: Input tensor. initial_state: List initial state tensors passed first call cell. mask: Binary tensor shape [batch_size, timesteps] indicating whether given timestep masked. individual TRUE entry indicates corresponding timestep utilized, FALSE entry indicates corresponding timestep ignored. training: Python boolean indicating whether layer behave training mode inference mode. argument passed cell calling . use cells use dropout.","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Base class for recurrent layers — layer_rnn","text":"3-D tensor shape (batch_size, timesteps, features).","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Base class for recurrent layers — layer_rnn","text":"return_state: list tensors. first tensor output. remaining tensors last states, shape (batch_size, state_size), state_size high dimension tensor shape. return_sequences: 3D tensor shape (batch_size, timesteps, output_size).","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"masking-","dir":"Reference","previous_headings":"","what":"Masking:","title":"Base class for recurrent layers — layer_rnn","text":"layer supports masking input data variable number timesteps. introduce masks data, use layer_embedding() layer mask_zero parameter set TRUE. Note using statefulness RNNs: can set RNN layers 'stateful', means states computed samples one batch reused initial states samples next batch. assumes one--one mapping samples different successive batches. enable statefulness: Specify stateful = TRUE layer constructor. Specify fixed batch size model, passing sequential model: input_batch_shape = c(...) keras_model_sequential() call. Else functional model 1 input layers: batch_shape = c(...) layer_input() call(s). expected shape inputs including batch size. list integers, e.g. c(32, 10, 100). Specify shuffle = FALSE calling fit(). reset states model, call reset_state() either specific layer, entire model. Note specifying initial state RNNs: can specify initial state RNN layers symbolically calling keyword argument initial_state. value initial_state tensor list tensors representing initial state RNN layer.","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Base class for recurrent layers — layer_rnn","text":"First, define RNN Cell, layer subclass.   use cell RNN layer:","code":"rnn_cell_minimal <- Layer(   \"MinimalRNNCell\",    initialize = function(units, ...) {     super$initialize(...)     self$units <- as.integer(units)     self$state_size <- as.integer(units)   },    build = function(input_shape) {     self$kernel <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = 'uniform',       name = 'kernel'     )     self$recurrent_kernel <- self$add_weight(       shape = shape(self$units, self$units),       initializer = 'uniform',       name = 'recurrent_kernel'     )     self$built <- TRUE   },    call = function(inputs, states) {     prev_output <- states[[1]]     h <- op_matmul(inputs, self$kernel)     output <- h + op_matmul(prev_output, self$recurrent_kernel)     list(output, list(output))   } ) cell <- rnn_cell_minimal(units = 32) x <- layer_input(shape = shape(NULL, 5)) layer <- layer_rnn(cell = cell) y <- layer(x) cells <- list(rnn_cell_minimal(units = 32), rnn_cell_minimal(units = 4)) x <- layer_input(shape = shape(NULL, 5)) layer <- layer_rnn(cell = cells) y <- layer(x)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"1D separable convolution layer. — layer_separable_conv_1d","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"layer performs depthwise convolution acts separately channels, followed pointwise convolution mixes channels. use_bias TRUE bias initializer provided, adds bias vector output. optionally applies activation function produce final output.","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"","code":"layer_separable_conv_1d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   depth_multiplier = 1L,   activation = NULL,   use_bias = TRUE,   depthwise_initializer = \"glorot_uniform\",   pointwise_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   depthwise_regularizer = NULL,   pointwise_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   depthwise_constraint = NULL,   pointwise_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimensionality output space (.e. number filters pointwise convolution). kernel_size int list 1 integers, specifying size depthwise convolution window. strides int list 1 integers, specifying stride length depthwise convolution. one int specified, stride size used dimensions. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated convolution. one int specified, dilation rate used dimensions. depth_multiplier number depthwise convolution output channels input channel. total number depthwise convolution output channels equal input_channel * depth_multiplier. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. depthwise_initializer initializer depthwise convolution kernel. NULL, default initializer (\"glorot_uniform\") used. pointwise_initializer initializer pointwise convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer initializer bias vector. NULL, default initializer ('\"zeros\"') used. depthwise_regularizer Optional regularizer depthwise convolution kernel. pointwise_regularizer Optional regularizer pointwise convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. depthwise_constraint Optional projection function applied depthwise kernel updated Optimizer (e.g. used norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). pointwise_constraint Optional projection function applied pointwise kernel updated Optimizer. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"3D tensor representing activation(separable_conv1d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, steps, channels) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, new_steps, filters) data_format=\"channels_first\": 3D tensor shape: (batch_shape, filters, new_steps)","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"","code":"x <- random_uniform(c(4, 10, 12)) y <- layer_separable_conv_1d(x, 3, 2, 2, activation='relu') shape(y) ## shape(4, 5, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"2D separable convolution layer. — layer_separable_conv_2d","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"layer performs depthwise convolution acts separately channels, followed pointwise convolution mixes channels. use_bias TRUE bias initializer provided, adds bias vector output. optionally applies activation function produce final output.","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"","code":"layer_separable_conv_2d(   object,   filters,   kernel_size,   strides = list(1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L),   depth_multiplier = 1L,   activation = NULL,   use_bias = TRUE,   depthwise_initializer = \"glorot_uniform\",   pointwise_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   depthwise_regularizer = NULL,   pointwise_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   depthwise_constraint = NULL,   pointwise_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimensionality output space (.e. number filters pointwise convolution). kernel_size int list 2 integers, specifying size depthwise convolution window. strides int list 2 integers, specifying stride length depthwise convolution. one int specified, stride size used dimensions. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 2 integers, specifying dilation rate use dilated convolution. one int specified, dilation rate used dimensions. depth_multiplier number depthwise convolution output channels input channel. total number depthwise convolution output channels equal input_channel * depth_multiplier. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. depthwise_initializer initializer depthwise convolution kernel. NULL, default initializer (\"glorot_uniform\") used. pointwise_initializer initializer pointwise convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer initializer bias vector. NULL, default initializer ('\"zeros\"') used. depthwise_regularizer Optional regularizer depthwise convolution kernel. pointwise_regularizer Optional regularizer pointwise convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. depthwise_constraint Optional projection function applied depthwise kernel updated Optimizer (e.g. used norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). pointwise_constraint Optional projection function applied pointwise kernel updated Optimizer. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"4D tensor representing activation(separable_conv2d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, height, width, channels) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, new_height, new_width, filters) data_format=\"channels_first\": 4D tensor shape: (batch_size, filters, new_height, new_width)","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"","code":"x <- random_uniform(c(4, 10, 10, 12)) y <- layer_separable_conv_2d(x, 3, c(4, 3), 2, activation='relu') shape(y) ## shape(4, 4, 4, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":null,"dir":"Reference","previous_headings":"","what":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"Fully-connected RNN output fed back new input.","code":""},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"","code":"layer_simple_rnn(   object,   units,   activation = \"tanh\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   unroll = FALSE,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"object Object compose layer . tensor, array, sequential model. units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state.  Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. activity_regularizer Regularizer function applied output layer (\"activation\"). Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix.  Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default: FALSE). TRUE, last state sample index batch used initial state sample index following batch. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences. seed Initial seed random number generator ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"sequence: 3D tensor, shape [batch, timesteps, feature]. mask: Binary tensor shape [batch, timesteps] indicating whether given timestep masked. individual TRUE entry indicates corresponding timestep utilized, FALSE entry indicates corresponding timestep ignored. training: Python boolean indicating whether layer behave training mode inference mode. argument passed cell calling . relevant dropout recurrent_dropout used. initial_state: List initial state tensors passed first call cell.","code":""},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"","code":"inputs <- random_uniform(c(32, 10, 8)) simple_rnn <- layer_simple_rnn(units = 4) output <- simple_rnn(inputs)  # The output has shape `(32, 4)`. simple_rnn <- layer_simple_rnn(     units = 4, return_sequences=TRUE, return_state=TRUE ) # whole_sequence_output has shape `(32, 10, 4)`. # final_state has shape `(32, 4)`. c(whole_sequence_output, final_state) %<-% simple_rnn(inputs)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"layer performs function Dropout, however, drops entire 1D feature maps instead individual elements. adjacent frames within feature maps strongly correlated (normally case early convolution layers) regular dropout regularize activations otherwise just result effective learning rate decrease. case, SpatialDropout1D help promote independence feature maps used instead.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"","code":"layer_spatial_dropout_1d(object, rate, seed = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. Fraction input units drop. seed Initial seed random number generator name String, name object dtype datatype (e.g., \"float32\").","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"inputs: 3D tensor. training: Python boolean indicating whether layer behave training mode (applying dropout) inference mode (pass-).","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"3D tensor shape: (samples, timesteps, channels)","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"input.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"Tompson et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"version performs function Dropout, however, drops entire 2D feature maps instead individual elements. adjacent pixels within feature maps strongly correlated (normally case early convolution layers) regular dropout regularize activations otherwise just result effective learning rate decrease. case, SpatialDropout2D help promote independence feature maps used instead.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"","code":"layer_spatial_dropout_2d(   object,   rate,   data_format = NULL,   seed = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. Fraction input units drop. data_format \"channels_first\" \"channels_last\". \"channels_first\" mode, channels dimension (depth) index 1, \"channels_last\" mode index 3. defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". seed Initial seed random number generator name String, name object dtype datatype (e.g., \"float32\").","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"inputs: 4D tensor. training: Python boolean indicating whether layer behave training mode (applying dropout) inference mode (pass-).","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"4D tensor shape: (samples, channels, rows, cols) data_format='channels_first' 4D tensor shape: (samples, rows, cols, channels) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"input.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"Tompson et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"version performs function Dropout, however, drops entire 3D feature maps instead individual elements. adjacent voxels within feature maps strongly correlated (normally case early convolution layers) regular dropout regularize activations otherwise just result effective learning rate decrease. case, SpatialDropout3D help promote independence feature maps used instead.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"","code":"layer_spatial_dropout_3d(   object,   rate,   data_format = NULL,   seed = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. Fraction input units drop. data_format \"channels_first\" \"channels_last\". \"channels_first\" mode, channels dimension (depth) index 1, \"channels_last\" mode index 4. defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". seed Initial seed random number generator name String, name object dtype datatype (e.g., \"float32\").","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"inputs: 5D tensor. training: Python boolean indicating whether layer behave training mode (applying dropout) inference mode (pass-).","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"5D tensor shape: (samples, channels, dim1, dim2, dim3) data_format='channels_first' 5D tensor shape: (samples, dim1, dim2, dim3, channels) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"input.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"Tompson et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"wrapper controls Lipschitz constant weights layer constraining spectral norm, can stabilize training GANs.","code":""},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"","code":"layer_spectral_normalization(object, layer, power_iterations = 1L, ...)"},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"object Object compose layer . tensor, array, sequential model. layer Layer instance either kernel (e.g. layer_conv_2d, layer_dense...) embeddings attribute (layer_embedding layer). power_iterations int, number iterations normalization. ... Base wrapper keyword arguments.","code":""},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"Wrap layer_conv_2d:     Wrap layer_dense:","code":"x <- random_uniform(c(1, 10, 10, 1)) conv2d <- layer_spectral_normalization(   layer = layer_conv_2d(filters = 2, kernel_size = 2) ) y <- conv2d(x) shape(y) ## shape(1, 9, 9, 2) x <- random_uniform(c(1, 10, 10, 1)) dense <- layer_spectral_normalization(layer = layer_dense(units = 10)) y <- dense(x) shape(y) ## shape(1, 10, 10, 10)"},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"Spectral Normalization GAN.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"layer translates set arbitrary strings integer output via table-based vocabulary lookup. layer perform splitting transformation input strings. layer can split tokenize natural language, see layer_text_vectorization layer. vocabulary layer must either supplied construction learned via adapt(). adapt(), layer analyze data set, determine frequency individual strings tokens, create vocabulary . vocabulary capped size, frequent tokens used create vocabulary others treated --vocabulary (OOV). two possible output modes layer. output_mode \"int\", input strings converted index vocabulary (integer). output_mode \"multi_hot\", \"count\", \"tf_idf\", input strings encoded array dimension corresponds element vocabulary. vocabulary can optionally contain mask token well OOV token (can optionally occupy multiple indices vocabulary, set num_oov_indices). position tokens vocabulary fixed. output_mode \"int\", vocabulary begin mask token (set), followed OOV indices, followed rest vocabulary. output_mode \"multi_hot\", \"count\", \"tf_idf\" vocabulary begin OOV indices instances mask token dropped. Note: layer uses TensorFlow internally. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"","code":"layer_string_lookup(   object,   max_tokens = NULL,   num_oov_indices = 1L,   mask_token = NULL,   oov_token = \"[UNK]\",   vocabulary = NULL,   idf_weights = NULL,   invert = FALSE,   output_mode = \"int\",   pad_to_max_tokens = FALSE,   sparse = FALSE,   encoding = \"utf-8\",   name = NULL,   ...,   vocabulary_dtype = NULL )"},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"object Object compose layer . tensor, array, sequential model. max_tokens Maximum size vocabulary layer. specified adapting vocabulary setting pad_to_max_tokens=TRUE. NULL, cap size vocabulary. Note size includes OOV mask tokens. Defaults NULL. num_oov_indices number --vocabulary tokens use. value 1, OOV inputs modulated determine OOV value. value 0, OOV inputs cause error calling layer. Defaults 1. mask_token token represents masked inputs. output_mode \"int\", token included vocabulary mapped index 0. output modes, token appear vocabulary instances mask token input dropped. set NULL, mask term added. Defaults NULL. oov_token used invert TRUE. token return OOV indices. Defaults \"[UNK]\". vocabulary Optional. Either array integers string path text file. passing array, can pass list, list, 1D NumPy array, 1D tensor containing integer vocbulary terms. passing file path, file contain one line per term vocabulary. argument set, need adapt() layer. idf_weights valid output_mode \"tf_idf\". list, list, 1D NumPy array, 1D tensor length vocabulary, containing floating point inverse document frequency weights, multiplied per sample term counts final TF-IDF weight. vocabulary argument set, output_mode \"tf_idf\", argument must supplied. invert valid output_mode \"int\". TRUE, layer map indices vocabulary items instead mapping vocabulary items indices. Defaults FALSE. output_mode Specification output layer. Values can \"int\", \"one_hot\", \"multi_hot\", \"count\", \"tf_idf\" configuring layer follows: \"int\": Return vocabulary indices input tokens. \"one_hot\": Encodes individual element input array size vocabulary, containing 1 element index. last dimension size 1, encode dimension. last dimension size 1, append new dimension encoded output. \"multi_hot\": Encodes sample input single array size vocabulary, containing 1 vocabulary term present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). \"count\": \"multi_hot\", int array contains count number times token index appeared sample. \"tf_idf\": \"multi_hot\", TF-IDF algorithm applied find value token slot. \"int\" output, shape input output supported. output modes, currently output rank 2 supported. Defaults \"int\". pad_to_max_tokens applicable output_mode \"multi_hot\", \"count\", \"tf_idf\". TRUE, output feature axis padded max_tokens even number unique tokens vocabulary less max_tokens, resulting tensor shape (batch_size, max_tokens) regardless vocabulary size. Defaults FALSE. sparse Boolean. applicable \"multi_hot\", \"count\", \"tf_idf\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. encoding Optional. text encoding use interpret input strings. Defaults \"utf-8\". name String, name object ... forward/backward compatability. vocabulary_dtype dtype vocabulary terms, example \"int64\" \"int32\". Defaults \"int64\".","code":""},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"Creating lookup layer known vocabulary example creates lookup layer pre-existing vocabulary.     Creating lookup layer adapted vocabulary example creates lookup layer generates vocabulary analyzing dataset.     Note OOV token \"[UNK]\" added vocabulary. remaining tokens sorted frequency (\"d\", 2 occurrences, first) inverse sort order.     Lookups multiple OOV indices example demonstrates use lookup layer multiple OOV indices.  layer created one OOV index, OOV values hashed number OOV buckets, distributing OOV values deterministic fashion across set.     Note output OOV value 'm' 0, output OOV value \"z\" 1. -vocab terms output index increased 1 earlier examples (maps 2, etc) order make space extra OOV value. One-hot output Configure layer output_mode='one_hot'. Note first num_oov_indices dimensions ont_hot encoding represent OOV values.     Multi-hot output Configure layer output_mode='multi_hot'. Note first num_oov_indices dimensions multi_hot encoding represent OOV values.     Token count output Configure layer output_mode='count'. multi_hot output, first num_oov_indices dimensions output represent OOV values.     TF-IDF output Configure layer output_mode=\"tf_idf\". multi_hot output, first num_oov_indices dimensions output represent OOV values. token bin output token_count * idf_weight, idf weights inverse document frequency weights per token. provided along vocabulary. Note idf_weight OOV values default average idf weights passed .     specify idf weights oov values, need pass entire vocabulary including leading oov token.     adapting layer \"tf_idf\" mode, input sample considered document, IDF weight per token calculated log(1 + num_documents / (1 + token_document_count)). Inverse lookup example demonstrates map indices strings using layer. (can also use adapt() inverse=TRUE, simplicity pass vocab example.)     Note first index correspond oov token default. Forward inverse lookup pairs example demonstrates use vocabulary standard lookup layer create inverse lookup layer.     example, input value \"z\" resulted output \"[UNK]\", since 1000 vocabulary - got represented OOV, OOV values returned \"[UNK]\" inverse layer. Also, note inverse work, must already set forward layer vocabulary either directly via adapt() calling get_vocabulary().","code":"vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\"), c(\"d\", \"z\", \"b\")) layer <- layer_string_lookup(vocabulary=vocab) layer(data) ## tf.Tensor( ## [[1 3 4] ##  [4 0 2]], shape=(2, 3), dtype=int64) data <- rbind(c(\"a\", \"c\", \"d\"), c(\"d\", \"z\", \"b\")) layer <- layer_string_lookup() layer %>% adapt(data) get_vocabulary(layer) ## [1] \"[UNK]\" \"d\"     \"z\"     \"c\"     \"b\"     \"a\" data <- rbind(c(\"a\", \"c\", \"d\"), c(\"d\", \"z\", \"b\")) layer <- layer_string_lookup() layer %>% adapt(data) layer(data) ## tf.Tensor( ## [[5 3 1] ##  [1 2 4]], shape=(2, 3), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\"), c(\"m\", \"z\", \"b\")) layer <- layer_string_lookup(vocabulary = vocab, num_oov_indices = 2) layer(data) ## tf.Tensor( ## [[2 4 5] ##  [0 1 3]], shape=(2, 3), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- c(\"a\", \"b\", \"c\", \"d\", \"z\") layer <- layer_string_lookup(vocabulary = vocab, output_mode = 'one_hot') layer(data) ## tf.Tensor( ## [[0 1 0 0 0] ##  [0 0 1 0 0] ##  [0 0 0 1 0] ##  [0 0 0 0 1] ##  [1 0 0 0 0]], shape=(5, 5), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\", \"d\"), c(\"d\", \"z\", \"b\", \"z\")) layer <- layer_string_lookup(vocabulary = vocab, output_mode = 'multi_hot') layer(data) ## tf.Tensor( ## [[0 1 0 1 1] ##  [1 0 1 0 1]], shape=(2, 5), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\", \"d\"), c(\"d\", \"z\", \"b\", \"z\")) layer <- layer_string_lookup(vocabulary = vocab, output_mode = 'count') layer(data) ## tf.Tensor( ## [[0 1 0 1 2] ##  [2 0 1 0 1]], shape=(2, 5), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") idf_weights <- c(0.25, 0.75, 0.6, 0.4) data <- rbind(c(\"a\", \"c\", \"d\", \"d\"), c(\"d\", \"z\", \"b\", \"z\")) layer <- layer_string_lookup(output_mode = \"tf_idf\") layer %>% set_vocabulary(vocab, idf_weights=idf_weights) layer(data) ## tf.Tensor( ## [[0.   0.25 0.   0.6  0.8 ] ##  [1.   0.   0.75 0.   0.4 ]], shape=(2, 5), dtype=float32) vocab <- c(\"[UNK]\", \"a\", \"b\", \"c\", \"d\") idf_weights <- c(0.9, 0.25, 0.75, 0.6, 0.4) data <- rbind(c(\"a\", \"c\", \"d\", \"d\"), c(\"d\", \"z\", \"b\", \"z\")) layer <- layer_string_lookup(output_mode = \"tf_idf\") layer %>% set_vocabulary(vocab, idf_weights=idf_weights) layer(data) ## tf.Tensor( ## [[0.   0.25 0.   0.6  0.8 ] ##  [1.8  0.   0.75 0.   0.4 ]], shape=(2, 5), dtype=float32) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(1, 3, 4), c(4, 0, 2)) layer <- layer_string_lookup(vocabulary = vocab, invert = TRUE) layer(data) ## tf.Tensor( ## [[b'a' b'c' b'd'] ##  [b'd' b'[UNK]' b'b']], shape=(2, 3), dtype=string) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\"), c(\"d\", \"z\", \"b\")) layer <- layer_string_lookup(vocabulary = vocab) i_layer <- layer_string_lookup(vocabulary = vocab, invert = TRUE) int_data <- layer(data) i_layer(int_data) ## tf.Tensor( ## [[b'a' b'c' b'd'] ##  [b'd' b'[UNK]' b'b']], shape=(2, 3), dtype=string)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs elementwise subtraction. — layer_subtract","title":"Performs elementwise subtraction. — layer_subtract","text":"takes input list tensors size 2 shape, returns single tensor (inputs[0] - inputs[1)) shape.","code":""},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs elementwise subtraction. — layer_subtract","text":"","code":"layer_subtract(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs elementwise subtraction. — layer_subtract","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs elementwise subtraction. — layer_subtract","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs elementwise subtraction. — layer_subtract","text":"Usage Keras model:","code":"input_shape <- c(2, 3, 4) x1 <- random_uniform(input_shape) x2 <- random_uniform(input_shape) y <- layer_subtract(list(x1, x2)) input1 <- layer_input(shape = 16) x1 <- layer_dense(input1, units = 8, activation = 'relu') input2 <- layer_input(shape = 32) x2 <- layer_dense(input2, units = 8, activation = 'relu') subtracted <- layer_subtract(list(x1, x2)) out <- layer_dense(subtracted, units = 4) model <- keras_model(inputs = list(input1, input2), outputs = out)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"layer basic options managing text Keras model. transforms batch strings (one example = one string) either list token indices (one example = 1D tensor integer token indices) dense representation (one example = 1D tensor float values representing data example's tokens). layer meant handle natural language inputs. handle simple string inputs (categorical strings pre-tokenized strings) see layer_string_lookup(). vocabulary layer must either supplied construction learned via adapt(). layer adapted, analyze dataset, determine frequency individual string values, create vocabulary . vocabulary can unlimited size capped, depending configuration options layer; unique values input maximum vocabulary size, frequent terms used create vocabulary. processing example contains following steps: Standardize example (usually lowercasing + punctuation stripping) Split example substrings (usually words) Recombine substrings tokens (usually ngrams) Index tokens (associate unique int value token) Transform example using index, either vector ints dense float vector. notes passing callables customize splitting normalization layer: callable can passed Layer, want serialize object pass functions registered Keras serializables (see register_keras_serializable() details). using custom callable standardize, data received callable exactly passed layer. callable return tensor shape input. using custom callable split, data received callable 1st dimension squeezed - instead list(\"string split\", \"another string split\"), Callable see c(\"string split\", \"another string split\"). callable return tf.Tensor dtype string first dimension containing split tokens - example, see something like list(c(\"string\", \"\", \"split\"), c(\"another\", \"string\", \"\", \"split\")). Note: layer uses TensorFlow internally. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"","code":"layer_text_vectorization(   object,   max_tokens = NULL,   standardize = \"lower_and_strip_punctuation\",   split = \"whitespace\",   ngrams = NULL,   output_mode = \"int\",   output_sequence_length = NULL,   pad_to_max_tokens = FALSE,   vocabulary = NULL,   idf_weights = NULL,   sparse = FALSE,   ragged = FALSE,   encoding = \"utf-8\",   name = NULL,   ... )  get_vocabulary(object, include_special_tokens = TRUE)  set_vocabulary(object, vocabulary, idf_weights = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"object Object compose layer . tensor, array, sequential model. max_tokens Maximum size vocabulary layer. specified adapting vocabulary setting pad_to_max_tokens=TRUE. Note vocabulary contains 1 OOV token, effective number tokens (max_tokens - 1 - (1 output_mode == \"int\" else 0)). standardize Optional specification standardization apply input text. Values can : NULL: standardization. \"lower_and_strip_punctuation\": Text lowercased punctuation removed. \"lower\": Text lowercased. \"strip_punctuation\": punctuation removed. Callable: Inputs passed callable function, standardized returned. split Optional specification splitting input text. Values can : NULL: splitting. \"whitespace\": Split whitespace. \"character\": Split unicode character. Callable: Standardized inputs passed callable function, split returned. ngrams Optional specification ngrams create possibly-split input text. Values can NULL, integer list integers; passing integer create ngrams integer, passing list integers create ngrams specified values list. Passing NULL means ngrams created. output_mode Optional specification output layer. Values can \"int\", \"multi_hot\", \"count\" \"tf_idf\", configuring layer follows: \"int\": Outputs integer indices, one integer index per split string token. output_mode == \"int\", 0 reserved masked locations; reduces vocab size max_tokens - 2 instead max_tokens - 1. \"multi_hot\": Outputs single int array per batch, either vocab_size max_tokens size, containing 1s elements token mapped index exists least batch item. \"count\": Like \"multi_hot\", int array contains count number times token index appeared batch item. \"tf_idf\": Like \"multi_hot\", TF-IDF algorithm applied find value token slot. \"int\" output, shape input output supported. output modes, currently rank 1 inputs (rank 2 outputs splitting) supported. output_sequence_length valid INT mode. set, output time dimension padded truncated exactly output_sequence_length values, resulting tensor shape (batch_size, output_sequence_length) regardless many tokens resulted splitting step. Defaults NULL. ragged TRUE output_sequence_length may still truncate output. pad_to_max_tokens valid  \"multi_hot\", \"count\", \"tf_idf\" modes. TRUE, output feature axis padded max_tokens even number unique tokens vocabulary less max_tokens, resulting tensor shape (batch_size, max_tokens) regardless vocabulary size. Defaults FALSE. vocabulary Optional. Either array strings string path text file. passing array, can pass list, list, 1D NumPy array, 1D tensor containing string vocabulary terms. passing file path, file contain one line per term vocabulary. argument set, need adapt() layer. idf_weights R vector, 1D numpy array, 1D tensor inverse document frequency weights equal length vocabulary. Must set output_mode \"tf_idf\". set otherwise. sparse Boolean. applicable \"multi_hot\", \"count\", \"tf_idf\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. ragged Boolean. applicable \"int\" output mode. supported TensorFlow backend. TRUE, returns RaggedTensor instead dense Tensor, sequence may different length string splitting. Defaults FALSE. encoding Optional. text encoding use interpret input strings. Defaults \"utf-8\". name String, name object ... forward/backward compatability. include_special_tokens TRUE, returned vocabulary include padding OOV tokens, term's index vocabulary equal term's index calling layer. FALSE, returned vocabulary include padding OOV tokens.","code":""},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"example instantiates TextVectorization layer lowercases text, splits whitespace, strips punctuation, outputs integer vocab indices.         example instantiates TextVectorization layer passing list vocabulary terms layer's initialize() method.","code":"max_tokens <- 5000  # Maximum vocab size. max_len <- 4  # Sequence length to pad the outputs to. # Create the layer. vectorize_layer <- layer_text_vectorization(     max_tokens = max_tokens,     output_mode = 'int',     output_sequence_length = max_len) # Now that the vocab layer has been created, call `adapt` on the # list of strings to create the vocabulary. vectorize_layer %>% adapt(c(\"foo bar\", \"bar baz\", \"baz bada boom\")) # Now, the layer can map strings to integers -- you can use an # embedding layer to map these integers to learned embeddings. input_data <- rbind(\"foo qux bar\", \"qux baz\") vectorize_layer(input_data) ## tf.Tensor( ## [[4 1 3 0] ##  [1 2 0 0]], shape=(2, 4), dtype=int64) vocab_data <- c(\"earth\", \"wind\", \"and\", \"fire\") max_len <- 4  # Sequence length to pad the outputs to. # Create the layer, passing the vocab directly. You can also pass the # vocabulary arg a path to a file containing one vocabulary word per # line. vectorize_layer <- layer_text_vectorization(     max_tokens = max_tokens,     output_mode = 'int',     output_sequence_length = max_len,     vocabulary = vocab_data) # Because we've passed the vocabulary directly, we don't need to adapt # the layer - the vocabulary is already set. The vocabulary contains the # padding token ('') and OOV token ('[UNK]') # as well as the passed tokens. vectorize_layer %>% get_vocabulary() ## [1] \"\"      \"[UNK]\" \"earth\" \"wind\"  \"and\"   \"fire\" # ['', '[UNK]', 'earth', 'wind', 'and', 'fire']"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"Reload Keras model/layer saved via export_savedmodel().","code":""},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"","code":"layer_tfsm(   object,   filepath,   call_endpoint = \"serve\",   call_training_endpoint = NULL,   trainable = TRUE,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"object Object compose layer . tensor, array, sequential model. filepath string, path SavedModel. call_endpoint Name endpoint use call() method reloaded layer. SavedModel created via export_savedmodel(), default endpoint name 'serve'. cases may named 'serving_default'. call_training_endpoint see description trainable see description name String, name object dtype datatype (e.g., \"float32\").","code":""},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"reloaded object can used like regular Keras layer, supports training/fine-tuning trainable weights. Note reloaded object retains none internal structure custom methods original object -- brand new layer created around saved function. Limitations: call endpoints single inputs tensor argument (may optionally named list/list tensors) supported. endpoints multiple separate input tensor arguments, consider subclassing layer_tfsm implementing call() method custom signature. need training-time behavior differ inference-time behavior (.e. need reloaded object support training=TRUE argument __call__()), make sure training-time call function saved standalone endpoint artifact, provide name layer_tfsm via call_training_endpoint argument.","code":"model <- keras_model_sequential(input_shape = c(784)) |> layer_dense(10) model |> export_savedmodel(\"path/to/artifact\") ## Saved artifact at 'path/to/artifact'. The following endpoints are available: ## ## * Endpoint 'serve' ##   args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 784), dtype=tf.float32, name='keras_tensor') ## Output Type: ##   TensorSpec(shape=(None, 10), dtype=tf.float32, name=None) ## Captures: ##   128255174032224: TensorSpec(shape=(), dtype=tf.resource, name=None) ##   128255174017264: TensorSpec(shape=(), dtype=tf.resource, name=None) reloaded_layer <- layer_tfsm(filepath = \"path/to/artifact\") input <- random_normal(c(2, 784)) output <- reloaded_layer(input) stopifnot(all.equal(as.array(output), as.array(model(input))))"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":null,"dir":"Reference","previous_headings":"","what":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"Every input least 3D, dimension index one first input considered temporal dimension. Consider batch 32 video samples, sample 128x128 RGB image channels_last data format, across 10 timesteps. batch input shape (32, 10, 128, 128, 3). can use TimeDistributed apply Conv2D layer 10 timesteps, independently:     layer_time_distributed applies instance layer_conv2d timestamps, set weights used timestamp.","code":"inputs <- keras_input(shape = c(10, 128, 128, 3), batch_size = 32) conv_2d_layer <- layer_conv_2d(filters = 64, kernel_size = c(3, 3)) outputs <- layer_time_distributed(inputs, layer = conv_2d_layer) shape(outputs) ## shape(32, 10, 126, 126, 64)"},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"","code":"layer_time_distributed(object, layer, ...)"},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"object Object compose layer . tensor, array, sequential model. layer Layer instance. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"inputs: Input tensor shape (batch, time, ...) nested tensors, shape (batch, time, ...). training: Boolean indicating whether layer behave training mode inference mode. argument passed wrapped layer (layer supports argument). mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked. argument passed wrapped layer (layer supports argument).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Torch module wrapper layer. — layer_torch_module_wrapper","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"layer_torch_module_wrapper wrapper class can turn torch.nn.Module Keras layer, particular making parameters trackable Keras.","code":""},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"","code":"layer_torch_module_wrapper(object, module, name = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"object Object compose layer . tensor, array, sequential model. module torch.nn.Module instance. LazyModule instance, parameters must initialized passing instance layer_torch_module_wrapper (e.g. calling ). name name layer (string). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"example layer_torch_module_wrapper() can used vanilla PyTorch modules.","code":"# reticulate::py_install( #   packages = c(\"torch\", \"torchvision\", \"torchaudio\"), #   envname = \"r-keras\", #   pip_options = c(\"--index-url https://download.pytorch.org/whl/cpu\") # ) library(keras3) use_backend(\"torch\") torch <- reticulate::import(\"torch\") nn <- reticulate::import(\"torch.nn\") nnf <- reticulate::import(\"torch.nn.functional\")  Classifier(keras$Model) \\%py_class\\% {   initialize <- function(...) {     super$initialize(...)      self$conv1 <- layer_torch_module_wrapper(module = nn$Conv2d(       in_channels = 1L,       out_channels = 32L,       kernel_size = tuple(3L, 3L)     ))     self$conv2 <- layer_torch_module_wrapper(module = nn$Conv2d(       in_channels = 32L,       out_channels = 64L,       kernel_size = tuple(3L, 3L)     ))     self$pool <- nn$MaxPool2d(kernel_size = tuple(2L, 2L))     self$flatten <- nn$Flatten()     self$dropout <- nn$Dropout(p = 0.5)     self$fc <-       layer_torch_module_wrapper(module = nn$Linear(1600L, 10L))   }    call <- function(inputs) {     x <- nnf$relu(self$conv1(inputs))     x <- self$pool(x)     x <- nnf$relu(self$conv2(x))     x <- self$pool(x)     x <- self$flatten(x)     x <- self$dropout(x)     x <- self$fc(x)     nnf$softmax(x, dim = 1L)   } } model <- Classifier() model$build(shape(1, 28, 28)) cat(\"Output shape:\", format(shape(model(torch$ones(1L, 1L, 28L, 28L)))))  model |> compile(loss = \"sparse_categorical_crossentropy\",                  optimizer = \"adam\",                  metrics = \"accuracy\") model |> fit(train_loader, epochs = 5)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Unit normalization layer. — layer_unit_normalization","title":"Unit normalization layer. — layer_unit_normalization","text":"Normalize batch inputs input batch L2 norm equal 1 (across axes specified axis).","code":""},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unit normalization layer. — layer_unit_normalization","text":"","code":"layer_unit_normalization(object, axis = -1L, ...)"},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unit normalization layer. — layer_unit_normalization","text":"object Object compose layer . tensor, array, sequential model. axis Integer list. axis axes normalize across. Typically, features axis axes. left-axes typically batch axis axes. -1 last dimension input. Defaults -1. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unit normalization layer. — layer_unit_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unit normalization layer. — layer_unit_normalization","text":"","code":"data <- op_reshape(1:6, newshape = c(2, 3)) normalized_data <- layer_unit_normalization(data) op_sum(normalized_data[1,]^2) ## tf.Tensor(0.9999999, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsampling layer for 1D inputs. — layer_upsampling_1d","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"Repeats temporal step size times along time axis.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"","code":"layer_upsampling_1d(object, size = 2L, ...)"},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"object Object compose layer . tensor, array, sequential model. size Integer. Upsampling factor. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"","code":"input_shape <- c(2, 2, 3) x <- seq_len(prod(input_shape)) %>% op_reshape(input_shape) x ## tf.Tensor( ## [[[ 1  2  3] ##   [ 4  5  6]] ## ##  [[ 7  8  9] ##   [10 11 12]]], shape=(2, 2, 3), dtype=int32) y <- layer_upsampling_1d(x, size = 2) y ## tf.Tensor( ## [[[ 1  2  3] ##   [ 1  2  3] ##   [ 4  5  6] ##   [ 4  5  6]] ## ##  [[ 7  8  9] ##   [ 7  8  9] ##   [10 11 12] ##   [10 11 12]]], shape=(2, 4, 3), dtype=int32)"},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"3D tensor shape: (batch_size, steps, features).","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"3D tensor shape: (batch_size, upsampled_steps, features).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsampling layer for 2D inputs. — layer_upsampling_2d","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"implementation uses interpolative resizing, given resize method (specified interpolation argument). Use interpolation=nearest repeat rows columns data.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"","code":"layer_upsampling_2d(   object,   size = list(2L, 2L),   data_format = NULL,   interpolation = \"nearest\",   ... )"},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"object Object compose layer . tensor, array, sequential model. size Int, list 2 integers. upsampling factors rows columns. data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists) else \"channels_last\". Defaults \"channels_last\". interpolation string, one \"bicubic\", \"bilinear\", \"lanczos3\", \"lanczos5\", \"nearest\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"","code":"input_shape <- c(2, 2, 1, 3) x <- op_reshape(seq_len(prod(input_shape)), input_shape) print(x) ## tf.Tensor( ## [[[[ 1  2  3]] ## ##   [[ 4  5  6]]] ## ## ##  [[[ 7  8  9]] ## ##   [[10 11 12]]]], shape=(2, 2, 1, 3), dtype=int32) y <- layer_upsampling_2d(x, size = c(1, 2)) print(y) ## tf.Tensor( ## [[[[ 1  2  3] ##    [ 1  2  3]] ## ##   [[ 4  5  6] ##    [ 4  5  6]]] ## ## ##  [[[ 7  8  9] ##    [ 7  8  9]] ## ##   [[10 11 12] ##    [10 11 12]]]], shape=(2, 2, 2, 3), dtype=int32)"},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, rows, cols, channels) data_format \"channels_first\": (batch_size, channels, rows, cols)","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, upsampled_rows, upsampled_cols, channels) data_format \"channels_first\": (batch_size, channels, upsampled_rows, upsampled_cols)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsampling layer for 3D inputs. — layer_upsampling_3d","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"Repeats 1st, 2nd 3rd dimensions data size[0], size[1] size[2] respectively.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"","code":"layer_upsampling_3d(object, size = list(2L, 2L, 2L), data_format = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"object Object compose layer . tensor, array, sequential model. size Int, list 3 integers. upsampling factors dim1, dim2 dim3. data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists) else \"channels_last\". Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"","code":"input_shape <- c(2, 1, 2, 1, 3) x <- array(1, dim = input_shape) y <- layer_upsampling_3d(x, size = c(2, 2, 2)) shape(y) ## shape(2, 2, 4, 2, 3)"},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, dim1, dim2, dim3, channels) data_format \"channels_first\": (batch_size, channels, dim1, dim2, dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels) data_format \"channels_first\": (batch_size, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"Zero-padding layer 1D input (e.g. temporal sequence).","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"","code":"layer_zero_padding_1d(object, padding = 1L, ...)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"object Object compose layer . tensor, array, sequential model. padding Int, list int (length 2), named listionary. int: many zeros add beginning end padding dimension (axis 1). list 2 ints: many zeros add beginning end padding dimension ((left_pad, right_pad)). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"","code":"input_shape <- c(2, 2, 3) x <- op_reshape(seq_len(prod(input_shape)), input_shape) x ## tf.Tensor( ## [[[ 1  2  3] ##   [ 4  5  6]] ## ##  [[ 7  8  9] ##   [10 11 12]]], shape=(2, 2, 3), dtype=int32) y <- layer_zero_padding_1d(x, padding = 2) y ## tf.Tensor( ## [[[ 0  0  0] ##   [ 0  0  0] ##   [ 1  2  3] ##   [ 4  5  6] ##   [ 0  0  0] ##   [ 0  0  0]] ## ##  [[ 0  0  0] ##   [ 0  0  0] ##   [ 7  8  9] ##   [10 11 12] ##   [ 0  0  0] ##   [ 0  0  0]]], shape=(2, 6, 3), dtype=int32)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"3D tensor shape (batch_size, axis_to_pad, features)","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"3D tensor shape (batch_size, padded_axis, features)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"layer can add rows columns zeros top, bottom, left right side image tensor.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"","code":"layer_zero_padding_2d(object, padding = list(1L, 1L), data_format = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"object Object compose layer . tensor, array, sequential model. padding Int, list 2 ints, list 2 lists 2 ints. int: symmetric padding applied height width. list 2 ints: interpreted two different symmetric padding values height width: (symmetric_height_pad, symmetric_width_pad). list 2 lists 2 ints: interpreted ((top_pad, bottom_pad), (left_pad, right_pad)). data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"","code":"input_shape <- c(1, 1, 2, 2) x <- op_reshape(seq_len(prod(input_shape)), input_shape) x ## tf.Tensor( ## [[[[1 2] ##    [3 4]]]], shape=(1, 1, 2, 2), dtype=int32) y <- layer_zero_padding_2d(x, padding = 1) y ## tf.Tensor( ## [[[[0 0] ##    [0 0] ##    [0 0] ##    [0 0]] ## ##   [[0 0] ##    [1 2] ##    [3 4] ##    [0 0]] ## ##   [[0 0] ##    [0 0] ##    [0 0] ##    [0 0]]]], shape=(1, 3, 4, 2), dtype=int32)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, height, width, channels) data_format \"channels_first\": (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, padded_height, padded_width, channels) data_format \"channels_first\": (batch_size, channels, padded_height, padded_width)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"Zero-padding layer 3D data (spatial spatio-temporal).","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"","code":"layer_zero_padding_3d(   object,   padding = list(list(1L, 1L), list(1L, 1L), list(1L, 1L)),   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"object Object compose layer . tensor, array, sequential model. padding Int, list 3 ints, list 3 lists 2 ints. int: symmetric padding applied depth, height, width. list 3 ints: interpreted three different symmetric padding values depth, height, width: (symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad). list 3 lists 2 ints: interpreted ((left_dim1_pad, right_dim1_pad), (left_dim2_pad, right_dim2_pad), (left_dim3_pad, right_dim3_pad)). data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"","code":"input_shape <- c(1, 1, 2, 2, 3) x <- op_reshape(seq_len(prod(input_shape)), input_shape) x ## tf.Tensor( ## [[[[[ 1  2  3] ##     [ 4  5  6]] ## ##    [[ 7  8  9] ##     [10 11 12]]]]], shape=(1, 1, 2, 2, 3), dtype=int32) y <- layer_zero_padding_3d(x, padding = 2) shape(y) ## shape(1, 5, 6, 6, 3)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad, depth) data_format \"channels_first\": (batch_size, depth, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad)","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, first_padded_axis, second_padded_axis, third_axis_to_pad, depth) data_format \"channels_first\": (batch_size, depth, first_padded_axis, second_padded_axis, third_axis_to_pad)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","title":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","text":"See Loshchilov & Hutter, ICLR2016, SGDR: Stochastic Gradient Descent Warm Restarts. idea linear warmup learning rate, see Goyal et al.. begin training model, often want initial increase learning rate followed decay. warmup_target int, schedule applies linear increase per optimizer step learning rate initial_learning_rate warmup_target duration warmup_steps. Afterwards, applies cosine decay function taking learning rate warmup_target alpha duration decay_steps. warmup_target NULL skip warmup decay take learning rate initial_learning_rate alpha. requires step value  compute learning rate. can just pass backend variable increment training step. schedule 1-arg callable produces warmup followed decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. warmup computed :   decay computed :   Example usage without warmup:   Example usage warmup:   can pass schedule directly optimizer learning rate. learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"warmup_learning_rate <- function(step) {   completed_fraction <- step / warmup_steps   total_delta <- target_warmup - initial_learning_rate   completed_fraction * total_delta } if (is.null(warmup_target)) {   initial_decay_lr <- initial_learning_rate } else {   initial_decay_lr <- warmup_target }  decayed_learning_rate <- function(step) {   step <- min(step, decay_steps)   cosine_decay <- 0.5 * (1 + cos(pi * step / decay_steps))   decayed <- (1 - alpha) * cosine_decay + alpha   initial_decay_lr * decayed } decay_steps <- 1000 initial_learning_rate <- 0.1 lr_decayed_fn <- learning_rate_schedule_cosine_decay(     initial_learning_rate, decay_steps) decay_steps <- 1000 initial_learning_rate <- 0 warmup_steps <- 1000 target_learning_rate <- 0.1 lr_warmup_decayed_fn <- learning_rate_schedule_cosine_decay(     initial_learning_rate, decay_steps, warmup_target = target_learning_rate,     warmup_steps = warmup_steps )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","text":"","code":"learning_rate_schedule_cosine_decay(   initial_learning_rate,   decay_steps,   alpha = 0,   name = \"CosineDecay\",   warmup_target = NULL,   warmup_steps = 0L )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","text":"initial_learning_rate float. initial learning rate. decay_steps int. Number steps decay . alpha float. Minimum learning rate value decay fraction initial_learning_rate. name String. Optional name operation.  Defaults \"CosineDecay\". warmup_target float. target learning rate warmup phase. cast initial_learning_rate datatype. Setting NULL skip warmup begins decay phase initial_learning_rate. Otherwise scheduler warmup initial_learning_rate warmup_target. warmup_steps int. Number steps warmup .","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"See Loshchilov & Hutter, ICLR2016, SGDR: Stochastic Gradient Descent Warm Restarts. training model, often useful lower learning rate training progresses. schedule applies cosine decay function restarts optimizer step, given provided initial learning rate. requires step value compute decayed learning rate. can just pass backend variable increment training step. schedule 1-arg callable produces decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. learning rate multiplier first decays 1 alpha first_decay_steps steps. , warm restart performed. new warm restart runs t_mul times steps m_mul times initial learning rate new learning rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"","code":"learning_rate_schedule_cosine_decay_restarts(   initial_learning_rate,   first_decay_steps,   t_mul = 2,   m_mul = 1,   alpha = 0,   name = \"SGDRDecay\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"initial_learning_rate float. initial learning rate. first_decay_steps integer. Number steps decay . t_mul float. Used derive number iterations -th period. m_mul float. Used derive initial learning rate -th period. alpha float. Minimum learning rate value fraction initial_learning_rate. name String. Optional name operation. Defaults \"SGDRDecay\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"can pass schedule directly optimizer learning rate. learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"first_decay_steps <- 1000 lr_decayed_fn <- learning_rate_schedule_cosine_decay_restarts(         0.001,         first_decay_steps)"},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"training model, often useful lower learning rate training progresses. schedule applies exponential decay function optimizer step, given provided initial learning rate. schedule 1-arg callable produces decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. computed :   argument staircase TRUE, step / decay_steps integer division decayed learning rate follows staircase function. can pass schedule directly optimizer learning rate.","code":"decayed_learning_rate <- function(step) {   initial_learning_rate * decay_rate ^ (step / decay_steps) }"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"","code":"learning_rate_schedule_exponential_decay(   initial_learning_rate,   decay_steps,   decay_rate,   staircase = FALSE,   name = \"ExponentialDecay\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"initial_learning_rate float. initial learning rate. decay_steps integer. Must positive. See decay computation . decay_rate float. decay rate. staircase Boolean.  TRUE decay learning rate discrete intervals. name String.  Optional name operation.  Defaults \"ExponentialDecay\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"fitting Keras model, decay every 100000 steps base 0.96:   learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"initial_learning_rate <- 0.1 lr_schedule <- learning_rate_schedule_exponential_decay(     initial_learning_rate,     decay_steps=100000,     decay_rate=0.96,     staircase=TRUE)  model %>% compile(   optimizer = optimizer_sgd(learning_rate = lr_schedule),   loss = 'sparse_categorical_crossentropy',   metrics = c('accuracy'))  model %>% fit(data, labels, epochs=5)"},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"training model, often useful lower learning rate training progresses. schedule applies inverse decay function optimizer step, given provided initial learning rate. requires step value compute decayed learning rate. can just pass backend variable increment training step. schedule 1-arg callable produces decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. computed :   , staircase TRUE, :   can pass schedule directly optimizer_* learning rate.","code":"decayed_learning_rate <- function(step) {   initial_learning_rate / (1 + decay_rate * step / decay_step) } decayed_learning_rate <- function(step) {   initial_learning_rate /            (1 + decay_rate * floor(step / decay_step)) }"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"","code":"learning_rate_schedule_inverse_time_decay(   initial_learning_rate,   decay_steps,   decay_rate,   staircase = FALSE,   name = \"InverseTimeDecay\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"initial_learning_rate float. initial learning rate. decay_steps often apply decay. decay_rate number.  decay rate. staircase Whether apply decay discrete staircase, o pposed continuous, fashion. name String.  Optional name operation.  Defaults \"InverseTimeDecay\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"Fit Keras model decaying 1/t rate 0.5:","code":"... initial_learning_rate <- 0.1 decay_steps <- 1.0 decay_rate <- 0.5 learning_rate_fn <- learning_rate_schedule_inverse_time_decay(     initial_learning_rate, decay_steps, decay_rate)  model %>% compile(   optimizer = optimizer_sgd(learning_rate=learning_rate_fn),   loss = 'sparse_categorical_crossentropy',   metrics = 'accuracy') )  model %>% fit(data, labels, epochs=5)"},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"function returns 1-arg callable compute piecewise constant passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"","code":"learning_rate_schedule_piecewise_constant_decay(   boundaries,   values,   name = \"PiecewiseConstant\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"boundaries list Python numbers strictly increasing entries, elements type optimizer step. values list Python numbers specifies values intervals defined boundaries. one element boundaries, elements type. name string. Optional name operation. Defaults \"PiecewiseConstant\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type boundary tensors. output 1-arg function takes step values[0] step <= boundaries[0], values[1] step > boundaries[0] step <= boundaries[1], ..., values[-1] step > boundaries[-1].","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"use learning rate 1.0 first 100001 steps, 0.5 next 10000 steps, 0.1 additional steps.   can pass schedule directly optimizer learning rate. learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"step <- 0 boundaries <- c(100000, 110000) values <- c(1.0, 0.5, 0.1) learning_rate_fn <- learning_rate_schedule_piecewise_constant_decay(   boundaries, values)  # Later, whenever we perform an optimization step, we pass in the step. learning_rate <- learning_rate_fn(step)"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"ValueError: number elements boundaries values lists match.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"commonly observed monotonically decreasing learning rate, whose degree change carefully chosen, results better performing model. schedule applies polynomial decay function optimizer step, given provided initial_learning_rate, reach end_learning_rate given decay_steps. requires step value compute decayed learning rate. can just pass backend variable increment training step. schedule 1-arg callable produces decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. computed :   cycle TRUE multiple decay_steps used, first one bigger step.   can pass schedule directly Optimizer learning rate.","code":"decayed_learning_rate <- function(step) {   step = min(step, decay_steps)   ((initial_learning_rate - end_learning_rate) *     (1 - step / decay_steps) ^ (power)) +     end_learning_rate } decayed_learning_rate <- function(step) {   decay_steps = decay_steps * ceil(step / decay_steps)   ((initial_learning_rate - end_learning_rate) *       (1 - step / decay_steps) ^ (power)) +     end_learning_rate }"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"","code":"learning_rate_schedule_polynomial_decay(   initial_learning_rate,   decay_steps,   end_learning_rate = 1e-04,   power = 1,   cycle = FALSE,   name = \"PolynomialDecay\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"initial_learning_rate float. initial learning rate. decay_steps integer. Must positive. See decay computation . end_learning_rate float. minimal end learning rate. power float. power polynomial. Defaults 1.0. cycle boolean, whether cycle beyond decay_steps. name String.  Optional name operation. Defaults \"PolynomialDecay\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"Fit model decaying 0.1 0.01 10000 steps using sqrt (.e. power=0.5):   learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"... starter_learning_rate <- 0.1 end_learning_rate <- 0.01 decay_steps <- 10000 learning_rate_fn <- learning_rate_schedule_polynomial_decay(     starter_learning_rate,     decay_steps,     end_learning_rate,     power=0.5)  model %>% compile(   optimizer = optimizer_sgd(learning_rate=learning_rate_fn),   loss = 'sparse_categorical_crossentropy',   metrics = 'accuracy' )  model %>% fit(data, labels, epochs=5)"},{"path":[]},{"path":"https://keras.posit.co/reference/load_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Loads a model saved via save_model(). — load_model","title":"Loads a model saved via save_model(). — load_model","text":"Loads model saved via save_model().","code":""},{"path":"https://keras.posit.co/reference/load_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loads a model saved via save_model(). — load_model","text":"","code":"load_model(model, custom_objects = NULL, compile = TRUE, safe_mode = TRUE)"},{"path":"https://keras.posit.co/reference/load_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loads a model saved via save_model(). — load_model","text":"model string, path saved model file, raw vector, returned save_model(filepath = NULL) custom_objects Optional named list mapping names custom classes functions considered deserialization. compile Boolean, whether compile model loading. safe_mode Boolean, whether disallow unsafe lambda deserialization. safe_mode=FALSE, loading object potential trigger arbitrary code execution. argument applicable Keras v3 model format. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/load_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loads a model saved via save_model(). — load_model","text":"Keras model instance. original model compiled, argument compile = TRUE set, returned model compiled. Otherwise, model left uncompiled.","code":""},{"path":"https://keras.posit.co/reference/load_model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Loads a model saved via save_model(). — load_model","text":"Note model variables may different name values (var$name property, e.g. \"dense_1/kernel:0\") reloaded. recommended use layer attributes access specific variables, e.g. model |> get_layer(\"dense_1\") |> _$kernel.","code":"model <- keras_model_sequential(input_shape = c(3)) |>   layer_dense(5) |>   layer_activation_softmax()  model |> save_model(\"model.keras\") loaded_model <- load_model(\"model.keras\") x <- random_uniform(c(10, 3)) stopifnot(all.equal(   model |> predict(x),   loaded_model |> predict(x) ))"},{"path":[]},{"path":"https://keras.posit.co/reference/load_model_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Load weights from a file saved via save_model_weights(). — load_model_weights","title":"Load weights from a file saved via save_model_weights(). — load_model_weights","text":"Weights loaded based network's topology. means architecture weights saved. Note layers weights taken account topological ordering, adding removing layers fine long weights. Partial weight loading modified model, instance adding new layer (weights) changing shape weights layer, can choose ignore errors continue loading setting skip_mismatch=TRUE. case layer mismatching weights skipped. warning displayed skipped layer.","code":""},{"path":"https://keras.posit.co/reference/load_model_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load weights from a file saved via save_model_weights(). — load_model_weights","text":"","code":"load_model_weights(model, filepath, skip_mismatch = FALSE, ...)"},{"path":"https://keras.posit.co/reference/load_model_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load weights from a file saved via save_model_weights(). — load_model_weights","text":"model keras model. filepath String, path weights file load. can either .weights.h5 file legacy .h5 weights file. skip_mismatch Boolean, whether skip loading layers mismatch number weights, mismatch shape weights. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/load_model_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load weights from a file saved via save_model_weights(). — load_model_weights","text":"called primarily side effects. model returned, invisibly, enable usage pipe.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"Use cross-entropy loss binary (0 1) classification applications. loss function requires following inputs: y_true (true label): either 0 1. y_pred (predicted value): model's prediction, .e, single floating-point value either represents logit, (.e, value [-inf, inf] from_logits=TRUE) probability (.e, value [0., 1.] from_logits=FALSE).","code":""},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"","code":"loss_binary_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"binary_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"y_true Ground truth values. shape = [batch_size, d0, .. dN]. y_pred predicted values. shape = [batch_size, d0, .. dN]. from_logits Whether interpret y_pred tensor logit values. default, assume y_pred probabilities (.e., values [0, 1)). label_smoothing Float range [0, 1]. 0, smoothing occurs. > 0, compute loss predicted labels smoothed version true labels, smoothing squeezes labels towards 0.5. Larger values label_smoothing correspond heavier smoothing. axis axis along compute crossentropy (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"Binary crossentropy loss value. shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"Recommended Usage: (set from_logits=TRUE) compile() API:   standalone function:                     Default Usage: (set from_logits=FALSE)","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_binary_crossentropy(y_true, y_pred) loss ## tf.Tensor([0.91629073 0.71355818], shape=(2), dtype=float64) model %>% compile(     loss = loss_binary_crossentropy(from_logits=TRUE),     ... ) # Example 1: (batch_size = 1, number of samples = 4) y_true <- op_array(c(0, 1, 0, 0)) y_pred <- op_array(c(-18.6, 0.51, 2.94, -12.8)) bce <- loss_binary_crossentropy(from_logits = TRUE) bce(y_true, y_pred) ## tf.Tensor(0.865458, shape=(), dtype=float32) # Example 2: (batch_size = 2, number of samples = 4) y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(-18.6, 0.51), c(2.94, -12.8)) # Using default 'auto'/'sum_over_batch_size' reduction type. bce <- loss_binary_crossentropy(from_logits = TRUE) bce(y_true, y_pred) ## tf.Tensor(0.865458, shape=(), dtype=float32) # Using 'sample_weight' attribute bce(y_true, y_pred, sample_weight = c(0.8, 0.2)) ## tf.Tensor(0.2436386, shape=(), dtype=float32) # 0.243 # Using 'sum' reduction` type. bce <- loss_binary_crossentropy(from_logits = TRUE, reduction = \"sum\") bce(y_true, y_pred) ## tf.Tensor(1.730916, shape=(), dtype=float32) # Using 'none' reduction type. bce <- loss_binary_crossentropy(from_logits = TRUE, reduction = NULL) bce(y_true, y_pred) ## tf.Tensor([0.23515666 1.4957594 ], shape=(2), dtype=float32) # Make the following updates to the above \"Recommended Usage\" section # 1. Set `from_logits=FALSE` loss_binary_crossentropy() # OR ...('from_logits=FALSE') ## <keras.src.losses.losses.BinaryCrossentropy object> ##  signature: (y_true, y_pred, sample_weight=None) # 2. Update `y_pred` to use probabilities instead of logits y_pred <- c(0.6, 0.3, 0.2, 0.8) # OR [[0.6, 0.3], [0.2, 0.8]]"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"According Lin et al., 2018, helps apply focal factor -weight easy examples focus hard examples. default, focal tensor computed follows: focal_factor = (1 - output)^gamma class 1 focal_factor = output^gamma class 0 gamma focusing parameter. gamma = 0, focal effect binary crossentropy loss. apply_class_balancing == TRUE, function also takes account weight balancing factor binary classes 0 1 follows: weight = alpha class 1 (target == 1) weight = 1 - alpha class 0 alpha float range [0, 1]. Binary cross-entropy loss often used binary (0 1) classification tasks. loss function requires following inputs: y_true (true label): either 0 1. y_pred (predicted value): model's prediction, .e, single floating-point value either represents logit, (.e, value [-inf, inf] from_logits=TRUE) probability (.e, value [0., 1.] from_logits=FALSE). According Lin et al., 2018, helps apply \"focal factor\" -weight easy examples focus hard examples. default, focal tensor computed follows: focal_factor = (1 - output) ** gamma class 1 focal_factor = output ** gamma class 0 gamma focusing parameter. gamma=0, function equivalent binary crossentropy loss.","code":""},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"","code":"loss_binary_focal_crossentropy(   y_true,   y_pred,   apply_class_balancing = FALSE,   alpha = 0.25,   gamma = 2,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"binary_focal_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"y_true Ground truth values, shape (batch_size, d0, .. dN). y_pred predicted values, shape (batch_size, d0, .. dN). apply_class_balancing bool, whether apply weight balancing binary classes 0 1. alpha weight balancing factor class 1, default 0.25 mentioned reference Lin et al., 2018.  weight class 0 1.0 - alpha. gamma focusing parameter used compute focal factor, default 2.0 mentioned reference Lin et al., 2018. from_logits Whether interpret y_pred tensor logit values. default, assume y_pred probabilities (.e., values [0, 1]). label_smoothing Float [0, 1]. 0, smoothing occurs. > 0, compute loss predicted labels smoothed version true labels, smoothing squeezes labels towards 0.5. Larger values label_smoothing correspond heavier smoothing. axis axis along compute crossentropy (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"Binary focal crossentropy loss value shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"compile() API:   standalone function:","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_binary_focal_crossentropy(y_true, y_pred, gamma = 2) loss ## tf.Tensor([0.32986466 0.20579838], shape=(2), dtype=float64) model %>% compile(     loss = loss_binary_focal_crossentropy(         gamma = 2.0, from_logits = TRUE),     ... ) # Example 1: (batch_size = 1, number of samples = 4) y_true <- op_array(c(0, 1, 0, 0)) y_pred <- op_array(c(-18.6, 0.51, 2.94, -12.8)) loss <- loss_binary_focal_crossentropy(gamma = 2, from_logits = TRUE) loss(y_true, y_pred) ## tf.Tensor(0.6912122, shape=(), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(   apply_class_balancing = TRUE, gamma = 2, from_logits = TRUE) loss(y_true, y_pred) ## tf.Tensor(0.5101333, shape=(), dtype=float32) # Example 2: (batch_size = 2, number of samples = 4) y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(-18.6, 0.51), c(2.94, -12.8)) # Using default 'auto'/'sum_over_batch_size' reduction type. loss <- loss_binary_focal_crossentropy(     gamma = 3, from_logits = TRUE) loss(y_true, y_pred) ## tf.Tensor(0.6469951, shape=(), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(      apply_class_balancing = TRUE, gamma = 3, from_logits = TRUE) loss(y_true, y_pred) ## tf.Tensor(0.48214132, shape=(), dtype=float32) # Using 'sample_weight' attribute with focal effect loss <- loss_binary_focal_crossentropy(     gamma = 3, from_logits = TRUE) loss(y_true, y_pred, sample_weight = c(0.8, 0.2)) ## tf.Tensor(0.13312504, shape=(), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(      apply_class_balancing = TRUE, gamma = 3, from_logits = TRUE) loss(y_true, y_pred, sample_weight = c(0.8, 0.2)) ## tf.Tensor(0.09735977, shape=(), dtype=float32) # Using 'sum' reduction` type. loss <- loss_binary_focal_crossentropy(     gamma = 4, from_logits = TRUE,     reduction = \"sum\") loss(y_true, y_pred) ## tf.Tensor(1.2218808, shape=(), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(     apply_class_balancing = TRUE, gamma = 4, from_logits = TRUE,     reduction = \"sum\") loss(y_true, y_pred) ## tf.Tensor(0.9140807, shape=(), dtype=float32) # Using 'none' reduction type. loss <- loss_binary_focal_crossentropy(     gamma = 5, from_logits = TRUE,     reduction = NULL) loss(y_true, y_pred) ## tf.Tensor([0.00174837 1.1561027 ], shape=(2), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(     apply_class_balancing = TRUE, gamma = 5, from_logits = TRUE,     reduction = NULL) loss(y_true, y_pred) ## tf.Tensor([4.3709317e-04 8.6707699e-01], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"Use crossentropy loss function two label classes. expect labels provided one_hot representation. want provide labels integers, please use SparseCategoricalCrossentropy loss. num_classes floating point values per feature, .e., shape y_pred y_true [batch_size, num_classes].","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"","code":"loss_categorical_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"categorical_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"y_true Tensor one-hot true targets. y_pred Tensor predicted targets. from_logits Whether y_pred expected logits tensor. default, assume y_pred encodes probability distribution. label_smoothing Float [0, 1]. > 0, label values smoothed, meaning confidence label values relaxed. example, 0.1, use 0.1 / num_classes non-target labels 0.9 + 0.1 / num_classes target labels. axis axis along compute crossentropy (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"Categorical crossentropy loss value.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"Standalone usage:                 Usage compile() API:","code":"y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) loss <- loss_categorical_crossentropy(y_true, y_pred) loss ## tf.Tensor([0.05129329 2.30258509], shape=(2), dtype=float64) y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) # Using 'auto'/'sum_over_batch_size' reduction type. cce <- loss_categorical_crossentropy() cce(y_true, y_pred) ## tf.Tensor(1.1769392, shape=(), dtype=float32) # Calling with 'sample_weight'. cce(y_true, y_pred, sample_weight = op_array(c(0.3, 0.7))) ## tf.Tensor(0.8135988, shape=(), dtype=float32) # Using 'sum' reduction type. cce <- loss_categorical_crossentropy(reduction = \"sum\") cce(y_true, y_pred) ## tf.Tensor(2.3538785, shape=(), dtype=float32) # Using 'none' reduction type. cce <- loss_categorical_crossentropy(reduction = NULL) cce(y_true, y_pred) ## tf.Tensor([0.05129331 2.3025851 ], shape=(2), dtype=float32) model %>% compile(optimizer = 'sgd',               loss=loss_categorical_crossentropy())"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"Use crossentropy loss function two label classes want handle class imbalance without using class_weights. expect labels provided one_hot representation. According Lin et al., 2018, helps apply focal factor -weight easy examples focus hard examples. general formula focal loss (FL) follows: FL(p_t) = (1 - p_t)^gamma * log(p_t) p_t defined follows: p_t = output y_true == 1, else 1 - output (1 - p_t)^gamma modulating_factor, gamma focusing parameter. gamma = 0, focal effect cross entropy. gamma reduces importance given simple examples smooth manner. authors use alpha-balanced variant focal loss (FL) paper: FL(p_t) = -alpha * (1 - p_t)^gamma * log(p_t) alpha weight factor classes. alpha = 1, loss able handle class imbalance properly classes weight. can constant list constants. alpha list, must length number classes. formula can generalized : FL(p_t) = alpha * (1 - p_t)^gamma * CrossEntropy(y_true, y_pred) minus comes CrossEntropy(y_true, y_pred) (CE). Extending multi-class case straightforward: FL(p_t) = alpha * (1 - p_t) ** gamma * CategoricalCE(y_true, y_pred) snippet , num_classes floating pointing values per example. shape y_pred y_true (batch_size, num_classes).","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"","code":"loss_categorical_focal_crossentropy(   y_true,   y_pred,   alpha = 0.25,   gamma = 2,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"categorical_focal_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"y_true Tensor one-hot true targets. y_pred Tensor predicted targets. alpha weight balancing factor classes, default 0.25 mentioned reference. can list floats scalar. multi-class case, alpha may set inverse class frequency using compute_class_weight sklearn.utils. gamma focusing parameter, default 2.0 mentioned reference. helps gradually reduce importance given simple examples smooth manner. gamma = 0, focal effect categorical crossentropy. from_logits Whether output expected logits tensor. default, consider output encodes probability distribution. label_smoothing Float [0, 1]. > 0, label values smoothed, meaning confidence label values relaxed. example, 0.1, use 0.1 / num_classes non-target labels 0.9 + 0.1 / num_classes target labels. axis axis along compute crossentropy (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"Categorical focal crossentropy loss value.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"Standalone usage:                 Usage compile() API:","code":"y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) loss <- loss_categorical_focal_crossentropy(y_true, y_pred) loss ## tf.Tensor([3.20583090e-05 4.66273481e-01], shape=(2), dtype=float64) y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) # Using 'auto'/'sum_over_batch_size' reduction type. cce <- loss_categorical_focal_crossentropy() cce(y_true, y_pred) ## tf.Tensor(0.23315276, shape=(), dtype=float32) # Calling with 'sample_weight'. cce(y_true, y_pred, sample_weight = op_array(c(0.3, 0.7))) ## tf.Tensor(0.16320053, shape=(), dtype=float32) # Using 'sum' reduction type. cce <- loss_categorical_focal_crossentropy(reduction = \"sum\") cce(y_true, y_pred) ## tf.Tensor(0.46630552, shape=(), dtype=float32) # Using 'none' reduction type. cce <- loss_categorical_focal_crossentropy(reduction = NULL) cce(y_true, y_pred) ## tf.Tensor([3.2058331e-05 4.6627346e-01], shape=(2), dtype=float32) model %>% compile(   optimizer = 'adam',   loss = loss_categorical_focal_crossentropy())"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"Formula:   neg=maximum((1-y_true)*y_pred) pos=sum(y_true*y_pred)","code":"loss <- maximum(neg - pos + 1, 0)"},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"","code":"loss_categorical_hinge(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"categorical_hinge\" )"},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"y_true ground truth values. y_true values expected either {-1, +1} {0, 1} (.e. one-hot-encoded tensor) shape <- [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"Categorical hinge loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_categorical_hinge(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"Formula:   Note number -1 1. negative number -1 0, 0 indicates orthogonality values closer -1 indicate greater similarity. makes usable loss function setting try maximize proximity predictions targets. either y_true y_pred zero vector, cosine similarity 0 regardless proximity predictions targets.","code":"loss <- -sum(l2_norm(y_true) * l2_norm(y_pred))"},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"","code":"loss_cosine_similarity(   y_true,   y_pred,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"cosine_similarity\" )"},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"y_true Tensor true targets. y_pred Tensor predicted targets. axis axis along cosine similarity computed (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"Cosine similarity tensor.","code":""},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"","code":"y_true <- rbind(c(0., 1.), c(1., 1.), c(1., 1.)) y_pred <- rbind(c(1., 0.), c(1., 1.), c(-1., -1.)) loss <- loss_cosine_similarity(y_true, y_pred, axis=-1) loss ## tf.Tensor([-0. -1.  1.], shape=(3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_ctc.html","id":null,"dir":"Reference","previous_headings":"","what":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","title":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","text":"CTC (Connectionist Temporal Classification) loss.","code":""},{"path":"https://keras.posit.co/reference/loss_ctc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","text":"","code":"loss_ctc(y_true, y_pred, ..., reduction = \"sum_over_batch_size\", name = \"ctc\")"},{"path":"https://keras.posit.co/reference/loss_ctc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","text":"y_true tensor shape (batch_size, target_max_length) containing true labels integer format. 0 always represents blank/mask index used classes. y_pred tensor shape (batch_size, output_max_length, num_classes) containing logits (output model). normalized via softmax. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name String, name object","code":""},{"path":"https://keras.posit.co/reference/loss_ctc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","text":"CTC loss value.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/loss_dice.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Dice loss value between y_true and y_pred. — loss_dice","title":"Computes the Dice loss value between y_true and y_pred. — loss_dice","text":"Formula:   Formula:","code":"loss = 1 - (2 * sum(y_true * y_pred)) / (sum(y_true) + sum(y_pred)) loss = 1 - (2 * sum(y_true * y_pred)) / (sum(y_true) + sum(y_pred))"},{"path":"https://keras.posit.co/reference/loss_dice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Dice loss value between y_true and y_pred. — loss_dice","text":"","code":"loss_dice(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"dice\" )"},{"path":"https://keras.posit.co/reference/loss_dice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Dice loss value between y_true and y_pred. — loss_dice","text":"y_true tensor true targets. y_pred tensor predicted targets. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name String, name object","code":""},{"path":"https://keras.posit.co/reference/loss_dice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Dice loss value between y_true and y_pred. — loss_dice","text":"y_true y_pred provided, Dice loss value. Otherwise, Loss() instance.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the hinge loss between y_true & y_pred. — loss_hinge","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"Formula:   y_true values expected -1 1. binary (0 1) labels provided convert -1 1.","code":"loss <- mean(maximum(1 - y_true * y_pred, 0), axis=-1)"},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"","code":"loss_hinge(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"hinge\" )"},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"y_true ground truth values. y_true values expected -1 1. binary (0 1) labels provided converted -1 1 shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"Hinge loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"","code":"y_true <- array(sample(c(-1,1), 6, replace = TRUE), dim = c(2, 3)) y_pred <- random_uniform(c(2, 3)) loss <- loss_hinge(y_true, y_pred) loss ## tf.Tensor([1.0610152  0.93285507], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_huber.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Huber loss between y_true & y_pred. — loss_huber","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"Formula:   See: Huber loss.","code":"for (x in error) {   if (abs(x) <= delta){     loss <- c(loss, (0.5 * x^2))   } else if (abs(x) > delta) {     loss <- c(loss, (delta * abs(x) - 0.5 * delta^2))   } } loss <- mean(loss)"},{"path":"https://keras.posit.co/reference/loss_huber.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"","code":"loss_huber(   y_true,   y_pred,   delta = 1,   ...,   reduction = \"sum_over_batch_size\",   name = \"huber_loss\" )"},{"path":"https://keras.posit.co/reference/loss_huber.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"y_true tensor true targets. y_pred tensor predicted targets. delta float, point Huber loss function changes quadratic linear. Defaults 1.0. ... forward/backward compatability. reduction Type reduction apply loss. Options \"sum\", \"sum_over_batch_size\" NULL. Defaults \"sum_over_batch_size\". name Optional name instance.","code":""},{"path":"https://keras.posit.co/reference/loss_huber.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"Tensor one scalar loss entry per sample.","code":""},{"path":"https://keras.posit.co/reference/loss_huber.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_huber(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"Formula:   y_true y_pred expected probability distributions, values 0 1. get clipped [0, 1] range.","code":"loss <- y_true * log(y_true / y_pred)"},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"","code":"loss_kl_divergence(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"kl_divergence\" )"},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"y_true Tensor true targets. y_pred Tensor predicted targets. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"KL Divergence loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2,3)) loss <- loss_kl_divergence(y_true, y_pred) loss ## tf.Tensor([3.5312676 0.2128672], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"Formula:   Note log(cosh(x)) approximately equal (x ** 2) / 2 small x abs(x) - log(2) large x. means 'logcosh' works mostly like mean squared error, strongly affected occasional wildly incorrect prediction.","code":"loss <- mean(log(cosh(y_pred - y_true)), axis=-1)"},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"","code":"loss_log_cosh(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"log_cosh\" )"},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. Options \"sum\", \"sum_over_batch_size\" NULL. Defaults \"sum_over_batch_size\". name Optional name instance.","code":""},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"Logcosh error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"","code":"y_true <- rbind(c(0., 1.), c(0., 0.)) y_pred <- rbind(c(1., 1.), c(0., 0.)) loss <- loss_log_cosh(y_true, y_pred) # 0.108"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"Formula:","code":"loss <- mean(abs(y_true - y_pred))"},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"","code":"loss_mean_absolute_error(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"mean_absolute_error\" )"},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"Mean absolute error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2, 3)) loss <- loss_mean_absolute_error(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"Formula:   Division zero prevented dividing max(y_true, epsilon) epsilon = config_epsilon() (default 1e-7).","code":"loss <- 100 * op_mean(op_abs((y_true - y_pred) / y_true),                       axis=-1)"},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"","code":"loss_mean_absolute_percentage_error(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"mean_absolute_percentage_error\" )"},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"Mean absolute percentage error values shape = [batch_size, d0, ..dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"","code":"y_true <- random_uniform(c(2, 3)) y_pred <- random_uniform(c(2, 3)) loss <- loss_mean_absolute_percentage_error(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"Formula:","code":"loss <- mean(square(y_true - y_pred))"},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"","code":"loss_mean_squared_error(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"mean_squared_error\" )"},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"Mean squared error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2, 3)) loss <- loss_mean_squared_error(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"Note y_pred y_true less equal 0. Negative values 0 values replaced config_epsilon() (default 1e-7). Formula:","code":"loss <- mean(square(log(y_true + 1) - log(y_pred + 1)))"},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"","code":"loss_mean_squared_logarithmic_error(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"mean_squared_logarithmic_error\" )"},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"Mean squared logarithmic error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2, 3)) loss <- loss_mean_squared_logarithmic_error(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"Formula:","code":"loss <- y_pred - y_true * log(y_pred)"},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"","code":"loss_poisson(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"poisson\" )"},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"y_true Ground truth values. shape = [batch_size, d0, .. dN]. y_pred predicted values. shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"Poisson loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2, 3)) loss <- loss_poisson(y_true, y_pred) loss ## tf.Tensor([2.5907533  0.66836613], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"Use crossentropy loss function two label classes.  expect labels provided integers. want provide labels using one-hot representation, please use CategoricalCrossentropy loss.  # classes floating point values per feature y_pred single floating point value per feature y_true. snippet , single floating point value per example y_true num_classes floating pointing values per example y_pred. shape y_true [batch_size] shape y_pred [batch_size, num_classes].","code":""},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"","code":"loss_sparse_categorical_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   ignore_class = NULL,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"sparse_categorical_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"y_true Ground truth values. y_pred predicted values. from_logits Whether y_pred expected logits tensor. default, assume y_pred encodes probability distribution. ignore_class Optional integer. ID class ignored loss computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. axis Defaults -1. dimension along entropy computed. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"Sparse categorical crossentropy loss value.","code":""},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"Usage compile() API:","code":"y_true <- c(1, 2) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) loss <- loss_sparse_categorical_crossentropy(y_true, y_pred) loss ## tf.Tensor([0.05129339 2.30258509], shape=(2), dtype=float64) y_true <- c(1, 2) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) # Using 'auto'/'sum_over_batch_size' reduction type. scce <- loss_sparse_categorical_crossentropy() scce(op_array(y_true), op_array(y_pred)) ## tf.Tensor(1.1769392, shape=(), dtype=float32) # 1.177 # Calling with 'sample_weight'. scce(op_array(y_true), op_array(y_pred), sample_weight = op_array(c(0.3, 0.7))) ## tf.Tensor(0.8135988, shape=(), dtype=float32) # Using 'sum' reduction type. scce <- loss_sparse_categorical_crossentropy(reduction=\"sum\") scce(op_array(y_true), op_array(y_pred)) ## tf.Tensor(2.3538785, shape=(), dtype=float32) # 2.354 # Using 'none' reduction type. scce <- loss_sparse_categorical_crossentropy(reduction=NULL) scce(op_array(y_true), op_array(y_pred)) ## tf.Tensor([0.05129344 2.3025851 ], shape=(2), dtype=float32) # array([0.0513, 2.303], dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = loss_sparse_categorical_crossentropy())"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"Formula:   y_true values expected -1 1. binary (0 1) labels provided convert -1 1.","code":"loss <- square(maximum(1 - y_true * y_pred, 0))"},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"","code":"loss_squared_hinge(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"squared_hinge\" )"},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"y_true ground truth values. y_true values expected -1 1. binary (0 1) labels provided convert -1 1 shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"Squared hinge loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"","code":"y_true <- array(sample(c(-1,1), 6, replace = TRUE), dim = c(2, 3)) y_pred <- random_uniform(c(2, 3)) loss <- loss_squared_hinge(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"loss function weighted alpha beta coefficients penalize false positives false negatives. alpha=0.5 beta=0.5, loss value becomes equivalent Dice Loss. loss function weighted alpha beta coefficients penalize false positives false negatives. alpha=0.5 beta=0.5, loss value becomes equivalent Dice Loss.","code":""},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"","code":"loss_tversky(   y_true,   y_pred,   ...,   alpha = 0.5,   beta = 0.5,   reduction = \"sum_over_batch_size\",   name = \"tversky\" )"},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"y_true tensor true targets. y_pred tensor predicted targets. ... forward/backward compatability. alpha coefficient controlling incidence false positives. beta coefficient controlling incidence false negatives. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name String, name object","code":""},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"Tversky loss value.","code":""},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"Salehi et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/mark_active.html","id":null,"dir":"Reference","previous_headings":"","what":"active_property — mark_active","title":"active_property — mark_active","text":"mark_active() alias active_property(). See ?active_property() full documentation.","code":""},{"path":"https://keras.posit.co/reference/mark_active.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"active_property — mark_active","text":"","code":"mark_active(fn)"},{"path":"https://keras.posit.co/reference/mark_active.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"active_property — mark_active","text":"fn R function","code":""},{"path":"https://keras.posit.co/reference/mark_active.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"active_property — mark_active","text":"fn, additional R attribute cause fn converted active property converted method custom subclass.","code":""},{"path":"https://keras.posit.co/reference/metric_auc.html","id":null,"dir":"Reference","previous_headings":"","what":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"AUC (Area curve) ROC (Receiver operating characteristic; default) PR (Precision Recall) curves quality measures binary classifiers. Unlike accuracy, like cross-entropy losses, ROC-AUC PR-AUC evaluate operational points model. class approximates AUCs using Riemann sum. metric accumulation phrase, predictions accumulated within predefined buckets value. AUC computed interpolating per-bucket averages. buckets define evaluated operational points. metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute AUC.  discretize AUC curve, linearly spaced set thresholds used compute pairs recall precision values. area ROC-curve therefore computed using height recall values false positive rate, area PR-curve computed using height precision values recall. value ultimately returned auc, idempotent operation computes area discretized curve precision versus recall values (computed using aforementioned variables). num_thresholds variable controls degree discretization larger numbers thresholds closely approximating true AUC. quality approximation may vary dramatically depending num_thresholds. thresholds parameter can used manually specify thresholds split predictions evenly. best approximation real AUC, predictions distributed approximately uniformly range [0, 1] (from_logits=FALSE). quality AUC approximation may poor case. Setting summation_method 'minoring' 'majoring' can help quantify error approximation providing lower upper bound estimate AUC. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_auc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"","code":"metric_auc(   ...,   num_thresholds = 200L,   curve = \"ROC\",   summation_method = \"interpolation\",   name = NULL,   dtype = NULL,   thresholds = NULL,   multi_label = FALSE,   num_labels = NULL,   label_weights = NULL,   from_logits = FALSE )"},{"path":"https://keras.posit.co/reference/metric_auc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"... forward/backward compatability. num_thresholds (Optional) number thresholds use discretizing roc curve. Values must > 1. Defaults 200. curve (Optional) Specifies name curve computed, 'ROC' (default) 'PR' Precision-Recall-curve. summation_method (Optional) Specifies Riemann summation method used. 'interpolation' (default) applies mid-point summation scheme ROC.  PR-AUC, interpolates (true/false) positives ratio precision (see Davis & Goadrich 2006 details); 'minoring' applies left summation increasing intervals right summation decreasing intervals; 'majoring' opposite. name (Optional) string name metric instance. dtype (Optional) data type metric result. thresholds (Optional) list floating point values use thresholds discretizing curve. set, num_thresholds parameter ignored. Values [0, 1]. Endpoint thresholds equal {-epsilon, 1+epsilon} small positive epsilon value automatically included correctly handle predictions equal exactly 0 1. multi_label boolean indicating whether multilabel data treated , wherein AUC computed separately label averaged across labels, (FALSE) data flattened single label AUC computation. latter case, multilabel data passed AUC, label-prediction pair treated individual data point. set `FALSE`` multi-class data. num_labels (Optional) number labels, used multi_label TRUE. num_labels specified, state variables get created first call update_state. label_weights (Optional) list, array, tensor non-negative weights used compute AUCs multilabel data. multi_label TRUE, weights applied individual label AUCs averaged produce multi-label AUC. FALSE, used weight individual label predictions computing confusion matrix flattened data. Note unlike class_weights class_weights weights example depending value label, whereas label_weights depends index label flattening; therefore label_weights used multi-class data. from_logits boolean indicating whether predictions (y_pred update_state) probabilities sigmoid logits. rule thumb, using keras loss, from_logits constructor argument loss match AUC from_logits constructor argument.","code":""},{"path":"https://keras.posit.co/reference/metric_auc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_auc.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_auc(num_thresholds = 3) m$update_state(c(0,   0,   1,   1),                c(0, 0.5, 0.3, 0.9)) # threshold values are [0 - 1e-7, 0.5, 1 + 1e-7] # tp = [2, 1, 0], fp = [2, 0, 0], fn = [0, 1, 2], tn = [0, 2, 2] # tp_rate = recall = [1, 0.5, 0], fp_rate = [1, 0, 0] # auc = ((((1 + 0.5) / 2) * (1 - 0)) + (((0.5 + 0) / 2) * (0 - 0))) #     = 0.75 m$result() ## tf.Tensor(0.75, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   1,   1),                c(0, 0.5, 0.3, 0.9),                sample_weight=c(1, 0, 0, 1)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32) # Reports the AUC of a model outputting a probability. model |> compile(   optimizer = 'sgd',   loss = loss_binary_crossentropy(),   metrics = list(metric_auc()) )  # Reports the AUC of a model outputting a logit. model |> compile(   optimizer = 'sgd',   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_auc(from_logits = TRUE)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates how often predictions match binary labels. — metric_binary_accuracy","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"metric creates two local variables, total count used compute frequency y_pred matches y_true. frequency ultimately returned binary accuracy: idempotent operation simply divides total count. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"","code":"metric_binary_accuracy(   y_true,   y_pred,   threshold = 0.5,   ...,   name = \"binary_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. threshold (Optional) Float representing threshold deciding whether prediction values 1 0. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"Standalone usage:             Usage compile() API:","code":"m <- metric_binary_accuracy() m$update_state(rbind(1, 1, 0, 0), rbind(0.98, 1, 0, 0.6)) m$result() ## tf.Tensor(0.75, shape=(), dtype=float32) # 0.75 m$reset_state() m$update_state(rbind(1, 1, 0, 0), rbind(0.98, 1, 0, 0.6),                sample_weight = c(1, 0, 0, 1)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) # 0.5 model %>% compile(optimizer='sgd',                   loss='binary_crossentropy',                   metrics=list(metric_binary_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"crossentropy metric class used two label classes (0 1).","code":""},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"","code":"metric_binary_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   name = \"binary_crossentropy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"y_true Ground truth values. shape = [batch_size, d0, .. dN]. y_pred predicted values. shape = [batch_size, d0, .. dN]. from_logits (Optional) Whether output expected logits tensor. default, consider output encodes probability distribution. label_smoothing (Optional) Float [0, 1]. > 0, label values smoothed, meaning confidence label values relaxed. e.g. label_smoothing=0.2 means use value 0.1 label \"0\" 0.9 label \"1\". axis axis along mean computed. Defaults -1. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_binary_crossentropy() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(0.8149245, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight=c(1, 0)) m$result() ## tf.Tensor(0.91629076, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_binary_crossentropy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"According Lin et al., 2018, helps apply focal factor -weight easy examples focus hard examples. default, focal tensor computed follows: focal_factor = (1 - output)^gamma class 1 focal_factor = output^gamma class 0 gamma focusing parameter. gamma = 0, focal effect binary crossentropy loss. apply_class_balancing == TRUE, function also takes account weight balancing factor binary classes 0 1 follows: weight = alpha class 1 (target == 1) weight = 1 - alpha class 0 alpha float range [0, 1].","code":""},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"","code":"metric_binary_focal_crossentropy(   y_true,   y_pred,   apply_class_balancing = FALSE,   alpha = 0.25,   gamma = 2,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"y_true Ground truth values, shape (batch_size, d0, .. dN). y_pred predicted values, shape (batch_size, d0, .. dN). apply_class_balancing bool, whether apply weight balancing binary classes 0 1. alpha weight balancing factor class 1, default 0.25 mentioned reference. weight class 0 1.0 - alpha. gamma focusing parameter, default 2.0 mentioned reference. from_logits Whether y_pred expected logits tensor. default, assume y_pred encodes probability distribution. label_smoothing Float [0, 1]. > 0 smooth labels squeezing towards 0.5, , using 1. - 0.5 * label_smoothing target class 0.5 * label_smoothing non-target class. axis axis along mean computed. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"Binary focal crossentropy loss value shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_binary_focal_crossentropy(y_true, y_pred, gamma=2) loss ## tf.Tensor([0.32986466 0.20579838], shape=(2), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class can used compute IoUs binary classification task predictions provided logits. First threshold applied predicted values threshold converted class 0 threshold converted class 1. IoUs classes 0 1 computed, mean IoUs classes specified target_class_ids returned.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"","code":"metric_binary_iou(   ...,   target_class_ids = list(0L, 1L),   threshold = 0.5,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"... forward/backward compatability. target_class_ids list list target class ids metric returned. Options 0, 1, c(0, 1). 0 (1), IoU metric class 0 (class 1, respectively) returned. c(0, 1), mean IoUs two classes returned. threshold threshold applies prediction logits convert either predicted class 0 logit threshold predicted class 1 logit threshold. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"threshold=0, metric behavior IoU.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_binary_iou(target_class_ids=c(0L, 1L), threshold = 0.3) m$update_state(c(0, 1, 0, 1), c(0.1, 0.2, 0.4, 0.7)) m$result() ## tf.Tensor(0.33333334, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 0, 1), c(0.1, 0.2, 0.4, 0.7),                sample_weight = c(0.2, 0.3, 0.4, 0.1)) m$result() ## tf.Tensor(0.17361109, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_binary_iou(         target_class_ids = 0L,         threshold = 0.5     )) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"can provide logits classes y_pred, since argmax logits probabilities . metric creates two local variables, total count used compute frequency y_pred matches y_true. frequency ultimately returned categorical accuracy: idempotent operation simply divides total count. y_pred y_true passed vectors probabilities, rather labels. necessary, use op_one_hot expand y_true vector. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"","code":"metric_categorical_accuracy(   y_true,   y_pred,   ...,   name = \"categorical_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"Standalone usage:           Usage compile() API:","code":"m <- metric_categorical_accuracy() m$update_state(rbind(c(0, 0, 1), c(0, 1, 0)), rbind(c(0.1, 0.9, 0.8),                 c(0.05, 0.95, 0))) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 0, 1), c(0, 1, 0)), rbind(c(0.1, 0.9, 0.8),                c(0.05, 0.95, 0)),                sample_weight = c(0.7, 0.3)) m$result() ## tf.Tensor(0.3, shape=(), dtype=float32) # 0.3 model %>% compile(optimizer = 'sgd',                   loss = 'categorical_crossentropy',                   metrics = list(metric_categorical_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"crossentropy metric class used multiple label classes (2 ). assumes labels one-hot encoded, e.g., labels values c(2, 0, 1), y_true rbind(c([0, 0, 1), c(1, 0, 0), c(0, 1, 0)).","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"","code":"metric_categorical_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   name = \"categorical_crossentropy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"y_true Tensor one-hot true targets. y_pred Tensor predicted targets. from_logits (Optional) Whether output expected logits tensor. default, consider output encodes probability distribution. label_smoothing (Optional) Float [0, 1]. > 0, label values smoothed, meaning confidence label values relaxed. e.g. label_smoothing=0.2 means use value 0.1 label \"0\" 0.9 label \"1\". axis (Optional) Defaults -1. dimension along entropy computed. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"Standalone usage:           Usage compile() API:","code":"# EPSILON = 1e-7, y = y_true, y` = y_pred # y` = clip_op_clip_by_value(output, EPSILON, 1. - EPSILON) # y` = rbind(c(0.05, 0.95, EPSILON), c(0.1, 0.8, 0.1)) # xent = -sum(y * log(y'), axis = -1) #      = -((log 0.95), (log 0.1)) #      = [0.051, 2.302] # Reduced xent = (0.051 + 2.302) / 2 m <- metric_categorical_crossentropy() m$update_state(rbind(c(0, 1, 0), c(0, 0, 1)),                rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1))) m$result() ## tf.Tensor(1.1769392, shape=(), dtype=float32) # 1.1769392 m$reset_state() m$update_state(rbind(c(0, 1, 0), c(0, 0, 1)),                rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)),                sample_weight = c(0.3, 0.7)) m$result() ## tf.Tensor(1.6271976, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_categorical_crossentropy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"Computes categorical focal crossentropy loss.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"","code":"metric_categorical_focal_crossentropy(   y_true,   y_pred,   alpha = 0.25,   gamma = 2,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"y_true Tensor one-hot true targets. y_pred Tensor predicted targets. alpha weight balancing factor classes, default 0.25 mentioned reference. can list floats scalar. multi-class case, alpha may set inverse class frequency using compute_class_weight sklearn.utils. gamma focusing parameter, default 2.0 mentioned reference. helps gradually reduce importance given simple examples smooth manner. gamma = 0, focal effect categorical crossentropy. from_logits Whether y_pred expected logits tensor. default, assume y_pred encodes probability distribution. label_smoothing Float [0, 1]. > 0 smooth labels. example, 0.1, use 0.1 / num_classes non-target labels 0.9 + 0.1 / num_classes target labels. axis Defaults -1. dimension along entropy computed.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"Categorical focal crossentropy loss value.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"","code":"y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.9, 0.05), c(0.1, 0.85, 0.05)) loss <- loss_categorical_focal_crossentropy(y_true, y_pred) loss ## tf.Tensor([2.63401289e-04 6.75912094e-01], shape=(2), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"Formula:   neg=maximum((1-y_true)*y_pred) pos=sum(y_true*y_pred)","code":"loss <- maximum(neg - pos + 1, 0)"},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"","code":"metric_categorical_hinge(   y_true,   y_pred,   ...,   name = \"categorical_hinge\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"y_true ground truth values. y_true values expected either {-1, +1} {0, 1} (.e. one-hot-encoded tensor) shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"Categorical hinge loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"Standalone usage:","code":"m <- metric_categorical_hinge() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(1.4000001, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(1.2, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"Formula:   See: Cosine Similarity. metric keeps average cosine similarity predictions labels stream data.","code":"loss <- sum(l2_norm(y_true) * l2_norm(y_pred))"},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"","code":"metric_cosine_similarity(   ...,   name = \"cosine_similarity\",   dtype = NULL,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result. axis (Optional) Defaults -1. dimension along cosine similarity computed.","code":""},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_cosine_similarity(axis=2) m$update_state(rbind(c(0., 1.), c(1., 1.)), rbind(c(1., 0.), c(1., 1.))) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0., 1.), c(1., 1.)), rbind(c(1., 0.), c(1., 1.)),                sample_weight = c(0.3, 0.7)) m$result() ## tf.Tensor(0.7, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_cosine_similarity(axis=2)))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes F-1 Score. — metric_f1_score","title":"Computes F-1 Score. — metric_f1_score","text":"Formula:   harmonic mean precision recall. output range [0, 1]. works multi-class multi-label classification.","code":"f1_score <- 2 * (precision * recall) / (precision + recall)"},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes F-1 Score. — metric_f1_score","text":"","code":"metric_f1_score(   ...,   average = NULL,   threshold = NULL,   name = \"f1_score\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes F-1 Score. — metric_f1_score","text":"... forward/backward compatability. average Type averaging performed data. Acceptable values NULL, \"micro\", \"macro\" \"weighted\". Defaults NULL. NULL, averaging performed result() return score class. \"micro\", compute metrics globally counting total true positives, false negatives false positives. \"macro\", compute metrics label, return unweighted mean. take label imbalance account. \"weighted\", compute metrics label, return average weighted support (number true instances label). alters \"macro\" account label imbalance. can result score precision recall. threshold Elements y_pred greater threshold converted 1, rest 0. threshold NULL, argmax y_pred converted 1, rest 0. name Optional. String name metric instance. dtype Optional. Data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes F-1 Score. — metric_f1_score","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes F-1 Score. — metric_f1_score","text":"","code":"metric <- metric_f1_score(threshold = 0.5) y_true <- rbind(c(1, 1, 1),                 c(1, 0, 0),                 c(1, 1, 0)) y_pred <- rbind(c(0.2, 0.6, 0.7),                 c(0.2, 0.6, 0.6),                 c(0.6, 0.8, 0.0)) metric$update_state(y_true, y_pred) result <- metric$result() result ## tf.Tensor([0.49999997 0.79999995 0.66666657], shape=(3), dtype=float32)"},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Computes F-1 Score. — metric_f1_score","text":"F-1 Score: float.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the number of false negatives. — metric_false_negatives","title":"Calculates the number of false negatives. — metric_false_negatives","text":"sample_weight given, calculates sum weights false negatives. metric creates one local variable, accumulator used keep track number false negatives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of false negatives. — metric_false_negatives","text":"","code":"metric_false_negatives(..., thresholds = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the number of false negatives. — metric_false_negatives","text":"... forward/backward compatability. thresholds (Optional) Defaults 0.5. float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the number of false negatives. — metric_false_negatives","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of false negatives. — metric_false_negatives","text":"Standalone usage:","code":"m <- metric_false_negatives() m$update_state(c(0, 1, 1, 1), c(0, 1, 0, 0)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 1, 1), c(0, 1, 0, 0), sample_weight=c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32) # 1.0"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the number of false positives. — metric_false_positives","title":"Calculates the number of false positives. — metric_false_positives","text":"sample_weight given, calculates sum weights false positives. metric creates one local variable, accumulator used keep track number false positives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of false positives. — metric_false_positives","text":"","code":"metric_false_positives(..., thresholds = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the number of false positives. — metric_false_positives","text":"... forward/backward compatability. thresholds (Optional) Defaults 0.5. float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the number of false positives. — metric_false_positives","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of false positives. — metric_false_positives","text":"Standalone usage:","code":"m <- metric_false_positives() m$update_state(c(0, 1, 0, 0), c(0, 0, 1, 1)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 0, 0), c(0, 0, 1, 1), sample_weight = c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes F-Beta score. — metric_fbeta_score","title":"Computes F-Beta score. — metric_fbeta_score","text":"Formula:   weighted harmonic mean precision recall. output range [0, 1]. works multi-class multi-label classification.","code":"b2 <- beta^2 f_beta_score <- (1 + b2) * (precision * recall) / (precision * b2 + recall)"},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes F-Beta score. — metric_fbeta_score","text":"","code":"metric_fbeta_score(   ...,   average = NULL,   beta = 1,   threshold = NULL,   name = \"fbeta_score\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes F-Beta score. — metric_fbeta_score","text":"... forward/backward compatability. average Type averaging performed across per-class results multi-class case. Acceptable values NULL, \"micro\", \"macro\" \"weighted\". Defaults NULL. NULL, averaging performed result() return score class. \"micro\", compute metrics globally counting total true positives, false negatives false positives. \"macro\", compute metrics label, return unweighted mean. take label imbalance account. \"weighted\", compute metrics label, return average weighted support (number true instances label). alters \"macro\" account label imbalance. can result score precision recall. beta Determines weight given recall harmonic mean precision recall (see pseudocode equation ). Defaults 1. threshold Elements y_pred greater threshold converted 1, rest 0. threshold NULL, argmax y_pred converted 1, rest 0. name Optional. String name metric instance. dtype Optional. Data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes F-Beta score. — metric_fbeta_score","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes F-Beta score. — metric_fbeta_score","text":"","code":"metric <- metric_fbeta_score(beta = 2.0, threshold = 0.5) y_true <- rbind(c(1, 1, 1),                 c(1, 0, 0),                 c(1, 1, 0)) y_pred <- rbind(c(0.2, 0.6, 0.7),                 c(0.2, 0.6, 0.6),                 c(0.6, 0.8, 0.0)) metric$update_state(y_true, y_pred) metric$result() ## tf.Tensor([0.3846154  0.90909094 0.8333332 ], shape=(3), dtype=float32)"},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Computes F-Beta score. — metric_fbeta_score","text":"F-Beta Score: float.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the hinge metric between y_true and y_pred. — metric_hinge","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"Formula:   y_true values expected -1 1. binary (0 1) labels provided convert -1 1.","code":"loss <- mean(maximum(1 - y_true * y_pred, 0), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"","code":"metric_hinge(y_true, y_pred, ..., name = \"hinge\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"y_true ground truth values. y_true values expected -1 1. binary (0 1) labels provided converted -1 1 shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"Standalone usage:","code":"m <- metric_hinge() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(1.3, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(1.1, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_huber.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes Huber loss value. — metric_huber","title":"Computes Huber loss value. — metric_huber","text":"Formula:   See: Huber loss.","code":"for (x in error) {   if (abs(x) <= delta){     loss <- c(loss, (0.5 * x^2))   } else if (abs(x) > delta) {     loss <- c(loss, (delta * abs(x) - 0.5 * delta^2))   } } loss <- mean(loss)"},{"path":"https://keras.posit.co/reference/metric_huber.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes Huber loss value. — metric_huber","text":"","code":"metric_huber(y_true, y_pred, delta = 1)"},{"path":"https://keras.posit.co/reference/metric_huber.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes Huber loss value. — metric_huber","text":"y_true tensor true targets. y_pred tensor predicted targets. delta float, point Huber loss function changes quadratic linear. Defaults 1.0.","code":""},{"path":"https://keras.posit.co/reference/metric_huber.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes Huber loss value. — metric_huber","text":"Tensor one scalar loss entry per sample.","code":""},{"path":"https://keras.posit.co/reference/metric_huber.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes Huber loss value. — metric_huber","text":"","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_huber(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. Note, class first computes IoUs individual classes, returns mean IoUs classes specified target_class_ids. target_class_ids one id value, IoU specific class returned.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"","code":"metric_iou(   ...,   num_classes,   target_class_ids,   name = NULL,   dtype = NULL,   ignore_class = NULL,   sparse_y_true = TRUE,   sparse_y_pred = TRUE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"... forward/backward compatability. num_classes possible number labels prediction task can . target_class_ids list target class ids metric returned. compute IoU specific class, list single id value provided. name (Optional) string name metric instance. dtype (Optional) data type metric result. ignore_class Optional integer. ID class ignored metric computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. sparse_y_true Whether labels encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. sparse_y_pred Whether predictions encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. axis (Optional) -1 dimension containing logits. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_iou(num_classes = 2L, target_class_ids = list(0L)) m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1)) m$result() ## tf.Tensor(0.3333333, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1),                sample_weight = c(0.3, 0.3, 0.3, 0.1)) m$result() ## tf.Tensor(0.33333325, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_iou(num_classes = 2L, target_class_ids = list(0L))))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"Formula:   y_true y_pred expected probability distributions, values 0 1. get clipped [0, 1] range.","code":"loss <- y_true * log(y_true / y_pred)"},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"","code":"metric_kl_divergence(y_true, y_pred, ..., name = \"kl_divergence\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"y_true Tensor true targets. y_pred Tensor predicted targets. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_kl_divergence() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(0.45814303, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.91628915, shape=(), dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = 'mse',                   metrics = list(metric_kl_divergence()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"Formula:   Note log(cosh(x)) approximately equal (x ** 2) / 2 small x abs(x) - log(2) large x. means 'logcosh' works mostly like mean squared error, strongly affected occasional wildly incorrect prediction.","code":"loss <- mean(log(cosh(y_pred - y_true)), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"","code":"metric_log_cosh(y_true, y_pred)"},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN].","code":""},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"Logcosh error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"","code":"y_true <- rbind(c(0., 1.), c(0., 0.)) y_pred <- rbind(c(1., 1.), c(0., 0.)) loss <- metric_log_cosh(y_true, y_pred) loss ## tf.Tensor([ 2.16890413e-01 -1.90465432e-09], shape=(2), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"Formula:","code":"error <- y_pred - y_true logcosh <- mean(log((exp(error) + exp(-error))/2), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"","code":"metric_log_cosh_error(..., name = \"logcosh\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_log_cosh_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.108445205, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.21689041, shape=(), dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = 'mse',                   metrics = list(metric_log_cosh_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the (weighted) mean of the given values. — metric_mean","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"example, values c(1, 3, 5, 7) mean 4. sample_weight specified c(1, 1, 0, 0) mean 2. metric creates two variables, total count. mean value returned simply total divided count.","code":""},{"path":"https://keras.posit.co/reference/metric_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"","code":"metric_mean(..., name = \"mean\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_mean.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"","code":"m <- metric_mean() m$update_state(c(1, 3, 5, 7)) m$result() ## tf.Tensor(4.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(1, 3, 5, 7), sample_weight = c(1, 1, 0, 0)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"Formula:","code":"loss <- mean(abs(y_true - y_pred))"},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"","code":"metric_mean_absolute_error(   y_true,   y_pred,   ...,   name = \"mean_absolute_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_mean_absolute_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.25, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_mean_absolute_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"Formula:   Division zero prevented dividing maximum(y_true, epsilon) epsilon = keras$backend$epsilon() (default 1e-7).","code":"loss <- 100 * mean(abs((y_true - y_pred) / y_true), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"","code":"metric_mean_absolute_percentage_error(   y_true,   y_pred,   ...,   name = \"mean_absolute_percentage_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_mean_absolute_percentage_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(250000000.0, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(500000000.0, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_mean_absolute_percentage_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. Note class first computes IoUs individual classes, returns mean values.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"","code":"metric_mean_iou(   ...,   num_classes,   name = NULL,   dtype = NULL,   ignore_class = NULL,   sparse_y_true = TRUE,   sparse_y_pred = TRUE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"... forward/backward compatability. num_classes possible number labels prediction task can . value must provided, since confusion matrix dimension = [num_classes, num_classes] allocated. name (Optional) string name metric instance. dtype (Optional) data type metric result. ignore_class Optional integer. ID class ignored metric computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. sparse_y_true Whether labels encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. sparse_y_pred Whether predictions encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. axis (Optional) dimension containing logits. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"Standalone usage:         Usage compile() API:","code":"# cm = [[1, 1], #        [1, 1]] # sum_row = [2, 2], sum_col = [2, 2], true_positives = [1, 1] # iou = true_positives / (sum_row + sum_col - true_positives)) # result = (1 / (2 + 2 - 1) + 1 / (2 + 2 - 1)) / 2 = 0.33 m <- metric_mean_iou(num_classes = 2) m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1)) m$result() ## tf.Tensor(0.33333334, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1),                sample_weight=c(0.3, 0.3, 0.3, 0.1)) m$result() ## tf.Tensor(0.2380952, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_mean_iou(num_classes=2)))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"Formula:","code":"loss <- mean(square(y_true - y_pred))"},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"","code":"metric_mean_squared_error(   y_true,   y_pred,   ...,   name = \"mean_squared_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"","code":"m <- metric_mean_squared_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.25, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"Formula:   Note y_pred y_true less equal 0. Negative values 0 values replaced keras$backend$epsilon() (default 1e-7).","code":"loss <- mean(square(log(y_true + 1) - log(y_pred + 1)), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"","code":"metric_mean_squared_logarithmic_error(   y_true,   y_pred,   ...,   name = \"mean_squared_logarithmic_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_mean_squared_logarithmic_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.12011322, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.24022643, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_mean_squared_logarithmic_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","title":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","text":"use class quickly build mean metric function. function needs signature fn(y_true, y_pred) return per-sample loss array. metric_mean_wrapper$result() return average metric value across samples seen far. example:","code":"mse <- function(y_true, y_pred) {   (y_true - y_pred)^2 }  mse_metric <- metric_mean_wrapper(fn = mse) mse_metric$update_state(c(0, 1), c(1, 1)) mse_metric$result() ## tf.Tensor(0.5, shape=(), dtype=float32)"},{"path":"https://keras.posit.co/reference/metric_mean_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","text":"","code":"metric_mean_wrapper(..., fn, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_mean_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","text":"... Keyword arguments pass fn. fn metric function wrap, signature fn(y_true, y_pred). name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class can used compute IoU multi-class classification tasks labels one-hot encoded (last axis one dimension per class). Note predictions also shape. compute IoU, first labels predictions converted back integer format taking argmax class axis. computation steps base IoU class apply. Note, one channel labels predictions, class class IoU. case, use IoU instead. Also, make sure num_classes equal number classes data, avoid \"labels bound\" error confusion matrix computed.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"","code":"metric_one_hot_iou(   ...,   num_classes,   target_class_ids,   name = NULL,   dtype = NULL,   ignore_class = NULL,   sparse_y_pred = FALSE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"... forward/backward compatability. num_classes possible number labels prediction task can . target_class_ids list list target class ids metric returned. compute IoU specific class, list (list) single id value provided. name (Optional) string name metric instance. dtype (Optional) data type metric result. ignore_class Optional integer. ID class ignored metric computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. sparse_y_pred Whether predictions encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. axis (Optional) dimension containing logits. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"Standalone usage:     Usage compile() API:","code":"y_true <- rbind(c(0, 0, 1), c(1, 0, 0), c(0, 1, 0), c(1, 0, 0)) y_pred <- rbind(c(0.2, 0.3, 0.5), c(0.1, 0.2, 0.7), c(0.5, 0.3, 0.1),                 c(0.1, 0.4, 0.5)) sample_weight <- c(0.1, 0.2, 0.3, 0.4) m <- metric_one_hot_iou(num_classes = 3, target_class_ids = c(0, 2)) m$update_state(     y_true = y_true, y_pred = y_pred, sample_weight = sample_weight) m$result() ## tf.Tensor(0.07142855, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_one_hot_iou(     num_classes = 3L,     target_class_id = list(1L)   )) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class can used compute mean IoU multi-class classification tasks labels one-hot encoded (last axis one dimension per class). Note predictions also shape. compute mean IoU, first labels predictions converted back integer format taking argmax class axis. computation steps base MeanIoU class apply. Note, one channel labels predictions, class class metric_mean_iou. case, use metric_mean_iou instead. Also, make sure num_classes equal number classes data, avoid \"labels bound\" error confusion matrix computed.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"","code":"metric_one_hot_mean_iou(   ...,   num_classes,   name = NULL,   dtype = NULL,   ignore_class = NULL,   sparse_y_pred = FALSE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"... forward/backward compatability. num_classes possible number labels prediction task can . name (Optional) string name metric instance. dtype (Optional) data type metric result. ignore_class Optional integer. ID class ignored metric computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. sparse_y_pred Whether predictions encoded using natural numbers probability distribution vectors. FALSE, argmax function used determine sample's likely associated label. axis (Optional) dimension containing logits. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"Standalone usage:     Usage compile() API:","code":"y_true <- rbind(c(0, 0, 1), c(1, 0, 0), c(0, 1, 0), c(1, 0, 0)) y_pred <- rbind(c(0.2, 0.3, 0.5), c(0.1, 0.2, 0.7), c(0.5, 0.3, 0.1),                 c(0.1, 0.4, 0.5)) sample_weight <- c(0.1, 0.2, 0.3, 0.4) m <- metric_one_hot_mean_iou(num_classes = 3L) m$update_state(     y_true = y_true, y_pred = y_pred, sample_weight = sample_weight) m$result() ## tf.Tensor(0.047619034, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_one_hot_mean_iou(num_classes = 3L)))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"Formula:","code":"metric <- y_pred - y_true * log(y_pred)"},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"","code":"metric_poisson(y_true, y_pred, ..., name = \"poisson\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"y_true Ground truth values. shape = [batch_size, d0, .. dN]. y_pred predicted values. shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_poisson() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.49999997, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.99999994, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_poisson()) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_precision.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the precision of the predictions with respect to the labels. — metric_precision","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"metric creates two local variables, true_positives false_positives used compute precision. value ultimately returned precision, idempotent operation simply divides true_positives sum true_positives false_positives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. top_k set, calculate precision often average class among top-k classes highest predicted values batch entry correct can found label entry. class_id specified, calculate precision considering entries batch class_id threshold /top-k highest predictions, computing fraction class_id indeed correct label.","code":""},{"path":"https://keras.posit.co/reference/metric_precision.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"","code":"metric_precision(   ...,   thresholds = NULL,   top_k = NULL,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_precision.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"... forward/backward compatability. thresholds (Optional) float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. neither thresholds top_k set, default calculate precision thresholds=0.5. top_k (Optional) Unset default. int value specifying top-k predictions consider calculating precision. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_precision.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_precision.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"Standalone usage:                 Usage compile() API:   Usage loss from_logits=TRUE:","code":"m <- metric_precision() m$update_state(c(0, 1, 1, 1),                c(1, 0, 1, 1)) m$result() |> as.double() |> signif() ## [1] 0.666667 m$reset_state() m$update_state(c(0, 1, 1, 1),                c(1, 0, 1, 1),                sample_weight = c(0, 0, 1, 0)) m$result() |> as.double() |> signif() ## [1] 1 # With top_k=2, it will calculate precision over y_true[1:2] # and y_pred[1:2] m <- metric_precision(top_k = 2) m$update_state(c(0, 0, 1, 1), c(1, 1, 1, 1)) m$result() ## tf.Tensor(0.0, shape=(), dtype=float32) # With top_k=4, it will calculate precision over y_true[1:4] # and y_pred[1:4] m <- metric_precision(top_k = 4) m$update_state(c(0, 0, 1, 1), c(1, 1, 1, 1)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_precision()) ) model |> compile(   optimizer = 'adam',   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_precision(thresholds = 0)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute precision given recall. threshold given recall value computed used evaluate corresponding precision. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class_id specified, calculate precision considering entries batch class_id threshold predictions, computing fraction class_id indeed correct label.","code":""},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"","code":"metric_precision_at_recall(   ...,   recall,   num_thresholds = 200L,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"... forward/backward compatability. recall scalar value range [0, 1]. num_thresholds (Optional) Defaults 200. number thresholds use matching given recall. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_precision_at_recall(recall = 0.5) m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8),                sample_weight = c(2, 2, 2, 1, 1)) m$result() ## tf.Tensor(0.33333334, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_precision_at_recall(recall = 0.8)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes R2 score. — metric_r2_score","title":"Computes R2 score. — metric_r2_score","text":"Formula:   also called coefficient determination. indicates close fitted regression line ground-truth data. highest score possible 1.0. indicates predictors perfectly accounts variation target. score 0.0 indicates predictors account variation target. can also negative model worse random. metric can also compute \"Adjusted R2\" score.","code":"sum_squares_residuals <- sum((y_true - y_pred) ** 2) sum_squares <- sum((y_true - mean(y_true)) ** 2) R2 <- 1 - sum_squares_residuals / sum_squares"},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes R2 score. — metric_r2_score","text":"","code":"metric_r2_score(   ...,   class_aggregation = \"uniform_average\",   num_regressors = 0L,   name = \"r2_score\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes R2 score. — metric_r2_score","text":"... forward/backward compatability. class_aggregation Specifies aggregate scores corresponding different output classes (target dimensions), .e. different dimensions last axis predictions. Equivalent multioutput argument Scikit-Learn. one NULL (aggregation), \"uniform_average\", \"variance_weighted_average\". num_regressors Number independent regressors used (\"Adjusted R2\" score). 0 standard R2 score. Defaults 0. name Optional. string name metric instance. dtype Optional. data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes R2 score. — metric_r2_score","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes R2 score. — metric_r2_score","text":"","code":"y_true <- rbind(1, 4, 3) y_pred <- rbind(2, 4, 4) metric <- metric_r2_score() metric$update_state(y_true, y_pred) metric$result() ## tf.Tensor(0.57142854, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_recall.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the recall of the predictions with respect to the labels. — metric_recall","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"metric creates two local variables, true_positives false_negatives, used compute recall. value ultimately returned recall, idempotent operation simply divides true_positives sum true_positives false_negatives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. top_k set, recall computed often average class among labels batch entry top-k predictions. class_id specified, calculate recall considering entries batch class_id label, computing fraction class_id threshold /top-k predictions.","code":""},{"path":"https://keras.posit.co/reference/metric_recall.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"","code":"metric_recall(   ...,   thresholds = NULL,   top_k = NULL,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_recall.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"... forward/backward compatability. thresholds (Optional) float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. neither thresholds top_k set, default calculate recall thresholds=0.5. top_k (Optional) Unset default. int value specifying top-k predictions consider calculating recall. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_recall.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_recall.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"Standalone usage:         Usage compile() API:   Usage loss from_logits=TRUE:","code":"m <- metric_recall() m$update_state(c(0, 1, 1, 1),                c(1, 0, 1, 1)) m$result() ## tf.Tensor(0.6666667, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 1, 1),                c(1, 0, 1, 1),                sample_weight = c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_recall()) ) model |> compile(   optimizer = 'adam',   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_recall(thresholds = 0)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"given score-label-distribution required precision might achievable, case 0.0 returned recall. metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute recall given precision. threshold given precision value computed used evaluate corresponding recall. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class_id specified, calculate precision considering entries batch class_id threshold predictions, computing fraction class_id indeed correct label.","code":""},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"","code":"metric_recall_at_precision(   ...,   precision,   num_thresholds = 200L,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"... forward/backward compatability. precision scalar value range [0, 1]. num_thresholds (Optional) Defaults 200. number thresholds use matching given precision. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_recall_at_precision(precision = 0.8) m$update_state(c(0,   0,   1,   1),                c(0, 0.5, 0.3, 0.9)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   1,   1),                c(0, 0.5, 0.3, 0.9),                sample_weight = c(1, 0, 0, 1)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_recall_at_precision(precision = 0.8)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"Formula:","code":"loss <- sqrt(mean((y_pred - y_true) ^ 2))"},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"","code":"metric_root_mean_squared_error(   ...,   name = \"root_mean_squared_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_root_mean_squared_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.70710677, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_root_mean_squared_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"Sensitivity measures proportion actual positives correctly identified (tp / (tp + fn)). Specificity measures proportion actual negatives correctly identified (tn / (tn + fp)). metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute sensitivity given specificity. threshold given specificity value computed used evaluate corresponding sensitivity. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class_id specified, calculate precision considering entries batch class_id threshold predictions, computing fraction class_id indeed correct label. additional information specificity sensitivity, see following.","code":""},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"","code":"metric_sensitivity_at_specificity(   ...,   specificity,   num_thresholds = 200L,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"... forward/backward compatability. specificity scalar value range [0, 1]. num_thresholds (Optional) Defaults 200. number thresholds use matching given specificity. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_sensitivity_at_specificity(specificity = 0.5) m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8),                sample_weight = c(1, 1, 2, 2, 1)) m$result() ## tf.Tensor(0.33333334, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_sensitivity_at_specificity()) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"can provide logits classes y_pred, since argmax logits probabilities . metric creates two local variables, total count used compute frequency y_pred matches y_true. frequency ultimately returned sparse categorical accuracy: idempotent operation simply divides total count. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":"acc <- sample_weight %*% (y_true == which.max(y_pred))"},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"","code":"metric_sparse_categorical_accuracy(   y_true,   y_pred,   ...,   name = \"sparse_categorical_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_sparse_categorical_accuracy() m$update_state(rbind(2L, 1L), rbind(c(0.1, 0.6, 0.3), c(0.05, 0.95, 0))) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(2L, 1L), rbind(c(0.1, 0.6, 0.3), c(0.05, 0.95, 0)),                sample_weight = c(0.7, 0.3)) m$result() ## tf.Tensor(0.3, shape=(), dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = 'sparse_categorical_crossentropy',                   metrics = list(metric_sparse_categorical_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"Use crossentropy metric two label classes. expects labels provided integers. want provide labels one-hot encoded, please use metric_categorical_crossentropy() metric instead. num_classes floating point values per feature y_pred single floating point value per feature y_true.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"","code":"metric_sparse_categorical_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   ignore_class = NULL,   axis = -1L,   ...,   name = \"sparse_categorical_crossentropy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"y_true Ground truth values. y_pred predicted values. from_logits (Optional) Whether output expected logits tensor. default, consider output encodes probability distribution. ignore_class Optional integer. ID class ignored loss computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. axis (Optional) Defaults -1. dimension along entropy computed. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"Standalone usage:           Usage compile() API:","code":"m <- metric_sparse_categorical_crossentropy() m$update_state(c(1, 2),                rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1))) m$result() ## tf.Tensor(1.1769392, shape=(), dtype=float32) m$reset_state() m$update_state(c(1, 2),                rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)),                sample_weight = c(0.3, 0.7)) m$result() ## tf.Tensor(1.6271976, shape=(), dtype=float32) # 1.6271976 model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_sparse_categorical_crossentropy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"Computes often integer targets top K predictions.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"","code":"metric_sparse_top_k_categorical_accuracy(   y_true,   y_pred,   k = 5L,   ...,   name = \"sparse_top_k_categorical_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. k (Optional) Number top elements look computing accuracy. Defaults 5. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_sparse_top_k_categorical_accuracy(k = 1L) m$update_state(   rbind(2, 1),   op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = \"float32\") ) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(   rbind(2, 1),   op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = \"float32\"),   sample_weight = c(0.7, 0.3) ) m$result() ## tf.Tensor(0.3, shape=(), dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = 'sparse_categorical_crossentropy',                   metrics = list(metric_sparse_top_k_categorical_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"Sensitivity measures proportion actual positives correctly identified (tp / (tp + fn)). Specificity measures proportion actual negatives correctly identified (tn / (tn + fp)). metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute specificity given sensitivity. threshold given sensitivity value computed used evaluate corresponding specificity. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class_id specified, calculate precision considering entries batch class_id threshold predictions, computing fraction class_id indeed correct label. additional information specificity sensitivity, see following.","code":""},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"","code":"metric_specificity_at_sensitivity(   ...,   sensitivity,   num_thresholds = 200L,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"... forward/backward compatability. sensitivity scalar value range [0, 1]. num_thresholds (Optional) Defaults 200. number thresholds use matching given sensitivity. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_specificity_at_sensitivity(sensitivity = 0.5) m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8)) m$result() ## tf.Tensor(0.6666667, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8),                sample_weight = c(1, 1, 2, 2, 2)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_sensitivity_at_specificity()) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"Formula:   y_true values expected -1 1. binary (0 1) labels provided convert -1 1.","code":"loss <- mean(square(maximum(1 - y_true * y_pred, 0)))"},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"","code":"metric_squared_hinge(y_true, y_pred, ..., name = \"squared_hinge\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"y_true ground truth values. y_true values expected -1 1. binary (0 1) labels provided convert -1 1 shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"Standalone usage:","code":"m <- metric_squared_hinge() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(1.86, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(1.46, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the (weighted) sum of the given values. — metric_sum","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"example, values [1, 3, 5, 7] sum 16. sample_weight specified [1, 1, 0, 0] sum 4. metric creates one variable, total. ultimately returned sum value.","code":""},{"path":"https://keras.posit.co/reference/metric_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"","code":"metric_sum(..., name = \"sum\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_sum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"","code":"m <- metric_sum() m$update_state(c(1, 3, 5, 7)) m$result() ## tf.Tensor(16.0, shape=(), dtype=float32) m <- metric_sum() m$update_state(c(1, 3, 5, 7), sample_weight = c(1, 1, 0, 0)) m$result() ## tf.Tensor(4.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"Computes often targets top K predictions.","code":""},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"","code":"metric_top_k_categorical_accuracy(   y_true,   y_pred,   k = 5L,   ...,   name = \"top_k_categorical_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. k (Optional) Number top elements look computing accuracy. Defaults 5. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_top_k_categorical_accuracy(k = 1) m$update_state(   rbind(c(0, 0, 1), c(0, 1, 0)),   op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = \"float32\") ) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(   rbind(c(0, 0, 1), c(0, 1, 0)),   op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = \"float32\"),   sample_weight = c(0.7, 0.3)) m$result() ## tf.Tensor(0.3, shape=(), dtype=float32) model.compile(optimizer = 'sgd',               loss = 'categorical_crossentropy',               metrics = list(metric_top_k_categorical_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the number of true negatives. — metric_true_negatives","title":"Calculates the number of true negatives. — metric_true_negatives","text":"sample_weight given, calculates sum weights true negatives. metric creates one local variable, accumulator used keep track number true negatives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of true negatives. — metric_true_negatives","text":"","code":"metric_true_negatives(..., thresholds = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the number of true negatives. — metric_true_negatives","text":"... forward/backward compatability. thresholds (Optional) Defaults 0.5. float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the number of true negatives. — metric_true_negatives","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of true negatives. — metric_true_negatives","text":"Standalone usage:","code":"m <- metric_true_negatives() m$update_state(c(0, 1, 0, 0), c(1, 1, 0, 0)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 0, 0), c(1, 1, 0, 0), sample_weight = c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the number of true positives. — metric_true_positives","title":"Calculates the number of true positives. — metric_true_positives","text":"sample_weight given, calculates sum weights true positives. metric creates one local variable, true_positives used keep track number true positives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of true positives. — metric_true_positives","text":"","code":"metric_true_positives(..., thresholds = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the number of true positives. — metric_true_positives","text":"... forward/backward compatability. thresholds (Optional) Defaults 0.5. float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the number of true positives. — metric_true_positives","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of true positives. — metric_true_positives","text":"Standalone usage:","code":"m <- metric_true_positives() m$update_state(c(0, 1, 1, 1), c(1, 0, 1, 1)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 1, 1), c(1, 0, 1, 1), sample_weight = c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/multi-assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Assign values to names — %<-%","title":"Assign values to names — %<-%","text":"See %<-% details.","code":""},{"path":"https://keras.posit.co/reference/multi-assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assign values to names — %<-%","text":"","code":"x %<-% value"},{"path":"https://keras.posit.co/reference/multi-assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assign values to names — %<-%","text":"right-hand-side argument, value, invisibly. called primarily side-effect assigning symbols current frame.","code":""},{"path":"https://keras.posit.co/reference/new_callback_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback — new_callback_class","title":"Callback — new_callback_class","text":"new_callback_class() alias Callback(). See ?Callback() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_callback_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback — new_callback_class","text":"","code":"new_callback_class(   classname,   on_epoch_begin = NULL,   on_epoch_end = NULL,   on_train_begin = NULL,   on_train_end = NULL,   on_train_batch_begin = NULL,   on_train_batch_end = NULL,   on_test_begin = NULL,   on_test_end = NULL,   on_test_batch_begin = NULL,   on_test_batch_end = NULL,   on_predict_begin = NULL,   on_predict_end = NULL,   on_predict_batch_begin = NULL,   on_predict_batch_end = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_callback_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback — new_callback_class","text":"classname String, name custom class. (Conventionally, CamelCase). on_epoch_begin   Called start epoch. Subclasses override actions run. function called TRAIN mode. Args: epoch: Integer, index epoch. logs: Named List. Currently data passed argument method may change future. on_epoch_end   Called end epoch. Subclasses override actions run. function called TRAIN mode. Args: epoch: Integer, index epoch. logs: Named List, metric results training epoch, validation epoch validation performed. Validation result keys prefixed val_. training epoch, values Model's metrics returned. Example: list(loss = 0.2, accuracy = 0.7). on_train_begin   Called beginning training. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_train_end   Called end training. Subclasses override actions run. Args: logs: Named list. Currently output last call on_epoch_end() passed argument method may change future. on_train_batch_begin   Called beginning training batch fit() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_train_batch_end   Called end training batch fit() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. on_test_begin   Called beginning evaluation validation. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_test_end   Called end evaluation validation. Subclasses override actions run. Args: logs: Named list. Currently output last call on_test_batch_end() passed argument method may change future. on_test_batch_begin   Called beginning batch evaluate() methods. Also called beginning validation batch fit() methods, validation data provided. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_test_batch_end   Called end batch evaluate() methods. Also called end validation batch fit() methods, validation data provided. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. on_predict_begin   Called beginning prediction. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_predict_end   Called end prediction. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_predict_batch_begin   Called beginning batch predict() methods. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_predict_batch_end   Called end batch predict() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"\\(epoch, logs = NULL) \\(epoch, logs = NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs=NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs = NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs = NULL)"},{"path":"https://keras.posit.co/reference/new_callback_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback — new_callback_class","text":"function returns custom Callback instances, similar builtin callback functions.","code":""},{"path":"https://keras.posit.co/reference/new_layer_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer — new_layer_class","title":"Layer — new_layer_class","text":"new_layer_class() alias Layer(). See ?Layer() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_layer_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer — new_layer_class","text":"","code":"new_layer_class(   classname,   initialize = NULL,   call = NULL,   build = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_layer_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer — new_layer_class","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, call, build, get_config Recommended methods implement. See description details sections. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/new_layer_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer — new_layer_class","text":"composing layer constructor, similar behavior layer functions like layer_dense(). first argument returned function object, enabling initialize()ing call() layer one step composing layer pipe, like   initialize() layer instance call() , pass missing NULL value object, pass arguments initialize() name.","code":"layer_foo <- Layer(\"Foo\", ....) output <- inputs |> layer_foo() layer <- layer_dense(units = 2, activation = \"relu\") layer <- layer_dense(NULL, 2, activation = \"relu\") layer <- layer_dense(, 2, activation = \"relu\")  # then you can call() the layer in a separate step outputs <- inputs |> layer()"},{"path":"https://keras.posit.co/reference/new_learning_rate_schedule_class.html","id":null,"dir":"Reference","previous_headings":"","what":"LearningRateSchedule — new_learning_rate_schedule_class","title":"LearningRateSchedule — new_learning_rate_schedule_class","text":"new_learning_rate_schedule_class() alias LearningRateSchedule(). See ?LearningRateSchedule() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_learning_rate_schedule_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LearningRateSchedule — new_learning_rate_schedule_class","text":"","code":"new_learning_rate_schedule_class(   classname,   call = NULL,   initialize = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_learning_rate_schedule_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LearningRateSchedule — new_learning_rate_schedule_class","text":"classname String, name custom class. (Conventionally, CamelCase). call, initialize, get_config Recommended methods implement. See description details sections. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/new_learning_rate_schedule_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"LearningRateSchedule — new_learning_rate_schedule_class","text":"function returns LearningRateSchedule instances, similar built-learning_rate_schedule_* family functions.","code":""},{"path":"https://keras.posit.co/reference/new_loss_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Loss — new_loss_class","title":"Loss — new_loss_class","text":"new_loss_class() alias Loss(). See ?Loss() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_loss_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loss — new_loss_class","text":"","code":"new_loss_class(   classname,   call = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_loss_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loss — new_loss_class","text":"classname String, name custom class. (Conventionally, CamelCase). call   Method implemented subclasses: Function contains logic loss calculation using y_true, y_pred. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"function(y_true, y_pred)"},{"path":"https://keras.posit.co/reference/new_loss_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loss — new_loss_class","text":"function returns Loss instances, similar builtin loss functions.","code":""},{"path":"https://keras.posit.co/reference/new_metric_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Metric — new_metric_class","title":"Metric — new_metric_class","text":"new_metric_class() alias Metric(). See ?Metric() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_metric_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metric — new_metric_class","text":"","code":"new_metric_class(   classname,   initialize = NULL,   update_state = NULL,   result = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_metric_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metric — new_metric_class","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, update_state, result Recommended methods implement. See description section. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/new_metric_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metric — new_metric_class","text":"function returns Metric instances, similar builtin metric functions.","code":""},{"path":"https://keras.posit.co/reference/new_model_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Model — new_model_class","title":"Model — new_model_class","text":"new_model_class() alias Model(). See ?Model() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_model_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model — new_model_class","text":"","code":"new_model_class(   classname,   initialize = NULL,   call = NULL,   train_step = NULL,   predict_step = NULL,   test_step = NULL,   compute_loss = NULL,   compute_metrics = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_model_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model — new_model_class","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, call, train_step, predict_step, test_step, compute_loss, compute_metrics Optional methods can overridden. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/new_model_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model — new_model_class","text":"model constructor function, can call create instance new model type.","code":""},{"path":"https://keras.posit.co/reference/normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalizes an array. — normalize","title":"Normalizes an array. — normalize","text":"input R array, R array returned. backend tensor, backend tensor returned.","code":""},{"path":"https://keras.posit.co/reference/normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalizes an array. — normalize","text":"","code":"normalize(x, axis = -1L, order = 2L)"},{"path":"https://keras.posit.co/reference/normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalizes an array. — normalize","text":"x Array normalize. axis axis along normalize. order Normalization order (e.g. order=2 L2 norm).","code":""},{"path":"https://keras.posit.co/reference/normalize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalizes an array. — normalize","text":"normalized copy array.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_abs.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the absolute value element-wise. — op_abs","title":"Compute the absolute value element-wise. — op_abs","text":"Compute absolute value element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_abs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the absolute value element-wise. — op_abs","text":"","code":"op_abs(x)"},{"path":"https://keras.posit.co/reference/op_abs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the absolute value element-wise. — op_abs","text":"x Input tensor","code":""},{"path":"https://keras.posit.co/reference/op_abs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the absolute value element-wise. — op_abs","text":"array containing absolute value element x.","code":""},{"path":"https://keras.posit.co/reference/op_abs.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Compute the absolute value element-wise. — op_abs","text":"","code":"x <- op_convert_to_tensor(c(-1.2, 1.2)) op_abs(x) ## tf.Tensor([1.2 1.2], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_add.html","id":null,"dir":"Reference","previous_headings":"","what":"Add arguments element-wise. — op_add","title":"Add arguments element-wise. — op_add","text":"Add arguments element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_add.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add arguments element-wise. — op_add","text":"","code":"op_add(x1, x2)"},{"path":"https://keras.posit.co/reference/op_add.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add arguments element-wise. — op_add","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_add.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add arguments element-wise. — op_add","text":"tensor containing element-wise sum x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_add.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add arguments element-wise. — op_add","text":"op_add also broadcasts shapes:     Note function automatically called using R operator + tensors.","code":"x1 <- op_convert_to_tensor(c(1, 4)) x2 <- op_convert_to_tensor(c(5, 6)) op_add(x1, x2) ## tf.Tensor([ 6. 10.], shape=(2), dtype=float32) # alias for x1 + x2 x1 + x2 ## tf.Tensor([ 6. 10.], shape=(2), dtype=float32) x1 <- op_convert_to_tensor(array(c(5, 5, 4, 6), dim =c(2, 2))) x2 <- op_convert_to_tensor(c(5, 6)) op_add(x1, x2) ## tf.Tensor( ## [[10. 10.] ##  [10. 12.]], shape=(2, 2), dtype=float64) x <- op_ones(c(3)) op_add(x, x) ## tf.Tensor([2. 2. 2.], shape=(3), dtype=float32) x + x ## tf.Tensor([2. 2. 2.], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_all.html","id":null,"dir":"Reference","previous_headings":"","what":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"Test whether array elements along given axis evaluate TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_all.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"","code":"op_all(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_all.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"x Input tensor. axis integer tuple integers represent axis along logical reduction performed. default (axis = NULL) perform logical dimensions input array. axis may negative, case counts last first axis. keepdims TRUE, axes reduced left result dimensions size one. option, result broadcast correctly input array. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_all.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"tensor containing logical reduction axis.","code":""},{"path":"https://keras.posit.co/reference/op_all.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"keepdims = TRUE outputs tensor dimensions reduced one.","code":"x <- op_convert_to_tensor(c(TRUE, FALSE)) op_all(x) ## tf.Tensor(False, shape=(), dtype=bool) (x <- op_convert_to_tensor(array(c(TRUE, FALSE, TRUE, TRUE, TRUE, TRUE), dim = c(3, 2)))) ## tf.Tensor( ## [[ True  True] ##  [False  True] ##  [ True  True]], shape=(3, 2), dtype=bool) op_all(x, axis = 1) ## tf.Tensor([False  True], shape=(2), dtype=bool) op_all(x, keepdims = TRUE) ## tf.Tensor([[False]], shape=(1, 1), dtype=bool)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_any.html","id":null,"dir":"Reference","previous_headings":"","what":"Test whether any array element along a given axis evaluates to TRUE. — op_any","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"Test whether array element along given axis evaluates TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_any.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"","code":"op_any(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_any.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"x Input tensor. axis integer tuple integers represent axis along logical reduction performed. default (axis = NULL) perform logical dimensions input array. axis may negative, case counts last first axis. keepdims TRUE, axes reduced left result dimensions size one. option, result broadcast correctly input array. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_any.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"tensor containing logical reduction axis.","code":""},{"path":"https://keras.posit.co/reference/op_any.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"keepdims = TRUE outputs tensor dimensions reduced one.","code":"x <- op_array(c(TRUE, FALSE)) op_any(x) ## tf.Tensor(True, shape=(), dtype=bool) (x <- op_reshape(c(FALSE, FALSE, FALSE,                    TRUE, FALSE, FALSE),                  c(2, 3))) ## tf.Tensor( ## [[False False False] ##  [ True False False]], shape=(2, 3), dtype=bool) op_any(x, axis = 1) ## tf.Tensor([ True False False], shape=(3), dtype=bool) op_any(x, axis = 2) ## tf.Tensor([False  True], shape=(2), dtype=bool) op_any(x, axis = -1) ## tf.Tensor([False  True], shape=(2), dtype=bool) op_any(x, keepdims = TRUE) ## tf.Tensor([[ True]], shape=(1, 1), dtype=bool) op_any(x, 1, keepdims = TRUE) ## tf.Tensor([[ True False False]], shape=(1, 3), dtype=bool)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_append.html","id":null,"dir":"Reference","previous_headings":"","what":"Append tensor x2 to the end of tensor x1. — op_append","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"Append tensor x2 end tensor x1.","code":""},{"path":"https://keras.posit.co/reference/op_append.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"","code":"op_append(x1, x2, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_append.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"x1 First input tensor. x2 Second input tensor. axis Axis along tensor x2 appended tensor x1. NULL, tensors flattened use.","code":""},{"path":"https://keras.posit.co/reference/op_append.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"tensor values x2 appended x1.","code":""},{"path":"https://keras.posit.co/reference/op_append.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"axis specified, x1 x2 must compatible shapes.","code":"x1 <- op_convert_to_tensor(c(1, 2, 3)) x2 <- op_convert_to_tensor(rbind(c(4, 5, 6), c(7, 8, 9))) op_append(x1, x2) ## tf.Tensor([1. 2. 3. 4. 5. 6. 7. 8. 9.], shape=(9), dtype=float64) x1 <- op_convert_to_tensor(rbind(c(1, 2, 3), c(4, 5, 6))) x2 <- op_convert_to_tensor(rbind(c(7, 8, 9))) op_append(x1, x2, axis = 1) ## tf.Tensor( ## [[1. 2. 3.] ##  [4. 5. 6.] ##  [7. 8. 9.]], shape=(3, 3), dtype=float64) x3 <- op_convert_to_tensor(c(7, 8, 9)) try(op_append(x1, x3, axis = 1)) ## Error in py_call_impl(callable, call_args$unnamed, call_args$named) : ##   tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__ConcatV2_N_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} ConcatOp : Ranks of all input tensors should match: shape[0] = [2,3] vs. shape[1] = [3] [Op:ConcatV2] name: concat"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arange.html","id":null,"dir":"Reference","previous_headings":"","what":"Return evenly spaced values within a given interval. — op_arange","title":"Return evenly spaced values within a given interval. — op_arange","text":"arange can called varying number positional arguments: arange(stop): Values generated within half-open interval [0, stop) (words, interval including start excluding stop). arange(start, stop): Values generated within half-open interval [start, stop). arange(start, stop, step): Values generated within half-open interval [start, stop), spacing values given step.","code":""},{"path":"https://keras.posit.co/reference/op_arange.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return evenly spaced values within a given interval. — op_arange","text":"","code":"op_arange(start, stop = NULL, step = 1L, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_arange.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return evenly spaced values within a given interval. — op_arange","text":"start Integer real, representing start interval. interval includes value. stop Integer real, representing end interval. interval include value, except cases step integer floating point round-affects length . Defaults NULL. step Integer real, represent spacing values. output , distance two adjacent values, [+1] - []. default step size 1. step specified position argument, start must also given. dtype type output array. dtype given, infer data type input arguments.","code":""},{"path":"https://keras.posit.co/reference/op_arange.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return evenly spaced values within a given interval. — op_arange","text":"Tensor evenly spaced values. floating point arguments, length result ceiling((stop - start)/step). floating point overflow, rule may result last element greater stop.","code":""},{"path":"https://keras.posit.co/reference/op_arange.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return evenly spaced values within a given interval. — op_arange","text":"","code":"op_arange(3L) ## tf.Tensor([0 1 2], shape=(3), dtype=int32) op_arange(3) # float ## tf.Tensor([0. 1. 2.], shape=(3), dtype=float32) op_arange(3, dtype = 'int32') #int ## tf.Tensor([0 1 2], shape=(3), dtype=int32) op_arange(3L, 7L) ## tf.Tensor([3 4 5 6], shape=(4), dtype=int32) op_arange(3L, 7L, 2L) ## tf.Tensor([3 5], shape=(2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arccos.html","id":null,"dir":"Reference","previous_headings":"","what":"Trigonometric inverse cosine, element-wise. — op_arccos","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"inverse cos , y = cos(x), x = arccos(y).","code":""},{"path":"https://keras.posit.co/reference/op_arccos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"","code":"op_arccos(x)"},{"path":"https://keras.posit.co/reference/op_arccos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arccos.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"Tensor angle ray intersecting unit circle given x-coordinate radians [0, pi].","code":""},{"path":"https://keras.posit.co/reference/op_arccos.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"","code":"x <- op_convert_to_tensor(c(1, -1)) op_arccos(x) ## tf.Tensor([0.        3.1415927], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse hyperbolic cosine, element-wise. — op_arccosh","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"Inverse hyperbolic cosine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"","code":"op_arccosh(x)"},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"Output tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"","code":"x <- op_convert_to_tensor(c(10, 100)) op_arccosh(x) ## tf.Tensor([2.993223 5.298292], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse sine, element-wise. — op_arcsin","title":"Inverse sine, element-wise. — op_arcsin","text":"Inverse sine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse sine, element-wise. — op_arcsin","text":"","code":"op_arcsin(x)"},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse sine, element-wise. — op_arcsin","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse sine, element-wise. — op_arcsin","text":"Tensor inverse sine element x, radians closed interval [-pi/2, pi/2].","code":""},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse sine, element-wise. — op_arcsin","text":"","code":"x <- op_convert_to_tensor(c(1, -1, 0)) op_arcsin(x) ## tf.Tensor([ 1.5707964 -1.5707964  0.       ], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse hyperbolic sine, element-wise. — op_arcsinh","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"Inverse hyperbolic sine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"","code":"op_arcsinh(x)"},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"Output tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"","code":"x <- op_convert_to_tensor(c(1, -1, 0)) op_arcsinh(x) ## tf.Tensor([ 0.8813736 -0.8813736  0.       ], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arctan.html","id":null,"dir":"Reference","previous_headings":"","what":"Trigonometric inverse tangent, element-wise. — op_arctan","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"Trigonometric inverse tangent, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arctan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"","code":"op_arctan(x)"},{"path":"https://keras.posit.co/reference/op_arctan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arctan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"Tensor inverse tangent element x, interval [-pi/2, pi/2].","code":""},{"path":"https://keras.posit.co/reference/op_arctan.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"","code":"x <- op_convert_to_tensor(c(0, 1)) op_arctan(x) ## tf.Tensor([0.        0.7853982], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":null,"dir":"Reference","previous_headings":"","what":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"quadrant (.e., branch) chosen arctan2(x1, x2) signed angle radians ray ending origin passing point (1, 0), ray ending origin passing point (x2, x1). (Note role reversal: \"y-coordinate\" first function parameter, \"x-coordinate\" second.) IEEE convention, function defined x2 = +/-0 either x1 x2 = +/-inf.","code":""},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"","code":"op_arctan2(x1, x2)"},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"Tensor angles radians, range [-pi, pi].","code":""},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"Consider four points different quadrants:     Note order parameters. arctan2 defined also x2 = 0 several points, obtaining values range [-pi, pi]:","code":"x <- op_array(c(-1, 1, 1, -1)) y <- op_array(c(-1, -1, 1, 1)) op_arctan2(y, x) * 180 / pi ## tf.Tensor([-135.  -45.   45.  135.], shape=(4), dtype=float32) op_arctan2(     op_array(c(1, -1)),     op_array(c(0, 0)) ) ## tf.Tensor([ 1.5707964 -1.5707964], shape=(2), dtype=float32) op_arctan2(     op_array(c(0, 0, Inf)),     op_array(c(+0, -0, Inf)) ) ## tf.Tensor([0.        3.1415927 0.7853982], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arctanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse hyperbolic tangent, element-wise. — op_arctanh","title":"Inverse hyperbolic tangent, element-wise. — op_arctanh","text":"Inverse hyperbolic tangent, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arctanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse hyperbolic tangent, element-wise. — op_arctanh","text":"","code":"op_arctanh(x)"},{"path":"https://keras.posit.co/reference/op_arctanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse hyperbolic tangent, element-wise. — op_arctanh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arctanh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse hyperbolic tangent, element-wise. — op_arctanh","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_argmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the indices of the maximum values along an axis. — op_argmax","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"Returns indices maximum values along axis.","code":""},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"","code":"op_argmax(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"x Input tensor. axis default, index flattened tensor, otherwise along specified axis. keepdims set TRUE, axes reduced left result dimensions size one. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"Tensor indices. shape x, dimension along axis removed. Note returned integer 0-based (.e., argmax first index position, returned value 0)","code":""},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"similar R max.col(x) - 1 case 2-d array (matrix), nd-array, apply(x, axis, .max) - 1","code":""},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"","code":"x <- op_arange(6L) |> op_reshape(c(2, 3)) |> op_add(10) x ## tf.Tensor( ## [[10. 11. 12.] ##  [13. 14. 15.]], shape=(2, 3), dtype=float32) op_argmax(x) ## tf.Tensor(5, shape=(), dtype=int32) op_argmax(x, axis = 1) ## tf.Tensor([1 1 1], shape=(3), dtype=int32) op_argmax(x, axis = 2) ## tf.Tensor([2 2], shape=(2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_argmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the indices of the minimum values along an axis. — op_argmin","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"Returns indices minimum values along axis.","code":""},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"","code":"op_argmin(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"x Input tensor. axis default, index flattened tensor, otherwise along specified axis. keepdims set TRUE, axes reduced left result dimensions size one. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"Tensor indices. shape x, dimension along axis removed.","code":""},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"similar R expression apply(x, axis, .min) - 1, x R array.","code":""},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"","code":"x <- op_arange(6L) |> op_reshape(c(2, 3)) |> op_add(10) x ## tf.Tensor( ## [[10. 11. 12.] ##  [13. 14. 15.]], shape=(2, 3), dtype=float32) op_argmin(x) ## tf.Tensor(0, shape=(), dtype=int32) op_argmin(x, axis = 1) ## tf.Tensor([0 0 0], shape=(3), dtype=int32) op_argmin(x, axis = 2) ## tf.Tensor([0 0], shape=(2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_argsort.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the indices that would sort a tensor. — op_argsort","title":"Returns the indices that would sort a tensor. — op_argsort","text":"Returns indices sort tensor.","code":""},{"path":"https://keras.posit.co/reference/op_argsort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the indices that would sort a tensor. — op_argsort","text":"","code":"op_argsort(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_argsort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the indices that would sort a tensor. — op_argsort","text":"x Input tensor. axis Axis along sort. Defaults -1 (last axis). NULL, flattened tensor used.","code":""},{"path":"https://keras.posit.co/reference/op_argsort.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the indices that would sort a tensor. — op_argsort","text":"Tensor indices sort x along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_argsort.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the indices that would sort a tensor. — op_argsort","text":"One dimensional array:     Two-dimensional array:","code":"x <- op_array(c(3, 1, 2)) op_argsort(x) ## tf.Tensor([1 2 0], shape=(3), dtype=int32) x <- op_array(rbind(c(0, 3),                    c(3, 2),                    c(4, 5)), dtype = \"int32\") op_argsort(x, axis = 1) ## tf.Tensor( ## [[0 1] ##  [1 0] ##  [2 2]], shape=(3, 2), dtype=int32) op_argsort(x, axis = 2) ## tf.Tensor( ## [[0 1] ##  [1 0] ##  [0 1]], shape=(3, 2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a tensor. — op_array","title":"Create a tensor. — op_array","text":"Create tensor.","code":""},{"path":"https://keras.posit.co/reference/op_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a tensor. — op_array","text":"","code":"op_array(x, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a tensor. — op_array","text":"x Input tensor. dtype desired data-type tensor.","code":""},{"path":"https://keras.posit.co/reference/op_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a tensor. — op_array","text":"tensor.","code":""},{"path":"https://keras.posit.co/reference/op_array.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a tensor. — op_array","text":"","code":"op_array(c(1, 2, 3)) ## tf.Tensor([1. 2. 3.], shape=(3), dtype=float32) op_array(c(1, 2, 3), dtype = \"float32\") ## tf.Tensor([1. 2. 3.], shape=(3), dtype=float32) op_array(c(1, 2, 3), dtype = \"int32\") ## tf.Tensor([1 2 3], shape=(3), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_average.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the weighted average along the specified axis. — op_average","title":"Compute the weighted average along the specified axis. — op_average","text":"Compute weighted average along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_average.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the weighted average along the specified axis. — op_average","text":"","code":"op_average(x, axis = NULL, weights = NULL)"},{"path":"https://keras.posit.co/reference/op_average.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the weighted average along the specified axis. — op_average","text":"x Input tensor. axis Integer along average x. default, axis = NULL, average elements input tensor. axis negative counts last first axis. weights Tensor wieghts associated values x. value x contributes average according associated weight. weights array can either 1-D (case length must size along given axis) shape x. weights = NULL (default), data x assumed weight equal one. 1-D calculation : avg = sum(* weights) / sum(weights). constraint weights sum(weights) must 0.","code":""},{"path":"https://keras.posit.co/reference/op_average.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the weighted average along the specified axis. — op_average","text":"Return average along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_average.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the weighted average along the specified axis. — op_average","text":"","code":"data <- op_arange(1, 5, dtype = \"int32\") data ## tf.Tensor([1 2 3 4], shape=(4), dtype=int32) op_average(data) ## tf.Tensor(2.5, shape=(), dtype=float32) op_average(   op_arange(1, 11),   weights = op_arange(10, 0, -1) ) ## tf.Tensor(4.0, shape=(), dtype=float32) data <- op_arange(6) |> op_reshape(c(3, 2)) data ## tf.Tensor( ## [[0. 1.] ##  [2. 3.] ##  [4. 5.]], shape=(3, 2), dtype=float32) op_average(   data,   axis = 2,   weights = op_array(c(1/4, 3/4)) ) ## tf.Tensor([0.75 2.75 4.75], shape=(3), dtype=float32) # Error: Axis must be specified when shapes of x and weights differ. try(op_average(   data,   weights = op_array(c(1/4, 3/4)) )) ## Error in op_average(data, weights = op_array(c(1/4, 3/4))) : ##   Axis must be specified when shapes of x and weights differ."},{"path":[]},{"path":"https://keras.posit.co/reference/op_average_pool.html","id":null,"dir":"Reference","previous_headings":"","what":"Average pooling operation. — op_average_pool","title":"Average pooling operation. — op_average_pool","text":"Average pooling operation.","code":""},{"path":"https://keras.posit.co/reference/op_average_pool.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average pooling operation. — op_average_pool","text":"","code":"op_average_pool(   inputs,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL )"},{"path":"https://keras.posit.co/reference/op_average_pool.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average pooling operation. — op_average_pool","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". Pooling happens spatial dimensions . pool_size int tuple/list integers size len(inputs_spatial_shape), specifying size pooling window spatial dimension input tensor. pool_size int, every spatial dimension shares pool_size. strides int tuple/list integers size len(inputs_spatial_shape). stride sliding window spatial dimension input tensor. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...).","code":""},{"path":"https://keras.posit.co/reference/op_average_pool.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average pooling operation. — op_average_pool","text":"tensor rank N+2, result average pooling operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalizes x by mean and variance. — op_batch_normalization","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"op typically used batch normalization step neural network. normalizes input tensor along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"","code":"op_batch_normalization(   x,   mean,   variance,   axis,   offset = NULL,   scale = NULL,   epsilon = 0.001 )"},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"x Input tensor. mean mean vector length axis dimension input thensor. variance variance vector length axis dimension input tensor. axis Integer, axis normalized. offset offset vector length axis dimension input tensor. NULL, offset added normalized tensor. Defaults NULL. scale scale vector length axis dimension input tensor. NULL, normalized tensor multiplied scale. Defaults NULL. epsilon Small float added variance avoid dividing zero. Defaults 1e-3.","code":""},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"normalized tensor.","code":""},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"","code":"x <- op_convert_to_tensor(rbind(c(0.1, 0.2, 0.3),                                 c(0.4, 0.5, 0.6),                                 c(0.7, 0.8, 0.9))) op_batch_normalization(   x,   mean = c(0.4, 0.5, 0.6),   variance = c(0.67, 0.67, 0.67),   axis = -1 ) ## tf.Tensor( ## [[-0.36623513 -0.36623513 -0.36623513] ##  [ 0.          0.          0.        ] ##  [ 0.36623513  0.36623513  0.36623513]], shape=(3, 3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"binary cross-entropy loss commonly used binary classification tasks input sample belongs one two classes. measures dissimilarity target output probabilities logits.","code":""},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"","code":"op_binary_crossentropy(target, output, from_logits = FALSE)"},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"target target tensor representing true binary labels. shape match shape output tensor. output output tensor representing predicted probabilities logits. shape match shape target tensor. from_logits (optional) Whether output tensor logits probabilities. Set TRUE output represents logits; otherwise, set FALSE output represents probabilities. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"Integer tensor: computed binary cross-entropy loss target output.","code":""},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"","code":"target <- op_array(c(0, 1, 1, 0)) output <- op_array(c(0.1, 0.9, 0.8, 0.2)) op_binary_crossentropy(target, output) ## tf.Tensor([0.10536055 0.10536055 0.22314353 0.22314353], shape=(4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_bincount.html","id":null,"dir":"Reference","previous_headings":"","what":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"Count number occurrences value tensor integers.","code":""},{"path":"https://keras.posit.co/reference/op_bincount.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"","code":"op_bincount(x, weights = NULL, minlength = 0L, sparse = FALSE)"},{"path":"https://keras.posit.co/reference/op_bincount.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"x Input tensor. must dimension 1, must contain non-negative integer(s). weights Weight tensor. must length x. default value NULL. specified, x weighted , .e. n = x[], [n] += weight[] instead default behavior [n] += 1. minlength integer. default value 0. specified, least number bins output tensor. greater max(x) + 1, value output index higher max(x) set 0. sparse Whether return sparse tensor; backends support sparse tensors.","code":""},{"path":"https://keras.posit.co/reference/op_bincount.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"1D tensor element gives number occurrence(s) index value x. length maximum max(x) + 1 minlength.","code":""},{"path":"https://keras.posit.co/reference/op_bincount.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"","code":"(x <- op_array(c(1, 2, 2, 3), dtype = \"uint8\")) ## tf.Tensor([1 2 2 3], shape=(4), dtype=uint8) op_bincount(x) ## tf.Tensor([0 1 2 1], shape=(4), dtype=int32) (weights <- x / 2) ## tf.Tensor([0.5 1.  1.  1.5], shape=(4), dtype=float32) op_bincount(x, weights = weights) ## tf.Tensor([0.  0.5 2.  1.5], shape=(4), dtype=float32) minlength <- as.integer(op_max(x) + 1 + 2) # 6 op_bincount(x, minlength = minlength) ## tf.Tensor([0 1 2 1 0 0], shape=(6), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":null,"dir":"Reference","previous_headings":"","what":"Broadcast a tensor to a new shape. — op_broadcast_to","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"Broadcast tensor new shape.","code":""},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"","code":"op_broadcast_to(x, shape)"},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"x tensor broadcast. shape shape desired tensor.","code":""},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"tensor desired shape.","code":""},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"","code":"x <- op_array(c(1, 2, 3)) op_broadcast_to(x, shape = c(3, 3)) ## tf.Tensor( ## [[1. 2. 3.] ##  [1. 2. 3.] ##  [1. 2. 3.]], shape=(3, 3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_cast.html","id":null,"dir":"Reference","previous_headings":"","what":"Cast a tensor to the desired dtype. — op_cast","title":"Cast a tensor to the desired dtype. — op_cast","text":"Cast tensor desired dtype.","code":""},{"path":"https://keras.posit.co/reference/op_cast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cast a tensor to the desired dtype. — op_cast","text":"","code":"op_cast(x, dtype)"},{"path":"https://keras.posit.co/reference/op_cast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cast a tensor to the desired dtype. — op_cast","text":"x tensor variable. dtype target type.","code":""},{"path":"https://keras.posit.co/reference/op_cast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cast a tensor to the desired dtype. — op_cast","text":"tensor specified dtype.","code":""},{"path":"https://keras.posit.co/reference/op_cast.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cast a tensor to the desired dtype. — op_cast","text":"","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_cast(x, dtype = \"float16\") ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float16)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"categorical cross-entropy loss commonly used multi-class classification tasks input sample can belong one multiple classes. measures dissimilarity target output probabilities logits.","code":""},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"","code":"op_categorical_crossentropy(target, output, from_logits = FALSE, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"target target tensor representing true categorical labels. shape match shape output tensor except last dimension. output output tensor representing predicted probabilities logits. shape match shape target tensor except last dimension. from_logits (optional) Whether output tensor logits probabilities. Set TRUE output represents logits; otherwise, set FALSE output represents probabilities. Defaults FALSE. axis (optional) axis along categorical cross-entropy computed. Defaults -1, corresponds last dimension tensors.","code":""},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"Integer tensor: computed categorical cross-entropy loss target output.","code":""},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"","code":"target <- op_array(rbind(c(1, 0, 0),                         c(0, 1, 0),                         c(0, 0, 1))) output <- op_array(rbind(c(0.9, 0.05, 0.05),                         c(0.1, 0.8, 0.1),                         c(0.2, 0.3, 0.5))) op_categorical_crossentropy(target, output) ## tf.Tensor([0.10536052 0.22314355 0.69314718], shape=(3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_ceil.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the ceiling of the input, element-wise. — op_ceil","title":"Return the ceiling of the input, element-wise. — op_ceil","text":"ceil scalar x smallest integer , >= x.","code":""},{"path":"https://keras.posit.co/reference/op_ceil.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the ceiling of the input, element-wise. — op_ceil","text":"","code":"op_ceil(x)"},{"path":"https://keras.posit.co/reference/op_ceil.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the ceiling of the input, element-wise. — op_ceil","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ceil.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the ceiling of the input, element-wise. — op_ceil","text":"ceiling element x, float dtype.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cholesky.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","title":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","text":"Computes Cholesky decomposition positive semi-definite matrix.","code":""},{"path":"https://keras.posit.co/reference/op_cholesky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","text":"","code":"op_cholesky(x)"},{"path":"https://keras.posit.co/reference/op_cholesky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_cholesky.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","text":"tensor shape (..., M, M) representing lower triangular Cholesky factor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_clip.html","id":null,"dir":"Reference","previous_headings":"","what":"Clip (limit) the values in a tensor. — op_clip","title":"Clip (limit) the values in a tensor. — op_clip","text":"Given interval, values outside interval clipped interval edges. example, interval [0, 1] specified, values smaller 0 become 0, values larger 1 become 1.","code":""},{"path":"https://keras.posit.co/reference/op_clip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clip (limit) the values in a tensor. — op_clip","text":"","code":"op_clip(x, x_min, x_max)"},{"path":"https://keras.posit.co/reference/op_clip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clip (limit) the values in a tensor. — op_clip","text":"x Input tensor. x_min Minimum value. x_max Maximum value.","code":""},{"path":"https://keras.posit.co/reference/op_clip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clip (limit) the values in a tensor. — op_clip","text":"clipped tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_concatenate.html","id":null,"dir":"Reference","previous_headings":"","what":"Join a sequence of tensors along an existing axis. — op_concatenate","title":"Join a sequence of tensors along an existing axis. — op_concatenate","text":"Join sequence tensors along existing axis.","code":""},{"path":"https://keras.posit.co/reference/op_concatenate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join a sequence of tensors along an existing axis. — op_concatenate","text":"","code":"op_concatenate(xs, axis = 1L)"},{"path":"https://keras.posit.co/reference/op_concatenate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join a sequence of tensors along an existing axis. — op_concatenate","text":"xs sequence tensors concatenate. axis axis along tensors joined. Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/op_concatenate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join a sequence of tensors along an existing axis. — op_concatenate","text":"concatenated tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cond.html","id":null,"dir":"Reference","previous_headings":"","what":"Conditionally applies true_fn or false_fn. — op_cond","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"Conditionally applies true_fn false_fn.","code":""},{"path":"https://keras.posit.co/reference/op_cond.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"","code":"op_cond(pred, true_fn, false_fn)"},{"path":"https://keras.posit.co/reference/op_cond.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"pred Boolean scalar type true_fn Callable returning output pred == TRUE case. false_fn Callable returning output pred == FALSE case.","code":""},{"path":"https://keras.posit.co/reference/op_cond.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"output either true_fn false_fn depending pred.","code":""},{"path":"https://keras.posit.co/reference/op_cond.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"","code":"fn <- tensorflow::tf_function(function(x) {   op_cond(x > 0,     true_fn = \\() x + 1,     false_fn = \\() x - 1) })  fn(tensorflow::as_tensor(1)) ## tf.Tensor(2.0, shape=(), dtype=float64) fn(tensorflow::as_tensor(-1)) ## tf.Tensor(-2.0, shape=(), dtype=float64) # # Conditional side-effect (print only, no return value). file <- tempfile(fileext = \".txt\") fn <- tensorflow::tf_function(function(epochs) {   op_fori_loop(     0, epochs,     body_fun = \\(epoch, state) {       op_cond(epoch %% 20 == 0,               \\() {                 tensorflow::tf$print(                   \"epoch:\", epoch,                   output_stream = paste0(\"file://\", file))                 NULL               },               \\() {NULL})       state     },     init_val = tensorflow::as_tensor(0)) })  fn(tensorflow::as_tensor(100)) ## tf.Tensor(0.0, shape=(), dtype=float64) readLines(file) ## [1] \"epoch: 0\"  \"epoch: 20\" \"epoch: 40\" \"epoch: 60\" \"epoch: 80\" # cleanup unlink(file)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_conj.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the complex conjugate, element-wise. — op_conj","title":"Returns the complex conjugate, element-wise. — op_conj","text":"complex conjugate complex number obtained changing sign imaginary part.","code":""},{"path":"https://keras.posit.co/reference/op_conj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the complex conjugate, element-wise. — op_conj","text":"","code":"op_conj(x)"},{"path":"https://keras.posit.co/reference/op_conj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the complex conjugate, element-wise. — op_conj","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_conj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the complex conjugate, element-wise. — op_conj","text":"complex conjugate element x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_conv.html","id":null,"dir":"Reference","previous_headings":"","what":"General N-D convolution. — op_conv","title":"General N-D convolution. — op_conv","text":"ops supports 1D, 2D 3D convolution.","code":""},{"path":"https://keras.posit.co/reference/op_conv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General N-D convolution. — op_conv","text":"","code":"op_conv(   inputs,   kernel,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L )"},{"path":"https://keras.posit.co/reference/op_conv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General N-D convolution. — op_conv","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". kernel Tensor rank N+2. kernel shape (kernel_spatial_shape, num_input_channels, num_output_channels). num_input_channels match number channels inputs. strides int int tuple/list len(inputs_spatial_shape), specifying strides convolution along spatial dimension. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...). dilation_rate int int tuple/list len(inputs_spatial_shape), specifying dilation rate use dilated convolution. dilation_rate int, every spatial dimension shares dilation_rate.","code":""},{"path":"https://keras.posit.co/reference/op_conv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General N-D convolution. — op_conv","text":"tensor rank N+2, result conv operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_conv_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"General N-D convolution transpose. — op_conv_transpose","title":"General N-D convolution transpose. — op_conv_transpose","text":"Also known de-convolution. ops supports 1D, 2D 3D convolution.","code":""},{"path":"https://keras.posit.co/reference/op_conv_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General N-D convolution transpose. — op_conv_transpose","text":"","code":"op_conv_transpose(   inputs,   kernel,   strides,   padding = \"valid\",   output_padding = NULL,   data_format = NULL,   dilation_rate = 1L )"},{"path":"https://keras.posit.co/reference/op_conv_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General N-D convolution transpose. — op_conv_transpose","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". kernel Tensor rank N+2. kernel shape [kernel_spatial_shape, num_output_channels, num_input_channels], num_input_channels match number channels inputs. strides int int tuple/list len(inputs_spatial_shape), specifying strides convolution along spatial dimension. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. output_padding int int tuple/list len(inputs_spatial_shape), specifying amount padding along height width output tensor. Can single integer specify value spatial dimensions. amount output padding along given dimension must lower stride along dimension. set NULL (default), output shape inferred. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...). dilation_rate int int tuple/list len(inputs_spatial_shape), specifying dilation rate use dilated convolution. dilation_rate int, every spatial dimension shares dilation_rate.","code":""},{"path":"https://keras.posit.co/reference/op_conv_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General N-D convolution transpose. — op_conv_transpose","text":"tensor rank N+2, result conv operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_convert_to_numpy.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a tensor to a NumPy array. — op_convert_to_numpy","title":"Convert a tensor to a NumPy array. — op_convert_to_numpy","text":"Convert tensor NumPy array.","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_numpy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a tensor to a NumPy array. — op_convert_to_numpy","text":"","code":"op_convert_to_numpy(x)"},{"path":"https://keras.posit.co/reference/op_convert_to_numpy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a tensor to a NumPy array. — op_convert_to_numpy","text":"x tensor.","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_numpy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a tensor to a NumPy array. — op_convert_to_numpy","text":"NumPy array.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert an array to a tensor. — op_convert_to_tensor","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"Convert array tensor.","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"","code":"op_convert_to_tensor(x, dtype = NULL, sparse = NULL)"},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"x array. dtype target type. sparse Whether keep sparse tensors. FALSE cause sparse tensors densified. default value NULL means sparse tensors kept backend supports .","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"tensor specified dtype.","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"","code":"x <- array(c(1, 2, 3)) y <- op_convert_to_tensor(x) y ## tf.Tensor([1. 2. 3.], shape=(3), dtype=float64) op_convert_to_tensor(c(1, 3, 2, 0), \"int32\") ## tf.Tensor([1 3 2 0], shape=(4), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_copy.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a copy of x. — op_copy","title":"Returns a copy of x. — op_copy","text":"Returns copy x.","code":""},{"path":"https://keras.posit.co/reference/op_copy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a copy of x. — op_copy","text":"","code":"op_copy(x)"},{"path":"https://keras.posit.co/reference/op_copy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a copy of x. — op_copy","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_copy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a copy of x. — op_copy","text":"copy x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_correlate.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","title":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","text":"Compute cross-correlation two 1-dimensional tensors.","code":""},{"path":"https://keras.posit.co/reference/op_correlate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","text":"","code":"op_correlate(x1, x2, mode = \"valid\")"},{"path":"https://keras.posit.co/reference/op_correlate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","text":"x1 First 1-dimensional input tensor length M. x2 Second 1-dimensional input tensor length N. mode Either \"valid\", \"\" \"full\". default mode set \"valid\", returns output length max(M, N) - min(M, N) + 1. \"\" returns output length max(M, N). \"full\" mode returns convolution point overlap, output length N+M-1.","code":""},{"path":"https://keras.posit.co/reference/op_correlate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","text":"Output tensor, cross-correlation x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cos.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosine, element-wise. — op_cos","title":"Cosine, element-wise. — op_cos","text":"Cosine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_cos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosine, element-wise. — op_cos","text":"","code":"op_cos(x)"},{"path":"https://keras.posit.co/reference/op_cos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosine, element-wise. — op_cos","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_cos.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cosine, element-wise. — op_cos","text":"corresponding cosine values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hyperbolic cosine, element-wise. — op_cosh","title":"Hyperbolic cosine, element-wise. — op_cosh","text":"Hyperbolic cosine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_cosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hyperbolic cosine, element-wise. — op_cosh","text":"","code":"op_cosh(x)"},{"path":"https://keras.posit.co/reference/op_cosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hyperbolic cosine, element-wise. — op_cosh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_cosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hyperbolic cosine, element-wise. — op_cosh","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"axis specified non-zeros tensor counted.","code":""},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"","code":"op_count_nonzero(x, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"x Input tensor. axis Axis tuple axes along count number non-zeros. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"integer tensor integers.","code":""},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"","code":"x <- op_array(rbind(c(0, 1, 7, 0),                    c(3, 0, 2, 19))) op_count_nonzero(x) ## tf.Tensor(5, shape=(), dtype=int32) op_count_nonzero(x, axis = 1) ## tf.Tensor([1 1 2 1], shape=(4), dtype=int32) op_count_nonzero(x, axis = 2) ## tf.Tensor([2 3], shape=(2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_cross.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the cross product of two (arrays of) vectors. — op_cross","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"cross product x1 x2 R^3 vector perpendicular x1 x2. x1 x2 arrays vectors, vectors defined last axis x1 x2 default, axes can dimensions 2 3. dimension either x1 x2 2, third component input vector assumed zero cross product calculated accordingly. cases input vectors dimension 2, z-component cross product returned.","code":""},{"path":"https://keras.posit.co/reference/op_cross.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"","code":"op_cross(x1, x2, axisa = -1L, axisb = -1L, axisc = -1L, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_cross.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"x1 Components first vector(s). x2 Components second vector(s). axisa Axis x1 defines vector(s). Defaults -1. axisb Axis x2 defines vector(s). Defaults -1. axisc Axis result containing cross product vector(s). Ignored input vectors dimension 2, return scalar. default, last axis. axis defined, axis x1, x2 result defines vector(s) cross product(s). Overrides axisa, axisb axisc.","code":""},{"path":"https://keras.posit.co/reference/op_cross.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"Vector cross product(s).","code":""},{"path":"https://keras.posit.co/reference/op_cross.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"Torch backend support two dimensional vectors, arguments axisa, axisb axisc. Use axis instead.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_ctc_decode.html","id":null,"dir":"Reference","previous_headings":"","what":"Decodes the output of a CTC model. — op_ctc_decode","title":"Decodes the output of a CTC model. — op_ctc_decode","text":"Decodes output CTC model.","code":""},{"path":"https://keras.posit.co/reference/op_ctc_decode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decodes the output of a CTC model. — op_ctc_decode","text":"","code":"op_ctc_decode(   inputs,   sequence_lengths,   strategy = \"greedy\",   beam_width = 100L,   top_paths = 1L,   merge_repeated = TRUE,   mask_index = NULL )"},{"path":"https://keras.posit.co/reference/op_ctc_decode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decodes the output of a CTC model. — op_ctc_decode","text":"inputs tensor shape (batch_size, max_length, num_classes) containing logits (output model). normalized via softmax. sequence_lengths tensor shape (batch_size) containing sequence lengths batch. strategy string decoding strategy. Supported values \"greedy\" \"beam_search\". beam_width integer scalar beam width used beam search. Defaults 100. top_paths integer scalar, number top paths return. Defaults 1. merge_repeated boolean scalar, whether merge repeated labels output. Defaults TRUE. mask_index integer scalar, index mask character vocabulary. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_ctc_decode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decodes the output of a CTC model. — op_ctc_decode","text":"list containing: tensor representing list decoded sequences. strategy=\"greedy\", shape (1, batch_size, max_length). strategy=\"beam_seatch\", shape (top_paths, batch_size, max_length). Note : -1 indicates blank label. strategy=\"greedy\", tensor shape (batch_size, 1) representing negative sum probability logits sequence. strategy=\"beam_seatch\", tensor shape (batch_size, top_paths) representing log probability sequence.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_ctc_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","title":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","text":"CTC (Connectionist Temporal Classification) loss.","code":""},{"path":"https://keras.posit.co/reference/op_ctc_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","text":"","code":"op_ctc_loss(target, output, target_length, output_length, mask_index = 0L)"},{"path":"https://keras.posit.co/reference/op_ctc_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","text":"target tensor shape (batch_size, max_length) containing true labels integer format. output tensor shape (batch_size, max_length, num_classes) containing logits (output model). target_length tensor shape (batch_size) containing true label lengths. output_length tensor shape (batch_size) containing output lengths. mask_index index mask character vocabulary. Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/op_ctc_loss.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","text":"tensor, shape (batch_size), loss values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cumprod.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the cumulative product of elements along a given axis. — op_cumprod","title":"Return the cumulative product of elements along a given axis. — op_cumprod","text":"Return cumulative product elements along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_cumprod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the cumulative product of elements along a given axis. — op_cumprod","text":"","code":"op_cumprod(x, axis = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_cumprod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the cumulative product of elements along a given axis. — op_cumprod","text":"x Input tensor. axis Axis along cumulative product computed. default input flattened. dtype dtype returned tensor. Defaults x$dtype.","code":""},{"path":"https://keras.posit.co/reference/op_cumprod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the cumulative product of elements along a given axis. — op_cumprod","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cumsum.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the cumulative sum of elements along a given axis. — op_cumsum","title":"Returns the cumulative sum of elements along a given axis. — op_cumsum","text":"Returns cumulative sum elements along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_cumsum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the cumulative sum of elements along a given axis. — op_cumsum","text":"","code":"op_cumsum(x, axis = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_cumsum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the cumulative sum of elements along a given axis. — op_cumsum","text":"x Input tensor. axis Axis along cumulative sum computed. default input flattened. dtype dtype returned tensor. Defaults x$dtype.","code":""},{"path":"https://keras.posit.co/reference/op_cumsum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the cumulative sum of elements along a given axis. — op_cumsum","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":null,"dir":"Reference","previous_headings":"","what":"Decorator to define a function with a custom gradient. — op_custom_gradient","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"decorator allows fine grained control gradients sequence operations. may useful multiple reasons, including providing efficient numerically stable gradient sequence operations.","code":""},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"","code":"op_custom_gradient(f)"},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"f Function f(...) returns tuple (output, grad_fn) : ... sequence unnamed arguments, tensor input nested structure tensor inputs function. output (potentially nested structure ) tensor outputs applying operations forward_fn f() .... grad_fn function signature grad_fn(..., upstream) returns list tensors size (flattened) ...: derivatives tensors output respect tensors .... upstream tensor sequence tensors holding initial value gradients tensor output.","code":""},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"function h(...) returns value f(...)[[1]] whose gradient determined f(...)[[2]].","code":""},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"Note grad function returns gradient computation requires ... well upstream named argument, depending backend set. JAX TensorFlow backends, requires one argument, whereas might use upstream argument case PyTorch backend. working TensorFlow/JAX backend, grad(upstream) sufficient. PyTorch, grad function requires ... well upstream, e.g. grad <- \\(..., upstream). Follow example use op_custom_gradient() way compatible backends.","code":""},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"Backend-agnostic example.","code":"log1pexp <- op_custom_gradient(\\(x) {      e <- op_exp(x)      grad <- function(..., upstream = NULL) {       upstream <- upstream %||% ..1       op_multiply(upstream, 1.0 - 1.0 / op_add(1, e))     }      tuple(op_log(1 + e), grad)  })  if(config_backend() == \"tensorflow\") {   tf <- tensorflow::tf   x <- op_convert_to_tensor(100.0)   with(tf$GradientTape() %as% tape, {     tape$watch(x)     y <- log1pexp(x)   })   dy_dx <- tape$gradient(y, x)   stopifnot(as.numeric(dy_dx) == 1) }"},{"path":[]},{"path":"https://keras.posit.co/reference/op_depthwise_conv.html","id":null,"dir":"Reference","previous_headings":"","what":"General N-D depthwise convolution. — op_depthwise_conv","title":"General N-D depthwise convolution. — op_depthwise_conv","text":"ops supports 1D 2D depthwise convolution.","code":""},{"path":"https://keras.posit.co/reference/op_depthwise_conv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General N-D depthwise convolution. — op_depthwise_conv","text":"","code":"op_depthwise_conv(   inputs,   kernel,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L )"},{"path":"https://keras.posit.co/reference/op_depthwise_conv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General N-D depthwise convolution. — op_depthwise_conv","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". kernel Tensor rank N+2. kernel shape [kernel_spatial_shape, num_input_channels, num_channels_multiplier], num_input_channels match number channels inputs. strides int int tuple/list len(inputs_spatial_shape), specifying strides convolution along spatial dimension. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...). dilation_rate int int tuple/list len(inputs_spatial_shape), specifying dilation rate use dilated convolution. dilation_rate int, every spatial dimension shares dilation_rate.","code":""},{"path":"https://keras.posit.co/reference/op_depthwise_conv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General N-D depthwise convolution. — op_depthwise_conv","text":"tensor rank N+2, result depthwise conv operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_det.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the determinant of a square tensor. — op_det","title":"Computes the determinant of a square tensor. — op_det","text":"Computes determinant square tensor.","code":""},{"path":"https://keras.posit.co/reference/op_det.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the determinant of a square tensor. — op_det","text":"","code":"op_det(x)"},{"path":"https://keras.posit.co/reference/op_det.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the determinant of a square tensor. — op_det","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_det.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the determinant of a square tensor. — op_det","text":"tensor shape (...) represeting determinant x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_diag.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a diagonal or construct a diagonal array. — op_diag","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"Extract diagonal construct diagonal array.","code":""},{"path":"https://keras.posit.co/reference/op_diag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"","code":"op_diag(x, k = 0L)"},{"path":"https://keras.posit.co/reference/op_diag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"x Input tensor. x 2-D, returns k-th diagonal x. x 1-D, return 2-D tensor x k-th diagonal. k diagonal consider. Defaults 0. Use k > 0 diagonals main diagonal, k < 0 diagonals main diagonal.","code":""},{"path":"https://keras.posit.co/reference/op_diag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"extracted diagonal constructed diagonal tensor.","code":""},{"path":"https://keras.posit.co/reference/op_diag.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"","code":"x <- op_arange(9L) |> op_reshape(c(3, 3)) x ## tf.Tensor( ## [[0 1 2] ##  [3 4 5] ##  [6 7 8]], shape=(3, 3), dtype=int32) op_diag(x) ## tf.Tensor([0 4 8], shape=(3), dtype=int32) op_diag(x, k = 1) ## tf.Tensor([1 5], shape=(2), dtype=int32) op_diag(x, k = -1) ## tf.Tensor([3 7], shape=(2), dtype=int32) op_diag(op_diag(x)) ## tf.Tensor( ## [[0 0 0] ##  [0 4 0] ##  [0 0 8]], shape=(3, 3), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return specified diagonals. — op_diagonal","title":"Return specified diagonals. — op_diagonal","text":"x 2-D, returns diagonal x given offset, .e., collection elements form x[, +offset]. x two dimensions, axes specified axis1 axis2 used determine 2-D sub-array whose diagonal returned. shape resulting array can determined removing axis1 axis2 appending index right equal size resulting diagonals.","code":""},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return specified diagonals. — op_diagonal","text":"","code":"op_diagonal(x, offset = 0L, axis1 = 1L, axis2 = 2L)"},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return specified diagonals. — op_diagonal","text":"x Input tensor. offset Offset diagonal main diagonal. Can positive negative. Defaults 0 (main diagonal). axis1 Axis used first axis 2-D sub-arrays. Defaults 1 (first axis). axis2 Axis used second axis 2-D sub-arrays. Defaults 2 (second axis).","code":""},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return specified diagonals. — op_diagonal","text":"Tensor diagonals.","code":""},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return specified diagonals. — op_diagonal","text":"","code":"x <- op_arange(4L) |> op_reshape(c(2, 2)) x ## tf.Tensor( ## [[0 1] ##  [2 3]], shape=(2, 2), dtype=int32) op_diagonal(x) ## tf.Tensor([0 3], shape=(2), dtype=int32) op_diagonal(x, offset = 1) ## tf.Tensor([1], shape=(1), dtype=int32) x <- op_array(1:8) |> op_reshape(c(2, 2, 2)) x ## tf.Tensor( ## [[[1 2] ##   [3 4]] ## ##  [[5 6] ##   [7 8]]], shape=(2, 2, 2), dtype=int32) x |> op_diagonal(0) ## tf.Tensor( ## [[1 7] ##  [2 8]], shape=(2, 2), dtype=int32) x |> op_diagonal(0, 1, 2) # same as above, the default ## tf.Tensor( ## [[1 7] ##  [2 8]], shape=(2, 2), dtype=int32) x |> op_diagonal(0, 2, 3) ## tf.Tensor( ## [[1 4] ##  [5 8]], shape=(2, 2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the n-th discrete difference along the given axis. — op_diff","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"first difference given [] = [+1] - [] along given axis, higher differences calculated using diff recursively.","code":""},{"path":"https://keras.posit.co/reference/op_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"","code":"op_diff(a, n = 1L, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"Input tensor. n number times values differenced. Defaults 1. axis Axis compute discrete difference(s) along. Defaults -1 (last axis).","code":""},{"path":"https://keras.posit.co/reference/op_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"Tensor diagonals.","code":""},{"path":"https://keras.posit.co/reference/op_diff.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"","code":"x <- op_array(c(1, 2, 4, 7, 0)) op_diff(x) ## tf.Tensor([ 1.  2.  3. -7.], shape=(4), dtype=float32) op_diff(x, n = 2) ## tf.Tensor([  1.   1. -10.], shape=(3), dtype=float32) x <- op_array(rbind(c(1, 3, 6, 10),                   c(0, 5, 6, 8))) op_diff(x) ## tf.Tensor( ## [[2. 3. 4.] ##  [5. 1. 2.]], shape=(2, 3), dtype=float64) op_diff(x, axis = 1) ## tf.Tensor([[-1.  2.  0. -2.]], shape=(1, 4), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_digitize.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the indices of the bins to which each value in x belongs. — op_digitize","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"Returns indices bins value x belongs.","code":""},{"path":"https://keras.posit.co/reference/op_digitize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"","code":"op_digitize(x, bins)"},{"path":"https://keras.posit.co/reference/op_digitize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"x Input array binned. bins Array bins. one-dimensional monotonically increasing.","code":""},{"path":"https://keras.posit.co/reference/op_digitize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"Output array indices, shape x.","code":""},{"path":"https://keras.posit.co/reference/op_digitize.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"","code":"x <- op_array(c(0.0, 1.0, 3.0, 1.6)) bins <- array(c(0.0, 3.0, 4.5, 7.0)) op_digitize(x, bins) ## tf.Tensor([1 1 2 1], shape=(4), dtype=int32) # array([1, 1, 2, 1])"},{"path":[]},{"path":"https://keras.posit.co/reference/op_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"Divide arguments element-wise. — op_divide","title":"Divide arguments element-wise. — op_divide","text":"Note function automatically called using R operator * tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_divide(x, 2) ## tf.Tensor([0.  0.5 1.  1.5], shape=(4), dtype=float32) x / 2 ## tf.Tensor([0.  0.5 1.  1.5], shape=(4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Divide arguments element-wise. — op_divide","text":"","code":"op_divide(x1, x2)"},{"path":"https://keras.posit.co/reference/op_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Divide arguments element-wise. — op_divide","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_divide.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Divide arguments element-wise. — op_divide","text":"Output tensor, quotient x1/x2, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_divide.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Divide arguments element-wise. — op_divide","text":"","code":"op_divide(3, 2) ## tf.Tensor(1.5, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_divide_no_nan.html","id":null,"dir":"Reference","previous_headings":"","what":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","title":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","text":"Safe element-wise division returns 0 denominator 0.","code":""},{"path":"https://keras.posit.co/reference/op_divide_no_nan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","text":"","code":"op_divide_no_nan(x1, x2)"},{"path":"https://keras.posit.co/reference/op_divide_no_nan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_divide_no_nan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","text":"quotient x1/x2, element-wise, zero x2 zero.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_dot.html","id":null,"dir":"Reference","previous_headings":"","what":"Dot product of two tensors. — op_dot","title":"Dot product of two tensors. — op_dot","text":"x1 x2 1-D tensors, inner product vectors (without complex conjugation). x1 x2 2-D tensors, matrix multiplication. either x1 x2 0-D (scalar), equivalent x1 * x2. x1 N-D tensor x2 1-D tensor, sum product last axis x1 x2. x1 N-D tensor x2 M-D tensor (M >= 2), sum product last axis x1 second--last axis x2: dot(x1, x2)[,j,k,m] = sum([,j,:] * b[k,:,m]).","code":""},{"path":"https://keras.posit.co/reference/op_dot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dot product of two tensors. — op_dot","text":"","code":"op_dot(x1, x2)"},{"path":"https://keras.posit.co/reference/op_dot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dot product of two tensors. — op_dot","text":"x1 First argument. x2 Second argument.","code":""},{"path":"https://keras.posit.co/reference/op_dot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dot product of two tensors. — op_dot","text":"Dot product x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_dot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Dot product of two tensors. — op_dot","text":"Torch backend accept 0-D tensors arguments.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_eig.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","title":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","text":"Computes eigenvalues eigenvectors square matrix.","code":""},{"path":"https://keras.posit.co/reference/op_eig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","text":"","code":"op_eig(x)"},{"path":"https://keras.posit.co/reference/op_eig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_eig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","text":"list two tensors: tensor shape (..., M) containing eigenvalues tensor shape (..., M, M) containing eigenvectors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_eigh.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","title":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","text":"Computes eigenvalues eigenvectors complex Hermitian.","code":""},{"path":"https://keras.posit.co/reference/op_eigh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","text":"","code":"op_eigh(x)"},{"path":"https://keras.posit.co/reference/op_eigh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_eigh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","text":"list two tensors: tensor shape (..., M) containing eigenvalues tensor shape (..., M, M) containing eigenvectors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_einsum.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluates the Einstein summation convention on the operands. — op_einsum","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"Evaluates Einstein summation convention operands.","code":""},{"path":"https://keras.posit.co/reference/op_einsum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"","code":"op_einsum(subscripts, ...)"},{"path":"https://keras.posit.co/reference/op_einsum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"subscripts Specifies subscripts summation comma separated list subscript labels. implicit (classical Einstein summation) calculation performed unless explicit indicator -> included well subscript labels precise output form. ... operands compute Einstein sum .","code":""},{"path":"https://keras.posit.co/reference/op_einsum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"calculation based Einstein summation convention.","code":""},{"path":"https://keras.posit.co/reference/op_einsum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"Trace matrix:     Extract diagonal:     Sum axis:     higher dimensional tensors summing single axis can done ellipsis:     Compute matrix transpose reorder number axes:         Matrix vector multiplication:","code":"a <- op_arange(25) |> op_reshape(c(5, 5)) b <- op_arange(5) c <- op_arange(6) |> op_reshape(c(2, 3)) op_einsum(\"ii\", a) op_trace(a) ## tf.Tensor(60.0, shape=(), dtype=float32) ## tf.Tensor(60.0, shape=(), dtype=float32) op_einsum(\"ii -> i\", a) op_diag(a) ## tf.Tensor([ 0.  6. 12. 18. 24.], shape=(5), dtype=float32) ## tf.Tensor([ 0.  6. 12. 18. 24.], shape=(5), dtype=float32) op_einsum(\"ij -> i\", a) op_sum(a, axis = 2) ## tf.Tensor([ 10.  35.  60.  85. 110.], shape=(5), dtype=float32) ## tf.Tensor([ 10.  35.  60.  85. 110.], shape=(5), dtype=float32) op_einsum(\"...j -> ...\", a) op_sum(a, axis = -1) ## tf.Tensor([ 10.  35.  60.  85. 110.], shape=(5), dtype=float32) ## tf.Tensor([ 10.  35.  60.  85. 110.], shape=(5), dtype=float32) op_einsum(\"ji\", c) # return c unchanged ## tf.Tensor( ## [[0. 1. 2.] ##  [3. 4. 5.]], shape=(2, 3), dtype=float32) op_einsum(\"ij -> ji\", c) # transpose op_transpose(c)          # same as above ## tf.Tensor( ## [[0. 3.] ##  [1. 4.] ##  [2. 5.]], shape=(3, 2), dtype=float32) ## tf.Tensor( ## [[0. 3.] ##  [1. 4.] ##  [2. 5.]], shape=(3, 2), dtype=float32) op_einsum(\"ij, j\", a, b) op_einsum(\"...j, j\", a, b) a %*% b op_matmul(a, b) ## tf.Tensor([ 30.  80. 130. 180. 230.], shape=(5), dtype=float32) ## tf.Tensor([ 30.  80. 130. 180. 230.], shape=(5), dtype=float32) ## tf.Tensor([ 30.  80. 130. 180. 230.], shape=(5), dtype=float32) ## tf.Tensor([ 30.  80. 130. 180. 230.], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"Exponential Linear Unit activation function. — op_elu","title":"Exponential Linear Unit activation function. — op_elu","text":"defined : f(x) =  alpha * (exp(x) - 1.) x < 0, f(x) = x x >= 0.","code":""},{"path":"https://keras.posit.co/reference/op_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exponential Linear Unit activation function. — op_elu","text":"","code":"op_elu(x, alpha = 1)"},{"path":"https://keras.posit.co/reference/op_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exponential Linear Unit activation function. — op_elu","text":"x Input tensor. alpha scalar, slope positive section. Defaults 1.0.","code":""},{"path":"https://keras.posit.co/reference/op_elu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exponential Linear Unit activation function. — op_elu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_elu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Exponential Linear Unit activation function. — op_elu","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_elu(x) ## tf.Tensor([-0.63212055  0.          1.        ], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_empty.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","title":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","text":"Return tensor given shape type filled uninitialized data.","code":""},{"path":"https://keras.posit.co/reference/op_empty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","text":"","code":"op_empty(shape, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_empty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","text":"shape Shape empty tensor. dtype Desired data type empty tensor.","code":""},{"path":"https://keras.posit.co/reference/op_empty.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","text":"empty tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns (x1 == x2) element-wise. — op_equal","title":"Returns (x1 == x2) element-wise. — op_equal","text":"Note function automatically called using R operator == tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_equal(x, 2) ## tf.Tensor([False False  True False], shape=(4), dtype=bool) x == 2 ## tf.Tensor([False False  True False], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns (x1 == x2) element-wise. — op_equal","text":"","code":"op_equal(x1, x2)"},{"path":"https://keras.posit.co/reference/op_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns (x1 == x2) element-wise. — op_equal","text":"x1 Tensor compare. x2 Tensor compare.","code":""},{"path":"https://keras.posit.co/reference/op_equal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns (x1 == x2) element-wise. — op_equal","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_erf.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the error function of x, element-wise. — op_erf","title":"Computes the error function of x, element-wise. — op_erf","text":"Computes error function x, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_erf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the error function of x, element-wise. — op_erf","text":"","code":"op_erf(x)"},{"path":"https://keras.posit.co/reference/op_erf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the error function of x, element-wise. — op_erf","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_erf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the error function of x, element-wise. — op_erf","text":"tensor dtype x.","code":""},{"path":"https://keras.posit.co/reference/op_erf.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the error function of x, element-wise. — op_erf","text":"","code":"x <- op_array(c(-3, -2, -1, 0, 1)) op_erf(x) ## tf.Tensor([-0.99997777 -0.9953222  -0.84270084  0.          0.84270084], shape=(5), dtype=float32) # array([-0.99998 , -0.99532, -0.842701,  0.,  0.842701], dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the inverse error function of x, element-wise. — op_erfinv","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"Computes inverse error function x, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"","code":"op_erfinv(x)"},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"tensor dtype x.","code":""},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"","code":"x <- op_array(c(-0.5, -0.2, -0.1, 0.0, 0.3)) op_erfinv(x) ## tf.Tensor([-0.4769363  -0.17914344 -0.088856    0.          0.27246267], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the exponential of all elements in the input tensor. — op_exp","title":"Calculate the exponential of all elements in the input tensor. — op_exp","text":"Calculate exponential elements input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the exponential of all elements in the input tensor. — op_exp","text":"","code":"op_exp(x)"},{"path":"https://keras.posit.co/reference/op_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the exponential of all elements in the input tensor. — op_exp","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_exp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the exponential of all elements in the input tensor. — op_exp","text":"Output tensor, element-wise exponential x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_expand_dims.html","id":null,"dir":"Reference","previous_headings":"","what":"Expand the shape of a tensor. — op_expand_dims","title":"Expand the shape of a tensor. — op_expand_dims","text":"Insert new axis axis position expanded tensor shape.","code":""},{"path":"https://keras.posit.co/reference/op_expand_dims.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expand the shape of a tensor. — op_expand_dims","text":"","code":"op_expand_dims(x, axis)"},{"path":"https://keras.posit.co/reference/op_expand_dims.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expand the shape of a tensor. — op_expand_dims","text":"x Input tensor. axis Position expanded axes new axis (axes) placed.","code":""},{"path":"https://keras.posit.co/reference/op_expand_dims.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expand the shape of a tensor. — op_expand_dims","text":"Output tensor number dimensions increased.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_expm1.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","title":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","text":"Calculate exp(x) - 1 elements tensor.","code":""},{"path":"https://keras.posit.co/reference/op_expm1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","text":"","code":"op_expm1(x)"},{"path":"https://keras.posit.co/reference/op_expm1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","text":"x Input values.","code":""},{"path":"https://keras.posit.co/reference/op_expm1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","text":"Output tensor, element-wise exponential minus one.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":null,"dir":"Reference","previous_headings":"","what":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"Slides window size sequence_length last axis input stride sequence_stride, replacing last axis [num_sequences, sequence_length] sequences. dimension along last axis N, number sequences can computed : num_sequences = 1 + (N - sequence_length) // sequence_stride","code":""},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"","code":"op_extract_sequences(x, sequence_length, sequence_stride)"},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"x Input tensor. sequence_length integer representing sequences length. sequence_stride integer representing sequences hop size.","code":""},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"tensor sequences shape [..., num_sequences, sequence_length].","code":""},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"","code":"x <- op_convert_to_tensor(1:6) op_extract_sequences(x, 3, 2) ## tf.Tensor( ## [[1 2 3] ##  [3 4 5]], shape=(2, 3), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_eye.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","title":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","text":"Return 2-D tensor ones diagonal zeros elsewhere.","code":""},{"path":"https://keras.posit.co/reference/op_eye.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","text":"","code":"op_eye(N, M = NULL, k = 0L, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_eye.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","text":"N Number rows output. M Number columns output. NULL, defaults N. k Index diagonal: 0 (default) refers main diagonal, positive value refers upper diagonal, negative value lower diagonal. dtype Data type returned tensor.","code":""},{"path":"https://keras.posit.co/reference/op_eye.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","text":"Tensor ones k-th diagonal zeros elsewhere.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_fft.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Fast Fourier Transform along last axis of input. — op_fft","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"Computes Fast Fourier Transform along last axis input.","code":""},{"path":"https://keras.posit.co/reference/op_fft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"","code":"op_fft(x)"},{"path":"https://keras.posit.co/reference/op_fft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"x list real imaginary parts input tensor. tensors provided floating type.","code":""},{"path":"https://keras.posit.co/reference/op_fft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"list containing two tensors - real imaginary parts output tensor.","code":""},{"path":"https://keras.posit.co/reference/op_fft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"","code":"x = c(op_array(c(1., 2.)),       op_array(c(0., 1.))) op_fft(x) ## [[1]] ## tf.Tensor([ 3. -1.], shape=(2), dtype=float32) ## ## [[2]] ## tf.Tensor([ 1. -1.], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_fft2.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"Computes 2D Fast Fourier Transform along last two axes input.","code":""},{"path":"https://keras.posit.co/reference/op_fft2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"","code":"op_fft2(x)"},{"path":"https://keras.posit.co/reference/op_fft2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"x list real imaginary parts input tensor. tensors provided floating type.","code":""},{"path":"https://keras.posit.co/reference/op_fft2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"list containing two tensors - real imaginary parts output.","code":""},{"path":"https://keras.posit.co/reference/op_fft2.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"","code":"x <- c(op_array(rbind(c(1, 2),                      c(2, 1))),        op_array(rbind(c(0, 1),                      c(1, 0)))) op_fft2(x) ## [[1]] ## tf.Tensor( ## [[ 6.  0.] ##  [ 0. -2.]], shape=(2, 2), dtype=float64) ## ## [[2]] ## tf.Tensor( ## [[ 2.  0.] ##  [ 0. -2.]], shape=(2, 2), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_flip.html","id":null,"dir":"Reference","previous_headings":"","what":"Reverse the order of elements in the tensor along the given axis. — op_flip","title":"Reverse the order of elements in the tensor along the given axis. — op_flip","text":"shape tensor preserved, elements reordered.","code":""},{"path":"https://keras.posit.co/reference/op_flip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reverse the order of elements in the tensor along the given axis. — op_flip","text":"","code":"op_flip(x, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_flip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reverse the order of elements in the tensor along the given axis. — op_flip","text":"x Input tensor. axis Axis axes along flip tensor. default, axis = NULL, flip axes input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_flip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reverse the order of elements in the tensor along the given axis. — op_flip","text":"Output tensor entries axis reversed.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_floor.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the floor of the input, element-wise. — op_floor","title":"Return the floor of the input, element-wise. — op_floor","text":"floor scalar x largest integer , <= x.","code":""},{"path":"https://keras.posit.co/reference/op_floor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the floor of the input, element-wise. — op_floor","text":"","code":"op_floor(x)"},{"path":"https://keras.posit.co/reference/op_floor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the floor of the input, element-wise. — op_floor","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_floor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the floor of the input, element-wise. — op_floor","text":"Output tensor, element-wise floor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_floor_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","title":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","text":"Note function automatically called using R operator %/% tensor.","code":"(x <- op_arange(10)) ## tf.Tensor([0. 1. 2. 3. 4. 5. 6. 7. 8. 9.], shape=(10), dtype=float32) op_floor_divide(x, 2) ## tf.Tensor([0. 0. 1. 1. 2. 2. 3. 3. 4. 4.], shape=(10), dtype=float32) x %/% 2 ## tf.Tensor([0. 0. 1. 1. 2. 2. 3. 3. 4. 4.], shape=(10), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_floor_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","text":"","code":"op_floor_divide(x1, x2)"},{"path":"https://keras.posit.co/reference/op_floor_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","text":"x1 Numerator. x2 Denominator.","code":""},{"path":"https://keras.posit.co/reference/op_floor_divide.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","text":"Output tensor, y <- floor(x1/x2)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":null,"dir":"Reference","previous_headings":"","what":"For loop implementation. — op_fori_loop","title":"For loop implementation. — op_fori_loop","text":"loop implementation.","code":""},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"For loop implementation. — op_fori_loop","text":"","code":"op_fori_loop(lower, upper, body_fun, init_val)"},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"For loop implementation. — op_fori_loop","text":"lower initial value loop variable. upper upper bound loop variable. body_fun callable represents loop body. Must take two arguments: loop variable loop state. loop state updated returned function. init_val initial value loop state.","code":""},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"For loop implementation. — op_fori_loop","text":"final state loop.","code":""},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"For loop implementation. — op_fori_loop","text":"","code":"lower <- 0L upper <- 10L body_fun <- function(i, state) state + i init_state <- 0L final_state <- op_fori_loop(lower, upper, body_fun, init_state) final_state ## tf.Tensor(45, shape=(), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_full.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a new tensor of given shape and type, filled with fill_value. — op_full","title":"Return a new tensor of given shape and type, filled with fill_value. — op_full","text":"Return new tensor given shape type, filled fill_value.","code":""},{"path":"https://keras.posit.co/reference/op_full.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a new tensor of given shape and type, filled with fill_value. — op_full","text":"","code":"op_full(shape, fill_value, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_full.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a new tensor of given shape and type, filled with fill_value. — op_full","text":"shape Shape new tensor. fill_value Fill value. dtype Desired data type tensor.","code":""},{"path":"https://keras.posit.co/reference/op_full.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a new tensor of given shape and type, filled with fill_value. — op_full","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_full_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","title":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","text":"Return full tensor shape type given tensor.","code":""},{"path":"https://keras.posit.co/reference/op_full_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","text":"","code":"op_full_like(x, fill_value, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_full_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","text":"x Input tensor. fill_value Fill value. dtype Overrides data type result.","code":""},{"path":"https://keras.posit.co/reference/op_full_like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","text":"Tensor fill_value shape type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_gelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"approximate TRUE, defined : f(x) = 0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * x^3))) approximate FALSE, defined : f(x) = x * P(X <= x) = 0.5 * x * (1 + erf(x / sqrt(2))), P(X) ~ N(0, 1).","code":""},{"path":"https://keras.posit.co/reference/op_gelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"","code":"op_gelu(x, approximate = TRUE)"},{"path":"https://keras.posit.co/reference/op_gelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"x Input tensor. approximate Approximate version GELU activation. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_gelu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_gelu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_gelu(x) ## tf.Tensor([-0.15880796  0.          0.841192  ], shape=(3), dtype=float32) op_gelu(x, FALSE) ## tf.Tensor([-0.15865526  0.          0.8413447 ], shape=(3), dtype=float32) x <- seq(-5, 5, .1) plot(x, op_gelu(x),      type = \"l\", #, frame.plot = FALSE,      panel.first = grid())"},{"path":[]},{"path":"https://keras.posit.co/reference/op_get_item.html","id":null,"dir":"Reference","previous_headings":"","what":"Return x[key]. — op_get_item","title":"Return x[key]. — op_get_item","text":"Return x[key].","code":""},{"path":"https://keras.posit.co/reference/op_get_item.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return x[key]. — op_get_item","text":"","code":"op_get_item(x, key)"},{"path":"https://keras.posit.co/reference/op_get_item.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return x[key]. — op_get_item","text":"x dictionary-like object key Generally, string, object __hash__ method acceptable.","code":""},{"path":"https://keras.posit.co/reference/op_get_item.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return x[key]. — op_get_item","text":"key.","code":""},{"path":"https://keras.posit.co/reference/op_get_item.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Return x[key]. — op_get_item","text":"Generally, calling x[[key]] x$key preferable.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_greater.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the truth value of x1 > x2 element-wise. — op_greater","title":"Return the truth value of x1 > x2 element-wise. — op_greater","text":"Note function automatically called using R operator > tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_greater(x, 2) ## tf.Tensor([False False False  True], shape=(4), dtype=bool) x > 2 ## tf.Tensor([False False False  True], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_greater.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the truth value of x1 > x2 element-wise. — op_greater","text":"","code":"op_greater(x1, x2)"},{"path":"https://keras.posit.co/reference/op_greater.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the truth value of x1 > x2 element-wise. — op_greater","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_greater.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the truth value of x1 > x2 element-wise. — op_greater","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_greater_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","title":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","text":"Note function automatically called using R operator >= tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_greater_equal(x, 2) ## tf.Tensor([False False  True  True], shape=(4), dtype=bool) x >= 2 ## tf.Tensor([False False  True  True], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_greater_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","text":"","code":"op_greater_equal(x1, x2)"},{"path":"https://keras.posit.co/reference/op_greater_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_greater_equal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Hard sigmoid activation function. — op_hard_sigmoid","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"defined : 0 x < -2.5, 1 x > 2.5, (0.2 * x) + 0.5 -2.5 <= x <= 2.5.","code":""},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"","code":"op_hard_sigmoid(x)"},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_hard_sigmoid(x) ## tf.Tensor([0.33333334 0.5        0.6666667 ], shape=(3), dtype=float32) x <- as.array(seq(-5, 5, .1)) plot(x, op_hard_sigmoid(x),      type = 'l', panel.first = grid(), frame.plot = FALSE)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"defined : 0 x < -3 x x > 3 x * (x + 3) / 6 -3 <= x <= 3 faster, piecewise linear approximation silu activation.","code":""},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"","code":"op_hard_silu(x)  op_hard_swish(x)"},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"","code":"x <- op_convert_to_tensor(c(-3.0, -1.0, 0.0, 1.0, 3.0)) op_hard_silu(x) ## tf.Tensor([-0.         -0.33333334  0.          0.6666667   3.        ], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_hstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Stack tensors in sequence horizontally (column wise). — op_hstack","title":"Stack tensors in sequence horizontally (column wise). — op_hstack","text":"equivalent concatenation along first axis 1-D tensors, along second axis tensors.","code":""},{"path":"https://keras.posit.co/reference/op_hstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stack tensors in sequence horizontally (column wise). — op_hstack","text":"","code":"op_hstack(xs)"},{"path":"https://keras.posit.co/reference/op_hstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stack tensors in sequence horizontally (column wise). — op_hstack","text":"xs Sequence tensors.","code":""},{"path":"https://keras.posit.co/reference/op_hstack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stack tensors in sequence horizontally (column wise). — op_hstack","text":"tensor formed stacking given tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the identity tensor. — op_identity","title":"Return the identity tensor. — op_identity","text":"identity tensor square tensor ones main diagonal zeros elsewhere.","code":""},{"path":"https://keras.posit.co/reference/op_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the identity tensor. — op_identity","text":"","code":"op_identity(n, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_identity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the identity tensor. — op_identity","text":"n Number rows (columns) n x n output tensor. dtype Data type output tensor.","code":""},{"path":"https://keras.posit.co/reference/op_identity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the identity tensor. — op_identity","text":"identity tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_imag.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the imaginary part of the complex argument. — op_imag","title":"Return the imaginary part of the complex argument. — op_imag","text":"Return imaginary part complex argument.","code":""},{"path":"https://keras.posit.co/reference/op_imag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the imaginary part of the complex argument. — op_imag","text":"","code":"op_imag(x)"},{"path":"https://keras.posit.co/reference/op_imag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the imaginary part of the complex argument. — op_imag","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_imag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the imaginary part of the complex argument. — op_imag","text":"imaginary component complex argument.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies the given transform(s) to the image(s). — op_image_affine_transform","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"Applies given transform(s) image(s).","code":""},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"","code":"op_image_affine_transform(   image,   transform,   interpolation = \"bilinear\",   fill_mode = \"constant\",   fill_value = 0L,   data_format = \"channels_last\" )"},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"image Input image batch images. Must 3D 4D. transform Projective transform matrix/matrices. vector length 8 tensor size N x 8. one row transform [a0, a1, a2, b0, b1, b2, c0, c1], maps output point (x, y) transformed input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k), k = c0 x + c1 y + 1. transform inverted compared transform mapping input points output points. Note gradients backpropagated transformation parameters. Note c0 c1 effective using TensorFlow backend considered 0 using backends. interpolation Interpolation method. Available methods \"nearest\", \"bilinear\". Defaults \"bilinear\". fill_mode Points outside boundaries input filled according given mode. Available methods \"constant\", \"nearest\", \"wrap\" \"reflect\". Defaults \"constant\". \"reflect\": (d c b | b c d | d c b ) input extended reflecting edge last pixel. \"constant\": (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k specified fill_value. \"wrap\": (b c d | b c d | b c d) input extended wrapping around opposite edge. \"nearest\": (| b c d | d d d d) input extended nearest pixel. fill_value Value used points outside boundaries input fill_mode = \"constant\". Defaults 0. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\".","code":""},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"Applied affine transform image batch images.","code":""},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"","code":"x <- random_uniform(c(2, 64, 80, 3)) # batch of 2 RGB images transform <- op_array(rbind(c(1.5, 0, -20, 0, 1.5, -16, 0, 0),  # zoom                            c(1, 0, -20, 0, 1, -16, 0, 0)))  # translation)) y <- op_image_affine_transform(x, transform) shape(y) ## shape(2, 64, 80, 3) # (2, 64, 80, 3) x <- random_uniform(c(64, 80, 3)) # single RGB image transform <- op_array(c(1.0, 0.5, -20, 0.5, 1.0, -16, 0, 0))  # shear y <- op_image_affine_transform(x, transform) shape(y) ## shape(64, 80, 3) # (64, 80, 3) x <- random_uniform(c(2, 3, 64, 80)) # batch of 2 RGB images transform <- op_array(rbind(   c(1.5, 0,-20, 0, 1.5,-16, 0, 0),  # zoom   c(1, 0,-20, 0, 1,-16, 0, 0)  # translation )) y <- op_image_affine_transform(x, transform, data_format = \"channels_first\") shape(y) ## shape(2, 3, 64, 80) # (2, 3, 64, 80)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":null,"dir":"Reference","previous_headings":"","what":"Crop images to a specified height and width. — op_image_crop","title":"Crop images to a specified height and width. — op_image_crop","text":"Crop images specified height width.","code":""},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Crop images to a specified height and width. — op_image_crop","text":"","code":"op_image_crop(   images,   top_cropping = NULL,   left_cropping = NULL,   target_height = NULL,   target_width = NULL,   bottom_cropping = NULL,   right_cropping = NULL )"},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Crop images to a specified height and width. — op_image_crop","text":"images 4-D batch images shape (batch, height, width, channels) 3-D single image shape (height, width, channels). top_cropping Number columns crop top. left_cropping Number columns crop left. target_height Height output images. target_width Width output images. bottom_cropping Number columns crop bottom. right_cropping Number columns crop right.","code":""},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Crop images to a specified height and width. — op_image_crop","text":"images 4D, 4D float Tensor shape (batch, target_height, target_width, channels) images 3D, 3D float Tensor shape (target_height, target_width, channels)","code":""},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Crop images to a specified height and width. — op_image_crop","text":"","code":"images <- op_reshape(op_arange(1, 28, dtype=\"float32\"), c(3, 3, 3)) images[, , 1] # print the first channel of the images  cropped_images <- op_image_crop(images, 0, 0, 2, 2) cropped_images[, , 1] # print the first channel of the cropped images"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":null,"dir":"Reference","previous_headings":"","what":"Extracts patches from the image(s). — op_image_extract_patches","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"Extracts patches image(s).","code":""},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"","code":"op_image_extract_patches(   image,   size,   strides = NULL,   dilation_rate = 1L,   padding = \"valid\",   data_format = \"channels_last\" )"},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"image Input image batch images. Must 3D 4D. size Patch size int list (patch_height, patch_width) strides strides along height width. specified, NULL, defaults value size. dilation_rate input stride, specifying far two consecutive patch samples input. value 1, strides must 1. NOTE: strides > 1 supported conjunction dilation_rate > 1 padding type padding algorithm use: \"\" \"valid\". data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\".","code":""},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"Extracted patches 3D (batched) 4D (batched)","code":""},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"","code":"image <- random_uniform(c(2, 20, 20, 3), dtype = \"float32\") # batch of 2 RGB images patches <- op_image_extract_patches(image, c(5, 5)) shape(patches) ## shape(2, 4, 4, 75) # (2, 4, 4, 75) image <- random_uniform(c(20, 20, 3), dtype = \"float32\") # 1 RGB image patches <- op_image_extract_patches(image, c(3, 3), c(1, 1)) shape(patches) ## shape(18, 18, 27) # (18, 18, 27)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_map_coordinates.html","id":null,"dir":"Reference","previous_headings":"","what":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","title":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","text":"Note interpolation near boundaries differs scipy function, fixed outstanding bug scipy/issues/2640.","code":""},{"path":"https://keras.posit.co/reference/op_image_map_coordinates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","text":"","code":"op_image_map_coordinates(   input,   coordinates,   order,   fill_mode = \"constant\",   fill_value = 0L )"},{"path":"https://keras.posit.co/reference/op_image_map_coordinates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","text":"input input array. coordinates coordinates input evaluated. order order spline interpolation. order must 0 1. 0 indicates nearest neighbor 1 indicates linear interpolation. fill_mode Points outside boundaries input filled according given mode. Available methods \"constant\", \"nearest\", \"wrap\" \"mirror\" \"reflect\". Defaults \"constant\". \"constant\": (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k specified fill_value. \"nearest\": (| b c d | d d d d) input extended nearest pixel. \"wrap\": (b c d | b c d | b c d) input extended wrapping around opposite edge. \"mirror\": (c d c b | b c d | c b b) input extended mirroring edge. \"reflect\": (d c b | b c d | d c b ) input extended reflecting edge last pixel. fill_value Value used points outside boundaries input fill_mode = \"constant\". Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/op_image_map_coordinates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","text":"Output image batch images.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":null,"dir":"Reference","previous_headings":"","what":"Pad images with zeros to the specified height and width. — op_image_pad","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"Pad images zeros specified height width.","code":""},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"","code":"op_image_pad(   images,   top_padding = NULL,   left_padding = NULL,   target_height = NULL,   target_width = NULL,   bottom_padding = NULL,   right_padding = NULL )"},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"images 4D Tensor shape (batch, height, width, channels) 3D Tensor shape (height, width, channels). top_padding Number rows zeros add top. left_padding Number columns zeros add left. target_height Height output images. target_width Width output images. bottom_padding Number rows zeros add bottom. right_padding Number columns zeros add right.","code":""},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"images 4D, 4D float Tensor shape (batch, target_height, target_width, channels) images 3D, 3D float Tensor shape (target_height, target_width, channels)","code":""},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"","code":"images <- random_uniform(c(15, 25, 3)) padded_images <- op_image_pad(     images, 2, 3, target_height = 20, target_width = 30 ) shape(padded_images) ## shape(20, 30, 3) batch_images <- random_uniform(c(2, 15, 25, 3)) padded_batch <- op_image_pad(batch_images, 2, 3,                              target_height = 20,                              target_width = 30) shape(padded_batch) ## shape(2, 20, 30, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":null,"dir":"Reference","previous_headings":"","what":"Resize images to size using the specified interpolation method. — op_image_resize","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"Resize images size using specified interpolation method.","code":""},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"","code":"op_image_resize(   image,   size,   interpolation = \"bilinear\",   antialias = FALSE,   crop_to_aspect_ratio = FALSE,   pad_to_aspect_ratio = FALSE,   fill_mode = \"constant\",   fill_value = 0,   data_format = \"channels_last\" )"},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"image Input image batch images. Must 3D 4D. size Size output image (height, width) format. interpolation Interpolation method. Available methods \"nearest\", \"bilinear\", \"bicubic\". Defaults \"bilinear\". antialias Whether use antialiasing filter downsampling image. Defaults FALSE. crop_to_aspect_ratio TRUE, resize images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image cropped return largest possible window image (size (height, width)) matches target aspect ratio. default (crop_to_aspect_ratio=FALSE), aspect ratio may preserved. pad_to_aspect_ratio TRUE, pad images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image evenly padded short side. fill_mode using pad_to_aspect_ratio=TRUE, padded areas filled according given mode. \"constant\" supported time (fill constant value, equal fill_value). fill_value Float. Padding value use pad_to_aspect_ratio=TRUE. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\".","code":""},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"Resized image batch images.","code":""},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"","code":"x <- random_uniform(c(2, 4, 4, 3)) # batch of 2 RGB images y <- op_image_resize(x, c(2, 2)) shape(y) ## shape(2, 2, 2, 3) x <- random_uniform(c(4, 4, 3)) # single RGB image y <- op_image_resize(x, c(2, 2)) shape(y) ## shape(2, 2, 3) x <- random_uniform(c(2, 3, 4, 4)) # batch of 2 RGB images y <- op_image_resize(x, c(2, 2), data_format = \"channels_first\") shape(y) ## shape(2, 3, 2, 2)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"function converts RGB images grayscale images. supports 3D 4D tensors, last dimension represents channels.","code":""},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"","code":"op_image_rgb_to_grayscale(image, data_format = \"channels_last\")"},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"image Input RGB image batch RGB images. Must 3D tensor shape (height, width, channels) 4D tensor shape (batch, height, width, channels). data_format string specifying data format input tensor. can either \"channels_last\" \"channels_first\". \"channels_last\" corresponds inputs shape (batch, height, width, channels), \"channels_first\" corresponds inputs shape (batch, channels, height, width). Defaults \"channels_last\".","code":""},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"Grayscale image batch grayscale images.","code":""},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"","code":"x <- random_uniform(c(2, 4, 4, 3)) y <- op_image_rgb_to_grayscale(x) shape(y) ## shape(2, 4, 4, 1) x <- random_uniform(c(4, 4, 3)) # Single RGB image y = op_image_rgb_to_grayscale(x) shape(y) ## shape(4, 4, 1) x <- random_uniform(c(2, 3, 4, 4)) y <- op_image_rgb_to_grayscale(x, data_format=\"channels_first\") shape(y) ## shape(2, 1, 4, 4)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if the targets are in the top-k predictions. — op_in_top_k","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"Checks targets top-k predictions.","code":""},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"","code":"op_in_top_k(targets, predictions, k)"},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"targets tensor true labels. predictions tensor predicted labels. k integer representing number predictions consider.","code":""},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"boolean tensor shape targets, element indicates whether corresponding target top-k predictions.","code":""},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"","code":"targets <- op_array(c(2, 5, 3), \"int32\") predictions <- op_array(dtype = \"float32\", rbind(   c(0.1, 0.4, 0.6, 0.9, 0.5),   c(0.1, 0.7, 0.9, 0.8, 0.3),   c(0.1, 0.6, 0.9, 0.9, 0.5) )) op_in_top_k(targets, predictions, k = 3L) ## tf.Tensor([ True False  True], shape=(3), dtype=bool)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_inv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the inverse of a square tensor. — op_inv","title":"Computes the inverse of a square tensor. — op_inv","text":"Computes inverse square tensor.","code":""},{"path":"https://keras.posit.co/reference/op_inv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the inverse of a square tensor. — op_inv","text":"","code":"op_inv(x)"},{"path":"https://keras.posit.co/reference/op_inv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the inverse of a square tensor. — op_inv","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_inv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the inverse of a square tensor. — op_inv","text":"tensor shape (..., M, M) representing inverse x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_irfft.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"Computes inverse 1D Discrete Fourier Transform real-valued signal inner-dimension input. inner-dimension input assumed result RFFT: fft_length / 2 + 1 unique components DFT real-valued signal. fft_length provided, computed size inner-dimension input (fft_length = 2 * (inner - 1)). FFT length used compute odd, provided since inferred properly. Along axis IRFFT computed , fft_length / 2 + 1 smaller corresponding dimension input, dimension cropped. larger, dimension padded zeros.","code":""},{"path":"https://keras.posit.co/reference/op_irfft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"","code":"op_irfft(x, fft_length = NULL)"},{"path":"https://keras.posit.co/reference/op_irfft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"x List real imaginary parts input tensor. tensors list floating type. fft_length integer representing number fft length. specified, inferred length last axis x. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_irfft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"tensor containing inverse real-valued Fast Fourier Transform along last axis x.","code":""},{"path":"https://keras.posit.co/reference/op_irfft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"","code":"real <- op_array(c(0, 1, 2, 3, 4)) imag <- op_array(c(0, 1, 2, 3, 4)) op_irfft(c(real, imag)) #> tf.Tensor( #> [ 2.         -2.0606601   0.5        -0.35355338  0.          0.06066012 #>  -0.5         0.35355338], shape=(8), dtype=float32) all.equal(op_irfft(op_rfft(real, 5), 5), real) #> [1] TRUE"},{"path":[]},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check whether the given object is a tensor. — op_is_tensor","title":"Check whether the given object is a tensor. — op_is_tensor","text":"Check whether given object tensor.","code":""},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check whether the given object is a tensor. — op_is_tensor","text":"","code":"op_is_tensor(x)"},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check whether the given object is a tensor. — op_is_tensor","text":"x variable.","code":""},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check whether the given object is a tensor. — op_is_tensor","text":"TRUE x tensor, otherwise FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Check whether the given object is a tensor. — op_is_tensor","text":"checks backend specific tensors passing TensorFlow tensor return FALSE backend PyTorch JAX.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_isclose.html","id":null,"dir":"Reference","previous_headings":"","what":"Return whether two tensors are element-wise almost equal. — op_isclose","title":"Return whether two tensors are element-wise almost equal. — op_isclose","text":"Return whether two tensors element-wise almost equal.","code":""},{"path":"https://keras.posit.co/reference/op_isclose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return whether two tensors are element-wise almost equal. — op_isclose","text":"","code":"op_isclose(x1, x2)"},{"path":"https://keras.posit.co/reference/op_isclose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return whether two tensors are element-wise almost equal. — op_isclose","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isclose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return whether two tensors are element-wise almost equal. — op_isclose","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_isfinite.html","id":null,"dir":"Reference","previous_headings":"","what":"Return whether a tensor is finite, element-wise. — op_isfinite","title":"Return whether a tensor is finite, element-wise. — op_isfinite","text":"Real values finite NaN, positive infinity, negative infinity. Complex values finite real imaginary parts finite.","code":""},{"path":"https://keras.posit.co/reference/op_isfinite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return whether a tensor is finite, element-wise. — op_isfinite","text":"","code":"op_isfinite(x)"},{"path":"https://keras.posit.co/reference/op_isfinite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return whether a tensor is finite, element-wise. — op_isfinite","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isfinite.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return whether a tensor is finite, element-wise. — op_isfinite","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_isinf.html","id":null,"dir":"Reference","previous_headings":"","what":"Test element-wise for positive or negative infinity. — op_isinf","title":"Test element-wise for positive or negative infinity. — op_isinf","text":"Test element-wise positive negative infinity.","code":""},{"path":"https://keras.posit.co/reference/op_isinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test element-wise for positive or negative infinity. — op_isinf","text":"","code":"op_isinf(x)"},{"path":"https://keras.posit.co/reference/op_isinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test element-wise for positive or negative infinity. — op_isinf","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isinf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test element-wise for positive or negative infinity. — op_isinf","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_isnan.html","id":null,"dir":"Reference","previous_headings":"","what":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","title":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","text":"Test element-wise NaN return result boolean tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isnan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","text":"","code":"op_isnan(x)"},{"path":"https://keras.posit.co/reference/op_isnan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isnan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_istft.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"reconstruct original waveform, parameters stft.","code":""},{"path":"https://keras.posit.co/reference/op_istft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"","code":"op_istft(   x,   sequence_length,   sequence_stride,   fft_length,   length = NULL,   window = \"hann\",   center = TRUE )"},{"path":"https://keras.posit.co/reference/op_istft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"x Tuple real imaginary parts input tensor. tensors list floating type. sequence_length integer representing sequence length. sequence_stride integer representing sequence hop size. fft_length integer representing size FFT produced stft. length integer representing output clipped exactly length. specified, padding clipping take place. Defaults NULL. window string, tensor window NULL. window string, available values \"hann\" \"hamming\". window tensor, used directly window length must sequence_length. window NULL, windowing used. Defaults \"hann\". center Whether x padded sides t-th sequence centered time t * sequence_stride. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_istft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"tensor containing inverse Short-Time Fourier Transform along last axis x.","code":""},{"path":"https://keras.posit.co/reference/op_istft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"","code":"x <- op_convert_to_tensor(c(0, 1, 2, 3, 4)) op_istft(op_stft(x, 1, 1, 1), 1, 1, 1) ## tf.Tensor([], shape=(0), dtype=float32) # array([0.0, 1.0, 2.0, 3.0, 4.0])"},{"path":[]},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"allows small gradient unit active, defined : f(x) = alpha * x x < 0 f(x) = x x >= 0.","code":""},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"","code":"op_leaky_relu(x, negative_slope = 0.2)"},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"x Input tensor. negative_slope Slope activation function x < 0. Defaults 0.2.","code":""},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_leaky_relu(x) ## tf.Tensor([-0.2  0.   1. ], shape=(3), dtype=float32) # array([-0.2,  0. ,  1. ], shape=(3,), dtype=float64) x <- seq(-5, 5, .1) plot(x, op_leaky_relu(x),      type = 'l', panel.first = grid())"},{"path":[]},{"path":"https://keras.posit.co/reference/op_less.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the truth value of x1 < x2 element-wise. — op_less","title":"Return the truth value of x1 < x2 element-wise. — op_less","text":"Note function automatically called using R operator < tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_less(x, 2) ## tf.Tensor([ True  True False False], shape=(4), dtype=bool) x < 2 ## tf.Tensor([ True  True False False], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_less.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the truth value of x1 < x2 element-wise. — op_less","text":"","code":"op_less(x1, x2)"},{"path":"https://keras.posit.co/reference/op_less.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the truth value of x1 < x2 element-wise. — op_less","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_less.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the truth value of x1 < x2 element-wise. — op_less","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_less_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","title":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","text":"Note function automatically called using R operator <= tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_less_equal(x, 2) ## tf.Tensor([ True  True  True False], shape=(4), dtype=bool) x <= 2 ## tf.Tensor([ True  True  True False], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_less_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","text":"","code":"op_less_equal(x1, x2)"},{"path":"https://keras.posit.co/reference/op_less_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_less_equal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_linspace.html","id":null,"dir":"Reference","previous_headings":"","what":"Return evenly spaced numbers over a specified interval. — op_linspace","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"Returns num evenly spaced samples, calculated interval [start, stop]. endpoint interval can optionally excluded.","code":""},{"path":"https://keras.posit.co/reference/op_linspace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"","code":"op_linspace(   start,   stop,   num = 50L,   endpoint = TRUE,   retstep = FALSE,   dtype = NULL,   axis = 1L )"},{"path":"https://keras.posit.co/reference/op_linspace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"start starting value sequence. stop end value sequence, unless endpoint set FALSE. case, sequence consists last num + 1 evenly spaced samples, stop excluded. Note step size changes endpoint FALSE. num Number samples generate. Defaults 50. Must non-negative. endpoint TRUE, stop last sample. Otherwise, included. Defaults TRUE. retstep TRUE, return (samples, step), step spacing samples. dtype type output tensor. axis axis result store samples. Relevant start stop array-like. Defaults 1, first axis.","code":""},{"path":"https://keras.posit.co/reference/op_linspace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"tensor evenly spaced numbers. retstep TRUE, returns (samples, step)","code":""},{"path":"https://keras.posit.co/reference/op_linspace.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"Torch backend support axis argument.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log.html","id":null,"dir":"Reference","previous_headings":"","what":"Natural logarithm, element-wise. — op_log","title":"Natural logarithm, element-wise. — op_log","text":"Natural logarithm, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_log.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Natural logarithm, element-wise. — op_log","text":"","code":"op_log(x)"},{"path":"https://keras.posit.co/reference/op_log.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Natural logarithm, element-wise. — op_log","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Natural logarithm, element-wise. — op_log","text":"Output tensor, element-wise natural logarithm x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log10.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","title":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","text":"Return base 10 logarithm input tensor, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_log10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","text":"","code":"op_log10(x)"},{"path":"https://keras.posit.co/reference/op_log10.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log10.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","text":"Output tensor, element-wise base 10 logarithm x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log1p.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","title":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","text":"Calculates log(1 + x).","code":""},{"path":"https://keras.posit.co/reference/op_log1p.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","text":"","code":"op_log1p(x)"},{"path":"https://keras.posit.co/reference/op_log1p.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log1p.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","text":"Output tensor, element-wise natural logarithm 1 + x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log2.html","id":null,"dir":"Reference","previous_headings":"","what":"Base-2 logarithm of x, element-wise. — op_log2","title":"Base-2 logarithm of x, element-wise. — op_log2","text":"Base-2 logarithm x, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_log2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Base-2 logarithm of x, element-wise. — op_log2","text":"","code":"op_log2(x)"},{"path":"https://keras.posit.co/reference/op_log2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Base-2 logarithm of x, element-wise. — op_log2","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Base-2 logarithm of x, element-wise. — op_log2","text":"Output tensor, element-wise base-2 logarithm x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Logarithm of the sigmoid activation function. — op_log_sigmoid","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"defined f(x) = log(1 / (1 + exp(-x))).","code":""},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"","code":"op_log_sigmoid(x)"},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"","code":"x <- op_convert_to_tensor(c(-0.541391, 0.0, 0.50, 5.0)) op_log_sigmoid(x) ## tf.Tensor([-1.0000418  -0.6931472  -0.474077   -0.00671535], shape=(4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Log-softmax activation function. — op_log_softmax","title":"Log-softmax activation function. — op_log_softmax","text":"defined : f(x) = x - max(x) - log(sum(exp(x - max(x))))","code":""},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log-softmax activation function. — op_log_softmax","text":"","code":"op_log_softmax(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log-softmax activation function. — op_log_softmax","text":"x Input tensor. axis Integer, axis along log-softmax applied. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Log-softmax activation function. — op_log_softmax","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Log-softmax activation function. — op_log_softmax","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_log_softmax(x) ## tf.Tensor([-2.407606   -1.4076059  -0.40760595], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_logaddexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","title":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","text":"Calculates log(exp(x1) + exp(x2)).","code":""},{"path":"https://keras.posit.co/reference/op_logaddexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","text":"","code":"op_logaddexp(x1, x2)"},{"path":"https://keras.posit.co/reference/op_logaddexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","text":"x1 Input tensor. x2 Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logaddexp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","text":"Output tensor, element-wise logarithm sum exponentiations inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"Zeros treated FALSE non-zeros treated TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"","code":"op_logical_and(x1, x2)"},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"x1 Input tensor. x2 Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"Output tensor, element-wise logical inputs.","code":""},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"Note function automatically called using R operator & tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logical_not.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the element-wise NOT of the given input tensor. — op_logical_not","title":"Computes the element-wise NOT of the given input tensor. — op_logical_not","text":"Zeros treated FALSE non-zeros treated TRUE. Note function automatically called using R operator ! tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_not.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the element-wise NOT of the given input tensor. — op_logical_not","text":"","code":"op_logical_not(x)"},{"path":"https://keras.posit.co/reference/op_logical_not.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the element-wise NOT of the given input tensor. — op_logical_not","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_not.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the element-wise NOT of the given input tensor. — op_logical_not","text":"Output tensor, element-wise logical input.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logical_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","title":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","text":"Zeros treated FALSE non-zeros treated TRUE. Note function automatically called using R operator | tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","text":"","code":"op_logical_or(x1, x2)"},{"path":"https://keras.posit.co/reference/op_logical_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","text":"x1 Input tensor. x2 Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_or.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","text":"Output tensor, element-wise logical inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logical_xor.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","title":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","text":"Compute truth value x1 XOR x2, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_logical_xor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","text":"","code":"op_logical_xor(x1, x2)"},{"path":"https://keras.posit.co/reference/op_logical_xor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_xor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logspace.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns numbers spaced evenly on a log scale. — op_logspace","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"linear space, sequence starts base ** start ends base ** stop (see endpoint ).","code":""},{"path":"https://keras.posit.co/reference/op_logspace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"","code":"op_logspace(   start,   stop,   num = 50L,   endpoint = TRUE,   base = 10L,   dtype = NULL,   axis = 1L )"},{"path":"https://keras.posit.co/reference/op_logspace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"start starting value sequence. stop final value sequence, unless endpoint FALSE. case, num + 1 values spaced interval log-space, last (sequence length num) returned. num Number samples generate. Defaults 50. endpoint TRUE, stop last sample. Otherwise, included. Defaults TRUE. base base log space. Defaults 10. dtype type output tensor. axis axis result store samples. Relevant start stop array-like.","code":""},{"path":"https://keras.posit.co/reference/op_logspace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"tensor evenly spaced samples log scale.","code":""},{"path":"https://keras.posit.co/reference/op_logspace.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"Torch backend support axis argument.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"Computes logarithm sum exponentials elements tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"","code":"op_logsumexp(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"x Input tensor. axis integer list integers specifying axis/axes along compute sum. NULL, sum computed elements. Defaults toNULL. keepdims boolean indicating whether keep dimensions input tensor computing sum. Defaults toFALSE.","code":""},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"tensor containing logarithm sum exponentials elements x.","code":""},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"","code":"x <- op_convert_to_tensor(c(1, 2, 3)) op_logsumexp(x) ## tf.Tensor(3.407606, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_lu_factor.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","title":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","text":"Computes lower-upper decomposition square matrix.","code":""},{"path":"https://keras.posit.co/reference/op_lu_factor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","text":"","code":"op_lu_factor(x)"},{"path":"https://keras.posit.co/reference/op_lu_factor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","text":"x tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_lu_factor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","text":"tuple two tensors: tensor shape (..., M, M) containing lower upper triangular matrices tensor shape (..., M) containing pivots.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_matmul.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix product of two tensors. — op_matmul","title":"Matrix product of two tensors. — op_matmul","text":"tensors 1-dimensional, dot product (scalar) returned. either tensor N-D, N > 2, treated stack matrices residing last two indexes broadcast accordingly. first tensor 1-D, promoted matrix prepending 1 dimensions. matrix multiplication prepended 1 removed. second tensor 1-D, promoted matrix appending 1 dimensions. matrix multiplication appended 1 removed.","code":""},{"path":"https://keras.posit.co/reference/op_matmul.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix product of two tensors. — op_matmul","text":"","code":"op_matmul(x1, x2)"},{"path":"https://keras.posit.co/reference/op_matmul.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix product of two tensors. — op_matmul","text":"x1 First tensor. x2 Second tensor.","code":""},{"path":"https://keras.posit.co/reference/op_matmul.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matrix product of two tensors. — op_matmul","text":"Output tensor, matrix product inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_max.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the maximum of a tensor or maximum along an axis. — op_max","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"Return maximum tensor maximum along axis.","code":""},{"path":"https://keras.posit.co/reference/op_max.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"","code":"op_max(x, axis = NULL, keepdims = FALSE, initial = NULL)"},{"path":"https://keras.posit.co/reference/op_max.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"x Input tensor. axis Axis axes along operate. default, flattened input used. keepdims set TRUE, axes reduced left result dimensions size one. Defaults FALSE. initial minimum value output element. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_max.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"Maximum x.","code":""},{"path":"https://keras.posit.co/reference/op_max.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"","code":"(x <- op_convert_to_tensor(rbind(c(1, 3, 5), c(1, 5, 2)))) ## tf.Tensor( ## [[1. 3. 5.] ##  [1. 5. 2.]], shape=(2, 3), dtype=float64) op_max(x) ## tf.Tensor(5.0, shape=(), dtype=float64) op_max(x, axis = 1) ## tf.Tensor([1. 5. 5.], shape=(3), dtype=float64) op_max(x, axis = 1, keepdims = TRUE) ## tf.Tensor([[1. 5. 5.]], shape=(1, 3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_max_pool.html","id":null,"dir":"Reference","previous_headings":"","what":"Max pooling operation. — op_max_pool","title":"Max pooling operation. — op_max_pool","text":"Max pooling operation.","code":""},{"path":"https://keras.posit.co/reference/op_max_pool.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max pooling operation. — op_max_pool","text":"","code":"op_max_pool(   inputs,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL )"},{"path":"https://keras.posit.co/reference/op_max_pool.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max pooling operation. — op_max_pool","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". Pooling happens spatial dimensions . pool_size int tuple/list integers size len(inputs_spatial_shape), specifying size pooling window spatial dimension input tensor. pool_size int, every spatial dimension shares pool_size. strides int tuple/list integers size len(inputs_spatial_shape). stride sliding window spatial dimension input tensor. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...).","code":""},{"path":"https://keras.posit.co/reference/op_max_pool.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Max pooling operation. — op_max_pool","text":"tensor rank N+2, result max pooling operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_maximum.html","id":null,"dir":"Reference","previous_headings":"","what":"Element-wise maximum of x1 and x2. — op_maximum","title":"Element-wise maximum of x1 and x2. — op_maximum","text":"Element-wise maximum x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_maximum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Element-wise maximum of x1 and x2. — op_maximum","text":"","code":"op_maximum(x1, x2)  op_pmax(x1, x2)"},{"path":"https://keras.posit.co/reference/op_maximum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Element-wise maximum of x1 and x2. — op_maximum","text":"x1 First tensor. x2 Second tensor.","code":""},{"path":"https://keras.posit.co/reference/op_maximum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Element-wise maximum of x1 and x2. — op_maximum","text":"Output tensor, element-wise maximum x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the arithmetic mean along the specified axes. — op_mean","title":"Compute the arithmetic mean along the specified axes. — op_mean","text":"Compute arithmetic mean along specified axes.","code":""},{"path":"https://keras.posit.co/reference/op_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the arithmetic mean along the specified axes. — op_mean","text":"","code":"op_mean(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the arithmetic mean along the specified axes. — op_mean","text":"x Input tensor. axis Axis axes along means computed. default compute mean flattened tensor. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the arithmetic mean along the specified axes. — op_mean","text":"Output tensor containing mean values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_median.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the median along the specified axis. — op_median","title":"Compute the median along the specified axis. — op_median","text":"Compute median along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_median.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the median along the specified axis. — op_median","text":"","code":"op_median(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_median.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the median along the specified axis. — op_median","text":"x Input tensor. axis Axis axes along medians computed. Defaults axis = NULL compute median(s) along flattened version array. keepdims set TRUE, axes reduce left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_median.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the median along the specified axis. — op_median","text":"output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"Given N 1-D tensors T0, T1, ..., TN-1 inputs corresponding lengths S0, S1, ..., SN-1, creates N N-dimensional tensors G0, G1, ..., GN-1 shape (S0, ..., SN-1) output Gi constructed expanding Ti result shape.","code":""},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"","code":"op_meshgrid(..., indexing = \"xy\")"},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"... 1-D tensors representing coordinates grid. indexing \"xy\" \"ij\". \"xy\" cartesian; \"ij\" matrix indexing output. Defaults \"xy\".","code":""},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"Sequence N tensors.","code":""},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"","code":"x <- op_array(c(1, 2, 3), \"int32\") y <- op_array(c(4, 5, 6), \"int32\") c(grid_x, grid_y) %<-% op_meshgrid(x, y, indexing = \"ij\") grid_x ## tf.Tensor( ## [[1 1 1] ##  [2 2 2] ##  [3 3 3]], shape=(3, 3), dtype=int32) # array([[1, 1, 1], #        [2, 2, 2], #        [3, 3, 3])) grid_y ## tf.Tensor( ## [[4 5 6] ##  [4 5 6] ##  [4 5 6]], shape=(3, 3), dtype=int32) # array([[4, 5, 6], #        [4, 5, 6], #        [4, 5, 6]))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_min.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the minimum of a tensor or minimum along an axis. — op_min","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"Return minimum tensor minimum along axis.","code":""},{"path":"https://keras.posit.co/reference/op_min.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"","code":"op_min(x, axis = NULL, keepdims = FALSE, initial = NULL)"},{"path":"https://keras.posit.co/reference/op_min.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"x Input tensor. axis Axis axes along operate. default, flattened input used. keepdims set TRUE, axes reduced left result dimensions size one. Defaults FALSE. initial maximum value output element. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_min.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"Minimum x.","code":""},{"path":"https://keras.posit.co/reference/op_min.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"","code":"(x <- op_convert_to_tensor(rbind(c(1, 3, 5), c(1, 5, 2)))) ## tf.Tensor( ## [[1. 3. 5.] ##  [1. 5. 2.]], shape=(2, 3), dtype=float64) op_min(x) ## tf.Tensor(1.0, shape=(), dtype=float64) op_min(x, axis = 1) ## tf.Tensor([1. 3. 2.], shape=(3), dtype=float64) op_min(x, axis = 1, keepdims = TRUE) ## tf.Tensor([[1. 3. 2.]], shape=(1, 3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_minimum.html","id":null,"dir":"Reference","previous_headings":"","what":"Element-wise minimum of x1 and x2. — op_minimum","title":"Element-wise minimum of x1 and x2. — op_minimum","text":"Element-wise minimum x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_minimum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Element-wise minimum of x1 and x2. — op_minimum","text":"","code":"op_minimum(x1, x2)  op_pmin(x1, x2)"},{"path":"https://keras.posit.co/reference/op_minimum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Element-wise minimum of x1 and x2. — op_minimum","text":"x1 First tensor. x2 Second tensor.","code":""},{"path":"https://keras.posit.co/reference/op_minimum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Element-wise minimum of x1 and x2. — op_minimum","text":"Output tensor, element-wise minimum x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_mod.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the element-wise remainder of division. — op_mod","title":"Returns the element-wise remainder of division. — op_mod","text":"Note function automatically called using R operator %% tensor.","code":"(x <- op_arange(10)) ## tf.Tensor([0. 1. 2. 3. 4. 5. 6. 7. 8. 9.], shape=(10), dtype=float32) op_mod(x, 3) ## tf.Tensor([0. 1. 2. 0. 1. 2. 0. 1. 2. 0.], shape=(10), dtype=float32) x %% 3 ## tf.Tensor([0. 1. 2. 0. 1. 2. 0. 1. 2. 0.], shape=(10), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_mod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the element-wise remainder of division. — op_mod","text":"","code":"op_mod(x1, x2)"},{"path":"https://keras.posit.co/reference/op_mod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the element-wise remainder of division. — op_mod","text":"x1 First tensor. x2 Second tensor.","code":""},{"path":"https://keras.posit.co/reference/op_mod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the element-wise remainder of division. — op_mod","text":"Output tensor, element-wise remainder division.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_moments.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the mean and variance of x. — op_moments","title":"Calculates the mean and variance of x. — op_moments","text":"mean variance calculated aggregating contents x across axes. x 1-D axes = c(1) just mean variance vector.","code":""},{"path":"https://keras.posit.co/reference/op_moments.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the mean and variance of x. — op_moments","text":"","code":"op_moments(x, axes, keepdims = FALSE, synchronized = FALSE)"},{"path":"https://keras.posit.co/reference/op_moments.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the mean and variance of x. — op_moments","text":"x Input tensor. axes list axes compute mean variance. keepdims set TRUE, axes reduced left result dimensions size one. synchronized applicable TensorFlow backend. TRUE, synchronizes global batch statistics (mean variance) across devices training step distributed training strategy. FALSE, replica uses local batch statistics.","code":""},{"path":"https://keras.posit.co/reference/op_moments.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the mean and variance of x. — op_moments","text":"list containing two tensors - mean variance.","code":""},{"path":"https://keras.posit.co/reference/op_moments.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculates the mean and variance of x. — op_moments","text":"","code":"x <- op_convert_to_tensor(c(0, 1, 2, 3, 100), dtype = \"float32\") op_moments(x, axes = c(1)) ## [[1]] ## tf.Tensor(21.2, shape=(), dtype=float32) ## ## [[2]] ## tf.Tensor(1553.3601, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_moveaxis.html","id":null,"dir":"Reference","previous_headings":"","what":"Move axes of a tensor to new positions. — op_moveaxis","title":"Move axes of a tensor to new positions. — op_moveaxis","text":"axes remain original order.","code":""},{"path":"https://keras.posit.co/reference/op_moveaxis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Move axes of a tensor to new positions. — op_moveaxis","text":"","code":"op_moveaxis(x, source, destination)"},{"path":"https://keras.posit.co/reference/op_moveaxis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Move axes of a tensor to new positions. — op_moveaxis","text":"x Tensor whose axes reordered. source Original positions axes move. must unique. destination Destinations positions original axes. must also unique.","code":""},{"path":"https://keras.posit.co/reference/op_moveaxis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Move axes of a tensor to new positions. — op_moveaxis","text":"Tensor moved axes.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":null,"dir":"Reference","previous_headings":"","what":"Encodes integer labels as multi-hot vectors. — op_multi_hot","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"function encodes integer labels multi-hot vectors, label mapped binary value resulting vector.","code":""},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"","code":"op_multi_hot(   inputs,   num_classes,   axis = -1L,   dtype = NULL,   sparse = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"inputs Tensor integer labels converted multi-hot vectors. num_classes Integer, total number unique classes. axis (optional) Axis along multi-hot encoding added. Defaults -1, corresponds last dimension. dtype (optional) data type resulting tensor. Default backend's float type. sparse Whether return sparse tensor; backends support sparse tensors. ... forward/backwards compatability","code":""},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"Tensor: multi-hot encoded tensor.","code":""},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"","code":"data <- op_convert_to_tensor(c(0, 4)) op_multi_hot(data, num_classes = 5) ## tf.Tensor([1. 0. 0. 0. 1.], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_multiply.html","id":null,"dir":"Reference","previous_headings":"","what":"Multiply arguments element-wise. — op_multiply","title":"Multiply arguments element-wise. — op_multiply","text":"Note function automatically called using R operator * tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_multiply(x, x) ## tf.Tensor([0. 1. 4. 9.], shape=(4), dtype=float32) x * x ## tf.Tensor([0. 1. 4. 9.], shape=(4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_multiply.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multiply arguments element-wise. — op_multiply","text":"","code":"op_multiply(x1, x2)"},{"path":"https://keras.posit.co/reference/op_multiply.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multiply arguments element-wise. — op_multiply","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_multiply.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multiply arguments element-wise. — op_multiply","text":"Output tensor, element-wise product x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"Replace NaN zero infinity large finite numbers.","code":""},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"","code":"op_nan_to_num(x, nan = 0, posinf = NULL, neginf = NULL)"},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"x Input data. nan Optional float int. Value replace NaN entries . posinf Optional float int. Value replace positive infinity . neginf Optional float int. Value replace negative infinity .","code":""},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"x, non-finite values replaced.","code":""},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"","code":"(x <- op_convert_to_tensor(c(1, NaN, -Inf, Inf))) ## tf.Tensor([  1.  nan -inf  inf], shape=(4), dtype=float32) op_nan_to_num(x) ## tf.Tensor([ 1.0000000e+00  0.0000000e+00 -3.4028235e+38  3.4028235e+38], shape=(4), dtype=float32) op_nan_to_num(x, nan = -1, posinf = 2, neginf = -2) ## tf.Tensor([ 1. -1. -2.  2.], shape=(4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_ndim.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the number of dimensions of a tensor. — op_ndim","title":"Return the number of dimensions of a tensor. — op_ndim","text":"Return number dimensions tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ndim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the number of dimensions of a tensor. — op_ndim","text":"","code":"op_ndim(x)"},{"path":"https://keras.posit.co/reference/op_ndim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the number of dimensions of a tensor. — op_ndim","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ndim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the number of dimensions of a tensor. — op_ndim","text":"number dimensions x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_negative.html","id":null,"dir":"Reference","previous_headings":"","what":"Numerical negative, element-wise. — op_negative","title":"Numerical negative, element-wise. — op_negative","text":"Note function automatically called using unary R operator - tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_negative(x) ## tf.Tensor([-0. -1. -2. -3.], shape=(4), dtype=float32) -x ## tf.Tensor([-0. -1. -2. -3.], shape=(4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_negative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Numerical negative, element-wise. — op_negative","text":"","code":"op_negative(x)"},{"path":"https://keras.posit.co/reference/op_negative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Numerical negative, element-wise. — op_negative","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_negative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Numerical negative, element-wise. — op_negative","text":"Output tensor, y = -x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the indices of the elements that are non-zero. — op_nonzero","title":"Return the indices of the elements that are non-zero. — op_nonzero","text":"Return indices elements non-zero.","code":""},{"path":"https://keras.posit.co/reference/op_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the indices of the elements that are non-zero. — op_nonzero","text":"","code":"op_nonzero(x)"},{"path":"https://keras.posit.co/reference/op_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the indices of the elements that are non-zero. — op_nonzero","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_nonzero.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the indices of the elements that are non-zero. — op_nonzero","text":"Indices elements non-zero.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix or vector norm. — op_norm","title":"Matrix or vector norm. — op_norm","text":"function able return one eight different matrix norms, one infinite number vector norms (described ), depending value ord parameter.","code":""},{"path":"https://keras.posit.co/reference/op_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix or vector norm. — op_norm","text":"","code":"op_norm(x, ord = NULL, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix or vector norm. — op_norm","text":"x Input tensor. ord Order norm (see table Notes). default NULL. axis axis integer, specifies axis x along compute vector norms. axis length 2 vector, specifies axes hold 2-D matrices, matrix norms matrices computed. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matrix or vector norm. — op_norm","text":"Norm matrix vector(s).","code":""},{"path":"https://keras.posit.co/reference/op_norm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Matrix or vector norm. — op_norm","text":"values ord < 1, result , strictly speaking, mathematical 'norm', may still useful various numerical purposes. following norms can calculated: matrices: ord=NULL: Frobenius norm ord=\"fro\": Frobenius norm ord=\"nuc\": nuclear norm ord=Inf: max(sum(abs(x), axis=2)) ord=-Inf: min(sum(abs(x), axis=2)) ord=0: supported ord=1: max(sum(abs(x), axis=1)) ord=-1: min(sum(abs(x), axis=1)) ord=2: 2-norm (largest sing. value) ord=-2: smallest singular value : supported vectors: ord=NULL: 2-norm ord=\"fro\": supported ord=\"nuc\": supported ord=Inf: max(abs(x)) ord=-Inf: min(abs(x)) ord=0: sum(x != 0) ord=1: ord=-1: ord=2: ord=-2: : sum(abs(x)^ord)^(1/ord)","code":""},{"path":"https://keras.posit.co/reference/op_norm.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrix or vector norm. — op_norm","text":"","code":"x <- op_reshape(op_arange(9, dtype=\"float32\") - 4, c(3, 3)) op_norm(x) ## tf.Tensor(7.745967, shape=(), dtype=float32) # 7.7459664"},{"path":[]},{"path":"https://keras.posit.co/reference/op_normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalizes x over the specified axis. — op_normalize","title":"Normalizes x over the specified axis. — op_normalize","text":"defined : normalize(x) = x / max(norm(x), epsilon).","code":""},{"path":"https://keras.posit.co/reference/op_normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalizes x over the specified axis. — op_normalize","text":"","code":"op_normalize(x, axis = -1L, order = 2L)"},{"path":"https://keras.posit.co/reference/op_normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalizes x over the specified axis. — op_normalize","text":"x Input tensor. axis axis axes along perform normalization. Default -1. order exponent value norm formulation. Defaults 2.","code":""},{"path":"https://keras.posit.co/reference/op_normalize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalizes x over the specified axis. — op_normalize","text":"normalized array.","code":""},{"path":"https://keras.posit.co/reference/op_normalize.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalizes x over the specified axis. — op_normalize","text":"","code":"x <- op_convert_to_tensor(rbind(c(1, 2, 3), c(4, 5, 6))) x_norm <- op_normalize(x) x_norm ## tf.Tensor( ## [[0.26726124 0.53452248 0.80178373] ##  [0.45584231 0.56980288 0.68376346]], shape=(2, 3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_not_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return (x1 != x2) element-wise. — op_not_equal","title":"Return (x1 != x2) element-wise. — op_not_equal","text":"Note function automatically called using R operator != tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_not_equal(x, 2) ## tf.Tensor([ True  True False  True], shape=(4), dtype=bool) x != 2 ## tf.Tensor([ True  True False  True], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_not_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return (x1 != x2) element-wise. — op_not_equal","text":"","code":"op_not_equal(x1, x2)"},{"path":"https://keras.posit.co/reference/op_not_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return (x1 != x2) element-wise. — op_not_equal","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_not_equal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return (x1 != x2) element-wise. — op_not_equal","text":"Output tensor, element-wise comparsion x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts integer tensor x into a one-hot tensor. — op_one_hot","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"one-hot encoding representation integer value converted binary vector length equal num_classes, index corresponding integer value marked 1, indices marked 0.","code":""},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"","code":"op_one_hot(x, num_classes, axis = -1L, dtype = NULL, sparse = FALSE)"},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"x Integer tensor encoded. shape can arbitrary, dtype integer. R factors coerced integer offset 0-based, .e., .integer(x) - 1L. num_classes Number classes one-hot encoding. axis Axis along encoding performed. Defaults -1, represents last axis. dtype (Optional) Data type output tensor. provided, defaults default data type backend. sparse Whether return sparse tensor; backends support sparse tensors.","code":""},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"Integer tensor: One-hot encoded tensor shape x except specified axis dimension, length num_classes. dtype output tensor determined dtype default data type backend.","code":""},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"","code":"x <- op_array(c(1, 3, 2, 0), \"int32\") op_one_hot(x, num_classes = 4) ## tf.Tensor( ## [[0. 1. 0. 0.] ##  [0. 0. 0. 1.] ##  [0. 0. 1. 0.] ##  [1. 0. 0. 0.]], shape=(4, 4), dtype=float32) # array([[0. 1. 0. 0.] #        [0. 0. 0. 1.] #        [0. 0. 1. 0.] #        [1. 0. 0. 0.]], shape=(4, 4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_ones.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a new tensor of given shape and type, filled with ones. — op_ones","title":"Return a new tensor of given shape and type, filled with ones. — op_ones","text":"Return new tensor given shape type, filled ones.","code":""},{"path":"https://keras.posit.co/reference/op_ones.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a new tensor of given shape and type, filled with ones. — op_ones","text":"","code":"op_ones(shape, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_ones.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a new tensor of given shape and type, filled with ones. — op_ones","text":"shape Shape new tensor. dtype Desired data type tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ones.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a new tensor of given shape and type, filled with ones. — op_ones","text":"Tensor ones given shape dtype.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_ones_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a tensor of ones with the same shape and type of x. — op_ones_like","title":"Return a tensor of ones with the same shape and type of x. — op_ones_like","text":"Return tensor ones shape type x.","code":""},{"path":"https://keras.posit.co/reference/op_ones_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a tensor of ones with the same shape and type of x. — op_ones_like","text":"","code":"op_ones_like(x, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_ones_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a tensor of ones with the same shape and type of x. — op_ones_like","text":"x Input tensor. dtype Overrides data type result.","code":""},{"path":"https://keras.posit.co/reference/op_ones_like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a tensor of ones with the same shape and type of x. — op_ones_like","text":"tensor ones shape type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_outer.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the outer product of two vectors. — op_outer","title":"Compute the outer product of two vectors. — op_outer","text":"Given two vectors x1 x2, outer product :","code":"out[i, j] = x1[i] * x2[j]"},{"path":"https://keras.posit.co/reference/op_outer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the outer product of two vectors. — op_outer","text":"","code":"op_outer(x1, x2)"},{"path":"https://keras.posit.co/reference/op_outer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the outer product of two vectors. — op_outer","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_outer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the outer product of two vectors. — op_outer","text":"Outer product x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_pad.html","id":null,"dir":"Reference","previous_headings":"","what":"Pad a tensor. — op_pad","title":"Pad a tensor. — op_pad","text":"Pad tensor.","code":""},{"path":"https://keras.posit.co/reference/op_pad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pad a tensor. — op_pad","text":"","code":"op_pad(x, pad_width, mode = \"constant\", constant_values = NULL)"},{"path":"https://keras.posit.co/reference/op_pad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pad a tensor. — op_pad","text":"x Tensor pad. pad_width Number values padded edges axis. ((before_1, after_1), ...(before_N, after_N)) unique pad widths axis. ((, ),) yields pad axis. (pad,) int shortcut = = pad width axes. mode One \"constant\", \"edge\", \"linear_ramp\", \"maximum\", \"mean\", \"median\", \"minimum\", \"reflect\", \"symmetric\", \"wrap\", \"empty\", \"circular\". Defaults \"constant\". constant_values Value pad mode == \"constant\". Defaults 0. ValueError raised NULL mode != \"constant\".","code":""},{"path":"https://keras.posit.co/reference/op_pad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pad a tensor. — op_pad","text":"Padded tensor.","code":""},{"path":"https://keras.posit.co/reference/op_pad.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Pad a tensor. — op_pad","text":"Torch backend supports modes \"constant\", \"reflect\", \"symmetric\" \"circular\". Torch backend supports \"circular\" mode. Note: Tensorflow backend supports modes \"constant\", \"reflect\" \"symmetric\".","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_power.html","id":null,"dir":"Reference","previous_headings":"","what":"First tensor elements raised to powers from second tensor, element-wise. — op_power","title":"First tensor elements raised to powers from second tensor, element-wise. — op_power","text":"Note function automatically called using R operator ^ tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_power(2, x) ## tf.Tensor([1. 2. 4. 8.], shape=(4), dtype=float32) 2 ^ x ## tf.Tensor([1. 2. 4. 8.], shape=(4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_power.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"First tensor elements raised to powers from second tensor, element-wise. — op_power","text":"","code":"op_power(x1, x2)"},{"path":"https://keras.posit.co/reference/op_power.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"First tensor elements raised to powers from second tensor, element-wise. — op_power","text":"x1 bases. x2 exponents.","code":""},{"path":"https://keras.posit.co/reference/op_power.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"First tensor elements raised to powers from second tensor, element-wise. — op_power","text":"Output tensor, bases x1 raised exponents x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_prod.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the product of tensor elements over a given axis. — op_prod","title":"Return the product of tensor elements over a given axis. — op_prod","text":"Return product tensor elements given axis.","code":""},{"path":"https://keras.posit.co/reference/op_prod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the product of tensor elements over a given axis. — op_prod","text":"","code":"op_prod(x, axis = NULL, keepdims = FALSE, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_prod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the product of tensor elements over a given axis. — op_prod","text":"x Input tensor. axis Axis axes along product performed. default, axis = NULL, compute product elements input tensor. keepdims set TRUE, axes reduce left result dimensions size one. dtype Data type returned tensor.","code":""},{"path":"https://keras.posit.co/reference/op_prod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the product of tensor elements over a given axis. — op_prod","text":"Product elements x given axis axes.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_psnr.html","id":null,"dir":"Reference","previous_headings":"","what":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"function computes Peak Signal--Noise Ratio two signals, x1 x2. PSNR measure quality reconstructed signal. higher PSNR, closer reconstructed signal original signal. Note can become negative signal power smaller noise power.","code":""},{"path":"https://keras.posit.co/reference/op_psnr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"","code":"op_psnr(x1, x2, max_val)"},{"path":"https://keras.posit.co/reference/op_psnr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"x1 first input signal. x2 second input signal. Must shape x1. max_val maximum possible value signals.","code":""},{"path":"https://keras.posit.co/reference/op_psnr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"float: PSNR value x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_psnr.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"","code":"x1 <- random_normal(c(2, 4, 4, 3)) x2 <- random_normal(c(2, 4, 4, 3)) max_val <- 1.0 op_psnr(x1, x2, max_val) ## tf.Tensor(-3.433579, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_qr.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the QR decomposition of a tensor. — op_qr","title":"Computes the QR decomposition of a tensor. — op_qr","text":"Computes QR decomposition tensor.","code":""},{"path":"https://keras.posit.co/reference/op_qr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the QR decomposition of a tensor. — op_qr","text":"","code":"op_qr(x, mode = \"reduced\")"},{"path":"https://keras.posit.co/reference/op_qr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the QR decomposition of a tensor. — op_qr","text":"x Input tensor shape (..., M, N). mode string specifying mode QR decomposition. 'reduced': Returns reduced QR decomposition. (default) 'complete': Returns complete QR decomposition.","code":""},{"path":"https://keras.posit.co/reference/op_qr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the QR decomposition of a tensor. — op_qr","text":"list containing two tensors. first tensor shape (..., M, K) orthogonal matrix q second tensor shape (..., K, N)upper triangular matrixr, K = min(M, N)`.","code":""},{"path":"https://keras.posit.co/reference/op_qr.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the QR decomposition of a tensor. — op_qr","text":"","code":"x <- op_convert_to_tensor(rbind(c(1, 2), c(3, 4), c(5, 6))) op_qr(x) ## $q ## tf.Tensor( ## [[-0.16903085  0.89708523] ##  [-0.50709255  0.27602622] ##  [-0.84515425 -0.34503278]], shape=(3, 2), dtype=float64) ## ## $r ## tf.Tensor( ## [[-5.91607978 -7.43735744] ##  [ 0.          0.82807867]], shape=(2, 2), dtype=float64) c(q, r) %<-% op_qr(x)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","title":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","text":"Compute q-th quantile(s) data along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","text":"","code":"op_quantile(x, q, axis = NULL, method = \"linear\", keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","text":"x Input tensor. q Probability sequence probabilities quantiles compute. Values must 0 1 inclusive. axis Axis axes along quantiles computed. Defaults axis=NULL compute quantile(s) along flattened version array. method string specifies method use estimating quantile. Available methods \"linear\", \"lower\", \"higher\", \"midpoint\", \"nearest\". Defaults \"linear\". desired quantile lies two data points < j: \"linear\": + (j - ) * fraction, fraction fractional part index surrounded j. \"lower\": . \"higher\": j. \"midpoint\": (+ j) / 2 \"nearest\": j, whichever nearest. keepdims set TRUE, axes reduce left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","text":"quantile(s). q single probability axis=NULL, result scalar. multiple probabilies levels given, first axis result corresponds quantiles. axes axes remain reduction x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_ravel.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a contiguous flattened tensor. — op_ravel","title":"Return a contiguous flattened tensor. — op_ravel","text":"1-D tensor, containing elements input, returned.","code":""},{"path":"https://keras.posit.co/reference/op_ravel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a contiguous flattened tensor. — op_ravel","text":"","code":"op_ravel(x)"},{"path":"https://keras.posit.co/reference/op_ravel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a contiguous flattened tensor. — op_ravel","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ravel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a contiguous flattened tensor. — op_ravel","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_real.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the real part of the complex argument. — op_real","title":"Return the real part of the complex argument. — op_real","text":"Return real part complex argument.","code":""},{"path":"https://keras.posit.co/reference/op_real.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the real part of the complex argument. — op_real","text":"","code":"op_real(x)"},{"path":"https://keras.posit.co/reference/op_real.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the real part of the complex argument. — op_real","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_real.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the real part of the complex argument. — op_real","text":"real component complex argument.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_reciprocal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the reciprocal of the argument, element-wise. — op_reciprocal","title":"Return the reciprocal of the argument, element-wise. — op_reciprocal","text":"Calculates 1/x.","code":""},{"path":"https://keras.posit.co/reference/op_reciprocal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the reciprocal of the argument, element-wise. — op_reciprocal","text":"","code":"op_reciprocal(x)"},{"path":"https://keras.posit.co/reference/op_reciprocal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the reciprocal of the argument, element-wise. — op_reciprocal","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_reciprocal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the reciprocal of the argument, element-wise. — op_reciprocal","text":"Output tensor, element-wise reciprocal x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Rectified linear unit activation function. — op_relu","title":"Rectified linear unit activation function. — op_relu","text":"defined f(x) = max(0, x).","code":""},{"path":"https://keras.posit.co/reference/op_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rectified linear unit activation function. — op_relu","text":"","code":"op_relu(x)"},{"path":"https://keras.posit.co/reference/op_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rectified linear unit activation function. — op_relu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rectified linear unit activation function. — op_relu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rectified linear unit activation function. — op_relu","text":"","code":"x1 <- op_convert_to_tensor(c(-1, 0, 1, 0.2)) op_relu(x1) ## tf.Tensor([0.  0.  1.  0.2], shape=(4), dtype=float32) x <- seq(-10, 10, .1) plot(x, op_relu(x))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_relu6.html","id":null,"dir":"Reference","previous_headings":"","what":"Rectified linear unit activation function with upper bound of 6. — op_relu6","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"defined f(x) = op_clip(x, 0, 6).","code":""},{"path":"https://keras.posit.co/reference/op_relu6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"","code":"op_relu6(x)"},{"path":"https://keras.posit.co/reference/op_relu6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_relu6.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_relu6.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"","code":"x <- op_convert_to_tensor(c(-3, -2, 0.1, 0.2, 6, 8)) op_relu6(x) ## tf.Tensor([0.  0.  0.1 0.2 6.  6. ], shape=(6), dtype=float32) x <- seq(-10, 10, .1) plot(x, op_relu6(x))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_repeat.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeat each element of a tensor after themselves. — op_repeat","title":"Repeat each element of a tensor after themselves. — op_repeat","text":"Repeat element tensor .","code":""},{"path":"https://keras.posit.co/reference/op_repeat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeat each element of a tensor after themselves. — op_repeat","text":"","code":"op_repeat(x, repeats, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_repeat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeat each element of a tensor after themselves. — op_repeat","text":"x Input tensor. repeats number repetitions element. axis axis along repeat values. default, use flattened input array, return flat output array.","code":""},{"path":"https://keras.posit.co/reference/op_repeat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Repeat each element of a tensor after themselves. — op_repeat","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_reshape.html","id":null,"dir":"Reference","previous_headings":"","what":"Gives a new shape to a tensor without changing its data. — op_reshape","title":"Gives a new shape to a tensor without changing its data. — op_reshape","text":"Gives new shape tensor without changing data.","code":""},{"path":"https://keras.posit.co/reference/op_reshape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gives a new shape to a tensor without changing its data. — op_reshape","text":"","code":"op_reshape(x, newshape)"},{"path":"https://keras.posit.co/reference/op_reshape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gives a new shape to a tensor without changing its data. — op_reshape","text":"x Input tensor. newshape new shape compatible original shape. One shape dimension can -1 case value inferred length array remaining dimensions.","code":""},{"path":"https://keras.posit.co/reference/op_reshape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gives a new shape to a tensor without changing its data. — op_reshape","text":"reshaped tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_rfft.html","id":null,"dir":"Reference","previous_headings":"","what":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"Computes 1D Discrete Fourier Transform real-valued signal inner-dimension input. Since Discrete Fourier Transform real-valued signal Hermitian-symmetric, RFFT returns fft_length / 2 + 1 unique components FFT: zero-frequency term, followed fft_length / 2 positive-frequency terms. Along axis RFFT computed , fft_length smaller corresponding dimension input, dimension cropped. larger, dimension padded zeros.","code":""},{"path":"https://keras.posit.co/reference/op_rfft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"","code":"op_rfft(x, fft_length = NULL)"},{"path":"https://keras.posit.co/reference/op_rfft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"x Input tensor. fft_length integer representing number fft length. specified, inferred length last axis x. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_rfft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"list containing two tensors - real imaginary parts output.","code":""},{"path":"https://keras.posit.co/reference/op_rfft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"","code":"x <- op_convert_to_tensor(c(0, 1, 2, 3, 4)) op_rfft(x) ## [[1]] ## tf.Tensor([10.  -2.5 -2.5], shape=(3), dtype=float32) ## ## [[2]] ## tf.Tensor([0.        3.440955  0.8122992], shape=(3), dtype=float32) op_rfft(x, 3) ## [[1]] ## tf.Tensor([ 3.  -1.5], shape=(2), dtype=float32) ## ## [[2]] ## tf.Tensor([0.        0.8660254], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_roll.html","id":null,"dir":"Reference","previous_headings":"","what":"Roll tensor elements along a given axis. — op_roll","title":"Roll tensor elements along a given axis. — op_roll","text":"Elements roll beyond last position re-introduced first.","code":""},{"path":"https://keras.posit.co/reference/op_roll.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Roll tensor elements along a given axis. — op_roll","text":"","code":"op_roll(x, shift, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_roll.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Roll tensor elements along a given axis. — op_roll","text":"x Input tensor. shift number places elements shifted. axis axis along elements shifted. default, array flattened shifting, original shape restored.","code":""},{"path":"https://keras.posit.co/reference/op_roll.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Roll tensor elements along a given axis. — op_roll","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_round.html","id":null,"dir":"Reference","previous_headings":"","what":"Evenly round to the given number of decimals. — op_round","title":"Evenly round to the given number of decimals. — op_round","text":"Evenly round given number decimals.","code":""},{"path":"https://keras.posit.co/reference/op_round.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evenly round to the given number of decimals. — op_round","text":"","code":"op_round(x, decimals = 0L)"},{"path":"https://keras.posit.co/reference/op_round.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evenly round to the given number of decimals. — op_round","text":"x Input tensor. decimals Number decimal places round . Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/op_round.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evenly round to the given number of decimals. — op_round","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes reciprocal of square root of x element-wise. — op_rsqrt","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"Computes reciprocal square root x element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"","code":"op_rsqrt(x)"},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"x input tensor","code":""},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"tensor dtype x.","code":""},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"","code":"x <- op_convert_to_tensor(c(1, 10, 100)) op_rsqrt(x) ## tf.Tensor([1.         0.31622776 0.1       ], shape=(3), dtype=float32) # array([1, 0.31622776, 0.1], dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_scatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a tensor of shape shape where indices are set to values. — op_scatter","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"high level, operation zeros[indices] = updates returns output. equivalent :","code":"output <- op_scatter_update(op_zeros(shape), indices, values)"},{"path":"https://keras.posit.co/reference/op_scatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"","code":"op_scatter(indices, values, shape)"},{"path":"https://keras.posit.co/reference/op_scatter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"indices tensor list specifying indices values values. values tensor, values set indices. shape Shape output tensor.","code":""},{"path":"https://keras.posit.co/reference/op_scatter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"tensor shape shape indices set values.","code":""},{"path":"https://keras.posit.co/reference/op_scatter.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"","code":"indices <- rbind(c(1, 2), c(2, 2)) values <- op_array(c(1, 1)) op_scatter(indices, values, shape= c(2, 2)) ## tf.Tensor( ## [[0. 1.] ##  [0. 1.]], shape=(2, 2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_scatter_update.html","id":null,"dir":"Reference","previous_headings":"","what":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","title":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","text":"high level, operation inputs[indices] <- updates. Assume inputs tensor shape (D1, D2, ..., Dn), 2 main usages scatter_update. indices 2D tensor shape (num_updates, n), num_updates number updates perform, updates 1D tensor shape (num_updates). example, inputs op_zeros(c(4, 4, 4)), want update inputs[2, 3, 4] inputs[1, 2, 4] 1, can use:     2 indices 2D tensor shape (num_updates, k), num_updates number updates perform, k (k <= n) size index indices. updates n - k-D tensor shape (num_updates, shape(inputs)[-(1:k)]). example, inputs <- op_zeros(c(4, 4, 4)), want update inputs[1, 2, ] inputs[2, 3, ] [1, 1, 1, 1], indices shape (num_updates, 2) (k = 2), updates shape (num_updates, 4) (shape(inputs)[3:4] == 4). See code :","code":"inputs <- op_zeros(c(4, 4, 4)) indices <- rbind(c(2, 3, 4),                  c(1, 2, 4)) updates <- op_array(c(1, 1), \"float32\") op_scatter_update(inputs, indices, updates) ## tf.Tensor( ## [[[0. 0. 0. 0.] ##   [0. 0. 0. 1.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 1.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]]], shape=(4, 4, 4), dtype=float32) inputs <- op_zeros(c(4, 4, 4)) indices <- rbind(c(2, 3),                  c(3, 4)) updates <- op_array(rbind(c(1, 1, 1, 1),                           c(1, 1, 1, 1)),                     \"float32\") op_scatter_update(inputs, indices, updates) ## tf.Tensor( ## [[[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [1. 1. 1. 1.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [1. 1. 1. 1.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]]], shape=(4, 4, 4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_scatter_update.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","text":"","code":"op_scatter_update(inputs, indices, updates)"},{"path":"https://keras.posit.co/reference/op_scatter_update.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","text":"inputs tensor, tensor updated. indices tensor list shape (N, inputs$ndim), specifying indices update. N number indices update, must equal first dimension updates. updates tensor, new values put inputs indices.","code":""},{"path":"https://keras.posit.co/reference/op_scatter_update.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","text":"tensor, shape dtype inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the max of segments in a tensor. — op_segment_max","title":"Computes the max of segments in a tensor. — op_segment_max","text":"Computes max segments tensor.","code":""},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the max of segments in a tensor. — op_segment_max","text":"","code":"op_segment_max(data, segment_ids, num_segments = NULL, sorted = FALSE)"},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the max of segments in a tensor. — op_segment_max","text":"data Input tensor. segment_ids 1-D tensor containing segment indices element data. num_segments integer representing total number segments. specified, inferred maximum value segment_ids. sorted boolean indicating whether segment_ids sorted. Defaults toFALSE.","code":""},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the max of segments in a tensor. — op_segment_max","text":"tensor containing max segments, element represents max corresponding segment data.","code":""},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the max of segments in a tensor. — op_segment_max","text":"","code":"data <- op_convert_to_tensor(c(1, 2, 10, 20, 100, 200)) segment_ids <- op_array(c(1, 1, 2, 2, 3, 3), \"int32\") num_segments <- 3 op_segment_max(data, segment_ids, num_segments) ## tf.Tensor([  2.  20. 200.], shape=(3), dtype=float32) # array([2, 20, 200], dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the sum of segments in a tensor. — op_segment_sum","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"Computes sum segments tensor.","code":""},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"","code":"op_segment_sum(data, segment_ids, num_segments = NULL, sorted = FALSE)"},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"data Input tensor. segment_ids 1-D tensor containing segment indices element data. num_segments integer representing total number segments. specified, inferred maximum value segment_ids. sorted boolean indicating whether segment_ids sorted. Defaults toFALSE.","code":""},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"tensor containing sum segments, element represents sum corresponding segment data.","code":""},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"","code":"data <- op_array(c(1, 2, 10, 20, 100, 200)) segment_ids <- op_array(c(1, 1, 2, 2, 3, 3), \"int32\") num_segments <- 3 op_segment_sum(data, segment_ids, num_segments) ## tf.Tensor([  3.  30. 300.], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Return elements from choicelist, based on conditions in condlist. — op_select","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"Return elements choicelist, based conditions condlist.","code":""},{"path":"https://keras.posit.co/reference/op_select.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"","code":"op_select(condlist, choicelist, default = 0L)"},{"path":"https://keras.posit.co/reference/op_select.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"condlist List boolean tensors. list conditions determine array choicelist output elements taken. multiple conditions satisfied, first one encountered condlist used. choicelist List tensors. list tensors output elements taken. list length condlist. default Optional scalar value. element inserted output conditions evaluate FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_select.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"Tensor output position m m-th element tensor choicelist m-th element corresponding tensor condlist TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_select.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"","code":"x <- op_arange(6L) condlist <- list(x < 3, x > 3) choicelist <- list(x, x^2) op_select(condlist, choicelist, 42) ## tf.Tensor([ 0  1  2 42 16 25], shape=(6), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"defined : f(x) =  scale * alpha * (exp(x) - 1.) x < 0, f(x) = scale * x x >= 0.","code":""},{"path":"https://keras.posit.co/reference/op_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"","code":"op_selu(x)"},{"path":"https://keras.posit.co/reference/op_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_selu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_selu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"","code":"x <- op_array(c(-1, 0, 1)) op_selu(x) ## tf.Tensor([-1.1113307  0.         1.050701 ], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_separable_conv.html","id":null,"dir":"Reference","previous_headings":"","what":"General N-D separable convolution. — op_separable_conv","title":"General N-D separable convolution. — op_separable_conv","text":"ops supports 1D 2D separable convolution. separable_conv depthwise conv followed pointwise conv.","code":""},{"path":"https://keras.posit.co/reference/op_separable_conv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General N-D separable convolution. — op_separable_conv","text":"","code":"op_separable_conv(   inputs,   depthwise_kernel,   pointwise_kernel,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L )"},{"path":"https://keras.posit.co/reference/op_separable_conv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General N-D separable convolution. — op_separable_conv","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format=\"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format=\"channels_first\". depthwise_kernel Tensor rank N+2. depthwise_kernel shape [kernel_spatial_shape, num_input_channels, num_channels_multiplier], num_input_channels match number channels inputs. pointwise_kernel Tensor rank N+2. pointwise_kernel shape (*ones_like(kernel_spatial_shape), num_input_channels * num_channels_multiplier, num_output_channels). strides int int tuple/list len(inputs_spatial_shape), specifying strides convolution along spatial dimension. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides=1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format=\"channels_last\", inputs shape (batch_size, ..., channels) data_format=\"channels_first\", inputs shape (batch_size, channels, ...). dilation_rate int int tuple/list len(inputs_spatial_shape), specifying dilation rate use dilated convolution. dilation_rate int, every spatial dimension shares dilation_rate.","code":""},{"path":"https://keras.posit.co/reference/op_separable_conv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General N-D separable convolution. — op_separable_conv","text":"tensor rank N+2, result depthwise conv operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_shape.html","id":null,"dir":"Reference","previous_headings":"","what":"Gets the shape of the tensor input. — op_shape","title":"Gets the shape of the tensor input. — op_shape","text":"Gets shape tensor input.","code":""},{"path":"https://keras.posit.co/reference/op_shape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gets the shape of the tensor input. — op_shape","text":"","code":"op_shape(x)"},{"path":"https://keras.posit.co/reference/op_shape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gets the shape of the tensor input. — op_shape","text":"x tensor. function try access shape attribute input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_shape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gets the shape of the tensor input. — op_shape","text":"list integers NULL values, indicating shape input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_shape.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Gets the shape of the tensor input. — op_shape","text":"TensorFlow backend, x tf.Tensor dynamic shape, dimensions dynamic context compiled function tf.Tensor value instead static integer value.","code":""},{"path":"https://keras.posit.co/reference/op_shape.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gets the shape of the tensor input. — op_shape","text":"","code":"x <- op_zeros(c(8, 12)) op_shape(x) ## shape(8, 12)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid activation function. — op_sigmoid","title":"Sigmoid activation function. — op_sigmoid","text":"defined f(x) = 1 / (1 + exp(-x)).","code":""},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid activation function. — op_sigmoid","text":"","code":"op_sigmoid(x)"},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid activation function. — op_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sigmoid activation function. — op_sigmoid","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sigmoid activation function. — op_sigmoid","text":"","code":"x <- op_convert_to_tensor(c(-6, 1, 0, 1, 6)) op_sigmoid(x) ## tf.Tensor([0.00247262 0.7310586  0.5        0.7310586  0.99752736], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_sign.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a tensor with the signs of the elements of x. — op_sign","title":"Returns a tensor with the signs of the elements of x. — op_sign","text":"Returns tensor signs elements x.","code":""},{"path":"https://keras.posit.co/reference/op_sign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a tensor with the signs of the elements of x. — op_sign","text":"","code":"op_sign(x)"},{"path":"https://keras.posit.co/reference/op_sign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a tensor with the signs of the elements of x. — op_sign","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a tensor with the signs of the elements of x. — op_sign","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"SiLU activation function computed sigmoid function multiplied input. defined f(x) = x * sigmoid(x).","code":""},{"path":"https://keras.posit.co/reference/op_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"","code":"op_silu(x)"},{"path":"https://keras.posit.co/reference/op_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_silu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_silu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"","code":"x <- op_convert_to_tensor(c(-6, 1, 0, 1, 6)) op_sigmoid(x) ## tf.Tensor([0.00247262 0.7310586  0.5        0.7310586  0.99752736], shape=(5), dtype=float32) op_silu(x) ## tf.Tensor([-0.01483574  0.7310586   0.          0.7310586   5.985164  ], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_sin.html","id":null,"dir":"Reference","previous_headings":"","what":"Trigonometric sine, element-wise. — op_sin","title":"Trigonometric sine, element-wise. — op_sin","text":"Trigonometric sine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_sin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trigonometric sine, element-wise. — op_sin","text":"","code":"op_sin(x)"},{"path":"https://keras.posit.co/reference/op_sin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trigonometric sine, element-wise. — op_sin","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trigonometric sine, element-wise. — op_sin","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hyperbolic sine, element-wise. — op_sinh","title":"Hyperbolic sine, element-wise. — op_sinh","text":"Hyperbolic sine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_sinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hyperbolic sine, element-wise. — op_sinh","text":"","code":"op_sinh(x)"},{"path":"https://keras.posit.co/reference/op_sinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hyperbolic sine, element-wise. — op_sinh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sinh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hyperbolic sine, element-wise. — op_sinh","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_size.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the number of elements in a tensor. — op_size","title":"Return the number of elements in a tensor. — op_size","text":"Return number elements tensor.","code":""},{"path":"https://keras.posit.co/reference/op_size.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the number of elements in a tensor. — op_size","text":"","code":"op_size(x)"},{"path":"https://keras.posit.co/reference/op_size.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the number of elements in a tensor. — op_size","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_size.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the number of elements in a tensor. — op_size","text":"Number elements x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a slice of an input tensor. — op_slice","title":"Return a slice of an input tensor. — op_slice","text":"high level, operation explicit replacement array slicing e.g. inputs[start_indices:(start_indices + shape)]. Unlike slicing via brackets, operation accept tensor start indices backends, useful indices dynamically computed via tensor operations.","code":"(inputs <- op_arange(5*5) |> op_reshape(c(5, 5))) ## tf.Tensor( ## [[ 0.  1.  2.  3.  4.] ##  [ 5.  6.  7.  8.  9.] ##  [10. 11. 12. 13. 14.] ##  [15. 16. 17. 18. 19.] ##  [20. 21. 22. 23. 24.]], shape=(5, 5), dtype=float32) start_indices <- c(3, 3) shape <- c(2, 2) op_slice(inputs, start_indices, shape) ## tf.Tensor( ## [[12. 13.] ##  [17. 18.]], shape=(2, 2), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a slice of an input tensor. — op_slice","text":"","code":"op_slice(inputs, start_indices, shape)"},{"path":"https://keras.posit.co/reference/op_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a slice of an input tensor. — op_slice","text":"inputs tensor, tensor sliced. start_indices list length inputs$ndim, specifying starting indices updating. shape full shape returned slice.","code":""},{"path":"https://keras.posit.co/reference/op_slice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a slice of an input tensor. — op_slice","text":"tensor, shape dtype inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_slice_update.html","id":null,"dir":"Reference","previous_headings":"","what":"Update an input by slicing in a tensor of updated values. — op_slice_update","title":"Update an input by slicing in a tensor of updated values. — op_slice_update","text":"high level, operation inputs[start_indices: start_indices + updates.shape] = updates. Assume inputs tensor shape (D1, D2, ..., Dn), start_indices must list n integers, specifying starting indices. updates must rank inputs, size dim must exceed Di - start_indices[]. example, 2D inputs inputs = op_zeros(c(5, 5)), want update intersection last 2 rows last 2 columns 1, .e., inputs[4:5, 4:5] = op_ones(c(2, 2)), can use code :","code":"inputs <- op_zeros(c(5, 5)) start_indices <- c(3, 3) updates <- op_ones(c(2, 2)) op_slice_update(inputs, start_indices, updates) ## tf.Tensor( ## [[0. 0. 0. 0. 0.] ##  [0. 0. 0. 0. 0.] ##  [0. 0. 1. 1. 0.] ##  [0. 0. 1. 1. 0.] ##  [0. 0. 0. 0. 0.]], shape=(5, 5), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_slice_update.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update an input by slicing in a tensor of updated values. — op_slice_update","text":"","code":"op_slice_update(inputs, start_indices, updates)"},{"path":"https://keras.posit.co/reference/op_slice_update.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update an input by slicing in a tensor of updated values. — op_slice_update","text":"inputs tensor, tensor updated. start_indices list length inputs$ndim, specifying starting indices updating. updates tensor, new values put inputs indices. updates must rank inputs.","code":""},{"path":"https://keras.posit.co/reference/op_slice_update.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update an input by slicing in a tensor of updated values. — op_slice_update","text":"tensor, shape dtype inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_slogdet.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","title":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","text":"Compute sign natural logarithm determinant matrix.","code":""},{"path":"https://keras.posit.co/reference/op_slogdet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","text":"","code":"op_slogdet(x)"},{"path":"https://keras.posit.co/reference/op_slogdet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","text":"x Input matrix. must 2D square.","code":""},{"path":"https://keras.posit.co/reference/op_slogdet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","text":"list: (sign, logabsdet). sign number representing sign determinant. real matrix, 1, 0, -1. complex matrix, complex number absolute value 1 (.e., unit circle), else 0. logabsdet natural log absolute value determinant.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax activation function. — op_softmax","title":"Softmax activation function. — op_softmax","text":"elements output vector lie within range (0, 1), total sum exactly 1 (excluding floating point rounding error). vector processed independently. axis argument specifies axis along function applied within input. defined : f(x) = exp(x) / sum(exp(x))","code":""},{"path":"https://keras.posit.co/reference/op_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax activation function. — op_softmax","text":"","code":"op_softmax(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax activation function. — op_softmax","text":"x Input tensor. axis Integer, axis along softmax applied.","code":""},{"path":"https://keras.posit.co/reference/op_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax activation function. — op_softmax","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_softmax.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmax activation function. — op_softmax","text":"","code":"x <- op_array(c(-1, 0, 1)) op_softmax(x) ## tf.Tensor([0.09003057 0.24472848 0.66524094], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_softplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Softplus activation function. — op_softplus","title":"Softplus activation function. — op_softplus","text":"defined f(x) = log(exp(x) + 1), log natural logarithm exp exponential function.","code":""},{"path":"https://keras.posit.co/reference/op_softplus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softplus activation function. — op_softplus","text":"","code":"op_softplus(x)"},{"path":"https://keras.posit.co/reference/op_softplus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softplus activation function. — op_softplus","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_softplus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softplus activation function. — op_softplus","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_softplus.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softplus activation function. — op_softplus","text":"","code":"x <- op_convert_to_tensor(c(-0.555, 0, 0.555)) op_softplus(x) ## tf.Tensor([0.45366603 0.6931472  1.008666  ], shape=(3), dtype=float32) x <- seq(-10, 10, .1) plot(x, op_softplus(x))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_softsign.html","id":null,"dir":"Reference","previous_headings":"","what":"Softsign activation function. — op_softsign","title":"Softsign activation function. — op_softsign","text":"defined f(x) = x / (abs(x) + 1).","code":""},{"path":"https://keras.posit.co/reference/op_softsign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softsign activation function. — op_softsign","text":"","code":"op_softsign(x)"},{"path":"https://keras.posit.co/reference/op_softsign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softsign activation function. — op_softsign","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_softsign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softsign activation function. — op_softsign","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_softsign.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softsign activation function. — op_softsign","text":"","code":"x <- op_convert_to_tensor(c(-0.100, -10.0, 1.0, 0.0, 100.0)) op_softsign(x) ## tf.Tensor([-0.09090909 -0.90909094  0.5         0.          0.990099  ], shape=(5), dtype=float32) x <- seq(-10, 10, .1) plot(x, op_softsign(x), ylim = c(-1, 1))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Solves a linear system of equations given by a x = b. — op_solve","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"Solves x equation %*% x == b.","code":""},{"path":"https://keras.posit.co/reference/op_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"","code":"op_solve(a, b)"},{"path":"https://keras.posit.co/reference/op_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"tensor shape (..., M, M) representing coefficients matrix. b tensor shape (..., M) (..., M, N) represeting right-hand side \"dependent variable\" matrix.","code":""},{"path":"https://keras.posit.co/reference/op_solve.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"tensor shape (..., M) (..., M, N) representing solution linear system. Returned shape identical b.","code":""},{"path":"https://keras.posit.co/reference/op_solve.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"","code":"a <- op_array(c(1, 2, 4, 5), dtype=\"float32\") |> op_reshape(c(2, 2)) b <- op_array(c(2, 4, 8, 10), dtype=\"float32\") |> op_reshape(c(2, 2)) op_solve(a, b) ## tf.Tensor( ## [[2. 0.] ##  [0. 2.]], shape=(2, 2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_solve_triangular.html","id":null,"dir":"Reference","previous_headings":"","what":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","title":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","text":"Solves linear system equations given %*% x = b.","code":""},{"path":"https://keras.posit.co/reference/op_solve_triangular.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","text":"","code":"op_solve_triangular(a, b, lower = FALSE)"},{"path":"https://keras.posit.co/reference/op_solve_triangular.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","text":"tensor shape (..., M, M) representing coefficients matrix. b tensor shape (..., M) (..., M, N) represeting right-hand side \"dependent variable\" matrix. lower logical. Use data contained lower triangle . Default use upper triangle.","code":""},{"path":"https://keras.posit.co/reference/op_solve_triangular.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","text":"tensor shape (..., M) (..., M, N) representing solution linear system. Returned shape identical b.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sort.html","id":null,"dir":"Reference","previous_headings":"","what":"Sorts the elements of x along a given axis in ascending order. — op_sort","title":"Sorts the elements of x along a given axis in ascending order. — op_sort","text":"Sorts elements x along given axis ascending order.","code":""},{"path":"https://keras.posit.co/reference/op_sort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sorts the elements of x along a given axis in ascending order. — op_sort","text":"","code":"op_sort(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_sort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sorts the elements of x along a given axis in ascending order. — op_sort","text":"x Input tensor. axis Axis along sort. NULL, tensor flattened sorting. Defaults -1; last axis.","code":""},{"path":"https://keras.posit.co/reference/op_sort.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sorts the elements of x along a given axis in ascending order. — op_sort","text":"Sorted tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"sparse categorical cross-entropy loss similar categorical cross-entropy, used target tensor contains integer class labels instead one-hot encoded vectors. measures dissimilarity target output probabilities logits.","code":""},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"","code":"op_sparse_categorical_crossentropy(   target,   output,   from_logits = FALSE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"target target tensor representing true class labels integers. shape match shape output tensor except last dimension. output output tensor representing predicted probabilities logits. shape match shape target tensor except last dimension. from_logits (optional) Whether output tensor logits probabilities. Set TRUE output represents logits; otherwise, set FALSE output represents probabilities. Defaults toFALSE. axis (optional) axis along sparse categorical cross-entropy computed. Defaults -1, corresponds last dimension tensors.","code":""},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"Integer tensor: computed sparse categorical cross-entropy loss target output.","code":""},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"","code":"target <- op_array(c(0, 1, 2), dtype=\"int32\") output <- op_array(rbind(c(0.9, 0.05, 0.05),                         c(0.1, 0.8,  0.1),                         c(0.2, 0.3,  0.5))) op_sparse_categorical_crossentropy(target, output) ## tf.Tensor([0.10536052 0.22314355 0.69314718], shape=(3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Split a tensor into chunks. — op_split","title":"Split a tensor into chunks. — op_split","text":"Split tensor chunks.","code":""},{"path":"https://keras.posit.co/reference/op_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split a tensor into chunks. — op_split","text":"","code":"op_split(x, indices_or_sections, axis = 1L)"},{"path":"https://keras.posit.co/reference/op_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split a tensor into chunks. — op_split","text":"x Input tensor. indices_or_sections integer, N, tensor split N equal sections along axis. 1-D array sorted integers, entries indicate indices tensor split along axis. axis Axis along split. Defaults 1, first axis.","code":""},{"path":"https://keras.posit.co/reference/op_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split a tensor into chunks. — op_split","text":"list tensors.","code":""},{"path":"https://keras.posit.co/reference/op_split.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Split a tensor into chunks. — op_split","text":"split result equal division using Torch backend.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sqrt.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","title":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","text":"Return non-negative square root tensor, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_sqrt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","text":"","code":"op_sqrt(x)"},{"path":"https://keras.posit.co/reference/op_sqrt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sqrt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","text":"Output tensor, non-negative square root x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_square.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the element-wise square of the input. — op_square","title":"Return the element-wise square of the input. — op_square","text":"Return element-wise square input.","code":""},{"path":"https://keras.posit.co/reference/op_square.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the element-wise square of the input. — op_square","text":"","code":"op_square(x)"},{"path":"https://keras.posit.co/reference/op_square.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the element-wise square of the input. — op_square","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_square.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the element-wise square of the input. — op_square","text":"Output tensor, square x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_squeeze.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove axes of length one from x. — op_squeeze","title":"Remove axes of length one from x. — op_squeeze","text":"Remove axes length one x.","code":""},{"path":"https://keras.posit.co/reference/op_squeeze.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove axes of length one from x. — op_squeeze","text":"","code":"op_squeeze(x, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_squeeze.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove axes of length one from x. — op_squeeze","text":"x Input tensor. axis Select subset entries length one shape.","code":""},{"path":"https://keras.posit.co/reference/op_squeeze.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove axes of length one from x. — op_squeeze","text":"input tensor subset dimensions length 1 removed.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Join a sequence of tensors along a new axis. — op_stack","title":"Join a sequence of tensors along a new axis. — op_stack","text":"axis parameter specifies index new axis dimensions result.","code":""},{"path":"https://keras.posit.co/reference/op_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join a sequence of tensors along a new axis. — op_stack","text":"","code":"op_stack(x, axis = 1L)"},{"path":"https://keras.posit.co/reference/op_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join a sequence of tensors along a new axis. — op_stack","text":"x sequence tensors. axis Axis along stack. Defaults 1, first axis.","code":""},{"path":"https://keras.posit.co/reference/op_stack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join a sequence of tensors along a new axis. — op_stack","text":"stacked tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_std.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the standard deviation along the specified axis. — op_std","title":"Compute the standard deviation along the specified axis. — op_std","text":"Compute standard deviation along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_std.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the standard deviation along the specified axis. — op_std","text":"","code":"op_std(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_std.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the standard deviation along the specified axis. — op_std","text":"x Input tensor. axis Axis along compute standard deviation. Default compute standard deviation flattened tensor. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_std.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the standard deviation along the specified axis. — op_std","text":"Output tensor containing standard deviation values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_stft.html","id":null,"dir":"Reference","previous_headings":"","what":"Short-Time Fourier Transform along the last axis of the input. — op_stft","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"STFT computes Fourier transform short overlapping windows input. giving frequency components signal change time.","code":""},{"path":"https://keras.posit.co/reference/op_stft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"","code":"op_stft(   x,   sequence_length,   sequence_stride,   fft_length,   window = \"hann\",   center = TRUE )"},{"path":"https://keras.posit.co/reference/op_stft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"x Input tensor. sequence_length integer representing sequence length. sequence_stride integer representing sequence hop size. fft_length integer representing size FFT apply. specified, uses smallest power 2 enclosing sequence_length. window string, tensor window NULL. window string, available values \"hann\" \"hamming\". window tensor, used directly window length must sequence_length. window NULL, windowing used. Defaults \"hann\". center Whether pad x sides t-th sequence centered time t * sequence_stride. Otherwise, t-th sequence begins time t * sequence_stride. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_stft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"list containing two tensors - real imaginary parts STFT output.","code":""},{"path":"https://keras.posit.co/reference/op_stft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"","code":"x <- op_array(c(0, 1, 2, 3, 4)) op_stft(x, 3, 2, 3) ## [[1]] ## tf.Tensor( ## [[ 0.  0.] ##  [ 2. -1.] ##  [ 4. -2.]], shape=(3, 2), dtype=float32) ## ## [[2]] ## tf.Tensor( ## [[ 0.         0.       ] ##  [ 0.        -1.7320508] ##  [ 0.        -3.4641016]], shape=(3, 2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":null,"dir":"Reference","previous_headings":"","what":"Stops gradient computation. — op_stop_gradient","title":"Stops gradient computation. — op_stop_gradient","text":"Stops gradient computation.","code":""},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stops gradient computation. — op_stop_gradient","text":"","code":"op_stop_gradient(variable)"},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stops gradient computation. — op_stop_gradient","text":"variable tensor variable gradient computation disabled.","code":""},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stops gradient computation. — op_stop_gradient","text":"variable gradient computation disabled.","code":""},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stops gradient computation. — op_stop_gradient","text":"","code":"var <- op_convert_to_tensor(c(1, 2, 3), dtype=\"float32\") var <- op_stop_gradient(var)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_subtract.html","id":null,"dir":"Reference","previous_headings":"","what":"Subtract arguments element-wise. — op_subtract","title":"Subtract arguments element-wise. — op_subtract","text":"Note function automatically called using R operator - tensor.","code":"x <- op_ones(c(3)) op_subtract(x, x) ## tf.Tensor([0. 0. 0.], shape=(3), dtype=float32) x - x ## tf.Tensor([0. 0. 0.], shape=(3), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_subtract.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subtract arguments element-wise. — op_subtract","text":"","code":"op_subtract(x1, x2)"},{"path":"https://keras.posit.co/reference/op_subtract.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subtract arguments element-wise. — op_subtract","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_subtract.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subtract arguments element-wise. — op_subtract","text":"Output tensor, element-wise difference x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Sum of a tensor over the given axes. — op_sum","title":"Sum of a tensor over the given axes. — op_sum","text":"Sum tensor given axes.","code":""},{"path":"https://keras.posit.co/reference/op_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sum of a tensor over the given axes. — op_sum","text":"","code":"op_sum(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sum of a tensor over the given axes. — op_sum","text":"x Input tensor. axis Axis axes along sum computed. default compute sum flattened tensor. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_sum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sum of a tensor over the given axes. — op_sum","text":"Output tensor containing sum.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_svd.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the singular value decomposition of a matrix. — op_svd","title":"Computes the singular value decomposition of a matrix. — op_svd","text":"Computes singular value decomposition matrix.","code":""},{"path":"https://keras.posit.co/reference/op_svd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the singular value decomposition of a matrix. — op_svd","text":"","code":"op_svd(x, full_matrices = TRUE, compute_uv = TRUE)"},{"path":"https://keras.posit.co/reference/op_svd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the singular value decomposition of a matrix. — op_svd","text":"x Input tensor shape (..., M, N). full_matrices Logical compute_uv Logical","code":""},{"path":"https://keras.posit.co/reference/op_svd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the singular value decomposition of a matrix. — op_svd","text":"list three tensors: tensor shape (..., M, M) containing left singular vectors, tensor shape (..., M, N) containing singular values tensor shape (..., N, N) containing right singular vectors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_swapaxes.html","id":null,"dir":"Reference","previous_headings":"","what":"Interchange two axes of a tensor. — op_swapaxes","title":"Interchange two axes of a tensor. — op_swapaxes","text":"Interchange two axes tensor.","code":""},{"path":"https://keras.posit.co/reference/op_swapaxes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interchange two axes of a tensor. — op_swapaxes","text":"","code":"op_swapaxes(x, axis1, axis2)"},{"path":"https://keras.posit.co/reference/op_swapaxes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interchange two axes of a tensor. — op_swapaxes","text":"x Input tensor. axis1 First axis. axis2 Second axis.","code":""},{"path":"https://keras.posit.co/reference/op_swapaxes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interchange two axes of a tensor. — op_swapaxes","text":"tensor axes swapped.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_take.html","id":null,"dir":"Reference","previous_headings":"","what":"Take elements from a tensor along an axis. — op_take","title":"Take elements from a tensor along an axis. — op_take","text":"Take elements tensor along axis.","code":""},{"path":"https://keras.posit.co/reference/op_take.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Take elements from a tensor along an axis. — op_take","text":"","code":"op_take(x, indices, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_take.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Take elements from a tensor along an axis. — op_take","text":"x Source tensor. indices indices values extract. axis axis select values. default, flattened input tensor used.","code":""},{"path":"https://keras.posit.co/reference/op_take.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Take elements from a tensor along an axis. — op_take","text":"corresponding tensor values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_take_along_axis.html","id":null,"dir":"Reference","previous_headings":"","what":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","title":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","text":"Select values x 1-D indices along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_take_along_axis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","text":"","code":"op_take_along_axis(x, indices, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_take_along_axis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","text":"x Source tensor. indices indices values extract. axis axis select values. default, flattened input tensor used.","code":""},{"path":"https://keras.posit.co/reference/op_take_along_axis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","text":"corresponding tensor values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tan.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute tangent, element-wise. — op_tan","title":"Compute tangent, element-wise. — op_tan","text":"Compute tangent, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_tan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute tangent, element-wise. — op_tan","text":"","code":"op_tan(x)"},{"path":"https://keras.posit.co/reference/op_tan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute tangent, element-wise. — op_tan","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_tan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute tangent, element-wise. — op_tan","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hyperbolic tangent, element-wise. — op_tanh","title":"Hyperbolic tangent, element-wise. — op_tanh","text":"Hyperbolic tangent, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_tanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hyperbolic tangent, element-wise. — op_tanh","text":"","code":"op_tanh(x)"},{"path":"https://keras.posit.co/reference/op_tanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hyperbolic tangent, element-wise. — op_tanh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_tanh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hyperbolic tangent, element-wise. — op_tanh","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tensordot.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the tensor dot product along specified axes. — op_tensordot","title":"Compute the tensor dot product along specified axes. — op_tensordot","text":"Compute tensor dot product along specified axes.","code":""},{"path":"https://keras.posit.co/reference/op_tensordot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the tensor dot product along specified axes. — op_tensordot","text":"","code":"op_tensordot(x1, x2, axes = 3L)"},{"path":"https://keras.posit.co/reference/op_tensordot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the tensor dot product along specified axes. — op_tensordot","text":"x1 First tensor. x2 Second tensor. axes integer, N, sum last N axes x1 first N axes x2 order. sizes corresponding axes must match. , list axes summed , first sequence applying x1, second x2. sequences must length.","code":""},{"path":"https://keras.posit.co/reference/op_tensordot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the tensor dot product along specified axes. — op_tensordot","text":"tensor dot product inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tile.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeat x the number of times given by repeats. — op_tile","title":"Repeat x the number of times given by repeats. — op_tile","text":"repeats length d, result dimension max(d, x.ndim). x.ndim < d, x promoted d-dimensional prepending new axes. x.ndim > d, repeats promoted x.ndim prepending 1's .","code":""},{"path":"https://keras.posit.co/reference/op_tile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeat x the number of times given by repeats. — op_tile","text":"","code":"op_tile(x, repeats)"},{"path":"https://keras.posit.co/reference/op_tile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeat x the number of times given by repeats. — op_tile","text":"x Input tensor. repeats number repetitions x along axis.","code":""},{"path":"https://keras.posit.co/reference/op_tile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Repeat x the number of times given by repeats. — op_tile","text":"tiled output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_top_k.html","id":null,"dir":"Reference","previous_headings":"","what":"Finds the top-k values and their indices in a tensor. — op_top_k","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"Finds top-k values indices tensor.","code":""},{"path":"https://keras.posit.co/reference/op_top_k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"","code":"op_top_k(x, k, sorted = TRUE)"},{"path":"https://keras.posit.co/reference/op_top_k.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"x Input tensor. k integer representing number top elements retrieve. sorted boolean indicating whether sort output descending order. Defaults toTRUE.","code":""},{"path":"https://keras.posit.co/reference/op_top_k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"list containing two tensors. first tensor contains top-k values, second tensor contains indices top-k values input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_top_k.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"","code":"x <- op_array(c(5, 2, 7, 1, 9, 3), \"int32\") op_top_k(x, k = 3) ## $values ## tf.Tensor([9 7 5], shape=(3), dtype=int32) ## ## $indices ## tf.Tensor([4 2 0], shape=(3), dtype=int32) c(values, indices) %<-% op_top_k(x, k = 3) values ## tf.Tensor([9 7 5], shape=(3), dtype=int32) indices ## tf.Tensor([4 2 0], shape=(3), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_trace.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the sum along diagonals of the tensor. — op_trace","title":"Return the sum along diagonals of the tensor. — op_trace","text":"x 2-D, sum along diagonal given offset returned, .e., sum elements x[, +offset] . two dimensions, axes specified axis1 axis2 used determine 2-D sub-arrays whose traces returned. shape resulting tensor x axis1 axis2 removed.","code":""},{"path":"https://keras.posit.co/reference/op_trace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the sum along diagonals of the tensor. — op_trace","text":"","code":"op_trace(x, offset = 0L, axis1 = 1L, axis2 = 2L)"},{"path":"https://keras.posit.co/reference/op_trace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the sum along diagonals of the tensor. — op_trace","text":"x Input tensor. offset Offset diagonal main diagonal. Can positive negative. Defaults 0. axis1 Axis used first axis 2-D sub-arrays. Defaults 1. (first axis). axis2 Axis used second axis 2-D sub-arrays. Defaults 2. (second axis).","code":""},{"path":"https://keras.posit.co/reference/op_trace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the sum along diagonals of the tensor. — op_trace","text":"x 2-D, sum diagonal returned. x larger dimensions, tensor sums along diagonals returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a tensor with axes transposed. — op_transpose","title":"Returns a tensor with axes transposed. — op_transpose","text":"Returns tensor axes transposed.","code":""},{"path":"https://keras.posit.co/reference/op_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a tensor with axes transposed. — op_transpose","text":"","code":"op_transpose(x, axes = NULL)"},{"path":"https://keras.posit.co/reference/op_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a tensor with axes transposed. — op_transpose","text":"x Input tensor. axes Sequence integers. Permutation dimensions x. default, order axes reversed.","code":""},{"path":"https://keras.posit.co/reference/op_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a tensor with axes transposed. — op_transpose","text":"x axes permuted.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tri.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","title":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","text":"Return tensor ones diagonal zeros elsewhere.","code":""},{"path":"https://keras.posit.co/reference/op_tri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","text":"","code":"op_tri(N, M = NULL, k = 0L, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_tri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","text":"N Number rows tensor. M Number columns tensor. k sub-diagonal array filled. k = 0 main diagonal, k < 0 , k > 0 . default 0. dtype Data type returned tensor. default \"float32\".","code":""},{"path":"https://keras.posit.co/reference/op_tri.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","text":"Tensor lower triangle filled ones zeros elsewhere. T[, j] == 1 j <= + k, 0 otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tril.html","id":null,"dir":"Reference","previous_headings":"","what":"Return lower triangle of a tensor. — op_tril","title":"Return lower triangle of a tensor. — op_tril","text":"tensors ndim exceeding 2, tril apply final two axes.","code":""},{"path":"https://keras.posit.co/reference/op_tril.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return lower triangle of a tensor. — op_tril","text":"","code":"op_tril(x, k = 0L)"},{"path":"https://keras.posit.co/reference/op_tril.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return lower triangle of a tensor. — op_tril","text":"x Input tensor. k Diagonal zero elements. Defaults 0. main diagonal. k < 0 , k > 0 .","code":""},{"path":"https://keras.posit.co/reference/op_tril.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return lower triangle of a tensor. — op_tril","text":"Lower triangle x, shape data type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_triu.html","id":null,"dir":"Reference","previous_headings":"","what":"Return upper triangle of a tensor. — op_triu","title":"Return upper triangle of a tensor. — op_triu","text":"tensors ndim exceeding 2, triu apply final two axes.","code":""},{"path":"https://keras.posit.co/reference/op_triu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return upper triangle of a tensor. — op_triu","text":"","code":"op_triu(x, k = 0L)"},{"path":"https://keras.posit.co/reference/op_triu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return upper triangle of a tensor. — op_triu","text":"x Input tensor. k Diagonal zero elements. Defaults 0. main diagonal. k < 0 , k > 0 .","code":""},{"path":"https://keras.posit.co/reference/op_triu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return upper triangle of a tensor. — op_triu","text":"Upper triangle x, shape data type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_unstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"Unpacks given dimension rank-R tensor rank-(R-1) tensors.","code":""},{"path":"https://keras.posit.co/reference/op_unstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"","code":"op_unstack(x, num = NULL, axis = 1L)"},{"path":"https://keras.posit.co/reference/op_unstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"x input tensor. num length dimension axis. Automatically inferred NULL. axis axis along unpack.","code":""},{"path":"https://keras.posit.co/reference/op_unstack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"list tensors unpacked along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_unstack.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"[3, 4))]: R:3,%204))","code":"x <- op_array(rbind(c(1, 2),                    c(3, 4))) op_unstack(x, axis=1) ## [[1]] ## tf.Tensor([1. 2.], shape=(2), dtype=float64) ## ## [[2]] ## tf.Tensor([3. 4.], shape=(2), dtype=float64) op_unstack(x, axis=2) ## [[1]] ## tf.Tensor([1. 3.], shape=(2), dtype=float64) ## ## [[2]] ## tf.Tensor([2. 4.], shape=(2), dtype=float64) all.equal(op_unstack(x),           op_unstack(x, axis = 1)) ## [1] TRUE all.equal(op_unstack(x, axis = -1),           op_unstack(x, axis = 2)) ## [1] TRUE # [array([1, 2)), array([3, 4))]"},{"path":[]},{"path":"https://keras.posit.co/reference/op_var.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the variance along the specified axes. — op_var","title":"Compute the variance along the specified axes. — op_var","text":"Compute variance along specified axes.","code":""},{"path":"https://keras.posit.co/reference/op_var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the variance along the specified axes. — op_var","text":"","code":"op_var(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the variance along the specified axes. — op_var","text":"x Input tensor. axis Axis axes along variance computed. default compute variance flattened tensor. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the variance along the specified axes. — op_var","text":"Output tensor containing variance.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_vdot.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the dot product of two vectors. — op_vdot","title":"Return the dot product of two vectors. — op_vdot","text":"first argument complex, complex conjugate first argument used calculation dot product. Multidimensional tensors flattened dot product taken.","code":""},{"path":"https://keras.posit.co/reference/op_vdot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the dot product of two vectors. — op_vdot","text":"","code":"op_vdot(x1, x2)"},{"path":"https://keras.posit.co/reference/op_vdot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the dot product of two vectors. — op_vdot","text":"x1 First input tensor. complex, complex conjugate taken calculation dot product. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_vdot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the dot product of two vectors. — op_vdot","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn a function into a vectorized function. — op_vectorize","title":"Turn a function into a vectorized function. — op_vectorize","text":"Turn function vectorized function.","code":""},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn a function into a vectorized function. — op_vectorize","text":"","code":"op_vectorize(func, ..., excluded = NULL, signature = NULL)"},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Turn a function into a vectorized function. — op_vectorize","text":"func Callable single tensor argument. ... forward/backward compatability. excluded Optional set integers representing positional arguments function vectorized. passed directly func unmodified. signature Optional generalized universal function signature, e.g., \"(m,n),(n)->(m)\" vectorized matrix-vector multiplication. provided, func called (expected return) arrays shapes given size corresponding core dimensions. default, func assumed take scalar tensors input output.","code":""},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn a function into a vectorized function. — op_vectorize","text":"new function applies func every element input along axis 1 (batch axis, first axis).","code":""},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Turn a function into a vectorized function. — op_vectorize","text":"","code":"# currently does not work w/ tensorflow backend if(config_backend() != \"tensorflow\") {    myfunc <- function(a, b) a + b    vfunc <- op_vectorize(myfunc)   y <- vfunc(c(1, 2, 3, 4), 2)   print(y)   # with Jax backend, y is:   # Array([3., 4., 5., 6.], dtype=float32) }"},{"path":[]},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":null,"dir":"Reference","previous_headings":"","what":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"Schematically, op_vectorized_map() maps first dimension provided tensors. elements list tensors, tensors required size first dimension, iterated together.","code":""},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"","code":"op_vectorized_map(elements, f)"},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"elements see description f function taking either tensor, list tensors.","code":""},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"tensor list tensors, result mapping f across elements.","code":""},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"Note f may traced compiled. Meaning, R function may evaluated symbolic tensors using Jax TensorFlow backends, eager tensors. See output str() examples:","code":"(x <- op_arange(12L) |> op_reshape(c(3, 4))) ## tf.Tensor( ## [[ 0  1  2  3] ##  [ 4  5  6  7] ##  [ 8  9 10 11]], shape=(3, 4), dtype=int32) x |> op_vectorized_map(\\(row) {row + 10}) ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) list(x, x, x) |> op_vectorized_map(\\(rows) Reduce(`+`, rows)) ## tf.Tensor( ## [[ 0  3  6  9] ##  [12 15 18 21] ##  [24 27 30 33]], shape=(3, 4), dtype=int32) # simplest case, map f over rows of x, # where .x is 1 row of x input <- x output <- op_vectorized_map(input, function(.x) {   str(.x)   .x + 10 }) ## <tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> output ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) # map f over two tensors simultaneously. Here, # `.x` is a list of two # tensors. The return values from each call of `f(row)` are stacked to form the # final output input <- list(x, x) output <- op_vectorized_map(input, function(.x) {   str(.x)   .x[[1]] + 10 }) ## List of 2 ##  $ :<tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> ##  $ :<tf.Tensor 'loop_body/GatherV2_1:0' shape=(4) dtype=int32> output ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) # same as above, but now returning two tensors in the final output output <- op_vectorized_map(input, function(.x) {   str(.x)   c(.x1, .x2) %<-% .x   list(.x1+10, .x2+20) }) ## List of 2 ##  $ :<tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> ##  $ :<tf.Tensor 'loop_body/GatherV2_1:0' shape=(4) dtype=int32> output ## [[1]] ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) ## ## [[2]] ## tf.Tensor( ## [[20 21 22 23] ##  [24 25 26 27] ##  [28 29 30 31]], shape=(3, 4), dtype=int32) # passing named lists. # WARNING: if passing a named list, the order of elements of `.x` supplied # to `f` is not stable. Only retrieve elements by name. input <- list(name1 = x, name2 = x) output <- op_vectorized_map(input, function(.x) {   str(.x)   list(outname1 = .x$name1 + 10,        outname2 = .x$name2 + 20) }) ## List of 2 ##  $ name1:<tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> ##  $ name2:<tf.Tensor 'loop_body/GatherV2_1:0' shape=(4) dtype=int32> output ## $outname1 ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) ## ## $outname2 ## tf.Tensor( ## [[20 21 22 23] ##  [24 25 26 27] ##  [28 29 30 31]], shape=(3, 4), dtype=int32) # passing a tuple() is equivalent to passing an unnamed list() input <- tuple(x, x) output <- op_vectorized_map(input, function(.x) {   str(.x)   list(.x[[1]] + 10) }) ## List of 2 ##  $ :<tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> ##  $ :<tf.Tensor 'loop_body/GatherV2_1:0' shape=(4) dtype=int32> output ## [[1]] ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32)"},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"debugging-f","dir":"Reference","previous_headings":"","what":"Debugging f","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"Even eager contexts, op_vectorized_map() may trace f. case, want eagerly debug f (e.g., browser()), can swap manual (slow) implementation op_vectorized_map(). Note example debug implementation handle edge cases op_vectorized_map(), particular, f returns structure multiple tensors.","code":"op_vectorized_map_debug <- function(elements, fn) {    if (!is.list(elements)) {     # `elements` is a single tensor     batch_size <- op_shape(elements)[[1]]     out <- elements |>       op_split(batch_size) |>       lapply(fn) |>       op_stack()     return(out)   }    # `elements` is a list of tensors   batch_size <- elements[[1]] |> op_shape() |> _[[1]]   elements |>     lapply(\\(e) op_split(e, batch_size)) |>     zip_lists() |>     lapply(fn) |>     op_stack()  }"},{"path":[]},{"path":"https://keras.posit.co/reference/op_vstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Stack tensors in sequence vertically (row wise). — op_vstack","title":"Stack tensors in sequence vertically (row wise). — op_vstack","text":"Stack tensors sequence vertically (row wise).","code":""},{"path":"https://keras.posit.co/reference/op_vstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stack tensors in sequence vertically (row wise). — op_vstack","text":"","code":"op_vstack(xs)"},{"path":"https://keras.posit.co/reference/op_vstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stack tensors in sequence vertically (row wise). — op_vstack","text":"xs Sequence tensors.","code":""},{"path":"https://keras.posit.co/reference/op_vstack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stack tensors in sequence vertically (row wise). — op_vstack","text":"Tensor formed stacking given tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_where.html","id":null,"dir":"Reference","previous_headings":"","what":"Return elements chosen from x1 or x2 depending on condition. — op_where","title":"Return elements chosen from x1 or x2 depending on condition. — op_where","text":"Return elements chosen x1 x2 depending condition.","code":""},{"path":"https://keras.posit.co/reference/op_where.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return elements chosen from x1 or x2 depending on condition. — op_where","text":"","code":"op_where(condition, x1 = NULL, x2 = NULL)"},{"path":"https://keras.posit.co/reference/op_where.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return elements chosen from x1 or x2 depending on condition. — op_where","text":"condition TRUE, yield x1, otherwise yield x2. x1 Values choose condition TRUE. x2 Values choose condition FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_where.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return elements chosen from x1 or x2 depending on condition. — op_where","text":"tensor elements x1 condition TRUE, elements x2 condition FALSE.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":null,"dir":"Reference","previous_headings":"","what":"While loop implementation. — op_while_loop","title":"While loop implementation. — op_while_loop","text":"loop implementation.","code":""},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"While loop implementation. — op_while_loop","text":"","code":"op_while_loop(cond, body, loop_vars, maximum_iterations = NULL)"},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"While loop implementation. — op_while_loop","text":"cond callable represents termination condition loop. Must accept loop_vars like structure argument. loop_vars tuple unnamed list, element loop_vars passed positionally callable. body callable represents loop body. Must accept loop_vars like structure argument, return update value structure. loop_vars tuple unnamed list, element loop_vars passed positionally callable. loop_vars arbitrary nested structure tensor state persist across loop iterations. maximum_iterations Optional maximum number iterations loop run. provided, cond output -ed additional condition ensuring number iterations executed greater maximum_iterations.","code":""},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"While loop implementation. — op_while_loop","text":"list tensors, shape dtype loop_vars.","code":""},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"While loop implementation. — op_while_loop","text":"","code":"i <- 0 loop_vars <- list(i)  # cond() must return a scalar bool cond <- function(i) i < 10L  # body must return same shape as loop_vars body <- function(i) list(i + 1L)  op_while_loop(cond, body, loop_vars) ## [[1]] ## tf.Tensor(10.0, shape=(), dtype=float32) x <- 0; y <- 1 cond <- \\(x, y) x < 10 body <- \\(x, y) list(x+1, y+1) op_while_loop(cond, body, list(x, y)) ## [[1]] ## tf.Tensor(10.0, shape=(), dtype=float32) ## ## [[2]] ## tf.Tensor(11.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_zeros.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","title":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","text":"Return new tensor given shape type, filled zeros.","code":""},{"path":"https://keras.posit.co/reference/op_zeros.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","text":"","code":"op_zeros(shape, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_zeros.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","text":"shape Shape new tensor. dtype Desired data type tensor.","code":""},{"path":"https://keras.posit.co/reference/op_zeros.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","text":"Tensor zeros given shape dtype.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_zeros_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","title":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","text":"Return tensor zeros shape type x.","code":""},{"path":"https://keras.posit.co/reference/op_zeros_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","text":"","code":"op_zeros_like(x, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_zeros_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","text":"x Input tensor. dtype Overrides data type result.","code":""},{"path":"https://keras.posit.co/reference/op_zeros_like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","text":"tensor zeros shape type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"Adadelta optimization stochastic gradient descent method based adaptive learning rate per dimension address two drawbacks: continual decay learning rates throughout training. need manually selected global learning rate. Adadelta robust extension Adagrad adapts learning rates based moving window gradient updates, instead accumulating past gradients. way, Adadelta continues learning even many updates done. Compared Adagrad, original version Adadelta set initial learning rate. version, initial learning rate can set, Keras optimizers.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"","code":"optimizer_adadelta(   learning_rate = 0.001,   rho = 0.95,   epsilon = 1e-07,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adadelta\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"learning_rate float, [LearningRateSchedule()] instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. Note Adadelta tends benefit higher initial learning rate values compared optimizers. match exact form original paper, use 1.0. rho floating point value. decay rate. Defaults 0.95. epsilon Small floating point value maintaining numerical stability. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema = TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema = TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"Zeiler, 2012","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"Adafactor commonly used NLP tasks, advantage taking less memory saves partial information previous gradients. default argument setup based original paper (see reference). gradients dimension > 2, Adafactor optimizer delete last 2 dimensions separately accumulator variables.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"","code":"optimizer_adafactor(   learning_rate = 0.001,   beta_2_decay = -0.8,   epsilon_1 = 1e-30,   epsilon_2 = 0.001,   clip_threshold = 1,   relative_step = TRUE,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adafactor\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_2_decay float, defaults -0.8. decay rate beta_2. epsilon_1 float, defaults 1e-30. small offset keep denominator away 0. epsilon_2 float, defaults 1e-3. small offset avoid learning rate becoming small time. clip_threshold float, defaults 1.0. Clipping threshold. part Adafactor algorithm, independent clipnorm, clipvalue, global_clipnorm. relative_step bool, defaults TRUE. learning_rate constant relative_step=TRUE, learning rate adjusted based current iterations. default learning rate decay Adafactor. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema = TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"Shazeer, Noam et al., 2018.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"Adagrad optimizer parameter-specific learning rates, adapted relative frequently parameter gets updated training. updates parameter receives, smaller updates.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"","code":"optimizer_adagrad(   learning_rate = 0.001,   initial_accumulator_value = 0.1,   epsilon = 1e-07,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adagrad\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. Note Adagrad tends benefit higher initial learning rate values compared optimizers. match exact form original paper, use 1.0. initial_accumulator_value Floating point value. Starting value accumulators (per-parameter momentum values). Must non-negative. epsilon Small floating point value maintaining numerical stability. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"Duchi et al., 2011.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adam.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adam algorithm. — optimizer_adam","title":"Optimizer that implements the Adam algorithm. — optimizer_adam","text":"Adam optimization stochastic gradient descent method based adaptive estimation first-order second-order moments. According Kingma et al., 2014, method \"computationally efficient, little memory requirement, invariant diagonal rescaling gradients, well suited problems large terms data/parameters\".","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adam algorithm. — optimizer_adam","text":"","code":"optimizer_adam(   learning_rate = 0.001,   beta_1 = 0.9,   beta_2 = 0.999,   epsilon = 1e-07,   amsgrad = FALSE,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adam\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adam algorithm. — optimizer_adam","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_1 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 1st moment estimates. Defaults 0.9. beta_2 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 2nd moment estimates. Defaults 0.999. epsilon small constant numerical stability. epsilon \"epsilon hat\" Kingma Ba paper (formula just Section 2.1), epsilon Algorithm 1 paper. Defaults 1e-7. amsgrad Boolean. Whether apply AMSGrad variant algorithm paper \"Convergence Adam beyond\". Defaults FALSE. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adam algorithm. — optimizer_adam","text":"Optimizer instance","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"AdamW optimization stochastic gradient descent method based adaptive estimation first-order second-order moments added method decay weights per techniques discussed paper, 'Decoupled Weight Decay Regularization' Loshchilov, Hutter et al., 2019. According Kingma et al., 2014, underying Adam method \"computationally efficient, little memory requirement, invariant diagonal rescaling gradients, well suited problems large terms data/parameters\".","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"","code":"optimizer_adam_w(   learning_rate = 0.001,   weight_decay = 0.004,   beta_1 = 0.9,   beta_2 = 0.999,   epsilon = 1e-07,   amsgrad = FALSE,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adamw\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. weight_decay Float. set, weight decay applied. beta_1 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 1st moment estimates. Defaults 0.9. beta_2 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 2nd moment estimates. Defaults 0.999. epsilon small constant numerical stability. epsilon \"epsilon hat\" Kingma Ba paper (formula just Section 2.1), epsilon Algorithm 1 paper. Defaults 1e-7. amsgrad Boolean. Whether apply AMSGrad variant algorithm paper \"Convergence Adam beyond\". Defaults FALSE. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"Loshchilov et al., 2019 Kingma et al., 2014 adam Reddi et al., 2018 amsgrad.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"Adamax, variant Adam based infinity norm, first-order gradient-based optimization method. Due capability adjusting learning rate based data characteristics, suited learn time-variant process, e.g., speech data dynamically changed noise conditions. Default parameters follow provided paper (see references ). Initialization:   update rule parameter w gradient g described end section 7.1 paper (see referenece section):","code":"m <- 0  # Initialize initial 1st moment vector u <- 0  # Initialize the exponentially weighted infinity norm t <- 0  # Initialize timestep t <-  t + 1 m <- beta1 * m + (1 - beta) * g u <- max(beta2 * u, abs(g)) current_lr <- learning_rate / (1 - beta1 ** t) w <- w - current_lr * m / (u + epsilon)"},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"","code":"optimizer_adamax(   learning_rate = 0.001,   beta_1 = 0.9,   beta_2 = 0.999,   epsilon = 1e-07,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adamax\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_1 float value constant float tensor. exponential decay rate 1st moment estimates. beta_2 float value constant float tensor. exponential decay rate exponentially weighted infinity norm. epsilon small constant numerical stability. name: String. name use momentum accumulator weights created optimizer. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String, name object ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"Kingma et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_ftrl.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","title":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","text":"\"Follow Regularized Leader\" (FTRL) optimization algorithm developed Google click-rate prediction early 2010s. suitable shallow models large sparse feature spaces. algorithm described McMahan et al., 2013. Keras version support online L2 regularization (L2 regularization described paper ) shrinkage-type L2 regularization (addition L2 penalty loss function). Initialization:   Update rule one variable w:   Notation: lr learning rate g gradient variable lambda_1 L1 regularization strength lambda_2 L2 regularization strength lr_power power scale n. Check documentation l2_shrinkage_regularization_strength parameter details shrinkage enabled, case gradient replaced gradient shrinkage.","code":"n <- 0 sigma <- 0 z <- 0 prev_n <- n n <- n + g^2 sigma <- (n^(-lr_power) - prev_n^(-lr_power)) / lr z <- z + g - sigma * w if (abs(z) < lambda_1) {   w <- 0 } else {   w <- (sgn(z) * lambda_1 - z) / ((beta + sqrt(n)) / alpha + lambda_2) }"},{"path":"https://keras.posit.co/reference/optimizer_ftrl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","text":"","code":"optimizer_ftrl(   learning_rate = 0.001,   learning_rate_power = -0.5,   initial_accumulator_value = 0.1,   l1_regularization_strength = 0,   l2_regularization_strength = 0,   l2_shrinkage_regularization_strength = 0,   beta = 0,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"ftrl\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_ftrl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. learning_rate_power float value, must less equal zero. Controls learning rate decreases training. Use zero fixed learning rate. initial_accumulator_value starting value accumulators. zero positive values allowed. l1_regularization_strength float value, must greater equal zero. Defaults 0.0. l2_regularization_strength float value, must greater equal zero. Defaults 0.0. l2_shrinkage_regularization_strength float value, must greater equal zero. differs L2 L2 stabilization penalty, whereas L2 shrinkage magnitude penalty. input sparse shrinkage happen active weights. beta float value, representing beta value paper. Defaults 0.0. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_ftrl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","text":"Optimizer instance","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Lion algorithm. — optimizer_lion","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"Lion optimizer stochastic-gradient-descent method uses sign operator control magnitude update, unlike adaptive optimizers Adam rely second-order moments. make Lion memory-efficient keeps track momentum. According authors (see reference), performance gain Adam grows batch size. update Lion produced sign operation, resulting larger norm, suitable learning rate Lion typically 3-10x smaller AdamW. weight decay Lion turn 3-10x larger AdamW maintain similar strength (lr * wd).","code":""},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"","code":"optimizer_lion(   learning_rate = 0.001,   beta_1 = 0.9,   beta_2 = 0.99,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"lion\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_1 float value constant float tensor, callable takes arguments returns actual value use. rate combine current gradient 1st moment estimate. Defaults 0.9. beta_2 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 1st moment estimate. Defaults 0.99. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"Chen et al., 2023 Authors' implementation","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_loss_scale.html","id":null,"dir":"Reference","previous_headings":"","what":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","title":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","text":"Loss scaling technique prevent numeric underflow intermediate gradients float16 used. prevent underflow, loss multiplied (\"scaled\") certain factor called \"loss scale\", causes intermediate gradients scaled loss scale well. final gradients divided (\"unscaled\") loss scale bring back original value. LossScaleOptimizer wraps another optimizer applies dynamic loss scaling . loss scale dynamically updated time follows: train step, nonfinite gradient encountered, loss scale halved, train step skipped. dynamic_growth_steps ocurred since last time loss scale updated, nonfinite gradients occurred, loss scale doubled.","code":""},{"path":"https://keras.posit.co/reference/optimizer_loss_scale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","text":"","code":"optimizer_loss_scale(   inner_optimizer,   initial_scale = 32768,   dynamic_growth_steps = 2000L,   ...,   name = NULL,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = NULL,   ema_momentum = NULL,   ema_overwrite_frequency = NULL,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_loss_scale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","text":"inner_optimizer keras Optimizer instance wrap. initial_scale Float. initial loss scale. scale updated training. recommended high number, loss scale high gets lowered far quickly loss scale low gets raised. dynamic_growth_steps Int. often update scale upwards. every dynamic_growth_steps steps finite gradients, loss scale doubled. ... forward/backward compatability. name String. name use momentum accumulator weights created optimizer. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_loss_scale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","text":"Optimizer instance","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"Much like Adam essentially RMSprop momentum, Nadam Adam Nesterov momentum.","code":""},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"","code":"optimizer_nadam(   learning_rate = 0.001,   beta_1 = 0.9,   beta_2 = 0.999,   epsilon = 1e-07,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"nadam\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_1 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 1st moment estimates. Defaults 0.9. beta_2 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 2nd moment estimates. Defaults 0.999. epsilon small constant numerical stability. epsilon \"epsilon hat\" Kingma Ba paper (formula just Section 2.1), epsilon Algorithm 1 paper. Defaults 1e-7. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"Dozat, 2015.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"gist RMSprop : Maintain moving (discounted) average square gradients Divide gradient root average implementation RMSprop uses plain momentum, Nesterov momentum. centered version additionally maintains moving average gradients, uses average estimate variance.","code":""},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"","code":"optimizer_rmsprop(   learning_rate = 0.001,   rho = 0.9,   momentum = 0,   epsilon = 1e-07,   centered = FALSE,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"rmsprop\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"learning_rate float, learning_rate_schedule_* instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. rho float, defaults 0.9. Discounting factor old gradients. momentum float, defaults 0.0. 0.0., optimizer tracks momentum value, decay rate equals 1 - momentum. epsilon small constant numerical stability. epsilon \"epsilon hat\" Kingma Ba paper (formula just Section 2.1), epsilon Algorithm 1 paper. Defaults 1e-7. centered Boolean. TRUE, gradients normalized estimated variance gradient; FALSE, uncentered second moment. Setting TRUE may help training, slightly expensive terms computation memory. Defaults FALSE. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"","code":"opt <- optimizer_rmsprop(learning_rate=0.1)"},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"Hinton, 2012","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_sgd.html","id":null,"dir":"Reference","previous_headings":"","what":"Gradient descent (with momentum) optimizer. — optimizer_sgd","title":"Gradient descent (with momentum) optimizer. — optimizer_sgd","text":"Update rule parameter w gradient g momentum 0:   Update rule momentum larger 0:   nesterov=TRUE, rule becomes:","code":"w <- w - learning_rate * g velocity <- momentum * velocity - learning_rate * g w <- w + velocity velocity <- momentum * velocity - learning_rate * g w <- w + momentum * velocity - learning_rate * g"},{"path":"https://keras.posit.co/reference/optimizer_sgd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gradient descent (with momentum) optimizer. — optimizer_sgd","text":"","code":"optimizer_sgd(   learning_rate = 0.01,   momentum = 0,   nesterov = FALSE,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"SGD\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_sgd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gradient descent (with momentum) optimizer. — optimizer_sgd","text":"learning_rate float, learning_rate_schedule_* instance, callable takes arguments returns actual value use. learning rate. Defaults 0.01. momentum float hyperparameter >= 0 accelerates gradient descent relevant direction dampens oscillations. 0 vanilla gradient descent. Defaults 0.0. nesterov boolean. Whether apply Nesterov momentum. Defaults FALSE. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_sgd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gradient descent (with momentum) optimizer. — optimizer_sgd","text":"Optimizer instance","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":null,"dir":"Reference","previous_headings":"","what":"Packs user-provided data into a list. — pack_x_y_sample_weight","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"convenience utility packing data list formats fit() uses.","code":""},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"","code":"pack_x_y_sample_weight(x, y = NULL, sample_weight = NULL)"},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"x Features pass Model. y Ground-truth targets pass Model. sample_weight Sample weight element.","code":""},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"List format used fit().","code":""},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"","code":"x <- op_ones(c(10, 1)) data <- pack_x_y_sample_weight(x)   y <- op_ones(c(10, 1)) data <- pack_x_y_sample_weight(x, y)"},{"path":[]},{"path":"https://keras.posit.co/reference/pad_sequences.html","id":null,"dir":"Reference","previous_headings":"","what":"Pads sequences to the same length. — pad_sequences","title":"Pads sequences to the same length. — pad_sequences","text":"function transforms list (length num_samples) sequences (lists integers) 2D NumPy array shape (num_samples, num_timesteps). num_timesteps either maxlen argument provided, length longest sequence list. Sequences shorter num_timesteps padded value num_timesteps long. Sequences longer num_timesteps truncated fit desired length. position padding truncation happens determined arguments padding truncating, respectively. Pre-padding removing values beginning sequence default.","code":"sequence <- list(c(1), c(2, 3), c(4, 5, 6)) pad_sequences(sequence) ##      [,1] [,2] [,3] ## [1,]    0    0    1 ## [2,]    0    2    3 ## [3,]    4    5    6 pad_sequences(sequence, value=-1) ##      [,1] [,2] [,3] ## [1,]   -1   -1    1 ## [2,]   -1    2    3 ## [3,]    4    5    6 pad_sequences(sequence, padding='post') ##      [,1] [,2] [,3] ## [1,]    1    0    0 ## [2,]    2    3    0 ## [3,]    4    5    6 pad_sequences(sequence, maxlen=2) ##      [,1] [,2] ## [1,]    0    1 ## [2,]    2    3 ## [3,]    5    6"},{"path":"https://keras.posit.co/reference/pad_sequences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pads sequences to the same length. — pad_sequences","text":"","code":"pad_sequences(   sequences,   maxlen = NULL,   dtype = \"int32\",   padding = \"pre\",   truncating = \"pre\",   value = 0 )"},{"path":"https://keras.posit.co/reference/pad_sequences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pads sequences to the same length. — pad_sequences","text":"sequences List sequences (sequence list integers). maxlen Optional Int, maximum length sequences. provided, sequences padded length longest individual sequence. dtype (Optional, defaults \"int32\"). Type output sequences. pad sequences variable length strings, can use object. padding String, \"pre\" \"post\" (optional, defaults \"pre\"): pad either sequence. truncating String, \"pre\" \"post\" (optional, defaults \"pre\"): remove values sequences larger maxlen, either beginning end sequences. value Float String, padding value. (Optional, defaults 0.)","code":""},{"path":"https://keras.posit.co/reference/pad_sequences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pads sequences to the same length. — pad_sequences","text":"Array shape (len(sequences), maxlen)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See %>% details.","code":""},{"path":"https://keras.posit.co/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://keras.posit.co/reference/pipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pipe operator — %>%","text":"commonly, result calling right hand side left hand side argument: rhs(lhs). See magritter vignette , advanced, usages.","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a Keras model — plot.keras.src.models.model.Model","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"Plot Keras model","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model plot(   x,   show_shapes = FALSE,   show_dtype = FALSE,   show_layer_names = FALSE,   ...,   rankdir = \"TB\",   expand_nested = FALSE,   dpi = 200,   layer_range = NULL,   show_layer_activations = FALSE,   show_trainable = NA,   to_file = NULL )"},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"x Keras model instance show_shapes whether display shape information. show_dtype whether display layer dtypes. show_layer_names whether display layer names. ... passed Python keras.utils.model_to_dot(). Used forward backward compatibility. rankdir string specifying format plot: 'TB' creates vertical plot; 'LR' creates horizontal plot. (argument passed PyDot) expand_nested Whether expand nested models clusters. dpi Dots per inch. Increase value image text appears excessively pixelated. layer_range list containing two character strings, starting layer name ending layer name (inclusive) indicating range layers plot generated. also accepts regex patterns instead exact name. case, start predicate first element matches layer_range[1] end predicate last element matches layer_range[2]. default NULL considers layers model. Note must pass range resultant subgraph must complete. show_layer_activations Display layer activations (layers activation property). show_trainable whether display layer trainable. to_file File name plot image. NULL (default), model drawn default graphics device. Otherwise, file saved.","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"Nothing, called side effects.","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"ValueError: plot(model) called model built, unless input_shape =  argument supplied keras_model_sequential().","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"requirements","dir":"Reference","previous_headings":"","what":"Requirements","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"function requires pydot graphviz. pydot default installed install_keras(), installed keras means, can install pydot directly :   can install graphviz directly : https://graphviz.gitlab.io/download/ Linux platforms, can install graphviz via package manager. example, Ubuntu/Debian can install   conda environment, can install graphviz :","code":"reticulate::py_install(\"pydot\", pip = TRUE) sudo apt install graphviz reticulate::conda_install(packages = \"graphviz\") # Restart the R session after install."},{"path":"https://keras.posit.co/reference/plot.keras_training_history.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot training history — plot.keras_training_history","title":"Plot training history — plot.keras_training_history","text":"Plots metrics recorded training.","code":""},{"path":"https://keras.posit.co/reference/plot.keras_training_history.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot training history — plot.keras_training_history","text":"","code":"# S3 method for keras_training_history plot(   x,   y,   metrics = NULL,   method = c(\"auto\", \"ggplot2\", \"base\"),   smooth = getOption(\"keras.plot.history.smooth\", TRUE),   theme_bw = getOption(\"keras.plot.history.theme_bw\", FALSE),   ... )"},{"path":"https://keras.posit.co/reference/plot.keras_training_history.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot training history — plot.keras_training_history","text":"x Training history object returned fit.keras.src.models.model.Model(). y Unused. metrics One metrics plot (e.g. c('loss', 'accuracy')). Defaults plotting captured metrics. method Method use plotting. default \"auto\" use ggplot2 available, otherwise use base graphics. smooth Whether loess smooth added plot, available ggplot2 method. number epochs smaller ten, forced false. theme_bw Use ggplot2::theme_bw() plot history black white. ... Additional parameters pass plot() method.","code":""},{"path":"https://keras.posit.co/reference/plot.keras_training_history.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot training history — plot.keras_training_history","text":"method == \"ggplot2\", ggplot object returned. method == \"base\", function draw graphics device return NULL, invisibly.","code":""},{"path":"https://keras.posit.co/reference/pop_layer.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove the last layer in a Sequential model — pop_layer","title":"Remove the last layer in a Sequential model — pop_layer","text":"Remove last layer Sequential model","code":""},{"path":"https://keras.posit.co/reference/pop_layer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove the last layer in a Sequential model — pop_layer","text":"","code":"pop_layer(object)"},{"path":"https://keras.posit.co/reference/pop_layer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove the last layer in a Sequential model — pop_layer","text":"object Sequential keras model object","code":""},{"path":"https://keras.posit.co/reference/pop_layer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove the last layer in a Sequential model — pop_layer","text":"input object, invisibly.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"Generates output predictions input samples.","code":""},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model predict(   object,   x,   ...,   batch_size = NULL,   verbose = getOption(\"keras.verbose\", default = \"auto\"),   steps = NULL,   callbacks = NULL )"},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"object Keras model object x Input samples. : array (array-like), list arrays (case model multiple inputs). tensor, list tensors (case model multiple inputs). TF Dataset. ... forward/backward compatability. batch_size Integer NULL. Number samples per batch. unspecified, batch_size default 32. specify batch_size data form TF Dataset generator (since generate batches). verbose \"auto\", 0, 1, 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. \"auto\" becomes 1 cases, 2 knitr render running distributed training server. Note progress bar particularly useful logged file, verbose=2 recommended running interactively (e.g., production environment). Defaults \"auto\". steps Total number steps (batches samples) declaring prediction round finished. Ignored default value NULL. x TF Dataset steps NULL, predict() run input dataset exhausted. callbacks List Callback instances. List callbacks apply prediction.","code":""},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"R array(s) predictions.","code":""},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"Computation done batches. method designed batch processing large numbers inputs. intended use inside loops iterate data process small numbers inputs time. small numbers inputs fit one batch, directly call model model$call faster execution, e.g., model(x), model(x, training = FALSE) layers BatchNormalization behave differently inference.","code":""},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"See FAQ entry details difference Model methods predict() call().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/predict_on_batch.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns predictions for a single batch of samples. — predict_on_batch","title":"Returns predictions for a single batch of samples. — predict_on_batch","text":"Returns predictions single batch samples.","code":""},{"path":"https://keras.posit.co/reference/predict_on_batch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns predictions for a single batch of samples. — predict_on_batch","text":"","code":"predict_on_batch(object, x)"},{"path":"https://keras.posit.co/reference/predict_on_batch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns predictions for a single batch of samples. — predict_on_batch","text":"object Keras model object x Input data. must array-like.","code":""},{"path":"https://keras.posit.co/reference/predict_on_batch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns predictions for a single batch of samples. — predict_on_batch","text":"Array(s) predictions.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/process_utils.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocessing and postprocessing utilities — process_utils","title":"Preprocessing and postprocessing utilities — process_utils","text":"functions used preprocess postprocess inputs outputs Keras applications.","code":""},{"path":"https://keras.posit.co/reference/process_utils.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocessing and postprocessing utilities — process_utils","text":"","code":"application_preprocess_inputs(model, x, ..., data_format = NULL)  application_decode_predictions(model, preds, top = 5L, ...)"},{"path":"https://keras.posit.co/reference/process_utils.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocessing and postprocessing utilities — process_utils","text":"model Keras model initialized using application_ function. x batch inputs model. ... Additional arguments passed preprocessing decoding function. data_format Optional data format image tensor/array. NULL means global setting config_image_data_format() used (unless changed , uses \"channels_last\"). Defaults NULL. preds batch outputs model. top number top predictions return.","code":""},{"path":"https://keras.posit.co/reference/process_utils.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocessing and postprocessing utilities — process_utils","text":"list decoded predictions case application_decode_predictions(). batch preprocessed inputs case application_preprocess_inputs().","code":""},{"path":"https://keras.posit.co/reference/process_utils.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Preprocessing and postprocessing utilities — process_utils","text":"application_preprocess_inputs(): Pre-process inputs used model application_decode_predictions(): Decode predictions model","code":""},{"path":"https://keras.posit.co/reference/process_utils.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preprocessing and postprocessing utilities — process_utils","text":"","code":"if (FALSE) { model <- application_convnext_tiny()  inputs <- random_normal(c(32, 224, 224, 3)) processed_inputs <- application_preprocess_inputs(model, inputs)  preds <- random_normal(c(32, 1000)) decoded_preds <- application_decode_predictions(model, preds)  }"},{"path":"https://keras.posit.co/reference/quantize_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantize the weights of a model. — quantize_weights","title":"Quantize the weights of a model. — quantize_weights","text":"Note model must built first calling method. quantize_weights() recursively call layer$quantize(mode) layers skipped layer implement function. Currently Dense EinsumDense layers support quantization.","code":""},{"path":"https://keras.posit.co/reference/quantize_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantize the weights of a model. — quantize_weights","text":"","code":"quantize_weights(object, mode)"},{"path":"https://keras.posit.co/reference/quantize_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantize the weights of a model. — quantize_weights","text":"object Keras Model Layer. mode mode quantization. 'int8' supported time.","code":""},{"path":"https://keras.posit.co/reference/quantize_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantize the weights of a model. — quantize_weights","text":"model, invisibly. Note just convenience usage |>, model modified -place.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_beta.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from a Beta distribution. — random_beta","title":"Draw samples from a Beta distribution. — random_beta","text":"values drawm Beta distribution parametrized alpha beta.","code":""},{"path":"https://keras.posit.co/reference/random_beta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from a Beta distribution. — random_beta","text":"","code":"random_beta(shape, alpha, beta, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_beta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from a Beta distribution. — random_beta","text":"shape shape random values generate. alpha Float array floats representing first parameter alpha. Must broadcastable beta shape. beta Float array floats representing second parameter beta. Must broadcastable alpha shape. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults \"float32\" unless configured otherwise (via config_set_floatx(float_dtype)). seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_beta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from a Beta distribution. — random_beta","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_binomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from a Binomial distribution. — random_binomial","title":"Draw samples from a Binomial distribution. — random_binomial","text":"values drawn Binomial distribution specified trial count probability success.","code":""},{"path":"https://keras.posit.co/reference/random_binomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from a Binomial distribution. — random_binomial","text":"","code":"random_binomial(shape, counts, probabilities, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_binomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from a Binomial distribution. — random_binomial","text":"shape shape random values generate. counts number array numbers representing number trials. must broadcastable probabilities. probabilities float array floats representing probability success individual event. must broadcastable counts. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults \"float32\" unless configured otherwise (via config_set_floatx(float_dtype)). seed Python integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer None (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_binomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from a Binomial distribution. — random_binomial","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_categorical.html","id":null,"dir":"Reference","previous_headings":"","what":"Draws samples from a categorical distribution. — random_categorical","title":"Draws samples from a categorical distribution. — random_categorical","text":"function takes input logits, 2-D input tensor shape (batch_size, num_classes). row input represents categorical distribution, column index containing log-probability given class. function output 2-D tensor shape (batch_size, num_samples), row contains samples corresponding row logits. column index contains independent samples drawn input distribution.","code":""},{"path":"https://keras.posit.co/reference/random_categorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draws samples from a categorical distribution. — random_categorical","text":"","code":"random_categorical(logits, num_samples, dtype = \"int32\", seed = NULL)"},{"path":"https://keras.posit.co/reference/random_categorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draws samples from a categorical distribution. — random_categorical","text":"logits 2-D Tensor shape (batch_size, num_classes). row define categorical distibution unnormalized log-probabilities classes. num_samples Int, number independent samples draw row input. second dimension output tensor's shape. dtype Optional dtype output tensor. seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_categorical.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draws samples from a categorical distribution. — random_categorical","text":"2-D tensor (batch_size, num_samples).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomly set some values in a tensor to 0. — random_dropout","title":"Randomly set some values in a tensor to 0. — random_dropout","text":"Randomly set portion values tensor 0.","code":""},{"path":"https://keras.posit.co/reference/random_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomly set some values in a tensor to 0. — random_dropout","text":"","code":"random_dropout(inputs, rate, noise_shape = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomly set some values in a tensor to 0. — random_dropout","text":"inputs tensor rate numeric noise_shape shape() value seed Initial seed random number generator","code":""},{"path":"https://keras.posit.co/reference/random_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Randomly set some values in a tensor to 0. — random_dropout","text":"tensor copy inputs values set 0.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_gamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw random samples from the Gamma distribution. — random_gamma","title":"Draw random samples from the Gamma distribution. — random_gamma","text":"Draw random samples Gamma distribution.","code":""},{"path":"https://keras.posit.co/reference/random_gamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw random samples from the Gamma distribution. — random_gamma","text":"","code":"random_gamma(shape, alpha, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_gamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw random samples from the Gamma distribution. — random_gamma","text":"shape shape random values generate. alpha Float, parameter distribution. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults float32 unless configured otherwise (via config_set_floatx(float_dtype)). seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_gamma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw random samples from the Gamma distribution. — random_gamma","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_integer.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw random integers from a uniform distribution. — random_integer","title":"Draw random integers from a uniform distribution. — random_integer","text":"generated values follow uniform distribution range [minval, maxval). lower bound minval included range, upper bound maxval excluded. dtype must integer type.","code":""},{"path":"https://keras.posit.co/reference/random_integer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw random integers from a uniform distribution. — random_integer","text":"","code":"random_integer(shape, minval, maxval, dtype = \"int32\", seed = NULL)"},{"path":"https://keras.posit.co/reference/random_integer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw random integers from a uniform distribution. — random_integer","text":"shape shape random values generate. minval integer, lower bound range random values generate (inclusive). maxval integer, upper bound range random values generate (exclusive). dtype Optional dtype tensor. integer types supported. specified, \"int32\" used. seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_integer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw random integers from a uniform distribution. — random_integer","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw random samples from a normal (Gaussian) distribution. — random_normal","title":"Draw random samples from a normal (Gaussian) distribution. — random_normal","text":"Draw random samples normal (Gaussian) distribution.","code":""},{"path":"https://keras.posit.co/reference/random_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw random samples from a normal (Gaussian) distribution. — random_normal","text":"","code":"random_normal(shape, mean = 0, stddev = 1, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw random samples from a normal (Gaussian) distribution. — random_normal","text":"shape shape random values generate. mean Float, defaults 0. Mean random values generate. stddev Float, defaults 1. Standard deviation random values generate. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults float32 unless configured otherwise (via config_set_floatx(float_dtype)). seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw random samples from a normal (Gaussian) distribution. — random_normal","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"Keras, RNG-using methods (random_normal()) stateless, meaning pass integer seed (seed = 42), return values call. order get different values call, must use SeedGenerator instead seed argument. SeedGenerator object stateful.","code":""},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"","code":"random_seed_generator(seed = NULL, name = NULL, ...)"},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"seed Initial seed random number generator name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"SeedGenerator instance, can passed seed = argument random tensor generators.","code":""},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"Usage layer:","code":"seed_gen <- random_seed_generator(seed = 42) values <- random_normal(shape = c(2, 3), seed = seed_gen) new_values <- random_normal(shape = c(2, 3), seed = seed_gen) layer_dropout2 <- new_layer_class(   \"dropout2\",   initialize = function(...) {     super$initialize(...)     self$seed_generator <- random_seed_generator(seed = 1337)   },   call = function(x, training = FALSE) {     if (training) {       return(random_dropout(x, rate = 0.5, seed = self$seed_generator))     }     return(x)   } )  out <- layer_dropout(rate = 0.8) out(op_ones(10), training = TRUE) ## tf.Tensor([0. 5. 5. 0. 0. 0. 0. 0. 0. 0.], shape=(10), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/random_shuffle.html","id":null,"dir":"Reference","previous_headings":"","what":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","title":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","text":"Shuffle elements tensor uniformly random along axis.","code":""},{"path":"https://keras.posit.co/reference/random_shuffle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","text":"","code":"random_shuffle(x, axis = 1L, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_shuffle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","text":"x tensor shuffled. axis integer specifying axis along shuffle. Defaults 0. seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_shuffle.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","text":"tensor, copy x axis axis shuffled.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_truncated_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from a truncated normal distribution. — random_truncated_normal","title":"Draw samples from a truncated normal distribution. — random_truncated_normal","text":"values drawn normal distribution specified mean standard deviation, discarding re-drawing samples two standard deviations mean.","code":""},{"path":"https://keras.posit.co/reference/random_truncated_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from a truncated normal distribution. — random_truncated_normal","text":"","code":"random_truncated_normal(shape, mean = 0, stddev = 1, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_truncated_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from a truncated normal distribution. — random_truncated_normal","text":"shape shape random values generate. mean Float, defaults 0. Mean random values generate. stddev Float, defaults 1. Standard deviation random values generate. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults float32 unless configured otherwise (via config_set_floatx(float_dtype)) seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_truncated_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from a truncated normal distribution. — random_truncated_normal","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from a uniform distribution. — random_uniform","title":"Draw samples from a uniform distribution. — random_uniform","text":"generated values follow uniform distribution range [minval, maxval). lower bound minval included range, upper bound maxval excluded. dtype must floating point type, default range [0, 1).","code":""},{"path":"https://keras.posit.co/reference/random_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from a uniform distribution. — random_uniform","text":"","code":"random_uniform(shape, minval = 0, maxval = 1, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from a uniform distribution. — random_uniform","text":"shape shape random values generate. minval Float, defaults 0. Lower bound range random values generate (inclusive). maxval Float, defaults 1. Upper bound range random values generate (exclusive). dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults float32 unless configured otherwise (via config_set_floatx(float_dtype)) seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from a uniform distribution. — random_uniform","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. generics compile, fit magrittr %<>% reticulate array_reshape, as_iterator, iter_next, iterate, np_array, tuple, use_python, use_virtualenv tensorflow all_dims, as_tensor, evaluate, export_savedmodel, tensorboard tfruns flag_boolean, flag_integer, flag_numeric, flag_string, flags, run_dir","code":""},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":null,"dir":"Reference","previous_headings":"","what":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"function registers custom class function Keras custom object registry, can serialized deserialized without needing entry user-provided custom_objects argument. also injects function Keras call get object's serializable string key. Note serialized deserialized, classes must implement get_config() method. Functions requirement. object registered key 'package>name' name, defaults object name passed.","code":""},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"","code":"register_keras_serializable(object, name = NULL, package = NULL)"},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"object keras object. name name serialize class package. package package class belongs . used key (\"package>name\") identify class. Defaults current package name, \"Custom\" outside package.","code":""},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"object returned invisibly, convenient piping. primarily called side effects.","code":""},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"","code":"# Note that `'my_package'` is used as the `package` argument here, and since # the `name` argument is not provided, `'MyDense'` is used as the `name`. layer_my_dense <- Layer(\"MyDense\") register_keras_serializable(layer_my_dense, package = \"my_package\")  MyDense <- environment(layer_my_dense)$`__class__` # the python class obj stopifnot(exprs = {   get_registered_object('my_package>MyDense') == MyDense   get_registered_name(MyDense) == 'my_package>MyDense' })"},{"path":[]},{"path":"https://keras.posit.co/reference/regularizer_l1.html","id":null,"dir":"Reference","previous_headings":"","what":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","title":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","text":"L1 regularization penalty computed : loss = l1 * reduce_sum(abs(x)) L1 may passed layer string identifier:   case, default value used l1=0.01.","code":"dense <- layer_dense(units = 3, kernel_regularizer = 'l1')"},{"path":"https://keras.posit.co/reference/regularizer_l1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","text":"","code":"regularizer_l1(l1 = 0.01)"},{"path":"https://keras.posit.co/reference/regularizer_l1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","text":"l1 float, L1 regularization factor.","code":""},{"path":"https://keras.posit.co/reference/regularizer_l1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","text":"Regularizer instance can passed layer constructors used standalone object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/regularizer_l1_l2.html","id":null,"dir":"Reference","previous_headings":"","what":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","title":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","text":"L1 regularization penalty computed : loss = l1 * reduce_sum(abs(x)) L2 regularization penalty computed loss = l2 * reduce_sum(square(x)) L1L2 may passed layer string identifier:   case, default values used l1=0.01 l2=0.01.","code":"dense <- layer_dense(units = 3, kernel_regularizer = 'L1L2')"},{"path":"https://keras.posit.co/reference/regularizer_l1_l2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","text":"","code":"regularizer_l1_l2(l1 = 0, l2 = 0)"},{"path":"https://keras.posit.co/reference/regularizer_l1_l2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","text":"l1 float, L1 regularization factor. l2 float, L2 regularization factor.","code":""},{"path":"https://keras.posit.co/reference/regularizer_l1_l2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","text":"Regularizer instance can passed layer constructors used standalone object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/regularizer_l2.html","id":null,"dir":"Reference","previous_headings":"","what":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","title":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","text":"L2 regularization penalty computed : loss = l2 * reduce_sum(square(x)) L2 may passed layer string identifier:   case, default value used l2=0.01.","code":"dense <- layer_dense(units = 3, kernel_regularizer='l2')"},{"path":"https://keras.posit.co/reference/regularizer_l2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","text":"","code":"regularizer_l2(l2 = 0.01)"},{"path":"https://keras.posit.co/reference/regularizer_l2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","text":"l2 float, L2 regularization factor.","code":""},{"path":"https://keras.posit.co/reference/regularizer_l2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","text":"Regularizer instance can passed layer constructors used standalone object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":null,"dir":"Reference","previous_headings":"","what":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"can applied either rows matrix (mode=\"rows\") columns (mode=\"columns\"). applied Dense kernel shape (input_dim, units), rows mode seek make feature vectors (.e. basis output space) orthogonal .","code":""},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"","code":"regularizer_orthogonal(factor = 0.01, mode = \"rows\")"},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"factor Float. regularization factor. regularization penalty proportional factor times mean dot products L2-normalized rows (mode=\"rows\", columns mode=\"columns\") inputs, excluding product row/column .  Defaults 0.01. mode String, one {\"rows\", \"columns\"}. Defaults \"rows\". rows mode, regularization effect seeks make rows input orthogonal . columns mode, seeks make columns input orthogonal .","code":""},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"Regularizer instance can passed layer constructors used standalone object.","code":""},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"","code":"regularizer <- regularizer_orthogonal(factor=0.01) layer <- layer_dense(units=4, kernel_regularizer=regularizer)"},{"path":[]},{"path":"https://keras.posit.co/reference/reset_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Reset the state for a model, layer or metric. — reset_state","title":"Reset the state for a model, layer or metric. — reset_state","text":"Reset state model, layer metric.","code":""},{"path":"https://keras.posit.co/reference/reset_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reset the state for a model, layer or metric. — reset_state","text":"","code":"reset_state(object)"},{"path":"https://keras.posit.co/reference/reset_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reset the state for a model, layer or metric. — reset_state","text":"object Model, Layer, Metric instance Layers resettable state (E.g., adapt()-able preprocessing layers rnn layers resettable state, layer_dense() ). Calling Layer instance without resettable-state error.","code":""},{"path":"https://keras.posit.co/reference/reset_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reset the state for a model, layer or metric. — reset_state","text":"object, invisibly.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell class for the GRU layer. — rnn_cell_gru","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"class processes one step within whole time sequence input, whereas layer_gru() processes whole sequence.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"","code":"rnn_cell_gru(   units,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   reset_after = TRUE,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). recurrent_activation Activation function use recurrent step. Default: sigmoid (sigmoid). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. reset_after GRU convention (whether apply reset gate matrix multiplication). FALSE = \"\", TRUE = \"\" (default cuDNN compatible). seed Random seed dropout. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"Layer instance, intended used layer_rnn().","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"inputs: 2D tensor, shape (batch, features). states: 2D tensor shape (batch, units), state previous time step. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout used.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"","code":"inputs <- random_uniform(c(32, 10, 8)) outputs <- inputs |> layer_rnn(rnn_cell_gru(4)) shape(outputs) ## shape(32, 4) rnn <- layer_rnn(    cell = rnn_cell_gru(4),    return_sequences=TRUE,    return_state=TRUE) c(whole_sequence_output, final_state) %<-% rnn(inputs) shape(whole_sequence_output) ## shape(32, 10, 4) shape(final_state) ## shape(32, 4)"},{"path":[]},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell class for the LSTM layer. — rnn_cell_lstm","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"class processes one step within whole time sequence input, whereas layer_lstm() processes whole sequence.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"","code":"rnn_cell_lstm(   units,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). recurrent_activation Activation function use recurrent step. Default: sigmoid (sigmoid). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". unit_forget_bias Boolean (default TRUE). TRUE, add 1 bias forget gate initialization. Setting TRUE also force bias_initializer=\"zeros\". recommended Jozefowicz et al. kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. seed Random seed dropout. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"Layer instance, intended used layer_rnn().","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"inputs: 2D tensor, shape (batch, features). states: 2D tensor shape (batch, units), state previous time step. training: Boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout used.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"","code":"inputs <- random_uniform(c(32, 10, 8)) output <- inputs |>   layer_rnn(cell = rnn_cell_lstm(4)) shape(output) ## shape(32, 4) rnn <- layer_rnn(cell = rnn_cell_lstm(4),                  return_sequences = T,                  return_state = T) c(whole_sequence_output, ...final_state) %<-% rnn(inputs) str(whole_sequence_output) ## <tf.Tensor: shape=(32, 10, 4), dtype=float32, numpy=…> str(final_state) ## List of 2 ##  $ :<tf.Tensor: shape=(32, 4), dtype=float32, numpy=…> ##  $ :<tf.Tensor: shape=(32, 4), dtype=float32, numpy=…>"},{"path":[]},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell class for SimpleRNN. — rnn_cell_simple","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"class processes one step within whole time sequence input, whereas layer_simple_rnn() processes whole sequence.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"","code":"rnn_cell_simple(   units,   activation = \"tanh\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. seed Random seed dropout. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"Layer instance, intended used layer_rnn().","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"sequence: 2D tensor, shape (batch, features). states: 2D tensor shape (batch, units), state previous time step. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout used.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"","code":"inputs <- random_uniform(c(32, 10, 8)) rnn <- layer_rnn(cell = rnn_cell_simple(units = 4)) output <- rnn(inputs)  # The output has shape `(32, 4)`. rnn <- layer_rnn(     cell = rnn_cell_simple(units = 4),     return_sequences=TRUE,     return_state=TRUE ) # whole_sequence_output has shape `(32, 10, 4)`. # final_state has shape `(32, 4)`. c(whole_sequence_output, final_state) %<-% rnn(inputs)"},{"path":[]},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"Used implement efficient stacked RNNs.","code":""},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"","code":"rnn_cells_stack(cells, ...)"},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"cells List RNN cell instances. ... Unnamed arguments treated additional cells. Named arguments passed underlying layer.","code":""},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"Layer instance, intended used layer_rnn().","code":""},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"","code":"batch_size <- 3 sentence_length <- 5 num_features <- 2 new_shape <- c(batch_size, sentence_length, num_features) x <- array(1:30, dim = new_shape)  rnn_cells <- lapply(1:2, function(x) rnn_cell_lstm(units = 128)) stacked_lstm <- rnn_cells_stack(rnn_cells) lstm_layer <- layer_rnn(cell = stacked_lstm)  result <- lstm_layer(x) str(result) ## <tf.Tensor: shape=(3, 128), dtype=float32, numpy=…>"},{"path":[]},{"path":"https://keras.posit.co/reference/save_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves a model as a .keras file. — save_model","title":"Saves a model as a .keras file. — save_model","text":"Saves model .keras file.","code":""},{"path":"https://keras.posit.co/reference/save_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves a model as a .keras file. — save_model","text":"","code":"save_model(model, filepath = NULL, overwrite = FALSE, ...)"},{"path":"https://keras.posit.co/reference/save_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves a model as a .keras file. — save_model","text":"model keras model. filepath string, Path save model. Must end .keras. overwrite Whether overwrite existing model target location, instead ask user via interactive prompt. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/save_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Saves a model as a .keras file. — save_model","text":"filepath provided, function called primarily side effects, model returned invisibly. filepath provided NULL, serialized model returned R raw vector.","code":""},{"path":"https://keras.posit.co/reference/save_model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Saves a model as a .keras file. — save_model","text":"saved .keras file contains: model's configuration (architecture) model's weights model's optimizer's state () Thus models can reinstantiated exact state.","code":"model <- keras_model_sequential(input_shape = c(3)) |>   layer_dense(5) |>   layer_activation_softmax()  model |> save_model(\"model.keras\") loaded_model <- load_model(\"model.keras\") x <- random_uniform(c(10, 3)) stopifnot(all.equal(   model |> predict(x),   loaded_model |> predict(x) )) zip::zip_list(\"model.keras\")[, \"filename\"] ## [1] \"metadata.json\"    \"config.json\"      \"model.weights.h5\""},{"path":[]},{"path":"https://keras.posit.co/reference/save_model_config.html","id":null,"dir":"Reference","previous_headings":"","what":"Save and load model configuration as JSON — save_model_config","title":"Save and load model configuration as JSON — save_model_config","text":"Save re-load models configurations JSON. Note representation include weights, architecture.","code":""},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Save and load model configuration as JSON — save_model_config","text":"","code":"save_model_config(model, filepath = NULL, overwrite = FALSE)  load_model_config(filepath, custom_objects = NULL)"},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Save and load model configuration as JSON — save_model_config","text":"model Model object save filepath path json file model config. overwrite Whether overwrite existing model configuration json filepath, instead ask user via interactive prompt. custom_objects Optional named list mapping names custom classes functions considered deserialization.","code":""},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Save and load model configuration as JSON — save_model_config","text":"called primarily side effects. model returned, invisibly, enable usage pipe.","code":""},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Save and load model configuration as JSON — save_model_config","text":"Note: save_model_config() serializes model JSON using serialize_keras_object(), get_config(). serialize_keras_object() returns superset get_config(), additional information needed create class object needed restore model. See example extract get_config() value saved model.","code":""},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Save and load model configuration as JSON — save_model_config","text":"","code":"model <- keras_model_sequential(input_shape = 10) |> layer_dense(10) file <- tempfile(\"model-config-\", fileext = \".json\") save_model_config(model, file)  # load a new model instance with the same architecture but different weights model2 <- load_model_config(file)  stopifnot(exprs = {   all.equal(get_config(model), get_config(model2))    # To extract the `get_config()` value from a saved model config:   all.equal(       get_config(model),       structure(jsonlite::read_json(file)$config,                 \"__class__\" = keras_model_sequential()$`__class__`)   ) })"},{"path":[]},{"path":"https://keras.posit.co/reference/save_model_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves all layer weights to a .weights.h5 file. — save_model_weights","title":"Saves all layer weights to a .weights.h5 file. — save_model_weights","text":"Saves layer weights .weights.h5 file.","code":""},{"path":"https://keras.posit.co/reference/save_model_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves all layer weights to a .weights.h5 file. — save_model_weights","text":"","code":"save_model_weights(model, filepath, overwrite = FALSE)"},{"path":"https://keras.posit.co/reference/save_model_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves all layer weights to a .weights.h5 file. — save_model_weights","text":"model keras Model object filepath string. Path save model. Must end .weights.h5. overwrite Whether overwrite existing model target location, instead ask user via interactive prompt.","code":""},{"path":"https://keras.posit.co/reference/save_model_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Saves all layer weights to a .weights.h5 file. — save_model_weights","text":"called primarily side effects. model returned, invisibly, enable usage pipe.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/serialize_keras_object.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","title":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","text":"serialize_keras_object() serializes Keras object named list represents object, reciprocal function deserialize_keras_object(). See deserialize_keras_object() information full config format.","code":""},{"path":"https://keras.posit.co/reference/serialize_keras_object.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","text":"","code":"serialize_keras_object(obj)"},{"path":"https://keras.posit.co/reference/serialize_keras_object.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","text":"obj Keras object serialize.","code":""},{"path":"https://keras.posit.co/reference/serialize_keras_object.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","text":"named list represents object config. config expected contain simple types , can saved json. object can deserialized config via deserialize_keras_object().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/set_random_seed.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","title":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","text":"can use utility make almost Keras program fully deterministic. limitations apply cases network communications involved (e.g. parameter server distribution), creates additional sources randomness, certain non-deterministic cuDNN ops involved. sets: R session seed: set.seed() Python session seed: import random; random.seed(seed) Python NumPy seed: import numpy; numpy.random.seed(seed) TensorFlow seed: tf$random$set_seed(seed) (TF installed) Torch seed: import(\"torch\")$manual_seed(seed) (backend torch) disables Python hash randomization. Note TensorFlow seed set even using TensorFlow backend framework, since many workflows leverage tf$data pipelines (feature random shuffling). Likewise many workflows might leverage NumPy APIs.","code":""},{"path":"https://keras.posit.co/reference/set_random_seed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","text":"","code":"set_random_seed(seed)"},{"path":"https://keras.posit.co/reference/set_random_seed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","text":"seed Integer, random seed use.","code":""},{"path":"https://keras.posit.co/reference/set_random_seed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/shape.html","id":null,"dir":"Reference","previous_headings":"","what":"Tensor shape utility — shape","title":"Tensor shape utility — shape","text":"function can used get create tensor shape.","code":""},{"path":"https://keras.posit.co/reference/shape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tensor shape utility — shape","text":"","code":"shape(...)  # S3 method for keras_shape format(x, ..., prefix = TRUE)  # S3 method for keras_shape print(x, ...)  # S3 method for keras_shape [(x, ...)  # S3 method for keras_shape as.integer(x, ...)  # S3 method for keras_shape as.list(x, ...)"},{"path":"https://keras.posit.co/reference/shape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tensor shape utility — shape","text":"... shape specification. Numerics, NULL tensors valid. NULL, NA, -1L can used specify unspecified dim size. Tensors dispatched op_shape() extract tensor shape. Values wrapped () used asis (see examples). objects coerced via .integer(). x keras_shape object. prefix Whether format shape object prefix. Defaults \"shape\".","code":""},{"path":"https://keras.posit.co/reference/shape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tensor shape utility — shape","text":"list \"keras_shape\" class attribute. element list either ) NULL, b) R integer c) scalar integer tensor (e.g., supplied TF tensor unspecified dimension function traced).","code":""},{"path":"https://keras.posit.co/reference/shape.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tensor shape utility — shape","text":"3 ways specify unknown dimension     functions take 'shape' argument also coerce shape()     can also use shape() get shape tensor (excepting scalar integer tensors).             Combine expand shapes         Scalar integer tensors treated axis values. commonly encountered tracing function graph mode, axis size might unknown.     useful pattern unpack shape() %<-%, like :                     want resolve shape tensor can potentially scalar integer, can wrap tensor (), use op_shape().","code":"shape(1, 2, 3) ## shape(1, 2, 3) shape(NA,   2, 3) shape(NULL, 2, 3) shape(-1,   2, 3) ## shape(NA, 2, 3) ## shape(NA, 2, 3) ## shape(NA, 2, 3) layer_input(c(1, 2, 3)) layer_input(shape(1, 2, 3)) ## <KerasTensor shape=(None, 1, 2, 3), dtype=float32, sparse=None, name=keras_tensor> ## <KerasTensor shape=(None, 1, 2, 3), dtype=float32, sparse=None, name=keras_tensor_1> symbolic_tensor <- layer_input(shape(1, 2, 3)) shape(symbolic_tensor) ## shape(NA, 1, 2, 3) eager_tensor <- op_ones(c(1,2,3)) shape(eager_tensor) ## shape(1, 2, 3) op_shape(eager_tensor) ## shape(1, 2, 3) shape(symbolic_tensor, 4) ## shape(NA, 1, 2, 3, 4) shape(5, symbolic_tensor, 4) ## shape(5, NA, 1, 2, 3, 4) tfn <- tensorflow::tf_function(function(x) {   print(op_shape(x))   x }, input_signature = list(tensorflow::tf$TensorSpec(shape(1, NA, 3)))) invisible(tfn(op_ones(shape(1, 2, 3)))) ## shape(1, Tensor(\"strided_slice:0\", shape=(), dtype=int32), 3) c(batch_size, seq_len, channels) %<-% shape(x)  # `%<-%` also has support for skipping values # during unpacking with `.` and `...`. For example, # To retrieve just the first and/or last dim: c(batch_size, ...) %<-% shape(x) c(batch_size, ., .) %<-% shape(x) c(..., channels) %<-% shape(x) c(batch_size, ..., channels) %<-% shape(x) c(batch_size, ., channels) %<-% shape(x) echo_print <- function(x) {   message(\"> \", deparse(substitute(x)));   if(!is.null(x)) print(x) } tfn <- tensorflow::tf_function(function(x) {   c(axis1, axis2, axis3) %<-% shape(x)   echo_print(str(list(axis1 = axis1, axis2 = axis2, axis3 = axis3)))    echo_print(shape(axis1))               # use axis1 tensor as axis value   echo_print(shape(axis1, axis2, axis3)) # use axis1 tensor as axis value    # use shape() to compose a new shape, e.g., in multihead attention   n_heads <- 4   echo_print(shape(axis1, axis2, n_heads, axis3/n_heads))    x }, input_signature = list(tensorflow::tf$TensorSpec(shape(NA, 4, 16)))) invisible(tfn(op_ones(shape(2, 4, 16)))) ## > str(list(axis1 = axis1, axis2 = axis2, axis3 = axis3)) ## List of 3 ##  $ axis1:<tf.Tensor 'strided_slice:0' shape=() dtype=int32> ##  $ axis2: int 4 ##  $ axis3: int 16 ## > shape(axis1) ## shape(Tensor(\"strided_slice:0\", shape=(), dtype=int32)) ## > shape(axis1, axis2, axis3) ## shape(Tensor(\"strided_slice:0\", shape=(), dtype=int32), 4, 16) ## > shape(axis1, axis2, n_heads, axis3/n_heads) ## shape(Tensor(\"strided_slice:0\", shape=(), dtype=int32), 4, 4, 4) (x <- op_convert_to_tensor(2L)) ## tf.Tensor(2, shape=(), dtype=int32) # by default, shape() treats scalar integer tensors as axis values shape(x) ## shape(tf.Tensor(2, shape=(), dtype=int32)) # to access the shape of a scalar integer, # call `op_shape()`, or protect with `I()` op_shape(x) ## shape() shape(I(x)) ## shape()"},{"path":[]},{"path":"https://keras.posit.co/reference/split_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"Splits dataset left half right half (e.g. train / test).","code":""},{"path":"https://keras.posit.co/reference/split_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"","code":"split_dataset(   dataset,   left_size = NULL,   right_size = NULL,   shuffle = FALSE,   seed = NULL )"},{"path":"https://keras.posit.co/reference/split_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"dataset tf$data$Dataset, torch$utils$data$Dataset object, list arrays length. left_size float (range [0, 1]), signifies fraction data pack left dataset. integer, signifies number samples pack left dataset. NULL, defaults complement right_size. Defaults NULL. right_size float (range [0, 1]), signifies fraction data pack right dataset. integer, signifies number samples pack right dataset. NULL, defaults complement left_size. Defaults NULL. shuffle Boolean, whether shuffle data splitting . seed random seed shuffling.","code":""},{"path":"https://keras.posit.co/reference/split_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"list two tf$data$Dataset objects: left right splits.","code":""},{"path":"https://keras.posit.co/reference/split_dataset.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"","code":"data <- random_uniform(c(1000, 4)) c(left_ds, right_ds) %<-% split_dataset(list(data$numpy()), left_size = 0.8) left_ds$cardinality() ## tf.Tensor(800, shape=(), dtype=int64) right_ds$cardinality() ## tf.Tensor(200, shape=(), dtype=int64)"},{"path":[]},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"Print summary Keras Model","code":""},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model summary(object, ...)  # S3 method for keras.src.models.model.Model format(   x,   line_length = getOption(\"width\"),   positions = NULL,   expand_nested = FALSE,   show_trainable = NA,   ...,   layer_range = NULL,   compact = TRUE )  # S3 method for keras.src.models.model.Model print(x, ...)"},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"object, x Keras model instance ... summary() print(), passed format(). format(), passed model$summary(). line_length Total length printed lines positions Relative absolute positions log elements line. provided, defaults c(0.33, 0.55, 0.67, 1.0). expand_nested Whether expand nested models. provided, defaults FALSE. show_trainable Whether show layer trainable. provided, defaults FALSE. layer_range list, tuple, vector 2 strings, starting layer name ending layer name (inclusive) indicating range layers printed summary. also accepts regex patterns instead exact name. case, start predicate first element matches layer_range[[1]] end predicate last element matches layer_range[[1]]. default NULL considers layers model. compact Whether remove white-space lines model summary. (Default TRUE)","code":""},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"format() returns length 1 character vector. print() returns model object invisibly. summary() returns output format() invisibly printing .","code":""},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":"enabling-color-output-in-knitr-rmarkdown-quarto-","dir":"Reference","previous_headings":"","what":"Enabling color output in Knitr (RMarkdown, Quarto)","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"order enable color output quarto rmarkdown document html output format (include revealjs presentations), need following setup chunk:","code":"```{r setup, include = FALSE}  options(cli.num_colors = 256)  fansi::set_knit_hooks(knitr::knit_hooks)  options(width = 75) # adjust as needed for format  ```"},{"path":[]},{"path":"https://keras.posit.co/reference/test_on_batch.html","id":null,"dir":"Reference","previous_headings":"","what":"Test the model on a single batch of samples. — test_on_batch","title":"Test the model on a single batch of samples. — test_on_batch","text":"Test model single batch samples.","code":""},{"path":"https://keras.posit.co/reference/test_on_batch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test the model on a single batch of samples. — test_on_batch","text":"","code":"test_on_batch(object, x, y = NULL, sample_weight = NULL, ...)"},{"path":"https://keras.posit.co/reference/test_on_batch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test the model on a single batch of samples. — test_on_batch","text":"object Keras model object x Input data. Must array-like. y Target data. Must array-like. sample_weight Optional array length x, containing weights apply model's loss sample. case temporal data, can pass 2D array shape (samples, sequence_length), apply different weight every timestep every sample. ... forward/backward compatability","code":""},{"path":"https://keras.posit.co/reference/test_on_batch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test the model on a single batch of samples. — test_on_batch","text":"scalar loss value (metrics), named list loss metric values (metrics).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/text_dataset_from_directory.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","title":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","text":"directory structure :   calling text_dataset_from_directory(main_directory, labels='inferred') return tf.data.Dataset yields batches texts subdirectories class_a class_b, together labels 0 1 (0 corresponding class_a 1 corresponding class_b). .txt files supported time.","code":"main_directory/ ...class_a/ ......a_text_1.txt ......a_text_2.txt ...class_b/ ......b_text_1.txt ......b_text_2.txt"},{"path":"https://keras.posit.co/reference/text_dataset_from_directory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","text":"","code":"text_dataset_from_directory(   directory,   labels = \"inferred\",   label_mode = \"int\",   class_names = NULL,   batch_size = 32L,   max_length = NULL,   shuffle = TRUE,   seed = NULL,   validation_split = NULL,   subset = NULL,   follow_links = FALSE,   verbose = TRUE )"},{"path":"https://keras.posit.co/reference/text_dataset_from_directory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","text":"directory Directory data located. labels \"inferred\", contain subdirectories, containing text files class. Otherwise, directory structure ignored. labels Either \"inferred\" (labels generated directory structure), NULL (labels), list/tuple integer labels size number text files found directory. Labels sorted according alphanumeric order text file paths (obtained via os.walk(directory) Python). label_mode String describing encoding labels. Options : \"int\": means labels encoded integers (e.g. sparse_categorical_crossentropy loss). \"categorical\" means labels encoded categorical vector (e.g. categorical_crossentropy loss). \"binary\" means labels (can 2) encoded float32 scalars values 0 1 (e.g. binary_crossentropy). NULL (labels). class_names valid \"labels\" \"inferred\". explicit list class names (must match names subdirectories). Used control order classes (otherwise alphanumerical order used). batch_size Size batches data. Defaults 32. NULL, data batched (dataset yield individual samples). max_length Maximum size text string. Texts longer truncated max_length. shuffle Whether shuffle data. Defaults TRUE. set FALSE, sorts data alphanumeric order. seed Optional random seed shuffling transformations. validation_split Optional float 0 1, fraction data reserve validation. subset Subset data return. One \"training\", \"validation\" \"\". used validation_split set. subset=\"\", utility returns tuple two datasets (training validation datasets respectively). follow_links Whether visits subdirectories pointed symlinks. Defaults FALSE. verbose Whether display number information classes number files found. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/text_dataset_from_directory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","text":"tf.data.Dataset object. label_mode NULL, yields string tensors shape (batch_size,), containing contents batch text files. Otherwise, yields tuple (texts, labels), texts shape (batch_size,) labels follows format described . Rules regarding labels format: label_mode int, labels int32 tensor shape (batch_size,). label_mode binary, labels float32 tensor 1s 0s shape (batch_size, 1). label_mode categorical, labels float32 tensor shape (batch_size, num_classes), representing one-hot encoding class index.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/time_distributed.html","id":null,"dir":"Reference","previous_headings":"","what":"layer_time_distributed — time_distributed","title":"layer_time_distributed — time_distributed","text":"time_distributed() alias layer_time_distributed(). See ?layer_time_distributed() full documentation.","code":""},{"path":"https://keras.posit.co/reference/time_distributed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"layer_time_distributed — time_distributed","text":"","code":"time_distributed(object, layer, ...)"},{"path":"https://keras.posit.co/reference/time_distributed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"layer_time_distributed — time_distributed","text":"object Object compose layer . tensor, array, sequential model. layer Layer instance. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/time_distributed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"layer_time_distributed — time_distributed","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/timeseries_dataset_from_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","title":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","text":"function takes sequence data-points gathered equal intervals, along time series parameters length sequences/windows, spacing two sequence/windows, etc., produce batches timeseries inputs targets.","code":""},{"path":"https://keras.posit.co/reference/timeseries_dataset_from_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","text":"","code":"timeseries_dataset_from_array(   data,   targets,   sequence_length,   sequence_stride = 1L,   sampling_rate = 1L,   batch_size = 128L,   shuffle = FALSE,   seed = NULL,   start_index = NULL,   end_index = NULL )"},{"path":"https://keras.posit.co/reference/timeseries_dataset_from_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","text":"data array eager tensor containing consecutive data points (timesteps). first dimension expected time dimension. targets Targets corresponding timesteps data. targets[] target corresponding window starts index (see example 2 ). Pass NULL target data (case dataset yield input data). sequence_length Length output sequences (number timesteps). sequence_stride Period successive output sequences. stride s, output samples start index data[], data[+ s], data[+ 2 * s], etc. sampling_rate Period successive individual timesteps within sequences. rate r, timesteps data[], data[+ r], ... data[+ sequence_length] used creating sample sequence. batch_size Number timeseries samples batch (except maybe last one). NULL, data batched (dataset yield individual samples). shuffle Whether shuffle output samples, instead draw chronological order. seed Optional int; random seed shuffling. start_index Optional int; data points earlier (exclusive) start_index used output sequences. useful reserve part data test validation. end_index Optional int; data points later (exclusive) end_index used output sequences. useful reserve part data test validation.","code":""},{"path":"https://keras.posit.co/reference/timeseries_dataset_from_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","text":"tf$data$Dataset instance. targets passed, dataset yields list (batch_of_sequences, batch_of_targets). , dataset yields batch_of_sequences. Example 1: Consider indices [0, 1, ... 98]. sequence_length=10,  sampling_rate=2, sequence_stride=3, shuffle=FALSE, dataset yield batches sequences composed following indices:   case last 2 data points discarded since full sequence can generated include (next sequence started index 81, thus last step gone 98). Example 2: Temporal regression. Consider array data scalar values, shape (steps,). generate dataset uses past 10 timesteps predict next timestep, use:     Example 3: Temporal regression many--many architectures. Consider two arrays scalar values X Y, shape (100,). resulting dataset consist samples 20 timestamps . samples overlap. generate dataset uses current timestamp predict corresponding target timestep, use:","code":"First sequence:  [0  2  4  6  8 10 12 14 16 18] Second sequence: [3  5  7  9 11 13 15 17 19 21] Third sequence:  [6  8 10 12 14 16 18 20 22 24] ... Last sequence:   [78 80 82 84 86 88 90 92 94 96] data <- op_array(1:20) input_data <- data[1:10] targets <- data[11:20] dataset <- timeseries_dataset_from_array(   input_data, targets, sequence_length=10) iter <- reticulate::as_iterator(dataset) reticulate::iter_next(iter) ## [[1]] ## tf.Tensor([[ 1  2  3  4  5  6  7  8  9 10]], shape=(1, 10), dtype=int32) ## ## [[2]] ## tf.Tensor([11], shape=(1), dtype=int32) X <- op_array(1:100) Y <- X*2  sample_length <- 20 input_dataset <- timeseries_dataset_from_array(     X, NULL, sequence_length=sample_length, sequence_stride=sample_length) target_dataset <- timeseries_dataset_from_array(     Y, NULL, sequence_length=sample_length, sequence_stride=sample_length)   inputs <- reticulate::as_iterator(input_dataset) %>% reticulate::iter_next() targets <- reticulate::as_iterator(target_dataset) %>% reticulate::iter_next()"},{"path":[]},{"path":"https://keras.posit.co/reference/to_categorical.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts a class vector (integers) to binary class matrix. — to_categorical","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"E.g. use loss_categorical_crossentropy().","code":""},{"path":"https://keras.posit.co/reference/to_categorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"","code":"to_categorical(x, num_classes = NULL)"},{"path":"https://keras.posit.co/reference/to_categorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"x Array-like class values converted matrix (integers 0 num_classes - 1). R factors coerced integer offset 0-based, .e., .integer(x) - 1L. num_classes Total number classes. NULL, inferred max(x) + 1. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/to_categorical.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"binary matrix representation input R array. class axis placed last.","code":""},{"path":"https://keras.posit.co/reference/to_categorical.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"","code":"a <- to_categorical(c(0, 1, 2, 3), num_classes=4) print(a) ##      [,1] [,2] [,3] [,4] ## [1,]    1    0    0    0 ## [2,]    0    1    0    0 ## [3,]    0    0    1    0 ## [4,]    0    0    0    1 b <- array(c(.9, .04, .03, .03,               .3, .45, .15, .13,               .04, .01, .94, .05,               .12, .21, .5, .17),               dim = c(4, 4)) loss <- op_categorical_crossentropy(a, b) loss ## tf.Tensor([0.41284522 0.45601739 0.54430155 0.80437282], shape=(4), dtype=float64) loss <- op_categorical_crossentropy(a, a) loss ## tf.Tensor([1.00000005e-07 1.00000005e-07 1.00000005e-07 1.00000005e-07], shape=(4), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/train_on_batch.html","id":null,"dir":"Reference","previous_headings":"","what":"Runs a single gradient update on a single batch of data. — train_on_batch","title":"Runs a single gradient update on a single batch of data. — train_on_batch","text":"Runs single gradient update single batch data.","code":""},{"path":"https://keras.posit.co/reference/train_on_batch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Runs a single gradient update on a single batch of data. — train_on_batch","text":"","code":"train_on_batch(object, x, y = NULL, sample_weight = NULL, class_weight = NULL)"},{"path":"https://keras.posit.co/reference/train_on_batch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Runs a single gradient update on a single batch of data. — train_on_batch","text":"object Keras model object x Input data. Must array-like. y Target data. Must array-like. sample_weight Optional array length x, containing weights apply model's loss sample. case temporal data, can pass 2D array shape (samples, sequence_length), apply different weight every timestep every sample. class_weight Optional named list mapping class indices (integers, 0-based) weight (float) apply model's loss samples class training. can useful tell model \"pay attention\" samples -represented class. class_weight specified targets rank 2 greater, either y must one-hot encoded, explicit final dimension 1 must included sparse class labels.","code":""},{"path":"https://keras.posit.co/reference/train_on_batch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Runs a single gradient update on a single batch of data. — train_on_batch","text":"scalar loss value (metrics), named list loss metric values (metrics). property model$metrics_names give display labels scalar outputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":null,"dir":"Reference","previous_headings":"","what":"Unpacks user-provided data list. — unpack_x_y_sample_weight","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"convenience utility used overriding $train_step, $test_step, $predict_step. utility makes easy support data form (x,), (x, y), (x, y, sample_weight).","code":""},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"","code":"unpack_x_y_sample_weight(data)"},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"data list form (x), (x, y), (x, y, sample_weight).","code":""},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"unpacked list, NULLs y sample_weight provided.","code":""},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":"example-","dir":"Reference","previous_headings":"","what":"Example:","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"can also equivalent providing default values %<-%","code":"features_batch <- op_ones(c(10, 5)) labels_batch <- op_zeros(c(10, 5)) data <- list(features_batch, labels_batch) # `y` and `sample_weight` will default to `NULL` if not provided. c(x, y, sample_weight) %<-% unpack_x_y_sample_weight(data) c(x, y = NULL, sample_weight = NULL) %<-% data"},{"path":[]},{"path":"https://keras.posit.co/reference/use_backend.html","id":null,"dir":"Reference","previous_headings":"","what":"Configure a Keras backend — use_backend","title":"Configure a Keras backend — use_backend","text":"Configure Keras backend","code":""},{"path":"https://keras.posit.co/reference/use_backend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Configure a Keras backend — use_backend","text":"","code":"use_backend(backend)"},{"path":"https://keras.posit.co/reference/use_backend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Configure a Keras backend — use_backend","text":"backend string, can \"tensorflow\", \"jax\", \"numpy\", \"torch\".","code":""},{"path":"https://keras.posit.co/reference/use_backend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Configure a Keras backend — use_backend","text":"Called primarily side effects. Returns provided backend, invisibly.","code":""},{"path":"https://keras.posit.co/reference/use_backend.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Configure a Keras backend — use_backend","text":"functions allow configuring backend keras use. Note one backend can configured time. function called library(keras3) calling functions within package (see example). experimental support changing backend keras initialized. using config_set_backend().","code":"library(keras3) use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":null,"dir":"Reference","previous_headings":"","what":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"Provide scope mappings names custom objects","code":""},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"","code":"with_custom_object_scope(objects, expr)"},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"objects Named list objects expr Expression evaluate","code":""},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"result evaluating expr within custom object scope.","code":""},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"many elements Keras models can customized user objects (e.g. losses, metrics, regularizers, etc.). loading saved models use functions typically need explicitly map names user objects via custom_objects parameter. with_custom_object_scope() function provides alternative lets create named alias user object applies entire block code, automatically recognized loading saved models.","code":""},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"","code":"# define custom metric metric_top_3_categorical_accuracy <-   custom_metric(\"top_3_categorical_accuracy\", function(y_true, y_pred) {     metric_top_k_categorical_accuracy(y_true, y_pred, k = 3)   })  with_custom_object_scope(c(top_k_acc = sparse_top_k_cat_acc), {    # ...define model...    # compile model (refer to \"top_k_acc\" by name)   model |> compile(     loss = \"binary_crossentropy\",     optimizer = optimizer_nadam(),     metrics = c(\"top_k_acc\")   )    # save the model   model |> save_model(\"my_model.keras\")    # loading the model within the custom object scope doesn't   # require explicitly providing the custom_object   reloaded_model <- load_model(\"my_model.keras\") })"},{"path":[]},{"path":"https://keras.posit.co/reference/zip_lists.html","id":null,"dir":"Reference","previous_headings":"","what":"Zip lists — zip_lists","title":"Zip lists — zip_lists","text":"conceptually similar zip() Python, R functions purrr::transpose() data.table::transpose() (albeit, accepting elements ... instead single list), one crucial difference: provided objects named, matching done names, positions.","code":""},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zip lists — zip_lists","text":"","code":"zip_lists(...)"},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zip lists — zip_lists","text":"... R lists atomic vectors, optionally named.","code":""},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zip lists — zip_lists","text":"inverted list","code":""},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Zip lists — zip_lists","text":"arguments supplied must length. positional matching required, arguments provided must unnamed. matching names, arguments must set names, can different orders.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Zip lists — zip_lists","text":"","code":"gradients <- list(\"grad_for_wt_1\", \"grad_for_wt_2\", \"grad_for_wt_3\") weights <- list(\"weight_1\", \"weight_2\", \"weight_3\") str(zip_lists(gradients, weights)) #> List of 3 #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_1\" #>   ..$ : chr \"weight_1\" #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_2\" #>   ..$ : chr \"weight_2\" #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_3\" #>   ..$ : chr \"weight_3\" str(zip_lists(gradient = gradients, weight = weights)) #> List of 3 #>  $ :List of 2 #>   ..$ gradient: chr \"grad_for_wt_1\" #>   ..$ weight  : chr \"weight_1\" #>  $ :List of 2 #>   ..$ gradient: chr \"grad_for_wt_2\" #>   ..$ weight  : chr \"weight_2\" #>  $ :List of 2 #>   ..$ gradient: chr \"grad_for_wt_3\" #>   ..$ weight  : chr \"weight_3\"  names(gradients) <- names(weights) <- paste0(\"layer_\", 1:3) str(zip_lists(gradients, weights[c(3, 1, 2)])) #> List of 3 #>  $ layer_1:List of 2 #>   ..$ : chr \"grad_for_wt_1\" #>   ..$ : chr \"weight_1\" #>  $ layer_2:List of 2 #>   ..$ : chr \"grad_for_wt_2\" #>   ..$ : chr \"weight_2\" #>  $ layer_3:List of 2 #>   ..$ : chr \"grad_for_wt_3\" #>   ..$ : chr \"weight_3\"  names(gradients) <- paste0(\"gradient_\", 1:3) try(zip_lists(gradients, weights)) # error, names don't match #> Error in zip_lists(gradients, weights) :  #>   All names of arguments provided to `zip_lists()` must match. Call `unname()` on each argument if you want positional matching # call unname directly for positional matching str(zip_lists(unname(gradients), unname(weights))) #> List of 3 #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_1\" #>   ..$ : chr \"weight_1\" #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_2\" #>   ..$ : chr \"weight_2\" #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_3\" #>   ..$ : chr \"weight_3\""},{"path":"https://keras.posit.co/news/index.html","id":"keras3-100","dir":"Changelog","previous_headings":"","what":"keras3 1.0.0","title":"keras3 1.0.0","text":"Chains layer_* calls |> now instantiate layers order %>% pipe chains: left-hand-side first (#1440). iterate(), iter_next() as_iterator() now reexported reticulate. User facing changes upstream Keras v3.3.2: new function: op_ctc_decode() new function: op_eigh() new function: op_select() new function: op_vectorize() new function: op_image_rgb_to_grayscale() new function: loss_tversky() new args: layer_resizing(pad_to_aspect_ratio, fill_mode, fill_value) new arg: layer_embedding(weights) providing initial weights matrix new args: op_nan_to_num(nan, posinf, neginf) new args: op_image_resize(crop_to_aspect_ratio, pad_to_aspect_ratio, fill_mode, fill_value) new args: op_argmax(keepdims) op_argmin(keepdims) new arg: clear_session(free_memory) clearing without invoking garbage collector. metric_kl_divergence() loss_kl_divergence() clip inputs (y_true y_pred) [0, 1] range. new Layer() attributes: metrics, dtype_policy Added initial support float8 training layer_conv_*d() layers now support LoRa op_digitize() now supports sparse tensors. Models layers now return owned metrics recursively. Add pickling support Keras models. (e.g., via reticulate::py_save_object()) Note pickling recommended, prefer using Keras saving APIs.","code":""},{"path":"https://keras.posit.co/news/index.html","id":"keras3-020","dir":"Changelog","previous_headings":"","what":"keras3 0.2.0","title":"keras3 0.2.0","text":"CRAN release: 2024-04-18 New functions: quantize_weights(): quantize model layer weights -place. Currently, Dense, EinsumDense, Embedding layers supported (enough cover majority transformers today) layer_mel_spectrogram() layer_flax_module_wrapper() layer_jax_model_wrapper() loss_dice() random_beta() random_binomial() config_set_backend(): change backend Keras initialized. config_dtype_policy() config_set_dtype_policy() New Ops op_custom_gradient() op_batch_normalization() op_image_crop() op_divide_no_nan() op_normalize() op_correlate() ` New family linear algebra ops op_cholesky() op_det() op_eig() op_inv() op_lu_factor() op_norm() op_erfinv() op_solve_triangular() op_svd() audio_dataset_from_directory(), image_dataset_from_directory() text_dataset_from_directory() gain verbose argument (default TRUE) image_dataset_from_directory() gains pad_to_aspect_ratio argument (default FALSE) to_categorical(), op_one_hot(), fit() can now accept R factors, offset 0-based (reported #1055). op_convert_to_numpy() now returns unconverted NumPy arrays. op_array() op_convert_to_tensor() longer error casting R doubles integer types. export_savedmodel() now works Jax backend. Metric()$add_variable() method gains arg: aggregration. Layer()$add_weight() method gains args: autocast, regularizer, aggregation. op_bincount(), op_multi_hot(), op_one_hot(), layer_category_encoding() now support sparse tensors. op_custom_gradient() now supports PyTorch backend layer_lstm() layer_gru() gain arg use_cudnn, default 'auto'. Fixed issue application_preprocess_inputs() error supplied R array input. Doc improvements.","code":""},{"path":"https://keras.posit.co/news/index.html","id":"keras3-010","dir":"Changelog","previous_headings":"","what":"keras3 0.1.0","title":"keras3 0.1.0","text":"CRAN release: 2024-02-17 package rebuilt Keras 3.0. Refer  overview https://keras.posit.co current --date documentation.","code":""}]

From 0b01d471dd222a07e26b0f1a5381ec6c0e7cf76b Mon Sep 17 00:00:00 2001
From: Tomasz Kalinowski <kalinowskit@gmail.com>
Date: Tue, 21 May 2024 11:46:46 -0400
Subject: [PATCH 08/11] remove  url redirect for CRAN

---
 docs/articles/getting_started.html | 2 +-
 vignettes-src/getting_started.Rmd  | 2 +-
 vignettes/getting_started.Rmd      | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/articles/getting_started.html b/docs/articles/getting_started.html
index 893a2f24c..eacff549f 100644
--- a/docs/articles/getting_started.html
+++ b/docs/articles/getting_started.html
@@ -303,7 +303,7 @@ <h3 id="training-and-evaluation">Training and Evaluation<a class="anchor" aria-l
 <h3 id="deep-learning-with-r-book">Deep Learning with R Book<a class="anchor" aria-label="anchor" href="#deep-learning-with-r-book"></a>
 </h3>
 <p>If you want a more comprehensive introduction to both Keras and the
-concepts and practice of deep learning, we recommend the <a href="https://rstd.io/dlwr-2e" class="external-link">Deep Learning with R, 2nd Edition</a>
+concepts and practice of deep learning, we recommend the <a href="https://www.manning.com/books/deep-learning-with-r-second-edition" class="external-link">Deep Learning with R, 2nd Edition</a>
 book from Manning. This book is a collaboration between François
 Chollet, the creator of (Python) Keras, J.J. Allaire, who wrote the
 original R interface to Keras, and Tomasz Kalinowski, the maintainer of
diff --git a/vignettes-src/getting_started.Rmd b/vignettes-src/getting_started.Rmd
index bdd486123..576d32ef5 100644
--- a/vignettes-src/getting_started.Rmd
+++ b/vignettes-src/getting_started.Rmd
@@ -168,7 +168,7 @@ Keras provides a vocabulary for building deep learning models that is simple, el
 
 ### Deep Learning with R Book
 
-If you want a more comprehensive introduction to both Keras and the concepts and practice of deep learning, we recommend the [Deep Learning with R, 2nd Edition](https://rstd.io/dlwr-2e) book from Manning. This book is a collaboration between François Chollet, the creator of (Python) Keras, J.J. Allaire, who wrote the original R interface to Keras, and Tomasz Kalinowski, the maintainer of the R interface to Keras.
+If you want a more comprehensive introduction to both Keras and the concepts and practice of deep learning, we recommend the [Deep Learning with R, 2nd Edition](https://www.manning.com/books/deep-learning-with-r-second-edition) book from Manning. This book is a collaboration between François Chollet, the creator of (Python) Keras, J.J. Allaire, who wrote the original R interface to Keras, and Tomasz Kalinowski, the maintainer of the R interface to Keras.
 
 The book presumes no significant knowledge of machine learning and deep learning, and goes all the way from basic theory to advanced practical applications, all using the R interface to Keras.
 
diff --git a/vignettes/getting_started.Rmd b/vignettes/getting_started.Rmd
index 91162507f..bc35a3ae2 100644
--- a/vignettes/getting_started.Rmd
+++ b/vignettes/getting_started.Rmd
@@ -233,7 +233,7 @@ Keras provides a vocabulary for building deep learning models that is simple, el
 
 ### Deep Learning with R Book
 
-If you want a more comprehensive introduction to both Keras and the concepts and practice of deep learning, we recommend the [Deep Learning with R, 2nd Edition](https://rstd.io/dlwr-2e) book from Manning. This book is a collaboration between François Chollet, the creator of (Python) Keras, J.J. Allaire, who wrote the original R interface to Keras, and Tomasz Kalinowski, the maintainer of the R interface to Keras.
+If you want a more comprehensive introduction to both Keras and the concepts and practice of deep learning, we recommend the [Deep Learning with R, 2nd Edition](https://www.manning.com/books/deep-learning-with-r-second-edition) book from Manning. This book is a collaboration between François Chollet, the creator of (Python) Keras, J.J. Allaire, who wrote the original R interface to Keras, and Tomasz Kalinowski, the maintainer of the R interface to Keras.
 
 The book presumes no significant knowledge of machine learning and deep learning, and goes all the way from basic theory to advanced practical applications, all using the R interface to Keras.
 

From 6a1efdd9bb79e26d27621b0141b202488cd464f4 Mon Sep 17 00:00:00 2001
From: Tomasz Kalinowski <kalinowskit@gmail.com>
Date: Tue, 21 May 2024 12:03:58 -0400
Subject: [PATCH 09/11] update news with 3.3.3 retether

---
 NEWS.md | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index 1c7100c6e..f069c2d83 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -5,6 +5,19 @@
 
 - `iterate()`, `iter_next()` and `as_iterator()` are now reexported from reticulate.
 
+
+User facing changes with upstream Keras v3.3.3:
+
+- new functions: `op_slogdet()`, `op_psnr()`
+
+- `clone_model()` gains new args: `call_function`, `recursive`
+  Updated example usage. 
+
+- `op_ctc_decode()` strategy argument has new default: `"greedy"`.
+  Updated docs. 
+
+- `loss_ctc()` default name fixed, changed to `"ctc"`
+
 User facing changes with upstream Keras v3.3.2:
 
 - new function: `op_ctc_decode()`

From 63a91fac2e18baaa1b9ce74b8b553922967e4e89 Mon Sep 17 00:00:00 2001
From: Tomasz Kalinowski <kalinowskit@gmail.com>
Date: Tue, 21 May 2024 12:15:45 -0400
Subject: [PATCH 10/11] rebuild

---
 docs/articles/getting_started.html | 10 +++++-----
 docs/news/index.html               |  5 +++++
 docs/pkgdown.yml                   |  2 +-
 docs/reference/layer_tfsm.html     |  4 ++--
 docs/search.json                   |  2 +-
 man/layer_tfsm.Rd                  |  4 ++--
 6 files changed, 16 insertions(+), 11 deletions(-)

diff --git a/docs/articles/getting_started.html b/docs/articles/getting_started.html
index eacff549f..bf4cd2a42 100644
--- a/docs/articles/getting_started.html
+++ b/docs/articles/getting_started.html
@@ -303,11 +303,11 @@ <h3 id="training-and-evaluation">Training and Evaluation<a class="anchor" aria-l
 <h3 id="deep-learning-with-r-book">Deep Learning with R Book<a class="anchor" aria-label="anchor" href="#deep-learning-with-r-book"></a>
 </h3>
 <p>If you want a more comprehensive introduction to both Keras and the
-concepts and practice of deep learning, we recommend the <a href="https://www.manning.com/books/deep-learning-with-r-second-edition" class="external-link">Deep Learning with R, 2nd Edition</a>
-book from Manning. This book is a collaboration between François
-Chollet, the creator of (Python) Keras, J.J. Allaire, who wrote the
-original R interface to Keras, and Tomasz Kalinowski, the maintainer of
-the R interface to Keras.</p>
+concepts and practice of deep learning, we recommend the <a href="https://www.manning.com/books/deep-learning-with-r-second-edition" class="external-link">Deep
+Learning with R, 2nd Edition</a> book from Manning. This book is a
+collaboration between François Chollet, the creator of (Python) Keras,
+J.J. Allaire, who wrote the original R interface to Keras, and Tomasz
+Kalinowski, the maintainer of the R interface to Keras.</p>
 <p>The book presumes no significant knowledge of machine learning and
 deep learning, and goes all the way from basic theory to advanced
 practical applications, all using the R interface to Keras.</p>
diff --git a/docs/news/index.html b/docs/news/index.html
index 02cb4ce31..85d5795aa 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -73,6 +73,11 @@ <h6 class="dropdown-header" data-toc-skip>Other topics</h6>
 <h2 class="pkg-version" data-toc-text="1.0.0" id="keras3-100">keras3 1.0.0<a class="anchor" aria-label="anchor" href="#keras3-100"></a></h2>
 <ul><li><p>Chains of <code>layer_*</code> calls with <code>|&gt;</code> now instantiate layers in the same order as <code>%&gt;%</code> pipe chains: left-hand-side first (<a href="https://github.com/rstudio/keras/issues/1440" class="external-link">#1440</a>).</p></li>
 <li><p><code><a href="https://rstudio.github.io/reticulate/reference/iterate.html" class="external-link">iterate()</a></code>, <code><a href="https://rstudio.github.io/reticulate/reference/iterate.html" class="external-link">iter_next()</a></code> and <code><a href="https://rstudio.github.io/reticulate/reference/iterate.html" class="external-link">as_iterator()</a></code> are now reexported from reticulate.</p></li>
+</ul><p>User facing changes with upstream Keras v3.3.3:</p>
+<ul><li><p>new functions: <code><a href="../reference/op_slogdet.html">op_slogdet()</a></code>, <code><a href="../reference/op_psnr.html">op_psnr()</a></code></p></li>
+<li><p><code><a href="../reference/clone_model.html">clone_model()</a></code> gains new args: <code>call_function</code>, <code>recursive</code> Updated example usage.</p></li>
+<li><p><code><a href="../reference/op_ctc_decode.html">op_ctc_decode()</a></code> strategy argument has new default: <code>"greedy"</code>. Updated docs.</p></li>
+<li><p><code><a href="../reference/loss_ctc.html">loss_ctc()</a></code> default name fixed, changed to <code>"ctc"</code></p></li>
 </ul><p>User facing changes with upstream Keras v3.3.2:</p>
 <ul><li><p>new function: <code><a href="../reference/op_ctc_decode.html">op_ctc_decode()</a></code></p></li>
 <li><p>new function: <code><a href="../reference/op_eigh.html">op_eigh()</a></code></p></li>
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 573d81e83..e3c9347a6 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -28,7 +28,7 @@ articles:
   understanding_masking_and_padding: understanding_masking_and_padding.html
   writing_a_custom_training_loop_in_tensorflow: writing_a_custom_training_loop_in_tensorflow.html
   writing_your_own_callbacks: writing_your_own_callbacks.html
-last_built: 2024-05-21T15:16Z
+last_built: 2024-05-21T16:08Z
 urls:
   reference: https://keras.posit.co/reference
   article: https://keras.posit.co/articles
diff --git a/docs/reference/layer_tfsm.html b/docs/reference/layer_tfsm.html
index a47c4fb59..0040b4282 100644
--- a/docs/reference/layer_tfsm.html
+++ b/docs/reference/layer_tfsm.html
@@ -143,8 +143,8 @@ <h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"
 <span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a><span class="do">## Output Type:</span></span>
 <span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a><span class="do">##   TensorSpec(shape=(None, 10), dtype=tf.float32, name=None)</span></span>
 <span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a><span class="do">## Captures:</span></span>
-<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a><span class="do">##   128255174032224: TensorSpec(shape=(), dtype=tf.resource, name=None)</span></span>
-<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a><span class="do">##   128255174017264: TensorSpec(shape=(), dtype=tf.resource, name=None)</span></span>
+<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a><span class="do">##   128695884597376: TensorSpec(shape=(), dtype=tf.resource, name=None)</span></span>
+<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a><span class="do">##   128695884598080: TensorSpec(shape=(), dtype=tf.resource, name=None)</span></span>
 <span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a></span></code></pre><p></p></div>
 <p></p><div class="sourceCode r"><pre><code><span><span class="va">reloaded_layer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/layer_tfsm.html">layer_tfsm</a></span><span class="op">(</span>filepath <span class="op">=</span> <span class="st">"path/to/artifact"</span><span class="op">)</span></span>
 <span><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/random_normal.html">random_normal</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">784</span><span class="op">)</span><span class="op">)</span></span>
diff --git a/docs/search.json b/docs/search.json
index d2cc629f0..ddc1660d8 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Customizing what happens in `fit()` with JAX","text":"’re supervised learning, can use fit() everything works smoothly. need take control every little detail, can write training loop entirely scratch. need custom training algorithm, still want benefit convenient features fit(), callbacks, built-distribution support, step fusing? core principle Keras progressive disclosure complexity. always able get lower-level workflows gradual way. shouldn’t fall cliff high-level functionality doesn’t exactly match use case. able gain control small details retaining commensurate amount high-level convenience. need customize fit() , override training step function Model class. function called fit() every batch data. able call fit() usual – running learning algorithm. Note pattern prevent building models Functional API. can whether ’re building Sequential models, Functional API models, subclassed models. Let’s see works.","code":""},{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Customizing what happens in `fit()` with JAX","text":"","code":"import os  # This guide can only be run with the JAX backend. os.environ[\"KERAS_BACKEND\"] = \"jax\"  import jax import keras import numpy as np"},{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"a-first-simple-example","dir":"Articles","previous_headings":"","what":"A first simple example","title":"Customizing what happens in `fit()` with JAX","text":"Let’s start simple example: create new class subclasses keras.Model. implement fully-stateless compute_loss_and_updates() method compute loss well updated values non-trainable variables model. Internally, calls stateless_call() built-compute_loss(). implement fully-stateless train_step() method compute current metric values (including loss) well updated values trainable variables, optimizer variables, metric variables. Note can also take account sample_weight argument : Unpacking data x, y, sample_weight = data Passing sample_weight compute_loss() Passing sample_weight alongside y y_pred metrics stateless_update_state() Let’s try :","code":"class CustomModel(keras.Model):     def compute_loss_and_updates(         self,         trainable_variables,         non_trainable_variables,         x,         y,         training=False,     ):         y_pred, non_trainable_variables = self.stateless_call(             trainable_variables,             non_trainable_variables,             x,             training=training,         )         loss = self.compute_loss(x, y, y_pred)         return loss, (y_pred, non_trainable_variables)      def train_step(self, state, data):         (             trainable_variables,             non_trainable_variables,             optimizer_variables,             metrics_variables,         ) = state         x, y = data          # Get the gradient function.         grad_fn = jax.value_and_grad(             self.compute_loss_and_updates, has_aux=True         )          # Compute the gradients.         (loss, (y_pred, non_trainable_variables)), grads = grad_fn(             trainable_variables,             non_trainable_variables,             x,             y,             training=True,         )          # Update trainable variables and optimizer variables.         (             trainable_variables,             optimizer_variables,         ) = self.optimizer.stateless_apply(             optimizer_variables, grads, trainable_variables         )          # Update metrics.         new_metrics_vars = []         for metric in self.metrics:             this_metric_vars = metrics_variables[                 len(new_metrics_vars) : len(new_metrics_vars)                 + len(metric.variables)             ]             if metric.name == \"loss\":                 this_metric_vars = metric.stateless_update_state(                     this_metric_vars, loss                 )             else:                 this_metric_vars = metric.stateless_update_state(                     this_metric_vars, y, y_pred                 )             logs = metric.stateless_result(this_metric_vars)             new_metrics_vars += this_metric_vars          # Return metric logs and updated state variables.         state = (             trainable_variables,             non_trainable_variables,             optimizer_variables,             new_metrics_vars,         )         return logs, state # Construct and compile an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])  # Just use `fit` as usual x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=3)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"going-lower-level","dir":"Articles","previous_headings":"","what":"Going lower-level","title":"Customizing what happens in `fit()` with JAX","text":"Naturally, just skip passing loss function compile(), instead everything manually train_step. Likewise metrics. ’s lower-level example, uses compile() configure optimizer:","code":"class CustomModel(keras.Model):     def __init__(self, *args, **kwargs):         super().__init__(*args, **kwargs)         self.loss_tracker = keras.metrics.Mean(name=\"loss\")         self.mae_metric = keras.metrics.MeanAbsoluteError(name=\"mae\")         self.loss_fn = keras.losses.MeanSquaredError()      def compute_loss_and_updates(         self,         trainable_variables,         non_trainable_variables,         x,         y,         training=False,     ):         y_pred, non_trainable_variables = self.stateless_call(             trainable_variables,             non_trainable_variables,             x,             training=training,         )         loss = self.loss_fn(y, y_pred)         return loss, (y_pred, non_trainable_variables)      def train_step(self, state, data):         (             trainable_variables,             non_trainable_variables,             optimizer_variables,             metrics_variables,         ) = state         x, y = data          # Get the gradient function.         grad_fn = jax.value_and_grad(             self.compute_loss_and_updates, has_aux=True         )          # Compute the gradients.         (loss, (y_pred, non_trainable_variables)), grads = grad_fn(             trainable_variables,             non_trainable_variables,             x,             y,             training=True,         )          # Update trainable variables and optimizer variables.         (             trainable_variables,             optimizer_variables,         ) = self.optimizer.stateless_apply(             optimizer_variables, grads, trainable_variables         )          # Update metrics.         loss_tracker_vars = metrics_variables[             : len(self.loss_tracker.variables)         ]         mae_metric_vars = metrics_variables[len(self.loss_tracker.variables) :]          loss_tracker_vars = self.loss_tracker.stateless_update_state(             loss_tracker_vars, loss         )         mae_metric_vars = self.mae_metric.stateless_update_state(             mae_metric_vars, y, y_pred         )          logs = {}         logs[self.loss_tracker.name] = self.loss_tracker.stateless_result(             loss_tracker_vars         )         logs[self.mae_metric.name] = self.mae_metric.stateless_result(             mae_metric_vars         )          new_metrics_vars = loss_tracker_vars + mae_metric_vars          # Return metric logs and updated state variables.         state = (             trainable_variables,             non_trainable_variables,             optimizer_variables,             new_metrics_vars,         )         return logs, state      @property     def metrics(self):         # We list our `Metric` objects here so that `reset_states()` can be         # called automatically at the start of each epoch         # or at the start of `evaluate()`.         return [self.loss_tracker, self.mae_metric]   # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs)  # We don't passs a loss or metrics here. model.compile(optimizer=\"adam\")  # Just use `fit` as usual -- you can use callbacks, etc. x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=5)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"providing-your-own-evaluation-step","dir":"Articles","previous_headings":"","what":"Providing your own evaluation step","title":"Customizing what happens in `fit()` with JAX","text":"want calls model.evaluate()? override test_step exactly way. ’s looks like: ’s !","code":"class CustomModel(keras.Model):     def test_step(self, state, data):         # Unpack the data.         x, y = data         (             trainable_variables,             non_trainable_variables,             metrics_variables,         ) = state          # Compute predictions and loss.         y_pred, non_trainable_variables = self.stateless_call(             trainable_variables,             non_trainable_variables,             x,             training=False,         )         loss = self.compute_loss(x, y, y_pred)          # Update metrics.         new_metrics_vars = []         for metric in self.metrics:             this_metric_vars = metrics_variables[                 len(new_metrics_vars) : len(new_metrics_vars)                 + len(metric.variables)             ]             if metric.name == \"loss\":                 this_metric_vars = metric.stateless_update_state(                     this_metric_vars, loss                 )             else:                 this_metric_vars = metric.stateless_update_state(                     this_metric_vars, y, y_pred                 )             logs = metric.stateless_result(this_metric_vars)             new_metrics_vars += this_metric_vars          # Return metric logs and updated state variables.         state = (             trainable_variables,             non_trainable_variables,             new_metrics_vars,         )         return logs, state   # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(loss=\"mse\", metrics=[\"mae\"])  # Evaluate with our custom test_step x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.evaluate(x, y)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Customizing what happens in `fit()` with TensorFlow","text":"’re supervised learning, can use fit() everything works smoothly. need take control every little detail, can write training loop entirely scratch. need custom training algorithm, still want benefit convenient features fit(), callbacks, built-distribution support, step fusing? core principle Keras progressive disclosure complexity. always able get lower-level workflows gradual way. shouldn’t fall cliff high-level functionality doesn’t exactly match use case. able gain control small details retaining commensurate amount high-level convenience. need customize fit() , override training step function Model class. function called fit() every batch data. able call fit() usual – running learning algorithm. Note pattern prevent building models Functional API. can whether ’re building Sequential models, Functional API models, subclassed models. Let’s see works.","code":""},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Customizing what happens in `fit()` with TensorFlow","text":"","code":"library(reticulate) library(tensorflow, exclude = c(\"set_random_seed\", \"shape\")) library(keras3)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"a-first-simple-example","dir":"Articles","previous_headings":"","what":"A first simple example","title":"Customizing what happens in `fit()` with TensorFlow","text":"Let’s start simple example: create new class subclasses Model. just override method train_step(self, data). return dictionary mapping metric names (including loss) current value. input argument data gets passed fit training data: pass arrays, calling fit(x, y, ...), data list (x, y) pass tf_dataset, calling fit(dataset, ...), data gets yielded dataset batch. body train_step() method, implement regular training update, similar already familiar . Importantly, compute loss via self.compute_loss(), wraps loss(es) function(s) passed compile(). Similarly, call metric$update_state(y, y_pred) metrics self$metrics, update state metrics passed compile(), query results self$metrics end retrieve current value. Let’s try :","code":"CustomModel <- new_model_class(   \"CustomModel\",   train_step = function(data) {     c(x, y = NULL, sample_weight = NULL) %<-% data      with(tf$GradientTape() %as% tape, {       y_pred <- self(x, training = TRUE)       loss <- self$compute_loss(y = y, y_pred = y_pred,                                 sample_weight = sample_weight)     })      # Compute gradients     trainable_vars <- self$trainable_variables     gradients <- tape$gradient(loss, trainable_vars)      # Update weights     self$optimizer$apply(gradients, trainable_vars)      # Update metrics (includes the metric that tracks the loss)     for (metric in self$metrics) {       if (metric$name == \"loss\")         metric$update_state(loss)       else         metric$update_state(y, y_pred)     }      # Return a dict mapping metric names to current value     metrics <- lapply(self$metrics, function(m) m$result())     metrics <- setNames(metrics, sapply(self$metrics, function(m) m$name))     metrics   } ) # Construct and compile an instance of CustomModel inputs <- keras_input(shape = 32) outputs <- layer_dense(inputs, 1) model <- CustomModel(inputs, outputs) model |> compile(optimizer = \"adam\", loss = \"mse\", metrics = \"mae\")  # Just use `fit` as usual x <- random_normal(c(1000, 32)) y <- random_normal(c(1000, 1)) model |> fit(x, y, epochs = 3) ## Epoch 1/3 ## 32/32 - 1s - 19ms/step - loss: 2.9118 - mae: 1.3597 ## Epoch 2/3 ## 32/32 - 0s - 1ms/step - loss: 2.6026 - mae: 1.2856 ## Epoch 3/3 ## 32/32 - 0s - 1ms/step - loss: 2.3378 - mae: 1.2193"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"going-lower-level","dir":"Articles","previous_headings":"","what":"Going lower-level","title":"Customizing what happens in `fit()` with TensorFlow","text":"Naturally, just skip passing loss function compile(), instead everything manually train_step. Likewise metrics. ’s lower-level example, uses compile() configure optimizer: start creating Metric instances track loss MAE score (__init__()). implement custom train_step() updates state metrics (calling update_state() ), query (via result()) return current average value, displayed progress bar pass callback. Note need call reset_states() metrics epoch! Otherwise calling result() return average since start training, whereas usually work per-epoch averages. Thankfully, framework can us: just list metric want reset metrics property model. model call reset_states() object listed beginning fit() epoch beginning call evaluate().","code":"CustomModel <- new_model_class(   \"CustomModel\",   initialize = function(...) {     super$initialize(...)     self$loss_tracker <- metric_mean(name = \"loss\")     self$mae_metric <- metric_mean_absolute_error(name = \"mae\")     self$loss_fn <- loss_mean_squared_error()   },   train_step = function(data) {     c(x, y = NULL, sample_weight = NULL) %<-% data      with(tf$GradientTape() %as% tape, {       y_pred <- self(x, training = TRUE)       loss <- self$loss_fn(y, y_pred, sample_weight = sample_weight)     })      # Compute gradients     trainable_vars <- self$trainable_variables     gradients <- tape$gradient(loss, trainable_vars)      # Update weights     self$optimizer$apply(gradients, trainable_vars)      # Compute our own metrics     self$loss_tracker$update_state(loss)     self$mae_metric$update_state(y, y_pred)      # Return a dict mapping metric names to current value     list(       loss = self$loss_tracker$result(),       mae = self$mae_metric$result()     )   },   metrics = mark_active(function() {     # We list our `Metric` objects here so that `reset_states()` can be     # called automatically at the start of each epoch     # or at the start of `evaluate()`.     list(self$loss_tracker, self$mae_metric)   }) )   # Construct and compile an instance of CustomModel inputs <- keras_input(shape = 32) outputs <- layer_dense(inputs, 1) model <- CustomModel(inputs, outputs)  # We don't pass a loss or metrics here. model |> compile(optimizer = \"adam\")  # Just use `fit` as usual x <- random_normal(c(1000, 32)) y <- random_normal(c(1000, 1)) model |> fit(x, y, epochs = 3) ## Epoch 1/3 ## 32/32 - 1s - 16ms/step - loss: 2.6540 - mae: 1.2901 ## Epoch 2/3 ## 32/32 - 0s - 1ms/step - loss: 2.4139 - mae: 1.2303 ## Epoch 3/3 ## 32/32 - 0s - 1ms/step - loss: 2.2080 - mae: 1.1761"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"supporting-sample_weight-class_weight","dir":"Articles","previous_headings":"","what":"Supporting sample_weight & class_weight","title":"Customizing what happens in `fit()` with TensorFlow","text":"may noticed first basic example didn’t make mention sample weighting. want support fit() arguments sample_weight class_weight, ’d simply following: Unpack sample_weight data argument Pass compute_loss & update_state (course, also just apply manually don’t rely compile() losses & metrics) ’s .","code":"CustomModel <- new_model_class(   \"CustomModel\",   train_step = function(data) {     c(x, y = NULL, sample_weight = NULL) %<-% data      with(tf$GradientTape() %as% tape, {       y_pred <- self(x, training = TRUE)       loss <- self$compute_loss(y = y, y_pred = y_pred,                                 sample_weight = sample_weight)     })      # Compute gradients     trainable_vars <- self$trainable_variables     gradients <- tape$gradient(loss, trainable_vars)      # Update weights     self$optimizer$apply_gradients(zip_lists(gradients, trainable_vars))      # Update metrics (includes the metric that tracks the loss)     for (metric in self$metrics) {       if (metric$name == \"loss\") {         metric$update_state(loss)       } else {         metric$update_state(y, y_pred, sample_weight = sample_weight)       }     }      # Return a dict mapping metric names to current value     metrics <- lapply(self$metrics, function(m) m$result())     metrics <- setNames(metrics, sapply(self$metrics, function(m) m$name))     metrics   } )   # Construct and compile an instance of CustomModel inputs <- keras_input(shape = 32) outputs <- layer_dense(inputs, units = 1) model <- CustomModel(inputs, outputs) model |> compile(optimizer = \"adam\", loss = \"mse\", metrics = \"mae\")  # You can now use sample_weight argument x <- random_normal(c(1000, 32)) y <- random_normal(c(1000, 1)) sw <- random_normal(c(1000, 1)) model |> fit(x, y, sample_weight = sw, epochs = 3) ## Epoch 1/3 ## 32/32 - 1s - 18ms/step - loss: 0.1607 - mae: 1.3018 ## Epoch 2/3 ## 32/32 - 0s - 1ms/step - loss: 0.1452 - mae: 1.2999 ## Epoch 3/3 ## 32/32 - 0s - 1ms/step - loss: 0.1335 - mae: 1.2986"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"providing-your-own-evaluation-step","dir":"Articles","previous_headings":"","what":"Providing your own evaluation step","title":"Customizing what happens in `fit()` with TensorFlow","text":"want calls model.evaluate()? override test_step exactly way. ’s looks like:","code":"CustomModel <- new_model_class(   \"CustomModel\",   test_step = function(data) {     # Unpack the data     c(x, y = NULL, sw = NULL) %<-% data     # Compute predictions     y_pred = self(x, training = FALSE)     # Updates the metrics tracking the loss     self$compute_loss(y = y, y_pred = y_pred, sample_weight = sw)     # Update the metrics.     for (metric in self$metrics) {       if (metric$name != \"loss\") {         metric$update_state(y, y_pred, sample_weight = sw)       }     }     # Return a dict mapping metric names to current value.     # Note that it will include the loss (tracked in self.metrics).     metrics <- lapply(self$metrics, function(m) m$result())     metrics <- setNames(metrics, sapply(self$metrics, function(m) m$name))     metrics   } )  # Construct an instance of CustomModel inputs <- keras_input(shape = 32) outputs <- layer_dense(inputs, 1) model <- CustomModel(inputs, outputs) model |> compile(loss = \"mse\", metrics = \"mae\")  # Evaluate with our custom test_step x <- random_normal(c(1000, 32)) y <- random_normal(c(1000, 1)) model |> evaluate(x, y) ## 32/32 - 0s - 8ms/step - loss: 0.0000e+00 - mae: 1.3947 ## $loss ## [1] 0 ## ## $mae ## [1] 1.394695"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"wrapping-up-an-end-to-end-gan-example","dir":"Articles","previous_headings":"","what":"Wrapping up: an end-to-end GAN example","title":"Customizing what happens in `fit()` with TensorFlow","text":"Let’s walk end--end example leverages everything just learned. Let’s consider: generator network meant generate 28x28x1 images. discriminator network meant classify 28x28x1 images two classes (“fake” “real”). One optimizer . loss function train discriminator. ’s feature-complete GAN class, overriding compile() use signature, implementing entire GAN algorithm 17 lines train_step: Let’s test-drive : ideas behind deep learning simple, implementation painful?","code":"# Create the discriminator discriminator <-   keras_model_sequential(name = \"discriminator\", input_shape = c(28, 28, 1)) |>    layer_conv_2d(filters = 64, kernel_size = c(3, 3),                 strides = c(2, 2),  padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_conv_2d(filters = 128, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_global_max_pooling_2d() |>   layer_dense(units = 1)   # Create the generator latent_dim <- 128 generator <-   keras_model_sequential(name = \"generator\", input_shape = latent_dim) |>    layer_dense(7 * 7 * 128) |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_reshape(target_shape = c(7, 7, 128)) |>    layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_conv_2d(filters = 1, kernel_size = c(7, 7), padding = \"same\",                 activation = \"sigmoid\") GAN <- Model(   classname = \"GAN\",    initialize = function(discriminator, generator, latent_dim, ...) {     super$initialize(...)     self$discriminator <- discriminator     self$generator <- generator     self$latent_dim <- as.integer(latent_dim)     self$d_loss_tracker <- metric_mean(name = \"d_loss\")     self$g_loss_tracker <- metric_mean(name = \"g_loss\")   },    compile = function(d_optimizer, g_optimizer, loss_fn, ...) {     super$compile(...)     self$d_optimizer <- d_optimizer     self$g_optimizer <- g_optimizer     self$loss_fn <- loss_fn   },   metrics = active_property(function() {     list(self$d_loss_tracker, self$g_loss_tracker)   }),    train_step = function(real_images) {      # Sample random points in the latent space     batch_size <- shape(real_images)[[1]]     random_latent_vectors <-       tf$random$normal(shape(batch_size, self$latent_dim))      # Decode them to fake images     generated_images <- self$generator(random_latent_vectors)      # Combine them with real images     combined_images <- op_concatenate(list(generated_images,                                            real_images))      # Assemble labels discriminating real from fake images     labels <- op_concatenate(list(op_ones(c(batch_size, 1)),                                   op_zeros(c(batch_size, 1))))      # Add random noise to the labels - important trick!     labels %<>% `+`(tf$random$uniform(shape(.), maxval = 0.05))      # Train the discriminator     with(tf$GradientTape() %as% tape, {       predictions <- self$discriminator(combined_images)       d_loss <- self$loss_fn(labels, predictions)     })     grads <- tape$gradient(d_loss, self$discriminator$trainable_weights)     self$d_optimizer$apply_gradients(       zip_lists(grads, self$discriminator$trainable_weights))      # Sample random points in the latent space     random_latent_vectors <-       tf$random$normal(shape(batch_size, self$latent_dim))      # Assemble labels that say \"all real images\"     misleading_labels <- op_zeros(c(batch_size, 1))      # Train the generator (note that we should *not* update the weights     # of the discriminator)!     with(tf$GradientTape() %as% tape, {       predictions <- self$discriminator(self$generator(random_latent_vectors))       g_loss <- self$loss_fn(misleading_labels, predictions)     })     grads <- tape$gradient(g_loss, self$generator$trainable_weights)     self$g_optimizer$apply_gradients(       zip_lists(grads, self$generator$trainable_weights))      list(d_loss = d_loss, g_loss = g_loss)   } ) batch_size <- 64 c(c(x_train, .), c(x_test, .)) %<-% dataset_mnist() all_digits <- op_concatenate(list(x_train, x_test)) all_digits <- op_reshape(all_digits, c(-1, 28, 28, 1)) dataset <- all_digits |>   tfdatasets::tensor_slices_dataset() |>   tfdatasets::dataset_map(\\(x) op_cast(x, \"float32\") / 255) |>   tfdatasets::dataset_shuffle(buffer_size = 1024) |>   tfdatasets::dataset_batch(batch_size = batch_size)  gan <- GAN(discriminator = discriminator,            generator = generator,            latent_dim = latent_dim)  gan |> compile(   d_optimizer = optimizer_adam(learning_rate = 0.0003),   g_optimizer = optimizer_adam(learning_rate = 0.0003),   loss_fn = loss_binary_crossentropy(from_logits = TRUE) )  # To limit the execution time, we only train on 100 batches. You can train on # the entire dataset. You will need about 20 epochs to get nice results. gan |> fit(   tfdatasets::dataset_take(dataset, 100),   epochs = 1 ) ## 100/100 - 5s - 47ms/step - d_loss: 0.0000e+00 - g_loss: 0.0000e+00"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Customizing what happens in `fit()` with PyTorch","text":"’re supervised learning, can use fit() everything works smoothly. need take control every little detail, can write training loop entirely scratch. need custom training algorithm, still want benefit convenient features fit(), callbacks, built-distribution support, step fusing? core principle Keras progressive disclosure complexity. always able get lower-level workflows gradual way. shouldn’t fall cliff high-level functionality doesn’t exactly match use case. able gain control small details retaining commensurate amount high-level convenience. need customize fit() , override training step function Model class. function called fit() every batch data. able call fit() usual – running learning algorithm. Note pattern prevent building models Functional API. can whether ’re building Sequential models, Functional API models, subclassed models. Let’s see works.","code":""},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Customizing what happens in `fit()` with PyTorch","text":"","code":"import os  # This guide can only be run with the torch backend. os.environ[\"KERAS_BACKEND\"] = \"torch\"  import torch import keras from keras import layers import numpy as np"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"a-first-simple-example","dir":"Articles","previous_headings":"","what":"A first simple example","title":"Customizing what happens in `fit()` with PyTorch","text":"Let’s start simple example: create new class subclasses keras.Model. just override method train_step(self, data). return dictionary mapping metric names (including loss) current value. input argument data gets passed fit training data: pass NumPy arrays, calling fit(x, y, ...), data tuple (x, y) pass torch.utils.data.DataLoader tf.data.Dataset, calling fit(dataset, ...), data gets yielded dataset batch. body train_step() method, implement regular training update, similar already familiar . Importantly, compute loss via self.compute_loss(), wraps loss(es) function(s) passed compile(). Similarly, call metric.update_state(y, y_pred) metrics self.metrics, update state metrics passed compile(), query results self.metrics end retrieve current value. Let’s try :","code":"class CustomModel(keras.Model):     def train_step(self, data):         # Unpack the data. Its structure depends on your model and         # on what you pass to `fit()`.         x, y = data          # Call torch.nn.Module.zero_grad() to clear the leftover gradients         # for the weights from the previous train step.         self.zero_grad()          # Compute loss         y_pred = self(x, training=True)  # Forward pass         loss = self.compute_loss(y=y, y_pred=y_pred)          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()          trainable_weights = [v for v in self.trainable_weights]         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             self.optimizer.apply(gradients, trainable_weights)          # Update metrics (includes the metric that tracks the loss)         for metric in self.metrics:             if metric.name == \"loss\":                 metric.update_state(loss)             else:                 metric.update_state(y, y_pred)          # Return a dict mapping metric names to current value         # Note that it will include the loss (tracked in self.metrics).         return {m.name: m.result() for m in self.metrics} # Construct and compile an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])  # Just use `fit` as usual x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=3)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"going-lower-level","dir":"Articles","previous_headings":"","what":"Going lower-level","title":"Customizing what happens in `fit()` with PyTorch","text":"Naturally, just skip passing loss function compile(), instead everything manually train_step. Likewise metrics. ’s lower-level example, uses compile() configure optimizer: start creating Metric instances track loss MAE score (__init__()). implement custom train_step() updates state metrics (calling update_state() ), query (via result()) return current average value, displayed progress bar pass callback. Note need call reset_states() metrics epoch! Otherwise calling result() return average since start training, whereas usually work per-epoch averages. Thankfully, framework can us: just list metric want reset metrics property model. model call reset_states() object listed beginning fit() epoch beginning call evaluate().","code":"class CustomModel(keras.Model):     def __init__(self, *args, **kwargs):         super().__init__(*args, **kwargs)         self.loss_tracker = keras.metrics.Mean(name=\"loss\")         self.mae_metric = keras.metrics.MeanAbsoluteError(name=\"mae\")         self.loss_fn = keras.losses.MeanSquaredError()      def train_step(self, data):         x, y = data          # Call torch.nn.Module.zero_grad() to clear the leftover gradients         # for the weights from the previous train step.         self.zero_grad()          # Compute loss         y_pred = self(x, training=True)  # Forward pass         loss = self.loss_fn(y, y_pred)          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()          trainable_weights = [v for v in self.trainable_weights]         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             self.optimizer.apply(gradients, trainable_weights)          # Compute our own metrics         self.loss_tracker.update_state(loss)         self.mae_metric.update_state(y, y_pred)         return {             \"loss\": self.loss_tracker.result(),             \"mae\": self.mae_metric.result(),         }      @property     def metrics(self):         # We list our `Metric` objects here so that `reset_states()` can be         # called automatically at the start of each epoch         # or at the start of `evaluate()`.         return [self.loss_tracker, self.mae_metric]   # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs)  # We don't passs a loss or metrics here. model.compile(optimizer=\"adam\")  # Just use `fit` as usual -- you can use callbacks, etc. x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=5)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"supporting-sample_weight-class_weight","dir":"Articles","previous_headings":"","what":"Supporting sample_weight & class_weight","title":"Customizing what happens in `fit()` with PyTorch","text":"may noticed first basic example didn’t make mention sample weighting. want support fit() arguments sample_weight class_weight, ’d simply following: Unpack sample_weight data argument Pass compute_loss & update_state (course, also just apply manually don’t rely compile() losses & metrics) ’s .","code":"class CustomModel(keras.Model):     def train_step(self, data):         # Unpack the data. Its structure depends on your model and         # on what you pass to `fit()`.         if len(data) == 3:             x, y, sample_weight = data         else:             sample_weight = None             x, y = data          # Call torch.nn.Module.zero_grad() to clear the leftover gradients         # for the weights from the previous train step.         self.zero_grad()          # Compute loss         y_pred = self(x, training=True)  # Forward pass         loss = self.compute_loss(             y=y,             y_pred=y_pred,             sample_weight=sample_weight,         )          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()          trainable_weights = [v for v in self.trainable_weights]         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             self.optimizer.apply(gradients, trainable_weights)          # Update metrics (includes the metric that tracks the loss)         for metric in self.metrics:             if metric.name == \"loss\":                 metric.update_state(loss)             else:                 metric.update_state(y, y_pred, sample_weight=sample_weight)          # Return a dict mapping metric names to current value         # Note that it will include the loss (tracked in self.metrics).         return {m.name: m.result() for m in self.metrics}   # Construct and compile an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])  # You can now use sample_weight argument x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) sw = np.random.random((1000, 1)) model.fit(x, y, sample_weight=sw, epochs=3)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"providing-your-own-evaluation-step","dir":"Articles","previous_headings":"","what":"Providing your own evaluation step","title":"Customizing what happens in `fit()` with PyTorch","text":"want calls model.evaluate()? override test_step exactly way. ’s looks like:","code":"class CustomModel(keras.Model):     def test_step(self, data):         # Unpack the data         x, y = data         # Compute predictions         y_pred = self(x, training=False)         # Updates the metrics tracking the loss         loss = self.compute_loss(y=y, y_pred=y_pred)         # Update the metrics.         for metric in self.metrics:             if metric.name == \"loss\":                 metric.update_state(loss)             else:                 metric.update_state(y, y_pred)         # Return a dict mapping metric names to current value.         # Note that it will include the loss (tracked in self.metrics).         return {m.name: m.result() for m in self.metrics}   # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(loss=\"mse\", metrics=[\"mae\"])  # Evaluate with our custom test_step x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.evaluate(x, y)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"wrapping-up-an-end-to-end-gan-example","dir":"Articles","previous_headings":"","what":"Wrapping up: an end-to-end GAN example","title":"Customizing what happens in `fit()` with PyTorch","text":"Let’s walk end--end example leverages everything just learned. Let’s consider: generator network meant generate 28x28x1 images. discriminator network meant classify 28x28x1 images two classes (“fake” “real”). One optimizer . loss function train discriminator. ’s feature-complete GAN class, overriding compile() use signature, implementing entire GAN algorithm 17 lines train_step: Let’s test-drive : ideas behind deep learning simple, implementation painful?","code":"# Create the discriminator discriminator = keras.Sequential(     [         keras.Input(shape=(28, 28, 1)),         layers.Conv2D(64, (3, 3), strides=(2, 2), padding=\"same\"),         layers.LeakyReLU(negative_slope=0.2),         layers.Conv2D(128, (3, 3), strides=(2, 2), padding=\"same\"),         layers.LeakyReLU(negative_slope=0.2),         layers.GlobalMaxPooling2D(),         layers.Dense(1),     ],     name=\"discriminator\", )  # Create the generator latent_dim = 128 generator = keras.Sequential(     [         keras.Input(shape=(latent_dim,)),         # We want to generate 128 coefficients to reshape into a 7x7x128 map         layers.Dense(7 * 7 * 128),         layers.LeakyReLU(negative_slope=0.2),         layers.Reshape((7, 7, 128)),         layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding=\"same\"),         layers.LeakyReLU(negative_slope=0.2),         layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding=\"same\"),         layers.LeakyReLU(negative_slope=0.2),         layers.Conv2D(1, (7, 7), padding=\"same\", activation=\"sigmoid\"),     ],     name=\"generator\", ) class GAN(keras.Model):     def __init__(self, discriminator, generator, latent_dim):         super().__init__()         self.discriminator = discriminator         self.generator = generator         self.latent_dim = latent_dim         self.d_loss_tracker = keras.metrics.Mean(name=\"d_loss\")         self.g_loss_tracker = keras.metrics.Mean(name=\"g_loss\")         self.seed_generator = keras.random.SeedGenerator(1337)         self.built = True      @property     def metrics(self):         return [self.d_loss_tracker, self.g_loss_tracker]      def compile(self, d_optimizer, g_optimizer, loss_fn):         super().compile()         self.d_optimizer = d_optimizer         self.g_optimizer = g_optimizer         self.loss_fn = loss_fn      def train_step(self, real_images):         if isinstance(real_images, tuple):             real_images = real_images[0]         # Sample random points in the latent space         batch_size = real_images.shape[0]         random_latent_vectors = keras.random.normal(             shape=(batch_size, self.latent_dim), seed=self.seed_generator         )          # Decode them to fake images         generated_images = self.generator(random_latent_vectors)          # Combine them with real images         real_images = torch.tensor(real_images)         combined_images = torch.concat([generated_images, real_images], axis=0)          # Assemble labels discriminating real from fake images         labels = torch.concat(             [torch.ones((batch_size, 1)), torch.zeros((batch_size, 1))], axis=0         )         # Add random noise to the labels - important trick!         labels += 0.05 * keras.random.uniform(             labels.shape, seed=self.seed_generator         )          # Train the discriminator         self.zero_grad()         predictions = self.discriminator(combined_images)         d_loss = self.loss_fn(labels, predictions)         d_loss.backward()         grads = [v.value.grad for v in self.discriminator.trainable_weights]         with torch.no_grad():             self.d_optimizer.apply(grads, self.discriminator.trainable_weights)          # Sample random points in the latent space         random_latent_vectors = keras.random.normal(             shape=(batch_size, self.latent_dim), seed=self.seed_generator         )          # Assemble labels that say \"all real images\"         misleading_labels = torch.zeros((batch_size, 1))          # Train the generator (note that we should *not* update the weights         # of the discriminator)!         self.zero_grad()         predictions = self.discriminator(self.generator(random_latent_vectors))         g_loss = self.loss_fn(misleading_labels, predictions)         grads = g_loss.backward()         grads = [v.value.grad for v in self.generator.trainable_weights]         with torch.no_grad():             self.g_optimizer.apply(grads, self.generator.trainable_weights)          # Update metrics and return their value.         self.d_loss_tracker.update_state(d_loss)         self.g_loss_tracker.update_state(g_loss)         return {             \"d_loss\": self.d_loss_tracker.result(),             \"g_loss\": self.g_loss_tracker.result(),         } # Prepare the dataset. We use both the training & test MNIST digits. batch_size = 64 (x_train, _), (x_test, _) = keras.datasets.mnist.load_data() all_digits = np.concatenate([x_train, x_test]) all_digits = all_digits.astype(\"float32\") / 255.0 all_digits = np.reshape(all_digits, (-1, 28, 28, 1))  # Create a TensorDataset dataset = torch.utils.data.TensorDataset(     torch.from_numpy(all_digits), torch.from_numpy(all_digits) ) # Create a DataLoader dataloader = torch.utils.data.DataLoader(     dataset, batch_size=batch_size, shuffle=True )  gan = GAN(     discriminator=discriminator, generator=generator, latent_dim=latent_dim ) gan.compile(     d_optimizer=keras.optimizers.Adam(learning_rate=0.0003),     g_optimizer=keras.optimizers.Adam(learning_rate=0.0003),     loss_fn=keras.losses.BinaryCrossentropy(from_logits=True), )  gan.fit(dataloader, epochs=1)"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Customizing Saving and Serialization","text":"guide covers advanced methods can customized Keras saving. users, methods outlined primary Serialize, save, export guide sufficient.","code":""},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"apis","dir":"Articles","previous_headings":"Introduction","what":"APIs","title":"Customizing Saving and Serialization","text":"cover following APIs: save_assets() load_assets() save_own_variables() load_own_variables() get_build_config() build_from_config() get_compile_config() compile_from_config() restoring model, get executed following order: build_from_config() compile_from_config() load_own_variables() load_assets()","code":""},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Customizing Saving and Serialization","text":"","code":"import os import numpy as np import keras"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"state-saving-customization","dir":"Articles","previous_headings":"","what":"State saving customization","title":"Customizing Saving and Serialization","text":"methods determine state model’s layers saved calling model.save(). can override take full control state saving process.","code":""},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"save_own_variables-and-load_own_variables","dir":"Articles","previous_headings":"State saving customization","what":"save_own_variables() and load_own_variables()","title":"Customizing Saving and Serialization","text":"methods save load state variables layer model.save() keras.models.load_model() called, respectively. default, state variables saved loaded weights layer (trainable non-trainable). default implementation save_own_variables(): store used methods dictionary can populated layer variables. Let’s take look example customizing . Example:","code":"def save_own_variables(self, store):     all_vars = self._trainable_weights + self._non_trainable_weights     for i, v in enumerate(all_vars):         store[f\"{i}\"] = v.numpy() @keras.utils.register_keras_serializable(package=\"my_custom_package\") class LayerWithCustomVariable(keras.layers.Dense):     def __init__(self, units, **kwargs):         super().__init__(units, **kwargs)         self.my_variable = keras.Variable(             np.random.random((units,)), name=\"my_variable\", dtype=\"float32\"         )      def save_own_variables(self, store):         super().save_own_variables(store)         # Stores the value of the variable upon saving         store[\"variables\"] = self.my_variable.numpy()      def load_own_variables(self, store):         # Assigns the value of the variable upon loading         self.my_variable.assign(store[\"variables\"])         # Load the remaining weights         for i, v in enumerate(self.weights):             v.assign(store[f\"{i}\"])         # Note: You must specify how all variables (including layer weights)         # are loaded in `load_own_variables.`      def call(self, inputs):         dense_out = super().call(inputs)         return dense_out + self.my_variable   model = keras.Sequential([LayerWithCustomVariable(1)])  ref_input = np.random.random((8, 10)) ref_output = np.random.random((8, 10)) model.compile(optimizer=\"adam\", loss=\"mean_squared_error\") model.fit(ref_input, ref_output)  model.save(\"custom_vars_model.keras\") restored_model = keras.models.load_model(\"custom_vars_model.keras\")  np.testing.assert_allclose(     model.layers[0].my_variable.numpy(),     restored_model.layers[0].my_variable.numpy(), )"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"save_assets-and-load_assets","dir":"Articles","previous_headings":"State saving customization","what":"save_assets() and load_assets()","title":"Customizing Saving and Serialization","text":"methods can added model class definition store load additional information model needs. example, NLP domain layers TextVectorization layers IndexLookup layers may need store associated vocabulary (lookup table) text file upon saving. Let’s take basics workflow simple file assets.txt. Example:","code":"@keras.saving.register_keras_serializable(package=\"my_custom_package\") class LayerWithCustomAssets(keras.layers.Dense):     def __init__(self, vocab=None, *args, **kwargs):         super().__init__(*args, **kwargs)         self.vocab = vocab      def save_assets(self, inner_path):         # Writes the vocab (sentence) to text file at save time.         with open(os.path.join(inner_path, \"vocabulary.txt\"), \"w\") as f:             f.write(self.vocab)      def load_assets(self, inner_path):         # Reads the vocab (sentence) from text file at load time.         with open(os.path.join(inner_path, \"vocabulary.txt\"), \"r\") as f:             text = f.read()         self.vocab = text.replace(\"<unk>\", \"little\")   model = keras.Sequential(     [LayerWithCustomAssets(vocab=\"Mary had a <unk> lamb.\", units=5)] )  x = np.random.random((10, 10)) y = model(x)  model.save(\"custom_assets_model.keras\") restored_model = keras.models.load_model(\"custom_assets_model.keras\")  np.testing.assert_string_equal(     restored_model.layers[0].vocab, \"Mary had a little lamb.\" )"},{"path":[]},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"get_build_config-and-build_from_config","dir":"Articles","previous_headings":"build and compile saving customization","what":"get_build_config() and build_from_config()","title":"Customizing Saving and Serialization","text":"methods work together save layer’s built states restore upon loading. default, includes build config dictionary layer’s input shape, overriding methods can used include Variables Lookup Tables can useful restore built model. Example:","code":"@keras.saving.register_keras_serializable(package=\"my_custom_package\") class LayerWithCustomBuild(keras.layers.Layer):     def __init__(self, units=32, **kwargs):         super().__init__(**kwargs)         self.units = units      def call(self, inputs):         return keras.ops.matmul(inputs, self.w) + self.b      def get_config(self):         return dict(units=self.units, **super().get_config())      def build(self, input_shape, layer_init):         # Note the overriding of `build()` to add an extra argument.         # Therefore, we will need to manually call build with `layer_init` argument         # before the first execution of `call()`.         super().build(input_shape)         self._input_shape = input_shape         self.w = self.add_weight(             shape=(input_shape[-1], self.units),             initializer=layer_init,             trainable=True,         )         self.b = self.add_weight(             shape=(self.units,),             initializer=layer_init,             trainable=True,         )         self.layer_init = layer_init      def get_build_config(self):         build_config = {             \"layer_init\": self.layer_init,             \"input_shape\": self._input_shape,         }  # Stores our initializer for `build()`         return build_config      def build_from_config(self, config):         # Calls `build()` with the parameters at loading time         self.build(config[\"input_shape\"], config[\"layer_init\"])   custom_layer = LayerWithCustomBuild(units=16) custom_layer.build(input_shape=(8,), layer_init=\"random_normal\")  model = keras.Sequential(     [         custom_layer,         keras.layers.Dense(1, activation=\"sigmoid\"),     ] )  x = np.random.random((16, 8)) y = model(x)  model.save(\"custom_build_model.keras\") restored_model = keras.models.load_model(\"custom_build_model.keras\")  np.testing.assert_equal(restored_model.layers[0].layer_init, \"random_normal\") np.testing.assert_equal(restored_model.built, True)"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"get_compile_config-and-compile_from_config","dir":"Articles","previous_headings":"build and compile saving customization","what":"get_compile_config() and compile_from_config()","title":"Customizing Saving and Serialization","text":"methods work together save information model compiled (optimizers, losses, etc.) restore re-compile model information. Overriding methods can useful compiling restored model custom optimizers, custom losses, etc., need deserialized prior calling model.compile compile_from_config(). Let’s take look example . Example:","code":"@keras.saving.register_keras_serializable(package=\"my_custom_package\") def small_square_sum_loss(y_true, y_pred):     loss = keras.ops.square(y_pred - y_true)     loss = loss / 10.0     loss = keras.ops.sum(loss, axis=1)     return loss   @keras.saving.register_keras_serializable(package=\"my_custom_package\") def mean_pred(y_true, y_pred):     return keras.ops.mean(y_pred)   @keras.saving.register_keras_serializable(package=\"my_custom_package\") class ModelWithCustomCompile(keras.Model):     def __init__(self, **kwargs):         super().__init__(**kwargs)         self.dense1 = keras.layers.Dense(8, activation=\"relu\")         self.dense2 = keras.layers.Dense(4, activation=\"softmax\")      def call(self, inputs):         x = self.dense1(inputs)         return self.dense2(x)      def compile(self, optimizer, loss_fn, metrics):         super().compile(optimizer=optimizer, loss=loss_fn, metrics=metrics)         self.model_optimizer = optimizer         self.loss_fn = loss_fn         self.loss_metrics = metrics      def get_compile_config(self):         # These parameters will be serialized at saving time.         return {             \"model_optimizer\": self.model_optimizer,             \"loss_fn\": self.loss_fn,             \"metric\": self.loss_metrics,         }      def compile_from_config(self, config):         # Deserializes the compile parameters (important, since many are custom)         optimizer = keras.utils.deserialize_keras_object(config[\"model_optimizer\"])         loss_fn = keras.utils.deserialize_keras_object(config[\"loss_fn\"])         metrics = keras.utils.deserialize_keras_object(config[\"metric\"])          # Calls compile with the deserialized parameters         self.compile(optimizer=optimizer, loss_fn=loss_fn, metrics=metrics)   model = ModelWithCustomCompile() model.compile(     optimizer=\"SGD\", loss_fn=small_square_sum_loss, metrics=[\"accuracy\", mean_pred] )  x = np.random.random((4, 8)) y = np.random.random((4,))  model.fit(x, y)  model.save(\"custom_compile_model.keras\") restored_model = keras.models.load_model(\"custom_compile_model.keras\")  np.testing.assert_equal(model.model_optimizer, restored_model.model_optimizer) np.testing.assert_equal(model.loss_fn, restored_model.loss_fn) np.testing.assert_equal(model.loss_metrics, restored_model.loss_metrics)"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"Customizing Saving and Serialization","text":"Using methods learned tutorial allows wide variety use cases, allowing saving loading complex models exotic assets state elements. recap: save_own_variables load_own_variables determine states saved loaded. save_assets load_assets can added store load additional information model needs. get_build_config build_from_config save restore model’s built states. get_compile_config compile_from_config save restore model’s compiled states.","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_jax.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Multi-GPU distributed training with JAX","text":"generally two ways distribute computation across multiple devices: Data parallelism, single model gets replicated multiple devices multiple machines. processes different batches data, merge results. exist many variants setup, differ different model replicas merge results, whether stay sync every batch whether loosely coupled, etc. Model parallelism, different parts single model run different devices, processing single batch data together. works best models naturally-parallel architecture, models feature multiple branches. guide focuses data parallelism, particular synchronous data parallelism, different replicas model stay sync batch process. Synchronicity keeps model convergence behavior identical see single-device training. Specifically, guide teaches use jax.sharding APIs train Keras models, minimal changes code, multiple GPUs TPUS (typically 2 16) installed single machine (single host, multi-device training). common setup researchers small-scale industry workflows.","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_jax.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Multi-GPU distributed training with JAX","text":"Let’s start defining function creates model train, function creates dataset train (MNIST case).","code":"import os  os.environ[\"KERAS_BACKEND\"] = \"jax\"  import jax import numpy as np import tensorflow as tf import keras  from jax.experimental import mesh_utils from jax.sharding import Mesh from jax.sharding import NamedSharding from jax.sharding import PartitionSpec as P   def get_model():     # Make a simple convnet with batch normalization and dropout.     inputs = keras.Input(shape=(28, 28, 1))     x = keras.layers.Rescaling(1.0 / 255.0)(inputs)     x = keras.layers.Conv2D(         filters=12, kernel_size=3, padding=\"same\", use_bias=False     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.Conv2D(         filters=24,         kernel_size=6,         use_bias=False,         strides=2,     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.Conv2D(         filters=32,         kernel_size=6,         padding=\"same\",         strides=2,         name=\"large_k\",     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.GlobalAveragePooling2D()(x)     x = keras.layers.Dense(256, activation=\"relu\")(x)     x = keras.layers.Dropout(0.5)(x)     outputs = keras.layers.Dense(10)(x)     model = keras.Model(inputs, outputs)     return model   def get_datasets():     # Load the data and split it between train and test sets     (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()      # Scale images to the [0, 1] range     x_train = x_train.astype(\"float32\")     x_test = x_test.astype(\"float32\")     # Make sure images have shape (28, 28, 1)     x_train = np.expand_dims(x_train, -1)     x_test = np.expand_dims(x_test, -1)     print(\"x_train shape:\", x_train.shape)     print(x_train.shape[0], \"train samples\")     print(x_test.shape[0], \"test samples\")      # Create TF Datasets     train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))     eval_data = tf.data.Dataset.from_tensor_slices((x_test, y_test))     return train_data, eval_data"},{"path":"https://keras.posit.co/articles/distributed_training_with_jax.html","id":"single-host-multi-device-synchronous-training","dir":"Articles","previous_headings":"","what":"Single-host, multi-device synchronous training","title":"Multi-GPU distributed training with JAX","text":"setup, one machine several GPUs TPUs (typically 2 16). device run copy model (called replica). simplicity, follows, ’ll assume ’re dealing 8 GPUs, loss generality. works step training: current batch data (called global batch) split 8 different sub-batches (called local batches). instance, global batch 512 samples, 8 local batches 64 samples. 8 replicas independently processes local batch: run forward pass, backward pass, outputting gradient weights respect loss model local batch. weight updates originating local gradients efficiently merged across 8 replicas. done end every step, replicas always stay sync. practice, process synchronously updating weights model replicas handled level individual weight variable. done using jax.sharding.NamedSharding configured replicate variables. use single-host, multi-device synchronous training Keras model, use jax.sharding features. ’s works: first create device mesh using mesh_utils.create_device_mesh. specify want replicate model optimizer variables across devices using spec axis. specify want shard data across devices using spec splits along batch dimension. use jax.device_put replicate model optimizer variables across devices. happens beginning. training loop, batch process, use jax.device_put split batch across devices invoking train step. ’s flow, step split utility function: ’s !","code":"# Config num_epochs = 2 batch_size = 64  train_data, eval_data = get_datasets() train_data = train_data.batch(batch_size, drop_remainder=True)  model = get_model() optimizer = keras.optimizers.Adam(1e-3) loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)  # Initialize all state with .build() (one_batch, one_batch_labels) = next(iter(train_data)) model.build(one_batch) optimizer.build(model.trainable_variables)   # This is the loss function that will be differentiated. # Keras provides a pure functional forward pass: model.stateless_call def compute_loss(trainable_variables, non_trainable_variables, x, y):     y_pred, updated_non_trainable_variables = model.stateless_call(         trainable_variables, non_trainable_variables, x     )     loss_value = loss(y, y_pred)     return loss_value, updated_non_trainable_variables   # Function to compute gradients compute_gradients = jax.value_and_grad(compute_loss, has_aux=True)   # Training step, Keras provides a pure functional optimizer.stateless_apply @jax.jit def train_step(train_state, x, y):     (         trainable_variables,         non_trainable_variables,         optimizer_variables,     ) = train_state     (loss_value, non_trainable_variables), grads = compute_gradients(         trainable_variables, non_trainable_variables, x, y     )      trainable_variables, optimizer_variables = optimizer.stateless_apply(         optimizer_variables, grads, trainable_variables     )      return loss_value, (         trainable_variables,         non_trainable_variables,         optimizer_variables,     )   # Replicate the model and optimizer variable on all devices def get_replicated_train_state(devices):     # All variables will be replicated on all devices     var_mesh = Mesh(devices, axis_names=(\"_\"))     # In NamedSharding, axes not mentioned are replicated (all axes here)     var_replication = NamedSharding(var_mesh, P())      # Apply the distribution settings to the model variables     trainable_variables = jax.device_put(         model.trainable_variables, var_replication     )     non_trainable_variables = jax.device_put(         model.non_trainable_variables, var_replication     )     optimizer_variables = jax.device_put(optimizer.variables, var_replication)      # Combine all state in a tuple     return (trainable_variables, non_trainable_variables, optimizer_variables)   num_devices = len(jax.local_devices()) print(f\"Running on {num_devices} devices: {jax.local_devices()}\") devices = mesh_utils.create_device_mesh((num_devices,))  # Data will be split along the batch axis data_mesh = Mesh(devices, axis_names=(\"batch\",))  # naming axes of the mesh data_sharding = NamedSharding(     data_mesh,     P(         \"batch\",     ), )  # naming axes of the sharded partition  # Display data sharding x, y = next(iter(train_data)) sharded_x = jax.device_put(x.numpy(), data_sharding) print(\"Data sharding\") jax.debug.visualize_array_sharding(jax.numpy.reshape(sharded_x, [-1, 28 * 28]))  train_state = get_replicated_train_state(devices)  # Custom training loop for epoch in range(num_epochs):     data_iter = iter(train_data)     for data in data_iter:         x, y = data         sharded_x = jax.device_put(x.numpy(), data_sharding)         loss_value, train_state = train_step(train_state, sharded_x, y.numpy())     print(\"Epoch\", epoch, \"loss:\", loss_value)  # Post-processing model state update to write them back into the model trainable_variables, non_trainable_variables, optimizer_variables = train_state for variable, value in zip(model.trainable_variables, trainable_variables):     variable.assign(value) for variable, value in zip(     model.non_trainable_variables, non_trainable_variables ):     variable.assign(value)"},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Multi-GPU distributed training with TensorFlow","text":"generally two ways distribute computation across multiple devices: Data parallelism, single model gets replicated multiple devices multiple machines. processes different batches data, merge results. exist many variants setup, differ different model replicas merge results, whether stay sync every batch whether loosely coupled, etc. Model parallelism, different parts single model run different devices, processing single batch data together. works best models naturally-parallel architecture, models feature multiple branches. guide focuses data parallelism, particular synchronous data parallelism, different replicas model stay sync batch process. Synchronicity keeps model convergence behavior identical see single-device training. Specifically, guide teaches use tf.distribute API train Keras models multiple GPUs, minimal changes code, multiple GPUs (typically 2 16) installed single machine (single host, multi-device training). common setup researchers small-scale industry workflows.","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Multi-GPU distributed training with TensorFlow","text":"","code":"library(keras3) library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")"},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"single-host-multi-device-synchronous-training","dir":"Articles","previous_headings":"","what":"Single-host, multi-device synchronous training","title":"Multi-GPU distributed training with TensorFlow","text":"setup, one machine several GPUs (typically 2 16). device run copy model (called replica). simplicity, follows, ’ll assume ’re dealing 8 GPUs, loss generality. works step training: current batch data (called global batch) split 8 different sub-batches (called local batches). instance, global batch 512 samples, 8 local batches 64 samples. 8 replicas independently processes local batch: run forward pass, backward pass, outputting gradient weights respect loss model local batch. weight updates originating local gradients efficiently merged across 8 replicas. done end every step, replicas always stay sync. practice, process synchronously updating weights model replicas handled level individual weight variable. done mirrored variable object. use single-host, multi-device synchronous training Keras model, use tf$distribute$MirroredStrategy API. ’s works: Instantiate MirroredStrategy, optionally configuring specific devices want use (default strategy use GPUs available). Use strategy object open scope, within scope, create Keras objects need contain variables. Typically, means creating & compiling model inside distribution scope. cases, first call fit() may also create variables, ’s good idea put fit() call scope well. Train model via fit() usual. Importantly, recommend use tf.data.Dataset objects load data multi-device distributed workflow. Schematically, looks like : ’s simple end--end runnable example:","code":"# Create a MirroredStrategy. strategy <- tf$distribute$MirroredStrategy() cat(sprintf('Number of devices: %d\\n', strategy$num_replicas_in_sync))  # Open a strategy scope. with(startegy$scope(), {   # Everything that creates variables should be under the strategy scope.   # In general this is only model construction & `compile()`.   model <- Model(...)   model |> compile(...)    # Train the model on all available devices.   model |> fit(train_dataset, validation_data=val_dataset, ...)    # Test the model on all available devices.   model |> evaluate(test_dataset) }) get_compiled_model <- function() {   inputs <- keras_input(shape = 784)   outputs <- inputs |>     layer_dense(units = 256, activation = \"relu\") |>     layer_dense(units = 256, activation = \"relu\") |>     layer_dense(units = 10)   model <- keras_model(inputs, outputs)   model |> compile(     optimizer = optimizer_adam(),     loss = loss_sparse_categorical_crossentropy(from_logits = TRUE),     metrics = list(metric_sparse_categorical_accuracy()),      # XLA compilation is temporarily disabled due to a bug     # https://github.com/keras-team/keras/issues/19005     jit_compile = FALSE   )   model }  get_dataset <- function(batch_size = 64) {    c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()   x_train <- array_reshape(x_train, c(-1, 784))   x_test <- array_reshape(x_test, c(-1, 784))    # Reserve 10,000 samples for validation.   val_i <- sample.int(nrow(x_train), 10000)   x_val <- x_train[val_i,]   y_val <- y_train[val_i]   x_train = x_train[-val_i,]   y_train = y_train[-val_i]    # Prepare the training dataset.   train_dataset <- list(x_train, y_train) |>     tensor_slices_dataset() |>     dataset_batch(batch_size)    # Prepare the validation dataset.   val_dataset <- list(x_val, y_val) |>     tensor_slices_dataset() |>     dataset_batch(batch_size)    # Prepare the test dataset.   test_dataset <- list(x_test, y_test) |>     tensor_slices_dataset() |>     dataset_batch(batch_size)    list(train_dataset, val_dataset, test_dataset) }  # Create a MirroredStrategy. strategy <- tf$distribute$MirroredStrategy() cat(sprintf('Number of devices: %d\\n', strategy$num_replicas_in_sync)) ## Number of devices: 2 # Open a strategy scope. with(strategy$scope(), {   # Everything that creates variables should be under the strategy scope.   # In general this is only model construction & `compile()`.   model <- get_compiled_model()    c(train_dataset, val_dataset, test_dataset) %<-% get_dataset()    # Train the model on all available devices.   model |> fit(train_dataset, epochs = 2, validation_data = val_dataset)    # Test the model on all available devices.   model |> evaluate(test_dataset)  }) ## Epoch 1/2 ## 782/782 - 5s - 7ms/step - loss: 3.0622 - sparse_categorical_accuracy: 0.8615 - val_loss: 1.1367 - val_sparse_categorical_accuracy: 0.9006 ## Epoch 2/2 ## 782/782 - 3s - 4ms/step - loss: 0.5774 - sparse_categorical_accuracy: 0.9259 - val_loss: 0.6612 - val_sparse_categorical_accuracy: 0.9210 ## 157/157 - 0s - 2ms/step - loss: 0.6729 - sparse_categorical_accuracy: 0.9150 ## $loss ## [1] 0.6728871 ## ## $sparse_categorical_accuracy ## [1] 0.915"},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"using-callbacks-to-ensure-fault-tolerance","dir":"Articles","previous_headings":"","what":"Using callbacks to ensure fault tolerance","title":"Multi-GPU distributed training with TensorFlow","text":"using distributed training, always make sure strategy recover failure (fault tolerance). simplest way handle pass ModelCheckpoint callback fit(), save model regular intervals (e.g. every 100 batches every epoch). can restart training saved model. ’s simple example:","code":"# Prepare a directory to store all the checkpoints. checkpoint_dir <- \"./ckpt\" if (!dir.exists(checkpoint_dir)) {   dir.create(checkpoint_dir) }  make_or_restore_model <- function() {   # Either restore the latest model, or create a fresh one   # if there is no checkpoint available.   checkpoints <- list.files(checkpoint_dir, #pattern = \"ckpt-.*\\\\.keras\",                             full.names = TRUE)    if (length(checkpoints) > 0) {     checkpoint_epochs <- as.integer(sub(\"ckpt-([0-9]+)\\\\.keras\", \"\\\\1\",                                         basename(checkpoints)))     latest_checkpoint <- checkpoints[which.max(checkpoint_epochs)]     load_model(latest_checkpoint)   } else {     get_compiled_model()   } }    run_training <- function(epochs = 1) {   # Create a MirroredStrategy.   strategy <- tf$distribute$MirroredStrategy()    # Open a strategy scope and create/restore the model   with(strategy$scope(), {     model <- make_or_restore_model()      callbacks <- list(       # This callback saves a SavedModel every epoch       # We include the current epoch in the folder name.       callback_model_checkpoint(         filepath = paste0(checkpoint_dir, \"/ckpt-{epoch}.keras\"),         save_freq = \"epoch\"       ))      model |> fit(       train_dataset,       epochs = epochs,       callbacks = callbacks,       validation_data = val_dataset,       verbose = 2     )   }) }  # Running the first time creates the model run_training(epochs = 1) ## 782/782 - 4s - 6ms/step - loss: 2.9519 - sparse_categorical_accuracy: 0.8655 - val_loss: 1.3110 - val_sparse_categorical_accuracy: 0.8836 # Calling the same function again will resume from where we left off run_training(epochs = 1) ## 782/782 - 3s - 4ms/step - loss: 0.5998 - sparse_categorical_accuracy: 0.9270 - val_loss: 0.8736 - val_sparse_categorical_accuracy: 0.9128"},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"tfdata-performance-tips","dir":"Articles","previous_headings":"","what":"tf$data performance tips","title":"Multi-GPU distributed training with TensorFlow","text":"distributed training, efficiency load data can often become critical. tips make sure tf$data pipelines run fast possible. Note dataset batching creating dataset, make sure batched global batch size. instance, 8 GPUs capable running batch 64 samples, call use global batch size 512. Calling dataset_cache() call dataset_cache() dataset, data cached running first iteration data. Every subsequent iteration use cached data. cache can memory (default) local file specify. can improve performance : data expected change iteration iteration reading data remote distributed filesystem reading data local disk, data fit memory workflow significantly IO-bound (e.g. reading & decoding image files). Calling dataset_prefetch(buffer_size) almost always call dataset_prefetch(buffer_size) creating dataset. means data pipeline run asynchronously model, new samples preprocessed stored buffer current batch samples used train model. next batch prefetched GPU memory time current batch . ’s !","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_torch.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Multi-GPU distributed training with PyTorch","text":"generally two ways distribute computation across multiple devices: Data parallelism, single model gets replicated multiple devices multiple machines. processes different batches data, merge results. exist many variants setup, differ different model replicas merge results, whether stay sync every batch whether loosely coupled, etc. Model parallelism, different parts single model run different devices, processing single batch data together. works best models naturally-parallel architecture, models feature multiple branches. guide focuses data parallelism, particular synchronous data parallelism, different replicas model stay sync batch process. Synchronicity keeps model convergence behavior identical see single-device training. Specifically, guide teaches use PyTorch’s DistributedDataParallel module wrapper train Keras, minimal changes code, multiple GPUs (typically 2 16) installed single machine (single host, multi-device training). common setup researchers small-scale industry workflows.","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_torch.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Multi-GPU distributed training with PyTorch","text":"Let’s start defining function creates model train, function creates dataset train (MNIST case). Next, let’s define simple PyTorch training loop targets GPU (note calls .cuda()).","code":"import os  os.environ[\"KERAS_BACKEND\"] = \"torch\"  import torch import numpy as np import keras   def get_model():     # Make a simple convnet with batch normalization and dropout.     inputs = keras.Input(shape=(28, 28, 1))     x = keras.layers.Rescaling(1.0 / 255.0)(inputs)     x = keras.layers.Conv2D(         filters=12, kernel_size=3, padding=\"same\", use_bias=False     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.Conv2D(         filters=24,         kernel_size=6,         use_bias=False,         strides=2,     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.Conv2D(         filters=32,         kernel_size=6,         padding=\"same\",         strides=2,         name=\"large_k\",     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.GlobalAveragePooling2D()(x)     x = keras.layers.Dense(256, activation=\"relu\")(x)     x = keras.layers.Dropout(0.5)(x)     outputs = keras.layers.Dense(10)(x)     model = keras.Model(inputs, outputs)     return model   def get_dataset():     # Load the data and split it between train and test sets     (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()      # Scale images to the [0, 1] range     x_train = x_train.astype(\"float32\")     x_test = x_test.astype(\"float32\")     # Make sure images have shape (28, 28, 1)     x_train = np.expand_dims(x_train, -1)     x_test = np.expand_dims(x_test, -1)     print(\"x_train shape:\", x_train.shape)      # Create a TensorDataset     dataset = torch.utils.data.TensorDataset(         torch.from_numpy(x_train), torch.from_numpy(y_train)     )     return dataset def train_model(model, dataloader, num_epochs, optimizer, loss_fn):     for epoch in range(num_epochs):         running_loss = 0.0         running_loss_count = 0         for batch_idx, (inputs, targets) in enumerate(dataloader):             inputs = inputs.cuda(non_blocking=True)             targets = targets.cuda(non_blocking=True)              # Forward pass             outputs = model(inputs)             loss = loss_fn(outputs, targets)              # Backward and optimize             optimizer.zero_grad()             loss.backward()             optimizer.step()              running_loss += loss.item()             running_loss_count += 1          # Print loss statistics         print(             f\"Epoch {epoch + 1}/{num_epochs}, \"             f\"Loss: {running_loss / running_loss_count}\"         )"},{"path":"https://keras.posit.co/articles/distributed_training_with_torch.html","id":"single-host-multi-device-synchronous-training","dir":"Articles","previous_headings":"","what":"Single-host, multi-device synchronous training","title":"Multi-GPU distributed training with PyTorch","text":"setup, one machine several GPUs (typically 2 16). device run copy model (called replica). simplicity, follows, ’ll assume ’re dealing 8 GPUs, loss generality. works step training: current batch data (called global batch) split 8 different sub-batches (called local batches). instance, global batch 512 samples, 8 local batches 64 samples. 8 replicas independently processes local batch: run forward pass, backward pass, outputting gradient weights respect loss model local batch. weight updates originating local gradients efficiently merged across 8 replicas. done end every step, replicas always stay sync. practice, process synchronously updating weights model replicas handled level individual weight variable. done mirrored variable object. use single-host, multi-device synchronous training Keras model, use torch.nn.parallel.DistributedDataParallel module wrapper. ’s works: use torch.multiprocessing.start_processes start multiple Python processes, one per device. process run per_device_launch_fn function. uses torch.distributed.init_process_group torch.cuda.set_device configure device used process. uses torch.utils.data.distributed.DistributedSampler torch.utils.data.DataLoader turn data distributed data loader. also uses torch.nn.parallel.DistributedDataParallel turn model distributed PyTorch module. calls train_model function. train_model function run process, model using separate device process. ’s flow, step split utility function: Time start multiple processes: ’s !","code":"# Config num_gpu = torch.cuda.device_count() num_epochs = 2 batch_size = 64 print(f\"Running on {num_gpu} GPUs\")   def setup_device(current_gpu_index, num_gpus):     # Device setup     os.environ[\"MASTER_ADDR\"] = \"localhost\"     os.environ[\"MASTER_PORT\"] = \"56492\"     device = torch.device(\"cuda:{}\".format(current_gpu_index))     torch.distributed.init_process_group(         backend=\"nccl\",         init_method=\"env://\",         world_size=num_gpus,         rank=current_gpu_index,     )     torch.cuda.set_device(device)   def cleanup():     torch.distributed.destroy_process_group()   def prepare_dataloader(dataset, current_gpu_index, num_gpus, batch_size):     sampler = torch.utils.data.distributed.DistributedSampler(         dataset,         num_replicas=num_gpus,         rank=current_gpu_index,         shuffle=False,     )     dataloader = torch.utils.data.DataLoader(         dataset,         sampler=sampler,         batch_size=batch_size,         shuffle=False,     )     return dataloader   def per_device_launch_fn(current_gpu_index, num_gpu):     # Setup the process groups     setup_device(current_gpu_index, num_gpu)      dataset = get_dataset()     model = get_model()      # prepare the dataloader     dataloader = prepare_dataloader(         dataset, current_gpu_index, num_gpu, batch_size     )      # Instantiate the torch optimizer     optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)      # Instantiate the torch loss function     loss_fn = torch.nn.CrossEntropyLoss()      # Put model on device     model = model.to(current_gpu_index)     ddp_model = torch.nn.parallel.DistributedDataParallel(         model, device_ids=[current_gpu_index], output_device=current_gpu_index     )      train_model(ddp_model, dataloader, num_epochs, optimizer, loss_fn)      cleanup() if __name__ == \"__main__\":     # We use the \"fork\" method rather than \"spawn\" to support notebooks     torch.multiprocessing.start_processes(         per_device_launch_fn,         args=(num_gpu,),         nprocs=num_gpu,         join=True,         start_method=\"fork\",     )"},{"path":"https://keras.posit.co/articles/distribution.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Distributed training with Keras 3","text":"Keras distribution API new interface designed facilitate distributed deep learning across variety backends like JAX, TensorFlow PyTorch. powerful API introduces suite tools enabling data model parallelism, allowing efficient scaling deep learning models multiple accelerators hosts. Whether leveraging power GPUs TPUs, API provides streamlined approach initializing distributed environments, defining device meshes, orchestrating layout tensors across computational resources. classes like DataParallel ModelParallel, abstracts complexity involved parallel computation, making easier developers accelerate machine learning workflows.","code":""},{"path":"https://keras.posit.co/articles/distribution.html","id":"how-it-works","dir":"Articles","previous_headings":"","what":"How it works","title":"Distributed training with Keras 3","text":"Keras distribution API provides global programming model allows developers compose applications operate tensors global context (working single device) automatically managing distribution across many devices. API leverages underlying framework (e.g. JAX) distribute program tensors according sharding directives procedure called single program, multiple data (SPMD) expansion. decoupling application sharding directives, API enables running application single device, multiple devices, even multiple clients, preserving global semantics.","code":""},{"path":"https://keras.posit.co/articles/distribution.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Distributed training with Keras 3","text":"","code":"# This guide assumes there are 8 GPUs available for testing. If you don't have # 8 gpus available locally, you can set the following envvar to # make xla initialize the CPU as 8 devices, to enable local testing Sys.setenv(\"CUDA_VISIBLE_DEVICES\" = \"\") Sys.setenv(\"XLA_FLAGS\" = \"--xla_force_host_platform_device_count=8\") library(keras3)  # The distribution API is only implemented for the JAX backend for now. use_backend(\"jax\") jax <- reticulate::import(\"jax\")  library(tfdatasets, exclude = \"shape\") # For dataset input."},{"path":"https://keras.posit.co/articles/distribution.html","id":"devicemesh-and-tensorlayout","dir":"Articles","previous_headings":"","what":"DeviceMesh and TensorLayout","title":"Distributed training with Keras 3","text":"keras$distribution$DeviceMesh class Keras distribution API represents cluster computational devices configured distributed computation. aligns similar concepts jax.sharding.Mesh tf.dtensor.Mesh, ’s used map physical devices logical mesh structure. TensorLayout class specifies tensors distributed across DeviceMesh, detailing sharding tensors along specified axes correspond names axes DeviceMesh. can find detailed concept explainers TensorFlow DTensor guide.","code":"# Retrieve the local available gpu devices. devices <- jax$devices() # \"gpu\" str(devices) ## List of 8 ##  $ :TFRT_CPU_0 ##  $ :TFRT_CPU_1 ##  $ :TFRT_CPU_2 ##  $ :TFRT_CPU_3 ##  $ :TFRT_CPU_4 ##  $ :TFRT_CPU_5 ##  $ :TFRT_CPU_6 ##  $ :TFRT_CPU_7 # Define a 2x4 device mesh with data and model parallel axes mesh <- keras$distribution$DeviceMesh(   shape = shape(2, 4),   axis_names = list(\"data\", \"model\"),   devices = devices )  # A 2D layout, which describes how a tensor is distributed across the # mesh. The layout can be visualized as a 2D grid with \"model\" as rows and # \"data\" as columns, and it is a [4, 2] grid when it mapped to the physical # devices on the mesh. layout_2d <- keras$distribution$TensorLayout(   axes = c(\"model\", \"data\"),   device_mesh = mesh )  # A 4D layout which could be used for data parallelism of an image input. replicated_layout_4d <- keras$distribution$TensorLayout(   axes = list(\"data\", NULL, NULL, NULL),   device_mesh = mesh )"},{"path":"https://keras.posit.co/articles/distribution.html","id":"distribution","dir":"Articles","previous_headings":"","what":"Distribution","title":"Distributed training with Keras 3","text":"Distribution class Keras serves foundational abstract class designed developing custom distribution strategies. encapsulates core logic needed distribute model’s variables, input data, intermediate computations across device mesh. end user, won’t interact directly class, subclasses like DataParallel ModelParallel.","code":""},{"path":"https://keras.posit.co/articles/distribution.html","id":"dataparallel","dir":"Articles","previous_headings":"","what":"DataParallel","title":"Distributed training with Keras 3","text":"DataParallel class Keras distribution API designed data parallelism strategy distributed training, model weights replicated across devices DeviceMesh, device processes portion input data. sample usage class.","code":"# Create DataParallel with list of devices. # As a shortcut, the devices can be skipped, # and Keras will detect all local available devices. # E.g. data_parallel <- DataParallel() data_parallel <- keras$distribution$DataParallel(devices = devices)  # Or you can choose to create DataParallel with a 1D `DeviceMesh`. mesh_1d <- keras$distribution$DeviceMesh(   shape = shape(8),   axis_names = list(\"data\"),   devices = devices ) data_parallel <- keras$distribution$DataParallel(device_mesh = mesh_1d)  inputs <- random_normal(c(128, 28, 28, 1)) labels <- random_normal(c(128, 10)) dataset <- tensor_slices_dataset(c(inputs, labels)) |>   dataset_batch(16)  # Set the global distribution. keras$distribution$set_distribution(data_parallel)  # Note that all the model weights from here on are replicated to # all the devices of the `DeviceMesh`. This includes the RNG # state, optimizer states, metrics, etc. The dataset fed into `model |> fit()` or # `model |> evaluate()` will be split evenly on the batch dimension, and sent to # all the devices. You don't have to do any manual aggregation of losses, # since all the computation happens in a global context. inputs <- keras_input(shape = c(28, 28, 1)) outputs <- inputs |>   layer_flatten() |>   layer_dense(units = 200, use_bias = FALSE, activation = \"relu\") |>   layer_dropout(0.4) |>   layer_dense(units = 10, activation = \"softmax\")  model <- keras_model(inputs = inputs, outputs = outputs)  model |> compile(loss = \"mse\") model |> fit(dataset, epochs = 3) ## Epoch 1/3 ## 8/8 - 0s - 37ms/step - loss: 1.0629 ## Epoch 2/3 ## 8/8 - 0s - 5ms/step - loss: 0.9712 ## Epoch 3/3 ## 8/8 - 0s - 5ms/step - loss: 0.9322 model |> evaluate(dataset) ## 8/8 - 0s - 7ms/step - loss: 0.8859 ## $loss ## [1] 0.8858577"},{"path":"https://keras.posit.co/articles/distribution.html","id":"modelparallel-and-layoutmap","dir":"Articles","previous_headings":"","what":"ModelParallel and LayoutMap","title":"Distributed training with Keras 3","text":"ModelParallel mostly useful model weights large fit single accelerator. setting allows spit model weights activation tensors across devices DeviceMesh, enable horizontal scaling large models. Unlike DataParallel model weights fully replicated, weights layout ModelParallel usually need customization best performances. introduce LayoutMap let specify TensorLayout weights intermediate tensors global perspective. LayoutMap dict-like object maps string TensorLayout instances. behaves differently normal dict string key treated regex retrieving value. class allows define naming schema TensorLayout retrieve corresponding TensorLayout instance. Typically, key used query variable$path attribute, identifier variable. shortcut, list axis names also allowed inserting value, converted TensorLayout. LayoutMap can also optionally contain DeviceMesh populate TensorLayout$device_mesh set. retrieving layout key, isn’t exact match, existing keys layout map treated regex matched input key . multiple matches, ValueError raised. matches found, NULL returned. also easy change mesh structure tune computation data parallel model parallel. can adjusting shape mesh. changes needed code.","code":"mesh_2d <- keras$distribution$DeviceMesh(   shape = shape(2, 4),   axis_names = c(\"data\", \"model\"),   devices = devices ) layout_map  <- keras$distribution$LayoutMap(mesh_2d)  # The rule below means that for any weights that match with d1/kernel, it # will be sharded with model dimensions (4 devices), same for the d1/bias. # All other weights will be fully replicated. layout_map[\"d1/kernel\"] <- tuple(NULL, \"model\") layout_map[\"d1/bias\"] <- tuple(\"model\")  # You can also set the layout for the layer output like layout_map[\"d2/output\"] <- tuple(\"data\", NULL)  model_parallel <- keras$distribution$ModelParallel(   mesh_2d, layout_map, batch_dim_name = \"data\" )  keras$distribution$set_distribution(model_parallel)  inputs <- layer_input(shape = c(28, 28, 1)) outputs <- inputs |>   layer_flatten() |>   layer_dense(units = 200, use_bias = FALSE,               activation = \"relu\", name = \"d1\") |>   layer_dropout(0.4) |>   layer_dense(units = 10,               activation = \"softmax\",               name = \"d2\")  model <- keras_model(inputs = inputs, outputs = outputs)  # The data will be sharded across the \"data\" dimension of the method, which # has 2 devices. model |> compile(loss = \"mse\") model |> fit(dataset, epochs = 3) ## Epoch 1/3 ## 8/8 - 0s - 29ms/step - loss: 1.0714 ## Epoch 2/3 ## 8/8 - 0s - 3ms/step - loss: 0.9744 ## Epoch 3/3 ## 8/8 - 0s - 4ms/step - loss: 0.9280 model |> evaluate(dataset) ## 8/8 - 0s - 7ms/step - loss: 0.8802 ## $loss ## [1] 0.8802156 full_data_parallel_mesh <- keras$distribution$DeviceMesh(   shape = shape(8, 1),   axis_names = list(\"data\", \"model\"),   devices = devices ) more_data_parallel_mesh <- keras$distribution$DeviceMesh(   shape = shape(4, 2),   axis_names = list(\"data\", \"model\"),   devices = devices ) more_model_parallel_mesh <- keras$distribution$DeviceMesh(   shape = shape(2, 4),   axis_names = list(\"data\", \"model\"),   devices = devices ) full_model_parallel_mesh <- keras$distribution$DeviceMesh(   shape = shape(1, 8),   axis_names = list(\"data\", \"model\"),   devices = devices )"},{"path":"https://keras.posit.co/articles/distribution.html","id":"further-reading","dir":"Articles","previous_headings":"ModelParallel and LayoutMap","what":"Further reading","title":"Distributed training with Keras 3","text":"JAX Distributed arrays automatic parallelization JAX sharding module TensorFlow Distributed training DTensors TensorFlow DTensor concepts Using DTensors tf.keras","code":""},{"path":"https://keras.posit.co/articles/examples/autoencoder.html","id":"introduction","dir":"Articles > Examples","previous_headings":"","what":"Introduction","title":"Convolutional autoencoder for image denoising","text":"example demonstrates implement deep convolutional autoencoder image denoising, mapping noisy digits images MNIST dataset clean digits images. implementation based original blog post titled Building Autoencoders Keras François Chollet.","code":""},{"path":"https://keras.posit.co/articles/examples/autoencoder.html","id":"setup","dir":"Articles > Examples","previous_headings":"","what":"Setup","title":"Convolutional autoencoder for image denoising","text":"","code":"library(keras3)  # Normalizes the supplied array and reshapes it. preprocess <- function(array) {   array_reshape(array/255, c(dim(array)[1], 28, 28, 1)) }  # Adds random noise to each image in the supplied array. noise <- function(array) {   noise_factor <- 0.4   noisy_array <- array + noise_factor * random_normal(dim(array))   op_clip(noisy_array, 0.0, 1.0) }  display <- function(array1, array2) {   n <- 2   indices <- sample.int(dim(array1)[1], n)   images1 <- as.array(array1)[indices, , , ]   images2 <- as.array(array2)[indices, , , ]    par(mfrow = c(2, n), mar = c(0, 0, 0, 0))   for (i in seq_len(n)) {     plot(as.raster(images1[i, , ]))     plot(as.raster(images2[i, , ]))   } }"},{"path":"https://keras.posit.co/articles/examples/autoencoder.html","id":"prepare-the-data","dir":"Articles > Examples","previous_headings":"","what":"Prepare the data","title":"Convolutional autoencoder for image denoising","text":"","code":"# Since we only need images from the dataset to encode and decode, we # won't use the labels. c(c(train_data, .), c(test_data, .)) %<-% dataset_mnist()  # Normalize and reshape the data train_data <- preprocess(train_data) test_data <- preprocess(test_data)  # Create a copy of the data with added noise noisy_train_data <- noise(train_data) noisy_test_data <- noise(test_data)  # Display the train data and a version of it with added noise display(train_data, noisy_train_data)"},{"path":"https://keras.posit.co/articles/examples/autoencoder.html","id":"build-the-autoencoder","dir":"Articles > Examples","previous_headings":"","what":"Build the autoencoder","title":"Convolutional autoencoder for image denoising","text":"going use Functional API build convolutional autoencoder. Now can train autoencoder using train_data input data target. Notice setting validation data using format. Let’s predict test dataset display original image together prediction autoencoder. Notice predictions pretty close original images, although quite . Now know autoencoder works, let’s retrain using noisy data input clean data target. want autoencoder learn denoise images. Let’s now predict noisy data display results autoencoder. Notice autoencoder amazing job removing noise input images.","code":"input <- keras_input(shape = c(28, 28, 1))  # Encoder enc <- input |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3),                 activation = \"relu\", padding = \"same\") |>   layer_max_pooling_2d(pool_size = c(2, 2), padding = \"same\") |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3),                 activation = \"relu\", padding = \"same\") |>   layer_max_pooling_2d(pool_size = c(2, 2), padding = \"same\")  # Decoder dec <- enc |>   layer_conv_2d_transpose(filters = 32, kernel_size = c(3, 3), strides = 2,                           activation = \"relu\", padding = \"same\") |>   layer_conv_2d_transpose(filters = 32, kernel_size = c(3, 3), strides = 2,                           activation = \"relu\", padding = \"same\") |>   layer_conv_2d(filters = 1, kernel_size = c(3, 3),                 activation = \"sigmoid\", padding = \"same\")  # Autoencoder autoencoder <- keras_model(input, dec) autoencoder |> compile(optimizer = \"adam\", loss = \"binary_crossentropy\") autoencoder |> summary() ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 28, 28, 32)     │           320 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 14, 14, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 14, 14, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 7, 7, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 14, 14, 32)     │         9,248 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 28, 28, 32)     │         9,248 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 28, 28, 1)      │           289 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 28,353 (110.75 KB) ##  Trainable params: 28,353 (110.75 KB) ##  Non-trainable params: 0 (0.00 B) autoencoder |> fit(   x = train_data,   y = train_data,   epochs = 50,   batch_size = 128,   shuffle = TRUE,   validation_data = list(test_data, test_data), ) ## Epoch 1/50 ## 469/469 - 5s - 10ms/step - loss: 0.1322 - val_loss: 0.0736 ## Epoch 2/50 ## 469/469 - 1s - 3ms/step - loss: 0.0721 - val_loss: 0.0698 ## Epoch 3/50 ## 469/469 - 1s - 3ms/step - loss: 0.0695 - val_loss: 0.0681 ## Epoch 4/50 ## 469/469 - 1s - 3ms/step - loss: 0.0682 - val_loss: 0.0671 ## Epoch 5/50 ## 469/469 - 1s - 3ms/step - loss: 0.0673 - val_loss: 0.0665 ## Epoch 6/50 ## 469/469 - 1s - 3ms/step - loss: 0.0667 - val_loss: 0.0660 ## Epoch 7/50 ## 469/469 - 1s - 3ms/step - loss: 0.0663 - val_loss: 0.0656 ## Epoch 8/50 ## 469/469 - 1s - 3ms/step - loss: 0.0659 - val_loss: 0.0652 ## Epoch 9/50 ## 469/469 - 1s - 3ms/step - loss: 0.0655 - val_loss: 0.0649 ## Epoch 10/50 ## 469/469 - 1s - 3ms/step - loss: 0.0652 - val_loss: 0.0646 ## Epoch 11/50 ## 469/469 - 1s - 3ms/step - loss: 0.0650 - val_loss: 0.0644 ## Epoch 12/50 ## 469/469 - 1s - 3ms/step - loss: 0.0647 - val_loss: 0.0642 ## Epoch 13/50 ## 469/469 - 1s - 3ms/step - loss: 0.0645 - val_loss: 0.0640 ## Epoch 14/50 ## 469/469 - 1s - 3ms/step - loss: 0.0643 - val_loss: 0.0638 ## Epoch 15/50 ## 469/469 - 1s - 3ms/step - loss: 0.0642 - val_loss: 0.0636 ## Epoch 16/50 ## 469/469 - 1s - 3ms/step - loss: 0.0640 - val_loss: 0.0635 ## Epoch 17/50 ## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0634 ## Epoch 18/50 ## 469/469 - 1s - 3ms/step - loss: 0.0638 - val_loss: 0.0633 ## Epoch 19/50 ## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0631 ## Epoch 20/50 ## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631 ## Epoch 21/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0630 ## Epoch 22/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0629 ## Epoch 23/50 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0628 ## Epoch 24/50 ## 469/469 - 1s - 3ms/step - loss: 0.0632 - val_loss: 0.0628 ## Epoch 25/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627 ## Epoch 26/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0626 ## Epoch 27/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0626 ## Epoch 28/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0625 ## Epoch 29/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0625 ## Epoch 30/50 ## 469/469 - 1s - 3ms/step - loss: 0.0628 - val_loss: 0.0624 ## Epoch 31/50 ## 469/469 - 1s - 3ms/step - loss: 0.0628 - val_loss: 0.0624 ## Epoch 32/50 ## 469/469 - 1s - 3ms/step - loss: 0.0627 - val_loss: 0.0623 ## Epoch 33/50 ## 469/469 - 1s - 3ms/step - loss: 0.0627 - val_loss: 0.0623 ## Epoch 34/50 ## 469/469 - 1s - 3ms/step - loss: 0.0627 - val_loss: 0.0623 ## Epoch 35/50 ## 469/469 - 1s - 3ms/step - loss: 0.0626 - val_loss: 0.0622 ## Epoch 36/50 ## 469/469 - 1s - 3ms/step - loss: 0.0626 - val_loss: 0.0622 ## Epoch 37/50 ## 469/469 - 1s - 3ms/step - loss: 0.0626 - val_loss: 0.0622 ## Epoch 38/50 ## 469/469 - 1s - 3ms/step - loss: 0.0625 - val_loss: 0.0622 ## Epoch 39/50 ## 469/469 - 1s - 3ms/step - loss: 0.0625 - val_loss: 0.0621 ## Epoch 40/50 ## 469/469 - 1s - 3ms/step - loss: 0.0625 - val_loss: 0.0621 ## Epoch 41/50 ## 469/469 - 1s - 3ms/step - loss: 0.0624 - val_loss: 0.0621 ## Epoch 42/50 ## 469/469 - 1s - 3ms/step - loss: 0.0624 - val_loss: 0.0621 ## Epoch 43/50 ## 469/469 - 1s - 3ms/step - loss: 0.0624 - val_loss: 0.0620 ## Epoch 44/50 ## 469/469 - 1s - 3ms/step - loss: 0.0624 - val_loss: 0.0620 ## Epoch 45/50 ## 469/469 - 1s - 3ms/step - loss: 0.0623 - val_loss: 0.0620 ## Epoch 46/50 ## 469/469 - 1s - 3ms/step - loss: 0.0623 - val_loss: 0.0620 ## Epoch 47/50 ## 469/469 - 1s - 3ms/step - loss: 0.0623 - val_loss: 0.0619 ## Epoch 48/50 ## 469/469 - 1s - 3ms/step - loss: 0.0623 - val_loss: 0.0619 ## Epoch 49/50 ## 469/469 - 1s - 3ms/step - loss: 0.0622 - val_loss: 0.0619 ## Epoch 50/50 ## 469/469 - 1s - 3ms/step - loss: 0.0622 - val_loss: 0.0619 predictions <- autoencoder |> predict(test_data) ## 313/313 - 1s - 2ms/step display(test_data, predictions) autoencoder |> fit(   x = noisy_train_data,   y = train_data,   epochs = 100,   batch_size = 128,   shuffle = TRUE,   validation_data = list(noisy_test_data, test_data), ) ## Epoch 1/100 ## 469/469 - 1s - 3ms/step - loss: 0.0998 - val_loss: 0.0928 ## Epoch 2/100 ## 469/469 - 1s - 3ms/step - loss: 0.0925 - val_loss: 0.0908 ## Epoch 3/100 ## 469/469 - 1s - 3ms/step - loss: 0.0910 - val_loss: 0.0898 ## Epoch 4/100 ## 469/469 - 1s - 3ms/step - loss: 0.0900 - val_loss: 0.0890 ## Epoch 5/100 ## 469/469 - 1s - 3ms/step - loss: 0.0893 - val_loss: 0.0883 ## Epoch 6/100 ## 469/469 - 1s - 3ms/step - loss: 0.0888 - val_loss: 0.0877 ## Epoch 7/100 ## 469/469 - 1s - 3ms/step - loss: 0.0883 - val_loss: 0.0873 ## Epoch 8/100 ## 469/469 - 1s - 3ms/step - loss: 0.0879 - val_loss: 0.0870 ## Epoch 9/100 ## 469/469 - 1s - 3ms/step - loss: 0.0876 - val_loss: 0.0868 ## Epoch 10/100 ## 469/469 - 1s - 3ms/step - loss: 0.0873 - val_loss: 0.0866 ## Epoch 11/100 ## 469/469 - 1s - 3ms/step - loss: 0.0871 - val_loss: 0.0863 ## Epoch 12/100 ## 469/469 - 1s - 3ms/step - loss: 0.0869 - val_loss: 0.0861 ## Epoch 13/100 ## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0860 ## Epoch 14/100 ## 469/469 - 1s - 3ms/step - loss: 0.0865 - val_loss: 0.0858 ## Epoch 15/100 ## 469/469 - 1s - 3ms/step - loss: 0.0864 - val_loss: 0.0857 ## Epoch 16/100 ## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0855 ## Epoch 17/100 ## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0854 ## Epoch 18/100 ## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0853 ## Epoch 19/100 ## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0852 ## Epoch 20/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0852 ## Epoch 21/100 ## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0851 ## Epoch 22/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0850 ## Epoch 23/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0850 ## Epoch 24/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0849 ## Epoch 25/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0849 ## Epoch 26/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0848 ## Epoch 27/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0848 ## Epoch 28/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0847 ## Epoch 29/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0847 ## Epoch 30/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0847 ## Epoch 31/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847 ## Epoch 32/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0846 ## Epoch 33/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0846 ## Epoch 34/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846 ## Epoch 35/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0845 ## Epoch 36/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0845 ## Epoch 37/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0845 ## Epoch 38/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0845 ## Epoch 39/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0845 ## Epoch 40/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0844 ## Epoch 41/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0844 ## Epoch 42/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0844 ## Epoch 43/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0844 ## Epoch 44/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0844 ## Epoch 45/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 46/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 47/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 48/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 49/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 50/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0843 ## Epoch 51/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 52/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 53/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 54/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 55/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 56/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0842 ## Epoch 57/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0842 ## Epoch 58/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0842 ## Epoch 59/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0842 ## Epoch 60/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0841 ## Epoch 61/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0841 ## Epoch 62/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0841 ## Epoch 63/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0841 ## Epoch 64/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0841 ## Epoch 65/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0841 ## Epoch 66/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0841 ## Epoch 67/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 68/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 69/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 70/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 71/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 72/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 73/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 74/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 75/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 76/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 77/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 78/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 79/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 80/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 81/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 82/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 83/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 84/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 85/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 86/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 87/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 88/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 89/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 90/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 91/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 92/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 93/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 94/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 95/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 96/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 97/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 98/100 ## 469/469 - 1s - 3ms/step - loss: 0.0841 - val_loss: 0.0839 ## Epoch 99/100 ## 469/469 - 1s - 3ms/step - loss: 0.0841 - val_loss: 0.0839 ## Epoch 100/100 ## 469/469 - 1s - 3ms/step - loss: 0.0841 - val_loss: 0.0839 predictions <- autoencoder |> predict(noisy_test_data) ## 313/313 - 0s - 581us/step display(noisy_test_data, predictions)"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"setup","dir":"Articles > Examples","previous_headings":"","what":"Setup","title":"Simple MNIST convnet","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"prepare-the-data","dir":"Articles > Examples","previous_headings":"","what":"Prepare the data","title":"Simple MNIST convnet","text":"","code":"# Model / data parameters num_classes <- 10 input_shape <- c(28, 28, 1)  # Load the data and split it between train and test sets c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()  # Scale images to the [0, 1] range x_train <- x_train / 255 x_test <- x_test / 255 # Make sure images have shape (28, 28, 1) x_train <- op_expand_dims(x_train, -1) x_test <- op_expand_dims(x_test, -1)   dim(x_train) ## [1] 60000    28    28     1 dim(x_test) ## [1] 10000    28    28     1 # convert class vectors to binary class matrices y_train <- to_categorical(y_train, num_classes) y_test <- to_categorical(y_test, num_classes)"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"build-the-model","dir":"Articles > Examples","previous_headings":"","what":"Build the model","title":"Simple MNIST convnet","text":"","code":"model <- keras_model_sequential(input_shape = input_shape) model |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_flatten() |>   layer_dropout(rate = 0.5) |>   layer_dense(units = num_classes, activation = \"softmax\")  summary(model) ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d_1 (Conv2D)               │ (None, 26, 26, 32)     │           320 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 13, 13, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 11, 11, 64)     │        18,496 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 5, 5, 64)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ flatten (Flatten)               │ (None, 1600)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 1600)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 10)             │        16,010 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 34,826 (136.04 KB) ##  Trainable params: 34,826 (136.04 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"train-the-model","dir":"Articles > Examples","previous_headings":"","what":"Train the model","title":"Simple MNIST convnet","text":"","code":"batch_size <- 128 epochs <- 15  model |> compile(   loss = \"categorical_crossentropy\",   optimizer = \"adam\",   metrics = \"accuracy\" )  model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = epochs,   validation_split = 0.1 ) ## Epoch 1/15 ## 422/422 - 4s - 11ms/step - accuracy: 0.8845 - loss: 0.3815 - val_accuracy: 0.9783 - val_loss: 0.0810 ## Epoch 2/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9644 - loss: 0.1151 - val_accuracy: 0.9863 - val_loss: 0.0548 ## Epoch 3/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9735 - loss: 0.0838 - val_accuracy: 0.9880 - val_loss: 0.0455 ## Epoch 4/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9781 - loss: 0.0689 - val_accuracy: 0.9893 - val_loss: 0.0413 ## Epoch 5/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9811 - loss: 0.0608 - val_accuracy: 0.9907 - val_loss: 0.0373 ## Epoch 6/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9824 - loss: 0.0561 - val_accuracy: 0.9908 - val_loss: 0.0359 ## Epoch 7/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9848 - loss: 0.0487 - val_accuracy: 0.9917 - val_loss: 0.0328 ## Epoch 8/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9845 - loss: 0.0484 - val_accuracy: 0.9915 - val_loss: 0.0334 ## Epoch 9/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9859 - loss: 0.0439 - val_accuracy: 0.9923 - val_loss: 0.0320 ## Epoch 10/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9868 - loss: 0.0405 - val_accuracy: 0.9925 - val_loss: 0.0319 ## Epoch 11/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9874 - loss: 0.0387 - val_accuracy: 0.9920 - val_loss: 0.0317 ## Epoch 12/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9882 - loss: 0.0367 - val_accuracy: 0.9925 - val_loss: 0.0301 ## Epoch 13/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9879 - loss: 0.0361 - val_accuracy: 0.9928 - val_loss: 0.0281 ## Epoch 14/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9889 - loss: 0.0332 - val_accuracy: 0.9930 - val_loss: 0.0284 ## Epoch 15/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9897 - loss: 0.0311 - val_accuracy: 0.9925 - val_loss: 0.0293"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"evaluate-the-trained-model","dir":"Articles > Examples","previous_headings":"","what":"Evaluate the trained model","title":"Simple MNIST convnet","text":"","code":"score <- model |> evaluate(x_test, y_test, verbose = 0) score ## $accuracy ## [1] 0.9911 ## ## $loss ## [1] 0.02564374"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"introduction","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Introduction","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"example, ’ll build sequence--sequence Transformer model, ’ll train English--Spanish machine translation task. ’ll learn : Vectorize text using layer_text_vectorization(). Implement layer_transformer_encoder(), layer_transformer_decoder(), layer_positional_embedding(). Prepare data training sequence--sequence model. Use trained model generate translations never-seen-input sentences (sequence--sequence inference). code featured adapted book Deep Learning R, Second Edition (chapter 11: Deep learning text). present example fairly barebones, detailed explanations building block works, well theory behind Transformers, recommend reading book.","code":""},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"setup","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Setup","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"","code":"library(glue)  library(tensorflow, exclude = c(\"set_random_seed\", \"shape\")) library(keras3)"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"downloading-the-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Downloading the data","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"’ll working English--Spanish translation dataset provided Anki. Let’s download :","code":"zipfile <- get_file(\"spa-eng.zip\", origin =   \"http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip\")  zip::zip_list(zipfile) # See what's in the zipfile ##             filename compressed_size uncompressed_size           timestamp ## 1           spa-eng/               0                 0 2018-06-13 12:21:20 ## 2 spa-eng/_about.txt             685              1441 2018-05-13 19:50:34 ## 3    spa-eng/spa.txt         2637619           8042772 2018-05-13 19:50:34 ##   permissions    crc32 offset ## 1         755 00000000      0 ## 2         644 4b18349d     38 ## 3         644 63c5c4a2    771 zip::unzip(zipfile, exdir = \".\") # unzip into the current directory  text_file <- fs::path(\"./spa-eng/spa.txt\")"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"parsing-the-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Parsing the data","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"line contains English sentence corresponding Spanish sentence. English sentence source sequence Spanish one target sequence. prepend token \"[start]\" append token \"[end]\" Spanish sentence. ’s sentence pairs look like: Now, let’s split sentence pairs training set, validation set, test set.","code":"text_file <- \"spa-eng/spa.txt\" text_pairs <- text_file %>%   readr::read_tsv(col_names = c(\"english\", \"spanish\"),                   col_types = c(\"cc\")) %>%   within(spanish %<>% paste(\"[start]\", ., \"[end]\")) df <- text_pairs[sample(nrow(text_pairs), 5), ] glue::glue_data(df, r\"(   english: {english}   spanish: {spanish} )\") |> cat(sep = \"\\n\\n\") ## english: I'm staying in Italy. ## spanish: [start] Me estoy quedando en Italia. [end] ## ## english: What's so strange about that? ## spanish: [start] ¿Qué es tan extraño acerca de eso? [end] ## ## english: All of the buses are full. ## spanish: [start] Todos los bondis están llenos. [end] ## ## english: Is this where your mother works? ## spanish: [start] ¿Es aquí donde trabaja tu madre? [end] ## ## english: Take precautions. ## spanish: [start] Ten cuidado. [end] random.shuffle(text_pairs) num_val_samples = int(0.15 * len(text_pairs)) num_train_samples = len(text_pairs) - 2 * num_val_samples train_pairs = text_pairs[:num_train_samples] val_pairs = text_pairs[num_train_samples : num_train_samples + num_val_samples] test_pairs = text_pairs[num_train_samples + num_val_samples :]  print(f\"{len(text_pairs)} total pairs\") print(f\"{len(train_pairs)} training pairs\") print(f\"{len(val_pairs)} validation pairs\") print(f\"{len(test_pairs)} test pairs\") num_test_samples <- num_val_samples <-   round(0.15 * nrow(text_pairs)) num_train_samples <- nrow(text_pairs) - num_val_samples - num_test_samples  pair_group <- sample(c(   rep(\"train\", num_train_samples),   rep(\"test\", num_test_samples),   rep(\"val\", num_val_samples) ))  train_pairs <- text_pairs[pair_group == \"train\", ] test_pairs <- text_pairs[pair_group == \"test\", ] val_pairs <- text_pairs[pair_group == \"val\", ] glue(r\"(   {nrow(text_pairs)} total pairs   {nrow(train_pairs)} training pairs   {nrow(val_pairs)} validation pairs   {nrow(test_pairs)} test pairs )\", .transformer = function(text, envir) {   val <- eval(str2lang(text), envir)   prettyNum(val, big.mark = \",\") }) ## 118,493 total pairs ## 82,945 training pairs ## 17,774 validation pairs ## 17,774 test pairs"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"vectorizing-the-text-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Vectorizing the text data","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"’ll use two instances layer_text_vectorization() vectorize text data (one English one Spanish), say, turn original strings integer sequences integer represents index word vocabulary. English layer use default string standardization (strip punctuation characters) splitting scheme (split whitespace), Spanish layer use custom standardization, add character \"¿\" set punctuation characters stripped. Note: production-grade machine translation model, recommend stripping punctuation characters either language. Instead, recommend turning punctuation character token, achieve providing custom split function layer_text_vectorization(). Next, ’ll format datasets. training step, model seek predict target words N+1 (beyond) using source sentence target words 1 N. , training dataset yield tuple (inputs, targets), : inputs dictionary (named list) keys (names) encoder_inputs decoder_inputs. encoder_inputs vectorized source sentence encoder_inputs target sentence “far”, say, words 0 N used predict word N+1 (beyond) target sentence. target target sentence offset one step: provides next words target sentence – model try predict. Let’s take quick look sequence shapes (batches 64 pairs, sequences 20 steps long):","code":"punctuation_regex <- \"[¡¿]|[^[:^punct:][\\\\]]\" # the regex explained: Match ¡, or ¿, or any punctuation character except ] # # [:^punct:]: is a negated POSIX character class. # [:punct:] matches any punctuation character, so [:^punct:] matches any # character that is not a punctuation character. # [^...] negates the whole character class # So [^[:^punct:]] would matche any character that is a punctuation character. # Putting this all together, [^[:^punct:][\\\\]] matches any # punctuation character except the ] character.  custom_standardization <- function(input_string) {   input_string %>%     tf$strings$lower() %>%     tf$strings$regex_replace(punctuation_regex, \"\") }  input_string <- as_tensor(\"[start] ¡corre! [end]\") custom_standardization(input_string) ## tf.Tensor(b'[start] corre [end]', shape=(), dtype=string) vocab_size <- 15000 sequence_length <- 20  # rename to eng_vectorization eng_vectorization <- layer_text_vectorization(   max_tokens = vocab_size,   output_mode = \"int\",   output_sequence_length = sequence_length )  spa_vectorization <- layer_text_vectorization(   max_tokens = vocab_size,   output_mode = \"int\",   output_sequence_length = sequence_length + 1,   standardize = custom_standardization )  adapt(eng_vectorization, train_pairs$english) adapt(spa_vectorization, train_pairs$spanish) format_pair <- function(pair) {   # the vectorization layers requrie batched inputs,   # reshape scalar string tensor to add a batch dim   pair %<>% lapply(op_expand_dims, 1)    # vectorize   eng <- eng_vectorization(pair$english)   spa <- spa_vectorization(pair$spanish)    # drop the batch dim   eng %<>% tf$ensure_shape(shape(1, sequence_length)) %>% op_squeeze(1)   spa %<>% tf$ensure_shape(shape(1, sequence_length+1)) %>% op_squeeze(1)    inputs <- list(encoder_inputs = eng,                  decoder_inputs = spa[NA:-2])   targets <- spa[2:NA]   list(inputs, targets) }   batch_size <- 64  library(tfdatasets, exclude = \"shape\") make_dataset <- function(pairs) {   tensor_slices_dataset(pairs) %>%     dataset_map(format_pair, num_parallel_calls = 4) %>%     dataset_cache() %>%     dataset_shuffle(2048) %>%     dataset_batch(batch_size) %>%     dataset_prefetch(2) } train_ds <- make_dataset(train_pairs) ## Warning: Negative numbers are interpreted python-style when subsetting tensorflow tensors. ## See: ?`[.tensorflow.tensor` for details. ## To turn off this warning, set `options(tensorflow.extract.warn_negatives_pythonic = FALSE)` val_ds <- make_dataset(val_pairs) c(inputs, targets) %<-% iter_next(as_iterator(train_ds)) str(inputs) ## List of 2 ##  $ encoder_inputs:<tf.Tensor: shape=(64, 20), dtype=int64, numpy=…> ##  $ decoder_inputs:<tf.Tensor: shape=(64, 20), dtype=int64, numpy=…> str(targets) ## <tf.Tensor: shape=(64, 20), dtype=int64, numpy=…>"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"building-the-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Building the model","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"sequence--sequence Transformer consists TransformerEncoder TransformerDecoder chained together. make model aware word order, also use PositionalEmbedding layer. source sequence pass TransformerEncoder, produce new representation . new representation passed TransformerDecoder, together target sequence far (target words 1 N). TransformerDecoder seek predict next words target sequence (N+1 beyond). key detail makes possible causal masking (see method get_causal_attention_mask() TransformerDecoder). TransformerDecoder sees entire sequences , thus must make sure uses information target tokens 0 N predicting token N+1 (otherwise, use information future, result model used inference time). Next, assemble end--end model.","code":"layer_transformer_encoder <- Layer(   classname = \"TransformerEncoder\",   initialize = function(embed_dim, dense_dim, num_heads, ...) {     super$initialize(...)     self$embed_dim <- embed_dim     self$dense_dim <- dense_dim     self$num_heads <- num_heads     self$attention <-       layer_multi_head_attention(num_heads = num_heads,                                  key_dim = embed_dim)      self$dense_proj <- keras_model_sequential() %>%       layer_dense(dense_dim, activation = \"relu\") %>%       layer_dense(embed_dim)      self$layernorm_1 <- layer_layer_normalization()     self$layernorm_2 <- layer_layer_normalization()     self$supports_masking <- TRUE   },    call = function(inputs, mask = NULL) {     if (!is.null(mask))       mask <- mask[, NULL, ] |> op_cast(\"int32\")      inputs %>%       { self$attention(., ., attention_mask = mask) + . } %>%       self$layernorm_1() %>%       { self$dense_proj(.) + . } %>%       self$layernorm_2()   },    get_config = function() {     config <- super$get_config()     for(name in c(\"embed_dim\", \"num_heads\", \"dense_dim\"))       config[[name]] <- self[[name]]     config   } )  layer_transformer_decoder <- Layer(   classname = \"TransformerDecoder\",    initialize = function(embed_dim, latent_dim, num_heads, ...) {     super$initialize(...)     self$embed_dim <- embed_dim     self$latent_dim <- latent_dim     self$num_heads <- num_heads     self$attention_1 <- layer_multi_head_attention(num_heads = num_heads,                                                    key_dim = embed_dim)     self$attention_2 <- layer_multi_head_attention(num_heads = num_heads,                                                    key_dim = embed_dim)     self$dense_proj <- keras_model_sequential() %>%       layer_dense(latent_dim, activation = \"relu\") %>%       layer_dense(embed_dim)      self$layernorm_1 <- layer_layer_normalization()     self$layernorm_2 <- layer_layer_normalization()     self$layernorm_3 <- layer_layer_normalization()     self$supports_masking <- TRUE   },    get_config = function() {     config <- super$get_config()     for (name in c(\"embed_dim\", \"num_heads\", \"latent_dim\"))       config[[name]] <- self[[name]]     config   },     get_causal_attention_mask = function(inputs) {     c(batch_size, sequence_length, encoding_length) %<-% op_shape(inputs)      x <- op_arange(sequence_length)     i <- x[, NULL]     j <- x[NULL, ]     mask <- op_cast(i >= j, \"int32\")      repeats <- op_stack(c(batch_size, 1L, 1L))     op_tile(mask[NULL, , ], repeats)   },   call = function(inputs, encoder_outputs, mask = NULL) {     causal_mask <- self$get_causal_attention_mask(inputs)      if (is.null(mask))       mask <- causal_mask     else       mask %<>% { op_minimum(op_cast(.[, NULL, ], \"int32\"),                              causal_mask) }      inputs %>%       { self$attention_1(query = ., value = ., key = .,                          attention_mask = causal_mask) + . } %>%       self$layernorm_1() %>%        { self$attention_2(query = .,                          value = encoder_outputs,                          key = encoder_outputs,                          attention_mask = mask) + . } %>%       self$layernorm_2() %>%        { self$dense_proj(.) + . } %>%       self$layernorm_3()    } )  layer_positional_embedding <- Layer(   classname = \"PositionalEmbedding\",    initialize = function(sequence_length, vocab_size, embed_dim, ...) {     super$initialize(...)     self$token_embeddings <- layer_embedding(       input_dim = vocab_size, output_dim = embed_dim     )     self$position_embeddings <- layer_embedding(       input_dim = sequence_length, output_dim = embed_dim     )     self$sequence_length <- sequence_length     self$vocab_size <- vocab_size     self$embed_dim <- embed_dim   },    call = function(inputs) {     c(., len) %<-% op_shape(inputs) # (batch_size, seq_len)     positions <- op_arange(0, len, dtype = \"int32\")     embedded_tokens <- self$token_embeddings(inputs)     embedded_positions <- self$position_embeddings(positions)     embedded_tokens + embedded_positions   },    compute_mask = function(inputs, mask = NULL) {     if (is.null(mask)) return (NULL)     inputs != 0L   },    get_config = function() {     config <- super$get_config()     for(name in c(\"sequence_length\", \"vocab_size\", \"embed_dim\"))       config[[name]] <- self[[name]]     config   } ) embed_dim <- 256 latent_dim <- 2048 num_heads <- 8  encoder_inputs <- layer_input(shape(NA), dtype = \"int64\",                               name = \"encoder_inputs\") encoder_outputs <- encoder_inputs %>%   layer_positional_embedding(sequence_length, vocab_size, embed_dim) %>%   layer_transformer_encoder(embed_dim, latent_dim, num_heads)  encoder <- keras_model(encoder_inputs, encoder_outputs)  decoder_inputs <-  layer_input(shape(NA), dtype = \"int64\",                                name = \"decoder_inputs\") encoded_seq_inputs <- layer_input(shape(NA, embed_dim),                                   name = \"decoder_state_inputs\")  transformer_decoder <- layer_transformer_decoder(NULL,   embed_dim, latent_dim, num_heads)  decoder_outputs <- decoder_inputs %>%   layer_positional_embedding(sequence_length, vocab_size, embed_dim) %>%   transformer_decoder(., encoded_seq_inputs) %>%   layer_dropout(0.5) %>%   layer_dense(vocab_size, activation=\"softmax\")  decoder <- keras_model(inputs = list(decoder_inputs, encoded_seq_inputs),                        outputs = decoder_outputs)  decoder_outputs = decoder(list(decoder_inputs, encoder_outputs))  transformer <- keras_model(list(encoder_inputs, decoder_inputs),                            decoder_outputs,                            name = \"transformer\")"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"training-our-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Training our model","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"’ll use accuracy quick way monitor training progress validation data. Note machine translation typically uses BLEU scores well metrics, rather accuracy. train 1 epoch, get model actually converge train least 30 epochs.","code":"epochs <- 1  # This should be at least 30 for convergence  transformer ## Model: \"transformer\" ## ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ ## │ encoder_inputs      │ (None, None)      │          0 │ -                 │ ## │ (InputLayer)        │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ positional_embeddi… │ (None, None, 256) │  3,845,120 │ encoder_inputs[0… │ ## │ (PositionalEmbeddi… │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ decoder_inputs      │ (None, None)      │          0 │ -                 │ ## │ (InputLayer)        │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ transformer_encoder │ (None, None, 256) │  3,155,456 │ positional_embed… │ ## │ (TransformerEncode… │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ functional_5        │ (None, None,      │ 12,959,640 │ decoder_inputs[0… │ ## │ (Functional)        │ 15000)            │            │ transformer_enco… │ ## └─────────────────────┴───────────────────┴────────────┴───────────────────┘ ##  Total params: 19,960,216 (76.14 MB) ##  Trainable params: 19,960,216 (76.14 MB) ##  Non-trainable params: 0 (0.00 B) transformer |> compile(   \"rmsprop\",   loss = \"sparse_categorical_crossentropy\",   metrics = \"accuracy\" ) transformer |> fit(train_ds, epochs = epochs,                    validation_data = val_ds) ## 1297/1297 - 58s - 44ms/step - accuracy: 0.7709 - loss: 1.5752 - val_accuracy: 0.7731 - val_loss: 1.4209"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"decoding-test-sentences","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Decoding test sentences","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"Finally, let’s demonstrate translate brand new English sentences. simply feed model vectorized English sentence well target token \"[start]\", repeatedly generated next token, hit token \"[end]\". 30 epochs, get results :","code":"spa_vocab <- spa_vectorization |> get_vocabulary() max_decoded_sentence_length <- 20 tf_decode_sequence <- tf_function(function(input_sentence) {   withr::local_options(tensorflow.extract.style = \"python\")    tokenized_input_sentence <- input_sentence %>%     as_tensor(shape = c(1, 1)) %>%     eng_vectorization()   spa_vocab <- as_tensor(spa_vocab)   decoded_sentence <- as_tensor(\"[start]\", shape = c(1, 1))    for (i in tf$range(as.integer(max_decoded_sentence_length))) {      tokenized_target_sentence <-       spa_vectorization(decoded_sentence)[,NA:-1]      next_token_predictions <-       transformer(list(tokenized_input_sentence,                        tokenized_target_sentence))      sampled_token_index <- tf$argmax(next_token_predictions[0, i, ])     sampled_token <- spa_vocab[sampled_token_index]     decoded_sentence <-       tf$strings$join(c(decoded_sentence, sampled_token),                       separator = \" \")      if (sampled_token == \"[end]\")       break   }    decoded_sentence  })  for (i in seq(20)) {      c(input_sentence, correct_translation) %<-%       test_pairs[sample.int(nrow(test_pairs), 1), ]     cat(\"-\\n\")     cat(\"English:\", input_sentence, \"\\n\")     cat(\"Correct Translation:\", tolower(correct_translation), \"\\n\")     cat(\"  Model Translation:\", input_sentence %>% as_tensor() %>%           tf_decode_sequence() %>% as.character(), \"\\n\") } English: I'm sure everything will be fine. Correct Translation: [start] estoy segura de que todo irá bien. [end]   Model Translation: [start] estoy seguro de que todo va bien [end]"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"introduction","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Introduction","title":"Text classification from scratch","text":"example shows text classification starting raw text (set text files disk). demonstrate workflow IMDB sentiment classification dataset (unprocessed version). use layer_text_vectorization() word splitting & indexing.","code":""},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"setup","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Setup","title":"Text classification from scratch","text":"","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\") library(keras3) use_virtualenv(\"r-keras\")"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"load-the-data-imdb-movie-review-sentiment-classification","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Load the data: IMDB movie review sentiment classification","title":"Text classification from scratch","text":"Let’s download data inspect structure. aclImdb folder contains train test subfolder: aclImdb/train/pos aclImdb/train/neg folders contain text files, represents one review (either positive negative): interested pos neg subfolders, let’s delete subfolder text files : can use utility text_dataset_from_directory() generate labeled tf_dataset object set text files disk filed class-specific folders. Let’s use generate training, validation, test datasets. validation training datasets generated two subsets train directory, 20% samples going validation dataset 80% going training dataset. validation dataset addition test dataset useful tuning hyperparameters, model architecture, test dataset used. putting model real world however, retrained using available training data (without creating validation dataset), performance maximized. using validation_split subset arguments, make sure either specify random seed, pass shuffle=FALSE, validation & training splits get overlap. Let’s preview samples:","code":"if (!dir.exists(\"datasets/aclImdb\")) {   dir.create(\"datasets\")   download.file(     \"https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\",     \"datasets/aclImdb_v1.tar.gz\"   )   untar(\"datasets/aclImdb_v1.tar.gz\", exdir = \"datasets\")   unlink(\"datasets/aclImdb/train/unsup\", recursive = TRUE) } head(list.files(\"datasets/aclImdb/test\")) ## [1] \"labeledBow.feat\" \"neg\"             \"pos\"             \"urls_neg.txt\" ## [5] \"urls_pos.txt\" head(list.files(\"datasets/aclImdb/train\")) ## [1] \"labeledBow.feat\" \"neg\"             \"pos\"             \"unsupBow.feat\" ## [5] \"urls_neg.txt\"    \"urls_pos.txt\" writeLines(strwrap(readLines(\"datasets/aclImdb/train/pos/4229_10.txt\"))) ## Don't waste time reading my review. Go out and see this ## astonishingly good episode, which may very well be the best Columbo ## ever written! Ruth Gordon is perfectly cast as the scheming yet ## charming mystery writer who murders her son-in-law to avenge his ## murder of her daughter. Columbo is his usual rumpled, befuddled and ## far-cleverer-than-he-seems self, and this particular installment ## features fantastic chemistry between Gordon and Falk. Ironically, ## this was not written by heralded creators Levinson or Link yet is ## possibly the densest, most thoroughly original and twist-laden ## Columbo plot ever. Utterly satisfying in nearly every department ## and overflowing with droll and witty dialogue and thinking. Truly ## unexpected and inventive climax tops all. 10/10...seek this one out ## on Netflix! unlink(\"datasets/aclImdb/train/unsup\", recursive = TRUE) batch_size <- 32  raw_train_ds <- text_dataset_from_directory(   \"datasets/aclImdb/train\",   batch_size = batch_size,   validation_split = 0.2,   subset = \"training\",   seed = 1337 ) ## Found 25000 files belonging to 2 classes. ## Using 20000 files for training. raw_val_ds <- text_dataset_from_directory(   \"datasets/aclImdb/train\",   batch_size = batch_size,   validation_split = 0.2,   subset = \"validation\",   seed = 1337 ) ## Found 25000 files belonging to 2 classes. ## Using 5000 files for validation. raw_test_ds <- text_dataset_from_directory(   \"datasets/aclImdb/test\",   batch_size = batch_size ) ## Found 25000 files belonging to 2 classes. cat(\"Number of batches in raw_train_ds:\", length(raw_train_ds), \"\\n\") ## Number of batches in raw_train_ds: 625 cat(\"Number of batches in raw_val_ds:\", length(raw_val_ds), \"\\n\") ## Number of batches in raw_val_ds: 157 cat(\"Number of batches in raw_test_ds:\", length(raw_test_ds), \"\\n\") ## Number of batches in raw_test_ds: 782 # It's important to take a look at your raw data to ensure your normalization # and tokenization will work as expected. We can do that by taking a few # examples from the training set and looking at them. # This is one of the places where eager execution shines: # we can just evaluate these tensors using .numpy() # instead of needing to evaluate them in a Session/Graph context. batch <- iter_next(as_iterator(raw_train_ds)) str(batch) ## List of 2 ##  $ :<tf.Tensor: shape=(32), dtype=string, numpy=…> ##  $ :<tf.Tensor: shape=(32), dtype=int32, numpy=…> c(text_batch, label_batch) %<-% batch for (i in 1:3) {   print(text_batch[i])   print(label_batch[i]) } ## tf.Tensor(b\"I have read the novel Reaper of Ben Mezrich a fews years ago and last night I accidentally came to see this adaption.<br /><br />Although it's been years since I read the story the first time, the differences between the novel and the movie are humongous. Very important elements, which made the whole thing plausible are just written out or changed to bad.<br /><br />If the plot sounds interesting to you: go and get the novel. Its much, much, much better.<br /><br />Still 4 out of 10 since it was hard to stop watching because of the great basic plot by Ben Mezrich.\", shape=(), dtype=string) ## tf.Tensor(0, shape=(), dtype=int32) ## tf.Tensor(b'After seeing all the Jesse James, Quantrill, jayhawkers,etc films in the fifties, it is quite a thrill to see this film with a new perspective by director Ang Lee. The scene of the attack of Lawrence, Kansas is awesome. The romantic relationship between Jewel and Toby Mcguire turns out to be one of the best parts and Jonathan Rhys-Meyers is outstanding as the bad guy. All the time this film makes you feel the horror of war, and the desperate situation of the main characters who do not know if they are going to survive the next hours. Definitely worth seeing.', shape=(), dtype=string) ## tf.Tensor(1, shape=(), dtype=int32) ## tf.Tensor(b'AG was an excellent presentation of drama, suspense and thriller that is so rare to American TV. Sheriff Lucas gave many a viewer the willies. We rooted for Caleb as he strove to resist the overtures of Sheriff Lucas. We became engrossed and fearful upon learning of the unthinkable connection between these two characters. The manipulations which weekly gave cause to fear what Lucas would do next were truly surprising. This show lived up to the \"Gothic\" moniker in ways American entertainment has so seldom attempted, much less mastered. The suits definitely made a big mistake in not supporting this show. This show puts shame to the current glut of \"reality\" shows- which are so less than satisfying viewing.The call for a DVD box set is well based. This show is quality viewing for a discerning market hungry for quality viewing. A public that is tiring of over-saturation of mind-numbing reality fare will welcome this gem of real storytelling. Bring on the DVD box set!!', shape=(), dtype=string) ## tf.Tensor(1, shape=(), dtype=int32)"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"prepare-the-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Prepare the data","title":"Text classification from scratch","text":"particular, remove <br /> tags.","code":"# Having looked at our data above, we see that the raw text contains HTML break # tags of the form '<br />'. These tags will not be removed by the default # standardizer (which doesn't strip HTML). Because of this, we will need to # create a custom standardization function. custom_standardization_fn <- function(string_tensor) {   string_tensor |>     tf$strings$lower() |> # convert to all lowercase     tf$strings$regex_replace(\"<br />\", \" \") |> # remove '<br />' HTML tag     tf$strings$regex_replace(\"[[:punct:]]\", \"\") # remove punctuation }   # Model constants. max_features <- 20000 embedding_dim <- 128 sequence_length <- 500  # Now that we have our custom standardization, we can instantiate our text # vectorization layer. We are using this layer to normalize, split, and map # strings to integers, so we set our 'output_mode' to 'int'. # Note that we're using the default split function, # and the custom standardization defined above. # We also set an explicit maximum sequence length, since the CNNs later in our # model won't support ragged sequences. vectorize_layer <- layer_text_vectorization(   standardize = custom_standardization_fn,   max_tokens = max_features,   output_mode = \"int\",   output_sequence_length = sequence_length, )  # Now that the vectorize_layer has been created, call `adapt` on a text-only # dataset to create the vocabulary. You don't have to batch, but for very large # datasets this means you're not keeping spare copies of the dataset in memory.  # Let's make a text-only dataset (no labels): text_ds <- raw_train_ds |>   dataset_map(\\(x, y) x) # Let's call `adapt`: vectorize_layer |> adapt(text_ds)"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"two-options-to-vectorize-the-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Two options to vectorize the data","title":"Text classification from scratch","text":"2 ways can use text vectorization layer: Option 1: Make part model, obtain model processes raw strings, like : Option 2: Apply text dataset obtain dataset word indices, feed model expects integer sequences inputs. important difference two option 2 enables asynchronous CPU processing buffering data training GPU. ’re training model GPU, probably want go option get best performance. . export model production, ’d ship model accepts raw strings input, like code snippet option 1 . can done training. last section.","code":"text_input <- keras_input(shape = c(1L), dtype = \"string\", name = 'text') x <- text_input |>   vectorize_layer() |>   layer_embedding(max_features + 1, embedding_dim) vectorize_text <- function(text, label) {   text <- text |>     op_expand_dims(-1) |>     vectorize_layer()   list(text, label) }  # Vectorize the data. train_ds <- raw_train_ds |> dataset_map(vectorize_text) val_ds   <- raw_val_ds   |> dataset_map(vectorize_text) test_ds  <- raw_test_ds  |> dataset_map(vectorize_text)  # Do async prefetching / buffering of the data for best performance on GPU. train_ds <- train_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10) val_ds <- val_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10) test_ds <- test_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10)"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"build-a-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Build a model","title":"Text classification from scratch","text":"choose simple 1D convnet starting Embedding layer.","code":"# A integer input for vocab indices. inputs <- keras_input(shape = c(NA), dtype = \"int64\")  predictions <- inputs |>   # Next, we add a layer to map those vocab indices into a space of dimensionality   # 'embedding_dim'.   layer_embedding(max_features, embedding_dim) |>   layer_dropout(0.5) |>   # Conv1D + global max pooling   layer_conv_1d(128, 7, padding = \"valid\", activation = \"relu\", strides = 3) |>   layer_conv_1d(128, 7, padding = \"valid\", activation = \"relu\", strides = 3) |>   layer_global_max_pooling_1d() |>   # We add a vanilla hidden layer:   layer_dense(128, activation = \"relu\") |>   layer_dropout(0.5) |>   # We project onto a single unit output layer, and squash it with a sigmoid:   layer_dense(1, activation = \"sigmoid\", name = \"predictions\")  model <- keras_model(inputs, predictions)  summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, None)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ embedding_1 (Embedding)         │ (None, None, 128)      │     2,560,000 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, None, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d (Conv1D)                 │ (None, None, 128)      │       114,816 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_1 (Conv1D)               │ (None, None, 128)      │       114,816 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling1d            │ (None, 128)            │             0 │ ## │ (GlobalMaxPooling1D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 128)            │        16,512 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, 128)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ predictions (Dense)             │ (None, 1)              │           129 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 2,806,273 (10.71 MB) ##  Trainable params: 2,806,273 (10.71 MB) ##  Non-trainable params: 0 (0.00 B) # Compile the model with binary crossentropy loss and an adam optimizer. model |> compile(loss = \"binary_crossentropy\",                  optimizer = \"adam\",                  metrics = \"accuracy\")"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"train-the-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Train the model","title":"Text classification from scratch","text":"","code":"epochs <- 3  # Fit the model using the train and test datasets. model |> fit(train_ds, validation_data = val_ds, epochs = epochs) ## Epoch 1/3 ## 625/625 - 6s - 10ms/step - accuracy: 0.6909 - loss: 0.5300 - val_accuracy: 0.8658 - val_loss: 0.3229 ## Epoch 2/3 ## 625/625 - 2s - 3ms/step - accuracy: 0.9047 - loss: 0.2412 - val_accuracy: 0.8742 - val_loss: 0.3202 ## Epoch 3/3 ## 625/625 - 2s - 3ms/step - accuracy: 0.9573 - loss: 0.1237 - val_accuracy: 0.8704 - val_loss: 0.3551"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"evaluate-the-model-on-the-test-set","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Evaluate the model on the test set","title":"Text classification from scratch","text":"","code":"model |> evaluate(test_ds) ## 782/782 - 1s - 2ms/step - accuracy: 0.8594 - loss: 0.3818 ## $accuracy ## [1] 0.85936 ## ## $loss ## [1] 0.381799"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"make-an-end-to-end-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Make an end-to-end model","title":"Text classification from scratch","text":"want obtain model capable processing raw strings, can simply create new model (using weights just trained):","code":"# A string input inputs <- keras_input(shape = c(1), dtype = \"string\") # Turn strings into vocab indices indices <- vectorize_layer(inputs) # Turn vocab indices into predictions outputs <- model(indices)  # Our end to end model end_to_end_model <- keras_model(inputs, outputs) end_to_end_model |> compile(   loss = \"binary_crossentropy\",   optimizer = \"adam\",   metrics = c(\"accuracy\") )  # Test it with `raw_test_ds`, which yields raw strings end_to_end_model |> evaluate(raw_test_ds) ## 782/782 - 3s - 4ms/step - accuracy: 0.8594 - loss: 0.0000e+00 ## $accuracy ## [1] 0.85936 ## ## $loss ## [1] 0"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"download-the-data","dir":"Articles > Examples","previous_headings":"","what":"Download the data","title":"Image segmentation with a U-Net-like architecture","text":"","code":"options(timeout = 5000) download.file(   \"https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\",   \"datasets/images.tar.gz\" ) download.file(   \"https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\",   \"datasets/annotations.tar.gz\" )  untar(\"datasets/images.tar.gz\", exdir = \"datasets\") untar(\"datasets/annotations.tar.gz\", exdir = \"datasets\")"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"prepare-paths-of-input-images-and-target-segmentation-masks","dir":"Articles > Examples","previous_headings":"","what":"Prepare paths of input images and target segmentation masks","title":"Image segmentation with a U-Net-like architecture","text":"","code":"library(keras3) input_dir <- \"datasets/images/\" target_dir <- \"datasets/annotations/trimaps/\" img_size <- c(160, 160) num_classes <- 3 batch_size <- 32  input_img_paths <- fs::dir_ls(input_dir, glob = \"*.jpg\") |> sort() target_img_paths <- fs::dir_ls(target_dir, glob = \"*.png\") |> sort()  cat(\"Number of samples:\", length(input_img_paths), \"\\n\") ## Number of samples: 7390 for (i in 1:10) {   cat(input_img_paths[i], \"|\", target_img_paths[i], \"\\n\") } ## datasets/images/Abyssinian_1.jpg | datasets/annotations/trimaps/Abyssinian_1.png ## datasets/images/Abyssinian_10.jpg | datasets/annotations/trimaps/Abyssinian_10.png ## datasets/images/Abyssinian_100.jpg | datasets/annotations/trimaps/Abyssinian_100.png ## datasets/images/Abyssinian_101.jpg | datasets/annotations/trimaps/Abyssinian_101.png ## datasets/images/Abyssinian_102.jpg | datasets/annotations/trimaps/Abyssinian_102.png ## datasets/images/Abyssinian_103.jpg | datasets/annotations/trimaps/Abyssinian_103.png ## datasets/images/Abyssinian_104.jpg | datasets/annotations/trimaps/Abyssinian_104.png ## datasets/images/Abyssinian_105.jpg | datasets/annotations/trimaps/Abyssinian_105.png ## datasets/images/Abyssinian_106.jpg | datasets/annotations/trimaps/Abyssinian_106.png ## datasets/images/Abyssinian_107.jpg | datasets/annotations/trimaps/Abyssinian_107.png"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"what-does-one-input-image-and-corresponding-segmentation-mask-look-like","dir":"Articles > Examples","previous_headings":"","what":"What does one input image and corresponding segmentation mask look like?","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Display input image #10 input_img_paths[10] |>   jpeg::readJPEG() |>   as.raster() |>   plot() target_img_paths[10] |>   png::readPNG() |>   magrittr::multiply_by(255)|>   as.raster(max = 3) |>   plot()"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"prepare-dataset-to-load-vectorize-batches-of-data","dir":"Articles > Examples","previous_headings":"","what":"Prepare dataset to load & vectorize batches of data","title":"Image segmentation with a U-Net-like architecture","text":"","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")   # Returns a tf_dataset get_dataset <- function(batch_size, img_size, input_img_paths, target_img_paths,                         max_dataset_len = NULL) {    img_size <- as.integer(img_size)    load_img_masks <- function(input_img_path, target_img_path) {     input_img <- input_img_path |>       tf$io$read_file() |>       tf$io$decode_jpeg(channels = 3) |>       tf$image$resize(img_size) |>       tf$image$convert_image_dtype(\"float32\")      target_img <- target_img_path |>       tf$io$read_file() |>       tf$io$decode_png(channels = 1) |>       tf$image$resize(img_size, method = \"nearest\") |>       tf$image$convert_image_dtype(\"uint8\")      # Ground truth labels are 1, 2, 3. Subtract one to make them 0, 1, 2:     target_img <- target_img - 1L      list(input_img, target_img)   }    if (!is.null(max_dataset_len)) {     input_img_paths <- input_img_paths[1:max_dataset_len]     target_img_paths <- target_img_paths[1:max_dataset_len]   }    list(input_img_paths, target_img_paths) |>     tensor_slices_dataset() |>     dataset_map(load_img_masks, num_parallel_calls = tf$data$AUTOTUNE)|>     dataset_batch(batch_size) }"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"prepare-u-net-xception-style-model","dir":"Articles > Examples","previous_headings":"","what":"Prepare U-Net Xception-style model","title":"Image segmentation with a U-Net-like architecture","text":"","code":"get_model <- function(img_size, num_classes) {    inputs <- keras_input(shape = c(img_size, 3))    ### [First half of the network: downsampling inputs] ###    # Entry block   x <- inputs |>     layer_conv_2d(filters = 32, kernel_size = 3, strides = 2, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation(\"relu\")    previous_block_activation <- x  # Set aside residual    for (filters in c(64, 128, 256)) {     x <- x |>       layer_activation(\"relu\") |>       layer_separable_conv_2d(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_activation(\"relu\") |>       layer_separable_conv_2d(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_max_pooling_2d(pool_size = 3, strides = 2, padding = \"same\")      residual <- previous_block_activation |>       layer_conv_2d(filters = filters, kernel_size = 1, strides = 2, padding = \"same\")      x <- layer_add(x, residual)  # Add back residual     previous_block_activation <- x  # Set aside next residual   }    ### [Second half of the network: upsampling inputs] ###    for (filters in c(256, 128, 64, 32)) {     x <- x |>       layer_activation(\"relu\") |>       layer_conv_2d_transpose(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_activation(\"relu\") |>       layer_conv_2d_transpose(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_upsampling_2d(size = 2)      # Project residual     residual <- previous_block_activation |>       layer_upsampling_2d(size = 2) |>       layer_conv_2d(filters = filters, kernel_size = 1, padding = \"same\")      x <- layer_add(x, residual)     # Add back residual     previous_block_activation <- x  # Set aside next residual   }    # Add a per-pixel classification layer   outputs <- x |>     layer_conv_2d(num_classes, 3, activation = \"softmax\", padding = \"same\")    # Define the model   keras_model(inputs, outputs) }  # Build model model <- get_model(img_size, num_classes) summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)      ┃ Output Shape    ┃   Param # ┃ Connected to   ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer       │ (None, 160,     │         0 │ -              │   -   │ ## │ (InputLayer)      │ 160, 3)         │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d (Conv2D)   │ (None, 80, 80,  │       896 │ input_layer[0… │   Y   │ ## │                   │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d[0][0]   │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation        │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_2      │ (None, 80, 80,  │         0 │ activation[0]… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 80, 80,  │     2,400 │ activation_2[… │   Y   │ ## │ (SeparableConv2D) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       256 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_1      │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d  │ (None, 80, 80,  │     4,736 │ activation_1[… │   Y   │ ## │ (SeparableConv2D) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       256 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d     │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_1 (Conv2D) │ (None, 40, 40,  │     2,112 │ activation[0]… │   Y   │ ## │                   │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add (Add)         │ (None, 40, 40,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 64)             │           │ conv2d_1[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_4      │ (None, 40, 40,  │         0 │ add[0][0]      │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 40, 40,  │     8,896 │ activation_4[… │   Y   │ ## │ (SeparableConv2D) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       512 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_3      │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 40, 40,  │    17,664 │ activation_3[… │   Y   │ ## │ (SeparableConv2D) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       512 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d_1   │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_2 (Conv2D) │ (None, 20, 20,  │     8,320 │ add[0][0]      │   Y   │ ## │                   │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_1 (Add)       │ (None, 20, 20,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 128)            │           │ conv2d_2[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_6      │ (None, 20, 20,  │         0 │ add_1[0][0]    │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 20, 20,  │    34,176 │ activation_6[… │   Y   │ ## │ (SeparableConv2D) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │     1,024 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_5      │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 20, 20,  │    68,096 │ activation_5[… │   Y   │ ## │ (SeparableConv2D) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │     1,024 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d_2   │ (None, 10, 10,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_3 (Conv2D) │ (None, 10, 10,  │    33,024 │ add_1[0][0]    │   Y   │ ## │                   │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_2 (Add)       │ (None, 10, 10,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 256)            │           │ conv2d_3[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_8      │ (None, 10, 10,  │         0 │ add_2[0][0]    │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 10, 10,  │   590,080 │ activation_8[… │   Y   │ ## │ (Conv2DTranspose) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 10, 10,  │     1,024 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_7      │ (None, 10, 10,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose  │ (None, 10, 10,  │   590,080 │ activation_7[… │   Y   │ ## │ (Conv2DTranspose) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 10, 10,  │     1,024 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_1   │ (None, 20, 20,  │         0 │ add_2[0][0]    │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d     │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_4 (Conv2D) │ (None, 20, 20,  │    65,792 │ up_sampling2d… │   Y   │ ## │                   │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_3 (Add)       │ (None, 20, 20,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 256)            │           │ conv2d_4[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_10     │ (None, 20, 20,  │         0 │ add_3[0][0]    │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 20, 20,  │   295,040 │ activation_10… │   Y   │ ## │ (Conv2DTranspose) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │       512 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_9      │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 20, 20,  │   147,584 │ activation_9[… │   Y   │ ## │ (Conv2DTranspose) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │       512 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_3   │ (None, 40, 40,  │         0 │ add_3[0][0]    │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_2   │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_5 (Conv2D) │ (None, 40, 40,  │    32,896 │ up_sampling2d… │   Y   │ ## │                   │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_4 (Add)       │ (None, 40, 40,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 128)            │           │ conv2d_5[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_12     │ (None, 40, 40,  │         0 │ add_4[0][0]    │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 40, 40,  │    73,792 │ activation_12… │   Y   │ ## │ (Conv2DTranspose) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       256 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_11     │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 40, 40,  │    36,928 │ activation_11… │   Y   │ ## │ (Conv2DTranspose) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       256 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_5   │ (None, 80, 80,  │         0 │ add_4[0][0]    │   -   │ ## │ (UpSampling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_4   │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_6 (Conv2D) │ (None, 80, 80,  │     8,256 │ up_sampling2d… │   Y   │ ## │                   │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_5 (Add)       │ (None, 80, 80,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 64)             │           │ conv2d_6[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_14     │ (None, 80, 80,  │         0 │ add_5[0][0]    │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 80, 80,  │    18,464 │ activation_14… │   Y   │ ## │ (Conv2DTranspose) │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_13     │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 80, 80,  │     9,248 │ activation_13… │   Y   │ ## │ (Conv2DTranspose) │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_7   │ (None, 160,     │         0 │ add_5[0][0]    │   -   │ ## │ (UpSampling2D)    │ 160, 64)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_6   │ (None, 160,     │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 160, 32)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_7 (Conv2D) │ (None, 160,     │     2,080 │ up_sampling2d… │   Y   │ ## │                   │ 160, 32)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_6 (Add)       │ (None, 160,     │         0 │ up_sampling2d… │   -   │ ## │                   │ 160, 32)        │           │ conv2d_7[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_8 (Conv2D) │ (None, 160,     │       867 │ add_6[0][0]    │   Y   │ ## │                   │ 160, 3)         │           │                │       │ ## └───────────────────┴─────────────────┴───────────┴────────────────┴───────┘ ##  Total params: 2,058,979 (7.85 MB) ##  Trainable params: 2,055,203 (7.84 MB) ##  Non-trainable params: 3,776 (14.75 KB)"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"set-aside-a-validation-split","dir":"Articles > Examples","previous_headings":"","what":"Set aside a validation split","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Split our img paths into a training and a validation set val_samples <- 1000 val_samples <- sample.int(length(input_img_paths), val_samples)  train_input_img_paths <- input_img_paths[-val_samples] train_target_img_paths <- target_img_paths[-val_samples]  val_input_img_paths <- input_img_paths[val_samples] val_target_img_paths <- target_img_paths[val_samples]  # Instantiate dataset for each split # Limit input files in `max_dataset_len` for faster epoch training time. # Remove the `max_dataset_len` arg when running with full dataset. train_dataset <- get_dataset(   batch_size,   img_size,   train_input_img_paths,   train_target_img_paths,   max_dataset_len = 1000 ) valid_dataset <- get_dataset(   batch_size, img_size, val_input_img_paths, val_target_img_paths )"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"train-the-model","dir":"Articles > Examples","previous_headings":"","what":"Train the model","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Configure the model for training. # We use the \"sparse\" version of categorical_crossentropy # because our target data is integers. model |> compile(   optimizer = optimizer_adam(1e-4),   loss = \"sparse_categorical_crossentropy\" )  callbacks <- list(   callback_model_checkpoint(     \"models/oxford_segmentation.keras\", save_best_only = TRUE   ) )  # Train the model, doing validation at the end of each epoch. epochs <- 50 model |> fit(     train_dataset,     epochs=epochs,     validation_data=valid_dataset,     callbacks=callbacks,     verbose=2 ) ## Epoch 1/50 ## 32/32 - 32s - 986ms/step - loss: 1.3044 - val_loss: 1.5502 ## Epoch 2/50 ## 32/32 - 2s - 60ms/step - loss: 0.8717 - val_loss: 1.9567 ## Epoch 3/50 ## 32/32 - 2s - 60ms/step - loss: 0.7695 - val_loss: 2.1948 ## Epoch 4/50 ## 32/32 - 2s - 60ms/step - loss: 0.7080 - val_loss: 2.4837 ## Epoch 5/50 ## 32/32 - 2s - 64ms/step - loss: 0.6689 - val_loss: 2.7751 ## Epoch 6/50 ## 32/32 - 2s - 61ms/step - loss: 0.6378 - val_loss: 3.1156 ## Epoch 7/50 ## 32/32 - 2s - 60ms/step - loss: 0.6129 - val_loss: 3.3611 ## Epoch 8/50 ## 32/32 - 2s - 60ms/step - loss: 0.5917 - val_loss: 3.5193 ## Epoch 9/50 ## 32/32 - 2s - 62ms/step - loss: 0.5723 - val_loss: 3.6237 ## Epoch 10/50 ## 32/32 - 2s - 62ms/step - loss: 0.5538 - val_loss: 3.7209 ## Epoch 11/50 ## 32/32 - 2s - 60ms/step - loss: 0.5352 - val_loss: 3.8216 ## Epoch 12/50 ## 32/32 - 2s - 61ms/step - loss: 0.5161 - val_loss: 3.9090 ## Epoch 13/50 ## 32/32 - 2s - 60ms/step - loss: 0.4958 - val_loss: 4.0158 ## Epoch 14/50 ## 32/32 - 2s - 60ms/step - loss: 0.4739 - val_loss: 4.0756 ## Epoch 15/50 ## 32/32 - 2s - 60ms/step - loss: 0.4501 - val_loss: 4.0083 ## Epoch 16/50 ## 32/32 - 2s - 60ms/step - loss: 0.4243 - val_loss: 3.7728 ## Epoch 17/50 ## 32/32 - 2s - 60ms/step - loss: 0.3971 - val_loss: 3.3484 ## Epoch 18/50 ## 32/32 - 2s - 61ms/step - loss: 0.3696 - val_loss: 2.6909 ## Epoch 19/50 ## 32/32 - 2s - 60ms/step - loss: 0.3428 - val_loss: 1.9726 ## Epoch 20/50 ## 32/32 - 2s - 65ms/step - loss: 0.3178 - val_loss: 1.4081 ## Epoch 21/50 ## 32/32 - 2s - 65ms/step - loss: 0.2954 - val_loss: 1.1136 ## Epoch 22/50 ## 32/32 - 2s - 65ms/step - loss: 0.2764 - val_loss: 1.0246 ## Epoch 23/50 ## 32/32 - 2s - 61ms/step - loss: 0.2610 - val_loss: 1.0390 ## Epoch 24/50 ## 32/32 - 2s - 60ms/step - loss: 0.2507 - val_loss: 1.1001 ## Epoch 25/50 ## 32/32 - 2s - 60ms/step - loss: 0.2525 - val_loss: 1.1915 ## Epoch 26/50 ## 32/32 - 2s - 61ms/step - loss: 0.2961 - val_loss: 1.2296 ## Epoch 27/50 ## 32/32 - 2s - 61ms/step - loss: 0.3545 - val_loss: 1.2625 ## Epoch 28/50 ## 32/32 - 2s - 65ms/step - loss: 0.3326 - val_loss: 0.9711 ## Epoch 29/50 ## 32/32 - 2s - 60ms/step - loss: 0.3302 - val_loss: 1.0251 ## Epoch 30/50 ## 32/32 - 2s - 60ms/step - loss: 0.3163 - val_loss: 1.0867 ## Epoch 31/50 ## 32/32 - 2s - 60ms/step - loss: 0.3131 - val_loss: 1.4197 ## Epoch 32/50 ## 32/32 - 2s - 60ms/step - loss: 0.3093 - val_loss: 1.0851 ## Epoch 33/50 ## 32/32 - 2s - 61ms/step - loss: 0.2939 - val_loss: 1.4807 ## Epoch 34/50 ## 32/32 - 2s - 60ms/step - loss: 0.2763 - val_loss: 1.1851 ## Epoch 35/50 ## 32/32 - 2s - 60ms/step - loss: 0.2702 - val_loss: 1.1337 ## Epoch 36/50 ## 32/32 - 2s - 61ms/step - loss: 0.2584 - val_loss: 1.0315 ## Epoch 37/50 ## 32/32 - 2s - 61ms/step - loss: 0.2440 - val_loss: 1.0631 ## Epoch 38/50 ## 32/32 - 2s - 60ms/step - loss: 0.2353 - val_loss: 1.1609 ## Epoch 39/50 ## 32/32 - 2s - 61ms/step - loss: 0.2285 - val_loss: 1.1839 ## Epoch 40/50 ## 32/32 - 2s - 60ms/step - loss: 0.2307 - val_loss: 1.1293 ## Epoch 41/50 ## 32/32 - 2s - 61ms/step - loss: 0.2357 - val_loss: 0.9948 ## Epoch 42/50 ## 32/32 - 2s - 60ms/step - loss: 0.2279 - val_loss: 1.1329 ## Epoch 43/50 ## 32/32 - 2s - 61ms/step - loss: 0.2174 - val_loss: 1.0418 ## Epoch 44/50 ## 32/32 - 2s - 61ms/step - loss: 0.2076 - val_loss: 1.1141 ## Epoch 45/50 ## 32/32 - 2s - 61ms/step - loss: 0.2004 - val_loss: 1.1162 ## Epoch 46/50 ## 32/32 - 2s - 60ms/step - loss: 0.1930 - val_loss: 1.0939 ## Epoch 47/50 ## 32/32 - 2s - 61ms/step - loss: 0.1852 - val_loss: 1.0499 ## Epoch 48/50 ## 32/32 - 2s - 60ms/step - loss: 0.1796 - val_loss: 1.0651 ## Epoch 49/50 ## 32/32 - 2s - 61ms/step - loss: 0.1762 - val_loss: 1.0679 ## Epoch 50/50 ## 32/32 - 2s - 60ms/step - loss: 0.1757 - val_loss: 1.1762"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"visualize-predictions","dir":"Articles > Examples","previous_headings":"","what":"Visualize predictions","title":"Image segmentation with a U-Net-like architecture","text":"","code":"model <- load_model(\"models/oxford_segmentation.keras\") # Generate predictions for all images in the validation set val_dataset <- get_dataset(   batch_size, img_size, val_input_img_paths, val_target_img_paths ) val_preds <- predict(model, val_dataset) ## 32/32 - 4s - 111ms/step display_mask <- function(i) {   # Quick utility to display a model's prediction.   mask <- val_preds[i,,,] %>%     apply(c(1,2), which.max) %>%     array_reshape(dim = c(img_size, 1))   mask <- abind::abind(mask, mask, mask, along = 3)   plot(as.raster(mask, max = 3)) }  # Display results for validation image #10 i <- 10  par(mfrow = c(1, 3)) # Display input image input_img_paths[i] |>   jpeg::readJPEG() |>   as.raster() |>   plot()  # Display ground-truth target mask target_img_paths[i] |>   png::readPNG() |>   magrittr::multiply_by(255)|>   as.raster(max = 3) |>   plot()  # Display mask predicted by our model display_mask(i)  # Note that the model only sees inputs at 150x150."},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"introduction","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Introduction","title":"Imbalanced classification: credit card fraud detection","text":"example looks Kaggle Credit Card Fraud Detection dataset demonstrate train classification model data highly imbalanced classes. can download data clicking “Download” link, ’re setup kaggle API key \"~/.kaggle/kagle.json\", can run following:","code":"reticulate::py_install(\"kaggle\", pip = TRUE) reticulate::py_available(TRUE) # ensure 'kaggle' is on the PATH system(\"kaggle datasets download -d mlg-ulb/creditcardfraud\") zip::unzip(\"creditcardfraud.zip\", files = \"creditcard.csv\")"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"first-load-the-data","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"First, load the data","title":"Imbalanced classification: credit card fraud detection","text":"","code":"library(readr) df <- read_csv(\"creditcard.csv\", col_types = cols(   Class = col_integer(),   .default = col_double() )) tibble::glimpse(df) ## Rows: 284,807 ## Columns: 31 ## $ Time   <dbl> 0, 0, 1, 1, 2, 2, 4, 7, 7, 9, 10, 10, 10, 11, 12, 12, 12, 1… ## $ V1     <dbl> -1.3598071, 1.1918571, -1.3583541, -0.9662717, -1.1582331, … ## $ V2     <dbl> -0.07278117, 0.26615071, -1.34016307, -0.18522601, 0.877736… ## $ V3     <dbl> 2.53634674, 0.16648011, 1.77320934, 1.79299334, 1.54871785,… ## $ V4     <dbl> 1.37815522, 0.44815408, 0.37977959, -0.86329128, 0.40303393… ## $ V5     <dbl> -0.33832077, 0.06001765, -0.50319813, -0.01030888, -0.40719… ## $ V6     <dbl> 0.46238778, -0.08236081, 1.80049938, 1.24720317, 0.09592146… ## $ V7     <dbl> 0.239598554, -0.078802983, 0.791460956, 0.237608940, 0.5929… ## $ V8     <dbl> 0.098697901, 0.085101655, 0.247675787, 0.377435875, -0.2705… ## $ V9     <dbl> 0.3637870, -0.2554251, -1.5146543, -1.3870241, 0.8177393, -… ## $ V10    <dbl> 0.09079417, -0.16697441, 0.20764287, -0.05495192, 0.7530744… ## $ V11    <dbl> -0.55159953, 1.61272666, 0.62450146, -0.22648726, -0.822842… ## $ V12    <dbl> -0.61780086, 1.06523531, 0.06608369, 0.17822823, 0.53819555… ## $ V13    <dbl> -0.99138985, 0.48909502, 0.71729273, 0.50775687, 1.34585159… ## $ V14    <dbl> -0.31116935, -0.14377230, -0.16594592, -0.28792375, -1.1196… ## $ V15    <dbl> 1.468176972, 0.635558093, 2.345864949, -0.631418118, 0.1751… ## $ V16    <dbl> -0.47040053, 0.46391704, -2.89008319, -1.05964725, -0.45144… ## $ V17    <dbl> 0.207971242, -0.114804663, 1.109969379, -0.684092786, -0.23… ## $ V18    <dbl> 0.02579058, -0.18336127, -0.12135931, 1.96577500, -0.038194… ## $ V19    <dbl> 0.40399296, -0.14578304, -2.26185710, -1.23262197, 0.803486… ## $ V20    <dbl> 0.25141210, -0.06908314, 0.52497973, -0.20803778, 0.4085423… ## $ V21    <dbl> -0.018306778, -0.225775248, 0.247998153, -0.108300452, -0.0… ## $ V22    <dbl> 0.277837576, -0.638671953, 0.771679402, 0.005273597, 0.7982… ## $ V23    <dbl> -0.110473910, 0.101288021, 0.909412262, -0.190320519, -0.13… ## $ V24    <dbl> 0.06692807, -0.33984648, -0.68928096, -1.17557533, 0.141266… ## $ V25    <dbl> 0.12853936, 0.16717040, -0.32764183, 0.64737603, -0.2060095… ## $ V26    <dbl> -0.18911484, 0.12589453, -0.13909657, -0.22192884, 0.502292… ## $ V27    <dbl> 0.133558377, -0.008983099, -0.055352794, 0.062722849, 0.219… ## $ V28    <dbl> -0.021053053, 0.014724169, -0.059751841, 0.061457629, 0.215… ## $ Amount <dbl> 149.62, 2.69, 378.66, 123.50, 69.99, 3.67, 4.99, 40.80, 93.… ## $ Class  <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"prepare-a-validation-set","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Prepare a validation set","title":"Imbalanced classification: credit card fraud detection","text":"","code":"val_idx <- nrow(df) %>% sample.int(., round( . * 0.2)) val_df <- df[val_idx, ] train_df <- df[-val_idx, ]  cat(\"Number of training samples:\", nrow(train_df), \"\\n\") ## Number of training samples: 227846 cat(\"Number of validation samples:\", nrow(val_df), \"\\n\") ## Number of validation samples: 56961"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"analyze-class-imbalance-in-the-targets","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Analyze class imbalance in the targets","title":"Imbalanced classification: credit card fraud detection","text":"","code":"counts <- table(train_df$Class) counts ## ##      0      1 ## 227450    396 cat(sprintf(\"Number of positive samples in training data: %i (%.2f%% of total)\",             counts[\"1\"], 100 * counts[\"1\"] / sum(counts))) ## Number of positive samples in training data: 396 (0.17% of total) weight_for_0 = 1 / counts[\"0\"] weight_for_1 = 1 / counts[\"1\"]"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"normalize-the-data-using-training-set-statistics","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Normalize the data using training set statistics","title":"Imbalanced classification: credit card fraud detection","text":"","code":"feature_names <- colnames(train_df) %>% setdiff(\"Class\")  train_features <- as.matrix(train_df[feature_names]) train_targets <- as.matrix(train_df$Class)  val_features <- as.matrix(val_df[feature_names]) val_targets <- as.matrix(val_df$Class)  train_features %<>% scale() val_features %<>% scale(center = attr(train_features, \"scaled:center\"),                         scale = attr(train_features, \"scaled:scale\"))"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"build-a-binary-classification-model","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Build a binary classification model","title":"Imbalanced classification: credit card fraud detection","text":"","code":"model <-   keras_model_sequential(input_shape = ncol(train_features)) |>   layer_dense(256, activation = \"relu\") |>   layer_dense(256, activation = \"relu\") |>   layer_dropout(0.3) |>   layer_dense(256, activation = \"relu\") |>   layer_dropout(0.3) |>   layer_dense(1, activation = \"sigmoid\")  model ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense (Dense)                   │ (None, 256)            │         7,936 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_1 (Dense)                 │ (None, 256)            │        65,792 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 256)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 256)            │        65,792 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, 256)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_3 (Dense)                 │ (None, 1)              │           257 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 139,777 (546.00 KB) ##  Trainable params: 139,777 (546.00 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"train-the-model-with-class_weight-argument","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Train the model with class_weight argument","title":"Imbalanced classification: credit card fraud detection","text":"","code":"metrics <- list(   metric_false_negatives(name = \"fn\"),   metric_false_positives(name = \"fp\"),   metric_true_negatives(name = \"tn\"),   metric_true_positives(name = \"tp\"),   metric_precision(name = \"precision\"),   metric_recall(name = \"recall\") ) model |> compile(   optimizer = optimizer_adam(1e-2),   loss = \"binary_crossentropy\",   metrics = metrics ) callbacks <- list(   callback_model_checkpoint(\"fraud_model_at_epoch_{epoch}.keras\") )  class_weight <- list(\"0\" = weight_for_0,                      \"1\" = weight_for_1)  model |> fit(   train_features, train_targets,   validation_data = list(val_features, val_targets),   class_weight = class_weight,   batch_size = 2048,   epochs = 30,   callbacks = callbacks,   verbose = 2 ) ## Epoch 1/30 ## 112/112 - 3s - 27ms/step - fn: 40.0000 - fp: 23705.0000 - loss: 2.2728e-06 - precision: 0.0148 - recall: 0.8990 - tn: 203745.0000 - tp: 356.0000 - val_fn: 8.0000 - val_fp: 1502.0000 - val_loss: 0.1089 - val_precision: 0.0553 - val_recall: 0.9167 - val_tn: 55363.0000 - val_tp: 88.0000 ## Epoch 2/30 ## 112/112 - 1s - 6ms/step - fn: 33.0000 - fp: 8009.0000 - loss: 1.4949e-06 - precision: 0.0434 - recall: 0.9167 - tn: 219441.0000 - tp: 363.0000 - val_fn: 10.0000 - val_fp: 1176.0000 - val_loss: 0.0912 - val_precision: 0.0681 - val_recall: 0.8958 - val_tn: 55689.0000 - val_tp: 86.0000 ## Epoch 3/30 ## 112/112 - 0s - 2ms/step - fn: 32.0000 - fp: 7704.0000 - loss: 1.3369e-06 - precision: 0.0451 - recall: 0.9192 - tn: 219746.0000 - tp: 364.0000 - val_fn: 9.0000 - val_fp: 1202.0000 - val_loss: 0.0870 - val_precision: 0.0675 - val_recall: 0.9062 - val_tn: 55663.0000 - val_tp: 87.0000 ## Epoch 4/30 ## 112/112 - 0s - 2ms/step - fn: 28.0000 - fp: 8366.0000 - loss: 1.2269e-06 - precision: 0.0421 - recall: 0.9293 - tn: 219084.0000 - tp: 368.0000 - val_fn: 10.0000 - val_fp: 1560.0000 - val_loss: 0.0967 - val_precision: 0.0522 - val_recall: 0.8958 - val_tn: 55305.0000 - val_tp: 86.0000 ## Epoch 5/30 ## 112/112 - 0s - 2ms/step - fn: 19.0000 - fp: 6217.0000 - loss: 8.5492e-07 - precision: 0.0572 - recall: 0.9520 - tn: 221233.0000 - tp: 377.0000 - val_fn: 9.0000 - val_fp: 2626.0000 - val_loss: 0.1236 - val_precision: 0.0321 - val_recall: 0.9062 - val_tn: 54239.0000 - val_tp: 87.0000 ## Epoch 6/30 ## 112/112 - 0s - 2ms/step - fn: 18.0000 - fp: 6566.0000 - loss: 7.4300e-07 - precision: 0.0544 - recall: 0.9545 - tn: 220884.0000 - tp: 378.0000 - val_fn: 8.0000 - val_fp: 2302.0000 - val_loss: 0.1081 - val_precision: 0.0368 - val_recall: 0.9167 - val_tn: 54563.0000 - val_tp: 88.0000 ## Epoch 7/30 ## 112/112 - 0s - 2ms/step - fn: 14.0000 - fp: 6819.0000 - loss: 7.1838e-07 - precision: 0.0530 - recall: 0.9646 - tn: 220631.0000 - tp: 382.0000 - val_fn: 7.0000 - val_fp: 1376.0000 - val_loss: 0.0800 - val_precision: 0.0608 - val_recall: 0.9271 - val_tn: 55489.0000 - val_tp: 89.0000 ## Epoch 8/30 ## 112/112 - 0s - 2ms/step - fn: 19.0000 - fp: 7345.0000 - loss: 8.3921e-07 - precision: 0.0488 - recall: 0.9520 - tn: 220105.0000 - tp: 377.0000 - val_fn: 10.0000 - val_fp: 416.0000 - val_loss: 0.0406 - val_precision: 0.1713 - val_recall: 0.8958 - val_tn: 56449.0000 - val_tp: 86.0000 ## Epoch 9/30 ## 112/112 - 0s - 2ms/step - fn: 13.0000 - fp: 6282.0000 - loss: 8.3488e-07 - precision: 0.0575 - recall: 0.9672 - tn: 221168.0000 - tp: 383.0000 - val_fn: 10.0000 - val_fp: 933.0000 - val_loss: 0.0450 - val_precision: 0.0844 - val_recall: 0.8958 - val_tn: 55932.0000 - val_tp: 86.0000 ## Epoch 10/30 ## 112/112 - 0s - 2ms/step - fn: 15.0000 - fp: 7597.0000 - loss: 1.1098e-06 - precision: 0.0478 - recall: 0.9621 - tn: 219853.0000 - tp: 381.0000 - val_fn: 10.0000 - val_fp: 2417.0000 - val_loss: 0.4261 - val_precision: 0.0344 - val_recall: 0.8958 - val_tn: 54448.0000 - val_tp: 86.0000 ## Epoch 11/30 ## 112/112 - 0s - 2ms/step - fn: 24.0000 - fp: 10269.0000 - loss: 1.9062e-06 - precision: 0.0350 - recall: 0.9394 - tn: 217181.0000 - tp: 372.0000 - val_fn: 9.0000 - val_fp: 1434.0000 - val_loss: 0.1261 - val_precision: 0.0572 - val_recall: 0.9062 - val_tn: 55431.0000 - val_tp: 87.0000 ## Epoch 12/30 ## 112/112 - 0s - 2ms/step - fn: 17.0000 - fp: 6634.0000 - loss: 1.0711e-06 - precision: 0.0540 - recall: 0.9571 - tn: 220816.0000 - tp: 379.0000 - val_fn: 8.0000 - val_fp: 2263.0000 - val_loss: 0.1654 - val_precision: 0.0374 - val_recall: 0.9167 - val_tn: 54602.0000 - val_tp: 88.0000 ## Epoch 13/30 ## 112/112 - 0s - 2ms/step - fn: 10.0000 - fp: 5620.0000 - loss: 5.3600e-07 - precision: 0.0643 - recall: 0.9747 - tn: 221830.0000 - tp: 386.0000 - val_fn: 12.0000 - val_fp: 914.0000 - val_loss: 0.0414 - val_precision: 0.0842 - val_recall: 0.8750 - val_tn: 55951.0000 - val_tp: 84.0000 ## Epoch 14/30 ## 112/112 - 0s - 2ms/step - fn: 8.0000 - fp: 5103.0000 - loss: 5.1168e-07 - precision: 0.0707 - recall: 0.9798 - tn: 222347.0000 - tp: 388.0000 - val_fn: 11.0000 - val_fp: 1382.0000 - val_loss: 0.0660 - val_precision: 0.0579 - val_recall: 0.8854 - val_tn: 55483.0000 - val_tp: 85.0000 ## Epoch 15/30 ## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 4497.0000 - loss: 4.4632e-07 - precision: 0.0796 - recall: 0.9823 - tn: 222953.0000 - tp: 389.0000 - val_fn: 10.0000 - val_fp: 1805.0000 - val_loss: 0.0786 - val_precision: 0.0455 - val_recall: 0.8958 - val_tn: 55060.0000 - val_tp: 86.0000 ## Epoch 16/30 ## 112/112 - 0s - 2ms/step - fn: 5.0000 - fp: 5056.0000 - loss: 6.1038e-07 - precision: 0.0718 - recall: 0.9874 - tn: 222394.0000 - tp: 391.0000 - val_fn: 13.0000 - val_fp: 889.0000 - val_loss: 0.0676 - val_precision: 0.0854 - val_recall: 0.8646 - val_tn: 55976.0000 - val_tp: 83.0000 ## Epoch 17/30 ## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 4639.0000 - loss: 4.5763e-07 - precision: 0.0781 - recall: 0.9924 - tn: 222811.0000 - tp: 393.0000 - val_fn: 11.0000 - val_fp: 1185.0000 - val_loss: 0.0669 - val_precision: 0.0669 - val_recall: 0.8854 - val_tn: 55680.0000 - val_tp: 85.0000 ## Epoch 18/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 4355.0000 - loss: 4.5299e-07 - precision: 0.0826 - recall: 0.9899 - tn: 223095.0000 - tp: 392.0000 - val_fn: 9.0000 - val_fp: 1405.0000 - val_loss: 0.0772 - val_precision: 0.0583 - val_recall: 0.9062 - val_tn: 55460.0000 - val_tp: 87.0000 ## Epoch 19/30 ## 112/112 - 0s - 2ms/step - fn: 6.0000 - fp: 5779.0000 - loss: 4.8324e-07 - precision: 0.0632 - recall: 0.9848 - tn: 221671.0000 - tp: 390.0000 - val_fn: 10.0000 - val_fp: 1135.0000 - val_loss: 0.0546 - val_precision: 0.0704 - val_recall: 0.8958 - val_tn: 55730.0000 - val_tp: 86.0000 ## Epoch 20/30 ## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 4748.0000 - loss: 3.7424e-07 - precision: 0.0764 - recall: 0.9924 - tn: 222702.0000 - tp: 393.0000 - val_fn: 9.0000 - val_fp: 895.0000 - val_loss: 0.0396 - val_precision: 0.0886 - val_recall: 0.9062 - val_tn: 55970.0000 - val_tp: 87.0000 ## Epoch 21/30 ## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 3798.0000 - loss: 3.1417e-07 - precision: 0.0938 - recall: 0.9924 - tn: 223652.0000 - tp: 393.0000 - val_fn: 10.0000 - val_fp: 652.0000 - val_loss: 0.0309 - val_precision: 0.1165 - val_recall: 0.8958 - val_tn: 56213.0000 - val_tp: 86.0000 ## Epoch 22/30 ## 112/112 - 0s - 2ms/step - fn: 0.0000e+00 - fp: 2188.0000 - loss: 1.8112e-07 - precision: 0.1533 - recall: 1.0000 - tn: 225262.0000 - tp: 396.0000 - val_fn: 10.0000 - val_fp: 427.0000 - val_loss: 0.0250 - val_precision: 0.1676 - val_recall: 0.8958 - val_tn: 56438.0000 - val_tp: 86.0000 ## Epoch 23/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 3066.0000 - loss: 3.4932e-07 - precision: 0.1134 - recall: 0.9899 - tn: 224384.0000 - tp: 392.0000 - val_fn: 11.0000 - val_fp: 1053.0000 - val_loss: 0.0494 - val_precision: 0.0747 - val_recall: 0.8854 - val_tn: 55812.0000 - val_tp: 85.0000 ## Epoch 24/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 3175.0000 - loss: 5.1994e-07 - precision: 0.1099 - recall: 0.9899 - tn: 224275.0000 - tp: 392.0000 - val_fn: 11.0000 - val_fp: 1012.0000 - val_loss: 0.0997 - val_precision: 0.0775 - val_recall: 0.8854 - val_tn: 55853.0000 - val_tp: 85.0000 ## Epoch 25/30 ## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 5874.0000 - loss: 6.5862e-07 - precision: 0.0621 - recall: 0.9823 - tn: 221576.0000 - tp: 389.0000 - val_fn: 9.0000 - val_fp: 3090.0000 - val_loss: 0.1296 - val_precision: 0.0274 - val_recall: 0.9062 - val_tn: 53775.0000 - val_tp: 87.0000 ## Epoch 26/30 ## 112/112 - 0s - 2ms/step - fn: 6.0000 - fp: 5395.0000 - loss: 5.9253e-07 - precision: 0.0674 - recall: 0.9848 - tn: 222055.0000 - tp: 390.0000 - val_fn: 9.0000 - val_fp: 1320.0000 - val_loss: 0.0928 - val_precision: 0.0618 - val_recall: 0.9062 - val_tn: 55545.0000 - val_tp: 87.0000 ## Epoch 27/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 3887.0000 - loss: 7.1278e-07 - precision: 0.0916 - recall: 0.9899 - tn: 223563.0000 - tp: 392.0000 - val_fn: 12.0000 - val_fp: 572.0000 - val_loss: 0.0352 - val_precision: 0.1280 - val_recall: 0.8750 - val_tn: 56293.0000 - val_tp: 84.0000 ## Epoch 28/30 ## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 2209.0000 - loss: 5.8153e-07 - precision: 0.1510 - recall: 0.9924 - tn: 225241.0000 - tp: 393.0000 - val_fn: 11.0000 - val_fp: 1373.0000 - val_loss: 0.0808 - val_precision: 0.0583 - val_recall: 0.8854 - val_tn: 55492.0000 - val_tp: 85.0000 ## Epoch 29/30 ## 112/112 - 0s - 2ms/step - fn: 1.0000 - fp: 2506.0000 - loss: 2.9031e-07 - precision: 0.1362 - recall: 0.9975 - tn: 224944.0000 - tp: 395.0000 - val_fn: 11.0000 - val_fp: 690.0000 - val_loss: 0.0401 - val_precision: 0.1097 - val_recall: 0.8854 - val_tn: 56175.0000 - val_tp: 85.0000 ## Epoch 30/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 2719.0000 - loss: 6.1097e-07 - precision: 0.1260 - recall: 0.9899 - tn: 224731.0000 - tp: 392.0000 - val_fn: 13.0000 - val_fp: 861.0000 - val_loss: 0.0362 - val_precision: 0.0879 - val_recall: 0.8646 - val_tn: 56004.0000 - val_tp: 83.0000 val_pred <- model %>%   predict(val_features) %>%   { as.integer(. > 0.5) } ## 1781/1781 - 1s - 311us/step pred_correct <- val_df$Class == val_pred cat(sprintf(\"Validation accuracy: %.2f\", mean(pred_correct))) ## Validation accuracy: 0.98 fraudulent <- val_df$Class == 1  n_fraudulent_detected <- sum(fraudulent & pred_correct) n_fraudulent_missed <- sum(fraudulent & !pred_correct) n_legitimate_flagged <- sum(!fraudulent & !pred_correct)"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"conclusions","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Conclusions","title":"Imbalanced classification: credit card fraud detection","text":"end training, 56,961 validation transactions, : Correctly identifying 83 fraudulent Missing 13 fraudulent transactions cost incorrectly flagging 861 legitimate transactions real world, one put even higher weight class 1, reflect False Negatives costly False Positives. Next time credit card gets declined online purchase – .","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"introduction","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Introduction","title":"Structured data classification with FeatureSpace","text":"example demonstrates structured data classification (also known tabular data classification), starting raw CSV file. data includes numerical features, integer categorical features, string categorical features. use utility layer_feature_space() index, preprocess, encode features. code adapted example Structured data classification scratch. previous example managed low-level feature preprocessing encoding Keras preprocessing layers, example delegate everything layer_feature_space(), making workflow extremely quick easy.","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"the-dataset","dir":"Articles > Examples > Structured_data","previous_headings":"Introduction","what":"The dataset","title":"Structured data classification with FeatureSpace","text":"dataset provided Cleveland Clinic Foundation Heart Disease. ’s CSV file 303 rows. row contains information patient (sample), column describes attribute patient (feature). use features predict whether patient heart disease (binary classification). ’s description feature:","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"setup","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Setup","title":"Structured data classification with FeatureSpace","text":"","code":"library(readr) library(dplyr, warn.conflicts = FALSE) library(keras3) library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")  conflicted::conflicts_prefer(   keras3::shape(),   keras3::set_random_seed(),   dplyr::filter(),   .quiet = TRUE )  use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"preparing-the-data","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Preparing the data","title":"Structured data classification with FeatureSpace","text":"Let’s download data load Pandas dataframe: dataset includes 303 samples 14 columns per sample (13 features, plus target label) ’s preview samples: last column, “target”, indicates whether patient heart disease (1) (0). Let’s split data training validation set: Let’s generate tf_dataset objects dataframe: tf_dataset yields tuple (input, target) input dictionary (named list) features target value 0 1: Let’s batch datasets:","code":"file_url <-   \"http://storage.googleapis.com/download.tensorflow.org/data/heart.csv\" df <- read_csv(file_url, col_types = cols(   oldpeak = col_double(),   thal = col_character(),   .default = col_integer() ))  # the dataset has two malformed rows, filter them out df <- df |> filter(!thal %in% c(\"1\", \"2\")) glimpse(df) ## Rows: 301 ## Columns: 14 ## $ age      <int> 63, 67, 67, 37, 41, 56, 62, 57, 63, 53, 57, 56, 56, 44, 5… ## $ sex      <int> 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, … ## $ cp       <int> 1, 4, 4, 3, 2, 2, 4, 4, 4, 4, 4, 2, 3, 2, 3, 3, 2, 4, 3, … ## $ trestbps <int> 145, 160, 120, 130, 130, 120, 140, 120, 130, 140, 140, 14… ## $ chol     <int> 233, 286, 229, 250, 204, 236, 268, 354, 254, 203, 192, 29… ## $ fbs      <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, … ## $ restecg  <int> 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, … ## $ thalach  <int> 150, 108, 129, 187, 172, 178, 160, 163, 147, 155, 148, 15… ## $ exang    <int> 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, … ## $ oldpeak  <dbl> 2.3, 1.5, 2.6, 3.5, 1.4, 0.8, 3.6, 0.6, 1.4, 3.1, 0.4, 1.… ## $ slope    <int> 3, 2, 2, 3, 1, 1, 3, 1, 2, 3, 2, 2, 2, 1, 1, 1, 3, 1, 1, … ## $ ca       <int> 0, 3, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, … ## $ thal     <chr> \"fixed\", \"normal\", \"reversible\", \"normal\", \"normal\", \"nor… ## $ target   <int> 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, … df ## # A tibble: 301 × 14 ##      age   sex    cp trestbps  chol   fbs restecg thalach exang oldpeak ##    <int> <int> <int>    <int> <int> <int>   <int>   <int> <int>   <dbl> ##  1    63     1     1      145   233     1       2     150     0     2.3 ##  2    67     1     4      160   286     0       2     108     1     1.5 ##  3    67     1     4      120   229     0       2     129     1     2.6 ##  4    37     1     3      130   250     0       0     187     0     3.5 ##  5    41     0     2      130   204     0       2     172     0     1.4 ##  6    56     1     2      120   236     0       0     178     0     0.8 ##  7    62     0     4      140   268     0       2     160     0     3.6 ##  8    57     0     4      120   354     0       0     163     1     0.6 ##  9    63     1     4      130   254     0       2     147     0     1.4 ## 10    53     1     4      140   203     1       2     155     1     3.1 ## # ℹ 291 more rows ## # ℹ 4 more variables: slope <int>, ca <int>, thal <chr>, target <int> val_idx <- nrow(df) %>% sample.int(., . * 0.2) val_df <- df[val_idx, ] train_df <- df[-val_idx, ]  cat(sprintf(   \"Using %d samples for training and %d for validation\",   nrow(train_df), nrow(val_df) )) ## Using 241 samples for training and 60 for validation dataframe_to_dataset <- function(df) {   labels <- df |> pull(target) |> as.integer()   inputs <- df |> select(-target) |> as.list()    ds <- tensor_slices_dataset(list(inputs, labels)) |>     dataset_shuffle(nrow(df))    ds }  train_ds <- dataframe_to_dataset(train_df) val_ds <- dataframe_to_dataset(val_df) c(x, y) %<-% iter_next(as_iterator(train_ds)) cat(\"Input: \"); str(x) cat(\"Target: \"); str(y) ## Input: List of 13 ##  $ age     :<tf.Tensor: shape=(), dtype=int32, numpy=59> ##  $ sex     :<tf.Tensor: shape=(), dtype=int32, numpy=1> ##  $ cp      :<tf.Tensor: shape=(), dtype=int32, numpy=4> ##  $ trestbps:<tf.Tensor: shape=(), dtype=int32, numpy=164> ##  $ chol    :<tf.Tensor: shape=(), dtype=int32, numpy=176> ##  $ fbs     :<tf.Tensor: shape=(), dtype=int32, numpy=1> ##  $ restecg :<tf.Tensor: shape=(), dtype=int32, numpy=2> ##  $ thalach :<tf.Tensor: shape=(), dtype=int32, numpy=90> ##  $ exang   :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ oldpeak :<tf.Tensor: shape=(), dtype=float32, numpy=1.0> ##  $ slope   :<tf.Tensor: shape=(), dtype=int32, numpy=2> ##  $ ca      :<tf.Tensor: shape=(), dtype=int32, numpy=2> ##  $ thal    :<tf.Tensor: shape=(), dtype=string, numpy=b'fixed'> ## Target: <tf.Tensor: shape=(), dtype=int32, numpy=1> train_ds <- train_ds |> dataset_batch(32) val_ds <- val_ds |> dataset_batch(32)"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"configuring-a-featurespace","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Configuring a FeatureSpace","title":"Structured data classification with FeatureSpace","text":"configure feature preprocessed, instantiate layer_feature_space(), pass dictionary (named list unique names) maps name features string describes feature type. “integer categorical” features \"FBS\", one “string categorical” feature (\"thal\"), numerical features, ’d like normalize – except \"age\", ’d like discretize number bins. also use crosses argument capture feature interactions categorical features, say, create additional features represent value co-occurrences categorical features. can compute feature crosses like arbitrary sets categorical features – just tuples two features. resulting co-occurences hashed fixed-sized vector, don’t need worry whether co-occurence space large.","code":"feature_space <- layer_feature_space(   features = list(     # Categorical features encoded as integers     sex = \"integer_categorical\",     cp = \"integer_categorical\",     fbs = \"integer_categorical\",     restecg = \"integer_categorical\",     exang = \"integer_categorical\",     ca = \"integer_categorical\",     # Categorical feature encoded as string     thal = \"string_categorical\",     # Numerical features to discretize     age = \"float_discretized\",     # Numerical features to normalize     trestbps = \"float_normalized\",     chol = \"float_normalized\",     thalach = \"float_normalized\",     oldpeak = \"float_normalized\",     slope = \"float_normalized\"   ),   # We create additional features by hashing   # value co-occurrences for the   # following groups of categorical features.   crosses = list(c(\"sex\", \"age\"), c(\"thal\", \"ca\")),   # The hashing space for these co-occurrences   # wil be 32-dimensional.   crossing_dim = 32,   # Our utility will one-hot encode all categorical   # features and concat all features into a single   # vector (one vector per sample).   output_mode = \"concat\" )"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"further-customizing-a-featurespace","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Further customizing a FeatureSpace","title":"Structured data classification with FeatureSpace","text":"Specifying feature type via string name quick easy, sometimes may want configure preprocessing feature. instance, case, categorical features don’t large set possible values – ’s handful values per feature (e.g. 1 0 feature \"FBS\"), possible values represented training set. result, don’t need reserve index represent “vocabulary” values features – default behavior. , just specify num_oov_indices=0 features tell feature preprocessor skip “vocabulary” indexing. customizations access include specifying number bins discretizing features type \"float_discretized\", dimensionality hashing space feature crossing.","code":"feature_space <- layer_feature_space(   features = list(     # Categorical features encoded as integers     sex       = feature_integer_categorical(num_oov_indices = 0),     cp        = feature_integer_categorical(num_oov_indices = 0),     fbs       = feature_integer_categorical(num_oov_indices = 0),     restecg   = feature_integer_categorical(num_oov_indices = 0),     exang     = feature_integer_categorical(num_oov_indices = 0),     ca        = feature_integer_categorical(num_oov_indices = 0),     # Categorical feature encoded as string     thal      = feature_string_categorical(num_oov_indices = 0),     # Numerical features to discretize     age       = feature_float_discretized(num_bins = 30),     # Numerical features to normalize     trestbps  = feature_float_normalized(),     chol      = feature_float_normalized(),     thalach   = feature_float_normalized(),     oldpeak   = feature_float_normalized(),     slope     = feature_float_normalized()   ),   # Specify feature cross with a custom crossing dim.   crosses = list(     feature_cross(       feature_names = c(\"sex\", \"age\"),       crossing_dim = 64     ),     feature_cross(       feature_names = c(\"thal\", \"ca\"),       crossing_dim = 16     )   ),   output_mode = \"concat\" )"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"adapt-the-featurespace-to-the-training-data","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Adapt the FeatureSpace to the training data","title":"Structured data classification with FeatureSpace","text":"start using FeatureSpace build model, adapt training data. adapt(), FeatureSpace : Index set possible values categorical features. Compute mean variance numerical features normalize. Compute value boundaries different bins numerical features discretize. Note adapt() called tf_dataset yields dicts (named lists) feature values – labels. point, FeatureSpace can called dict raw feature values, return single concatenate vector sample, combining encoded features feature crosses.","code":"train_ds_with_no_labels <- train_ds |> dataset_map(\\(x, y) x) feature_space |> adapt(train_ds_with_no_labels) c(x, y) %<-% iter_next(as_iterator(train_ds)) preprocessed_x <- feature_space(x) preprocessed_x ## tf.Tensor( ## [[0. 0. 1. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  ... ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.]], shape=(32, 136), dtype=float32)"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"two-ways-to-manage-preprocessing-as-part-of-the-tf-data-pipeline-or-in-the-model-itself","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","title":"Structured data classification with FeatureSpace","text":"two ways can leverage FeatureSpace:","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"asynchronous-preprocessing-in-tf-data","dir":"Articles > Examples > Structured_data","previous_headings":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","what":"Asynchronous preprocessing in tf.data","title":"Structured data classification with FeatureSpace","text":"can make part data pipeline, model. enables asynchronous parallel preprocessing data CPU hits model. ’re training GPU TPU, want speed preprocessing. Usually, always right thing training.","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"synchronous-preprocessing-in-the-model","dir":"Articles > Examples > Structured_data","previous_headings":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","what":"Synchronous preprocessing in the model","title":"Structured data classification with FeatureSpace","text":"can make part model. means model expect dicts raw feature values, preprocessing batch done synchronously (blocking manner) rest forward pass. want end--end model can process raw feature values – keep mind model able run CPU, since types feature preprocessing (e.g. string preprocessing) GPU TPU compatible. GPU / TPU performance-sensitive settings. general, want -model preprocessing inference CPU. case, apply FeatureSpace tf.data pipeline training, inference end--end model includes FeatureSpace. Let’s create training validation dataset preprocessed batches:","code":"preprocessed_train_ds <- train_ds |>   dataset_map(\\(x, y) list(feature_space(x), y),               num_parallel_calls = tf$data$AUTOTUNE) |>   dataset_prefetch(tf$data$AUTOTUNE)  preprocessed_val_ds <- val_ds |>   dataset_map(\\(x, y) list(feature_space(x), y),               num_parallel_calls = tf$data$AUTOTUNE) |>   dataset_prefetch(tf$data$AUTOTUNE)"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"build-a-model","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Build a model","title":"Structured data classification with FeatureSpace","text":"Time build model – rather two models: training model expects preprocessed features (one sample = one vector) inference model expects raw features (one sample = dict raw feature values)","code":"dict_inputs <- feature_space$get_inputs() encoded_features <- feature_space$get_encoded_features()  predictions <- encoded_features |>   layer_dense(32, activation=\"relu\") |>   layer_dropout(0.5) |>   layer_dense(1, activation=\"sigmoid\")  training_model <- keras_model(inputs = encoded_features,                               outputs = predictions) training_model |> compile(optimizer = \"adam\",                           loss = \"binary_crossentropy\",                           metrics = \"accuracy\")  inference_model <- keras_model(inputs = dict_inputs,                                outputs = predictions)"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"train-the-model","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Train the model","title":"Structured data classification with FeatureSpace","text":"Let’s train model 20 epochs. Note feature preprocessing happening part tf.data pipeline, part model. quickly get 80% validation accuracy.","code":"training_model |> fit(   preprocessed_train_ds,   epochs = 20,   validation_data = preprocessed_val_ds,   verbose = 2 ) ## Epoch 1/20 ## 8/8 - 3s - 326ms/step - accuracy: 0.4315 - loss: 0.7427 - val_accuracy: 0.5333 - val_loss: 0.7048 ## Epoch 2/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.5311 - loss: 0.6984 - val_accuracy: 0.6500 - val_loss: 0.6438 ## Epoch 3/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.6473 - loss: 0.6316 - val_accuracy: 0.6833 - val_loss: 0.5937 ## Epoch 4/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.6639 - loss: 0.6134 - val_accuracy: 0.7500 - val_loss: 0.5524 ## Epoch 5/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7178 - loss: 0.5820 - val_accuracy: 0.7667 - val_loss: 0.5176 ## Epoch 6/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.7718 - loss: 0.5573 - val_accuracy: 0.7500 - val_loss: 0.4897 ## Epoch 7/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7718 - loss: 0.5200 - val_accuracy: 0.8167 - val_loss: 0.4640 ## Epoch 8/20 ## 8/8 - 0s - 14ms/step - accuracy: 0.7759 - loss: 0.5068 - val_accuracy: 0.8167 - val_loss: 0.4388 ## Epoch 9/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8174 - loss: 0.4724 - val_accuracy: 0.8333 - val_loss: 0.4162 ## Epoch 10/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8050 - loss: 0.4545 - val_accuracy: 0.8167 - val_loss: 0.3960 ## Epoch 11/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8091 - loss: 0.4514 - val_accuracy: 0.8500 - val_loss: 0.3786 ## Epoch 12/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8423 - loss: 0.4291 - val_accuracy: 0.8500 - val_loss: 0.3647 ## Epoch 13/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8465 - loss: 0.4028 - val_accuracy: 0.8667 - val_loss: 0.3499 ## Epoch 14/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8340 - loss: 0.4037 - val_accuracy: 0.8667 - val_loss: 0.3369 ## Epoch 15/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8548 - loss: 0.3928 - val_accuracy: 0.8667 - val_loss: 0.3289 ## Epoch 16/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8589 - loss: 0.3745 - val_accuracy: 0.8667 - val_loss: 0.3206 ## Epoch 17/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8257 - loss: 0.3820 - val_accuracy: 0.8667 - val_loss: 0.3129 ## Epoch 18/20 ## 8/8 - 0s - 14ms/step - accuracy: 0.8631 - loss: 0.3650 - val_accuracy: 0.8667 - val_loss: 0.3079 ## Epoch 19/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8382 - loss: 0.3635 - val_accuracy: 0.8667 - val_loss: 0.3024 ## Epoch 20/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8631 - loss: 0.3524 - val_accuracy: 0.8833 - val_loss: 0.2970"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"inference-on-new-data-with-the-end-to-end-model","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Inference on new data with the end-to-end model","title":"Structured data classification with FeatureSpace","text":"Now, can use inference model (includes FeatureSpace) make predictions based dicts raw features values, follows:","code":"sample <- list(   age = 60,   sex = 1,   cp = 1,   trestbps = 145,   chol = 233,   fbs = 1,   restecg = 2,   thalach = 150,   exang = 0,   oldpeak = 2.3,   slope = 3,   ca = 0,   thal = \"fixed\" )  input_dict <- lapply(sample, \\(x) op_convert_to_tensor(array(x))) predictions <- inference_model |> predict(input_dict) ## 1/1 - 0s - 257ms/step glue::glue(r\"---(   This particular patient had a {(100 * predictions) |> signif(3)}% probability   of having a heart disease, as evaluated by our model. )---\") ## This particular patient had a 49.7% probability ## of having a heart disease, as evaluated by our model."},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"introduction","dir":"Articles > Examples","previous_headings":"","what":"Introduction","title":"Structured data classification with FeatureSpace","text":"example demonstrates structured data classification (also known tabular data classification), starting raw CSV file. data includes numerical features, integer categorical features, string categorical features. use utility layer_feature_space() index, preprocess, encode features. code adapted example Structured data classification scratch. previous example managed low-level feature preprocessing encoding Keras preprocessing layers, example delegate everything layer_feature_space(), making workflow extremely quick easy.","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"the-dataset","dir":"Articles > Examples","previous_headings":"Introduction","what":"The dataset","title":"Structured data classification with FeatureSpace","text":"dataset provided Cleveland Clinic Foundation Heart Disease. ’s CSV file 303 rows. row contains information patient (sample), column describes attribute patient (feature). use features predict whether patient heart disease (binary classification). ’s description feature:","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"setup","dir":"Articles > Examples","previous_headings":"","what":"Setup","title":"Structured data classification with FeatureSpace","text":"","code":"library(readr) library(dplyr, warn.conflicts = FALSE) library(keras3) library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")  conflicted::conflicts_prefer(   keras3::shape(),   keras3::set_random_seed(),   dplyr::filter(),   .quiet = TRUE )  use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"preparing-the-data","dir":"Articles > Examples","previous_headings":"","what":"Preparing the data","title":"Structured data classification with FeatureSpace","text":"Let’s download data load Pandas dataframe: dataset includes 303 samples 14 columns per sample (13 features, plus target label) ’s preview samples: last column, “target”, indicates whether patient heart disease (1) (0). Let’s split data training validation set: Let’s generate tf_dataset objects dataframe: tf_dataset yields tuple (input, target) input dictionary (named list) features target value 0 1: Let’s batch datasets:","code":"file_url <-   \"http://storage.googleapis.com/download.tensorflow.org/data/heart.csv\" df <- read_csv(file_url, col_types = cols(   oldpeak = col_double(),   thal = col_character(),   .default = col_integer() ))  # the dataset has two malformed rows, filter them out df <- df |> filter(!thal %in% c(\"1\", \"2\")) glimpse(df) ## Rows: 301 ## Columns: 14 ## $ age      <int> 63, 67, 67, 37, 41, 56, 62, 57, 63, 53, 57, 56, 56, 44, 5… ## $ sex      <int> 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, … ## $ cp       <int> 1, 4, 4, 3, 2, 2, 4, 4, 4, 4, 4, 2, 3, 2, 3, 3, 2, 4, 3, … ## $ trestbps <int> 145, 160, 120, 130, 130, 120, 140, 120, 130, 140, 140, 14… ## $ chol     <int> 233, 286, 229, 250, 204, 236, 268, 354, 254, 203, 192, 29… ## $ fbs      <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, … ## $ restecg  <int> 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, … ## $ thalach  <int> 150, 108, 129, 187, 172, 178, 160, 163, 147, 155, 148, 15… ## $ exang    <int> 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, … ## $ oldpeak  <dbl> 2.3, 1.5, 2.6, 3.5, 1.4, 0.8, 3.6, 0.6, 1.4, 3.1, 0.4, 1.… ## $ slope    <int> 3, 2, 2, 3, 1, 1, 3, 1, 2, 3, 2, 2, 2, 1, 1, 1, 3, 1, 1, … ## $ ca       <int> 0, 3, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, … ## $ thal     <chr> \"fixed\", \"normal\", \"reversible\", \"normal\", \"normal\", \"nor… ## $ target   <int> 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, … df ## # A tibble: 301 × 14 ##      age   sex    cp trestbps  chol   fbs restecg thalach exang oldpeak ##    <int> <int> <int>    <int> <int> <int>   <int>   <int> <int>   <dbl> ##  1    63     1     1      145   233     1       2     150     0     2.3 ##  2    67     1     4      160   286     0       2     108     1     1.5 ##  3    67     1     4      120   229     0       2     129     1     2.6 ##  4    37     1     3      130   250     0       0     187     0     3.5 ##  5    41     0     2      130   204     0       2     172     0     1.4 ##  6    56     1     2      120   236     0       0     178     0     0.8 ##  7    62     0     4      140   268     0       2     160     0     3.6 ##  8    57     0     4      120   354     0       0     163     1     0.6 ##  9    63     1     4      130   254     0       2     147     0     1.4 ## 10    53     1     4      140   203     1       2     155     1     3.1 ## # ℹ 291 more rows ## # ℹ 4 more variables: slope <int>, ca <int>, thal <chr>, target <int> val_idx <- nrow(df) %>% sample.int(., . * 0.2) val_df <- df[val_idx, ] train_df <- df[-val_idx, ]  cat(sprintf(   \"Using %d samples for training and %d for validation\",   nrow(train_df), nrow(val_df) )) ## Using 241 samples for training and 60 for validation dataframe_to_dataset <- function(df) {   labels <- df |> pull(target) |> as.integer()   inputs <- df |> select(-target) |> as.list()    ds <- tensor_slices_dataset(list(inputs, labels)) |>     dataset_shuffle(nrow(df))    ds }  train_ds <- dataframe_to_dataset(train_df) val_ds <- dataframe_to_dataset(val_df) c(x, y) %<-% iter_next(as_iterator(train_ds)) cat(\"Input: \"); str(x) cat(\"Target: \"); str(y) ## Input: List of 13 ##  $ age     :<tf.Tensor: shape=(), dtype=int32, numpy=57> ##  $ sex     :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ cp      :<tf.Tensor: shape=(), dtype=int32, numpy=4> ##  $ trestbps:<tf.Tensor: shape=(), dtype=int32, numpy=140> ##  $ chol    :<tf.Tensor: shape=(), dtype=int32, numpy=241> ##  $ fbs     :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ restecg :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ thalach :<tf.Tensor: shape=(), dtype=int32, numpy=123> ##  $ exang   :<tf.Tensor: shape=(), dtype=int32, numpy=1> ##  $ oldpeak :<tf.Tensor: shape=(), dtype=float32, numpy=0.2> ##  $ slope   :<tf.Tensor: shape=(), dtype=int32, numpy=2> ##  $ ca      :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ thal    :<tf.Tensor: shape=(), dtype=string, numpy=b'reversible'> ## Target: <tf.Tensor: shape=(), dtype=int32, numpy=0> train_ds <- train_ds |> dataset_batch(32) val_ds <- val_ds |> dataset_batch(32)"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"configuring-a-featurespace","dir":"Articles > Examples","previous_headings":"","what":"Configuring a FeatureSpace","title":"Structured data classification with FeatureSpace","text":"configure feature preprocessed, instantiate layer_feature_space, pass dictionary (named list unique names) maps name features string describes feature type. “integer categorical” features \"FBS\", one “string categorical” feature (\"thal\"), numerical features, ’d like normalize – except \"age\", ’d like discretize number bins. also use crosses argument capture feature interactions categorical features, say, create additional features represent value co-occurrences categorical features. can compute feature crosses like arbitrary sets categorical features – just tuples two features. resulting co-occurences hashed fixed-sized vector, don’t need worry whether co-occurence space large.","code":"feature_space <- layer_feature_space(   features = list(     # Categorical features encoded as integers     sex = \"integer_categorical\",     cp = \"integer_categorical\",     fbs = \"integer_categorical\",     restecg = \"integer_categorical\",     exang = \"integer_categorical\",     ca = \"integer_categorical\",     # Categorical feature encoded as string     thal = \"string_categorical\",     # Numerical features to discretize     age = \"float_discretized\",     # Numerical features to normalize     trestbps = \"float_normalized\",     chol = \"float_normalized\",     thalach = \"float_normalized\",     oldpeak = \"float_normalized\",     slope = \"float_normalized\"   ),   # We create additional features by hashing   # value co-occurrences for the   # following groups of categorical features.   crosses = list(c(\"sex\", \"age\"), c(\"thal\", \"ca\")),   # The hashing space for these co-occurrences   # wil be 32-dimensional.   crossing_dim = 32,   # Our utility will one-hot encode all categorical   # features and concat all features into a single   # vector (one vector per sample).   output_mode = \"concat\" )"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"further-customizing-a-featurespace","dir":"Articles > Examples","previous_headings":"","what":"Further customizing a FeatureSpace","title":"Structured data classification with FeatureSpace","text":"Specifying feature type via string name quick easy, sometimes may want configure preprocessing feature. instance, case, categorical features don’t large set possible values – ’s handful values per feature (e.g. 1 0 feature \"FBS\"), possible values represented training set. result, don’t need reserve index represent “vocabulary” values features – default behavior. , just specify num_oov_indices=0 features tell feature preprocessor skip “vocabulary” indexing. customizations access include specifying number bins discretizing features type \"float_discretized\", dimensionality hashing space feature crossing.","code":"feature_space <- layer_feature_space(   features = list(     # Categorical features encoded as integers     sex       = feature_integer_categorical(num_oov_indices = 0),     cp        = feature_integer_categorical(num_oov_indices = 0),     fbs       = feature_integer_categorical(num_oov_indices = 0),     restecg   = feature_integer_categorical(num_oov_indices = 0),     exang     = feature_integer_categorical(num_oov_indices = 0),     ca        = feature_integer_categorical(num_oov_indices = 0),     # Categorical feature encoded as string     thal      = feature_string_categorical(num_oov_indices = 0),     # Numerical features to discretize     age       = feature_float_discretized(num_bins = 30),     # Numerical features to normalize     trestbps  = feature_float_normalized(),     chol      = feature_float_normalized(),     thalach   = feature_float_normalized(),     oldpeak   = feature_float_normalized(),     slope     = feature_float_normalized()   ),   # Specify feature cross with a custom crossing dim.   crosses = list(     feature_cross(       feature_names = c(\"sex\", \"age\"),       crossing_dim = 64     ),     feature_cross(       feature_names = c(\"thal\", \"ca\"),       crossing_dim = 16     )   ),   output_mode = \"concat\" )"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"adapt-the-featurespace-to-the-training-data","dir":"Articles > Examples","previous_headings":"","what":"Adapt the FeatureSpace to the training data","title":"Structured data classification with FeatureSpace","text":"start using FeatureSpace build model, adapt training data. adapt(), FeatureSpace : Index set possible values categorical features. Compute mean variance numerical features normalize. Compute value boundaries different bins numerical features discretize. Note adapt() called tf_dataset yields dicts (named lists) feature values – labels. point, FeatureSpace can called dict raw feature values, return single concatenate vector sample, combining encoded features feature crosses.","code":"train_ds_with_no_labels <- train_ds |> dataset_map(\\(x, y) x) feature_space |> adapt(train_ds_with_no_labels) c(x, y) %<-% iter_next(as_iterator(train_ds)) preprocessed_x <- feature_space(x) preprocessed_x ## tf.Tensor( ## [[0. 0. 0. ... 1. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  ... ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.]], shape=(32, 136), dtype=float32)"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"two-ways-to-manage-preprocessing-as-part-of-the-tf-data-pipeline-or-in-the-model-itself","dir":"Articles > Examples","previous_headings":"","what":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","title":"Structured data classification with FeatureSpace","text":"two ways can leverage FeatureSpace:","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"asynchronous-preprocessing-in-tf-data","dir":"Articles > Examples","previous_headings":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","what":"Asynchronous preprocessing in tf.data","title":"Structured data classification with FeatureSpace","text":"can make part data pipeline, model. enables asynchronous parallel preprocessing data CPU hits model. ’re training GPU TPU, want speed preprocessing. Usually, always right thing training.","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"synchronous-preprocessing-in-the-model","dir":"Articles > Examples","previous_headings":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","what":"Synchronous preprocessing in the model","title":"Structured data classification with FeatureSpace","text":"can make part model. means model expect dicts raw feature values, preprocessing batch done synchronously (blocking manner) rest forward pass. want end--end model can process raw feature values – keep mind model able run CPU, since types feature preprocessing (e.g. string preprocessing) GPU TPU compatible. GPU / TPU performance-sensitive settings. general, want -model preprocessing inference CPU. case, apply FeatureSpace tf.data pipeline training, inference end--end model includes FeatureSpace. Let’s create training validation dataset preprocessed batches:","code":"preprocessed_train_ds <- train_ds |>   dataset_map(\\(x, y) list(feature_space(x), y),               num_parallel_calls = tf$data$AUTOTUNE) |>   dataset_prefetch(tf$data$AUTOTUNE)  preprocessed_val_ds <- val_ds |>   dataset_map(\\(x, y) list(feature_space(x), y),               num_parallel_calls = tf$data$AUTOTUNE) |>   dataset_prefetch(tf$data$AUTOTUNE)"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"build-a-model","dir":"Articles > Examples","previous_headings":"","what":"Build a model","title":"Structured data classification with FeatureSpace","text":"Time build model – rather two models: training model expects preprocessed features (one sample = one vector) inference model expects raw features (one sample = dict raw feature values)","code":"dict_inputs <- feature_space$get_inputs() encoded_features <- feature_space$get_encoded_features()  predictions <- encoded_features |>   layer_dense(32, activation=\"relu\") |>   layer_dropout(0.5) |>   layer_dense(1, activation=\"sigmoid\")  training_model <- keras_model(inputs = encoded_features,                               outputs = predictions) training_model |> compile(optimizer = \"adam\",                           loss = \"binary_crossentropy\",                           metrics = \"accuracy\")  inference_model <- keras_model(inputs = dict_inputs,                                outputs = predictions)"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"train-the-model","dir":"Articles > Examples","previous_headings":"","what":"Train the model","title":"Structured data classification with FeatureSpace","text":"Let’s train model 20 epochs. Note feature preprocessing happening part tf.data pipeline, part model. quickly get 80% validation accuracy.","code":"training_model |> fit(   preprocessed_train_ds,   epochs = 20,   validation_data = preprocessed_val_ds,   verbose = 2 ) ## Epoch 1/20 ## 8/8 - 3s - 343ms/step - accuracy: 0.5685 - loss: 0.7120 - val_accuracy: 0.6500 - val_loss: 0.6607 ## Epoch 2/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.6722 - loss: 0.6131 - val_accuracy: 0.6500 - val_loss: 0.6212 ## Epoch 3/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7137 - loss: 0.5685 - val_accuracy: 0.6500 - val_loss: 0.5961 ## Epoch 4/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.7718 - loss: 0.4981 - val_accuracy: 0.6667 - val_loss: 0.5758 ## Epoch 5/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7635 - loss: 0.4804 - val_accuracy: 0.6500 - val_loss: 0.5575 ## Epoch 6/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7676 - loss: 0.4739 - val_accuracy: 0.6667 - val_loss: 0.5309 ## Epoch 7/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.7842 - loss: 0.4141 - val_accuracy: 0.6667 - val_loss: 0.5161 ## Epoch 8/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8133 - loss: 0.3841 - val_accuracy: 0.6667 - val_loss: 0.5080 ## Epoch 9/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8133 - loss: 0.3997 - val_accuracy: 0.6667 - val_loss: 0.4980 ## Epoch 10/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8340 - loss: 0.3618 - val_accuracy: 0.6833 - val_loss: 0.4882 ## Epoch 11/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8548 - loss: 0.3335 - val_accuracy: 0.6833 - val_loss: 0.4814 ## Epoch 12/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8465 - loss: 0.3535 - val_accuracy: 0.6833 - val_loss: 0.4818 ## Epoch 13/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8340 - loss: 0.3363 - val_accuracy: 0.7000 - val_loss: 0.4755 ## Epoch 14/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8548 - loss: 0.3224 - val_accuracy: 0.7333 - val_loss: 0.4745 ## Epoch 15/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8714 - loss: 0.3083 - val_accuracy: 0.7500 - val_loss: 0.4813 ## Epoch 16/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8797 - loss: 0.3270 - val_accuracy: 0.7500 - val_loss: 0.4690 ## Epoch 17/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8631 - loss: 0.2955 - val_accuracy: 0.7500 - val_loss: 0.4782 ## Epoch 18/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8797 - loss: 0.2831 - val_accuracy: 0.7667 - val_loss: 0.4818 ## Epoch 19/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8797 - loss: 0.2872 - val_accuracy: 0.7667 - val_loss: 0.4767 ## Epoch 20/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8631 - loss: 0.2877 - val_accuracy: 0.7833 - val_loss: 0.4781"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"inference-on-new-data-with-the-end-to-end-model","dir":"Articles > Examples","previous_headings":"","what":"Inference on new data with the end-to-end model","title":"Structured data classification with FeatureSpace","text":"Now, can use inference model (includes FeatureSpace) make predictions based dicts raw features values, follows:","code":"sample <- list(   age = 60,   sex = 1,   cp = 1,   trestbps = 145,   chol = 233,   fbs = 1,   restecg = 2,   thalach = 150,   exang = 0,   oldpeak = 2.3,   slope = 3,   ca = 0,   thal = \"fixed\" )  input_dict <- lapply(sample, \\(x) op_convert_to_tensor(array(x))) predictions <- inference_model |> predict(input_dict) ## 1/1 - 1s - 548ms/step glue::glue(r\"---(   This particular patient had a {(100 * predictions) |> signif(3)}% probability   of having a heart disease, as evaluated by our model. )---\") ## This particular patient had a 55% probability ## of having a heart disease, as evaluated by our model."},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"introduction","dir":"Articles > Examples","previous_headings":"","what":"Introduction","title":"Text classification from scratch","text":"example shows text classification starting raw text (set text files disk). demonstrate workflow IMDB sentiment classification dataset (unprocessed version). use [layer_text_vectorization()] word splitting & indexing.","code":""},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"setup","dir":"Articles > Examples","previous_headings":"","what":"Setup","title":"Text classification from scratch","text":"","code":"options(conflicts.policy = \"strict\") library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\") library(keras3) use_virtualenv(\"r-keras\")"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"load-the-data-imdb-movie-review-sentiment-classification","dir":"Articles > Examples","previous_headings":"","what":"Load the data: IMDB movie review sentiment classification","title":"Text classification from scratch","text":"Let’s download data inspect structure. aclImdb folder contains train test subfolder: aclImdb/train/pos aclImdb/train/neg folders contain text files, represents one review (either positive negative): interested pos neg subfolders, let’s delete subfolder text files : can use utility text_dataset_from_directory() generate labeled tf_dataset object set text files disk filed class-specific folders. Let’s use generate training, validation, test datasets. validation training datasets generated two subsets train directory, 20% samples going validation dataset 80% going training dataset. validation dataset addition test dataset useful tuning hyperparameters, model architecture, test dataset used. putting model real world however, retrained using available training data (without creating validation dataset), performance maximized. using validation_split subset arguments, make sure either specify random seed, pass shuffle=FALSE, validation & training splits get overlap. Let’s preview samples:","code":"if (!dir.exists(\"datasets/aclImdb\")) {   dir.create(\"datasets\")   download.file(     \"https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\",     \"datasets/aclImdb_v1.tar.gz\"   )   untar(\"datasets/aclImdb_v1.tar.gz\", exdir = \"datasets\")   unlink(\"datasets/aclImdb/train/unsup\", recursive = TRUE) } head(list.files(\"datasets/aclImdb/test\")) ## [1] \"labeledBow.feat\" \"neg\"             \"pos\"             \"urls_neg.txt\" ## [5] \"urls_pos.txt\" head(list.files(\"datasets/aclImdb/train\")) ## [1] \"labeledBow.feat\" \"neg\"             \"pos\"             \"unsupBow.feat\" ## [5] \"urls_neg.txt\"    \"urls_pos.txt\" cat(readLines(\"datasets/aclImdb/train/pos/6248_7.txt\")) ## Being an Austrian myself this has been a straight knock in my face. Fortunately I don't live nowhere near the place where this movie takes place but unfortunately it portrays everything that the rest of Austria hates about Viennese people (or people close to that region). And it is very easy to read that this is exactly the directors intention: to let your head sink into your hands and say \"Oh my god, how can THAT be possible!\". No, not with me, the (in my opinion) totally exaggerated uncensored swinger club scene is not necessary, I watch porn, sure, but in this context I was rather disgusted than put in the right context.<br /><br />This movie tells a story about how misled people who suffer from lack of education or bad company try to survive and live in a world of redundancy and boring horizons. A girl who is treated like a whore by her super-jealous boyfriend (and still keeps coming back), a female teacher who discovers her masochism by putting the life of her super-cruel \"lover\" on the line, an old couple who has an almost mathematical daily cycle (she is the \"official replacement\" of his ex wife), a couple that has just divorced and has the ex husband suffer under the acts of his former wife obviously having a relationship with her masseuse and finally a crazy hitchhiker who asks her drivers the most unusual questions and stretches their nerves by just being super-annoying.<br /><br />After having seen it you feel almost nothing. You're not even shocked, sad, depressed or feel like doing anything... Maybe that's why I gave it 7 points, it made me react in a way I never reacted before. If that's good or bad is up to you! unlink(\"datasets/aclImdb/train/unsup\", recursive = TRUE) batch_size <- 32  raw_train_ds <- text_dataset_from_directory(   \"datasets/aclImdb/train\",   batch_size = batch_size,   validation_split = 0.2,   subset = \"training\",   seed = 1337 ) ## Found 25000 files belonging to 2 classes. ## Using 20000 files for training. raw_val_ds <- text_dataset_from_directory(   \"datasets/aclImdb/train\",   batch_size = batch_size,   validation_split = 0.2,   subset = \"validation\",   seed = 1337 ) ## Found 25000 files belonging to 2 classes. ## Using 5000 files for validation. raw_test_ds <- text_dataset_from_directory(   \"datasets/aclImdb/test\",   batch_size = batch_size ) ## Found 25000 files belonging to 2 classes. cat(\"Number of batches in raw_train_ds:\", length(raw_train_ds), \"\\n\") ## Number of batches in raw_train_ds: 625 cat(\"Number of batches in raw_val_ds:\", length(raw_val_ds), \"\\n\") ## Number of batches in raw_val_ds: 157 cat(\"Number of batches in raw_test_ds:\", length(raw_test_ds), \"\\n\") ## Number of batches in raw_test_ds: 782 # It's important to take a look at your raw data to ensure your normalization # and tokenization will work as expected. We can do that by taking a few # examples from the training set and looking at them. # This is one of the places where eager execution shines: # we can just evaluate these tensors using .numpy() # instead of needing to evaluate them in a Session/Graph context. batch <- iter_next(as_iterator(raw_train_ds)) str(batch) ## List of 2 ##  $ :<tf.Tensor: shape=(32), dtype=string, numpy=…> ##  $ :<tf.Tensor: shape=(32), dtype=int32, numpy=…> c(text_batch, label_batch) %<-% batch for (i in 1:3) {   print(text_batch[i])   print(label_batch[i]) } ## tf.Tensor(b\"I have read the novel Reaper of Ben Mezrich a fews years ago and last night I accidentally came to see this adaption.<br /><br />Although it's been years since I read the story the first time, the differences between the novel and the movie are humongous. Very important elements, which made the whole thing plausible are just written out or changed to bad.<br /><br />If the plot sounds interesting to you: go and get the novel. Its much, much, much better.<br /><br />Still 4 out of 10 since it was hard to stop watching because of the great basic plot by Ben Mezrich.\", shape=(), dtype=string) ## tf.Tensor(0, shape=(), dtype=int32) ## tf.Tensor(b'After seeing all the Jesse James, Quantrill, jayhawkers,etc films in the fifties, it is quite a thrill to see this film with a new perspective by director Ang Lee. The scene of the attack of Lawrence, Kansas is awesome. The romantic relationship between Jewel and Toby Mcguire turns out to be one of the best parts and Jonathan Rhys-Meyers is outstanding as the bad guy. All the time this film makes you feel the horror of war, and the desperate situation of the main characters who do not know if they are going to survive the next hours. Definitely worth seeing.', shape=(), dtype=string) ## tf.Tensor(1, shape=(), dtype=int32) ## tf.Tensor(b'AG was an excellent presentation of drama, suspense and thriller that is so rare to American TV. Sheriff Lucas gave many a viewer the willies. We rooted for Caleb as he strove to resist the overtures of Sheriff Lucas. We became engrossed and fearful upon learning of the unthinkable connection between these two characters. The manipulations which weekly gave cause to fear what Lucas would do next were truly surprising. This show lived up to the \"Gothic\" moniker in ways American entertainment has so seldom attempted, much less mastered. The suits definitely made a big mistake in not supporting this show. This show puts shame to the current glut of \"reality\" shows- which are so less than satisfying viewing.The call for a DVD box set is well based. This show is quality viewing for a discerning market hungry for quality viewing. A public that is tiring of over-saturation of mind-numbing reality fare will welcome this gem of real storytelling. Bring on the DVD box set!!', shape=(), dtype=string) ## tf.Tensor(1, shape=(), dtype=int32)"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"prepare-the-data","dir":"Articles > Examples","previous_headings":"","what":"Prepare the data","title":"Text classification from scratch","text":"particular, remove <br /> tags.","code":"# Having looked at our data above, we see that the raw text contains HTML break # tags of the form '<br />'. These tags will not be removed by the default # standardizer (which doesn't strip HTML). Because of this, we will need to # create a custom standardization function. custom_standardization_fn <- function(string_tensor) {   string_tensor |>     tf$strings$lower() |> # convert to all lowercase     tf$strings$regex_replace(\"<br />\", \" \") |> # remove '<br />' HTML tag     tf$strings$regex_replace(\"[[:punct:]]\", \"\") # remove punctuation }   # Model constants. max_features <- 20000 embedding_dim <- 128 sequence_length <- 500  # Now that we have our custom standardization, we can instantiate our text # vectorization layer. We are using this layer to normalize, split, and map # strings to integers, so we set our 'output_mode' to 'int'. # Note that we're using the default split function, # and the custom standardization defined above. # We also set an explicit maximum sequence length, since the CNNs later in our # model won't support ragged sequences. vectorize_layer <- layer_text_vectorization(   standardize = custom_standardization_fn,   max_tokens = max_features,   output_mode = \"int\",   output_sequence_length = sequence_length, )  # Now that the vectorize_layer has been created, call `adapt` on a text-only # dataset to create the vocabulary. You don't have to batch, but for very large # datasets this means you're not keeping spare copies of the dataset in memory.  # Let's make a text-only dataset (no labels): text_ds <- raw_train_ds |>   dataset_map(\\(x, y) x) # Let's call `adapt`: vectorize_layer |> adapt(text_ds)"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"two-options-to-vectorize-the-data","dir":"Articles > Examples","previous_headings":"","what":"Two options to vectorize the data","title":"Text classification from scratch","text":"2 ways can use text vectorization layer: Option 1: Make part model, obtain model processes raw strings, like : Option 2: Apply text dataset obtain dataset word indices, feed model expects integer sequences inputs. important difference two option 2 enables asynchronous CPU processing buffering data training GPU. ’re training model GPU, probably want go option get best performance. . export model production, ’d ship model accepts raw strings input, like code snippet option 1 . can done training. last section.","code":"text_input <- keras_input(shape = c(1L), dtype = \"string\", name = 'text') x <- text_input |>   vectorize_layer() |>   layer_embedding(max_features + 1, embedding_dim) vectorize_text <- function(text, label) {   text <- text |>     op_expand_dims(-1) |>     vectorize_layer()   list(text, label) }  # Vectorize the data. train_ds <- raw_train_ds |> dataset_map(vectorize_text) val_ds   <- raw_val_ds   |> dataset_map(vectorize_text) test_ds  <- raw_test_ds  |> dataset_map(vectorize_text)  # Do async prefetching / buffering of the data for best performance on GPU. train_ds <- train_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10) val_ds <- val_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10) test_ds <- test_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10)"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"build-a-model","dir":"Articles > Examples","previous_headings":"","what":"Build a model","title":"Text classification from scratch","text":"choose simple 1D convnet starting Embedding layer.","code":"# A integer input for vocab indices. inputs <- keras_input(shape = c(NA), dtype = \"int64\")  predictions <- inputs |>   # Next, we add a layer to map those vocab indices into a space of dimensionality   # 'embedding_dim'.   layer_embedding(max_features, embedding_dim) |>   layer_dropout(0.5) |>   # Conv1D + global max pooling   layer_conv_1d(128, 7, padding = \"valid\", activation = \"relu\", strides = 3) |>   layer_conv_1d(128, 7, padding = \"valid\", activation = \"relu\", strides = 3) |>   layer_global_max_pooling_1d() |>   # We add a vanilla hidden layer:   layer_dense(128, activation = \"relu\") |>   layer_dropout(0.5) |>   # We project onto a single unit output layer, and squash it with a sigmoid:   layer_dense(1, activation = \"sigmoid\", name = \"predictions\")  model <- keras_model(inputs, predictions)  summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, None)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ embedding_1 (Embedding)         │ (None, None, 128)      │     2,560,000 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, None, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_1 (Conv1D)               │ (None, None, 128)      │       114,816 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d (Conv1D)                 │ (None, None, 128)      │       114,816 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling1d            │ (None, 128)            │             0 │ ## │ (GlobalMaxPooling1D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 128)            │        16,512 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 128)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ predictions (Dense)             │ (None, 1)              │           129 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 2,806,273 (10.71 MB) ##  Trainable params: 2,806,273 (10.71 MB) ##  Non-trainable params: 0 (0.00 B) # Compile the model with binary crossentropy loss and an adam optimizer. model |> compile(loss = \"binary_crossentropy\",                  optimizer = \"adam\",                  metrics = \"accuracy\")"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"train-the-model","dir":"Articles > Examples","previous_headings":"","what":"Train the model","title":"Text classification from scratch","text":"","code":"epochs <- 3  # Fit the model using the train and test datasets. model |> fit(train_ds, validation_data = val_ds, epochs = epochs) ## Epoch 1/3 ## 625/625 - 6s - 9ms/step - accuracy: 0.7327 - loss: 0.4894 - val_accuracy: 0.8638 - val_loss: 0.3162 ## Epoch 2/3 ## 625/625 - 2s - 2ms/step - accuracy: 0.9116 - loss: 0.2230 - val_accuracy: 0.8780 - val_loss: 0.3131 ## Epoch 3/3 ## 625/625 - 2s - 2ms/step - accuracy: 0.9603 - loss: 0.1118 - val_accuracy: 0.8646 - val_loss: 0.3735"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"evaluate-the-model-on-the-test-set","dir":"Articles > Examples","previous_headings":"","what":"Evaluate the model on the test set","title":"Text classification from scratch","text":"","code":"model |> evaluate(test_ds) ## 782/782 - 1s - 2ms/step - accuracy: 0.8532 - loss: 0.4071 ## $accuracy ## [1] 0.8532 ## ## $loss ## [1] 0.407085"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"make-an-end-to-end-model","dir":"Articles > Examples","previous_headings":"","what":"Make an end-to-end model","title":"Text classification from scratch","text":"want obtain model capable processing raw strings, can simply create new model (using weights just trained):","code":"# A string input inputs <- keras_input(shape = c(1), dtype = \"string\") # Turn strings into vocab indices indices <- vectorize_layer(inputs) # Turn vocab indices into predictions outputs <- model(indices)  # Our end to end model end_to_end_model <- keras_model(inputs, outputs) end_to_end_model |> compile(   loss = \"binary_crossentropy\",   optimizer = \"adam\",   metrics = c(\"accuracy\") )  # Test it with `raw_test_ds`, which yields raw strings end_to_end_model |> evaluate(raw_test_ds) ## 782/782 - 3s - 4ms/step - accuracy: 0.8532 - loss: 0.4063 ## $accuracy ## [1] 0.8532 ## ## $loss ## [1] 0.4062699"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"introduction","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Introduction","title":"Timeseries anomaly detection using an Autoencoder","text":"script demonstrates can use reconstruction convolutional autoencoder model detect anomalies timeseries data.","code":""},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"setup","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Setup","title":"Timeseries anomaly detection using an Autoencoder","text":"","code":"library(dplyr, warn.conflicts = FALSE) library(ggplot2) theme_set(theme_minimal())  library(listarrays) library(tfdatasets, exclude = c(\"shape\")) library(keras3)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"load-the-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Load the data","title":"Timeseries anomaly detection using an Autoencoder","text":"use Numenta Anomaly Benchmark(NAB) dataset. provides artificial timeseries data containing labeled anomalous periods behavior. Data ordered, timestamped, single-valued metrics. use art_daily_small_noise.csv file training art_daily_jumpsup.csv file testing. simplicity dataset allows us demonstrate anomaly detection.","code":"get_data <- function(url_suffix) {   url_root <- \"https://raw.githubusercontent.com/numenta/NAB/master/data/\"   url <- paste0(url_root, url_suffix)   file <- get_file(origin = url) # cache file locally   # parse csv; 2 columns with types: datetime (T), double (d)   readr::read_csv(file, col_types = \"Td\") }  df_small_noise   <- get_data(\"artificialNoAnomaly/art_daily_small_noise.csv\") df_daily_jumpsup <- get_data(\"artificialWithAnomaly/art_daily_jumpsup.csv\")"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"quick-look-at-the-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Quick look at the data","title":"Timeseries anomaly detection using an Autoencoder","text":"","code":"df_small_noise ## # A tibble: 4,032 × 2 ##    timestamp           value ##    <dttm>              <dbl> ##  1 2014-04-01 00:00:00  18.3 ##  2 2014-04-01 00:05:00  22.0 ##  3 2014-04-01 00:10:00  18.6 ##  4 2014-04-01 00:15:00  22.0 ##  5 2014-04-01 00:20:00  21.9 ##  6 2014-04-01 00:25:00  21.2 ##  7 2014-04-01 00:30:00  20.6 ##  8 2014-04-01 00:35:00  20.3 ##  9 2014-04-01 00:40:00  21.5 ## 10 2014-04-01 00:45:00  19.2 ## # ℹ 4,022 more rows df_daily_jumpsup ## # A tibble: 4,032 × 2 ##    timestamp           value ##    <dttm>              <dbl> ##  1 2014-04-01 00:00:00  19.8 ##  2 2014-04-01 00:05:00  20.5 ##  3 2014-04-01 00:10:00  20.0 ##  4 2014-04-01 00:15:00  21.5 ##  5 2014-04-01 00:20:00  20.2 ##  6 2014-04-01 00:25:00  19.9 ##  7 2014-04-01 00:30:00  21.7 ##  8 2014-04-01 00:35:00  20.9 ##  9 2014-04-01 00:40:00  18.4 ## 10 2014-04-01 00:45:00  18.7 ## # ℹ 4,022 more rows"},{"path":[]},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"timeseries-data-without-anomalies","dir":"Articles > Examples > Timeseries","previous_headings":"Visualize the data","what":"Timeseries data without anomalies","title":"Timeseries anomaly detection using an Autoencoder","text":"use following data training.","code":"plot_ts <- function(df) {   ggplot(df, aes(x = timestamp, y = value)) + geom_line() +     scale_x_datetime(date_breaks = \"1 day\", date_labels = \"%b-%d\") }  plot_ts(df_small_noise) + ggtitle(\"Without Anomaly\")"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"timeseries-data-with-anomalies","dir":"Articles > Examples > Timeseries","previous_headings":"Visualize the data","what":"Timeseries data with anomalies","title":"Timeseries anomaly detection using an Autoencoder","text":"use following data testing see sudden jump data detected anomaly.","code":"plot_ts(df_daily_jumpsup) + ggtitle(\"With Anomaly\")"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"prepare-training-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Prepare training data","title":"Timeseries anomaly detection using an Autoencoder","text":"Get data values training timeseries data file normalize value data. value every 5 mins 14 days. 24 * 60 / 5 = 288 timesteps per day 288 * 14 = 4032 data points total","code":"df_train <- df_small_noise |>   mutate(value = (value - mean(value)) / sd(value))  cat(\"Number of training samples:\", nrow(df_train), \"\\n\") ## Number of training samples: 4032"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"create-sequences","dir":"Articles > Examples > Timeseries","previous_headings":"Prepare training data","what":"Create sequences","title":"Timeseries anomaly detection using an Autoencoder","text":"Create sequences combining TIME_STEPS contiguous data values training data.","code":"TIME_STEPS <- 288  as_dataset <- function(df) {   x <- as.matrix(df$value)   ds <- timeseries_dataset_from_array(x, NULL, sequence_length = TIME_STEPS)   # Because the dataset is small, cast TF Dataset to an R array for convenience.   ds |> as_array_iterator() |> iterate() |> bind_on_rows() }  x_train <- as_dataset(df_train) writeLines(sprintf(\"Training input shape: (%s)\", toString(dim(x_train)))) ## Training input shape: (3745, 288, 1)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"build-a-model","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Build a model","title":"Timeseries anomaly detection using an Autoencoder","text":"build convolutional reconstruction autoencoder model. model take input shape (batch_size, sequence_length, num_features) return output shape. case, sequence_length 288 num_features 1.","code":"model <- keras_model_sequential(input_shape = c(TIME_STEPS, 1)) |>   layer_conv_1d(     filters = 32, kernel_size = 7, padding = \"same\",     strides = 2, activation = \"relu\"   ) |>   layer_dropout(rate = 0.2) |>   layer_conv_1d(     filters = 16, kernel_size = 7, padding = \"same\",     strides = 2, activation = \"relu\"   ) |>   layer_conv_1d_transpose(     filters = 16, kernel_size = 7, padding = \"same\",     strides = 2, activation = \"relu\"   ) |>   layer_dropout(rate = 0.2) |>   layer_conv_1d_transpose(     filters = 32, kernel_size = 7, padding = \"same\",     strides = 2, activation = \"relu\"   ) |>   layer_conv_1d_transpose(filters = 1, kernel_size = 7, padding = \"same\")  model |> compile(optimizer=optimizer_adam(learning_rate=0.001), loss=\"mse\") model ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv1d (Conv1D)                 │ (None, 144, 32)        │           256 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 144, 32)        │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_1 (Conv1D)               │ (None, 72, 16)         │         3,600 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_transpose                │ (None, 144, 16)        │         1,808 │ ## │ (Conv1DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, 144, 16)        │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_transpose_1              │ (None, 288, 32)        │         3,616 │ ## │ (Conv1DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_transpose_2              │ (None, 288, 1)         │           225 │ ## │ (Conv1DTranspose)               │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 9,505 (37.13 KB) ##  Trainable params: 9,505 (37.13 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"train-the-model","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Train the model","title":"Timeseries anomaly detection using an Autoencoder","text":"Please note using x_train input target since reconstruction model. Let’s plot training validation loss see training went.","code":"history = model |> fit(   x_train, x_train,   epochs = 50,   validation_split = 0.1,   callbacks = c(     callback_early_stopping(       monitor = \"val_loss\", patience = 5, mode = \"min\"     )   ) ) ## Epoch 1/50 ## 106/106 - 7s - 62ms/step - loss: 0.1863 - val_loss: 0.0316 ## Epoch 2/50 ## 106/106 - 0s - 2ms/step - loss: 0.0333 - val_loss: 0.0233 ## Epoch 3/50 ## 106/106 - 0s - 2ms/step - loss: 0.0248 - val_loss: 0.0201 ## Epoch 4/50 ## 106/106 - 0s - 2ms/step - loss: 0.0209 - val_loss: 0.0186 ## Epoch 5/50 ## 106/106 - 0s - 2ms/step - loss: 0.0179 - val_loss: 0.0146 ## Epoch 6/50 ## 106/106 - 0s - 2ms/step - loss: 0.0150 - val_loss: 0.0113 ## Epoch 7/50 ## 106/106 - 0s - 2ms/step - loss: 0.0127 - val_loss: 0.0094 ## Epoch 8/50 ## 106/106 - 0s - 2ms/step - loss: 0.0109 - val_loss: 0.0088 ## Epoch 9/50 ## 106/106 - 0s - 3ms/step - loss: 0.0096 - val_loss: 0.0084 ## Epoch 10/50 ## 106/106 - 0s - 2ms/step - loss: 0.0086 - val_loss: 0.0069 ## Epoch 11/50 ## 106/106 - 0s - 2ms/step - loss: 0.0078 - val_loss: 0.0062 ## Epoch 12/50 ## 106/106 - 0s - 2ms/step - loss: 0.0073 - val_loss: 0.0058 ## Epoch 13/50 ## 106/106 - 0s - 2ms/step - loss: 0.0067 - val_loss: 0.0054 ## Epoch 14/50 ## 106/106 - 0s - 2ms/step - loss: 0.0063 - val_loss: 0.0051 ## Epoch 15/50 ## 106/106 - 0s - 2ms/step - loss: 0.0060 - val_loss: 0.0045 ## Epoch 16/50 ## 106/106 - 0s - 2ms/step - loss: 0.0058 - val_loss: 0.0039 ## Epoch 17/50 ## 106/106 - 0s - 2ms/step - loss: 0.0055 - val_loss: 0.0041 ## Epoch 18/50 ## 106/106 - 0s - 2ms/step - loss: 0.0052 - val_loss: 0.0036 ## Epoch 19/50 ## 106/106 - 0s - 2ms/step - loss: 0.0050 - val_loss: 0.0037 ## Epoch 20/50 ## 106/106 - 0s - 2ms/step - loss: 0.0048 - val_loss: 0.0033 ## Epoch 21/50 ## 106/106 - 0s - 2ms/step - loss: 0.0046 - val_loss: 0.0031 ## Epoch 22/50 ## 106/106 - 0s - 2ms/step - loss: 0.0044 - val_loss: 0.0032 ## Epoch 23/50 ## 106/106 - 0s - 2ms/step - loss: 0.0042 - val_loss: 0.0033 ## Epoch 24/50 ## 106/106 - 0s - 2ms/step - loss: 0.0040 - val_loss: 0.0031 ## Epoch 25/50 ## 106/106 - 0s - 2ms/step - loss: 0.0038 - val_loss: 0.0032 ## Epoch 26/50 ## 106/106 - 0s - 2ms/step - loss: 0.0036 - val_loss: 0.0031 ## Epoch 27/50 ## 106/106 - 0s - 2ms/step - loss: 0.0035 - val_loss: 0.0031 ## Epoch 28/50 ## 106/106 - 0s - 2ms/step - loss: 0.0033 - val_loss: 0.0025 ## Epoch 29/50 ## 106/106 - 0s - 2ms/step - loss: 0.0032 - val_loss: 0.0026 ## Epoch 30/50 ## 106/106 - 0s - 2ms/step - loss: 0.0031 - val_loss: 0.0029 ## Epoch 31/50 ## 106/106 - 0s - 2ms/step - loss: 0.0030 - val_loss: 0.0024 ## Epoch 32/50 ## 106/106 - 0s - 2ms/step - loss: 0.0029 - val_loss: 0.0025 ## Epoch 33/50 ## 106/106 - 0s - 2ms/step - loss: 0.0028 - val_loss: 0.0025 ## Epoch 34/50 ## 106/106 - 0s - 2ms/step - loss: 0.0027 - val_loss: 0.0022 ## Epoch 35/50 ## 106/106 - 0s - 2ms/step - loss: 0.0026 - val_loss: 0.0023 ## Epoch 36/50 ## 106/106 - 0s - 2ms/step - loss: 0.0026 - val_loss: 0.0025 ## Epoch 37/50 ## 106/106 - 0s - 2ms/step - loss: 0.0025 - val_loss: 0.0025 ## Epoch 38/50 ## 106/106 - 0s - 2ms/step - loss: 0.0025 - val_loss: 0.0023 ## Epoch 39/50 ## 106/106 - 0s - 2ms/step - loss: 0.0024 - val_loss: 0.0023 plot(history)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"detecting-anomalies","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Detecting anomalies","title":"Timeseries anomaly detection using an Autoencoder","text":"detect anomalies determining well model can reconstruct input data. Find MAE loss training samples. Find max MAE loss value. worst model performed trying reconstruct sample. make threshold anomaly detection. reconstruction loss sample greater threshold value can infer model seeing pattern isn’t familiar . label sample anomaly.","code":"# Get train MAE loss. x_train_pred <- model |> predict(x_train) ## 118/118 - 0s - 3ms/step train_mae_loss <- apply(abs(x_train_pred - x_train), 1, mean)  hist(train_mae_loss, breaks = 50) # Get reconstruction loss threshold. threshold <- max(train_mae_loss) cat(\"Reconstruction error threshold: \", threshold, \"\\n\") ## Reconstruction error threshold:  0.03930207"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"compare-recontruction","dir":"Articles > Examples > Timeseries","previous_headings":"Detecting anomalies","what":"Compare recontruction","title":"Timeseries anomaly detection using an Autoencoder","text":"Just fun, let’s see model recontructed first sample. 288 timesteps day 1 training dataset.","code":"# Checking how the first sequence is learnt plot(NULL, NULL, ylab = 'Value',      xlim = c(0, TIME_STEPS),      ylim = range(c(x_train[1,,], x_train_pred[1,,]))) lines(x_train[1,,]) lines(x_train_pred[1,,], col = 'red') legend(\"topleft\", lty = 1,        legend = c(\"actual\", \"predicted\"),        col = c(\"black\", \"red\"))"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"prepare-test-data","dir":"Articles > Examples > Timeseries","previous_headings":"Detecting anomalies","what":"Prepare test data","title":"Timeseries anomaly detection using an Autoencoder","text":"","code":"df_test <- df_daily_jumpsup |>   mutate(value =            (value - mean(df_small_noise$value)) /              sd(df_small_noise$value))  df_test |> head() plot_ts(df_test) # Create sequences from test values. x_test <- as_dataset(df_test)  # Get test MAE loss. x_test_pred <- model |> predict(x_test) test_mae_loss <- apply(abs(x_test_pred - x_test), 1, mean)  hist(test_mae_loss, breaks = 50, xlab = \"test MAE loss\", ylab = \"No of samples\") # Detect all the samples which are anomalies. anomalies <- test_mae_loss > threshold cat(\"Number of anomaly samples:\", sum(anomalies), \"\\n\") cat(\"Indices of anomaly samples:\", which(anomalies), \"\\n\", fill = TRUE) ## # A tibble: 6 × 2 ##   timestamp            value ##   <dttm>               <dbl> ## 1 2014-04-01 00:00:00 -0.808 ## 2 2014-04-01 00:05:00 -0.781 ## 3 2014-04-01 00:10:00 -0.801 ## 4 2014-04-01 00:15:00 -0.746 ## 5 2014-04-01 00:20:00 -0.792 ## 6 2014-04-01 00:25:00 -0.802 ## 118/118 - 0s - 763us/step ## Number of anomaly samples: 486 ## Indices of anomaly samples: 216 218 220 396 507 793 795 1659 1945 1946 1947 ## 1949 2013 2016 2017 2021 2025 2029 2033 2037 2041 2045 2046 2049 2052 2053 ## 2054 2056 2057 2058 2060 2061 2062 2064 2065 2066 2068 2069 2070 2072 2073 ## 2074 2076 2077 2078 2080 2081 2082 2084 2085 2086 2088 2089 2090 2092 2093 ## 2094 2096 2097 2098 2100 2101 2102 2105 2108 2109 2112 2113 2117 2121 2124 ## 2126 2129 2141 2145 2153 2521 2522 2523 2525 2538 2542 2546 2550 2698 2700 ## 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 ## 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 ## 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 ## 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 ## 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 ## 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 ## 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 ## 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 ## 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 ## 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 ## 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 ## 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 ## 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 ## 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 ## 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 ## 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 ## 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 ## 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 ## 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 ## 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 ## 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 ## 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 ## 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 ## 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 ## 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 ## 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 ## 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"plot-anomalies","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Plot anomalies","title":"Timeseries anomaly detection using an Autoencoder","text":"now know samples data anomalies. , find corresponding timestamps original test data. using following method : Let’s say time_steps = 3 10 training values. x_train look like : 0, 1, 2 1, 2, 3 2, 3, 4 3, 4, 5 4, 5, 6 5, 6, 7 6, 7, 8 7, 8, 9 except initial final time_steps-1 data values, appear time_steps number samples. , know samples [(3, 4, 5), (4, 5, 6), (5, 6, 7)] anomalies, can say data point 5 anomaly. Let’s overlay anomalies original test data plot.","code":"is_anomaly <- test_mae_loss > threshold is_anomaly <- is_anomaly &   zoo::rollsum(is_anomaly, TIME_STEPS,                align = \"right\", na.pad = TRUE) >= TIME_STEPS  with(df_test, {   plot(value ~ timestamp, type = 'l', xaxt = 'n', las = 2)   axis.POSIXct(1, at = seq(timestamp[1], tail(timestamp, 1), by = \"days\"),                format = \"%b-%d\") })  with(df_test[which(is_anomaly),], {   points(value ~ timestamp, col = \"red\") })"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"introduction","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Introduction","title":"Timeseries classification from scratch","text":"example shows timeseries classification scratch, starting raw CSV timeseries files disk. demonstrate workflow FordA dataset UCR/UEA archive.","code":""},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"setup","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Setup","title":"Timeseries classification from scratch","text":"","code":"library(keras3) use_backend(\"jax\")"},{"path":[]},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"dataset-description","dir":"Articles > Examples > Timeseries","previous_headings":"Load the data: the FordA dataset","what":"Dataset description","title":"Timeseries classification from scratch","text":"dataset using called FordA. data comes UCR archive. dataset contains 3601 training instances another 1320 testing instances. timeseries corresponds measurement engine noise captured motor sensor. task, goal automatically detect presence specific issue engine. problem balanced binary classification task. full description dataset can found .","code":""},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"read-the-tsv-data","dir":"Articles > Examples > Timeseries","previous_headings":"Load the data: the FordA dataset","what":"Read the TSV data","title":"Timeseries classification from scratch","text":"use FordA_TRAIN file training FordA_TEST file testing. simplicity dataset allows us demonstrate effectively use ConvNets timeseries classification. file, first column corresponds label.","code":"get_data <- function(path) {   if(path |> startsWith(\"https://\"))     path <- get_file(origin = path)  # cache file locally    data <- readr::read_tsv(     path, col_names = FALSE,     # Each row is: one integer (the label),     # followed by 500 doubles (the timeseries)     col_types = paste0(\"i\", strrep(\"d\", 500))   )    y <- as.matrix(data[[1]])   x <- as.matrix(data[,-1])   dimnames(x) <- dimnames(y) <- NULL    list(x, y) }  root_url <- \"https://raw.githubusercontent.com/hfawaz/cd-diagram/master/FordA/\" c(x_train, y_train) %<-% get_data(paste0(root_url, \"FordA_TRAIN.tsv\")) c(x_test, y_test) %<-% get_data(paste0(root_url, \"FordA_TEST.tsv\"))  str(keras3:::named_list(   x_train, y_train,   x_test, y_test )) ## List of 4 ##  $ x_train: num [1:3601, 1:500] -0.797 0.805 0.728 -0.234 -0.171 ... ##  $ y_train: int [1:3601, 1] -1 1 -1 -1 -1 1 1 1 1 1 ... ##  $ x_test : num [1:1320, 1:500] -0.14 0.334 0.717 1.24 -1.159 ... ##  $ y_test : int [1:1320, 1] -1 -1 -1 1 -1 1 -1 -1 1 1 ..."},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"visualize-the-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Visualize the data","title":"Timeseries classification from scratch","text":"visualize one timeseries example class dataset.","code":"plot(NULL, main = \"Timeseries Data\",      xlab = \"Timepoints\",  ylab = \"Values\",      xlim = c(1, ncol(x_test)),      ylim = range(x_test)) grid() lines(x_test[match(-1, y_test), ], col = \"blue\") lines(x_test[match( 1, y_test), ], col = \"red\") legend(\"topright\", legend=c(\"label -1\", \"label 1\"), col=c(\"blue\", \"red\"), lty=1)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"standardize-the-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Standardize the data","title":"Timeseries classification from scratch","text":"timeseries already single length (500). However, values usually various ranges. ideal neural network; general seek make input values normalized. specific dataset, data already z-normalized: timeseries sample mean equal zero standard deviation equal one. type normalization common timeseries classification problems, see Bagnall et al. (2016). Note timeseries data used univariate, meaning one channel per timeseries example. therefore transform timeseries multivariate one one channel using simple reshaping via numpy. allow us construct model easily applicable multivariate time series. Finally, order use sparse_categorical_crossentropy, count number classes beforehand. Now shuffle training set using validation_split option later training. Standardize labels positive integers. expected labels 0 1.","code":"dim(x_train) <- c(dim(x_train), 1) dim(x_test) <- c(dim(x_test), 1) num_classes <- length(unique(y_train)) c(x_train, y_train) %<-% listarrays::shuffle_rows(x_train, y_train) # idx <- sample.int(nrow(x_train)) # x_train %<>% .[idx,, ,drop = FALSE] # y_train %<>% .[idx,  ,drop = FALSE] y_train[y_train == -1L] <- 0L y_test[y_test == -1L] <- 0L"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"build-a-model","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Build a model","title":"Timeseries classification from scratch","text":"build Fully Convolutional Neural Network originally proposed paper. implementation based TF 2 version provided . following hyperparameters (kernel_size, filters, usage BatchNorm) found via random search using KerasTuner. plot chunk unnamed-chunk-9","code":"make_model <- function(input_shape) {   inputs <- keras_input(input_shape)    outputs <- inputs |>     # conv1     layer_conv_1d(filters = 64, kernel_size = 3, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation_relu() |>     # conv2     layer_conv_1d(filters = 64, kernel_size = 3, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation_relu() |>     # conv3     layer_conv_1d(filters = 64, kernel_size = 3, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation_relu() |>     # pooling     layer_global_average_pooling_1d() |>     # final output     layer_dense(num_classes, activation = \"softmax\")    keras_model(inputs, outputs) }  model <- make_model(input_shape = dim(x_train)[-1]) model ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)                ┃ Output Shape          ┃    Param # ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer (InputLayer)    │ (None, 500, 1)        │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ conv1d (Conv1D)             │ (None, 500, 64)       │        256 │   Y   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ batch_normalization         │ (None, 500, 64)       │        256 │   Y   │ ## │ (BatchNormalization)        │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ re_lu (ReLU)                │ (None, 500, 64)       │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ conv1d_1 (Conv1D)           │ (None, 500, 64)       │     12,352 │   Y   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ batch_normalization_1       │ (None, 500, 64)       │        256 │   Y   │ ## │ (BatchNormalization)        │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ re_lu_1 (ReLU)              │ (None, 500, 64)       │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ conv1d_2 (Conv1D)           │ (None, 500, 64)       │     12,352 │   Y   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ batch_normalization_2       │ (None, 500, 64)       │        256 │   Y   │ ## │ (BatchNormalization)        │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ re_lu_2 (ReLU)              │ (None, 500, 64)       │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ global_average_pooling1d    │ (None, 64)            │          0 │   -   │ ## │ (GlobalAveragePooling1D)    │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense (Dense)               │ (None, 2)             │        130 │   Y   │ ## └─────────────────────────────┴───────────────────────┴────────────┴───────┘ ##  Total params: 25,858 (101.01 KB) ##  Trainable params: 25,474 (99.51 KB) ##  Non-trainable params: 384 (1.50 KB) plot(model, show_shapes = TRUE)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"train-the-model","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Train the model","title":"Timeseries classification from scratch","text":"","code":"epochs <- 500 batch_size <- 32  callbacks <- c(   callback_model_checkpoint(     \"best_model.keras\", save_best_only = TRUE,     monitor = \"val_loss\"   ),   callback_reduce_lr_on_plateau(     monitor = \"val_loss\", factor = 0.5,     patience = 20, min_lr = 0.0001   ),   callback_early_stopping(     monitor = \"val_loss\", patience = 50,     verbose = 1   ) )   model |> compile(   optimizer = \"adam\",   loss = \"sparse_categorical_crossentropy\",   metrics = \"sparse_categorical_accuracy\" )  history <- model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = epochs,   callbacks = callbacks,   validation_split = 0.2 ) ## Epoch 1/500 ## 90/90 - 2s - 24ms/step - loss: 0.5557 - sparse_categorical_accuracy: 0.7073 - val_loss: 0.8517 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010 ## Epoch 2/500 ## 90/90 - 1s - 6ms/step - loss: 0.4850 - sparse_categorical_accuracy: 0.7611 - val_loss: 0.9464 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010 ## Epoch 3/500 ## 90/90 - 0s - 2ms/step - loss: 0.4713 - sparse_categorical_accuracy: 0.7705 - val_loss: 0.7593 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010 ## Epoch 4/500 ## 90/90 - 0s - 2ms/step - loss: 0.4239 - sparse_categorical_accuracy: 0.7885 - val_loss: 0.6721 - val_sparse_categorical_accuracy: 0.5021 - learning_rate: 0.0010 ## Epoch 5/500 ## 90/90 - 0s - 2ms/step - loss: 0.4218 - sparse_categorical_accuracy: 0.7885 - val_loss: 0.5647 - val_sparse_categorical_accuracy: 0.7198 - learning_rate: 0.0010 ## Epoch 6/500 ## 90/90 - 0s - 2ms/step - loss: 0.4060 - sparse_categorical_accuracy: 0.8028 - val_loss: 0.4563 - val_sparse_categorical_accuracy: 0.8141 - learning_rate: 0.0010 ## Epoch 7/500 ## 90/90 - 0s - 2ms/step - loss: 0.4032 - sparse_categorical_accuracy: 0.8017 - val_loss: 0.4209 - val_sparse_categorical_accuracy: 0.7892 - learning_rate: 0.0010 ## Epoch 8/500 ## 90/90 - 0s - 2ms/step - loss: 0.3920 - sparse_categorical_accuracy: 0.8003 - val_loss: 0.3938 - val_sparse_categorical_accuracy: 0.8239 - learning_rate: 0.0010 ## Epoch 9/500 ## 90/90 - 0s - 2ms/step - loss: 0.3892 - sparse_categorical_accuracy: 0.8135 - val_loss: 0.3849 - val_sparse_categorical_accuracy: 0.8225 - learning_rate: 0.0010 ## Epoch 10/500 ## 90/90 - 0s - 2ms/step - loss: 0.3874 - sparse_categorical_accuracy: 0.8021 - val_loss: 0.4934 - val_sparse_categorical_accuracy: 0.7503 - learning_rate: 0.0010 ## Epoch 11/500 ## 90/90 - 0s - 1ms/step - loss: 0.3798 - sparse_categorical_accuracy: 0.8149 - val_loss: 0.5693 - val_sparse_categorical_accuracy: 0.6990 - learning_rate: 0.0010 ## Epoch 12/500 ## 90/90 - 0s - 1ms/step - loss: 0.3671 - sparse_categorical_accuracy: 0.8215 - val_loss: 0.3998 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010 ## Epoch 13/500 ## 90/90 - 0s - 1ms/step - loss: 0.3555 - sparse_categorical_accuracy: 0.8389 - val_loss: 0.4145 - val_sparse_categorical_accuracy: 0.8031 - learning_rate: 0.0010 ## Epoch 14/500 ## 90/90 - 0s - 1ms/step - loss: 0.3628 - sparse_categorical_accuracy: 0.8253 - val_loss: 0.3886 - val_sparse_categorical_accuracy: 0.8211 - learning_rate: 0.0010 ## Epoch 15/500 ## 90/90 - 0s - 1ms/step - loss: 0.3433 - sparse_categorical_accuracy: 0.8406 - val_loss: 0.7763 - val_sparse_categorical_accuracy: 0.6019 - learning_rate: 0.0010 ## Epoch 16/500 ## 90/90 - 0s - 2ms/step - loss: 0.3455 - sparse_categorical_accuracy: 0.8434 - val_loss: 0.3537 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010 ## Epoch 17/500 ## 90/90 - 0s - 1ms/step - loss: 0.3336 - sparse_categorical_accuracy: 0.8462 - val_loss: 0.5375 - val_sparse_categorical_accuracy: 0.7074 - learning_rate: 0.0010 ## Epoch 18/500 ## 90/90 - 0s - 1ms/step - loss: 0.3263 - sparse_categorical_accuracy: 0.8503 - val_loss: 0.4134 - val_sparse_categorical_accuracy: 0.7892 - learning_rate: 0.0010 ## Epoch 19/500 ## 90/90 - 0s - 2ms/step - loss: 0.3195 - sparse_categorical_accuracy: 0.8601 - val_loss: 0.3432 - val_sparse_categorical_accuracy: 0.8433 - learning_rate: 0.0010 ## Epoch 20/500 ## 90/90 - 0s - 1ms/step - loss: 0.3136 - sparse_categorical_accuracy: 0.8674 - val_loss: 0.9287 - val_sparse_categorical_accuracy: 0.5950 - learning_rate: 0.0010 ## Epoch 21/500 ## 90/90 - 0s - 1ms/step - loss: 0.3001 - sparse_categorical_accuracy: 0.8691 - val_loss: 0.3926 - val_sparse_categorical_accuracy: 0.7795 - learning_rate: 0.0010 ## Epoch 22/500 ## 90/90 - 0s - 2ms/step - loss: 0.2984 - sparse_categorical_accuracy: 0.8719 - val_loss: 0.3023 - val_sparse_categorical_accuracy: 0.8655 - learning_rate: 0.0010 ## Epoch 23/500 ## 90/90 - 0s - 1ms/step - loss: 0.2930 - sparse_categorical_accuracy: 0.8767 - val_loss: 0.3501 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010 ## Epoch 24/500 ## 90/90 - 0s - 1ms/step - loss: 0.2861 - sparse_categorical_accuracy: 0.8799 - val_loss: 0.9187 - val_sparse_categorical_accuracy: 0.5992 - learning_rate: 0.0010 ## Epoch 25/500 ## 90/90 - 0s - 1ms/step - loss: 0.2843 - sparse_categorical_accuracy: 0.8816 - val_loss: 0.3731 - val_sparse_categorical_accuracy: 0.8363 - learning_rate: 0.0010 ## Epoch 26/500 ## 90/90 - 0s - 1ms/step - loss: 0.3182 - sparse_categorical_accuracy: 0.8569 - val_loss: 0.5553 - val_sparse_categorical_accuracy: 0.7115 - learning_rate: 0.0010 ## Epoch 27/500 ## 90/90 - 0s - 1ms/step - loss: 0.2732 - sparse_categorical_accuracy: 0.8875 - val_loss: 0.3950 - val_sparse_categorical_accuracy: 0.7628 - learning_rate: 0.0010 ## Epoch 28/500 ## 90/90 - 0s - 1ms/step - loss: 0.2943 - sparse_categorical_accuracy: 0.8705 - val_loss: 0.8138 - val_sparse_categorical_accuracy: 0.7254 - learning_rate: 0.0010 ## Epoch 29/500 ## 90/90 - 0s - 1ms/step - loss: 0.2772 - sparse_categorical_accuracy: 0.8781 - val_loss: 0.3712 - val_sparse_categorical_accuracy: 0.8405 - learning_rate: 0.0010 ## Epoch 30/500 ## 90/90 - 0s - 1ms/step - loss: 0.2714 - sparse_categorical_accuracy: 0.8833 - val_loss: 0.4209 - val_sparse_categorical_accuracy: 0.7975 - learning_rate: 0.0010 ## Epoch 31/500 ## 90/90 - 0s - 2ms/step - loss: 0.2720 - sparse_categorical_accuracy: 0.8854 - val_loss: 1.9298 - val_sparse_categorical_accuracy: 0.5104 - learning_rate: 0.0010 ## Epoch 32/500 ## 90/90 - 0s - 2ms/step - loss: 0.2699 - sparse_categorical_accuracy: 0.8833 - val_loss: 0.3155 - val_sparse_categorical_accuracy: 0.8516 - learning_rate: 0.0010 ## Epoch 33/500 ## 90/90 - 0s - 2ms/step - loss: 0.2586 - sparse_categorical_accuracy: 0.8951 - val_loss: 0.3628 - val_sparse_categorical_accuracy: 0.7947 - learning_rate: 0.0010 ## Epoch 34/500 ## 90/90 - 0s - 1ms/step - loss: 0.2490 - sparse_categorical_accuracy: 0.9003 - val_loss: 0.5844 - val_sparse_categorical_accuracy: 0.7309 - learning_rate: 0.0010 ## Epoch 35/500 ## 90/90 - 0s - 1ms/step - loss: 0.2627 - sparse_categorical_accuracy: 0.8938 - val_loss: 0.3480 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010 ## Epoch 36/500 ## 90/90 - 0s - 1ms/step - loss: 0.2558 - sparse_categorical_accuracy: 0.8948 - val_loss: 0.3793 - val_sparse_categorical_accuracy: 0.8128 - learning_rate: 0.0010 ## Epoch 37/500 ## 90/90 - 0s - 1ms/step - loss: 0.2515 - sparse_categorical_accuracy: 0.8958 - val_loss: 0.4828 - val_sparse_categorical_accuracy: 0.7503 - learning_rate: 0.0010 ## Epoch 38/500 ## 90/90 - 0s - 2ms/step - loss: 0.2541 - sparse_categorical_accuracy: 0.8986 - val_loss: 0.2950 - val_sparse_categorical_accuracy: 0.8669 - learning_rate: 0.0010 ## Epoch 39/500 ## 90/90 - 0s - 1ms/step - loss: 0.2505 - sparse_categorical_accuracy: 0.8976 - val_loss: 0.7151 - val_sparse_categorical_accuracy: 0.7087 - learning_rate: 0.0010 ## Epoch 40/500 ## 90/90 - 0s - 1ms/step - loss: 0.2490 - sparse_categorical_accuracy: 0.8979 - val_loss: 0.3170 - val_sparse_categorical_accuracy: 0.8627 - learning_rate: 0.0010 ## Epoch 41/500 ## 90/90 - 0s - 2ms/step - loss: 0.2325 - sparse_categorical_accuracy: 0.9062 - val_loss: 0.2560 - val_sparse_categorical_accuracy: 0.8904 - learning_rate: 0.0010 ## Epoch 42/500 ## 90/90 - 0s - 1ms/step - loss: 0.2312 - sparse_categorical_accuracy: 0.9076 - val_loss: 0.3396 - val_sparse_categorical_accuracy: 0.8405 - learning_rate: 0.0010 ## Epoch 43/500 ## 90/90 - 0s - 1ms/step - loss: 0.2402 - sparse_categorical_accuracy: 0.8976 - val_loss: 0.3650 - val_sparse_categorical_accuracy: 0.8294 - learning_rate: 0.0010 ## Epoch 44/500 ## 90/90 - 0s - 1ms/step - loss: 0.2342 - sparse_categorical_accuracy: 0.9038 - val_loss: 1.0563 - val_sparse_categorical_accuracy: 0.6172 - learning_rate: 0.0010 ## Epoch 45/500 ## 90/90 - 0s - 1ms/step - loss: 0.2320 - sparse_categorical_accuracy: 0.9080 - val_loss: 0.2506 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010 ## Epoch 46/500 ## 90/90 - 0s - 1ms/step - loss: 0.2317 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.4473 - val_sparse_categorical_accuracy: 0.7920 - learning_rate: 0.0010 ## Epoch 47/500 ## 90/90 - 0s - 1ms/step - loss: 0.2164 - sparse_categorical_accuracy: 0.9125 - val_loss: 0.6324 - val_sparse_categorical_accuracy: 0.7240 - learning_rate: 0.0010 ## Epoch 48/500 ## 90/90 - 0s - 2ms/step - loss: 0.2186 - sparse_categorical_accuracy: 0.9122 - val_loss: 0.2525 - val_sparse_categorical_accuracy: 0.9001 - learning_rate: 0.0010 ## Epoch 49/500 ## 90/90 - 0s - 2ms/step - loss: 0.2119 - sparse_categorical_accuracy: 0.9118 - val_loss: 0.2401 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010 ## Epoch 50/500 ## 90/90 - 0s - 1ms/step - loss: 0.2134 - sparse_categorical_accuracy: 0.9191 - val_loss: 0.2410 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010 ## Epoch 51/500 ## 90/90 - 0s - 2ms/step - loss: 0.2120 - sparse_categorical_accuracy: 0.9174 - val_loss: 0.6803 - val_sparse_categorical_accuracy: 0.6935 - learning_rate: 0.0010 ## Epoch 52/500 ## 90/90 - 0s - 2ms/step - loss: 0.2023 - sparse_categorical_accuracy: 0.9205 - val_loss: 0.2938 - val_sparse_categorical_accuracy: 0.8779 - learning_rate: 0.0010 ## Epoch 53/500 ## 90/90 - 0s - 2ms/step - loss: 0.1980 - sparse_categorical_accuracy: 0.9243 - val_loss: 0.5380 - val_sparse_categorical_accuracy: 0.7531 - learning_rate: 0.0010 ## Epoch 54/500 ## 90/90 - 0s - 2ms/step - loss: 0.1883 - sparse_categorical_accuracy: 0.9271 - val_loss: 0.2272 - val_sparse_categorical_accuracy: 0.9071 - learning_rate: 0.0010 ## Epoch 55/500 ## 90/90 - 0s - 2ms/step - loss: 0.1761 - sparse_categorical_accuracy: 0.9385 - val_loss: 0.2707 - val_sparse_categorical_accuracy: 0.8821 - learning_rate: 0.0010 ## Epoch 56/500 ## 90/90 - 0s - 1ms/step - loss: 0.1683 - sparse_categorical_accuracy: 0.9417 - val_loss: 0.2584 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010 ## Epoch 57/500 ## 90/90 - 0s - 1ms/step - loss: 0.1581 - sparse_categorical_accuracy: 0.9490 - val_loss: 1.4305 - val_sparse_categorical_accuracy: 0.7184 - learning_rate: 0.0010 ## Epoch 58/500 ## 90/90 - 0s - 1ms/step - loss: 0.1518 - sparse_categorical_accuracy: 0.9497 - val_loss: 0.7991 - val_sparse_categorical_accuracy: 0.7531 - learning_rate: 0.0010 ## Epoch 59/500 ## 90/90 - 0s - 1ms/step - loss: 0.1529 - sparse_categorical_accuracy: 0.9479 - val_loss: 0.3389 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 0.0010 ## Epoch 60/500 ## 90/90 - 0s - 1ms/step - loss: 0.1784 - sparse_categorical_accuracy: 0.9368 - val_loss: 0.2534 - val_sparse_categorical_accuracy: 0.8974 - learning_rate: 0.0010 ## Epoch 61/500 ## 90/90 - 0s - 1ms/step - loss: 0.1335 - sparse_categorical_accuracy: 0.9566 - val_loss: 0.2323 - val_sparse_categorical_accuracy: 0.9098 - learning_rate: 0.0010 ## Epoch 62/500 ## 90/90 - 0s - 1ms/step - loss: 0.1401 - sparse_categorical_accuracy: 0.9531 - val_loss: 0.3259 - val_sparse_categorical_accuracy: 0.8696 - learning_rate: 0.0010 ## Epoch 63/500 ## 90/90 - 0s - 1ms/step - loss: 0.1404 - sparse_categorical_accuracy: 0.9521 - val_loss: 0.2970 - val_sparse_categorical_accuracy: 0.8627 - learning_rate: 0.0010 ## Epoch 64/500 ## 90/90 - 0s - 1ms/step - loss: 0.1375 - sparse_categorical_accuracy: 0.9559 - val_loss: 0.3416 - val_sparse_categorical_accuracy: 0.8183 - learning_rate: 0.0010 ## Epoch 65/500 ## 90/90 - 0s - 1ms/step - loss: 0.1303 - sparse_categorical_accuracy: 0.9583 - val_loss: 0.2586 - val_sparse_categorical_accuracy: 0.8766 - learning_rate: 0.0010 ## Epoch 66/500 ## 90/90 - 0s - 1ms/step - loss: 0.1199 - sparse_categorical_accuracy: 0.9608 - val_loss: 1.2218 - val_sparse_categorical_accuracy: 0.7337 - learning_rate: 0.0010 ## Epoch 67/500 ## 90/90 - 0s - 2ms/step - loss: 0.1186 - sparse_categorical_accuracy: 0.9615 - val_loss: 0.1464 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010 ## Epoch 68/500 ## 90/90 - 0s - 2ms/step - loss: 0.1176 - sparse_categorical_accuracy: 0.9632 - val_loss: 0.1451 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010 ## Epoch 69/500 ## 90/90 - 0s - 2ms/step - loss: 0.1295 - sparse_categorical_accuracy: 0.9583 - val_loss: 1.7723 - val_sparse_categorical_accuracy: 0.5395 - learning_rate: 0.0010 ## Epoch 70/500 ## 90/90 - 0s - 2ms/step - loss: 0.1232 - sparse_categorical_accuracy: 0.9587 - val_loss: 0.1802 - val_sparse_categorical_accuracy: 0.9237 - learning_rate: 0.0010 ## Epoch 71/500 ## 90/90 - 0s - 1ms/step - loss: 0.1202 - sparse_categorical_accuracy: 0.9573 - val_loss: 0.1932 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010 ## Epoch 72/500 ## 90/90 - 0s - 1ms/step - loss: 0.1124 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.4178 - val_sparse_categorical_accuracy: 0.8003 - learning_rate: 0.0010 ## Epoch 73/500 ## 90/90 - 0s - 1ms/step - loss: 0.1068 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1517 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 0.0010 ## Epoch 74/500 ## 90/90 - 0s - 1ms/step - loss: 0.1085 - sparse_categorical_accuracy: 0.9639 - val_loss: 0.2126 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 0.0010 ## Epoch 75/500 ## 90/90 - 0s - 1ms/step - loss: 0.1263 - sparse_categorical_accuracy: 0.9580 - val_loss: 0.1689 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 0.0010 ## Epoch 76/500 ## 90/90 - 0s - 1ms/step - loss: 0.1169 - sparse_categorical_accuracy: 0.9597 - val_loss: 0.2955 - val_sparse_categorical_accuracy: 0.8460 - learning_rate: 0.0010 ## Epoch 77/500 ## 90/90 - 0s - 1ms/step - loss: 0.1089 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.6333 - val_sparse_categorical_accuracy: 0.6241 - learning_rate: 0.0010 ## Epoch 78/500 ## 90/90 - 0s - 2ms/step - loss: 0.1017 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.1416 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 0.0010 ## Epoch 79/500 ## 90/90 - 0s - 2ms/step - loss: 0.1099 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.3434 - val_sparse_categorical_accuracy: 0.8544 - learning_rate: 0.0010 ## Epoch 80/500 ## 90/90 - 0s - 2ms/step - loss: 0.1046 - sparse_categorical_accuracy: 0.9653 - val_loss: 0.4430 - val_sparse_categorical_accuracy: 0.8280 - learning_rate: 0.0010 ## Epoch 81/500 ## 90/90 - 0s - 1ms/step - loss: 0.1098 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.1632 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 0.0010 ## Epoch 82/500 ## 90/90 - 0s - 1ms/step - loss: 0.1021 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.3984 - val_sparse_categorical_accuracy: 0.7906 - learning_rate: 0.0010 ## Epoch 83/500 ## 90/90 - 0s - 1ms/step - loss: 0.1086 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.1976 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010 ## Epoch 84/500 ## 90/90 - 0s - 1ms/step - loss: 0.1026 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.2123 - val_sparse_categorical_accuracy: 0.9251 - learning_rate: 0.0010 ## Epoch 85/500 ## 90/90 - 0s - 1ms/step - loss: 0.1032 - sparse_categorical_accuracy: 0.9632 - val_loss: 0.3491 - val_sparse_categorical_accuracy: 0.8613 - learning_rate: 0.0010 ## Epoch 86/500 ## 90/90 - 0s - 1ms/step - loss: 0.1136 - sparse_categorical_accuracy: 0.9576 - val_loss: 0.1498 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 0.0010 ## Epoch 87/500 ## 90/90 - 0s - 2ms/step - loss: 0.1107 - sparse_categorical_accuracy: 0.9608 - val_loss: 0.1406 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 0.0010 ## Epoch 88/500 ## 90/90 - 0s - 1ms/step - loss: 0.0995 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.6864 - val_sparse_categorical_accuracy: 0.7656 - learning_rate: 0.0010 ## Epoch 89/500 ## 90/90 - 0s - 1ms/step - loss: 0.1013 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.8218 - val_sparse_categorical_accuracy: 0.7476 - learning_rate: 0.0010 ## Epoch 90/500 ## 90/90 - 0s - 1ms/step - loss: 0.1002 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.2009 - val_sparse_categorical_accuracy: 0.9209 - learning_rate: 0.0010 ## Epoch 91/500 ## 90/90 - 0s - 1ms/step - loss: 0.1010 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.2869 - val_sparse_categorical_accuracy: 0.8849 - learning_rate: 0.0010 ## Epoch 92/500 ## 90/90 - 0s - 1ms/step - loss: 0.1012 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.5128 - val_sparse_categorical_accuracy: 0.7434 - learning_rate: 0.0010 ## Epoch 93/500 ## 90/90 - 0s - 1ms/step - loss: 0.1009 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.5212 - val_sparse_categorical_accuracy: 0.7753 - learning_rate: 0.0010 ## Epoch 94/500 ## 90/90 - 0s - 1ms/step - loss: 0.1016 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.3146 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 0.0010 ## Epoch 95/500 ## 90/90 - 0s - 1ms/step - loss: 0.0975 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1750 - val_sparse_categorical_accuracy: 0.9376 - learning_rate: 0.0010 ## Epoch 96/500 ## 90/90 - 0s - 1ms/step - loss: 0.1121 - sparse_categorical_accuracy: 0.9583 - val_loss: 0.2399 - val_sparse_categorical_accuracy: 0.9112 - learning_rate: 0.0010 ## Epoch 97/500 ## 90/90 - 0s - 1ms/step - loss: 0.1000 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.5611 - val_sparse_categorical_accuracy: 0.7101 - learning_rate: 0.0010 ## Epoch 98/500 ## 90/90 - 0s - 1ms/step - loss: 0.0949 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1864 - val_sparse_categorical_accuracy: 0.9279 - learning_rate: 0.0010 ## Epoch 99/500 ## 90/90 - 0s - 2ms/step - loss: 0.0925 - sparse_categorical_accuracy: 0.9667 - val_loss: 0.1228 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010 ## Epoch 100/500 ## 90/90 - 0s - 1ms/step - loss: 0.0922 - sparse_categorical_accuracy: 0.9691 - val_loss: 1.9448 - val_sparse_categorical_accuracy: 0.7115 - learning_rate: 0.0010 ## Epoch 101/500 ## 90/90 - 0s - 1ms/step - loss: 0.0887 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.2127 - val_sparse_categorical_accuracy: 0.9071 - learning_rate: 0.0010 ## Epoch 102/500 ## 90/90 - 0s - 1ms/step - loss: 0.0999 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.1903 - val_sparse_categorical_accuracy: 0.9154 - learning_rate: 0.0010 ## Epoch 103/500 ## 90/90 - 0s - 1ms/step - loss: 0.0929 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.2647 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010 ## Epoch 104/500 ## 90/90 - 0s - 1ms/step - loss: 0.1008 - sparse_categorical_accuracy: 0.9642 - val_loss: 1.1019 - val_sparse_categorical_accuracy: 0.6019 - learning_rate: 0.0010 ## Epoch 105/500 ## 90/90 - 0s - 1ms/step - loss: 0.0969 - sparse_categorical_accuracy: 0.9681 - val_loss: 1.1464 - val_sparse_categorical_accuracy: 0.5645 - learning_rate: 0.0010 ## Epoch 106/500 ## 90/90 - 0s - 2ms/step - loss: 0.0924 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.2535 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010 ## Epoch 107/500 ## 90/90 - 0s - 2ms/step - loss: 0.0930 - sparse_categorical_accuracy: 0.9677 - val_loss: 1.5288 - val_sparse_categorical_accuracy: 0.5118 - learning_rate: 0.0010 ## Epoch 108/500 ## 90/90 - 0s - 1ms/step - loss: 0.0893 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1496 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 0.0010 ## Epoch 109/500 ## 90/90 - 0s - 1ms/step - loss: 0.0891 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.2799 - val_sparse_categorical_accuracy: 0.8835 - learning_rate: 0.0010 ## Epoch 110/500 ## 90/90 - 0s - 1ms/step - loss: 0.0963 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.4827 - val_sparse_categorical_accuracy: 0.8155 - learning_rate: 0.0010 ## Epoch 111/500 ## 90/90 - 0s - 2ms/step - loss: 0.0841 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1203 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 0.0010 ## Epoch 112/500 ## 90/90 - 0s - 1ms/step - loss: 0.0861 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.5081 - val_sparse_categorical_accuracy: 0.7559 - learning_rate: 0.0010 ## Epoch 113/500 ## 90/90 - 0s - 1ms/step - loss: 0.0831 - sparse_categorical_accuracy: 0.9729 - val_loss: 1.2423 - val_sparse_categorical_accuracy: 0.5687 - learning_rate: 0.0010 ## Epoch 114/500 ## 90/90 - 0s - 1ms/step - loss: 0.1094 - sparse_categorical_accuracy: 0.9653 - val_loss: 0.3459 - val_sparse_categorical_accuracy: 0.8252 - learning_rate: 0.0010 ## Epoch 115/500 ## 90/90 - 0s - 1ms/step - loss: 0.0886 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.7117 - val_sparse_categorical_accuracy: 0.7226 - learning_rate: 0.0010 ## Epoch 116/500 ## 90/90 - 0s - 1ms/step - loss: 0.0989 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.1669 - val_sparse_categorical_accuracy: 0.9334 - learning_rate: 0.0010 ## Epoch 117/500 ## 90/90 - 0s - 1ms/step - loss: 0.0891 - sparse_categorical_accuracy: 0.9705 - val_loss: 0.2413 - val_sparse_categorical_accuracy: 0.9154 - learning_rate: 0.0010 ## Epoch 118/500 ## 90/90 - 0s - 1ms/step - loss: 0.0902 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.7796 - val_sparse_categorical_accuracy: 0.6741 - learning_rate: 0.0010 ## Epoch 119/500 ## 90/90 - 0s - 1ms/step - loss: 0.0877 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.1806 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 0.0010 ## Epoch 120/500 ## 90/90 - 0s - 2ms/step - loss: 0.0880 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.1146 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 0.0010 ## Epoch 121/500 ## 90/90 - 0s - 1ms/step - loss: 0.0896 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.1279 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 0.0010 ## Epoch 122/500 ## 90/90 - 0s - 1ms/step - loss: 0.0883 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.3067 - val_sparse_categorical_accuracy: 0.8918 - learning_rate: 0.0010 ## Epoch 123/500 ## 90/90 - 0s - 2ms/step - loss: 0.0855 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.1060 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 0.0010 ## Epoch 124/500 ## 90/90 - 0s - 1ms/step - loss: 0.0917 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.2169 - val_sparse_categorical_accuracy: 0.9112 - learning_rate: 0.0010 ## Epoch 125/500 ## 90/90 - 0s - 1ms/step - loss: 0.0976 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.1407 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 0.0010 ## Epoch 126/500 ## 90/90 - 0s - 1ms/step - loss: 0.0866 - sparse_categorical_accuracy: 0.9722 - val_loss: 1.4019 - val_sparse_categorical_accuracy: 0.7295 - learning_rate: 0.0010 ## Epoch 127/500 ## 90/90 - 0s - 1ms/step - loss: 0.0877 - sparse_categorical_accuracy: 0.9747 - val_loss: 0.2518 - val_sparse_categorical_accuracy: 0.8918 - learning_rate: 0.0010 ## Epoch 128/500 ## 90/90 - 0s - 1ms/step - loss: 0.0942 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.1465 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 0.0010 ## Epoch 129/500 ## 90/90 - 0s - 1ms/step - loss: 0.0904 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.1546 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 0.0010 ## Epoch 130/500 ## 90/90 - 0s - 1ms/step - loss: 0.0953 - sparse_categorical_accuracy: 0.9705 - val_loss: 0.1201 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 0.0010 ## Epoch 131/500 ## 90/90 - 0s - 1ms/step - loss: 0.0846 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.1980 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 0.0010 ## Epoch 132/500 ## 90/90 - 0s - 1ms/step - loss: 0.0812 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.4080 - val_sparse_categorical_accuracy: 0.8599 - learning_rate: 0.0010 ## Epoch 133/500 ## 90/90 - 0s - 1ms/step - loss: 0.0785 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.3284 - val_sparse_categorical_accuracy: 0.8835 - learning_rate: 0.0010 ## Epoch 134/500 ## 90/90 - 0s - 1ms/step - loss: 0.0885 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.5050 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010 ## Epoch 135/500 ## 90/90 - 0s - 1ms/step - loss: 0.0859 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.4296 - val_sparse_categorical_accuracy: 0.8141 - learning_rate: 0.0010 ## Epoch 136/500 ## 90/90 - 0s - 1ms/step - loss: 0.0885 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.1295 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 0.0010 ## Epoch 137/500 ## 90/90 - 0s - 1ms/step - loss: 0.0892 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.4052 - val_sparse_categorical_accuracy: 0.8031 - learning_rate: 0.0010 ## Epoch 138/500 ## 90/90 - 0s - 1ms/step - loss: 0.0835 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.1160 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 0.0010 ## Epoch 139/500 ## 90/90 - 0s - 1ms/step - loss: 0.0877 - sparse_categorical_accuracy: 0.9708 - val_loss: 3.0631 - val_sparse_categorical_accuracy: 0.7032 - learning_rate: 0.0010 ## Epoch 140/500 ## 90/90 - 0s - 1ms/step - loss: 0.0867 - sparse_categorical_accuracy: 0.9719 - val_loss: 1.3638 - val_sparse_categorical_accuracy: 0.7143 - learning_rate: 0.0010 ## Epoch 141/500 ## 90/90 - 0s - 1ms/step - loss: 0.0857 - sparse_categorical_accuracy: 0.9715 - val_loss: 0.2599 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010 ## Epoch 142/500 ## 90/90 - 0s - 1ms/step - loss: 0.0771 - sparse_categorical_accuracy: 0.9764 - val_loss: 0.2860 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010 ## Epoch 143/500 ## 90/90 - 0s - 1ms/step - loss: 0.0835 - sparse_categorical_accuracy: 0.9694 - val_loss: 0.1252 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 0.0010 ## Epoch 144/500 ## 90/90 - 1s - 7ms/step - loss: 0.0674 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.1234 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 5.0000e-04 ## Epoch 145/500 ## 90/90 - 0s - 1ms/step - loss: 0.0680 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1147 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 5.0000e-04 ## Epoch 146/500 ## 90/90 - 0s - 2ms/step - loss: 0.0702 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1042 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 5.0000e-04 ## Epoch 147/500 ## 90/90 - 0s - 1ms/step - loss: 0.0742 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.1511 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 5.0000e-04 ## Epoch 148/500 ## 90/90 - 0s - 1ms/step - loss: 0.0801 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.1219 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 5.0000e-04 ## Epoch 149/500 ## 90/90 - 0s - 2ms/step - loss: 0.0725 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04 ## Epoch 150/500 ## 90/90 - 0s - 1ms/step - loss: 0.0738 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.2021 - val_sparse_categorical_accuracy: 0.9334 - learning_rate: 5.0000e-04 ## Epoch 151/500 ## 90/90 - 0s - 1ms/step - loss: 0.0707 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.1358 - val_sparse_categorical_accuracy: 0.9376 - learning_rate: 5.0000e-04 ## Epoch 152/500 ## 90/90 - 0s - 1ms/step - loss: 0.0727 - sparse_categorical_accuracy: 0.9740 - val_loss: 0.1989 - val_sparse_categorical_accuracy: 0.9098 - learning_rate: 5.0000e-04 ## Epoch 153/500 ## 90/90 - 0s - 1ms/step - loss: 0.0671 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.1250 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04 ## Epoch 154/500 ## 90/90 - 0s - 1ms/step - loss: 0.0687 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1973 - val_sparse_categorical_accuracy: 0.9237 - learning_rate: 5.0000e-04 ## Epoch 155/500 ## 90/90 - 0s - 1ms/step - loss: 0.0697 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1190 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 5.0000e-04 ## Epoch 156/500 ## 90/90 - 0s - 1ms/step - loss: 0.0731 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.1409 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 5.0000e-04 ## Epoch 157/500 ## 90/90 - 0s - 1ms/step - loss: 0.0719 - sparse_categorical_accuracy: 0.9740 - val_loss: 0.4024 - val_sparse_categorical_accuracy: 0.8655 - learning_rate: 5.0000e-04 ## Epoch 158/500 ## 90/90 - 0s - 1ms/step - loss: 0.0722 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.3254 - val_sparse_categorical_accuracy: 0.8821 - learning_rate: 5.0000e-04 ## Epoch 159/500 ## 90/90 - 0s - 1ms/step - loss: 0.0706 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.2014 - val_sparse_categorical_accuracy: 0.9265 - learning_rate: 5.0000e-04 ## Epoch 160/500 ## 90/90 - 0s - 1ms/step - loss: 0.0707 - sparse_categorical_accuracy: 0.9753 - val_loss: 0.1128 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 5.0000e-04 ## Epoch 161/500 ## 90/90 - 0s - 1ms/step - loss: 0.0727 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.1118 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 5.0000e-04 ## Epoch 162/500 ## 90/90 - 0s - 1ms/step - loss: 0.0660 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.2972 - val_sparse_categorical_accuracy: 0.8960 - learning_rate: 5.0000e-04 ## Epoch 163/500 ## 90/90 - 0s - 1ms/step - loss: 0.0619 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.1079 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 5.0000e-04 ## Epoch 164/500 ## 90/90 - 0s - 1ms/step - loss: 0.0670 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.2169 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 5.0000e-04 ## Epoch 165/500 ## 90/90 - 0s - 1ms/step - loss: 0.0690 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.2980 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 5.0000e-04 ## Epoch 166/500 ## 90/90 - 0s - 1ms/step - loss: 0.0650 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.1257 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 5.0000e-04 ## Epoch 167/500 ## 90/90 - 0s - 1ms/step - loss: 0.0656 - sparse_categorical_accuracy: 0.9788 - val_loss: 0.1451 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 5.0000e-04 ## Epoch 168/500 ## 90/90 - 0s - 1ms/step - loss: 0.0750 - sparse_categorical_accuracy: 0.9736 - val_loss: 0.3402 - val_sparse_categorical_accuracy: 0.8350 - learning_rate: 5.0000e-04 ## Epoch 169/500 ## 90/90 - 0s - 1ms/step - loss: 0.0664 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.1295 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 5.0000e-04 ## Epoch 170/500 ## 90/90 - 0s - 2ms/step - loss: 0.0635 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04 ## Epoch 171/500 ## 90/90 - 0s - 1ms/step - loss: 0.0593 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1230 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 2.5000e-04 ## Epoch 172/500 ## 90/90 - 0s - 1ms/step - loss: 0.0591 - sparse_categorical_accuracy: 0.9799 - val_loss: 0.1079 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04 ## Epoch 173/500 ## 90/90 - 0s - 1ms/step - loss: 0.0593 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1073 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04 ## Epoch 174/500 ## 90/90 - 0s - 2ms/step - loss: 0.0605 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 2.5000e-04 ## Epoch 175/500 ## 90/90 - 0s - 1ms/step - loss: 0.0616 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.2084 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 2.5000e-04 ## Epoch 176/500 ## 90/90 - 0s - 1ms/step - loss: 0.0584 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.0993 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04 ## Epoch 177/500 ## 90/90 - 0s - 1ms/step - loss: 0.0582 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1498 - val_sparse_categorical_accuracy: 0.9487 - learning_rate: 2.5000e-04 ## Epoch 178/500 ## 90/90 - 0s - 1ms/step - loss: 0.0566 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1165 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 2.5000e-04 ## Epoch 179/500 ## 90/90 - 0s - 2ms/step - loss: 0.0566 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1714 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 2.5000e-04 ## Epoch 180/500 ## 90/90 - 0s - 2ms/step - loss: 0.0563 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1042 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04 ## Epoch 181/500 ## 90/90 - 0s - 2ms/step - loss: 0.0609 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.2017 - val_sparse_categorical_accuracy: 0.9209 - learning_rate: 2.5000e-04 ## Epoch 182/500 ## 90/90 - 0s - 1ms/step - loss: 0.0559 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1059 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 2.5000e-04 ## Epoch 183/500 ## 90/90 - 0s - 1ms/step - loss: 0.0597 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.1279 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 2.5000e-04 ## Epoch 184/500 ## 90/90 - 0s - 1ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04 ## Epoch 185/500 ## 90/90 - 0s - 1ms/step - loss: 0.0570 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.1022 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04 ## Epoch 186/500 ## 90/90 - 0s - 1ms/step - loss: 0.0633 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04 ## Epoch 187/500 ## 90/90 - 0s - 2ms/step - loss: 0.0576 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0969 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04 ## Epoch 188/500 ## 90/90 - 0s - 1ms/step - loss: 0.0567 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1043 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04 ## Epoch 189/500 ## 90/90 - 0s - 1ms/step - loss: 0.0546 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1734 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 2.5000e-04 ## Epoch 190/500 ## 90/90 - 0s - 1ms/step - loss: 0.0639 - sparse_categorical_accuracy: 0.9799 - val_loss: 0.2847 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 2.5000e-04 ## Epoch 191/500 ## 90/90 - 0s - 1ms/step - loss: 0.0551 - sparse_categorical_accuracy: 0.9799 - val_loss: 0.4059 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 2.5000e-04 ## Epoch 192/500 ## 90/90 - 0s - 1ms/step - loss: 0.0613 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.3821 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 2.5000e-04 ## Epoch 193/500 ## 90/90 - 0s - 1ms/step - loss: 0.0559 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04 ## Epoch 194/500 ## 90/90 - 0s - 1ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1008 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04 ## Epoch 195/500 ## 90/90 - 0s - 1ms/step - loss: 0.0557 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0996 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 2.5000e-04 ## Epoch 196/500 ## 90/90 - 0s - 1ms/step - loss: 0.0581 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.1143 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 2.5000e-04 ## Epoch 197/500 ## 90/90 - 0s - 1ms/step - loss: 0.0544 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1027 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 2.5000e-04 ## Epoch 198/500 ## 90/90 - 0s - 1ms/step - loss: 0.0533 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1310 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04 ## Epoch 199/500 ## 90/90 - 0s - 1ms/step - loss: 0.0578 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.1236 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 2.5000e-04 ## Epoch 200/500 ## 90/90 - 0s - 1ms/step - loss: 0.0582 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 2.5000e-04 ## Epoch 201/500 ## 90/90 - 0s - 1ms/step - loss: 0.0551 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1327 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04 ## Epoch 202/500 ## 90/90 - 0s - 1ms/step - loss: 0.0543 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.2115 - val_sparse_categorical_accuracy: 0.9140 - learning_rate: 2.5000e-04 ## Epoch 203/500 ## 90/90 - 0s - 1ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.2015 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 2.5000e-04 ## Epoch 204/500 ## 90/90 - 0s - 1ms/step - loss: 0.0508 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04 ## Epoch 205/500 ## 90/90 - 0s - 1ms/step - loss: 0.0542 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0996 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04 ## Epoch 206/500 ## 90/90 - 0s - 1ms/step - loss: 0.0559 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1172 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 2.5000e-04 ## Epoch 207/500 ## 90/90 - 0s - 2ms/step - loss: 0.0570 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 2.5000e-04 ## Epoch 208/500 ## 90/90 - 0s - 2ms/step - loss: 0.0521 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1000 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 209/500 ## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1286 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04 ## Epoch 210/500 ## 90/90 - 0s - 1ms/step - loss: 0.0541 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 211/500 ## 90/90 - 0s - 1ms/step - loss: 0.0509 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0993 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 212/500 ## 90/90 - 0s - 1ms/step - loss: 0.0503 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04 ## Epoch 213/500 ## 90/90 - 0s - 2ms/step - loss: 0.0534 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0968 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 214/500 ## 90/90 - 0s - 1ms/step - loss: 0.0532 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04 ## Epoch 215/500 ## 90/90 - 0s - 1ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1013 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04 ## Epoch 216/500 ## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1244 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04 ## Epoch 217/500 ## 90/90 - 0s - 2ms/step - loss: 0.0495 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.1141 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.2500e-04 ## Epoch 218/500 ## 90/90 - 0s - 2ms/step - loss: 0.0547 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04 ## Epoch 219/500 ## 90/90 - 0s - 2ms/step - loss: 0.0480 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1387 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.2500e-04 ## Epoch 220/500 ## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1161 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 221/500 ## 90/90 - 0s - 1ms/step - loss: 0.0516 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.1338 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04 ## Epoch 222/500 ## 90/90 - 0s - 1ms/step - loss: 0.0525 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.0995 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 223/500 ## 90/90 - 0s - 1ms/step - loss: 0.0504 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04 ## Epoch 224/500 ## 90/90 - 0s - 1ms/step - loss: 0.0471 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0994 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04 ## Epoch 225/500 ## 90/90 - 0s - 1ms/step - loss: 0.0536 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1145 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04 ## Epoch 226/500 ## 90/90 - 0s - 1ms/step - loss: 0.0487 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1047 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.2500e-04 ## Epoch 227/500 ## 90/90 - 0s - 1ms/step - loss: 0.0529 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.1087 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04 ## Epoch 228/500 ## 90/90 - 0s - 1ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 229/500 ## 90/90 - 0s - 1ms/step - loss: 0.0499 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1064 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04 ## Epoch 230/500 ## 90/90 - 0s - 2ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0967 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04 ## Epoch 231/500 ## 90/90 - 0s - 1ms/step - loss: 0.0527 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1730 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 1.2500e-04 ## Epoch 232/500 ## 90/90 - 0s - 1ms/step - loss: 0.0496 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1109 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.2500e-04 ## Epoch 233/500 ## 90/90 - 0s - 2ms/step - loss: 0.0483 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0963 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 234/500 ## 90/90 - 0s - 1ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1158 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 235/500 ## 90/90 - 0s - 1ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1057 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04 ## Epoch 236/500 ## 90/90 - 0s - 1ms/step - loss: 0.0502 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0982 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04 ## Epoch 237/500 ## 90/90 - 0s - 1ms/step - loss: 0.0501 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 238/500 ## 90/90 - 0s - 1ms/step - loss: 0.0487 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1031 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.2500e-04 ## Epoch 239/500 ## 90/90 - 0s - 1ms/step - loss: 0.0491 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1139 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 1.2500e-04 ## Epoch 240/500 ## 90/90 - 0s - 2ms/step - loss: 0.0478 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0990 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04 ## Epoch 241/500 ## 90/90 - 0s - 2ms/step - loss: 0.0498 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0994 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04 ## Epoch 242/500 ## 90/90 - 0s - 2ms/step - loss: 0.0490 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1118 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04 ## Epoch 243/500 ## 90/90 - 0s - 2ms/step - loss: 0.0523 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1052 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 244/500 ## 90/90 - 0s - 2ms/step - loss: 0.0527 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1055 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 245/500 ## 90/90 - 0s - 2ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1045 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04 ## Epoch 246/500 ## 90/90 - 0s - 1ms/step - loss: 0.0518 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1214 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 1.2500e-04 ## Epoch 247/500 ## 90/90 - 0s - 1ms/step - loss: 0.0457 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0964 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 248/500 ## 90/90 - 0s - 1ms/step - loss: 0.0450 - sparse_categorical_accuracy: 0.9885 - val_loss: 0.1161 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04 ## Epoch 249/500 ## 90/90 - 0s - 1ms/step - loss: 0.0492 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1282 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04 ## Epoch 250/500 ## 90/90 - 0s - 2ms/step - loss: 0.0510 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0972 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 251/500 ## 90/90 - 0s - 1ms/step - loss: 0.0506 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0986 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 252/500 ## 90/90 - 0s - 2ms/step - loss: 0.0503 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.1075 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 253/500 ## 90/90 - 0s - 2ms/step - loss: 0.0507 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0999 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 254/500 ## 90/90 - 0s - 2ms/step - loss: 0.0478 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0971 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.0000e-04 ## Epoch 255/500 ## 90/90 - 0s - 2ms/step - loss: 0.0464 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0954 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 256/500 ## 90/90 - 0s - 2ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0954 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 257/500 ## 90/90 - 0s - 1ms/step - loss: 0.0472 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1047 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.0000e-04 ## Epoch 258/500 ## 90/90 - 0s - 1ms/step - loss: 0.0448 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 259/500 ## 90/90 - 0s - 1ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1245 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04 ## Epoch 260/500 ## 90/90 - 0s - 1ms/step - loss: 0.0454 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1113 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04 ## Epoch 261/500 ## 90/90 - 0s - 1ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1697 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 1.0000e-04 ## Epoch 262/500 ## 90/90 - 0s - 1ms/step - loss: 0.0450 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 263/500 ## 90/90 - 0s - 1ms/step - loss: 0.0439 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0975 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 264/500 ## 90/90 - 0s - 1ms/step - loss: 0.0498 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1111 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 265/500 ## 90/90 - 0s - 1ms/step - loss: 0.0523 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.0983 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 266/500 ## 90/90 - 0s - 1ms/step - loss: 0.0459 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0997 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 267/500 ## 90/90 - 0s - 1ms/step - loss: 0.0462 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1002 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 268/500 ## 90/90 - 0s - 1ms/step - loss: 0.0438 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.1058 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04 ## Epoch 269/500 ## 90/90 - 0s - 2ms/step - loss: 0.0491 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1289 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.0000e-04 ## Epoch 270/500 ## 90/90 - 0s - 2ms/step - loss: 0.0430 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.0000e-04 ## Epoch 271/500 ## 90/90 - 0s - 1ms/step - loss: 0.0454 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1096 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04 ## Epoch 272/500 ## 90/90 - 0s - 1ms/step - loss: 0.0455 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1262 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04 ## Epoch 273/500 ## 90/90 - 0s - 1ms/step - loss: 0.0469 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1129 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04 ## Epoch 274/500 ## 90/90 - 0s - 1ms/step - loss: 0.0429 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.1119 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 275/500 ## 90/90 - 0s - 1ms/step - loss: 0.0430 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1392 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04 ## Epoch 276/500 ## 90/90 - 0s - 1ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 277/500 ## 90/90 - 0s - 1ms/step - loss: 0.0457 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 278/500 ## 90/90 - 0s - 1ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1205 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 279/500 ## 90/90 - 0s - 1ms/step - loss: 0.0473 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0958 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 280/500 ## 90/90 - 0s - 2ms/step - loss: 0.0459 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0947 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 281/500 ## 90/90 - 0s - 1ms/step - loss: 0.0438 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1216 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04 ## Epoch 282/500 ## 90/90 - 0s - 1ms/step - loss: 0.0419 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.1008 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 283/500 ## 90/90 - 0s - 1ms/step - loss: 0.0481 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1129 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 284/500 ## 90/90 - 0s - 1ms/step - loss: 0.0480 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.1418 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04 ## Epoch 285/500 ## 90/90 - 0s - 1ms/step - loss: 0.0467 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1058 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 286/500 ## 90/90 - 0s - 1ms/step - loss: 0.0449 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1039 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 1.0000e-04 ## Epoch 287/500 ## 90/90 - 0s - 1ms/step - loss: 0.0450 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1015 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 288/500 ## 90/90 - 0s - 2ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1156 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 289/500 ## 90/90 - 0s - 1ms/step - loss: 0.0460 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1000 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04 ## Epoch 290/500 ## 90/90 - 0s - 1ms/step - loss: 0.0482 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0967 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 291/500 ## 90/90 - 0s - 1ms/step - loss: 0.0433 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.1002 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04 ## Epoch 292/500 ## 90/90 - 0s - 1ms/step - loss: 0.0506 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1102 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04 ## Epoch 293/500 ## 90/90 - 0s - 1ms/step - loss: 0.0473 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1139 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04 ## Epoch 294/500 ## 90/90 - 0s - 1ms/step - loss: 0.0459 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1005 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04 ## Epoch 295/500 ## 90/90 - 0s - 1ms/step - loss: 0.0502 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.0978 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 296/500 ## 90/90 - 0s - 1ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1006 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 ## Epoch 297/500 ## 90/90 - 0s - 1ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0978 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 298/500 ## 90/90 - 0s - 1ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0953 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 ## Epoch 299/500 ## 90/90 - 0s - 1ms/step - loss: 0.0410 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.1107 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 300/500 ## 90/90 - 0s - 1ms/step - loss: 0.0423 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.0000e-04 ## Epoch 301/500 ## 90/90 - 0s - 1ms/step - loss: 0.0457 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0983 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04 ## Epoch 302/500 ## 90/90 - 0s - 1ms/step - loss: 0.0422 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1233 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04 ## Epoch 303/500 ## 90/90 - 0s - 2ms/step - loss: 0.0417 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.1175 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04 ## Epoch 304/500 ## 90/90 - 0s - 2ms/step - loss: 0.0467 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1007 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 1.0000e-04 ## Epoch 305/500 ## 90/90 - 0s - 2ms/step - loss: 0.0466 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1058 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 306/500 ## 90/90 - 0s - 1ms/step - loss: 0.0402 - sparse_categorical_accuracy: 0.9899 - val_loss: 0.0999 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 ## Epoch 307/500 ## 90/90 - 0s - 2ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1130 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 1.0000e-04 ## Epoch 308/500 ## 90/90 - 0s - 1ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04 ## Epoch 309/500 ## 90/90 - 0s - 1ms/step - loss: 0.0472 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1179 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 310/500 ## 90/90 - 0s - 1ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 311/500 ## 90/90 - 0s - 1ms/step - loss: 0.0448 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1012 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04 ## Epoch 312/500 ## 90/90 - 0s - 1ms/step - loss: 0.0442 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1172 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04 ## Epoch 313/500 ## 90/90 - 0s - 2ms/step - loss: 0.0438 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0990 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 314/500 ## 90/90 - 0s - 2ms/step - loss: 0.0432 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 315/500 ## 90/90 - 0s - 1ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0980 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04 ## Epoch 316/500 ## 90/90 - 0s - 1ms/step - loss: 0.0429 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1030 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04 ## Epoch 317/500 ## 90/90 - 0s - 3ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1018 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04 ## Epoch 318/500 ## 90/90 - 0s - 2ms/step - loss: 0.0423 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1024 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 1.0000e-04 ## Epoch 319/500 ## 90/90 - 0s - 2ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1197 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 ## Epoch 320/500 ## 90/90 - 0s - 2ms/step - loss: 0.0405 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0994 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.0000e-04 ## Epoch 321/500 ## 90/90 - 0s - 2ms/step - loss: 0.0442 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.1499 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 1.0000e-04 ## Epoch 322/500 ## 90/90 - 0s - 2ms/step - loss: 0.0404 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1253 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04 ## Epoch 323/500 ## 90/90 - 0s - 1ms/step - loss: 0.0468 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1081 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 324/500 ## 90/90 - 0s - 1ms/step - loss: 0.0418 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1016 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04 ## Epoch 325/500 ## 90/90 - 0s - 1ms/step - loss: 0.0420 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1290 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 1.0000e-04 ## Epoch 326/500 ## 90/90 - 0s - 2ms/step - loss: 0.0436 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1270 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 1.0000e-04 ## Epoch 327/500 ## 90/90 - 0s - 1ms/step - loss: 0.0417 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1009 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 328/500 ## 90/90 - 0s - 2ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1141 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 ## Epoch 329/500 ## 90/90 - 0s - 2ms/step - loss: 0.0422 - sparse_categorical_accuracy: 0.9885 - val_loss: 0.1198 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04 ## Epoch 330/500 ## 90/90 - 0s - 3ms/step - loss: 0.0435 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0988 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 330: early stopping"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"evaluate-model-on-test-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Evaluate model on test data","title":"Timeseries classification from scratch","text":"","code":"model <- load_model(\"best_model.keras\")  results <- model |> evaluate(x_test, y_test) ## 42/42 - 1s - 15ms/step - loss: 0.0980 - sparse_categorical_accuracy: 0.9682 str(results) ## List of 2 ##  $ loss                       : num 0.098 ##  $ sparse_categorical_accuracy: num 0.968 cat(   \"Test accuracy: \", results$sparse_categorical_accuracy, \"\\n\",   \"Test loss: \", results$loss, \"\\n\",   sep = \"\" ) ## Test accuracy: 0.9681818 ## Test loss: 0.09795157"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"plot-the-models-training-history","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Plot the model’s training history","title":"Timeseries classification from scratch","text":"Plot just training validation accuracy: can see training accuracy reaches almost 0.95 100 epochs. However, observing validation accuracy can see network still needs training reaches almost 0.97 validation training accuracy 200 epochs. Beyond 200th epoch, continue training, validation accuracy start decreasing training accuracy continue increasing: model starts overfitting.","code":"plot(history) plot(history, metric = \"sparse_categorical_accuracy\") +   # scale x axis to actual number of epochs run before early stopping   ggplot2::xlim(0, length(history$metrics$loss))"},{"path":"https://keras.posit.co/articles/examples/vision/autoencoder.html","id":"introduction","dir":"Articles > Examples > Vision","previous_headings":"","what":"Introduction","title":"Convolutional autoencoder for image denoising","text":"example demonstrates implement deep convolutional autoencoder image denoising, mapping noisy digits images MNIST dataset clean digits images. implementation based original blog post titled Building Autoencoders Keras François Chollet.","code":""},{"path":"https://keras.posit.co/articles/examples/vision/autoencoder.html","id":"setup","dir":"Articles > Examples > Vision","previous_headings":"","what":"Setup","title":"Convolutional autoencoder for image denoising","text":"","code":"library(keras3)  # Normalizes the supplied array and reshapes it. preprocess <- function(array) {   array_reshape(array/255, c(dim(array)[1], 28, 28, 1)) }  # Adds random noise to each image in the supplied array. noise <- function(array) {   noise_factor <- 0.4   noisy_array <- array + noise_factor * random_normal(dim(array))   op_clip(noisy_array, 0.0, 1.0) }  display <- function(array1, array2) {   n <- 2   indices <- sample.int(dim(array1)[1], n)   images1 <- as.array(array1)[indices, , , ]   images2 <- as.array(array2)[indices, , , ]    par(mfrow = c(2, n), mar = c(0, 0, 0, 0))   for (i in seq_len(n)) {     plot(as.raster(images1[i, , ]))     plot(as.raster(images2[i, , ]))   } }"},{"path":"https://keras.posit.co/articles/examples/vision/autoencoder.html","id":"prepare-the-data","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare the data","title":"Convolutional autoencoder for image denoising","text":"","code":"# Since we only need images from the dataset to encode and decode, we # won't use the labels. c(c(train_data, .), c(test_data, .)) %<-% dataset_mnist()  # Normalize and reshape the data train_data <- preprocess(train_data) test_data <- preprocess(test_data)  # Create a copy of the data with added noise noisy_train_data <- noise(train_data) noisy_test_data <- noise(test_data)  # Display the train data and a version of it with added noise display(train_data, noisy_train_data)"},{"path":"https://keras.posit.co/articles/examples/vision/autoencoder.html","id":"build-the-autoencoder","dir":"Articles > Examples > Vision","previous_headings":"","what":"Build the autoencoder","title":"Convolutional autoencoder for image denoising","text":"going use Functional API build convolutional autoencoder. Now can train autoencoder using train_data input data target. Notice setting validation data using format. Let’s predict test dataset display original image together prediction autoencoder. Notice predictions pretty close original images, although quite . Now know autoencoder works, let’s retrain using noisy data input clean data target. want autoencoder learn denoise images. Let’s now predict noisy data display results autoencoder. Notice autoencoder amazing job removing noise input images.","code":"input <- keras_input(shape = c(28, 28, 1))  # Encoder enc <- input |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3),                 activation = \"relu\", padding = \"same\") |>   layer_max_pooling_2d(pool_size = c(2, 2), padding = \"same\") |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3),                 activation = \"relu\", padding = \"same\") |>   layer_max_pooling_2d(pool_size = c(2, 2), padding = \"same\")  # Decoder dec <- enc |>   layer_conv_2d_transpose(filters = 32, kernel_size = c(3, 3), strides = 2,                           activation = \"relu\", padding = \"same\") |>   layer_conv_2d_transpose(filters = 32, kernel_size = c(3, 3), strides = 2,                           activation = \"relu\", padding = \"same\") |>   layer_conv_2d(filters = 1, kernel_size = c(3, 3),                 activation = \"sigmoid\", padding = \"same\")  # Autoencoder autoencoder <- keras_model(input, dec) autoencoder |> compile(optimizer = \"adam\", loss = \"binary_crossentropy\") autoencoder |> summary() ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 28, 28, 32)     │           320 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 14, 14, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 14, 14, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 7, 7, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 14, 14, 32)     │         9,248 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 28, 28, 32)     │         9,248 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 28, 28, 1)      │           289 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 28,353 (110.75 KB) ##  Trainable params: 28,353 (110.75 KB) ##  Non-trainable params: 0 (0.00 B) autoencoder |> fit(   x = train_data,   y = train_data,   epochs = 50,   batch_size = 128,   shuffle = TRUE,   validation_data = list(test_data, test_data), ) ## Epoch 1/50 ## 469/469 - 5s - 10ms/step - loss: 0.1371 - val_loss: 0.0727 ## Epoch 2/50 ## 469/469 - 1s - 3ms/step - loss: 0.0712 - val_loss: 0.0692 ## Epoch 3/50 ## 469/469 - 1s - 3ms/step - loss: 0.0690 - val_loss: 0.0679 ## Epoch 4/50 ## 469/469 - 1s - 3ms/step - loss: 0.0679 - val_loss: 0.0671 ## Epoch 5/50 ## 469/469 - 1s - 3ms/step - loss: 0.0672 - val_loss: 0.0665 ## Epoch 6/50 ## 469/469 - 1s - 3ms/step - loss: 0.0667 - val_loss: 0.0660 ## Epoch 7/50 ## 469/469 - 1s - 3ms/step - loss: 0.0662 - val_loss: 0.0656 ## Epoch 8/50 ## 469/469 - 1s - 3ms/step - loss: 0.0659 - val_loss: 0.0653 ## Epoch 9/50 ## 469/469 - 2s - 3ms/step - loss: 0.0656 - val_loss: 0.0650 ## Epoch 10/50 ## 469/469 - 2s - 3ms/step - loss: 0.0653 - val_loss: 0.0648 ## Epoch 11/50 ## 469/469 - 1s - 3ms/step - loss: 0.0651 - val_loss: 0.0646 ## Epoch 12/50 ## 469/469 - 1s - 3ms/step - loss: 0.0650 - val_loss: 0.0644 ## Epoch 13/50 ## 469/469 - 1s - 3ms/step - loss: 0.0648 - val_loss: 0.0643 ## Epoch 14/50 ## 469/469 - 2s - 3ms/step - loss: 0.0646 - val_loss: 0.0642 ## Epoch 15/50 ## 469/469 - 1s - 3ms/step - loss: 0.0645 - val_loss: 0.0640 ## Epoch 16/50 ## 469/469 - 1s - 3ms/step - loss: 0.0644 - val_loss: 0.0639 ## Epoch 17/50 ## 469/469 - 1s - 3ms/step - loss: 0.0643 - val_loss: 0.0638 ## Epoch 18/50 ## 469/469 - 1s - 3ms/step - loss: 0.0642 - val_loss: 0.0637 ## Epoch 19/50 ## 469/469 - 1s - 3ms/step - loss: 0.0641 - val_loss: 0.0637 ## Epoch 20/50 ## 469/469 - 1s - 3ms/step - loss: 0.0640 - val_loss: 0.0636 ## Epoch 21/50 ## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0635 ## Epoch 22/50 ## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0634 ## Epoch 23/50 ## 469/469 - 1s - 3ms/step - loss: 0.0638 - val_loss: 0.0634 ## Epoch 24/50 ## 469/469 - 1s - 3ms/step - loss: 0.0637 - val_loss: 0.0633 ## Epoch 25/50 ## 469/469 - 1s - 3ms/step - loss: 0.0637 - val_loss: 0.0633 ## Epoch 26/50 ## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0632 ## Epoch 27/50 ## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0632 ## Epoch 28/50 ## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631 ## Epoch 29/50 ## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631 ## Epoch 30/50 ## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631 ## Epoch 31/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0630 ## Epoch 32/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0630 ## Epoch 33/50 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0629 ## Epoch 34/50 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0629 ## Epoch 35/50 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0629 ## Epoch 36/50 ## 469/469 - 1s - 3ms/step - loss: 0.0632 - val_loss: 0.0629 ## Epoch 37/50 ## 469/469 - 1s - 3ms/step - loss: 0.0632 - val_loss: 0.0628 ## Epoch 38/50 ## 469/469 - 1s - 3ms/step - loss: 0.0632 - val_loss: 0.0628 ## Epoch 39/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0628 ## Epoch 40/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627 ## Epoch 41/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627 ## Epoch 42/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627 ## Epoch 43/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627 ## Epoch 44/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627 ## Epoch 45/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0626 ## Epoch 46/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0626 ## Epoch 47/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626 ## Epoch 48/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626 ## Epoch 49/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626 ## Epoch 50/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0625 predictions <- autoencoder |> predict(test_data) ## 313/313 - 1s - 2ms/step display(test_data, predictions) autoencoder |> fit(   x = noisy_train_data,   y = train_data,   epochs = 100,   batch_size = 128,   shuffle = TRUE,   validation_data = list(noisy_test_data, test_data), ) ## Epoch 1/100 ## 469/469 - 1s - 3ms/step - loss: 0.1006 - val_loss: 0.0941 ## Epoch 2/100 ## 469/469 - 1s - 3ms/step - loss: 0.0938 - val_loss: 0.0922 ## Epoch 3/100 ## 469/469 - 1s - 3ms/step - loss: 0.0922 - val_loss: 0.0910 ## Epoch 4/100 ## 469/469 - 1s - 3ms/step - loss: 0.0912 - val_loss: 0.0901 ## Epoch 5/100 ## 469/469 - 1s - 3ms/step - loss: 0.0905 - val_loss: 0.0895 ## Epoch 6/100 ## 469/469 - 1s - 3ms/step - loss: 0.0899 - val_loss: 0.0891 ## Epoch 7/100 ## 469/469 - 1s - 3ms/step - loss: 0.0895 - val_loss: 0.0887 ## Epoch 8/100 ## 469/469 - 1s - 3ms/step - loss: 0.0891 - val_loss: 0.0883 ## Epoch 9/100 ## 469/469 - 1s - 3ms/step - loss: 0.0888 - val_loss: 0.0881 ## Epoch 10/100 ## 469/469 - 1s - 3ms/step - loss: 0.0885 - val_loss: 0.0878 ## Epoch 11/100 ## 469/469 - 1s - 3ms/step - loss: 0.0882 - val_loss: 0.0876 ## Epoch 12/100 ## 469/469 - 1s - 3ms/step - loss: 0.0880 - val_loss: 0.0874 ## Epoch 13/100 ## 469/469 - 1s - 3ms/step - loss: 0.0878 - val_loss: 0.0873 ## Epoch 14/100 ## 469/469 - 1s - 3ms/step - loss: 0.0877 - val_loss: 0.0871 ## Epoch 15/100 ## 469/469 - 1s - 3ms/step - loss: 0.0875 - val_loss: 0.0869 ## Epoch 16/100 ## 469/469 - 1s - 3ms/step - loss: 0.0874 - val_loss: 0.0868 ## Epoch 17/100 ## 469/469 - 1s - 3ms/step - loss: 0.0872 - val_loss: 0.0867 ## Epoch 18/100 ## 469/469 - 1s - 3ms/step - loss: 0.0871 - val_loss: 0.0866 ## Epoch 19/100 ## 469/469 - 1s - 3ms/step - loss: 0.0870 - val_loss: 0.0865 ## Epoch 20/100 ## 469/469 - 1s - 3ms/step - loss: 0.0869 - val_loss: 0.0864 ## Epoch 21/100 ## 469/469 - 1s - 3ms/step - loss: 0.0868 - val_loss: 0.0863 ## Epoch 22/100 ## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0862 ## Epoch 23/100 ## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0861 ## Epoch 24/100 ## 469/469 - 1s - 3ms/step - loss: 0.0866 - val_loss: 0.0861 ## Epoch 25/100 ## 469/469 - 2s - 4ms/step - loss: 0.0865 - val_loss: 0.0860 ## Epoch 26/100 ## 469/469 - 2s - 4ms/step - loss: 0.0865 - val_loss: 0.0860 ## Epoch 27/100 ## 469/469 - 2s - 4ms/step - loss: 0.0864 - val_loss: 0.0859 ## Epoch 28/100 ## 469/469 - 2s - 4ms/step - loss: 0.0863 - val_loss: 0.0858 ## Epoch 29/100 ## 469/469 - 1s - 3ms/step - loss: 0.0863 - val_loss: 0.0858 ## Epoch 30/100 ## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0858 ## Epoch 31/100 ## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0857 ## Epoch 32/100 ## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0857 ## Epoch 33/100 ## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0856 ## Epoch 34/100 ## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0856 ## Epoch 35/100 ## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0855 ## Epoch 36/100 ## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0855 ## Epoch 37/100 ## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0855 ## Epoch 38/100 ## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0855 ## Epoch 39/100 ## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0854 ## Epoch 40/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854 ## Epoch 41/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854 ## Epoch 42/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854 ## Epoch 43/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0853 ## Epoch 44/100 ## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853 ## Epoch 45/100 ## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853 ## Epoch 46/100 ## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853 ## Epoch 47/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852 ## Epoch 48/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852 ## Epoch 49/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852 ## Epoch 50/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852 ## Epoch 51/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851 ## Epoch 52/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851 ## Epoch 53/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851 ## Epoch 54/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851 ## Epoch 55/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0851 ## Epoch 56/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850 ## Epoch 57/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850 ## Epoch 58/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850 ## Epoch 59/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850 ## Epoch 60/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0850 ## Epoch 61/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0850 ## Epoch 62/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849 ## Epoch 63/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849 ## Epoch 64/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849 ## Epoch 65/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849 ## Epoch 66/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849 ## Epoch 67/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849 ## Epoch 68/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849 ## Epoch 69/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849 ## Epoch 70/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848 ## Epoch 71/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848 ## Epoch 72/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 73/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 74/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 75/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 76/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 77/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 78/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847 ## Epoch 79/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847 ## Epoch 80/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 81/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 82/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 83/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 84/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 85/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 86/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 87/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 88/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846 ## Epoch 89/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 90/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 91/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 92/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 93/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 94/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 95/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 96/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 97/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 98/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 99/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 100/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 predictions <- autoencoder |> predict(noisy_test_data) ## 313/313 - 0s - 673us/step display(noisy_test_data, predictions)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"setup","dir":"Articles > Examples > Vision","previous_headings":"","what":"Setup","title":"Simple MNIST convnet","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"prepare-the-data","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare the data","title":"Simple MNIST convnet","text":"","code":"# Model / data parameters num_classes <- 10 input_shape <- c(28, 28, 1)  # Load the data and split it between train and test sets c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()  # Scale images to the [0, 1] range x_train <- x_train / 255 x_test <- x_test / 255 # Make sure images have shape (28, 28, 1) x_train <- op_expand_dims(x_train, -1) x_test <- op_expand_dims(x_test, -1)   dim(x_train) ## [1] 60000    28    28     1 dim(x_test) ## [1] 10000    28    28     1 # convert class vectors to binary class matrices y_train <- to_categorical(y_train, num_classes) y_test <- to_categorical(y_test, num_classes)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"build-the-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Build the model","title":"Simple MNIST convnet","text":"","code":"model <- keras_model_sequential(input_shape = input_shape) model |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_flatten() |>   layer_dropout(rate = 0.5) |>   layer_dense(units = num_classes, activation = \"softmax\")  summary(model) ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 26, 26, 32)     │           320 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 13, 13, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 11, 11, 64)     │        18,496 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 5, 5, 64)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ flatten (Flatten)               │ (None, 1600)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 1600)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 10)             │        16,010 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 34,826 (136.04 KB) ##  Trainable params: 34,826 (136.04 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"train-the-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Train the model","title":"Simple MNIST convnet","text":"","code":"batch_size <- 128 epochs <- 15  model |> compile(   loss = \"categorical_crossentropy\",   optimizer = \"adam\",   metrics = \"accuracy\" )  model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = epochs,   validation_split = 0.1 ) ## Epoch 1/15 ## 422/422 - 5s - 11ms/step - accuracy: 0.8895 - loss: 0.3636 - val_accuracy: 0.9787 - val_loss: 0.0792 ## Epoch 2/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9664 - loss: 0.1111 - val_accuracy: 0.9850 - val_loss: 0.0550 ## Epoch 3/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9743 - loss: 0.0824 - val_accuracy: 0.9882 - val_loss: 0.0441 ## Epoch 4/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9786 - loss: 0.0695 - val_accuracy: 0.9895 - val_loss: 0.0400 ## Epoch 5/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9804 - loss: 0.0626 - val_accuracy: 0.9900 - val_loss: 0.0355 ## Epoch 6/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9824 - loss: 0.0558 - val_accuracy: 0.9912 - val_loss: 0.0333 ## Epoch 7/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9835 - loss: 0.0501 - val_accuracy: 0.9918 - val_loss: 0.0310 ## Epoch 8/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9851 - loss: 0.0479 - val_accuracy: 0.9922 - val_loss: 0.0310 ## Epoch 9/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9862 - loss: 0.0444 - val_accuracy: 0.9920 - val_loss: 0.0300 ## Epoch 10/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9863 - loss: 0.0438 - val_accuracy: 0.9912 - val_loss: 0.0294 ## Epoch 11/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9873 - loss: 0.0394 - val_accuracy: 0.9913 - val_loss: 0.0304 ## Epoch 12/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9875 - loss: 0.0371 - val_accuracy: 0.9927 - val_loss: 0.0287 ## Epoch 13/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9891 - loss: 0.0346 - val_accuracy: 0.9920 - val_loss: 0.0292 ## Epoch 14/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9891 - loss: 0.0343 - val_accuracy: 0.9922 - val_loss: 0.0284 ## Epoch 15/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9895 - loss: 0.0320 - val_accuracy: 0.9920 - val_loss: 0.0282"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"evaluate-the-trained-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Evaluate the trained model","title":"Simple MNIST convnet","text":"","code":"score <- model |> evaluate(x_test, y_test, verbose = 0) score ## $accuracy ## [1] 0.9914 ## ## $loss ## [1] 0.02402576"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"introduction","dir":"Articles > Examples > Vision","previous_headings":"","what":"Introduction","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"Siamese Networks neural networks share weights two sister networks, producing embedding vectors respective inputs. supervised similarity learning, networks trained maximize contrast (distance) embeddings inputs different classes, minimizing distance embeddings similar classes, resulting embedding spaces reflect class segmentation training inputs. implementation loosely follows Hadsell-et-al.’06 [1] (see paper mode details) euclidean distance replaced subtraction layer one fully-connect (FC) layer. [1] “Dimensionality Reduction Learning Invariant Mapping” https://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf Gets 98.11% test accuracy 20 epochs. 3 seconds per epoch AMD Ryzen 7 PRO 4750U (CPU)","code":"library(keras3) contrastive_loss <- function(y_true, y_pred) {     # Contrastive loss from Hadsell-et-al.'06     # https://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf     margin = 1     margin_square = op_square(op_maximum(margin - (y_pred), 0))     op_mean((1 - y_true) * op_square(y_pred) + (y_true) * margin_square) }"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"create-pairs-of-images","dir":"Articles > Examples > Vision","previous_headings":"","what":"Create pairs of images","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"train model differentiate digits different classes. example, digit 0 needs differentiated rest digits (1 9), digit 1 - 0 2 9, . carry , select N random images class (example, digit 0) pair N random images another class B (example, digit 1). , can repeat process classes digits (digit 9). paired digit 0 digits, can repeat process remaining classes rest digits (1 9).","code":"create_pairs <- function(x, y) {     # Positive and negative pair creation.     # Alternates between positive and negative pairs.     digit_indices <- tapply(1:length(y), y, list)     y1 <- y     y2 <- sapply(y, function(a) sample(0:9,1,prob=0.1+0.8*(0:9==a)))     idx1 <- 1:nrow(x)     idx2 <- sapply(as.character(y2), function(a) sample(digit_indices[[a]],1))     is_same  <- 1*(y1==y2)     list(pair1 = x[idx1,], pair2 = x[idx2,], y = is_same) }  compute_accuracy <- function(predictions, labels) {     # Compute classification accuracy with a fixed threshold on distances.     mean(labels[predictions > 0.5]) } # the data, shuffled and split between train and test sets mnist   <- dataset_mnist() x_train <- mnist$train$x y_train <- mnist$train$y x_test  <- mnist$test$x y_test  <- mnist$test$y x_train <- array_reshape(x_train, c(nrow(x_train), 784)) x_test  <- array_reshape(x_test, c(nrow(x_test), 784)) x_train <- x_train / 255 x_test  <- x_test / 255  # create training+test positive and negative pairs tr <- create_pairs(x_train, y_train) te <- create_pairs(x_test,  y_test)  names(tr) ## [1] \"pair1\" \"pair2\" \"y\""},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"network-definition","dir":"Articles > Examples > Vision","previous_headings":"","what":"Network definition","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"","code":"# input layers input_dim = 784 input_1 <- layer_input(shape = c(input_dim)) input_2 <- layer_input(shape = c(input_dim))  # definition of the base network that will be shared base_network <- keras_model_sequential() %>%     layer_dense(units = 128, activation = 'relu') %>%     layer_dropout(rate = 0.1) %>%     layer_dense(units = 128, activation = 'relu') %>%     layer_dropout(rate = 0.1) %>%     layer_dense(units = 128, activation = 'relu')  # because we re-use the same instance `base_network`, the weights of # the network will be shared across the two branches branch_1 <- base_network(input_1) branch_2 <- base_network(input_2)  # merging layer out <- layer_subtract(list(branch_1, branch_2)) %>%     layer_dropout(rate = 0.1) %>%     layer_dense(units = 16, activation = 'relu') %>%     layer_dense(1, activation = \"sigmoid\")  # create and compile model model <- keras_model(list(input_1, input_2), out)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"train","dir":"Articles > Examples > Vision","previous_headings":"","what":"Train","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"","code":"model %>% compile(     optimizer = \"rmsprop\",     #loss = \"binary_crossentropy\",     loss = contrastive_loss,     metrics = metric_binary_accuracy )  history <- model %>% fit(     list(tr$pair1, tr$pair2), tr$y,     batch_size = 128,     epochs = 20,     validation_data = list(         list(te$pair1, te$pair2),         te$y     ) ) ## Epoch 1/20 ## 469/469 - 17s - 36ms/step - binary_accuracy: 0.7566 - loss: 0.1644 - val_binary_accuracy: 0.8753 - val_loss: 0.1001 ## Epoch 2/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.8896 - loss: 0.0867 - val_binary_accuracy: 0.9248 - val_loss: 0.0614 ## Epoch 3/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9261 - loss: 0.0579 - val_binary_accuracy: 0.9409 - val_loss: 0.0461 ## Epoch 4/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9474 - loss: 0.0419 - val_binary_accuracy: 0.9529 - val_loss: 0.0382 ## Epoch 5/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9589 - loss: 0.0328 - val_binary_accuracy: 0.9603 - val_loss: 0.0316 ## Epoch 6/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9660 - loss: 0.0272 - val_binary_accuracy: 0.9617 - val_loss: 0.0297 ## Epoch 7/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9708 - loss: 0.0234 - val_binary_accuracy: 0.9665 - val_loss: 0.0270 ## Epoch 8/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9747 - loss: 0.0201 - val_binary_accuracy: 0.9674 - val_loss: 0.0259 ## Epoch 9/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9787 - loss: 0.0175 - val_binary_accuracy: 0.9697 - val_loss: 0.0247 ## Epoch 10/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9807 - loss: 0.0157 - val_binary_accuracy: 0.9684 - val_loss: 0.0251 ## Epoch 11/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9810 - loss: 0.0152 - val_binary_accuracy: 0.9710 - val_loss: 0.0230 ## Epoch 12/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9831 - loss: 0.0138 - val_binary_accuracy: 0.9726 - val_loss: 0.0224 ## Epoch 13/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9854 - loss: 0.0121 - val_binary_accuracy: 0.9724 - val_loss: 0.0224 ## Epoch 14/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9861 - loss: 0.0114 - val_binary_accuracy: 0.9738 - val_loss: 0.0217 ## Epoch 15/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9864 - loss: 0.0110 - val_binary_accuracy: 0.9738 - val_loss: 0.0213 ## Epoch 16/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9881 - loss: 0.0101 - val_binary_accuracy: 0.9754 - val_loss: 0.0206 ## Epoch 17/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9883 - loss: 0.0097 - val_binary_accuracy: 0.9733 - val_loss: 0.0214 ## Epoch 18/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9881 - loss: 0.0097 - val_binary_accuracy: 0.9723 - val_loss: 0.0216 ## Epoch 19/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9891 - loss: 0.0090 - val_binary_accuracy: 0.9749 - val_loss: 0.0202 ## Epoch 20/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9898 - loss: 0.0085 - val_binary_accuracy: 0.9752 - val_loss: 0.0205 plot(history)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"evaluate","dir":"Articles > Examples > Vision","previous_headings":"","what":"Evaluate","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"","code":"# compute final accuracy on training and test sets  tr_pred <- predict(model, list(tr$pair1, tr$pair2))[,1] ## 1875/1875 - 1s - 793us/step tr_acc  <- compute_accuracy(tr_pred, tr$y) te_pred <- predict(model, list(te$pair1, te$pair2))[,1] ## 313/313 - 0s - 1ms/step te_acc  <- compute_accuracy(te_pred, te$y)  sprintf('* Accuracy on training set: %0.2f%%', (100 * tr_acc)) ## [1] \"* Accuracy on training set: 99.63%\" sprintf('* Accuracy on test set: %0.2f%%', (100 * te_acc)) ## [1] \"* Accuracy on test set: 97.93%\""},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"plots","dir":"Articles > Examples > Vision","previous_headings":"","what":"Plots","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"","code":"par(mfrow=c(1,1)) vioplot::vioplot( te_pred ~ te$y ) i=3 visualizePair <- function(i) {     image(rbind(matrix( te$pair1[i,],28,28)[,28:1], matrix( te$pair2[i,],28,28)[,28:1]))     title(paste(\"true:\", te$y[i],\"|  pred:\", round(te_pred[i],5))) } par(mfrow=c(3,3)) lapply(1:9, visualizePair) ## [[1]] ## NULL ## ## [[2]] ## NULL ## ## [[3]] ## NULL ## ## [[4]] ## NULL ## ## [[5]] ## NULL ## ## [[6]] ## NULL ## ## [[7]] ## NULL ## ## [[8]] ## NULL ## ## [[9]] ## NULL"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"download-the-data","dir":"Articles > Examples > Vision","previous_headings":"","what":"Download the data","title":"Image segmentation with a U-Net-like architecture","text":"","code":"options(timeout = 5000) download.file(   \"https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\",   \"datasets/images.tar.gz\" ) download.file(   \"https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\",   \"datasets/annotations.tar.gz\" )  untar(\"datasets/images.tar.gz\", exdir = \"datasets\") untar(\"datasets/annotations.tar.gz\", exdir = \"datasets\")"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"prepare-paths-of-input-images-and-target-segmentation-masks","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare paths of input images and target segmentation masks","title":"Image segmentation with a U-Net-like architecture","text":"","code":"library(keras3) input_dir <- \"datasets/images/\" target_dir <- \"datasets/annotations/trimaps/\" img_size <- c(160, 160) num_classes <- 3 batch_size <- 32  input_img_paths <- fs::dir_ls(input_dir, glob = \"*.jpg\") |> sort() target_img_paths <- fs::dir_ls(target_dir, glob = \"*.png\") |> sort()  cat(\"Number of samples:\", length(input_img_paths), \"\\n\") ## Number of samples: 7390 for (i in 1:10) {   cat(input_img_paths[i], \"|\", target_img_paths[i], \"\\n\") } ## datasets/images/Abyssinian_1.jpg | datasets/annotations/trimaps/Abyssinian_1.png ## datasets/images/Abyssinian_10.jpg | datasets/annotations/trimaps/Abyssinian_10.png ## datasets/images/Abyssinian_100.jpg | datasets/annotations/trimaps/Abyssinian_100.png ## datasets/images/Abyssinian_101.jpg | datasets/annotations/trimaps/Abyssinian_101.png ## datasets/images/Abyssinian_102.jpg | datasets/annotations/trimaps/Abyssinian_102.png ## datasets/images/Abyssinian_103.jpg | datasets/annotations/trimaps/Abyssinian_103.png ## datasets/images/Abyssinian_104.jpg | datasets/annotations/trimaps/Abyssinian_104.png ## datasets/images/Abyssinian_105.jpg | datasets/annotations/trimaps/Abyssinian_105.png ## datasets/images/Abyssinian_106.jpg | datasets/annotations/trimaps/Abyssinian_106.png ## datasets/images/Abyssinian_107.jpg | datasets/annotations/trimaps/Abyssinian_107.png"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"what-does-one-input-image-and-corresponding-segmentation-mask-look-like","dir":"Articles > Examples > Vision","previous_headings":"","what":"What does one input image and corresponding segmentation mask look like?","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Display input image #10 input_img_paths[10] |>   jpeg::readJPEG() |>   as.raster() |>   plot() target_img_paths[10] |>   png::readPNG() |>   magrittr::multiply_by(255)|>   as.raster(max = 3) |>   plot()"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"prepare-dataset-to-load-vectorize-batches-of-data","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare dataset to load & vectorize batches of data","title":"Image segmentation with a U-Net-like architecture","text":"","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")   # Returns a tf_dataset get_dataset <- function(batch_size, img_size, input_img_paths, target_img_paths,                         max_dataset_len = NULL) {    img_size <- as.integer(img_size)    load_img_masks <- function(input_img_path, target_img_path) {     input_img <- input_img_path |>       tf$io$read_file() |>       tf$io$decode_jpeg(channels = 3) |>       tf$image$resize(img_size) |>       tf$image$convert_image_dtype(\"float32\")      target_img <- target_img_path |>       tf$io$read_file() |>       tf$io$decode_png(channels = 1) |>       tf$image$resize(img_size, method = \"nearest\") |>       tf$image$convert_image_dtype(\"uint8\")      # Ground truth labels are 1, 2, 3. Subtract one to make them 0, 1, 2:     target_img <- target_img - 1L      list(input_img, target_img)   }    if (!is.null(max_dataset_len)) {     input_img_paths <- input_img_paths[1:max_dataset_len]     target_img_paths <- target_img_paths[1:max_dataset_len]   }    list(input_img_paths, target_img_paths) |>     tensor_slices_dataset() |>     dataset_map(load_img_masks, num_parallel_calls = tf$data$AUTOTUNE)|>     dataset_batch(batch_size) }"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"prepare-u-net-xception-style-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare U-Net Xception-style model","title":"Image segmentation with a U-Net-like architecture","text":"","code":"get_model <- function(img_size, num_classes) {    inputs <- keras_input(shape = c(img_size, 3))    ### [First half of the network: downsampling inputs] ###    # Entry block   x <- inputs |>     layer_conv_2d(filters = 32, kernel_size = 3, strides = 2, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation(\"relu\")    previous_block_activation <- x  # Set aside residual    for (filters in c(64, 128, 256)) {     x <- x |>       layer_activation(\"relu\") |>       layer_separable_conv_2d(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_activation(\"relu\") |>       layer_separable_conv_2d(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_max_pooling_2d(pool_size = 3, strides = 2, padding = \"same\")      residual <- previous_block_activation |>       layer_conv_2d(filters = filters, kernel_size = 1, strides = 2, padding = \"same\")      x <- layer_add(x, residual)  # Add back residual     previous_block_activation <- x  # Set aside next residual   }    ### [Second half of the network: upsampling inputs] ###    for (filters in c(256, 128, 64, 32)) {     x <- x |>       layer_activation(\"relu\") |>       layer_conv_2d_transpose(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_activation(\"relu\") |>       layer_conv_2d_transpose(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_upsampling_2d(size = 2)      # Project residual     residual <- previous_block_activation |>       layer_upsampling_2d(size = 2) |>       layer_conv_2d(filters = filters, kernel_size = 1, padding = \"same\")      x <- layer_add(x, residual)     # Add back residual     previous_block_activation <- x  # Set aside next residual   }    # Add a per-pixel classification layer   outputs <- x |>     layer_conv_2d(num_classes, 3, activation = \"softmax\", padding = \"same\")    # Define the model   keras_model(inputs, outputs) }  # Build model model <- get_model(img_size, num_classes) summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)      ┃ Output Shape    ┃   Param # ┃ Connected to   ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer       │ (None, 160,     │         0 │ -              │   -   │ ## │ (InputLayer)      │ 160, 3)         │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d (Conv2D)   │ (None, 80, 80,  │       896 │ input_layer[0… │   Y   │ ## │                   │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d[0][0]   │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation        │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_1      │ (None, 80, 80,  │         0 │ activation[0]… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d  │ (None, 80, 80,  │     2,400 │ activation_1[… │   Y   │ ## │ (SeparableConv2D) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       256 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_2      │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 80, 80,  │     4,736 │ activation_2[… │   Y   │ ## │ (SeparableConv2D) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       256 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d     │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_1 (Conv2D) │ (None, 40, 40,  │     2,112 │ activation[0]… │   Y   │ ## │                   │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add (Add)         │ (None, 40, 40,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 64)             │           │ conv2d_1[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_3      │ (None, 40, 40,  │         0 │ add[0][0]      │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 40, 40,  │     8,896 │ activation_3[… │   Y   │ ## │ (SeparableConv2D) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       512 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_4      │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 40, 40,  │    17,664 │ activation_4[… │   Y   │ ## │ (SeparableConv2D) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       512 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d_1   │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_2 (Conv2D) │ (None, 20, 20,  │     8,320 │ add[0][0]      │   Y   │ ## │                   │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_1 (Add)       │ (None, 20, 20,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 128)            │           │ conv2d_2[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_5      │ (None, 20, 20,  │         0 │ add_1[0][0]    │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 20, 20,  │    34,176 │ activation_5[… │   Y   │ ## │ (SeparableConv2D) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │     1,024 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_6      │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 20, 20,  │    68,096 │ activation_6[… │   Y   │ ## │ (SeparableConv2D) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │     1,024 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d_2   │ (None, 10, 10,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_3 (Conv2D) │ (None, 10, 10,  │    33,024 │ add_1[0][0]    │   Y   │ ## │                   │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_2 (Add)       │ (None, 10, 10,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 256)            │           │ conv2d_3[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_7      │ (None, 10, 10,  │         0 │ add_2[0][0]    │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose  │ (None, 10, 10,  │   590,080 │ activation_7[… │   Y   │ ## │ (Conv2DTranspose) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 10, 10,  │     1,024 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_8      │ (None, 10, 10,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 10, 10,  │   590,080 │ activation_8[… │   Y   │ ## │ (Conv2DTranspose) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 10, 10,  │     1,024 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_1   │ (None, 20, 20,  │         0 │ add_2[0][0]    │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d     │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_4 (Conv2D) │ (None, 20, 20,  │    65,792 │ up_sampling2d… │   Y   │ ## │                   │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_3 (Add)       │ (None, 20, 20,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 256)            │           │ conv2d_4[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_9      │ (None, 20, 20,  │         0 │ add_3[0][0]    │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 20, 20,  │   295,040 │ activation_9[… │   Y   │ ## │ (Conv2DTranspose) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │       512 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_10     │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 20, 20,  │   147,584 │ activation_10… │   Y   │ ## │ (Conv2DTranspose) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │       512 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_3   │ (None, 40, 40,  │         0 │ add_3[0][0]    │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_2   │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_5 (Conv2D) │ (None, 40, 40,  │    32,896 │ up_sampling2d… │   Y   │ ## │                   │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_4 (Add)       │ (None, 40, 40,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 128)            │           │ conv2d_5[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_11     │ (None, 40, 40,  │         0 │ add_4[0][0]    │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 40, 40,  │    73,792 │ activation_11… │   Y   │ ## │ (Conv2DTranspose) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       256 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_12     │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 40, 40,  │    36,928 │ activation_12… │   Y   │ ## │ (Conv2DTranspose) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       256 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_5   │ (None, 80, 80,  │         0 │ add_4[0][0]    │   -   │ ## │ (UpSampling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_4   │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_6 (Conv2D) │ (None, 80, 80,  │     8,256 │ up_sampling2d… │   Y   │ ## │                   │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_5 (Add)       │ (None, 80, 80,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 64)             │           │ conv2d_6[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_13     │ (None, 80, 80,  │         0 │ add_5[0][0]    │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 80, 80,  │    18,464 │ activation_13… │   Y   │ ## │ (Conv2DTranspose) │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_14     │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 80, 80,  │     9,248 │ activation_14… │   Y   │ ## │ (Conv2DTranspose) │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_7   │ (None, 160,     │         0 │ add_5[0][0]    │   -   │ ## │ (UpSampling2D)    │ 160, 64)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_6   │ (None, 160,     │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 160, 32)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_7 (Conv2D) │ (None, 160,     │     2,080 │ up_sampling2d… │   Y   │ ## │                   │ 160, 32)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_6 (Add)       │ (None, 160,     │         0 │ up_sampling2d… │   -   │ ## │                   │ 160, 32)        │           │ conv2d_7[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_8 (Conv2D) │ (None, 160,     │       867 │ add_6[0][0]    │   Y   │ ## │                   │ 160, 3)         │           │                │       │ ## └───────────────────┴─────────────────┴───────────┴────────────────┴───────┘ ##  Total params: 2,058,979 (7.85 MB) ##  Trainable params: 2,055,203 (7.84 MB) ##  Non-trainable params: 3,776 (14.75 KB)"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"set-aside-a-validation-split","dir":"Articles > Examples > Vision","previous_headings":"","what":"Set aside a validation split","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Split our img paths into a training and a validation set val_samples <- 1000 val_samples <- sample.int(length(input_img_paths), val_samples)  train_input_img_paths <- input_img_paths[-val_samples] train_target_img_paths <- target_img_paths[-val_samples]  val_input_img_paths <- input_img_paths[val_samples] val_target_img_paths <- target_img_paths[val_samples]  # Instantiate dataset for each split # Limit input files in `max_dataset_len` for faster epoch training time. # Remove the `max_dataset_len` arg when running with full dataset. train_dataset <- get_dataset(   batch_size,   img_size,   train_input_img_paths,   train_target_img_paths,   max_dataset_len = 1000 ) valid_dataset <- get_dataset(   batch_size, img_size, val_input_img_paths, val_target_img_paths )"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"train-the-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Train the model","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Configure the model for training. # We use the \"sparse\" version of categorical_crossentropy # because our target data is integers. model |> compile(   optimizer = optimizer_adam(1e-4),   loss = \"sparse_categorical_crossentropy\" )  callbacks <- list(   callback_model_checkpoint(     \"models/oxford_segmentation.keras\", save_best_only = TRUE   ) )  # Train the model, doing validation at the end of each epoch. epochs <- 50 model |> fit(     train_dataset,     epochs=epochs,     validation_data=valid_dataset,     callbacks=callbacks,     verbose=2 ) ## Epoch 1/50 ## 32/32 - 29s - 914ms/step - loss: 1.4283 - val_loss: 1.5499 ## Epoch 2/50 ## 32/32 - 2s - 60ms/step - loss: 0.9221 - val_loss: 1.9887 ## Epoch 3/50 ## 32/32 - 2s - 60ms/step - loss: 0.7764 - val_loss: 2.5144 ## Epoch 4/50 ## 32/32 - 2s - 61ms/step - loss: 0.7200 - val_loss: 3.0185 ## Epoch 5/50 ## 32/32 - 2s - 61ms/step - loss: 0.6847 - val_loss: 3.2947 ## Epoch 6/50 ## 32/32 - 2s - 68ms/step - loss: 0.6556 - val_loss: 3.4559 ## Epoch 7/50 ## 32/32 - 2s - 76ms/step - loss: 0.6303 - val_loss: 3.5671 ## Epoch 8/50 ## 32/32 - 2s - 62ms/step - loss: 0.6083 - val_loss: 3.6620 ## Epoch 9/50 ## 32/32 - 2s - 63ms/step - loss: 0.5895 - val_loss: 3.7374 ## Epoch 10/50 ## 32/32 - 2s - 62ms/step - loss: 0.5726 - val_loss: 3.8015 ## Epoch 11/50 ## 32/32 - 2s - 63ms/step - loss: 0.5567 - val_loss: 3.8311 ## Epoch 12/50 ## 32/32 - 2s - 63ms/step - loss: 0.5407 - val_loss: 3.8089 ## Epoch 13/50 ## 32/32 - 2s - 62ms/step - loss: 0.5242 - val_loss: 3.7293 ## Epoch 14/50 ## 32/32 - 2s - 61ms/step - loss: 0.5062 - val_loss: 3.5953 ## Epoch 15/50 ## 32/32 - 2s - 61ms/step - loss: 0.4860 - val_loss: 3.4506 ## Epoch 16/50 ## 32/32 - 2s - 60ms/step - loss: 0.4637 - val_loss: 3.2588 ## Epoch 17/50 ## 32/32 - 2s - 61ms/step - loss: 0.4396 - val_loss: 3.0063 ## Epoch 18/50 ## 32/32 - 2s - 61ms/step - loss: 0.4140 - val_loss: 2.7031 ## Epoch 19/50 ## 32/32 - 2s - 61ms/step - loss: 0.3877 - val_loss: 2.3504 ## Epoch 20/50 ## 32/32 - 2s - 61ms/step - loss: 0.3623 - val_loss: 1.9645 ## Epoch 21/50 ## 32/32 - 2s - 62ms/step - loss: 0.3392 - val_loss: 1.6364 ## Epoch 22/50 ## 32/32 - 2s - 66ms/step - loss: 0.3203 - val_loss: 1.3376 ## Epoch 23/50 ## 32/32 - 2s - 67ms/step - loss: 0.3082 - val_loss: 1.0917 ## Epoch 24/50 ## 32/32 - 2s - 66ms/step - loss: 0.3084 - val_loss: 1.0189 ## Epoch 25/50 ## 32/32 - 2s - 66ms/step - loss: 0.3461 - val_loss: 0.9181 ## Epoch 26/50 ## 32/32 - 2s - 61ms/step - loss: 0.3649 - val_loss: 1.0231 ## Epoch 27/50 ## 32/32 - 2s - 66ms/step - loss: 0.3294 - val_loss: 0.8574 ## Epoch 28/50 ## 32/32 - 2s - 61ms/step - loss: 0.2891 - val_loss: 1.0065 ## Epoch 29/50 ## 32/32 - 2s - 61ms/step - loss: 0.2731 - val_loss: 1.1668 ## Epoch 30/50 ## 32/32 - 2s - 61ms/step - loss: 0.2681 - val_loss: 1.1974 ## Epoch 31/50 ## 32/32 - 2s - 61ms/step - loss: 0.2723 - val_loss: 1.1259 ## Epoch 32/50 ## 32/32 - 2s - 61ms/step - loss: 0.2872 - val_loss: 1.1112 ## Epoch 33/50 ## 32/32 - 2s - 61ms/step - loss: 0.3146 - val_loss: 1.2479 ## Epoch 34/50 ## 32/32 - 2s - 61ms/step - loss: 0.3034 - val_loss: 1.1946 ## Epoch 35/50 ## 32/32 - 2s - 61ms/step - loss: 0.2880 - val_loss: 1.0699 ## Epoch 36/50 ## 32/32 - 2s - 61ms/step - loss: 0.2770 - val_loss: 1.0195 ## Epoch 37/50 ## 32/32 - 2s - 61ms/step - loss: 0.2669 - val_loss: 1.2986 ## Epoch 38/50 ## 32/32 - 2s - 61ms/step - loss: 0.2544 - val_loss: 1.0411 ## Epoch 39/50 ## 32/32 - 2s - 61ms/step - loss: 0.2383 - val_loss: 1.3622 ## Epoch 40/50 ## 32/32 - 2s - 61ms/step - loss: 0.2378 - val_loss: 1.3429 ## Epoch 41/50 ## 32/32 - 2s - 61ms/step - loss: 0.2439 - val_loss: 1.2881 ## Epoch 42/50 ## 32/32 - 2s - 61ms/step - loss: 0.2406 - val_loss: 1.5361 ## Epoch 43/50 ## 32/32 - 2s - 61ms/step - loss: 0.2417 - val_loss: 1.2444 ## Epoch 44/50 ## 32/32 - 2s - 61ms/step - loss: 0.2275 - val_loss: 1.1713 ## Epoch 45/50 ## 32/32 - 2s - 61ms/step - loss: 0.2168 - val_loss: 1.0482 ## Epoch 46/50 ## 32/32 - 2s - 62ms/step - loss: 0.2096 - val_loss: 1.1095 ## Epoch 47/50 ## 32/32 - 2s - 61ms/step - loss: 0.2026 - val_loss: 1.1017 ## Epoch 48/50 ## 32/32 - 2s - 61ms/step - loss: 0.1986 - val_loss: 1.1087 ## Epoch 49/50 ## 32/32 - 2s - 61ms/step - loss: 0.1924 - val_loss: 1.1055 ## Epoch 50/50 ## 32/32 - 2s - 61ms/step - loss: 0.1846 - val_loss: 1.0708"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"visualize-predictions","dir":"Articles > Examples > Vision","previous_headings":"","what":"Visualize predictions","title":"Image segmentation with a U-Net-like architecture","text":"","code":"model <- load_model(\"models/oxford_segmentation.keras\") # Generate predictions for all images in the validation set val_dataset <- get_dataset(   batch_size, img_size, val_input_img_paths, val_target_img_paths ) val_preds <- predict(model, val_dataset) ## 32/32 - 3s - 86ms/step display_mask <- function(i) {   # Quick utility to display a model's prediction.   mask <- val_preds[i,,,] %>%     apply(c(1,2), which.max) %>%     array_reshape(dim = c(img_size, 1))   mask <- abind::abind(mask, mask, mask, along = 3)   plot(as.raster(mask, max = 3)) }  # Display results for validation image #10 i <- 10  par(mfrow = c(1, 3)) # Display input image input_img_paths[i] |>   jpeg::readJPEG() |>   as.raster() |>   plot()  # Display ground-truth target mask target_img_paths[i] |>   png::readPNG() |>   magrittr::multiply_by(255)|>   as.raster(max = 3) |>   plot()  # Display mask predicted by our model display_mask(i)  # Note that the model only sees inputs at 150x150."},{"path":"https://keras.posit.co/articles/functional_api.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"The Functional API","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"The Functional API","text":"Keras functional API way create models flexible sequential API. functional API can handle models non-linear topology, shared layers, even multiple inputs outputs. main idea deep learning model usually directed acyclic graph (DAG) layers. functional API way build graphs layers. Consider following model: basic graph three layers. build model using functional API, start creating input node: shape data set 784-dimensional vector. batch size always omitted since shape sample specified. , example, image input shape (32, 32, 3), use: inputs returned contains information shape dtype input data feed model. ’s shape: ’s dtype: create new node graph layers calling layer inputs object: “layer call” action like drawing arrow “inputs” layer created. ’re “passing” inputs dense layer, get x output. Let’s add layers graph layers: point, can create Model specifying inputs outputs graph layers: Let’s check model summary looks like: can also plot model graph:  , optionally, display input output shapes layer plotted graph:  figure code almost identical. code version, connection arrows replaced call operation. “graph layers” intuitive mental image deep learning model, functional API way create models closely mirrors .","code":"(input: 784-dimensional vectors)        ↧ [Dense (64 units, relu activation)]        ↧ [Dense (64 units, relu activation)]        ↧ [Dense (10 units, softmax activation)]        ↧ (output: logits of a probability distribution over 10 classes) inputs <- keras_input(shape = c(784)) # Just for demonstration purposes. img_inputs <- keras_input(shape = c(32, 32, 3)) shape(inputs) ## shape(NA, 784) inputs$dtype ## [1] \"float32\" dense <- layer_dense(units = 64, activation=\"relu\") x <- dense(inputs) outputs <- x |>   layer_dense(units = 64, activation = \"relu\") |>   layer_dense(units = 10) model <- keras_model(inputs = inputs, outputs = outputs, name = \"mnist_model\") summary(model) ## Model: \"mnist_model\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, 784)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 64)             │        50,240 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_1 (Dense)                 │ (None, 64)             │         4,160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 10)             │           650 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 55,050 (215.04 KB) ##  Trainable params: 55,050 (215.04 KB) ##  Non-trainable params: 0 (0.00 B) plot(model) plot(model, show_shapes = TRUE)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"training-evaluation-and-inference","dir":"Articles","previous_headings":"","what":"Training, evaluation, and inference","title":"The Functional API","text":"Training, evaluation, inference work exactly way models built using functional API Sequential models. Model class offers built-training loop (fit() method) built-evaluation loop (evaluate() method). Note can easily customize loops implement training routines beyond supervised learning (e.g. GANs). , load MNIST image data, reshape vectors, fit model data (monitoring performance validation split), evaluate model test data: reading, see training evaluation guide.","code":"c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()  x_train <- array_reshape(x_train, c(60000, 784)) / 255 x_test <- array_reshape(x_test, c(10000, 784)) / 255  model |> compile(   loss = loss_sparse_categorical_crossentropy(from_logits = TRUE),   optimizer = optimizer_rmsprop(),   metrics = \"accuracy\" )  history <- model |> fit(     x_train, y_train, batch_size = 64, epochs = 2, validation_split = 0.2 ) ## Epoch 1/2 ## 750/750 - 2s - 2ms/step - accuracy: 0.8979 - loss: 0.3540 - val_accuracy: 0.9448 - val_loss: 0.1903 ## Epoch 2/2 ## 750/750 - 1s - 773us/step - accuracy: 0.9511 - loss: 0.1634 - val_accuracy: 0.9605 - val_loss: 0.1386 test_scores <- model |> evaluate(x_test, y_test, verbose=2) ## 313/313 - 0s - 980us/step - accuracy: 0.9593 - loss: 0.1323 cat(\"Test loss:\", test_scores$loss, \"\\n\") cat(\"Test accuracy:\", test_scores$accuracy, \"\\n\") ## Test loss: 0.1323339 ## Test accuracy: 0.9593"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"save-and-serialize","dir":"Articles","previous_headings":"","what":"Save and serialize","title":"The Functional API","text":"Saving model serialization work way models built using functional API Sequential models. standard way save functional model call model.save() save entire model single file. can later recreate model file, even code built model longer available. saved file includes : - model architecture - model weight values (learned training) - model training config, (passed compile()) - optimizer state, (restart training left ) details, read model serialization & saving guide.","code":"model |> save_model(\"my_model.keras\") rm(model) # Recreate the exact same model purely from the file: model <- load_model(\"my_model.keras\")"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"use-the-same-graph-of-layers-to-define-multiple-models","dir":"Articles","previous_headings":"","what":"Use the same graph of layers to define multiple models","title":"The Functional API","text":"functional API, models created specifying inputs outputs graph layers. means single graph layers can used generate multiple models. example , use stack layers instantiate two models: encoder model turns image inputs 16-dimensional vectors, end--end autoencoder model training. , decoding architecture strictly symmetrical encoding architecture, output shape input shape (28, 28, 1). reverse conv_2d layer conv_2d_transpose layer, reverse max_pooling_2d layer upsampling_2d layer.","code":"encoder_input <- keras_input(shape = c(28, 28, 1), name=\"img\") encoder_output <- encoder_input |>   layer_conv_2d(16, 3, activation = \"relu\") |>   layer_conv_2d(32, 3, activation = \"relu\") |>   layer_max_pooling_2d(3) |>   layer_conv_2d(32, 3, activation = \"relu\") |>   layer_conv_2d(16, 3, activation = \"relu\") |>   layer_global_max_pooling_2d()  encoder <- keras_model(encoder_input, encoder_output, name=\"encoder\") summary(encoder) ## Model: \"encoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)                │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 26, 26, 16)     │           160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 24, 24, 32)     │         4,640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 8, 8, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 6, 6, 32)       │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_3 (Conv2D)               │ (None, 4, 4, 16)       │         4,624 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d            │ (None, 16)             │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 18,672 (72.94 KB) ##  Trainable params: 18,672 (72.94 KB) ##  Non-trainable params: 0 (0.00 B) decoder_output <- encoder_output |>   layer_reshape(c(4, 4, 1)) |>   layer_conv_2d_transpose(16, 3, activation = \"relu\") |>   layer_conv_2d_transpose(32, 3, activation = \"relu\") |>   layer_upsampling_2d(3) |>   layer_conv_2d_transpose(16, 3, activation = \"relu\") |>   layer_conv_2d_transpose(1, 3, activation = \"relu\")  autoencoder <- keras_model(encoder_input, decoder_output, name=\"autoencoder\") summary(autoencoder) ## Model: \"autoencoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)                │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 26, 26, 16)     │           160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 24, 24, 32)     │         4,640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 8, 8, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 6, 6, 32)       │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_3 (Conv2D)               │ (None, 4, 4, 16)       │         4,624 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d            │ (None, 16)             │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ reshape (Reshape)               │ (None, 4, 4, 1)        │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 6, 6, 16)       │           160 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 8, 8, 32)       │         4,640 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ up_sampling2d (UpSampling2D)    │ (None, 24, 24, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_2              │ (None, 26, 26, 16)     │         4,624 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_3              │ (None, 28, 28, 1)      │           145 │ ## │ (Conv2DTranspose)               │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 28,241 (110.32 KB) ##  Trainable params: 28,241 (110.32 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"all-models-are-callable-just-like-layers","dir":"Articles","previous_headings":"","what":"All models are callable, just like layers","title":"The Functional API","text":"can treat model layer invoking Input output another layer. calling model aren’t just reusing architecture model, ’re also reusing weights. see action, ’s different take autoencoder example creates encoder model, decoder model, chains two calls obtain autoencoder model: can see, model can nested: model can contain sub-models (since model just like layer). common use case model nesting ensembling. example, ’s ensemble set models single model averages predictions:","code":"encoder_input <- keras_input(shape = c(28, 28, 1), name=\"img\") encoder_output <- encoder_input |>   layer_conv_2d(16, 3, activation = \"relu\") |>   layer_conv_2d(32, 3, activation = \"relu\") |>   layer_max_pooling_2d(3) |>   layer_conv_2d(32, 3, activation = \"relu\") |>   layer_conv_2d(16, 3, activation = \"relu\") |>   layer_global_max_pooling_2d()  encoder <- keras_model(encoder_input, encoder_output, name=\"encoder\") summary(encoder) ## Model: \"encoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)                │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_4 (Conv2D)               │ (None, 26, 26, 16)     │           160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_5 (Conv2D)               │ (None, 24, 24, 32)     │         4,640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 8, 8, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_6 (Conv2D)               │ (None, 6, 6, 32)       │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_7 (Conv2D)               │ (None, 4, 4, 16)       │         4,624 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d_1          │ (None, 16)             │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 18,672 (72.94 KB) ##  Trainable params: 18,672 (72.94 KB) ##  Non-trainable params: 0 (0.00 B) decoder_input <- keras_input(shape = c(16), name = \"encoded_img\") decoder_output <- decoder_input |>   layer_reshape(c(4, 4, 1)) |>   layer_conv_2d_transpose(16, 3, activation = \"relu\") |>   layer_conv_2d_transpose(32, 3, activation = \"relu\") |>   layer_upsampling_2d(3) |>   layer_conv_2d_transpose(16, 3, activation = \"relu\") |>   layer_conv_2d_transpose(1, 3, activation = \"relu\")  decoder <- keras_model(decoder_input, decoder_output, name = \"decoder\") summary(decoder) ## Model: \"decoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ encoded_img (InputLayer)        │ (None, 16)             │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ reshape_1 (Reshape)             │ (None, 4, 4, 1)        │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_4              │ (None, 6, 6, 16)       │           160 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_5              │ (None, 8, 8, 32)       │         4,640 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ up_sampling2d_1 (UpSampling2D)  │ (None, 24, 24, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_6              │ (None, 26, 26, 16)     │         4,624 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_7              │ (None, 28, 28, 1)      │           145 │ ## │ (Conv2DTranspose)               │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 9,569 (37.38 KB) ##  Trainable params: 9,569 (37.38 KB) ##  Non-trainable params: 0 (0.00 B) autoencoder_input <- keras_input(shape = c(28, 28, 1), name = \"img\") encoded_img <- encoder(autoencoder_input) decoded_img <- decoder(encoded_img) autoencoder <- keras_model(autoencoder_input, decoded_img,                            name = \"autoencoder\") summary(autoencoder) ## Model: \"autoencoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)                │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ encoder (Functional)            │ (None, 16)             │        18,672 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ decoder (Functional)            │ (None, 28, 28, 1)      │         9,569 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 28,241 (110.32 KB) ##  Trainable params: 28,241 (110.32 KB) ##  Non-trainable params: 0 (0.00 B) get_model <- function() {   inputs <- keras_input(shape = 128)   outputs <- inputs |> layer_dense(1)   keras_model(inputs, outputs) }  model1 <- get_model() model2 <- get_model() model3 <- get_model()  inputs <- keras_input(shape = 128) y1 <- model1(inputs) y2 <- model2(inputs) y3 <- model3(inputs) outputs <- layer_average(list(y1, y2, y3)) ensemble_model <- keras_model(inputs = inputs, outputs = outputs)"},{"path":[]},{"path":"https://keras.posit.co/articles/functional_api.html","id":"models-with-multiple-inputs-and-outputs","dir":"Articles","previous_headings":"Manipulate complex graph topologies","what":"Models with multiple inputs and outputs","title":"The Functional API","text":"functional API makes easy manipulate multiple inputs outputs. handled Sequential API. example, ’re building system ranking customer issue tickets priority routing correct department, model three inputs: title ticket (text input), text body ticket (text input), tags added user (categorical input) model two outputs: priority score 0 1 (scalar sigmoid output), department handle ticket (softmax output set departments). can build model lines functional API: Now plot model:  compiling model, can assign different losses output. can even assign different weights loss – modulate contribution total training loss. Since output layers different names, also specify losses loss weights corresponding layer names: Train model passing lists NumPy arrays inputs targets: calling fit Dataset object, yield either list lists like list(list(title_data, body_data, tags_data), list(priority_targets, dept_targets)) list named lists like list(list(title = title_data, body = body_data, tags = tags_data), list(priority = priority_targets, department = dept_targets)). detailed explanation, refer training evaluation guide.","code":"num_tags <- 12  # Number of unique issue tags num_words <- 10000  # Size of vocabulary obtained when preprocessing text data num_departments <- 4  # Number of departments for predictions  title_input <- # Variable-length sequence of ints   keras_input(shape(NA), name = \"title\") body_input <-  # Variable-length sequence of ints   keras_input(shape(NA), name = \"body\") tags_input <-  # Binary vectors of size `num_tags`   keras_input(shape = num_tags, name = \"tags\")  # Embed each word in the title into a 64-dimensional vector title_features <- layer_embedding(title_input, num_words, 64) # Embed each word in the text into a 64-dimensional vector body_features <- layer_embedding(body_input, num_words, 64)  # Reduce sequence of embedded words in the title # into a single 128-dimensional vector title_features <- layer_lstm(title_features, 128) # Reduce sequence of embedded words in the body # into a single 32-dimensional vector body_features <- layer_lstm(body_features, 32)  # Merge all available features into a single large vector via concatenation x <- layer_concatenate(title_features, body_features, tags_input)  # Stick a logistic regression for priority prediction on top of the features priority_pred <- layer_dense(x, 1, name = \"priority\")  # Stick a department classifier on top of the features department_pred <- layer_dense(x, num_departments, name = \"department\")  # Instantiate an end-to-end model predicting both priority and department model <- keras_model(   inputs = list(title_input, body_input, tags_input),   outputs = list(priority = priority_pred, department = department_pred) ) plot(model, show_shapes = TRUE) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     loss_binary_crossentropy(from_logits = TRUE),     loss_categorical_crossentropy(from_logits = TRUE)   ),   loss_weights = c(1.0, 0.2) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     priority = loss_binary_crossentropy(from_logits = TRUE),     department = loss_categorical_crossentropy(from_logits = TRUE)   ),   loss_weights = list(priority = 1.0, department = 0.2) ) # Dummy input data title_data <- random_integer(c(1280, 10), 0, num_words) body_data <- random_integer(c(1280, 100), 0, num_words) tags_data <- random_integer(c(1280, num_tags), 0, 2)  # Dummy target data priority_targets <- random_normal(c(1280, 1)) dept_targets <- random_integer(c(1280, num_departments), 0, 2)  model |> fit(   list(title = title_data, body = body_data, tags = tags_data),   list(priority = priority_targets, department = dept_targets),   epochs = 2,   batch_size = 32 ) ## Epoch 1/2 ## 40/40 - 2s - 57ms/step - loss: 0.3948 ## Epoch 2/2 ## 40/40 - 0s - 5ms/step - loss: 0.1971"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"a-toy-resnet-model","dir":"Articles","previous_headings":"Manipulate complex graph topologies","what":"A toy ResNet model","title":"The Functional API","text":"addition models multiple inputs outputs, functional API makes easy manipulate non-linear connectivity topologies – models layers connected sequentially, Sequential API handle. common use case residual connections. Let’s build toy ResNet model CIFAR10 demonstrate : Plot model:  Now train model:","code":"inputs <- keras_input(shape = c(32, 32, 3), name = \"img\") block_1_output <- inputs |>   layer_conv_2d(32, kernel_size = 3, activation = \"relu\") |>   layer_conv_2d(64, kernel_size = 3, activation = \"relu\") |>   layer_max_pooling_2d(pool_size = 3)  block_2_output <- block_1_output |>   layer_conv_2d(32, kernel_size = 3, activation = \"relu\", padding = \"same\") |>   layer_conv_2d(64, kernel_size = 3, activation = \"relu\", padding = \"same\") |>   layer_add(block_1_output)  block_3_output <- block_2_output |>   layer_conv_2d(64, kernel_size = 3, activation = \"relu\", padding = \"same\") |>   layer_conv_2d(64, kernel_size = 3, activation = \"relu\", padding = \"same\") |>   layer_add(block_2_output)  outputs <- block_3_output |>   layer_conv_2d(64, 3, activation = \"relu\") |>   layer_global_average_pooling_2d() |>   layer_dense(256, activation = \"relu\") |>   layer_dropout(0.5) |>   layer_dense(10)  model <- keras_model(inputs, outputs, name = \"toy_resnet\") summary(model) ## Model: \"toy_resnet\" ## ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)    │ (None, 32, 32, 3) │          0 │ -                 │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_8 (Conv2D)   │ (None, 30, 30,    │        896 │ img[0][0]         │ ## │                     │ 32)               │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_9 (Conv2D)   │ (None, 28, 28,    │     18,496 │ conv2d_8[0][0]    │ ## │                     │ 64)               │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ max_pooling2d_2     │ (None, 9, 9, 64)  │          0 │ conv2d_9[0][0]    │ ## │ (MaxPooling2D)      │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_10 (Conv2D)  │ (None, 9, 9, 32)  │     18,464 │ max_pooling2d_2[… │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_11 (Conv2D)  │ (None, 9, 9, 64)  │     18,496 │ conv2d_10[0][0]   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ add (Add)           │ (None, 9, 9, 64)  │          0 │ conv2d_11[0][0],  │ ## │                     │                   │            │ max_pooling2d_2[… │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_12 (Conv2D)  │ (None, 9, 9, 64)  │     36,928 │ add[0][0]         │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_13 (Conv2D)  │ (None, 9, 9, 64)  │     36,928 │ conv2d_12[0][0]   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ add_1 (Add)         │ (None, 9, 9, 64)  │          0 │ conv2d_13[0][0],  │ ## │                     │                   │            │ add[0][0]         │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_14 (Conv2D)  │ (None, 7, 7, 64)  │     36,928 │ add_1[0][0]       │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ global_average_poo… │ (None, 64)        │          0 │ conv2d_14[0][0]   │ ## │ (GlobalAveragePool… │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ dense_6 (Dense)     │ (None, 256)       │     16,640 │ global_average_p… │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ dropout (Dropout)   │ (None, 256)       │          0 │ dense_6[0][0]     │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ dense_7 (Dense)     │ (None, 10)        │      2,570 │ dropout[0][0]     │ ## └─────────────────────┴───────────────────┴────────────┴───────────────────┘ ##  Total params: 186,346 (727.91 KB) ##  Trainable params: 186,346 (727.91 KB) ##  Non-trainable params: 0 (0.00 B) plot(model, show_shapes = TRUE) c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_cifar10()  x_train <- x_train / 255.0 x_test <- x_test / 255.0  model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = loss_sparse_categorical_crossentropy(from_logits = TRUE),   metrics = \"acc\" ) # We restrict the data to the first 1000 samples so as to limit the # guide render time. # Try to train on the entire dataset until convergence! model |> fit(   x_train[1:1000, , , ],   y_train[1:1000, ],   batch_size = 64,   epochs = 1,   validation_split = 0.2 ) ## 13/13 - 5s - 373ms/step - acc: 0.1238 - loss: 2.2995 - val_acc: 0.1300 - val_loss: 2.2957"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"shared-layers","dir":"Articles","previous_headings":"","what":"Shared layers","title":"The Functional API","text":"Another good use functional API models use shared layers. Shared layers layer instances reused multiple times model – learn features correspond multiple paths graph--layers. Shared layers often used encode inputs similar spaces (say, two different pieces text feature similar vocabulary). enable sharing information across different inputs, make possible train model less data. given word seen one inputs, benefit processing inputs pass shared layer. share layer functional API, call layer instance multiple times. instance, ’s Embedding layer shared across two different text inputs:","code":"# Embedding for 1000 unique words mapped to 128-dimensional vectors shared_embedding <- layer_embedding(input_dim = 1000, output_dim = 128)  # Variable-length sequence of integers text_input_a <- keras_input(shape = shape(NA), dtype=\"int32\")  # Variable-length sequence of integers text_input_b <- keras_input(shape = shape(NA), dtype=\"int32\")  # Reuse the same layer to encode both inputs encoded_input_a <- shared_embedding(text_input_a) encoded_input_b <- shared_embedding(text_input_b)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"extract-and-reuse-nodes-in-the-graph-of-layers","dir":"Articles","previous_headings":"","what":"Extract and reuse nodes in the graph of layers","title":"The Functional API","text":"graph layers manipulating static data structure, can accessed inspected. able plot functional models images. also means can access activations intermediate layers (“nodes” graph) reuse elsewhere – useful something like feature extraction. Let’s look example. VGG19 model weights pretrained ImageNet: intermediate activations model, obtained querying graph data structure: Use features create new feature-extraction model returns values intermediate layer activations: comes handy tasks like neural style transfer, among things.","code":"vgg19 <- application_vgg19() features_list <- lapply(vgg19$layers, function(x) x$output) feat_extraction_model <- keras_model(inputs = vgg19$input,                                      outputs = features_list)  img <- random_normal(c(1, 224, 224, 3)) extracted_features <- feat_extraction_model(img)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"extend-the-api-using-custom-layers","dir":"Articles","previous_headings":"","what":"Extend the API using custom layers","title":"The Functional API","text":"keras includes wide range built-layers, example: Convolutional layers: conv_1d, conv_2d, conv_3d, conv_2d_transpose Pooling layers: max_pooling_1d, max_pooling_2d, max_pooling_3d, average_pooling_3d RNN layers: gru, lstm, conv_lstm_2d batch_normalization, dropout, embedding, etc. don’t find need, ’s easy extend API creating layers. layers subclass Layer class implement: call method, specifies computation done layer. build method, creates weights layer (just style convention since can create weights initialize, well). learn creating layers scratch, read custom layers models guide. following basic implementation layer_dense(): serialization support custom layer, define get_config() method returns constructor arguments layer instance: Optionally, implement class method from_config(cls, config) used recreating layer instance given config dictionary. default implementation from_config :","code":"custom_dense <- Layer(   classname = \"CustomDense\",   initialize = function(units = 32) {     super$initialize()     self$units <- as.integer(units)   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(input_shape[[2]], self$units),       initializer = \"random_normal\",       trainable = TRUE,     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer=\"random_normal\",       trainable = TRUE     )   },   call = function(inputs) {     op_matmul(inputs, self$w) + self$b   } )  inputs <- keras_input(c(4)) outputs <- custom_dense(inputs, 10)  model <- keras_model(inputs, outputs) custom_dense <- Layer(   classname = \"CustomDense\",    initialize = function(units = 32, ...) {     super$initialize()     self$units <- as.integer(units)   },    build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(input_shape[[2]], self$units),       initializer = \"random_normal\",       trainable = TRUE,     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer=\"random_normal\",       trainable = TRUE     )   },    call = function(inputs) {     op_matmul(inputs, self$w) + self$b   },    get_config = function() {     list(units = self$units)   } )  inputs <- keras_input(c(4)) outputs <- custom_dense(inputs, 10)  model <- keras_model(inputs, outputs) config <- get_config(model)  new_model <-   from_config(config, custom_objects = list(CustomDense = custom_dense)) from_config <- function(cls, config) {   do.call(cls, config) }"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"when-to-use-the-functional-api","dir":"Articles","previous_headings":"","what":"When to use the functional API","title":"The Functional API","text":"use Keras functional API create new model, just subclass Model class directly? general, functional API higher-level, easier safer, number features subclassed models support. However, model subclassing provides greater flexibility building models easily expressible directed acyclic graphs layers. example, implement Tree-RNN functional API subclass Model directly. -depth look differences functional API model subclassing, read Symbolic Imperative APIs TensorFlow 2.0?.","code":""},{"path":"https://keras.posit.co/articles/functional_api.html","id":"functional-api-strengths","dir":"Articles","previous_headings":"When to use the functional API","what":"Functional API strengths:","title":"The Functional API","text":"following properties also true Sequential models (also data structures), true subclassed models (R Python (byte)code, data structures).","code":""},{"path":"https://keras.posit.co/articles/functional_api.html","id":"less-verbose","dir":"Articles","previous_headings":"When to use the functional API > Functional API strengths:","what":"Less verbose","title":"The Functional API","text":"super$initialize(...), call = function(...), self$..., etc. Compare: subclassed version:","code":"inputs <- keras_input(shape = shape(32)) outputs <- inputs |>   layer_dense(64, activation = \"relu\") |>   layer_dense(10) mlp <- keras_model(inputs, outputs) MLP <- Model(   classname = \"MLP\",   initialize = function(...) {     super$initialize(...)     self$dense_1 <- layer_dense(units = 64, activation = \"relu\")     self$dense_2 <- layer_dense(units = 10)   },   call = function(inputs) {     inputs |>       self$dense_1() |>       self$dense_2()   } )  # Instantiate the model. mlp <- MLP() # Necessary to create the model's state. # The model doesn't have a state until it's called at least once. out <- mlp(op_zeros(c(1, 32)))"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"model-validation-while-defining-its-connectivity-graph","dir":"Articles","previous_headings":"When to use the functional API > Functional API strengths:","what":"Model validation while defining its connectivity graph","title":"The Functional API","text":"functional API, input specification (shape dtype) created advance (using Input). Every time call layer, layer checks specification passed matches assumptions, raise helpful error message . guarantees model can build functional API run. debugging – convergence-related debugging – happens statically model construction execution time. similar type checking compiler.","code":""},{"path":"https://keras.posit.co/articles/functional_api.html","id":"a-functional-model-is-plottable-and-inspectable","dir":"Articles","previous_headings":"When to use the functional API > Functional API strengths:","what":"A functional model is plottable and inspectable","title":"The Functional API","text":"can plot model graph, can easily access intermediate nodes graph. example, extract reuse activations intermediate layers (seen previous example):","code":"features_list <- lapply(vgg19$layers, function(x) x$output) feat_extraction_model <- keras_model(inputs = vgg19$input,                                      outputs = features_list)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"a-functional-model-can-be-serialized-or-cloned","dir":"Articles","previous_headings":"When to use the functional API > Functional API strengths:","what":"A functional model can be serialized or cloned","title":"The Functional API","text":"functional model data structure rather piece code, safely serializable can saved single file allows recreate exact model without access original code. See serialization & saving guide. serialize subclassed model, necessary implementer specify get_config() from_config() method model level.","code":""},{"path":[]},{"path":"https://keras.posit.co/articles/functional_api.html","id":"it-does-not-support-dynamic-architectures","dir":"Articles","previous_headings":"When to use the functional API > Functional API weakness:","what":"It does not support dynamic architectures","title":"The Functional API","text":"functional API treats models DAGs layers. true deep learning architectures, – example, recursive networks Tree RNNs follow assumption implemented functional API.","code":""},{"path":"https://keras.posit.co/articles/functional_api.html","id":"mix-and-match-api-styles","dir":"Articles","previous_headings":"","what":"Mix-and-match API styles","title":"The Functional API","text":"Choosing functional API Model subclassing isn’t binary decision restricts one category models. models keras API can interact , whether ’re Sequential models, functional models, subclassed models written scratch. can always use functional model Sequential model part subclassed model layer: can use subclassed layer model functional API long implements call method follows one following patterns: call(inputs, ...) – inputs tensor nested structure tensors (e.g. list tensors), ... non-tensor arguments (non-inputs). call(inputs, training = NULL, ...) – training boolean indicating whether layer behave training mode inference mode. call(inputs, mask = NULL, ...) – mask boolean mask tensor (useful RNNs, instance). call(inputs, training = NULL, mask = NULL, ...) – course, can masking training-specific behavior time. Additionally, implement get_config() method custom Layer model, functional models create still serializable cloneable. ’s quick example custom RNN, written scratch, used functional model:","code":"units <- 32 timesteps <- 10 input_dim <- 5  # Define a Functional model inputs <- keras_input(shape(NA, units)) outputs <- inputs |>   layer_global_average_pooling_1d() |>   layer_dense(units = 1)  model <- keras_model(inputs, outputs)  layer_custom_rnn <- Layer(   classname = \"CustomRNN\",   initialize = function(...) {     super$initialize(...)     self$units <- units     self$projection_1 <- layer_dense(units = units, activation = \"tanh\")     self$projection_2 <- layer_dense(units = units, activation = \"tanh\")     self$classifier <- model   },   call = function(inputs, ...) {     outputs <- list()     state <- op_zeros(c(shape(inputs)[[1]], self$units))     for (t in 1:(shape(inputs)[[2]])) {       x <- inputs[, t, ]       h <- self$projection_1(x)       y <- h + self$projection_2(state)       state <- y       outputs[[t]] <- y     }     features <- op_stack(outputs, axis = 2)     self$classifier(features)   } )  rnn <- layer_custom_rnn() out <- rnn(op_zeros(c(1, timesteps, input_dim))) units <- 32 timesteps <- 10 input_dim <- 5 batch_size <- 16  layer_custom_rnn <- Layer(   \"custom_rnn\",   initialize = function(...) {     super$initialize(...)     self$units <- units     self$projection_1 <- layer_dense(units = units, activation = \"tanh\")     self$projection_2 <- layer_dense(units = units, activation = \"tanh\")     self$classifier <- layer_dense(units = 1)   },   call = function(inputs, ...) {     outputs <- list()     state <- op_zeros(c(shape(inputs)[[1]], self$units))     for (t in 1:(shape(inputs)[[2]])) {       x <- inputs[, t, ]       h <- self$projection_1(x)       y <- h + self$projection_2(state)       state <- y       outputs[[t]] <- y     }     features <- op_stack(outputs, axis = 2)     self$classifier(features)   } )  # Note that you specify a static batch size for the inputs with the `batch_shape` # arg, because the inner computation of `layer_custom_rnn()` requires a static batch size # (when you create the `state` zeros tensor). inputs <- keras_input(batch_shape = shape(batch_size, timesteps, input_dim)) outputs <- inputs |>   layer_conv_1d(filters = 32, kernel_size = 3) |>   layer_custom_rnn()  model <- keras_model(inputs, outputs) out <- model(op_zeros(c(1, 10, 5)))"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Getting Started with Keras","text":"Keras high-level neural networks API developed focus enabling fast experimentation. able go idea result least possible delay key good research. Keras following key features: Allows code run CPU GPU, seamlessly. User-friendly API makes easy quickly prototype deep learning models. Built-support convolutional networks (computer vision), recurrent networks (sequence processing), combination . Supports arbitrary network architectures: multi-input multi-output models, layer sharing, model sharing, etc. means Keras appropriate building essentially deep learning model, memory network neural Turing machine. website provides documentation R interface Keras. See main Keras website https://keras.io additional information project.","code":""},{"path":"https://keras.posit.co/articles/getting_started.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Getting Started with Keras","text":"First, install keras R package: install development version : Keras R interface requires backend engine installed. TensorFlow default. provide default installation Keras TensorFlow GPU capable, GPU available. want customized installation, e.g. see documentation install_keras() installation section.","code":"install.packages(\"keras3\") remotes::install_github(\"rstudio/keras\") keras3::install_keras(backend = \"tensorflow\")"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"mnist-example","dir":"Articles","previous_headings":"","what":"MNIST Example","title":"Getting Started with Keras","text":"can learn basics Keras walking simple example: recognizing handwritten digits MNIST dataset. MNIST consists 28 x 28 grayscale images handwritten digits like :  dataset also includes labels image, telling us digit . example, labels images 5, 0, 4, 1.","code":""},{"path":"https://keras.posit.co/articles/getting_started.html","id":"preparing-the-data","dir":"Articles","previous_headings":"MNIST Example","what":"Preparing the Data","title":"Getting Started with Keras","text":"MNIST dataset included Keras can accessed using dataset_mnist() function. load dataset create variables test training data: x data 3-d array (images, width, height) grayscale values. prepare data training convert 3-d arrays matrices reshaping width height single dimension (28x28 images flattened length 784 vectors). , convert grayscale values integers ranging 0 255 floating point values ranging 0 1: Note use array_reshape() function rather dim<-() function reshape array. data re-interpreted using row-major semantics (opposed R’s default column-major semantics), turn compatible way numerical libraries called Keras interpret array dimensions. y data integer vector values ranging 0 9. prepare data training one-hot encode vectors binary class matrices using Keras to_categorical() function:","code":"library(keras3) mnist <- dataset_mnist() x_train <- mnist$train$x y_train <- mnist$train$y x_test <- mnist$test$x y_test <- mnist$test$y # reshape x_train <- array_reshape(x_train, c(nrow(x_train), 784)) x_test <- array_reshape(x_test, c(nrow(x_test), 784)) # rescale x_train <- x_train / 255 x_test <- x_test / 255 y_train <- to_categorical(y_train, 10) y_test <- to_categorical(y_test, 10)"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"defining-the-model","dir":"Articles","previous_headings":"MNIST Example","what":"Defining the Model","title":"Getting Started with Keras","text":"core data structure Keras model, way organize layers. simplest type model Sequential model, linear stack layers. begin creating sequential model adding layers using pipe (|>) operator: input_shape argument first layer specifies shape input data (length 784 numeric vector representing grayscale image). final layer outputs length 10 numeric vector (probabilities digit) using softmax activation function. Use summary() function print details model: Next, compile model appropriate loss function, optimizer, metrics:","code":"model <- keras_model_sequential(input_shape = c(784)) model |>   layer_dense(units = 256, activation = 'relu') |>   layer_dropout(rate = 0.4) |>   layer_dense(units = 128, activation = 'relu') |>   layer_dropout(rate = 0.3) |>   layer_dense(units = 10, activation = 'softmax') summary(model) ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense (Dense)                   │ (None, 256)            │       200,960 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 256)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_1 (Dense)                 │ (None, 128)            │        32,896 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, 128)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 10)             │         1,290 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 235,146 (918.54 KB) ##  Trainable params: 235,146 (918.54 KB) ##  Non-trainable params: 0 (0.00 B) plot(model) model |> compile(   loss = 'categorical_crossentropy',   optimizer = optimizer_rmsprop(),   metrics = c('accuracy') )"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"training-and-evaluation","dir":"Articles","previous_headings":"MNIST Example","what":"Training and Evaluation","title":"Getting Started with Keras","text":"Use fit() function train model 30 epochs using batches 128 images: history object returned fit() includes loss accuracy metrics can plot: Evaluate model’s performance test data: Generate predictions new data: Keras provides vocabulary building deep learning models simple, elegant, intuitive. Building question answering system, image classification model, neural Turing machine, model just straightforward.","code":"history <- model |> fit(   x_train, y_train,   epochs = 30, batch_size = 128,   validation_split = 0.2 ) plot(history) model |> evaluate(x_test, y_test) ## 313/313 - 1s - 2ms/step - accuracy: 0.9814 - loss: 0.0844 ## $accuracy ## [1] 0.9814 ## ## $loss ## [1] 0.08441389 probs <- model |> predict(x_test) ## 313/313 - 0s - 1ms/step max.col(probs) - 1L ##   [1] 7 2 1 0 4 1 4 9 6 9 0 6 9 0 1 5 9 7 3 4 9 6 6 5 4 0 7 4 0 1 3 1 3 4 7 ##  [36] 2 7 1 2 1 1 7 4 2 3 5 1 2 4 4 6 3 5 5 6 0 4 1 9 5 7 8 9 3 7 4 6 4 3 0 ##  [71] 7 0 2 9 1 7 3 2 9 7 7 6 2 7 8 4 7 3 6 1 3 6 9 3 1 4 1 7 6 9 ##  [ reached getOption(\"max.print\") -- omitted 9900 entries ]"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"deep-learning-with-r-book","dir":"Articles","previous_headings":"MNIST Example","what":"Deep Learning with R Book","title":"Getting Started with Keras","text":"want comprehensive introduction Keras concepts practice deep learning, recommend Deep Learning R, 2nd Edition book Manning. book collaboration François Chollet, creator (Python) Keras, J.J. Allaire, wrote original R interface Keras, Tomasz Kalinowski, maintainer R interface Keras. book presumes significant knowledge machine learning deep learning, goes way basic theory advanced practical applications, using R interface Keras.","code":""},{"path":"https://keras.posit.co/articles/getting_started.html","id":"why-this-name-keras","dir":"Articles","previous_headings":"","what":"Why this name, Keras?","title":"Getting Started with Keras","text":"Keras (κέρας) means horn Greek. reference literary image ancient Greek Latin literature, first found Odyssey, dream spirits (Oneiroi, singular Oneiros) divided deceive men false visions, arrive Earth gate ivory, announce future come pass, arrive gate horn. ’s play words κέρας (horn) / κραίνω (fulfill), ἐλέφας (ivory) / ἐλεφαίρομαι (deceive). Keras initially developed part research effort project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System). “Oneiroi beyond unravelling –can sure tale tell? men look comes pass. Two gates give passage fleeting Oneiroi; one made horn, one ivory. Oneiroi pass sawn ivory deceitful, bearing message fulfilled; come polished horn truth behind , accomplished men see .” Homer, Odyssey 19. 562 ff (Shewring translation).","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Introduction to Keras for engineers","text":"Keras 3 deep learning framework works TensorFlow, JAX, PyTorch interchangeably. notebook walk key Keras 3 workflows. Let’s start installing Keras 3: pip install keras –upgrade –quiet","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Introduction to Keras for engineers","text":"’re going using tensorflow backend – can edit string \"jax\" \"torch\" hit “Restart runtime”, whole notebook run just ! entire guide backend-agnostic.","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(keras3)  # Note that you must configure the backend # before calling any other keras functions. # The backend cannot be changed once the # package is imported. use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"a-first-example-a-mnist-convnet","dir":"Articles","previous_headings":"","what":"A first example: A MNIST convnet","title":"Introduction to Keras for engineers","text":"Let’s start Hello World ML: training convnet classify MNIST digits. ’s data: ’s model. Different model-building options Keras offers include: Sequential API (use ) Functional API (typical) Writing models via subclassing (advanced use cases) ’s model summary: use compile() method specify optimizer, loss function, metrics monitor. Note JAX TensorFlow backends, XLA compilation turned default. Let’s train evaluate model. ’ll set aside validation split 15% data training monitor generalization unseen data. training, saving model end epoch. can also save model latest state like : reload like : Next, can query predictions class probabilities predict(): ’s basics!","code":"# Load the data and split it between train and test sets c(c(x_train, y_train), c(x_test, y_test)) %<-% keras3::dataset_mnist()  # Scale images to the [0, 1] range x_train <- x_train / 255 x_test <- x_test / 255 # Make sure images have shape (28, 28, 1) x_train <- op_expand_dims(x_train, -1) x_test <- op_expand_dims(x_test, -1)  dim(x_train) ## [1] 60000    28    28     1 dim(x_test) ## [1] 10000    28    28     1 # Model parameters num_classes <- 10 input_shape <- c(28, 28, 1)  model <- keras_model_sequential(input_shape = input_shape) model |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = \"relu\") |>   layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = \"relu\") |>   layer_global_average_pooling_2d() |>   layer_dropout(rate = 0.5) |>   layer_dense(units = num_classes, activation = \"softmax\") summary(model) ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 26, 26, 64)     │           640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 24, 24, 64)     │        36,928 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 12, 12, 64)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 10, 10, 128)    │        73,856 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_3 (Conv2D)               │ (None, 8, 8, 128)      │       147,584 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_average_pooling2d        │ (None, 128)            │             0 │ ## │ (GlobalAveragePooling2D)        │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 128)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 10)             │         1,290 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 260,298 (1016.79 KB) ##  Trainable params: 260,298 (1016.79 KB) ##  Non-trainable params: 0 (0.00 B) model |> compile(   optimizer = \"adam\",   loss = \"sparse_categorical_crossentropy\",   metrics = list(     metric_sparse_categorical_accuracy(name = \"acc\")   ) ) batch_size <- 128 epochs <- 10  callbacks <- list(   callback_model_checkpoint(filepath=\"model_at_epoch_{epoch}.keras\"),   callback_early_stopping(monitor=\"val_loss\", patience=2) )  model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = epochs,   validation_split = 0.15,   callbacks = callbacks ) ## Epoch 1/10 ## 399/399 - 6s - 16ms/step - acc: 0.7450 - loss: 0.7521 - val_acc: 0.9642 - val_loss: 0.1234 ## Epoch 2/10 ## 399/399 - 2s - 5ms/step - acc: 0.9387 - loss: 0.2054 - val_acc: 0.9769 - val_loss: 0.0751 ## Epoch 3/10 ## 399/399 - 2s - 5ms/step - acc: 0.9575 - loss: 0.1455 - val_acc: 0.9824 - val_loss: 0.0609 ## Epoch 4/10 ## 399/399 - 2s - 5ms/step - acc: 0.9662 - loss: 0.1148 - val_acc: 0.9863 - val_loss: 0.0483 ## Epoch 5/10 ## 399/399 - 2s - 5ms/step - acc: 0.9726 - loss: 0.0972 - val_acc: 0.9869 - val_loss: 0.0479 ## Epoch 6/10 ## 399/399 - 2s - 5ms/step - acc: 0.9750 - loss: 0.0857 - val_acc: 0.9892 - val_loss: 0.0379 ## Epoch 7/10 ## 399/399 - 2s - 5ms/step - acc: 0.9768 - loss: 0.0768 - val_acc: 0.9906 - val_loss: 0.0358 ## Epoch 8/10 ## 399/399 - 2s - 5ms/step - acc: 0.9794 - loss: 0.0671 - val_acc: 0.9876 - val_loss: 0.0422 ## Epoch 9/10 ## 399/399 - 2s - 5ms/step - acc: 0.9808 - loss: 0.0639 - val_acc: 0.9887 - val_loss: 0.0387 score <- model |> evaluate(x_test, y_test, verbose = 0) save_model(model, \"final_model.keras\", overwrite=TRUE) model <- load_model(\"final_model.keras\") predictions <- model |> predict(x_test) ## 313/313 - 0s - 1ms/step dim(predictions) ## [1] 10000    10"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"writing-cross-framework-custom-components","dir":"Articles","previous_headings":"","what":"Writing cross-framework custom components","title":"Introduction to Keras for engineers","text":"Keras enables write custom Layers, Models, Metrics, Losses, Optimizers work across TensorFlow, JAX, PyTorch codebase. Let’s take look custom layers first. op_ namespace contains: implementation NumPy API, e.g. op_stack op_matmul. set neural network specific ops absent NumPy, op_conv op_binary_crossentropy. Let’s make custom Dense layer works backends: Next, let’s make custom Dropout layer relies random_* namespace: Next, let’s write custom subclassed model uses two custom layers: Let’s compile fit :","code":"layer_my_dense <- Layer(   classname = \"MyDense\",   initialize = function(units, activation = NULL, name = NULL, ...) {     super$initialize(name = name, ...)     self$units <- units     self$activation <- activation   },   build = function(input_shape) {     input_dim <- tail(input_shape, 1)     self$w <- self$add_weight(       shape = shape(input_dim, self$units),       initializer = initializer_glorot_normal(),       name = \"kernel\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = initializer_zeros(),       name = \"bias\",       trainable = TRUE     )   },   call = function(inputs) {     # Use Keras ops to create backend-agnostic layers/metrics/etc.     x <- op_matmul(inputs, self$w) + self$b     if (!is.null(self$activation))       x <- self$activation(x)     x   } ) layer_my_dropout <- Layer(   \"MyDropout\",   initialize = function(rate, name = NULL, seed = NULL, ...) {     super$initialize(name = name)     self$rate <- rate     # Use seed_generator for managing RNG state.     # It is a state element and its seed variable is     # tracked as part of `layer$variables`.     self$seed_generator <- random_seed_generator(seed)   },   call = function(inputs) {     # Use `keras3::random_*` for random ops.     random_dropout(inputs, self$rate, seed = self$seed_generator)   } ) MyModel <- Model(   \"MyModel\",   initialize = function(num_classes, ...) {     super$initialize(...)     self$conv_base <-       keras_model_sequential() |>       layer_conv_2d(64, kernel_size = c(3, 3), activation = \"relu\") |>       layer_conv_2d(64, kernel_size = c(3, 3), activation = \"relu\") |>       layer_max_pooling_2d(pool_size = c(2, 2)) |>       layer_conv_2d(128, kernel_size = c(3, 3), activation = \"relu\") |>       layer_conv_2d(128, kernel_size = c(3, 3), activation = \"relu\") |>       layer_global_average_pooling_2d()      self$dp <- layer_my_dropout(rate = 0.5)     self$dense <- layer_my_dense(units = num_classes,                                  activation = activation_softmax)   },   call = function(inputs) {     inputs |>       self$conv_base() |>       self$dp() |>       self$dense()   } ) model <- MyModel(num_classes = 10) model |> compile(   loss = loss_sparse_categorical_crossentropy(),   optimizer = optimizer_adam(learning_rate = 1e-3),   metrics = list(     metric_sparse_categorical_accuracy(name = \"acc\")   ) )  model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = 1, # For speed   validation_split = 0.15 ) ## 399/399 - 6s - 16ms/step - acc: 0.7356 - loss: 0.7726 - val_acc: 0.9301 - val_loss: 0.2358"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"training-models-on-arbitrary-data-sources","dir":"Articles","previous_headings":"","what":"Training models on arbitrary data sources","title":"Introduction to Keras for engineers","text":"Keras models can trained evaluated wide variety data sources, independently backend ’re using. includes: Arrays Dataframes TensorFlow tf_dataset objects PyTorch DataLoader objects Keras PyDataset objects work whether ’re using TensorFlow, JAX, PyTorch Keras backend. Let’s try tf_dataset:","code":"library(tfdatasets, exclude = \"shape\")  train_dataset <- list(x_train, y_train) |>   tensor_slices_dataset() |>   dataset_batch(batch_size) |>   dataset_prefetch(buffer_size = tf$data$AUTOTUNE)  test_dataset <- list(x_test, y_test) |>   tensor_slices_dataset() |>   dataset_batch(batch_size) |>   dataset_prefetch(buffer_size = tf$data$AUTOTUNE)  model <- MyModel(num_classes = 10) model |> compile(   loss = loss_sparse_categorical_crossentropy(),   optimizer = optimizer_adam(learning_rate = 1e-3),   metrics = list(     metric_sparse_categorical_accuracy(name = \"acc\")   ) )  model |> fit(train_dataset, epochs = 1, validation_data = test_dataset) ## 469/469 - 7s - 16ms/step - acc: 0.7542 - loss: 0.7342 - val_acc: 0.9026 - val_loss: 0.3192"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"further-reading","dir":"Articles","previous_headings":"","what":"Further reading","title":"Introduction to Keras for engineers","text":"concludes short overview new multi-backend capabilities Keras 3. Next, can learn :","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"how-to-customize-what-happens-in-fit","dir":"Articles","previous_headings":"Further reading","what":"How to customize what happens in fit()","title":"Introduction to Keras for engineers","text":"Want implement non-standard training algorithm still want benefit power usability fit()? ’s easy customize fit() support arbitrary use cases: Customizing happens fit() TensorFlow","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"how-to-write-custom-training-loops","dir":"Articles","previous_headings":"","what":"How to write custom training loops","title":"Introduction to Keras for engineers","text":"Writing training loop scratch TensorFlow","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"how-to-distribute-training","dir":"Articles","previous_headings":"","what":"How to distribute training","title":"Introduction to Keras for engineers","text":"Guide distributed training TensorFlow Enjoy library! 🚀","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Introduction to Keras for Researchers","text":"","code":"library(keras3) library(tensorflow)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Introduction to Keras for Researchers","text":"machine learning researcher? publish NeurIPS push state---art CV NLP? guide serve first introduction core Keras & TensorFlow API concepts. guide, learn : Tensors, variables, gradients TensorFlow Creating layers subclassing [Layer] class Writing low-level training loops Tracking losses created layers via add_loss() method Tracking metrics low-level training loop Speeding execution compiled [tensorflow::tf_function()] Executing layers training inference mode Keras Functional API also see Keras API action two end--end research examples: Variational Autoencoder, Hypernetwork.","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"tensors","dir":"Articles","previous_headings":"","what":"Tensors","title":"Introduction to Keras for Researchers","text":"TensorFlow infrastructure layer differentiable programming. heart, ’s framework manipulating N-dimensional arrays (tensors), much like NumPy. However, three key differences NumPy TensorFlow: TensorFlow can leverage hardware accelerators GPUs TPUs. TensorFlow can automatically compute gradient arbitrary differentiable tensor expressions. TensorFlow computation can distributed large numbers devices single machine, large number machines (potentially multiple devices ). Let’s take look object core TensorFlow: Tensor. ’s constant tensor: can get value R array calling .array(): features attributes dtype shape: common way create constant tensors via tf$ones tf$zeros: can also create random constant tensors:","code":"x <- tf$constant(rbind(c(5, 2), c(1, 3))) print(x) ## tf.Tensor( ## [[5. 2.] ##  [1. 3.]], shape=(2, 2), dtype=float64) as.array(x) ##      [,1] [,2] ## [1,]    5    2 ## [2,]    1    3 x$dtype ## tf.float64 x$shape ## TensorShape([2, 2]) tf$ones(shape = shape(2, 1)) ## tf.Tensor( ## [[1.] ##  [1.]], shape=(2, 1), dtype=float32) tf$zeros(shape = shape(2, 1)) ## tf.Tensor( ## [[0.] ##  [0.]], shape=(2, 1), dtype=float32) x <- random_normal(shape = c(2, 2), mean = 0.0, stddev = 1.0) x <- random_uniform(shape = c(2, 2), minval = 0, maxval = 10)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"variables","dir":"Articles","previous_headings":"","what":"Variables","title":"Introduction to Keras for Researchers","text":"Variables special tensors used store mutable state (weights neural network). create Variable using initial value: update value Variable using methods $assign(value), $assign_add(increment), $assign_sub(decrement):","code":"initial_value <- random_normal(shape=c(2, 2)) a <- tf$Variable(initial_value) print(a) ## <tf.Variable 'Variable:0' shape=(2, 2) dtype=float32, numpy= ## array([[ 0.9057419 ,  0.7916686 ], ##        [ 0.28754202, -0.5408822 ]], dtype=float32)> new_value <- random_normal(shape=c(2, 2)) a$assign(new_value) ## <tf.Variable 'UnreadVariable' shape=(2, 2) dtype=float32, numpy= ## array([[-0.3405368 , -2.1463926 ], ##        [ 1.2602988 ,  0.12241419]], dtype=float32)> added_value <- random_normal(shape=c(2, 2)) a$assign_add(added_value) ## <tf.Variable 'UnreadVariable' shape=(2, 2) dtype=float32, numpy= ## array([[ 0.04820395, -2.6854615 ], ##        [ 0.23246336,  1.4535258 ]], dtype=float32)>"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"doing-math-in-tensorflow","dir":"Articles","previous_headings":"","what":"Doing math in TensorFlow","title":"Introduction to Keras for Researchers","text":"’ve used NumPy, math TensorFlow look familiar. main difference TensorFlow code can run GPU TPU.","code":"a <- random_normal(shape=c(2, 2)) b <- random_normal(shape=c(2, 2))  c <- a + b d <- tf$square(c) e <- tf$exp(d)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"gradients","dir":"Articles","previous_headings":"","what":"Gradients","title":"Introduction to Keras for Researchers","text":"’s another big difference R: can automatically retrieve gradient differentiable expression. Just open GradientTape, start “watching” tensor via tape$watch(), compose differentiable expression using tensor input: default, variables watched automatically, don’t need manually watch : Note can compute higher-order derivatives nesting tapes:","code":"a <- random_normal(shape=c(2, 2)) b <- random_normal(shape=c(2, 2))  with(tf$GradientTape() %as% tape, {   tape$watch(a)  # Start recording the history of operations applied to `a`   c <- tf$sqrt(tf$square(a) + tf$square(b))  # Do some math using `a`   # What's the gradient of `c` with respect to `a`?   dc_da <- tape$gradient(c, a)   print(dc_da) }) ## tf.Tensor( ## [[ 0.9969011  -0.7707146 ] ##  [ 0.23378514  0.96255165]], shape=(2, 2), dtype=float32) a <- tf$Variable(a)  with(tf$GradientTape() %as% tape, {   c <- tf$sqrt(tf$square(a) + tf$square(b))   dc_da <- tape$gradient(c, a)   print(dc_da) }) ## tf.Tensor( ## [[ 0.9969011  -0.7707146 ] ##  [ 0.23378514  0.96255165]], shape=(2, 2), dtype=float32) with(tf$GradientTape() %as% outer_tape, {   with(tf$GradientTape() %as% tape, {     c <- tf$sqrt(tf$square(a) + tf$square(b))     dc_da <- tape$gradient(c, a)   })   d2c_da2 <- outer_tape$gradient(dc_da, a)   print(d2c_da2) }) ## tf.Tensor( ## [[3.3447742e-03 7.1282005e-01] ##  [5.7464113e+00 5.5013180e-02]], shape=(2, 2), dtype=float32)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"keras-layers","dir":"Articles","previous_headings":"","what":"Keras layers","title":"Introduction to Keras for Researchers","text":"TensorFlow infrastructure layer differentiable programming, dealing tensors, variables, gradients, Keras user interface deep learning, dealing layers, models, optimizers, loss functions, metrics, . Keras serves high-level API TensorFlow: Keras makes TensorFlow simple productive. Layer class fundamental abstraction Keras. Layer encapsulates state (weights) computation (defined call method). simple layer looks like . self$add_weight() method gives shortcut creating weights: use Layer instance much like R function: weight variables (created initialize) automatically tracked weights property: many built-layers available, Dense Conv2D LSTM fancier ones like Conv3DTranspose ConvLSTM2D. smart reusing built-functionality.","code":"Linear <- new_layer_class(   \"Linear\",   initialize = function(units = 32, input_dim = 32) {     super$initialize()     self$w <- self$add_weight(       shape = shape(input_dim, units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     tf$matmul(inputs, self$w) + self$b   } ) # Instantiate our layer. linear_layer <- Linear(units=4, input_dim=2)  # The layer can be treated as a function. # Here we call it on some data. y <- linear_layer(tf$ones(shape(2, 2))) linear_layer$weights ## [[1]] ## <KerasVariable shape=(2, 4), dtype=float32, path=linear/variable> ## ## [[2]] ## <KerasVariable shape=(4), dtype=float32, path=linear/variable_1>"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"layer-weight-creation-in-buildinput_shape","dir":"Articles","previous_headings":"","what":"Layer weight creation in build(input_shape)","title":"Introduction to Keras for Researchers","text":"’s often good idea defer weight creation build() method, don’t need specify input dim/shape layer construction time:","code":"Linear <- new_layer_class(   \"Linear\",   initialize = function(units = 32) {     super$initialize()     self$units <- units   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(input_shape[-1], self$units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     tf$matmul(inputs, self$w) + self$b   } )  # Instantiate our layer. linear_layer <- Linear(units = 4)  # This will also call `build(input_shape)` and create the weights. y <- linear_layer(tf$ones(shape(2, 2)))"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"layer-gradients","dir":"Articles","previous_headings":"","what":"Layer gradients","title":"Introduction to Keras for Researchers","text":"can automatically retrieve gradients weights layer calling inside GradientTape. Using gradients, can update weights layer, either manually, using optimizer object. course, can modify gradients using , need .","code":"# Prepare a dataset. c(c(x_train, y_train), .) %<-% dataset_mnist()  x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(list(x_train, y_train)) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   tfdatasets::dataset_batch(64)  # Instantiate our linear layer (defined above) with 10 units. linear_layer <- Linear(units = 10)  # Instantiate a logistic loss function that expects integer targets. loss_fn <- loss_sparse_categorical_crossentropy(from_logits=TRUE)  # Instantiate an optimizer. optimizer <- optimizer_sgd(learning_rate=1e-3)  # Iterate over the batches of the dataset. coro::loop(for(data in dataset) {   # Open a GradientTape.   with(tf$GradientTape() %as% tape, {     # Forward pass.     logits <- linear_layer(data[[1]])      # Loss value for this batch.     loss_value <- loss_fn(data[[2]], logits)   })    # Get gradients of the loss wrt the weights.   gradients <- tape$gradient(loss_value, linear_layer$trainable_weights)    # Update the weights of our linear layer.   optimizer$apply_gradients(zip_lists(gradients, linear_layer$trainable_weights)) }) loss_value ## tf.Tensor(1.2819729, shape=(), dtype=float32)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"trainable-and-non-trainable-weights","dir":"Articles","previous_headings":"","what":"Trainable and non-trainable weights","title":"Introduction to Keras for Researchers","text":"Weights created layers can either trainable non-trainable. ’re exposed trainable_weights non_trainable_weights respectively. ’s layer non-trainable weight:","code":"ComputeSum <- new_layer_class(   \"ComputeSum\",   initialize = function(input_dim) {     super$initialize()     # Create a non-trainable weight.     self$total <- self$add_weight(       initializer = \"zeros\",       shape = shape(input_dim),       trainable = FALSE     )   },   call = function(inputs) {     self$total$assign_add(tf$reduce_sum(inputs, axis=0L))     self$total   } )  my_sum <- ComputeSum(input_dim = 2) x <- tf$ones(shape(2, 2))  as.array(my_sum(x)) ## [1] 2 2 as.array(my_sum(x)) ## [1] 4 4 my_sum$trainable_weights ## list()"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"layers-that-own-layers","dir":"Articles","previous_headings":"","what":"Layers that own layers","title":"Introduction to Keras for Researchers","text":"Layers can recursively nested create bigger computation blocks. layer track weights sublayers (trainable non-trainable). Note manually-created MLP equivalent following built-option:","code":"# Let's reuse the Linear class # with a `build` method that we defined above.  MLP <- new_layer_class(   \"MLP\",   initialize = function() {     super$initialize()     self$linear_1 <- Linear(units = 32)     self$linear_2 <- Linear(units = 32)     self$linear_3 <- Linear(units = 10)   },   call = function(inputs) {     x <- self$linear_1(inputs)     x <- tf$nn$relu(x)     x <- self$linear_2(x)     x <- tf$nn$relu(x)     return(self$linear_3(x))   } )  mlp <- MLP()  # The first call to the `mlp` object will create the weights. y <- mlp(tf$ones(shape=shape(3, 64)))  # Weights are recursively tracked. length(mlp$weights) ## [1] 6 mlp <- keras_model_sequential() %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"tracking-losses-created-by-layers","dir":"Articles","previous_headings":"","what":"Tracking losses created by layers","title":"Introduction to Keras for Researchers","text":"Layers can create losses forward pass via add_loss() method. especially useful regularization losses. losses created sublayers recursively tracked parent layers. ’s layer creates activity regularization loss: model incorporating layer track regularization loss: losses cleared top-level layer start forward pass – don’t accumulate. layer.losses always contains losses created last forward pass. typically use losses summing computing gradients writing training loop.","code":"# A layer that creates an activity sparsity regularization loss ActivityRegularization <- new_layer_class(   \"ActivityRegularization\",   initialize = function(rate=1e-2) {     super$initialize()     self$rate <- rate   },   call = function(inputs) {     self$add_loss(self$rate * tf$reduce_sum(tf$abs(inputs)))     inputs   } ) # Let's use the loss layer in a MLP block. SparseMLP <- new_layer_class(   \"SparseMLP\",   initialize = function() {     super$initialize()     self$linear_1 <- Linear(units = 32)     self$reg <- ActivityRegularization(rate = 1e-2)     self$linear_3 <- Linear(units = 10)   },   call = function(inputs) {     x <- self$linear_1(inputs)     x <- tf$nn$relu(x)     x <- self$reg(x)     return(self$linear_3(x))   } )  mlp <- SparseMLP() y <- mlp(tf$ones(shape(10, 10)))  mlp$losses  # List containing one float32 scalar ## [[1]] ## tf.Tensor(0.18065463, shape=(), dtype=float32) # Losses correspond to the *last* forward pass. mlp <- SparseMLP() mlp(tf$ones(shape(10, 10))) ## tf.Tensor( ## [[ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621]], shape=(10, 10), dtype=float32) length(mlp$losses) ## [1] 1 mlp(tf$ones(shape(10, 10))) ## tf.Tensor( ## [[ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621]], shape=(10, 10), dtype=float32) length(mlp$losses)  # No accumulation. ## [1] 1 # Let's demonstrate how to use these losses in a training loop.  # Prepare a dataset. c(c(x_train, y_train), .) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(list(x_train, y_train)) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   tfdatasets::dataset_batch(64)  # A new MLP. mlp <- SparseMLP()  # Loss and optimizer. loss_fn <- loss_sparse_categorical_crossentropy(from_logits=TRUE) optimizer <- optimizer_sgd(learning_rate=1e-3)  coro::loop(for(data in dataset) {   x <- data[[1]]   y <- data[[2]]   with(tf$GradientTape() %as% tape, {     # Forward pass.     logits <- mlp(x)      # External loss value for this batch.     loss <- loss_fn(y, logits)      # Add the losses created during the forward pass.     loss <- loss + Reduce(`+`, mlp$losses)      # Get gradients of the loss wrt the weights.     gradients <- tape$gradient(loss, mlp$trainable_weights)      # Update the weights of our linear layer.     optimizer$apply_gradients(zip_lists(gradients, mlp$trainable_weights))   }) })"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"keeping-track-of-training-metrics","dir":"Articles","previous_headings":"","what":"Keeping track of training metrics","title":"Introduction to Keras for Researchers","text":"Keras offers broad range built-metrics, like metric_auc metric_precision_at_recall. ’s also easy create metrics lines code. use metric custom training loop, : Instantiate metric object, e.g. metric = metric_auc() Call metric$udpate_state(targets, predictions) method batch data Query result via metric$result() Reset metric’s state end epoch start evaluation via metric$reset_state() ’s simple example: can also define metrics subclassing keras.metrics.Metric. need override three functions called : Override update_state() update statistic values. Override result() return metric value. Override reset_state() reset metric initial state. example implement F1-score metric (support sample weighting). Let’s test-drive :","code":"# Instantiate a metric object accuracy <- metric_sparse_categorical_accuracy()  # Prepare our layer, loss, and optimizer. model <- keras_model_sequential() %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10) loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE) optimizer <- optimizer_adam(learning_rate=1e-3)  for (epoch in seq_len(2)) {   coro::loop(for (data in dataset) {     x <- data[[1]]     y <- data[[2]]     with(tf$GradientTape() %as% tape, {       # Forward pass.       logits <- model(x)        # External loss value for this batch.       loss_value <- loss_fn(y, logits)     })      # Update the state of the `accuracy` metric.     accuracy$update_state(y, logits)      # Update the weights of the model to minimize the loss value.     gradients <- tape$gradient(loss_value, model$trainable_weights)     optimizer$apply_gradients(zip_lists(gradients, model$trainable_weights))    })   cat(\"Epoch:\", epoch, \"Accuracy:\", as.numeric(accuracy$result()), \"\\n\")   accuracy$reset_state() } ## Epoch: 1 Accuracy: 0.8757833 ## Epoch: 2 Accuracy: 0.93915 F1Score <- new_metric_class(   \"F1Score\",   initialize = function(self, name=\"f1_score\", dtype=\"float32\", threshold=0.5, ...) {     super$initialize(name=name, dtype=dtype, ...)     self$threshold <- threshold     self$true_positives <- self$add_weight(       name=\"tp\", dtype=dtype, initializer=\"zeros\"     )     self$false_positives <- self$add_weight(       name=\"fp\", dtype=dtype, initializer=\"zeros\"     )     self$false_negatives <- self$add_weight(       name=\"fn\", dtype=dtype, initializer=\"zeros\"     )   },   update_state = function(y_true, y_pred, sample_weight=NULL) {     y_pred <- tf$math$greater_equal(y_pred, self$threshold)     y_true <- tf$cast(y_true, tf$bool)     y_pred <- tf$cast(y_pred, tf$bool)      true_positives <- tf$cast(y_true & y_pred, self$dtype)     false_positives <- tf$cast((!y_true) & y_pred, self$dtype)     false_negatives <- tf$cast(y_true & (!y_pred), self$dtype)      if (!is.null(sample_weight)) {       sample_weight <- tf$cast(sample_weight, self$dtype)       true_positives <- true_positives * sample_weight       false_positives <- false_positives * sample_weight       false_negatives <- false_negatives * sample_weight     }      self$true_positives$assign_add(tf$reduce_sum(true_positives))     self$false_positives$assign_add(tf$reduce_sum(false_positives))     self$false_negatives$assign_add(tf$reduce_sum(false_negatives))   },    result = function() {     precision <- self$true_positives / (self$true_positives + self$false_positives)     recall <- self$true_positives / (self$true_positives + self$false_negatives)     f1_score <- 2 * precision * recall / (precision + recall)     f1_score   },    reset_state = function() {     self$true_positives$assign(0)     self$false_positives$assign(0)     self$false_negatives$assign(0)   } ) m <- F1Score() m$update_state(c(0, 1, 0, 0), c(0.3, 0.5, 0.8, 0.9)) cat(\"Intermediate result:\", as.numeric(m$result()), \"\\n\") ## Intermediate result: 0.5 m$update_state(c(1, 1, 1, 1), c(0.1, 0.7, 0.6, 0.0)) cat(\"Final result:\", as.numeric(m$result()), \"\\n\") ## Final result: 0.6"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"compiled-functions","dir":"Articles","previous_headings":"","what":"Compiled functions","title":"Introduction to Keras for Researchers","text":"Running eagerly great debugging, get better performance compiling computation static graphs. Static graphs researcher’s best friends. can compile function wrapping tf.function decorator.","code":"# Prepare our layer, loss, and optimizer. model <- keras_model_sequential() %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10) loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE) optimizer <- optimizer_adam(learning_rate=1e-3)  # Create a training step function. train_on_batch <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     # Forward pass.     logits <- model(x)     # External loss value for this batch.     loss_value <- loss_fn(y, logits)   })   # Update the weights of the model to minimize the loss value.   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply_gradients(zip_lists(gradients, model$trainable_weights))   loss_value })   # Prepare a dataset. c(c(x_train, y_train), .) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(list(x_train, y_train)) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   tfdatasets::dataset_batch(64)  i <- 0 coro::loop(for (data in dataset) {   i <- i + 1   x <- data[[1]]   y <- data[[2]]   loss <- train_on_batch(x, y)   if (i %% 100 == 0)     cat(\"Loss:\", as.numeric(loss), \"\\n\") }) ## Loss: 0.551749 ## Loss: 0.2131135 ## Loss: 0.2765952 ## Loss: 0.1296219 ## Loss: 0.2657076 ## Loss: 0.2683381 ## Loss: 0.1570166 ## Loss: 0.3139241 ## Loss: 0.08981849"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"training-mode-inference-mode","dir":"Articles","previous_headings":"","what":"Training mode & inference mode","title":"Introduction to Keras for Researchers","text":"layers, particular BatchNormalization layer Dropout layer, different behaviors training inference. layers, standard practice expose training (boolean) argument call method. exposing argument call, enable built-training evaluation loops (e.g. fit) correctly use layer training inference modes.","code":"Dropout <- new_layer_class(   \"Dropout\",   initialize = function(rate) {     super$initialize()     self$rate <- rate   },   call = function(inputs, training = NULL) {     if (!is.null(training) && training) {       return(tf$nn$dropout(inputs, rate = self$rate))     }     inputs   } )  MLPWithDropout <- new_layer_class(   \"MLPWithDropout\",   initialize = function() {     super$initialize()     self$linear_1 <- Linear(units = 32)     self$dropout <- Dropout(rate = 0.5)     self$linear_3 <- Linear(units = 10)   },   call = function(inputs, training = NULL) {     x <- self$linear_1(inputs)     x <- tf$nn$relu(x)     x <- self$dropout(x, training = training)     self$linear_3(x)   } )  mlp <- MLPWithDropout() y_train <- mlp(tf$ones(shape(2, 2)), training=TRUE) y_test <- mlp(tf$ones(shape(2, 2)), training=FALSE)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"the-functional-api-for-model-building","dir":"Articles","previous_headings":"","what":"The Functional API for model-building","title":"Introduction to Keras for Researchers","text":"build deep learning models, don’t use object-oriented programming time. layers ’ve seen far can also composed functionally, like (call “Functional API”): Functional API tends concise subclassing, provides advantages (generally advantages functional, typed languages provide untyped OO development). However, can used define DAGs layers – recursive networks defined Layer subclasses instead. Learn Functional API . research workflows, may often find mix--matching OO models Functional models. Note Model class also features built-training & evaluation loops: fit(), predict() evaluate() (configured via compile() method). built-functions give access following built-training infrastructure features: Callbacks. can leverage built-callbacks early-stopping, model checkpointing, monitoring training TensorBoard. can also implement custom callbacks needed. Distributed training. can easily scale training multiple GPUs, TPU, even multiple machines tf.distribute API – changes code. Step fusing. steps_per_execution argument Model.compile(), can process multiple batches single tf.function call, greatly improves device utilization TPUs. won’t go details, provide simple code example . leverages built-training infrastructure implement MNIST example . can always subclass Model class (works exactly like subclassing Layer) want leverage built-training loops OO models. Just override Model$train_step() customize happens fit() retaining support built-infrastructure features outlined – callbacks, zero-code distribution support, step fusing support. may also override test_step() customize happens evaluate(), override predict_step() customize happens predict(). information, please refer guide.","code":"# We use an `Input` object to describe the shape and dtype of the inputs. # This is the deep learning equivalent of *declaring a type*. # The shape argument is per-sample; it does not include the batch size. # The functional API focused on defining per-sample transformations. # The model we create will automatically batch the per-sample transformations, # so that it can be called on batches of data. inputs <- layer_input(shape = 16, dtype = \"float32\")  # We call layers on these \"type\" objects # and they return updated types (new shapes/dtypes). outputs <- inputs %>%   Linear(units = 32) %>% # We are reusing the Linear layer we defined earlier.   Dropout(rate = 0.5) %>% # We are reusing the Dropout layer we defined earlier.   Linear(units = 10)  # A functional `Model` can be defined by specifying inputs and outputs. # A model is itself a layer like any other. model <- keras_model(inputs, outputs)  # A functional model already has weights, before being called on any data. # That's because we defined its input shape in advance (in `Input`). length(model$weights) ## [1] 4 # Let's call our model on some data, for fun. y <- model(tf$ones(shape(2, 16))) y$shape ## TensorShape([2, 10]) # You can pass a `training` argument in `__call__` # (it will get passed down to the Dropout layer). y <- model(tf$ones(shape(2, 16)), training=TRUE) inputs <- layer_input(shape = 784, dtype=\"float32\") outputs <- inputs %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10) model <- keras_model(inputs, outputs)  # Specify the loss, optimizer, and metrics with `compile()`. model %>% compile(     loss = loss_sparse_categorical_crossentropy(from_logits=TRUE),     optimizer=optimizer_adam(learning_rate=1e-3),     metrics=list(metric_sparse_categorical_accuracy()), )  # Train the model with the dataset for 2 epochs. model %>% fit(dataset, epochs=2) ## Epoch 1/2 ## 938/938 - 4s - 4ms/step - loss: 0.3958 - sparse_categorical_accuracy: 0.8866 ## Epoch 2/2 ## 938/938 - 1s - 960us/step - loss: 0.1888 - sparse_categorical_accuracy: 0.9443 predictions <- model %>% predict(dataset) ## 938/938 - 1s - 1ms/step model %>% evaluate(dataset) ## 938/938 - 1s - 1ms/step - loss: 0.1763 - sparse_categorical_accuracy: 0.9454 ## $loss ## [1] 0.1763445 ## ## $sparse_categorical_accuracy ## [1] 0.9454167 CustomModel <- new_model_class(   \"CustomModel\",   initialize = function(...) {     super$initialize(...)     self$loss_tracker <- metric_mean(name=\"loss\")     self$accuracy <- metric_sparse_categorical_accuracy()     self$loss_fn <- loss_sparse_categorical_crossentropy(from_logits=TRUE)     self$optimizer <- optimizer_adam(learning_rate=1e-3)   },   train_step = function(data) {     c(x, y = NULL, sample_weight = NULL) %<-% data     with(tf$GradientTape() %as% tape, {       y_pred <- self(x, training=TRUE)       loss <- self$loss_fn(y = y, y_pred = y_pred, sample_weight=sample_weight)     })     gradients <- tape$gradient(loss, self$trainable_variables)     self$optimizer$apply_gradients(       zip_lists(gradients, self$trainable_variables)     )      # Update metrics (includes the metric that tracks the loss)     self$loss_tracker$update_state(loss)     self$accuracy$update_state(y, y_pred, sample_weight=sample_weight)     # Return a list mapping metric names to current value     list(       loss = self$loss_tracker$result(),       accuracy = self$accuracy$result()     )   },   metrics = mark_active(function() {     list(self$loss_tracker, self$accuracy)   }) )  inputs <- layer_input(shape = 784, dtype=\"float32\") outputs <- inputs %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10) model <- CustomModel(inputs, outputs) model %>% compile() model %>% fit(dataset, epochs=2) ## Epoch 1/2 ## 938/938 - 2s - 2ms/step - loss: 0.3869 - sparse_categorical_accuracy: 0.8924 ## Epoch 2/2 ## 938/938 - 1s - 1ms/step - loss: 0.2163 - sparse_categorical_accuracy: 0.9370"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"end-to-end-experiment-example-1-variational-autoencoders-","dir":"Articles","previous_headings":"","what":"End-to-end experiment example 1: variational autoencoders.","title":"Introduction to Keras for Researchers","text":"things ’ve learned far: Layer encapsulates state (created __init__ build) computation (defined call). Layers can recursively nested create new, bigger computation blocks. can easily write highly hackable training loops opening GradientTape, calling model inside tape’s scope, retrieving gradients applying via optimizer. can speed training loops using @tf.function decorator. Layers can create track losses (typically regularization losses) via self.add_loss(). Let’s put things together end--end example: ’re going implement Variational AutoEncoder (VAE). ’ll train MNIST digits. VAE subclass Layer, built nested composition layers subclass Layer. feature regularization loss (KL divergence). model definition. First, Encoder class, uses Sampling layer map MNIST digit latent-space triplet (z_mean, z_log_var, z). Next, Decoder class, maps probabilistic latent space coordinates back MNIST digit. Finally, VariationalAutoEncoder composes together encoder decoder, creates KL divergence regularization loss via add_loss(). Now, let’s write training loop. training step decorated @tf.function compile super fast graph function. can see, building training type model Keras quick painless.","code":"Sampling <- new_layer_class(   \"Sampling\",   call = function(inputs) {     c(z_mean, z_log_var) %<-% inputs     batch <- op_shape(z_mean)[[1]]     dim <- op_shape(z_mean)[[2]]     epsilon <- random_normal(shape = c(batch, dim))     z_mean + op_exp(0.5 * z_log_var) * epsilon   } )  Encoder <- new_layer_class(   \"Encoder\",   initialize = function(latent_dim = 32, intermediate_dim = 64, ...) {     super$initialize(...)     self$dense_proj <- layer_dense(units = intermediate_dim, activation = \"relu\")     self$dense_mean <- layer_dense(units = latent_dim)     self$dense_log_var <- layer_dense(units = latent_dim)     self$sampling <- Sampling()   },   call = function(inputs) {     x <- self$dense_proj(inputs)     z_mean <- self$dense_mean(x)     z_log_var <- self$dense_log_var(x)     z <- self$sampling(list(z_mean, z_log_var))     list(z_mean, z_log_var, z)   } ) Decoder <- new_layer_class(   \"Decoder\",   initialize = function(original_dim, intermediate_dim = 64, ...) {     super$initialize(...)     self$dense_proj <- layer_dense(units = intermediate_dim, activation = \"relu\")     self$dense_output <- layer_dense(units = original_dim, activation = \"sigmoid\")   },   call = function(inputs) {     x <- self$dense_proj(inputs)     self$dense_output(x)   } ) VariationalAutoEncoder <- new_model_class(   \"VariationalAutoEncoder\",   initialize = function(original_dim,         intermediate_dim=64,         latent_dim=32,         name=\"autoencoder\", ...) {     super$initialize(name = name, ...)     self$original_dim <- original_dim     self$encoder <- Encoder(       latent_dim = latent_dim,       intermediate_dim = intermediate_dim     )     self$decoder <- Decoder(       original_dim = original_dim,       intermediate_dim = intermediate_dim     )   },   call = function(inputs) {     c(z_mean, z_log_var, z) %<-% self$encoder(inputs)     reconstructed <- self$decoder(z)     # Add KL divergence regularization loss.     kl_loss <- -0.5 * op_mean(       z_log_var - op_square(z_mean) - op_exp(z_log_var) + 1     )     self$add_loss(kl_loss)     reconstructed   } ) # Our model. vae <- VariationalAutoEncoder(   original_dim = 784,   intermediate_dim = 64,   latent_dim = 32 )  # Loss and optimizer. loss_fn <- loss_mean_squared_error() optimizer = optimizer_adam(learning_rate=1e-3)  # Prepare a dataset. c(c(x_train, .), .) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(x_train) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   tfdatasets::dataset_batch(32)   training_step <- tf_function(function(x) {   with(tf$GradientTape() %as% tape, {     reconstructed <- vae(x)  # Compute input reconstruction.     # Compute loss.     loss <- loss_fn(x, reconstructed)     loss <- loss + op_sum(vae$losses)  # Add KLD term.   })   # Update the weights of the VAE.   grads <- tape$gradient(loss, vae$trainable_weights)   optimizer$apply_gradients(zip_lists(grads, vae$trainable_weights))   loss })  losses <- c()  # Keep track of the losses over time. coro::loop(for(data in dataset) {   loss <- training_step(data)    # Logging.   losses[length(losses) + 1] <- as.numeric(loss)   if (length(losses) %% 100 == 0) {     cat(\"Step:\", length(losses), \"Loss:\", mean(losses), \"\\n\")   }   # Stop after 1000 steps.   # Training the model to convergence is left   # as an exercise to the reader.   if (length(losses) >= 1000) {     break   } }) ## Step: 100 Loss: 0.1270978 ## Step: 200 Loss: 0.1003238 ## Step: 300 Loss: 0.09001128 ## Step: 400 Loss: 0.08493649 ## Step: 500 Loss: 0.08171404 ## Step: 600 Loss: 0.07926706 ## Step: 700 Loss: 0.07790599 ## Step: 800 Loss: 0.07670419 ## Step: 900 Loss: 0.07570736 ## Step: 1000 Loss: 0.07476593"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"end-to-end-experiment-example-2-hypernetworks-","dir":"Articles","previous_headings":"","what":"End-to-end experiment example 2: hypernetworks.","title":"Introduction to Keras for Researchers","text":"Let’s take look another kind research experiment: hypernetworks. idea use small deep neural network (hypernetwork) generate weights larger network (main network). Let’s implement really trivial hypernetwork: ’ll use small 2-layer network generate weights larger 3-layer network. training loop. batch data: use hypernetwork generate array weight coefficients, weights_pred reshape coefficients kernel & bias tensors main_network run forward pass main_network compute actual MNIST predictions run backprop weights hypernetwork minimize final classification loss Implementing arbitrary research ideas Keras straightforward highly productive. Imagine trying 25 ideas per day (20 minutes per experiment average)! Keras designed go idea results fast possible, believe key great research. hope enjoyed quick introduction. Let us know build Keras!","code":"input_dim <- 784 classes <- 10  # This is the main network we'll actually use to predict labels. inputs <- layer_input(shape = input_dim) dense1 <- layer_dense(units = 64, activation = \"relu\") dense1$built <- TRUE  dense2 <- layer_dense(units = classes) dense2$built <- TRUE  outputs <- inputs %>% dense1() %>% dense2() main_network <- keras_model(inputs, outputs)  # This is the number of weight coefficients to generate. Each layer in the # main network requires output_dim * input_dim + output_dim coefficients. num_weights_to_generate <- (classes * 64 + classes) + (64 * input_dim + 64)  # This is the hypernetwork that generates the weights of the `main_network` above. hypernetwork <- keras_model_sequential() %>%   layer_dense(units=16, activation=\"relu\") %>%   layer_dense(units=num_weights_to_generate, activation=\"sigmoid\") # Loss and optimizer. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE) optimizer <- optimizer_adam(learning_rate=1e-4)  # Prepare a dataset. c(c(x_train, y_train), .) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(list(x_train, y_train)) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   # We'll use a batch size of 1 for this experiment.   tfdatasets::dataset_batch(1)  train_step <- function(x, y) {   with(tf$GradientTape() %as% tape, {     weights_pred <- hypernetwork(x)      # Reshape them to the expected shapes for w and b for the outer model.     # Layer 1 kernel.     start_index <- 1     w1_shape <- c(input_dim, 64)     w1_coeffs <- weights_pred[, start_index:(start_index + prod(w1_shape) - 1)]     w1 <- tf$reshape(w1_coeffs, as.integer(w1_shape))     start_index <- start_index + prod(w1_shape)      # Layer 1 bias.     b1_shape <- c(64)     b1_coeffs <- weights_pred[, start_index:(start_index + prod(b1_shape) - 1)]     b1 <- tf$reshape(b1_coeffs, as.integer(b1_shape))     start_index <- start_index + prod(b1_shape)      # Layer 2 kernel.     w2_shape <- c(64, classes)     w2_coeffs <- weights_pred[, start_index:(start_index + prod(w2_shape) - 1)]     w2 <- tf$reshape(w2_coeffs, as.integer(w2_shape))     start_index <- start_index + prod(w2_shape)      # Layer 2 bias.     b2_shape <- c(classes)     b2_coeffs <- weights_pred[, start_index:(start_index + prod(b2_shape) - 1)]     b2 <- tf$reshape(b2_coeffs, as.integer(b2_shape))     start_index <- start_index + prod(b2_shape)      # Set the weight predictions as the weight variables on the outer model.     dense1$kernel <- w1     dense1$bias <- b1     dense2$kernel <- w2     dense2$bias <- b2      # Inference on the outer model.     preds <- main_network(x)     loss <- loss_fn(y, preds)   })    grads <- tape$gradient(loss, hypernetwork$trainable_weights)   optimizer$apply_gradients(zip_lists(grads, hypernetwork$trainable_weights))   loss }  losses <- c()  # Keep track of the losses over time. coro::loop(for (data in dataset) {   x <- data[[1]]   y <- data[[2]]   loss <- train_step(x, y)    # Logging.   losses[length(losses) + 1] <- as.numeric(loss)   if (length(losses) %% 100 == 0) {     cat(\"Step:\", length(losses), \"Loss:\", mean(losses), \"\\n\")   }   # Stop after 1000 steps.   # Training the model to convergence is left   # as an exercise to the reader.   if (length(losses) >= 1000) {     break   } }) ## Step: 100 Loss: 2.536778 ## Step: 200 Loss: 2.236472 ## Step: 300 Loss: 2.119417 ## Step: 400 Loss: 2.040341 ## Step: 500 Loss: 1.949125 ## Step: 600 Loss: 1.859384 ## Step: 700 Loss: 1.845726 ## Step: 800 Loss: 1.820594 ## Step: 900 Loss: 1.771334 ## Step: 1000 Loss: 1.730648"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Making new layers and models via subclassing","text":"guide cover everything need know build subclassed layers models. particular, ’ll learn following features: Layer class add_weight() method Trainable non-trainable weights build() method Making sure layers can used backend add_loss() method training argument call() mask argument call() Making sure layers can serialized Let’s dive .","code":""},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Making new layers and models via subclassing","text":"","code":"library(keras3) library(tensorflow, exclude = c(\"set_random_seed\", \"shape\")) library(tfdatasets, exclude = \"shape\")"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"the-layer-class-the-combination-of-state-weights-and-some-computation","dir":"Articles","previous_headings":"","what":"The Layer class: the combination of state (weights) and some computation","title":"Making new layers and models via subclassing","text":"One central abstractions Keras Layer class. layer encapsulates state (layer’s “weights”) transformation inputs outputs (“call”, layer’s forward pass). ’s densely-connected layer. two state variables: variables w b. use layer calling tensor input(s), much like R function. Note weights w b automatically tracked layer upon set layer attributes:","code":"layer_linear <- Layer(\"Linear\",    initialize = function(units = 32, input_dim = 32, ...) {     super$initialize(...)     self$w <- self$add_weight(       shape = shape(input_dim, units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(units),       initializer = \"zeros\",       trainable = TRUE     )   },    call = function(inputs) {     op_matmul(inputs, self$w) + self$b   } ) x <- op_ones(c(2, 2)) linear_layer <- layer_linear(units = 4, input_dim = 2) y <- linear_layer(x) print(y) ## tf.Tensor( ## [[0.02153057 0.15450525 0.0205495  0.04493225] ##  [0.02153057 0.15450525 0.0205495  0.04493225]], shape=(2, 4), dtype=float32) linear_layer$weights ## [[1]] ## <KerasVariable shape=(2, 4), dtype=float32, path=linear/variable> ## ## [[2]] ## <KerasVariable shape=(4), dtype=float32, path=linear/variable_1>"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"layers-can-have-non-trainable-weights","dir":"Articles","previous_headings":"","what":"Layers can have non-trainable weights","title":"Making new layers and models via subclassing","text":"Besides trainable weights, can add non-trainable weights layer well. weights meant taken account backpropagation, training layer. ’s add use non-trainable weight: ’s part layer$weights, gets categorized non-trainable weight:","code":"layer_compute_sum <- Layer(   \"ComputeSum\",   initialize = function(input_dim) {     super$initialize()     self$total <- self$add_weight(       initializer = \"zeros\",       shape = shape(input_dim),       trainable = FALSE     )   },   call = function(inputs) {     self$total$assign_add(op_sum(inputs, axis = 1))     self$total   } )  x <- op_ones(c(2, 2)) my_sum <- layer_compute_sum(input_dim = 2) y <- my_sum(x) print(as.array(y)) ## [1] 2 2 y <- my_sum(x) print(as.array(y)) ## [1] 4 4 cat(\"weights:\", length(my_sum$weights)) ## weights: 1 cat(\"non-trainable weights:\", length(my_sum$non_trainable_weights)) ## non-trainable weights: 1 # It's not included in the trainable weights: cat(\"trainable_weights:\", length(my_sum$trainable_weights)) ## trainable_weights: 0"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"best-practice-deferring-weight-creation-until-the-shape-of-the-inputs-is-known","dir":"Articles","previous_headings":"","what":"Best practice: deferring weight creation until the shape of the inputs is known","title":"Making new layers and models via subclassing","text":"Linear layer took input_dim argument used compute shape weights w b initialize(): many cases, may know advance size inputs, like lazily create weights value becomes known, time instantiating layer. Keras API, recommend creating layer weights build(self, inputs_shape) method layer. Like : call() method layer automatically run build first time called. now layer ’s lazy thus easier use: Implementing build() separately shown nicely separates creating weights using weights every call.","code":"layer_linear <- Layer(\"Linear\",    initialize = function(units = 32, input_dim = 32, ...) {     super$initialize(...)     self$w <- self$add_weight(       shape = shape(input_dim, units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(units),       initializer = \"zeros\",       trainable = TRUE     )   },    call = function(inputs) {     op_matmul(inputs, self$w) + self$b   } ) layer_linear <- Layer(   \"Linear\",   initialize = function(units = 32, ...) {     self$units <- as.integer(units)     super$initialize(...)   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     op_matmul(inputs, self$w) + self$b   } ) # At instantiation, we don't know on what inputs this is going to get called linear_layer <- layer_linear(units = 32)  # The layer's weights are created dynamically the first time the layer is called y <- linear_layer(x)"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"layers-are-recursively-composable","dir":"Articles","previous_headings":"","what":"Layers are recursively composable","title":"Making new layers and models via subclassing","text":"assign Layer instance attribute another Layer, outer layer start tracking weights created inner layer. recommend creating sublayers initialize() method leave first call() trigger building weights.","code":"MLPBlock <- Layer(   \"MLPBlock\",   initialize = function() {     super$initialize()     self$linear_1 <- layer_linear(units = 32)     self$linear_2 <- layer_linear(units = 32)     self$linear_3 <- layer_linear(units = 1)   },   call = function(inputs) {     inputs |>       self$linear_1() |>       activation_relu() |>       self$linear_2() |>       activation_relu() |>       self$linear_3()   } )  mlp <- MLPBlock() # The first call to the `mlp` will create the weights y <- mlp(op_ones(shape = c(3, 64)))  cat(\"weights:\", length(mlp$weights), \"\\n\") ## weights: 6 cat(\"trainable weights:\", length(mlp$trainable_weights), \"\\n\") ## trainable weights: 6"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"backend-agnostic-layers-and-backend-specific-layers","dir":"Articles","previous_headings":"","what":"Backend-agnostic layers and backend-specific layers","title":"Making new layers and models via subclassing","text":"long layer uses APIs ops namespace (ie. using functions starting op_), (Keras namespaces activations_*, random_*, layer_*), can used backend – TensorFlow, JAX, PyTorch. layers ’ve seen far guide work Keras backends. ops namespace gives access : NumPy API, e.g. op_matmul, op_sum, op_reshape, op_stack, etc. Neural networks-specific APIs op_softmax, op_conv, op_binary_crossentropy, op_relu, etc. can also use backend-native APIs layers (tf$nn functions), , layer usable backend question. instance, write following JAX-specific layer using jax$numpy: equivalent TensorFlow-specific layer: equivalent PyTorch-specific layer: cross-backend compatibility tremendously useful property, strongly recommend seek always make layers backend-agnostic leveraging Keras APIs.","code":"# keras3::install_keras(backend = c(\"jax\")) jax <- reticulate::import(\"jax\")  Linear <- new_layer_class(   ...   call = function(inputs) {     jax$numpy$matmul(inputs, self$w) + self$b   } ) library(tensorflow)  Linear <- new_layer_class(   ...   call = function(inputs) {     tf$matmul(inputs, self$w) + self$b   } ) torch <- reticulate::import(\"torch\")  Linear <- new_layer_class(   ...   call = function(inputs) {     torch$matmul(inputs, self$w) + self$b   } )"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"the-add_loss-method","dir":"Articles","previous_headings":"","what":"The add_loss() method","title":"Making new layers and models via subclassing","text":"writing call() method layer, can create loss tensors want use later, writing training loop. doable calling self$add_loss(value): losses (including created inner layer) can retrieved via layer$losses. property reset start every call top-level layer, layer$losses always contains loss values created last forward pass. addition, loss property also contains regularization losses created weights inner layer: losses meant taken account writing custom training loops. also work seamlessly fit() (get automatically summed added main loss, ):","code":"# A layer that creates an activity regularization loss layer_activity_regularization <- Layer(   \"ActivityRegularizationLayer\",   initialize = function(rate = 1e-2) {     self$rate <- as.numeric(rate)     super$initialize()   },   call = function(inputs) {     self$add_loss(self$rate * op_mean(inputs))     inputs   } ) layer_outer <- Layer(   \"OuterLayer\",   initialize = function() {     super$initialize()     self$activity_reg <- layer_activity_regularization(rate = 1e-2)   },   call = function(inputs) {     self$activity_reg(inputs)     inputs   } )  layer <- layer_outer() # No losses yet since the layer has never been called cat(\"losses:\", length(layer$losses), \"\\n\") ## losses: 0 x <- layer(op_zeros(c(1, 1))) # We created one loss value cat(\"losses:\", length(layer$losses), \"\\n\") ## losses: 1 # `layer$losses` gets reset at the start of each call x <- layer(op_zeros(c(1, 1))) # This is the loss created during the call above cat(\"losses:\", length(layer$losses), \"\\n\") ## losses: 1 layer_outer_with_kernel_regularizer <- Layer(   \"OuterLayerWithKernelRegularizer\",   initialize = function() {     super$initialize()     self$dense <- layer_dense(units = 32,                               kernel_regularizer = regularizer_l2(1e-3))   },   call = function(inputs) {     self$dense(inputs)   } )  layer <- layer_outer_with_kernel_regularizer() x <- layer(op_zeros(c(1, 1)))  # This is `1e-3 * sum(layer$dense$kernel ** 2)`, # created by the `kernel_regularizer` above. print(layer$losses) ## [[1]] ## tf.Tensor(0.002025157, shape=(), dtype=float32) inputs <- keras_input(shape = 3) outputs <- inputs |> layer_activity_regularization() model <- keras_model(inputs, outputs)  # If there is a loss passed in `compile`, the regularization # losses get added to it model |> compile(optimizer = \"adam\", loss = \"mse\") model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1) ## 1/1 - 0s - 118ms/step - loss: 1.8971 # It's also possible not to pass any loss in `compile`, # since the model already has a loss to minimize, via the `add_loss` # call during the forward pass! model |> compile(optimizer = \"adam\") model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1) ## 1/1 - 0s - 73ms/step - loss: -3.3344e-03"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"you-can-optionally-enable-serialization-on-your-layers","dir":"Articles","previous_headings":"","what":"You can optionally enable serialization on your layers","title":"Making new layers and models via subclassing","text":"need custom layers serializable part Functional model, can optionally implement get_config() method: Note initialize() method base Layer class takes keyword arguments, particular name dtype. ’s good practice pass arguments parent class initialize() include layer config: need flexibility deserializing layer config, can also override from_config() class method. base implementation from_config(): learn serialization saving, see complete guide saving serializing models.","code":"layer_linear <- Layer(   \"Linear\",   initialize = function(units = 32) {     self$units <- as.integer(units)     super$initialize()   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     op_matmul(inputs, self$w) + self$b   },   get_config = function() {     list(units = self$units)   } )  # Now you can recreate the layer from its config: layer <- layer_linear(units = 64) config <- get_config(layer) str(config) ## List of 1 ##  $ units: int 64 ##  - attr(*, \"__class__\")=<class '<r-globalenv>.Linear'> new_layer <- from_config(config) Linear <- new_layer_class(   \"Linear\",   initialize = function(units = 32, ...) {     self$units <- as.integer(units)     super$initialize(...)   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     op_matmul(inputs, self$w) + self$b   },   get_config = function() {     list(units = self$units)   } )  layer <- Linear(units = 64) config <- get_config(layer) str(config) ## List of 1 ##  $ units: int 64 ##  - attr(*, \"__class__\")=<class '<r-globalenv>.Linear'> new_layer <- from_config(config) Layer(   ...,   from_config = function(config) {     # calling `__class__`() creates a new instance and calls initialize()     do.call(`__class__`, config)   } )"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"privileged-training-argument-in-the-call-method","dir":"Articles","previous_headings":"","what":"Privileged training argument in the call() method","title":"Making new layers and models via subclassing","text":"layers, particular BatchNormalization layer Dropout layer, different behaviors training inference. layers, standard practice expose training (boolean) argument call() method. exposing argument call(), enable built-training evaluation loops (e.g. fit()) correctly use layer training inference.","code":"layer_custom_dropout <- Layer(   \"CustomDropout\",   initialize = function(rate, ...) {     super$initialize(...)     self$rate <- rate     self$seed_generator <- random_seed_generator(1337)   },   call = function(inputs, training = NULL) {     if (isTRUE(training))       return(random_dropout(inputs, rate = self$rate,                             seed = self.seed_generator))     inputs   } )"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"privileged-mask-argument-in-the-call-method","dir":"Articles","previous_headings":"","what":"Privileged mask argument in the call() method","title":"Making new layers and models via subclassing","text":"privileged argument supported call() mask argument. find Keras RNN layers. mask boolean tensor (one boolean value per timestep input) used skip certain input timesteps processing timeseries data. Keras automatically pass correct mask argument call() layers support , mask generated prior layer. Mask-generating layers Embedding layer configured mask_zero = TRUE, Masking layer.","code":""},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"the-model-class","dir":"Articles","previous_headings":"","what":"The Model class","title":"Making new layers and models via subclassing","text":"general, use Layer class define inner computation blocks, use Model class define outer model – object train. instance, ResNet50 model, several ResNet blocks subclassing Layer, single Model encompassing entire ResNet50 network. Model class API Layer, following differences: exposes built-training, evaluation, prediction loops (fit(), evaluate(), predict()). exposes list inner layers, via model$layers property. exposes saving serialization APIs (save(), save_weights()…) Effectively, Layer class corresponds refer literature “layer” (“convolution layer” “recurrent layer”) “block” (“ResNet block” “Inception block”). Meanwhile, Model class corresponds referred literature “model” (“deep learning model”) “network” (“deep neural network”). ’re wondering, “use Layer class Model class?”, ask : need call fit() ? need call save() ? , go Model. (either class just block bigger system, writing training & saving code ), use Layer. instance, take mini-resnet example , use build Model train fit(), save save_weights():","code":"ResNet <- Model(   \"ResNet\",   initialize = function(num_classes = 1000, ...) {     super$initialize(...)     self$block_1 <- layer_resnet_block()     self$block_2 <- layer_resnet_block()     self$global_pool <- layer_global_average_pooling_2d()     self$classifier <- layer_dense(num_classes)   },   call = function(inputs) {     inputs |>       self$block_1() |>       self$block_2() |>       self$global_pool() |>       self$classifier()   } )  resnet <- ResNet() dataset <- ... resnet |> fit(dataset, epochs=10) resnet |> save_model(\"filepath.keras\")"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"putting-it-all-together-an-end-to-end-example","dir":"Articles","previous_headings":"","what":"Putting it all together: an end-to-end example","title":"Making new layers and models via subclassing","text":"’s ’ve learned far: Layer encapsulate state (created initialize() build()) computation (defined call()). Layers can recursively nested create new, bigger computation blocks. Layers backend-agnostic long use Keras APIs. can use backend-native APIs (jax$numpy, torch$nn tf$nn), layer usable specific backend. Layers can create track losses (typically regularization losses) via add_loss(). outer container, thing want train, Model. Model just like Layer, added training serialization utilities. Let’s put things together end--end example: ’re going implement Variational AutoEncoder (VAE) backend-agnostic fashion – runs TensorFlow, JAX, PyTorch. ’ll train MNIST digits. VAE subclass Model, built nested composition layers subclass Layer. feature regularization loss (KL divergence). Let’s train MNIST using fit() API:","code":"layer_sampling <- Layer(   \"Sampling\",   initialize = function(...) {     super$initialize(...)     self$seed_generator <- random_seed_generator(1337)   },   call = function(inputs) {     c(z_mean, z_log_var) %<-% inputs     batch <- op_shape(z_mean)[[1]]     dim <- op_shape(z_mean)[[2]]     epsilon <- random_normal(shape = c(batch, dim),                              seed=self$seed_generator)     z_mean + op_exp(0.5 * z_log_var) * epsilon   } )  # Maps MNIST digits to a triplet (z_mean, z_log_var, z). layer_encoder <- Layer(   \"Encoder\",   initialize = function(latent_dim = 32, intermediate_dim = 64, ...) {     super$initialize(...)     self$dense_proj <-       layer_dense(units = intermediate_dim,  activation = \"relu\")     self$dense_mean <- layer_dense(units = latent_dim)     self$dense_log_var <- layer_dense(units = latent_dim)     self$sampling <- layer_sampling()   },   call = function(inputs) {     x <- self$dense_proj(inputs)     z_mean <- self$dense_mean(x)     z_log_var <- self$dense_log_var(x)     z <- self$sampling(list(z_mean, z_log_var))     list(z_mean, z_log_var, z)   } )  # Converts z, the encoded digit vector, back into a readable digit. layer_decoder <- Layer(   \"Decoder\",   initialize = function(original_dim, intermediate_dim = 64, ...) {     super$initialize(...)     self$dense_proj <-       layer_dense(units = intermediate_dim, activation = \"relu\")     self$dense_output <-       layer_dense(units = original_dim, activation = \"sigmoid\")   },   call = function(inputs) {     x <- self$dense_proj(inputs)     self$dense_output(x)   } )  # Combines the encoder and decoder into an end-to-end model for training. VariationalAutoEncoder <- Model(   \"VariationalAutoEncoder\",    initialize = function(original_dim, intermediate_dim = 64, latent_dim = 32,                         name = \"autoencoder\", ...) {     super$initialize(name = name, ...)     self$original_dim <- original_dim     self$encoder <- layer_encoder(latent_dim = latent_dim,                             intermediate_dim = intermediate_dim)     self$decoder <- layer_decoder(original_dim = original_dim,                             intermediate_dim = intermediate_dim)   },    call = function(inputs) {     c(z_mean, z_log_var, z) %<-% self$encoder(inputs)     reconstructed <- self$decoder(z)     # Add KL divergence regularization loss.     kl_loss <- -0.5 * op_mean(z_log_var - op_square(z_mean) - op_exp(z_log_var) + 1)     self$add_loss(kl_loss)     reconstructed   } ) c(c(x_train, .), .) %<-% dataset_mnist() x_train <- x_train |>   op_reshape(c(60000, 784)) |>   op_cast(\"float32\") |>   op_divide(255)  original_dim <- 784 vae <- VariationalAutoEncoder(   original_dim = 784,   intermediate_dim = 64,   latent_dim = 32 )  optimizer <- optimizer_adam(learning_rate = 1e-3) vae |> compile(optimizer, loss = loss_mean_squared_error())  vae |> fit(x_train, x_train, epochs = 2, batch_size = 64) ## Epoch 1/2 ## 938/938 - 4s - 5ms/step - loss: 0.0748 ## Epoch 2/2 ## 938/938 - 1s - 815us/step - loss: 0.0676"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"The Sequential model","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"when-to-use-a-sequential-model","dir":"Articles","previous_headings":"","what":"When to use a Sequential model","title":"The Sequential model","text":"Sequential model appropriate plain stack layers layer exactly one input tensor one output tensor. Schematically, following Sequential model: equivalent function: Sequential model appropriate : model multiple inputs multiple outputs layers multiple inputs multiple outputs need layer sharing want non-linear topology (e.g. residual connection, multi-branch model)","code":"model <- keras_model_sequential() |>   layer_dense(units = 2, activation = \"relu\", name = \"layer1\") |>   layer_dense(units = 3, activation = \"relu\", name = \"layer2\") |>   layer_dense(units = 4, name = \"layer3\")  # Call model on a test input x <- op_ones(c(3, 3)) y <- model(x) # Create 3 layers layer1 <- layer_dense(units = 2, activation=\"relu\", name=\"layer1\") layer2 <- layer_dense(units = 3, activation=\"relu\", name=\"layer2\") layer3 <- layer_dense(units = 4, name=\"layer3\")  # Call layers on a test input x <- op_ones(c(3, 3)) y <- x |> layer1() |> layer2() |> layer3()"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"creating-a-sequential-model","dir":"Articles","previous_headings":"","what":"Creating a Sequential model","title":"The Sequential model","text":"can create Sequential model piping layers keras_model_sequential() object: passing list layers keras_model_sequential(): layers accessible via layers attribute: can also create Sequential model incrementally: Note ’s also corresponding pop_layer() method remove layers: Sequential model behaves much like stack layers. Also note Sequential constructor accepts name argument, just like layer model Keras. useful annotate TensorBoard graphs semantically meaningful names.","code":"model <- keras_model_sequential() |>   layer_dense(units = 2, activation = \"relu\") |>   layer_dense(units = 3, activation = \"relu\") |>   layer_dense(units = 4) model <- keras_model_sequential(layers = list(   layer_dense(units = 2, activation = \"relu\"),   layer_dense(units = 3, activation = \"relu\"),   layer_dense(units = 4) )) model$layers ## [[1]] ## <Dense name=dense_3, built=False> ##  signature: (*args, **kwargs) ## ## [[2]] ## <Dense name=dense_4, built=False> ##  signature: (*args, **kwargs) ## ## [[3]] ## <Dense name=dense_5, built=False> ##  signature: (*args, **kwargs) model <- keras_model_sequential() model |> layer_dense(units = 2, activation=\"relu\") model |> layer_dense(units = 3, activation=\"relu\") model |> layer_dense(units = 4) model |> pop_layer() length(model$layers)  # 2 ## [1] 2 model <- keras_model_sequential(name = \"my_sequential\") model |> layer_dense(units = 2, activation=\"relu\", name = \"layer1\") model |> layer_dense(units = 3, activation=\"relu\", name = \"layer2\") model |> layer_dense(units = 4, name = \"layer3\")"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"specifying-the-input-shape-in-advance","dir":"Articles","previous_headings":"","what":"Specifying the input shape in advance","title":"The Sequential model","text":"Generally, layers Keras need know shape inputs order able create weights. create layer like , initially, weights: creates weights first time called input, since shape weights depends shape inputs: Naturally, also applies Sequential models. instantiate Sequential model without input shape, isn’t “built”: weights (calling model$weights results error stating just ). weights created model first sees input data: model “built”, can call summary() method display contents: However, can useful building Sequential model incrementally able display summary model far, including current output shape. case, start model passing input_shape argument model, knows input shape start: Models built predefined input shape like always weights (even seeing data) always defined output shape. general, ’s recommended best practice always specify input shape Sequential model advance know .","code":"layer <- layer_dense(units = 3) layer$weights  # Empty ## list() # Call layer on a test input x <- op_ones(c(1, 4)) y <- layer(x) layer$weights  # Now it has weights, of shape (4, 3) and (3,) ## [[1]] ## <KerasVariable shape=(4, 3), dtype=float32, path=dense_9/kernel> ## ## [[2]] ## <KerasVariable shape=(3), dtype=float32, path=dense_9/bias> model <- keras_model_sequential() |>   layer_dense(units = 2, activation = \"relu\") |>   layer_dense(units = 3, activation = \"relu\") |>   layer_dense(units = 4) # No weights at this stage!  # At this point, you can't do this: # model$weights   # Call the model on a test input x <- op_ones(c(1, 4)) y <- model(x) length(model$weights) ## [1] 6 summary(model) ## Model: \"sequential_4\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_10 (Dense)                │ (1, 2)                 │            10 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_11 (Dense)                │ (1, 3)                 │             9 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_12 (Dense)                │ (1, 4)                 │            16 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 35 (140.00 B) ##  Trainable params: 35 (140.00 B) ##  Non-trainable params: 0 (0.00 B) model <- keras_model_sequential(input_shape = 4) |>   layer_dense(units = 2, activation = \"relu\") summary(model) ## Model: \"sequential_5\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_13 (Dense)                │ (None, 2)              │            10 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 10 (40.00 B) ##  Trainable params: 10 (40.00 B) ##  Non-trainable params: 0 (0.00 B) model$layers ## [[1]] ## <Dense name=dense_13, built=True> ##  signature: (*args, **kwargs)"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"a-common-debugging-workflow-add-layers-summary","dir":"Articles","previous_headings":"","what":"A common debugging workflow: add layers + summary()","title":"The Sequential model","text":"building new Sequential architecture, ’s useful incrementally stack layers |> frequently print model summaries. instance, enables monitor stack Conv2D MaxPooling2D layers downsampling image feature maps: practical, right? Note |> equivalent calling model$add(), modifies model -place, don’t need reassign model symbol step.","code":"model <- keras_model_sequential(input_shape = c(250, 250, 3)) |>   layer_conv_2d(filters = 32, kernel_size = 5, strides = 2, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(3, 3))  # Can you guess what the current output shape is at this point? Probably not. # Let's just print it: summary(model) ## Model: \"sequential_6\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 123, 123, 32)   │         2,432 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 121, 121, 32)   │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 40, 40, 32)     │             0 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 11,680 (45.62 KB) ##  Trainable params: 11,680 (45.62 KB) ##  Non-trainable params: 0 (0.00 B) # The answer was: (40, 40, 32), so we can keep downsampling...  model |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_max_pooling_2d(pool_size = 3) |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_max_pooling_2d(pool_size = 2)  # And now? summary(model) ## Model: \"sequential_6\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 123, 123, 32)   │         2,432 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 121, 121, 32)   │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 40, 40, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 38, 38, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_3 (Conv2D)               │ (None, 36, 36, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 12, 12, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_4 (Conv2D)               │ (None, 10, 10, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_5 (Conv2D)               │ (None, 8, 8, 32)       │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_2 (MaxPooling2D)  │ (None, 4, 4, 32)       │             0 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 48,672 (190.12 KB) ##  Trainable params: 48,672 (190.12 KB) ##  Non-trainable params: 0 (0.00 B) # Now that we have 4x4 feature maps, time to apply global max pooling. model |>   layer_global_max_pooling_2d()  # Finally, we add a classification layer. model |>   layer_dense(units = 10, activation = \"softmax\")"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"what-to-do-once-you-have-a-model","dir":"Articles","previous_headings":"","what":"What to do once you have a model","title":"The Sequential model","text":"model architecture ready, want : Train model, evaluate , run inference. See guide training & evaluation built-loops Save model disk restore . See guide serialization & saving.","code":""},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"feature-extraction-with-a-sequential-model","dir":"Articles","previous_headings":"","what":"Feature extraction with a Sequential model","title":"The Sequential model","text":"Sequential model built, behaves like Functional API model. means every layer input output attribute. attributes can used neat things, like quickly creating model extracts outputs intermediate layers Sequential model: ’s similar example extract features one layer:","code":"initial_model <- keras_model_sequential(input_shape = c(250, 250, 3)) |>   layer_conv_2d(filters = 32, kernel_size = 5, strides = 2, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\")   feature_extractor <- keras_model(     inputs = initial_model$inputs,     outputs = lapply(initial_model$layers, function(x) x$output), )  # Call feature extractor on test input. x <- op_ones(c(1, 250, 250, 3)) features <- feature_extractor(x) initial_model <-   keras_model_sequential(input_shape = c(250, 250, 3)) |>   layer_conv_2d(filters = 32, kernel_size = 5, strides = 2,                 activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\",                 name = \"my_intermediate_layer\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\")  feature_extractor <- keras_model(   inputs = initial_model$inputs,   outputs = get_layer(initial_model, \"my_intermediate_layer\")$output, )  # Call feature extractor on test input. x <- op_ones(c(1, 250, 250, 3)) features <- feature_extractor(x)"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"transfer-learning-with-a-sequential-model","dir":"Articles","previous_headings":"","what":"Transfer learning with a Sequential model","title":"The Sequential model","text":"Transfer learning consists freezing bottom layers model training top layers. aren’t familiar , make sure read guide transfer learning. two common transfer learning blueprint involving Sequential models. First, let’s say Sequential model, want freeze layers except last one. case, can call freeze_weights(). Alternatively, can iterate model$layers set layer$trainable <- FALSE layer, except last one. Like : Another common blueprint use Sequential model stack pre-trained model freshly initialized classification layers. Like : transfer learning, probably find frequently using two patterns. ’s need know Sequential models! find building models Keras, see: Guide Functional API Guide making new Layers & Models via subclassing","code":"model <- keras_model_sequential(input_shape = 784) |>   layer_dense(units = 32, activation = \"relu\") |>   layer_dense(units = 32, activation = \"relu\") |>   layer_dense(units = 32, activation = \"relu\") |>   layer_dense(units = 10) # Presumably you would want to first load pre-trained weights. model |> load_model_weights(...) # Freeze all layers except the last one. model |> freeze_weights(from = 1, to = -2) model # note the \"Trainable\" column now visible in the summary table ## Model: \"sequential_9\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)                ┃ Output Shape          ┃    Param # ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩ ## │ dense_15 (Dense)            │ (None, 32)            │     25,120 │   N   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_16 (Dense)            │ (None, 32)            │      1,056 │   N   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_17 (Dense)            │ (None, 32)            │      1,056 │   N   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_18 (Dense)            │ (None, 10)            │        330 │   Y   │ ## └─────────────────────────────┴───────────────────────┴────────────┴───────┘ ##  Total params: 27,562 (107.66 KB) ##  Trainable params: 330 (1.29 KB) ##  Non-trainable params: 27,232 (106.38 KB) # Another way to freeze all layers except the last one. for (layer in model$layers[-length(model$layers)]) {   layer$trainable <- FALSE }  # Recompile and train (this will only update the weights of the last layer). model |> compile(...) model |> fit(...) # Load a convolutional base with pre-trained weights base_model <- application_xception(weights = 'imagenet',                                    include_top = FALSE,                                    pooling = 'avg')  # Freeze the base model freeze_weights(base_model)  # Use a Sequential model to add a trainable classifier on top model <- keras_model_sequential() |>   base_model() |>   layer_dense(1000) # Compile & train model |> compile(...) model |> fit(...)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Save, serialize, and export models","text":"Keras model consists multiple components: architecture, configuration, specifies layers model contain, ’re connected. set weights values (“state model”). optimizer (defined compiling model). set losses metrics (defined compiling model). Keras API saves pieces together unified format, marked .keras extension. zip archive consisting following: JSON-based configuration file (config.json): Records model, layer, trackables’ configuration. H5-based state file, model.weights.h5 (whole model), directory keys layers weights. metadata file JSON, storing things current Keras version. Let’s take look works.","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"how-to-save-and-load-a-model","dir":"Articles","previous_headings":"","what":"How to save and load a model","title":"Save, serialize, and export models","text":"10 seconds read guide, ’s need know. Saving Keras model: Loading model back: Now, let’s look details.","code":"# Get model (Sequential, Functional Model, or Model subclass) model <- ...  # The filename needs to end with the .keras extension model |> save_model('path/to/location.keras') model <- load_model('path/to/location.keras')"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Save, serialize, and export models","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"saving","dir":"Articles","previous_headings":"","what":"Saving","title":"Save, serialize, and export models","text":"section saving entire model single file. file include: model’s architecture/config model’s weight values (learned training) model’s compilation information (compile() called) optimizer state, (enables restart training left)","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"apis","dir":"Articles","previous_headings":"Saving","what":"APIs","title":"Save, serialize, and export models","text":"can save model save_model(). can load back load_model(). supported format Keras 3 “Keras v3” format, uses .keras extension. Example:","code":"get_model <- function() {   # Create a simple model.   inputs <- keras_input(shape(32))   outputs <- inputs |> layer_dense(1)   model <-  keras_model(inputs, outputs)   model |> compile(optimizer = optimizer_adam(), loss = \"mean_squared_error\")   model }  model <- get_model()  # Train the model. test_input <- random_uniform(c(128, 32)) test_target <- random_uniform(c(128, 1)) model |> fit(test_input, test_target)  # Calling `save('my_model.keras')` creates a zip archive `my_model.keras`. model |> save_model(\"my_model.keras\")  # It can be used to reconstruct the model identically. reconstructed_model <- load_model(\"my_model.keras\")  # Let's check: stopifnot(all.equal(   model |> predict(test_input),   reconstructed_model |> predict(test_input) ))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"custom-objects","dir":"Articles","previous_headings":"Saving","what":"Custom objects","title":"Save, serialize, and export models","text":"section covers basic workflows handling custom layers, functions, models Keras saving reloading. saving model includes custom objects, subclassed Layer, must define get_config() method object class. arguments passed constructor (initialize() method) custom object aren’t simple objects (anything types like ints, strings, etc.), must also explicitly deserialize arguments from_config() class method. Like : Please see Defining config methods section details examples. saved .keras file lightweight store Python code custom objects. Therefore, reload model, load_model requires access definition custom objects used one following methods: Registering custom objects (preferred), Passing custom objects directly loading, Using custom object scope examples workflow:","code":"layer_custom <- Layer(   \"CustomLayer\",   initialize = function(sublayer, ...) {     super$initialize(...)     self$sublayer <- sublayer   },   call = function(x) {     self$sublayer(x)   },   get_config = function() {     base_config <- super$get_config()     config <- list(       sublayer = serialize_keras_object(self$sublayer)     )     c(base_config, config)   },   from_config = function(cls, config) {     sublayer_config <- config$sublayer     sublayer <- deserialize_keras_object(sublayer_config)     cls(sublayer, !!!config)   } )"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"registering-custom-objects-preferred","dir":"Articles","previous_headings":"Saving > Custom objects","what":"Registering custom objects (preferred)","title":"Save, serialize, and export models","text":"preferred method, custom object registration greatly simplifies saving loading code. Calling register_keras_serializable() custom object registers object globally master list, allowing Keras recognize object loading model. Let’s create custom model involving custom layer custom activation function demonstrate . Example:","code":"# Clear all previously registered custom objects set_custom_objects(clear = TRUE) ## named list() layer_custom <- Layer(   \"CustomLayer\",   initialize = function(self, factor) {     super$initialize()     self$factor = factor   },    call = function(self, x) {     x * self$factor   },    get_config = function(self) {     list(factor = self$factor)   } )  # Upon registration, you can optionally specify a package or a name. # If left blank, the package defaults to \"Custom\" and the name defaults to # the class name. register_keras_serializable(layer_custom, package = \"MyLayers\")  custom_fn <- keras3:::py_func2(function(x) x^2, name = \"custom_fn\", convert = TRUE)  register_keras_serializable(custom_fn, name=\"custom_fn\", package=\"my_package\")   # Create the model. get_model <- function() {   inputs <- keras_input(shape(4))   mid <- inputs |> layer_custom(0.5)   outputs <- mid |> layer_dense(1, activation = custom_fn)   model <- keras_model(inputs, outputs)   model |> compile(optimizer = \"rmsprop\", loss = \"mean_squared_error\")   model }   # Train the model. train_model <- function(model) {   input <- random_uniform(c(4, 4))   target <- random_uniform(c(4, 1))   model |> fit(input, target, verbose = FALSE, epochs = 1)   model }  test_input <- random_uniform(c(4, 4)) test_target <- random_uniform(c(4, 1))  model <- get_model() |> train_model() model |> save_model(\"custom_model.keras\", overwrite = TRUE)  # Now, we can simply load without worrying about our custom objects. reconstructed_model <- load_model(\"custom_model.keras\")  # Let's check: stopifnot(all.equal(   model |> predict(test_input, verbose = FALSE),   reconstructed_model |> predict(test_input, verbose = FALSE) ))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"passing-custom-objects-to-load_model","dir":"Articles","previous_headings":"Saving > Custom objects","what":"Passing custom objects to load_model()","title":"Save, serialize, and export models","text":"","code":"model <- get_model() |> train_model()  # Calling `save_model('my_model.keras')` creates a zip archive `my_model.keras`. model |> save_model(\"custom_model.keras\", overwrite = TRUE)  # Upon loading, pass a named list containing the custom objects used in the # `custom_objects` argument of `load_model()`. reconstructed_model <-  load_model(  \"custom_model.keras\",   custom_objects = list(CustomLayer = layer_custom,                         custom_fn = custom_fn), )  # Let's check: stopifnot(all.equal(   model |> predict(test_input, verbose = FALSE),   reconstructed_model |> predict(test_input, verbose = FALSE) ))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"using-a-custom-object-scope","dir":"Articles","previous_headings":"Saving > Custom objects","what":"Using a custom object scope","title":"Save, serialize, and export models","text":"code within custom object scope able recognize custom objects passed scope argument. Therefore, loading model within scope allow loading custom objects. Example:","code":"model <- get_model() |> train_model() model |> save_model(\"custom_model.keras\", overwrite = TRUE)  # Pass the custom objects dictionary to a custom object scope and place # the `keras.models.load_model()` call within the scope. custom_objects <- list(CustomLayer = layer_custom, custom_fn = custom_fn)  with_custom_object_scope(custom_objects, {   reconstructed_model <- load_model(\"custom_model.keras\") })  # Let's check: stopifnot(all.equal(   model |> predict(test_input, verbose = FALSE),   reconstructed_model |> predict(test_input, verbose = FALSE) ))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"model-serialization","dir":"Articles","previous_headings":"Saving","what":"Model serialization","title":"Save, serialize, and export models","text":"section saving model’s configuration, without state. model’s configuration (architecture) specifies layers model contains, layers connected. configuration model, model can created freshly initialized state (weights compilation information).","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"apis-1","dir":"Articles","previous_headings":"Saving > Model serialization","what":"APIs","title":"Save, serialize, and export models","text":"following serialization APIs available: clone_model(model): make (randomly initialized) copy model. get_config() cls.from_config(): retrieve configuration layer model, recreate model instance config, respectively. keras.models.model_to_json() keras.models.model_from_json(): similar, JSON strings. keras.saving.serialize_keras_object(): retrieve configuration arbitrary Keras object. keras.saving.deserialize_keras_object(): recreate object instance configuration.","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"in-memory-model-cloning","dir":"Articles","previous_headings":"Saving > Model serialization","what":"In-memory model cloning","title":"Save, serialize, and export models","text":"can -memory cloning model via clone_model(). equivalent getting config recreating model config (preserve compilation information layer weights values). Example:","code":"new_model <- clone_model(model)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"get_config-and-from_config","dir":"Articles","previous_headings":"Saving > Model serialization","what":"get_config() and from_config()","title":"Save, serialize, and export models","text":"Calling get_config(model) get_config(layer) return named list containing configuration model layer, respectively. define get_config() contain arguments needed initialize() method model layer. loading time, from_config(config) method call initialize() arguments reconstruct model layer. Layer example: Now let’s reconstruct layer using from_config() method: Sequential model example: Functional model example:","code":"layer <- layer_dense(, 3, activation=\"relu\") layer_config <- get_config(layer) str(layer_config) ## List of 12 ##  $ name              : chr \"dense_4\" ##  $ trainable         : logi TRUE ##  $ dtype             : chr \"float32\" ##  $ units             : int 3 ##  $ activation        : chr \"relu\" ##  $ use_bias          : logi TRUE ##  $ kernel_initializer:List of 4 ##   ..$ module         : chr \"keras.initializers\" ##   ..$ class_name     : chr \"GlorotUniform\" ##   ..$ config         :List of 1 ##   .. ..$ seed: NULL ##   ..$ registered_name: NULL ##  $ bias_initializer  :List of 4 ##   ..$ module         : chr \"keras.initializers\" ##   ..$ class_name     : chr \"Zeros\" ##   ..$ config         : Named list() ##   ..$ registered_name: NULL ##  $ kernel_regularizer: NULL ##  $ bias_regularizer  : NULL ##  $ kernel_constraint : NULL ##  $ bias_constraint   : NULL ##  - attr(*, \"__class__\")=<class 'keras.src.layers.core.dense.Dense'> new_layer <- from_config(layer_config) model <- keras_model_sequential(input_shape = c(32)) |>   layer_dense(1) config <- get_config(model) new_model <- from_config(config) inputs <- keras_input(c(32)) outputs <- inputs |> layer_dense(1) model <- keras_model(inputs, outputs) config <- get_config(model) new_model <- from_config(config)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"save_model_config-and-load_model_config","dir":"Articles","previous_headings":"Saving > Model serialization","what":"save_model_config() and load_model_config()","title":"Save, serialize, and export models","text":"similar get_config / from_config, except turns model JSON file, can loaded without original model class. also specific models, isn’t meant layers. Example:","code":"model <- keras_model_sequential(input_shape = c(32)) |>   layer_dense(1) save_model_config(model, \"model_config.json\") new_model <- load_model_config(\"model_config.json\") unlink(\"model_config.json\")"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"arbitrary-object-serialization-and-deserialization","dir":"Articles","previous_headings":"Saving > Model serialization","what":"Arbitrary object serialization and deserialization","title":"Save, serialize, and export models","text":"serialize_keras_object() deserialize_keras_object() APIs general-purpose APIs can used serialize deserialize Keras object custom object. foundation saving model architecture behind serialize()/deserialize() calls keras. Example: Note serialization format containing necessary information proper reconstruction: module containing name Keras module identifying module object comes class_name containing name object’s class. config information needed reconstruct object registered_name custom objects. See . Now can reconstruct regularizer.","code":"my_reg <- regularizer_l1(0.005) config <- serialize_keras_object(my_reg) str(config) ## List of 4 ##  $ module         : chr \"keras.regularizers\" ##  $ class_name     : chr \"L1\" ##  $ config         :List of 1 ##   ..$ l1: num 0.005 ##  $ registered_name: NULL new_reg <- deserialize_keras_object(config) new_reg ## <keras.src.regularizers.regularizers.L1 object> ##  signature: (x)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"model-weights-saving","dir":"Articles","previous_headings":"Saving","what":"Model weights saving","title":"Save, serialize, and export models","text":"can choose save & load model’s weights. can useful : need model inference: case won’t need restart training, don’t need compilation information optimizer state. transfer learning: case training new model reusing state prior model, don’t need compilation information prior model.","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"apis-for-in-memory-weight-transfer","dir":"Articles","previous_headings":"Saving > Model weights saving","what":"APIs for in-memory weight transfer","title":"Save, serialize, and export models","text":"Weights can copied different objects using get_weights() set_weights(): get_weights(<layer>): Returns list arrays weight values. set_weights(<layer>weights): Sets model/layer weights values provided (arrays). Examples: Transferring weights one layer another, memory Transferring weights one model another model compatible architecture, memory case stateless layers stateless layers change order number weights, models can compatible architectures even extra/missing stateless layers.","code":"create_layer <- function() {   layer <- layer_dense(, 64, activation = \"relu\", name = \"dense_2\")   layer$build(shape(NA, 784))   layer }  layer_1 <- create_layer() layer_2 <- create_layer()  # Copy weights from layer 1 to layer 2 layer_2 |> set_weights(get_weights(layer_1)) # Create a simple functional model inputs <- keras_input(shape=c(784), name=\"digits\") outputs <- inputs |>   layer_dense(64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(10, name = \"predictions\") functional_model <- keras_model(inputs = inputs, outputs = outputs,                                name = \"3_layer_mlp\")  # Define a subclassed model with the same architecture SubclassedModel <- new_model_class(   \"SubclassedModel\",   initialize = function(output_dim, name = NULL) {     super$initialize(name = name)     self$output_dim <- output_dim |> as.integer()     self$dense_1 <- layer_dense(, 64, activation = \"relu\",                                 name = \"dense_1\")     self$dense_2 <- layer_dense(, 64, activation = \"relu\",                                 name = \"dense_2\")     self$dense_3 <- layer_dense(, self$output_dim,                                 name = \"predictions\")   },    call = function(inputs) {     inputs |>       self$dense_1() |>       self$dense_2() |>       self$dense_3()   },    get_config = function(self) {     list(output_dim = self$output_dim,          name = self$name)   } )   subclassed_model <- SubclassedModel(10) # Call the subclassed model once to create the weights. subclassed_model(op_ones(c(1, 784))) |> invisible()  # Copy weights from functional_model to subclassed_model. set_weights(subclassed_model, get_weights(functional_model))  stopifnot(all.equal(   get_weights(functional_model),   get_weights(subclassed_model) )) input <- keras_input(shape = c(784), name = \"digits\") output <- input |>   layer_dense(64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(10, name = \"predictions\") functional_model <- keras_model(inputs, outputs,                                 name = \"3_layer_mlp\")  input <- keras_input(shape = c(784), name = \"digits\") output <- input |>   layer_dense(64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(64, activation = \"relu\", name = \"dense_2\") |>   # Add a dropout layer, which does not contain any weights.   layer_dropout(0.5) |>   layer_dense(10, name = \"predictions\")  functional_model_with_dropout <-   keras_model(input, output, name = \"3_layer_mlp\")  set_weights(functional_model_with_dropout,             get_weights(functional_model))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"apis-for-saving-weights-to-disk-loading-them-back","dir":"Articles","previous_headings":"Saving > Model weights saving","what":"APIs for saving weights to disk & loading them back","title":"Save, serialize, and export models","text":"Weights can saved disk calling save_model_weights(filepath). filename end .weights.h5. Example: Note using freeze_weights() may result different output get_weights(layer) ordering model contains nested layers.","code":"sequential_model = keras_model_sequential(input_shape = c(784),                                           input_name = \"digits\") |>   layer_dense(64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(10, name = \"predictions\") sequential_model |> save_model_weights(\"my_model.weights.h5\") sequential_model |> load_model_weights(\"my_model.weights.h5\")"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"transfer-learning-example","dir":"Articles","previous_headings":"Saving > Model weights saving > APIs for saving weights to disk & loading them back","what":"Transfer learning example","title":"Save, serialize, and export models","text":"loading pretrained weights weights file, recommended load weights original checkpointed model, extract desired weights/layers new model. Example:","code":"create_functional_model <- function() {   inputs <- keras_input(shape = c(784), name = \"digits\")   outputs <- inputs |>     layer_dense(64, activation = \"relu\", name = \"dense_1\") |>     layer_dense(64, activation = \"relu\", name = \"dense_2\") |>     layer_dense(10, name = \"predictions\")   keras_model(inputs, outputs, name = \"3_layer_mlp\") }  functional_model <- create_functional_model() functional_model |> save_model_weights(\"pretrained.weights.h5\")  # In a separate program: pretrained_model <- create_functional_model() pretrained_model |> load_model_weights(\"pretrained.weights.h5\")  # Create a new model by extracting layers from the original model: extracted_layers <- pretrained_model$layers |> head(-1) model <- keras_model_sequential(layers = extracted_layers) |>   layer_dense(5, name = \"dense_3\") summary(model) ## Model: \"sequential_4\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_1 (Dense)                 │ (None, 64)             │        50,240 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 64)             │         4,160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_3 (Dense)                 │ (None, 5)              │           325 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 54,725 (213.77 KB) ##  Trainable params: 54,725 (213.77 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":[]},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"defining-the-config-methods","dir":"Articles","previous_headings":"Saving > Appendix: Handling custom objects","what":"Defining the config methods","title":"Save, serialize, and export models","text":"Specifications: get_config() return JSON-serializable named list order compatible Keras architecture model-saving APIs. from_config(config) (class method) return new layer model object created config. default implementation returns .call(cls, config). NOTE: constructor arguments already serializable, e.g. strings ints, non-custom Keras objects, overriding from_config() necessary. However, complex objects layers models passed initialize(), deserialization must handled explicitly either initialize overriding from_config() method. Example: Note overriding from_config unnecessary MyDense hidden_units, kernel_initializer, kernel_regularizer ints, strings, built-Keras object, respectively. means default from_config implementation cls(!!!config) work intended. complex objects, layers models passed initialize(), example, must explicitly deserialize objects. Let’s take look example model from_config override necessary. Example:","code":"layer_my_dense <- register_keras_serializable(   package = \"MyLayers\", name = \"KernelMult\",   object = Layer(     \"MyDense\",     initialize = function(units,                           ...,                           kernel_regularizer = NULL,                           kernel_initializer = NULL,                           nested_model = NULL) {       super$initialize(...)       self$hidden_units <- units       self$kernel_regularizer <- kernel_regularizer       self$kernel_initializer <- kernel_initializer       self$nested_model <- nested_model     },     get_config = function() {       config <- super$get_config()       # Update the config with the custom layer's parameters       config <- modifyList(config, list(         units = self$hidden_units,         kernel_regularizer = self$kernel_regularizer,         kernel_initializer = self$kernel_initializer,         nested_model = self$nested_model       ))       config     },     build = function(input_shape) {       input_units <- tail(input_shape, 1)       self$kernel <- self$add_weight(         name = \"kernel\",         shape = shape(input_units, self$hidden_units),         regularizer = self$kernel_regularizer,         initializer = self$kernel_initializer,       )     },     call = function(inputs) {       op_matmul(inputs, self$kernel)     }   ) )   layer <- layer_my_dense(units = 16,                         kernel_regularizer = \"l1\",                         kernel_initializer = \"ones\") layer3 <- layer_my_dense(units = 64, nested_model = layer)  config <- serialize_keras_object(layer3) str(config) ## List of 4 ##  $ module         : chr \"<r-globalenv>\" ##  $ class_name     : chr \"MyDense\" ##  $ config         :List of 5 ##   ..$ name        : chr \"my_dense_1\" ##   ..$ trainable   : logi TRUE ##   ..$ dtype       : chr \"float32\" ##   ..$ units       : num 64 ##   ..$ nested_model:List of 4 ##   .. ..$ module         : chr \"<r-globalenv>\" ##   .. ..$ class_name     : chr \"MyDense\" ##   .. ..$ config         :List of 6 ##   .. .. ..$ name              : chr \"my_dense\" ##   .. .. ..$ trainable         : logi TRUE ##   .. .. ..$ dtype             : chr \"float32\" ##   .. .. ..$ units             : num 16 ##   .. .. ..$ kernel_regularizer: chr \"l1\" ##   .. .. ..$ kernel_initializer: chr \"ones\" ##   .. ..$ registered_name: chr \"MyLayers>KernelMult\" ##  $ registered_name: chr \"MyLayers>KernelMult\" new_layer <- deserialize_keras_object(config) new_layer ## <MyDense name=my_dense_1, built=False> ##  signature: (*args, **kwargs) `%||%` <- \\(x, y) if(is.null(x)) y else x layer_custom_model <- register_keras_serializable(   package = \"ComplexModels\",   object = Layer(     \"CustomModel\",     initialize = function(first_layer, second_layer = NULL, ...) {       super$initialize(...)       self$first_layer <- first_layer       self$second_layer <- second_layer %||% layer_dense(, 8)     },      get_config = function() {       config <- super$get_config()       config <- modifyList(config, list(         first_layer = self$first_layer,         second_layer = self$second_layer       ))       config     },      from_config = function(config) {       config$first_layer %<>% deserialize_keras_object()       config$second_layer %<>% deserialize_keras_object()       # note that the class is available in methods under the classname symbol,       # (`CustomModel` for this class), and also under the symbol `__class__`       cls(!!!config)       # CustomModel(!!!config)     },     call = function(self, inputs) {       inputs |>         self$first_layer() |>         self$second_layer()     }   ) )  # Let's make our first layer the custom layer from the previous example (MyDense) inputs <- keras_input(c(32)) outputs <-  inputs |> layer_custom_model(first_layer=layer) model <- keras_model(inputs, outputs)  config <- get_config(model) new_model <- from_config(config)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"how-custom-objects-are-serialized","dir":"Articles","previous_headings":"Saving > Appendix: Handling custom objects","what":"How custom objects are serialized","title":"Save, serialize, and export models","text":"serialization format special key custom objects registered via register_keras_serializable(). registered_name key allows easy retrieval loading/deserialization time also allowing users add custom naming. Let’s take look config serializing custom layer MyDense defined . Example: shown, registered_name key contains lookup information Keras master list, including package MyLayers custom name KernelMult gave calling register_keras_serializables(). Take look custom class definition/registration . Note class_name key contains original name class, allowing proper re-initialization from_config. Additionally, note module key NULL since custom object.","code":"layer <- layer_my_dense(   units = 16,   kernel_regularizer = regularizer_l1_l2(l1 = 1e-5, l2 = 1e-4),   kernel_initializer = \"ones\", ) config <- serialize_keras_object(layer) str(config) ## List of 4 ##  $ module         : chr \"<r-globalenv>\" ##  $ class_name     : chr \"MyDense\" ##  $ config         :List of 6 ##   ..$ name              : chr \"my_dense_2\" ##   ..$ trainable         : logi TRUE ##   ..$ dtype             : chr \"float32\" ##   ..$ units             : num 16 ##   ..$ kernel_regularizer:List of 4 ##   .. ..$ module         : chr \"keras.regularizers\" ##   .. ..$ class_name     : chr \"L1L2\" ##   .. ..$ config         :List of 2 ##   .. .. ..$ l1: num 1e-05 ##   .. .. ..$ l2: num 1e-04 ##   .. ..$ registered_name: NULL ##   ..$ kernel_initializer: chr \"ones\" ##  $ registered_name: chr \"MyLayers>KernelMult\""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Training & evaluation with the built-in methods","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Training & evaluation with the built-in methods","text":"guide covers training, evaluation, prediction (inference) models using built-APIs training & validation (fit(), evaluate() predict()). interested leveraging fit() specifying training step function, see Customizing happens fit() guide. interested writing training & evaluation loops scratch, see guide Writing training loop scratch. general, whether using built-loops writing , model training & evaluation works strictly way across every kind Keras model – Sequential models, models built Functional API, models written scratch via model subclassing.","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"api-overview-a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"API overview: a first end-to-end example","title":"Training & evaluation with the built-in methods","text":"passing data built-training loops model, either use: Arrays (data small fits memory) tf_dataset objects PyTorch DataLoader instances next paragraphs, ’ll use MNIST dataset NumPy arrays, order demonstrate use optimizers, losses, metrics. Afterwards, ’ll take close look options. Let’s consider following model (, build Functional API, Sequential model subclassed model well): ’s typical end--end workflow looks like, consisting : Training Validation holdout set generated original training data Evaluation test data ’ll use MNIST data example. specify training configuration (optimizer, loss, metrics): call fit(), train model slicing data “batches” size batch_size, repeatedly iterating entire dataset given number epochs. returned history object holds record loss values metric values training: evaluate model test data via evaluate(): Now, let’s review piece workflow detail.","code":"inputs <- keras_input(shape = 784, name=\"digits\") outputs <- inputs |>   layer_dense(units = 64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(units = 64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(units = 10, activation = \"softmax\", name = \"predictions\") model <- keras_model(inputs = inputs, outputs = outputs) summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ digits (InputLayer)             │ (None, 784)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_1 (Dense)                 │ (None, 64)             │        50,240 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 64)             │         4,160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ predictions (Dense)             │ (None, 10)             │           650 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 55,050 (215.04 KB) ##  Trainable params: 55,050 (215.04 KB) ##  Non-trainable params: 0 (0.00 B) c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()  # Preprocess the data (these are NumPy arrays) x_train <- array_reshape(x_train, c(60000, 784)) / 255 x_test <- array_reshape(x_test, c(10000, 784)) / 255  # Reserve 10,000 samples for validation x_val <- x_train[1:10000,] y_val <- y_train[1:10000] x_train <- x_train[-c(1:10000),] y_train <- y_train[-c(1:10000)] model |> compile(   # Optimizer   optimizer = optimizer_rmsprop(),   # Loss function to minimize   loss = loss_sparse_categorical_crossentropy(),   # List of metrics to monitor   metrics = list(metric_sparse_categorical_accuracy()) ) history <- model |> fit(   x_train, y_train,   batch_size = 64,   epochs = 2,   # We pass some validation for   # monitoring validation loss and metrics   # at the end of each epoch   validation_data = list(x_val, y_val) ) ## Epoch 1/2 ## 782/782 - 3s - 3ms/step - loss: 0.3410 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.1869 - val_sparse_categorical_accuracy: 0.9455 ## Epoch 2/2 ## 782/782 - 1s - 1ms/step - loss: 0.1588 - sparse_categorical_accuracy: 0.9532 - val_loss: 0.1303 - val_sparse_categorical_accuracy: 0.9626 history ## ## Final epoch (plot to see history): ##                            loss: 0.1588 ##     sparse_categorical_accuracy: 0.9532 ##                        val_loss: 0.1303 ## val_sparse_categorical_accuracy: 0.9626 # Evaluate the model on the test data using `evaluate` results <- model |> evaluate(x_test, y_test, batch_size=128) ## 79/79 - 0s - 3ms/step - loss: 0.1258 - sparse_categorical_accuracy: 0.9625 str(results) ## List of 2 ##  $ loss                       : num 0.126 ##  $ sparse_categorical_accuracy: num 0.962 # Generate predictions (probabilities -- the output of the last layer) # on new data using `predict` predictions <- model |> predict(x_test[1:2,]) ## 1/1 - 0s - 136ms/step dim(predictions) ## [1]  2 10"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"the-compile-method-specifying-a-loss-metrics-and-an-optimizer","dir":"Articles","previous_headings":"","what":"The compile() method: specifying a loss, metrics, and an optimizer","title":"Training & evaluation with the built-in methods","text":"train model fit(), need specify loss function, optimizer, optionally, metrics monitor. pass model arguments compile() method: metrics argument list – model can number metrics. model multiple outputs, can specify different losses metrics output, can modulate contribution output total loss model. find details Passing data multi-input, multi-output models section. Note ’re satisfied default settings, many cases optimizer, loss, metrics can specified via string identifiers shortcut: later reuse, let’s put model definition compile step functions; call several times across different examples guide.","code":"model |> compile(   optimizer = optimizer_rmsprop(learning_rate = 1e-3),   loss = loss_sparse_categorical_crossentropy(),   metrics = list(metric_sparse_categorical_accuracy()) ) model |> compile(   optimizer = \"rmsprop\",   loss = \"sparse_categorical_crossentropy\",   metrics = c(\"sparse_categorical_accuracy\") ) get_uncompiled_model <- function() {   inputs <- keras_input(shape = 784, name = \"digits\")   outputs <- inputs |>     layer_dense(units = 64, activation = \"relu\", name = \"dense_1\") |>     layer_dense(units = 64, activation = \"relu\", name = \"dense_2\") |>     layer_dense(units = 10, activation = \"softmax\", name = \"predictions\")   keras_model(inputs = inputs, outputs = outputs) }  get_compiled_model <- function() {   model <- get_uncompiled_model()   model |> compile(     optimizer = \"rmsprop\",     loss = \"sparse_categorical_crossentropy\",     metrics = c(\"sparse_categorical_accuracy\")   )   model }"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"many-built-in-optimizers-losses-and-metrics-are-available","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Many built-in optimizers, losses, and metrics are available","title":"Training & evaluation with the built-in methods","text":"general, won’t create losses, metrics, optimizers scratch, need likely already part Keras API: Optimizers: [optimizer_sgd()] (without momentum) [optimizer_rmsprop()] [optimizer_adam()] etc. Losses: [loss_mean_squared_error()] [loss_kl_divergence()] [loss_cosine_similarity()] etc. Metrics: [metric_auc()] [metric_precision()] [metric_recall()] etc.","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"custom-losses","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Custom losses","title":"Training & evaluation with the built-in methods","text":"need create custom loss, Keras provides three ways . first method involves creating function accepts inputs y_true y_pred. following example shows loss function computes mean squared error real data predictions: need loss function takes parameters beside y_true y_pred, can subclass Keras base Loss class using [Loss()] implement following two methods: initialize(): accept parameters pass call loss function call(y_true, y_pred): use targets (y_true) model predictions (y_pred) compute model’s loss Let’s say want use mean squared error, added term de-incentivize prediction values far 0.5 (assume categorical targets one-hot encoded take values 0 1). creates incentive model confident, may help reduce overfitting (won’t know works try!). ’s :","code":"custom_mean_squared_error <- function(y_true, y_pred) {   op_mean(op_square(y_true - y_pred), axis = -1) }  model <- get_uncompiled_model() model |> compile(optimizer = \"adam\",                  loss = custom_mean_squared_error)  # We need to one-hot encode the labels to use MSE y_train_one_hot <- op_one_hot(y_train, num_classes = 10) model |> fit(x_train, y_train_one_hot, batch_size = 64, epochs = 2) ## Epoch 1/2 ## 782/782 - 2s - 2ms/step - loss: 0.0161 ## Epoch 2/2 ## 782/782 - 0s - 623us/step - loss: 0.0078 loss_custom_mse <- Loss(   classname = \"CustomMSE\",   initialize = function(regularization_factor = 0.1, name = \"custom_mse\") {     super$initialize(name = name)     self$regularization_factor <- regularization_factor   },   call = function(y_true, y_pred) {     mse <- op_mean(op_square(y_true - y_pred), axis = -1)     reg <- op_mean(op_square(0.5 - y_pred), axis = -1)     mse + reg * self$regularization_factor   } )  model <- get_uncompiled_model() model |> compile(optimizer=\"adam\", loss = loss_custom_mse())  y_train_one_hot <- op_one_hot(y_train, num_classes=10) model |> fit(x_train, y_train_one_hot, batch_size=64, epochs=1) ## 782/782 - 2s - 2ms/step - loss: 0.0390"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"custom-metrics","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Custom metrics","title":"Training & evaluation with the built-in methods","text":"need metric isn’t part API, can easily create custom metrics subclassing Keras base Metric class using [Metric()]. need implement 4 methods: initialize(), create state variables metric. update_state(y_true, y_pred, sample_weight = NULL), uses targets y_true model predictions y_pred update state variables. result(), uses state variables compute final results. reset_state(), reinitializes state metric. State update results computation kept separate (update_state() result(), respectively) cases, results computation might expensive done periodically. ’s simple example showing implement CategoricalTruePositives metric counts many samples correctly classified belonging given class:","code":"metric_categorical_true_positives <- Metric(   \"CategoricalTruePositives\",    initialize = function(name = \"categorical_true_positives\", ...) {     super$initialize(name = name, ...)     self$true_positives <- self$add_variable(shape = shape(),                                              name = \"ctp\",                                              initializer = \"zeros\")   },    update_state = function(y_true, y_pred, sample_weight = NULL) {     y_pred <- op_argmax(y_pred, axis = 2) |> op_reshape(c(-1, 1))     values <- op_cast(y_true, \"int32\") == op_cast(y_pred, \"int32\")     values <- op_cast(values, \"float32\")     if (!is.null(sample_weight)) {       sample_weight <- op_cast(sample_weight, \"float32\")       values <- op_multiply(values, sample_weight)     }     self$true_positives$assign_add(op_sum(values))   },    result = function() {     self$true_positives$value   },    reset_state = function() {     self$true_positives$assign(0.0)   } )  model <- get_uncompiled_model() model |> compile(   optimizer = optimizer_rmsprop(learning_rate = 1e-3),   loss = loss_sparse_categorical_crossentropy(),   metrics = c(metric_categorical_true_positives()) ) history <- model |> fit(x_train, y_train, batch_size = 64, epochs = 3) ## Epoch 1/3 ## 782/782 - 1s - 2ms/step - categorical_true_positives: 360502.0000 - loss: 0.3444 ## Epoch 2/3 ## 782/782 - 1s - 964us/step - categorical_true_positives: 362616.0000 - loss: 0.1656 ## Epoch 3/3 ## 782/782 - 1s - 1ms/step - categorical_true_positives: 363187.0000 - loss: 0.1203"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"handling-losses-and-metrics-that-dont-fit-the-standard-signature","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Handling losses and metrics that don’t fit the standard signature","title":"Training & evaluation with the built-in methods","text":"overwhelming majority losses metrics can computed y_true y_pred, y_pred output model – . instance, regularization loss may require activation layer (targets case), activation may model output. cases, can call self$add_loss(loss_value) inside call method custom layer. Losses added way get added “main” loss training (one passed compile()). ’s simple example adds activity regularization (note activity regularization built-Keras layers – layer just sake providing concrete example): Note pass losses via add_loss(), becomes possible call compile() without loss function, since model already loss minimize. Consider following LogisticEndpoint layer: takes inputs targets & logits, tracks crossentropy loss via add_loss(). can use model two inputs (input data & targets), compiled without loss argument, like : information training multi-input models, see section Passing data multi-input, multi-output models.","code":"layer_custom_activity_regularizer <- Layer(   \"ActivityRegularization\",   call = function(inputs) {     self$add_loss(op_sum(inputs) * 0.1)     inputs  # Pass-through layer.   } )  inputs <- keras_input(shape = 784, name = \"digits\") outputs <- inputs |>   layer_dense(units = 32, activation = \"relu\", name = \"dense_1\") |>   layer_custom_activity_regularizer() |>   layer_dense(units = 64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(units = 10, name = \"predictions\")  model <- keras_model(inputs = inputs, outputs = outputs) model |> compile(optimizer = optimizer_rmsprop(learning_rate = 1e-3),                  loss = loss_sparse_categorical_crossentropy(from_logits = TRUE))  # The displayed loss will be much higher than before # due to the regularization component. model |> fit(x_train, y_train, batch_size = 64, epochs = 1) ## 782/782 - 2s - 2ms/step - loss: 2.3721 layer_logistic_endpoint <- Layer(   \"LogisticEndpoint\",   initialize = function(name = NULL) {     super$initialize(name = name)     self$loss_fn <- loss_binary_crossentropy(from_logits = TRUE)   },   call = function(targets, logits, sample_weights = NULL) {     # Compute the training-time loss value and add it     # to the layer using `self.add_loss()`.     loss <- self$loss_fn(targets, logits, sample_weights)     self$add_loss(loss)      # Return the inference-time prediction tensor (for `predict()`).     op_softmax(logits)   } ) inputs <- keras_input(shape = 3, name = \"inputs\") targets <- keras_input(shape = 10, name = \"targets\")  logits <- inputs |> layer_dense(10) predictions <- layer_logistic_endpoint(name = \"predictions\")(targets, logits)  model <- keras_model(inputs = list(inputs, targets),                      outputs = predictions) model |> compile(optimizer = \"adam\")  # No loss argument!  data <- list(   inputs = random_normal(c(3, 3)),   targets = random_normal(c(3, 10)) ) model |> fit(data, epochs = 1) ## 1/1 - 1s - 508ms/step - loss: 1.0566"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"automatically-setting-apart-a-validation-holdout-set","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Automatically setting apart a validation holdout set","title":"Training & evaluation with the built-in methods","text":"first end--end example saw, used validation_data argument pass list arrays list(x_val, y_val) model evaluating validation loss validation metrics end epoch. ’s another option: argument validation_split allows automatically reserve part training data validation. argument value represents fraction data reserved validation, set number higher 0 lower 1. instance, validation_split = 0.2 means “use 20% data validation”, validation_split = 0.6 means “use 60% data validation”. way validation computed taking last x% samples arrays received fit() call, shuffling. Note can use validation_split training NumPy data.","code":"model <- get_compiled_model() model |> fit(x_train, y_train,              batch_size = 64,              validation_split = 0.2, epochs = 1) ## 625/625 - 2s - 3ms/step - loss: 0.3817 - sparse_categorical_accuracy: 0.8919 - val_loss: 0.1953 - val_sparse_categorical_accuracy: 0.9431"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"training-evaluation-using-tf-dataset-objects","dir":"Articles","previous_headings":"","what":"Training & evaluation using TF Dataset objects","title":"Training & evaluation with the built-in methods","text":"past paragraphs, ’ve seen handle losses, metrics, optimizers, ’ve seen use validation_data validation_split arguments fit(), data passed arrays. Another option use iterator-like, tf.data.Dataset, PyTorch DataLoader, R generator function. Let’s take look former. tfdatasets R package containes set utilities loading preprocessing data way ’s fast scalable. complete guide creating Datasets, see tf.data documentation. can use tf.data train Keras models regardless backend ’re using – whether ’s JAX, PyTorch, TensorFlow. can pass Dataset instance directly methods fit(), evaluate(), predict(): Note Dataset reset end epoch, can reused next epoch. want run training specific number batches Dataset, can pass steps_per_epoch argument, specifies many training steps model run using Dataset moving next epoch. can also pass Dataset instance validation_data argument fit(): end epoch, model iterate validation dataset compute validation loss validation metrics. want run validation specific number batches dataset, can pass validation_steps argument, specifies many validation steps model run validation dataset interrupting validation moving next epoch: Note validation dataset reset use (always evaluating samples epoch epoch). argument validation_split (generating holdout set training data) supported training Dataset objects, since feature requires ability index samples datasets, possible general Dataset API.","code":"library(tfdatasets, exclude = \"shape\") model <- get_compiled_model()  # First, let's create a training Dataset instance. # For the sake of our example, we'll use the same MNIST data as before. train_dataset <- tensor_slices_dataset(list(x_train, y_train))  # Shuffle and slice the dataset. train_dataset <- train_dataset |>   dataset_shuffle(buffer_size=1024) |>   dataset_batch(64)  # Now we get a test dataset. test_dataset <-   tensor_slices_dataset(list(x_test, y_test)) |>   dataset_batch(64)  # Since the dataset already takes care of batching, # we don't pass a `batch_size` argument. model |> fit(train_dataset, epochs = 3) ## Epoch 1/3 ## 782/782 - 2s - 2ms/step - loss: 0.3365 - sparse_categorical_accuracy: 0.9041 ## Epoch 2/3 ## 782/782 - 1s - 681us/step - loss: 0.1605 - sparse_categorical_accuracy: 0.9524 ## Epoch 3/3 ## 782/782 - 1s - 1ms/step - loss: 0.1185 - sparse_categorical_accuracy: 0.9647 # You can also evaluate or predict on a dataset. result <- model |> evaluate(test_dataset) ## 157/157 - 1s - 4ms/step - loss: 0.1152 - sparse_categorical_accuracy: 0.9627 result ## $loss ## [1] 0.1151979 ## ## $sparse_categorical_accuracy ## [1] 0.9627 model <- get_compiled_model()  # Prepare the training dataset train_dataset <- tensor_slices_dataset(list(x_train, y_train)) train_dataset <- train_dataset |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(64)  # Only use the 100 batches per epoch (that's 64 * 100 samples) model |> fit(train_dataset, epochs = 3, steps_per_epoch = 100) ## Epoch 1/3 ## 100/100 - 1s - 7ms/step - loss: 0.8017 - sparse_categorical_accuracy: 0.7806 ## Epoch 2/3 ## 100/100 - 0s - 682us/step - loss: 0.3661 - sparse_categorical_accuracy: 0.9006 ## Epoch 3/3 ## 100/100 - 0s - 740us/step - loss: 0.3009 - sparse_categorical_accuracy: 0.9106 model <- get_compiled_model()  # Prepare the training dataset train_dataset <- tensor_slices_dataset(list(x_train, y_train)) train_dataset <- train_dataset |>   dataset_shuffle(buffer_size=1024) |>   dataset_batch(64)  # Prepare the validation dataset val_dataset <- tensor_slices_dataset(list(x_val, y_val)) val_dataset <- val_dataset |> dataset_batch(64)  model |> fit(train_dataset, epochs = 1, validation_data = val_dataset) ## 782/782 - 2s - 3ms/step - loss: 0.3428 - sparse_categorical_accuracy: 0.9022 - val_loss: 0.2337 - val_sparse_categorical_accuracy: 0.9291 model  <- get_compiled_model()  # Prepare the training dataset train_dataset <- tensor_slices_dataset(list(x_train, y_train)) train_dataset <- train_dataset |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(64)  # Prepare the validation dataset val_dataset <- tensor_slices_dataset(list(x_val, y_val)) val_dataset <- val_dataset |> dataset_batch(64)  model %>% fit(   train_dataset,   epochs = 1,   # Only run validation using the first 10 batches of the dataset   # using the `validation_steps` argument   validation_data = val_dataset,   validation_steps = 10, ) ## 782/782 - 2s - 2ms/step - loss: 0.3391 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.1997 - val_sparse_categorical_accuracy: 0.9391"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-sample-weighting-and-class-weighting","dir":"Articles","previous_headings":"","what":"Using sample weighting and class weighting","title":"Training & evaluation with the built-in methods","text":"default settings weight sample decided frequency dataset. two methods weight data, independent sample frequency: Class weights Sample weights","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"class-weights","dir":"Articles","previous_headings":"Using sample weighting and class weighting","what":"Class weights","title":"Training & evaluation with the built-in methods","text":"set passing named list class_weight argument fit(). list maps class indices weight used samples belonging class. can used balance classes without resampling, train model gives importance particular class. instance, class “0” half represented class “1” data, use model |> fit(..., class_weight = c(\"0\" = 1, \"1\" = 0.5)). ’s R example use class weights sample weights give importance correct classification class #5 (digit “5” MNIST dataset).","code":"class_weight <- c(     \"0\" = 1.0,     \"1\" = 1.0,     \"2\" = 1.0,     \"3\" = 1.0,     \"4\" = 1.0,     # Set weight \"2\" for class \"5\",     # making this class 2x more important     \"5\" = 2.0,     \"6\" = 1.0,     \"7\" = 1.0,     \"8\" = 1.0,     \"9\" = 1.0 )  model <- get_compiled_model() model |> fit(x_train, y_train,              class_weight = class_weight,              batch_size = 64, epochs = 1) ## 782/782 - 1s - 2ms/step - loss: 0.3713 - sparse_categorical_accuracy: 0.9018"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"sample-weights","dir":"Articles","previous_headings":"Using sample weighting and class weighting","what":"Sample weights","title":"Training & evaluation with the built-in methods","text":"fine grained control, building classifier, can use sample_weights. training R arrays: Pass sample_weight argument fit(). training tf_dataset sort iterator: yield (input_batch, label_batch, sample_weight_batch) tuples. “sample weights” array array numbers specify much weight sample batch computing total loss. commonly used imbalanced classification problems (idea give weight rarely-seen classes). weights used ones zeros, array can used mask loss function (entirely discarding contribution certain samples total loss). ’s matching Dataset example:","code":"sample_weight <- rep(1.0, length(y_train)) sample_weight[y_train == 5] <- 2.0  model <- get_compiled_model() model |> fit(   x_train, y_train,   sample_weight = sample_weight,   batch_size = 64, epochs = 1 ) ## 782/782 - 2s - 2ms/step - loss: 0.3740 - sparse_categorical_accuracy: 0.9015 sample_weight <- rep(1.0, length(y_train)) sample_weight[y_train == 5] <- 2.0  # Create a Dataset that includes sample weights # (3rd element in the return tuple). train_dataset <- tensor_slices_dataset(list(     x_train, y_train, sample_weight ))  # Shuffle and slice the dataset. train_dataset <- train_dataset |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(64)  model <- get_compiled_model() model |> fit(train_dataset, epochs = 1) ## 782/782 - 2s - 2ms/step - loss: 0.3654 - sparse_categorical_accuracy: 0.9057"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"passing-data-to-multi-input-multi-output-models","dir":"Articles","previous_headings":"","what":"Passing data to multi-input, multi-output models","title":"Training & evaluation with the built-in methods","text":"previous examples, considering model single input (tensor shape (764)) single output (prediction tensor shape (10)). models multiple inputs outputs? Consider following model, image input shape (32, 32, 3) (’s (height, width, channels)) time series input shape (NA, 10) (’s (timesteps, features)). model two outputs computed combination inputs: “score” (shape (1)) probability distribution five classes (shape (5)). Let’s plot model, can clearly see ’re (note shapes shown plot batch shapes, rather per-sample shapes). plot chunk unnamed-chunk-26 compilation time, can specify different losses different outputs, passing loss functions list: passed single loss function model, loss function applied every output (appropriate ). Likewise metrics: Since gave names output layers, also specify per-output losses metrics via named list: recommend use names 2 outputs. ’s possible give different weights different output-specific losses (instance, one might wish privilege “score” loss example, giving 2x importance class loss), using loss_weights argument: also choose compute loss certain outputs, outputs meant prediction training: Passing data multi-input multi-output model fit() works similar way specifying loss function compile: can pass lists arrays (1:1 mapping outputs received loss function) dicts mapping output names arrays. ’s Dataset use case: similarly R arrays, Dataset return tuple named lists (dicts).","code":"image_input <- keras_input(c(32, 32, 3), name = \"img_input\") timeseries_input <- keras_input(c(NA, 10), name = \"ts_input\")  x1 <- image_input |>   layer_conv_2d(filters = 3, kernel_size = c(3, 3)) |>   layer_global_max_pooling_2d()  x2 <- timeseries_input |>   layer_conv_1d(filters = 3, kernel_size = 3) |>   layer_global_max_pooling_1d()  x <- layer_concatenate(x1, x2)  score_output <- layer_dense(x, 1, name = \"score_output\") class_output <- layer_dense(x, 5, name = \"class_output\")  model <- keras_model(   inputs = list(image_input, timeseries_input),   outputs = list(score_output, class_output) ) plot(model, show_shapes = TRUE) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     loss_mean_squared_error(),     loss_categorical_crossentropy()   ) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     loss_mean_squared_error(),     loss_categorical_crossentropy()   ),   metrics = list(     list(       metric_mean_absolute_error(),       metric_mean_absolute_percentage_error()     ),     list(metric_categorical_accuracy())   ) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     score_output = loss_mean_squared_error(),     class_output = loss_categorical_crossentropy()   ),   metrics = list(     score_output = list(       metric_mean_absolute_error(),       metric_mean_absolute_percentage_error()     ),     class_output = list(metric_categorical_accuracy())   ) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     score_output = loss_mean_squared_error(),     class_output = loss_categorical_crossentropy()   ),   metrics = list(     score_output = list(       metric_mean_absolute_error(),       metric_mean_absolute_percentage_error()     ),     class_output = list(metric_categorical_accuracy())   ),   loss_weights = list(score_output = 2.0, class_output = 1.0) ) # loss list, positional version model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(NULL, loss_categorical_crossentropy()) )  # Or loss list, named version model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(class_output = loss_categorical_crossentropy()) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     loss_mean_squared_error(),     loss_categorical_crossentropy()   ) )  # Generate dummy data img_data <- random_normal(c(100, 32, 32, 3)) ts_data <- random_normal(c(100, 20, 10)) score_targets <- random_normal(c(100, 1)) class_targets <- random_normal(c(100, 5))  # Fit on unnamed lists (positional matching) model |> fit(     list(img_data, ts_data),     list(score_targets, class_targets),     batch_size=32,     epochs=1 ) ## 4/4 - 2s - 458ms/step - loss: 1.3788 # Alternatively, fit on named lists (names matching) model |> fit(   list(img_input = img_data, ts_input = ts_data),   list(score_output = score_targets, class_output = class_targets),   batch_size = 32,   epochs = 1 ) ## 4/4 - 1s - 230ms/step - loss: 0.2857 train_dataset <- tensor_slices_dataset(list(   list(img_input = img_data, ts_input = ts_data),   list(score_output = score_targets, class_output = class_targets) )) train_dataset <- train_dataset |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(64)  model |> fit(train_dataset, epochs = 1) ## 2/2 - 1s - 629ms/step - loss: 0.5600"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-callbacks","dir":"Articles","previous_headings":"","what":"Using callbacks","title":"Training & evaluation with the built-in methods","text":"Callbacks Keras objects called different points training (start epoch, end batch, end epoch, etc.). can used implement certain behaviors, : validation different points training (beyond built-per-epoch validation) Checkpointing model regular intervals exceeds certain accuracy threshold Changing learning rate model training seems plateauing fine-tuning top layers training seems plateauing Sending email instant message notifications training ends certain performance threshold exceeded Etc. Callbacks can passed list call fit():","code":"model <- get_compiled_model()  callbacks <- list(   callback_early_stopping(     # Stop training when `val_loss` is no longer improving     monitor = \"val_loss\",     # \"no longer improving\" being defined as \"no better than 1e-2 less\"     min_delta = 1e-2,     # \"no longer improving\" being further defined as \"for at least 2 epochs\"     patience = 2,     verbose = 1   ) ) model |> fit(   x_train,   y_train,   epochs = 20,   batch_size = 64,   callbacks = callbacks,   validation_split = 0.2, ) ## Epoch 1/20 ## 625/625 - 1s - 2ms/step - loss: 0.3695 - sparse_categorical_accuracy: 0.8961 - val_loss: 0.1873 - val_sparse_categorical_accuracy: 0.9469 ## Epoch 2/20 ## 625/625 - 1s - 974us/step - loss: 0.1751 - sparse_categorical_accuracy: 0.9489 - val_loss: 0.1403 - val_sparse_categorical_accuracy: 0.9579 ## Epoch 3/20 ## 625/625 - 1s - 903us/step - loss: 0.1277 - sparse_categorical_accuracy: 0.9625 - val_loss: 0.1218 - val_sparse_categorical_accuracy: 0.9651 ## Epoch 4/20 ## 625/625 - 1s - 889us/step - loss: 0.1007 - sparse_categorical_accuracy: 0.9700 - val_loss: 0.1153 - val_sparse_categorical_accuracy: 0.9661 ## Epoch 5/20 ## 625/625 - 1s - 1ms/step - loss: 0.0822 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1104 - val_sparse_categorical_accuracy: 0.9670 ## Epoch 6/20 ## 625/625 - 1s - 899us/step - loss: 0.0683 - sparse_categorical_accuracy: 0.9801 - val_loss: 0.1098 - val_sparse_categorical_accuracy: 0.9689 ## Epoch 7/20 ## 625/625 - 1s - 1ms/step - loss: 0.0571 - sparse_categorical_accuracy: 0.9838 - val_loss: 0.1116 - val_sparse_categorical_accuracy: 0.9698 ## Epoch 8/20 ## 625/625 - 1s - 976us/step - loss: 0.0485 - sparse_categorical_accuracy: 0.9864 - val_loss: 0.1126 - val_sparse_categorical_accuracy: 0.9702 ## Epoch 8: early stopping"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"many-built-in-callbacks-are-available","dir":"Articles","previous_headings":"Using callbacks","what":"Many built-in callbacks are available","title":"Training & evaluation with the built-in methods","text":"many built-callbacks already available Keras, : callback_model_checkpoint(): Periodically save model. callback_early_stopping(): Stop training training longer improving validation metrics. callback_tensorboard(): periodically write model logs can visualized TensorBoard (details section “Visualization”). callback_csv_logger(): streams loss metrics data CSV file. etc. See callbacks documentation complete list.","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"writing-your-own-callback","dir":"Articles","previous_headings":"Using callbacks","what":"Writing your own callback","title":"Training & evaluation with the built-in methods","text":"can create custom callback subclassing base [Callback()] class. callback access associated model class property self$model. Make sure read complete guide writing custom callbacks. ’s simple example saving list per-batch loss values training:","code":"callback_loss_history <- Callback(   classname = \"LossHistory\",   initialize = function(file = \"per_training_batch_losses.txt\", ...) {     super$initialize(...)     private$file <- file   },   on_train_begin = function(logs = NULL) {     private$per_batch_losses <- fastmap::faststack()   },   on_train_batch_begin = function(batch, logs = NULL) {     private$per_batch_losses$push(logs$loss)   },   on_train_end = function(logs = NULL) {     per_batch_losses <- private$per_batch_losses$as_list() |> as.numeric()     write(per_batch_losses, private$file)   } )"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"checkpointing-models","dir":"Articles","previous_headings":"","what":"Checkpointing models","title":"Training & evaluation with the built-in methods","text":"’re training model relatively large datasets, ’s crucial save checkpoints model frequent intervals. easiest way achieve [callback_model_checkpoint()]: ModelCheckpoint callback can used implement fault-tolerance: ability restart training last saved state model case training gets randomly interrupted. ’s basic example: call also write callback saving restoring models. complete guide serialization saving, see guide saving serializing Models.","code":"model <- get_compiled_model()  callbacks <- list(   callback_model_checkpoint(     # Path where to save the model     # The two parameters below mean that we will overwrite     # the current checkpoint if and only if     # the `val_loss` score has improved.     # The saved model name will include the current epoch.     filepath = \"mymodel_{epoch}.keras\",     save_best_only = TRUE,     # Only save a model if `val_loss` has improved.     monitor = \"val_loss\",     verbose = 1   ) ) model |> fit(   x_train, y_train,   epochs = 2, batch_size = 64,   callbacks = callbacks,   validation_split = 0.2 ) ## Epoch 1/2 ## ## Epoch 1: val_loss improved from inf to 0.19344, saving model to mymodel_1.keras ## 625/625 - 1s - 2ms/step - loss: 0.3787 - sparse_categorical_accuracy: 0.8940 - val_loss: 0.1934 - val_sparse_categorical_accuracy: 0.9441 ## Epoch 2/2 ## ## Epoch 2: val_loss improved from 0.19344 to 0.14251, saving model to mymodel_2.keras ## 625/625 - 1s - 842us/step - loss: 0.1768 - sparse_categorical_accuracy: 0.9478 - val_loss: 0.1425 - val_sparse_categorical_accuracy: 0.9600 # Prepare a directory to store all the checkpoints. checkpoint_dir <- \"./ckpt\" fs::dir_create(checkpoint_dir)  make_or_restore_model <- function() {   # Either restore the latest (best) model, or create a fresh one   # if there is no checkpoint available.   checkpoints <- Sys.glob(file.path(checkpoint_dir, \"model-loss=*.keras\"))    if (length(checkpoints) > 0) {     checkpoint_losses <- sub(\"^model-loss=([0-9.]+)\\\\.keras$\", \"\\\\1\",                              basename(checkpoints)) |> as.numeric()     best_checkpoint <- checkpoints[which.min(checkpoint_losses)]     load_model(best_checkpoint)   } else {     get_compiled_model()   } }  model <- make_or_restore_model() callbacks <- list(   # This callback saves the model every 100 batches.   # We include the training loss in the saved model name.   callback_model_checkpoint(     filepath = file.path(checkpoint_dir, \"model-loss={loss:.2f}.keras\"),     save_freq = 100   ) ) model |> fit(x_train, y_train, epochs = 1, callbacks = callbacks) ## 1563/1563 - 2s - 2ms/step - loss: 0.2932 - sparse_categorical_accuracy: 0.9145"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-learning-rate-schedules","dir":"Articles","previous_headings":"","what":"Using learning rate schedules","title":"Training & evaluation with the built-in methods","text":"common pattern training deep learning models gradually reduce learning training progresses. generally known “learning rate decay”. learning decay schedule static (fixed advance, function current epoch current batch index), dynamic (responding current behavior model, particular validation loss).","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"passing-a-schedule-to-an-optimizer","dir":"Articles","previous_headings":"Using learning rate schedules","what":"Passing a schedule to an optimizer","title":"Training & evaluation with the built-in methods","text":"can easily use static learning rate decay schedule passing schedule object learning_rate argument optimizer: Several built-schedules available: ExponentialDecay, PiecewiseConstantDecay, PolynomialDecay, InverseTimeDecay.","code":"initial_learning_rate <- 0.1 lr_schedule <- learning_rate_schedule_exponential_decay(     initial_learning_rate, decay_steps=100000, decay_rate=0.96,     staircase=TRUE )  optimizer <- optimizer_rmsprop(learning_rate = lr_schedule)"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-callbacks-to-implement-a-dynamic-learning-rate-schedule","dir":"Articles","previous_headings":"Using learning rate schedules","what":"Using callbacks to implement a dynamic learning rate schedule","title":"Training & evaluation with the built-in methods","text":"dynamic learning rate schedule (instance, decreasing learning rate validation loss longer improving) achieved schedule objects, since optimizer access validation metrics. However, callbacks access metrics, including validation metrics! can thus achieve pattern using callback modifies current learning rate optimizer. fact, even built-[callback_reduce_lr_on_plateau()].","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"visualizing-loss-and-metrics-during-training-with-tensorboard","dir":"Articles","previous_headings":"","what":"Visualizing loss and metrics during training with TensorBoard","title":"Training & evaluation with the built-in methods","text":"best way keep eye model training use TensorBoard – browser-based application can run locally provides : Live plots loss metrics training evaluation (optionally) Visualizations histograms layer activations (optionally) 3D visualizations embedding spaces learned layer_embedding() installed TensorFlow pip, able launch TensorBoard command line: R using:","code":"tensorboard --logdir=/full_path_to_your_logs tensorflow::tensorboard(logdir = \"/full_path_to_your_logs\")"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-the-tensorboard-callback","dir":"Articles","previous_headings":"Visualizing loss and metrics during training with TensorBoard","what":"Using the TensorBoard callback","title":"Training & evaluation with the built-in methods","text":"easiest way use TensorBoard Keras model fit() method [callback_tensorboard()]. simplest case, just specify want callback write logs, ’re good go: information, see callback_tensorboard().","code":"tb_callback <- callback_tensorboard(   log_dir = \"/full_path_to_your_logs\",   histogram_freq = 0, # How often to log histogram visualizations   embeddings_freq = 0, # How often to log embedding visualizations   update_freq = \"epoch\", # How often to write logs (default: once per epoch) )"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Transfer learning & fine-tuning","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Transfer learning & fine-tuning","text":"Transfer learning consists taking features learned one problem, leveraging new, similar problem. instance, features model learned identify raccoon may useful kick-start model meant identify tanukis. Transfer learning usually done tasks dataset little data train full-scale model scratch. common incarnation transfer learning context deep learning following workflow: Take layers previously trained model. Freeze , avoid destroying information contain future training rounds. Add new, trainable layers top frozen layers. learn turn old features predictions new dataset. Train new layers dataset. last, optional step, fine-tuning, consists unfreezing entire model obtained (part ), re-training new data low learning rate. can potentially achieve meaningful improvements, incrementally adapting pretrained features new data. First, go Keras trainable API detail, underlies transfer learning & fine-tuning workflows. , ’ll demonstrate typical workflow taking model pretrained ImageNet dataset, retraining Kaggle “cats vs dogs” classification dataset. adapted Deep Learning Python 2016 blog post “building powerful image classification models using little data”.","code":""},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"freezing-layers-understanding-the-trainable-attribute","dir":"Articles","previous_headings":"","what":"Freezing layers: understanding the trainable attribute","title":"Transfer learning & fine-tuning","text":"Layers & models three weight attributes: weights list weights variables layer. trainable_weights list meant updated (via gradient descent) minimize loss training. non_trainable_weights list aren’t meant trained. Typically updated model forward pass. Example: Dense layer 2 trainable weights (kernel & bias) general, weights trainable weights. built-layer non-trainable weights [layer_batch_normalization()]. uses non-trainable weights keep track mean variance inputs training. learn use non-trainable weights custom layers, see guide writing new layers scratch. Example: BatchNormalization layer 2 trainable weights 2 non-trainable weights Layers & models also feature boolean attribute trainable. value can changed. Setting layer$trainable FALSE moves layer’s weights trainable non-trainable. called “freezing” layer: state frozen layer won’t updated training (either training fit() training custom loop relies trainable_weights apply gradient updates). Example: setting trainable False trainable weight becomes non-trainable, value longer updated training. confuse layer$trainable attribute argument training layer$call() (controls whether layer run forward pass inference mode training mode). information, see Keras FAQ.","code":"layer <- layer_dense(units = 3) layer$build(shape(NULL, 4))  # Create the weights  length(layer$weights) ## [1] 2 length(layer$trainable_weights) ## [1] 2 length(layer$non_trainable_weights) ## [1] 0 layer <- layer_batch_normalization() layer$build(shape(NA, 4))  # Create the weights  length(layer$weights) ## [1] 4 length(layer$trainable_weights) ## [1] 2 length(layer$non_trainable_weights) ## [1] 2 layer <- layer_dense(units = 3) layer$build(shape(NULL, 4))  # Create the weights layer$trainable <- FALSE  # Freeze the layer  length(layer$weights) ## [1] 2 length(layer$trainable_weights) ## [1] 0 length(layer$non_trainable_weights) ## [1] 2 # Make a model with 2 layers layer1 <- layer_dense(units = 3, activation = \"relu\") layer2 <- layer_dense(units = 3, activation = \"sigmoid\") model <- keras_model_sequential(input_shape = 3) |>   layer1() |>   layer2()  # Freeze the first layer layer1$trainable <- FALSE  # Keep a copy of the weights of layer1 for later reference # (get_weights() returns a list of R arrays, #  layer$weights returns a list of KerasVariables) initial_layer1_weights_values <- get_weights(layer1)  # Train the model model |> compile(optimizer = \"adam\", loss = \"mse\") model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1) ## 1/1 - 1s - 544ms/step - loss: 2.1868 # Check that the weights of layer1 have not changed during training final_layer1_weights_values <- get_weights(layer1)  all.equal(initial_layer1_weights_values,           final_layer1_weights_values) ## [1] TRUE"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"recursive-setting-of-the-trainable-attribute","dir":"Articles","previous_headings":"","what":"Recursive setting of the trainable attribute","title":"Transfer learning & fine-tuning","text":"set $trainable <- FALSE model layer sublayers, children layers become non-trainable well. Example:","code":"inner_model <- keras_model_sequential(input_shape = 3) |>   layer_dense(units = 3, activation = \"relu\") |>   layer_dense(units = 3, activation = \"relu\")  model <- keras_model_sequential(input_shape = 3) |>   inner_model() |>   layer_dense(units = 3, activation = \"sigmoid\")  model$trainable <- FALSE  # Freeze the outer model  inner_model$trainable  # All layers in `model` are now frozen ## [1] FALSE inner_model$layers[[1]]$trainable  # `trainable` is propagated recursively ## [1] FALSE"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"the-typical-transfer-learning-workflow","dir":"Articles","previous_headings":"","what":"The typical transfer-learning workflow","title":"Transfer learning & fine-tuning","text":"leads us typical transfer learning workflow can implemented Keras: Instantiate base model load pre-trained weights . Freeze layers base model setting trainable <- FALSE. Create new model top output one (several) layers base model. Train new model new dataset. Note alternative, lightweight workflow also : Instantiate base model load pre-trained weights . Run new dataset record output one (several) layers base model. called feature extraction. Use output input data new, smaller model. key advantage second workflow run base model data, rather per epoch training. ’s lot faster & cheaper. issue second workflow, though, doesn’t allow dynamically modify input data new model training, required data augmentation, instance. Transfer learning typically used tasks new dataset little data train full-scale model scratch, scenarios data augmentation important. follows, focus first workflow. ’s first workflow looks like Keras: First, instantiate base model pre-trained weights. , freeze base model. Create new model top. Train model new data.","code":"base_model <- application_xception(   weights = 'imagenet', # Load weights pre-trained on ImageNet.   input_shape = c(150, 150, 3),   include_top = FALSE  # Do not include the ImageNet classifier at the top. ) base_model$trainable <- FALSE inputs <- keras_input(shape = c(150, 150, 3)) # We make sure that the base_model is running in inference mode here, # by passing `training <- FALSE`. This is important for fine-tuning, as you will # learn in a few paragraphs. outputs <- inputs |>   base_model(training = FALSE) |>   # Convert features of shape `base_model$output_shape[-1]` to vectors   layer_global_average_pooling_2d() |>   # A Dense classifier with a single unit (binary classification)   layer_dense(1)  model <- keras_model(inputs, outputs) model |> compile(   optimizer = optimizer_adam(),   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_binary_accuracy()) ) model |> fit(new_dataset, epochs = 20,              callbacks = ..., validation_data = ...)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"fine-tuning","dir":"Articles","previous_headings":"","what":"Fine-tuning","title":"Transfer learning & fine-tuning","text":"model converged new data, can try unfreeze part base model retrain whole model end--end low learning rate. optional last step can potentially give incremental improvements. also potentially lead quick overfitting – keep mind. critical step model frozen layers trained convergence. mix randomly-initialized trainable layers trainable layers hold pre-trained features, randomly-initialized layers cause large gradient updates training, destroy pre-trained features. ’s also critical use low learning rate stage, training much larger model first round training, dataset typically small. result, risk overfitting quickly apply large weight updates. , want readapt pretrained weights incremental way. implement fine-tuning whole base model: Important note compile() trainable Calling compile() model meant “freeze” behavior model. implies trainable attribute values time model compiled preserved throughout lifetime model, compile called . Hence, change trainable value, make sure call compile() model changes taken account. Important notes BatchNormalization layer Many image models contain BatchNormalization layers. layer special case every imaginable count. things keep mind. BatchNormalization contains 2 non-trainable weights get updated training. variables tracking mean variance inputs. set bn_layer$trainable <- FALSE, BatchNormalization layer run inference mode, update mean & variance statistics. case layers general, weight trainability & inference/training modes two orthogonal concepts. two tied case BatchNormalization layer. unfreeze model contains BatchNormalization layers order fine-tuning, keep BatchNormalization layers inference mode passing training = FALSE calling base model. Otherwise updates applied non-trainable weights suddenly destroy model learned. ’ll see pattern action end--end example end guide.","code":"# Unfreeze the base model base_model$trainable <- TRUE  # It's important to recompile your model after you make any changes # to the `trainable` attribute of any inner layer, so that your changes # are take into account model |> compile(   optimizer = optimizer_adam(1e-5), # Very low learning rate   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = c(metric_binary_accuracy()) )  # Train end-to-end. Be careful to stop before you overfit! model |> fit(new_dataset, epochs = 10,              callbacks = ..., validation_data = ...)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"an-end-to-end-example-fine-tuning-an-image-classification-model-on-a-cats-vs--dogs-dataset","dir":"Articles","previous_headings":"","what":"An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset","title":"Transfer learning & fine-tuning","text":"solidify concepts, let’s walk concrete end--end transfer learning & fine-tuning example. load Xception model, pre-trained ImageNet, use Kaggle “cats vs. dogs” classification dataset.","code":""},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"getting-the-data","dir":"Articles","previous_headings":"An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset","what":"Getting the data","title":"Transfer learning & fine-tuning","text":"First, let’s fetch cats vs. dogs dataset using TFDS. dataset, ’ll probably want use utility [image_dataset_from_directory()] generate similar labeled dataset objects set images disk filed class-specific folders. Transfer learning useful working small datasets. keep dataset small, use 40% original training data (25,000 images) training, 10% validation, 10% testing. first 9 images training dataset – can see, ’re different sizes. can also see label 1 “dog” label 0 “cat”.","code":"# reticulate::py_install(\"tensorflow-datasets\") tfds <- reticulate::import(\"tensorflow_datasets\")  c(train_ds, validation_ds, test_ds) %<-% tfds$load(   \"cats_vs_dogs\",   # Reserve 10% for validation and 10% for test   split = c(\"train[:40%]\", \"train[40%:50%]\", \"train[50%:60%]\"),   as_supervised = TRUE  # Include labels )  length(train_ds) ## [1] 9305 library(tfdatasets, exclude = \"shape\")  par(mfrow = c(3, 3), mar = c(1,0,1.5,0)) train_ds |>   dataset_take(9) |>   as_array_iterator() |>   iterate(function(batch) {     c(image, label) %<-% batch     plot(as.raster(image, max = 255L))     title(sprintf(       \"label: %s   size: %s\",       label, paste(dim(image), collapse = \" x \")))   })"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"standardizing-the-data","dir":"Articles","previous_headings":"An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset","what":"Standardizing the data","title":"Transfer learning & fine-tuning","text":"raw images variety sizes. addition, pixel consists 3 integer values 0 255 (RGB level values). isn’t great fit feeding neural network. need 2 things: Standardize fixed image size. pick 150x150. Normalize pixel values -1 1. ’ll using Normalization layer part model . general, ’s good practice develop models take raw data input, opposed models take already-preprocessed data. reason , model expects preprocessed data, time export model use elsewhere (web browser, mobile app), ’ll need reimplement exact preprocessing pipeline. gets tricky quickly. least possible amount preprocessing hitting model. , ’ll image resizing data pipeline (deep neural network can process contiguous batches data), ’ll input value scaling part model, create . Let’s resize images 150x150:","code":"resize_fn <- layer_resizing(width = 150, height = 150) resize_pair <- function(x, y) list(resize_fn(x), y)  train_ds <- train_ds |> dataset_map(resize_pair) validation_ds <- validation_ds |> dataset_map(resize_pair) test_ds <- test_ds |> dataset_map(resize_pair)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"using-random-data-augmentation","dir":"Articles","previous_headings":"An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset","what":"Using random data augmentation","title":"Transfer learning & fine-tuning","text":"don’t large image dataset, ’s good practice artificially introduce sample diversity applying random yet realistic transformations training images, random horizontal flipping small random rotations. helps expose model different aspects training data slowing overfitting. Let’s batch data use prefetching optimize loading speed. Let’s visualize first image first batch looks like various random transformations:","code":"data_augmentation <- keras_model_sequential() |>   layer_random_flip(\"horizontal\") |>   layer_random_rotation(.1)  train_ds <- train_ds %>%   dataset_map(function(x, y) list(data_augmentation(x), y)) library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) batch_size <- 64  train_ds <- train_ds |>   dataset_batch(batch_size) |>   dataset_prefetch()  validation_ds <- validation_ds |>   dataset_batch(batch_size) |>   dataset_prefetch()  test_ds <- test_ds |>   dataset_batch(batch_size) |>   dataset_prefetch() batch <- train_ds |>   dataset_take(1) |>   as_iterator() |>   iter_next()  c(images, labels) %<-% batch first_image <- images[1, all_dims(), drop = TRUE] augmented_image <- data_augmentation(first_image, training = TRUE)  plot_image <- function(image, main = deparse1(substitute(image))) {   image |>     as.array() |> # convert from tensor to R array     as.raster(max = 255) |>     plot()    if(!is.null(main))     title(main) }  par(mfrow = c(2, 2), mar = c(1, 1, 1.5, 1)) plot_image(first_image) plot_image(augmented_image) plot_image(data_augmentation(first_image, training = TRUE), \"augmented 2\") plot_image(data_augmentation(first_image, training = TRUE), \"augmented 3\")"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"build-a-model","dir":"Articles","previous_headings":"","what":"Build a model","title":"Transfer learning & fine-tuning","text":"Now let’s built model follows blueprint ’ve explained earlier. Note : add Rescaling layer scale input values (initially [0, 255] range) [-1, 1] range. add Dropout layer classification layer, regularization. make sure pass training=FALSE calling base model, runs inference mode, batchnorm statistics don’t get updated even unfreeze base model fine-tuning.","code":"base_model <- application_xception(   weights = \"imagenet\", # Load weights pre-trained on ImageNet.   input_shape = c(150, 150, 3),   include_top = FALSE, # Do not include the ImageNet classifier at the top. )  # Freeze the base_model base_model$trainable <- FALSE  # Create new model on top inputs <- keras_input(shape = c(150, 150, 3))  # Pre-trained Xception weights requires that input be scaled # from (0, 255) to a range of (-1., +1.), the rescaling layer # outputs: `(inputs * scale) + offset` scale_layer <- layer_rescaling(scale = 1 / 127.5, offset = -1) x <- scale_layer(inputs)  # The base model contains batchnorm layers. We want to keep them in inference mode # when we unfreeze the base model for fine-tuning, so we make sure that the # base_model is running in inference mode here. outputs <- x |>   base_model(training = FALSE) |>   layer_global_average_pooling_2d() |>   layer_dropout(0.2) |>   layer_dense(1)  model <- keras_model(inputs, outputs)  summary(model, show_trainable = TRUE) ## Model: \"functional_10\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)                ┃ Output Shape          ┃    Param # ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer_7 (InputLayer)  │ (None, 150, 150, 3)   │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ rescaling (Rescaling)       │ (None, 150, 150, 3)   │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ xception (Functional)       │ (None, 5, 5, 2048)    │ 20,861,480 │   N   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ global_average_pooling2d_1  │ (None, 2048)          │          0 │   -   │ ## │ (GlobalAveragePooling2D)    │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dropout (Dropout)           │ (None, 2048)          │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_8 (Dense)             │ (None, 1)             │      2,049 │   Y   │ ## └─────────────────────────────┴───────────────────────┴────────────┴───────┘ ##  Total params: 20,863,529 (79.59 MB) ##  Trainable params: 2,049 (8.00 KB) ##  Non-trainable params: 20,861,480 (79.58 MB)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"train-the-top-layer","dir":"Articles","previous_headings":"","what":"Train the top layer","title":"Transfer learning & fine-tuning","text":"","code":"model |> compile(   optimizer = optimizer_adam(),   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_binary_accuracy()) )  epochs <- 1 model |> fit(train_ds, epochs = epochs, validation_data = validation_ds) ## 146/146 - 45s - 306ms/step - binary_accuracy: 0.9183 - loss: 0.1887 - val_binary_accuracy: 0.9669 - val_loss: 0.0926"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"do-a-round-of-fine-tuning-of-the-entire-model","dir":"Articles","previous_headings":"","what":"Do a round of fine-tuning of the entire model","title":"Transfer learning & fine-tuning","text":"Finally, let’s unfreeze base model train entire model end--end low learning rate. Importantly, although base model becomes trainable, still running inference mode since passed training=FALSE calling built model. means batch normalization layers inside won’t update batch statistics. , wreck havoc representations learned model far. 10 epochs, fine-tuning gains us nice improvement . Let’s evaluate model test dataset:","code":"# Unfreeze the base_model. Note that it keeps running in inference mode # since we passed `training=FALSE` when calling it. This means that # the batchnorm layers will not update their batch statistics. # This prevents the batchnorm layers from undoing all the training # we've done so far. base_model$trainable <- TRUE summary(model, show_trainable = TRUE) ## Model: \"functional_10\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)                ┃ Output Shape          ┃    Param # ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer_7 (InputLayer)  │ (None, 150, 150, 3)   │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ rescaling (Rescaling)       │ (None, 150, 150, 3)   │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ xception (Functional)       │ (None, 5, 5, 2048)    │ 20,861,480 │   Y   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ global_average_pooling2d_1  │ (None, 2048)          │          0 │   -   │ ## │ (GlobalAveragePooling2D)    │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dropout (Dropout)           │ (None, 2048)          │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_8 (Dense)             │ (None, 1)             │      2,049 │   Y   │ ## └─────────────────────────────┴───────────────────────┴────────────┴───────┘ ##  Total params: 20,867,629 (79.60 MB) ##  Trainable params: 20,809,001 (79.38 MB) ##  Non-trainable params: 54,528 (213.00 KB) ##  Optimizer params: 4,100 (16.02 KB) model |> compile(   optimizer = optimizer_adam(1e-5), # Low learning rate   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_binary_accuracy()) )  epochs <- 1 model |> fit(train_ds, epochs = epochs, validation_data = validation_ds) ## 146/146 - 82s - 564ms/step - binary_accuracy: 0.8660 - loss: 0.3213 - val_binary_accuracy: 0.9652 - val_loss: 0.1022 model |> evaluate(test_ds) ## 37/37 - 2s - 42ms/step - binary_accuracy: 0.9540 - loss: 0.1103 ## $binary_accuracy ## [1] 0.9539983 ## ## $loss ## [1] 0.1102556"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Understanding masking & padding","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Understanding masking & padding","text":"Masking way tell sequence-processing layers certain timesteps input missing, thus skipped processing data. Padding special form masking masked steps start end sequence. Padding comes need encode sequence data contiguous batches: order make sequences batch fit given standard length, necessary pad truncate sequences. Let’s take close look.","code":""},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"padding-sequence-data","dir":"Articles","previous_headings":"","what":"Padding sequence data","title":"Understanding masking & padding","text":"processing sequence data, common individual samples different lengths. Consider following example (text tokenized words): vocabulary lookup, data might vectorized integers, e.g.: data nested list individual samples length 3, 5, 6, respectively. Since input data deep learning model must single tensor (shape e.g. (batch_size, 6, vocab_size) case), samples shorter longest item need padded placeholder value (alternatively, one might also truncate long samples padding short samples). Keras provides utility function truncate pad Python lists common length: pad_sequences.","code":"data <- list(   c(\"Hello\", \"world\", \"!\"),   c(\"How\", \"are\", \"you\", \"doing\", \"today\"),   c(\"The\", \"weather\", \"will\", \"be\", \"nice\", \"tomorrow\") ) data <- list(   c(71, 1331, 4231),   c(73, 8, 3215, 55, 927),   c(83, 91, 1, 645, 1253, 927) ) raw_inputs <- list(   c(711, 632, 71),   c(73, 8, 3215, 55, 927),   c(83, 91, 1, 645, 1253, 927) )  # By default, this will pad using 0s; it is configurable via the # \"value\" parameter. # Note that you could use \"pre\" padding (at the beginning) or # \"post\" padding (at the end). # We recommend using \"post\" padding when working with RNN layers # (in order to be able to use the # CuDNN implementation of the layers). padded_inputs <- pad_sequences(raw_inputs, padding=\"post\") padded_inputs ##      [,1] [,2] [,3] [,4] [,5] [,6] ## [1,]  711  632   71    0    0    0 ## [2,]   73    8 3215   55  927    0 ## [3,]   83   91    1  645 1253  927"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"masking","dir":"Articles","previous_headings":"","what":"Masking","title":"Understanding masking & padding","text":"Now samples uniform length, model must informed part data actually padding ignored. mechanism masking. three ways introduce input masks Keras models: Add layer_masking layer. Configure layer_embedding layer mask_zero=TRUE. Pass mask argument manually calling layers support argument (e.g. RNN layers).","code":""},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"mask-generating-layers-embedding-and-masking","dir":"Articles","previous_headings":"","what":"Mask-generating layers: Embedding and Masking","title":"Understanding masking & padding","text":"hood, layers create mask tensor (2D tensor shape (batch, sequence_length)), attach tensor output returned Masking Embedding layer. can see printed result, mask 2D boolean tensor shape (batch_size, sequence_length), individual FALSE entry indicates corresponding timestep ignored processing.","code":"embedding <- layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) masked_output <- embedding(padded_inputs)  masked_output$`_keras_mask` ## tf.Tensor( ## [[ True  True  True False False False] ##  [ True  True  True  True  True False] ##  [ True  True  True  True  True  True]], shape=(3, 6), dtype=bool) masking_layer <- layer_masking() # Simulate the embedding lookup by expanding the 2D input to 3D, # with embedding dimension of 10. unmasked_embedding <- op_cast(     op_tile(op_expand_dims(padded_inputs, axis=-1), c(1L, 1L, 10L)),     dtype=\"float32\" )  masked_embedding <- masking_layer(unmasked_embedding) masked_embedding$`_keras_mask` ## tf.Tensor( ## [[ True  True  True False False False] ##  [ True  True  True  True  True False] ##  [ True  True  True  True  True  True]], shape=(3, 6), dtype=bool)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"mask-propagation-in-the-functional-api-and-sequential-api","dir":"Articles","previous_headings":"","what":"Mask propagation in the Functional API and Sequential API","title":"Understanding masking & padding","text":"using Functional API Sequential API, mask generated Embedding Masking layer propagated network layer capable using (example, RNN layers). Keras automatically fetch mask corresponding input pass layer knows use . instance, following Sequential model, LSTM layer automatically receive mask, means ignore padded values: also case following Functional API model:","code":"model <- keras_model_sequential() %>%   layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%   layer_lstm(units=32) inputs <- keras_input(shape = shape(NULL), dtype=\"int32\") outputs <- inputs %>%   layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%   layer_lstm(units=32)  model <- keras_model(inputs, outputs)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"passing-mask-tensors-directly-to-layers","dir":"Articles","previous_headings":"","what":"Passing mask tensors directly to layers","title":"Understanding masking & padding","text":"Layers can handle masks (LSTM layer) mask argument call method. Meanwhile, layers produce mask (e.g. Embedding) expose compute_mask(input, previous_mask) method can call. Thus, can pass output compute_mask() method mask-producing layer call method mask-consuming layer, like :","code":"MyLayer <- new_layer_class(   \"MyLayer\",   initialize = function(...) {     super$initialize(...)     self$embedding <- layer_embedding(       input_dim=5000, output_dim=16, mask_zero=TRUE     )     self$lstm <- layer_lstm(units=32)   },   call = function(inputs) {     inputs %>%       self$embedding() %>%       # Note that you could also prepare a `mask` tensor manually.       # It only needs to be a boolean tensor       # with the right shape, i.e. (batch_size, timesteps).       self$lstm(mask=self$embedding$compute_mask(inputs))   } )  layer <- MyLayer() x <- random_integer(c(32, 10), 0, 100) layer(x) ## tf.Tensor( ## [[ 0.00130048 -0.00113367 -0.00715671 ... -0.00107615 -0.00162071 ##    0.00135018] ##  [-0.004185    0.00726349  0.00520932 ...  0.00119117  0.00230441 ##    0.00174123] ##  [-0.00537032 -0.00164898 -0.00238435 ... -0.00154158 -0.0038603 ##   -0.00105811] ##  ... ##  [ 0.00622133 -0.00905907 -0.00599518 ...  0.00025823 -0.00142478 ##   -0.00125036] ##  [-0.00523904  0.00336683 -0.00299453 ...  0.00876719  0.00172074 ##    0.00903089] ##  [-0.00393721  0.00058538  0.00503809 ... -0.00203075  0.00325885 ##   -0.00299755]], shape=(32, 32), dtype=float32)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"supporting-masking-in-your-custom-layers","dir":"Articles","previous_headings":"","what":"Supporting masking in your custom layers","title":"Understanding masking & padding","text":"Sometimes, may need write layers generate mask (like Embedding), layers need modify current mask. instance, layer produces tensor different time dimension input, Concatenate layer concatenates time dimension, need modify current mask downstream layers able properly take masked timesteps account. , layer implement layer.compute_mask() method, produces new mask given input current mask. example TemporalSplit layer needs modify current mask. another example CustomEmbedding layer capable generating mask input values: Note: details format limitations related masking, see serialization guide.","code":"TemporalSplit <- new_layer_class(   \"TemporalSplit\",   call = function(inputs) {     # Expect the input to be 3D and mask to be 2D, split the input tensor into 2     # subtensors along the time axis (axis 1).     op_split(inputs, 2, axis=2)   },   compute_mask = function(inputs, mask = NULL) {     # Also split the mask into 2 if it presents.     if (!is.null(mask)) {       op_split(mask, 2, axis=2)     } else {       NULL     }   } )  c(first_half, second_half) %<-% TemporalSplit(masked_embedding) first_half$`_keras_mask` ## tf.Tensor( ## [[ True  True  True] ##  [ True  True  True] ##  [ True  True  True]], shape=(3, 3), dtype=bool) second_half$`_keras_mask` ## tf.Tensor( ## [[False False False] ##  [ True  True False] ##  [ True  True  True]], shape=(3, 3), dtype=bool) CustomEmbedding <- new_layer_class(   \"CustomEmbedding\",   initialize = function(input_dim, output_dim, mask_zero=FALSE, ...) {     super$initialize(...)     self$input_dim <- as.integer(input_dim)     self$output_dim <- as.integer(output_dim)     self$mask_zero <- mask_zero   },   build = function(input_shape) {     self$embeddings <- self$add_weight(       shape=c(self$input_dim, self$output_dim),       initializer=\"random_normal\",       dtype=\"float32\"     )   },   call = function(inputs) {     inputs <- op_cast(inputs, \"int32\")     op_take(self$embeddings, inputs)   },   compute_mask = function(inputs, mask=NULL) {     if (!self$mask_zero) {       NULL     } else {       op_not_equal(inputs, 0)     }   } )  layer <- CustomEmbedding(input_dim = 10, output_dim = 32, mask_zero=TRUE) x <- random_integer(c(3, 10), 0, 9)  y <- layer(x) mask <- layer$compute_mask(x)  mask ## tf.Tensor( ## [[ True  True  True  True  True  True  True  True  True  True] ##  [ True  True  True  True  True  True  True  True  True  True] ##  [False False  True  True  True  True  True  True  True  True]], shape=(3, 10), dtype=bool)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"opting-in-to-mask-propagation-on-compatible-layers","dir":"Articles","previous_headings":"","what":"Opting-in to mask propagation on compatible layers","title":"Understanding masking & padding","text":"layers don’t modify time dimension, don’t need modify current mask. However, may still want able propagate current mask, unchanged, next layer. opt-behavior. default, custom layer destroy current mask (since framework way tell whether propagating mask safe ). custom layer modify time dimension, want able propagate current input mask, set self.supports_masking = True layer constructor. case, default behavior compute_mask() just pass current mask . ’s example layer whitelisted mask propagation: can now use custom layer -mask-generating layer (like Embedding) mask-consuming layer (like LSTM), pass mask along reaches mask-consuming layer.","code":"MyActivation <- new_layer_class(   \"MyActivation\",   initialize = function(...) {     super$initialize(...)     self$supports_masking <- TRUE   },   call = function(inputs) {     op_relu(inputs)   } ) inputs <- keras_input(shape = shape(NULL), dtype=\"int32\") outputs <- inputs %>%   layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%   MyActivation() %>%   layer_lstm(units=32)  model <- keras_model(inputs, outputs) y <- model(random_integer(c(32, 100), 0, 5000))"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"writing-layers-that-need-mask-information","dir":"Articles","previous_headings":"","what":"Writing layers that need mask information","title":"Understanding masking & padding","text":"layers mask consumers: accept mask argument call use determine whether skip certain time steps. write layer, can simply add mask=None argument call signature. mask associated inputs passed layer whenever available. ’s simple example : layer computes softmax time dimension (axis 1) input sequence, discarding masked timesteps.","code":"TemporalSoftmax <- new_layer_class(   \"TemporalSoftmax\",   initialize = function(...) {     super$initialize(...)     self$supports_masking <- TRUE   },   call = function(inputs, mask=NULL) {     if (is.null(mask)) {       stop(\"`TemporalSoftmax` layer requires a previous layer to support masking.\")     }     broadcast_float_mask <- op_expand_dims(op_cast(mask, \"float32\"), -1)     inputs_exp <- op_exp(inputs) * broadcast_float_mask     inputs_sum <- op_sum(inputs_exp * broadcast_float_mask, axis=-1, keepdims=TRUE)     inputs_exp / inputs_sum   } )  inputs <- keras_input(shape = shape(NULL), dtype=\"int32\") outputs <- inputs %>%   layer_embedding(input_dim=10, output_dim=32, mask_zero=TRUE) %>%   layer_dense(1) %>%   TemporalSoftmax()  model <- keras_model(inputs, outputs) y <- model(random_integer(c(32, 100), 0, 10))"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Understanding masking & padding","text":"need know padding & masking Keras. recap: “Masking” layers able know skip / ignore certain timesteps sequence inputs. layers mask-generators: Embedding can generate mask input values (mask_zero=TRUE), can Masking layer. layers mask-consumers: expose mask argument call method. case RNN layers. Functional API Sequential API, mask information propagated automatically. using layers standalone way, can pass mask arguments layers manually. can easily write layers modify current mask, generate new mask, consume mask associated inputs.","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing a training loop from scratch in JAX","text":"","code":"import os  # This guide can only be run with the jax backend. os.environ[\"KERAS_BACKEND\"] = \"jax\"  import jax  # We import TF so we can use tf.data. import tensorflow as tf import keras import numpy as np"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing a training loop from scratch in JAX","text":"Keras provides default training evaluation loops, fit() evaluate(). usage covered guide Training & evaluation built-methods. want customize learning algorithm model still leveraging convenience fit() (instance, train GAN using fit()), can subclass Model class implement train_step() method, called repeatedly fit(). Now, want low-level control training & evaluation, write training & evaluation loops scratch. guide .","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"A first end-to-end example","title":"Writing a training loop from scratch in JAX","text":"write custom training loop, need following ingredients: model train, course. optimizer. either use optimizer keras.optimizers, one optax package. loss function. dataset. standard JAX ecosystem load data via tf.data, ’s ’ll use. Let’s line . First, let’s get model MNIST dataset: Next, ’s loss function optimizer. ’ll use Keras optimizer case.","code":"def get_model():     inputs = keras.Input(shape=(784,), name=\"digits\")     x1 = keras.layers.Dense(64, activation=\"relu\")(inputs)     x2 = keras.layers.Dense(64, activation=\"relu\")(x1)     outputs = keras.layers.Dense(10, name=\"predictions\")(x2)     model = keras.Model(inputs=inputs, outputs=outputs)     return model   model = get_model()  # Prepare the training dataset. batch_size = 32 (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() x_train = np.reshape(x_train, (-1, 784)).astype(\"float32\") x_test = np.reshape(x_test, (-1, 784)).astype(\"float32\") y_train = keras.utils.to_categorical(y_train) y_test = keras.utils.to_categorical(y_test)  # Reserve 10,000 samples for validation. x_val = x_train[-10000:] y_val = y_train[-10000:] x_train = x_train[:-10000] y_train = y_train[:-10000]  # Prepare the training dataset. train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)) train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size)  # Prepare the validation dataset. val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val)) val_dataset = val_dataset.batch(batch_size) # Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  # Instantiate an optimizer. optimizer = keras.optimizers.Adam(learning_rate=1e-3)"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"getting-gradients-in-jax","dir":"Articles","previous_headings":"A first end-to-end example","what":"Getting gradients in JAX","title":"Writing a training loop from scratch in JAX","text":"Let’s train model using mini-batch gradient custom training loop. JAX, gradients computed via metaprogramming: call jax.grad (jax.value_and_grad function order create gradient-computing function first function. first thing need function returns loss value. ’s function ’ll use generate gradient function. Something like : function, can compute gradients via metaprogramming : Typically, don’t just want get gradient values, also want get loss value. can using jax.value_and_grad instead jax.grad:","code":"def compute_loss(x, y):     ...     return loss grad_fn = jax.grad(compute_loss) grads = grad_fn(x, y) grad_fn = jax.value_and_grad(compute_loss) loss, grads = grad_fn(x, y)"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"jax-computation-is-purely-stateless","dir":"Articles","previous_headings":"A first end-to-end example","what":"JAX computation is purely stateless","title":"Writing a training loop from scratch in JAX","text":"JAX, everything must stateless function – loss computation function must stateless well. means Keras variables (e.g. weight tensors) must passed function inputs, variable updated forward pass must returned function output. function side effect. forward pass, non-trainable variables Keras model might get updated. variables , instance, RNG seed state variables BatchNormalization statistics. ’re going need return . need something like : function, can get gradient function specifying hax_aux value_and_grad: tells JAX loss computation function returns outputs just loss. Note loss always first output. Now established basics, let’s implement compute_loss_and_updates function. Keras models stateless_call method come handy . works just like model.__call__, requires explicitly pass value variables model, returns just __call__ outputs also (potentially updated) non-trainable variables. Let’s get gradient function:","code":"def compute_loss_and_updates(trainable_variables, non_trainable_variables, x, y):     ...     return loss, non_trainable_variables grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True) (loss, non_trainable_variables), grads = grad_fn(     trainable_variables, non_trainable_variables, x, y ) def compute_loss_and_updates(     trainable_variables, non_trainable_variables, x, y ):     y_pred, non_trainable_variables = model.stateless_call(         trainable_variables, non_trainable_variables, x     )     loss = loss_fn(y, y_pred)     return loss, non_trainable_variables grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True)"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"the-training-step-function","dir":"Articles","previous_headings":"A first end-to-end example","what":"The training step function","title":"Writing a training loop from scratch in JAX","text":"Next, let’s implement end--end training step, function run forward pass, compute loss, compute gradients, also use optimizer update trainable variables. function also needs stateless, get input state tuple includes every state element ’re going use: trainable_variables non_trainable_variables: model’s variables. optimizer_variables: optimizer’s state variables, momentum accumulators. update trainable variables, use optimizer’s stateless method stateless_apply. ’s equivalent optimizer.apply(), requires always passing trainable_variables optimizer_variables. returns updated trainable variables updated optimizer_variables.","code":"def train_step(state, data):     trainable_variables, non_trainable_variables, optimizer_variables = state     x, y = data     (loss, non_trainable_variables), grads = grad_fn(         trainable_variables, non_trainable_variables, x, y     )     trainable_variables, optimizer_variables = optimizer.stateless_apply(         optimizer_variables, grads, trainable_variables     )     # Return updated state     return loss, (         trainable_variables,         non_trainable_variables,         optimizer_variables,     )"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"make-it-fast-with-jax-jit","dir":"Articles","previous_headings":"A first end-to-end example","what":"Make it fast with jax.jit","title":"Writing a training loop from scratch in JAX","text":"default, JAX operations run eagerly, just like TensorFlow eager mode PyTorch eager mode. just like TensorFlow eager mode PyTorch eager mode, ’s pretty slow – eager mode better used debugging environment, way actual work. let’s make train_step fast compiling . stateless JAX function, can compile XLA via @jax.jit decorator. get traced first execution, subsequent executions executing traced graph (just like @tf.function(jit_compile=True). Let’s try : ’re now ready train model. training loop trivial: just repeatedly call loss, state = train_step(state, data). Note: convert TF tensors yielded tf.data.Dataset NumPy passing JAX function. variables must built beforehand: model must built optimizer must built. Since ’re using Functional API model, ’s already built, subclassed model ’d need call batch data build . key thing notice loop entirely stateless – variables attached model (model.weights) never getting updated loop. new values stored state tuple. means point, saving model, attaching new variable values back model. Just call variable.assign(new_value) model variable want update:","code":"@jax.jit def train_step(state, data):     trainable_variables, non_trainable_variables, optimizer_variables = state     x, y = data     (loss, non_trainable_variables), grads = grad_fn(         trainable_variables, non_trainable_variables, x, y     )     trainable_variables, optimizer_variables = optimizer.stateless_apply(         optimizer_variables, grads, trainable_variables     )     # Return updated state     return loss, (         trainable_variables,         non_trainable_variables,         optimizer_variables,     ) # Build optimizer variables. optimizer.build(model.trainable_variables)  trainable_variables = model.trainable_variables non_trainable_variables = model.non_trainable_variables optimizer_variables = optimizer.variables state = trainable_variables, non_trainable_variables, optimizer_variables  # Training loop for step, data in enumerate(train_dataset):     data = (data[0].numpy(), data[1].numpy())     loss, state = train_step(state, data)     # Log every 100 batches.     if step % 100 == 0:         print(f\"Training loss (for 1 batch) at step {step}: {float(loss):.4f}\")         print(f\"Seen so far: {(step + 1) * batch_size} samples\") trainable_variables, non_trainable_variables, optimizer_variables = state for variable, value in zip(model.trainable_variables, trainable_variables):     variable.assign(value) for variable, value in zip(     model.non_trainable_variables, non_trainable_variables ):     variable.assign(value)"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"low-level-handling-of-metrics","dir":"Articles","previous_headings":"","what":"Low-level handling of metrics","title":"Writing a training loop from scratch in JAX","text":"Let’s add metrics monitoring basic training loop. can readily reuse built-Keras metrics (custom ones wrote) training loops written scratch. ’s flow: Instantiate metric start loop Include metric_variables train_step arguments compute_loss_and_updates arguments. Call metric.stateless_update_state() compute_loss_and_updates function. ’s equivalent update_state() – stateless. need display current value metric, outside train_step (eager scope), attach new metric variable values metric object vall metric.result(). Call metric.reset_state() need clear state metric (typically end epoch) Let’s use knowledge compute CategoricalAccuracy training validation data end training: ’ll also prepare evaluation step function: loops:","code":"# Get a fresh model model = get_model()  # Instantiate an optimizer to train the model. optimizer = keras.optimizers.Adam(learning_rate=1e-3) # Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  # Prepare the metrics. train_acc_metric = keras.metrics.CategoricalAccuracy() val_acc_metric = keras.metrics.CategoricalAccuracy()   def compute_loss_and_updates(     trainable_variables, non_trainable_variables, metric_variables, x, y ):     y_pred, non_trainable_variables = model.stateless_call(         trainable_variables, non_trainable_variables, x     )     loss = loss_fn(y, y_pred)     metric_variables = train_acc_metric.stateless_update_state(         metric_variables, y, y_pred     )     return loss, (non_trainable_variables, metric_variables)   grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True)   @jax.jit def train_step(state, data):     (         trainable_variables,         non_trainable_variables,         optimizer_variables,         metric_variables,     ) = state     x, y = data     (loss, (non_trainable_variables, metric_variables)), grads = grad_fn(         trainable_variables, non_trainable_variables, metric_variables, x, y     )     trainable_variables, optimizer_variables = optimizer.stateless_apply(         optimizer_variables, grads, trainable_variables     )     # Return updated state     return loss, (         trainable_variables,         non_trainable_variables,         optimizer_variables,         metric_variables,     ) @jax.jit def eval_step(state, data):     trainable_variables, non_trainable_variables, metric_variables = state     x, y = data     y_pred, non_trainable_variables = model.stateless_call(         trainable_variables, non_trainable_variables, x     )     loss = loss_fn(y, y_pred)     metric_variables = val_acc_metric.stateless_update_state(         metric_variables, y, y_pred     )     return loss, (         trainable_variables,         non_trainable_variables,         metric_variables,     ) # Build optimizer variables. optimizer.build(model.trainable_variables)  trainable_variables = model.trainable_variables non_trainable_variables = model.non_trainable_variables optimizer_variables = optimizer.variables metric_variables = train_acc_metric.variables state = (     trainable_variables,     non_trainable_variables,     optimizer_variables,     metric_variables, )  # Training loop for step, data in enumerate(train_dataset):     data = (data[0].numpy(), data[1].numpy())     loss, state = train_step(state, data)     # Log every 100 batches.     if step % 100 == 0:         print(f\"Training loss (for 1 batch) at step {step}: {float(loss):.4f}\")         _, _, _, metric_variables = state         for variable, value in zip(             train_acc_metric.variables, metric_variables         ):             variable.assign(value)         print(f\"Training accuracy: {train_acc_metric.result()}\")         print(f\"Seen so far: {(step + 1) * batch_size} samples\")  metric_variables = val_acc_metric.variables (     trainable_variables,     non_trainable_variables,     optimizer_variables,     metric_variables, ) = state state = trainable_variables, non_trainable_variables, metric_variables  # Eval loop for step, data in enumerate(val_dataset):     data = (data[0].numpy(), data[1].numpy())     loss, state = eval_step(state, data)     # Log every 100 batches.     if step % 100 == 0:         print(             f\"Validation loss (for 1 batch) at step {step}: {float(loss):.4f}\"         )         _, _, metric_variables = state         for variable, value in zip(val_acc_metric.variables, metric_variables):             variable.assign(value)         print(f\"Validation accuracy: {val_acc_metric.result()}\")         print(f\"Seen so far: {(step + 1) * batch_size} samples\")"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"low-level-handling-of-losses-tracked-by-the-model","dir":"Articles","previous_headings":"","what":"Low-level handling of losses tracked by the model","title":"Writing a training loop from scratch in JAX","text":"Layers & models recursively track losses created forward pass layers call self.add_loss(value). resulting list scalar loss values available via property model.losses end forward pass. want using loss components, sum add main loss training step. Consider layer, creates activity regularization loss: Let’s build really simple model uses : ’s compute_loss_and_updates function look like now: Pass return_losses=True model.stateless_call(). Sum resulting losses add main loss. ’s !","code":"class ActivityRegularizationLayer(keras.layers.Layer):     def call(self, inputs):         self.add_loss(1e-2 * jax.numpy.sum(inputs))         return inputs inputs = keras.Input(shape=(784,), name=\"digits\") x = keras.layers.Dense(64, activation=\"relu\")(inputs) # Insert activity regularization as a layer x = ActivityRegularizationLayer()(x) x = keras.layers.Dense(64, activation=\"relu\")(x) outputs = keras.layers.Dense(10, name=\"predictions\")(x)  model = keras.Model(inputs=inputs, outputs=outputs) def compute_loss_and_updates(     trainable_variables, non_trainable_variables, metric_variables, x, y ):     y_pred, non_trainable_variables, losses = model.stateless_call(         trainable_variables, non_trainable_variables, x, return_losses=True     )     loss = loss_fn(y, y_pred)     if losses:         loss += jax.numpy.sum(losses)     metric_variables = train_acc_metric.stateless_update_state(         metric_variables, y, y_pred     )     return loss, non_trainable_variables, metric_variables"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing a training loop from scratch in TensorFlow","text":"","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\") library(keras3)  # This guide can only be run with the TensorFlow backend. use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing a training loop from scratch in TensorFlow","text":"Keras provides default training evaluation loops, fit() evaluate(). usage covered guide Training & evaluation built-methods. want customize learning algorithm model still leveraging convenience fit() (instance, train GAN using fit()), can subclass Model class implement train_step() method, called repeatedly fit(). covered guide Customizing happens fit(). Now, want low-level control training & evaluation, write training & evaluation loops scratch. guide .","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"using-the-gradienttape-a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"Using the GradientTape: a first end-to-end example","title":"Writing a training loop from scratch in TensorFlow","text":"Calling model inside GradientTape scope enables retrieve gradients trainable weights layer respect loss value. Using optimizer instance, can use gradients update variables (can retrieve using model$trainable_weights). Let’s consider simple MNIST model: Let’s train using mini-batch gradient custom training loop. First, ’re going need optimizer, loss function, dataset: ’s training loop: open loop iterates epochs epoch, iterate dataset, batches batch, open GradientTape() scope Inside scope, call model (forward pass) compute loss Outside scope, retrieve gradients weights model regard loss Finally, use optimizer update weights model based gradients","code":"get_model <- function() {   inputs <- keras_input(shape = 784, name = \"digits\")   outputs <- inputs |>     layer_dense(units = 64, activation = \"relu\") |>     layer_dense(units = 64, activation = \"relu\") |>     layer_dense(units = 10, name = \"predictions\")   keras_model(inputs = inputs, outputs = outputs) }  model <- get_model() # Instantiate an optimizer. optimizer <- optimizer_adam(learning_rate = 1e-3) # Instantiate a loss function. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE)  # Prepare the training dataset. batch_size <- 64 c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(-1, 784)) x_test <- array_reshape(x_test, c(-1, 784))  # Reserve 10,000 samples for validation. val_i <- sample.int(nrow(x_train), 10000) x_val <- x_train[val_i,] y_val <- y_train[val_i] x_train %<>% .[-val_i,] y_train %<>% .[-val_i]  # Prepare the training dataset. train_dataset <- list(x_train, y_train) |>   tensor_slices_dataset() |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(batch_size)  # Prepare the validation dataset. val_dataset <- list(x_val, y_val) |>   tensor_slices_dataset() |>   dataset_batch(batch_size) epochs <- 3 for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   iterator <- as_iterator(train_dataset)   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1     # Open a GradientTape to record the operations run     # during the forward pass, which enables auto-differentiation.     with(tf$GradientTape() %as% tape, {       # Run the forward pass of the layer.       # The operations that the layer applies       # to its inputs are going to be recorded       # on the GradientTape.       logits <- model(x_batch_train, training = TRUE) # Logits for this minibatch        # Compute the loss value for this minibatch.       loss_value <- loss_fn(y_batch_train, logits)     })      # Use the gradient tape to automatically retrieve     # the gradients of the trainable variables with respect to the loss.     gradients <- tape$gradient(loss_value, model$trainable_weights)      # Run one step of gradient descent by updating     # the value of the variables to minimize the loss.     optimizer$apply(gradients, model$trainable_weights)      # Log every 200 batches.     if (step %% 200 == 0) {       cat(sprintf(\"Training loss (for one batch) at step %d: %.4f\\n\",                   step, loss_value))       cat(sprintf(\"Seen so far: %d samples\\n\", (step * batch_size)))     }   } } ## Start of epoch  1 ## Training loss (for one batch) at step 200: 1.1675 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 1.6450 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.6477 ## Seen so far: 38400 samples ## Start of epoch  2 ## Training loss (for one batch) at step 200: 0.6421 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.1827 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.4249 ## Seen so far: 38400 samples ## Start of epoch  3 ## Training loss (for one batch) at step 200: 0.2715 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.3106 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.2905 ## Seen so far: 38400 samples"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"low-level-handling-of-metrics","dir":"Articles","previous_headings":"","what":"Low-level handling of metrics","title":"Writing a training loop from scratch in TensorFlow","text":"Let’s add metrics monitoring basic loop. can readily reuse built-metrics (custom ones wrote) training loops written scratch. ’s flow: Instantiate metric start loop Call metric$update_state() batch Call metric$result() need display current value metric Call metric$reset_state() need clear state metric (typically end epoch) Let’s use knowledge compute SparseCategoricalAccuracy validation data end epoch: ’s training & evaluation loop:","code":"# Get a fresh model model <- get_model()  # Instantiate an optimizer to train the model. optimizer <- optimizer_adam(learning_rate = 1e-3) # Instantiate a loss function. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE)  # Prepare the metrics. train_acc_metric <- metric_sparse_categorical_accuracy() val_acc_metric <- metric_sparse_categorical_accuracy() epochs <- 2 time <- Sys.time() for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   train_dataset_iterator <- as_iterator(train_dataset)   while (!is.null(train_batch <- iter_next(train_dataset_iterator))) {     c(x_batch_train, y_batch_train) %<-% train_batch     step <- step + 1      with(tf$GradientTape() %as% tape, {       logits <- model(x_batch_train, training = TRUE)       loss_value <- loss_fn(y_batch_train, logits)     })     gradients <- tape$gradient(loss_value, model$trainable_weights)     optimizer$apply(gradients, model$trainable_weights)      # Update training metric.     train_acc_metric$update_state(y_batch_train, logits)     if (step %% 200 == 0) {       cat(sprintf(         \"Training loss (for one batch) at step %d: %.4f\\n\", step, loss_value))       cat(sprintf(\"Seen so far: %d samples \\n\", (step * batch_size)))     }   }    # Display metrics at the end of each epoch.   train_acc <- train_acc_metric$result()   cat(sprintf(\"Training acc over epoch: %.4f\\n\", train_acc))    # Reset training metrics at the end of each epoch   train_acc_metric$reset_state()    # Run a validation loop at the end of each epoch.   iterate(val_dataset, function(val_batch) {     c(x_batch_val, y_batch_val) %<-% val_batch     val_logits <- model(x_batch_val, training = FALSE)     val_acc_metric$update_state(y_batch_val, val_logits)   })   val_acc <- val_acc_metric$result()   val_acc_metric$reset_state()   cat(sprintf(\"Validation acc: %.4f\\n\", val_acc)) } ## Start of epoch  1 ## Training loss (for one batch) at step 200: 1.6268 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 1.2241 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.4987 ## Seen so far: 38400 samples ## Training acc over epoch: 0.7844 ## Validation acc: 0.8680 ## Start of epoch  2 ## Training loss (for one batch) at step 200: 0.4626 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.4654 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.5022 ## Seen so far: 38400 samples ## Training acc over epoch: 0.8837 ## Validation acc: 0.9031 Sys.time() - time ## Time difference of 44.04362 secs"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"speeding-up-your-training-step-with-tf_function","dir":"Articles","previous_headings":"","what":"Speeding-up your training step with tf_function()","title":"Writing a training loop from scratch in TensorFlow","text":"default runtime TensorFlow 2 eager execution. , training loop executes eagerly. great debugging, graph compilation definite performance advantage. Describing computation static graph enables framework apply global performance optimizations. impossible framework constrained greedily execute one operation another, knowledge comes next. can compile static graph function takes tensors input. Just add @tf.function decorator , like : Let’s evaluation step: Now, let’s re-run training loop compiled training step: Much faster, isn’t ?","code":"train_step <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     logits <- model(x, training = TRUE)     loss_value <- loss_fn(y, logits)   })   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply(gradients, model$trainable_weights)   train_acc_metric$update_state(y, logits)   invisible(NULL) # return nothing }) test_step <- tf_function(function(x, y) {   val_logits <- model(x, training=FALSE)   val_acc_metric$update_state(y, val_logits)   invisible(NULL) # return nothing }) epochs <- 2 time <- Sys.time() for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1     train_step(x_batch_train, y_batch_train)      if (step %% 200 == 0) {       cat(sprintf(         \"Training loss (for one batch) at step %d: %.4f\\n\", step, loss_value       ))       cat(sprintf(\"Seen so far: %d samples \\n\", (step * batch_size)))     }   }    # Display metrics at the end of each epoch.   train_acc <- train_acc_metric$result()   cat(sprintf(\"Training acc over epoch: %.4f\\n\", train_acc))     # Reset training metrics at the end of each epoch   train_acc_metric$reset_state()    # Run a validation loop at the end of each epoch.    iterate(val_dataset, function(val_batch) {     c(x_batch_val, y_batch_val) %<-% val_batch     test_step(x_batch_val, y_batch_val)   })    val_acc <- val_acc_metric$result()   val_acc_metric$reset_state()   cat(sprintf(\"Validation acc: %.4f\\n\", val_acc)) } ## Start of epoch  1 ## Training acc over epoch: 0.0000 ## Validation acc: 0.9031 ## Start of epoch  2 ## Training acc over epoch: 0.0000 ## Validation acc: 0.9031 Sys.time() - time ## Time difference of 0.4195933 secs"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"low-level-handling-of-losses-tracked-by-the-model","dir":"Articles","previous_headings":"","what":"Low-level handling of losses tracked by the model","title":"Writing a training loop from scratch in TensorFlow","text":"Layers models recursively track losses created forward pass layers call self$add_loss(value). resulting list scalar loss values available via property model$losses end forward pass. want using loss components, sum add main loss training step. Consider layer, creates activity regularization loss: Let’s build really simple model uses : ’s training step look like now:","code":"layer_activity_regularization <- Layer(   \"ActivityRegularizationLayer\",   call = function(inputs) {     self$add_loss(0.1 * op_mean(inputs))     inputs   } ) inputs <- keras_input(shape = 784, name=\"digits\") outputs <- inputs |>   layer_dense(units = 64, activation = \"relu\") |>   layer_activity_regularization() |>   layer_dense(units = 64, activation = \"relu\") |>   layer_dense(units = 10, name = \"predictions\") model <- keras_model(inputs = inputs, outputs = outputs) train_step <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     logits <- model(x, training = TRUE)     loss_value <- Reduce(`+`, c(loss_fn(y, logits),                                 model$losses))   })   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply(gradients, model$trainable_weights)   train_acc_metric$update_state(y, logits)   invisible(NULL) })"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Writing a training loop from scratch in TensorFlow","text":"Now know everything know using built-training loops writing scratch. conclude, ’s simple end--end example ties together everything ’ve learned guide: DCGAN trained MNIST digits.","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"end-to-end-example-a-gan-training-loop-from-scratch","dir":"Articles","previous_headings":"","what":"End-to-end example: a GAN training loop from scratch","title":"Writing a training loop from scratch in TensorFlow","text":"may familiar Generative Adversarial Networks (GANs). GANs can generate new images look almost real, learning latent distribution training dataset images (“latent space” images). GAN made two parts: “generator” model maps points latent space points image space, “discriminator” model, classifier can tell difference real images (training dataset) fake images (output generator network). GAN training loop looks like : Train discriminator. Sample batch random points latent space. Turn points fake images via “generator” model. Get batch real images combine generated images. Train “discriminator” model classify generated vs. real images. Train generator. Sample random points latent space. Turn points fake images via “generator” network. Get batch real images combine generated images. Train “generator” model “fool” discriminator classify fake images real. much detailed overview GANs works, see Deep Learning Python. Let’s implement training loop. First, create discriminator meant classify fake vs real digits: let’s create generator network, turns latent vectors outputs shape (28, 28, 1) (representing MNIST digits): ’s key bit: training loop. can see quite straightforward. training step function takes 17 lines. Let’s train GAN, repeatedly calling train_step batches images. Since discriminator generator convnets, ’re going want run code GPU. ’s ! ’ll get nice-looking fake MNIST digits just ~30s training GPU.","code":"# Create the discriminator discriminator <-   keras_model_sequential(name = \"discriminator\",                          input_shape = c(28, 28, 1)) |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d(filters = 128, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_global_max_pooling_2d() |>   layer_dense(units = 1)  summary(discriminator) ## Model: \"discriminator\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 14, 14, 64)     │           640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu (LeakyReLU)         │ (None, 14, 14, 64)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 7, 7, 128)      │        73,856 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_1 (LeakyReLU)       │ (None, 7, 7, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d            │ (None, 128)            │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_6 (Dense)                 │ (None, 1)              │           129 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 74,625 (291.50 KB) ##  Trainable params: 74,625 (291.50 KB) ##  Non-trainable params: 0 (0.00 B) latent_dim <- 128L  generator <-   keras_model_sequential(name = \"generator\",                          input_shape = latent_dim) |>   layer_dense(7 * 7 * 128) |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_reshape(target_shape = c(7, 7, 128)) |>   layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d(filters = 1, kernel_size = c(7, 7), padding = \"same\",                 activation = \"sigmoid\")  summary(generator) ## Model: \"generator\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_7 (Dense)                 │ (None, 6272)           │       809,088 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_2 (LeakyReLU)       │ (None, 6272)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ reshape (Reshape)               │ (None, 7, 7, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 14, 14, 128)    │       262,272 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_3 (LeakyReLU)       │ (None, 14, 14, 128)    │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 28, 28, 128)    │       262,272 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_4 (LeakyReLU)       │ (None, 28, 28, 128)    │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 28, 28, 1)      │         6,273 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 1,339,905 (5.11 MB) ##  Trainable params: 1,339,905 (5.11 MB) ##  Non-trainable params: 0 (0.00 B) # Instantiate one optimizer for the discriminator and another for the generator. d_optimizer <- optimizer_adam(learning_rate = 0.0003) g_optimizer <- optimizer_adam(learning_rate = 0.0004)  # Instantiate a loss function. loss_fn <- loss_binary_crossentropy(from_logits = TRUE)  train_step <- tf_function(function(real_images) {   # Sample random points in the latent space   c(batch_size, ...) %<-% op_shape(real_images)   random_latent_vectors <-     tf$random$normal(shape(batch_size, latent_dim))    # Decode them to fake images   generated_images <- generator(random_latent_vectors)    # Combine them with real images   combined_images <- tf$concat(list(generated_images, real_images),                                axis = 0L)    # Assemble labels discriminating real from fake images   labels <- tf$concat(list(tf$ones(shape(batch_size, 1)),                            tf$zeros(shape(batch_size, 1))),                       axis = 0L)    # Add random noise to the labels - important trick!   labels %<>% `+`(tf$random$uniform(tf$shape(.), maxval = 0.05))    # Train the discriminator   with(tf$GradientTape() %as% tape, {     predictions <- discriminator(combined_images)     d_loss <- loss_fn(labels, predictions)   })   grads <- tape$gradient(d_loss, discriminator$trainable_weights)   d_optimizer$apply(grads, discriminator$trainable_weights)    # Sample random points in the latent space   random_latent_vectors <-     tf$random$normal(shape(batch_size, latent_dim))    # Assemble labels that say \"all real images\"   misleading_labels <- tf$zeros(shape(batch_size, 1))    # Train the generator (note that we should *not* update the weights   # of the discriminator)!   with(tf$GradientTape() %as% tape, {     predictions <- discriminator(generator(random_latent_vectors))     g_loss <- loss_fn(misleading_labels, predictions)   })    grads <- tape$gradient(g_loss, generator$trainable_weights)   g_optimizer$apply(grads, generator$trainable_weights)    list(d_loss, g_loss, generated_images) }) # Prepare the dataset. We use both the training & test MNIST digits. batch_size <- 64 c(c(x_train, .), c(x_test, .)) %<-% dataset_mnist() all_digits <- op_concatenate(list(x_train, x_test)) all_digits <- op_reshape(all_digits, c(-1, 28, 28, 1)) dataset <- all_digits |>   tensor_slices_dataset() |>   dataset_map(\\(x) op_cast(x, \"float32\") / 255) |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(batch_size = batch_size)  epochs <- 1 # In practice you need at least 20 epochs to generate nice digits. save_dir <- \"./\"  for (epoch in seq_len(epochs)) {   cat(\"Start epoch: \", epoch, \"\\n\")   step <- 0   train_iterator <- as_iterator(dataset)   while (!is.null(real_images <- iter_next(train_iterator))) {     step <- step + 1     # Train the discriminator & generator on one batch of real images.     c(d_loss, g_loss, generated_images) %<-% train_step(real_images)      # Logging.     if (step %% 200 == 0) {       # Print metrics       cat(sprintf(\"discriminator loss at step %d: %.2f\\n\", step, d_loss))       cat(sprintf(\"adversarial loss at step %d: %.2f\\n\", step, g_loss))     }      # To limit execution time we stop after 10 steps.     # Remove the lines below to actually train the model!     if (step > 10)       break   } } ## Start epoch:  1"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing a training loop from scratch in PyTorch","text":"","code":"import os  # This guide can only be run with the torch backend. os.environ[\"KERAS_BACKEND\"] = \"torch\"  import torch import keras import numpy as np"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing a training loop from scratch in PyTorch","text":"Keras provides default training evaluation loops, fit() evaluate(). usage covered guide Training & evaluation built-methods. want customize learning algorithm model still leveraging convenience fit() (instance, train GAN using fit()), can subclass Model class implement train_step() method, called repeatedly fit(). Now, want low-level control training & evaluation, write training & evaluation loops scratch. guide .","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"A first end-to-end example","title":"Writing a training loop from scratch in PyTorch","text":"write custom training loop, need following ingredients: model train, course. optimizer. either use keras.optimizers optimizer, native PyTorch optimizer torch.optim. loss function. either use keras.losses loss, native PyTorch loss torch.nn. dataset. use format: tf.data.Dataset, PyTorch DataLoader, Python generator, etc. Let’s line . ’ll use torch-native objects case – except, course, Keras model. First, let’s get model MNIST dataset: Next, ’s PyTorch optimizer PyTorch loss function: Let’s train model using mini-batch gradient custom training loop. Calling loss.backward() loss tensor triggers backpropagation. ’s done, optimizer magically aware gradients variable can update variables, done via optimizer.step(). Tensors, variables, optimizers interconnected one another via hidden global state. Also, don’t forget call model.zero_grad() loss.backward(), won’t get right gradients variables. ’s training loop, step step: open loop iterates epochs epoch, open loop iterates dataset, batches batch, call model input data retrive predictions, use compute loss value call loss.backward() Outside scope, retrieve gradients weights model regard loss Finally, use optimizer update weights model based gradients alternative, let’s look loop looks like using Keras optimizer Keras loss function. Important differences: retrieve gradients variables via v.value.grad, called trainable variable. update variables via optimizer.apply(), must called torch.no_grad() scope. Also, big gotcha: NumPy/TensorFlow/JAX/Keras APIs well Python unittest APIs use argument order convention fn(y_true, y_pred) (reference values first, predicted values second), PyTorch actually uses fn(y_pred, y_true) losses. make sure invert order logits targets.","code":"# Let's consider a simple MNIST model def get_model():     inputs = keras.Input(shape=(784,), name=\"digits\")     x1 = keras.layers.Dense(64, activation=\"relu\")(inputs)     x2 = keras.layers.Dense(64, activation=\"relu\")(x1)     outputs = keras.layers.Dense(10, name=\"predictions\")(x2)     model = keras.Model(inputs=inputs, outputs=outputs)     return model   # Create load up the MNIST dataset and put it in a torch DataLoader # Prepare the training dataset. batch_size = 32 (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() x_train = np.reshape(x_train, (-1, 784)).astype(\"float32\") x_test = np.reshape(x_test, (-1, 784)).astype(\"float32\") y_train = keras.utils.to_categorical(y_train) y_test = keras.utils.to_categorical(y_test)  # Reserve 10,000 samples for validation. x_val = x_train[-10000:] y_val = y_train[-10000:] x_train = x_train[:-10000] y_train = y_train[:-10000]  # Create torch Datasets train_dataset = torch.utils.data.TensorDataset(     torch.from_numpy(x_train), torch.from_numpy(y_train) ) val_dataset = torch.utils.data.TensorDataset(     torch.from_numpy(x_val), torch.from_numpy(y_val) )  # Create DataLoaders for the Datasets train_dataloader = torch.utils.data.DataLoader(     train_dataset, batch_size=batch_size, shuffle=True ) val_dataloader = torch.utils.data.DataLoader(     val_dataset, batch_size=batch_size, shuffle=False ) # Instantiate a torch optimizer model = get_model() optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)  # Instantiate a torch loss function loss_fn = torch.nn.CrossEntropyLoss() epochs = 3 for epoch in range(epochs):     for step, (inputs, targets) in enumerate(train_dataloader):         # Forward pass         logits = model(inputs)         loss = loss_fn(logits, targets)          # Backward pass         model.zero_grad()         loss.backward()          # Optimizer variable updates         optimizer.step()          # Log every 100 batches.         if step % 100 == 0:             print(                 f\"Training loss (for 1 batch) at step {step}: {loss.detach().numpy():.4f}\"             )             print(f\"Seen so far: {(step + 1) * batch_size} samples\") model = get_model() optimizer = keras.optimizers.Adam(learning_rate=1e-3) loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  for epoch in range(epochs):     print(f\"\\nStart of epoch {epoch}\")     for step, (inputs, targets) in enumerate(train_dataloader):         # Forward pass         logits = model(inputs)         loss = loss_fn(targets, logits)          # Backward pass         model.zero_grad()         trainable_weights = [v for v in model.trainable_weights]          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             optimizer.apply(gradients, trainable_weights)          # Log every 100 batches.         if step % 100 == 0:             print(                 f\"Training loss (for 1 batch) at step {step}: {loss.detach().numpy():.4f}\"             )             print(f\"Seen so far: {(step + 1) * batch_size} samples\")"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"low-level-handling-of-metrics","dir":"Articles","previous_headings":"","what":"Low-level handling of metrics","title":"Writing a training loop from scratch in PyTorch","text":"Let’s add metrics monitoring basic training loop. can readily reuse built-Keras metrics (custom ones wrote) training loops written scratch. ’s flow: Instantiate metric start loop Call metric.update_state() batch Call metric.result() need display current value metric Call metric.reset_state() need clear state metric (typically end epoch) Let’s use knowledge compute CategoricalAccuracy training validation data end epoch: ’s training & evaluation loop:","code":"# Get a fresh model model = get_model()  # Instantiate an optimizer to train the model. optimizer = keras.optimizers.Adam(learning_rate=1e-3) # Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  # Prepare the metrics. train_acc_metric = keras.metrics.CategoricalAccuracy() val_acc_metric = keras.metrics.CategoricalAccuracy() for epoch in range(epochs):     print(f\"\\nStart of epoch {epoch}\")     for step, (inputs, targets) in enumerate(train_dataloader):         # Forward pass         logits = model(inputs)         loss = loss_fn(targets, logits)          # Backward pass         model.zero_grad()         trainable_weights = [v for v in model.trainable_weights]          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             optimizer.apply(gradients, trainable_weights)          # Update training metric.         train_acc_metric.update_state(targets, logits)          # Log every 100 batches.         if step % 100 == 0:             print(                 f\"Training loss (for 1 batch) at step {step}: {loss.detach().numpy():.4f}\"             )             print(f\"Seen so far: {(step + 1) * batch_size} samples\")      # Display metrics at the end of each epoch.     train_acc = train_acc_metric.result()     print(f\"Training acc over epoch: {float(train_acc):.4f}\")      # Reset training metrics at the end of each epoch     train_acc_metric.reset_state()      # Run a validation loop at the end of each epoch.     for x_batch_val, y_batch_val in val_dataloader:         val_logits = model(x_batch_val, training=False)         # Update val metrics         val_acc_metric.update_state(y_batch_val, val_logits)     val_acc = val_acc_metric.result()     val_acc_metric.reset_state()     print(f\"Validation acc: {float(val_acc):.4f}\")"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"low-level-handling-of-losses-tracked-by-the-model","dir":"Articles","previous_headings":"","what":"Low-level handling of losses tracked by the model","title":"Writing a training loop from scratch in PyTorch","text":"Layers & models recursively track losses created forward pass layers call self.add_loss(value). resulting list scalar loss values available via property model.losses end forward pass. want using loss components, sum add main loss training step. Consider layer, creates activity regularization loss: Let’s build really simple model uses : ’s training loop look like now: ’s !","code":"class ActivityRegularizationLayer(keras.layers.Layer):     def call(self, inputs):         self.add_loss(1e-2 * torch.sum(inputs))         return inputs inputs = keras.Input(shape=(784,), name=\"digits\") x = keras.layers.Dense(64, activation=\"relu\")(inputs) # Insert activity regularization as a layer x = ActivityRegularizationLayer()(x) x = keras.layers.Dense(64, activation=\"relu\")(x) outputs = keras.layers.Dense(10, name=\"predictions\")(x)  model = keras.Model(inputs=inputs, outputs=outputs) # Get a fresh model model = get_model()  # Instantiate an optimizer to train the model. optimizer = keras.optimizers.Adam(learning_rate=1e-3) # Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  # Prepare the metrics. train_acc_metric = keras.metrics.CategoricalAccuracy() val_acc_metric = keras.metrics.CategoricalAccuracy()  for epoch in range(epochs):     print(f\"\\nStart of epoch {epoch}\")     for step, (inputs, targets) in enumerate(train_dataloader):         # Forward pass         logits = model(inputs)         loss = loss_fn(targets, logits)         if model.losses:             loss = loss + torch.sum(*model.losses)          # Backward pass         model.zero_grad()         trainable_weights = [v for v in model.trainable_weights]          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             optimizer.apply(gradients, trainable_weights)          # Update training metric.         train_acc_metric.update_state(targets, logits)          # Log every 100 batches.         if step % 100 == 0:             print(                 f\"Training loss (for 1 batch) at step {step}: {loss.detach().numpy():.4f}\"             )             print(f\"Seen so far: {(step + 1) * batch_size} samples\")      # Display metrics at the end of each epoch.     train_acc = train_acc_metric.result()     print(f\"Training acc over epoch: {float(train_acc):.4f}\")      # Reset training metrics at the end of each epoch     train_acc_metric.reset_state()      # Run a validation loop at the end of each epoch.     for x_batch_val, y_batch_val in val_dataloader:         val_logits = model(x_batch_val, training=False)         # Update val metrics         val_acc_metric.update_state(y_batch_val, val_logits)     val_acc = val_acc_metric.result()     val_acc_metric.reset_state()     print(f\"Validation acc: {float(val_acc):.4f}\")"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing a training loop from scratch in TensorFlow","text":"","code":"library(tensorflow) library(tfdatasets) library(keras3, warn.conflicts = FALSE)  # This guide can only be run with the TensorFlow backend. use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing a training loop from scratch in TensorFlow","text":"Keras provides default training evaluation loops, fit() evaluate(). usage covered guide Training & evaluation built-methods. want customize learning algorithm model still leveraging convenience fit() (instance, train GAN using fit()), can subclass Model class implement train_step() method, called repeatedly fit(). covered guide Customizing happens fit(). Now, want low-level control training & evaluation, write training & evaluation loops scratch. guide .","code":""},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"using-the-gradienttape-a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"Using the GradientTape: a first end-to-end example","title":"Writing a training loop from scratch in TensorFlow","text":"Calling model inside GradientTape scope enables retrieve gradients trainable weights layer respect loss value. Using optimizer instance, can use gradients update variables (can retrieve using model$trainable_weights). Let’s consider simple MNIST model: Let’s train using mini-batch gradient custom training loop. First, ’re going need optimizer, loss function, dataset: ’s training loop: open loop iterates epochs epoch, iterate dataset, batches batch, open GradientTape() scope Inside scope, call model (forward pass) compute loss Outside scope, retrieve gradients weights model regard loss Finally, use optimizer update weights model based gradients","code":"get_model <- function() {   inputs <- keras_input(shape = 784, name = \"digits\")   outputs <- inputs |>     layer_dense(units = 64, activation = \"relu\") |>     layer_dense(units = 64, activation = \"relu\") |>     layer_dense(units = 10, name = \"predictions\")   keras_model(inputs = inputs, outputs = outputs) }  model <- get_model() # Instantiate an optimizer. optimizer <- optimizer_adam(learning_rate = 1e-3) # Instantiate a loss function. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE)  # Prepare the training dataset. batch_size <- 64 c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(-1, 784)) x_test <- array_reshape(x_test, c(-1, 784))  # Reserve 10,000 samples for validation. x_val <- x_train[1:10000, ] y_val <- y_train[1:10000] x_train = x_train[-c(1:10000), ] y_train = y_train[-c(1:10000)]  # Prepare the training dataset. train_dataset <- list(x_train, y_train) |>   tensor_slices_dataset() |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(batch_size)  # Prepare the validation dataset. val_dataset <- list(x_val, y_val) |>   tensor_slices_dataset() |>   dataset_batch(batch_size) epochs <- 3 for (epoch in seq_len(epochs)) {   cat(\"\\nStart of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   iterator <- as_iterator(train_dataset)   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1     # Open a GradientTape to record the operations run     # during the forward pass, which enables auto-differentiation.     with(tf$GradientTape() %as% tape, {       # Run the forward pass of the layer.       # The operations that the layer applies       # to its inputs are going to be recorded       # on the GradientTape.       logits <- model(x_batch_train, training = TRUE) # Logits for this minibatch        # Compute the loss value for this minibatch.       loss_value <- loss_fn(y_batch_train, logits)     })      # Use the gradient tape to automatically retrieve     # the gradients of the trainable variables with respect to the loss.     gradients <- tape$gradient(loss_value, model$trainable_weights)      # Run one step of gradient descent by updating     # the value of the variables to minimize the loss.     optimizer$apply(gradients, model$trainable_weights)      # Log every 200 batches.     if (step %% 200 == 0) {       cat(sprintf(\"Training loss (for one batch) at step %d: %.4f\\n\",                   step, loss_value))       cat(sprintf(\"Seen so far: %d samples\\n\", (step * batch_size)))     }   } } ## ## Start of epoch  1 ## Training loss (for one batch) at step 200: 1.9342 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.8244 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.8011 ## Seen so far: 38400 samples ## ## Start of epoch  2 ## Training loss (for one batch) at step 200: 0.3505 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.6878 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.3928 ## Seen so far: 38400 samples ## ## Start of epoch  3 ## Training loss (for one batch) at step 200: 0.3366 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.1102 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.3629 ## Seen so far: 38400 samples"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"low-level-handling-of-metrics","dir":"Articles","previous_headings":"","what":"Low-level handling of metrics","title":"Writing a training loop from scratch in TensorFlow","text":"Let’s add metrics monitoring basic loop. can readily reuse built-metrics (custom ones wrote) training loops written scratch. ’s flow: Instantiate metric start loop Call metric$update_state() batch Call metric$result() need display current value metric Call metric$reset_state() need clear state metric (typically end epoch) Let’s use knowledge compute SparseCategoricalAccuracy validation data end epoch: ’s training & evaluation loop:","code":"# Get model model <- get_model()  # Instantiate an optimizer to train the model. optimizer <- optimizer_adam(learning_rate = 1e-3) # Instantiate a loss function. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE)  # Prepare the metrics. train_acc_metric <- metric_sparse_categorical_accuracy() val_acc_metric <- metric_sparse_categorical_accuracy() epochs <- 2 time <- Sys.time() for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   iterator <- as_iterator(train_dataset)   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1      with(tf$GradientTape() %as% tape, {       logits <- model(x_batch_train, training = TRUE)       loss_value <- loss_fn(y_batch_train, logits)     })     gradients <- tape$gradient(loss_value, model$trainable_weights)     optimizer$apply(gradients, model$trainable_weights)      # Update training metric.     train_acc_metric$update_state(y_batch_train, logits)     if (step %% 200 == 0) {       cat(sprintf(         \"Training loss (for one batch) at step %d: %.4f\\n\", step, loss_value))       cat(sprintf(\"Seen so far: %d samples \\n\", (step * batch_size)))     }   }    # Display metrics at the end of each epoch.   train_acc <- train_acc_metric$result()   cat(sprintf(\"Training acc over epoch: %.4f\\n\", train_acc))    # Reset training metrics at the end of each epoch   train_acc_metric$reset_state()    # Run a validation loop at the end of each epoch.   iterate(val_dataset, function(val_batch) {     c(x_batch_val, y_batch_val) %<-% val_batch     val_logits <- model(x_batch_val, training = FALSE)     val_acc_metric$update_state(y_batch_val, val_logits)   })   val_acc <- val_acc_metric$result()   val_acc_metric$reset_state()   cat(sprintf(\"Validation acc: %.4f\\n\", val_acc)) } ## Start of epoch  1 ## Training loss (for one batch) at step 200: 2.4070 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.8216 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 1.2056 ## Seen so far: 38400 samples ## Training acc over epoch: 0.8094 ## Validation acc: 0.8687 ## Start of epoch  2 ## Training loss (for one batch) at step 200: 0.1857 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.3803 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.3399 ## Seen so far: 38400 samples ## Training acc over epoch: 0.8859 ## Validation acc: 0.9002 Sys.time() - time ## Time difference of 44.61786 secs"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"speeding-up-your-training-step-with-tf_function","dir":"Articles","previous_headings":"","what":"Speeding-up your training step with tf_function()","title":"Writing a training loop from scratch in TensorFlow","text":"default runtime TensorFlow 2 eager execution. , training loop executes eagerly. great debugging, graph compilation definite performance advantage. Describing computation static graph enables framework apply global performance optimizations. impossible framework constrained greedily execute one operation another, knowledge comes next. can compile static graph function takes tensors input. Just add @tf.function decorator , like : Let’s evaluation step: Now, let’s re-run training loop compiled training step: Much faster, isn’t ?","code":"train_step <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     logits <- model(x, training = TRUE)     loss_value <- loss_fn(y, logits)   })   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply(gradients, model$trainable_weights)   train_acc_metric$update_state(y, logits)   invisible(NULL) # return nothing }) test_step <- tf_function(function(x, y) {   val_logits <- model(x, training=FALSE)   val_acc_metric$update_state(y, val_logits)   invisible(NULL) # return nothing }) epochs <- 2 time <- Sys.time() for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1     train_step(x_batch_train, y_batch_train)      if (step %% 200 == 0) {       cat(sprintf(         \"Training loss (for one batch) at step %d: %.4f\\n\", step, loss_value       ))       cat(sprintf(\"Seen so far: %d samples \\n\", (step * batch_size)))     }   }    # Display metrics at the end of each epoch.   train_acc <- train_acc_metric$result()   cat(sprintf(\"Training acc over epoch: %.4f\\n\", train_acc))     # Reset training metrics at the end of each epoch   train_acc_metric$reset_state()    # Run a validation loop at the end of each epoch.    iterate(val_dataset, function(val_batch) {     c(x_batch_val, y_batch_val) %<-% val_batch     test_step(x_batch_val, y_batch_val)   })    val_acc <- val_acc_metric$result()   val_acc_metric$reset_state()   cat(sprintf(\"Validation acc: %.4f\\n\", val_acc)) } ## Start of epoch  1 ## Training acc over epoch: 0.0000 ## Validation acc: 0.9002 ## Start of epoch  2 ## Training acc over epoch: 0.0000 ## Validation acc: 0.9002 Sys.time() - time ## Time difference of 0.5360565 secs"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"low-level-handling-of-losses-tracked-by-the-model","dir":"Articles","previous_headings":"","what":"Low-level handling of losses tracked by the model","title":"Writing a training loop from scratch in TensorFlow","text":"Layers models recursively track losses created forward pass layers call self$add_loss(value). resulting list scalar loss values available via property model$losses end forward pass. want using loss components, sum add main loss training step. Consider layer, creates activity regularization loss: Let’s build really simple model uses : ’s training step look like now:","code":"layer_activity_regularization <- Layer(   \"ActivityRegularizationLayer\",   call = function(inputs) {     self$add_loss(0.1 * op_mean(inputs))     inputs   } ) inputs <- keras_input(shape = 784, name=\"digits\") outputs <- inputs |>   layer_dense(units = 64, activation = \"relu\") |>   layer_activity_regularization() |>   layer_dense(units = 64, activation = \"relu\") |>   layer_dense(units = 10, name = \"predictions\") model <- keras_model(inputs = inputs, outputs = outputs) train_step <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     logits <- model(x, training = TRUE)     loss_value <- Reduce(`+`, c(loss_fn(y, logits),                                 model$losses))   })   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply(gradients, model$trainable_weights)   train_acc_metric$update_state(y, logits)   invisible(NULL) })"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Writing a training loop from scratch in TensorFlow","text":"Now know everything know using built-training loops writing scratch. conclude, ’s simple end--end example ties together everything ’ve learned guide: DCGAN trained MNIST digits.","code":""},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"end-to-end-example-a-gan-training-loop-from-scratch","dir":"Articles","previous_headings":"","what":"End-to-end example: a GAN training loop from scratch","title":"Writing a training loop from scratch in TensorFlow","text":"may familiar Generative Adversarial Networks (GANs). GANs can generate new images look almost real, learning latent distribution training dataset images (“latent space” images). GAN made two parts: “generator” model maps points latent space points image space, “discriminator” model, classifier can tell difference real images (training dataset) fake images (output generator network). GAN training loop looks like : Train discriminator. Sample batch random points latent space. Turn points fake images via “generator” model. Get batch real images combine generated images. Train “discriminator” model classify generated vs. real images. Train generator. Sample random points latent space. Turn points fake images via “generator” network. Get batch real images combine generated images. Train “generator” model “fool” discriminator classify fake images real. much detailed overview GANs works, see Deep Learning Python. Let’s implement training loop. First, create discriminator meant classify fake vs real digits: let’s create generator network, turns latent vectors outputs shape (28, 28, 1) (representing MNIST digits): ’s key bit: training loop. can see quite straightforward. training step function takes 17 lines. Let’s train GAN, repeatedly calling train_step batches images. Since discriminator generator convnets, ’re going want run code GPU. ’s ! ’ll get nice-looking fake MNIST digits just ~30s training Colab GPU.","code":"# Create the discriminator discriminator <-   keras_model_sequential(name = \"discriminator\",                          input_shape = c(28, 28, 1)) |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d(filters = 128, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_global_max_pooling_2d() |>   layer_dense(units = 1)  summary(discriminator) ## Model: \"discriminator\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d_1 (Conv2D)               │ (None, 14, 14, 64)     │           640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_1 (LeakyReLU)       │ (None, 14, 14, 64)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 7, 7, 128)      │        73,856 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu (LeakyReLU)         │ (None, 7, 7, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d            │ (None, 128)            │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_6 (Dense)                 │ (None, 1)              │           129 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 74,625 (291.50 KB) ##  Trainable params: 74,625 (291.50 KB) ##  Non-trainable params: 0 (0.00 B) latent_dim <- 128L  generator <-   keras_model_sequential(name = \"generator\",                          input_shape = latent_dim) |>   layer_dense(7 * 7 * 128) |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_reshape(target_shape = c(7, 7, 128)) |>   layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d(filters = 1, kernel_size = c(7, 7), padding = \"same\",                 activation = \"sigmoid\")  summary(generator) ## Model: \"generator\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_7 (Dense)                 │ (None, 6272)           │       809,088 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_4 (LeakyReLU)       │ (None, 6272)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ reshape (Reshape)               │ (None, 7, 7, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 14, 14, 128)    │       262,272 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_3 (LeakyReLU)       │ (None, 14, 14, 128)    │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 28, 28, 128)    │       262,272 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_2 (LeakyReLU)       │ (None, 28, 28, 128)    │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 28, 28, 1)      │         6,273 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 1,339,905 (5.11 MB) ##  Trainable params: 1,339,905 (5.11 MB) ##  Non-trainable params: 0 (0.00 B) # Instantiate one optimizer for the discriminator and another for the generator. d_optimizer <- optimizer_adam(learning_rate = 0.0003) g_optimizer <- optimizer_adam(learning_rate = 0.0004)  # Instantiate a loss function. loss_fn <- loss_binary_crossentropy(from_logits = TRUE)  train_step <- tf_function(function(real_images) {   # Sample random points in the latent space   c(batch_size, ...) %<-% op_shape(real_images)   random_latent_vectors <-     tf$random$normal(shape(batch_size, latent_dim))    # Decode them to fake images   generated_images <- generator(random_latent_vectors)    # Combine them with real images   combined_images <- tf$concat(list(generated_images, real_images),                                axis = 0L)    # Assemble labels discriminating real from fake images   labels <- tf$concat(list(tf$ones(shape(batch_size, 1)),                            tf$zeros(shape(batch_size, 1))),                       axis = 0L)    # Add random noise to the labels - important trick!   labels %<>% `+`(tf$random$uniform(tf$shape(.), maxval = 0.05))    # Train the discriminator   with(tf$GradientTape() %as% tape, {     predictions <- discriminator(combined_images)     d_loss <- loss_fn(labels, predictions)   })   grads <- tape$gradient(d_loss, discriminator$trainable_weights)   d_optimizer$apply(grads, discriminator$trainable_weights)    # Sample random points in the latent space   random_latent_vectors <-     tf$random$normal(shape(batch_size, latent_dim))    # Assemble labels that say \"all real images\"   misleading_labels <- tf$zeros(shape(batch_size, 1))    # Train the generator (note that we should *not* update the weights   # of the discriminator)!   with(tf$GradientTape() %as% tape, {     predictions <- discriminator(generator(random_latent_vectors))     g_loss <- loss_fn(misleading_labels, predictions)   })    grads <- tape$gradient(g_loss, generator$trainable_weights)   g_optimizer$apply(grads, generator$trainable_weights)    list(d_loss, g_loss, generated_images) }) # Prepare the dataset. We use both the training & test MNIST digits. batch_size <- 64 c(c(x_train, .), c(x_test, .)) %<-% dataset_mnist() all_digits <- op_concatenate(list(x_train, x_test)) all_digits <- op_reshape(all_digits, c(-1, 28, 28, 1)) dataset <- all_digits |>   tfdatasets::tensor_slices_dataset() |>   tfdatasets::dataset_map(function(x) tf$cast(x, \"float\") / 255) |>   tfdatasets::dataset_shuffle(buffer_size = 1024) |>   tfdatasets::dataset_batch(batch_size = batch_size)  epochs <- 1 # In practice you need at least 20 epochs to generate nice digits. save_dir <- \"./\"  for (epoch in seq_len(epochs)) {   cat(\"Start epoch: \", epoch, \"\\n\")   step <- 0   train_iterator <- as_iterator(dataset)   while (!is.null(real_images <- iter_next(train_iterator))) {     step <- step + 1     # Train the discriminator & generator on one batch of real images.     c(d_loss, g_loss, generated_images) %<-% train_step(real_images)      # Logging.     if (step %% 200 == 0) {       # Print metrics       cat(sprintf(\"discriminator loss at step %d: %.2f\\n\", step, d_loss))       cat(sprintf(\"adversarial loss at step %d: %.2f\\n\", step, g_loss))     }      # To limit execution time we stop after 10 steps.     # Remove the lines below to actually train the model!     if (step > 10)       break   } } ## Start epoch:  1"},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing your own callbacks","text":"callback powerful tool customize behavior Keras model training, evaluation, inference. Examples include keras.callbacks.TensorBoard visualize training progress results TensorBoard, keras.callbacks.ModelCheckpoint periodically save model training. guide, learn Keras callback , can , can build . provide demos simple callback applications get started.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing your own callbacks","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"keras-callbacks-overview","dir":"Articles","previous_headings":"","what":"Keras callbacks overview","title":"Writing your own callbacks","text":"callbacks subclass keras.callbacks.Callback class, override set methods called various stages training, testing, predicting. Callbacks useful get view internal states statistics model training. can pass list callbacks (keyword argument callbacks) following model methods: fit() evaluate() predict()","code":""},{"path":[]},{"path":[]},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_traintestpredict_beginlogs-null","dir":"Articles","previous_headings":"An overview of callback methods > Global methods","what":"on_(train|test|predict)_begin(logs = NULL)","title":"Writing your own callbacks","text":"Called beginning fit/evaluate/predict.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_traintestpredict_endlogs-null","dir":"Articles","previous_headings":"An overview of callback methods > Global methods","what":"on_(train|test|predict)_end(logs = NULL)","title":"Writing your own callbacks","text":"Called end fit/evaluate/predict.","code":""},{"path":[]},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_traintestpredict_batch_beginbatch-logs-null","dir":"Articles","previous_headings":"An overview of callback methods > Batch-level methods for training/testing/predicting","what":"on_(train|test|predict)_batch_begin(batch, logs = NULL)","title":"Writing your own callbacks","text":"Called right processing batch training/testing/predicting.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_traintestpredict_batch_endbatch-logs-null","dir":"Articles","previous_headings":"An overview of callback methods > Batch-level methods for training/testing/predicting","what":"on_(train|test|predict)_batch_end(batch, logs = NULL)","title":"Writing your own callbacks","text":"Called end training/testing/predicting batch. Within method, logs named list containing metrics results.","code":""},{"path":[]},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_epoch_beginepoch-logs-null","dir":"Articles","previous_headings":"An overview of callback methods > Epoch-level methods (training only)","what":"on_epoch_begin(epoch, logs = NULL)","title":"Writing your own callbacks","text":"Called beginning epoch training.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_epoch_endepoch-logs-null","dir":"Articles","previous_headings":"An overview of callback methods > Epoch-level methods (training only)","what":"on_epoch_end(epoch, logs = NULL)","title":"Writing your own callbacks","text":"Called end epoch training.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"a-basic-example","dir":"Articles","previous_headings":"","what":"A basic example","title":"Writing your own callbacks","text":"Let’s take look concrete example. get started, let’s import tensorflow define simple Sequential Keras model: , load MNIST data training testing Keras datasets API: Now, define simple custom callback logs: fit/evaluate/predict starts & ends epoch starts & ends training batch starts & ends evaluation (test) batch starts & ends inference (prediction) batch starts & ends Let’s try :","code":"# Define the Keras model to add callbacks to get_model <- function() {   model <- keras_model_sequential()   model |> layer_dense(units = 1)   model |> compile(     optimizer = optimizer_rmsprop(learning_rate = 0.1),     loss = \"mean_squared_error\",     metrics = \"mean_absolute_error\"   )   model } # Load example MNIST data and pre-process it mnist <- dataset_mnist()  flatten_and_rescale <- function(x) {   x <- array_reshape(x, c(-1, 784))   x <- x / 255   x }  mnist$train$x <- flatten_and_rescale(mnist$train$x) mnist$test$x  <- flatten_and_rescale(mnist$test$x)  # limit to 1000 samples n <- 1000 mnist$train$x <- mnist$train$x[1:n,] mnist$train$y <- mnist$train$y[1:n] mnist$test$x  <- mnist$test$x[1:n,] mnist$test$y  <- mnist$test$y[1:n] show <- function(msg, logs) {   cat(glue::glue(msg, .envir = parent.frame()),       \"got logs: \", sep = \"; \")   str(logs); cat(\"\\n\") }  callback_custom <- Callback(   \"CustomCallback\",   on_train_begin         = \\(logs = NULL)        show(\"Starting training\", logs),   on_epoch_begin         = \\(epoch, logs = NULL) show(\"Start epoch {epoch} of training\", logs),   on_train_batch_begin   = \\(batch, logs = NULL) show(\"...Training: start of batch {batch}\", logs),   on_train_batch_end     = \\(batch, logs = NULL) show(\"...Training: end of batch {batch}\",  logs),   on_epoch_end           = \\(epoch, logs = NULL) show(\"End epoch {epoch} of training\", logs),   on_train_end           = \\(logs = NULL)        show(\"Stop training\", logs),     on_test_begin          = \\(logs = NULL)        show(\"Start testing\", logs),   on_test_batch_begin    = \\(batch, logs = NULL) show(\"...Evaluating: start of batch {batch}\", logs),   on_test_batch_end      = \\(batch, logs = NULL) show(\"...Evaluating: end of batch {batch}\", logs),   on_test_end            = \\(logs = NULL)        show(\"Stop testing\", logs),    on_predict_begin       = \\(logs = NULL)        show(\"Start predicting\", logs),   on_predict_end         = \\(logs = NULL)        show(\"Stop predicting\", logs),   on_predict_batch_begin = \\(batch, logs = NULL) show(\"...Predicting: start of batch {batch}\", logs),   on_predict_batch_end   = \\(batch, logs = NULL) show(\"...Predicting: end of batch {batch}\", logs), ) model <- get_model() model |> fit(   mnist$train$x, mnist$train$y,   batch_size = 128,   epochs = 2,   verbose = 0,   validation_split = 0.5,   callbacks = list(callback_custom()) ) ## Starting training; got logs:  Named list() ## ## Start epoch 1 of training; got logs:  Named list() ## ## ...Training: start of batch 1; got logs:  Named list() ## ## ...Training: end of batch 1; got logs: List of 2 ##  $ loss               : num 25.9 ##  $ mean_absolute_error: num 4.19 ## ## ...Training: start of batch 2; got logs:  Named list() ## ## ...Training: end of batch 2; got logs: List of 2 ##  $ loss               : num 433 ##  $ mean_absolute_error: num 15.5 ## ## ...Training: start of batch 3; got logs:  Named list() ## ## ...Training: end of batch 3; got logs: List of 2 ##  $ loss               : num 297 ##  $ mean_absolute_error: num 11.8 ## ## ...Training: start of batch 4; got logs:  Named list() ## ## ...Training: end of batch 4; got logs: List of 2 ##  $ loss               : num 231 ##  $ mean_absolute_error: num 9.68 ## ## Start testing; got logs:  Named list() ## ## ...Evaluating: start of batch 1; got logs:  Named list() ## ## ...Evaluating: end of batch 1; got logs: List of 2 ##  $ loss               : num 8.1 ##  $ mean_absolute_error: num 2.3 ## ## ...Evaluating: start of batch 2; got logs:  Named list() ## ## ...Evaluating: end of batch 2; got logs: List of 2 ##  $ loss               : num 7.58 ##  $ mean_absolute_error: num 2.23 ## ## ...Evaluating: start of batch 3; got logs:  Named list() ## ## ...Evaluating: end of batch 3; got logs: List of 2 ##  $ loss               : num 7.38 ##  $ mean_absolute_error: num 2.21 ## ## ...Evaluating: start of batch 4; got logs:  Named list() ## ## ...Evaluating: end of batch 4; got logs: List of 2 ##  $ loss               : num 7.3 ##  $ mean_absolute_error: num 2.21 ## ## Stop testing; got logs: List of 2 ##  $ loss               : num 7.3 ##  $ mean_absolute_error: num 2.21 ## ## End epoch 1 of training; got logs: List of 4 ##  $ loss                   : num 231 ##  $ mean_absolute_error    : num 9.68 ##  $ val_loss               : num 7.3 ##  $ val_mean_absolute_error: num 2.21 ## ## Start epoch 2 of training; got logs:  Named list() ## ## ...Training: start of batch 1; got logs:  Named list() ## ## ...Training: end of batch 1; got logs: List of 2 ##  $ loss               : num 7.44 ##  $ mean_absolute_error: num 2.27 ## ## ...Training: start of batch 2; got logs:  Named list() ## ## ...Training: end of batch 2; got logs: List of 2 ##  $ loss               : num 6.81 ##  $ mean_absolute_error: num 2.16 ## ## ...Training: start of batch 3; got logs:  Named list() ## ## ...Training: end of batch 3; got logs: List of 2 ##  $ loss               : num 6.12 ##  $ mean_absolute_error: num 2.06 ## ## ...Training: start of batch 4; got logs:  Named list() ## ## ...Training: end of batch 4; got logs: List of 2 ##  $ loss               : num 6.08 ##  $ mean_absolute_error: num 2.04 ## ## Start testing; got logs:  Named list() ## ## ...Evaluating: start of batch 1; got logs:  Named list() ## ## ...Evaluating: end of batch 1; got logs: List of 2 ##  $ loss               : num 5.54 ##  $ mean_absolute_error: num 1.92 ## ## ...Evaluating: start of batch 2; got logs:  Named list() ## ## ...Evaluating: end of batch 2; got logs: List of 2 ##  $ loss               : num 5.31 ##  $ mean_absolute_error: num 1.87 ## ## ...Evaluating: start of batch 3; got logs:  Named list() ## ## ...Evaluating: end of batch 3; got logs: List of 2 ##  $ loss               : num 5.11 ##  $ mean_absolute_error: num 1.8 ## ## ...Evaluating: start of batch 4; got logs:  Named list() ## ## ...Evaluating: end of batch 4; got logs: List of 2 ##  $ loss               : num 5.15 ##  $ mean_absolute_error: num 1.82 ## ## Stop testing; got logs: List of 2 ##  $ loss               : num 5.15 ##  $ mean_absolute_error: num 1.82 ## ## End epoch 2 of training; got logs: List of 4 ##  $ loss                   : num 6.08 ##  $ mean_absolute_error    : num 2.04 ##  $ val_loss               : num 5.15 ##  $ val_mean_absolute_error: num 1.82 ## ## Stop training; got logs: List of 4 ##  $ loss                   : num 6.08 ##  $ mean_absolute_error    : num 2.04 ##  $ val_loss               : num 5.15 ##  $ val_mean_absolute_error: num 1.82 res <- model |> evaluate(   mnist$test$x, mnist$test$y,   batch_size = 128, verbose = 0,   callbacks = list(callback_custom()) ) ## Start testing; got logs:  Named list() ## ## ...Evaluating: start of batch 1; got logs:  Named list() ## ## ...Evaluating: end of batch 1; got logs: List of 2 ##  $ loss               : num 5.2 ##  $ mean_absolute_error: num 1.84 ## ## ...Evaluating: start of batch 2; got logs:  Named list() ## ## ...Evaluating: end of batch 2; got logs: List of 2 ##  $ loss               : num 4.62 ##  $ mean_absolute_error: num 1.73 ## ## ...Evaluating: start of batch 3; got logs:  Named list() ## ## ...Evaluating: end of batch 3; got logs: List of 2 ##  $ loss               : num 4.61 ##  $ mean_absolute_error: num 1.74 ## ## ...Evaluating: start of batch 4; got logs:  Named list() ## ## ...Evaluating: end of batch 4; got logs: List of 2 ##  $ loss               : num 4.65 ##  $ mean_absolute_error: num 1.75 ## ## ...Evaluating: start of batch 5; got logs:  Named list() ## ## ...Evaluating: end of batch 5; got logs: List of 2 ##  $ loss               : num 4.84 ##  $ mean_absolute_error: num 1.77 ## ## ...Evaluating: start of batch 6; got logs:  Named list() ## ## ...Evaluating: end of batch 6; got logs: List of 2 ##  $ loss               : num 4.76 ##  $ mean_absolute_error: num 1.76 ## ## ...Evaluating: start of batch 7; got logs:  Named list() ## ## ...Evaluating: end of batch 7; got logs: List of 2 ##  $ loss               : num 4.74 ##  $ mean_absolute_error: num 1.76 ## ## ...Evaluating: start of batch 8; got logs:  Named list() ## ## ...Evaluating: end of batch 8; got logs: List of 2 ##  $ loss               : num 4.67 ##  $ mean_absolute_error: num 1.75 ## ## Stop testing; got logs: List of 2 ##  $ loss               : num 4.67 ##  $ mean_absolute_error: num 1.75 res <- model |> predict(   mnist$test$x,   batch_size = 128, verbose = 0,   callbacks = list(callback_custom()) ) ## Start predicting; got logs:  Named list() ## ## ...Predicting: start of batch 1; got logs:  Named list() ## ## ...Predicting: end of batch 1; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 2; got logs:  Named list() ## ## ...Predicting: end of batch 2; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 3; got logs:  Named list() ## ## ...Predicting: end of batch 3; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 4; got logs:  Named list() ## ## ...Predicting: end of batch 4; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 5; got logs:  Named list() ## ## ...Predicting: end of batch 5; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 6; got logs:  Named list() ## ## ...Predicting: end of batch 6; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 7; got logs:  Named list() ## ## ...Predicting: end of batch 7; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 8; got logs:  Named list() ## ## ...Predicting: end of batch 8; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(104, 1), dtype=float32, numpy=…> ## ## Stop predicting; got logs:  Named list()"},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"usage-of-logs-list","dir":"Articles","previous_headings":"A basic example","what":"Usage of logs list","title":"Writing your own callbacks","text":"logs named list contains loss value, metrics end batch epoch. Example includes loss mean absolute error. information callbacks, can check Keras callback API documentation.","code":"callback_print_loss_and_mae <- Callback(   \"LossAndErrorPrintingCallback\",    on_train_batch_end = function(batch, logs = NULL)     cat(sprintf(\"Up to batch %i, the average loss is %7.2f.\\n\",                 batch,  logs$loss)),    on_test_batch_end = function(batch, logs = NULL)     cat(sprintf(\"Up to batch %i, the average loss is %7.2f.\\n\",                 batch, logs$loss)),    on_epoch_end = function(epoch, logs = NULL)     cat(sprintf(       \"The average loss for epoch %2i is %9.2f and mean absolute error is %7.2f.\\n\",       epoch, logs$loss, logs$mean_absolute_error     )) )   model <- get_model() model |> fit(   mnist$train$x, mnist$train$y,   epochs = 2, verbose = 0, batch_size = 128,   callbacks = list(callback_print_loss_and_mae()) ) ## Up to batch 1, the average loss is   25.12. ## Up to batch 2, the average loss is  398.92. ## Up to batch 3, the average loss is  274.04. ## Up to batch 4, the average loss is  208.32. ## Up to batch 5, the average loss is  168.15. ## Up to batch 6, the average loss is  141.31. ## Up to batch 7, the average loss is  122.19. ## Up to batch 8, the average loss is  110.05. ## The average loss for epoch  1 is    110.05 and mean absolute error is    5.79. ## Up to batch 1, the average loss is    4.71. ## Up to batch 2, the average loss is    4.74. ## Up to batch 3, the average loss is    4.81. ## Up to batch 4, the average loss is    5.07. ## Up to batch 5, the average loss is    5.08. ## Up to batch 6, the average loss is    5.09. ## Up to batch 7, the average loss is    5.19. ## Up to batch 8, the average loss is    5.51. ## The average loss for epoch  2 is      5.51 and mean absolute error is    1.90. res = model |> evaluate(   mnist$test$x, mnist$test$y,   verbose = 0, batch_size = 128,   callbacks = list(callback_print_loss_and_mae()) ) ## Up to batch 1, the average loss is   15.86. ## Up to batch 2, the average loss is   16.13. ## Up to batch 3, the average loss is   16.02. ## Up to batch 4, the average loss is   16.11. ## Up to batch 5, the average loss is   16.23. ## Up to batch 6, the average loss is   16.68. ## Up to batch 7, the average loss is   16.61. ## Up to batch 8, the average loss is   16.54."},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"usage-of-selfmodel-attribute","dir":"Articles","previous_headings":"","what":"Usage of self$model attribute","title":"Writing your own callbacks","text":"addition receiving log information one methods called, callbacks access model associated current round training/evaluation/inference: self$model. things can self$model callback: Set self$model$stop_training <- TRUE immediately interrupt training. Mutate hyperparameters optimizer (available self$model$optimizer), self$model$optimizer$learning_rate. Save model period intervals. Record output model |> predict() test samples end epoch, use sanity check training. Extract visualizations intermediate features end epoch, monitor model learning time. etc. Let’s see action couple examples.","code":""},{"path":[]},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"early-stopping-at-minimum-loss","dir":"Articles","previous_headings":"Examples of Keras callback applications","what":"Early stopping at minimum loss","title":"Writing your own callbacks","text":"first example shows creation Callback stops training minimum loss reached, setting attribute self$model$stop_training (boolean). Optionally, can provide argument patience specify many epochs wait stopping reached local minimum. callback_early_stopping() provides complete general implementation.","code":"callback_early_stopping_at_min_loss <- Callback(   \"EarlyStoppingAtMinLoss\",   `__doc__` =     \"Stop training when the loss is at its min, i.e. the loss stops decreasing.      Arguments:         patience: Number of epochs to wait after min has been hit. After this         number of no improvement, training stops.     \",    initialize = function(patience = 0) {     super$initialize()     self$patience <- patience     # best_weights to store the weights at which the minimum loss occurs.     self$best_weights <- NULL   },    on_train_begin = function(logs = NULL) {     # The number of epoch it has waited when loss is no longer minimum.     self$wait <- 0     # The epoch the training stops at.     self$stopped_epoch <- 0     # Initialize the best as infinity.     self$best <- Inf   },    on_epoch_end = function(epoch, logs = NULL) {     current <- logs$loss     if (current < self$best) {       self$best <- current       self$wait <- 0L       # Record the best weights if current results is better (less).       self$best_weights <- get_weights(self$model)     } else {       add(self$wait) <- 1L       if (self$wait >= self$patience) {         self$stopped_epoch <- epoch         self$model$stop_training <- TRUE         cat(\"Restoring model weights from the end of the best epoch.\\n\")         model$set_weights(self$best_weights)       }     }   },    on_train_end = function(logs = NULL)     if (self$stopped_epoch > 0)       cat(sprintf(\"Epoch %05d: early stopping\\n\", self$stopped_epoch + 1)) ) `add<-` <- `+`   model <- get_model() model |> fit(   mnist$train$x,   mnist$train$y,   epochs = 30,   batch_size = 64,   verbose = 0,   callbacks = list(callback_print_loss_and_mae(),                    callback_early_stopping_at_min_loss()) ) ## Up to batch 1, the average loss is   30.54. ## Up to batch 2, the average loss is  513.27. ## Up to batch 3, the average loss is  352.60. ## Up to batch 4, the average loss is  266.37. ## Up to batch 5, the average loss is  214.68. ## Up to batch 6, the average loss is  179.97. ## Up to batch 7, the average loss is  155.06. ## Up to batch 8, the average loss is  136.59. ## Up to batch 9, the average loss is  121.96. ## Up to batch 10, the average loss is  110.28. ## Up to batch 11, the average loss is  100.72. ## Up to batch 12, the average loss is   92.71. ## Up to batch 13, the average loss is   85.95. ## Up to batch 14, the average loss is   80.21. ## Up to batch 15, the average loss is   75.17. ## Up to batch 16, the average loss is   72.48. ## The average loss for epoch  1 is     72.48 and mean absolute error is    4.08. ## Up to batch 1, the average loss is    7.98. ## Up to batch 2, the average loss is    9.92. ## Up to batch 3, the average loss is   12.88. ## Up to batch 4, the average loss is   16.61. ## Up to batch 5, the average loss is   20.49. ## Up to batch 6, the average loss is   26.14. ## Up to batch 7, the average loss is   30.44. ## Up to batch 8, the average loss is   33.76. ## Up to batch 9, the average loss is   36.32. ## Up to batch 10, the average loss is   35.26. ## Up to batch 11, the average loss is   34.22. ## Up to batch 12, the average loss is   33.53. ## Up to batch 13, the average loss is   32.84. ## Up to batch 14, the average loss is   31.80. ## Up to batch 15, the average loss is   31.39. ## Up to batch 16, the average loss is   31.45. ## The average loss for epoch  2 is     31.45 and mean absolute error is    4.82. ## Up to batch 1, the average loss is   39.60. ## Up to batch 2, the average loss is   41.95. ## Up to batch 3, the average loss is   41.29. ## Up to batch 4, the average loss is   36.77. ## Up to batch 5, the average loss is   32.08. ## Up to batch 6, the average loss is   28.17. ## Up to batch 7, the average loss is   25.33. ## Up to batch 8, the average loss is   23.56. ## Up to batch 9, the average loss is   22.28. ## Up to batch 10, the average loss is   21.22. ## Up to batch 11, the average loss is   20.87. ## Up to batch 12, the average loss is   22.25. ## Up to batch 13, the average loss is   25.08. ## Up to batch 14, the average loss is   27.87. ## Up to batch 15, the average loss is   31.72. ## Up to batch 16, the average loss is   33.21. ## The average loss for epoch  3 is     33.21 and mean absolute error is    4.79. ## Restoring model weights from the end of the best epoch. ## Epoch 00004: early stopping"},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"learning-rate-scheduling","dir":"Articles","previous_headings":"Examples of Keras callback applications","what":"Learning rate scheduling","title":"Writing your own callbacks","text":"example, show custom Callback can used dynamically change learning rate optimizer course training. See keras$callbacks$LearningRateScheduler general implementations (RStudio, press F1 cursor LearningRateScheduler browser open page).","code":"callback_custom_learning_rate_scheduler <- Callback(   \"CustomLearningRateScheduler\",   `__doc__` =   \"Learning rate scheduler which sets the learning rate according to schedule.      Arguments:         schedule: a function that takes an epoch index             (integer, indexed from 0) and current learning rate             as inputs and returns a new learning rate as output (float).     \",    initialize = function(schedule) {     super$initialize()     self$schedule <- schedule   },    on_epoch_begin = function(epoch, logs = NULL) {     ## When in doubt about what types of objects are in scope (e.g., self$model)     ## use a debugger to interact with the actual objects at the console!     # browser()      if (!\"learning_rate\" %in% names(self$model$optimizer))       stop('Optimizer must have a \"learning_rate\" attribute.')      # # Get the current learning rate from model's optimizer.     # use as.numeric() to convert the keras variablea to an R numeric     lr <- as.numeric(self$model$optimizer$learning_rate)     # # Call schedule function to get the scheduled learning rate.     scheduled_lr <- self$schedule(epoch, lr)     # # Set the value back to the optimizer before this epoch starts     optimizer <- self$model$optimizer     optimizer$learning_rate <- scheduled_lr     cat(sprintf(\"\\nEpoch %03d: Learning rate is %6.4f.\\n\", epoch, scheduled_lr))   } )  LR_SCHEDULE <- tibble::tribble(   ~start_epoch, ~learning_rate,              0,            0.1,              3,           0.05,              6,           0.01,              9,          0.005,             12,          0.001,   )  last <- function(x) x[length(x)] lr_schedule <- function(epoch, learning_rate) {   \"Helper function to retrieve the scheduled learning rate based on epoch.\"   with(LR_SCHEDULE, learning_rate[last(which(epoch >= start_epoch))]) }  model <- get_model() model |> fit(   mnist$train$x,   mnist$train$y,   epochs = 14,   batch_size = 64,   verbose = 0,   callbacks = list(     callback_print_loss_and_mae(),     callback_custom_learning_rate_scheduler(lr_schedule)   ) ) ## ## Epoch 001: Learning rate is 0.1000. ## Up to batch 1, the average loss is   29.36. ## Up to batch 2, the average loss is  513.95. ## Up to batch 3, the average loss is  352.70. ## Up to batch 4, the average loss is  266.46. ## Up to batch 5, the average loss is  214.73. ## Up to batch 6, the average loss is  180.00. ## Up to batch 7, the average loss is  155.05. ## Up to batch 8, the average loss is  136.64. ## Up to batch 9, the average loss is  121.97. ## Up to batch 10, the average loss is  110.30. ## Up to batch 11, the average loss is  100.76. ## Up to batch 12, the average loss is   92.74. ## Up to batch 13, the average loss is   85.95. ## Up to batch 14, the average loss is   80.18. ## Up to batch 15, the average loss is   75.11. ## Up to batch 16, the average loss is   72.38. ## The average loss for epoch  1 is     72.38 and mean absolute error is    4.04. ## ## Epoch 002: Learning rate is 0.1000. ## Up to batch 1, the average loss is    6.95. ## Up to batch 2, the average loss is    8.71. ## Up to batch 3, the average loss is   11.42. ## Up to batch 4, the average loss is   15.15. ## Up to batch 5, the average loss is   19.28. ## Up to batch 6, the average loss is   25.54. ## Up to batch 7, the average loss is   30.38. ## Up to batch 8, the average loss is   33.95. ## Up to batch 9, the average loss is   36.58. ## Up to batch 10, the average loss is   35.46. ## Up to batch 11, the average loss is   34.34. ## Up to batch 12, the average loss is   33.51. ## Up to batch 13, the average loss is   32.67. ## Up to batch 14, the average loss is   31.54. ## Up to batch 15, the average loss is   31.05. ## Up to batch 16, the average loss is   31.09. ## The average loss for epoch  2 is     31.09 and mean absolute error is    4.77. ## ## Epoch 003: Learning rate is 0.0500. ## Up to batch 1, the average loss is   40.40. ## Up to batch 2, the average loss is   22.33. ## Up to batch 3, the average loss is   16.18. ## Up to batch 4, the average loss is   13.09. ## Up to batch 5, the average loss is   11.48. ## Up to batch 6, the average loss is   10.21. ## Up to batch 7, the average loss is    9.22. ## Up to batch 8, the average loss is    8.70. ## Up to batch 9, the average loss is    8.16. ## Up to batch 10, the average loss is    7.80. ## Up to batch 11, the average loss is    7.50. ## Up to batch 12, the average loss is    7.17. ## Up to batch 13, the average loss is    6.89. ## Up to batch 14, the average loss is    6.70. ## Up to batch 15, the average loss is    6.52. ## Up to batch 16, the average loss is    6.54. ## The average loss for epoch  3 is      6.54 and mean absolute error is    1.93. ## ## Epoch 004: Learning rate is 0.0500. ## Up to batch 1, the average loss is    8.74. ## Up to batch 2, the average loss is    8.34. ## Up to batch 3, the average loss is    9.09. ## Up to batch 4, the average loss is    9.72. ## Up to batch 5, the average loss is   10.48. ## Up to batch 6, the average loss is   11.69. ## Up to batch 7, the average loss is   11.83. ## Up to batch 8, the average loss is   11.56. ## Up to batch 9, the average loss is   11.24. ## Up to batch 10, the average loss is   10.84. ## Up to batch 11, the average loss is   10.66. ## Up to batch 12, the average loss is   10.44. ## Up to batch 13, the average loss is   10.21. ## Up to batch 14, the average loss is   10.06. ## Up to batch 15, the average loss is   10.00. ## Up to batch 16, the average loss is   10.20. ## The average loss for epoch  4 is     10.20 and mean absolute error is    2.71. ## ## Epoch 005: Learning rate is 0.0500. ## Up to batch 1, the average loss is   17.26. ## Up to batch 2, the average loss is   14.09. ## Up to batch 3, the average loss is   12.67. ## Up to batch 4, the average loss is   11.44. ## Up to batch 5, the average loss is   10.54. ## Up to batch 6, the average loss is   10.10. ## Up to batch 7, the average loss is    9.53. ## Up to batch 8, the average loss is    9.17. ## Up to batch 9, the average loss is    8.78. ## Up to batch 10, the average loss is    8.49. ## Up to batch 11, the average loss is    8.50. ## Up to batch 12, the average loss is    8.59. ## Up to batch 13, the average loss is    8.68. ## Up to batch 14, the average loss is    8.86. ## Up to batch 15, the average loss is    9.17. ## Up to batch 16, the average loss is    9.53. ## The average loss for epoch  5 is      9.53 and mean absolute error is    2.58. ## ## Epoch 006: Learning rate is 0.0100. ## Up to batch 1, the average loss is   17.04. ## Up to batch 2, the average loss is   14.85. ## Up to batch 3, the average loss is   11.53. ## Up to batch 4, the average loss is    9.65. ## Up to batch 5, the average loss is    8.44. ## Up to batch 6, the average loss is    7.50. ## Up to batch 7, the average loss is    6.74. ## Up to batch 8, the average loss is    6.56. ## Up to batch 9, the average loss is    6.18. ## Up to batch 10, the average loss is    5.87. ## Up to batch 11, the average loss is    5.63. ## Up to batch 12, the average loss is    5.45. ## Up to batch 13, the average loss is    5.23. ## Up to batch 14, the average loss is    5.12. ## Up to batch 15, the average loss is    4.96. ## Up to batch 16, the average loss is    4.91. ## The average loss for epoch  6 is      4.91 and mean absolute error is    1.67. ## ## Epoch 007: Learning rate is 0.0100. ## Up to batch 1, the average loss is    3.65. ## Up to batch 2, the average loss is    3.04. ## Up to batch 3, the average loss is    2.88. ## Up to batch 4, the average loss is    2.85. ## Up to batch 5, the average loss is    2.88. ## Up to batch 6, the average loss is    2.81. ## Up to batch 7, the average loss is    2.70. ## Up to batch 8, the average loss is    2.96. ## Up to batch 9, the average loss is    2.96. ## Up to batch 10, the average loss is    2.93. ## Up to batch 11, the average loss is    2.95. ## Up to batch 12, the average loss is    2.98. ## Up to batch 13, the average loss is    2.97. ## Up to batch 14, the average loss is    3.01. ## Up to batch 15, the average loss is    3.00. ## Up to batch 16, the average loss is    3.05. ## The average loss for epoch  7 is      3.05 and mean absolute error is    1.34. ## ## Epoch 008: Learning rate is 0.0100. ## Up to batch 1, the average loss is    3.69. ## Up to batch 2, the average loss is    3.21. ## Up to batch 3, the average loss is    3.00. ## Up to batch 4, the average loss is    2.91. ## Up to batch 5, the average loss is    2.94. ## Up to batch 6, the average loss is    2.85. ## Up to batch 7, the average loss is    2.72. ## Up to batch 8, the average loss is    2.95. ## Up to batch 9, the average loss is    2.97. ## Up to batch 10, the average loss is    2.93. ## Up to batch 11, the average loss is    2.96. ## Up to batch 12, the average loss is    2.98. ## Up to batch 13, the average loss is    2.99. ## Up to batch 14, the average loss is    3.05. ## Up to batch 15, the average loss is    3.08. ## Up to batch 16, the average loss is    3.14. ## The average loss for epoch  8 is      3.14 and mean absolute error is    1.36. ## ## Epoch 009: Learning rate is 0.0050. ## Up to batch 1, the average loss is    3.71. ## Up to batch 2, the average loss is    2.93. ## Up to batch 3, the average loss is    2.76. ## Up to batch 4, the average loss is    2.70. ## Up to batch 5, the average loss is    2.76. ## Up to batch 6, the average loss is    2.69. ## Up to batch 7, the average loss is    2.57. ## Up to batch 8, the average loss is    2.79. ## Up to batch 9, the average loss is    2.80. ## Up to batch 10, the average loss is    2.77. ## Up to batch 11, the average loss is    2.79. ## Up to batch 12, the average loss is    2.80. ## Up to batch 13, the average loss is    2.78. ## Up to batch 14, the average loss is    2.81. ## Up to batch 15, the average loss is    2.80. ## Up to batch 16, the average loss is    2.83. ## The average loss for epoch  9 is      2.83 and mean absolute error is    1.28. ## ## Epoch 010: Learning rate is 0.0050. ## Up to batch 1, the average loss is    3.02. ## Up to batch 2, the average loss is    2.69. ## Up to batch 3, the average loss is    2.58. ## Up to batch 4, the average loss is    2.57. ## Up to batch 5, the average loss is    2.65. ## Up to batch 6, the average loss is    2.60. ## Up to batch 7, the average loss is    2.48. ## Up to batch 8, the average loss is    2.72. ## Up to batch 9, the average loss is    2.74. ## Up to batch 10, the average loss is    2.71. ## Up to batch 11, the average loss is    2.74. ## Up to batch 12, the average loss is    2.75. ## Up to batch 13, the average loss is    2.74. ## Up to batch 14, the average loss is    2.77. ## Up to batch 15, the average loss is    2.77. ## Up to batch 16, the average loss is    2.80. ## The average loss for epoch 10 is      2.80 and mean absolute error is    1.27. ## ## Epoch 011: Learning rate is 0.0050. ## Up to batch 1, the average loss is    3.01. ## Up to batch 2, the average loss is    2.69. ## Up to batch 3, the average loss is    2.58. ## Up to batch 4, the average loss is    2.56. ## Up to batch 5, the average loss is    2.63. ## Up to batch 6, the average loss is    2.58. ## Up to batch 7, the average loss is    2.47. ## Up to batch 8, the average loss is    2.70. ## Up to batch 9, the average loss is    2.72. ## Up to batch 10, the average loss is    2.69. ## Up to batch 11, the average loss is    2.71. ## Up to batch 12, the average loss is    2.72. ## Up to batch 13, the average loss is    2.71. ## Up to batch 14, the average loss is    2.75. ## Up to batch 15, the average loss is    2.74. ## Up to batch 16, the average loss is    2.77. ## The average loss for epoch 11 is      2.77 and mean absolute error is    1.27. ## ## Epoch 012: Learning rate is 0.0010. ## Up to batch 1, the average loss is    2.96. ## Up to batch 2, the average loss is    2.53. ## Up to batch 3, the average loss is    2.47. ## Up to batch 4, the average loss is    2.46. ## Up to batch 5, the average loss is    2.54. ## Up to batch 6, the average loss is    2.48. ## Up to batch 7, the average loss is    2.39. ## Up to batch 8, the average loss is    2.60. ## Up to batch 9, the average loss is    2.62. ## Up to batch 10, the average loss is    2.59. ## Up to batch 11, the average loss is    2.61. ## Up to batch 12, the average loss is    2.62. ## Up to batch 13, the average loss is    2.60. ## Up to batch 14, the average loss is    2.64. ## Up to batch 15, the average loss is    2.62. ## Up to batch 16, the average loss is    2.64. ## The average loss for epoch 12 is      2.64 and mean absolute error is    1.24. ## ## Epoch 013: Learning rate is 0.0010. ## Up to batch 1, the average loss is    2.82. ## Up to batch 2, the average loss is    2.46. ## Up to batch 3, the average loss is    2.42. ## Up to batch 4, the average loss is    2.42. ## Up to batch 5, the average loss is    2.50. ## Up to batch 6, the average loss is    2.45. ## Up to batch 7, the average loss is    2.36. ## Up to batch 8, the average loss is    2.57. ## Up to batch 9, the average loss is    2.59. ## Up to batch 10, the average loss is    2.57. ## Up to batch 11, the average loss is    2.59. ## Up to batch 12, the average loss is    2.60. ## Up to batch 13, the average loss is    2.59. ## Up to batch 14, the average loss is    2.62. ## Up to batch 15, the average loss is    2.61. ## Up to batch 16, the average loss is    2.63. ## The average loss for epoch 13 is      2.63 and mean absolute error is    1.23. ## ## Epoch 014: Learning rate is 0.0010. ## Up to batch 1, the average loss is    2.79. ## Up to batch 2, the average loss is    2.44. ## Up to batch 3, the average loss is    2.40. ## Up to batch 4, the average loss is    2.41. ## Up to batch 5, the average loss is    2.49. ## Up to batch 6, the average loss is    2.44. ## Up to batch 7, the average loss is    2.34. ## Up to batch 8, the average loss is    2.56. ## Up to batch 9, the average loss is    2.58. ## Up to batch 10, the average loss is    2.56. ## Up to batch 11, the average loss is    2.58. ## Up to batch 12, the average loss is    2.59. ## Up to batch 13, the average loss is    2.58. ## Up to batch 14, the average loss is    2.61. ## Up to batch 15, the average loss is    2.60. ## Up to batch 16, the average loss is    2.62. ## The average loss for epoch 14 is      2.62 and mean absolute error is    1.23."},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"built-in-keras-callbacks","dir":"Articles","previous_headings":"Examples of Keras callback applications","what":"Built-in Keras callbacks","title":"Writing your own callbacks","text":"sure check existing Keras callbacks reading API docs. Applications include logging CSV, saving model, visualizing metrics TensorBoard, lot !","code":""},{"path":"https://keras.posit.co/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Tomasz Kalinowski. Author, copyright holder, maintainer. Daniel Falbel. Contributor, copyright holder. JJ Allaire. Author, copyright holder. François Chollet. Author, copyright holder. Posit Software, PBC. Copyright holder, funder. Google. Copyright holder, funder. Yuan Tang. Contributor, copyright holder. Wouter Van Der Bijl. Contributor, copyright holder. Martin Studer. Contributor, copyright holder. Sigrid Keydana. Contributor.","code":""},{"path":"https://keras.posit.co/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Kalinowski T, Allaire J, Chollet F (2024). keras3: R Interface 'Keras'. R package version 1.0.0, https://github.com/rstudio/keras, https://keras.posit.co/.","code":"@Manual{,   title = {keras3: R Interface to 'Keras'},   author = {Tomasz Kalinowski and JJ Allaire and François Chollet},   year = {2024},   note = {R package version 1.0.0, https://github.com/rstudio/keras},   url = {https://keras.posit.co/}, }"},{"path":"https://keras.posit.co/index.html","id":"r-interface-to-keras","dir":"","previous_headings":"","what":"R Interface to Keras","title":"R Interface to Keras","text":"Keras high-level neural networks API developed focus enabling fast experimentation. able go idea result least possible delay key good research. Keras following key features: Allows code run CPU GPU, seamlessly. User-friendly API makes easy quickly prototype deep learning models. Built-support convolutional networks (computer vision), recurrent networks (sequence processing), combination . Supports arbitrary network architectures: multi-input multi-output models, layer sharing, model sharing, etc. means Keras appropriate building essentially deep learning model, memory network neural Turing machine. See package website https://keras.posit.co complete documentation.","code":""},{"path":"https://keras.posit.co/reference/Callback.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a custom Callback class — Callback","title":"Define a custom Callback class — Callback","text":"Callbacks can passed keras methods fit(), evaluate(), predict() order hook various stages model training, evaluation, inference lifecycle. create custom callback, call Callback() override method associated stage interest.","code":""},{"path":"https://keras.posit.co/reference/Callback.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a custom Callback class — Callback","text":"","code":"Callback(   classname,   on_epoch_begin = NULL,   on_epoch_end = NULL,   on_train_begin = NULL,   on_train_end = NULL,   on_train_batch_begin = NULL,   on_train_batch_end = NULL,   on_test_begin = NULL,   on_test_end = NULL,   on_test_batch_begin = NULL,   on_test_batch_end = NULL,   on_predict_begin = NULL,   on_predict_end = NULL,   on_predict_batch_begin = NULL,   on_predict_batch_end = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Callback.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a custom Callback class — Callback","text":"classname String, name custom class. (Conventionally, CamelCase). on_epoch_begin   Called start epoch. Subclasses override actions run. function called TRAIN mode. Args: epoch: Integer, index epoch. logs: Named List. Currently data passed argument method may change future. on_epoch_end   Called end epoch. Subclasses override actions run. function called TRAIN mode. Args: epoch: Integer, index epoch. logs: Named List, metric results training epoch, validation epoch validation performed. Validation result keys prefixed val_. training epoch, values Model's metrics returned. Example: list(loss = 0.2, accuracy = 0.7). on_train_begin   Called beginning training. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_train_end   Called end training. Subclasses override actions run. Args: logs: Named list. Currently output last call on_epoch_end() passed argument method may change future. on_train_batch_begin   Called beginning training batch fit() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_train_batch_end   Called end training batch fit() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. on_test_begin   Called beginning evaluation validation. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_test_end   Called end evaluation validation. Subclasses override actions run. Args: logs: Named list. Currently output last call on_test_batch_end() passed argument method may change future. on_test_batch_begin   Called beginning batch evaluate() methods. Also called beginning validation batch fit() methods, validation data provided. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_test_batch_end   Called end batch evaluate() methods. Also called end validation batch fit() methods, validation data provided. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. on_predict_begin   Called beginning prediction. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_predict_end   Called end prediction. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_predict_batch_begin   Called beginning batch predict() methods. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_predict_batch_end   Called end batch predict() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"\\(epoch, logs = NULL) \\(epoch, logs = NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs=NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs = NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs = NULL)"},{"path":"https://keras.posit.co/reference/Callback.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a custom Callback class — Callback","text":"function returns custom Callback instances, similar builtin callback functions.","code":""},{"path":"https://keras.posit.co/reference/Callback.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define a custom Callback class — Callback","text":"R function custom methods (public private) following symbols scope: self: Layer instance. super: Layer superclass. private: R environment specific class instance. objects defined invisible Keras framework. __class__ current class type object. also available alias symbol, value supplied Layer(classname = )","code":"training_finished <- FALSE callback_mark_finished <- Callback(\"MarkFinished\",   on_train_end = function(logs = NULL) {     training_finished <<- TRUE   } )  model <- keras_model_sequential(input_shape = c(1)) |>   layer_dense(1) model |> compile(loss = 'mean_squared_error') model |> fit(op_ones(c(1, 1)), op_ones(c(1, 1)),              callbacks = callback_mark_finished()) stopifnot(isTRUE(training_finished))"},{"path":"https://keras.posit.co/reference/Callback.html","id":"attributes-accessible-via-self-","dir":"Reference","previous_headings":"","what":"Attributes (accessible via self$)","title":"Define a custom Callback class — Callback","text":"params: Named list, Training parameters (e.g. verbosity, batch size, number epochs, ...). model: Instance Model. Reference model trained. logs named list callback methods take argument contain keys quantities relevant current batch epoch (see method-specific docstrings).","code":""},{"path":"https://keras.posit.co/reference/Callback.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Define a custom Callback class — Callback","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Constraint.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a custom Constraint class — Constraint","title":"Define a custom Constraint class — Constraint","text":"Base class weight constraints. Constraint() instance works like stateless function. Users subclass Constraint class override call() method, takes single weight parameter return projected version parameter (e.g. normalized clipped). Constraints can used various Keras layers via kernel_constraint bias_constraint arguments. simple example non-negative weight constraint:     Usage layer:","code":"constraint_nonnegative <- Constraint(\"NonNegative\",   call = function(w) {     w * op_cast(w >= 0, dtype = w$dtype)   } ) weight <- op_convert_to_tensor(c(-1, 1)) constraint_nonnegative()(weight) ## tf.Tensor([-0.  1.], shape=(2), dtype=float32) layer_dense(units = 4, kernel_constraint = constraint_nonnegative()) ## <Dense name=dense, built=False> ##  signature: (*args, **kwargs)"},{"path":"https://keras.posit.co/reference/Constraint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a custom Constraint class — Constraint","text":"","code":"Constraint(   classname,   call = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Constraint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a custom Constraint class — Constraint","text":"classname String, name custom class. (Conventionally, CamelCase). call   Applies constraint input weight variable. default, inputs weight variable modified. Users override method implement projection function. Args: w: Input weight variable. Returns: Projected variable (default, returns unmodified inputs). get_config   Function returns named list object config. constraint config named list (JSON-serializable) can used reinstantiate object (via .call(<constraint_class>, <config>)). ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"\\(w) \\()"},{"path":"https://keras.posit.co/reference/Constraint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a custom Constraint class — Constraint","text":"function returns Constraint instances, similar builtin constraint functions like constraint_maxnorm().","code":""},{"path":"https://keras.posit.co/reference/Constraint.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Define a custom Constraint class — Constraint","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Layer.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a custom Layer class. — Layer","title":"Define a custom Layer class. — Layer","text":"layer callable object takes input one tensors outputs one tensors. involves computation, defined call() method, state (weight variables). State can created: initialize(), instance via self$add_weight(); optional build() method, invoked first call() layer, supplies shape(s) input(s), may known initialization time. Layers recursively composable: assign Layer instance attribute another Layer, outer layer start tracking weights created inner layer. Nested layers instantiated initialize() method build() method. Users just instantiate layer treat callable.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a custom Layer class. — Layer","text":"","code":"Layer(   classname,   initialize = NULL,   call = NULL,   build = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Layer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a custom Layer class. — Layer","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, call, build, get_config Recommended methods implement. See description details sections. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a custom Layer class. — Layer","text":"composing layer constructor, similar behavior layer functions like layer_dense(). first argument returned function object, enabling initialize()ing call() layer one step composing layer pipe, like   initialize() layer instance call() , pass missing NULL value object, pass arguments initialize() name.","code":"layer_foo <- Layer(\"Foo\", ....) output <- inputs |> layer_foo() layer <- layer_dense(units = 2, activation = \"relu\") layer <- layer_dense(NULL, 2, activation = \"relu\") layer <- layer_dense(, 2, activation = \"relu\")  # then you can call() the layer in a separate step outputs <- inputs |> layer()"},{"path":"https://keras.posit.co/reference/Layer.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Define a custom Layer class. — Layer","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Define a custom Layer class. — Layer","text":"name: name layer (string). dtype: Dtype layer's weights. Alias layer$variable_dtype. variable_dtype: Dtype layer's weights. compute_dtype: dtype layer's computations. Layers automatically cast inputs dtype, causes computations output also dtype. mixed precision used keras$mixed_precision$DTypePolicy, different variable_dtype. trainable_weights: List variables included backprop. non_trainable_weights: List variables included backprop. weights: concatenation lists trainable_weights non_trainable_weights (order). trainable: Whether layer trained (boolean), .e. whether potentially-trainable weights returned part layer$trainable_weights. input_spec: Optional (list ) InputSpec object(s) specifying constraints inputs can accepted layer. recommend custom Layers implement following methods: initialize(): Defines custom layer attributes, creates layer weights depend input shapes, using add_weight(), state. build(input_shape): method can used create weights depend shape(s) input(s), using add_weight(), state. Calling call() automatically build layer (built yet) calling build(). call(...): Method called making sure build() called. call() performs logic applying layer input arguments. Two reserved arguments can optionally use call() : training (boolean, whether call inference mode training mode). mask (boolean tensor encoding masked timesteps input, used e.g. RNN layers). typical signature method call(inputs), user optionally add training mask layer need . get_config(): Returns named list containing configuration used initialize layer. list names differ arguments initialize(), override from_config() well. method used saving layer model contains layer.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define a custom Layer class. — Layer","text":"basic example: layer two variables, w b, returns y <- (w %*% x) + b. shows implement build() call(). Variables set attributes layer tracked weights layers (layer$weights).   Besides trainable weights, updated via backpropagation training, layers can also non-trainable weights. weights meant updated manually call(). example layer computes running sum inputs:","code":"layer_simple_dense <- Layer(   \"SimpleDense\",   initialize = function(units = 32) {     super$initialize()     self$units <- units   },    # Create the state of the layer (weights)   build = function(input_shape) {     self$kernel <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = \"glorot_uniform\",       trainable = TRUE,       name = \"kernel\"     )     self$bias = self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE,       name = \"bias\"     )   },    # Defines the computation   call = function(self, inputs) {     op_matmul(inputs, self$kernel) + self$bias   } )  # Instantiates the layer. # Supply missing `object` arg to skip invoking `call()` and instead return # the Layer instance linear_layer <- layer_simple_dense(, 4)  # This will call `build(input_shape)` and create the weights, # and then invoke `call()`. y <- linear_layer(op_ones(c(2, 2))) stopifnot(length(linear_layer$weights) == 2)  # These weights are trainable, so they're listed in `trainable_weights`: stopifnot(length(linear_layer$trainable_weights) == 2) layer_compute_sum <- Layer(   classname = \"ComputeSum\",    initialize = function(input_dim) {     super$initialize()      # Create a non-trainable weight.     self$total <- self$add_weight(       shape = shape(),       initializer = \"zeros\",       trainable = FALSE,       name = \"total\"     )   },    call = function(inputs) {     self$total$assign(self$total + op_sum(inputs))     self$total   } )  my_sum <- layer_compute_sum(, 2) x <- op_ones(c(2, 2)) y <- my_sum(x)  stopifnot(exprs = {   all.equal(my_sum$weights,               list(my_sum$total))   all.equal(my_sum$non_trainable_weights, list(my_sum$total))   all.equal(my_sum$trainable_weights,     list()) })"},{"path":"https://keras.posit.co/reference/Layer.html","id":"methods-available","dir":"Reference","previous_headings":"","what":"Methods available","title":"Define a custom Layer class. — Layer","text":"Initialize self. method typically called custom initialize() method. Example:   Args: trainable: Boolean, whether layer's variables trainable. name: String name layer. dtype: dtype layer's computations weights. Can also keras$DTypePolicy, allows computation weight dtype differ. Defaults NULL. NULL means use config_dtype_policy(), \"float32\" policy unless set different value (via config_set_dtype_policy()).    Can called inside call() method add scalar loss. Example:         Add weight variable layer. Alias add_weight().    Add weight variable layer. Args: shape: shape variable (defined shape()) Must fully-defined (NA/NULL/-1 entries). Defaults () (scalar) unspecified. initializer: Initializer object use populate initial variable value, string name built-initializer (e.g. \"random_normal\"). unspecified, defaults \"glorot_uniform\" floating-point variables \"zeros\" types (e.g. int, bool). dtype: Dtype variable create, e.g. \"float32\". unspecified, defaults layer's variable dtype (defaults \"float32\" unspecified). trainable: Boolean, whether variable trainable via backprop whether updates managed manually. Defaults TRUE. autocast: Boolean, whether autocast layers variables accessing . Defaults TRUE. regularizer: Regularizer object call apply penalty weight. penalties summed loss function optimization. Defaults NULL. constraint: Constraint object call variable optimizer update, string name built-constraint. Defaults NULL. aggregation: String, one 'mean', 'sum', 'only_first_replica'. Annotates variable type multi-replica aggregation used variable writing custom data parallel training loops. name: String name variable. Useful debugging purposes. Returns: backend tensor, wrapped KerasVariable class. KerasVariable class Methods: assign(value) assign_add(value) assign_sub(value) numpy() (calling .array(<variable>) preferred) Properties/Attributes: value dtype ndim shape (calling shape(<variable>) preferred) trainable       Builds layer's states supplied config (named list args). default, method calls .call(build, config$input_shape) method, creates weights based layer's input shape supplied config. config contains information needed load layer's state, override method. Args: config: Named list containing input shape associated layer.    See description             Count total number scalars composing weights. Returns: integer count.    Returns named list layer's input shape. method returns config (named list) can used build_from_config(config) create states (e.g. Variables Lookup tables) needed layer. default, config contains input shape layer built . writing custom layer creates state unusual way, override method make sure state already created Keras attempts load value upon model loading. Returns: named list containing input shape associated layer.    Returns config object. object config named list (serializable) containing information needed re-instantiate . config expected serializable JSON, expected consist (potentially complex, nested) structure names lists consisting simple objects like strings, ints.    Return values layer$weights list R NumPy arrays.    Currently, Dense EinsumDense layers support -place quantization via quantize() method. Example:         Loads state layer. can override method take full control state layer loaded upon calling load_model(). Args: store: Named list state model loaded.    Saves state layer. can override method take full control state layer saved upon calling save_model(). Args: store: Named list state model saved.    Sets values weights list R NumPy arrays.    Call layer without side effects. Args: trainable_variables: List trainable variables model. non_trainable_variables: List non-trainable variables model. ...: Positional named arguments passed call(). return_losses: TRUE, stateless_call() return list losses created call() part return values. Returns: unnamed list. default, returns list(outputs, non_trainable_variables). return_losses = TRUE, returns list(outputs, non_trainable_variables, losses). Note: non_trainable_variables include non-trainable weights BatchNormalization statistics, also RNG seed state (random operations part layer, dropout), Metric state (metrics attached layer). elements state layer. Example:         Creates layer config. class method, meaning, R function self symbol (class instance) scope. Use __class__ classname symbol provided Layer() constructed) resolve class definition. default implementation :   method reverse get_config(), capable instantiating layer config named list. handle layer connectivity (handled Network), weights (handled set_weights()). Args: config: named list, typically output get_config(). Returns: layer instance.","code":"initialize(...,            activity_regularizer = NULL,            trainable = TRUE,            dtype = NULL,            autocast = TRUE,            name = NULL) layer_my_layer <- Layer(\"MyLayer\",   initialize = function(units, ..., dtype = NULL, name = NULL) {     super$initialize(..., dtype = dtype, name = name)     # .... finish initializing `self` instance   } ) add_loss(loss) Layer(\"MyLayer\",   ...   call = function(x) {     self$add_loss(op_sum(x))     x   } add_metric() add_variable(...) add_weight(shape = NULL,            initializer = NULL,            dtype = NULL,            trainable = TRUE,            autocast = TRUE,            regularizer = NULL,            constraint = NULL,            aggregation = 'mean',            name = NULL) build(input_shape) build_from_config(config) call(...) compute_mask(inputs, previous_mask) compute_output_shape(...) compute_output_spec(...) count_params() get_build_config() get_config() get_weights() quantize(mode) model$quantize(\"int8\") # quantize model in-place model |> predict(data) # faster inference quantized_call(...) load_own_variables(store) save_own_variables(store) set_weights(weights) stateless_call(trainable_variables, non_trainable_variables,                ..., return_losses = FALSE) model <- ... data <- ... trainable_variables <- model$trainable_variables non_trainable_variables <- model$non_trainable_variables # Call the model with zero side effects c(outputs, non_trainable_variables) %<-% model$stateless_call(     trainable_variables,     non_trainable_variables,     data ) # Attach the updated state to the model # (until you do this, the model is still in its pre-call state). purrr::walk2(   model$non_trainable_variables, non_trainable_variables,   \\(variable, value) variable$assign(value)) symbolic_call(...) from_config(config) from_config = function(config) {   do.call(`__class__`, config) }"},{"path":"https://keras.posit.co/reference/Layer.html","id":"readonly-properties-","dir":"Reference","previous_headings":"","what":"Readonly properties:","title":"Define a custom Layer class. — Layer","text":"compute_dtype dtype computations performed layer. dtype Alias layer$variable_dtype. input_dtype dtype layer inputs converted . losses List scalar losses add_loss(), regularizers sublayers. metrics List metrics. metrics_variables List metric variables. non_trainable_variables List non-trainable layer state. extends layer$non_trainable_weights include state used layer including state metrics SeedGenerators. non_trainable_weights List non-trainable weight variables layer. weights updated optimizer training. Unlike, layer$non_trainable_variables excludes metric state random seeds. trainable_variables List trainable layer state. equivalent layer$trainable_weights. trainable_weights List trainable weight variables layer. weights get updated optimizer training. variable_dtype dtype state (weights) layer. variables List layer state, including random seeds. extends layer$weights include state used layer including SeedGenerators. Note metrics variables included , use metrics_variables visit metric variables. weights List weight variables layer. Unlike, layer$variables excludes metric state random seeds. input Retrieves input tensor(s) symbolic operation. returns tensor(s) corresponding first time operation called. Returns: Input tensor list input tensors. output Retrieves output tensor(s) layer. returns tensor(s) corresponding first time operation called. Returns: Output tensor list output tensors.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"data-descriptors-attributes-","dir":"Reference","previous_headings":"","what":"Data descriptors (Attributes):","title":"Define a custom Layer class. — Layer","text":"dtype_policy input_spec supports_masking Whether layer supports computing mask using compute_mask. trainable Settable boolean, whether layer trainable .","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a custom LearningRateSchedule class — LearningRateSchedule","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"Subclass Keras LearningRateSchedule base class. can use learning rate schedule modulate learning rate optimizer changes time. Several built-learning rate schedules available, learning_rate_schedule_exponential_decay() learning_rate_schedule_piecewise_constant_decay():   LearningRateSchedule() instance can passed learning_rate argument optimizer. implement schedule object, implement call method, takes step argument (scalar integer backend tensor, current training step count). Note step 0-based (.e., first step 0). Like Keras object, can also optionally make object serializable implementing get_config() from_config() methods.","code":"lr_schedule <- learning_rate_schedule_exponential_decay(   initial_learning_rate = 1e-2,   decay_steps = 10000,   decay_rate = 0.9 ) optimizer <- optimizer_sgd(learning_rate = lr_schedule)"},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"","code":"LearningRateSchedule(   classname,   call = NULL,   initialize = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"classname String, name custom class. (Conventionally, CamelCase). call, initialize, get_config Recommended methods implement. See description details sections. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"function returns LearningRateSchedule instances, similar built-learning_rate_schedule_* family functions.","code":""},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"","code":"my_custom_learning_rate_schedule <- LearningRateSchedule(   classname = \"MyLRSchedule\",    initialize = function(initial_learning_rate) {     self$initial_learning_rate <- initial_learning_rate   },    call = function(step) {     # note that `step` is a tensor     # and call() will be traced via tf_function() or similar.      str(step) # <KerasVariable shape=(), dtype=int64, path=SGD/iteration>      # print 'step' every 1000 steps     op_cond((step %% 1000) == 0,             \\() {tensorflow::tf$print(step); NULL},             \\() {NULL})     self$initial_learning_rate / (step + 1)   } )  optimizer <- optimizer_sgd(   learning_rate = my_custom_learning_rate_schedule(0.1) )  # You can also call schedule instances directly # (e.g., for interactive testing, or if implementing a custom optimizer) schedule <- my_custom_learning_rate_schedule(0.1) step <- keras$Variable(initializer = op_ones,                        shape = shape(),                        dtype = \"int64\") schedule(step) ## <KerasVariable shape=(), dtype=int64, path=variable> ## tf.Tensor(0.0, shape=(), dtype=float64)"},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"methods-available-","dir":"Reference","previous_headings":"","what":"Methods available:","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"","code":"get_config()"},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Subclass the base Loss class — Loss","title":"Subclass the base Loss class — Loss","text":"Use define custom loss class. Note, cases need subclass Loss define custom loss: can also pass bare R function, named R function defined custom_metric(), loss function compile().","code":""},{"path":"https://keras.posit.co/reference/Loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subclass the base Loss class — Loss","text":"","code":"Loss(   classname,   call = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subclass the base Loss class — Loss","text":"classname String, name custom class. (Conventionally, CamelCase). call   Method implemented subclasses: Function contains logic loss calculation using y_true, y_pred. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"function(y_true, y_pred)"},{"path":"https://keras.posit.co/reference/Loss.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subclass the base Loss class — Loss","text":"function returns Loss instances, similar builtin loss functions.","code":""},{"path":"https://keras.posit.co/reference/Loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Subclass the base Loss class — Loss","text":"Example subclass implementation:","code":"loss_custom_mse <- Loss(   classname = \"CustomMeanSquaredError\",   call = function(y_true, y_pred) {     op_mean(op_square(y_pred - y_true), axis = -1)   } )  # Usage in compile() model <- keras_model_sequential(input_shape = 10) |> layer_dense(10) model |> compile(loss = loss_custom_mse())  # Standalone usage mse <- loss_custom_mse(name = \"my_custom_mse_instance\")  y_true <- op_arange(20) |> op_reshape(c(4, 5)) y_pred <- op_arange(20) |> op_reshape(c(4, 5)) * 2 (loss <- mse(y_true, y_pred)) ## tf.Tensor(123.5, shape=(), dtype=float32) loss2 <- (y_pred - y_true)^2 |>   op_mean(axis = -1) |>   op_mean()  stopifnot(all.equal(as.array(loss), as.array(loss2)))  sample_weight <-array(c(.25, .25, 1, 1)) (weighted_loss <- mse(y_true, y_pred, sample_weight = sample_weight)) ## tf.Tensor(112.8125, shape=(), dtype=float32) weighted_loss2 <- (y_true - y_pred)^2 |>   op_mean(axis = -1) |>   op_multiply(sample_weight) |>   op_mean()  stopifnot(all.equal(as.array(weighted_loss),                     as.array(weighted_loss2)))"},{"path":"https://keras.posit.co/reference/Loss.html","id":"methods-defined-by-base-loss-class-","dir":"Reference","previous_headings":"","what":"Methods defined by base Loss class:","title":"Subclass the base Loss class — Loss","text":"Args: name reduction: Valid values one {\"sum_over_batch_size\", \"sum\", NULL, \"none\"} dtype    Call loss instance function, optionally sample_weight.","code":"initialize(name=NULL, reduction=\"sum_over_batch_size\", dtype=NULL) __call__(y_true, y_pred, sample_weight=NULL) get_config()"},{"path":"https://keras.posit.co/reference/Loss.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Subclass the base Loss class — Loss","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Metric.html","id":null,"dir":"Reference","previous_headings":"","what":"Subclass the base Metric class — Metric","title":"Subclass the base Metric class — Metric","text":"Metric object encapsulates metric logic state can used track model performance training. returned family metric functions start prefix metric_*, well returned custom metrics defined Metric().","code":""},{"path":"https://keras.posit.co/reference/Metric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subclass the base Metric class — Metric","text":"","code":"Metric(   classname,   initialize = NULL,   update_state = NULL,   result = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Metric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subclass the base Metric class — Metric","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, update_state, result Recommended methods implement. See description section. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/Metric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subclass the base Metric class — Metric","text":"function returns Metric instances, similar builtin metric functions.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Metric.html","id":"usage-with-compile-","dir":"Reference","previous_headings":"","what":"Usage with compile():","title":"Subclass the base Metric class — Metric","text":"","code":"model |> compile(   optimizer = 'sgd',   loss = 'mse',   metrics = c(metric_SOME_METRIC(), metric_SOME_OTHER_METRIC()) )"},{"path":"https://keras.posit.co/reference/Metric.html","id":"standalone-usage-","dir":"Reference","previous_headings":"","what":"Standalone usage:","title":"Subclass the base Metric class — Metric","text":"","code":"m <- metric_SOME_METRIC() for (e in seq(epochs)) {   for (i in seq(train_steps)) {     c(y_true, y_pred, sample_weight = NULL) %<-% ...     m$update_state(y_true, y_pred, sample_weight)   }   cat('Final epoch result: ', as.numeric(m$result()), \"\\n\")   m$reset_state() }"},{"path":[]},{"path":"https://keras.posit.co/reference/Metric.html","id":"usage-with-compile--1","dir":"Reference","previous_headings":"","what":"Usage with compile():","title":"Subclass the base Metric class — Metric","text":"implemented subclasses (custom metrics): initialize(): state variables created method calling self$add_variable() like: self$var <- self$add_variable(...). update_state(): Updates state variables like: self$var$assign(...). result(): Computes returns scalar value named list scalar values metric state variables. Example subclass implementation:","code":"model <- keras_model_sequential() model |>   layer_dense(64, activation = \"relu\") |>   layer_dense(64, activation = \"relu\") |>   layer_dense(10, activation = \"softmax\") model |>   compile(optimizer = optimizer_rmsprop(0.01),           loss = loss_categorical_crossentropy(),           metrics = metric_categorical_accuracy())  data <- random_uniform(c(1000, 32)) labels <- random_uniform(c(1000, 10))  model |> fit(data, labels, verbose = 0) metric_binary_true_positives <- Metric(   classname = \"BinaryTruePositives\",    initialize = function(name = 'binary_true_positives', ...) {     super$initialize(name = name, ...)     self$true_positives <-       self$add_weight(shape = shape(),                       initializer = 'zeros',                       name = 'true_positives')   },    update_state = function(y_true, y_pred, sample_weight = NULL) {     y_true <- op_cast(y_true, \"bool\")     y_pred <- op_cast(y_pred, \"bool\")      values <- y_true & y_pred # `&` calls op_logical_and()     values <- op_cast(values, self$dtype)     if (!is.null(sample_weight)) {       sample_weight <- op_cast(sample_weight, self$dtype)       sample_weight <- op_broadcast_to(sample_weight, shape(values))       values <- values * sample_weight # `*` calls op_multiply()     }     self$true_positives$assign(self$true_positives + op_sum(values))   },    result = function() {     self$true_positives   } ) model <- keras_model_sequential(input_shape = 32) |> layer_dense(10) model |> compile(loss = loss_binary_crossentropy(),                  metrics = list(metric_binary_true_positives())) model |> fit(data, labels, verbose = 0)"},{"path":"https://keras.posit.co/reference/Metric.html","id":"methods-defined-by-the-base-metric-class-","dir":"Reference","previous_headings":"","what":"Methods defined by the base Metric class:","title":"Subclass the base Metric class — Metric","text":"Calling metric instance self like m(...) equivalent calling:      Initialize self. Args: name: (Optional) string name metric instance. dtype: (Optional) data type metric result.          Return serializable config metric.    Reset metric state variables. function called epochs/steps, metric evaluated training.    Compute current metric value. Returns: scalar tensor, named list scalar tensors.             Accumulate statistics metric.","code":"__call__(...) function(...) {   m$update_state(...)   m$result() } initialize(dtype=NULL, name=NULL) add_variable(shape, initializer, dtype=NULL, aggregation = 'sum', name=NULL) add_weight(shape=shape(), initializer=NULL, dtype=NULL, name=NULL) get_config() reset_state() result() stateless_result(metric_variables) stateless_reset_state() stateless_update_state(metric_variables, ...) update_state(...)"},{"path":"https://keras.posit.co/reference/Metric.html","id":"readonly-properties","dir":"Reference","previous_headings":"","what":"Readonly properties","title":"Subclass the base Metric class — Metric","text":"dtype variables","code":""},{"path":"https://keras.posit.co/reference/Metric.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Subclass the base Metric class — Metric","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Subclass the base Keras Model Class — Model","title":"Subclass the base Keras Model Class — Model","text":"advanced use cases need subclass base Model type, e.g., want override train_step() method. just want create define keras model, prefer keras_model() keras_model_sequential(). just want encapsulate custom logic state, need customize training behavior (besides calling self$add_loss() call() method), prefer Layer().","code":""},{"path":"https://keras.posit.co/reference/Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subclass the base Keras Model Class — Model","text":"","code":"Model(   classname,   initialize = NULL,   call = NULL,   train_step = NULL,   predict_step = NULL,   test_step = NULL,   compute_loss = NULL,   compute_metrics = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subclass the base Keras Model Class — Model","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, call, train_step, predict_step, test_step, compute_loss, compute_metrics Optional methods can overridden. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subclass the base Keras Model Class — Model","text":"model constructor function, can call create instance new model type.","code":""},{"path":"https://keras.posit.co/reference/Model.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Subclass the base Keras Model Class — Model","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"Exponential Linear Unit. — activation_elu","title":"Exponential Linear Unit. — activation_elu","text":"exponential linear unit (ELU) alpha > 0 defined : x x > 0 alpha * exp(x) - 1 x < 0 ELUs negative values pushes mean activations closer zero. Mean activations closer zero enable faster learning bring gradient closer natural gradient. ELUs saturate negative value argument gets smaller. Saturation means small derivative decreases variation information propagated next layer.","code":""},{"path":"https://keras.posit.co/reference/activation_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exponential Linear Unit. — activation_elu","text":"","code":"activation_elu(x, alpha = 1)"},{"path":"https://keras.posit.co/reference/activation_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exponential Linear Unit. — activation_elu","text":"x Input tensor. alpha Numeric. See description details.","code":""},{"path":"https://keras.posit.co/reference/activation_elu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exponential Linear Unit. — activation_elu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_elu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Exponential Linear Unit. — activation_elu","text":"Clevert et al., 2016","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_exponential.html","id":null,"dir":"Reference","previous_headings":"","what":"Exponential activation function. — activation_exponential","title":"Exponential activation function. — activation_exponential","text":"Exponential activation function.","code":""},{"path":"https://keras.posit.co/reference/activation_exponential.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exponential activation function. — activation_exponential","text":"","code":"activation_exponential(x)"},{"path":"https://keras.posit.co/reference/activation_exponential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exponential activation function. — activation_exponential","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_exponential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exponential activation function. — activation_exponential","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian error linear unit (GELU) activation function. — activation_gelu","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"Gaussian error linear unit (GELU) defined : gelu(x) = x * P(X <= x) P(X) ~ N(0, 1), .e. gelu(x) = 0.5 * x * (1 + erf(x / sqrt(2))). GELU weights inputs value, rather gating inputs sign ReLU.","code":""},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"","code":"activation_gelu(x, approximate = FALSE)"},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"x Input tensor. approximate bool, whether enable approximation.","code":""},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"Hendrycks et al., 2016","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Hard sigmoid activation function. — activation_hard_sigmoid","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"hard sigmoid activation defined : 0 x <= -3 1 x >= 3 (x/6) + 0.5 -3 < x < 3 faster, piecewise linear approximation sigmoid activation.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"","code":"activation_hard_sigmoid(x)"},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"Wikipedia \"Hard sigmoid\"","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"defined : 0 x < -3 x x > 3 x * (x + 3) / 6 -3 <= x <= 3 faster, piecewise linear approximation silu activation.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"","code":"activation_hard_silu(x)  activation_hard_swish(x)"},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"Howard, 2019","code":""},{"path":"https://keras.posit.co/reference/activation_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaky relu activation function. — activation_leaky_relu","title":"Leaky relu activation function. — activation_leaky_relu","text":"Leaky relu activation function.","code":""},{"path":"https://keras.posit.co/reference/activation_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leaky relu activation function. — activation_leaky_relu","text":"","code":"activation_leaky_relu(x, negative_slope = 0.2)"},{"path":"https://keras.posit.co/reference/activation_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leaky relu activation function. — activation_leaky_relu","text":"x Input tensor. negative_slope float controls slope values lower threshold.","code":""},{"path":"https://keras.posit.co/reference/activation_leaky_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Leaky relu activation function. — activation_leaky_relu","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_linear.html","id":null,"dir":"Reference","previous_headings":"","what":"Linear activation function (pass-through). — activation_linear","title":"Linear activation function (pass-through). — activation_linear","text":"\"linear\" activation identity function: returns input, unmodified.","code":""},{"path":"https://keras.posit.co/reference/activation_linear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linear activation function (pass-through). — activation_linear","text":"","code":"activation_linear(x)"},{"path":"https://keras.posit.co/reference/activation_linear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linear activation function (pass-through). — activation_linear","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_linear.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Linear activation function (pass-through). — activation_linear","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_log_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Log-Softmax activation function. — activation_log_softmax","title":"Log-Softmax activation function. — activation_log_softmax","text":"input vector handled independently. axis argument sets axis input function applied along.","code":""},{"path":"https://keras.posit.co/reference/activation_log_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log-Softmax activation function. — activation_log_softmax","text":"","code":"activation_log_softmax(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/activation_log_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log-Softmax activation function. — activation_log_softmax","text":"x Input tensor. axis Integer, axis along softmax applied.","code":""},{"path":"https://keras.posit.co/reference/activation_log_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Log-Softmax activation function. — activation_log_softmax","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_mish.html","id":null,"dir":"Reference","previous_headings":"","what":"Mish activation function. — activation_mish","title":"Mish activation function. — activation_mish","text":"defined : mish(x) = x * tanh(softplus(x)) softplus defined : softplus(x) = log(exp(x) + 1)","code":""},{"path":"https://keras.posit.co/reference/activation_mish.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mish activation function. — activation_mish","text":"","code":"activation_mish(x)"},{"path":"https://keras.posit.co/reference/activation_mish.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mish activation function. — activation_mish","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_mish.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mish activation function. — activation_mish","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_mish.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Mish activation function. — activation_mish","text":"Misra, 2019","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies the rectified linear unit activation function. — activation_relu","title":"Applies the rectified linear unit activation function. — activation_relu","text":"default values, returns standard ReLU activation: max(x, 0), element-wise maximum 0 input tensor. Modifying default parameters allows use non-zero thresholds, change max value activation, use non-zero multiple input values threshold.","code":""},{"path":"https://keras.posit.co/reference/activation_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies the rectified linear unit activation function. — activation_relu","text":"","code":"activation_relu(x, negative_slope = 0, max_value = NULL, threshold = 0)"},{"path":"https://keras.posit.co/reference/activation_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies the rectified linear unit activation function. — activation_relu","text":"x Input tensor. negative_slope numeric controls slope values lower threshold. max_value numeric sets saturation threshold (largest value function return). threshold numeric giving threshold value activation function values damped set zero.","code":""},{"path":"https://keras.posit.co/reference/activation_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies the rectified linear unit activation function. — activation_relu","text":"tensor shape dtype input x.","code":""},{"path":"https://keras.posit.co/reference/activation_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies the rectified linear unit activation function. — activation_relu","text":"","code":"x <- c(-10, -5, 0, 5, 10) activation_relu(x) ## tf.Tensor([ 0.  0.  0.  5. 10.], shape=(5), dtype=float32) activation_relu(x, negative_slope = 0.5) ## tf.Tensor([-5.  -2.5  0.   5.  10. ], shape=(5), dtype=float32) activation_relu(x, max_value = 5) ## tf.Tensor([0. 0. 0. 5. 5.], shape=(5), dtype=float32) activation_relu(x, threshold = 5) ## tf.Tensor([-0. -0.  0.  0. 10.], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/activation_relu6.html","id":null,"dir":"Reference","previous_headings":"","what":"Relu6 activation function. — activation_relu6","title":"Relu6 activation function. — activation_relu6","text":"ReLU function, truncated maximum value 6.","code":""},{"path":"https://keras.posit.co/reference/activation_relu6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relu6 activation function. — activation_relu6","text":"","code":"activation_relu6(x)"},{"path":"https://keras.posit.co/reference/activation_relu6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relu6 activation function. — activation_relu6","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_relu6.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Relu6 activation function. — activation_relu6","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"Scaled Exponential Linear Unit (SELU). — activation_selu","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"Scaled Exponential Linear Unit (SELU) activation function defined : scale * x x > 0 scale * alpha * (exp(x) - 1) x < 0 alpha scale pre-defined constants (alpha = 1.67326324 scale = 1.05070098). Basically, SELU activation function multiplies scale (> 1) output activation_elu function ensure slope larger one positive inputs. values alpha scale chosen mean variance inputs preserved two consecutive layers long weights initialized correctly (see initializer_lecun_normal()) number input units \"large enough\" (see reference paper information).","code":""},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"","code":"activation_selu(x)"},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"notes","dir":"Reference","previous_headings":"","what":"Notes","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"used together initializer_lecun_normal(). used together dropout variant layer_alpha_dropout() (legacy, depracated).","code":""},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"Klambauer et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid activation function. — activation_sigmoid","title":"Sigmoid activation function. — activation_sigmoid","text":"defined : sigmoid(x) = 1 / (1 + exp(-x)). small values (<-5), sigmoid returns value close zero, large values (>5) result function gets close 1. Sigmoid equivalent 2-element softmax, second element assumed zero. sigmoid function always returns value 0 1.","code":""},{"path":"https://keras.posit.co/reference/activation_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid activation function. — activation_sigmoid","text":"","code":"activation_sigmoid(x)"},{"path":"https://keras.posit.co/reference/activation_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid activation function. — activation_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sigmoid activation function. — activation_sigmoid","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Swish (or Silu) activation function. — activation_silu","title":"Swish (or Silu) activation function. — activation_silu","text":"defined : swish(x) = x * sigmoid(x). Swish (Silu) activation function smooth, non-monotonic function unbounded bounded .","code":""},{"path":"https://keras.posit.co/reference/activation_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Swish (or Silu) activation function. — activation_silu","text":"","code":"activation_silu(x)"},{"path":"https://keras.posit.co/reference/activation_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Swish (or Silu) activation function. — activation_silu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_silu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Swish (or Silu) activation function. — activation_silu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_silu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Swish (or Silu) activation function. — activation_silu","text":"Ramachandran et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax converts a vector of values to a probability distribution. — activation_softmax","title":"Softmax converts a vector of values to a probability distribution. — activation_softmax","text":"elements output vector range [0, 1] sum 1. input vector handled independently. axis argument sets axis input function applied along. Softmax often used activation last layer classification network result interpreted probability distribution. softmax vector x computed exp(x) / sum(exp(x)). input values log-odds resulting probability.","code":""},{"path":"https://keras.posit.co/reference/activation_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax converts a vector of values to a probability distribution. — activation_softmax","text":"","code":"activation_softmax(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/activation_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax converts a vector of values to a probability distribution. — activation_softmax","text":"x Input tensor. axis Integer, axis along softmax applied.","code":""},{"path":"https://keras.posit.co/reference/activation_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax converts a vector of values to a probability distribution. — activation_softmax","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_softplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Softplus activation function. — activation_softplus","title":"Softplus activation function. — activation_softplus","text":"defined : softplus(x) = log(exp(x) + 1).","code":""},{"path":"https://keras.posit.co/reference/activation_softplus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softplus activation function. — activation_softplus","text":"","code":"activation_softplus(x)"},{"path":"https://keras.posit.co/reference/activation_softplus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softplus activation function. — activation_softplus","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_softplus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softplus activation function. — activation_softplus","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_softsign.html","id":null,"dir":"Reference","previous_headings":"","what":"Softsign activation function. — activation_softsign","title":"Softsign activation function. — activation_softsign","text":"Softsign defined : softsign(x) = x / (abs(x) + 1).","code":""},{"path":"https://keras.posit.co/reference/activation_softsign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softsign activation function. — activation_softsign","text":"","code":"activation_softsign(x)"},{"path":"https://keras.posit.co/reference/activation_softsign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softsign activation function. — activation_softsign","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_softsign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softsign activation function. — activation_softsign","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_tanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hyperbolic tangent activation function. — activation_tanh","title":"Hyperbolic tangent activation function. — activation_tanh","text":"defined : tanh(x) = sinh(x) / cosh(x), .e. tanh(x) = ((exp(x) - exp(-x)) / (exp(x) + exp(-x))).","code":""},{"path":"https://keras.posit.co/reference/activation_tanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hyperbolic tangent activation function. — activation_tanh","text":"","code":"activation_tanh(x)"},{"path":"https://keras.posit.co/reference/activation_tanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hyperbolic tangent activation function. — activation_tanh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_tanh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hyperbolic tangent activation function. — activation_tanh","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/active_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an active property class method — active_property","title":"Create an active property class method — active_property","text":"Create active property class method","code":""},{"path":"https://keras.posit.co/reference/active_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an active property class method — active_property","text":"","code":"active_property(fn)"},{"path":"https://keras.posit.co/reference/active_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an active property class method — active_property","text":"fn R function","code":""},{"path":"https://keras.posit.co/reference/active_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an active property class method — active_property","text":"fn, additional R attribute cause fn converted active property converted method custom subclass.","code":""},{"path":"https://keras.posit.co/reference/active_property.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Create an active property class method — active_property","text":"","code":"layer_foo <- Model(\"Foo\", ...,   metrics = active_property(function() {     list(self$d_loss_metric,          self$g_loss_metric)   }))"},{"path":"https://keras.posit.co/reference/adapt.html","id":null,"dir":"Reference","previous_headings":"","what":"Fits the state of the preprocessing layer to the data being passed — adapt","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"Fits state preprocessing layer data passed","code":""},{"path":"https://keras.posit.co/reference/adapt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"","code":"adapt(object, data, ..., batch_size = NULL, steps = NULL)"},{"path":"https://keras.posit.co/reference/adapt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"object Preprocessing layer object data data train . can passed either tf.data.Dataset R array. ... Used forwards backwards compatibility. Passed underlying method. batch_size Integer NULL. Number asamples per state update. unspecified, batch_size default 32. specify batch_size data form TF Dataset generator (since generate batches). steps Integer NULL. Total number steps (batches samples) training input tensors TensorFlow data tensors, default NULL equal number samples dataset divided batch size, 1 determined. x tf.data.Dataset, steps NULL, epoch run input dataset exhausted. passing infinitely repeating dataset, must specify steps argument. argument supported array inputs.","code":""},{"path":"https://keras.posit.co/reference/adapt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"Returns object, invisibly.","code":""},{"path":"https://keras.posit.co/reference/adapt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"calling adapt layer, preprocessing layer's state update training. order make preprocessing layers efficient distribution context, kept constant respect compiled tf.Graphs call layer. affect layer use adapting layer , adapt layer multiple times need take care re-compile compiled functions follows: adding preprocessing layer keras model, need call compile(model) subsequent call adapt(). calling preprocessing layer inside tfdatasets::dataset_map(), call dataset_map() input Dataset adapt(). using tensorflow::tf_function() directly calls preprocessing layer, need call tf_function() callable subsequent call adapt(). keras_model() example multiple adapts:         tfdatasets example multiple adapts:","code":"layer <- layer_normalization(axis = NULL) adapt(layer, c(0, 2)) model <- keras_model_sequential() |> layer() predict(model, c(0, 1, 2), verbose = FALSE) # [1] -1  0  1 ## [1] -1  0  1 adapt(layer, c(-1, 1)) compile(model)  # This is needed to re-compile model.predict! predict(model, c(0, 1, 2), verbose = FALSE) # [1] 0 1 2 ## [1] 0 1 2 layer <- layer_normalization(axis = NULL) adapt(layer, c(0, 2)) input_ds <- tfdatasets::range_dataset(0, 3) normalized_ds <- input_ds |>   tfdatasets::dataset_map(layer) str(tfdatasets::iterate(normalized_ds)) ## List of 3 ##  $ :<tf.Tensor: shape=(1), dtype=float32, numpy=array([-1.], dtype=float32)> ##  $ :<tf.Tensor: shape=(1), dtype=float32, numpy=array([0.], dtype=float32)> ##  $ :<tf.Tensor: shape=(1), dtype=float32, numpy=array([1.], dtype=float32)> adapt(layer, c(-1, 1)) normalized_ds <- input_ds |>   tfdatasets::dataset_map(layer) # Re-map over the input dataset.  normalized_ds |>   tfdatasets::as_array_iterator() |>   tfdatasets::iterate(simplify = FALSE) |>   str() ## List of 3 ##  $ : num [1(1d)] 0 ##  $ : num [1(1d)] 1 ##  $ : num [1(1d)] 2"},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"Instantiates ConvNeXtBase architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"","code":"application_convnext_base(   model_name = \"convnext_base\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"Instantiates ConvNeXtLarge architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"","code":"application_convnext_large(   model_name = \"convnext_large\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"Instantiates ConvNeXtSmall architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"","code":"application_convnext_small(   model_name = \"convnext_small\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"Instantiates ConvNeXtTiny architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"","code":"application_convnext_tiny(   model_name = \"convnext_tiny\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"Instantiates ConvNeXtXLarge architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"","code":"application_convnext_xlarge(   model_name = \"convnext_xlarge\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Densenet121 architecture. — application_densenet121","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"Instantiates Densenet121 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"","code":"application_densenet121(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) ('channels_last' data format) (3, 224, 224) ('channels_first' data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"Densely Connected Convolutional Networks (CVPR 2017) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"Keras Application expects specific kind input preprocessing. DenseNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Densenet169 architecture. — application_densenet169","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"Instantiates Densenet169 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"","code":"application_densenet169(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) ('channels_last' data format) (3, 224, 224) ('channels_first' data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"Densely Connected Convolutional Networks (CVPR 2017) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"Keras Application expects specific kind input preprocessing. DenseNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Densenet201 architecture. — application_densenet201","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"Instantiates Densenet201 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"","code":"application_densenet201(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) ('channels_last' data format) (3, 224, 224) ('channels_first' data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"Densely Connected Convolutional Networks (CVPR 2017) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"Keras Application expects specific kind input preprocessing. DenseNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"Instantiates EfficientNetB0 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"","code":"application_efficientnet_b0(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"Instantiates EfficientNetB1 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"","code":"application_efficientnet_b1(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"Instantiates EfficientNetB2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"","code":"application_efficientnet_b2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"Instantiates EfficientNetB3 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"","code":"application_efficientnet_b3(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"Instantiates EfficientNetB4 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"","code":"application_efficientnet_b4(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"Instantiates EfficientNetB5 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"","code":"application_efficientnet_b5(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"Instantiates EfficientNetB6 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"","code":"application_efficientnet_b6(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"Instantiates EfficientNetB7 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"","code":"application_efficientnet_b7(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"Instantiates EfficientNetV2B0 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"","code":"application_efficientnet_v2b0(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"Instantiates EfficientNetV2B1 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"","code":"application_efficientnet_v2b1(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"Instantiates EfficientNetV2B2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"","code":"application_efficientnet_v2b2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"Instantiates EfficientNetV2B3 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"","code":"application_efficientnet_v2b3(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"Instantiates EfficientNetV2L architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"","code":"application_efficientnet_v2l(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"Instantiates EfficientNetV2M architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"","code":"application_efficientnet_v2m(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"Instantiates EfficientNetV2S architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"","code":"application_efficientnet_v2s(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"Instantiates Inception-ResNet v2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"","code":"application_inception_resnet_v2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (299, 299, 3) ('channels_last' data format) (3, 299, 299) ('channels_first' data format). exactly 3 inputs channels, width height smaller 75. E.g. (150, 150, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. 'avg' means global average pooling applied output last convolutional block, thus output model 2D tensor. 'max' means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"Inception-v4, Inception-ResNet Impact Residual Connections Learning (AAAI 2017) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"Keras Application expects specific kind input preprocessing. InceptionResNetV2, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Inception v3 architecture. — application_inception_v3","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"Instantiates Inception v3 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"","code":"application_inception_v3(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"include_top Boolean, whether include fully-connected layer top, last layer network. Defaults TRUE. weights One NULL (random initialization), imagenet (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_tensor useful sharing inputs multiple different networks. Defaults NULL. input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (299, 299, 3) (channels_last data format) (3, 299, 299) (channels_first data format). exactly 3 inputs channels, width height smaller 75. E.g. (150, 150, 3) one valid value. input_shape ignored input_tensor provided. pooling Optional pooling mode feature extraction include_top FALSE. NULL (default) means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"Rethinking Inception Architecture Computer Vision (CVPR 2016) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"Keras Application expects specific kind input preprocessing. InceptionV3, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the MobileNet architecture. — application_mobilenet","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"Instantiates MobileNet architecture.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"","code":"application_mobilenet(   input_shape = NULL,   alpha = 1,   depth_multiplier = 1L,   dropout = 0.001,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. Defaults NULL. input_shape ignored input_tensor provided. alpha Controls width network. known width multiplier MobileNet paper. alpha < 1.0, proportionally decreases number filters layer. alpha > 1.0, proportionally increases number filters layer. alpha == 1, default number filters paper used layer. Defaults 1.0. depth_multiplier Depth multiplier depthwise convolution. called resolution multiplier MobileNet paper. Defaults 1.0. dropout Dropout rate. Defaults 0.001. include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_tensor useful sharing inputs multiple different networks. Defaults NULL. pooling Optional pooling mode feature extraction include_top FALSE. NULL (default) means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"MobileNets: Efficient Convolutional Neural Networks Mobile Vision Applications function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"Keras Application expects specific kind input preprocessing. MobileNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"MobileNetV2 similar original MobileNet, except uses inverted residual blocks bottlenecking features. drastically lower parameter count original MobileNet. MobileNets support input size greater 32 x 32, larger image sizes offering better performance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"","code":"application_mobilenet_v2(   input_shape = NULL,   alpha = 1,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. Defaults NULL. input_shape ignored input_tensor provided. alpha Controls width network. known width multiplier MobileNet paper. alpha < 1.0, proportionally decreases number filters layer. alpha > 1.0, proportionally increases number filters layer. alpha == 1, default number filters paper used layer. Defaults 1.0. include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_tensor useful sharing inputs multiple different networks. Defaults NULL. pooling Optional pooling mode feature extraction include_top FALSE. NULL (default) means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"MobileNetV2: Inverted Residuals Linear Bottlenecks (CVPR 2018) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"Keras Application expects specific kind input preprocessing. MobileNetV2, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"Instantiates MobileNetV3Large architecture.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"","code":"application_mobilenet_v3_large(   input_shape = NULL,   alpha = 1,   minimalistic = FALSE,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   classes = 1000L,   pooling = NULL,   dropout_rate = 0.2,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"input_shape Optional shape tuple, specified like use model input image resolution (224, 224, 3). exactly 3 inputs channels. can also omit option like infer input_shape input_tensor. choose include input_tensor input_shape input_shape used match, shapes match throw error. E.g. (160, 160, 3) one valid value. alpha controls width network. known depth multiplier MobileNetV3 paper, name kept consistency MobileNetV1 Keras. alpha < 1.0, proportionally decreases number filters layer. alpha > 1.0, proportionally increases number filters layer. alpha == 1, default number filters paper used layer. minimalistic addition large small models module also contains -called minimalistic models, models per-layer dimensions characteristic MobilenetV3 however, utilize advanced blocks (squeeze--excite units, hard-swish, 5x5 convolutions). models less efficient CPU, much performant GPU/DSP. include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights String, one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. classes Integer, optional number classes classify images , specified include_top TRUE, weights argument specified. pooling String, optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. dropout_rate fraction input units drop last layer. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer (Rescaling) bottom network. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"Searching MobileNetV3 (ICCV 2019)","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"the-following-table-describes-the-performance-of-mobilenets-v-","dir":"Reference","previous_headings":"","what":"The following table describes the performance of MobileNets v3:","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"MACs stands Multiply Adds image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"Keras Application expects specific kind input preprocessing. MobileNetV3, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, MobileNetV3 models expect inputs float tensors pixels values [0-255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled MobileNetV3 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"inputs: floating point numpy.array backend-native tensor, 4D 3 color channels, values range [0, 255] include_preprocessing TRUE range [-1, 1] otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"Instantiates MobileNetV3Small architecture.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"","code":"application_mobilenet_v3_small(   input_shape = NULL,   alpha = 1,   minimalistic = FALSE,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   classes = 1000L,   pooling = NULL,   dropout_rate = 0.2,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"input_shape Optional shape tuple, specified like use model input image resolution (224, 224, 3). exactly 3 inputs channels. can also omit option like infer input_shape input_tensor. choose include input_tensor input_shape input_shape used match, shapes match throw error. E.g. (160, 160, 3) one valid value. alpha controls width network. known depth multiplier MobileNetV3 paper, name kept consistency MobileNetV1 Keras. alpha < 1.0, proportionally decreases number filters layer. alpha > 1.0, proportionally increases number filters layer. alpha == 1, default number filters paper used layer. minimalistic addition large small models module also contains -called minimalistic models, models per-layer dimensions characteristic MobilenetV3 however, utilize advanced blocks (squeeze--excite units, hard-swish, 5x5 convolutions). models less efficient CPU, much performant GPU/DSP. include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights String, one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. classes Integer, optional number classes classify images , specified include_top TRUE, weights argument specified. pooling String, optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. dropout_rate fraction input units drop last layer. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer (Rescaling) bottom network. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"Searching MobileNetV3 (ICCV 2019)","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"the-following-table-describes-the-performance-of-mobilenets-v-","dir":"Reference","previous_headings":"","what":"The following table describes the performance of MobileNets v3:","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"MACs stands Multiply Adds image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"Keras Application expects specific kind input preprocessing. MobileNetV3, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, MobileNetV3 models expect inputs float tensors pixels values [0-255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled MobileNetV3 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"inputs: floating point numpy.array backend-native tensor, 4D 3 color channels, values range [0, 255] include_preprocessing TRUE range [-1, 1] otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"Instantiates NASNet model ImageNet mode.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"","code":"application_nasnetlarge(   input_shape = NULL,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (331, 331, 3) NASNetLarge. exactly 3 inputs channels, width height smaller 32. E.g. (224, 224, 3) one valid value. include_top Whether include fully-connected layer top network. weights NULL (random initialization) imagenet (ImageNet weights).  loading imagenet weights, input_shape (331, 331, 3) input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"Learning Transferable Architectures Scalable Image Recognition (CVPR 2018) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"Keras Application expects specific kind input preprocessing. NASNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"Instantiates Mobile NASNet model ImageNet mode.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"","code":"application_nasnetmobile(   input_shape = NULL,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) NASNetMobile exactly 3 inputs channels, width height smaller 32. E.g. (224, 224, 3) one valid value. include_top Whether include fully-connected layer top network. weights NULL (random initialization) imagenet (ImageNet weights). loading imagenet weights, input_shape (224, 224, 3) input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"Learning Transferable Architectures Scalable Image Recognition (CVPR 2018) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"Keras Application expects specific kind input preprocessing. NASNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet101 architecture. — application_resnet101","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"Instantiates ResNet101 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"","code":"application_resnet101(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"Deep Residual Learning Image Recognition (CVPR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"Instantiates ResNet101V2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"","code":"application_resnet101_v2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"Identity Mappings Deep Residual Networks (CVPR 2016) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet152 architecture. — application_resnet152","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"Instantiates ResNet152 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"","code":"application_resnet152(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"Deep Residual Learning Image Recognition (CVPR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"Instantiates ResNet152V2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"","code":"application_resnet152_v2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"Identity Mappings Deep Residual Networks (CVPR 2016) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet50 architecture. — application_resnet50","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"Instantiates ResNet50 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"","code":"application_resnet50(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"Deep Residual Learning Image Recognition (CVPR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"Instantiates ResNet50V2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"","code":"application_resnet50_v2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"Identity Mappings Deep Residual Networks (CVPR 2016) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the VGG16 model. — application_vgg16","title":"Instantiates the VGG16 model. — application_vgg16","text":"Instantiates VGG16 model.","code":""},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the VGG16 model. — application_vgg16","text":"","code":"application_vgg16(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the VGG16 model. — application_vgg16","text":"include_top whether include 3 fully-connected layers top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (channels_last data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 input channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the VGG16 model. — application_vgg16","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the VGG16 model. — application_vgg16","text":"Deep Convolutional Networks Large-Scale Image Recognition (ICLR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. default input size model 224x224.","code":""},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the VGG16 model. — application_vgg16","text":"Keras Application expects specific kind input preprocessing. VGG16, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the VGG19 model. — application_vgg19","title":"Instantiates the VGG19 model. — application_vgg19","text":"Instantiates VGG19 model.","code":""},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the VGG19 model. — application_vgg19","text":"","code":"application_vgg19(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the VGG19 model. — application_vgg19","text":"include_top whether include 3 fully-connected layers top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (channels_last data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 input channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the VGG19 model. — application_vgg19","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the VGG19 model. — application_vgg19","text":"Deep Convolutional Networks Large-Scale Image Recognition (ICLR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. default input size model 224x224.","code":""},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the VGG19 model. — application_vgg19","text":"Keras Application expects specific kind input preprocessing. VGG19, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_xception.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Xception architecture. — application_xception","title":"Instantiates the Xception architecture. — application_xception","text":"Instantiates Xception architecture.","code":""},{"path":"https://keras.posit.co/reference/application_xception.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Xception architecture. — application_xception","text":"","code":"application_xception(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_xception.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Xception architecture. — application_xception","text":"include_top whether include 3 fully-connected layers top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (299, 299, 3). exactly 3 inputs channels, width height smaller 71. E.g. (150, 150, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_xception.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Xception architecture. — application_xception","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_xception.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Xception architecture. — application_xception","text":"Xception: Deep Learning Depthwise Separable Convolutions (CVPR 2017) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. default input image size model 299x299.","code":""},{"path":"https://keras.posit.co/reference/application_xception.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Xception architecture. — application_xception","text":"Keras Application expects specific kind input preprocessing. Xception, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/audio_dataset_from_directory.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","title":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","text":"directory structure :   calling audio_dataset_from_directory(main_directory, labels = 'inferred') return tf.data.Dataset yields batches audio files subdirectories class_a class_b, together labels 0 1 (0 corresponding class_a 1 corresponding class_b). .wav files supported time.","code":"main_directory/ ...class_a/ ......a_audio_1.wav ......a_audio_2.wav ...class_b/ ......b_audio_1.wav ......b_audio_2.wav"},{"path":"https://keras.posit.co/reference/audio_dataset_from_directory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","text":"","code":"audio_dataset_from_directory(   directory,   labels = \"inferred\",   label_mode = \"int\",   class_names = NULL,   batch_size = 32L,   sampling_rate = NULL,   output_sequence_length = NULL,   ragged = FALSE,   shuffle = TRUE,   seed = NULL,   validation_split = NULL,   subset = NULL,   follow_links = FALSE,   verbose = TRUE )"},{"path":"https://keras.posit.co/reference/audio_dataset_from_directory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","text":"directory Directory data located. labels \"inferred\", contain subdirectories, containing audio files class. Otherwise, directory structure ignored. labels Either \"inferred\" (labels generated directory structure), NULL (labels), list/tuple integer labels size number audio files found directory. Labels sorted according alphanumeric order audio file paths (obtained via os.walk(directory) Python). label_mode String describing encoding labels. Options : \"int\": means labels encoded integers (e.g. sparse_categorical_crossentropy loss). \"categorical\" means labels encoded categorical vector (e.g. categorical_crossentropy loss) \"binary\" means labels (can 2) encoded float32 scalars values 0 1 (e.g. binary_crossentropy). NULL (labels). class_names valid \"labels\" \"inferred\". explicit list class names (must match names subdirectories). Used control order classes (otherwise alphanumerical order used). batch_size Size batches data. Default: 32. NULL, data batched (dataset yield individual samples). sampling_rate Audio sampling rate (samples per second). output_sequence_length Maximum length audio sequence. Audio files longer truncated output_sequence_length. set NULL, sequences batch padded length longest sequence batch. ragged Whether return Ragged dataset (sequence length). Defaults FALSE. shuffle Whether shuffle data. Defaults TRUE. set FALSE, sorts data alphanumeric order. seed Optional random seed shuffling transformations. validation_split Optional float 0 1, fraction data reserve validation. subset Subset data return. One \"training\", \"validation\" \"\". used validation_split set. follow_links Whether visits subdirectories pointed symlinks. Defaults FALSE. verbose Whether display number information classes number files found. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/audio_dataset_from_directory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","text":"tf.data.Dataset object. label_mode NULL, yields string tensors shape (batch_size,), containing contents batch audio files. Otherwise, yields tuple (audio, labels), audio shape (batch_size, sequence_length, num_channels) labels follows format described . Rules regarding labels format: label_mode int, labels int32 tensor shape (batch_size,). label_mode binary, labels float32 tensor 1s 0s shape (batch_size, 1). label_mode categorical, labels float32 tensor shape (batch_size, num_classes), representing one-hot encoding class index.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/bidirectional.html","id":null,"dir":"Reference","previous_headings":"","what":"layer_bidirectional — bidirectional","title":"layer_bidirectional — bidirectional","text":"bidirectional() alias layer_bidirectional(). See ?layer_bidirectional() full documentation.","code":""},{"path":"https://keras.posit.co/reference/bidirectional.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"layer_bidirectional — bidirectional","text":"","code":"bidirectional(   object,   layer,   merge_mode = \"concat\",   weights = NULL,   backward_layer = NULL,   ... )"},{"path":"https://keras.posit.co/reference/bidirectional.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"layer_bidirectional — bidirectional","text":"object Object compose layer . tensor, array, sequential model. layer RNN instance, layer_lstm() layer_gru(). also Layer() instance meets following criteria: sequence-processing layer (accepts 3D+ inputs). go_backwards, return_sequences return_state attribute (semantics RNN class). input_spec attribute. Implement serialization via get_config() from_config(). Note recommended way create new RNN layers write custom RNN cell use layer_rnn(), instead subclassing Layer() directly. return_sequences TRUE, output masked timestep zero regardless layer's original zero_output_for_mask value. merge_mode Mode outputs forward backward RNNs combined. One {\"sum\", \"mul\", \"concat\", \"ave\", NULL}. NULL, outputs combined, returned list. Defaults \"concat\". weights see description backward_layer Optional RNN, Layer() instance used handle backwards input processing. backward_layer provided, layer instance passed layer argument used generate backward layer automatically. Note provided backward_layer layer properties matching layer argument, particular values stateful, return_states, return_sequences, etc. addition, backward_layer layer different go_backwards argument values. ValueError raised requirements met. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/bidirectional.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"layer_bidirectional — bidirectional","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback to back up and restore the training state. — callback_backup_and_restore","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"callback_backup_and_restore() callback intended recover training interruption happened middle fit execution, backing training states temporary checkpoint file, end epoch. backup overwrites previously written checkpoint file, given time one checkpoint file backup/restoring purpose. training restarts completion, training state (includes model weights epoch number) restored recently saved state beginning new fit run. completion fit run, temporary checkpoint file deleted. Note user responsible bring jobs back interruption. callback important backup restore mechanism fault tolerance purpose, model restored previous checkpoint expected one used back . user changes arguments passed compile fit, checkpoint saved fault tolerance can become invalid.","code":""},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"","code":"callback_backup_and_restore(   backup_dir,   save_freq = \"epoch\",   delete_checkpoint = TRUE )"},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"backup_dir String, path directory store data needed restore model. directory reused elsewhere store files, e.g. backup_and_restore callback another training run, another callback (e.g. callback_model_checkpoint) training run. save_freq \"epoch\", integer, FALSE. set \"epoch\", callback saves checkpoint end epoch. set integer, callback saves checkpoint every save_freq batches. Set save_freq = FALSE using preemption checkpointing (.e. save_before_preemption = TRUE). delete_checkpoint Boolean, defaults TRUE. backup_and_restore callback works saving checkpoint back training state. delete_checkpoint = TRUE, checkpoint deleted training finished. Use FALSE like keep checkpoint future usage.","code":""},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"","code":"callback_interrupting <- new_callback_class(   \"InterruptingCallback\",   on_epoch_begin = function(epoch, logs = NULL) {     if (epoch == 4) {       stop('Interrupting!')     }   } )  backup_dir <- tempfile() callback <- callback_backup_and_restore(backup_dir = backup_dir) model <- keras_model_sequential() %>%   layer_dense(10) model %>% compile(optimizer = optimizer_sgd(), loss = 'mse')  # ensure model is built (i.e., weights are initialized) for # callback_backup_and_restore() model(op_ones(c(5, 20))) |> invisible()  tryCatch({   model %>% fit(x = op_ones(c(5, 20)),                 y = op_zeros(5),                 epochs = 10, batch_size = 1,                 callbacks = list(callback, callback_interrupting()),                 verbose = 0) }, python.builtin.RuntimeError = function(e) message(\"Interrupted!\")) ## Interrupted! model$history$epoch ## [1] 0 1 2 # model$history %>% keras3:::to_keras_training_history() %>% as.data.frame() %>% print()  history <- model %>% fit(x = op_ones(c(5, 20)),                          y = op_zeros(5),                          epochs = 10, batch_size = 1,                          callbacks = list(callback),                          verbose = 0)  # Only 6 more epochs are run, since first training got interrupted at # zero-indexed epoch 4, second training will continue from 4 to 9. nrow(as.data.frame(history)) ## [1] 10"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback that streams epoch results to a CSV file. — callback_csv_logger","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"Supports values can represented string, including 1D iterables atomic vectors.","code":""},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"","code":"callback_csv_logger(filename, separator = \",\", append = FALSE)"},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"filename Filename CSV file, e.g. 'run/log.csv'. separator String used separate elements CSV file. append Boolean. TRUE: append file exists (useful continuing training). FALSE: overwrite existing file.","code":""},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"","code":"csv_logger <- callback_csv_logger('training.log') model %>% fit(X_train, Y_train, callbacks = list(csv_logger))"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":null,"dir":"Reference","previous_headings":"","what":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"Assuming goal training minimize loss. , metric monitored 'loss', mode 'min'. model$fit() training loop check end every epoch whether loss longer decreasing, considering min_delta patience applicable. found longer decreasing, model$stop_training marked TRUE training terminates. quantity monitored needs available logs list. make , pass loss metrics model$compile().","code":""},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"","code":"callback_early_stopping(   monitor = \"val_loss\",   min_delta = 0L,   patience = 0L,   verbose = 0L,   mode = \"auto\",   baseline = NULL,   restore_best_weights = FALSE,   start_from_epoch = 0L )"},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"monitor Quantity monitored. Defaults \"val_loss\". min_delta Minimum change monitored quantity qualify improvement, .e. absolute change less min_delta, count improvement. Defaults 0. patience Number epochs improvement training stopped. Defaults 0. verbose Verbosity mode, 0 1. Mode 0 silent, mode 1 displays messages callback takes action. Defaults 0. mode One {\"auto\", \"min\", \"max\"}. min mode, training stop quantity monitored stopped decreasing; \"max\" mode stop quantity monitored stopped increasing; \"auto\" mode, direction automatically inferred name monitored quantity. Defaults \"auto\". baseline Baseline value monitored quantity. NULL, training stop model show improvement baseline. Defaults NULL. restore_best_weights Whether restore model weights epoch best value monitored quantity. FALSE, model weights obtained last step training used. epoch restored regardless performance relative baseline. epoch improves baseline, training run patience epochs restore weights best epoch set. Defaults FALSE. start_from_epoch Number epochs wait starting monitor improvement. allows warm-period improvement expected thus training stopped. Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"","code":"callback <- callback_early_stopping(monitor = 'loss',                                    patience = 3) # This callback will stop the training when there is no improvement in # the loss for three consecutive epochs. model <- keras_model_sequential() %>%   layer_dense(10) model %>% compile(optimizer = optimizer_sgd(), loss = 'mse') history <- model %>% fit(x = op_ones(c(5, 20)),                          y = op_zeros(5),                          epochs = 10, batch_size = 1,                          callbacks = list(callback),                          verbose = 0) nrow(as.data.frame(history))  # Only 4 epochs are run. ## [1] 10"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"callback constructed anonymous functions called appropriate time (Model.{fit | evaluate | predict}). Note callbacks expects positional arguments, : on_epoch_begin on_epoch_end expect two positional arguments: epoch, logs on_train_begin on_train_end expect one positional argument: logs on_train_batch_begin on_train_batch_end expect two positional arguments: batch, logs See Callback class definition full list functions expected arguments.","code":""},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"","code":"callback_lambda(   on_epoch_begin = NULL,   on_epoch_end = NULL,   on_train_begin = NULL,   on_train_end = NULL,   on_train_batch_begin = NULL,   on_train_batch_end = NULL,   ... )"},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"on_epoch_begin called beginning every epoch. on_epoch_end called end every epoch. on_train_begin called beginning model training. on_train_end called end model training. on_train_batch_begin called beginning every train batch. on_train_batch_end called end every train batch. ... function Callback() want override passing function_name = function. example, callback_lambda(.., on_train_end = train_end_fn). custom function needs arguments ones defined Callback().","code":""},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"","code":"# Print the batch number at the beginning of every batch. batch_print_callback <- callback_lambda(   on_train_batch_begin = function(batch, logs) {     print(batch)   } )  # Stream the epoch loss to a file in new-line delimited JSON format # (one valid JSON object per line) json_log <- file('loss_log.json', open = 'wt') json_logging_callback <- callback_lambda(   on_epoch_end = function(epoch, logs) {     jsonlite::write_json(       list(epoch = epoch, loss = logs$loss),       json_log,       append = TRUE     )   },   on_train_end = function(logs) {     close(json_log)   } )  # Terminate some processes after having finished model training. processes <- ... cleanup_callback <- callback_lambda(   on_train_end = function(logs) {     for (p in processes) {       if (is_alive(p)) {         terminate(p)       }     }   } )  model %>% fit(   ...,   callbacks = list(     batch_print_callback,     json_logging_callback,     cleanup_callback   ) )"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":null,"dir":"Reference","previous_headings":"","what":"Learning rate scheduler. — callback_learning_rate_scheduler","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"beginning every epoch, callback gets updated learning rate value schedule function provided, current epoch current learning rate, applies updated learning rate optimizer.","code":""},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"","code":"callback_learning_rate_scheduler(schedule, verbose = 0L)"},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"schedule function takes epoch index (integer, indexed 0) current learning rate (float) inputs returns new learning rate output (float). verbose Integer. 0: quiet, 1: log update messages.","code":""},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"","code":"# This function keeps the initial learning rate steady for the first ten epochs # and decreases it exponentially after that. scheduler <- function(epoch, lr) {   if (epoch < 10)     return(lr)   else     return(lr * exp(-0.1)) }  model <- keras_model_sequential() |> layer_dense(units = 10) model |> compile(optimizer = optimizer_sgd(), loss = 'mse') model$optimizer$learning_rate |> as.array() |> round(5) ## [1] 0.01 callback <- callback_learning_rate_scheduler(schedule = scheduler) history <- model |> fit(x = array(runif(100), c(5, 20)),                         y = array(0, c(5, 1)),                         epochs = 15, callbacks = list(callback), verbose = 0) model$optimizer$learning_rate |> as.array() |> round(5) ## [1] 0.00607"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"callback_model_checkpoint() used conjunction training using model |> fit() save model weights (checkpoint file) interval, model weights can loaded later continue training state saved. options callback provides include: Whether keep model achieved \"best performance\" far, whether save model end every epoch regardless performance. Definition \"best\"; quantity monitor whether maximized minimized. frequency save . Currently, callback supports saving end every epoch, fixed number training batches. Whether weights saved, whole model saved.","code":""},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"","code":"callback_model_checkpoint(   filepath,   monitor = \"val_loss\",   verbose = 0L,   save_best_only = FALSE,   save_weights_only = FALSE,   mode = \"auto\",   save_freq = \"epoch\",   initial_value_threshold = NULL )"},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"filepath string, path save model file. filepath can contain named formatting options, filled value epoch keys logs (passed on_epoch_end). filepath name needs end \".weights.h5\" save_weights_only = TRUE end \".keras\" checkpoint saving whole model (default). example: filepath \"{epoch:02d}-{val_loss:.2f}.keras\", model checkpoints saved epoch number validation loss filename. directory filepath reused callbacks avoid conflicts. monitor metric name monitor. Typically metrics set model |> compile() method. Note: Prefix name \"val_\" monitor validation metrics. Use \"loss\" \"val_loss\" monitor model's total loss. specify metrics strings, like \"accuracy\", pass string (without \"val_\" prefix). pass Metric objects (created one metric_*()), monitor set metric$name. sure metric names can check contents history$metrics list returned history <- model |> fit() Multi-output models set additional prefixes metric names. verbose Verbosity mode, 0 1. Mode 0 silent, mode 1 displays messages callback takes action. save_best_only save_best_only = TRUE, saves model considered \"best\" latest best model according quantity monitored overwritten. filepath contain formatting options like {epoch} filepath overwritten new better model. save_weights_only TRUE, model's weights saved (model |> save_model_weights(filepath)), else full model saved (model |> save_model(filepath)). mode one {\"auto\", \"min\", \"max\"}. save_best_only = TRUE, decision overwrite current save file made based either maximization minimization monitored quantity. val_acc, \"max\", val_loss \"min\", etc. \"auto\" mode, mode set \"max\" quantities monitored \"acc\" start \"fmeasure\" set \"min\" rest quantities. save_freq \"epoch\" integer. using \"epoch\", callback saves model epoch. using integer, callback saves model end many batches. Model compiled steps_per_execution = N, saving criteria checked every Nth batch. Note saving aligned epochs, monitored metric may potentially less reliable (reflect little 1 batch, since metrics get reset every epoch). Defaults \"epoch\". initial_value_threshold Floating point initial \"best\" value metric monitored. applies save_best_value = TRUE. overwrites model weights already saved performance current model better value.","code":""},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"","code":"model <- keras_model_sequential(input_shape = c(10)) |>   layer_dense(1, activation = \"sigmoid\") |>   compile(loss = \"binary_crossentropy\", optimizer = \"adam\",           metrics = c('accuracy'))  EPOCHS <- 10 checkpoint_filepath <- tempfile('checkpoint-model-', fileext = \".keras\") model_checkpoint_callback <- callback_model_checkpoint(   filepath = checkpoint_filepath,   monitor = 'val_accuracy',   mode = 'max',   save_best_only = TRUE )  # Model is saved at the end of every epoch, if it's the best seen so far. model |> fit(x = random_uniform(c(2, 10)), y = op_ones(2, 1),              epochs = EPOCHS, validation_split = .5, verbose = 0,              callbacks = list(model_checkpoint_callback))  # The model (that are considered the best) can be loaded as - load_model(checkpoint_filepath) ## Model: \"sequential\" ## +---------------------------------+------------------------+---------------+ ## | Layer (type)                    | Output Shape           |       Param # | ## +=================================+========================+===============+ ## | dense (Dense)                   | (None, 1)              |            11 | ## +---------------------------------+------------------------+---------------+ ##  Total params: 35 (144.00 B) ##  Trainable params: 11 (44.00 B) ##  Non-trainable params: 0 (0.00 B) ##  Optimizer params: 24 (100.00 B) # Alternatively, one could checkpoint just the model weights as - checkpoint_filepath <- tempfile('checkpoint-', fileext = \".weights.h5\") model_checkpoint_callback <- callback_model_checkpoint(   filepath = checkpoint_filepath,   save_weights_only = TRUE,   monitor = 'val_accuracy',   mode = 'max',   save_best_only = TRUE )  # Model weights are saved at the end of every epoch, if it's the best seen # so far. # same as above model |> fit(x = random_uniform(c(2, 10)), y = op_ones(2, 1),              epochs = EPOCHS, validation_split = .5, verbose = 0,              callbacks = list(model_checkpoint_callback))  # The model weights (that are considered the best) can be loaded model |> load_model_weights(checkpoint_filepath)"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":null,"dir":"Reference","previous_headings":"","what":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"Models often benefit reducing learning rate factor 2-10 learning stagnates. callback monitors quantity improvement seen 'patience' number epochs, learning rate reduced.","code":""},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"","code":"callback_reduce_lr_on_plateau(   monitor = \"val_loss\",   factor = 0.1,   patience = 10L,   verbose = 0L,   mode = \"auto\",   min_delta = 1e-04,   cooldown = 0L,   min_lr = 0,   ... )"},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"monitor String. Quantity monitored. factor Float. Factor learning rate reduced. new_lr = lr * factor. patience Integer. Number epochs improvement learning rate reduced. verbose Integer. 0: quiet, 1: update messages. mode String. One {'auto', 'min', 'max'}. 'min' mode, learning rate reduced quantity monitored stopped decreasing; 'max' mode reduced quantity monitored stopped increasing; 'auto' mode, direction automatically inferred name monitored quantity. min_delta Float. Threshold measuring new optimum, focus significant changes. cooldown Integer. Number epochs wait resuming normal operation learning rate reduced. min_lr Float. Lower bound learning rate. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"","code":"reduce_lr <- callback_reduce_lr_on_plateau(monitor = 'val_loss', factor = 0.2,                                            patience = 5, min_lr = 0.001) model %>% fit(x_train, y_train, callbacks = list(reduce_lr))"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_remote_monitor.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback used to stream events to a server. — callback_remote_monitor","title":"Callback used to stream events to a server. — callback_remote_monitor","text":"Requires requests library. Events sent root + '/publish/epoch/end/' default. Calls HTTP POST, data argument JSON-encoded named list event data. send_as_json = TRUE, content type request \"application/json\". Otherwise serialized JSON sent within form.","code":""},{"path":"https://keras.posit.co/reference/callback_remote_monitor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback used to stream events to a server. — callback_remote_monitor","text":"","code":"callback_remote_monitor(   root = \"http://localhost:9000\",   path = \"/publish/epoch/end/\",   field = \"data\",   headers = NULL,   send_as_json = FALSE )"},{"path":"https://keras.posit.co/reference/callback_remote_monitor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback used to stream events to a server. — callback_remote_monitor","text":"root String; root url target server. path String; path relative root events sent. field String; JSON field data stored. field used payload sent within form (.e. send_as_json = FALSE). headers Named list; optional custom HTTP headers. send_as_json Boolean; whether request sent \"application/json\".","code":""},{"path":"https://keras.posit.co/reference/callback_remote_monitor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback used to stream events to a server. — callback_remote_monitor","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"callbacks replaces model's weight values values optimizer's EMA weights (exponential moving average past model weights values, implementing \"Polyak averaging\") model evaluation, restores previous weights evaluation. SwapEMAWeights callback used conjunction optimizer sets use_ema = TRUE. Note weights swapped -place order save memory. behavior undefined modify EMA weights model weights callbacks.","code":""},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"","code":"callback_swap_ema_weights(swap_on_epoch = FALSE)"},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"swap_on_epoch Whether perform swapping on_epoch_begin() on_epoch_end(). useful want use EMA weights callbacks callback_model_checkpoint(). Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"","code":"# Remember to set `use_ema=TRUE` in the optimizer optimizer <- optimizer_sgd(use_ema = TRUE) model |> compile(optimizer = optimizer, loss = ..., metrics = ...)  # Metrics will be computed with EMA weights model |> fit(X_train, Y_train,              callbacks = c(callback_swap_ema_weights()))  # If you want to save model checkpoint with EMA weights, you can set # `swap_on_epoch=TRUE` and place ModelCheckpoint after SwapEMAWeights. model |> fit(   X_train, Y_train,   callbacks = c(     callback_swap_ema_weights(swap_on_epoch = TRUE),     callback_model_checkpoint(...)   ) )"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":null,"dir":"Reference","previous_headings":"","what":"Enable visualizations for TensorBoard. — callback_tensorboard","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"TensorBoard visualization tool provided TensorFlow. TensorFlow installation required use callback. callback logs events TensorBoard, including: Metrics summary plots Training graph visualization Weight histograms Sampled profiling used model |> evaluate() regular validation addition epoch summaries, summary records evaluation metrics vs model$optimizer$iterations written. metric names prepended evaluation, model$optimizer$iterations step visualized TensorBoard. installed TensorFlow pip reticulate::py_install(), able launch TensorBoard command line:   R tensorflow::tensorboard(). can find information TensorBoard .","code":"tensorboard --logdir=path_to_your_logs"},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"","code":"callback_tensorboard(   log_dir = \"logs\",   histogram_freq = 0L,   write_graph = TRUE,   write_images = FALSE,   write_steps_per_second = FALSE,   update_freq = \"epoch\",   profile_batch = 0L,   embeddings_freq = 0L,   embeddings_metadata = NULL )"},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"log_dir path directory save log files parsed TensorBoard. e.g., log_dir = file.path(working_dir, 'logs'). directory reused callbacks. histogram_freq frequency (epochs) compute weight histograms layers model. set 0, histograms computed. Validation data (split) must specified histogram visualizations. write_graph (supported time) Whether visualize graph TensorBoard. Note log file can become quite large write_graph set TRUE. write_images whether write model weights visualize image TensorBoard. write_steps_per_second whether log training steps per second TensorBoard. supports epoch batch frequency logging. update_freq \"batch\" \"epoch\" integer. using \"epoch\", writes losses metrics TensorBoard every epoch. using integer, say 1000, metrics losses (including custom ones added Model.compile) logged TensorBoard every 1000 batches. \"batch\" synonym 1, meaning written every batch. Note however writing frequently TensorBoard can slow training, especially used distribution strategies incur additional synchronization overhead. Batch-level summary writing also available via train_step override. Please see TensorBoard Scalars tutorial  # noqa: E501 details. profile_batch (supported time) Profile batch(es) sample compute characteristics. profile_batch must non-negative integer tuple integers. pair positive integers signify range batches profile. default, profiling disabled. embeddings_freq frequency (epochs) embedding layers visualized. set 0, embeddings visualized. embeddings_metadata Named list maps embedding layer names filename file save metadata embedding layer. case metadata file used embedding layers, single filename can passed.","code":""},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"Custom batch-level summaries subclassed Model:   Custom batch-level summaries Functional API Model:   Profiling:","code":"tensorboard_callback <- callback_tensorboard(log_dir = \"./logs\") model %>% fit(x_train, y_train, epochs = 2, callbacks = list(tensorboard_callback)) # Then run the tensorboard command to view the visualizations. MyModel <- new_model_class(\"MyModel\",   initialize = function() {     self$dense <- layer_dense(units = 10)   },   call = function(x) {     outputs <- x |> self$dense()     tf$summary$histogram('outputs', outputs)     outputs   } )  model <- MyModel() model |> compile(optimizer = 'sgd', loss = 'mse')  # Make sure to set `update_freq = N` to log a batch-level summary every N # batches. In addition to any `tf$summary` contained in `model$call()`, # metrics added in `model |>compile` will be logged every N batches. tb_callback <- callback_tensorboard(log_dir = './logs', update_freq = 1) model |> fit(x_train, y_train, callbacks = list(tb_callback)) my_summary <- function(x) {   tf$summary$histogram('x', x)   x }  inputs <- layer_input(10) outputs <- inputs |>   layer_dense(10) |>   layer_lambda(my_summary)  model <- keras_model(inputs, outputs) model |> compile(optimizer = 'sgd', loss = 'mse')  # Make sure to set `update_freq = N` to log a batch-level summary every N # batches. In addition to any `tf.summary` contained in `Model.call`, # metrics added in `Model.compile` will be logged every N batches. tb_callback <- callback_tensorboard(log_dir = './logs', update_freq = 1) model |> fit(x_train, y_train, callbacks = list(tb_callback)) # Profile a single batch, e.g. the 5th batch. tensorboard_callback <- callback_tensorboard(   log_dir = './logs', profile_batch = 5) model |> fit(x_train, y_train, epochs = 2,              callbacks = list(tensorboard_callback))  # Profile a range of batches, e.g. from 10 to 20. tensorboard_callback <- callback_tensorboard(   log_dir = './logs', profile_batch = c(10, 20)) model |> fit(x_train, y_train, epochs = 2,              callbacks = list(tensorboard_callback))"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_terminate_on_nan.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback that terminates training when a NaN loss is encountered. — callback_terminate_on_nan","title":"Callback that terminates training when a NaN loss is encountered. — callback_terminate_on_nan","text":"Callback terminates training NaN loss encountered.","code":""},{"path":"https://keras.posit.co/reference/callback_terminate_on_nan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback that terminates training when a NaN loss is encountered. — callback_terminate_on_nan","text":"","code":"callback_terminate_on_nan()"},{"path":"https://keras.posit.co/reference/callback_terminate_on_nan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback that terminates training when a NaN loss is encountered. — callback_terminate_on_nan","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/clear_session.html","id":null,"dir":"Reference","previous_headings":"","what":"Resets all state generated by Keras. — clear_session","title":"Resets all state generated by Keras. — clear_session","text":"Keras manages global state, uses implement Functional model-building API uniquify autogenerated layer names. creating many models loop, global state consume increasing amount memory time, may want clear . Calling clear_session() releases global state: helps avoid clutter old models layers, especially memory limited. Example 1: calling clear_session() creating models loop   Example 2: resetting layer name generation counter","code":"for (i in 1:100) {   # Without `clear_session()`, each iteration of this loop will   # slightly increase the size of the global state managed by Keras   model <- keras_model_sequential()   for (j in 1:10) {     model <- model |> layer_dense(units = 10)   } }  for (i in 1:100) {   # With `clear_session()` called at the beginning,   # Keras starts with a blank state at each iteration   # and memory consumption is constant over time.   clear_session()   model <- keras_model_sequential()   for (j in 1:10) {     model <- model |> layer_dense(units = 10)   } } layers <- lapply(1:10, \\(i) layer_dense(units = 10))  new_layer <- layer_dense(units = 10) print(new_layer$name) ## [1] \"dense_10\" clear_session() new_layer <- layer_dense(units = 10) print(new_layer$name) ## [1] \"dense\""},{"path":"https://keras.posit.co/reference/clear_session.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resets all state generated by Keras. — clear_session","text":"","code":"clear_session(free_memory = TRUE)"},{"path":"https://keras.posit.co/reference/clear_session.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resets all state generated by Keras. — clear_session","text":"free_memory Whether call Python garbage collection. usually good practice call make sure memory used deleted objects immediately freed. However, may take seconds execute, using clear_session() short loop, may want skip .","code":""},{"path":"https://keras.posit.co/reference/clear_session.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resets all state generated by Keras. — clear_session","text":"NULL, invisibly, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/clone_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Clone a Functional or Sequential Model instance. — clone_model","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"Model cloning similar calling model new inputs, except creates new layers (thus new weights) instead sharing weights existing layers. Note clone_model() preserve uniqueness shared objects within model (e.g. single variable attached two distinct layers restored two separate variables).","code":""},{"path":"https://keras.posit.co/reference/clone_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"","code":"clone_model(   model,   input_tensors = NULL,   clone_function = NULL,   call_function = NULL,   recursive = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/clone_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"model Instance Model (Functional model Sequential model). input_tensors Optional list input tensors build model upon. provided, new keras_input() objects created. clone_function Callable signature function(layer) used clone layer target model (except Input instances). takes argument layer instance cloned, returns corresponding layer instance used model copy. unspecified, callable defaults following serialization/deserialization function: function(layer) layer$`__class__`$from_config(layer$get_config()). passing custom callable, can customize copy model, e.g. wrapping certain layers interest (might want replace LSTM instances equivalent Bidirectional(LSTM(...)) instances, example). Defaults NULL. call_function Callable signature function(layer, ...) used call cloned layer set inputs. takes layer instance, call arguments, returns call outputs. unspecified, callable defaults regular call() method: function(layer, ...) .call(layer, list(...)). passing custom callable, can insert new layers given layer. recursive Note, argument can used Functional models. Boolean. Whether recursively clone Sequential Functional models encountered original Sequential/Functional model. FALSE, inner models cloned calling clone_function(). TRUE, inner models cloned calling clone_model() clone_function, call_function, recursive arguments. Note case, call_function propagated Sequential model (since applicable Sequential models). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/clone_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"instance Model reproducing behavior original model, top new inputs tensors, using newly instantiated weights. cloned model may behave differently original model custom clone_function call_function modifies layer layer call.","code":""},{"path":"https://keras.posit.co/reference/clone_model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"Using clone_function make model deterministic setting random seed everywhere:   Using call_function add Dropout layer Dense layer (without recreating new layers):     Note subclassed models cloned default, since internal layer structure known. achieve equivalent functionality clone_model case subclassed model, simply make sure model class implements get_config() (optionally from_config()), call:   case subclassed model, using custom clone_function.","code":"# Create a test Sequential model. model <- keras_model_sequential(input_shape = c(728)) |>   layer_dense(32, activation = 'relu') |>   layer_dense(1, activation = 'sigmoid')  # Create a copy of the test model (with freshly initialized weights). new_model <- clone_model(model) clone_function <- function(layer) {   config <- layer$get_config()   if (\"seed\" %in% names(config))     config$seed <- 1337L   layer$`__class__`$from_config(config) }  new_model <- clone_model(model, clone_function = clone_function) call_function <- function(layer, ...) {   out <- layer(...)   if (inherits(layer, keras$layers$Dense))     out <- out |> layer_dropout(0.5)   out }  inputs <- keras_input(c(728)) outputs <- inputs |>   layer_dense(32, activation = 'relu') |>   layer_dense(1, activation = 'sigmoid') model <- keras_model(inputs, outputs)  new_model <- clone_model(   model,   clone_function = function(x) x, # Reuse the same layers.   call_function = call_function, ) new_model ## Model: \"functional_5\" ## +-----------------------------------+--------------------------+---------------+ ## | Layer (type)                      | Output Shape             |       Param # | ## +===================================+==========================+===============+ ## | keras_tensor_8 (InputLayer)       | (None, 728)              |             0 | ## +-----------------------------------+--------------------------+---------------+ ## | dense_2 (Dense)                   | (None, 32)               |        23,328 | ## +-----------------------------------+--------------------------+---------------+ ## | dropout (Dropout)                 | (None, 32)               |             0 | ## +-----------------------------------+--------------------------+---------------+ ## | dense_3 (Dense)                   | (None, 1)                |            33 | ## +-----------------------------------+--------------------------+---------------+ ## | dropout_1 (Dropout)               | (None, 1)                |             0 | ## +-----------------------------------+--------------------------+---------------+ ##  Total params: 23,361 (91.25 KB) ##  Trainable params: 23,361 (91.25 KB) ##  Non-trainable params: 0 (0.00 B) new_model <- model$`__class__`$from_config(model$get_config())"},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Configure a model for training. — compile.keras.src.models.model.Model","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"Configure model training.","code":""},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model compile(   object,   optimizer = \"rmsprop\",   loss = NULL,   metrics = NULL,   ...,   loss_weights = NULL,   weighted_metrics = NULL,   run_eagerly = FALSE,   steps_per_execution = 1L,   jit_compile = \"auto\",   auto_scale_loss = TRUE )"},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"object Keras model object optimizer String (name optimizer) optimizer instance. See optimizer_* family. loss Loss function. May : string (name builtin loss function), custom function, Loss instance (returned loss_* family functions). loss function callable signature loss = fn(y_true, y_pred), y_true ground truth values, y_pred model's predictions. y_true shape (batch_size, d1, .. dN) (except case sparse loss functions sparse categorical crossentropy expects integer arrays shape (batch_size, d1, .. dN-1)). y_pred shape (batch_size, d1, .. dN). loss function return float tensor. metrics List metrics evaluated model training testing. can : string (name built-function), function, optionally \"name\" attribute Metric() instance. See metric_* family functions. Typically use metrics = c('accuracy'). function callable signature result = fn(y_true, y_pred). specify different metrics different outputs multi-output model, also pass named list, metrics = list(= 'accuracy', b = c('accuracy', 'mse')). can also pass list specify metric list metrics output, metrics = list(c('accuracy'), c('accuracy', 'mse')) metrics = list('accuracy', c('accuracy', 'mse')). pass strings 'accuracy' 'acc', convert one metric_binary_accuracy(), metric_categorical_accuracy(), metric_sparse_categorical_accuracy() based shapes targets model output. similar conversion done strings \"crossentropy\" \"ce\" well. metrics passed evaluated without sample weighting; like sample weighting apply, can specify metrics via weighted_metrics argument instead. providing anonymous R function, can customize printed name training assigning attr(<fn>, \"name\") <- \"my_custom_metric_name\", calling custom_metric(\"my_custom_metric_name\", <fn>) ... Additional arguments passed compile() model method. loss_weights Optional list (named unnamed) specifying scalar coefficients (R numerics) weight loss contributions different model outputs. loss value minimized model weighted sum individual losses, weighted loss_weights coefficients.  unnamed list, expected 1:1 mapping model's outputs. named list, expected map output names (strings) scalar coefficients. weighted_metrics List metrics evaluated weighted sample_weight class_weight training testing. run_eagerly Bool. TRUE, model's forward pass never compiled. recommended leave FALSE training (best performance), set TRUE debugging. steps_per_execution Int. number batches run single compiled function call. Running multiple batches inside single compiled function call can greatly improve performance TPUs small models large R/Python overhead. , one full epoch run execution. number larger size epoch passed, execution truncated size epoch. Note steps_per_execution set N, Callback$on_batch_begin Callback$on_batch_end methods called every N batches (.e. /compiled function execution). supported PyTorch backend. jit_compile Bool \"auto\". Whether use XLA compilation compiling model. jax tensorflow backends, jit_compile=\"auto\" enables XLA compilation model supports , disabled otherwise. torch backend, \"auto\" default eager execution jit_compile=True run torch.compile \"inductor\" backend. auto_scale_loss Bool. TRUE model dtype policy \"mixed_float16\", passed optimizer automatically wrapped LossScaleOptimizer, dynamically scale loss prevent underflow.","code":""},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"called primarily side effect modifying object -place. first argument object also returned, invisibly, enable usage pipe.","code":""},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"","code":"model |> compile(   optimizer = optimizer_adam(learning_rate = 1e-3),   loss = loss_binary_crossentropy(),   metrics = c(metric_binary_accuracy(),               metric_false_negatives()) )"},{"path":[]},{"path":"https://keras.posit.co/reference/config_backend.html","id":null,"dir":"Reference","previous_headings":"","what":"Publicly accessible method for determining the current backend. — config_backend","title":"Publicly accessible method for determining the current backend. — config_backend","text":"Publicly accessible method determining current backend.","code":""},{"path":"https://keras.posit.co/reference/config_backend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Publicly accessible method for determining the current backend. — config_backend","text":"","code":"config_backend()"},{"path":"https://keras.posit.co/reference/config_backend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Publicly accessible method for determining the current backend. — config_backend","text":"String, name backend Keras currently using. One \"tensorflow\", \"torch\", \"jax\".","code":""},{"path":"https://keras.posit.co/reference/config_backend.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Publicly accessible method for determining the current backend. — config_backend","text":"","code":"config_backend() ## [1] \"tensorflow\""},{"path":[]},{"path":"https://keras.posit.co/reference/config_disable_interactive_logging.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn off interactive logging. — config_disable_interactive_logging","title":"Turn off interactive logging. — config_disable_interactive_logging","text":"interactive logging disabled, Keras sends logs absl.logging. best option using Keras non-interactive way, running training inference job server.","code":""},{"path":"https://keras.posit.co/reference/config_disable_interactive_logging.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn off interactive logging. — config_disable_interactive_logging","text":"","code":"config_disable_interactive_logging()"},{"path":"https://keras.posit.co/reference/config_disable_interactive_logging.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn off interactive logging. — config_disable_interactive_logging","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_disable_traceback_filtering.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn off traceback filtering. — config_disable_traceback_filtering","title":"Turn off traceback filtering. — config_disable_traceback_filtering","text":"Raw Keras tracebacks (also known stack traces) involve many internal frames, can challenging read , actionable end users. default, Keras filters internal frames exceptions raises, keep traceback short, readable, focused actionable (code). See also config_enable_traceback_filtering() config_is_traceback_filtering_enabled(). previously disabled traceback filtering via config_disable_traceback_filtering(), can re-enable via config_enable_traceback_filtering().","code":""},{"path":"https://keras.posit.co/reference/config_disable_traceback_filtering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn off traceback filtering. — config_disable_traceback_filtering","text":"","code":"config_disable_traceback_filtering()"},{"path":"https://keras.posit.co/reference/config_disable_traceback_filtering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn off traceback filtering. — config_disable_traceback_filtering","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_dtype_policy.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the current default dtype policy object. — config_dtype_policy","title":"Returns the current default dtype policy object. — config_dtype_policy","text":"Returns current default dtype policy object.","code":""},{"path":"https://keras.posit.co/reference/config_dtype_policy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the current default dtype policy object. — config_dtype_policy","text":"","code":"config_dtype_policy()"},{"path":"https://keras.posit.co/reference/config_dtype_policy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the current default dtype policy object. — config_dtype_policy","text":"DTypePolicy object.","code":""},{"path":"https://keras.posit.co/reference/config_enable_interactive_logging.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn on interactive logging. — config_enable_interactive_logging","title":"Turn on interactive logging. — config_enable_interactive_logging","text":"interactive logging enabled, Keras displays logs via stdout. provides best experience using Keras interactive environment shell notebook.","code":""},{"path":"https://keras.posit.co/reference/config_enable_interactive_logging.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn on interactive logging. — config_enable_interactive_logging","text":"","code":"config_enable_interactive_logging()"},{"path":"https://keras.posit.co/reference/config_enable_interactive_logging.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn on interactive logging. — config_enable_interactive_logging","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_enable_traceback_filtering.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn on traceback filtering. — config_enable_traceback_filtering","title":"Turn on traceback filtering. — config_enable_traceback_filtering","text":"Raw Keras tracebacks (also known stack traces) involve many internal frames, can challenging read , actionable end users. default, Keras filters internal frames exceptions raises, keep traceback short, readable, focused actionable (code). See also config_disable_traceback_filtering() config_is_traceback_filtering_enabled(). previously disabled traceback filtering via config_disable_traceback_filtering(), can re-enable via config_enable_traceback_filtering().","code":""},{"path":"https://keras.posit.co/reference/config_enable_traceback_filtering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn on traceback filtering. — config_enable_traceback_filtering","text":"","code":"config_enable_traceback_filtering()"},{"path":"https://keras.posit.co/reference/config_enable_traceback_filtering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn on traceback filtering. — config_enable_traceback_filtering","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_enable_unsafe_deserialization.html","id":null,"dir":"Reference","previous_headings":"","what":"Disables safe mode globally, allowing deserialization of lambdas. — config_enable_unsafe_deserialization","title":"Disables safe mode globally, allowing deserialization of lambdas. — config_enable_unsafe_deserialization","text":"Disables safe mode globally, allowing deserialization lambdas.","code":""},{"path":"https://keras.posit.co/reference/config_enable_unsafe_deserialization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Disables safe mode globally, allowing deserialization of lambdas. — config_enable_unsafe_deserialization","text":"","code":"config_enable_unsafe_deserialization()"},{"path":"https://keras.posit.co/reference/config_enable_unsafe_deserialization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Disables safe mode globally, allowing deserialization of lambdas. — config_enable_unsafe_deserialization","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_epsilon.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","title":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","text":"Return value fuzz factor used numeric expressions.","code":""},{"path":"https://keras.posit.co/reference/config_epsilon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","text":"","code":"config_epsilon()"},{"path":"https://keras.posit.co/reference/config_epsilon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","text":"float.","code":""},{"path":"https://keras.posit.co/reference/config_epsilon.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","text":"","code":"config_epsilon() ## [1] 1e-07"},{"path":[]},{"path":"https://keras.posit.co/reference/config_floatx.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the default float type, as a string. — config_floatx","title":"Return the default float type, as a string. — config_floatx","text":"E.g. 'bfloat16' 'float16', 'float32', 'float64'.","code":""},{"path":"https://keras.posit.co/reference/config_floatx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the default float type, as a string. — config_floatx","text":"","code":"config_floatx()"},{"path":"https://keras.posit.co/reference/config_floatx.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the default float type, as a string. — config_floatx","text":"String, current default float type.","code":""},{"path":"https://keras.posit.co/reference/config_floatx.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the default float type, as a string. — config_floatx","text":"","code":"keras3::config_floatx() ## [1] \"float32\""},{"path":[]},{"path":"https://keras.posit.co/reference/config_image_data_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the default image data format convention. — config_image_data_format","title":"Return the default image data format convention. — config_image_data_format","text":"Return default image data format convention.","code":""},{"path":"https://keras.posit.co/reference/config_image_data_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the default image data format convention. — config_image_data_format","text":"","code":"config_image_data_format()"},{"path":"https://keras.posit.co/reference/config_image_data_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the default image data format convention. — config_image_data_format","text":"string, either 'channels_first' 'channels_last'.","code":""},{"path":"https://keras.posit.co/reference/config_image_data_format.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the default image data format convention. — config_image_data_format","text":"","code":"config_image_data_format() ## [1] \"channels_last\""},{"path":[]},{"path":"https://keras.posit.co/reference/config_is_interactive_logging_enabled.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if interactive logging is enabled. — config_is_interactive_logging_enabled","title":"Check if interactive logging is enabled. — config_is_interactive_logging_enabled","text":"switch writing logs stdout absl.logging, may use config_enable_interactive_logging() config_disable_interactive_logging().","code":""},{"path":"https://keras.posit.co/reference/config_is_interactive_logging_enabled.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if interactive logging is enabled. — config_is_interactive_logging_enabled","text":"","code":"config_is_interactive_logging_enabled()"},{"path":"https://keras.posit.co/reference/config_is_interactive_logging_enabled.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if interactive logging is enabled. — config_is_interactive_logging_enabled","text":"Boolean, TRUE interactive logging enabled, FALSE otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_is_traceback_filtering_enabled.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if traceback filtering is enabled. — config_is_traceback_filtering_enabled","title":"Check if traceback filtering is enabled. — config_is_traceback_filtering_enabled","text":"Raw Keras tracebacks (also known stack traces) involve many internal frames, can challenging read , actionable end users. default, Keras filters internal frames exceptions raises, keep traceback short, readable, focused actionable (code). See also config_enable_traceback_filtering() config_disable_traceback_filtering(). previously disabled traceback filtering via config_disable_traceback_filtering(), can re-enable via config_enable_traceback_filtering().","code":""},{"path":"https://keras.posit.co/reference/config_is_traceback_filtering_enabled.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if traceback filtering is enabled. — config_is_traceback_filtering_enabled","text":"","code":"config_is_traceback_filtering_enabled()"},{"path":"https://keras.posit.co/reference/config_is_traceback_filtering_enabled.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if traceback filtering is enabled. — config_is_traceback_filtering_enabled","text":"Boolean, TRUE traceback filtering enabled, FALSE otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":null,"dir":"Reference","previous_headings":"","what":"Reload the backend (and the Keras package). — config_set_backend","title":"Reload the backend (and the Keras package). — config_set_backend","text":"Reload backend (Keras package).","code":""},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reload the backend (and the Keras package). — config_set_backend","text":"","code":"config_set_backend(backend)"},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reload the backend (and the Keras package). — config_set_backend","text":"backend String","code":""},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reload the backend (and the Keras package). — config_set_backend","text":"Nothing, function called side effect.","code":""},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reload the backend (and the Keras package). — config_set_backend","text":"","code":"config_set_backend(\"jax\")"},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"warning","dir":"Reference","previous_headings":"","what":"WARNING","title":"Reload the backend (and the Keras package). — config_set_backend","text":"Using function dangerous done carefully. Changing backend convert type already-instantiated objects. Thus, layers / tensors / etc. already created longer usable without errors. strongly recommended keep around Keras-originated objects instances created calling config_set_backend(). includes function class instance uses Keras functionality. code needs re-executed calling config_set_backend().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets the default dtype policy globally. — config_set_dtype_policy","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"Sets default dtype policy globally.","code":""},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"","code":"config_set_dtype_policy(policy)"},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"policy string DTypePolicy object.","code":""},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"return value, called side effects.","code":""},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"","code":"config_set_dtype_policy(\"mixed_float16\")"},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"Set value fuzz factor used numeric expressions.","code":""},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"","code":"config_set_epsilon(value)"},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"value float. New value epsilon.","code":""},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"return value, called side effects.","code":""},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"","code":"config_epsilon() ## [1] 1e-07 config_set_epsilon(1e-5) config_epsilon() ## [1] 1e-05 # Set it back to the default value. config_set_epsilon(1e-7)"},{"path":[]},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the default float dtype. — config_set_floatx","title":"Set the default float dtype. — config_set_floatx","text":"Set default float dtype.","code":""},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the default float dtype. — config_set_floatx","text":"","code":"config_set_floatx(value)"},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the default float dtype. — config_set_floatx","text":"value String; 'bfloat16', 'float16', 'float32', 'float64'.","code":""},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the default float dtype. — config_set_floatx","text":"return value, called side effects.","code":""},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Set the default float dtype. — config_set_floatx","text":"recommended set \"float16\" training, likely cause numeric stability issues. Instead, mixed precision, leverages mix float16 float32. can configured calling keras3::keras$mixed_precision$set_dtype_policy('mixed_float16').","code":""},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set the default float dtype. — config_set_floatx","text":"","code":"config_floatx() ## [1] \"float32\" config_set_floatx('float64') config_floatx() ## [1] \"float64\" # Set it back to float32 config_set_floatx('float32')"},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"Set the default float dtype. — config_set_floatx","text":"ValueError: case invalid value.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the value of the image data format convention. — config_set_image_data_format","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"Set value image data format convention.","code":""},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"","code":"config_set_image_data_format(data_format)"},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"data_format string. 'channels_first' 'channels_last'.","code":""},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"return value, called side effects.","code":""},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"","code":"config_image_data_format() ## [1] \"channels_last\" # 'channels_last' keras3::config_set_image_data_format('channels_first') config_image_data_format() ## [1] \"channels_first\" # Set it back to `'channels_last'` keras3::config_set_image_data_format('channels_last')"},{"path":[]},{"path":"https://keras.posit.co/reference/constraint_maxnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"MaxNorm weight constraint. — constraint_maxnorm","title":"MaxNorm weight constraint. — constraint_maxnorm","text":"Constrains weights incident hidden unit norm less equal desired value.","code":""},{"path":"https://keras.posit.co/reference/constraint_maxnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MaxNorm weight constraint. — constraint_maxnorm","text":"","code":"constraint_maxnorm(max_value = 2L, axis = 1L)"},{"path":"https://keras.posit.co/reference/constraint_maxnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MaxNorm weight constraint. — constraint_maxnorm","text":"max_value maximum norm value incoming weights. axis integer, axis along calculate weight norms. instance, Dense layer weight matrix shape (input_dim, output_dim), set axis 0 constrain weight vector length (input_dim,). Conv2D layer data_format = \"channels_last\", weight tensor shape (rows, cols, input_depth, output_depth), set axis [0, 1, 2] constrain weights filter tensor size (rows, cols, input_depth).","code":""},{"path":"https://keras.posit.co/reference/constraint_maxnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MaxNorm weight constraint. — constraint_maxnorm","text":"Constraint instance, callable can passed layer constructors used directly calling tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/constraint_minmaxnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"MinMaxNorm weight constraint. — constraint_minmaxnorm","title":"MinMaxNorm weight constraint. — constraint_minmaxnorm","text":"Constrains weights incident hidden unit norm lower bound upper bound.","code":""},{"path":"https://keras.posit.co/reference/constraint_minmaxnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MinMaxNorm weight constraint. — constraint_minmaxnorm","text":"","code":"constraint_minmaxnorm(min_value = 0, max_value = 1, rate = 1, axis = 1L)"},{"path":"https://keras.posit.co/reference/constraint_minmaxnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MinMaxNorm weight constraint. — constraint_minmaxnorm","text":"min_value minimum norm incoming weights. max_value maximum norm incoming weights. rate rate enforcing constraint: weights rescaled yield op_clip? (1 - rate) * norm + rate * op_clip(norm, min_value, max_value). Effectively, means rate = 1.0 stands strict enforcement constraint, rate<1.0 means weights rescaled step slowly move towards value inside desired interval. axis integer, axis along calculate weight norms. instance, Dense layer weight matrix shape (input_dim, output_dim), set axis 0 constrain weight vector length (input_dim,). Conv2D layer data_format = \"channels_last\", weight tensor shape (rows, cols, input_depth, output_depth), set axis [0, 1, 2] constrain weights filter tensor size (rows, cols, input_depth).","code":""},{"path":"https://keras.posit.co/reference/constraint_minmaxnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MinMaxNorm weight constraint. — constraint_minmaxnorm","text":"Constraint instance, callable can passed layer constructors used directly calling tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/constraint_nonneg.html","id":null,"dir":"Reference","previous_headings":"","what":"Constrains the weights to be non-negative. — constraint_nonneg","title":"Constrains the weights to be non-negative. — constraint_nonneg","text":"Constrains weights non-negative.","code":""},{"path":"https://keras.posit.co/reference/constraint_nonneg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constrains the weights to be non-negative. — constraint_nonneg","text":"","code":"constraint_nonneg()"},{"path":"https://keras.posit.co/reference/constraint_nonneg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constrains the weights to be non-negative. — constraint_nonneg","text":"Constraint instance, callable can passed layer constructors used directly calling tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/constraint_unitnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","title":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","text":"Constrains weights incident hidden unit unit norm.","code":""},{"path":"https://keras.posit.co/reference/constraint_unitnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","text":"","code":"constraint_unitnorm(axis = 1L)"},{"path":"https://keras.posit.co/reference/constraint_unitnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","text":"axis integer, axis along calculate weight norms. instance, Dense layer weight matrix shape (input_dim, output_dim), set axis 0 constrain weight vector length (input_dim,). Conv2D layer data_format = \"channels_last\", weight tensor shape (rows, cols, input_depth, output_depth), set axis [0, 1, 2] constrain weights filter tensor size (rows, cols, input_depth).","code":""},{"path":"https://keras.posit.co/reference/constraint_unitnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","text":"Constraint instance, callable can passed layer constructors used directly calling tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/count_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Count the total number of scalars composing the weights. — count_params","title":"Count the total number of scalars composing the weights. — count_params","text":"Count total number scalars composing weights.","code":""},{"path":"https://keras.posit.co/reference/count_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count the total number of scalars composing the weights. — count_params","text":"","code":"count_params(object)"},{"path":"https://keras.posit.co/reference/count_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count the total number of scalars composing the weights. — count_params","text":"object Layer model object","code":""},{"path":"https://keras.posit.co/reference/count_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count the total number of scalars composing the weights. — count_params","text":"integer count","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/custom_metric.html","id":null,"dir":"Reference","previous_headings":"","what":"Custom metric function — custom_metric","title":"Custom metric function — custom_metric","text":"Custom metric function","code":""},{"path":"https://keras.posit.co/reference/custom_metric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Custom metric function — custom_metric","text":"","code":"custom_metric(name, metric_fn)"},{"path":"https://keras.posit.co/reference/custom_metric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Custom metric function — custom_metric","text":"name name used show training progress output metric_fn R function signature function(y_true, y_pred) accepts tensors.","code":""},{"path":"https://keras.posit.co/reference/custom_metric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Custom metric function — custom_metric","text":"callable function __name__ attribute.","code":""},{"path":"https://keras.posit.co/reference/custom_metric.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Custom metric function — custom_metric","text":"can provide arbitrary R function custom metric. Note y_true y_pred parameters tensors, computations use op_* tensor functions. Use custom_metric() function define custom metric. Note name ('mean_pred') provided custom metric function: name used within training progress output. want save load model custom metrics, also call register_keras_serializable(), specify metric call load_model(). example: load_model(\"my_model.keras\", c('mean_pred' = metric_mean_pred)). Alternatively, can wrap code call with_custom_object_scope() allow refer metric name just like built keras metrics. Alternative ways supplying custom metrics: custom_metric(): Arbitrary R function. metric_mean_wrapper(): Wrap arbitrary R function Metric instance. Create custom Metric() subclass.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_boston_housing.html","id":null,"dir":"Reference","previous_headings":"","what":"Boston housing price regression dataset — dataset_boston_housing","title":"Boston housing price regression dataset — dataset_boston_housing","text":"Dataset taken StatLib library maintained Carnegie Mellon University.","code":""},{"path":"https://keras.posit.co/reference/dataset_boston_housing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Boston housing price regression dataset — dataset_boston_housing","text":"","code":"dataset_boston_housing(   path = \"boston_housing.npz\",   test_split = 0.2,   seed = 113L )"},{"path":"https://keras.posit.co/reference/dataset_boston_housing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Boston housing price regression dataset — dataset_boston_housing","text":"path Path cache dataset locally (relative ~/.keras/datasets). test_split fraction data reserve test set. seed Random seed shuffling data computing test split.","code":""},{"path":"https://keras.posit.co/reference/dataset_boston_housing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Boston housing price regression dataset — dataset_boston_housing","text":"Lists training test data: train$x, train$y, test$x, test$y. Samples contain 13 attributes houses different locations around Boston suburbs late 1970s. Targets median values houses location (k$).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_cifar10.html","id":null,"dir":"Reference","previous_headings":"","what":"CIFAR10 small image classification — dataset_cifar10","title":"CIFAR10 small image classification — dataset_cifar10","text":"Dataset 50,000 32x32 color training images, labeled 10 categories, 10,000 test images.","code":""},{"path":"https://keras.posit.co/reference/dataset_cifar10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIFAR10 small image classification — dataset_cifar10","text":"","code":"dataset_cifar10()"},{"path":"https://keras.posit.co/reference/dataset_cifar10.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIFAR10 small image classification — dataset_cifar10","text":"Lists training test data: train$x, train$y, test$x, test$y. x data array RGB image data shape (num_samples, 3, 32, 32). y data array category labels (integers range 0-9) shape (num_samples).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_cifar100.html","id":null,"dir":"Reference","previous_headings":"","what":"CIFAR100 small image classification — dataset_cifar100","title":"CIFAR100 small image classification — dataset_cifar100","text":"Dataset 50,000 32x32 color training images, labeled 100 categories, 10,000 test images.","code":""},{"path":"https://keras.posit.co/reference/dataset_cifar100.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIFAR100 small image classification — dataset_cifar100","text":"","code":"dataset_cifar100(label_mode = c(\"fine\", \"coarse\"))"},{"path":"https://keras.posit.co/reference/dataset_cifar100.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIFAR100 small image classification — dataset_cifar100","text":"label_mode one \"fine\", \"coarse\".","code":""},{"path":"https://keras.posit.co/reference/dataset_cifar100.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIFAR100 small image classification — dataset_cifar100","text":"Lists training test data: train$x, train$y, test$x, test$y. x data array RGB image data shape (num_samples, 3, 32, 32). y data array category labels shape (num_samples).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_fashion_mnist.html","id":null,"dir":"Reference","previous_headings":"","what":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","title":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","text":"Dataset 60,000 28x28 grayscale images 10 fashion article classes, along test set 10,000 images. dataset can used drop-replacement MNIST. class labels encoded integers 0-9 correspond T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt,","code":""},{"path":"https://keras.posit.co/reference/dataset_fashion_mnist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","text":"","code":"dataset_fashion_mnist()"},{"path":"https://keras.posit.co/reference/dataset_fashion_mnist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","text":"Lists training test data: train$x, train$y, test$x, test$y, x array grayscale image data shape (num_samples, 28, 28) y array article labels (integers range 0-9) shape (num_samples).","code":""},{"path":"https://keras.posit.co/reference/dataset_fashion_mnist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","text":"Dataset 60,000 28x28 grayscale images 10 fashion categories, along test set 10,000 images. dataset can used drop-replacement MNIST. class labels : 0 - T-shirt/top 1 - Trouser 2 - Pullover 3 - Dress 4 - Coat 5 - Sandal 6 - Shirt 7 - Sneaker 8 - Bag 9 - Ankle boot","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":null,"dir":"Reference","previous_headings":"","what":"IMDB Movie reviews sentiment classification — dataset_imdb","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"Dataset 25,000 movies reviews IMDB, labeled sentiment (positive/negative). Reviews preprocessed, review encoded sequence word indexes (integers). convenience, words indexed overall frequency dataset, instance integer \"3\" encodes 3rd frequent word data. allows quick filtering operations : \"consider top 10,000 common words, eliminate top 20 common words\".","code":""},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"","code":"dataset_imdb(   path = \"imdb.npz\",   num_words = NULL,   skip_top = 0L,   maxlen = NULL,   seed = 113L,   start_char = 1L,   oov_char = 2L,   index_from = 3L )  dataset_imdb_word_index(path = \"imdb_word_index.json\")"},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"path cache data (relative ~/.keras/dataset). num_words Max number words include. Words ranked often occur (training set) frequent words kept skip_top Skip top N frequently occuring words (may informative). maxlen sequences longer filtered . seed random seed sample shuffling. start_char start sequence marked character. Set 1 0 usually padding character. oov_char Words cut num_words skip_top limit replaced character. index_from Index actual words index higher.","code":""},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"Lists training test data: train$x, train$y, test$x, test$y. x data includes integer sequences. num_words argument specific, maximum possible index value num_words-1. maxlen argument specified, largest possible sequence length maxlen. y data includes set integer labels (0 1). dataset_imdb_word_index() function returns list names words values integer.","code":""},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"convention, \"0\" stand specific word, instead used encode unknown word.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_mnist.html","id":null,"dir":"Reference","previous_headings":"","what":"MNIST database of handwritten digits — dataset_mnist","title":"MNIST database of handwritten digits — dataset_mnist","text":"Dataset 60,000 28x28 grayscale images 10 digits, along test set 10,000 images.","code":""},{"path":"https://keras.posit.co/reference/dataset_mnist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MNIST database of handwritten digits — dataset_mnist","text":"","code":"dataset_mnist(path = \"mnist.npz\")"},{"path":"https://keras.posit.co/reference/dataset_mnist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MNIST database of handwritten digits — dataset_mnist","text":"path Path cache dataset locally (relative ~/.keras/datasets).","code":""},{"path":"https://keras.posit.co/reference/dataset_mnist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MNIST database of handwritten digits — dataset_mnist","text":"Lists training test data: train$x, train$y, test$x, test$y, x array grayscale image data shape (num_samples, 28, 28) y array digit labels (integers range 0-9) shape (num_samples).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_reuters.html","id":null,"dir":"Reference","previous_headings":"","what":"Reuters newswire topics classification — dataset_reuters","title":"Reuters newswire topics classification — dataset_reuters","text":"Dataset 11,228 newswires Reuters, labeled 46 topics. dataset_imdb() , wire encoded sequence word indexes (conventions).","code":""},{"path":"https://keras.posit.co/reference/dataset_reuters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reuters newswire topics classification — dataset_reuters","text":"","code":"dataset_reuters(   path = \"reuters.npz\",   num_words = NULL,   skip_top = 0L,   maxlen = NULL,   test_split = 0.2,   seed = 113L,   start_char = 1L,   oov_char = 2L,   index_from = 3L )  dataset_reuters_word_index(path = \"reuters_word_index.pkl\")"},{"path":"https://keras.posit.co/reference/dataset_reuters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reuters newswire topics classification — dataset_reuters","text":"path cache data (relative ~/.keras/dataset). num_words Max number words include. Words ranked often occur (training set) frequent words kept skip_top Skip top N frequently occuring words (may informative). maxlen Truncate sequences length. test_split Fraction dataset used test data. seed Random seed sample shuffling. start_char start sequence marked character. Set 1 0 usually padding character. oov_char words cut num_words skip_top limit replaced character. index_from index actual words index higher.","code":""},{"path":"https://keras.posit.co/reference/dataset_reuters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reuters newswire topics classification — dataset_reuters","text":"Lists training test data: train$x, train$y, test$x, test$y format dataset_imdb(). dataset_reuters_word_index() function returns list names words values integer. e.g. word_index[[\"giraffe\"]] might return 1234.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/deserialize_keras_object.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","title":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","text":"config dict Python dictionary consists set key-value pairs, represents Keras object, Optimizer, Layer, Metrics, etc. saving loading library uses following keys record information Keras object: class_name: String. name class, exactly defined source code, \"LossesContainer\". config: Named List. Library-defined user-defined key-value pairs store configuration object, obtained object$get_config(). module: String. path python module. Built-Keras classes expect prefix keras. registered_name: String. key class registered via register_keras_serializable(package, name) API. key format '{package}>{name}', package name arguments passed register_keras_serializable(). name provided, uses class name. registered_name successfully resolves class (registered), class_name config values config dict used. registered_name used non-built-classes. example, following config list represents built-Adam optimizer relevant config:     class exported Keras namespace, library tracks module class_name. example:   following config represents user-customized MeanSquaredError loss:","code":"config <- list(   class_name = \"Adam\",   config = list(     amsgrad = FALSE,     beta_1 = 0.8999999761581421,     beta_2 = 0.9990000128746033,     epsilon = 1e-07,     learning_rate = 0.0010000000474974513,     name = \"Adam\"   ),   module = \"keras.optimizers\",   registered_name = NULL ) # Returns an `Adam` instance identical to the original one. deserialize_keras_object(config) ## <keras.src.optimizers.adam.Adam object> config <- list(   class_name = \"MetricsList\",   config =  list(     ...   ),   module = \"keras.trainers.compile_utils\",   registered_name = \"MetricsList\" )  # Returns a `MetricsList` instance identical to the original one. deserialize_keras_object(config) # define a custom object loss_modified_mse <- Loss(   \"ModifiedMeanSquaredError\",   inherit = loss_mean_squared_error)  # register the custom object register_keras_serializable(loss_modified_mse)  # confirm object is registered get_custom_objects() ## $`keras3>ModifiedMeanSquaredError` ## <class '<r-namespace:keras3>.ModifiedMeanSquaredError'> ##  signature: (reduction='sum_over_batch_size', name='mean_squared_error') get_registered_name(loss_modified_mse) ## [1] \"keras3>ModifiedMeanSquaredError\" # now custom object instances can be serialized full_config <- serialize_keras_object(loss_modified_mse())  # the `config` arguments will be passed to loss_modified_mse() str(full_config) ## List of 4 ##  $ module         : chr \"<r-namespace:keras3>\" ##  $ class_name     : chr \"ModifiedMeanSquaredError\" ##  $ config         :List of 2 ##   ..$ name     : chr \"mean_squared_error\" ##   ..$ reduction: chr \"sum_over_batch_size\" ##  $ registered_name: chr \"keras3>ModifiedMeanSquaredError\" # and custom object instances can be deserialized deserialize_keras_object(full_config) ## <<r-namespace:keras3>.ModifiedMeanSquaredError object> ##  signature: (y_true, y_pred, sample_weight=None) # Returns the `ModifiedMeanSquaredError` object"},{"path":"https://keras.posit.co/reference/deserialize_keras_object.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","text":"","code":"deserialize_keras_object(config, custom_objects = NULL, safe_mode = TRUE, ...)"},{"path":"https://keras.posit.co/reference/deserialize_keras_object.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","text":"config Named list describing object. custom_objects Named list containing mapping custom object names corresponding classes functions. safe_mode Boolean, whether disallow unsafe lambda deserialization. safe_mode=FALSE, loading object potential trigger arbitrary code execution. argument applicable Keras v3 model format. Defaults TRUE. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/deserialize_keras_object.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","text":"object described config dictionary.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/evaluate.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","title":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","text":"functions returns loss value metrics values model test mode. Computation done batches (see batch_size arg.)","code":""},{"path":"https://keras.posit.co/reference/evaluate.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model evaluate(   object,   x = NULL,   y = NULL,   ...,   batch_size = NULL,   verbose = getOption(\"keras.verbose\", default = \"auto\"),   sample_weight = NULL,   steps = NULL,   callbacks = NULL )"},{"path":"https://keras.posit.co/reference/evaluate.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","text":"object Keras model object x Input data. : R array (array-like), list arrays (case model multiple inputs). tensor, list tensors (case model multiple inputs). named list mapping input names corresponding array/tensors, model named inputs. tf.data.Dataset. return tuple either (inputs, targets) (inputs, targets, sample_weights). generator returning (inputs, targets) (inputs, targets, sample_weights). y Target data. Like input data x, either R array(s) backend-native tensor(s). x tf.data.Dataset generator function, y specified (since targets obtained iterator/dataset). ... forward/backward compatability. batch_size Integer NULL. Number samples per batch computation. unspecified, batch_size default 32. specify batch_size data form tf dataset generator (since generate batches). verbose \"auto\", 0, 1, 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. \"auto\" becomes 1 cases, 2 knitr render running distributed training server. Note progress bar particularly useful logged file, verbose=2 recommended running interactively (e.g. production environment). Defaults \"auto\". sample_weight Optional array weights test samples, used weighting loss function. can either pass flat (1D) R array length input samples (1:1 mapping weights samples), case temporal data, can pass 2D array shape (samples, sequence_length), apply different weight every timestep every sample. argument supported x tfdataset, instead pass sample weights third element x. steps Integer NULL. Total number steps (batches samples) declaring evaluation round finished. Ignored default value NULL. x tf.data.Dataset steps NULL, evaluation run dataset exhausted. callbacks List Callback instances. List callbacks apply evaluation.","code":""},{"path":"https://keras.posit.co/reference/evaluate.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","text":"Scalar test loss (model single output metrics) list scalars (model multiple outputs /metrics). attribute model$metrics_names give display labels scalar outputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"(e.g. via TF-Serving). Note: can currently used TensorFlow JAX backends. method lets export model lightweight SavedModel artifact contains model's forward pass (call() method) can served via e.g. TF-Serving. forward pass registered name serve() (see example ). original code model (including custom layers may used) longer necessary reload artifact -- entirely standalone.","code":""},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model export_savedmodel(object, export_dir_base, ...)"},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"object keras model. export_dir_base string, file path save artifact. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"called primarily side effect exporting object. first argument, object also returned, invisibly, enable usage pipe.","code":""},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"","code":"# Create the artifact model |> tensorflow::export_savedmodel(\"path/to/location\")  # Later, in a different process / environment... library(tensorflow) reloaded_artifact <- tf$saved_model$load(\"path/to/location\") predictions <- reloaded_artifact$serve(input_data)  # see tfdeploy::serve_savedmodel() for serving a model over a local web api."},{"path":[]},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"Train model fixed number epochs (dataset iterations).","code":""},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model fit(   object,   x = NULL,   y = NULL,   ...,   batch_size = NULL,   epochs = 1L,   callbacks = NULL,   validation_split = 0,   validation_data = NULL,   shuffle = TRUE,   class_weight = NULL,   sample_weight = NULL,   initial_epoch = 1L,   steps_per_epoch = NULL,   validation_steps = NULL,   validation_batch_size = NULL,   validation_freq = 1L,   verbose = getOption(\"keras.verbose\", default = \"auto\"),   view_metrics = getOption(\"keras.view_metrics\", default = \"auto\") )"},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"object Keras model object x Input data. : array (array-like), list arrays (case model multiple inputs). tensor, list tensors (case model multiple inputs). named list mapping input names corresponding array/tensors, model named inputs. tf.data.Dataset. return tuple either (inputs, targets) (inputs, targets, sample_weights). generator returning (inputs, targets) (inputs, targets, sample_weights). y Target data. Like input data x, either array(s) backend-native tensor(s). x TF Dataset generator, y specified (since targets obtained x). ... Additional arguments passed model fit() method. batch_size Integer NULL. Number samples per gradient update. unspecified, batch_size default 32. specify batch_size data form TF Datasets generators, (since generate batches). epochs Integer. Number epochs train model. epoch iteration entire x y data provided (unless steps_per_epoch flag set something NULL). Note conjunction initial_epoch, epochs understood \"final epoch\". model trained number iterations given epochs, merely epoch index epochs reached. callbacks List Callback() instances. List callbacks apply training. See callback_*. validation_split Float 0 1. Fraction training data used validation data. model set apart fraction training data, train , evaluate loss model metrics data end epoch. validation data selected last samples x y data provided, shuffling. argument supported x TF Dataset generator. validation_data validation_split provided, validation_data override validation_split. validation_data Data evaluate loss model metrics end epoch. model trained data. Thus, note fact validation loss data provided using validation_split validation_data affected regularization layers like noise dropout. validation_data override validation_split. : tuple (x_val, y_val) arrays tensors. tuple (x_val, y_val, val_sample_weights) arrays. generator returning (inputs, targets) (inputs, targets, sample_weights). shuffle Boolean, whether shuffle training data epoch. argument ignored x generator TF Dataset. class_weight Optional named list mapping class indices (integers, 0-based) weight (float) value, used weighting loss function (training ). can useful tell model \"pay attention\" samples -represented class. class_weight specified targets rank 2 greater, either y must one-hot encoded, explicit final dimension 1 must included sparse class labels. sample_weight Optional array weights training samples, used weighting loss function (training ). can either pass flat (1D) array/vector length input samples (1:1 mapping weights samples), case temporal data, can pass 2D array (matrix) shape (samples, sequence_length), apply different weight every timestep every sample. argument supported x TF Dataset generator, instead provide sample_weights third element x. Note sample weighting apply metrics specified via metrics argument compile(). apply sample weighting metrics, can specify via weighted_metrics compile() instead. initial_epoch Integer. Epoch start training (useful resuming previous training run). steps_per_epoch Integer NULL. Total number steps (batches samples) declaring one epoch finished starting next epoch. training input tensors backend-native tensors, default NULL equal number samples dataset divided batch size, 1 determined. x TF Dataset, steps_per_epoch NULL, epoch run input dataset exhausted.  passing infinitely repeating dataset, must specify steps_per_epoch argument. steps_per_epoch = -1 training run indefinitely infinitely repeating dataset. validation_steps relevant validation_data provided. Total number steps (batches samples) draw stopping performing validation end every epoch. validation_steps NULL, validation run validation_data dataset exhausted. case infinitely repeated dataset, run infinite loop. validation_steps specified part dataset consumed, evaluation start beginning dataset epoch. ensures validation samples used every time. validation_batch_size Integer NULL. Number samples per validation batch. unspecified, default batch_size. specify validation_batch_size data form TF Datasets generator instances (since generate batches). validation_freq relevant validation data provided. Specifies many training epochs run new validation run performed, e.g. validation_freq=2 runs validation every 2 epochs. verbose \"auto\", 0, 1, 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. \"auto\" becomes 1 cases, 2 knitr render running distributed training server. Note progress bar particularly useful logged file, verbose=2 recommended running interactively (e.g., production environment). Defaults \"auto\". view_metrics View realtime plot training metrics (epoch). default (\"auto\") display plot running within RStudio, metrics specified model compile(), epochs > 1 verbose > 0. Set global options(keras.view_metrics = ) option establish different default.","code":""},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"keras_training_history object, named list: list(params = <params>, metrics = <metrics>\"), S3 methods print(), plot(), .data.frame(). metrics field record training loss values metrics values successive epochs, well validation loss values validation metrics values (applicable).","code":""},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"Unpacking behavior iterator-like inputs: common pattern pass iterator like object tf.data.Dataset generator fit(), fact yield features (x) optionally targets (y) sample weights (sample_weight). Keras requires output iterator-likes unambiguous. iterator return tuple() length 1, 2, 3, optional second third elements used y sample_weight respectively. type provided wrapped length-one tuple(), effectively treating everything x. yielding named lists, still adhere top-level tuple structure, e.g. tuple(list(x0 = x0, x = x1), y). Keras attempt separate features, targets, weights keys single dict.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Freeze and unfreeze weights — freeze_weights","title":"Freeze and unfreeze weights — freeze_weights","text":"Freeze weights model layer longer trainable.","code":""},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Freeze and unfreeze weights — freeze_weights","text":"","code":"freeze_weights(object, from = NULL, to = NULL, which = NULL)  unfreeze_weights(object, from = NULL, to = NULL, which = NULL)"},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Freeze and unfreeze weights — freeze_weights","text":"object Keras model layer object Layer instance, layer name, layer index within model Layer instance, layer name, layer index within model layer names, integer positions, layers, logical vector (length(object$layers)), function returning logical vector.","code":""},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Freeze and unfreeze weights — freeze_weights","text":"input object frozen weights returned, invisibly. Note, object modified place, return value provided make usage pipe convenient.","code":""},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Freeze and unfreeze weights — freeze_weights","text":"layer arguments inclusive. applied model, freeze unfreeze global operation layers model (.e. layers within specified range set opposite value, e.g. unfrozen call freeze). Models must compiled weights frozen unfrozen.","code":""},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Freeze and unfreeze weights — freeze_weights","text":"","code":"# instantiate a VGG16 model conv_base <- application_vgg16(   weights = \"imagenet\",   include_top = FALSE,   input_shape = c(150, 150, 3) )  # freeze it's weights freeze_weights(conv_base)  # Note the \"Trainable\" column conv_base ## Model: \"vgg16\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | input_layer (InputLayer)    | (None, 150, 150, 3)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv1 (Conv2D)       | (None, 150, 150, 64)  |      1,792 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv2 (Conv2D)       | (None, 150, 150, 64)  |     36,928 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_pool (MaxPooling2D)  | (None, 75, 75, 64)    |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv1 (Conv2D)       | (None, 75, 75, 128)   |     73,856 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv2 (Conv2D)       | (None, 75, 75, 128)   |    147,584 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_pool (MaxPooling2D)  | (None, 37, 37, 128)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv1 (Conv2D)       | (None, 37, 37, 256)   |    295,168 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv2 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv3 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_pool (MaxPooling2D)  | (None, 18, 18, 256)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv1 (Conv2D)       | (None, 18, 18, 512)   |  1,180,160 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv2 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv3 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_pool (MaxPooling2D)  | (None, 9, 9, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv1 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv2 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv3 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_pool (MaxPooling2D)  | (None, 4, 4, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 14,714,688 (56.13 MB) ##  Trainable params: 0 (0.00 B) ##  Non-trainable params: 14,714,688 (56.13 MB) # create a composite model that includes the base + more layers model <- keras_model_sequential(input_batch_shape = shape(conv_base$input)) |>   conv_base() |>   layer_flatten() |>   layer_dense(units = 256, activation = \"relu\") |>   layer_dense(units = 1, activation = \"sigmoid\")  # compile model |> compile(   loss = \"binary_crossentropy\",   optimizer = optimizer_rmsprop(learning_rate = 2e-5),   metrics = c(\"accuracy\") )  model ## Model: \"sequential\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | vgg16 (Functional)          | (None, 4, 4, 512)     | 14,714,688 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | flatten (Flatten)           | (None, 8192)          |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense (Dense)               | (None, 256)           |  2,097,408 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense_1 (Dense)             | (None, 1)             |        257 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 16,812,353 (64.13 MB) ##  Trainable params: 2,097,665 (8.00 MB) ##  Non-trainable params: 14,714,688 (56.13 MB) print(model, expand_nested = TRUE) ## Model: \"sequential\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | vgg16 (Functional)          | (None, 4, 4, 512)     | 14,714,688 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > input_layer            | (None, 150, 150, 3)   |          0 |   -   | ## | (InputLayer)                |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_conv1 (Conv2D)  | (None, 150, 150, 64)  |      1,792 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_conv2 (Conv2D)  | (None, 150, 150, 64)  |     36,928 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_pool            | (None, 75, 75, 64)    |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_conv1 (Conv2D)  | (None, 75, 75, 128)   |     73,856 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_conv2 (Conv2D)  | (None, 75, 75, 128)   |    147,584 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_pool            | (None, 37, 37, 128)   |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv1 (Conv2D)  | (None, 37, 37, 256)   |    295,168 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv2 (Conv2D)  | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv3 (Conv2D)  | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_pool            | (None, 18, 18, 256)   |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv1 (Conv2D)  | (None, 18, 18, 512)   |  1,180,160 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv2 (Conv2D)  | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv3 (Conv2D)  | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_pool            | (None, 9, 9, 512)     |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv1 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv2 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv3 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_pool            | (None, 4, 4, 512)     |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## | flatten (Flatten)           | (None, 8192)          |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense (Dense)               | (None, 256)           |  2,097,408 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense_1 (Dense)             | (None, 1)             |        257 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 16,812,353 (64.13 MB) ##  Trainable params: 2,097,665 (8.00 MB) ##  Non-trainable params: 14,714,688 (56.13 MB) # unfreeze weights from \"block5_conv1\" on unfreeze_weights(conv_base, from = \"block5_conv1\")  # compile again since we froze or unfroze weights model |> compile(   loss = \"binary_crossentropy\",   optimizer = optimizer_rmsprop(learning_rate = 2e-5),   metrics = c(\"accuracy\") )  conv_base ## Model: \"vgg16\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | input_layer (InputLayer)    | (None, 150, 150, 3)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv1 (Conv2D)       | (None, 150, 150, 64)  |      1,792 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv2 (Conv2D)       | (None, 150, 150, 64)  |     36,928 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_pool (MaxPooling2D)  | (None, 75, 75, 64)    |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv1 (Conv2D)       | (None, 75, 75, 128)   |     73,856 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv2 (Conv2D)       | (None, 75, 75, 128)   |    147,584 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_pool (MaxPooling2D)  | (None, 37, 37, 128)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv1 (Conv2D)       | (None, 37, 37, 256)   |    295,168 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv2 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv3 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_pool (MaxPooling2D)  | (None, 18, 18, 256)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv1 (Conv2D)       | (None, 18, 18, 512)   |  1,180,160 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv2 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv3 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_pool (MaxPooling2D)  | (None, 9, 9, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv1 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv2 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv3 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_pool (MaxPooling2D)  | (None, 4, 4, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 14,714,688 (56.13 MB) ##  Trainable params: 7,079,424 (27.01 MB) ##  Non-trainable params: 7,635,264 (29.13 MB) print(model, expand_nested = TRUE) ## Model: \"sequential\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | vgg16 (Functional)          | (None, 4, 4, 512)     | 14,714,688 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > input_layer            | (None, 150, 150, 3)   |          0 |   -   | ## | (InputLayer)                |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_conv1 (Conv2D)  | (None, 150, 150, 64)  |      1,792 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_conv2 (Conv2D)  | (None, 150, 150, 64)  |     36,928 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_pool            | (None, 75, 75, 64)    |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_conv1 (Conv2D)  | (None, 75, 75, 128)   |     73,856 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_conv2 (Conv2D)  | (None, 75, 75, 128)   |    147,584 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_pool            | (None, 37, 37, 128)   |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv1 (Conv2D)  | (None, 37, 37, 256)   |    295,168 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv2 (Conv2D)  | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv3 (Conv2D)  | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_pool            | (None, 18, 18, 256)   |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv1 (Conv2D)  | (None, 18, 18, 512)   |  1,180,160 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv2 (Conv2D)  | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv3 (Conv2D)  | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_pool            | (None, 9, 9, 512)     |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv1 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv2 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv3 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_pool            | (None, 4, 4, 512)     |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## | flatten (Flatten)           | (None, 8192)          |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense (Dense)               | (None, 256)           |  2,097,408 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense_1 (Dense)             | (None, 1)             |        257 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 16,812,353 (64.13 MB) ##  Trainable params: 9,177,089 (35.01 MB) ##  Non-trainable params: 7,635,264 (29.13 MB) # freeze only the last 5 layers freeze_weights(conv_base, from = -5) conv_base ## Model: \"vgg16\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | input_layer (InputLayer)    | (None, 150, 150, 3)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv1 (Conv2D)       | (None, 150, 150, 64)  |      1,792 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv2 (Conv2D)       | (None, 150, 150, 64)  |     36,928 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_pool (MaxPooling2D)  | (None, 75, 75, 64)    |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv1 (Conv2D)       | (None, 75, 75, 128)   |     73,856 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv2 (Conv2D)       | (None, 75, 75, 128)   |    147,584 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_pool (MaxPooling2D)  | (None, 37, 37, 128)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv1 (Conv2D)       | (None, 37, 37, 256)   |    295,168 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv2 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv3 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_pool (MaxPooling2D)  | (None, 18, 18, 256)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv1 (Conv2D)       | (None, 18, 18, 512)   |  1,180,160 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv2 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv3 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_pool (MaxPooling2D)  | (None, 9, 9, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv1 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv2 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv3 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_pool (MaxPooling2D)  | (None, 4, 4, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 14,714,688 (56.13 MB) ##  Trainable params: 7,635,264 (29.13 MB) ##  Non-trainable params: 7,079,424 (27.01 MB) # freeze only the last 5 layers, a different way unfreeze_weights(conv_base, to = -6) conv_base ## Model: \"vgg16\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | input_layer (InputLayer)    | (None, 150, 150, 3)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv1 (Conv2D)       | (None, 150, 150, 64)  |      1,792 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv2 (Conv2D)       | (None, 150, 150, 64)  |     36,928 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_pool (MaxPooling2D)  | (None, 75, 75, 64)    |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv1 (Conv2D)       | (None, 75, 75, 128)   |     73,856 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv2 (Conv2D)       | (None, 75, 75, 128)   |    147,584 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_pool (MaxPooling2D)  | (None, 37, 37, 128)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv1 (Conv2D)       | (None, 37, 37, 256)   |    295,168 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv2 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv3 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_pool (MaxPooling2D)  | (None, 18, 18, 256)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv1 (Conv2D)       | (None, 18, 18, 512)   |  1,180,160 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv2 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv3 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_pool (MaxPooling2D)  | (None, 9, 9, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv1 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv2 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv3 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_pool (MaxPooling2D)  | (None, 4, 4, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 14,714,688 (56.13 MB) ##  Trainable params: 7,635,264 (29.13 MB) ##  Non-trainable params: 7,079,424 (27.01 MB) # Freeze only layers of a certain type, e.g, BatchNorm layers batch_norm_layer_class_name <- class(layer_batch_normalization())[1] is_batch_norm_layer <- function(x) inherits(x, batch_norm_layer_class_name)  model <- application_efficientnet_b0() freeze_weights(model, which = is_batch_norm_layer) # print(model)  # equivalent to: for(layer in model$layers) {   if(is_batch_norm_layer(layer))     layer$trainable <- FALSE   else     layer$trainable <- TRUE }"},{"path":"https://keras.posit.co/reference/get_config.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer/Model configuration — get_config","title":"Layer/Model configuration — get_config","text":"layer config object returned get_config() contains configuration layer model. layer model can reinstantiated later (without trained weights) configuration using from_config(). config include connectivity information, class name (handled externally).","code":""},{"path":"https://keras.posit.co/reference/get_config.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer/Model configuration — get_config","text":"","code":"get_config(object)  from_config(config, custom_objects = NULL)"},{"path":"https://keras.posit.co/reference/get_config.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer/Model configuration — get_config","text":"object Layer model object config Object layer model configuration custom_objects list custom objects needed instantiate layer, e.g., custom layers defined new_layer_class() similar.","code":""},{"path":"https://keras.posit.co/reference/get_config.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer/Model configuration — get_config","text":"get_config() returns object configuration, from_config() returns re-instantiation object.","code":""},{"path":"https://keras.posit.co/reference/get_config.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layer/Model configuration — get_config","text":"Objects returned get_config() serializable via RDS. want save restore model across sessions, can use save_model_config() (model configuration , weights) save_model() save model configuration weights filesystem.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":null,"dir":"Reference","previous_headings":"","what":"Get/set the currently registered custom objects. — get_custom_objects","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"Custom objects set using custom_object_scope() added global list custom objects, appear returned list.","code":""},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"","code":"get_custom_objects()  set_custom_objects(objects = named_list(), clear = TRUE)"},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"objects named list custom objects, returned get_custom_objects() set_custom_objects(). clear bool, whether clear custom object registry populating objects.","code":""},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"R named list mapping registered names registered objects. set_custom_objects() returns registry values updating, invisibly.","code":""},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"register_keras_serializable() preferred set_custom_objects() registering new objects.","code":""},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"can use set_custom_objects() restore previous registry state.","code":"get_custom_objects() # within a function, if you want to temporarily modify the registry, function() {   orig_objects <- set_custom_objects(clear = TRUE)   on.exit(set_custom_objects(orig_objects))    ## temporarily modify the global registry   # register_keras_serializable(....)   # ....  <do work>   # on.exit(), the previous registry state is restored. }"},{"path":[]},{"path":"https://keras.posit.co/reference/get_file.html","id":null,"dir":"Reference","previous_headings":"","what":"Downloads a file from a URL if it not already in the cache. — get_file","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"default file url origin downloaded cache_dir ~/.keras, placed cache_subdir datasets, given filename fname. final location file example.txt therefore ~/.keras/datasets/example.txt. Files .tar, .tar.gz, .tar.bz, .zip formats can also extracted. Passing hash verify file download. command line programs shasum sha256sum can compute hash.","code":""},{"path":"https://keras.posit.co/reference/get_file.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"","code":"get_file(   fname = NULL,   origin = NULL,   ...,   file_hash = NULL,   cache_subdir = \"datasets\",   hash_algorithm = \"auto\",   extract = FALSE,   archive_format = \"auto\",   cache_dir = NULL,   force_download = FALSE )"},{"path":"https://keras.posit.co/reference/get_file.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"fname Name file. absolute path, e.g. \"/path//file.txt\" specified, file saved location. NULL, name file origin used. origin Original URL file. ... forward/backward compatability. file_hash expected hash string file download. sha256 md5 hash algorithms supported. cache_subdir Subdirectory Keras cache dir file saved. absolute path, e.g. \"/path//folder\" specified, file saved location. hash_algorithm Select hash algorithm verify file. options \"md5', \"sha256', \"auto'. default 'auto' detects hash algorithm use. extract TRUE tries extracting file Archive, like tar zip. archive_format Archive format try extracting file. Options \"auto', \"tar', \"zip', NULL. \"tar\" includes tar, tar.gz, tar.bz files. default \"auto\" corresponds c(\"tar\", \"zip\"). NULL empty list return matches found. cache_dir Location store cached files, NULL defaults Sys.getenv(\"KERAS_HOME\", \"~/.keras/\"). force_download TRUE, file always re-downloaded regardless cache state.","code":""},{"path":"https://keras.posit.co/reference/get_file.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"Path downloaded file. ** Warning malicious downloads ** Downloading something Internet carries risk. NEVER download file/archive trust source. recommend specify file_hash argument (hash source file known) make sure file getting one expect.","code":""},{"path":"https://keras.posit.co/reference/get_file.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"","code":"path_to_downloaded_file <- get_file(     origin = \"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\",     extract = TRUE )"},{"path":[]},{"path":"https://keras.posit.co/reference/get_layer.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieves a layer based on either its name (unique) or index. — get_layer","title":"Retrieves a layer based on either its name (unique) or index. — get_layer","text":"Indices based order horizontal graph traversal (bottom-) 1-based. name index provided, index take precedence.","code":""},{"path":"https://keras.posit.co/reference/get_layer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieves a layer based on either its name (unique) or index. — get_layer","text":"","code":"get_layer(object, name = NULL, index = NULL)"},{"path":"https://keras.posit.co/reference/get_layer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieves a layer based on either its name (unique) or index. — get_layer","text":"object Keras model object name String, name layer. index Integer, index layer (1-based). Also valid negative values, count end model.","code":""},{"path":"https://keras.posit.co/reference/get_layer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieves a layer based on either its name (unique) or index. — get_layer","text":"layer instance.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/get_registered_name.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the name registered to an object within the Keras framework. — get_registered_name","title":"Returns the name registered to an object within the Keras framework. — get_registered_name","text":"function part Keras serialization deserialization framework. maps objects string names associated objects serialization/deserialization.","code":""},{"path":"https://keras.posit.co/reference/get_registered_name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the name registered to an object within the Keras framework. — get_registered_name","text":"","code":"get_registered_name(obj)"},{"path":"https://keras.posit.co/reference/get_registered_name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the name registered to an object within the Keras framework. — get_registered_name","text":"obj object look .","code":""},{"path":"https://keras.posit.co/reference/get_registered_name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the name registered to an object within the Keras framework. — get_registered_name","text":"name associated object, default name object registered.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the class associated with name if it is registered with Keras. — get_registered_object","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"function part Keras serialization deserialization framework. maps strings objects associated serialization/deserialization.","code":""},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"","code":"get_registered_object(name, custom_objects = NULL, module_objects = NULL)"},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"name name look . custom_objects named list custom objects look name . Generally, custom_objects provided user. module_objects named list custom objects look name . Generally, module_objects provided midlevel library implementers.","code":""},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"instantiable class associated name, NULL class exists.","code":""},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"","code":"from_config <- function(cls, config, custom_objects = NULL) {   if ('my_custom_object_name' \\%in\\% names(config)) {     config$hidden_cls <- get_registered_object(       config$my_custom_object_name,       custom_objects = custom_objects)   } }"},{"path":[]},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"Output always list tensors (potentially 1 element).","code":""},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"","code":"get_source_inputs(tensor)"},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"tensor tensor start .","code":""},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"List input tensors.","code":""},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"","code":"input <- keras_input(c(3)) output <- input |> layer_dense(4) |> op_multiply(5) reticulate::py_id(get_source_inputs(output)[[1]]) == reticulate::py_id(input) ## [1] TRUE"},{"path":[]},{"path":"https://keras.posit.co/reference/get_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer/Model weights as R arrays — get_weights","title":"Layer/Model weights as R arrays — get_weights","text":"Layer/Model weights R arrays","code":""},{"path":"https://keras.posit.co/reference/get_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer/Model weights as R arrays — get_weights","text":"","code":"get_weights(object, trainable = NA)  set_weights(object, weights)"},{"path":"https://keras.posit.co/reference/get_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer/Model weights as R arrays — get_weights","text":"object Layer model object trainable NA (default), weights returned. TRUE, weights trainable variables returned. FALSE, weights non-trainable variables returned. weights Weights R array","code":""},{"path":"https://keras.posit.co/reference/get_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer/Model weights as R arrays — get_weights","text":"list R arrays.","code":""},{"path":"https://keras.posit.co/reference/get_weights.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layer/Model weights as R arrays — get_weights","text":"can access Layer/Model KerasVariables (also backend-native tensors like tf.Variable) object$weights, object$trainable_weights, object$non_trainable_weights","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":null,"dir":"Reference","previous_headings":"","what":"Make a python class constructor — %py_class%","title":"Make a python class constructor — %py_class%","text":"Make python class constructor","code":""},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make a python class constructor — %py_class%","text":"","code":"spec %py_class% body"},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make a python class constructor — %py_class%","text":"spec bare symbol MyClassName, call MyClassName(SuperClass) body expression can evaluated construct class methods.","code":""},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make a python class constructor — %py_class%","text":"python class constructor, invisibly. Note, constructor also assigned parent frame.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make a python class constructor — %py_class%","text":"","code":"if (FALSE) { MyClass %py_class% {   initialize <- function(x) {     print(\"Hi from MyClass$initialize()!\")     self$x <- x   }   my_method <- function() {     self$x   } }  my_class_instance <- MyClass(42) my_class_instance$my_method()  MyClass2(MyClass) %py_class% {   \"This will be a __doc__ string for MyClass2\"    initialize <- function(...) {     \"This will be the __doc__ string for the MyClass2.__init__() method\"     print(\"Hi from MyClass2$initialize()!\")     super$initialize(...)   } }  my_class_instance2 <- MyClass2(42) my_class_instance2$my_method()  reticulate::py_help(MyClass2) # see the __doc__ strings and more!  # In addition to `self`, there is also `private` available. # This is an R environment unique to each class instance, where you can # store objects that you don't want converted to Python, but still want # available from methods. You can also assign methods to private, and # `self` and `private` will be available in private methods.  MyClass %py_class% {    initialize <- function(x) {     print(\"Hi from MyClass$initialize()!\")     private$y <- paste(\"A Private field:\", x)   }    get_private_field <- function() {     private$y   }    private$a_private_method <- function() {     cat(\"a_private_method() was called.\\n\")     cat(\"private$y is \", sQuote(private$y), \"\\n\")   }    call_private_method <- function()     private$a_private_method()    # equivalent of @property decorator in python   an_active_property %<-active% function(x = NULL) {     if(!is.null(x)) {       cat(\"`an_active_property` was assigned\", x, \"\\n\")       return(x)     } else {       cat(\"`an_active_property` was accessed\\n\")       return(42)     }   } }  inst1 <- MyClass(1) inst2 <- MyClass(2) inst1$get_private_field() inst2$get_private_field() inst1$call_private_method() inst2$call_private_method() inst1$an_active_property inst1$an_active_property <- 11 }"},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":null,"dir":"Reference","previous_headings":"","what":"Make an Active Binding — %<-active%","title":"Make an Active Binding — %<-active%","text":"Make Active Binding","code":""},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make an Active Binding — %<-active%","text":"","code":"sym %<-active% value"},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make an Active Binding — %<-active%","text":"sym symbol bind value function call value sym accessed.","code":""},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make an Active Binding — %<-active%","text":"value, invisibly","code":""},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make an Active Binding — %<-active%","text":"Active bindings defined %py_class% converted @property decorated methods.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make an Active Binding — %<-active%","text":"","code":"set.seed(1234) x %<-active% function(value) {   message(\"Evaluating function of active binding\")   if(missing(value))     runif(1)   else    message(\"Received: \", value) } x #> Evaluating function of active binding #> [1] 0.1137034 x #> Evaluating function of active binding #> [1] 0.6222994 x <- \"foo\" #> Evaluating function of active binding #> Received: foo x <- \"foo\" #> Evaluating function of active binding #> Received: foo x #> Evaluating function of active binding #> [1] 0.6092747 rm(x) # cleanup"},{"path":"https://keras.posit.co/reference/image_array_save.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves an image stored as an array to a path or file object. — image_array_save","title":"Saves an image stored as an array to a path or file object. — image_array_save","text":"Saves image stored array path file object.","code":""},{"path":"https://keras.posit.co/reference/image_array_save.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves an image stored as an array to a path or file object. — image_array_save","text":"","code":"image_array_save(   x,   path,   data_format = NULL,   file_format = NULL,   scale = TRUE,   ... )"},{"path":"https://keras.posit.co/reference/image_array_save.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves an image stored as an array to a path or file object. — image_array_save","text":"x array. path Path file object. data_format Image data format, either \"channels_first\" \"channels_last\". file_format Optional file format override. omitted, format use determined filename extension. file object used instead filename, parameter always used. scale Whether rescale image values within [0, 255]. ... Additional keyword arguments passed PIL.Image.save().","code":""},{"path":"https://keras.posit.co/reference/image_array_save.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Saves an image stored as an array to a path or file object. — image_array_save","text":"Called primarily side effects. input x returned, invisibly, enable usage pipe.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/image_dataset_from_directory.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","title":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","text":"directory structure :   calling image_dataset_from_directory(main_directory, labels = 'inferred') return tf.data.Dataset yields batches images subdirectories class_a class_b, together labels 0 1 (0 corresponding class_a 1 corresponding class_b). Supported image formats: .jpeg, .jpg, .png, .bmp, .gif. Animated gifs truncated first frame.","code":"main_directory/ ...class_a/ ......a_image_1.jpg ......a_image_2.jpg ...class_b/ ......b_image_1.jpg ......b_image_2.jpg"},{"path":"https://keras.posit.co/reference/image_dataset_from_directory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","text":"","code":"image_dataset_from_directory(   directory,   labels = \"inferred\",   label_mode = \"int\",   class_names = NULL,   color_mode = \"rgb\",   batch_size = 32L,   image_size = c(256L, 256L),   shuffle = TRUE,   seed = NULL,   validation_split = NULL,   subset = NULL,   interpolation = \"bilinear\",   follow_links = FALSE,   crop_to_aspect_ratio = FALSE,   pad_to_aspect_ratio = FALSE,   data_format = NULL,   verbose = TRUE )"},{"path":"https://keras.posit.co/reference/image_dataset_from_directory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","text":"directory Directory data located. labels \"inferred\", contain subdirectories, containing images class. Otherwise, directory structure ignored. labels Either \"inferred\" (labels generated directory structure), NULL (labels), list/tuple integer labels size number image files found directory. Labels sorted according alphanumeric order image file paths (obtained via os.walk(directory) Python). label_mode String describing encoding labels. Options : \"int\": means labels encoded integers (e.g. sparse_categorical_crossentropy loss). \"categorical\" means labels encoded categorical vector (e.g. categorical_crossentropy loss). \"binary\" means labels (can 2) encoded float32 scalars values 0 1 (e.g. binary_crossentropy). NULL (labels). class_names valid labels \"inferred\". explicit list class names (must match names subdirectories). Used control order classes (otherwise alphanumerical order used). color_mode One \"grayscale\", \"rgb\", \"rgba\". Defaults \"rgb\". Whether images converted 1, 3, 4 channels. batch_size Size batches data. Defaults 32. NULL, data batched (dataset yield individual samples). image_size Size resize images read disk, specified (height, width). Defaults (256, 256). Since pipeline processes batches images must size, must provided. shuffle Whether shuffle data. Defaults TRUE. set FALSE, sorts data alphanumeric order. seed Optional random seed shuffling transformations. validation_split Optional float 0 1, fraction data reserve validation. subset Subset data return. One \"training\", \"validation\", \"\". used validation_split set. subset = \"\", utility returns tuple two datasets (training validation datasets respectively). interpolation String, interpolation method used resizing images. Defaults \"bilinear\". Supports \"bilinear\", \"nearest\", \"bicubic\", \"area\", \"lanczos3\", \"lanczos5\", \"gaussian\", \"mitchellcubic\". follow_links Whether visit subdirectories pointed symlinks. Defaults FALSE. crop_to_aspect_ratio TRUE, resize images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image cropped return largest possible window image (size image_size) matches target aspect ratio. default (crop_to_aspect_ratio = FALSE), aspect ratio may preserved. pad_to_aspect_ratio TRUE, resize images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image padded return largest possible window image (size image_size) matches target aspect ratio. default (pad_to_aspect_ratio=FALSE), aspect ratio may preserved. data_format NULL uses config_image_data_format() otherwise either 'channel_last' 'channel_first'. verbose Whether display number information classes number files found. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/image_dataset_from_directory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","text":"tf.data.Dataset object. label_mode NULL, yields float32 tensors shape (batch_size, image_size[1], image_size[2], num_channels), encoding images (see rules regarding num_channels). Otherwise, yields tuple (images, labels), images shape (batch_size, image_size[1], image_size[2], num_channels), labels follows format described . Rules regarding labels format: label_mode \"int\", labels int32 tensor shape (batch_size,). label_mode \"binary\", labels float32 tensor 1s 0s shape (batch_size, 1). label_mode \"categorical\", labels float32 tensor shape (batch_size, num_classes), representing one-hot encoding class index. Rules regarding number channels yielded images: color_mode \"grayscale\", 1 channel image tensors. color_mode \"rgb\", 3 channels image tensors. color_mode \"rgba\", 4 channels image tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/image_from_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts a 3D array to a PIL Image instance. — image_from_array","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"Converts 3D array PIL Image instance.","code":""},{"path":"https://keras.posit.co/reference/image_from_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"","code":"image_from_array(x, data_format = NULL, scale = TRUE, dtype = NULL)"},{"path":"https://keras.posit.co/reference/image_from_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"x Input data, form can converted array. data_format Image data format, can either \"channels_first\" \"channels_last\". Defaults NULL, case global setting config_image_data_format() used (unless changed , defaults \"channels_last\"). scale Whether rescale image minimum maximum values 0 255 respectively. Defaults TRUE. dtype Dtype use. NULL means global setting config_floatx() used (unless changed , defaults \"float32\"). Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/image_from_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"PIL Image instance.","code":""},{"path":"https://keras.posit.co/reference/image_from_array.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"","code":"img <- array(runif(30000), dim = c(100, 100, 3)) pil_img <- image_from_array(img) pil_img ## <PIL.Image.Image image mode=RGB size=100x100>"},{"path":[]},{"path":"https://keras.posit.co/reference/image_load.html","id":null,"dir":"Reference","previous_headings":"","what":"Loads an image into PIL format. — image_load","title":"Loads an image into PIL format. — image_load","text":"Loads image PIL format.","code":""},{"path":"https://keras.posit.co/reference/image_load.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loads an image into PIL format. — image_load","text":"","code":"image_load(   path,   color_mode = \"rgb\",   target_size = NULL,   interpolation = \"nearest\",   keep_aspect_ratio = FALSE )"},{"path":"https://keras.posit.co/reference/image_load.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loads an image into PIL format. — image_load","text":"path Path image file. color_mode One \"grayscale\", \"rgb\", \"rgba\". Default: \"rgb\". desired image format. target_size Either NULL (default original size) tuple ints (img_height, img_width). interpolation Interpolation method used resample image target size different loaded image. Supported methods \"nearest\", \"bilinear\", \"bicubic\". PIL version 1.1.3 newer installed, \"lanczos\" also supported. PIL version 3.4.0 newer installed, \"box\" \"hamming\" also supported. default, \"nearest\" used. keep_aspect_ratio Boolean, whether resize images target size without aspect ratio distortion. image cropped center target aspect ratio resizing.","code":""},{"path":"https://keras.posit.co/reference/image_load.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loads an image into PIL format. — image_load","text":"PIL Image instance.","code":""},{"path":"https://keras.posit.co/reference/image_load.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Loads an image into PIL format. — image_load","text":"","code":"image_path <- get_file(origin = \"https://www.r-project.org/logo/Rlogo.png\") (image <- image_load(image_path)) ## <PIL.Image.Image image mode=RGB size=724x561> input_arr <- image_to_array(image) str(input_arr) ##  num [1:561, 1:724, 1:3] 0 0 0 0 0 0 0 0 0 0 ... input_arr %<>% array_reshape(dim = c(1, dim(input_arr))) # Convert single image to a batch. model |> predict(input_arr)"},{"path":[]},{"path":"https://keras.posit.co/reference/image_smart_resize.html","id":null,"dir":"Reference","previous_headings":"","what":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","title":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","text":"Image datasets typically yield images different size. However, images need batched can processed Keras layers. batched, images need share height width. simply , TF (JAX equivalent):   However, , distort aspect ratio images, since general aspect ratio size. fine many cases, always (e.g. image generation models can problem). Note passing argument preserve_aspect_ratio = TRUE tf$image$resize() preserve aspect ratio, cost longer respecting provided target size. calls :   output images actually (200, 200), distorted. Instead, parts image fit within target size get cropped . resizing process : Take largest centered crop image aspect ratio target size. instance, size = c(200, 200) input image size (340, 500), take crop (340, 340) centered along width. Resize cropped image target size. example , resize (340, 340) crop (200, 200).","code":"size <- c(200, 200) ds <- ds$map(\\(img) tf$image$resize(img, size)) size <- c(200, 200) ds <- ds$map(\\(img) image_smart_resize(img, size))"},{"path":"https://keras.posit.co/reference/image_smart_resize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","text":"","code":"image_smart_resize(   x,   size,   interpolation = \"bilinear\",   data_format = \"channels_last\",   backend_module = NULL )"},{"path":"https://keras.posit.co/reference/image_smart_resize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","text":"x Input image batch images (tensor array). Must format (height, width, channels) (batch_size, height, width, channels). size Tuple (height, width) integer. Target size. interpolation String, interpolation use resizing. Defaults 'bilinear'. Supports bilinear, nearest, bicubic, lanczos3, lanczos5. data_format \"channels_last\" \"channels_first\". backend_module Backend module use (different default backend).","code":""},{"path":"https://keras.posit.co/reference/image_smart_resize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","text":"Array shape (size[1], size[2], channels). input image array, output array, backend-native tensor, output backend-native tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/image_to_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts a PIL Image instance to a matrix. — image_to_array","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"Converts PIL Image instance matrix.","code":""},{"path":"https://keras.posit.co/reference/image_to_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"","code":"image_to_array(img, data_format = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/image_to_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"img Input PIL Image instance. data_format Image data format, can either \"channels_first\" \"channels_last\". Defaults NULL, case global setting config_image_data_format() used (unless changed , defaults \"channels_last\"). dtype Dtype use. NULL means global setting config_floatx() used (unless changed , defaults \"float32\").","code":""},{"path":"https://keras.posit.co/reference/image_to_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"3D array.","code":""},{"path":"https://keras.posit.co/reference/image_to_array.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"","code":"image_path <- get_file(origin = \"https://www.r-project.org/logo/Rlogo.png\") (img <- image_load(image_path)) ## <PIL.Image.Image image mode=RGB size=724x561> array <- image_to_array(img) str(array) ##  num [1:561, 1:724, 1:3] 0 0 0 0 0 0 0 0 0 0 ..."},{"path":[]},{"path":"https://keras.posit.co/reference/imagenet_decode_predictions.html","id":null,"dir":"Reference","previous_headings":"","what":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","title":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","text":"Decodes prediction ImageNet model.","code":""},{"path":"https://keras.posit.co/reference/imagenet_decode_predictions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","text":"","code":"imagenet_decode_predictions(preds, top = 5)"},{"path":"https://keras.posit.co/reference/imagenet_decode_predictions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","text":"preds Tensor encoding batch predictions. top integer, many top-guesses return.","code":""},{"path":"https://keras.posit.co/reference/imagenet_decode_predictions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","text":"List data frames variables class_name, class_description, score (one data frame per sample batch input).","code":""},{"path":"https://keras.posit.co/reference/imagenet_preprocess_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","title":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","text":"Preprocesses tensor array encoding batch images.","code":""},{"path":"https://keras.posit.co/reference/imagenet_preprocess_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","text":"","code":"imagenet_preprocess_input(x, data_format = NULL, mode = \"caffe\")"},{"path":"https://keras.posit.co/reference/imagenet_preprocess_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","text":"x Input Numpy symbolic tensor, 3D 4D. data_format Data format image tensor/array. mode One \"caffe\", \"tf\", \"torch\" caffe: convert images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling. tf: scale pixels -1 1, sample-wise. torch: scale pixels 0 1 normalize channel respect ImageNet dataset.","code":""},{"path":"https://keras.posit.co/reference/imagenet_preprocess_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","text":"Preprocessed tensor array.","code":""},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates tensors with constant values. — initializer_constant","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"scalar values allowed. constant value provided must convertible dtype requested calling initializer.","code":""},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"","code":"initializer_constant(value = 0)"},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"value numeric scalar.","code":""},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"","code":"# Standalone usage: initializer <- initializer_constant(10) values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_constant(10) layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"Draws samples truncated normal distribution centered 0 stddev = sqrt(2 / (fan_in + fan_out)) fan_in number input units weight tensor fan_out number output units weight tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"","code":"initializer_glorot_normal(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"","code":"# Standalone usage: initializer <- initializer_glorot_normal() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_glorot_normal() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"Glorot et al., 2010","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"Draws samples uniform distribution within [-limit, limit], limit = sqrt(6 / (fan_in + fan_out)) (fan_in number input units weight tensor fan_out number output units).","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"","code":"initializer_glorot_uniform(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"","code":"# Standalone usage: initializer <- initializer_glorot_uniform() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_glorot_uniform() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"Glorot et al., 2010","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"He normal initializer. — initializer_he_normal","title":"He normal initializer. — initializer_he_normal","text":"draws samples truncated normal distribution centered 0 stddev = sqrt(2 / fan_in) fan_in number input units weight tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"He normal initializer. — initializer_he_normal","text":"","code":"initializer_he_normal(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"He normal initializer. — initializer_he_normal","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"He normal initializer. — initializer_he_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"He normal initializer. — initializer_he_normal","text":"","code":"# Standalone usage: initializer <- initializer_he_normal() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_he_normal() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"He normal initializer. — initializer_he_normal","text":"et al., 2015","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"He uniform variance scaling initializer. — initializer_he_uniform","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"Draws samples uniform distribution within [-limit, limit], limit = sqrt(6 / fan_in) (fan_in number input units weight tensor).","code":""},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"","code":"initializer_he_uniform(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"","code":"# Standalone usage: initializer <- initializer_he_uniform() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_he_uniform() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"et al., 2015","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates the identity matrix. — initializer_identity","title":"Initializer that generates the identity matrix. — initializer_identity","text":"usable generating 2D matrices.","code":""},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates the identity matrix. — initializer_identity","text":"","code":"initializer_identity(gain = 1)"},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that generates the identity matrix. — initializer_identity","text":"gain Multiplicative factor apply identity matrix.","code":""},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates the identity matrix. — initializer_identity","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates the identity matrix. — initializer_identity","text":"","code":"# Standalone usage: initializer <- initializer_identity() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_identity() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Lecun normal initializer. — initializer_lecun_normal","title":"Lecun normal initializer. — initializer_lecun_normal","text":"Initializers allow pre-specify initialization strategy, encoded Initializer object, without knowing shape dtype variable initialized. Draws samples truncated normal distribution centered 0 stddev = sqrt(1 / fan_in) fan_in number input units weight tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lecun normal initializer. — initializer_lecun_normal","text":"","code":"initializer_lecun_normal(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lecun normal initializer. — initializer_lecun_normal","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Lecun normal initializer. — initializer_lecun_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lecun normal initializer. — initializer_lecun_normal","text":"","code":"# Standalone usage: initializer <- initializer_lecun_normal() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_lecun_normal() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Lecun normal initializer. — initializer_lecun_normal","text":"Klambauer et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"Lecun uniform initializer. — initializer_lecun_uniform","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"Draws samples uniform distribution within [-limit, limit], limit = sqrt(3 / fan_in) (fan_in number input units weight tensor).","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"","code":"initializer_lecun_uniform(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"","code":"# Standalone usage: initializer <- initializer_lecun_uniform() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_lecun_uniform() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"Klambauer et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_ones.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates tensors initialized to 1. — initializer_ones","title":"Initializer that generates tensors initialized to 1. — initializer_ones","text":"Also available via shortcut function ones.","code":""},{"path":"https://keras.posit.co/reference/initializer_ones.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates tensors initialized to 1. — initializer_ones","text":"","code":"initializer_ones()"},{"path":"https://keras.posit.co/reference/initializer_ones.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates tensors initialized to 1. — initializer_ones","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_ones.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates tensors initialized to 1. — initializer_ones","text":"","code":"# Standalone usage: initializer <- initializer_ones() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_ones() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"shape tensor initialize two-dimensional, initialized orthogonal matrix obtained QR decomposition matrix random numbers drawn normal distribution. matrix fewer rows columns output orthogonal rows. Otherwise, output orthogonal columns. shape tensor initialize two-dimensional, matrix shape (shape[1] * ... * shape[n - 1], shape[n]) initialized, n length shape vector. matrix subsequently reshaped give tensor desired shape.","code":""},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"","code":"initializer_orthogonal(gain = 1, seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"gain Multiplicative factor apply orthogonal matrix. seed integer. Used make behavior initializer deterministic.","code":""},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"","code":"# Standalone usage: initializer <- initializer_orthogonal() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_orthogonal() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"Saxe et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Random normal initializer. — initializer_random_normal","title":"Random normal initializer. — initializer_random_normal","text":"Draws samples normal distribution given parameters.","code":""},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random normal initializer. — initializer_random_normal","text":"","code":"initializer_random_normal(mean = 0, stddev = 0.05, seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random normal initializer. — initializer_random_normal","text":"mean numeric scalar. Mean random values generate. stddev numeric scalar. Standard deviation random values generate. seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random normal initializer. — initializer_random_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random normal initializer. — initializer_random_normal","text":"","code":"# Standalone usage: initializer <- initializer_random_normal(mean = 0.0, stddev = 1.0) values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_random_normal(mean = 0.0, stddev = 1.0) layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"Random uniform initializer. — initializer_random_uniform","title":"Random uniform initializer. — initializer_random_uniform","text":"Draws samples uniform distribution given parameters.","code":""},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random uniform initializer. — initializer_random_uniform","text":"","code":"initializer_random_uniform(minval = -0.05, maxval = 0.05, seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random uniform initializer. — initializer_random_uniform","text":"minval numeric scalar scalar keras tensor. Lower bound range random values generate (inclusive). maxval numeric scalar scalar keras tensor. Upper bound range random values generate (exclusive). seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random uniform initializer. — initializer_random_uniform","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random uniform initializer. — initializer_random_uniform","text":"","code":"# Standalone usage: initializer <- initializer_random_uniform(minval = 0.0, maxval = 1.0) values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_random_uniform(minval = 0.0, maxval = 1.0) layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"values generated similar values RandomNormal initializer, except values two standard deviations mean discarded re-drawn.","code":""},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"","code":"initializer_truncated_normal(mean = 0, stddev = 0.05, seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"mean numeric scalar. Mean random values generate. stddev numeric scalar. Standard deviation random values generate. seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"","code":"# Standalone usage: initializer <- initializer_truncated_normal(mean = 0, stddev = 1) values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_truncated_normal(mean = 0, stddev = 1) layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"distribution = \"truncated_normal\" \"untruncated_normal\", samples drawn truncated/untruncated normal distribution mean zero standard deviation (truncation, used) stddev = sqrt(scale / n), n : number input units weight tensor, mode = \"fan_in\" number output units, mode = \"fan_out\" average numbers input output units, mode = \"fan_avg\" distribution = \"uniform\", samples drawn uniform distribution within [-limit, limit], limit = sqrt(3 * scale / n).","code":""},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"","code":"initializer_variance_scaling(   scale = 1,   mode = \"fan_in\",   distribution = \"truncated_normal\",   seed = NULL )"},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"scale Scaling factor (positive float). mode One \"fan_in\", \"fan_out\", \"fan_avg\". distribution Random distribution use. One \"truncated_normal\", \"untruncated_normal\", \"uniform\". seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"","code":"# Standalone usage: initializer <- initializer_variance_scaling(scale = 0.1, mode = 'fan_in',                                             distribution = 'uniform') values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_variance_scaling(scale = 0.1, mode = 'fan_in',                                             distribution = 'uniform') layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_zeros.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates tensors initialized to 0. — initializer_zeros","title":"Initializer that generates tensors initialized to 0. — initializer_zeros","text":"Initializer generates tensors initialized 0.","code":""},{"path":"https://keras.posit.co/reference/initializer_zeros.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates tensors initialized to 0. — initializer_zeros","text":"","code":"initializer_zeros()"},{"path":"https://keras.posit.co/reference/initializer_zeros.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates tensors initialized to 0. — initializer_zeros","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_zeros.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates tensors initialized to 0. — initializer_zeros","text":"","code":"# Standalone usage: initializer <- initializer_zeros() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_zeros() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/install_keras.html","id":null,"dir":"Reference","previous_headings":"","what":"Install Keras — install_keras","title":"Install Keras — install_keras","text":"function install Keras along selected backend, including Python dependencies.","code":""},{"path":"https://keras.posit.co/reference/install_keras.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Install Keras — install_keras","text":"","code":"install_keras(   envname = \"r-keras\",   ...,   extra_packages = c(\"scipy\", \"pandas\", \"Pillow\", \"pydot\", \"ipython\",     \"tensorflow_datasets\"),   python_version = \">=3.9,<=3.11\",   backend = c(\"tensorflow\", \"jax\"),   gpu = NA,   restart_session = TRUE )"},{"path":"https://keras.posit.co/reference/install_keras.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Install Keras — install_keras","text":"envname Name path Python virtual environment ... reserved future compatibility. extra_packages Additional Python packages install alongside Keras python_version Passed reticulate::virtualenv_starter() backend backend(s) install. Accepted values include \"tensorflow\", \"jax\" \"torch\" gpu whether install GPU capable version backend. restart_session Whether restart R session installing (note occur within RStudio).","code":""},{"path":"https://keras.posit.co/reference/install_keras.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Install Keras — install_keras","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/keras.html","id":null,"dir":"Reference","previous_headings":"","what":"Main Keras module — keras","title":"Main Keras module — keras","text":"keras module object equivalent reticulate::import(\"keras\") provided mainly convenience.","code":""},{"path":"https://keras.posit.co/reference/keras.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Main Keras module — keras","text":"object class python.builtin.module","code":""},{"path":"https://keras.posit.co/reference/keras.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Main Keras module — keras","text":"keras Python module","code":""},{"path":"https://keras.posit.co/reference/keras3-package.html","id":null,"dir":"Reference","previous_headings":"","what":"keras3: R Interface to 'Keras' — keras3-package","title":"keras3: R Interface to 'Keras' — keras3-package","text":"Interface 'Keras' https://keras.io, high-level neural networks API. 'Keras' developed focus enabling fast experimentation, supports convolution based networks recurrent networks (well combinations two), runs seamlessly CPU GPU devices. Keras high-level neural networks API, developed focus enabling fast experimentation. Keras following key features:","code":""},{"path":"https://keras.posit.co/reference/keras3-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"keras3: R Interface to 'Keras' — keras3-package","text":"Allows code run CPU GPU, seamlessly. User-friendly API makes easy quickly prototype deep learning models. Built-support convolutional networks (computer vision), recurrent networks (sequence processing), combination . Supports arbitrary network architectures: multi-input multi-output models, layer sharing, model sharing, etc. means Keras appropriate building essentially deep learning model, memory network neural Turing machine. capable running top multiple back-ends including TensorFlow, Jax, PyTorch. See package website https://keras.posit.co complete documentation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/keras3-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"keras3: R Interface to 'Keras' — keras3-package","text":"Maintainer: Tomasz Kalinowski tomasz@posit.co [copyright holder] Authors: JJ Allaire [copyright holder] François Chollet [copyright holder] contributors: Daniel Falbel daniel@posit.co [contributor, copyright holder] Posit Software, PBC [copyright holder, funder] Google [copyright holder, funder] Yuan Tang terrytangyuan@gmail.com (ORCID) [contributor, copyright holder] Wouter Van Der Bijl [contributor, copyright holder] Martin Studer [contributor, copyright holder] Sigrid Keydana [contributor]","code":""},{"path":"https://keras.posit.co/reference/keras_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Keras tensor (Functional API input). — keras_input","title":"Create a Keras tensor (Functional API input). — keras_input","text":"Keras tensor symbolic tensor-like object, augment certain attributes allow us build Keras model just knowing inputs outputs model. instance, , b c Keras tensors, becomes possible : model <- keras_model(input = c(, b), output = c)","code":""},{"path":"https://keras.posit.co/reference/keras_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Keras tensor (Functional API input). — keras_input","text":"","code":"keras_input(   shape = NULL,   batch_size = NULL,   dtype = NULL,   sparse = NULL,   batch_shape = NULL,   name = NULL,   tensor = NULL )"},{"path":"https://keras.posit.co/reference/keras_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Keras tensor (Functional API input). — keras_input","text":"shape shape list (list integers NULL objects), including batch size. instance, shape = c(32) indicates expected input batches 32-dimensional vectors. Elements list can NULL NA; NULL/NA elements represent dimensions shape known may vary (e.g. sequence length). batch_size Optional static batch size (integer). dtype data type expected input, string (e.g. \"float32\", \"int32\"...) sparse boolean specifying whether expected input sparse tensors. Note , sparse FALSE, sparse tensors can still passed input - densified default value 0. feature supported TensorFlow backend. Defaults FALSE. batch_shape Shape, including batch dim. name Optional name string layer. unique model (reuse name twice). autogenerated provided. tensor Optional existing tensor wrap Input layer. set, layer use tensor rather creating new placeholder tensor.","code":""},{"path":"https://keras.posit.co/reference/keras_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Keras tensor (Functional API input). — keras_input","text":"Keras tensor, can passed inputs argument (keras_model()).","code":""},{"path":"https://keras.posit.co/reference/keras_input.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Keras tensor (Functional API input). — keras_input","text":"","code":"# This is a logistic regression in Keras input <- layer_input(shape=c(32)) output <- input |> layer_dense(16, activation='softmax') model <- keras_model(input, output)"},{"path":[]},{"path":"https://keras.posit.co/reference/keras_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Keras Model (Functional API) — keras_model","title":"Keras Model (Functional API) — keras_model","text":"model directed acyclic graph layers.","code":""},{"path":"https://keras.posit.co/reference/keras_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keras Model (Functional API) — keras_model","text":"","code":"keras_model(inputs = NULL, outputs = NULL, ...)"},{"path":"https://keras.posit.co/reference/keras_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keras Model (Functional API) — keras_model","text":"inputs Input tensor(s) (keras_input()) outputs Output tensors (calling layers inputs) ... additional arguments","code":""},{"path":"https://keras.posit.co/reference/keras_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keras Model (Functional API) — keras_model","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/keras_model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Keras Model (Functional API) — keras_model","text":"","code":"library(keras3)  # input tensor inputs <- keras_input(shape = c(784))  # outputs compose input + dense layers predictions <- inputs |>   layer_dense(units = 64, activation = 'relu') |>   layer_dense(units = 64, activation = 'relu') |>   layer_dense(units = 10, activation = 'softmax')  # create and compile model model <- keras_model(inputs = inputs, outputs = predictions) model |> compile(   optimizer = 'rmsprop',   loss = 'categorical_crossentropy',   metrics = c('accuracy') )"},{"path":[]},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":null,"dir":"Reference","previous_headings":"","what":"Keras Model composed of a linear stack of layers — keras_model_sequential","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"Keras Model composed linear stack layers","code":""},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"","code":"keras_model_sequential(   input_shape = NULL,   name = NULL,   ...,   input_dtype = NULL,   input_batch_size = NULL,   input_sparse = NULL,   input_batch_shape = NULL,   input_name = NULL,   input_tensor = NULL,   trainable = TRUE,   layers = list() )"},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"input_shape shape integer vector, including batch size. instance, shape=c(32) indicates expected input batches 32-dimensional vectors. Elements shape can NA; NA elements represent dimensions shape known may vary (e.g. sequence length). name Name model ... additional arguments passed keras.layers.InputLayer. input_dtype data type expected input, string (e.g. \"float32\", \"int32\"...) input_batch_size Optional static batch size (integer). input_sparse boolean specifying whether expected input sparse tensors. Note , sparse FALSE, sparse tensors can still passed input - densified default value 0. feature supported TensorFlow backend. Defaults FALSE. input_batch_shape optional way specify batch_size input_shape one argument. input_name Optional name string input layer. unique model (reuse name twice). autogenerated provided. input_tensor Optional existing tensor wrap InputLayer. set, layer use tensor rather creating new placeholder tensor. trainable Boolean, whether model's variables trainable. can also change trainable status model/layer freeze_weights() unfreeze_weights(). layers List layers add model.","code":""},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"Sequential model instance.","code":""},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"input_shape omitted, model layer shapes, including final model output shape, known model built, either calling model input tensor/array like model(input), (possibly via fit()/evaluate()/predict()), explicitly calling model$build(input_shape).","code":""},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"","code":"model <- keras_model_sequential(input_shape = c(784)) model |>   layer_dense(units = 32) |>   layer_activation('relu') |>   layer_dense(units = 10) |>   layer_activation('softmax')  model |> compile(   optimizer = 'rmsprop',   loss = 'categorical_crossentropy',   metrics = c('accuracy') )  model ## Model: \"sequential\" ## +---------------------------------+------------------------+---------------+ ## | Layer (type)                    | Output Shape           |       Param # | ## +=================================+========================+===============+ ## | dense (Dense)                   | (None, 32)             |        25,120 | ## +---------------------------------+------------------------+---------------+ ## | activation (Activation)         | (None, 32)             |             0 | ## +---------------------------------+------------------------+---------------+ ## | dense_1 (Dense)                 | (None, 10)             |           330 | ## +---------------------------------+------------------------+---------------+ ## | activation_1 (Activation)       | (None, 10)             |             0 | ## +---------------------------------+------------------------+---------------+ ##  Total params: 25,450 (99.41 KB) ##  Trainable params: 25,450 (99.41 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies an activation function to an output. — layer_activation","title":"Applies an activation function to an output. — layer_activation","text":"Applies activation function output.","code":""},{"path":"https://keras.posit.co/reference/layer_activation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies an activation function to an output. — layer_activation","text":"","code":"layer_activation(object, activation, ...)"},{"path":"https://keras.posit.co/reference/layer_activation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies an activation function to an output. — layer_activation","text":"object Object compose layer . tensor, array, sequential model. activation Activation function. callable, name activation keras3::activation_* namespace. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies an activation function to an output. — layer_activation","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_activation.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies an activation function to an output. — layer_activation","text":"","code":"x <- array(c(-3, -1, 0, 2)) layer <- layer_activation(activation = 'relu') layer(x) ## tf.Tensor([0. 0. 0. 2.], shape=(4), dtype=float32) layer <- layer_activation(activation = activation_relu) layer(x) ## tf.Tensor([0. 0. 0. 2.], shape=(4), dtype=float32) layer <- layer_activation(activation = op_relu) layer(x) ## tf.Tensor([0. 0. 0. 2.], shape=(4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","title":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","text":"Formula:","code":"f(x) = alpha * (exp(x) - 1.) for x < 0 f(x) = x for x >= 0"},{"path":"https://keras.posit.co/reference/layer_activation_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","text":"","code":"layer_activation_elu(object, alpha = 1, ...)"},{"path":"https://keras.posit.co/reference/layer_activation_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","text":"object Object compose layer . tensor, array, sequential model. alpha float, slope negative section. Defaults 1.0. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_elu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"layer allows small gradient unit active. Formula:","code":"f <- function(x) ifelse(x >= 0, x, alpha * x)"},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"","code":"layer_activation_leaky_relu(object, negative_slope = 0.3, ...)"},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"object Object compose layer . tensor, array, sequential model. negative_slope Float >= 0.0. Negative slope coefficient. Defaults 0.3. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"","code":"leaky_relu_layer <- layer_activation_leaky_relu(negative_slope=0.5) input <- array(c(-10, -5, 0.0, 5, 10)) result <- leaky_relu_layer(input) as.array(result) ## [1] -5.0 -2.5  0.0  5.0 10.0"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_parametric_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","title":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","text":"Formula:   alpha learned array shape x.","code":"f <- function(x) ifelse(x >= 0, x, alpha * x)"},{"path":"https://keras.posit.co/reference/layer_activation_parametric_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","text":"","code":"layer_activation_parametric_relu(   object,   alpha_initializer = \"Zeros\",   alpha_regularizer = NULL,   alpha_constraint = NULL,   shared_axes = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_activation_parametric_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","text":"object Object compose layer . tensor, array, sequential model. alpha_initializer Initializer function weights. alpha_regularizer Regularizer weights. alpha_constraint Constraint weights. shared_axes axes along share learnable parameters activation function. example, incoming feature maps 2D convolution output shape (batch, height, width, channels), wish share parameters across space filter one set parameters, set shared_axes=[1, 2]. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_parametric_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Rectified Linear Unit activation function layer. — layer_activation_relu","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"Formula:","code":"f <- function(x, max_value = Inf, negative_slope = 0, threshold = 0) {  x <- max(x,0)  if (x >= max_value)    max_value  else if (threshold <= x && x < max_value)    x  else    negative_slope * (x - threshold) }"},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"","code":"layer_activation_relu(   object,   max_value = NULL,   negative_slope = 0,   threshold = 0,   ... )"},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"object Object compose layer . tensor, array, sequential model. max_value Float >= 0. Maximum activation value. NULL means unlimited. Defaults NULL. negative_slope Float >= 0. Negative slope coefficient. Defaults 0.0. threshold Float >= 0. Threshold value thresholded activation. Defaults 0.0. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"","code":"relu_layer <- layer_activation_relu(max_value = 10,                                     negative_slope = 0.5,                                     threshold = 0) input <- array(c(-10, -5, 0.0, 5, 10)) result <- relu_layer(input) as.array(result) ## [1] -5.0 -2.5  0.0  5.0 10.0"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax activation layer. — layer_activation_softmax","title":"Softmax activation layer. — layer_activation_softmax","text":"Formula:","code":"exp_x = exp(x - max(x)) f(x) = exp_x / sum(exp_x)"},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax activation layer. — layer_activation_softmax","text":"","code":"layer_activation_softmax(object, axis = -1L, ...)"},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax activation layer. — layer_activation_softmax","text":"object Object compose layer . tensor, array, sequential model. axis Integer, list Integers, axis along softmax normalization applied. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax activation layer. — layer_activation_softmax","text":"Softmaxed output shape inputs.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmax activation layer. — layer_activation_softmax","text":"","code":"softmax_layer <- layer_activation_softmax() input <- op_array(c(1, 2, 1)) softmax_layer(input) ## tf.Tensor([0.21194157 0.5761169  0.21194157], shape=(3), dtype=float32)"},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Softmax activation layer. — layer_activation_softmax","text":"inputs: inputs (logits) softmax layer. mask: boolean mask shape inputs. mask specifies 1 keep 0 mask. Defaults NULL.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"Layer applies update cost function based input activity.","code":""},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"","code":"layer_activity_regularization(object, l1 = 0, l2 = 0, ...)"},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"object Object compose layer . tensor, array, sequential model. l1 L1 regularization factor (positive float). l2 L2 regularization factor (positive float). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"Arbitrary. Use keyword argument input_shape (tuple integers, include samples axis) using layer first layer model.","code":""},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"shape input.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_add.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs elementwise addition operation. — layer_add","title":"Performs elementwise addition operation. — layer_add","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_add.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs elementwise addition operation. — layer_add","text":"","code":"layer_add(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_add.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs elementwise addition operation. — layer_add","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_add.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs elementwise addition operation. — layer_add","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_add.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs elementwise addition operation. — layer_add","text":"Usage Keras model:","code":"input_shape <- c(1, 2, 3) x1 <- op_ones(input_shape) x2 <- op_ones(input_shape) layer_add(x1, x2) ## tf.Tensor( ## [[[2. 2. 2.] ##   [2. 2. 2.]]], shape=(1, 2, 3), dtype=float32) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu')  input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu')  # equivalent to `added = layer_add([x1, x2))` added <- layer_add(x1, x2) output <- added |> layer_dense(4)  model <- keras_model(inputs = c(input1, input2), outputs = output)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"Inputs list 2 3 elements: query tensor shape (batch_size, Tq, dim). value tensor shape (batch_size, Tv, dim). optional key tensor shape (batch_size, Tv, dim). none supplied, value used key. calculation follows steps: Calculate attention scores using query key shape (batch_size, Tq, Tv) non-linear sum scores = reduce_sum(tanh(query + key), axis=-1). Use scores calculate softmax distribution shape (batch_size, Tq, Tv). Use softmax distribution create linear combination value shape (batch_size, Tq, dim).","code":""},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"","code":"layer_additive_attention(object, use_scale = TRUE, dropout = 0, ...)"},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"object Object compose layer . tensor, array, sequential model. use_scale TRUE, create scalar variable scale attention scores. dropout Float 0 1. Fraction units drop attention scores. Defaults 0.0. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"inputs: List following tensors: query: Query tensor shape (batch_size, Tq, dim). value: Value tensor shape (batch_size, Tv, dim). key: Optional key tensor shape (batch_size, Tv, dim). given, use value key value, common case. mask: List following tensors: query_mask: boolean mask tensor shape (batch_size, Tq). given, output zero positions mask==FALSE. value_mask: boolean mask tensor shape (batch_size, Tv). given, apply mask values positions mask==FALSE contribute result. return_attention_scores: bool, TRUE, returns attention scores (masking softmax) additional output argument. training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (dropout). use_causal_mask: Boolean. Set TRUE decoder self-attention. Adds mask position attend positions j > . prevents flow information future towards past. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"output","dir":"Reference","previous_headings":"","what":"Output","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"Attention outputs shape (batch_size, Tq, dim). (Optional) Attention scores masking softmax shape (batch_size, Tq, Tv).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies Alpha Dropout to the input. — layer_alpha_dropout","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"Alpha Dropout Dropout keeps mean variance inputs original values, order ensure self-normalizing property even dropout. Alpha Dropout fits well Scaled Exponential Linear Units (SELU) randomly setting activations negative saturation value.","code":""},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"","code":"layer_alpha_dropout(object, rate, noise_shape = NULL, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. multiplicative noise standard deviation sqrt(rate / (1 - rate)). noise_shape 1D integer tensor representing shape binary alpha dropout mask multiplied input. instance, inputs shape (batch_size, timesteps, features) want alpha dropout mask timesteps, can use noise_shape = (batch_size, 1, features). seed integer use random seed. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"inputs: Input tensor (rank). training: R boolean indicating whether layer behave training mode (adding alpha dropout) inference mode (nothing).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"Inputs list 2 3 elements: query tensor shape (batch_size, Tq, dim). value tensor shape (batch_size, Tv, dim). optional key tensor shape (batch_size, Tv, dim). none supplied, value used key. calculation follows steps: Calculate attention scores using query key shape (batch_size, Tq, Tv). Use scores calculate softmax distribution shape (batch_size, Tq, Tv). Use softmax distribution create linear combination value shape (batch_size, Tq, dim).","code":""},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"","code":"layer_attention(   object,   use_scale = FALSE,   score_mode = \"dot\",   dropout = 0,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"object Object compose layer . tensor, array, sequential model. use_scale TRUE, create scalar variable scale attention scores. score_mode Function use compute attention scores, one {\"dot\", \"concat\"}. \"dot\" refers dot product query key vectors. \"concat\" refers hyperbolic tangent concatenation query key vectors. dropout Float 0 1. Fraction units drop attention scores. Defaults 0.0. seed integer use random seed incase dropout. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"inputs: List following tensors: query: Query tensor shape (batch_size, Tq, dim). value: Value tensor shape (batch_size, Tv, dim). key: Optional key tensor shape (batch_size, Tv, dim). given, use value key value, common case. mask: List following tensors: query_mask: boolean mask tensor shape (batch_size, Tq). given, output zero positions mask==FALSE. value_mask: boolean mask tensor shape (batch_size, Tv). given, apply mask values positions mask==FALSE contribute result. return_attention_scores: bool, TRUE, returns attention scores (masking softmax) additional output argument. training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (dropout). use_causal_mask: Boolean. Set TRUE decoder self-attention. Adds mask position attend positions j > . prevents flow information future towards past. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"output","dir":"Reference","previous_headings":"","what":"Output","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"Attention outputs shape (batch_size, Tq, dim). (Optional) Attention scores masking softmax shape (batch_size, Tq, Tv).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_average.html","id":null,"dir":"Reference","previous_headings":"","what":"Averages a list of inputs element-wise.. — layer_average","title":"Averages a list of inputs element-wise.. — layer_average","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_average.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Averages a list of inputs element-wise.. — layer_average","text":"","code":"layer_average(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_average.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Averages a list of inputs element-wise.. — layer_average","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_average.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Averages a list of inputs element-wise.. — layer_average","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_average.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Averages a list of inputs element-wise.. — layer_average","text":"Usage Keras model:","code":"input_shape <- c(1, 2, 3) x1 <- op_ones(input_shape) x2 <- op_zeros(input_shape) layer_average(x1, x2) ## tf.Tensor( ## [[[0.5 0.5 0.5] ##   [0.5 0.5 0.5]]], shape=(1, 2, 3), dtype=float32) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu')  input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu')  added <- layer_average(x1, x2) output <- added |> layer_dense(4)  model <- keras_model(inputs = c(input1, input2), outputs = output)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Average pooling for temporal data. — layer_average_pooling_1d","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"Downsamples input representation taking average value window defined pool_size. window shifted strides.  resulting output using \"valid\" padding option shape : output_shape = (input_shape - pool_size + 1) / strides) resulting output shape using \"\" padding option : output_shape = input_shape / strides","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"","code":"layer_average_pooling_1d(   object,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"object Object compose layer . tensor, array, sequential model. pool_size int, size max pooling window. strides int NULL. Specifies much pooling window moves pooling step. NULL, default pool_size. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"data_format=\"channels_last\": 3D tensor shape (batch_size, steps, features). data_format=\"channels_first\": 3D tensor shape (batch_size, features, steps).","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"data_format=\"channels_last\": 3D tensor shape (batch_size, downsampled_steps, features). data_format=\"channels_first\": 3D tensor shape (batch_size, features, downsampled_steps).","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"strides=1 padding=\"valid\":     strides=2 padding=\"valid\":     strides=1 padding=\"\":","code":"x <- op_array(c(1., 2., 3., 4., 5.)) |> op_reshape(c(1, 5, 1)) output <- x |>   layer_average_pooling_1d(pool_size = 2,                            strides = 1,                            padding = \"valid\") output ## tf.Tensor( ## [[[1.5] ##   [2.5] ##   [3.5] ##   [4.5]]], shape=(1, 4, 1), dtype=float32) x <- op_array(c(1., 2., 3., 4., 5.)) |> op_reshape(c(1, 5, 1)) output <- x |>   layer_average_pooling_1d(pool_size = 2,                            strides = 2,                            padding = \"valid\") output ## tf.Tensor( ## [[[1.5] ##   [3.5]]], shape=(1, 2, 1), dtype=float32) x <- op_array(c(1., 2., 3., 4., 5.)) |> op_reshape(c(1, 5, 1)) output <- x |>   layer_average_pooling_1d(pool_size = 2,                            strides = 1,                            padding = \"same\") output ## tf.Tensor( ## [[[1.5] ##   [2.5] ##   [3.5] ##   [4.5] ##   [5. ]]], shape=(1, 5, 1), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"Downsamples input along spatial dimensions (height width) taking average value input window (size defined pool_size) channel input. window shifted strides along dimension. resulting output using \"valid\" padding option spatial shape (number rows columns) : output_shape = math.floor((input_shape - pool_size) / strides) + 1 (input_shape >= pool_size) resulting output shape using \"\" padding option : output_shape = math.floor((input_shape - 1) / strides) + 1","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"","code":"layer_average_pooling_2d(   object,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"object Object compose layer . tensor, array, sequential model. pool_size int list 2 integers, factors downscale (dim1, dim2). one integer specified, window length used dimensions. strides int list 2 integers, NULL. Strides values. NULL, default pool_size. one int specified, stride size used dimensions. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"data_format=\"channels_last\": 4D tensor shape (batch_size, height, width, channels). data_format=\"channels_first\": 4D tensor shape (batch_size, channels, height, width).","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"data_format=\"channels_last\": 4D tensor shape (batch_size, pooled_height, pooled_width, channels). data_format=\"channels_first\": 4D tensor shape (batch_size, channels, pooled_height, pooled_width).","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"strides=(1, 1) padding=\"valid\":     strides=(2, 2) padding=\"valid\":     stride=(1, 1) padding=\"\":","code":"x <- op_array(1:9, \"float32\") |> op_reshape(c(1, 3, 3, 1)) output <- x |>   layer_average_pooling_2d(pool_size = c(2, 2),                            strides = c(1, 1),                            padding = \"valid\") output ## tf.Tensor( ## [[[[3.] ##    [4.]] ## ##   [[6.] ##    [7.]]]], shape=(1, 2, 2, 1), dtype=float32) x <- op_array(1:12, \"float32\") |> op_reshape(c(1, 3, 4, 1)) output <- x |>   layer_average_pooling_2d(pool_size = c(2, 2),                            strides = c(2, 2),                            padding = \"valid\") output ## tf.Tensor( ## [[[[3.5] ##    [5.5]]]], shape=(1, 1, 2, 1), dtype=float32) x <- op_array(1:9, \"float32\") |> op_reshape(c(1, 3, 3, 1)) output <- x |>   layer_average_pooling_2d(pool_size = c(2, 2),                            strides = c(1, 1),                            padding = \"same\") output ## tf.Tensor( ## [[[[3. ] ##    [4. ] ##    [4.5]] ## ##   [[6. ] ##    [7. ] ##    [7.5]] ## ##   [[7.5] ##    [8.5] ##    [9. ]]]], shape=(1, 3, 3, 1), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"Downsamples input along spatial dimensions (depth, height, width) taking average value input window (size defined pool_size) channel input. window shifted strides along dimension.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"","code":"layer_average_pooling_3d(   object,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"object Object compose layer . tensor, array, sequential model. pool_size int list 3 integers, factors downscale (dim1, dim2, dim3). one integer specified, window length used dimensions. strides int list 3 integers, NULL. Strides values. NULL, default pool_size. one int specified, stride size used dimensions. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"","code":"depth <- height <- width <- 30 channels <- 3  inputs <- layer_input(shape = c(depth, height, width, channels)) outputs <- inputs |> layer_average_pooling_3d(pool_size = 3) outputs # Shape: (batch_size, 10, 10, 10, 3) ## <KerasTensor shape=(None, 10, 10, 10, 3), dtype=float32, sparse=False, name=keras_tensor_1>"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer that normalizes its inputs. — layer_batch_normalization","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"Batch normalization applies transformation maintains mean output close 0 output standard deviation close 1. Importantly, batch normalization works differently training inference. training (.e. using fit() calling layer/model argument training = TRUE), layer normalizes output using mean standard deviation current batch inputs. say, channel normalized, layer returns gamma * (batch - mean(batch)) / sqrt(var(batch) + epsilon) + beta, : epsilon small constant (configurable part constructor arguments) gamma learned scaling factor (initialized 1), can disabled passing scale = FALSE constructor. beta learned offset factor (initialized 0), can disabled passing center = FALSE constructor. inference (.e. using evaluate() predict() calling layer/model argument training = FALSE (default), layer normalizes output using moving average mean standard deviation batches seen training. say, returns gamma * (batch - self$moving_mean) / sqrt(self$moving_var+epsilon) + beta. self$moving_mean self$moving_var non-trainable variables updated time layer called training mode, : moving_mean = moving_mean * momentum + mean(batch) * (1 - momentum) moving_var = moving_var * momentum + var(batch) * (1 - momentum) , layer normalize inputs inference trained data similar statistics inference data. setting layer$trainable <- FALSE BatchNormalization layer: meaning setting layer$trainable <- FALSE freeze layer, .e. internal state change training: trainable weights updated fit() train_on_batch(), state updates run. Usually, necessarily mean layer run inference mode (normally controlled training argument can passed calling layer). \"Frozen state\" \"inference mode\" two separate concepts. However, case BatchNormalization layer, setting trainable <- FALSE layer means layer subsequently run inference mode (meaning use moving mean moving variance normalize current batch, rather using mean variance current batch). Note : Setting trainable model containing layers recursively set trainable value inner layers. value trainable attribute changed calling compile() model, new value take effect model compile() called .","code":""},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"","code":"layer_batch_normalization(   object,   axis = -1L,   momentum = 0.99,   epsilon = 0.001,   center = TRUE,   scale = TRUE,   beta_initializer = \"zeros\",   gamma_initializer = \"ones\",   moving_mean_initializer = \"zeros\",   moving_variance_initializer = \"ones\",   beta_regularizer = NULL,   gamma_regularizer = NULL,   beta_constraint = NULL,   gamma_constraint = NULL,   synchronized = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"object Object compose layer . tensor, array, sequential model. axis Integer, axis normalized (typically features axis). instance, Conv2D layer data_format = \"channels_first\", use axis = 2. momentum Momentum moving average. epsilon Small float added variance avoid dividing zero. center TRUE, add offset beta normalized tensor. FALSE, beta ignored. scale TRUE, multiply gamma. FALSE, gamma used. next layer linear can disabled since scaling done next layer. beta_initializer Initializer beta weight. gamma_initializer Initializer gamma weight. moving_mean_initializer Initializer moving mean. moving_variance_initializer Initializer moving variance. beta_regularizer Optional regularizer beta weight. gamma_regularizer Optional regularizer gamma weight. beta_constraint Optional constraint beta weight. gamma_constraint Optional constraint gamma weight. synchronized applicable TensorFlow backend. TRUE, synchronizes global batch statistics (mean variance) layer across devices training step distributed training strategy. FALSE, replica uses local batch statistics. ... Base layer keyword arguments (e.g. name dtype).","code":""},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"inputs: Input tensor (rank). training: R boolean indicating whether layer behave training mode inference mode. training = TRUE: layer normalize inputs using mean variance current batch inputs. training = FALSE: layer normalize inputs using mean variance moving statistics, learned training. mask: Binary tensor shape broadcastable inputs tensor, TRUE values indicating positions mean variance computed. Masked elements current inputs taken account mean variance computation training. prior unmasked element values taken account momentum expires.","code":""},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"Ioffe Szegedy, 2015.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":null,"dir":"Reference","previous_headings":"","what":"Bidirectional wrapper for RNNs. — layer_bidirectional","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"Bidirectional wrapper RNNs.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"","code":"layer_bidirectional(   object,   layer,   merge_mode = \"concat\",   weights = NULL,   backward_layer = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"object Object compose layer . tensor, array, sequential model. layer RNN instance, layer_lstm() layer_gru(). also Layer() instance meets following criteria: sequence-processing layer (accepts 3D+ inputs). go_backwards, return_sequences return_state attribute (semantics RNN class). input_spec attribute. Implement serialization via get_config() from_config(). Note recommended way create new RNN layers write custom RNN cell use layer_rnn(), instead subclassing Layer() directly. return_sequences TRUE, output masked timestep zero regardless layer's original zero_output_for_mask value. merge_mode Mode outputs forward backward RNNs combined. One {\"sum\", \"mul\", \"concat\", \"ave\", NULL}. NULL, outputs combined, returned list. Defaults \"concat\". weights see description backward_layer Optional RNN, Layer() instance used handle backwards input processing. backward_layer provided, layer instance passed layer argument used generate backward layer automatically. Note provided backward_layer layer properties matching layer argument, particular values stateful, return_states, return_sequences, etc. addition, backward_layer layer different go_backwards argument values. ValueError raised requirements met. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"call arguments layer wrapped RNN layer. Beware passing initial_state argument call layer, first half list elements initial_state list passed forward RNN call last half list elements passed backward RNN call.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"instantiating Bidirectional layer existing RNN layer instance reuse weights state RNN layer instance -- Bidirectional layer freshly initialized weights.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"","code":"model <- keras_model_sequential(input_shape = c(5, 10)) %>%   layer_bidirectional(layer_lstm(units = 10, return_sequences = TRUE)) %>%   layer_bidirectional(layer_lstm(units = 10)) %>%   layer_dense(5, activation = \"softmax\")  model %>% compile(loss = \"categorical_crossentropy\",                   optimizer = \"rmsprop\")  # With custom backward layer forward_layer <- layer_lstm(units = 10, return_sequences = TRUE) backward_layer <- layer_lstm(units = 10, activation = \"relu\",                              return_sequences = TRUE, go_backwards = TRUE)  model <- keras_model_sequential(input_shape = c(5, 10)) %>%   bidirectional(forward_layer, backward_layer = backward_layer) %>%   layer_dense(5, activation = \"softmax\")  model %>% compile(loss = \"categorical_crossentropy\",                   optimizer = \"rmsprop\")"},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"states","dir":"Reference","previous_headings":"","what":"States","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"Bidirectional layer instance property states, can access layer$states. can also reset states using reset_state()","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which encodes integer features. — layer_category_encoding","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"layer provides options condensing data categorical encoding total number tokens known advance. accepts integer values inputs, outputs dense sparse representation inputs. integer inputs total number tokens known, use layer_integer_lookup() instead. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"","code":"layer_category_encoding(   object,   num_tokens = NULL,   output_mode = \"multi_hot\",   sparse = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"object Object compose layer . tensor, array, sequential model. num_tokens total number tokens layer support. inputs layer must integers range 0 <= value < num_tokens, error thrown. output_mode Specification output layer. Values can \"one_hot\", \"multi_hot\" \"count\", configuring layer follows: - \"one_hot\": Encodes individual element input array num_tokens size, containing 1 element index. last dimension size 1, encode dimension. last dimension size 1, append new dimension encoded output. - \"multi_hot\": Encodes sample input single array num_tokens size, containing 1 vocabulary term present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). - \"count\": Like \"multi_hot\", int array contains count number times token index appeared sample. output modes, currently output rank 2 supported. Defaults \"multi_hot\". sparse Whether return sparse tensor; backends support sparse tensors. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"One-hot encoding data     Multi-hot encoding data     Using weighted inputs \"count\" mode","code":"layer <- layer_category_encoding(num_tokens = 4, output_mode = \"one_hot\") x <- op_array(c(3, 2, 0, 1), \"int32\") layer(x) ## tf.Tensor( ## [[0. 0. 0. 1.] ##  [0. 0. 1. 0.] ##  [1. 0. 0. 0.] ##  [0. 1. 0. 0.]], shape=(4, 4), dtype=float32) layer <- layer_category_encoding(num_tokens = 4, output_mode = \"multi_hot\") x <- op_array(rbind(c(0, 1),                    c(0, 0),                    c(1, 2),                    c(3, 1)), \"int32\") layer(x) ## tf.Tensor( ## [[1. 1. 0. 0.] ##  [1. 0. 0. 0.] ##  [0. 1. 1. 0.] ##  [0. 1. 0. 1.]], shape=(4, 4), dtype=float32) layer <- layer_category_encoding(num_tokens = 4, output_mode = \"count\") count_weights <- op_array(rbind(c(.1, .2),                                c(.1, .1),                                c(.2, .3),                                c(.4, .2))) x <- op_array(rbind(c(0, 1),                    c(0, 0),                    c(1, 2),                    c(3, 1)), \"int32\") layer(x, count_weights = count_weights) #   array([[01, 02, 0. , 0. ], #          [02, 0. , 0. , 0. ], #          [0. , 02, 03, 0. ], #          [0. , 02, 0. , 04]]>"},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"inputs: 1D 2D tensor integer inputs. count_weights: tensor shape inputs indicating weight sample value summing count mode. used \"multi_hot\" \"one_hot\" modes.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which crops images. — layer_center_crop","title":"A preprocessing layer which crops images. — layer_center_crop","text":"layers crops central portion images target size. image smaller target size, resized cropped return largest possible window image matches target aspect ratio. Input pixel values can range (e.g. [0., 1.) [0, 255]).","code":""},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which crops images. — layer_center_crop","text":"","code":"layer_center_crop(object, height, width, data_format = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which crops images. — layer_center_crop","text":"object Object compose layer . tensor, array, sequential model. height Integer, height output shape. width Integer, width output shape. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which crops images. — layer_center_crop","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which crops images. — layer_center_crop","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format, (..., channels, height, width), \"channels_first\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which crops images. — layer_center_crop","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels), (..., channels, target_height, target_width), \"channels_first\" format. input height/width even target height/width odd (inversely), input image left-padded 1 pixel. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":null,"dir":"Reference","previous_headings":"","what":"Concatenates a list of inputs. — layer_concatenate","title":"Concatenates a list of inputs. — layer_concatenate","text":"takes input list tensors, shape except concatenation axis, returns single tensor concatenation inputs.","code":""},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Concatenates a list of inputs. — layer_concatenate","text":"","code":"layer_concatenate(inputs, ..., axis = -1L)"},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Concatenates a list of inputs. — layer_concatenate","text":"inputs layers combine ... Standard layer keyword arguments. axis Axis along concatenate.","code":""},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Concatenates a list of inputs. — layer_concatenate","text":"tensor, concatenation inputs alongside axis axis.","code":""},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Concatenates a list of inputs. — layer_concatenate","text":"Usage Keras model:","code":"x <- op_arange(20) |> op_reshape(c(2, 2, 5)) y <- op_arange(20, 40) |> op_reshape(c(2, 2, 5)) layer_concatenate(x, y, axis = 2) ## tf.Tensor( ## [[[ 0.  1.  2.  3.  4.] ##   [ 5.  6.  7.  8.  9.] ##   [20. 21. 22. 23. 24.] ##   [25. 26. 27. 28. 29.]] ## ##  [[10. 11. 12. 13. 14.] ##   [15. 16. 17. 18. 19.] ##   [30. 31. 32. 33. 34.] ##   [35. 36. 37. 38. 39.]]], shape=(2, 4, 5), dtype=float32) x1 <- op_arange(10)     |> op_reshape(c(5, 2)) |> layer_dense(8) x2 <- op_arange(10, 20) |> op_reshape(c(5, 2)) |> layer_dense(8) y <- layer_concatenate(x1, x2)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"layer creates convolution kernel convolved layer input single spatial (temporal) dimension produce tensor outputs. use_bias TRUE, bias vector created added outputs. Finally, activation NULL, applied outputs well.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"","code":"layer_conv_1d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   groups = 1L,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int list 1 integer, specifying size convolution window. strides int list 1 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, \"valid\", \"\" \"causal\"(case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. \"causal\" results causal (dilated) convolutions, e.g. output[t] depend ontail(input, t+1). Useful modeling temporal data model violate temporal order. See WaveNet: Generative Model Raw Audio, section2.1. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated convolution. groups positive int specifying number groups input split along channel axis. group convolved separately filters // groups filters. output concatenation groups results along channel axis. Input channels filters must divisible groups. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"3D tensor representing activation(conv1d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, steps, channels) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, new_steps, filters) data_format=\"channels_first\": 3D tensor shape: (batch_shape, filters, new_steps)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"","code":"# The inputs are 128-length vectors with 10 timesteps, and the # batch size is 4. x <- random_uniform(c(4, 10, 128)) y <- x |> layer_conv_1d(32, 3, activation='relu') shape(y) ## shape(4, 8, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"1D transposed convolution layer. — layer_conv_1d_transpose","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"need transposed convolutions generally arise desire use transformation going opposite direction normal convolution, .e., something shape output convolution something shape input maintaining connectivity pattern compatible said convolution.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"","code":"layer_conv_1d_transpose(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters transpose convolution). kernel_size int list 1 integer, specifying size transposed convolution window. strides int list 1 integer, specifying stride length transposed convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated transposed convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"3D tensor representing activation(conv1d_transpose(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, steps, channels) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, new_steps, filters) data_format=\"channels_first\": 3D tensor shape: (batch_shape, filters, new_steps)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"guide convolution arithmetic deep learning Deconvolutional Networks","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"","code":"x <- random_uniform(c(4, 10, 128)) y <- x |> layer_conv_1d_transpose(32, 3, 2, activation='relu') shape(y) ## shape(4, 21, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"2D convolution layer. — layer_conv_2d","title":"2D convolution layer. — layer_conv_2d","text":"layer creates convolution kernel convolved layer input single spatial (temporal) dimension produce tensor outputs. use_bias TRUE, bias vector created added outputs. Finally, activation NULL, applied outputs well.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D convolution layer. — layer_conv_2d","text":"","code":"layer_conv_2d(   object,   filters,   kernel_size,   strides = list(1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L),   groups = 1L,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D convolution layer. — layer_conv_2d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int list 2 integer, specifying size convolution window. strides int list 2 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 2 integers, specifying dilation rate use dilated convolution. groups positive int specifying number groups input split along channel axis. group convolved separately filters // groups filters. output concatenation groups results along channel axis. Input channels filters must divisible groups. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D convolution layer. — layer_conv_2d","text":"4D tensor representing activation(conv2d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D convolution layer. — layer_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, height, width, channels) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D convolution layer. — layer_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, new_height, new_width, filters) data_format=\"channels_first\": 4D tensor shape: (batch_size, filters, new_height, new_width)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"2D convolution layer. — layer_conv_2d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"2D convolution layer. — layer_conv_2d","text":"","code":"x <- random_uniform(c(4, 10, 10, 128)) y <- x |> layer_conv_2d(32, 3, activation='relu') shape(y) ## shape(4, 8, 8, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"2D transposed convolution layer. — layer_conv_2d_transpose","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"need transposed convolutions generally arise desire use transformation going opposite direction normal convolution, .e., something shape output convolution something shape input maintaining connectivity pattern compatible said convolution.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"","code":"layer_conv_2d_transpose(   object,   filters,   kernel_size,   strides = list(1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L),   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters transposed convolution). kernel_size int list 1 integer, specifying size transposed convolution window. strides int list 1 integer, specifying stride length transposed convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated transposed convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"4D tensor representing activation(conv2d_transpose(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, height, width, channels) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, new_height, new_width, filters) data_format=\"channels_first\": 4D tensor shape: (batch_size, filters, new_height, new_width)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"guide convolution arithmetic deep learning Deconvolutional Networks","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"","code":"x <- random_uniform(c(4, 10, 8, 128)) y <- x |> layer_conv_2d_transpose(32, 2, 2, activation='relu') shape(y) ## shape(4, 20, 16, 32) # (4, 20, 16, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"3D convolution layer. — layer_conv_3d","title":"3D convolution layer. — layer_conv_3d","text":"layer creates convolution kernel convolved layer input single spatial (temporal) dimension produce tensor outputs. use_bias TRUE, bias vector created added outputs. Finally, activation NULL, applied outputs well.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"3D convolution layer. — layer_conv_3d","text":"","code":"layer_conv_3d(   object,   filters,   kernel_size,   strides = list(1L, 1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L, 1L),   groups = 1L,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"3D convolution layer. — layer_conv_3d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int list 3 integer, specifying size convolution window. strides int list 3 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 3 integers, specifying dilation rate use dilated convolution. groups positive int specifying number groups input split along channel axis. group convolved separately filters %/% groups filters. output concatenation groups results along channel axis. Input channels filters must divisible groups. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"3D convolution layer. — layer_conv_3d","text":"5D tensor representing activation(conv3d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"3D convolution layer. — layer_conv_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"3D convolution layer. — layer_conv_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3, filters) data_format=\"channels_first\": 5D tensor shape: (batch_size, filters, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"3D convolution layer. — layer_conv_3d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"3D convolution layer. — layer_conv_3d","text":"","code":"x <- random_uniform(c(4, 10, 10, 10, 128)) y <- x |> layer_conv_3d(32, 3, activation = 'relu') shape(y) ## shape(4, 8, 8, 8, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"3D transposed convolution layer. — layer_conv_3d_transpose","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"need transposed convolutions generally arise desire use transformation going opposite direction normal convolution, .e., something shape output convolution something shape input maintaining connectivity pattern compatible said convolution.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"","code":"layer_conv_3d_transpose(   object,   filters,   kernel_size,   strides = list(1L, 1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L, 1L),   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters transposed convolution). kernel_size int list 1 integer, specifying size transposed convolution window. strides int list 1 integer, specifying stride length transposed convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated transposed convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"5D tensor representing activation(conv3d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3, filters) data_format=\"channels_first\": 5D tensor shape: (batch_size, filters, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"guide convolution arithmetic deep learning Deconvolutional Networks","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"","code":"x <- random_uniform(c(4, 10, 8, 12, 128)) y <- x |> layer_conv_3d_transpose(32, 2, 2, activation = 'relu') shape(y) ## shape(4, 20, 16, 24, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"1D Convolutional LSTM. — layer_conv_lstm_1d","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"Similar LSTM layer, input transformations recurrent transformations convolutional.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"","code":"layer_conv_lstm_1d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   ...,   unroll = NULL )"},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int tuple/list 1 integer, specifying size convolution window. strides int tuple/list 1 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int tuple/list 1 integers, specifying dilation rate use dilated convolution. activation Activation function use. default hyperbolic tangent activation function applied (tanh(x)). recurrent_activation Activation function use recurrent step. use_bias Boolean, whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. bias_initializer Initializer bias vector. unit_forget_bias Boolean. TRUE, add 1 bias forget gate initialization. Use combination bias_initializer=\"zeros\". recommended Jozefowicz et al., 2015 kernel_regularizer Regularizer function applied kernel weights matrix. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. bias_regularizer Regularizer function applied bias vector. activity_regularizer Regularizer function applied . kernel_constraint Constraint function applied kernel weights matrix. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. bias_constraint Constraint function applied bias vector. dropout Float 0 1. Fraction units drop linear transformation inputs. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default FALSE). TRUE, last state sample index batch used initial state sample index following batch. ... forward/backward compatability. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"inputs: 4D tensor. initial_state: List initial state tensors passed first call cell. mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout set.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"data_format=\"channels_first\": 4D tensor shape: (samples, time, channels, rows) data_format=\"channels_last\": 4D tensor shape: (samples, time, rows, channels)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"return_state: list tensors. first tensor output. remaining tensors last states, 3D tensor shape: (samples, filters, new_rows) data_format='channels_first' shape: (samples, new_rows, filters) data_format='channels_last'. rows values might changed due padding. return_sequences: 4D tensor shape: (samples, timesteps, filters, new_rows) data_format='channels_first' shape: (samples, timesteps, new_rows, filters) data_format='channels_last'. Else, 3D tensor shape: (samples, filters, new_rows) data_format='channels_first' shape: (samples, new_rows, filters) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"Shi et al., 2015 (current implementation include feedback loop cells output).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"2D Convolutional LSTM. — layer_conv_lstm_2d","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"Similar LSTM layer, input transformations recurrent transformations convolutional.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"","code":"layer_conv_lstm_2d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   ...,   unroll = NULL )"},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int tuple/list 2 integers, specifying size convolution window. strides int tuple/list 2 integers, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int tuple/list 2 integers, specifying dilation rate use dilated convolution. activation Activation function use. default hyperbolic tangent activation function applied (tanh(x)). recurrent_activation Activation function use recurrent step. use_bias Boolean, whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. bias_initializer Initializer bias vector. unit_forget_bias Boolean. TRUE, add 1 bias forget gate initialization. Use combination bias_initializer=\"zeros\". recommended Jozefowicz et al., 2015 kernel_regularizer Regularizer function applied kernel weights matrix. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. bias_regularizer Regularizer function applied bias vector. activity_regularizer Regularizer function applied . kernel_constraint Constraint function applied kernel weights matrix. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. bias_constraint Constraint function applied bias vector. dropout Float 0 1. Fraction units drop linear transformation inputs. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default FALSE). TRUE, last state sample index batch used initial state sample index following batch. ... forward/backward compatability. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"inputs: 5D tensor. mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout set. initial_state: List initial state tensors passed first call cell.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"data_format='channels_first': 5D tensor shape: (samples, time, channels, rows, cols) data_format='channels_last': 5D tensor shape: (samples, time, rows, cols, channels)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"return_state: list tensors. first tensor output. remaining tensors last states, 4D tensor shape: (samples, filters, new_rows, new_cols) data_format='channels_first' shape: (samples, new_rows, new_cols, filters) data_format='channels_last'. rows cols values might changed due padding. return_sequences: 5D tensor shape: (samples, timesteps, filters, new_rows, new_cols) data_format='channels_first' shape: (samples, timesteps, new_rows, new_cols, filters) data_format='channels_last'. Else, 4D tensor shape: (samples, filters, new_rows, new_cols) data_format='channels_first' shape: (samples, new_rows, new_cols, filters) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"Shi et al., 2015 (current implementation include feedback loop cells output).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"3D Convolutional LSTM. — layer_conv_lstm_3d","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"Similar LSTM layer, input transformations recurrent transformations convolutional.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"","code":"layer_conv_lstm_3d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   ...,   unroll = NULL )"},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int tuple/list 3 integers, specifying size convolution window. strides int tuple/list 3 integers, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int tuple/list 3 integers, specifying dilation rate use dilated convolution. activation Activation function use. default hyperbolic tangent activation function applied (tanh(x)). recurrent_activation Activation function use recurrent step. use_bias Boolean, whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. bias_initializer Initializer bias vector. unit_forget_bias Boolean. TRUE, add 1 bias forget gate initialization. Use combination bias_initializer=\"zeros\". recommended Jozefowicz et al., 2015 kernel_regularizer Regularizer function applied kernel weights matrix. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. bias_regularizer Regularizer function applied bias vector. activity_regularizer Regularizer function applied . kernel_constraint Constraint function applied kernel weights matrix. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. bias_constraint Constraint function applied bias vector. dropout Float 0 1. Fraction units drop linear transformation inputs. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default FALSE). TRUE, last state sample index batch used initial state sample index following batch. ... forward/backward compatability. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"inputs: 6D tensor. mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout set. initial_state: List initial state tensors passed first call cell.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"data_format='channels_first': 5D tensor shape: (samples, time, channels, *spatial_dims) data_format='channels_last': 5D tensor shape: (samples, time, *spatial_dims, channels)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"return_state: list tensors. first tensor output. remaining tensors last states, 4D tensor shape: (samples, filters, *spatial_dims) data_format='channels_first' shape: (samples, *spatial_dims, filters) data_format='channels_last'. return_sequences: 5D tensor shape: (samples, timesteps, filters, *spatial_dims) data_format='channels_first' shape: (samples, timesteps, *spatial_dims, filters) data_format='channels_last'. Else, 4D tensor shape: (samples, filters, *spatial_dims) data_format='channels_first' shape: (samples, *spatial_dims, filters) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"Shi et al., 2015 (current implementation include feedback loop cells output).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"crops along time dimension (axis 2).","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"","code":"layer_cropping_1d(object, cropping = list(1L, 1L), ...)"},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"object Object compose layer . tensor, array, sequential model. cropping Int, list int (length 2). int: many units trimmed beginning end cropping dimension (axis 1). list 2 ints: many units trimmed beginning end cropping dimension ((left_crop, right_crop)). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"","code":"input_shape <- c(2, 3, 2) x <- op_arange(prod(input_shape)) |> op_reshape(input_shape) x ## tf.Tensor( ## [[[ 0.  1.] ##   [ 2.  3.] ##   [ 4.  5.]] ## ##  [[ 6.  7.] ##   [ 8.  9.] ##   [10. 11.]]], shape=(2, 3, 2), dtype=float32) y <- x |> layer_cropping_1d(cropping = 1) y ## tf.Tensor( ## [[[2. 3.]] ## ##  [[8. 9.]]], shape=(2, 1, 2), dtype=float32)"},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"3D tensor shape (batch_size, axis_to_crop, features)","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"3D tensor shape (batch_size, cropped_axis, features)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"crops along spatial dimensions, .e. height width.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"","code":"layer_cropping_2d(   object,   cropping = list(list(0L, 0L), list(0L, 0L)),   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"object Object compose layer . tensor, array, sequential model. cropping Int, list 2 ints, list 2 lists 2 ints. int: symmetric cropping applied height width. list 2 ints: interpreted two different symmetric cropping values height width: (symmetric_height_crop, symmetric_width_crop). list 2 lists 2 ints: interpreted ((top_crop, bottom_crop), (left_crop, right_crop)). data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"","code":"input_shape <- c(2, 28, 28, 3) x <- op_arange(prod(input_shape), dtype ='int32') |> op_reshape(input_shape) y <- x |> layer_cropping_2d(cropping=list(c(2, 2), c(4, 4))) shape(y) ## shape(2, 24, 20, 3)"},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, height, width, channels) data_format \"channels_first\": (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, cropped_height, cropped_width, channels) data_format \"channels_first\": (batch_size, channels, cropped_height, cropped_width)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"Cropping layer 3D data (e.g. spatial spatio-temporal).","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"","code":"layer_cropping_3d(   object,   cropping = list(list(1L, 1L), list(1L, 1L), list(1L, 1L)),   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"object Object compose layer . tensor, array, sequential model. cropping Int, list 3 ints, list 3 lists 2 ints. int: symmetric cropping applied depth, height, width. list 3 ints: interpreted three different symmetric cropping values depth, height, width: (symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop). list 3 lists 2 ints: interpreted ((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_crop)). data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"","code":"input_shape <- c(2, 28, 28, 10, 3) x <- input_shape %>% { op_reshape(seq(prod(.)), .) } y <- x |> layer_cropping_3d(cropping = c(2, 4, 2)) shape(y) ## shape(2, 24, 20, 6, 3)"},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop, channels) data_format \"channels_first\": (batch_size, channels, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, first_cropped_axis, second_cropped_axis, third_cropped_axis, channels) data_format \"channels_first\": (batch_size, channels, first_cropped_axis, second_cropped_axis, third_cropped_axis)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_dense.html","id":null,"dir":"Reference","previous_headings":"","what":"Just your regular densely-connected NN layer. — layer_dense","title":"Just your regular densely-connected NN layer. — layer_dense","text":"Dense implements operation: output = activation(dot(input, kernel) + bias) activation element-wise activation function passed activation argument, kernel weights matrix created layer, bias bias vector created layer (applicable use_bias TRUE).","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Just your regular densely-connected NN layer. — layer_dense","text":"","code":"layer_dense(   object,   units,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   lora_rank = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Just your regular densely-connected NN layer. — layer_dense","text":"object Object compose layer . tensor, array, sequential model. units Positive integer, dimensionality output space. activation Activation function use. specify anything, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix. bias_initializer Initializer bias vector. kernel_regularizer Regularizer function applied kernel weights matrix. bias_regularizer Regularizer function applied bias vector. activity_regularizer Regularizer function applied output layer (\"activation\"). kernel_constraint Constraint function applied kernel weights matrix. bias_constraint Constraint function applied bias vector. lora_rank Optional integer. set, layer's forward pass implement LoRA (Low-Rank Adaptation) provided rank. LoRA sets layer's kernel non-trainable replaces delta original kernel, obtained via multiplying two lower-rank trainable matrices. can useful reduce computation cost fine-tuning large dense layers. can also enable LoRA existing Dense layer calling layer$enable_lora(rank). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Just your regular densely-connected NN layer. — layer_dense","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Just your regular densely-connected NN layer. — layer_dense","text":"input layer rank greater 2, Dense computes dot product inputs kernel along last axis inputs axis 0 kernel (using tf.tensordot). example, input dimensions (batch_size, d0, d1), create kernel shape (d1, units), kernel operates along axis 2 input, every sub-tensor shape (1, 1, d1) (batch_size * d0 sub-tensors). output case shape (batch_size, d0, units).","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Just your regular densely-connected NN layer. — layer_dense","text":"N-D tensor shape: (batch_size, ..., input_dim). common situation 2D input shape (batch_size, input_dim).","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Just your regular densely-connected NN layer. — layer_dense","text":"N-D tensor shape: (batch_size, ..., units). instance, 2D input shape (batch_size, input_dim), output shape (batch_size, units).","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Just your regular densely-connected NN layer. — layer_dense","text":"","code":"enable_lora(   rank,   a_initializer = 'he_uniform',   b_initializer = 'zeros' )"},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"readonly-properties-","dir":"Reference","previous_headings":"","what":"Readonly properties:","title":"Just your regular densely-connected NN layer. — layer_dense","text":"kernel","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"1D depthwise convolution layer. — layer_depthwise_conv_1d","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"Depthwise convolution type convolution input channel convolved different kernel (called depthwise kernel). can understand depthwise convolution first step depthwise separable convolution. implemented via following steps: Split input individual channels. Convolve channel individual depthwise kernel depth_multiplier output channels. Concatenate convolved outputs along channels axis. Unlike regular 1D convolution, depthwise convolution mix information across different input channels. depth_multiplier argument determines many filters applied one input channel. , controls amount output channels generated per input channel depthwise step.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"","code":"layer_depthwise_conv_1d(   object,   kernel_size,   strides = 1L,   padding = \"valid\",   depth_multiplier = 1L,   data_format = NULL,   dilation_rate = 1L,   activation = NULL,   use_bias = TRUE,   depthwise_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   depthwise_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   depthwise_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"object Object compose layer . tensor, array, sequential model. kernel_size int list 1 integer, specifying size depthwise convolution window. strides int list 1 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. depth_multiplier number depthwise convolution output channels input channel. total number depthwise convolution output channels equal input_channel * depth_multiplier. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. depthwise_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. depthwise_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. depthwise_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"3D tensor representing activation(depthwise_conv1d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, steps, channels) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, new_steps, channels * depth_multiplier) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels * depth_multiplier, new_steps)","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"","code":"x <- random_uniform(c(4, 10, 12)) y <- x |> layer_depthwise_conv_1d(   kernel_size = 3,   depth_multiplier = 3,   activation = 'relu' ) shape(y) ## shape(4, 8, 36)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"2D depthwise convolution layer. — layer_depthwise_conv_2d","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"Depthwise convolution type convolution input channel convolved different kernel (called depthwise kernel). can understand depthwise convolution first step depthwise separable convolution. implemented via following steps: Split input individual channels. Convolve channel individual depthwise kernel depth_multiplier output channels. Concatenate convolved outputs along channels axis. Unlike regular 2D convolution, depthwise convolution mix information across different input channels. depth_multiplier argument determines many filters applied one input channel. , controls amount output channels generated per input channel depthwise step.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"","code":"layer_depthwise_conv_2d(   object,   kernel_size,   strides = list(1L, 1L),   padding = \"valid\",   depth_multiplier = 1L,   data_format = NULL,   dilation_rate = list(1L, 1L),   activation = NULL,   use_bias = TRUE,   depthwise_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   depthwise_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   depthwise_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"object Object compose layer . tensor, array, sequential model. kernel_size int list 2 integer, specifying size depthwise convolution window. strides int list 2 integer, specifying stride length depthwise convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. depth_multiplier number depthwise convolution output channels input channel. total number depthwise convolution output channels equal input_channel * depth_multiplier. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 2 integers, specifying dilation rate use dilated convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. depthwise_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. depthwise_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. depthwise_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"4D tensor representing activation(depthwise_conv2d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, height, width, channels) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, new_height, new_width, channels * depth_multiplier) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels * depth_multiplier, new_height, new_width)","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"","code":"x <- random_uniform(c(4, 10, 10, 12)) y <- x |> layer_depthwise_conv_2d(3, 3, activation = 'relu') shape(y) ## shape(4, 3, 3, 12)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"layer place element input data one several contiguous ranges output integer index indicating range element placed . Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"","code":"layer_discretization(   object,   bin_boundaries = NULL,   num_bins = NULL,   epsilon = 0.01,   output_mode = \"int\",   sparse = FALSE,   dtype = NULL,   name = NULL )"},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"object Object compose layer . tensor, array, sequential model. bin_boundaries list bin boundaries. leftmost rightmost bins always extend -Inf Inf, bin_boundaries = c(0, 1, 2) generates bins (-Inf, 0), [0, 1), [1, 2), [2, +Inf). option set, adapt() called. num_bins integer number bins compute. option set, adapt() called learn bin boundaries. epsilon Error tolerance, typically small fraction close zero (e.g. 0.01). Higher values epsilon increase quantile approximation, hence result unequal buckets, improve performance resource consumption. output_mode Specification output layer. Values can \"int\", \"one_hot\", \"multi_hot\", \"count\" configuring layer follows: \"int\": Return discretized bin indices directly. \"one_hot\": Encodes individual element input array size num_bins, containing 1 input's bin index. last dimension size 1, encode dimension.  last dimension size 1, append new dimension encoded output. \"multi_hot\": Encodes sample input single array size num_bins, containing 1 bin index index present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). \"count\": \"multi_hot\", int array contains count number times bin index appeared sample. Defaults \"int\". sparse Boolean. applicable \"one_hot\", \"multi_hot\", \"count\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. dtype datatype (e.g., \"float32\"). name String, name object","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"array dimension 2 higher.","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"input shape.","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"Discretize float values based provided buckets.     Discretize float values based number buckets compute.","code":"input <- op_array(rbind(c(-1.5, 1, 3.4, 0.5),                        c(0, 3, 1.3, 0),                        c(-.5, 0, .5, 1),                        c(1.5, 2, 2.5, 3))) output <- input |> layer_discretization(bin_boundaries = c(0, 1, 2)) output ## tf.Tensor( ## [[0 2 3 1] ##  [1 3 2 1] ##  [0 1 1 2] ##  [2 3 3 3]], shape=(4, 4), dtype=int64) layer <- layer_discretization(num_bins = 4, epsilon = 0.01) layer |> adapt(input) layer(input) ## tf.Tensor( ## [[0 2 3 1] ##  [1 3 2 1] ##  [0 1 1 2] ##  [2 3 3 3]], shape=(4, 4), dtype=int64)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_dot.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes element-wise dot product of two tensors. — layer_dot","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"takes list inputs size 2, axes corresponding input along dot product performed. say x y two input tensors shapes (2, 3, 5) (2, 10, 3). batch dimension size inputs, axes correspond dimensions size corresponding inputs. e.g. axes = c(1, 2), dot product x, y result tensor shape (2, 5, 10)","code":""},{"path":"https://keras.posit.co/reference/layer_dot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"","code":"layer_dot(inputs, ..., axes, normalize = FALSE)"},{"path":"https://keras.posit.co/reference/layer_dot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"inputs layers combine ... Standard layer keyword arguments. axes Integer list integers, axis axes along take dot product. list, two integers corresponding desired axis first input desired axis second input, respectively. Note size two selected axes must match. normalize Whether L2-normalize samples along dot product axis taking dot product. set TRUE, output dot product cosine proximity two samples.","code":""},{"path":"https://keras.posit.co/reference/layer_dot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"tensor, dot product samples inputs.","code":""},{"path":"https://keras.posit.co/reference/layer_dot.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"Usage Keras model:","code":"x <- op_reshape(0:9,   c(1, 5, 2)) y <- op_reshape(10:19, c(1, 2, 5)) layer_dot(x, y, axes=c(2, 3)) ## tf.Tensor( ## [[[260 360] ##   [320 445]]], shape=(1, 2, 2), dtype=int32) x1 <- op_reshape(0:9, c(5, 2)) |> layer_dense(8) x2 <- op_reshape(10:19, c(5, 2)) |> layer_dense(8) shape(x1) ## shape(5, 8) shape(x2) ## shape(5, 8) y <- layer_dot(x1, x2, axes=2) shape(y) ## shape(5, 1)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies dropout to the input. — layer_dropout","title":"Applies dropout to the input. — layer_dropout","text":"Dropout layer randomly sets input units 0 frequency rate step training time, helps prevent overfitting. Inputs set 0 scaled 1 / (1 - rate) sum inputs unchanged. Note Dropout layer applies training set TRUE call(), values dropped inference. using model.fit, training appropriately set TRUE automatically. contexts, can set argument explicitly TRUE calling layer. (contrast setting trainable=FALSE Dropout layer. trainable affect layer's behavior, Dropout variables/weights can frozen training.)","code":""},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies dropout to the input. — layer_dropout","text":"","code":"layer_dropout(object, rate, noise_shape = NULL, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies dropout to the input. — layer_dropout","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. Fraction input units drop. noise_shape 1D integer tensor representing shape binary dropout mask multiplied input. instance, inputs shape (batch_size, timesteps, features) want dropout mask timesteps, can use noise_shape=(batch_size, 1, features). seed R integer use random seed. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies dropout to the input. — layer_dropout","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Applies dropout to the input. — layer_dropout","text":"inputs: Input tensor (rank). training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (nothing).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":null,"dir":"Reference","previous_headings":"","what":"A layer that uses einsum as the backing computation. — layer_einsum_dense","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"layer can perform einsum calculations arbitrary dimensionality.","code":""},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"","code":"layer_einsum_dense(   object,   equation,   output_shape,   activation = NULL,   bias_axes = NULL,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   lora_rank = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"object Object compose layer . tensor, array, sequential model. equation equation describing einsum perform. equation must valid einsum string form ab,bc->ac, ...ab,bc->...ac, ab...,bc->ac... 'ab', 'bc', 'ac' can valid einsum axis expression sequence. output_shape expected shape output tensor (excluding batch dimension dimensions represented ellipses). can specify NA NULL dimension unknown can inferred input shape. activation Activation function use. specify anything, activation applied (, \"linear\" activation: (x) = x). bias_axes string containing output dimension(s) apply bias . character bias_axes string correspond character output portion equation string. kernel_initializer Initializer kernel weights matrix. bias_initializer Initializer bias vector. kernel_regularizer Regularizer function applied kernel weights matrix. bias_regularizer Regularizer function applied bias vector. kernel_constraint Constraint function applied kernel weights matrix. bias_constraint Constraint function applied bias vector. lora_rank Optional integer. set, layer's forward pass implement LoRA (Low-Rank Adaptation) provided rank. LoRA sets layer's kernel non-trainable replaces delta original kernel, obtained via multiplying two lower-rank trainable matrices (factorization happens last dimension). can useful reduce computation cost fine-tuning large dense layers. can also enable LoRA existing EinsumDense layer calling layer$enable_lora(rank). ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"Biased dense layer einsums example shows instantiate standard Keras dense layer using einsum operations. example equivalent layer_Dense(64, use_bias=TRUE).     Applying dense layer sequence example shows instantiate layer applies dense operation every element sequence. , output_shape two values (since two non-batch dimensions output); first dimension output_shape NA, sequence dimension b unknown shape.     Applying dense layer sequence using ellipses example shows instantiate layer applies dense operation every element sequence, uses ellipsis notation instead specifying batch sequence dimensions. using ellipsis notation specified one axis, output_shape arg single value. instantiated way, layer can handle number sequence dimensions - including case sequence dimension exists.","code":"input <- layer_input(shape = c(32)) output <- input |>   layer_einsum_dense(\"ab,bc->ac\",                      output_shape = 64,                      bias_axes = \"c\") output # shape(NA, 64) ## <KerasTensor shape=(None, 64), dtype=float32, sparse=False, name=keras_tensor_1> input <- layer_input(shape = c(32, 128)) output <- input |>   layer_einsum_dense(\"abc,cd->abd\",                      output_shape = c(NA, 64),                      bias_axes = \"d\") output  # shape(NA, 32, 64) ## <KerasTensor shape=(None, None, 64), dtype=float32, sparse=False, name=keras_tensor_3> input <- layer_input(shape = c(32, 128)) output <- input |>   layer_einsum_dense(\"...x,xy->...y\",                      output_shape = 64,                      bias_axes = \"y\")  output  # shape(NA, 32, 64) ## <KerasTensor shape=(None, 32, 64), dtype=float32, sparse=False, name=keras_tensor_5>"},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"","code":"enable_lora(   rank,   a_initializer = 'he_uniform',   b_initializer = 'zeros' ) quantize(mode)"},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"readonly-properties-","dir":"Reference","previous_headings":"","what":"Readonly properties:","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"kernel","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":null,"dir":"Reference","previous_headings":"","what":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"e.g. rbind(4L, 20L) \\(\\rightarrow\\) rbind(c(0.25, 0.1), c(0.6, -0.2)) layer can used positive integer inputs fixed range.","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"","code":"layer_embedding(   object,   input_dim,   output_dim,   embeddings_initializer = \"uniform\",   embeddings_regularizer = NULL,   embeddings_constraint = NULL,   mask_zero = FALSE,   weights = NULL,   lora_rank = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"object Object compose layer . tensor, array, sequential model. input_dim Integer. Size vocabulary, .e. maximum integer index + 1. output_dim Integer. Dimension dense embedding. embeddings_initializer Initializer embeddings matrix (see keras3::initializer_*). embeddings_regularizer Regularizer function applied embeddings matrix (see keras3::regularizer_*). embeddings_constraint Constraint function applied embeddings matrix (see keras3::constraint_*). mask_zero Boolean, whether input value 0 special \"padding\" value masked . useful using recurrent layers may take variable length input. TRUE, subsequent layers model need support masking exception raised. mask_zero set TRUE, consequence, index 0 used vocabulary (input_dim equal size vocabulary + 1). weights Optional floating-point matrix size (input_dim, output_dim). initial embeddings values use. lora_rank Optional integer. set, layer's forward pass implement LoRA (Low-Rank Adaptation) provided rank. LoRA sets layer's embeddings matrix non-trainable replaces delta original matrix, obtained via multiplying two lower-rank trainable matrices. can useful reduce computation cost fine-tuning large embedding layers. can also enable LoRA existing Embedding layer instance calling layer$enable_lora(rank). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"","code":"model <- keras_model_sequential() |>   layer_embedding(1000, 64)  # The model will take as input an integer matrix of size (batch,input_length), # and the largest integer (i.e. word index) in the input # should be no larger than 999 (vocabulary size). # Now model$output_shape is (NA, 10, 64), where `NA` is the batch # dimension.  input_array <- random_integer(shape = c(32, 10), minval = 0, maxval = 1000) model |> compile('rmsprop', 'mse') output_array <- model |> predict(input_array, verbose = 0) dim(output_array)    # (32, 10, 64) ## [1] 32 10 64"},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"2D tensor shape: (batch_size, input_length).","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"3D tensor shape: (batch_size, input_length, output_dim).","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"","code":"enable_lora(   rank,   a_initializer = 'he_uniform',   b_initializer = 'zeros' ) quantize(mode) quantized_build(input_shape, mode) quantized_call(inputs)"},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"readonly-properties-","dir":"Reference","previous_headings":"","what":"Readonly properties:","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"embeddings","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":null,"dir":"Reference","previous_headings":"","what":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"Available feature types: Note features can referred string name, e.g. \"integer_categorical\". using string name, default argument values used.","code":"# Plain float values. feature_float(name = NULL)  # Float values to be preprocessed via featurewise standardization # (i.e. via a `layer_normalization()` layer). feature_float_normalized(name = NULL)  # Float values to be preprocessed via linear rescaling # (i.e. via a `layer_rescaling` layer). feature_float_rescaled(scale = 1., offset = 0., name = NULL)  # Float values to be discretized. By default, the discrete # representation will then be one-hot encoded. feature_float_discretized(   num_bins,   bin_boundaries = NULL,   output_mode = \"one_hot\",   name = NULL )  # Integer values to be indexed. By default, the discrete # representation will then be one-hot encoded. feature_integer_categorical(   max_tokens = NULL,   num_oov_indices = 1,   output_mode = \"one_hot\",   name = NULL )  # String values to be indexed. By default, the discrete # representation will then be one-hot encoded. feature_string_categorical(   max_tokens = NULL,   num_oov_indices = 1,   output_mode = \"one_hot\",   name = NULL )  # Integer values to be hashed into a fixed number of bins. # By default, the discrete representation will then be one-hot encoded. feature_integer_hashed(num_bins, output_mode = \"one_hot\", name = NULL)  # String values to be hashed into a fixed number of bins. # By default, the discrete representation will then be one-hot encoded. feature_string_hashed(num_bins, output_mode = \"one_hot\", name = NULL)"},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"","code":"layer_feature_space(   object,   features,   output_mode = \"concat\",   crosses = NULL,   crossing_dim = 32L,   hashing_dim = 32L,   num_discretization_bins = 32L,   name = NULL,   feature_names = NULL )  feature_cross(feature_names, crossing_dim, output_mode = \"one_hot\")  feature_custom(dtype, preprocessor, output_mode)  feature_float(name = NULL)  feature_float_rescaled(scale = 1, offset = 0, name = NULL)  feature_float_normalized(name = NULL)  feature_float_discretized(   num_bins,   bin_boundaries = NULL,   output_mode = \"one_hot\",   name = NULL )  feature_integer_categorical(   max_tokens = NULL,   num_oov_indices = 1,   output_mode = \"one_hot\",   name = NULL )  feature_string_categorical(   max_tokens = NULL,   num_oov_indices = 1,   output_mode = \"one_hot\",   name = NULL )  feature_string_hashed(num_bins, output_mode = \"one_hot\", name = NULL)  feature_integer_hashed(num_bins, output_mode = \"one_hot\", name = NULL)"},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"object see description features see description output_mode string. layer_feature_space(), one \"concat\" \"dict\". concat mode, features get concatenated together single vector. dict mode, FeatureSpace returns named list individually encoded features (names input list names). feature_* functions, one : \"int\" \"one_hot\" \"float\". crosses List features crossed together, e.g. crosses=list(c(\"feature_1\", \"feature_2\")). features \"crossed\" hashing combined value fixed-length vector. crossing_dim Default vector size hashing crossed features. Defaults 32. hashing_dim Default vector size hashing features type \"integer_hashed\" \"string_hashed\". Defaults 32. num_discretization_bins Default number bins used discretizing features type \"float_discretized\". Defaults 32. name String, name object feature_names Named list mapping names features type specification, e.g. list(my_feature = \"integer_categorical\") list(my_feature = feature_integer_categorical()). complete list supported types, see \"Available feature types\" paragraph . dtype string, output dtype feature. E.g., \"float32\". preprocessor callable. scale, offset Passed layer_rescaling() num_bins, bin_boundaries Passed layer_discretization() max_tokens, num_oov_indices Passed layer_integer_lookup() feature_integer_categorical() layer_string_lookup() feature_string_categorical().","code":""},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"Basic usage named list input data:   Basic usage tf.data:   Basic usage Keras Functional API:           Customizing feature feature cross:   Returning dict (named list) integer-encoded features:   Specifying Keras preprocessing layer:   Retrieving underlying Keras preprocessing layers:   Saving reloading FeatureSpace:","code":"raw_data <- list(   float_values = c(0.0, 0.1, 0.2, 0.3),   string_values = c(\"zero\", \"one\", \"two\", \"three\"),   int_values = as.integer(c(0, 1, 2, 3)) )  dataset <- tfdatasets::tensor_slices_dataset(raw_data)  feature_space <- layer_feature_space(   features = list(     float_values = \"float_normalized\",     string_values = \"string_categorical\",     int_values = \"integer_categorical\"   ),   crosses = list(c(\"string_values\", \"int_values\")),   output_mode = \"concat\" )  # Before you start using the feature_space(), # you must `adapt()` it on some data. feature_space |> adapt(dataset)  # You can call the feature_space() on a named list of # data (batched or unbatched). output_vector <- feature_space(raw_data) library(tfdatasets) # Unlabeled data preprocessed_ds <- unlabeled_dataset |>   dataset_map(feature_space)  # Labeled data preprocessed_ds <- labeled_dataset |>   dataset_map(function(x, y) tuple(feature_space(x), y)) # Retrieve a named list of Keras layer_input() objects (inputs <- feature_space$get_inputs()) ## $float_values ## <KerasTensor shape=(None, 1), dtype=float32, sparse=None, name=float_values> ## ## $string_values ## <KerasTensor shape=(None, 1), dtype=string, sparse=None, name=string_values> ## ## $int_values ## <KerasTensor shape=(None, 1), dtype=int32, sparse=None, name=int_values> # Retrieve the corresponding encoded Keras tensors (encoded_features <- feature_space$get_encoded_features()) ## <KerasTensor shape=(None, 43), dtype=float32, sparse=False, name=keras_tensor_7> # Build a Functional model outputs <- encoded_features |> layer_dense(1, activation = \"sigmoid\") model <- keras_model(inputs, outputs) feature_space <- layer_feature_space(   features = list(     float_values = feature_float_normalized(),     string_values = feature_string_categorical(max_tokens = 10),     int_values = feature_integer_categorical(max_tokens = 10)   ),   crosses = list(     feature_cross(c(\"string_values\", \"int_values\"), crossing_dim = 32)   ),   output_mode = \"concat\" ) feature_space <- layer_feature_space(   features = list(     \"string_values\" = feature_string_categorical(output_mode = \"int\"),     \"int_values\" = feature_integer_categorical(output_mode = \"int\")   ),   crosses = list(     feature_cross(       feature_names = c(\"string_values\", \"int_values\"),       crossing_dim = 32,       output_mode = \"int\"     )   ),   output_mode = \"dict\" ) # Let's say that one of the features is a short text paragraph that # we want to encode as a vector (one vector per paragraph) via TF-IDF. data <- list(text = c(\"1st string\", \"2nd string\", \"3rd string\"))  # There's a Keras layer for this: layer_text_vectorization() custom_layer <- layer_text_vectorization(output_mode = \"tf_idf\")  # We can use feature_custom() to create a custom feature # that will use our preprocessing layer. feature_space <- layer_feature_space(   features = list(     text = feature_custom(preprocessor = custom_layer,                           dtype = \"string\",                           output_mode = \"float\"     )   ),   output_mode = \"concat\" ) feature_space |> adapt(tfdatasets::tensor_slices_dataset(data)) output_vector <- feature_space(data) # The preprocessing layer of each feature is available in `$preprocessors`. preprocessing_layer <- feature_space$preprocessors$feature1  # The crossing layer of each feature cross is available in `$crossers`. # It's an instance of layer_hashed_crossing() crossing_layer <- feature_space$crossers[[\"feature1_X_feature2\"]] feature_space$save(\"featurespace.keras\") reloaded_feature_space <- keras$models$load_model(\"featurespace.keras\")"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":null,"dir":"Reference","previous_headings":"","what":"Flattens the input. Does not affect the batch size. — layer_flatten","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"Flattens input. affect batch size.","code":""},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"","code":"layer_flatten(object, data_format = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"object Object compose layer . tensor, array, sequential model. data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, ..., channels) \"channels_first\" corresponds inputs shape (batch, channels, ...). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"inputs shaped (batch) without feature axis, flattening adds extra channel dimension output shape (batch, 1).","code":""},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"","code":"x <- layer_input(shape=c(10, 64)) y <- x |> layer_flatten() shape(y) ## shape(NA, 640)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_flax_module_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","title":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","text":"layer enables use Flax components form flax.linen.Module instances within Keras using JAX backend Keras. module method use forward pass can specified via method argument __call__ default. method must take following arguments exact names: self method bound module, case default __call__, module otherwise pass module. inputs: inputs model, JAX array PyTree arrays. training (optional): argument specifying training mode inference mode, TRUE passed training mode. FlaxLayer handles non-trainable state model required RNGs automatically. Note mutable parameter flax.linen.Module.apply() set DenyList([\"params\"]), therefore making assumption variables outside \"params\" collection non-trainable weights. example shows create FlaxLayer Flax Module default __call__ method training argument:   example shows wrap module method conform required signature. allows multiple input arguments training argument different name values. additionally shows use function bound module.","code":"# keras3::use_backend(\"jax\") # py_install(\"flax\", \"r-keras\")  if(config_backend() == \"jax\" &&    reticulate::py_module_available(\"flax\")) {  flax <- import(\"flax\")  MyFlaxModule(flax$linen$Module) %py_class% {   `__call__` <- flax$linen$compact(\\(self, inputs) {     inputs |>       (flax$linen$Conv(features = 32L, kernel_size = tuple(3L, 3L)))() |>       flax$linen$relu() |>       flax$linen$avg_pool(window_shape = tuple(2L, 2L),                           strides = tuple(2L, 2L)) |>       # flatten all except batch_size axis       (\\(x) x$reshape(tuple(x$shape[[1]], -1L)))() |>       (flax$linen$Dense(features = 200L))() |>       flax$linen$relu() |>       (flax$linen$Dense(features = 10L))() |>       flax$linen$softmax()   }) }  # typical usage: input <- keras_input(c(28, 28, 3)) output <- input |>   layer_flax_module_wrapper(MyFlaxModule())  model <- keras_model(input, output)  # to instantiate the layer before composing: flax_module <- MyFlaxModule() keras_layer <- layer_flax_module_wrapper(module = flax_module)  input <- keras_input(c(28, 28, 3)) output <- input |>   keras_layer()  model <- keras_model(input, output)  } flax <- import(\"flax\")  MyFlaxModule(flax$linen$Module) \\%py_class\\% {   forward <-     flax$linen$compact(\\(self, inputs1, input2, deterministic) {       # do work ....       outputs # return     }) }  my_flax_module_wrapper <- function(module, inputs, training) {   c(input1, input2) \\%<-\\% inputs   module$forward(input1, input2,!training) }  flax_module <- MyFlaxModule() keras_layer <- layer_flax_module_wrapper(module = flax_module,                                          method = my_flax_module_wrapper)"},{"path":"https://keras.posit.co/reference/layer_flax_module_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","text":"","code":"layer_flax_module_wrapper(object, module, method = NULL, variables = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_flax_module_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","text":"object Object compose layer . tensor, array, sequential model. module instance flax.linen.Module subclass. method method call model. generally method Module. provided, __call__ method used. method can also function defined Module, case must take Module first argument. used Module.init Module.apply. Details documented method argument flax.linen.Module.apply(). variables dict (named R list) containing variables module format returned flax.linen.Module.init(). contain \"params\" key , applicable, keys collections variables non-trainable state. allows passing trained parameters learned non-trainable state controlling initialization. NULL passed, module's init function called build time initialize variables model. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_flax_module_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"regularization layer, active training time.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"","code":"layer_gaussian_dropout(object, rate, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"object Object compose layer . tensor, array, sequential model. rate Float, drop probability (Dropout). multiplicative noise standard deviation sqrt(rate / (1 - rate)). seed Integer, optional random seed enable deterministic behavior. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"inputs: Input tensor (rank). training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (nothing).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"useful mitigate overfitting (see form random data augmentation). Gaussian Noise (GS) natural choice corruption process real valued inputs. regularization layer, active training time.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"","code":"layer_gaussian_noise(object, stddev, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"object Object compose layer . tensor, array, sequential model. stddev Float, standard deviation noise distribution. seed Integer, optional random seed enable deterministic behavior. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"inputs: Input tensor (rank). training: Python boolean indicating whether layer behave training mode (adding noise) inference mode (nothing).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"Global average pooling operation temporal data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"","code":"layer_global_average_pooling_1d(   object,   data_format = NULL,   keepdims = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, temporal dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"inputs: 3D tensor. mask: Binary tensor shape (batch_size, steps) indicating whether given step masked (excluded average).","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"data_format='channels_last': 3D tensor shape: (batch_size, steps, features) data_format='channels_first': 3D tensor shape: (batch_size, features, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"keepdims=FALSE: 2D tensor shape (batch_size, features). keepdims=TRUE: data_format=\"channels_last\": 3D tensor shape (batch_size, 1, features) data_format=\"channels_first\": 3D tensor shape (batch_size, features, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"","code":"x <- random_uniform(c(2, 3, 4)) y <- x |> layer_global_average_pooling_1d() shape(y) ## shape(2, 4)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"Global average pooling operation 2D data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"","code":"layer_global_average_pooling_2d(   object,   data_format = NULL,   keepdims = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, features, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, spatial dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"data_format='channels_last': 4D tensor shape: (batch_size, height, width, channels) data_format='channels_first': 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"keepdims=FALSE: 2D tensor shape (batch_size, channels). keepdims=TRUE: data_format=\"channels_last\": 4D tensor shape (batch_size, 1, 1, channels) data_format=\"channels_first\": 4D tensor shape (batch_size, channels, 1, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"","code":"x <- random_uniform(c(2, 4, 5, 3)) y <- x |> layer_global_average_pooling_2d() shape(y) ## shape(2, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"Global average pooling operation 3D data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"","code":"layer_global_average_pooling_3d(   object,   data_format = NULL,   keepdims = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, spatial dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"data_format='channels_last': 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format='channels_first': 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"keepdims=FALSE: 2D tensor shape (batch_size, channels). keepdims=TRUE: data_format=\"channels_last\": 5D tensor shape (batch_size, 1, 1, 1, channels) data_format=\"channels_first\": 5D tensor shape (batch_size, channels, 1, 1, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"","code":"x <- random_uniform(c(2, 4, 5, 4, 3)) y <- x |> layer_global_average_pooling_3d() shape(y) ## shape(2, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"Global max pooling operation temporal data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"","code":"layer_global_max_pooling_1d(object, data_format = NULL, keepdims = FALSE, ...)"},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, temporal dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"data_format='channels_last': 3D tensor shape: (batch_size, steps, features) data_format='channels_first': 3D tensor shape: (batch_size, features, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"keepdims=FALSE: 2D tensor shape (batch_size, features). keepdims=TRUE: data_format=\"channels_last\": 3D tensor shape (batch_size, 1, features) data_format=\"channels_first\": 3D tensor shape (batch_size, features, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"","code":"x <- random_uniform(c(2, 3, 4)) y <- x |> layer_global_max_pooling_1d() shape(y) ## shape(2, 4)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"Global max pooling operation 2D data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"","code":"layer_global_max_pooling_2d(object, data_format = NULL, keepdims = FALSE, ...)"},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, features, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, spatial dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"data_format='channels_last': 4D tensor shape: (batch_size, height, width, channels) data_format='channels_first': 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"keepdims=FALSE: 2D tensor shape (batch_size, channels). keepdims=TRUE: data_format=\"channels_last\": 4D tensor shape (batch_size, 1, 1, channels) data_format=\"channels_first\": 4D tensor shape (batch_size, channels, 1, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"","code":"x <- random_uniform(c(2, 4, 5, 3)) y <- x |> layer_global_max_pooling_2d() shape(y) ## shape(2, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"Global max pooling operation 3D data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"","code":"layer_global_max_pooling_3d(object, data_format = NULL, keepdims = FALSE, ...)"},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, spatial dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"data_format='channels_last': 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format='channels_first': 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"keepdims=FALSE: 2D tensor shape (batch_size, channels). keepdims=TRUE: data_format=\"channels_last\": 5D tensor shape (batch_size, 1, 1, 1, channels) data_format=\"channels_first\": 5D tensor shape (batch_size, channels, 1, 1, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"","code":"x <- random_uniform(c(2, 4, 5, 4, 3)) y <- x |> layer_global_max_pooling_3d() shape(y) ## shape(2, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Group normalization layer. — layer_group_normalization","title":"Group normalization layer. — layer_group_normalization","text":"Group Normalization divides channels groups computes within group mean variance normalization. Empirically, accuracy stable batch norm wide range small batch sizes, learning rate adjusted linearly batch sizes. Relation Layer Normalization: number groups set 1, operation becomes nearly identical Layer Normalization (see Layer Normalization docs details). Relation Instance Normalization: number groups set input dimension (number groups equal number channels), operation becomes identical Instance Normalization. can achieve via groups=-1.","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Group normalization layer. — layer_group_normalization","text":"","code":"layer_group_normalization(   object,   groups = 32L,   axis = -1L,   epsilon = 0.001,   center = TRUE,   scale = TRUE,   beta_initializer = \"zeros\",   gamma_initializer = \"ones\",   beta_regularizer = NULL,   gamma_regularizer = NULL,   beta_constraint = NULL,   gamma_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Group normalization layer. — layer_group_normalization","text":"object Object compose layer . tensor, array, sequential model. groups Integer, number groups Group Normalization. Can range [1, N] N input dimension. input dimension must divisible number groups. Defaults 32. axis Integer List/Tuple. axis axes normalize across. Typically, features axis/axes. left-axes typically batch axis/axes. -1 last dimension input. Defaults -1. epsilon Small float added variance avoid dividing zero. Defaults 1e-3. center TRUE, add offset beta normalized tensor. FALSE, beta ignored. Defaults TRUE. scale TRUE, multiply gamma. FALSE, gamma used. next layer linear (also e.g. relu), can disabled since scaling done next layer. Defaults TRUE. beta_initializer Initializer beta weight. Defaults zeros. gamma_initializer Initializer gamma weight. Defaults ones. beta_regularizer Optional regularizer beta weight. NULL default. gamma_regularizer Optional regularizer gamma weight. NULL default. beta_constraint Optional constraint beta weight. NULL default. gamma_constraint Optional constraint gamma weight. NULL default. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Group normalization layer. — layer_group_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Group normalization layer. — layer_group_normalization","text":"Arbitrary. Use keyword argument input_shape (tuple integers, include samples axis) using layer first layer model.","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Group normalization layer. — layer_group_normalization","text":"shape input. **kwargs: Base layer keyword arguments (e.g. name dtype).","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Group normalization layer. — layer_group_normalization","text":"Yuxin Wu & Kaiming , 2018","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"Grouped Query Attention layer. — layer_group_query_attention","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"implementation grouped-query attention introduced Ainslie et al., 2023. num_key_value_heads denotes number groups, setting num_key_value_heads 1 equivalent multi-query attention, num_key_value_heads equal num_query_heads equivalent multi-head attention. layer first projects query, key, value tensors. , key value repeated match number heads query. , query scaled dot-producted key tensors. softmaxed obtain attention probabilities. value tensors interpolated probabilities concatenated back single tensor.","code":""},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"","code":"layer_group_query_attention(   object,   head_dim,   num_query_heads,   num_key_value_heads,   dropout = 0,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"object Object compose layer . tensor, array, sequential model. head_dim Size attention head. num_query_heads Number query attention heads. num_key_value_heads Number key value attention heads. dropout Dropout probability. use_bias Boolean, whether dense layers use bias vectors/matrices. kernel_initializer Initializer dense layer kernels. bias_initializer Initializer dense layer biases. kernel_regularizer Regularizer dense layer kernels. bias_regularizer Regularizer dense layer biases. activity_regularizer Regularizer dense layer activity. kernel_constraint Constraint dense layer kernels. bias_constraint Constraint dense layer kernels. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"attention_output: Result computation, shape (batch_dim, target_seq_len, feature_dim), target_seq_len target sequence length feature_dim query input last dim. attention_scores: (Optional) attention coefficients shape (batch_dim, num_query_heads, target_seq_len, source_seq_len).","code":""},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"query: Query tensor shape (batch_dim, target_seq_len, feature_dim), batch_dim batch size, target_seq_len length target sequence, feature_dim dimension feature. value: Value tensor shape (batch_dim, source_seq_len, feature_dim), batch_dim batch size, source_seq_len length source sequence, feature_dim dimension feature. key: Optional key tensor shape (batch_dim, source_seq_len, feature_dim). given, use value key value, common case. attention_mask: boolean mask shape (batch_dim, target_seq_len, source_seq_len), prevents attention certain positions. boolean mask specifies query elements can attend key elements, 1 indicates attention 0 indicates attention. Broadcasting can happen missing batch dimensions head dimension. return_attention_scores: boolean indicate whether output (attention_output, attention_scores) TRUE, attention_output FALSE. Defaults FALSE. training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (dropout). go either using training mode parent layer/model FALSE (inference) parent layer. use_causal_mask: boolean indicate whether apply causal mask prevent tokens attending future tokens (e.g., used decoder Transformer).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_gru.html","id":null,"dir":"Reference","previous_headings":"","what":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"Based available runtime hardware constraints, layer choose different implementations (cuDNN-based backend-native) maximize performance. GPU available arguments layer meet requirement cuDNN kernel (see details), layer use fast cuDNN implementation using TensorFlow backend. requirements use cuDNN implementation : activation == tanh recurrent_activation == sigmoid dropout == 0 recurrent_dropout == 0 unroll FALSE use_bias TRUE reset_after TRUE Inputs, use masking, strictly right-padded. Eager execution enabled outermost context. two variants GRU implementation. default one based v3 reset gate applied hidden state matrix multiplication. one based original order reversed. second variant compatible CuDNNGRU (GPU-) allows inference CPU. Thus separate biases kernel recurrent_kernel. use variant, set reset_after=TRUE recurrent_activation='sigmoid'. example:","code":"inputs <- random_uniform(c(32, 10, 8)) outputs <- inputs |> layer_gru(4) shape(outputs) ## shape(32, 4) # (32, 4) gru <- layer_gru(, 4, return_sequences = TRUE, return_state = TRUE) c(whole_sequence_output, final_state) %<-% gru(inputs) shape(whole_sequence_output) ## shape(32, 10, 4) shape(final_state) ## shape(32, 4)"},{"path":"https://keras.posit.co/reference/layer_gru.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"","code":"layer_gru(   object,   units,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   unroll = FALSE,   reset_after = TRUE,   use_cudnn = \"auto\",   ... )"},{"path":"https://keras.posit.co/reference/layer_gru.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"object Object compose layer . tensor, array, sequential model. units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). recurrent_activation Activation function use recurrent step. Default: sigmoid (sigmoid). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. activity_regularizer Regularizer function applied output layer (\"activation\"). Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default: FALSE). TRUE, last state sample index batch used initial state sample index following batch. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences. reset_after GRU convention (whether apply reset gate matrix multiplication). FALSE \"\", TRUE \"\" (default cuDNN compatible). use_cudnn Whether use cuDNN-backed implementation. \"auto\" attempt use cuDNN feasible, fallback default implementation . ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_gru.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_gru.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"inputs: 3D tensor, shape (batch, timesteps, feature). mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked  (optional). individual TRUE entry indicates corresponding timestep utilized, FALSE entry indicates corresponding timestep ignored. Defaults NULL. training: Python boolean indicating whether layer behave training mode inference mode. argument passed cell calling . relevant dropout recurrent_dropout used  (optional). Defaults NULL. initial_state: List initial state tensors passed first call cell (optional, NULL causes creation zero-filled initial state tensors). Defaults NULL.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which crosses features using the ","title":"A preprocessing layer which crosses features using the ","text":"layer performs crosses categorical features using \"hashing trick\". Conceptually, transformation can thought : hash(concatenate(features)) %% num_bins. layer currently performs crosses scalar inputs batches scalar inputs. Valid input shapes (batch_size, 1), (batch_size) (). Note: layer wraps tf.keras.layers.HashedCrossing. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tfdatasets pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which crosses features using the ","text":"","code":"layer_hashed_crossing(   object,   num_bins,   output_mode = \"int\",   sparse = FALSE,   name = NULL,   dtype = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which crosses features using the ","text":"object Object compose layer . tensor, array, sequential model. num_bins Number hash bins. output_mode Specification output layer. Values can \"int\", \"one_hot\" configuring layer follows: \"int\": Return integer bin indices directly. \"one_hot\": Encodes individual element input array size num_bins, containing 1 input's bin index. Defaults \"int\". sparse Boolean. applicable \"one_hot\" mode valid using TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. name String, name object dtype datatype (e.g., \"float32\"). ... Keyword arguments construct layer.","code":""},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which crosses features using the ","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which crosses features using the ","text":"Crossing two scalar features.     Crossing one-hotting two scalar features.","code":"feat1 <- c('A', 'B', 'A', 'B', 'A') |> as.array() feat2 <- c(101, 101, 101, 102, 102) |> as.integer() |> as.array() layer <- layer_hashed_crossing(num_bins = 5) layer(list(feat1, feat2)) ## tf.Tensor([1 4 1 1 3], shape=(5), dtype=int64) layer <- layer_hashed_crossing(num_bins = 5, output_mode = 'one_hot') layer(list(feat1, feat2)) ## tf.Tensor( ## [[0. 1. 0. 0. 0.] ##  [0. 0. 0. 0. 1.] ##  [0. 1. 0. 0. 0.] ##  [0. 1. 0. 0. 0.] ##  [0. 0. 0. 1. 0.]], shape=(5, 5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"layer transforms categorical inputs hashed output. element-wise converts ints strings ints fixed range. stable hash function uses tensorflow::ops::Fingerprint produce output consistently across platforms. layer uses FarmHash64 default, provides consistent hashed output across different platforms stable across invocations, regardless device context, mixing input bits thoroughly. want obfuscate hashed output, can also pass random salt argument constructor. case, layer use SipHash64 hash function, salt value serving additional input hash function. Note: layer internally uses TensorFlow. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tf.data pipeline (independently backend using). Example (FarmHash64)     Example (FarmHash64) mask value     Example (SipHash64)     Example (Siphash64 single integer, salt=[133, 133])","code":"layer <- layer_hashing(num_bins = 3) inp <- c('A', 'B', 'C', 'D', 'E') |> array(dim = c(5, 1)) layer(inp) ## tf.Tensor( ## [[1] ##  [0] ##  [1] ##  [1] ##  [2]], shape=(5, 1), dtype=int64) layer <- layer_hashing(num_bins=3, mask_value='') inp <- c('A', 'B', '', 'C', 'D') |> array(dim = c(5, 1)) layer(inp) ## tf.Tensor( ## [[1] ##  [1] ##  [0] ##  [2] ##  [2]], shape=(5, 1), dtype=int64) layer <- layer_hashing(num_bins=3, salt=c(133, 137)) inp <- c('A', 'B', 'C', 'D', 'E') |> array(dim = c(5, 1)) layer(inp) ## tf.Tensor( ## [[1] ##  [2] ##  [1] ##  [0] ##  [2]], shape=(5, 1), dtype=int64) layer <- layer_hashing(num_bins=3, salt=133) inp <- c('A', 'B', 'C', 'D', 'E') |> array(dim = c(5, 1)) layer(inp) ## tf.Tensor( ## [[0] ##  [0] ##  [2] ##  [1] ##  [0]], shape=(5, 1), dtype=int64)"},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"","code":"layer_hashing(   object,   num_bins,   mask_value = NULL,   salt = NULL,   output_mode = \"int\",   sparse = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"object Object compose layer . tensor, array, sequential model. num_bins Number hash bins. Note includes mask_value bin, effective number bins (num_bins - 1) mask_value set. mask_value value represents masked inputs, mapped index 0. NULL means mask term added hashing start index 0. Defaults NULL. salt single unsigned integer NULL. passed, hash function used SipHash64, values used additional input (known \"salt\" cryptography). non-zero. NULL, uses FarmHash64 hash function. also supports list 2 unsigned integer numbers, see reference paper details. Defaults NULL. output_mode Specification output layer. Values can \"int\", \"one_hot\", \"multi_hot\", \"count\" configuring layer follows: \"int\": Return integer bin indices directly. \"one_hot\": Encodes individual element input array size num_bins, containing 1 input's bin index. last dimension size 1, encode dimension. last dimension size 1, append new dimension encoded output. \"multi_hot\": Encodes sample input single array size num_bins, containing 1 bin index index present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). \"count\": \"multi_hot\", int array contains count number times bin index appeared sample. Defaults \"int\". sparse Boolean. applicable \"one_hot\", \"multi_hot\", \"count\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. ... Keyword arguments construct layer.","code":""},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"single string, list strings, int32 int64 tensor shape (batch_size, ...,).","code":""},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"int32 tensor shape (batch_size, ...).","code":""},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"SipHash salt","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Identity layer. — layer_identity","title":"Identity layer. — layer_identity","text":"layer used placeholder operation performed. layer just returns inputs argument output.","code":""},{"path":"https://keras.posit.co/reference/layer_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identity layer. — layer_identity","text":"","code":"layer_identity(object, ...)"},{"path":"https://keras.posit.co/reference/layer_identity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identity layer. — layer_identity","text":"object Object compose layer . tensor, array, sequential model. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_identity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identity layer. — layer_identity","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_input.html","id":null,"dir":"Reference","previous_headings":"","what":"keras_input — layer_input","title":"keras_input — layer_input","text":"layer_input() alias keras_input(). See ?keras_input() full documentation.","code":""},{"path":"https://keras.posit.co/reference/layer_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"keras_input — layer_input","text":"","code":"layer_input(   shape = NULL,   batch_size = NULL,   dtype = NULL,   sparse = NULL,   batch_shape = NULL,   name = NULL,   tensor = NULL )"},{"path":"https://keras.posit.co/reference/layer_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"keras_input — layer_input","text":"shape shape list (list integers NULL objects), including batch size. instance, shape = c(32) indicates expected input batches 32-dimensional vectors. Elements list can NULL NA; NULL/NA elements represent dimensions shape known may vary (e.g. sequence length). batch_size Optional static batch size (integer). dtype data type expected input, string (e.g. \"float32\", \"int32\"...) sparse boolean specifying whether expected input sparse tensors. Note , sparse FALSE, sparse tensors can still passed input - densified default value 0. feature supported TensorFlow backend. Defaults FALSE. batch_shape Shape, including batch dim. name Optional name string layer. unique model (reuse name twice). autogenerated provided. tensor Optional existing tensor wrap Input layer. set, layer use tensor rather creating new placeholder tensor.","code":""},{"path":"https://keras.posit.co/reference/layer_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"keras_input — layer_input","text":"Keras tensor, can passed inputs argument (keras_model()).","code":""},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"layer maps set arbitrary integer input tokens indexed integer output via table-based vocabulary lookup. layer's output indices contiguously arranged maximum vocab size, even input tokens non-continguous unbounded. layer supports multiple options encoding output via output_mode, optional support --vocabulary (OOV) tokens masking. vocabulary layer must either supplied construction learned via adapt(). adapt(), layer analyze data set, determine frequency individual integer tokens, create vocabulary . vocabulary capped size, frequent tokens used create vocabulary others treated OOV. two possible output modes layer.  output_mode \"int\", input integers converted index vocabulary (integer).  output_mode \"multi_hot\", \"count\", \"tf_idf\", input integers encoded array dimension corresponds element vocabulary. vocabulary can optionally contain mask token well OOV token (can optionally occupy multiple indices vocabulary, set num_oov_indices). position tokens vocabulary fixed. output_mode \"int\", vocabulary begin mask token index 0, followed OOV indices, followed rest vocabulary. output_mode \"multi_hot\", \"count\", \"tf_idf\" vocabulary begin OOV indices instances mask token dropped. Note: layer uses TensorFlow internally. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"","code":"layer_integer_lookup(   object,   max_tokens = NULL,   num_oov_indices = 1L,   mask_token = NULL,   oov_token = -1L,   vocabulary = NULL,   vocabulary_dtype = \"int64\",   idf_weights = NULL,   invert = FALSE,   output_mode = \"int\",   sparse = FALSE,   pad_to_max_tokens = FALSE,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"object Object compose layer . tensor, array, sequential model. max_tokens Maximum size vocabulary layer. specified adapting vocabulary setting pad_to_max_tokens=TRUE. NULL, cap size vocabulary. Note size includes OOV mask tokens. Defaults NULL. num_oov_indices number --vocabulary tokens use. value 1, OOV inputs modulated determine OOV value. value 0, OOV inputs cause error calling layer. Defaults 1. mask_token integer token represents masked inputs. output_mode \"int\", token included vocabulary mapped index 0. output modes, token appear vocabulary instances mask token input dropped. set NULL, mask term added. Defaults NULL. oov_token used invert TRUE. token return OOV indices. Defaults -1. vocabulary Optional. Either array integers string path text file. passing array, can pass list, list, 1D NumPy array, 1D tensor containing integer vocbulary terms. passing file path, file contain one line per term vocabulary. argument set, need adapt() layer. vocabulary_dtype dtype vocabulary terms, example \"int64\" \"int32\". Defaults \"int64\". idf_weights valid output_mode \"tf_idf\". list, list, 1D NumPy array, 1D tensor length vocabulary, containing floating point inverse document frequency weights, multiplied per sample term counts final TF-IDF weight. vocabulary argument set, output_mode \"tf_idf\", argument must supplied. invert valid output_mode \"int\". TRUE, layer map indices vocabulary items instead mapping vocabulary items indices. Defaults FALSE. output_mode Specification output layer. Values can \"int\", \"one_hot\", \"multi_hot\", \"count\", \"tf_idf\" configuring layer follows: \"int\": Return vocabulary indices input tokens. \"one_hot\": Encodes individual element input array size vocabulary, containing 1 element index. last dimension size 1, encode dimension. last dimension size 1, append new dimension encoded output. \"multi_hot\": Encodes sample input single array size vocabulary, containing 1 vocabulary term present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). \"count\": \"multi_hot\", int array contains count number times token index appeared sample. \"tf_idf\": \"multi_hot\", TF-IDF algorithm applied find value token slot. \"int\" output, shape input output supported. output modes, currently output rank 2 supported. Defaults \"int\". sparse Boolean. applicable \"multi_hot\", \"count\", \"tf_idf\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. pad_to_max_tokens applicable output_mode \"multi_hot\", \"count\", \"tf_idf\". TRUE, output feature axis padded max_tokens even number unique tokens vocabulary less max_tokens, resulting tensor shape (batch_size, max_tokens) regardless vocabulary size. Defaults FALSE. name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"Creating lookup layer known vocabulary example creates lookup layer pre-existing vocabulary.     Creating lookup layer adapted vocabulary example creates lookup layer generates vocabulary analyzing dataset.     Note OOV token -1 added vocabulary. remaining tokens sorted frequency (42, 2 occurrences, first) inverse sort order.     Lookups multiple OOV indices example demonstrates use lookup layer multiple OOV indices.  layer created one OOV index, OOV tokens hashed number OOV buckets, distributing OOV tokens deterministic fashion across set.     Note output OOV token 37 1, output OOV token 1000 0. -vocab terms output index increased 1 earlier examples (12 maps 2, etc) order make space extra OOV token. One-hot output Configure layer output_mode='one_hot'. Note first num_oov_indices dimensions ont_hot encoding represent OOV values.     Multi-hot output Configure layer output_mode = 'multi_hot'. Note first num_oov_indices dimensions multi_hot encoding represent OOV tokens     Token count output Configure layer output_mode='count'. multi_hot output, first num_oov_indices dimensions output represent OOV tokens.     TF-IDF output Configure layer output_mode='tf_idf'. multi_hot output, first num_oov_indices dimensions output represent OOV tokens. token bin output token_count * idf_weight, idf weights inverse document frequency weights per token. provided along vocabulary. Note idf_weight OOV tokens default average idf weights passed .     specify idf weights oov tokens, need pass entire vocabulary including leading oov token.     adapting layer \"tf_idf\" mode, input sample considered document, IDF weight per token calculated : log(1 + num_documents / (1 + token_document_count)). Inverse lookup example demonstrates map indices tokens using layer. (can also use adapt() inverse = TRUE, simplicity pass vocab example.)     Note first index correspond oov token default. Forward inverse lookup pairs example demonstrates use vocabulary standard lookup layer create inverse lookup layer.     example, input token 1000 resulted output -1, since 1000 vocabulary - got represented OOV, OOV tokens returned -1 inverse layer. Also, note inverse work, must already set forward layer vocabulary either directly via adapt() calling get_vocabulary().","code":"vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(rbind(c(12, 1138, 42),                       c(42, 1000, 36)))  # Note OOV tokens out <- data |> layer_integer_lookup(vocabulary = vocab) out ## tf.Tensor( ## [[1 3 4] ##  [4 0 2]], shape=(2, 3), dtype=int64) data <- op_array(rbind(c(12, 1138, 42),                       c(42, 1000, 36)))  # Note OOV tokens layer <- layer_integer_lookup() layer |> adapt(data) layer |> get_vocabulary() |> str() ## List of 6 ##  $ : int -1 ##  $ : num 42 ##  $ : num 1138 ##  $ : num 1000 ##  $ : num 36 ##  $ : num 12 layer(data) ## tf.Tensor( ## [[5 2 1] ##  [1 3 4]], shape=(2, 3), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(rbind(c(12, 1138, 42),                       c(37, 1000, 36)))  # Note OOV tokens out <- data |>   layer_integer_lookup(vocabulary = vocab,                        num_oov_indices = 2) out ## tf.Tensor( ## [[2 4 5] ##  [1 0 3]], shape=(2, 3), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(c(12, 36, 1138, 42, 7), 'int32')  # Note OOV tokens layer <- layer_integer_lookup(vocabulary = vocab,                               output_mode = 'one_hot') layer(data) ## tf.Tensor( ## [[0 1 0 0 0] ##  [0 0 1 0 0] ##  [0 0 0 1 0] ##  [0 0 0 0 1] ##  [1 0 0 0 0]], shape=(5, 5), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(rbind(c(12, 1138, 42, 42),                       c(42,    7, 36,  7)), \"int64\")  # Note OOV tokens layer <- layer_integer_lookup(vocabulary = vocab,                               output_mode = 'multi_hot') layer(data) ## tf.Tensor( ## [[0 1 0 1 1] ##  [1 0 1 0 1]], shape=(2, 5), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- rbind(c(12, 1138, 42, 42),               c(42,    7, 36,  7)) |> op_array(\"int64\") layer <- layer_integer_lookup(vocabulary = vocab,                               output_mode = 'count') layer(data) ## tf.Tensor( ## [[0 1 0 1 2] ##  [2 0 1 0 1]], shape=(2, 5), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() idf_weights <- c(0.25, 0.75, 0.6, 0.4) data <- rbind(c(12, 1138, 42, 42),               c(42,    7, 36,  7)) |> op_array(\"int64\") layer <- layer_integer_lookup(output_mode = 'tf_idf',                               vocabulary = vocab,                               idf_weights = idf_weights) layer(data) ## tf.Tensor( ## [[0.   0.25 0.   0.6  0.8 ] ##  [1.   0.   0.75 0.   0.4 ]], shape=(2, 5), dtype=float32) vocab <- c(-1, 12, 36, 1138, 42) |> as.integer() idf_weights <- c(0.9, 0.25, 0.75, 0.6, 0.4) data <- rbind(c(12, 1138, 42, 42),               c(42,    7, 36,  7)) |> op_array(\"int64\") layer <- layer_integer_lookup(output_mode = 'tf_idf',                               vocabulary = vocab,                               idf_weights = idf_weights) layer(data) ## tf.Tensor( ## [[0.   0.25 0.   0.6  0.8 ] ##  [1.8  0.   0.75 0.   0.4 ]], shape=(2, 5), dtype=float32) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(c(1, 3, 4,                   4, 0, 2)) |> op_reshape(c(2,-1)) |> op_cast(\"int32\") layer <- layer_integer_lookup(vocabulary = vocab, invert = TRUE) layer(data) ## tf.Tensor( ## [[  12 1138   42] ##  [  42   -1   36]], shape=(2, 3), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(rbind(c(12, 1138, 42), c(42, 1000, 36)), \"int32\") layer <- layer_integer_lookup(vocabulary = vocab) i_layer <- layer_integer_lookup(vocabulary = get_vocabulary(layer),                                 invert = TRUE) int_data <- layer(data) i_layer(int_data) ## tf.Tensor( ## [[  12 1138   42] ##  [  42   -1   36]], shape=(2, 3), dtype=int64)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"layer enables use JAX components within Keras using JAX backend Keras.","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"","code":"layer_jax_model_wrapper(   object,   call_fn,   init_fn = NULL,   params = NULL,   state = NULL,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"object Object compose layer . tensor, array, sequential model. call_fn function call model. See description list arguments takes outputs returns. init_fn function call initialize model. See description list arguments takes ouputs returns. NULL, params /state must provided. params PyTree containing model trainable parameters. allows passing trained parameters controlling initialization. params state NULL, init_fn() called build time initialize trainable parameters model. state PyTree containing model non-trainable state. allows passing learned state controlling initialization. params state NULL, call_fn() takes state argument, init_fn() called build time initialize non-trainable state model. seed Seed random number generator. Optional. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"model-function","dir":"Reference","previous_headings":"","what":"Model function","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"layer accepts JAX models form function, call_fn(), must take following arguments exact names: params: trainable parameters model. state (optional): non-trainable state model. Can omitted model non-trainable state. rng (optional): jax.random.PRNGKey instance. Can omitted model need RNGs, neither training inference. inputs: inputs model, JAX array PyTree arrays. training (optional): argument specifying training mode inference mode, TRUE passed training mode. Can omitted model behaves training mode inference mode. inputs argument mandatory. Inputs model must provided via single argument. JAX model takes multiple inputs separate arguments, must combined single structure, instance tuple() dict().","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"model-weights-initialization","dir":"Reference","previous_headings":"","what":"Model weights initialization","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"initialization params state model can handled layer, case init_fn() argument must provided. allows model initialized dynamically right shape. Alternatively, shape known, params argument optionally state argument can used create already initialized model. init_fn() function, provided, must take following arguments exact names: rng: jax.random.PRNGKey instance. inputs: JAX array PyTree arrays placeholder values provide shape inputs. training (optional): argument specifying training mode inference mode. True always passed init_fn. Can omitted regardless whether call_fn training argument.","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"models-with-non-trainable-state","dir":"Reference","previous_headings":"","what":"Models with non-trainable state","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"JAX models non-trainable state: call_fn() must state argument call_fn() must return tuple() containing outputs model new non-trainable state model init_fn() must return tuple() containing initial trainable params model initial non-trainable state model. code shows possible combination call_fn() init_fn() signatures model non-trainable state. example, model training argument rng argument call_fn().","code":"stateful_call <- function(params, state, rng, inputs, training) {   outputs <- ....   new_state <- ....   tuple(outputs, new_state) }  stateful_init <- function(rng, inputs) {   initial_params <- ....   initial_state <- ....   tuple(initial_params, initial_state) }"},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"models-without-non-trainable-state","dir":"Reference","previous_headings":"","what":"Models without non-trainable state","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"JAX models non-trainable state: call_fn() must state argument call_fn() must return outputs model init_fn() must return initial trainable params model. code shows possible combination call_fn() init_fn() signatures model without non-trainable state. example, model training argument rng argument call_fn().","code":"stateful_call <- function(pparams, inputs) {   outputs <- ....   outputs }  stateful_init <- function(rng, inputs) {   initial_params <- ....   initial_params }"},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"conforming-to-the-required-signature","dir":"Reference","previous_headings":"","what":"Conforming to the required signature","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"model different signature one required JaxLayer, one can easily write wrapper method adapt arguments. example shows model multiple inputs separate arguments, expects multiple RNGs dict, deterministic argument opposite meaning training. conform, inputs combined single structure using tuple, RNG split used populate expected dict, Boolean flag negated:","code":"jax <- import(\"jax\") my_model_fn <- function(params, rngs, input1, input2, deterministic) {   ....   if (!deterministic) {     dropout_rng <- rngs$dropout     keep <- jax$random$bernoulli(dropout_rng, dropout_rate, x$shape)     x <- jax$numpy$where(keep, x / dropout_rate, 0)     ....   }   ....   return(outputs) }  my_model_wrapper_fn <- function(params, rng, inputs, training) {   c(input1, input2) %<-% inputs   c(rng1, rng2) %<-% jax$random$split(rng)   rngs <-  list(dropout = rng1, preprocessing = rng2)   deterministic <-  !training   my_model_fn(params, rngs, input1, input2, deterministic) }  keras_layer <- layer_jax_model_wrapper(call_fn = my_model_wrapper_fn,                                        params = initial_params)"},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"usage-with-haiku-modules","dir":"Reference","previous_headings":"","what":"Usage with Haiku modules","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"JaxLayer enables use Haiku components form haiku.Module. achieved transforming module per Haiku pattern passing module.apply call_fn parameter module.init init_fn parameter needed. model non-trainable state, transformed haiku.transform_with_state. model non-trainable state, transformed haiku.transform. Additionally, optionally, module use RNGs \"apply\", can transformed haiku.without_apply_rng. following example shows create JaxLayer Haiku module uses random number generators via hk.next_rng_key() takes training positional argument:","code":"# reticulate::py_install(\"haiku\", \"r-keras\") hk <- import(\"haiku\") MyHaikuModule(hk$Module) \\%py_class\\% {    `__call__` <- \\(self, x, training) {     x <- hk$Conv2D(32L, tuple(3L, 3L))(x)     x <- jax$nn$relu(x)     x <- hk$AvgPool(tuple(1L, 2L, 2L, 1L),                     tuple(1L, 2L, 2L, 1L), \"VALID\")(x)     x <- hk$Flatten()(x)     x <- hk$Linear(200L)(x)     if (training)       x <- hk$dropout(rng = hk$next_rng_key(), rate = 0.3, x = x)     x <- jax$nn$relu(x)     x <- hk$Linear(10L)(x)     x <- jax$nn$softmax(x)     x   }  }  my_haiku_module_fn <- function(inputs, training) {   module <- MyHaikuModule()   module(inputs, training) }  transformed_module <- hk$transform(my_haiku_module_fn)  keras_layer <-   layer_jax_model_wrapper(call_fn = transformed_module$apply,                           init_fn = transformed_module$init)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":null,"dir":"Reference","previous_headings":"","what":"Wraps arbitrary expressions as a Layer object. — layer_lambda","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"layer_lambda() layer exists arbitrary expressions can used Layer constructing Sequential Functional API models. Lambda layers best suited simple operations quick experimentation. advanced use cases, prefer writing new subclasses Layer using new_layer_class().","code":""},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"","code":"layer_lambda(   object,   f,   output_shape = NULL,   mask = NULL,   arguments = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"object Object compose layer . tensor, array, sequential model. f function evaluated. Takes input tensor first argument. output_shape Expected output shape function. argument can usually inferred explicitly provided. Can list function. list, specifies first dimension onward; sample dimension assumed either input: output_shape = c(input_shape[1], output_shape) , input NULL sample dimension also NULL: output_shape = c(NA, output_shape). function, specifies entire shape function input shape: output_shape = f(input_shape). mask Either NULL (indicating masking) callable signature compute_mask layer method, tensor returned output mask regardless input . arguments Optional named list arguments passed function. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"","code":"# add a x -> x^2 layer model <- keras_model_sequential() model |> layer_lambda(\\(x) x^2)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"Normalize activations previous layer given example batch independently, rather across batch like Batch Normalization. .e. applies transformation maintains mean activation within example close 0 activation standard deviation close 1. scale center enabled, layer scale normalized outputs broadcasting trainable variable gamma, center outputs broadcasting trainable variable beta. gamma default ones tensor beta default zeros tensor, centering scaling -ops training begun. , scaling centering enabled normalization equations follows: Let intermediate activations mini-batch inputs. sample x batch inputs, compute mean variance sample, normalize value sample (including small factor epsilon numerical stability), finally, transform normalized output gamma beta, learned parameters:   gamma beta span axes inputs specified axis, part inputs' shape must fully defined. example:         Note implementations layer normalization may choose define gamma beta separate set axes axes normalized across. example, Group Normalization (Wu et al. 2018) group size 1 corresponds layer_layer_normalization() normalizes across height, width, channel gamma beta span channel dimension. , layer_layer_normalization() implementation match layer_group_normalization() layer group size set 1.","code":"outputs <- inputs |> apply(1, function(x) {   x_normalized <- (x - mean(x)) /                   sqrt(var(x) + epsilon)   x_normalized * gamma + beta }) layer <- layer_layer_normalization(axis = c(2, 3, 4))  layer(op_ones(c(5, 20, 30, 40))) |> invisible() # build() shape(layer$beta) ## shape(20, 30, 40) shape(layer$gamma) ## shape(20, 30, 40)"},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"","code":"layer_layer_normalization(   object,   axis = -1L,   epsilon = 0.001,   center = TRUE,   scale = TRUE,   rms_scaling = FALSE,   beta_initializer = \"zeros\",   gamma_initializer = \"ones\",   beta_regularizer = NULL,   gamma_regularizer = NULL,   beta_constraint = NULL,   gamma_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"object Object compose layer . tensor, array, sequential model. axis Integer list. axis axes normalize across. Typically, features axis/axes. left-axes typically batch axis/axes. -1 last dimension input. Defaults -1. epsilon Small float added variance avoid dividing zero. Defaults 1e-3. center TRUE, add offset beta normalized tensor. FALSE, beta ignored. Defaults TRUE. scale TRUE, multiply gamma. FALSE, gamma used. next layer linear (also e.g. layer_activation_relu()), can disabled since scaling done next layer. Defaults TRUE. rms_scaling TRUE, center scale ignored, inputs scaled gamma inverse square root square inputs. approximate faster approach avoids ever computing mean input. beta_initializer Initializer beta weight. Defaults zeros. gamma_initializer Initializer gamma weight. Defaults ones. beta_regularizer Optional regularizer beta weight. NULL default. gamma_regularizer Optional regularizer gamma weight. NULL default. beta_constraint Optional constraint beta weight. NULL default. gamma_constraint Optional constraint gamma weight. NULL default. ... Base layer keyword arguments (e.g. name dtype).","code":""},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"Lei Ba et al., 2016.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":null,"dir":"Reference","previous_headings":"","what":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"Based available runtime hardware constraints, layer choose different implementations (cuDNN-based backend-native) maximize performance. GPU available arguments layer meet requirement cuDNN kernel (see details), layer use fast cuDNN implementation using TensorFlow backend. requirements use cuDNN implementation : activation == tanh recurrent_activation == sigmoid dropout == 0 recurrent_dropout == 0 unroll FALSE use_bias TRUE Inputs, use masking, strictly right-padded. Eager execution enabled outermost context. example:","code":"input <- random_uniform(c(32, 10, 8)) output <- input |> layer_lstm(4) shape(output) ## shape(32, 4) lstm <- layer_lstm(units = 4, return_sequences = TRUE, return_state = TRUE) c(whole_seq_output, final_memory_state, final_carry_state) %<-% lstm(input) shape(whole_seq_output) ## shape(32, 10, 4) shape(final_memory_state) ## shape(32, 4) shape(final_carry_state) ## shape(32, 4)"},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"","code":"layer_lstm(   object,   units,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   unroll = FALSE,   use_cudnn = \"auto\",   ... )"},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"object Object compose layer . tensor, array, sequential model. units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). recurrent_activation Activation function use recurrent step. Default: sigmoid (sigmoid). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". unit_forget_bias Boolean (default TRUE). TRUE, add 1 bias forget gate initialization. Setting TRUE also force bias_initializer=\"zeros\". recommended Jozefowicz et al. kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. activity_regularizer Regularizer function applied output layer (\"activation\"). Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default: FALSE). TRUE, last state sample index batch used initial state sample index following batch. unroll Boolean (default FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences. use_cudnn Whether use cuDNN-backed implementation. \"auto\" attempt use cuDNN feasible, fallback default implementation . ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"inputs: 3D tensor, shape (batch, timesteps, feature). mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked  (optional). individual TRUE entry indicates corresponding timestep utilized, FALSE entry indicates corresponding timestep ignored. Defaults NULL. training: Boolean indicating whether layer behave training mode inference mode. argument passed cell calling . relevant dropout recurrent_dropout used  (optional). Defaults NULL. initial_state: List initial state tensors passed first call cell (optional, NULL causes creation zero-filled initial state tensors). Defaults NULL.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_masking.html","id":null,"dir":"Reference","previous_headings":"","what":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"timestep input tensor (dimension #1 tensor), values input tensor timestep equal mask_value, timestep masked (skipped) downstream layers (long support masking). downstream layer support masking yet receives input mask, exception raised.","code":""},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"","code":"layer_masking(object, mask_value = 0, ...)"},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"object Object compose layer . tensor, array, sequential model. mask_value see description ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"Consider array x shape c(samples, timesteps, features), fed LSTM layer. want mask timestep #3 #5 lack data timesteps. can: Set x[, 3, ] <- 0. x[, 5, ] <- 0. Insert layer_masking() layer mask_value = 0. LSTM layer:","code":"c(samples, timesteps, features) %<-% c(32, 10, 8) inputs <- c(samples, timesteps, features) %>% { array(runif(prod(.)), dim = .) } inputs[, 3, ] <- 0 inputs[, 5, ] <- 0  model <- keras_model_sequential() %>%   layer_masking(mask_value = 0) %>%   layer_lstm(32)  output <- model(inputs) # The time step 3 and 5 will be skipped from LSTM calculation."},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"Keras masking convention, masked timestep denoted mask value FALSE, non-masked (.e. usable) timestep denoted mask value TRUE.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"Downsamples input representation taking maximum value spatial window size pool_size. window shifted strides. resulting output using \"valid\" padding option shape : output_shape = (input_shape - pool_size + 1) / strides). resulting output shape using \"\" padding option : output_shape = input_shape / strides","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"","code":"layer_max_pooling_1d(   object,   pool_size = 2L,   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"object Object compose layer . tensor, array, sequential model. pool_size int, size max pooling window. strides int NULL. Specifies much pooling window moves pooling step. NULL, default pool_size. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"data_format=\"channels_last\": 3D tensor shape (batch_size, steps, features). data_format=\"channels_first\": 3D tensor shape (batch_size, features, steps).","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"data_format=\"channels_last\": 3D tensor shape (batch_size, downsampled_steps, features). data_format=\"channels_first\": 3D tensor shape (batch_size, features, downsampled_steps).","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"strides=1 padding=\"valid\":     strides=2 padding=\"valid\":     strides=1 padding=\"\":","code":"x <- op_reshape(c(1, 2, 3, 4, 5),                c(1, 5, 1)) max_pool_1d <- layer_max_pooling_1d(pool_size = 2,                                     strides = 1,                                     padding = \"valid\") max_pool_1d(x) ## tf.Tensor( ## [[[2.] ##   [3.] ##   [4.] ##   [5.]]], shape=(1, 4, 1), dtype=float32) x <- op_reshape(c(1, 2, 3, 4, 5),                c(1, 5, 1)) max_pool_1d <- layer_max_pooling_1d(pool_size = 2,                                     strides = 2,                                     padding = \"valid\") max_pool_1d(x) ## tf.Tensor( ## [[[2.] ##   [4.]]], shape=(1, 2, 1), dtype=float32) x <- op_reshape(c(1, 2, 3, 4, 5),                c(1, 5, 1)) max_pool_1d <- layer_max_pooling_1d(pool_size = 2,                                     strides = 1,                                     padding = \"same\") max_pool_1d(x) ## tf.Tensor( ## [[[2.] ##   [3.] ##   [4.] ##   [5.] ##   [5.]]], shape=(1, 5, 1), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"Downsamples input along spatial dimensions (height width) taking maximum value input window (size defined pool_size) channel input. window shifted strides along dimension. resulting output using \"valid\" padding option spatial shape (number rows columns) : output_shape = floor((input_shape - pool_size) / strides) + 1 (input_shape >= pool_size) resulting output shape using \"\" padding option : output_shape = floor((input_shape - 1) / strides) + 1","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"","code":"layer_max_pooling_2d(   object,   pool_size = list(2L, 2L),   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"object Object compose layer . tensor, array, sequential model. pool_size int list 2 integers, factors downscale (dim1, dim2). one integer specified, window length used dimensions. strides int list 2 integers, NULL. Strides values. NULL, default pool_size. one int specified, stride size used dimensions. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"data_format=\"channels_last\": 4D tensor shape (batch_size, height, width, channels). data_format=\"channels_first\": 4D tensor shape (batch_size, channels, height, width).","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"data_format=\"channels_last\": 4D tensor shape (batch_size, pooled_height, pooled_width, channels). data_format=\"channels_first\": 4D tensor shape (batch_size, channels, pooled_height, pooled_width).","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"strides = (1, 1) padding = \"valid\":     strides = c(2, 2) padding = \"valid\":     stride = (1, 1) padding = \"\":","code":"x <- rbind(c(1., 2., 3.),            c(4., 5., 6.),            c(7., 8., 9.)) |> op_reshape(c(1, 3, 3, 1)) max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),                                     strides = c(1, 1),                                     padding = \"valid\") max_pool_2d(x) ## tf.Tensor( ## [[[[5.] ##    [6.]] ## ##   [[8.] ##    [9.]]]], shape=(1, 2, 2, 1), dtype=float32) x <- rbind(c(1., 2., 3., 4.),            c(5., 6., 7., 8.),            c(9., 10., 11., 12.)) |> op_reshape(c(1, 3, 4, 1)) max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),                                     strides = c(2, 2),                                     padding = \"valid\") max_pool_2d(x) ## tf.Tensor( ## [[[[6.] ##    [8.]]]], shape=(1, 1, 2, 1), dtype=float32) x <- rbind(c(1., 2., 3.),            c(4., 5., 6.),            c(7., 8., 9.)) |> op_reshape(c(1, 3, 3, 1)) max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),                                     strides = c(1, 1),                                     padding = \"same\") max_pool_2d(x) ## tf.Tensor( ## [[[[5.] ##    [6.] ##    [6.]] ## ##   [[8.] ##    [9.] ##    [9.]] ## ##   [[8.] ##    [9.] ##    [9.]]]], shape=(1, 3, 3, 1), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"Downsamples input along spatial dimensions (depth, height, width) taking maximum value input window (size defined pool_size) channel input. window shifted strides along dimension.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"","code":"layer_max_pooling_3d(   object,   pool_size = list(2L, 2L, 2L),   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"object Object compose layer . tensor, array, sequential model. pool_size int list 3 integers, factors downscale (dim1, dim2, dim3). one integer specified, window length used dimensions. strides int list 3 integers, NULL. Strides values. NULL, default pool_size. one int specified, stride size used dimensions. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"","code":"depth <- 30 height <- 30 width <- 30 channels <- 3  inputs <- layer_input(shape=c(depth, height, width, channels)) layer <- layer_max_pooling_3d(pool_size=3) outputs <- inputs |> layer() outputs ## <KerasTensor shape=(None, 10, 10, 10, 3), dtype=float32, sparse=False, name=keras_tensor_1>"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes element-wise maximum on a list of inputs. — layer_maximum","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"","code":"layer_maximum(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"Usage Keras model:","code":"input_shape <- c(2, 3, 4) x1 <- random_uniform(input_shape) x2 <- random_uniform(input_shape) y <- layer_maximum(x1, x2) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu') input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu') # equivalent to `y <- layer_maximum(x1, x2)` y <- layer_maximum(x1, x2) out <- y |> layer_dense(4) model <- keras_model(inputs = c(input1, input2), outputs = out)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"layer takes float32/float64 single batched audio signal inputs computes Mel spectrogram using Short-Time Fourier Transform Mel scaling. input 1D (unbatched) 2D (batched) tensor representing audio signals. output 2D 3D tensor representing Mel spectrograms. spectrogram image-like representation shows frequency spectrum signal time. uses x-axis represent time, y-axis represent frequency, pixel represent intensity. Mel spectrograms special type spectrogram use mel scale, approximates humans perceive sound. commonly used speech music processing tasks like speech recognition, speaker identification, music genre classification.","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"","code":"layer_mel_spectrogram(   object,   fft_length = 2048L,   sequence_stride = 512L,   sequence_length = NULL,   window = \"hann\",   sampling_rate = 16000L,   num_mel_bins = 128L,   min_freq = 20,   max_freq = NULL,   power_to_db = TRUE,   top_db = 80,   mag_exp = 2,   min_power = 1e-10,   ref_power = 1,   ... )"},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"object Object compose layer . tensor, array, sequential model. fft_length Integer, size FFT window. sequence_stride Integer, number samples successive STFT columns. sequence_length Integer, size window used applying window audio frame. NULL, defaults fft_length. window String, name window function use. Available values \"hann\" \"hamming\". window tensor, used directly window length must sequence_length. window NULL, windowing used. Defaults \"hann\". sampling_rate Integer, sample rate input signal. num_mel_bins Integer, number mel bins generate. min_freq Float, minimum frequency mel bins. max_freq Float, maximum frequency mel bins. NULL, defaults sampling_rate / 2. power_to_db TRUE, convert power spectrogram decibels. top_db Float, minimum negative cut-max(10 * log10(S)) - top_db. mag_exp Float, exponent magnitude spectrogram. 1 magnitude, 2 power, etc. Default 2. min_power Float, minimum value power ref_power. ref_power Float, power scaled relative 10 * log10(S / ref_power). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"Spectrogram, Mel scale.","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"Unbatched audio signal   Batched audio signal","code":"layer <- layer_mel_spectrogram(   num_mel_bins = 64,   sampling_rate = 8000,   sequence_stride = 256,   fft_length = 2048 ) layer(random_uniform(shape = c(16000))) |> shape() layer <- layer_mel_spectrogram(   num_mel_bins = 80,   sampling_rate = 8000,   sequence_stride = 128,   fft_length = 2048 ) layer(random_uniform(shape = c(2, 16000))) |> shape()"},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"1D (unbatched) 2D (batched) tensor shape:(..., samples).","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"2D (unbatched) 3D (batched) tensor shape:(..., num_mel_bins, time).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes elementwise minimum on a list of inputs. — layer_minimum","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"","code":"layer_minimum(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"Usage Keras model:","code":"input_shape <- c(2, 3, 4) x1 <- random_uniform(input_shape) x2 <- random_uniform(input_shape) y <- layer_minimum(x1, x2) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu') input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu') # equivalent to `y <- layer_minimum(x1, x2)` y <- layer_minimum(x1, x2) out <- y |> layer_dense(4) model <- keras_model(inputs = c(input1, input2), outputs = out)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi Head Attention layer. — layer_multi_head_attention","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"implementation multi-headed attention described paper \"Attention Need\" Vaswani et al., 2017. query, key, value , self-attention. timestep query attends corresponding sequence key, returns fixed-width vector. layer first projects query, key value. (effectively) list tensors length num_attention_heads, corresponding shapes (batch_size, <query dimensions>, key_dim), (batch_size, <key/value dimensions>, key_dim), (batch_size, <key/value dimensions>, value_dim). , query key tensors dot-producted scaled. softmaxed obtain attention probabilities. value tensors interpolated probabilities, concatenated back single tensor. Finally, result tensor last dimension value_dim can take linear projection return.","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"","code":"layer_multi_head_attention(   inputs,   num_heads,   key_dim,   value_dim = NULL,   dropout = 0,   use_bias = TRUE,   output_shape = NULL,   attention_axes = NULL,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"inputs see description num_heads Number attention heads. key_dim Size attention head query key. value_dim Size attention head value. dropout Dropout probability. use_bias Boolean, whether dense layers use bias vectors/matrices. output_shape expected shape output tensor, besides batch sequence dims. specified, projects back query feature dim (query input's last dimension). attention_axes axes attention applied. NULL means attention axes, batch, heads, features. kernel_initializer Initializer dense layer kernels. bias_initializer Initializer dense layer biases. kernel_regularizer Regularizer dense layer kernels. bias_regularizer Regularizer dense layer biases. activity_regularizer Regularizer dense layer activity. kernel_constraint Constraint dense layer kernels. bias_constraint Constraint dense layer kernels. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"query: Query tensor shape (B, T, dim), B batch size, T target sequence length, dim feature dimension. value: Value tensor shape (B, S, dim), B batch size, S source sequence length, dim feature dimension. key: Optional key tensor shape (B, S, dim). given, use value key value, common case. attention_mask: boolean mask shape (B, T, S), prevents attention certain positions. boolean mask specifies query elements can attend key elements, 1 indicates attention 0 indicates attention. Broadcasting can happen missing batch dimensions head dimension. return_attention_scores: boolean indicate whether output (attention_output, attention_scores) TRUE, attention_output FALSE. Defaults FALSE. training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (dropout). go either using training mode parent layer/model, FALSE (inference) parent layer. use_causal_mask: boolean indicate whether apply causal mask prevent tokens attending future tokens (e.g., used decoder Transformer).","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"call-return","dir":"Reference","previous_headings":"","what":"Call return","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"attention_output: result computation, shape (B, T, E), T target sequence shapes E query input last dimension output_shape NULL. Otherwise, multi-head outputs projected shape specified output_shape. attention_scores: (Optional) multi-head attention coefficients attention axes.","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"properties","dir":"Reference","previous_headings":"","what":"Properties","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"MultiHeadAttention Layer instance following additional read-properties: attention_axes dropout key_dense key_dim num_heads output_dense output_shape query_dense use_bias value_dense value_dim","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs elementwise multiplication. — layer_multiply","title":"Performs elementwise multiplication. — layer_multiply","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs elementwise multiplication. — layer_multiply","text":"","code":"layer_multiply(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs elementwise multiplication. — layer_multiply","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs elementwise multiplication. — layer_multiply","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs elementwise multiplication. — layer_multiply","text":"Usage Keras model:","code":"input_shape <- c(2, 3, 4) x1 <- random_uniform(input_shape) x2 <- random_uniform(input_shape) y <- layer_multiply(x1, x2) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu') input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu') # equivalent to `y <- layer_multiply(x1, x2)` y <- layer_multiply(x1, x2) out <- y |> layer_dense(4) model <- keras_model(inputs = c(input1, input2), outputs = out)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer that normalizes continuous features. — layer_normalization","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"layer shift scale inputs distribution centered around 0 standard deviation 1. accomplishes precomputing mean variance data, calling (input - mean) / sqrt(var) runtime. mean variance values layer must either supplied construction learned via adapt(). adapt() compute mean variance data store layer's weights. adapt() called fit(), evaluate(), predict().","code":""},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"","code":"layer_normalization(   object,   axis = -1L,   mean = NULL,   variance = NULL,   invert = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"object Object compose layer . tensor, array, sequential model. axis Integer, list integers, NULL. axis axes separate mean variance index shape. example, shape (NULL, 5) axis=1, layer track 5 separate mean variance values last axis. axis set NULL, layer normalize elements input scalar mean variance. -1, last axis input assumed feature dimension normalized per index. Note specific case batched scalar inputs axis batch axis, default normalize index batch separately. case, consider passing axis=NULL. Defaults -1. mean mean value(s) use normalization. passed value(s) broadcast shape kept axes ; value(s) broadcast, error raised layer's build() method called. variance variance value(s) use normalization. passed value(s) broadcast shape kept axes ; value(s) broadcast, error raised layer's build() method called. invert TRUE, layer apply inverse transformation inputs: turn normalized input back original form. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"Calculate global mean variance analyzing dataset adapt().     Calculate mean variance index last axis.     Pass mean variance directly.     Use layer de-normalize inputs (adapting layer).","code":"adapt_data <- op_array(c(1., 2., 3., 4., 5.), dtype='float32') input_data <- op_array(c(1., 2., 3.), dtype='float32') layer <- layer_normalization(axis = NULL) layer %>% adapt(adapt_data) layer(input_data) ## tf.Tensor([-1.4142135  -0.70710677  0.        ], shape=(3), dtype=float32) adapt_data <- op_array(rbind(c(0., 7., 4.),                        c(2., 9., 6.),                        c(0., 7., 4.),                        c(2., 9., 6.)), dtype='float32') input_data <- op_array(matrix(c(0., 7., 4.), nrow = 1), dtype='float32') layer <- layer_normalization(axis=-1) layer %>% adapt(adapt_data) layer(input_data) ## tf.Tensor([[-1. -1. -1.]], shape=(1, 3), dtype=float32) input_data <- op_array(rbind(1, 2, 3), dtype='float32') layer <- layer_normalization(mean=3., variance=2.) layer(input_data) ## tf.Tensor( ## [[-1.4142135 ] ##  [-0.70710677] ##  [ 0.        ]], shape=(3, 1), dtype=float32) adapt_data <- op_array(rbind(c(0., 7., 4.),                        c(2., 9., 6.),                        c(0., 7., 4.),                        c(2., 9., 6.)), dtype='float32') input_data <- op_array(c(1., 2., 3.), dtype='float32') layer <- layer_normalization(axis=-1, invert=TRUE) layer %>% adapt(adapt_data) layer(input_data) ## tf.Tensor([[ 2. 10.  8.]], shape=(1, 3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_permute.html","id":null,"dir":"Reference","previous_headings":"","what":"Permutes the dimensions of the input according to a given pattern. — layer_permute","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"Useful e.g. connecting RNNs convnets.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"","code":"layer_permute(object, dims, ...)"},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"object Object compose layer . tensor, array, sequential model. dims List integers. Permutation pattern include batch dimension. Indexing starts 1. instance, c(2, 1) permutes first second dimensions input. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"Arbitrary.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"input shape, dimensions re-ordered according specified pattern.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"","code":"x <- layer_input(shape=c(10, 64)) y <- layer_permute(x, c(2, 1)) shape(y) ## shape(NA, 64, 10)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"layer randomly increase/reduce brightness input RGB images. inference time, output identical input. Call layer training=TRUE adjust brightness input. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"","code":"layer_random_brightness(   object,   factor,   value_range = list(0L, 255L),   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"object Object compose layer . tensor, array, sequential model. factor Float list 2 floats -1.0 1.0. factor used determine lower bound upper bound brightness adjustment. float value chosen randomly limits. -1.0 chosen, output image black, 1.0 chosen, image fully white. one float provided, eg, 0.2, -0.2 used lower bound 0.2 used upper bound. value_range Optional list 2 floats lower upper limit values input data. make change, use c(0.0, 1.0), e.g., image input scaled layer. Defaults c(0.0, 255.0). brightness adjustment scaled range, output values clipped range. seed optional integer, fixed RNG behavior. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"3D (HWC) 4D (NHWC) tensor, float int dtype. Input pixel values can range (e.g. [0., 1.) [0, 255])","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"output","dir":"Reference","previous_headings":"","what":"Output","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"3D (HWC) 4D (NHWC) tensor brightness adjusted based factor. default, layer output floats. output value clipped range [0, 255], valid range RGB colors, rescaled based value_range needed.","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"","code":"random_bright <- layer_random_brightness(factor=0.2, seed = 1)  # An image with shape [2, 2, 3] image <- array(1:12, dim=c(2, 2, 3))  # Assume we randomly select the factor to be 0.1, then it will apply # 0.1 * 255 to all the channel output <- random_bright(image, training=TRUE) output ## tf.Tensor( ## [[[39.605797 43.605797 47.605797] ##   [41.605797 45.605797 49.605797]] ## ##  [[40.605797 44.605797 48.605797] ##   [42.605797 46.605797 50.605797]]], shape=(2, 2, 3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"layer randomly adjust contrast image images random factor. Contrast adjusted independently channel image training. channel, layer computes mean image pixels channel adjusts component x pixel (x - mean) * contrast_factor + mean. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"","code":"layer_random_contrast(object, factor, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"object Object compose layer . tensor, array, sequential model. factor positive float represented fraction value, tuple size 2 representing lower upper bound. represented single float, lower = upper. contrast factor randomly picked [1.0 - lower, 1.0 + upper]. pixel x channel, output (x - mean) * factor + mean mean mean value channel. seed Integer. Used create random seed. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly crops images during training. — layer_random_crop","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"training, layer randomly choose location crop images target size. layer crop images batch cropping location. inference time, training input image smaller target size, input resized cropped return largest possible window image matches target aspect ratio. need apply random cropping inference time, set training TRUE calling layer. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"","code":"layer_random_crop(   object,   height,   width,   seed = NULL,   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"object Object compose layer . tensor, array, sequential model. height Integer, height output shape. width Integer, width output shape. seed Integer. Used create random seed. data_format see description name String, name object ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly flips images during training. — layer_random_flip","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"layer flip images horizontally vertically based mode attribute. inference time, output identical input. Call layer training=TRUE flip input. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"","code":"layer_random_flip(object, mode = \"horizontal_and_vertical\", seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"object Object compose layer . tensor, array, sequential model. mode String indicating flip mode use. Can \"horizontal\", \"vertical\", \"horizontal_and_vertical\". \"horizontal\" left-right flip \"vertical\" top-bottom flip. Defaults \"horizontal_and_vertical\" seed Integer. Used create random seed. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"layer apply random rotations image, filling empty space according fill_mode. default, random rotations applied training. inference time, layer nothing. need apply random rotations inference time, pass training = TRUE calling layer. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"","code":"layer_random_rotation(   object,   factor,   fill_mode = \"reflect\",   interpolation = \"bilinear\",   seed = NULL,   fill_value = 0,   value_range = list(0L, 255L),   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"object Object compose layer . tensor, array, sequential model. factor float represented fraction 2 Pi, tuple size 2 representing lower upper bound rotating clockwise counter-clockwise. positive values means rotating counter clock-wise, negative value means clock-wise. represented single float, value used upper lower bound. instance, factor=(-0.2, 0.3) results output rotation random amount range [-20% * 2pi, 30% * 2pi]. factor=0.2 results output rotating random amount range [-20% * 2pi, 20% * 2pi]. fill_mode Points outside boundaries input filled according given mode (one {\"constant\", \"reflect\", \"wrap\", \"nearest\"}). reflect: (d c b | b c d | d c b ) input extended reflecting edge last pixel. constant: (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k = 0. wrap: (b c d | b c d | b c d) input extended wrapping around opposite edge. nearest: (| b c d | d d d d) input extended nearest pixel. interpolation Interpolation mode. Supported values: \"nearest\", \"bilinear\". seed Integer. Used create random seed. fill_value float represents value filled outside boundaries fill_mode=\"constant\". value_range see description data_format see description ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format","code":""},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly translates images during training. — layer_random_translation","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"layer apply random translations image training, filling empty space according fill_mode. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats.","code":""},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"","code":"layer_random_translation(   object,   height_factor,   width_factor,   fill_mode = \"reflect\",   interpolation = \"bilinear\",   seed = NULL,   fill_value = 0,   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"object Object compose layer . tensor, array, sequential model. height_factor float represented fraction value, tuple size 2 representing lower upper bound shifting vertically. negative value means shifting image , positive value means shifting image . represented single positive float, value used upper lower bound. instance, height_factor=(-0.2, 0.3) results output shifted random amount range [-20%, +30%]. height_factor=0.2 results output height shifted random amount range [-20%, +20%]. width_factor float represented fraction value, tuple size 2 representing lower upper bound shifting horizontally. negative value means shifting image left, positive value means shifting image right. represented single positive float, value used upper lower bound. instance, width_factor=(-0.2, 0.3) results output shifted left 20%, shifted right 30%. width_factor=0.2 results output height shifted left right 20%. fill_mode Points outside boundaries input filled according given mode. Available methods \"constant\", \"nearest\", \"wrap\" \"reflect\". Defaults \"constant\". \"reflect\": (d c b | b c d | d c b ) input extended reflecting edge last pixel. \"constant\": (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k specified fill_value. \"wrap\": (b c d | b c d | b c d) input extended wrapping around opposite edge. \"nearest\": (| b c d | d d d d) input extended nearest pixel. Note using torch backend, \"reflect\" redirected \"mirror\" (c d c b | b c d | c b b) torch support \"reflect\". Note torch backend support \"wrap\". interpolation Interpolation mode. Supported values: \"nearest\", \"bilinear\". seed Integer. Used create random seed. fill_value float represents value filled outside boundaries fill_mode=\"constant\". data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format, (..., channels, height, width), \"channels_first\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels), (..., channels, target_height, target_width), \"channels_first\" format. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"layer randomly zoom axis image independently, filling empty space according fill_mode. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats.","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"","code":"layer_random_zoom(   object,   height_factor,   width_factor = NULL,   fill_mode = \"reflect\",   interpolation = \"bilinear\",   seed = NULL,   fill_value = 0,   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"object Object compose layer . tensor, array, sequential model. height_factor float represented fraction value, list size 2 representing lower upper bound zooming vertically. represented single float, value used upper lower bound. positive value means zooming , negative value means zooming . instance, height_factor=c(0.2, 0.3) result output zoomed random amount range [+20%, +30%]. height_factor=c(-0.3, -0.2) result output zoomed random amount range [+20%, +30%]. width_factor float represented fraction value, list size 2 representing lower upper bound zooming horizontally. represented single float, value used upper lower bound. instance, width_factor=c(0.2, 0.3) result output zooming 20% 30%. width_factor=c(-0.3, -0.2) result output zooming 20% 30%. NULL means .e., zooming vertical horizontal directions preserving aspect ratio. Defaults NULL. fill_mode Points outside boundaries input filled according given mode. Available methods \"constant\", \"nearest\", \"wrap\" \"reflect\". Defaults \"constant\". \"reflect\": (d c b | b c d | d c b ) input extended reflecting edge last pixel. \"constant\": (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k specified fill_value. \"wrap\": (b c d | b c d | b c d) input extended wrapping around opposite edge. \"nearest\": (| b c d | d d d d) input extended nearest pixel. Note using torch backend, \"reflect\" redirected \"mirror\" (c d c b | b c d | c b b) torch support \"reflect\". Note torch backend support \"wrap\". interpolation Interpolation mode. Supported values: \"nearest\", \"bilinear\". seed Integer. Used create random seed. fill_value float represents value filled outside boundaries fill_mode=\"constant\". data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format, (..., channels, height, width), \"channels_first\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels), (..., channels, target_height, target_width), \"channels_first\" format. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"","code":"input_img <- random_uniform(c(32, 224, 224, 3)) layer <- layer_random_zoom(height_factor = .5, width_factor = .2) out_img <- layer(input_img)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeats the input n times. — layer_repeat_vector","title":"Repeats the input n times. — layer_repeat_vector","text":"Repeats input n times.","code":""},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeats the input n times. — layer_repeat_vector","text":"","code":"layer_repeat_vector(object, n, ...)"},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeats the input n times. — layer_repeat_vector","text":"object Object compose layer . tensor, array, sequential model. n Integer, repetition factor. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Repeats the input n times. — layer_repeat_vector","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Repeats the input n times. — layer_repeat_vector","text":"","code":"x <- layer_input(shape = 32) y <- layer_repeat_vector(x, n = 3) shape(y) ## shape(NA, 3, 32)"},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Repeats the input n times. — layer_repeat_vector","text":"2D tensor shape (batch_size, features).","code":""},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Repeats the input n times. — layer_repeat_vector","text":"3D tensor shape (batch_size, n, features).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_rescaling.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","title":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","text":"layer rescales every value input (often image) multiplying scale adding offset. instance: rescale input [0, 255] range [0, 1] range, pass scale=1./255. rescale input [0, 255] range [-1, 1] range, pass scale=1./127.5, offset=-1. rescaling applied training inference. Inputs can integer floating point dtype, default layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_rescaling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","text":"","code":"layer_rescaling(object, scale, offset = 0, ...)"},{"path":"https://keras.posit.co/reference/layer_rescaling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","text":"object Object compose layer . tensor, array, sequential model. scale Float, scale apply inputs. offset Float, offset apply inputs. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_rescaling.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer that reshapes inputs into the given shape. — layer_reshape","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"Layer reshapes inputs given shape.","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"","code":"layer_reshape(object, target_shape, ...)"},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"object Object compose layer . tensor, array, sequential model. target_shape Target shape. List integers, include samples dimension (batch size). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"Arbitrary, although dimensions input shape must known/fixed. Use keyword argument input_shape (list integers, include samples/batch size axis) using layer first layer model.","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"(batch_size, *target_shape)","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"","code":"x <- layer_input(shape = 12) y <- layer_reshape(x, c(3, 4)) shape(y) ## shape(NA, 3, 4) # also supports shape inference using `-1` as dimension y <- layer_reshape(x, c(-1, 2, 2)) shape(y) ## shape(NA, 3, 2, 2)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which resizes images. — layer_resizing","title":"A preprocessing layer which resizes images. — layer_resizing","text":"layer resizes image input target height width. input 4D (batched) 3D (unbatched) tensor \"channels_last\" format. Input pixel values can range (e.g. [0., 1.) [0, 255]).","code":""},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which resizes images. — layer_resizing","text":"","code":"layer_resizing(   object,   height,   width,   interpolation = \"bilinear\",   crop_to_aspect_ratio = FALSE,   pad_to_aspect_ratio = FALSE,   fill_mode = \"constant\",   fill_value = 0,   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which resizes images. — layer_resizing","text":"object Object compose layer . tensor, array, sequential model. height Integer, height output shape. width Integer, width output shape. interpolation String, interpolation method. Supports \"bilinear\", \"nearest\", \"bicubic\", \"lanczos3\", \"lanczos5\". Defaults \"bilinear\". crop_to_aspect_ratio TRUE, resize images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image cropped return largest possible window image (size (height, width)) matches target aspect ratio. default (crop_to_aspect_ratio=FALSE), aspect ratio may preserved. pad_to_aspect_ratio TRUE, pad images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image evenly padded short side. fill_mode using pad_to_aspect_ratio=TRUE, padded areas filled according given mode. \"constant\" supported time (fill constant value, equal fill_value). fill_value Float. Padding value use pad_to_aspect_ratio=TRUE. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which resizes images. — layer_resizing","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which resizes images. — layer_resizing","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format, (..., channels, height, width), \"channels_first\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which resizes images. — layer_resizing","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels), (..., channels, target_height, target_width), \"channels_first\" format. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":null,"dir":"Reference","previous_headings":"","what":"Base class for recurrent layers — layer_rnn","title":"Base class for recurrent layers — layer_rnn","text":"Base class recurrent layers","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Base class for recurrent layers — layer_rnn","text":"","code":"layer_rnn(   object,   cell,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   unroll = FALSE,   zero_output_for_mask = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Base class for recurrent layers — layer_rnn","text":"object Object compose layer . tensor, array, sequential model. cell RNN cell instance list RNN cell instances. RNN cell class : call(input_at_t, states_at_t) method, returning (output_at_t, states_at_t_plus_1). call method cell can also take optional argument constants, see section \"Note passing external constants\" . state_size attribute. can single integer (single state) case size recurrent state. can also list integers (one size per state). output_size attribute, single integer. get_initial_state(batch_size=NULL) method creates tensor meant fed call() initial state, user specify initial state via means. returned initial state shape (batch_size, cell.state_size). cell might choose create tensor full zeros, values based cell's implementation. inputs input tensor RNN layer, shape (batch_size, timesteps, features). method implemented cell, RNN layer create zero filled tensor shape (batch_size, cell$state_size). case cell list RNN cell instances, cells stacked top RNN, resulting efficient stacked RNN. return_sequences Boolean (default FALSE). Whether return last output output sequence, full sequence. return_state Boolean (default FALSE). Whether return last state addition output. go_backwards Boolean (default FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default FALSE). TRUE, last state sample index batch used initial state sample index following batch. unroll Boolean (default FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences. zero_output_for_mask Boolean (default FALSE). Whether output use zeros masked timesteps. Note field used return_sequences TRUE mask provided. can useful want reuse raw output sequence RNN without interference masked timesteps, e.g., merging bidirectional RNNs. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Base class for recurrent layers — layer_rnn","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Base class for recurrent layers — layer_rnn","text":"inputs: Input tensor. initial_state: List initial state tensors passed first call cell. mask: Binary tensor shape [batch_size, timesteps] indicating whether given timestep masked. individual TRUE entry indicates corresponding timestep utilized, FALSE entry indicates corresponding timestep ignored. training: Python boolean indicating whether layer behave training mode inference mode. argument passed cell calling . use cells use dropout.","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Base class for recurrent layers — layer_rnn","text":"3-D tensor shape (batch_size, timesteps, features).","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Base class for recurrent layers — layer_rnn","text":"return_state: list tensors. first tensor output. remaining tensors last states, shape (batch_size, state_size), state_size high dimension tensor shape. return_sequences: 3D tensor shape (batch_size, timesteps, output_size).","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"masking-","dir":"Reference","previous_headings":"","what":"Masking:","title":"Base class for recurrent layers — layer_rnn","text":"layer supports masking input data variable number timesteps. introduce masks data, use layer_embedding() layer mask_zero parameter set TRUE. Note using statefulness RNNs: can set RNN layers 'stateful', means states computed samples one batch reused initial states samples next batch. assumes one--one mapping samples different successive batches. enable statefulness: Specify stateful = TRUE layer constructor. Specify fixed batch size model, passing sequential model: input_batch_shape = c(...) keras_model_sequential() call. Else functional model 1 input layers: batch_shape = c(...) layer_input() call(s). expected shape inputs including batch size. list integers, e.g. c(32, 10, 100). Specify shuffle = FALSE calling fit(). reset states model, call reset_state() either specific layer, entire model. Note specifying initial state RNNs: can specify initial state RNN layers symbolically calling keyword argument initial_state. value initial_state tensor list tensors representing initial state RNN layer.","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Base class for recurrent layers — layer_rnn","text":"First, define RNN Cell, layer subclass.   use cell RNN layer:","code":"rnn_cell_minimal <- Layer(   \"MinimalRNNCell\",    initialize = function(units, ...) {     super$initialize(...)     self$units <- as.integer(units)     self$state_size <- as.integer(units)   },    build = function(input_shape) {     self$kernel <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = 'uniform',       name = 'kernel'     )     self$recurrent_kernel <- self$add_weight(       shape = shape(self$units, self$units),       initializer = 'uniform',       name = 'recurrent_kernel'     )     self$built <- TRUE   },    call = function(inputs, states) {     prev_output <- states[[1]]     h <- op_matmul(inputs, self$kernel)     output <- h + op_matmul(prev_output, self$recurrent_kernel)     list(output, list(output))   } ) cell <- rnn_cell_minimal(units = 32) x <- layer_input(shape = shape(NULL, 5)) layer <- layer_rnn(cell = cell) y <- layer(x) cells <- list(rnn_cell_minimal(units = 32), rnn_cell_minimal(units = 4)) x <- layer_input(shape = shape(NULL, 5)) layer <- layer_rnn(cell = cells) y <- layer(x)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"1D separable convolution layer. — layer_separable_conv_1d","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"layer performs depthwise convolution acts separately channels, followed pointwise convolution mixes channels. use_bias TRUE bias initializer provided, adds bias vector output. optionally applies activation function produce final output.","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"","code":"layer_separable_conv_1d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   depth_multiplier = 1L,   activation = NULL,   use_bias = TRUE,   depthwise_initializer = \"glorot_uniform\",   pointwise_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   depthwise_regularizer = NULL,   pointwise_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   depthwise_constraint = NULL,   pointwise_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimensionality output space (.e. number filters pointwise convolution). kernel_size int list 1 integers, specifying size depthwise convolution window. strides int list 1 integers, specifying stride length depthwise convolution. one int specified, stride size used dimensions. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated convolution. one int specified, dilation rate used dimensions. depth_multiplier number depthwise convolution output channels input channel. total number depthwise convolution output channels equal input_channel * depth_multiplier. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. depthwise_initializer initializer depthwise convolution kernel. NULL, default initializer (\"glorot_uniform\") used. pointwise_initializer initializer pointwise convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer initializer bias vector. NULL, default initializer ('\"zeros\"') used. depthwise_regularizer Optional regularizer depthwise convolution kernel. pointwise_regularizer Optional regularizer pointwise convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. depthwise_constraint Optional projection function applied depthwise kernel updated Optimizer (e.g. used norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). pointwise_constraint Optional projection function applied pointwise kernel updated Optimizer. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"3D tensor representing activation(separable_conv1d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, steps, channels) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, new_steps, filters) data_format=\"channels_first\": 3D tensor shape: (batch_shape, filters, new_steps)","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"","code":"x <- random_uniform(c(4, 10, 12)) y <- layer_separable_conv_1d(x, 3, 2, 2, activation='relu') shape(y) ## shape(4, 5, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"2D separable convolution layer. — layer_separable_conv_2d","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"layer performs depthwise convolution acts separately channels, followed pointwise convolution mixes channels. use_bias TRUE bias initializer provided, adds bias vector output. optionally applies activation function produce final output.","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"","code":"layer_separable_conv_2d(   object,   filters,   kernel_size,   strides = list(1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L),   depth_multiplier = 1L,   activation = NULL,   use_bias = TRUE,   depthwise_initializer = \"glorot_uniform\",   pointwise_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   depthwise_regularizer = NULL,   pointwise_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   depthwise_constraint = NULL,   pointwise_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimensionality output space (.e. number filters pointwise convolution). kernel_size int list 2 integers, specifying size depthwise convolution window. strides int list 2 integers, specifying stride length depthwise convolution. one int specified, stride size used dimensions. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 2 integers, specifying dilation rate use dilated convolution. one int specified, dilation rate used dimensions. depth_multiplier number depthwise convolution output channels input channel. total number depthwise convolution output channels equal input_channel * depth_multiplier. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. depthwise_initializer initializer depthwise convolution kernel. NULL, default initializer (\"glorot_uniform\") used. pointwise_initializer initializer pointwise convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer initializer bias vector. NULL, default initializer ('\"zeros\"') used. depthwise_regularizer Optional regularizer depthwise convolution kernel. pointwise_regularizer Optional regularizer pointwise convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. depthwise_constraint Optional projection function applied depthwise kernel updated Optimizer (e.g. used norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). pointwise_constraint Optional projection function applied pointwise kernel updated Optimizer. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"4D tensor representing activation(separable_conv2d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, height, width, channels) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, new_height, new_width, filters) data_format=\"channels_first\": 4D tensor shape: (batch_size, filters, new_height, new_width)","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"","code":"x <- random_uniform(c(4, 10, 10, 12)) y <- layer_separable_conv_2d(x, 3, c(4, 3), 2, activation='relu') shape(y) ## shape(4, 4, 4, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":null,"dir":"Reference","previous_headings":"","what":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"Fully-connected RNN output fed back new input.","code":""},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"","code":"layer_simple_rnn(   object,   units,   activation = \"tanh\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   unroll = FALSE,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"object Object compose layer . tensor, array, sequential model. units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state.  Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. activity_regularizer Regularizer function applied output layer (\"activation\"). Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix.  Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default: FALSE). TRUE, last state sample index batch used initial state sample index following batch. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences. seed Initial seed random number generator ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"sequence: 3D tensor, shape [batch, timesteps, feature]. mask: Binary tensor shape [batch, timesteps] indicating whether given timestep masked. individual TRUE entry indicates corresponding timestep utilized, FALSE entry indicates corresponding timestep ignored. training: Python boolean indicating whether layer behave training mode inference mode. argument passed cell calling . relevant dropout recurrent_dropout used. initial_state: List initial state tensors passed first call cell.","code":""},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"","code":"inputs <- random_uniform(c(32, 10, 8)) simple_rnn <- layer_simple_rnn(units = 4) output <- simple_rnn(inputs)  # The output has shape `(32, 4)`. simple_rnn <- layer_simple_rnn(     units = 4, return_sequences=TRUE, return_state=TRUE ) # whole_sequence_output has shape `(32, 10, 4)`. # final_state has shape `(32, 4)`. c(whole_sequence_output, final_state) %<-% simple_rnn(inputs)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"layer performs function Dropout, however, drops entire 1D feature maps instead individual elements. adjacent frames within feature maps strongly correlated (normally case early convolution layers) regular dropout regularize activations otherwise just result effective learning rate decrease. case, SpatialDropout1D help promote independence feature maps used instead.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"","code":"layer_spatial_dropout_1d(object, rate, seed = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. Fraction input units drop. seed Initial seed random number generator name String, name object dtype datatype (e.g., \"float32\").","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"inputs: 3D tensor. training: Python boolean indicating whether layer behave training mode (applying dropout) inference mode (pass-).","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"3D tensor shape: (samples, timesteps, channels)","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"input.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"Tompson et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"version performs function Dropout, however, drops entire 2D feature maps instead individual elements. adjacent pixels within feature maps strongly correlated (normally case early convolution layers) regular dropout regularize activations otherwise just result effective learning rate decrease. case, SpatialDropout2D help promote independence feature maps used instead.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"","code":"layer_spatial_dropout_2d(   object,   rate,   data_format = NULL,   seed = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. Fraction input units drop. data_format \"channels_first\" \"channels_last\". \"channels_first\" mode, channels dimension (depth) index 1, \"channels_last\" mode index 3. defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". seed Initial seed random number generator name String, name object dtype datatype (e.g., \"float32\").","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"inputs: 4D tensor. training: Python boolean indicating whether layer behave training mode (applying dropout) inference mode (pass-).","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"4D tensor shape: (samples, channels, rows, cols) data_format='channels_first' 4D tensor shape: (samples, rows, cols, channels) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"input.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"Tompson et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"version performs function Dropout, however, drops entire 3D feature maps instead individual elements. adjacent voxels within feature maps strongly correlated (normally case early convolution layers) regular dropout regularize activations otherwise just result effective learning rate decrease. case, SpatialDropout3D help promote independence feature maps used instead.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"","code":"layer_spatial_dropout_3d(   object,   rate,   data_format = NULL,   seed = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. Fraction input units drop. data_format \"channels_first\" \"channels_last\". \"channels_first\" mode, channels dimension (depth) index 1, \"channels_last\" mode index 4. defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". seed Initial seed random number generator name String, name object dtype datatype (e.g., \"float32\").","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"inputs: 5D tensor. training: Python boolean indicating whether layer behave training mode (applying dropout) inference mode (pass-).","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"5D tensor shape: (samples, channels, dim1, dim2, dim3) data_format='channels_first' 5D tensor shape: (samples, dim1, dim2, dim3, channels) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"input.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"Tompson et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"wrapper controls Lipschitz constant weights layer constraining spectral norm, can stabilize training GANs.","code":""},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"","code":"layer_spectral_normalization(object, layer, power_iterations = 1L, ...)"},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"object Object compose layer . tensor, array, sequential model. layer Layer instance either kernel (e.g. layer_conv_2d, layer_dense...) embeddings attribute (layer_embedding layer). power_iterations int, number iterations normalization. ... Base wrapper keyword arguments.","code":""},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"Wrap layer_conv_2d:     Wrap layer_dense:","code":"x <- random_uniform(c(1, 10, 10, 1)) conv2d <- layer_spectral_normalization(   layer = layer_conv_2d(filters = 2, kernel_size = 2) ) y <- conv2d(x) shape(y) ## shape(1, 9, 9, 2) x <- random_uniform(c(1, 10, 10, 1)) dense <- layer_spectral_normalization(layer = layer_dense(units = 10)) y <- dense(x) shape(y) ## shape(1, 10, 10, 10)"},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"Spectral Normalization GAN.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"layer translates set arbitrary strings integer output via table-based vocabulary lookup. layer perform splitting transformation input strings. layer can split tokenize natural language, see layer_text_vectorization layer. vocabulary layer must either supplied construction learned via adapt(). adapt(), layer analyze data set, determine frequency individual strings tokens, create vocabulary . vocabulary capped size, frequent tokens used create vocabulary others treated --vocabulary (OOV). two possible output modes layer. output_mode \"int\", input strings converted index vocabulary (integer). output_mode \"multi_hot\", \"count\", \"tf_idf\", input strings encoded array dimension corresponds element vocabulary. vocabulary can optionally contain mask token well OOV token (can optionally occupy multiple indices vocabulary, set num_oov_indices). position tokens vocabulary fixed. output_mode \"int\", vocabulary begin mask token (set), followed OOV indices, followed rest vocabulary. output_mode \"multi_hot\", \"count\", \"tf_idf\" vocabulary begin OOV indices instances mask token dropped. Note: layer uses TensorFlow internally. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"","code":"layer_string_lookup(   object,   max_tokens = NULL,   num_oov_indices = 1L,   mask_token = NULL,   oov_token = \"[UNK]\",   vocabulary = NULL,   idf_weights = NULL,   invert = FALSE,   output_mode = \"int\",   pad_to_max_tokens = FALSE,   sparse = FALSE,   encoding = \"utf-8\",   name = NULL,   ...,   vocabulary_dtype = NULL )"},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"object Object compose layer . tensor, array, sequential model. max_tokens Maximum size vocabulary layer. specified adapting vocabulary setting pad_to_max_tokens=TRUE. NULL, cap size vocabulary. Note size includes OOV mask tokens. Defaults NULL. num_oov_indices number --vocabulary tokens use. value 1, OOV inputs modulated determine OOV value. value 0, OOV inputs cause error calling layer. Defaults 1. mask_token token represents masked inputs. output_mode \"int\", token included vocabulary mapped index 0. output modes, token appear vocabulary instances mask token input dropped. set NULL, mask term added. Defaults NULL. oov_token used invert TRUE. token return OOV indices. Defaults \"[UNK]\". vocabulary Optional. Either array integers string path text file. passing array, can pass list, list, 1D NumPy array, 1D tensor containing integer vocbulary terms. passing file path, file contain one line per term vocabulary. argument set, need adapt() layer. idf_weights valid output_mode \"tf_idf\". list, list, 1D NumPy array, 1D tensor length vocabulary, containing floating point inverse document frequency weights, multiplied per sample term counts final TF-IDF weight. vocabulary argument set, output_mode \"tf_idf\", argument must supplied. invert valid output_mode \"int\". TRUE, layer map indices vocabulary items instead mapping vocabulary items indices. Defaults FALSE. output_mode Specification output layer. Values can \"int\", \"one_hot\", \"multi_hot\", \"count\", \"tf_idf\" configuring layer follows: \"int\": Return vocabulary indices input tokens. \"one_hot\": Encodes individual element input array size vocabulary, containing 1 element index. last dimension size 1, encode dimension. last dimension size 1, append new dimension encoded output. \"multi_hot\": Encodes sample input single array size vocabulary, containing 1 vocabulary term present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). \"count\": \"multi_hot\", int array contains count number times token index appeared sample. \"tf_idf\": \"multi_hot\", TF-IDF algorithm applied find value token slot. \"int\" output, shape input output supported. output modes, currently output rank 2 supported. Defaults \"int\". pad_to_max_tokens applicable output_mode \"multi_hot\", \"count\", \"tf_idf\". TRUE, output feature axis padded max_tokens even number unique tokens vocabulary less max_tokens, resulting tensor shape (batch_size, max_tokens) regardless vocabulary size. Defaults FALSE. sparse Boolean. applicable \"multi_hot\", \"count\", \"tf_idf\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. encoding Optional. text encoding use interpret input strings. Defaults \"utf-8\". name String, name object ... forward/backward compatability. vocabulary_dtype dtype vocabulary terms, example \"int64\" \"int32\". Defaults \"int64\".","code":""},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"Creating lookup layer known vocabulary example creates lookup layer pre-existing vocabulary.     Creating lookup layer adapted vocabulary example creates lookup layer generates vocabulary analyzing dataset.     Note OOV token \"[UNK]\" added vocabulary. remaining tokens sorted frequency (\"d\", 2 occurrences, first) inverse sort order.     Lookups multiple OOV indices example demonstrates use lookup layer multiple OOV indices.  layer created one OOV index, OOV values hashed number OOV buckets, distributing OOV values deterministic fashion across set.     Note output OOV value 'm' 0, output OOV value \"z\" 1. -vocab terms output index increased 1 earlier examples (maps 2, etc) order make space extra OOV value. One-hot output Configure layer output_mode='one_hot'. Note first num_oov_indices dimensions ont_hot encoding represent OOV values.     Multi-hot output Configure layer output_mode='multi_hot'. Note first num_oov_indices dimensions multi_hot encoding represent OOV values.     Token count output Configure layer output_mode='count'. multi_hot output, first num_oov_indices dimensions output represent OOV values.     TF-IDF output Configure layer output_mode=\"tf_idf\". multi_hot output, first num_oov_indices dimensions output represent OOV values. token bin output token_count * idf_weight, idf weights inverse document frequency weights per token. provided along vocabulary. Note idf_weight OOV values default average idf weights passed .     specify idf weights oov values, need pass entire vocabulary including leading oov token.     adapting layer \"tf_idf\" mode, input sample considered document, IDF weight per token calculated log(1 + num_documents / (1 + token_document_count)). Inverse lookup example demonstrates map indices strings using layer. (can also use adapt() inverse=TRUE, simplicity pass vocab example.)     Note first index correspond oov token default. Forward inverse lookup pairs example demonstrates use vocabulary standard lookup layer create inverse lookup layer.     example, input value \"z\" resulted output \"[UNK]\", since 1000 vocabulary - got represented OOV, OOV values returned \"[UNK]\" inverse layer. Also, note inverse work, must already set forward layer vocabulary either directly via adapt() calling get_vocabulary().","code":"vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\"), c(\"d\", \"z\", \"b\")) layer <- layer_string_lookup(vocabulary=vocab) layer(data) ## tf.Tensor( ## [[1 3 4] ##  [4 0 2]], shape=(2, 3), dtype=int64) data <- rbind(c(\"a\", \"c\", \"d\"), c(\"d\", \"z\", \"b\")) layer <- layer_string_lookup() layer %>% adapt(data) get_vocabulary(layer) ## [1] \"[UNK]\" \"d\"     \"z\"     \"c\"     \"b\"     \"a\" data <- rbind(c(\"a\", \"c\", \"d\"), c(\"d\", \"z\", \"b\")) layer <- layer_string_lookup() layer %>% adapt(data) layer(data) ## tf.Tensor( ## [[5 3 1] ##  [1 2 4]], shape=(2, 3), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\"), c(\"m\", \"z\", \"b\")) layer <- layer_string_lookup(vocabulary = vocab, num_oov_indices = 2) layer(data) ## tf.Tensor( ## [[2 4 5] ##  [0 1 3]], shape=(2, 3), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- c(\"a\", \"b\", \"c\", \"d\", \"z\") layer <- layer_string_lookup(vocabulary = vocab, output_mode = 'one_hot') layer(data) ## tf.Tensor( ## [[0 1 0 0 0] ##  [0 0 1 0 0] ##  [0 0 0 1 0] ##  [0 0 0 0 1] ##  [1 0 0 0 0]], shape=(5, 5), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\", \"d\"), c(\"d\", \"z\", \"b\", \"z\")) layer <- layer_string_lookup(vocabulary = vocab, output_mode = 'multi_hot') layer(data) ## tf.Tensor( ## [[0 1 0 1 1] ##  [1 0 1 0 1]], shape=(2, 5), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\", \"d\"), c(\"d\", \"z\", \"b\", \"z\")) layer <- layer_string_lookup(vocabulary = vocab, output_mode = 'count') layer(data) ## tf.Tensor( ## [[0 1 0 1 2] ##  [2 0 1 0 1]], shape=(2, 5), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") idf_weights <- c(0.25, 0.75, 0.6, 0.4) data <- rbind(c(\"a\", \"c\", \"d\", \"d\"), c(\"d\", \"z\", \"b\", \"z\")) layer <- layer_string_lookup(output_mode = \"tf_idf\") layer %>% set_vocabulary(vocab, idf_weights=idf_weights) layer(data) ## tf.Tensor( ## [[0.   0.25 0.   0.6  0.8 ] ##  [1.   0.   0.75 0.   0.4 ]], shape=(2, 5), dtype=float32) vocab <- c(\"[UNK]\", \"a\", \"b\", \"c\", \"d\") idf_weights <- c(0.9, 0.25, 0.75, 0.6, 0.4) data <- rbind(c(\"a\", \"c\", \"d\", \"d\"), c(\"d\", \"z\", \"b\", \"z\")) layer <- layer_string_lookup(output_mode = \"tf_idf\") layer %>% set_vocabulary(vocab, idf_weights=idf_weights) layer(data) ## tf.Tensor( ## [[0.   0.25 0.   0.6  0.8 ] ##  [1.8  0.   0.75 0.   0.4 ]], shape=(2, 5), dtype=float32) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(1, 3, 4), c(4, 0, 2)) layer <- layer_string_lookup(vocabulary = vocab, invert = TRUE) layer(data) ## tf.Tensor( ## [[b'a' b'c' b'd'] ##  [b'd' b'[UNK]' b'b']], shape=(2, 3), dtype=string) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\"), c(\"d\", \"z\", \"b\")) layer <- layer_string_lookup(vocabulary = vocab) i_layer <- layer_string_lookup(vocabulary = vocab, invert = TRUE) int_data <- layer(data) i_layer(int_data) ## tf.Tensor( ## [[b'a' b'c' b'd'] ##  [b'd' b'[UNK]' b'b']], shape=(2, 3), dtype=string)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs elementwise subtraction. — layer_subtract","title":"Performs elementwise subtraction. — layer_subtract","text":"takes input list tensors size 2 shape, returns single tensor (inputs[0] - inputs[1)) shape.","code":""},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs elementwise subtraction. — layer_subtract","text":"","code":"layer_subtract(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs elementwise subtraction. — layer_subtract","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs elementwise subtraction. — layer_subtract","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs elementwise subtraction. — layer_subtract","text":"Usage Keras model:","code":"input_shape <- c(2, 3, 4) x1 <- random_uniform(input_shape) x2 <- random_uniform(input_shape) y <- layer_subtract(list(x1, x2)) input1 <- layer_input(shape = 16) x1 <- layer_dense(input1, units = 8, activation = 'relu') input2 <- layer_input(shape = 32) x2 <- layer_dense(input2, units = 8, activation = 'relu') subtracted <- layer_subtract(list(x1, x2)) out <- layer_dense(subtracted, units = 4) model <- keras_model(inputs = list(input1, input2), outputs = out)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"layer basic options managing text Keras model. transforms batch strings (one example = one string) either list token indices (one example = 1D tensor integer token indices) dense representation (one example = 1D tensor float values representing data example's tokens). layer meant handle natural language inputs. handle simple string inputs (categorical strings pre-tokenized strings) see layer_string_lookup(). vocabulary layer must either supplied construction learned via adapt(). layer adapted, analyze dataset, determine frequency individual string values, create vocabulary . vocabulary can unlimited size capped, depending configuration options layer; unique values input maximum vocabulary size, frequent terms used create vocabulary. processing example contains following steps: Standardize example (usually lowercasing + punctuation stripping) Split example substrings (usually words) Recombine substrings tokens (usually ngrams) Index tokens (associate unique int value token) Transform example using index, either vector ints dense float vector. notes passing callables customize splitting normalization layer: callable can passed Layer, want serialize object pass functions registered Keras serializables (see register_keras_serializable() details). using custom callable standardize, data received callable exactly passed layer. callable return tensor shape input. using custom callable split, data received callable 1st dimension squeezed - instead list(\"string split\", \"another string split\"), Callable see c(\"string split\", \"another string split\"). callable return tf.Tensor dtype string first dimension containing split tokens - example, see something like list(c(\"string\", \"\", \"split\"), c(\"another\", \"string\", \"\", \"split\")). Note: layer uses TensorFlow internally. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"","code":"layer_text_vectorization(   object,   max_tokens = NULL,   standardize = \"lower_and_strip_punctuation\",   split = \"whitespace\",   ngrams = NULL,   output_mode = \"int\",   output_sequence_length = NULL,   pad_to_max_tokens = FALSE,   vocabulary = NULL,   idf_weights = NULL,   sparse = FALSE,   ragged = FALSE,   encoding = \"utf-8\",   name = NULL,   ... )  get_vocabulary(object, include_special_tokens = TRUE)  set_vocabulary(object, vocabulary, idf_weights = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"object Object compose layer . tensor, array, sequential model. max_tokens Maximum size vocabulary layer. specified adapting vocabulary setting pad_to_max_tokens=TRUE. Note vocabulary contains 1 OOV token, effective number tokens (max_tokens - 1 - (1 output_mode == \"int\" else 0)). standardize Optional specification standardization apply input text. Values can : NULL: standardization. \"lower_and_strip_punctuation\": Text lowercased punctuation removed. \"lower\": Text lowercased. \"strip_punctuation\": punctuation removed. Callable: Inputs passed callable function, standardized returned. split Optional specification splitting input text. Values can : NULL: splitting. \"whitespace\": Split whitespace. \"character\": Split unicode character. Callable: Standardized inputs passed callable function, split returned. ngrams Optional specification ngrams create possibly-split input text. Values can NULL, integer list integers; passing integer create ngrams integer, passing list integers create ngrams specified values list. Passing NULL means ngrams created. output_mode Optional specification output layer. Values can \"int\", \"multi_hot\", \"count\" \"tf_idf\", configuring layer follows: \"int\": Outputs integer indices, one integer index per split string token. output_mode == \"int\", 0 reserved masked locations; reduces vocab size max_tokens - 2 instead max_tokens - 1. \"multi_hot\": Outputs single int array per batch, either vocab_size max_tokens size, containing 1s elements token mapped index exists least batch item. \"count\": Like \"multi_hot\", int array contains count number times token index appeared batch item. \"tf_idf\": Like \"multi_hot\", TF-IDF algorithm applied find value token slot. \"int\" output, shape input output supported. output modes, currently rank 1 inputs (rank 2 outputs splitting) supported. output_sequence_length valid INT mode. set, output time dimension padded truncated exactly output_sequence_length values, resulting tensor shape (batch_size, output_sequence_length) regardless many tokens resulted splitting step. Defaults NULL. ragged TRUE output_sequence_length may still truncate output. pad_to_max_tokens valid  \"multi_hot\", \"count\", \"tf_idf\" modes. TRUE, output feature axis padded max_tokens even number unique tokens vocabulary less max_tokens, resulting tensor shape (batch_size, max_tokens) regardless vocabulary size. Defaults FALSE. vocabulary Optional. Either array strings string path text file. passing array, can pass list, list, 1D NumPy array, 1D tensor containing string vocabulary terms. passing file path, file contain one line per term vocabulary. argument set, need adapt() layer. idf_weights R vector, 1D numpy array, 1D tensor inverse document frequency weights equal length vocabulary. Must set output_mode \"tf_idf\". set otherwise. sparse Boolean. applicable \"multi_hot\", \"count\", \"tf_idf\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. ragged Boolean. applicable \"int\" output mode. supported TensorFlow backend. TRUE, returns RaggedTensor instead dense Tensor, sequence may different length string splitting. Defaults FALSE. encoding Optional. text encoding use interpret input strings. Defaults \"utf-8\". name String, name object ... forward/backward compatability. include_special_tokens TRUE, returned vocabulary include padding OOV tokens, term's index vocabulary equal term's index calling layer. FALSE, returned vocabulary include padding OOV tokens.","code":""},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"example instantiates TextVectorization layer lowercases text, splits whitespace, strips punctuation, outputs integer vocab indices.         example instantiates TextVectorization layer passing list vocabulary terms layer's initialize() method.","code":"max_tokens <- 5000  # Maximum vocab size. max_len <- 4  # Sequence length to pad the outputs to. # Create the layer. vectorize_layer <- layer_text_vectorization(     max_tokens = max_tokens,     output_mode = 'int',     output_sequence_length = max_len) # Now that the vocab layer has been created, call `adapt` on the # list of strings to create the vocabulary. vectorize_layer %>% adapt(c(\"foo bar\", \"bar baz\", \"baz bada boom\")) # Now, the layer can map strings to integers -- you can use an # embedding layer to map these integers to learned embeddings. input_data <- rbind(\"foo qux bar\", \"qux baz\") vectorize_layer(input_data) ## tf.Tensor( ## [[4 1 3 0] ##  [1 2 0 0]], shape=(2, 4), dtype=int64) vocab_data <- c(\"earth\", \"wind\", \"and\", \"fire\") max_len <- 4  # Sequence length to pad the outputs to. # Create the layer, passing the vocab directly. You can also pass the # vocabulary arg a path to a file containing one vocabulary word per # line. vectorize_layer <- layer_text_vectorization(     max_tokens = max_tokens,     output_mode = 'int',     output_sequence_length = max_len,     vocabulary = vocab_data) # Because we've passed the vocabulary directly, we don't need to adapt # the layer - the vocabulary is already set. The vocabulary contains the # padding token ('') and OOV token ('[UNK]') # as well as the passed tokens. vectorize_layer %>% get_vocabulary() ## [1] \"\"      \"[UNK]\" \"earth\" \"wind\"  \"and\"   \"fire\" # ['', '[UNK]', 'earth', 'wind', 'and', 'fire']"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"Reload Keras model/layer saved via export_savedmodel().","code":""},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"","code":"layer_tfsm(   object,   filepath,   call_endpoint = \"serve\",   call_training_endpoint = NULL,   trainable = TRUE,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"object Object compose layer . tensor, array, sequential model. filepath string, path SavedModel. call_endpoint Name endpoint use call() method reloaded layer. SavedModel created via export_savedmodel(), default endpoint name 'serve'. cases may named 'serving_default'. call_training_endpoint see description trainable see description name String, name object dtype datatype (e.g., \"float32\").","code":""},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"reloaded object can used like regular Keras layer, supports training/fine-tuning trainable weights. Note reloaded object retains none internal structure custom methods original object -- brand new layer created around saved function. Limitations: call endpoints single inputs tensor argument (may optionally named list/list tensors) supported. endpoints multiple separate input tensor arguments, consider subclassing layer_tfsm implementing call() method custom signature. need training-time behavior differ inference-time behavior (.e. need reloaded object support training=TRUE argument __call__()), make sure training-time call function saved standalone endpoint artifact, provide name layer_tfsm via call_training_endpoint argument.","code":"model <- keras_model_sequential(input_shape = c(784)) |> layer_dense(10) model |> export_savedmodel(\"path/to/artifact\") ## Saved artifact at 'path/to/artifact'. The following endpoints are available: ## ## * Endpoint 'serve' ##   args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 784), dtype=tf.float32, name='keras_tensor') ## Output Type: ##   TensorSpec(shape=(None, 10), dtype=tf.float32, name=None) ## Captures: ##   128255174032224: TensorSpec(shape=(), dtype=tf.resource, name=None) ##   128255174017264: TensorSpec(shape=(), dtype=tf.resource, name=None) reloaded_layer <- layer_tfsm(filepath = \"path/to/artifact\") input <- random_normal(c(2, 784)) output <- reloaded_layer(input) stopifnot(all.equal(as.array(output), as.array(model(input))))"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":null,"dir":"Reference","previous_headings":"","what":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"Every input least 3D, dimension index one first input considered temporal dimension. Consider batch 32 video samples, sample 128x128 RGB image channels_last data format, across 10 timesteps. batch input shape (32, 10, 128, 128, 3). can use TimeDistributed apply Conv2D layer 10 timesteps, independently:     layer_time_distributed applies instance layer_conv2d timestamps, set weights used timestamp.","code":"inputs <- keras_input(shape = c(10, 128, 128, 3), batch_size = 32) conv_2d_layer <- layer_conv_2d(filters = 64, kernel_size = c(3, 3)) outputs <- layer_time_distributed(inputs, layer = conv_2d_layer) shape(outputs) ## shape(32, 10, 126, 126, 64)"},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"","code":"layer_time_distributed(object, layer, ...)"},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"object Object compose layer . tensor, array, sequential model. layer Layer instance. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"inputs: Input tensor shape (batch, time, ...) nested tensors, shape (batch, time, ...). training: Boolean indicating whether layer behave training mode inference mode. argument passed wrapped layer (layer supports argument). mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked. argument passed wrapped layer (layer supports argument).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Torch module wrapper layer. — layer_torch_module_wrapper","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"layer_torch_module_wrapper wrapper class can turn torch.nn.Module Keras layer, particular making parameters trackable Keras.","code":""},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"","code":"layer_torch_module_wrapper(object, module, name = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"object Object compose layer . tensor, array, sequential model. module torch.nn.Module instance. LazyModule instance, parameters must initialized passing instance layer_torch_module_wrapper (e.g. calling ). name name layer (string). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"example layer_torch_module_wrapper() can used vanilla PyTorch modules.","code":"# reticulate::py_install( #   packages = c(\"torch\", \"torchvision\", \"torchaudio\"), #   envname = \"r-keras\", #   pip_options = c(\"--index-url https://download.pytorch.org/whl/cpu\") # ) library(keras3) use_backend(\"torch\") torch <- reticulate::import(\"torch\") nn <- reticulate::import(\"torch.nn\") nnf <- reticulate::import(\"torch.nn.functional\")  Classifier(keras$Model) \\%py_class\\% {   initialize <- function(...) {     super$initialize(...)      self$conv1 <- layer_torch_module_wrapper(module = nn$Conv2d(       in_channels = 1L,       out_channels = 32L,       kernel_size = tuple(3L, 3L)     ))     self$conv2 <- layer_torch_module_wrapper(module = nn$Conv2d(       in_channels = 32L,       out_channels = 64L,       kernel_size = tuple(3L, 3L)     ))     self$pool <- nn$MaxPool2d(kernel_size = tuple(2L, 2L))     self$flatten <- nn$Flatten()     self$dropout <- nn$Dropout(p = 0.5)     self$fc <-       layer_torch_module_wrapper(module = nn$Linear(1600L, 10L))   }    call <- function(inputs) {     x <- nnf$relu(self$conv1(inputs))     x <- self$pool(x)     x <- nnf$relu(self$conv2(x))     x <- self$pool(x)     x <- self$flatten(x)     x <- self$dropout(x)     x <- self$fc(x)     nnf$softmax(x, dim = 1L)   } } model <- Classifier() model$build(shape(1, 28, 28)) cat(\"Output shape:\", format(shape(model(torch$ones(1L, 1L, 28L, 28L)))))  model |> compile(loss = \"sparse_categorical_crossentropy\",                  optimizer = \"adam\",                  metrics = \"accuracy\") model |> fit(train_loader, epochs = 5)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Unit normalization layer. — layer_unit_normalization","title":"Unit normalization layer. — layer_unit_normalization","text":"Normalize batch inputs input batch L2 norm equal 1 (across axes specified axis).","code":""},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unit normalization layer. — layer_unit_normalization","text":"","code":"layer_unit_normalization(object, axis = -1L, ...)"},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unit normalization layer. — layer_unit_normalization","text":"object Object compose layer . tensor, array, sequential model. axis Integer list. axis axes normalize across. Typically, features axis axes. left-axes typically batch axis axes. -1 last dimension input. Defaults -1. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unit normalization layer. — layer_unit_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unit normalization layer. — layer_unit_normalization","text":"","code":"data <- op_reshape(1:6, newshape = c(2, 3)) normalized_data <- layer_unit_normalization(data) op_sum(normalized_data[1,]^2) ## tf.Tensor(0.9999999, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsampling layer for 1D inputs. — layer_upsampling_1d","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"Repeats temporal step size times along time axis.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"","code":"layer_upsampling_1d(object, size = 2L, ...)"},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"object Object compose layer . tensor, array, sequential model. size Integer. Upsampling factor. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"","code":"input_shape <- c(2, 2, 3) x <- seq_len(prod(input_shape)) %>% op_reshape(input_shape) x ## tf.Tensor( ## [[[ 1  2  3] ##   [ 4  5  6]] ## ##  [[ 7  8  9] ##   [10 11 12]]], shape=(2, 2, 3), dtype=int32) y <- layer_upsampling_1d(x, size = 2) y ## tf.Tensor( ## [[[ 1  2  3] ##   [ 1  2  3] ##   [ 4  5  6] ##   [ 4  5  6]] ## ##  [[ 7  8  9] ##   [ 7  8  9] ##   [10 11 12] ##   [10 11 12]]], shape=(2, 4, 3), dtype=int32)"},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"3D tensor shape: (batch_size, steps, features).","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"3D tensor shape: (batch_size, upsampled_steps, features).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsampling layer for 2D inputs. — layer_upsampling_2d","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"implementation uses interpolative resizing, given resize method (specified interpolation argument). Use interpolation=nearest repeat rows columns data.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"","code":"layer_upsampling_2d(   object,   size = list(2L, 2L),   data_format = NULL,   interpolation = \"nearest\",   ... )"},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"object Object compose layer . tensor, array, sequential model. size Int, list 2 integers. upsampling factors rows columns. data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists) else \"channels_last\". Defaults \"channels_last\". interpolation string, one \"bicubic\", \"bilinear\", \"lanczos3\", \"lanczos5\", \"nearest\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"","code":"input_shape <- c(2, 2, 1, 3) x <- op_reshape(seq_len(prod(input_shape)), input_shape) print(x) ## tf.Tensor( ## [[[[ 1  2  3]] ## ##   [[ 4  5  6]]] ## ## ##  [[[ 7  8  9]] ## ##   [[10 11 12]]]], shape=(2, 2, 1, 3), dtype=int32) y <- layer_upsampling_2d(x, size = c(1, 2)) print(y) ## tf.Tensor( ## [[[[ 1  2  3] ##    [ 1  2  3]] ## ##   [[ 4  5  6] ##    [ 4  5  6]]] ## ## ##  [[[ 7  8  9] ##    [ 7  8  9]] ## ##   [[10 11 12] ##    [10 11 12]]]], shape=(2, 2, 2, 3), dtype=int32)"},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, rows, cols, channels) data_format \"channels_first\": (batch_size, channels, rows, cols)","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, upsampled_rows, upsampled_cols, channels) data_format \"channels_first\": (batch_size, channels, upsampled_rows, upsampled_cols)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsampling layer for 3D inputs. — layer_upsampling_3d","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"Repeats 1st, 2nd 3rd dimensions data size[0], size[1] size[2] respectively.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"","code":"layer_upsampling_3d(object, size = list(2L, 2L, 2L), data_format = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"object Object compose layer . tensor, array, sequential model. size Int, list 3 integers. upsampling factors dim1, dim2 dim3. data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists) else \"channels_last\". Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"","code":"input_shape <- c(2, 1, 2, 1, 3) x <- array(1, dim = input_shape) y <- layer_upsampling_3d(x, size = c(2, 2, 2)) shape(y) ## shape(2, 2, 4, 2, 3)"},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, dim1, dim2, dim3, channels) data_format \"channels_first\": (batch_size, channels, dim1, dim2, dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels) data_format \"channels_first\": (batch_size, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"Zero-padding layer 1D input (e.g. temporal sequence).","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"","code":"layer_zero_padding_1d(object, padding = 1L, ...)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"object Object compose layer . tensor, array, sequential model. padding Int, list int (length 2), named listionary. int: many zeros add beginning end padding dimension (axis 1). list 2 ints: many zeros add beginning end padding dimension ((left_pad, right_pad)). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"","code":"input_shape <- c(2, 2, 3) x <- op_reshape(seq_len(prod(input_shape)), input_shape) x ## tf.Tensor( ## [[[ 1  2  3] ##   [ 4  5  6]] ## ##  [[ 7  8  9] ##   [10 11 12]]], shape=(2, 2, 3), dtype=int32) y <- layer_zero_padding_1d(x, padding = 2) y ## tf.Tensor( ## [[[ 0  0  0] ##   [ 0  0  0] ##   [ 1  2  3] ##   [ 4  5  6] ##   [ 0  0  0] ##   [ 0  0  0]] ## ##  [[ 0  0  0] ##   [ 0  0  0] ##   [ 7  8  9] ##   [10 11 12] ##   [ 0  0  0] ##   [ 0  0  0]]], shape=(2, 6, 3), dtype=int32)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"3D tensor shape (batch_size, axis_to_pad, features)","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"3D tensor shape (batch_size, padded_axis, features)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"layer can add rows columns zeros top, bottom, left right side image tensor.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"","code":"layer_zero_padding_2d(object, padding = list(1L, 1L), data_format = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"object Object compose layer . tensor, array, sequential model. padding Int, list 2 ints, list 2 lists 2 ints. int: symmetric padding applied height width. list 2 ints: interpreted two different symmetric padding values height width: (symmetric_height_pad, symmetric_width_pad). list 2 lists 2 ints: interpreted ((top_pad, bottom_pad), (left_pad, right_pad)). data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"","code":"input_shape <- c(1, 1, 2, 2) x <- op_reshape(seq_len(prod(input_shape)), input_shape) x ## tf.Tensor( ## [[[[1 2] ##    [3 4]]]], shape=(1, 1, 2, 2), dtype=int32) y <- layer_zero_padding_2d(x, padding = 1) y ## tf.Tensor( ## [[[[0 0] ##    [0 0] ##    [0 0] ##    [0 0]] ## ##   [[0 0] ##    [1 2] ##    [3 4] ##    [0 0]] ## ##   [[0 0] ##    [0 0] ##    [0 0] ##    [0 0]]]], shape=(1, 3, 4, 2), dtype=int32)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, height, width, channels) data_format \"channels_first\": (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, padded_height, padded_width, channels) data_format \"channels_first\": (batch_size, channels, padded_height, padded_width)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"Zero-padding layer 3D data (spatial spatio-temporal).","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"","code":"layer_zero_padding_3d(   object,   padding = list(list(1L, 1L), list(1L, 1L), list(1L, 1L)),   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"object Object compose layer . tensor, array, sequential model. padding Int, list 3 ints, list 3 lists 2 ints. int: symmetric padding applied depth, height, width. list 3 ints: interpreted three different symmetric padding values depth, height, width: (symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad). list 3 lists 2 ints: interpreted ((left_dim1_pad, right_dim1_pad), (left_dim2_pad, right_dim2_pad), (left_dim3_pad, right_dim3_pad)). data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"","code":"input_shape <- c(1, 1, 2, 2, 3) x <- op_reshape(seq_len(prod(input_shape)), input_shape) x ## tf.Tensor( ## [[[[[ 1  2  3] ##     [ 4  5  6]] ## ##    [[ 7  8  9] ##     [10 11 12]]]]], shape=(1, 1, 2, 2, 3), dtype=int32) y <- layer_zero_padding_3d(x, padding = 2) shape(y) ## shape(1, 5, 6, 6, 3)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad, depth) data_format \"channels_first\": (batch_size, depth, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad)","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, first_padded_axis, second_padded_axis, third_axis_to_pad, depth) data_format \"channels_first\": (batch_size, depth, first_padded_axis, second_padded_axis, third_axis_to_pad)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","title":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","text":"See Loshchilov & Hutter, ICLR2016, SGDR: Stochastic Gradient Descent Warm Restarts. idea linear warmup learning rate, see Goyal et al.. begin training model, often want initial increase learning rate followed decay. warmup_target int, schedule applies linear increase per optimizer step learning rate initial_learning_rate warmup_target duration warmup_steps. Afterwards, applies cosine decay function taking learning rate warmup_target alpha duration decay_steps. warmup_target NULL skip warmup decay take learning rate initial_learning_rate alpha. requires step value  compute learning rate. can just pass backend variable increment training step. schedule 1-arg callable produces warmup followed decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. warmup computed :   decay computed :   Example usage without warmup:   Example usage warmup:   can pass schedule directly optimizer learning rate. learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"warmup_learning_rate <- function(step) {   completed_fraction <- step / warmup_steps   total_delta <- target_warmup - initial_learning_rate   completed_fraction * total_delta } if (is.null(warmup_target)) {   initial_decay_lr <- initial_learning_rate } else {   initial_decay_lr <- warmup_target }  decayed_learning_rate <- function(step) {   step <- min(step, decay_steps)   cosine_decay <- 0.5 * (1 + cos(pi * step / decay_steps))   decayed <- (1 - alpha) * cosine_decay + alpha   initial_decay_lr * decayed } decay_steps <- 1000 initial_learning_rate <- 0.1 lr_decayed_fn <- learning_rate_schedule_cosine_decay(     initial_learning_rate, decay_steps) decay_steps <- 1000 initial_learning_rate <- 0 warmup_steps <- 1000 target_learning_rate <- 0.1 lr_warmup_decayed_fn <- learning_rate_schedule_cosine_decay(     initial_learning_rate, decay_steps, warmup_target = target_learning_rate,     warmup_steps = warmup_steps )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","text":"","code":"learning_rate_schedule_cosine_decay(   initial_learning_rate,   decay_steps,   alpha = 0,   name = \"CosineDecay\",   warmup_target = NULL,   warmup_steps = 0L )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","text":"initial_learning_rate float. initial learning rate. decay_steps int. Number steps decay . alpha float. Minimum learning rate value decay fraction initial_learning_rate. name String. Optional name operation.  Defaults \"CosineDecay\". warmup_target float. target learning rate warmup phase. cast initial_learning_rate datatype. Setting NULL skip warmup begins decay phase initial_learning_rate. Otherwise scheduler warmup initial_learning_rate warmup_target. warmup_steps int. Number steps warmup .","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"See Loshchilov & Hutter, ICLR2016, SGDR: Stochastic Gradient Descent Warm Restarts. training model, often useful lower learning rate training progresses. schedule applies cosine decay function restarts optimizer step, given provided initial learning rate. requires step value compute decayed learning rate. can just pass backend variable increment training step. schedule 1-arg callable produces decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. learning rate multiplier first decays 1 alpha first_decay_steps steps. , warm restart performed. new warm restart runs t_mul times steps m_mul times initial learning rate new learning rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"","code":"learning_rate_schedule_cosine_decay_restarts(   initial_learning_rate,   first_decay_steps,   t_mul = 2,   m_mul = 1,   alpha = 0,   name = \"SGDRDecay\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"initial_learning_rate float. initial learning rate. first_decay_steps integer. Number steps decay . t_mul float. Used derive number iterations -th period. m_mul float. Used derive initial learning rate -th period. alpha float. Minimum learning rate value fraction initial_learning_rate. name String. Optional name operation. Defaults \"SGDRDecay\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"can pass schedule directly optimizer learning rate. learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"first_decay_steps <- 1000 lr_decayed_fn <- learning_rate_schedule_cosine_decay_restarts(         0.001,         first_decay_steps)"},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"training model, often useful lower learning rate training progresses. schedule applies exponential decay function optimizer step, given provided initial learning rate. schedule 1-arg callable produces decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. computed :   argument staircase TRUE, step / decay_steps integer division decayed learning rate follows staircase function. can pass schedule directly optimizer learning rate.","code":"decayed_learning_rate <- function(step) {   initial_learning_rate * decay_rate ^ (step / decay_steps) }"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"","code":"learning_rate_schedule_exponential_decay(   initial_learning_rate,   decay_steps,   decay_rate,   staircase = FALSE,   name = \"ExponentialDecay\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"initial_learning_rate float. initial learning rate. decay_steps integer. Must positive. See decay computation . decay_rate float. decay rate. staircase Boolean.  TRUE decay learning rate discrete intervals. name String.  Optional name operation.  Defaults \"ExponentialDecay\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"fitting Keras model, decay every 100000 steps base 0.96:   learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"initial_learning_rate <- 0.1 lr_schedule <- learning_rate_schedule_exponential_decay(     initial_learning_rate,     decay_steps=100000,     decay_rate=0.96,     staircase=TRUE)  model %>% compile(   optimizer = optimizer_sgd(learning_rate = lr_schedule),   loss = 'sparse_categorical_crossentropy',   metrics = c('accuracy'))  model %>% fit(data, labels, epochs=5)"},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"training model, often useful lower learning rate training progresses. schedule applies inverse decay function optimizer step, given provided initial learning rate. requires step value compute decayed learning rate. can just pass backend variable increment training step. schedule 1-arg callable produces decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. computed :   , staircase TRUE, :   can pass schedule directly optimizer_* learning rate.","code":"decayed_learning_rate <- function(step) {   initial_learning_rate / (1 + decay_rate * step / decay_step) } decayed_learning_rate <- function(step) {   initial_learning_rate /            (1 + decay_rate * floor(step / decay_step)) }"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"","code":"learning_rate_schedule_inverse_time_decay(   initial_learning_rate,   decay_steps,   decay_rate,   staircase = FALSE,   name = \"InverseTimeDecay\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"initial_learning_rate float. initial learning rate. decay_steps often apply decay. decay_rate number.  decay rate. staircase Whether apply decay discrete staircase, o pposed continuous, fashion. name String.  Optional name operation.  Defaults \"InverseTimeDecay\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"Fit Keras model decaying 1/t rate 0.5:","code":"... initial_learning_rate <- 0.1 decay_steps <- 1.0 decay_rate <- 0.5 learning_rate_fn <- learning_rate_schedule_inverse_time_decay(     initial_learning_rate, decay_steps, decay_rate)  model %>% compile(   optimizer = optimizer_sgd(learning_rate=learning_rate_fn),   loss = 'sparse_categorical_crossentropy',   metrics = 'accuracy') )  model %>% fit(data, labels, epochs=5)"},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"function returns 1-arg callable compute piecewise constant passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"","code":"learning_rate_schedule_piecewise_constant_decay(   boundaries,   values,   name = \"PiecewiseConstant\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"boundaries list Python numbers strictly increasing entries, elements type optimizer step. values list Python numbers specifies values intervals defined boundaries. one element boundaries, elements type. name string. Optional name operation. Defaults \"PiecewiseConstant\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type boundary tensors. output 1-arg function takes step values[0] step <= boundaries[0], values[1] step > boundaries[0] step <= boundaries[1], ..., values[-1] step > boundaries[-1].","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"use learning rate 1.0 first 100001 steps, 0.5 next 10000 steps, 0.1 additional steps.   can pass schedule directly optimizer learning rate. learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"step <- 0 boundaries <- c(100000, 110000) values <- c(1.0, 0.5, 0.1) learning_rate_fn <- learning_rate_schedule_piecewise_constant_decay(   boundaries, values)  # Later, whenever we perform an optimization step, we pass in the step. learning_rate <- learning_rate_fn(step)"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"ValueError: number elements boundaries values lists match.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"commonly observed monotonically decreasing learning rate, whose degree change carefully chosen, results better performing model. schedule applies polynomial decay function optimizer step, given provided initial_learning_rate, reach end_learning_rate given decay_steps. requires step value compute decayed learning rate. can just pass backend variable increment training step. schedule 1-arg callable produces decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. computed :   cycle TRUE multiple decay_steps used, first one bigger step.   can pass schedule directly Optimizer learning rate.","code":"decayed_learning_rate <- function(step) {   step = min(step, decay_steps)   ((initial_learning_rate - end_learning_rate) *     (1 - step / decay_steps) ^ (power)) +     end_learning_rate } decayed_learning_rate <- function(step) {   decay_steps = decay_steps * ceil(step / decay_steps)   ((initial_learning_rate - end_learning_rate) *       (1 - step / decay_steps) ^ (power)) +     end_learning_rate }"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"","code":"learning_rate_schedule_polynomial_decay(   initial_learning_rate,   decay_steps,   end_learning_rate = 1e-04,   power = 1,   cycle = FALSE,   name = \"PolynomialDecay\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"initial_learning_rate float. initial learning rate. decay_steps integer. Must positive. See decay computation . end_learning_rate float. minimal end learning rate. power float. power polynomial. Defaults 1.0. cycle boolean, whether cycle beyond decay_steps. name String.  Optional name operation. Defaults \"PolynomialDecay\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"Fit model decaying 0.1 0.01 10000 steps using sqrt (.e. power=0.5):   learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"... starter_learning_rate <- 0.1 end_learning_rate <- 0.01 decay_steps <- 10000 learning_rate_fn <- learning_rate_schedule_polynomial_decay(     starter_learning_rate,     decay_steps,     end_learning_rate,     power=0.5)  model %>% compile(   optimizer = optimizer_sgd(learning_rate=learning_rate_fn),   loss = 'sparse_categorical_crossentropy',   metrics = 'accuracy' )  model %>% fit(data, labels, epochs=5)"},{"path":[]},{"path":"https://keras.posit.co/reference/load_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Loads a model saved via save_model(). — load_model","title":"Loads a model saved via save_model(). — load_model","text":"Loads model saved via save_model().","code":""},{"path":"https://keras.posit.co/reference/load_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loads a model saved via save_model(). — load_model","text":"","code":"load_model(model, custom_objects = NULL, compile = TRUE, safe_mode = TRUE)"},{"path":"https://keras.posit.co/reference/load_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loads a model saved via save_model(). — load_model","text":"model string, path saved model file, raw vector, returned save_model(filepath = NULL) custom_objects Optional named list mapping names custom classes functions considered deserialization. compile Boolean, whether compile model loading. safe_mode Boolean, whether disallow unsafe lambda deserialization. safe_mode=FALSE, loading object potential trigger arbitrary code execution. argument applicable Keras v3 model format. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/load_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loads a model saved via save_model(). — load_model","text":"Keras model instance. original model compiled, argument compile = TRUE set, returned model compiled. Otherwise, model left uncompiled.","code":""},{"path":"https://keras.posit.co/reference/load_model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Loads a model saved via save_model(). — load_model","text":"Note model variables may different name values (var$name property, e.g. \"dense_1/kernel:0\") reloaded. recommended use layer attributes access specific variables, e.g. model |> get_layer(\"dense_1\") |> _$kernel.","code":"model <- keras_model_sequential(input_shape = c(3)) |>   layer_dense(5) |>   layer_activation_softmax()  model |> save_model(\"model.keras\") loaded_model <- load_model(\"model.keras\") x <- random_uniform(c(10, 3)) stopifnot(all.equal(   model |> predict(x),   loaded_model |> predict(x) ))"},{"path":[]},{"path":"https://keras.posit.co/reference/load_model_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Load weights from a file saved via save_model_weights(). — load_model_weights","title":"Load weights from a file saved via save_model_weights(). — load_model_weights","text":"Weights loaded based network's topology. means architecture weights saved. Note layers weights taken account topological ordering, adding removing layers fine long weights. Partial weight loading modified model, instance adding new layer (weights) changing shape weights layer, can choose ignore errors continue loading setting skip_mismatch=TRUE. case layer mismatching weights skipped. warning displayed skipped layer.","code":""},{"path":"https://keras.posit.co/reference/load_model_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load weights from a file saved via save_model_weights(). — load_model_weights","text":"","code":"load_model_weights(model, filepath, skip_mismatch = FALSE, ...)"},{"path":"https://keras.posit.co/reference/load_model_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load weights from a file saved via save_model_weights(). — load_model_weights","text":"model keras model. filepath String, path weights file load. can either .weights.h5 file legacy .h5 weights file. skip_mismatch Boolean, whether skip loading layers mismatch number weights, mismatch shape weights. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/load_model_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load weights from a file saved via save_model_weights(). — load_model_weights","text":"called primarily side effects. model returned, invisibly, enable usage pipe.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"Use cross-entropy loss binary (0 1) classification applications. loss function requires following inputs: y_true (true label): either 0 1. y_pred (predicted value): model's prediction, .e, single floating-point value either represents logit, (.e, value [-inf, inf] from_logits=TRUE) probability (.e, value [0., 1.] from_logits=FALSE).","code":""},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"","code":"loss_binary_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"binary_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"y_true Ground truth values. shape = [batch_size, d0, .. dN]. y_pred predicted values. shape = [batch_size, d0, .. dN]. from_logits Whether interpret y_pred tensor logit values. default, assume y_pred probabilities (.e., values [0, 1)). label_smoothing Float range [0, 1]. 0, smoothing occurs. > 0, compute loss predicted labels smoothed version true labels, smoothing squeezes labels towards 0.5. Larger values label_smoothing correspond heavier smoothing. axis axis along compute crossentropy (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"Binary crossentropy loss value. shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"Recommended Usage: (set from_logits=TRUE) compile() API:   standalone function:                     Default Usage: (set from_logits=FALSE)","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_binary_crossentropy(y_true, y_pred) loss ## tf.Tensor([0.91629073 0.71355818], shape=(2), dtype=float64) model %>% compile(     loss = loss_binary_crossentropy(from_logits=TRUE),     ... ) # Example 1: (batch_size = 1, number of samples = 4) y_true <- op_array(c(0, 1, 0, 0)) y_pred <- op_array(c(-18.6, 0.51, 2.94, -12.8)) bce <- loss_binary_crossentropy(from_logits = TRUE) bce(y_true, y_pred) ## tf.Tensor(0.865458, shape=(), dtype=float32) # Example 2: (batch_size = 2, number of samples = 4) y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(-18.6, 0.51), c(2.94, -12.8)) # Using default 'auto'/'sum_over_batch_size' reduction type. bce <- loss_binary_crossentropy(from_logits = TRUE) bce(y_true, y_pred) ## tf.Tensor(0.865458, shape=(), dtype=float32) # Using 'sample_weight' attribute bce(y_true, y_pred, sample_weight = c(0.8, 0.2)) ## tf.Tensor(0.2436386, shape=(), dtype=float32) # 0.243 # Using 'sum' reduction` type. bce <- loss_binary_crossentropy(from_logits = TRUE, reduction = \"sum\") bce(y_true, y_pred) ## tf.Tensor(1.730916, shape=(), dtype=float32) # Using 'none' reduction type. bce <- loss_binary_crossentropy(from_logits = TRUE, reduction = NULL) bce(y_true, y_pred) ## tf.Tensor([0.23515666 1.4957594 ], shape=(2), dtype=float32) # Make the following updates to the above \"Recommended Usage\" section # 1. Set `from_logits=FALSE` loss_binary_crossentropy() # OR ...('from_logits=FALSE') ## <keras.src.losses.losses.BinaryCrossentropy object> ##  signature: (y_true, y_pred, sample_weight=None) # 2. Update `y_pred` to use probabilities instead of logits y_pred <- c(0.6, 0.3, 0.2, 0.8) # OR [[0.6, 0.3], [0.2, 0.8]]"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"According Lin et al., 2018, helps apply focal factor -weight easy examples focus hard examples. default, focal tensor computed follows: focal_factor = (1 - output)^gamma class 1 focal_factor = output^gamma class 0 gamma focusing parameter. gamma = 0, focal effect binary crossentropy loss. apply_class_balancing == TRUE, function also takes account weight balancing factor binary classes 0 1 follows: weight = alpha class 1 (target == 1) weight = 1 - alpha class 0 alpha float range [0, 1]. Binary cross-entropy loss often used binary (0 1) classification tasks. loss function requires following inputs: y_true (true label): either 0 1. y_pred (predicted value): model's prediction, .e, single floating-point value either represents logit, (.e, value [-inf, inf] from_logits=TRUE) probability (.e, value [0., 1.] from_logits=FALSE). According Lin et al., 2018, helps apply \"focal factor\" -weight easy examples focus hard examples. default, focal tensor computed follows: focal_factor = (1 - output) ** gamma class 1 focal_factor = output ** gamma class 0 gamma focusing parameter. gamma=0, function equivalent binary crossentropy loss.","code":""},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"","code":"loss_binary_focal_crossentropy(   y_true,   y_pred,   apply_class_balancing = FALSE,   alpha = 0.25,   gamma = 2,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"binary_focal_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"y_true Ground truth values, shape (batch_size, d0, .. dN). y_pred predicted values, shape (batch_size, d0, .. dN). apply_class_balancing bool, whether apply weight balancing binary classes 0 1. alpha weight balancing factor class 1, default 0.25 mentioned reference Lin et al., 2018.  weight class 0 1.0 - alpha. gamma focusing parameter used compute focal factor, default 2.0 mentioned reference Lin et al., 2018. from_logits Whether interpret y_pred tensor logit values. default, assume y_pred probabilities (.e., values [0, 1]). label_smoothing Float [0, 1]. 0, smoothing occurs. > 0, compute loss predicted labels smoothed version true labels, smoothing squeezes labels towards 0.5. Larger values label_smoothing correspond heavier smoothing. axis axis along compute crossentropy (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"Binary focal crossentropy loss value shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"compile() API:   standalone function:","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_binary_focal_crossentropy(y_true, y_pred, gamma = 2) loss ## tf.Tensor([0.32986466 0.20579838], shape=(2), dtype=float64) model %>% compile(     loss = loss_binary_focal_crossentropy(         gamma = 2.0, from_logits = TRUE),     ... ) # Example 1: (batch_size = 1, number of samples = 4) y_true <- op_array(c(0, 1, 0, 0)) y_pred <- op_array(c(-18.6, 0.51, 2.94, -12.8)) loss <- loss_binary_focal_crossentropy(gamma = 2, from_logits = TRUE) loss(y_true, y_pred) ## tf.Tensor(0.6912122, shape=(), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(   apply_class_balancing = TRUE, gamma = 2, from_logits = TRUE) loss(y_true, y_pred) ## tf.Tensor(0.5101333, shape=(), dtype=float32) # Example 2: (batch_size = 2, number of samples = 4) y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(-18.6, 0.51), c(2.94, -12.8)) # Using default 'auto'/'sum_over_batch_size' reduction type. loss <- loss_binary_focal_crossentropy(     gamma = 3, from_logits = TRUE) loss(y_true, y_pred) ## tf.Tensor(0.6469951, shape=(), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(      apply_class_balancing = TRUE, gamma = 3, from_logits = TRUE) loss(y_true, y_pred) ## tf.Tensor(0.48214132, shape=(), dtype=float32) # Using 'sample_weight' attribute with focal effect loss <- loss_binary_focal_crossentropy(     gamma = 3, from_logits = TRUE) loss(y_true, y_pred, sample_weight = c(0.8, 0.2)) ## tf.Tensor(0.13312504, shape=(), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(      apply_class_balancing = TRUE, gamma = 3, from_logits = TRUE) loss(y_true, y_pred, sample_weight = c(0.8, 0.2)) ## tf.Tensor(0.09735977, shape=(), dtype=float32) # Using 'sum' reduction` type. loss <- loss_binary_focal_crossentropy(     gamma = 4, from_logits = TRUE,     reduction = \"sum\") loss(y_true, y_pred) ## tf.Tensor(1.2218808, shape=(), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(     apply_class_balancing = TRUE, gamma = 4, from_logits = TRUE,     reduction = \"sum\") loss(y_true, y_pred) ## tf.Tensor(0.9140807, shape=(), dtype=float32) # Using 'none' reduction type. loss <- loss_binary_focal_crossentropy(     gamma = 5, from_logits = TRUE,     reduction = NULL) loss(y_true, y_pred) ## tf.Tensor([0.00174837 1.1561027 ], shape=(2), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(     apply_class_balancing = TRUE, gamma = 5, from_logits = TRUE,     reduction = NULL) loss(y_true, y_pred) ## tf.Tensor([4.3709317e-04 8.6707699e-01], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"Use crossentropy loss function two label classes. expect labels provided one_hot representation. want provide labels integers, please use SparseCategoricalCrossentropy loss. num_classes floating point values per feature, .e., shape y_pred y_true [batch_size, num_classes].","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"","code":"loss_categorical_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"categorical_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"y_true Tensor one-hot true targets. y_pred Tensor predicted targets. from_logits Whether y_pred expected logits tensor. default, assume y_pred encodes probability distribution. label_smoothing Float [0, 1]. > 0, label values smoothed, meaning confidence label values relaxed. example, 0.1, use 0.1 / num_classes non-target labels 0.9 + 0.1 / num_classes target labels. axis axis along compute crossentropy (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"Categorical crossentropy loss value.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"Standalone usage:                 Usage compile() API:","code":"y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) loss <- loss_categorical_crossentropy(y_true, y_pred) loss ## tf.Tensor([0.05129329 2.30258509], shape=(2), dtype=float64) y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) # Using 'auto'/'sum_over_batch_size' reduction type. cce <- loss_categorical_crossentropy() cce(y_true, y_pred) ## tf.Tensor(1.1769392, shape=(), dtype=float32) # Calling with 'sample_weight'. cce(y_true, y_pred, sample_weight = op_array(c(0.3, 0.7))) ## tf.Tensor(0.8135988, shape=(), dtype=float32) # Using 'sum' reduction type. cce <- loss_categorical_crossentropy(reduction = \"sum\") cce(y_true, y_pred) ## tf.Tensor(2.3538785, shape=(), dtype=float32) # Using 'none' reduction type. cce <- loss_categorical_crossentropy(reduction = NULL) cce(y_true, y_pred) ## tf.Tensor([0.05129331 2.3025851 ], shape=(2), dtype=float32) model %>% compile(optimizer = 'sgd',               loss=loss_categorical_crossentropy())"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"Use crossentropy loss function two label classes want handle class imbalance without using class_weights. expect labels provided one_hot representation. According Lin et al., 2018, helps apply focal factor -weight easy examples focus hard examples. general formula focal loss (FL) follows: FL(p_t) = (1 - p_t)^gamma * log(p_t) p_t defined follows: p_t = output y_true == 1, else 1 - output (1 - p_t)^gamma modulating_factor, gamma focusing parameter. gamma = 0, focal effect cross entropy. gamma reduces importance given simple examples smooth manner. authors use alpha-balanced variant focal loss (FL) paper: FL(p_t) = -alpha * (1 - p_t)^gamma * log(p_t) alpha weight factor classes. alpha = 1, loss able handle class imbalance properly classes weight. can constant list constants. alpha list, must length number classes. formula can generalized : FL(p_t) = alpha * (1 - p_t)^gamma * CrossEntropy(y_true, y_pred) minus comes CrossEntropy(y_true, y_pred) (CE). Extending multi-class case straightforward: FL(p_t) = alpha * (1 - p_t) ** gamma * CategoricalCE(y_true, y_pred) snippet , num_classes floating pointing values per example. shape y_pred y_true (batch_size, num_classes).","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"","code":"loss_categorical_focal_crossentropy(   y_true,   y_pred,   alpha = 0.25,   gamma = 2,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"categorical_focal_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"y_true Tensor one-hot true targets. y_pred Tensor predicted targets. alpha weight balancing factor classes, default 0.25 mentioned reference. can list floats scalar. multi-class case, alpha may set inverse class frequency using compute_class_weight sklearn.utils. gamma focusing parameter, default 2.0 mentioned reference. helps gradually reduce importance given simple examples smooth manner. gamma = 0, focal effect categorical crossentropy. from_logits Whether output expected logits tensor. default, consider output encodes probability distribution. label_smoothing Float [0, 1]. > 0, label values smoothed, meaning confidence label values relaxed. example, 0.1, use 0.1 / num_classes non-target labels 0.9 + 0.1 / num_classes target labels. axis axis along compute crossentropy (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"Categorical focal crossentropy loss value.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"Standalone usage:                 Usage compile() API:","code":"y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) loss <- loss_categorical_focal_crossentropy(y_true, y_pred) loss ## tf.Tensor([3.20583090e-05 4.66273481e-01], shape=(2), dtype=float64) y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) # Using 'auto'/'sum_over_batch_size' reduction type. cce <- loss_categorical_focal_crossentropy() cce(y_true, y_pred) ## tf.Tensor(0.23315276, shape=(), dtype=float32) # Calling with 'sample_weight'. cce(y_true, y_pred, sample_weight = op_array(c(0.3, 0.7))) ## tf.Tensor(0.16320053, shape=(), dtype=float32) # Using 'sum' reduction type. cce <- loss_categorical_focal_crossentropy(reduction = \"sum\") cce(y_true, y_pred) ## tf.Tensor(0.46630552, shape=(), dtype=float32) # Using 'none' reduction type. cce <- loss_categorical_focal_crossentropy(reduction = NULL) cce(y_true, y_pred) ## tf.Tensor([3.2058331e-05 4.6627346e-01], shape=(2), dtype=float32) model %>% compile(   optimizer = 'adam',   loss = loss_categorical_focal_crossentropy())"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"Formula:   neg=maximum((1-y_true)*y_pred) pos=sum(y_true*y_pred)","code":"loss <- maximum(neg - pos + 1, 0)"},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"","code":"loss_categorical_hinge(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"categorical_hinge\" )"},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"y_true ground truth values. y_true values expected either {-1, +1} {0, 1} (.e. one-hot-encoded tensor) shape <- [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"Categorical hinge loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_categorical_hinge(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"Formula:   Note number -1 1. negative number -1 0, 0 indicates orthogonality values closer -1 indicate greater similarity. makes usable loss function setting try maximize proximity predictions targets. either y_true y_pred zero vector, cosine similarity 0 regardless proximity predictions targets.","code":"loss <- -sum(l2_norm(y_true) * l2_norm(y_pred))"},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"","code":"loss_cosine_similarity(   y_true,   y_pred,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"cosine_similarity\" )"},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"y_true Tensor true targets. y_pred Tensor predicted targets. axis axis along cosine similarity computed (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"Cosine similarity tensor.","code":""},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"","code":"y_true <- rbind(c(0., 1.), c(1., 1.), c(1., 1.)) y_pred <- rbind(c(1., 0.), c(1., 1.), c(-1., -1.)) loss <- loss_cosine_similarity(y_true, y_pred, axis=-1) loss ## tf.Tensor([-0. -1.  1.], shape=(3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_ctc.html","id":null,"dir":"Reference","previous_headings":"","what":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","title":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","text":"CTC (Connectionist Temporal Classification) loss.","code":""},{"path":"https://keras.posit.co/reference/loss_ctc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","text":"","code":"loss_ctc(y_true, y_pred, ..., reduction = \"sum_over_batch_size\", name = \"ctc\")"},{"path":"https://keras.posit.co/reference/loss_ctc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","text":"y_true tensor shape (batch_size, target_max_length) containing true labels integer format. 0 always represents blank/mask index used classes. y_pred tensor shape (batch_size, output_max_length, num_classes) containing logits (output model). normalized via softmax. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name String, name object","code":""},{"path":"https://keras.posit.co/reference/loss_ctc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","text":"CTC loss value.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/loss_dice.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Dice loss value between y_true and y_pred. — loss_dice","title":"Computes the Dice loss value between y_true and y_pred. — loss_dice","text":"Formula:   Formula:","code":"loss = 1 - (2 * sum(y_true * y_pred)) / (sum(y_true) + sum(y_pred)) loss = 1 - (2 * sum(y_true * y_pred)) / (sum(y_true) + sum(y_pred))"},{"path":"https://keras.posit.co/reference/loss_dice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Dice loss value between y_true and y_pred. — loss_dice","text":"","code":"loss_dice(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"dice\" )"},{"path":"https://keras.posit.co/reference/loss_dice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Dice loss value between y_true and y_pred. — loss_dice","text":"y_true tensor true targets. y_pred tensor predicted targets. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name String, name object","code":""},{"path":"https://keras.posit.co/reference/loss_dice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Dice loss value between y_true and y_pred. — loss_dice","text":"y_true y_pred provided, Dice loss value. Otherwise, Loss() instance.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the hinge loss between y_true & y_pred. — loss_hinge","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"Formula:   y_true values expected -1 1. binary (0 1) labels provided convert -1 1.","code":"loss <- mean(maximum(1 - y_true * y_pred, 0), axis=-1)"},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"","code":"loss_hinge(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"hinge\" )"},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"y_true ground truth values. y_true values expected -1 1. binary (0 1) labels provided converted -1 1 shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"Hinge loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"","code":"y_true <- array(sample(c(-1,1), 6, replace = TRUE), dim = c(2, 3)) y_pred <- random_uniform(c(2, 3)) loss <- loss_hinge(y_true, y_pred) loss ## tf.Tensor([1.0610152  0.93285507], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_huber.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Huber loss between y_true & y_pred. — loss_huber","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"Formula:   See: Huber loss.","code":"for (x in error) {   if (abs(x) <= delta){     loss <- c(loss, (0.5 * x^2))   } else if (abs(x) > delta) {     loss <- c(loss, (delta * abs(x) - 0.5 * delta^2))   } } loss <- mean(loss)"},{"path":"https://keras.posit.co/reference/loss_huber.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"","code":"loss_huber(   y_true,   y_pred,   delta = 1,   ...,   reduction = \"sum_over_batch_size\",   name = \"huber_loss\" )"},{"path":"https://keras.posit.co/reference/loss_huber.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"y_true tensor true targets. y_pred tensor predicted targets. delta float, point Huber loss function changes quadratic linear. Defaults 1.0. ... forward/backward compatability. reduction Type reduction apply loss. Options \"sum\", \"sum_over_batch_size\" NULL. Defaults \"sum_over_batch_size\". name Optional name instance.","code":""},{"path":"https://keras.posit.co/reference/loss_huber.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"Tensor one scalar loss entry per sample.","code":""},{"path":"https://keras.posit.co/reference/loss_huber.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_huber(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"Formula:   y_true y_pred expected probability distributions, values 0 1. get clipped [0, 1] range.","code":"loss <- y_true * log(y_true / y_pred)"},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"","code":"loss_kl_divergence(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"kl_divergence\" )"},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"y_true Tensor true targets. y_pred Tensor predicted targets. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"KL Divergence loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2,3)) loss <- loss_kl_divergence(y_true, y_pred) loss ## tf.Tensor([3.5312676 0.2128672], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"Formula:   Note log(cosh(x)) approximately equal (x ** 2) / 2 small x abs(x) - log(2) large x. means 'logcosh' works mostly like mean squared error, strongly affected occasional wildly incorrect prediction.","code":"loss <- mean(log(cosh(y_pred - y_true)), axis=-1)"},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"","code":"loss_log_cosh(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"log_cosh\" )"},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. Options \"sum\", \"sum_over_batch_size\" NULL. Defaults \"sum_over_batch_size\". name Optional name instance.","code":""},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"Logcosh error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"","code":"y_true <- rbind(c(0., 1.), c(0., 0.)) y_pred <- rbind(c(1., 1.), c(0., 0.)) loss <- loss_log_cosh(y_true, y_pred) # 0.108"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"Formula:","code":"loss <- mean(abs(y_true - y_pred))"},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"","code":"loss_mean_absolute_error(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"mean_absolute_error\" )"},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"Mean absolute error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2, 3)) loss <- loss_mean_absolute_error(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"Formula:   Division zero prevented dividing max(y_true, epsilon) epsilon = config_epsilon() (default 1e-7).","code":"loss <- 100 * op_mean(op_abs((y_true - y_pred) / y_true),                       axis=-1)"},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"","code":"loss_mean_absolute_percentage_error(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"mean_absolute_percentage_error\" )"},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"Mean absolute percentage error values shape = [batch_size, d0, ..dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"","code":"y_true <- random_uniform(c(2, 3)) y_pred <- random_uniform(c(2, 3)) loss <- loss_mean_absolute_percentage_error(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"Formula:","code":"loss <- mean(square(y_true - y_pred))"},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"","code":"loss_mean_squared_error(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"mean_squared_error\" )"},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"Mean squared error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2, 3)) loss <- loss_mean_squared_error(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"Note y_pred y_true less equal 0. Negative values 0 values replaced config_epsilon() (default 1e-7). Formula:","code":"loss <- mean(square(log(y_true + 1) - log(y_pred + 1)))"},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"","code":"loss_mean_squared_logarithmic_error(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"mean_squared_logarithmic_error\" )"},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"Mean squared logarithmic error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2, 3)) loss <- loss_mean_squared_logarithmic_error(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"Formula:","code":"loss <- y_pred - y_true * log(y_pred)"},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"","code":"loss_poisson(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"poisson\" )"},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"y_true Ground truth values. shape = [batch_size, d0, .. dN]. y_pred predicted values. shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"Poisson loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2, 3)) loss <- loss_poisson(y_true, y_pred) loss ## tf.Tensor([2.5907533  0.66836613], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"Use crossentropy loss function two label classes.  expect labels provided integers. want provide labels using one-hot representation, please use CategoricalCrossentropy loss.  # classes floating point values per feature y_pred single floating point value per feature y_true. snippet , single floating point value per example y_true num_classes floating pointing values per example y_pred. shape y_true [batch_size] shape y_pred [batch_size, num_classes].","code":""},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"","code":"loss_sparse_categorical_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   ignore_class = NULL,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"sparse_categorical_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"y_true Ground truth values. y_pred predicted values. from_logits Whether y_pred expected logits tensor. default, assume y_pred encodes probability distribution. ignore_class Optional integer. ID class ignored loss computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. axis Defaults -1. dimension along entropy computed. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"Sparse categorical crossentropy loss value.","code":""},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"Usage compile() API:","code":"y_true <- c(1, 2) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) loss <- loss_sparse_categorical_crossentropy(y_true, y_pred) loss ## tf.Tensor([0.05129339 2.30258509], shape=(2), dtype=float64) y_true <- c(1, 2) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) # Using 'auto'/'sum_over_batch_size' reduction type. scce <- loss_sparse_categorical_crossentropy() scce(op_array(y_true), op_array(y_pred)) ## tf.Tensor(1.1769392, shape=(), dtype=float32) # 1.177 # Calling with 'sample_weight'. scce(op_array(y_true), op_array(y_pred), sample_weight = op_array(c(0.3, 0.7))) ## tf.Tensor(0.8135988, shape=(), dtype=float32) # Using 'sum' reduction type. scce <- loss_sparse_categorical_crossentropy(reduction=\"sum\") scce(op_array(y_true), op_array(y_pred)) ## tf.Tensor(2.3538785, shape=(), dtype=float32) # 2.354 # Using 'none' reduction type. scce <- loss_sparse_categorical_crossentropy(reduction=NULL) scce(op_array(y_true), op_array(y_pred)) ## tf.Tensor([0.05129344 2.3025851 ], shape=(2), dtype=float32) # array([0.0513, 2.303], dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = loss_sparse_categorical_crossentropy())"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"Formula:   y_true values expected -1 1. binary (0 1) labels provided convert -1 1.","code":"loss <- square(maximum(1 - y_true * y_pred, 0))"},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"","code":"loss_squared_hinge(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"squared_hinge\" )"},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"y_true ground truth values. y_true values expected -1 1. binary (0 1) labels provided convert -1 1 shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"Squared hinge loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"","code":"y_true <- array(sample(c(-1,1), 6, replace = TRUE), dim = c(2, 3)) y_pred <- random_uniform(c(2, 3)) loss <- loss_squared_hinge(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"loss function weighted alpha beta coefficients penalize false positives false negatives. alpha=0.5 beta=0.5, loss value becomes equivalent Dice Loss. loss function weighted alpha beta coefficients penalize false positives false negatives. alpha=0.5 beta=0.5, loss value becomes equivalent Dice Loss.","code":""},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"","code":"loss_tversky(   y_true,   y_pred,   ...,   alpha = 0.5,   beta = 0.5,   reduction = \"sum_over_batch_size\",   name = \"tversky\" )"},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"y_true tensor true targets. y_pred tensor predicted targets. ... forward/backward compatability. alpha coefficient controlling incidence false positives. beta coefficient controlling incidence false negatives. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name String, name object","code":""},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"Tversky loss value.","code":""},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"Salehi et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/mark_active.html","id":null,"dir":"Reference","previous_headings":"","what":"active_property — mark_active","title":"active_property — mark_active","text":"mark_active() alias active_property(). See ?active_property() full documentation.","code":""},{"path":"https://keras.posit.co/reference/mark_active.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"active_property — mark_active","text":"","code":"mark_active(fn)"},{"path":"https://keras.posit.co/reference/mark_active.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"active_property — mark_active","text":"fn R function","code":""},{"path":"https://keras.posit.co/reference/mark_active.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"active_property — mark_active","text":"fn, additional R attribute cause fn converted active property converted method custom subclass.","code":""},{"path":"https://keras.posit.co/reference/metric_auc.html","id":null,"dir":"Reference","previous_headings":"","what":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"AUC (Area curve) ROC (Receiver operating characteristic; default) PR (Precision Recall) curves quality measures binary classifiers. Unlike accuracy, like cross-entropy losses, ROC-AUC PR-AUC evaluate operational points model. class approximates AUCs using Riemann sum. metric accumulation phrase, predictions accumulated within predefined buckets value. AUC computed interpolating per-bucket averages. buckets define evaluated operational points. metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute AUC.  discretize AUC curve, linearly spaced set thresholds used compute pairs recall precision values. area ROC-curve therefore computed using height recall values false positive rate, area PR-curve computed using height precision values recall. value ultimately returned auc, idempotent operation computes area discretized curve precision versus recall values (computed using aforementioned variables). num_thresholds variable controls degree discretization larger numbers thresholds closely approximating true AUC. quality approximation may vary dramatically depending num_thresholds. thresholds parameter can used manually specify thresholds split predictions evenly. best approximation real AUC, predictions distributed approximately uniformly range [0, 1] (from_logits=FALSE). quality AUC approximation may poor case. Setting summation_method 'minoring' 'majoring' can help quantify error approximation providing lower upper bound estimate AUC. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_auc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"","code":"metric_auc(   ...,   num_thresholds = 200L,   curve = \"ROC\",   summation_method = \"interpolation\",   name = NULL,   dtype = NULL,   thresholds = NULL,   multi_label = FALSE,   num_labels = NULL,   label_weights = NULL,   from_logits = FALSE )"},{"path":"https://keras.posit.co/reference/metric_auc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"... forward/backward compatability. num_thresholds (Optional) number thresholds use discretizing roc curve. Values must > 1. Defaults 200. curve (Optional) Specifies name curve computed, 'ROC' (default) 'PR' Precision-Recall-curve. summation_method (Optional) Specifies Riemann summation method used. 'interpolation' (default) applies mid-point summation scheme ROC.  PR-AUC, interpolates (true/false) positives ratio precision (see Davis & Goadrich 2006 details); 'minoring' applies left summation increasing intervals right summation decreasing intervals; 'majoring' opposite. name (Optional) string name metric instance. dtype (Optional) data type metric result. thresholds (Optional) list floating point values use thresholds discretizing curve. set, num_thresholds parameter ignored. Values [0, 1]. Endpoint thresholds equal {-epsilon, 1+epsilon} small positive epsilon value automatically included correctly handle predictions equal exactly 0 1. multi_label boolean indicating whether multilabel data treated , wherein AUC computed separately label averaged across labels, (FALSE) data flattened single label AUC computation. latter case, multilabel data passed AUC, label-prediction pair treated individual data point. set `FALSE`` multi-class data. num_labels (Optional) number labels, used multi_label TRUE. num_labels specified, state variables get created first call update_state. label_weights (Optional) list, array, tensor non-negative weights used compute AUCs multilabel data. multi_label TRUE, weights applied individual label AUCs averaged produce multi-label AUC. FALSE, used weight individual label predictions computing confusion matrix flattened data. Note unlike class_weights class_weights weights example depending value label, whereas label_weights depends index label flattening; therefore label_weights used multi-class data. from_logits boolean indicating whether predictions (y_pred update_state) probabilities sigmoid logits. rule thumb, using keras loss, from_logits constructor argument loss match AUC from_logits constructor argument.","code":""},{"path":"https://keras.posit.co/reference/metric_auc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_auc.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_auc(num_thresholds = 3) m$update_state(c(0,   0,   1,   1),                c(0, 0.5, 0.3, 0.9)) # threshold values are [0 - 1e-7, 0.5, 1 + 1e-7] # tp = [2, 1, 0], fp = [2, 0, 0], fn = [0, 1, 2], tn = [0, 2, 2] # tp_rate = recall = [1, 0.5, 0], fp_rate = [1, 0, 0] # auc = ((((1 + 0.5) / 2) * (1 - 0)) + (((0.5 + 0) / 2) * (0 - 0))) #     = 0.75 m$result() ## tf.Tensor(0.75, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   1,   1),                c(0, 0.5, 0.3, 0.9),                sample_weight=c(1, 0, 0, 1)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32) # Reports the AUC of a model outputting a probability. model |> compile(   optimizer = 'sgd',   loss = loss_binary_crossentropy(),   metrics = list(metric_auc()) )  # Reports the AUC of a model outputting a logit. model |> compile(   optimizer = 'sgd',   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_auc(from_logits = TRUE)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates how often predictions match binary labels. — metric_binary_accuracy","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"metric creates two local variables, total count used compute frequency y_pred matches y_true. frequency ultimately returned binary accuracy: idempotent operation simply divides total count. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"","code":"metric_binary_accuracy(   y_true,   y_pred,   threshold = 0.5,   ...,   name = \"binary_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. threshold (Optional) Float representing threshold deciding whether prediction values 1 0. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"Standalone usage:             Usage compile() API:","code":"m <- metric_binary_accuracy() m$update_state(rbind(1, 1, 0, 0), rbind(0.98, 1, 0, 0.6)) m$result() ## tf.Tensor(0.75, shape=(), dtype=float32) # 0.75 m$reset_state() m$update_state(rbind(1, 1, 0, 0), rbind(0.98, 1, 0, 0.6),                sample_weight = c(1, 0, 0, 1)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) # 0.5 model %>% compile(optimizer='sgd',                   loss='binary_crossentropy',                   metrics=list(metric_binary_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"crossentropy metric class used two label classes (0 1).","code":""},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"","code":"metric_binary_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   name = \"binary_crossentropy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"y_true Ground truth values. shape = [batch_size, d0, .. dN]. y_pred predicted values. shape = [batch_size, d0, .. dN]. from_logits (Optional) Whether output expected logits tensor. default, consider output encodes probability distribution. label_smoothing (Optional) Float [0, 1]. > 0, label values smoothed, meaning confidence label values relaxed. e.g. label_smoothing=0.2 means use value 0.1 label \"0\" 0.9 label \"1\". axis axis along mean computed. Defaults -1. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_binary_crossentropy() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(0.8149245, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight=c(1, 0)) m$result() ## tf.Tensor(0.91629076, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_binary_crossentropy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"According Lin et al., 2018, helps apply focal factor -weight easy examples focus hard examples. default, focal tensor computed follows: focal_factor = (1 - output)^gamma class 1 focal_factor = output^gamma class 0 gamma focusing parameter. gamma = 0, focal effect binary crossentropy loss. apply_class_balancing == TRUE, function also takes account weight balancing factor binary classes 0 1 follows: weight = alpha class 1 (target == 1) weight = 1 - alpha class 0 alpha float range [0, 1].","code":""},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"","code":"metric_binary_focal_crossentropy(   y_true,   y_pred,   apply_class_balancing = FALSE,   alpha = 0.25,   gamma = 2,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"y_true Ground truth values, shape (batch_size, d0, .. dN). y_pred predicted values, shape (batch_size, d0, .. dN). apply_class_balancing bool, whether apply weight balancing binary classes 0 1. alpha weight balancing factor class 1, default 0.25 mentioned reference. weight class 0 1.0 - alpha. gamma focusing parameter, default 2.0 mentioned reference. from_logits Whether y_pred expected logits tensor. default, assume y_pred encodes probability distribution. label_smoothing Float [0, 1]. > 0 smooth labels squeezing towards 0.5, , using 1. - 0.5 * label_smoothing target class 0.5 * label_smoothing non-target class. axis axis along mean computed. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"Binary focal crossentropy loss value shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_binary_focal_crossentropy(y_true, y_pred, gamma=2) loss ## tf.Tensor([0.32986466 0.20579838], shape=(2), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class can used compute IoUs binary classification task predictions provided logits. First threshold applied predicted values threshold converted class 0 threshold converted class 1. IoUs classes 0 1 computed, mean IoUs classes specified target_class_ids returned.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"","code":"metric_binary_iou(   ...,   target_class_ids = list(0L, 1L),   threshold = 0.5,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"... forward/backward compatability. target_class_ids list list target class ids metric returned. Options 0, 1, c(0, 1). 0 (1), IoU metric class 0 (class 1, respectively) returned. c(0, 1), mean IoUs two classes returned. threshold threshold applies prediction logits convert either predicted class 0 logit threshold predicted class 1 logit threshold. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"threshold=0, metric behavior IoU.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_binary_iou(target_class_ids=c(0L, 1L), threshold = 0.3) m$update_state(c(0, 1, 0, 1), c(0.1, 0.2, 0.4, 0.7)) m$result() ## tf.Tensor(0.33333334, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 0, 1), c(0.1, 0.2, 0.4, 0.7),                sample_weight = c(0.2, 0.3, 0.4, 0.1)) m$result() ## tf.Tensor(0.17361109, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_binary_iou(         target_class_ids = 0L,         threshold = 0.5     )) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"can provide logits classes y_pred, since argmax logits probabilities . metric creates two local variables, total count used compute frequency y_pred matches y_true. frequency ultimately returned categorical accuracy: idempotent operation simply divides total count. y_pred y_true passed vectors probabilities, rather labels. necessary, use op_one_hot expand y_true vector. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"","code":"metric_categorical_accuracy(   y_true,   y_pred,   ...,   name = \"categorical_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"Standalone usage:           Usage compile() API:","code":"m <- metric_categorical_accuracy() m$update_state(rbind(c(0, 0, 1), c(0, 1, 0)), rbind(c(0.1, 0.9, 0.8),                 c(0.05, 0.95, 0))) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 0, 1), c(0, 1, 0)), rbind(c(0.1, 0.9, 0.8),                c(0.05, 0.95, 0)),                sample_weight = c(0.7, 0.3)) m$result() ## tf.Tensor(0.3, shape=(), dtype=float32) # 0.3 model %>% compile(optimizer = 'sgd',                   loss = 'categorical_crossentropy',                   metrics = list(metric_categorical_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"crossentropy metric class used multiple label classes (2 ). assumes labels one-hot encoded, e.g., labels values c(2, 0, 1), y_true rbind(c([0, 0, 1), c(1, 0, 0), c(0, 1, 0)).","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"","code":"metric_categorical_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   name = \"categorical_crossentropy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"y_true Tensor one-hot true targets. y_pred Tensor predicted targets. from_logits (Optional) Whether output expected logits tensor. default, consider output encodes probability distribution. label_smoothing (Optional) Float [0, 1]. > 0, label values smoothed, meaning confidence label values relaxed. e.g. label_smoothing=0.2 means use value 0.1 label \"0\" 0.9 label \"1\". axis (Optional) Defaults -1. dimension along entropy computed. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"Standalone usage:           Usage compile() API:","code":"# EPSILON = 1e-7, y = y_true, y` = y_pred # y` = clip_op_clip_by_value(output, EPSILON, 1. - EPSILON) # y` = rbind(c(0.05, 0.95, EPSILON), c(0.1, 0.8, 0.1)) # xent = -sum(y * log(y'), axis = -1) #      = -((log 0.95), (log 0.1)) #      = [0.051, 2.302] # Reduced xent = (0.051 + 2.302) / 2 m <- metric_categorical_crossentropy() m$update_state(rbind(c(0, 1, 0), c(0, 0, 1)),                rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1))) m$result() ## tf.Tensor(1.1769392, shape=(), dtype=float32) # 1.1769392 m$reset_state() m$update_state(rbind(c(0, 1, 0), c(0, 0, 1)),                rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)),                sample_weight = c(0.3, 0.7)) m$result() ## tf.Tensor(1.6271976, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_categorical_crossentropy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"Computes categorical focal crossentropy loss.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"","code":"metric_categorical_focal_crossentropy(   y_true,   y_pred,   alpha = 0.25,   gamma = 2,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"y_true Tensor one-hot true targets. y_pred Tensor predicted targets. alpha weight balancing factor classes, default 0.25 mentioned reference. can list floats scalar. multi-class case, alpha may set inverse class frequency using compute_class_weight sklearn.utils. gamma focusing parameter, default 2.0 mentioned reference. helps gradually reduce importance given simple examples smooth manner. gamma = 0, focal effect categorical crossentropy. from_logits Whether y_pred expected logits tensor. default, assume y_pred encodes probability distribution. label_smoothing Float [0, 1]. > 0 smooth labels. example, 0.1, use 0.1 / num_classes non-target labels 0.9 + 0.1 / num_classes target labels. axis Defaults -1. dimension along entropy computed.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"Categorical focal crossentropy loss value.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"","code":"y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.9, 0.05), c(0.1, 0.85, 0.05)) loss <- loss_categorical_focal_crossentropy(y_true, y_pred) loss ## tf.Tensor([2.63401289e-04 6.75912094e-01], shape=(2), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"Formula:   neg=maximum((1-y_true)*y_pred) pos=sum(y_true*y_pred)","code":"loss <- maximum(neg - pos + 1, 0)"},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"","code":"metric_categorical_hinge(   y_true,   y_pred,   ...,   name = \"categorical_hinge\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"y_true ground truth values. y_true values expected either {-1, +1} {0, 1} (.e. one-hot-encoded tensor) shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"Categorical hinge loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"Standalone usage:","code":"m <- metric_categorical_hinge() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(1.4000001, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(1.2, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"Formula:   See: Cosine Similarity. metric keeps average cosine similarity predictions labels stream data.","code":"loss <- sum(l2_norm(y_true) * l2_norm(y_pred))"},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"","code":"metric_cosine_similarity(   ...,   name = \"cosine_similarity\",   dtype = NULL,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result. axis (Optional) Defaults -1. dimension along cosine similarity computed.","code":""},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_cosine_similarity(axis=2) m$update_state(rbind(c(0., 1.), c(1., 1.)), rbind(c(1., 0.), c(1., 1.))) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0., 1.), c(1., 1.)), rbind(c(1., 0.), c(1., 1.)),                sample_weight = c(0.3, 0.7)) m$result() ## tf.Tensor(0.7, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_cosine_similarity(axis=2)))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes F-1 Score. — metric_f1_score","title":"Computes F-1 Score. — metric_f1_score","text":"Formula:   harmonic mean precision recall. output range [0, 1]. works multi-class multi-label classification.","code":"f1_score <- 2 * (precision * recall) / (precision + recall)"},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes F-1 Score. — metric_f1_score","text":"","code":"metric_f1_score(   ...,   average = NULL,   threshold = NULL,   name = \"f1_score\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes F-1 Score. — metric_f1_score","text":"... forward/backward compatability. average Type averaging performed data. Acceptable values NULL, \"micro\", \"macro\" \"weighted\". Defaults NULL. NULL, averaging performed result() return score class. \"micro\", compute metrics globally counting total true positives, false negatives false positives. \"macro\", compute metrics label, return unweighted mean. take label imbalance account. \"weighted\", compute metrics label, return average weighted support (number true instances label). alters \"macro\" account label imbalance. can result score precision recall. threshold Elements y_pred greater threshold converted 1, rest 0. threshold NULL, argmax y_pred converted 1, rest 0. name Optional. String name metric instance. dtype Optional. Data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes F-1 Score. — metric_f1_score","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes F-1 Score. — metric_f1_score","text":"","code":"metric <- metric_f1_score(threshold = 0.5) y_true <- rbind(c(1, 1, 1),                 c(1, 0, 0),                 c(1, 1, 0)) y_pred <- rbind(c(0.2, 0.6, 0.7),                 c(0.2, 0.6, 0.6),                 c(0.6, 0.8, 0.0)) metric$update_state(y_true, y_pred) result <- metric$result() result ## tf.Tensor([0.49999997 0.79999995 0.66666657], shape=(3), dtype=float32)"},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Computes F-1 Score. — metric_f1_score","text":"F-1 Score: float.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the number of false negatives. — metric_false_negatives","title":"Calculates the number of false negatives. — metric_false_negatives","text":"sample_weight given, calculates sum weights false negatives. metric creates one local variable, accumulator used keep track number false negatives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of false negatives. — metric_false_negatives","text":"","code":"metric_false_negatives(..., thresholds = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the number of false negatives. — metric_false_negatives","text":"... forward/backward compatability. thresholds (Optional) Defaults 0.5. float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the number of false negatives. — metric_false_negatives","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of false negatives. — metric_false_negatives","text":"Standalone usage:","code":"m <- metric_false_negatives() m$update_state(c(0, 1, 1, 1), c(0, 1, 0, 0)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 1, 1), c(0, 1, 0, 0), sample_weight=c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32) # 1.0"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the number of false positives. — metric_false_positives","title":"Calculates the number of false positives. — metric_false_positives","text":"sample_weight given, calculates sum weights false positives. metric creates one local variable, accumulator used keep track number false positives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of false positives. — metric_false_positives","text":"","code":"metric_false_positives(..., thresholds = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the number of false positives. — metric_false_positives","text":"... forward/backward compatability. thresholds (Optional) Defaults 0.5. float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the number of false positives. — metric_false_positives","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of false positives. — metric_false_positives","text":"Standalone usage:","code":"m <- metric_false_positives() m$update_state(c(0, 1, 0, 0), c(0, 0, 1, 1)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 0, 0), c(0, 0, 1, 1), sample_weight = c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes F-Beta score. — metric_fbeta_score","title":"Computes F-Beta score. — metric_fbeta_score","text":"Formula:   weighted harmonic mean precision recall. output range [0, 1]. works multi-class multi-label classification.","code":"b2 <- beta^2 f_beta_score <- (1 + b2) * (precision * recall) / (precision * b2 + recall)"},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes F-Beta score. — metric_fbeta_score","text":"","code":"metric_fbeta_score(   ...,   average = NULL,   beta = 1,   threshold = NULL,   name = \"fbeta_score\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes F-Beta score. — metric_fbeta_score","text":"... forward/backward compatability. average Type averaging performed across per-class results multi-class case. Acceptable values NULL, \"micro\", \"macro\" \"weighted\". Defaults NULL. NULL, averaging performed result() return score class. \"micro\", compute metrics globally counting total true positives, false negatives false positives. \"macro\", compute metrics label, return unweighted mean. take label imbalance account. \"weighted\", compute metrics label, return average weighted support (number true instances label). alters \"macro\" account label imbalance. can result score precision recall. beta Determines weight given recall harmonic mean precision recall (see pseudocode equation ). Defaults 1. threshold Elements y_pred greater threshold converted 1, rest 0. threshold NULL, argmax y_pred converted 1, rest 0. name Optional. String name metric instance. dtype Optional. Data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes F-Beta score. — metric_fbeta_score","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes F-Beta score. — metric_fbeta_score","text":"","code":"metric <- metric_fbeta_score(beta = 2.0, threshold = 0.5) y_true <- rbind(c(1, 1, 1),                 c(1, 0, 0),                 c(1, 1, 0)) y_pred <- rbind(c(0.2, 0.6, 0.7),                 c(0.2, 0.6, 0.6),                 c(0.6, 0.8, 0.0)) metric$update_state(y_true, y_pred) metric$result() ## tf.Tensor([0.3846154  0.90909094 0.8333332 ], shape=(3), dtype=float32)"},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Computes F-Beta score. — metric_fbeta_score","text":"F-Beta Score: float.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the hinge metric between y_true and y_pred. — metric_hinge","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"Formula:   y_true values expected -1 1. binary (0 1) labels provided convert -1 1.","code":"loss <- mean(maximum(1 - y_true * y_pred, 0), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"","code":"metric_hinge(y_true, y_pred, ..., name = \"hinge\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"y_true ground truth values. y_true values expected -1 1. binary (0 1) labels provided converted -1 1 shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"Standalone usage:","code":"m <- metric_hinge() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(1.3, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(1.1, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_huber.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes Huber loss value. — metric_huber","title":"Computes Huber loss value. — metric_huber","text":"Formula:   See: Huber loss.","code":"for (x in error) {   if (abs(x) <= delta){     loss <- c(loss, (0.5 * x^2))   } else if (abs(x) > delta) {     loss <- c(loss, (delta * abs(x) - 0.5 * delta^2))   } } loss <- mean(loss)"},{"path":"https://keras.posit.co/reference/metric_huber.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes Huber loss value. — metric_huber","text":"","code":"metric_huber(y_true, y_pred, delta = 1)"},{"path":"https://keras.posit.co/reference/metric_huber.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes Huber loss value. — metric_huber","text":"y_true tensor true targets. y_pred tensor predicted targets. delta float, point Huber loss function changes quadratic linear. Defaults 1.0.","code":""},{"path":"https://keras.posit.co/reference/metric_huber.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes Huber loss value. — metric_huber","text":"Tensor one scalar loss entry per sample.","code":""},{"path":"https://keras.posit.co/reference/metric_huber.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes Huber loss value. — metric_huber","text":"","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_huber(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. Note, class first computes IoUs individual classes, returns mean IoUs classes specified target_class_ids. target_class_ids one id value, IoU specific class returned.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"","code":"metric_iou(   ...,   num_classes,   target_class_ids,   name = NULL,   dtype = NULL,   ignore_class = NULL,   sparse_y_true = TRUE,   sparse_y_pred = TRUE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"... forward/backward compatability. num_classes possible number labels prediction task can . target_class_ids list target class ids metric returned. compute IoU specific class, list single id value provided. name (Optional) string name metric instance. dtype (Optional) data type metric result. ignore_class Optional integer. ID class ignored metric computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. sparse_y_true Whether labels encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. sparse_y_pred Whether predictions encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. axis (Optional) -1 dimension containing logits. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_iou(num_classes = 2L, target_class_ids = list(0L)) m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1)) m$result() ## tf.Tensor(0.3333333, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1),                sample_weight = c(0.3, 0.3, 0.3, 0.1)) m$result() ## tf.Tensor(0.33333325, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_iou(num_classes = 2L, target_class_ids = list(0L))))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"Formula:   y_true y_pred expected probability distributions, values 0 1. get clipped [0, 1] range.","code":"loss <- y_true * log(y_true / y_pred)"},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"","code":"metric_kl_divergence(y_true, y_pred, ..., name = \"kl_divergence\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"y_true Tensor true targets. y_pred Tensor predicted targets. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_kl_divergence() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(0.45814303, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.91628915, shape=(), dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = 'mse',                   metrics = list(metric_kl_divergence()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"Formula:   Note log(cosh(x)) approximately equal (x ** 2) / 2 small x abs(x) - log(2) large x. means 'logcosh' works mostly like mean squared error, strongly affected occasional wildly incorrect prediction.","code":"loss <- mean(log(cosh(y_pred - y_true)), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"","code":"metric_log_cosh(y_true, y_pred)"},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN].","code":""},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"Logcosh error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"","code":"y_true <- rbind(c(0., 1.), c(0., 0.)) y_pred <- rbind(c(1., 1.), c(0., 0.)) loss <- metric_log_cosh(y_true, y_pred) loss ## tf.Tensor([ 2.16890413e-01 -1.90465432e-09], shape=(2), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"Formula:","code":"error <- y_pred - y_true logcosh <- mean(log((exp(error) + exp(-error))/2), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"","code":"metric_log_cosh_error(..., name = \"logcosh\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_log_cosh_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.108445205, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.21689041, shape=(), dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = 'mse',                   metrics = list(metric_log_cosh_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the (weighted) mean of the given values. — metric_mean","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"example, values c(1, 3, 5, 7) mean 4. sample_weight specified c(1, 1, 0, 0) mean 2. metric creates two variables, total count. mean value returned simply total divided count.","code":""},{"path":"https://keras.posit.co/reference/metric_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"","code":"metric_mean(..., name = \"mean\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_mean.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"","code":"m <- metric_mean() m$update_state(c(1, 3, 5, 7)) m$result() ## tf.Tensor(4.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(1, 3, 5, 7), sample_weight = c(1, 1, 0, 0)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"Formula:","code":"loss <- mean(abs(y_true - y_pred))"},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"","code":"metric_mean_absolute_error(   y_true,   y_pred,   ...,   name = \"mean_absolute_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_mean_absolute_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.25, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_mean_absolute_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"Formula:   Division zero prevented dividing maximum(y_true, epsilon) epsilon = keras$backend$epsilon() (default 1e-7).","code":"loss <- 100 * mean(abs((y_true - y_pred) / y_true), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"","code":"metric_mean_absolute_percentage_error(   y_true,   y_pred,   ...,   name = \"mean_absolute_percentage_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_mean_absolute_percentage_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(250000000.0, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(500000000.0, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_mean_absolute_percentage_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. Note class first computes IoUs individual classes, returns mean values.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"","code":"metric_mean_iou(   ...,   num_classes,   name = NULL,   dtype = NULL,   ignore_class = NULL,   sparse_y_true = TRUE,   sparse_y_pred = TRUE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"... forward/backward compatability. num_classes possible number labels prediction task can . value must provided, since confusion matrix dimension = [num_classes, num_classes] allocated. name (Optional) string name metric instance. dtype (Optional) data type metric result. ignore_class Optional integer. ID class ignored metric computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. sparse_y_true Whether labels encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. sparse_y_pred Whether predictions encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. axis (Optional) dimension containing logits. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"Standalone usage:         Usage compile() API:","code":"# cm = [[1, 1], #        [1, 1]] # sum_row = [2, 2], sum_col = [2, 2], true_positives = [1, 1] # iou = true_positives / (sum_row + sum_col - true_positives)) # result = (1 / (2 + 2 - 1) + 1 / (2 + 2 - 1)) / 2 = 0.33 m <- metric_mean_iou(num_classes = 2) m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1)) m$result() ## tf.Tensor(0.33333334, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1),                sample_weight=c(0.3, 0.3, 0.3, 0.1)) m$result() ## tf.Tensor(0.2380952, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_mean_iou(num_classes=2)))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"Formula:","code":"loss <- mean(square(y_true - y_pred))"},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"","code":"metric_mean_squared_error(   y_true,   y_pred,   ...,   name = \"mean_squared_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"","code":"m <- metric_mean_squared_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.25, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"Formula:   Note y_pred y_true less equal 0. Negative values 0 values replaced keras$backend$epsilon() (default 1e-7).","code":"loss <- mean(square(log(y_true + 1) - log(y_pred + 1)), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"","code":"metric_mean_squared_logarithmic_error(   y_true,   y_pred,   ...,   name = \"mean_squared_logarithmic_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_mean_squared_logarithmic_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.12011322, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.24022643, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_mean_squared_logarithmic_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","title":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","text":"use class quickly build mean metric function. function needs signature fn(y_true, y_pred) return per-sample loss array. metric_mean_wrapper$result() return average metric value across samples seen far. example:","code":"mse <- function(y_true, y_pred) {   (y_true - y_pred)^2 }  mse_metric <- metric_mean_wrapper(fn = mse) mse_metric$update_state(c(0, 1), c(1, 1)) mse_metric$result() ## tf.Tensor(0.5, shape=(), dtype=float32)"},{"path":"https://keras.posit.co/reference/metric_mean_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","text":"","code":"metric_mean_wrapper(..., fn, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_mean_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","text":"... Keyword arguments pass fn. fn metric function wrap, signature fn(y_true, y_pred). name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class can used compute IoU multi-class classification tasks labels one-hot encoded (last axis one dimension per class). Note predictions also shape. compute IoU, first labels predictions converted back integer format taking argmax class axis. computation steps base IoU class apply. Note, one channel labels predictions, class class IoU. case, use IoU instead. Also, make sure num_classes equal number classes data, avoid \"labels bound\" error confusion matrix computed.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"","code":"metric_one_hot_iou(   ...,   num_classes,   target_class_ids,   name = NULL,   dtype = NULL,   ignore_class = NULL,   sparse_y_pred = FALSE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"... forward/backward compatability. num_classes possible number labels prediction task can . target_class_ids list list target class ids metric returned. compute IoU specific class, list (list) single id value provided. name (Optional) string name metric instance. dtype (Optional) data type metric result. ignore_class Optional integer. ID class ignored metric computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. sparse_y_pred Whether predictions encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. axis (Optional) dimension containing logits. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"Standalone usage:     Usage compile() API:","code":"y_true <- rbind(c(0, 0, 1), c(1, 0, 0), c(0, 1, 0), c(1, 0, 0)) y_pred <- rbind(c(0.2, 0.3, 0.5), c(0.1, 0.2, 0.7), c(0.5, 0.3, 0.1),                 c(0.1, 0.4, 0.5)) sample_weight <- c(0.1, 0.2, 0.3, 0.4) m <- metric_one_hot_iou(num_classes = 3, target_class_ids = c(0, 2)) m$update_state(     y_true = y_true, y_pred = y_pred, sample_weight = sample_weight) m$result() ## tf.Tensor(0.07142855, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_one_hot_iou(     num_classes = 3L,     target_class_id = list(1L)   )) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class can used compute mean IoU multi-class classification tasks labels one-hot encoded (last axis one dimension per class). Note predictions also shape. compute mean IoU, first labels predictions converted back integer format taking argmax class axis. computation steps base MeanIoU class apply. Note, one channel labels predictions, class class metric_mean_iou. case, use metric_mean_iou instead. Also, make sure num_classes equal number classes data, avoid \"labels bound\" error confusion matrix computed.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"","code":"metric_one_hot_mean_iou(   ...,   num_classes,   name = NULL,   dtype = NULL,   ignore_class = NULL,   sparse_y_pred = FALSE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"... forward/backward compatability. num_classes possible number labels prediction task can . name (Optional) string name metric instance. dtype (Optional) data type metric result. ignore_class Optional integer. ID class ignored metric computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. sparse_y_pred Whether predictions encoded using natural numbers probability distribution vectors. FALSE, argmax function used determine sample's likely associated label. axis (Optional) dimension containing logits. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"Standalone usage:     Usage compile() API:","code":"y_true <- rbind(c(0, 0, 1), c(1, 0, 0), c(0, 1, 0), c(1, 0, 0)) y_pred <- rbind(c(0.2, 0.3, 0.5), c(0.1, 0.2, 0.7), c(0.5, 0.3, 0.1),                 c(0.1, 0.4, 0.5)) sample_weight <- c(0.1, 0.2, 0.3, 0.4) m <- metric_one_hot_mean_iou(num_classes = 3L) m$update_state(     y_true = y_true, y_pred = y_pred, sample_weight = sample_weight) m$result() ## tf.Tensor(0.047619034, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_one_hot_mean_iou(num_classes = 3L)))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"Formula:","code":"metric <- y_pred - y_true * log(y_pred)"},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"","code":"metric_poisson(y_true, y_pred, ..., name = \"poisson\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"y_true Ground truth values. shape = [batch_size, d0, .. dN]. y_pred predicted values. shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_poisson() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.49999997, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.99999994, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_poisson()) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_precision.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the precision of the predictions with respect to the labels. — metric_precision","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"metric creates two local variables, true_positives false_positives used compute precision. value ultimately returned precision, idempotent operation simply divides true_positives sum true_positives false_positives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. top_k set, calculate precision often average class among top-k classes highest predicted values batch entry correct can found label entry. class_id specified, calculate precision considering entries batch class_id threshold /top-k highest predictions, computing fraction class_id indeed correct label.","code":""},{"path":"https://keras.posit.co/reference/metric_precision.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"","code":"metric_precision(   ...,   thresholds = NULL,   top_k = NULL,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_precision.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"... forward/backward compatability. thresholds (Optional) float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. neither thresholds top_k set, default calculate precision thresholds=0.5. top_k (Optional) Unset default. int value specifying top-k predictions consider calculating precision. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_precision.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_precision.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"Standalone usage:                 Usage compile() API:   Usage loss from_logits=TRUE:","code":"m <- metric_precision() m$update_state(c(0, 1, 1, 1),                c(1, 0, 1, 1)) m$result() |> as.double() |> signif() ## [1] 0.666667 m$reset_state() m$update_state(c(0, 1, 1, 1),                c(1, 0, 1, 1),                sample_weight = c(0, 0, 1, 0)) m$result() |> as.double() |> signif() ## [1] 1 # With top_k=2, it will calculate precision over y_true[1:2] # and y_pred[1:2] m <- metric_precision(top_k = 2) m$update_state(c(0, 0, 1, 1), c(1, 1, 1, 1)) m$result() ## tf.Tensor(0.0, shape=(), dtype=float32) # With top_k=4, it will calculate precision over y_true[1:4] # and y_pred[1:4] m <- metric_precision(top_k = 4) m$update_state(c(0, 0, 1, 1), c(1, 1, 1, 1)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_precision()) ) model |> compile(   optimizer = 'adam',   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_precision(thresholds = 0)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute precision given recall. threshold given recall value computed used evaluate corresponding precision. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class_id specified, calculate precision considering entries batch class_id threshold predictions, computing fraction class_id indeed correct label.","code":""},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"","code":"metric_precision_at_recall(   ...,   recall,   num_thresholds = 200L,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"... forward/backward compatability. recall scalar value range [0, 1]. num_thresholds (Optional) Defaults 200. number thresholds use matching given recall. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_precision_at_recall(recall = 0.5) m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8),                sample_weight = c(2, 2, 2, 1, 1)) m$result() ## tf.Tensor(0.33333334, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_precision_at_recall(recall = 0.8)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes R2 score. — metric_r2_score","title":"Computes R2 score. — metric_r2_score","text":"Formula:   also called coefficient determination. indicates close fitted regression line ground-truth data. highest score possible 1.0. indicates predictors perfectly accounts variation target. score 0.0 indicates predictors account variation target. can also negative model worse random. metric can also compute \"Adjusted R2\" score.","code":"sum_squares_residuals <- sum((y_true - y_pred) ** 2) sum_squares <- sum((y_true - mean(y_true)) ** 2) R2 <- 1 - sum_squares_residuals / sum_squares"},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes R2 score. — metric_r2_score","text":"","code":"metric_r2_score(   ...,   class_aggregation = \"uniform_average\",   num_regressors = 0L,   name = \"r2_score\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes R2 score. — metric_r2_score","text":"... forward/backward compatability. class_aggregation Specifies aggregate scores corresponding different output classes (target dimensions), .e. different dimensions last axis predictions. Equivalent multioutput argument Scikit-Learn. one NULL (aggregation), \"uniform_average\", \"variance_weighted_average\". num_regressors Number independent regressors used (\"Adjusted R2\" score). 0 standard R2 score. Defaults 0. name Optional. string name metric instance. dtype Optional. data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes R2 score. — metric_r2_score","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes R2 score. — metric_r2_score","text":"","code":"y_true <- rbind(1, 4, 3) y_pred <- rbind(2, 4, 4) metric <- metric_r2_score() metric$update_state(y_true, y_pred) metric$result() ## tf.Tensor(0.57142854, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_recall.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the recall of the predictions with respect to the labels. — metric_recall","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"metric creates two local variables, true_positives false_negatives, used compute recall. value ultimately returned recall, idempotent operation simply divides true_positives sum true_positives false_negatives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. top_k set, recall computed often average class among labels batch entry top-k predictions. class_id specified, calculate recall considering entries batch class_id label, computing fraction class_id threshold /top-k predictions.","code":""},{"path":"https://keras.posit.co/reference/metric_recall.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"","code":"metric_recall(   ...,   thresholds = NULL,   top_k = NULL,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_recall.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"... forward/backward compatability. thresholds (Optional) float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. neither thresholds top_k set, default calculate recall thresholds=0.5. top_k (Optional) Unset default. int value specifying top-k predictions consider calculating recall. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_recall.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_recall.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"Standalone usage:         Usage compile() API:   Usage loss from_logits=TRUE:","code":"m <- metric_recall() m$update_state(c(0, 1, 1, 1),                c(1, 0, 1, 1)) m$result() ## tf.Tensor(0.6666667, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 1, 1),                c(1, 0, 1, 1),                sample_weight = c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_recall()) ) model |> compile(   optimizer = 'adam',   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_recall(thresholds = 0)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"given score-label-distribution required precision might achievable, case 0.0 returned recall. metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute recall given precision. threshold given precision value computed used evaluate corresponding recall. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class_id specified, calculate precision considering entries batch class_id threshold predictions, computing fraction class_id indeed correct label.","code":""},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"","code":"metric_recall_at_precision(   ...,   precision,   num_thresholds = 200L,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"... forward/backward compatability. precision scalar value range [0, 1]. num_thresholds (Optional) Defaults 200. number thresholds use matching given precision. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_recall_at_precision(precision = 0.8) m$update_state(c(0,   0,   1,   1),                c(0, 0.5, 0.3, 0.9)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   1,   1),                c(0, 0.5, 0.3, 0.9),                sample_weight = c(1, 0, 0, 1)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_recall_at_precision(precision = 0.8)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"Formula:","code":"loss <- sqrt(mean((y_pred - y_true) ^ 2))"},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"","code":"metric_root_mean_squared_error(   ...,   name = \"root_mean_squared_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_root_mean_squared_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.70710677, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_root_mean_squared_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"Sensitivity measures proportion actual positives correctly identified (tp / (tp + fn)). Specificity measures proportion actual negatives correctly identified (tn / (tn + fp)). metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute sensitivity given specificity. threshold given specificity value computed used evaluate corresponding sensitivity. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class_id specified, calculate precision considering entries batch class_id threshold predictions, computing fraction class_id indeed correct label. additional information specificity sensitivity, see following.","code":""},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"","code":"metric_sensitivity_at_specificity(   ...,   specificity,   num_thresholds = 200L,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"... forward/backward compatability. specificity scalar value range [0, 1]. num_thresholds (Optional) Defaults 200. number thresholds use matching given specificity. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_sensitivity_at_specificity(specificity = 0.5) m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8),                sample_weight = c(1, 1, 2, 2, 1)) m$result() ## tf.Tensor(0.33333334, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_sensitivity_at_specificity()) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"can provide logits classes y_pred, since argmax logits probabilities . metric creates two local variables, total count used compute frequency y_pred matches y_true. frequency ultimately returned sparse categorical accuracy: idempotent operation simply divides total count. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":"acc <- sample_weight %*% (y_true == which.max(y_pred))"},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"","code":"metric_sparse_categorical_accuracy(   y_true,   y_pred,   ...,   name = \"sparse_categorical_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_sparse_categorical_accuracy() m$update_state(rbind(2L, 1L), rbind(c(0.1, 0.6, 0.3), c(0.05, 0.95, 0))) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(2L, 1L), rbind(c(0.1, 0.6, 0.3), c(0.05, 0.95, 0)),                sample_weight = c(0.7, 0.3)) m$result() ## tf.Tensor(0.3, shape=(), dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = 'sparse_categorical_crossentropy',                   metrics = list(metric_sparse_categorical_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"Use crossentropy metric two label classes. expects labels provided integers. want provide labels one-hot encoded, please use metric_categorical_crossentropy() metric instead. num_classes floating point values per feature y_pred single floating point value per feature y_true.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"","code":"metric_sparse_categorical_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   ignore_class = NULL,   axis = -1L,   ...,   name = \"sparse_categorical_crossentropy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"y_true Ground truth values. y_pred predicted values. from_logits (Optional) Whether output expected logits tensor. default, consider output encodes probability distribution. ignore_class Optional integer. ID class ignored loss computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. axis (Optional) Defaults -1. dimension along entropy computed. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"Standalone usage:           Usage compile() API:","code":"m <- metric_sparse_categorical_crossentropy() m$update_state(c(1, 2),                rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1))) m$result() ## tf.Tensor(1.1769392, shape=(), dtype=float32) m$reset_state() m$update_state(c(1, 2),                rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)),                sample_weight = c(0.3, 0.7)) m$result() ## tf.Tensor(1.6271976, shape=(), dtype=float32) # 1.6271976 model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_sparse_categorical_crossentropy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"Computes often integer targets top K predictions.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"","code":"metric_sparse_top_k_categorical_accuracy(   y_true,   y_pred,   k = 5L,   ...,   name = \"sparse_top_k_categorical_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. k (Optional) Number top elements look computing accuracy. Defaults 5. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_sparse_top_k_categorical_accuracy(k = 1L) m$update_state(   rbind(2, 1),   op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = \"float32\") ) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(   rbind(2, 1),   op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = \"float32\"),   sample_weight = c(0.7, 0.3) ) m$result() ## tf.Tensor(0.3, shape=(), dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = 'sparse_categorical_crossentropy',                   metrics = list(metric_sparse_top_k_categorical_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"Sensitivity measures proportion actual positives correctly identified (tp / (tp + fn)). Specificity measures proportion actual negatives correctly identified (tn / (tn + fp)). metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute specificity given sensitivity. threshold given sensitivity value computed used evaluate corresponding specificity. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class_id specified, calculate precision considering entries batch class_id threshold predictions, computing fraction class_id indeed correct label. additional information specificity sensitivity, see following.","code":""},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"","code":"metric_specificity_at_sensitivity(   ...,   sensitivity,   num_thresholds = 200L,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"... forward/backward compatability. sensitivity scalar value range [0, 1]. num_thresholds (Optional) Defaults 200. number thresholds use matching given sensitivity. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_specificity_at_sensitivity(sensitivity = 0.5) m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8)) m$result() ## tf.Tensor(0.6666667, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8),                sample_weight = c(1, 1, 2, 2, 2)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_sensitivity_at_specificity()) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"Formula:   y_true values expected -1 1. binary (0 1) labels provided convert -1 1.","code":"loss <- mean(square(maximum(1 - y_true * y_pred, 0)))"},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"","code":"metric_squared_hinge(y_true, y_pred, ..., name = \"squared_hinge\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"y_true ground truth values. y_true values expected -1 1. binary (0 1) labels provided convert -1 1 shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"Standalone usage:","code":"m <- metric_squared_hinge() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(1.86, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(1.46, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the (weighted) sum of the given values. — metric_sum","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"example, values [1, 3, 5, 7] sum 16. sample_weight specified [1, 1, 0, 0] sum 4. metric creates one variable, total. ultimately returned sum value.","code":""},{"path":"https://keras.posit.co/reference/metric_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"","code":"metric_sum(..., name = \"sum\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_sum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"","code":"m <- metric_sum() m$update_state(c(1, 3, 5, 7)) m$result() ## tf.Tensor(16.0, shape=(), dtype=float32) m <- metric_sum() m$update_state(c(1, 3, 5, 7), sample_weight = c(1, 1, 0, 0)) m$result() ## tf.Tensor(4.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"Computes often targets top K predictions.","code":""},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"","code":"metric_top_k_categorical_accuracy(   y_true,   y_pred,   k = 5L,   ...,   name = \"top_k_categorical_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. k (Optional) Number top elements look computing accuracy. Defaults 5. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_top_k_categorical_accuracy(k = 1) m$update_state(   rbind(c(0, 0, 1), c(0, 1, 0)),   op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = \"float32\") ) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(   rbind(c(0, 0, 1), c(0, 1, 0)),   op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = \"float32\"),   sample_weight = c(0.7, 0.3)) m$result() ## tf.Tensor(0.3, shape=(), dtype=float32) model.compile(optimizer = 'sgd',               loss = 'categorical_crossentropy',               metrics = list(metric_top_k_categorical_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the number of true negatives. — metric_true_negatives","title":"Calculates the number of true negatives. — metric_true_negatives","text":"sample_weight given, calculates sum weights true negatives. metric creates one local variable, accumulator used keep track number true negatives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of true negatives. — metric_true_negatives","text":"","code":"metric_true_negatives(..., thresholds = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the number of true negatives. — metric_true_negatives","text":"... forward/backward compatability. thresholds (Optional) Defaults 0.5. float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the number of true negatives. — metric_true_negatives","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of true negatives. — metric_true_negatives","text":"Standalone usage:","code":"m <- metric_true_negatives() m$update_state(c(0, 1, 0, 0), c(1, 1, 0, 0)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 0, 0), c(1, 1, 0, 0), sample_weight = c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the number of true positives. — metric_true_positives","title":"Calculates the number of true positives. — metric_true_positives","text":"sample_weight given, calculates sum weights true positives. metric creates one local variable, true_positives used keep track number true positives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of true positives. — metric_true_positives","text":"","code":"metric_true_positives(..., thresholds = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the number of true positives. — metric_true_positives","text":"... forward/backward compatability. thresholds (Optional) Defaults 0.5. float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the number of true positives. — metric_true_positives","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of true positives. — metric_true_positives","text":"Standalone usage:","code":"m <- metric_true_positives() m$update_state(c(0, 1, 1, 1), c(1, 0, 1, 1)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 1, 1), c(1, 0, 1, 1), sample_weight = c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/multi-assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Assign values to names — %<-%","title":"Assign values to names — %<-%","text":"See %<-% details.","code":""},{"path":"https://keras.posit.co/reference/multi-assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assign values to names — %<-%","text":"","code":"x %<-% value"},{"path":"https://keras.posit.co/reference/multi-assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assign values to names — %<-%","text":"right-hand-side argument, value, invisibly. called primarily side-effect assigning symbols current frame.","code":""},{"path":"https://keras.posit.co/reference/new_callback_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback — new_callback_class","title":"Callback — new_callback_class","text":"new_callback_class() alias Callback(). See ?Callback() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_callback_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback — new_callback_class","text":"","code":"new_callback_class(   classname,   on_epoch_begin = NULL,   on_epoch_end = NULL,   on_train_begin = NULL,   on_train_end = NULL,   on_train_batch_begin = NULL,   on_train_batch_end = NULL,   on_test_begin = NULL,   on_test_end = NULL,   on_test_batch_begin = NULL,   on_test_batch_end = NULL,   on_predict_begin = NULL,   on_predict_end = NULL,   on_predict_batch_begin = NULL,   on_predict_batch_end = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_callback_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback — new_callback_class","text":"classname String, name custom class. (Conventionally, CamelCase). on_epoch_begin   Called start epoch. Subclasses override actions run. function called TRAIN mode. Args: epoch: Integer, index epoch. logs: Named List. Currently data passed argument method may change future. on_epoch_end   Called end epoch. Subclasses override actions run. function called TRAIN mode. Args: epoch: Integer, index epoch. logs: Named List, metric results training epoch, validation epoch validation performed. Validation result keys prefixed val_. training epoch, values Model's metrics returned. Example: list(loss = 0.2, accuracy = 0.7). on_train_begin   Called beginning training. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_train_end   Called end training. Subclasses override actions run. Args: logs: Named list. Currently output last call on_epoch_end() passed argument method may change future. on_train_batch_begin   Called beginning training batch fit() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_train_batch_end   Called end training batch fit() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. on_test_begin   Called beginning evaluation validation. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_test_end   Called end evaluation validation. Subclasses override actions run. Args: logs: Named list. Currently output last call on_test_batch_end() passed argument method may change future. on_test_batch_begin   Called beginning batch evaluate() methods. Also called beginning validation batch fit() methods, validation data provided. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_test_batch_end   Called end batch evaluate() methods. Also called end validation batch fit() methods, validation data provided. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. on_predict_begin   Called beginning prediction. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_predict_end   Called end prediction. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_predict_batch_begin   Called beginning batch predict() methods. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_predict_batch_end   Called end batch predict() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"\\(epoch, logs = NULL) \\(epoch, logs = NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs=NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs = NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs = NULL)"},{"path":"https://keras.posit.co/reference/new_callback_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback — new_callback_class","text":"function returns custom Callback instances, similar builtin callback functions.","code":""},{"path":"https://keras.posit.co/reference/new_layer_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer — new_layer_class","title":"Layer — new_layer_class","text":"new_layer_class() alias Layer(). See ?Layer() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_layer_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer — new_layer_class","text":"","code":"new_layer_class(   classname,   initialize = NULL,   call = NULL,   build = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_layer_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer — new_layer_class","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, call, build, get_config Recommended methods implement. See description details sections. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/new_layer_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer — new_layer_class","text":"composing layer constructor, similar behavior layer functions like layer_dense(). first argument returned function object, enabling initialize()ing call() layer one step composing layer pipe, like   initialize() layer instance call() , pass missing NULL value object, pass arguments initialize() name.","code":"layer_foo <- Layer(\"Foo\", ....) output <- inputs |> layer_foo() layer <- layer_dense(units = 2, activation = \"relu\") layer <- layer_dense(NULL, 2, activation = \"relu\") layer <- layer_dense(, 2, activation = \"relu\")  # then you can call() the layer in a separate step outputs <- inputs |> layer()"},{"path":"https://keras.posit.co/reference/new_learning_rate_schedule_class.html","id":null,"dir":"Reference","previous_headings":"","what":"LearningRateSchedule — new_learning_rate_schedule_class","title":"LearningRateSchedule — new_learning_rate_schedule_class","text":"new_learning_rate_schedule_class() alias LearningRateSchedule(). See ?LearningRateSchedule() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_learning_rate_schedule_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LearningRateSchedule — new_learning_rate_schedule_class","text":"","code":"new_learning_rate_schedule_class(   classname,   call = NULL,   initialize = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_learning_rate_schedule_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LearningRateSchedule — new_learning_rate_schedule_class","text":"classname String, name custom class. (Conventionally, CamelCase). call, initialize, get_config Recommended methods implement. See description details sections. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/new_learning_rate_schedule_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"LearningRateSchedule — new_learning_rate_schedule_class","text":"function returns LearningRateSchedule instances, similar built-learning_rate_schedule_* family functions.","code":""},{"path":"https://keras.posit.co/reference/new_loss_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Loss — new_loss_class","title":"Loss — new_loss_class","text":"new_loss_class() alias Loss(). See ?Loss() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_loss_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loss — new_loss_class","text":"","code":"new_loss_class(   classname,   call = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_loss_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loss — new_loss_class","text":"classname String, name custom class. (Conventionally, CamelCase). call   Method implemented subclasses: Function contains logic loss calculation using y_true, y_pred. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"function(y_true, y_pred)"},{"path":"https://keras.posit.co/reference/new_loss_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loss — new_loss_class","text":"function returns Loss instances, similar builtin loss functions.","code":""},{"path":"https://keras.posit.co/reference/new_metric_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Metric — new_metric_class","title":"Metric — new_metric_class","text":"new_metric_class() alias Metric(). See ?Metric() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_metric_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metric — new_metric_class","text":"","code":"new_metric_class(   classname,   initialize = NULL,   update_state = NULL,   result = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_metric_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metric — new_metric_class","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, update_state, result Recommended methods implement. See description section. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/new_metric_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metric — new_metric_class","text":"function returns Metric instances, similar builtin metric functions.","code":""},{"path":"https://keras.posit.co/reference/new_model_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Model — new_model_class","title":"Model — new_model_class","text":"new_model_class() alias Model(). See ?Model() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_model_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model — new_model_class","text":"","code":"new_model_class(   classname,   initialize = NULL,   call = NULL,   train_step = NULL,   predict_step = NULL,   test_step = NULL,   compute_loss = NULL,   compute_metrics = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_model_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model — new_model_class","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, call, train_step, predict_step, test_step, compute_loss, compute_metrics Optional methods can overridden. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/new_model_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model — new_model_class","text":"model constructor function, can call create instance new model type.","code":""},{"path":"https://keras.posit.co/reference/normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalizes an array. — normalize","title":"Normalizes an array. — normalize","text":"input R array, R array returned. backend tensor, backend tensor returned.","code":""},{"path":"https://keras.posit.co/reference/normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalizes an array. — normalize","text":"","code":"normalize(x, axis = -1L, order = 2L)"},{"path":"https://keras.posit.co/reference/normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalizes an array. — normalize","text":"x Array normalize. axis axis along normalize. order Normalization order (e.g. order=2 L2 norm).","code":""},{"path":"https://keras.posit.co/reference/normalize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalizes an array. — normalize","text":"normalized copy array.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_abs.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the absolute value element-wise. — op_abs","title":"Compute the absolute value element-wise. — op_abs","text":"Compute absolute value element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_abs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the absolute value element-wise. — op_abs","text":"","code":"op_abs(x)"},{"path":"https://keras.posit.co/reference/op_abs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the absolute value element-wise. — op_abs","text":"x Input tensor","code":""},{"path":"https://keras.posit.co/reference/op_abs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the absolute value element-wise. — op_abs","text":"array containing absolute value element x.","code":""},{"path":"https://keras.posit.co/reference/op_abs.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Compute the absolute value element-wise. — op_abs","text":"","code":"x <- op_convert_to_tensor(c(-1.2, 1.2)) op_abs(x) ## tf.Tensor([1.2 1.2], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_add.html","id":null,"dir":"Reference","previous_headings":"","what":"Add arguments element-wise. — op_add","title":"Add arguments element-wise. — op_add","text":"Add arguments element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_add.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add arguments element-wise. — op_add","text":"","code":"op_add(x1, x2)"},{"path":"https://keras.posit.co/reference/op_add.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add arguments element-wise. — op_add","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_add.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add arguments element-wise. — op_add","text":"tensor containing element-wise sum x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_add.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add arguments element-wise. — op_add","text":"op_add also broadcasts shapes:     Note function automatically called using R operator + tensors.","code":"x1 <- op_convert_to_tensor(c(1, 4)) x2 <- op_convert_to_tensor(c(5, 6)) op_add(x1, x2) ## tf.Tensor([ 6. 10.], shape=(2), dtype=float32) # alias for x1 + x2 x1 + x2 ## tf.Tensor([ 6. 10.], shape=(2), dtype=float32) x1 <- op_convert_to_tensor(array(c(5, 5, 4, 6), dim =c(2, 2))) x2 <- op_convert_to_tensor(c(5, 6)) op_add(x1, x2) ## tf.Tensor( ## [[10. 10.] ##  [10. 12.]], shape=(2, 2), dtype=float64) x <- op_ones(c(3)) op_add(x, x) ## tf.Tensor([2. 2. 2.], shape=(3), dtype=float32) x + x ## tf.Tensor([2. 2. 2.], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_all.html","id":null,"dir":"Reference","previous_headings":"","what":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"Test whether array elements along given axis evaluate TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_all.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"","code":"op_all(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_all.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"x Input tensor. axis integer tuple integers represent axis along logical reduction performed. default (axis = NULL) perform logical dimensions input array. axis may negative, case counts last first axis. keepdims TRUE, axes reduced left result dimensions size one. option, result broadcast correctly input array. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_all.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"tensor containing logical reduction axis.","code":""},{"path":"https://keras.posit.co/reference/op_all.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"keepdims = TRUE outputs tensor dimensions reduced one.","code":"x <- op_convert_to_tensor(c(TRUE, FALSE)) op_all(x) ## tf.Tensor(False, shape=(), dtype=bool) (x <- op_convert_to_tensor(array(c(TRUE, FALSE, TRUE, TRUE, TRUE, TRUE), dim = c(3, 2)))) ## tf.Tensor( ## [[ True  True] ##  [False  True] ##  [ True  True]], shape=(3, 2), dtype=bool) op_all(x, axis = 1) ## tf.Tensor([False  True], shape=(2), dtype=bool) op_all(x, keepdims = TRUE) ## tf.Tensor([[False]], shape=(1, 1), dtype=bool)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_any.html","id":null,"dir":"Reference","previous_headings":"","what":"Test whether any array element along a given axis evaluates to TRUE. — op_any","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"Test whether array element along given axis evaluates TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_any.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"","code":"op_any(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_any.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"x Input tensor. axis integer tuple integers represent axis along logical reduction performed. default (axis = NULL) perform logical dimensions input array. axis may negative, case counts last first axis. keepdims TRUE, axes reduced left result dimensions size one. option, result broadcast correctly input array. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_any.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"tensor containing logical reduction axis.","code":""},{"path":"https://keras.posit.co/reference/op_any.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"keepdims = TRUE outputs tensor dimensions reduced one.","code":"x <- op_array(c(TRUE, FALSE)) op_any(x) ## tf.Tensor(True, shape=(), dtype=bool) (x <- op_reshape(c(FALSE, FALSE, FALSE,                    TRUE, FALSE, FALSE),                  c(2, 3))) ## tf.Tensor( ## [[False False False] ##  [ True False False]], shape=(2, 3), dtype=bool) op_any(x, axis = 1) ## tf.Tensor([ True False False], shape=(3), dtype=bool) op_any(x, axis = 2) ## tf.Tensor([False  True], shape=(2), dtype=bool) op_any(x, axis = -1) ## tf.Tensor([False  True], shape=(2), dtype=bool) op_any(x, keepdims = TRUE) ## tf.Tensor([[ True]], shape=(1, 1), dtype=bool) op_any(x, 1, keepdims = TRUE) ## tf.Tensor([[ True False False]], shape=(1, 3), dtype=bool)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_append.html","id":null,"dir":"Reference","previous_headings":"","what":"Append tensor x2 to the end of tensor x1. — op_append","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"Append tensor x2 end tensor x1.","code":""},{"path":"https://keras.posit.co/reference/op_append.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"","code":"op_append(x1, x2, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_append.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"x1 First input tensor. x2 Second input tensor. axis Axis along tensor x2 appended tensor x1. NULL, tensors flattened use.","code":""},{"path":"https://keras.posit.co/reference/op_append.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"tensor values x2 appended x1.","code":""},{"path":"https://keras.posit.co/reference/op_append.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"axis specified, x1 x2 must compatible shapes.","code":"x1 <- op_convert_to_tensor(c(1, 2, 3)) x2 <- op_convert_to_tensor(rbind(c(4, 5, 6), c(7, 8, 9))) op_append(x1, x2) ## tf.Tensor([1. 2. 3. 4. 5. 6. 7. 8. 9.], shape=(9), dtype=float64) x1 <- op_convert_to_tensor(rbind(c(1, 2, 3), c(4, 5, 6))) x2 <- op_convert_to_tensor(rbind(c(7, 8, 9))) op_append(x1, x2, axis = 1) ## tf.Tensor( ## [[1. 2. 3.] ##  [4. 5. 6.] ##  [7. 8. 9.]], shape=(3, 3), dtype=float64) x3 <- op_convert_to_tensor(c(7, 8, 9)) try(op_append(x1, x3, axis = 1)) ## Error in py_call_impl(callable, call_args$unnamed, call_args$named) : ##   tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__ConcatV2_N_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} ConcatOp : Ranks of all input tensors should match: shape[0] = [2,3] vs. shape[1] = [3] [Op:ConcatV2] name: concat"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arange.html","id":null,"dir":"Reference","previous_headings":"","what":"Return evenly spaced values within a given interval. — op_arange","title":"Return evenly spaced values within a given interval. — op_arange","text":"arange can called varying number positional arguments: arange(stop): Values generated within half-open interval [0, stop) (words, interval including start excluding stop). arange(start, stop): Values generated within half-open interval [start, stop). arange(start, stop, step): Values generated within half-open interval [start, stop), spacing values given step.","code":""},{"path":"https://keras.posit.co/reference/op_arange.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return evenly spaced values within a given interval. — op_arange","text":"","code":"op_arange(start, stop = NULL, step = 1L, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_arange.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return evenly spaced values within a given interval. — op_arange","text":"start Integer real, representing start interval. interval includes value. stop Integer real, representing end interval. interval include value, except cases step integer floating point round-affects length . Defaults NULL. step Integer real, represent spacing values. output , distance two adjacent values, [+1] - []. default step size 1. step specified position argument, start must also given. dtype type output array. dtype given, infer data type input arguments.","code":""},{"path":"https://keras.posit.co/reference/op_arange.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return evenly spaced values within a given interval. — op_arange","text":"Tensor evenly spaced values. floating point arguments, length result ceiling((stop - start)/step). floating point overflow, rule may result last element greater stop.","code":""},{"path":"https://keras.posit.co/reference/op_arange.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return evenly spaced values within a given interval. — op_arange","text":"","code":"op_arange(3L) ## tf.Tensor([0 1 2], shape=(3), dtype=int32) op_arange(3) # float ## tf.Tensor([0. 1. 2.], shape=(3), dtype=float32) op_arange(3, dtype = 'int32') #int ## tf.Tensor([0 1 2], shape=(3), dtype=int32) op_arange(3L, 7L) ## tf.Tensor([3 4 5 6], shape=(4), dtype=int32) op_arange(3L, 7L, 2L) ## tf.Tensor([3 5], shape=(2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arccos.html","id":null,"dir":"Reference","previous_headings":"","what":"Trigonometric inverse cosine, element-wise. — op_arccos","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"inverse cos , y = cos(x), x = arccos(y).","code":""},{"path":"https://keras.posit.co/reference/op_arccos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"","code":"op_arccos(x)"},{"path":"https://keras.posit.co/reference/op_arccos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arccos.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"Tensor angle ray intersecting unit circle given x-coordinate radians [0, pi].","code":""},{"path":"https://keras.posit.co/reference/op_arccos.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"","code":"x <- op_convert_to_tensor(c(1, -1)) op_arccos(x) ## tf.Tensor([0.        3.1415927], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse hyperbolic cosine, element-wise. — op_arccosh","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"Inverse hyperbolic cosine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"","code":"op_arccosh(x)"},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"Output tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"","code":"x <- op_convert_to_tensor(c(10, 100)) op_arccosh(x) ## tf.Tensor([2.993223 5.298292], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse sine, element-wise. — op_arcsin","title":"Inverse sine, element-wise. — op_arcsin","text":"Inverse sine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse sine, element-wise. — op_arcsin","text":"","code":"op_arcsin(x)"},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse sine, element-wise. — op_arcsin","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse sine, element-wise. — op_arcsin","text":"Tensor inverse sine element x, radians closed interval [-pi/2, pi/2].","code":""},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse sine, element-wise. — op_arcsin","text":"","code":"x <- op_convert_to_tensor(c(1, -1, 0)) op_arcsin(x) ## tf.Tensor([ 1.5707964 -1.5707964  0.       ], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse hyperbolic sine, element-wise. — op_arcsinh","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"Inverse hyperbolic sine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"","code":"op_arcsinh(x)"},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"Output tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"","code":"x <- op_convert_to_tensor(c(1, -1, 0)) op_arcsinh(x) ## tf.Tensor([ 0.8813736 -0.8813736  0.       ], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arctan.html","id":null,"dir":"Reference","previous_headings":"","what":"Trigonometric inverse tangent, element-wise. — op_arctan","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"Trigonometric inverse tangent, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arctan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"","code":"op_arctan(x)"},{"path":"https://keras.posit.co/reference/op_arctan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arctan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"Tensor inverse tangent element x, interval [-pi/2, pi/2].","code":""},{"path":"https://keras.posit.co/reference/op_arctan.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"","code":"x <- op_convert_to_tensor(c(0, 1)) op_arctan(x) ## tf.Tensor([0.        0.7853982], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":null,"dir":"Reference","previous_headings":"","what":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"quadrant (.e., branch) chosen arctan2(x1, x2) signed angle radians ray ending origin passing point (1, 0), ray ending origin passing point (x2, x1). (Note role reversal: \"y-coordinate\" first function parameter, \"x-coordinate\" second.) IEEE convention, function defined x2 = +/-0 either x1 x2 = +/-inf.","code":""},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"","code":"op_arctan2(x1, x2)"},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"Tensor angles radians, range [-pi, pi].","code":""},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"Consider four points different quadrants:     Note order parameters. arctan2 defined also x2 = 0 several points, obtaining values range [-pi, pi]:","code":"x <- op_array(c(-1, 1, 1, -1)) y <- op_array(c(-1, -1, 1, 1)) op_arctan2(y, x) * 180 / pi ## tf.Tensor([-135.  -45.   45.  135.], shape=(4), dtype=float32) op_arctan2(     op_array(c(1, -1)),     op_array(c(0, 0)) ) ## tf.Tensor([ 1.5707964 -1.5707964], shape=(2), dtype=float32) op_arctan2(     op_array(c(0, 0, Inf)),     op_array(c(+0, -0, Inf)) ) ## tf.Tensor([0.        3.1415927 0.7853982], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arctanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse hyperbolic tangent, element-wise. — op_arctanh","title":"Inverse hyperbolic tangent, element-wise. — op_arctanh","text":"Inverse hyperbolic tangent, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arctanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse hyperbolic tangent, element-wise. — op_arctanh","text":"","code":"op_arctanh(x)"},{"path":"https://keras.posit.co/reference/op_arctanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse hyperbolic tangent, element-wise. — op_arctanh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arctanh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse hyperbolic tangent, element-wise. — op_arctanh","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_argmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the indices of the maximum values along an axis. — op_argmax","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"Returns indices maximum values along axis.","code":""},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"","code":"op_argmax(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"x Input tensor. axis default, index flattened tensor, otherwise along specified axis. keepdims set TRUE, axes reduced left result dimensions size one. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"Tensor indices. shape x, dimension along axis removed. Note returned integer 0-based (.e., argmax first index position, returned value 0)","code":""},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"similar R max.col(x) - 1 case 2-d array (matrix), nd-array, apply(x, axis, .max) - 1","code":""},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"","code":"x <- op_arange(6L) |> op_reshape(c(2, 3)) |> op_add(10) x ## tf.Tensor( ## [[10. 11. 12.] ##  [13. 14. 15.]], shape=(2, 3), dtype=float32) op_argmax(x) ## tf.Tensor(5, shape=(), dtype=int32) op_argmax(x, axis = 1) ## tf.Tensor([1 1 1], shape=(3), dtype=int32) op_argmax(x, axis = 2) ## tf.Tensor([2 2], shape=(2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_argmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the indices of the minimum values along an axis. — op_argmin","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"Returns indices minimum values along axis.","code":""},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"","code":"op_argmin(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"x Input tensor. axis default, index flattened tensor, otherwise along specified axis. keepdims set TRUE, axes reduced left result dimensions size one. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"Tensor indices. shape x, dimension along axis removed.","code":""},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"similar R expression apply(x, axis, .min) - 1, x R array.","code":""},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"","code":"x <- op_arange(6L) |> op_reshape(c(2, 3)) |> op_add(10) x ## tf.Tensor( ## [[10. 11. 12.] ##  [13. 14. 15.]], shape=(2, 3), dtype=float32) op_argmin(x) ## tf.Tensor(0, shape=(), dtype=int32) op_argmin(x, axis = 1) ## tf.Tensor([0 0 0], shape=(3), dtype=int32) op_argmin(x, axis = 2) ## tf.Tensor([0 0], shape=(2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_argsort.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the indices that would sort a tensor. — op_argsort","title":"Returns the indices that would sort a tensor. — op_argsort","text":"Returns indices sort tensor.","code":""},{"path":"https://keras.posit.co/reference/op_argsort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the indices that would sort a tensor. — op_argsort","text":"","code":"op_argsort(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_argsort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the indices that would sort a tensor. — op_argsort","text":"x Input tensor. axis Axis along sort. Defaults -1 (last axis). NULL, flattened tensor used.","code":""},{"path":"https://keras.posit.co/reference/op_argsort.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the indices that would sort a tensor. — op_argsort","text":"Tensor indices sort x along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_argsort.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the indices that would sort a tensor. — op_argsort","text":"One dimensional array:     Two-dimensional array:","code":"x <- op_array(c(3, 1, 2)) op_argsort(x) ## tf.Tensor([1 2 0], shape=(3), dtype=int32) x <- op_array(rbind(c(0, 3),                    c(3, 2),                    c(4, 5)), dtype = \"int32\") op_argsort(x, axis = 1) ## tf.Tensor( ## [[0 1] ##  [1 0] ##  [2 2]], shape=(3, 2), dtype=int32) op_argsort(x, axis = 2) ## tf.Tensor( ## [[0 1] ##  [1 0] ##  [0 1]], shape=(3, 2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a tensor. — op_array","title":"Create a tensor. — op_array","text":"Create tensor.","code":""},{"path":"https://keras.posit.co/reference/op_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a tensor. — op_array","text":"","code":"op_array(x, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a tensor. — op_array","text":"x Input tensor. dtype desired data-type tensor.","code":""},{"path":"https://keras.posit.co/reference/op_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a tensor. — op_array","text":"tensor.","code":""},{"path":"https://keras.posit.co/reference/op_array.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a tensor. — op_array","text":"","code":"op_array(c(1, 2, 3)) ## tf.Tensor([1. 2. 3.], shape=(3), dtype=float32) op_array(c(1, 2, 3), dtype = \"float32\") ## tf.Tensor([1. 2. 3.], shape=(3), dtype=float32) op_array(c(1, 2, 3), dtype = \"int32\") ## tf.Tensor([1 2 3], shape=(3), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_average.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the weighted average along the specified axis. — op_average","title":"Compute the weighted average along the specified axis. — op_average","text":"Compute weighted average along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_average.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the weighted average along the specified axis. — op_average","text":"","code":"op_average(x, axis = NULL, weights = NULL)"},{"path":"https://keras.posit.co/reference/op_average.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the weighted average along the specified axis. — op_average","text":"x Input tensor. axis Integer along average x. default, axis = NULL, average elements input tensor. axis negative counts last first axis. weights Tensor wieghts associated values x. value x contributes average according associated weight. weights array can either 1-D (case length must size along given axis) shape x. weights = NULL (default), data x assumed weight equal one. 1-D calculation : avg = sum(* weights) / sum(weights). constraint weights sum(weights) must 0.","code":""},{"path":"https://keras.posit.co/reference/op_average.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the weighted average along the specified axis. — op_average","text":"Return average along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_average.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the weighted average along the specified axis. — op_average","text":"","code":"data <- op_arange(1, 5, dtype = \"int32\") data ## tf.Tensor([1 2 3 4], shape=(4), dtype=int32) op_average(data) ## tf.Tensor(2.5, shape=(), dtype=float32) op_average(   op_arange(1, 11),   weights = op_arange(10, 0, -1) ) ## tf.Tensor(4.0, shape=(), dtype=float32) data <- op_arange(6) |> op_reshape(c(3, 2)) data ## tf.Tensor( ## [[0. 1.] ##  [2. 3.] ##  [4. 5.]], shape=(3, 2), dtype=float32) op_average(   data,   axis = 2,   weights = op_array(c(1/4, 3/4)) ) ## tf.Tensor([0.75 2.75 4.75], shape=(3), dtype=float32) # Error: Axis must be specified when shapes of x and weights differ. try(op_average(   data,   weights = op_array(c(1/4, 3/4)) )) ## Error in op_average(data, weights = op_array(c(1/4, 3/4))) : ##   Axis must be specified when shapes of x and weights differ."},{"path":[]},{"path":"https://keras.posit.co/reference/op_average_pool.html","id":null,"dir":"Reference","previous_headings":"","what":"Average pooling operation. — op_average_pool","title":"Average pooling operation. — op_average_pool","text":"Average pooling operation.","code":""},{"path":"https://keras.posit.co/reference/op_average_pool.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average pooling operation. — op_average_pool","text":"","code":"op_average_pool(   inputs,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL )"},{"path":"https://keras.posit.co/reference/op_average_pool.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average pooling operation. — op_average_pool","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". Pooling happens spatial dimensions . pool_size int tuple/list integers size len(inputs_spatial_shape), specifying size pooling window spatial dimension input tensor. pool_size int, every spatial dimension shares pool_size. strides int tuple/list integers size len(inputs_spatial_shape). stride sliding window spatial dimension input tensor. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...).","code":""},{"path":"https://keras.posit.co/reference/op_average_pool.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average pooling operation. — op_average_pool","text":"tensor rank N+2, result average pooling operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalizes x by mean and variance. — op_batch_normalization","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"op typically used batch normalization step neural network. normalizes input tensor along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"","code":"op_batch_normalization(   x,   mean,   variance,   axis,   offset = NULL,   scale = NULL,   epsilon = 0.001 )"},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"x Input tensor. mean mean vector length axis dimension input thensor. variance variance vector length axis dimension input tensor. axis Integer, axis normalized. offset offset vector length axis dimension input tensor. NULL, offset added normalized tensor. Defaults NULL. scale scale vector length axis dimension input tensor. NULL, normalized tensor multiplied scale. Defaults NULL. epsilon Small float added variance avoid dividing zero. Defaults 1e-3.","code":""},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"normalized tensor.","code":""},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"","code":"x <- op_convert_to_tensor(rbind(c(0.1, 0.2, 0.3),                                 c(0.4, 0.5, 0.6),                                 c(0.7, 0.8, 0.9))) op_batch_normalization(   x,   mean = c(0.4, 0.5, 0.6),   variance = c(0.67, 0.67, 0.67),   axis = -1 ) ## tf.Tensor( ## [[-0.36623513 -0.36623513 -0.36623513] ##  [ 0.          0.          0.        ] ##  [ 0.36623513  0.36623513  0.36623513]], shape=(3, 3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"binary cross-entropy loss commonly used binary classification tasks input sample belongs one two classes. measures dissimilarity target output probabilities logits.","code":""},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"","code":"op_binary_crossentropy(target, output, from_logits = FALSE)"},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"target target tensor representing true binary labels. shape match shape output tensor. output output tensor representing predicted probabilities logits. shape match shape target tensor. from_logits (optional) Whether output tensor logits probabilities. Set TRUE output represents logits; otherwise, set FALSE output represents probabilities. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"Integer tensor: computed binary cross-entropy loss target output.","code":""},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"","code":"target <- op_array(c(0, 1, 1, 0)) output <- op_array(c(0.1, 0.9, 0.8, 0.2)) op_binary_crossentropy(target, output) ## tf.Tensor([0.10536055 0.10536055 0.22314353 0.22314353], shape=(4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_bincount.html","id":null,"dir":"Reference","previous_headings":"","what":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"Count number occurrences value tensor integers.","code":""},{"path":"https://keras.posit.co/reference/op_bincount.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"","code":"op_bincount(x, weights = NULL, minlength = 0L, sparse = FALSE)"},{"path":"https://keras.posit.co/reference/op_bincount.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"x Input tensor. must dimension 1, must contain non-negative integer(s). weights Weight tensor. must length x. default value NULL. specified, x weighted , .e. n = x[], [n] += weight[] instead default behavior [n] += 1. minlength integer. default value 0. specified, least number bins output tensor. greater max(x) + 1, value output index higher max(x) set 0. sparse Whether return sparse tensor; backends support sparse tensors.","code":""},{"path":"https://keras.posit.co/reference/op_bincount.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"1D tensor element gives number occurrence(s) index value x. length maximum max(x) + 1 minlength.","code":""},{"path":"https://keras.posit.co/reference/op_bincount.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"","code":"(x <- op_array(c(1, 2, 2, 3), dtype = \"uint8\")) ## tf.Tensor([1 2 2 3], shape=(4), dtype=uint8) op_bincount(x) ## tf.Tensor([0 1 2 1], shape=(4), dtype=int32) (weights <- x / 2) ## tf.Tensor([0.5 1.  1.  1.5], shape=(4), dtype=float32) op_bincount(x, weights = weights) ## tf.Tensor([0.  0.5 2.  1.5], shape=(4), dtype=float32) minlength <- as.integer(op_max(x) + 1 + 2) # 6 op_bincount(x, minlength = minlength) ## tf.Tensor([0 1 2 1 0 0], shape=(6), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":null,"dir":"Reference","previous_headings":"","what":"Broadcast a tensor to a new shape. — op_broadcast_to","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"Broadcast tensor new shape.","code":""},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"","code":"op_broadcast_to(x, shape)"},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"x tensor broadcast. shape shape desired tensor.","code":""},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"tensor desired shape.","code":""},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"","code":"x <- op_array(c(1, 2, 3)) op_broadcast_to(x, shape = c(3, 3)) ## tf.Tensor( ## [[1. 2. 3.] ##  [1. 2. 3.] ##  [1. 2. 3.]], shape=(3, 3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_cast.html","id":null,"dir":"Reference","previous_headings":"","what":"Cast a tensor to the desired dtype. — op_cast","title":"Cast a tensor to the desired dtype. — op_cast","text":"Cast tensor desired dtype.","code":""},{"path":"https://keras.posit.co/reference/op_cast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cast a tensor to the desired dtype. — op_cast","text":"","code":"op_cast(x, dtype)"},{"path":"https://keras.posit.co/reference/op_cast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cast a tensor to the desired dtype. — op_cast","text":"x tensor variable. dtype target type.","code":""},{"path":"https://keras.posit.co/reference/op_cast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cast a tensor to the desired dtype. — op_cast","text":"tensor specified dtype.","code":""},{"path":"https://keras.posit.co/reference/op_cast.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cast a tensor to the desired dtype. — op_cast","text":"","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_cast(x, dtype = \"float16\") ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float16)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"categorical cross-entropy loss commonly used multi-class classification tasks input sample can belong one multiple classes. measures dissimilarity target output probabilities logits.","code":""},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"","code":"op_categorical_crossentropy(target, output, from_logits = FALSE, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"target target tensor representing true categorical labels. shape match shape output tensor except last dimension. output output tensor representing predicted probabilities logits. shape match shape target tensor except last dimension. from_logits (optional) Whether output tensor logits probabilities. Set TRUE output represents logits; otherwise, set FALSE output represents probabilities. Defaults FALSE. axis (optional) axis along categorical cross-entropy computed. Defaults -1, corresponds last dimension tensors.","code":""},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"Integer tensor: computed categorical cross-entropy loss target output.","code":""},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"","code":"target <- op_array(rbind(c(1, 0, 0),                         c(0, 1, 0),                         c(0, 0, 1))) output <- op_array(rbind(c(0.9, 0.05, 0.05),                         c(0.1, 0.8, 0.1),                         c(0.2, 0.3, 0.5))) op_categorical_crossentropy(target, output) ## tf.Tensor([0.10536052 0.22314355 0.69314718], shape=(3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_ceil.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the ceiling of the input, element-wise. — op_ceil","title":"Return the ceiling of the input, element-wise. — op_ceil","text":"ceil scalar x smallest integer , >= x.","code":""},{"path":"https://keras.posit.co/reference/op_ceil.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the ceiling of the input, element-wise. — op_ceil","text":"","code":"op_ceil(x)"},{"path":"https://keras.posit.co/reference/op_ceil.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the ceiling of the input, element-wise. — op_ceil","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ceil.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the ceiling of the input, element-wise. — op_ceil","text":"ceiling element x, float dtype.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cholesky.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","title":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","text":"Computes Cholesky decomposition positive semi-definite matrix.","code":""},{"path":"https://keras.posit.co/reference/op_cholesky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","text":"","code":"op_cholesky(x)"},{"path":"https://keras.posit.co/reference/op_cholesky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_cholesky.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","text":"tensor shape (..., M, M) representing lower triangular Cholesky factor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_clip.html","id":null,"dir":"Reference","previous_headings":"","what":"Clip (limit) the values in a tensor. — op_clip","title":"Clip (limit) the values in a tensor. — op_clip","text":"Given interval, values outside interval clipped interval edges. example, interval [0, 1] specified, values smaller 0 become 0, values larger 1 become 1.","code":""},{"path":"https://keras.posit.co/reference/op_clip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clip (limit) the values in a tensor. — op_clip","text":"","code":"op_clip(x, x_min, x_max)"},{"path":"https://keras.posit.co/reference/op_clip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clip (limit) the values in a tensor. — op_clip","text":"x Input tensor. x_min Minimum value. x_max Maximum value.","code":""},{"path":"https://keras.posit.co/reference/op_clip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clip (limit) the values in a tensor. — op_clip","text":"clipped tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_concatenate.html","id":null,"dir":"Reference","previous_headings":"","what":"Join a sequence of tensors along an existing axis. — op_concatenate","title":"Join a sequence of tensors along an existing axis. — op_concatenate","text":"Join sequence tensors along existing axis.","code":""},{"path":"https://keras.posit.co/reference/op_concatenate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join a sequence of tensors along an existing axis. — op_concatenate","text":"","code":"op_concatenate(xs, axis = 1L)"},{"path":"https://keras.posit.co/reference/op_concatenate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join a sequence of tensors along an existing axis. — op_concatenate","text":"xs sequence tensors concatenate. axis axis along tensors joined. Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/op_concatenate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join a sequence of tensors along an existing axis. — op_concatenate","text":"concatenated tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cond.html","id":null,"dir":"Reference","previous_headings":"","what":"Conditionally applies true_fn or false_fn. — op_cond","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"Conditionally applies true_fn false_fn.","code":""},{"path":"https://keras.posit.co/reference/op_cond.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"","code":"op_cond(pred, true_fn, false_fn)"},{"path":"https://keras.posit.co/reference/op_cond.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"pred Boolean scalar type true_fn Callable returning output pred == TRUE case. false_fn Callable returning output pred == FALSE case.","code":""},{"path":"https://keras.posit.co/reference/op_cond.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"output either true_fn false_fn depending pred.","code":""},{"path":"https://keras.posit.co/reference/op_cond.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"","code":"fn <- tensorflow::tf_function(function(x) {   op_cond(x > 0,     true_fn = \\() x + 1,     false_fn = \\() x - 1) })  fn(tensorflow::as_tensor(1)) ## tf.Tensor(2.0, shape=(), dtype=float64) fn(tensorflow::as_tensor(-1)) ## tf.Tensor(-2.0, shape=(), dtype=float64) # # Conditional side-effect (print only, no return value). file <- tempfile(fileext = \".txt\") fn <- tensorflow::tf_function(function(epochs) {   op_fori_loop(     0, epochs,     body_fun = \\(epoch, state) {       op_cond(epoch %% 20 == 0,               \\() {                 tensorflow::tf$print(                   \"epoch:\", epoch,                   output_stream = paste0(\"file://\", file))                 NULL               },               \\() {NULL})       state     },     init_val = tensorflow::as_tensor(0)) })  fn(tensorflow::as_tensor(100)) ## tf.Tensor(0.0, shape=(), dtype=float64) readLines(file) ## [1] \"epoch: 0\"  \"epoch: 20\" \"epoch: 40\" \"epoch: 60\" \"epoch: 80\" # cleanup unlink(file)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_conj.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the complex conjugate, element-wise. — op_conj","title":"Returns the complex conjugate, element-wise. — op_conj","text":"complex conjugate complex number obtained changing sign imaginary part.","code":""},{"path":"https://keras.posit.co/reference/op_conj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the complex conjugate, element-wise. — op_conj","text":"","code":"op_conj(x)"},{"path":"https://keras.posit.co/reference/op_conj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the complex conjugate, element-wise. — op_conj","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_conj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the complex conjugate, element-wise. — op_conj","text":"complex conjugate element x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_conv.html","id":null,"dir":"Reference","previous_headings":"","what":"General N-D convolution. — op_conv","title":"General N-D convolution. — op_conv","text":"ops supports 1D, 2D 3D convolution.","code":""},{"path":"https://keras.posit.co/reference/op_conv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General N-D convolution. — op_conv","text":"","code":"op_conv(   inputs,   kernel,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L )"},{"path":"https://keras.posit.co/reference/op_conv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General N-D convolution. — op_conv","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". kernel Tensor rank N+2. kernel shape (kernel_spatial_shape, num_input_channels, num_output_channels). num_input_channels match number channels inputs. strides int int tuple/list len(inputs_spatial_shape), specifying strides convolution along spatial dimension. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...). dilation_rate int int tuple/list len(inputs_spatial_shape), specifying dilation rate use dilated convolution. dilation_rate int, every spatial dimension shares dilation_rate.","code":""},{"path":"https://keras.posit.co/reference/op_conv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General N-D convolution. — op_conv","text":"tensor rank N+2, result conv operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_conv_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"General N-D convolution transpose. — op_conv_transpose","title":"General N-D convolution transpose. — op_conv_transpose","text":"Also known de-convolution. ops supports 1D, 2D 3D convolution.","code":""},{"path":"https://keras.posit.co/reference/op_conv_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General N-D convolution transpose. — op_conv_transpose","text":"","code":"op_conv_transpose(   inputs,   kernel,   strides,   padding = \"valid\",   output_padding = NULL,   data_format = NULL,   dilation_rate = 1L )"},{"path":"https://keras.posit.co/reference/op_conv_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General N-D convolution transpose. — op_conv_transpose","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". kernel Tensor rank N+2. kernel shape [kernel_spatial_shape, num_output_channels, num_input_channels], num_input_channels match number channels inputs. strides int int tuple/list len(inputs_spatial_shape), specifying strides convolution along spatial dimension. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. output_padding int int tuple/list len(inputs_spatial_shape), specifying amount padding along height width output tensor. Can single integer specify value spatial dimensions. amount output padding along given dimension must lower stride along dimension. set NULL (default), output shape inferred. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...). dilation_rate int int tuple/list len(inputs_spatial_shape), specifying dilation rate use dilated convolution. dilation_rate int, every spatial dimension shares dilation_rate.","code":""},{"path":"https://keras.posit.co/reference/op_conv_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General N-D convolution transpose. — op_conv_transpose","text":"tensor rank N+2, result conv operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_convert_to_numpy.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a tensor to a NumPy array. — op_convert_to_numpy","title":"Convert a tensor to a NumPy array. — op_convert_to_numpy","text":"Convert tensor NumPy array.","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_numpy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a tensor to a NumPy array. — op_convert_to_numpy","text":"","code":"op_convert_to_numpy(x)"},{"path":"https://keras.posit.co/reference/op_convert_to_numpy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a tensor to a NumPy array. — op_convert_to_numpy","text":"x tensor.","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_numpy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a tensor to a NumPy array. — op_convert_to_numpy","text":"NumPy array.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert an array to a tensor. — op_convert_to_tensor","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"Convert array tensor.","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"","code":"op_convert_to_tensor(x, dtype = NULL, sparse = NULL)"},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"x array. dtype target type. sparse Whether keep sparse tensors. FALSE cause sparse tensors densified. default value NULL means sparse tensors kept backend supports .","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"tensor specified dtype.","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"","code":"x <- array(c(1, 2, 3)) y <- op_convert_to_tensor(x) y ## tf.Tensor([1. 2. 3.], shape=(3), dtype=float64) op_convert_to_tensor(c(1, 3, 2, 0), \"int32\") ## tf.Tensor([1 3 2 0], shape=(4), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_copy.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a copy of x. — op_copy","title":"Returns a copy of x. — op_copy","text":"Returns copy x.","code":""},{"path":"https://keras.posit.co/reference/op_copy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a copy of x. — op_copy","text":"","code":"op_copy(x)"},{"path":"https://keras.posit.co/reference/op_copy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a copy of x. — op_copy","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_copy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a copy of x. — op_copy","text":"copy x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_correlate.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","title":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","text":"Compute cross-correlation two 1-dimensional tensors.","code":""},{"path":"https://keras.posit.co/reference/op_correlate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","text":"","code":"op_correlate(x1, x2, mode = \"valid\")"},{"path":"https://keras.posit.co/reference/op_correlate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","text":"x1 First 1-dimensional input tensor length M. x2 Second 1-dimensional input tensor length N. mode Either \"valid\", \"\" \"full\". default mode set \"valid\", returns output length max(M, N) - min(M, N) + 1. \"\" returns output length max(M, N). \"full\" mode returns convolution point overlap, output length N+M-1.","code":""},{"path":"https://keras.posit.co/reference/op_correlate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","text":"Output tensor, cross-correlation x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cos.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosine, element-wise. — op_cos","title":"Cosine, element-wise. — op_cos","text":"Cosine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_cos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosine, element-wise. — op_cos","text":"","code":"op_cos(x)"},{"path":"https://keras.posit.co/reference/op_cos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosine, element-wise. — op_cos","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_cos.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cosine, element-wise. — op_cos","text":"corresponding cosine values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hyperbolic cosine, element-wise. — op_cosh","title":"Hyperbolic cosine, element-wise. — op_cosh","text":"Hyperbolic cosine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_cosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hyperbolic cosine, element-wise. — op_cosh","text":"","code":"op_cosh(x)"},{"path":"https://keras.posit.co/reference/op_cosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hyperbolic cosine, element-wise. — op_cosh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_cosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hyperbolic cosine, element-wise. — op_cosh","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"axis specified non-zeros tensor counted.","code":""},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"","code":"op_count_nonzero(x, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"x Input tensor. axis Axis tuple axes along count number non-zeros. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"integer tensor integers.","code":""},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"","code":"x <- op_array(rbind(c(0, 1, 7, 0),                    c(3, 0, 2, 19))) op_count_nonzero(x) ## tf.Tensor(5, shape=(), dtype=int32) op_count_nonzero(x, axis = 1) ## tf.Tensor([1 1 2 1], shape=(4), dtype=int32) op_count_nonzero(x, axis = 2) ## tf.Tensor([2 3], shape=(2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_cross.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the cross product of two (arrays of) vectors. — op_cross","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"cross product x1 x2 R^3 vector perpendicular x1 x2. x1 x2 arrays vectors, vectors defined last axis x1 x2 default, axes can dimensions 2 3. dimension either x1 x2 2, third component input vector assumed zero cross product calculated accordingly. cases input vectors dimension 2, z-component cross product returned.","code":""},{"path":"https://keras.posit.co/reference/op_cross.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"","code":"op_cross(x1, x2, axisa = -1L, axisb = -1L, axisc = -1L, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_cross.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"x1 Components first vector(s). x2 Components second vector(s). axisa Axis x1 defines vector(s). Defaults -1. axisb Axis x2 defines vector(s). Defaults -1. axisc Axis result containing cross product vector(s). Ignored input vectors dimension 2, return scalar. default, last axis. axis defined, axis x1, x2 result defines vector(s) cross product(s). Overrides axisa, axisb axisc.","code":""},{"path":"https://keras.posit.co/reference/op_cross.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"Vector cross product(s).","code":""},{"path":"https://keras.posit.co/reference/op_cross.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"Torch backend support two dimensional vectors, arguments axisa, axisb axisc. Use axis instead.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_ctc_decode.html","id":null,"dir":"Reference","previous_headings":"","what":"Decodes the output of a CTC model. — op_ctc_decode","title":"Decodes the output of a CTC model. — op_ctc_decode","text":"Decodes output CTC model.","code":""},{"path":"https://keras.posit.co/reference/op_ctc_decode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decodes the output of a CTC model. — op_ctc_decode","text":"","code":"op_ctc_decode(   inputs,   sequence_lengths,   strategy = \"greedy\",   beam_width = 100L,   top_paths = 1L,   merge_repeated = TRUE,   mask_index = NULL )"},{"path":"https://keras.posit.co/reference/op_ctc_decode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decodes the output of a CTC model. — op_ctc_decode","text":"inputs tensor shape (batch_size, max_length, num_classes) containing logits (output model). normalized via softmax. sequence_lengths tensor shape (batch_size) containing sequence lengths batch. strategy string decoding strategy. Supported values \"greedy\" \"beam_search\". beam_width integer scalar beam width used beam search. Defaults 100. top_paths integer scalar, number top paths return. Defaults 1. merge_repeated boolean scalar, whether merge repeated labels output. Defaults TRUE. mask_index integer scalar, index mask character vocabulary. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_ctc_decode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decodes the output of a CTC model. — op_ctc_decode","text":"list containing: tensor representing list decoded sequences. strategy=\"greedy\", shape (1, batch_size, max_length). strategy=\"beam_seatch\", shape (top_paths, batch_size, max_length). Note : -1 indicates blank label. strategy=\"greedy\", tensor shape (batch_size, 1) representing negative sum probability logits sequence. strategy=\"beam_seatch\", tensor shape (batch_size, top_paths) representing log probability sequence.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_ctc_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","title":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","text":"CTC (Connectionist Temporal Classification) loss.","code":""},{"path":"https://keras.posit.co/reference/op_ctc_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","text":"","code":"op_ctc_loss(target, output, target_length, output_length, mask_index = 0L)"},{"path":"https://keras.posit.co/reference/op_ctc_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","text":"target tensor shape (batch_size, max_length) containing true labels integer format. output tensor shape (batch_size, max_length, num_classes) containing logits (output model). target_length tensor shape (batch_size) containing true label lengths. output_length tensor shape (batch_size) containing output lengths. mask_index index mask character vocabulary. Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/op_ctc_loss.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","text":"tensor, shape (batch_size), loss values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cumprod.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the cumulative product of elements along a given axis. — op_cumprod","title":"Return the cumulative product of elements along a given axis. — op_cumprod","text":"Return cumulative product elements along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_cumprod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the cumulative product of elements along a given axis. — op_cumprod","text":"","code":"op_cumprod(x, axis = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_cumprod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the cumulative product of elements along a given axis. — op_cumprod","text":"x Input tensor. axis Axis along cumulative product computed. default input flattened. dtype dtype returned tensor. Defaults x$dtype.","code":""},{"path":"https://keras.posit.co/reference/op_cumprod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the cumulative product of elements along a given axis. — op_cumprod","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cumsum.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the cumulative sum of elements along a given axis. — op_cumsum","title":"Returns the cumulative sum of elements along a given axis. — op_cumsum","text":"Returns cumulative sum elements along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_cumsum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the cumulative sum of elements along a given axis. — op_cumsum","text":"","code":"op_cumsum(x, axis = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_cumsum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the cumulative sum of elements along a given axis. — op_cumsum","text":"x Input tensor. axis Axis along cumulative sum computed. default input flattened. dtype dtype returned tensor. Defaults x$dtype.","code":""},{"path":"https://keras.posit.co/reference/op_cumsum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the cumulative sum of elements along a given axis. — op_cumsum","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":null,"dir":"Reference","previous_headings":"","what":"Decorator to define a function with a custom gradient. — op_custom_gradient","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"decorator allows fine grained control gradients sequence operations. may useful multiple reasons, including providing efficient numerically stable gradient sequence operations.","code":""},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"","code":"op_custom_gradient(f)"},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"f Function f(...) returns tuple (output, grad_fn) : ... sequence unnamed arguments, tensor input nested structure tensor inputs function. output (potentially nested structure ) tensor outputs applying operations forward_fn f() .... grad_fn function signature grad_fn(..., upstream) returns list tensors size (flattened) ...: derivatives tensors output respect tensors .... upstream tensor sequence tensors holding initial value gradients tensor output.","code":""},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"function h(...) returns value f(...)[[1]] whose gradient determined f(...)[[2]].","code":""},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"Note grad function returns gradient computation requires ... well upstream named argument, depending backend set. JAX TensorFlow backends, requires one argument, whereas might use upstream argument case PyTorch backend. working TensorFlow/JAX backend, grad(upstream) sufficient. PyTorch, grad function requires ... well upstream, e.g. grad <- \\(..., upstream). Follow example use op_custom_gradient() way compatible backends.","code":""},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"Backend-agnostic example.","code":"log1pexp <- op_custom_gradient(\\(x) {      e <- op_exp(x)      grad <- function(..., upstream = NULL) {       upstream <- upstream %||% ..1       op_multiply(upstream, 1.0 - 1.0 / op_add(1, e))     }      tuple(op_log(1 + e), grad)  })  if(config_backend() == \"tensorflow\") {   tf <- tensorflow::tf   x <- op_convert_to_tensor(100.0)   with(tf$GradientTape() %as% tape, {     tape$watch(x)     y <- log1pexp(x)   })   dy_dx <- tape$gradient(y, x)   stopifnot(as.numeric(dy_dx) == 1) }"},{"path":[]},{"path":"https://keras.posit.co/reference/op_depthwise_conv.html","id":null,"dir":"Reference","previous_headings":"","what":"General N-D depthwise convolution. — op_depthwise_conv","title":"General N-D depthwise convolution. — op_depthwise_conv","text":"ops supports 1D 2D depthwise convolution.","code":""},{"path":"https://keras.posit.co/reference/op_depthwise_conv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General N-D depthwise convolution. — op_depthwise_conv","text":"","code":"op_depthwise_conv(   inputs,   kernel,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L )"},{"path":"https://keras.posit.co/reference/op_depthwise_conv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General N-D depthwise convolution. — op_depthwise_conv","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". kernel Tensor rank N+2. kernel shape [kernel_spatial_shape, num_input_channels, num_channels_multiplier], num_input_channels match number channels inputs. strides int int tuple/list len(inputs_spatial_shape), specifying strides convolution along spatial dimension. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...). dilation_rate int int tuple/list len(inputs_spatial_shape), specifying dilation rate use dilated convolution. dilation_rate int, every spatial dimension shares dilation_rate.","code":""},{"path":"https://keras.posit.co/reference/op_depthwise_conv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General N-D depthwise convolution. — op_depthwise_conv","text":"tensor rank N+2, result depthwise conv operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_det.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the determinant of a square tensor. — op_det","title":"Computes the determinant of a square tensor. — op_det","text":"Computes determinant square tensor.","code":""},{"path":"https://keras.posit.co/reference/op_det.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the determinant of a square tensor. — op_det","text":"","code":"op_det(x)"},{"path":"https://keras.posit.co/reference/op_det.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the determinant of a square tensor. — op_det","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_det.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the determinant of a square tensor. — op_det","text":"tensor shape (...) represeting determinant x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_diag.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a diagonal or construct a diagonal array. — op_diag","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"Extract diagonal construct diagonal array.","code":""},{"path":"https://keras.posit.co/reference/op_diag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"","code":"op_diag(x, k = 0L)"},{"path":"https://keras.posit.co/reference/op_diag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"x Input tensor. x 2-D, returns k-th diagonal x. x 1-D, return 2-D tensor x k-th diagonal. k diagonal consider. Defaults 0. Use k > 0 diagonals main diagonal, k < 0 diagonals main diagonal.","code":""},{"path":"https://keras.posit.co/reference/op_diag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"extracted diagonal constructed diagonal tensor.","code":""},{"path":"https://keras.posit.co/reference/op_diag.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"","code":"x <- op_arange(9L) |> op_reshape(c(3, 3)) x ## tf.Tensor( ## [[0 1 2] ##  [3 4 5] ##  [6 7 8]], shape=(3, 3), dtype=int32) op_diag(x) ## tf.Tensor([0 4 8], shape=(3), dtype=int32) op_diag(x, k = 1) ## tf.Tensor([1 5], shape=(2), dtype=int32) op_diag(x, k = -1) ## tf.Tensor([3 7], shape=(2), dtype=int32) op_diag(op_diag(x)) ## tf.Tensor( ## [[0 0 0] ##  [0 4 0] ##  [0 0 8]], shape=(3, 3), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return specified diagonals. — op_diagonal","title":"Return specified diagonals. — op_diagonal","text":"x 2-D, returns diagonal x given offset, .e., collection elements form x[, +offset]. x two dimensions, axes specified axis1 axis2 used determine 2-D sub-array whose diagonal returned. shape resulting array can determined removing axis1 axis2 appending index right equal size resulting diagonals.","code":""},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return specified diagonals. — op_diagonal","text":"","code":"op_diagonal(x, offset = 0L, axis1 = 1L, axis2 = 2L)"},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return specified diagonals. — op_diagonal","text":"x Input tensor. offset Offset diagonal main diagonal. Can positive negative. Defaults 0 (main diagonal). axis1 Axis used first axis 2-D sub-arrays. Defaults 1 (first axis). axis2 Axis used second axis 2-D sub-arrays. Defaults 2 (second axis).","code":""},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return specified diagonals. — op_diagonal","text":"Tensor diagonals.","code":""},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return specified diagonals. — op_diagonal","text":"","code":"x <- op_arange(4L) |> op_reshape(c(2, 2)) x ## tf.Tensor( ## [[0 1] ##  [2 3]], shape=(2, 2), dtype=int32) op_diagonal(x) ## tf.Tensor([0 3], shape=(2), dtype=int32) op_diagonal(x, offset = 1) ## tf.Tensor([1], shape=(1), dtype=int32) x <- op_array(1:8) |> op_reshape(c(2, 2, 2)) x ## tf.Tensor( ## [[[1 2] ##   [3 4]] ## ##  [[5 6] ##   [7 8]]], shape=(2, 2, 2), dtype=int32) x |> op_diagonal(0) ## tf.Tensor( ## [[1 7] ##  [2 8]], shape=(2, 2), dtype=int32) x |> op_diagonal(0, 1, 2) # same as above, the default ## tf.Tensor( ## [[1 7] ##  [2 8]], shape=(2, 2), dtype=int32) x |> op_diagonal(0, 2, 3) ## tf.Tensor( ## [[1 4] ##  [5 8]], shape=(2, 2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the n-th discrete difference along the given axis. — op_diff","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"first difference given [] = [+1] - [] along given axis, higher differences calculated using diff recursively.","code":""},{"path":"https://keras.posit.co/reference/op_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"","code":"op_diff(a, n = 1L, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"Input tensor. n number times values differenced. Defaults 1. axis Axis compute discrete difference(s) along. Defaults -1 (last axis).","code":""},{"path":"https://keras.posit.co/reference/op_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"Tensor diagonals.","code":""},{"path":"https://keras.posit.co/reference/op_diff.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"","code":"x <- op_array(c(1, 2, 4, 7, 0)) op_diff(x) ## tf.Tensor([ 1.  2.  3. -7.], shape=(4), dtype=float32) op_diff(x, n = 2) ## tf.Tensor([  1.   1. -10.], shape=(3), dtype=float32) x <- op_array(rbind(c(1, 3, 6, 10),                   c(0, 5, 6, 8))) op_diff(x) ## tf.Tensor( ## [[2. 3. 4.] ##  [5. 1. 2.]], shape=(2, 3), dtype=float64) op_diff(x, axis = 1) ## tf.Tensor([[-1.  2.  0. -2.]], shape=(1, 4), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_digitize.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the indices of the bins to which each value in x belongs. — op_digitize","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"Returns indices bins value x belongs.","code":""},{"path":"https://keras.posit.co/reference/op_digitize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"","code":"op_digitize(x, bins)"},{"path":"https://keras.posit.co/reference/op_digitize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"x Input array binned. bins Array bins. one-dimensional monotonically increasing.","code":""},{"path":"https://keras.posit.co/reference/op_digitize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"Output array indices, shape x.","code":""},{"path":"https://keras.posit.co/reference/op_digitize.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"","code":"x <- op_array(c(0.0, 1.0, 3.0, 1.6)) bins <- array(c(0.0, 3.0, 4.5, 7.0)) op_digitize(x, bins) ## tf.Tensor([1 1 2 1], shape=(4), dtype=int32) # array([1, 1, 2, 1])"},{"path":[]},{"path":"https://keras.posit.co/reference/op_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"Divide arguments element-wise. — op_divide","title":"Divide arguments element-wise. — op_divide","text":"Note function automatically called using R operator * tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_divide(x, 2) ## tf.Tensor([0.  0.5 1.  1.5], shape=(4), dtype=float32) x / 2 ## tf.Tensor([0.  0.5 1.  1.5], shape=(4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Divide arguments element-wise. — op_divide","text":"","code":"op_divide(x1, x2)"},{"path":"https://keras.posit.co/reference/op_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Divide arguments element-wise. — op_divide","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_divide.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Divide arguments element-wise. — op_divide","text":"Output tensor, quotient x1/x2, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_divide.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Divide arguments element-wise. — op_divide","text":"","code":"op_divide(3, 2) ## tf.Tensor(1.5, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_divide_no_nan.html","id":null,"dir":"Reference","previous_headings":"","what":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","title":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","text":"Safe element-wise division returns 0 denominator 0.","code":""},{"path":"https://keras.posit.co/reference/op_divide_no_nan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","text":"","code":"op_divide_no_nan(x1, x2)"},{"path":"https://keras.posit.co/reference/op_divide_no_nan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_divide_no_nan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","text":"quotient x1/x2, element-wise, zero x2 zero.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_dot.html","id":null,"dir":"Reference","previous_headings":"","what":"Dot product of two tensors. — op_dot","title":"Dot product of two tensors. — op_dot","text":"x1 x2 1-D tensors, inner product vectors (without complex conjugation). x1 x2 2-D tensors, matrix multiplication. either x1 x2 0-D (scalar), equivalent x1 * x2. x1 N-D tensor x2 1-D tensor, sum product last axis x1 x2. x1 N-D tensor x2 M-D tensor (M >= 2), sum product last axis x1 second--last axis x2: dot(x1, x2)[,j,k,m] = sum([,j,:] * b[k,:,m]).","code":""},{"path":"https://keras.posit.co/reference/op_dot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dot product of two tensors. — op_dot","text":"","code":"op_dot(x1, x2)"},{"path":"https://keras.posit.co/reference/op_dot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dot product of two tensors. — op_dot","text":"x1 First argument. x2 Second argument.","code":""},{"path":"https://keras.posit.co/reference/op_dot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dot product of two tensors. — op_dot","text":"Dot product x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_dot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Dot product of two tensors. — op_dot","text":"Torch backend accept 0-D tensors arguments.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_eig.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","title":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","text":"Computes eigenvalues eigenvectors square matrix.","code":""},{"path":"https://keras.posit.co/reference/op_eig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","text":"","code":"op_eig(x)"},{"path":"https://keras.posit.co/reference/op_eig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_eig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","text":"list two tensors: tensor shape (..., M) containing eigenvalues tensor shape (..., M, M) containing eigenvectors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_eigh.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","title":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","text":"Computes eigenvalues eigenvectors complex Hermitian.","code":""},{"path":"https://keras.posit.co/reference/op_eigh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","text":"","code":"op_eigh(x)"},{"path":"https://keras.posit.co/reference/op_eigh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_eigh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","text":"list two tensors: tensor shape (..., M) containing eigenvalues tensor shape (..., M, M) containing eigenvectors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_einsum.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluates the Einstein summation convention on the operands. — op_einsum","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"Evaluates Einstein summation convention operands.","code":""},{"path":"https://keras.posit.co/reference/op_einsum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"","code":"op_einsum(subscripts, ...)"},{"path":"https://keras.posit.co/reference/op_einsum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"subscripts Specifies subscripts summation comma separated list subscript labels. implicit (classical Einstein summation) calculation performed unless explicit indicator -> included well subscript labels precise output form. ... operands compute Einstein sum .","code":""},{"path":"https://keras.posit.co/reference/op_einsum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"calculation based Einstein summation convention.","code":""},{"path":"https://keras.posit.co/reference/op_einsum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"Trace matrix:     Extract diagonal:     Sum axis:     higher dimensional tensors summing single axis can done ellipsis:     Compute matrix transpose reorder number axes:         Matrix vector multiplication:","code":"a <- op_arange(25) |> op_reshape(c(5, 5)) b <- op_arange(5) c <- op_arange(6) |> op_reshape(c(2, 3)) op_einsum(\"ii\", a) op_trace(a) ## tf.Tensor(60.0, shape=(), dtype=float32) ## tf.Tensor(60.0, shape=(), dtype=float32) op_einsum(\"ii -> i\", a) op_diag(a) ## tf.Tensor([ 0.  6. 12. 18. 24.], shape=(5), dtype=float32) ## tf.Tensor([ 0.  6. 12. 18. 24.], shape=(5), dtype=float32) op_einsum(\"ij -> i\", a) op_sum(a, axis = 2) ## tf.Tensor([ 10.  35.  60.  85. 110.], shape=(5), dtype=float32) ## tf.Tensor([ 10.  35.  60.  85. 110.], shape=(5), dtype=float32) op_einsum(\"...j -> ...\", a) op_sum(a, axis = -1) ## tf.Tensor([ 10.  35.  60.  85. 110.], shape=(5), dtype=float32) ## tf.Tensor([ 10.  35.  60.  85. 110.], shape=(5), dtype=float32) op_einsum(\"ji\", c) # return c unchanged ## tf.Tensor( ## [[0. 1. 2.] ##  [3. 4. 5.]], shape=(2, 3), dtype=float32) op_einsum(\"ij -> ji\", c) # transpose op_transpose(c)          # same as above ## tf.Tensor( ## [[0. 3.] ##  [1. 4.] ##  [2. 5.]], shape=(3, 2), dtype=float32) ## tf.Tensor( ## [[0. 3.] ##  [1. 4.] ##  [2. 5.]], shape=(3, 2), dtype=float32) op_einsum(\"ij, j\", a, b) op_einsum(\"...j, j\", a, b) a %*% b op_matmul(a, b) ## tf.Tensor([ 30.  80. 130. 180. 230.], shape=(5), dtype=float32) ## tf.Tensor([ 30.  80. 130. 180. 230.], shape=(5), dtype=float32) ## tf.Tensor([ 30.  80. 130. 180. 230.], shape=(5), dtype=float32) ## tf.Tensor([ 30.  80. 130. 180. 230.], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"Exponential Linear Unit activation function. — op_elu","title":"Exponential Linear Unit activation function. — op_elu","text":"defined : f(x) =  alpha * (exp(x) - 1.) x < 0, f(x) = x x >= 0.","code":""},{"path":"https://keras.posit.co/reference/op_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exponential Linear Unit activation function. — op_elu","text":"","code":"op_elu(x, alpha = 1)"},{"path":"https://keras.posit.co/reference/op_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exponential Linear Unit activation function. — op_elu","text":"x Input tensor. alpha scalar, slope positive section. Defaults 1.0.","code":""},{"path":"https://keras.posit.co/reference/op_elu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exponential Linear Unit activation function. — op_elu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_elu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Exponential Linear Unit activation function. — op_elu","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_elu(x) ## tf.Tensor([-0.63212055  0.          1.        ], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_empty.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","title":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","text":"Return tensor given shape type filled uninitialized data.","code":""},{"path":"https://keras.posit.co/reference/op_empty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","text":"","code":"op_empty(shape, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_empty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","text":"shape Shape empty tensor. dtype Desired data type empty tensor.","code":""},{"path":"https://keras.posit.co/reference/op_empty.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","text":"empty tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns (x1 == x2) element-wise. — op_equal","title":"Returns (x1 == x2) element-wise. — op_equal","text":"Note function automatically called using R operator == tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_equal(x, 2) ## tf.Tensor([False False  True False], shape=(4), dtype=bool) x == 2 ## tf.Tensor([False False  True False], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns (x1 == x2) element-wise. — op_equal","text":"","code":"op_equal(x1, x2)"},{"path":"https://keras.posit.co/reference/op_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns (x1 == x2) element-wise. — op_equal","text":"x1 Tensor compare. x2 Tensor compare.","code":""},{"path":"https://keras.posit.co/reference/op_equal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns (x1 == x2) element-wise. — op_equal","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_erf.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the error function of x, element-wise. — op_erf","title":"Computes the error function of x, element-wise. — op_erf","text":"Computes error function x, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_erf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the error function of x, element-wise. — op_erf","text":"","code":"op_erf(x)"},{"path":"https://keras.posit.co/reference/op_erf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the error function of x, element-wise. — op_erf","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_erf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the error function of x, element-wise. — op_erf","text":"tensor dtype x.","code":""},{"path":"https://keras.posit.co/reference/op_erf.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the error function of x, element-wise. — op_erf","text":"","code":"x <- op_array(c(-3, -2, -1, 0, 1)) op_erf(x) ## tf.Tensor([-0.99997777 -0.9953222  -0.84270084  0.          0.84270084], shape=(5), dtype=float32) # array([-0.99998 , -0.99532, -0.842701,  0.,  0.842701], dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the inverse error function of x, element-wise. — op_erfinv","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"Computes inverse error function x, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"","code":"op_erfinv(x)"},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"tensor dtype x.","code":""},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"","code":"x <- op_array(c(-0.5, -0.2, -0.1, 0.0, 0.3)) op_erfinv(x) ## tf.Tensor([-0.4769363  -0.17914344 -0.088856    0.          0.27246267], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the exponential of all elements in the input tensor. — op_exp","title":"Calculate the exponential of all elements in the input tensor. — op_exp","text":"Calculate exponential elements input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the exponential of all elements in the input tensor. — op_exp","text":"","code":"op_exp(x)"},{"path":"https://keras.posit.co/reference/op_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the exponential of all elements in the input tensor. — op_exp","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_exp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the exponential of all elements in the input tensor. — op_exp","text":"Output tensor, element-wise exponential x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_expand_dims.html","id":null,"dir":"Reference","previous_headings":"","what":"Expand the shape of a tensor. — op_expand_dims","title":"Expand the shape of a tensor. — op_expand_dims","text":"Insert new axis axis position expanded tensor shape.","code":""},{"path":"https://keras.posit.co/reference/op_expand_dims.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expand the shape of a tensor. — op_expand_dims","text":"","code":"op_expand_dims(x, axis)"},{"path":"https://keras.posit.co/reference/op_expand_dims.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expand the shape of a tensor. — op_expand_dims","text":"x Input tensor. axis Position expanded axes new axis (axes) placed.","code":""},{"path":"https://keras.posit.co/reference/op_expand_dims.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expand the shape of a tensor. — op_expand_dims","text":"Output tensor number dimensions increased.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_expm1.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","title":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","text":"Calculate exp(x) - 1 elements tensor.","code":""},{"path":"https://keras.posit.co/reference/op_expm1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","text":"","code":"op_expm1(x)"},{"path":"https://keras.posit.co/reference/op_expm1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","text":"x Input values.","code":""},{"path":"https://keras.posit.co/reference/op_expm1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","text":"Output tensor, element-wise exponential minus one.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":null,"dir":"Reference","previous_headings":"","what":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"Slides window size sequence_length last axis input stride sequence_stride, replacing last axis [num_sequences, sequence_length] sequences. dimension along last axis N, number sequences can computed : num_sequences = 1 + (N - sequence_length) // sequence_stride","code":""},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"","code":"op_extract_sequences(x, sequence_length, sequence_stride)"},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"x Input tensor. sequence_length integer representing sequences length. sequence_stride integer representing sequences hop size.","code":""},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"tensor sequences shape [..., num_sequences, sequence_length].","code":""},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"","code":"x <- op_convert_to_tensor(1:6) op_extract_sequences(x, 3, 2) ## tf.Tensor( ## [[1 2 3] ##  [3 4 5]], shape=(2, 3), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_eye.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","title":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","text":"Return 2-D tensor ones diagonal zeros elsewhere.","code":""},{"path":"https://keras.posit.co/reference/op_eye.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","text":"","code":"op_eye(N, M = NULL, k = 0L, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_eye.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","text":"N Number rows output. M Number columns output. NULL, defaults N. k Index diagonal: 0 (default) refers main diagonal, positive value refers upper diagonal, negative value lower diagonal. dtype Data type returned tensor.","code":""},{"path":"https://keras.posit.co/reference/op_eye.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","text":"Tensor ones k-th diagonal zeros elsewhere.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_fft.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Fast Fourier Transform along last axis of input. — op_fft","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"Computes Fast Fourier Transform along last axis input.","code":""},{"path":"https://keras.posit.co/reference/op_fft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"","code":"op_fft(x)"},{"path":"https://keras.posit.co/reference/op_fft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"x list real imaginary parts input tensor. tensors provided floating type.","code":""},{"path":"https://keras.posit.co/reference/op_fft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"list containing two tensors - real imaginary parts output tensor.","code":""},{"path":"https://keras.posit.co/reference/op_fft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"","code":"x = c(op_array(c(1., 2.)),       op_array(c(0., 1.))) op_fft(x) ## [[1]] ## tf.Tensor([ 3. -1.], shape=(2), dtype=float32) ## ## [[2]] ## tf.Tensor([ 1. -1.], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_fft2.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"Computes 2D Fast Fourier Transform along last two axes input.","code":""},{"path":"https://keras.posit.co/reference/op_fft2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"","code":"op_fft2(x)"},{"path":"https://keras.posit.co/reference/op_fft2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"x list real imaginary parts input tensor. tensors provided floating type.","code":""},{"path":"https://keras.posit.co/reference/op_fft2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"list containing two tensors - real imaginary parts output.","code":""},{"path":"https://keras.posit.co/reference/op_fft2.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"","code":"x <- c(op_array(rbind(c(1, 2),                      c(2, 1))),        op_array(rbind(c(0, 1),                      c(1, 0)))) op_fft2(x) ## [[1]] ## tf.Tensor( ## [[ 6.  0.] ##  [ 0. -2.]], shape=(2, 2), dtype=float64) ## ## [[2]] ## tf.Tensor( ## [[ 2.  0.] ##  [ 0. -2.]], shape=(2, 2), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_flip.html","id":null,"dir":"Reference","previous_headings":"","what":"Reverse the order of elements in the tensor along the given axis. — op_flip","title":"Reverse the order of elements in the tensor along the given axis. — op_flip","text":"shape tensor preserved, elements reordered.","code":""},{"path":"https://keras.posit.co/reference/op_flip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reverse the order of elements in the tensor along the given axis. — op_flip","text":"","code":"op_flip(x, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_flip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reverse the order of elements in the tensor along the given axis. — op_flip","text":"x Input tensor. axis Axis axes along flip tensor. default, axis = NULL, flip axes input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_flip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reverse the order of elements in the tensor along the given axis. — op_flip","text":"Output tensor entries axis reversed.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_floor.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the floor of the input, element-wise. — op_floor","title":"Return the floor of the input, element-wise. — op_floor","text":"floor scalar x largest integer , <= x.","code":""},{"path":"https://keras.posit.co/reference/op_floor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the floor of the input, element-wise. — op_floor","text":"","code":"op_floor(x)"},{"path":"https://keras.posit.co/reference/op_floor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the floor of the input, element-wise. — op_floor","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_floor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the floor of the input, element-wise. — op_floor","text":"Output tensor, element-wise floor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_floor_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","title":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","text":"Note function automatically called using R operator %/% tensor.","code":"(x <- op_arange(10)) ## tf.Tensor([0. 1. 2. 3. 4. 5. 6. 7. 8. 9.], shape=(10), dtype=float32) op_floor_divide(x, 2) ## tf.Tensor([0. 0. 1. 1. 2. 2. 3. 3. 4. 4.], shape=(10), dtype=float32) x %/% 2 ## tf.Tensor([0. 0. 1. 1. 2. 2. 3. 3. 4. 4.], shape=(10), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_floor_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","text":"","code":"op_floor_divide(x1, x2)"},{"path":"https://keras.posit.co/reference/op_floor_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","text":"x1 Numerator. x2 Denominator.","code":""},{"path":"https://keras.posit.co/reference/op_floor_divide.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","text":"Output tensor, y <- floor(x1/x2)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":null,"dir":"Reference","previous_headings":"","what":"For loop implementation. — op_fori_loop","title":"For loop implementation. — op_fori_loop","text":"loop implementation.","code":""},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"For loop implementation. — op_fori_loop","text":"","code":"op_fori_loop(lower, upper, body_fun, init_val)"},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"For loop implementation. — op_fori_loop","text":"lower initial value loop variable. upper upper bound loop variable. body_fun callable represents loop body. Must take two arguments: loop variable loop state. loop state updated returned function. init_val initial value loop state.","code":""},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"For loop implementation. — op_fori_loop","text":"final state loop.","code":""},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"For loop implementation. — op_fori_loop","text":"","code":"lower <- 0L upper <- 10L body_fun <- function(i, state) state + i init_state <- 0L final_state <- op_fori_loop(lower, upper, body_fun, init_state) final_state ## tf.Tensor(45, shape=(), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_full.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a new tensor of given shape and type, filled with fill_value. — op_full","title":"Return a new tensor of given shape and type, filled with fill_value. — op_full","text":"Return new tensor given shape type, filled fill_value.","code":""},{"path":"https://keras.posit.co/reference/op_full.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a new tensor of given shape and type, filled with fill_value. — op_full","text":"","code":"op_full(shape, fill_value, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_full.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a new tensor of given shape and type, filled with fill_value. — op_full","text":"shape Shape new tensor. fill_value Fill value. dtype Desired data type tensor.","code":""},{"path":"https://keras.posit.co/reference/op_full.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a new tensor of given shape and type, filled with fill_value. — op_full","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_full_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","title":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","text":"Return full tensor shape type given tensor.","code":""},{"path":"https://keras.posit.co/reference/op_full_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","text":"","code":"op_full_like(x, fill_value, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_full_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","text":"x Input tensor. fill_value Fill value. dtype Overrides data type result.","code":""},{"path":"https://keras.posit.co/reference/op_full_like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","text":"Tensor fill_value shape type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_gelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"approximate TRUE, defined : f(x) = 0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * x^3))) approximate FALSE, defined : f(x) = x * P(X <= x) = 0.5 * x * (1 + erf(x / sqrt(2))), P(X) ~ N(0, 1).","code":""},{"path":"https://keras.posit.co/reference/op_gelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"","code":"op_gelu(x, approximate = TRUE)"},{"path":"https://keras.posit.co/reference/op_gelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"x Input tensor. approximate Approximate version GELU activation. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_gelu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_gelu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_gelu(x) ## tf.Tensor([-0.15880796  0.          0.841192  ], shape=(3), dtype=float32) op_gelu(x, FALSE) ## tf.Tensor([-0.15865526  0.          0.8413447 ], shape=(3), dtype=float32) x <- seq(-5, 5, .1) plot(x, op_gelu(x),      type = \"l\", #, frame.plot = FALSE,      panel.first = grid())"},{"path":[]},{"path":"https://keras.posit.co/reference/op_get_item.html","id":null,"dir":"Reference","previous_headings":"","what":"Return x[key]. — op_get_item","title":"Return x[key]. — op_get_item","text":"Return x[key].","code":""},{"path":"https://keras.posit.co/reference/op_get_item.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return x[key]. — op_get_item","text":"","code":"op_get_item(x, key)"},{"path":"https://keras.posit.co/reference/op_get_item.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return x[key]. — op_get_item","text":"x dictionary-like object key Generally, string, object __hash__ method acceptable.","code":""},{"path":"https://keras.posit.co/reference/op_get_item.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return x[key]. — op_get_item","text":"key.","code":""},{"path":"https://keras.posit.co/reference/op_get_item.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Return x[key]. — op_get_item","text":"Generally, calling x[[key]] x$key preferable.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_greater.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the truth value of x1 > x2 element-wise. — op_greater","title":"Return the truth value of x1 > x2 element-wise. — op_greater","text":"Note function automatically called using R operator > tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_greater(x, 2) ## tf.Tensor([False False False  True], shape=(4), dtype=bool) x > 2 ## tf.Tensor([False False False  True], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_greater.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the truth value of x1 > x2 element-wise. — op_greater","text":"","code":"op_greater(x1, x2)"},{"path":"https://keras.posit.co/reference/op_greater.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the truth value of x1 > x2 element-wise. — op_greater","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_greater.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the truth value of x1 > x2 element-wise. — op_greater","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_greater_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","title":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","text":"Note function automatically called using R operator >= tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_greater_equal(x, 2) ## tf.Tensor([False False  True  True], shape=(4), dtype=bool) x >= 2 ## tf.Tensor([False False  True  True], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_greater_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","text":"","code":"op_greater_equal(x1, x2)"},{"path":"https://keras.posit.co/reference/op_greater_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_greater_equal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Hard sigmoid activation function. — op_hard_sigmoid","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"defined : 0 x < -2.5, 1 x > 2.5, (0.2 * x) + 0.5 -2.5 <= x <= 2.5.","code":""},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"","code":"op_hard_sigmoid(x)"},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_hard_sigmoid(x) ## tf.Tensor([0.33333334 0.5        0.6666667 ], shape=(3), dtype=float32) x <- as.array(seq(-5, 5, .1)) plot(x, op_hard_sigmoid(x),      type = 'l', panel.first = grid(), frame.plot = FALSE)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"defined : 0 x < -3 x x > 3 x * (x + 3) / 6 -3 <= x <= 3 faster, piecewise linear approximation silu activation.","code":""},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"","code":"op_hard_silu(x)  op_hard_swish(x)"},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"","code":"x <- op_convert_to_tensor(c(-3.0, -1.0, 0.0, 1.0, 3.0)) op_hard_silu(x) ## tf.Tensor([-0.         -0.33333334  0.          0.6666667   3.        ], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_hstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Stack tensors in sequence horizontally (column wise). — op_hstack","title":"Stack tensors in sequence horizontally (column wise). — op_hstack","text":"equivalent concatenation along first axis 1-D tensors, along second axis tensors.","code":""},{"path":"https://keras.posit.co/reference/op_hstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stack tensors in sequence horizontally (column wise). — op_hstack","text":"","code":"op_hstack(xs)"},{"path":"https://keras.posit.co/reference/op_hstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stack tensors in sequence horizontally (column wise). — op_hstack","text":"xs Sequence tensors.","code":""},{"path":"https://keras.posit.co/reference/op_hstack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stack tensors in sequence horizontally (column wise). — op_hstack","text":"tensor formed stacking given tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the identity tensor. — op_identity","title":"Return the identity tensor. — op_identity","text":"identity tensor square tensor ones main diagonal zeros elsewhere.","code":""},{"path":"https://keras.posit.co/reference/op_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the identity tensor. — op_identity","text":"","code":"op_identity(n, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_identity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the identity tensor. — op_identity","text":"n Number rows (columns) n x n output tensor. dtype Data type output tensor.","code":""},{"path":"https://keras.posit.co/reference/op_identity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the identity tensor. — op_identity","text":"identity tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_imag.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the imaginary part of the complex argument. — op_imag","title":"Return the imaginary part of the complex argument. — op_imag","text":"Return imaginary part complex argument.","code":""},{"path":"https://keras.posit.co/reference/op_imag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the imaginary part of the complex argument. — op_imag","text":"","code":"op_imag(x)"},{"path":"https://keras.posit.co/reference/op_imag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the imaginary part of the complex argument. — op_imag","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_imag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the imaginary part of the complex argument. — op_imag","text":"imaginary component complex argument.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies the given transform(s) to the image(s). — op_image_affine_transform","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"Applies given transform(s) image(s).","code":""},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"","code":"op_image_affine_transform(   image,   transform,   interpolation = \"bilinear\",   fill_mode = \"constant\",   fill_value = 0L,   data_format = \"channels_last\" )"},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"image Input image batch images. Must 3D 4D. transform Projective transform matrix/matrices. vector length 8 tensor size N x 8. one row transform [a0, a1, a2, b0, b1, b2, c0, c1], maps output point (x, y) transformed input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k), k = c0 x + c1 y + 1. transform inverted compared transform mapping input points output points. Note gradients backpropagated transformation parameters. Note c0 c1 effective using TensorFlow backend considered 0 using backends. interpolation Interpolation method. Available methods \"nearest\", \"bilinear\". Defaults \"bilinear\". fill_mode Points outside boundaries input filled according given mode. Available methods \"constant\", \"nearest\", \"wrap\" \"reflect\". Defaults \"constant\". \"reflect\": (d c b | b c d | d c b ) input extended reflecting edge last pixel. \"constant\": (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k specified fill_value. \"wrap\": (b c d | b c d | b c d) input extended wrapping around opposite edge. \"nearest\": (| b c d | d d d d) input extended nearest pixel. fill_value Value used points outside boundaries input fill_mode = \"constant\". Defaults 0. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\".","code":""},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"Applied affine transform image batch images.","code":""},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"","code":"x <- random_uniform(c(2, 64, 80, 3)) # batch of 2 RGB images transform <- op_array(rbind(c(1.5, 0, -20, 0, 1.5, -16, 0, 0),  # zoom                            c(1, 0, -20, 0, 1, -16, 0, 0)))  # translation)) y <- op_image_affine_transform(x, transform) shape(y) ## shape(2, 64, 80, 3) # (2, 64, 80, 3) x <- random_uniform(c(64, 80, 3)) # single RGB image transform <- op_array(c(1.0, 0.5, -20, 0.5, 1.0, -16, 0, 0))  # shear y <- op_image_affine_transform(x, transform) shape(y) ## shape(64, 80, 3) # (64, 80, 3) x <- random_uniform(c(2, 3, 64, 80)) # batch of 2 RGB images transform <- op_array(rbind(   c(1.5, 0,-20, 0, 1.5,-16, 0, 0),  # zoom   c(1, 0,-20, 0, 1,-16, 0, 0)  # translation )) y <- op_image_affine_transform(x, transform, data_format = \"channels_first\") shape(y) ## shape(2, 3, 64, 80) # (2, 3, 64, 80)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":null,"dir":"Reference","previous_headings":"","what":"Crop images to a specified height and width. — op_image_crop","title":"Crop images to a specified height and width. — op_image_crop","text":"Crop images specified height width.","code":""},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Crop images to a specified height and width. — op_image_crop","text":"","code":"op_image_crop(   images,   top_cropping = NULL,   left_cropping = NULL,   target_height = NULL,   target_width = NULL,   bottom_cropping = NULL,   right_cropping = NULL )"},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Crop images to a specified height and width. — op_image_crop","text":"images 4-D batch images shape (batch, height, width, channels) 3-D single image shape (height, width, channels). top_cropping Number columns crop top. left_cropping Number columns crop left. target_height Height output images. target_width Width output images. bottom_cropping Number columns crop bottom. right_cropping Number columns crop right.","code":""},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Crop images to a specified height and width. — op_image_crop","text":"images 4D, 4D float Tensor shape (batch, target_height, target_width, channels) images 3D, 3D float Tensor shape (target_height, target_width, channels)","code":""},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Crop images to a specified height and width. — op_image_crop","text":"","code":"images <- op_reshape(op_arange(1, 28, dtype=\"float32\"), c(3, 3, 3)) images[, , 1] # print the first channel of the images  cropped_images <- op_image_crop(images, 0, 0, 2, 2) cropped_images[, , 1] # print the first channel of the cropped images"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":null,"dir":"Reference","previous_headings":"","what":"Extracts patches from the image(s). — op_image_extract_patches","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"Extracts patches image(s).","code":""},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"","code":"op_image_extract_patches(   image,   size,   strides = NULL,   dilation_rate = 1L,   padding = \"valid\",   data_format = \"channels_last\" )"},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"image Input image batch images. Must 3D 4D. size Patch size int list (patch_height, patch_width) strides strides along height width. specified, NULL, defaults value size. dilation_rate input stride, specifying far two consecutive patch samples input. value 1, strides must 1. NOTE: strides > 1 supported conjunction dilation_rate > 1 padding type padding algorithm use: \"\" \"valid\". data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\".","code":""},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"Extracted patches 3D (batched) 4D (batched)","code":""},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"","code":"image <- random_uniform(c(2, 20, 20, 3), dtype = \"float32\") # batch of 2 RGB images patches <- op_image_extract_patches(image, c(5, 5)) shape(patches) ## shape(2, 4, 4, 75) # (2, 4, 4, 75) image <- random_uniform(c(20, 20, 3), dtype = \"float32\") # 1 RGB image patches <- op_image_extract_patches(image, c(3, 3), c(1, 1)) shape(patches) ## shape(18, 18, 27) # (18, 18, 27)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_map_coordinates.html","id":null,"dir":"Reference","previous_headings":"","what":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","title":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","text":"Note interpolation near boundaries differs scipy function, fixed outstanding bug scipy/issues/2640.","code":""},{"path":"https://keras.posit.co/reference/op_image_map_coordinates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","text":"","code":"op_image_map_coordinates(   input,   coordinates,   order,   fill_mode = \"constant\",   fill_value = 0L )"},{"path":"https://keras.posit.co/reference/op_image_map_coordinates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","text":"input input array. coordinates coordinates input evaluated. order order spline interpolation. order must 0 1. 0 indicates nearest neighbor 1 indicates linear interpolation. fill_mode Points outside boundaries input filled according given mode. Available methods \"constant\", \"nearest\", \"wrap\" \"mirror\" \"reflect\". Defaults \"constant\". \"constant\": (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k specified fill_value. \"nearest\": (| b c d | d d d d) input extended nearest pixel. \"wrap\": (b c d | b c d | b c d) input extended wrapping around opposite edge. \"mirror\": (c d c b | b c d | c b b) input extended mirroring edge. \"reflect\": (d c b | b c d | d c b ) input extended reflecting edge last pixel. fill_value Value used points outside boundaries input fill_mode = \"constant\". Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/op_image_map_coordinates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","text":"Output image batch images.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":null,"dir":"Reference","previous_headings":"","what":"Pad images with zeros to the specified height and width. — op_image_pad","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"Pad images zeros specified height width.","code":""},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"","code":"op_image_pad(   images,   top_padding = NULL,   left_padding = NULL,   target_height = NULL,   target_width = NULL,   bottom_padding = NULL,   right_padding = NULL )"},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"images 4D Tensor shape (batch, height, width, channels) 3D Tensor shape (height, width, channels). top_padding Number rows zeros add top. left_padding Number columns zeros add left. target_height Height output images. target_width Width output images. bottom_padding Number rows zeros add bottom. right_padding Number columns zeros add right.","code":""},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"images 4D, 4D float Tensor shape (batch, target_height, target_width, channels) images 3D, 3D float Tensor shape (target_height, target_width, channels)","code":""},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"","code":"images <- random_uniform(c(15, 25, 3)) padded_images <- op_image_pad(     images, 2, 3, target_height = 20, target_width = 30 ) shape(padded_images) ## shape(20, 30, 3) batch_images <- random_uniform(c(2, 15, 25, 3)) padded_batch <- op_image_pad(batch_images, 2, 3,                              target_height = 20,                              target_width = 30) shape(padded_batch) ## shape(2, 20, 30, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":null,"dir":"Reference","previous_headings":"","what":"Resize images to size using the specified interpolation method. — op_image_resize","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"Resize images size using specified interpolation method.","code":""},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"","code":"op_image_resize(   image,   size,   interpolation = \"bilinear\",   antialias = FALSE,   crop_to_aspect_ratio = FALSE,   pad_to_aspect_ratio = FALSE,   fill_mode = \"constant\",   fill_value = 0,   data_format = \"channels_last\" )"},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"image Input image batch images. Must 3D 4D. size Size output image (height, width) format. interpolation Interpolation method. Available methods \"nearest\", \"bilinear\", \"bicubic\". Defaults \"bilinear\". antialias Whether use antialiasing filter downsampling image. Defaults FALSE. crop_to_aspect_ratio TRUE, resize images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image cropped return largest possible window image (size (height, width)) matches target aspect ratio. default (crop_to_aspect_ratio=FALSE), aspect ratio may preserved. pad_to_aspect_ratio TRUE, pad images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image evenly padded short side. fill_mode using pad_to_aspect_ratio=TRUE, padded areas filled according given mode. \"constant\" supported time (fill constant value, equal fill_value). fill_value Float. Padding value use pad_to_aspect_ratio=TRUE. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\".","code":""},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"Resized image batch images.","code":""},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"","code":"x <- random_uniform(c(2, 4, 4, 3)) # batch of 2 RGB images y <- op_image_resize(x, c(2, 2)) shape(y) ## shape(2, 2, 2, 3) x <- random_uniform(c(4, 4, 3)) # single RGB image y <- op_image_resize(x, c(2, 2)) shape(y) ## shape(2, 2, 3) x <- random_uniform(c(2, 3, 4, 4)) # batch of 2 RGB images y <- op_image_resize(x, c(2, 2), data_format = \"channels_first\") shape(y) ## shape(2, 3, 2, 2)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"function converts RGB images grayscale images. supports 3D 4D tensors, last dimension represents channels.","code":""},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"","code":"op_image_rgb_to_grayscale(image, data_format = \"channels_last\")"},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"image Input RGB image batch RGB images. Must 3D tensor shape (height, width, channels) 4D tensor shape (batch, height, width, channels). data_format string specifying data format input tensor. can either \"channels_last\" \"channels_first\". \"channels_last\" corresponds inputs shape (batch, height, width, channels), \"channels_first\" corresponds inputs shape (batch, channels, height, width). Defaults \"channels_last\".","code":""},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"Grayscale image batch grayscale images.","code":""},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"","code":"x <- random_uniform(c(2, 4, 4, 3)) y <- op_image_rgb_to_grayscale(x) shape(y) ## shape(2, 4, 4, 1) x <- random_uniform(c(4, 4, 3)) # Single RGB image y = op_image_rgb_to_grayscale(x) shape(y) ## shape(4, 4, 1) x <- random_uniform(c(2, 3, 4, 4)) y <- op_image_rgb_to_grayscale(x, data_format=\"channels_first\") shape(y) ## shape(2, 1, 4, 4)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if the targets are in the top-k predictions. — op_in_top_k","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"Checks targets top-k predictions.","code":""},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"","code":"op_in_top_k(targets, predictions, k)"},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"targets tensor true labels. predictions tensor predicted labels. k integer representing number predictions consider.","code":""},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"boolean tensor shape targets, element indicates whether corresponding target top-k predictions.","code":""},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"","code":"targets <- op_array(c(2, 5, 3), \"int32\") predictions <- op_array(dtype = \"float32\", rbind(   c(0.1, 0.4, 0.6, 0.9, 0.5),   c(0.1, 0.7, 0.9, 0.8, 0.3),   c(0.1, 0.6, 0.9, 0.9, 0.5) )) op_in_top_k(targets, predictions, k = 3L) ## tf.Tensor([ True False  True], shape=(3), dtype=bool)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_inv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the inverse of a square tensor. — op_inv","title":"Computes the inverse of a square tensor. — op_inv","text":"Computes inverse square tensor.","code":""},{"path":"https://keras.posit.co/reference/op_inv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the inverse of a square tensor. — op_inv","text":"","code":"op_inv(x)"},{"path":"https://keras.posit.co/reference/op_inv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the inverse of a square tensor. — op_inv","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_inv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the inverse of a square tensor. — op_inv","text":"tensor shape (..., M, M) representing inverse x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_irfft.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"Computes inverse 1D Discrete Fourier Transform real-valued signal inner-dimension input. inner-dimension input assumed result RFFT: fft_length / 2 + 1 unique components DFT real-valued signal. fft_length provided, computed size inner-dimension input (fft_length = 2 * (inner - 1)). FFT length used compute odd, provided since inferred properly. Along axis IRFFT computed , fft_length / 2 + 1 smaller corresponding dimension input, dimension cropped. larger, dimension padded zeros.","code":""},{"path":"https://keras.posit.co/reference/op_irfft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"","code":"op_irfft(x, fft_length = NULL)"},{"path":"https://keras.posit.co/reference/op_irfft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"x List real imaginary parts input tensor. tensors list floating type. fft_length integer representing number fft length. specified, inferred length last axis x. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_irfft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"tensor containing inverse real-valued Fast Fourier Transform along last axis x.","code":""},{"path":"https://keras.posit.co/reference/op_irfft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"","code":"real <- op_array(c(0, 1, 2, 3, 4)) imag <- op_array(c(0, 1, 2, 3, 4)) op_irfft(c(real, imag)) #> tf.Tensor( #> [ 2.         -2.0606601   0.5        -0.35355338  0.          0.06066012 #>  -0.5         0.35355338], shape=(8), dtype=float32) all.equal(op_irfft(op_rfft(real, 5), 5), real) #> [1] TRUE"},{"path":[]},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check whether the given object is a tensor. — op_is_tensor","title":"Check whether the given object is a tensor. — op_is_tensor","text":"Check whether given object tensor.","code":""},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check whether the given object is a tensor. — op_is_tensor","text":"","code":"op_is_tensor(x)"},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check whether the given object is a tensor. — op_is_tensor","text":"x variable.","code":""},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check whether the given object is a tensor. — op_is_tensor","text":"TRUE x tensor, otherwise FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Check whether the given object is a tensor. — op_is_tensor","text":"checks backend specific tensors passing TensorFlow tensor return FALSE backend PyTorch JAX.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_isclose.html","id":null,"dir":"Reference","previous_headings":"","what":"Return whether two tensors are element-wise almost equal. — op_isclose","title":"Return whether two tensors are element-wise almost equal. — op_isclose","text":"Return whether two tensors element-wise almost equal.","code":""},{"path":"https://keras.posit.co/reference/op_isclose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return whether two tensors are element-wise almost equal. — op_isclose","text":"","code":"op_isclose(x1, x2)"},{"path":"https://keras.posit.co/reference/op_isclose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return whether two tensors are element-wise almost equal. — op_isclose","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isclose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return whether two tensors are element-wise almost equal. — op_isclose","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_isfinite.html","id":null,"dir":"Reference","previous_headings":"","what":"Return whether a tensor is finite, element-wise. — op_isfinite","title":"Return whether a tensor is finite, element-wise. — op_isfinite","text":"Real values finite NaN, positive infinity, negative infinity. Complex values finite real imaginary parts finite.","code":""},{"path":"https://keras.posit.co/reference/op_isfinite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return whether a tensor is finite, element-wise. — op_isfinite","text":"","code":"op_isfinite(x)"},{"path":"https://keras.posit.co/reference/op_isfinite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return whether a tensor is finite, element-wise. — op_isfinite","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isfinite.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return whether a tensor is finite, element-wise. — op_isfinite","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_isinf.html","id":null,"dir":"Reference","previous_headings":"","what":"Test element-wise for positive or negative infinity. — op_isinf","title":"Test element-wise for positive or negative infinity. — op_isinf","text":"Test element-wise positive negative infinity.","code":""},{"path":"https://keras.posit.co/reference/op_isinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test element-wise for positive or negative infinity. — op_isinf","text":"","code":"op_isinf(x)"},{"path":"https://keras.posit.co/reference/op_isinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test element-wise for positive or negative infinity. — op_isinf","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isinf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test element-wise for positive or negative infinity. — op_isinf","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_isnan.html","id":null,"dir":"Reference","previous_headings":"","what":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","title":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","text":"Test element-wise NaN return result boolean tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isnan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","text":"","code":"op_isnan(x)"},{"path":"https://keras.posit.co/reference/op_isnan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isnan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_istft.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"reconstruct original waveform, parameters stft.","code":""},{"path":"https://keras.posit.co/reference/op_istft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"","code":"op_istft(   x,   sequence_length,   sequence_stride,   fft_length,   length = NULL,   window = \"hann\",   center = TRUE )"},{"path":"https://keras.posit.co/reference/op_istft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"x Tuple real imaginary parts input tensor. tensors list floating type. sequence_length integer representing sequence length. sequence_stride integer representing sequence hop size. fft_length integer representing size FFT produced stft. length integer representing output clipped exactly length. specified, padding clipping take place. Defaults NULL. window string, tensor window NULL. window string, available values \"hann\" \"hamming\". window tensor, used directly window length must sequence_length. window NULL, windowing used. Defaults \"hann\". center Whether x padded sides t-th sequence centered time t * sequence_stride. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_istft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"tensor containing inverse Short-Time Fourier Transform along last axis x.","code":""},{"path":"https://keras.posit.co/reference/op_istft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"","code":"x <- op_convert_to_tensor(c(0, 1, 2, 3, 4)) op_istft(op_stft(x, 1, 1, 1), 1, 1, 1) ## tf.Tensor([], shape=(0), dtype=float32) # array([0.0, 1.0, 2.0, 3.0, 4.0])"},{"path":[]},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"allows small gradient unit active, defined : f(x) = alpha * x x < 0 f(x) = x x >= 0.","code":""},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"","code":"op_leaky_relu(x, negative_slope = 0.2)"},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"x Input tensor. negative_slope Slope activation function x < 0. Defaults 0.2.","code":""},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_leaky_relu(x) ## tf.Tensor([-0.2  0.   1. ], shape=(3), dtype=float32) # array([-0.2,  0. ,  1. ], shape=(3,), dtype=float64) x <- seq(-5, 5, .1) plot(x, op_leaky_relu(x),      type = 'l', panel.first = grid())"},{"path":[]},{"path":"https://keras.posit.co/reference/op_less.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the truth value of x1 < x2 element-wise. — op_less","title":"Return the truth value of x1 < x2 element-wise. — op_less","text":"Note function automatically called using R operator < tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_less(x, 2) ## tf.Tensor([ True  True False False], shape=(4), dtype=bool) x < 2 ## tf.Tensor([ True  True False False], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_less.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the truth value of x1 < x2 element-wise. — op_less","text":"","code":"op_less(x1, x2)"},{"path":"https://keras.posit.co/reference/op_less.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the truth value of x1 < x2 element-wise. — op_less","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_less.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the truth value of x1 < x2 element-wise. — op_less","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_less_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","title":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","text":"Note function automatically called using R operator <= tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_less_equal(x, 2) ## tf.Tensor([ True  True  True False], shape=(4), dtype=bool) x <= 2 ## tf.Tensor([ True  True  True False], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_less_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","text":"","code":"op_less_equal(x1, x2)"},{"path":"https://keras.posit.co/reference/op_less_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_less_equal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_linspace.html","id":null,"dir":"Reference","previous_headings":"","what":"Return evenly spaced numbers over a specified interval. — op_linspace","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"Returns num evenly spaced samples, calculated interval [start, stop]. endpoint interval can optionally excluded.","code":""},{"path":"https://keras.posit.co/reference/op_linspace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"","code":"op_linspace(   start,   stop,   num = 50L,   endpoint = TRUE,   retstep = FALSE,   dtype = NULL,   axis = 1L )"},{"path":"https://keras.posit.co/reference/op_linspace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"start starting value sequence. stop end value sequence, unless endpoint set FALSE. case, sequence consists last num + 1 evenly spaced samples, stop excluded. Note step size changes endpoint FALSE. num Number samples generate. Defaults 50. Must non-negative. endpoint TRUE, stop last sample. Otherwise, included. Defaults TRUE. retstep TRUE, return (samples, step), step spacing samples. dtype type output tensor. axis axis result store samples. Relevant start stop array-like. Defaults 1, first axis.","code":""},{"path":"https://keras.posit.co/reference/op_linspace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"tensor evenly spaced numbers. retstep TRUE, returns (samples, step)","code":""},{"path":"https://keras.posit.co/reference/op_linspace.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"Torch backend support axis argument.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log.html","id":null,"dir":"Reference","previous_headings":"","what":"Natural logarithm, element-wise. — op_log","title":"Natural logarithm, element-wise. — op_log","text":"Natural logarithm, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_log.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Natural logarithm, element-wise. — op_log","text":"","code":"op_log(x)"},{"path":"https://keras.posit.co/reference/op_log.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Natural logarithm, element-wise. — op_log","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Natural logarithm, element-wise. — op_log","text":"Output tensor, element-wise natural logarithm x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log10.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","title":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","text":"Return base 10 logarithm input tensor, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_log10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","text":"","code":"op_log10(x)"},{"path":"https://keras.posit.co/reference/op_log10.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log10.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","text":"Output tensor, element-wise base 10 logarithm x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log1p.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","title":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","text":"Calculates log(1 + x).","code":""},{"path":"https://keras.posit.co/reference/op_log1p.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","text":"","code":"op_log1p(x)"},{"path":"https://keras.posit.co/reference/op_log1p.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log1p.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","text":"Output tensor, element-wise natural logarithm 1 + x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log2.html","id":null,"dir":"Reference","previous_headings":"","what":"Base-2 logarithm of x, element-wise. — op_log2","title":"Base-2 logarithm of x, element-wise. — op_log2","text":"Base-2 logarithm x, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_log2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Base-2 logarithm of x, element-wise. — op_log2","text":"","code":"op_log2(x)"},{"path":"https://keras.posit.co/reference/op_log2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Base-2 logarithm of x, element-wise. — op_log2","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Base-2 logarithm of x, element-wise. — op_log2","text":"Output tensor, element-wise base-2 logarithm x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Logarithm of the sigmoid activation function. — op_log_sigmoid","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"defined f(x) = log(1 / (1 + exp(-x))).","code":""},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"","code":"op_log_sigmoid(x)"},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"","code":"x <- op_convert_to_tensor(c(-0.541391, 0.0, 0.50, 5.0)) op_log_sigmoid(x) ## tf.Tensor([-1.0000418  -0.6931472  -0.474077   -0.00671535], shape=(4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Log-softmax activation function. — op_log_softmax","title":"Log-softmax activation function. — op_log_softmax","text":"defined : f(x) = x - max(x) - log(sum(exp(x - max(x))))","code":""},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log-softmax activation function. — op_log_softmax","text":"","code":"op_log_softmax(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log-softmax activation function. — op_log_softmax","text":"x Input tensor. axis Integer, axis along log-softmax applied. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Log-softmax activation function. — op_log_softmax","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Log-softmax activation function. — op_log_softmax","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_log_softmax(x) ## tf.Tensor([-2.407606   -1.4076059  -0.40760595], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_logaddexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","title":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","text":"Calculates log(exp(x1) + exp(x2)).","code":""},{"path":"https://keras.posit.co/reference/op_logaddexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","text":"","code":"op_logaddexp(x1, x2)"},{"path":"https://keras.posit.co/reference/op_logaddexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","text":"x1 Input tensor. x2 Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logaddexp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","text":"Output tensor, element-wise logarithm sum exponentiations inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"Zeros treated FALSE non-zeros treated TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"","code":"op_logical_and(x1, x2)"},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"x1 Input tensor. x2 Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"Output tensor, element-wise logical inputs.","code":""},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"Note function automatically called using R operator & tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logical_not.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the element-wise NOT of the given input tensor. — op_logical_not","title":"Computes the element-wise NOT of the given input tensor. — op_logical_not","text":"Zeros treated FALSE non-zeros treated TRUE. Note function automatically called using R operator ! tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_not.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the element-wise NOT of the given input tensor. — op_logical_not","text":"","code":"op_logical_not(x)"},{"path":"https://keras.posit.co/reference/op_logical_not.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the element-wise NOT of the given input tensor. — op_logical_not","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_not.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the element-wise NOT of the given input tensor. — op_logical_not","text":"Output tensor, element-wise logical input.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logical_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","title":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","text":"Zeros treated FALSE non-zeros treated TRUE. Note function automatically called using R operator | tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","text":"","code":"op_logical_or(x1, x2)"},{"path":"https://keras.posit.co/reference/op_logical_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","text":"x1 Input tensor. x2 Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_or.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","text":"Output tensor, element-wise logical inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logical_xor.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","title":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","text":"Compute truth value x1 XOR x2, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_logical_xor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","text":"","code":"op_logical_xor(x1, x2)"},{"path":"https://keras.posit.co/reference/op_logical_xor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_xor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logspace.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns numbers spaced evenly on a log scale. — op_logspace","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"linear space, sequence starts base ** start ends base ** stop (see endpoint ).","code":""},{"path":"https://keras.posit.co/reference/op_logspace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"","code":"op_logspace(   start,   stop,   num = 50L,   endpoint = TRUE,   base = 10L,   dtype = NULL,   axis = 1L )"},{"path":"https://keras.posit.co/reference/op_logspace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"start starting value sequence. stop final value sequence, unless endpoint FALSE. case, num + 1 values spaced interval log-space, last (sequence length num) returned. num Number samples generate. Defaults 50. endpoint TRUE, stop last sample. Otherwise, included. Defaults TRUE. base base log space. Defaults 10. dtype type output tensor. axis axis result store samples. Relevant start stop array-like.","code":""},{"path":"https://keras.posit.co/reference/op_logspace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"tensor evenly spaced samples log scale.","code":""},{"path":"https://keras.posit.co/reference/op_logspace.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"Torch backend support axis argument.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"Computes logarithm sum exponentials elements tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"","code":"op_logsumexp(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"x Input tensor. axis integer list integers specifying axis/axes along compute sum. NULL, sum computed elements. Defaults toNULL. keepdims boolean indicating whether keep dimensions input tensor computing sum. Defaults toFALSE.","code":""},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"tensor containing logarithm sum exponentials elements x.","code":""},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"","code":"x <- op_convert_to_tensor(c(1, 2, 3)) op_logsumexp(x) ## tf.Tensor(3.407606, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_lu_factor.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","title":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","text":"Computes lower-upper decomposition square matrix.","code":""},{"path":"https://keras.posit.co/reference/op_lu_factor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","text":"","code":"op_lu_factor(x)"},{"path":"https://keras.posit.co/reference/op_lu_factor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","text":"x tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_lu_factor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","text":"tuple two tensors: tensor shape (..., M, M) containing lower upper triangular matrices tensor shape (..., M) containing pivots.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_matmul.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix product of two tensors. — op_matmul","title":"Matrix product of two tensors. — op_matmul","text":"tensors 1-dimensional, dot product (scalar) returned. either tensor N-D, N > 2, treated stack matrices residing last two indexes broadcast accordingly. first tensor 1-D, promoted matrix prepending 1 dimensions. matrix multiplication prepended 1 removed. second tensor 1-D, promoted matrix appending 1 dimensions. matrix multiplication appended 1 removed.","code":""},{"path":"https://keras.posit.co/reference/op_matmul.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix product of two tensors. — op_matmul","text":"","code":"op_matmul(x1, x2)"},{"path":"https://keras.posit.co/reference/op_matmul.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix product of two tensors. — op_matmul","text":"x1 First tensor. x2 Second tensor.","code":""},{"path":"https://keras.posit.co/reference/op_matmul.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matrix product of two tensors. — op_matmul","text":"Output tensor, matrix product inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_max.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the maximum of a tensor or maximum along an axis. — op_max","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"Return maximum tensor maximum along axis.","code":""},{"path":"https://keras.posit.co/reference/op_max.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"","code":"op_max(x, axis = NULL, keepdims = FALSE, initial = NULL)"},{"path":"https://keras.posit.co/reference/op_max.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"x Input tensor. axis Axis axes along operate. default, flattened input used. keepdims set TRUE, axes reduced left result dimensions size one. Defaults FALSE. initial minimum value output element. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_max.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"Maximum x.","code":""},{"path":"https://keras.posit.co/reference/op_max.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"","code":"(x <- op_convert_to_tensor(rbind(c(1, 3, 5), c(1, 5, 2)))) ## tf.Tensor( ## [[1. 3. 5.] ##  [1. 5. 2.]], shape=(2, 3), dtype=float64) op_max(x) ## tf.Tensor(5.0, shape=(), dtype=float64) op_max(x, axis = 1) ## tf.Tensor([1. 5. 5.], shape=(3), dtype=float64) op_max(x, axis = 1, keepdims = TRUE) ## tf.Tensor([[1. 5. 5.]], shape=(1, 3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_max_pool.html","id":null,"dir":"Reference","previous_headings":"","what":"Max pooling operation. — op_max_pool","title":"Max pooling operation. — op_max_pool","text":"Max pooling operation.","code":""},{"path":"https://keras.posit.co/reference/op_max_pool.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max pooling operation. — op_max_pool","text":"","code":"op_max_pool(   inputs,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL )"},{"path":"https://keras.posit.co/reference/op_max_pool.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max pooling operation. — op_max_pool","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". Pooling happens spatial dimensions . pool_size int tuple/list integers size len(inputs_spatial_shape), specifying size pooling window spatial dimension input tensor. pool_size int, every spatial dimension shares pool_size. strides int tuple/list integers size len(inputs_spatial_shape). stride sliding window spatial dimension input tensor. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...).","code":""},{"path":"https://keras.posit.co/reference/op_max_pool.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Max pooling operation. — op_max_pool","text":"tensor rank N+2, result max pooling operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_maximum.html","id":null,"dir":"Reference","previous_headings":"","what":"Element-wise maximum of x1 and x2. — op_maximum","title":"Element-wise maximum of x1 and x2. — op_maximum","text":"Element-wise maximum x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_maximum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Element-wise maximum of x1 and x2. — op_maximum","text":"","code":"op_maximum(x1, x2)  op_pmax(x1, x2)"},{"path":"https://keras.posit.co/reference/op_maximum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Element-wise maximum of x1 and x2. — op_maximum","text":"x1 First tensor. x2 Second tensor.","code":""},{"path":"https://keras.posit.co/reference/op_maximum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Element-wise maximum of x1 and x2. — op_maximum","text":"Output tensor, element-wise maximum x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the arithmetic mean along the specified axes. — op_mean","title":"Compute the arithmetic mean along the specified axes. — op_mean","text":"Compute arithmetic mean along specified axes.","code":""},{"path":"https://keras.posit.co/reference/op_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the arithmetic mean along the specified axes. — op_mean","text":"","code":"op_mean(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the arithmetic mean along the specified axes. — op_mean","text":"x Input tensor. axis Axis axes along means computed. default compute mean flattened tensor. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the arithmetic mean along the specified axes. — op_mean","text":"Output tensor containing mean values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_median.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the median along the specified axis. — op_median","title":"Compute the median along the specified axis. — op_median","text":"Compute median along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_median.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the median along the specified axis. — op_median","text":"","code":"op_median(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_median.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the median along the specified axis. — op_median","text":"x Input tensor. axis Axis axes along medians computed. Defaults axis = NULL compute median(s) along flattened version array. keepdims set TRUE, axes reduce left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_median.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the median along the specified axis. — op_median","text":"output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"Given N 1-D tensors T0, T1, ..., TN-1 inputs corresponding lengths S0, S1, ..., SN-1, creates N N-dimensional tensors G0, G1, ..., GN-1 shape (S0, ..., SN-1) output Gi constructed expanding Ti result shape.","code":""},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"","code":"op_meshgrid(..., indexing = \"xy\")"},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"... 1-D tensors representing coordinates grid. indexing \"xy\" \"ij\". \"xy\" cartesian; \"ij\" matrix indexing output. Defaults \"xy\".","code":""},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"Sequence N tensors.","code":""},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"","code":"x <- op_array(c(1, 2, 3), \"int32\") y <- op_array(c(4, 5, 6), \"int32\") c(grid_x, grid_y) %<-% op_meshgrid(x, y, indexing = \"ij\") grid_x ## tf.Tensor( ## [[1 1 1] ##  [2 2 2] ##  [3 3 3]], shape=(3, 3), dtype=int32) # array([[1, 1, 1], #        [2, 2, 2], #        [3, 3, 3])) grid_y ## tf.Tensor( ## [[4 5 6] ##  [4 5 6] ##  [4 5 6]], shape=(3, 3), dtype=int32) # array([[4, 5, 6], #        [4, 5, 6], #        [4, 5, 6]))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_min.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the minimum of a tensor or minimum along an axis. — op_min","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"Return minimum tensor minimum along axis.","code":""},{"path":"https://keras.posit.co/reference/op_min.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"","code":"op_min(x, axis = NULL, keepdims = FALSE, initial = NULL)"},{"path":"https://keras.posit.co/reference/op_min.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"x Input tensor. axis Axis axes along operate. default, flattened input used. keepdims set TRUE, axes reduced left result dimensions size one. Defaults FALSE. initial maximum value output element. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_min.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"Minimum x.","code":""},{"path":"https://keras.posit.co/reference/op_min.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"","code":"(x <- op_convert_to_tensor(rbind(c(1, 3, 5), c(1, 5, 2)))) ## tf.Tensor( ## [[1. 3. 5.] ##  [1. 5. 2.]], shape=(2, 3), dtype=float64) op_min(x) ## tf.Tensor(1.0, shape=(), dtype=float64) op_min(x, axis = 1) ## tf.Tensor([1. 3. 2.], shape=(3), dtype=float64) op_min(x, axis = 1, keepdims = TRUE) ## tf.Tensor([[1. 3. 2.]], shape=(1, 3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_minimum.html","id":null,"dir":"Reference","previous_headings":"","what":"Element-wise minimum of x1 and x2. — op_minimum","title":"Element-wise minimum of x1 and x2. — op_minimum","text":"Element-wise minimum x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_minimum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Element-wise minimum of x1 and x2. — op_minimum","text":"","code":"op_minimum(x1, x2)  op_pmin(x1, x2)"},{"path":"https://keras.posit.co/reference/op_minimum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Element-wise minimum of x1 and x2. — op_minimum","text":"x1 First tensor. x2 Second tensor.","code":""},{"path":"https://keras.posit.co/reference/op_minimum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Element-wise minimum of x1 and x2. — op_minimum","text":"Output tensor, element-wise minimum x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_mod.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the element-wise remainder of division. — op_mod","title":"Returns the element-wise remainder of division. — op_mod","text":"Note function automatically called using R operator %% tensor.","code":"(x <- op_arange(10)) ## tf.Tensor([0. 1. 2. 3. 4. 5. 6. 7. 8. 9.], shape=(10), dtype=float32) op_mod(x, 3) ## tf.Tensor([0. 1. 2. 0. 1. 2. 0. 1. 2. 0.], shape=(10), dtype=float32) x %% 3 ## tf.Tensor([0. 1. 2. 0. 1. 2. 0. 1. 2. 0.], shape=(10), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_mod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the element-wise remainder of division. — op_mod","text":"","code":"op_mod(x1, x2)"},{"path":"https://keras.posit.co/reference/op_mod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the element-wise remainder of division. — op_mod","text":"x1 First tensor. x2 Second tensor.","code":""},{"path":"https://keras.posit.co/reference/op_mod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the element-wise remainder of division. — op_mod","text":"Output tensor, element-wise remainder division.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_moments.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the mean and variance of x. — op_moments","title":"Calculates the mean and variance of x. — op_moments","text":"mean variance calculated aggregating contents x across axes. x 1-D axes = c(1) just mean variance vector.","code":""},{"path":"https://keras.posit.co/reference/op_moments.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the mean and variance of x. — op_moments","text":"","code":"op_moments(x, axes, keepdims = FALSE, synchronized = FALSE)"},{"path":"https://keras.posit.co/reference/op_moments.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the mean and variance of x. — op_moments","text":"x Input tensor. axes list axes compute mean variance. keepdims set TRUE, axes reduced left result dimensions size one. synchronized applicable TensorFlow backend. TRUE, synchronizes global batch statistics (mean variance) across devices training step distributed training strategy. FALSE, replica uses local batch statistics.","code":""},{"path":"https://keras.posit.co/reference/op_moments.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the mean and variance of x. — op_moments","text":"list containing two tensors - mean variance.","code":""},{"path":"https://keras.posit.co/reference/op_moments.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculates the mean and variance of x. — op_moments","text":"","code":"x <- op_convert_to_tensor(c(0, 1, 2, 3, 100), dtype = \"float32\") op_moments(x, axes = c(1)) ## [[1]] ## tf.Tensor(21.2, shape=(), dtype=float32) ## ## [[2]] ## tf.Tensor(1553.3601, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_moveaxis.html","id":null,"dir":"Reference","previous_headings":"","what":"Move axes of a tensor to new positions. — op_moveaxis","title":"Move axes of a tensor to new positions. — op_moveaxis","text":"axes remain original order.","code":""},{"path":"https://keras.posit.co/reference/op_moveaxis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Move axes of a tensor to new positions. — op_moveaxis","text":"","code":"op_moveaxis(x, source, destination)"},{"path":"https://keras.posit.co/reference/op_moveaxis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Move axes of a tensor to new positions. — op_moveaxis","text":"x Tensor whose axes reordered. source Original positions axes move. must unique. destination Destinations positions original axes. must also unique.","code":""},{"path":"https://keras.posit.co/reference/op_moveaxis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Move axes of a tensor to new positions. — op_moveaxis","text":"Tensor moved axes.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":null,"dir":"Reference","previous_headings":"","what":"Encodes integer labels as multi-hot vectors. — op_multi_hot","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"function encodes integer labels multi-hot vectors, label mapped binary value resulting vector.","code":""},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"","code":"op_multi_hot(   inputs,   num_classes,   axis = -1L,   dtype = NULL,   sparse = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"inputs Tensor integer labels converted multi-hot vectors. num_classes Integer, total number unique classes. axis (optional) Axis along multi-hot encoding added. Defaults -1, corresponds last dimension. dtype (optional) data type resulting tensor. Default backend's float type. sparse Whether return sparse tensor; backends support sparse tensors. ... forward/backwards compatability","code":""},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"Tensor: multi-hot encoded tensor.","code":""},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"","code":"data <- op_convert_to_tensor(c(0, 4)) op_multi_hot(data, num_classes = 5) ## tf.Tensor([1. 0. 0. 0. 1.], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_multiply.html","id":null,"dir":"Reference","previous_headings":"","what":"Multiply arguments element-wise. — op_multiply","title":"Multiply arguments element-wise. — op_multiply","text":"Note function automatically called using R operator * tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_multiply(x, x) ## tf.Tensor([0. 1. 4. 9.], shape=(4), dtype=float32) x * x ## tf.Tensor([0. 1. 4. 9.], shape=(4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_multiply.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multiply arguments element-wise. — op_multiply","text":"","code":"op_multiply(x1, x2)"},{"path":"https://keras.posit.co/reference/op_multiply.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multiply arguments element-wise. — op_multiply","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_multiply.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multiply arguments element-wise. — op_multiply","text":"Output tensor, element-wise product x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"Replace NaN zero infinity large finite numbers.","code":""},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"","code":"op_nan_to_num(x, nan = 0, posinf = NULL, neginf = NULL)"},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"x Input data. nan Optional float int. Value replace NaN entries . posinf Optional float int. Value replace positive infinity . neginf Optional float int. Value replace negative infinity .","code":""},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"x, non-finite values replaced.","code":""},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"","code":"(x <- op_convert_to_tensor(c(1, NaN, -Inf, Inf))) ## tf.Tensor([  1.  nan -inf  inf], shape=(4), dtype=float32) op_nan_to_num(x) ## tf.Tensor([ 1.0000000e+00  0.0000000e+00 -3.4028235e+38  3.4028235e+38], shape=(4), dtype=float32) op_nan_to_num(x, nan = -1, posinf = 2, neginf = -2) ## tf.Tensor([ 1. -1. -2.  2.], shape=(4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_ndim.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the number of dimensions of a tensor. — op_ndim","title":"Return the number of dimensions of a tensor. — op_ndim","text":"Return number dimensions tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ndim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the number of dimensions of a tensor. — op_ndim","text":"","code":"op_ndim(x)"},{"path":"https://keras.posit.co/reference/op_ndim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the number of dimensions of a tensor. — op_ndim","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ndim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the number of dimensions of a tensor. — op_ndim","text":"number dimensions x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_negative.html","id":null,"dir":"Reference","previous_headings":"","what":"Numerical negative, element-wise. — op_negative","title":"Numerical negative, element-wise. — op_negative","text":"Note function automatically called using unary R operator - tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_negative(x) ## tf.Tensor([-0. -1. -2. -3.], shape=(4), dtype=float32) -x ## tf.Tensor([-0. -1. -2. -3.], shape=(4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_negative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Numerical negative, element-wise. — op_negative","text":"","code":"op_negative(x)"},{"path":"https://keras.posit.co/reference/op_negative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Numerical negative, element-wise. — op_negative","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_negative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Numerical negative, element-wise. — op_negative","text":"Output tensor, y = -x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the indices of the elements that are non-zero. — op_nonzero","title":"Return the indices of the elements that are non-zero. — op_nonzero","text":"Return indices elements non-zero.","code":""},{"path":"https://keras.posit.co/reference/op_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the indices of the elements that are non-zero. — op_nonzero","text":"","code":"op_nonzero(x)"},{"path":"https://keras.posit.co/reference/op_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the indices of the elements that are non-zero. — op_nonzero","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_nonzero.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the indices of the elements that are non-zero. — op_nonzero","text":"Indices elements non-zero.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix or vector norm. — op_norm","title":"Matrix or vector norm. — op_norm","text":"function able return one eight different matrix norms, one infinite number vector norms (described ), depending value ord parameter.","code":""},{"path":"https://keras.posit.co/reference/op_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix or vector norm. — op_norm","text":"","code":"op_norm(x, ord = NULL, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix or vector norm. — op_norm","text":"x Input tensor. ord Order norm (see table Notes). default NULL. axis axis integer, specifies axis x along compute vector norms. axis length 2 vector, specifies axes hold 2-D matrices, matrix norms matrices computed. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matrix or vector norm. — op_norm","text":"Norm matrix vector(s).","code":""},{"path":"https://keras.posit.co/reference/op_norm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Matrix or vector norm. — op_norm","text":"values ord < 1, result , strictly speaking, mathematical 'norm', may still useful various numerical purposes. following norms can calculated: matrices: ord=NULL: Frobenius norm ord=\"fro\": Frobenius norm ord=\"nuc\": nuclear norm ord=Inf: max(sum(abs(x), axis=2)) ord=-Inf: min(sum(abs(x), axis=2)) ord=0: supported ord=1: max(sum(abs(x), axis=1)) ord=-1: min(sum(abs(x), axis=1)) ord=2: 2-norm (largest sing. value) ord=-2: smallest singular value : supported vectors: ord=NULL: 2-norm ord=\"fro\": supported ord=\"nuc\": supported ord=Inf: max(abs(x)) ord=-Inf: min(abs(x)) ord=0: sum(x != 0) ord=1: ord=-1: ord=2: ord=-2: : sum(abs(x)^ord)^(1/ord)","code":""},{"path":"https://keras.posit.co/reference/op_norm.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrix or vector norm. — op_norm","text":"","code":"x <- op_reshape(op_arange(9, dtype=\"float32\") - 4, c(3, 3)) op_norm(x) ## tf.Tensor(7.745967, shape=(), dtype=float32) # 7.7459664"},{"path":[]},{"path":"https://keras.posit.co/reference/op_normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalizes x over the specified axis. — op_normalize","title":"Normalizes x over the specified axis. — op_normalize","text":"defined : normalize(x) = x / max(norm(x), epsilon).","code":""},{"path":"https://keras.posit.co/reference/op_normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalizes x over the specified axis. — op_normalize","text":"","code":"op_normalize(x, axis = -1L, order = 2L)"},{"path":"https://keras.posit.co/reference/op_normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalizes x over the specified axis. — op_normalize","text":"x Input tensor. axis axis axes along perform normalization. Default -1. order exponent value norm formulation. Defaults 2.","code":""},{"path":"https://keras.posit.co/reference/op_normalize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalizes x over the specified axis. — op_normalize","text":"normalized array.","code":""},{"path":"https://keras.posit.co/reference/op_normalize.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalizes x over the specified axis. — op_normalize","text":"","code":"x <- op_convert_to_tensor(rbind(c(1, 2, 3), c(4, 5, 6))) x_norm <- op_normalize(x) x_norm ## tf.Tensor( ## [[0.26726124 0.53452248 0.80178373] ##  [0.45584231 0.56980288 0.68376346]], shape=(2, 3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_not_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return (x1 != x2) element-wise. — op_not_equal","title":"Return (x1 != x2) element-wise. — op_not_equal","text":"Note function automatically called using R operator != tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_not_equal(x, 2) ## tf.Tensor([ True  True False  True], shape=(4), dtype=bool) x != 2 ## tf.Tensor([ True  True False  True], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_not_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return (x1 != x2) element-wise. — op_not_equal","text":"","code":"op_not_equal(x1, x2)"},{"path":"https://keras.posit.co/reference/op_not_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return (x1 != x2) element-wise. — op_not_equal","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_not_equal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return (x1 != x2) element-wise. — op_not_equal","text":"Output tensor, element-wise comparsion x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts integer tensor x into a one-hot tensor. — op_one_hot","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"one-hot encoding representation integer value converted binary vector length equal num_classes, index corresponding integer value marked 1, indices marked 0.","code":""},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"","code":"op_one_hot(x, num_classes, axis = -1L, dtype = NULL, sparse = FALSE)"},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"x Integer tensor encoded. shape can arbitrary, dtype integer. R factors coerced integer offset 0-based, .e., .integer(x) - 1L. num_classes Number classes one-hot encoding. axis Axis along encoding performed. Defaults -1, represents last axis. dtype (Optional) Data type output tensor. provided, defaults default data type backend. sparse Whether return sparse tensor; backends support sparse tensors.","code":""},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"Integer tensor: One-hot encoded tensor shape x except specified axis dimension, length num_classes. dtype output tensor determined dtype default data type backend.","code":""},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"","code":"x <- op_array(c(1, 3, 2, 0), \"int32\") op_one_hot(x, num_classes = 4) ## tf.Tensor( ## [[0. 1. 0. 0.] ##  [0. 0. 0. 1.] ##  [0. 0. 1. 0.] ##  [1. 0. 0. 0.]], shape=(4, 4), dtype=float32) # array([[0. 1. 0. 0.] #        [0. 0. 0. 1.] #        [0. 0. 1. 0.] #        [1. 0. 0. 0.]], shape=(4, 4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_ones.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a new tensor of given shape and type, filled with ones. — op_ones","title":"Return a new tensor of given shape and type, filled with ones. — op_ones","text":"Return new tensor given shape type, filled ones.","code":""},{"path":"https://keras.posit.co/reference/op_ones.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a new tensor of given shape and type, filled with ones. — op_ones","text":"","code":"op_ones(shape, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_ones.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a new tensor of given shape and type, filled with ones. — op_ones","text":"shape Shape new tensor. dtype Desired data type tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ones.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a new tensor of given shape and type, filled with ones. — op_ones","text":"Tensor ones given shape dtype.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_ones_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a tensor of ones with the same shape and type of x. — op_ones_like","title":"Return a tensor of ones with the same shape and type of x. — op_ones_like","text":"Return tensor ones shape type x.","code":""},{"path":"https://keras.posit.co/reference/op_ones_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a tensor of ones with the same shape and type of x. — op_ones_like","text":"","code":"op_ones_like(x, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_ones_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a tensor of ones with the same shape and type of x. — op_ones_like","text":"x Input tensor. dtype Overrides data type result.","code":""},{"path":"https://keras.posit.co/reference/op_ones_like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a tensor of ones with the same shape and type of x. — op_ones_like","text":"tensor ones shape type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_outer.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the outer product of two vectors. — op_outer","title":"Compute the outer product of two vectors. — op_outer","text":"Given two vectors x1 x2, outer product :","code":"out[i, j] = x1[i] * x2[j]"},{"path":"https://keras.posit.co/reference/op_outer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the outer product of two vectors. — op_outer","text":"","code":"op_outer(x1, x2)"},{"path":"https://keras.posit.co/reference/op_outer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the outer product of two vectors. — op_outer","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_outer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the outer product of two vectors. — op_outer","text":"Outer product x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_pad.html","id":null,"dir":"Reference","previous_headings":"","what":"Pad a tensor. — op_pad","title":"Pad a tensor. — op_pad","text":"Pad tensor.","code":""},{"path":"https://keras.posit.co/reference/op_pad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pad a tensor. — op_pad","text":"","code":"op_pad(x, pad_width, mode = \"constant\", constant_values = NULL)"},{"path":"https://keras.posit.co/reference/op_pad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pad a tensor. — op_pad","text":"x Tensor pad. pad_width Number values padded edges axis. ((before_1, after_1), ...(before_N, after_N)) unique pad widths axis. ((, ),) yields pad axis. (pad,) int shortcut = = pad width axes. mode One \"constant\", \"edge\", \"linear_ramp\", \"maximum\", \"mean\", \"median\", \"minimum\", \"reflect\", \"symmetric\", \"wrap\", \"empty\", \"circular\". Defaults \"constant\". constant_values Value pad mode == \"constant\". Defaults 0. ValueError raised NULL mode != \"constant\".","code":""},{"path":"https://keras.posit.co/reference/op_pad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pad a tensor. — op_pad","text":"Padded tensor.","code":""},{"path":"https://keras.posit.co/reference/op_pad.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Pad a tensor. — op_pad","text":"Torch backend supports modes \"constant\", \"reflect\", \"symmetric\" \"circular\". Torch backend supports \"circular\" mode. Note: Tensorflow backend supports modes \"constant\", \"reflect\" \"symmetric\".","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_power.html","id":null,"dir":"Reference","previous_headings":"","what":"First tensor elements raised to powers from second tensor, element-wise. — op_power","title":"First tensor elements raised to powers from second tensor, element-wise. — op_power","text":"Note function automatically called using R operator ^ tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_power(2, x) ## tf.Tensor([1. 2. 4. 8.], shape=(4), dtype=float32) 2 ^ x ## tf.Tensor([1. 2. 4. 8.], shape=(4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_power.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"First tensor elements raised to powers from second tensor, element-wise. — op_power","text":"","code":"op_power(x1, x2)"},{"path":"https://keras.posit.co/reference/op_power.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"First tensor elements raised to powers from second tensor, element-wise. — op_power","text":"x1 bases. x2 exponents.","code":""},{"path":"https://keras.posit.co/reference/op_power.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"First tensor elements raised to powers from second tensor, element-wise. — op_power","text":"Output tensor, bases x1 raised exponents x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_prod.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the product of tensor elements over a given axis. — op_prod","title":"Return the product of tensor elements over a given axis. — op_prod","text":"Return product tensor elements given axis.","code":""},{"path":"https://keras.posit.co/reference/op_prod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the product of tensor elements over a given axis. — op_prod","text":"","code":"op_prod(x, axis = NULL, keepdims = FALSE, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_prod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the product of tensor elements over a given axis. — op_prod","text":"x Input tensor. axis Axis axes along product performed. default, axis = NULL, compute product elements input tensor. keepdims set TRUE, axes reduce left result dimensions size one. dtype Data type returned tensor.","code":""},{"path":"https://keras.posit.co/reference/op_prod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the product of tensor elements over a given axis. — op_prod","text":"Product elements x given axis axes.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_psnr.html","id":null,"dir":"Reference","previous_headings":"","what":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"function computes Peak Signal--Noise Ratio two signals, x1 x2. PSNR measure quality reconstructed signal. higher PSNR, closer reconstructed signal original signal. Note can become negative signal power smaller noise power.","code":""},{"path":"https://keras.posit.co/reference/op_psnr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"","code":"op_psnr(x1, x2, max_val)"},{"path":"https://keras.posit.co/reference/op_psnr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"x1 first input signal. x2 second input signal. Must shape x1. max_val maximum possible value signals.","code":""},{"path":"https://keras.posit.co/reference/op_psnr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"float: PSNR value x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_psnr.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"","code":"x1 <- random_normal(c(2, 4, 4, 3)) x2 <- random_normal(c(2, 4, 4, 3)) max_val <- 1.0 op_psnr(x1, x2, max_val) ## tf.Tensor(-3.433579, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_qr.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the QR decomposition of a tensor. — op_qr","title":"Computes the QR decomposition of a tensor. — op_qr","text":"Computes QR decomposition tensor.","code":""},{"path":"https://keras.posit.co/reference/op_qr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the QR decomposition of a tensor. — op_qr","text":"","code":"op_qr(x, mode = \"reduced\")"},{"path":"https://keras.posit.co/reference/op_qr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the QR decomposition of a tensor. — op_qr","text":"x Input tensor shape (..., M, N). mode string specifying mode QR decomposition. 'reduced': Returns reduced QR decomposition. (default) 'complete': Returns complete QR decomposition.","code":""},{"path":"https://keras.posit.co/reference/op_qr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the QR decomposition of a tensor. — op_qr","text":"list containing two tensors. first tensor shape (..., M, K) orthogonal matrix q second tensor shape (..., K, N)upper triangular matrixr, K = min(M, N)`.","code":""},{"path":"https://keras.posit.co/reference/op_qr.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the QR decomposition of a tensor. — op_qr","text":"","code":"x <- op_convert_to_tensor(rbind(c(1, 2), c(3, 4), c(5, 6))) op_qr(x) ## $q ## tf.Tensor( ## [[-0.16903085  0.89708523] ##  [-0.50709255  0.27602622] ##  [-0.84515425 -0.34503278]], shape=(3, 2), dtype=float64) ## ## $r ## tf.Tensor( ## [[-5.91607978 -7.43735744] ##  [ 0.          0.82807867]], shape=(2, 2), dtype=float64) c(q, r) %<-% op_qr(x)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","title":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","text":"Compute q-th quantile(s) data along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","text":"","code":"op_quantile(x, q, axis = NULL, method = \"linear\", keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","text":"x Input tensor. q Probability sequence probabilities quantiles compute. Values must 0 1 inclusive. axis Axis axes along quantiles computed. Defaults axis=NULL compute quantile(s) along flattened version array. method string specifies method use estimating quantile. Available methods \"linear\", \"lower\", \"higher\", \"midpoint\", \"nearest\". Defaults \"linear\". desired quantile lies two data points < j: \"linear\": + (j - ) * fraction, fraction fractional part index surrounded j. \"lower\": . \"higher\": j. \"midpoint\": (+ j) / 2 \"nearest\": j, whichever nearest. keepdims set TRUE, axes reduce left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","text":"quantile(s). q single probability axis=NULL, result scalar. multiple probabilies levels given, first axis result corresponds quantiles. axes axes remain reduction x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_ravel.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a contiguous flattened tensor. — op_ravel","title":"Return a contiguous flattened tensor. — op_ravel","text":"1-D tensor, containing elements input, returned.","code":""},{"path":"https://keras.posit.co/reference/op_ravel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a contiguous flattened tensor. — op_ravel","text":"","code":"op_ravel(x)"},{"path":"https://keras.posit.co/reference/op_ravel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a contiguous flattened tensor. — op_ravel","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ravel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a contiguous flattened tensor. — op_ravel","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_real.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the real part of the complex argument. — op_real","title":"Return the real part of the complex argument. — op_real","text":"Return real part complex argument.","code":""},{"path":"https://keras.posit.co/reference/op_real.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the real part of the complex argument. — op_real","text":"","code":"op_real(x)"},{"path":"https://keras.posit.co/reference/op_real.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the real part of the complex argument. — op_real","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_real.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the real part of the complex argument. — op_real","text":"real component complex argument.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_reciprocal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the reciprocal of the argument, element-wise. — op_reciprocal","title":"Return the reciprocal of the argument, element-wise. — op_reciprocal","text":"Calculates 1/x.","code":""},{"path":"https://keras.posit.co/reference/op_reciprocal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the reciprocal of the argument, element-wise. — op_reciprocal","text":"","code":"op_reciprocal(x)"},{"path":"https://keras.posit.co/reference/op_reciprocal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the reciprocal of the argument, element-wise. — op_reciprocal","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_reciprocal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the reciprocal of the argument, element-wise. — op_reciprocal","text":"Output tensor, element-wise reciprocal x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Rectified linear unit activation function. — op_relu","title":"Rectified linear unit activation function. — op_relu","text":"defined f(x) = max(0, x).","code":""},{"path":"https://keras.posit.co/reference/op_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rectified linear unit activation function. — op_relu","text":"","code":"op_relu(x)"},{"path":"https://keras.posit.co/reference/op_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rectified linear unit activation function. — op_relu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rectified linear unit activation function. — op_relu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rectified linear unit activation function. — op_relu","text":"","code":"x1 <- op_convert_to_tensor(c(-1, 0, 1, 0.2)) op_relu(x1) ## tf.Tensor([0.  0.  1.  0.2], shape=(4), dtype=float32) x <- seq(-10, 10, .1) plot(x, op_relu(x))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_relu6.html","id":null,"dir":"Reference","previous_headings":"","what":"Rectified linear unit activation function with upper bound of 6. — op_relu6","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"defined f(x) = op_clip(x, 0, 6).","code":""},{"path":"https://keras.posit.co/reference/op_relu6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"","code":"op_relu6(x)"},{"path":"https://keras.posit.co/reference/op_relu6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_relu6.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_relu6.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"","code":"x <- op_convert_to_tensor(c(-3, -2, 0.1, 0.2, 6, 8)) op_relu6(x) ## tf.Tensor([0.  0.  0.1 0.2 6.  6. ], shape=(6), dtype=float32) x <- seq(-10, 10, .1) plot(x, op_relu6(x))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_repeat.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeat each element of a tensor after themselves. — op_repeat","title":"Repeat each element of a tensor after themselves. — op_repeat","text":"Repeat element tensor .","code":""},{"path":"https://keras.posit.co/reference/op_repeat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeat each element of a tensor after themselves. — op_repeat","text":"","code":"op_repeat(x, repeats, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_repeat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeat each element of a tensor after themselves. — op_repeat","text":"x Input tensor. repeats number repetitions element. axis axis along repeat values. default, use flattened input array, return flat output array.","code":""},{"path":"https://keras.posit.co/reference/op_repeat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Repeat each element of a tensor after themselves. — op_repeat","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_reshape.html","id":null,"dir":"Reference","previous_headings":"","what":"Gives a new shape to a tensor without changing its data. — op_reshape","title":"Gives a new shape to a tensor without changing its data. — op_reshape","text":"Gives new shape tensor without changing data.","code":""},{"path":"https://keras.posit.co/reference/op_reshape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gives a new shape to a tensor without changing its data. — op_reshape","text":"","code":"op_reshape(x, newshape)"},{"path":"https://keras.posit.co/reference/op_reshape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gives a new shape to a tensor without changing its data. — op_reshape","text":"x Input tensor. newshape new shape compatible original shape. One shape dimension can -1 case value inferred length array remaining dimensions.","code":""},{"path":"https://keras.posit.co/reference/op_reshape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gives a new shape to a tensor without changing its data. — op_reshape","text":"reshaped tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_rfft.html","id":null,"dir":"Reference","previous_headings":"","what":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"Computes 1D Discrete Fourier Transform real-valued signal inner-dimension input. Since Discrete Fourier Transform real-valued signal Hermitian-symmetric, RFFT returns fft_length / 2 + 1 unique components FFT: zero-frequency term, followed fft_length / 2 positive-frequency terms. Along axis RFFT computed , fft_length smaller corresponding dimension input, dimension cropped. larger, dimension padded zeros.","code":""},{"path":"https://keras.posit.co/reference/op_rfft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"","code":"op_rfft(x, fft_length = NULL)"},{"path":"https://keras.posit.co/reference/op_rfft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"x Input tensor. fft_length integer representing number fft length. specified, inferred length last axis x. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_rfft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"list containing two tensors - real imaginary parts output.","code":""},{"path":"https://keras.posit.co/reference/op_rfft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"","code":"x <- op_convert_to_tensor(c(0, 1, 2, 3, 4)) op_rfft(x) ## [[1]] ## tf.Tensor([10.  -2.5 -2.5], shape=(3), dtype=float32) ## ## [[2]] ## tf.Tensor([0.        3.440955  0.8122992], shape=(3), dtype=float32) op_rfft(x, 3) ## [[1]] ## tf.Tensor([ 3.  -1.5], shape=(2), dtype=float32) ## ## [[2]] ## tf.Tensor([0.        0.8660254], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_roll.html","id":null,"dir":"Reference","previous_headings":"","what":"Roll tensor elements along a given axis. — op_roll","title":"Roll tensor elements along a given axis. — op_roll","text":"Elements roll beyond last position re-introduced first.","code":""},{"path":"https://keras.posit.co/reference/op_roll.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Roll tensor elements along a given axis. — op_roll","text":"","code":"op_roll(x, shift, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_roll.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Roll tensor elements along a given axis. — op_roll","text":"x Input tensor. shift number places elements shifted. axis axis along elements shifted. default, array flattened shifting, original shape restored.","code":""},{"path":"https://keras.posit.co/reference/op_roll.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Roll tensor elements along a given axis. — op_roll","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_round.html","id":null,"dir":"Reference","previous_headings":"","what":"Evenly round to the given number of decimals. — op_round","title":"Evenly round to the given number of decimals. — op_round","text":"Evenly round given number decimals.","code":""},{"path":"https://keras.posit.co/reference/op_round.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evenly round to the given number of decimals. — op_round","text":"","code":"op_round(x, decimals = 0L)"},{"path":"https://keras.posit.co/reference/op_round.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evenly round to the given number of decimals. — op_round","text":"x Input tensor. decimals Number decimal places round . Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/op_round.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evenly round to the given number of decimals. — op_round","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes reciprocal of square root of x element-wise. — op_rsqrt","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"Computes reciprocal square root x element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"","code":"op_rsqrt(x)"},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"x input tensor","code":""},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"tensor dtype x.","code":""},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"","code":"x <- op_convert_to_tensor(c(1, 10, 100)) op_rsqrt(x) ## tf.Tensor([1.         0.31622776 0.1       ], shape=(3), dtype=float32) # array([1, 0.31622776, 0.1], dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_scatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a tensor of shape shape where indices are set to values. — op_scatter","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"high level, operation zeros[indices] = updates returns output. equivalent :","code":"output <- op_scatter_update(op_zeros(shape), indices, values)"},{"path":"https://keras.posit.co/reference/op_scatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"","code":"op_scatter(indices, values, shape)"},{"path":"https://keras.posit.co/reference/op_scatter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"indices tensor list specifying indices values values. values tensor, values set indices. shape Shape output tensor.","code":""},{"path":"https://keras.posit.co/reference/op_scatter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"tensor shape shape indices set values.","code":""},{"path":"https://keras.posit.co/reference/op_scatter.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"","code":"indices <- rbind(c(1, 2), c(2, 2)) values <- op_array(c(1, 1)) op_scatter(indices, values, shape= c(2, 2)) ## tf.Tensor( ## [[0. 1.] ##  [0. 1.]], shape=(2, 2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_scatter_update.html","id":null,"dir":"Reference","previous_headings":"","what":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","title":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","text":"high level, operation inputs[indices] <- updates. Assume inputs tensor shape (D1, D2, ..., Dn), 2 main usages scatter_update. indices 2D tensor shape (num_updates, n), num_updates number updates perform, updates 1D tensor shape (num_updates). example, inputs op_zeros(c(4, 4, 4)), want update inputs[2, 3, 4] inputs[1, 2, 4] 1, can use:     2 indices 2D tensor shape (num_updates, k), num_updates number updates perform, k (k <= n) size index indices. updates n - k-D tensor shape (num_updates, shape(inputs)[-(1:k)]). example, inputs <- op_zeros(c(4, 4, 4)), want update inputs[1, 2, ] inputs[2, 3, ] [1, 1, 1, 1], indices shape (num_updates, 2) (k = 2), updates shape (num_updates, 4) (shape(inputs)[3:4] == 4). See code :","code":"inputs <- op_zeros(c(4, 4, 4)) indices <- rbind(c(2, 3, 4),                  c(1, 2, 4)) updates <- op_array(c(1, 1), \"float32\") op_scatter_update(inputs, indices, updates) ## tf.Tensor( ## [[[0. 0. 0. 0.] ##   [0. 0. 0. 1.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 1.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]]], shape=(4, 4, 4), dtype=float32) inputs <- op_zeros(c(4, 4, 4)) indices <- rbind(c(2, 3),                  c(3, 4)) updates <- op_array(rbind(c(1, 1, 1, 1),                           c(1, 1, 1, 1)),                     \"float32\") op_scatter_update(inputs, indices, updates) ## tf.Tensor( ## [[[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [1. 1. 1. 1.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [1. 1. 1. 1.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]]], shape=(4, 4, 4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_scatter_update.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","text":"","code":"op_scatter_update(inputs, indices, updates)"},{"path":"https://keras.posit.co/reference/op_scatter_update.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","text":"inputs tensor, tensor updated. indices tensor list shape (N, inputs$ndim), specifying indices update. N number indices update, must equal first dimension updates. updates tensor, new values put inputs indices.","code":""},{"path":"https://keras.posit.co/reference/op_scatter_update.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","text":"tensor, shape dtype inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the max of segments in a tensor. — op_segment_max","title":"Computes the max of segments in a tensor. — op_segment_max","text":"Computes max segments tensor.","code":""},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the max of segments in a tensor. — op_segment_max","text":"","code":"op_segment_max(data, segment_ids, num_segments = NULL, sorted = FALSE)"},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the max of segments in a tensor. — op_segment_max","text":"data Input tensor. segment_ids 1-D tensor containing segment indices element data. num_segments integer representing total number segments. specified, inferred maximum value segment_ids. sorted boolean indicating whether segment_ids sorted. Defaults toFALSE.","code":""},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the max of segments in a tensor. — op_segment_max","text":"tensor containing max segments, element represents max corresponding segment data.","code":""},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the max of segments in a tensor. — op_segment_max","text":"","code":"data <- op_convert_to_tensor(c(1, 2, 10, 20, 100, 200)) segment_ids <- op_array(c(1, 1, 2, 2, 3, 3), \"int32\") num_segments <- 3 op_segment_max(data, segment_ids, num_segments) ## tf.Tensor([  2.  20. 200.], shape=(3), dtype=float32) # array([2, 20, 200], dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the sum of segments in a tensor. — op_segment_sum","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"Computes sum segments tensor.","code":""},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"","code":"op_segment_sum(data, segment_ids, num_segments = NULL, sorted = FALSE)"},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"data Input tensor. segment_ids 1-D tensor containing segment indices element data. num_segments integer representing total number segments. specified, inferred maximum value segment_ids. sorted boolean indicating whether segment_ids sorted. Defaults toFALSE.","code":""},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"tensor containing sum segments, element represents sum corresponding segment data.","code":""},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"","code":"data <- op_array(c(1, 2, 10, 20, 100, 200)) segment_ids <- op_array(c(1, 1, 2, 2, 3, 3), \"int32\") num_segments <- 3 op_segment_sum(data, segment_ids, num_segments) ## tf.Tensor([  3.  30. 300.], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Return elements from choicelist, based on conditions in condlist. — op_select","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"Return elements choicelist, based conditions condlist.","code":""},{"path":"https://keras.posit.co/reference/op_select.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"","code":"op_select(condlist, choicelist, default = 0L)"},{"path":"https://keras.posit.co/reference/op_select.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"condlist List boolean tensors. list conditions determine array choicelist output elements taken. multiple conditions satisfied, first one encountered condlist used. choicelist List tensors. list tensors output elements taken. list length condlist. default Optional scalar value. element inserted output conditions evaluate FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_select.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"Tensor output position m m-th element tensor choicelist m-th element corresponding tensor condlist TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_select.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"","code":"x <- op_arange(6L) condlist <- list(x < 3, x > 3) choicelist <- list(x, x^2) op_select(condlist, choicelist, 42) ## tf.Tensor([ 0  1  2 42 16 25], shape=(6), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"defined : f(x) =  scale * alpha * (exp(x) - 1.) x < 0, f(x) = scale * x x >= 0.","code":""},{"path":"https://keras.posit.co/reference/op_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"","code":"op_selu(x)"},{"path":"https://keras.posit.co/reference/op_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_selu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_selu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"","code":"x <- op_array(c(-1, 0, 1)) op_selu(x) ## tf.Tensor([-1.1113307  0.         1.050701 ], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_separable_conv.html","id":null,"dir":"Reference","previous_headings":"","what":"General N-D separable convolution. — op_separable_conv","title":"General N-D separable convolution. — op_separable_conv","text":"ops supports 1D 2D separable convolution. separable_conv depthwise conv followed pointwise conv.","code":""},{"path":"https://keras.posit.co/reference/op_separable_conv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General N-D separable convolution. — op_separable_conv","text":"","code":"op_separable_conv(   inputs,   depthwise_kernel,   pointwise_kernel,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L )"},{"path":"https://keras.posit.co/reference/op_separable_conv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General N-D separable convolution. — op_separable_conv","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format=\"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format=\"channels_first\". depthwise_kernel Tensor rank N+2. depthwise_kernel shape [kernel_spatial_shape, num_input_channels, num_channels_multiplier], num_input_channels match number channels inputs. pointwise_kernel Tensor rank N+2. pointwise_kernel shape (*ones_like(kernel_spatial_shape), num_input_channels * num_channels_multiplier, num_output_channels). strides int int tuple/list len(inputs_spatial_shape), specifying strides convolution along spatial dimension. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides=1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format=\"channels_last\", inputs shape (batch_size, ..., channels) data_format=\"channels_first\", inputs shape (batch_size, channels, ...). dilation_rate int int tuple/list len(inputs_spatial_shape), specifying dilation rate use dilated convolution. dilation_rate int, every spatial dimension shares dilation_rate.","code":""},{"path":"https://keras.posit.co/reference/op_separable_conv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General N-D separable convolution. — op_separable_conv","text":"tensor rank N+2, result depthwise conv operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_shape.html","id":null,"dir":"Reference","previous_headings":"","what":"Gets the shape of the tensor input. — op_shape","title":"Gets the shape of the tensor input. — op_shape","text":"Gets shape tensor input.","code":""},{"path":"https://keras.posit.co/reference/op_shape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gets the shape of the tensor input. — op_shape","text":"","code":"op_shape(x)"},{"path":"https://keras.posit.co/reference/op_shape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gets the shape of the tensor input. — op_shape","text":"x tensor. function try access shape attribute input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_shape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gets the shape of the tensor input. — op_shape","text":"list integers NULL values, indicating shape input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_shape.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Gets the shape of the tensor input. — op_shape","text":"TensorFlow backend, x tf.Tensor dynamic shape, dimensions dynamic context compiled function tf.Tensor value instead static integer value.","code":""},{"path":"https://keras.posit.co/reference/op_shape.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gets the shape of the tensor input. — op_shape","text":"","code":"x <- op_zeros(c(8, 12)) op_shape(x) ## shape(8, 12)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid activation function. — op_sigmoid","title":"Sigmoid activation function. — op_sigmoid","text":"defined f(x) = 1 / (1 + exp(-x)).","code":""},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid activation function. — op_sigmoid","text":"","code":"op_sigmoid(x)"},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid activation function. — op_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sigmoid activation function. — op_sigmoid","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sigmoid activation function. — op_sigmoid","text":"","code":"x <- op_convert_to_tensor(c(-6, 1, 0, 1, 6)) op_sigmoid(x) ## tf.Tensor([0.00247262 0.7310586  0.5        0.7310586  0.99752736], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_sign.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a tensor with the signs of the elements of x. — op_sign","title":"Returns a tensor with the signs of the elements of x. — op_sign","text":"Returns tensor signs elements x.","code":""},{"path":"https://keras.posit.co/reference/op_sign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a tensor with the signs of the elements of x. — op_sign","text":"","code":"op_sign(x)"},{"path":"https://keras.posit.co/reference/op_sign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a tensor with the signs of the elements of x. — op_sign","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a tensor with the signs of the elements of x. — op_sign","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"SiLU activation function computed sigmoid function multiplied input. defined f(x) = x * sigmoid(x).","code":""},{"path":"https://keras.posit.co/reference/op_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"","code":"op_silu(x)"},{"path":"https://keras.posit.co/reference/op_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_silu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_silu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"","code":"x <- op_convert_to_tensor(c(-6, 1, 0, 1, 6)) op_sigmoid(x) ## tf.Tensor([0.00247262 0.7310586  0.5        0.7310586  0.99752736], shape=(5), dtype=float32) op_silu(x) ## tf.Tensor([-0.01483574  0.7310586   0.          0.7310586   5.985164  ], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_sin.html","id":null,"dir":"Reference","previous_headings":"","what":"Trigonometric sine, element-wise. — op_sin","title":"Trigonometric sine, element-wise. — op_sin","text":"Trigonometric sine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_sin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trigonometric sine, element-wise. — op_sin","text":"","code":"op_sin(x)"},{"path":"https://keras.posit.co/reference/op_sin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trigonometric sine, element-wise. — op_sin","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trigonometric sine, element-wise. — op_sin","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hyperbolic sine, element-wise. — op_sinh","title":"Hyperbolic sine, element-wise. — op_sinh","text":"Hyperbolic sine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_sinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hyperbolic sine, element-wise. — op_sinh","text":"","code":"op_sinh(x)"},{"path":"https://keras.posit.co/reference/op_sinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hyperbolic sine, element-wise. — op_sinh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sinh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hyperbolic sine, element-wise. — op_sinh","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_size.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the number of elements in a tensor. — op_size","title":"Return the number of elements in a tensor. — op_size","text":"Return number elements tensor.","code":""},{"path":"https://keras.posit.co/reference/op_size.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the number of elements in a tensor. — op_size","text":"","code":"op_size(x)"},{"path":"https://keras.posit.co/reference/op_size.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the number of elements in a tensor. — op_size","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_size.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the number of elements in a tensor. — op_size","text":"Number elements x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a slice of an input tensor. — op_slice","title":"Return a slice of an input tensor. — op_slice","text":"high level, operation explicit replacement array slicing e.g. inputs[start_indices:(start_indices + shape)]. Unlike slicing via brackets, operation accept tensor start indices backends, useful indices dynamically computed via tensor operations.","code":"(inputs <- op_arange(5*5) |> op_reshape(c(5, 5))) ## tf.Tensor( ## [[ 0.  1.  2.  3.  4.] ##  [ 5.  6.  7.  8.  9.] ##  [10. 11. 12. 13. 14.] ##  [15. 16. 17. 18. 19.] ##  [20. 21. 22. 23. 24.]], shape=(5, 5), dtype=float32) start_indices <- c(3, 3) shape <- c(2, 2) op_slice(inputs, start_indices, shape) ## tf.Tensor( ## [[12. 13.] ##  [17. 18.]], shape=(2, 2), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a slice of an input tensor. — op_slice","text":"","code":"op_slice(inputs, start_indices, shape)"},{"path":"https://keras.posit.co/reference/op_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a slice of an input tensor. — op_slice","text":"inputs tensor, tensor sliced. start_indices list length inputs$ndim, specifying starting indices updating. shape full shape returned slice.","code":""},{"path":"https://keras.posit.co/reference/op_slice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a slice of an input tensor. — op_slice","text":"tensor, shape dtype inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_slice_update.html","id":null,"dir":"Reference","previous_headings":"","what":"Update an input by slicing in a tensor of updated values. — op_slice_update","title":"Update an input by slicing in a tensor of updated values. — op_slice_update","text":"high level, operation inputs[start_indices: start_indices + updates.shape] = updates. Assume inputs tensor shape (D1, D2, ..., Dn), start_indices must list n integers, specifying starting indices. updates must rank inputs, size dim must exceed Di - start_indices[]. example, 2D inputs inputs = op_zeros(c(5, 5)), want update intersection last 2 rows last 2 columns 1, .e., inputs[4:5, 4:5] = op_ones(c(2, 2)), can use code :","code":"inputs <- op_zeros(c(5, 5)) start_indices <- c(3, 3) updates <- op_ones(c(2, 2)) op_slice_update(inputs, start_indices, updates) ## tf.Tensor( ## [[0. 0. 0. 0. 0.] ##  [0. 0. 0. 0. 0.] ##  [0. 0. 1. 1. 0.] ##  [0. 0. 1. 1. 0.] ##  [0. 0. 0. 0. 0.]], shape=(5, 5), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_slice_update.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update an input by slicing in a tensor of updated values. — op_slice_update","text":"","code":"op_slice_update(inputs, start_indices, updates)"},{"path":"https://keras.posit.co/reference/op_slice_update.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update an input by slicing in a tensor of updated values. — op_slice_update","text":"inputs tensor, tensor updated. start_indices list length inputs$ndim, specifying starting indices updating. updates tensor, new values put inputs indices. updates must rank inputs.","code":""},{"path":"https://keras.posit.co/reference/op_slice_update.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update an input by slicing in a tensor of updated values. — op_slice_update","text":"tensor, shape dtype inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_slogdet.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","title":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","text":"Compute sign natural logarithm determinant matrix.","code":""},{"path":"https://keras.posit.co/reference/op_slogdet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","text":"","code":"op_slogdet(x)"},{"path":"https://keras.posit.co/reference/op_slogdet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","text":"x Input matrix. must 2D square.","code":""},{"path":"https://keras.posit.co/reference/op_slogdet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","text":"list: (sign, logabsdet). sign number representing sign determinant. real matrix, 1, 0, -1. complex matrix, complex number absolute value 1 (.e., unit circle), else 0. logabsdet natural log absolute value determinant.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax activation function. — op_softmax","title":"Softmax activation function. — op_softmax","text":"elements output vector lie within range (0, 1), total sum exactly 1 (excluding floating point rounding error). vector processed independently. axis argument specifies axis along function applied within input. defined : f(x) = exp(x) / sum(exp(x))","code":""},{"path":"https://keras.posit.co/reference/op_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax activation function. — op_softmax","text":"","code":"op_softmax(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax activation function. — op_softmax","text":"x Input tensor. axis Integer, axis along softmax applied.","code":""},{"path":"https://keras.posit.co/reference/op_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax activation function. — op_softmax","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_softmax.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmax activation function. — op_softmax","text":"","code":"x <- op_array(c(-1, 0, 1)) op_softmax(x) ## tf.Tensor([0.09003057 0.24472848 0.66524094], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_softplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Softplus activation function. — op_softplus","title":"Softplus activation function. — op_softplus","text":"defined f(x) = log(exp(x) + 1), log natural logarithm exp exponential function.","code":""},{"path":"https://keras.posit.co/reference/op_softplus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softplus activation function. — op_softplus","text":"","code":"op_softplus(x)"},{"path":"https://keras.posit.co/reference/op_softplus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softplus activation function. — op_softplus","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_softplus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softplus activation function. — op_softplus","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_softplus.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softplus activation function. — op_softplus","text":"","code":"x <- op_convert_to_tensor(c(-0.555, 0, 0.555)) op_softplus(x) ## tf.Tensor([0.45366603 0.6931472  1.008666  ], shape=(3), dtype=float32) x <- seq(-10, 10, .1) plot(x, op_softplus(x))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_softsign.html","id":null,"dir":"Reference","previous_headings":"","what":"Softsign activation function. — op_softsign","title":"Softsign activation function. — op_softsign","text":"defined f(x) = x / (abs(x) + 1).","code":""},{"path":"https://keras.posit.co/reference/op_softsign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softsign activation function. — op_softsign","text":"","code":"op_softsign(x)"},{"path":"https://keras.posit.co/reference/op_softsign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softsign activation function. — op_softsign","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_softsign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softsign activation function. — op_softsign","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_softsign.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softsign activation function. — op_softsign","text":"","code":"x <- op_convert_to_tensor(c(-0.100, -10.0, 1.0, 0.0, 100.0)) op_softsign(x) ## tf.Tensor([-0.09090909 -0.90909094  0.5         0.          0.990099  ], shape=(5), dtype=float32) x <- seq(-10, 10, .1) plot(x, op_softsign(x), ylim = c(-1, 1))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Solves a linear system of equations given by a x = b. — op_solve","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"Solves x equation %*% x == b.","code":""},{"path":"https://keras.posit.co/reference/op_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"","code":"op_solve(a, b)"},{"path":"https://keras.posit.co/reference/op_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"tensor shape (..., M, M) representing coefficients matrix. b tensor shape (..., M) (..., M, N) represeting right-hand side \"dependent variable\" matrix.","code":""},{"path":"https://keras.posit.co/reference/op_solve.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"tensor shape (..., M) (..., M, N) representing solution linear system. Returned shape identical b.","code":""},{"path":"https://keras.posit.co/reference/op_solve.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"","code":"a <- op_array(c(1, 2, 4, 5), dtype=\"float32\") |> op_reshape(c(2, 2)) b <- op_array(c(2, 4, 8, 10), dtype=\"float32\") |> op_reshape(c(2, 2)) op_solve(a, b) ## tf.Tensor( ## [[2. 0.] ##  [0. 2.]], shape=(2, 2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_solve_triangular.html","id":null,"dir":"Reference","previous_headings":"","what":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","title":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","text":"Solves linear system equations given %*% x = b.","code":""},{"path":"https://keras.posit.co/reference/op_solve_triangular.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","text":"","code":"op_solve_triangular(a, b, lower = FALSE)"},{"path":"https://keras.posit.co/reference/op_solve_triangular.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","text":"tensor shape (..., M, M) representing coefficients matrix. b tensor shape (..., M) (..., M, N) represeting right-hand side \"dependent variable\" matrix. lower logical. Use data contained lower triangle . Default use upper triangle.","code":""},{"path":"https://keras.posit.co/reference/op_solve_triangular.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","text":"tensor shape (..., M) (..., M, N) representing solution linear system. Returned shape identical b.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sort.html","id":null,"dir":"Reference","previous_headings":"","what":"Sorts the elements of x along a given axis in ascending order. — op_sort","title":"Sorts the elements of x along a given axis in ascending order. — op_sort","text":"Sorts elements x along given axis ascending order.","code":""},{"path":"https://keras.posit.co/reference/op_sort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sorts the elements of x along a given axis in ascending order. — op_sort","text":"","code":"op_sort(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_sort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sorts the elements of x along a given axis in ascending order. — op_sort","text":"x Input tensor. axis Axis along sort. NULL, tensor flattened sorting. Defaults -1; last axis.","code":""},{"path":"https://keras.posit.co/reference/op_sort.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sorts the elements of x along a given axis in ascending order. — op_sort","text":"Sorted tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"sparse categorical cross-entropy loss similar categorical cross-entropy, used target tensor contains integer class labels instead one-hot encoded vectors. measures dissimilarity target output probabilities logits.","code":""},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"","code":"op_sparse_categorical_crossentropy(   target,   output,   from_logits = FALSE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"target target tensor representing true class labels integers. shape match shape output tensor except last dimension. output output tensor representing predicted probabilities logits. shape match shape target tensor except last dimension. from_logits (optional) Whether output tensor logits probabilities. Set TRUE output represents logits; otherwise, set FALSE output represents probabilities. Defaults toFALSE. axis (optional) axis along sparse categorical cross-entropy computed. Defaults -1, corresponds last dimension tensors.","code":""},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"Integer tensor: computed sparse categorical cross-entropy loss target output.","code":""},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"","code":"target <- op_array(c(0, 1, 2), dtype=\"int32\") output <- op_array(rbind(c(0.9, 0.05, 0.05),                         c(0.1, 0.8,  0.1),                         c(0.2, 0.3,  0.5))) op_sparse_categorical_crossentropy(target, output) ## tf.Tensor([0.10536052 0.22314355 0.69314718], shape=(3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Split a tensor into chunks. — op_split","title":"Split a tensor into chunks. — op_split","text":"Split tensor chunks.","code":""},{"path":"https://keras.posit.co/reference/op_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split a tensor into chunks. — op_split","text":"","code":"op_split(x, indices_or_sections, axis = 1L)"},{"path":"https://keras.posit.co/reference/op_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split a tensor into chunks. — op_split","text":"x Input tensor. indices_or_sections integer, N, tensor split N equal sections along axis. 1-D array sorted integers, entries indicate indices tensor split along axis. axis Axis along split. Defaults 1, first axis.","code":""},{"path":"https://keras.posit.co/reference/op_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split a tensor into chunks. — op_split","text":"list tensors.","code":""},{"path":"https://keras.posit.co/reference/op_split.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Split a tensor into chunks. — op_split","text":"split result equal division using Torch backend.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sqrt.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","title":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","text":"Return non-negative square root tensor, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_sqrt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","text":"","code":"op_sqrt(x)"},{"path":"https://keras.posit.co/reference/op_sqrt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sqrt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","text":"Output tensor, non-negative square root x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_square.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the element-wise square of the input. — op_square","title":"Return the element-wise square of the input. — op_square","text":"Return element-wise square input.","code":""},{"path":"https://keras.posit.co/reference/op_square.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the element-wise square of the input. — op_square","text":"","code":"op_square(x)"},{"path":"https://keras.posit.co/reference/op_square.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the element-wise square of the input. — op_square","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_square.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the element-wise square of the input. — op_square","text":"Output tensor, square x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_squeeze.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove axes of length one from x. — op_squeeze","title":"Remove axes of length one from x. — op_squeeze","text":"Remove axes length one x.","code":""},{"path":"https://keras.posit.co/reference/op_squeeze.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove axes of length one from x. — op_squeeze","text":"","code":"op_squeeze(x, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_squeeze.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove axes of length one from x. — op_squeeze","text":"x Input tensor. axis Select subset entries length one shape.","code":""},{"path":"https://keras.posit.co/reference/op_squeeze.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove axes of length one from x. — op_squeeze","text":"input tensor subset dimensions length 1 removed.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Join a sequence of tensors along a new axis. — op_stack","title":"Join a sequence of tensors along a new axis. — op_stack","text":"axis parameter specifies index new axis dimensions result.","code":""},{"path":"https://keras.posit.co/reference/op_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join a sequence of tensors along a new axis. — op_stack","text":"","code":"op_stack(x, axis = 1L)"},{"path":"https://keras.posit.co/reference/op_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join a sequence of tensors along a new axis. — op_stack","text":"x sequence tensors. axis Axis along stack. Defaults 1, first axis.","code":""},{"path":"https://keras.posit.co/reference/op_stack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join a sequence of tensors along a new axis. — op_stack","text":"stacked tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_std.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the standard deviation along the specified axis. — op_std","title":"Compute the standard deviation along the specified axis. — op_std","text":"Compute standard deviation along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_std.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the standard deviation along the specified axis. — op_std","text":"","code":"op_std(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_std.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the standard deviation along the specified axis. — op_std","text":"x Input tensor. axis Axis along compute standard deviation. Default compute standard deviation flattened tensor. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_std.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the standard deviation along the specified axis. — op_std","text":"Output tensor containing standard deviation values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_stft.html","id":null,"dir":"Reference","previous_headings":"","what":"Short-Time Fourier Transform along the last axis of the input. — op_stft","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"STFT computes Fourier transform short overlapping windows input. giving frequency components signal change time.","code":""},{"path":"https://keras.posit.co/reference/op_stft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"","code":"op_stft(   x,   sequence_length,   sequence_stride,   fft_length,   window = \"hann\",   center = TRUE )"},{"path":"https://keras.posit.co/reference/op_stft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"x Input tensor. sequence_length integer representing sequence length. sequence_stride integer representing sequence hop size. fft_length integer representing size FFT apply. specified, uses smallest power 2 enclosing sequence_length. window string, tensor window NULL. window string, available values \"hann\" \"hamming\". window tensor, used directly window length must sequence_length. window NULL, windowing used. Defaults \"hann\". center Whether pad x sides t-th sequence centered time t * sequence_stride. Otherwise, t-th sequence begins time t * sequence_stride. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_stft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"list containing two tensors - real imaginary parts STFT output.","code":""},{"path":"https://keras.posit.co/reference/op_stft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"","code":"x <- op_array(c(0, 1, 2, 3, 4)) op_stft(x, 3, 2, 3) ## [[1]] ## tf.Tensor( ## [[ 0.  0.] ##  [ 2. -1.] ##  [ 4. -2.]], shape=(3, 2), dtype=float32) ## ## [[2]] ## tf.Tensor( ## [[ 0.         0.       ] ##  [ 0.        -1.7320508] ##  [ 0.        -3.4641016]], shape=(3, 2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":null,"dir":"Reference","previous_headings":"","what":"Stops gradient computation. — op_stop_gradient","title":"Stops gradient computation. — op_stop_gradient","text":"Stops gradient computation.","code":""},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stops gradient computation. — op_stop_gradient","text":"","code":"op_stop_gradient(variable)"},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stops gradient computation. — op_stop_gradient","text":"variable tensor variable gradient computation disabled.","code":""},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stops gradient computation. — op_stop_gradient","text":"variable gradient computation disabled.","code":""},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stops gradient computation. — op_stop_gradient","text":"","code":"var <- op_convert_to_tensor(c(1, 2, 3), dtype=\"float32\") var <- op_stop_gradient(var)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_subtract.html","id":null,"dir":"Reference","previous_headings":"","what":"Subtract arguments element-wise. — op_subtract","title":"Subtract arguments element-wise. — op_subtract","text":"Note function automatically called using R operator - tensor.","code":"x <- op_ones(c(3)) op_subtract(x, x) ## tf.Tensor([0. 0. 0.], shape=(3), dtype=float32) x - x ## tf.Tensor([0. 0. 0.], shape=(3), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_subtract.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subtract arguments element-wise. — op_subtract","text":"","code":"op_subtract(x1, x2)"},{"path":"https://keras.posit.co/reference/op_subtract.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subtract arguments element-wise. — op_subtract","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_subtract.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subtract arguments element-wise. — op_subtract","text":"Output tensor, element-wise difference x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Sum of a tensor over the given axes. — op_sum","title":"Sum of a tensor over the given axes. — op_sum","text":"Sum tensor given axes.","code":""},{"path":"https://keras.posit.co/reference/op_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sum of a tensor over the given axes. — op_sum","text":"","code":"op_sum(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sum of a tensor over the given axes. — op_sum","text":"x Input tensor. axis Axis axes along sum computed. default compute sum flattened tensor. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_sum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sum of a tensor over the given axes. — op_sum","text":"Output tensor containing sum.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_svd.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the singular value decomposition of a matrix. — op_svd","title":"Computes the singular value decomposition of a matrix. — op_svd","text":"Computes singular value decomposition matrix.","code":""},{"path":"https://keras.posit.co/reference/op_svd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the singular value decomposition of a matrix. — op_svd","text":"","code":"op_svd(x, full_matrices = TRUE, compute_uv = TRUE)"},{"path":"https://keras.posit.co/reference/op_svd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the singular value decomposition of a matrix. — op_svd","text":"x Input tensor shape (..., M, N). full_matrices Logical compute_uv Logical","code":""},{"path":"https://keras.posit.co/reference/op_svd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the singular value decomposition of a matrix. — op_svd","text":"list three tensors: tensor shape (..., M, M) containing left singular vectors, tensor shape (..., M, N) containing singular values tensor shape (..., N, N) containing right singular vectors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_swapaxes.html","id":null,"dir":"Reference","previous_headings":"","what":"Interchange two axes of a tensor. — op_swapaxes","title":"Interchange two axes of a tensor. — op_swapaxes","text":"Interchange two axes tensor.","code":""},{"path":"https://keras.posit.co/reference/op_swapaxes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interchange two axes of a tensor. — op_swapaxes","text":"","code":"op_swapaxes(x, axis1, axis2)"},{"path":"https://keras.posit.co/reference/op_swapaxes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interchange two axes of a tensor. — op_swapaxes","text":"x Input tensor. axis1 First axis. axis2 Second axis.","code":""},{"path":"https://keras.posit.co/reference/op_swapaxes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interchange two axes of a tensor. — op_swapaxes","text":"tensor axes swapped.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_take.html","id":null,"dir":"Reference","previous_headings":"","what":"Take elements from a tensor along an axis. — op_take","title":"Take elements from a tensor along an axis. — op_take","text":"Take elements tensor along axis.","code":""},{"path":"https://keras.posit.co/reference/op_take.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Take elements from a tensor along an axis. — op_take","text":"","code":"op_take(x, indices, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_take.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Take elements from a tensor along an axis. — op_take","text":"x Source tensor. indices indices values extract. axis axis select values. default, flattened input tensor used.","code":""},{"path":"https://keras.posit.co/reference/op_take.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Take elements from a tensor along an axis. — op_take","text":"corresponding tensor values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_take_along_axis.html","id":null,"dir":"Reference","previous_headings":"","what":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","title":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","text":"Select values x 1-D indices along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_take_along_axis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","text":"","code":"op_take_along_axis(x, indices, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_take_along_axis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","text":"x Source tensor. indices indices values extract. axis axis select values. default, flattened input tensor used.","code":""},{"path":"https://keras.posit.co/reference/op_take_along_axis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","text":"corresponding tensor values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tan.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute tangent, element-wise. — op_tan","title":"Compute tangent, element-wise. — op_tan","text":"Compute tangent, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_tan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute tangent, element-wise. — op_tan","text":"","code":"op_tan(x)"},{"path":"https://keras.posit.co/reference/op_tan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute tangent, element-wise. — op_tan","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_tan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute tangent, element-wise. — op_tan","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hyperbolic tangent, element-wise. — op_tanh","title":"Hyperbolic tangent, element-wise. — op_tanh","text":"Hyperbolic tangent, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_tanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hyperbolic tangent, element-wise. — op_tanh","text":"","code":"op_tanh(x)"},{"path":"https://keras.posit.co/reference/op_tanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hyperbolic tangent, element-wise. — op_tanh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_tanh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hyperbolic tangent, element-wise. — op_tanh","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tensordot.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the tensor dot product along specified axes. — op_tensordot","title":"Compute the tensor dot product along specified axes. — op_tensordot","text":"Compute tensor dot product along specified axes.","code":""},{"path":"https://keras.posit.co/reference/op_tensordot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the tensor dot product along specified axes. — op_tensordot","text":"","code":"op_tensordot(x1, x2, axes = 3L)"},{"path":"https://keras.posit.co/reference/op_tensordot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the tensor dot product along specified axes. — op_tensordot","text":"x1 First tensor. x2 Second tensor. axes integer, N, sum last N axes x1 first N axes x2 order. sizes corresponding axes must match. , list axes summed , first sequence applying x1, second x2. sequences must length.","code":""},{"path":"https://keras.posit.co/reference/op_tensordot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the tensor dot product along specified axes. — op_tensordot","text":"tensor dot product inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tile.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeat x the number of times given by repeats. — op_tile","title":"Repeat x the number of times given by repeats. — op_tile","text":"repeats length d, result dimension max(d, x.ndim). x.ndim < d, x promoted d-dimensional prepending new axes. x.ndim > d, repeats promoted x.ndim prepending 1's .","code":""},{"path":"https://keras.posit.co/reference/op_tile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeat x the number of times given by repeats. — op_tile","text":"","code":"op_tile(x, repeats)"},{"path":"https://keras.posit.co/reference/op_tile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeat x the number of times given by repeats. — op_tile","text":"x Input tensor. repeats number repetitions x along axis.","code":""},{"path":"https://keras.posit.co/reference/op_tile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Repeat x the number of times given by repeats. — op_tile","text":"tiled output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_top_k.html","id":null,"dir":"Reference","previous_headings":"","what":"Finds the top-k values and their indices in a tensor. — op_top_k","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"Finds top-k values indices tensor.","code":""},{"path":"https://keras.posit.co/reference/op_top_k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"","code":"op_top_k(x, k, sorted = TRUE)"},{"path":"https://keras.posit.co/reference/op_top_k.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"x Input tensor. k integer representing number top elements retrieve. sorted boolean indicating whether sort output descending order. Defaults toTRUE.","code":""},{"path":"https://keras.posit.co/reference/op_top_k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"list containing two tensors. first tensor contains top-k values, second tensor contains indices top-k values input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_top_k.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"","code":"x <- op_array(c(5, 2, 7, 1, 9, 3), \"int32\") op_top_k(x, k = 3) ## $values ## tf.Tensor([9 7 5], shape=(3), dtype=int32) ## ## $indices ## tf.Tensor([4 2 0], shape=(3), dtype=int32) c(values, indices) %<-% op_top_k(x, k = 3) values ## tf.Tensor([9 7 5], shape=(3), dtype=int32) indices ## tf.Tensor([4 2 0], shape=(3), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_trace.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the sum along diagonals of the tensor. — op_trace","title":"Return the sum along diagonals of the tensor. — op_trace","text":"x 2-D, sum along diagonal given offset returned, .e., sum elements x[, +offset] . two dimensions, axes specified axis1 axis2 used determine 2-D sub-arrays whose traces returned. shape resulting tensor x axis1 axis2 removed.","code":""},{"path":"https://keras.posit.co/reference/op_trace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the sum along diagonals of the tensor. — op_trace","text":"","code":"op_trace(x, offset = 0L, axis1 = 1L, axis2 = 2L)"},{"path":"https://keras.posit.co/reference/op_trace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the sum along diagonals of the tensor. — op_trace","text":"x Input tensor. offset Offset diagonal main diagonal. Can positive negative. Defaults 0. axis1 Axis used first axis 2-D sub-arrays. Defaults 1. (first axis). axis2 Axis used second axis 2-D sub-arrays. Defaults 2. (second axis).","code":""},{"path":"https://keras.posit.co/reference/op_trace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the sum along diagonals of the tensor. — op_trace","text":"x 2-D, sum diagonal returned. x larger dimensions, tensor sums along diagonals returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a tensor with axes transposed. — op_transpose","title":"Returns a tensor with axes transposed. — op_transpose","text":"Returns tensor axes transposed.","code":""},{"path":"https://keras.posit.co/reference/op_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a tensor with axes transposed. — op_transpose","text":"","code":"op_transpose(x, axes = NULL)"},{"path":"https://keras.posit.co/reference/op_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a tensor with axes transposed. — op_transpose","text":"x Input tensor. axes Sequence integers. Permutation dimensions x. default, order axes reversed.","code":""},{"path":"https://keras.posit.co/reference/op_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a tensor with axes transposed. — op_transpose","text":"x axes permuted.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tri.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","title":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","text":"Return tensor ones diagonal zeros elsewhere.","code":""},{"path":"https://keras.posit.co/reference/op_tri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","text":"","code":"op_tri(N, M = NULL, k = 0L, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_tri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","text":"N Number rows tensor. M Number columns tensor. k sub-diagonal array filled. k = 0 main diagonal, k < 0 , k > 0 . default 0. dtype Data type returned tensor. default \"float32\".","code":""},{"path":"https://keras.posit.co/reference/op_tri.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","text":"Tensor lower triangle filled ones zeros elsewhere. T[, j] == 1 j <= + k, 0 otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tril.html","id":null,"dir":"Reference","previous_headings":"","what":"Return lower triangle of a tensor. — op_tril","title":"Return lower triangle of a tensor. — op_tril","text":"tensors ndim exceeding 2, tril apply final two axes.","code":""},{"path":"https://keras.posit.co/reference/op_tril.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return lower triangle of a tensor. — op_tril","text":"","code":"op_tril(x, k = 0L)"},{"path":"https://keras.posit.co/reference/op_tril.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return lower triangle of a tensor. — op_tril","text":"x Input tensor. k Diagonal zero elements. Defaults 0. main diagonal. k < 0 , k > 0 .","code":""},{"path":"https://keras.posit.co/reference/op_tril.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return lower triangle of a tensor. — op_tril","text":"Lower triangle x, shape data type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_triu.html","id":null,"dir":"Reference","previous_headings":"","what":"Return upper triangle of a tensor. — op_triu","title":"Return upper triangle of a tensor. — op_triu","text":"tensors ndim exceeding 2, triu apply final two axes.","code":""},{"path":"https://keras.posit.co/reference/op_triu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return upper triangle of a tensor. — op_triu","text":"","code":"op_triu(x, k = 0L)"},{"path":"https://keras.posit.co/reference/op_triu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return upper triangle of a tensor. — op_triu","text":"x Input tensor. k Diagonal zero elements. Defaults 0. main diagonal. k < 0 , k > 0 .","code":""},{"path":"https://keras.posit.co/reference/op_triu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return upper triangle of a tensor. — op_triu","text":"Upper triangle x, shape data type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_unstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"Unpacks given dimension rank-R tensor rank-(R-1) tensors.","code":""},{"path":"https://keras.posit.co/reference/op_unstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"","code":"op_unstack(x, num = NULL, axis = 1L)"},{"path":"https://keras.posit.co/reference/op_unstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"x input tensor. num length dimension axis. Automatically inferred NULL. axis axis along unpack.","code":""},{"path":"https://keras.posit.co/reference/op_unstack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"list tensors unpacked along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_unstack.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"[3, 4))]: R:3,%204))","code":"x <- op_array(rbind(c(1, 2),                    c(3, 4))) op_unstack(x, axis=1) ## [[1]] ## tf.Tensor([1. 2.], shape=(2), dtype=float64) ## ## [[2]] ## tf.Tensor([3. 4.], shape=(2), dtype=float64) op_unstack(x, axis=2) ## [[1]] ## tf.Tensor([1. 3.], shape=(2), dtype=float64) ## ## [[2]] ## tf.Tensor([2. 4.], shape=(2), dtype=float64) all.equal(op_unstack(x),           op_unstack(x, axis = 1)) ## [1] TRUE all.equal(op_unstack(x, axis = -1),           op_unstack(x, axis = 2)) ## [1] TRUE # [array([1, 2)), array([3, 4))]"},{"path":[]},{"path":"https://keras.posit.co/reference/op_var.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the variance along the specified axes. — op_var","title":"Compute the variance along the specified axes. — op_var","text":"Compute variance along specified axes.","code":""},{"path":"https://keras.posit.co/reference/op_var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the variance along the specified axes. — op_var","text":"","code":"op_var(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the variance along the specified axes. — op_var","text":"x Input tensor. axis Axis axes along variance computed. default compute variance flattened tensor. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the variance along the specified axes. — op_var","text":"Output tensor containing variance.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_vdot.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the dot product of two vectors. — op_vdot","title":"Return the dot product of two vectors. — op_vdot","text":"first argument complex, complex conjugate first argument used calculation dot product. Multidimensional tensors flattened dot product taken.","code":""},{"path":"https://keras.posit.co/reference/op_vdot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the dot product of two vectors. — op_vdot","text":"","code":"op_vdot(x1, x2)"},{"path":"https://keras.posit.co/reference/op_vdot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the dot product of two vectors. — op_vdot","text":"x1 First input tensor. complex, complex conjugate taken calculation dot product. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_vdot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the dot product of two vectors. — op_vdot","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn a function into a vectorized function. — op_vectorize","title":"Turn a function into a vectorized function. — op_vectorize","text":"Turn function vectorized function.","code":""},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn a function into a vectorized function. — op_vectorize","text":"","code":"op_vectorize(func, ..., excluded = NULL, signature = NULL)"},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Turn a function into a vectorized function. — op_vectorize","text":"func Callable single tensor argument. ... forward/backward compatability. excluded Optional set integers representing positional arguments function vectorized. passed directly func unmodified. signature Optional generalized universal function signature, e.g., \"(m,n),(n)->(m)\" vectorized matrix-vector multiplication. provided, func called (expected return) arrays shapes given size corresponding core dimensions. default, func assumed take scalar tensors input output.","code":""},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn a function into a vectorized function. — op_vectorize","text":"new function applies func every element input along axis 1 (batch axis, first axis).","code":""},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Turn a function into a vectorized function. — op_vectorize","text":"","code":"# currently does not work w/ tensorflow backend if(config_backend() != \"tensorflow\") {    myfunc <- function(a, b) a + b    vfunc <- op_vectorize(myfunc)   y <- vfunc(c(1, 2, 3, 4), 2)   print(y)   # with Jax backend, y is:   # Array([3., 4., 5., 6.], dtype=float32) }"},{"path":[]},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":null,"dir":"Reference","previous_headings":"","what":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"Schematically, op_vectorized_map() maps first dimension provided tensors. elements list tensors, tensors required size first dimension, iterated together.","code":""},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"","code":"op_vectorized_map(elements, f)"},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"elements see description f function taking either tensor, list tensors.","code":""},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"tensor list tensors, result mapping f across elements.","code":""},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"Note f may traced compiled. Meaning, R function may evaluated symbolic tensors using Jax TensorFlow backends, eager tensors. See output str() examples:","code":"(x <- op_arange(12L) |> op_reshape(c(3, 4))) ## tf.Tensor( ## [[ 0  1  2  3] ##  [ 4  5  6  7] ##  [ 8  9 10 11]], shape=(3, 4), dtype=int32) x |> op_vectorized_map(\\(row) {row + 10}) ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) list(x, x, x) |> op_vectorized_map(\\(rows) Reduce(`+`, rows)) ## tf.Tensor( ## [[ 0  3  6  9] ##  [12 15 18 21] ##  [24 27 30 33]], shape=(3, 4), dtype=int32) # simplest case, map f over rows of x, # where .x is 1 row of x input <- x output <- op_vectorized_map(input, function(.x) {   str(.x)   .x + 10 }) ## <tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> output ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) # map f over two tensors simultaneously. Here, # `.x` is a list of two # tensors. The return values from each call of `f(row)` are stacked to form the # final output input <- list(x, x) output <- op_vectorized_map(input, function(.x) {   str(.x)   .x[[1]] + 10 }) ## List of 2 ##  $ :<tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> ##  $ :<tf.Tensor 'loop_body/GatherV2_1:0' shape=(4) dtype=int32> output ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) # same as above, but now returning two tensors in the final output output <- op_vectorized_map(input, function(.x) {   str(.x)   c(.x1, .x2) %<-% .x   list(.x1+10, .x2+20) }) ## List of 2 ##  $ :<tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> ##  $ :<tf.Tensor 'loop_body/GatherV2_1:0' shape=(4) dtype=int32> output ## [[1]] ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) ## ## [[2]] ## tf.Tensor( ## [[20 21 22 23] ##  [24 25 26 27] ##  [28 29 30 31]], shape=(3, 4), dtype=int32) # passing named lists. # WARNING: if passing a named list, the order of elements of `.x` supplied # to `f` is not stable. Only retrieve elements by name. input <- list(name1 = x, name2 = x) output <- op_vectorized_map(input, function(.x) {   str(.x)   list(outname1 = .x$name1 + 10,        outname2 = .x$name2 + 20) }) ## List of 2 ##  $ name1:<tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> ##  $ name2:<tf.Tensor 'loop_body/GatherV2_1:0' shape=(4) dtype=int32> output ## $outname1 ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) ## ## $outname2 ## tf.Tensor( ## [[20 21 22 23] ##  [24 25 26 27] ##  [28 29 30 31]], shape=(3, 4), dtype=int32) # passing a tuple() is equivalent to passing an unnamed list() input <- tuple(x, x) output <- op_vectorized_map(input, function(.x) {   str(.x)   list(.x[[1]] + 10) }) ## List of 2 ##  $ :<tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> ##  $ :<tf.Tensor 'loop_body/GatherV2_1:0' shape=(4) dtype=int32> output ## [[1]] ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32)"},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"debugging-f","dir":"Reference","previous_headings":"","what":"Debugging f","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"Even eager contexts, op_vectorized_map() may trace f. case, want eagerly debug f (e.g., browser()), can swap manual (slow) implementation op_vectorized_map(). Note example debug implementation handle edge cases op_vectorized_map(), particular, f returns structure multiple tensors.","code":"op_vectorized_map_debug <- function(elements, fn) {    if (!is.list(elements)) {     # `elements` is a single tensor     batch_size <- op_shape(elements)[[1]]     out <- elements |>       op_split(batch_size) |>       lapply(fn) |>       op_stack()     return(out)   }    # `elements` is a list of tensors   batch_size <- elements[[1]] |> op_shape() |> _[[1]]   elements |>     lapply(\\(e) op_split(e, batch_size)) |>     zip_lists() |>     lapply(fn) |>     op_stack()  }"},{"path":[]},{"path":"https://keras.posit.co/reference/op_vstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Stack tensors in sequence vertically (row wise). — op_vstack","title":"Stack tensors in sequence vertically (row wise). — op_vstack","text":"Stack tensors sequence vertically (row wise).","code":""},{"path":"https://keras.posit.co/reference/op_vstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stack tensors in sequence vertically (row wise). — op_vstack","text":"","code":"op_vstack(xs)"},{"path":"https://keras.posit.co/reference/op_vstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stack tensors in sequence vertically (row wise). — op_vstack","text":"xs Sequence tensors.","code":""},{"path":"https://keras.posit.co/reference/op_vstack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stack tensors in sequence vertically (row wise). — op_vstack","text":"Tensor formed stacking given tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_where.html","id":null,"dir":"Reference","previous_headings":"","what":"Return elements chosen from x1 or x2 depending on condition. — op_where","title":"Return elements chosen from x1 or x2 depending on condition. — op_where","text":"Return elements chosen x1 x2 depending condition.","code":""},{"path":"https://keras.posit.co/reference/op_where.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return elements chosen from x1 or x2 depending on condition. — op_where","text":"","code":"op_where(condition, x1 = NULL, x2 = NULL)"},{"path":"https://keras.posit.co/reference/op_where.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return elements chosen from x1 or x2 depending on condition. — op_where","text":"condition TRUE, yield x1, otherwise yield x2. x1 Values choose condition TRUE. x2 Values choose condition FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_where.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return elements chosen from x1 or x2 depending on condition. — op_where","text":"tensor elements x1 condition TRUE, elements x2 condition FALSE.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":null,"dir":"Reference","previous_headings":"","what":"While loop implementation. — op_while_loop","title":"While loop implementation. — op_while_loop","text":"loop implementation.","code":""},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"While loop implementation. — op_while_loop","text":"","code":"op_while_loop(cond, body, loop_vars, maximum_iterations = NULL)"},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"While loop implementation. — op_while_loop","text":"cond callable represents termination condition loop. Must accept loop_vars like structure argument. loop_vars tuple unnamed list, element loop_vars passed positionally callable. body callable represents loop body. Must accept loop_vars like structure argument, return update value structure. loop_vars tuple unnamed list, element loop_vars passed positionally callable. loop_vars arbitrary nested structure tensor state persist across loop iterations. maximum_iterations Optional maximum number iterations loop run. provided, cond output -ed additional condition ensuring number iterations executed greater maximum_iterations.","code":""},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"While loop implementation. — op_while_loop","text":"list tensors, shape dtype loop_vars.","code":""},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"While loop implementation. — op_while_loop","text":"","code":"i <- 0 loop_vars <- list(i)  # cond() must return a scalar bool cond <- function(i) i < 10L  # body must return same shape as loop_vars body <- function(i) list(i + 1L)  op_while_loop(cond, body, loop_vars) ## [[1]] ## tf.Tensor(10.0, shape=(), dtype=float32) x <- 0; y <- 1 cond <- \\(x, y) x < 10 body <- \\(x, y) list(x+1, y+1) op_while_loop(cond, body, list(x, y)) ## [[1]] ## tf.Tensor(10.0, shape=(), dtype=float32) ## ## [[2]] ## tf.Tensor(11.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_zeros.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","title":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","text":"Return new tensor given shape type, filled zeros.","code":""},{"path":"https://keras.posit.co/reference/op_zeros.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","text":"","code":"op_zeros(shape, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_zeros.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","text":"shape Shape new tensor. dtype Desired data type tensor.","code":""},{"path":"https://keras.posit.co/reference/op_zeros.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","text":"Tensor zeros given shape dtype.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_zeros_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","title":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","text":"Return tensor zeros shape type x.","code":""},{"path":"https://keras.posit.co/reference/op_zeros_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","text":"","code":"op_zeros_like(x, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_zeros_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","text":"x Input tensor. dtype Overrides data type result.","code":""},{"path":"https://keras.posit.co/reference/op_zeros_like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","text":"tensor zeros shape type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"Adadelta optimization stochastic gradient descent method based adaptive learning rate per dimension address two drawbacks: continual decay learning rates throughout training. need manually selected global learning rate. Adadelta robust extension Adagrad adapts learning rates based moving window gradient updates, instead accumulating past gradients. way, Adadelta continues learning even many updates done. Compared Adagrad, original version Adadelta set initial learning rate. version, initial learning rate can set, Keras optimizers.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"","code":"optimizer_adadelta(   learning_rate = 0.001,   rho = 0.95,   epsilon = 1e-07,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adadelta\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"learning_rate float, [LearningRateSchedule()] instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. Note Adadelta tends benefit higher initial learning rate values compared optimizers. match exact form original paper, use 1.0. rho floating point value. decay rate. Defaults 0.95. epsilon Small floating point value maintaining numerical stability. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema = TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema = TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"Zeiler, 2012","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"Adafactor commonly used NLP tasks, advantage taking less memory saves partial information previous gradients. default argument setup based original paper (see reference). gradients dimension > 2, Adafactor optimizer delete last 2 dimensions separately accumulator variables.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"","code":"optimizer_adafactor(   learning_rate = 0.001,   beta_2_decay = -0.8,   epsilon_1 = 1e-30,   epsilon_2 = 0.001,   clip_threshold = 1,   relative_step = TRUE,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adafactor\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_2_decay float, defaults -0.8. decay rate beta_2. epsilon_1 float, defaults 1e-30. small offset keep denominator away 0. epsilon_2 float, defaults 1e-3. small offset avoid learning rate becoming small time. clip_threshold float, defaults 1.0. Clipping threshold. part Adafactor algorithm, independent clipnorm, clipvalue, global_clipnorm. relative_step bool, defaults TRUE. learning_rate constant relative_step=TRUE, learning rate adjusted based current iterations. default learning rate decay Adafactor. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema = TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"Shazeer, Noam et al., 2018.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"Adagrad optimizer parameter-specific learning rates, adapted relative frequently parameter gets updated training. updates parameter receives, smaller updates.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"","code":"optimizer_adagrad(   learning_rate = 0.001,   initial_accumulator_value = 0.1,   epsilon = 1e-07,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adagrad\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. Note Adagrad tends benefit higher initial learning rate values compared optimizers. match exact form original paper, use 1.0. initial_accumulator_value Floating point value. Starting value accumulators (per-parameter momentum values). Must non-negative. epsilon Small floating point value maintaining numerical stability. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"Duchi et al., 2011.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adam.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adam algorithm. — optimizer_adam","title":"Optimizer that implements the Adam algorithm. — optimizer_adam","text":"Adam optimization stochastic gradient descent method based adaptive estimation first-order second-order moments. According Kingma et al., 2014, method \"computationally efficient, little memory requirement, invariant diagonal rescaling gradients, well suited problems large terms data/parameters\".","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adam algorithm. — optimizer_adam","text":"","code":"optimizer_adam(   learning_rate = 0.001,   beta_1 = 0.9,   beta_2 = 0.999,   epsilon = 1e-07,   amsgrad = FALSE,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adam\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adam algorithm. — optimizer_adam","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_1 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 1st moment estimates. Defaults 0.9. beta_2 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 2nd moment estimates. Defaults 0.999. epsilon small constant numerical stability. epsilon \"epsilon hat\" Kingma Ba paper (formula just Section 2.1), epsilon Algorithm 1 paper. Defaults 1e-7. amsgrad Boolean. Whether apply AMSGrad variant algorithm paper \"Convergence Adam beyond\". Defaults FALSE. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adam algorithm. — optimizer_adam","text":"Optimizer instance","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"AdamW optimization stochastic gradient descent method based adaptive estimation first-order second-order moments added method decay weights per techniques discussed paper, 'Decoupled Weight Decay Regularization' Loshchilov, Hutter et al., 2019. According Kingma et al., 2014, underying Adam method \"computationally efficient, little memory requirement, invariant diagonal rescaling gradients, well suited problems large terms data/parameters\".","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"","code":"optimizer_adam_w(   learning_rate = 0.001,   weight_decay = 0.004,   beta_1 = 0.9,   beta_2 = 0.999,   epsilon = 1e-07,   amsgrad = FALSE,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adamw\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. weight_decay Float. set, weight decay applied. beta_1 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 1st moment estimates. Defaults 0.9. beta_2 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 2nd moment estimates. Defaults 0.999. epsilon small constant numerical stability. epsilon \"epsilon hat\" Kingma Ba paper (formula just Section 2.1), epsilon Algorithm 1 paper. Defaults 1e-7. amsgrad Boolean. Whether apply AMSGrad variant algorithm paper \"Convergence Adam beyond\". Defaults FALSE. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"Loshchilov et al., 2019 Kingma et al., 2014 adam Reddi et al., 2018 amsgrad.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"Adamax, variant Adam based infinity norm, first-order gradient-based optimization method. Due capability adjusting learning rate based data characteristics, suited learn time-variant process, e.g., speech data dynamically changed noise conditions. Default parameters follow provided paper (see references ). Initialization:   update rule parameter w gradient g described end section 7.1 paper (see referenece section):","code":"m <- 0  # Initialize initial 1st moment vector u <- 0  # Initialize the exponentially weighted infinity norm t <- 0  # Initialize timestep t <-  t + 1 m <- beta1 * m + (1 - beta) * g u <- max(beta2 * u, abs(g)) current_lr <- learning_rate / (1 - beta1 ** t) w <- w - current_lr * m / (u + epsilon)"},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"","code":"optimizer_adamax(   learning_rate = 0.001,   beta_1 = 0.9,   beta_2 = 0.999,   epsilon = 1e-07,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adamax\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_1 float value constant float tensor. exponential decay rate 1st moment estimates. beta_2 float value constant float tensor. exponential decay rate exponentially weighted infinity norm. epsilon small constant numerical stability. name: String. name use momentum accumulator weights created optimizer. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String, name object ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"Kingma et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_ftrl.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","title":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","text":"\"Follow Regularized Leader\" (FTRL) optimization algorithm developed Google click-rate prediction early 2010s. suitable shallow models large sparse feature spaces. algorithm described McMahan et al., 2013. Keras version support online L2 regularization (L2 regularization described paper ) shrinkage-type L2 regularization (addition L2 penalty loss function). Initialization:   Update rule one variable w:   Notation: lr learning rate g gradient variable lambda_1 L1 regularization strength lambda_2 L2 regularization strength lr_power power scale n. Check documentation l2_shrinkage_regularization_strength parameter details shrinkage enabled, case gradient replaced gradient shrinkage.","code":"n <- 0 sigma <- 0 z <- 0 prev_n <- n n <- n + g^2 sigma <- (n^(-lr_power) - prev_n^(-lr_power)) / lr z <- z + g - sigma * w if (abs(z) < lambda_1) {   w <- 0 } else {   w <- (sgn(z) * lambda_1 - z) / ((beta + sqrt(n)) / alpha + lambda_2) }"},{"path":"https://keras.posit.co/reference/optimizer_ftrl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","text":"","code":"optimizer_ftrl(   learning_rate = 0.001,   learning_rate_power = -0.5,   initial_accumulator_value = 0.1,   l1_regularization_strength = 0,   l2_regularization_strength = 0,   l2_shrinkage_regularization_strength = 0,   beta = 0,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"ftrl\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_ftrl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. learning_rate_power float value, must less equal zero. Controls learning rate decreases training. Use zero fixed learning rate. initial_accumulator_value starting value accumulators. zero positive values allowed. l1_regularization_strength float value, must greater equal zero. Defaults 0.0. l2_regularization_strength float value, must greater equal zero. Defaults 0.0. l2_shrinkage_regularization_strength float value, must greater equal zero. differs L2 L2 stabilization penalty, whereas L2 shrinkage magnitude penalty. input sparse shrinkage happen active weights. beta float value, representing beta value paper. Defaults 0.0. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_ftrl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","text":"Optimizer instance","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Lion algorithm. — optimizer_lion","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"Lion optimizer stochastic-gradient-descent method uses sign operator control magnitude update, unlike adaptive optimizers Adam rely second-order moments. make Lion memory-efficient keeps track momentum. According authors (see reference), performance gain Adam grows batch size. update Lion produced sign operation, resulting larger norm, suitable learning rate Lion typically 3-10x smaller AdamW. weight decay Lion turn 3-10x larger AdamW maintain similar strength (lr * wd).","code":""},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"","code":"optimizer_lion(   learning_rate = 0.001,   beta_1 = 0.9,   beta_2 = 0.99,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"lion\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_1 float value constant float tensor, callable takes arguments returns actual value use. rate combine current gradient 1st moment estimate. Defaults 0.9. beta_2 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 1st moment estimate. Defaults 0.99. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"Chen et al., 2023 Authors' implementation","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_loss_scale.html","id":null,"dir":"Reference","previous_headings":"","what":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","title":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","text":"Loss scaling technique prevent numeric underflow intermediate gradients float16 used. prevent underflow, loss multiplied (\"scaled\") certain factor called \"loss scale\", causes intermediate gradients scaled loss scale well. final gradients divided (\"unscaled\") loss scale bring back original value. LossScaleOptimizer wraps another optimizer applies dynamic loss scaling . loss scale dynamically updated time follows: train step, nonfinite gradient encountered, loss scale halved, train step skipped. dynamic_growth_steps ocurred since last time loss scale updated, nonfinite gradients occurred, loss scale doubled.","code":""},{"path":"https://keras.posit.co/reference/optimizer_loss_scale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","text":"","code":"optimizer_loss_scale(   inner_optimizer,   initial_scale = 32768,   dynamic_growth_steps = 2000L,   ...,   name = NULL,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = NULL,   ema_momentum = NULL,   ema_overwrite_frequency = NULL,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_loss_scale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","text":"inner_optimizer keras Optimizer instance wrap. initial_scale Float. initial loss scale. scale updated training. recommended high number, loss scale high gets lowered far quickly loss scale low gets raised. dynamic_growth_steps Int. often update scale upwards. every dynamic_growth_steps steps finite gradients, loss scale doubled. ... forward/backward compatability. name String. name use momentum accumulator weights created optimizer. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_loss_scale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","text":"Optimizer instance","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"Much like Adam essentially RMSprop momentum, Nadam Adam Nesterov momentum.","code":""},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"","code":"optimizer_nadam(   learning_rate = 0.001,   beta_1 = 0.9,   beta_2 = 0.999,   epsilon = 1e-07,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"nadam\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_1 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 1st moment estimates. Defaults 0.9. beta_2 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 2nd moment estimates. Defaults 0.999. epsilon small constant numerical stability. epsilon \"epsilon hat\" Kingma Ba paper (formula just Section 2.1), epsilon Algorithm 1 paper. Defaults 1e-7. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"Dozat, 2015.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"gist RMSprop : Maintain moving (discounted) average square gradients Divide gradient root average implementation RMSprop uses plain momentum, Nesterov momentum. centered version additionally maintains moving average gradients, uses average estimate variance.","code":""},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"","code":"optimizer_rmsprop(   learning_rate = 0.001,   rho = 0.9,   momentum = 0,   epsilon = 1e-07,   centered = FALSE,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"rmsprop\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"learning_rate float, learning_rate_schedule_* instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. rho float, defaults 0.9. Discounting factor old gradients. momentum float, defaults 0.0. 0.0., optimizer tracks momentum value, decay rate equals 1 - momentum. epsilon small constant numerical stability. epsilon \"epsilon hat\" Kingma Ba paper (formula just Section 2.1), epsilon Algorithm 1 paper. Defaults 1e-7. centered Boolean. TRUE, gradients normalized estimated variance gradient; FALSE, uncentered second moment. Setting TRUE may help training, slightly expensive terms computation memory. Defaults FALSE. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"","code":"opt <- optimizer_rmsprop(learning_rate=0.1)"},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"Hinton, 2012","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_sgd.html","id":null,"dir":"Reference","previous_headings":"","what":"Gradient descent (with momentum) optimizer. — optimizer_sgd","title":"Gradient descent (with momentum) optimizer. — optimizer_sgd","text":"Update rule parameter w gradient g momentum 0:   Update rule momentum larger 0:   nesterov=TRUE, rule becomes:","code":"w <- w - learning_rate * g velocity <- momentum * velocity - learning_rate * g w <- w + velocity velocity <- momentum * velocity - learning_rate * g w <- w + momentum * velocity - learning_rate * g"},{"path":"https://keras.posit.co/reference/optimizer_sgd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gradient descent (with momentum) optimizer. — optimizer_sgd","text":"","code":"optimizer_sgd(   learning_rate = 0.01,   momentum = 0,   nesterov = FALSE,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"SGD\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_sgd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gradient descent (with momentum) optimizer. — optimizer_sgd","text":"learning_rate float, learning_rate_schedule_* instance, callable takes arguments returns actual value use. learning rate. Defaults 0.01. momentum float hyperparameter >= 0 accelerates gradient descent relevant direction dampens oscillations. 0 vanilla gradient descent. Defaults 0.0. nesterov boolean. Whether apply Nesterov momentum. Defaults FALSE. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_sgd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gradient descent (with momentum) optimizer. — optimizer_sgd","text":"Optimizer instance","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":null,"dir":"Reference","previous_headings":"","what":"Packs user-provided data into a list. — pack_x_y_sample_weight","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"convenience utility packing data list formats fit() uses.","code":""},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"","code":"pack_x_y_sample_weight(x, y = NULL, sample_weight = NULL)"},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"x Features pass Model. y Ground-truth targets pass Model. sample_weight Sample weight element.","code":""},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"List format used fit().","code":""},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"","code":"x <- op_ones(c(10, 1)) data <- pack_x_y_sample_weight(x)   y <- op_ones(c(10, 1)) data <- pack_x_y_sample_weight(x, y)"},{"path":[]},{"path":"https://keras.posit.co/reference/pad_sequences.html","id":null,"dir":"Reference","previous_headings":"","what":"Pads sequences to the same length. — pad_sequences","title":"Pads sequences to the same length. — pad_sequences","text":"function transforms list (length num_samples) sequences (lists integers) 2D NumPy array shape (num_samples, num_timesteps). num_timesteps either maxlen argument provided, length longest sequence list. Sequences shorter num_timesteps padded value num_timesteps long. Sequences longer num_timesteps truncated fit desired length. position padding truncation happens determined arguments padding truncating, respectively. Pre-padding removing values beginning sequence default.","code":"sequence <- list(c(1), c(2, 3), c(4, 5, 6)) pad_sequences(sequence) ##      [,1] [,2] [,3] ## [1,]    0    0    1 ## [2,]    0    2    3 ## [3,]    4    5    6 pad_sequences(sequence, value=-1) ##      [,1] [,2] [,3] ## [1,]   -1   -1    1 ## [2,]   -1    2    3 ## [3,]    4    5    6 pad_sequences(sequence, padding='post') ##      [,1] [,2] [,3] ## [1,]    1    0    0 ## [2,]    2    3    0 ## [3,]    4    5    6 pad_sequences(sequence, maxlen=2) ##      [,1] [,2] ## [1,]    0    1 ## [2,]    2    3 ## [3,]    5    6"},{"path":"https://keras.posit.co/reference/pad_sequences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pads sequences to the same length. — pad_sequences","text":"","code":"pad_sequences(   sequences,   maxlen = NULL,   dtype = \"int32\",   padding = \"pre\",   truncating = \"pre\",   value = 0 )"},{"path":"https://keras.posit.co/reference/pad_sequences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pads sequences to the same length. — pad_sequences","text":"sequences List sequences (sequence list integers). maxlen Optional Int, maximum length sequences. provided, sequences padded length longest individual sequence. dtype (Optional, defaults \"int32\"). Type output sequences. pad sequences variable length strings, can use object. padding String, \"pre\" \"post\" (optional, defaults \"pre\"): pad either sequence. truncating String, \"pre\" \"post\" (optional, defaults \"pre\"): remove values sequences larger maxlen, either beginning end sequences. value Float String, padding value. (Optional, defaults 0.)","code":""},{"path":"https://keras.posit.co/reference/pad_sequences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pads sequences to the same length. — pad_sequences","text":"Array shape (len(sequences), maxlen)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See %>% details.","code":""},{"path":"https://keras.posit.co/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://keras.posit.co/reference/pipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pipe operator — %>%","text":"commonly, result calling right hand side left hand side argument: rhs(lhs). See magritter vignette , advanced, usages.","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a Keras model — plot.keras.src.models.model.Model","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"Plot Keras model","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model plot(   x,   show_shapes = FALSE,   show_dtype = FALSE,   show_layer_names = FALSE,   ...,   rankdir = \"TB\",   expand_nested = FALSE,   dpi = 200,   layer_range = NULL,   show_layer_activations = FALSE,   show_trainable = NA,   to_file = NULL )"},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"x Keras model instance show_shapes whether display shape information. show_dtype whether display layer dtypes. show_layer_names whether display layer names. ... passed Python keras.utils.model_to_dot(). Used forward backward compatibility. rankdir string specifying format plot: 'TB' creates vertical plot; 'LR' creates horizontal plot. (argument passed PyDot) expand_nested Whether expand nested models clusters. dpi Dots per inch. Increase value image text appears excessively pixelated. layer_range list containing two character strings, starting layer name ending layer name (inclusive) indicating range layers plot generated. also accepts regex patterns instead exact name. case, start predicate first element matches layer_range[1] end predicate last element matches layer_range[2]. default NULL considers layers model. Note must pass range resultant subgraph must complete. show_layer_activations Display layer activations (layers activation property). show_trainable whether display layer trainable. to_file File name plot image. NULL (default), model drawn default graphics device. Otherwise, file saved.","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"Nothing, called side effects.","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"ValueError: plot(model) called model built, unless input_shape =  argument supplied keras_model_sequential().","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"requirements","dir":"Reference","previous_headings":"","what":"Requirements","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"function requires pydot graphviz. pydot default installed install_keras(), installed keras means, can install pydot directly :   can install graphviz directly : https://graphviz.gitlab.io/download/ Linux platforms, can install graphviz via package manager. example, Ubuntu/Debian can install   conda environment, can install graphviz :","code":"reticulate::py_install(\"pydot\", pip = TRUE) sudo apt install graphviz reticulate::conda_install(packages = \"graphviz\") # Restart the R session after install."},{"path":"https://keras.posit.co/reference/plot.keras_training_history.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot training history — plot.keras_training_history","title":"Plot training history — plot.keras_training_history","text":"Plots metrics recorded training.","code":""},{"path":"https://keras.posit.co/reference/plot.keras_training_history.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot training history — plot.keras_training_history","text":"","code":"# S3 method for keras_training_history plot(   x,   y,   metrics = NULL,   method = c(\"auto\", \"ggplot2\", \"base\"),   smooth = getOption(\"keras.plot.history.smooth\", TRUE),   theme_bw = getOption(\"keras.plot.history.theme_bw\", FALSE),   ... )"},{"path":"https://keras.posit.co/reference/plot.keras_training_history.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot training history — plot.keras_training_history","text":"x Training history object returned fit.keras.src.models.model.Model(). y Unused. metrics One metrics plot (e.g. c('loss', 'accuracy')). Defaults plotting captured metrics. method Method use plotting. default \"auto\" use ggplot2 available, otherwise use base graphics. smooth Whether loess smooth added plot, available ggplot2 method. number epochs smaller ten, forced false. theme_bw Use ggplot2::theme_bw() plot history black white. ... Additional parameters pass plot() method.","code":""},{"path":"https://keras.posit.co/reference/plot.keras_training_history.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot training history — plot.keras_training_history","text":"method == \"ggplot2\", ggplot object returned. method == \"base\", function draw graphics device return NULL, invisibly.","code":""},{"path":"https://keras.posit.co/reference/pop_layer.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove the last layer in a Sequential model — pop_layer","title":"Remove the last layer in a Sequential model — pop_layer","text":"Remove last layer Sequential model","code":""},{"path":"https://keras.posit.co/reference/pop_layer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove the last layer in a Sequential model — pop_layer","text":"","code":"pop_layer(object)"},{"path":"https://keras.posit.co/reference/pop_layer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove the last layer in a Sequential model — pop_layer","text":"object Sequential keras model object","code":""},{"path":"https://keras.posit.co/reference/pop_layer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove the last layer in a Sequential model — pop_layer","text":"input object, invisibly.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"Generates output predictions input samples.","code":""},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model predict(   object,   x,   ...,   batch_size = NULL,   verbose = getOption(\"keras.verbose\", default = \"auto\"),   steps = NULL,   callbacks = NULL )"},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"object Keras model object x Input samples. : array (array-like), list arrays (case model multiple inputs). tensor, list tensors (case model multiple inputs). TF Dataset. ... forward/backward compatability. batch_size Integer NULL. Number samples per batch. unspecified, batch_size default 32. specify batch_size data form TF Dataset generator (since generate batches). verbose \"auto\", 0, 1, 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. \"auto\" becomes 1 cases, 2 knitr render running distributed training server. Note progress bar particularly useful logged file, verbose=2 recommended running interactively (e.g., production environment). Defaults \"auto\". steps Total number steps (batches samples) declaring prediction round finished. Ignored default value NULL. x TF Dataset steps NULL, predict() run input dataset exhausted. callbacks List Callback instances. List callbacks apply prediction.","code":""},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"R array(s) predictions.","code":""},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"Computation done batches. method designed batch processing large numbers inputs. intended use inside loops iterate data process small numbers inputs time. small numbers inputs fit one batch, directly call model model$call faster execution, e.g., model(x), model(x, training = FALSE) layers BatchNormalization behave differently inference.","code":""},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"See FAQ entry details difference Model methods predict() call().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/predict_on_batch.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns predictions for a single batch of samples. — predict_on_batch","title":"Returns predictions for a single batch of samples. — predict_on_batch","text":"Returns predictions single batch samples.","code":""},{"path":"https://keras.posit.co/reference/predict_on_batch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns predictions for a single batch of samples. — predict_on_batch","text":"","code":"predict_on_batch(object, x)"},{"path":"https://keras.posit.co/reference/predict_on_batch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns predictions for a single batch of samples. — predict_on_batch","text":"object Keras model object x Input data. must array-like.","code":""},{"path":"https://keras.posit.co/reference/predict_on_batch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns predictions for a single batch of samples. — predict_on_batch","text":"Array(s) predictions.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/process_utils.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocessing and postprocessing utilities — process_utils","title":"Preprocessing and postprocessing utilities — process_utils","text":"functions used preprocess postprocess inputs outputs Keras applications.","code":""},{"path":"https://keras.posit.co/reference/process_utils.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocessing and postprocessing utilities — process_utils","text":"","code":"application_preprocess_inputs(model, x, ..., data_format = NULL)  application_decode_predictions(model, preds, top = 5L, ...)"},{"path":"https://keras.posit.co/reference/process_utils.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocessing and postprocessing utilities — process_utils","text":"model Keras model initialized using application_ function. x batch inputs model. ... Additional arguments passed preprocessing decoding function. data_format Optional data format image tensor/array. NULL means global setting config_image_data_format() used (unless changed , uses \"channels_last\"). Defaults NULL. preds batch outputs model. top number top predictions return.","code":""},{"path":"https://keras.posit.co/reference/process_utils.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocessing and postprocessing utilities — process_utils","text":"list decoded predictions case application_decode_predictions(). batch preprocessed inputs case application_preprocess_inputs().","code":""},{"path":"https://keras.posit.co/reference/process_utils.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Preprocessing and postprocessing utilities — process_utils","text":"application_preprocess_inputs(): Pre-process inputs used model application_decode_predictions(): Decode predictions model","code":""},{"path":"https://keras.posit.co/reference/process_utils.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preprocessing and postprocessing utilities — process_utils","text":"","code":"if (FALSE) { model <- application_convnext_tiny()  inputs <- random_normal(c(32, 224, 224, 3)) processed_inputs <- application_preprocess_inputs(model, inputs)  preds <- random_normal(c(32, 1000)) decoded_preds <- application_decode_predictions(model, preds)  }"},{"path":"https://keras.posit.co/reference/quantize_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantize the weights of a model. — quantize_weights","title":"Quantize the weights of a model. — quantize_weights","text":"Note model must built first calling method. quantize_weights() recursively call layer$quantize(mode) layers skipped layer implement function. Currently Dense EinsumDense layers support quantization.","code":""},{"path":"https://keras.posit.co/reference/quantize_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantize the weights of a model. — quantize_weights","text":"","code":"quantize_weights(object, mode)"},{"path":"https://keras.posit.co/reference/quantize_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantize the weights of a model. — quantize_weights","text":"object Keras Model Layer. mode mode quantization. 'int8' supported time.","code":""},{"path":"https://keras.posit.co/reference/quantize_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantize the weights of a model. — quantize_weights","text":"model, invisibly. Note just convenience usage |>, model modified -place.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_beta.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from a Beta distribution. — random_beta","title":"Draw samples from a Beta distribution. — random_beta","text":"values drawm Beta distribution parametrized alpha beta.","code":""},{"path":"https://keras.posit.co/reference/random_beta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from a Beta distribution. — random_beta","text":"","code":"random_beta(shape, alpha, beta, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_beta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from a Beta distribution. — random_beta","text":"shape shape random values generate. alpha Float array floats representing first parameter alpha. Must broadcastable beta shape. beta Float array floats representing second parameter beta. Must broadcastable alpha shape. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults \"float32\" unless configured otherwise (via config_set_floatx(float_dtype)). seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_beta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from a Beta distribution. — random_beta","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_binomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from a Binomial distribution. — random_binomial","title":"Draw samples from a Binomial distribution. — random_binomial","text":"values drawn Binomial distribution specified trial count probability success.","code":""},{"path":"https://keras.posit.co/reference/random_binomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from a Binomial distribution. — random_binomial","text":"","code":"random_binomial(shape, counts, probabilities, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_binomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from a Binomial distribution. — random_binomial","text":"shape shape random values generate. counts number array numbers representing number trials. must broadcastable probabilities. probabilities float array floats representing probability success individual event. must broadcastable counts. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults \"float32\" unless configured otherwise (via config_set_floatx(float_dtype)). seed Python integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer None (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_binomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from a Binomial distribution. — random_binomial","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_categorical.html","id":null,"dir":"Reference","previous_headings":"","what":"Draws samples from a categorical distribution. — random_categorical","title":"Draws samples from a categorical distribution. — random_categorical","text":"function takes input logits, 2-D input tensor shape (batch_size, num_classes). row input represents categorical distribution, column index containing log-probability given class. function output 2-D tensor shape (batch_size, num_samples), row contains samples corresponding row logits. column index contains independent samples drawn input distribution.","code":""},{"path":"https://keras.posit.co/reference/random_categorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draws samples from a categorical distribution. — random_categorical","text":"","code":"random_categorical(logits, num_samples, dtype = \"int32\", seed = NULL)"},{"path":"https://keras.posit.co/reference/random_categorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draws samples from a categorical distribution. — random_categorical","text":"logits 2-D Tensor shape (batch_size, num_classes). row define categorical distibution unnormalized log-probabilities classes. num_samples Int, number independent samples draw row input. second dimension output tensor's shape. dtype Optional dtype output tensor. seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_categorical.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draws samples from a categorical distribution. — random_categorical","text":"2-D tensor (batch_size, num_samples).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomly set some values in a tensor to 0. — random_dropout","title":"Randomly set some values in a tensor to 0. — random_dropout","text":"Randomly set portion values tensor 0.","code":""},{"path":"https://keras.posit.co/reference/random_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomly set some values in a tensor to 0. — random_dropout","text":"","code":"random_dropout(inputs, rate, noise_shape = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomly set some values in a tensor to 0. — random_dropout","text":"inputs tensor rate numeric noise_shape shape() value seed Initial seed random number generator","code":""},{"path":"https://keras.posit.co/reference/random_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Randomly set some values in a tensor to 0. — random_dropout","text":"tensor copy inputs values set 0.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_gamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw random samples from the Gamma distribution. — random_gamma","title":"Draw random samples from the Gamma distribution. — random_gamma","text":"Draw random samples Gamma distribution.","code":""},{"path":"https://keras.posit.co/reference/random_gamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw random samples from the Gamma distribution. — random_gamma","text":"","code":"random_gamma(shape, alpha, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_gamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw random samples from the Gamma distribution. — random_gamma","text":"shape shape random values generate. alpha Float, parameter distribution. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults float32 unless configured otherwise (via config_set_floatx(float_dtype)). seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_gamma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw random samples from the Gamma distribution. — random_gamma","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_integer.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw random integers from a uniform distribution. — random_integer","title":"Draw random integers from a uniform distribution. — random_integer","text":"generated values follow uniform distribution range [minval, maxval). lower bound minval included range, upper bound maxval excluded. dtype must integer type.","code":""},{"path":"https://keras.posit.co/reference/random_integer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw random integers from a uniform distribution. — random_integer","text":"","code":"random_integer(shape, minval, maxval, dtype = \"int32\", seed = NULL)"},{"path":"https://keras.posit.co/reference/random_integer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw random integers from a uniform distribution. — random_integer","text":"shape shape random values generate. minval integer, lower bound range random values generate (inclusive). maxval integer, upper bound range random values generate (exclusive). dtype Optional dtype tensor. integer types supported. specified, \"int32\" used. seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_integer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw random integers from a uniform distribution. — random_integer","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw random samples from a normal (Gaussian) distribution. — random_normal","title":"Draw random samples from a normal (Gaussian) distribution. — random_normal","text":"Draw random samples normal (Gaussian) distribution.","code":""},{"path":"https://keras.posit.co/reference/random_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw random samples from a normal (Gaussian) distribution. — random_normal","text":"","code":"random_normal(shape, mean = 0, stddev = 1, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw random samples from a normal (Gaussian) distribution. — random_normal","text":"shape shape random values generate. mean Float, defaults 0. Mean random values generate. stddev Float, defaults 1. Standard deviation random values generate. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults float32 unless configured otherwise (via config_set_floatx(float_dtype)). seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw random samples from a normal (Gaussian) distribution. — random_normal","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"Keras, RNG-using methods (random_normal()) stateless, meaning pass integer seed (seed = 42), return values call. order get different values call, must use SeedGenerator instead seed argument. SeedGenerator object stateful.","code":""},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"","code":"random_seed_generator(seed = NULL, name = NULL, ...)"},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"seed Initial seed random number generator name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"SeedGenerator instance, can passed seed = argument random tensor generators.","code":""},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"Usage layer:","code":"seed_gen <- random_seed_generator(seed = 42) values <- random_normal(shape = c(2, 3), seed = seed_gen) new_values <- random_normal(shape = c(2, 3), seed = seed_gen) layer_dropout2 <- new_layer_class(   \"dropout2\",   initialize = function(...) {     super$initialize(...)     self$seed_generator <- random_seed_generator(seed = 1337)   },   call = function(x, training = FALSE) {     if (training) {       return(random_dropout(x, rate = 0.5, seed = self$seed_generator))     }     return(x)   } )  out <- layer_dropout(rate = 0.8) out(op_ones(10), training = TRUE) ## tf.Tensor([0. 5. 5. 0. 0. 0. 0. 0. 0. 0.], shape=(10), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/random_shuffle.html","id":null,"dir":"Reference","previous_headings":"","what":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","title":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","text":"Shuffle elements tensor uniformly random along axis.","code":""},{"path":"https://keras.posit.co/reference/random_shuffle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","text":"","code":"random_shuffle(x, axis = 1L, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_shuffle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","text":"x tensor shuffled. axis integer specifying axis along shuffle. Defaults 0. seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_shuffle.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","text":"tensor, copy x axis axis shuffled.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_truncated_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from a truncated normal distribution. — random_truncated_normal","title":"Draw samples from a truncated normal distribution. — random_truncated_normal","text":"values drawn normal distribution specified mean standard deviation, discarding re-drawing samples two standard deviations mean.","code":""},{"path":"https://keras.posit.co/reference/random_truncated_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from a truncated normal distribution. — random_truncated_normal","text":"","code":"random_truncated_normal(shape, mean = 0, stddev = 1, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_truncated_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from a truncated normal distribution. — random_truncated_normal","text":"shape shape random values generate. mean Float, defaults 0. Mean random values generate. stddev Float, defaults 1. Standard deviation random values generate. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults float32 unless configured otherwise (via config_set_floatx(float_dtype)) seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_truncated_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from a truncated normal distribution. — random_truncated_normal","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from a uniform distribution. — random_uniform","title":"Draw samples from a uniform distribution. — random_uniform","text":"generated values follow uniform distribution range [minval, maxval). lower bound minval included range, upper bound maxval excluded. dtype must floating point type, default range [0, 1).","code":""},{"path":"https://keras.posit.co/reference/random_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from a uniform distribution. — random_uniform","text":"","code":"random_uniform(shape, minval = 0, maxval = 1, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from a uniform distribution. — random_uniform","text":"shape shape random values generate. minval Float, defaults 0. Lower bound range random values generate (inclusive). maxval Float, defaults 1. Upper bound range random values generate (exclusive). dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults float32 unless configured otherwise (via config_set_floatx(float_dtype)) seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from a uniform distribution. — random_uniform","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. generics compile, fit magrittr %<>% reticulate array_reshape, as_iterator, iter_next, iterate, np_array, tuple, use_python, use_virtualenv tensorflow all_dims, as_tensor, evaluate, export_savedmodel, tensorboard tfruns flag_boolean, flag_integer, flag_numeric, flag_string, flags, run_dir","code":""},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":null,"dir":"Reference","previous_headings":"","what":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"function registers custom class function Keras custom object registry, can serialized deserialized without needing entry user-provided custom_objects argument. also injects function Keras call get object's serializable string key. Note serialized deserialized, classes must implement get_config() method. Functions requirement. object registered key 'package>name' name, defaults object name passed.","code":""},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"","code":"register_keras_serializable(object, name = NULL, package = NULL)"},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"object keras object. name name serialize class package. package package class belongs . used key (\"package>name\") identify class. Defaults current package name, \"Custom\" outside package.","code":""},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"object returned invisibly, convenient piping. primarily called side effects.","code":""},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"","code":"# Note that `'my_package'` is used as the `package` argument here, and since # the `name` argument is not provided, `'MyDense'` is used as the `name`. layer_my_dense <- Layer(\"MyDense\") register_keras_serializable(layer_my_dense, package = \"my_package\")  MyDense <- environment(layer_my_dense)$`__class__` # the python class obj stopifnot(exprs = {   get_registered_object('my_package>MyDense') == MyDense   get_registered_name(MyDense) == 'my_package>MyDense' })"},{"path":[]},{"path":"https://keras.posit.co/reference/regularizer_l1.html","id":null,"dir":"Reference","previous_headings":"","what":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","title":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","text":"L1 regularization penalty computed : loss = l1 * reduce_sum(abs(x)) L1 may passed layer string identifier:   case, default value used l1=0.01.","code":"dense <- layer_dense(units = 3, kernel_regularizer = 'l1')"},{"path":"https://keras.posit.co/reference/regularizer_l1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","text":"","code":"regularizer_l1(l1 = 0.01)"},{"path":"https://keras.posit.co/reference/regularizer_l1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","text":"l1 float, L1 regularization factor.","code":""},{"path":"https://keras.posit.co/reference/regularizer_l1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","text":"Regularizer instance can passed layer constructors used standalone object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/regularizer_l1_l2.html","id":null,"dir":"Reference","previous_headings":"","what":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","title":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","text":"L1 regularization penalty computed : loss = l1 * reduce_sum(abs(x)) L2 regularization penalty computed loss = l2 * reduce_sum(square(x)) L1L2 may passed layer string identifier:   case, default values used l1=0.01 l2=0.01.","code":"dense <- layer_dense(units = 3, kernel_regularizer = 'L1L2')"},{"path":"https://keras.posit.co/reference/regularizer_l1_l2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","text":"","code":"regularizer_l1_l2(l1 = 0, l2 = 0)"},{"path":"https://keras.posit.co/reference/regularizer_l1_l2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","text":"l1 float, L1 regularization factor. l2 float, L2 regularization factor.","code":""},{"path":"https://keras.posit.co/reference/regularizer_l1_l2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","text":"Regularizer instance can passed layer constructors used standalone object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/regularizer_l2.html","id":null,"dir":"Reference","previous_headings":"","what":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","title":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","text":"L2 regularization penalty computed : loss = l2 * reduce_sum(square(x)) L2 may passed layer string identifier:   case, default value used l2=0.01.","code":"dense <- layer_dense(units = 3, kernel_regularizer='l2')"},{"path":"https://keras.posit.co/reference/regularizer_l2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","text":"","code":"regularizer_l2(l2 = 0.01)"},{"path":"https://keras.posit.co/reference/regularizer_l2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","text":"l2 float, L2 regularization factor.","code":""},{"path":"https://keras.posit.co/reference/regularizer_l2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","text":"Regularizer instance can passed layer constructors used standalone object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":null,"dir":"Reference","previous_headings":"","what":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"can applied either rows matrix (mode=\"rows\") columns (mode=\"columns\"). applied Dense kernel shape (input_dim, units), rows mode seek make feature vectors (.e. basis output space) orthogonal .","code":""},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"","code":"regularizer_orthogonal(factor = 0.01, mode = \"rows\")"},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"factor Float. regularization factor. regularization penalty proportional factor times mean dot products L2-normalized rows (mode=\"rows\", columns mode=\"columns\") inputs, excluding product row/column .  Defaults 0.01. mode String, one {\"rows\", \"columns\"}. Defaults \"rows\". rows mode, regularization effect seeks make rows input orthogonal . columns mode, seeks make columns input orthogonal .","code":""},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"Regularizer instance can passed layer constructors used standalone object.","code":""},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"","code":"regularizer <- regularizer_orthogonal(factor=0.01) layer <- layer_dense(units=4, kernel_regularizer=regularizer)"},{"path":[]},{"path":"https://keras.posit.co/reference/reset_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Reset the state for a model, layer or metric. — reset_state","title":"Reset the state for a model, layer or metric. — reset_state","text":"Reset state model, layer metric.","code":""},{"path":"https://keras.posit.co/reference/reset_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reset the state for a model, layer or metric. — reset_state","text":"","code":"reset_state(object)"},{"path":"https://keras.posit.co/reference/reset_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reset the state for a model, layer or metric. — reset_state","text":"object Model, Layer, Metric instance Layers resettable state (E.g., adapt()-able preprocessing layers rnn layers resettable state, layer_dense() ). Calling Layer instance without resettable-state error.","code":""},{"path":"https://keras.posit.co/reference/reset_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reset the state for a model, layer or metric. — reset_state","text":"object, invisibly.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell class for the GRU layer. — rnn_cell_gru","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"class processes one step within whole time sequence input, whereas layer_gru() processes whole sequence.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"","code":"rnn_cell_gru(   units,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   reset_after = TRUE,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). recurrent_activation Activation function use recurrent step. Default: sigmoid (sigmoid). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. reset_after GRU convention (whether apply reset gate matrix multiplication). FALSE = \"\", TRUE = \"\" (default cuDNN compatible). seed Random seed dropout. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"Layer instance, intended used layer_rnn().","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"inputs: 2D tensor, shape (batch, features). states: 2D tensor shape (batch, units), state previous time step. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout used.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"","code":"inputs <- random_uniform(c(32, 10, 8)) outputs <- inputs |> layer_rnn(rnn_cell_gru(4)) shape(outputs) ## shape(32, 4) rnn <- layer_rnn(    cell = rnn_cell_gru(4),    return_sequences=TRUE,    return_state=TRUE) c(whole_sequence_output, final_state) %<-% rnn(inputs) shape(whole_sequence_output) ## shape(32, 10, 4) shape(final_state) ## shape(32, 4)"},{"path":[]},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell class for the LSTM layer. — rnn_cell_lstm","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"class processes one step within whole time sequence input, whereas layer_lstm() processes whole sequence.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"","code":"rnn_cell_lstm(   units,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). recurrent_activation Activation function use recurrent step. Default: sigmoid (sigmoid). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". unit_forget_bias Boolean (default TRUE). TRUE, add 1 bias forget gate initialization. Setting TRUE also force bias_initializer=\"zeros\". recommended Jozefowicz et al. kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. seed Random seed dropout. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"Layer instance, intended used layer_rnn().","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"inputs: 2D tensor, shape (batch, features). states: 2D tensor shape (batch, units), state previous time step. training: Boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout used.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"","code":"inputs <- random_uniform(c(32, 10, 8)) output <- inputs |>   layer_rnn(cell = rnn_cell_lstm(4)) shape(output) ## shape(32, 4) rnn <- layer_rnn(cell = rnn_cell_lstm(4),                  return_sequences = T,                  return_state = T) c(whole_sequence_output, ...final_state) %<-% rnn(inputs) str(whole_sequence_output) ## <tf.Tensor: shape=(32, 10, 4), dtype=float32, numpy=…> str(final_state) ## List of 2 ##  $ :<tf.Tensor: shape=(32, 4), dtype=float32, numpy=…> ##  $ :<tf.Tensor: shape=(32, 4), dtype=float32, numpy=…>"},{"path":[]},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell class for SimpleRNN. — rnn_cell_simple","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"class processes one step within whole time sequence input, whereas layer_simple_rnn() processes whole sequence.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"","code":"rnn_cell_simple(   units,   activation = \"tanh\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. seed Random seed dropout. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"Layer instance, intended used layer_rnn().","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"sequence: 2D tensor, shape (batch, features). states: 2D tensor shape (batch, units), state previous time step. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout used.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"","code":"inputs <- random_uniform(c(32, 10, 8)) rnn <- layer_rnn(cell = rnn_cell_simple(units = 4)) output <- rnn(inputs)  # The output has shape `(32, 4)`. rnn <- layer_rnn(     cell = rnn_cell_simple(units = 4),     return_sequences=TRUE,     return_state=TRUE ) # whole_sequence_output has shape `(32, 10, 4)`. # final_state has shape `(32, 4)`. c(whole_sequence_output, final_state) %<-% rnn(inputs)"},{"path":[]},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"Used implement efficient stacked RNNs.","code":""},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"","code":"rnn_cells_stack(cells, ...)"},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"cells List RNN cell instances. ... Unnamed arguments treated additional cells. Named arguments passed underlying layer.","code":""},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"Layer instance, intended used layer_rnn().","code":""},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"","code":"batch_size <- 3 sentence_length <- 5 num_features <- 2 new_shape <- c(batch_size, sentence_length, num_features) x <- array(1:30, dim = new_shape)  rnn_cells <- lapply(1:2, function(x) rnn_cell_lstm(units = 128)) stacked_lstm <- rnn_cells_stack(rnn_cells) lstm_layer <- layer_rnn(cell = stacked_lstm)  result <- lstm_layer(x) str(result) ## <tf.Tensor: shape=(3, 128), dtype=float32, numpy=…>"},{"path":[]},{"path":"https://keras.posit.co/reference/save_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves a model as a .keras file. — save_model","title":"Saves a model as a .keras file. — save_model","text":"Saves model .keras file.","code":""},{"path":"https://keras.posit.co/reference/save_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves a model as a .keras file. — save_model","text":"","code":"save_model(model, filepath = NULL, overwrite = FALSE, ...)"},{"path":"https://keras.posit.co/reference/save_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves a model as a .keras file. — save_model","text":"model keras model. filepath string, Path save model. Must end .keras. overwrite Whether overwrite existing model target location, instead ask user via interactive prompt. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/save_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Saves a model as a .keras file. — save_model","text":"filepath provided, function called primarily side effects, model returned invisibly. filepath provided NULL, serialized model returned R raw vector.","code":""},{"path":"https://keras.posit.co/reference/save_model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Saves a model as a .keras file. — save_model","text":"saved .keras file contains: model's configuration (architecture) model's weights model's optimizer's state () Thus models can reinstantiated exact state.","code":"model <- keras_model_sequential(input_shape = c(3)) |>   layer_dense(5) |>   layer_activation_softmax()  model |> save_model(\"model.keras\") loaded_model <- load_model(\"model.keras\") x <- random_uniform(c(10, 3)) stopifnot(all.equal(   model |> predict(x),   loaded_model |> predict(x) )) zip::zip_list(\"model.keras\")[, \"filename\"] ## [1] \"metadata.json\"    \"config.json\"      \"model.weights.h5\""},{"path":[]},{"path":"https://keras.posit.co/reference/save_model_config.html","id":null,"dir":"Reference","previous_headings":"","what":"Save and load model configuration as JSON — save_model_config","title":"Save and load model configuration as JSON — save_model_config","text":"Save re-load models configurations JSON. Note representation include weights, architecture.","code":""},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Save and load model configuration as JSON — save_model_config","text":"","code":"save_model_config(model, filepath = NULL, overwrite = FALSE)  load_model_config(filepath, custom_objects = NULL)"},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Save and load model configuration as JSON — save_model_config","text":"model Model object save filepath path json file model config. overwrite Whether overwrite existing model configuration json filepath, instead ask user via interactive prompt. custom_objects Optional named list mapping names custom classes functions considered deserialization.","code":""},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Save and load model configuration as JSON — save_model_config","text":"called primarily side effects. model returned, invisibly, enable usage pipe.","code":""},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Save and load model configuration as JSON — save_model_config","text":"Note: save_model_config() serializes model JSON using serialize_keras_object(), get_config(). serialize_keras_object() returns superset get_config(), additional information needed create class object needed restore model. See example extract get_config() value saved model.","code":""},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Save and load model configuration as JSON — save_model_config","text":"","code":"model <- keras_model_sequential(input_shape = 10) |> layer_dense(10) file <- tempfile(\"model-config-\", fileext = \".json\") save_model_config(model, file)  # load a new model instance with the same architecture but different weights model2 <- load_model_config(file)  stopifnot(exprs = {   all.equal(get_config(model), get_config(model2))    # To extract the `get_config()` value from a saved model config:   all.equal(       get_config(model),       structure(jsonlite::read_json(file)$config,                 \"__class__\" = keras_model_sequential()$`__class__`)   ) })"},{"path":[]},{"path":"https://keras.posit.co/reference/save_model_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves all layer weights to a .weights.h5 file. — save_model_weights","title":"Saves all layer weights to a .weights.h5 file. — save_model_weights","text":"Saves layer weights .weights.h5 file.","code":""},{"path":"https://keras.posit.co/reference/save_model_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves all layer weights to a .weights.h5 file. — save_model_weights","text":"","code":"save_model_weights(model, filepath, overwrite = FALSE)"},{"path":"https://keras.posit.co/reference/save_model_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves all layer weights to a .weights.h5 file. — save_model_weights","text":"model keras Model object filepath string. Path save model. Must end .weights.h5. overwrite Whether overwrite existing model target location, instead ask user via interactive prompt.","code":""},{"path":"https://keras.posit.co/reference/save_model_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Saves all layer weights to a .weights.h5 file. — save_model_weights","text":"called primarily side effects. model returned, invisibly, enable usage pipe.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/serialize_keras_object.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","title":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","text":"serialize_keras_object() serializes Keras object named list represents object, reciprocal function deserialize_keras_object(). See deserialize_keras_object() information full config format.","code":""},{"path":"https://keras.posit.co/reference/serialize_keras_object.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","text":"","code":"serialize_keras_object(obj)"},{"path":"https://keras.posit.co/reference/serialize_keras_object.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","text":"obj Keras object serialize.","code":""},{"path":"https://keras.posit.co/reference/serialize_keras_object.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","text":"named list represents object config. config expected contain simple types , can saved json. object can deserialized config via deserialize_keras_object().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/set_random_seed.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","title":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","text":"can use utility make almost Keras program fully deterministic. limitations apply cases network communications involved (e.g. parameter server distribution), creates additional sources randomness, certain non-deterministic cuDNN ops involved. sets: R session seed: set.seed() Python session seed: import random; random.seed(seed) Python NumPy seed: import numpy; numpy.random.seed(seed) TensorFlow seed: tf$random$set_seed(seed) (TF installed) Torch seed: import(\"torch\")$manual_seed(seed) (backend torch) disables Python hash randomization. Note TensorFlow seed set even using TensorFlow backend framework, since many workflows leverage tf$data pipelines (feature random shuffling). Likewise many workflows might leverage NumPy APIs.","code":""},{"path":"https://keras.posit.co/reference/set_random_seed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","text":"","code":"set_random_seed(seed)"},{"path":"https://keras.posit.co/reference/set_random_seed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","text":"seed Integer, random seed use.","code":""},{"path":"https://keras.posit.co/reference/set_random_seed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/shape.html","id":null,"dir":"Reference","previous_headings":"","what":"Tensor shape utility — shape","title":"Tensor shape utility — shape","text":"function can used get create tensor shape.","code":""},{"path":"https://keras.posit.co/reference/shape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tensor shape utility — shape","text":"","code":"shape(...)  # S3 method for keras_shape format(x, ..., prefix = TRUE)  # S3 method for keras_shape print(x, ...)  # S3 method for keras_shape [(x, ...)  # S3 method for keras_shape as.integer(x, ...)  # S3 method for keras_shape as.list(x, ...)"},{"path":"https://keras.posit.co/reference/shape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tensor shape utility — shape","text":"... shape specification. Numerics, NULL tensors valid. NULL, NA, -1L can used specify unspecified dim size. Tensors dispatched op_shape() extract tensor shape. Values wrapped () used asis (see examples). objects coerced via .integer(). x keras_shape object. prefix Whether format shape object prefix. Defaults \"shape\".","code":""},{"path":"https://keras.posit.co/reference/shape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tensor shape utility — shape","text":"list \"keras_shape\" class attribute. element list either ) NULL, b) R integer c) scalar integer tensor (e.g., supplied TF tensor unspecified dimension function traced).","code":""},{"path":"https://keras.posit.co/reference/shape.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tensor shape utility — shape","text":"3 ways specify unknown dimension     functions take 'shape' argument also coerce shape()     can also use shape() get shape tensor (excepting scalar integer tensors).             Combine expand shapes         Scalar integer tensors treated axis values. commonly encountered tracing function graph mode, axis size might unknown.     useful pattern unpack shape() %<-%, like :                     want resolve shape tensor can potentially scalar integer, can wrap tensor (), use op_shape().","code":"shape(1, 2, 3) ## shape(1, 2, 3) shape(NA,   2, 3) shape(NULL, 2, 3) shape(-1,   2, 3) ## shape(NA, 2, 3) ## shape(NA, 2, 3) ## shape(NA, 2, 3) layer_input(c(1, 2, 3)) layer_input(shape(1, 2, 3)) ## <KerasTensor shape=(None, 1, 2, 3), dtype=float32, sparse=None, name=keras_tensor> ## <KerasTensor shape=(None, 1, 2, 3), dtype=float32, sparse=None, name=keras_tensor_1> symbolic_tensor <- layer_input(shape(1, 2, 3)) shape(symbolic_tensor) ## shape(NA, 1, 2, 3) eager_tensor <- op_ones(c(1,2,3)) shape(eager_tensor) ## shape(1, 2, 3) op_shape(eager_tensor) ## shape(1, 2, 3) shape(symbolic_tensor, 4) ## shape(NA, 1, 2, 3, 4) shape(5, symbolic_tensor, 4) ## shape(5, NA, 1, 2, 3, 4) tfn <- tensorflow::tf_function(function(x) {   print(op_shape(x))   x }, input_signature = list(tensorflow::tf$TensorSpec(shape(1, NA, 3)))) invisible(tfn(op_ones(shape(1, 2, 3)))) ## shape(1, Tensor(\"strided_slice:0\", shape=(), dtype=int32), 3) c(batch_size, seq_len, channels) %<-% shape(x)  # `%<-%` also has support for skipping values # during unpacking with `.` and `...`. For example, # To retrieve just the first and/or last dim: c(batch_size, ...) %<-% shape(x) c(batch_size, ., .) %<-% shape(x) c(..., channels) %<-% shape(x) c(batch_size, ..., channels) %<-% shape(x) c(batch_size, ., channels) %<-% shape(x) echo_print <- function(x) {   message(\"> \", deparse(substitute(x)));   if(!is.null(x)) print(x) } tfn <- tensorflow::tf_function(function(x) {   c(axis1, axis2, axis3) %<-% shape(x)   echo_print(str(list(axis1 = axis1, axis2 = axis2, axis3 = axis3)))    echo_print(shape(axis1))               # use axis1 tensor as axis value   echo_print(shape(axis1, axis2, axis3)) # use axis1 tensor as axis value    # use shape() to compose a new shape, e.g., in multihead attention   n_heads <- 4   echo_print(shape(axis1, axis2, n_heads, axis3/n_heads))    x }, input_signature = list(tensorflow::tf$TensorSpec(shape(NA, 4, 16)))) invisible(tfn(op_ones(shape(2, 4, 16)))) ## > str(list(axis1 = axis1, axis2 = axis2, axis3 = axis3)) ## List of 3 ##  $ axis1:<tf.Tensor 'strided_slice:0' shape=() dtype=int32> ##  $ axis2: int 4 ##  $ axis3: int 16 ## > shape(axis1) ## shape(Tensor(\"strided_slice:0\", shape=(), dtype=int32)) ## > shape(axis1, axis2, axis3) ## shape(Tensor(\"strided_slice:0\", shape=(), dtype=int32), 4, 16) ## > shape(axis1, axis2, n_heads, axis3/n_heads) ## shape(Tensor(\"strided_slice:0\", shape=(), dtype=int32), 4, 4, 4) (x <- op_convert_to_tensor(2L)) ## tf.Tensor(2, shape=(), dtype=int32) # by default, shape() treats scalar integer tensors as axis values shape(x) ## shape(tf.Tensor(2, shape=(), dtype=int32)) # to access the shape of a scalar integer, # call `op_shape()`, or protect with `I()` op_shape(x) ## shape() shape(I(x)) ## shape()"},{"path":[]},{"path":"https://keras.posit.co/reference/split_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"Splits dataset left half right half (e.g. train / test).","code":""},{"path":"https://keras.posit.co/reference/split_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"","code":"split_dataset(   dataset,   left_size = NULL,   right_size = NULL,   shuffle = FALSE,   seed = NULL )"},{"path":"https://keras.posit.co/reference/split_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"dataset tf$data$Dataset, torch$utils$data$Dataset object, list arrays length. left_size float (range [0, 1]), signifies fraction data pack left dataset. integer, signifies number samples pack left dataset. NULL, defaults complement right_size. Defaults NULL. right_size float (range [0, 1]), signifies fraction data pack right dataset. integer, signifies number samples pack right dataset. NULL, defaults complement left_size. Defaults NULL. shuffle Boolean, whether shuffle data splitting . seed random seed shuffling.","code":""},{"path":"https://keras.posit.co/reference/split_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"list two tf$data$Dataset objects: left right splits.","code":""},{"path":"https://keras.posit.co/reference/split_dataset.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"","code":"data <- random_uniform(c(1000, 4)) c(left_ds, right_ds) %<-% split_dataset(list(data$numpy()), left_size = 0.8) left_ds$cardinality() ## tf.Tensor(800, shape=(), dtype=int64) right_ds$cardinality() ## tf.Tensor(200, shape=(), dtype=int64)"},{"path":[]},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"Print summary Keras Model","code":""},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model summary(object, ...)  # S3 method for keras.src.models.model.Model format(   x,   line_length = getOption(\"width\"),   positions = NULL,   expand_nested = FALSE,   show_trainable = NA,   ...,   layer_range = NULL,   compact = TRUE )  # S3 method for keras.src.models.model.Model print(x, ...)"},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"object, x Keras model instance ... summary() print(), passed format(). format(), passed model$summary(). line_length Total length printed lines positions Relative absolute positions log elements line. provided, defaults c(0.33, 0.55, 0.67, 1.0). expand_nested Whether expand nested models. provided, defaults FALSE. show_trainable Whether show layer trainable. provided, defaults FALSE. layer_range list, tuple, vector 2 strings, starting layer name ending layer name (inclusive) indicating range layers printed summary. also accepts regex patterns instead exact name. case, start predicate first element matches layer_range[[1]] end predicate last element matches layer_range[[1]]. default NULL considers layers model. compact Whether remove white-space lines model summary. (Default TRUE)","code":""},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"format() returns length 1 character vector. print() returns model object invisibly. summary() returns output format() invisibly printing .","code":""},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":"enabling-color-output-in-knitr-rmarkdown-quarto-","dir":"Reference","previous_headings":"","what":"Enabling color output in Knitr (RMarkdown, Quarto)","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"order enable color output quarto rmarkdown document html output format (include revealjs presentations), need following setup chunk:","code":"```{r setup, include = FALSE}  options(cli.num_colors = 256)  fansi::set_knit_hooks(knitr::knit_hooks)  options(width = 75) # adjust as needed for format  ```"},{"path":[]},{"path":"https://keras.posit.co/reference/test_on_batch.html","id":null,"dir":"Reference","previous_headings":"","what":"Test the model on a single batch of samples. — test_on_batch","title":"Test the model on a single batch of samples. — test_on_batch","text":"Test model single batch samples.","code":""},{"path":"https://keras.posit.co/reference/test_on_batch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test the model on a single batch of samples. — test_on_batch","text":"","code":"test_on_batch(object, x, y = NULL, sample_weight = NULL, ...)"},{"path":"https://keras.posit.co/reference/test_on_batch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test the model on a single batch of samples. — test_on_batch","text":"object Keras model object x Input data. Must array-like. y Target data. Must array-like. sample_weight Optional array length x, containing weights apply model's loss sample. case temporal data, can pass 2D array shape (samples, sequence_length), apply different weight every timestep every sample. ... forward/backward compatability","code":""},{"path":"https://keras.posit.co/reference/test_on_batch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test the model on a single batch of samples. — test_on_batch","text":"scalar loss value (metrics), named list loss metric values (metrics).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/text_dataset_from_directory.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","title":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","text":"directory structure :   calling text_dataset_from_directory(main_directory, labels='inferred') return tf.data.Dataset yields batches texts subdirectories class_a class_b, together labels 0 1 (0 corresponding class_a 1 corresponding class_b). .txt files supported time.","code":"main_directory/ ...class_a/ ......a_text_1.txt ......a_text_2.txt ...class_b/ ......b_text_1.txt ......b_text_2.txt"},{"path":"https://keras.posit.co/reference/text_dataset_from_directory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","text":"","code":"text_dataset_from_directory(   directory,   labels = \"inferred\",   label_mode = \"int\",   class_names = NULL,   batch_size = 32L,   max_length = NULL,   shuffle = TRUE,   seed = NULL,   validation_split = NULL,   subset = NULL,   follow_links = FALSE,   verbose = TRUE )"},{"path":"https://keras.posit.co/reference/text_dataset_from_directory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","text":"directory Directory data located. labels \"inferred\", contain subdirectories, containing text files class. Otherwise, directory structure ignored. labels Either \"inferred\" (labels generated directory structure), NULL (labels), list/tuple integer labels size number text files found directory. Labels sorted according alphanumeric order text file paths (obtained via os.walk(directory) Python). label_mode String describing encoding labels. Options : \"int\": means labels encoded integers (e.g. sparse_categorical_crossentropy loss). \"categorical\" means labels encoded categorical vector (e.g. categorical_crossentropy loss). \"binary\" means labels (can 2) encoded float32 scalars values 0 1 (e.g. binary_crossentropy). NULL (labels). class_names valid \"labels\" \"inferred\". explicit list class names (must match names subdirectories). Used control order classes (otherwise alphanumerical order used). batch_size Size batches data. Defaults 32. NULL, data batched (dataset yield individual samples). max_length Maximum size text string. Texts longer truncated max_length. shuffle Whether shuffle data. Defaults TRUE. set FALSE, sorts data alphanumeric order. seed Optional random seed shuffling transformations. validation_split Optional float 0 1, fraction data reserve validation. subset Subset data return. One \"training\", \"validation\" \"\". used validation_split set. subset=\"\", utility returns tuple two datasets (training validation datasets respectively). follow_links Whether visits subdirectories pointed symlinks. Defaults FALSE. verbose Whether display number information classes number files found. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/text_dataset_from_directory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","text":"tf.data.Dataset object. label_mode NULL, yields string tensors shape (batch_size,), containing contents batch text files. Otherwise, yields tuple (texts, labels), texts shape (batch_size,) labels follows format described . Rules regarding labels format: label_mode int, labels int32 tensor shape (batch_size,). label_mode binary, labels float32 tensor 1s 0s shape (batch_size, 1). label_mode categorical, labels float32 tensor shape (batch_size, num_classes), representing one-hot encoding class index.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/time_distributed.html","id":null,"dir":"Reference","previous_headings":"","what":"layer_time_distributed — time_distributed","title":"layer_time_distributed — time_distributed","text":"time_distributed() alias layer_time_distributed(). See ?layer_time_distributed() full documentation.","code":""},{"path":"https://keras.posit.co/reference/time_distributed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"layer_time_distributed — time_distributed","text":"","code":"time_distributed(object, layer, ...)"},{"path":"https://keras.posit.co/reference/time_distributed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"layer_time_distributed — time_distributed","text":"object Object compose layer . tensor, array, sequential model. layer Layer instance. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/time_distributed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"layer_time_distributed — time_distributed","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/timeseries_dataset_from_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","title":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","text":"function takes sequence data-points gathered equal intervals, along time series parameters length sequences/windows, spacing two sequence/windows, etc., produce batches timeseries inputs targets.","code":""},{"path":"https://keras.posit.co/reference/timeseries_dataset_from_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","text":"","code":"timeseries_dataset_from_array(   data,   targets,   sequence_length,   sequence_stride = 1L,   sampling_rate = 1L,   batch_size = 128L,   shuffle = FALSE,   seed = NULL,   start_index = NULL,   end_index = NULL )"},{"path":"https://keras.posit.co/reference/timeseries_dataset_from_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","text":"data array eager tensor containing consecutive data points (timesteps). first dimension expected time dimension. targets Targets corresponding timesteps data. targets[] target corresponding window starts index (see example 2 ). Pass NULL target data (case dataset yield input data). sequence_length Length output sequences (number timesteps). sequence_stride Period successive output sequences. stride s, output samples start index data[], data[+ s], data[+ 2 * s], etc. sampling_rate Period successive individual timesteps within sequences. rate r, timesteps data[], data[+ r], ... data[+ sequence_length] used creating sample sequence. batch_size Number timeseries samples batch (except maybe last one). NULL, data batched (dataset yield individual samples). shuffle Whether shuffle output samples, instead draw chronological order. seed Optional int; random seed shuffling. start_index Optional int; data points earlier (exclusive) start_index used output sequences. useful reserve part data test validation. end_index Optional int; data points later (exclusive) end_index used output sequences. useful reserve part data test validation.","code":""},{"path":"https://keras.posit.co/reference/timeseries_dataset_from_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","text":"tf$data$Dataset instance. targets passed, dataset yields list (batch_of_sequences, batch_of_targets). , dataset yields batch_of_sequences. Example 1: Consider indices [0, 1, ... 98]. sequence_length=10,  sampling_rate=2, sequence_stride=3, shuffle=FALSE, dataset yield batches sequences composed following indices:   case last 2 data points discarded since full sequence can generated include (next sequence started index 81, thus last step gone 98). Example 2: Temporal regression. Consider array data scalar values, shape (steps,). generate dataset uses past 10 timesteps predict next timestep, use:     Example 3: Temporal regression many--many architectures. Consider two arrays scalar values X Y, shape (100,). resulting dataset consist samples 20 timestamps . samples overlap. generate dataset uses current timestamp predict corresponding target timestep, use:","code":"First sequence:  [0  2  4  6  8 10 12 14 16 18] Second sequence: [3  5  7  9 11 13 15 17 19 21] Third sequence:  [6  8 10 12 14 16 18 20 22 24] ... Last sequence:   [78 80 82 84 86 88 90 92 94 96] data <- op_array(1:20) input_data <- data[1:10] targets <- data[11:20] dataset <- timeseries_dataset_from_array(   input_data, targets, sequence_length=10) iter <- reticulate::as_iterator(dataset) reticulate::iter_next(iter) ## [[1]] ## tf.Tensor([[ 1  2  3  4  5  6  7  8  9 10]], shape=(1, 10), dtype=int32) ## ## [[2]] ## tf.Tensor([11], shape=(1), dtype=int32) X <- op_array(1:100) Y <- X*2  sample_length <- 20 input_dataset <- timeseries_dataset_from_array(     X, NULL, sequence_length=sample_length, sequence_stride=sample_length) target_dataset <- timeseries_dataset_from_array(     Y, NULL, sequence_length=sample_length, sequence_stride=sample_length)   inputs <- reticulate::as_iterator(input_dataset) %>% reticulate::iter_next() targets <- reticulate::as_iterator(target_dataset) %>% reticulate::iter_next()"},{"path":[]},{"path":"https://keras.posit.co/reference/to_categorical.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts a class vector (integers) to binary class matrix. — to_categorical","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"E.g. use loss_categorical_crossentropy().","code":""},{"path":"https://keras.posit.co/reference/to_categorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"","code":"to_categorical(x, num_classes = NULL)"},{"path":"https://keras.posit.co/reference/to_categorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"x Array-like class values converted matrix (integers 0 num_classes - 1). R factors coerced integer offset 0-based, .e., .integer(x) - 1L. num_classes Total number classes. NULL, inferred max(x) + 1. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/to_categorical.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"binary matrix representation input R array. class axis placed last.","code":""},{"path":"https://keras.posit.co/reference/to_categorical.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"","code":"a <- to_categorical(c(0, 1, 2, 3), num_classes=4) print(a) ##      [,1] [,2] [,3] [,4] ## [1,]    1    0    0    0 ## [2,]    0    1    0    0 ## [3,]    0    0    1    0 ## [4,]    0    0    0    1 b <- array(c(.9, .04, .03, .03,               .3, .45, .15, .13,               .04, .01, .94, .05,               .12, .21, .5, .17),               dim = c(4, 4)) loss <- op_categorical_crossentropy(a, b) loss ## tf.Tensor([0.41284522 0.45601739 0.54430155 0.80437282], shape=(4), dtype=float64) loss <- op_categorical_crossentropy(a, a) loss ## tf.Tensor([1.00000005e-07 1.00000005e-07 1.00000005e-07 1.00000005e-07], shape=(4), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/train_on_batch.html","id":null,"dir":"Reference","previous_headings":"","what":"Runs a single gradient update on a single batch of data. — train_on_batch","title":"Runs a single gradient update on a single batch of data. — train_on_batch","text":"Runs single gradient update single batch data.","code":""},{"path":"https://keras.posit.co/reference/train_on_batch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Runs a single gradient update on a single batch of data. — train_on_batch","text":"","code":"train_on_batch(object, x, y = NULL, sample_weight = NULL, class_weight = NULL)"},{"path":"https://keras.posit.co/reference/train_on_batch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Runs a single gradient update on a single batch of data. — train_on_batch","text":"object Keras model object x Input data. Must array-like. y Target data. Must array-like. sample_weight Optional array length x, containing weights apply model's loss sample. case temporal data, can pass 2D array shape (samples, sequence_length), apply different weight every timestep every sample. class_weight Optional named list mapping class indices (integers, 0-based) weight (float) apply model's loss samples class training. can useful tell model \"pay attention\" samples -represented class. class_weight specified targets rank 2 greater, either y must one-hot encoded, explicit final dimension 1 must included sparse class labels.","code":""},{"path":"https://keras.posit.co/reference/train_on_batch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Runs a single gradient update on a single batch of data. — train_on_batch","text":"scalar loss value (metrics), named list loss metric values (metrics). property model$metrics_names give display labels scalar outputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":null,"dir":"Reference","previous_headings":"","what":"Unpacks user-provided data list. — unpack_x_y_sample_weight","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"convenience utility used overriding $train_step, $test_step, $predict_step. utility makes easy support data form (x,), (x, y), (x, y, sample_weight).","code":""},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"","code":"unpack_x_y_sample_weight(data)"},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"data list form (x), (x, y), (x, y, sample_weight).","code":""},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"unpacked list, NULLs y sample_weight provided.","code":""},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":"example-","dir":"Reference","previous_headings":"","what":"Example:","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"can also equivalent providing default values %<-%","code":"features_batch <- op_ones(c(10, 5)) labels_batch <- op_zeros(c(10, 5)) data <- list(features_batch, labels_batch) # `y` and `sample_weight` will default to `NULL` if not provided. c(x, y, sample_weight) %<-% unpack_x_y_sample_weight(data) c(x, y = NULL, sample_weight = NULL) %<-% data"},{"path":[]},{"path":"https://keras.posit.co/reference/use_backend.html","id":null,"dir":"Reference","previous_headings":"","what":"Configure a Keras backend — use_backend","title":"Configure a Keras backend — use_backend","text":"Configure Keras backend","code":""},{"path":"https://keras.posit.co/reference/use_backend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Configure a Keras backend — use_backend","text":"","code":"use_backend(backend)"},{"path":"https://keras.posit.co/reference/use_backend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Configure a Keras backend — use_backend","text":"backend string, can \"tensorflow\", \"jax\", \"numpy\", \"torch\".","code":""},{"path":"https://keras.posit.co/reference/use_backend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Configure a Keras backend — use_backend","text":"Called primarily side effects. Returns provided backend, invisibly.","code":""},{"path":"https://keras.posit.co/reference/use_backend.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Configure a Keras backend — use_backend","text":"functions allow configuring backend keras use. Note one backend can configured time. function called library(keras3) calling functions within package (see example). experimental support changing backend keras initialized. using config_set_backend().","code":"library(keras3) use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":null,"dir":"Reference","previous_headings":"","what":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"Provide scope mappings names custom objects","code":""},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"","code":"with_custom_object_scope(objects, expr)"},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"objects Named list objects expr Expression evaluate","code":""},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"result evaluating expr within custom object scope.","code":""},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"many elements Keras models can customized user objects (e.g. losses, metrics, regularizers, etc.). loading saved models use functions typically need explicitly map names user objects via custom_objects parameter. with_custom_object_scope() function provides alternative lets create named alias user object applies entire block code, automatically recognized loading saved models.","code":""},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"","code":"# define custom metric metric_top_3_categorical_accuracy <-   custom_metric(\"top_3_categorical_accuracy\", function(y_true, y_pred) {     metric_top_k_categorical_accuracy(y_true, y_pred, k = 3)   })  with_custom_object_scope(c(top_k_acc = sparse_top_k_cat_acc), {    # ...define model...    # compile model (refer to \"top_k_acc\" by name)   model |> compile(     loss = \"binary_crossentropy\",     optimizer = optimizer_nadam(),     metrics = c(\"top_k_acc\")   )    # save the model   model |> save_model(\"my_model.keras\")    # loading the model within the custom object scope doesn't   # require explicitly providing the custom_object   reloaded_model <- load_model(\"my_model.keras\") })"},{"path":[]},{"path":"https://keras.posit.co/reference/zip_lists.html","id":null,"dir":"Reference","previous_headings":"","what":"Zip lists — zip_lists","title":"Zip lists — zip_lists","text":"conceptually similar zip() Python, R functions purrr::transpose() data.table::transpose() (albeit, accepting elements ... instead single list), one crucial difference: provided objects named, matching done names, positions.","code":""},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zip lists — zip_lists","text":"","code":"zip_lists(...)"},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zip lists — zip_lists","text":"... R lists atomic vectors, optionally named.","code":""},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zip lists — zip_lists","text":"inverted list","code":""},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Zip lists — zip_lists","text":"arguments supplied must length. positional matching required, arguments provided must unnamed. matching names, arguments must set names, can different orders.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Zip lists — zip_lists","text":"","code":"gradients <- list(\"grad_for_wt_1\", \"grad_for_wt_2\", \"grad_for_wt_3\") weights <- list(\"weight_1\", \"weight_2\", \"weight_3\") str(zip_lists(gradients, weights)) #> List of 3 #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_1\" #>   ..$ : chr \"weight_1\" #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_2\" #>   ..$ : chr \"weight_2\" #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_3\" #>   ..$ : chr \"weight_3\" str(zip_lists(gradient = gradients, weight = weights)) #> List of 3 #>  $ :List of 2 #>   ..$ gradient: chr \"grad_for_wt_1\" #>   ..$ weight  : chr \"weight_1\" #>  $ :List of 2 #>   ..$ gradient: chr \"grad_for_wt_2\" #>   ..$ weight  : chr \"weight_2\" #>  $ :List of 2 #>   ..$ gradient: chr \"grad_for_wt_3\" #>   ..$ weight  : chr \"weight_3\"  names(gradients) <- names(weights) <- paste0(\"layer_\", 1:3) str(zip_lists(gradients, weights[c(3, 1, 2)])) #> List of 3 #>  $ layer_1:List of 2 #>   ..$ : chr \"grad_for_wt_1\" #>   ..$ : chr \"weight_1\" #>  $ layer_2:List of 2 #>   ..$ : chr \"grad_for_wt_2\" #>   ..$ : chr \"weight_2\" #>  $ layer_3:List of 2 #>   ..$ : chr \"grad_for_wt_3\" #>   ..$ : chr \"weight_3\"  names(gradients) <- paste0(\"gradient_\", 1:3) try(zip_lists(gradients, weights)) # error, names don't match #> Error in zip_lists(gradients, weights) :  #>   All names of arguments provided to `zip_lists()` must match. Call `unname()` on each argument if you want positional matching # call unname directly for positional matching str(zip_lists(unname(gradients), unname(weights))) #> List of 3 #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_1\" #>   ..$ : chr \"weight_1\" #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_2\" #>   ..$ : chr \"weight_2\" #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_3\" #>   ..$ : chr \"weight_3\""},{"path":"https://keras.posit.co/news/index.html","id":"keras3-100","dir":"Changelog","previous_headings":"","what":"keras3 1.0.0","title":"keras3 1.0.0","text":"Chains layer_* calls |> now instantiate layers order %>% pipe chains: left-hand-side first (#1440). iterate(), iter_next() as_iterator() now reexported reticulate. User facing changes upstream Keras v3.3.2: new function: op_ctc_decode() new function: op_eigh() new function: op_select() new function: op_vectorize() new function: op_image_rgb_to_grayscale() new function: loss_tversky() new args: layer_resizing(pad_to_aspect_ratio, fill_mode, fill_value) new arg: layer_embedding(weights) providing initial weights matrix new args: op_nan_to_num(nan, posinf, neginf) new args: op_image_resize(crop_to_aspect_ratio, pad_to_aspect_ratio, fill_mode, fill_value) new args: op_argmax(keepdims) op_argmin(keepdims) new arg: clear_session(free_memory) clearing without invoking garbage collector. metric_kl_divergence() loss_kl_divergence() clip inputs (y_true y_pred) [0, 1] range. new Layer() attributes: metrics, dtype_policy Added initial support float8 training layer_conv_*d() layers now support LoRa op_digitize() now supports sparse tensors. Models layers now return owned metrics recursively. Add pickling support Keras models. (e.g., via reticulate::py_save_object()) Note pickling recommended, prefer using Keras saving APIs.","code":""},{"path":"https://keras.posit.co/news/index.html","id":"keras3-020","dir":"Changelog","previous_headings":"","what":"keras3 0.2.0","title":"keras3 0.2.0","text":"CRAN release: 2024-04-18 New functions: quantize_weights(): quantize model layer weights -place. Currently, Dense, EinsumDense, Embedding layers supported (enough cover majority transformers today) layer_mel_spectrogram() layer_flax_module_wrapper() layer_jax_model_wrapper() loss_dice() random_beta() random_binomial() config_set_backend(): change backend Keras initialized. config_dtype_policy() config_set_dtype_policy() New Ops op_custom_gradient() op_batch_normalization() op_image_crop() op_divide_no_nan() op_normalize() op_correlate() ` New family linear algebra ops op_cholesky() op_det() op_eig() op_inv() op_lu_factor() op_norm() op_erfinv() op_solve_triangular() op_svd() audio_dataset_from_directory(), image_dataset_from_directory() text_dataset_from_directory() gain verbose argument (default TRUE) image_dataset_from_directory() gains pad_to_aspect_ratio argument (default FALSE) to_categorical(), op_one_hot(), fit() can now accept R factors, offset 0-based (reported #1055). op_convert_to_numpy() now returns unconverted NumPy arrays. op_array() op_convert_to_tensor() longer error casting R doubles integer types. export_savedmodel() now works Jax backend. Metric()$add_variable() method gains arg: aggregration. Layer()$add_weight() method gains args: autocast, regularizer, aggregation. op_bincount(), op_multi_hot(), op_one_hot(), layer_category_encoding() now support sparse tensors. op_custom_gradient() now supports PyTorch backend layer_lstm() layer_gru() gain arg use_cudnn, default 'auto'. Fixed issue application_preprocess_inputs() error supplied R array input. Doc improvements.","code":""},{"path":"https://keras.posit.co/news/index.html","id":"keras3-010","dir":"Changelog","previous_headings":"","what":"keras3 0.1.0","title":"keras3 0.1.0","text":"CRAN release: 2024-02-17 package rebuilt Keras 3.0. Refer  overview https://keras.posit.co current --date documentation.","code":""}]
+[{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Customizing what happens in `fit()` with JAX","text":"’re supervised learning, can use fit() everything works smoothly. need take control every little detail, can write training loop entirely scratch. need custom training algorithm, still want benefit convenient features fit(), callbacks, built-distribution support, step fusing? core principle Keras progressive disclosure complexity. always able get lower-level workflows gradual way. shouldn’t fall cliff high-level functionality doesn’t exactly match use case. able gain control small details retaining commensurate amount high-level convenience. need customize fit() , override training step function Model class. function called fit() every batch data. able call fit() usual – running learning algorithm. Note pattern prevent building models Functional API. can whether ’re building Sequential models, Functional API models, subclassed models. Let’s see works.","code":""},{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Customizing what happens in `fit()` with JAX","text":"","code":"import os  # This guide can only be run with the JAX backend. os.environ[\"KERAS_BACKEND\"] = \"jax\"  import jax import keras import numpy as np"},{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"a-first-simple-example","dir":"Articles","previous_headings":"","what":"A first simple example","title":"Customizing what happens in `fit()` with JAX","text":"Let’s start simple example: create new class subclasses keras.Model. implement fully-stateless compute_loss_and_updates() method compute loss well updated values non-trainable variables model. Internally, calls stateless_call() built-compute_loss(). implement fully-stateless train_step() method compute current metric values (including loss) well updated values trainable variables, optimizer variables, metric variables. Note can also take account sample_weight argument : Unpacking data x, y, sample_weight = data Passing sample_weight compute_loss() Passing sample_weight alongside y y_pred metrics stateless_update_state() Let’s try :","code":"class CustomModel(keras.Model):     def compute_loss_and_updates(         self,         trainable_variables,         non_trainable_variables,         x,         y,         training=False,     ):         y_pred, non_trainable_variables = self.stateless_call(             trainable_variables,             non_trainable_variables,             x,             training=training,         )         loss = self.compute_loss(x, y, y_pred)         return loss, (y_pred, non_trainable_variables)      def train_step(self, state, data):         (             trainable_variables,             non_trainable_variables,             optimizer_variables,             metrics_variables,         ) = state         x, y = data          # Get the gradient function.         grad_fn = jax.value_and_grad(             self.compute_loss_and_updates, has_aux=True         )          # Compute the gradients.         (loss, (y_pred, non_trainable_variables)), grads = grad_fn(             trainable_variables,             non_trainable_variables,             x,             y,             training=True,         )          # Update trainable variables and optimizer variables.         (             trainable_variables,             optimizer_variables,         ) = self.optimizer.stateless_apply(             optimizer_variables, grads, trainable_variables         )          # Update metrics.         new_metrics_vars = []         for metric in self.metrics:             this_metric_vars = metrics_variables[                 len(new_metrics_vars) : len(new_metrics_vars)                 + len(metric.variables)             ]             if metric.name == \"loss\":                 this_metric_vars = metric.stateless_update_state(                     this_metric_vars, loss                 )             else:                 this_metric_vars = metric.stateless_update_state(                     this_metric_vars, y, y_pred                 )             logs = metric.stateless_result(this_metric_vars)             new_metrics_vars += this_metric_vars          # Return metric logs and updated state variables.         state = (             trainable_variables,             non_trainable_variables,             optimizer_variables,             new_metrics_vars,         )         return logs, state # Construct and compile an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])  # Just use `fit` as usual x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=3)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"going-lower-level","dir":"Articles","previous_headings":"","what":"Going lower-level","title":"Customizing what happens in `fit()` with JAX","text":"Naturally, just skip passing loss function compile(), instead everything manually train_step. Likewise metrics. ’s lower-level example, uses compile() configure optimizer:","code":"class CustomModel(keras.Model):     def __init__(self, *args, **kwargs):         super().__init__(*args, **kwargs)         self.loss_tracker = keras.metrics.Mean(name=\"loss\")         self.mae_metric = keras.metrics.MeanAbsoluteError(name=\"mae\")         self.loss_fn = keras.losses.MeanSquaredError()      def compute_loss_and_updates(         self,         trainable_variables,         non_trainable_variables,         x,         y,         training=False,     ):         y_pred, non_trainable_variables = self.stateless_call(             trainable_variables,             non_trainable_variables,             x,             training=training,         )         loss = self.loss_fn(y, y_pred)         return loss, (y_pred, non_trainable_variables)      def train_step(self, state, data):         (             trainable_variables,             non_trainable_variables,             optimizer_variables,             metrics_variables,         ) = state         x, y = data          # Get the gradient function.         grad_fn = jax.value_and_grad(             self.compute_loss_and_updates, has_aux=True         )          # Compute the gradients.         (loss, (y_pred, non_trainable_variables)), grads = grad_fn(             trainable_variables,             non_trainable_variables,             x,             y,             training=True,         )          # Update trainable variables and optimizer variables.         (             trainable_variables,             optimizer_variables,         ) = self.optimizer.stateless_apply(             optimizer_variables, grads, trainable_variables         )          # Update metrics.         loss_tracker_vars = metrics_variables[             : len(self.loss_tracker.variables)         ]         mae_metric_vars = metrics_variables[len(self.loss_tracker.variables) :]          loss_tracker_vars = self.loss_tracker.stateless_update_state(             loss_tracker_vars, loss         )         mae_metric_vars = self.mae_metric.stateless_update_state(             mae_metric_vars, y, y_pred         )          logs = {}         logs[self.loss_tracker.name] = self.loss_tracker.stateless_result(             loss_tracker_vars         )         logs[self.mae_metric.name] = self.mae_metric.stateless_result(             mae_metric_vars         )          new_metrics_vars = loss_tracker_vars + mae_metric_vars          # Return metric logs and updated state variables.         state = (             trainable_variables,             non_trainable_variables,             optimizer_variables,             new_metrics_vars,         )         return logs, state      @property     def metrics(self):         # We list our `Metric` objects here so that `reset_states()` can be         # called automatically at the start of each epoch         # or at the start of `evaluate()`.         return [self.loss_tracker, self.mae_metric]   # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs)  # We don't passs a loss or metrics here. model.compile(optimizer=\"adam\")  # Just use `fit` as usual -- you can use callbacks, etc. x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=5)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_jax.html","id":"providing-your-own-evaluation-step","dir":"Articles","previous_headings":"","what":"Providing your own evaluation step","title":"Customizing what happens in `fit()` with JAX","text":"want calls model.evaluate()? override test_step exactly way. ’s looks like: ’s !","code":"class CustomModel(keras.Model):     def test_step(self, state, data):         # Unpack the data.         x, y = data         (             trainable_variables,             non_trainable_variables,             metrics_variables,         ) = state          # Compute predictions and loss.         y_pred, non_trainable_variables = self.stateless_call(             trainable_variables,             non_trainable_variables,             x,             training=False,         )         loss = self.compute_loss(x, y, y_pred)          # Update metrics.         new_metrics_vars = []         for metric in self.metrics:             this_metric_vars = metrics_variables[                 len(new_metrics_vars) : len(new_metrics_vars)                 + len(metric.variables)             ]             if metric.name == \"loss\":                 this_metric_vars = metric.stateless_update_state(                     this_metric_vars, loss                 )             else:                 this_metric_vars = metric.stateless_update_state(                     this_metric_vars, y, y_pred                 )             logs = metric.stateless_result(this_metric_vars)             new_metrics_vars += this_metric_vars          # Return metric logs and updated state variables.         state = (             trainable_variables,             non_trainable_variables,             new_metrics_vars,         )         return logs, state   # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(loss=\"mse\", metrics=[\"mae\"])  # Evaluate with our custom test_step x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.evaluate(x, y)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Customizing what happens in `fit()` with TensorFlow","text":"’re supervised learning, can use fit() everything works smoothly. need take control every little detail, can write training loop entirely scratch. need custom training algorithm, still want benefit convenient features fit(), callbacks, built-distribution support, step fusing? core principle Keras progressive disclosure complexity. always able get lower-level workflows gradual way. shouldn’t fall cliff high-level functionality doesn’t exactly match use case. able gain control small details retaining commensurate amount high-level convenience. need customize fit() , override training step function Model class. function called fit() every batch data. able call fit() usual – running learning algorithm. Note pattern prevent building models Functional API. can whether ’re building Sequential models, Functional API models, subclassed models. Let’s see works.","code":""},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Customizing what happens in `fit()` with TensorFlow","text":"","code":"library(reticulate) library(tensorflow, exclude = c(\"set_random_seed\", \"shape\")) library(keras3)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"a-first-simple-example","dir":"Articles","previous_headings":"","what":"A first simple example","title":"Customizing what happens in `fit()` with TensorFlow","text":"Let’s start simple example: create new class subclasses Model. just override method train_step(self, data). return dictionary mapping metric names (including loss) current value. input argument data gets passed fit training data: pass arrays, calling fit(x, y, ...), data list (x, y) pass tf_dataset, calling fit(dataset, ...), data gets yielded dataset batch. body train_step() method, implement regular training update, similar already familiar . Importantly, compute loss via self.compute_loss(), wraps loss(es) function(s) passed compile(). Similarly, call metric$update_state(y, y_pred) metrics self$metrics, update state metrics passed compile(), query results self$metrics end retrieve current value. Let’s try :","code":"CustomModel <- new_model_class(   \"CustomModel\",   train_step = function(data) {     c(x, y = NULL, sample_weight = NULL) %<-% data      with(tf$GradientTape() %as% tape, {       y_pred <- self(x, training = TRUE)       loss <- self$compute_loss(y = y, y_pred = y_pred,                                 sample_weight = sample_weight)     })      # Compute gradients     trainable_vars <- self$trainable_variables     gradients <- tape$gradient(loss, trainable_vars)      # Update weights     self$optimizer$apply(gradients, trainable_vars)      # Update metrics (includes the metric that tracks the loss)     for (metric in self$metrics) {       if (metric$name == \"loss\")         metric$update_state(loss)       else         metric$update_state(y, y_pred)     }      # Return a dict mapping metric names to current value     metrics <- lapply(self$metrics, function(m) m$result())     metrics <- setNames(metrics, sapply(self$metrics, function(m) m$name))     metrics   } ) # Construct and compile an instance of CustomModel inputs <- keras_input(shape = 32) outputs <- layer_dense(inputs, 1) model <- CustomModel(inputs, outputs) model |> compile(optimizer = \"adam\", loss = \"mse\", metrics = \"mae\")  # Just use `fit` as usual x <- random_normal(c(1000, 32)) y <- random_normal(c(1000, 1)) model |> fit(x, y, epochs = 3) ## Epoch 1/3 ## 32/32 - 1s - 19ms/step - loss: 2.9118 - mae: 1.3597 ## Epoch 2/3 ## 32/32 - 0s - 1ms/step - loss: 2.6026 - mae: 1.2856 ## Epoch 3/3 ## 32/32 - 0s - 1ms/step - loss: 2.3378 - mae: 1.2193"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"going-lower-level","dir":"Articles","previous_headings":"","what":"Going lower-level","title":"Customizing what happens in `fit()` with TensorFlow","text":"Naturally, just skip passing loss function compile(), instead everything manually train_step. Likewise metrics. ’s lower-level example, uses compile() configure optimizer: start creating Metric instances track loss MAE score (__init__()). implement custom train_step() updates state metrics (calling update_state() ), query (via result()) return current average value, displayed progress bar pass callback. Note need call reset_states() metrics epoch! Otherwise calling result() return average since start training, whereas usually work per-epoch averages. Thankfully, framework can us: just list metric want reset metrics property model. model call reset_states() object listed beginning fit() epoch beginning call evaluate().","code":"CustomModel <- new_model_class(   \"CustomModel\",   initialize = function(...) {     super$initialize(...)     self$loss_tracker <- metric_mean(name = \"loss\")     self$mae_metric <- metric_mean_absolute_error(name = \"mae\")     self$loss_fn <- loss_mean_squared_error()   },   train_step = function(data) {     c(x, y = NULL, sample_weight = NULL) %<-% data      with(tf$GradientTape() %as% tape, {       y_pred <- self(x, training = TRUE)       loss <- self$loss_fn(y, y_pred, sample_weight = sample_weight)     })      # Compute gradients     trainable_vars <- self$trainable_variables     gradients <- tape$gradient(loss, trainable_vars)      # Update weights     self$optimizer$apply(gradients, trainable_vars)      # Compute our own metrics     self$loss_tracker$update_state(loss)     self$mae_metric$update_state(y, y_pred)      # Return a dict mapping metric names to current value     list(       loss = self$loss_tracker$result(),       mae = self$mae_metric$result()     )   },   metrics = mark_active(function() {     # We list our `Metric` objects here so that `reset_states()` can be     # called automatically at the start of each epoch     # or at the start of `evaluate()`.     list(self$loss_tracker, self$mae_metric)   }) )   # Construct and compile an instance of CustomModel inputs <- keras_input(shape = 32) outputs <- layer_dense(inputs, 1) model <- CustomModel(inputs, outputs)  # We don't pass a loss or metrics here. model |> compile(optimizer = \"adam\")  # Just use `fit` as usual x <- random_normal(c(1000, 32)) y <- random_normal(c(1000, 1)) model |> fit(x, y, epochs = 3) ## Epoch 1/3 ## 32/32 - 1s - 16ms/step - loss: 2.6540 - mae: 1.2901 ## Epoch 2/3 ## 32/32 - 0s - 1ms/step - loss: 2.4139 - mae: 1.2303 ## Epoch 3/3 ## 32/32 - 0s - 1ms/step - loss: 2.2080 - mae: 1.1761"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"supporting-sample_weight-class_weight","dir":"Articles","previous_headings":"","what":"Supporting sample_weight & class_weight","title":"Customizing what happens in `fit()` with TensorFlow","text":"may noticed first basic example didn’t make mention sample weighting. want support fit() arguments sample_weight class_weight, ’d simply following: Unpack sample_weight data argument Pass compute_loss & update_state (course, also just apply manually don’t rely compile() losses & metrics) ’s .","code":"CustomModel <- new_model_class(   \"CustomModel\",   train_step = function(data) {     c(x, y = NULL, sample_weight = NULL) %<-% data      with(tf$GradientTape() %as% tape, {       y_pred <- self(x, training = TRUE)       loss <- self$compute_loss(y = y, y_pred = y_pred,                                 sample_weight = sample_weight)     })      # Compute gradients     trainable_vars <- self$trainable_variables     gradients <- tape$gradient(loss, trainable_vars)      # Update weights     self$optimizer$apply_gradients(zip_lists(gradients, trainable_vars))      # Update metrics (includes the metric that tracks the loss)     for (metric in self$metrics) {       if (metric$name == \"loss\") {         metric$update_state(loss)       } else {         metric$update_state(y, y_pred, sample_weight = sample_weight)       }     }      # Return a dict mapping metric names to current value     metrics <- lapply(self$metrics, function(m) m$result())     metrics <- setNames(metrics, sapply(self$metrics, function(m) m$name))     metrics   } )   # Construct and compile an instance of CustomModel inputs <- keras_input(shape = 32) outputs <- layer_dense(inputs, units = 1) model <- CustomModel(inputs, outputs) model |> compile(optimizer = \"adam\", loss = \"mse\", metrics = \"mae\")  # You can now use sample_weight argument x <- random_normal(c(1000, 32)) y <- random_normal(c(1000, 1)) sw <- random_normal(c(1000, 1)) model |> fit(x, y, sample_weight = sw, epochs = 3) ## Epoch 1/3 ## 32/32 - 1s - 18ms/step - loss: 0.1607 - mae: 1.3018 ## Epoch 2/3 ## 32/32 - 0s - 1ms/step - loss: 0.1452 - mae: 1.2999 ## Epoch 3/3 ## 32/32 - 0s - 1ms/step - loss: 0.1335 - mae: 1.2986"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"providing-your-own-evaluation-step","dir":"Articles","previous_headings":"","what":"Providing your own evaluation step","title":"Customizing what happens in `fit()` with TensorFlow","text":"want calls model.evaluate()? override test_step exactly way. ’s looks like:","code":"CustomModel <- new_model_class(   \"CustomModel\",   test_step = function(data) {     # Unpack the data     c(x, y = NULL, sw = NULL) %<-% data     # Compute predictions     y_pred = self(x, training = FALSE)     # Updates the metrics tracking the loss     self$compute_loss(y = y, y_pred = y_pred, sample_weight = sw)     # Update the metrics.     for (metric in self$metrics) {       if (metric$name != \"loss\") {         metric$update_state(y, y_pred, sample_weight = sw)       }     }     # Return a dict mapping metric names to current value.     # Note that it will include the loss (tracked in self.metrics).     metrics <- lapply(self$metrics, function(m) m$result())     metrics <- setNames(metrics, sapply(self$metrics, function(m) m$name))     metrics   } )  # Construct an instance of CustomModel inputs <- keras_input(shape = 32) outputs <- layer_dense(inputs, 1) model <- CustomModel(inputs, outputs) model |> compile(loss = \"mse\", metrics = \"mae\")  # Evaluate with our custom test_step x <- random_normal(c(1000, 32)) y <- random_normal(c(1000, 1)) model |> evaluate(x, y) ## 32/32 - 0s - 8ms/step - loss: 0.0000e+00 - mae: 1.3947 ## $loss ## [1] 0 ## ## $mae ## [1] 1.394695"},{"path":"https://keras.posit.co/articles/custom_train_step_in_tensorflow.html","id":"wrapping-up-an-end-to-end-gan-example","dir":"Articles","previous_headings":"","what":"Wrapping up: an end-to-end GAN example","title":"Customizing what happens in `fit()` with TensorFlow","text":"Let’s walk end--end example leverages everything just learned. Let’s consider: generator network meant generate 28x28x1 images. discriminator network meant classify 28x28x1 images two classes (“fake” “real”). One optimizer . loss function train discriminator. ’s feature-complete GAN class, overriding compile() use signature, implementing entire GAN algorithm 17 lines train_step: Let’s test-drive : ideas behind deep learning simple, implementation painful?","code":"# Create the discriminator discriminator <-   keras_model_sequential(name = \"discriminator\", input_shape = c(28, 28, 1)) |>    layer_conv_2d(filters = 64, kernel_size = c(3, 3),                 strides = c(2, 2),  padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_conv_2d(filters = 128, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_global_max_pooling_2d() |>   layer_dense(units = 1)   # Create the generator latent_dim <- 128 generator <-   keras_model_sequential(name = \"generator\", input_shape = latent_dim) |>    layer_dense(7 * 7 * 128) |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_reshape(target_shape = c(7, 7, 128)) |>    layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>    layer_conv_2d(filters = 1, kernel_size = c(7, 7), padding = \"same\",                 activation = \"sigmoid\") GAN <- Model(   classname = \"GAN\",    initialize = function(discriminator, generator, latent_dim, ...) {     super$initialize(...)     self$discriminator <- discriminator     self$generator <- generator     self$latent_dim <- as.integer(latent_dim)     self$d_loss_tracker <- metric_mean(name = \"d_loss\")     self$g_loss_tracker <- metric_mean(name = \"g_loss\")   },    compile = function(d_optimizer, g_optimizer, loss_fn, ...) {     super$compile(...)     self$d_optimizer <- d_optimizer     self$g_optimizer <- g_optimizer     self$loss_fn <- loss_fn   },   metrics = active_property(function() {     list(self$d_loss_tracker, self$g_loss_tracker)   }),    train_step = function(real_images) {      # Sample random points in the latent space     batch_size <- shape(real_images)[[1]]     random_latent_vectors <-       tf$random$normal(shape(batch_size, self$latent_dim))      # Decode them to fake images     generated_images <- self$generator(random_latent_vectors)      # Combine them with real images     combined_images <- op_concatenate(list(generated_images,                                            real_images))      # Assemble labels discriminating real from fake images     labels <- op_concatenate(list(op_ones(c(batch_size, 1)),                                   op_zeros(c(batch_size, 1))))      # Add random noise to the labels - important trick!     labels %<>% `+`(tf$random$uniform(shape(.), maxval = 0.05))      # Train the discriminator     with(tf$GradientTape() %as% tape, {       predictions <- self$discriminator(combined_images)       d_loss <- self$loss_fn(labels, predictions)     })     grads <- tape$gradient(d_loss, self$discriminator$trainable_weights)     self$d_optimizer$apply_gradients(       zip_lists(grads, self$discriminator$trainable_weights))      # Sample random points in the latent space     random_latent_vectors <-       tf$random$normal(shape(batch_size, self$latent_dim))      # Assemble labels that say \"all real images\"     misleading_labels <- op_zeros(c(batch_size, 1))      # Train the generator (note that we should *not* update the weights     # of the discriminator)!     with(tf$GradientTape() %as% tape, {       predictions <- self$discriminator(self$generator(random_latent_vectors))       g_loss <- self$loss_fn(misleading_labels, predictions)     })     grads <- tape$gradient(g_loss, self$generator$trainable_weights)     self$g_optimizer$apply_gradients(       zip_lists(grads, self$generator$trainable_weights))      list(d_loss = d_loss, g_loss = g_loss)   } ) batch_size <- 64 c(c(x_train, .), c(x_test, .)) %<-% dataset_mnist() all_digits <- op_concatenate(list(x_train, x_test)) all_digits <- op_reshape(all_digits, c(-1, 28, 28, 1)) dataset <- all_digits |>   tfdatasets::tensor_slices_dataset() |>   tfdatasets::dataset_map(\\(x) op_cast(x, \"float32\") / 255) |>   tfdatasets::dataset_shuffle(buffer_size = 1024) |>   tfdatasets::dataset_batch(batch_size = batch_size)  gan <- GAN(discriminator = discriminator,            generator = generator,            latent_dim = latent_dim)  gan |> compile(   d_optimizer = optimizer_adam(learning_rate = 0.0003),   g_optimizer = optimizer_adam(learning_rate = 0.0003),   loss_fn = loss_binary_crossentropy(from_logits = TRUE) )  # To limit the execution time, we only train on 100 batches. You can train on # the entire dataset. You will need about 20 epochs to get nice results. gan |> fit(   tfdatasets::dataset_take(dataset, 100),   epochs = 1 ) ## 100/100 - 5s - 47ms/step - d_loss: 0.0000e+00 - g_loss: 0.0000e+00"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Customizing what happens in `fit()` with PyTorch","text":"’re supervised learning, can use fit() everything works smoothly. need take control every little detail, can write training loop entirely scratch. need custom training algorithm, still want benefit convenient features fit(), callbacks, built-distribution support, step fusing? core principle Keras progressive disclosure complexity. always able get lower-level workflows gradual way. shouldn’t fall cliff high-level functionality doesn’t exactly match use case. able gain control small details retaining commensurate amount high-level convenience. need customize fit() , override training step function Model class. function called fit() every batch data. able call fit() usual – running learning algorithm. Note pattern prevent building models Functional API. can whether ’re building Sequential models, Functional API models, subclassed models. Let’s see works.","code":""},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Customizing what happens in `fit()` with PyTorch","text":"","code":"import os  # This guide can only be run with the torch backend. os.environ[\"KERAS_BACKEND\"] = \"torch\"  import torch import keras from keras import layers import numpy as np"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"a-first-simple-example","dir":"Articles","previous_headings":"","what":"A first simple example","title":"Customizing what happens in `fit()` with PyTorch","text":"Let’s start simple example: create new class subclasses keras.Model. just override method train_step(self, data). return dictionary mapping metric names (including loss) current value. input argument data gets passed fit training data: pass NumPy arrays, calling fit(x, y, ...), data tuple (x, y) pass torch.utils.data.DataLoader tf.data.Dataset, calling fit(dataset, ...), data gets yielded dataset batch. body train_step() method, implement regular training update, similar already familiar . Importantly, compute loss via self.compute_loss(), wraps loss(es) function(s) passed compile(). Similarly, call metric.update_state(y, y_pred) metrics self.metrics, update state metrics passed compile(), query results self.metrics end retrieve current value. Let’s try :","code":"class CustomModel(keras.Model):     def train_step(self, data):         # Unpack the data. Its structure depends on your model and         # on what you pass to `fit()`.         x, y = data          # Call torch.nn.Module.zero_grad() to clear the leftover gradients         # for the weights from the previous train step.         self.zero_grad()          # Compute loss         y_pred = self(x, training=True)  # Forward pass         loss = self.compute_loss(y=y, y_pred=y_pred)          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()          trainable_weights = [v for v in self.trainable_weights]         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             self.optimizer.apply(gradients, trainable_weights)          # Update metrics (includes the metric that tracks the loss)         for metric in self.metrics:             if metric.name == \"loss\":                 metric.update_state(loss)             else:                 metric.update_state(y, y_pred)          # Return a dict mapping metric names to current value         # Note that it will include the loss (tracked in self.metrics).         return {m.name: m.result() for m in self.metrics} # Construct and compile an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])  # Just use `fit` as usual x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=3)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"going-lower-level","dir":"Articles","previous_headings":"","what":"Going lower-level","title":"Customizing what happens in `fit()` with PyTorch","text":"Naturally, just skip passing loss function compile(), instead everything manually train_step. Likewise metrics. ’s lower-level example, uses compile() configure optimizer: start creating Metric instances track loss MAE score (__init__()). implement custom train_step() updates state metrics (calling update_state() ), query (via result()) return current average value, displayed progress bar pass callback. Note need call reset_states() metrics epoch! Otherwise calling result() return average since start training, whereas usually work per-epoch averages. Thankfully, framework can us: just list metric want reset metrics property model. model call reset_states() object listed beginning fit() epoch beginning call evaluate().","code":"class CustomModel(keras.Model):     def __init__(self, *args, **kwargs):         super().__init__(*args, **kwargs)         self.loss_tracker = keras.metrics.Mean(name=\"loss\")         self.mae_metric = keras.metrics.MeanAbsoluteError(name=\"mae\")         self.loss_fn = keras.losses.MeanSquaredError()      def train_step(self, data):         x, y = data          # Call torch.nn.Module.zero_grad() to clear the leftover gradients         # for the weights from the previous train step.         self.zero_grad()          # Compute loss         y_pred = self(x, training=True)  # Forward pass         loss = self.loss_fn(y, y_pred)          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()          trainable_weights = [v for v in self.trainable_weights]         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             self.optimizer.apply(gradients, trainable_weights)          # Compute our own metrics         self.loss_tracker.update_state(loss)         self.mae_metric.update_state(y, y_pred)         return {             \"loss\": self.loss_tracker.result(),             \"mae\": self.mae_metric.result(),         }      @property     def metrics(self):         # We list our `Metric` objects here so that `reset_states()` can be         # called automatically at the start of each epoch         # or at the start of `evaluate()`.         return [self.loss_tracker, self.mae_metric]   # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs)  # We don't passs a loss or metrics here. model.compile(optimizer=\"adam\")  # Just use `fit` as usual -- you can use callbacks, etc. x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.fit(x, y, epochs=5)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"supporting-sample_weight-class_weight","dir":"Articles","previous_headings":"","what":"Supporting sample_weight & class_weight","title":"Customizing what happens in `fit()` with PyTorch","text":"may noticed first basic example didn’t make mention sample weighting. want support fit() arguments sample_weight class_weight, ’d simply following: Unpack sample_weight data argument Pass compute_loss & update_state (course, also just apply manually don’t rely compile() losses & metrics) ’s .","code":"class CustomModel(keras.Model):     def train_step(self, data):         # Unpack the data. Its structure depends on your model and         # on what you pass to `fit()`.         if len(data) == 3:             x, y, sample_weight = data         else:             sample_weight = None             x, y = data          # Call torch.nn.Module.zero_grad() to clear the leftover gradients         # for the weights from the previous train step.         self.zero_grad()          # Compute loss         y_pred = self(x, training=True)  # Forward pass         loss = self.compute_loss(             y=y,             y_pred=y_pred,             sample_weight=sample_weight,         )          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()          trainable_weights = [v for v in self.trainable_weights]         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             self.optimizer.apply(gradients, trainable_weights)          # Update metrics (includes the metric that tracks the loss)         for metric in self.metrics:             if metric.name == \"loss\":                 metric.update_state(loss)             else:                 metric.update_state(y, y_pred, sample_weight=sample_weight)          # Return a dict mapping metric names to current value         # Note that it will include the loss (tracked in self.metrics).         return {m.name: m.result() for m in self.metrics}   # Construct and compile an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])  # You can now use sample_weight argument x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) sw = np.random.random((1000, 1)) model.fit(x, y, sample_weight=sw, epochs=3)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"providing-your-own-evaluation-step","dir":"Articles","previous_headings":"","what":"Providing your own evaluation step","title":"Customizing what happens in `fit()` with PyTorch","text":"want calls model.evaluate()? override test_step exactly way. ’s looks like:","code":"class CustomModel(keras.Model):     def test_step(self, data):         # Unpack the data         x, y = data         # Compute predictions         y_pred = self(x, training=False)         # Updates the metrics tracking the loss         loss = self.compute_loss(y=y, y_pred=y_pred)         # Update the metrics.         for metric in self.metrics:             if metric.name == \"loss\":                 metric.update_state(loss)             else:                 metric.update_state(y, y_pred)         # Return a dict mapping metric names to current value.         # Note that it will include the loss (tracked in self.metrics).         return {m.name: m.result() for m in self.metrics}   # Construct an instance of CustomModel inputs = keras.Input(shape=(32,)) outputs = keras.layers.Dense(1)(inputs) model = CustomModel(inputs, outputs) model.compile(loss=\"mse\", metrics=[\"mae\"])  # Evaluate with our custom test_step x = np.random.random((1000, 32)) y = np.random.random((1000, 1)) model.evaluate(x, y)"},{"path":"https://keras.posit.co/articles/custom_train_step_in_torch.html","id":"wrapping-up-an-end-to-end-gan-example","dir":"Articles","previous_headings":"","what":"Wrapping up: an end-to-end GAN example","title":"Customizing what happens in `fit()` with PyTorch","text":"Let’s walk end--end example leverages everything just learned. Let’s consider: generator network meant generate 28x28x1 images. discriminator network meant classify 28x28x1 images two classes (“fake” “real”). One optimizer . loss function train discriminator. ’s feature-complete GAN class, overriding compile() use signature, implementing entire GAN algorithm 17 lines train_step: Let’s test-drive : ideas behind deep learning simple, implementation painful?","code":"# Create the discriminator discriminator = keras.Sequential(     [         keras.Input(shape=(28, 28, 1)),         layers.Conv2D(64, (3, 3), strides=(2, 2), padding=\"same\"),         layers.LeakyReLU(negative_slope=0.2),         layers.Conv2D(128, (3, 3), strides=(2, 2), padding=\"same\"),         layers.LeakyReLU(negative_slope=0.2),         layers.GlobalMaxPooling2D(),         layers.Dense(1),     ],     name=\"discriminator\", )  # Create the generator latent_dim = 128 generator = keras.Sequential(     [         keras.Input(shape=(latent_dim,)),         # We want to generate 128 coefficients to reshape into a 7x7x128 map         layers.Dense(7 * 7 * 128),         layers.LeakyReLU(negative_slope=0.2),         layers.Reshape((7, 7, 128)),         layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding=\"same\"),         layers.LeakyReLU(negative_slope=0.2),         layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding=\"same\"),         layers.LeakyReLU(negative_slope=0.2),         layers.Conv2D(1, (7, 7), padding=\"same\", activation=\"sigmoid\"),     ],     name=\"generator\", ) class GAN(keras.Model):     def __init__(self, discriminator, generator, latent_dim):         super().__init__()         self.discriminator = discriminator         self.generator = generator         self.latent_dim = latent_dim         self.d_loss_tracker = keras.metrics.Mean(name=\"d_loss\")         self.g_loss_tracker = keras.metrics.Mean(name=\"g_loss\")         self.seed_generator = keras.random.SeedGenerator(1337)         self.built = True      @property     def metrics(self):         return [self.d_loss_tracker, self.g_loss_tracker]      def compile(self, d_optimizer, g_optimizer, loss_fn):         super().compile()         self.d_optimizer = d_optimizer         self.g_optimizer = g_optimizer         self.loss_fn = loss_fn      def train_step(self, real_images):         if isinstance(real_images, tuple):             real_images = real_images[0]         # Sample random points in the latent space         batch_size = real_images.shape[0]         random_latent_vectors = keras.random.normal(             shape=(batch_size, self.latent_dim), seed=self.seed_generator         )          # Decode them to fake images         generated_images = self.generator(random_latent_vectors)          # Combine them with real images         real_images = torch.tensor(real_images)         combined_images = torch.concat([generated_images, real_images], axis=0)          # Assemble labels discriminating real from fake images         labels = torch.concat(             [torch.ones((batch_size, 1)), torch.zeros((batch_size, 1))], axis=0         )         # Add random noise to the labels - important trick!         labels += 0.05 * keras.random.uniform(             labels.shape, seed=self.seed_generator         )          # Train the discriminator         self.zero_grad()         predictions = self.discriminator(combined_images)         d_loss = self.loss_fn(labels, predictions)         d_loss.backward()         grads = [v.value.grad for v in self.discriminator.trainable_weights]         with torch.no_grad():             self.d_optimizer.apply(grads, self.discriminator.trainable_weights)          # Sample random points in the latent space         random_latent_vectors = keras.random.normal(             shape=(batch_size, self.latent_dim), seed=self.seed_generator         )          # Assemble labels that say \"all real images\"         misleading_labels = torch.zeros((batch_size, 1))          # Train the generator (note that we should *not* update the weights         # of the discriminator)!         self.zero_grad()         predictions = self.discriminator(self.generator(random_latent_vectors))         g_loss = self.loss_fn(misleading_labels, predictions)         grads = g_loss.backward()         grads = [v.value.grad for v in self.generator.trainable_weights]         with torch.no_grad():             self.g_optimizer.apply(grads, self.generator.trainable_weights)          # Update metrics and return their value.         self.d_loss_tracker.update_state(d_loss)         self.g_loss_tracker.update_state(g_loss)         return {             \"d_loss\": self.d_loss_tracker.result(),             \"g_loss\": self.g_loss_tracker.result(),         } # Prepare the dataset. We use both the training & test MNIST digits. batch_size = 64 (x_train, _), (x_test, _) = keras.datasets.mnist.load_data() all_digits = np.concatenate([x_train, x_test]) all_digits = all_digits.astype(\"float32\") / 255.0 all_digits = np.reshape(all_digits, (-1, 28, 28, 1))  # Create a TensorDataset dataset = torch.utils.data.TensorDataset(     torch.from_numpy(all_digits), torch.from_numpy(all_digits) ) # Create a DataLoader dataloader = torch.utils.data.DataLoader(     dataset, batch_size=batch_size, shuffle=True )  gan = GAN(     discriminator=discriminator, generator=generator, latent_dim=latent_dim ) gan.compile(     d_optimizer=keras.optimizers.Adam(learning_rate=0.0003),     g_optimizer=keras.optimizers.Adam(learning_rate=0.0003),     loss_fn=keras.losses.BinaryCrossentropy(from_logits=True), )  gan.fit(dataloader, epochs=1)"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Customizing Saving and Serialization","text":"guide covers advanced methods can customized Keras saving. users, methods outlined primary Serialize, save, export guide sufficient.","code":""},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"apis","dir":"Articles","previous_headings":"Introduction","what":"APIs","title":"Customizing Saving and Serialization","text":"cover following APIs: save_assets() load_assets() save_own_variables() load_own_variables() get_build_config() build_from_config() get_compile_config() compile_from_config() restoring model, get executed following order: build_from_config() compile_from_config() load_own_variables() load_assets()","code":""},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Customizing Saving and Serialization","text":"","code":"import os import numpy as np import keras"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"state-saving-customization","dir":"Articles","previous_headings":"","what":"State saving customization","title":"Customizing Saving and Serialization","text":"methods determine state model’s layers saved calling model.save(). can override take full control state saving process.","code":""},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"save_own_variables-and-load_own_variables","dir":"Articles","previous_headings":"State saving customization","what":"save_own_variables() and load_own_variables()","title":"Customizing Saving and Serialization","text":"methods save load state variables layer model.save() keras.models.load_model() called, respectively. default, state variables saved loaded weights layer (trainable non-trainable). default implementation save_own_variables(): store used methods dictionary can populated layer variables. Let’s take look example customizing . Example:","code":"def save_own_variables(self, store):     all_vars = self._trainable_weights + self._non_trainable_weights     for i, v in enumerate(all_vars):         store[f\"{i}\"] = v.numpy() @keras.utils.register_keras_serializable(package=\"my_custom_package\") class LayerWithCustomVariable(keras.layers.Dense):     def __init__(self, units, **kwargs):         super().__init__(units, **kwargs)         self.my_variable = keras.Variable(             np.random.random((units,)), name=\"my_variable\", dtype=\"float32\"         )      def save_own_variables(self, store):         super().save_own_variables(store)         # Stores the value of the variable upon saving         store[\"variables\"] = self.my_variable.numpy()      def load_own_variables(self, store):         # Assigns the value of the variable upon loading         self.my_variable.assign(store[\"variables\"])         # Load the remaining weights         for i, v in enumerate(self.weights):             v.assign(store[f\"{i}\"])         # Note: You must specify how all variables (including layer weights)         # are loaded in `load_own_variables.`      def call(self, inputs):         dense_out = super().call(inputs)         return dense_out + self.my_variable   model = keras.Sequential([LayerWithCustomVariable(1)])  ref_input = np.random.random((8, 10)) ref_output = np.random.random((8, 10)) model.compile(optimizer=\"adam\", loss=\"mean_squared_error\") model.fit(ref_input, ref_output)  model.save(\"custom_vars_model.keras\") restored_model = keras.models.load_model(\"custom_vars_model.keras\")  np.testing.assert_allclose(     model.layers[0].my_variable.numpy(),     restored_model.layers[0].my_variable.numpy(), )"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"save_assets-and-load_assets","dir":"Articles","previous_headings":"State saving customization","what":"save_assets() and load_assets()","title":"Customizing Saving and Serialization","text":"methods can added model class definition store load additional information model needs. example, NLP domain layers TextVectorization layers IndexLookup layers may need store associated vocabulary (lookup table) text file upon saving. Let’s take basics workflow simple file assets.txt. Example:","code":"@keras.saving.register_keras_serializable(package=\"my_custom_package\") class LayerWithCustomAssets(keras.layers.Dense):     def __init__(self, vocab=None, *args, **kwargs):         super().__init__(*args, **kwargs)         self.vocab = vocab      def save_assets(self, inner_path):         # Writes the vocab (sentence) to text file at save time.         with open(os.path.join(inner_path, \"vocabulary.txt\"), \"w\") as f:             f.write(self.vocab)      def load_assets(self, inner_path):         # Reads the vocab (sentence) from text file at load time.         with open(os.path.join(inner_path, \"vocabulary.txt\"), \"r\") as f:             text = f.read()         self.vocab = text.replace(\"<unk>\", \"little\")   model = keras.Sequential(     [LayerWithCustomAssets(vocab=\"Mary had a <unk> lamb.\", units=5)] )  x = np.random.random((10, 10)) y = model(x)  model.save(\"custom_assets_model.keras\") restored_model = keras.models.load_model(\"custom_assets_model.keras\")  np.testing.assert_string_equal(     restored_model.layers[0].vocab, \"Mary had a little lamb.\" )"},{"path":[]},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"get_build_config-and-build_from_config","dir":"Articles","previous_headings":"build and compile saving customization","what":"get_build_config() and build_from_config()","title":"Customizing Saving and Serialization","text":"methods work together save layer’s built states restore upon loading. default, includes build config dictionary layer’s input shape, overriding methods can used include Variables Lookup Tables can useful restore built model. Example:","code":"@keras.saving.register_keras_serializable(package=\"my_custom_package\") class LayerWithCustomBuild(keras.layers.Layer):     def __init__(self, units=32, **kwargs):         super().__init__(**kwargs)         self.units = units      def call(self, inputs):         return keras.ops.matmul(inputs, self.w) + self.b      def get_config(self):         return dict(units=self.units, **super().get_config())      def build(self, input_shape, layer_init):         # Note the overriding of `build()` to add an extra argument.         # Therefore, we will need to manually call build with `layer_init` argument         # before the first execution of `call()`.         super().build(input_shape)         self._input_shape = input_shape         self.w = self.add_weight(             shape=(input_shape[-1], self.units),             initializer=layer_init,             trainable=True,         )         self.b = self.add_weight(             shape=(self.units,),             initializer=layer_init,             trainable=True,         )         self.layer_init = layer_init      def get_build_config(self):         build_config = {             \"layer_init\": self.layer_init,             \"input_shape\": self._input_shape,         }  # Stores our initializer for `build()`         return build_config      def build_from_config(self, config):         # Calls `build()` with the parameters at loading time         self.build(config[\"input_shape\"], config[\"layer_init\"])   custom_layer = LayerWithCustomBuild(units=16) custom_layer.build(input_shape=(8,), layer_init=\"random_normal\")  model = keras.Sequential(     [         custom_layer,         keras.layers.Dense(1, activation=\"sigmoid\"),     ] )  x = np.random.random((16, 8)) y = model(x)  model.save(\"custom_build_model.keras\") restored_model = keras.models.load_model(\"custom_build_model.keras\")  np.testing.assert_equal(restored_model.layers[0].layer_init, \"random_normal\") np.testing.assert_equal(restored_model.built, True)"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"get_compile_config-and-compile_from_config","dir":"Articles","previous_headings":"build and compile saving customization","what":"get_compile_config() and compile_from_config()","title":"Customizing Saving and Serialization","text":"methods work together save information model compiled (optimizers, losses, etc.) restore re-compile model information. Overriding methods can useful compiling restored model custom optimizers, custom losses, etc., need deserialized prior calling model.compile compile_from_config(). Let’s take look example . Example:","code":"@keras.saving.register_keras_serializable(package=\"my_custom_package\") def small_square_sum_loss(y_true, y_pred):     loss = keras.ops.square(y_pred - y_true)     loss = loss / 10.0     loss = keras.ops.sum(loss, axis=1)     return loss   @keras.saving.register_keras_serializable(package=\"my_custom_package\") def mean_pred(y_true, y_pred):     return keras.ops.mean(y_pred)   @keras.saving.register_keras_serializable(package=\"my_custom_package\") class ModelWithCustomCompile(keras.Model):     def __init__(self, **kwargs):         super().__init__(**kwargs)         self.dense1 = keras.layers.Dense(8, activation=\"relu\")         self.dense2 = keras.layers.Dense(4, activation=\"softmax\")      def call(self, inputs):         x = self.dense1(inputs)         return self.dense2(x)      def compile(self, optimizer, loss_fn, metrics):         super().compile(optimizer=optimizer, loss=loss_fn, metrics=metrics)         self.model_optimizer = optimizer         self.loss_fn = loss_fn         self.loss_metrics = metrics      def get_compile_config(self):         # These parameters will be serialized at saving time.         return {             \"model_optimizer\": self.model_optimizer,             \"loss_fn\": self.loss_fn,             \"metric\": self.loss_metrics,         }      def compile_from_config(self, config):         # Deserializes the compile parameters (important, since many are custom)         optimizer = keras.utils.deserialize_keras_object(config[\"model_optimizer\"])         loss_fn = keras.utils.deserialize_keras_object(config[\"loss_fn\"])         metrics = keras.utils.deserialize_keras_object(config[\"metric\"])          # Calls compile with the deserialized parameters         self.compile(optimizer=optimizer, loss_fn=loss_fn, metrics=metrics)   model = ModelWithCustomCompile() model.compile(     optimizer=\"SGD\", loss_fn=small_square_sum_loss, metrics=[\"accuracy\", mean_pred] )  x = np.random.random((4, 8)) y = np.random.random((4,))  model.fit(x, y)  model.save(\"custom_compile_model.keras\") restored_model = keras.models.load_model(\"custom_compile_model.keras\")  np.testing.assert_equal(model.model_optimizer, restored_model.model_optimizer) np.testing.assert_equal(model.loss_fn, restored_model.loss_fn) np.testing.assert_equal(model.loss_metrics, restored_model.loss_metrics)"},{"path":"https://keras.posit.co/articles/customizing_saving_and_serialization.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"Customizing Saving and Serialization","text":"Using methods learned tutorial allows wide variety use cases, allowing saving loading complex models exotic assets state elements. recap: save_own_variables load_own_variables determine states saved loaded. save_assets load_assets can added store load additional information model needs. get_build_config build_from_config save restore model’s built states. get_compile_config compile_from_config save restore model’s compiled states.","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_jax.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Multi-GPU distributed training with JAX","text":"generally two ways distribute computation across multiple devices: Data parallelism, single model gets replicated multiple devices multiple machines. processes different batches data, merge results. exist many variants setup, differ different model replicas merge results, whether stay sync every batch whether loosely coupled, etc. Model parallelism, different parts single model run different devices, processing single batch data together. works best models naturally-parallel architecture, models feature multiple branches. guide focuses data parallelism, particular synchronous data parallelism, different replicas model stay sync batch process. Synchronicity keeps model convergence behavior identical see single-device training. Specifically, guide teaches use jax.sharding APIs train Keras models, minimal changes code, multiple GPUs TPUS (typically 2 16) installed single machine (single host, multi-device training). common setup researchers small-scale industry workflows.","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_jax.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Multi-GPU distributed training with JAX","text":"Let’s start defining function creates model train, function creates dataset train (MNIST case).","code":"import os  os.environ[\"KERAS_BACKEND\"] = \"jax\"  import jax import numpy as np import tensorflow as tf import keras  from jax.experimental import mesh_utils from jax.sharding import Mesh from jax.sharding import NamedSharding from jax.sharding import PartitionSpec as P   def get_model():     # Make a simple convnet with batch normalization and dropout.     inputs = keras.Input(shape=(28, 28, 1))     x = keras.layers.Rescaling(1.0 / 255.0)(inputs)     x = keras.layers.Conv2D(         filters=12, kernel_size=3, padding=\"same\", use_bias=False     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.Conv2D(         filters=24,         kernel_size=6,         use_bias=False,         strides=2,     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.Conv2D(         filters=32,         kernel_size=6,         padding=\"same\",         strides=2,         name=\"large_k\",     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.GlobalAveragePooling2D()(x)     x = keras.layers.Dense(256, activation=\"relu\")(x)     x = keras.layers.Dropout(0.5)(x)     outputs = keras.layers.Dense(10)(x)     model = keras.Model(inputs, outputs)     return model   def get_datasets():     # Load the data and split it between train and test sets     (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()      # Scale images to the [0, 1] range     x_train = x_train.astype(\"float32\")     x_test = x_test.astype(\"float32\")     # Make sure images have shape (28, 28, 1)     x_train = np.expand_dims(x_train, -1)     x_test = np.expand_dims(x_test, -1)     print(\"x_train shape:\", x_train.shape)     print(x_train.shape[0], \"train samples\")     print(x_test.shape[0], \"test samples\")      # Create TF Datasets     train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))     eval_data = tf.data.Dataset.from_tensor_slices((x_test, y_test))     return train_data, eval_data"},{"path":"https://keras.posit.co/articles/distributed_training_with_jax.html","id":"single-host-multi-device-synchronous-training","dir":"Articles","previous_headings":"","what":"Single-host, multi-device synchronous training","title":"Multi-GPU distributed training with JAX","text":"setup, one machine several GPUs TPUs (typically 2 16). device run copy model (called replica). simplicity, follows, ’ll assume ’re dealing 8 GPUs, loss generality. works step training: current batch data (called global batch) split 8 different sub-batches (called local batches). instance, global batch 512 samples, 8 local batches 64 samples. 8 replicas independently processes local batch: run forward pass, backward pass, outputting gradient weights respect loss model local batch. weight updates originating local gradients efficiently merged across 8 replicas. done end every step, replicas always stay sync. practice, process synchronously updating weights model replicas handled level individual weight variable. done using jax.sharding.NamedSharding configured replicate variables. use single-host, multi-device synchronous training Keras model, use jax.sharding features. ’s works: first create device mesh using mesh_utils.create_device_mesh. specify want replicate model optimizer variables across devices using spec axis. specify want shard data across devices using spec splits along batch dimension. use jax.device_put replicate model optimizer variables across devices. happens beginning. training loop, batch process, use jax.device_put split batch across devices invoking train step. ’s flow, step split utility function: ’s !","code":"# Config num_epochs = 2 batch_size = 64  train_data, eval_data = get_datasets() train_data = train_data.batch(batch_size, drop_remainder=True)  model = get_model() optimizer = keras.optimizers.Adam(1e-3) loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)  # Initialize all state with .build() (one_batch, one_batch_labels) = next(iter(train_data)) model.build(one_batch) optimizer.build(model.trainable_variables)   # This is the loss function that will be differentiated. # Keras provides a pure functional forward pass: model.stateless_call def compute_loss(trainable_variables, non_trainable_variables, x, y):     y_pred, updated_non_trainable_variables = model.stateless_call(         trainable_variables, non_trainable_variables, x     )     loss_value = loss(y, y_pred)     return loss_value, updated_non_trainable_variables   # Function to compute gradients compute_gradients = jax.value_and_grad(compute_loss, has_aux=True)   # Training step, Keras provides a pure functional optimizer.stateless_apply @jax.jit def train_step(train_state, x, y):     (         trainable_variables,         non_trainable_variables,         optimizer_variables,     ) = train_state     (loss_value, non_trainable_variables), grads = compute_gradients(         trainable_variables, non_trainable_variables, x, y     )      trainable_variables, optimizer_variables = optimizer.stateless_apply(         optimizer_variables, grads, trainable_variables     )      return loss_value, (         trainable_variables,         non_trainable_variables,         optimizer_variables,     )   # Replicate the model and optimizer variable on all devices def get_replicated_train_state(devices):     # All variables will be replicated on all devices     var_mesh = Mesh(devices, axis_names=(\"_\"))     # In NamedSharding, axes not mentioned are replicated (all axes here)     var_replication = NamedSharding(var_mesh, P())      # Apply the distribution settings to the model variables     trainable_variables = jax.device_put(         model.trainable_variables, var_replication     )     non_trainable_variables = jax.device_put(         model.non_trainable_variables, var_replication     )     optimizer_variables = jax.device_put(optimizer.variables, var_replication)      # Combine all state in a tuple     return (trainable_variables, non_trainable_variables, optimizer_variables)   num_devices = len(jax.local_devices()) print(f\"Running on {num_devices} devices: {jax.local_devices()}\") devices = mesh_utils.create_device_mesh((num_devices,))  # Data will be split along the batch axis data_mesh = Mesh(devices, axis_names=(\"batch\",))  # naming axes of the mesh data_sharding = NamedSharding(     data_mesh,     P(         \"batch\",     ), )  # naming axes of the sharded partition  # Display data sharding x, y = next(iter(train_data)) sharded_x = jax.device_put(x.numpy(), data_sharding) print(\"Data sharding\") jax.debug.visualize_array_sharding(jax.numpy.reshape(sharded_x, [-1, 28 * 28]))  train_state = get_replicated_train_state(devices)  # Custom training loop for epoch in range(num_epochs):     data_iter = iter(train_data)     for data in data_iter:         x, y = data         sharded_x = jax.device_put(x.numpy(), data_sharding)         loss_value, train_state = train_step(train_state, sharded_x, y.numpy())     print(\"Epoch\", epoch, \"loss:\", loss_value)  # Post-processing model state update to write them back into the model trainable_variables, non_trainable_variables, optimizer_variables = train_state for variable, value in zip(model.trainable_variables, trainable_variables):     variable.assign(value) for variable, value in zip(     model.non_trainable_variables, non_trainable_variables ):     variable.assign(value)"},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Multi-GPU distributed training with TensorFlow","text":"generally two ways distribute computation across multiple devices: Data parallelism, single model gets replicated multiple devices multiple machines. processes different batches data, merge results. exist many variants setup, differ different model replicas merge results, whether stay sync every batch whether loosely coupled, etc. Model parallelism, different parts single model run different devices, processing single batch data together. works best models naturally-parallel architecture, models feature multiple branches. guide focuses data parallelism, particular synchronous data parallelism, different replicas model stay sync batch process. Synchronicity keeps model convergence behavior identical see single-device training. Specifically, guide teaches use tf.distribute API train Keras models multiple GPUs, minimal changes code, multiple GPUs (typically 2 16) installed single machine (single host, multi-device training). common setup researchers small-scale industry workflows.","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Multi-GPU distributed training with TensorFlow","text":"","code":"library(keras3) library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")"},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"single-host-multi-device-synchronous-training","dir":"Articles","previous_headings":"","what":"Single-host, multi-device synchronous training","title":"Multi-GPU distributed training with TensorFlow","text":"setup, one machine several GPUs (typically 2 16). device run copy model (called replica). simplicity, follows, ’ll assume ’re dealing 8 GPUs, loss generality. works step training: current batch data (called global batch) split 8 different sub-batches (called local batches). instance, global batch 512 samples, 8 local batches 64 samples. 8 replicas independently processes local batch: run forward pass, backward pass, outputting gradient weights respect loss model local batch. weight updates originating local gradients efficiently merged across 8 replicas. done end every step, replicas always stay sync. practice, process synchronously updating weights model replicas handled level individual weight variable. done mirrored variable object. use single-host, multi-device synchronous training Keras model, use tf$distribute$MirroredStrategy API. ’s works: Instantiate MirroredStrategy, optionally configuring specific devices want use (default strategy use GPUs available). Use strategy object open scope, within scope, create Keras objects need contain variables. Typically, means creating & compiling model inside distribution scope. cases, first call fit() may also create variables, ’s good idea put fit() call scope well. Train model via fit() usual. Importantly, recommend use tf.data.Dataset objects load data multi-device distributed workflow. Schematically, looks like : ’s simple end--end runnable example:","code":"# Create a MirroredStrategy. strategy <- tf$distribute$MirroredStrategy() cat(sprintf('Number of devices: %d\\n', strategy$num_replicas_in_sync))  # Open a strategy scope. with(startegy$scope(), {   # Everything that creates variables should be under the strategy scope.   # In general this is only model construction & `compile()`.   model <- Model(...)   model |> compile(...)    # Train the model on all available devices.   model |> fit(train_dataset, validation_data=val_dataset, ...)    # Test the model on all available devices.   model |> evaluate(test_dataset) }) get_compiled_model <- function() {   inputs <- keras_input(shape = 784)   outputs <- inputs |>     layer_dense(units = 256, activation = \"relu\") |>     layer_dense(units = 256, activation = \"relu\") |>     layer_dense(units = 10)   model <- keras_model(inputs, outputs)   model |> compile(     optimizer = optimizer_adam(),     loss = loss_sparse_categorical_crossentropy(from_logits = TRUE),     metrics = list(metric_sparse_categorical_accuracy()),      # XLA compilation is temporarily disabled due to a bug     # https://github.com/keras-team/keras/issues/19005     jit_compile = FALSE   )   model }  get_dataset <- function(batch_size = 64) {    c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()   x_train <- array_reshape(x_train, c(-1, 784))   x_test <- array_reshape(x_test, c(-1, 784))    # Reserve 10,000 samples for validation.   val_i <- sample.int(nrow(x_train), 10000)   x_val <- x_train[val_i,]   y_val <- y_train[val_i]   x_train = x_train[-val_i,]   y_train = y_train[-val_i]    # Prepare the training dataset.   train_dataset <- list(x_train, y_train) |>     tensor_slices_dataset() |>     dataset_batch(batch_size)    # Prepare the validation dataset.   val_dataset <- list(x_val, y_val) |>     tensor_slices_dataset() |>     dataset_batch(batch_size)    # Prepare the test dataset.   test_dataset <- list(x_test, y_test) |>     tensor_slices_dataset() |>     dataset_batch(batch_size)    list(train_dataset, val_dataset, test_dataset) }  # Create a MirroredStrategy. strategy <- tf$distribute$MirroredStrategy() cat(sprintf('Number of devices: %d\\n', strategy$num_replicas_in_sync)) ## Number of devices: 2 # Open a strategy scope. with(strategy$scope(), {   # Everything that creates variables should be under the strategy scope.   # In general this is only model construction & `compile()`.   model <- get_compiled_model()    c(train_dataset, val_dataset, test_dataset) %<-% get_dataset()    # Train the model on all available devices.   model |> fit(train_dataset, epochs = 2, validation_data = val_dataset)    # Test the model on all available devices.   model |> evaluate(test_dataset)  }) ## Epoch 1/2 ## 782/782 - 5s - 7ms/step - loss: 3.0622 - sparse_categorical_accuracy: 0.8615 - val_loss: 1.1367 - val_sparse_categorical_accuracy: 0.9006 ## Epoch 2/2 ## 782/782 - 3s - 4ms/step - loss: 0.5774 - sparse_categorical_accuracy: 0.9259 - val_loss: 0.6612 - val_sparse_categorical_accuracy: 0.9210 ## 157/157 - 0s - 2ms/step - loss: 0.6729 - sparse_categorical_accuracy: 0.9150 ## $loss ## [1] 0.6728871 ## ## $sparse_categorical_accuracy ## [1] 0.915"},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"using-callbacks-to-ensure-fault-tolerance","dir":"Articles","previous_headings":"","what":"Using callbacks to ensure fault tolerance","title":"Multi-GPU distributed training with TensorFlow","text":"using distributed training, always make sure strategy recover failure (fault tolerance). simplest way handle pass ModelCheckpoint callback fit(), save model regular intervals (e.g. every 100 batches every epoch). can restart training saved model. ’s simple example:","code":"# Prepare a directory to store all the checkpoints. checkpoint_dir <- \"./ckpt\" if (!dir.exists(checkpoint_dir)) {   dir.create(checkpoint_dir) }  make_or_restore_model <- function() {   # Either restore the latest model, or create a fresh one   # if there is no checkpoint available.   checkpoints <- list.files(checkpoint_dir, #pattern = \"ckpt-.*\\\\.keras\",                             full.names = TRUE)    if (length(checkpoints) > 0) {     checkpoint_epochs <- as.integer(sub(\"ckpt-([0-9]+)\\\\.keras\", \"\\\\1\",                                         basename(checkpoints)))     latest_checkpoint <- checkpoints[which.max(checkpoint_epochs)]     load_model(latest_checkpoint)   } else {     get_compiled_model()   } }    run_training <- function(epochs = 1) {   # Create a MirroredStrategy.   strategy <- tf$distribute$MirroredStrategy()    # Open a strategy scope and create/restore the model   with(strategy$scope(), {     model <- make_or_restore_model()      callbacks <- list(       # This callback saves a SavedModel every epoch       # We include the current epoch in the folder name.       callback_model_checkpoint(         filepath = paste0(checkpoint_dir, \"/ckpt-{epoch}.keras\"),         save_freq = \"epoch\"       ))      model |> fit(       train_dataset,       epochs = epochs,       callbacks = callbacks,       validation_data = val_dataset,       verbose = 2     )   }) }  # Running the first time creates the model run_training(epochs = 1) ## 782/782 - 4s - 6ms/step - loss: 2.9519 - sparse_categorical_accuracy: 0.8655 - val_loss: 1.3110 - val_sparse_categorical_accuracy: 0.8836 # Calling the same function again will resume from where we left off run_training(epochs = 1) ## 782/782 - 3s - 4ms/step - loss: 0.5998 - sparse_categorical_accuracy: 0.9270 - val_loss: 0.8736 - val_sparse_categorical_accuracy: 0.9128"},{"path":"https://keras.posit.co/articles/distributed_training_with_tensorflow.html","id":"tfdata-performance-tips","dir":"Articles","previous_headings":"","what":"tf$data performance tips","title":"Multi-GPU distributed training with TensorFlow","text":"distributed training, efficiency load data can often become critical. tips make sure tf$data pipelines run fast possible. Note dataset batching creating dataset, make sure batched global batch size. instance, 8 GPUs capable running batch 64 samples, call use global batch size 512. Calling dataset_cache() call dataset_cache() dataset, data cached running first iteration data. Every subsequent iteration use cached data. cache can memory (default) local file specify. can improve performance : data expected change iteration iteration reading data remote distributed filesystem reading data local disk, data fit memory workflow significantly IO-bound (e.g. reading & decoding image files). Calling dataset_prefetch(buffer_size) almost always call dataset_prefetch(buffer_size) creating dataset. means data pipeline run asynchronously model, new samples preprocessed stored buffer current batch samples used train model. next batch prefetched GPU memory time current batch . ’s !","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_torch.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Multi-GPU distributed training with PyTorch","text":"generally two ways distribute computation across multiple devices: Data parallelism, single model gets replicated multiple devices multiple machines. processes different batches data, merge results. exist many variants setup, differ different model replicas merge results, whether stay sync every batch whether loosely coupled, etc. Model parallelism, different parts single model run different devices, processing single batch data together. works best models naturally-parallel architecture, models feature multiple branches. guide focuses data parallelism, particular synchronous data parallelism, different replicas model stay sync batch process. Synchronicity keeps model convergence behavior identical see single-device training. Specifically, guide teaches use PyTorch’s DistributedDataParallel module wrapper train Keras, minimal changes code, multiple GPUs (typically 2 16) installed single machine (single host, multi-device training). common setup researchers small-scale industry workflows.","code":""},{"path":"https://keras.posit.co/articles/distributed_training_with_torch.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Multi-GPU distributed training with PyTorch","text":"Let’s start defining function creates model train, function creates dataset train (MNIST case). Next, let’s define simple PyTorch training loop targets GPU (note calls .cuda()).","code":"import os  os.environ[\"KERAS_BACKEND\"] = \"torch\"  import torch import numpy as np import keras   def get_model():     # Make a simple convnet with batch normalization and dropout.     inputs = keras.Input(shape=(28, 28, 1))     x = keras.layers.Rescaling(1.0 / 255.0)(inputs)     x = keras.layers.Conv2D(         filters=12, kernel_size=3, padding=\"same\", use_bias=False     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.Conv2D(         filters=24,         kernel_size=6,         use_bias=False,         strides=2,     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.Conv2D(         filters=32,         kernel_size=6,         padding=\"same\",         strides=2,         name=\"large_k\",     )(x)     x = keras.layers.BatchNormalization(scale=False, center=True)(x)     x = keras.layers.ReLU()(x)     x = keras.layers.GlobalAveragePooling2D()(x)     x = keras.layers.Dense(256, activation=\"relu\")(x)     x = keras.layers.Dropout(0.5)(x)     outputs = keras.layers.Dense(10)(x)     model = keras.Model(inputs, outputs)     return model   def get_dataset():     # Load the data and split it between train and test sets     (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()      # Scale images to the [0, 1] range     x_train = x_train.astype(\"float32\")     x_test = x_test.astype(\"float32\")     # Make sure images have shape (28, 28, 1)     x_train = np.expand_dims(x_train, -1)     x_test = np.expand_dims(x_test, -1)     print(\"x_train shape:\", x_train.shape)      # Create a TensorDataset     dataset = torch.utils.data.TensorDataset(         torch.from_numpy(x_train), torch.from_numpy(y_train)     )     return dataset def train_model(model, dataloader, num_epochs, optimizer, loss_fn):     for epoch in range(num_epochs):         running_loss = 0.0         running_loss_count = 0         for batch_idx, (inputs, targets) in enumerate(dataloader):             inputs = inputs.cuda(non_blocking=True)             targets = targets.cuda(non_blocking=True)              # Forward pass             outputs = model(inputs)             loss = loss_fn(outputs, targets)              # Backward and optimize             optimizer.zero_grad()             loss.backward()             optimizer.step()              running_loss += loss.item()             running_loss_count += 1          # Print loss statistics         print(             f\"Epoch {epoch + 1}/{num_epochs}, \"             f\"Loss: {running_loss / running_loss_count}\"         )"},{"path":"https://keras.posit.co/articles/distributed_training_with_torch.html","id":"single-host-multi-device-synchronous-training","dir":"Articles","previous_headings":"","what":"Single-host, multi-device synchronous training","title":"Multi-GPU distributed training with PyTorch","text":"setup, one machine several GPUs (typically 2 16). device run copy model (called replica). simplicity, follows, ’ll assume ’re dealing 8 GPUs, loss generality. works step training: current batch data (called global batch) split 8 different sub-batches (called local batches). instance, global batch 512 samples, 8 local batches 64 samples. 8 replicas independently processes local batch: run forward pass, backward pass, outputting gradient weights respect loss model local batch. weight updates originating local gradients efficiently merged across 8 replicas. done end every step, replicas always stay sync. practice, process synchronously updating weights model replicas handled level individual weight variable. done mirrored variable object. use single-host, multi-device synchronous training Keras model, use torch.nn.parallel.DistributedDataParallel module wrapper. ’s works: use torch.multiprocessing.start_processes start multiple Python processes, one per device. process run per_device_launch_fn function. uses torch.distributed.init_process_group torch.cuda.set_device configure device used process. uses torch.utils.data.distributed.DistributedSampler torch.utils.data.DataLoader turn data distributed data loader. also uses torch.nn.parallel.DistributedDataParallel turn model distributed PyTorch module. calls train_model function. train_model function run process, model using separate device process. ’s flow, step split utility function: Time start multiple processes: ’s !","code":"# Config num_gpu = torch.cuda.device_count() num_epochs = 2 batch_size = 64 print(f\"Running on {num_gpu} GPUs\")   def setup_device(current_gpu_index, num_gpus):     # Device setup     os.environ[\"MASTER_ADDR\"] = \"localhost\"     os.environ[\"MASTER_PORT\"] = \"56492\"     device = torch.device(\"cuda:{}\".format(current_gpu_index))     torch.distributed.init_process_group(         backend=\"nccl\",         init_method=\"env://\",         world_size=num_gpus,         rank=current_gpu_index,     )     torch.cuda.set_device(device)   def cleanup():     torch.distributed.destroy_process_group()   def prepare_dataloader(dataset, current_gpu_index, num_gpus, batch_size):     sampler = torch.utils.data.distributed.DistributedSampler(         dataset,         num_replicas=num_gpus,         rank=current_gpu_index,         shuffle=False,     )     dataloader = torch.utils.data.DataLoader(         dataset,         sampler=sampler,         batch_size=batch_size,         shuffle=False,     )     return dataloader   def per_device_launch_fn(current_gpu_index, num_gpu):     # Setup the process groups     setup_device(current_gpu_index, num_gpu)      dataset = get_dataset()     model = get_model()      # prepare the dataloader     dataloader = prepare_dataloader(         dataset, current_gpu_index, num_gpu, batch_size     )      # Instantiate the torch optimizer     optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)      # Instantiate the torch loss function     loss_fn = torch.nn.CrossEntropyLoss()      # Put model on device     model = model.to(current_gpu_index)     ddp_model = torch.nn.parallel.DistributedDataParallel(         model, device_ids=[current_gpu_index], output_device=current_gpu_index     )      train_model(ddp_model, dataloader, num_epochs, optimizer, loss_fn)      cleanup() if __name__ == \"__main__\":     # We use the \"fork\" method rather than \"spawn\" to support notebooks     torch.multiprocessing.start_processes(         per_device_launch_fn,         args=(num_gpu,),         nprocs=num_gpu,         join=True,         start_method=\"fork\",     )"},{"path":"https://keras.posit.co/articles/distribution.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Distributed training with Keras 3","text":"Keras distribution API new interface designed facilitate distributed deep learning across variety backends like JAX, TensorFlow PyTorch. powerful API introduces suite tools enabling data model parallelism, allowing efficient scaling deep learning models multiple accelerators hosts. Whether leveraging power GPUs TPUs, API provides streamlined approach initializing distributed environments, defining device meshes, orchestrating layout tensors across computational resources. classes like DataParallel ModelParallel, abstracts complexity involved parallel computation, making easier developers accelerate machine learning workflows.","code":""},{"path":"https://keras.posit.co/articles/distribution.html","id":"how-it-works","dir":"Articles","previous_headings":"","what":"How it works","title":"Distributed training with Keras 3","text":"Keras distribution API provides global programming model allows developers compose applications operate tensors global context (working single device) automatically managing distribution across many devices. API leverages underlying framework (e.g. JAX) distribute program tensors according sharding directives procedure called single program, multiple data (SPMD) expansion. decoupling application sharding directives, API enables running application single device, multiple devices, even multiple clients, preserving global semantics.","code":""},{"path":"https://keras.posit.co/articles/distribution.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Distributed training with Keras 3","text":"","code":"# This guide assumes there are 8 GPUs available for testing. If you don't have # 8 gpus available locally, you can set the following envvar to # make xla initialize the CPU as 8 devices, to enable local testing Sys.setenv(\"CUDA_VISIBLE_DEVICES\" = \"\") Sys.setenv(\"XLA_FLAGS\" = \"--xla_force_host_platform_device_count=8\") library(keras3)  # The distribution API is only implemented for the JAX backend for now. use_backend(\"jax\") jax <- reticulate::import(\"jax\")  library(tfdatasets, exclude = \"shape\") # For dataset input."},{"path":"https://keras.posit.co/articles/distribution.html","id":"devicemesh-and-tensorlayout","dir":"Articles","previous_headings":"","what":"DeviceMesh and TensorLayout","title":"Distributed training with Keras 3","text":"keras$distribution$DeviceMesh class Keras distribution API represents cluster computational devices configured distributed computation. aligns similar concepts jax.sharding.Mesh tf.dtensor.Mesh, ’s used map physical devices logical mesh structure. TensorLayout class specifies tensors distributed across DeviceMesh, detailing sharding tensors along specified axes correspond names axes DeviceMesh. can find detailed concept explainers TensorFlow DTensor guide.","code":"# Retrieve the local available gpu devices. devices <- jax$devices() # \"gpu\" str(devices) ## List of 8 ##  $ :TFRT_CPU_0 ##  $ :TFRT_CPU_1 ##  $ :TFRT_CPU_2 ##  $ :TFRT_CPU_3 ##  $ :TFRT_CPU_4 ##  $ :TFRT_CPU_5 ##  $ :TFRT_CPU_6 ##  $ :TFRT_CPU_7 # Define a 2x4 device mesh with data and model parallel axes mesh <- keras$distribution$DeviceMesh(   shape = shape(2, 4),   axis_names = list(\"data\", \"model\"),   devices = devices )  # A 2D layout, which describes how a tensor is distributed across the # mesh. The layout can be visualized as a 2D grid with \"model\" as rows and # \"data\" as columns, and it is a [4, 2] grid when it mapped to the physical # devices on the mesh. layout_2d <- keras$distribution$TensorLayout(   axes = c(\"model\", \"data\"),   device_mesh = mesh )  # A 4D layout which could be used for data parallelism of an image input. replicated_layout_4d <- keras$distribution$TensorLayout(   axes = list(\"data\", NULL, NULL, NULL),   device_mesh = mesh )"},{"path":"https://keras.posit.co/articles/distribution.html","id":"distribution","dir":"Articles","previous_headings":"","what":"Distribution","title":"Distributed training with Keras 3","text":"Distribution class Keras serves foundational abstract class designed developing custom distribution strategies. encapsulates core logic needed distribute model’s variables, input data, intermediate computations across device mesh. end user, won’t interact directly class, subclasses like DataParallel ModelParallel.","code":""},{"path":"https://keras.posit.co/articles/distribution.html","id":"dataparallel","dir":"Articles","previous_headings":"","what":"DataParallel","title":"Distributed training with Keras 3","text":"DataParallel class Keras distribution API designed data parallelism strategy distributed training, model weights replicated across devices DeviceMesh, device processes portion input data. sample usage class.","code":"# Create DataParallel with list of devices. # As a shortcut, the devices can be skipped, # and Keras will detect all local available devices. # E.g. data_parallel <- DataParallel() data_parallel <- keras$distribution$DataParallel(devices = devices)  # Or you can choose to create DataParallel with a 1D `DeviceMesh`. mesh_1d <- keras$distribution$DeviceMesh(   shape = shape(8),   axis_names = list(\"data\"),   devices = devices ) data_parallel <- keras$distribution$DataParallel(device_mesh = mesh_1d)  inputs <- random_normal(c(128, 28, 28, 1)) labels <- random_normal(c(128, 10)) dataset <- tensor_slices_dataset(c(inputs, labels)) |>   dataset_batch(16)  # Set the global distribution. keras$distribution$set_distribution(data_parallel)  # Note that all the model weights from here on are replicated to # all the devices of the `DeviceMesh`. This includes the RNG # state, optimizer states, metrics, etc. The dataset fed into `model |> fit()` or # `model |> evaluate()` will be split evenly on the batch dimension, and sent to # all the devices. You don't have to do any manual aggregation of losses, # since all the computation happens in a global context. inputs <- keras_input(shape = c(28, 28, 1)) outputs <- inputs |>   layer_flatten() |>   layer_dense(units = 200, use_bias = FALSE, activation = \"relu\") |>   layer_dropout(0.4) |>   layer_dense(units = 10, activation = \"softmax\")  model <- keras_model(inputs = inputs, outputs = outputs)  model |> compile(loss = \"mse\") model |> fit(dataset, epochs = 3) ## Epoch 1/3 ## 8/8 - 0s - 37ms/step - loss: 1.0629 ## Epoch 2/3 ## 8/8 - 0s - 5ms/step - loss: 0.9712 ## Epoch 3/3 ## 8/8 - 0s - 5ms/step - loss: 0.9322 model |> evaluate(dataset) ## 8/8 - 0s - 7ms/step - loss: 0.8859 ## $loss ## [1] 0.8858577"},{"path":"https://keras.posit.co/articles/distribution.html","id":"modelparallel-and-layoutmap","dir":"Articles","previous_headings":"","what":"ModelParallel and LayoutMap","title":"Distributed training with Keras 3","text":"ModelParallel mostly useful model weights large fit single accelerator. setting allows spit model weights activation tensors across devices DeviceMesh, enable horizontal scaling large models. Unlike DataParallel model weights fully replicated, weights layout ModelParallel usually need customization best performances. introduce LayoutMap let specify TensorLayout weights intermediate tensors global perspective. LayoutMap dict-like object maps string TensorLayout instances. behaves differently normal dict string key treated regex retrieving value. class allows define naming schema TensorLayout retrieve corresponding TensorLayout instance. Typically, key used query variable$path attribute, identifier variable. shortcut, list axis names also allowed inserting value, converted TensorLayout. LayoutMap can also optionally contain DeviceMesh populate TensorLayout$device_mesh set. retrieving layout key, isn’t exact match, existing keys layout map treated regex matched input key . multiple matches, ValueError raised. matches found, NULL returned. also easy change mesh structure tune computation data parallel model parallel. can adjusting shape mesh. changes needed code.","code":"mesh_2d <- keras$distribution$DeviceMesh(   shape = shape(2, 4),   axis_names = c(\"data\", \"model\"),   devices = devices ) layout_map  <- keras$distribution$LayoutMap(mesh_2d)  # The rule below means that for any weights that match with d1/kernel, it # will be sharded with model dimensions (4 devices), same for the d1/bias. # All other weights will be fully replicated. layout_map[\"d1/kernel\"] <- tuple(NULL, \"model\") layout_map[\"d1/bias\"] <- tuple(\"model\")  # You can also set the layout for the layer output like layout_map[\"d2/output\"] <- tuple(\"data\", NULL)  model_parallel <- keras$distribution$ModelParallel(   mesh_2d, layout_map, batch_dim_name = \"data\" )  keras$distribution$set_distribution(model_parallel)  inputs <- layer_input(shape = c(28, 28, 1)) outputs <- inputs |>   layer_flatten() |>   layer_dense(units = 200, use_bias = FALSE,               activation = \"relu\", name = \"d1\") |>   layer_dropout(0.4) |>   layer_dense(units = 10,               activation = \"softmax\",               name = \"d2\")  model <- keras_model(inputs = inputs, outputs = outputs)  # The data will be sharded across the \"data\" dimension of the method, which # has 2 devices. model |> compile(loss = \"mse\") model |> fit(dataset, epochs = 3) ## Epoch 1/3 ## 8/8 - 0s - 29ms/step - loss: 1.0714 ## Epoch 2/3 ## 8/8 - 0s - 3ms/step - loss: 0.9744 ## Epoch 3/3 ## 8/8 - 0s - 4ms/step - loss: 0.9280 model |> evaluate(dataset) ## 8/8 - 0s - 7ms/step - loss: 0.8802 ## $loss ## [1] 0.8802156 full_data_parallel_mesh <- keras$distribution$DeviceMesh(   shape = shape(8, 1),   axis_names = list(\"data\", \"model\"),   devices = devices ) more_data_parallel_mesh <- keras$distribution$DeviceMesh(   shape = shape(4, 2),   axis_names = list(\"data\", \"model\"),   devices = devices ) more_model_parallel_mesh <- keras$distribution$DeviceMesh(   shape = shape(2, 4),   axis_names = list(\"data\", \"model\"),   devices = devices ) full_model_parallel_mesh <- keras$distribution$DeviceMesh(   shape = shape(1, 8),   axis_names = list(\"data\", \"model\"),   devices = devices )"},{"path":"https://keras.posit.co/articles/distribution.html","id":"further-reading","dir":"Articles","previous_headings":"ModelParallel and LayoutMap","what":"Further reading","title":"Distributed training with Keras 3","text":"JAX Distributed arrays automatic parallelization JAX sharding module TensorFlow Distributed training DTensors TensorFlow DTensor concepts Using DTensors tf.keras","code":""},{"path":"https://keras.posit.co/articles/examples/autoencoder.html","id":"introduction","dir":"Articles > Examples","previous_headings":"","what":"Introduction","title":"Convolutional autoencoder for image denoising","text":"example demonstrates implement deep convolutional autoencoder image denoising, mapping noisy digits images MNIST dataset clean digits images. implementation based original blog post titled Building Autoencoders Keras François Chollet.","code":""},{"path":"https://keras.posit.co/articles/examples/autoencoder.html","id":"setup","dir":"Articles > Examples","previous_headings":"","what":"Setup","title":"Convolutional autoencoder for image denoising","text":"","code":"library(keras3)  # Normalizes the supplied array and reshapes it. preprocess <- function(array) {   array_reshape(array/255, c(dim(array)[1], 28, 28, 1)) }  # Adds random noise to each image in the supplied array. noise <- function(array) {   noise_factor <- 0.4   noisy_array <- array + noise_factor * random_normal(dim(array))   op_clip(noisy_array, 0.0, 1.0) }  display <- function(array1, array2) {   n <- 2   indices <- sample.int(dim(array1)[1], n)   images1 <- as.array(array1)[indices, , , ]   images2 <- as.array(array2)[indices, , , ]    par(mfrow = c(2, n), mar = c(0, 0, 0, 0))   for (i in seq_len(n)) {     plot(as.raster(images1[i, , ]))     plot(as.raster(images2[i, , ]))   } }"},{"path":"https://keras.posit.co/articles/examples/autoencoder.html","id":"prepare-the-data","dir":"Articles > Examples","previous_headings":"","what":"Prepare the data","title":"Convolutional autoencoder for image denoising","text":"","code":"# Since we only need images from the dataset to encode and decode, we # won't use the labels. c(c(train_data, .), c(test_data, .)) %<-% dataset_mnist()  # Normalize and reshape the data train_data <- preprocess(train_data) test_data <- preprocess(test_data)  # Create a copy of the data with added noise noisy_train_data <- noise(train_data) noisy_test_data <- noise(test_data)  # Display the train data and a version of it with added noise display(train_data, noisy_train_data)"},{"path":"https://keras.posit.co/articles/examples/autoencoder.html","id":"build-the-autoencoder","dir":"Articles > Examples","previous_headings":"","what":"Build the autoencoder","title":"Convolutional autoencoder for image denoising","text":"going use Functional API build convolutional autoencoder. Now can train autoencoder using train_data input data target. Notice setting validation data using format. Let’s predict test dataset display original image together prediction autoencoder. Notice predictions pretty close original images, although quite . Now know autoencoder works, let’s retrain using noisy data input clean data target. want autoencoder learn denoise images. Let’s now predict noisy data display results autoencoder. Notice autoencoder amazing job removing noise input images.","code":"input <- keras_input(shape = c(28, 28, 1))  # Encoder enc <- input |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3),                 activation = \"relu\", padding = \"same\") |>   layer_max_pooling_2d(pool_size = c(2, 2), padding = \"same\") |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3),                 activation = \"relu\", padding = \"same\") |>   layer_max_pooling_2d(pool_size = c(2, 2), padding = \"same\")  # Decoder dec <- enc |>   layer_conv_2d_transpose(filters = 32, kernel_size = c(3, 3), strides = 2,                           activation = \"relu\", padding = \"same\") |>   layer_conv_2d_transpose(filters = 32, kernel_size = c(3, 3), strides = 2,                           activation = \"relu\", padding = \"same\") |>   layer_conv_2d(filters = 1, kernel_size = c(3, 3),                 activation = \"sigmoid\", padding = \"same\")  # Autoencoder autoencoder <- keras_model(input, dec) autoencoder |> compile(optimizer = \"adam\", loss = \"binary_crossentropy\") autoencoder |> summary() ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 28, 28, 32)     │           320 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 14, 14, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 14, 14, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 7, 7, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 14, 14, 32)     │         9,248 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 28, 28, 32)     │         9,248 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 28, 28, 1)      │           289 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 28,353 (110.75 KB) ##  Trainable params: 28,353 (110.75 KB) ##  Non-trainable params: 0 (0.00 B) autoencoder |> fit(   x = train_data,   y = train_data,   epochs = 50,   batch_size = 128,   shuffle = TRUE,   validation_data = list(test_data, test_data), ) ## Epoch 1/50 ## 469/469 - 5s - 10ms/step - loss: 0.1322 - val_loss: 0.0736 ## Epoch 2/50 ## 469/469 - 1s - 3ms/step - loss: 0.0721 - val_loss: 0.0698 ## Epoch 3/50 ## 469/469 - 1s - 3ms/step - loss: 0.0695 - val_loss: 0.0681 ## Epoch 4/50 ## 469/469 - 1s - 3ms/step - loss: 0.0682 - val_loss: 0.0671 ## Epoch 5/50 ## 469/469 - 1s - 3ms/step - loss: 0.0673 - val_loss: 0.0665 ## Epoch 6/50 ## 469/469 - 1s - 3ms/step - loss: 0.0667 - val_loss: 0.0660 ## Epoch 7/50 ## 469/469 - 1s - 3ms/step - loss: 0.0663 - val_loss: 0.0656 ## Epoch 8/50 ## 469/469 - 1s - 3ms/step - loss: 0.0659 - val_loss: 0.0652 ## Epoch 9/50 ## 469/469 - 1s - 3ms/step - loss: 0.0655 - val_loss: 0.0649 ## Epoch 10/50 ## 469/469 - 1s - 3ms/step - loss: 0.0652 - val_loss: 0.0646 ## Epoch 11/50 ## 469/469 - 1s - 3ms/step - loss: 0.0650 - val_loss: 0.0644 ## Epoch 12/50 ## 469/469 - 1s - 3ms/step - loss: 0.0647 - val_loss: 0.0642 ## Epoch 13/50 ## 469/469 - 1s - 3ms/step - loss: 0.0645 - val_loss: 0.0640 ## Epoch 14/50 ## 469/469 - 1s - 3ms/step - loss: 0.0643 - val_loss: 0.0638 ## Epoch 15/50 ## 469/469 - 1s - 3ms/step - loss: 0.0642 - val_loss: 0.0636 ## Epoch 16/50 ## 469/469 - 1s - 3ms/step - loss: 0.0640 - val_loss: 0.0635 ## Epoch 17/50 ## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0634 ## Epoch 18/50 ## 469/469 - 1s - 3ms/step - loss: 0.0638 - val_loss: 0.0633 ## Epoch 19/50 ## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0631 ## Epoch 20/50 ## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631 ## Epoch 21/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0630 ## Epoch 22/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0629 ## Epoch 23/50 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0628 ## Epoch 24/50 ## 469/469 - 1s - 3ms/step - loss: 0.0632 - val_loss: 0.0628 ## Epoch 25/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627 ## Epoch 26/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0626 ## Epoch 27/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0626 ## Epoch 28/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0625 ## Epoch 29/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0625 ## Epoch 30/50 ## 469/469 - 1s - 3ms/step - loss: 0.0628 - val_loss: 0.0624 ## Epoch 31/50 ## 469/469 - 1s - 3ms/step - loss: 0.0628 - val_loss: 0.0624 ## Epoch 32/50 ## 469/469 - 1s - 3ms/step - loss: 0.0627 - val_loss: 0.0623 ## Epoch 33/50 ## 469/469 - 1s - 3ms/step - loss: 0.0627 - val_loss: 0.0623 ## Epoch 34/50 ## 469/469 - 1s - 3ms/step - loss: 0.0627 - val_loss: 0.0623 ## Epoch 35/50 ## 469/469 - 1s - 3ms/step - loss: 0.0626 - val_loss: 0.0622 ## Epoch 36/50 ## 469/469 - 1s - 3ms/step - loss: 0.0626 - val_loss: 0.0622 ## Epoch 37/50 ## 469/469 - 1s - 3ms/step - loss: 0.0626 - val_loss: 0.0622 ## Epoch 38/50 ## 469/469 - 1s - 3ms/step - loss: 0.0625 - val_loss: 0.0622 ## Epoch 39/50 ## 469/469 - 1s - 3ms/step - loss: 0.0625 - val_loss: 0.0621 ## Epoch 40/50 ## 469/469 - 1s - 3ms/step - loss: 0.0625 - val_loss: 0.0621 ## Epoch 41/50 ## 469/469 - 1s - 3ms/step - loss: 0.0624 - val_loss: 0.0621 ## Epoch 42/50 ## 469/469 - 1s - 3ms/step - loss: 0.0624 - val_loss: 0.0621 ## Epoch 43/50 ## 469/469 - 1s - 3ms/step - loss: 0.0624 - val_loss: 0.0620 ## Epoch 44/50 ## 469/469 - 1s - 3ms/step - loss: 0.0624 - val_loss: 0.0620 ## Epoch 45/50 ## 469/469 - 1s - 3ms/step - loss: 0.0623 - val_loss: 0.0620 ## Epoch 46/50 ## 469/469 - 1s - 3ms/step - loss: 0.0623 - val_loss: 0.0620 ## Epoch 47/50 ## 469/469 - 1s - 3ms/step - loss: 0.0623 - val_loss: 0.0619 ## Epoch 48/50 ## 469/469 - 1s - 3ms/step - loss: 0.0623 - val_loss: 0.0619 ## Epoch 49/50 ## 469/469 - 1s - 3ms/step - loss: 0.0622 - val_loss: 0.0619 ## Epoch 50/50 ## 469/469 - 1s - 3ms/step - loss: 0.0622 - val_loss: 0.0619 predictions <- autoencoder |> predict(test_data) ## 313/313 - 1s - 2ms/step display(test_data, predictions) autoencoder |> fit(   x = noisy_train_data,   y = train_data,   epochs = 100,   batch_size = 128,   shuffle = TRUE,   validation_data = list(noisy_test_data, test_data), ) ## Epoch 1/100 ## 469/469 - 1s - 3ms/step - loss: 0.0998 - val_loss: 0.0928 ## Epoch 2/100 ## 469/469 - 1s - 3ms/step - loss: 0.0925 - val_loss: 0.0908 ## Epoch 3/100 ## 469/469 - 1s - 3ms/step - loss: 0.0910 - val_loss: 0.0898 ## Epoch 4/100 ## 469/469 - 1s - 3ms/step - loss: 0.0900 - val_loss: 0.0890 ## Epoch 5/100 ## 469/469 - 1s - 3ms/step - loss: 0.0893 - val_loss: 0.0883 ## Epoch 6/100 ## 469/469 - 1s - 3ms/step - loss: 0.0888 - val_loss: 0.0877 ## Epoch 7/100 ## 469/469 - 1s - 3ms/step - loss: 0.0883 - val_loss: 0.0873 ## Epoch 8/100 ## 469/469 - 1s - 3ms/step - loss: 0.0879 - val_loss: 0.0870 ## Epoch 9/100 ## 469/469 - 1s - 3ms/step - loss: 0.0876 - val_loss: 0.0868 ## Epoch 10/100 ## 469/469 - 1s - 3ms/step - loss: 0.0873 - val_loss: 0.0866 ## Epoch 11/100 ## 469/469 - 1s - 3ms/step - loss: 0.0871 - val_loss: 0.0863 ## Epoch 12/100 ## 469/469 - 1s - 3ms/step - loss: 0.0869 - val_loss: 0.0861 ## Epoch 13/100 ## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0860 ## Epoch 14/100 ## 469/469 - 1s - 3ms/step - loss: 0.0865 - val_loss: 0.0858 ## Epoch 15/100 ## 469/469 - 1s - 3ms/step - loss: 0.0864 - val_loss: 0.0857 ## Epoch 16/100 ## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0855 ## Epoch 17/100 ## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0854 ## Epoch 18/100 ## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0853 ## Epoch 19/100 ## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0852 ## Epoch 20/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0852 ## Epoch 21/100 ## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0851 ## Epoch 22/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0850 ## Epoch 23/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0850 ## Epoch 24/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0849 ## Epoch 25/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0849 ## Epoch 26/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0848 ## Epoch 27/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0848 ## Epoch 28/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0847 ## Epoch 29/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0847 ## Epoch 30/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0847 ## Epoch 31/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847 ## Epoch 32/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0846 ## Epoch 33/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0846 ## Epoch 34/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846 ## Epoch 35/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0845 ## Epoch 36/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0845 ## Epoch 37/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0845 ## Epoch 38/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0845 ## Epoch 39/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0845 ## Epoch 40/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0844 ## Epoch 41/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0844 ## Epoch 42/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0844 ## Epoch 43/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0844 ## Epoch 44/100 ## 469/469 - 1s - 3ms/step - loss: 0.0848 - val_loss: 0.0844 ## Epoch 45/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 46/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 47/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 48/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 49/100 ## 469/469 - 1s - 3ms/step - loss: 0.0847 - val_loss: 0.0843 ## Epoch 50/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0843 ## Epoch 51/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 52/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 53/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 54/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 55/100 ## 469/469 - 1s - 3ms/step - loss: 0.0846 - val_loss: 0.0842 ## Epoch 56/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0842 ## Epoch 57/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0842 ## Epoch 58/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0842 ## Epoch 59/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0842 ## Epoch 60/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0841 ## Epoch 61/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0841 ## Epoch 62/100 ## 469/469 - 1s - 3ms/step - loss: 0.0845 - val_loss: 0.0841 ## Epoch 63/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0841 ## Epoch 64/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0841 ## Epoch 65/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0841 ## Epoch 66/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0841 ## Epoch 67/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 68/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 69/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 70/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 71/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 72/100 ## 469/469 - 1s - 3ms/step - loss: 0.0844 - val_loss: 0.0840 ## Epoch 73/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 74/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 75/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 76/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 77/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 78/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 79/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 80/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 81/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 82/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 83/100 ## 469/469 - 1s - 3ms/step - loss: 0.0843 - val_loss: 0.0840 ## Epoch 84/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 85/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 86/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 87/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 88/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 89/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 90/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 91/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 92/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 93/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 94/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 95/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 96/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 97/100 ## 469/469 - 1s - 3ms/step - loss: 0.0842 - val_loss: 0.0839 ## Epoch 98/100 ## 469/469 - 1s - 3ms/step - loss: 0.0841 - val_loss: 0.0839 ## Epoch 99/100 ## 469/469 - 1s - 3ms/step - loss: 0.0841 - val_loss: 0.0839 ## Epoch 100/100 ## 469/469 - 1s - 3ms/step - loss: 0.0841 - val_loss: 0.0839 predictions <- autoencoder |> predict(noisy_test_data) ## 313/313 - 0s - 581us/step display(noisy_test_data, predictions)"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"setup","dir":"Articles > Examples","previous_headings":"","what":"Setup","title":"Simple MNIST convnet","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"prepare-the-data","dir":"Articles > Examples","previous_headings":"","what":"Prepare the data","title":"Simple MNIST convnet","text":"","code":"# Model / data parameters num_classes <- 10 input_shape <- c(28, 28, 1)  # Load the data and split it between train and test sets c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()  # Scale images to the [0, 1] range x_train <- x_train / 255 x_test <- x_test / 255 # Make sure images have shape (28, 28, 1) x_train <- op_expand_dims(x_train, -1) x_test <- op_expand_dims(x_test, -1)   dim(x_train) ## [1] 60000    28    28     1 dim(x_test) ## [1] 10000    28    28     1 # convert class vectors to binary class matrices y_train <- to_categorical(y_train, num_classes) y_test <- to_categorical(y_test, num_classes)"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"build-the-model","dir":"Articles > Examples","previous_headings":"","what":"Build the model","title":"Simple MNIST convnet","text":"","code":"model <- keras_model_sequential(input_shape = input_shape) model |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_flatten() |>   layer_dropout(rate = 0.5) |>   layer_dense(units = num_classes, activation = \"softmax\")  summary(model) ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d_1 (Conv2D)               │ (None, 26, 26, 32)     │           320 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 13, 13, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 11, 11, 64)     │        18,496 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 5, 5, 64)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ flatten (Flatten)               │ (None, 1600)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 1600)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 10)             │        16,010 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 34,826 (136.04 KB) ##  Trainable params: 34,826 (136.04 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"train-the-model","dir":"Articles > Examples","previous_headings":"","what":"Train the model","title":"Simple MNIST convnet","text":"","code":"batch_size <- 128 epochs <- 15  model |> compile(   loss = \"categorical_crossentropy\",   optimizer = \"adam\",   metrics = \"accuracy\" )  model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = epochs,   validation_split = 0.1 ) ## Epoch 1/15 ## 422/422 - 4s - 11ms/step - accuracy: 0.8845 - loss: 0.3815 - val_accuracy: 0.9783 - val_loss: 0.0810 ## Epoch 2/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9644 - loss: 0.1151 - val_accuracy: 0.9863 - val_loss: 0.0548 ## Epoch 3/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9735 - loss: 0.0838 - val_accuracy: 0.9880 - val_loss: 0.0455 ## Epoch 4/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9781 - loss: 0.0689 - val_accuracy: 0.9893 - val_loss: 0.0413 ## Epoch 5/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9811 - loss: 0.0608 - val_accuracy: 0.9907 - val_loss: 0.0373 ## Epoch 6/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9824 - loss: 0.0561 - val_accuracy: 0.9908 - val_loss: 0.0359 ## Epoch 7/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9848 - loss: 0.0487 - val_accuracy: 0.9917 - val_loss: 0.0328 ## Epoch 8/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9845 - loss: 0.0484 - val_accuracy: 0.9915 - val_loss: 0.0334 ## Epoch 9/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9859 - loss: 0.0439 - val_accuracy: 0.9923 - val_loss: 0.0320 ## Epoch 10/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9868 - loss: 0.0405 - val_accuracy: 0.9925 - val_loss: 0.0319 ## Epoch 11/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9874 - loss: 0.0387 - val_accuracy: 0.9920 - val_loss: 0.0317 ## Epoch 12/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9882 - loss: 0.0367 - val_accuracy: 0.9925 - val_loss: 0.0301 ## Epoch 13/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9879 - loss: 0.0361 - val_accuracy: 0.9928 - val_loss: 0.0281 ## Epoch 14/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9889 - loss: 0.0332 - val_accuracy: 0.9930 - val_loss: 0.0284 ## Epoch 15/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9897 - loss: 0.0311 - val_accuracy: 0.9925 - val_loss: 0.0293"},{"path":"https://keras.posit.co/articles/examples/mnist_convnet.html","id":"evaluate-the-trained-model","dir":"Articles > Examples","previous_headings":"","what":"Evaluate the trained model","title":"Simple MNIST convnet","text":"","code":"score <- model |> evaluate(x_test, y_test, verbose = 0) score ## $accuracy ## [1] 0.9911 ## ## $loss ## [1] 0.02564374"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"introduction","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Introduction","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"example, ’ll build sequence--sequence Transformer model, ’ll train English--Spanish machine translation task. ’ll learn : Vectorize text using layer_text_vectorization(). Implement layer_transformer_encoder(), layer_transformer_decoder(), layer_positional_embedding(). Prepare data training sequence--sequence model. Use trained model generate translations never-seen-input sentences (sequence--sequence inference). code featured adapted book Deep Learning R, Second Edition (chapter 11: Deep learning text). present example fairly barebones, detailed explanations building block works, well theory behind Transformers, recommend reading book.","code":""},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"setup","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Setup","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"","code":"library(glue)  library(tensorflow, exclude = c(\"set_random_seed\", \"shape\")) library(keras3)"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"downloading-the-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Downloading the data","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"’ll working English--Spanish translation dataset provided Anki. Let’s download :","code":"zipfile <- get_file(\"spa-eng.zip\", origin =   \"http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip\")  zip::zip_list(zipfile) # See what's in the zipfile ##             filename compressed_size uncompressed_size           timestamp ## 1           spa-eng/               0                 0 2018-06-13 12:21:20 ## 2 spa-eng/_about.txt             685              1441 2018-05-13 19:50:34 ## 3    spa-eng/spa.txt         2637619           8042772 2018-05-13 19:50:34 ##   permissions    crc32 offset ## 1         755 00000000      0 ## 2         644 4b18349d     38 ## 3         644 63c5c4a2    771 zip::unzip(zipfile, exdir = \".\") # unzip into the current directory  text_file <- fs::path(\"./spa-eng/spa.txt\")"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"parsing-the-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Parsing the data","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"line contains English sentence corresponding Spanish sentence. English sentence source sequence Spanish one target sequence. prepend token \"[start]\" append token \"[end]\" Spanish sentence. ’s sentence pairs look like: Now, let’s split sentence pairs training set, validation set, test set.","code":"text_file <- \"spa-eng/spa.txt\" text_pairs <- text_file %>%   readr::read_tsv(col_names = c(\"english\", \"spanish\"),                   col_types = c(\"cc\")) %>%   within(spanish %<>% paste(\"[start]\", ., \"[end]\")) df <- text_pairs[sample(nrow(text_pairs), 5), ] glue::glue_data(df, r\"(   english: {english}   spanish: {spanish} )\") |> cat(sep = \"\\n\\n\") ## english: I'm staying in Italy. ## spanish: [start] Me estoy quedando en Italia. [end] ## ## english: What's so strange about that? ## spanish: [start] ¿Qué es tan extraño acerca de eso? [end] ## ## english: All of the buses are full. ## spanish: [start] Todos los bondis están llenos. [end] ## ## english: Is this where your mother works? ## spanish: [start] ¿Es aquí donde trabaja tu madre? [end] ## ## english: Take precautions. ## spanish: [start] Ten cuidado. [end] random.shuffle(text_pairs) num_val_samples = int(0.15 * len(text_pairs)) num_train_samples = len(text_pairs) - 2 * num_val_samples train_pairs = text_pairs[:num_train_samples] val_pairs = text_pairs[num_train_samples : num_train_samples + num_val_samples] test_pairs = text_pairs[num_train_samples + num_val_samples :]  print(f\"{len(text_pairs)} total pairs\") print(f\"{len(train_pairs)} training pairs\") print(f\"{len(val_pairs)} validation pairs\") print(f\"{len(test_pairs)} test pairs\") num_test_samples <- num_val_samples <-   round(0.15 * nrow(text_pairs)) num_train_samples <- nrow(text_pairs) - num_val_samples - num_test_samples  pair_group <- sample(c(   rep(\"train\", num_train_samples),   rep(\"test\", num_test_samples),   rep(\"val\", num_val_samples) ))  train_pairs <- text_pairs[pair_group == \"train\", ] test_pairs <- text_pairs[pair_group == \"test\", ] val_pairs <- text_pairs[pair_group == \"val\", ] glue(r\"(   {nrow(text_pairs)} total pairs   {nrow(train_pairs)} training pairs   {nrow(val_pairs)} validation pairs   {nrow(test_pairs)} test pairs )\", .transformer = function(text, envir) {   val <- eval(str2lang(text), envir)   prettyNum(val, big.mark = \",\") }) ## 118,493 total pairs ## 82,945 training pairs ## 17,774 validation pairs ## 17,774 test pairs"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"vectorizing-the-text-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Vectorizing the text data","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"’ll use two instances layer_text_vectorization() vectorize text data (one English one Spanish), say, turn original strings integer sequences integer represents index word vocabulary. English layer use default string standardization (strip punctuation characters) splitting scheme (split whitespace), Spanish layer use custom standardization, add character \"¿\" set punctuation characters stripped. Note: production-grade machine translation model, recommend stripping punctuation characters either language. Instead, recommend turning punctuation character token, achieve providing custom split function layer_text_vectorization(). Next, ’ll format datasets. training step, model seek predict target words N+1 (beyond) using source sentence target words 1 N. , training dataset yield tuple (inputs, targets), : inputs dictionary (named list) keys (names) encoder_inputs decoder_inputs. encoder_inputs vectorized source sentence encoder_inputs target sentence “far”, say, words 0 N used predict word N+1 (beyond) target sentence. target target sentence offset one step: provides next words target sentence – model try predict. Let’s take quick look sequence shapes (batches 64 pairs, sequences 20 steps long):","code":"punctuation_regex <- \"[¡¿]|[^[:^punct:][\\\\]]\" # the regex explained: Match ¡, or ¿, or any punctuation character except ] # # [:^punct:]: is a negated POSIX character class. # [:punct:] matches any punctuation character, so [:^punct:] matches any # character that is not a punctuation character. # [^...] negates the whole character class # So [^[:^punct:]] would matche any character that is a punctuation character. # Putting this all together, [^[:^punct:][\\\\]] matches any # punctuation character except the ] character.  custom_standardization <- function(input_string) {   input_string %>%     tf$strings$lower() %>%     tf$strings$regex_replace(punctuation_regex, \"\") }  input_string <- as_tensor(\"[start] ¡corre! [end]\") custom_standardization(input_string) ## tf.Tensor(b'[start] corre [end]', shape=(), dtype=string) vocab_size <- 15000 sequence_length <- 20  # rename to eng_vectorization eng_vectorization <- layer_text_vectorization(   max_tokens = vocab_size,   output_mode = \"int\",   output_sequence_length = sequence_length )  spa_vectorization <- layer_text_vectorization(   max_tokens = vocab_size,   output_mode = \"int\",   output_sequence_length = sequence_length + 1,   standardize = custom_standardization )  adapt(eng_vectorization, train_pairs$english) adapt(spa_vectorization, train_pairs$spanish) format_pair <- function(pair) {   # the vectorization layers requrie batched inputs,   # reshape scalar string tensor to add a batch dim   pair %<>% lapply(op_expand_dims, 1)    # vectorize   eng <- eng_vectorization(pair$english)   spa <- spa_vectorization(pair$spanish)    # drop the batch dim   eng %<>% tf$ensure_shape(shape(1, sequence_length)) %>% op_squeeze(1)   spa %<>% tf$ensure_shape(shape(1, sequence_length+1)) %>% op_squeeze(1)    inputs <- list(encoder_inputs = eng,                  decoder_inputs = spa[NA:-2])   targets <- spa[2:NA]   list(inputs, targets) }   batch_size <- 64  library(tfdatasets, exclude = \"shape\") make_dataset <- function(pairs) {   tensor_slices_dataset(pairs) %>%     dataset_map(format_pair, num_parallel_calls = 4) %>%     dataset_cache() %>%     dataset_shuffle(2048) %>%     dataset_batch(batch_size) %>%     dataset_prefetch(2) } train_ds <- make_dataset(train_pairs) ## Warning: Negative numbers are interpreted python-style when subsetting tensorflow tensors. ## See: ?`[.tensorflow.tensor` for details. ## To turn off this warning, set `options(tensorflow.extract.warn_negatives_pythonic = FALSE)` val_ds <- make_dataset(val_pairs) c(inputs, targets) %<-% iter_next(as_iterator(train_ds)) str(inputs) ## List of 2 ##  $ encoder_inputs:<tf.Tensor: shape=(64, 20), dtype=int64, numpy=…> ##  $ decoder_inputs:<tf.Tensor: shape=(64, 20), dtype=int64, numpy=…> str(targets) ## <tf.Tensor: shape=(64, 20), dtype=int64, numpy=…>"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"building-the-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Building the model","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"sequence--sequence Transformer consists TransformerEncoder TransformerDecoder chained together. make model aware word order, also use PositionalEmbedding layer. source sequence pass TransformerEncoder, produce new representation . new representation passed TransformerDecoder, together target sequence far (target words 1 N). TransformerDecoder seek predict next words target sequence (N+1 beyond). key detail makes possible causal masking (see method get_causal_attention_mask() TransformerDecoder). TransformerDecoder sees entire sequences , thus must make sure uses information target tokens 0 N predicting token N+1 (otherwise, use information future, result model used inference time). Next, assemble end--end model.","code":"layer_transformer_encoder <- Layer(   classname = \"TransformerEncoder\",   initialize = function(embed_dim, dense_dim, num_heads, ...) {     super$initialize(...)     self$embed_dim <- embed_dim     self$dense_dim <- dense_dim     self$num_heads <- num_heads     self$attention <-       layer_multi_head_attention(num_heads = num_heads,                                  key_dim = embed_dim)      self$dense_proj <- keras_model_sequential() %>%       layer_dense(dense_dim, activation = \"relu\") %>%       layer_dense(embed_dim)      self$layernorm_1 <- layer_layer_normalization()     self$layernorm_2 <- layer_layer_normalization()     self$supports_masking <- TRUE   },    call = function(inputs, mask = NULL) {     if (!is.null(mask))       mask <- mask[, NULL, ] |> op_cast(\"int32\")      inputs %>%       { self$attention(., ., attention_mask = mask) + . } %>%       self$layernorm_1() %>%       { self$dense_proj(.) + . } %>%       self$layernorm_2()   },    get_config = function() {     config <- super$get_config()     for(name in c(\"embed_dim\", \"num_heads\", \"dense_dim\"))       config[[name]] <- self[[name]]     config   } )  layer_transformer_decoder <- Layer(   classname = \"TransformerDecoder\",    initialize = function(embed_dim, latent_dim, num_heads, ...) {     super$initialize(...)     self$embed_dim <- embed_dim     self$latent_dim <- latent_dim     self$num_heads <- num_heads     self$attention_1 <- layer_multi_head_attention(num_heads = num_heads,                                                    key_dim = embed_dim)     self$attention_2 <- layer_multi_head_attention(num_heads = num_heads,                                                    key_dim = embed_dim)     self$dense_proj <- keras_model_sequential() %>%       layer_dense(latent_dim, activation = \"relu\") %>%       layer_dense(embed_dim)      self$layernorm_1 <- layer_layer_normalization()     self$layernorm_2 <- layer_layer_normalization()     self$layernorm_3 <- layer_layer_normalization()     self$supports_masking <- TRUE   },    get_config = function() {     config <- super$get_config()     for (name in c(\"embed_dim\", \"num_heads\", \"latent_dim\"))       config[[name]] <- self[[name]]     config   },     get_causal_attention_mask = function(inputs) {     c(batch_size, sequence_length, encoding_length) %<-% op_shape(inputs)      x <- op_arange(sequence_length)     i <- x[, NULL]     j <- x[NULL, ]     mask <- op_cast(i >= j, \"int32\")      repeats <- op_stack(c(batch_size, 1L, 1L))     op_tile(mask[NULL, , ], repeats)   },   call = function(inputs, encoder_outputs, mask = NULL) {     causal_mask <- self$get_causal_attention_mask(inputs)      if (is.null(mask))       mask <- causal_mask     else       mask %<>% { op_minimum(op_cast(.[, NULL, ], \"int32\"),                              causal_mask) }      inputs %>%       { self$attention_1(query = ., value = ., key = .,                          attention_mask = causal_mask) + . } %>%       self$layernorm_1() %>%        { self$attention_2(query = .,                          value = encoder_outputs,                          key = encoder_outputs,                          attention_mask = mask) + . } %>%       self$layernorm_2() %>%        { self$dense_proj(.) + . } %>%       self$layernorm_3()    } )  layer_positional_embedding <- Layer(   classname = \"PositionalEmbedding\",    initialize = function(sequence_length, vocab_size, embed_dim, ...) {     super$initialize(...)     self$token_embeddings <- layer_embedding(       input_dim = vocab_size, output_dim = embed_dim     )     self$position_embeddings <- layer_embedding(       input_dim = sequence_length, output_dim = embed_dim     )     self$sequence_length <- sequence_length     self$vocab_size <- vocab_size     self$embed_dim <- embed_dim   },    call = function(inputs) {     c(., len) %<-% op_shape(inputs) # (batch_size, seq_len)     positions <- op_arange(0, len, dtype = \"int32\")     embedded_tokens <- self$token_embeddings(inputs)     embedded_positions <- self$position_embeddings(positions)     embedded_tokens + embedded_positions   },    compute_mask = function(inputs, mask = NULL) {     if (is.null(mask)) return (NULL)     inputs != 0L   },    get_config = function() {     config <- super$get_config()     for(name in c(\"sequence_length\", \"vocab_size\", \"embed_dim\"))       config[[name]] <- self[[name]]     config   } ) embed_dim <- 256 latent_dim <- 2048 num_heads <- 8  encoder_inputs <- layer_input(shape(NA), dtype = \"int64\",                               name = \"encoder_inputs\") encoder_outputs <- encoder_inputs %>%   layer_positional_embedding(sequence_length, vocab_size, embed_dim) %>%   layer_transformer_encoder(embed_dim, latent_dim, num_heads)  encoder <- keras_model(encoder_inputs, encoder_outputs)  decoder_inputs <-  layer_input(shape(NA), dtype = \"int64\",                                name = \"decoder_inputs\") encoded_seq_inputs <- layer_input(shape(NA, embed_dim),                                   name = \"decoder_state_inputs\")  transformer_decoder <- layer_transformer_decoder(NULL,   embed_dim, latent_dim, num_heads)  decoder_outputs <- decoder_inputs %>%   layer_positional_embedding(sequence_length, vocab_size, embed_dim) %>%   transformer_decoder(., encoded_seq_inputs) %>%   layer_dropout(0.5) %>%   layer_dense(vocab_size, activation=\"softmax\")  decoder <- keras_model(inputs = list(decoder_inputs, encoded_seq_inputs),                        outputs = decoder_outputs)  decoder_outputs = decoder(list(decoder_inputs, encoder_outputs))  transformer <- keras_model(list(encoder_inputs, decoder_inputs),                            decoder_outputs,                            name = \"transformer\")"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"training-our-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Training our model","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"’ll use accuracy quick way monitor training progress validation data. Note machine translation typically uses BLEU scores well metrics, rather accuracy. train 1 epoch, get model actually converge train least 30 epochs.","code":"epochs <- 1  # This should be at least 30 for convergence  transformer ## Model: \"transformer\" ## ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ ## │ encoder_inputs      │ (None, None)      │          0 │ -                 │ ## │ (InputLayer)        │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ positional_embeddi… │ (None, None, 256) │  3,845,120 │ encoder_inputs[0… │ ## │ (PositionalEmbeddi… │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ decoder_inputs      │ (None, None)      │          0 │ -                 │ ## │ (InputLayer)        │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ transformer_encoder │ (None, None, 256) │  3,155,456 │ positional_embed… │ ## │ (TransformerEncode… │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ functional_5        │ (None, None,      │ 12,959,640 │ decoder_inputs[0… │ ## │ (Functional)        │ 15000)            │            │ transformer_enco… │ ## └─────────────────────┴───────────────────┴────────────┴───────────────────┘ ##  Total params: 19,960,216 (76.14 MB) ##  Trainable params: 19,960,216 (76.14 MB) ##  Non-trainable params: 0 (0.00 B) transformer |> compile(   \"rmsprop\",   loss = \"sparse_categorical_crossentropy\",   metrics = \"accuracy\" ) transformer |> fit(train_ds, epochs = epochs,                    validation_data = val_ds) ## 1297/1297 - 58s - 44ms/step - accuracy: 0.7709 - loss: 1.5752 - val_accuracy: 0.7731 - val_loss: 1.4209"},{"path":"https://keras.posit.co/articles/examples/nlp/neural_machine_translation_with_transformer.html","id":"decoding-test-sentences","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Decoding test sentences","title":"English-to-Spanish translation with a sequence-to-sequence Transformer","text":"Finally, let’s demonstrate translate brand new English sentences. simply feed model vectorized English sentence well target token \"[start]\", repeatedly generated next token, hit token \"[end]\". 30 epochs, get results :","code":"spa_vocab <- spa_vectorization |> get_vocabulary() max_decoded_sentence_length <- 20 tf_decode_sequence <- tf_function(function(input_sentence) {   withr::local_options(tensorflow.extract.style = \"python\")    tokenized_input_sentence <- input_sentence %>%     as_tensor(shape = c(1, 1)) %>%     eng_vectorization()   spa_vocab <- as_tensor(spa_vocab)   decoded_sentence <- as_tensor(\"[start]\", shape = c(1, 1))    for (i in tf$range(as.integer(max_decoded_sentence_length))) {      tokenized_target_sentence <-       spa_vectorization(decoded_sentence)[,NA:-1]      next_token_predictions <-       transformer(list(tokenized_input_sentence,                        tokenized_target_sentence))      sampled_token_index <- tf$argmax(next_token_predictions[0, i, ])     sampled_token <- spa_vocab[sampled_token_index]     decoded_sentence <-       tf$strings$join(c(decoded_sentence, sampled_token),                       separator = \" \")      if (sampled_token == \"[end]\")       break   }    decoded_sentence  })  for (i in seq(20)) {      c(input_sentence, correct_translation) %<-%       test_pairs[sample.int(nrow(test_pairs), 1), ]     cat(\"-\\n\")     cat(\"English:\", input_sentence, \"\\n\")     cat(\"Correct Translation:\", tolower(correct_translation), \"\\n\")     cat(\"  Model Translation:\", input_sentence %>% as_tensor() %>%           tf_decode_sequence() %>% as.character(), \"\\n\") } English: I'm sure everything will be fine. Correct Translation: [start] estoy segura de que todo irá bien. [end]   Model Translation: [start] estoy seguro de que todo va bien [end]"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"introduction","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Introduction","title":"Text classification from scratch","text":"example shows text classification starting raw text (set text files disk). demonstrate workflow IMDB sentiment classification dataset (unprocessed version). use layer_text_vectorization() word splitting & indexing.","code":""},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"setup","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Setup","title":"Text classification from scratch","text":"","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\") library(keras3) use_virtualenv(\"r-keras\")"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"load-the-data-imdb-movie-review-sentiment-classification","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Load the data: IMDB movie review sentiment classification","title":"Text classification from scratch","text":"Let’s download data inspect structure. aclImdb folder contains train test subfolder: aclImdb/train/pos aclImdb/train/neg folders contain text files, represents one review (either positive negative): interested pos neg subfolders, let’s delete subfolder text files : can use utility text_dataset_from_directory() generate labeled tf_dataset object set text files disk filed class-specific folders. Let’s use generate training, validation, test datasets. validation training datasets generated two subsets train directory, 20% samples going validation dataset 80% going training dataset. validation dataset addition test dataset useful tuning hyperparameters, model architecture, test dataset used. putting model real world however, retrained using available training data (without creating validation dataset), performance maximized. using validation_split subset arguments, make sure either specify random seed, pass shuffle=FALSE, validation & training splits get overlap. Let’s preview samples:","code":"if (!dir.exists(\"datasets/aclImdb\")) {   dir.create(\"datasets\")   download.file(     \"https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\",     \"datasets/aclImdb_v1.tar.gz\"   )   untar(\"datasets/aclImdb_v1.tar.gz\", exdir = \"datasets\")   unlink(\"datasets/aclImdb/train/unsup\", recursive = TRUE) } head(list.files(\"datasets/aclImdb/test\")) ## [1] \"labeledBow.feat\" \"neg\"             \"pos\"             \"urls_neg.txt\" ## [5] \"urls_pos.txt\" head(list.files(\"datasets/aclImdb/train\")) ## [1] \"labeledBow.feat\" \"neg\"             \"pos\"             \"unsupBow.feat\" ## [5] \"urls_neg.txt\"    \"urls_pos.txt\" writeLines(strwrap(readLines(\"datasets/aclImdb/train/pos/4229_10.txt\"))) ## Don't waste time reading my review. Go out and see this ## astonishingly good episode, which may very well be the best Columbo ## ever written! Ruth Gordon is perfectly cast as the scheming yet ## charming mystery writer who murders her son-in-law to avenge his ## murder of her daughter. Columbo is his usual rumpled, befuddled and ## far-cleverer-than-he-seems self, and this particular installment ## features fantastic chemistry between Gordon and Falk. Ironically, ## this was not written by heralded creators Levinson or Link yet is ## possibly the densest, most thoroughly original and twist-laden ## Columbo plot ever. Utterly satisfying in nearly every department ## and overflowing with droll and witty dialogue and thinking. Truly ## unexpected and inventive climax tops all. 10/10...seek this one out ## on Netflix! unlink(\"datasets/aclImdb/train/unsup\", recursive = TRUE) batch_size <- 32  raw_train_ds <- text_dataset_from_directory(   \"datasets/aclImdb/train\",   batch_size = batch_size,   validation_split = 0.2,   subset = \"training\",   seed = 1337 ) ## Found 25000 files belonging to 2 classes. ## Using 20000 files for training. raw_val_ds <- text_dataset_from_directory(   \"datasets/aclImdb/train\",   batch_size = batch_size,   validation_split = 0.2,   subset = \"validation\",   seed = 1337 ) ## Found 25000 files belonging to 2 classes. ## Using 5000 files for validation. raw_test_ds <- text_dataset_from_directory(   \"datasets/aclImdb/test\",   batch_size = batch_size ) ## Found 25000 files belonging to 2 classes. cat(\"Number of batches in raw_train_ds:\", length(raw_train_ds), \"\\n\") ## Number of batches in raw_train_ds: 625 cat(\"Number of batches in raw_val_ds:\", length(raw_val_ds), \"\\n\") ## Number of batches in raw_val_ds: 157 cat(\"Number of batches in raw_test_ds:\", length(raw_test_ds), \"\\n\") ## Number of batches in raw_test_ds: 782 # It's important to take a look at your raw data to ensure your normalization # and tokenization will work as expected. We can do that by taking a few # examples from the training set and looking at them. # This is one of the places where eager execution shines: # we can just evaluate these tensors using .numpy() # instead of needing to evaluate them in a Session/Graph context. batch <- iter_next(as_iterator(raw_train_ds)) str(batch) ## List of 2 ##  $ :<tf.Tensor: shape=(32), dtype=string, numpy=…> ##  $ :<tf.Tensor: shape=(32), dtype=int32, numpy=…> c(text_batch, label_batch) %<-% batch for (i in 1:3) {   print(text_batch[i])   print(label_batch[i]) } ## tf.Tensor(b\"I have read the novel Reaper of Ben Mezrich a fews years ago and last night I accidentally came to see this adaption.<br /><br />Although it's been years since I read the story the first time, the differences between the novel and the movie are humongous. Very important elements, which made the whole thing plausible are just written out or changed to bad.<br /><br />If the plot sounds interesting to you: go and get the novel. Its much, much, much better.<br /><br />Still 4 out of 10 since it was hard to stop watching because of the great basic plot by Ben Mezrich.\", shape=(), dtype=string) ## tf.Tensor(0, shape=(), dtype=int32) ## tf.Tensor(b'After seeing all the Jesse James, Quantrill, jayhawkers,etc films in the fifties, it is quite a thrill to see this film with a new perspective by director Ang Lee. The scene of the attack of Lawrence, Kansas is awesome. The romantic relationship between Jewel and Toby Mcguire turns out to be one of the best parts and Jonathan Rhys-Meyers is outstanding as the bad guy. All the time this film makes you feel the horror of war, and the desperate situation of the main characters who do not know if they are going to survive the next hours. Definitely worth seeing.', shape=(), dtype=string) ## tf.Tensor(1, shape=(), dtype=int32) ## tf.Tensor(b'AG was an excellent presentation of drama, suspense and thriller that is so rare to American TV. Sheriff Lucas gave many a viewer the willies. We rooted for Caleb as he strove to resist the overtures of Sheriff Lucas. We became engrossed and fearful upon learning of the unthinkable connection between these two characters. The manipulations which weekly gave cause to fear what Lucas would do next were truly surprising. This show lived up to the \"Gothic\" moniker in ways American entertainment has so seldom attempted, much less mastered. The suits definitely made a big mistake in not supporting this show. This show puts shame to the current glut of \"reality\" shows- which are so less than satisfying viewing.The call for a DVD box set is well based. This show is quality viewing for a discerning market hungry for quality viewing. A public that is tiring of over-saturation of mind-numbing reality fare will welcome this gem of real storytelling. Bring on the DVD box set!!', shape=(), dtype=string) ## tf.Tensor(1, shape=(), dtype=int32)"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"prepare-the-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Prepare the data","title":"Text classification from scratch","text":"particular, remove <br /> tags.","code":"# Having looked at our data above, we see that the raw text contains HTML break # tags of the form '<br />'. These tags will not be removed by the default # standardizer (which doesn't strip HTML). Because of this, we will need to # create a custom standardization function. custom_standardization_fn <- function(string_tensor) {   string_tensor |>     tf$strings$lower() |> # convert to all lowercase     tf$strings$regex_replace(\"<br />\", \" \") |> # remove '<br />' HTML tag     tf$strings$regex_replace(\"[[:punct:]]\", \"\") # remove punctuation }   # Model constants. max_features <- 20000 embedding_dim <- 128 sequence_length <- 500  # Now that we have our custom standardization, we can instantiate our text # vectorization layer. We are using this layer to normalize, split, and map # strings to integers, so we set our 'output_mode' to 'int'. # Note that we're using the default split function, # and the custom standardization defined above. # We also set an explicit maximum sequence length, since the CNNs later in our # model won't support ragged sequences. vectorize_layer <- layer_text_vectorization(   standardize = custom_standardization_fn,   max_tokens = max_features,   output_mode = \"int\",   output_sequence_length = sequence_length, )  # Now that the vectorize_layer has been created, call `adapt` on a text-only # dataset to create the vocabulary. You don't have to batch, but for very large # datasets this means you're not keeping spare copies of the dataset in memory.  # Let's make a text-only dataset (no labels): text_ds <- raw_train_ds |>   dataset_map(\\(x, y) x) # Let's call `adapt`: vectorize_layer |> adapt(text_ds)"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"two-options-to-vectorize-the-data","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Two options to vectorize the data","title":"Text classification from scratch","text":"2 ways can use text vectorization layer: Option 1: Make part model, obtain model processes raw strings, like : Option 2: Apply text dataset obtain dataset word indices, feed model expects integer sequences inputs. important difference two option 2 enables asynchronous CPU processing buffering data training GPU. ’re training model GPU, probably want go option get best performance. . export model production, ’d ship model accepts raw strings input, like code snippet option 1 . can done training. last section.","code":"text_input <- keras_input(shape = c(1L), dtype = \"string\", name = 'text') x <- text_input |>   vectorize_layer() |>   layer_embedding(max_features + 1, embedding_dim) vectorize_text <- function(text, label) {   text <- text |>     op_expand_dims(-1) |>     vectorize_layer()   list(text, label) }  # Vectorize the data. train_ds <- raw_train_ds |> dataset_map(vectorize_text) val_ds   <- raw_val_ds   |> dataset_map(vectorize_text) test_ds  <- raw_test_ds  |> dataset_map(vectorize_text)  # Do async prefetching / buffering of the data for best performance on GPU. train_ds <- train_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10) val_ds <- val_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10) test_ds <- test_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10)"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"build-a-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Build a model","title":"Text classification from scratch","text":"choose simple 1D convnet starting Embedding layer.","code":"# A integer input for vocab indices. inputs <- keras_input(shape = c(NA), dtype = \"int64\")  predictions <- inputs |>   # Next, we add a layer to map those vocab indices into a space of dimensionality   # 'embedding_dim'.   layer_embedding(max_features, embedding_dim) |>   layer_dropout(0.5) |>   # Conv1D + global max pooling   layer_conv_1d(128, 7, padding = \"valid\", activation = \"relu\", strides = 3) |>   layer_conv_1d(128, 7, padding = \"valid\", activation = \"relu\", strides = 3) |>   layer_global_max_pooling_1d() |>   # We add a vanilla hidden layer:   layer_dense(128, activation = \"relu\") |>   layer_dropout(0.5) |>   # We project onto a single unit output layer, and squash it with a sigmoid:   layer_dense(1, activation = \"sigmoid\", name = \"predictions\")  model <- keras_model(inputs, predictions)  summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, None)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ embedding_1 (Embedding)         │ (None, None, 128)      │     2,560,000 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, None, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d (Conv1D)                 │ (None, None, 128)      │       114,816 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_1 (Conv1D)               │ (None, None, 128)      │       114,816 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling1d            │ (None, 128)            │             0 │ ## │ (GlobalMaxPooling1D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 128)            │        16,512 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, 128)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ predictions (Dense)             │ (None, 1)              │           129 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 2,806,273 (10.71 MB) ##  Trainable params: 2,806,273 (10.71 MB) ##  Non-trainable params: 0 (0.00 B) # Compile the model with binary crossentropy loss and an adam optimizer. model |> compile(loss = \"binary_crossentropy\",                  optimizer = \"adam\",                  metrics = \"accuracy\")"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"train-the-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Train the model","title":"Text classification from scratch","text":"","code":"epochs <- 3  # Fit the model using the train and test datasets. model |> fit(train_ds, validation_data = val_ds, epochs = epochs) ## Epoch 1/3 ## 625/625 - 6s - 10ms/step - accuracy: 0.6909 - loss: 0.5300 - val_accuracy: 0.8658 - val_loss: 0.3229 ## Epoch 2/3 ## 625/625 - 2s - 3ms/step - accuracy: 0.9047 - loss: 0.2412 - val_accuracy: 0.8742 - val_loss: 0.3202 ## Epoch 3/3 ## 625/625 - 2s - 3ms/step - accuracy: 0.9573 - loss: 0.1237 - val_accuracy: 0.8704 - val_loss: 0.3551"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"evaluate-the-model-on-the-test-set","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Evaluate the model on the test set","title":"Text classification from scratch","text":"","code":"model |> evaluate(test_ds) ## 782/782 - 1s - 2ms/step - accuracy: 0.8594 - loss: 0.3818 ## $accuracy ## [1] 0.85936 ## ## $loss ## [1] 0.381799"},{"path":"https://keras.posit.co/articles/examples/nlp/text_classification_from_scratch.html","id":"make-an-end-to-end-model","dir":"Articles > Examples > Nlp","previous_headings":"","what":"Make an end-to-end model","title":"Text classification from scratch","text":"want obtain model capable processing raw strings, can simply create new model (using weights just trained):","code":"# A string input inputs <- keras_input(shape = c(1), dtype = \"string\") # Turn strings into vocab indices indices <- vectorize_layer(inputs) # Turn vocab indices into predictions outputs <- model(indices)  # Our end to end model end_to_end_model <- keras_model(inputs, outputs) end_to_end_model |> compile(   loss = \"binary_crossentropy\",   optimizer = \"adam\",   metrics = c(\"accuracy\") )  # Test it with `raw_test_ds`, which yields raw strings end_to_end_model |> evaluate(raw_test_ds) ## 782/782 - 3s - 4ms/step - accuracy: 0.8594 - loss: 0.0000e+00 ## $accuracy ## [1] 0.85936 ## ## $loss ## [1] 0"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"download-the-data","dir":"Articles > Examples","previous_headings":"","what":"Download the data","title":"Image segmentation with a U-Net-like architecture","text":"","code":"options(timeout = 5000) download.file(   \"https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\",   \"datasets/images.tar.gz\" ) download.file(   \"https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\",   \"datasets/annotations.tar.gz\" )  untar(\"datasets/images.tar.gz\", exdir = \"datasets\") untar(\"datasets/annotations.tar.gz\", exdir = \"datasets\")"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"prepare-paths-of-input-images-and-target-segmentation-masks","dir":"Articles > Examples","previous_headings":"","what":"Prepare paths of input images and target segmentation masks","title":"Image segmentation with a U-Net-like architecture","text":"","code":"library(keras3) input_dir <- \"datasets/images/\" target_dir <- \"datasets/annotations/trimaps/\" img_size <- c(160, 160) num_classes <- 3 batch_size <- 32  input_img_paths <- fs::dir_ls(input_dir, glob = \"*.jpg\") |> sort() target_img_paths <- fs::dir_ls(target_dir, glob = \"*.png\") |> sort()  cat(\"Number of samples:\", length(input_img_paths), \"\\n\") ## Number of samples: 7390 for (i in 1:10) {   cat(input_img_paths[i], \"|\", target_img_paths[i], \"\\n\") } ## datasets/images/Abyssinian_1.jpg | datasets/annotations/trimaps/Abyssinian_1.png ## datasets/images/Abyssinian_10.jpg | datasets/annotations/trimaps/Abyssinian_10.png ## datasets/images/Abyssinian_100.jpg | datasets/annotations/trimaps/Abyssinian_100.png ## datasets/images/Abyssinian_101.jpg | datasets/annotations/trimaps/Abyssinian_101.png ## datasets/images/Abyssinian_102.jpg | datasets/annotations/trimaps/Abyssinian_102.png ## datasets/images/Abyssinian_103.jpg | datasets/annotations/trimaps/Abyssinian_103.png ## datasets/images/Abyssinian_104.jpg | datasets/annotations/trimaps/Abyssinian_104.png ## datasets/images/Abyssinian_105.jpg | datasets/annotations/trimaps/Abyssinian_105.png ## datasets/images/Abyssinian_106.jpg | datasets/annotations/trimaps/Abyssinian_106.png ## datasets/images/Abyssinian_107.jpg | datasets/annotations/trimaps/Abyssinian_107.png"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"what-does-one-input-image-and-corresponding-segmentation-mask-look-like","dir":"Articles > Examples","previous_headings":"","what":"What does one input image and corresponding segmentation mask look like?","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Display input image #10 input_img_paths[10] |>   jpeg::readJPEG() |>   as.raster() |>   plot() target_img_paths[10] |>   png::readPNG() |>   magrittr::multiply_by(255)|>   as.raster(max = 3) |>   plot()"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"prepare-dataset-to-load-vectorize-batches-of-data","dir":"Articles > Examples","previous_headings":"","what":"Prepare dataset to load & vectorize batches of data","title":"Image segmentation with a U-Net-like architecture","text":"","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")   # Returns a tf_dataset get_dataset <- function(batch_size, img_size, input_img_paths, target_img_paths,                         max_dataset_len = NULL) {    img_size <- as.integer(img_size)    load_img_masks <- function(input_img_path, target_img_path) {     input_img <- input_img_path |>       tf$io$read_file() |>       tf$io$decode_jpeg(channels = 3) |>       tf$image$resize(img_size) |>       tf$image$convert_image_dtype(\"float32\")      target_img <- target_img_path |>       tf$io$read_file() |>       tf$io$decode_png(channels = 1) |>       tf$image$resize(img_size, method = \"nearest\") |>       tf$image$convert_image_dtype(\"uint8\")      # Ground truth labels are 1, 2, 3. Subtract one to make them 0, 1, 2:     target_img <- target_img - 1L      list(input_img, target_img)   }    if (!is.null(max_dataset_len)) {     input_img_paths <- input_img_paths[1:max_dataset_len]     target_img_paths <- target_img_paths[1:max_dataset_len]   }    list(input_img_paths, target_img_paths) |>     tensor_slices_dataset() |>     dataset_map(load_img_masks, num_parallel_calls = tf$data$AUTOTUNE)|>     dataset_batch(batch_size) }"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"prepare-u-net-xception-style-model","dir":"Articles > Examples","previous_headings":"","what":"Prepare U-Net Xception-style model","title":"Image segmentation with a U-Net-like architecture","text":"","code":"get_model <- function(img_size, num_classes) {    inputs <- keras_input(shape = c(img_size, 3))    ### [First half of the network: downsampling inputs] ###    # Entry block   x <- inputs |>     layer_conv_2d(filters = 32, kernel_size = 3, strides = 2, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation(\"relu\")    previous_block_activation <- x  # Set aside residual    for (filters in c(64, 128, 256)) {     x <- x |>       layer_activation(\"relu\") |>       layer_separable_conv_2d(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_activation(\"relu\") |>       layer_separable_conv_2d(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_max_pooling_2d(pool_size = 3, strides = 2, padding = \"same\")      residual <- previous_block_activation |>       layer_conv_2d(filters = filters, kernel_size = 1, strides = 2, padding = \"same\")      x <- layer_add(x, residual)  # Add back residual     previous_block_activation <- x  # Set aside next residual   }    ### [Second half of the network: upsampling inputs] ###    for (filters in c(256, 128, 64, 32)) {     x <- x |>       layer_activation(\"relu\") |>       layer_conv_2d_transpose(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_activation(\"relu\") |>       layer_conv_2d_transpose(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_upsampling_2d(size = 2)      # Project residual     residual <- previous_block_activation |>       layer_upsampling_2d(size = 2) |>       layer_conv_2d(filters = filters, kernel_size = 1, padding = \"same\")      x <- layer_add(x, residual)     # Add back residual     previous_block_activation <- x  # Set aside next residual   }    # Add a per-pixel classification layer   outputs <- x |>     layer_conv_2d(num_classes, 3, activation = \"softmax\", padding = \"same\")    # Define the model   keras_model(inputs, outputs) }  # Build model model <- get_model(img_size, num_classes) summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)      ┃ Output Shape    ┃   Param # ┃ Connected to   ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer       │ (None, 160,     │         0 │ -              │   -   │ ## │ (InputLayer)      │ 160, 3)         │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d (Conv2D)   │ (None, 80, 80,  │       896 │ input_layer[0… │   Y   │ ## │                   │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d[0][0]   │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation        │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_2      │ (None, 80, 80,  │         0 │ activation[0]… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 80, 80,  │     2,400 │ activation_2[… │   Y   │ ## │ (SeparableConv2D) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       256 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_1      │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d  │ (None, 80, 80,  │     4,736 │ activation_1[… │   Y   │ ## │ (SeparableConv2D) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       256 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d     │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_1 (Conv2D) │ (None, 40, 40,  │     2,112 │ activation[0]… │   Y   │ ## │                   │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add (Add)         │ (None, 40, 40,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 64)             │           │ conv2d_1[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_4      │ (None, 40, 40,  │         0 │ add[0][0]      │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 40, 40,  │     8,896 │ activation_4[… │   Y   │ ## │ (SeparableConv2D) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       512 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_3      │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 40, 40,  │    17,664 │ activation_3[… │   Y   │ ## │ (SeparableConv2D) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       512 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d_1   │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_2 (Conv2D) │ (None, 20, 20,  │     8,320 │ add[0][0]      │   Y   │ ## │                   │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_1 (Add)       │ (None, 20, 20,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 128)            │           │ conv2d_2[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_6      │ (None, 20, 20,  │         0 │ add_1[0][0]    │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 20, 20,  │    34,176 │ activation_6[… │   Y   │ ## │ (SeparableConv2D) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │     1,024 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_5      │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 20, 20,  │    68,096 │ activation_5[… │   Y   │ ## │ (SeparableConv2D) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │     1,024 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d_2   │ (None, 10, 10,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_3 (Conv2D) │ (None, 10, 10,  │    33,024 │ add_1[0][0]    │   Y   │ ## │                   │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_2 (Add)       │ (None, 10, 10,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 256)            │           │ conv2d_3[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_8      │ (None, 10, 10,  │         0 │ add_2[0][0]    │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 10, 10,  │   590,080 │ activation_8[… │   Y   │ ## │ (Conv2DTranspose) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 10, 10,  │     1,024 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_7      │ (None, 10, 10,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose  │ (None, 10, 10,  │   590,080 │ activation_7[… │   Y   │ ## │ (Conv2DTranspose) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 10, 10,  │     1,024 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_1   │ (None, 20, 20,  │         0 │ add_2[0][0]    │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d     │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_4 (Conv2D) │ (None, 20, 20,  │    65,792 │ up_sampling2d… │   Y   │ ## │                   │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_3 (Add)       │ (None, 20, 20,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 256)            │           │ conv2d_4[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_10     │ (None, 20, 20,  │         0 │ add_3[0][0]    │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 20, 20,  │   295,040 │ activation_10… │   Y   │ ## │ (Conv2DTranspose) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │       512 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_9      │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 20, 20,  │   147,584 │ activation_9[… │   Y   │ ## │ (Conv2DTranspose) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │       512 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_3   │ (None, 40, 40,  │         0 │ add_3[0][0]    │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_2   │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_5 (Conv2D) │ (None, 40, 40,  │    32,896 │ up_sampling2d… │   Y   │ ## │                   │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_4 (Add)       │ (None, 40, 40,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 128)            │           │ conv2d_5[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_12     │ (None, 40, 40,  │         0 │ add_4[0][0]    │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 40, 40,  │    73,792 │ activation_12… │   Y   │ ## │ (Conv2DTranspose) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       256 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_11     │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 40, 40,  │    36,928 │ activation_11… │   Y   │ ## │ (Conv2DTranspose) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       256 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_5   │ (None, 80, 80,  │         0 │ add_4[0][0]    │   -   │ ## │ (UpSampling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_4   │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_6 (Conv2D) │ (None, 80, 80,  │     8,256 │ up_sampling2d… │   Y   │ ## │                   │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_5 (Add)       │ (None, 80, 80,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 64)             │           │ conv2d_6[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_14     │ (None, 80, 80,  │         0 │ add_5[0][0]    │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 80, 80,  │    18,464 │ activation_14… │   Y   │ ## │ (Conv2DTranspose) │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_13     │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 80, 80,  │     9,248 │ activation_13… │   Y   │ ## │ (Conv2DTranspose) │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_7   │ (None, 160,     │         0 │ add_5[0][0]    │   -   │ ## │ (UpSampling2D)    │ 160, 64)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_6   │ (None, 160,     │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 160, 32)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_7 (Conv2D) │ (None, 160,     │     2,080 │ up_sampling2d… │   Y   │ ## │                   │ 160, 32)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_6 (Add)       │ (None, 160,     │         0 │ up_sampling2d… │   -   │ ## │                   │ 160, 32)        │           │ conv2d_7[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_8 (Conv2D) │ (None, 160,     │       867 │ add_6[0][0]    │   Y   │ ## │                   │ 160, 3)         │           │                │       │ ## └───────────────────┴─────────────────┴───────────┴────────────────┴───────┘ ##  Total params: 2,058,979 (7.85 MB) ##  Trainable params: 2,055,203 (7.84 MB) ##  Non-trainable params: 3,776 (14.75 KB)"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"set-aside-a-validation-split","dir":"Articles > Examples","previous_headings":"","what":"Set aside a validation split","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Split our img paths into a training and a validation set val_samples <- 1000 val_samples <- sample.int(length(input_img_paths), val_samples)  train_input_img_paths <- input_img_paths[-val_samples] train_target_img_paths <- target_img_paths[-val_samples]  val_input_img_paths <- input_img_paths[val_samples] val_target_img_paths <- target_img_paths[val_samples]  # Instantiate dataset for each split # Limit input files in `max_dataset_len` for faster epoch training time. # Remove the `max_dataset_len` arg when running with full dataset. train_dataset <- get_dataset(   batch_size,   img_size,   train_input_img_paths,   train_target_img_paths,   max_dataset_len = 1000 ) valid_dataset <- get_dataset(   batch_size, img_size, val_input_img_paths, val_target_img_paths )"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"train-the-model","dir":"Articles > Examples","previous_headings":"","what":"Train the model","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Configure the model for training. # We use the \"sparse\" version of categorical_crossentropy # because our target data is integers. model |> compile(   optimizer = optimizer_adam(1e-4),   loss = \"sparse_categorical_crossentropy\" )  callbacks <- list(   callback_model_checkpoint(     \"models/oxford_segmentation.keras\", save_best_only = TRUE   ) )  # Train the model, doing validation at the end of each epoch. epochs <- 50 model |> fit(     train_dataset,     epochs=epochs,     validation_data=valid_dataset,     callbacks=callbacks,     verbose=2 ) ## Epoch 1/50 ## 32/32 - 32s - 986ms/step - loss: 1.3044 - val_loss: 1.5502 ## Epoch 2/50 ## 32/32 - 2s - 60ms/step - loss: 0.8717 - val_loss: 1.9567 ## Epoch 3/50 ## 32/32 - 2s - 60ms/step - loss: 0.7695 - val_loss: 2.1948 ## Epoch 4/50 ## 32/32 - 2s - 60ms/step - loss: 0.7080 - val_loss: 2.4837 ## Epoch 5/50 ## 32/32 - 2s - 64ms/step - loss: 0.6689 - val_loss: 2.7751 ## Epoch 6/50 ## 32/32 - 2s - 61ms/step - loss: 0.6378 - val_loss: 3.1156 ## Epoch 7/50 ## 32/32 - 2s - 60ms/step - loss: 0.6129 - val_loss: 3.3611 ## Epoch 8/50 ## 32/32 - 2s - 60ms/step - loss: 0.5917 - val_loss: 3.5193 ## Epoch 9/50 ## 32/32 - 2s - 62ms/step - loss: 0.5723 - val_loss: 3.6237 ## Epoch 10/50 ## 32/32 - 2s - 62ms/step - loss: 0.5538 - val_loss: 3.7209 ## Epoch 11/50 ## 32/32 - 2s - 60ms/step - loss: 0.5352 - val_loss: 3.8216 ## Epoch 12/50 ## 32/32 - 2s - 61ms/step - loss: 0.5161 - val_loss: 3.9090 ## Epoch 13/50 ## 32/32 - 2s - 60ms/step - loss: 0.4958 - val_loss: 4.0158 ## Epoch 14/50 ## 32/32 - 2s - 60ms/step - loss: 0.4739 - val_loss: 4.0756 ## Epoch 15/50 ## 32/32 - 2s - 60ms/step - loss: 0.4501 - val_loss: 4.0083 ## Epoch 16/50 ## 32/32 - 2s - 60ms/step - loss: 0.4243 - val_loss: 3.7728 ## Epoch 17/50 ## 32/32 - 2s - 60ms/step - loss: 0.3971 - val_loss: 3.3484 ## Epoch 18/50 ## 32/32 - 2s - 61ms/step - loss: 0.3696 - val_loss: 2.6909 ## Epoch 19/50 ## 32/32 - 2s - 60ms/step - loss: 0.3428 - val_loss: 1.9726 ## Epoch 20/50 ## 32/32 - 2s - 65ms/step - loss: 0.3178 - val_loss: 1.4081 ## Epoch 21/50 ## 32/32 - 2s - 65ms/step - loss: 0.2954 - val_loss: 1.1136 ## Epoch 22/50 ## 32/32 - 2s - 65ms/step - loss: 0.2764 - val_loss: 1.0246 ## Epoch 23/50 ## 32/32 - 2s - 61ms/step - loss: 0.2610 - val_loss: 1.0390 ## Epoch 24/50 ## 32/32 - 2s - 60ms/step - loss: 0.2507 - val_loss: 1.1001 ## Epoch 25/50 ## 32/32 - 2s - 60ms/step - loss: 0.2525 - val_loss: 1.1915 ## Epoch 26/50 ## 32/32 - 2s - 61ms/step - loss: 0.2961 - val_loss: 1.2296 ## Epoch 27/50 ## 32/32 - 2s - 61ms/step - loss: 0.3545 - val_loss: 1.2625 ## Epoch 28/50 ## 32/32 - 2s - 65ms/step - loss: 0.3326 - val_loss: 0.9711 ## Epoch 29/50 ## 32/32 - 2s - 60ms/step - loss: 0.3302 - val_loss: 1.0251 ## Epoch 30/50 ## 32/32 - 2s - 60ms/step - loss: 0.3163 - val_loss: 1.0867 ## Epoch 31/50 ## 32/32 - 2s - 60ms/step - loss: 0.3131 - val_loss: 1.4197 ## Epoch 32/50 ## 32/32 - 2s - 60ms/step - loss: 0.3093 - val_loss: 1.0851 ## Epoch 33/50 ## 32/32 - 2s - 61ms/step - loss: 0.2939 - val_loss: 1.4807 ## Epoch 34/50 ## 32/32 - 2s - 60ms/step - loss: 0.2763 - val_loss: 1.1851 ## Epoch 35/50 ## 32/32 - 2s - 60ms/step - loss: 0.2702 - val_loss: 1.1337 ## Epoch 36/50 ## 32/32 - 2s - 61ms/step - loss: 0.2584 - val_loss: 1.0315 ## Epoch 37/50 ## 32/32 - 2s - 61ms/step - loss: 0.2440 - val_loss: 1.0631 ## Epoch 38/50 ## 32/32 - 2s - 60ms/step - loss: 0.2353 - val_loss: 1.1609 ## Epoch 39/50 ## 32/32 - 2s - 61ms/step - loss: 0.2285 - val_loss: 1.1839 ## Epoch 40/50 ## 32/32 - 2s - 60ms/step - loss: 0.2307 - val_loss: 1.1293 ## Epoch 41/50 ## 32/32 - 2s - 61ms/step - loss: 0.2357 - val_loss: 0.9948 ## Epoch 42/50 ## 32/32 - 2s - 60ms/step - loss: 0.2279 - val_loss: 1.1329 ## Epoch 43/50 ## 32/32 - 2s - 61ms/step - loss: 0.2174 - val_loss: 1.0418 ## Epoch 44/50 ## 32/32 - 2s - 61ms/step - loss: 0.2076 - val_loss: 1.1141 ## Epoch 45/50 ## 32/32 - 2s - 61ms/step - loss: 0.2004 - val_loss: 1.1162 ## Epoch 46/50 ## 32/32 - 2s - 60ms/step - loss: 0.1930 - val_loss: 1.0939 ## Epoch 47/50 ## 32/32 - 2s - 61ms/step - loss: 0.1852 - val_loss: 1.0499 ## Epoch 48/50 ## 32/32 - 2s - 60ms/step - loss: 0.1796 - val_loss: 1.0651 ## Epoch 49/50 ## 32/32 - 2s - 61ms/step - loss: 0.1762 - val_loss: 1.0679 ## Epoch 50/50 ## 32/32 - 2s - 60ms/step - loss: 0.1757 - val_loss: 1.1762"},{"path":"https://keras.posit.co/articles/examples/oxford_pets_image_segmentation.html","id":"visualize-predictions","dir":"Articles > Examples","previous_headings":"","what":"Visualize predictions","title":"Image segmentation with a U-Net-like architecture","text":"","code":"model <- load_model(\"models/oxford_segmentation.keras\") # Generate predictions for all images in the validation set val_dataset <- get_dataset(   batch_size, img_size, val_input_img_paths, val_target_img_paths ) val_preds <- predict(model, val_dataset) ## 32/32 - 4s - 111ms/step display_mask <- function(i) {   # Quick utility to display a model's prediction.   mask <- val_preds[i,,,] %>%     apply(c(1,2), which.max) %>%     array_reshape(dim = c(img_size, 1))   mask <- abind::abind(mask, mask, mask, along = 3)   plot(as.raster(mask, max = 3)) }  # Display results for validation image #10 i <- 10  par(mfrow = c(1, 3)) # Display input image input_img_paths[i] |>   jpeg::readJPEG() |>   as.raster() |>   plot()  # Display ground-truth target mask target_img_paths[i] |>   png::readPNG() |>   magrittr::multiply_by(255)|>   as.raster(max = 3) |>   plot()  # Display mask predicted by our model display_mask(i)  # Note that the model only sees inputs at 150x150."},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"introduction","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Introduction","title":"Imbalanced classification: credit card fraud detection","text":"example looks Kaggle Credit Card Fraud Detection dataset demonstrate train classification model data highly imbalanced classes. can download data clicking “Download” link, ’re setup kaggle API key \"~/.kaggle/kagle.json\", can run following:","code":"reticulate::py_install(\"kaggle\", pip = TRUE) reticulate::py_available(TRUE) # ensure 'kaggle' is on the PATH system(\"kaggle datasets download -d mlg-ulb/creditcardfraud\") zip::unzip(\"creditcardfraud.zip\", files = \"creditcard.csv\")"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"first-load-the-data","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"First, load the data","title":"Imbalanced classification: credit card fraud detection","text":"","code":"library(readr) df <- read_csv(\"creditcard.csv\", col_types = cols(   Class = col_integer(),   .default = col_double() )) tibble::glimpse(df) ## Rows: 284,807 ## Columns: 31 ## $ Time   <dbl> 0, 0, 1, 1, 2, 2, 4, 7, 7, 9, 10, 10, 10, 11, 12, 12, 12, 1… ## $ V1     <dbl> -1.3598071, 1.1918571, -1.3583541, -0.9662717, -1.1582331, … ## $ V2     <dbl> -0.07278117, 0.26615071, -1.34016307, -0.18522601, 0.877736… ## $ V3     <dbl> 2.53634674, 0.16648011, 1.77320934, 1.79299334, 1.54871785,… ## $ V4     <dbl> 1.37815522, 0.44815408, 0.37977959, -0.86329128, 0.40303393… ## $ V5     <dbl> -0.33832077, 0.06001765, -0.50319813, -0.01030888, -0.40719… ## $ V6     <dbl> 0.46238778, -0.08236081, 1.80049938, 1.24720317, 0.09592146… ## $ V7     <dbl> 0.239598554, -0.078802983, 0.791460956, 0.237608940, 0.5929… ## $ V8     <dbl> 0.098697901, 0.085101655, 0.247675787, 0.377435875, -0.2705… ## $ V9     <dbl> 0.3637870, -0.2554251, -1.5146543, -1.3870241, 0.8177393, -… ## $ V10    <dbl> 0.09079417, -0.16697441, 0.20764287, -0.05495192, 0.7530744… ## $ V11    <dbl> -0.55159953, 1.61272666, 0.62450146, -0.22648726, -0.822842… ## $ V12    <dbl> -0.61780086, 1.06523531, 0.06608369, 0.17822823, 0.53819555… ## $ V13    <dbl> -0.99138985, 0.48909502, 0.71729273, 0.50775687, 1.34585159… ## $ V14    <dbl> -0.31116935, -0.14377230, -0.16594592, -0.28792375, -1.1196… ## $ V15    <dbl> 1.468176972, 0.635558093, 2.345864949, -0.631418118, 0.1751… ## $ V16    <dbl> -0.47040053, 0.46391704, -2.89008319, -1.05964725, -0.45144… ## $ V17    <dbl> 0.207971242, -0.114804663, 1.109969379, -0.684092786, -0.23… ## $ V18    <dbl> 0.02579058, -0.18336127, -0.12135931, 1.96577500, -0.038194… ## $ V19    <dbl> 0.40399296, -0.14578304, -2.26185710, -1.23262197, 0.803486… ## $ V20    <dbl> 0.25141210, -0.06908314, 0.52497973, -0.20803778, 0.4085423… ## $ V21    <dbl> -0.018306778, -0.225775248, 0.247998153, -0.108300452, -0.0… ## $ V22    <dbl> 0.277837576, -0.638671953, 0.771679402, 0.005273597, 0.7982… ## $ V23    <dbl> -0.110473910, 0.101288021, 0.909412262, -0.190320519, -0.13… ## $ V24    <dbl> 0.06692807, -0.33984648, -0.68928096, -1.17557533, 0.141266… ## $ V25    <dbl> 0.12853936, 0.16717040, -0.32764183, 0.64737603, -0.2060095… ## $ V26    <dbl> -0.18911484, 0.12589453, -0.13909657, -0.22192884, 0.502292… ## $ V27    <dbl> 0.133558377, -0.008983099, -0.055352794, 0.062722849, 0.219… ## $ V28    <dbl> -0.021053053, 0.014724169, -0.059751841, 0.061457629, 0.215… ## $ Amount <dbl> 149.62, 2.69, 378.66, 123.50, 69.99, 3.67, 4.99, 40.80, 93.… ## $ Class  <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"prepare-a-validation-set","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Prepare a validation set","title":"Imbalanced classification: credit card fraud detection","text":"","code":"val_idx <- nrow(df) %>% sample.int(., round( . * 0.2)) val_df <- df[val_idx, ] train_df <- df[-val_idx, ]  cat(\"Number of training samples:\", nrow(train_df), \"\\n\") ## Number of training samples: 227846 cat(\"Number of validation samples:\", nrow(val_df), \"\\n\") ## Number of validation samples: 56961"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"analyze-class-imbalance-in-the-targets","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Analyze class imbalance in the targets","title":"Imbalanced classification: credit card fraud detection","text":"","code":"counts <- table(train_df$Class) counts ## ##      0      1 ## 227450    396 cat(sprintf(\"Number of positive samples in training data: %i (%.2f%% of total)\",             counts[\"1\"], 100 * counts[\"1\"] / sum(counts))) ## Number of positive samples in training data: 396 (0.17% of total) weight_for_0 = 1 / counts[\"0\"] weight_for_1 = 1 / counts[\"1\"]"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"normalize-the-data-using-training-set-statistics","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Normalize the data using training set statistics","title":"Imbalanced classification: credit card fraud detection","text":"","code":"feature_names <- colnames(train_df) %>% setdiff(\"Class\")  train_features <- as.matrix(train_df[feature_names]) train_targets <- as.matrix(train_df$Class)  val_features <- as.matrix(val_df[feature_names]) val_targets <- as.matrix(val_df$Class)  train_features %<>% scale() val_features %<>% scale(center = attr(train_features, \"scaled:center\"),                         scale = attr(train_features, \"scaled:scale\"))"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"build-a-binary-classification-model","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Build a binary classification model","title":"Imbalanced classification: credit card fraud detection","text":"","code":"model <-   keras_model_sequential(input_shape = ncol(train_features)) |>   layer_dense(256, activation = \"relu\") |>   layer_dense(256, activation = \"relu\") |>   layer_dropout(0.3) |>   layer_dense(256, activation = \"relu\") |>   layer_dropout(0.3) |>   layer_dense(1, activation = \"sigmoid\")  model ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense (Dense)                   │ (None, 256)            │         7,936 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_1 (Dense)                 │ (None, 256)            │        65,792 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 256)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 256)            │        65,792 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, 256)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_3 (Dense)                 │ (None, 1)              │           257 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 139,777 (546.00 KB) ##  Trainable params: 139,777 (546.00 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"train-the-model-with-class_weight-argument","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Train the model with class_weight argument","title":"Imbalanced classification: credit card fraud detection","text":"","code":"metrics <- list(   metric_false_negatives(name = \"fn\"),   metric_false_positives(name = \"fp\"),   metric_true_negatives(name = \"tn\"),   metric_true_positives(name = \"tp\"),   metric_precision(name = \"precision\"),   metric_recall(name = \"recall\") ) model |> compile(   optimizer = optimizer_adam(1e-2),   loss = \"binary_crossentropy\",   metrics = metrics ) callbacks <- list(   callback_model_checkpoint(\"fraud_model_at_epoch_{epoch}.keras\") )  class_weight <- list(\"0\" = weight_for_0,                      \"1\" = weight_for_1)  model |> fit(   train_features, train_targets,   validation_data = list(val_features, val_targets),   class_weight = class_weight,   batch_size = 2048,   epochs = 30,   callbacks = callbacks,   verbose = 2 ) ## Epoch 1/30 ## 112/112 - 3s - 27ms/step - fn: 40.0000 - fp: 23705.0000 - loss: 2.2728e-06 - precision: 0.0148 - recall: 0.8990 - tn: 203745.0000 - tp: 356.0000 - val_fn: 8.0000 - val_fp: 1502.0000 - val_loss: 0.1089 - val_precision: 0.0553 - val_recall: 0.9167 - val_tn: 55363.0000 - val_tp: 88.0000 ## Epoch 2/30 ## 112/112 - 1s - 6ms/step - fn: 33.0000 - fp: 8009.0000 - loss: 1.4949e-06 - precision: 0.0434 - recall: 0.9167 - tn: 219441.0000 - tp: 363.0000 - val_fn: 10.0000 - val_fp: 1176.0000 - val_loss: 0.0912 - val_precision: 0.0681 - val_recall: 0.8958 - val_tn: 55689.0000 - val_tp: 86.0000 ## Epoch 3/30 ## 112/112 - 0s - 2ms/step - fn: 32.0000 - fp: 7704.0000 - loss: 1.3369e-06 - precision: 0.0451 - recall: 0.9192 - tn: 219746.0000 - tp: 364.0000 - val_fn: 9.0000 - val_fp: 1202.0000 - val_loss: 0.0870 - val_precision: 0.0675 - val_recall: 0.9062 - val_tn: 55663.0000 - val_tp: 87.0000 ## Epoch 4/30 ## 112/112 - 0s - 2ms/step - fn: 28.0000 - fp: 8366.0000 - loss: 1.2269e-06 - precision: 0.0421 - recall: 0.9293 - tn: 219084.0000 - tp: 368.0000 - val_fn: 10.0000 - val_fp: 1560.0000 - val_loss: 0.0967 - val_precision: 0.0522 - val_recall: 0.8958 - val_tn: 55305.0000 - val_tp: 86.0000 ## Epoch 5/30 ## 112/112 - 0s - 2ms/step - fn: 19.0000 - fp: 6217.0000 - loss: 8.5492e-07 - precision: 0.0572 - recall: 0.9520 - tn: 221233.0000 - tp: 377.0000 - val_fn: 9.0000 - val_fp: 2626.0000 - val_loss: 0.1236 - val_precision: 0.0321 - val_recall: 0.9062 - val_tn: 54239.0000 - val_tp: 87.0000 ## Epoch 6/30 ## 112/112 - 0s - 2ms/step - fn: 18.0000 - fp: 6566.0000 - loss: 7.4300e-07 - precision: 0.0544 - recall: 0.9545 - tn: 220884.0000 - tp: 378.0000 - val_fn: 8.0000 - val_fp: 2302.0000 - val_loss: 0.1081 - val_precision: 0.0368 - val_recall: 0.9167 - val_tn: 54563.0000 - val_tp: 88.0000 ## Epoch 7/30 ## 112/112 - 0s - 2ms/step - fn: 14.0000 - fp: 6819.0000 - loss: 7.1838e-07 - precision: 0.0530 - recall: 0.9646 - tn: 220631.0000 - tp: 382.0000 - val_fn: 7.0000 - val_fp: 1376.0000 - val_loss: 0.0800 - val_precision: 0.0608 - val_recall: 0.9271 - val_tn: 55489.0000 - val_tp: 89.0000 ## Epoch 8/30 ## 112/112 - 0s - 2ms/step - fn: 19.0000 - fp: 7345.0000 - loss: 8.3921e-07 - precision: 0.0488 - recall: 0.9520 - tn: 220105.0000 - tp: 377.0000 - val_fn: 10.0000 - val_fp: 416.0000 - val_loss: 0.0406 - val_precision: 0.1713 - val_recall: 0.8958 - val_tn: 56449.0000 - val_tp: 86.0000 ## Epoch 9/30 ## 112/112 - 0s - 2ms/step - fn: 13.0000 - fp: 6282.0000 - loss: 8.3488e-07 - precision: 0.0575 - recall: 0.9672 - tn: 221168.0000 - tp: 383.0000 - val_fn: 10.0000 - val_fp: 933.0000 - val_loss: 0.0450 - val_precision: 0.0844 - val_recall: 0.8958 - val_tn: 55932.0000 - val_tp: 86.0000 ## Epoch 10/30 ## 112/112 - 0s - 2ms/step - fn: 15.0000 - fp: 7597.0000 - loss: 1.1098e-06 - precision: 0.0478 - recall: 0.9621 - tn: 219853.0000 - tp: 381.0000 - val_fn: 10.0000 - val_fp: 2417.0000 - val_loss: 0.4261 - val_precision: 0.0344 - val_recall: 0.8958 - val_tn: 54448.0000 - val_tp: 86.0000 ## Epoch 11/30 ## 112/112 - 0s - 2ms/step - fn: 24.0000 - fp: 10269.0000 - loss: 1.9062e-06 - precision: 0.0350 - recall: 0.9394 - tn: 217181.0000 - tp: 372.0000 - val_fn: 9.0000 - val_fp: 1434.0000 - val_loss: 0.1261 - val_precision: 0.0572 - val_recall: 0.9062 - val_tn: 55431.0000 - val_tp: 87.0000 ## Epoch 12/30 ## 112/112 - 0s - 2ms/step - fn: 17.0000 - fp: 6634.0000 - loss: 1.0711e-06 - precision: 0.0540 - recall: 0.9571 - tn: 220816.0000 - tp: 379.0000 - val_fn: 8.0000 - val_fp: 2263.0000 - val_loss: 0.1654 - val_precision: 0.0374 - val_recall: 0.9167 - val_tn: 54602.0000 - val_tp: 88.0000 ## Epoch 13/30 ## 112/112 - 0s - 2ms/step - fn: 10.0000 - fp: 5620.0000 - loss: 5.3600e-07 - precision: 0.0643 - recall: 0.9747 - tn: 221830.0000 - tp: 386.0000 - val_fn: 12.0000 - val_fp: 914.0000 - val_loss: 0.0414 - val_precision: 0.0842 - val_recall: 0.8750 - val_tn: 55951.0000 - val_tp: 84.0000 ## Epoch 14/30 ## 112/112 - 0s - 2ms/step - fn: 8.0000 - fp: 5103.0000 - loss: 5.1168e-07 - precision: 0.0707 - recall: 0.9798 - tn: 222347.0000 - tp: 388.0000 - val_fn: 11.0000 - val_fp: 1382.0000 - val_loss: 0.0660 - val_precision: 0.0579 - val_recall: 0.8854 - val_tn: 55483.0000 - val_tp: 85.0000 ## Epoch 15/30 ## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 4497.0000 - loss: 4.4632e-07 - precision: 0.0796 - recall: 0.9823 - tn: 222953.0000 - tp: 389.0000 - val_fn: 10.0000 - val_fp: 1805.0000 - val_loss: 0.0786 - val_precision: 0.0455 - val_recall: 0.8958 - val_tn: 55060.0000 - val_tp: 86.0000 ## Epoch 16/30 ## 112/112 - 0s - 2ms/step - fn: 5.0000 - fp: 5056.0000 - loss: 6.1038e-07 - precision: 0.0718 - recall: 0.9874 - tn: 222394.0000 - tp: 391.0000 - val_fn: 13.0000 - val_fp: 889.0000 - val_loss: 0.0676 - val_precision: 0.0854 - val_recall: 0.8646 - val_tn: 55976.0000 - val_tp: 83.0000 ## Epoch 17/30 ## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 4639.0000 - loss: 4.5763e-07 - precision: 0.0781 - recall: 0.9924 - tn: 222811.0000 - tp: 393.0000 - val_fn: 11.0000 - val_fp: 1185.0000 - val_loss: 0.0669 - val_precision: 0.0669 - val_recall: 0.8854 - val_tn: 55680.0000 - val_tp: 85.0000 ## Epoch 18/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 4355.0000 - loss: 4.5299e-07 - precision: 0.0826 - recall: 0.9899 - tn: 223095.0000 - tp: 392.0000 - val_fn: 9.0000 - val_fp: 1405.0000 - val_loss: 0.0772 - val_precision: 0.0583 - val_recall: 0.9062 - val_tn: 55460.0000 - val_tp: 87.0000 ## Epoch 19/30 ## 112/112 - 0s - 2ms/step - fn: 6.0000 - fp: 5779.0000 - loss: 4.8324e-07 - precision: 0.0632 - recall: 0.9848 - tn: 221671.0000 - tp: 390.0000 - val_fn: 10.0000 - val_fp: 1135.0000 - val_loss: 0.0546 - val_precision: 0.0704 - val_recall: 0.8958 - val_tn: 55730.0000 - val_tp: 86.0000 ## Epoch 20/30 ## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 4748.0000 - loss: 3.7424e-07 - precision: 0.0764 - recall: 0.9924 - tn: 222702.0000 - tp: 393.0000 - val_fn: 9.0000 - val_fp: 895.0000 - val_loss: 0.0396 - val_precision: 0.0886 - val_recall: 0.9062 - val_tn: 55970.0000 - val_tp: 87.0000 ## Epoch 21/30 ## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 3798.0000 - loss: 3.1417e-07 - precision: 0.0938 - recall: 0.9924 - tn: 223652.0000 - tp: 393.0000 - val_fn: 10.0000 - val_fp: 652.0000 - val_loss: 0.0309 - val_precision: 0.1165 - val_recall: 0.8958 - val_tn: 56213.0000 - val_tp: 86.0000 ## Epoch 22/30 ## 112/112 - 0s - 2ms/step - fn: 0.0000e+00 - fp: 2188.0000 - loss: 1.8112e-07 - precision: 0.1533 - recall: 1.0000 - tn: 225262.0000 - tp: 396.0000 - val_fn: 10.0000 - val_fp: 427.0000 - val_loss: 0.0250 - val_precision: 0.1676 - val_recall: 0.8958 - val_tn: 56438.0000 - val_tp: 86.0000 ## Epoch 23/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 3066.0000 - loss: 3.4932e-07 - precision: 0.1134 - recall: 0.9899 - tn: 224384.0000 - tp: 392.0000 - val_fn: 11.0000 - val_fp: 1053.0000 - val_loss: 0.0494 - val_precision: 0.0747 - val_recall: 0.8854 - val_tn: 55812.0000 - val_tp: 85.0000 ## Epoch 24/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 3175.0000 - loss: 5.1994e-07 - precision: 0.1099 - recall: 0.9899 - tn: 224275.0000 - tp: 392.0000 - val_fn: 11.0000 - val_fp: 1012.0000 - val_loss: 0.0997 - val_precision: 0.0775 - val_recall: 0.8854 - val_tn: 55853.0000 - val_tp: 85.0000 ## Epoch 25/30 ## 112/112 - 0s - 2ms/step - fn: 7.0000 - fp: 5874.0000 - loss: 6.5862e-07 - precision: 0.0621 - recall: 0.9823 - tn: 221576.0000 - tp: 389.0000 - val_fn: 9.0000 - val_fp: 3090.0000 - val_loss: 0.1296 - val_precision: 0.0274 - val_recall: 0.9062 - val_tn: 53775.0000 - val_tp: 87.0000 ## Epoch 26/30 ## 112/112 - 0s - 2ms/step - fn: 6.0000 - fp: 5395.0000 - loss: 5.9253e-07 - precision: 0.0674 - recall: 0.9848 - tn: 222055.0000 - tp: 390.0000 - val_fn: 9.0000 - val_fp: 1320.0000 - val_loss: 0.0928 - val_precision: 0.0618 - val_recall: 0.9062 - val_tn: 55545.0000 - val_tp: 87.0000 ## Epoch 27/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 3887.0000 - loss: 7.1278e-07 - precision: 0.0916 - recall: 0.9899 - tn: 223563.0000 - tp: 392.0000 - val_fn: 12.0000 - val_fp: 572.0000 - val_loss: 0.0352 - val_precision: 0.1280 - val_recall: 0.8750 - val_tn: 56293.0000 - val_tp: 84.0000 ## Epoch 28/30 ## 112/112 - 0s - 2ms/step - fn: 3.0000 - fp: 2209.0000 - loss: 5.8153e-07 - precision: 0.1510 - recall: 0.9924 - tn: 225241.0000 - tp: 393.0000 - val_fn: 11.0000 - val_fp: 1373.0000 - val_loss: 0.0808 - val_precision: 0.0583 - val_recall: 0.8854 - val_tn: 55492.0000 - val_tp: 85.0000 ## Epoch 29/30 ## 112/112 - 0s - 2ms/step - fn: 1.0000 - fp: 2506.0000 - loss: 2.9031e-07 - precision: 0.1362 - recall: 0.9975 - tn: 224944.0000 - tp: 395.0000 - val_fn: 11.0000 - val_fp: 690.0000 - val_loss: 0.0401 - val_precision: 0.1097 - val_recall: 0.8854 - val_tn: 56175.0000 - val_tp: 85.0000 ## Epoch 30/30 ## 112/112 - 0s - 2ms/step - fn: 4.0000 - fp: 2719.0000 - loss: 6.1097e-07 - precision: 0.1260 - recall: 0.9899 - tn: 224731.0000 - tp: 392.0000 - val_fn: 13.0000 - val_fp: 861.0000 - val_loss: 0.0362 - val_precision: 0.0879 - val_recall: 0.8646 - val_tn: 56004.0000 - val_tp: 83.0000 val_pred <- model %>%   predict(val_features) %>%   { as.integer(. > 0.5) } ## 1781/1781 - 1s - 311us/step pred_correct <- val_df$Class == val_pred cat(sprintf(\"Validation accuracy: %.2f\", mean(pred_correct))) ## Validation accuracy: 0.98 fraudulent <- val_df$Class == 1  n_fraudulent_detected <- sum(fraudulent & pred_correct) n_fraudulent_missed <- sum(fraudulent & !pred_correct) n_legitimate_flagged <- sum(!fraudulent & !pred_correct)"},{"path":"https://keras.posit.co/articles/examples/structured_data/imbalanced_classification.html","id":"conclusions","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Conclusions","title":"Imbalanced classification: credit card fraud detection","text":"end training, 56,961 validation transactions, : Correctly identifying 83 fraudulent Missing 13 fraudulent transactions cost incorrectly flagging 861 legitimate transactions real world, one put even higher weight class 1, reflect False Negatives costly False Positives. Next time credit card gets declined online purchase – .","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"introduction","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Introduction","title":"Structured data classification with FeatureSpace","text":"example demonstrates structured data classification (also known tabular data classification), starting raw CSV file. data includes numerical features, integer categorical features, string categorical features. use utility layer_feature_space() index, preprocess, encode features. code adapted example Structured data classification scratch. previous example managed low-level feature preprocessing encoding Keras preprocessing layers, example delegate everything layer_feature_space(), making workflow extremely quick easy.","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"the-dataset","dir":"Articles > Examples > Structured_data","previous_headings":"Introduction","what":"The dataset","title":"Structured data classification with FeatureSpace","text":"dataset provided Cleveland Clinic Foundation Heart Disease. ’s CSV file 303 rows. row contains information patient (sample), column describes attribute patient (feature). use features predict whether patient heart disease (binary classification). ’s description feature:","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"setup","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Setup","title":"Structured data classification with FeatureSpace","text":"","code":"library(readr) library(dplyr, warn.conflicts = FALSE) library(keras3) library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")  conflicted::conflicts_prefer(   keras3::shape(),   keras3::set_random_seed(),   dplyr::filter(),   .quiet = TRUE )  use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"preparing-the-data","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Preparing the data","title":"Structured data classification with FeatureSpace","text":"Let’s download data load Pandas dataframe: dataset includes 303 samples 14 columns per sample (13 features, plus target label) ’s preview samples: last column, “target”, indicates whether patient heart disease (1) (0). Let’s split data training validation set: Let’s generate tf_dataset objects dataframe: tf_dataset yields tuple (input, target) input dictionary (named list) features target value 0 1: Let’s batch datasets:","code":"file_url <-   \"http://storage.googleapis.com/download.tensorflow.org/data/heart.csv\" df <- read_csv(file_url, col_types = cols(   oldpeak = col_double(),   thal = col_character(),   .default = col_integer() ))  # the dataset has two malformed rows, filter them out df <- df |> filter(!thal %in% c(\"1\", \"2\")) glimpse(df) ## Rows: 301 ## Columns: 14 ## $ age      <int> 63, 67, 67, 37, 41, 56, 62, 57, 63, 53, 57, 56, 56, 44, 5… ## $ sex      <int> 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, … ## $ cp       <int> 1, 4, 4, 3, 2, 2, 4, 4, 4, 4, 4, 2, 3, 2, 3, 3, 2, 4, 3, … ## $ trestbps <int> 145, 160, 120, 130, 130, 120, 140, 120, 130, 140, 140, 14… ## $ chol     <int> 233, 286, 229, 250, 204, 236, 268, 354, 254, 203, 192, 29… ## $ fbs      <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, … ## $ restecg  <int> 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, … ## $ thalach  <int> 150, 108, 129, 187, 172, 178, 160, 163, 147, 155, 148, 15… ## $ exang    <int> 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, … ## $ oldpeak  <dbl> 2.3, 1.5, 2.6, 3.5, 1.4, 0.8, 3.6, 0.6, 1.4, 3.1, 0.4, 1.… ## $ slope    <int> 3, 2, 2, 3, 1, 1, 3, 1, 2, 3, 2, 2, 2, 1, 1, 1, 3, 1, 1, … ## $ ca       <int> 0, 3, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, … ## $ thal     <chr> \"fixed\", \"normal\", \"reversible\", \"normal\", \"normal\", \"nor… ## $ target   <int> 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, … df ## # A tibble: 301 × 14 ##      age   sex    cp trestbps  chol   fbs restecg thalach exang oldpeak ##    <int> <int> <int>    <int> <int> <int>   <int>   <int> <int>   <dbl> ##  1    63     1     1      145   233     1       2     150     0     2.3 ##  2    67     1     4      160   286     0       2     108     1     1.5 ##  3    67     1     4      120   229     0       2     129     1     2.6 ##  4    37     1     3      130   250     0       0     187     0     3.5 ##  5    41     0     2      130   204     0       2     172     0     1.4 ##  6    56     1     2      120   236     0       0     178     0     0.8 ##  7    62     0     4      140   268     0       2     160     0     3.6 ##  8    57     0     4      120   354     0       0     163     1     0.6 ##  9    63     1     4      130   254     0       2     147     0     1.4 ## 10    53     1     4      140   203     1       2     155     1     3.1 ## # ℹ 291 more rows ## # ℹ 4 more variables: slope <int>, ca <int>, thal <chr>, target <int> val_idx <- nrow(df) %>% sample.int(., . * 0.2) val_df <- df[val_idx, ] train_df <- df[-val_idx, ]  cat(sprintf(   \"Using %d samples for training and %d for validation\",   nrow(train_df), nrow(val_df) )) ## Using 241 samples for training and 60 for validation dataframe_to_dataset <- function(df) {   labels <- df |> pull(target) |> as.integer()   inputs <- df |> select(-target) |> as.list()    ds <- tensor_slices_dataset(list(inputs, labels)) |>     dataset_shuffle(nrow(df))    ds }  train_ds <- dataframe_to_dataset(train_df) val_ds <- dataframe_to_dataset(val_df) c(x, y) %<-% iter_next(as_iterator(train_ds)) cat(\"Input: \"); str(x) cat(\"Target: \"); str(y) ## Input: List of 13 ##  $ age     :<tf.Tensor: shape=(), dtype=int32, numpy=59> ##  $ sex     :<tf.Tensor: shape=(), dtype=int32, numpy=1> ##  $ cp      :<tf.Tensor: shape=(), dtype=int32, numpy=4> ##  $ trestbps:<tf.Tensor: shape=(), dtype=int32, numpy=164> ##  $ chol    :<tf.Tensor: shape=(), dtype=int32, numpy=176> ##  $ fbs     :<tf.Tensor: shape=(), dtype=int32, numpy=1> ##  $ restecg :<tf.Tensor: shape=(), dtype=int32, numpy=2> ##  $ thalach :<tf.Tensor: shape=(), dtype=int32, numpy=90> ##  $ exang   :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ oldpeak :<tf.Tensor: shape=(), dtype=float32, numpy=1.0> ##  $ slope   :<tf.Tensor: shape=(), dtype=int32, numpy=2> ##  $ ca      :<tf.Tensor: shape=(), dtype=int32, numpy=2> ##  $ thal    :<tf.Tensor: shape=(), dtype=string, numpy=b'fixed'> ## Target: <tf.Tensor: shape=(), dtype=int32, numpy=1> train_ds <- train_ds |> dataset_batch(32) val_ds <- val_ds |> dataset_batch(32)"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"configuring-a-featurespace","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Configuring a FeatureSpace","title":"Structured data classification with FeatureSpace","text":"configure feature preprocessed, instantiate layer_feature_space(), pass dictionary (named list unique names) maps name features string describes feature type. “integer categorical” features \"FBS\", one “string categorical” feature (\"thal\"), numerical features, ’d like normalize – except \"age\", ’d like discretize number bins. also use crosses argument capture feature interactions categorical features, say, create additional features represent value co-occurrences categorical features. can compute feature crosses like arbitrary sets categorical features – just tuples two features. resulting co-occurences hashed fixed-sized vector, don’t need worry whether co-occurence space large.","code":"feature_space <- layer_feature_space(   features = list(     # Categorical features encoded as integers     sex = \"integer_categorical\",     cp = \"integer_categorical\",     fbs = \"integer_categorical\",     restecg = \"integer_categorical\",     exang = \"integer_categorical\",     ca = \"integer_categorical\",     # Categorical feature encoded as string     thal = \"string_categorical\",     # Numerical features to discretize     age = \"float_discretized\",     # Numerical features to normalize     trestbps = \"float_normalized\",     chol = \"float_normalized\",     thalach = \"float_normalized\",     oldpeak = \"float_normalized\",     slope = \"float_normalized\"   ),   # We create additional features by hashing   # value co-occurrences for the   # following groups of categorical features.   crosses = list(c(\"sex\", \"age\"), c(\"thal\", \"ca\")),   # The hashing space for these co-occurrences   # wil be 32-dimensional.   crossing_dim = 32,   # Our utility will one-hot encode all categorical   # features and concat all features into a single   # vector (one vector per sample).   output_mode = \"concat\" )"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"further-customizing-a-featurespace","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Further customizing a FeatureSpace","title":"Structured data classification with FeatureSpace","text":"Specifying feature type via string name quick easy, sometimes may want configure preprocessing feature. instance, case, categorical features don’t large set possible values – ’s handful values per feature (e.g. 1 0 feature \"FBS\"), possible values represented training set. result, don’t need reserve index represent “vocabulary” values features – default behavior. , just specify num_oov_indices=0 features tell feature preprocessor skip “vocabulary” indexing. customizations access include specifying number bins discretizing features type \"float_discretized\", dimensionality hashing space feature crossing.","code":"feature_space <- layer_feature_space(   features = list(     # Categorical features encoded as integers     sex       = feature_integer_categorical(num_oov_indices = 0),     cp        = feature_integer_categorical(num_oov_indices = 0),     fbs       = feature_integer_categorical(num_oov_indices = 0),     restecg   = feature_integer_categorical(num_oov_indices = 0),     exang     = feature_integer_categorical(num_oov_indices = 0),     ca        = feature_integer_categorical(num_oov_indices = 0),     # Categorical feature encoded as string     thal      = feature_string_categorical(num_oov_indices = 0),     # Numerical features to discretize     age       = feature_float_discretized(num_bins = 30),     # Numerical features to normalize     trestbps  = feature_float_normalized(),     chol      = feature_float_normalized(),     thalach   = feature_float_normalized(),     oldpeak   = feature_float_normalized(),     slope     = feature_float_normalized()   ),   # Specify feature cross with a custom crossing dim.   crosses = list(     feature_cross(       feature_names = c(\"sex\", \"age\"),       crossing_dim = 64     ),     feature_cross(       feature_names = c(\"thal\", \"ca\"),       crossing_dim = 16     )   ),   output_mode = \"concat\" )"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"adapt-the-featurespace-to-the-training-data","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Adapt the FeatureSpace to the training data","title":"Structured data classification with FeatureSpace","text":"start using FeatureSpace build model, adapt training data. adapt(), FeatureSpace : Index set possible values categorical features. Compute mean variance numerical features normalize. Compute value boundaries different bins numerical features discretize. Note adapt() called tf_dataset yields dicts (named lists) feature values – labels. point, FeatureSpace can called dict raw feature values, return single concatenate vector sample, combining encoded features feature crosses.","code":"train_ds_with_no_labels <- train_ds |> dataset_map(\\(x, y) x) feature_space |> adapt(train_ds_with_no_labels) c(x, y) %<-% iter_next(as_iterator(train_ds)) preprocessed_x <- feature_space(x) preprocessed_x ## tf.Tensor( ## [[0. 0. 1. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  ... ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.]], shape=(32, 136), dtype=float32)"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"two-ways-to-manage-preprocessing-as-part-of-the-tf-data-pipeline-or-in-the-model-itself","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","title":"Structured data classification with FeatureSpace","text":"two ways can leverage FeatureSpace:","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"asynchronous-preprocessing-in-tf-data","dir":"Articles > Examples > Structured_data","previous_headings":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","what":"Asynchronous preprocessing in tf.data","title":"Structured data classification with FeatureSpace","text":"can make part data pipeline, model. enables asynchronous parallel preprocessing data CPU hits model. ’re training GPU TPU, want speed preprocessing. Usually, always right thing training.","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"synchronous-preprocessing-in-the-model","dir":"Articles > Examples > Structured_data","previous_headings":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","what":"Synchronous preprocessing in the model","title":"Structured data classification with FeatureSpace","text":"can make part model. means model expect dicts raw feature values, preprocessing batch done synchronously (blocking manner) rest forward pass. want end--end model can process raw feature values – keep mind model able run CPU, since types feature preprocessing (e.g. string preprocessing) GPU TPU compatible. GPU / TPU performance-sensitive settings. general, want -model preprocessing inference CPU. case, apply FeatureSpace tf.data pipeline training, inference end--end model includes FeatureSpace. Let’s create training validation dataset preprocessed batches:","code":"preprocessed_train_ds <- train_ds |>   dataset_map(\\(x, y) list(feature_space(x), y),               num_parallel_calls = tf$data$AUTOTUNE) |>   dataset_prefetch(tf$data$AUTOTUNE)  preprocessed_val_ds <- val_ds |>   dataset_map(\\(x, y) list(feature_space(x), y),               num_parallel_calls = tf$data$AUTOTUNE) |>   dataset_prefetch(tf$data$AUTOTUNE)"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"build-a-model","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Build a model","title":"Structured data classification with FeatureSpace","text":"Time build model – rather two models: training model expects preprocessed features (one sample = one vector) inference model expects raw features (one sample = dict raw feature values)","code":"dict_inputs <- feature_space$get_inputs() encoded_features <- feature_space$get_encoded_features()  predictions <- encoded_features |>   layer_dense(32, activation=\"relu\") |>   layer_dropout(0.5) |>   layer_dense(1, activation=\"sigmoid\")  training_model <- keras_model(inputs = encoded_features,                               outputs = predictions) training_model |> compile(optimizer = \"adam\",                           loss = \"binary_crossentropy\",                           metrics = \"accuracy\")  inference_model <- keras_model(inputs = dict_inputs,                                outputs = predictions)"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"train-the-model","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Train the model","title":"Structured data classification with FeatureSpace","text":"Let’s train model 20 epochs. Note feature preprocessing happening part tf.data pipeline, part model. quickly get 80% validation accuracy.","code":"training_model |> fit(   preprocessed_train_ds,   epochs = 20,   validation_data = preprocessed_val_ds,   verbose = 2 ) ## Epoch 1/20 ## 8/8 - 3s - 326ms/step - accuracy: 0.4315 - loss: 0.7427 - val_accuracy: 0.5333 - val_loss: 0.7048 ## Epoch 2/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.5311 - loss: 0.6984 - val_accuracy: 0.6500 - val_loss: 0.6438 ## Epoch 3/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.6473 - loss: 0.6316 - val_accuracy: 0.6833 - val_loss: 0.5937 ## Epoch 4/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.6639 - loss: 0.6134 - val_accuracy: 0.7500 - val_loss: 0.5524 ## Epoch 5/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7178 - loss: 0.5820 - val_accuracy: 0.7667 - val_loss: 0.5176 ## Epoch 6/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.7718 - loss: 0.5573 - val_accuracy: 0.7500 - val_loss: 0.4897 ## Epoch 7/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7718 - loss: 0.5200 - val_accuracy: 0.8167 - val_loss: 0.4640 ## Epoch 8/20 ## 8/8 - 0s - 14ms/step - accuracy: 0.7759 - loss: 0.5068 - val_accuracy: 0.8167 - val_loss: 0.4388 ## Epoch 9/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8174 - loss: 0.4724 - val_accuracy: 0.8333 - val_loss: 0.4162 ## Epoch 10/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8050 - loss: 0.4545 - val_accuracy: 0.8167 - val_loss: 0.3960 ## Epoch 11/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8091 - loss: 0.4514 - val_accuracy: 0.8500 - val_loss: 0.3786 ## Epoch 12/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8423 - loss: 0.4291 - val_accuracy: 0.8500 - val_loss: 0.3647 ## Epoch 13/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8465 - loss: 0.4028 - val_accuracy: 0.8667 - val_loss: 0.3499 ## Epoch 14/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8340 - loss: 0.4037 - val_accuracy: 0.8667 - val_loss: 0.3369 ## Epoch 15/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8548 - loss: 0.3928 - val_accuracy: 0.8667 - val_loss: 0.3289 ## Epoch 16/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8589 - loss: 0.3745 - val_accuracy: 0.8667 - val_loss: 0.3206 ## Epoch 17/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8257 - loss: 0.3820 - val_accuracy: 0.8667 - val_loss: 0.3129 ## Epoch 18/20 ## 8/8 - 0s - 14ms/step - accuracy: 0.8631 - loss: 0.3650 - val_accuracy: 0.8667 - val_loss: 0.3079 ## Epoch 19/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8382 - loss: 0.3635 - val_accuracy: 0.8667 - val_loss: 0.3024 ## Epoch 20/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8631 - loss: 0.3524 - val_accuracy: 0.8833 - val_loss: 0.2970"},{"path":"https://keras.posit.co/articles/examples/structured_data/structured_data_classification_with_feature_space.html","id":"inference-on-new-data-with-the-end-to-end-model","dir":"Articles > Examples > Structured_data","previous_headings":"","what":"Inference on new data with the end-to-end model","title":"Structured data classification with FeatureSpace","text":"Now, can use inference model (includes FeatureSpace) make predictions based dicts raw features values, follows:","code":"sample <- list(   age = 60,   sex = 1,   cp = 1,   trestbps = 145,   chol = 233,   fbs = 1,   restecg = 2,   thalach = 150,   exang = 0,   oldpeak = 2.3,   slope = 3,   ca = 0,   thal = \"fixed\" )  input_dict <- lapply(sample, \\(x) op_convert_to_tensor(array(x))) predictions <- inference_model |> predict(input_dict) ## 1/1 - 0s - 257ms/step glue::glue(r\"---(   This particular patient had a {(100 * predictions) |> signif(3)}% probability   of having a heart disease, as evaluated by our model. )---\") ## This particular patient had a 49.7% probability ## of having a heart disease, as evaluated by our model."},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"introduction","dir":"Articles > Examples","previous_headings":"","what":"Introduction","title":"Structured data classification with FeatureSpace","text":"example demonstrates structured data classification (also known tabular data classification), starting raw CSV file. data includes numerical features, integer categorical features, string categorical features. use utility layer_feature_space() index, preprocess, encode features. code adapted example Structured data classification scratch. previous example managed low-level feature preprocessing encoding Keras preprocessing layers, example delegate everything layer_feature_space(), making workflow extremely quick easy.","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"the-dataset","dir":"Articles > Examples","previous_headings":"Introduction","what":"The dataset","title":"Structured data classification with FeatureSpace","text":"dataset provided Cleveland Clinic Foundation Heart Disease. ’s CSV file 303 rows. row contains information patient (sample), column describes attribute patient (feature). use features predict whether patient heart disease (binary classification). ’s description feature:","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"setup","dir":"Articles > Examples","previous_headings":"","what":"Setup","title":"Structured data classification with FeatureSpace","text":"","code":"library(readr) library(dplyr, warn.conflicts = FALSE) library(keras3) library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")  conflicted::conflicts_prefer(   keras3::shape(),   keras3::set_random_seed(),   dplyr::filter(),   .quiet = TRUE )  use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"preparing-the-data","dir":"Articles > Examples","previous_headings":"","what":"Preparing the data","title":"Structured data classification with FeatureSpace","text":"Let’s download data load Pandas dataframe: dataset includes 303 samples 14 columns per sample (13 features, plus target label) ’s preview samples: last column, “target”, indicates whether patient heart disease (1) (0). Let’s split data training validation set: Let’s generate tf_dataset objects dataframe: tf_dataset yields tuple (input, target) input dictionary (named list) features target value 0 1: Let’s batch datasets:","code":"file_url <-   \"http://storage.googleapis.com/download.tensorflow.org/data/heart.csv\" df <- read_csv(file_url, col_types = cols(   oldpeak = col_double(),   thal = col_character(),   .default = col_integer() ))  # the dataset has two malformed rows, filter them out df <- df |> filter(!thal %in% c(\"1\", \"2\")) glimpse(df) ## Rows: 301 ## Columns: 14 ## $ age      <int> 63, 67, 67, 37, 41, 56, 62, 57, 63, 53, 57, 56, 56, 44, 5… ## $ sex      <int> 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, … ## $ cp       <int> 1, 4, 4, 3, 2, 2, 4, 4, 4, 4, 4, 2, 3, 2, 3, 3, 2, 4, 3, … ## $ trestbps <int> 145, 160, 120, 130, 130, 120, 140, 120, 130, 140, 140, 14… ## $ chol     <int> 233, 286, 229, 250, 204, 236, 268, 354, 254, 203, 192, 29… ## $ fbs      <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, … ## $ restecg  <int> 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, … ## $ thalach  <int> 150, 108, 129, 187, 172, 178, 160, 163, 147, 155, 148, 15… ## $ exang    <int> 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, … ## $ oldpeak  <dbl> 2.3, 1.5, 2.6, 3.5, 1.4, 0.8, 3.6, 0.6, 1.4, 3.1, 0.4, 1.… ## $ slope    <int> 3, 2, 2, 3, 1, 1, 3, 1, 2, 3, 2, 2, 2, 1, 1, 1, 3, 1, 1, … ## $ ca       <int> 0, 3, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, … ## $ thal     <chr> \"fixed\", \"normal\", \"reversible\", \"normal\", \"normal\", \"nor… ## $ target   <int> 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, … df ## # A tibble: 301 × 14 ##      age   sex    cp trestbps  chol   fbs restecg thalach exang oldpeak ##    <int> <int> <int>    <int> <int> <int>   <int>   <int> <int>   <dbl> ##  1    63     1     1      145   233     1       2     150     0     2.3 ##  2    67     1     4      160   286     0       2     108     1     1.5 ##  3    67     1     4      120   229     0       2     129     1     2.6 ##  4    37     1     3      130   250     0       0     187     0     3.5 ##  5    41     0     2      130   204     0       2     172     0     1.4 ##  6    56     1     2      120   236     0       0     178     0     0.8 ##  7    62     0     4      140   268     0       2     160     0     3.6 ##  8    57     0     4      120   354     0       0     163     1     0.6 ##  9    63     1     4      130   254     0       2     147     0     1.4 ## 10    53     1     4      140   203     1       2     155     1     3.1 ## # ℹ 291 more rows ## # ℹ 4 more variables: slope <int>, ca <int>, thal <chr>, target <int> val_idx <- nrow(df) %>% sample.int(., . * 0.2) val_df <- df[val_idx, ] train_df <- df[-val_idx, ]  cat(sprintf(   \"Using %d samples for training and %d for validation\",   nrow(train_df), nrow(val_df) )) ## Using 241 samples for training and 60 for validation dataframe_to_dataset <- function(df) {   labels <- df |> pull(target) |> as.integer()   inputs <- df |> select(-target) |> as.list()    ds <- tensor_slices_dataset(list(inputs, labels)) |>     dataset_shuffle(nrow(df))    ds }  train_ds <- dataframe_to_dataset(train_df) val_ds <- dataframe_to_dataset(val_df) c(x, y) %<-% iter_next(as_iterator(train_ds)) cat(\"Input: \"); str(x) cat(\"Target: \"); str(y) ## Input: List of 13 ##  $ age     :<tf.Tensor: shape=(), dtype=int32, numpy=57> ##  $ sex     :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ cp      :<tf.Tensor: shape=(), dtype=int32, numpy=4> ##  $ trestbps:<tf.Tensor: shape=(), dtype=int32, numpy=140> ##  $ chol    :<tf.Tensor: shape=(), dtype=int32, numpy=241> ##  $ fbs     :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ restecg :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ thalach :<tf.Tensor: shape=(), dtype=int32, numpy=123> ##  $ exang   :<tf.Tensor: shape=(), dtype=int32, numpy=1> ##  $ oldpeak :<tf.Tensor: shape=(), dtype=float32, numpy=0.2> ##  $ slope   :<tf.Tensor: shape=(), dtype=int32, numpy=2> ##  $ ca      :<tf.Tensor: shape=(), dtype=int32, numpy=0> ##  $ thal    :<tf.Tensor: shape=(), dtype=string, numpy=b'reversible'> ## Target: <tf.Tensor: shape=(), dtype=int32, numpy=0> train_ds <- train_ds |> dataset_batch(32) val_ds <- val_ds |> dataset_batch(32)"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"configuring-a-featurespace","dir":"Articles > Examples","previous_headings":"","what":"Configuring a FeatureSpace","title":"Structured data classification with FeatureSpace","text":"configure feature preprocessed, instantiate layer_feature_space, pass dictionary (named list unique names) maps name features string describes feature type. “integer categorical” features \"FBS\", one “string categorical” feature (\"thal\"), numerical features, ’d like normalize – except \"age\", ’d like discretize number bins. also use crosses argument capture feature interactions categorical features, say, create additional features represent value co-occurrences categorical features. can compute feature crosses like arbitrary sets categorical features – just tuples two features. resulting co-occurences hashed fixed-sized vector, don’t need worry whether co-occurence space large.","code":"feature_space <- layer_feature_space(   features = list(     # Categorical features encoded as integers     sex = \"integer_categorical\",     cp = \"integer_categorical\",     fbs = \"integer_categorical\",     restecg = \"integer_categorical\",     exang = \"integer_categorical\",     ca = \"integer_categorical\",     # Categorical feature encoded as string     thal = \"string_categorical\",     # Numerical features to discretize     age = \"float_discretized\",     # Numerical features to normalize     trestbps = \"float_normalized\",     chol = \"float_normalized\",     thalach = \"float_normalized\",     oldpeak = \"float_normalized\",     slope = \"float_normalized\"   ),   # We create additional features by hashing   # value co-occurrences for the   # following groups of categorical features.   crosses = list(c(\"sex\", \"age\"), c(\"thal\", \"ca\")),   # The hashing space for these co-occurrences   # wil be 32-dimensional.   crossing_dim = 32,   # Our utility will one-hot encode all categorical   # features and concat all features into a single   # vector (one vector per sample).   output_mode = \"concat\" )"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"further-customizing-a-featurespace","dir":"Articles > Examples","previous_headings":"","what":"Further customizing a FeatureSpace","title":"Structured data classification with FeatureSpace","text":"Specifying feature type via string name quick easy, sometimes may want configure preprocessing feature. instance, case, categorical features don’t large set possible values – ’s handful values per feature (e.g. 1 0 feature \"FBS\"), possible values represented training set. result, don’t need reserve index represent “vocabulary” values features – default behavior. , just specify num_oov_indices=0 features tell feature preprocessor skip “vocabulary” indexing. customizations access include specifying number bins discretizing features type \"float_discretized\", dimensionality hashing space feature crossing.","code":"feature_space <- layer_feature_space(   features = list(     # Categorical features encoded as integers     sex       = feature_integer_categorical(num_oov_indices = 0),     cp        = feature_integer_categorical(num_oov_indices = 0),     fbs       = feature_integer_categorical(num_oov_indices = 0),     restecg   = feature_integer_categorical(num_oov_indices = 0),     exang     = feature_integer_categorical(num_oov_indices = 0),     ca        = feature_integer_categorical(num_oov_indices = 0),     # Categorical feature encoded as string     thal      = feature_string_categorical(num_oov_indices = 0),     # Numerical features to discretize     age       = feature_float_discretized(num_bins = 30),     # Numerical features to normalize     trestbps  = feature_float_normalized(),     chol      = feature_float_normalized(),     thalach   = feature_float_normalized(),     oldpeak   = feature_float_normalized(),     slope     = feature_float_normalized()   ),   # Specify feature cross with a custom crossing dim.   crosses = list(     feature_cross(       feature_names = c(\"sex\", \"age\"),       crossing_dim = 64     ),     feature_cross(       feature_names = c(\"thal\", \"ca\"),       crossing_dim = 16     )   ),   output_mode = \"concat\" )"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"adapt-the-featurespace-to-the-training-data","dir":"Articles > Examples","previous_headings":"","what":"Adapt the FeatureSpace to the training data","title":"Structured data classification with FeatureSpace","text":"start using FeatureSpace build model, adapt training data. adapt(), FeatureSpace : Index set possible values categorical features. Compute mean variance numerical features normalize. Compute value boundaries different bins numerical features discretize. Note adapt() called tf_dataset yields dicts (named lists) feature values – labels. point, FeatureSpace can called dict raw feature values, return single concatenate vector sample, combining encoded features feature crosses.","code":"train_ds_with_no_labels <- train_ds |> dataset_map(\\(x, y) x) feature_space |> adapt(train_ds_with_no_labels) c(x, y) %<-% iter_next(as_iterator(train_ds)) preprocessed_x <- feature_space(x) preprocessed_x ## tf.Tensor( ## [[0. 0. 0. ... 1. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  ... ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.] ##  [0. 0. 0. ... 0. 0. 0.]], shape=(32, 136), dtype=float32)"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"two-ways-to-manage-preprocessing-as-part-of-the-tf-data-pipeline-or-in-the-model-itself","dir":"Articles > Examples","previous_headings":"","what":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","title":"Structured data classification with FeatureSpace","text":"two ways can leverage FeatureSpace:","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"asynchronous-preprocessing-in-tf-data","dir":"Articles > Examples","previous_headings":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","what":"Asynchronous preprocessing in tf.data","title":"Structured data classification with FeatureSpace","text":"can make part data pipeline, model. enables asynchronous parallel preprocessing data CPU hits model. ’re training GPU TPU, want speed preprocessing. Usually, always right thing training.","code":""},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"synchronous-preprocessing-in-the-model","dir":"Articles > Examples","previous_headings":"Two ways to manage preprocessing: as part of the tf.data pipeline, or in the model itself","what":"Synchronous preprocessing in the model","title":"Structured data classification with FeatureSpace","text":"can make part model. means model expect dicts raw feature values, preprocessing batch done synchronously (blocking manner) rest forward pass. want end--end model can process raw feature values – keep mind model able run CPU, since types feature preprocessing (e.g. string preprocessing) GPU TPU compatible. GPU / TPU performance-sensitive settings. general, want -model preprocessing inference CPU. case, apply FeatureSpace tf.data pipeline training, inference end--end model includes FeatureSpace. Let’s create training validation dataset preprocessed batches:","code":"preprocessed_train_ds <- train_ds |>   dataset_map(\\(x, y) list(feature_space(x), y),               num_parallel_calls = tf$data$AUTOTUNE) |>   dataset_prefetch(tf$data$AUTOTUNE)  preprocessed_val_ds <- val_ds |>   dataset_map(\\(x, y) list(feature_space(x), y),               num_parallel_calls = tf$data$AUTOTUNE) |>   dataset_prefetch(tf$data$AUTOTUNE)"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"build-a-model","dir":"Articles > Examples","previous_headings":"","what":"Build a model","title":"Structured data classification with FeatureSpace","text":"Time build model – rather two models: training model expects preprocessed features (one sample = one vector) inference model expects raw features (one sample = dict raw feature values)","code":"dict_inputs <- feature_space$get_inputs() encoded_features <- feature_space$get_encoded_features()  predictions <- encoded_features |>   layer_dense(32, activation=\"relu\") |>   layer_dropout(0.5) |>   layer_dense(1, activation=\"sigmoid\")  training_model <- keras_model(inputs = encoded_features,                               outputs = predictions) training_model |> compile(optimizer = \"adam\",                           loss = \"binary_crossentropy\",                           metrics = \"accuracy\")  inference_model <- keras_model(inputs = dict_inputs,                                outputs = predictions)"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"train-the-model","dir":"Articles > Examples","previous_headings":"","what":"Train the model","title":"Structured data classification with FeatureSpace","text":"Let’s train model 20 epochs. Note feature preprocessing happening part tf.data pipeline, part model. quickly get 80% validation accuracy.","code":"training_model |> fit(   preprocessed_train_ds,   epochs = 20,   validation_data = preprocessed_val_ds,   verbose = 2 ) ## Epoch 1/20 ## 8/8 - 3s - 343ms/step - accuracy: 0.5685 - loss: 0.7120 - val_accuracy: 0.6500 - val_loss: 0.6607 ## Epoch 2/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.6722 - loss: 0.6131 - val_accuracy: 0.6500 - val_loss: 0.6212 ## Epoch 3/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7137 - loss: 0.5685 - val_accuracy: 0.6500 - val_loss: 0.5961 ## Epoch 4/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.7718 - loss: 0.4981 - val_accuracy: 0.6667 - val_loss: 0.5758 ## Epoch 5/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7635 - loss: 0.4804 - val_accuracy: 0.6500 - val_loss: 0.5575 ## Epoch 6/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.7676 - loss: 0.4739 - val_accuracy: 0.6667 - val_loss: 0.5309 ## Epoch 7/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.7842 - loss: 0.4141 - val_accuracy: 0.6667 - val_loss: 0.5161 ## Epoch 8/20 ## 8/8 - 0s - 13ms/step - accuracy: 0.8133 - loss: 0.3841 - val_accuracy: 0.6667 - val_loss: 0.5080 ## Epoch 9/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8133 - loss: 0.3997 - val_accuracy: 0.6667 - val_loss: 0.4980 ## Epoch 10/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8340 - loss: 0.3618 - val_accuracy: 0.6833 - val_loss: 0.4882 ## Epoch 11/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8548 - loss: 0.3335 - val_accuracy: 0.6833 - val_loss: 0.4814 ## Epoch 12/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8465 - loss: 0.3535 - val_accuracy: 0.6833 - val_loss: 0.4818 ## Epoch 13/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8340 - loss: 0.3363 - val_accuracy: 0.7000 - val_loss: 0.4755 ## Epoch 14/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8548 - loss: 0.3224 - val_accuracy: 0.7333 - val_loss: 0.4745 ## Epoch 15/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8714 - loss: 0.3083 - val_accuracy: 0.7500 - val_loss: 0.4813 ## Epoch 16/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8797 - loss: 0.3270 - val_accuracy: 0.7500 - val_loss: 0.4690 ## Epoch 17/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8631 - loss: 0.2955 - val_accuracy: 0.7500 - val_loss: 0.4782 ## Epoch 18/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8797 - loss: 0.2831 - val_accuracy: 0.7667 - val_loss: 0.4818 ## Epoch 19/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8797 - loss: 0.2872 - val_accuracy: 0.7667 - val_loss: 0.4767 ## Epoch 20/20 ## 8/8 - 0s - 12ms/step - accuracy: 0.8631 - loss: 0.2877 - val_accuracy: 0.7833 - val_loss: 0.4781"},{"path":"https://keras.posit.co/articles/examples/structured_data_classification_with_feature_space.html","id":"inference-on-new-data-with-the-end-to-end-model","dir":"Articles > Examples","previous_headings":"","what":"Inference on new data with the end-to-end model","title":"Structured data classification with FeatureSpace","text":"Now, can use inference model (includes FeatureSpace) make predictions based dicts raw features values, follows:","code":"sample <- list(   age = 60,   sex = 1,   cp = 1,   trestbps = 145,   chol = 233,   fbs = 1,   restecg = 2,   thalach = 150,   exang = 0,   oldpeak = 2.3,   slope = 3,   ca = 0,   thal = \"fixed\" )  input_dict <- lapply(sample, \\(x) op_convert_to_tensor(array(x))) predictions <- inference_model |> predict(input_dict) ## 1/1 - 1s - 548ms/step glue::glue(r\"---(   This particular patient had a {(100 * predictions) |> signif(3)}% probability   of having a heart disease, as evaluated by our model. )---\") ## This particular patient had a 55% probability ## of having a heart disease, as evaluated by our model."},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"introduction","dir":"Articles > Examples","previous_headings":"","what":"Introduction","title":"Text classification from scratch","text":"example shows text classification starting raw text (set text files disk). demonstrate workflow IMDB sentiment classification dataset (unprocessed version). use [layer_text_vectorization()] word splitting & indexing.","code":""},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"setup","dir":"Articles > Examples","previous_headings":"","what":"Setup","title":"Text classification from scratch","text":"","code":"options(conflicts.policy = \"strict\") library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\") library(keras3) use_virtualenv(\"r-keras\")"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"load-the-data-imdb-movie-review-sentiment-classification","dir":"Articles > Examples","previous_headings":"","what":"Load the data: IMDB movie review sentiment classification","title":"Text classification from scratch","text":"Let’s download data inspect structure. aclImdb folder contains train test subfolder: aclImdb/train/pos aclImdb/train/neg folders contain text files, represents one review (either positive negative): interested pos neg subfolders, let’s delete subfolder text files : can use utility text_dataset_from_directory() generate labeled tf_dataset object set text files disk filed class-specific folders. Let’s use generate training, validation, test datasets. validation training datasets generated two subsets train directory, 20% samples going validation dataset 80% going training dataset. validation dataset addition test dataset useful tuning hyperparameters, model architecture, test dataset used. putting model real world however, retrained using available training data (without creating validation dataset), performance maximized. using validation_split subset arguments, make sure either specify random seed, pass shuffle=FALSE, validation & training splits get overlap. Let’s preview samples:","code":"if (!dir.exists(\"datasets/aclImdb\")) {   dir.create(\"datasets\")   download.file(     \"https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\",     \"datasets/aclImdb_v1.tar.gz\"   )   untar(\"datasets/aclImdb_v1.tar.gz\", exdir = \"datasets\")   unlink(\"datasets/aclImdb/train/unsup\", recursive = TRUE) } head(list.files(\"datasets/aclImdb/test\")) ## [1] \"labeledBow.feat\" \"neg\"             \"pos\"             \"urls_neg.txt\" ## [5] \"urls_pos.txt\" head(list.files(\"datasets/aclImdb/train\")) ## [1] \"labeledBow.feat\" \"neg\"             \"pos\"             \"unsupBow.feat\" ## [5] \"urls_neg.txt\"    \"urls_pos.txt\" cat(readLines(\"datasets/aclImdb/train/pos/6248_7.txt\")) ## Being an Austrian myself this has been a straight knock in my face. Fortunately I don't live nowhere near the place where this movie takes place but unfortunately it portrays everything that the rest of Austria hates about Viennese people (or people close to that region). And it is very easy to read that this is exactly the directors intention: to let your head sink into your hands and say \"Oh my god, how can THAT be possible!\". No, not with me, the (in my opinion) totally exaggerated uncensored swinger club scene is not necessary, I watch porn, sure, but in this context I was rather disgusted than put in the right context.<br /><br />This movie tells a story about how misled people who suffer from lack of education or bad company try to survive and live in a world of redundancy and boring horizons. A girl who is treated like a whore by her super-jealous boyfriend (and still keeps coming back), a female teacher who discovers her masochism by putting the life of her super-cruel \"lover\" on the line, an old couple who has an almost mathematical daily cycle (she is the \"official replacement\" of his ex wife), a couple that has just divorced and has the ex husband suffer under the acts of his former wife obviously having a relationship with her masseuse and finally a crazy hitchhiker who asks her drivers the most unusual questions and stretches their nerves by just being super-annoying.<br /><br />After having seen it you feel almost nothing. You're not even shocked, sad, depressed or feel like doing anything... Maybe that's why I gave it 7 points, it made me react in a way I never reacted before. If that's good or bad is up to you! unlink(\"datasets/aclImdb/train/unsup\", recursive = TRUE) batch_size <- 32  raw_train_ds <- text_dataset_from_directory(   \"datasets/aclImdb/train\",   batch_size = batch_size,   validation_split = 0.2,   subset = \"training\",   seed = 1337 ) ## Found 25000 files belonging to 2 classes. ## Using 20000 files for training. raw_val_ds <- text_dataset_from_directory(   \"datasets/aclImdb/train\",   batch_size = batch_size,   validation_split = 0.2,   subset = \"validation\",   seed = 1337 ) ## Found 25000 files belonging to 2 classes. ## Using 5000 files for validation. raw_test_ds <- text_dataset_from_directory(   \"datasets/aclImdb/test\",   batch_size = batch_size ) ## Found 25000 files belonging to 2 classes. cat(\"Number of batches in raw_train_ds:\", length(raw_train_ds), \"\\n\") ## Number of batches in raw_train_ds: 625 cat(\"Number of batches in raw_val_ds:\", length(raw_val_ds), \"\\n\") ## Number of batches in raw_val_ds: 157 cat(\"Number of batches in raw_test_ds:\", length(raw_test_ds), \"\\n\") ## Number of batches in raw_test_ds: 782 # It's important to take a look at your raw data to ensure your normalization # and tokenization will work as expected. We can do that by taking a few # examples from the training set and looking at them. # This is one of the places where eager execution shines: # we can just evaluate these tensors using .numpy() # instead of needing to evaluate them in a Session/Graph context. batch <- iter_next(as_iterator(raw_train_ds)) str(batch) ## List of 2 ##  $ :<tf.Tensor: shape=(32), dtype=string, numpy=…> ##  $ :<tf.Tensor: shape=(32), dtype=int32, numpy=…> c(text_batch, label_batch) %<-% batch for (i in 1:3) {   print(text_batch[i])   print(label_batch[i]) } ## tf.Tensor(b\"I have read the novel Reaper of Ben Mezrich a fews years ago and last night I accidentally came to see this adaption.<br /><br />Although it's been years since I read the story the first time, the differences between the novel and the movie are humongous. Very important elements, which made the whole thing plausible are just written out or changed to bad.<br /><br />If the plot sounds interesting to you: go and get the novel. Its much, much, much better.<br /><br />Still 4 out of 10 since it was hard to stop watching because of the great basic plot by Ben Mezrich.\", shape=(), dtype=string) ## tf.Tensor(0, shape=(), dtype=int32) ## tf.Tensor(b'After seeing all the Jesse James, Quantrill, jayhawkers,etc films in the fifties, it is quite a thrill to see this film with a new perspective by director Ang Lee. The scene of the attack of Lawrence, Kansas is awesome. The romantic relationship between Jewel and Toby Mcguire turns out to be one of the best parts and Jonathan Rhys-Meyers is outstanding as the bad guy. All the time this film makes you feel the horror of war, and the desperate situation of the main characters who do not know if they are going to survive the next hours. Definitely worth seeing.', shape=(), dtype=string) ## tf.Tensor(1, shape=(), dtype=int32) ## tf.Tensor(b'AG was an excellent presentation of drama, suspense and thriller that is so rare to American TV. Sheriff Lucas gave many a viewer the willies. We rooted for Caleb as he strove to resist the overtures of Sheriff Lucas. We became engrossed and fearful upon learning of the unthinkable connection between these two characters. The manipulations which weekly gave cause to fear what Lucas would do next were truly surprising. This show lived up to the \"Gothic\" moniker in ways American entertainment has so seldom attempted, much less mastered. The suits definitely made a big mistake in not supporting this show. This show puts shame to the current glut of \"reality\" shows- which are so less than satisfying viewing.The call for a DVD box set is well based. This show is quality viewing for a discerning market hungry for quality viewing. A public that is tiring of over-saturation of mind-numbing reality fare will welcome this gem of real storytelling. Bring on the DVD box set!!', shape=(), dtype=string) ## tf.Tensor(1, shape=(), dtype=int32)"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"prepare-the-data","dir":"Articles > Examples","previous_headings":"","what":"Prepare the data","title":"Text classification from scratch","text":"particular, remove <br /> tags.","code":"# Having looked at our data above, we see that the raw text contains HTML break # tags of the form '<br />'. These tags will not be removed by the default # standardizer (which doesn't strip HTML). Because of this, we will need to # create a custom standardization function. custom_standardization_fn <- function(string_tensor) {   string_tensor |>     tf$strings$lower() |> # convert to all lowercase     tf$strings$regex_replace(\"<br />\", \" \") |> # remove '<br />' HTML tag     tf$strings$regex_replace(\"[[:punct:]]\", \"\") # remove punctuation }   # Model constants. max_features <- 20000 embedding_dim <- 128 sequence_length <- 500  # Now that we have our custom standardization, we can instantiate our text # vectorization layer. We are using this layer to normalize, split, and map # strings to integers, so we set our 'output_mode' to 'int'. # Note that we're using the default split function, # and the custom standardization defined above. # We also set an explicit maximum sequence length, since the CNNs later in our # model won't support ragged sequences. vectorize_layer <- layer_text_vectorization(   standardize = custom_standardization_fn,   max_tokens = max_features,   output_mode = \"int\",   output_sequence_length = sequence_length, )  # Now that the vectorize_layer has been created, call `adapt` on a text-only # dataset to create the vocabulary. You don't have to batch, but for very large # datasets this means you're not keeping spare copies of the dataset in memory.  # Let's make a text-only dataset (no labels): text_ds <- raw_train_ds |>   dataset_map(\\(x, y) x) # Let's call `adapt`: vectorize_layer |> adapt(text_ds)"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"two-options-to-vectorize-the-data","dir":"Articles > Examples","previous_headings":"","what":"Two options to vectorize the data","title":"Text classification from scratch","text":"2 ways can use text vectorization layer: Option 1: Make part model, obtain model processes raw strings, like : Option 2: Apply text dataset obtain dataset word indices, feed model expects integer sequences inputs. important difference two option 2 enables asynchronous CPU processing buffering data training GPU. ’re training model GPU, probably want go option get best performance. . export model production, ’d ship model accepts raw strings input, like code snippet option 1 . can done training. last section.","code":"text_input <- keras_input(shape = c(1L), dtype = \"string\", name = 'text') x <- text_input |>   vectorize_layer() |>   layer_embedding(max_features + 1, embedding_dim) vectorize_text <- function(text, label) {   text <- text |>     op_expand_dims(-1) |>     vectorize_layer()   list(text, label) }  # Vectorize the data. train_ds <- raw_train_ds |> dataset_map(vectorize_text) val_ds   <- raw_val_ds   |> dataset_map(vectorize_text) test_ds  <- raw_test_ds  |> dataset_map(vectorize_text)  # Do async prefetching / buffering of the data for best performance on GPU. train_ds <- train_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10) val_ds <- val_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10) test_ds <- test_ds |>   dataset_cache() |>   dataset_prefetch(buffer_size = 10)"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"build-a-model","dir":"Articles > Examples","previous_headings":"","what":"Build a model","title":"Text classification from scratch","text":"choose simple 1D convnet starting Embedding layer.","code":"# A integer input for vocab indices. inputs <- keras_input(shape = c(NA), dtype = \"int64\")  predictions <- inputs |>   # Next, we add a layer to map those vocab indices into a space of dimensionality   # 'embedding_dim'.   layer_embedding(max_features, embedding_dim) |>   layer_dropout(0.5) |>   # Conv1D + global max pooling   layer_conv_1d(128, 7, padding = \"valid\", activation = \"relu\", strides = 3) |>   layer_conv_1d(128, 7, padding = \"valid\", activation = \"relu\", strides = 3) |>   layer_global_max_pooling_1d() |>   # We add a vanilla hidden layer:   layer_dense(128, activation = \"relu\") |>   layer_dropout(0.5) |>   # We project onto a single unit output layer, and squash it with a sigmoid:   layer_dense(1, activation = \"sigmoid\", name = \"predictions\")  model <- keras_model(inputs, predictions)  summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, None)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ embedding_1 (Embedding)         │ (None, None, 128)      │     2,560,000 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, None, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_1 (Conv1D)               │ (None, None, 128)      │       114,816 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d (Conv1D)                 │ (None, None, 128)      │       114,816 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling1d            │ (None, 128)            │             0 │ ## │ (GlobalMaxPooling1D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 128)            │        16,512 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 128)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ predictions (Dense)             │ (None, 1)              │           129 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 2,806,273 (10.71 MB) ##  Trainable params: 2,806,273 (10.71 MB) ##  Non-trainable params: 0 (0.00 B) # Compile the model with binary crossentropy loss and an adam optimizer. model |> compile(loss = \"binary_crossentropy\",                  optimizer = \"adam\",                  metrics = \"accuracy\")"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"train-the-model","dir":"Articles > Examples","previous_headings":"","what":"Train the model","title":"Text classification from scratch","text":"","code":"epochs <- 3  # Fit the model using the train and test datasets. model |> fit(train_ds, validation_data = val_ds, epochs = epochs) ## Epoch 1/3 ## 625/625 - 6s - 9ms/step - accuracy: 0.7327 - loss: 0.4894 - val_accuracy: 0.8638 - val_loss: 0.3162 ## Epoch 2/3 ## 625/625 - 2s - 2ms/step - accuracy: 0.9116 - loss: 0.2230 - val_accuracy: 0.8780 - val_loss: 0.3131 ## Epoch 3/3 ## 625/625 - 2s - 2ms/step - accuracy: 0.9603 - loss: 0.1118 - val_accuracy: 0.8646 - val_loss: 0.3735"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"evaluate-the-model-on-the-test-set","dir":"Articles > Examples","previous_headings":"","what":"Evaluate the model on the test set","title":"Text classification from scratch","text":"","code":"model |> evaluate(test_ds) ## 782/782 - 1s - 2ms/step - accuracy: 0.8532 - loss: 0.4071 ## $accuracy ## [1] 0.8532 ## ## $loss ## [1] 0.407085"},{"path":"https://keras.posit.co/articles/examples/text_classification_from_scratch.html","id":"make-an-end-to-end-model","dir":"Articles > Examples","previous_headings":"","what":"Make an end-to-end model","title":"Text classification from scratch","text":"want obtain model capable processing raw strings, can simply create new model (using weights just trained):","code":"# A string input inputs <- keras_input(shape = c(1), dtype = \"string\") # Turn strings into vocab indices indices <- vectorize_layer(inputs) # Turn vocab indices into predictions outputs <- model(indices)  # Our end to end model end_to_end_model <- keras_model(inputs, outputs) end_to_end_model |> compile(   loss = \"binary_crossentropy\",   optimizer = \"adam\",   metrics = c(\"accuracy\") )  # Test it with `raw_test_ds`, which yields raw strings end_to_end_model |> evaluate(raw_test_ds) ## 782/782 - 3s - 4ms/step - accuracy: 0.8532 - loss: 0.4063 ## $accuracy ## [1] 0.8532 ## ## $loss ## [1] 0.4062699"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"introduction","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Introduction","title":"Timeseries anomaly detection using an Autoencoder","text":"script demonstrates can use reconstruction convolutional autoencoder model detect anomalies timeseries data.","code":""},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"setup","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Setup","title":"Timeseries anomaly detection using an Autoencoder","text":"","code":"library(dplyr, warn.conflicts = FALSE) library(ggplot2) theme_set(theme_minimal())  library(listarrays) library(tfdatasets, exclude = c(\"shape\")) library(keras3)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"load-the-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Load the data","title":"Timeseries anomaly detection using an Autoencoder","text":"use Numenta Anomaly Benchmark(NAB) dataset. provides artificial timeseries data containing labeled anomalous periods behavior. Data ordered, timestamped, single-valued metrics. use art_daily_small_noise.csv file training art_daily_jumpsup.csv file testing. simplicity dataset allows us demonstrate anomaly detection.","code":"get_data <- function(url_suffix) {   url_root <- \"https://raw.githubusercontent.com/numenta/NAB/master/data/\"   url <- paste0(url_root, url_suffix)   file <- get_file(origin = url) # cache file locally   # parse csv; 2 columns with types: datetime (T), double (d)   readr::read_csv(file, col_types = \"Td\") }  df_small_noise   <- get_data(\"artificialNoAnomaly/art_daily_small_noise.csv\") df_daily_jumpsup <- get_data(\"artificialWithAnomaly/art_daily_jumpsup.csv\")"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"quick-look-at-the-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Quick look at the data","title":"Timeseries anomaly detection using an Autoencoder","text":"","code":"df_small_noise ## # A tibble: 4,032 × 2 ##    timestamp           value ##    <dttm>              <dbl> ##  1 2014-04-01 00:00:00  18.3 ##  2 2014-04-01 00:05:00  22.0 ##  3 2014-04-01 00:10:00  18.6 ##  4 2014-04-01 00:15:00  22.0 ##  5 2014-04-01 00:20:00  21.9 ##  6 2014-04-01 00:25:00  21.2 ##  7 2014-04-01 00:30:00  20.6 ##  8 2014-04-01 00:35:00  20.3 ##  9 2014-04-01 00:40:00  21.5 ## 10 2014-04-01 00:45:00  19.2 ## # ℹ 4,022 more rows df_daily_jumpsup ## # A tibble: 4,032 × 2 ##    timestamp           value ##    <dttm>              <dbl> ##  1 2014-04-01 00:00:00  19.8 ##  2 2014-04-01 00:05:00  20.5 ##  3 2014-04-01 00:10:00  20.0 ##  4 2014-04-01 00:15:00  21.5 ##  5 2014-04-01 00:20:00  20.2 ##  6 2014-04-01 00:25:00  19.9 ##  7 2014-04-01 00:30:00  21.7 ##  8 2014-04-01 00:35:00  20.9 ##  9 2014-04-01 00:40:00  18.4 ## 10 2014-04-01 00:45:00  18.7 ## # ℹ 4,022 more rows"},{"path":[]},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"timeseries-data-without-anomalies","dir":"Articles > Examples > Timeseries","previous_headings":"Visualize the data","what":"Timeseries data without anomalies","title":"Timeseries anomaly detection using an Autoencoder","text":"use following data training.","code":"plot_ts <- function(df) {   ggplot(df, aes(x = timestamp, y = value)) + geom_line() +     scale_x_datetime(date_breaks = \"1 day\", date_labels = \"%b-%d\") }  plot_ts(df_small_noise) + ggtitle(\"Without Anomaly\")"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"timeseries-data-with-anomalies","dir":"Articles > Examples > Timeseries","previous_headings":"Visualize the data","what":"Timeseries data with anomalies","title":"Timeseries anomaly detection using an Autoencoder","text":"use following data testing see sudden jump data detected anomaly.","code":"plot_ts(df_daily_jumpsup) + ggtitle(\"With Anomaly\")"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"prepare-training-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Prepare training data","title":"Timeseries anomaly detection using an Autoencoder","text":"Get data values training timeseries data file normalize value data. value every 5 mins 14 days. 24 * 60 / 5 = 288 timesteps per day 288 * 14 = 4032 data points total","code":"df_train <- df_small_noise |>   mutate(value = (value - mean(value)) / sd(value))  cat(\"Number of training samples:\", nrow(df_train), \"\\n\") ## Number of training samples: 4032"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"create-sequences","dir":"Articles > Examples > Timeseries","previous_headings":"Prepare training data","what":"Create sequences","title":"Timeseries anomaly detection using an Autoencoder","text":"Create sequences combining TIME_STEPS contiguous data values training data.","code":"TIME_STEPS <- 288  as_dataset <- function(df) {   x <- as.matrix(df$value)   ds <- timeseries_dataset_from_array(x, NULL, sequence_length = TIME_STEPS)   # Because the dataset is small, cast TF Dataset to an R array for convenience.   ds |> as_array_iterator() |> iterate() |> bind_on_rows() }  x_train <- as_dataset(df_train) writeLines(sprintf(\"Training input shape: (%s)\", toString(dim(x_train)))) ## Training input shape: (3745, 288, 1)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"build-a-model","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Build a model","title":"Timeseries anomaly detection using an Autoencoder","text":"build convolutional reconstruction autoencoder model. model take input shape (batch_size, sequence_length, num_features) return output shape. case, sequence_length 288 num_features 1.","code":"model <- keras_model_sequential(input_shape = c(TIME_STEPS, 1)) |>   layer_conv_1d(     filters = 32, kernel_size = 7, padding = \"same\",     strides = 2, activation = \"relu\"   ) |>   layer_dropout(rate = 0.2) |>   layer_conv_1d(     filters = 16, kernel_size = 7, padding = \"same\",     strides = 2, activation = \"relu\"   ) |>   layer_conv_1d_transpose(     filters = 16, kernel_size = 7, padding = \"same\",     strides = 2, activation = \"relu\"   ) |>   layer_dropout(rate = 0.2) |>   layer_conv_1d_transpose(     filters = 32, kernel_size = 7, padding = \"same\",     strides = 2, activation = \"relu\"   ) |>   layer_conv_1d_transpose(filters = 1, kernel_size = 7, padding = \"same\")  model |> compile(optimizer=optimizer_adam(learning_rate=0.001), loss=\"mse\") model ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv1d (Conv1D)                 │ (None, 144, 32)        │           256 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 144, 32)        │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_1 (Conv1D)               │ (None, 72, 16)         │         3,600 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_transpose                │ (None, 144, 16)        │         1,808 │ ## │ (Conv1DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, 144, 16)        │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_transpose_1              │ (None, 288, 32)        │         3,616 │ ## │ (Conv1DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv1d_transpose_2              │ (None, 288, 1)         │           225 │ ## │ (Conv1DTranspose)               │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 9,505 (37.13 KB) ##  Trainable params: 9,505 (37.13 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"train-the-model","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Train the model","title":"Timeseries anomaly detection using an Autoencoder","text":"Please note using x_train input target since reconstruction model. Let’s plot training validation loss see training went.","code":"history = model |> fit(   x_train, x_train,   epochs = 50,   validation_split = 0.1,   callbacks = c(     callback_early_stopping(       monitor = \"val_loss\", patience = 5, mode = \"min\"     )   ) ) ## Epoch 1/50 ## 106/106 - 7s - 62ms/step - loss: 0.1863 - val_loss: 0.0316 ## Epoch 2/50 ## 106/106 - 0s - 2ms/step - loss: 0.0333 - val_loss: 0.0233 ## Epoch 3/50 ## 106/106 - 0s - 2ms/step - loss: 0.0248 - val_loss: 0.0201 ## Epoch 4/50 ## 106/106 - 0s - 2ms/step - loss: 0.0209 - val_loss: 0.0186 ## Epoch 5/50 ## 106/106 - 0s - 2ms/step - loss: 0.0179 - val_loss: 0.0146 ## Epoch 6/50 ## 106/106 - 0s - 2ms/step - loss: 0.0150 - val_loss: 0.0113 ## Epoch 7/50 ## 106/106 - 0s - 2ms/step - loss: 0.0127 - val_loss: 0.0094 ## Epoch 8/50 ## 106/106 - 0s - 2ms/step - loss: 0.0109 - val_loss: 0.0088 ## Epoch 9/50 ## 106/106 - 0s - 3ms/step - loss: 0.0096 - val_loss: 0.0084 ## Epoch 10/50 ## 106/106 - 0s - 2ms/step - loss: 0.0086 - val_loss: 0.0069 ## Epoch 11/50 ## 106/106 - 0s - 2ms/step - loss: 0.0078 - val_loss: 0.0062 ## Epoch 12/50 ## 106/106 - 0s - 2ms/step - loss: 0.0073 - val_loss: 0.0058 ## Epoch 13/50 ## 106/106 - 0s - 2ms/step - loss: 0.0067 - val_loss: 0.0054 ## Epoch 14/50 ## 106/106 - 0s - 2ms/step - loss: 0.0063 - val_loss: 0.0051 ## Epoch 15/50 ## 106/106 - 0s - 2ms/step - loss: 0.0060 - val_loss: 0.0045 ## Epoch 16/50 ## 106/106 - 0s - 2ms/step - loss: 0.0058 - val_loss: 0.0039 ## Epoch 17/50 ## 106/106 - 0s - 2ms/step - loss: 0.0055 - val_loss: 0.0041 ## Epoch 18/50 ## 106/106 - 0s - 2ms/step - loss: 0.0052 - val_loss: 0.0036 ## Epoch 19/50 ## 106/106 - 0s - 2ms/step - loss: 0.0050 - val_loss: 0.0037 ## Epoch 20/50 ## 106/106 - 0s - 2ms/step - loss: 0.0048 - val_loss: 0.0033 ## Epoch 21/50 ## 106/106 - 0s - 2ms/step - loss: 0.0046 - val_loss: 0.0031 ## Epoch 22/50 ## 106/106 - 0s - 2ms/step - loss: 0.0044 - val_loss: 0.0032 ## Epoch 23/50 ## 106/106 - 0s - 2ms/step - loss: 0.0042 - val_loss: 0.0033 ## Epoch 24/50 ## 106/106 - 0s - 2ms/step - loss: 0.0040 - val_loss: 0.0031 ## Epoch 25/50 ## 106/106 - 0s - 2ms/step - loss: 0.0038 - val_loss: 0.0032 ## Epoch 26/50 ## 106/106 - 0s - 2ms/step - loss: 0.0036 - val_loss: 0.0031 ## Epoch 27/50 ## 106/106 - 0s - 2ms/step - loss: 0.0035 - val_loss: 0.0031 ## Epoch 28/50 ## 106/106 - 0s - 2ms/step - loss: 0.0033 - val_loss: 0.0025 ## Epoch 29/50 ## 106/106 - 0s - 2ms/step - loss: 0.0032 - val_loss: 0.0026 ## Epoch 30/50 ## 106/106 - 0s - 2ms/step - loss: 0.0031 - val_loss: 0.0029 ## Epoch 31/50 ## 106/106 - 0s - 2ms/step - loss: 0.0030 - val_loss: 0.0024 ## Epoch 32/50 ## 106/106 - 0s - 2ms/step - loss: 0.0029 - val_loss: 0.0025 ## Epoch 33/50 ## 106/106 - 0s - 2ms/step - loss: 0.0028 - val_loss: 0.0025 ## Epoch 34/50 ## 106/106 - 0s - 2ms/step - loss: 0.0027 - val_loss: 0.0022 ## Epoch 35/50 ## 106/106 - 0s - 2ms/step - loss: 0.0026 - val_loss: 0.0023 ## Epoch 36/50 ## 106/106 - 0s - 2ms/step - loss: 0.0026 - val_loss: 0.0025 ## Epoch 37/50 ## 106/106 - 0s - 2ms/step - loss: 0.0025 - val_loss: 0.0025 ## Epoch 38/50 ## 106/106 - 0s - 2ms/step - loss: 0.0025 - val_loss: 0.0023 ## Epoch 39/50 ## 106/106 - 0s - 2ms/step - loss: 0.0024 - val_loss: 0.0023 plot(history)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"detecting-anomalies","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Detecting anomalies","title":"Timeseries anomaly detection using an Autoencoder","text":"detect anomalies determining well model can reconstruct input data. Find MAE loss training samples. Find max MAE loss value. worst model performed trying reconstruct sample. make threshold anomaly detection. reconstruction loss sample greater threshold value can infer model seeing pattern isn’t familiar . label sample anomaly.","code":"# Get train MAE loss. x_train_pred <- model |> predict(x_train) ## 118/118 - 0s - 3ms/step train_mae_loss <- apply(abs(x_train_pred - x_train), 1, mean)  hist(train_mae_loss, breaks = 50) # Get reconstruction loss threshold. threshold <- max(train_mae_loss) cat(\"Reconstruction error threshold: \", threshold, \"\\n\") ## Reconstruction error threshold:  0.03930207"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"compare-recontruction","dir":"Articles > Examples > Timeseries","previous_headings":"Detecting anomalies","what":"Compare recontruction","title":"Timeseries anomaly detection using an Autoencoder","text":"Just fun, let’s see model recontructed first sample. 288 timesteps day 1 training dataset.","code":"# Checking how the first sequence is learnt plot(NULL, NULL, ylab = 'Value',      xlim = c(0, TIME_STEPS),      ylim = range(c(x_train[1,,], x_train_pred[1,,]))) lines(x_train[1,,]) lines(x_train_pred[1,,], col = 'red') legend(\"topleft\", lty = 1,        legend = c(\"actual\", \"predicted\"),        col = c(\"black\", \"red\"))"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"prepare-test-data","dir":"Articles > Examples > Timeseries","previous_headings":"Detecting anomalies","what":"Prepare test data","title":"Timeseries anomaly detection using an Autoencoder","text":"","code":"df_test <- df_daily_jumpsup |>   mutate(value =            (value - mean(df_small_noise$value)) /              sd(df_small_noise$value))  df_test |> head() plot_ts(df_test) # Create sequences from test values. x_test <- as_dataset(df_test)  # Get test MAE loss. x_test_pred <- model |> predict(x_test) test_mae_loss <- apply(abs(x_test_pred - x_test), 1, mean)  hist(test_mae_loss, breaks = 50, xlab = \"test MAE loss\", ylab = \"No of samples\") # Detect all the samples which are anomalies. anomalies <- test_mae_loss > threshold cat(\"Number of anomaly samples:\", sum(anomalies), \"\\n\") cat(\"Indices of anomaly samples:\", which(anomalies), \"\\n\", fill = TRUE) ## # A tibble: 6 × 2 ##   timestamp            value ##   <dttm>               <dbl> ## 1 2014-04-01 00:00:00 -0.808 ## 2 2014-04-01 00:05:00 -0.781 ## 3 2014-04-01 00:10:00 -0.801 ## 4 2014-04-01 00:15:00 -0.746 ## 5 2014-04-01 00:20:00 -0.792 ## 6 2014-04-01 00:25:00 -0.802 ## 118/118 - 0s - 763us/step ## Number of anomaly samples: 486 ## Indices of anomaly samples: 216 218 220 396 507 793 795 1659 1945 1946 1947 ## 1949 2013 2016 2017 2021 2025 2029 2033 2037 2041 2045 2046 2049 2052 2053 ## 2054 2056 2057 2058 2060 2061 2062 2064 2065 2066 2068 2069 2070 2072 2073 ## 2074 2076 2077 2078 2080 2081 2082 2084 2085 2086 2088 2089 2090 2092 2093 ## 2094 2096 2097 2098 2100 2101 2102 2105 2108 2109 2112 2113 2117 2121 2124 ## 2126 2129 2141 2145 2153 2521 2522 2523 2525 2538 2542 2546 2550 2698 2700 ## 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 ## 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 ## 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 ## 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 ## 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 ## 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 ## 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 ## 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 ## 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 ## 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 ## 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 ## 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 ## 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 ## 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 ## 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 ## 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 ## 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 ## 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 ## 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 ## 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 ## 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 ## 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 ## 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 ## 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 ## 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 ## 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 ## 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_anomaly_detection.html","id":"plot-anomalies","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Plot anomalies","title":"Timeseries anomaly detection using an Autoencoder","text":"now know samples data anomalies. , find corresponding timestamps original test data. using following method : Let’s say time_steps = 3 10 training values. x_train look like : 0, 1, 2 1, 2, 3 2, 3, 4 3, 4, 5 4, 5, 6 5, 6, 7 6, 7, 8 7, 8, 9 except initial final time_steps-1 data values, appear time_steps number samples. , know samples [(3, 4, 5), (4, 5, 6), (5, 6, 7)] anomalies, can say data point 5 anomaly. Let’s overlay anomalies original test data plot.","code":"is_anomaly <- test_mae_loss > threshold is_anomaly <- is_anomaly &   zoo::rollsum(is_anomaly, TIME_STEPS,                align = \"right\", na.pad = TRUE) >= TIME_STEPS  with(df_test, {   plot(value ~ timestamp, type = 'l', xaxt = 'n', las = 2)   axis.POSIXct(1, at = seq(timestamp[1], tail(timestamp, 1), by = \"days\"),                format = \"%b-%d\") })  with(df_test[which(is_anomaly),], {   points(value ~ timestamp, col = \"red\") })"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"introduction","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Introduction","title":"Timeseries classification from scratch","text":"example shows timeseries classification scratch, starting raw CSV timeseries files disk. demonstrate workflow FordA dataset UCR/UEA archive.","code":""},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"setup","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Setup","title":"Timeseries classification from scratch","text":"","code":"library(keras3) use_backend(\"jax\")"},{"path":[]},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"dataset-description","dir":"Articles > Examples > Timeseries","previous_headings":"Load the data: the FordA dataset","what":"Dataset description","title":"Timeseries classification from scratch","text":"dataset using called FordA. data comes UCR archive. dataset contains 3601 training instances another 1320 testing instances. timeseries corresponds measurement engine noise captured motor sensor. task, goal automatically detect presence specific issue engine. problem balanced binary classification task. full description dataset can found .","code":""},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"read-the-tsv-data","dir":"Articles > Examples > Timeseries","previous_headings":"Load the data: the FordA dataset","what":"Read the TSV data","title":"Timeseries classification from scratch","text":"use FordA_TRAIN file training FordA_TEST file testing. simplicity dataset allows us demonstrate effectively use ConvNets timeseries classification. file, first column corresponds label.","code":"get_data <- function(path) {   if(path |> startsWith(\"https://\"))     path <- get_file(origin = path)  # cache file locally    data <- readr::read_tsv(     path, col_names = FALSE,     # Each row is: one integer (the label),     # followed by 500 doubles (the timeseries)     col_types = paste0(\"i\", strrep(\"d\", 500))   )    y <- as.matrix(data[[1]])   x <- as.matrix(data[,-1])   dimnames(x) <- dimnames(y) <- NULL    list(x, y) }  root_url <- \"https://raw.githubusercontent.com/hfawaz/cd-diagram/master/FordA/\" c(x_train, y_train) %<-% get_data(paste0(root_url, \"FordA_TRAIN.tsv\")) c(x_test, y_test) %<-% get_data(paste0(root_url, \"FordA_TEST.tsv\"))  str(keras3:::named_list(   x_train, y_train,   x_test, y_test )) ## List of 4 ##  $ x_train: num [1:3601, 1:500] -0.797 0.805 0.728 -0.234 -0.171 ... ##  $ y_train: int [1:3601, 1] -1 1 -1 -1 -1 1 1 1 1 1 ... ##  $ x_test : num [1:1320, 1:500] -0.14 0.334 0.717 1.24 -1.159 ... ##  $ y_test : int [1:1320, 1] -1 -1 -1 1 -1 1 -1 -1 1 1 ..."},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"visualize-the-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Visualize the data","title":"Timeseries classification from scratch","text":"visualize one timeseries example class dataset.","code":"plot(NULL, main = \"Timeseries Data\",      xlab = \"Timepoints\",  ylab = \"Values\",      xlim = c(1, ncol(x_test)),      ylim = range(x_test)) grid() lines(x_test[match(-1, y_test), ], col = \"blue\") lines(x_test[match( 1, y_test), ], col = \"red\") legend(\"topright\", legend=c(\"label -1\", \"label 1\"), col=c(\"blue\", \"red\"), lty=1)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"standardize-the-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Standardize the data","title":"Timeseries classification from scratch","text":"timeseries already single length (500). However, values usually various ranges. ideal neural network; general seek make input values normalized. specific dataset, data already z-normalized: timeseries sample mean equal zero standard deviation equal one. type normalization common timeseries classification problems, see Bagnall et al. (2016). Note timeseries data used univariate, meaning one channel per timeseries example. therefore transform timeseries multivariate one one channel using simple reshaping via numpy. allow us construct model easily applicable multivariate time series. Finally, order use sparse_categorical_crossentropy, count number classes beforehand. Now shuffle training set using validation_split option later training. Standardize labels positive integers. expected labels 0 1.","code":"dim(x_train) <- c(dim(x_train), 1) dim(x_test) <- c(dim(x_test), 1) num_classes <- length(unique(y_train)) c(x_train, y_train) %<-% listarrays::shuffle_rows(x_train, y_train) # idx <- sample.int(nrow(x_train)) # x_train %<>% .[idx,, ,drop = FALSE] # y_train %<>% .[idx,  ,drop = FALSE] y_train[y_train == -1L] <- 0L y_test[y_test == -1L] <- 0L"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"build-a-model","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Build a model","title":"Timeseries classification from scratch","text":"build Fully Convolutional Neural Network originally proposed paper. implementation based TF 2 version provided . following hyperparameters (kernel_size, filters, usage BatchNorm) found via random search using KerasTuner. plot chunk unnamed-chunk-9","code":"make_model <- function(input_shape) {   inputs <- keras_input(input_shape)    outputs <- inputs |>     # conv1     layer_conv_1d(filters = 64, kernel_size = 3, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation_relu() |>     # conv2     layer_conv_1d(filters = 64, kernel_size = 3, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation_relu() |>     # conv3     layer_conv_1d(filters = 64, kernel_size = 3, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation_relu() |>     # pooling     layer_global_average_pooling_1d() |>     # final output     layer_dense(num_classes, activation = \"softmax\")    keras_model(inputs, outputs) }  model <- make_model(input_shape = dim(x_train)[-1]) model ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)                ┃ Output Shape          ┃    Param # ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer (InputLayer)    │ (None, 500, 1)        │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ conv1d (Conv1D)             │ (None, 500, 64)       │        256 │   Y   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ batch_normalization         │ (None, 500, 64)       │        256 │   Y   │ ## │ (BatchNormalization)        │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ re_lu (ReLU)                │ (None, 500, 64)       │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ conv1d_1 (Conv1D)           │ (None, 500, 64)       │     12,352 │   Y   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ batch_normalization_1       │ (None, 500, 64)       │        256 │   Y   │ ## │ (BatchNormalization)        │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ re_lu_1 (ReLU)              │ (None, 500, 64)       │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ conv1d_2 (Conv1D)           │ (None, 500, 64)       │     12,352 │   Y   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ batch_normalization_2       │ (None, 500, 64)       │        256 │   Y   │ ## │ (BatchNormalization)        │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ re_lu_2 (ReLU)              │ (None, 500, 64)       │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ global_average_pooling1d    │ (None, 64)            │          0 │   -   │ ## │ (GlobalAveragePooling1D)    │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense (Dense)               │ (None, 2)             │        130 │   Y   │ ## └─────────────────────────────┴───────────────────────┴────────────┴───────┘ ##  Total params: 25,858 (101.01 KB) ##  Trainable params: 25,474 (99.51 KB) ##  Non-trainable params: 384 (1.50 KB) plot(model, show_shapes = TRUE)"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"train-the-model","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Train the model","title":"Timeseries classification from scratch","text":"","code":"epochs <- 500 batch_size <- 32  callbacks <- c(   callback_model_checkpoint(     \"best_model.keras\", save_best_only = TRUE,     monitor = \"val_loss\"   ),   callback_reduce_lr_on_plateau(     monitor = \"val_loss\", factor = 0.5,     patience = 20, min_lr = 0.0001   ),   callback_early_stopping(     monitor = \"val_loss\", patience = 50,     verbose = 1   ) )   model |> compile(   optimizer = \"adam\",   loss = \"sparse_categorical_crossentropy\",   metrics = \"sparse_categorical_accuracy\" )  history <- model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = epochs,   callbacks = callbacks,   validation_split = 0.2 ) ## Epoch 1/500 ## 90/90 - 2s - 24ms/step - loss: 0.5557 - sparse_categorical_accuracy: 0.7073 - val_loss: 0.8517 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010 ## Epoch 2/500 ## 90/90 - 1s - 6ms/step - loss: 0.4850 - sparse_categorical_accuracy: 0.7611 - val_loss: 0.9464 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010 ## Epoch 3/500 ## 90/90 - 0s - 2ms/step - loss: 0.4713 - sparse_categorical_accuracy: 0.7705 - val_loss: 0.7593 - val_sparse_categorical_accuracy: 0.4896 - learning_rate: 0.0010 ## Epoch 4/500 ## 90/90 - 0s - 2ms/step - loss: 0.4239 - sparse_categorical_accuracy: 0.7885 - val_loss: 0.6721 - val_sparse_categorical_accuracy: 0.5021 - learning_rate: 0.0010 ## Epoch 5/500 ## 90/90 - 0s - 2ms/step - loss: 0.4218 - sparse_categorical_accuracy: 0.7885 - val_loss: 0.5647 - val_sparse_categorical_accuracy: 0.7198 - learning_rate: 0.0010 ## Epoch 6/500 ## 90/90 - 0s - 2ms/step - loss: 0.4060 - sparse_categorical_accuracy: 0.8028 - val_loss: 0.4563 - val_sparse_categorical_accuracy: 0.8141 - learning_rate: 0.0010 ## Epoch 7/500 ## 90/90 - 0s - 2ms/step - loss: 0.4032 - sparse_categorical_accuracy: 0.8017 - val_loss: 0.4209 - val_sparse_categorical_accuracy: 0.7892 - learning_rate: 0.0010 ## Epoch 8/500 ## 90/90 - 0s - 2ms/step - loss: 0.3920 - sparse_categorical_accuracy: 0.8003 - val_loss: 0.3938 - val_sparse_categorical_accuracy: 0.8239 - learning_rate: 0.0010 ## Epoch 9/500 ## 90/90 - 0s - 2ms/step - loss: 0.3892 - sparse_categorical_accuracy: 0.8135 - val_loss: 0.3849 - val_sparse_categorical_accuracy: 0.8225 - learning_rate: 0.0010 ## Epoch 10/500 ## 90/90 - 0s - 2ms/step - loss: 0.3874 - sparse_categorical_accuracy: 0.8021 - val_loss: 0.4934 - val_sparse_categorical_accuracy: 0.7503 - learning_rate: 0.0010 ## Epoch 11/500 ## 90/90 - 0s - 1ms/step - loss: 0.3798 - sparse_categorical_accuracy: 0.8149 - val_loss: 0.5693 - val_sparse_categorical_accuracy: 0.6990 - learning_rate: 0.0010 ## Epoch 12/500 ## 90/90 - 0s - 1ms/step - loss: 0.3671 - sparse_categorical_accuracy: 0.8215 - val_loss: 0.3998 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010 ## Epoch 13/500 ## 90/90 - 0s - 1ms/step - loss: 0.3555 - sparse_categorical_accuracy: 0.8389 - val_loss: 0.4145 - val_sparse_categorical_accuracy: 0.8031 - learning_rate: 0.0010 ## Epoch 14/500 ## 90/90 - 0s - 1ms/step - loss: 0.3628 - sparse_categorical_accuracy: 0.8253 - val_loss: 0.3886 - val_sparse_categorical_accuracy: 0.8211 - learning_rate: 0.0010 ## Epoch 15/500 ## 90/90 - 0s - 1ms/step - loss: 0.3433 - sparse_categorical_accuracy: 0.8406 - val_loss: 0.7763 - val_sparse_categorical_accuracy: 0.6019 - learning_rate: 0.0010 ## Epoch 16/500 ## 90/90 - 0s - 2ms/step - loss: 0.3455 - sparse_categorical_accuracy: 0.8434 - val_loss: 0.3537 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010 ## Epoch 17/500 ## 90/90 - 0s - 1ms/step - loss: 0.3336 - sparse_categorical_accuracy: 0.8462 - val_loss: 0.5375 - val_sparse_categorical_accuracy: 0.7074 - learning_rate: 0.0010 ## Epoch 18/500 ## 90/90 - 0s - 1ms/step - loss: 0.3263 - sparse_categorical_accuracy: 0.8503 - val_loss: 0.4134 - val_sparse_categorical_accuracy: 0.7892 - learning_rate: 0.0010 ## Epoch 19/500 ## 90/90 - 0s - 2ms/step - loss: 0.3195 - sparse_categorical_accuracy: 0.8601 - val_loss: 0.3432 - val_sparse_categorical_accuracy: 0.8433 - learning_rate: 0.0010 ## Epoch 20/500 ## 90/90 - 0s - 1ms/step - loss: 0.3136 - sparse_categorical_accuracy: 0.8674 - val_loss: 0.9287 - val_sparse_categorical_accuracy: 0.5950 - learning_rate: 0.0010 ## Epoch 21/500 ## 90/90 - 0s - 1ms/step - loss: 0.3001 - sparse_categorical_accuracy: 0.8691 - val_loss: 0.3926 - val_sparse_categorical_accuracy: 0.7795 - learning_rate: 0.0010 ## Epoch 22/500 ## 90/90 - 0s - 2ms/step - loss: 0.2984 - sparse_categorical_accuracy: 0.8719 - val_loss: 0.3023 - val_sparse_categorical_accuracy: 0.8655 - learning_rate: 0.0010 ## Epoch 23/500 ## 90/90 - 0s - 1ms/step - loss: 0.2930 - sparse_categorical_accuracy: 0.8767 - val_loss: 0.3501 - val_sparse_categorical_accuracy: 0.8086 - learning_rate: 0.0010 ## Epoch 24/500 ## 90/90 - 0s - 1ms/step - loss: 0.2861 - sparse_categorical_accuracy: 0.8799 - val_loss: 0.9187 - val_sparse_categorical_accuracy: 0.5992 - learning_rate: 0.0010 ## Epoch 25/500 ## 90/90 - 0s - 1ms/step - loss: 0.2843 - sparse_categorical_accuracy: 0.8816 - val_loss: 0.3731 - val_sparse_categorical_accuracy: 0.8363 - learning_rate: 0.0010 ## Epoch 26/500 ## 90/90 - 0s - 1ms/step - loss: 0.3182 - sparse_categorical_accuracy: 0.8569 - val_loss: 0.5553 - val_sparse_categorical_accuracy: 0.7115 - learning_rate: 0.0010 ## Epoch 27/500 ## 90/90 - 0s - 1ms/step - loss: 0.2732 - sparse_categorical_accuracy: 0.8875 - val_loss: 0.3950 - val_sparse_categorical_accuracy: 0.7628 - learning_rate: 0.0010 ## Epoch 28/500 ## 90/90 - 0s - 1ms/step - loss: 0.2943 - sparse_categorical_accuracy: 0.8705 - val_loss: 0.8138 - val_sparse_categorical_accuracy: 0.7254 - learning_rate: 0.0010 ## Epoch 29/500 ## 90/90 - 0s - 1ms/step - loss: 0.2772 - sparse_categorical_accuracy: 0.8781 - val_loss: 0.3712 - val_sparse_categorical_accuracy: 0.8405 - learning_rate: 0.0010 ## Epoch 30/500 ## 90/90 - 0s - 1ms/step - loss: 0.2714 - sparse_categorical_accuracy: 0.8833 - val_loss: 0.4209 - val_sparse_categorical_accuracy: 0.7975 - learning_rate: 0.0010 ## Epoch 31/500 ## 90/90 - 0s - 2ms/step - loss: 0.2720 - sparse_categorical_accuracy: 0.8854 - val_loss: 1.9298 - val_sparse_categorical_accuracy: 0.5104 - learning_rate: 0.0010 ## Epoch 32/500 ## 90/90 - 0s - 2ms/step - loss: 0.2699 - sparse_categorical_accuracy: 0.8833 - val_loss: 0.3155 - val_sparse_categorical_accuracy: 0.8516 - learning_rate: 0.0010 ## Epoch 33/500 ## 90/90 - 0s - 2ms/step - loss: 0.2586 - sparse_categorical_accuracy: 0.8951 - val_loss: 0.3628 - val_sparse_categorical_accuracy: 0.7947 - learning_rate: 0.0010 ## Epoch 34/500 ## 90/90 - 0s - 1ms/step - loss: 0.2490 - sparse_categorical_accuracy: 0.9003 - val_loss: 0.5844 - val_sparse_categorical_accuracy: 0.7309 - learning_rate: 0.0010 ## Epoch 35/500 ## 90/90 - 0s - 1ms/step - loss: 0.2627 - sparse_categorical_accuracy: 0.8938 - val_loss: 0.3480 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010 ## Epoch 36/500 ## 90/90 - 0s - 1ms/step - loss: 0.2558 - sparse_categorical_accuracy: 0.8948 - val_loss: 0.3793 - val_sparse_categorical_accuracy: 0.8128 - learning_rate: 0.0010 ## Epoch 37/500 ## 90/90 - 0s - 1ms/step - loss: 0.2515 - sparse_categorical_accuracy: 0.8958 - val_loss: 0.4828 - val_sparse_categorical_accuracy: 0.7503 - learning_rate: 0.0010 ## Epoch 38/500 ## 90/90 - 0s - 2ms/step - loss: 0.2541 - sparse_categorical_accuracy: 0.8986 - val_loss: 0.2950 - val_sparse_categorical_accuracy: 0.8669 - learning_rate: 0.0010 ## Epoch 39/500 ## 90/90 - 0s - 1ms/step - loss: 0.2505 - sparse_categorical_accuracy: 0.8976 - val_loss: 0.7151 - val_sparse_categorical_accuracy: 0.7087 - learning_rate: 0.0010 ## Epoch 40/500 ## 90/90 - 0s - 1ms/step - loss: 0.2490 - sparse_categorical_accuracy: 0.8979 - val_loss: 0.3170 - val_sparse_categorical_accuracy: 0.8627 - learning_rate: 0.0010 ## Epoch 41/500 ## 90/90 - 0s - 2ms/step - loss: 0.2325 - sparse_categorical_accuracy: 0.9062 - val_loss: 0.2560 - val_sparse_categorical_accuracy: 0.8904 - learning_rate: 0.0010 ## Epoch 42/500 ## 90/90 - 0s - 1ms/step - loss: 0.2312 - sparse_categorical_accuracy: 0.9076 - val_loss: 0.3396 - val_sparse_categorical_accuracy: 0.8405 - learning_rate: 0.0010 ## Epoch 43/500 ## 90/90 - 0s - 1ms/step - loss: 0.2402 - sparse_categorical_accuracy: 0.8976 - val_loss: 0.3650 - val_sparse_categorical_accuracy: 0.8294 - learning_rate: 0.0010 ## Epoch 44/500 ## 90/90 - 0s - 1ms/step - loss: 0.2342 - sparse_categorical_accuracy: 0.9038 - val_loss: 1.0563 - val_sparse_categorical_accuracy: 0.6172 - learning_rate: 0.0010 ## Epoch 45/500 ## 90/90 - 0s - 1ms/step - loss: 0.2320 - sparse_categorical_accuracy: 0.9080 - val_loss: 0.2506 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010 ## Epoch 46/500 ## 90/90 - 0s - 1ms/step - loss: 0.2317 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.4473 - val_sparse_categorical_accuracy: 0.7920 - learning_rate: 0.0010 ## Epoch 47/500 ## 90/90 - 0s - 1ms/step - loss: 0.2164 - sparse_categorical_accuracy: 0.9125 - val_loss: 0.6324 - val_sparse_categorical_accuracy: 0.7240 - learning_rate: 0.0010 ## Epoch 48/500 ## 90/90 - 0s - 2ms/step - loss: 0.2186 - sparse_categorical_accuracy: 0.9122 - val_loss: 0.2525 - val_sparse_categorical_accuracy: 0.9001 - learning_rate: 0.0010 ## Epoch 49/500 ## 90/90 - 0s - 2ms/step - loss: 0.2119 - sparse_categorical_accuracy: 0.9118 - val_loss: 0.2401 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010 ## Epoch 50/500 ## 90/90 - 0s - 1ms/step - loss: 0.2134 - sparse_categorical_accuracy: 0.9191 - val_loss: 0.2410 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010 ## Epoch 51/500 ## 90/90 - 0s - 2ms/step - loss: 0.2120 - sparse_categorical_accuracy: 0.9174 - val_loss: 0.6803 - val_sparse_categorical_accuracy: 0.6935 - learning_rate: 0.0010 ## Epoch 52/500 ## 90/90 - 0s - 2ms/step - loss: 0.2023 - sparse_categorical_accuracy: 0.9205 - val_loss: 0.2938 - val_sparse_categorical_accuracy: 0.8779 - learning_rate: 0.0010 ## Epoch 53/500 ## 90/90 - 0s - 2ms/step - loss: 0.1980 - sparse_categorical_accuracy: 0.9243 - val_loss: 0.5380 - val_sparse_categorical_accuracy: 0.7531 - learning_rate: 0.0010 ## Epoch 54/500 ## 90/90 - 0s - 2ms/step - loss: 0.1883 - sparse_categorical_accuracy: 0.9271 - val_loss: 0.2272 - val_sparse_categorical_accuracy: 0.9071 - learning_rate: 0.0010 ## Epoch 55/500 ## 90/90 - 0s - 2ms/step - loss: 0.1761 - sparse_categorical_accuracy: 0.9385 - val_loss: 0.2707 - val_sparse_categorical_accuracy: 0.8821 - learning_rate: 0.0010 ## Epoch 56/500 ## 90/90 - 0s - 1ms/step - loss: 0.1683 - sparse_categorical_accuracy: 0.9417 - val_loss: 0.2584 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010 ## Epoch 57/500 ## 90/90 - 0s - 1ms/step - loss: 0.1581 - sparse_categorical_accuracy: 0.9490 - val_loss: 1.4305 - val_sparse_categorical_accuracy: 0.7184 - learning_rate: 0.0010 ## Epoch 58/500 ## 90/90 - 0s - 1ms/step - loss: 0.1518 - sparse_categorical_accuracy: 0.9497 - val_loss: 0.7991 - val_sparse_categorical_accuracy: 0.7531 - learning_rate: 0.0010 ## Epoch 59/500 ## 90/90 - 0s - 1ms/step - loss: 0.1529 - sparse_categorical_accuracy: 0.9479 - val_loss: 0.3389 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 0.0010 ## Epoch 60/500 ## 90/90 - 0s - 1ms/step - loss: 0.1784 - sparse_categorical_accuracy: 0.9368 - val_loss: 0.2534 - val_sparse_categorical_accuracy: 0.8974 - learning_rate: 0.0010 ## Epoch 61/500 ## 90/90 - 0s - 1ms/step - loss: 0.1335 - sparse_categorical_accuracy: 0.9566 - val_loss: 0.2323 - val_sparse_categorical_accuracy: 0.9098 - learning_rate: 0.0010 ## Epoch 62/500 ## 90/90 - 0s - 1ms/step - loss: 0.1401 - sparse_categorical_accuracy: 0.9531 - val_loss: 0.3259 - val_sparse_categorical_accuracy: 0.8696 - learning_rate: 0.0010 ## Epoch 63/500 ## 90/90 - 0s - 1ms/step - loss: 0.1404 - sparse_categorical_accuracy: 0.9521 - val_loss: 0.2970 - val_sparse_categorical_accuracy: 0.8627 - learning_rate: 0.0010 ## Epoch 64/500 ## 90/90 - 0s - 1ms/step - loss: 0.1375 - sparse_categorical_accuracy: 0.9559 - val_loss: 0.3416 - val_sparse_categorical_accuracy: 0.8183 - learning_rate: 0.0010 ## Epoch 65/500 ## 90/90 - 0s - 1ms/step - loss: 0.1303 - sparse_categorical_accuracy: 0.9583 - val_loss: 0.2586 - val_sparse_categorical_accuracy: 0.8766 - learning_rate: 0.0010 ## Epoch 66/500 ## 90/90 - 0s - 1ms/step - loss: 0.1199 - sparse_categorical_accuracy: 0.9608 - val_loss: 1.2218 - val_sparse_categorical_accuracy: 0.7337 - learning_rate: 0.0010 ## Epoch 67/500 ## 90/90 - 0s - 2ms/step - loss: 0.1186 - sparse_categorical_accuracy: 0.9615 - val_loss: 0.1464 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010 ## Epoch 68/500 ## 90/90 - 0s - 2ms/step - loss: 0.1176 - sparse_categorical_accuracy: 0.9632 - val_loss: 0.1451 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010 ## Epoch 69/500 ## 90/90 - 0s - 2ms/step - loss: 0.1295 - sparse_categorical_accuracy: 0.9583 - val_loss: 1.7723 - val_sparse_categorical_accuracy: 0.5395 - learning_rate: 0.0010 ## Epoch 70/500 ## 90/90 - 0s - 2ms/step - loss: 0.1232 - sparse_categorical_accuracy: 0.9587 - val_loss: 0.1802 - val_sparse_categorical_accuracy: 0.9237 - learning_rate: 0.0010 ## Epoch 71/500 ## 90/90 - 0s - 1ms/step - loss: 0.1202 - sparse_categorical_accuracy: 0.9573 - val_loss: 0.1932 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010 ## Epoch 72/500 ## 90/90 - 0s - 1ms/step - loss: 0.1124 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.4178 - val_sparse_categorical_accuracy: 0.8003 - learning_rate: 0.0010 ## Epoch 73/500 ## 90/90 - 0s - 1ms/step - loss: 0.1068 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1517 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 0.0010 ## Epoch 74/500 ## 90/90 - 0s - 1ms/step - loss: 0.1085 - sparse_categorical_accuracy: 0.9639 - val_loss: 0.2126 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 0.0010 ## Epoch 75/500 ## 90/90 - 0s - 1ms/step - loss: 0.1263 - sparse_categorical_accuracy: 0.9580 - val_loss: 0.1689 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 0.0010 ## Epoch 76/500 ## 90/90 - 0s - 1ms/step - loss: 0.1169 - sparse_categorical_accuracy: 0.9597 - val_loss: 0.2955 - val_sparse_categorical_accuracy: 0.8460 - learning_rate: 0.0010 ## Epoch 77/500 ## 90/90 - 0s - 1ms/step - loss: 0.1089 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.6333 - val_sparse_categorical_accuracy: 0.6241 - learning_rate: 0.0010 ## Epoch 78/500 ## 90/90 - 0s - 2ms/step - loss: 0.1017 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.1416 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 0.0010 ## Epoch 79/500 ## 90/90 - 0s - 2ms/step - loss: 0.1099 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.3434 - val_sparse_categorical_accuracy: 0.8544 - learning_rate: 0.0010 ## Epoch 80/500 ## 90/90 - 0s - 2ms/step - loss: 0.1046 - sparse_categorical_accuracy: 0.9653 - val_loss: 0.4430 - val_sparse_categorical_accuracy: 0.8280 - learning_rate: 0.0010 ## Epoch 81/500 ## 90/90 - 0s - 1ms/step - loss: 0.1098 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.1632 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 0.0010 ## Epoch 82/500 ## 90/90 - 0s - 1ms/step - loss: 0.1021 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.3984 - val_sparse_categorical_accuracy: 0.7906 - learning_rate: 0.0010 ## Epoch 83/500 ## 90/90 - 0s - 1ms/step - loss: 0.1086 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.1976 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010 ## Epoch 84/500 ## 90/90 - 0s - 1ms/step - loss: 0.1026 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.2123 - val_sparse_categorical_accuracy: 0.9251 - learning_rate: 0.0010 ## Epoch 85/500 ## 90/90 - 0s - 1ms/step - loss: 0.1032 - sparse_categorical_accuracy: 0.9632 - val_loss: 0.3491 - val_sparse_categorical_accuracy: 0.8613 - learning_rate: 0.0010 ## Epoch 86/500 ## 90/90 - 0s - 1ms/step - loss: 0.1136 - sparse_categorical_accuracy: 0.9576 - val_loss: 0.1498 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 0.0010 ## Epoch 87/500 ## 90/90 - 0s - 2ms/step - loss: 0.1107 - sparse_categorical_accuracy: 0.9608 - val_loss: 0.1406 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 0.0010 ## Epoch 88/500 ## 90/90 - 0s - 1ms/step - loss: 0.0995 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.6864 - val_sparse_categorical_accuracy: 0.7656 - learning_rate: 0.0010 ## Epoch 89/500 ## 90/90 - 0s - 1ms/step - loss: 0.1013 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.8218 - val_sparse_categorical_accuracy: 0.7476 - learning_rate: 0.0010 ## Epoch 90/500 ## 90/90 - 0s - 1ms/step - loss: 0.1002 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.2009 - val_sparse_categorical_accuracy: 0.9209 - learning_rate: 0.0010 ## Epoch 91/500 ## 90/90 - 0s - 1ms/step - loss: 0.1010 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.2869 - val_sparse_categorical_accuracy: 0.8849 - learning_rate: 0.0010 ## Epoch 92/500 ## 90/90 - 0s - 1ms/step - loss: 0.1012 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.5128 - val_sparse_categorical_accuracy: 0.7434 - learning_rate: 0.0010 ## Epoch 93/500 ## 90/90 - 0s - 1ms/step - loss: 0.1009 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.5212 - val_sparse_categorical_accuracy: 0.7753 - learning_rate: 0.0010 ## Epoch 94/500 ## 90/90 - 0s - 1ms/step - loss: 0.1016 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.3146 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 0.0010 ## Epoch 95/500 ## 90/90 - 0s - 1ms/step - loss: 0.0975 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1750 - val_sparse_categorical_accuracy: 0.9376 - learning_rate: 0.0010 ## Epoch 96/500 ## 90/90 - 0s - 1ms/step - loss: 0.1121 - sparse_categorical_accuracy: 0.9583 - val_loss: 0.2399 - val_sparse_categorical_accuracy: 0.9112 - learning_rate: 0.0010 ## Epoch 97/500 ## 90/90 - 0s - 1ms/step - loss: 0.1000 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.5611 - val_sparse_categorical_accuracy: 0.7101 - learning_rate: 0.0010 ## Epoch 98/500 ## 90/90 - 0s - 1ms/step - loss: 0.0949 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1864 - val_sparse_categorical_accuracy: 0.9279 - learning_rate: 0.0010 ## Epoch 99/500 ## 90/90 - 0s - 2ms/step - loss: 0.0925 - sparse_categorical_accuracy: 0.9667 - val_loss: 0.1228 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 0.0010 ## Epoch 100/500 ## 90/90 - 0s - 1ms/step - loss: 0.0922 - sparse_categorical_accuracy: 0.9691 - val_loss: 1.9448 - val_sparse_categorical_accuracy: 0.7115 - learning_rate: 0.0010 ## Epoch 101/500 ## 90/90 - 0s - 1ms/step - loss: 0.0887 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.2127 - val_sparse_categorical_accuracy: 0.9071 - learning_rate: 0.0010 ## Epoch 102/500 ## 90/90 - 0s - 1ms/step - loss: 0.0999 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.1903 - val_sparse_categorical_accuracy: 0.9154 - learning_rate: 0.0010 ## Epoch 103/500 ## 90/90 - 0s - 1ms/step - loss: 0.0929 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.2647 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010 ## Epoch 104/500 ## 90/90 - 0s - 1ms/step - loss: 0.1008 - sparse_categorical_accuracy: 0.9642 - val_loss: 1.1019 - val_sparse_categorical_accuracy: 0.6019 - learning_rate: 0.0010 ## Epoch 105/500 ## 90/90 - 0s - 1ms/step - loss: 0.0969 - sparse_categorical_accuracy: 0.9681 - val_loss: 1.1464 - val_sparse_categorical_accuracy: 0.5645 - learning_rate: 0.0010 ## Epoch 106/500 ## 90/90 - 0s - 2ms/step - loss: 0.0924 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.2535 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 0.0010 ## Epoch 107/500 ## 90/90 - 0s - 2ms/step - loss: 0.0930 - sparse_categorical_accuracy: 0.9677 - val_loss: 1.5288 - val_sparse_categorical_accuracy: 0.5118 - learning_rate: 0.0010 ## Epoch 108/500 ## 90/90 - 0s - 1ms/step - loss: 0.0893 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1496 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 0.0010 ## Epoch 109/500 ## 90/90 - 0s - 1ms/step - loss: 0.0891 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.2799 - val_sparse_categorical_accuracy: 0.8835 - learning_rate: 0.0010 ## Epoch 110/500 ## 90/90 - 0s - 1ms/step - loss: 0.0963 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.4827 - val_sparse_categorical_accuracy: 0.8155 - learning_rate: 0.0010 ## Epoch 111/500 ## 90/90 - 0s - 2ms/step - loss: 0.0841 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1203 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 0.0010 ## Epoch 112/500 ## 90/90 - 0s - 1ms/step - loss: 0.0861 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.5081 - val_sparse_categorical_accuracy: 0.7559 - learning_rate: 0.0010 ## Epoch 113/500 ## 90/90 - 0s - 1ms/step - loss: 0.0831 - sparse_categorical_accuracy: 0.9729 - val_loss: 1.2423 - val_sparse_categorical_accuracy: 0.5687 - learning_rate: 0.0010 ## Epoch 114/500 ## 90/90 - 0s - 1ms/step - loss: 0.1094 - sparse_categorical_accuracy: 0.9653 - val_loss: 0.3459 - val_sparse_categorical_accuracy: 0.8252 - learning_rate: 0.0010 ## Epoch 115/500 ## 90/90 - 0s - 1ms/step - loss: 0.0886 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.7117 - val_sparse_categorical_accuracy: 0.7226 - learning_rate: 0.0010 ## Epoch 116/500 ## 90/90 - 0s - 1ms/step - loss: 0.0989 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.1669 - val_sparse_categorical_accuracy: 0.9334 - learning_rate: 0.0010 ## Epoch 117/500 ## 90/90 - 0s - 1ms/step - loss: 0.0891 - sparse_categorical_accuracy: 0.9705 - val_loss: 0.2413 - val_sparse_categorical_accuracy: 0.9154 - learning_rate: 0.0010 ## Epoch 118/500 ## 90/90 - 0s - 1ms/step - loss: 0.0902 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.7796 - val_sparse_categorical_accuracy: 0.6741 - learning_rate: 0.0010 ## Epoch 119/500 ## 90/90 - 0s - 1ms/step - loss: 0.0877 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.1806 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 0.0010 ## Epoch 120/500 ## 90/90 - 0s - 2ms/step - loss: 0.0880 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.1146 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 0.0010 ## Epoch 121/500 ## 90/90 - 0s - 1ms/step - loss: 0.0896 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.1279 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 0.0010 ## Epoch 122/500 ## 90/90 - 0s - 1ms/step - loss: 0.0883 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.3067 - val_sparse_categorical_accuracy: 0.8918 - learning_rate: 0.0010 ## Epoch 123/500 ## 90/90 - 0s - 2ms/step - loss: 0.0855 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.1060 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 0.0010 ## Epoch 124/500 ## 90/90 - 0s - 1ms/step - loss: 0.0917 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.2169 - val_sparse_categorical_accuracy: 0.9112 - learning_rate: 0.0010 ## Epoch 125/500 ## 90/90 - 0s - 1ms/step - loss: 0.0976 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.1407 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 0.0010 ## Epoch 126/500 ## 90/90 - 0s - 1ms/step - loss: 0.0866 - sparse_categorical_accuracy: 0.9722 - val_loss: 1.4019 - val_sparse_categorical_accuracy: 0.7295 - learning_rate: 0.0010 ## Epoch 127/500 ## 90/90 - 0s - 1ms/step - loss: 0.0877 - sparse_categorical_accuracy: 0.9747 - val_loss: 0.2518 - val_sparse_categorical_accuracy: 0.8918 - learning_rate: 0.0010 ## Epoch 128/500 ## 90/90 - 0s - 1ms/step - loss: 0.0942 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.1465 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 0.0010 ## Epoch 129/500 ## 90/90 - 0s - 1ms/step - loss: 0.0904 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.1546 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 0.0010 ## Epoch 130/500 ## 90/90 - 0s - 1ms/step - loss: 0.0953 - sparse_categorical_accuracy: 0.9705 - val_loss: 0.1201 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 0.0010 ## Epoch 131/500 ## 90/90 - 0s - 1ms/step - loss: 0.0846 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.1980 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 0.0010 ## Epoch 132/500 ## 90/90 - 0s - 1ms/step - loss: 0.0812 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.4080 - val_sparse_categorical_accuracy: 0.8599 - learning_rate: 0.0010 ## Epoch 133/500 ## 90/90 - 0s - 1ms/step - loss: 0.0785 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.3284 - val_sparse_categorical_accuracy: 0.8835 - learning_rate: 0.0010 ## Epoch 134/500 ## 90/90 - 0s - 1ms/step - loss: 0.0885 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.5050 - val_sparse_categorical_accuracy: 0.8419 - learning_rate: 0.0010 ## Epoch 135/500 ## 90/90 - 0s - 1ms/step - loss: 0.0859 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.4296 - val_sparse_categorical_accuracy: 0.8141 - learning_rate: 0.0010 ## Epoch 136/500 ## 90/90 - 0s - 1ms/step - loss: 0.0885 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.1295 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 0.0010 ## Epoch 137/500 ## 90/90 - 0s - 1ms/step - loss: 0.0892 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.4052 - val_sparse_categorical_accuracy: 0.8031 - learning_rate: 0.0010 ## Epoch 138/500 ## 90/90 - 0s - 1ms/step - loss: 0.0835 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.1160 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 0.0010 ## Epoch 139/500 ## 90/90 - 0s - 1ms/step - loss: 0.0877 - sparse_categorical_accuracy: 0.9708 - val_loss: 3.0631 - val_sparse_categorical_accuracy: 0.7032 - learning_rate: 0.0010 ## Epoch 140/500 ## 90/90 - 0s - 1ms/step - loss: 0.0867 - sparse_categorical_accuracy: 0.9719 - val_loss: 1.3638 - val_sparse_categorical_accuracy: 0.7143 - learning_rate: 0.0010 ## Epoch 141/500 ## 90/90 - 0s - 1ms/step - loss: 0.0857 - sparse_categorical_accuracy: 0.9715 - val_loss: 0.2599 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 0.0010 ## Epoch 142/500 ## 90/90 - 0s - 1ms/step - loss: 0.0771 - sparse_categorical_accuracy: 0.9764 - val_loss: 0.2860 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010 ## Epoch 143/500 ## 90/90 - 0s - 1ms/step - loss: 0.0835 - sparse_categorical_accuracy: 0.9694 - val_loss: 0.1252 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 0.0010 ## Epoch 144/500 ## 90/90 - 1s - 7ms/step - loss: 0.0674 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.1234 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 5.0000e-04 ## Epoch 145/500 ## 90/90 - 0s - 1ms/step - loss: 0.0680 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1147 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 5.0000e-04 ## Epoch 146/500 ## 90/90 - 0s - 2ms/step - loss: 0.0702 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1042 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 5.0000e-04 ## Epoch 147/500 ## 90/90 - 0s - 1ms/step - loss: 0.0742 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.1511 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 5.0000e-04 ## Epoch 148/500 ## 90/90 - 0s - 1ms/step - loss: 0.0801 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.1219 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 5.0000e-04 ## Epoch 149/500 ## 90/90 - 0s - 2ms/step - loss: 0.0725 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04 ## Epoch 150/500 ## 90/90 - 0s - 1ms/step - loss: 0.0738 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.2021 - val_sparse_categorical_accuracy: 0.9334 - learning_rate: 5.0000e-04 ## Epoch 151/500 ## 90/90 - 0s - 1ms/step - loss: 0.0707 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.1358 - val_sparse_categorical_accuracy: 0.9376 - learning_rate: 5.0000e-04 ## Epoch 152/500 ## 90/90 - 0s - 1ms/step - loss: 0.0727 - sparse_categorical_accuracy: 0.9740 - val_loss: 0.1989 - val_sparse_categorical_accuracy: 0.9098 - learning_rate: 5.0000e-04 ## Epoch 153/500 ## 90/90 - 0s - 1ms/step - loss: 0.0671 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.1250 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04 ## Epoch 154/500 ## 90/90 - 0s - 1ms/step - loss: 0.0687 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1973 - val_sparse_categorical_accuracy: 0.9237 - learning_rate: 5.0000e-04 ## Epoch 155/500 ## 90/90 - 0s - 1ms/step - loss: 0.0697 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1190 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 5.0000e-04 ## Epoch 156/500 ## 90/90 - 0s - 1ms/step - loss: 0.0731 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.1409 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 5.0000e-04 ## Epoch 157/500 ## 90/90 - 0s - 1ms/step - loss: 0.0719 - sparse_categorical_accuracy: 0.9740 - val_loss: 0.4024 - val_sparse_categorical_accuracy: 0.8655 - learning_rate: 5.0000e-04 ## Epoch 158/500 ## 90/90 - 0s - 1ms/step - loss: 0.0722 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.3254 - val_sparse_categorical_accuracy: 0.8821 - learning_rate: 5.0000e-04 ## Epoch 159/500 ## 90/90 - 0s - 1ms/step - loss: 0.0706 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.2014 - val_sparse_categorical_accuracy: 0.9265 - learning_rate: 5.0000e-04 ## Epoch 160/500 ## 90/90 - 0s - 1ms/step - loss: 0.0707 - sparse_categorical_accuracy: 0.9753 - val_loss: 0.1128 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 5.0000e-04 ## Epoch 161/500 ## 90/90 - 0s - 1ms/step - loss: 0.0727 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.1118 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 5.0000e-04 ## Epoch 162/500 ## 90/90 - 0s - 1ms/step - loss: 0.0660 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.2972 - val_sparse_categorical_accuracy: 0.8960 - learning_rate: 5.0000e-04 ## Epoch 163/500 ## 90/90 - 0s - 1ms/step - loss: 0.0619 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.1079 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 5.0000e-04 ## Epoch 164/500 ## 90/90 - 0s - 1ms/step - loss: 0.0670 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.2169 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 5.0000e-04 ## Epoch 165/500 ## 90/90 - 0s - 1ms/step - loss: 0.0690 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.2980 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 5.0000e-04 ## Epoch 166/500 ## 90/90 - 0s - 1ms/step - loss: 0.0650 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.1257 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 5.0000e-04 ## Epoch 167/500 ## 90/90 - 0s - 1ms/step - loss: 0.0656 - sparse_categorical_accuracy: 0.9788 - val_loss: 0.1451 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 5.0000e-04 ## Epoch 168/500 ## 90/90 - 0s - 1ms/step - loss: 0.0750 - sparse_categorical_accuracy: 0.9736 - val_loss: 0.3402 - val_sparse_categorical_accuracy: 0.8350 - learning_rate: 5.0000e-04 ## Epoch 169/500 ## 90/90 - 0s - 1ms/step - loss: 0.0664 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.1295 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 5.0000e-04 ## Epoch 170/500 ## 90/90 - 0s - 2ms/step - loss: 0.0635 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04 ## Epoch 171/500 ## 90/90 - 0s - 1ms/step - loss: 0.0593 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1230 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 2.5000e-04 ## Epoch 172/500 ## 90/90 - 0s - 1ms/step - loss: 0.0591 - sparse_categorical_accuracy: 0.9799 - val_loss: 0.1079 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04 ## Epoch 173/500 ## 90/90 - 0s - 1ms/step - loss: 0.0593 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1073 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04 ## Epoch 174/500 ## 90/90 - 0s - 2ms/step - loss: 0.0605 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 2.5000e-04 ## Epoch 175/500 ## 90/90 - 0s - 1ms/step - loss: 0.0616 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.2084 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 2.5000e-04 ## Epoch 176/500 ## 90/90 - 0s - 1ms/step - loss: 0.0584 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.0993 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04 ## Epoch 177/500 ## 90/90 - 0s - 1ms/step - loss: 0.0582 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1498 - val_sparse_categorical_accuracy: 0.9487 - learning_rate: 2.5000e-04 ## Epoch 178/500 ## 90/90 - 0s - 1ms/step - loss: 0.0566 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1165 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 2.5000e-04 ## Epoch 179/500 ## 90/90 - 0s - 2ms/step - loss: 0.0566 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1714 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 2.5000e-04 ## Epoch 180/500 ## 90/90 - 0s - 2ms/step - loss: 0.0563 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1042 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04 ## Epoch 181/500 ## 90/90 - 0s - 2ms/step - loss: 0.0609 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.2017 - val_sparse_categorical_accuracy: 0.9209 - learning_rate: 2.5000e-04 ## Epoch 182/500 ## 90/90 - 0s - 1ms/step - loss: 0.0559 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1059 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 2.5000e-04 ## Epoch 183/500 ## 90/90 - 0s - 1ms/step - loss: 0.0597 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.1279 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 2.5000e-04 ## Epoch 184/500 ## 90/90 - 0s - 1ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04 ## Epoch 185/500 ## 90/90 - 0s - 1ms/step - loss: 0.0570 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.1022 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04 ## Epoch 186/500 ## 90/90 - 0s - 1ms/step - loss: 0.0633 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 2.5000e-04 ## Epoch 187/500 ## 90/90 - 0s - 2ms/step - loss: 0.0576 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0969 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04 ## Epoch 188/500 ## 90/90 - 0s - 1ms/step - loss: 0.0567 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1043 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04 ## Epoch 189/500 ## 90/90 - 0s - 1ms/step - loss: 0.0546 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1734 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 2.5000e-04 ## Epoch 190/500 ## 90/90 - 0s - 1ms/step - loss: 0.0639 - sparse_categorical_accuracy: 0.9799 - val_loss: 0.2847 - val_sparse_categorical_accuracy: 0.9015 - learning_rate: 2.5000e-04 ## Epoch 191/500 ## 90/90 - 0s - 1ms/step - loss: 0.0551 - sparse_categorical_accuracy: 0.9799 - val_loss: 0.4059 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 2.5000e-04 ## Epoch 192/500 ## 90/90 - 0s - 1ms/step - loss: 0.0613 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.3821 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 2.5000e-04 ## Epoch 193/500 ## 90/90 - 0s - 1ms/step - loss: 0.0559 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04 ## Epoch 194/500 ## 90/90 - 0s - 1ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1008 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04 ## Epoch 195/500 ## 90/90 - 0s - 1ms/step - loss: 0.0557 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0996 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 2.5000e-04 ## Epoch 196/500 ## 90/90 - 0s - 1ms/step - loss: 0.0581 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.1143 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 2.5000e-04 ## Epoch 197/500 ## 90/90 - 0s - 1ms/step - loss: 0.0544 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1027 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 2.5000e-04 ## Epoch 198/500 ## 90/90 - 0s - 1ms/step - loss: 0.0533 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1310 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04 ## Epoch 199/500 ## 90/90 - 0s - 1ms/step - loss: 0.0578 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.1236 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 2.5000e-04 ## Epoch 200/500 ## 90/90 - 0s - 1ms/step - loss: 0.0582 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 2.5000e-04 ## Epoch 201/500 ## 90/90 - 0s - 1ms/step - loss: 0.0551 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1327 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04 ## Epoch 202/500 ## 90/90 - 0s - 1ms/step - loss: 0.0543 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.2115 - val_sparse_categorical_accuracy: 0.9140 - learning_rate: 2.5000e-04 ## Epoch 203/500 ## 90/90 - 0s - 1ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.2015 - val_sparse_categorical_accuracy: 0.9223 - learning_rate: 2.5000e-04 ## Epoch 204/500 ## 90/90 - 0s - 1ms/step - loss: 0.0508 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04 ## Epoch 205/500 ## 90/90 - 0s - 1ms/step - loss: 0.0542 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0996 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04 ## Epoch 206/500 ## 90/90 - 0s - 1ms/step - loss: 0.0559 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1172 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 2.5000e-04 ## Epoch 207/500 ## 90/90 - 0s - 2ms/step - loss: 0.0570 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 2.5000e-04 ## Epoch 208/500 ## 90/90 - 0s - 2ms/step - loss: 0.0521 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1000 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 209/500 ## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1286 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04 ## Epoch 210/500 ## 90/90 - 0s - 1ms/step - loss: 0.0541 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 211/500 ## 90/90 - 0s - 1ms/step - loss: 0.0509 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0993 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 212/500 ## 90/90 - 0s - 1ms/step - loss: 0.0503 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04 ## Epoch 213/500 ## 90/90 - 0s - 2ms/step - loss: 0.0534 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0968 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 214/500 ## 90/90 - 0s - 1ms/step - loss: 0.0532 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04 ## Epoch 215/500 ## 90/90 - 0s - 1ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1013 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.2500e-04 ## Epoch 216/500 ## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1244 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04 ## Epoch 217/500 ## 90/90 - 0s - 2ms/step - loss: 0.0495 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.1141 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.2500e-04 ## Epoch 218/500 ## 90/90 - 0s - 2ms/step - loss: 0.0547 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04 ## Epoch 219/500 ## 90/90 - 0s - 2ms/step - loss: 0.0480 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1387 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.2500e-04 ## Epoch 220/500 ## 90/90 - 0s - 1ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1161 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 221/500 ## 90/90 - 0s - 1ms/step - loss: 0.0516 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.1338 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04 ## Epoch 222/500 ## 90/90 - 0s - 1ms/step - loss: 0.0525 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.0995 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 223/500 ## 90/90 - 0s - 1ms/step - loss: 0.0504 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04 ## Epoch 224/500 ## 90/90 - 0s - 1ms/step - loss: 0.0471 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0994 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04 ## Epoch 225/500 ## 90/90 - 0s - 1ms/step - loss: 0.0536 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1145 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04 ## Epoch 226/500 ## 90/90 - 0s - 1ms/step - loss: 0.0487 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1047 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.2500e-04 ## Epoch 227/500 ## 90/90 - 0s - 1ms/step - loss: 0.0529 - sparse_categorical_accuracy: 0.9813 - val_loss: 0.1087 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04 ## Epoch 228/500 ## 90/90 - 0s - 1ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 229/500 ## 90/90 - 0s - 1ms/step - loss: 0.0499 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1064 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04 ## Epoch 230/500 ## 90/90 - 0s - 2ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0967 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04 ## Epoch 231/500 ## 90/90 - 0s - 1ms/step - loss: 0.0527 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1730 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 1.2500e-04 ## Epoch 232/500 ## 90/90 - 0s - 1ms/step - loss: 0.0496 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1109 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.2500e-04 ## Epoch 233/500 ## 90/90 - 0s - 2ms/step - loss: 0.0483 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0963 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 234/500 ## 90/90 - 0s - 1ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1158 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 235/500 ## 90/90 - 0s - 1ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1057 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04 ## Epoch 236/500 ## 90/90 - 0s - 1ms/step - loss: 0.0502 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0982 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04 ## Epoch 237/500 ## 90/90 - 0s - 1ms/step - loss: 0.0501 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0992 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 238/500 ## 90/90 - 0s - 1ms/step - loss: 0.0487 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1031 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.2500e-04 ## Epoch 239/500 ## 90/90 - 0s - 1ms/step - loss: 0.0491 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1139 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 1.2500e-04 ## Epoch 240/500 ## 90/90 - 0s - 2ms/step - loss: 0.0478 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0990 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.2500e-04 ## Epoch 241/500 ## 90/90 - 0s - 2ms/step - loss: 0.0498 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0994 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04 ## Epoch 242/500 ## 90/90 - 0s - 2ms/step - loss: 0.0490 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1118 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04 ## Epoch 243/500 ## 90/90 - 0s - 2ms/step - loss: 0.0523 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1052 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 244/500 ## 90/90 - 0s - 2ms/step - loss: 0.0527 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1055 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 ## Epoch 245/500 ## 90/90 - 0s - 2ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1045 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04 ## Epoch 246/500 ## 90/90 - 0s - 1ms/step - loss: 0.0518 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1214 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 1.2500e-04 ## Epoch 247/500 ## 90/90 - 0s - 1ms/step - loss: 0.0457 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0964 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 248/500 ## 90/90 - 0s - 1ms/step - loss: 0.0450 - sparse_categorical_accuracy: 0.9885 - val_loss: 0.1161 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04 ## Epoch 249/500 ## 90/90 - 0s - 1ms/step - loss: 0.0492 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1282 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.2500e-04 ## Epoch 250/500 ## 90/90 - 0s - 2ms/step - loss: 0.0510 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0972 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 251/500 ## 90/90 - 0s - 1ms/step - loss: 0.0506 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0986 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 252/500 ## 90/90 - 0s - 2ms/step - loss: 0.0503 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.1075 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 ## Epoch 253/500 ## 90/90 - 0s - 2ms/step - loss: 0.0507 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0999 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.2500e-04 ## Epoch 254/500 ## 90/90 - 0s - 2ms/step - loss: 0.0478 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0971 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.0000e-04 ## Epoch 255/500 ## 90/90 - 0s - 2ms/step - loss: 0.0464 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0954 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 256/500 ## 90/90 - 0s - 2ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0954 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 257/500 ## 90/90 - 0s - 1ms/step - loss: 0.0472 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1047 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.0000e-04 ## Epoch 258/500 ## 90/90 - 0s - 1ms/step - loss: 0.0448 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 259/500 ## 90/90 - 0s - 1ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1245 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04 ## Epoch 260/500 ## 90/90 - 0s - 1ms/step - loss: 0.0454 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1113 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04 ## Epoch 261/500 ## 90/90 - 0s - 1ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1697 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 1.0000e-04 ## Epoch 262/500 ## 90/90 - 0s - 1ms/step - loss: 0.0450 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 263/500 ## 90/90 - 0s - 1ms/step - loss: 0.0439 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0975 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 264/500 ## 90/90 - 0s - 1ms/step - loss: 0.0498 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1111 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 265/500 ## 90/90 - 0s - 1ms/step - loss: 0.0523 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.0983 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 266/500 ## 90/90 - 0s - 1ms/step - loss: 0.0459 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0997 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 267/500 ## 90/90 - 0s - 1ms/step - loss: 0.0462 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1002 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 268/500 ## 90/90 - 0s - 1ms/step - loss: 0.0438 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.1058 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04 ## Epoch 269/500 ## 90/90 - 0s - 2ms/step - loss: 0.0491 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1289 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.0000e-04 ## Epoch 270/500 ## 90/90 - 0s - 2ms/step - loss: 0.0430 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 1.0000e-04 ## Epoch 271/500 ## 90/90 - 0s - 1ms/step - loss: 0.0454 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1096 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04 ## Epoch 272/500 ## 90/90 - 0s - 1ms/step - loss: 0.0455 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1262 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04 ## Epoch 273/500 ## 90/90 - 0s - 1ms/step - loss: 0.0469 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1129 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04 ## Epoch 274/500 ## 90/90 - 0s - 1ms/step - loss: 0.0429 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.1119 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 275/500 ## 90/90 - 0s - 1ms/step - loss: 0.0430 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1392 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04 ## Epoch 276/500 ## 90/90 - 0s - 1ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 277/500 ## 90/90 - 0s - 1ms/step - loss: 0.0457 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 278/500 ## 90/90 - 0s - 1ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1205 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 279/500 ## 90/90 - 0s - 1ms/step - loss: 0.0473 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0958 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 280/500 ## 90/90 - 0s - 2ms/step - loss: 0.0459 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0947 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 281/500 ## 90/90 - 0s - 1ms/step - loss: 0.0438 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1216 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04 ## Epoch 282/500 ## 90/90 - 0s - 1ms/step - loss: 0.0419 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.1008 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 283/500 ## 90/90 - 0s - 1ms/step - loss: 0.0481 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1129 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 284/500 ## 90/90 - 0s - 1ms/step - loss: 0.0480 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.1418 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04 ## Epoch 285/500 ## 90/90 - 0s - 1ms/step - loss: 0.0467 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1058 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 286/500 ## 90/90 - 0s - 1ms/step - loss: 0.0449 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1039 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 1.0000e-04 ## Epoch 287/500 ## 90/90 - 0s - 1ms/step - loss: 0.0450 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1015 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 288/500 ## 90/90 - 0s - 2ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1156 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 289/500 ## 90/90 - 0s - 1ms/step - loss: 0.0460 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1000 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04 ## Epoch 290/500 ## 90/90 - 0s - 1ms/step - loss: 0.0482 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0967 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 291/500 ## 90/90 - 0s - 1ms/step - loss: 0.0433 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.1002 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04 ## Epoch 292/500 ## 90/90 - 0s - 1ms/step - loss: 0.0506 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1102 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04 ## Epoch 293/500 ## 90/90 - 0s - 1ms/step - loss: 0.0473 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1139 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.0000e-04 ## Epoch 294/500 ## 90/90 - 0s - 1ms/step - loss: 0.0459 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1005 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04 ## Epoch 295/500 ## 90/90 - 0s - 1ms/step - loss: 0.0502 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.0978 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 296/500 ## 90/90 - 0s - 1ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1006 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 ## Epoch 297/500 ## 90/90 - 0s - 1ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0978 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 298/500 ## 90/90 - 0s - 1ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0953 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 ## Epoch 299/500 ## 90/90 - 0s - 1ms/step - loss: 0.0410 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.1107 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 ## Epoch 300/500 ## 90/90 - 0s - 1ms/step - loss: 0.0423 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.1099 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.0000e-04 ## Epoch 301/500 ## 90/90 - 0s - 1ms/step - loss: 0.0457 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0983 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04 ## Epoch 302/500 ## 90/90 - 0s - 1ms/step - loss: 0.0422 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1233 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04 ## Epoch 303/500 ## 90/90 - 0s - 2ms/step - loss: 0.0417 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.1175 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04 ## Epoch 304/500 ## 90/90 - 0s - 2ms/step - loss: 0.0467 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1007 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 1.0000e-04 ## Epoch 305/500 ## 90/90 - 0s - 2ms/step - loss: 0.0466 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1058 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 306/500 ## 90/90 - 0s - 1ms/step - loss: 0.0402 - sparse_categorical_accuracy: 0.9899 - val_loss: 0.0999 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 ## Epoch 307/500 ## 90/90 - 0s - 2ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1130 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 1.0000e-04 ## Epoch 308/500 ## 90/90 - 0s - 1ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04 ## Epoch 309/500 ## 90/90 - 0s - 1ms/step - loss: 0.0472 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1179 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 310/500 ## 90/90 - 0s - 1ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 311/500 ## 90/90 - 0s - 1ms/step - loss: 0.0448 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1012 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.0000e-04 ## Epoch 312/500 ## 90/90 - 0s - 1ms/step - loss: 0.0442 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1172 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.0000e-04 ## Epoch 313/500 ## 90/90 - 0s - 2ms/step - loss: 0.0438 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0990 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 314/500 ## 90/90 - 0s - 2ms/step - loss: 0.0432 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 315/500 ## 90/90 - 0s - 1ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0980 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04 ## Epoch 316/500 ## 90/90 - 0s - 1ms/step - loss: 0.0429 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1030 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04 ## Epoch 317/500 ## 90/90 - 0s - 3ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1018 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04 ## Epoch 318/500 ## 90/90 - 0s - 2ms/step - loss: 0.0423 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1024 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 1.0000e-04 ## Epoch 319/500 ## 90/90 - 0s - 2ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1197 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 ## Epoch 320/500 ## 90/90 - 0s - 2ms/step - loss: 0.0405 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0994 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 1.0000e-04 ## Epoch 321/500 ## 90/90 - 0s - 2ms/step - loss: 0.0442 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.1499 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 1.0000e-04 ## Epoch 322/500 ## 90/90 - 0s - 2ms/step - loss: 0.0404 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1253 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.0000e-04 ## Epoch 323/500 ## 90/90 - 0s - 1ms/step - loss: 0.0468 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.1081 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 ## Epoch 324/500 ## 90/90 - 0s - 1ms/step - loss: 0.0418 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1016 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04 ## Epoch 325/500 ## 90/90 - 0s - 1ms/step - loss: 0.0420 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1290 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 1.0000e-04 ## Epoch 326/500 ## 90/90 - 0s - 2ms/step - loss: 0.0436 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1270 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 1.0000e-04 ## Epoch 327/500 ## 90/90 - 0s - 1ms/step - loss: 0.0417 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1009 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.0000e-04 ## Epoch 328/500 ## 90/90 - 0s - 2ms/step - loss: 0.0447 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1141 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 ## Epoch 329/500 ## 90/90 - 0s - 2ms/step - loss: 0.0422 - sparse_categorical_accuracy: 0.9885 - val_loss: 0.1198 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 1.0000e-04 ## Epoch 330/500 ## 90/90 - 0s - 3ms/step - loss: 0.0435 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0988 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 1.0000e-04 ## Epoch 330: early stopping"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"evaluate-model-on-test-data","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Evaluate model on test data","title":"Timeseries classification from scratch","text":"","code":"model <- load_model(\"best_model.keras\")  results <- model |> evaluate(x_test, y_test) ## 42/42 - 1s - 15ms/step - loss: 0.0980 - sparse_categorical_accuracy: 0.9682 str(results) ## List of 2 ##  $ loss                       : num 0.098 ##  $ sparse_categorical_accuracy: num 0.968 cat(   \"Test accuracy: \", results$sparse_categorical_accuracy, \"\\n\",   \"Test loss: \", results$loss, \"\\n\",   sep = \"\" ) ## Test accuracy: 0.9681818 ## Test loss: 0.09795157"},{"path":"https://keras.posit.co/articles/examples/timeseries/timeseries_classification_from_scratch.html","id":"plot-the-models-training-history","dir":"Articles > Examples > Timeseries","previous_headings":"","what":"Plot the model’s training history","title":"Timeseries classification from scratch","text":"Plot just training validation accuracy: can see training accuracy reaches almost 0.95 100 epochs. However, observing validation accuracy can see network still needs training reaches almost 0.97 validation training accuracy 200 epochs. Beyond 200th epoch, continue training, validation accuracy start decreasing training accuracy continue increasing: model starts overfitting.","code":"plot(history) plot(history, metric = \"sparse_categorical_accuracy\") +   # scale x axis to actual number of epochs run before early stopping   ggplot2::xlim(0, length(history$metrics$loss))"},{"path":"https://keras.posit.co/articles/examples/vision/autoencoder.html","id":"introduction","dir":"Articles > Examples > Vision","previous_headings":"","what":"Introduction","title":"Convolutional autoencoder for image denoising","text":"example demonstrates implement deep convolutional autoencoder image denoising, mapping noisy digits images MNIST dataset clean digits images. implementation based original blog post titled Building Autoencoders Keras François Chollet.","code":""},{"path":"https://keras.posit.co/articles/examples/vision/autoencoder.html","id":"setup","dir":"Articles > Examples > Vision","previous_headings":"","what":"Setup","title":"Convolutional autoencoder for image denoising","text":"","code":"library(keras3)  # Normalizes the supplied array and reshapes it. preprocess <- function(array) {   array_reshape(array/255, c(dim(array)[1], 28, 28, 1)) }  # Adds random noise to each image in the supplied array. noise <- function(array) {   noise_factor <- 0.4   noisy_array <- array + noise_factor * random_normal(dim(array))   op_clip(noisy_array, 0.0, 1.0) }  display <- function(array1, array2) {   n <- 2   indices <- sample.int(dim(array1)[1], n)   images1 <- as.array(array1)[indices, , , ]   images2 <- as.array(array2)[indices, , , ]    par(mfrow = c(2, n), mar = c(0, 0, 0, 0))   for (i in seq_len(n)) {     plot(as.raster(images1[i, , ]))     plot(as.raster(images2[i, , ]))   } }"},{"path":"https://keras.posit.co/articles/examples/vision/autoencoder.html","id":"prepare-the-data","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare the data","title":"Convolutional autoencoder for image denoising","text":"","code":"# Since we only need images from the dataset to encode and decode, we # won't use the labels. c(c(train_data, .), c(test_data, .)) %<-% dataset_mnist()  # Normalize and reshape the data train_data <- preprocess(train_data) test_data <- preprocess(test_data)  # Create a copy of the data with added noise noisy_train_data <- noise(train_data) noisy_test_data <- noise(test_data)  # Display the train data and a version of it with added noise display(train_data, noisy_train_data)"},{"path":"https://keras.posit.co/articles/examples/vision/autoencoder.html","id":"build-the-autoencoder","dir":"Articles > Examples > Vision","previous_headings":"","what":"Build the autoencoder","title":"Convolutional autoencoder for image denoising","text":"going use Functional API build convolutional autoencoder. Now can train autoencoder using train_data input data target. Notice setting validation data using format. Let’s predict test dataset display original image together prediction autoencoder. Notice predictions pretty close original images, although quite . Now know autoencoder works, let’s retrain using noisy data input clean data target. want autoencoder learn denoise images. Let’s now predict noisy data display results autoencoder. Notice autoencoder amazing job removing noise input images.","code":"input <- keras_input(shape = c(28, 28, 1))  # Encoder enc <- input |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3),                 activation = \"relu\", padding = \"same\") |>   layer_max_pooling_2d(pool_size = c(2, 2), padding = \"same\") |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3),                 activation = \"relu\", padding = \"same\") |>   layer_max_pooling_2d(pool_size = c(2, 2), padding = \"same\")  # Decoder dec <- enc |>   layer_conv_2d_transpose(filters = 32, kernel_size = c(3, 3), strides = 2,                           activation = \"relu\", padding = \"same\") |>   layer_conv_2d_transpose(filters = 32, kernel_size = c(3, 3), strides = 2,                           activation = \"relu\", padding = \"same\") |>   layer_conv_2d(filters = 1, kernel_size = c(3, 3),                 activation = \"sigmoid\", padding = \"same\")  # Autoencoder autoencoder <- keras_model(input, dec) autoencoder |> compile(optimizer = \"adam\", loss = \"binary_crossentropy\") autoencoder |> summary() ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 28, 28, 32)     │           320 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 14, 14, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 14, 14, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 7, 7, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 14, 14, 32)     │         9,248 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 28, 28, 32)     │         9,248 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 28, 28, 1)      │           289 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 28,353 (110.75 KB) ##  Trainable params: 28,353 (110.75 KB) ##  Non-trainable params: 0 (0.00 B) autoencoder |> fit(   x = train_data,   y = train_data,   epochs = 50,   batch_size = 128,   shuffle = TRUE,   validation_data = list(test_data, test_data), ) ## Epoch 1/50 ## 469/469 - 5s - 10ms/step - loss: 0.1371 - val_loss: 0.0727 ## Epoch 2/50 ## 469/469 - 1s - 3ms/step - loss: 0.0712 - val_loss: 0.0692 ## Epoch 3/50 ## 469/469 - 1s - 3ms/step - loss: 0.0690 - val_loss: 0.0679 ## Epoch 4/50 ## 469/469 - 1s - 3ms/step - loss: 0.0679 - val_loss: 0.0671 ## Epoch 5/50 ## 469/469 - 1s - 3ms/step - loss: 0.0672 - val_loss: 0.0665 ## Epoch 6/50 ## 469/469 - 1s - 3ms/step - loss: 0.0667 - val_loss: 0.0660 ## Epoch 7/50 ## 469/469 - 1s - 3ms/step - loss: 0.0662 - val_loss: 0.0656 ## Epoch 8/50 ## 469/469 - 1s - 3ms/step - loss: 0.0659 - val_loss: 0.0653 ## Epoch 9/50 ## 469/469 - 2s - 3ms/step - loss: 0.0656 - val_loss: 0.0650 ## Epoch 10/50 ## 469/469 - 2s - 3ms/step - loss: 0.0653 - val_loss: 0.0648 ## Epoch 11/50 ## 469/469 - 1s - 3ms/step - loss: 0.0651 - val_loss: 0.0646 ## Epoch 12/50 ## 469/469 - 1s - 3ms/step - loss: 0.0650 - val_loss: 0.0644 ## Epoch 13/50 ## 469/469 - 1s - 3ms/step - loss: 0.0648 - val_loss: 0.0643 ## Epoch 14/50 ## 469/469 - 2s - 3ms/step - loss: 0.0646 - val_loss: 0.0642 ## Epoch 15/50 ## 469/469 - 1s - 3ms/step - loss: 0.0645 - val_loss: 0.0640 ## Epoch 16/50 ## 469/469 - 1s - 3ms/step - loss: 0.0644 - val_loss: 0.0639 ## Epoch 17/50 ## 469/469 - 1s - 3ms/step - loss: 0.0643 - val_loss: 0.0638 ## Epoch 18/50 ## 469/469 - 1s - 3ms/step - loss: 0.0642 - val_loss: 0.0637 ## Epoch 19/50 ## 469/469 - 1s - 3ms/step - loss: 0.0641 - val_loss: 0.0637 ## Epoch 20/50 ## 469/469 - 1s - 3ms/step - loss: 0.0640 - val_loss: 0.0636 ## Epoch 21/50 ## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0635 ## Epoch 22/50 ## 469/469 - 1s - 3ms/step - loss: 0.0639 - val_loss: 0.0634 ## Epoch 23/50 ## 469/469 - 1s - 3ms/step - loss: 0.0638 - val_loss: 0.0634 ## Epoch 24/50 ## 469/469 - 1s - 3ms/step - loss: 0.0637 - val_loss: 0.0633 ## Epoch 25/50 ## 469/469 - 1s - 3ms/step - loss: 0.0637 - val_loss: 0.0633 ## Epoch 26/50 ## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0632 ## Epoch 27/50 ## 469/469 - 1s - 3ms/step - loss: 0.0636 - val_loss: 0.0632 ## Epoch 28/50 ## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631 ## Epoch 29/50 ## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631 ## Epoch 30/50 ## 469/469 - 1s - 3ms/step - loss: 0.0635 - val_loss: 0.0631 ## Epoch 31/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0630 ## Epoch 32/50 ## 469/469 - 1s - 3ms/step - loss: 0.0634 - val_loss: 0.0630 ## Epoch 33/50 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0629 ## Epoch 34/50 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0629 ## Epoch 35/50 ## 469/469 - 1s - 3ms/step - loss: 0.0633 - val_loss: 0.0629 ## Epoch 36/50 ## 469/469 - 1s - 3ms/step - loss: 0.0632 - val_loss: 0.0629 ## Epoch 37/50 ## 469/469 - 1s - 3ms/step - loss: 0.0632 - val_loss: 0.0628 ## Epoch 38/50 ## 469/469 - 1s - 3ms/step - loss: 0.0632 - val_loss: 0.0628 ## Epoch 39/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0628 ## Epoch 40/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627 ## Epoch 41/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627 ## Epoch 42/50 ## 469/469 - 1s - 3ms/step - loss: 0.0631 - val_loss: 0.0627 ## Epoch 43/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627 ## Epoch 44/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0627 ## Epoch 45/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0626 ## Epoch 46/50 ## 469/469 - 1s - 3ms/step - loss: 0.0630 - val_loss: 0.0626 ## Epoch 47/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626 ## Epoch 48/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626 ## Epoch 49/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0626 ## Epoch 50/50 ## 469/469 - 1s - 3ms/step - loss: 0.0629 - val_loss: 0.0625 predictions <- autoencoder |> predict(test_data) ## 313/313 - 1s - 2ms/step display(test_data, predictions) autoencoder |> fit(   x = noisy_train_data,   y = train_data,   epochs = 100,   batch_size = 128,   shuffle = TRUE,   validation_data = list(noisy_test_data, test_data), ) ## Epoch 1/100 ## 469/469 - 1s - 3ms/step - loss: 0.1006 - val_loss: 0.0941 ## Epoch 2/100 ## 469/469 - 1s - 3ms/step - loss: 0.0938 - val_loss: 0.0922 ## Epoch 3/100 ## 469/469 - 1s - 3ms/step - loss: 0.0922 - val_loss: 0.0910 ## Epoch 4/100 ## 469/469 - 1s - 3ms/step - loss: 0.0912 - val_loss: 0.0901 ## Epoch 5/100 ## 469/469 - 1s - 3ms/step - loss: 0.0905 - val_loss: 0.0895 ## Epoch 6/100 ## 469/469 - 1s - 3ms/step - loss: 0.0899 - val_loss: 0.0891 ## Epoch 7/100 ## 469/469 - 1s - 3ms/step - loss: 0.0895 - val_loss: 0.0887 ## Epoch 8/100 ## 469/469 - 1s - 3ms/step - loss: 0.0891 - val_loss: 0.0883 ## Epoch 9/100 ## 469/469 - 1s - 3ms/step - loss: 0.0888 - val_loss: 0.0881 ## Epoch 10/100 ## 469/469 - 1s - 3ms/step - loss: 0.0885 - val_loss: 0.0878 ## Epoch 11/100 ## 469/469 - 1s - 3ms/step - loss: 0.0882 - val_loss: 0.0876 ## Epoch 12/100 ## 469/469 - 1s - 3ms/step - loss: 0.0880 - val_loss: 0.0874 ## Epoch 13/100 ## 469/469 - 1s - 3ms/step - loss: 0.0878 - val_loss: 0.0873 ## Epoch 14/100 ## 469/469 - 1s - 3ms/step - loss: 0.0877 - val_loss: 0.0871 ## Epoch 15/100 ## 469/469 - 1s - 3ms/step - loss: 0.0875 - val_loss: 0.0869 ## Epoch 16/100 ## 469/469 - 1s - 3ms/step - loss: 0.0874 - val_loss: 0.0868 ## Epoch 17/100 ## 469/469 - 1s - 3ms/step - loss: 0.0872 - val_loss: 0.0867 ## Epoch 18/100 ## 469/469 - 1s - 3ms/step - loss: 0.0871 - val_loss: 0.0866 ## Epoch 19/100 ## 469/469 - 1s - 3ms/step - loss: 0.0870 - val_loss: 0.0865 ## Epoch 20/100 ## 469/469 - 1s - 3ms/step - loss: 0.0869 - val_loss: 0.0864 ## Epoch 21/100 ## 469/469 - 1s - 3ms/step - loss: 0.0868 - val_loss: 0.0863 ## Epoch 22/100 ## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0862 ## Epoch 23/100 ## 469/469 - 1s - 3ms/step - loss: 0.0867 - val_loss: 0.0861 ## Epoch 24/100 ## 469/469 - 1s - 3ms/step - loss: 0.0866 - val_loss: 0.0861 ## Epoch 25/100 ## 469/469 - 2s - 4ms/step - loss: 0.0865 - val_loss: 0.0860 ## Epoch 26/100 ## 469/469 - 2s - 4ms/step - loss: 0.0865 - val_loss: 0.0860 ## Epoch 27/100 ## 469/469 - 2s - 4ms/step - loss: 0.0864 - val_loss: 0.0859 ## Epoch 28/100 ## 469/469 - 2s - 4ms/step - loss: 0.0863 - val_loss: 0.0858 ## Epoch 29/100 ## 469/469 - 1s - 3ms/step - loss: 0.0863 - val_loss: 0.0858 ## Epoch 30/100 ## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0858 ## Epoch 31/100 ## 469/469 - 1s - 3ms/step - loss: 0.0862 - val_loss: 0.0857 ## Epoch 32/100 ## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0857 ## Epoch 33/100 ## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0856 ## Epoch 34/100 ## 469/469 - 1s - 3ms/step - loss: 0.0861 - val_loss: 0.0856 ## Epoch 35/100 ## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0855 ## Epoch 36/100 ## 469/469 - 1s - 3ms/step - loss: 0.0860 - val_loss: 0.0855 ## Epoch 37/100 ## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0855 ## Epoch 38/100 ## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0855 ## Epoch 39/100 ## 469/469 - 1s - 3ms/step - loss: 0.0859 - val_loss: 0.0854 ## Epoch 40/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854 ## Epoch 41/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854 ## Epoch 42/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0854 ## Epoch 43/100 ## 469/469 - 1s - 3ms/step - loss: 0.0858 - val_loss: 0.0853 ## Epoch 44/100 ## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853 ## Epoch 45/100 ## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853 ## Epoch 46/100 ## 469/469 - 1s - 3ms/step - loss: 0.0857 - val_loss: 0.0853 ## Epoch 47/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852 ## Epoch 48/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852 ## Epoch 49/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852 ## Epoch 50/100 ## 469/469 - 1s - 3ms/step - loss: 0.0856 - val_loss: 0.0852 ## Epoch 51/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851 ## Epoch 52/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851 ## Epoch 53/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851 ## Epoch 54/100 ## 469/469 - 1s - 3ms/step - loss: 0.0855 - val_loss: 0.0851 ## Epoch 55/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0851 ## Epoch 56/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850 ## Epoch 57/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850 ## Epoch 58/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850 ## Epoch 59/100 ## 469/469 - 1s - 3ms/step - loss: 0.0854 - val_loss: 0.0850 ## Epoch 60/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0850 ## Epoch 61/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0850 ## Epoch 62/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849 ## Epoch 63/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849 ## Epoch 64/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849 ## Epoch 65/100 ## 469/469 - 1s - 3ms/step - loss: 0.0853 - val_loss: 0.0849 ## Epoch 66/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849 ## Epoch 67/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849 ## Epoch 68/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849 ## Epoch 69/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0849 ## Epoch 70/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848 ## Epoch 71/100 ## 469/469 - 1s - 3ms/step - loss: 0.0852 - val_loss: 0.0848 ## Epoch 72/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 73/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 74/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 75/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 76/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 77/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0848 ## Epoch 78/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847 ## Epoch 79/100 ## 469/469 - 1s - 3ms/step - loss: 0.0851 - val_loss: 0.0847 ## Epoch 80/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 81/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 82/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 83/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 84/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 85/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 86/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 87/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0847 ## Epoch 88/100 ## 469/469 - 1s - 3ms/step - loss: 0.0850 - val_loss: 0.0846 ## Epoch 89/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 90/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 91/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 92/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 93/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 94/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 95/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 96/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 97/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 98/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 99/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 ## Epoch 100/100 ## 469/469 - 1s - 3ms/step - loss: 0.0849 - val_loss: 0.0846 predictions <- autoencoder |> predict(noisy_test_data) ## 313/313 - 0s - 673us/step display(noisy_test_data, predictions)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"setup","dir":"Articles > Examples > Vision","previous_headings":"","what":"Setup","title":"Simple MNIST convnet","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"prepare-the-data","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare the data","title":"Simple MNIST convnet","text":"","code":"# Model / data parameters num_classes <- 10 input_shape <- c(28, 28, 1)  # Load the data and split it between train and test sets c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()  # Scale images to the [0, 1] range x_train <- x_train / 255 x_test <- x_test / 255 # Make sure images have shape (28, 28, 1) x_train <- op_expand_dims(x_train, -1) x_test <- op_expand_dims(x_test, -1)   dim(x_train) ## [1] 60000    28    28     1 dim(x_test) ## [1] 10000    28    28     1 # convert class vectors to binary class matrices y_train <- to_categorical(y_train, num_classes) y_test <- to_categorical(y_test, num_classes)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"build-the-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Build the model","title":"Simple MNIST convnet","text":"","code":"model <- keras_model_sequential(input_shape = input_shape) model |>   layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_flatten() |>   layer_dropout(rate = 0.5) |>   layer_dense(units = num_classes, activation = \"softmax\")  summary(model) ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 26, 26, 32)     │           320 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 13, 13, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 11, 11, 64)     │        18,496 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 5, 5, 64)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ flatten (Flatten)               │ (None, 1600)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 1600)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 10)             │        16,010 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 34,826 (136.04 KB) ##  Trainable params: 34,826 (136.04 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"train-the-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Train the model","title":"Simple MNIST convnet","text":"","code":"batch_size <- 128 epochs <- 15  model |> compile(   loss = \"categorical_crossentropy\",   optimizer = \"adam\",   metrics = \"accuracy\" )  model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = epochs,   validation_split = 0.1 ) ## Epoch 1/15 ## 422/422 - 5s - 11ms/step - accuracy: 0.8895 - loss: 0.3636 - val_accuracy: 0.9787 - val_loss: 0.0792 ## Epoch 2/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9664 - loss: 0.1111 - val_accuracy: 0.9850 - val_loss: 0.0550 ## Epoch 3/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9743 - loss: 0.0824 - val_accuracy: 0.9882 - val_loss: 0.0441 ## Epoch 4/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9786 - loss: 0.0695 - val_accuracy: 0.9895 - val_loss: 0.0400 ## Epoch 5/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9804 - loss: 0.0626 - val_accuracy: 0.9900 - val_loss: 0.0355 ## Epoch 6/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9824 - loss: 0.0558 - val_accuracy: 0.9912 - val_loss: 0.0333 ## Epoch 7/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9835 - loss: 0.0501 - val_accuracy: 0.9918 - val_loss: 0.0310 ## Epoch 8/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9851 - loss: 0.0479 - val_accuracy: 0.9922 - val_loss: 0.0310 ## Epoch 9/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9862 - loss: 0.0444 - val_accuracy: 0.9920 - val_loss: 0.0300 ## Epoch 10/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9863 - loss: 0.0438 - val_accuracy: 0.9912 - val_loss: 0.0294 ## Epoch 11/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9873 - loss: 0.0394 - val_accuracy: 0.9913 - val_loss: 0.0304 ## Epoch 12/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9875 - loss: 0.0371 - val_accuracy: 0.9927 - val_loss: 0.0287 ## Epoch 13/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9891 - loss: 0.0346 - val_accuracy: 0.9920 - val_loss: 0.0292 ## Epoch 14/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9891 - loss: 0.0343 - val_accuracy: 0.9922 - val_loss: 0.0284 ## Epoch 15/15 ## 422/422 - 1s - 2ms/step - accuracy: 0.9895 - loss: 0.0320 - val_accuracy: 0.9920 - val_loss: 0.0282"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_convnet.html","id":"evaluate-the-trained-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Evaluate the trained model","title":"Simple MNIST convnet","text":"","code":"score <- model |> evaluate(x_test, y_test, verbose = 0) score ## $accuracy ## [1] 0.9914 ## ## $loss ## [1] 0.02402576"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"introduction","dir":"Articles > Examples > Vision","previous_headings":"","what":"Introduction","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"Siamese Networks neural networks share weights two sister networks, producing embedding vectors respective inputs. supervised similarity learning, networks trained maximize contrast (distance) embeddings inputs different classes, minimizing distance embeddings similar classes, resulting embedding spaces reflect class segmentation training inputs. implementation loosely follows Hadsell-et-al.’06 [1] (see paper mode details) euclidean distance replaced subtraction layer one fully-connect (FC) layer. [1] “Dimensionality Reduction Learning Invariant Mapping” https://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf Gets 98.11% test accuracy 20 epochs. 3 seconds per epoch AMD Ryzen 7 PRO 4750U (CPU)","code":"library(keras3) contrastive_loss <- function(y_true, y_pred) {     # Contrastive loss from Hadsell-et-al.'06     # https://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf     margin = 1     margin_square = op_square(op_maximum(margin - (y_pred), 0))     op_mean((1 - y_true) * op_square(y_pred) + (y_true) * margin_square) }"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"create-pairs-of-images","dir":"Articles > Examples > Vision","previous_headings":"","what":"Create pairs of images","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"train model differentiate digits different classes. example, digit 0 needs differentiated rest digits (1 9), digit 1 - 0 2 9, . carry , select N random images class (example, digit 0) pair N random images another class B (example, digit 1). , can repeat process classes digits (digit 9). paired digit 0 digits, can repeat process remaining classes rest digits (1 9).","code":"create_pairs <- function(x, y) {     # Positive and negative pair creation.     # Alternates between positive and negative pairs.     digit_indices <- tapply(1:length(y), y, list)     y1 <- y     y2 <- sapply(y, function(a) sample(0:9,1,prob=0.1+0.8*(0:9==a)))     idx1 <- 1:nrow(x)     idx2 <- sapply(as.character(y2), function(a) sample(digit_indices[[a]],1))     is_same  <- 1*(y1==y2)     list(pair1 = x[idx1,], pair2 = x[idx2,], y = is_same) }  compute_accuracy <- function(predictions, labels) {     # Compute classification accuracy with a fixed threshold on distances.     mean(labels[predictions > 0.5]) } # the data, shuffled and split between train and test sets mnist   <- dataset_mnist() x_train <- mnist$train$x y_train <- mnist$train$y x_test  <- mnist$test$x y_test  <- mnist$test$y x_train <- array_reshape(x_train, c(nrow(x_train), 784)) x_test  <- array_reshape(x_test, c(nrow(x_test), 784)) x_train <- x_train / 255 x_test  <- x_test / 255  # create training+test positive and negative pairs tr <- create_pairs(x_train, y_train) te <- create_pairs(x_test,  y_test)  names(tr) ## [1] \"pair1\" \"pair2\" \"y\""},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"network-definition","dir":"Articles > Examples > Vision","previous_headings":"","what":"Network definition","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"","code":"# input layers input_dim = 784 input_1 <- layer_input(shape = c(input_dim)) input_2 <- layer_input(shape = c(input_dim))  # definition of the base network that will be shared base_network <- keras_model_sequential() %>%     layer_dense(units = 128, activation = 'relu') %>%     layer_dropout(rate = 0.1) %>%     layer_dense(units = 128, activation = 'relu') %>%     layer_dropout(rate = 0.1) %>%     layer_dense(units = 128, activation = 'relu')  # because we re-use the same instance `base_network`, the weights of # the network will be shared across the two branches branch_1 <- base_network(input_1) branch_2 <- base_network(input_2)  # merging layer out <- layer_subtract(list(branch_1, branch_2)) %>%     layer_dropout(rate = 0.1) %>%     layer_dense(units = 16, activation = 'relu') %>%     layer_dense(1, activation = \"sigmoid\")  # create and compile model model <- keras_model(list(input_1, input_2), out)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"train","dir":"Articles > Examples > Vision","previous_headings":"","what":"Train","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"","code":"model %>% compile(     optimizer = \"rmsprop\",     #loss = \"binary_crossentropy\",     loss = contrastive_loss,     metrics = metric_binary_accuracy )  history <- model %>% fit(     list(tr$pair1, tr$pair2), tr$y,     batch_size = 128,     epochs = 20,     validation_data = list(         list(te$pair1, te$pair2),         te$y     ) ) ## Epoch 1/20 ## 469/469 - 17s - 36ms/step - binary_accuracy: 0.7566 - loss: 0.1644 - val_binary_accuracy: 0.8753 - val_loss: 0.1001 ## Epoch 2/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.8896 - loss: 0.0867 - val_binary_accuracy: 0.9248 - val_loss: 0.0614 ## Epoch 3/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9261 - loss: 0.0579 - val_binary_accuracy: 0.9409 - val_loss: 0.0461 ## Epoch 4/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9474 - loss: 0.0419 - val_binary_accuracy: 0.9529 - val_loss: 0.0382 ## Epoch 5/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9589 - loss: 0.0328 - val_binary_accuracy: 0.9603 - val_loss: 0.0316 ## Epoch 6/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9660 - loss: 0.0272 - val_binary_accuracy: 0.9617 - val_loss: 0.0297 ## Epoch 7/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9708 - loss: 0.0234 - val_binary_accuracy: 0.9665 - val_loss: 0.0270 ## Epoch 8/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9747 - loss: 0.0201 - val_binary_accuracy: 0.9674 - val_loss: 0.0259 ## Epoch 9/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9787 - loss: 0.0175 - val_binary_accuracy: 0.9697 - val_loss: 0.0247 ## Epoch 10/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9807 - loss: 0.0157 - val_binary_accuracy: 0.9684 - val_loss: 0.0251 ## Epoch 11/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9810 - loss: 0.0152 - val_binary_accuracy: 0.9710 - val_loss: 0.0230 ## Epoch 12/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9831 - loss: 0.0138 - val_binary_accuracy: 0.9726 - val_loss: 0.0224 ## Epoch 13/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9854 - loss: 0.0121 - val_binary_accuracy: 0.9724 - val_loss: 0.0224 ## Epoch 14/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9861 - loss: 0.0114 - val_binary_accuracy: 0.9738 - val_loss: 0.0217 ## Epoch 15/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9864 - loss: 0.0110 - val_binary_accuracy: 0.9738 - val_loss: 0.0213 ## Epoch 16/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9881 - loss: 0.0101 - val_binary_accuracy: 0.9754 - val_loss: 0.0206 ## Epoch 17/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9883 - loss: 0.0097 - val_binary_accuracy: 0.9733 - val_loss: 0.0214 ## Epoch 18/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9881 - loss: 0.0097 - val_binary_accuracy: 0.9723 - val_loss: 0.0216 ## Epoch 19/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9891 - loss: 0.0090 - val_binary_accuracy: 0.9749 - val_loss: 0.0202 ## Epoch 20/20 ## 469/469 - 1s - 1ms/step - binary_accuracy: 0.9898 - loss: 0.0085 - val_binary_accuracy: 0.9752 - val_loss: 0.0205 plot(history)"},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"evaluate","dir":"Articles > Examples > Vision","previous_headings":"","what":"Evaluate","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"","code":"# compute final accuracy on training and test sets  tr_pred <- predict(model, list(tr$pair1, tr$pair2))[,1] ## 1875/1875 - 1s - 793us/step tr_acc  <- compute_accuracy(tr_pred, tr$y) te_pred <- predict(model, list(te$pair1, te$pair2))[,1] ## 313/313 - 0s - 1ms/step te_acc  <- compute_accuracy(te_pred, te$y)  sprintf('* Accuracy on training set: %0.2f%%', (100 * tr_acc)) ## [1] \"* Accuracy on training set: 99.63%\" sprintf('* Accuracy on test set: %0.2f%%', (100 * te_acc)) ## [1] \"* Accuracy on test set: 97.93%\""},{"path":"https://keras.posit.co/articles/examples/vision/mnist_siamese_graph.html","id":"plots","dir":"Articles > Examples > Vision","previous_headings":"","what":"Plots","title":"Train a Siamese MLP on pairs of digits from the MNIST dataset.","text":"","code":"par(mfrow=c(1,1)) vioplot::vioplot( te_pred ~ te$y ) i=3 visualizePair <- function(i) {     image(rbind(matrix( te$pair1[i,],28,28)[,28:1], matrix( te$pair2[i,],28,28)[,28:1]))     title(paste(\"true:\", te$y[i],\"|  pred:\", round(te_pred[i],5))) } par(mfrow=c(3,3)) lapply(1:9, visualizePair) ## [[1]] ## NULL ## ## [[2]] ## NULL ## ## [[3]] ## NULL ## ## [[4]] ## NULL ## ## [[5]] ## NULL ## ## [[6]] ## NULL ## ## [[7]] ## NULL ## ## [[8]] ## NULL ## ## [[9]] ## NULL"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"download-the-data","dir":"Articles > Examples > Vision","previous_headings":"","what":"Download the data","title":"Image segmentation with a U-Net-like architecture","text":"","code":"options(timeout = 5000) download.file(   \"https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\",   \"datasets/images.tar.gz\" ) download.file(   \"https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\",   \"datasets/annotations.tar.gz\" )  untar(\"datasets/images.tar.gz\", exdir = \"datasets\") untar(\"datasets/annotations.tar.gz\", exdir = \"datasets\")"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"prepare-paths-of-input-images-and-target-segmentation-masks","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare paths of input images and target segmentation masks","title":"Image segmentation with a U-Net-like architecture","text":"","code":"library(keras3) input_dir <- \"datasets/images/\" target_dir <- \"datasets/annotations/trimaps/\" img_size <- c(160, 160) num_classes <- 3 batch_size <- 32  input_img_paths <- fs::dir_ls(input_dir, glob = \"*.jpg\") |> sort() target_img_paths <- fs::dir_ls(target_dir, glob = \"*.png\") |> sort()  cat(\"Number of samples:\", length(input_img_paths), \"\\n\") ## Number of samples: 7390 for (i in 1:10) {   cat(input_img_paths[i], \"|\", target_img_paths[i], \"\\n\") } ## datasets/images/Abyssinian_1.jpg | datasets/annotations/trimaps/Abyssinian_1.png ## datasets/images/Abyssinian_10.jpg | datasets/annotations/trimaps/Abyssinian_10.png ## datasets/images/Abyssinian_100.jpg | datasets/annotations/trimaps/Abyssinian_100.png ## datasets/images/Abyssinian_101.jpg | datasets/annotations/trimaps/Abyssinian_101.png ## datasets/images/Abyssinian_102.jpg | datasets/annotations/trimaps/Abyssinian_102.png ## datasets/images/Abyssinian_103.jpg | datasets/annotations/trimaps/Abyssinian_103.png ## datasets/images/Abyssinian_104.jpg | datasets/annotations/trimaps/Abyssinian_104.png ## datasets/images/Abyssinian_105.jpg | datasets/annotations/trimaps/Abyssinian_105.png ## datasets/images/Abyssinian_106.jpg | datasets/annotations/trimaps/Abyssinian_106.png ## datasets/images/Abyssinian_107.jpg | datasets/annotations/trimaps/Abyssinian_107.png"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"what-does-one-input-image-and-corresponding-segmentation-mask-look-like","dir":"Articles > Examples > Vision","previous_headings":"","what":"What does one input image and corresponding segmentation mask look like?","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Display input image #10 input_img_paths[10] |>   jpeg::readJPEG() |>   as.raster() |>   plot() target_img_paths[10] |>   png::readPNG() |>   magrittr::multiply_by(255)|>   as.raster(max = 3) |>   plot()"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"prepare-dataset-to-load-vectorize-batches-of-data","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare dataset to load & vectorize batches of data","title":"Image segmentation with a U-Net-like architecture","text":"","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\")   # Returns a tf_dataset get_dataset <- function(batch_size, img_size, input_img_paths, target_img_paths,                         max_dataset_len = NULL) {    img_size <- as.integer(img_size)    load_img_masks <- function(input_img_path, target_img_path) {     input_img <- input_img_path |>       tf$io$read_file() |>       tf$io$decode_jpeg(channels = 3) |>       tf$image$resize(img_size) |>       tf$image$convert_image_dtype(\"float32\")      target_img <- target_img_path |>       tf$io$read_file() |>       tf$io$decode_png(channels = 1) |>       tf$image$resize(img_size, method = \"nearest\") |>       tf$image$convert_image_dtype(\"uint8\")      # Ground truth labels are 1, 2, 3. Subtract one to make them 0, 1, 2:     target_img <- target_img - 1L      list(input_img, target_img)   }    if (!is.null(max_dataset_len)) {     input_img_paths <- input_img_paths[1:max_dataset_len]     target_img_paths <- target_img_paths[1:max_dataset_len]   }    list(input_img_paths, target_img_paths) |>     tensor_slices_dataset() |>     dataset_map(load_img_masks, num_parallel_calls = tf$data$AUTOTUNE)|>     dataset_batch(batch_size) }"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"prepare-u-net-xception-style-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Prepare U-Net Xception-style model","title":"Image segmentation with a U-Net-like architecture","text":"","code":"get_model <- function(img_size, num_classes) {    inputs <- keras_input(shape = c(img_size, 3))    ### [First half of the network: downsampling inputs] ###    # Entry block   x <- inputs |>     layer_conv_2d(filters = 32, kernel_size = 3, strides = 2, padding = \"same\") |>     layer_batch_normalization() |>     layer_activation(\"relu\")    previous_block_activation <- x  # Set aside residual    for (filters in c(64, 128, 256)) {     x <- x |>       layer_activation(\"relu\") |>       layer_separable_conv_2d(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_activation(\"relu\") |>       layer_separable_conv_2d(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_max_pooling_2d(pool_size = 3, strides = 2, padding = \"same\")      residual <- previous_block_activation |>       layer_conv_2d(filters = filters, kernel_size = 1, strides = 2, padding = \"same\")      x <- layer_add(x, residual)  # Add back residual     previous_block_activation <- x  # Set aside next residual   }    ### [Second half of the network: upsampling inputs] ###    for (filters in c(256, 128, 64, 32)) {     x <- x |>       layer_activation(\"relu\") |>       layer_conv_2d_transpose(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_activation(\"relu\") |>       layer_conv_2d_transpose(filters = filters, kernel_size = 3, padding = \"same\") |>       layer_batch_normalization() |>        layer_upsampling_2d(size = 2)      # Project residual     residual <- previous_block_activation |>       layer_upsampling_2d(size = 2) |>       layer_conv_2d(filters = filters, kernel_size = 1, padding = \"same\")      x <- layer_add(x, residual)     # Add back residual     previous_block_activation <- x  # Set aside next residual   }    # Add a per-pixel classification layer   outputs <- x |>     layer_conv_2d(num_classes, 3, activation = \"softmax\", padding = \"same\")    # Define the model   keras_model(inputs, outputs) }  # Build model model <- get_model(img_size, num_classes) summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)      ┃ Output Shape    ┃   Param # ┃ Connected to   ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer       │ (None, 160,     │         0 │ -              │   -   │ ## │ (InputLayer)      │ 160, 3)         │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d (Conv2D)   │ (None, 80, 80,  │       896 │ input_layer[0… │   Y   │ ## │                   │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d[0][0]   │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation        │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_1      │ (None, 80, 80,  │         0 │ activation[0]… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d  │ (None, 80, 80,  │     2,400 │ activation_1[… │   Y   │ ## │ (SeparableConv2D) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       256 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_2      │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 80, 80,  │     4,736 │ activation_2[… │   Y   │ ## │ (SeparableConv2D) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       256 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d     │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_1 (Conv2D) │ (None, 40, 40,  │     2,112 │ activation[0]… │   Y   │ ## │                   │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add (Add)         │ (None, 40, 40,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 64)             │           │ conv2d_1[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_3      │ (None, 40, 40,  │         0 │ add[0][0]      │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 40, 40,  │     8,896 │ activation_3[… │   Y   │ ## │ (SeparableConv2D) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       512 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_4      │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 40, 40,  │    17,664 │ activation_4[… │   Y   │ ## │ (SeparableConv2D) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       512 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d_1   │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_2 (Conv2D) │ (None, 20, 20,  │     8,320 │ add[0][0]      │   Y   │ ## │                   │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_1 (Add)       │ (None, 20, 20,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 128)            │           │ conv2d_2[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_5      │ (None, 20, 20,  │         0 │ add_1[0][0]    │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 20, 20,  │    34,176 │ activation_5[… │   Y   │ ## │ (SeparableConv2D) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │     1,024 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_6      │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ separable_conv2d… │ (None, 20, 20,  │    68,096 │ activation_6[… │   Y   │ ## │ (SeparableConv2D) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │     1,024 │ separable_con… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ max_pooling2d_2   │ (None, 10, 10,  │         0 │ batch_normali… │   -   │ ## │ (MaxPooling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_3 (Conv2D) │ (None, 10, 10,  │    33,024 │ add_1[0][0]    │   Y   │ ## │                   │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_2 (Add)       │ (None, 10, 10,  │         0 │ max_pooling2d… │   -   │ ## │                   │ 256)            │           │ conv2d_3[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_7      │ (None, 10, 10,  │         0 │ add_2[0][0]    │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose  │ (None, 10, 10,  │   590,080 │ activation_7[… │   Y   │ ## │ (Conv2DTranspose) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 10, 10,  │     1,024 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_8      │ (None, 10, 10,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 10, 10,  │   590,080 │ activation_8[… │   Y   │ ## │ (Conv2DTranspose) │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 10, 10,  │     1,024 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_1   │ (None, 20, 20,  │         0 │ add_2[0][0]    │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d     │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_4 (Conv2D) │ (None, 20, 20,  │    65,792 │ up_sampling2d… │   Y   │ ## │                   │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_3 (Add)       │ (None, 20, 20,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 256)            │           │ conv2d_4[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_9      │ (None, 20, 20,  │         0 │ add_3[0][0]    │   -   │ ## │ (Activation)      │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 20, 20,  │   295,040 │ activation_9[… │   Y   │ ## │ (Conv2DTranspose) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │       512 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_10     │ (None, 20, 20,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 20, 20,  │   147,584 │ activation_10… │   Y   │ ## │ (Conv2DTranspose) │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 20, 20,  │       512 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_3   │ (None, 40, 40,  │         0 │ add_3[0][0]    │   -   │ ## │ (UpSampling2D)    │ 256)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_2   │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_5 (Conv2D) │ (None, 40, 40,  │    32,896 │ up_sampling2d… │   Y   │ ## │                   │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_4 (Add)       │ (None, 40, 40,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 128)            │           │ conv2d_5[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_11     │ (None, 40, 40,  │         0 │ add_4[0][0]    │   -   │ ## │ (Activation)      │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 40, 40,  │    73,792 │ activation_11… │   Y   │ ## │ (Conv2DTranspose) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       256 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_12     │ (None, 40, 40,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 40, 40,  │    36,928 │ activation_12… │   Y   │ ## │ (Conv2DTranspose) │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 40, 40,  │       256 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_5   │ (None, 80, 80,  │         0 │ add_4[0][0]    │   -   │ ## │ (UpSampling2D)    │ 128)            │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_4   │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_6 (Conv2D) │ (None, 80, 80,  │     8,256 │ up_sampling2d… │   Y   │ ## │                   │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_5 (Add)       │ (None, 80, 80,  │         0 │ up_sampling2d… │   -   │ ## │                   │ 64)             │           │ conv2d_6[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_13     │ (None, 80, 80,  │         0 │ add_5[0][0]    │   -   │ ## │ (Activation)      │ 64)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 80, 80,  │    18,464 │ activation_13… │   Y   │ ## │ (Conv2DTranspose) │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ activation_14     │ (None, 80, 80,  │         0 │ batch_normali… │   -   │ ## │ (Activation)      │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_transpose… │ (None, 80, 80,  │     9,248 │ activation_14… │   Y   │ ## │ (Conv2DTranspose) │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ batch_normalizat… │ (None, 80, 80,  │       128 │ conv2d_transp… │   Y   │ ## │ (BatchNormalizat… │ 32)             │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_7   │ (None, 160,     │         0 │ add_5[0][0]    │   -   │ ## │ (UpSampling2D)    │ 160, 64)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ up_sampling2d_6   │ (None, 160,     │         0 │ batch_normali… │   -   │ ## │ (UpSampling2D)    │ 160, 32)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_7 (Conv2D) │ (None, 160,     │     2,080 │ up_sampling2d… │   Y   │ ## │                   │ 160, 32)        │           │                │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ add_6 (Add)       │ (None, 160,     │         0 │ up_sampling2d… │   -   │ ## │                   │ 160, 32)        │           │ conv2d_7[0][0] │       │ ## ├───────────────────┼─────────────────┼───────────┼────────────────┼───────┤ ## │ conv2d_8 (Conv2D) │ (None, 160,     │       867 │ add_6[0][0]    │   Y   │ ## │                   │ 160, 3)         │           │                │       │ ## └───────────────────┴─────────────────┴───────────┴────────────────┴───────┘ ##  Total params: 2,058,979 (7.85 MB) ##  Trainable params: 2,055,203 (7.84 MB) ##  Non-trainable params: 3,776 (14.75 KB)"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"set-aside-a-validation-split","dir":"Articles > Examples > Vision","previous_headings":"","what":"Set aside a validation split","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Split our img paths into a training and a validation set val_samples <- 1000 val_samples <- sample.int(length(input_img_paths), val_samples)  train_input_img_paths <- input_img_paths[-val_samples] train_target_img_paths <- target_img_paths[-val_samples]  val_input_img_paths <- input_img_paths[val_samples] val_target_img_paths <- target_img_paths[val_samples]  # Instantiate dataset for each split # Limit input files in `max_dataset_len` for faster epoch training time. # Remove the `max_dataset_len` arg when running with full dataset. train_dataset <- get_dataset(   batch_size,   img_size,   train_input_img_paths,   train_target_img_paths,   max_dataset_len = 1000 ) valid_dataset <- get_dataset(   batch_size, img_size, val_input_img_paths, val_target_img_paths )"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"train-the-model","dir":"Articles > Examples > Vision","previous_headings":"","what":"Train the model","title":"Image segmentation with a U-Net-like architecture","text":"","code":"# Configure the model for training. # We use the \"sparse\" version of categorical_crossentropy # because our target data is integers. model |> compile(   optimizer = optimizer_adam(1e-4),   loss = \"sparse_categorical_crossentropy\" )  callbacks <- list(   callback_model_checkpoint(     \"models/oxford_segmentation.keras\", save_best_only = TRUE   ) )  # Train the model, doing validation at the end of each epoch. epochs <- 50 model |> fit(     train_dataset,     epochs=epochs,     validation_data=valid_dataset,     callbacks=callbacks,     verbose=2 ) ## Epoch 1/50 ## 32/32 - 29s - 914ms/step - loss: 1.4283 - val_loss: 1.5499 ## Epoch 2/50 ## 32/32 - 2s - 60ms/step - loss: 0.9221 - val_loss: 1.9887 ## Epoch 3/50 ## 32/32 - 2s - 60ms/step - loss: 0.7764 - val_loss: 2.5144 ## Epoch 4/50 ## 32/32 - 2s - 61ms/step - loss: 0.7200 - val_loss: 3.0185 ## Epoch 5/50 ## 32/32 - 2s - 61ms/step - loss: 0.6847 - val_loss: 3.2947 ## Epoch 6/50 ## 32/32 - 2s - 68ms/step - loss: 0.6556 - val_loss: 3.4559 ## Epoch 7/50 ## 32/32 - 2s - 76ms/step - loss: 0.6303 - val_loss: 3.5671 ## Epoch 8/50 ## 32/32 - 2s - 62ms/step - loss: 0.6083 - val_loss: 3.6620 ## Epoch 9/50 ## 32/32 - 2s - 63ms/step - loss: 0.5895 - val_loss: 3.7374 ## Epoch 10/50 ## 32/32 - 2s - 62ms/step - loss: 0.5726 - val_loss: 3.8015 ## Epoch 11/50 ## 32/32 - 2s - 63ms/step - loss: 0.5567 - val_loss: 3.8311 ## Epoch 12/50 ## 32/32 - 2s - 63ms/step - loss: 0.5407 - val_loss: 3.8089 ## Epoch 13/50 ## 32/32 - 2s - 62ms/step - loss: 0.5242 - val_loss: 3.7293 ## Epoch 14/50 ## 32/32 - 2s - 61ms/step - loss: 0.5062 - val_loss: 3.5953 ## Epoch 15/50 ## 32/32 - 2s - 61ms/step - loss: 0.4860 - val_loss: 3.4506 ## Epoch 16/50 ## 32/32 - 2s - 60ms/step - loss: 0.4637 - val_loss: 3.2588 ## Epoch 17/50 ## 32/32 - 2s - 61ms/step - loss: 0.4396 - val_loss: 3.0063 ## Epoch 18/50 ## 32/32 - 2s - 61ms/step - loss: 0.4140 - val_loss: 2.7031 ## Epoch 19/50 ## 32/32 - 2s - 61ms/step - loss: 0.3877 - val_loss: 2.3504 ## Epoch 20/50 ## 32/32 - 2s - 61ms/step - loss: 0.3623 - val_loss: 1.9645 ## Epoch 21/50 ## 32/32 - 2s - 62ms/step - loss: 0.3392 - val_loss: 1.6364 ## Epoch 22/50 ## 32/32 - 2s - 66ms/step - loss: 0.3203 - val_loss: 1.3376 ## Epoch 23/50 ## 32/32 - 2s - 67ms/step - loss: 0.3082 - val_loss: 1.0917 ## Epoch 24/50 ## 32/32 - 2s - 66ms/step - loss: 0.3084 - val_loss: 1.0189 ## Epoch 25/50 ## 32/32 - 2s - 66ms/step - loss: 0.3461 - val_loss: 0.9181 ## Epoch 26/50 ## 32/32 - 2s - 61ms/step - loss: 0.3649 - val_loss: 1.0231 ## Epoch 27/50 ## 32/32 - 2s - 66ms/step - loss: 0.3294 - val_loss: 0.8574 ## Epoch 28/50 ## 32/32 - 2s - 61ms/step - loss: 0.2891 - val_loss: 1.0065 ## Epoch 29/50 ## 32/32 - 2s - 61ms/step - loss: 0.2731 - val_loss: 1.1668 ## Epoch 30/50 ## 32/32 - 2s - 61ms/step - loss: 0.2681 - val_loss: 1.1974 ## Epoch 31/50 ## 32/32 - 2s - 61ms/step - loss: 0.2723 - val_loss: 1.1259 ## Epoch 32/50 ## 32/32 - 2s - 61ms/step - loss: 0.2872 - val_loss: 1.1112 ## Epoch 33/50 ## 32/32 - 2s - 61ms/step - loss: 0.3146 - val_loss: 1.2479 ## Epoch 34/50 ## 32/32 - 2s - 61ms/step - loss: 0.3034 - val_loss: 1.1946 ## Epoch 35/50 ## 32/32 - 2s - 61ms/step - loss: 0.2880 - val_loss: 1.0699 ## Epoch 36/50 ## 32/32 - 2s - 61ms/step - loss: 0.2770 - val_loss: 1.0195 ## Epoch 37/50 ## 32/32 - 2s - 61ms/step - loss: 0.2669 - val_loss: 1.2986 ## Epoch 38/50 ## 32/32 - 2s - 61ms/step - loss: 0.2544 - val_loss: 1.0411 ## Epoch 39/50 ## 32/32 - 2s - 61ms/step - loss: 0.2383 - val_loss: 1.3622 ## Epoch 40/50 ## 32/32 - 2s - 61ms/step - loss: 0.2378 - val_loss: 1.3429 ## Epoch 41/50 ## 32/32 - 2s - 61ms/step - loss: 0.2439 - val_loss: 1.2881 ## Epoch 42/50 ## 32/32 - 2s - 61ms/step - loss: 0.2406 - val_loss: 1.5361 ## Epoch 43/50 ## 32/32 - 2s - 61ms/step - loss: 0.2417 - val_loss: 1.2444 ## Epoch 44/50 ## 32/32 - 2s - 61ms/step - loss: 0.2275 - val_loss: 1.1713 ## Epoch 45/50 ## 32/32 - 2s - 61ms/step - loss: 0.2168 - val_loss: 1.0482 ## Epoch 46/50 ## 32/32 - 2s - 62ms/step - loss: 0.2096 - val_loss: 1.1095 ## Epoch 47/50 ## 32/32 - 2s - 61ms/step - loss: 0.2026 - val_loss: 1.1017 ## Epoch 48/50 ## 32/32 - 2s - 61ms/step - loss: 0.1986 - val_loss: 1.1087 ## Epoch 49/50 ## 32/32 - 2s - 61ms/step - loss: 0.1924 - val_loss: 1.1055 ## Epoch 50/50 ## 32/32 - 2s - 61ms/step - loss: 0.1846 - val_loss: 1.0708"},{"path":"https://keras.posit.co/articles/examples/vision/oxford_pets_image_segmentation.html","id":"visualize-predictions","dir":"Articles > Examples > Vision","previous_headings":"","what":"Visualize predictions","title":"Image segmentation with a U-Net-like architecture","text":"","code":"model <- load_model(\"models/oxford_segmentation.keras\") # Generate predictions for all images in the validation set val_dataset <- get_dataset(   batch_size, img_size, val_input_img_paths, val_target_img_paths ) val_preds <- predict(model, val_dataset) ## 32/32 - 3s - 86ms/step display_mask <- function(i) {   # Quick utility to display a model's prediction.   mask <- val_preds[i,,,] %>%     apply(c(1,2), which.max) %>%     array_reshape(dim = c(img_size, 1))   mask <- abind::abind(mask, mask, mask, along = 3)   plot(as.raster(mask, max = 3)) }  # Display results for validation image #10 i <- 10  par(mfrow = c(1, 3)) # Display input image input_img_paths[i] |>   jpeg::readJPEG() |>   as.raster() |>   plot()  # Display ground-truth target mask target_img_paths[i] |>   png::readPNG() |>   magrittr::multiply_by(255)|>   as.raster(max = 3) |>   plot()  # Display mask predicted by our model display_mask(i)  # Note that the model only sees inputs at 150x150."},{"path":"https://keras.posit.co/articles/functional_api.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"The Functional API","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"The Functional API","text":"Keras functional API way create models flexible sequential API. functional API can handle models non-linear topology, shared layers, even multiple inputs outputs. main idea deep learning model usually directed acyclic graph (DAG) layers. functional API way build graphs layers. Consider following model: basic graph three layers. build model using functional API, start creating input node: shape data set 784-dimensional vector. batch size always omitted since shape sample specified. , example, image input shape (32, 32, 3), use: inputs returned contains information shape dtype input data feed model. ’s shape: ’s dtype: create new node graph layers calling layer inputs object: “layer call” action like drawing arrow “inputs” layer created. ’re “passing” inputs dense layer, get x output. Let’s add layers graph layers: point, can create Model specifying inputs outputs graph layers: Let’s check model summary looks like: can also plot model graph:  , optionally, display input output shapes layer plotted graph:  figure code almost identical. code version, connection arrows replaced call operation. “graph layers” intuitive mental image deep learning model, functional API way create models closely mirrors .","code":"(input: 784-dimensional vectors)        ↧ [Dense (64 units, relu activation)]        ↧ [Dense (64 units, relu activation)]        ↧ [Dense (10 units, softmax activation)]        ↧ (output: logits of a probability distribution over 10 classes) inputs <- keras_input(shape = c(784)) # Just for demonstration purposes. img_inputs <- keras_input(shape = c(32, 32, 3)) shape(inputs) ## shape(NA, 784) inputs$dtype ## [1] \"float32\" dense <- layer_dense(units = 64, activation=\"relu\") x <- dense(inputs) outputs <- x |>   layer_dense(units = 64, activation = \"relu\") |>   layer_dense(units = 10) model <- keras_model(inputs = inputs, outputs = outputs, name = \"mnist_model\") summary(model) ## Model: \"mnist_model\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ input_layer (InputLayer)        │ (None, 784)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 64)             │        50,240 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_1 (Dense)                 │ (None, 64)             │         4,160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 10)             │           650 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 55,050 (215.04 KB) ##  Trainable params: 55,050 (215.04 KB) ##  Non-trainable params: 0 (0.00 B) plot(model) plot(model, show_shapes = TRUE)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"training-evaluation-and-inference","dir":"Articles","previous_headings":"","what":"Training, evaluation, and inference","title":"The Functional API","text":"Training, evaluation, inference work exactly way models built using functional API Sequential models. Model class offers built-training loop (fit() method) built-evaluation loop (evaluate() method). Note can easily customize loops implement training routines beyond supervised learning (e.g. GANs). , load MNIST image data, reshape vectors, fit model data (monitoring performance validation split), evaluate model test data: reading, see training evaluation guide.","code":"c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()  x_train <- array_reshape(x_train, c(60000, 784)) / 255 x_test <- array_reshape(x_test, c(10000, 784)) / 255  model |> compile(   loss = loss_sparse_categorical_crossentropy(from_logits = TRUE),   optimizer = optimizer_rmsprop(),   metrics = \"accuracy\" )  history <- model |> fit(     x_train, y_train, batch_size = 64, epochs = 2, validation_split = 0.2 ) ## Epoch 1/2 ## 750/750 - 2s - 2ms/step - accuracy: 0.8979 - loss: 0.3540 - val_accuracy: 0.9448 - val_loss: 0.1903 ## Epoch 2/2 ## 750/750 - 1s - 773us/step - accuracy: 0.9511 - loss: 0.1634 - val_accuracy: 0.9605 - val_loss: 0.1386 test_scores <- model |> evaluate(x_test, y_test, verbose=2) ## 313/313 - 0s - 980us/step - accuracy: 0.9593 - loss: 0.1323 cat(\"Test loss:\", test_scores$loss, \"\\n\") cat(\"Test accuracy:\", test_scores$accuracy, \"\\n\") ## Test loss: 0.1323339 ## Test accuracy: 0.9593"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"save-and-serialize","dir":"Articles","previous_headings":"","what":"Save and serialize","title":"The Functional API","text":"Saving model serialization work way models built using functional API Sequential models. standard way save functional model call model.save() save entire model single file. can later recreate model file, even code built model longer available. saved file includes : - model architecture - model weight values (learned training) - model training config, (passed compile()) - optimizer state, (restart training left ) details, read model serialization & saving guide.","code":"model |> save_model(\"my_model.keras\") rm(model) # Recreate the exact same model purely from the file: model <- load_model(\"my_model.keras\")"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"use-the-same-graph-of-layers-to-define-multiple-models","dir":"Articles","previous_headings":"","what":"Use the same graph of layers to define multiple models","title":"The Functional API","text":"functional API, models created specifying inputs outputs graph layers. means single graph layers can used generate multiple models. example , use stack layers instantiate two models: encoder model turns image inputs 16-dimensional vectors, end--end autoencoder model training. , decoding architecture strictly symmetrical encoding architecture, output shape input shape (28, 28, 1). reverse conv_2d layer conv_2d_transpose layer, reverse max_pooling_2d layer upsampling_2d layer.","code":"encoder_input <- keras_input(shape = c(28, 28, 1), name=\"img\") encoder_output <- encoder_input |>   layer_conv_2d(16, 3, activation = \"relu\") |>   layer_conv_2d(32, 3, activation = \"relu\") |>   layer_max_pooling_2d(3) |>   layer_conv_2d(32, 3, activation = \"relu\") |>   layer_conv_2d(16, 3, activation = \"relu\") |>   layer_global_max_pooling_2d()  encoder <- keras_model(encoder_input, encoder_output, name=\"encoder\") summary(encoder) ## Model: \"encoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)                │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 26, 26, 16)     │           160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 24, 24, 32)     │         4,640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 8, 8, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 6, 6, 32)       │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_3 (Conv2D)               │ (None, 4, 4, 16)       │         4,624 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d            │ (None, 16)             │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 18,672 (72.94 KB) ##  Trainable params: 18,672 (72.94 KB) ##  Non-trainable params: 0 (0.00 B) decoder_output <- encoder_output |>   layer_reshape(c(4, 4, 1)) |>   layer_conv_2d_transpose(16, 3, activation = \"relu\") |>   layer_conv_2d_transpose(32, 3, activation = \"relu\") |>   layer_upsampling_2d(3) |>   layer_conv_2d_transpose(16, 3, activation = \"relu\") |>   layer_conv_2d_transpose(1, 3, activation = \"relu\")  autoencoder <- keras_model(encoder_input, decoder_output, name=\"autoencoder\") summary(autoencoder) ## Model: \"autoencoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)                │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 26, 26, 16)     │           160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 24, 24, 32)     │         4,640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 8, 8, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 6, 6, 32)       │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_3 (Conv2D)               │ (None, 4, 4, 16)       │         4,624 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d            │ (None, 16)             │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ reshape (Reshape)               │ (None, 4, 4, 1)        │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 6, 6, 16)       │           160 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 8, 8, 32)       │         4,640 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ up_sampling2d (UpSampling2D)    │ (None, 24, 24, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_2              │ (None, 26, 26, 16)     │         4,624 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_3              │ (None, 28, 28, 1)      │           145 │ ## │ (Conv2DTranspose)               │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 28,241 (110.32 KB) ##  Trainable params: 28,241 (110.32 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"all-models-are-callable-just-like-layers","dir":"Articles","previous_headings":"","what":"All models are callable, just like layers","title":"The Functional API","text":"can treat model layer invoking Input output another layer. calling model aren’t just reusing architecture model, ’re also reusing weights. see action, ’s different take autoencoder example creates encoder model, decoder model, chains two calls obtain autoencoder model: can see, model can nested: model can contain sub-models (since model just like layer). common use case model nesting ensembling. example, ’s ensemble set models single model averages predictions:","code":"encoder_input <- keras_input(shape = c(28, 28, 1), name=\"img\") encoder_output <- encoder_input |>   layer_conv_2d(16, 3, activation = \"relu\") |>   layer_conv_2d(32, 3, activation = \"relu\") |>   layer_max_pooling_2d(3) |>   layer_conv_2d(32, 3, activation = \"relu\") |>   layer_conv_2d(16, 3, activation = \"relu\") |>   layer_global_max_pooling_2d()  encoder <- keras_model(encoder_input, encoder_output, name=\"encoder\") summary(encoder) ## Model: \"encoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)                │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_4 (Conv2D)               │ (None, 26, 26, 16)     │           160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_5 (Conv2D)               │ (None, 24, 24, 32)     │         4,640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 8, 8, 32)       │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_6 (Conv2D)               │ (None, 6, 6, 32)       │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_7 (Conv2D)               │ (None, 4, 4, 16)       │         4,624 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d_1          │ (None, 16)             │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 18,672 (72.94 KB) ##  Trainable params: 18,672 (72.94 KB) ##  Non-trainable params: 0 (0.00 B) decoder_input <- keras_input(shape = c(16), name = \"encoded_img\") decoder_output <- decoder_input |>   layer_reshape(c(4, 4, 1)) |>   layer_conv_2d_transpose(16, 3, activation = \"relu\") |>   layer_conv_2d_transpose(32, 3, activation = \"relu\") |>   layer_upsampling_2d(3) |>   layer_conv_2d_transpose(16, 3, activation = \"relu\") |>   layer_conv_2d_transpose(1, 3, activation = \"relu\")  decoder <- keras_model(decoder_input, decoder_output, name = \"decoder\") summary(decoder) ## Model: \"decoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ encoded_img (InputLayer)        │ (None, 16)             │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ reshape_1 (Reshape)             │ (None, 4, 4, 1)        │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_4              │ (None, 6, 6, 16)       │           160 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_5              │ (None, 8, 8, 32)       │         4,640 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ up_sampling2d_1 (UpSampling2D)  │ (None, 24, 24, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_6              │ (None, 26, 26, 16)     │         4,624 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_7              │ (None, 28, 28, 1)      │           145 │ ## │ (Conv2DTranspose)               │                        │               │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 9,569 (37.38 KB) ##  Trainable params: 9,569 (37.38 KB) ##  Non-trainable params: 0 (0.00 B) autoencoder_input <- keras_input(shape = c(28, 28, 1), name = \"img\") encoded_img <- encoder(autoencoder_input) decoded_img <- decoder(encoded_img) autoencoder <- keras_model(autoencoder_input, decoded_img,                            name = \"autoencoder\") summary(autoencoder) ## Model: \"autoencoder\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)                │ (None, 28, 28, 1)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ encoder (Functional)            │ (None, 16)             │        18,672 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ decoder (Functional)            │ (None, 28, 28, 1)      │         9,569 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 28,241 (110.32 KB) ##  Trainable params: 28,241 (110.32 KB) ##  Non-trainable params: 0 (0.00 B) get_model <- function() {   inputs <- keras_input(shape = 128)   outputs <- inputs |> layer_dense(1)   keras_model(inputs, outputs) }  model1 <- get_model() model2 <- get_model() model3 <- get_model()  inputs <- keras_input(shape = 128) y1 <- model1(inputs) y2 <- model2(inputs) y3 <- model3(inputs) outputs <- layer_average(list(y1, y2, y3)) ensemble_model <- keras_model(inputs = inputs, outputs = outputs)"},{"path":[]},{"path":"https://keras.posit.co/articles/functional_api.html","id":"models-with-multiple-inputs-and-outputs","dir":"Articles","previous_headings":"Manipulate complex graph topologies","what":"Models with multiple inputs and outputs","title":"The Functional API","text":"functional API makes easy manipulate multiple inputs outputs. handled Sequential API. example, ’re building system ranking customer issue tickets priority routing correct department, model three inputs: title ticket (text input), text body ticket (text input), tags added user (categorical input) model two outputs: priority score 0 1 (scalar sigmoid output), department handle ticket (softmax output set departments). can build model lines functional API: Now plot model:  compiling model, can assign different losses output. can even assign different weights loss – modulate contribution total training loss. Since output layers different names, also specify losses loss weights corresponding layer names: Train model passing lists NumPy arrays inputs targets: calling fit Dataset object, yield either list lists like list(list(title_data, body_data, tags_data), list(priority_targets, dept_targets)) list named lists like list(list(title = title_data, body = body_data, tags = tags_data), list(priority = priority_targets, department = dept_targets)). detailed explanation, refer training evaluation guide.","code":"num_tags <- 12  # Number of unique issue tags num_words <- 10000  # Size of vocabulary obtained when preprocessing text data num_departments <- 4  # Number of departments for predictions  title_input <- # Variable-length sequence of ints   keras_input(shape(NA), name = \"title\") body_input <-  # Variable-length sequence of ints   keras_input(shape(NA), name = \"body\") tags_input <-  # Binary vectors of size `num_tags`   keras_input(shape = num_tags, name = \"tags\")  # Embed each word in the title into a 64-dimensional vector title_features <- layer_embedding(title_input, num_words, 64) # Embed each word in the text into a 64-dimensional vector body_features <- layer_embedding(body_input, num_words, 64)  # Reduce sequence of embedded words in the title # into a single 128-dimensional vector title_features <- layer_lstm(title_features, 128) # Reduce sequence of embedded words in the body # into a single 32-dimensional vector body_features <- layer_lstm(body_features, 32)  # Merge all available features into a single large vector via concatenation x <- layer_concatenate(title_features, body_features, tags_input)  # Stick a logistic regression for priority prediction on top of the features priority_pred <- layer_dense(x, 1, name = \"priority\")  # Stick a department classifier on top of the features department_pred <- layer_dense(x, num_departments, name = \"department\")  # Instantiate an end-to-end model predicting both priority and department model <- keras_model(   inputs = list(title_input, body_input, tags_input),   outputs = list(priority = priority_pred, department = department_pred) ) plot(model, show_shapes = TRUE) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     loss_binary_crossentropy(from_logits = TRUE),     loss_categorical_crossentropy(from_logits = TRUE)   ),   loss_weights = c(1.0, 0.2) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     priority = loss_binary_crossentropy(from_logits = TRUE),     department = loss_categorical_crossentropy(from_logits = TRUE)   ),   loss_weights = list(priority = 1.0, department = 0.2) ) # Dummy input data title_data <- random_integer(c(1280, 10), 0, num_words) body_data <- random_integer(c(1280, 100), 0, num_words) tags_data <- random_integer(c(1280, num_tags), 0, 2)  # Dummy target data priority_targets <- random_normal(c(1280, 1)) dept_targets <- random_integer(c(1280, num_departments), 0, 2)  model |> fit(   list(title = title_data, body = body_data, tags = tags_data),   list(priority = priority_targets, department = dept_targets),   epochs = 2,   batch_size = 32 ) ## Epoch 1/2 ## 40/40 - 2s - 57ms/step - loss: 0.3948 ## Epoch 2/2 ## 40/40 - 0s - 5ms/step - loss: 0.1971"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"a-toy-resnet-model","dir":"Articles","previous_headings":"Manipulate complex graph topologies","what":"A toy ResNet model","title":"The Functional API","text":"addition models multiple inputs outputs, functional API makes easy manipulate non-linear connectivity topologies – models layers connected sequentially, Sequential API handle. common use case residual connections. Let’s build toy ResNet model CIFAR10 demonstrate : Plot model:  Now train model:","code":"inputs <- keras_input(shape = c(32, 32, 3), name = \"img\") block_1_output <- inputs |>   layer_conv_2d(32, kernel_size = 3, activation = \"relu\") |>   layer_conv_2d(64, kernel_size = 3, activation = \"relu\") |>   layer_max_pooling_2d(pool_size = 3)  block_2_output <- block_1_output |>   layer_conv_2d(32, kernel_size = 3, activation = \"relu\", padding = \"same\") |>   layer_conv_2d(64, kernel_size = 3, activation = \"relu\", padding = \"same\") |>   layer_add(block_1_output)  block_3_output <- block_2_output |>   layer_conv_2d(64, kernel_size = 3, activation = \"relu\", padding = \"same\") |>   layer_conv_2d(64, kernel_size = 3, activation = \"relu\", padding = \"same\") |>   layer_add(block_2_output)  outputs <- block_3_output |>   layer_conv_2d(64, 3, activation = \"relu\") |>   layer_global_average_pooling_2d() |>   layer_dense(256, activation = \"relu\") |>   layer_dropout(0.5) |>   layer_dense(10)  model <- keras_model(inputs, outputs, name = \"toy_resnet\") summary(model) ## Model: \"toy_resnet\" ## ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ ## │ img (InputLayer)    │ (None, 32, 32, 3) │          0 │ -                 │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_8 (Conv2D)   │ (None, 30, 30,    │        896 │ img[0][0]         │ ## │                     │ 32)               │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_9 (Conv2D)   │ (None, 28, 28,    │     18,496 │ conv2d_8[0][0]    │ ## │                     │ 64)               │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ max_pooling2d_2     │ (None, 9, 9, 64)  │          0 │ conv2d_9[0][0]    │ ## │ (MaxPooling2D)      │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_10 (Conv2D)  │ (None, 9, 9, 32)  │     18,464 │ max_pooling2d_2[… │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_11 (Conv2D)  │ (None, 9, 9, 64)  │     18,496 │ conv2d_10[0][0]   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ add (Add)           │ (None, 9, 9, 64)  │          0 │ conv2d_11[0][0],  │ ## │                     │                   │            │ max_pooling2d_2[… │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_12 (Conv2D)  │ (None, 9, 9, 64)  │     36,928 │ add[0][0]         │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_13 (Conv2D)  │ (None, 9, 9, 64)  │     36,928 │ conv2d_12[0][0]   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ add_1 (Add)         │ (None, 9, 9, 64)  │          0 │ conv2d_13[0][0],  │ ## │                     │                   │            │ add[0][0]         │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ conv2d_14 (Conv2D)  │ (None, 7, 7, 64)  │     36,928 │ add_1[0][0]       │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ global_average_poo… │ (None, 64)        │          0 │ conv2d_14[0][0]   │ ## │ (GlobalAveragePool… │                   │            │                   │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ dense_6 (Dense)     │ (None, 256)       │     16,640 │ global_average_p… │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ dropout (Dropout)   │ (None, 256)       │          0 │ dense_6[0][0]     │ ## ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ ## │ dense_7 (Dense)     │ (None, 10)        │      2,570 │ dropout[0][0]     │ ## └─────────────────────┴───────────────────┴────────────┴───────────────────┘ ##  Total params: 186,346 (727.91 KB) ##  Trainable params: 186,346 (727.91 KB) ##  Non-trainable params: 0 (0.00 B) plot(model, show_shapes = TRUE) c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_cifar10()  x_train <- x_train / 255.0 x_test <- x_test / 255.0  model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = loss_sparse_categorical_crossentropy(from_logits = TRUE),   metrics = \"acc\" ) # We restrict the data to the first 1000 samples so as to limit the # guide render time. # Try to train on the entire dataset until convergence! model |> fit(   x_train[1:1000, , , ],   y_train[1:1000, ],   batch_size = 64,   epochs = 1,   validation_split = 0.2 ) ## 13/13 - 5s - 373ms/step - acc: 0.1238 - loss: 2.2995 - val_acc: 0.1300 - val_loss: 2.2957"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"shared-layers","dir":"Articles","previous_headings":"","what":"Shared layers","title":"The Functional API","text":"Another good use functional API models use shared layers. Shared layers layer instances reused multiple times model – learn features correspond multiple paths graph--layers. Shared layers often used encode inputs similar spaces (say, two different pieces text feature similar vocabulary). enable sharing information across different inputs, make possible train model less data. given word seen one inputs, benefit processing inputs pass shared layer. share layer functional API, call layer instance multiple times. instance, ’s Embedding layer shared across two different text inputs:","code":"# Embedding for 1000 unique words mapped to 128-dimensional vectors shared_embedding <- layer_embedding(input_dim = 1000, output_dim = 128)  # Variable-length sequence of integers text_input_a <- keras_input(shape = shape(NA), dtype=\"int32\")  # Variable-length sequence of integers text_input_b <- keras_input(shape = shape(NA), dtype=\"int32\")  # Reuse the same layer to encode both inputs encoded_input_a <- shared_embedding(text_input_a) encoded_input_b <- shared_embedding(text_input_b)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"extract-and-reuse-nodes-in-the-graph-of-layers","dir":"Articles","previous_headings":"","what":"Extract and reuse nodes in the graph of layers","title":"The Functional API","text":"graph layers manipulating static data structure, can accessed inspected. able plot functional models images. also means can access activations intermediate layers (“nodes” graph) reuse elsewhere – useful something like feature extraction. Let’s look example. VGG19 model weights pretrained ImageNet: intermediate activations model, obtained querying graph data structure: Use features create new feature-extraction model returns values intermediate layer activations: comes handy tasks like neural style transfer, among things.","code":"vgg19 <- application_vgg19() features_list <- lapply(vgg19$layers, function(x) x$output) feat_extraction_model <- keras_model(inputs = vgg19$input,                                      outputs = features_list)  img <- random_normal(c(1, 224, 224, 3)) extracted_features <- feat_extraction_model(img)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"extend-the-api-using-custom-layers","dir":"Articles","previous_headings":"","what":"Extend the API using custom layers","title":"The Functional API","text":"keras includes wide range built-layers, example: Convolutional layers: conv_1d, conv_2d, conv_3d, conv_2d_transpose Pooling layers: max_pooling_1d, max_pooling_2d, max_pooling_3d, average_pooling_3d RNN layers: gru, lstm, conv_lstm_2d batch_normalization, dropout, embedding, etc. don’t find need, ’s easy extend API creating layers. layers subclass Layer class implement: call method, specifies computation done layer. build method, creates weights layer (just style convention since can create weights initialize, well). learn creating layers scratch, read custom layers models guide. following basic implementation layer_dense(): serialization support custom layer, define get_config() method returns constructor arguments layer instance: Optionally, implement class method from_config(cls, config) used recreating layer instance given config dictionary. default implementation from_config :","code":"custom_dense <- Layer(   classname = \"CustomDense\",   initialize = function(units = 32) {     super$initialize()     self$units <- as.integer(units)   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(input_shape[[2]], self$units),       initializer = \"random_normal\",       trainable = TRUE,     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer=\"random_normal\",       trainable = TRUE     )   },   call = function(inputs) {     op_matmul(inputs, self$w) + self$b   } )  inputs <- keras_input(c(4)) outputs <- custom_dense(inputs, 10)  model <- keras_model(inputs, outputs) custom_dense <- Layer(   classname = \"CustomDense\",    initialize = function(units = 32, ...) {     super$initialize()     self$units <- as.integer(units)   },    build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(input_shape[[2]], self$units),       initializer = \"random_normal\",       trainable = TRUE,     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer=\"random_normal\",       trainable = TRUE     )   },    call = function(inputs) {     op_matmul(inputs, self$w) + self$b   },    get_config = function() {     list(units = self$units)   } )  inputs <- keras_input(c(4)) outputs <- custom_dense(inputs, 10)  model <- keras_model(inputs, outputs) config <- get_config(model)  new_model <-   from_config(config, custom_objects = list(CustomDense = custom_dense)) from_config <- function(cls, config) {   do.call(cls, config) }"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"when-to-use-the-functional-api","dir":"Articles","previous_headings":"","what":"When to use the functional API","title":"The Functional API","text":"use Keras functional API create new model, just subclass Model class directly? general, functional API higher-level, easier safer, number features subclassed models support. However, model subclassing provides greater flexibility building models easily expressible directed acyclic graphs layers. example, implement Tree-RNN functional API subclass Model directly. -depth look differences functional API model subclassing, read Symbolic Imperative APIs TensorFlow 2.0?.","code":""},{"path":"https://keras.posit.co/articles/functional_api.html","id":"functional-api-strengths","dir":"Articles","previous_headings":"When to use the functional API","what":"Functional API strengths:","title":"The Functional API","text":"following properties also true Sequential models (also data structures), true subclassed models (R Python (byte)code, data structures).","code":""},{"path":"https://keras.posit.co/articles/functional_api.html","id":"less-verbose","dir":"Articles","previous_headings":"When to use the functional API > Functional API strengths:","what":"Less verbose","title":"The Functional API","text":"super$initialize(...), call = function(...), self$..., etc. Compare: subclassed version:","code":"inputs <- keras_input(shape = shape(32)) outputs <- inputs |>   layer_dense(64, activation = \"relu\") |>   layer_dense(10) mlp <- keras_model(inputs, outputs) MLP <- Model(   classname = \"MLP\",   initialize = function(...) {     super$initialize(...)     self$dense_1 <- layer_dense(units = 64, activation = \"relu\")     self$dense_2 <- layer_dense(units = 10)   },   call = function(inputs) {     inputs |>       self$dense_1() |>       self$dense_2()   } )  # Instantiate the model. mlp <- MLP() # Necessary to create the model's state. # The model doesn't have a state until it's called at least once. out <- mlp(op_zeros(c(1, 32)))"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"model-validation-while-defining-its-connectivity-graph","dir":"Articles","previous_headings":"When to use the functional API > Functional API strengths:","what":"Model validation while defining its connectivity graph","title":"The Functional API","text":"functional API, input specification (shape dtype) created advance (using Input). Every time call layer, layer checks specification passed matches assumptions, raise helpful error message . guarantees model can build functional API run. debugging – convergence-related debugging – happens statically model construction execution time. similar type checking compiler.","code":""},{"path":"https://keras.posit.co/articles/functional_api.html","id":"a-functional-model-is-plottable-and-inspectable","dir":"Articles","previous_headings":"When to use the functional API > Functional API strengths:","what":"A functional model is plottable and inspectable","title":"The Functional API","text":"can plot model graph, can easily access intermediate nodes graph. example, extract reuse activations intermediate layers (seen previous example):","code":"features_list <- lapply(vgg19$layers, function(x) x$output) feat_extraction_model <- keras_model(inputs = vgg19$input,                                      outputs = features_list)"},{"path":"https://keras.posit.co/articles/functional_api.html","id":"a-functional-model-can-be-serialized-or-cloned","dir":"Articles","previous_headings":"When to use the functional API > Functional API strengths:","what":"A functional model can be serialized or cloned","title":"The Functional API","text":"functional model data structure rather piece code, safely serializable can saved single file allows recreate exact model without access original code. See serialization & saving guide. serialize subclassed model, necessary implementer specify get_config() from_config() method model level.","code":""},{"path":[]},{"path":"https://keras.posit.co/articles/functional_api.html","id":"it-does-not-support-dynamic-architectures","dir":"Articles","previous_headings":"When to use the functional API > Functional API weakness:","what":"It does not support dynamic architectures","title":"The Functional API","text":"functional API treats models DAGs layers. true deep learning architectures, – example, recursive networks Tree RNNs follow assumption implemented functional API.","code":""},{"path":"https://keras.posit.co/articles/functional_api.html","id":"mix-and-match-api-styles","dir":"Articles","previous_headings":"","what":"Mix-and-match API styles","title":"The Functional API","text":"Choosing functional API Model subclassing isn’t binary decision restricts one category models. models keras API can interact , whether ’re Sequential models, functional models, subclassed models written scratch. can always use functional model Sequential model part subclassed model layer: can use subclassed layer model functional API long implements call method follows one following patterns: call(inputs, ...) – inputs tensor nested structure tensors (e.g. list tensors), ... non-tensor arguments (non-inputs). call(inputs, training = NULL, ...) – training boolean indicating whether layer behave training mode inference mode. call(inputs, mask = NULL, ...) – mask boolean mask tensor (useful RNNs, instance). call(inputs, training = NULL, mask = NULL, ...) – course, can masking training-specific behavior time. Additionally, implement get_config() method custom Layer model, functional models create still serializable cloneable. ’s quick example custom RNN, written scratch, used functional model:","code":"units <- 32 timesteps <- 10 input_dim <- 5  # Define a Functional model inputs <- keras_input(shape(NA, units)) outputs <- inputs |>   layer_global_average_pooling_1d() |>   layer_dense(units = 1)  model <- keras_model(inputs, outputs)  layer_custom_rnn <- Layer(   classname = \"CustomRNN\",   initialize = function(...) {     super$initialize(...)     self$units <- units     self$projection_1 <- layer_dense(units = units, activation = \"tanh\")     self$projection_2 <- layer_dense(units = units, activation = \"tanh\")     self$classifier <- model   },   call = function(inputs, ...) {     outputs <- list()     state <- op_zeros(c(shape(inputs)[[1]], self$units))     for (t in 1:(shape(inputs)[[2]])) {       x <- inputs[, t, ]       h <- self$projection_1(x)       y <- h + self$projection_2(state)       state <- y       outputs[[t]] <- y     }     features <- op_stack(outputs, axis = 2)     self$classifier(features)   } )  rnn <- layer_custom_rnn() out <- rnn(op_zeros(c(1, timesteps, input_dim))) units <- 32 timesteps <- 10 input_dim <- 5 batch_size <- 16  layer_custom_rnn <- Layer(   \"custom_rnn\",   initialize = function(...) {     super$initialize(...)     self$units <- units     self$projection_1 <- layer_dense(units = units, activation = \"tanh\")     self$projection_2 <- layer_dense(units = units, activation = \"tanh\")     self$classifier <- layer_dense(units = 1)   },   call = function(inputs, ...) {     outputs <- list()     state <- op_zeros(c(shape(inputs)[[1]], self$units))     for (t in 1:(shape(inputs)[[2]])) {       x <- inputs[, t, ]       h <- self$projection_1(x)       y <- h + self$projection_2(state)       state <- y       outputs[[t]] <- y     }     features <- op_stack(outputs, axis = 2)     self$classifier(features)   } )  # Note that you specify a static batch size for the inputs with the `batch_shape` # arg, because the inner computation of `layer_custom_rnn()` requires a static batch size # (when you create the `state` zeros tensor). inputs <- keras_input(batch_shape = shape(batch_size, timesteps, input_dim)) outputs <- inputs |>   layer_conv_1d(filters = 32, kernel_size = 3) |>   layer_custom_rnn()  model <- keras_model(inputs, outputs) out <- model(op_zeros(c(1, 10, 5)))"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Getting Started with Keras","text":"Keras high-level neural networks API developed focus enabling fast experimentation. able go idea result least possible delay key good research. Keras following key features: Allows code run CPU GPU, seamlessly. User-friendly API makes easy quickly prototype deep learning models. Built-support convolutional networks (computer vision), recurrent networks (sequence processing), combination . Supports arbitrary network architectures: multi-input multi-output models, layer sharing, model sharing, etc. means Keras appropriate building essentially deep learning model, memory network neural Turing machine. website provides documentation R interface Keras. See main Keras website https://keras.io additional information project.","code":""},{"path":"https://keras.posit.co/articles/getting_started.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Getting Started with Keras","text":"First, install keras R package: install development version : Keras R interface requires backend engine installed. TensorFlow default. provide default installation Keras TensorFlow GPU capable, GPU available. want customized installation, e.g. see documentation install_keras() installation section.","code":"install.packages(\"keras3\") remotes::install_github(\"rstudio/keras\") keras3::install_keras(backend = \"tensorflow\")"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"mnist-example","dir":"Articles","previous_headings":"","what":"MNIST Example","title":"Getting Started with Keras","text":"can learn basics Keras walking simple example: recognizing handwritten digits MNIST dataset. MNIST consists 28 x 28 grayscale images handwritten digits like :  dataset also includes labels image, telling us digit . example, labels images 5, 0, 4, 1.","code":""},{"path":"https://keras.posit.co/articles/getting_started.html","id":"preparing-the-data","dir":"Articles","previous_headings":"MNIST Example","what":"Preparing the Data","title":"Getting Started with Keras","text":"MNIST dataset included Keras can accessed using dataset_mnist() function. load dataset create variables test training data: x data 3-d array (images, width, height) grayscale values. prepare data training convert 3-d arrays matrices reshaping width height single dimension (28x28 images flattened length 784 vectors). , convert grayscale values integers ranging 0 255 floating point values ranging 0 1: Note use array_reshape() function rather dim<-() function reshape array. data re-interpreted using row-major semantics (opposed R’s default column-major semantics), turn compatible way numerical libraries called Keras interpret array dimensions. y data integer vector values ranging 0 9. prepare data training one-hot encode vectors binary class matrices using Keras to_categorical() function:","code":"library(keras3) mnist <- dataset_mnist() x_train <- mnist$train$x y_train <- mnist$train$y x_test <- mnist$test$x y_test <- mnist$test$y # reshape x_train <- array_reshape(x_train, c(nrow(x_train), 784)) x_test <- array_reshape(x_test, c(nrow(x_test), 784)) # rescale x_train <- x_train / 255 x_test <- x_test / 255 y_train <- to_categorical(y_train, 10) y_test <- to_categorical(y_test, 10)"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"defining-the-model","dir":"Articles","previous_headings":"MNIST Example","what":"Defining the Model","title":"Getting Started with Keras","text":"core data structure Keras model, way organize layers. simplest type model Sequential model, linear stack layers. begin creating sequential model adding layers using pipe (|>) operator: input_shape argument first layer specifies shape input data (length 784 numeric vector representing grayscale image). final layer outputs length 10 numeric vector (probabilities digit) using softmax activation function. Use summary() function print details model: Next, compile model appropriate loss function, optimizer, metrics:","code":"model <- keras_model_sequential(input_shape = c(784)) model |>   layer_dense(units = 256, activation = 'relu') |>   layer_dropout(rate = 0.4) |>   layer_dense(units = 128, activation = 'relu') |>   layer_dropout(rate = 0.3) |>   layer_dense(units = 10, activation = 'softmax') summary(model) ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense (Dense)                   │ (None, 256)            │       200,960 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 256)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_1 (Dense)                 │ (None, 128)            │        32,896 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout_1 (Dropout)             │ (None, 128)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 10)             │         1,290 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 235,146 (918.54 KB) ##  Trainable params: 235,146 (918.54 KB) ##  Non-trainable params: 0 (0.00 B) plot(model) model |> compile(   loss = 'categorical_crossentropy',   optimizer = optimizer_rmsprop(),   metrics = c('accuracy') )"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"training-and-evaluation","dir":"Articles","previous_headings":"MNIST Example","what":"Training and Evaluation","title":"Getting Started with Keras","text":"Use fit() function train model 30 epochs using batches 128 images: history object returned fit() includes loss accuracy metrics can plot: Evaluate model’s performance test data: Generate predictions new data: Keras provides vocabulary building deep learning models simple, elegant, intuitive. Building question answering system, image classification model, neural Turing machine, model just straightforward.","code":"history <- model |> fit(   x_train, y_train,   epochs = 30, batch_size = 128,   validation_split = 0.2 ) plot(history) model |> evaluate(x_test, y_test) ## 313/313 - 1s - 2ms/step - accuracy: 0.9814 - loss: 0.0844 ## $accuracy ## [1] 0.9814 ## ## $loss ## [1] 0.08441389 probs <- model |> predict(x_test) ## 313/313 - 0s - 1ms/step max.col(probs) - 1L ##   [1] 7 2 1 0 4 1 4 9 6 9 0 6 9 0 1 5 9 7 3 4 9 6 6 5 4 0 7 4 0 1 3 1 3 4 7 ##  [36] 2 7 1 2 1 1 7 4 2 3 5 1 2 4 4 6 3 5 5 6 0 4 1 9 5 7 8 9 3 7 4 6 4 3 0 ##  [71] 7 0 2 9 1 7 3 2 9 7 7 6 2 7 8 4 7 3 6 1 3 6 9 3 1 4 1 7 6 9 ##  [ reached getOption(\"max.print\") -- omitted 9900 entries ]"},{"path":"https://keras.posit.co/articles/getting_started.html","id":"deep-learning-with-r-book","dir":"Articles","previous_headings":"MNIST Example","what":"Deep Learning with R Book","title":"Getting Started with Keras","text":"want comprehensive introduction Keras concepts practice deep learning, recommend Deep Learning R, 2nd Edition book Manning. book collaboration François Chollet, creator (Python) Keras, J.J. Allaire, wrote original R interface Keras, Tomasz Kalinowski, maintainer R interface Keras. book presumes significant knowledge machine learning deep learning, goes way basic theory advanced practical applications, using R interface Keras.","code":""},{"path":"https://keras.posit.co/articles/getting_started.html","id":"why-this-name-keras","dir":"Articles","previous_headings":"","what":"Why this name, Keras?","title":"Getting Started with Keras","text":"Keras (κέρας) means horn Greek. reference literary image ancient Greek Latin literature, first found Odyssey, dream spirits (Oneiroi, singular Oneiros) divided deceive men false visions, arrive Earth gate ivory, announce future come pass, arrive gate horn. ’s play words κέρας (horn) / κραίνω (fulfill), ἐλέφας (ivory) / ἐλεφαίρομαι (deceive). Keras initially developed part research effort project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System). “Oneiroi beyond unravelling –can sure tale tell? men look comes pass. Two gates give passage fleeting Oneiroi; one made horn, one ivory. Oneiroi pass sawn ivory deceitful, bearing message fulfilled; come polished horn truth behind , accomplished men see .” Homer, Odyssey 19. 562 ff (Shewring translation).","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Introduction to Keras for engineers","text":"Keras 3 deep learning framework works TensorFlow, JAX, PyTorch interchangeably. notebook walk key Keras 3 workflows. Let’s start installing Keras 3: pip install keras –upgrade –quiet","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Introduction to Keras for engineers","text":"’re going using tensorflow backend – can edit string \"jax\" \"torch\" hit “Restart runtime”, whole notebook run just ! entire guide backend-agnostic.","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(keras3)  # Note that you must configure the backend # before calling any other keras functions. # The backend cannot be changed once the # package is imported. use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"a-first-example-a-mnist-convnet","dir":"Articles","previous_headings":"","what":"A first example: A MNIST convnet","title":"Introduction to Keras for engineers","text":"Let’s start Hello World ML: training convnet classify MNIST digits. ’s data: ’s model. Different model-building options Keras offers include: Sequential API (use ) Functional API (typical) Writing models via subclassing (advanced use cases) ’s model summary: use compile() method specify optimizer, loss function, metrics monitor. Note JAX TensorFlow backends, XLA compilation turned default. Let’s train evaluate model. ’ll set aside validation split 15% data training monitor generalization unseen data. training, saving model end epoch. can also save model latest state like : reload like : Next, can query predictions class probabilities predict(): ’s basics!","code":"# Load the data and split it between train and test sets c(c(x_train, y_train), c(x_test, y_test)) %<-% keras3::dataset_mnist()  # Scale images to the [0, 1] range x_train <- x_train / 255 x_test <- x_test / 255 # Make sure images have shape (28, 28, 1) x_train <- op_expand_dims(x_train, -1) x_test <- op_expand_dims(x_test, -1)  dim(x_train) ## [1] 60000    28    28     1 dim(x_test) ## [1] 10000    28    28     1 # Model parameters num_classes <- 10 input_shape <- c(28, 28, 1)  model <- keras_model_sequential(input_shape = input_shape) model |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(2, 2)) |>   layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = \"relu\") |>   layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = \"relu\") |>   layer_global_average_pooling_2d() |>   layer_dropout(rate = 0.5) |>   layer_dense(units = num_classes, activation = \"softmax\") summary(model) ## Model: \"sequential\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 26, 26, 64)     │           640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 24, 24, 64)     │        36,928 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 12, 12, 64)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 10, 10, 128)    │        73,856 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_3 (Conv2D)               │ (None, 8, 8, 128)      │       147,584 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_average_pooling2d        │ (None, 128)            │             0 │ ## │ (GlobalAveragePooling2D)        │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dropout (Dropout)               │ (None, 128)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense (Dense)                   │ (None, 10)             │         1,290 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 260,298 (1016.79 KB) ##  Trainable params: 260,298 (1016.79 KB) ##  Non-trainable params: 0 (0.00 B) model |> compile(   optimizer = \"adam\",   loss = \"sparse_categorical_crossentropy\",   metrics = list(     metric_sparse_categorical_accuracy(name = \"acc\")   ) ) batch_size <- 128 epochs <- 10  callbacks <- list(   callback_model_checkpoint(filepath=\"model_at_epoch_{epoch}.keras\"),   callback_early_stopping(monitor=\"val_loss\", patience=2) )  model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = epochs,   validation_split = 0.15,   callbacks = callbacks ) ## Epoch 1/10 ## 399/399 - 6s - 16ms/step - acc: 0.7450 - loss: 0.7521 - val_acc: 0.9642 - val_loss: 0.1234 ## Epoch 2/10 ## 399/399 - 2s - 5ms/step - acc: 0.9387 - loss: 0.2054 - val_acc: 0.9769 - val_loss: 0.0751 ## Epoch 3/10 ## 399/399 - 2s - 5ms/step - acc: 0.9575 - loss: 0.1455 - val_acc: 0.9824 - val_loss: 0.0609 ## Epoch 4/10 ## 399/399 - 2s - 5ms/step - acc: 0.9662 - loss: 0.1148 - val_acc: 0.9863 - val_loss: 0.0483 ## Epoch 5/10 ## 399/399 - 2s - 5ms/step - acc: 0.9726 - loss: 0.0972 - val_acc: 0.9869 - val_loss: 0.0479 ## Epoch 6/10 ## 399/399 - 2s - 5ms/step - acc: 0.9750 - loss: 0.0857 - val_acc: 0.9892 - val_loss: 0.0379 ## Epoch 7/10 ## 399/399 - 2s - 5ms/step - acc: 0.9768 - loss: 0.0768 - val_acc: 0.9906 - val_loss: 0.0358 ## Epoch 8/10 ## 399/399 - 2s - 5ms/step - acc: 0.9794 - loss: 0.0671 - val_acc: 0.9876 - val_loss: 0.0422 ## Epoch 9/10 ## 399/399 - 2s - 5ms/step - acc: 0.9808 - loss: 0.0639 - val_acc: 0.9887 - val_loss: 0.0387 score <- model |> evaluate(x_test, y_test, verbose = 0) save_model(model, \"final_model.keras\", overwrite=TRUE) model <- load_model(\"final_model.keras\") predictions <- model |> predict(x_test) ## 313/313 - 0s - 1ms/step dim(predictions) ## [1] 10000    10"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"writing-cross-framework-custom-components","dir":"Articles","previous_headings":"","what":"Writing cross-framework custom components","title":"Introduction to Keras for engineers","text":"Keras enables write custom Layers, Models, Metrics, Losses, Optimizers work across TensorFlow, JAX, PyTorch codebase. Let’s take look custom layers first. op_ namespace contains: implementation NumPy API, e.g. op_stack op_matmul. set neural network specific ops absent NumPy, op_conv op_binary_crossentropy. Let’s make custom Dense layer works backends: Next, let’s make custom Dropout layer relies random_* namespace: Next, let’s write custom subclassed model uses two custom layers: Let’s compile fit :","code":"layer_my_dense <- Layer(   classname = \"MyDense\",   initialize = function(units, activation = NULL, name = NULL, ...) {     super$initialize(name = name, ...)     self$units <- units     self$activation <- activation   },   build = function(input_shape) {     input_dim <- tail(input_shape, 1)     self$w <- self$add_weight(       shape = shape(input_dim, self$units),       initializer = initializer_glorot_normal(),       name = \"kernel\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = initializer_zeros(),       name = \"bias\",       trainable = TRUE     )   },   call = function(inputs) {     # Use Keras ops to create backend-agnostic layers/metrics/etc.     x <- op_matmul(inputs, self$w) + self$b     if (!is.null(self$activation))       x <- self$activation(x)     x   } ) layer_my_dropout <- Layer(   \"MyDropout\",   initialize = function(rate, name = NULL, seed = NULL, ...) {     super$initialize(name = name)     self$rate <- rate     # Use seed_generator for managing RNG state.     # It is a state element and its seed variable is     # tracked as part of `layer$variables`.     self$seed_generator <- random_seed_generator(seed)   },   call = function(inputs) {     # Use `keras3::random_*` for random ops.     random_dropout(inputs, self$rate, seed = self$seed_generator)   } ) MyModel <- Model(   \"MyModel\",   initialize = function(num_classes, ...) {     super$initialize(...)     self$conv_base <-       keras_model_sequential() |>       layer_conv_2d(64, kernel_size = c(3, 3), activation = \"relu\") |>       layer_conv_2d(64, kernel_size = c(3, 3), activation = \"relu\") |>       layer_max_pooling_2d(pool_size = c(2, 2)) |>       layer_conv_2d(128, kernel_size = c(3, 3), activation = \"relu\") |>       layer_conv_2d(128, kernel_size = c(3, 3), activation = \"relu\") |>       layer_global_average_pooling_2d()      self$dp <- layer_my_dropout(rate = 0.5)     self$dense <- layer_my_dense(units = num_classes,                                  activation = activation_softmax)   },   call = function(inputs) {     inputs |>       self$conv_base() |>       self$dp() |>       self$dense()   } ) model <- MyModel(num_classes = 10) model |> compile(   loss = loss_sparse_categorical_crossentropy(),   optimizer = optimizer_adam(learning_rate = 1e-3),   metrics = list(     metric_sparse_categorical_accuracy(name = \"acc\")   ) )  model |> fit(   x_train, y_train,   batch_size = batch_size,   epochs = 1, # For speed   validation_split = 0.15 ) ## 399/399 - 6s - 16ms/step - acc: 0.7356 - loss: 0.7726 - val_acc: 0.9301 - val_loss: 0.2358"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"training-models-on-arbitrary-data-sources","dir":"Articles","previous_headings":"","what":"Training models on arbitrary data sources","title":"Introduction to Keras for engineers","text":"Keras models can trained evaluated wide variety data sources, independently backend ’re using. includes: Arrays Dataframes TensorFlow tf_dataset objects PyTorch DataLoader objects Keras PyDataset objects work whether ’re using TensorFlow, JAX, PyTorch Keras backend. Let’s try tf_dataset:","code":"library(tfdatasets, exclude = \"shape\")  train_dataset <- list(x_train, y_train) |>   tensor_slices_dataset() |>   dataset_batch(batch_size) |>   dataset_prefetch(buffer_size = tf$data$AUTOTUNE)  test_dataset <- list(x_test, y_test) |>   tensor_slices_dataset() |>   dataset_batch(batch_size) |>   dataset_prefetch(buffer_size = tf$data$AUTOTUNE)  model <- MyModel(num_classes = 10) model |> compile(   loss = loss_sparse_categorical_crossentropy(),   optimizer = optimizer_adam(learning_rate = 1e-3),   metrics = list(     metric_sparse_categorical_accuracy(name = \"acc\")   ) )  model |> fit(train_dataset, epochs = 1, validation_data = test_dataset) ## 469/469 - 7s - 16ms/step - acc: 0.7542 - loss: 0.7342 - val_acc: 0.9026 - val_loss: 0.3192"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"further-reading","dir":"Articles","previous_headings":"","what":"Further reading","title":"Introduction to Keras for engineers","text":"concludes short overview new multi-backend capabilities Keras 3. Next, can learn :","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"how-to-customize-what-happens-in-fit","dir":"Articles","previous_headings":"Further reading","what":"How to customize what happens in fit()","title":"Introduction to Keras for engineers","text":"Want implement non-standard training algorithm still want benefit power usability fit()? ’s easy customize fit() support arbitrary use cases: Customizing happens fit() TensorFlow","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"how-to-write-custom-training-loops","dir":"Articles","previous_headings":"","what":"How to write custom training loops","title":"Introduction to Keras for engineers","text":"Writing training loop scratch TensorFlow","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_engineers.html","id":"how-to-distribute-training","dir":"Articles","previous_headings":"","what":"How to distribute training","title":"Introduction to Keras for engineers","text":"Guide distributed training TensorFlow Enjoy library! 🚀","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Introduction to Keras for Researchers","text":"","code":"library(keras3) library(tensorflow)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Introduction to Keras for Researchers","text":"machine learning researcher? publish NeurIPS push state---art CV NLP? guide serve first introduction core Keras & TensorFlow API concepts. guide, learn : Tensors, variables, gradients TensorFlow Creating layers subclassing [Layer] class Writing low-level training loops Tracking losses created layers via add_loss() method Tracking metrics low-level training loop Speeding execution compiled [tensorflow::tf_function()] Executing layers training inference mode Keras Functional API also see Keras API action two end--end research examples: Variational Autoencoder, Hypernetwork.","code":""},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"tensors","dir":"Articles","previous_headings":"","what":"Tensors","title":"Introduction to Keras for Researchers","text":"TensorFlow infrastructure layer differentiable programming. heart, ’s framework manipulating N-dimensional arrays (tensors), much like NumPy. However, three key differences NumPy TensorFlow: TensorFlow can leverage hardware accelerators GPUs TPUs. TensorFlow can automatically compute gradient arbitrary differentiable tensor expressions. TensorFlow computation can distributed large numbers devices single machine, large number machines (potentially multiple devices ). Let’s take look object core TensorFlow: Tensor. ’s constant tensor: can get value R array calling .array(): features attributes dtype shape: common way create constant tensors via tf$ones tf$zeros: can also create random constant tensors:","code":"x <- tf$constant(rbind(c(5, 2), c(1, 3))) print(x) ## tf.Tensor( ## [[5. 2.] ##  [1. 3.]], shape=(2, 2), dtype=float64) as.array(x) ##      [,1] [,2] ## [1,]    5    2 ## [2,]    1    3 x$dtype ## tf.float64 x$shape ## TensorShape([2, 2]) tf$ones(shape = shape(2, 1)) ## tf.Tensor( ## [[1.] ##  [1.]], shape=(2, 1), dtype=float32) tf$zeros(shape = shape(2, 1)) ## tf.Tensor( ## [[0.] ##  [0.]], shape=(2, 1), dtype=float32) x <- random_normal(shape = c(2, 2), mean = 0.0, stddev = 1.0) x <- random_uniform(shape = c(2, 2), minval = 0, maxval = 10)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"variables","dir":"Articles","previous_headings":"","what":"Variables","title":"Introduction to Keras for Researchers","text":"Variables special tensors used store mutable state (weights neural network). create Variable using initial value: update value Variable using methods $assign(value), $assign_add(increment), $assign_sub(decrement):","code":"initial_value <- random_normal(shape=c(2, 2)) a <- tf$Variable(initial_value) print(a) ## <tf.Variable 'Variable:0' shape=(2, 2) dtype=float32, numpy= ## array([[ 0.9057419 ,  0.7916686 ], ##        [ 0.28754202, -0.5408822 ]], dtype=float32)> new_value <- random_normal(shape=c(2, 2)) a$assign(new_value) ## <tf.Variable 'UnreadVariable' shape=(2, 2) dtype=float32, numpy= ## array([[-0.3405368 , -2.1463926 ], ##        [ 1.2602988 ,  0.12241419]], dtype=float32)> added_value <- random_normal(shape=c(2, 2)) a$assign_add(added_value) ## <tf.Variable 'UnreadVariable' shape=(2, 2) dtype=float32, numpy= ## array([[ 0.04820395, -2.6854615 ], ##        [ 0.23246336,  1.4535258 ]], dtype=float32)>"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"doing-math-in-tensorflow","dir":"Articles","previous_headings":"","what":"Doing math in TensorFlow","title":"Introduction to Keras for Researchers","text":"’ve used NumPy, math TensorFlow look familiar. main difference TensorFlow code can run GPU TPU.","code":"a <- random_normal(shape=c(2, 2)) b <- random_normal(shape=c(2, 2))  c <- a + b d <- tf$square(c) e <- tf$exp(d)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"gradients","dir":"Articles","previous_headings":"","what":"Gradients","title":"Introduction to Keras for Researchers","text":"’s another big difference R: can automatically retrieve gradient differentiable expression. Just open GradientTape, start “watching” tensor via tape$watch(), compose differentiable expression using tensor input: default, variables watched automatically, don’t need manually watch : Note can compute higher-order derivatives nesting tapes:","code":"a <- random_normal(shape=c(2, 2)) b <- random_normal(shape=c(2, 2))  with(tf$GradientTape() %as% tape, {   tape$watch(a)  # Start recording the history of operations applied to `a`   c <- tf$sqrt(tf$square(a) + tf$square(b))  # Do some math using `a`   # What's the gradient of `c` with respect to `a`?   dc_da <- tape$gradient(c, a)   print(dc_da) }) ## tf.Tensor( ## [[ 0.9969011  -0.7707146 ] ##  [ 0.23378514  0.96255165]], shape=(2, 2), dtype=float32) a <- tf$Variable(a)  with(tf$GradientTape() %as% tape, {   c <- tf$sqrt(tf$square(a) + tf$square(b))   dc_da <- tape$gradient(c, a)   print(dc_da) }) ## tf.Tensor( ## [[ 0.9969011  -0.7707146 ] ##  [ 0.23378514  0.96255165]], shape=(2, 2), dtype=float32) with(tf$GradientTape() %as% outer_tape, {   with(tf$GradientTape() %as% tape, {     c <- tf$sqrt(tf$square(a) + tf$square(b))     dc_da <- tape$gradient(c, a)   })   d2c_da2 <- outer_tape$gradient(dc_da, a)   print(d2c_da2) }) ## tf.Tensor( ## [[3.3447742e-03 7.1282005e-01] ##  [5.7464113e+00 5.5013180e-02]], shape=(2, 2), dtype=float32)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"keras-layers","dir":"Articles","previous_headings":"","what":"Keras layers","title":"Introduction to Keras for Researchers","text":"TensorFlow infrastructure layer differentiable programming, dealing tensors, variables, gradients, Keras user interface deep learning, dealing layers, models, optimizers, loss functions, metrics, . Keras serves high-level API TensorFlow: Keras makes TensorFlow simple productive. Layer class fundamental abstraction Keras. Layer encapsulates state (weights) computation (defined call method). simple layer looks like . self$add_weight() method gives shortcut creating weights: use Layer instance much like R function: weight variables (created initialize) automatically tracked weights property: many built-layers available, Dense Conv2D LSTM fancier ones like Conv3DTranspose ConvLSTM2D. smart reusing built-functionality.","code":"Linear <- new_layer_class(   \"Linear\",   initialize = function(units = 32, input_dim = 32) {     super$initialize()     self$w <- self$add_weight(       shape = shape(input_dim, units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     tf$matmul(inputs, self$w) + self$b   } ) # Instantiate our layer. linear_layer <- Linear(units=4, input_dim=2)  # The layer can be treated as a function. # Here we call it on some data. y <- linear_layer(tf$ones(shape(2, 2))) linear_layer$weights ## [[1]] ## <KerasVariable shape=(2, 4), dtype=float32, path=linear/variable> ## ## [[2]] ## <KerasVariable shape=(4), dtype=float32, path=linear/variable_1>"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"layer-weight-creation-in-buildinput_shape","dir":"Articles","previous_headings":"","what":"Layer weight creation in build(input_shape)","title":"Introduction to Keras for Researchers","text":"’s often good idea defer weight creation build() method, don’t need specify input dim/shape layer construction time:","code":"Linear <- new_layer_class(   \"Linear\",   initialize = function(units = 32) {     super$initialize()     self$units <- units   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(input_shape[-1], self$units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     tf$matmul(inputs, self$w) + self$b   } )  # Instantiate our layer. linear_layer <- Linear(units = 4)  # This will also call `build(input_shape)` and create the weights. y <- linear_layer(tf$ones(shape(2, 2)))"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"layer-gradients","dir":"Articles","previous_headings":"","what":"Layer gradients","title":"Introduction to Keras for Researchers","text":"can automatically retrieve gradients weights layer calling inside GradientTape. Using gradients, can update weights layer, either manually, using optimizer object. course, can modify gradients using , need .","code":"# Prepare a dataset. c(c(x_train, y_train), .) %<-% dataset_mnist()  x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(list(x_train, y_train)) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   tfdatasets::dataset_batch(64)  # Instantiate our linear layer (defined above) with 10 units. linear_layer <- Linear(units = 10)  # Instantiate a logistic loss function that expects integer targets. loss_fn <- loss_sparse_categorical_crossentropy(from_logits=TRUE)  # Instantiate an optimizer. optimizer <- optimizer_sgd(learning_rate=1e-3)  # Iterate over the batches of the dataset. coro::loop(for(data in dataset) {   # Open a GradientTape.   with(tf$GradientTape() %as% tape, {     # Forward pass.     logits <- linear_layer(data[[1]])      # Loss value for this batch.     loss_value <- loss_fn(data[[2]], logits)   })    # Get gradients of the loss wrt the weights.   gradients <- tape$gradient(loss_value, linear_layer$trainable_weights)    # Update the weights of our linear layer.   optimizer$apply_gradients(zip_lists(gradients, linear_layer$trainable_weights)) }) loss_value ## tf.Tensor(1.2819729, shape=(), dtype=float32)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"trainable-and-non-trainable-weights","dir":"Articles","previous_headings":"","what":"Trainable and non-trainable weights","title":"Introduction to Keras for Researchers","text":"Weights created layers can either trainable non-trainable. ’re exposed trainable_weights non_trainable_weights respectively. ’s layer non-trainable weight:","code":"ComputeSum <- new_layer_class(   \"ComputeSum\",   initialize = function(input_dim) {     super$initialize()     # Create a non-trainable weight.     self$total <- self$add_weight(       initializer = \"zeros\",       shape = shape(input_dim),       trainable = FALSE     )   },   call = function(inputs) {     self$total$assign_add(tf$reduce_sum(inputs, axis=0L))     self$total   } )  my_sum <- ComputeSum(input_dim = 2) x <- tf$ones(shape(2, 2))  as.array(my_sum(x)) ## [1] 2 2 as.array(my_sum(x)) ## [1] 4 4 my_sum$trainable_weights ## list()"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"layers-that-own-layers","dir":"Articles","previous_headings":"","what":"Layers that own layers","title":"Introduction to Keras for Researchers","text":"Layers can recursively nested create bigger computation blocks. layer track weights sublayers (trainable non-trainable). Note manually-created MLP equivalent following built-option:","code":"# Let's reuse the Linear class # with a `build` method that we defined above.  MLP <- new_layer_class(   \"MLP\",   initialize = function() {     super$initialize()     self$linear_1 <- Linear(units = 32)     self$linear_2 <- Linear(units = 32)     self$linear_3 <- Linear(units = 10)   },   call = function(inputs) {     x <- self$linear_1(inputs)     x <- tf$nn$relu(x)     x <- self$linear_2(x)     x <- tf$nn$relu(x)     return(self$linear_3(x))   } )  mlp <- MLP()  # The first call to the `mlp` object will create the weights. y <- mlp(tf$ones(shape=shape(3, 64)))  # Weights are recursively tracked. length(mlp$weights) ## [1] 6 mlp <- keras_model_sequential() %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"tracking-losses-created-by-layers","dir":"Articles","previous_headings":"","what":"Tracking losses created by layers","title":"Introduction to Keras for Researchers","text":"Layers can create losses forward pass via add_loss() method. especially useful regularization losses. losses created sublayers recursively tracked parent layers. ’s layer creates activity regularization loss: model incorporating layer track regularization loss: losses cleared top-level layer start forward pass – don’t accumulate. layer.losses always contains losses created last forward pass. typically use losses summing computing gradients writing training loop.","code":"# A layer that creates an activity sparsity regularization loss ActivityRegularization <- new_layer_class(   \"ActivityRegularization\",   initialize = function(rate=1e-2) {     super$initialize()     self$rate <- rate   },   call = function(inputs) {     self$add_loss(self$rate * tf$reduce_sum(tf$abs(inputs)))     inputs   } ) # Let's use the loss layer in a MLP block. SparseMLP <- new_layer_class(   \"SparseMLP\",   initialize = function() {     super$initialize()     self$linear_1 <- Linear(units = 32)     self$reg <- ActivityRegularization(rate = 1e-2)     self$linear_3 <- Linear(units = 10)   },   call = function(inputs) {     x <- self$linear_1(inputs)     x <- tf$nn$relu(x)     x <- self$reg(x)     return(self$linear_3(x))   } )  mlp <- SparseMLP() y <- mlp(tf$ones(shape(10, 10)))  mlp$losses  # List containing one float32 scalar ## [[1]] ## tf.Tensor(0.18065463, shape=(), dtype=float32) # Losses correspond to the *last* forward pass. mlp <- SparseMLP() mlp(tf$ones(shape(10, 10))) ## tf.Tensor( ## [[ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621]], shape=(10, 10), dtype=float32) length(mlp$losses) ## [1] 1 mlp(tf$ones(shape(10, 10))) ## tf.Tensor( ## [[ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621] ##  [ 0.0388482  -0.03920118  0.01624808 -0.01361975 -0.01354899  0.07107338 ##   -0.01077365  0.05688906 -0.02838149 -0.04084621]], shape=(10, 10), dtype=float32) length(mlp$losses)  # No accumulation. ## [1] 1 # Let's demonstrate how to use these losses in a training loop.  # Prepare a dataset. c(c(x_train, y_train), .) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(list(x_train, y_train)) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   tfdatasets::dataset_batch(64)  # A new MLP. mlp <- SparseMLP()  # Loss and optimizer. loss_fn <- loss_sparse_categorical_crossentropy(from_logits=TRUE) optimizer <- optimizer_sgd(learning_rate=1e-3)  coro::loop(for(data in dataset) {   x <- data[[1]]   y <- data[[2]]   with(tf$GradientTape() %as% tape, {     # Forward pass.     logits <- mlp(x)      # External loss value for this batch.     loss <- loss_fn(y, logits)      # Add the losses created during the forward pass.     loss <- loss + Reduce(`+`, mlp$losses)      # Get gradients of the loss wrt the weights.     gradients <- tape$gradient(loss, mlp$trainable_weights)      # Update the weights of our linear layer.     optimizer$apply_gradients(zip_lists(gradients, mlp$trainable_weights))   }) })"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"keeping-track-of-training-metrics","dir":"Articles","previous_headings":"","what":"Keeping track of training metrics","title":"Introduction to Keras for Researchers","text":"Keras offers broad range built-metrics, like metric_auc metric_precision_at_recall. ’s also easy create metrics lines code. use metric custom training loop, : Instantiate metric object, e.g. metric = metric_auc() Call metric$udpate_state(targets, predictions) method batch data Query result via metric$result() Reset metric’s state end epoch start evaluation via metric$reset_state() ’s simple example: can also define metrics subclassing keras.metrics.Metric. need override three functions called : Override update_state() update statistic values. Override result() return metric value. Override reset_state() reset metric initial state. example implement F1-score metric (support sample weighting). Let’s test-drive :","code":"# Instantiate a metric object accuracy <- metric_sparse_categorical_accuracy()  # Prepare our layer, loss, and optimizer. model <- keras_model_sequential() %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10) loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE) optimizer <- optimizer_adam(learning_rate=1e-3)  for (epoch in seq_len(2)) {   coro::loop(for (data in dataset) {     x <- data[[1]]     y <- data[[2]]     with(tf$GradientTape() %as% tape, {       # Forward pass.       logits <- model(x)        # External loss value for this batch.       loss_value <- loss_fn(y, logits)     })      # Update the state of the `accuracy` metric.     accuracy$update_state(y, logits)      # Update the weights of the model to minimize the loss value.     gradients <- tape$gradient(loss_value, model$trainable_weights)     optimizer$apply_gradients(zip_lists(gradients, model$trainable_weights))    })   cat(\"Epoch:\", epoch, \"Accuracy:\", as.numeric(accuracy$result()), \"\\n\")   accuracy$reset_state() } ## Epoch: 1 Accuracy: 0.8757833 ## Epoch: 2 Accuracy: 0.93915 F1Score <- new_metric_class(   \"F1Score\",   initialize = function(self, name=\"f1_score\", dtype=\"float32\", threshold=0.5, ...) {     super$initialize(name=name, dtype=dtype, ...)     self$threshold <- threshold     self$true_positives <- self$add_weight(       name=\"tp\", dtype=dtype, initializer=\"zeros\"     )     self$false_positives <- self$add_weight(       name=\"fp\", dtype=dtype, initializer=\"zeros\"     )     self$false_negatives <- self$add_weight(       name=\"fn\", dtype=dtype, initializer=\"zeros\"     )   },   update_state = function(y_true, y_pred, sample_weight=NULL) {     y_pred <- tf$math$greater_equal(y_pred, self$threshold)     y_true <- tf$cast(y_true, tf$bool)     y_pred <- tf$cast(y_pred, tf$bool)      true_positives <- tf$cast(y_true & y_pred, self$dtype)     false_positives <- tf$cast((!y_true) & y_pred, self$dtype)     false_negatives <- tf$cast(y_true & (!y_pred), self$dtype)      if (!is.null(sample_weight)) {       sample_weight <- tf$cast(sample_weight, self$dtype)       true_positives <- true_positives * sample_weight       false_positives <- false_positives * sample_weight       false_negatives <- false_negatives * sample_weight     }      self$true_positives$assign_add(tf$reduce_sum(true_positives))     self$false_positives$assign_add(tf$reduce_sum(false_positives))     self$false_negatives$assign_add(tf$reduce_sum(false_negatives))   },    result = function() {     precision <- self$true_positives / (self$true_positives + self$false_positives)     recall <- self$true_positives / (self$true_positives + self$false_negatives)     f1_score <- 2 * precision * recall / (precision + recall)     f1_score   },    reset_state = function() {     self$true_positives$assign(0)     self$false_positives$assign(0)     self$false_negatives$assign(0)   } ) m <- F1Score() m$update_state(c(0, 1, 0, 0), c(0.3, 0.5, 0.8, 0.9)) cat(\"Intermediate result:\", as.numeric(m$result()), \"\\n\") ## Intermediate result: 0.5 m$update_state(c(1, 1, 1, 1), c(0.1, 0.7, 0.6, 0.0)) cat(\"Final result:\", as.numeric(m$result()), \"\\n\") ## Final result: 0.6"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"compiled-functions","dir":"Articles","previous_headings":"","what":"Compiled functions","title":"Introduction to Keras for Researchers","text":"Running eagerly great debugging, get better performance compiling computation static graphs. Static graphs researcher’s best friends. can compile function wrapping tf.function decorator.","code":"# Prepare our layer, loss, and optimizer. model <- keras_model_sequential() %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10) loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE) optimizer <- optimizer_adam(learning_rate=1e-3)  # Create a training step function. train_on_batch <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     # Forward pass.     logits <- model(x)     # External loss value for this batch.     loss_value <- loss_fn(y, logits)   })   # Update the weights of the model to minimize the loss value.   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply_gradients(zip_lists(gradients, model$trainable_weights))   loss_value })   # Prepare a dataset. c(c(x_train, y_train), .) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(list(x_train, y_train)) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   tfdatasets::dataset_batch(64)  i <- 0 coro::loop(for (data in dataset) {   i <- i + 1   x <- data[[1]]   y <- data[[2]]   loss <- train_on_batch(x, y)   if (i %% 100 == 0)     cat(\"Loss:\", as.numeric(loss), \"\\n\") }) ## Loss: 0.551749 ## Loss: 0.2131135 ## Loss: 0.2765952 ## Loss: 0.1296219 ## Loss: 0.2657076 ## Loss: 0.2683381 ## Loss: 0.1570166 ## Loss: 0.3139241 ## Loss: 0.08981849"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"training-mode-inference-mode","dir":"Articles","previous_headings":"","what":"Training mode & inference mode","title":"Introduction to Keras for Researchers","text":"layers, particular BatchNormalization layer Dropout layer, different behaviors training inference. layers, standard practice expose training (boolean) argument call method. exposing argument call, enable built-training evaluation loops (e.g. fit) correctly use layer training inference modes.","code":"Dropout <- new_layer_class(   \"Dropout\",   initialize = function(rate) {     super$initialize()     self$rate <- rate   },   call = function(inputs, training = NULL) {     if (!is.null(training) && training) {       return(tf$nn$dropout(inputs, rate = self$rate))     }     inputs   } )  MLPWithDropout <- new_layer_class(   \"MLPWithDropout\",   initialize = function() {     super$initialize()     self$linear_1 <- Linear(units = 32)     self$dropout <- Dropout(rate = 0.5)     self$linear_3 <- Linear(units = 10)   },   call = function(inputs, training = NULL) {     x <- self$linear_1(inputs)     x <- tf$nn$relu(x)     x <- self$dropout(x, training = training)     self$linear_3(x)   } )  mlp <- MLPWithDropout() y_train <- mlp(tf$ones(shape(2, 2)), training=TRUE) y_test <- mlp(tf$ones(shape(2, 2)), training=FALSE)"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"the-functional-api-for-model-building","dir":"Articles","previous_headings":"","what":"The Functional API for model-building","title":"Introduction to Keras for Researchers","text":"build deep learning models, don’t use object-oriented programming time. layers ’ve seen far can also composed functionally, like (call “Functional API”): Functional API tends concise subclassing, provides advantages (generally advantages functional, typed languages provide untyped OO development). However, can used define DAGs layers – recursive networks defined Layer subclasses instead. Learn Functional API . research workflows, may often find mix--matching OO models Functional models. Note Model class also features built-training & evaluation loops: fit(), predict() evaluate() (configured via compile() method). built-functions give access following built-training infrastructure features: Callbacks. can leverage built-callbacks early-stopping, model checkpointing, monitoring training TensorBoard. can also implement custom callbacks needed. Distributed training. can easily scale training multiple GPUs, TPU, even multiple machines tf.distribute API – changes code. Step fusing. steps_per_execution argument Model.compile(), can process multiple batches single tf.function call, greatly improves device utilization TPUs. won’t go details, provide simple code example . leverages built-training infrastructure implement MNIST example . can always subclass Model class (works exactly like subclassing Layer) want leverage built-training loops OO models. Just override Model$train_step() customize happens fit() retaining support built-infrastructure features outlined – callbacks, zero-code distribution support, step fusing support. may also override test_step() customize happens evaluate(), override predict_step() customize happens predict(). information, please refer guide.","code":"# We use an `Input` object to describe the shape and dtype of the inputs. # This is the deep learning equivalent of *declaring a type*. # The shape argument is per-sample; it does not include the batch size. # The functional API focused on defining per-sample transformations. # The model we create will automatically batch the per-sample transformations, # so that it can be called on batches of data. inputs <- layer_input(shape = 16, dtype = \"float32\")  # We call layers on these \"type\" objects # and they return updated types (new shapes/dtypes). outputs <- inputs %>%   Linear(units = 32) %>% # We are reusing the Linear layer we defined earlier.   Dropout(rate = 0.5) %>% # We are reusing the Dropout layer we defined earlier.   Linear(units = 10)  # A functional `Model` can be defined by specifying inputs and outputs. # A model is itself a layer like any other. model <- keras_model(inputs, outputs)  # A functional model already has weights, before being called on any data. # That's because we defined its input shape in advance (in `Input`). length(model$weights) ## [1] 4 # Let's call our model on some data, for fun. y <- model(tf$ones(shape(2, 16))) y$shape ## TensorShape([2, 10]) # You can pass a `training` argument in `__call__` # (it will get passed down to the Dropout layer). y <- model(tf$ones(shape(2, 16)), training=TRUE) inputs <- layer_input(shape = 784, dtype=\"float32\") outputs <- inputs %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10) model <- keras_model(inputs, outputs)  # Specify the loss, optimizer, and metrics with `compile()`. model %>% compile(     loss = loss_sparse_categorical_crossentropy(from_logits=TRUE),     optimizer=optimizer_adam(learning_rate=1e-3),     metrics=list(metric_sparse_categorical_accuracy()), )  # Train the model with the dataset for 2 epochs. model %>% fit(dataset, epochs=2) ## Epoch 1/2 ## 938/938 - 4s - 4ms/step - loss: 0.3958 - sparse_categorical_accuracy: 0.8866 ## Epoch 2/2 ## 938/938 - 1s - 960us/step - loss: 0.1888 - sparse_categorical_accuracy: 0.9443 predictions <- model %>% predict(dataset) ## 938/938 - 1s - 1ms/step model %>% evaluate(dataset) ## 938/938 - 1s - 1ms/step - loss: 0.1763 - sparse_categorical_accuracy: 0.9454 ## $loss ## [1] 0.1763445 ## ## $sparse_categorical_accuracy ## [1] 0.9454167 CustomModel <- new_model_class(   \"CustomModel\",   initialize = function(...) {     super$initialize(...)     self$loss_tracker <- metric_mean(name=\"loss\")     self$accuracy <- metric_sparse_categorical_accuracy()     self$loss_fn <- loss_sparse_categorical_crossentropy(from_logits=TRUE)     self$optimizer <- optimizer_adam(learning_rate=1e-3)   },   train_step = function(data) {     c(x, y = NULL, sample_weight = NULL) %<-% data     with(tf$GradientTape() %as% tape, {       y_pred <- self(x, training=TRUE)       loss <- self$loss_fn(y = y, y_pred = y_pred, sample_weight=sample_weight)     })     gradients <- tape$gradient(loss, self$trainable_variables)     self$optimizer$apply_gradients(       zip_lists(gradients, self$trainable_variables)     )      # Update metrics (includes the metric that tracks the loss)     self$loss_tracker$update_state(loss)     self$accuracy$update_state(y, y_pred, sample_weight=sample_weight)     # Return a list mapping metric names to current value     list(       loss = self$loss_tracker$result(),       accuracy = self$accuracy$result()     )   },   metrics = mark_active(function() {     list(self$loss_tracker, self$accuracy)   }) )  inputs <- layer_input(shape = 784, dtype=\"float32\") outputs <- inputs %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 32, activation = \"relu\") %>%   layer_dense(units = 10) model <- CustomModel(inputs, outputs) model %>% compile() model %>% fit(dataset, epochs=2) ## Epoch 1/2 ## 938/938 - 2s - 2ms/step - loss: 0.3869 - sparse_categorical_accuracy: 0.8924 ## Epoch 2/2 ## 938/938 - 1s - 1ms/step - loss: 0.2163 - sparse_categorical_accuracy: 0.9370"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"end-to-end-experiment-example-1-variational-autoencoders-","dir":"Articles","previous_headings":"","what":"End-to-end experiment example 1: variational autoencoders.","title":"Introduction to Keras for Researchers","text":"things ’ve learned far: Layer encapsulates state (created __init__ build) computation (defined call). Layers can recursively nested create new, bigger computation blocks. can easily write highly hackable training loops opening GradientTape, calling model inside tape’s scope, retrieving gradients applying via optimizer. can speed training loops using @tf.function decorator. Layers can create track losses (typically regularization losses) via self.add_loss(). Let’s put things together end--end example: ’re going implement Variational AutoEncoder (VAE). ’ll train MNIST digits. VAE subclass Layer, built nested composition layers subclass Layer. feature regularization loss (KL divergence). model definition. First, Encoder class, uses Sampling layer map MNIST digit latent-space triplet (z_mean, z_log_var, z). Next, Decoder class, maps probabilistic latent space coordinates back MNIST digit. Finally, VariationalAutoEncoder composes together encoder decoder, creates KL divergence regularization loss via add_loss(). Now, let’s write training loop. training step decorated @tf.function compile super fast graph function. can see, building training type model Keras quick painless.","code":"Sampling <- new_layer_class(   \"Sampling\",   call = function(inputs) {     c(z_mean, z_log_var) %<-% inputs     batch <- op_shape(z_mean)[[1]]     dim <- op_shape(z_mean)[[2]]     epsilon <- random_normal(shape = c(batch, dim))     z_mean + op_exp(0.5 * z_log_var) * epsilon   } )  Encoder <- new_layer_class(   \"Encoder\",   initialize = function(latent_dim = 32, intermediate_dim = 64, ...) {     super$initialize(...)     self$dense_proj <- layer_dense(units = intermediate_dim, activation = \"relu\")     self$dense_mean <- layer_dense(units = latent_dim)     self$dense_log_var <- layer_dense(units = latent_dim)     self$sampling <- Sampling()   },   call = function(inputs) {     x <- self$dense_proj(inputs)     z_mean <- self$dense_mean(x)     z_log_var <- self$dense_log_var(x)     z <- self$sampling(list(z_mean, z_log_var))     list(z_mean, z_log_var, z)   } ) Decoder <- new_layer_class(   \"Decoder\",   initialize = function(original_dim, intermediate_dim = 64, ...) {     super$initialize(...)     self$dense_proj <- layer_dense(units = intermediate_dim, activation = \"relu\")     self$dense_output <- layer_dense(units = original_dim, activation = \"sigmoid\")   },   call = function(inputs) {     x <- self$dense_proj(inputs)     self$dense_output(x)   } ) VariationalAutoEncoder <- new_model_class(   \"VariationalAutoEncoder\",   initialize = function(original_dim,         intermediate_dim=64,         latent_dim=32,         name=\"autoencoder\", ...) {     super$initialize(name = name, ...)     self$original_dim <- original_dim     self$encoder <- Encoder(       latent_dim = latent_dim,       intermediate_dim = intermediate_dim     )     self$decoder <- Decoder(       original_dim = original_dim,       intermediate_dim = intermediate_dim     )   },   call = function(inputs) {     c(z_mean, z_log_var, z) %<-% self$encoder(inputs)     reconstructed <- self$decoder(z)     # Add KL divergence regularization loss.     kl_loss <- -0.5 * op_mean(       z_log_var - op_square(z_mean) - op_exp(z_log_var) + 1     )     self$add_loss(kl_loss)     reconstructed   } ) # Our model. vae <- VariationalAutoEncoder(   original_dim = 784,   intermediate_dim = 64,   latent_dim = 32 )  # Loss and optimizer. loss_fn <- loss_mean_squared_error() optimizer = optimizer_adam(learning_rate=1e-3)  # Prepare a dataset. c(c(x_train, .), .) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(x_train) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   tfdatasets::dataset_batch(32)   training_step <- tf_function(function(x) {   with(tf$GradientTape() %as% tape, {     reconstructed <- vae(x)  # Compute input reconstruction.     # Compute loss.     loss <- loss_fn(x, reconstructed)     loss <- loss + op_sum(vae$losses)  # Add KLD term.   })   # Update the weights of the VAE.   grads <- tape$gradient(loss, vae$trainable_weights)   optimizer$apply_gradients(zip_lists(grads, vae$trainable_weights))   loss })  losses <- c()  # Keep track of the losses over time. coro::loop(for(data in dataset) {   loss <- training_step(data)    # Logging.   losses[length(losses) + 1] <- as.numeric(loss)   if (length(losses) %% 100 == 0) {     cat(\"Step:\", length(losses), \"Loss:\", mean(losses), \"\\n\")   }   # Stop after 1000 steps.   # Training the model to convergence is left   # as an exercise to the reader.   if (length(losses) >= 1000) {     break   } }) ## Step: 100 Loss: 0.1270978 ## Step: 200 Loss: 0.1003238 ## Step: 300 Loss: 0.09001128 ## Step: 400 Loss: 0.08493649 ## Step: 500 Loss: 0.08171404 ## Step: 600 Loss: 0.07926706 ## Step: 700 Loss: 0.07790599 ## Step: 800 Loss: 0.07670419 ## Step: 900 Loss: 0.07570736 ## Step: 1000 Loss: 0.07476593"},{"path":"https://keras.posit.co/articles/intro_to_keras_for_researchers.html","id":"end-to-end-experiment-example-2-hypernetworks-","dir":"Articles","previous_headings":"","what":"End-to-end experiment example 2: hypernetworks.","title":"Introduction to Keras for Researchers","text":"Let’s take look another kind research experiment: hypernetworks. idea use small deep neural network (hypernetwork) generate weights larger network (main network). Let’s implement really trivial hypernetwork: ’ll use small 2-layer network generate weights larger 3-layer network. training loop. batch data: use hypernetwork generate array weight coefficients, weights_pred reshape coefficients kernel & bias tensors main_network run forward pass main_network compute actual MNIST predictions run backprop weights hypernetwork minimize final classification loss Implementing arbitrary research ideas Keras straightforward highly productive. Imagine trying 25 ideas per day (20 minutes per experiment average)! Keras designed go idea results fast possible, believe key great research. hope enjoyed quick introduction. Let us know build Keras!","code":"input_dim <- 784 classes <- 10  # This is the main network we'll actually use to predict labels. inputs <- layer_input(shape = input_dim) dense1 <- layer_dense(units = 64, activation = \"relu\") dense1$built <- TRUE  dense2 <- layer_dense(units = classes) dense2$built <- TRUE  outputs <- inputs %>% dense1() %>% dense2() main_network <- keras_model(inputs, outputs)  # This is the number of weight coefficients to generate. Each layer in the # main network requires output_dim * input_dim + output_dim coefficients. num_weights_to_generate <- (classes * 64 + classes) + (64 * input_dim + 64)  # This is the hypernetwork that generates the weights of the `main_network` above. hypernetwork <- keras_model_sequential() %>%   layer_dense(units=16, activation=\"relu\") %>%   layer_dense(units=num_weights_to_generate, activation=\"sigmoid\") # Loss and optimizer. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE) optimizer <- optimizer_adam(learning_rate=1e-4)  # Prepare a dataset. c(c(x_train, y_train), .) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(60000, 784)) / 255  dataset <- tfdatasets::tensor_slices_dataset(list(x_train, y_train)) %>%   tfdatasets::dataset_shuffle(buffer_size=1024) %>%   # We'll use a batch size of 1 for this experiment.   tfdatasets::dataset_batch(1)  train_step <- function(x, y) {   with(tf$GradientTape() %as% tape, {     weights_pred <- hypernetwork(x)      # Reshape them to the expected shapes for w and b for the outer model.     # Layer 1 kernel.     start_index <- 1     w1_shape <- c(input_dim, 64)     w1_coeffs <- weights_pred[, start_index:(start_index + prod(w1_shape) - 1)]     w1 <- tf$reshape(w1_coeffs, as.integer(w1_shape))     start_index <- start_index + prod(w1_shape)      # Layer 1 bias.     b1_shape <- c(64)     b1_coeffs <- weights_pred[, start_index:(start_index + prod(b1_shape) - 1)]     b1 <- tf$reshape(b1_coeffs, as.integer(b1_shape))     start_index <- start_index + prod(b1_shape)      # Layer 2 kernel.     w2_shape <- c(64, classes)     w2_coeffs <- weights_pred[, start_index:(start_index + prod(w2_shape) - 1)]     w2 <- tf$reshape(w2_coeffs, as.integer(w2_shape))     start_index <- start_index + prod(w2_shape)      # Layer 2 bias.     b2_shape <- c(classes)     b2_coeffs <- weights_pred[, start_index:(start_index + prod(b2_shape) - 1)]     b2 <- tf$reshape(b2_coeffs, as.integer(b2_shape))     start_index <- start_index + prod(b2_shape)      # Set the weight predictions as the weight variables on the outer model.     dense1$kernel <- w1     dense1$bias <- b1     dense2$kernel <- w2     dense2$bias <- b2      # Inference on the outer model.     preds <- main_network(x)     loss <- loss_fn(y, preds)   })    grads <- tape$gradient(loss, hypernetwork$trainable_weights)   optimizer$apply_gradients(zip_lists(grads, hypernetwork$trainable_weights))   loss }  losses <- c()  # Keep track of the losses over time. coro::loop(for (data in dataset) {   x <- data[[1]]   y <- data[[2]]   loss <- train_step(x, y)    # Logging.   losses[length(losses) + 1] <- as.numeric(loss)   if (length(losses) %% 100 == 0) {     cat(\"Step:\", length(losses), \"Loss:\", mean(losses), \"\\n\")   }   # Stop after 1000 steps.   # Training the model to convergence is left   # as an exercise to the reader.   if (length(losses) >= 1000) {     break   } }) ## Step: 100 Loss: 2.536778 ## Step: 200 Loss: 2.236472 ## Step: 300 Loss: 2.119417 ## Step: 400 Loss: 2.040341 ## Step: 500 Loss: 1.949125 ## Step: 600 Loss: 1.859384 ## Step: 700 Loss: 1.845726 ## Step: 800 Loss: 1.820594 ## Step: 900 Loss: 1.771334 ## Step: 1000 Loss: 1.730648"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Making new layers and models via subclassing","text":"guide cover everything need know build subclassed layers models. particular, ’ll learn following features: Layer class add_weight() method Trainable non-trainable weights build() method Making sure layers can used backend add_loss() method training argument call() mask argument call() Making sure layers can serialized Let’s dive .","code":""},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Making new layers and models via subclassing","text":"","code":"library(keras3) library(tensorflow, exclude = c(\"set_random_seed\", \"shape\")) library(tfdatasets, exclude = \"shape\")"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"the-layer-class-the-combination-of-state-weights-and-some-computation","dir":"Articles","previous_headings":"","what":"The Layer class: the combination of state (weights) and some computation","title":"Making new layers and models via subclassing","text":"One central abstractions Keras Layer class. layer encapsulates state (layer’s “weights”) transformation inputs outputs (“call”, layer’s forward pass). ’s densely-connected layer. two state variables: variables w b. use layer calling tensor input(s), much like R function. Note weights w b automatically tracked layer upon set layer attributes:","code":"layer_linear <- Layer(\"Linear\",    initialize = function(units = 32, input_dim = 32, ...) {     super$initialize(...)     self$w <- self$add_weight(       shape = shape(input_dim, units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(units),       initializer = \"zeros\",       trainable = TRUE     )   },    call = function(inputs) {     op_matmul(inputs, self$w) + self$b   } ) x <- op_ones(c(2, 2)) linear_layer <- layer_linear(units = 4, input_dim = 2) y <- linear_layer(x) print(y) ## tf.Tensor( ## [[0.02153057 0.15450525 0.0205495  0.04493225] ##  [0.02153057 0.15450525 0.0205495  0.04493225]], shape=(2, 4), dtype=float32) linear_layer$weights ## [[1]] ## <KerasVariable shape=(2, 4), dtype=float32, path=linear/variable> ## ## [[2]] ## <KerasVariable shape=(4), dtype=float32, path=linear/variable_1>"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"layers-can-have-non-trainable-weights","dir":"Articles","previous_headings":"","what":"Layers can have non-trainable weights","title":"Making new layers and models via subclassing","text":"Besides trainable weights, can add non-trainable weights layer well. weights meant taken account backpropagation, training layer. ’s add use non-trainable weight: ’s part layer$weights, gets categorized non-trainable weight:","code":"layer_compute_sum <- Layer(   \"ComputeSum\",   initialize = function(input_dim) {     super$initialize()     self$total <- self$add_weight(       initializer = \"zeros\",       shape = shape(input_dim),       trainable = FALSE     )   },   call = function(inputs) {     self$total$assign_add(op_sum(inputs, axis = 1))     self$total   } )  x <- op_ones(c(2, 2)) my_sum <- layer_compute_sum(input_dim = 2) y <- my_sum(x) print(as.array(y)) ## [1] 2 2 y <- my_sum(x) print(as.array(y)) ## [1] 4 4 cat(\"weights:\", length(my_sum$weights)) ## weights: 1 cat(\"non-trainable weights:\", length(my_sum$non_trainable_weights)) ## non-trainable weights: 1 # It's not included in the trainable weights: cat(\"trainable_weights:\", length(my_sum$trainable_weights)) ## trainable_weights: 0"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"best-practice-deferring-weight-creation-until-the-shape-of-the-inputs-is-known","dir":"Articles","previous_headings":"","what":"Best practice: deferring weight creation until the shape of the inputs is known","title":"Making new layers and models via subclassing","text":"Linear layer took input_dim argument used compute shape weights w b initialize(): many cases, may know advance size inputs, like lazily create weights value becomes known, time instantiating layer. Keras API, recommend creating layer weights build(self, inputs_shape) method layer. Like : call() method layer automatically run build first time called. now layer ’s lazy thus easier use: Implementing build() separately shown nicely separates creating weights using weights every call.","code":"layer_linear <- Layer(\"Linear\",    initialize = function(units = 32, input_dim = 32, ...) {     super$initialize(...)     self$w <- self$add_weight(       shape = shape(input_dim, units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(units),       initializer = \"zeros\",       trainable = TRUE     )   },    call = function(inputs) {     op_matmul(inputs, self$w) + self$b   } ) layer_linear <- Layer(   \"Linear\",   initialize = function(units = 32, ...) {     self$units <- as.integer(units)     super$initialize(...)   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     op_matmul(inputs, self$w) + self$b   } ) # At instantiation, we don't know on what inputs this is going to get called linear_layer <- layer_linear(units = 32)  # The layer's weights are created dynamically the first time the layer is called y <- linear_layer(x)"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"layers-are-recursively-composable","dir":"Articles","previous_headings":"","what":"Layers are recursively composable","title":"Making new layers and models via subclassing","text":"assign Layer instance attribute another Layer, outer layer start tracking weights created inner layer. recommend creating sublayers initialize() method leave first call() trigger building weights.","code":"MLPBlock <- Layer(   \"MLPBlock\",   initialize = function() {     super$initialize()     self$linear_1 <- layer_linear(units = 32)     self$linear_2 <- layer_linear(units = 32)     self$linear_3 <- layer_linear(units = 1)   },   call = function(inputs) {     inputs |>       self$linear_1() |>       activation_relu() |>       self$linear_2() |>       activation_relu() |>       self$linear_3()   } )  mlp <- MLPBlock() # The first call to the `mlp` will create the weights y <- mlp(op_ones(shape = c(3, 64)))  cat(\"weights:\", length(mlp$weights), \"\\n\") ## weights: 6 cat(\"trainable weights:\", length(mlp$trainable_weights), \"\\n\") ## trainable weights: 6"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"backend-agnostic-layers-and-backend-specific-layers","dir":"Articles","previous_headings":"","what":"Backend-agnostic layers and backend-specific layers","title":"Making new layers and models via subclassing","text":"long layer uses APIs ops namespace (ie. using functions starting op_), (Keras namespaces activations_*, random_*, layer_*), can used backend – TensorFlow, JAX, PyTorch. layers ’ve seen far guide work Keras backends. ops namespace gives access : NumPy API, e.g. op_matmul, op_sum, op_reshape, op_stack, etc. Neural networks-specific APIs op_softmax, op_conv, op_binary_crossentropy, op_relu, etc. can also use backend-native APIs layers (tf$nn functions), , layer usable backend question. instance, write following JAX-specific layer using jax$numpy: equivalent TensorFlow-specific layer: equivalent PyTorch-specific layer: cross-backend compatibility tremendously useful property, strongly recommend seek always make layers backend-agnostic leveraging Keras APIs.","code":"# keras3::install_keras(backend = c(\"jax\")) jax <- reticulate::import(\"jax\")  Linear <- new_layer_class(   ...   call = function(inputs) {     jax$numpy$matmul(inputs, self$w) + self$b   } ) library(tensorflow)  Linear <- new_layer_class(   ...   call = function(inputs) {     tf$matmul(inputs, self$w) + self$b   } ) torch <- reticulate::import(\"torch\")  Linear <- new_layer_class(   ...   call = function(inputs) {     torch$matmul(inputs, self$w) + self$b   } )"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"the-add_loss-method","dir":"Articles","previous_headings":"","what":"The add_loss() method","title":"Making new layers and models via subclassing","text":"writing call() method layer, can create loss tensors want use later, writing training loop. doable calling self$add_loss(value): losses (including created inner layer) can retrieved via layer$losses. property reset start every call top-level layer, layer$losses always contains loss values created last forward pass. addition, loss property also contains regularization losses created weights inner layer: losses meant taken account writing custom training loops. also work seamlessly fit() (get automatically summed added main loss, ):","code":"# A layer that creates an activity regularization loss layer_activity_regularization <- Layer(   \"ActivityRegularizationLayer\",   initialize = function(rate = 1e-2) {     self$rate <- as.numeric(rate)     super$initialize()   },   call = function(inputs) {     self$add_loss(self$rate * op_mean(inputs))     inputs   } ) layer_outer <- Layer(   \"OuterLayer\",   initialize = function() {     super$initialize()     self$activity_reg <- layer_activity_regularization(rate = 1e-2)   },   call = function(inputs) {     self$activity_reg(inputs)     inputs   } )  layer <- layer_outer() # No losses yet since the layer has never been called cat(\"losses:\", length(layer$losses), \"\\n\") ## losses: 0 x <- layer(op_zeros(c(1, 1))) # We created one loss value cat(\"losses:\", length(layer$losses), \"\\n\") ## losses: 1 # `layer$losses` gets reset at the start of each call x <- layer(op_zeros(c(1, 1))) # This is the loss created during the call above cat(\"losses:\", length(layer$losses), \"\\n\") ## losses: 1 layer_outer_with_kernel_regularizer <- Layer(   \"OuterLayerWithKernelRegularizer\",   initialize = function() {     super$initialize()     self$dense <- layer_dense(units = 32,                               kernel_regularizer = regularizer_l2(1e-3))   },   call = function(inputs) {     self$dense(inputs)   } )  layer <- layer_outer_with_kernel_regularizer() x <- layer(op_zeros(c(1, 1)))  # This is `1e-3 * sum(layer$dense$kernel ** 2)`, # created by the `kernel_regularizer` above. print(layer$losses) ## [[1]] ## tf.Tensor(0.002025157, shape=(), dtype=float32) inputs <- keras_input(shape = 3) outputs <- inputs |> layer_activity_regularization() model <- keras_model(inputs, outputs)  # If there is a loss passed in `compile`, the regularization # losses get added to it model |> compile(optimizer = \"adam\", loss = \"mse\") model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1) ## 1/1 - 0s - 118ms/step - loss: 1.8971 # It's also possible not to pass any loss in `compile`, # since the model already has a loss to minimize, via the `add_loss` # call during the forward pass! model |> compile(optimizer = \"adam\") model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1) ## 1/1 - 0s - 73ms/step - loss: -3.3344e-03"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"you-can-optionally-enable-serialization-on-your-layers","dir":"Articles","previous_headings":"","what":"You can optionally enable serialization on your layers","title":"Making new layers and models via subclassing","text":"need custom layers serializable part Functional model, can optionally implement get_config() method: Note initialize() method base Layer class takes keyword arguments, particular name dtype. ’s good practice pass arguments parent class initialize() include layer config: need flexibility deserializing layer config, can also override from_config() class method. base implementation from_config(): learn serialization saving, see complete guide saving serializing models.","code":"layer_linear <- Layer(   \"Linear\",   initialize = function(units = 32) {     self$units <- as.integer(units)     super$initialize()   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     op_matmul(inputs, self$w) + self$b   },   get_config = function() {     list(units = self$units)   } )  # Now you can recreate the layer from its config: layer <- layer_linear(units = 64) config <- get_config(layer) str(config) ## List of 1 ##  $ units: int 64 ##  - attr(*, \"__class__\")=<class '<r-globalenv>.Linear'> new_layer <- from_config(config) Linear <- new_layer_class(   \"Linear\",   initialize = function(units = 32, ...) {     self$units <- as.integer(units)     super$initialize(...)   },   build = function(input_shape) {     self$w <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = \"random_normal\",       trainable = TRUE     )     self$b <- self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE     )   },   call = function(inputs) {     op_matmul(inputs, self$w) + self$b   },   get_config = function() {     list(units = self$units)   } )  layer <- Linear(units = 64) config <- get_config(layer) str(config) ## List of 1 ##  $ units: int 64 ##  - attr(*, \"__class__\")=<class '<r-globalenv>.Linear'> new_layer <- from_config(config) Layer(   ...,   from_config = function(config) {     # calling `__class__`() creates a new instance and calls initialize()     do.call(`__class__`, config)   } )"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"privileged-training-argument-in-the-call-method","dir":"Articles","previous_headings":"","what":"Privileged training argument in the call() method","title":"Making new layers and models via subclassing","text":"layers, particular BatchNormalization layer Dropout layer, different behaviors training inference. layers, standard practice expose training (boolean) argument call() method. exposing argument call(), enable built-training evaluation loops (e.g. fit()) correctly use layer training inference.","code":"layer_custom_dropout <- Layer(   \"CustomDropout\",   initialize = function(rate, ...) {     super$initialize(...)     self$rate <- rate     self$seed_generator <- random_seed_generator(1337)   },   call = function(inputs, training = NULL) {     if (isTRUE(training))       return(random_dropout(inputs, rate = self$rate,                             seed = self.seed_generator))     inputs   } )"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"privileged-mask-argument-in-the-call-method","dir":"Articles","previous_headings":"","what":"Privileged mask argument in the call() method","title":"Making new layers and models via subclassing","text":"privileged argument supported call() mask argument. find Keras RNN layers. mask boolean tensor (one boolean value per timestep input) used skip certain input timesteps processing timeseries data. Keras automatically pass correct mask argument call() layers support , mask generated prior layer. Mask-generating layers Embedding layer configured mask_zero = TRUE, Masking layer.","code":""},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"the-model-class","dir":"Articles","previous_headings":"","what":"The Model class","title":"Making new layers and models via subclassing","text":"general, use Layer class define inner computation blocks, use Model class define outer model – object train. instance, ResNet50 model, several ResNet blocks subclassing Layer, single Model encompassing entire ResNet50 network. Model class API Layer, following differences: exposes built-training, evaluation, prediction loops (fit(), evaluate(), predict()). exposes list inner layers, via model$layers property. exposes saving serialization APIs (save(), save_weights()…) Effectively, Layer class corresponds refer literature “layer” (“convolution layer” “recurrent layer”) “block” (“ResNet block” “Inception block”). Meanwhile, Model class corresponds referred literature “model” (“deep learning model”) “network” (“deep neural network”). ’re wondering, “use Layer class Model class?”, ask : need call fit() ? need call save() ? , go Model. (either class just block bigger system, writing training & saving code ), use Layer. instance, take mini-resnet example , use build Model train fit(), save save_weights():","code":"ResNet <- Model(   \"ResNet\",   initialize = function(num_classes = 1000, ...) {     super$initialize(...)     self$block_1 <- layer_resnet_block()     self$block_2 <- layer_resnet_block()     self$global_pool <- layer_global_average_pooling_2d()     self$classifier <- layer_dense(num_classes)   },   call = function(inputs) {     inputs |>       self$block_1() |>       self$block_2() |>       self$global_pool() |>       self$classifier()   } )  resnet <- ResNet() dataset <- ... resnet |> fit(dataset, epochs=10) resnet |> save_model(\"filepath.keras\")"},{"path":"https://keras.posit.co/articles/making_new_layers_and_models_via_subclassing.html","id":"putting-it-all-together-an-end-to-end-example","dir":"Articles","previous_headings":"","what":"Putting it all together: an end-to-end example","title":"Making new layers and models via subclassing","text":"’s ’ve learned far: Layer encapsulate state (created initialize() build()) computation (defined call()). Layers can recursively nested create new, bigger computation blocks. Layers backend-agnostic long use Keras APIs. can use backend-native APIs (jax$numpy, torch$nn tf$nn), layer usable specific backend. Layers can create track losses (typically regularization losses) via add_loss(). outer container, thing want train, Model. Model just like Layer, added training serialization utilities. Let’s put things together end--end example: ’re going implement Variational AutoEncoder (VAE) backend-agnostic fashion – runs TensorFlow, JAX, PyTorch. ’ll train MNIST digits. VAE subclass Model, built nested composition layers subclass Layer. feature regularization loss (KL divergence). Let’s train MNIST using fit() API:","code":"layer_sampling <- Layer(   \"Sampling\",   initialize = function(...) {     super$initialize(...)     self$seed_generator <- random_seed_generator(1337)   },   call = function(inputs) {     c(z_mean, z_log_var) %<-% inputs     batch <- op_shape(z_mean)[[1]]     dim <- op_shape(z_mean)[[2]]     epsilon <- random_normal(shape = c(batch, dim),                              seed=self$seed_generator)     z_mean + op_exp(0.5 * z_log_var) * epsilon   } )  # Maps MNIST digits to a triplet (z_mean, z_log_var, z). layer_encoder <- Layer(   \"Encoder\",   initialize = function(latent_dim = 32, intermediate_dim = 64, ...) {     super$initialize(...)     self$dense_proj <-       layer_dense(units = intermediate_dim,  activation = \"relu\")     self$dense_mean <- layer_dense(units = latent_dim)     self$dense_log_var <- layer_dense(units = latent_dim)     self$sampling <- layer_sampling()   },   call = function(inputs) {     x <- self$dense_proj(inputs)     z_mean <- self$dense_mean(x)     z_log_var <- self$dense_log_var(x)     z <- self$sampling(list(z_mean, z_log_var))     list(z_mean, z_log_var, z)   } )  # Converts z, the encoded digit vector, back into a readable digit. layer_decoder <- Layer(   \"Decoder\",   initialize = function(original_dim, intermediate_dim = 64, ...) {     super$initialize(...)     self$dense_proj <-       layer_dense(units = intermediate_dim, activation = \"relu\")     self$dense_output <-       layer_dense(units = original_dim, activation = \"sigmoid\")   },   call = function(inputs) {     x <- self$dense_proj(inputs)     self$dense_output(x)   } )  # Combines the encoder and decoder into an end-to-end model for training. VariationalAutoEncoder <- Model(   \"VariationalAutoEncoder\",    initialize = function(original_dim, intermediate_dim = 64, latent_dim = 32,                         name = \"autoencoder\", ...) {     super$initialize(name = name, ...)     self$original_dim <- original_dim     self$encoder <- layer_encoder(latent_dim = latent_dim,                             intermediate_dim = intermediate_dim)     self$decoder <- layer_decoder(original_dim = original_dim,                             intermediate_dim = intermediate_dim)   },    call = function(inputs) {     c(z_mean, z_log_var, z) %<-% self$encoder(inputs)     reconstructed <- self$decoder(z)     # Add KL divergence regularization loss.     kl_loss <- -0.5 * op_mean(z_log_var - op_square(z_mean) - op_exp(z_log_var) + 1)     self$add_loss(kl_loss)     reconstructed   } ) c(c(x_train, .), .) %<-% dataset_mnist() x_train <- x_train |>   op_reshape(c(60000, 784)) |>   op_cast(\"float32\") |>   op_divide(255)  original_dim <- 784 vae <- VariationalAutoEncoder(   original_dim = 784,   intermediate_dim = 64,   latent_dim = 32 )  optimizer <- optimizer_adam(learning_rate = 1e-3) vae |> compile(optimizer, loss = loss_mean_squared_error())  vae |> fit(x_train, x_train, epochs = 2, batch_size = 64) ## Epoch 1/2 ## 938/938 - 4s - 5ms/step - loss: 0.0748 ## Epoch 2/2 ## 938/938 - 1s - 815us/step - loss: 0.0676"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"The Sequential model","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"when-to-use-a-sequential-model","dir":"Articles","previous_headings":"","what":"When to use a Sequential model","title":"The Sequential model","text":"Sequential model appropriate plain stack layers layer exactly one input tensor one output tensor. Schematically, following Sequential model: equivalent function: Sequential model appropriate : model multiple inputs multiple outputs layers multiple inputs multiple outputs need layer sharing want non-linear topology (e.g. residual connection, multi-branch model)","code":"model <- keras_model_sequential() |>   layer_dense(units = 2, activation = \"relu\", name = \"layer1\") |>   layer_dense(units = 3, activation = \"relu\", name = \"layer2\") |>   layer_dense(units = 4, name = \"layer3\")  # Call model on a test input x <- op_ones(c(3, 3)) y <- model(x) # Create 3 layers layer1 <- layer_dense(units = 2, activation=\"relu\", name=\"layer1\") layer2 <- layer_dense(units = 3, activation=\"relu\", name=\"layer2\") layer3 <- layer_dense(units = 4, name=\"layer3\")  # Call layers on a test input x <- op_ones(c(3, 3)) y <- x |> layer1() |> layer2() |> layer3()"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"creating-a-sequential-model","dir":"Articles","previous_headings":"","what":"Creating a Sequential model","title":"The Sequential model","text":"can create Sequential model piping layers keras_model_sequential() object: passing list layers keras_model_sequential(): layers accessible via layers attribute: can also create Sequential model incrementally: Note ’s also corresponding pop_layer() method remove layers: Sequential model behaves much like stack layers. Also note Sequential constructor accepts name argument, just like layer model Keras. useful annotate TensorBoard graphs semantically meaningful names.","code":"model <- keras_model_sequential() |>   layer_dense(units = 2, activation = \"relu\") |>   layer_dense(units = 3, activation = \"relu\") |>   layer_dense(units = 4) model <- keras_model_sequential(layers = list(   layer_dense(units = 2, activation = \"relu\"),   layer_dense(units = 3, activation = \"relu\"),   layer_dense(units = 4) )) model$layers ## [[1]] ## <Dense name=dense_3, built=False> ##  signature: (*args, **kwargs) ## ## [[2]] ## <Dense name=dense_4, built=False> ##  signature: (*args, **kwargs) ## ## [[3]] ## <Dense name=dense_5, built=False> ##  signature: (*args, **kwargs) model <- keras_model_sequential() model |> layer_dense(units = 2, activation=\"relu\") model |> layer_dense(units = 3, activation=\"relu\") model |> layer_dense(units = 4) model |> pop_layer() length(model$layers)  # 2 ## [1] 2 model <- keras_model_sequential(name = \"my_sequential\") model |> layer_dense(units = 2, activation=\"relu\", name = \"layer1\") model |> layer_dense(units = 3, activation=\"relu\", name = \"layer2\") model |> layer_dense(units = 4, name = \"layer3\")"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"specifying-the-input-shape-in-advance","dir":"Articles","previous_headings":"","what":"Specifying the input shape in advance","title":"The Sequential model","text":"Generally, layers Keras need know shape inputs order able create weights. create layer like , initially, weights: creates weights first time called input, since shape weights depends shape inputs: Naturally, also applies Sequential models. instantiate Sequential model without input shape, isn’t “built”: weights (calling model$weights results error stating just ). weights created model first sees input data: model “built”, can call summary() method display contents: However, can useful building Sequential model incrementally able display summary model far, including current output shape. case, start model passing input_shape argument model, knows input shape start: Models built predefined input shape like always weights (even seeing data) always defined output shape. general, ’s recommended best practice always specify input shape Sequential model advance know .","code":"layer <- layer_dense(units = 3) layer$weights  # Empty ## list() # Call layer on a test input x <- op_ones(c(1, 4)) y <- layer(x) layer$weights  # Now it has weights, of shape (4, 3) and (3,) ## [[1]] ## <KerasVariable shape=(4, 3), dtype=float32, path=dense_9/kernel> ## ## [[2]] ## <KerasVariable shape=(3), dtype=float32, path=dense_9/bias> model <- keras_model_sequential() |>   layer_dense(units = 2, activation = \"relu\") |>   layer_dense(units = 3, activation = \"relu\") |>   layer_dense(units = 4) # No weights at this stage!  # At this point, you can't do this: # model$weights   # Call the model on a test input x <- op_ones(c(1, 4)) y <- model(x) length(model$weights) ## [1] 6 summary(model) ## Model: \"sequential_4\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_10 (Dense)                │ (1, 2)                 │            10 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_11 (Dense)                │ (1, 3)                 │             9 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_12 (Dense)                │ (1, 4)                 │            16 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 35 (140.00 B) ##  Trainable params: 35 (140.00 B) ##  Non-trainable params: 0 (0.00 B) model <- keras_model_sequential(input_shape = 4) |>   layer_dense(units = 2, activation = \"relu\") summary(model) ## Model: \"sequential_5\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_13 (Dense)                │ (None, 2)              │            10 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 10 (40.00 B) ##  Trainable params: 10 (40.00 B) ##  Non-trainable params: 0 (0.00 B) model$layers ## [[1]] ## <Dense name=dense_13, built=True> ##  signature: (*args, **kwargs)"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"a-common-debugging-workflow-add-layers-summary","dir":"Articles","previous_headings":"","what":"A common debugging workflow: add layers + summary()","title":"The Sequential model","text":"building new Sequential architecture, ’s useful incrementally stack layers |> frequently print model summaries. instance, enables monitor stack Conv2D MaxPooling2D layers downsampling image feature maps: practical, right? Note |> equivalent calling model$add(), modifies model -place, don’t need reassign model symbol step.","code":"model <- keras_model_sequential(input_shape = c(250, 250, 3)) |>   layer_conv_2d(filters = 32, kernel_size = 5, strides = 2, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_max_pooling_2d(pool_size = c(3, 3))  # Can you guess what the current output shape is at this point? Probably not. # Let's just print it: summary(model) ## Model: \"sequential_6\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 123, 123, 32)   │         2,432 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 121, 121, 32)   │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 40, 40, 32)     │             0 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 11,680 (45.62 KB) ##  Trainable params: 11,680 (45.62 KB) ##  Non-trainable params: 0 (0.00 B) # The answer was: (40, 40, 32), so we can keep downsampling...  model |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_max_pooling_2d(pool_size = 3) |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_max_pooling_2d(pool_size = 2)  # And now? summary(model) ## Model: \"sequential_6\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 123, 123, 32)   │         2,432 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 121, 121, 32)   │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d (MaxPooling2D)    │ (None, 40, 40, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 38, 38, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_3 (Conv2D)               │ (None, 36, 36, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_1 (MaxPooling2D)  │ (None, 12, 12, 32)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_4 (Conv2D)               │ (None, 10, 10, 32)     │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_5 (Conv2D)               │ (None, 8, 8, 32)       │         9,248 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ max_pooling2d_2 (MaxPooling2D)  │ (None, 4, 4, 32)       │             0 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 48,672 (190.12 KB) ##  Trainable params: 48,672 (190.12 KB) ##  Non-trainable params: 0 (0.00 B) # Now that we have 4x4 feature maps, time to apply global max pooling. model |>   layer_global_max_pooling_2d()  # Finally, we add a classification layer. model |>   layer_dense(units = 10, activation = \"softmax\")"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"what-to-do-once-you-have-a-model","dir":"Articles","previous_headings":"","what":"What to do once you have a model","title":"The Sequential model","text":"model architecture ready, want : Train model, evaluate , run inference. See guide training & evaluation built-loops Save model disk restore . See guide serialization & saving.","code":""},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"feature-extraction-with-a-sequential-model","dir":"Articles","previous_headings":"","what":"Feature extraction with a Sequential model","title":"The Sequential model","text":"Sequential model built, behaves like Functional API model. means every layer input output attribute. attributes can used neat things, like quickly creating model extracts outputs intermediate layers Sequential model: ’s similar example extract features one layer:","code":"initial_model <- keras_model_sequential(input_shape = c(250, 250, 3)) |>   layer_conv_2d(filters = 32, kernel_size = 5, strides = 2, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\")   feature_extractor <- keras_model(     inputs = initial_model$inputs,     outputs = lapply(initial_model$layers, function(x) x$output), )  # Call feature extractor on test input. x <- op_ones(c(1, 250, 250, 3)) features <- feature_extractor(x) initial_model <-   keras_model_sequential(input_shape = c(250, 250, 3)) |>   layer_conv_2d(filters = 32, kernel_size = 5, strides = 2,                 activation = \"relu\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\",                 name = \"my_intermediate_layer\") |>   layer_conv_2d(filters = 32, kernel_size = 3, activation = \"relu\")  feature_extractor <- keras_model(   inputs = initial_model$inputs,   outputs = get_layer(initial_model, \"my_intermediate_layer\")$output, )  # Call feature extractor on test input. x <- op_ones(c(1, 250, 250, 3)) features <- feature_extractor(x)"},{"path":"https://keras.posit.co/articles/sequential_model.html","id":"transfer-learning-with-a-sequential-model","dir":"Articles","previous_headings":"","what":"Transfer learning with a Sequential model","title":"The Sequential model","text":"Transfer learning consists freezing bottom layers model training top layers. aren’t familiar , make sure read guide transfer learning. two common transfer learning blueprint involving Sequential models. First, let’s say Sequential model, want freeze layers except last one. case, can call freeze_weights(). Alternatively, can iterate model$layers set layer$trainable <- FALSE layer, except last one. Like : Another common blueprint use Sequential model stack pre-trained model freshly initialized classification layers. Like : transfer learning, probably find frequently using two patterns. ’s need know Sequential models! find building models Keras, see: Guide Functional API Guide making new Layers & Models via subclassing","code":"model <- keras_model_sequential(input_shape = 784) |>   layer_dense(units = 32, activation = \"relu\") |>   layer_dense(units = 32, activation = \"relu\") |>   layer_dense(units = 32, activation = \"relu\") |>   layer_dense(units = 10) # Presumably you would want to first load pre-trained weights. model |> load_model_weights(...) # Freeze all layers except the last one. model |> freeze_weights(from = 1, to = -2) model # note the \"Trainable\" column now visible in the summary table ## Model: \"sequential_9\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)                ┃ Output Shape          ┃    Param # ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩ ## │ dense_15 (Dense)            │ (None, 32)            │     25,120 │   N   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_16 (Dense)            │ (None, 32)            │      1,056 │   N   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_17 (Dense)            │ (None, 32)            │      1,056 │   N   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_18 (Dense)            │ (None, 10)            │        330 │   Y   │ ## └─────────────────────────────┴───────────────────────┴────────────┴───────┘ ##  Total params: 27,562 (107.66 KB) ##  Trainable params: 330 (1.29 KB) ##  Non-trainable params: 27,232 (106.38 KB) # Another way to freeze all layers except the last one. for (layer in model$layers[-length(model$layers)]) {   layer$trainable <- FALSE }  # Recompile and train (this will only update the weights of the last layer). model |> compile(...) model |> fit(...) # Load a convolutional base with pre-trained weights base_model <- application_xception(weights = 'imagenet',                                    include_top = FALSE,                                    pooling = 'avg')  # Freeze the base model freeze_weights(base_model)  # Use a Sequential model to add a trainable classifier on top model <- keras_model_sequential() |>   base_model() |>   layer_dense(1000) # Compile & train model |> compile(...) model |> fit(...)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Save, serialize, and export models","text":"Keras model consists multiple components: architecture, configuration, specifies layers model contain, ’re connected. set weights values (“state model”). optimizer (defined compiling model). set losses metrics (defined compiling model). Keras API saves pieces together unified format, marked .keras extension. zip archive consisting following: JSON-based configuration file (config.json): Records model, layer, trackables’ configuration. H5-based state file, model.weights.h5 (whole model), directory keys layers weights. metadata file JSON, storing things current Keras version. Let’s take look works.","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"how-to-save-and-load-a-model","dir":"Articles","previous_headings":"","what":"How to save and load a model","title":"Save, serialize, and export models","text":"10 seconds read guide, ’s need know. Saving Keras model: Loading model back: Now, let’s look details.","code":"# Get model (Sequential, Functional Model, or Model subclass) model <- ...  # The filename needs to end with the .keras extension model |> save_model('path/to/location.keras') model <- load_model('path/to/location.keras')"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Save, serialize, and export models","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"saving","dir":"Articles","previous_headings":"","what":"Saving","title":"Save, serialize, and export models","text":"section saving entire model single file. file include: model’s architecture/config model’s weight values (learned training) model’s compilation information (compile() called) optimizer state, (enables restart training left)","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"apis","dir":"Articles","previous_headings":"Saving","what":"APIs","title":"Save, serialize, and export models","text":"can save model save_model(). can load back load_model(). supported format Keras 3 “Keras v3” format, uses .keras extension. Example:","code":"get_model <- function() {   # Create a simple model.   inputs <- keras_input(shape(32))   outputs <- inputs |> layer_dense(1)   model <-  keras_model(inputs, outputs)   model |> compile(optimizer = optimizer_adam(), loss = \"mean_squared_error\")   model }  model <- get_model()  # Train the model. test_input <- random_uniform(c(128, 32)) test_target <- random_uniform(c(128, 1)) model |> fit(test_input, test_target)  # Calling `save('my_model.keras')` creates a zip archive `my_model.keras`. model |> save_model(\"my_model.keras\")  # It can be used to reconstruct the model identically. reconstructed_model <- load_model(\"my_model.keras\")  # Let's check: stopifnot(all.equal(   model |> predict(test_input),   reconstructed_model |> predict(test_input) ))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"custom-objects","dir":"Articles","previous_headings":"Saving","what":"Custom objects","title":"Save, serialize, and export models","text":"section covers basic workflows handling custom layers, functions, models Keras saving reloading. saving model includes custom objects, subclassed Layer, must define get_config() method object class. arguments passed constructor (initialize() method) custom object aren’t simple objects (anything types like ints, strings, etc.), must also explicitly deserialize arguments from_config() class method. Like : Please see Defining config methods section details examples. saved .keras file lightweight store Python code custom objects. Therefore, reload model, load_model requires access definition custom objects used one following methods: Registering custom objects (preferred), Passing custom objects directly loading, Using custom object scope examples workflow:","code":"layer_custom <- Layer(   \"CustomLayer\",   initialize = function(sublayer, ...) {     super$initialize(...)     self$sublayer <- sublayer   },   call = function(x) {     self$sublayer(x)   },   get_config = function() {     base_config <- super$get_config()     config <- list(       sublayer = serialize_keras_object(self$sublayer)     )     c(base_config, config)   },   from_config = function(cls, config) {     sublayer_config <- config$sublayer     sublayer <- deserialize_keras_object(sublayer_config)     cls(sublayer, !!!config)   } )"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"registering-custom-objects-preferred","dir":"Articles","previous_headings":"Saving > Custom objects","what":"Registering custom objects (preferred)","title":"Save, serialize, and export models","text":"preferred method, custom object registration greatly simplifies saving loading code. Calling register_keras_serializable() custom object registers object globally master list, allowing Keras recognize object loading model. Let’s create custom model involving custom layer custom activation function demonstrate . Example:","code":"# Clear all previously registered custom objects set_custom_objects(clear = TRUE) ## named list() layer_custom <- Layer(   \"CustomLayer\",   initialize = function(self, factor) {     super$initialize()     self$factor = factor   },    call = function(self, x) {     x * self$factor   },    get_config = function(self) {     list(factor = self$factor)   } )  # Upon registration, you can optionally specify a package or a name. # If left blank, the package defaults to \"Custom\" and the name defaults to # the class name. register_keras_serializable(layer_custom, package = \"MyLayers\")  custom_fn <- keras3:::py_func2(function(x) x^2, name = \"custom_fn\", convert = TRUE)  register_keras_serializable(custom_fn, name=\"custom_fn\", package=\"my_package\")   # Create the model. get_model <- function() {   inputs <- keras_input(shape(4))   mid <- inputs |> layer_custom(0.5)   outputs <- mid |> layer_dense(1, activation = custom_fn)   model <- keras_model(inputs, outputs)   model |> compile(optimizer = \"rmsprop\", loss = \"mean_squared_error\")   model }   # Train the model. train_model <- function(model) {   input <- random_uniform(c(4, 4))   target <- random_uniform(c(4, 1))   model |> fit(input, target, verbose = FALSE, epochs = 1)   model }  test_input <- random_uniform(c(4, 4)) test_target <- random_uniform(c(4, 1))  model <- get_model() |> train_model() model |> save_model(\"custom_model.keras\", overwrite = TRUE)  # Now, we can simply load without worrying about our custom objects. reconstructed_model <- load_model(\"custom_model.keras\")  # Let's check: stopifnot(all.equal(   model |> predict(test_input, verbose = FALSE),   reconstructed_model |> predict(test_input, verbose = FALSE) ))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"passing-custom-objects-to-load_model","dir":"Articles","previous_headings":"Saving > Custom objects","what":"Passing custom objects to load_model()","title":"Save, serialize, and export models","text":"","code":"model <- get_model() |> train_model()  # Calling `save_model('my_model.keras')` creates a zip archive `my_model.keras`. model |> save_model(\"custom_model.keras\", overwrite = TRUE)  # Upon loading, pass a named list containing the custom objects used in the # `custom_objects` argument of `load_model()`. reconstructed_model <-  load_model(  \"custom_model.keras\",   custom_objects = list(CustomLayer = layer_custom,                         custom_fn = custom_fn), )  # Let's check: stopifnot(all.equal(   model |> predict(test_input, verbose = FALSE),   reconstructed_model |> predict(test_input, verbose = FALSE) ))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"using-a-custom-object-scope","dir":"Articles","previous_headings":"Saving > Custom objects","what":"Using a custom object scope","title":"Save, serialize, and export models","text":"code within custom object scope able recognize custom objects passed scope argument. Therefore, loading model within scope allow loading custom objects. Example:","code":"model <- get_model() |> train_model() model |> save_model(\"custom_model.keras\", overwrite = TRUE)  # Pass the custom objects dictionary to a custom object scope and place # the `keras.models.load_model()` call within the scope. custom_objects <- list(CustomLayer = layer_custom, custom_fn = custom_fn)  with_custom_object_scope(custom_objects, {   reconstructed_model <- load_model(\"custom_model.keras\") })  # Let's check: stopifnot(all.equal(   model |> predict(test_input, verbose = FALSE),   reconstructed_model |> predict(test_input, verbose = FALSE) ))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"model-serialization","dir":"Articles","previous_headings":"Saving","what":"Model serialization","title":"Save, serialize, and export models","text":"section saving model’s configuration, without state. model’s configuration (architecture) specifies layers model contains, layers connected. configuration model, model can created freshly initialized state (weights compilation information).","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"apis-1","dir":"Articles","previous_headings":"Saving > Model serialization","what":"APIs","title":"Save, serialize, and export models","text":"following serialization APIs available: clone_model(model): make (randomly initialized) copy model. get_config() cls.from_config(): retrieve configuration layer model, recreate model instance config, respectively. keras.models.model_to_json() keras.models.model_from_json(): similar, JSON strings. keras.saving.serialize_keras_object(): retrieve configuration arbitrary Keras object. keras.saving.deserialize_keras_object(): recreate object instance configuration.","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"in-memory-model-cloning","dir":"Articles","previous_headings":"Saving > Model serialization","what":"In-memory model cloning","title":"Save, serialize, and export models","text":"can -memory cloning model via clone_model(). equivalent getting config recreating model config (preserve compilation information layer weights values). Example:","code":"new_model <- clone_model(model)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"get_config-and-from_config","dir":"Articles","previous_headings":"Saving > Model serialization","what":"get_config() and from_config()","title":"Save, serialize, and export models","text":"Calling get_config(model) get_config(layer) return named list containing configuration model layer, respectively. define get_config() contain arguments needed initialize() method model layer. loading time, from_config(config) method call initialize() arguments reconstruct model layer. Layer example: Now let’s reconstruct layer using from_config() method: Sequential model example: Functional model example:","code":"layer <- layer_dense(, 3, activation=\"relu\") layer_config <- get_config(layer) str(layer_config) ## List of 12 ##  $ name              : chr \"dense_4\" ##  $ trainable         : logi TRUE ##  $ dtype             : chr \"float32\" ##  $ units             : int 3 ##  $ activation        : chr \"relu\" ##  $ use_bias          : logi TRUE ##  $ kernel_initializer:List of 4 ##   ..$ module         : chr \"keras.initializers\" ##   ..$ class_name     : chr \"GlorotUniform\" ##   ..$ config         :List of 1 ##   .. ..$ seed: NULL ##   ..$ registered_name: NULL ##  $ bias_initializer  :List of 4 ##   ..$ module         : chr \"keras.initializers\" ##   ..$ class_name     : chr \"Zeros\" ##   ..$ config         : Named list() ##   ..$ registered_name: NULL ##  $ kernel_regularizer: NULL ##  $ bias_regularizer  : NULL ##  $ kernel_constraint : NULL ##  $ bias_constraint   : NULL ##  - attr(*, \"__class__\")=<class 'keras.src.layers.core.dense.Dense'> new_layer <- from_config(layer_config) model <- keras_model_sequential(input_shape = c(32)) |>   layer_dense(1) config <- get_config(model) new_model <- from_config(config) inputs <- keras_input(c(32)) outputs <- inputs |> layer_dense(1) model <- keras_model(inputs, outputs) config <- get_config(model) new_model <- from_config(config)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"save_model_config-and-load_model_config","dir":"Articles","previous_headings":"Saving > Model serialization","what":"save_model_config() and load_model_config()","title":"Save, serialize, and export models","text":"similar get_config / from_config, except turns model JSON file, can loaded without original model class. also specific models, isn’t meant layers. Example:","code":"model <- keras_model_sequential(input_shape = c(32)) |>   layer_dense(1) save_model_config(model, \"model_config.json\") new_model <- load_model_config(\"model_config.json\") unlink(\"model_config.json\")"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"arbitrary-object-serialization-and-deserialization","dir":"Articles","previous_headings":"Saving > Model serialization","what":"Arbitrary object serialization and deserialization","title":"Save, serialize, and export models","text":"serialize_keras_object() deserialize_keras_object() APIs general-purpose APIs can used serialize deserialize Keras object custom object. foundation saving model architecture behind serialize()/deserialize() calls keras. Example: Note serialization format containing necessary information proper reconstruction: module containing name Keras module identifying module object comes class_name containing name object’s class. config information needed reconstruct object registered_name custom objects. See . Now can reconstruct regularizer.","code":"my_reg <- regularizer_l1(0.005) config <- serialize_keras_object(my_reg) str(config) ## List of 4 ##  $ module         : chr \"keras.regularizers\" ##  $ class_name     : chr \"L1\" ##  $ config         :List of 1 ##   ..$ l1: num 0.005 ##  $ registered_name: NULL new_reg <- deserialize_keras_object(config) new_reg ## <keras.src.regularizers.regularizers.L1 object> ##  signature: (x)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"model-weights-saving","dir":"Articles","previous_headings":"Saving","what":"Model weights saving","title":"Save, serialize, and export models","text":"can choose save & load model’s weights. can useful : need model inference: case won’t need restart training, don’t need compilation information optimizer state. transfer learning: case training new model reusing state prior model, don’t need compilation information prior model.","code":""},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"apis-for-in-memory-weight-transfer","dir":"Articles","previous_headings":"Saving > Model weights saving","what":"APIs for in-memory weight transfer","title":"Save, serialize, and export models","text":"Weights can copied different objects using get_weights() set_weights(): get_weights(<layer>): Returns list arrays weight values. set_weights(<layer>weights): Sets model/layer weights values provided (arrays). Examples: Transferring weights one layer another, memory Transferring weights one model another model compatible architecture, memory case stateless layers stateless layers change order number weights, models can compatible architectures even extra/missing stateless layers.","code":"create_layer <- function() {   layer <- layer_dense(, 64, activation = \"relu\", name = \"dense_2\")   layer$build(shape(NA, 784))   layer }  layer_1 <- create_layer() layer_2 <- create_layer()  # Copy weights from layer 1 to layer 2 layer_2 |> set_weights(get_weights(layer_1)) # Create a simple functional model inputs <- keras_input(shape=c(784), name=\"digits\") outputs <- inputs |>   layer_dense(64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(10, name = \"predictions\") functional_model <- keras_model(inputs = inputs, outputs = outputs,                                name = \"3_layer_mlp\")  # Define a subclassed model with the same architecture SubclassedModel <- new_model_class(   \"SubclassedModel\",   initialize = function(output_dim, name = NULL) {     super$initialize(name = name)     self$output_dim <- output_dim |> as.integer()     self$dense_1 <- layer_dense(, 64, activation = \"relu\",                                 name = \"dense_1\")     self$dense_2 <- layer_dense(, 64, activation = \"relu\",                                 name = \"dense_2\")     self$dense_3 <- layer_dense(, self$output_dim,                                 name = \"predictions\")   },    call = function(inputs) {     inputs |>       self$dense_1() |>       self$dense_2() |>       self$dense_3()   },    get_config = function(self) {     list(output_dim = self$output_dim,          name = self$name)   } )   subclassed_model <- SubclassedModel(10) # Call the subclassed model once to create the weights. subclassed_model(op_ones(c(1, 784))) |> invisible()  # Copy weights from functional_model to subclassed_model. set_weights(subclassed_model, get_weights(functional_model))  stopifnot(all.equal(   get_weights(functional_model),   get_weights(subclassed_model) )) input <- keras_input(shape = c(784), name = \"digits\") output <- input |>   layer_dense(64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(10, name = \"predictions\") functional_model <- keras_model(inputs, outputs,                                 name = \"3_layer_mlp\")  input <- keras_input(shape = c(784), name = \"digits\") output <- input |>   layer_dense(64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(64, activation = \"relu\", name = \"dense_2\") |>   # Add a dropout layer, which does not contain any weights.   layer_dropout(0.5) |>   layer_dense(10, name = \"predictions\")  functional_model_with_dropout <-   keras_model(input, output, name = \"3_layer_mlp\")  set_weights(functional_model_with_dropout,             get_weights(functional_model))"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"apis-for-saving-weights-to-disk-loading-them-back","dir":"Articles","previous_headings":"Saving > Model weights saving","what":"APIs for saving weights to disk & loading them back","title":"Save, serialize, and export models","text":"Weights can saved disk calling save_model_weights(filepath). filename end .weights.h5. Example: Note using freeze_weights() may result different output get_weights(layer) ordering model contains nested layers.","code":"sequential_model = keras_model_sequential(input_shape = c(784),                                           input_name = \"digits\") |>   layer_dense(64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(10, name = \"predictions\") sequential_model |> save_model_weights(\"my_model.weights.h5\") sequential_model |> load_model_weights(\"my_model.weights.h5\")"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"transfer-learning-example","dir":"Articles","previous_headings":"Saving > Model weights saving > APIs for saving weights to disk & loading them back","what":"Transfer learning example","title":"Save, serialize, and export models","text":"loading pretrained weights weights file, recommended load weights original checkpointed model, extract desired weights/layers new model. Example:","code":"create_functional_model <- function() {   inputs <- keras_input(shape = c(784), name = \"digits\")   outputs <- inputs |>     layer_dense(64, activation = \"relu\", name = \"dense_1\") |>     layer_dense(64, activation = \"relu\", name = \"dense_2\") |>     layer_dense(10, name = \"predictions\")   keras_model(inputs, outputs, name = \"3_layer_mlp\") }  functional_model <- create_functional_model() functional_model |> save_model_weights(\"pretrained.weights.h5\")  # In a separate program: pretrained_model <- create_functional_model() pretrained_model |> load_model_weights(\"pretrained.weights.h5\")  # Create a new model by extracting layers from the original model: extracted_layers <- pretrained_model$layers |> head(-1) model <- keras_model_sequential(layers = extracted_layers) |>   layer_dense(5, name = \"dense_3\") summary(model) ## Model: \"sequential_4\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_1 (Dense)                 │ (None, 64)             │        50,240 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 64)             │         4,160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_3 (Dense)                 │ (None, 5)              │           325 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 54,725 (213.77 KB) ##  Trainable params: 54,725 (213.77 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":[]},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"defining-the-config-methods","dir":"Articles","previous_headings":"Saving > Appendix: Handling custom objects","what":"Defining the config methods","title":"Save, serialize, and export models","text":"Specifications: get_config() return JSON-serializable named list order compatible Keras architecture model-saving APIs. from_config(config) (class method) return new layer model object created config. default implementation returns .call(cls, config). NOTE: constructor arguments already serializable, e.g. strings ints, non-custom Keras objects, overriding from_config() necessary. However, complex objects layers models passed initialize(), deserialization must handled explicitly either initialize overriding from_config() method. Example: Note overriding from_config unnecessary MyDense hidden_units, kernel_initializer, kernel_regularizer ints, strings, built-Keras object, respectively. means default from_config implementation cls(!!!config) work intended. complex objects, layers models passed initialize(), example, must explicitly deserialize objects. Let’s take look example model from_config override necessary. Example:","code":"layer_my_dense <- register_keras_serializable(   package = \"MyLayers\", name = \"KernelMult\",   object = Layer(     \"MyDense\",     initialize = function(units,                           ...,                           kernel_regularizer = NULL,                           kernel_initializer = NULL,                           nested_model = NULL) {       super$initialize(...)       self$hidden_units <- units       self$kernel_regularizer <- kernel_regularizer       self$kernel_initializer <- kernel_initializer       self$nested_model <- nested_model     },     get_config = function() {       config <- super$get_config()       # Update the config with the custom layer's parameters       config <- modifyList(config, list(         units = self$hidden_units,         kernel_regularizer = self$kernel_regularizer,         kernel_initializer = self$kernel_initializer,         nested_model = self$nested_model       ))       config     },     build = function(input_shape) {       input_units <- tail(input_shape, 1)       self$kernel <- self$add_weight(         name = \"kernel\",         shape = shape(input_units, self$hidden_units),         regularizer = self$kernel_regularizer,         initializer = self$kernel_initializer,       )     },     call = function(inputs) {       op_matmul(inputs, self$kernel)     }   ) )   layer <- layer_my_dense(units = 16,                         kernel_regularizer = \"l1\",                         kernel_initializer = \"ones\") layer3 <- layer_my_dense(units = 64, nested_model = layer)  config <- serialize_keras_object(layer3) str(config) ## List of 4 ##  $ module         : chr \"<r-globalenv>\" ##  $ class_name     : chr \"MyDense\" ##  $ config         :List of 5 ##   ..$ name        : chr \"my_dense_1\" ##   ..$ trainable   : logi TRUE ##   ..$ dtype       : chr \"float32\" ##   ..$ units       : num 64 ##   ..$ nested_model:List of 4 ##   .. ..$ module         : chr \"<r-globalenv>\" ##   .. ..$ class_name     : chr \"MyDense\" ##   .. ..$ config         :List of 6 ##   .. .. ..$ name              : chr \"my_dense\" ##   .. .. ..$ trainable         : logi TRUE ##   .. .. ..$ dtype             : chr \"float32\" ##   .. .. ..$ units             : num 16 ##   .. .. ..$ kernel_regularizer: chr \"l1\" ##   .. .. ..$ kernel_initializer: chr \"ones\" ##   .. ..$ registered_name: chr \"MyLayers>KernelMult\" ##  $ registered_name: chr \"MyLayers>KernelMult\" new_layer <- deserialize_keras_object(config) new_layer ## <MyDense name=my_dense_1, built=False> ##  signature: (*args, **kwargs) `%||%` <- \\(x, y) if(is.null(x)) y else x layer_custom_model <- register_keras_serializable(   package = \"ComplexModels\",   object = Layer(     \"CustomModel\",     initialize = function(first_layer, second_layer = NULL, ...) {       super$initialize(...)       self$first_layer <- first_layer       self$second_layer <- second_layer %||% layer_dense(, 8)     },      get_config = function() {       config <- super$get_config()       config <- modifyList(config, list(         first_layer = self$first_layer,         second_layer = self$second_layer       ))       config     },      from_config = function(config) {       config$first_layer %<>% deserialize_keras_object()       config$second_layer %<>% deserialize_keras_object()       # note that the class is available in methods under the classname symbol,       # (`CustomModel` for this class), and also under the symbol `__class__`       cls(!!!config)       # CustomModel(!!!config)     },     call = function(self, inputs) {       inputs |>         self$first_layer() |>         self$second_layer()     }   ) )  # Let's make our first layer the custom layer from the previous example (MyDense) inputs <- keras_input(c(32)) outputs <-  inputs |> layer_custom_model(first_layer=layer) model <- keras_model(inputs, outputs)  config <- get_config(model) new_model <- from_config(config)"},{"path":"https://keras.posit.co/articles/serialization_and_saving.html","id":"how-custom-objects-are-serialized","dir":"Articles","previous_headings":"Saving > Appendix: Handling custom objects","what":"How custom objects are serialized","title":"Save, serialize, and export models","text":"serialization format special key custom objects registered via register_keras_serializable(). registered_name key allows easy retrieval loading/deserialization time also allowing users add custom naming. Let’s take look config serializing custom layer MyDense defined . Example: shown, registered_name key contains lookup information Keras master list, including package MyLayers custom name KernelMult gave calling register_keras_serializables(). Take look custom class definition/registration . Note class_name key contains original name class, allowing proper re-initialization from_config. Additionally, note module key NULL since custom object.","code":"layer <- layer_my_dense(   units = 16,   kernel_regularizer = regularizer_l1_l2(l1 = 1e-5, l2 = 1e-4),   kernel_initializer = \"ones\", ) config <- serialize_keras_object(layer) str(config) ## List of 4 ##  $ module         : chr \"<r-globalenv>\" ##  $ class_name     : chr \"MyDense\" ##  $ config         :List of 6 ##   ..$ name              : chr \"my_dense_2\" ##   ..$ trainable         : logi TRUE ##   ..$ dtype             : chr \"float32\" ##   ..$ units             : num 16 ##   ..$ kernel_regularizer:List of 4 ##   .. ..$ module         : chr \"keras.regularizers\" ##   .. ..$ class_name     : chr \"L1L2\" ##   .. ..$ config         :List of 2 ##   .. .. ..$ l1: num 1e-05 ##   .. .. ..$ l2: num 1e-04 ##   .. ..$ registered_name: NULL ##   ..$ kernel_initializer: chr \"ones\" ##  $ registered_name: chr \"MyLayers>KernelMult\""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Training & evaluation with the built-in methods","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Training & evaluation with the built-in methods","text":"guide covers training, evaluation, prediction (inference) models using built-APIs training & validation (fit(), evaluate() predict()). interested leveraging fit() specifying training step function, see Customizing happens fit() guide. interested writing training & evaluation loops scratch, see guide Writing training loop scratch. general, whether using built-loops writing , model training & evaluation works strictly way across every kind Keras model – Sequential models, models built Functional API, models written scratch via model subclassing.","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"api-overview-a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"API overview: a first end-to-end example","title":"Training & evaluation with the built-in methods","text":"passing data built-training loops model, either use: Arrays (data small fits memory) tf_dataset objects PyTorch DataLoader instances next paragraphs, ’ll use MNIST dataset NumPy arrays, order demonstrate use optimizers, losses, metrics. Afterwards, ’ll take close look options. Let’s consider following model (, build Functional API, Sequential model subclassed model well): ’s typical end--end workflow looks like, consisting : Training Validation holdout set generated original training data Evaluation test data ’ll use MNIST data example. specify training configuration (optimizer, loss, metrics): call fit(), train model slicing data “batches” size batch_size, repeatedly iterating entire dataset given number epochs. returned history object holds record loss values metric values training: evaluate model test data via evaluate(): Now, let’s review piece workflow detail.","code":"inputs <- keras_input(shape = 784, name=\"digits\") outputs <- inputs |>   layer_dense(units = 64, activation = \"relu\", name = \"dense_1\") |>   layer_dense(units = 64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(units = 10, activation = \"softmax\", name = \"predictions\") model <- keras_model(inputs = inputs, outputs = outputs) summary(model) ## Model: \"functional_1\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ digits (InputLayer)             │ (None, 784)            │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_1 (Dense)                 │ (None, 64)             │        50,240 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_2 (Dense)                 │ (None, 64)             │         4,160 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ predictions (Dense)             │ (None, 10)             │           650 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 55,050 (215.04 KB) ##  Trainable params: 55,050 (215.04 KB) ##  Non-trainable params: 0 (0.00 B) c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()  # Preprocess the data (these are NumPy arrays) x_train <- array_reshape(x_train, c(60000, 784)) / 255 x_test <- array_reshape(x_test, c(10000, 784)) / 255  # Reserve 10,000 samples for validation x_val <- x_train[1:10000,] y_val <- y_train[1:10000] x_train <- x_train[-c(1:10000),] y_train <- y_train[-c(1:10000)] model |> compile(   # Optimizer   optimizer = optimizer_rmsprop(),   # Loss function to minimize   loss = loss_sparse_categorical_crossentropy(),   # List of metrics to monitor   metrics = list(metric_sparse_categorical_accuracy()) ) history <- model |> fit(   x_train, y_train,   batch_size = 64,   epochs = 2,   # We pass some validation for   # monitoring validation loss and metrics   # at the end of each epoch   validation_data = list(x_val, y_val) ) ## Epoch 1/2 ## 782/782 - 3s - 3ms/step - loss: 0.3410 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.1869 - val_sparse_categorical_accuracy: 0.9455 ## Epoch 2/2 ## 782/782 - 1s - 1ms/step - loss: 0.1588 - sparse_categorical_accuracy: 0.9532 - val_loss: 0.1303 - val_sparse_categorical_accuracy: 0.9626 history ## ## Final epoch (plot to see history): ##                            loss: 0.1588 ##     sparse_categorical_accuracy: 0.9532 ##                        val_loss: 0.1303 ## val_sparse_categorical_accuracy: 0.9626 # Evaluate the model on the test data using `evaluate` results <- model |> evaluate(x_test, y_test, batch_size=128) ## 79/79 - 0s - 3ms/step - loss: 0.1258 - sparse_categorical_accuracy: 0.9625 str(results) ## List of 2 ##  $ loss                       : num 0.126 ##  $ sparse_categorical_accuracy: num 0.962 # Generate predictions (probabilities -- the output of the last layer) # on new data using `predict` predictions <- model |> predict(x_test[1:2,]) ## 1/1 - 0s - 136ms/step dim(predictions) ## [1]  2 10"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"the-compile-method-specifying-a-loss-metrics-and-an-optimizer","dir":"Articles","previous_headings":"","what":"The compile() method: specifying a loss, metrics, and an optimizer","title":"Training & evaluation with the built-in methods","text":"train model fit(), need specify loss function, optimizer, optionally, metrics monitor. pass model arguments compile() method: metrics argument list – model can number metrics. model multiple outputs, can specify different losses metrics output, can modulate contribution output total loss model. find details Passing data multi-input, multi-output models section. Note ’re satisfied default settings, many cases optimizer, loss, metrics can specified via string identifiers shortcut: later reuse, let’s put model definition compile step functions; call several times across different examples guide.","code":"model |> compile(   optimizer = optimizer_rmsprop(learning_rate = 1e-3),   loss = loss_sparse_categorical_crossentropy(),   metrics = list(metric_sparse_categorical_accuracy()) ) model |> compile(   optimizer = \"rmsprop\",   loss = \"sparse_categorical_crossentropy\",   metrics = c(\"sparse_categorical_accuracy\") ) get_uncompiled_model <- function() {   inputs <- keras_input(shape = 784, name = \"digits\")   outputs <- inputs |>     layer_dense(units = 64, activation = \"relu\", name = \"dense_1\") |>     layer_dense(units = 64, activation = \"relu\", name = \"dense_2\") |>     layer_dense(units = 10, activation = \"softmax\", name = \"predictions\")   keras_model(inputs = inputs, outputs = outputs) }  get_compiled_model <- function() {   model <- get_uncompiled_model()   model |> compile(     optimizer = \"rmsprop\",     loss = \"sparse_categorical_crossentropy\",     metrics = c(\"sparse_categorical_accuracy\")   )   model }"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"many-built-in-optimizers-losses-and-metrics-are-available","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Many built-in optimizers, losses, and metrics are available","title":"Training & evaluation with the built-in methods","text":"general, won’t create losses, metrics, optimizers scratch, need likely already part Keras API: Optimizers: [optimizer_sgd()] (without momentum) [optimizer_rmsprop()] [optimizer_adam()] etc. Losses: [loss_mean_squared_error()] [loss_kl_divergence()] [loss_cosine_similarity()] etc. Metrics: [metric_auc()] [metric_precision()] [metric_recall()] etc.","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"custom-losses","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Custom losses","title":"Training & evaluation with the built-in methods","text":"need create custom loss, Keras provides three ways . first method involves creating function accepts inputs y_true y_pred. following example shows loss function computes mean squared error real data predictions: need loss function takes parameters beside y_true y_pred, can subclass Keras base Loss class using [Loss()] implement following two methods: initialize(): accept parameters pass call loss function call(y_true, y_pred): use targets (y_true) model predictions (y_pred) compute model’s loss Let’s say want use mean squared error, added term de-incentivize prediction values far 0.5 (assume categorical targets one-hot encoded take values 0 1). creates incentive model confident, may help reduce overfitting (won’t know works try!). ’s :","code":"custom_mean_squared_error <- function(y_true, y_pred) {   op_mean(op_square(y_true - y_pred), axis = -1) }  model <- get_uncompiled_model() model |> compile(optimizer = \"adam\",                  loss = custom_mean_squared_error)  # We need to one-hot encode the labels to use MSE y_train_one_hot <- op_one_hot(y_train, num_classes = 10) model |> fit(x_train, y_train_one_hot, batch_size = 64, epochs = 2) ## Epoch 1/2 ## 782/782 - 2s - 2ms/step - loss: 0.0161 ## Epoch 2/2 ## 782/782 - 0s - 623us/step - loss: 0.0078 loss_custom_mse <- Loss(   classname = \"CustomMSE\",   initialize = function(regularization_factor = 0.1, name = \"custom_mse\") {     super$initialize(name = name)     self$regularization_factor <- regularization_factor   },   call = function(y_true, y_pred) {     mse <- op_mean(op_square(y_true - y_pred), axis = -1)     reg <- op_mean(op_square(0.5 - y_pred), axis = -1)     mse + reg * self$regularization_factor   } )  model <- get_uncompiled_model() model |> compile(optimizer=\"adam\", loss = loss_custom_mse())  y_train_one_hot <- op_one_hot(y_train, num_classes=10) model |> fit(x_train, y_train_one_hot, batch_size=64, epochs=1) ## 782/782 - 2s - 2ms/step - loss: 0.0390"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"custom-metrics","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Custom metrics","title":"Training & evaluation with the built-in methods","text":"need metric isn’t part API, can easily create custom metrics subclassing Keras base Metric class using [Metric()]. need implement 4 methods: initialize(), create state variables metric. update_state(y_true, y_pred, sample_weight = NULL), uses targets y_true model predictions y_pred update state variables. result(), uses state variables compute final results. reset_state(), reinitializes state metric. State update results computation kept separate (update_state() result(), respectively) cases, results computation might expensive done periodically. ’s simple example showing implement CategoricalTruePositives metric counts many samples correctly classified belonging given class:","code":"metric_categorical_true_positives <- Metric(   \"CategoricalTruePositives\",    initialize = function(name = \"categorical_true_positives\", ...) {     super$initialize(name = name, ...)     self$true_positives <- self$add_variable(shape = shape(),                                              name = \"ctp\",                                              initializer = \"zeros\")   },    update_state = function(y_true, y_pred, sample_weight = NULL) {     y_pred <- op_argmax(y_pred, axis = 2) |> op_reshape(c(-1, 1))     values <- op_cast(y_true, \"int32\") == op_cast(y_pred, \"int32\")     values <- op_cast(values, \"float32\")     if (!is.null(sample_weight)) {       sample_weight <- op_cast(sample_weight, \"float32\")       values <- op_multiply(values, sample_weight)     }     self$true_positives$assign_add(op_sum(values))   },    result = function() {     self$true_positives$value   },    reset_state = function() {     self$true_positives$assign(0.0)   } )  model <- get_uncompiled_model() model |> compile(   optimizer = optimizer_rmsprop(learning_rate = 1e-3),   loss = loss_sparse_categorical_crossentropy(),   metrics = c(metric_categorical_true_positives()) ) history <- model |> fit(x_train, y_train, batch_size = 64, epochs = 3) ## Epoch 1/3 ## 782/782 - 1s - 2ms/step - categorical_true_positives: 360502.0000 - loss: 0.3444 ## Epoch 2/3 ## 782/782 - 1s - 964us/step - categorical_true_positives: 362616.0000 - loss: 0.1656 ## Epoch 3/3 ## 782/782 - 1s - 1ms/step - categorical_true_positives: 363187.0000 - loss: 0.1203"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"handling-losses-and-metrics-that-dont-fit-the-standard-signature","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Handling losses and metrics that don’t fit the standard signature","title":"Training & evaluation with the built-in methods","text":"overwhelming majority losses metrics can computed y_true y_pred, y_pred output model – . instance, regularization loss may require activation layer (targets case), activation may model output. cases, can call self$add_loss(loss_value) inside call method custom layer. Losses added way get added “main” loss training (one passed compile()). ’s simple example adds activity regularization (note activity regularization built-Keras layers – layer just sake providing concrete example): Note pass losses via add_loss(), becomes possible call compile() without loss function, since model already loss minimize. Consider following LogisticEndpoint layer: takes inputs targets & logits, tracks crossentropy loss via add_loss(). can use model two inputs (input data & targets), compiled without loss argument, like : information training multi-input models, see section Passing data multi-input, multi-output models.","code":"layer_custom_activity_regularizer <- Layer(   \"ActivityRegularization\",   call = function(inputs) {     self$add_loss(op_sum(inputs) * 0.1)     inputs  # Pass-through layer.   } )  inputs <- keras_input(shape = 784, name = \"digits\") outputs <- inputs |>   layer_dense(units = 32, activation = \"relu\", name = \"dense_1\") |>   layer_custom_activity_regularizer() |>   layer_dense(units = 64, activation = \"relu\", name = \"dense_2\") |>   layer_dense(units = 10, name = \"predictions\")  model <- keras_model(inputs = inputs, outputs = outputs) model |> compile(optimizer = optimizer_rmsprop(learning_rate = 1e-3),                  loss = loss_sparse_categorical_crossentropy(from_logits = TRUE))  # The displayed loss will be much higher than before # due to the regularization component. model |> fit(x_train, y_train, batch_size = 64, epochs = 1) ## 782/782 - 2s - 2ms/step - loss: 2.3721 layer_logistic_endpoint <- Layer(   \"LogisticEndpoint\",   initialize = function(name = NULL) {     super$initialize(name = name)     self$loss_fn <- loss_binary_crossentropy(from_logits = TRUE)   },   call = function(targets, logits, sample_weights = NULL) {     # Compute the training-time loss value and add it     # to the layer using `self.add_loss()`.     loss <- self$loss_fn(targets, logits, sample_weights)     self$add_loss(loss)      # Return the inference-time prediction tensor (for `predict()`).     op_softmax(logits)   } ) inputs <- keras_input(shape = 3, name = \"inputs\") targets <- keras_input(shape = 10, name = \"targets\")  logits <- inputs |> layer_dense(10) predictions <- layer_logistic_endpoint(name = \"predictions\")(targets, logits)  model <- keras_model(inputs = list(inputs, targets),                      outputs = predictions) model |> compile(optimizer = \"adam\")  # No loss argument!  data <- list(   inputs = random_normal(c(3, 3)),   targets = random_normal(c(3, 10)) ) model |> fit(data, epochs = 1) ## 1/1 - 1s - 508ms/step - loss: 1.0566"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"automatically-setting-apart-a-validation-holdout-set","dir":"Articles","previous_headings":"The compile() method: specifying a loss, metrics, and an optimizer","what":"Automatically setting apart a validation holdout set","title":"Training & evaluation with the built-in methods","text":"first end--end example saw, used validation_data argument pass list arrays list(x_val, y_val) model evaluating validation loss validation metrics end epoch. ’s another option: argument validation_split allows automatically reserve part training data validation. argument value represents fraction data reserved validation, set number higher 0 lower 1. instance, validation_split = 0.2 means “use 20% data validation”, validation_split = 0.6 means “use 60% data validation”. way validation computed taking last x% samples arrays received fit() call, shuffling. Note can use validation_split training NumPy data.","code":"model <- get_compiled_model() model |> fit(x_train, y_train,              batch_size = 64,              validation_split = 0.2, epochs = 1) ## 625/625 - 2s - 3ms/step - loss: 0.3817 - sparse_categorical_accuracy: 0.8919 - val_loss: 0.1953 - val_sparse_categorical_accuracy: 0.9431"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"training-evaluation-using-tf-dataset-objects","dir":"Articles","previous_headings":"","what":"Training & evaluation using TF Dataset objects","title":"Training & evaluation with the built-in methods","text":"past paragraphs, ’ve seen handle losses, metrics, optimizers, ’ve seen use validation_data validation_split arguments fit(), data passed arrays. Another option use iterator-like, tf.data.Dataset, PyTorch DataLoader, R generator function. Let’s take look former. tfdatasets R package containes set utilities loading preprocessing data way ’s fast scalable. complete guide creating Datasets, see tf.data documentation. can use tf.data train Keras models regardless backend ’re using – whether ’s JAX, PyTorch, TensorFlow. can pass Dataset instance directly methods fit(), evaluate(), predict(): Note Dataset reset end epoch, can reused next epoch. want run training specific number batches Dataset, can pass steps_per_epoch argument, specifies many training steps model run using Dataset moving next epoch. can also pass Dataset instance validation_data argument fit(): end epoch, model iterate validation dataset compute validation loss validation metrics. want run validation specific number batches dataset, can pass validation_steps argument, specifies many validation steps model run validation dataset interrupting validation moving next epoch: Note validation dataset reset use (always evaluating samples epoch epoch). argument validation_split (generating holdout set training data) supported training Dataset objects, since feature requires ability index samples datasets, possible general Dataset API.","code":"library(tfdatasets, exclude = \"shape\") model <- get_compiled_model()  # First, let's create a training Dataset instance. # For the sake of our example, we'll use the same MNIST data as before. train_dataset <- tensor_slices_dataset(list(x_train, y_train))  # Shuffle and slice the dataset. train_dataset <- train_dataset |>   dataset_shuffle(buffer_size=1024) |>   dataset_batch(64)  # Now we get a test dataset. test_dataset <-   tensor_slices_dataset(list(x_test, y_test)) |>   dataset_batch(64)  # Since the dataset already takes care of batching, # we don't pass a `batch_size` argument. model |> fit(train_dataset, epochs = 3) ## Epoch 1/3 ## 782/782 - 2s - 2ms/step - loss: 0.3365 - sparse_categorical_accuracy: 0.9041 ## Epoch 2/3 ## 782/782 - 1s - 681us/step - loss: 0.1605 - sparse_categorical_accuracy: 0.9524 ## Epoch 3/3 ## 782/782 - 1s - 1ms/step - loss: 0.1185 - sparse_categorical_accuracy: 0.9647 # You can also evaluate or predict on a dataset. result <- model |> evaluate(test_dataset) ## 157/157 - 1s - 4ms/step - loss: 0.1152 - sparse_categorical_accuracy: 0.9627 result ## $loss ## [1] 0.1151979 ## ## $sparse_categorical_accuracy ## [1] 0.9627 model <- get_compiled_model()  # Prepare the training dataset train_dataset <- tensor_slices_dataset(list(x_train, y_train)) train_dataset <- train_dataset |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(64)  # Only use the 100 batches per epoch (that's 64 * 100 samples) model |> fit(train_dataset, epochs = 3, steps_per_epoch = 100) ## Epoch 1/3 ## 100/100 - 1s - 7ms/step - loss: 0.8017 - sparse_categorical_accuracy: 0.7806 ## Epoch 2/3 ## 100/100 - 0s - 682us/step - loss: 0.3661 - sparse_categorical_accuracy: 0.9006 ## Epoch 3/3 ## 100/100 - 0s - 740us/step - loss: 0.3009 - sparse_categorical_accuracy: 0.9106 model <- get_compiled_model()  # Prepare the training dataset train_dataset <- tensor_slices_dataset(list(x_train, y_train)) train_dataset <- train_dataset |>   dataset_shuffle(buffer_size=1024) |>   dataset_batch(64)  # Prepare the validation dataset val_dataset <- tensor_slices_dataset(list(x_val, y_val)) val_dataset <- val_dataset |> dataset_batch(64)  model |> fit(train_dataset, epochs = 1, validation_data = val_dataset) ## 782/782 - 2s - 3ms/step - loss: 0.3428 - sparse_categorical_accuracy: 0.9022 - val_loss: 0.2337 - val_sparse_categorical_accuracy: 0.9291 model  <- get_compiled_model()  # Prepare the training dataset train_dataset <- tensor_slices_dataset(list(x_train, y_train)) train_dataset <- train_dataset |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(64)  # Prepare the validation dataset val_dataset <- tensor_slices_dataset(list(x_val, y_val)) val_dataset <- val_dataset |> dataset_batch(64)  model %>% fit(   train_dataset,   epochs = 1,   # Only run validation using the first 10 batches of the dataset   # using the `validation_steps` argument   validation_data = val_dataset,   validation_steps = 10, ) ## 782/782 - 2s - 2ms/step - loss: 0.3391 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.1997 - val_sparse_categorical_accuracy: 0.9391"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-sample-weighting-and-class-weighting","dir":"Articles","previous_headings":"","what":"Using sample weighting and class weighting","title":"Training & evaluation with the built-in methods","text":"default settings weight sample decided frequency dataset. two methods weight data, independent sample frequency: Class weights Sample weights","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"class-weights","dir":"Articles","previous_headings":"Using sample weighting and class weighting","what":"Class weights","title":"Training & evaluation with the built-in methods","text":"set passing named list class_weight argument fit(). list maps class indices weight used samples belonging class. can used balance classes without resampling, train model gives importance particular class. instance, class “0” half represented class “1” data, use model |> fit(..., class_weight = c(\"0\" = 1, \"1\" = 0.5)). ’s R example use class weights sample weights give importance correct classification class #5 (digit “5” MNIST dataset).","code":"class_weight <- c(     \"0\" = 1.0,     \"1\" = 1.0,     \"2\" = 1.0,     \"3\" = 1.0,     \"4\" = 1.0,     # Set weight \"2\" for class \"5\",     # making this class 2x more important     \"5\" = 2.0,     \"6\" = 1.0,     \"7\" = 1.0,     \"8\" = 1.0,     \"9\" = 1.0 )  model <- get_compiled_model() model |> fit(x_train, y_train,              class_weight = class_weight,              batch_size = 64, epochs = 1) ## 782/782 - 1s - 2ms/step - loss: 0.3713 - sparse_categorical_accuracy: 0.9018"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"sample-weights","dir":"Articles","previous_headings":"Using sample weighting and class weighting","what":"Sample weights","title":"Training & evaluation with the built-in methods","text":"fine grained control, building classifier, can use sample_weights. training R arrays: Pass sample_weight argument fit(). training tf_dataset sort iterator: yield (input_batch, label_batch, sample_weight_batch) tuples. “sample weights” array array numbers specify much weight sample batch computing total loss. commonly used imbalanced classification problems (idea give weight rarely-seen classes). weights used ones zeros, array can used mask loss function (entirely discarding contribution certain samples total loss). ’s matching Dataset example:","code":"sample_weight <- rep(1.0, length(y_train)) sample_weight[y_train == 5] <- 2.0  model <- get_compiled_model() model |> fit(   x_train, y_train,   sample_weight = sample_weight,   batch_size = 64, epochs = 1 ) ## 782/782 - 2s - 2ms/step - loss: 0.3740 - sparse_categorical_accuracy: 0.9015 sample_weight <- rep(1.0, length(y_train)) sample_weight[y_train == 5] <- 2.0  # Create a Dataset that includes sample weights # (3rd element in the return tuple). train_dataset <- tensor_slices_dataset(list(     x_train, y_train, sample_weight ))  # Shuffle and slice the dataset. train_dataset <- train_dataset |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(64)  model <- get_compiled_model() model |> fit(train_dataset, epochs = 1) ## 782/782 - 2s - 2ms/step - loss: 0.3654 - sparse_categorical_accuracy: 0.9057"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"passing-data-to-multi-input-multi-output-models","dir":"Articles","previous_headings":"","what":"Passing data to multi-input, multi-output models","title":"Training & evaluation with the built-in methods","text":"previous examples, considering model single input (tensor shape (764)) single output (prediction tensor shape (10)). models multiple inputs outputs? Consider following model, image input shape (32, 32, 3) (’s (height, width, channels)) time series input shape (NA, 10) (’s (timesteps, features)). model two outputs computed combination inputs: “score” (shape (1)) probability distribution five classes (shape (5)). Let’s plot model, can clearly see ’re (note shapes shown plot batch shapes, rather per-sample shapes). plot chunk unnamed-chunk-26 compilation time, can specify different losses different outputs, passing loss functions list: passed single loss function model, loss function applied every output (appropriate ). Likewise metrics: Since gave names output layers, also specify per-output losses metrics via named list: recommend use names 2 outputs. ’s possible give different weights different output-specific losses (instance, one might wish privilege “score” loss example, giving 2x importance class loss), using loss_weights argument: also choose compute loss certain outputs, outputs meant prediction training: Passing data multi-input multi-output model fit() works similar way specifying loss function compile: can pass lists arrays (1:1 mapping outputs received loss function) dicts mapping output names arrays. ’s Dataset use case: similarly R arrays, Dataset return tuple named lists (dicts).","code":"image_input <- keras_input(c(32, 32, 3), name = \"img_input\") timeseries_input <- keras_input(c(NA, 10), name = \"ts_input\")  x1 <- image_input |>   layer_conv_2d(filters = 3, kernel_size = c(3, 3)) |>   layer_global_max_pooling_2d()  x2 <- timeseries_input |>   layer_conv_1d(filters = 3, kernel_size = 3) |>   layer_global_max_pooling_1d()  x <- layer_concatenate(x1, x2)  score_output <- layer_dense(x, 1, name = \"score_output\") class_output <- layer_dense(x, 5, name = \"class_output\")  model <- keras_model(   inputs = list(image_input, timeseries_input),   outputs = list(score_output, class_output) ) plot(model, show_shapes = TRUE) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     loss_mean_squared_error(),     loss_categorical_crossentropy()   ) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     loss_mean_squared_error(),     loss_categorical_crossentropy()   ),   metrics = list(     list(       metric_mean_absolute_error(),       metric_mean_absolute_percentage_error()     ),     list(metric_categorical_accuracy())   ) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     score_output = loss_mean_squared_error(),     class_output = loss_categorical_crossentropy()   ),   metrics = list(     score_output = list(       metric_mean_absolute_error(),       metric_mean_absolute_percentage_error()     ),     class_output = list(metric_categorical_accuracy())   ) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     score_output = loss_mean_squared_error(),     class_output = loss_categorical_crossentropy()   ),   metrics = list(     score_output = list(       metric_mean_absolute_error(),       metric_mean_absolute_percentage_error()     ),     class_output = list(metric_categorical_accuracy())   ),   loss_weights = list(score_output = 2.0, class_output = 1.0) ) # loss list, positional version model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(NULL, loss_categorical_crossentropy()) )  # Or loss list, named version model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(class_output = loss_categorical_crossentropy()) ) model |> compile(   optimizer = optimizer_rmsprop(1e-3),   loss = list(     loss_mean_squared_error(),     loss_categorical_crossentropy()   ) )  # Generate dummy data img_data <- random_normal(c(100, 32, 32, 3)) ts_data <- random_normal(c(100, 20, 10)) score_targets <- random_normal(c(100, 1)) class_targets <- random_normal(c(100, 5))  # Fit on unnamed lists (positional matching) model |> fit(     list(img_data, ts_data),     list(score_targets, class_targets),     batch_size=32,     epochs=1 ) ## 4/4 - 2s - 458ms/step - loss: 1.3788 # Alternatively, fit on named lists (names matching) model |> fit(   list(img_input = img_data, ts_input = ts_data),   list(score_output = score_targets, class_output = class_targets),   batch_size = 32,   epochs = 1 ) ## 4/4 - 1s - 230ms/step - loss: 0.2857 train_dataset <- tensor_slices_dataset(list(   list(img_input = img_data, ts_input = ts_data),   list(score_output = score_targets, class_output = class_targets) )) train_dataset <- train_dataset |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(64)  model |> fit(train_dataset, epochs = 1) ## 2/2 - 1s - 629ms/step - loss: 0.5600"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-callbacks","dir":"Articles","previous_headings":"","what":"Using callbacks","title":"Training & evaluation with the built-in methods","text":"Callbacks Keras objects called different points training (start epoch, end batch, end epoch, etc.). can used implement certain behaviors, : validation different points training (beyond built-per-epoch validation) Checkpointing model regular intervals exceeds certain accuracy threshold Changing learning rate model training seems plateauing fine-tuning top layers training seems plateauing Sending email instant message notifications training ends certain performance threshold exceeded Etc. Callbacks can passed list call fit():","code":"model <- get_compiled_model()  callbacks <- list(   callback_early_stopping(     # Stop training when `val_loss` is no longer improving     monitor = \"val_loss\",     # \"no longer improving\" being defined as \"no better than 1e-2 less\"     min_delta = 1e-2,     # \"no longer improving\" being further defined as \"for at least 2 epochs\"     patience = 2,     verbose = 1   ) ) model |> fit(   x_train,   y_train,   epochs = 20,   batch_size = 64,   callbacks = callbacks,   validation_split = 0.2, ) ## Epoch 1/20 ## 625/625 - 1s - 2ms/step - loss: 0.3695 - sparse_categorical_accuracy: 0.8961 - val_loss: 0.1873 - val_sparse_categorical_accuracy: 0.9469 ## Epoch 2/20 ## 625/625 - 1s - 974us/step - loss: 0.1751 - sparse_categorical_accuracy: 0.9489 - val_loss: 0.1403 - val_sparse_categorical_accuracy: 0.9579 ## Epoch 3/20 ## 625/625 - 1s - 903us/step - loss: 0.1277 - sparse_categorical_accuracy: 0.9625 - val_loss: 0.1218 - val_sparse_categorical_accuracy: 0.9651 ## Epoch 4/20 ## 625/625 - 1s - 889us/step - loss: 0.1007 - sparse_categorical_accuracy: 0.9700 - val_loss: 0.1153 - val_sparse_categorical_accuracy: 0.9661 ## Epoch 5/20 ## 625/625 - 1s - 1ms/step - loss: 0.0822 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1104 - val_sparse_categorical_accuracy: 0.9670 ## Epoch 6/20 ## 625/625 - 1s - 899us/step - loss: 0.0683 - sparse_categorical_accuracy: 0.9801 - val_loss: 0.1098 - val_sparse_categorical_accuracy: 0.9689 ## Epoch 7/20 ## 625/625 - 1s - 1ms/step - loss: 0.0571 - sparse_categorical_accuracy: 0.9838 - val_loss: 0.1116 - val_sparse_categorical_accuracy: 0.9698 ## Epoch 8/20 ## 625/625 - 1s - 976us/step - loss: 0.0485 - sparse_categorical_accuracy: 0.9864 - val_loss: 0.1126 - val_sparse_categorical_accuracy: 0.9702 ## Epoch 8: early stopping"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"many-built-in-callbacks-are-available","dir":"Articles","previous_headings":"Using callbacks","what":"Many built-in callbacks are available","title":"Training & evaluation with the built-in methods","text":"many built-callbacks already available Keras, : callback_model_checkpoint(): Periodically save model. callback_early_stopping(): Stop training training longer improving validation metrics. callback_tensorboard(): periodically write model logs can visualized TensorBoard (details section “Visualization”). callback_csv_logger(): streams loss metrics data CSV file. etc. See callbacks documentation complete list.","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"writing-your-own-callback","dir":"Articles","previous_headings":"Using callbacks","what":"Writing your own callback","title":"Training & evaluation with the built-in methods","text":"can create custom callback subclassing base [Callback()] class. callback access associated model class property self$model. Make sure read complete guide writing custom callbacks. ’s simple example saving list per-batch loss values training:","code":"callback_loss_history <- Callback(   classname = \"LossHistory\",   initialize = function(file = \"per_training_batch_losses.txt\", ...) {     super$initialize(...)     private$file <- file   },   on_train_begin = function(logs = NULL) {     private$per_batch_losses <- fastmap::faststack()   },   on_train_batch_begin = function(batch, logs = NULL) {     private$per_batch_losses$push(logs$loss)   },   on_train_end = function(logs = NULL) {     per_batch_losses <- private$per_batch_losses$as_list() |> as.numeric()     write(per_batch_losses, private$file)   } )"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"checkpointing-models","dir":"Articles","previous_headings":"","what":"Checkpointing models","title":"Training & evaluation with the built-in methods","text":"’re training model relatively large datasets, ’s crucial save checkpoints model frequent intervals. easiest way achieve [callback_model_checkpoint()]: ModelCheckpoint callback can used implement fault-tolerance: ability restart training last saved state model case training gets randomly interrupted. ’s basic example: call also write callback saving restoring models. complete guide serialization saving, see guide saving serializing Models.","code":"model <- get_compiled_model()  callbacks <- list(   callback_model_checkpoint(     # Path where to save the model     # The two parameters below mean that we will overwrite     # the current checkpoint if and only if     # the `val_loss` score has improved.     # The saved model name will include the current epoch.     filepath = \"mymodel_{epoch}.keras\",     save_best_only = TRUE,     # Only save a model if `val_loss` has improved.     monitor = \"val_loss\",     verbose = 1   ) ) model |> fit(   x_train, y_train,   epochs = 2, batch_size = 64,   callbacks = callbacks,   validation_split = 0.2 ) ## Epoch 1/2 ## ## Epoch 1: val_loss improved from inf to 0.19344, saving model to mymodel_1.keras ## 625/625 - 1s - 2ms/step - loss: 0.3787 - sparse_categorical_accuracy: 0.8940 - val_loss: 0.1934 - val_sparse_categorical_accuracy: 0.9441 ## Epoch 2/2 ## ## Epoch 2: val_loss improved from 0.19344 to 0.14251, saving model to mymodel_2.keras ## 625/625 - 1s - 842us/step - loss: 0.1768 - sparse_categorical_accuracy: 0.9478 - val_loss: 0.1425 - val_sparse_categorical_accuracy: 0.9600 # Prepare a directory to store all the checkpoints. checkpoint_dir <- \"./ckpt\" fs::dir_create(checkpoint_dir)  make_or_restore_model <- function() {   # Either restore the latest (best) model, or create a fresh one   # if there is no checkpoint available.   checkpoints <- Sys.glob(file.path(checkpoint_dir, \"model-loss=*.keras\"))    if (length(checkpoints) > 0) {     checkpoint_losses <- sub(\"^model-loss=([0-9.]+)\\\\.keras$\", \"\\\\1\",                              basename(checkpoints)) |> as.numeric()     best_checkpoint <- checkpoints[which.min(checkpoint_losses)]     load_model(best_checkpoint)   } else {     get_compiled_model()   } }  model <- make_or_restore_model() callbacks <- list(   # This callback saves the model every 100 batches.   # We include the training loss in the saved model name.   callback_model_checkpoint(     filepath = file.path(checkpoint_dir, \"model-loss={loss:.2f}.keras\"),     save_freq = 100   ) ) model |> fit(x_train, y_train, epochs = 1, callbacks = callbacks) ## 1563/1563 - 2s - 2ms/step - loss: 0.2932 - sparse_categorical_accuracy: 0.9145"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-learning-rate-schedules","dir":"Articles","previous_headings":"","what":"Using learning rate schedules","title":"Training & evaluation with the built-in methods","text":"common pattern training deep learning models gradually reduce learning training progresses. generally known “learning rate decay”. learning decay schedule static (fixed advance, function current epoch current batch index), dynamic (responding current behavior model, particular validation loss).","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"passing-a-schedule-to-an-optimizer","dir":"Articles","previous_headings":"Using learning rate schedules","what":"Passing a schedule to an optimizer","title":"Training & evaluation with the built-in methods","text":"can easily use static learning rate decay schedule passing schedule object learning_rate argument optimizer: Several built-schedules available: ExponentialDecay, PiecewiseConstantDecay, PolynomialDecay, InverseTimeDecay.","code":"initial_learning_rate <- 0.1 lr_schedule <- learning_rate_schedule_exponential_decay(     initial_learning_rate, decay_steps=100000, decay_rate=0.96,     staircase=TRUE )  optimizer <- optimizer_rmsprop(learning_rate = lr_schedule)"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-callbacks-to-implement-a-dynamic-learning-rate-schedule","dir":"Articles","previous_headings":"Using learning rate schedules","what":"Using callbacks to implement a dynamic learning rate schedule","title":"Training & evaluation with the built-in methods","text":"dynamic learning rate schedule (instance, decreasing learning rate validation loss longer improving) achieved schedule objects, since optimizer access validation metrics. However, callbacks access metrics, including validation metrics! can thus achieve pattern using callback modifies current learning rate optimizer. fact, even built-[callback_reduce_lr_on_plateau()].","code":""},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"visualizing-loss-and-metrics-during-training-with-tensorboard","dir":"Articles","previous_headings":"","what":"Visualizing loss and metrics during training with TensorBoard","title":"Training & evaluation with the built-in methods","text":"best way keep eye model training use TensorBoard – browser-based application can run locally provides : Live plots loss metrics training evaluation (optionally) Visualizations histograms layer activations (optionally) 3D visualizations embedding spaces learned layer_embedding() installed TensorFlow pip, able launch TensorBoard command line: R using:","code":"tensorboard --logdir=/full_path_to_your_logs tensorflow::tensorboard(logdir = \"/full_path_to_your_logs\")"},{"path":"https://keras.posit.co/articles/training_with_built_in_methods.html","id":"using-the-tensorboard-callback","dir":"Articles","previous_headings":"Visualizing loss and metrics during training with TensorBoard","what":"Using the TensorBoard callback","title":"Training & evaluation with the built-in methods","text":"easiest way use TensorBoard Keras model fit() method [callback_tensorboard()]. simplest case, just specify want callback write logs, ’re good go: information, see callback_tensorboard().","code":"tb_callback <- callback_tensorboard(   log_dir = \"/full_path_to_your_logs\",   histogram_freq = 0, # How often to log histogram visualizations   embeddings_freq = 0, # How often to log embedding visualizations   update_freq = \"epoch\", # How often to write logs (default: once per epoch) )"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Transfer learning & fine-tuning","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Transfer learning & fine-tuning","text":"Transfer learning consists taking features learned one problem, leveraging new, similar problem. instance, features model learned identify raccoon may useful kick-start model meant identify tanukis. Transfer learning usually done tasks dataset little data train full-scale model scratch. common incarnation transfer learning context deep learning following workflow: Take layers previously trained model. Freeze , avoid destroying information contain future training rounds. Add new, trainable layers top frozen layers. learn turn old features predictions new dataset. Train new layers dataset. last, optional step, fine-tuning, consists unfreezing entire model obtained (part ), re-training new data low learning rate. can potentially achieve meaningful improvements, incrementally adapting pretrained features new data. First, go Keras trainable API detail, underlies transfer learning & fine-tuning workflows. , ’ll demonstrate typical workflow taking model pretrained ImageNet dataset, retraining Kaggle “cats vs dogs” classification dataset. adapted Deep Learning Python 2016 blog post “building powerful image classification models using little data”.","code":""},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"freezing-layers-understanding-the-trainable-attribute","dir":"Articles","previous_headings":"","what":"Freezing layers: understanding the trainable attribute","title":"Transfer learning & fine-tuning","text":"Layers & models three weight attributes: weights list weights variables layer. trainable_weights list meant updated (via gradient descent) minimize loss training. non_trainable_weights list aren’t meant trained. Typically updated model forward pass. Example: Dense layer 2 trainable weights (kernel & bias) general, weights trainable weights. built-layer non-trainable weights [layer_batch_normalization()]. uses non-trainable weights keep track mean variance inputs training. learn use non-trainable weights custom layers, see guide writing new layers scratch. Example: BatchNormalization layer 2 trainable weights 2 non-trainable weights Layers & models also feature boolean attribute trainable. value can changed. Setting layer$trainable FALSE moves layer’s weights trainable non-trainable. called “freezing” layer: state frozen layer won’t updated training (either training fit() training custom loop relies trainable_weights apply gradient updates). Example: setting trainable False trainable weight becomes non-trainable, value longer updated training. confuse layer$trainable attribute argument training layer$call() (controls whether layer run forward pass inference mode training mode). information, see Keras FAQ.","code":"layer <- layer_dense(units = 3) layer$build(shape(NULL, 4))  # Create the weights  length(layer$weights) ## [1] 2 length(layer$trainable_weights) ## [1] 2 length(layer$non_trainable_weights) ## [1] 0 layer <- layer_batch_normalization() layer$build(shape(NA, 4))  # Create the weights  length(layer$weights) ## [1] 4 length(layer$trainable_weights) ## [1] 2 length(layer$non_trainable_weights) ## [1] 2 layer <- layer_dense(units = 3) layer$build(shape(NULL, 4))  # Create the weights layer$trainable <- FALSE  # Freeze the layer  length(layer$weights) ## [1] 2 length(layer$trainable_weights) ## [1] 0 length(layer$non_trainable_weights) ## [1] 2 # Make a model with 2 layers layer1 <- layer_dense(units = 3, activation = \"relu\") layer2 <- layer_dense(units = 3, activation = \"sigmoid\") model <- keras_model_sequential(input_shape = 3) |>   layer1() |>   layer2()  # Freeze the first layer layer1$trainable <- FALSE  # Keep a copy of the weights of layer1 for later reference # (get_weights() returns a list of R arrays, #  layer$weights returns a list of KerasVariables) initial_layer1_weights_values <- get_weights(layer1)  # Train the model model |> compile(optimizer = \"adam\", loss = \"mse\") model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1) ## 1/1 - 1s - 544ms/step - loss: 2.1868 # Check that the weights of layer1 have not changed during training final_layer1_weights_values <- get_weights(layer1)  all.equal(initial_layer1_weights_values,           final_layer1_weights_values) ## [1] TRUE"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"recursive-setting-of-the-trainable-attribute","dir":"Articles","previous_headings":"","what":"Recursive setting of the trainable attribute","title":"Transfer learning & fine-tuning","text":"set $trainable <- FALSE model layer sublayers, children layers become non-trainable well. Example:","code":"inner_model <- keras_model_sequential(input_shape = 3) |>   layer_dense(units = 3, activation = \"relu\") |>   layer_dense(units = 3, activation = \"relu\")  model <- keras_model_sequential(input_shape = 3) |>   inner_model() |>   layer_dense(units = 3, activation = \"sigmoid\")  model$trainable <- FALSE  # Freeze the outer model  inner_model$trainable  # All layers in `model` are now frozen ## [1] FALSE inner_model$layers[[1]]$trainable  # `trainable` is propagated recursively ## [1] FALSE"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"the-typical-transfer-learning-workflow","dir":"Articles","previous_headings":"","what":"The typical transfer-learning workflow","title":"Transfer learning & fine-tuning","text":"leads us typical transfer learning workflow can implemented Keras: Instantiate base model load pre-trained weights . Freeze layers base model setting trainable <- FALSE. Create new model top output one (several) layers base model. Train new model new dataset. Note alternative, lightweight workflow also : Instantiate base model load pre-trained weights . Run new dataset record output one (several) layers base model. called feature extraction. Use output input data new, smaller model. key advantage second workflow run base model data, rather per epoch training. ’s lot faster & cheaper. issue second workflow, though, doesn’t allow dynamically modify input data new model training, required data augmentation, instance. Transfer learning typically used tasks new dataset little data train full-scale model scratch, scenarios data augmentation important. follows, focus first workflow. ’s first workflow looks like Keras: First, instantiate base model pre-trained weights. , freeze base model. Create new model top. Train model new data.","code":"base_model <- application_xception(   weights = 'imagenet', # Load weights pre-trained on ImageNet.   input_shape = c(150, 150, 3),   include_top = FALSE  # Do not include the ImageNet classifier at the top. ) base_model$trainable <- FALSE inputs <- keras_input(shape = c(150, 150, 3)) # We make sure that the base_model is running in inference mode here, # by passing `training <- FALSE`. This is important for fine-tuning, as you will # learn in a few paragraphs. outputs <- inputs |>   base_model(training = FALSE) |>   # Convert features of shape `base_model$output_shape[-1]` to vectors   layer_global_average_pooling_2d() |>   # A Dense classifier with a single unit (binary classification)   layer_dense(1)  model <- keras_model(inputs, outputs) model |> compile(   optimizer = optimizer_adam(),   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_binary_accuracy()) ) model |> fit(new_dataset, epochs = 20,              callbacks = ..., validation_data = ...)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"fine-tuning","dir":"Articles","previous_headings":"","what":"Fine-tuning","title":"Transfer learning & fine-tuning","text":"model converged new data, can try unfreeze part base model retrain whole model end--end low learning rate. optional last step can potentially give incremental improvements. also potentially lead quick overfitting – keep mind. critical step model frozen layers trained convergence. mix randomly-initialized trainable layers trainable layers hold pre-trained features, randomly-initialized layers cause large gradient updates training, destroy pre-trained features. ’s also critical use low learning rate stage, training much larger model first round training, dataset typically small. result, risk overfitting quickly apply large weight updates. , want readapt pretrained weights incremental way. implement fine-tuning whole base model: Important note compile() trainable Calling compile() model meant “freeze” behavior model. implies trainable attribute values time model compiled preserved throughout lifetime model, compile called . Hence, change trainable value, make sure call compile() model changes taken account. Important notes BatchNormalization layer Many image models contain BatchNormalization layers. layer special case every imaginable count. things keep mind. BatchNormalization contains 2 non-trainable weights get updated training. variables tracking mean variance inputs. set bn_layer$trainable <- FALSE, BatchNormalization layer run inference mode, update mean & variance statistics. case layers general, weight trainability & inference/training modes two orthogonal concepts. two tied case BatchNormalization layer. unfreeze model contains BatchNormalization layers order fine-tuning, keep BatchNormalization layers inference mode passing training = FALSE calling base model. Otherwise updates applied non-trainable weights suddenly destroy model learned. ’ll see pattern action end--end example end guide.","code":"# Unfreeze the base model base_model$trainable <- TRUE  # It's important to recompile your model after you make any changes # to the `trainable` attribute of any inner layer, so that your changes # are take into account model |> compile(   optimizer = optimizer_adam(1e-5), # Very low learning rate   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = c(metric_binary_accuracy()) )  # Train end-to-end. Be careful to stop before you overfit! model |> fit(new_dataset, epochs = 10,              callbacks = ..., validation_data = ...)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"an-end-to-end-example-fine-tuning-an-image-classification-model-on-a-cats-vs--dogs-dataset","dir":"Articles","previous_headings":"","what":"An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset","title":"Transfer learning & fine-tuning","text":"solidify concepts, let’s walk concrete end--end transfer learning & fine-tuning example. load Xception model, pre-trained ImageNet, use Kaggle “cats vs. dogs” classification dataset.","code":""},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"getting-the-data","dir":"Articles","previous_headings":"An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset","what":"Getting the data","title":"Transfer learning & fine-tuning","text":"First, let’s fetch cats vs. dogs dataset using TFDS. dataset, ’ll probably want use utility [image_dataset_from_directory()] generate similar labeled dataset objects set images disk filed class-specific folders. Transfer learning useful working small datasets. keep dataset small, use 40% original training data (25,000 images) training, 10% validation, 10% testing. first 9 images training dataset – can see, ’re different sizes. can also see label 1 “dog” label 0 “cat”.","code":"# reticulate::py_install(\"tensorflow-datasets\") tfds <- reticulate::import(\"tensorflow_datasets\")  c(train_ds, validation_ds, test_ds) %<-% tfds$load(   \"cats_vs_dogs\",   # Reserve 10% for validation and 10% for test   split = c(\"train[:40%]\", \"train[40%:50%]\", \"train[50%:60%]\"),   as_supervised = TRUE  # Include labels )  length(train_ds) ## [1] 9305 library(tfdatasets, exclude = \"shape\")  par(mfrow = c(3, 3), mar = c(1,0,1.5,0)) train_ds |>   dataset_take(9) |>   as_array_iterator() |>   iterate(function(batch) {     c(image, label) %<-% batch     plot(as.raster(image, max = 255L))     title(sprintf(       \"label: %s   size: %s\",       label, paste(dim(image), collapse = \" x \")))   })"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"standardizing-the-data","dir":"Articles","previous_headings":"An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset","what":"Standardizing the data","title":"Transfer learning & fine-tuning","text":"raw images variety sizes. addition, pixel consists 3 integer values 0 255 (RGB level values). isn’t great fit feeding neural network. need 2 things: Standardize fixed image size. pick 150x150. Normalize pixel values -1 1. ’ll using Normalization layer part model . general, ’s good practice develop models take raw data input, opposed models take already-preprocessed data. reason , model expects preprocessed data, time export model use elsewhere (web browser, mobile app), ’ll need reimplement exact preprocessing pipeline. gets tricky quickly. least possible amount preprocessing hitting model. , ’ll image resizing data pipeline (deep neural network can process contiguous batches data), ’ll input value scaling part model, create . Let’s resize images 150x150:","code":"resize_fn <- layer_resizing(width = 150, height = 150) resize_pair <- function(x, y) list(resize_fn(x), y)  train_ds <- train_ds |> dataset_map(resize_pair) validation_ds <- validation_ds |> dataset_map(resize_pair) test_ds <- test_ds |> dataset_map(resize_pair)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"using-random-data-augmentation","dir":"Articles","previous_headings":"An end-to-end example: fine-tuning an image classification model on a cats vs. dogs dataset","what":"Using random data augmentation","title":"Transfer learning & fine-tuning","text":"don’t large image dataset, ’s good practice artificially introduce sample diversity applying random yet realistic transformations training images, random horizontal flipping small random rotations. helps expose model different aspects training data slowing overfitting. Let’s batch data use prefetching optimize loading speed. Let’s visualize first image first batch looks like various random transformations:","code":"data_augmentation <- keras_model_sequential() |>   layer_random_flip(\"horizontal\") |>   layer_random_rotation(.1)  train_ds <- train_ds %>%   dataset_map(function(x, y) list(data_augmentation(x), y)) library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) batch_size <- 64  train_ds <- train_ds |>   dataset_batch(batch_size) |>   dataset_prefetch()  validation_ds <- validation_ds |>   dataset_batch(batch_size) |>   dataset_prefetch()  test_ds <- test_ds |>   dataset_batch(batch_size) |>   dataset_prefetch() batch <- train_ds |>   dataset_take(1) |>   as_iterator() |>   iter_next()  c(images, labels) %<-% batch first_image <- images[1, all_dims(), drop = TRUE] augmented_image <- data_augmentation(first_image, training = TRUE)  plot_image <- function(image, main = deparse1(substitute(image))) {   image |>     as.array() |> # convert from tensor to R array     as.raster(max = 255) |>     plot()    if(!is.null(main))     title(main) }  par(mfrow = c(2, 2), mar = c(1, 1, 1.5, 1)) plot_image(first_image) plot_image(augmented_image) plot_image(data_augmentation(first_image, training = TRUE), \"augmented 2\") plot_image(data_augmentation(first_image, training = TRUE), \"augmented 3\")"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"build-a-model","dir":"Articles","previous_headings":"","what":"Build a model","title":"Transfer learning & fine-tuning","text":"Now let’s built model follows blueprint ’ve explained earlier. Note : add Rescaling layer scale input values (initially [0, 255] range) [-1, 1] range. add Dropout layer classification layer, regularization. make sure pass training=FALSE calling base model, runs inference mode, batchnorm statistics don’t get updated even unfreeze base model fine-tuning.","code":"base_model <- application_xception(   weights = \"imagenet\", # Load weights pre-trained on ImageNet.   input_shape = c(150, 150, 3),   include_top = FALSE, # Do not include the ImageNet classifier at the top. )  # Freeze the base_model base_model$trainable <- FALSE  # Create new model on top inputs <- keras_input(shape = c(150, 150, 3))  # Pre-trained Xception weights requires that input be scaled # from (0, 255) to a range of (-1., +1.), the rescaling layer # outputs: `(inputs * scale) + offset` scale_layer <- layer_rescaling(scale = 1 / 127.5, offset = -1) x <- scale_layer(inputs)  # The base model contains batchnorm layers. We want to keep them in inference mode # when we unfreeze the base model for fine-tuning, so we make sure that the # base_model is running in inference mode here. outputs <- x |>   base_model(training = FALSE) |>   layer_global_average_pooling_2d() |>   layer_dropout(0.2) |>   layer_dense(1)  model <- keras_model(inputs, outputs)  summary(model, show_trainable = TRUE) ## Model: \"functional_10\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)                ┃ Output Shape          ┃    Param # ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer_7 (InputLayer)  │ (None, 150, 150, 3)   │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ rescaling (Rescaling)       │ (None, 150, 150, 3)   │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ xception (Functional)       │ (None, 5, 5, 2048)    │ 20,861,480 │   N   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ global_average_pooling2d_1  │ (None, 2048)          │          0 │   -   │ ## │ (GlobalAveragePooling2D)    │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dropout (Dropout)           │ (None, 2048)          │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_8 (Dense)             │ (None, 1)             │      2,049 │   Y   │ ## └─────────────────────────────┴───────────────────────┴────────────┴───────┘ ##  Total params: 20,863,529 (79.59 MB) ##  Trainable params: 2,049 (8.00 KB) ##  Non-trainable params: 20,861,480 (79.58 MB)"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"train-the-top-layer","dir":"Articles","previous_headings":"","what":"Train the top layer","title":"Transfer learning & fine-tuning","text":"","code":"model |> compile(   optimizer = optimizer_adam(),   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_binary_accuracy()) )  epochs <- 1 model |> fit(train_ds, epochs = epochs, validation_data = validation_ds) ## 146/146 - 45s - 306ms/step - binary_accuracy: 0.9183 - loss: 0.1887 - val_binary_accuracy: 0.9669 - val_loss: 0.0926"},{"path":"https://keras.posit.co/articles/transfer_learning.html","id":"do-a-round-of-fine-tuning-of-the-entire-model","dir":"Articles","previous_headings":"","what":"Do a round of fine-tuning of the entire model","title":"Transfer learning & fine-tuning","text":"Finally, let’s unfreeze base model train entire model end--end low learning rate. Importantly, although base model becomes trainable, still running inference mode since passed training=FALSE calling built model. means batch normalization layers inside won’t update batch statistics. , wreck havoc representations learned model far. 10 epochs, fine-tuning gains us nice improvement . Let’s evaluate model test dataset:","code":"# Unfreeze the base_model. Note that it keeps running in inference mode # since we passed `training=FALSE` when calling it. This means that # the batchnorm layers will not update their batch statistics. # This prevents the batchnorm layers from undoing all the training # we've done so far. base_model$trainable <- TRUE summary(model, show_trainable = TRUE) ## Model: \"functional_10\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓ ## ┃ Layer (type)                ┃ Output Shape          ┃    Param # ┃ Trai… ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩ ## │ input_layer_7 (InputLayer)  │ (None, 150, 150, 3)   │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ rescaling (Rescaling)       │ (None, 150, 150, 3)   │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ xception (Functional)       │ (None, 5, 5, 2048)    │ 20,861,480 │   Y   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ global_average_pooling2d_1  │ (None, 2048)          │          0 │   -   │ ## │ (GlobalAveragePooling2D)    │                       │            │       │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dropout (Dropout)           │ (None, 2048)          │          0 │   -   │ ## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤ ## │ dense_8 (Dense)             │ (None, 1)             │      2,049 │   Y   │ ## └─────────────────────────────┴───────────────────────┴────────────┴───────┘ ##  Total params: 20,867,629 (79.60 MB) ##  Trainable params: 20,809,001 (79.38 MB) ##  Non-trainable params: 54,528 (213.00 KB) ##  Optimizer params: 4,100 (16.02 KB) model |> compile(   optimizer = optimizer_adam(1e-5), # Low learning rate   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_binary_accuracy()) )  epochs <- 1 model |> fit(train_ds, epochs = epochs, validation_data = validation_ds) ## 146/146 - 82s - 564ms/step - binary_accuracy: 0.8660 - loss: 0.3213 - val_binary_accuracy: 0.9652 - val_loss: 0.1022 model |> evaluate(test_ds) ## 37/37 - 2s - 42ms/step - binary_accuracy: 0.9540 - loss: 0.1103 ## $binary_accuracy ## [1] 0.9539983 ## ## $loss ## [1] 0.1102556"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Understanding masking & padding","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Understanding masking & padding","text":"Masking way tell sequence-processing layers certain timesteps input missing, thus skipped processing data. Padding special form masking masked steps start end sequence. Padding comes need encode sequence data contiguous batches: order make sequences batch fit given standard length, necessary pad truncate sequences. Let’s take close look.","code":""},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"padding-sequence-data","dir":"Articles","previous_headings":"","what":"Padding sequence data","title":"Understanding masking & padding","text":"processing sequence data, common individual samples different lengths. Consider following example (text tokenized words): vocabulary lookup, data might vectorized integers, e.g.: data nested list individual samples length 3, 5, 6, respectively. Since input data deep learning model must single tensor (shape e.g. (batch_size, 6, vocab_size) case), samples shorter longest item need padded placeholder value (alternatively, one might also truncate long samples padding short samples). Keras provides utility function truncate pad Python lists common length: pad_sequences.","code":"data <- list(   c(\"Hello\", \"world\", \"!\"),   c(\"How\", \"are\", \"you\", \"doing\", \"today\"),   c(\"The\", \"weather\", \"will\", \"be\", \"nice\", \"tomorrow\") ) data <- list(   c(71, 1331, 4231),   c(73, 8, 3215, 55, 927),   c(83, 91, 1, 645, 1253, 927) ) raw_inputs <- list(   c(711, 632, 71),   c(73, 8, 3215, 55, 927),   c(83, 91, 1, 645, 1253, 927) )  # By default, this will pad using 0s; it is configurable via the # \"value\" parameter. # Note that you could use \"pre\" padding (at the beginning) or # \"post\" padding (at the end). # We recommend using \"post\" padding when working with RNN layers # (in order to be able to use the # CuDNN implementation of the layers). padded_inputs <- pad_sequences(raw_inputs, padding=\"post\") padded_inputs ##      [,1] [,2] [,3] [,4] [,5] [,6] ## [1,]  711  632   71    0    0    0 ## [2,]   73    8 3215   55  927    0 ## [3,]   83   91    1  645 1253  927"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"masking","dir":"Articles","previous_headings":"","what":"Masking","title":"Understanding masking & padding","text":"Now samples uniform length, model must informed part data actually padding ignored. mechanism masking. three ways introduce input masks Keras models: Add layer_masking layer. Configure layer_embedding layer mask_zero=TRUE. Pass mask argument manually calling layers support argument (e.g. RNN layers).","code":""},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"mask-generating-layers-embedding-and-masking","dir":"Articles","previous_headings":"","what":"Mask-generating layers: Embedding and Masking","title":"Understanding masking & padding","text":"hood, layers create mask tensor (2D tensor shape (batch, sequence_length)), attach tensor output returned Masking Embedding layer. can see printed result, mask 2D boolean tensor shape (batch_size, sequence_length), individual FALSE entry indicates corresponding timestep ignored processing.","code":"embedding <- layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) masked_output <- embedding(padded_inputs)  masked_output$`_keras_mask` ## tf.Tensor( ## [[ True  True  True False False False] ##  [ True  True  True  True  True False] ##  [ True  True  True  True  True  True]], shape=(3, 6), dtype=bool) masking_layer <- layer_masking() # Simulate the embedding lookup by expanding the 2D input to 3D, # with embedding dimension of 10. unmasked_embedding <- op_cast(     op_tile(op_expand_dims(padded_inputs, axis=-1), c(1L, 1L, 10L)),     dtype=\"float32\" )  masked_embedding <- masking_layer(unmasked_embedding) masked_embedding$`_keras_mask` ## tf.Tensor( ## [[ True  True  True False False False] ##  [ True  True  True  True  True False] ##  [ True  True  True  True  True  True]], shape=(3, 6), dtype=bool)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"mask-propagation-in-the-functional-api-and-sequential-api","dir":"Articles","previous_headings":"","what":"Mask propagation in the Functional API and Sequential API","title":"Understanding masking & padding","text":"using Functional API Sequential API, mask generated Embedding Masking layer propagated network layer capable using (example, RNN layers). Keras automatically fetch mask corresponding input pass layer knows use . instance, following Sequential model, LSTM layer automatically receive mask, means ignore padded values: also case following Functional API model:","code":"model <- keras_model_sequential() %>%   layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%   layer_lstm(units=32) inputs <- keras_input(shape = shape(NULL), dtype=\"int32\") outputs <- inputs %>%   layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%   layer_lstm(units=32)  model <- keras_model(inputs, outputs)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"passing-mask-tensors-directly-to-layers","dir":"Articles","previous_headings":"","what":"Passing mask tensors directly to layers","title":"Understanding masking & padding","text":"Layers can handle masks (LSTM layer) mask argument call method. Meanwhile, layers produce mask (e.g. Embedding) expose compute_mask(input, previous_mask) method can call. Thus, can pass output compute_mask() method mask-producing layer call method mask-consuming layer, like :","code":"MyLayer <- new_layer_class(   \"MyLayer\",   initialize = function(...) {     super$initialize(...)     self$embedding <- layer_embedding(       input_dim=5000, output_dim=16, mask_zero=TRUE     )     self$lstm <- layer_lstm(units=32)   },   call = function(inputs) {     inputs %>%       self$embedding() %>%       # Note that you could also prepare a `mask` tensor manually.       # It only needs to be a boolean tensor       # with the right shape, i.e. (batch_size, timesteps).       self$lstm(mask=self$embedding$compute_mask(inputs))   } )  layer <- MyLayer() x <- random_integer(c(32, 10), 0, 100) layer(x) ## tf.Tensor( ## [[ 0.00130048 -0.00113367 -0.00715671 ... -0.00107615 -0.00162071 ##    0.00135018] ##  [-0.004185    0.00726349  0.00520932 ...  0.00119117  0.00230441 ##    0.00174123] ##  [-0.00537032 -0.00164898 -0.00238435 ... -0.00154158 -0.0038603 ##   -0.00105811] ##  ... ##  [ 0.00622133 -0.00905907 -0.00599518 ...  0.00025823 -0.00142478 ##   -0.00125036] ##  [-0.00523904  0.00336683 -0.00299453 ...  0.00876719  0.00172074 ##    0.00903089] ##  [-0.00393721  0.00058538  0.00503809 ... -0.00203075  0.00325885 ##   -0.00299755]], shape=(32, 32), dtype=float32)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"supporting-masking-in-your-custom-layers","dir":"Articles","previous_headings":"","what":"Supporting masking in your custom layers","title":"Understanding masking & padding","text":"Sometimes, may need write layers generate mask (like Embedding), layers need modify current mask. instance, layer produces tensor different time dimension input, Concatenate layer concatenates time dimension, need modify current mask downstream layers able properly take masked timesteps account. , layer implement layer.compute_mask() method, produces new mask given input current mask. example TemporalSplit layer needs modify current mask. another example CustomEmbedding layer capable generating mask input values: Note: details format limitations related masking, see serialization guide.","code":"TemporalSplit <- new_layer_class(   \"TemporalSplit\",   call = function(inputs) {     # Expect the input to be 3D and mask to be 2D, split the input tensor into 2     # subtensors along the time axis (axis 1).     op_split(inputs, 2, axis=2)   },   compute_mask = function(inputs, mask = NULL) {     # Also split the mask into 2 if it presents.     if (!is.null(mask)) {       op_split(mask, 2, axis=2)     } else {       NULL     }   } )  c(first_half, second_half) %<-% TemporalSplit(masked_embedding) first_half$`_keras_mask` ## tf.Tensor( ## [[ True  True  True] ##  [ True  True  True] ##  [ True  True  True]], shape=(3, 3), dtype=bool) second_half$`_keras_mask` ## tf.Tensor( ## [[False False False] ##  [ True  True False] ##  [ True  True  True]], shape=(3, 3), dtype=bool) CustomEmbedding <- new_layer_class(   \"CustomEmbedding\",   initialize = function(input_dim, output_dim, mask_zero=FALSE, ...) {     super$initialize(...)     self$input_dim <- as.integer(input_dim)     self$output_dim <- as.integer(output_dim)     self$mask_zero <- mask_zero   },   build = function(input_shape) {     self$embeddings <- self$add_weight(       shape=c(self$input_dim, self$output_dim),       initializer=\"random_normal\",       dtype=\"float32\"     )   },   call = function(inputs) {     inputs <- op_cast(inputs, \"int32\")     op_take(self$embeddings, inputs)   },   compute_mask = function(inputs, mask=NULL) {     if (!self$mask_zero) {       NULL     } else {       op_not_equal(inputs, 0)     }   } )  layer <- CustomEmbedding(input_dim = 10, output_dim = 32, mask_zero=TRUE) x <- random_integer(c(3, 10), 0, 9)  y <- layer(x) mask <- layer$compute_mask(x)  mask ## tf.Tensor( ## [[ True  True  True  True  True  True  True  True  True  True] ##  [ True  True  True  True  True  True  True  True  True  True] ##  [False False  True  True  True  True  True  True  True  True]], shape=(3, 10), dtype=bool)"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"opting-in-to-mask-propagation-on-compatible-layers","dir":"Articles","previous_headings":"","what":"Opting-in to mask propagation on compatible layers","title":"Understanding masking & padding","text":"layers don’t modify time dimension, don’t need modify current mask. However, may still want able propagate current mask, unchanged, next layer. opt-behavior. default, custom layer destroy current mask (since framework way tell whether propagating mask safe ). custom layer modify time dimension, want able propagate current input mask, set self.supports_masking = True layer constructor. case, default behavior compute_mask() just pass current mask . ’s example layer whitelisted mask propagation: can now use custom layer -mask-generating layer (like Embedding) mask-consuming layer (like LSTM), pass mask along reaches mask-consuming layer.","code":"MyActivation <- new_layer_class(   \"MyActivation\",   initialize = function(...) {     super$initialize(...)     self$supports_masking <- TRUE   },   call = function(inputs) {     op_relu(inputs)   } ) inputs <- keras_input(shape = shape(NULL), dtype=\"int32\") outputs <- inputs %>%   layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%   MyActivation() %>%   layer_lstm(units=32)  model <- keras_model(inputs, outputs) y <- model(random_integer(c(32, 100), 0, 5000))"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"writing-layers-that-need-mask-information","dir":"Articles","previous_headings":"","what":"Writing layers that need mask information","title":"Understanding masking & padding","text":"layers mask consumers: accept mask argument call use determine whether skip certain time steps. write layer, can simply add mask=None argument call signature. mask associated inputs passed layer whenever available. ’s simple example : layer computes softmax time dimension (axis 1) input sequence, discarding masked timesteps.","code":"TemporalSoftmax <- new_layer_class(   \"TemporalSoftmax\",   initialize = function(...) {     super$initialize(...)     self$supports_masking <- TRUE   },   call = function(inputs, mask=NULL) {     if (is.null(mask)) {       stop(\"`TemporalSoftmax` layer requires a previous layer to support masking.\")     }     broadcast_float_mask <- op_expand_dims(op_cast(mask, \"float32\"), -1)     inputs_exp <- op_exp(inputs) * broadcast_float_mask     inputs_sum <- op_sum(inputs_exp * broadcast_float_mask, axis=-1, keepdims=TRUE)     inputs_exp / inputs_sum   } )  inputs <- keras_input(shape = shape(NULL), dtype=\"int32\") outputs <- inputs %>%   layer_embedding(input_dim=10, output_dim=32, mask_zero=TRUE) %>%   layer_dense(1) %>%   TemporalSoftmax()  model <- keras_model(inputs, outputs) y <- model(random_integer(c(32, 100), 0, 10))"},{"path":"https://keras.posit.co/articles/understanding_masking_and_padding.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Understanding masking & padding","text":"need know padding & masking Keras. recap: “Masking” layers able know skip / ignore certain timesteps sequence inputs. layers mask-generators: Embedding can generate mask input values (mask_zero=TRUE), can Masking layer. layers mask-consumers: expose mask argument call method. case RNN layers. Functional API Sequential API, mask information propagated automatically. using layers standalone way, can pass mask arguments layers manually. can easily write layers modify current mask, generate new mask, consume mask associated inputs.","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing a training loop from scratch in JAX","text":"","code":"import os  # This guide can only be run with the jax backend. os.environ[\"KERAS_BACKEND\"] = \"jax\"  import jax  # We import TF so we can use tf.data. import tensorflow as tf import keras import numpy as np"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing a training loop from scratch in JAX","text":"Keras provides default training evaluation loops, fit() evaluate(). usage covered guide Training & evaluation built-methods. want customize learning algorithm model still leveraging convenience fit() (instance, train GAN using fit()), can subclass Model class implement train_step() method, called repeatedly fit(). Now, want low-level control training & evaluation, write training & evaluation loops scratch. guide .","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"A first end-to-end example","title":"Writing a training loop from scratch in JAX","text":"write custom training loop, need following ingredients: model train, course. optimizer. either use optimizer keras.optimizers, one optax package. loss function. dataset. standard JAX ecosystem load data via tf.data, ’s ’ll use. Let’s line . First, let’s get model MNIST dataset: Next, ’s loss function optimizer. ’ll use Keras optimizer case.","code":"def get_model():     inputs = keras.Input(shape=(784,), name=\"digits\")     x1 = keras.layers.Dense(64, activation=\"relu\")(inputs)     x2 = keras.layers.Dense(64, activation=\"relu\")(x1)     outputs = keras.layers.Dense(10, name=\"predictions\")(x2)     model = keras.Model(inputs=inputs, outputs=outputs)     return model   model = get_model()  # Prepare the training dataset. batch_size = 32 (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() x_train = np.reshape(x_train, (-1, 784)).astype(\"float32\") x_test = np.reshape(x_test, (-1, 784)).astype(\"float32\") y_train = keras.utils.to_categorical(y_train) y_test = keras.utils.to_categorical(y_test)  # Reserve 10,000 samples for validation. x_val = x_train[-10000:] y_val = y_train[-10000:] x_train = x_train[:-10000] y_train = y_train[:-10000]  # Prepare the training dataset. train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)) train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size)  # Prepare the validation dataset. val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val)) val_dataset = val_dataset.batch(batch_size) # Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  # Instantiate an optimizer. optimizer = keras.optimizers.Adam(learning_rate=1e-3)"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"getting-gradients-in-jax","dir":"Articles","previous_headings":"A first end-to-end example","what":"Getting gradients in JAX","title":"Writing a training loop from scratch in JAX","text":"Let’s train model using mini-batch gradient custom training loop. JAX, gradients computed via metaprogramming: call jax.grad (jax.value_and_grad function order create gradient-computing function first function. first thing need function returns loss value. ’s function ’ll use generate gradient function. Something like : function, can compute gradients via metaprogramming : Typically, don’t just want get gradient values, also want get loss value. can using jax.value_and_grad instead jax.grad:","code":"def compute_loss(x, y):     ...     return loss grad_fn = jax.grad(compute_loss) grads = grad_fn(x, y) grad_fn = jax.value_and_grad(compute_loss) loss, grads = grad_fn(x, y)"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"jax-computation-is-purely-stateless","dir":"Articles","previous_headings":"A first end-to-end example","what":"JAX computation is purely stateless","title":"Writing a training loop from scratch in JAX","text":"JAX, everything must stateless function – loss computation function must stateless well. means Keras variables (e.g. weight tensors) must passed function inputs, variable updated forward pass must returned function output. function side effect. forward pass, non-trainable variables Keras model might get updated. variables , instance, RNG seed state variables BatchNormalization statistics. ’re going need return . need something like : function, can get gradient function specifying hax_aux value_and_grad: tells JAX loss computation function returns outputs just loss. Note loss always first output. Now established basics, let’s implement compute_loss_and_updates function. Keras models stateless_call method come handy . works just like model.__call__, requires explicitly pass value variables model, returns just __call__ outputs also (potentially updated) non-trainable variables. Let’s get gradient function:","code":"def compute_loss_and_updates(trainable_variables, non_trainable_variables, x, y):     ...     return loss, non_trainable_variables grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True) (loss, non_trainable_variables), grads = grad_fn(     trainable_variables, non_trainable_variables, x, y ) def compute_loss_and_updates(     trainable_variables, non_trainable_variables, x, y ):     y_pred, non_trainable_variables = model.stateless_call(         trainable_variables, non_trainable_variables, x     )     loss = loss_fn(y, y_pred)     return loss, non_trainable_variables grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True)"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"the-training-step-function","dir":"Articles","previous_headings":"A first end-to-end example","what":"The training step function","title":"Writing a training loop from scratch in JAX","text":"Next, let’s implement end--end training step, function run forward pass, compute loss, compute gradients, also use optimizer update trainable variables. function also needs stateless, get input state tuple includes every state element ’re going use: trainable_variables non_trainable_variables: model’s variables. optimizer_variables: optimizer’s state variables, momentum accumulators. update trainable variables, use optimizer’s stateless method stateless_apply. ’s equivalent optimizer.apply(), requires always passing trainable_variables optimizer_variables. returns updated trainable variables updated optimizer_variables.","code":"def train_step(state, data):     trainable_variables, non_trainable_variables, optimizer_variables = state     x, y = data     (loss, non_trainable_variables), grads = grad_fn(         trainable_variables, non_trainable_variables, x, y     )     trainable_variables, optimizer_variables = optimizer.stateless_apply(         optimizer_variables, grads, trainable_variables     )     # Return updated state     return loss, (         trainable_variables,         non_trainable_variables,         optimizer_variables,     )"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"make-it-fast-with-jax-jit","dir":"Articles","previous_headings":"A first end-to-end example","what":"Make it fast with jax.jit","title":"Writing a training loop from scratch in JAX","text":"default, JAX operations run eagerly, just like TensorFlow eager mode PyTorch eager mode. just like TensorFlow eager mode PyTorch eager mode, ’s pretty slow – eager mode better used debugging environment, way actual work. let’s make train_step fast compiling . stateless JAX function, can compile XLA via @jax.jit decorator. get traced first execution, subsequent executions executing traced graph (just like @tf.function(jit_compile=True). Let’s try : ’re now ready train model. training loop trivial: just repeatedly call loss, state = train_step(state, data). Note: convert TF tensors yielded tf.data.Dataset NumPy passing JAX function. variables must built beforehand: model must built optimizer must built. Since ’re using Functional API model, ’s already built, subclassed model ’d need call batch data build . key thing notice loop entirely stateless – variables attached model (model.weights) never getting updated loop. new values stored state tuple. means point, saving model, attaching new variable values back model. Just call variable.assign(new_value) model variable want update:","code":"@jax.jit def train_step(state, data):     trainable_variables, non_trainable_variables, optimizer_variables = state     x, y = data     (loss, non_trainable_variables), grads = grad_fn(         trainable_variables, non_trainable_variables, x, y     )     trainable_variables, optimizer_variables = optimizer.stateless_apply(         optimizer_variables, grads, trainable_variables     )     # Return updated state     return loss, (         trainable_variables,         non_trainable_variables,         optimizer_variables,     ) # Build optimizer variables. optimizer.build(model.trainable_variables)  trainable_variables = model.trainable_variables non_trainable_variables = model.non_trainable_variables optimizer_variables = optimizer.variables state = trainable_variables, non_trainable_variables, optimizer_variables  # Training loop for step, data in enumerate(train_dataset):     data = (data[0].numpy(), data[1].numpy())     loss, state = train_step(state, data)     # Log every 100 batches.     if step % 100 == 0:         print(f\"Training loss (for 1 batch) at step {step}: {float(loss):.4f}\")         print(f\"Seen so far: {(step + 1) * batch_size} samples\") trainable_variables, non_trainable_variables, optimizer_variables = state for variable, value in zip(model.trainable_variables, trainable_variables):     variable.assign(value) for variable, value in zip(     model.non_trainable_variables, non_trainable_variables ):     variable.assign(value)"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"low-level-handling-of-metrics","dir":"Articles","previous_headings":"","what":"Low-level handling of metrics","title":"Writing a training loop from scratch in JAX","text":"Let’s add metrics monitoring basic training loop. can readily reuse built-Keras metrics (custom ones wrote) training loops written scratch. ’s flow: Instantiate metric start loop Include metric_variables train_step arguments compute_loss_and_updates arguments. Call metric.stateless_update_state() compute_loss_and_updates function. ’s equivalent update_state() – stateless. need display current value metric, outside train_step (eager scope), attach new metric variable values metric object vall metric.result(). Call metric.reset_state() need clear state metric (typically end epoch) Let’s use knowledge compute CategoricalAccuracy training validation data end training: ’ll also prepare evaluation step function: loops:","code":"# Get a fresh model model = get_model()  # Instantiate an optimizer to train the model. optimizer = keras.optimizers.Adam(learning_rate=1e-3) # Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  # Prepare the metrics. train_acc_metric = keras.metrics.CategoricalAccuracy() val_acc_metric = keras.metrics.CategoricalAccuracy()   def compute_loss_and_updates(     trainable_variables, non_trainable_variables, metric_variables, x, y ):     y_pred, non_trainable_variables = model.stateless_call(         trainable_variables, non_trainable_variables, x     )     loss = loss_fn(y, y_pred)     metric_variables = train_acc_metric.stateless_update_state(         metric_variables, y, y_pred     )     return loss, (non_trainable_variables, metric_variables)   grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True)   @jax.jit def train_step(state, data):     (         trainable_variables,         non_trainable_variables,         optimizer_variables,         metric_variables,     ) = state     x, y = data     (loss, (non_trainable_variables, metric_variables)), grads = grad_fn(         trainable_variables, non_trainable_variables, metric_variables, x, y     )     trainable_variables, optimizer_variables = optimizer.stateless_apply(         optimizer_variables, grads, trainable_variables     )     # Return updated state     return loss, (         trainable_variables,         non_trainable_variables,         optimizer_variables,         metric_variables,     ) @jax.jit def eval_step(state, data):     trainable_variables, non_trainable_variables, metric_variables = state     x, y = data     y_pred, non_trainable_variables = model.stateless_call(         trainable_variables, non_trainable_variables, x     )     loss = loss_fn(y, y_pred)     metric_variables = val_acc_metric.stateless_update_state(         metric_variables, y, y_pred     )     return loss, (         trainable_variables,         non_trainable_variables,         metric_variables,     ) # Build optimizer variables. optimizer.build(model.trainable_variables)  trainable_variables = model.trainable_variables non_trainable_variables = model.non_trainable_variables optimizer_variables = optimizer.variables metric_variables = train_acc_metric.variables state = (     trainable_variables,     non_trainable_variables,     optimizer_variables,     metric_variables, )  # Training loop for step, data in enumerate(train_dataset):     data = (data[0].numpy(), data[1].numpy())     loss, state = train_step(state, data)     # Log every 100 batches.     if step % 100 == 0:         print(f\"Training loss (for 1 batch) at step {step}: {float(loss):.4f}\")         _, _, _, metric_variables = state         for variable, value in zip(             train_acc_metric.variables, metric_variables         ):             variable.assign(value)         print(f\"Training accuracy: {train_acc_metric.result()}\")         print(f\"Seen so far: {(step + 1) * batch_size} samples\")  metric_variables = val_acc_metric.variables (     trainable_variables,     non_trainable_variables,     optimizer_variables,     metric_variables, ) = state state = trainable_variables, non_trainable_variables, metric_variables  # Eval loop for step, data in enumerate(val_dataset):     data = (data[0].numpy(), data[1].numpy())     loss, state = eval_step(state, data)     # Log every 100 batches.     if step % 100 == 0:         print(             f\"Validation loss (for 1 batch) at step {step}: {float(loss):.4f}\"         )         _, _, metric_variables = state         for variable, value in zip(val_acc_metric.variables, metric_variables):             variable.assign(value)         print(f\"Validation accuracy: {val_acc_metric.result()}\")         print(f\"Seen so far: {(step + 1) * batch_size} samples\")"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_jax.html","id":"low-level-handling-of-losses-tracked-by-the-model","dir":"Articles","previous_headings":"","what":"Low-level handling of losses tracked by the model","title":"Writing a training loop from scratch in JAX","text":"Layers & models recursively track losses created forward pass layers call self.add_loss(value). resulting list scalar loss values available via property model.losses end forward pass. want using loss components, sum add main loss training step. Consider layer, creates activity regularization loss: Let’s build really simple model uses : ’s compute_loss_and_updates function look like now: Pass return_losses=True model.stateless_call(). Sum resulting losses add main loss. ’s !","code":"class ActivityRegularizationLayer(keras.layers.Layer):     def call(self, inputs):         self.add_loss(1e-2 * jax.numpy.sum(inputs))         return inputs inputs = keras.Input(shape=(784,), name=\"digits\") x = keras.layers.Dense(64, activation=\"relu\")(inputs) # Insert activity regularization as a layer x = ActivityRegularizationLayer()(x) x = keras.layers.Dense(64, activation=\"relu\")(x) outputs = keras.layers.Dense(10, name=\"predictions\")(x)  model = keras.Model(inputs=inputs, outputs=outputs) def compute_loss_and_updates(     trainable_variables, non_trainable_variables, metric_variables, x, y ):     y_pred, non_trainable_variables, losses = model.stateless_call(         trainable_variables, non_trainable_variables, x, return_losses=True     )     loss = loss_fn(y, y_pred)     if losses:         loss += jax.numpy.sum(losses)     metric_variables = train_acc_metric.stateless_update_state(         metric_variables, y, y_pred     )     return loss, non_trainable_variables, metric_variables"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing a training loop from scratch in TensorFlow","text":"","code":"library(tensorflow, exclude = c(\"shape\", \"set_random_seed\")) library(tfdatasets, exclude = \"shape\") library(keras3)  # This guide can only be run with the TensorFlow backend. use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing a training loop from scratch in TensorFlow","text":"Keras provides default training evaluation loops, fit() evaluate(). usage covered guide Training & evaluation built-methods. want customize learning algorithm model still leveraging convenience fit() (instance, train GAN using fit()), can subclass Model class implement train_step() method, called repeatedly fit(). covered guide Customizing happens fit(). Now, want low-level control training & evaluation, write training & evaluation loops scratch. guide .","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"using-the-gradienttape-a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"Using the GradientTape: a first end-to-end example","title":"Writing a training loop from scratch in TensorFlow","text":"Calling model inside GradientTape scope enables retrieve gradients trainable weights layer respect loss value. Using optimizer instance, can use gradients update variables (can retrieve using model$trainable_weights). Let’s consider simple MNIST model: Let’s train using mini-batch gradient custom training loop. First, ’re going need optimizer, loss function, dataset: ’s training loop: open loop iterates epochs epoch, iterate dataset, batches batch, open GradientTape() scope Inside scope, call model (forward pass) compute loss Outside scope, retrieve gradients weights model regard loss Finally, use optimizer update weights model based gradients","code":"get_model <- function() {   inputs <- keras_input(shape = 784, name = \"digits\")   outputs <- inputs |>     layer_dense(units = 64, activation = \"relu\") |>     layer_dense(units = 64, activation = \"relu\") |>     layer_dense(units = 10, name = \"predictions\")   keras_model(inputs = inputs, outputs = outputs) }  model <- get_model() # Instantiate an optimizer. optimizer <- optimizer_adam(learning_rate = 1e-3) # Instantiate a loss function. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE)  # Prepare the training dataset. batch_size <- 64 c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(-1, 784)) x_test <- array_reshape(x_test, c(-1, 784))  # Reserve 10,000 samples for validation. val_i <- sample.int(nrow(x_train), 10000) x_val <- x_train[val_i,] y_val <- y_train[val_i] x_train %<>% .[-val_i,] y_train %<>% .[-val_i]  # Prepare the training dataset. train_dataset <- list(x_train, y_train) |>   tensor_slices_dataset() |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(batch_size)  # Prepare the validation dataset. val_dataset <- list(x_val, y_val) |>   tensor_slices_dataset() |>   dataset_batch(batch_size) epochs <- 3 for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   iterator <- as_iterator(train_dataset)   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1     # Open a GradientTape to record the operations run     # during the forward pass, which enables auto-differentiation.     with(tf$GradientTape() %as% tape, {       # Run the forward pass of the layer.       # The operations that the layer applies       # to its inputs are going to be recorded       # on the GradientTape.       logits <- model(x_batch_train, training = TRUE) # Logits for this minibatch        # Compute the loss value for this minibatch.       loss_value <- loss_fn(y_batch_train, logits)     })      # Use the gradient tape to automatically retrieve     # the gradients of the trainable variables with respect to the loss.     gradients <- tape$gradient(loss_value, model$trainable_weights)      # Run one step of gradient descent by updating     # the value of the variables to minimize the loss.     optimizer$apply(gradients, model$trainable_weights)      # Log every 200 batches.     if (step %% 200 == 0) {       cat(sprintf(\"Training loss (for one batch) at step %d: %.4f\\n\",                   step, loss_value))       cat(sprintf(\"Seen so far: %d samples\\n\", (step * batch_size)))     }   } } ## Start of epoch  1 ## Training loss (for one batch) at step 200: 1.1675 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 1.6450 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.6477 ## Seen so far: 38400 samples ## Start of epoch  2 ## Training loss (for one batch) at step 200: 0.6421 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.1827 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.4249 ## Seen so far: 38400 samples ## Start of epoch  3 ## Training loss (for one batch) at step 200: 0.2715 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.3106 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.2905 ## Seen so far: 38400 samples"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"low-level-handling-of-metrics","dir":"Articles","previous_headings":"","what":"Low-level handling of metrics","title":"Writing a training loop from scratch in TensorFlow","text":"Let’s add metrics monitoring basic loop. can readily reuse built-metrics (custom ones wrote) training loops written scratch. ’s flow: Instantiate metric start loop Call metric$update_state() batch Call metric$result() need display current value metric Call metric$reset_state() need clear state metric (typically end epoch) Let’s use knowledge compute SparseCategoricalAccuracy validation data end epoch: ’s training & evaluation loop:","code":"# Get a fresh model model <- get_model()  # Instantiate an optimizer to train the model. optimizer <- optimizer_adam(learning_rate = 1e-3) # Instantiate a loss function. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE)  # Prepare the metrics. train_acc_metric <- metric_sparse_categorical_accuracy() val_acc_metric <- metric_sparse_categorical_accuracy() epochs <- 2 time <- Sys.time() for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   train_dataset_iterator <- as_iterator(train_dataset)   while (!is.null(train_batch <- iter_next(train_dataset_iterator))) {     c(x_batch_train, y_batch_train) %<-% train_batch     step <- step + 1      with(tf$GradientTape() %as% tape, {       logits <- model(x_batch_train, training = TRUE)       loss_value <- loss_fn(y_batch_train, logits)     })     gradients <- tape$gradient(loss_value, model$trainable_weights)     optimizer$apply(gradients, model$trainable_weights)      # Update training metric.     train_acc_metric$update_state(y_batch_train, logits)     if (step %% 200 == 0) {       cat(sprintf(         \"Training loss (for one batch) at step %d: %.4f\\n\", step, loss_value))       cat(sprintf(\"Seen so far: %d samples \\n\", (step * batch_size)))     }   }    # Display metrics at the end of each epoch.   train_acc <- train_acc_metric$result()   cat(sprintf(\"Training acc over epoch: %.4f\\n\", train_acc))    # Reset training metrics at the end of each epoch   train_acc_metric$reset_state()    # Run a validation loop at the end of each epoch.   iterate(val_dataset, function(val_batch) {     c(x_batch_val, y_batch_val) %<-% val_batch     val_logits <- model(x_batch_val, training = FALSE)     val_acc_metric$update_state(y_batch_val, val_logits)   })   val_acc <- val_acc_metric$result()   val_acc_metric$reset_state()   cat(sprintf(\"Validation acc: %.4f\\n\", val_acc)) } ## Start of epoch  1 ## Training loss (for one batch) at step 200: 1.6268 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 1.2241 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.4987 ## Seen so far: 38400 samples ## Training acc over epoch: 0.7844 ## Validation acc: 0.8680 ## Start of epoch  2 ## Training loss (for one batch) at step 200: 0.4626 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.4654 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.5022 ## Seen so far: 38400 samples ## Training acc over epoch: 0.8837 ## Validation acc: 0.9031 Sys.time() - time ## Time difference of 44.04362 secs"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"speeding-up-your-training-step-with-tf_function","dir":"Articles","previous_headings":"","what":"Speeding-up your training step with tf_function()","title":"Writing a training loop from scratch in TensorFlow","text":"default runtime TensorFlow 2 eager execution. , training loop executes eagerly. great debugging, graph compilation definite performance advantage. Describing computation static graph enables framework apply global performance optimizations. impossible framework constrained greedily execute one operation another, knowledge comes next. can compile static graph function takes tensors input. Just add @tf.function decorator , like : Let’s evaluation step: Now, let’s re-run training loop compiled training step: Much faster, isn’t ?","code":"train_step <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     logits <- model(x, training = TRUE)     loss_value <- loss_fn(y, logits)   })   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply(gradients, model$trainable_weights)   train_acc_metric$update_state(y, logits)   invisible(NULL) # return nothing }) test_step <- tf_function(function(x, y) {   val_logits <- model(x, training=FALSE)   val_acc_metric$update_state(y, val_logits)   invisible(NULL) # return nothing }) epochs <- 2 time <- Sys.time() for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1     train_step(x_batch_train, y_batch_train)      if (step %% 200 == 0) {       cat(sprintf(         \"Training loss (for one batch) at step %d: %.4f\\n\", step, loss_value       ))       cat(sprintf(\"Seen so far: %d samples \\n\", (step * batch_size)))     }   }    # Display metrics at the end of each epoch.   train_acc <- train_acc_metric$result()   cat(sprintf(\"Training acc over epoch: %.4f\\n\", train_acc))     # Reset training metrics at the end of each epoch   train_acc_metric$reset_state()    # Run a validation loop at the end of each epoch.    iterate(val_dataset, function(val_batch) {     c(x_batch_val, y_batch_val) %<-% val_batch     test_step(x_batch_val, y_batch_val)   })    val_acc <- val_acc_metric$result()   val_acc_metric$reset_state()   cat(sprintf(\"Validation acc: %.4f\\n\", val_acc)) } ## Start of epoch  1 ## Training acc over epoch: 0.0000 ## Validation acc: 0.9031 ## Start of epoch  2 ## Training acc over epoch: 0.0000 ## Validation acc: 0.9031 Sys.time() - time ## Time difference of 0.4195933 secs"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"low-level-handling-of-losses-tracked-by-the-model","dir":"Articles","previous_headings":"","what":"Low-level handling of losses tracked by the model","title":"Writing a training loop from scratch in TensorFlow","text":"Layers models recursively track losses created forward pass layers call self$add_loss(value). resulting list scalar loss values available via property model$losses end forward pass. want using loss components, sum add main loss training step. Consider layer, creates activity regularization loss: Let’s build really simple model uses : ’s training step look like now:","code":"layer_activity_regularization <- Layer(   \"ActivityRegularizationLayer\",   call = function(inputs) {     self$add_loss(0.1 * op_mean(inputs))     inputs   } ) inputs <- keras_input(shape = 784, name=\"digits\") outputs <- inputs |>   layer_dense(units = 64, activation = \"relu\") |>   layer_activity_regularization() |>   layer_dense(units = 64, activation = \"relu\") |>   layer_dense(units = 10, name = \"predictions\") model <- keras_model(inputs = inputs, outputs = outputs) train_step <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     logits <- model(x, training = TRUE)     loss_value <- Reduce(`+`, c(loss_fn(y, logits),                                 model$losses))   })   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply(gradients, model$trainable_weights)   train_acc_metric$update_state(y, logits)   invisible(NULL) })"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Writing a training loop from scratch in TensorFlow","text":"Now know everything know using built-training loops writing scratch. conclude, ’s simple end--end example ties together everything ’ve learned guide: DCGAN trained MNIST digits.","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_tensorflow.html","id":"end-to-end-example-a-gan-training-loop-from-scratch","dir":"Articles","previous_headings":"","what":"End-to-end example: a GAN training loop from scratch","title":"Writing a training loop from scratch in TensorFlow","text":"may familiar Generative Adversarial Networks (GANs). GANs can generate new images look almost real, learning latent distribution training dataset images (“latent space” images). GAN made two parts: “generator” model maps points latent space points image space, “discriminator” model, classifier can tell difference real images (training dataset) fake images (output generator network). GAN training loop looks like : Train discriminator. Sample batch random points latent space. Turn points fake images via “generator” model. Get batch real images combine generated images. Train “discriminator” model classify generated vs. real images. Train generator. Sample random points latent space. Turn points fake images via “generator” network. Get batch real images combine generated images. Train “generator” model “fool” discriminator classify fake images real. much detailed overview GANs works, see Deep Learning Python. Let’s implement training loop. First, create discriminator meant classify fake vs real digits: let’s create generator network, turns latent vectors outputs shape (28, 28, 1) (representing MNIST digits): ’s key bit: training loop. can see quite straightforward. training step function takes 17 lines. Let’s train GAN, repeatedly calling train_step batches images. Since discriminator generator convnets, ’re going want run code GPU. ’s ! ’ll get nice-looking fake MNIST digits just ~30s training GPU.","code":"# Create the discriminator discriminator <-   keras_model_sequential(name = \"discriminator\",                          input_shape = c(28, 28, 1)) |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d(filters = 128, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_global_max_pooling_2d() |>   layer_dense(units = 1)  summary(discriminator) ## Model: \"discriminator\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d (Conv2D)                 │ (None, 14, 14, 64)     │           640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu (LeakyReLU)         │ (None, 14, 14, 64)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_1 (Conv2D)               │ (None, 7, 7, 128)      │        73,856 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_1 (LeakyReLU)       │ (None, 7, 7, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d            │ (None, 128)            │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_6 (Dense)                 │ (None, 1)              │           129 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 74,625 (291.50 KB) ##  Trainable params: 74,625 (291.50 KB) ##  Non-trainable params: 0 (0.00 B) latent_dim <- 128L  generator <-   keras_model_sequential(name = \"generator\",                          input_shape = latent_dim) |>   layer_dense(7 * 7 * 128) |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_reshape(target_shape = c(7, 7, 128)) |>   layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d(filters = 1, kernel_size = c(7, 7), padding = \"same\",                 activation = \"sigmoid\")  summary(generator) ## Model: \"generator\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_7 (Dense)                 │ (None, 6272)           │       809,088 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_2 (LeakyReLU)       │ (None, 6272)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ reshape (Reshape)               │ (None, 7, 7, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 14, 14, 128)    │       262,272 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_3 (LeakyReLU)       │ (None, 14, 14, 128)    │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 28, 28, 128)    │       262,272 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_4 (LeakyReLU)       │ (None, 28, 28, 128)    │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 28, 28, 1)      │         6,273 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 1,339,905 (5.11 MB) ##  Trainable params: 1,339,905 (5.11 MB) ##  Non-trainable params: 0 (0.00 B) # Instantiate one optimizer for the discriminator and another for the generator. d_optimizer <- optimizer_adam(learning_rate = 0.0003) g_optimizer <- optimizer_adam(learning_rate = 0.0004)  # Instantiate a loss function. loss_fn <- loss_binary_crossentropy(from_logits = TRUE)  train_step <- tf_function(function(real_images) {   # Sample random points in the latent space   c(batch_size, ...) %<-% op_shape(real_images)   random_latent_vectors <-     tf$random$normal(shape(batch_size, latent_dim))    # Decode them to fake images   generated_images <- generator(random_latent_vectors)    # Combine them with real images   combined_images <- tf$concat(list(generated_images, real_images),                                axis = 0L)    # Assemble labels discriminating real from fake images   labels <- tf$concat(list(tf$ones(shape(batch_size, 1)),                            tf$zeros(shape(batch_size, 1))),                       axis = 0L)    # Add random noise to the labels - important trick!   labels %<>% `+`(tf$random$uniform(tf$shape(.), maxval = 0.05))    # Train the discriminator   with(tf$GradientTape() %as% tape, {     predictions <- discriminator(combined_images)     d_loss <- loss_fn(labels, predictions)   })   grads <- tape$gradient(d_loss, discriminator$trainable_weights)   d_optimizer$apply(grads, discriminator$trainable_weights)    # Sample random points in the latent space   random_latent_vectors <-     tf$random$normal(shape(batch_size, latent_dim))    # Assemble labels that say \"all real images\"   misleading_labels <- tf$zeros(shape(batch_size, 1))    # Train the generator (note that we should *not* update the weights   # of the discriminator)!   with(tf$GradientTape() %as% tape, {     predictions <- discriminator(generator(random_latent_vectors))     g_loss <- loss_fn(misleading_labels, predictions)   })    grads <- tape$gradient(g_loss, generator$trainable_weights)   g_optimizer$apply(grads, generator$trainable_weights)    list(d_loss, g_loss, generated_images) }) # Prepare the dataset. We use both the training & test MNIST digits. batch_size <- 64 c(c(x_train, .), c(x_test, .)) %<-% dataset_mnist() all_digits <- op_concatenate(list(x_train, x_test)) all_digits <- op_reshape(all_digits, c(-1, 28, 28, 1)) dataset <- all_digits |>   tensor_slices_dataset() |>   dataset_map(\\(x) op_cast(x, \"float32\") / 255) |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(batch_size = batch_size)  epochs <- 1 # In practice you need at least 20 epochs to generate nice digits. save_dir <- \"./\"  for (epoch in seq_len(epochs)) {   cat(\"Start epoch: \", epoch, \"\\n\")   step <- 0   train_iterator <- as_iterator(dataset)   while (!is.null(real_images <- iter_next(train_iterator))) {     step <- step + 1     # Train the discriminator & generator on one batch of real images.     c(d_loss, g_loss, generated_images) %<-% train_step(real_images)      # Logging.     if (step %% 200 == 0) {       # Print metrics       cat(sprintf(\"discriminator loss at step %d: %.2f\\n\", step, d_loss))       cat(sprintf(\"adversarial loss at step %d: %.2f\\n\", step, g_loss))     }      # To limit execution time we stop after 10 steps.     # Remove the lines below to actually train the model!     if (step > 10)       break   } } ## Start epoch:  1"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing a training loop from scratch in PyTorch","text":"","code":"import os  # This guide can only be run with the torch backend. os.environ[\"KERAS_BACKEND\"] = \"torch\"  import torch import keras import numpy as np"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing a training loop from scratch in PyTorch","text":"Keras provides default training evaluation loops, fit() evaluate(). usage covered guide Training & evaluation built-methods. want customize learning algorithm model still leveraging convenience fit() (instance, train GAN using fit()), can subclass Model class implement train_step() method, called repeatedly fit(). Now, want low-level control training & evaluation, write training & evaluation loops scratch. guide .","code":""},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"A first end-to-end example","title":"Writing a training loop from scratch in PyTorch","text":"write custom training loop, need following ingredients: model train, course. optimizer. either use keras.optimizers optimizer, native PyTorch optimizer torch.optim. loss function. either use keras.losses loss, native PyTorch loss torch.nn. dataset. use format: tf.data.Dataset, PyTorch DataLoader, Python generator, etc. Let’s line . ’ll use torch-native objects case – except, course, Keras model. First, let’s get model MNIST dataset: Next, ’s PyTorch optimizer PyTorch loss function: Let’s train model using mini-batch gradient custom training loop. Calling loss.backward() loss tensor triggers backpropagation. ’s done, optimizer magically aware gradients variable can update variables, done via optimizer.step(). Tensors, variables, optimizers interconnected one another via hidden global state. Also, don’t forget call model.zero_grad() loss.backward(), won’t get right gradients variables. ’s training loop, step step: open loop iterates epochs epoch, open loop iterates dataset, batches batch, call model input data retrive predictions, use compute loss value call loss.backward() Outside scope, retrieve gradients weights model regard loss Finally, use optimizer update weights model based gradients alternative, let’s look loop looks like using Keras optimizer Keras loss function. Important differences: retrieve gradients variables via v.value.grad, called trainable variable. update variables via optimizer.apply(), must called torch.no_grad() scope. Also, big gotcha: NumPy/TensorFlow/JAX/Keras APIs well Python unittest APIs use argument order convention fn(y_true, y_pred) (reference values first, predicted values second), PyTorch actually uses fn(y_pred, y_true) losses. make sure invert order logits targets.","code":"# Let's consider a simple MNIST model def get_model():     inputs = keras.Input(shape=(784,), name=\"digits\")     x1 = keras.layers.Dense(64, activation=\"relu\")(inputs)     x2 = keras.layers.Dense(64, activation=\"relu\")(x1)     outputs = keras.layers.Dense(10, name=\"predictions\")(x2)     model = keras.Model(inputs=inputs, outputs=outputs)     return model   # Create load up the MNIST dataset and put it in a torch DataLoader # Prepare the training dataset. batch_size = 32 (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() x_train = np.reshape(x_train, (-1, 784)).astype(\"float32\") x_test = np.reshape(x_test, (-1, 784)).astype(\"float32\") y_train = keras.utils.to_categorical(y_train) y_test = keras.utils.to_categorical(y_test)  # Reserve 10,000 samples for validation. x_val = x_train[-10000:] y_val = y_train[-10000:] x_train = x_train[:-10000] y_train = y_train[:-10000]  # Create torch Datasets train_dataset = torch.utils.data.TensorDataset(     torch.from_numpy(x_train), torch.from_numpy(y_train) ) val_dataset = torch.utils.data.TensorDataset(     torch.from_numpy(x_val), torch.from_numpy(y_val) )  # Create DataLoaders for the Datasets train_dataloader = torch.utils.data.DataLoader(     train_dataset, batch_size=batch_size, shuffle=True ) val_dataloader = torch.utils.data.DataLoader(     val_dataset, batch_size=batch_size, shuffle=False ) # Instantiate a torch optimizer model = get_model() optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)  # Instantiate a torch loss function loss_fn = torch.nn.CrossEntropyLoss() epochs = 3 for epoch in range(epochs):     for step, (inputs, targets) in enumerate(train_dataloader):         # Forward pass         logits = model(inputs)         loss = loss_fn(logits, targets)          # Backward pass         model.zero_grad()         loss.backward()          # Optimizer variable updates         optimizer.step()          # Log every 100 batches.         if step % 100 == 0:             print(                 f\"Training loss (for 1 batch) at step {step}: {loss.detach().numpy():.4f}\"             )             print(f\"Seen so far: {(step + 1) * batch_size} samples\") model = get_model() optimizer = keras.optimizers.Adam(learning_rate=1e-3) loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  for epoch in range(epochs):     print(f\"\\nStart of epoch {epoch}\")     for step, (inputs, targets) in enumerate(train_dataloader):         # Forward pass         logits = model(inputs)         loss = loss_fn(targets, logits)          # Backward pass         model.zero_grad()         trainable_weights = [v for v in model.trainable_weights]          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             optimizer.apply(gradients, trainable_weights)          # Log every 100 batches.         if step % 100 == 0:             print(                 f\"Training loss (for 1 batch) at step {step}: {loss.detach().numpy():.4f}\"             )             print(f\"Seen so far: {(step + 1) * batch_size} samples\")"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"low-level-handling-of-metrics","dir":"Articles","previous_headings":"","what":"Low-level handling of metrics","title":"Writing a training loop from scratch in PyTorch","text":"Let’s add metrics monitoring basic training loop. can readily reuse built-Keras metrics (custom ones wrote) training loops written scratch. ’s flow: Instantiate metric start loop Call metric.update_state() batch Call metric.result() need display current value metric Call metric.reset_state() need clear state metric (typically end epoch) Let’s use knowledge compute CategoricalAccuracy training validation data end epoch: ’s training & evaluation loop:","code":"# Get a fresh model model = get_model()  # Instantiate an optimizer to train the model. optimizer = keras.optimizers.Adam(learning_rate=1e-3) # Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  # Prepare the metrics. train_acc_metric = keras.metrics.CategoricalAccuracy() val_acc_metric = keras.metrics.CategoricalAccuracy() for epoch in range(epochs):     print(f\"\\nStart of epoch {epoch}\")     for step, (inputs, targets) in enumerate(train_dataloader):         # Forward pass         logits = model(inputs)         loss = loss_fn(targets, logits)          # Backward pass         model.zero_grad()         trainable_weights = [v for v in model.trainable_weights]          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             optimizer.apply(gradients, trainable_weights)          # Update training metric.         train_acc_metric.update_state(targets, logits)          # Log every 100 batches.         if step % 100 == 0:             print(                 f\"Training loss (for 1 batch) at step {step}: {loss.detach().numpy():.4f}\"             )             print(f\"Seen so far: {(step + 1) * batch_size} samples\")      # Display metrics at the end of each epoch.     train_acc = train_acc_metric.result()     print(f\"Training acc over epoch: {float(train_acc):.4f}\")      # Reset training metrics at the end of each epoch     train_acc_metric.reset_state()      # Run a validation loop at the end of each epoch.     for x_batch_val, y_batch_val in val_dataloader:         val_logits = model(x_batch_val, training=False)         # Update val metrics         val_acc_metric.update_state(y_batch_val, val_logits)     val_acc = val_acc_metric.result()     val_acc_metric.reset_state()     print(f\"Validation acc: {float(val_acc):.4f}\")"},{"path":"https://keras.posit.co/articles/writing_a_custom_training_loop_in_torch.html","id":"low-level-handling-of-losses-tracked-by-the-model","dir":"Articles","previous_headings":"","what":"Low-level handling of losses tracked by the model","title":"Writing a training loop from scratch in PyTorch","text":"Layers & models recursively track losses created forward pass layers call self.add_loss(value). resulting list scalar loss values available via property model.losses end forward pass. want using loss components, sum add main loss training step. Consider layer, creates activity regularization loss: Let’s build really simple model uses : ’s training loop look like now: ’s !","code":"class ActivityRegularizationLayer(keras.layers.Layer):     def call(self, inputs):         self.add_loss(1e-2 * torch.sum(inputs))         return inputs inputs = keras.Input(shape=(784,), name=\"digits\") x = keras.layers.Dense(64, activation=\"relu\")(inputs) # Insert activity regularization as a layer x = ActivityRegularizationLayer()(x) x = keras.layers.Dense(64, activation=\"relu\")(x) outputs = keras.layers.Dense(10, name=\"predictions\")(x)  model = keras.Model(inputs=inputs, outputs=outputs) # Get a fresh model model = get_model()  # Instantiate an optimizer to train the model. optimizer = keras.optimizers.Adam(learning_rate=1e-3) # Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)  # Prepare the metrics. train_acc_metric = keras.metrics.CategoricalAccuracy() val_acc_metric = keras.metrics.CategoricalAccuracy()  for epoch in range(epochs):     print(f\"\\nStart of epoch {epoch}\")     for step, (inputs, targets) in enumerate(train_dataloader):         # Forward pass         logits = model(inputs)         loss = loss_fn(targets, logits)         if model.losses:             loss = loss + torch.sum(*model.losses)          # Backward pass         model.zero_grad()         trainable_weights = [v for v in model.trainable_weights]          # Call torch.Tensor.backward() on the loss to compute gradients         # for the weights.         loss.backward()         gradients = [v.value.grad for v in trainable_weights]          # Update weights         with torch.no_grad():             optimizer.apply(gradients, trainable_weights)          # Update training metric.         train_acc_metric.update_state(targets, logits)          # Log every 100 batches.         if step % 100 == 0:             print(                 f\"Training loss (for 1 batch) at step {step}: {loss.detach().numpy():.4f}\"             )             print(f\"Seen so far: {(step + 1) * batch_size} samples\")      # Display metrics at the end of each epoch.     train_acc = train_acc_metric.result()     print(f\"Training acc over epoch: {float(train_acc):.4f}\")      # Reset training metrics at the end of each epoch     train_acc_metric.reset_state()      # Run a validation loop at the end of each epoch.     for x_batch_val, y_batch_val in val_dataloader:         val_logits = model(x_batch_val, training=False)         # Update val metrics         val_acc_metric.update_state(y_batch_val, val_logits)     val_acc = val_acc_metric.result()     val_acc_metric.reset_state()     print(f\"Validation acc: {float(val_acc):.4f}\")"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing a training loop from scratch in TensorFlow","text":"","code":"library(tensorflow) library(tfdatasets) library(keras3, warn.conflicts = FALSE)  # This guide can only be run with the TensorFlow backend. use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing a training loop from scratch in TensorFlow","text":"Keras provides default training evaluation loops, fit() evaluate(). usage covered guide Training & evaluation built-methods. want customize learning algorithm model still leveraging convenience fit() (instance, train GAN using fit()), can subclass Model class implement train_step() method, called repeatedly fit(). covered guide Customizing happens fit(). Now, want low-level control training & evaluation, write training & evaluation loops scratch. guide .","code":""},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"using-the-gradienttape-a-first-end-to-end-example","dir":"Articles","previous_headings":"","what":"Using the GradientTape: a first end-to-end example","title":"Writing a training loop from scratch in TensorFlow","text":"Calling model inside GradientTape scope enables retrieve gradients trainable weights layer respect loss value. Using optimizer instance, can use gradients update variables (can retrieve using model$trainable_weights). Let’s consider simple MNIST model: Let’s train using mini-batch gradient custom training loop. First, ’re going need optimizer, loss function, dataset: ’s training loop: open loop iterates epochs epoch, iterate dataset, batches batch, open GradientTape() scope Inside scope, call model (forward pass) compute loss Outside scope, retrieve gradients weights model regard loss Finally, use optimizer update weights model based gradients","code":"get_model <- function() {   inputs <- keras_input(shape = 784, name = \"digits\")   outputs <- inputs |>     layer_dense(units = 64, activation = \"relu\") |>     layer_dense(units = 64, activation = \"relu\") |>     layer_dense(units = 10, name = \"predictions\")   keras_model(inputs = inputs, outputs = outputs) }  model <- get_model() # Instantiate an optimizer. optimizer <- optimizer_adam(learning_rate = 1e-3) # Instantiate a loss function. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE)  # Prepare the training dataset. batch_size <- 64 c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist() x_train <- array_reshape(x_train, c(-1, 784)) x_test <- array_reshape(x_test, c(-1, 784))  # Reserve 10,000 samples for validation. x_val <- x_train[1:10000, ] y_val <- y_train[1:10000] x_train = x_train[-c(1:10000), ] y_train = y_train[-c(1:10000)]  # Prepare the training dataset. train_dataset <- list(x_train, y_train) |>   tensor_slices_dataset() |>   dataset_shuffle(buffer_size = 1024) |>   dataset_batch(batch_size)  # Prepare the validation dataset. val_dataset <- list(x_val, y_val) |>   tensor_slices_dataset() |>   dataset_batch(batch_size) epochs <- 3 for (epoch in seq_len(epochs)) {   cat(\"\\nStart of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   iterator <- as_iterator(train_dataset)   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1     # Open a GradientTape to record the operations run     # during the forward pass, which enables auto-differentiation.     with(tf$GradientTape() %as% tape, {       # Run the forward pass of the layer.       # The operations that the layer applies       # to its inputs are going to be recorded       # on the GradientTape.       logits <- model(x_batch_train, training = TRUE) # Logits for this minibatch        # Compute the loss value for this minibatch.       loss_value <- loss_fn(y_batch_train, logits)     })      # Use the gradient tape to automatically retrieve     # the gradients of the trainable variables with respect to the loss.     gradients <- tape$gradient(loss_value, model$trainable_weights)      # Run one step of gradient descent by updating     # the value of the variables to minimize the loss.     optimizer$apply(gradients, model$trainable_weights)      # Log every 200 batches.     if (step %% 200 == 0) {       cat(sprintf(\"Training loss (for one batch) at step %d: %.4f\\n\",                   step, loss_value))       cat(sprintf(\"Seen so far: %d samples\\n\", (step * batch_size)))     }   } } ## ## Start of epoch  1 ## Training loss (for one batch) at step 200: 1.9342 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.8244 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.8011 ## Seen so far: 38400 samples ## ## Start of epoch  2 ## Training loss (for one batch) at step 200: 0.3505 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.6878 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.3928 ## Seen so far: 38400 samples ## ## Start of epoch  3 ## Training loss (for one batch) at step 200: 0.3366 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.1102 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.3629 ## Seen so far: 38400 samples"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"low-level-handling-of-metrics","dir":"Articles","previous_headings":"","what":"Low-level handling of metrics","title":"Writing a training loop from scratch in TensorFlow","text":"Let’s add metrics monitoring basic loop. can readily reuse built-metrics (custom ones wrote) training loops written scratch. ’s flow: Instantiate metric start loop Call metric$update_state() batch Call metric$result() need display current value metric Call metric$reset_state() need clear state metric (typically end epoch) Let’s use knowledge compute SparseCategoricalAccuracy validation data end epoch: ’s training & evaluation loop:","code":"# Get model model <- get_model()  # Instantiate an optimizer to train the model. optimizer <- optimizer_adam(learning_rate = 1e-3) # Instantiate a loss function. loss_fn <- loss_sparse_categorical_crossentropy(from_logits = TRUE)  # Prepare the metrics. train_acc_metric <- metric_sparse_categorical_accuracy() val_acc_metric <- metric_sparse_categorical_accuracy() epochs <- 2 time <- Sys.time() for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   iterator <- as_iterator(train_dataset)   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1      with(tf$GradientTape() %as% tape, {       logits <- model(x_batch_train, training = TRUE)       loss_value <- loss_fn(y_batch_train, logits)     })     gradients <- tape$gradient(loss_value, model$trainable_weights)     optimizer$apply(gradients, model$trainable_weights)      # Update training metric.     train_acc_metric$update_state(y_batch_train, logits)     if (step %% 200 == 0) {       cat(sprintf(         \"Training loss (for one batch) at step %d: %.4f\\n\", step, loss_value))       cat(sprintf(\"Seen so far: %d samples \\n\", (step * batch_size)))     }   }    # Display metrics at the end of each epoch.   train_acc <- train_acc_metric$result()   cat(sprintf(\"Training acc over epoch: %.4f\\n\", train_acc))    # Reset training metrics at the end of each epoch   train_acc_metric$reset_state()    # Run a validation loop at the end of each epoch.   iterate(val_dataset, function(val_batch) {     c(x_batch_val, y_batch_val) %<-% val_batch     val_logits <- model(x_batch_val, training = FALSE)     val_acc_metric$update_state(y_batch_val, val_logits)   })   val_acc <- val_acc_metric$result()   val_acc_metric$reset_state()   cat(sprintf(\"Validation acc: %.4f\\n\", val_acc)) } ## Start of epoch  1 ## Training loss (for one batch) at step 200: 2.4070 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.8216 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 1.2056 ## Seen so far: 38400 samples ## Training acc over epoch: 0.8094 ## Validation acc: 0.8687 ## Start of epoch  2 ## Training loss (for one batch) at step 200: 0.1857 ## Seen so far: 12800 samples ## Training loss (for one batch) at step 400: 0.3803 ## Seen so far: 25600 samples ## Training loss (for one batch) at step 600: 0.3399 ## Seen so far: 38400 samples ## Training acc over epoch: 0.8859 ## Validation acc: 0.9002 Sys.time() - time ## Time difference of 44.61786 secs"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"speeding-up-your-training-step-with-tf_function","dir":"Articles","previous_headings":"","what":"Speeding-up your training step with tf_function()","title":"Writing a training loop from scratch in TensorFlow","text":"default runtime TensorFlow 2 eager execution. , training loop executes eagerly. great debugging, graph compilation definite performance advantage. Describing computation static graph enables framework apply global performance optimizations. impossible framework constrained greedily execute one operation another, knowledge comes next. can compile static graph function takes tensors input. Just add @tf.function decorator , like : Let’s evaluation step: Now, let’s re-run training loop compiled training step: Much faster, isn’t ?","code":"train_step <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     logits <- model(x, training = TRUE)     loss_value <- loss_fn(y, logits)   })   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply(gradients, model$trainable_weights)   train_acc_metric$update_state(y, logits)   invisible(NULL) # return nothing }) test_step <- tf_function(function(x, y) {   val_logits <- model(x, training=FALSE)   val_acc_metric$update_state(y, val_logits)   invisible(NULL) # return nothing }) epochs <- 2 time <- Sys.time() for (epoch in seq_len(epochs)) {   cat(\"Start of epoch \", epoch, \"\\n\")    # Iterate over the batches of the dataset.   step <- 0   while (!is.null(batch <- iter_next(iterator))) {     c(x_batch_train, y_batch_train) %<-% batch     step <- step + 1     train_step(x_batch_train, y_batch_train)      if (step %% 200 == 0) {       cat(sprintf(         \"Training loss (for one batch) at step %d: %.4f\\n\", step, loss_value       ))       cat(sprintf(\"Seen so far: %d samples \\n\", (step * batch_size)))     }   }    # Display metrics at the end of each epoch.   train_acc <- train_acc_metric$result()   cat(sprintf(\"Training acc over epoch: %.4f\\n\", train_acc))     # Reset training metrics at the end of each epoch   train_acc_metric$reset_state()    # Run a validation loop at the end of each epoch.    iterate(val_dataset, function(val_batch) {     c(x_batch_val, y_batch_val) %<-% val_batch     test_step(x_batch_val, y_batch_val)   })    val_acc <- val_acc_metric$result()   val_acc_metric$reset_state()   cat(sprintf(\"Validation acc: %.4f\\n\", val_acc)) } ## Start of epoch  1 ## Training acc over epoch: 0.0000 ## Validation acc: 0.9002 ## Start of epoch  2 ## Training acc over epoch: 0.0000 ## Validation acc: 0.9002 Sys.time() - time ## Time difference of 0.5360565 secs"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"low-level-handling-of-losses-tracked-by-the-model","dir":"Articles","previous_headings":"","what":"Low-level handling of losses tracked by the model","title":"Writing a training loop from scratch in TensorFlow","text":"Layers models recursively track losses created forward pass layers call self$add_loss(value). resulting list scalar loss values available via property model$losses end forward pass. want using loss components, sum add main loss training step. Consider layer, creates activity regularization loss: Let’s build really simple model uses : ’s training step look like now:","code":"layer_activity_regularization <- Layer(   \"ActivityRegularizationLayer\",   call = function(inputs) {     self$add_loss(0.1 * op_mean(inputs))     inputs   } ) inputs <- keras_input(shape = 784, name=\"digits\") outputs <- inputs |>   layer_dense(units = 64, activation = \"relu\") |>   layer_activity_regularization() |>   layer_dense(units = 64, activation = \"relu\") |>   layer_dense(units = 10, name = \"predictions\") model <- keras_model(inputs = inputs, outputs = outputs) train_step <- tf_function(function(x, y) {   with(tf$GradientTape() %as% tape, {     logits <- model(x, training = TRUE)     loss_value <- Reduce(`+`, c(loss_fn(y, logits),                                 model$losses))   })   gradients <- tape$gradient(loss_value, model$trainable_weights)   optimizer$apply(gradients, model$trainable_weights)   train_acc_metric$update_state(y, logits)   invisible(NULL) })"},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Writing a training loop from scratch in TensorFlow","text":"Now know everything know using built-training loops writing scratch. conclude, ’s simple end--end example ties together everything ’ve learned guide: DCGAN trained MNIST digits.","code":""},{"path":"https://keras.posit.co/articles/writing_a_training_loop_from_scratch.html","id":"end-to-end-example-a-gan-training-loop-from-scratch","dir":"Articles","previous_headings":"","what":"End-to-end example: a GAN training loop from scratch","title":"Writing a training loop from scratch in TensorFlow","text":"may familiar Generative Adversarial Networks (GANs). GANs can generate new images look almost real, learning latent distribution training dataset images (“latent space” images). GAN made two parts: “generator” model maps points latent space points image space, “discriminator” model, classifier can tell difference real images (training dataset) fake images (output generator network). GAN training loop looks like : Train discriminator. Sample batch random points latent space. Turn points fake images via “generator” model. Get batch real images combine generated images. Train “discriminator” model classify generated vs. real images. Train generator. Sample random points latent space. Turn points fake images via “generator” network. Get batch real images combine generated images. Train “generator” model “fool” discriminator classify fake images real. much detailed overview GANs works, see Deep Learning Python. Let’s implement training loop. First, create discriminator meant classify fake vs real digits: let’s create generator network, turns latent vectors outputs shape (28, 28, 1) (representing MNIST digits): ’s key bit: training loop. can see quite straightforward. training step function takes 17 lines. Let’s train GAN, repeatedly calling train_step batches images. Since discriminator generator convnets, ’re going want run code GPU. ’s ! ’ll get nice-looking fake MNIST digits just ~30s training Colab GPU.","code":"# Create the discriminator discriminator <-   keras_model_sequential(name = \"discriminator\",                          input_shape = c(28, 28, 1)) |>   layer_conv_2d(filters = 64, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d(filters = 128, kernel_size = c(3, 3),                 strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_global_max_pooling_2d() |>   layer_dense(units = 1)  summary(discriminator) ## Model: \"discriminator\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ conv2d_1 (Conv2D)               │ (None, 14, 14, 64)     │           640 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_1 (LeakyReLU)       │ (None, 14, 14, 64)     │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d (Conv2D)                 │ (None, 7, 7, 128)      │        73,856 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu (LeakyReLU)         │ (None, 7, 7, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ global_max_pooling2d            │ (None, 128)            │             0 │ ## │ (GlobalMaxPooling2D)            │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ dense_6 (Dense)                 │ (None, 1)              │           129 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 74,625 (291.50 KB) ##  Trainable params: 74,625 (291.50 KB) ##  Non-trainable params: 0 (0.00 B) latent_dim <- 128L  generator <-   keras_model_sequential(name = \"generator\",                          input_shape = latent_dim) |>   layer_dense(7 * 7 * 128) |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_reshape(target_shape = c(7, 7, 128)) |>   layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d_transpose(filters = 128, kernel_size = c(4, 4),                           strides = c(2, 2), padding = \"same\") |>   layer_activation_leaky_relu(negative_slope = 0.2) |>   layer_conv_2d(filters = 1, kernel_size = c(7, 7), padding = \"same\",                 activation = \"sigmoid\")  summary(generator) ## Model: \"generator\" ## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ## ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃ ## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ ## │ dense_7 (Dense)                 │ (None, 6272)           │       809,088 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_4 (LeakyReLU)       │ (None, 6272)           │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ reshape (Reshape)               │ (None, 7, 7, 128)      │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose_1              │ (None, 14, 14, 128)    │       262,272 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_3 (LeakyReLU)       │ (None, 14, 14, 128)    │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_transpose                │ (None, 28, 28, 128)    │       262,272 │ ## │ (Conv2DTranspose)               │                        │               │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ leaky_re_lu_2 (LeakyReLU)       │ (None, 28, 28, 128)    │             0 │ ## ├─────────────────────────────────┼────────────────────────┼───────────────┤ ## │ conv2d_2 (Conv2D)               │ (None, 28, 28, 1)      │         6,273 │ ## └─────────────────────────────────┴────────────────────────┴───────────────┘ ##  Total params: 1,339,905 (5.11 MB) ##  Trainable params: 1,339,905 (5.11 MB) ##  Non-trainable params: 0 (0.00 B) # Instantiate one optimizer for the discriminator and another for the generator. d_optimizer <- optimizer_adam(learning_rate = 0.0003) g_optimizer <- optimizer_adam(learning_rate = 0.0004)  # Instantiate a loss function. loss_fn <- loss_binary_crossentropy(from_logits = TRUE)  train_step <- tf_function(function(real_images) {   # Sample random points in the latent space   c(batch_size, ...) %<-% op_shape(real_images)   random_latent_vectors <-     tf$random$normal(shape(batch_size, latent_dim))    # Decode them to fake images   generated_images <- generator(random_latent_vectors)    # Combine them with real images   combined_images <- tf$concat(list(generated_images, real_images),                                axis = 0L)    # Assemble labels discriminating real from fake images   labels <- tf$concat(list(tf$ones(shape(batch_size, 1)),                            tf$zeros(shape(batch_size, 1))),                       axis = 0L)    # Add random noise to the labels - important trick!   labels %<>% `+`(tf$random$uniform(tf$shape(.), maxval = 0.05))    # Train the discriminator   with(tf$GradientTape() %as% tape, {     predictions <- discriminator(combined_images)     d_loss <- loss_fn(labels, predictions)   })   grads <- tape$gradient(d_loss, discriminator$trainable_weights)   d_optimizer$apply(grads, discriminator$trainable_weights)    # Sample random points in the latent space   random_latent_vectors <-     tf$random$normal(shape(batch_size, latent_dim))    # Assemble labels that say \"all real images\"   misleading_labels <- tf$zeros(shape(batch_size, 1))    # Train the generator (note that we should *not* update the weights   # of the discriminator)!   with(tf$GradientTape() %as% tape, {     predictions <- discriminator(generator(random_latent_vectors))     g_loss <- loss_fn(misleading_labels, predictions)   })    grads <- tape$gradient(g_loss, generator$trainable_weights)   g_optimizer$apply(grads, generator$trainable_weights)    list(d_loss, g_loss, generated_images) }) # Prepare the dataset. We use both the training & test MNIST digits. batch_size <- 64 c(c(x_train, .), c(x_test, .)) %<-% dataset_mnist() all_digits <- op_concatenate(list(x_train, x_test)) all_digits <- op_reshape(all_digits, c(-1, 28, 28, 1)) dataset <- all_digits |>   tfdatasets::tensor_slices_dataset() |>   tfdatasets::dataset_map(function(x) tf$cast(x, \"float\") / 255) |>   tfdatasets::dataset_shuffle(buffer_size = 1024) |>   tfdatasets::dataset_batch(batch_size = batch_size)  epochs <- 1 # In practice you need at least 20 epochs to generate nice digits. save_dir <- \"./\"  for (epoch in seq_len(epochs)) {   cat(\"Start epoch: \", epoch, \"\\n\")   step <- 0   train_iterator <- as_iterator(dataset)   while (!is.null(real_images <- iter_next(train_iterator))) {     step <- step + 1     # Train the discriminator & generator on one batch of real images.     c(d_loss, g_loss, generated_images) %<-% train_step(real_images)      # Logging.     if (step %% 200 == 0) {       # Print metrics       cat(sprintf(\"discriminator loss at step %d: %.2f\\n\", step, d_loss))       cat(sprintf(\"adversarial loss at step %d: %.2f\\n\", step, g_loss))     }      # To limit execution time we stop after 10 steps.     # Remove the lines below to actually train the model!     if (step > 10)       break   } } ## Start epoch:  1"},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Writing your own callbacks","text":"callback powerful tool customize behavior Keras model training, evaluation, inference. Examples include keras.callbacks.TensorBoard visualize training progress results TensorBoard, keras.callbacks.ModelCheckpoint periodically save model training. guide, learn Keras callback , can , can build . provide demos simple callback applications get started.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Writing your own callbacks","text":"","code":"library(keras3)"},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"keras-callbacks-overview","dir":"Articles","previous_headings":"","what":"Keras callbacks overview","title":"Writing your own callbacks","text":"callbacks subclass keras.callbacks.Callback class, override set methods called various stages training, testing, predicting. Callbacks useful get view internal states statistics model training. can pass list callbacks (keyword argument callbacks) following model methods: fit() evaluate() predict()","code":""},{"path":[]},{"path":[]},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_traintestpredict_beginlogs-null","dir":"Articles","previous_headings":"An overview of callback methods > Global methods","what":"on_(train|test|predict)_begin(logs = NULL)","title":"Writing your own callbacks","text":"Called beginning fit/evaluate/predict.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_traintestpredict_endlogs-null","dir":"Articles","previous_headings":"An overview of callback methods > Global methods","what":"on_(train|test|predict)_end(logs = NULL)","title":"Writing your own callbacks","text":"Called end fit/evaluate/predict.","code":""},{"path":[]},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_traintestpredict_batch_beginbatch-logs-null","dir":"Articles","previous_headings":"An overview of callback methods > Batch-level methods for training/testing/predicting","what":"on_(train|test|predict)_batch_begin(batch, logs = NULL)","title":"Writing your own callbacks","text":"Called right processing batch training/testing/predicting.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_traintestpredict_batch_endbatch-logs-null","dir":"Articles","previous_headings":"An overview of callback methods > Batch-level methods for training/testing/predicting","what":"on_(train|test|predict)_batch_end(batch, logs = NULL)","title":"Writing your own callbacks","text":"Called end training/testing/predicting batch. Within method, logs named list containing metrics results.","code":""},{"path":[]},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_epoch_beginepoch-logs-null","dir":"Articles","previous_headings":"An overview of callback methods > Epoch-level methods (training only)","what":"on_epoch_begin(epoch, logs = NULL)","title":"Writing your own callbacks","text":"Called beginning epoch training.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"on_epoch_endepoch-logs-null","dir":"Articles","previous_headings":"An overview of callback methods > Epoch-level methods (training only)","what":"on_epoch_end(epoch, logs = NULL)","title":"Writing your own callbacks","text":"Called end epoch training.","code":""},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"a-basic-example","dir":"Articles","previous_headings":"","what":"A basic example","title":"Writing your own callbacks","text":"Let’s take look concrete example. get started, let’s import tensorflow define simple Sequential Keras model: , load MNIST data training testing Keras datasets API: Now, define simple custom callback logs: fit/evaluate/predict starts & ends epoch starts & ends training batch starts & ends evaluation (test) batch starts & ends inference (prediction) batch starts & ends Let’s try :","code":"# Define the Keras model to add callbacks to get_model <- function() {   model <- keras_model_sequential()   model |> layer_dense(units = 1)   model |> compile(     optimizer = optimizer_rmsprop(learning_rate = 0.1),     loss = \"mean_squared_error\",     metrics = \"mean_absolute_error\"   )   model } # Load example MNIST data and pre-process it mnist <- dataset_mnist()  flatten_and_rescale <- function(x) {   x <- array_reshape(x, c(-1, 784))   x <- x / 255   x }  mnist$train$x <- flatten_and_rescale(mnist$train$x) mnist$test$x  <- flatten_and_rescale(mnist$test$x)  # limit to 1000 samples n <- 1000 mnist$train$x <- mnist$train$x[1:n,] mnist$train$y <- mnist$train$y[1:n] mnist$test$x  <- mnist$test$x[1:n,] mnist$test$y  <- mnist$test$y[1:n] show <- function(msg, logs) {   cat(glue::glue(msg, .envir = parent.frame()),       \"got logs: \", sep = \"; \")   str(logs); cat(\"\\n\") }  callback_custom <- Callback(   \"CustomCallback\",   on_train_begin         = \\(logs = NULL)        show(\"Starting training\", logs),   on_epoch_begin         = \\(epoch, logs = NULL) show(\"Start epoch {epoch} of training\", logs),   on_train_batch_begin   = \\(batch, logs = NULL) show(\"...Training: start of batch {batch}\", logs),   on_train_batch_end     = \\(batch, logs = NULL) show(\"...Training: end of batch {batch}\",  logs),   on_epoch_end           = \\(epoch, logs = NULL) show(\"End epoch {epoch} of training\", logs),   on_train_end           = \\(logs = NULL)        show(\"Stop training\", logs),     on_test_begin          = \\(logs = NULL)        show(\"Start testing\", logs),   on_test_batch_begin    = \\(batch, logs = NULL) show(\"...Evaluating: start of batch {batch}\", logs),   on_test_batch_end      = \\(batch, logs = NULL) show(\"...Evaluating: end of batch {batch}\", logs),   on_test_end            = \\(logs = NULL)        show(\"Stop testing\", logs),    on_predict_begin       = \\(logs = NULL)        show(\"Start predicting\", logs),   on_predict_end         = \\(logs = NULL)        show(\"Stop predicting\", logs),   on_predict_batch_begin = \\(batch, logs = NULL) show(\"...Predicting: start of batch {batch}\", logs),   on_predict_batch_end   = \\(batch, logs = NULL) show(\"...Predicting: end of batch {batch}\", logs), ) model <- get_model() model |> fit(   mnist$train$x, mnist$train$y,   batch_size = 128,   epochs = 2,   verbose = 0,   validation_split = 0.5,   callbacks = list(callback_custom()) ) ## Starting training; got logs:  Named list() ## ## Start epoch 1 of training; got logs:  Named list() ## ## ...Training: start of batch 1; got logs:  Named list() ## ## ...Training: end of batch 1; got logs: List of 2 ##  $ loss               : num 25.9 ##  $ mean_absolute_error: num 4.19 ## ## ...Training: start of batch 2; got logs:  Named list() ## ## ...Training: end of batch 2; got logs: List of 2 ##  $ loss               : num 433 ##  $ mean_absolute_error: num 15.5 ## ## ...Training: start of batch 3; got logs:  Named list() ## ## ...Training: end of batch 3; got logs: List of 2 ##  $ loss               : num 297 ##  $ mean_absolute_error: num 11.8 ## ## ...Training: start of batch 4; got logs:  Named list() ## ## ...Training: end of batch 4; got logs: List of 2 ##  $ loss               : num 231 ##  $ mean_absolute_error: num 9.68 ## ## Start testing; got logs:  Named list() ## ## ...Evaluating: start of batch 1; got logs:  Named list() ## ## ...Evaluating: end of batch 1; got logs: List of 2 ##  $ loss               : num 8.1 ##  $ mean_absolute_error: num 2.3 ## ## ...Evaluating: start of batch 2; got logs:  Named list() ## ## ...Evaluating: end of batch 2; got logs: List of 2 ##  $ loss               : num 7.58 ##  $ mean_absolute_error: num 2.23 ## ## ...Evaluating: start of batch 3; got logs:  Named list() ## ## ...Evaluating: end of batch 3; got logs: List of 2 ##  $ loss               : num 7.38 ##  $ mean_absolute_error: num 2.21 ## ## ...Evaluating: start of batch 4; got logs:  Named list() ## ## ...Evaluating: end of batch 4; got logs: List of 2 ##  $ loss               : num 7.3 ##  $ mean_absolute_error: num 2.21 ## ## Stop testing; got logs: List of 2 ##  $ loss               : num 7.3 ##  $ mean_absolute_error: num 2.21 ## ## End epoch 1 of training; got logs: List of 4 ##  $ loss                   : num 231 ##  $ mean_absolute_error    : num 9.68 ##  $ val_loss               : num 7.3 ##  $ val_mean_absolute_error: num 2.21 ## ## Start epoch 2 of training; got logs:  Named list() ## ## ...Training: start of batch 1; got logs:  Named list() ## ## ...Training: end of batch 1; got logs: List of 2 ##  $ loss               : num 7.44 ##  $ mean_absolute_error: num 2.27 ## ## ...Training: start of batch 2; got logs:  Named list() ## ## ...Training: end of batch 2; got logs: List of 2 ##  $ loss               : num 6.81 ##  $ mean_absolute_error: num 2.16 ## ## ...Training: start of batch 3; got logs:  Named list() ## ## ...Training: end of batch 3; got logs: List of 2 ##  $ loss               : num 6.12 ##  $ mean_absolute_error: num 2.06 ## ## ...Training: start of batch 4; got logs:  Named list() ## ## ...Training: end of batch 4; got logs: List of 2 ##  $ loss               : num 6.08 ##  $ mean_absolute_error: num 2.04 ## ## Start testing; got logs:  Named list() ## ## ...Evaluating: start of batch 1; got logs:  Named list() ## ## ...Evaluating: end of batch 1; got logs: List of 2 ##  $ loss               : num 5.54 ##  $ mean_absolute_error: num 1.92 ## ## ...Evaluating: start of batch 2; got logs:  Named list() ## ## ...Evaluating: end of batch 2; got logs: List of 2 ##  $ loss               : num 5.31 ##  $ mean_absolute_error: num 1.87 ## ## ...Evaluating: start of batch 3; got logs:  Named list() ## ## ...Evaluating: end of batch 3; got logs: List of 2 ##  $ loss               : num 5.11 ##  $ mean_absolute_error: num 1.8 ## ## ...Evaluating: start of batch 4; got logs:  Named list() ## ## ...Evaluating: end of batch 4; got logs: List of 2 ##  $ loss               : num 5.15 ##  $ mean_absolute_error: num 1.82 ## ## Stop testing; got logs: List of 2 ##  $ loss               : num 5.15 ##  $ mean_absolute_error: num 1.82 ## ## End epoch 2 of training; got logs: List of 4 ##  $ loss                   : num 6.08 ##  $ mean_absolute_error    : num 2.04 ##  $ val_loss               : num 5.15 ##  $ val_mean_absolute_error: num 1.82 ## ## Stop training; got logs: List of 4 ##  $ loss                   : num 6.08 ##  $ mean_absolute_error    : num 2.04 ##  $ val_loss               : num 5.15 ##  $ val_mean_absolute_error: num 1.82 res <- model |> evaluate(   mnist$test$x, mnist$test$y,   batch_size = 128, verbose = 0,   callbacks = list(callback_custom()) ) ## Start testing; got logs:  Named list() ## ## ...Evaluating: start of batch 1; got logs:  Named list() ## ## ...Evaluating: end of batch 1; got logs: List of 2 ##  $ loss               : num 5.2 ##  $ mean_absolute_error: num 1.84 ## ## ...Evaluating: start of batch 2; got logs:  Named list() ## ## ...Evaluating: end of batch 2; got logs: List of 2 ##  $ loss               : num 4.62 ##  $ mean_absolute_error: num 1.73 ## ## ...Evaluating: start of batch 3; got logs:  Named list() ## ## ...Evaluating: end of batch 3; got logs: List of 2 ##  $ loss               : num 4.61 ##  $ mean_absolute_error: num 1.74 ## ## ...Evaluating: start of batch 4; got logs:  Named list() ## ## ...Evaluating: end of batch 4; got logs: List of 2 ##  $ loss               : num 4.65 ##  $ mean_absolute_error: num 1.75 ## ## ...Evaluating: start of batch 5; got logs:  Named list() ## ## ...Evaluating: end of batch 5; got logs: List of 2 ##  $ loss               : num 4.84 ##  $ mean_absolute_error: num 1.77 ## ## ...Evaluating: start of batch 6; got logs:  Named list() ## ## ...Evaluating: end of batch 6; got logs: List of 2 ##  $ loss               : num 4.76 ##  $ mean_absolute_error: num 1.76 ## ## ...Evaluating: start of batch 7; got logs:  Named list() ## ## ...Evaluating: end of batch 7; got logs: List of 2 ##  $ loss               : num 4.74 ##  $ mean_absolute_error: num 1.76 ## ## ...Evaluating: start of batch 8; got logs:  Named list() ## ## ...Evaluating: end of batch 8; got logs: List of 2 ##  $ loss               : num 4.67 ##  $ mean_absolute_error: num 1.75 ## ## Stop testing; got logs: List of 2 ##  $ loss               : num 4.67 ##  $ mean_absolute_error: num 1.75 res <- model |> predict(   mnist$test$x,   batch_size = 128, verbose = 0,   callbacks = list(callback_custom()) ) ## Start predicting; got logs:  Named list() ## ## ...Predicting: start of batch 1; got logs:  Named list() ## ## ...Predicting: end of batch 1; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 2; got logs:  Named list() ## ## ...Predicting: end of batch 2; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 3; got logs:  Named list() ## ## ...Predicting: end of batch 3; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 4; got logs:  Named list() ## ## ...Predicting: end of batch 4; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 5; got logs:  Named list() ## ## ...Predicting: end of batch 5; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 6; got logs:  Named list() ## ## ...Predicting: end of batch 6; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 7; got logs:  Named list() ## ## ...Predicting: end of batch 7; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…> ## ## ...Predicting: start of batch 8; got logs:  Named list() ## ## ...Predicting: end of batch 8; got logs: List of 1 ##  $ outputs:<tf.Tensor: shape=(104, 1), dtype=float32, numpy=…> ## ## Stop predicting; got logs:  Named list()"},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"usage-of-logs-list","dir":"Articles","previous_headings":"A basic example","what":"Usage of logs list","title":"Writing your own callbacks","text":"logs named list contains loss value, metrics end batch epoch. Example includes loss mean absolute error. information callbacks, can check Keras callback API documentation.","code":"callback_print_loss_and_mae <- Callback(   \"LossAndErrorPrintingCallback\",    on_train_batch_end = function(batch, logs = NULL)     cat(sprintf(\"Up to batch %i, the average loss is %7.2f.\\n\",                 batch,  logs$loss)),    on_test_batch_end = function(batch, logs = NULL)     cat(sprintf(\"Up to batch %i, the average loss is %7.2f.\\n\",                 batch, logs$loss)),    on_epoch_end = function(epoch, logs = NULL)     cat(sprintf(       \"The average loss for epoch %2i is %9.2f and mean absolute error is %7.2f.\\n\",       epoch, logs$loss, logs$mean_absolute_error     )) )   model <- get_model() model |> fit(   mnist$train$x, mnist$train$y,   epochs = 2, verbose = 0, batch_size = 128,   callbacks = list(callback_print_loss_and_mae()) ) ## Up to batch 1, the average loss is   25.12. ## Up to batch 2, the average loss is  398.92. ## Up to batch 3, the average loss is  274.04. ## Up to batch 4, the average loss is  208.32. ## Up to batch 5, the average loss is  168.15. ## Up to batch 6, the average loss is  141.31. ## Up to batch 7, the average loss is  122.19. ## Up to batch 8, the average loss is  110.05. ## The average loss for epoch  1 is    110.05 and mean absolute error is    5.79. ## Up to batch 1, the average loss is    4.71. ## Up to batch 2, the average loss is    4.74. ## Up to batch 3, the average loss is    4.81. ## Up to batch 4, the average loss is    5.07. ## Up to batch 5, the average loss is    5.08. ## Up to batch 6, the average loss is    5.09. ## Up to batch 7, the average loss is    5.19. ## Up to batch 8, the average loss is    5.51. ## The average loss for epoch  2 is      5.51 and mean absolute error is    1.90. res = model |> evaluate(   mnist$test$x, mnist$test$y,   verbose = 0, batch_size = 128,   callbacks = list(callback_print_loss_and_mae()) ) ## Up to batch 1, the average loss is   15.86. ## Up to batch 2, the average loss is   16.13. ## Up to batch 3, the average loss is   16.02. ## Up to batch 4, the average loss is   16.11. ## Up to batch 5, the average loss is   16.23. ## Up to batch 6, the average loss is   16.68. ## Up to batch 7, the average loss is   16.61. ## Up to batch 8, the average loss is   16.54."},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"usage-of-selfmodel-attribute","dir":"Articles","previous_headings":"","what":"Usage of self$model attribute","title":"Writing your own callbacks","text":"addition receiving log information one methods called, callbacks access model associated current round training/evaluation/inference: self$model. things can self$model callback: Set self$model$stop_training <- TRUE immediately interrupt training. Mutate hyperparameters optimizer (available self$model$optimizer), self$model$optimizer$learning_rate. Save model period intervals. Record output model |> predict() test samples end epoch, use sanity check training. Extract visualizations intermediate features end epoch, monitor model learning time. etc. Let’s see action couple examples.","code":""},{"path":[]},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"early-stopping-at-minimum-loss","dir":"Articles","previous_headings":"Examples of Keras callback applications","what":"Early stopping at minimum loss","title":"Writing your own callbacks","text":"first example shows creation Callback stops training minimum loss reached, setting attribute self$model$stop_training (boolean). Optionally, can provide argument patience specify many epochs wait stopping reached local minimum. callback_early_stopping() provides complete general implementation.","code":"callback_early_stopping_at_min_loss <- Callback(   \"EarlyStoppingAtMinLoss\",   `__doc__` =     \"Stop training when the loss is at its min, i.e. the loss stops decreasing.      Arguments:         patience: Number of epochs to wait after min has been hit. After this         number of no improvement, training stops.     \",    initialize = function(patience = 0) {     super$initialize()     self$patience <- patience     # best_weights to store the weights at which the minimum loss occurs.     self$best_weights <- NULL   },    on_train_begin = function(logs = NULL) {     # The number of epoch it has waited when loss is no longer minimum.     self$wait <- 0     # The epoch the training stops at.     self$stopped_epoch <- 0     # Initialize the best as infinity.     self$best <- Inf   },    on_epoch_end = function(epoch, logs = NULL) {     current <- logs$loss     if (current < self$best) {       self$best <- current       self$wait <- 0L       # Record the best weights if current results is better (less).       self$best_weights <- get_weights(self$model)     } else {       add(self$wait) <- 1L       if (self$wait >= self$patience) {         self$stopped_epoch <- epoch         self$model$stop_training <- TRUE         cat(\"Restoring model weights from the end of the best epoch.\\n\")         model$set_weights(self$best_weights)       }     }   },    on_train_end = function(logs = NULL)     if (self$stopped_epoch > 0)       cat(sprintf(\"Epoch %05d: early stopping\\n\", self$stopped_epoch + 1)) ) `add<-` <- `+`   model <- get_model() model |> fit(   mnist$train$x,   mnist$train$y,   epochs = 30,   batch_size = 64,   verbose = 0,   callbacks = list(callback_print_loss_and_mae(),                    callback_early_stopping_at_min_loss()) ) ## Up to batch 1, the average loss is   30.54. ## Up to batch 2, the average loss is  513.27. ## Up to batch 3, the average loss is  352.60. ## Up to batch 4, the average loss is  266.37. ## Up to batch 5, the average loss is  214.68. ## Up to batch 6, the average loss is  179.97. ## Up to batch 7, the average loss is  155.06. ## Up to batch 8, the average loss is  136.59. ## Up to batch 9, the average loss is  121.96. ## Up to batch 10, the average loss is  110.28. ## Up to batch 11, the average loss is  100.72. ## Up to batch 12, the average loss is   92.71. ## Up to batch 13, the average loss is   85.95. ## Up to batch 14, the average loss is   80.21. ## Up to batch 15, the average loss is   75.17. ## Up to batch 16, the average loss is   72.48. ## The average loss for epoch  1 is     72.48 and mean absolute error is    4.08. ## Up to batch 1, the average loss is    7.98. ## Up to batch 2, the average loss is    9.92. ## Up to batch 3, the average loss is   12.88. ## Up to batch 4, the average loss is   16.61. ## Up to batch 5, the average loss is   20.49. ## Up to batch 6, the average loss is   26.14. ## Up to batch 7, the average loss is   30.44. ## Up to batch 8, the average loss is   33.76. ## Up to batch 9, the average loss is   36.32. ## Up to batch 10, the average loss is   35.26. ## Up to batch 11, the average loss is   34.22. ## Up to batch 12, the average loss is   33.53. ## Up to batch 13, the average loss is   32.84. ## Up to batch 14, the average loss is   31.80. ## Up to batch 15, the average loss is   31.39. ## Up to batch 16, the average loss is   31.45. ## The average loss for epoch  2 is     31.45 and mean absolute error is    4.82. ## Up to batch 1, the average loss is   39.60. ## Up to batch 2, the average loss is   41.95. ## Up to batch 3, the average loss is   41.29. ## Up to batch 4, the average loss is   36.77. ## Up to batch 5, the average loss is   32.08. ## Up to batch 6, the average loss is   28.17. ## Up to batch 7, the average loss is   25.33. ## Up to batch 8, the average loss is   23.56. ## Up to batch 9, the average loss is   22.28. ## Up to batch 10, the average loss is   21.22. ## Up to batch 11, the average loss is   20.87. ## Up to batch 12, the average loss is   22.25. ## Up to batch 13, the average loss is   25.08. ## Up to batch 14, the average loss is   27.87. ## Up to batch 15, the average loss is   31.72. ## Up to batch 16, the average loss is   33.21. ## The average loss for epoch  3 is     33.21 and mean absolute error is    4.79. ## Restoring model weights from the end of the best epoch. ## Epoch 00004: early stopping"},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"learning-rate-scheduling","dir":"Articles","previous_headings":"Examples of Keras callback applications","what":"Learning rate scheduling","title":"Writing your own callbacks","text":"example, show custom Callback can used dynamically change learning rate optimizer course training. See keras$callbacks$LearningRateScheduler general implementations (RStudio, press F1 cursor LearningRateScheduler browser open page).","code":"callback_custom_learning_rate_scheduler <- Callback(   \"CustomLearningRateScheduler\",   `__doc__` =   \"Learning rate scheduler which sets the learning rate according to schedule.      Arguments:         schedule: a function that takes an epoch index             (integer, indexed from 0) and current learning rate             as inputs and returns a new learning rate as output (float).     \",    initialize = function(schedule) {     super$initialize()     self$schedule <- schedule   },    on_epoch_begin = function(epoch, logs = NULL) {     ## When in doubt about what types of objects are in scope (e.g., self$model)     ## use a debugger to interact with the actual objects at the console!     # browser()      if (!\"learning_rate\" %in% names(self$model$optimizer))       stop('Optimizer must have a \"learning_rate\" attribute.')      # # Get the current learning rate from model's optimizer.     # use as.numeric() to convert the keras variablea to an R numeric     lr <- as.numeric(self$model$optimizer$learning_rate)     # # Call schedule function to get the scheduled learning rate.     scheduled_lr <- self$schedule(epoch, lr)     # # Set the value back to the optimizer before this epoch starts     optimizer <- self$model$optimizer     optimizer$learning_rate <- scheduled_lr     cat(sprintf(\"\\nEpoch %03d: Learning rate is %6.4f.\\n\", epoch, scheduled_lr))   } )  LR_SCHEDULE <- tibble::tribble(   ~start_epoch, ~learning_rate,              0,            0.1,              3,           0.05,              6,           0.01,              9,          0.005,             12,          0.001,   )  last <- function(x) x[length(x)] lr_schedule <- function(epoch, learning_rate) {   \"Helper function to retrieve the scheduled learning rate based on epoch.\"   with(LR_SCHEDULE, learning_rate[last(which(epoch >= start_epoch))]) }  model <- get_model() model |> fit(   mnist$train$x,   mnist$train$y,   epochs = 14,   batch_size = 64,   verbose = 0,   callbacks = list(     callback_print_loss_and_mae(),     callback_custom_learning_rate_scheduler(lr_schedule)   ) ) ## ## Epoch 001: Learning rate is 0.1000. ## Up to batch 1, the average loss is   29.36. ## Up to batch 2, the average loss is  513.95. ## Up to batch 3, the average loss is  352.70. ## Up to batch 4, the average loss is  266.46. ## Up to batch 5, the average loss is  214.73. ## Up to batch 6, the average loss is  180.00. ## Up to batch 7, the average loss is  155.05. ## Up to batch 8, the average loss is  136.64. ## Up to batch 9, the average loss is  121.97. ## Up to batch 10, the average loss is  110.30. ## Up to batch 11, the average loss is  100.76. ## Up to batch 12, the average loss is   92.74. ## Up to batch 13, the average loss is   85.95. ## Up to batch 14, the average loss is   80.18. ## Up to batch 15, the average loss is   75.11. ## Up to batch 16, the average loss is   72.38. ## The average loss for epoch  1 is     72.38 and mean absolute error is    4.04. ## ## Epoch 002: Learning rate is 0.1000. ## Up to batch 1, the average loss is    6.95. ## Up to batch 2, the average loss is    8.71. ## Up to batch 3, the average loss is   11.42. ## Up to batch 4, the average loss is   15.15. ## Up to batch 5, the average loss is   19.28. ## Up to batch 6, the average loss is   25.54. ## Up to batch 7, the average loss is   30.38. ## Up to batch 8, the average loss is   33.95. ## Up to batch 9, the average loss is   36.58. ## Up to batch 10, the average loss is   35.46. ## Up to batch 11, the average loss is   34.34. ## Up to batch 12, the average loss is   33.51. ## Up to batch 13, the average loss is   32.67. ## Up to batch 14, the average loss is   31.54. ## Up to batch 15, the average loss is   31.05. ## Up to batch 16, the average loss is   31.09. ## The average loss for epoch  2 is     31.09 and mean absolute error is    4.77. ## ## Epoch 003: Learning rate is 0.0500. ## Up to batch 1, the average loss is   40.40. ## Up to batch 2, the average loss is   22.33. ## Up to batch 3, the average loss is   16.18. ## Up to batch 4, the average loss is   13.09. ## Up to batch 5, the average loss is   11.48. ## Up to batch 6, the average loss is   10.21. ## Up to batch 7, the average loss is    9.22. ## Up to batch 8, the average loss is    8.70. ## Up to batch 9, the average loss is    8.16. ## Up to batch 10, the average loss is    7.80. ## Up to batch 11, the average loss is    7.50. ## Up to batch 12, the average loss is    7.17. ## Up to batch 13, the average loss is    6.89. ## Up to batch 14, the average loss is    6.70. ## Up to batch 15, the average loss is    6.52. ## Up to batch 16, the average loss is    6.54. ## The average loss for epoch  3 is      6.54 and mean absolute error is    1.93. ## ## Epoch 004: Learning rate is 0.0500. ## Up to batch 1, the average loss is    8.74. ## Up to batch 2, the average loss is    8.34. ## Up to batch 3, the average loss is    9.09. ## Up to batch 4, the average loss is    9.72. ## Up to batch 5, the average loss is   10.48. ## Up to batch 6, the average loss is   11.69. ## Up to batch 7, the average loss is   11.83. ## Up to batch 8, the average loss is   11.56. ## Up to batch 9, the average loss is   11.24. ## Up to batch 10, the average loss is   10.84. ## Up to batch 11, the average loss is   10.66. ## Up to batch 12, the average loss is   10.44. ## Up to batch 13, the average loss is   10.21. ## Up to batch 14, the average loss is   10.06. ## Up to batch 15, the average loss is   10.00. ## Up to batch 16, the average loss is   10.20. ## The average loss for epoch  4 is     10.20 and mean absolute error is    2.71. ## ## Epoch 005: Learning rate is 0.0500. ## Up to batch 1, the average loss is   17.26. ## Up to batch 2, the average loss is   14.09. ## Up to batch 3, the average loss is   12.67. ## Up to batch 4, the average loss is   11.44. ## Up to batch 5, the average loss is   10.54. ## Up to batch 6, the average loss is   10.10. ## Up to batch 7, the average loss is    9.53. ## Up to batch 8, the average loss is    9.17. ## Up to batch 9, the average loss is    8.78. ## Up to batch 10, the average loss is    8.49. ## Up to batch 11, the average loss is    8.50. ## Up to batch 12, the average loss is    8.59. ## Up to batch 13, the average loss is    8.68. ## Up to batch 14, the average loss is    8.86. ## Up to batch 15, the average loss is    9.17. ## Up to batch 16, the average loss is    9.53. ## The average loss for epoch  5 is      9.53 and mean absolute error is    2.58. ## ## Epoch 006: Learning rate is 0.0100. ## Up to batch 1, the average loss is   17.04. ## Up to batch 2, the average loss is   14.85. ## Up to batch 3, the average loss is   11.53. ## Up to batch 4, the average loss is    9.65. ## Up to batch 5, the average loss is    8.44. ## Up to batch 6, the average loss is    7.50. ## Up to batch 7, the average loss is    6.74. ## Up to batch 8, the average loss is    6.56. ## Up to batch 9, the average loss is    6.18. ## Up to batch 10, the average loss is    5.87. ## Up to batch 11, the average loss is    5.63. ## Up to batch 12, the average loss is    5.45. ## Up to batch 13, the average loss is    5.23. ## Up to batch 14, the average loss is    5.12. ## Up to batch 15, the average loss is    4.96. ## Up to batch 16, the average loss is    4.91. ## The average loss for epoch  6 is      4.91 and mean absolute error is    1.67. ## ## Epoch 007: Learning rate is 0.0100. ## Up to batch 1, the average loss is    3.65. ## Up to batch 2, the average loss is    3.04. ## Up to batch 3, the average loss is    2.88. ## Up to batch 4, the average loss is    2.85. ## Up to batch 5, the average loss is    2.88. ## Up to batch 6, the average loss is    2.81. ## Up to batch 7, the average loss is    2.70. ## Up to batch 8, the average loss is    2.96. ## Up to batch 9, the average loss is    2.96. ## Up to batch 10, the average loss is    2.93. ## Up to batch 11, the average loss is    2.95. ## Up to batch 12, the average loss is    2.98. ## Up to batch 13, the average loss is    2.97. ## Up to batch 14, the average loss is    3.01. ## Up to batch 15, the average loss is    3.00. ## Up to batch 16, the average loss is    3.05. ## The average loss for epoch  7 is      3.05 and mean absolute error is    1.34. ## ## Epoch 008: Learning rate is 0.0100. ## Up to batch 1, the average loss is    3.69. ## Up to batch 2, the average loss is    3.21. ## Up to batch 3, the average loss is    3.00. ## Up to batch 4, the average loss is    2.91. ## Up to batch 5, the average loss is    2.94. ## Up to batch 6, the average loss is    2.85. ## Up to batch 7, the average loss is    2.72. ## Up to batch 8, the average loss is    2.95. ## Up to batch 9, the average loss is    2.97. ## Up to batch 10, the average loss is    2.93. ## Up to batch 11, the average loss is    2.96. ## Up to batch 12, the average loss is    2.98. ## Up to batch 13, the average loss is    2.99. ## Up to batch 14, the average loss is    3.05. ## Up to batch 15, the average loss is    3.08. ## Up to batch 16, the average loss is    3.14. ## The average loss for epoch  8 is      3.14 and mean absolute error is    1.36. ## ## Epoch 009: Learning rate is 0.0050. ## Up to batch 1, the average loss is    3.71. ## Up to batch 2, the average loss is    2.93. ## Up to batch 3, the average loss is    2.76. ## Up to batch 4, the average loss is    2.70. ## Up to batch 5, the average loss is    2.76. ## Up to batch 6, the average loss is    2.69. ## Up to batch 7, the average loss is    2.57. ## Up to batch 8, the average loss is    2.79. ## Up to batch 9, the average loss is    2.80. ## Up to batch 10, the average loss is    2.77. ## Up to batch 11, the average loss is    2.79. ## Up to batch 12, the average loss is    2.80. ## Up to batch 13, the average loss is    2.78. ## Up to batch 14, the average loss is    2.81. ## Up to batch 15, the average loss is    2.80. ## Up to batch 16, the average loss is    2.83. ## The average loss for epoch  9 is      2.83 and mean absolute error is    1.28. ## ## Epoch 010: Learning rate is 0.0050. ## Up to batch 1, the average loss is    3.02. ## Up to batch 2, the average loss is    2.69. ## Up to batch 3, the average loss is    2.58. ## Up to batch 4, the average loss is    2.57. ## Up to batch 5, the average loss is    2.65. ## Up to batch 6, the average loss is    2.60. ## Up to batch 7, the average loss is    2.48. ## Up to batch 8, the average loss is    2.72. ## Up to batch 9, the average loss is    2.74. ## Up to batch 10, the average loss is    2.71. ## Up to batch 11, the average loss is    2.74. ## Up to batch 12, the average loss is    2.75. ## Up to batch 13, the average loss is    2.74. ## Up to batch 14, the average loss is    2.77. ## Up to batch 15, the average loss is    2.77. ## Up to batch 16, the average loss is    2.80. ## The average loss for epoch 10 is      2.80 and mean absolute error is    1.27. ## ## Epoch 011: Learning rate is 0.0050. ## Up to batch 1, the average loss is    3.01. ## Up to batch 2, the average loss is    2.69. ## Up to batch 3, the average loss is    2.58. ## Up to batch 4, the average loss is    2.56. ## Up to batch 5, the average loss is    2.63. ## Up to batch 6, the average loss is    2.58. ## Up to batch 7, the average loss is    2.47. ## Up to batch 8, the average loss is    2.70. ## Up to batch 9, the average loss is    2.72. ## Up to batch 10, the average loss is    2.69. ## Up to batch 11, the average loss is    2.71. ## Up to batch 12, the average loss is    2.72. ## Up to batch 13, the average loss is    2.71. ## Up to batch 14, the average loss is    2.75. ## Up to batch 15, the average loss is    2.74. ## Up to batch 16, the average loss is    2.77. ## The average loss for epoch 11 is      2.77 and mean absolute error is    1.27. ## ## Epoch 012: Learning rate is 0.0010. ## Up to batch 1, the average loss is    2.96. ## Up to batch 2, the average loss is    2.53. ## Up to batch 3, the average loss is    2.47. ## Up to batch 4, the average loss is    2.46. ## Up to batch 5, the average loss is    2.54. ## Up to batch 6, the average loss is    2.48. ## Up to batch 7, the average loss is    2.39. ## Up to batch 8, the average loss is    2.60. ## Up to batch 9, the average loss is    2.62. ## Up to batch 10, the average loss is    2.59. ## Up to batch 11, the average loss is    2.61. ## Up to batch 12, the average loss is    2.62. ## Up to batch 13, the average loss is    2.60. ## Up to batch 14, the average loss is    2.64. ## Up to batch 15, the average loss is    2.62. ## Up to batch 16, the average loss is    2.64. ## The average loss for epoch 12 is      2.64 and mean absolute error is    1.24. ## ## Epoch 013: Learning rate is 0.0010. ## Up to batch 1, the average loss is    2.82. ## Up to batch 2, the average loss is    2.46. ## Up to batch 3, the average loss is    2.42. ## Up to batch 4, the average loss is    2.42. ## Up to batch 5, the average loss is    2.50. ## Up to batch 6, the average loss is    2.45. ## Up to batch 7, the average loss is    2.36. ## Up to batch 8, the average loss is    2.57. ## Up to batch 9, the average loss is    2.59. ## Up to batch 10, the average loss is    2.57. ## Up to batch 11, the average loss is    2.59. ## Up to batch 12, the average loss is    2.60. ## Up to batch 13, the average loss is    2.59. ## Up to batch 14, the average loss is    2.62. ## Up to batch 15, the average loss is    2.61. ## Up to batch 16, the average loss is    2.63. ## The average loss for epoch 13 is      2.63 and mean absolute error is    1.23. ## ## Epoch 014: Learning rate is 0.0010. ## Up to batch 1, the average loss is    2.79. ## Up to batch 2, the average loss is    2.44. ## Up to batch 3, the average loss is    2.40. ## Up to batch 4, the average loss is    2.41. ## Up to batch 5, the average loss is    2.49. ## Up to batch 6, the average loss is    2.44. ## Up to batch 7, the average loss is    2.34. ## Up to batch 8, the average loss is    2.56. ## Up to batch 9, the average loss is    2.58. ## Up to batch 10, the average loss is    2.56. ## Up to batch 11, the average loss is    2.58. ## Up to batch 12, the average loss is    2.59. ## Up to batch 13, the average loss is    2.58. ## Up to batch 14, the average loss is    2.61. ## Up to batch 15, the average loss is    2.60. ## Up to batch 16, the average loss is    2.62. ## The average loss for epoch 14 is      2.62 and mean absolute error is    1.23."},{"path":"https://keras.posit.co/articles/writing_your_own_callbacks.html","id":"built-in-keras-callbacks","dir":"Articles","previous_headings":"Examples of Keras callback applications","what":"Built-in Keras callbacks","title":"Writing your own callbacks","text":"sure check existing Keras callbacks reading API docs. Applications include logging CSV, saving model, visualizing metrics TensorBoard, lot !","code":""},{"path":"https://keras.posit.co/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Tomasz Kalinowski. Author, copyright holder, maintainer. Daniel Falbel. Contributor, copyright holder. JJ Allaire. Author, copyright holder. François Chollet. Author, copyright holder. Posit Software, PBC. Copyright holder, funder. Google. Copyright holder, funder. Yuan Tang. Contributor, copyright holder. Wouter Van Der Bijl. Contributor, copyright holder. Martin Studer. Contributor, copyright holder. Sigrid Keydana. Contributor.","code":""},{"path":"https://keras.posit.co/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Kalinowski T, Allaire J, Chollet F (2024). keras3: R Interface 'Keras'. R package version 1.0.0, https://github.com/rstudio/keras, https://keras.posit.co/.","code":"@Manual{,   title = {keras3: R Interface to 'Keras'},   author = {Tomasz Kalinowski and JJ Allaire and François Chollet},   year = {2024},   note = {R package version 1.0.0, https://github.com/rstudio/keras},   url = {https://keras.posit.co/}, }"},{"path":"https://keras.posit.co/index.html","id":"r-interface-to-keras","dir":"","previous_headings":"","what":"R Interface to Keras","title":"R Interface to Keras","text":"Keras high-level neural networks API developed focus enabling fast experimentation. able go idea result least possible delay key good research. Keras following key features: Allows code run CPU GPU, seamlessly. User-friendly API makes easy quickly prototype deep learning models. Built-support convolutional networks (computer vision), recurrent networks (sequence processing), combination . Supports arbitrary network architectures: multi-input multi-output models, layer sharing, model sharing, etc. means Keras appropriate building essentially deep learning model, memory network neural Turing machine. See package website https://keras.posit.co complete documentation.","code":""},{"path":"https://keras.posit.co/reference/Callback.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a custom Callback class — Callback","title":"Define a custom Callback class — Callback","text":"Callbacks can passed keras methods fit(), evaluate(), predict() order hook various stages model training, evaluation, inference lifecycle. create custom callback, call Callback() override method associated stage interest.","code":""},{"path":"https://keras.posit.co/reference/Callback.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a custom Callback class — Callback","text":"","code":"Callback(   classname,   on_epoch_begin = NULL,   on_epoch_end = NULL,   on_train_begin = NULL,   on_train_end = NULL,   on_train_batch_begin = NULL,   on_train_batch_end = NULL,   on_test_begin = NULL,   on_test_end = NULL,   on_test_batch_begin = NULL,   on_test_batch_end = NULL,   on_predict_begin = NULL,   on_predict_end = NULL,   on_predict_batch_begin = NULL,   on_predict_batch_end = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Callback.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a custom Callback class — Callback","text":"classname String, name custom class. (Conventionally, CamelCase). on_epoch_begin   Called start epoch. Subclasses override actions run. function called TRAIN mode. Args: epoch: Integer, index epoch. logs: Named List. Currently data passed argument method may change future. on_epoch_end   Called end epoch. Subclasses override actions run. function called TRAIN mode. Args: epoch: Integer, index epoch. logs: Named List, metric results training epoch, validation epoch validation performed. Validation result keys prefixed val_. training epoch, values Model's metrics returned. Example: list(loss = 0.2, accuracy = 0.7). on_train_begin   Called beginning training. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_train_end   Called end training. Subclasses override actions run. Args: logs: Named list. Currently output last call on_epoch_end() passed argument method may change future. on_train_batch_begin   Called beginning training batch fit() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_train_batch_end   Called end training batch fit() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. on_test_begin   Called beginning evaluation validation. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_test_end   Called end evaluation validation. Subclasses override actions run. Args: logs: Named list. Currently output last call on_test_batch_end() passed argument method may change future. on_test_batch_begin   Called beginning batch evaluate() methods. Also called beginning validation batch fit() methods, validation data provided. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_test_batch_end   Called end batch evaluate() methods. Also called end validation batch fit() methods, validation data provided. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. on_predict_begin   Called beginning prediction. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_predict_end   Called end prediction. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_predict_batch_begin   Called beginning batch predict() methods. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_predict_batch_end   Called end batch predict() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"\\(epoch, logs = NULL) \\(epoch, logs = NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs=NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs = NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs = NULL)"},{"path":"https://keras.posit.co/reference/Callback.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a custom Callback class — Callback","text":"function returns custom Callback instances, similar builtin callback functions.","code":""},{"path":"https://keras.posit.co/reference/Callback.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define a custom Callback class — Callback","text":"R function custom methods (public private) following symbols scope: self: Layer instance. super: Layer superclass. private: R environment specific class instance. objects defined invisible Keras framework. __class__ current class type object. also available alias symbol, value supplied Layer(classname = )","code":"training_finished <- FALSE callback_mark_finished <- Callback(\"MarkFinished\",   on_train_end = function(logs = NULL) {     training_finished <<- TRUE   } )  model <- keras_model_sequential(input_shape = c(1)) |>   layer_dense(1) model |> compile(loss = 'mean_squared_error') model |> fit(op_ones(c(1, 1)), op_ones(c(1, 1)),              callbacks = callback_mark_finished()) stopifnot(isTRUE(training_finished))"},{"path":"https://keras.posit.co/reference/Callback.html","id":"attributes-accessible-via-self-","dir":"Reference","previous_headings":"","what":"Attributes (accessible via self$)","title":"Define a custom Callback class — Callback","text":"params: Named list, Training parameters (e.g. verbosity, batch size, number epochs, ...). model: Instance Model. Reference model trained. logs named list callback methods take argument contain keys quantities relevant current batch epoch (see method-specific docstrings).","code":""},{"path":"https://keras.posit.co/reference/Callback.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Define a custom Callback class — Callback","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Constraint.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a custom Constraint class — Constraint","title":"Define a custom Constraint class — Constraint","text":"Base class weight constraints. Constraint() instance works like stateless function. Users subclass Constraint class override call() method, takes single weight parameter return projected version parameter (e.g. normalized clipped). Constraints can used various Keras layers via kernel_constraint bias_constraint arguments. simple example non-negative weight constraint:     Usage layer:","code":"constraint_nonnegative <- Constraint(\"NonNegative\",   call = function(w) {     w * op_cast(w >= 0, dtype = w$dtype)   } ) weight <- op_convert_to_tensor(c(-1, 1)) constraint_nonnegative()(weight) ## tf.Tensor([-0.  1.], shape=(2), dtype=float32) layer_dense(units = 4, kernel_constraint = constraint_nonnegative()) ## <Dense name=dense, built=False> ##  signature: (*args, **kwargs)"},{"path":"https://keras.posit.co/reference/Constraint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a custom Constraint class — Constraint","text":"","code":"Constraint(   classname,   call = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Constraint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a custom Constraint class — Constraint","text":"classname String, name custom class. (Conventionally, CamelCase). call   Applies constraint input weight variable. default, inputs weight variable modified. Users override method implement projection function. Args: w: Input weight variable. Returns: Projected variable (default, returns unmodified inputs). get_config   Function returns named list object config. constraint config named list (JSON-serializable) can used reinstantiate object (via .call(<constraint_class>, <config>)). ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"\\(w) \\()"},{"path":"https://keras.posit.co/reference/Constraint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a custom Constraint class — Constraint","text":"function returns Constraint instances, similar builtin constraint functions like constraint_maxnorm().","code":""},{"path":"https://keras.posit.co/reference/Constraint.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Define a custom Constraint class — Constraint","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Layer.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a custom Layer class. — Layer","title":"Define a custom Layer class. — Layer","text":"layer callable object takes input one tensors outputs one tensors. involves computation, defined call() method, state (weight variables). State can created: initialize(), instance via self$add_weight(); optional build() method, invoked first call() layer, supplies shape(s) input(s), may known initialization time. Layers recursively composable: assign Layer instance attribute another Layer, outer layer start tracking weights created inner layer. Nested layers instantiated initialize() method build() method. Users just instantiate layer treat callable.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a custom Layer class. — Layer","text":"","code":"Layer(   classname,   initialize = NULL,   call = NULL,   build = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Layer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a custom Layer class. — Layer","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, call, build, get_config Recommended methods implement. See description details sections. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a custom Layer class. — Layer","text":"composing layer constructor, similar behavior layer functions like layer_dense(). first argument returned function object, enabling initialize()ing call() layer one step composing layer pipe, like   initialize() layer instance call() , pass missing NULL value object, pass arguments initialize() name.","code":"layer_foo <- Layer(\"Foo\", ....) output <- inputs |> layer_foo() layer <- layer_dense(units = 2, activation = \"relu\") layer <- layer_dense(NULL, 2, activation = \"relu\") layer <- layer_dense(, 2, activation = \"relu\")  # then you can call() the layer in a separate step outputs <- inputs |> layer()"},{"path":"https://keras.posit.co/reference/Layer.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Define a custom Layer class. — Layer","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Define a custom Layer class. — Layer","text":"name: name layer (string). dtype: Dtype layer's weights. Alias layer$variable_dtype. variable_dtype: Dtype layer's weights. compute_dtype: dtype layer's computations. Layers automatically cast inputs dtype, causes computations output also dtype. mixed precision used keras$mixed_precision$DTypePolicy, different variable_dtype. trainable_weights: List variables included backprop. non_trainable_weights: List variables included backprop. weights: concatenation lists trainable_weights non_trainable_weights (order). trainable: Whether layer trained (boolean), .e. whether potentially-trainable weights returned part layer$trainable_weights. input_spec: Optional (list ) InputSpec object(s) specifying constraints inputs can accepted layer. recommend custom Layers implement following methods: initialize(): Defines custom layer attributes, creates layer weights depend input shapes, using add_weight(), state. build(input_shape): method can used create weights depend shape(s) input(s), using add_weight(), state. Calling call() automatically build layer (built yet) calling build(). call(...): Method called making sure build() called. call() performs logic applying layer input arguments. Two reserved arguments can optionally use call() : training (boolean, whether call inference mode training mode). mask (boolean tensor encoding masked timesteps input, used e.g. RNN layers). typical signature method call(inputs), user optionally add training mask layer need . get_config(): Returns named list containing configuration used initialize layer. list names differ arguments initialize(), override from_config() well. method used saving layer model contains layer.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define a custom Layer class. — Layer","text":"basic example: layer two variables, w b, returns y <- (w %*% x) + b. shows implement build() call(). Variables set attributes layer tracked weights layers (layer$weights).   Besides trainable weights, updated via backpropagation training, layers can also non-trainable weights. weights meant updated manually call(). example layer computes running sum inputs:","code":"layer_simple_dense <- Layer(   \"SimpleDense\",   initialize = function(units = 32) {     super$initialize()     self$units <- units   },    # Create the state of the layer (weights)   build = function(input_shape) {     self$kernel <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = \"glorot_uniform\",       trainable = TRUE,       name = \"kernel\"     )     self$bias = self$add_weight(       shape = shape(self$units),       initializer = \"zeros\",       trainable = TRUE,       name = \"bias\"     )   },    # Defines the computation   call = function(self, inputs) {     op_matmul(inputs, self$kernel) + self$bias   } )  # Instantiates the layer. # Supply missing `object` arg to skip invoking `call()` and instead return # the Layer instance linear_layer <- layer_simple_dense(, 4)  # This will call `build(input_shape)` and create the weights, # and then invoke `call()`. y <- linear_layer(op_ones(c(2, 2))) stopifnot(length(linear_layer$weights) == 2)  # These weights are trainable, so they're listed in `trainable_weights`: stopifnot(length(linear_layer$trainable_weights) == 2) layer_compute_sum <- Layer(   classname = \"ComputeSum\",    initialize = function(input_dim) {     super$initialize()      # Create a non-trainable weight.     self$total <- self$add_weight(       shape = shape(),       initializer = \"zeros\",       trainable = FALSE,       name = \"total\"     )   },    call = function(inputs) {     self$total$assign(self$total + op_sum(inputs))     self$total   } )  my_sum <- layer_compute_sum(, 2) x <- op_ones(c(2, 2)) y <- my_sum(x)  stopifnot(exprs = {   all.equal(my_sum$weights,               list(my_sum$total))   all.equal(my_sum$non_trainable_weights, list(my_sum$total))   all.equal(my_sum$trainable_weights,     list()) })"},{"path":"https://keras.posit.co/reference/Layer.html","id":"methods-available","dir":"Reference","previous_headings":"","what":"Methods available","title":"Define a custom Layer class. — Layer","text":"Initialize self. method typically called custom initialize() method. Example:   Args: trainable: Boolean, whether layer's variables trainable. name: String name layer. dtype: dtype layer's computations weights. Can also keras$DTypePolicy, allows computation weight dtype differ. Defaults NULL. NULL means use config_dtype_policy(), \"float32\" policy unless set different value (via config_set_dtype_policy()).    Can called inside call() method add scalar loss. Example:         Add weight variable layer. Alias add_weight().    Add weight variable layer. Args: shape: shape variable (defined shape()) Must fully-defined (NA/NULL/-1 entries). Defaults () (scalar) unspecified. initializer: Initializer object use populate initial variable value, string name built-initializer (e.g. \"random_normal\"). unspecified, defaults \"glorot_uniform\" floating-point variables \"zeros\" types (e.g. int, bool). dtype: Dtype variable create, e.g. \"float32\". unspecified, defaults layer's variable dtype (defaults \"float32\" unspecified). trainable: Boolean, whether variable trainable via backprop whether updates managed manually. Defaults TRUE. autocast: Boolean, whether autocast layers variables accessing . Defaults TRUE. regularizer: Regularizer object call apply penalty weight. penalties summed loss function optimization. Defaults NULL. constraint: Constraint object call variable optimizer update, string name built-constraint. Defaults NULL. aggregation: String, one 'mean', 'sum', 'only_first_replica'. Annotates variable type multi-replica aggregation used variable writing custom data parallel training loops. name: String name variable. Useful debugging purposes. Returns: backend tensor, wrapped KerasVariable class. KerasVariable class Methods: assign(value) assign_add(value) assign_sub(value) numpy() (calling .array(<variable>) preferred) Properties/Attributes: value dtype ndim shape (calling shape(<variable>) preferred) trainable       Builds layer's states supplied config (named list args). default, method calls .call(build, config$input_shape) method, creates weights based layer's input shape supplied config. config contains information needed load layer's state, override method. Args: config: Named list containing input shape associated layer.    See description             Count total number scalars composing weights. Returns: integer count.    Returns named list layer's input shape. method returns config (named list) can used build_from_config(config) create states (e.g. Variables Lookup tables) needed layer. default, config contains input shape layer built . writing custom layer creates state unusual way, override method make sure state already created Keras attempts load value upon model loading. Returns: named list containing input shape associated layer.    Returns config object. object config named list (serializable) containing information needed re-instantiate . config expected serializable JSON, expected consist (potentially complex, nested) structure names lists consisting simple objects like strings, ints.    Return values layer$weights list R NumPy arrays.    Currently, Dense EinsumDense layers support -place quantization via quantize() method. Example:         Loads state layer. can override method take full control state layer loaded upon calling load_model(). Args: store: Named list state model loaded.    Saves state layer. can override method take full control state layer saved upon calling save_model(). Args: store: Named list state model saved.    Sets values weights list R NumPy arrays.    Call layer without side effects. Args: trainable_variables: List trainable variables model. non_trainable_variables: List non-trainable variables model. ...: Positional named arguments passed call(). return_losses: TRUE, stateless_call() return list losses created call() part return values. Returns: unnamed list. default, returns list(outputs, non_trainable_variables). return_losses = TRUE, returns list(outputs, non_trainable_variables, losses). Note: non_trainable_variables include non-trainable weights BatchNormalization statistics, also RNG seed state (random operations part layer, dropout), Metric state (metrics attached layer). elements state layer. Example:         Creates layer config. class method, meaning, R function self symbol (class instance) scope. Use __class__ classname symbol provided Layer() constructed) resolve class definition. default implementation :   method reverse get_config(), capable instantiating layer config named list. handle layer connectivity (handled Network), weights (handled set_weights()). Args: config: named list, typically output get_config(). Returns: layer instance.","code":"initialize(...,            activity_regularizer = NULL,            trainable = TRUE,            dtype = NULL,            autocast = TRUE,            name = NULL) layer_my_layer <- Layer(\"MyLayer\",   initialize = function(units, ..., dtype = NULL, name = NULL) {     super$initialize(..., dtype = dtype, name = name)     # .... finish initializing `self` instance   } ) add_loss(loss) Layer(\"MyLayer\",   ...   call = function(x) {     self$add_loss(op_sum(x))     x   } add_metric() add_variable(...) add_weight(shape = NULL,            initializer = NULL,            dtype = NULL,            trainable = TRUE,            autocast = TRUE,            regularizer = NULL,            constraint = NULL,            aggregation = 'mean',            name = NULL) build(input_shape) build_from_config(config) call(...) compute_mask(inputs, previous_mask) compute_output_shape(...) compute_output_spec(...) count_params() get_build_config() get_config() get_weights() quantize(mode) model$quantize(\"int8\") # quantize model in-place model |> predict(data) # faster inference quantized_call(...) load_own_variables(store) save_own_variables(store) set_weights(weights) stateless_call(trainable_variables, non_trainable_variables,                ..., return_losses = FALSE) model <- ... data <- ... trainable_variables <- model$trainable_variables non_trainable_variables <- model$non_trainable_variables # Call the model with zero side effects c(outputs, non_trainable_variables) %<-% model$stateless_call(     trainable_variables,     non_trainable_variables,     data ) # Attach the updated state to the model # (until you do this, the model is still in its pre-call state). purrr::walk2(   model$non_trainable_variables, non_trainable_variables,   \\(variable, value) variable$assign(value)) symbolic_call(...) from_config(config) from_config = function(config) {   do.call(`__class__`, config) }"},{"path":"https://keras.posit.co/reference/Layer.html","id":"readonly-properties-","dir":"Reference","previous_headings":"","what":"Readonly properties:","title":"Define a custom Layer class. — Layer","text":"compute_dtype dtype computations performed layer. dtype Alias layer$variable_dtype. input_dtype dtype layer inputs converted . losses List scalar losses add_loss(), regularizers sublayers. metrics List metrics. metrics_variables List metric variables. non_trainable_variables List non-trainable layer state. extends layer$non_trainable_weights include state used layer including state metrics SeedGenerators. non_trainable_weights List non-trainable weight variables layer. weights updated optimizer training. Unlike, layer$non_trainable_variables excludes metric state random seeds. trainable_variables List trainable layer state. equivalent layer$trainable_weights. trainable_weights List trainable weight variables layer. weights get updated optimizer training. variable_dtype dtype state (weights) layer. variables List layer state, including random seeds. extends layer$weights include state used layer including SeedGenerators. Note metrics variables included , use metrics_variables visit metric variables. weights List weight variables layer. Unlike, layer$variables excludes metric state random seeds. input Retrieves input tensor(s) symbolic operation. returns tensor(s) corresponding first time operation called. Returns: Input tensor list input tensors. output Retrieves output tensor(s) layer. returns tensor(s) corresponding first time operation called. Returns: Output tensor list output tensors.","code":""},{"path":"https://keras.posit.co/reference/Layer.html","id":"data-descriptors-attributes-","dir":"Reference","previous_headings":"","what":"Data descriptors (Attributes):","title":"Define a custom Layer class. — Layer","text":"dtype_policy input_spec supports_masking Whether layer supports computing mask using compute_mask. trainable Settable boolean, whether layer trainable .","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a custom LearningRateSchedule class — LearningRateSchedule","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"Subclass Keras LearningRateSchedule base class. can use learning rate schedule modulate learning rate optimizer changes time. Several built-learning rate schedules available, learning_rate_schedule_exponential_decay() learning_rate_schedule_piecewise_constant_decay():   LearningRateSchedule() instance can passed learning_rate argument optimizer. implement schedule object, implement call method, takes step argument (scalar integer backend tensor, current training step count). Note step 0-based (.e., first step 0). Like Keras object, can also optionally make object serializable implementing get_config() from_config() methods.","code":"lr_schedule <- learning_rate_schedule_exponential_decay(   initial_learning_rate = 1e-2,   decay_steps = 10000,   decay_rate = 0.9 ) optimizer <- optimizer_sgd(learning_rate = lr_schedule)"},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"","code":"LearningRateSchedule(   classname,   call = NULL,   initialize = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"classname String, name custom class. (Conventionally, CamelCase). call, initialize, get_config Recommended methods implement. See description details sections. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"function returns LearningRateSchedule instances, similar built-learning_rate_schedule_* family functions.","code":""},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"","code":"my_custom_learning_rate_schedule <- LearningRateSchedule(   classname = \"MyLRSchedule\",    initialize = function(initial_learning_rate) {     self$initial_learning_rate <- initial_learning_rate   },    call = function(step) {     # note that `step` is a tensor     # and call() will be traced via tf_function() or similar.      str(step) # <KerasVariable shape=(), dtype=int64, path=SGD/iteration>      # print 'step' every 1000 steps     op_cond((step %% 1000) == 0,             \\() {tensorflow::tf$print(step); NULL},             \\() {NULL})     self$initial_learning_rate / (step + 1)   } )  optimizer <- optimizer_sgd(   learning_rate = my_custom_learning_rate_schedule(0.1) )  # You can also call schedule instances directly # (e.g., for interactive testing, or if implementing a custom optimizer) schedule <- my_custom_learning_rate_schedule(0.1) step <- keras$Variable(initializer = op_ones,                        shape = shape(),                        dtype = \"int64\") schedule(step) ## <KerasVariable shape=(), dtype=int64, path=variable> ## tf.Tensor(0.0, shape=(), dtype=float64)"},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"methods-available-","dir":"Reference","previous_headings":"","what":"Methods available:","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"","code":"get_config()"},{"path":"https://keras.posit.co/reference/LearningRateSchedule.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Define a custom LearningRateSchedule class — LearningRateSchedule","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Subclass the base Loss class — Loss","title":"Subclass the base Loss class — Loss","text":"Use define custom loss class. Note, cases need subclass Loss define custom loss: can also pass bare R function, named R function defined custom_metric(), loss function compile().","code":""},{"path":"https://keras.posit.co/reference/Loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subclass the base Loss class — Loss","text":"","code":"Loss(   classname,   call = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subclass the base Loss class — Loss","text":"classname String, name custom class. (Conventionally, CamelCase). call   Method implemented subclasses: Function contains logic loss calculation using y_true, y_pred. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"function(y_true, y_pred)"},{"path":"https://keras.posit.co/reference/Loss.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subclass the base Loss class — Loss","text":"function returns Loss instances, similar builtin loss functions.","code":""},{"path":"https://keras.posit.co/reference/Loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Subclass the base Loss class — Loss","text":"Example subclass implementation:","code":"loss_custom_mse <- Loss(   classname = \"CustomMeanSquaredError\",   call = function(y_true, y_pred) {     op_mean(op_square(y_pred - y_true), axis = -1)   } )  # Usage in compile() model <- keras_model_sequential(input_shape = 10) |> layer_dense(10) model |> compile(loss = loss_custom_mse())  # Standalone usage mse <- loss_custom_mse(name = \"my_custom_mse_instance\")  y_true <- op_arange(20) |> op_reshape(c(4, 5)) y_pred <- op_arange(20) |> op_reshape(c(4, 5)) * 2 (loss <- mse(y_true, y_pred)) ## tf.Tensor(123.5, shape=(), dtype=float32) loss2 <- (y_pred - y_true)^2 |>   op_mean(axis = -1) |>   op_mean()  stopifnot(all.equal(as.array(loss), as.array(loss2)))  sample_weight <-array(c(.25, .25, 1, 1)) (weighted_loss <- mse(y_true, y_pred, sample_weight = sample_weight)) ## tf.Tensor(112.8125, shape=(), dtype=float32) weighted_loss2 <- (y_true - y_pred)^2 |>   op_mean(axis = -1) |>   op_multiply(sample_weight) |>   op_mean()  stopifnot(all.equal(as.array(weighted_loss),                     as.array(weighted_loss2)))"},{"path":"https://keras.posit.co/reference/Loss.html","id":"methods-defined-by-base-loss-class-","dir":"Reference","previous_headings":"","what":"Methods defined by base Loss class:","title":"Subclass the base Loss class — Loss","text":"Args: name reduction: Valid values one {\"sum_over_batch_size\", \"sum\", NULL, \"none\"} dtype    Call loss instance function, optionally sample_weight.","code":"initialize(name=NULL, reduction=\"sum_over_batch_size\", dtype=NULL) __call__(y_true, y_pred, sample_weight=NULL) get_config()"},{"path":"https://keras.posit.co/reference/Loss.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Subclass the base Loss class — Loss","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Metric.html","id":null,"dir":"Reference","previous_headings":"","what":"Subclass the base Metric class — Metric","title":"Subclass the base Metric class — Metric","text":"Metric object encapsulates metric logic state can used track model performance training. returned family metric functions start prefix metric_*, well returned custom metrics defined Metric().","code":""},{"path":"https://keras.posit.co/reference/Metric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subclass the base Metric class — Metric","text":"","code":"Metric(   classname,   initialize = NULL,   update_state = NULL,   result = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Metric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subclass the base Metric class — Metric","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, update_state, result Recommended methods implement. See description section. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/Metric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subclass the base Metric class — Metric","text":"function returns Metric instances, similar builtin metric functions.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Metric.html","id":"usage-with-compile-","dir":"Reference","previous_headings":"","what":"Usage with compile():","title":"Subclass the base Metric class — Metric","text":"","code":"model |> compile(   optimizer = 'sgd',   loss = 'mse',   metrics = c(metric_SOME_METRIC(), metric_SOME_OTHER_METRIC()) )"},{"path":"https://keras.posit.co/reference/Metric.html","id":"standalone-usage-","dir":"Reference","previous_headings":"","what":"Standalone usage:","title":"Subclass the base Metric class — Metric","text":"","code":"m <- metric_SOME_METRIC() for (e in seq(epochs)) {   for (i in seq(train_steps)) {     c(y_true, y_pred, sample_weight = NULL) %<-% ...     m$update_state(y_true, y_pred, sample_weight)   }   cat('Final epoch result: ', as.numeric(m$result()), \"\\n\")   m$reset_state() }"},{"path":[]},{"path":"https://keras.posit.co/reference/Metric.html","id":"usage-with-compile--1","dir":"Reference","previous_headings":"","what":"Usage with compile():","title":"Subclass the base Metric class — Metric","text":"implemented subclasses (custom metrics): initialize(): state variables created method calling self$add_variable() like: self$var <- self$add_variable(...). update_state(): Updates state variables like: self$var$assign(...). result(): Computes returns scalar value named list scalar values metric state variables. Example subclass implementation:","code":"model <- keras_model_sequential() model |>   layer_dense(64, activation = \"relu\") |>   layer_dense(64, activation = \"relu\") |>   layer_dense(10, activation = \"softmax\") model |>   compile(optimizer = optimizer_rmsprop(0.01),           loss = loss_categorical_crossentropy(),           metrics = metric_categorical_accuracy())  data <- random_uniform(c(1000, 32)) labels <- random_uniform(c(1000, 10))  model |> fit(data, labels, verbose = 0) metric_binary_true_positives <- Metric(   classname = \"BinaryTruePositives\",    initialize = function(name = 'binary_true_positives', ...) {     super$initialize(name = name, ...)     self$true_positives <-       self$add_weight(shape = shape(),                       initializer = 'zeros',                       name = 'true_positives')   },    update_state = function(y_true, y_pred, sample_weight = NULL) {     y_true <- op_cast(y_true, \"bool\")     y_pred <- op_cast(y_pred, \"bool\")      values <- y_true & y_pred # `&` calls op_logical_and()     values <- op_cast(values, self$dtype)     if (!is.null(sample_weight)) {       sample_weight <- op_cast(sample_weight, self$dtype)       sample_weight <- op_broadcast_to(sample_weight, shape(values))       values <- values * sample_weight # `*` calls op_multiply()     }     self$true_positives$assign(self$true_positives + op_sum(values))   },    result = function() {     self$true_positives   } ) model <- keras_model_sequential(input_shape = 32) |> layer_dense(10) model |> compile(loss = loss_binary_crossentropy(),                  metrics = list(metric_binary_true_positives())) model |> fit(data, labels, verbose = 0)"},{"path":"https://keras.posit.co/reference/Metric.html","id":"methods-defined-by-the-base-metric-class-","dir":"Reference","previous_headings":"","what":"Methods defined by the base Metric class:","title":"Subclass the base Metric class — Metric","text":"Calling metric instance self like m(...) equivalent calling:      Initialize self. Args: name: (Optional) string name metric instance. dtype: (Optional) data type metric result.          Return serializable config metric.    Reset metric state variables. function called epochs/steps, metric evaluated training.    Compute current metric value. Returns: scalar tensor, named list scalar tensors.             Accumulate statistics metric.","code":"__call__(...) function(...) {   m$update_state(...)   m$result() } initialize(dtype=NULL, name=NULL) add_variable(shape, initializer, dtype=NULL, aggregation = 'sum', name=NULL) add_weight(shape=shape(), initializer=NULL, dtype=NULL, name=NULL) get_config() reset_state() result() stateless_result(metric_variables) stateless_reset_state() stateless_update_state(metric_variables, ...) update_state(...)"},{"path":"https://keras.posit.co/reference/Metric.html","id":"readonly-properties","dir":"Reference","previous_headings":"","what":"Readonly properties","title":"Subclass the base Metric class — Metric","text":"dtype variables","code":""},{"path":"https://keras.posit.co/reference/Metric.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Subclass the base Metric class — Metric","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Subclass the base Keras Model Class — Model","title":"Subclass the base Keras Model Class — Model","text":"advanced use cases need subclass base Model type, e.g., want override train_step() method. just want create define keras model, prefer keras_model() keras_model_sequential(). just want encapsulate custom logic state, need customize training behavior (besides calling self$add_loss() call() method), prefer Layer().","code":""},{"path":"https://keras.posit.co/reference/Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subclass the base Keras Model Class — Model","text":"","code":"Model(   classname,   initialize = NULL,   call = NULL,   train_step = NULL,   predict_step = NULL,   test_step = NULL,   compute_loss = NULL,   compute_metrics = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subclass the base Keras Model Class — Model","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, call, train_step, predict_step, test_step, compute_loss, compute_metrics Optional methods can overridden. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subclass the base Keras Model Class — Model","text":"model constructor function, can call create instance new model type.","code":""},{"path":"https://keras.posit.co/reference/Model.html","id":"symbols-in-scope","dir":"Reference","previous_headings":"","what":"Symbols in scope","title":"Subclass the base Keras Model Class — Model","text":"R function custom methods (public private) following symbols scope: self: custom class instance. super: custom class superclass. private: R environment specific class instance. objects assigned invisible Keras framework. __class__ .symbol(classname): custom class type object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"Exponential Linear Unit. — activation_elu","title":"Exponential Linear Unit. — activation_elu","text":"exponential linear unit (ELU) alpha > 0 defined : x x > 0 alpha * exp(x) - 1 x < 0 ELUs negative values pushes mean activations closer zero. Mean activations closer zero enable faster learning bring gradient closer natural gradient. ELUs saturate negative value argument gets smaller. Saturation means small derivative decreases variation information propagated next layer.","code":""},{"path":"https://keras.posit.co/reference/activation_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exponential Linear Unit. — activation_elu","text":"","code":"activation_elu(x, alpha = 1)"},{"path":"https://keras.posit.co/reference/activation_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exponential Linear Unit. — activation_elu","text":"x Input tensor. alpha Numeric. See description details.","code":""},{"path":"https://keras.posit.co/reference/activation_elu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exponential Linear Unit. — activation_elu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_elu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Exponential Linear Unit. — activation_elu","text":"Clevert et al., 2016","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_exponential.html","id":null,"dir":"Reference","previous_headings":"","what":"Exponential activation function. — activation_exponential","title":"Exponential activation function. — activation_exponential","text":"Exponential activation function.","code":""},{"path":"https://keras.posit.co/reference/activation_exponential.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exponential activation function. — activation_exponential","text":"","code":"activation_exponential(x)"},{"path":"https://keras.posit.co/reference/activation_exponential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exponential activation function. — activation_exponential","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_exponential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exponential activation function. — activation_exponential","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian error linear unit (GELU) activation function. — activation_gelu","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"Gaussian error linear unit (GELU) defined : gelu(x) = x * P(X <= x) P(X) ~ N(0, 1), .e. gelu(x) = 0.5 * x * (1 + erf(x / sqrt(2))). GELU weights inputs value, rather gating inputs sign ReLU.","code":""},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"","code":"activation_gelu(x, approximate = FALSE)"},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"x Input tensor. approximate bool, whether enable approximation.","code":""},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_gelu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Gaussian error linear unit (GELU) activation function. — activation_gelu","text":"Hendrycks et al., 2016","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Hard sigmoid activation function. — activation_hard_sigmoid","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"hard sigmoid activation defined : 0 x <= -3 1 x >= 3 (x/6) + 0.5 -3 < x < 3 faster, piecewise linear approximation sigmoid activation.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"","code":"activation_hard_sigmoid(x)"},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_sigmoid.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Hard sigmoid activation function. — activation_hard_sigmoid","text":"Wikipedia \"Hard sigmoid\"","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"defined : 0 x < -3 x x > 3 x * (x + 3) / 6 -3 <= x <= 3 faster, piecewise linear approximation silu activation.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"","code":"activation_hard_silu(x)  activation_hard_swish(x)"},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_hard_silu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Hard SiLU activation function, also known as Hard Swish. — activation_hard_silu","text":"Howard, 2019","code":""},{"path":"https://keras.posit.co/reference/activation_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaky relu activation function. — activation_leaky_relu","title":"Leaky relu activation function. — activation_leaky_relu","text":"Leaky relu activation function.","code":""},{"path":"https://keras.posit.co/reference/activation_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leaky relu activation function. — activation_leaky_relu","text":"","code":"activation_leaky_relu(x, negative_slope = 0.2)"},{"path":"https://keras.posit.co/reference/activation_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leaky relu activation function. — activation_leaky_relu","text":"x Input tensor. negative_slope float controls slope values lower threshold.","code":""},{"path":"https://keras.posit.co/reference/activation_leaky_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Leaky relu activation function. — activation_leaky_relu","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_linear.html","id":null,"dir":"Reference","previous_headings":"","what":"Linear activation function (pass-through). — activation_linear","title":"Linear activation function (pass-through). — activation_linear","text":"\"linear\" activation identity function: returns input, unmodified.","code":""},{"path":"https://keras.posit.co/reference/activation_linear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linear activation function (pass-through). — activation_linear","text":"","code":"activation_linear(x)"},{"path":"https://keras.posit.co/reference/activation_linear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linear activation function (pass-through). — activation_linear","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_linear.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Linear activation function (pass-through). — activation_linear","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_log_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Log-Softmax activation function. — activation_log_softmax","title":"Log-Softmax activation function. — activation_log_softmax","text":"input vector handled independently. axis argument sets axis input function applied along.","code":""},{"path":"https://keras.posit.co/reference/activation_log_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log-Softmax activation function. — activation_log_softmax","text":"","code":"activation_log_softmax(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/activation_log_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log-Softmax activation function. — activation_log_softmax","text":"x Input tensor. axis Integer, axis along softmax applied.","code":""},{"path":"https://keras.posit.co/reference/activation_log_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Log-Softmax activation function. — activation_log_softmax","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_mish.html","id":null,"dir":"Reference","previous_headings":"","what":"Mish activation function. — activation_mish","title":"Mish activation function. — activation_mish","text":"defined : mish(x) = x * tanh(softplus(x)) softplus defined : softplus(x) = log(exp(x) + 1)","code":""},{"path":"https://keras.posit.co/reference/activation_mish.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mish activation function. — activation_mish","text":"","code":"activation_mish(x)"},{"path":"https://keras.posit.co/reference/activation_mish.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mish activation function. — activation_mish","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_mish.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mish activation function. — activation_mish","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_mish.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Mish activation function. — activation_mish","text":"Misra, 2019","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies the rectified linear unit activation function. — activation_relu","title":"Applies the rectified linear unit activation function. — activation_relu","text":"default values, returns standard ReLU activation: max(x, 0), element-wise maximum 0 input tensor. Modifying default parameters allows use non-zero thresholds, change max value activation, use non-zero multiple input values threshold.","code":""},{"path":"https://keras.posit.co/reference/activation_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies the rectified linear unit activation function. — activation_relu","text":"","code":"activation_relu(x, negative_slope = 0, max_value = NULL, threshold = 0)"},{"path":"https://keras.posit.co/reference/activation_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies the rectified linear unit activation function. — activation_relu","text":"x Input tensor. negative_slope numeric controls slope values lower threshold. max_value numeric sets saturation threshold (largest value function return). threshold numeric giving threshold value activation function values damped set zero.","code":""},{"path":"https://keras.posit.co/reference/activation_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies the rectified linear unit activation function. — activation_relu","text":"tensor shape dtype input x.","code":""},{"path":"https://keras.posit.co/reference/activation_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies the rectified linear unit activation function. — activation_relu","text":"","code":"x <- c(-10, -5, 0, 5, 10) activation_relu(x) ## tf.Tensor([ 0.  0.  0.  5. 10.], shape=(5), dtype=float32) activation_relu(x, negative_slope = 0.5) ## tf.Tensor([-5.  -2.5  0.   5.  10. ], shape=(5), dtype=float32) activation_relu(x, max_value = 5) ## tf.Tensor([0. 0. 0. 5. 5.], shape=(5), dtype=float32) activation_relu(x, threshold = 5) ## tf.Tensor([-0. -0.  0.  0. 10.], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/activation_relu6.html","id":null,"dir":"Reference","previous_headings":"","what":"Relu6 activation function. — activation_relu6","title":"Relu6 activation function. — activation_relu6","text":"ReLU function, truncated maximum value 6.","code":""},{"path":"https://keras.posit.co/reference/activation_relu6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relu6 activation function. — activation_relu6","text":"","code":"activation_relu6(x)"},{"path":"https://keras.posit.co/reference/activation_relu6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relu6 activation function. — activation_relu6","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_relu6.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Relu6 activation function. — activation_relu6","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"Scaled Exponential Linear Unit (SELU). — activation_selu","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"Scaled Exponential Linear Unit (SELU) activation function defined : scale * x x > 0 scale * alpha * (exp(x) - 1) x < 0 alpha scale pre-defined constants (alpha = 1.67326324 scale = 1.05070098). Basically, SELU activation function multiplies scale (> 1) output activation_elu function ensure slope larger one positive inputs. values alpha scale chosen mean variance inputs preserved two consecutive layers long weights initialized correctly (see initializer_lecun_normal()) number input units \"large enough\" (see reference paper information).","code":""},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"","code":"activation_selu(x)"},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"notes","dir":"Reference","previous_headings":"","what":"Notes","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"used together initializer_lecun_normal(). used together dropout variant layer_alpha_dropout() (legacy, depracated).","code":""},{"path":"https://keras.posit.co/reference/activation_selu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Scaled Exponential Linear Unit (SELU). — activation_selu","text":"Klambauer et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid activation function. — activation_sigmoid","title":"Sigmoid activation function. — activation_sigmoid","text":"defined : sigmoid(x) = 1 / (1 + exp(-x)). small values (<-5), sigmoid returns value close zero, large values (>5) result function gets close 1. Sigmoid equivalent 2-element softmax, second element assumed zero. sigmoid function always returns value 0 1.","code":""},{"path":"https://keras.posit.co/reference/activation_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid activation function. — activation_sigmoid","text":"","code":"activation_sigmoid(x)"},{"path":"https://keras.posit.co/reference/activation_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid activation function. — activation_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sigmoid activation function. — activation_sigmoid","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Swish (or Silu) activation function. — activation_silu","title":"Swish (or Silu) activation function. — activation_silu","text":"defined : swish(x) = x * sigmoid(x). Swish (Silu) activation function smooth, non-monotonic function unbounded bounded .","code":""},{"path":"https://keras.posit.co/reference/activation_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Swish (or Silu) activation function. — activation_silu","text":"","code":"activation_silu(x)"},{"path":"https://keras.posit.co/reference/activation_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Swish (or Silu) activation function. — activation_silu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_silu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Swish (or Silu) activation function. — activation_silu","text":"tensor, result applying activation input tensor x.","code":""},{"path":"https://keras.posit.co/reference/activation_silu.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Swish (or Silu) activation function. — activation_silu","text":"Ramachandran et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax converts a vector of values to a probability distribution. — activation_softmax","title":"Softmax converts a vector of values to a probability distribution. — activation_softmax","text":"elements output vector range [0, 1] sum 1. input vector handled independently. axis argument sets axis input function applied along. Softmax often used activation last layer classification network result interpreted probability distribution. softmax vector x computed exp(x) / sum(exp(x)). input values log-odds resulting probability.","code":""},{"path":"https://keras.posit.co/reference/activation_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax converts a vector of values to a probability distribution. — activation_softmax","text":"","code":"activation_softmax(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/activation_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax converts a vector of values to a probability distribution. — activation_softmax","text":"x Input tensor. axis Integer, axis along softmax applied.","code":""},{"path":"https://keras.posit.co/reference/activation_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax converts a vector of values to a probability distribution. — activation_softmax","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_softplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Softplus activation function. — activation_softplus","title":"Softplus activation function. — activation_softplus","text":"defined : softplus(x) = log(exp(x) + 1).","code":""},{"path":"https://keras.posit.co/reference/activation_softplus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softplus activation function. — activation_softplus","text":"","code":"activation_softplus(x)"},{"path":"https://keras.posit.co/reference/activation_softplus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softplus activation function. — activation_softplus","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_softplus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softplus activation function. — activation_softplus","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_softsign.html","id":null,"dir":"Reference","previous_headings":"","what":"Softsign activation function. — activation_softsign","title":"Softsign activation function. — activation_softsign","text":"Softsign defined : softsign(x) = x / (abs(x) + 1).","code":""},{"path":"https://keras.posit.co/reference/activation_softsign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softsign activation function. — activation_softsign","text":"","code":"activation_softsign(x)"},{"path":"https://keras.posit.co/reference/activation_softsign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softsign activation function. — activation_softsign","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_softsign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softsign activation function. — activation_softsign","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/activation_tanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hyperbolic tangent activation function. — activation_tanh","title":"Hyperbolic tangent activation function. — activation_tanh","text":"defined : tanh(x) = sinh(x) / cosh(x), .e. tanh(x) = ((exp(x) - exp(-x)) / (exp(x) + exp(-x))).","code":""},{"path":"https://keras.posit.co/reference/activation_tanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hyperbolic tangent activation function. — activation_tanh","text":"","code":"activation_tanh(x)"},{"path":"https://keras.posit.co/reference/activation_tanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hyperbolic tangent activation function. — activation_tanh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/activation_tanh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hyperbolic tangent activation function. — activation_tanh","text":"tensor, result applying activation input tensor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/active_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an active property class method — active_property","title":"Create an active property class method — active_property","text":"Create active property class method","code":""},{"path":"https://keras.posit.co/reference/active_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an active property class method — active_property","text":"","code":"active_property(fn)"},{"path":"https://keras.posit.co/reference/active_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an active property class method — active_property","text":"fn R function","code":""},{"path":"https://keras.posit.co/reference/active_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an active property class method — active_property","text":"fn, additional R attribute cause fn converted active property converted method custom subclass.","code":""},{"path":"https://keras.posit.co/reference/active_property.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Create an active property class method — active_property","text":"","code":"layer_foo <- Model(\"Foo\", ...,   metrics = active_property(function() {     list(self$d_loss_metric,          self$g_loss_metric)   }))"},{"path":"https://keras.posit.co/reference/adapt.html","id":null,"dir":"Reference","previous_headings":"","what":"Fits the state of the preprocessing layer to the data being passed — adapt","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"Fits state preprocessing layer data passed","code":""},{"path":"https://keras.posit.co/reference/adapt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"","code":"adapt(object, data, ..., batch_size = NULL, steps = NULL)"},{"path":"https://keras.posit.co/reference/adapt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"object Preprocessing layer object data data train . can passed either tf.data.Dataset R array. ... Used forwards backwards compatibility. Passed underlying method. batch_size Integer NULL. Number asamples per state update. unspecified, batch_size default 32. specify batch_size data form TF Dataset generator (since generate batches). steps Integer NULL. Total number steps (batches samples) training input tensors TensorFlow data tensors, default NULL equal number samples dataset divided batch size, 1 determined. x tf.data.Dataset, steps NULL, epoch run input dataset exhausted. passing infinitely repeating dataset, must specify steps argument. argument supported array inputs.","code":""},{"path":"https://keras.posit.co/reference/adapt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"Returns object, invisibly.","code":""},{"path":"https://keras.posit.co/reference/adapt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fits the state of the preprocessing layer to the data being passed — adapt","text":"calling adapt layer, preprocessing layer's state update training. order make preprocessing layers efficient distribution context, kept constant respect compiled tf.Graphs call layer. affect layer use adapting layer , adapt layer multiple times need take care re-compile compiled functions follows: adding preprocessing layer keras model, need call compile(model) subsequent call adapt(). calling preprocessing layer inside tfdatasets::dataset_map(), call dataset_map() input Dataset adapt(). using tensorflow::tf_function() directly calls preprocessing layer, need call tf_function() callable subsequent call adapt(). keras_model() example multiple adapts:         tfdatasets example multiple adapts:","code":"layer <- layer_normalization(axis = NULL) adapt(layer, c(0, 2)) model <- keras_model_sequential() |> layer() predict(model, c(0, 1, 2), verbose = FALSE) # [1] -1  0  1 ## [1] -1  0  1 adapt(layer, c(-1, 1)) compile(model)  # This is needed to re-compile model.predict! predict(model, c(0, 1, 2), verbose = FALSE) # [1] 0 1 2 ## [1] 0 1 2 layer <- layer_normalization(axis = NULL) adapt(layer, c(0, 2)) input_ds <- tfdatasets::range_dataset(0, 3) normalized_ds <- input_ds |>   tfdatasets::dataset_map(layer) str(tfdatasets::iterate(normalized_ds)) ## List of 3 ##  $ :<tf.Tensor: shape=(1), dtype=float32, numpy=array([-1.], dtype=float32)> ##  $ :<tf.Tensor: shape=(1), dtype=float32, numpy=array([0.], dtype=float32)> ##  $ :<tf.Tensor: shape=(1), dtype=float32, numpy=array([1.], dtype=float32)> adapt(layer, c(-1, 1)) normalized_ds <- input_ds |>   tfdatasets::dataset_map(layer) # Re-map over the input dataset.  normalized_ds |>   tfdatasets::as_array_iterator() |>   tfdatasets::iterate(simplify = FALSE) |>   str() ## List of 3 ##  $ : num [1(1d)] 0 ##  $ : num [1(1d)] 1 ##  $ : num [1(1d)] 2"},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"Instantiates ConvNeXtBase architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"","code":"application_convnext_base(   model_name = \"convnext_base\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_base.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtBase architecture. — application_convnext_base","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"Instantiates ConvNeXtLarge architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"","code":"application_convnext_large(   model_name = \"convnext_large\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_large.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtLarge architecture. — application_convnext_large","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"Instantiates ConvNeXtSmall architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"","code":"application_convnext_small(   model_name = \"convnext_small\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_small.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtSmall architecture. — application_convnext_small","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"Instantiates ConvNeXtTiny architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"","code":"application_convnext_tiny(   model_name = \"convnext_tiny\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_tiny.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtTiny architecture. — application_convnext_tiny","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"Instantiates ConvNeXtXLarge architecture.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"","code":"application_convnext_xlarge(   model_name = \"convnext_xlarge\",   include_top = TRUE,   include_preprocessing = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"model_name String, name model. include_top Whether include fully-connected layer top network. Defaults TRUE. include_preprocessing Boolean, whether include preprocessing layer bottom network. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet-1k), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"ConvNet 2020s (CVPR 2022) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. base, large, xlarge models first pre-trained ImageNet-21k dataset fine-tuned ImageNet-1k dataset. pre-trained parameters models assembled official repository. get sense parameters converted Keras compatible parameters, please refer repository.","code":""},{"path":"https://keras.posit.co/reference/application_convnext_xlarge.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ConvNeXtXLarge architecture. — application_convnext_xlarge","text":"Keras Application expects specific kind input preprocessing. ConvNeXt, preprocessing included model using Normalization layer.  ConvNeXt models expect inputs float uint8 tensors pixels values [0-255] range. calling summary() method instantiating ConvNeXt model, prefer setting expand_nested argument summary() TRUE better investigate instantiated model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Densenet121 architecture. — application_densenet121","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"Instantiates Densenet121 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"","code":"application_densenet121(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) ('channels_last' data format) (3, 224, 224) ('channels_first' data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"Densely Connected Convolutional Networks (CVPR 2017) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_densenet121.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Densenet121 architecture. — application_densenet121","text":"Keras Application expects specific kind input preprocessing. DenseNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Densenet169 architecture. — application_densenet169","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"Instantiates Densenet169 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"","code":"application_densenet169(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) ('channels_last' data format) (3, 224, 224) ('channels_first' data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"Densely Connected Convolutional Networks (CVPR 2017) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_densenet169.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Densenet169 architecture. — application_densenet169","text":"Keras Application expects specific kind input preprocessing. DenseNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Densenet201 architecture. — application_densenet201","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"Instantiates Densenet201 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"","code":"application_densenet201(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) ('channels_last' data format) (3, 224, 224) ('channels_first' data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"Densely Connected Convolutional Networks (CVPR 2017) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_densenet201.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Densenet201 architecture. — application_densenet201","text":"Keras Application expects specific kind input preprocessing. DenseNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"Instantiates EfficientNetB0 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"","code":"application_efficientnet_b0(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b0.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB0 architecture. — application_efficientnet_b0","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"Instantiates EfficientNetB1 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"","code":"application_efficientnet_b1(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b1.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB1 architecture. — application_efficientnet_b1","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"Instantiates EfficientNetB2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"","code":"application_efficientnet_b2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB2 architecture. — application_efficientnet_b2","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"Instantiates EfficientNetB3 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"","code":"application_efficientnet_b3(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b3.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB3 architecture. — application_efficientnet_b3","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"Instantiates EfficientNetB4 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"","code":"application_efficientnet_b4(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b4.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB4 architecture. — application_efficientnet_b4","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"Instantiates EfficientNetB5 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"","code":"application_efficientnet_b5(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b5.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB5 architecture. — application_efficientnet_b5","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"Instantiates EfficientNetB6 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"","code":"application_efficientnet_b6(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b6.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB6 architecture. — application_efficientnet_b6","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"Instantiates EfficientNetB7 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"","code":"application_efficientnet_b7(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   ... )"},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"include_top Whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. 1000 many ImageNet classes . Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults 'softmax'. loading pretrained weights, classifier_activation can NULL \"softmax\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"EfficientNet: Rethinking Model Scaling Convolutional Neural Networks (ICML 2019) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_b7.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetB7 architecture. — application_efficientnet_b7","text":"Keras Application expects specific kind input preprocessing. EfficientNet, input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. EfficientNet models expect inputs float tensors pixels values [0-255] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"Instantiates EfficientNetV2B0 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"","code":"application_efficientnet_v2b0(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b0.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2B0 architecture. — application_efficientnet_v2b0","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"Instantiates EfficientNetV2B1 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"","code":"application_efficientnet_v2b1(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b1.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2B1 architecture. — application_efficientnet_v2b1","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"Instantiates EfficientNetV2B2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"","code":"application_efficientnet_v2b2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2B2 architecture. — application_efficientnet_v2b2","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"Instantiates EfficientNetV2B3 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"","code":"application_efficientnet_v2b3(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2b3.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2B3 architecture. — application_efficientnet_v2b3","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"Instantiates EfficientNetV2L architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"","code":"application_efficientnet_v2l(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2l.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2L architecture. — application_efficientnet_v2l","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"Instantiates EfficientNetV2M architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"","code":"application_efficientnet_v2m(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2m.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2M architecture. — application_efficientnet_v2m","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"Instantiates EfficientNetV2S architecture.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"","code":"application_efficientnet_v2s(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_shape Optional shape tuple, specified include_top FALSE. exactly 3 inputs channels. pooling Optional pooling mode feature extraction include_top FALSE. Defaults NULL. NULL means output model 4D tensor output last convolutional layer. \"avg\" means global average pooling applied output last convolutional layer, thus output model 2D tensor. \"max\" means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000 (number ImageNet classes). classifier_activation string callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. Defaults \"softmax\". loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer bottom network.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"EfficientNetV2: Smaller Models Faster Training (ICML 2021) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_efficientnet_v2s.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the EfficientNetV2S architecture. — application_efficientnet_v2s","text":"Keras Application expects specific kind input preprocessing. EfficientNetV2, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, EfficientNetV2 models expect inputs float tensors pixels values [0, 255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled EfficientNetV2 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"Instantiates Inception-ResNet v2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"","code":"application_inception_resnet_v2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (299, 299, 3) ('channels_last' data format) (3, 299, 299) ('channels_first' data format). exactly 3 inputs channels, width height smaller 75. E.g. (150, 150, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. 'avg' means global average pooling applied output last convolutional block, thus output model 2D tensor. 'max' means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"Inception-v4, Inception-ResNet Impact Residual Connections Learning (AAAI 2017) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_inception_resnet_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Inception-ResNet v2 architecture. — application_inception_resnet_v2","text":"Keras Application expects specific kind input preprocessing. InceptionResNetV2, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Inception v3 architecture. — application_inception_v3","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"Instantiates Inception v3 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"","code":"application_inception_v3(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"include_top Boolean, whether include fully-connected layer top, last layer network. Defaults TRUE. weights One NULL (random initialization), imagenet (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_tensor useful sharing inputs multiple different networks. Defaults NULL. input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (299, 299, 3) (channels_last data format) (3, 299, 299) (channels_first data format). exactly 3 inputs channels, width height smaller 75. E.g. (150, 150, 3) one valid value. input_shape ignored input_tensor provided. pooling Optional pooling mode feature extraction include_top FALSE. NULL (default) means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"Rethinking Inception Architecture Computer Vision (CVPR 2016) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_inception_v3.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Inception v3 architecture. — application_inception_v3","text":"Keras Application expects specific kind input preprocessing. InceptionV3, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the MobileNet architecture. — application_mobilenet","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"Instantiates MobileNet architecture.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"","code":"application_mobilenet(   input_shape = NULL,   alpha = 1,   depth_multiplier = 1L,   dropout = 0.001,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. Defaults NULL. input_shape ignored input_tensor provided. alpha Controls width network. known width multiplier MobileNet paper. alpha < 1.0, proportionally decreases number filters layer. alpha > 1.0, proportionally increases number filters layer. alpha == 1, default number filters paper used layer. Defaults 1.0. depth_multiplier Depth multiplier depthwise convolution. called resolution multiplier MobileNet paper. Defaults 1.0. dropout Dropout rate. Defaults 0.001. include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_tensor useful sharing inputs multiple different networks. Defaults NULL. pooling Optional pooling mode feature extraction include_top FALSE. NULL (default) means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"MobileNets: Efficient Convolutional Neural Networks Mobile Vision Applications function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the MobileNet architecture. — application_mobilenet","text":"Keras Application expects specific kind input preprocessing. MobileNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"MobileNetV2 similar original MobileNet, except uses inverted residual blocks bottlenecking features. drastically lower parameter count original MobileNet. MobileNets support input size greater 32 x 32, larger image sizes offering better performance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"","code":"application_mobilenet_v2(   input_shape = NULL,   alpha = 1,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. Defaults NULL. input_shape ignored input_tensor provided. alpha Controls width network. known width multiplier MobileNet paper. alpha < 1.0, proportionally decreases number filters layer. alpha > 1.0, proportionally increases number filters layer. alpha == 1, default number filters paper used layer. Defaults 1.0. include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights One NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. Defaults \"imagenet\". input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. input_tensor useful sharing inputs multiple different networks. Defaults NULL. pooling Optional pooling mode feature extraction include_top FALSE. NULL (default) means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. Defaults 1000. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"MobileNetV2: Inverted Residuals Linear Bottlenecks (CVPR 2018) function returns Keras image classification model, optionally loaded weights pre-trained ImageNet. image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the MobileNetV2 architecture. — application_mobilenet_v2","text":"Keras Application expects specific kind input preprocessing. MobileNetV2, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"Instantiates MobileNetV3Large architecture.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"","code":"application_mobilenet_v3_large(   input_shape = NULL,   alpha = 1,   minimalistic = FALSE,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   classes = 1000L,   pooling = NULL,   dropout_rate = 0.2,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"input_shape Optional shape tuple, specified like use model input image resolution (224, 224, 3). exactly 3 inputs channels. can also omit option like infer input_shape input_tensor. choose include input_tensor input_shape input_shape used match, shapes match throw error. E.g. (160, 160, 3) one valid value. alpha controls width network. known depth multiplier MobileNetV3 paper, name kept consistency MobileNetV1 Keras. alpha < 1.0, proportionally decreases number filters layer. alpha > 1.0, proportionally increases number filters layer. alpha == 1, default number filters paper used layer. minimalistic addition large small models module also contains -called minimalistic models, models per-layer dimensions characteristic MobilenetV3 however, utilize advanced blocks (squeeze--excite units, hard-swish, 5x5 convolutions). models less efficient CPU, much performant GPU/DSP. include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights String, one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. classes Integer, optional number classes classify images , specified include_top TRUE, weights argument specified. pooling String, optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. dropout_rate fraction input units drop last layer. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer (Rescaling) bottom network. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"Searching MobileNetV3 (ICCV 2019)","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"the-following-table-describes-the-performance-of-mobilenets-v-","dir":"Reference","previous_headings":"","what":"The following table describes the performance of MobileNets v3:","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"MACs stands Multiply Adds image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"Keras Application expects specific kind input preprocessing. MobileNetV3, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, MobileNetV3 models expect inputs float tensors pixels values [0-255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled MobileNetV3 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_large.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Instantiates the MobileNetV3Large architecture. — application_mobilenet_v3_large","text":"inputs: floating point numpy.array backend-native tensor, 4D 3 color channels, values range [0, 255] include_preprocessing TRUE range [-1, 1] otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"Instantiates MobileNetV3Small architecture.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"","code":"application_mobilenet_v3_small(   input_shape = NULL,   alpha = 1,   minimalistic = FALSE,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   classes = 1000L,   pooling = NULL,   dropout_rate = 0.2,   classifier_activation = \"softmax\",   include_preprocessing = TRUE )"},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"input_shape Optional shape tuple, specified like use model input image resolution (224, 224, 3). exactly 3 inputs channels. can also omit option like infer input_shape input_tensor. choose include input_tensor input_shape input_shape used match, shapes match throw error. E.g. (160, 160, 3) one valid value. alpha controls width network. known depth multiplier MobileNetV3 paper, name kept consistency MobileNetV1 Keras. alpha < 1.0, proportionally decreases number filters layer. alpha > 1.0, proportionally increases number filters layer. alpha == 1, default number filters paper used layer. minimalistic addition large small models module also contains -called minimalistic models, models per-layer dimensions characteristic MobilenetV3 however, utilize advanced blocks (squeeze--excite units, hard-swish, 5x5 convolutions). models less efficient CPU, much performant GPU/DSP. include_top Boolean, whether include fully-connected layer top network. Defaults TRUE. weights String, one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. classes Integer, optional number classes classify images , specified include_top TRUE, weights argument specified. pooling String, optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. dropout_rate fraction input units drop last layer. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\". include_preprocessing Boolean, whether include preprocessing layer (Rescaling) bottom network. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"Searching MobileNetV3 (ICCV 2019)","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"the-following-table-describes-the-performance-of-mobilenets-v-","dir":"Reference","previous_headings":"","what":"The following table describes the performance of MobileNets v3:","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"MACs stands Multiply Adds image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"Keras Application expects specific kind input preprocessing. MobileNetV3, default input preprocessing included part model (Rescaling layer), thus application_preprocess_inputs() actually pass-function. use case, MobileNetV3 models expect inputs float tensors pixels values [0-255] range. time, preprocessing part model (.e. Rescaling layer) can disabled setting include_preprocessing argument FALSE. preprocessing disabled MobileNetV3 models expect inputs float tensors pixels values [-1, 1] range.","code":""},{"path":"https://keras.posit.co/reference/application_mobilenet_v3_small.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Instantiates the MobileNetV3Small architecture. — application_mobilenet_v3_small","text":"inputs: floating point numpy.array backend-native tensor, 4D 3 color channels, values range [0, 255] include_preprocessing TRUE range [-1, 1] otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"Instantiates NASNet model ImageNet mode.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"","code":"application_nasnetlarge(   input_shape = NULL,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (331, 331, 3) NASNetLarge. exactly 3 inputs channels, width height smaller 32. E.g. (224, 224, 3) one valid value. include_top Whether include fully-connected layer top network. weights NULL (random initialization) imagenet (ImageNet weights).  loading imagenet weights, input_shape (331, 331, 3) input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"Learning Transferable Architectures Scalable Image Recognition (CVPR 2018) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetlarge.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates a NASNet model in ImageNet mode. — application_nasnetlarge","text":"Keras Application expects specific kind input preprocessing. NASNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"Instantiates Mobile NASNet model ImageNet mode.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"","code":"application_nasnetmobile(   input_shape = NULL,   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"input_shape Optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) NASNetMobile exactly 3 inputs channels, width height smaller 32. E.g. (224, 224, 3) one valid value. include_top Whether include fully-connected layer top network. weights NULL (random initialization) imagenet (ImageNet weights). loading imagenet weights, input_shape (224, 224, 3) input_tensor Optional Keras tensor (.e. output layers.Input()) use image input model. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional layer. avg means global average pooling applied output last convolutional layer, thus output model 2D tensor. max means global max pooling applied. classes Optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"Keras model instance.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"Learning Transferable Architectures Scalable Image Recognition (CVPR 2018) Optionally loads weights pre-trained ImageNet. Note data format convention used model one specified Keras config ~/.keras/keras.json.","code":""},{"path":"https://keras.posit.co/reference/application_nasnetmobile.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates a Mobile NASNet model in ImageNet mode. — application_nasnetmobile","text":"Keras Application expects specific kind input preprocessing. NASNet, call application_preprocess_inputs() inputs passing model.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet101 architecture. — application_resnet101","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"Instantiates ResNet101 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"","code":"application_resnet101(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"Deep Residual Learning Image Recognition (CVPR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet101 architecture. — application_resnet101","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"Instantiates ResNet101V2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"","code":"application_resnet101_v2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"Identity Mappings Deep Residual Networks (CVPR 2016) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet101_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet101V2 architecture. — application_resnet101_v2","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet152 architecture. — application_resnet152","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"Instantiates ResNet152 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"","code":"application_resnet152(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"Deep Residual Learning Image Recognition (CVPR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet152 architecture. — application_resnet152","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"Instantiates ResNet152V2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"","code":"application_resnet152_v2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"Identity Mappings Deep Residual Networks (CVPR 2016) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet152_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet152V2 architecture. — application_resnet152_v2","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet50 architecture. — application_resnet50","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"Instantiates ResNet50 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"","code":"application_resnet50(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"Deep Residual Learning Image Recognition (CVPR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet50 architecture. — application_resnet50","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"Instantiates ResNet50V2 architecture.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"","code":"application_resnet50_v2(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"include_top whether include fully-connected layer top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (\"channels_last\" data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 inputs channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer. loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"Identity Mappings Deep Residual Networks (CVPR 2016) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning.","code":""},{"path":"https://keras.posit.co/reference/application_resnet50_v2.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the ResNet50V2 architecture. — application_resnet50_v2","text":"Keras Application expects specific kind input preprocessing. ResNet, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the VGG16 model. — application_vgg16","title":"Instantiates the VGG16 model. — application_vgg16","text":"Instantiates VGG16 model.","code":""},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the VGG16 model. — application_vgg16","text":"","code":"application_vgg16(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the VGG16 model. — application_vgg16","text":"include_top whether include 3 fully-connected layers top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (channels_last data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 input channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the VGG16 model. — application_vgg16","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the VGG16 model. — application_vgg16","text":"Deep Convolutional Networks Large-Scale Image Recognition (ICLR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. default input size model 224x224.","code":""},{"path":"https://keras.posit.co/reference/application_vgg16.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the VGG16 model. — application_vgg16","text":"Keras Application expects specific kind input preprocessing. VGG16, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the VGG19 model. — application_vgg19","title":"Instantiates the VGG19 model. — application_vgg19","text":"Instantiates VGG19 model.","code":""},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the VGG19 model. — application_vgg19","text":"","code":"application_vgg19(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the VGG19 model. — application_vgg19","text":"include_top whether include 3 fully-connected layers top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (224, 224, 3) (channels_last data format) (3, 224, 224) (\"channels_first\" data format). exactly 3 input channels, width height smaller 32. E.g. (200, 200, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the VGG19 model. — application_vgg19","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the VGG19 model. — application_vgg19","text":"Deep Convolutional Networks Large-Scale Image Recognition (ICLR 2015) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. default input size model 224x224.","code":""},{"path":"https://keras.posit.co/reference/application_vgg19.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the VGG19 model. — application_vgg19","text":"Keras Application expects specific kind input preprocessing. VGG19, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() convert input images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/application_xception.html","id":null,"dir":"Reference","previous_headings":"","what":"Instantiates the Xception architecture. — application_xception","title":"Instantiates the Xception architecture. — application_xception","text":"Instantiates Xception architecture.","code":""},{"path":"https://keras.posit.co/reference/application_xception.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instantiates the Xception architecture. — application_xception","text":"","code":"application_xception(   include_top = TRUE,   weights = \"imagenet\",   input_tensor = NULL,   input_shape = NULL,   pooling = NULL,   classes = 1000L,   classifier_activation = \"softmax\" )"},{"path":"https://keras.posit.co/reference/application_xception.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instantiates the Xception architecture. — application_xception","text":"include_top whether include 3 fully-connected layers top network. weights one NULL (random initialization), \"imagenet\" (pre-training ImageNet), path weights file loaded. input_tensor optional Keras tensor (.e. output layers.Input()) use image input model. input_shape optional shape tuple, specified include_top FALSE (otherwise input shape (299, 299, 3). exactly 3 inputs channels, width height smaller 71. E.g. (150, 150, 3) one valid value. pooling Optional pooling mode feature extraction include_top FALSE. NULL means output model 4D tensor output last convolutional block. avg means global average pooling applied output last convolutional block, thus output model 2D tensor. max means global max pooling applied. classes optional number classes classify images , specified include_top TRUE, weights argument specified. classifier_activation str callable. activation function use \"top\" layer. Ignored unless include_top=TRUE. Set classifier_activation=NULL return logits \"top\" layer.  loading pretrained weights, classifier_activation can NULL \"softmax\".","code":""},{"path":"https://keras.posit.co/reference/application_xception.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Instantiates the Xception architecture. — application_xception","text":"model instance.","code":""},{"path":"https://keras.posit.co/reference/application_xception.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Instantiates the Xception architecture. — application_xception","text":"Xception: Deep Learning Depthwise Separable Convolutions (CVPR 2017) image classification use cases, see page detailed examples. transfer learning use cases, make sure read guide transfer learning & fine-tuning. default input image size model 299x299.","code":""},{"path":"https://keras.posit.co/reference/application_xception.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Instantiates the Xception architecture. — application_xception","text":"Keras Application expects specific kind input preprocessing. Xception, call application_preprocess_inputs() inputs passing model. application_preprocess_inputs() scale input pixels -1 1.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/audio_dataset_from_directory.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","title":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","text":"directory structure :   calling audio_dataset_from_directory(main_directory, labels = 'inferred') return tf.data.Dataset yields batches audio files subdirectories class_a class_b, together labels 0 1 (0 corresponding class_a 1 corresponding class_b). .wav files supported time.","code":"main_directory/ ...class_a/ ......a_audio_1.wav ......a_audio_2.wav ...class_b/ ......b_audio_1.wav ......b_audio_2.wav"},{"path":"https://keras.posit.co/reference/audio_dataset_from_directory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","text":"","code":"audio_dataset_from_directory(   directory,   labels = \"inferred\",   label_mode = \"int\",   class_names = NULL,   batch_size = 32L,   sampling_rate = NULL,   output_sequence_length = NULL,   ragged = FALSE,   shuffle = TRUE,   seed = NULL,   validation_split = NULL,   subset = NULL,   follow_links = FALSE,   verbose = TRUE )"},{"path":"https://keras.posit.co/reference/audio_dataset_from_directory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","text":"directory Directory data located. labels \"inferred\", contain subdirectories, containing audio files class. Otherwise, directory structure ignored. labels Either \"inferred\" (labels generated directory structure), NULL (labels), list/tuple integer labels size number audio files found directory. Labels sorted according alphanumeric order audio file paths (obtained via os.walk(directory) Python). label_mode String describing encoding labels. Options : \"int\": means labels encoded integers (e.g. sparse_categorical_crossentropy loss). \"categorical\" means labels encoded categorical vector (e.g. categorical_crossentropy loss) \"binary\" means labels (can 2) encoded float32 scalars values 0 1 (e.g. binary_crossentropy). NULL (labels). class_names valid \"labels\" \"inferred\". explicit list class names (must match names subdirectories). Used control order classes (otherwise alphanumerical order used). batch_size Size batches data. Default: 32. NULL, data batched (dataset yield individual samples). sampling_rate Audio sampling rate (samples per second). output_sequence_length Maximum length audio sequence. Audio files longer truncated output_sequence_length. set NULL, sequences batch padded length longest sequence batch. ragged Whether return Ragged dataset (sequence length). Defaults FALSE. shuffle Whether shuffle data. Defaults TRUE. set FALSE, sorts data alphanumeric order. seed Optional random seed shuffling transformations. validation_split Optional float 0 1, fraction data reserve validation. subset Subset data return. One \"training\", \"validation\" \"\". used validation_split set. follow_links Whether visits subdirectories pointed symlinks. Defaults FALSE. verbose Whether display number information classes number files found. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/audio_dataset_from_directory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a tf.data.Dataset from audio files in a directory. — audio_dataset_from_directory","text":"tf.data.Dataset object. label_mode NULL, yields string tensors shape (batch_size,), containing contents batch audio files. Otherwise, yields tuple (audio, labels), audio shape (batch_size, sequence_length, num_channels) labels follows format described . Rules regarding labels format: label_mode int, labels int32 tensor shape (batch_size,). label_mode binary, labels float32 tensor 1s 0s shape (batch_size, 1). label_mode categorical, labels float32 tensor shape (batch_size, num_classes), representing one-hot encoding class index.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/bidirectional.html","id":null,"dir":"Reference","previous_headings":"","what":"layer_bidirectional — bidirectional","title":"layer_bidirectional — bidirectional","text":"bidirectional() alias layer_bidirectional(). See ?layer_bidirectional() full documentation.","code":""},{"path":"https://keras.posit.co/reference/bidirectional.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"layer_bidirectional — bidirectional","text":"","code":"bidirectional(   object,   layer,   merge_mode = \"concat\",   weights = NULL,   backward_layer = NULL,   ... )"},{"path":"https://keras.posit.co/reference/bidirectional.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"layer_bidirectional — bidirectional","text":"object Object compose layer . tensor, array, sequential model. layer RNN instance, layer_lstm() layer_gru(). also Layer() instance meets following criteria: sequence-processing layer (accepts 3D+ inputs). go_backwards, return_sequences return_state attribute (semantics RNN class). input_spec attribute. Implement serialization via get_config() from_config(). Note recommended way create new RNN layers write custom RNN cell use layer_rnn(), instead subclassing Layer() directly. return_sequences TRUE, output masked timestep zero regardless layer's original zero_output_for_mask value. merge_mode Mode outputs forward backward RNNs combined. One {\"sum\", \"mul\", \"concat\", \"ave\", NULL}. NULL, outputs combined, returned list. Defaults \"concat\". weights see description backward_layer Optional RNN, Layer() instance used handle backwards input processing. backward_layer provided, layer instance passed layer argument used generate backward layer automatically. Note provided backward_layer layer properties matching layer argument, particular values stateful, return_states, return_sequences, etc. addition, backward_layer layer different go_backwards argument values. ValueError raised requirements met. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/bidirectional.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"layer_bidirectional — bidirectional","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback to back up and restore the training state. — callback_backup_and_restore","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"callback_backup_and_restore() callback intended recover training interruption happened middle fit execution, backing training states temporary checkpoint file, end epoch. backup overwrites previously written checkpoint file, given time one checkpoint file backup/restoring purpose. training restarts completion, training state (includes model weights epoch number) restored recently saved state beginning new fit run. completion fit run, temporary checkpoint file deleted. Note user responsible bring jobs back interruption. callback important backup restore mechanism fault tolerance purpose, model restored previous checkpoint expected one used back . user changes arguments passed compile fit, checkpoint saved fault tolerance can become invalid.","code":""},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"","code":"callback_backup_and_restore(   backup_dir,   save_freq = \"epoch\",   delete_checkpoint = TRUE )"},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"backup_dir String, path directory store data needed restore model. directory reused elsewhere store files, e.g. backup_and_restore callback another training run, another callback (e.g. callback_model_checkpoint) training run. save_freq \"epoch\", integer, FALSE. set \"epoch\", callback saves checkpoint end epoch. set integer, callback saves checkpoint every save_freq batches. Set save_freq = FALSE using preemption checkpointing (.e. save_before_preemption = TRUE). delete_checkpoint Boolean, defaults TRUE. backup_and_restore callback works saving checkpoint back training state. delete_checkpoint = TRUE, checkpoint deleted training finished. Use FALSE like keep checkpoint future usage.","code":""},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_backup_and_restore.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Callback to back up and restore the training state. — callback_backup_and_restore","text":"","code":"callback_interrupting <- new_callback_class(   \"InterruptingCallback\",   on_epoch_begin = function(epoch, logs = NULL) {     if (epoch == 4) {       stop('Interrupting!')     }   } )  backup_dir <- tempfile() callback <- callback_backup_and_restore(backup_dir = backup_dir) model <- keras_model_sequential() %>%   layer_dense(10) model %>% compile(optimizer = optimizer_sgd(), loss = 'mse')  # ensure model is built (i.e., weights are initialized) for # callback_backup_and_restore() model(op_ones(c(5, 20))) |> invisible()  tryCatch({   model %>% fit(x = op_ones(c(5, 20)),                 y = op_zeros(5),                 epochs = 10, batch_size = 1,                 callbacks = list(callback, callback_interrupting()),                 verbose = 0) }, python.builtin.RuntimeError = function(e) message(\"Interrupted!\")) ## Interrupted! model$history$epoch ## [1] 0 1 2 # model$history %>% keras3:::to_keras_training_history() %>% as.data.frame() %>% print()  history <- model %>% fit(x = op_ones(c(5, 20)),                          y = op_zeros(5),                          epochs = 10, batch_size = 1,                          callbacks = list(callback),                          verbose = 0)  # Only 6 more epochs are run, since first training got interrupted at # zero-indexed epoch 4, second training will continue from 4 to 9. nrow(as.data.frame(history)) ## [1] 10"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback that streams epoch results to a CSV file. — callback_csv_logger","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"Supports values can represented string, including 1D iterables atomic vectors.","code":""},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"","code":"callback_csv_logger(filename, separator = \",\", append = FALSE)"},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"filename Filename CSV file, e.g. 'run/log.csv'. separator String used separate elements CSV file. append Boolean. TRUE: append file exists (useful continuing training). FALSE: overwrite existing file.","code":""},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_csv_logger.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Callback that streams epoch results to a CSV file. — callback_csv_logger","text":"","code":"csv_logger <- callback_csv_logger('training.log') model %>% fit(X_train, Y_train, callbacks = list(csv_logger))"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":null,"dir":"Reference","previous_headings":"","what":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"Assuming goal training minimize loss. , metric monitored 'loss', mode 'min'. model$fit() training loop check end every epoch whether loss longer decreasing, considering min_delta patience applicable. found longer decreasing, model$stop_training marked TRUE training terminates. quantity monitored needs available logs list. make , pass loss metrics model$compile().","code":""},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"","code":"callback_early_stopping(   monitor = \"val_loss\",   min_delta = 0L,   patience = 0L,   verbose = 0L,   mode = \"auto\",   baseline = NULL,   restore_best_weights = FALSE,   start_from_epoch = 0L )"},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"monitor Quantity monitored. Defaults \"val_loss\". min_delta Minimum change monitored quantity qualify improvement, .e. absolute change less min_delta, count improvement. Defaults 0. patience Number epochs improvement training stopped. Defaults 0. verbose Verbosity mode, 0 1. Mode 0 silent, mode 1 displays messages callback takes action. Defaults 0. mode One {\"auto\", \"min\", \"max\"}. min mode, training stop quantity monitored stopped decreasing; \"max\" mode stop quantity monitored stopped increasing; \"auto\" mode, direction automatically inferred name monitored quantity. Defaults \"auto\". baseline Baseline value monitored quantity. NULL, training stop model show improvement baseline. Defaults NULL. restore_best_weights Whether restore model weights epoch best value monitored quantity. FALSE, model weights obtained last step training used. epoch restored regardless performance relative baseline. epoch improves baseline, training run patience epochs restore weights best epoch set. Defaults FALSE. start_from_epoch Number epochs wait starting monitor improvement. allows warm-period improvement expected thus training stopped. Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_early_stopping.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stop training when a monitored metric has stopped improving. — callback_early_stopping","text":"","code":"callback <- callback_early_stopping(monitor = 'loss',                                    patience = 3) # This callback will stop the training when there is no improvement in # the loss for three consecutive epochs. model <- keras_model_sequential() %>%   layer_dense(10) model %>% compile(optimizer = optimizer_sgd(), loss = 'mse') history <- model %>% fit(x = op_ones(c(5, 20)),                          y = op_zeros(5),                          epochs = 10, batch_size = 1,                          callbacks = list(callback),                          verbose = 0) nrow(as.data.frame(history))  # Only 4 epochs are run. ## [1] 10"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"callback constructed anonymous functions called appropriate time (Model.{fit | evaluate | predict}). Note callbacks expects positional arguments, : on_epoch_begin on_epoch_end expect two positional arguments: epoch, logs on_train_begin on_train_end expect one positional argument: logs on_train_batch_begin on_train_batch_end expect two positional arguments: batch, logs See Callback class definition full list functions expected arguments.","code":""},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"","code":"callback_lambda(   on_epoch_begin = NULL,   on_epoch_end = NULL,   on_train_begin = NULL,   on_train_end = NULL,   on_train_batch_begin = NULL,   on_train_batch_end = NULL,   ... )"},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"on_epoch_begin called beginning every epoch. on_epoch_end called end every epoch. on_train_begin called beginning model training. on_train_end called end model training. on_train_batch_begin called beginning every train batch. on_train_batch_end called end every train batch. ... function Callback() want override passing function_name = function. example, callback_lambda(.., on_train_end = train_end_fn). custom function needs arguments ones defined Callback().","code":""},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_lambda.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Callback for creating simple, custom callbacks on-the-fly. — callback_lambda","text":"","code":"# Print the batch number at the beginning of every batch. batch_print_callback <- callback_lambda(   on_train_batch_begin = function(batch, logs) {     print(batch)   } )  # Stream the epoch loss to a file in new-line delimited JSON format # (one valid JSON object per line) json_log <- file('loss_log.json', open = 'wt') json_logging_callback <- callback_lambda(   on_epoch_end = function(epoch, logs) {     jsonlite::write_json(       list(epoch = epoch, loss = logs$loss),       json_log,       append = TRUE     )   },   on_train_end = function(logs) {     close(json_log)   } )  # Terminate some processes after having finished model training. processes <- ... cleanup_callback <- callback_lambda(   on_train_end = function(logs) {     for (p in processes) {       if (is_alive(p)) {         terminate(p)       }     }   } )  model %>% fit(   ...,   callbacks = list(     batch_print_callback,     json_logging_callback,     cleanup_callback   ) )"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":null,"dir":"Reference","previous_headings":"","what":"Learning rate scheduler. — callback_learning_rate_scheduler","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"beginning every epoch, callback gets updated learning rate value schedule function provided, current epoch current learning rate, applies updated learning rate optimizer.","code":""},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"","code":"callback_learning_rate_scheduler(schedule, verbose = 0L)"},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"schedule function takes epoch index (integer, indexed 0) current learning rate (float) inputs returns new learning rate output (float). verbose Integer. 0: quiet, 1: log update messages.","code":""},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_learning_rate_scheduler.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Learning rate scheduler. — callback_learning_rate_scheduler","text":"","code":"# This function keeps the initial learning rate steady for the first ten epochs # and decreases it exponentially after that. scheduler <- function(epoch, lr) {   if (epoch < 10)     return(lr)   else     return(lr * exp(-0.1)) }  model <- keras_model_sequential() |> layer_dense(units = 10) model |> compile(optimizer = optimizer_sgd(), loss = 'mse') model$optimizer$learning_rate |> as.array() |> round(5) ## [1] 0.01 callback <- callback_learning_rate_scheduler(schedule = scheduler) history <- model |> fit(x = array(runif(100), c(5, 20)),                         y = array(0, c(5, 1)),                         epochs = 15, callbacks = list(callback), verbose = 0) model$optimizer$learning_rate |> as.array() |> round(5) ## [1] 0.00607"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"callback_model_checkpoint() used conjunction training using model |> fit() save model weights (checkpoint file) interval, model weights can loaded later continue training state saved. options callback provides include: Whether keep model achieved \"best performance\" far, whether save model end every epoch regardless performance. Definition \"best\"; quantity monitor whether maximized minimized. frequency save . Currently, callback supports saving end every epoch, fixed number training batches. Whether weights saved, whole model saved.","code":""},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"","code":"callback_model_checkpoint(   filepath,   monitor = \"val_loss\",   verbose = 0L,   save_best_only = FALSE,   save_weights_only = FALSE,   mode = \"auto\",   save_freq = \"epoch\",   initial_value_threshold = NULL )"},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"filepath string, path save model file. filepath can contain named formatting options, filled value epoch keys logs (passed on_epoch_end). filepath name needs end \".weights.h5\" save_weights_only = TRUE end \".keras\" checkpoint saving whole model (default). example: filepath \"{epoch:02d}-{val_loss:.2f}.keras\", model checkpoints saved epoch number validation loss filename. directory filepath reused callbacks avoid conflicts. monitor metric name monitor. Typically metrics set model |> compile() method. Note: Prefix name \"val_\" monitor validation metrics. Use \"loss\" \"val_loss\" monitor model's total loss. specify metrics strings, like \"accuracy\", pass string (without \"val_\" prefix). pass Metric objects (created one metric_*()), monitor set metric$name. sure metric names can check contents history$metrics list returned history <- model |> fit() Multi-output models set additional prefixes metric names. verbose Verbosity mode, 0 1. Mode 0 silent, mode 1 displays messages callback takes action. save_best_only save_best_only = TRUE, saves model considered \"best\" latest best model according quantity monitored overwritten. filepath contain formatting options like {epoch} filepath overwritten new better model. save_weights_only TRUE, model's weights saved (model |> save_model_weights(filepath)), else full model saved (model |> save_model(filepath)). mode one {\"auto\", \"min\", \"max\"}. save_best_only = TRUE, decision overwrite current save file made based either maximization minimization monitored quantity. val_acc, \"max\", val_loss \"min\", etc. \"auto\" mode, mode set \"max\" quantities monitored \"acc\" start \"fmeasure\" set \"min\" rest quantities. save_freq \"epoch\" integer. using \"epoch\", callback saves model epoch. using integer, callback saves model end many batches. Model compiled steps_per_execution = N, saving criteria checked every Nth batch. Note saving aligned epochs, monitored metric may potentially less reliable (reflect little 1 batch, since metrics get reset every epoch). Defaults \"epoch\". initial_value_threshold Floating point initial \"best\" value metric monitored. applies save_best_value = TRUE. overwrites model weights already saved performance current model better value.","code":""},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_model_checkpoint.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Callback to save the Keras model or model weights at some frequency. — callback_model_checkpoint","text":"","code":"model <- keras_model_sequential(input_shape = c(10)) |>   layer_dense(1, activation = \"sigmoid\") |>   compile(loss = \"binary_crossentropy\", optimizer = \"adam\",           metrics = c('accuracy'))  EPOCHS <- 10 checkpoint_filepath <- tempfile('checkpoint-model-', fileext = \".keras\") model_checkpoint_callback <- callback_model_checkpoint(   filepath = checkpoint_filepath,   monitor = 'val_accuracy',   mode = 'max',   save_best_only = TRUE )  # Model is saved at the end of every epoch, if it's the best seen so far. model |> fit(x = random_uniform(c(2, 10)), y = op_ones(2, 1),              epochs = EPOCHS, validation_split = .5, verbose = 0,              callbacks = list(model_checkpoint_callback))  # The model (that are considered the best) can be loaded as - load_model(checkpoint_filepath) ## Model: \"sequential\" ## +---------------------------------+------------------------+---------------+ ## | Layer (type)                    | Output Shape           |       Param # | ## +=================================+========================+===============+ ## | dense (Dense)                   | (None, 1)              |            11 | ## +---------------------------------+------------------------+---------------+ ##  Total params: 35 (144.00 B) ##  Trainable params: 11 (44.00 B) ##  Non-trainable params: 0 (0.00 B) ##  Optimizer params: 24 (100.00 B) # Alternatively, one could checkpoint just the model weights as - checkpoint_filepath <- tempfile('checkpoint-', fileext = \".weights.h5\") model_checkpoint_callback <- callback_model_checkpoint(   filepath = checkpoint_filepath,   save_weights_only = TRUE,   monitor = 'val_accuracy',   mode = 'max',   save_best_only = TRUE )  # Model weights are saved at the end of every epoch, if it's the best seen # so far. # same as above model |> fit(x = random_uniform(c(2, 10)), y = op_ones(2, 1),              epochs = EPOCHS, validation_split = .5, verbose = 0,              callbacks = list(model_checkpoint_callback))  # The model weights (that are considered the best) can be loaded model |> load_model_weights(checkpoint_filepath)"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":null,"dir":"Reference","previous_headings":"","what":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"Models often benefit reducing learning rate factor 2-10 learning stagnates. callback monitors quantity improvement seen 'patience' number epochs, learning rate reduced.","code":""},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"","code":"callback_reduce_lr_on_plateau(   monitor = \"val_loss\",   factor = 0.1,   patience = 10L,   verbose = 0L,   mode = \"auto\",   min_delta = 1e-04,   cooldown = 0L,   min_lr = 0,   ... )"},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"monitor String. Quantity monitored. factor Float. Factor learning rate reduced. new_lr = lr * factor. patience Integer. Number epochs improvement learning rate reduced. verbose Integer. 0: quiet, 1: update messages. mode String. One {'auto', 'min', 'max'}. 'min' mode, learning rate reduced quantity monitored stopped decreasing; 'max' mode reduced quantity monitored stopped increasing; 'auto' mode, direction automatically inferred name monitored quantity. min_delta Float. Threshold measuring new optimum, focus significant changes. cooldown Integer. Number epochs wait resuming normal operation learning rate reduced. min_lr Float. Lower bound learning rate. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_reduce_lr_on_plateau.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reduce learning rate when a metric has stopped improving. — callback_reduce_lr_on_plateau","text":"","code":"reduce_lr <- callback_reduce_lr_on_plateau(monitor = 'val_loss', factor = 0.2,                                            patience = 5, min_lr = 0.001) model %>% fit(x_train, y_train, callbacks = list(reduce_lr))"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_remote_monitor.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback used to stream events to a server. — callback_remote_monitor","title":"Callback used to stream events to a server. — callback_remote_monitor","text":"Requires requests library. Events sent root + '/publish/epoch/end/' default. Calls HTTP POST, data argument JSON-encoded named list event data. send_as_json = TRUE, content type request \"application/json\". Otherwise serialized JSON sent within form.","code":""},{"path":"https://keras.posit.co/reference/callback_remote_monitor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback used to stream events to a server. — callback_remote_monitor","text":"","code":"callback_remote_monitor(   root = \"http://localhost:9000\",   path = \"/publish/epoch/end/\",   field = \"data\",   headers = NULL,   send_as_json = FALSE )"},{"path":"https://keras.posit.co/reference/callback_remote_monitor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback used to stream events to a server. — callback_remote_monitor","text":"root String; root url target server. path String; path relative root events sent. field String; JSON field data stored. field used payload sent within form (.e. send_as_json = FALSE). headers Named list; optional custom HTTP headers. send_as_json Boolean; whether request sent \"application/json\".","code":""},{"path":"https://keras.posit.co/reference/callback_remote_monitor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback used to stream events to a server. — callback_remote_monitor","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"callbacks replaces model's weight values values optimizer's EMA weights (exponential moving average past model weights values, implementing \"Polyak averaging\") model evaluation, restores previous weights evaluation. SwapEMAWeights callback used conjunction optimizer sets use_ema = TRUE. Note weights swapped -place order save memory. behavior undefined modify EMA weights model weights callbacks.","code":""},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"","code":"callback_swap_ema_weights(swap_on_epoch = FALSE)"},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"swap_on_epoch Whether perform swapping on_epoch_begin() on_epoch_end(). useful want use EMA weights callbacks callback_model_checkpoint(). Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_swap_ema_weights.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Swaps model weights and EMA weights before and after evaluation. — callback_swap_ema_weights","text":"","code":"# Remember to set `use_ema=TRUE` in the optimizer optimizer <- optimizer_sgd(use_ema = TRUE) model |> compile(optimizer = optimizer, loss = ..., metrics = ...)  # Metrics will be computed with EMA weights model |> fit(X_train, Y_train,              callbacks = c(callback_swap_ema_weights()))  # If you want to save model checkpoint with EMA weights, you can set # `swap_on_epoch=TRUE` and place ModelCheckpoint after SwapEMAWeights. model |> fit(   X_train, Y_train,   callbacks = c(     callback_swap_ema_weights(swap_on_epoch = TRUE),     callback_model_checkpoint(...)   ) )"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":null,"dir":"Reference","previous_headings":"","what":"Enable visualizations for TensorBoard. — callback_tensorboard","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"TensorBoard visualization tool provided TensorFlow. TensorFlow installation required use callback. callback logs events TensorBoard, including: Metrics summary plots Training graph visualization Weight histograms Sampled profiling used model |> evaluate() regular validation addition epoch summaries, summary records evaluation metrics vs model$optimizer$iterations written. metric names prepended evaluation, model$optimizer$iterations step visualized TensorBoard. installed TensorFlow pip reticulate::py_install(), able launch TensorBoard command line:   R tensorflow::tensorboard(). can find information TensorBoard .","code":"tensorboard --logdir=path_to_your_logs"},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"","code":"callback_tensorboard(   log_dir = \"logs\",   histogram_freq = 0L,   write_graph = TRUE,   write_images = FALSE,   write_steps_per_second = FALSE,   update_freq = \"epoch\",   profile_batch = 0L,   embeddings_freq = 0L,   embeddings_metadata = NULL )"},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"log_dir path directory save log files parsed TensorBoard. e.g., log_dir = file.path(working_dir, 'logs'). directory reused callbacks. histogram_freq frequency (epochs) compute weight histograms layers model. set 0, histograms computed. Validation data (split) must specified histogram visualizations. write_graph (supported time) Whether visualize graph TensorBoard. Note log file can become quite large write_graph set TRUE. write_images whether write model weights visualize image TensorBoard. write_steps_per_second whether log training steps per second TensorBoard. supports epoch batch frequency logging. update_freq \"batch\" \"epoch\" integer. using \"epoch\", writes losses metrics TensorBoard every epoch. using integer, say 1000, metrics losses (including custom ones added Model.compile) logged TensorBoard every 1000 batches. \"batch\" synonym 1, meaning written every batch. Note however writing frequently TensorBoard can slow training, especially used distribution strategies incur additional synchronization overhead. Batch-level summary writing also available via train_step override. Please see TensorBoard Scalars tutorial  # noqa: E501 details. profile_batch (supported time) Profile batch(es) sample compute characteristics. profile_batch must non-negative integer tuple integers. pair positive integers signify range batches profile. default, profiling disabled. embeddings_freq frequency (epochs) embedding layers visualized. set 0, embeddings visualized. embeddings_metadata Named list maps embedding layer names filename file save metadata embedding layer. case metadata file used embedding layers, single filename can passed.","code":""},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":"https://keras.posit.co/reference/callback_tensorboard.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Enable visualizations for TensorBoard. — callback_tensorboard","text":"Custom batch-level summaries subclassed Model:   Custom batch-level summaries Functional API Model:   Profiling:","code":"tensorboard_callback <- callback_tensorboard(log_dir = \"./logs\") model %>% fit(x_train, y_train, epochs = 2, callbacks = list(tensorboard_callback)) # Then run the tensorboard command to view the visualizations. MyModel <- new_model_class(\"MyModel\",   initialize = function() {     self$dense <- layer_dense(units = 10)   },   call = function(x) {     outputs <- x |> self$dense()     tf$summary$histogram('outputs', outputs)     outputs   } )  model <- MyModel() model |> compile(optimizer = 'sgd', loss = 'mse')  # Make sure to set `update_freq = N` to log a batch-level summary every N # batches. In addition to any `tf$summary` contained in `model$call()`, # metrics added in `model |>compile` will be logged every N batches. tb_callback <- callback_tensorboard(log_dir = './logs', update_freq = 1) model |> fit(x_train, y_train, callbacks = list(tb_callback)) my_summary <- function(x) {   tf$summary$histogram('x', x)   x }  inputs <- layer_input(10) outputs <- inputs |>   layer_dense(10) |>   layer_lambda(my_summary)  model <- keras_model(inputs, outputs) model |> compile(optimizer = 'sgd', loss = 'mse')  # Make sure to set `update_freq = N` to log a batch-level summary every N # batches. In addition to any `tf.summary` contained in `Model.call`, # metrics added in `Model.compile` will be logged every N batches. tb_callback <- callback_tensorboard(log_dir = './logs', update_freq = 1) model |> fit(x_train, y_train, callbacks = list(tb_callback)) # Profile a single batch, e.g. the 5th batch. tensorboard_callback <- callback_tensorboard(   log_dir = './logs', profile_batch = 5) model |> fit(x_train, y_train, epochs = 2,              callbacks = list(tensorboard_callback))  # Profile a range of batches, e.g. from 10 to 20. tensorboard_callback <- callback_tensorboard(   log_dir = './logs', profile_batch = c(10, 20)) model |> fit(x_train, y_train, epochs = 2,              callbacks = list(tensorboard_callback))"},{"path":[]},{"path":"https://keras.posit.co/reference/callback_terminate_on_nan.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback that terminates training when a NaN loss is encountered. — callback_terminate_on_nan","title":"Callback that terminates training when a NaN loss is encountered. — callback_terminate_on_nan","text":"Callback terminates training NaN loss encountered.","code":""},{"path":"https://keras.posit.co/reference/callback_terminate_on_nan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback that terminates training when a NaN loss is encountered. — callback_terminate_on_nan","text":"","code":"callback_terminate_on_nan()"},{"path":"https://keras.posit.co/reference/callback_terminate_on_nan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback that terminates training when a NaN loss is encountered. — callback_terminate_on_nan","text":"Callback instance can passed fit.keras.src.models.model.Model().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/clear_session.html","id":null,"dir":"Reference","previous_headings":"","what":"Resets all state generated by Keras. — clear_session","title":"Resets all state generated by Keras. — clear_session","text":"Keras manages global state, uses implement Functional model-building API uniquify autogenerated layer names. creating many models loop, global state consume increasing amount memory time, may want clear . Calling clear_session() releases global state: helps avoid clutter old models layers, especially memory limited. Example 1: calling clear_session() creating models loop   Example 2: resetting layer name generation counter","code":"for (i in 1:100) {   # Without `clear_session()`, each iteration of this loop will   # slightly increase the size of the global state managed by Keras   model <- keras_model_sequential()   for (j in 1:10) {     model <- model |> layer_dense(units = 10)   } }  for (i in 1:100) {   # With `clear_session()` called at the beginning,   # Keras starts with a blank state at each iteration   # and memory consumption is constant over time.   clear_session()   model <- keras_model_sequential()   for (j in 1:10) {     model <- model |> layer_dense(units = 10)   } } layers <- lapply(1:10, \\(i) layer_dense(units = 10))  new_layer <- layer_dense(units = 10) print(new_layer$name) ## [1] \"dense_10\" clear_session() new_layer <- layer_dense(units = 10) print(new_layer$name) ## [1] \"dense\""},{"path":"https://keras.posit.co/reference/clear_session.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resets all state generated by Keras. — clear_session","text":"","code":"clear_session(free_memory = TRUE)"},{"path":"https://keras.posit.co/reference/clear_session.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resets all state generated by Keras. — clear_session","text":"free_memory Whether call Python garbage collection. usually good practice call make sure memory used deleted objects immediately freed. However, may take seconds execute, using clear_session() short loop, may want skip .","code":""},{"path":"https://keras.posit.co/reference/clear_session.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resets all state generated by Keras. — clear_session","text":"NULL, invisibly, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/clone_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Clone a Functional or Sequential Model instance. — clone_model","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"Model cloning similar calling model new inputs, except creates new layers (thus new weights) instead sharing weights existing layers. Note clone_model() preserve uniqueness shared objects within model (e.g. single variable attached two distinct layers restored two separate variables).","code":""},{"path":"https://keras.posit.co/reference/clone_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"","code":"clone_model(   model,   input_tensors = NULL,   clone_function = NULL,   call_function = NULL,   recursive = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/clone_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"model Instance Model (Functional model Sequential model). input_tensors Optional list input tensors build model upon. provided, new keras_input() objects created. clone_function Callable signature function(layer) used clone layer target model (except Input instances). takes argument layer instance cloned, returns corresponding layer instance used model copy. unspecified, callable defaults following serialization/deserialization function: function(layer) layer$`__class__`$from_config(layer$get_config()). passing custom callable, can customize copy model, e.g. wrapping certain layers interest (might want replace LSTM instances equivalent Bidirectional(LSTM(...)) instances, example). Defaults NULL. call_function Callable signature function(layer, ...) used call cloned layer set inputs. takes layer instance, call arguments, returns call outputs. unspecified, callable defaults regular call() method: function(layer, ...) .call(layer, list(...)). passing custom callable, can insert new layers given layer. recursive Note, argument can used Functional models. Boolean. Whether recursively clone Sequential Functional models encountered original Sequential/Functional model. FALSE, inner models cloned calling clone_function(). TRUE, inner models cloned calling clone_model() clone_function, call_function, recursive arguments. Note case, call_function propagated Sequential model (since applicable Sequential models). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/clone_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"instance Model reproducing behavior original model, top new inputs tensors, using newly instantiated weights. cloned model may behave differently original model custom clone_function call_function modifies layer layer call.","code":""},{"path":"https://keras.posit.co/reference/clone_model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clone a Functional or Sequential Model instance. — clone_model","text":"Using clone_function make model deterministic setting random seed everywhere:   Using call_function add Dropout layer Dense layer (without recreating new layers):     Note subclassed models cloned default, since internal layer structure known. achieve equivalent functionality clone_model case subclassed model, simply make sure model class implements get_config() (optionally from_config()), call:   case subclassed model, using custom clone_function.","code":"# Create a test Sequential model. model <- keras_model_sequential(input_shape = c(728)) |>   layer_dense(32, activation = 'relu') |>   layer_dense(1, activation = 'sigmoid')  # Create a copy of the test model (with freshly initialized weights). new_model <- clone_model(model) clone_function <- function(layer) {   config <- layer$get_config()   if (\"seed\" %in% names(config))     config$seed <- 1337L   layer$`__class__`$from_config(config) }  new_model <- clone_model(model, clone_function = clone_function) call_function <- function(layer, ...) {   out <- layer(...)   if (inherits(layer, keras$layers$Dense))     out <- out |> layer_dropout(0.5)   out }  inputs <- keras_input(c(728)) outputs <- inputs |>   layer_dense(32, activation = 'relu') |>   layer_dense(1, activation = 'sigmoid') model <- keras_model(inputs, outputs)  new_model <- clone_model(   model,   clone_function = function(x) x, # Reuse the same layers.   call_function = call_function, ) new_model ## Model: \"functional_5\" ## +-----------------------------------+--------------------------+---------------+ ## | Layer (type)                      | Output Shape             |       Param # | ## +===================================+==========================+===============+ ## | keras_tensor_8 (InputLayer)       | (None, 728)              |             0 | ## +-----------------------------------+--------------------------+---------------+ ## | dense_2 (Dense)                   | (None, 32)               |        23,328 | ## +-----------------------------------+--------------------------+---------------+ ## | dropout (Dropout)                 | (None, 32)               |             0 | ## +-----------------------------------+--------------------------+---------------+ ## | dense_3 (Dense)                   | (None, 1)                |            33 | ## +-----------------------------------+--------------------------+---------------+ ## | dropout_1 (Dropout)               | (None, 1)                |             0 | ## +-----------------------------------+--------------------------+---------------+ ##  Total params: 23,361 (91.25 KB) ##  Trainable params: 23,361 (91.25 KB) ##  Non-trainable params: 0 (0.00 B) new_model <- model$`__class__`$from_config(model$get_config())"},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Configure a model for training. — compile.keras.src.models.model.Model","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"Configure model training.","code":""},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model compile(   object,   optimizer = \"rmsprop\",   loss = NULL,   metrics = NULL,   ...,   loss_weights = NULL,   weighted_metrics = NULL,   run_eagerly = FALSE,   steps_per_execution = 1L,   jit_compile = \"auto\",   auto_scale_loss = TRUE )"},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"object Keras model object optimizer String (name optimizer) optimizer instance. See optimizer_* family. loss Loss function. May : string (name builtin loss function), custom function, Loss instance (returned loss_* family functions). loss function callable signature loss = fn(y_true, y_pred), y_true ground truth values, y_pred model's predictions. y_true shape (batch_size, d1, .. dN) (except case sparse loss functions sparse categorical crossentropy expects integer arrays shape (batch_size, d1, .. dN-1)). y_pred shape (batch_size, d1, .. dN). loss function return float tensor. metrics List metrics evaluated model training testing. can : string (name built-function), function, optionally \"name\" attribute Metric() instance. See metric_* family functions. Typically use metrics = c('accuracy'). function callable signature result = fn(y_true, y_pred). specify different metrics different outputs multi-output model, also pass named list, metrics = list(= 'accuracy', b = c('accuracy', 'mse')). can also pass list specify metric list metrics output, metrics = list(c('accuracy'), c('accuracy', 'mse')) metrics = list('accuracy', c('accuracy', 'mse')). pass strings 'accuracy' 'acc', convert one metric_binary_accuracy(), metric_categorical_accuracy(), metric_sparse_categorical_accuracy() based shapes targets model output. similar conversion done strings \"crossentropy\" \"ce\" well. metrics passed evaluated without sample weighting; like sample weighting apply, can specify metrics via weighted_metrics argument instead. providing anonymous R function, can customize printed name training assigning attr(<fn>, \"name\") <- \"my_custom_metric_name\", calling custom_metric(\"my_custom_metric_name\", <fn>) ... Additional arguments passed compile() model method. loss_weights Optional list (named unnamed) specifying scalar coefficients (R numerics) weight loss contributions different model outputs. loss value minimized model weighted sum individual losses, weighted loss_weights coefficients.  unnamed list, expected 1:1 mapping model's outputs. named list, expected map output names (strings) scalar coefficients. weighted_metrics List metrics evaluated weighted sample_weight class_weight training testing. run_eagerly Bool. TRUE, model's forward pass never compiled. recommended leave FALSE training (best performance), set TRUE debugging. steps_per_execution Int. number batches run single compiled function call. Running multiple batches inside single compiled function call can greatly improve performance TPUs small models large R/Python overhead. , one full epoch run execution. number larger size epoch passed, execution truncated size epoch. Note steps_per_execution set N, Callback$on_batch_begin Callback$on_batch_end methods called every N batches (.e. /compiled function execution). supported PyTorch backend. jit_compile Bool \"auto\". Whether use XLA compilation compiling model. jax tensorflow backends, jit_compile=\"auto\" enables XLA compilation model supports , disabled otherwise. torch backend, \"auto\" default eager execution jit_compile=True run torch.compile \"inductor\" backend. auto_scale_loss Bool. TRUE model dtype policy \"mixed_float16\", passed optimizer automatically wrapped LossScaleOptimizer, dynamically scale loss prevent underflow.","code":""},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"called primarily side effect modifying object -place. first argument object also returned, invisibly, enable usage pipe.","code":""},{"path":"https://keras.posit.co/reference/compile.keras.src.models.model.Model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Configure a model for training. — compile.keras.src.models.model.Model","text":"","code":"model |> compile(   optimizer = optimizer_adam(learning_rate = 1e-3),   loss = loss_binary_crossentropy(),   metrics = c(metric_binary_accuracy(),               metric_false_negatives()) )"},{"path":[]},{"path":"https://keras.posit.co/reference/config_backend.html","id":null,"dir":"Reference","previous_headings":"","what":"Publicly accessible method for determining the current backend. — config_backend","title":"Publicly accessible method for determining the current backend. — config_backend","text":"Publicly accessible method determining current backend.","code":""},{"path":"https://keras.posit.co/reference/config_backend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Publicly accessible method for determining the current backend. — config_backend","text":"","code":"config_backend()"},{"path":"https://keras.posit.co/reference/config_backend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Publicly accessible method for determining the current backend. — config_backend","text":"String, name backend Keras currently using. One \"tensorflow\", \"torch\", \"jax\".","code":""},{"path":"https://keras.posit.co/reference/config_backend.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Publicly accessible method for determining the current backend. — config_backend","text":"","code":"config_backend() ## [1] \"tensorflow\""},{"path":[]},{"path":"https://keras.posit.co/reference/config_disable_interactive_logging.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn off interactive logging. — config_disable_interactive_logging","title":"Turn off interactive logging. — config_disable_interactive_logging","text":"interactive logging disabled, Keras sends logs absl.logging. best option using Keras non-interactive way, running training inference job server.","code":""},{"path":"https://keras.posit.co/reference/config_disable_interactive_logging.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn off interactive logging. — config_disable_interactive_logging","text":"","code":"config_disable_interactive_logging()"},{"path":"https://keras.posit.co/reference/config_disable_interactive_logging.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn off interactive logging. — config_disable_interactive_logging","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_disable_traceback_filtering.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn off traceback filtering. — config_disable_traceback_filtering","title":"Turn off traceback filtering. — config_disable_traceback_filtering","text":"Raw Keras tracebacks (also known stack traces) involve many internal frames, can challenging read , actionable end users. default, Keras filters internal frames exceptions raises, keep traceback short, readable, focused actionable (code). See also config_enable_traceback_filtering() config_is_traceback_filtering_enabled(). previously disabled traceback filtering via config_disable_traceback_filtering(), can re-enable via config_enable_traceback_filtering().","code":""},{"path":"https://keras.posit.co/reference/config_disable_traceback_filtering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn off traceback filtering. — config_disable_traceback_filtering","text":"","code":"config_disable_traceback_filtering()"},{"path":"https://keras.posit.co/reference/config_disable_traceback_filtering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn off traceback filtering. — config_disable_traceback_filtering","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_dtype_policy.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the current default dtype policy object. — config_dtype_policy","title":"Returns the current default dtype policy object. — config_dtype_policy","text":"Returns current default dtype policy object.","code":""},{"path":"https://keras.posit.co/reference/config_dtype_policy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the current default dtype policy object. — config_dtype_policy","text":"","code":"config_dtype_policy()"},{"path":"https://keras.posit.co/reference/config_dtype_policy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the current default dtype policy object. — config_dtype_policy","text":"DTypePolicy object.","code":""},{"path":"https://keras.posit.co/reference/config_enable_interactive_logging.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn on interactive logging. — config_enable_interactive_logging","title":"Turn on interactive logging. — config_enable_interactive_logging","text":"interactive logging enabled, Keras displays logs via stdout. provides best experience using Keras interactive environment shell notebook.","code":""},{"path":"https://keras.posit.co/reference/config_enable_interactive_logging.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn on interactive logging. — config_enable_interactive_logging","text":"","code":"config_enable_interactive_logging()"},{"path":"https://keras.posit.co/reference/config_enable_interactive_logging.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn on interactive logging. — config_enable_interactive_logging","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_enable_traceback_filtering.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn on traceback filtering. — config_enable_traceback_filtering","title":"Turn on traceback filtering. — config_enable_traceback_filtering","text":"Raw Keras tracebacks (also known stack traces) involve many internal frames, can challenging read , actionable end users. default, Keras filters internal frames exceptions raises, keep traceback short, readable, focused actionable (code). See also config_disable_traceback_filtering() config_is_traceback_filtering_enabled(). previously disabled traceback filtering via config_disable_traceback_filtering(), can re-enable via config_enable_traceback_filtering().","code":""},{"path":"https://keras.posit.co/reference/config_enable_traceback_filtering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn on traceback filtering. — config_enable_traceback_filtering","text":"","code":"config_enable_traceback_filtering()"},{"path":"https://keras.posit.co/reference/config_enable_traceback_filtering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn on traceback filtering. — config_enable_traceback_filtering","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_enable_unsafe_deserialization.html","id":null,"dir":"Reference","previous_headings":"","what":"Disables safe mode globally, allowing deserialization of lambdas. — config_enable_unsafe_deserialization","title":"Disables safe mode globally, allowing deserialization of lambdas. — config_enable_unsafe_deserialization","text":"Disables safe mode globally, allowing deserialization lambdas.","code":""},{"path":"https://keras.posit.co/reference/config_enable_unsafe_deserialization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Disables safe mode globally, allowing deserialization of lambdas. — config_enable_unsafe_deserialization","text":"","code":"config_enable_unsafe_deserialization()"},{"path":"https://keras.posit.co/reference/config_enable_unsafe_deserialization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Disables safe mode globally, allowing deserialization of lambdas. — config_enable_unsafe_deserialization","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_epsilon.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","title":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","text":"Return value fuzz factor used numeric expressions.","code":""},{"path":"https://keras.posit.co/reference/config_epsilon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","text":"","code":"config_epsilon()"},{"path":"https://keras.posit.co/reference/config_epsilon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","text":"float.","code":""},{"path":"https://keras.posit.co/reference/config_epsilon.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the value of the fuzz factor used in numeric expressions. — config_epsilon","text":"","code":"config_epsilon() ## [1] 1e-07"},{"path":[]},{"path":"https://keras.posit.co/reference/config_floatx.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the default float type, as a string. — config_floatx","title":"Return the default float type, as a string. — config_floatx","text":"E.g. 'bfloat16' 'float16', 'float32', 'float64'.","code":""},{"path":"https://keras.posit.co/reference/config_floatx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the default float type, as a string. — config_floatx","text":"","code":"config_floatx()"},{"path":"https://keras.posit.co/reference/config_floatx.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the default float type, as a string. — config_floatx","text":"String, current default float type.","code":""},{"path":"https://keras.posit.co/reference/config_floatx.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the default float type, as a string. — config_floatx","text":"","code":"keras3::config_floatx() ## [1] \"float32\""},{"path":[]},{"path":"https://keras.posit.co/reference/config_image_data_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the default image data format convention. — config_image_data_format","title":"Return the default image data format convention. — config_image_data_format","text":"Return default image data format convention.","code":""},{"path":"https://keras.posit.co/reference/config_image_data_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the default image data format convention. — config_image_data_format","text":"","code":"config_image_data_format()"},{"path":"https://keras.posit.co/reference/config_image_data_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the default image data format convention. — config_image_data_format","text":"string, either 'channels_first' 'channels_last'.","code":""},{"path":"https://keras.posit.co/reference/config_image_data_format.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the default image data format convention. — config_image_data_format","text":"","code":"config_image_data_format() ## [1] \"channels_last\""},{"path":[]},{"path":"https://keras.posit.co/reference/config_is_interactive_logging_enabled.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if interactive logging is enabled. — config_is_interactive_logging_enabled","title":"Check if interactive logging is enabled. — config_is_interactive_logging_enabled","text":"switch writing logs stdout absl.logging, may use config_enable_interactive_logging() config_disable_interactive_logging().","code":""},{"path":"https://keras.posit.co/reference/config_is_interactive_logging_enabled.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if interactive logging is enabled. — config_is_interactive_logging_enabled","text":"","code":"config_is_interactive_logging_enabled()"},{"path":"https://keras.posit.co/reference/config_is_interactive_logging_enabled.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if interactive logging is enabled. — config_is_interactive_logging_enabled","text":"Boolean, TRUE interactive logging enabled, FALSE otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_is_traceback_filtering_enabled.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if traceback filtering is enabled. — config_is_traceback_filtering_enabled","title":"Check if traceback filtering is enabled. — config_is_traceback_filtering_enabled","text":"Raw Keras tracebacks (also known stack traces) involve many internal frames, can challenging read , actionable end users. default, Keras filters internal frames exceptions raises, keep traceback short, readable, focused actionable (code). See also config_enable_traceback_filtering() config_disable_traceback_filtering(). previously disabled traceback filtering via config_disable_traceback_filtering(), can re-enable via config_enable_traceback_filtering().","code":""},{"path":"https://keras.posit.co/reference/config_is_traceback_filtering_enabled.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if traceback filtering is enabled. — config_is_traceback_filtering_enabled","text":"","code":"config_is_traceback_filtering_enabled()"},{"path":"https://keras.posit.co/reference/config_is_traceback_filtering_enabled.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if traceback filtering is enabled. — config_is_traceback_filtering_enabled","text":"Boolean, TRUE traceback filtering enabled, FALSE otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":null,"dir":"Reference","previous_headings":"","what":"Reload the backend (and the Keras package). — config_set_backend","title":"Reload the backend (and the Keras package). — config_set_backend","text":"Reload backend (Keras package).","code":""},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reload the backend (and the Keras package). — config_set_backend","text":"","code":"config_set_backend(backend)"},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reload the backend (and the Keras package). — config_set_backend","text":"backend String","code":""},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reload the backend (and the Keras package). — config_set_backend","text":"Nothing, function called side effect.","code":""},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reload the backend (and the Keras package). — config_set_backend","text":"","code":"config_set_backend(\"jax\")"},{"path":"https://keras.posit.co/reference/config_set_backend.html","id":"warning","dir":"Reference","previous_headings":"","what":"WARNING","title":"Reload the backend (and the Keras package). — config_set_backend","text":"Using function dangerous done carefully. Changing backend convert type already-instantiated objects. Thus, layers / tensors / etc. already created longer usable without errors. strongly recommended keep around Keras-originated objects instances created calling config_set_backend(). includes function class instance uses Keras functionality. code needs re-executed calling config_set_backend().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets the default dtype policy globally. — config_set_dtype_policy","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"Sets default dtype policy globally.","code":""},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"","code":"config_set_dtype_policy(policy)"},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"policy string DTypePolicy object.","code":""},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"return value, called side effects.","code":""},{"path":"https://keras.posit.co/reference/config_set_dtype_policy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sets the default dtype policy globally. — config_set_dtype_policy","text":"","code":"config_set_dtype_policy(\"mixed_float16\")"},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"Set value fuzz factor used numeric expressions.","code":""},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"","code":"config_set_epsilon(value)"},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"value float. New value epsilon.","code":""},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"return value, called side effects.","code":""},{"path":"https://keras.posit.co/reference/config_set_epsilon.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set the value of the fuzz factor used in numeric expressions. — config_set_epsilon","text":"","code":"config_epsilon() ## [1] 1e-07 config_set_epsilon(1e-5) config_epsilon() ## [1] 1e-05 # Set it back to the default value. config_set_epsilon(1e-7)"},{"path":[]},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the default float dtype. — config_set_floatx","title":"Set the default float dtype. — config_set_floatx","text":"Set default float dtype.","code":""},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the default float dtype. — config_set_floatx","text":"","code":"config_set_floatx(value)"},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the default float dtype. — config_set_floatx","text":"value String; 'bfloat16', 'float16', 'float32', 'float64'.","code":""},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the default float dtype. — config_set_floatx","text":"return value, called side effects.","code":""},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Set the default float dtype. — config_set_floatx","text":"recommended set \"float16\" training, likely cause numeric stability issues. Instead, mixed precision, leverages mix float16 float32. can configured calling keras3::keras$mixed_precision$set_dtype_policy('mixed_float16').","code":""},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set the default float dtype. — config_set_floatx","text":"","code":"config_floatx() ## [1] \"float32\" config_set_floatx('float64') config_floatx() ## [1] \"float64\" # Set it back to float32 config_set_floatx('float32')"},{"path":"https://keras.posit.co/reference/config_set_floatx.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"Set the default float dtype. — config_set_floatx","text":"ValueError: case invalid value.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the value of the image data format convention. — config_set_image_data_format","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"Set value image data format convention.","code":""},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"","code":"config_set_image_data_format(data_format)"},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"data_format string. 'channels_first' 'channels_last'.","code":""},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"return value, called side effects.","code":""},{"path":"https://keras.posit.co/reference/config_set_image_data_format.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set the value of the image data format convention. — config_set_image_data_format","text":"","code":"config_image_data_format() ## [1] \"channels_last\" # 'channels_last' keras3::config_set_image_data_format('channels_first') config_image_data_format() ## [1] \"channels_first\" # Set it back to `'channels_last'` keras3::config_set_image_data_format('channels_last')"},{"path":[]},{"path":"https://keras.posit.co/reference/constraint_maxnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"MaxNorm weight constraint. — constraint_maxnorm","title":"MaxNorm weight constraint. — constraint_maxnorm","text":"Constrains weights incident hidden unit norm less equal desired value.","code":""},{"path":"https://keras.posit.co/reference/constraint_maxnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MaxNorm weight constraint. — constraint_maxnorm","text":"","code":"constraint_maxnorm(max_value = 2L, axis = 1L)"},{"path":"https://keras.posit.co/reference/constraint_maxnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MaxNorm weight constraint. — constraint_maxnorm","text":"max_value maximum norm value incoming weights. axis integer, axis along calculate weight norms. instance, Dense layer weight matrix shape (input_dim, output_dim), set axis 0 constrain weight vector length (input_dim,). Conv2D layer data_format = \"channels_last\", weight tensor shape (rows, cols, input_depth, output_depth), set axis [0, 1, 2] constrain weights filter tensor size (rows, cols, input_depth).","code":""},{"path":"https://keras.posit.co/reference/constraint_maxnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MaxNorm weight constraint. — constraint_maxnorm","text":"Constraint instance, callable can passed layer constructors used directly calling tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/constraint_minmaxnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"MinMaxNorm weight constraint. — constraint_minmaxnorm","title":"MinMaxNorm weight constraint. — constraint_minmaxnorm","text":"Constrains weights incident hidden unit norm lower bound upper bound.","code":""},{"path":"https://keras.posit.co/reference/constraint_minmaxnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MinMaxNorm weight constraint. — constraint_minmaxnorm","text":"","code":"constraint_minmaxnorm(min_value = 0, max_value = 1, rate = 1, axis = 1L)"},{"path":"https://keras.posit.co/reference/constraint_minmaxnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MinMaxNorm weight constraint. — constraint_minmaxnorm","text":"min_value minimum norm incoming weights. max_value maximum norm incoming weights. rate rate enforcing constraint: weights rescaled yield op_clip? (1 - rate) * norm + rate * op_clip(norm, min_value, max_value). Effectively, means rate = 1.0 stands strict enforcement constraint, rate<1.0 means weights rescaled step slowly move towards value inside desired interval. axis integer, axis along calculate weight norms. instance, Dense layer weight matrix shape (input_dim, output_dim), set axis 0 constrain weight vector length (input_dim,). Conv2D layer data_format = \"channels_last\", weight tensor shape (rows, cols, input_depth, output_depth), set axis [0, 1, 2] constrain weights filter tensor size (rows, cols, input_depth).","code":""},{"path":"https://keras.posit.co/reference/constraint_minmaxnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MinMaxNorm weight constraint. — constraint_minmaxnorm","text":"Constraint instance, callable can passed layer constructors used directly calling tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/constraint_nonneg.html","id":null,"dir":"Reference","previous_headings":"","what":"Constrains the weights to be non-negative. — constraint_nonneg","title":"Constrains the weights to be non-negative. — constraint_nonneg","text":"Constrains weights non-negative.","code":""},{"path":"https://keras.posit.co/reference/constraint_nonneg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constrains the weights to be non-negative. — constraint_nonneg","text":"","code":"constraint_nonneg()"},{"path":"https://keras.posit.co/reference/constraint_nonneg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constrains the weights to be non-negative. — constraint_nonneg","text":"Constraint instance, callable can passed layer constructors used directly calling tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/constraint_unitnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","title":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","text":"Constrains weights incident hidden unit unit norm.","code":""},{"path":"https://keras.posit.co/reference/constraint_unitnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","text":"","code":"constraint_unitnorm(axis = 1L)"},{"path":"https://keras.posit.co/reference/constraint_unitnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","text":"axis integer, axis along calculate weight norms. instance, Dense layer weight matrix shape (input_dim, output_dim), set axis 0 constrain weight vector length (input_dim,). Conv2D layer data_format = \"channels_last\", weight tensor shape (rows, cols, input_depth, output_depth), set axis [0, 1, 2] constrain weights filter tensor size (rows, cols, input_depth).","code":""},{"path":"https://keras.posit.co/reference/constraint_unitnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constrains the weights incident to each hidden unit to have unit norm. — constraint_unitnorm","text":"Constraint instance, callable can passed layer constructors used directly calling tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/count_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Count the total number of scalars composing the weights. — count_params","title":"Count the total number of scalars composing the weights. — count_params","text":"Count total number scalars composing weights.","code":""},{"path":"https://keras.posit.co/reference/count_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count the total number of scalars composing the weights. — count_params","text":"","code":"count_params(object)"},{"path":"https://keras.posit.co/reference/count_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count the total number of scalars composing the weights. — count_params","text":"object Layer model object","code":""},{"path":"https://keras.posit.co/reference/count_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count the total number of scalars composing the weights. — count_params","text":"integer count","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/custom_metric.html","id":null,"dir":"Reference","previous_headings":"","what":"Custom metric function — custom_metric","title":"Custom metric function — custom_metric","text":"Custom metric function","code":""},{"path":"https://keras.posit.co/reference/custom_metric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Custom metric function — custom_metric","text":"","code":"custom_metric(name, metric_fn)"},{"path":"https://keras.posit.co/reference/custom_metric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Custom metric function — custom_metric","text":"name name used show training progress output metric_fn R function signature function(y_true, y_pred) accepts tensors.","code":""},{"path":"https://keras.posit.co/reference/custom_metric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Custom metric function — custom_metric","text":"callable function __name__ attribute.","code":""},{"path":"https://keras.posit.co/reference/custom_metric.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Custom metric function — custom_metric","text":"can provide arbitrary R function custom metric. Note y_true y_pred parameters tensors, computations use op_* tensor functions. Use custom_metric() function define custom metric. Note name ('mean_pred') provided custom metric function: name used within training progress output. want save load model custom metrics, also call register_keras_serializable(), specify metric call load_model(). example: load_model(\"my_model.keras\", c('mean_pred' = metric_mean_pred)). Alternatively, can wrap code call with_custom_object_scope() allow refer metric name just like built keras metrics. Alternative ways supplying custom metrics: custom_metric(): Arbitrary R function. metric_mean_wrapper(): Wrap arbitrary R function Metric instance. Create custom Metric() subclass.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_boston_housing.html","id":null,"dir":"Reference","previous_headings":"","what":"Boston housing price regression dataset — dataset_boston_housing","title":"Boston housing price regression dataset — dataset_boston_housing","text":"Dataset taken StatLib library maintained Carnegie Mellon University.","code":""},{"path":"https://keras.posit.co/reference/dataset_boston_housing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Boston housing price regression dataset — dataset_boston_housing","text":"","code":"dataset_boston_housing(   path = \"boston_housing.npz\",   test_split = 0.2,   seed = 113L )"},{"path":"https://keras.posit.co/reference/dataset_boston_housing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Boston housing price regression dataset — dataset_boston_housing","text":"path Path cache dataset locally (relative ~/.keras/datasets). test_split fraction data reserve test set. seed Random seed shuffling data computing test split.","code":""},{"path":"https://keras.posit.co/reference/dataset_boston_housing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Boston housing price regression dataset — dataset_boston_housing","text":"Lists training test data: train$x, train$y, test$x, test$y. Samples contain 13 attributes houses different locations around Boston suburbs late 1970s. Targets median values houses location (k$).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_cifar10.html","id":null,"dir":"Reference","previous_headings":"","what":"CIFAR10 small image classification — dataset_cifar10","title":"CIFAR10 small image classification — dataset_cifar10","text":"Dataset 50,000 32x32 color training images, labeled 10 categories, 10,000 test images.","code":""},{"path":"https://keras.posit.co/reference/dataset_cifar10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIFAR10 small image classification — dataset_cifar10","text":"","code":"dataset_cifar10()"},{"path":"https://keras.posit.co/reference/dataset_cifar10.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIFAR10 small image classification — dataset_cifar10","text":"Lists training test data: train$x, train$y, test$x, test$y. x data array RGB image data shape (num_samples, 3, 32, 32). y data array category labels (integers range 0-9) shape (num_samples).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_cifar100.html","id":null,"dir":"Reference","previous_headings":"","what":"CIFAR100 small image classification — dataset_cifar100","title":"CIFAR100 small image classification — dataset_cifar100","text":"Dataset 50,000 32x32 color training images, labeled 100 categories, 10,000 test images.","code":""},{"path":"https://keras.posit.co/reference/dataset_cifar100.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIFAR100 small image classification — dataset_cifar100","text":"","code":"dataset_cifar100(label_mode = c(\"fine\", \"coarse\"))"},{"path":"https://keras.posit.co/reference/dataset_cifar100.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIFAR100 small image classification — dataset_cifar100","text":"label_mode one \"fine\", \"coarse\".","code":""},{"path":"https://keras.posit.co/reference/dataset_cifar100.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIFAR100 small image classification — dataset_cifar100","text":"Lists training test data: train$x, train$y, test$x, test$y. x data array RGB image data shape (num_samples, 3, 32, 32). y data array category labels shape (num_samples).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_fashion_mnist.html","id":null,"dir":"Reference","previous_headings":"","what":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","title":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","text":"Dataset 60,000 28x28 grayscale images 10 fashion article classes, along test set 10,000 images. dataset can used drop-replacement MNIST. class labels encoded integers 0-9 correspond T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt,","code":""},{"path":"https://keras.posit.co/reference/dataset_fashion_mnist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","text":"","code":"dataset_fashion_mnist()"},{"path":"https://keras.posit.co/reference/dataset_fashion_mnist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","text":"Lists training test data: train$x, train$y, test$x, test$y, x array grayscale image data shape (num_samples, 28, 28) y array article labels (integers range 0-9) shape (num_samples).","code":""},{"path":"https://keras.posit.co/reference/dataset_fashion_mnist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fashion-MNIST database of fashion articles — dataset_fashion_mnist","text":"Dataset 60,000 28x28 grayscale images 10 fashion categories, along test set 10,000 images. dataset can used drop-replacement MNIST. class labels : 0 - T-shirt/top 1 - Trouser 2 - Pullover 3 - Dress 4 - Coat 5 - Sandal 6 - Shirt 7 - Sneaker 8 - Bag 9 - Ankle boot","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":null,"dir":"Reference","previous_headings":"","what":"IMDB Movie reviews sentiment classification — dataset_imdb","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"Dataset 25,000 movies reviews IMDB, labeled sentiment (positive/negative). Reviews preprocessed, review encoded sequence word indexes (integers). convenience, words indexed overall frequency dataset, instance integer \"3\" encodes 3rd frequent word data. allows quick filtering operations : \"consider top 10,000 common words, eliminate top 20 common words\".","code":""},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"","code":"dataset_imdb(   path = \"imdb.npz\",   num_words = NULL,   skip_top = 0L,   maxlen = NULL,   seed = 113L,   start_char = 1L,   oov_char = 2L,   index_from = 3L )  dataset_imdb_word_index(path = \"imdb_word_index.json\")"},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"path cache data (relative ~/.keras/dataset). num_words Max number words include. Words ranked often occur (training set) frequent words kept skip_top Skip top N frequently occuring words (may informative). maxlen sequences longer filtered . seed random seed sample shuffling. start_char start sequence marked character. Set 1 0 usually padding character. oov_char Words cut num_words skip_top limit replaced character. index_from Index actual words index higher.","code":""},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"Lists training test data: train$x, train$y, test$x, test$y. x data includes integer sequences. num_words argument specific, maximum possible index value num_words-1. maxlen argument specified, largest possible sequence length maxlen. y data includes set integer labels (0 1). dataset_imdb_word_index() function returns list names words values integer.","code":""},{"path":"https://keras.posit.co/reference/dataset_imdb.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"IMDB Movie reviews sentiment classification — dataset_imdb","text":"convention, \"0\" stand specific word, instead used encode unknown word.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_mnist.html","id":null,"dir":"Reference","previous_headings":"","what":"MNIST database of handwritten digits — dataset_mnist","title":"MNIST database of handwritten digits — dataset_mnist","text":"Dataset 60,000 28x28 grayscale images 10 digits, along test set 10,000 images.","code":""},{"path":"https://keras.posit.co/reference/dataset_mnist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MNIST database of handwritten digits — dataset_mnist","text":"","code":"dataset_mnist(path = \"mnist.npz\")"},{"path":"https://keras.posit.co/reference/dataset_mnist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MNIST database of handwritten digits — dataset_mnist","text":"path Path cache dataset locally (relative ~/.keras/datasets).","code":""},{"path":"https://keras.posit.co/reference/dataset_mnist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MNIST database of handwritten digits — dataset_mnist","text":"Lists training test data: train$x, train$y, test$x, test$y, x array grayscale image data shape (num_samples, 28, 28) y array digit labels (integers range 0-9) shape (num_samples).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/dataset_reuters.html","id":null,"dir":"Reference","previous_headings":"","what":"Reuters newswire topics classification — dataset_reuters","title":"Reuters newswire topics classification — dataset_reuters","text":"Dataset 11,228 newswires Reuters, labeled 46 topics. dataset_imdb() , wire encoded sequence word indexes (conventions).","code":""},{"path":"https://keras.posit.co/reference/dataset_reuters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reuters newswire topics classification — dataset_reuters","text":"","code":"dataset_reuters(   path = \"reuters.npz\",   num_words = NULL,   skip_top = 0L,   maxlen = NULL,   test_split = 0.2,   seed = 113L,   start_char = 1L,   oov_char = 2L,   index_from = 3L )  dataset_reuters_word_index(path = \"reuters_word_index.pkl\")"},{"path":"https://keras.posit.co/reference/dataset_reuters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reuters newswire topics classification — dataset_reuters","text":"path cache data (relative ~/.keras/dataset). num_words Max number words include. Words ranked often occur (training set) frequent words kept skip_top Skip top N frequently occuring words (may informative). maxlen Truncate sequences length. test_split Fraction dataset used test data. seed Random seed sample shuffling. start_char start sequence marked character. Set 1 0 usually padding character. oov_char words cut num_words skip_top limit replaced character. index_from index actual words index higher.","code":""},{"path":"https://keras.posit.co/reference/dataset_reuters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reuters newswire topics classification — dataset_reuters","text":"Lists training test data: train$x, train$y, test$x, test$y format dataset_imdb(). dataset_reuters_word_index() function returns list names words values integer. e.g. word_index[[\"giraffe\"]] might return 1234.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/deserialize_keras_object.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","title":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","text":"config dict Python dictionary consists set key-value pairs, represents Keras object, Optimizer, Layer, Metrics, etc. saving loading library uses following keys record information Keras object: class_name: String. name class, exactly defined source code, \"LossesContainer\". config: Named List. Library-defined user-defined key-value pairs store configuration object, obtained object$get_config(). module: String. path python module. Built-Keras classes expect prefix keras. registered_name: String. key class registered via register_keras_serializable(package, name) API. key format '{package}>{name}', package name arguments passed register_keras_serializable(). name provided, uses class name. registered_name successfully resolves class (registered), class_name config values config dict used. registered_name used non-built-classes. example, following config list represents built-Adam optimizer relevant config:     class exported Keras namespace, library tracks module class_name. example:   following config represents user-customized MeanSquaredError loss:","code":"config <- list(   class_name = \"Adam\",   config = list(     amsgrad = FALSE,     beta_1 = 0.8999999761581421,     beta_2 = 0.9990000128746033,     epsilon = 1e-07,     learning_rate = 0.0010000000474974513,     name = \"Adam\"   ),   module = \"keras.optimizers\",   registered_name = NULL ) # Returns an `Adam` instance identical to the original one. deserialize_keras_object(config) ## <keras.src.optimizers.adam.Adam object> config <- list(   class_name = \"MetricsList\",   config =  list(     ...   ),   module = \"keras.trainers.compile_utils\",   registered_name = \"MetricsList\" )  # Returns a `MetricsList` instance identical to the original one. deserialize_keras_object(config) # define a custom object loss_modified_mse <- Loss(   \"ModifiedMeanSquaredError\",   inherit = loss_mean_squared_error)  # register the custom object register_keras_serializable(loss_modified_mse)  # confirm object is registered get_custom_objects() ## $`keras3>ModifiedMeanSquaredError` ## <class '<r-namespace:keras3>.ModifiedMeanSquaredError'> ##  signature: (reduction='sum_over_batch_size', name='mean_squared_error') get_registered_name(loss_modified_mse) ## [1] \"keras3>ModifiedMeanSquaredError\" # now custom object instances can be serialized full_config <- serialize_keras_object(loss_modified_mse())  # the `config` arguments will be passed to loss_modified_mse() str(full_config) ## List of 4 ##  $ module         : chr \"<r-namespace:keras3>\" ##  $ class_name     : chr \"ModifiedMeanSquaredError\" ##  $ config         :List of 2 ##   ..$ name     : chr \"mean_squared_error\" ##   ..$ reduction: chr \"sum_over_batch_size\" ##  $ registered_name: chr \"keras3>ModifiedMeanSquaredError\" # and custom object instances can be deserialized deserialize_keras_object(full_config) ## <<r-namespace:keras3>.ModifiedMeanSquaredError object> ##  signature: (y_true, y_pred, sample_weight=None) # Returns the `ModifiedMeanSquaredError` object"},{"path":"https://keras.posit.co/reference/deserialize_keras_object.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","text":"","code":"deserialize_keras_object(config, custom_objects = NULL, safe_mode = TRUE, ...)"},{"path":"https://keras.posit.co/reference/deserialize_keras_object.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","text":"config Named list describing object. custom_objects Named list containing mapping custom object names corresponding classes functions. safe_mode Boolean, whether disallow unsafe lambda deserialization. safe_mode=FALSE, loading object potential trigger arbitrary code execution. argument applicable Keras v3 model format. Defaults TRUE. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/deserialize_keras_object.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve the object by deserializing the config dict. — deserialize_keras_object","text":"object described config dictionary.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/evaluate.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","title":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","text":"functions returns loss value metrics values model test mode. Computation done batches (see batch_size arg.)","code":""},{"path":"https://keras.posit.co/reference/evaluate.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model evaluate(   object,   x = NULL,   y = NULL,   ...,   batch_size = NULL,   verbose = getOption(\"keras.verbose\", default = \"auto\"),   sample_weight = NULL,   steps = NULL,   callbacks = NULL )"},{"path":"https://keras.posit.co/reference/evaluate.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","text":"object Keras model object x Input data. : R array (array-like), list arrays (case model multiple inputs). tensor, list tensors (case model multiple inputs). named list mapping input names corresponding array/tensors, model named inputs. tf.data.Dataset. return tuple either (inputs, targets) (inputs, targets, sample_weights). generator returning (inputs, targets) (inputs, targets, sample_weights). y Target data. Like input data x, either R array(s) backend-native tensor(s). x tf.data.Dataset generator function, y specified (since targets obtained iterator/dataset). ... forward/backward compatability. batch_size Integer NULL. Number samples per batch computation. unspecified, batch_size default 32. specify batch_size data form tf dataset generator (since generate batches). verbose \"auto\", 0, 1, 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. \"auto\" becomes 1 cases, 2 knitr render running distributed training server. Note progress bar particularly useful logged file, verbose=2 recommended running interactively (e.g. production environment). Defaults \"auto\". sample_weight Optional array weights test samples, used weighting loss function. can either pass flat (1D) R array length input samples (1:1 mapping weights samples), case temporal data, can pass 2D array shape (samples, sequence_length), apply different weight every timestep every sample. argument supported x tfdataset, instead pass sample weights third element x. steps Integer NULL. Total number steps (batches samples) declaring evaluation round finished. Ignored default value NULL. x tf.data.Dataset steps NULL, evaluation run dataset exhausted. callbacks List Callback instances. List callbacks apply evaluation.","code":""},{"path":"https://keras.posit.co/reference/evaluate.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluate a Keras Model — evaluate.keras.src.models.model.Model","text":"Scalar test loss (model single output metrics) list scalars (model multiple outputs /metrics). attribute model$metrics_names give display labels scalar outputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"(e.g. via TF-Serving). Note: can currently used TensorFlow JAX backends. method lets export model lightweight SavedModel artifact contains model's forward pass (call() method) can served via e.g. TF-Serving. forward pass registered name serve() (see example ). original code model (including custom layers may used) longer necessary reload artifact -- entirely standalone.","code":""},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model export_savedmodel(object, export_dir_base, ...)"},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"object keras model. export_dir_base string, file path save artifact. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"called primarily side effect exporting object. first argument, object also returned, invisibly, enable usage pipe.","code":""},{"path":"https://keras.posit.co/reference/export_savedmodel.keras.src.models.model.Model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a TF SavedModel artifact for inference (e.g. via TF-Serving). — export_savedmodel.keras.src.models.model.Model","text":"","code":"# Create the artifact model |> tensorflow::export_savedmodel(\"path/to/location\")  # Later, in a different process / environment... library(tensorflow) reloaded_artifact <- tf$saved_model$load(\"path/to/location\") predictions <- reloaded_artifact$serve(input_data)  # see tfdeploy::serve_savedmodel() for serving a model over a local web api."},{"path":[]},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"Train model fixed number epochs (dataset iterations).","code":""},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model fit(   object,   x = NULL,   y = NULL,   ...,   batch_size = NULL,   epochs = 1L,   callbacks = NULL,   validation_split = 0,   validation_data = NULL,   shuffle = TRUE,   class_weight = NULL,   sample_weight = NULL,   initial_epoch = 1L,   steps_per_epoch = NULL,   validation_steps = NULL,   validation_batch_size = NULL,   validation_freq = 1L,   verbose = getOption(\"keras.verbose\", default = \"auto\"),   view_metrics = getOption(\"keras.view_metrics\", default = \"auto\") )"},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"object Keras model object x Input data. : array (array-like), list arrays (case model multiple inputs). tensor, list tensors (case model multiple inputs). named list mapping input names corresponding array/tensors, model named inputs. tf.data.Dataset. return tuple either (inputs, targets) (inputs, targets, sample_weights). generator returning (inputs, targets) (inputs, targets, sample_weights). y Target data. Like input data x, either array(s) backend-native tensor(s). x TF Dataset generator, y specified (since targets obtained x). ... Additional arguments passed model fit() method. batch_size Integer NULL. Number samples per gradient update. unspecified, batch_size default 32. specify batch_size data form TF Datasets generators, (since generate batches). epochs Integer. Number epochs train model. epoch iteration entire x y data provided (unless steps_per_epoch flag set something NULL). Note conjunction initial_epoch, epochs understood \"final epoch\". model trained number iterations given epochs, merely epoch index epochs reached. callbacks List Callback() instances. List callbacks apply training. See callback_*. validation_split Float 0 1. Fraction training data used validation data. model set apart fraction training data, train , evaluate loss model metrics data end epoch. validation data selected last samples x y data provided, shuffling. argument supported x TF Dataset generator. validation_data validation_split provided, validation_data override validation_split. validation_data Data evaluate loss model metrics end epoch. model trained data. Thus, note fact validation loss data provided using validation_split validation_data affected regularization layers like noise dropout. validation_data override validation_split. : tuple (x_val, y_val) arrays tensors. tuple (x_val, y_val, val_sample_weights) arrays. generator returning (inputs, targets) (inputs, targets, sample_weights). shuffle Boolean, whether shuffle training data epoch. argument ignored x generator TF Dataset. class_weight Optional named list mapping class indices (integers, 0-based) weight (float) value, used weighting loss function (training ). can useful tell model \"pay attention\" samples -represented class. class_weight specified targets rank 2 greater, either y must one-hot encoded, explicit final dimension 1 must included sparse class labels. sample_weight Optional array weights training samples, used weighting loss function (training ). can either pass flat (1D) array/vector length input samples (1:1 mapping weights samples), case temporal data, can pass 2D array (matrix) shape (samples, sequence_length), apply different weight every timestep every sample. argument supported x TF Dataset generator, instead provide sample_weights third element x. Note sample weighting apply metrics specified via metrics argument compile(). apply sample weighting metrics, can specify via weighted_metrics compile() instead. initial_epoch Integer. Epoch start training (useful resuming previous training run). steps_per_epoch Integer NULL. Total number steps (batches samples) declaring one epoch finished starting next epoch. training input tensors backend-native tensors, default NULL equal number samples dataset divided batch size, 1 determined. x TF Dataset, steps_per_epoch NULL, epoch run input dataset exhausted.  passing infinitely repeating dataset, must specify steps_per_epoch argument. steps_per_epoch = -1 training run indefinitely infinitely repeating dataset. validation_steps relevant validation_data provided. Total number steps (batches samples) draw stopping performing validation end every epoch. validation_steps NULL, validation run validation_data dataset exhausted. case infinitely repeated dataset, run infinite loop. validation_steps specified part dataset consumed, evaluation start beginning dataset epoch. ensures validation samples used every time. validation_batch_size Integer NULL. Number samples per validation batch. unspecified, default batch_size. specify validation_batch_size data form TF Datasets generator instances (since generate batches). validation_freq relevant validation data provided. Specifies many training epochs run new validation run performed, e.g. validation_freq=2 runs validation every 2 epochs. verbose \"auto\", 0, 1, 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. \"auto\" becomes 1 cases, 2 knitr render running distributed training server. Note progress bar particularly useful logged file, verbose=2 recommended running interactively (e.g., production environment). Defaults \"auto\". view_metrics View realtime plot training metrics (epoch). default (\"auto\") display plot running within RStudio, metrics specified model compile(), epochs > 1 verbose > 0. Set global options(keras.view_metrics = ) option establish different default.","code":""},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"keras_training_history object, named list: list(params = <params>, metrics = <metrics>\"), S3 methods print(), plot(), .data.frame(). metrics field record training loss values metrics values successive epochs, well validation loss values validation metrics values (applicable).","code":""},{"path":"https://keras.posit.co/reference/fit.keras.src.models.model.Model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Train a model for a fixed number of epochs (dataset iterations). — fit.keras.src.models.model.Model","text":"Unpacking behavior iterator-like inputs: common pattern pass iterator like object tf.data.Dataset generator fit(), fact yield features (x) optionally targets (y) sample weights (sample_weight). Keras requires output iterator-likes unambiguous. iterator return tuple() length 1, 2, 3, optional second third elements used y sample_weight respectively. type provided wrapped length-one tuple(), effectively treating everything x. yielding named lists, still adhere top-level tuple structure, e.g. tuple(list(x0 = x0, x = x1), y). Keras attempt separate features, targets, weights keys single dict.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Freeze and unfreeze weights — freeze_weights","title":"Freeze and unfreeze weights — freeze_weights","text":"Freeze weights model layer longer trainable.","code":""},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Freeze and unfreeze weights — freeze_weights","text":"","code":"freeze_weights(object, from = NULL, to = NULL, which = NULL)  unfreeze_weights(object, from = NULL, to = NULL, which = NULL)"},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Freeze and unfreeze weights — freeze_weights","text":"object Keras model layer object Layer instance, layer name, layer index within model Layer instance, layer name, layer index within model layer names, integer positions, layers, logical vector (length(object$layers)), function returning logical vector.","code":""},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Freeze and unfreeze weights — freeze_weights","text":"input object frozen weights returned, invisibly. Note, object modified place, return value provided make usage pipe convenient.","code":""},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Freeze and unfreeze weights — freeze_weights","text":"layer arguments inclusive. applied model, freeze unfreeze global operation layers model (.e. layers within specified range set opposite value, e.g. unfrozen call freeze). Models must compiled weights frozen unfrozen.","code":""},{"path":"https://keras.posit.co/reference/freeze_weights.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Freeze and unfreeze weights — freeze_weights","text":"","code":"# instantiate a VGG16 model conv_base <- application_vgg16(   weights = \"imagenet\",   include_top = FALSE,   input_shape = c(150, 150, 3) )  # freeze it's weights freeze_weights(conv_base)  # Note the \"Trainable\" column conv_base ## Model: \"vgg16\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | input_layer (InputLayer)    | (None, 150, 150, 3)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv1 (Conv2D)       | (None, 150, 150, 64)  |      1,792 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv2 (Conv2D)       | (None, 150, 150, 64)  |     36,928 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_pool (MaxPooling2D)  | (None, 75, 75, 64)    |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv1 (Conv2D)       | (None, 75, 75, 128)   |     73,856 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv2 (Conv2D)       | (None, 75, 75, 128)   |    147,584 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_pool (MaxPooling2D)  | (None, 37, 37, 128)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv1 (Conv2D)       | (None, 37, 37, 256)   |    295,168 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv2 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv3 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_pool (MaxPooling2D)  | (None, 18, 18, 256)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv1 (Conv2D)       | (None, 18, 18, 512)   |  1,180,160 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv2 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv3 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_pool (MaxPooling2D)  | (None, 9, 9, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv1 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv2 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv3 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_pool (MaxPooling2D)  | (None, 4, 4, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 14,714,688 (56.13 MB) ##  Trainable params: 0 (0.00 B) ##  Non-trainable params: 14,714,688 (56.13 MB) # create a composite model that includes the base + more layers model <- keras_model_sequential(input_batch_shape = shape(conv_base$input)) |>   conv_base() |>   layer_flatten() |>   layer_dense(units = 256, activation = \"relu\") |>   layer_dense(units = 1, activation = \"sigmoid\")  # compile model |> compile(   loss = \"binary_crossentropy\",   optimizer = optimizer_rmsprop(learning_rate = 2e-5),   metrics = c(\"accuracy\") )  model ## Model: \"sequential\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | vgg16 (Functional)          | (None, 4, 4, 512)     | 14,714,688 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | flatten (Flatten)           | (None, 8192)          |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense (Dense)               | (None, 256)           |  2,097,408 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense_1 (Dense)             | (None, 1)             |        257 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 16,812,353 (64.13 MB) ##  Trainable params: 2,097,665 (8.00 MB) ##  Non-trainable params: 14,714,688 (56.13 MB) print(model, expand_nested = TRUE) ## Model: \"sequential\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | vgg16 (Functional)          | (None, 4, 4, 512)     | 14,714,688 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > input_layer            | (None, 150, 150, 3)   |          0 |   -   | ## | (InputLayer)                |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_conv1 (Conv2D)  | (None, 150, 150, 64)  |      1,792 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_conv2 (Conv2D)  | (None, 150, 150, 64)  |     36,928 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_pool            | (None, 75, 75, 64)    |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_conv1 (Conv2D)  | (None, 75, 75, 128)   |     73,856 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_conv2 (Conv2D)  | (None, 75, 75, 128)   |    147,584 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_pool            | (None, 37, 37, 128)   |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv1 (Conv2D)  | (None, 37, 37, 256)   |    295,168 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv2 (Conv2D)  | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv3 (Conv2D)  | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_pool            | (None, 18, 18, 256)   |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv1 (Conv2D)  | (None, 18, 18, 512)   |  1,180,160 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv2 (Conv2D)  | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv3 (Conv2D)  | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_pool            | (None, 9, 9, 512)     |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv1 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv2 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv3 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_pool            | (None, 4, 4, 512)     |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## | flatten (Flatten)           | (None, 8192)          |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense (Dense)               | (None, 256)           |  2,097,408 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense_1 (Dense)             | (None, 1)             |        257 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 16,812,353 (64.13 MB) ##  Trainable params: 2,097,665 (8.00 MB) ##  Non-trainable params: 14,714,688 (56.13 MB) # unfreeze weights from \"block5_conv1\" on unfreeze_weights(conv_base, from = \"block5_conv1\")  # compile again since we froze or unfroze weights model |> compile(   loss = \"binary_crossentropy\",   optimizer = optimizer_rmsprop(learning_rate = 2e-5),   metrics = c(\"accuracy\") )  conv_base ## Model: \"vgg16\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | input_layer (InputLayer)    | (None, 150, 150, 3)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv1 (Conv2D)       | (None, 150, 150, 64)  |      1,792 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv2 (Conv2D)       | (None, 150, 150, 64)  |     36,928 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_pool (MaxPooling2D)  | (None, 75, 75, 64)    |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv1 (Conv2D)       | (None, 75, 75, 128)   |     73,856 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv2 (Conv2D)       | (None, 75, 75, 128)   |    147,584 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_pool (MaxPooling2D)  | (None, 37, 37, 128)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv1 (Conv2D)       | (None, 37, 37, 256)   |    295,168 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv2 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv3 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_pool (MaxPooling2D)  | (None, 18, 18, 256)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv1 (Conv2D)       | (None, 18, 18, 512)   |  1,180,160 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv2 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv3 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_pool (MaxPooling2D)  | (None, 9, 9, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv1 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv2 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv3 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_pool (MaxPooling2D)  | (None, 4, 4, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 14,714,688 (56.13 MB) ##  Trainable params: 7,079,424 (27.01 MB) ##  Non-trainable params: 7,635,264 (29.13 MB) print(model, expand_nested = TRUE) ## Model: \"sequential\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | vgg16 (Functional)          | (None, 4, 4, 512)     | 14,714,688 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > input_layer            | (None, 150, 150, 3)   |          0 |   -   | ## | (InputLayer)                |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_conv1 (Conv2D)  | (None, 150, 150, 64)  |      1,792 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_conv2 (Conv2D)  | (None, 150, 150, 64)  |     36,928 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block1_pool            | (None, 75, 75, 64)    |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_conv1 (Conv2D)  | (None, 75, 75, 128)   |     73,856 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_conv2 (Conv2D)  | (None, 75, 75, 128)   |    147,584 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block2_pool            | (None, 37, 37, 128)   |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv1 (Conv2D)  | (None, 37, 37, 256)   |    295,168 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv2 (Conv2D)  | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_conv3 (Conv2D)  | (None, 37, 37, 256)   |    590,080 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block3_pool            | (None, 18, 18, 256)   |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv1 (Conv2D)  | (None, 18, 18, 512)   |  1,180,160 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv2 (Conv2D)  | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_conv3 (Conv2D)  | (None, 18, 18, 512)   |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block4_pool            | (None, 9, 9, 512)     |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv1 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv2 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_conv3 (Conv2D)  | (None, 9, 9, 512)     |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## |    > block5_pool            | (None, 4, 4, 512)     |          0 |   -   | ## | (MaxPooling2D)              |                       |            |       | ## +-----------------------------+-----------------------+------------+-------+ ## | flatten (Flatten)           | (None, 8192)          |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense (Dense)               | (None, 256)           |  2,097,408 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | dense_1 (Dense)             | (None, 1)             |        257 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 16,812,353 (64.13 MB) ##  Trainable params: 9,177,089 (35.01 MB) ##  Non-trainable params: 7,635,264 (29.13 MB) # freeze only the last 5 layers freeze_weights(conv_base, from = -5) conv_base ## Model: \"vgg16\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | input_layer (InputLayer)    | (None, 150, 150, 3)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv1 (Conv2D)       | (None, 150, 150, 64)  |      1,792 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv2 (Conv2D)       | (None, 150, 150, 64)  |     36,928 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_pool (MaxPooling2D)  | (None, 75, 75, 64)    |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv1 (Conv2D)       | (None, 75, 75, 128)   |     73,856 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv2 (Conv2D)       | (None, 75, 75, 128)   |    147,584 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_pool (MaxPooling2D)  | (None, 37, 37, 128)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv1 (Conv2D)       | (None, 37, 37, 256)   |    295,168 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv2 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv3 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_pool (MaxPooling2D)  | (None, 18, 18, 256)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv1 (Conv2D)       | (None, 18, 18, 512)   |  1,180,160 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv2 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv3 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_pool (MaxPooling2D)  | (None, 9, 9, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv1 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv2 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv3 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_pool (MaxPooling2D)  | (None, 4, 4, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 14,714,688 (56.13 MB) ##  Trainable params: 7,635,264 (29.13 MB) ##  Non-trainable params: 7,079,424 (27.01 MB) # freeze only the last 5 layers, a different way unfreeze_weights(conv_base, to = -6) conv_base ## Model: \"vgg16\" ## +-----------------------------+-----------------------+------------+-------+ ## | Layer (type)                | Output Shape          |    Param # | Trai… | ## +=============================+=======================+============+=======+ ## | input_layer (InputLayer)    | (None, 150, 150, 3)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv1 (Conv2D)       | (None, 150, 150, 64)  |      1,792 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_conv2 (Conv2D)       | (None, 150, 150, 64)  |     36,928 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block1_pool (MaxPooling2D)  | (None, 75, 75, 64)    |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv1 (Conv2D)       | (None, 75, 75, 128)   |     73,856 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_conv2 (Conv2D)       | (None, 75, 75, 128)   |    147,584 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block2_pool (MaxPooling2D)  | (None, 37, 37, 128)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv1 (Conv2D)       | (None, 37, 37, 256)   |    295,168 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv2 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_conv3 (Conv2D)       | (None, 37, 37, 256)   |    590,080 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block3_pool (MaxPooling2D)  | (None, 18, 18, 256)   |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv1 (Conv2D)       | (None, 18, 18, 512)   |  1,180,160 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv2 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_conv3 (Conv2D)       | (None, 18, 18, 512)   |  2,359,808 |   Y   | ## +-----------------------------+-----------------------+------------+-------+ ## | block4_pool (MaxPooling2D)  | (None, 9, 9, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv1 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv2 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_conv3 (Conv2D)       | (None, 9, 9, 512)     |  2,359,808 |   N   | ## +-----------------------------+-----------------------+------------+-------+ ## | block5_pool (MaxPooling2D)  | (None, 4, 4, 512)     |          0 |   -   | ## +-----------------------------+-----------------------+------------+-------+ ##  Total params: 14,714,688 (56.13 MB) ##  Trainable params: 7,635,264 (29.13 MB) ##  Non-trainable params: 7,079,424 (27.01 MB) # Freeze only layers of a certain type, e.g, BatchNorm layers batch_norm_layer_class_name <- class(layer_batch_normalization())[1] is_batch_norm_layer <- function(x) inherits(x, batch_norm_layer_class_name)  model <- application_efficientnet_b0() freeze_weights(model, which = is_batch_norm_layer) # print(model)  # equivalent to: for(layer in model$layers) {   if(is_batch_norm_layer(layer))     layer$trainable <- FALSE   else     layer$trainable <- TRUE }"},{"path":"https://keras.posit.co/reference/get_config.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer/Model configuration — get_config","title":"Layer/Model configuration — get_config","text":"layer config object returned get_config() contains configuration layer model. layer model can reinstantiated later (without trained weights) configuration using from_config(). config include connectivity information, class name (handled externally).","code":""},{"path":"https://keras.posit.co/reference/get_config.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer/Model configuration — get_config","text":"","code":"get_config(object)  from_config(config, custom_objects = NULL)"},{"path":"https://keras.posit.co/reference/get_config.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer/Model configuration — get_config","text":"object Layer model object config Object layer model configuration custom_objects list custom objects needed instantiate layer, e.g., custom layers defined new_layer_class() similar.","code":""},{"path":"https://keras.posit.co/reference/get_config.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer/Model configuration — get_config","text":"get_config() returns object configuration, from_config() returns re-instantiation object.","code":""},{"path":"https://keras.posit.co/reference/get_config.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layer/Model configuration — get_config","text":"Objects returned get_config() serializable via RDS. want save restore model across sessions, can use save_model_config() (model configuration , weights) save_model() save model configuration weights filesystem.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":null,"dir":"Reference","previous_headings":"","what":"Get/set the currently registered custom objects. — get_custom_objects","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"Custom objects set using custom_object_scope() added global list custom objects, appear returned list.","code":""},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"","code":"get_custom_objects()  set_custom_objects(objects = named_list(), clear = TRUE)"},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"objects named list custom objects, returned get_custom_objects() set_custom_objects(). clear bool, whether clear custom object registry populating objects.","code":""},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"R named list mapping registered names registered objects. set_custom_objects() returns registry values updating, invisibly.","code":""},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"register_keras_serializable() preferred set_custom_objects() registering new objects.","code":""},{"path":"https://keras.posit.co/reference/get_custom_objects.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get/set the currently registered custom objects. — get_custom_objects","text":"can use set_custom_objects() restore previous registry state.","code":"get_custom_objects() # within a function, if you want to temporarily modify the registry, function() {   orig_objects <- set_custom_objects(clear = TRUE)   on.exit(set_custom_objects(orig_objects))    ## temporarily modify the global registry   # register_keras_serializable(....)   # ....  <do work>   # on.exit(), the previous registry state is restored. }"},{"path":[]},{"path":"https://keras.posit.co/reference/get_file.html","id":null,"dir":"Reference","previous_headings":"","what":"Downloads a file from a URL if it not already in the cache. — get_file","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"default file url origin downloaded cache_dir ~/.keras, placed cache_subdir datasets, given filename fname. final location file example.txt therefore ~/.keras/datasets/example.txt. Files .tar, .tar.gz, .tar.bz, .zip formats can also extracted. Passing hash verify file download. command line programs shasum sha256sum can compute hash.","code":""},{"path":"https://keras.posit.co/reference/get_file.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"","code":"get_file(   fname = NULL,   origin = NULL,   ...,   file_hash = NULL,   cache_subdir = \"datasets\",   hash_algorithm = \"auto\",   extract = FALSE,   archive_format = \"auto\",   cache_dir = NULL,   force_download = FALSE )"},{"path":"https://keras.posit.co/reference/get_file.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"fname Name file. absolute path, e.g. \"/path//file.txt\" specified, file saved location. NULL, name file origin used. origin Original URL file. ... forward/backward compatability. file_hash expected hash string file download. sha256 md5 hash algorithms supported. cache_subdir Subdirectory Keras cache dir file saved. absolute path, e.g. \"/path//folder\" specified, file saved location. hash_algorithm Select hash algorithm verify file. options \"md5', \"sha256', \"auto'. default 'auto' detects hash algorithm use. extract TRUE tries extracting file Archive, like tar zip. archive_format Archive format try extracting file. Options \"auto', \"tar', \"zip', NULL. \"tar\" includes tar, tar.gz, tar.bz files. default \"auto\" corresponds c(\"tar\", \"zip\"). NULL empty list return matches found. cache_dir Location store cached files, NULL defaults Sys.getenv(\"KERAS_HOME\", \"~/.keras/\"). force_download TRUE, file always re-downloaded regardless cache state.","code":""},{"path":"https://keras.posit.co/reference/get_file.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"Path downloaded file. ** Warning malicious downloads ** Downloading something Internet carries risk. NEVER download file/archive trust source. recommend specify file_hash argument (hash source file known) make sure file getting one expect.","code":""},{"path":"https://keras.posit.co/reference/get_file.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Downloads a file from a URL if it not already in the cache. — get_file","text":"","code":"path_to_downloaded_file <- get_file(     origin = \"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\",     extract = TRUE )"},{"path":[]},{"path":"https://keras.posit.co/reference/get_layer.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieves a layer based on either its name (unique) or index. — get_layer","title":"Retrieves a layer based on either its name (unique) or index. — get_layer","text":"Indices based order horizontal graph traversal (bottom-) 1-based. name index provided, index take precedence.","code":""},{"path":"https://keras.posit.co/reference/get_layer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieves a layer based on either its name (unique) or index. — get_layer","text":"","code":"get_layer(object, name = NULL, index = NULL)"},{"path":"https://keras.posit.co/reference/get_layer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieves a layer based on either its name (unique) or index. — get_layer","text":"object Keras model object name String, name layer. index Integer, index layer (1-based). Also valid negative values, count end model.","code":""},{"path":"https://keras.posit.co/reference/get_layer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieves a layer based on either its name (unique) or index. — get_layer","text":"layer instance.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/get_registered_name.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the name registered to an object within the Keras framework. — get_registered_name","title":"Returns the name registered to an object within the Keras framework. — get_registered_name","text":"function part Keras serialization deserialization framework. maps objects string names associated objects serialization/deserialization.","code":""},{"path":"https://keras.posit.co/reference/get_registered_name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the name registered to an object within the Keras framework. — get_registered_name","text":"","code":"get_registered_name(obj)"},{"path":"https://keras.posit.co/reference/get_registered_name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the name registered to an object within the Keras framework. — get_registered_name","text":"obj object look .","code":""},{"path":"https://keras.posit.co/reference/get_registered_name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the name registered to an object within the Keras framework. — get_registered_name","text":"name associated object, default name object registered.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the class associated with name if it is registered with Keras. — get_registered_object","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"function part Keras serialization deserialization framework. maps strings objects associated serialization/deserialization.","code":""},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"","code":"get_registered_object(name, custom_objects = NULL, module_objects = NULL)"},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"name name look . custom_objects named list custom objects look name . Generally, custom_objects provided user. module_objects named list custom objects look name . Generally, module_objects provided midlevel library implementers.","code":""},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"instantiable class associated name, NULL class exists.","code":""},{"path":"https://keras.posit.co/reference/get_registered_object.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the class associated with name if it is registered with Keras. — get_registered_object","text":"","code":"from_config <- function(cls, config, custom_objects = NULL) {   if ('my_custom_object_name' \\%in\\% names(config)) {     config$hidden_cls <- get_registered_object(       config$my_custom_object_name,       custom_objects = custom_objects)   } }"},{"path":[]},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"Output always list tensors (potentially 1 element).","code":""},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"","code":"get_source_inputs(tensor)"},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"tensor tensor start .","code":""},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"List input tensors.","code":""},{"path":"https://keras.posit.co/reference/get_source_inputs.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Returns the list of input tensors necessary to compute tensor. — get_source_inputs","text":"","code":"input <- keras_input(c(3)) output <- input |> layer_dense(4) |> op_multiply(5) reticulate::py_id(get_source_inputs(output)[[1]]) == reticulate::py_id(input) ## [1] TRUE"},{"path":[]},{"path":"https://keras.posit.co/reference/get_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer/Model weights as R arrays — get_weights","title":"Layer/Model weights as R arrays — get_weights","text":"Layer/Model weights R arrays","code":""},{"path":"https://keras.posit.co/reference/get_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer/Model weights as R arrays — get_weights","text":"","code":"get_weights(object, trainable = NA)  set_weights(object, weights)"},{"path":"https://keras.posit.co/reference/get_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer/Model weights as R arrays — get_weights","text":"object Layer model object trainable NA (default), weights returned. TRUE, weights trainable variables returned. FALSE, weights non-trainable variables returned. weights Weights R array","code":""},{"path":"https://keras.posit.co/reference/get_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer/Model weights as R arrays — get_weights","text":"list R arrays.","code":""},{"path":"https://keras.posit.co/reference/get_weights.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layer/Model weights as R arrays — get_weights","text":"can access Layer/Model KerasVariables (also backend-native tensors like tf.Variable) object$weights, object$trainable_weights, object$non_trainable_weights","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":null,"dir":"Reference","previous_headings":"","what":"Make a python class constructor — %py_class%","title":"Make a python class constructor — %py_class%","text":"Make python class constructor","code":""},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make a python class constructor — %py_class%","text":"","code":"spec %py_class% body"},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make a python class constructor — %py_class%","text":"spec bare symbol MyClassName, call MyClassName(SuperClass) body expression can evaluated construct class methods.","code":""},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make a python class constructor — %py_class%","text":"python class constructor, invisibly. Note, constructor also assigned parent frame.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/grapes-py_class-grapes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make a python class constructor — %py_class%","text":"","code":"if (FALSE) { MyClass %py_class% {   initialize <- function(x) {     print(\"Hi from MyClass$initialize()!\")     self$x <- x   }   my_method <- function() {     self$x   } }  my_class_instance <- MyClass(42) my_class_instance$my_method()  MyClass2(MyClass) %py_class% {   \"This will be a __doc__ string for MyClass2\"    initialize <- function(...) {     \"This will be the __doc__ string for the MyClass2.__init__() method\"     print(\"Hi from MyClass2$initialize()!\")     super$initialize(...)   } }  my_class_instance2 <- MyClass2(42) my_class_instance2$my_method()  reticulate::py_help(MyClass2) # see the __doc__ strings and more!  # In addition to `self`, there is also `private` available. # This is an R environment unique to each class instance, where you can # store objects that you don't want converted to Python, but still want # available from methods. You can also assign methods to private, and # `self` and `private` will be available in private methods.  MyClass %py_class% {    initialize <- function(x) {     print(\"Hi from MyClass$initialize()!\")     private$y <- paste(\"A Private field:\", x)   }    get_private_field <- function() {     private$y   }    private$a_private_method <- function() {     cat(\"a_private_method() was called.\\n\")     cat(\"private$y is \", sQuote(private$y), \"\\n\")   }    call_private_method <- function()     private$a_private_method()    # equivalent of @property decorator in python   an_active_property %<-active% function(x = NULL) {     if(!is.null(x)) {       cat(\"`an_active_property` was assigned\", x, \"\\n\")       return(x)     } else {       cat(\"`an_active_property` was accessed\\n\")       return(42)     }   } }  inst1 <- MyClass(1) inst2 <- MyClass(2) inst1$get_private_field() inst2$get_private_field() inst1$call_private_method() inst2$call_private_method() inst1$an_active_property inst1$an_active_property <- 11 }"},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":null,"dir":"Reference","previous_headings":"","what":"Make an Active Binding — %<-active%","title":"Make an Active Binding — %<-active%","text":"Make Active Binding","code":""},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make an Active Binding — %<-active%","text":"","code":"sym %<-active% value"},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make an Active Binding — %<-active%","text":"sym symbol bind value function call value sym accessed.","code":""},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make an Active Binding — %<-active%","text":"value, invisibly","code":""},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make an Active Binding — %<-active%","text":"Active bindings defined %py_class% converted @property decorated methods.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/grapes-set-active-grapes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make an Active Binding — %<-active%","text":"","code":"set.seed(1234) x %<-active% function(value) {   message(\"Evaluating function of active binding\")   if(missing(value))     runif(1)   else    message(\"Received: \", value) } x #> Evaluating function of active binding #> [1] 0.1137034 x #> Evaluating function of active binding #> [1] 0.6222994 x <- \"foo\" #> Evaluating function of active binding #> Received: foo x <- \"foo\" #> Evaluating function of active binding #> Received: foo x #> Evaluating function of active binding #> [1] 0.6092747 rm(x) # cleanup"},{"path":"https://keras.posit.co/reference/image_array_save.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves an image stored as an array to a path or file object. — image_array_save","title":"Saves an image stored as an array to a path or file object. — image_array_save","text":"Saves image stored array path file object.","code":""},{"path":"https://keras.posit.co/reference/image_array_save.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves an image stored as an array to a path or file object. — image_array_save","text":"","code":"image_array_save(   x,   path,   data_format = NULL,   file_format = NULL,   scale = TRUE,   ... )"},{"path":"https://keras.posit.co/reference/image_array_save.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves an image stored as an array to a path or file object. — image_array_save","text":"x array. path Path file object. data_format Image data format, either \"channels_first\" \"channels_last\". file_format Optional file format override. omitted, format use determined filename extension. file object used instead filename, parameter always used. scale Whether rescale image values within [0, 255]. ... Additional keyword arguments passed PIL.Image.save().","code":""},{"path":"https://keras.posit.co/reference/image_array_save.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Saves an image stored as an array to a path or file object. — image_array_save","text":"Called primarily side effects. input x returned, invisibly, enable usage pipe.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/image_dataset_from_directory.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","title":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","text":"directory structure :   calling image_dataset_from_directory(main_directory, labels = 'inferred') return tf.data.Dataset yields batches images subdirectories class_a class_b, together labels 0 1 (0 corresponding class_a 1 corresponding class_b). Supported image formats: .jpeg, .jpg, .png, .bmp, .gif. Animated gifs truncated first frame.","code":"main_directory/ ...class_a/ ......a_image_1.jpg ......a_image_2.jpg ...class_b/ ......b_image_1.jpg ......b_image_2.jpg"},{"path":"https://keras.posit.co/reference/image_dataset_from_directory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","text":"","code":"image_dataset_from_directory(   directory,   labels = \"inferred\",   label_mode = \"int\",   class_names = NULL,   color_mode = \"rgb\",   batch_size = 32L,   image_size = c(256L, 256L),   shuffle = TRUE,   seed = NULL,   validation_split = NULL,   subset = NULL,   interpolation = \"bilinear\",   follow_links = FALSE,   crop_to_aspect_ratio = FALSE,   pad_to_aspect_ratio = FALSE,   data_format = NULL,   verbose = TRUE )"},{"path":"https://keras.posit.co/reference/image_dataset_from_directory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","text":"directory Directory data located. labels \"inferred\", contain subdirectories, containing images class. Otherwise, directory structure ignored. labels Either \"inferred\" (labels generated directory structure), NULL (labels), list/tuple integer labels size number image files found directory. Labels sorted according alphanumeric order image file paths (obtained via os.walk(directory) Python). label_mode String describing encoding labels. Options : \"int\": means labels encoded integers (e.g. sparse_categorical_crossentropy loss). \"categorical\" means labels encoded categorical vector (e.g. categorical_crossentropy loss). \"binary\" means labels (can 2) encoded float32 scalars values 0 1 (e.g. binary_crossentropy). NULL (labels). class_names valid labels \"inferred\". explicit list class names (must match names subdirectories). Used control order classes (otherwise alphanumerical order used). color_mode One \"grayscale\", \"rgb\", \"rgba\". Defaults \"rgb\". Whether images converted 1, 3, 4 channels. batch_size Size batches data. Defaults 32. NULL, data batched (dataset yield individual samples). image_size Size resize images read disk, specified (height, width). Defaults (256, 256). Since pipeline processes batches images must size, must provided. shuffle Whether shuffle data. Defaults TRUE. set FALSE, sorts data alphanumeric order. seed Optional random seed shuffling transformations. validation_split Optional float 0 1, fraction data reserve validation. subset Subset data return. One \"training\", \"validation\", \"\". used validation_split set. subset = \"\", utility returns tuple two datasets (training validation datasets respectively). interpolation String, interpolation method used resizing images. Defaults \"bilinear\". Supports \"bilinear\", \"nearest\", \"bicubic\", \"area\", \"lanczos3\", \"lanczos5\", \"gaussian\", \"mitchellcubic\". follow_links Whether visit subdirectories pointed symlinks. Defaults FALSE. crop_to_aspect_ratio TRUE, resize images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image cropped return largest possible window image (size image_size) matches target aspect ratio. default (crop_to_aspect_ratio = FALSE), aspect ratio may preserved. pad_to_aspect_ratio TRUE, resize images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image padded return largest possible window image (size image_size) matches target aspect ratio. default (pad_to_aspect_ratio=FALSE), aspect ratio may preserved. data_format NULL uses config_image_data_format() otherwise either 'channel_last' 'channel_first'. verbose Whether display number information classes number files found. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/image_dataset_from_directory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a tf.data.Dataset from image files in a directory. — image_dataset_from_directory","text":"tf.data.Dataset object. label_mode NULL, yields float32 tensors shape (batch_size, image_size[1], image_size[2], num_channels), encoding images (see rules regarding num_channels). Otherwise, yields tuple (images, labels), images shape (batch_size, image_size[1], image_size[2], num_channels), labels follows format described . Rules regarding labels format: label_mode \"int\", labels int32 tensor shape (batch_size,). label_mode \"binary\", labels float32 tensor 1s 0s shape (batch_size, 1). label_mode \"categorical\", labels float32 tensor shape (batch_size, num_classes), representing one-hot encoding class index. Rules regarding number channels yielded images: color_mode \"grayscale\", 1 channel image tensors. color_mode \"rgb\", 3 channels image tensors. color_mode \"rgba\", 4 channels image tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/image_from_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts a 3D array to a PIL Image instance. — image_from_array","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"Converts 3D array PIL Image instance.","code":""},{"path":"https://keras.posit.co/reference/image_from_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"","code":"image_from_array(x, data_format = NULL, scale = TRUE, dtype = NULL)"},{"path":"https://keras.posit.co/reference/image_from_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"x Input data, form can converted array. data_format Image data format, can either \"channels_first\" \"channels_last\". Defaults NULL, case global setting config_image_data_format() used (unless changed , defaults \"channels_last\"). scale Whether rescale image minimum maximum values 0 255 respectively. Defaults TRUE. dtype Dtype use. NULL means global setting config_floatx() used (unless changed , defaults \"float32\"). Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/image_from_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"PIL Image instance.","code":""},{"path":"https://keras.posit.co/reference/image_from_array.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Converts a 3D array to a PIL Image instance. — image_from_array","text":"","code":"img <- array(runif(30000), dim = c(100, 100, 3)) pil_img <- image_from_array(img) pil_img ## <PIL.Image.Image image mode=RGB size=100x100>"},{"path":[]},{"path":"https://keras.posit.co/reference/image_load.html","id":null,"dir":"Reference","previous_headings":"","what":"Loads an image into PIL format. — image_load","title":"Loads an image into PIL format. — image_load","text":"Loads image PIL format.","code":""},{"path":"https://keras.posit.co/reference/image_load.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loads an image into PIL format. — image_load","text":"","code":"image_load(   path,   color_mode = \"rgb\",   target_size = NULL,   interpolation = \"nearest\",   keep_aspect_ratio = FALSE )"},{"path":"https://keras.posit.co/reference/image_load.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loads an image into PIL format. — image_load","text":"path Path image file. color_mode One \"grayscale\", \"rgb\", \"rgba\". Default: \"rgb\". desired image format. target_size Either NULL (default original size) tuple ints (img_height, img_width). interpolation Interpolation method used resample image target size different loaded image. Supported methods \"nearest\", \"bilinear\", \"bicubic\". PIL version 1.1.3 newer installed, \"lanczos\" also supported. PIL version 3.4.0 newer installed, \"box\" \"hamming\" also supported. default, \"nearest\" used. keep_aspect_ratio Boolean, whether resize images target size without aspect ratio distortion. image cropped center target aspect ratio resizing.","code":""},{"path":"https://keras.posit.co/reference/image_load.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loads an image into PIL format. — image_load","text":"PIL Image instance.","code":""},{"path":"https://keras.posit.co/reference/image_load.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Loads an image into PIL format. — image_load","text":"","code":"image_path <- get_file(origin = \"https://www.r-project.org/logo/Rlogo.png\") (image <- image_load(image_path)) ## <PIL.Image.Image image mode=RGB size=724x561> input_arr <- image_to_array(image) str(input_arr) ##  num [1:561, 1:724, 1:3] 0 0 0 0 0 0 0 0 0 0 ... input_arr %<>% array_reshape(dim = c(1, dim(input_arr))) # Convert single image to a batch. model |> predict(input_arr)"},{"path":[]},{"path":"https://keras.posit.co/reference/image_smart_resize.html","id":null,"dir":"Reference","previous_headings":"","what":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","title":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","text":"Image datasets typically yield images different size. However, images need batched can processed Keras layers. batched, images need share height width. simply , TF (JAX equivalent):   However, , distort aspect ratio images, since general aspect ratio size. fine many cases, always (e.g. image generation models can problem). Note passing argument preserve_aspect_ratio = TRUE tf$image$resize() preserve aspect ratio, cost longer respecting provided target size. calls :   output images actually (200, 200), distorted. Instead, parts image fit within target size get cropped . resizing process : Take largest centered crop image aspect ratio target size. instance, size = c(200, 200) input image size (340, 500), take crop (340, 340) centered along width. Resize cropped image target size. example , resize (340, 340) crop (200, 200).","code":"size <- c(200, 200) ds <- ds$map(\\(img) tf$image$resize(img, size)) size <- c(200, 200) ds <- ds$map(\\(img) image_smart_resize(img, size))"},{"path":"https://keras.posit.co/reference/image_smart_resize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","text":"","code":"image_smart_resize(   x,   size,   interpolation = \"bilinear\",   data_format = \"channels_last\",   backend_module = NULL )"},{"path":"https://keras.posit.co/reference/image_smart_resize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","text":"x Input image batch images (tensor array). Must format (height, width, channels) (batch_size, height, width, channels). size Tuple (height, width) integer. Target size. interpolation String, interpolation use resizing. Defaults 'bilinear'. Supports bilinear, nearest, bicubic, lanczos3, lanczos5. data_format \"channels_last\" \"channels_first\". backend_module Backend module use (different default backend).","code":""},{"path":"https://keras.posit.co/reference/image_smart_resize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resize images to a target size without aspect ratio distortion. — image_smart_resize","text":"Array shape (size[1], size[2], channels). input image array, output array, backend-native tensor, output backend-native tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/image_to_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts a PIL Image instance to a matrix. — image_to_array","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"Converts PIL Image instance matrix.","code":""},{"path":"https://keras.posit.co/reference/image_to_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"","code":"image_to_array(img, data_format = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/image_to_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"img Input PIL Image instance. data_format Image data format, can either \"channels_first\" \"channels_last\". Defaults NULL, case global setting config_image_data_format() used (unless changed , defaults \"channels_last\"). dtype Dtype use. NULL means global setting config_floatx() used (unless changed , defaults \"float32\").","code":""},{"path":"https://keras.posit.co/reference/image_to_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"3D array.","code":""},{"path":"https://keras.posit.co/reference/image_to_array.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Converts a PIL Image instance to a matrix. — image_to_array","text":"","code":"image_path <- get_file(origin = \"https://www.r-project.org/logo/Rlogo.png\") (img <- image_load(image_path)) ## <PIL.Image.Image image mode=RGB size=724x561> array <- image_to_array(img) str(array) ##  num [1:561, 1:724, 1:3] 0 0 0 0 0 0 0 0 0 0 ..."},{"path":[]},{"path":"https://keras.posit.co/reference/imagenet_decode_predictions.html","id":null,"dir":"Reference","previous_headings":"","what":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","title":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","text":"Decodes prediction ImageNet model.","code":""},{"path":"https://keras.posit.co/reference/imagenet_decode_predictions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","text":"","code":"imagenet_decode_predictions(preds, top = 5)"},{"path":"https://keras.posit.co/reference/imagenet_decode_predictions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","text":"preds Tensor encoding batch predictions. top integer, many top-guesses return.","code":""},{"path":"https://keras.posit.co/reference/imagenet_decode_predictions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decodes the prediction of an ImageNet model. — imagenet_decode_predictions","text":"List data frames variables class_name, class_description, score (one data frame per sample batch input).","code":""},{"path":"https://keras.posit.co/reference/imagenet_preprocess_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","title":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","text":"Preprocesses tensor array encoding batch images.","code":""},{"path":"https://keras.posit.co/reference/imagenet_preprocess_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","text":"","code":"imagenet_preprocess_input(x, data_format = NULL, mode = \"caffe\")"},{"path":"https://keras.posit.co/reference/imagenet_preprocess_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","text":"x Input Numpy symbolic tensor, 3D 4D. data_format Data format image tensor/array. mode One \"caffe\", \"tf\", \"torch\" caffe: convert images RGB BGR, zero-center color channel respect ImageNet dataset, without scaling. tf: scale pixels -1 1, sample-wise. torch: scale pixels 0 1 normalize channel respect ImageNet dataset.","code":""},{"path":"https://keras.posit.co/reference/imagenet_preprocess_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocesses a tensor or array encoding a batch of images. — imagenet_preprocess_input","text":"Preprocessed tensor array.","code":""},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates tensors with constant values. — initializer_constant","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"scalar values allowed. constant value provided must convertible dtype requested calling initializer.","code":""},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"","code":"initializer_constant(value = 0)"},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"value numeric scalar.","code":""},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_constant.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates tensors with constant values. — initializer_constant","text":"","code":"# Standalone usage: initializer <- initializer_constant(10) values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_constant(10) layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"Draws samples truncated normal distribution centered 0 stddev = sqrt(2 / (fan_in + fan_out)) fan_in number input units weight tensor fan_out number output units weight tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"","code":"initializer_glorot_normal(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"","code":"# Standalone usage: initializer <- initializer_glorot_normal() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_glorot_normal() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_glorot_normal.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"The Glorot normal initializer, also called Xavier normal initializer. — initializer_glorot_normal","text":"Glorot et al., 2010","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"Draws samples uniform distribution within [-limit, limit], limit = sqrt(6 / (fan_in + fan_out)) (fan_in number input units weight tensor fan_out number output units).","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"","code":"initializer_glorot_uniform(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"","code":"# Standalone usage: initializer <- initializer_glorot_uniform() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_glorot_uniform() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_glorot_uniform.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"The Glorot uniform initializer, also called Xavier uniform initializer. — initializer_glorot_uniform","text":"Glorot et al., 2010","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"He normal initializer. — initializer_he_normal","title":"He normal initializer. — initializer_he_normal","text":"draws samples truncated normal distribution centered 0 stddev = sqrt(2 / fan_in) fan_in number input units weight tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"He normal initializer. — initializer_he_normal","text":"","code":"initializer_he_normal(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"He normal initializer. — initializer_he_normal","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"He normal initializer. — initializer_he_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"He normal initializer. — initializer_he_normal","text":"","code":"# Standalone usage: initializer <- initializer_he_normal() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_he_normal() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_he_normal.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"He normal initializer. — initializer_he_normal","text":"et al., 2015","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"He uniform variance scaling initializer. — initializer_he_uniform","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"Draws samples uniform distribution within [-limit, limit], limit = sqrt(6 / fan_in) (fan_in number input units weight tensor).","code":""},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"","code":"initializer_he_uniform(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"","code":"# Standalone usage: initializer <- initializer_he_uniform() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_he_uniform() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_he_uniform.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"He uniform variance scaling initializer. — initializer_he_uniform","text":"et al., 2015","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates the identity matrix. — initializer_identity","title":"Initializer that generates the identity matrix. — initializer_identity","text":"usable generating 2D matrices.","code":""},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates the identity matrix. — initializer_identity","text":"","code":"initializer_identity(gain = 1)"},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that generates the identity matrix. — initializer_identity","text":"gain Multiplicative factor apply identity matrix.","code":""},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates the identity matrix. — initializer_identity","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_identity.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates the identity matrix. — initializer_identity","text":"","code":"# Standalone usage: initializer <- initializer_identity() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_identity() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Lecun normal initializer. — initializer_lecun_normal","title":"Lecun normal initializer. — initializer_lecun_normal","text":"Initializers allow pre-specify initialization strategy, encoded Initializer object, without knowing shape dtype variable initialized. Draws samples truncated normal distribution centered 0 stddev = sqrt(1 / fan_in) fan_in number input units weight tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lecun normal initializer. — initializer_lecun_normal","text":"","code":"initializer_lecun_normal(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lecun normal initializer. — initializer_lecun_normal","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Lecun normal initializer. — initializer_lecun_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lecun normal initializer. — initializer_lecun_normal","text":"","code":"# Standalone usage: initializer <- initializer_lecun_normal() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_lecun_normal() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_lecun_normal.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Lecun normal initializer. — initializer_lecun_normal","text":"Klambauer et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"Lecun uniform initializer. — initializer_lecun_uniform","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"Draws samples uniform distribution within [-limit, limit], limit = sqrt(3 / fan_in) (fan_in number input units weight tensor).","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"","code":"initializer_lecun_uniform(seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"","code":"# Standalone usage: initializer <- initializer_lecun_uniform() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_lecun_uniform() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_lecun_uniform.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Lecun uniform initializer. — initializer_lecun_uniform","text":"Klambauer et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_ones.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates tensors initialized to 1. — initializer_ones","title":"Initializer that generates tensors initialized to 1. — initializer_ones","text":"Also available via shortcut function ones.","code":""},{"path":"https://keras.posit.co/reference/initializer_ones.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates tensors initialized to 1. — initializer_ones","text":"","code":"initializer_ones()"},{"path":"https://keras.posit.co/reference/initializer_ones.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates tensors initialized to 1. — initializer_ones","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_ones.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates tensors initialized to 1. — initializer_ones","text":"","code":"# Standalone usage: initializer <- initializer_ones() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_ones() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"shape tensor initialize two-dimensional, initialized orthogonal matrix obtained QR decomposition matrix random numbers drawn normal distribution. matrix fewer rows columns output orthogonal rows. Otherwise, output orthogonal columns. shape tensor initialize two-dimensional, matrix shape (shape[1] * ... * shape[n - 1], shape[n]) initialized, n length shape vector. matrix subsequently reshaped give tensor desired shape.","code":""},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"","code":"initializer_orthogonal(gain = 1, seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"gain Multiplicative factor apply orthogonal matrix. seed integer. Used make behavior initializer deterministic.","code":""},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"","code":"# Standalone usage: initializer <- initializer_orthogonal() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_orthogonal() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":"https://keras.posit.co/reference/initializer_orthogonal.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Initializer that generates an orthogonal matrix. — initializer_orthogonal","text":"Saxe et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Random normal initializer. — initializer_random_normal","title":"Random normal initializer. — initializer_random_normal","text":"Draws samples normal distribution given parameters.","code":""},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random normal initializer. — initializer_random_normal","text":"","code":"initializer_random_normal(mean = 0, stddev = 0.05, seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random normal initializer. — initializer_random_normal","text":"mean numeric scalar. Mean random values generate. stddev numeric scalar. Standard deviation random values generate. seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random normal initializer. — initializer_random_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_random_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random normal initializer. — initializer_random_normal","text":"","code":"# Standalone usage: initializer <- initializer_random_normal(mean = 0.0, stddev = 1.0) values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_random_normal(mean = 0.0, stddev = 1.0) layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"Random uniform initializer. — initializer_random_uniform","title":"Random uniform initializer. — initializer_random_uniform","text":"Draws samples uniform distribution given parameters.","code":""},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random uniform initializer. — initializer_random_uniform","text":"","code":"initializer_random_uniform(minval = -0.05, maxval = 0.05, seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random uniform initializer. — initializer_random_uniform","text":"minval numeric scalar scalar keras tensor. Lower bound range random values generate (inclusive). maxval numeric scalar scalar keras tensor. Upper bound range random values generate (exclusive). seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random uniform initializer. — initializer_random_uniform","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_random_uniform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random uniform initializer. — initializer_random_uniform","text":"","code":"# Standalone usage: initializer <- initializer_random_uniform(minval = 0.0, maxval = 1.0) values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_random_uniform(minval = 0.0, maxval = 1.0) layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"values generated similar values RandomNormal initializer, except values two standard deviations mean discarded re-drawn.","code":""},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"","code":"initializer_truncated_normal(mean = 0, stddev = 0.05, seed = NULL)"},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"mean numeric scalar. Mean random values generate. stddev numeric scalar. Standard deviation random values generate. seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_truncated_normal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates a truncated normal distribution. — initializer_truncated_normal","text":"","code":"# Standalone usage: initializer <- initializer_truncated_normal(mean = 0, stddev = 1) values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_truncated_normal(mean = 0, stddev = 1) layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"distribution = \"truncated_normal\" \"untruncated_normal\", samples drawn truncated/untruncated normal distribution mean zero standard deviation (truncation, used) stddev = sqrt(scale / n), n : number input units weight tensor, mode = \"fan_in\" number output units, mode = \"fan_out\" average numbers input output units, mode = \"fan_avg\" distribution = \"uniform\", samples drawn uniform distribution within [-limit, limit], limit = sqrt(3 * scale / n).","code":""},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"","code":"initializer_variance_scaling(   scale = 1,   mode = \"fan_in\",   distribution = \"truncated_normal\",   seed = NULL )"},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"scale Scaling factor (positive float). mode One \"fan_in\", \"fan_out\", \"fan_avg\". distribution Random distribution use. One \"truncated_normal\", \"untruncated_normal\", \"uniform\". seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_variance_scaling.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that adapts its scale to the shape of its input tensors. — initializer_variance_scaling","text":"","code":"# Standalone usage: initializer <- initializer_variance_scaling(scale = 0.1, mode = 'fan_in',                                             distribution = 'uniform') values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_variance_scaling(scale = 0.1, mode = 'fan_in',                                             distribution = 'uniform') layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/initializer_zeros.html","id":null,"dir":"Reference","previous_headings":"","what":"Initializer that generates tensors initialized to 0. — initializer_zeros","title":"Initializer that generates tensors initialized to 0. — initializer_zeros","text":"Initializer generates tensors initialized 0.","code":""},{"path":"https://keras.posit.co/reference/initializer_zeros.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initializer that generates tensors initialized to 0. — initializer_zeros","text":"","code":"initializer_zeros()"},{"path":"https://keras.posit.co/reference/initializer_zeros.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initializer that generates tensors initialized to 0. — initializer_zeros","text":"Initializer instance can passed layer variable constructors, called directly shape return Tensor.","code":""},{"path":"https://keras.posit.co/reference/initializer_zeros.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initializer that generates tensors initialized to 0. — initializer_zeros","text":"","code":"# Standalone usage: initializer <- initializer_zeros() values <- initializer(shape = c(2, 2)) # Usage in a Keras layer: initializer <- initializer_zeros() layer <- layer_dense(units = 3, kernel_initializer = initializer)"},{"path":[]},{"path":"https://keras.posit.co/reference/install_keras.html","id":null,"dir":"Reference","previous_headings":"","what":"Install Keras — install_keras","title":"Install Keras — install_keras","text":"function install Keras along selected backend, including Python dependencies.","code":""},{"path":"https://keras.posit.co/reference/install_keras.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Install Keras — install_keras","text":"","code":"install_keras(   envname = \"r-keras\",   ...,   extra_packages = c(\"scipy\", \"pandas\", \"Pillow\", \"pydot\", \"ipython\",     \"tensorflow_datasets\"),   python_version = \">=3.9,<=3.11\",   backend = c(\"tensorflow\", \"jax\"),   gpu = NA,   restart_session = TRUE )"},{"path":"https://keras.posit.co/reference/install_keras.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Install Keras — install_keras","text":"envname Name path Python virtual environment ... reserved future compatibility. extra_packages Additional Python packages install alongside Keras python_version Passed reticulate::virtualenv_starter() backend backend(s) install. Accepted values include \"tensorflow\", \"jax\" \"torch\" gpu whether install GPU capable version backend. restart_session Whether restart R session installing (note occur within RStudio).","code":""},{"path":"https://keras.posit.co/reference/install_keras.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Install Keras — install_keras","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/keras.html","id":null,"dir":"Reference","previous_headings":"","what":"Main Keras module — keras","title":"Main Keras module — keras","text":"keras module object equivalent reticulate::import(\"keras\") provided mainly convenience.","code":""},{"path":"https://keras.posit.co/reference/keras.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Main Keras module — keras","text":"object class python.builtin.module","code":""},{"path":"https://keras.posit.co/reference/keras.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Main Keras module — keras","text":"keras Python module","code":""},{"path":"https://keras.posit.co/reference/keras3-package.html","id":null,"dir":"Reference","previous_headings":"","what":"keras3: R Interface to 'Keras' — keras3-package","title":"keras3: R Interface to 'Keras' — keras3-package","text":"Interface 'Keras' https://keras.io, high-level neural networks API. 'Keras' developed focus enabling fast experimentation, supports convolution based networks recurrent networks (well combinations two), runs seamlessly CPU GPU devices. Keras high-level neural networks API, developed focus enabling fast experimentation. Keras following key features:","code":""},{"path":"https://keras.posit.co/reference/keras3-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"keras3: R Interface to 'Keras' — keras3-package","text":"Allows code run CPU GPU, seamlessly. User-friendly API makes easy quickly prototype deep learning models. Built-support convolutional networks (computer vision), recurrent networks (sequence processing), combination . Supports arbitrary network architectures: multi-input multi-output models, layer sharing, model sharing, etc. means Keras appropriate building essentially deep learning model, memory network neural Turing machine. capable running top multiple back-ends including TensorFlow, Jax, PyTorch. See package website https://keras.posit.co complete documentation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/keras3-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"keras3: R Interface to 'Keras' — keras3-package","text":"Maintainer: Tomasz Kalinowski tomasz@posit.co [copyright holder] Authors: JJ Allaire [copyright holder] François Chollet [copyright holder] contributors: Daniel Falbel daniel@posit.co [contributor, copyright holder] Posit Software, PBC [copyright holder, funder] Google [copyright holder, funder] Yuan Tang terrytangyuan@gmail.com (ORCID) [contributor, copyright holder] Wouter Van Der Bijl [contributor, copyright holder] Martin Studer [contributor, copyright holder] Sigrid Keydana [contributor]","code":""},{"path":"https://keras.posit.co/reference/keras_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Keras tensor (Functional API input). — keras_input","title":"Create a Keras tensor (Functional API input). — keras_input","text":"Keras tensor symbolic tensor-like object, augment certain attributes allow us build Keras model just knowing inputs outputs model. instance, , b c Keras tensors, becomes possible : model <- keras_model(input = c(, b), output = c)","code":""},{"path":"https://keras.posit.co/reference/keras_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Keras tensor (Functional API input). — keras_input","text":"","code":"keras_input(   shape = NULL,   batch_size = NULL,   dtype = NULL,   sparse = NULL,   batch_shape = NULL,   name = NULL,   tensor = NULL )"},{"path":"https://keras.posit.co/reference/keras_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Keras tensor (Functional API input). — keras_input","text":"shape shape list (list integers NULL objects), including batch size. instance, shape = c(32) indicates expected input batches 32-dimensional vectors. Elements list can NULL NA; NULL/NA elements represent dimensions shape known may vary (e.g. sequence length). batch_size Optional static batch size (integer). dtype data type expected input, string (e.g. \"float32\", \"int32\"...) sparse boolean specifying whether expected input sparse tensors. Note , sparse FALSE, sparse tensors can still passed input - densified default value 0. feature supported TensorFlow backend. Defaults FALSE. batch_shape Shape, including batch dim. name Optional name string layer. unique model (reuse name twice). autogenerated provided. tensor Optional existing tensor wrap Input layer. set, layer use tensor rather creating new placeholder tensor.","code":""},{"path":"https://keras.posit.co/reference/keras_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Keras tensor (Functional API input). — keras_input","text":"Keras tensor, can passed inputs argument (keras_model()).","code":""},{"path":"https://keras.posit.co/reference/keras_input.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Keras tensor (Functional API input). — keras_input","text":"","code":"# This is a logistic regression in Keras input <- layer_input(shape=c(32)) output <- input |> layer_dense(16, activation='softmax') model <- keras_model(input, output)"},{"path":[]},{"path":"https://keras.posit.co/reference/keras_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Keras Model (Functional API) — keras_model","title":"Keras Model (Functional API) — keras_model","text":"model directed acyclic graph layers.","code":""},{"path":"https://keras.posit.co/reference/keras_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keras Model (Functional API) — keras_model","text":"","code":"keras_model(inputs = NULL, outputs = NULL, ...)"},{"path":"https://keras.posit.co/reference/keras_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keras Model (Functional API) — keras_model","text":"inputs Input tensor(s) (keras_input()) outputs Output tensors (calling layers inputs) ... additional arguments","code":""},{"path":"https://keras.posit.co/reference/keras_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keras Model (Functional API) — keras_model","text":"Model instance.","code":""},{"path":"https://keras.posit.co/reference/keras_model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Keras Model (Functional API) — keras_model","text":"","code":"library(keras3)  # input tensor inputs <- keras_input(shape = c(784))  # outputs compose input + dense layers predictions <- inputs |>   layer_dense(units = 64, activation = 'relu') |>   layer_dense(units = 64, activation = 'relu') |>   layer_dense(units = 10, activation = 'softmax')  # create and compile model model <- keras_model(inputs = inputs, outputs = predictions) model |> compile(   optimizer = 'rmsprop',   loss = 'categorical_crossentropy',   metrics = c('accuracy') )"},{"path":[]},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":null,"dir":"Reference","previous_headings":"","what":"Keras Model composed of a linear stack of layers — keras_model_sequential","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"Keras Model composed linear stack layers","code":""},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"","code":"keras_model_sequential(   input_shape = NULL,   name = NULL,   ...,   input_dtype = NULL,   input_batch_size = NULL,   input_sparse = NULL,   input_batch_shape = NULL,   input_name = NULL,   input_tensor = NULL,   trainable = TRUE,   layers = list() )"},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"input_shape shape integer vector, including batch size. instance, shape=c(32) indicates expected input batches 32-dimensional vectors. Elements shape can NA; NA elements represent dimensions shape known may vary (e.g. sequence length). name Name model ... additional arguments passed keras.layers.InputLayer. input_dtype data type expected input, string (e.g. \"float32\", \"int32\"...) input_batch_size Optional static batch size (integer). input_sparse boolean specifying whether expected input sparse tensors. Note , sparse FALSE, sparse tensors can still passed input - densified default value 0. feature supported TensorFlow backend. Defaults FALSE. input_batch_shape optional way specify batch_size input_shape one argument. input_name Optional name string input layer. unique model (reuse name twice). autogenerated provided. input_tensor Optional existing tensor wrap InputLayer. set, layer use tensor rather creating new placeholder tensor. trainable Boolean, whether model's variables trainable. can also change trainable status model/layer freeze_weights() unfreeze_weights(). layers List layers add model.","code":""},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"Sequential model instance.","code":""},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"input_shape omitted, model layer shapes, including final model output shape, known model built, either calling model input tensor/array like model(input), (possibly via fit()/evaluate()/predict()), explicitly calling model$build(input_shape).","code":""},{"path":"https://keras.posit.co/reference/keras_model_sequential.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Keras Model composed of a linear stack of layers — keras_model_sequential","text":"","code":"model <- keras_model_sequential(input_shape = c(784)) model |>   layer_dense(units = 32) |>   layer_activation('relu') |>   layer_dense(units = 10) |>   layer_activation('softmax')  model |> compile(   optimizer = 'rmsprop',   loss = 'categorical_crossentropy',   metrics = c('accuracy') )  model ## Model: \"sequential\" ## +---------------------------------+------------------------+---------------+ ## | Layer (type)                    | Output Shape           |       Param # | ## +=================================+========================+===============+ ## | dense (Dense)                   | (None, 32)             |        25,120 | ## +---------------------------------+------------------------+---------------+ ## | activation (Activation)         | (None, 32)             |             0 | ## +---------------------------------+------------------------+---------------+ ## | dense_1 (Dense)                 | (None, 10)             |           330 | ## +---------------------------------+------------------------+---------------+ ## | activation_1 (Activation)       | (None, 10)             |             0 | ## +---------------------------------+------------------------+---------------+ ##  Total params: 25,450 (99.41 KB) ##  Trainable params: 25,450 (99.41 KB) ##  Non-trainable params: 0 (0.00 B)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies an activation function to an output. — layer_activation","title":"Applies an activation function to an output. — layer_activation","text":"Applies activation function output.","code":""},{"path":"https://keras.posit.co/reference/layer_activation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies an activation function to an output. — layer_activation","text":"","code":"layer_activation(object, activation, ...)"},{"path":"https://keras.posit.co/reference/layer_activation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies an activation function to an output. — layer_activation","text":"object Object compose layer . tensor, array, sequential model. activation Activation function. callable, name activation keras3::activation_* namespace. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies an activation function to an output. — layer_activation","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_activation.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies an activation function to an output. — layer_activation","text":"","code":"x <- array(c(-3, -1, 0, 2)) layer <- layer_activation(activation = 'relu') layer(x) ## tf.Tensor([0. 0. 0. 2.], shape=(4), dtype=float32) layer <- layer_activation(activation = activation_relu) layer(x) ## tf.Tensor([0. 0. 0. 2.], shape=(4), dtype=float32) layer <- layer_activation(activation = op_relu) layer(x) ## tf.Tensor([0. 0. 0. 2.], shape=(4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","title":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","text":"Formula:","code":"f(x) = alpha * (exp(x) - 1.) for x < 0 f(x) = x for x >= 0"},{"path":"https://keras.posit.co/reference/layer_activation_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","text":"","code":"layer_activation_elu(object, alpha = 1, ...)"},{"path":"https://keras.posit.co/reference/layer_activation_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","text":"object Object compose layer . tensor, array, sequential model. alpha float, slope negative section. Defaults 1.0. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_elu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies an Exponential Linear Unit function to an output. — layer_activation_elu","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"layer allows small gradient unit active. Formula:","code":"f <- function(x) ifelse(x >= 0, x, alpha * x)"},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"","code":"layer_activation_leaky_relu(object, negative_slope = 0.3, ...)"},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"object Object compose layer . tensor, array, sequential model. negative_slope Float >= 0.0. Negative slope coefficient. Defaults 0.3. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_leaky_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Leaky version of a Rectified Linear Unit activation layer. — layer_activation_leaky_relu","text":"","code":"leaky_relu_layer <- layer_activation_leaky_relu(negative_slope=0.5) input <- array(c(-10, -5, 0.0, 5, 10)) result <- leaky_relu_layer(input) as.array(result) ## [1] -5.0 -2.5  0.0  5.0 10.0"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_parametric_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","title":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","text":"Formula:   alpha learned array shape x.","code":"f <- function(x) ifelse(x >= 0, x, alpha * x)"},{"path":"https://keras.posit.co/reference/layer_activation_parametric_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","text":"","code":"layer_activation_parametric_relu(   object,   alpha_initializer = \"Zeros\",   alpha_regularizer = NULL,   alpha_constraint = NULL,   shared_axes = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_activation_parametric_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","text":"object Object compose layer . tensor, array, sequential model. alpha_initializer Initializer function weights. alpha_regularizer Regularizer weights. alpha_constraint Constraint weights. shared_axes axes along share learnable parameters activation function. example, incoming feature maps 2D convolution output shape (batch, height, width, channels), wish share parameters across space filter one set parameters, set shared_axes=[1, 2]. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_parametric_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parametric Rectified Linear Unit activation layer. — layer_activation_parametric_relu","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Rectified Linear Unit activation function layer. — layer_activation_relu","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"Formula:","code":"f <- function(x, max_value = Inf, negative_slope = 0, threshold = 0) {  x <- max(x,0)  if (x >= max_value)    max_value  else if (threshold <= x && x < max_value)    x  else    negative_slope * (x - threshold) }"},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"","code":"layer_activation_relu(   object,   max_value = NULL,   negative_slope = 0,   threshold = 0,   ... )"},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"object Object compose layer . tensor, array, sequential model. max_value Float >= 0. Maximum activation value. NULL means unlimited. Defaults NULL. negative_slope Float >= 0. Negative slope coefficient. Defaults 0.0. threshold Float >= 0. Threshold value thresholded activation. Defaults 0.0. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rectified Linear Unit activation function layer. — layer_activation_relu","text":"","code":"relu_layer <- layer_activation_relu(max_value = 10,                                     negative_slope = 0.5,                                     threshold = 0) input <- array(c(-10, -5, 0.0, 5, 10)) result <- relu_layer(input) as.array(result) ## [1] -5.0 -2.5  0.0  5.0 10.0"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax activation layer. — layer_activation_softmax","title":"Softmax activation layer. — layer_activation_softmax","text":"Formula:","code":"exp_x = exp(x - max(x)) f(x) = exp_x / sum(exp_x)"},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax activation layer. — layer_activation_softmax","text":"","code":"layer_activation_softmax(object, axis = -1L, ...)"},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax activation layer. — layer_activation_softmax","text":"object Object compose layer . tensor, array, sequential model. axis Integer, list Integers, axis along softmax normalization applied. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax activation layer. — layer_activation_softmax","text":"Softmaxed output shape inputs.","code":""},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmax activation layer. — layer_activation_softmax","text":"","code":"softmax_layer <- layer_activation_softmax() input <- op_array(c(1, 2, 1)) softmax_layer(input) ## tf.Tensor([0.21194157 0.5761169  0.21194157], shape=(3), dtype=float32)"},{"path":"https://keras.posit.co/reference/layer_activation_softmax.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Softmax activation layer. — layer_activation_softmax","text":"inputs: inputs (logits) softmax layer. mask: boolean mask shape inputs. mask specifies 1 keep 0 mask. Defaults NULL.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"Layer applies update cost function based input activity.","code":""},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"","code":"layer_activity_regularization(object, l1 = 0, l2 = 0, ...)"},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"object Object compose layer . tensor, array, sequential model. l1 L1 regularization factor (positive float). l2 L2 regularization factor (positive float). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"Arbitrary. Use keyword argument input_shape (tuple integers, include samples axis) using layer first layer model.","code":""},{"path":"https://keras.posit.co/reference/layer_activity_regularization.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Layer that applies an update to the cost function based input activity. — layer_activity_regularization","text":"shape input.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_add.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs elementwise addition operation. — layer_add","title":"Performs elementwise addition operation. — layer_add","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_add.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs elementwise addition operation. — layer_add","text":"","code":"layer_add(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_add.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs elementwise addition operation. — layer_add","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_add.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs elementwise addition operation. — layer_add","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_add.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs elementwise addition operation. — layer_add","text":"Usage Keras model:","code":"input_shape <- c(1, 2, 3) x1 <- op_ones(input_shape) x2 <- op_ones(input_shape) layer_add(x1, x2) ## tf.Tensor( ## [[[2. 2. 2.] ##   [2. 2. 2.]]], shape=(1, 2, 3), dtype=float32) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu')  input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu')  # equivalent to `added = layer_add([x1, x2))` added <- layer_add(x1, x2) output <- added |> layer_dense(4)  model <- keras_model(inputs = c(input1, input2), outputs = output)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"Inputs list 2 3 elements: query tensor shape (batch_size, Tq, dim). value tensor shape (batch_size, Tv, dim). optional key tensor shape (batch_size, Tv, dim). none supplied, value used key. calculation follows steps: Calculate attention scores using query key shape (batch_size, Tq, Tv) non-linear sum scores = reduce_sum(tanh(query + key), axis=-1). Use scores calculate softmax distribution shape (batch_size, Tq, Tv). Use softmax distribution create linear combination value shape (batch_size, Tq, dim).","code":""},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"","code":"layer_additive_attention(object, use_scale = TRUE, dropout = 0, ...)"},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"object Object compose layer . tensor, array, sequential model. use_scale TRUE, create scalar variable scale attention scores. dropout Float 0 1. Fraction units drop attention scores. Defaults 0.0. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"inputs: List following tensors: query: Query tensor shape (batch_size, Tq, dim). value: Value tensor shape (batch_size, Tv, dim). key: Optional key tensor shape (batch_size, Tv, dim). given, use value key value, common case. mask: List following tensors: query_mask: boolean mask tensor shape (batch_size, Tq). given, output zero positions mask==FALSE. value_mask: boolean mask tensor shape (batch_size, Tv). given, apply mask values positions mask==FALSE contribute result. return_attention_scores: bool, TRUE, returns attention scores (masking softmax) additional output argument. training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (dropout). use_causal_mask: Boolean. Set TRUE decoder self-attention. Adds mask position attend positions j > . prevents flow information future towards past. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/layer_additive_attention.html","id":"output","dir":"Reference","previous_headings":"","what":"Output","title":"Additive attention layer, a.k.a. Bahdanau-style attention. — layer_additive_attention","text":"Attention outputs shape (batch_size, Tq, dim). (Optional) Attention scores masking softmax shape (batch_size, Tq, Tv).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies Alpha Dropout to the input. — layer_alpha_dropout","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"Alpha Dropout Dropout keeps mean variance inputs original values, order ensure self-normalizing property even dropout. Alpha Dropout fits well Scaled Exponential Linear Units (SELU) randomly setting activations negative saturation value.","code":""},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"","code":"layer_alpha_dropout(object, rate, noise_shape = NULL, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. multiplicative noise standard deviation sqrt(rate / (1 - rate)). noise_shape 1D integer tensor representing shape binary alpha dropout mask multiplied input. instance, inputs shape (batch_size, timesteps, features) want alpha dropout mask timesteps, can use noise_shape = (batch_size, 1, features). seed integer use random seed. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_alpha_dropout.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Applies Alpha Dropout to the input. — layer_alpha_dropout","text":"inputs: Input tensor (rank). training: R boolean indicating whether layer behave training mode (adding alpha dropout) inference mode (nothing).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"Inputs list 2 3 elements: query tensor shape (batch_size, Tq, dim). value tensor shape (batch_size, Tv, dim). optional key tensor shape (batch_size, Tv, dim). none supplied, value used key. calculation follows steps: Calculate attention scores using query key shape (batch_size, Tq, Tv). Use scores calculate softmax distribution shape (batch_size, Tq, Tv). Use softmax distribution create linear combination value shape (batch_size, Tq, dim).","code":""},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"","code":"layer_attention(   object,   use_scale = FALSE,   score_mode = \"dot\",   dropout = 0,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"object Object compose layer . tensor, array, sequential model. use_scale TRUE, create scalar variable scale attention scores. score_mode Function use compute attention scores, one {\"dot\", \"concat\"}. \"dot\" refers dot product query key vectors. \"concat\" refers hyperbolic tangent concatenation query key vectors. dropout Float 0 1. Fraction units drop attention scores. Defaults 0.0. seed integer use random seed incase dropout. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"inputs: List following tensors: query: Query tensor shape (batch_size, Tq, dim). value: Value tensor shape (batch_size, Tv, dim). key: Optional key tensor shape (batch_size, Tv, dim). given, use value key value, common case. mask: List following tensors: query_mask: boolean mask tensor shape (batch_size, Tq). given, output zero positions mask==FALSE. value_mask: boolean mask tensor shape (batch_size, Tv). given, apply mask values positions mask==FALSE contribute result. return_attention_scores: bool, TRUE, returns attention scores (masking softmax) additional output argument. training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (dropout). use_causal_mask: Boolean. Set TRUE decoder self-attention. Adds mask position attend positions j > . prevents flow information future towards past. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/layer_attention.html","id":"output","dir":"Reference","previous_headings":"","what":"Output","title":"Dot-product attention layer, a.k.a. Luong-style attention. — layer_attention","text":"Attention outputs shape (batch_size, Tq, dim). (Optional) Attention scores masking softmax shape (batch_size, Tq, Tv).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_average.html","id":null,"dir":"Reference","previous_headings":"","what":"Averages a list of inputs element-wise.. — layer_average","title":"Averages a list of inputs element-wise.. — layer_average","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_average.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Averages a list of inputs element-wise.. — layer_average","text":"","code":"layer_average(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_average.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Averages a list of inputs element-wise.. — layer_average","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_average.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Averages a list of inputs element-wise.. — layer_average","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_average.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Averages a list of inputs element-wise.. — layer_average","text":"Usage Keras model:","code":"input_shape <- c(1, 2, 3) x1 <- op_ones(input_shape) x2 <- op_zeros(input_shape) layer_average(x1, x2) ## tf.Tensor( ## [[[0.5 0.5 0.5] ##   [0.5 0.5 0.5]]], shape=(1, 2, 3), dtype=float32) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu')  input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu')  added <- layer_average(x1, x2) output <- added |> layer_dense(4)  model <- keras_model(inputs = c(input1, input2), outputs = output)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Average pooling for temporal data. — layer_average_pooling_1d","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"Downsamples input representation taking average value window defined pool_size. window shifted strides.  resulting output using \"valid\" padding option shape : output_shape = (input_shape - pool_size + 1) / strides) resulting output shape using \"\" padding option : output_shape = input_shape / strides","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"","code":"layer_average_pooling_1d(   object,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"object Object compose layer . tensor, array, sequential model. pool_size int, size max pooling window. strides int NULL. Specifies much pooling window moves pooling step. NULL, default pool_size. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"data_format=\"channels_last\": 3D tensor shape (batch_size, steps, features). data_format=\"channels_first\": 3D tensor shape (batch_size, features, steps).","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"data_format=\"channels_last\": 3D tensor shape (batch_size, downsampled_steps, features). data_format=\"channels_first\": 3D tensor shape (batch_size, features, downsampled_steps).","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_1d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average pooling for temporal data. — layer_average_pooling_1d","text":"strides=1 padding=\"valid\":     strides=2 padding=\"valid\":     strides=1 padding=\"\":","code":"x <- op_array(c(1., 2., 3., 4., 5.)) |> op_reshape(c(1, 5, 1)) output <- x |>   layer_average_pooling_1d(pool_size = 2,                            strides = 1,                            padding = \"valid\") output ## tf.Tensor( ## [[[1.5] ##   [2.5] ##   [3.5] ##   [4.5]]], shape=(1, 4, 1), dtype=float32) x <- op_array(c(1., 2., 3., 4., 5.)) |> op_reshape(c(1, 5, 1)) output <- x |>   layer_average_pooling_1d(pool_size = 2,                            strides = 2,                            padding = \"valid\") output ## tf.Tensor( ## [[[1.5] ##   [3.5]]], shape=(1, 2, 1), dtype=float32) x <- op_array(c(1., 2., 3., 4., 5.)) |> op_reshape(c(1, 5, 1)) output <- x |>   layer_average_pooling_1d(pool_size = 2,                            strides = 1,                            padding = \"same\") output ## tf.Tensor( ## [[[1.5] ##   [2.5] ##   [3.5] ##   [4.5] ##   [5. ]]], shape=(1, 5, 1), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"Downsamples input along spatial dimensions (height width) taking average value input window (size defined pool_size) channel input. window shifted strides along dimension. resulting output using \"valid\" padding option spatial shape (number rows columns) : output_shape = math.floor((input_shape - pool_size) / strides) + 1 (input_shape >= pool_size) resulting output shape using \"\" padding option : output_shape = math.floor((input_shape - 1) / strides) + 1","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"","code":"layer_average_pooling_2d(   object,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"object Object compose layer . tensor, array, sequential model. pool_size int list 2 integers, factors downscale (dim1, dim2). one integer specified, window length used dimensions. strides int list 2 integers, NULL. Strides values. NULL, default pool_size. one int specified, stride size used dimensions. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"data_format=\"channels_last\": 4D tensor shape (batch_size, height, width, channels). data_format=\"channels_first\": 4D tensor shape (batch_size, channels, height, width).","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"data_format=\"channels_last\": 4D tensor shape (batch_size, pooled_height, pooled_width, channels). data_format=\"channels_first\": 4D tensor shape (batch_size, channels, pooled_height, pooled_width).","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_2d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average pooling operation for 2D spatial data. — layer_average_pooling_2d","text":"strides=(1, 1) padding=\"valid\":     strides=(2, 2) padding=\"valid\":     stride=(1, 1) padding=\"\":","code":"x <- op_array(1:9, \"float32\") |> op_reshape(c(1, 3, 3, 1)) output <- x |>   layer_average_pooling_2d(pool_size = c(2, 2),                            strides = c(1, 1),                            padding = \"valid\") output ## tf.Tensor( ## [[[[3.] ##    [4.]] ## ##   [[6.] ##    [7.]]]], shape=(1, 2, 2, 1), dtype=float32) x <- op_array(1:12, \"float32\") |> op_reshape(c(1, 3, 4, 1)) output <- x |>   layer_average_pooling_2d(pool_size = c(2, 2),                            strides = c(2, 2),                            padding = \"valid\") output ## tf.Tensor( ## [[[[3.5] ##    [5.5]]]], shape=(1, 1, 2, 1), dtype=float32) x <- op_array(1:9, \"float32\") |> op_reshape(c(1, 3, 3, 1)) output <- x |>   layer_average_pooling_2d(pool_size = c(2, 2),                            strides = c(1, 1),                            padding = \"same\") output ## tf.Tensor( ## [[[[3. ] ##    [4. ] ##    [4.5]] ## ##   [[6. ] ##    [7. ] ##    [7.5]] ## ##   [[7.5] ##    [8.5] ##    [9. ]]]], shape=(1, 3, 3, 1), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"Downsamples input along spatial dimensions (depth, height, width) taking average value input window (size defined pool_size) channel input. window shifted strides along dimension.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"","code":"layer_average_pooling_3d(   object,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"object Object compose layer . tensor, array, sequential model. pool_size int list 3 integers, factors downscale (dim1, dim2, dim3). one integer specified, window length used dimensions. strides int list 3 integers, NULL. Strides values. NULL, default pool_size. one int specified, stride size used dimensions. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_average_pooling_3d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average pooling operation for 3D data (spatial or spatio-temporal). — layer_average_pooling_3d","text":"","code":"depth <- height <- width <- 30 channels <- 3  inputs <- layer_input(shape = c(depth, height, width, channels)) outputs <- inputs |> layer_average_pooling_3d(pool_size = 3) outputs # Shape: (batch_size, 10, 10, 10, 3) ## <KerasTensor shape=(None, 10, 10, 10, 3), dtype=float32, sparse=False, name=keras_tensor_1>"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer that normalizes its inputs. — layer_batch_normalization","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"Batch normalization applies transformation maintains mean output close 0 output standard deviation close 1. Importantly, batch normalization works differently training inference. training (.e. using fit() calling layer/model argument training = TRUE), layer normalizes output using mean standard deviation current batch inputs. say, channel normalized, layer returns gamma * (batch - mean(batch)) / sqrt(var(batch) + epsilon) + beta, : epsilon small constant (configurable part constructor arguments) gamma learned scaling factor (initialized 1), can disabled passing scale = FALSE constructor. beta learned offset factor (initialized 0), can disabled passing center = FALSE constructor. inference (.e. using evaluate() predict() calling layer/model argument training = FALSE (default), layer normalizes output using moving average mean standard deviation batches seen training. say, returns gamma * (batch - self$moving_mean) / sqrt(self$moving_var+epsilon) + beta. self$moving_mean self$moving_var non-trainable variables updated time layer called training mode, : moving_mean = moving_mean * momentum + mean(batch) * (1 - momentum) moving_var = moving_var * momentum + var(batch) * (1 - momentum) , layer normalize inputs inference trained data similar statistics inference data. setting layer$trainable <- FALSE BatchNormalization layer: meaning setting layer$trainable <- FALSE freeze layer, .e. internal state change training: trainable weights updated fit() train_on_batch(), state updates run. Usually, necessarily mean layer run inference mode (normally controlled training argument can passed calling layer). \"Frozen state\" \"inference mode\" two separate concepts. However, case BatchNormalization layer, setting trainable <- FALSE layer means layer subsequently run inference mode (meaning use moving mean moving variance normalize current batch, rather using mean variance current batch). Note : Setting trainable model containing layers recursively set trainable value inner layers. value trainable attribute changed calling compile() model, new value take effect model compile() called .","code":""},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"","code":"layer_batch_normalization(   object,   axis = -1L,   momentum = 0.99,   epsilon = 0.001,   center = TRUE,   scale = TRUE,   beta_initializer = \"zeros\",   gamma_initializer = \"ones\",   moving_mean_initializer = \"zeros\",   moving_variance_initializer = \"ones\",   beta_regularizer = NULL,   gamma_regularizer = NULL,   beta_constraint = NULL,   gamma_constraint = NULL,   synchronized = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"object Object compose layer . tensor, array, sequential model. axis Integer, axis normalized (typically features axis). instance, Conv2D layer data_format = \"channels_first\", use axis = 2. momentum Momentum moving average. epsilon Small float added variance avoid dividing zero. center TRUE, add offset beta normalized tensor. FALSE, beta ignored. scale TRUE, multiply gamma. FALSE, gamma used. next layer linear can disabled since scaling done next layer. beta_initializer Initializer beta weight. gamma_initializer Initializer gamma weight. moving_mean_initializer Initializer moving mean. moving_variance_initializer Initializer moving variance. beta_regularizer Optional regularizer beta weight. gamma_regularizer Optional regularizer gamma weight. beta_constraint Optional constraint beta weight. gamma_constraint Optional constraint gamma weight. synchronized applicable TensorFlow backend. TRUE, synchronizes global batch statistics (mean variance) layer across devices training step distributed training strategy. FALSE, replica uses local batch statistics. ... Base layer keyword arguments (e.g. name dtype).","code":""},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"inputs: Input tensor (rank). training: R boolean indicating whether layer behave training mode inference mode. training = TRUE: layer normalize inputs using mean variance current batch inputs. training = FALSE: layer normalize inputs using mean variance moving statistics, learned training. mask: Binary tensor shape broadcastable inputs tensor, TRUE values indicating positions mean variance computed. Masked elements current inputs taken account mean variance computation training. prior unmasked element values taken account momentum expires.","code":""},{"path":"https://keras.posit.co/reference/layer_batch_normalization.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Layer that normalizes its inputs. — layer_batch_normalization","text":"Ioffe Szegedy, 2015.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":null,"dir":"Reference","previous_headings":"","what":"Bidirectional wrapper for RNNs. — layer_bidirectional","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"Bidirectional wrapper RNNs.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"","code":"layer_bidirectional(   object,   layer,   merge_mode = \"concat\",   weights = NULL,   backward_layer = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"object Object compose layer . tensor, array, sequential model. layer RNN instance, layer_lstm() layer_gru(). also Layer() instance meets following criteria: sequence-processing layer (accepts 3D+ inputs). go_backwards, return_sequences return_state attribute (semantics RNN class). input_spec attribute. Implement serialization via get_config() from_config(). Note recommended way create new RNN layers write custom RNN cell use layer_rnn(), instead subclassing Layer() directly. return_sequences TRUE, output masked timestep zero regardless layer's original zero_output_for_mask value. merge_mode Mode outputs forward backward RNNs combined. One {\"sum\", \"mul\", \"concat\", \"ave\", NULL}. NULL, outputs combined, returned list. Defaults \"concat\". weights see description backward_layer Optional RNN, Layer() instance used handle backwards input processing. backward_layer provided, layer instance passed layer argument used generate backward layer automatically. Note provided backward_layer layer properties matching layer argument, particular values stateful, return_states, return_sequences, etc. addition, backward_layer layer different go_backwards argument values. ValueError raised requirements met. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"call arguments layer wrapped RNN layer. Beware passing initial_state argument call layer, first half list elements initial_state list passed forward RNN call last half list elements passed backward RNN call.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"instantiating Bidirectional layer existing RNN layer instance reuse weights state RNN layer instance -- Bidirectional layer freshly initialized weights.","code":""},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"","code":"model <- keras_model_sequential(input_shape = c(5, 10)) %>%   layer_bidirectional(layer_lstm(units = 10, return_sequences = TRUE)) %>%   layer_bidirectional(layer_lstm(units = 10)) %>%   layer_dense(5, activation = \"softmax\")  model %>% compile(loss = \"categorical_crossentropy\",                   optimizer = \"rmsprop\")  # With custom backward layer forward_layer <- layer_lstm(units = 10, return_sequences = TRUE) backward_layer <- layer_lstm(units = 10, activation = \"relu\",                              return_sequences = TRUE, go_backwards = TRUE)  model <- keras_model_sequential(input_shape = c(5, 10)) %>%   bidirectional(forward_layer, backward_layer = backward_layer) %>%   layer_dense(5, activation = \"softmax\")  model %>% compile(loss = \"categorical_crossentropy\",                   optimizer = \"rmsprop\")"},{"path":"https://keras.posit.co/reference/layer_bidirectional.html","id":"states","dir":"Reference","previous_headings":"","what":"States","title":"Bidirectional wrapper for RNNs. — layer_bidirectional","text":"Bidirectional layer instance property states, can access layer$states. can also reset states using reset_state()","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which encodes integer features. — layer_category_encoding","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"layer provides options condensing data categorical encoding total number tokens known advance. accepts integer values inputs, outputs dense sparse representation inputs. integer inputs total number tokens known, use layer_integer_lookup() instead. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"","code":"layer_category_encoding(   object,   num_tokens = NULL,   output_mode = \"multi_hot\",   sparse = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"object Object compose layer . tensor, array, sequential model. num_tokens total number tokens layer support. inputs layer must integers range 0 <= value < num_tokens, error thrown. output_mode Specification output layer. Values can \"one_hot\", \"multi_hot\" \"count\", configuring layer follows: - \"one_hot\": Encodes individual element input array num_tokens size, containing 1 element index. last dimension size 1, encode dimension. last dimension size 1, append new dimension encoded output. - \"multi_hot\": Encodes sample input single array num_tokens size, containing 1 vocabulary term present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). - \"count\": Like \"multi_hot\", int array contains count number times token index appeared sample. output modes, currently output rank 2 supported. Defaults \"multi_hot\". sparse Whether return sparse tensor; backends support sparse tensors. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"One-hot encoding data     Multi-hot encoding data     Using weighted inputs \"count\" mode","code":"layer <- layer_category_encoding(num_tokens = 4, output_mode = \"one_hot\") x <- op_array(c(3, 2, 0, 1), \"int32\") layer(x) ## tf.Tensor( ## [[0. 0. 0. 1.] ##  [0. 0. 1. 0.] ##  [1. 0. 0. 0.] ##  [0. 1. 0. 0.]], shape=(4, 4), dtype=float32) layer <- layer_category_encoding(num_tokens = 4, output_mode = \"multi_hot\") x <- op_array(rbind(c(0, 1),                    c(0, 0),                    c(1, 2),                    c(3, 1)), \"int32\") layer(x) ## tf.Tensor( ## [[1. 1. 0. 0.] ##  [1. 0. 0. 0.] ##  [0. 1. 1. 0.] ##  [0. 1. 0. 1.]], shape=(4, 4), dtype=float32) layer <- layer_category_encoding(num_tokens = 4, output_mode = \"count\") count_weights <- op_array(rbind(c(.1, .2),                                c(.1, .1),                                c(.2, .3),                                c(.4, .2))) x <- op_array(rbind(c(0, 1),                    c(0, 0),                    c(1, 2),                    c(3, 1)), \"int32\") layer(x, count_weights = count_weights) #   array([[01, 02, 0. , 0. ], #          [02, 0. , 0. , 0. ], #          [0. , 02, 03, 0. ], #          [0. , 02, 0. , 04]]>"},{"path":"https://keras.posit.co/reference/layer_category_encoding.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"A preprocessing layer which encodes integer features. — layer_category_encoding","text":"inputs: 1D 2D tensor integer inputs. count_weights: tensor shape inputs indicating weight sample value summing count mode. used \"multi_hot\" \"one_hot\" modes.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which crops images. — layer_center_crop","title":"A preprocessing layer which crops images. — layer_center_crop","text":"layers crops central portion images target size. image smaller target size, resized cropped return largest possible window image matches target aspect ratio. Input pixel values can range (e.g. [0., 1.) [0, 255]).","code":""},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which crops images. — layer_center_crop","text":"","code":"layer_center_crop(object, height, width, data_format = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which crops images. — layer_center_crop","text":"object Object compose layer . tensor, array, sequential model. height Integer, height output shape. width Integer, width output shape. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which crops images. — layer_center_crop","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which crops images. — layer_center_crop","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format, (..., channels, height, width), \"channels_first\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_center_crop.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which crops images. — layer_center_crop","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels), (..., channels, target_height, target_width), \"channels_first\" format. input height/width even target height/width odd (inversely), input image left-padded 1 pixel. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":null,"dir":"Reference","previous_headings":"","what":"Concatenates a list of inputs. — layer_concatenate","title":"Concatenates a list of inputs. — layer_concatenate","text":"takes input list tensors, shape except concatenation axis, returns single tensor concatenation inputs.","code":""},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Concatenates a list of inputs. — layer_concatenate","text":"","code":"layer_concatenate(inputs, ..., axis = -1L)"},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Concatenates a list of inputs. — layer_concatenate","text":"inputs layers combine ... Standard layer keyword arguments. axis Axis along concatenate.","code":""},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Concatenates a list of inputs. — layer_concatenate","text":"tensor, concatenation inputs alongside axis axis.","code":""},{"path":"https://keras.posit.co/reference/layer_concatenate.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Concatenates a list of inputs. — layer_concatenate","text":"Usage Keras model:","code":"x <- op_arange(20) |> op_reshape(c(2, 2, 5)) y <- op_arange(20, 40) |> op_reshape(c(2, 2, 5)) layer_concatenate(x, y, axis = 2) ## tf.Tensor( ## [[[ 0.  1.  2.  3.  4.] ##   [ 5.  6.  7.  8.  9.] ##   [20. 21. 22. 23. 24.] ##   [25. 26. 27. 28. 29.]] ## ##  [[10. 11. 12. 13. 14.] ##   [15. 16. 17. 18. 19.] ##   [30. 31. 32. 33. 34.] ##   [35. 36. 37. 38. 39.]]], shape=(2, 4, 5), dtype=float32) x1 <- op_arange(10)     |> op_reshape(c(5, 2)) |> layer_dense(8) x2 <- op_arange(10, 20) |> op_reshape(c(5, 2)) |> layer_dense(8) y <- layer_concatenate(x1, x2)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"layer creates convolution kernel convolved layer input single spatial (temporal) dimension produce tensor outputs. use_bias TRUE, bias vector created added outputs. Finally, activation NULL, applied outputs well.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"","code":"layer_conv_1d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   groups = 1L,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int list 1 integer, specifying size convolution window. strides int list 1 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, \"valid\", \"\" \"causal\"(case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. \"causal\" results causal (dilated) convolutions, e.g. output[t] depend ontail(input, t+1). Useful modeling temporal data model violate temporal order. See WaveNet: Generative Model Raw Audio, section2.1. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated convolution. groups positive int specifying number groups input split along channel axis. group convolved separately filters // groups filters. output concatenation groups results along channel axis. Input channels filters must divisible groups. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"3D tensor representing activation(conv1d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, steps, channels) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, new_steps, filters) data_format=\"channels_first\": 3D tensor shape: (batch_shape, filters, new_steps)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"1D convolution layer (e.g. temporal convolution). — layer_conv_1d","text":"","code":"# The inputs are 128-length vectors with 10 timesteps, and the # batch size is 4. x <- random_uniform(c(4, 10, 128)) y <- x |> layer_conv_1d(32, 3, activation='relu') shape(y) ## shape(4, 8, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"1D transposed convolution layer. — layer_conv_1d_transpose","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"need transposed convolutions generally arise desire use transformation going opposite direction normal convolution, .e., something shape output convolution something shape input maintaining connectivity pattern compatible said convolution.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"","code":"layer_conv_1d_transpose(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters transpose convolution). kernel_size int list 1 integer, specifying size transposed convolution window. strides int list 1 integer, specifying stride length transposed convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated transposed convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"3D tensor representing activation(conv1d_transpose(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, steps, channels) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, new_steps, filters) data_format=\"channels_first\": 3D tensor shape: (batch_shape, filters, new_steps)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"guide convolution arithmetic deep learning Deconvolutional Networks","code":""},{"path":"https://keras.posit.co/reference/layer_conv_1d_transpose.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"1D transposed convolution layer. — layer_conv_1d_transpose","text":"","code":"x <- random_uniform(c(4, 10, 128)) y <- x |> layer_conv_1d_transpose(32, 3, 2, activation='relu') shape(y) ## shape(4, 21, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"2D convolution layer. — layer_conv_2d","title":"2D convolution layer. — layer_conv_2d","text":"layer creates convolution kernel convolved layer input single spatial (temporal) dimension produce tensor outputs. use_bias TRUE, bias vector created added outputs. Finally, activation NULL, applied outputs well.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D convolution layer. — layer_conv_2d","text":"","code":"layer_conv_2d(   object,   filters,   kernel_size,   strides = list(1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L),   groups = 1L,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D convolution layer. — layer_conv_2d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int list 2 integer, specifying size convolution window. strides int list 2 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 2 integers, specifying dilation rate use dilated convolution. groups positive int specifying number groups input split along channel axis. group convolved separately filters // groups filters. output concatenation groups results along channel axis. Input channels filters must divisible groups. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D convolution layer. — layer_conv_2d","text":"4D tensor representing activation(conv2d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D convolution layer. — layer_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, height, width, channels) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D convolution layer. — layer_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, new_height, new_width, filters) data_format=\"channels_first\": 4D tensor shape: (batch_size, filters, new_height, new_width)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"2D convolution layer. — layer_conv_2d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"2D convolution layer. — layer_conv_2d","text":"","code":"x <- random_uniform(c(4, 10, 10, 128)) y <- x |> layer_conv_2d(32, 3, activation='relu') shape(y) ## shape(4, 8, 8, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"2D transposed convolution layer. — layer_conv_2d_transpose","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"need transposed convolutions generally arise desire use transformation going opposite direction normal convolution, .e., something shape output convolution something shape input maintaining connectivity pattern compatible said convolution.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"","code":"layer_conv_2d_transpose(   object,   filters,   kernel_size,   strides = list(1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L),   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters transposed convolution). kernel_size int list 1 integer, specifying size transposed convolution window. strides int list 1 integer, specifying stride length transposed convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated transposed convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"4D tensor representing activation(conv2d_transpose(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, height, width, channels) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, new_height, new_width, filters) data_format=\"channels_first\": 4D tensor shape: (batch_size, filters, new_height, new_width)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"guide convolution arithmetic deep learning Deconvolutional Networks","code":""},{"path":"https://keras.posit.co/reference/layer_conv_2d_transpose.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"2D transposed convolution layer. — layer_conv_2d_transpose","text":"","code":"x <- random_uniform(c(4, 10, 8, 128)) y <- x |> layer_conv_2d_transpose(32, 2, 2, activation='relu') shape(y) ## shape(4, 20, 16, 32) # (4, 20, 16, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"3D convolution layer. — layer_conv_3d","title":"3D convolution layer. — layer_conv_3d","text":"layer creates convolution kernel convolved layer input single spatial (temporal) dimension produce tensor outputs. use_bias TRUE, bias vector created added outputs. Finally, activation NULL, applied outputs well.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"3D convolution layer. — layer_conv_3d","text":"","code":"layer_conv_3d(   object,   filters,   kernel_size,   strides = list(1L, 1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L, 1L),   groups = 1L,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"3D convolution layer. — layer_conv_3d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int list 3 integer, specifying size convolution window. strides int list 3 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 3 integers, specifying dilation rate use dilated convolution. groups positive int specifying number groups input split along channel axis. group convolved separately filters %/% groups filters. output concatenation groups results along channel axis. Input channels filters must divisible groups. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"3D convolution layer. — layer_conv_3d","text":"5D tensor representing activation(conv3d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"3D convolution layer. — layer_conv_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"3D convolution layer. — layer_conv_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3, filters) data_format=\"channels_first\": 5D tensor shape: (batch_size, filters, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"3D convolution layer. — layer_conv_3d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"3D convolution layer. — layer_conv_3d","text":"","code":"x <- random_uniform(c(4, 10, 10, 10, 128)) y <- x |> layer_conv_3d(32, 3, activation = 'relu') shape(y) ## shape(4, 8, 8, 8, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"3D transposed convolution layer. — layer_conv_3d_transpose","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"need transposed convolutions generally arise desire use transformation going opposite direction normal convolution, .e., something shape output convolution something shape input maintaining connectivity pattern compatible said convolution.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"","code":"layer_conv_3d_transpose(   object,   filters,   kernel_size,   strides = list(1L, 1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L, 1L),   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters transposed convolution). kernel_size int list 1 integer, specifying size transposed convolution window. strides int list 1 integer, specifying stride length transposed convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated transposed convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. kernel_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. kernel_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. kernel_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"5D tensor representing activation(conv3d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3, filters) data_format=\"channels_first\": 5D tensor shape: (batch_size, filters, new_spatial_dim1, new_spatial_dim2, new_spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"guide convolution arithmetic deep learning Deconvolutional Networks","code":""},{"path":"https://keras.posit.co/reference/layer_conv_3d_transpose.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"3D transposed convolution layer. — layer_conv_3d_transpose","text":"","code":"x <- random_uniform(c(4, 10, 8, 12, 128)) y <- x |> layer_conv_3d_transpose(32, 2, 2, activation = 'relu') shape(y) ## shape(4, 20, 16, 24, 32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"1D Convolutional LSTM. — layer_conv_lstm_1d","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"Similar LSTM layer, input transformations recurrent transformations convolutional.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"","code":"layer_conv_lstm_1d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   ...,   unroll = NULL )"},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int tuple/list 1 integer, specifying size convolution window. strides int tuple/list 1 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int tuple/list 1 integers, specifying dilation rate use dilated convolution. activation Activation function use. default hyperbolic tangent activation function applied (tanh(x)). recurrent_activation Activation function use recurrent step. use_bias Boolean, whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. bias_initializer Initializer bias vector. unit_forget_bias Boolean. TRUE, add 1 bias forget gate initialization. Use combination bias_initializer=\"zeros\". recommended Jozefowicz et al., 2015 kernel_regularizer Regularizer function applied kernel weights matrix. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. bias_regularizer Regularizer function applied bias vector. activity_regularizer Regularizer function applied . kernel_constraint Constraint function applied kernel weights matrix. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. bias_constraint Constraint function applied bias vector. dropout Float 0 1. Fraction units drop linear transformation inputs. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default FALSE). TRUE, last state sample index batch used initial state sample index following batch. ... forward/backward compatability. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"inputs: 4D tensor. initial_state: List initial state tensors passed first call cell. mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout set.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"data_format=\"channels_first\": 4D tensor shape: (samples, time, channels, rows) data_format=\"channels_last\": 4D tensor shape: (samples, time, rows, channels)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"return_state: list tensors. first tensor output. remaining tensors last states, 3D tensor shape: (samples, filters, new_rows) data_format='channels_first' shape: (samples, new_rows, filters) data_format='channels_last'. rows values might changed due padding. return_sequences: 4D tensor shape: (samples, timesteps, filters, new_rows) data_format='channels_first' shape: (samples, timesteps, new_rows, filters) data_format='channels_last'. Else, 3D tensor shape: (samples, filters, new_rows) data_format='channels_first' shape: (samples, new_rows, filters) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_1d.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"1D Convolutional LSTM. — layer_conv_lstm_1d","text":"Shi et al., 2015 (current implementation include feedback loop cells output).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"2D Convolutional LSTM. — layer_conv_lstm_2d","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"Similar LSTM layer, input transformations recurrent transformations convolutional.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"","code":"layer_conv_lstm_2d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   ...,   unroll = NULL )"},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int tuple/list 2 integers, specifying size convolution window. strides int tuple/list 2 integers, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int tuple/list 2 integers, specifying dilation rate use dilated convolution. activation Activation function use. default hyperbolic tangent activation function applied (tanh(x)). recurrent_activation Activation function use recurrent step. use_bias Boolean, whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. bias_initializer Initializer bias vector. unit_forget_bias Boolean. TRUE, add 1 bias forget gate initialization. Use combination bias_initializer=\"zeros\". recommended Jozefowicz et al., 2015 kernel_regularizer Regularizer function applied kernel weights matrix. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. bias_regularizer Regularizer function applied bias vector. activity_regularizer Regularizer function applied . kernel_constraint Constraint function applied kernel weights matrix. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. bias_constraint Constraint function applied bias vector. dropout Float 0 1. Fraction units drop linear transformation inputs. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default FALSE). TRUE, last state sample index batch used initial state sample index following batch. ... forward/backward compatability. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"inputs: 5D tensor. mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout set. initial_state: List initial state tensors passed first call cell.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"data_format='channels_first': 5D tensor shape: (samples, time, channels, rows, cols) data_format='channels_last': 5D tensor shape: (samples, time, rows, cols, channels)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"return_state: list tensors. first tensor output. remaining tensors last states, 4D tensor shape: (samples, filters, new_rows, new_cols) data_format='channels_first' shape: (samples, new_rows, new_cols, filters) data_format='channels_last'. rows cols values might changed due padding. return_sequences: 5D tensor shape: (samples, timesteps, filters, new_rows, new_cols) data_format='channels_first' shape: (samples, timesteps, new_rows, new_cols, filters) data_format='channels_last'. Else, 4D tensor shape: (samples, filters, new_rows, new_cols) data_format='channels_first' shape: (samples, new_rows, new_cols, filters) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_2d.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"2D Convolutional LSTM. — layer_conv_lstm_2d","text":"Shi et al., 2015 (current implementation include feedback loop cells output).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"3D Convolutional LSTM. — layer_conv_lstm_3d","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"Similar LSTM layer, input transformations recurrent transformations convolutional.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"","code":"layer_conv_lstm_3d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   ...,   unroll = NULL )"},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimension output space (number filters convolution). kernel_size int tuple/list 3 integers, specifying size convolution window. strides int tuple/list 3 integers, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int tuple/list 3 integers, specifying dilation rate use dilated convolution. activation Activation function use. default hyperbolic tangent activation function applied (tanh(x)). recurrent_activation Activation function use recurrent step. use_bias Boolean, whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. bias_initializer Initializer bias vector. unit_forget_bias Boolean. TRUE, add 1 bias forget gate initialization. Use combination bias_initializer=\"zeros\". recommended Jozefowicz et al., 2015 kernel_regularizer Regularizer function applied kernel weights matrix. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. bias_regularizer Regularizer function applied bias vector. activity_regularizer Regularizer function applied . kernel_constraint Constraint function applied kernel weights matrix. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. bias_constraint Constraint function applied bias vector. dropout Float 0 1. Fraction units drop linear transformation inputs. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default FALSE). TRUE, last state sample index batch used initial state sample index following batch. ... forward/backward compatability. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"inputs: 6D tensor. mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout set. initial_state: List initial state tensors passed first call cell.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"data_format='channels_first': 5D tensor shape: (samples, time, channels, *spatial_dims) data_format='channels_last': 5D tensor shape: (samples, time, *spatial_dims, channels)","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"return_state: list tensors. first tensor output. remaining tensors last states, 4D tensor shape: (samples, filters, *spatial_dims) data_format='channels_first' shape: (samples, *spatial_dims, filters) data_format='channels_last'. return_sequences: 5D tensor shape: (samples, timesteps, filters, *spatial_dims) data_format='channels_first' shape: (samples, timesteps, *spatial_dims, filters) data_format='channels_last'. Else, 4D tensor shape: (samples, filters, *spatial_dims) data_format='channels_first' shape: (samples, *spatial_dims, filters) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_conv_lstm_3d.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"3D Convolutional LSTM. — layer_conv_lstm_3d","text":"Shi et al., 2015 (current implementation include feedback loop cells output).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"crops along time dimension (axis 2).","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"","code":"layer_cropping_1d(object, cropping = list(1L, 1L), ...)"},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"object Object compose layer . tensor, array, sequential model. cropping Int, list int (length 2). int: many units trimmed beginning end cropping dimension (axis 1). list 2 ints: many units trimmed beginning end cropping dimension ((left_crop, right_crop)). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"","code":"input_shape <- c(2, 3, 2) x <- op_arange(prod(input_shape)) |> op_reshape(input_shape) x ## tf.Tensor( ## [[[ 0.  1.] ##   [ 2.  3.] ##   [ 4.  5.]] ## ##  [[ 6.  7.] ##   [ 8.  9.] ##   [10. 11.]]], shape=(2, 3, 2), dtype=float32) y <- x |> layer_cropping_1d(cropping = 1) y ## tf.Tensor( ## [[[2. 3.]] ## ##  [[8. 9.]]], shape=(2, 1, 2), dtype=float32)"},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"3D tensor shape (batch_size, axis_to_crop, features)","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Cropping layer for 1D input (e.g. temporal sequence). — layer_cropping_1d","text":"3D tensor shape (batch_size, cropped_axis, features)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"crops along spatial dimensions, .e. height width.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"","code":"layer_cropping_2d(   object,   cropping = list(list(0L, 0L), list(0L, 0L)),   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"object Object compose layer . tensor, array, sequential model. cropping Int, list 2 ints, list 2 lists 2 ints. int: symmetric cropping applied height width. list 2 ints: interpreted two different symmetric cropping values height width: (symmetric_height_crop, symmetric_width_crop). list 2 lists 2 ints: interpreted ((top_crop, bottom_crop), (left_crop, right_crop)). data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"","code":"input_shape <- c(2, 28, 28, 3) x <- op_arange(prod(input_shape), dtype ='int32') |> op_reshape(input_shape) y <- x |> layer_cropping_2d(cropping=list(c(2, 2), c(4, 4))) shape(y) ## shape(2, 24, 20, 3)"},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, height, width, channels) data_format \"channels_first\": (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Cropping layer for 2D input (e.g. picture). — layer_cropping_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, cropped_height, cropped_width, channels) data_format \"channels_first\": (batch_size, channels, cropped_height, cropped_width)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"Cropping layer 3D data (e.g. spatial spatio-temporal).","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"","code":"layer_cropping_3d(   object,   cropping = list(list(1L, 1L), list(1L, 1L), list(1L, 1L)),   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"object Object compose layer . tensor, array, sequential model. cropping Int, list 3 ints, list 3 lists 2 ints. int: symmetric cropping applied depth, height, width. list 3 ints: interpreted three different symmetric cropping values depth, height, width: (symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop). list 3 lists 2 ints: interpreted ((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_crop)). data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"","code":"input_shape <- c(2, 28, 28, 10, 3) x <- input_shape %>% { op_reshape(seq(prod(.)), .) } y <- x |> layer_cropping_3d(cropping = c(2, 4, 2)) shape(y) ## shape(2, 24, 20, 6, 3)"},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop, channels) data_format \"channels_first\": (batch_size, channels, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)","code":""},{"path":"https://keras.posit.co/reference/layer_cropping_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Cropping layer for 3D data (e.g. spatial or spatio-temporal). — layer_cropping_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, first_cropped_axis, second_cropped_axis, third_cropped_axis, channels) data_format \"channels_first\": (batch_size, channels, first_cropped_axis, second_cropped_axis, third_cropped_axis)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_dense.html","id":null,"dir":"Reference","previous_headings":"","what":"Just your regular densely-connected NN layer. — layer_dense","title":"Just your regular densely-connected NN layer. — layer_dense","text":"Dense implements operation: output = activation(dot(input, kernel) + bias) activation element-wise activation function passed activation argument, kernel weights matrix created layer, bias bias vector created layer (applicable use_bias TRUE).","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Just your regular densely-connected NN layer. — layer_dense","text":"","code":"layer_dense(   object,   units,   activation = NULL,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   lora_rank = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Just your regular densely-connected NN layer. — layer_dense","text":"object Object compose layer . tensor, array, sequential model. units Positive integer, dimensionality output space. activation Activation function use. specify anything, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix. bias_initializer Initializer bias vector. kernel_regularizer Regularizer function applied kernel weights matrix. bias_regularizer Regularizer function applied bias vector. activity_regularizer Regularizer function applied output layer (\"activation\"). kernel_constraint Constraint function applied kernel weights matrix. bias_constraint Constraint function applied bias vector. lora_rank Optional integer. set, layer's forward pass implement LoRA (Low-Rank Adaptation) provided rank. LoRA sets layer's kernel non-trainable replaces delta original kernel, obtained via multiplying two lower-rank trainable matrices. can useful reduce computation cost fine-tuning large dense layers. can also enable LoRA existing Dense layer calling layer$enable_lora(rank). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Just your regular densely-connected NN layer. — layer_dense","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Just your regular densely-connected NN layer. — layer_dense","text":"input layer rank greater 2, Dense computes dot product inputs kernel along last axis inputs axis 0 kernel (using tf.tensordot). example, input dimensions (batch_size, d0, d1), create kernel shape (d1, units), kernel operates along axis 2 input, every sub-tensor shape (1, 1, d1) (batch_size * d0 sub-tensors). output case shape (batch_size, d0, units).","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Just your regular densely-connected NN layer. — layer_dense","text":"N-D tensor shape: (batch_size, ..., input_dim). common situation 2D input shape (batch_size, input_dim).","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Just your regular densely-connected NN layer. — layer_dense","text":"N-D tensor shape: (batch_size, ..., units). instance, 2D input shape (batch_size, input_dim), output shape (batch_size, units).","code":""},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Just your regular densely-connected NN layer. — layer_dense","text":"","code":"enable_lora(   rank,   a_initializer = 'he_uniform',   b_initializer = 'zeros' )"},{"path":"https://keras.posit.co/reference/layer_dense.html","id":"readonly-properties-","dir":"Reference","previous_headings":"","what":"Readonly properties:","title":"Just your regular densely-connected NN layer. — layer_dense","text":"kernel","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"1D depthwise convolution layer. — layer_depthwise_conv_1d","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"Depthwise convolution type convolution input channel convolved different kernel (called depthwise kernel). can understand depthwise convolution first step depthwise separable convolution. implemented via following steps: Split input individual channels. Convolve channel individual depthwise kernel depth_multiplier output channels. Concatenate convolved outputs along channels axis. Unlike regular 1D convolution, depthwise convolution mix information across different input channels. depth_multiplier argument determines many filters applied one input channel. , controls amount output channels generated per input channel depthwise step.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"","code":"layer_depthwise_conv_1d(   object,   kernel_size,   strides = 1L,   padding = \"valid\",   depth_multiplier = 1L,   data_format = NULL,   dilation_rate = 1L,   activation = NULL,   use_bias = TRUE,   depthwise_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   depthwise_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   depthwise_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"object Object compose layer . tensor, array, sequential model. kernel_size int list 1 integer, specifying size depthwise convolution window. strides int list 1 integer, specifying stride length convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. depth_multiplier number depthwise convolution output channels input channel. total number depthwise convolution output channels equal input_channel * depth_multiplier. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. depthwise_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. depthwise_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. depthwise_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"3D tensor representing activation(depthwise_conv1d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, steps, channels) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, new_steps, channels * depth_multiplier) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels * depth_multiplier, new_steps)","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"1D depthwise convolution layer. — layer_depthwise_conv_1d","text":"","code":"x <- random_uniform(c(4, 10, 12)) y <- x |> layer_depthwise_conv_1d(   kernel_size = 3,   depth_multiplier = 3,   activation = 'relu' ) shape(y) ## shape(4, 8, 36)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"2D depthwise convolution layer. — layer_depthwise_conv_2d","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"Depthwise convolution type convolution input channel convolved different kernel (called depthwise kernel). can understand depthwise convolution first step depthwise separable convolution. implemented via following steps: Split input individual channels. Convolve channel individual depthwise kernel depth_multiplier output channels. Concatenate convolved outputs along channels axis. Unlike regular 2D convolution, depthwise convolution mix information across different input channels. depth_multiplier argument determines many filters applied one input channel. , controls amount output channels generated per input channel depthwise step.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"","code":"layer_depthwise_conv_2d(   object,   kernel_size,   strides = list(1L, 1L),   padding = \"valid\",   depth_multiplier = 1L,   data_format = NULL,   dilation_rate = list(1L, 1L),   activation = NULL,   use_bias = TRUE,   depthwise_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   depthwise_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   depthwise_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"object Object compose layer . tensor, array, sequential model. kernel_size int list 2 integer, specifying size depthwise convolution window. strides int list 2 integer, specifying stride length depthwise convolution. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. depth_multiplier number depthwise convolution output channels input channel. total number depthwise convolution output channels equal input_channel * depth_multiplier. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 2 integers, specifying dilation rate use dilated convolution. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. depthwise_initializer Initializer convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer Initializer bias vector. NULL, default initializer (\"zeros\") used. depthwise_regularizer Optional regularizer convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. depthwise_constraint Optional projection function applied kernel updated Optimizer (e.g. used implement norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). Constraints safe use asynchronous distributed training. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"4D tensor representing activation(depthwise_conv2d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, height, width, channels) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, new_height, new_width, channels * depth_multiplier) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels * depth_multiplier, new_height, new_width)","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"ValueError: strides > 1 dilation_rate > 1.","code":""},{"path":"https://keras.posit.co/reference/layer_depthwise_conv_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"2D depthwise convolution layer. — layer_depthwise_conv_2d","text":"","code":"x <- random_uniform(c(4, 10, 10, 12)) y <- x |> layer_depthwise_conv_2d(3, 3, activation = 'relu') shape(y) ## shape(4, 3, 3, 12)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"layer place element input data one several contiguous ranges output integer index indicating range element placed . Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"","code":"layer_discretization(   object,   bin_boundaries = NULL,   num_bins = NULL,   epsilon = 0.01,   output_mode = \"int\",   sparse = FALSE,   dtype = NULL,   name = NULL )"},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"object Object compose layer . tensor, array, sequential model. bin_boundaries list bin boundaries. leftmost rightmost bins always extend -Inf Inf, bin_boundaries = c(0, 1, 2) generates bins (-Inf, 0), [0, 1), [1, 2), [2, +Inf). option set, adapt() called. num_bins integer number bins compute. option set, adapt() called learn bin boundaries. epsilon Error tolerance, typically small fraction close zero (e.g. 0.01). Higher values epsilon increase quantile approximation, hence result unequal buckets, improve performance resource consumption. output_mode Specification output layer. Values can \"int\", \"one_hot\", \"multi_hot\", \"count\" configuring layer follows: \"int\": Return discretized bin indices directly. \"one_hot\": Encodes individual element input array size num_bins, containing 1 input's bin index. last dimension size 1, encode dimension.  last dimension size 1, append new dimension encoded output. \"multi_hot\": Encodes sample input single array size num_bins, containing 1 bin index index present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). \"count\": \"multi_hot\", int array contains count number times bin index appeared sample. Defaults \"int\". sparse Boolean. applicable \"one_hot\", \"multi_hot\", \"count\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. dtype datatype (e.g., \"float32\"). name String, name object","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"array dimension 2 higher.","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"input shape.","code":""},{"path":"https://keras.posit.co/reference/layer_discretization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which buckets continuous features by ranges. — layer_discretization","text":"Discretize float values based provided buckets.     Discretize float values based number buckets compute.","code":"input <- op_array(rbind(c(-1.5, 1, 3.4, 0.5),                        c(0, 3, 1.3, 0),                        c(-.5, 0, .5, 1),                        c(1.5, 2, 2.5, 3))) output <- input |> layer_discretization(bin_boundaries = c(0, 1, 2)) output ## tf.Tensor( ## [[0 2 3 1] ##  [1 3 2 1] ##  [0 1 1 2] ##  [2 3 3 3]], shape=(4, 4), dtype=int64) layer <- layer_discretization(num_bins = 4, epsilon = 0.01) layer |> adapt(input) layer(input) ## tf.Tensor( ## [[0 2 3 1] ##  [1 3 2 1] ##  [0 1 1 2] ##  [2 3 3 3]], shape=(4, 4), dtype=int64)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_dot.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes element-wise dot product of two tensors. — layer_dot","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"takes list inputs size 2, axes corresponding input along dot product performed. say x y two input tensors shapes (2, 3, 5) (2, 10, 3). batch dimension size inputs, axes correspond dimensions size corresponding inputs. e.g. axes = c(1, 2), dot product x, y result tensor shape (2, 5, 10)","code":""},{"path":"https://keras.posit.co/reference/layer_dot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"","code":"layer_dot(inputs, ..., axes, normalize = FALSE)"},{"path":"https://keras.posit.co/reference/layer_dot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"inputs layers combine ... Standard layer keyword arguments. axes Integer list integers, axis axes along take dot product. list, two integers corresponding desired axis first input desired axis second input, respectively. Note size two selected axes must match. normalize Whether L2-normalize samples along dot product axis taking dot product. set TRUE, output dot product cosine proximity two samples.","code":""},{"path":"https://keras.posit.co/reference/layer_dot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"tensor, dot product samples inputs.","code":""},{"path":"https://keras.posit.co/reference/layer_dot.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes element-wise dot product of two tensors. — layer_dot","text":"Usage Keras model:","code":"x <- op_reshape(0:9,   c(1, 5, 2)) y <- op_reshape(10:19, c(1, 2, 5)) layer_dot(x, y, axes=c(2, 3)) ## tf.Tensor( ## [[[260 360] ##   [320 445]]], shape=(1, 2, 2), dtype=int32) x1 <- op_reshape(0:9, c(5, 2)) |> layer_dense(8) x2 <- op_reshape(10:19, c(5, 2)) |> layer_dense(8) shape(x1) ## shape(5, 8) shape(x2) ## shape(5, 8) y <- layer_dot(x1, x2, axes=2) shape(y) ## shape(5, 1)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies dropout to the input. — layer_dropout","title":"Applies dropout to the input. — layer_dropout","text":"Dropout layer randomly sets input units 0 frequency rate step training time, helps prevent overfitting. Inputs set 0 scaled 1 / (1 - rate) sum inputs unchanged. Note Dropout layer applies training set TRUE call(), values dropped inference. using model.fit, training appropriately set TRUE automatically. contexts, can set argument explicitly TRUE calling layer. (contrast setting trainable=FALSE Dropout layer. trainable affect layer's behavior, Dropout variables/weights can frozen training.)","code":""},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies dropout to the input. — layer_dropout","text":"","code":"layer_dropout(object, rate, noise_shape = NULL, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies dropout to the input. — layer_dropout","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. Fraction input units drop. noise_shape 1D integer tensor representing shape binary dropout mask multiplied input. instance, inputs shape (batch_size, timesteps, features) want dropout mask timesteps, can use noise_shape=(batch_size, 1, features). seed R integer use random seed. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies dropout to the input. — layer_dropout","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_dropout.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Applies dropout to the input. — layer_dropout","text":"inputs: Input tensor (rank). training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (nothing).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":null,"dir":"Reference","previous_headings":"","what":"A layer that uses einsum as the backing computation. — layer_einsum_dense","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"layer can perform einsum calculations arbitrary dimensionality.","code":""},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"","code":"layer_einsum_dense(   object,   equation,   output_shape,   activation = NULL,   bias_axes = NULL,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   lora_rank = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"object Object compose layer . tensor, array, sequential model. equation equation describing einsum perform. equation must valid einsum string form ab,bc->ac, ...ab,bc->...ac, ab...,bc->ac... 'ab', 'bc', 'ac' can valid einsum axis expression sequence. output_shape expected shape output tensor (excluding batch dimension dimensions represented ellipses). can specify NA NULL dimension unknown can inferred input shape. activation Activation function use. specify anything, activation applied (, \"linear\" activation: (x) = x). bias_axes string containing output dimension(s) apply bias . character bias_axes string correspond character output portion equation string. kernel_initializer Initializer kernel weights matrix. bias_initializer Initializer bias vector. kernel_regularizer Regularizer function applied kernel weights matrix. bias_regularizer Regularizer function applied bias vector. kernel_constraint Constraint function applied kernel weights matrix. bias_constraint Constraint function applied bias vector. lora_rank Optional integer. set, layer's forward pass implement LoRA (Low-Rank Adaptation) provided rank. LoRA sets layer's kernel non-trainable replaces delta original kernel, obtained via multiplying two lower-rank trainable matrices (factorization happens last dimension). can useful reduce computation cost fine-tuning large dense layers. can also enable LoRA existing EinsumDense layer calling layer$enable_lora(rank). ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"Biased dense layer einsums example shows instantiate standard Keras dense layer using einsum operations. example equivalent layer_Dense(64, use_bias=TRUE).     Applying dense layer sequence example shows instantiate layer applies dense operation every element sequence. , output_shape two values (since two non-batch dimensions output); first dimension output_shape NA, sequence dimension b unknown shape.     Applying dense layer sequence using ellipses example shows instantiate layer applies dense operation every element sequence, uses ellipsis notation instead specifying batch sequence dimensions. using ellipsis notation specified one axis, output_shape arg single value. instantiated way, layer can handle number sequence dimensions - including case sequence dimension exists.","code":"input <- layer_input(shape = c(32)) output <- input |>   layer_einsum_dense(\"ab,bc->ac\",                      output_shape = 64,                      bias_axes = \"c\") output # shape(NA, 64) ## <KerasTensor shape=(None, 64), dtype=float32, sparse=False, name=keras_tensor_1> input <- layer_input(shape = c(32, 128)) output <- input |>   layer_einsum_dense(\"abc,cd->abd\",                      output_shape = c(NA, 64),                      bias_axes = \"d\") output  # shape(NA, 32, 64) ## <KerasTensor shape=(None, None, 64), dtype=float32, sparse=False, name=keras_tensor_3> input <- layer_input(shape = c(32, 128)) output <- input |>   layer_einsum_dense(\"...x,xy->...y\",                      output_shape = 64,                      bias_axes = \"y\")  output  # shape(NA, 32, 64) ## <KerasTensor shape=(None, 32, 64), dtype=float32, sparse=False, name=keras_tensor_5>"},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"","code":"enable_lora(   rank,   a_initializer = 'he_uniform',   b_initializer = 'zeros' ) quantize(mode)"},{"path":"https://keras.posit.co/reference/layer_einsum_dense.html","id":"readonly-properties-","dir":"Reference","previous_headings":"","what":"Readonly properties:","title":"A layer that uses einsum as the backing computation. — layer_einsum_dense","text":"kernel","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":null,"dir":"Reference","previous_headings":"","what":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"e.g. rbind(4L, 20L) \\(\\rightarrow\\) rbind(c(0.25, 0.1), c(0.6, -0.2)) layer can used positive integer inputs fixed range.","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"","code":"layer_embedding(   object,   input_dim,   output_dim,   embeddings_initializer = \"uniform\",   embeddings_regularizer = NULL,   embeddings_constraint = NULL,   mask_zero = FALSE,   weights = NULL,   lora_rank = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"object Object compose layer . tensor, array, sequential model. input_dim Integer. Size vocabulary, .e. maximum integer index + 1. output_dim Integer. Dimension dense embedding. embeddings_initializer Initializer embeddings matrix (see keras3::initializer_*). embeddings_regularizer Regularizer function applied embeddings matrix (see keras3::regularizer_*). embeddings_constraint Constraint function applied embeddings matrix (see keras3::constraint_*). mask_zero Boolean, whether input value 0 special \"padding\" value masked . useful using recurrent layers may take variable length input. TRUE, subsequent layers model need support masking exception raised. mask_zero set TRUE, consequence, index 0 used vocabulary (input_dim equal size vocabulary + 1). weights Optional floating-point matrix size (input_dim, output_dim). initial embeddings values use. lora_rank Optional integer. set, layer's forward pass implement LoRA (Low-Rank Adaptation) provided rank. LoRA sets layer's embeddings matrix non-trainable replaces delta original matrix, obtained via multiplying two lower-rank trainable matrices. can useful reduce computation cost fine-tuning large embedding layers. can also enable LoRA existing Embedding layer instance calling layer$enable_lora(rank). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"","code":"model <- keras_model_sequential() |>   layer_embedding(1000, 64)  # The model will take as input an integer matrix of size (batch,input_length), # and the largest integer (i.e. word index) in the input # should be no larger than 999 (vocabulary size). # Now model$output_shape is (NA, 10, 64), where `NA` is the batch # dimension.  input_array <- random_integer(shape = c(32, 10), minval = 0, maxval = 1000) model |> compile('rmsprop', 'mse') output_array <- model |> predict(input_array, verbose = 0) dim(output_array)    # (32, 10, 64) ## [1] 32 10 64"},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"2D tensor shape: (batch_size, input_length).","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"3D tensor shape: (batch_size, input_length, output_dim).","code":""},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"","code":"enable_lora(   rank,   a_initializer = 'he_uniform',   b_initializer = 'zeros' ) quantize(mode) quantized_build(input_shape, mode) quantized_call(inputs)"},{"path":"https://keras.posit.co/reference/layer_embedding.html","id":"readonly-properties-","dir":"Reference","previous_headings":"","what":"Readonly properties:","title":"Turns positive integers (indexes) into dense vectors of fixed size. — layer_embedding","text":"embeddings","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":null,"dir":"Reference","previous_headings":"","what":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"Available feature types: Note features can referred string name, e.g. \"integer_categorical\". using string name, default argument values used.","code":"# Plain float values. feature_float(name = NULL)  # Float values to be preprocessed via featurewise standardization # (i.e. via a `layer_normalization()` layer). feature_float_normalized(name = NULL)  # Float values to be preprocessed via linear rescaling # (i.e. via a `layer_rescaling` layer). feature_float_rescaled(scale = 1., offset = 0., name = NULL)  # Float values to be discretized. By default, the discrete # representation will then be one-hot encoded. feature_float_discretized(   num_bins,   bin_boundaries = NULL,   output_mode = \"one_hot\",   name = NULL )  # Integer values to be indexed. By default, the discrete # representation will then be one-hot encoded. feature_integer_categorical(   max_tokens = NULL,   num_oov_indices = 1,   output_mode = \"one_hot\",   name = NULL )  # String values to be indexed. By default, the discrete # representation will then be one-hot encoded. feature_string_categorical(   max_tokens = NULL,   num_oov_indices = 1,   output_mode = \"one_hot\",   name = NULL )  # Integer values to be hashed into a fixed number of bins. # By default, the discrete representation will then be one-hot encoded. feature_integer_hashed(num_bins, output_mode = \"one_hot\", name = NULL)  # String values to be hashed into a fixed number of bins. # By default, the discrete representation will then be one-hot encoded. feature_string_hashed(num_bins, output_mode = \"one_hot\", name = NULL)"},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"","code":"layer_feature_space(   object,   features,   output_mode = \"concat\",   crosses = NULL,   crossing_dim = 32L,   hashing_dim = 32L,   num_discretization_bins = 32L,   name = NULL,   feature_names = NULL )  feature_cross(feature_names, crossing_dim, output_mode = \"one_hot\")  feature_custom(dtype, preprocessor, output_mode)  feature_float(name = NULL)  feature_float_rescaled(scale = 1, offset = 0, name = NULL)  feature_float_normalized(name = NULL)  feature_float_discretized(   num_bins,   bin_boundaries = NULL,   output_mode = \"one_hot\",   name = NULL )  feature_integer_categorical(   max_tokens = NULL,   num_oov_indices = 1,   output_mode = \"one_hot\",   name = NULL )  feature_string_categorical(   max_tokens = NULL,   num_oov_indices = 1,   output_mode = \"one_hot\",   name = NULL )  feature_string_hashed(num_bins, output_mode = \"one_hot\", name = NULL)  feature_integer_hashed(num_bins, output_mode = \"one_hot\", name = NULL)"},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"object see description features see description output_mode string. layer_feature_space(), one \"concat\" \"dict\". concat mode, features get concatenated together single vector. dict mode, FeatureSpace returns named list individually encoded features (names input list names). feature_* functions, one : \"int\" \"one_hot\" \"float\". crosses List features crossed together, e.g. crosses=list(c(\"feature_1\", \"feature_2\")). features \"crossed\" hashing combined value fixed-length vector. crossing_dim Default vector size hashing crossed features. Defaults 32. hashing_dim Default vector size hashing features type \"integer_hashed\" \"string_hashed\". Defaults 32. num_discretization_bins Default number bins used discretizing features type \"float_discretized\". Defaults 32. name String, name object feature_names Named list mapping names features type specification, e.g. list(my_feature = \"integer_categorical\") list(my_feature = feature_integer_categorical()). complete list supported types, see \"Available feature types\" paragraph . dtype string, output dtype feature. E.g., \"float32\". preprocessor callable. scale, offset Passed layer_rescaling() num_bins, bin_boundaries Passed layer_discretization() max_tokens, num_oov_indices Passed layer_integer_lookup() feature_integer_categorical() layer_string_lookup() feature_string_categorical().","code":""},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_feature_space.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"One-stop utility for preprocessing and encoding structured data. — layer_feature_space","text":"Basic usage named list input data:   Basic usage tf.data:   Basic usage Keras Functional API:           Customizing feature feature cross:   Returning dict (named list) integer-encoded features:   Specifying Keras preprocessing layer:   Retrieving underlying Keras preprocessing layers:   Saving reloading FeatureSpace:","code":"raw_data <- list(   float_values = c(0.0, 0.1, 0.2, 0.3),   string_values = c(\"zero\", \"one\", \"two\", \"three\"),   int_values = as.integer(c(0, 1, 2, 3)) )  dataset <- tfdatasets::tensor_slices_dataset(raw_data)  feature_space <- layer_feature_space(   features = list(     float_values = \"float_normalized\",     string_values = \"string_categorical\",     int_values = \"integer_categorical\"   ),   crosses = list(c(\"string_values\", \"int_values\")),   output_mode = \"concat\" )  # Before you start using the feature_space(), # you must `adapt()` it on some data. feature_space |> adapt(dataset)  # You can call the feature_space() on a named list of # data (batched or unbatched). output_vector <- feature_space(raw_data) library(tfdatasets) # Unlabeled data preprocessed_ds <- unlabeled_dataset |>   dataset_map(feature_space)  # Labeled data preprocessed_ds <- labeled_dataset |>   dataset_map(function(x, y) tuple(feature_space(x), y)) # Retrieve a named list of Keras layer_input() objects (inputs <- feature_space$get_inputs()) ## $float_values ## <KerasTensor shape=(None, 1), dtype=float32, sparse=None, name=float_values> ## ## $string_values ## <KerasTensor shape=(None, 1), dtype=string, sparse=None, name=string_values> ## ## $int_values ## <KerasTensor shape=(None, 1), dtype=int32, sparse=None, name=int_values> # Retrieve the corresponding encoded Keras tensors (encoded_features <- feature_space$get_encoded_features()) ## <KerasTensor shape=(None, 43), dtype=float32, sparse=False, name=keras_tensor_7> # Build a Functional model outputs <- encoded_features |> layer_dense(1, activation = \"sigmoid\") model <- keras_model(inputs, outputs) feature_space <- layer_feature_space(   features = list(     float_values = feature_float_normalized(),     string_values = feature_string_categorical(max_tokens = 10),     int_values = feature_integer_categorical(max_tokens = 10)   ),   crosses = list(     feature_cross(c(\"string_values\", \"int_values\"), crossing_dim = 32)   ),   output_mode = \"concat\" ) feature_space <- layer_feature_space(   features = list(     \"string_values\" = feature_string_categorical(output_mode = \"int\"),     \"int_values\" = feature_integer_categorical(output_mode = \"int\")   ),   crosses = list(     feature_cross(       feature_names = c(\"string_values\", \"int_values\"),       crossing_dim = 32,       output_mode = \"int\"     )   ),   output_mode = \"dict\" ) # Let's say that one of the features is a short text paragraph that # we want to encode as a vector (one vector per paragraph) via TF-IDF. data <- list(text = c(\"1st string\", \"2nd string\", \"3rd string\"))  # There's a Keras layer for this: layer_text_vectorization() custom_layer <- layer_text_vectorization(output_mode = \"tf_idf\")  # We can use feature_custom() to create a custom feature # that will use our preprocessing layer. feature_space <- layer_feature_space(   features = list(     text = feature_custom(preprocessor = custom_layer,                           dtype = \"string\",                           output_mode = \"float\"     )   ),   output_mode = \"concat\" ) feature_space |> adapt(tfdatasets::tensor_slices_dataset(data)) output_vector <- feature_space(data) # The preprocessing layer of each feature is available in `$preprocessors`. preprocessing_layer <- feature_space$preprocessors$feature1  # The crossing layer of each feature cross is available in `$crossers`. # It's an instance of layer_hashed_crossing() crossing_layer <- feature_space$crossers[[\"feature1_X_feature2\"]] feature_space$save(\"featurespace.keras\") reloaded_feature_space <- keras$models$load_model(\"featurespace.keras\")"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":null,"dir":"Reference","previous_headings":"","what":"Flattens the input. Does not affect the batch size. — layer_flatten","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"Flattens input. affect batch size.","code":""},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"","code":"layer_flatten(object, data_format = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"object Object compose layer . tensor, array, sequential model. data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, ..., channels) \"channels_first\" corresponds inputs shape (batch, channels, ...). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"inputs shaped (batch) without feature axis, flattening adds extra channel dimension output shape (batch, 1).","code":""},{"path":"https://keras.posit.co/reference/layer_flatten.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Flattens the input. Does not affect the batch size. — layer_flatten","text":"","code":"x <- layer_input(shape=c(10, 64)) y <- x |> layer_flatten() shape(y) ## shape(NA, 640)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_flax_module_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","title":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","text":"layer enables use Flax components form flax.linen.Module instances within Keras using JAX backend Keras. module method use forward pass can specified via method argument __call__ default. method must take following arguments exact names: self method bound module, case default __call__, module otherwise pass module. inputs: inputs model, JAX array PyTree arrays. training (optional): argument specifying training mode inference mode, TRUE passed training mode. FlaxLayer handles non-trainable state model required RNGs automatically. Note mutable parameter flax.linen.Module.apply() set DenyList([\"params\"]), therefore making assumption variables outside \"params\" collection non-trainable weights. example shows create FlaxLayer Flax Module default __call__ method training argument:   example shows wrap module method conform required signature. allows multiple input arguments training argument different name values. additionally shows use function bound module.","code":"# keras3::use_backend(\"jax\") # py_install(\"flax\", \"r-keras\")  if(config_backend() == \"jax\" &&    reticulate::py_module_available(\"flax\")) {  flax <- import(\"flax\")  MyFlaxModule(flax$linen$Module) %py_class% {   `__call__` <- flax$linen$compact(\\(self, inputs) {     inputs |>       (flax$linen$Conv(features = 32L, kernel_size = tuple(3L, 3L)))() |>       flax$linen$relu() |>       flax$linen$avg_pool(window_shape = tuple(2L, 2L),                           strides = tuple(2L, 2L)) |>       # flatten all except batch_size axis       (\\(x) x$reshape(tuple(x$shape[[1]], -1L)))() |>       (flax$linen$Dense(features = 200L))() |>       flax$linen$relu() |>       (flax$linen$Dense(features = 10L))() |>       flax$linen$softmax()   }) }  # typical usage: input <- keras_input(c(28, 28, 3)) output <- input |>   layer_flax_module_wrapper(MyFlaxModule())  model <- keras_model(input, output)  # to instantiate the layer before composing: flax_module <- MyFlaxModule() keras_layer <- layer_flax_module_wrapper(module = flax_module)  input <- keras_input(c(28, 28, 3)) output <- input |>   keras_layer()  model <- keras_model(input, output)  } flax <- import(\"flax\")  MyFlaxModule(flax$linen$Module) \\%py_class\\% {   forward <-     flax$linen$compact(\\(self, inputs1, input2, deterministic) {       # do work ....       outputs # return     }) }  my_flax_module_wrapper <- function(module, inputs, training) {   c(input1, input2) \\%<-\\% inputs   module$forward(input1, input2,!training) }  flax_module <- MyFlaxModule() keras_layer <- layer_flax_module_wrapper(module = flax_module,                                          method = my_flax_module_wrapper)"},{"path":"https://keras.posit.co/reference/layer_flax_module_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","text":"","code":"layer_flax_module_wrapper(object, module, method = NULL, variables = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_flax_module_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","text":"object Object compose layer . tensor, array, sequential model. module instance flax.linen.Module subclass. method method call model. generally method Module. provided, __call__ method used. method can also function defined Module, case must take Module first argument. used Module.init Module.apply. Details documented method argument flax.linen.Module.apply(). variables dict (named R list) containing variables module format returned flax.linen.Module.init(). contain \"params\" key , applicable, keys collections variables non-trainable state. allows passing trained parameters learned non-trainable state controlling initialization. NULL passed, module's init function called build time initialize variables model. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_flax_module_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keras Layer that wraps a Flax module. — layer_flax_module_wrapper","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"regularization layer, active training time.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"","code":"layer_gaussian_dropout(object, rate, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"object Object compose layer . tensor, array, sequential model. rate Float, drop probability (Dropout). multiplicative noise standard deviation sqrt(rate / (1 - rate)). seed Integer, optional random seed enable deterministic behavior. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_dropout.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Apply multiplicative 1-centered Gaussian noise. — layer_gaussian_dropout","text":"inputs: Input tensor (rank). training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (nothing).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"useful mitigate overfitting (see form random data augmentation). Gaussian Noise (GS) natural choice corruption process real valued inputs. regularization layer, active training time.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"","code":"layer_gaussian_noise(object, stddev, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"object Object compose layer . tensor, array, sequential model. stddev Float, standard deviation noise distribution. seed Integer, optional random seed enable deterministic behavior. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_gaussian_noise.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Apply additive zero-centered Gaussian noise. — layer_gaussian_noise","text":"inputs: Input tensor (rank). training: Python boolean indicating whether layer behave training mode (adding noise) inference mode (nothing).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"Global average pooling operation temporal data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"","code":"layer_global_average_pooling_1d(   object,   data_format = NULL,   keepdims = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, temporal dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"inputs: 3D tensor. mask: Binary tensor shape (batch_size, steps) indicating whether given step masked (excluded average).","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"data_format='channels_last': 3D tensor shape: (batch_size, steps, features) data_format='channels_first': 3D tensor shape: (batch_size, features, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"keepdims=FALSE: 2D tensor shape (batch_size, features). keepdims=TRUE: data_format=\"channels_last\": 3D tensor shape (batch_size, 1, features) data_format=\"channels_first\": 3D tensor shape (batch_size, features, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_1d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global average pooling operation for temporal data. — layer_global_average_pooling_1d","text":"","code":"x <- random_uniform(c(2, 3, 4)) y <- x |> layer_global_average_pooling_1d() shape(y) ## shape(2, 4)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"Global average pooling operation 2D data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"","code":"layer_global_average_pooling_2d(   object,   data_format = NULL,   keepdims = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, features, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, spatial dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"data_format='channels_last': 4D tensor shape: (batch_size, height, width, channels) data_format='channels_first': 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"keepdims=FALSE: 2D tensor shape (batch_size, channels). keepdims=TRUE: data_format=\"channels_last\": 4D tensor shape (batch_size, 1, 1, channels) data_format=\"channels_first\": 4D tensor shape (batch_size, channels, 1, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_2d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global average pooling operation for 2D data. — layer_global_average_pooling_2d","text":"","code":"x <- random_uniform(c(2, 4, 5, 3)) y <- x |> layer_global_average_pooling_2d() shape(y) ## shape(2, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"Global average pooling operation 3D data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"","code":"layer_global_average_pooling_3d(   object,   data_format = NULL,   keepdims = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, spatial dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"data_format='channels_last': 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format='channels_first': 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"keepdims=FALSE: 2D tensor shape (batch_size, channels). keepdims=TRUE: data_format=\"channels_last\": 5D tensor shape (batch_size, 1, 1, 1, channels) data_format=\"channels_first\": 5D tensor shape (batch_size, channels, 1, 1, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_average_pooling_3d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global average pooling operation for 3D data. — layer_global_average_pooling_3d","text":"","code":"x <- random_uniform(c(2, 4, 5, 4, 3)) y <- x |> layer_global_average_pooling_3d() shape(y) ## shape(2, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"Global max pooling operation temporal data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"","code":"layer_global_max_pooling_1d(object, data_format = NULL, keepdims = FALSE, ...)"},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, temporal dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"data_format='channels_last': 3D tensor shape: (batch_size, steps, features) data_format='channels_first': 3D tensor shape: (batch_size, features, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"keepdims=FALSE: 2D tensor shape (batch_size, features). keepdims=TRUE: data_format=\"channels_last\": 3D tensor shape (batch_size, 1, features) data_format=\"channels_first\": 3D tensor shape (batch_size, features, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_1d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global max pooling operation for temporal data. — layer_global_max_pooling_1d","text":"","code":"x <- random_uniform(c(2, 3, 4)) y <- x |> layer_global_max_pooling_1d() shape(y) ## shape(2, 4)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"Global max pooling operation 2D data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"","code":"layer_global_max_pooling_2d(object, data_format = NULL, keepdims = FALSE, ...)"},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, features, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, spatial dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"data_format='channels_last': 4D tensor shape: (batch_size, height, width, channels) data_format='channels_first': 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"keepdims=FALSE: 2D tensor shape (batch_size, channels). keepdims=TRUE: data_format=\"channels_last\": 4D tensor shape (batch_size, 1, 1, channels) data_format=\"channels_first\": 4D tensor shape (batch_size, channels, 1, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_2d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global max pooling operation for 2D data. — layer_global_max_pooling_2d","text":"","code":"x <- random_uniform(c(2, 4, 5, 3)) y <- x |> layer_global_max_pooling_2d() shape(y) ## shape(2, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"Global max pooling operation 3D data.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"","code":"layer_global_max_pooling_3d(object, data_format = NULL, keepdims = FALSE, ...)"},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"object Object compose layer . tensor, array, sequential model. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". keepdims boolean, whether keep temporal dimension . keepdims FALSE (default), rank tensor reduced spatial dimensions. keepdims TRUE, spatial dimension retained length 1. behavior tf$reduce_mean() op_mean(). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"data_format='channels_last': 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format='channels_first': 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"keepdims=FALSE: 2D tensor shape (batch_size, channels). keepdims=TRUE: data_format=\"channels_last\": 5D tensor shape (batch_size, 1, 1, 1, channels) data_format=\"channels_first\": 5D tensor shape (batch_size, channels, 1, 1, 1)","code":""},{"path":"https://keras.posit.co/reference/layer_global_max_pooling_3d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global max pooling operation for 3D data. — layer_global_max_pooling_3d","text":"","code":"x <- random_uniform(c(2, 4, 5, 4, 3)) y <- x |> layer_global_max_pooling_3d() shape(y) ## shape(2, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Group normalization layer. — layer_group_normalization","title":"Group normalization layer. — layer_group_normalization","text":"Group Normalization divides channels groups computes within group mean variance normalization. Empirically, accuracy stable batch norm wide range small batch sizes, learning rate adjusted linearly batch sizes. Relation Layer Normalization: number groups set 1, operation becomes nearly identical Layer Normalization (see Layer Normalization docs details). Relation Instance Normalization: number groups set input dimension (number groups equal number channels), operation becomes identical Instance Normalization. can achieve via groups=-1.","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Group normalization layer. — layer_group_normalization","text":"","code":"layer_group_normalization(   object,   groups = 32L,   axis = -1L,   epsilon = 0.001,   center = TRUE,   scale = TRUE,   beta_initializer = \"zeros\",   gamma_initializer = \"ones\",   beta_regularizer = NULL,   gamma_regularizer = NULL,   beta_constraint = NULL,   gamma_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Group normalization layer. — layer_group_normalization","text":"object Object compose layer . tensor, array, sequential model. groups Integer, number groups Group Normalization. Can range [1, N] N input dimension. input dimension must divisible number groups. Defaults 32. axis Integer List/Tuple. axis axes normalize across. Typically, features axis/axes. left-axes typically batch axis/axes. -1 last dimension input. Defaults -1. epsilon Small float added variance avoid dividing zero. Defaults 1e-3. center TRUE, add offset beta normalized tensor. FALSE, beta ignored. Defaults TRUE. scale TRUE, multiply gamma. FALSE, gamma used. next layer linear (also e.g. relu), can disabled since scaling done next layer. Defaults TRUE. beta_initializer Initializer beta weight. Defaults zeros. gamma_initializer Initializer gamma weight. Defaults ones. beta_regularizer Optional regularizer beta weight. NULL default. gamma_regularizer Optional regularizer gamma weight. NULL default. beta_constraint Optional constraint beta weight. NULL default. gamma_constraint Optional constraint gamma weight. NULL default. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Group normalization layer. — layer_group_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Group normalization layer. — layer_group_normalization","text":"Arbitrary. Use keyword argument input_shape (tuple integers, include samples axis) using layer first layer model.","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Group normalization layer. — layer_group_normalization","text":"shape input. **kwargs: Base layer keyword arguments (e.g. name dtype).","code":""},{"path":"https://keras.posit.co/reference/layer_group_normalization.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Group normalization layer. — layer_group_normalization","text":"Yuxin Wu & Kaiming , 2018","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"Grouped Query Attention layer. — layer_group_query_attention","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"implementation grouped-query attention introduced Ainslie et al., 2023. num_key_value_heads denotes number groups, setting num_key_value_heads 1 equivalent multi-query attention, num_key_value_heads equal num_query_heads equivalent multi-head attention. layer first projects query, key, value tensors. , key value repeated match number heads query. , query scaled dot-producted key tensors. softmaxed obtain attention probabilities. value tensors interpolated probabilities concatenated back single tensor.","code":""},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"","code":"layer_group_query_attention(   object,   head_dim,   num_query_heads,   num_key_value_heads,   dropout = 0,   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"object Object compose layer . tensor, array, sequential model. head_dim Size attention head. num_query_heads Number query attention heads. num_key_value_heads Number key value attention heads. dropout Dropout probability. use_bias Boolean, whether dense layers use bias vectors/matrices. kernel_initializer Initializer dense layer kernels. bias_initializer Initializer dense layer biases. kernel_regularizer Regularizer dense layer kernels. bias_regularizer Regularizer dense layer biases. activity_regularizer Regularizer dense layer activity. kernel_constraint Constraint dense layer kernels. bias_constraint Constraint dense layer kernels. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"attention_output: Result computation, shape (batch_dim, target_seq_len, feature_dim), target_seq_len target sequence length feature_dim query input last dim. attention_scores: (Optional) attention coefficients shape (batch_dim, num_query_heads, target_seq_len, source_seq_len).","code":""},{"path":"https://keras.posit.co/reference/layer_group_query_attention.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Grouped Query Attention layer. — layer_group_query_attention","text":"query: Query tensor shape (batch_dim, target_seq_len, feature_dim), batch_dim batch size, target_seq_len length target sequence, feature_dim dimension feature. value: Value tensor shape (batch_dim, source_seq_len, feature_dim), batch_dim batch size, source_seq_len length source sequence, feature_dim dimension feature. key: Optional key tensor shape (batch_dim, source_seq_len, feature_dim). given, use value key value, common case. attention_mask: boolean mask shape (batch_dim, target_seq_len, source_seq_len), prevents attention certain positions. boolean mask specifies query elements can attend key elements, 1 indicates attention 0 indicates attention. Broadcasting can happen missing batch dimensions head dimension. return_attention_scores: boolean indicate whether output (attention_output, attention_scores) TRUE, attention_output FALSE. Defaults FALSE. training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (dropout). go either using training mode parent layer/model FALSE (inference) parent layer. use_causal_mask: boolean indicate whether apply causal mask prevent tokens attending future tokens (e.g., used decoder Transformer).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_gru.html","id":null,"dir":"Reference","previous_headings":"","what":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"Based available runtime hardware constraints, layer choose different implementations (cuDNN-based backend-native) maximize performance. GPU available arguments layer meet requirement cuDNN kernel (see details), layer use fast cuDNN implementation using TensorFlow backend. requirements use cuDNN implementation : activation == tanh recurrent_activation == sigmoid dropout == 0 recurrent_dropout == 0 unroll FALSE use_bias TRUE reset_after TRUE Inputs, use masking, strictly right-padded. Eager execution enabled outermost context. two variants GRU implementation. default one based v3 reset gate applied hidden state matrix multiplication. one based original order reversed. second variant compatible CuDNNGRU (GPU-) allows inference CPU. Thus separate biases kernel recurrent_kernel. use variant, set reset_after=TRUE recurrent_activation='sigmoid'. example:","code":"inputs <- random_uniform(c(32, 10, 8)) outputs <- inputs |> layer_gru(4) shape(outputs) ## shape(32, 4) # (32, 4) gru <- layer_gru(, 4, return_sequences = TRUE, return_state = TRUE) c(whole_sequence_output, final_state) %<-% gru(inputs) shape(whole_sequence_output) ## shape(32, 10, 4) shape(final_state) ## shape(32, 4)"},{"path":"https://keras.posit.co/reference/layer_gru.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"","code":"layer_gru(   object,   units,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   unroll = FALSE,   reset_after = TRUE,   use_cudnn = \"auto\",   ... )"},{"path":"https://keras.posit.co/reference/layer_gru.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"object Object compose layer . tensor, array, sequential model. units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). recurrent_activation Activation function use recurrent step. Default: sigmoid (sigmoid). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. activity_regularizer Regularizer function applied output layer (\"activation\"). Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default: FALSE). TRUE, last state sample index batch used initial state sample index following batch. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences. reset_after GRU convention (whether apply reset gate matrix multiplication). FALSE \"\", TRUE \"\" (default cuDNN compatible). use_cudnn Whether use cuDNN-backed implementation. \"auto\" attempt use cuDNN feasible, fallback default implementation . ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_gru.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_gru.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Gated Recurrent Unit - Cho et al. 2014. — layer_gru","text":"inputs: 3D tensor, shape (batch, timesteps, feature). mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked  (optional). individual TRUE entry indicates corresponding timestep utilized, FALSE entry indicates corresponding timestep ignored. Defaults NULL. training: Python boolean indicating whether layer behave training mode inference mode. argument passed cell calling . relevant dropout recurrent_dropout used  (optional). Defaults NULL. initial_state: List initial state tensors passed first call cell (optional, NULL causes creation zero-filled initial state tensors). Defaults NULL.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which crosses features using the ","title":"A preprocessing layer which crosses features using the ","text":"layer performs crosses categorical features using \"hashing trick\". Conceptually, transformation can thought : hash(concatenate(features)) %% num_bins. layer currently performs crosses scalar inputs batches scalar inputs. Valid input shapes (batch_size, 1), (batch_size) (). Note: layer wraps tf.keras.layers.HashedCrossing. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tfdatasets pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which crosses features using the ","text":"","code":"layer_hashed_crossing(   object,   num_bins,   output_mode = \"int\",   sparse = FALSE,   name = NULL,   dtype = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which crosses features using the ","text":"object Object compose layer . tensor, array, sequential model. num_bins Number hash bins. output_mode Specification output layer. Values can \"int\", \"one_hot\" configuring layer follows: \"int\": Return integer bin indices directly. \"one_hot\": Encodes individual element input array size num_bins, containing 1 input's bin index. Defaults \"int\". sparse Boolean. applicable \"one_hot\" mode valid using TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. name String, name object dtype datatype (e.g., \"float32\"). ... Keyword arguments construct layer.","code":""},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which crosses features using the ","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_hashed_crossing.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which crosses features using the ","text":"Crossing two scalar features.     Crossing one-hotting two scalar features.","code":"feat1 <- c('A', 'B', 'A', 'B', 'A') |> as.array() feat2 <- c(101, 101, 101, 102, 102) |> as.integer() |> as.array() layer <- layer_hashed_crossing(num_bins = 5) layer(list(feat1, feat2)) ## tf.Tensor([1 4 1 1 3], shape=(5), dtype=int64) layer <- layer_hashed_crossing(num_bins = 5, output_mode = 'one_hot') layer(list(feat1, feat2)) ## tf.Tensor( ## [[0. 1. 0. 0. 0.] ##  [0. 0. 0. 0. 1.] ##  [0. 1. 0. 0. 0.] ##  [0. 1. 0. 0. 0.] ##  [0. 0. 0. 1. 0.]], shape=(5, 5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"layer transforms categorical inputs hashed output. element-wise converts ints strings ints fixed range. stable hash function uses tensorflow::ops::Fingerprint produce output consistently across platforms. layer uses FarmHash64 default, provides consistent hashed output across different platforms stable across invocations, regardless device context, mixing input bits thoroughly. want obfuscate hashed output, can also pass random salt argument constructor. case, layer use SipHash64 hash function, salt value serving additional input hash function. Note: layer internally uses TensorFlow. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tf.data pipeline (independently backend using). Example (FarmHash64)     Example (FarmHash64) mask value     Example (SipHash64)     Example (Siphash64 single integer, salt=[133, 133])","code":"layer <- layer_hashing(num_bins = 3) inp <- c('A', 'B', 'C', 'D', 'E') |> array(dim = c(5, 1)) layer(inp) ## tf.Tensor( ## [[1] ##  [0] ##  [1] ##  [1] ##  [2]], shape=(5, 1), dtype=int64) layer <- layer_hashing(num_bins=3, mask_value='') inp <- c('A', 'B', '', 'C', 'D') |> array(dim = c(5, 1)) layer(inp) ## tf.Tensor( ## [[1] ##  [1] ##  [0] ##  [2] ##  [2]], shape=(5, 1), dtype=int64) layer <- layer_hashing(num_bins=3, salt=c(133, 137)) inp <- c('A', 'B', 'C', 'D', 'E') |> array(dim = c(5, 1)) layer(inp) ## tf.Tensor( ## [[1] ##  [2] ##  [1] ##  [0] ##  [2]], shape=(5, 1), dtype=int64) layer <- layer_hashing(num_bins=3, salt=133) inp <- c('A', 'B', 'C', 'D', 'E') |> array(dim = c(5, 1)) layer(inp) ## tf.Tensor( ## [[0] ##  [0] ##  [2] ##  [1] ##  [0]], shape=(5, 1), dtype=int64)"},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"","code":"layer_hashing(   object,   num_bins,   mask_value = NULL,   salt = NULL,   output_mode = \"int\",   sparse = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"object Object compose layer . tensor, array, sequential model. num_bins Number hash bins. Note includes mask_value bin, effective number bins (num_bins - 1) mask_value set. mask_value value represents masked inputs, mapped index 0. NULL means mask term added hashing start index 0. Defaults NULL. salt single unsigned integer NULL. passed, hash function used SipHash64, values used additional input (known \"salt\" cryptography). non-zero. NULL, uses FarmHash64 hash function. also supports list 2 unsigned integer numbers, see reference paper details. Defaults NULL. output_mode Specification output layer. Values can \"int\", \"one_hot\", \"multi_hot\", \"count\" configuring layer follows: \"int\": Return integer bin indices directly. \"one_hot\": Encodes individual element input array size num_bins, containing 1 input's bin index. last dimension size 1, encode dimension. last dimension size 1, append new dimension encoded output. \"multi_hot\": Encodes sample input single array size num_bins, containing 1 bin index index present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). \"count\": \"multi_hot\", int array contains count number times bin index appeared sample. Defaults \"int\". sparse Boolean. applicable \"one_hot\", \"multi_hot\", \"count\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. ... Keyword arguments construct layer.","code":""},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"single string, list strings, int32 int64 tensor shape (batch_size, ...,).","code":""},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"int32 tensor shape (batch_size, ...).","code":""},{"path":"https://keras.posit.co/reference/layer_hashing.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"A preprocessing layer which hashes and bins categorical features. — layer_hashing","text":"SipHash salt","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Identity layer. — layer_identity","title":"Identity layer. — layer_identity","text":"layer used placeholder operation performed. layer just returns inputs argument output.","code":""},{"path":"https://keras.posit.co/reference/layer_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identity layer. — layer_identity","text":"","code":"layer_identity(object, ...)"},{"path":"https://keras.posit.co/reference/layer_identity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identity layer. — layer_identity","text":"object Object compose layer . tensor, array, sequential model. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_identity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Identity layer. — layer_identity","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_input.html","id":null,"dir":"Reference","previous_headings":"","what":"keras_input — layer_input","title":"keras_input — layer_input","text":"layer_input() alias keras_input(). See ?keras_input() full documentation.","code":""},{"path":"https://keras.posit.co/reference/layer_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"keras_input — layer_input","text":"","code":"layer_input(   shape = NULL,   batch_size = NULL,   dtype = NULL,   sparse = NULL,   batch_shape = NULL,   name = NULL,   tensor = NULL )"},{"path":"https://keras.posit.co/reference/layer_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"keras_input — layer_input","text":"shape shape list (list integers NULL objects), including batch size. instance, shape = c(32) indicates expected input batches 32-dimensional vectors. Elements list can NULL NA; NULL/NA elements represent dimensions shape known may vary (e.g. sequence length). batch_size Optional static batch size (integer). dtype data type expected input, string (e.g. \"float32\", \"int32\"...) sparse boolean specifying whether expected input sparse tensors. Note , sparse FALSE, sparse tensors can still passed input - densified default value 0. feature supported TensorFlow backend. Defaults FALSE. batch_shape Shape, including batch dim. name Optional name string layer. unique model (reuse name twice). autogenerated provided. tensor Optional existing tensor wrap Input layer. set, layer use tensor rather creating new placeholder tensor.","code":""},{"path":"https://keras.posit.co/reference/layer_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"keras_input — layer_input","text":"Keras tensor, can passed inputs argument (keras_model()).","code":""},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"layer maps set arbitrary integer input tokens indexed integer output via table-based vocabulary lookup. layer's output indices contiguously arranged maximum vocab size, even input tokens non-continguous unbounded. layer supports multiple options encoding output via output_mode, optional support --vocabulary (OOV) tokens masking. vocabulary layer must either supplied construction learned via adapt(). adapt(), layer analyze data set, determine frequency individual integer tokens, create vocabulary . vocabulary capped size, frequent tokens used create vocabulary others treated OOV. two possible output modes layer.  output_mode \"int\", input integers converted index vocabulary (integer).  output_mode \"multi_hot\", \"count\", \"tf_idf\", input integers encoded array dimension corresponds element vocabulary. vocabulary can optionally contain mask token well OOV token (can optionally occupy multiple indices vocabulary, set num_oov_indices). position tokens vocabulary fixed. output_mode \"int\", vocabulary begin mask token index 0, followed OOV indices, followed rest vocabulary. output_mode \"multi_hot\", \"count\", \"tf_idf\" vocabulary begin OOV indices instances mask token dropped. Note: layer uses TensorFlow internally. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"","code":"layer_integer_lookup(   object,   max_tokens = NULL,   num_oov_indices = 1L,   mask_token = NULL,   oov_token = -1L,   vocabulary = NULL,   vocabulary_dtype = \"int64\",   idf_weights = NULL,   invert = FALSE,   output_mode = \"int\",   sparse = FALSE,   pad_to_max_tokens = FALSE,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"object Object compose layer . tensor, array, sequential model. max_tokens Maximum size vocabulary layer. specified adapting vocabulary setting pad_to_max_tokens=TRUE. NULL, cap size vocabulary. Note size includes OOV mask tokens. Defaults NULL. num_oov_indices number --vocabulary tokens use. value 1, OOV inputs modulated determine OOV value. value 0, OOV inputs cause error calling layer. Defaults 1. mask_token integer token represents masked inputs. output_mode \"int\", token included vocabulary mapped index 0. output modes, token appear vocabulary instances mask token input dropped. set NULL, mask term added. Defaults NULL. oov_token used invert TRUE. token return OOV indices. Defaults -1. vocabulary Optional. Either array integers string path text file. passing array, can pass list, list, 1D NumPy array, 1D tensor containing integer vocbulary terms. passing file path, file contain one line per term vocabulary. argument set, need adapt() layer. vocabulary_dtype dtype vocabulary terms, example \"int64\" \"int32\". Defaults \"int64\". idf_weights valid output_mode \"tf_idf\". list, list, 1D NumPy array, 1D tensor length vocabulary, containing floating point inverse document frequency weights, multiplied per sample term counts final TF-IDF weight. vocabulary argument set, output_mode \"tf_idf\", argument must supplied. invert valid output_mode \"int\". TRUE, layer map indices vocabulary items instead mapping vocabulary items indices. Defaults FALSE. output_mode Specification output layer. Values can \"int\", \"one_hot\", \"multi_hot\", \"count\", \"tf_idf\" configuring layer follows: \"int\": Return vocabulary indices input tokens. \"one_hot\": Encodes individual element input array size vocabulary, containing 1 element index. last dimension size 1, encode dimension. last dimension size 1, append new dimension encoded output. \"multi_hot\": Encodes sample input single array size vocabulary, containing 1 vocabulary term present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). \"count\": \"multi_hot\", int array contains count number times token index appeared sample. \"tf_idf\": \"multi_hot\", TF-IDF algorithm applied find value token slot. \"int\" output, shape input output supported. output modes, currently output rank 2 supported. Defaults \"int\". sparse Boolean. applicable \"multi_hot\", \"count\", \"tf_idf\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. pad_to_max_tokens applicable output_mode \"multi_hot\", \"count\", \"tf_idf\". TRUE, output feature axis padded max_tokens even number unique tokens vocabulary less max_tokens, resulting tensor shape (batch_size, max_tokens) regardless vocabulary size. Defaults FALSE. name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_integer_lookup.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer that maps integers to (possibly encoded) indices. — layer_integer_lookup","text":"Creating lookup layer known vocabulary example creates lookup layer pre-existing vocabulary.     Creating lookup layer adapted vocabulary example creates lookup layer generates vocabulary analyzing dataset.     Note OOV token -1 added vocabulary. remaining tokens sorted frequency (42, 2 occurrences, first) inverse sort order.     Lookups multiple OOV indices example demonstrates use lookup layer multiple OOV indices.  layer created one OOV index, OOV tokens hashed number OOV buckets, distributing OOV tokens deterministic fashion across set.     Note output OOV token 37 1, output OOV token 1000 0. -vocab terms output index increased 1 earlier examples (12 maps 2, etc) order make space extra OOV token. One-hot output Configure layer output_mode='one_hot'. Note first num_oov_indices dimensions ont_hot encoding represent OOV values.     Multi-hot output Configure layer output_mode = 'multi_hot'. Note first num_oov_indices dimensions multi_hot encoding represent OOV tokens     Token count output Configure layer output_mode='count'. multi_hot output, first num_oov_indices dimensions output represent OOV tokens.     TF-IDF output Configure layer output_mode='tf_idf'. multi_hot output, first num_oov_indices dimensions output represent OOV tokens. token bin output token_count * idf_weight, idf weights inverse document frequency weights per token. provided along vocabulary. Note idf_weight OOV tokens default average idf weights passed .     specify idf weights oov tokens, need pass entire vocabulary including leading oov token.     adapting layer \"tf_idf\" mode, input sample considered document, IDF weight per token calculated : log(1 + num_documents / (1 + token_document_count)). Inverse lookup example demonstrates map indices tokens using layer. (can also use adapt() inverse = TRUE, simplicity pass vocab example.)     Note first index correspond oov token default. Forward inverse lookup pairs example demonstrates use vocabulary standard lookup layer create inverse lookup layer.     example, input token 1000 resulted output -1, since 1000 vocabulary - got represented OOV, OOV tokens returned -1 inverse layer. Also, note inverse work, must already set forward layer vocabulary either directly via adapt() calling get_vocabulary().","code":"vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(rbind(c(12, 1138, 42),                       c(42, 1000, 36)))  # Note OOV tokens out <- data |> layer_integer_lookup(vocabulary = vocab) out ## tf.Tensor( ## [[1 3 4] ##  [4 0 2]], shape=(2, 3), dtype=int64) data <- op_array(rbind(c(12, 1138, 42),                       c(42, 1000, 36)))  # Note OOV tokens layer <- layer_integer_lookup() layer |> adapt(data) layer |> get_vocabulary() |> str() ## List of 6 ##  $ : int -1 ##  $ : num 42 ##  $ : num 1138 ##  $ : num 1000 ##  $ : num 36 ##  $ : num 12 layer(data) ## tf.Tensor( ## [[5 2 1] ##  [1 3 4]], shape=(2, 3), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(rbind(c(12, 1138, 42),                       c(37, 1000, 36)))  # Note OOV tokens out <- data |>   layer_integer_lookup(vocabulary = vocab,                        num_oov_indices = 2) out ## tf.Tensor( ## [[2 4 5] ##  [1 0 3]], shape=(2, 3), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(c(12, 36, 1138, 42, 7), 'int32')  # Note OOV tokens layer <- layer_integer_lookup(vocabulary = vocab,                               output_mode = 'one_hot') layer(data) ## tf.Tensor( ## [[0 1 0 0 0] ##  [0 0 1 0 0] ##  [0 0 0 1 0] ##  [0 0 0 0 1] ##  [1 0 0 0 0]], shape=(5, 5), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(rbind(c(12, 1138, 42, 42),                       c(42,    7, 36,  7)), \"int64\")  # Note OOV tokens layer <- layer_integer_lookup(vocabulary = vocab,                               output_mode = 'multi_hot') layer(data) ## tf.Tensor( ## [[0 1 0 1 1] ##  [1 0 1 0 1]], shape=(2, 5), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- rbind(c(12, 1138, 42, 42),               c(42,    7, 36,  7)) |> op_array(\"int64\") layer <- layer_integer_lookup(vocabulary = vocab,                               output_mode = 'count') layer(data) ## tf.Tensor( ## [[0 1 0 1 2] ##  [2 0 1 0 1]], shape=(2, 5), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() idf_weights <- c(0.25, 0.75, 0.6, 0.4) data <- rbind(c(12, 1138, 42, 42),               c(42,    7, 36,  7)) |> op_array(\"int64\") layer <- layer_integer_lookup(output_mode = 'tf_idf',                               vocabulary = vocab,                               idf_weights = idf_weights) layer(data) ## tf.Tensor( ## [[0.   0.25 0.   0.6  0.8 ] ##  [1.   0.   0.75 0.   0.4 ]], shape=(2, 5), dtype=float32) vocab <- c(-1, 12, 36, 1138, 42) |> as.integer() idf_weights <- c(0.9, 0.25, 0.75, 0.6, 0.4) data <- rbind(c(12, 1138, 42, 42),               c(42,    7, 36,  7)) |> op_array(\"int64\") layer <- layer_integer_lookup(output_mode = 'tf_idf',                               vocabulary = vocab,                               idf_weights = idf_weights) layer(data) ## tf.Tensor( ## [[0.   0.25 0.   0.6  0.8 ] ##  [1.8  0.   0.75 0.   0.4 ]], shape=(2, 5), dtype=float32) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(c(1, 3, 4,                   4, 0, 2)) |> op_reshape(c(2,-1)) |> op_cast(\"int32\") layer <- layer_integer_lookup(vocabulary = vocab, invert = TRUE) layer(data) ## tf.Tensor( ## [[  12 1138   42] ##  [  42   -1   36]], shape=(2, 3), dtype=int64) vocab <- c(12, 36, 1138, 42) |> as.integer() data <- op_array(rbind(c(12, 1138, 42), c(42, 1000, 36)), \"int32\") layer <- layer_integer_lookup(vocabulary = vocab) i_layer <- layer_integer_lookup(vocabulary = get_vocabulary(layer),                                 invert = TRUE) int_data <- layer(data) i_layer(int_data) ## tf.Tensor( ## [[  12 1138   42] ##  [  42   -1   36]], shape=(2, 3), dtype=int64)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"layer enables use JAX components within Keras using JAX backend Keras.","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"","code":"layer_jax_model_wrapper(   object,   call_fn,   init_fn = NULL,   params = NULL,   state = NULL,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"object Object compose layer . tensor, array, sequential model. call_fn function call model. See description list arguments takes outputs returns. init_fn function call initialize model. See description list arguments takes ouputs returns. NULL, params /state must provided. params PyTree containing model trainable parameters. allows passing trained parameters controlling initialization. params state NULL, init_fn() called build time initialize trainable parameters model. state PyTree containing model non-trainable state. allows passing learned state controlling initialization. params state NULL, call_fn() takes state argument, init_fn() called build time initialize non-trainable state model. seed Seed random number generator. Optional. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"model-function","dir":"Reference","previous_headings":"","what":"Model function","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"layer accepts JAX models form function, call_fn(), must take following arguments exact names: params: trainable parameters model. state (optional): non-trainable state model. Can omitted model non-trainable state. rng (optional): jax.random.PRNGKey instance. Can omitted model need RNGs, neither training inference. inputs: inputs model, JAX array PyTree arrays. training (optional): argument specifying training mode inference mode, TRUE passed training mode. Can omitted model behaves training mode inference mode. inputs argument mandatory. Inputs model must provided via single argument. JAX model takes multiple inputs separate arguments, must combined single structure, instance tuple() dict().","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"model-weights-initialization","dir":"Reference","previous_headings":"","what":"Model weights initialization","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"initialization params state model can handled layer, case init_fn() argument must provided. allows model initialized dynamically right shape. Alternatively, shape known, params argument optionally state argument can used create already initialized model. init_fn() function, provided, must take following arguments exact names: rng: jax.random.PRNGKey instance. inputs: JAX array PyTree arrays placeholder values provide shape inputs. training (optional): argument specifying training mode inference mode. True always passed init_fn. Can omitted regardless whether call_fn training argument.","code":""},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"models-with-non-trainable-state","dir":"Reference","previous_headings":"","what":"Models with non-trainable state","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"JAX models non-trainable state: call_fn() must state argument call_fn() must return tuple() containing outputs model new non-trainable state model init_fn() must return tuple() containing initial trainable params model initial non-trainable state model. code shows possible combination call_fn() init_fn() signatures model non-trainable state. example, model training argument rng argument call_fn().","code":"stateful_call <- function(params, state, rng, inputs, training) {   outputs <- ....   new_state <- ....   tuple(outputs, new_state) }  stateful_init <- function(rng, inputs) {   initial_params <- ....   initial_state <- ....   tuple(initial_params, initial_state) }"},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"models-without-non-trainable-state","dir":"Reference","previous_headings":"","what":"Models without non-trainable state","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"JAX models non-trainable state: call_fn() must state argument call_fn() must return outputs model init_fn() must return initial trainable params model. code shows possible combination call_fn() init_fn() signatures model without non-trainable state. example, model training argument rng argument call_fn().","code":"stateful_call <- function(pparams, inputs) {   outputs <- ....   outputs }  stateful_init <- function(rng, inputs) {   initial_params <- ....   initial_params }"},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"conforming-to-the-required-signature","dir":"Reference","previous_headings":"","what":"Conforming to the required signature","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"model different signature one required JaxLayer, one can easily write wrapper method adapt arguments. example shows model multiple inputs separate arguments, expects multiple RNGs dict, deterministic argument opposite meaning training. conform, inputs combined single structure using tuple, RNG split used populate expected dict, Boolean flag negated:","code":"jax <- import(\"jax\") my_model_fn <- function(params, rngs, input1, input2, deterministic) {   ....   if (!deterministic) {     dropout_rng <- rngs$dropout     keep <- jax$random$bernoulli(dropout_rng, dropout_rate, x$shape)     x <- jax$numpy$where(keep, x / dropout_rate, 0)     ....   }   ....   return(outputs) }  my_model_wrapper_fn <- function(params, rng, inputs, training) {   c(input1, input2) %<-% inputs   c(rng1, rng2) %<-% jax$random$split(rng)   rngs <-  list(dropout = rng1, preprocessing = rng2)   deterministic <-  !training   my_model_fn(params, rngs, input1, input2, deterministic) }  keras_layer <- layer_jax_model_wrapper(call_fn = my_model_wrapper_fn,                                        params = initial_params)"},{"path":"https://keras.posit.co/reference/layer_jax_model_wrapper.html","id":"usage-with-haiku-modules","dir":"Reference","previous_headings":"","what":"Usage with Haiku modules","title":"Keras Layer that wraps a JAX model. — layer_jax_model_wrapper","text":"JaxLayer enables use Haiku components form haiku.Module. achieved transforming module per Haiku pattern passing module.apply call_fn parameter module.init init_fn parameter needed. model non-trainable state, transformed haiku.transform_with_state. model non-trainable state, transformed haiku.transform. Additionally, optionally, module use RNGs \"apply\", can transformed haiku.without_apply_rng. following example shows create JaxLayer Haiku module uses random number generators via hk.next_rng_key() takes training positional argument:","code":"# reticulate::py_install(\"haiku\", \"r-keras\") hk <- import(\"haiku\") MyHaikuModule(hk$Module) \\%py_class\\% {    `__call__` <- \\(self, x, training) {     x <- hk$Conv2D(32L, tuple(3L, 3L))(x)     x <- jax$nn$relu(x)     x <- hk$AvgPool(tuple(1L, 2L, 2L, 1L),                     tuple(1L, 2L, 2L, 1L), \"VALID\")(x)     x <- hk$Flatten()(x)     x <- hk$Linear(200L)(x)     if (training)       x <- hk$dropout(rng = hk$next_rng_key(), rate = 0.3, x = x)     x <- jax$nn$relu(x)     x <- hk$Linear(10L)(x)     x <- jax$nn$softmax(x)     x   }  }  my_haiku_module_fn <- function(inputs, training) {   module <- MyHaikuModule()   module(inputs, training) }  transformed_module <- hk$transform(my_haiku_module_fn)  keras_layer <-   layer_jax_model_wrapper(call_fn = transformed_module$apply,                           init_fn = transformed_module$init)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":null,"dir":"Reference","previous_headings":"","what":"Wraps arbitrary expressions as a Layer object. — layer_lambda","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"layer_lambda() layer exists arbitrary expressions can used Layer constructing Sequential Functional API models. Lambda layers best suited simple operations quick experimentation. advanced use cases, prefer writing new subclasses Layer using new_layer_class().","code":""},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"","code":"layer_lambda(   object,   f,   output_shape = NULL,   mask = NULL,   arguments = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"object Object compose layer . tensor, array, sequential model. f function evaluated. Takes input tensor first argument. output_shape Expected output shape function. argument can usually inferred explicitly provided. Can list function. list, specifies first dimension onward; sample dimension assumed either input: output_shape = c(input_shape[1], output_shape) , input NULL sample dimension also NULL: output_shape = c(NA, output_shape). function, specifies entire shape function input shape: output_shape = f(input_shape). mask Either NULL (indicating masking) callable signature compute_mask layer method, tensor returned output mask regardless input . arguments Optional named list arguments passed function. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_lambda.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wraps arbitrary expressions as a Layer object. — layer_lambda","text":"","code":"# add a x -> x^2 layer model <- keras_model_sequential() model |> layer_lambda(\\(x) x^2)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"Normalize activations previous layer given example batch independently, rather across batch like Batch Normalization. .e. applies transformation maintains mean activation within example close 0 activation standard deviation close 1. scale center enabled, layer scale normalized outputs broadcasting trainable variable gamma, center outputs broadcasting trainable variable beta. gamma default ones tensor beta default zeros tensor, centering scaling -ops training begun. , scaling centering enabled normalization equations follows: Let intermediate activations mini-batch inputs. sample x batch inputs, compute mean variance sample, normalize value sample (including small factor epsilon numerical stability), finally, transform normalized output gamma beta, learned parameters:   gamma beta span axes inputs specified axis, part inputs' shape must fully defined. example:         Note implementations layer normalization may choose define gamma beta separate set axes axes normalized across. example, Group Normalization (Wu et al. 2018) group size 1 corresponds layer_layer_normalization() normalizes across height, width, channel gamma beta span channel dimension. , layer_layer_normalization() implementation match layer_group_normalization() layer group size set 1.","code":"outputs <- inputs |> apply(1, function(x) {   x_normalized <- (x - mean(x)) /                   sqrt(var(x) + epsilon)   x_normalized * gamma + beta }) layer <- layer_layer_normalization(axis = c(2, 3, 4))  layer(op_ones(c(5, 20, 30, 40))) |> invisible() # build() shape(layer$beta) ## shape(20, 30, 40) shape(layer$gamma) ## shape(20, 30, 40)"},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"","code":"layer_layer_normalization(   object,   axis = -1L,   epsilon = 0.001,   center = TRUE,   scale = TRUE,   rms_scaling = FALSE,   beta_initializer = \"zeros\",   gamma_initializer = \"ones\",   beta_regularizer = NULL,   gamma_regularizer = NULL,   beta_constraint = NULL,   gamma_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"object Object compose layer . tensor, array, sequential model. axis Integer list. axis axes normalize across. Typically, features axis/axes. left-axes typically batch axis/axes. -1 last dimension input. Defaults -1. epsilon Small float added variance avoid dividing zero. Defaults 1e-3. center TRUE, add offset beta normalized tensor. FALSE, beta ignored. Defaults TRUE. scale TRUE, multiply gamma. FALSE, gamma used. next layer linear (also e.g. layer_activation_relu()), can disabled since scaling done next layer. Defaults TRUE. rms_scaling TRUE, center scale ignored, inputs scaled gamma inverse square root square inputs. approximate faster approach avoids ever computing mean input. beta_initializer Initializer beta weight. Defaults zeros. gamma_initializer Initializer gamma weight. Defaults ones. beta_regularizer Optional regularizer beta weight. NULL default. gamma_regularizer Optional regularizer gamma weight. NULL default. beta_constraint Optional constraint beta weight. NULL default. gamma_constraint Optional constraint gamma weight. NULL default. ... Base layer keyword arguments (e.g. name dtype).","code":""},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_layer_normalization.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Layer normalization layer (Ba et al., 2016). — layer_layer_normalization","text":"Lei Ba et al., 2016.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":null,"dir":"Reference","previous_headings":"","what":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"Based available runtime hardware constraints, layer choose different implementations (cuDNN-based backend-native) maximize performance. GPU available arguments layer meet requirement cuDNN kernel (see details), layer use fast cuDNN implementation using TensorFlow backend. requirements use cuDNN implementation : activation == tanh recurrent_activation == sigmoid dropout == 0 recurrent_dropout == 0 unroll FALSE use_bias TRUE Inputs, use masking, strictly right-padded. Eager execution enabled outermost context. example:","code":"input <- random_uniform(c(32, 10, 8)) output <- input |> layer_lstm(4) shape(output) ## shape(32, 4) lstm <- layer_lstm(units = 4, return_sequences = TRUE, return_state = TRUE) c(whole_seq_output, final_memory_state, final_carry_state) %<-% lstm(input) shape(whole_seq_output) ## shape(32, 10, 4) shape(final_memory_state) ## shape(32, 4) shape(final_carry_state) ## shape(32, 4)"},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"","code":"layer_lstm(   object,   units,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   unroll = FALSE,   use_cudnn = \"auto\",   ... )"},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"object Object compose layer . tensor, array, sequential model. units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). recurrent_activation Activation function use recurrent step. Default: sigmoid (sigmoid). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". unit_forget_bias Boolean (default TRUE). TRUE, add 1 bias forget gate initialization. Setting TRUE also force bias_initializer=\"zeros\". recommended Jozefowicz et al. kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. activity_regularizer Regularizer function applied output layer (\"activation\"). Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. seed Random seed dropout. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default: FALSE). TRUE, last state sample index batch used initial state sample index following batch. unroll Boolean (default FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences. use_cudnn Whether use cuDNN-backed implementation. \"auto\" attempt use cuDNN feasible, fallback default implementation . ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_lstm.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Long Short-Term Memory layer - Hochreiter 1997. — layer_lstm","text":"inputs: 3D tensor, shape (batch, timesteps, feature). mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked  (optional). individual TRUE entry indicates corresponding timestep utilized, FALSE entry indicates corresponding timestep ignored. Defaults NULL. training: Boolean indicating whether layer behave training mode inference mode. argument passed cell calling . relevant dropout recurrent_dropout used  (optional). Defaults NULL. initial_state: List initial state tensors passed first call cell (optional, NULL causes creation zero-filled initial state tensors). Defaults NULL.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_masking.html","id":null,"dir":"Reference","previous_headings":"","what":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"timestep input tensor (dimension #1 tensor), values input tensor timestep equal mask_value, timestep masked (skipped) downstream layers (long support masking). downstream layer support masking yet receives input mask, exception raised.","code":""},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"","code":"layer_masking(object, mask_value = 0, ...)"},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"object Object compose layer . tensor, array, sequential model. mask_value see description ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"Consider array x shape c(samples, timesteps, features), fed LSTM layer. want mask timestep #3 #5 lack data timesteps. can: Set x[, 3, ] <- 0. x[, 5, ] <- 0. Insert layer_masking() layer mask_value = 0. LSTM layer:","code":"c(samples, timesteps, features) %<-% c(32, 10, 8) inputs <- c(samples, timesteps, features) %>% { array(runif(prod(.)), dim = .) } inputs[, 3, ] <- 0 inputs[, 5, ] <- 0  model <- keras_model_sequential() %>%   layer_masking(mask_value = 0) %>%   layer_lstm(32)  output <- model(inputs) # The time step 3 and 5 will be skipped from LSTM calculation."},{"path":"https://keras.posit.co/reference/layer_masking.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Masks a sequence by using a mask value to skip timesteps. — layer_masking","text":"Keras masking convention, masked timestep denoted mask value FALSE, non-masked (.e. usable) timestep denoted mask value TRUE.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"Downsamples input representation taking maximum value spatial window size pool_size. window shifted strides. resulting output using \"valid\" padding option shape : output_shape = (input_shape - pool_size + 1) / strides). resulting output shape using \"\" padding option : output_shape = input_shape / strides","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"","code":"layer_max_pooling_1d(   object,   pool_size = 2L,   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"object Object compose layer . tensor, array, sequential model. pool_size int, size max pooling window. strides int NULL. Specifies much pooling window moves pooling step. NULL, default pool_size. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"data_format=\"channels_last\": 3D tensor shape (batch_size, steps, features). data_format=\"channels_first\": 3D tensor shape (batch_size, features, steps).","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"data_format=\"channels_last\": 3D tensor shape (batch_size, downsampled_steps, features). data_format=\"channels_first\": 3D tensor shape (batch_size, features, downsampled_steps).","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_1d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Max pooling operation for 1D temporal data. — layer_max_pooling_1d","text":"strides=1 padding=\"valid\":     strides=2 padding=\"valid\":     strides=1 padding=\"\":","code":"x <- op_reshape(c(1, 2, 3, 4, 5),                c(1, 5, 1)) max_pool_1d <- layer_max_pooling_1d(pool_size = 2,                                     strides = 1,                                     padding = \"valid\") max_pool_1d(x) ## tf.Tensor( ## [[[2.] ##   [3.] ##   [4.] ##   [5.]]], shape=(1, 4, 1), dtype=float32) x <- op_reshape(c(1, 2, 3, 4, 5),                c(1, 5, 1)) max_pool_1d <- layer_max_pooling_1d(pool_size = 2,                                     strides = 2,                                     padding = \"valid\") max_pool_1d(x) ## tf.Tensor( ## [[[2.] ##   [4.]]], shape=(1, 2, 1), dtype=float32) x <- op_reshape(c(1, 2, 3, 4, 5),                c(1, 5, 1)) max_pool_1d <- layer_max_pooling_1d(pool_size = 2,                                     strides = 1,                                     padding = \"same\") max_pool_1d(x) ## tf.Tensor( ## [[[2.] ##   [3.] ##   [4.] ##   [5.] ##   [5.]]], shape=(1, 5, 1), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"Downsamples input along spatial dimensions (height width) taking maximum value input window (size defined pool_size) channel input. window shifted strides along dimension. resulting output using \"valid\" padding option spatial shape (number rows columns) : output_shape = floor((input_shape - pool_size) / strides) + 1 (input_shape >= pool_size) resulting output shape using \"\" padding option : output_shape = floor((input_shape - 1) / strides) + 1","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"","code":"layer_max_pooling_2d(   object,   pool_size = list(2L, 2L),   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"object Object compose layer . tensor, array, sequential model. pool_size int list 2 integers, factors downscale (dim1, dim2). one integer specified, window length used dimensions. strides int list 2 integers, NULL. Strides values. NULL, default pool_size. one int specified, stride size used dimensions. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"data_format=\"channels_last\": 4D tensor shape (batch_size, height, width, channels). data_format=\"channels_first\": 4D tensor shape (batch_size, channels, height, width).","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"data_format=\"channels_last\": 4D tensor shape (batch_size, pooled_height, pooled_width, channels). data_format=\"channels_first\": 4D tensor shape (batch_size, channels, pooled_height, pooled_width).","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_2d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Max pooling operation for 2D spatial data. — layer_max_pooling_2d","text":"strides = (1, 1) padding = \"valid\":     strides = c(2, 2) padding = \"valid\":     stride = (1, 1) padding = \"\":","code":"x <- rbind(c(1., 2., 3.),            c(4., 5., 6.),            c(7., 8., 9.)) |> op_reshape(c(1, 3, 3, 1)) max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),                                     strides = c(1, 1),                                     padding = \"valid\") max_pool_2d(x) ## tf.Tensor( ## [[[[5.] ##    [6.]] ## ##   [[8.] ##    [9.]]]], shape=(1, 2, 2, 1), dtype=float32) x <- rbind(c(1., 2., 3., 4.),            c(5., 6., 7., 8.),            c(9., 10., 11., 12.)) |> op_reshape(c(1, 3, 4, 1)) max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),                                     strides = c(2, 2),                                     padding = \"valid\") max_pool_2d(x) ## tf.Tensor( ## [[[[6.] ##    [8.]]]], shape=(1, 1, 2, 1), dtype=float32) x <- rbind(c(1., 2., 3.),            c(4., 5., 6.),            c(7., 8., 9.)) |> op_reshape(c(1, 3, 3, 1)) max_pool_2d <- layer_max_pooling_2d(pool_size = c(2, 2),                                     strides = c(1, 1),                                     padding = \"same\") max_pool_2d(x) ## tf.Tensor( ## [[[[5.] ##    [6.] ##    [6.]] ## ##   [[8.] ##    [9.] ##    [9.]] ## ##   [[8.] ##    [9.] ##    [9.]]]], shape=(1, 3, 3, 1), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"Downsamples input along spatial dimensions (depth, height, width) taking maximum value input window (size defined pool_size) channel input. window shifted strides along dimension.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"","code":"layer_max_pooling_3d(   object,   pool_size = list(2L, 2L, 2L),   strides = NULL,   padding = \"valid\",   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"object Object compose layer . tensor, array, sequential model. pool_size int list 3 integers, factors downscale (dim1, dim2, dim3). one integer specified, window length used dimensions. strides int list 3 integers, NULL. Strides values. NULL, default pool_size. one int specified, stride size used dimensions. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input output height/width dimension input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"data_format=\"channels_last\": 5D tensor shape: (batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels) data_format=\"channels_first\": 5D tensor shape: (batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_max_pooling_3d.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Max pooling operation for 3D data (spatial or spatio-temporal). — layer_max_pooling_3d","text":"","code":"depth <- 30 height <- 30 width <- 30 channels <- 3  inputs <- layer_input(shape=c(depth, height, width, channels)) layer <- layer_max_pooling_3d(pool_size=3) outputs <- inputs |> layer() outputs ## <KerasTensor shape=(None, 10, 10, 10, 3), dtype=float32, sparse=False, name=keras_tensor_1>"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes element-wise maximum on a list of inputs. — layer_maximum","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"","code":"layer_maximum(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_maximum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes element-wise maximum on a list of inputs. — layer_maximum","text":"Usage Keras model:","code":"input_shape <- c(2, 3, 4) x1 <- random_uniform(input_shape) x2 <- random_uniform(input_shape) y <- layer_maximum(x1, x2) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu') input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu') # equivalent to `y <- layer_maximum(x1, x2)` y <- layer_maximum(x1, x2) out <- y |> layer_dense(4) model <- keras_model(inputs = c(input1, input2), outputs = out)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"layer takes float32/float64 single batched audio signal inputs computes Mel spectrogram using Short-Time Fourier Transform Mel scaling. input 1D (unbatched) 2D (batched) tensor representing audio signals. output 2D 3D tensor representing Mel spectrograms. spectrogram image-like representation shows frequency spectrum signal time. uses x-axis represent time, y-axis represent frequency, pixel represent intensity. Mel spectrograms special type spectrogram use mel scale, approximates humans perceive sound. commonly used speech music processing tasks like speech recognition, speaker identification, music genre classification.","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"","code":"layer_mel_spectrogram(   object,   fft_length = 2048L,   sequence_stride = 512L,   sequence_length = NULL,   window = \"hann\",   sampling_rate = 16000L,   num_mel_bins = 128L,   min_freq = 20,   max_freq = NULL,   power_to_db = TRUE,   top_db = 80,   mag_exp = 2,   min_power = 1e-10,   ref_power = 1,   ... )"},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"object Object compose layer . tensor, array, sequential model. fft_length Integer, size FFT window. sequence_stride Integer, number samples successive STFT columns. sequence_length Integer, size window used applying window audio frame. NULL, defaults fft_length. window String, name window function use. Available values \"hann\" \"hamming\". window tensor, used directly window length must sequence_length. window NULL, windowing used. Defaults \"hann\". sampling_rate Integer, sample rate input signal. num_mel_bins Integer, number mel bins generate. min_freq Float, minimum frequency mel bins. max_freq Float, maximum frequency mel bins. NULL, defaults sampling_rate / 2. power_to_db TRUE, convert power spectrogram decibels. top_db Float, minimum negative cut-max(10 * log10(S)) - top_db. mag_exp Float, exponent magnitude spectrogram. 1 magnitude, 2 power, etc. Default 2. min_power Float, minimum value power ref_power. ref_power Float, power scaled relative 10 * log10(S / ref_power). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"Spectrogram, Mel scale.","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"Unbatched audio signal   Batched audio signal","code":"layer <- layer_mel_spectrogram(   num_mel_bins = 64,   sampling_rate = 8000,   sequence_stride = 256,   fft_length = 2048 ) layer(random_uniform(shape = c(16000))) |> shape() layer <- layer_mel_spectrogram(   num_mel_bins = 80,   sampling_rate = 8000,   sequence_stride = 128,   fft_length = 2048 ) layer(random_uniform(shape = c(2, 16000))) |> shape()"},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"1D (unbatched) 2D (batched) tensor shape:(..., samples).","code":""},{"path":"https://keras.posit.co/reference/layer_mel_spectrogram.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer to convert raw audio signals to Mel spectrograms. — layer_mel_spectrogram","text":"2D (unbatched) 3D (batched) tensor shape:(..., num_mel_bins, time).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes elementwise minimum on a list of inputs. — layer_minimum","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"","code":"layer_minimum(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_minimum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes elementwise minimum on a list of inputs. — layer_minimum","text":"Usage Keras model:","code":"input_shape <- c(2, 3, 4) x1 <- random_uniform(input_shape) x2 <- random_uniform(input_shape) y <- layer_minimum(x1, x2) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu') input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu') # equivalent to `y <- layer_minimum(x1, x2)` y <- layer_minimum(x1, x2) out <- y |> layer_dense(4) model <- keras_model(inputs = c(input1, input2), outputs = out)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi Head Attention layer. — layer_multi_head_attention","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"implementation multi-headed attention described paper \"Attention Need\" Vaswani et al., 2017. query, key, value , self-attention. timestep query attends corresponding sequence key, returns fixed-width vector. layer first projects query, key value. (effectively) list tensors length num_attention_heads, corresponding shapes (batch_size, <query dimensions>, key_dim), (batch_size, <key/value dimensions>, key_dim), (batch_size, <key/value dimensions>, value_dim). , query key tensors dot-producted scaled. softmaxed obtain attention probabilities. value tensors interpolated probabilities, concatenated back single tensor. Finally, result tensor last dimension value_dim can take linear projection return.","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"","code":"layer_multi_head_attention(   inputs,   num_heads,   key_dim,   value_dim = NULL,   dropout = 0,   use_bias = TRUE,   output_shape = NULL,   attention_axes = NULL,   kernel_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"inputs see description num_heads Number attention heads. key_dim Size attention head query key. value_dim Size attention head value. dropout Dropout probability. use_bias Boolean, whether dense layers use bias vectors/matrices. output_shape expected shape output tensor, besides batch sequence dims. specified, projects back query feature dim (query input's last dimension). attention_axes axes attention applied. NULL means attention axes, batch, heads, features. kernel_initializer Initializer dense layer kernels. bias_initializer Initializer dense layer biases. kernel_regularizer Regularizer dense layer kernels. bias_regularizer Regularizer dense layer biases. activity_regularizer Regularizer dense layer activity. kernel_constraint Constraint dense layer kernels. bias_constraint Constraint dense layer kernels. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"query: Query tensor shape (B, T, dim), B batch size, T target sequence length, dim feature dimension. value: Value tensor shape (B, S, dim), B batch size, S source sequence length, dim feature dimension. key: Optional key tensor shape (B, S, dim). given, use value key value, common case. attention_mask: boolean mask shape (B, T, S), prevents attention certain positions. boolean mask specifies query elements can attend key elements, 1 indicates attention 0 indicates attention. Broadcasting can happen missing batch dimensions head dimension. return_attention_scores: boolean indicate whether output (attention_output, attention_scores) TRUE, attention_output FALSE. Defaults FALSE. training: Python boolean indicating whether layer behave training mode (adding dropout) inference mode (dropout). go either using training mode parent layer/model, FALSE (inference) parent layer. use_causal_mask: boolean indicate whether apply causal mask prevent tokens attending future tokens (e.g., used decoder Transformer).","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"call-return","dir":"Reference","previous_headings":"","what":"Call return","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"attention_output: result computation, shape (B, T, E), T target sequence shapes E query input last dimension output_shape NULL. Otherwise, multi-head outputs projected shape specified output_shape. attention_scores: (Optional) multi-head attention coefficients attention axes.","code":""},{"path":"https://keras.posit.co/reference/layer_multi_head_attention.html","id":"properties","dir":"Reference","previous_headings":"","what":"Properties","title":"Multi Head Attention layer. — layer_multi_head_attention","text":"MultiHeadAttention Layer instance following additional read-properties: attention_axes dropout key_dense key_dim num_heads output_dense output_shape query_dense use_bias value_dense value_dim","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs elementwise multiplication. — layer_multiply","title":"Performs elementwise multiplication. — layer_multiply","text":"takes input list tensors, shape, returns single tensor (also shape).","code":""},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs elementwise multiplication. — layer_multiply","text":"","code":"layer_multiply(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs elementwise multiplication. — layer_multiply","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs elementwise multiplication. — layer_multiply","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_multiply.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs elementwise multiplication. — layer_multiply","text":"Usage Keras model:","code":"input_shape <- c(2, 3, 4) x1 <- random_uniform(input_shape) x2 <- random_uniform(input_shape) y <- layer_multiply(x1, x2) input1 <- layer_input(shape = c(16)) x1 <- input1 |> layer_dense(8, activation = 'relu') input2 <- layer_input(shape = c(32)) x2 <- input2 |> layer_dense(8, activation = 'relu') # equivalent to `y <- layer_multiply(x1, x2)` y <- layer_multiply(x1, x2) out <- y |> layer_dense(4) model <- keras_model(inputs = c(input1, input2), outputs = out)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer that normalizes continuous features. — layer_normalization","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"layer shift scale inputs distribution centered around 0 standard deviation 1. accomplishes precomputing mean variance data, calling (input - mean) / sqrt(var) runtime. mean variance values layer must either supplied construction learned via adapt(). adapt() compute mean variance data store layer's weights. adapt() called fit(), evaluate(), predict().","code":""},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"","code":"layer_normalization(   object,   axis = -1L,   mean = NULL,   variance = NULL,   invert = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"object Object compose layer . tensor, array, sequential model. axis Integer, list integers, NULL. axis axes separate mean variance index shape. example, shape (NULL, 5) axis=1, layer track 5 separate mean variance values last axis. axis set NULL, layer normalize elements input scalar mean variance. -1, last axis input assumed feature dimension normalized per index. Note specific case batched scalar inputs axis batch axis, default normalize index batch separately. case, consider passing axis=NULL. Defaults -1. mean mean value(s) use normalization. passed value(s) broadcast shape kept axes ; value(s) broadcast, error raised layer's build() method called. variance variance value(s) use normalization. passed value(s) broadcast shape kept axes ; value(s) broadcast, error raised layer's build() method called. invert TRUE, layer apply inverse transformation inputs: turn normalized input back original form. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_normalization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer that normalizes continuous features. — layer_normalization","text":"Calculate global mean variance analyzing dataset adapt().     Calculate mean variance index last axis.     Pass mean variance directly.     Use layer de-normalize inputs (adapting layer).","code":"adapt_data <- op_array(c(1., 2., 3., 4., 5.), dtype='float32') input_data <- op_array(c(1., 2., 3.), dtype='float32') layer <- layer_normalization(axis = NULL) layer %>% adapt(adapt_data) layer(input_data) ## tf.Tensor([-1.4142135  -0.70710677  0.        ], shape=(3), dtype=float32) adapt_data <- op_array(rbind(c(0., 7., 4.),                        c(2., 9., 6.),                        c(0., 7., 4.),                        c(2., 9., 6.)), dtype='float32') input_data <- op_array(matrix(c(0., 7., 4.), nrow = 1), dtype='float32') layer <- layer_normalization(axis=-1) layer %>% adapt(adapt_data) layer(input_data) ## tf.Tensor([[-1. -1. -1.]], shape=(1, 3), dtype=float32) input_data <- op_array(rbind(1, 2, 3), dtype='float32') layer <- layer_normalization(mean=3., variance=2.) layer(input_data) ## tf.Tensor( ## [[-1.4142135 ] ##  [-0.70710677] ##  [ 0.        ]], shape=(3, 1), dtype=float32) adapt_data <- op_array(rbind(c(0., 7., 4.),                        c(2., 9., 6.),                        c(0., 7., 4.),                        c(2., 9., 6.)), dtype='float32') input_data <- op_array(c(1., 2., 3.), dtype='float32') layer <- layer_normalization(axis=-1, invert=TRUE) layer %>% adapt(adapt_data) layer(input_data) ## tf.Tensor([[ 2. 10.  8.]], shape=(1, 3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_permute.html","id":null,"dir":"Reference","previous_headings":"","what":"Permutes the dimensions of the input according to a given pattern. — layer_permute","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"Useful e.g. connecting RNNs convnets.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"","code":"layer_permute(object, dims, ...)"},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"object Object compose layer . tensor, array, sequential model. dims List integers. Permutation pattern include batch dimension. Indexing starts 1. instance, c(2, 1) permutes first second dimensions input. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"Arbitrary.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"input shape, dimensions re-ordered according specified pattern.","code":""},{"path":"https://keras.posit.co/reference/layer_permute.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Permutes the dimensions of the input according to a given pattern. — layer_permute","text":"","code":"x <- layer_input(shape=c(10, 64)) y <- layer_permute(x, c(2, 1)) shape(y) ## shape(NA, 64, 10)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"layer randomly increase/reduce brightness input RGB images. inference time, output identical input. Call layer training=TRUE adjust brightness input. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"","code":"layer_random_brightness(   object,   factor,   value_range = list(0L, 255L),   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"object Object compose layer . tensor, array, sequential model. factor Float list 2 floats -1.0 1.0. factor used determine lower bound upper bound brightness adjustment. float value chosen randomly limits. -1.0 chosen, output image black, 1.0 chosen, image fully white. one float provided, eg, 0.2, -0.2 used lower bound 0.2 used upper bound. value_range Optional list 2 floats lower upper limit values input data. make change, use c(0.0, 1.0), e.g., image input scaled layer. Defaults c(0.0, 255.0). brightness adjustment scaled range, output values clipped range. seed optional integer, fixed RNG behavior. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"3D (HWC) 4D (NHWC) tensor, float int dtype. Input pixel values can range (e.g. [0., 1.) [0, 255])","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"output","dir":"Reference","previous_headings":"","what":"Output","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"3D (HWC) 4D (NHWC) tensor brightness adjusted based factor. default, layer output floats. output value clipped range [0, 255], valid range RGB colors, rescaled based value_range needed.","code":""},{"path":"https://keras.posit.co/reference/layer_random_brightness.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"A preprocessing layer which randomly adjusts brightness during training. — layer_random_brightness","text":"","code":"random_bright <- layer_random_brightness(factor=0.2, seed = 1)  # An image with shape [2, 2, 3] image <- array(1:12, dim=c(2, 2, 3))  # Assume we randomly select the factor to be 0.1, then it will apply # 0.1 * 255 to all the channel output <- random_bright(image, training=TRUE) output ## tf.Tensor( ## [[[39.605797 43.605797 47.605797] ##   [41.605797 45.605797 49.605797]] ## ##  [[40.605797 44.605797 48.605797] ##   [42.605797 46.605797 50.605797]]], shape=(2, 2, 3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"layer randomly adjust contrast image images random factor. Contrast adjusted independently channel image training. channel, layer computes mean image pixels channel adjusts component x pixel (x - mean) * contrast_factor + mean. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"","code":"layer_random_contrast(object, factor, seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"object Object compose layer . tensor, array, sequential model. factor positive float represented fraction value, tuple size 2 representing lower upper bound. represented single float, lower = upper. contrast factor randomly picked [1.0 - lower, 1.0 + upper]. pixel x channel, output (x - mean) * factor + mean mean mean value channel. seed Integer. Used create random seed. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_contrast.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly adjusts contrast during training. — layer_random_contrast","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly crops images during training. — layer_random_crop","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"training, layer randomly choose location crop images target size. layer crop images batch cropping location. inference time, training input image smaller target size, input resized cropped return largest possible window image matches target aspect ratio. need apply random cropping inference time, set training TRUE calling layer. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"","code":"layer_random_crop(   object,   height,   width,   seed = NULL,   data_format = NULL,   name = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"object Object compose layer . tensor, array, sequential model. height Integer, height output shape. width Integer, width output shape. seed Integer. Used create random seed. data_format see description name String, name object ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_crop.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly crops images during training. — layer_random_crop","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly flips images during training. — layer_random_flip","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"layer flip images horizontally vertically based mode attribute. inference time, output identical input. Call layer training=TRUE flip input. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"","code":"layer_random_flip(object, mode = \"horizontal_and_vertical\", seed = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"object Object compose layer . tensor, array, sequential model. mode String indicating flip mode use. Can \"horizontal\", \"vertical\", \"horizontal_and_vertical\". \"horizontal\" left-right flip \"vertical\" top-bottom flip. Defaults \"horizontal_and_vertical\" seed Integer. Used create random seed. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_flip.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly flips images during training. — layer_random_flip","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"layer apply random rotations image, filling empty space according fill_mode. default, random rotations applied training. inference time, layer nothing. need apply random rotations inference time, pass training = TRUE calling layer. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"","code":"layer_random_rotation(   object,   factor,   fill_mode = \"reflect\",   interpolation = \"bilinear\",   seed = NULL,   fill_value = 0,   value_range = list(0L, 255L),   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"object Object compose layer . tensor, array, sequential model. factor float represented fraction 2 Pi, tuple size 2 representing lower upper bound rotating clockwise counter-clockwise. positive values means rotating counter clock-wise, negative value means clock-wise. represented single float, value used upper lower bound. instance, factor=(-0.2, 0.3) results output rotation random amount range [-20% * 2pi, 30% * 2pi]. factor=0.2 results output rotating random amount range [-20% * 2pi, 20% * 2pi]. fill_mode Points outside boundaries input filled according given mode (one {\"constant\", \"reflect\", \"wrap\", \"nearest\"}). reflect: (d c b | b c d | d c b ) input extended reflecting edge last pixel. constant: (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k = 0. wrap: (b c d | b c d | b c d) input extended wrapping around opposite edge. nearest: (| b c d | d d d d) input extended nearest pixel. interpolation Interpolation mode. Supported values: \"nearest\", \"bilinear\". seed Integer. Used create random seed. fill_value float represents value filled outside boundaries fill_mode=\"constant\". value_range see description data_format see description ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format","code":""},{"path":"https://keras.posit.co/reference/layer_random_rotation.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly rotates images during training. — layer_random_rotation","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly translates images during training. — layer_random_translation","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"layer apply random translations image training, filling empty space according fill_mode. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats.","code":""},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"","code":"layer_random_translation(   object,   height_factor,   width_factor,   fill_mode = \"reflect\",   interpolation = \"bilinear\",   seed = NULL,   fill_value = 0,   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"object Object compose layer . tensor, array, sequential model. height_factor float represented fraction value, tuple size 2 representing lower upper bound shifting vertically. negative value means shifting image , positive value means shifting image . represented single positive float, value used upper lower bound. instance, height_factor=(-0.2, 0.3) results output shifted random amount range [-20%, +30%]. height_factor=0.2 results output height shifted random amount range [-20%, +20%]. width_factor float represented fraction value, tuple size 2 representing lower upper bound shifting horizontally. negative value means shifting image left, positive value means shifting image right. represented single positive float, value used upper lower bound. instance, width_factor=(-0.2, 0.3) results output shifted left 20%, shifted right 30%. width_factor=0.2 results output height shifted left right 20%. fill_mode Points outside boundaries input filled according given mode. Available methods \"constant\", \"nearest\", \"wrap\" \"reflect\". Defaults \"constant\". \"reflect\": (d c b | b c d | d c b ) input extended reflecting edge last pixel. \"constant\": (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k specified fill_value. \"wrap\": (b c d | b c d | b c d) input extended wrapping around opposite edge. \"nearest\": (| b c d | d d d d) input extended nearest pixel. Note using torch backend, \"reflect\" redirected \"mirror\" (c d c b | b c d | c b b) torch support \"reflect\". Note torch backend support \"wrap\". interpolation Interpolation mode. Supported values: \"nearest\", \"bilinear\". seed Integer. Used create random seed. fill_value float represents value filled outside boundaries fill_mode=\"constant\". data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format, (..., channels, height, width), \"channels_first\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_translation.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly translates images during training. — layer_random_translation","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels), (..., channels, target_height, target_width), \"channels_first\" format. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"layer randomly zoom axis image independently, filling empty space according fill_mode. Input pixel values can range (e.g. [0., 1.) [0, 255]) integer floating point dtype. default, layer output floats.","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"","code":"layer_random_zoom(   object,   height_factor,   width_factor = NULL,   fill_mode = \"reflect\",   interpolation = \"bilinear\",   seed = NULL,   fill_value = 0,   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"object Object compose layer . tensor, array, sequential model. height_factor float represented fraction value, list size 2 representing lower upper bound zooming vertically. represented single float, value used upper lower bound. positive value means zooming , negative value means zooming . instance, height_factor=c(0.2, 0.3) result output zoomed random amount range [+20%, +30%]. height_factor=c(-0.3, -0.2) result output zoomed random amount range [+20%, +30%]. width_factor float represented fraction value, list size 2 representing lower upper bound zooming horizontally. represented single float, value used upper lower bound. instance, width_factor=c(0.2, 0.3) result output zooming 20% 30%. width_factor=c(-0.3, -0.2) result output zooming 20% 30%. NULL means .e., zooming vertical horizontal directions preserving aspect ratio. Defaults NULL. fill_mode Points outside boundaries input filled according given mode. Available methods \"constant\", \"nearest\", \"wrap\" \"reflect\". Defaults \"constant\". \"reflect\": (d c b | b c d | d c b ) input extended reflecting edge last pixel. \"constant\": (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k specified fill_value. \"wrap\": (b c d | b c d | b c d) input extended wrapping around opposite edge. \"nearest\": (| b c d | d d d d) input extended nearest pixel. Note using torch backend, \"reflect\" redirected \"mirror\" (c d c b | b c d | c b b) torch support \"reflect\". Note torch backend support \"wrap\". interpolation Interpolation mode. Supported values: \"nearest\", \"bilinear\". seed Integer. Used create random seed. fill_value float represents value filled outside boundaries fill_mode=\"constant\". data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format, (..., channels, height, width), \"channels_first\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels), (..., channels, target_height, target_width), \"channels_first\" format. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_random_zoom.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which randomly zooms images during training. — layer_random_zoom","text":"","code":"input_img <- random_uniform(c(32, 224, 224, 3)) layer <- layer_random_zoom(height_factor = .5, width_factor = .2) out_img <- layer(input_img)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeats the input n times. — layer_repeat_vector","title":"Repeats the input n times. — layer_repeat_vector","text":"Repeats input n times.","code":""},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeats the input n times. — layer_repeat_vector","text":"","code":"layer_repeat_vector(object, n, ...)"},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeats the input n times. — layer_repeat_vector","text":"object Object compose layer . tensor, array, sequential model. n Integer, repetition factor. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Repeats the input n times. — layer_repeat_vector","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Repeats the input n times. — layer_repeat_vector","text":"","code":"x <- layer_input(shape = 32) y <- layer_repeat_vector(x, n = 3) shape(y) ## shape(NA, 3, 32)"},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Repeats the input n times. — layer_repeat_vector","text":"2D tensor shape (batch_size, features).","code":""},{"path":"https://keras.posit.co/reference/layer_repeat_vector.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Repeats the input n times. — layer_repeat_vector","text":"3D tensor shape (batch_size, n, features).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_rescaling.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","title":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","text":"layer rescales every value input (often image) multiplying scale adding offset. instance: rescale input [0, 255] range [0, 1] range, pass scale=1./255. rescale input [0, 255] range [-1, 1] range, pass scale=1./127.5, offset=-1. rescaling applied training inference. Inputs can integer floating point dtype, default layer output floats. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_rescaling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","text":"","code":"layer_rescaling(object, scale, offset = 0, ...)"},{"path":"https://keras.posit.co/reference/layer_rescaling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","text":"object Object compose layer . tensor, array, sequential model. scale Float, scale apply inputs. offset Float, offset apply inputs. ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_rescaling.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which rescales input values to a new range. — layer_rescaling","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer that reshapes inputs into the given shape. — layer_reshape","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"Layer reshapes inputs given shape.","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"","code":"layer_reshape(object, target_shape, ...)"},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"object Object compose layer . tensor, array, sequential model. target_shape Target shape. List integers, include samples dimension (batch size). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"Arbitrary, although dimensions input shape must known/fixed. Use keyword argument input_shape (list integers, include samples/batch size axis) using layer first layer model.","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"(batch_size, *target_shape)","code":""},{"path":"https://keras.posit.co/reference/layer_reshape.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Layer that reshapes inputs into the given shape. — layer_reshape","text":"","code":"x <- layer_input(shape = 12) y <- layer_reshape(x, c(3, 4)) shape(y) ## shape(NA, 3, 4) # also supports shape inference using `-1` as dimension y <- layer_reshape(x, c(-1, 2, 2)) shape(y) ## shape(NA, 3, 2, 2)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which resizes images. — layer_resizing","title":"A preprocessing layer which resizes images. — layer_resizing","text":"layer resizes image input target height width. input 4D (batched) 3D (unbatched) tensor \"channels_last\" format. Input pixel values can range (e.g. [0., 1.) [0, 255]).","code":""},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which resizes images. — layer_resizing","text":"","code":"layer_resizing(   object,   height,   width,   interpolation = \"bilinear\",   crop_to_aspect_ratio = FALSE,   pad_to_aspect_ratio = FALSE,   fill_mode = \"constant\",   fill_value = 0,   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which resizes images. — layer_resizing","text":"object Object compose layer . tensor, array, sequential model. height Integer, height output shape. width Integer, width output shape. interpolation String, interpolation method. Supports \"bilinear\", \"nearest\", \"bicubic\", \"lanczos3\", \"lanczos5\". Defaults \"bilinear\". crop_to_aspect_ratio TRUE, resize images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image cropped return largest possible window image (size (height, width)) matches target aspect ratio. default (crop_to_aspect_ratio=FALSE), aspect ratio may preserved. pad_to_aspect_ratio TRUE, pad images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image evenly padded short side. fill_mode using pad_to_aspect_ratio=TRUE, padded areas filled according given mode. \"constant\" supported time (fill constant value, equal fill_value). fill_value Float. Padding value use pad_to_aspect_ratio=TRUE. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". ... Base layer keyword arguments, name dtype.","code":""},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which resizes images. — layer_resizing","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"A preprocessing layer which resizes images. — layer_resizing","text":"3D (unbatched) 4D (batched) tensor shape: (..., height, width, channels), \"channels_last\" format, (..., channels, height, width), \"channels_first\" format.","code":""},{"path":"https://keras.posit.co/reference/layer_resizing.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"A preprocessing layer which resizes images. — layer_resizing","text":"3D (unbatched) 4D (batched) tensor shape: (..., target_height, target_width, channels), (..., channels, target_height, target_width), \"channels_first\" format. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":null,"dir":"Reference","previous_headings":"","what":"Base class for recurrent layers — layer_rnn","title":"Base class for recurrent layers — layer_rnn","text":"Base class recurrent layers","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Base class for recurrent layers — layer_rnn","text":"","code":"layer_rnn(   object,   cell,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   unroll = FALSE,   zero_output_for_mask = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Base class for recurrent layers — layer_rnn","text":"object Object compose layer . tensor, array, sequential model. cell RNN cell instance list RNN cell instances. RNN cell class : call(input_at_t, states_at_t) method, returning (output_at_t, states_at_t_plus_1). call method cell can also take optional argument constants, see section \"Note passing external constants\" . state_size attribute. can single integer (single state) case size recurrent state. can also list integers (one size per state). output_size attribute, single integer. get_initial_state(batch_size=NULL) method creates tensor meant fed call() initial state, user specify initial state via means. returned initial state shape (batch_size, cell.state_size). cell might choose create tensor full zeros, values based cell's implementation. inputs input tensor RNN layer, shape (batch_size, timesteps, features). method implemented cell, RNN layer create zero filled tensor shape (batch_size, cell$state_size). case cell list RNN cell instances, cells stacked top RNN, resulting efficient stacked RNN. return_sequences Boolean (default FALSE). Whether return last output output sequence, full sequence. return_state Boolean (default FALSE). Whether return last state addition output. go_backwards Boolean (default FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default FALSE). TRUE, last state sample index batch used initial state sample index following batch. unroll Boolean (default FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences. zero_output_for_mask Boolean (default FALSE). Whether output use zeros masked timesteps. Note field used return_sequences TRUE mask provided. can useful want reuse raw output sequence RNN without interference masked timesteps, e.g., merging bidirectional RNNs. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Base class for recurrent layers — layer_rnn","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Base class for recurrent layers — layer_rnn","text":"inputs: Input tensor. initial_state: List initial state tensors passed first call cell. mask: Binary tensor shape [batch_size, timesteps] indicating whether given timestep masked. individual TRUE entry indicates corresponding timestep utilized, FALSE entry indicates corresponding timestep ignored. training: Python boolean indicating whether layer behave training mode inference mode. argument passed cell calling . use cells use dropout.","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Base class for recurrent layers — layer_rnn","text":"3-D tensor shape (batch_size, timesteps, features).","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Base class for recurrent layers — layer_rnn","text":"return_state: list tensors. first tensor output. remaining tensors last states, shape (batch_size, state_size), state_size high dimension tensor shape. return_sequences: 3D tensor shape (batch_size, timesteps, output_size).","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"masking-","dir":"Reference","previous_headings":"","what":"Masking:","title":"Base class for recurrent layers — layer_rnn","text":"layer supports masking input data variable number timesteps. introduce masks data, use layer_embedding() layer mask_zero parameter set TRUE. Note using statefulness RNNs: can set RNN layers 'stateful', means states computed samples one batch reused initial states samples next batch. assumes one--one mapping samples different successive batches. enable statefulness: Specify stateful = TRUE layer constructor. Specify fixed batch size model, passing sequential model: input_batch_shape = c(...) keras_model_sequential() call. Else functional model 1 input layers: batch_shape = c(...) layer_input() call(s). expected shape inputs including batch size. list integers, e.g. c(32, 10, 100). Specify shuffle = FALSE calling fit(). reset states model, call reset_state() either specific layer, entire model. Note specifying initial state RNNs: can specify initial state RNN layers symbolically calling keyword argument initial_state. value initial_state tensor list tensors representing initial state RNN layer.","code":""},{"path":"https://keras.posit.co/reference/layer_rnn.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Base class for recurrent layers — layer_rnn","text":"First, define RNN Cell, layer subclass.   use cell RNN layer:","code":"rnn_cell_minimal <- Layer(   \"MinimalRNNCell\",    initialize = function(units, ...) {     super$initialize(...)     self$units <- as.integer(units)     self$state_size <- as.integer(units)   },    build = function(input_shape) {     self$kernel <- self$add_weight(       shape = shape(tail(input_shape, 1), self$units),       initializer = 'uniform',       name = 'kernel'     )     self$recurrent_kernel <- self$add_weight(       shape = shape(self$units, self$units),       initializer = 'uniform',       name = 'recurrent_kernel'     )     self$built <- TRUE   },    call = function(inputs, states) {     prev_output <- states[[1]]     h <- op_matmul(inputs, self$kernel)     output <- h + op_matmul(prev_output, self$recurrent_kernel)     list(output, list(output))   } ) cell <- rnn_cell_minimal(units = 32) x <- layer_input(shape = shape(NULL, 5)) layer <- layer_rnn(cell = cell) y <- layer(x) cells <- list(rnn_cell_minimal(units = 32), rnn_cell_minimal(units = 4)) x <- layer_input(shape = shape(NULL, 5)) layer <- layer_rnn(cell = cells) y <- layer(x)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"1D separable convolution layer. — layer_separable_conv_1d","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"layer performs depthwise convolution acts separately channels, followed pointwise convolution mixes channels. use_bias TRUE bias initializer provided, adds bias vector output. optionally applies activation function produce final output.","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"","code":"layer_separable_conv_1d(   object,   filters,   kernel_size,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L,   depth_multiplier = 1L,   activation = NULL,   use_bias = TRUE,   depthwise_initializer = \"glorot_uniform\",   pointwise_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   depthwise_regularizer = NULL,   pointwise_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   depthwise_constraint = NULL,   pointwise_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimensionality output space (.e. number filters pointwise convolution). kernel_size int list 1 integers, specifying size depthwise convolution window. strides int list 1 integers, specifying stride length depthwise convolution. one int specified, stride size used dimensions. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, steps, features) \"channels_first\" corresponds inputs shape (batch, features, steps). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 1 integers, specifying dilation rate use dilated convolution. one int specified, dilation rate used dimensions. depth_multiplier number depthwise convolution output channels input channel. total number depthwise convolution output channels equal input_channel * depth_multiplier. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. depthwise_initializer initializer depthwise convolution kernel. NULL, default initializer (\"glorot_uniform\") used. pointwise_initializer initializer pointwise convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer initializer bias vector. NULL, default initializer ('\"zeros\"') used. depthwise_regularizer Optional regularizer depthwise convolution kernel. pointwise_regularizer Optional regularizer pointwise convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. depthwise_constraint Optional projection function applied depthwise kernel updated Optimizer (e.g. used norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). pointwise_constraint Optional projection function applied pointwise kernel updated Optimizer. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"3D tensor representing activation(separable_conv1d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, steps, channels) data_format=\"channels_first\": 3D tensor shape: (batch_shape, channels, steps)","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"data_format=\"channels_last\": 3D tensor shape: (batch_shape, new_steps, filters) data_format=\"channels_first\": 3D tensor shape: (batch_shape, filters, new_steps)","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"1D separable convolution layer. — layer_separable_conv_1d","text":"","code":"x <- random_uniform(c(4, 10, 12)) y <- layer_separable_conv_1d(x, 3, 2, 2, activation='relu') shape(y) ## shape(4, 5, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"2D separable convolution layer. — layer_separable_conv_2d","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"layer performs depthwise convolution acts separately channels, followed pointwise convolution mixes channels. use_bias TRUE bias initializer provided, adds bias vector output. optionally applies activation function produce final output.","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"","code":"layer_separable_conv_2d(   object,   filters,   kernel_size,   strides = list(1L, 1L),   padding = \"valid\",   data_format = NULL,   dilation_rate = list(1L, 1L),   depth_multiplier = 1L,   activation = NULL,   use_bias = TRUE,   depthwise_initializer = \"glorot_uniform\",   pointwise_initializer = \"glorot_uniform\",   bias_initializer = \"zeros\",   depthwise_regularizer = NULL,   pointwise_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   depthwise_constraint = NULL,   pointwise_constraint = NULL,   bias_constraint = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"object Object compose layer . tensor, array, sequential model. filters int, dimensionality output space (.e. number filters pointwise convolution). kernel_size int list 2 integers, specifying size depthwise convolution window. strides int list 2 integers, specifying stride length depthwise convolution. one int specified, stride size used dimensions. strides > 1 incompatible dilation_rate > 1. padding string, either \"valid\" \"\" (case-insensitive). \"valid\" means padding. \"\" results padding evenly left/right /input. padding=\"\" strides=1, output size input. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, width). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". dilation_rate int list 2 integers, specifying dilation rate use dilated convolution. one int specified, dilation rate used dimensions. depth_multiplier number depthwise convolution output channels input channel. total number depthwise convolution output channels equal input_channel * depth_multiplier. activation Activation function. NULL, activation applied. use_bias bool, TRUE, bias added output. depthwise_initializer initializer depthwise convolution kernel. NULL, default initializer (\"glorot_uniform\") used. pointwise_initializer initializer pointwise convolution kernel. NULL, default initializer (\"glorot_uniform\") used. bias_initializer initializer bias vector. NULL, default initializer ('\"zeros\"') used. depthwise_regularizer Optional regularizer depthwise convolution kernel. pointwise_regularizer Optional regularizer pointwise convolution kernel. bias_regularizer Optional regularizer bias vector. activity_regularizer Optional regularizer function output. depthwise_constraint Optional projection function applied depthwise kernel updated Optimizer (e.g. used norm constraints value constraints layer weights). function must take input unprojected variable must return projected variable (must shape). pointwise_constraint Optional projection function applied pointwise kernel updated Optimizer. bias_constraint Optional projection function applied bias updated Optimizer. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"4D tensor representing activation(separable_conv2d(inputs, kernel) + bias).","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, height, width, channels) data_format=\"channels_first\": 4D tensor shape: (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"data_format=\"channels_last\": 4D tensor shape: (batch_size, new_height, new_width, filters) data_format=\"channels_first\": 4D tensor shape: (batch_size, filters, new_height, new_width)","code":""},{"path":"https://keras.posit.co/reference/layer_separable_conv_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"2D separable convolution layer. — layer_separable_conv_2d","text":"","code":"x <- random_uniform(c(4, 10, 10, 12)) y <- layer_separable_conv_2d(x, 3, c(4, 3), 2, activation='relu') shape(y) ## shape(4, 4, 4, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":null,"dir":"Reference","previous_headings":"","what":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"Fully-connected RNN output fed back new input.","code":""},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"","code":"layer_simple_rnn(   object,   units,   activation = \"tanh\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   activity_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   return_sequences = FALSE,   return_state = FALSE,   go_backwards = FALSE,   stateful = FALSE,   unroll = FALSE,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"object Object compose layer . tensor, array, sequential model. units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer uses bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state.  Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. activity_regularizer Regularizer function applied output layer (\"activation\"). Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix.  Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. return_sequences Boolean. Whether return last output output sequence, full sequence. Default: FALSE. return_state Boolean. Whether return last state addition output. Default: FALSE. go_backwards Boolean (default: FALSE). TRUE, process input sequence backwards return reversed sequence. stateful Boolean (default: FALSE). TRUE, last state sample index batch used initial state sample index following batch. unroll Boolean (default: FALSE). TRUE, network unrolled, else symbolic loop used. Unrolling can speed-RNN, although tends memory-intensive. Unrolling suitable short sequences. seed Initial seed random number generator ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"sequence: 3D tensor, shape [batch, timesteps, feature]. mask: Binary tensor shape [batch, timesteps] indicating whether given timestep masked. individual TRUE entry indicates corresponding timestep utilized, FALSE entry indicates corresponding timestep ignored. training: Python boolean indicating whether layer behave training mode inference mode. argument passed cell calling . relevant dropout recurrent_dropout used. initial_state: List initial state tensors passed first call cell.","code":""},{"path":"https://keras.posit.co/reference/layer_simple_rnn.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fully-connected RNN where the output is to be fed back as the new input. — layer_simple_rnn","text":"","code":"inputs <- random_uniform(c(32, 10, 8)) simple_rnn <- layer_simple_rnn(units = 4) output <- simple_rnn(inputs)  # The output has shape `(32, 4)`. simple_rnn <- layer_simple_rnn(     units = 4, return_sequences=TRUE, return_state=TRUE ) # whole_sequence_output has shape `(32, 10, 4)`. # final_state has shape `(32, 4)`. c(whole_sequence_output, final_state) %<-% simple_rnn(inputs)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"layer performs function Dropout, however, drops entire 1D feature maps instead individual elements. adjacent frames within feature maps strongly correlated (normally case early convolution layers) regular dropout regularize activations otherwise just result effective learning rate decrease. case, SpatialDropout1D help promote independence feature maps used instead.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"","code":"layer_spatial_dropout_1d(object, rate, seed = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. Fraction input units drop. seed Initial seed random number generator name String, name object dtype datatype (e.g., \"float32\").","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"inputs: 3D tensor. training: Python boolean indicating whether layer behave training mode (applying dropout) inference mode (pass-).","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"3D tensor shape: (samples, timesteps, channels)","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"input.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_1d.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Spatial 1D version of Dropout. — layer_spatial_dropout_1d","text":"Tompson et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"version performs function Dropout, however, drops entire 2D feature maps instead individual elements. adjacent pixels within feature maps strongly correlated (normally case early convolution layers) regular dropout regularize activations otherwise just result effective learning rate decrease. case, SpatialDropout2D help promote independence feature maps used instead.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"","code":"layer_spatial_dropout_2d(   object,   rate,   data_format = NULL,   seed = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. Fraction input units drop. data_format \"channels_first\" \"channels_last\". \"channels_first\" mode, channels dimension (depth) index 1, \"channels_last\" mode index 3. defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". seed Initial seed random number generator name String, name object dtype datatype (e.g., \"float32\").","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"inputs: 4D tensor. training: Python boolean indicating whether layer behave training mode (applying dropout) inference mode (pass-).","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"4D tensor shape: (samples, channels, rows, cols) data_format='channels_first' 4D tensor shape: (samples, rows, cols, channels) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"input.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_2d.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Spatial 2D version of Dropout. — layer_spatial_dropout_2d","text":"Tompson et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"version performs function Dropout, however, drops entire 3D feature maps instead individual elements. adjacent voxels within feature maps strongly correlated (normally case early convolution layers) regular dropout regularize activations otherwise just result effective learning rate decrease. case, SpatialDropout3D help promote independence feature maps used instead.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"","code":"layer_spatial_dropout_3d(   object,   rate,   data_format = NULL,   seed = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"object Object compose layer . tensor, array, sequential model. rate Float 0 1. Fraction input units drop. data_format \"channels_first\" \"channels_last\". \"channels_first\" mode, channels dimension (depth) index 1, \"channels_last\" mode index 4. defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\". seed Initial seed random number generator name String, name object dtype datatype (e.g., \"float32\").","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"inputs: 5D tensor. training: Python boolean indicating whether layer behave training mode (applying dropout) inference mode (pass-).","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"5D tensor shape: (samples, channels, dim1, dim2, dim3) data_format='channels_first' 5D tensor shape: (samples, dim1, dim2, dim3, channels) data_format='channels_last'.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"input.","code":""},{"path":"https://keras.posit.co/reference/layer_spatial_dropout_3d.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Spatial 3D version of Dropout. — layer_spatial_dropout_3d","text":"Tompson et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"wrapper controls Lipschitz constant weights layer constraining spectral norm, can stabilize training GANs.","code":""},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"","code":"layer_spectral_normalization(object, layer, power_iterations = 1L, ...)"},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"object Object compose layer . tensor, array, sequential model. layer Layer instance either kernel (e.g. layer_conv_2d, layer_dense...) embeddings attribute (layer_embedding layer). power_iterations int, number iterations normalization. ... Base wrapper keyword arguments.","code":""},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"Wrap layer_conv_2d:     Wrap layer_dense:","code":"x <- random_uniform(c(1, 10, 10, 1)) conv2d <- layer_spectral_normalization(   layer = layer_conv_2d(filters = 2, kernel_size = 2) ) y <- conv2d(x) shape(y) ## shape(1, 9, 9, 2) x <- random_uniform(c(1, 10, 10, 1)) dense <- layer_spectral_normalization(layer = layer_dense(units = 10)) y <- dense(x) shape(y) ## shape(1, 10, 10, 10)"},{"path":"https://keras.posit.co/reference/layer_spectral_normalization.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Performs spectral normalization on the weights of a target layer. — layer_spectral_normalization","text":"Spectral Normalization GAN.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"layer translates set arbitrary strings integer output via table-based vocabulary lookup. layer perform splitting transformation input strings. layer can split tokenize natural language, see layer_text_vectorization layer. vocabulary layer must either supplied construction learned via adapt(). adapt(), layer analyze data set, determine frequency individual strings tokens, create vocabulary . vocabulary capped size, frequent tokens used create vocabulary others treated --vocabulary (OOV). two possible output modes layer. output_mode \"int\", input strings converted index vocabulary (integer). output_mode \"multi_hot\", \"count\", \"tf_idf\", input strings encoded array dimension corresponds element vocabulary. vocabulary can optionally contain mask token well OOV token (can optionally occupy multiple indices vocabulary, set num_oov_indices). position tokens vocabulary fixed. output_mode \"int\", vocabulary begin mask token (set), followed OOV indices, followed rest vocabulary. output_mode \"multi_hot\", \"count\", \"tf_idf\" vocabulary begin OOV indices instances mask token dropped. Note: layer uses TensorFlow internally. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"","code":"layer_string_lookup(   object,   max_tokens = NULL,   num_oov_indices = 1L,   mask_token = NULL,   oov_token = \"[UNK]\",   vocabulary = NULL,   idf_weights = NULL,   invert = FALSE,   output_mode = \"int\",   pad_to_max_tokens = FALSE,   sparse = FALSE,   encoding = \"utf-8\",   name = NULL,   ...,   vocabulary_dtype = NULL )"},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"object Object compose layer . tensor, array, sequential model. max_tokens Maximum size vocabulary layer. specified adapting vocabulary setting pad_to_max_tokens=TRUE. NULL, cap size vocabulary. Note size includes OOV mask tokens. Defaults NULL. num_oov_indices number --vocabulary tokens use. value 1, OOV inputs modulated determine OOV value. value 0, OOV inputs cause error calling layer. Defaults 1. mask_token token represents masked inputs. output_mode \"int\", token included vocabulary mapped index 0. output modes, token appear vocabulary instances mask token input dropped. set NULL, mask term added. Defaults NULL. oov_token used invert TRUE. token return OOV indices. Defaults \"[UNK]\". vocabulary Optional. Either array integers string path text file. passing array, can pass list, list, 1D NumPy array, 1D tensor containing integer vocbulary terms. passing file path, file contain one line per term vocabulary. argument set, need adapt() layer. idf_weights valid output_mode \"tf_idf\". list, list, 1D NumPy array, 1D tensor length vocabulary, containing floating point inverse document frequency weights, multiplied per sample term counts final TF-IDF weight. vocabulary argument set, output_mode \"tf_idf\", argument must supplied. invert valid output_mode \"int\". TRUE, layer map indices vocabulary items instead mapping vocabulary items indices. Defaults FALSE. output_mode Specification output layer. Values can \"int\", \"one_hot\", \"multi_hot\", \"count\", \"tf_idf\" configuring layer follows: \"int\": Return vocabulary indices input tokens. \"one_hot\": Encodes individual element input array size vocabulary, containing 1 element index. last dimension size 1, encode dimension. last dimension size 1, append new dimension encoded output. \"multi_hot\": Encodes sample input single array size vocabulary, containing 1 vocabulary term present sample. Treats last dimension sample dimension, input shape (..., sample_length), output shape (..., num_tokens). \"count\": \"multi_hot\", int array contains count number times token index appeared sample. \"tf_idf\": \"multi_hot\", TF-IDF algorithm applied find value token slot. \"int\" output, shape input output supported. output modes, currently output rank 2 supported. Defaults \"int\". pad_to_max_tokens applicable output_mode \"multi_hot\", \"count\", \"tf_idf\". TRUE, output feature axis padded max_tokens even number unique tokens vocabulary less max_tokens, resulting tensor shape (batch_size, max_tokens) regardless vocabulary size. Defaults FALSE. sparse Boolean. applicable \"multi_hot\", \"count\", \"tf_idf\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. encoding Optional. text encoding use interpret input strings. Defaults \"utf-8\". name String, name object ... forward/backward compatability. vocabulary_dtype dtype vocabulary terms, example \"int64\" \"int32\". Defaults \"int64\".","code":""},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_string_lookup.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer that maps strings to (possibly encoded) indices. — layer_string_lookup","text":"Creating lookup layer known vocabulary example creates lookup layer pre-existing vocabulary.     Creating lookup layer adapted vocabulary example creates lookup layer generates vocabulary analyzing dataset.     Note OOV token \"[UNK]\" added vocabulary. remaining tokens sorted frequency (\"d\", 2 occurrences, first) inverse sort order.     Lookups multiple OOV indices example demonstrates use lookup layer multiple OOV indices.  layer created one OOV index, OOV values hashed number OOV buckets, distributing OOV values deterministic fashion across set.     Note output OOV value 'm' 0, output OOV value \"z\" 1. -vocab terms output index increased 1 earlier examples (maps 2, etc) order make space extra OOV value. One-hot output Configure layer output_mode='one_hot'. Note first num_oov_indices dimensions ont_hot encoding represent OOV values.     Multi-hot output Configure layer output_mode='multi_hot'. Note first num_oov_indices dimensions multi_hot encoding represent OOV values.     Token count output Configure layer output_mode='count'. multi_hot output, first num_oov_indices dimensions output represent OOV values.     TF-IDF output Configure layer output_mode=\"tf_idf\". multi_hot output, first num_oov_indices dimensions output represent OOV values. token bin output token_count * idf_weight, idf weights inverse document frequency weights per token. provided along vocabulary. Note idf_weight OOV values default average idf weights passed .     specify idf weights oov values, need pass entire vocabulary including leading oov token.     adapting layer \"tf_idf\" mode, input sample considered document, IDF weight per token calculated log(1 + num_documents / (1 + token_document_count)). Inverse lookup example demonstrates map indices strings using layer. (can also use adapt() inverse=TRUE, simplicity pass vocab example.)     Note first index correspond oov token default. Forward inverse lookup pairs example demonstrates use vocabulary standard lookup layer create inverse lookup layer.     example, input value \"z\" resulted output \"[UNK]\", since 1000 vocabulary - got represented OOV, OOV values returned \"[UNK]\" inverse layer. Also, note inverse work, must already set forward layer vocabulary either directly via adapt() calling get_vocabulary().","code":"vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\"), c(\"d\", \"z\", \"b\")) layer <- layer_string_lookup(vocabulary=vocab) layer(data) ## tf.Tensor( ## [[1 3 4] ##  [4 0 2]], shape=(2, 3), dtype=int64) data <- rbind(c(\"a\", \"c\", \"d\"), c(\"d\", \"z\", \"b\")) layer <- layer_string_lookup() layer %>% adapt(data) get_vocabulary(layer) ## [1] \"[UNK]\" \"d\"     \"z\"     \"c\"     \"b\"     \"a\" data <- rbind(c(\"a\", \"c\", \"d\"), c(\"d\", \"z\", \"b\")) layer <- layer_string_lookup() layer %>% adapt(data) layer(data) ## tf.Tensor( ## [[5 3 1] ##  [1 2 4]], shape=(2, 3), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\"), c(\"m\", \"z\", \"b\")) layer <- layer_string_lookup(vocabulary = vocab, num_oov_indices = 2) layer(data) ## tf.Tensor( ## [[2 4 5] ##  [0 1 3]], shape=(2, 3), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- c(\"a\", \"b\", \"c\", \"d\", \"z\") layer <- layer_string_lookup(vocabulary = vocab, output_mode = 'one_hot') layer(data) ## tf.Tensor( ## [[0 1 0 0 0] ##  [0 0 1 0 0] ##  [0 0 0 1 0] ##  [0 0 0 0 1] ##  [1 0 0 0 0]], shape=(5, 5), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\", \"d\"), c(\"d\", \"z\", \"b\", \"z\")) layer <- layer_string_lookup(vocabulary = vocab, output_mode = 'multi_hot') layer(data) ## tf.Tensor( ## [[0 1 0 1 1] ##  [1 0 1 0 1]], shape=(2, 5), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\", \"d\"), c(\"d\", \"z\", \"b\", \"z\")) layer <- layer_string_lookup(vocabulary = vocab, output_mode = 'count') layer(data) ## tf.Tensor( ## [[0 1 0 1 2] ##  [2 0 1 0 1]], shape=(2, 5), dtype=int64) vocab <- c(\"a\", \"b\", \"c\", \"d\") idf_weights <- c(0.25, 0.75, 0.6, 0.4) data <- rbind(c(\"a\", \"c\", \"d\", \"d\"), c(\"d\", \"z\", \"b\", \"z\")) layer <- layer_string_lookup(output_mode = \"tf_idf\") layer %>% set_vocabulary(vocab, idf_weights=idf_weights) layer(data) ## tf.Tensor( ## [[0.   0.25 0.   0.6  0.8 ] ##  [1.   0.   0.75 0.   0.4 ]], shape=(2, 5), dtype=float32) vocab <- c(\"[UNK]\", \"a\", \"b\", \"c\", \"d\") idf_weights <- c(0.9, 0.25, 0.75, 0.6, 0.4) data <- rbind(c(\"a\", \"c\", \"d\", \"d\"), c(\"d\", \"z\", \"b\", \"z\")) layer <- layer_string_lookup(output_mode = \"tf_idf\") layer %>% set_vocabulary(vocab, idf_weights=idf_weights) layer(data) ## tf.Tensor( ## [[0.   0.25 0.   0.6  0.8 ] ##  [1.8  0.   0.75 0.   0.4 ]], shape=(2, 5), dtype=float32) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(1, 3, 4), c(4, 0, 2)) layer <- layer_string_lookup(vocabulary = vocab, invert = TRUE) layer(data) ## tf.Tensor( ## [[b'a' b'c' b'd'] ##  [b'd' b'[UNK]' b'b']], shape=(2, 3), dtype=string) vocab <- c(\"a\", \"b\", \"c\", \"d\") data <- rbind(c(\"a\", \"c\", \"d\"), c(\"d\", \"z\", \"b\")) layer <- layer_string_lookup(vocabulary = vocab) i_layer <- layer_string_lookup(vocabulary = vocab, invert = TRUE) int_data <- layer(data) i_layer(int_data) ## tf.Tensor( ## [[b'a' b'c' b'd'] ##  [b'd' b'[UNK]' b'b']], shape=(2, 3), dtype=string)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs elementwise subtraction. — layer_subtract","title":"Performs elementwise subtraction. — layer_subtract","text":"takes input list tensors size 2 shape, returns single tensor (inputs[0] - inputs[1)) shape.","code":""},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs elementwise subtraction. — layer_subtract","text":"","code":"layer_subtract(inputs, ...)"},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs elementwise subtraction. — layer_subtract","text":"inputs layers combine ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs elementwise subtraction. — layer_subtract","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_subtract.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs elementwise subtraction. — layer_subtract","text":"Usage Keras model:","code":"input_shape <- c(2, 3, 4) x1 <- random_uniform(input_shape) x2 <- random_uniform(input_shape) y <- layer_subtract(list(x1, x2)) input1 <- layer_input(shape = 16) x1 <- layer_dense(input1, units = 8, activation = 'relu') input2 <- layer_input(shape = 32) x2 <- layer_dense(input2, units = 8, activation = 'relu') subtracted <- layer_subtract(list(x1, x2)) out <- layer_dense(subtracted, units = 4) model <- keras_model(inputs = list(input1, input2), outputs = out)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":null,"dir":"Reference","previous_headings":"","what":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"layer basic options managing text Keras model. transforms batch strings (one example = one string) either list token indices (one example = 1D tensor integer token indices) dense representation (one example = 1D tensor float values representing data example's tokens). layer meant handle natural language inputs. handle simple string inputs (categorical strings pre-tokenized strings) see layer_string_lookup(). vocabulary layer must either supplied construction learned via adapt(). layer adapted, analyze dataset, determine frequency individual string values, create vocabulary . vocabulary can unlimited size capped, depending configuration options layer; unique values input maximum vocabulary size, frequent terms used create vocabulary. processing example contains following steps: Standardize example (usually lowercasing + punctuation stripping) Split example substrings (usually words) Recombine substrings tokens (usually ngrams) Index tokens (associate unique int value token) Transform example using index, either vector ints dense float vector. notes passing callables customize splitting normalization layer: callable can passed Layer, want serialize object pass functions registered Keras serializables (see register_keras_serializable() details). using custom callable standardize, data received callable exactly passed layer. callable return tensor shape input. using custom callable split, data received callable 1st dimension squeezed - instead list(\"string split\", \"another string split\"), Callable see c(\"string split\", \"another string split\"). callable return tf.Tensor dtype string first dimension containing split tokens - example, see something like list(c(\"string\", \"\", \"split\"), c(\"another\", \"string\", \"\", \"split\")). Note: layer uses TensorFlow internally. used part compiled computation graph model backend TensorFlow. can however used backend running eagerly. can also always used part input preprocessing pipeline backend (outside model ), recommend use layer. Note: layer safe use inside tf.data pipeline (independently backend using).","code":""},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"","code":"layer_text_vectorization(   object,   max_tokens = NULL,   standardize = \"lower_and_strip_punctuation\",   split = \"whitespace\",   ngrams = NULL,   output_mode = \"int\",   output_sequence_length = NULL,   pad_to_max_tokens = FALSE,   vocabulary = NULL,   idf_weights = NULL,   sparse = FALSE,   ragged = FALSE,   encoding = \"utf-8\",   name = NULL,   ... )  get_vocabulary(object, include_special_tokens = TRUE)  set_vocabulary(object, vocabulary, idf_weights = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"object Object compose layer . tensor, array, sequential model. max_tokens Maximum size vocabulary layer. specified adapting vocabulary setting pad_to_max_tokens=TRUE. Note vocabulary contains 1 OOV token, effective number tokens (max_tokens - 1 - (1 output_mode == \"int\" else 0)). standardize Optional specification standardization apply input text. Values can : NULL: standardization. \"lower_and_strip_punctuation\": Text lowercased punctuation removed. \"lower\": Text lowercased. \"strip_punctuation\": punctuation removed. Callable: Inputs passed callable function, standardized returned. split Optional specification splitting input text. Values can : NULL: splitting. \"whitespace\": Split whitespace. \"character\": Split unicode character. Callable: Standardized inputs passed callable function, split returned. ngrams Optional specification ngrams create possibly-split input text. Values can NULL, integer list integers; passing integer create ngrams integer, passing list integers create ngrams specified values list. Passing NULL means ngrams created. output_mode Optional specification output layer. Values can \"int\", \"multi_hot\", \"count\" \"tf_idf\", configuring layer follows: \"int\": Outputs integer indices, one integer index per split string token. output_mode == \"int\", 0 reserved masked locations; reduces vocab size max_tokens - 2 instead max_tokens - 1. \"multi_hot\": Outputs single int array per batch, either vocab_size max_tokens size, containing 1s elements token mapped index exists least batch item. \"count\": Like \"multi_hot\", int array contains count number times token index appeared batch item. \"tf_idf\": Like \"multi_hot\", TF-IDF algorithm applied find value token slot. \"int\" output, shape input output supported. output modes, currently rank 1 inputs (rank 2 outputs splitting) supported. output_sequence_length valid INT mode. set, output time dimension padded truncated exactly output_sequence_length values, resulting tensor shape (batch_size, output_sequence_length) regardless many tokens resulted splitting step. Defaults NULL. ragged TRUE output_sequence_length may still truncate output. pad_to_max_tokens valid  \"multi_hot\", \"count\", \"tf_idf\" modes. TRUE, output feature axis padded max_tokens even number unique tokens vocabulary less max_tokens, resulting tensor shape (batch_size, max_tokens) regardless vocabulary size. Defaults FALSE. vocabulary Optional. Either array strings string path text file. passing array, can pass list, list, 1D NumPy array, 1D tensor containing string vocabulary terms. passing file path, file contain one line per term vocabulary. argument set, need adapt() layer. idf_weights R vector, 1D numpy array, 1D tensor inverse document frequency weights equal length vocabulary. Must set output_mode \"tf_idf\". set otherwise. sparse Boolean. applicable \"multi_hot\", \"count\", \"tf_idf\" output modes. supported TensorFlow backend. TRUE, returns SparseTensor instead dense Tensor. Defaults FALSE. ragged Boolean. applicable \"int\" output mode. supported TensorFlow backend. TRUE, returns RaggedTensor instead dense Tensor, sequence may different length string splitting. Defaults FALSE. encoding Optional. text encoding use interpret input strings. Defaults \"utf-8\". name String, name object ... forward/backward compatability. include_special_tokens TRUE, returned vocabulary include padding OOV tokens, term's index vocabulary equal term's index calling layer. FALSE, returned vocabulary include padding OOV tokens.","code":""},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_text_vectorization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A preprocessing layer which maps text features to integer sequences. — layer_text_vectorization","text":"example instantiates TextVectorization layer lowercases text, splits whitespace, strips punctuation, outputs integer vocab indices.         example instantiates TextVectorization layer passing list vocabulary terms layer's initialize() method.","code":"max_tokens <- 5000  # Maximum vocab size. max_len <- 4  # Sequence length to pad the outputs to. # Create the layer. vectorize_layer <- layer_text_vectorization(     max_tokens = max_tokens,     output_mode = 'int',     output_sequence_length = max_len) # Now that the vocab layer has been created, call `adapt` on the # list of strings to create the vocabulary. vectorize_layer %>% adapt(c(\"foo bar\", \"bar baz\", \"baz bada boom\")) # Now, the layer can map strings to integers -- you can use an # embedding layer to map these integers to learned embeddings. input_data <- rbind(\"foo qux bar\", \"qux baz\") vectorize_layer(input_data) ## tf.Tensor( ## [[4 1 3 0] ##  [1 2 0 0]], shape=(2, 4), dtype=int64) vocab_data <- c(\"earth\", \"wind\", \"and\", \"fire\") max_len <- 4  # Sequence length to pad the outputs to. # Create the layer, passing the vocab directly. You can also pass the # vocabulary arg a path to a file containing one vocabulary word per # line. vectorize_layer <- layer_text_vectorization(     max_tokens = max_tokens,     output_mode = 'int',     output_sequence_length = max_len,     vocabulary = vocab_data) # Because we've passed the vocabulary directly, we don't need to adapt # the layer - the vocabulary is already set. The vocabulary contains the # padding token ('') and OOV token ('[UNK]') # as well as the passed tokens. vectorize_layer %>% get_vocabulary() ## [1] \"\"      \"[UNK]\" \"earth\" \"wind\"  \"and\"   \"fire\" # ['', '[UNK]', 'earth', 'wind', 'and', 'fire']"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"Reload Keras model/layer saved via export_savedmodel().","code":""},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"","code":"layer_tfsm(   object,   filepath,   call_endpoint = \"serve\",   call_training_endpoint = NULL,   trainable = TRUE,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"object Object compose layer . tensor, array, sequential model. filepath string, path SavedModel. call_endpoint Name endpoint use call() method reloaded layer. SavedModel created via export_savedmodel(), default endpoint name 'serve'. cases may named 'serving_default'. call_training_endpoint see description trainable see description name String, name object dtype datatype (e.g., \"float32\").","code":""},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_tfsm.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reload a Keras model/layer that was saved via export_savedmodel(). — layer_tfsm","text":"reloaded object can used like regular Keras layer, supports training/fine-tuning trainable weights. Note reloaded object retains none internal structure custom methods original object -- brand new layer created around saved function. Limitations: call endpoints single inputs tensor argument (may optionally named list/list tensors) supported. endpoints multiple separate input tensor arguments, consider subclassing layer_tfsm implementing call() method custom signature. need training-time behavior differ inference-time behavior (.e. need reloaded object support training=TRUE argument __call__()), make sure training-time call function saved standalone endpoint artifact, provide name layer_tfsm via call_training_endpoint argument.","code":"model <- keras_model_sequential(input_shape = c(784)) |> layer_dense(10) model |> export_savedmodel(\"path/to/artifact\") ## Saved artifact at 'path/to/artifact'. The following endpoints are available: ## ## * Endpoint 'serve' ##   args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 784), dtype=tf.float32, name='keras_tensor') ## Output Type: ##   TensorSpec(shape=(None, 10), dtype=tf.float32, name=None) ## Captures: ##   128695884597376: TensorSpec(shape=(), dtype=tf.resource, name=None) ##   128695884598080: TensorSpec(shape=(), dtype=tf.resource, name=None) reloaded_layer <- layer_tfsm(filepath = \"path/to/artifact\") input <- random_normal(c(2, 784)) output <- reloaded_layer(input) stopifnot(all.equal(as.array(output), as.array(model(input))))"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":null,"dir":"Reference","previous_headings":"","what":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"Every input least 3D, dimension index one first input considered temporal dimension. Consider batch 32 video samples, sample 128x128 RGB image channels_last data format, across 10 timesteps. batch input shape (32, 10, 128, 128, 3). can use TimeDistributed apply Conv2D layer 10 timesteps, independently:     layer_time_distributed applies instance layer_conv2d timestamps, set weights used timestamp.","code":"inputs <- keras_input(shape = c(10, 128, 128, 3), batch_size = 32) conv_2d_layer <- layer_conv_2d(filters = 64, kernel_size = c(3, 3)) outputs <- layer_time_distributed(inputs, layer = conv_2d_layer) shape(outputs) ## shape(32, 10, 126, 126, 64)"},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"","code":"layer_time_distributed(object, layer, ...)"},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"object Object compose layer . tensor, array, sequential model. layer Layer instance. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_time_distributed.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"This wrapper allows to apply a layer to every temporal slice of an input. — layer_time_distributed","text":"inputs: Input tensor shape (batch, time, ...) nested tensors, shape (batch, time, ...). training: Boolean indicating whether layer behave training mode inference mode. argument passed wrapped layer (layer supports argument). mask: Binary tensor shape (samples, timesteps) indicating whether given timestep masked. argument passed wrapped layer (layer supports argument).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Torch module wrapper layer. — layer_torch_module_wrapper","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"layer_torch_module_wrapper wrapper class can turn torch.nn.Module Keras layer, particular making parameters trackable Keras.","code":""},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"","code":"layer_torch_module_wrapper(object, module, name = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"object Object compose layer . tensor, array, sequential model. module torch.nn.Module instance. LazyModule instance, parameters must initialized passing instance layer_torch_module_wrapper (e.g. calling ). name name layer (string). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_torch_module_wrapper.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Torch module wrapper layer. — layer_torch_module_wrapper","text":"example layer_torch_module_wrapper() can used vanilla PyTorch modules.","code":"# reticulate::py_install( #   packages = c(\"torch\", \"torchvision\", \"torchaudio\"), #   envname = \"r-keras\", #   pip_options = c(\"--index-url https://download.pytorch.org/whl/cpu\") # ) library(keras3) use_backend(\"torch\") torch <- reticulate::import(\"torch\") nn <- reticulate::import(\"torch.nn\") nnf <- reticulate::import(\"torch.nn.functional\")  Classifier(keras$Model) \\%py_class\\% {   initialize <- function(...) {     super$initialize(...)      self$conv1 <- layer_torch_module_wrapper(module = nn$Conv2d(       in_channels = 1L,       out_channels = 32L,       kernel_size = tuple(3L, 3L)     ))     self$conv2 <- layer_torch_module_wrapper(module = nn$Conv2d(       in_channels = 32L,       out_channels = 64L,       kernel_size = tuple(3L, 3L)     ))     self$pool <- nn$MaxPool2d(kernel_size = tuple(2L, 2L))     self$flatten <- nn$Flatten()     self$dropout <- nn$Dropout(p = 0.5)     self$fc <-       layer_torch_module_wrapper(module = nn$Linear(1600L, 10L))   }    call <- function(inputs) {     x <- nnf$relu(self$conv1(inputs))     x <- self$pool(x)     x <- nnf$relu(self$conv2(x))     x <- self$pool(x)     x <- self$flatten(x)     x <- self$dropout(x)     x <- self$fc(x)     nnf$softmax(x, dim = 1L)   } } model <- Classifier() model$build(shape(1, 28, 28)) cat(\"Output shape:\", format(shape(model(torch$ones(1L, 1L, 28L, 28L)))))  model |> compile(loss = \"sparse_categorical_crossentropy\",                  optimizer = \"adam\",                  metrics = \"accuracy\") model |> fit(train_loader, epochs = 5)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Unit normalization layer. — layer_unit_normalization","title":"Unit normalization layer. — layer_unit_normalization","text":"Normalize batch inputs input batch L2 norm equal 1 (across axes specified axis).","code":""},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unit normalization layer. — layer_unit_normalization","text":"","code":"layer_unit_normalization(object, axis = -1L, ...)"},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unit normalization layer. — layer_unit_normalization","text":"object Object compose layer . tensor, array, sequential model. axis Integer list. axis axes normalize across. Typically, features axis axes. left-axes typically batch axis axes. -1 last dimension input. Defaults -1. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unit normalization layer. — layer_unit_normalization","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_unit_normalization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unit normalization layer. — layer_unit_normalization","text":"","code":"data <- op_reshape(1:6, newshape = c(2, 3)) normalized_data <- layer_unit_normalization(data) op_sum(normalized_data[1,]^2) ## tf.Tensor(0.9999999, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsampling layer for 1D inputs. — layer_upsampling_1d","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"Repeats temporal step size times along time axis.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"","code":"layer_upsampling_1d(object, size = 2L, ...)"},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"object Object compose layer . tensor, array, sequential model. size Integer. Upsampling factor. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"","code":"input_shape <- c(2, 2, 3) x <- seq_len(prod(input_shape)) %>% op_reshape(input_shape) x ## tf.Tensor( ## [[[ 1  2  3] ##   [ 4  5  6]] ## ##  [[ 7  8  9] ##   [10 11 12]]], shape=(2, 2, 3), dtype=int32) y <- layer_upsampling_1d(x, size = 2) y ## tf.Tensor( ## [[[ 1  2  3] ##   [ 1  2  3] ##   [ 4  5  6] ##   [ 4  5  6]] ## ##  [[ 7  8  9] ##   [ 7  8  9] ##   [10 11 12] ##   [10 11 12]]], shape=(2, 4, 3), dtype=int32)"},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"3D tensor shape: (batch_size, steps, features).","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Upsampling layer for 1D inputs. — layer_upsampling_1d","text":"3D tensor shape: (batch_size, upsampled_steps, features).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsampling layer for 2D inputs. — layer_upsampling_2d","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"implementation uses interpolative resizing, given resize method (specified interpolation argument). Use interpolation=nearest repeat rows columns data.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"","code":"layer_upsampling_2d(   object,   size = list(2L, 2L),   data_format = NULL,   interpolation = \"nearest\",   ... )"},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"object Object compose layer . tensor, array, sequential model. size Int, list 2 integers. upsampling factors rows columns. data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists) else \"channels_last\". Defaults \"channels_last\". interpolation string, one \"bicubic\", \"bilinear\", \"lanczos3\", \"lanczos5\", \"nearest\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"","code":"input_shape <- c(2, 2, 1, 3) x <- op_reshape(seq_len(prod(input_shape)), input_shape) print(x) ## tf.Tensor( ## [[[[ 1  2  3]] ## ##   [[ 4  5  6]]] ## ## ##  [[[ 7  8  9]] ## ##   [[10 11 12]]]], shape=(2, 2, 1, 3), dtype=int32) y <- layer_upsampling_2d(x, size = c(1, 2)) print(y) ## tf.Tensor( ## [[[[ 1  2  3] ##    [ 1  2  3]] ## ##   [[ 4  5  6] ##    [ 4  5  6]]] ## ## ##  [[[ 7  8  9] ##    [ 7  8  9]] ## ##   [[10 11 12] ##    [10 11 12]]]], shape=(2, 2, 2, 3), dtype=int32)"},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, rows, cols, channels) data_format \"channels_first\": (batch_size, channels, rows, cols)","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Upsampling layer for 2D inputs. — layer_upsampling_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, upsampled_rows, upsampled_cols, channels) data_format \"channels_first\": (batch_size, channels, upsampled_rows, upsampled_cols)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsampling layer for 3D inputs. — layer_upsampling_3d","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"Repeats 1st, 2nd 3rd dimensions data size[0], size[1] size[2] respectively.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"","code":"layer_upsampling_3d(object, size = list(2L, 2L, 2L), data_format = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"object Object compose layer . tensor, array, sequential model. size Int, list 3 integers. upsampling factors dim1, dim2 dim3. data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists) else \"channels_last\". Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"","code":"input_shape <- c(2, 1, 2, 1, 3) x <- array(1, dim = input_shape) y <- layer_upsampling_3d(x, size = c(2, 2, 2)) shape(y) ## shape(2, 2, 4, 2, 3)"},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, dim1, dim2, dim3, channels) data_format \"channels_first\": (batch_size, channels, dim1, dim2, dim3)","code":""},{"path":"https://keras.posit.co/reference/layer_upsampling_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Upsampling layer for 3D inputs. — layer_upsampling_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels) data_format \"channels_first\": (batch_size, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"Zero-padding layer 1D input (e.g. temporal sequence).","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"","code":"layer_zero_padding_1d(object, padding = 1L, ...)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"object Object compose layer . tensor, array, sequential model. padding Int, list int (length 2), named listionary. int: many zeros add beginning end padding dimension (axis 1). list 2 ints: many zeros add beginning end padding dimension ((left_pad, right_pad)). ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"","code":"input_shape <- c(2, 2, 3) x <- op_reshape(seq_len(prod(input_shape)), input_shape) x ## tf.Tensor( ## [[[ 1  2  3] ##   [ 4  5  6]] ## ##  [[ 7  8  9] ##   [10 11 12]]], shape=(2, 2, 3), dtype=int32) y <- layer_zero_padding_1d(x, padding = 2) y ## tf.Tensor( ## [[[ 0  0  0] ##   [ 0  0  0] ##   [ 1  2  3] ##   [ 4  5  6] ##   [ 0  0  0] ##   [ 0  0  0]] ## ##  [[ 0  0  0] ##   [ 0  0  0] ##   [ 7  8  9] ##   [10 11 12] ##   [ 0  0  0] ##   [ 0  0  0]]], shape=(2, 6, 3), dtype=int32)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"3D tensor shape (batch_size, axis_to_pad, features)","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_1d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Zero-padding layer for 1D input (e.g. temporal sequence). — layer_zero_padding_1d","text":"3D tensor shape (batch_size, padded_axis, features)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"layer can add rows columns zeros top, bottom, left right side image tensor.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"","code":"layer_zero_padding_2d(object, padding = list(1L, 1L), data_format = NULL, ...)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"object Object compose layer . tensor, array, sequential model. padding Int, list 2 ints, list 2 lists 2 ints. int: symmetric padding applied height width. list 2 ints: interpreted two different symmetric padding values height width: (symmetric_height_pad, symmetric_width_pad). list 2 lists 2 ints: interpreted ((top_pad, bottom_pad), (left_pad, right_pad)). data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, height, width, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, height, width). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"","code":"input_shape <- c(1, 1, 2, 2) x <- op_reshape(seq_len(prod(input_shape)), input_shape) x ## tf.Tensor( ## [[[[1 2] ##    [3 4]]]], shape=(1, 1, 2, 2), dtype=int32) y <- layer_zero_padding_2d(x, padding = 1) y ## tf.Tensor( ## [[[[0 0] ##    [0 0] ##    [0 0] ##    [0 0]] ## ##   [[0 0] ##    [1 2] ##    [3 4] ##    [0 0]] ## ##   [[0 0] ##    [0 0] ##    [0 0] ##    [0 0]]]], shape=(1, 3, 4, 2), dtype=int32)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, height, width, channels) data_format \"channels_first\": (batch_size, channels, height, width)","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_2d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Zero-padding layer for 2D input (e.g. picture). — layer_zero_padding_2d","text":"4D tensor shape: data_format \"channels_last\": (batch_size, padded_height, padded_width, channels) data_format \"channels_first\": (batch_size, channels, padded_height, padded_width)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"Zero-padding layer 3D data (spatial spatio-temporal).","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"","code":"layer_zero_padding_3d(   object,   padding = list(list(1L, 1L), list(1L, 1L), list(1L, 1L)),   data_format = NULL,   ... )"},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"object Object compose layer . tensor, array, sequential model. padding Int, list 3 ints, list 3 lists 2 ints. int: symmetric padding applied depth, height, width. list 3 ints: interpreted three different symmetric padding values depth, height, width: (symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad). list 3 lists 2 ints: interpreted ((left_dim1_pad, right_dim1_pad), (left_dim2_pad, right_dim2_pad), (left_dim3_pad, right_dim3_pad)). data_format string, one \"channels_last\" (default) \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) \"channels_first\" corresponds inputs shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). unspecified, uses image_data_format value found Keras config file ~/.keras/keras.json (exists). Defaults \"channels_last\". ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"","code":"input_shape <- c(1, 1, 2, 2, 3) x <- op_reshape(seq_len(prod(input_shape)), input_shape) x ## tf.Tensor( ## [[[[[ 1  2  3] ##     [ 4  5  6]] ## ##    [[ 7  8  9] ##     [10 11 12]]]]], shape=(1, 1, 2, 2, 3), dtype=int32) y <- layer_zero_padding_3d(x, padding = 2) shape(y) ## shape(1, 5, 6, 6, 3)"},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"input-shape","dir":"Reference","previous_headings":"","what":"Input Shape","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad, depth) data_format \"channels_first\": (batch_size, depth, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad)","code":""},{"path":"https://keras.posit.co/reference/layer_zero_padding_3d.html","id":"output-shape","dir":"Reference","previous_headings":"","what":"Output Shape","title":"Zero-padding layer for 3D data (spatial or spatio-temporal). — layer_zero_padding_3d","text":"5D tensor shape: data_format \"channels_last\": (batch_size, first_padded_axis, second_padded_axis, third_axis_to_pad, depth) data_format \"channels_first\": (batch_size, depth, first_padded_axis, second_padded_axis, third_axis_to_pad)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","title":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","text":"See Loshchilov & Hutter, ICLR2016, SGDR: Stochastic Gradient Descent Warm Restarts. idea linear warmup learning rate, see Goyal et al.. begin training model, often want initial increase learning rate followed decay. warmup_target int, schedule applies linear increase per optimizer step learning rate initial_learning_rate warmup_target duration warmup_steps. Afterwards, applies cosine decay function taking learning rate warmup_target alpha duration decay_steps. warmup_target NULL skip warmup decay take learning rate initial_learning_rate alpha. requires step value  compute learning rate. can just pass backend variable increment training step. schedule 1-arg callable produces warmup followed decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. warmup computed :   decay computed :   Example usage without warmup:   Example usage warmup:   can pass schedule directly optimizer learning rate. learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"warmup_learning_rate <- function(step) {   completed_fraction <- step / warmup_steps   total_delta <- target_warmup - initial_learning_rate   completed_fraction * total_delta } if (is.null(warmup_target)) {   initial_decay_lr <- initial_learning_rate } else {   initial_decay_lr <- warmup_target }  decayed_learning_rate <- function(step) {   step <- min(step, decay_steps)   cosine_decay <- 0.5 * (1 + cos(pi * step / decay_steps))   decayed <- (1 - alpha) * cosine_decay + alpha   initial_decay_lr * decayed } decay_steps <- 1000 initial_learning_rate <- 0.1 lr_decayed_fn <- learning_rate_schedule_cosine_decay(     initial_learning_rate, decay_steps) decay_steps <- 1000 initial_learning_rate <- 0 warmup_steps <- 1000 target_learning_rate <- 0.1 lr_warmup_decayed_fn <- learning_rate_schedule_cosine_decay(     initial_learning_rate, decay_steps, warmup_target = target_learning_rate,     warmup_steps = warmup_steps )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","text":"","code":"learning_rate_schedule_cosine_decay(   initial_learning_rate,   decay_steps,   alpha = 0,   name = \"CosineDecay\",   warmup_target = NULL,   warmup_steps = 0L )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","text":"initial_learning_rate float. initial learning rate. decay_steps int. Number steps decay . alpha float. Minimum learning rate value decay fraction initial_learning_rate. name String. Optional name operation.  Defaults \"CosineDecay\". warmup_target float. target learning rate warmup phase. cast initial_learning_rate datatype. Setting NULL skip warmup begins decay phase initial_learning_rate. Otherwise scheduler warmup initial_learning_rate warmup_target. warmup_steps int. Number steps warmup .","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses a cosine decay with optional warmup. — learning_rate_schedule_cosine_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"See Loshchilov & Hutter, ICLR2016, SGDR: Stochastic Gradient Descent Warm Restarts. training model, often useful lower learning rate training progresses. schedule applies cosine decay function restarts optimizer step, given provided initial learning rate. requires step value compute decayed learning rate. can just pass backend variable increment training step. schedule 1-arg callable produces decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. learning rate multiplier first decays 1 alpha first_decay_steps steps. , warm restart performed. new warm restart runs t_mul times steps m_mul times initial learning rate new learning rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"","code":"learning_rate_schedule_cosine_decay_restarts(   initial_learning_rate,   first_decay_steps,   t_mul = 2,   m_mul = 1,   alpha = 0,   name = \"SGDRDecay\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"initial_learning_rate float. initial learning rate. first_decay_steps integer. Number steps decay . t_mul float. Used derive number iterations -th period. m_mul float. Used derive initial learning rate -th period. alpha float. Minimum learning rate value fraction initial_learning_rate. name String. Optional name operation. Defaults \"SGDRDecay\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_cosine_decay_restarts.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"A LearningRateSchedule that uses a cosine decay schedule with restarts. — learning_rate_schedule_cosine_decay_restarts","text":"can pass schedule directly optimizer learning rate. learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"first_decay_steps <- 1000 lr_decayed_fn <- learning_rate_schedule_cosine_decay_restarts(         0.001,         first_decay_steps)"},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"training model, often useful lower learning rate training progresses. schedule applies exponential decay function optimizer step, given provided initial learning rate. schedule 1-arg callable produces decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. computed :   argument staircase TRUE, step / decay_steps integer division decayed learning rate follows staircase function. can pass schedule directly optimizer learning rate.","code":"decayed_learning_rate <- function(step) {   initial_learning_rate * decay_rate ^ (step / decay_steps) }"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"","code":"learning_rate_schedule_exponential_decay(   initial_learning_rate,   decay_steps,   decay_rate,   staircase = FALSE,   name = \"ExponentialDecay\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"initial_learning_rate float. initial learning rate. decay_steps integer. Must positive. See decay computation . decay_rate float. decay rate. staircase Boolean.  TRUE decay learning rate discrete intervals. name String.  Optional name operation.  Defaults \"ExponentialDecay\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_exponential_decay.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A LearningRateSchedule that uses an exponential decay schedule. — learning_rate_schedule_exponential_decay","text":"fitting Keras model, decay every 100000 steps base 0.96:   learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"initial_learning_rate <- 0.1 lr_schedule <- learning_rate_schedule_exponential_decay(     initial_learning_rate,     decay_steps=100000,     decay_rate=0.96,     staircase=TRUE)  model %>% compile(   optimizer = optimizer_sgd(learning_rate = lr_schedule),   loss = 'sparse_categorical_crossentropy',   metrics = c('accuracy'))  model %>% fit(data, labels, epochs=5)"},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"training model, often useful lower learning rate training progresses. schedule applies inverse decay function optimizer step, given provided initial learning rate. requires step value compute decayed learning rate. can just pass backend variable increment training step. schedule 1-arg callable produces decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. computed :   , staircase TRUE, :   can pass schedule directly optimizer_* learning rate.","code":"decayed_learning_rate <- function(step) {   initial_learning_rate / (1 + decay_rate * step / decay_step) } decayed_learning_rate <- function(step) {   initial_learning_rate /            (1 + decay_rate * floor(step / decay_step)) }"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"","code":"learning_rate_schedule_inverse_time_decay(   initial_learning_rate,   decay_steps,   decay_rate,   staircase = FALSE,   name = \"InverseTimeDecay\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"initial_learning_rate float. initial learning rate. decay_steps often apply decay. decay_rate number.  decay rate. staircase Whether apply decay discrete staircase, o pposed continuous, fashion. name String.  Optional name operation.  Defaults \"InverseTimeDecay\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_inverse_time_decay.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A LearningRateSchedule that uses an inverse time decay schedule. — learning_rate_schedule_inverse_time_decay","text":"Fit Keras model decaying 1/t rate 0.5:","code":"... initial_learning_rate <- 0.1 decay_steps <- 1.0 decay_rate <- 0.5 learning_rate_fn <- learning_rate_schedule_inverse_time_decay(     initial_learning_rate, decay_steps, decay_rate)  model %>% compile(   optimizer = optimizer_sgd(learning_rate=learning_rate_fn),   loss = 'sparse_categorical_crossentropy',   metrics = 'accuracy') )  model %>% fit(data, labels, epochs=5)"},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"function returns 1-arg callable compute piecewise constant passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"","code":"learning_rate_schedule_piecewise_constant_decay(   boundaries,   values,   name = \"PiecewiseConstant\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"boundaries list Python numbers strictly increasing entries, elements type optimizer step. values list Python numbers specifies values intervals defined boundaries. one element boundaries, elements type. name string. Optional name operation. Defaults \"PiecewiseConstant\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type boundary tensors. output 1-arg function takes step values[0] step <= boundaries[0], values[1] step > boundaries[0] step <= boundaries[1], ..., values[-1] step > boundaries[-1].","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"use learning rate 1.0 first 100001 steps, 0.5 next 10000 steps, 0.1 additional steps.   can pass schedule directly optimizer learning rate. learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"step <- 0 boundaries <- c(100000, 110000) values <- c(1.0, 0.5, 0.1) learning_rate_fn <- learning_rate_schedule_piecewise_constant_decay(   boundaries, values)  # Later, whenever we perform an optimization step, we pass in the step. learning_rate <- learning_rate_fn(step)"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_piecewise_constant_decay.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"A LearningRateSchedule that uses a piecewise constant decay schedule. — learning_rate_schedule_piecewise_constant_decay","text":"ValueError: number elements boundaries values lists match.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":null,"dir":"Reference","previous_headings":"","what":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"commonly observed monotonically decreasing learning rate, whose degree change carefully chosen, results better performing model. schedule applies polynomial decay function optimizer step, given provided initial_learning_rate, reach end_learning_rate given decay_steps. requires step value compute decayed learning rate. can just pass backend variable increment training step. schedule 1-arg callable produces decayed learning rate passed current optimizer step. can useful changing learning rate value across different invocations optimizer functions. computed :   cycle TRUE multiple decay_steps used, first one bigger step.   can pass schedule directly Optimizer learning rate.","code":"decayed_learning_rate <- function(step) {   step = min(step, decay_steps)   ((initial_learning_rate - end_learning_rate) *     (1 - step / decay_steps) ^ (power)) +     end_learning_rate } decayed_learning_rate <- function(step) {   decay_steps = decay_steps * ceil(step / decay_steps)   ((initial_learning_rate - end_learning_rate) *       (1 - step / decay_steps) ^ (power)) +     end_learning_rate }"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"","code":"learning_rate_schedule_polynomial_decay(   initial_learning_rate,   decay_steps,   end_learning_rate = 1e-04,   power = 1,   cycle = FALSE,   name = \"PolynomialDecay\" )"},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"initial_learning_rate float. initial learning rate. decay_steps integer. Must positive. See decay computation . end_learning_rate float. minimal end learning rate. power float. power polynomial. Defaults 1.0. cycle boolean, whether cycle beyond decay_steps. name String.  Optional name operation. Defaults \"PolynomialDecay\".","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"1-arg callable learning rate schedule takes current optimizer step outputs decayed learning rate, scalar tensor type initial_learning_rate.","code":""},{"path":"https://keras.posit.co/reference/learning_rate_schedule_polynomial_decay.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A LearningRateSchedule that uses a polynomial decay schedule. — learning_rate_schedule_polynomial_decay","text":"Fit model decaying 0.1 0.01 10000 steps using sqrt (.e. power=0.5):   learning rate schedule also serializable deserializable using keras$optimizers$schedules$serialize keras$optimizers$schedules$deserialize.","code":"... starter_learning_rate <- 0.1 end_learning_rate <- 0.01 decay_steps <- 10000 learning_rate_fn <- learning_rate_schedule_polynomial_decay(     starter_learning_rate,     decay_steps,     end_learning_rate,     power=0.5)  model %>% compile(   optimizer = optimizer_sgd(learning_rate=learning_rate_fn),   loss = 'sparse_categorical_crossentropy',   metrics = 'accuracy' )  model %>% fit(data, labels, epochs=5)"},{"path":[]},{"path":"https://keras.posit.co/reference/load_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Loads a model saved via save_model(). — load_model","title":"Loads a model saved via save_model(). — load_model","text":"Loads model saved via save_model().","code":""},{"path":"https://keras.posit.co/reference/load_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loads a model saved via save_model(). — load_model","text":"","code":"load_model(model, custom_objects = NULL, compile = TRUE, safe_mode = TRUE)"},{"path":"https://keras.posit.co/reference/load_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loads a model saved via save_model(). — load_model","text":"model string, path saved model file, raw vector, returned save_model(filepath = NULL) custom_objects Optional named list mapping names custom classes functions considered deserialization. compile Boolean, whether compile model loading. safe_mode Boolean, whether disallow unsafe lambda deserialization. safe_mode=FALSE, loading object potential trigger arbitrary code execution. argument applicable Keras v3 model format. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/load_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loads a model saved via save_model(). — load_model","text":"Keras model instance. original model compiled, argument compile = TRUE set, returned model compiled. Otherwise, model left uncompiled.","code":""},{"path":"https://keras.posit.co/reference/load_model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Loads a model saved via save_model(). — load_model","text":"Note model variables may different name values (var$name property, e.g. \"dense_1/kernel:0\") reloaded. recommended use layer attributes access specific variables, e.g. model |> get_layer(\"dense_1\") |> _$kernel.","code":"model <- keras_model_sequential(input_shape = c(3)) |>   layer_dense(5) |>   layer_activation_softmax()  model |> save_model(\"model.keras\") loaded_model <- load_model(\"model.keras\") x <- random_uniform(c(10, 3)) stopifnot(all.equal(   model |> predict(x),   loaded_model |> predict(x) ))"},{"path":[]},{"path":"https://keras.posit.co/reference/load_model_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Load weights from a file saved via save_model_weights(). — load_model_weights","title":"Load weights from a file saved via save_model_weights(). — load_model_weights","text":"Weights loaded based network's topology. means architecture weights saved. Note layers weights taken account topological ordering, adding removing layers fine long weights. Partial weight loading modified model, instance adding new layer (weights) changing shape weights layer, can choose ignore errors continue loading setting skip_mismatch=TRUE. case layer mismatching weights skipped. warning displayed skipped layer.","code":""},{"path":"https://keras.posit.co/reference/load_model_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load weights from a file saved via save_model_weights(). — load_model_weights","text":"","code":"load_model_weights(model, filepath, skip_mismatch = FALSE, ...)"},{"path":"https://keras.posit.co/reference/load_model_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load weights from a file saved via save_model_weights(). — load_model_weights","text":"model keras model. filepath String, path weights file load. can either .weights.h5 file legacy .h5 weights file. skip_mismatch Boolean, whether skip loading layers mismatch number weights, mismatch shape weights. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/load_model_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load weights from a file saved via save_model_weights(). — load_model_weights","text":"called primarily side effects. model returned, invisibly, enable usage pipe.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"Use cross-entropy loss binary (0 1) classification applications. loss function requires following inputs: y_true (true label): either 0 1. y_pred (predicted value): model's prediction, .e, single floating-point value either represents logit, (.e, value [-inf, inf] from_logits=TRUE) probability (.e, value [0., 1.] from_logits=FALSE).","code":""},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"","code":"loss_binary_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"binary_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"y_true Ground truth values. shape = [batch_size, d0, .. dN]. y_pred predicted values. shape = [batch_size, d0, .. dN]. from_logits Whether interpret y_pred tensor logit values. default, assume y_pred probabilities (.e., values [0, 1)). label_smoothing Float range [0, 1]. 0, smoothing occurs. > 0, compute loss predicted labels smoothed version true labels, smoothing squeezes labels towards 0.5. Larger values label_smoothing correspond heavier smoothing. axis axis along compute crossentropy (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"Binary crossentropy loss value. shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_binary_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the cross-entropy loss between true labels and predicted labels. — loss_binary_crossentropy","text":"Recommended Usage: (set from_logits=TRUE) compile() API:   standalone function:                     Default Usage: (set from_logits=FALSE)","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_binary_crossentropy(y_true, y_pred) loss ## tf.Tensor([0.91629073 0.71355818], shape=(2), dtype=float64) model %>% compile(     loss = loss_binary_crossentropy(from_logits=TRUE),     ... ) # Example 1: (batch_size = 1, number of samples = 4) y_true <- op_array(c(0, 1, 0, 0)) y_pred <- op_array(c(-18.6, 0.51, 2.94, -12.8)) bce <- loss_binary_crossentropy(from_logits = TRUE) bce(y_true, y_pred) ## tf.Tensor(0.865458, shape=(), dtype=float32) # Example 2: (batch_size = 2, number of samples = 4) y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(-18.6, 0.51), c(2.94, -12.8)) # Using default 'auto'/'sum_over_batch_size' reduction type. bce <- loss_binary_crossentropy(from_logits = TRUE) bce(y_true, y_pred) ## tf.Tensor(0.865458, shape=(), dtype=float32) # Using 'sample_weight' attribute bce(y_true, y_pred, sample_weight = c(0.8, 0.2)) ## tf.Tensor(0.2436386, shape=(), dtype=float32) # 0.243 # Using 'sum' reduction` type. bce <- loss_binary_crossentropy(from_logits = TRUE, reduction = \"sum\") bce(y_true, y_pred) ## tf.Tensor(1.730916, shape=(), dtype=float32) # Using 'none' reduction type. bce <- loss_binary_crossentropy(from_logits = TRUE, reduction = NULL) bce(y_true, y_pred) ## tf.Tensor([0.23515666 1.4957594 ], shape=(2), dtype=float32) # Make the following updates to the above \"Recommended Usage\" section # 1. Set `from_logits=FALSE` loss_binary_crossentropy() # OR ...('from_logits=FALSE') ## <keras.src.losses.losses.BinaryCrossentropy object> ##  signature: (y_true, y_pred, sample_weight=None) # 2. Update `y_pred` to use probabilities instead of logits y_pred <- c(0.6, 0.3, 0.2, 0.8) # OR [[0.6, 0.3], [0.2, 0.8]]"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"According Lin et al., 2018, helps apply focal factor -weight easy examples focus hard examples. default, focal tensor computed follows: focal_factor = (1 - output)^gamma class 1 focal_factor = output^gamma class 0 gamma focusing parameter. gamma = 0, focal effect binary crossentropy loss. apply_class_balancing == TRUE, function also takes account weight balancing factor binary classes 0 1 follows: weight = alpha class 1 (target == 1) weight = 1 - alpha class 0 alpha float range [0, 1]. Binary cross-entropy loss often used binary (0 1) classification tasks. loss function requires following inputs: y_true (true label): either 0 1. y_pred (predicted value): model's prediction, .e, single floating-point value either represents logit, (.e, value [-inf, inf] from_logits=TRUE) probability (.e, value [0., 1.] from_logits=FALSE). According Lin et al., 2018, helps apply \"focal factor\" -weight easy examples focus hard examples. default, focal tensor computed follows: focal_factor = (1 - output) ** gamma class 1 focal_factor = output ** gamma class 0 gamma focusing parameter. gamma=0, function equivalent binary crossentropy loss.","code":""},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"","code":"loss_binary_focal_crossentropy(   y_true,   y_pred,   apply_class_balancing = FALSE,   alpha = 0.25,   gamma = 2,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"binary_focal_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"y_true Ground truth values, shape (batch_size, d0, .. dN). y_pred predicted values, shape (batch_size, d0, .. dN). apply_class_balancing bool, whether apply weight balancing binary classes 0 1. alpha weight balancing factor class 1, default 0.25 mentioned reference Lin et al., 2018.  weight class 0 1.0 - alpha. gamma focusing parameter used compute focal factor, default 2.0 mentioned reference Lin et al., 2018. from_logits Whether interpret y_pred tensor logit values. default, assume y_pred probabilities (.e., values [0, 1]). label_smoothing Float [0, 1]. 0, smoothing occurs. > 0, compute loss predicted labels smoothed version true labels, smoothing squeezes labels towards 0.5. Larger values label_smoothing correspond heavier smoothing. axis axis along compute crossentropy (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"Binary focal crossentropy loss value shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_binary_focal_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes focal cross-entropy loss between true labels and predictions. — loss_binary_focal_crossentropy","text":"compile() API:   standalone function:","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_binary_focal_crossentropy(y_true, y_pred, gamma = 2) loss ## tf.Tensor([0.32986466 0.20579838], shape=(2), dtype=float64) model %>% compile(     loss = loss_binary_focal_crossentropy(         gamma = 2.0, from_logits = TRUE),     ... ) # Example 1: (batch_size = 1, number of samples = 4) y_true <- op_array(c(0, 1, 0, 0)) y_pred <- op_array(c(-18.6, 0.51, 2.94, -12.8)) loss <- loss_binary_focal_crossentropy(gamma = 2, from_logits = TRUE) loss(y_true, y_pred) ## tf.Tensor(0.6912122, shape=(), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(   apply_class_balancing = TRUE, gamma = 2, from_logits = TRUE) loss(y_true, y_pred) ## tf.Tensor(0.5101333, shape=(), dtype=float32) # Example 2: (batch_size = 2, number of samples = 4) y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(-18.6, 0.51), c(2.94, -12.8)) # Using default 'auto'/'sum_over_batch_size' reduction type. loss <- loss_binary_focal_crossentropy(     gamma = 3, from_logits = TRUE) loss(y_true, y_pred) ## tf.Tensor(0.6469951, shape=(), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(      apply_class_balancing = TRUE, gamma = 3, from_logits = TRUE) loss(y_true, y_pred) ## tf.Tensor(0.48214132, shape=(), dtype=float32) # Using 'sample_weight' attribute with focal effect loss <- loss_binary_focal_crossentropy(     gamma = 3, from_logits = TRUE) loss(y_true, y_pred, sample_weight = c(0.8, 0.2)) ## tf.Tensor(0.13312504, shape=(), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(      apply_class_balancing = TRUE, gamma = 3, from_logits = TRUE) loss(y_true, y_pred, sample_weight = c(0.8, 0.2)) ## tf.Tensor(0.09735977, shape=(), dtype=float32) # Using 'sum' reduction` type. loss <- loss_binary_focal_crossentropy(     gamma = 4, from_logits = TRUE,     reduction = \"sum\") loss(y_true, y_pred) ## tf.Tensor(1.2218808, shape=(), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(     apply_class_balancing = TRUE, gamma = 4, from_logits = TRUE,     reduction = \"sum\") loss(y_true, y_pred) ## tf.Tensor(0.9140807, shape=(), dtype=float32) # Using 'none' reduction type. loss <- loss_binary_focal_crossentropy(     gamma = 5, from_logits = TRUE,     reduction = NULL) loss(y_true, y_pred) ## tf.Tensor([0.00174837 1.1561027 ], shape=(2), dtype=float32) # Apply class weight loss <- loss_binary_focal_crossentropy(     apply_class_balancing = TRUE, gamma = 5, from_logits = TRUE,     reduction = NULL) loss(y_true, y_pred) ## tf.Tensor([4.3709317e-04 8.6707699e-01], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"Use crossentropy loss function two label classes. expect labels provided one_hot representation. want provide labels integers, please use SparseCategoricalCrossentropy loss. num_classes floating point values per feature, .e., shape y_pred y_true [batch_size, num_classes].","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"","code":"loss_categorical_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"categorical_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"y_true Tensor one-hot true targets. y_pred Tensor predicted targets. from_logits Whether y_pred expected logits tensor. default, assume y_pred encodes probability distribution. label_smoothing Float [0, 1]. > 0, label values smoothed, meaning confidence label values relaxed. example, 0.1, use 0.1 / num_classes non-target labels 0.9 + 0.1 / num_classes target labels. axis axis along compute crossentropy (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"Categorical crossentropy loss value.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy loss between the labels and predictions. — loss_categorical_crossentropy","text":"Standalone usage:                 Usage compile() API:","code":"y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) loss <- loss_categorical_crossentropy(y_true, y_pred) loss ## tf.Tensor([0.05129329 2.30258509], shape=(2), dtype=float64) y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) # Using 'auto'/'sum_over_batch_size' reduction type. cce <- loss_categorical_crossentropy() cce(y_true, y_pred) ## tf.Tensor(1.1769392, shape=(), dtype=float32) # Calling with 'sample_weight'. cce(y_true, y_pred, sample_weight = op_array(c(0.3, 0.7))) ## tf.Tensor(0.8135988, shape=(), dtype=float32) # Using 'sum' reduction type. cce <- loss_categorical_crossentropy(reduction = \"sum\") cce(y_true, y_pred) ## tf.Tensor(2.3538785, shape=(), dtype=float32) # Using 'none' reduction type. cce <- loss_categorical_crossentropy(reduction = NULL) cce(y_true, y_pred) ## tf.Tensor([0.05129331 2.3025851 ], shape=(2), dtype=float32) model %>% compile(optimizer = 'sgd',               loss=loss_categorical_crossentropy())"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"Use crossentropy loss function two label classes want handle class imbalance without using class_weights. expect labels provided one_hot representation. According Lin et al., 2018, helps apply focal factor -weight easy examples focus hard examples. general formula focal loss (FL) follows: FL(p_t) = (1 - p_t)^gamma * log(p_t) p_t defined follows: p_t = output y_true == 1, else 1 - output (1 - p_t)^gamma modulating_factor, gamma focusing parameter. gamma = 0, focal effect cross entropy. gamma reduces importance given simple examples smooth manner. authors use alpha-balanced variant focal loss (FL) paper: FL(p_t) = -alpha * (1 - p_t)^gamma * log(p_t) alpha weight factor classes. alpha = 1, loss able handle class imbalance properly classes weight. can constant list constants. alpha list, must length number classes. formula can generalized : FL(p_t) = alpha * (1 - p_t)^gamma * CrossEntropy(y_true, y_pred) minus comes CrossEntropy(y_true, y_pred) (CE). Extending multi-class case straightforward: FL(p_t) = alpha * (1 - p_t) ** gamma * CategoricalCE(y_true, y_pred) snippet , num_classes floating pointing values per example. shape y_pred y_true (batch_size, num_classes).","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"","code":"loss_categorical_focal_crossentropy(   y_true,   y_pred,   alpha = 0.25,   gamma = 2,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"categorical_focal_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"y_true Tensor one-hot true targets. y_pred Tensor predicted targets. alpha weight balancing factor classes, default 0.25 mentioned reference. can list floats scalar. multi-class case, alpha may set inverse class frequency using compute_class_weight sklearn.utils. gamma focusing parameter, default 2.0 mentioned reference. helps gradually reduce importance given simple examples smooth manner. gamma = 0, focal effect categorical crossentropy. from_logits Whether output expected logits tensor. default, consider output encodes probability distribution. label_smoothing Float [0, 1]. > 0, label values smoothed, meaning confidence label values relaxed. example, 0.1, use 0.1 / num_classes non-target labels 0.9 + 0.1 / num_classes target labels. axis axis along compute crossentropy (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"Categorical focal crossentropy loss value.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_focal_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the alpha balanced focal crossentropy loss. — loss_categorical_focal_crossentropy","text":"Standalone usage:                 Usage compile() API:","code":"y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) loss <- loss_categorical_focal_crossentropy(y_true, y_pred) loss ## tf.Tensor([3.20583090e-05 4.66273481e-01], shape=(2), dtype=float64) y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) # Using 'auto'/'sum_over_batch_size' reduction type. cce <- loss_categorical_focal_crossentropy() cce(y_true, y_pred) ## tf.Tensor(0.23315276, shape=(), dtype=float32) # Calling with 'sample_weight'. cce(y_true, y_pred, sample_weight = op_array(c(0.3, 0.7))) ## tf.Tensor(0.16320053, shape=(), dtype=float32) # Using 'sum' reduction type. cce <- loss_categorical_focal_crossentropy(reduction = \"sum\") cce(y_true, y_pred) ## tf.Tensor(0.46630552, shape=(), dtype=float32) # Using 'none' reduction type. cce <- loss_categorical_focal_crossentropy(reduction = NULL) cce(y_true, y_pred) ## tf.Tensor([3.2058331e-05 4.6627346e-01], shape=(2), dtype=float32) model %>% compile(   optimizer = 'adam',   loss = loss_categorical_focal_crossentropy())"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"Formula:   neg=maximum((1-y_true)*y_pred) pos=sum(y_true*y_pred)","code":"loss <- maximum(neg - pos + 1, 0)"},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"","code":"loss_categorical_hinge(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"categorical_hinge\" )"},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"y_true ground truth values. y_true values expected either {-1, +1} {0, 1} (.e. one-hot-encoded tensor) shape <- [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"Categorical hinge loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_categorical_hinge.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the categorical hinge loss between y_true & y_pred. — loss_categorical_hinge","text":"","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_categorical_hinge(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"Formula:   Note number -1 1. negative number -1 0, 0 indicates orthogonality values closer -1 indicate greater similarity. makes usable loss function setting try maximize proximity predictions targets. either y_true y_pred zero vector, cosine similarity 0 regardless proximity predictions targets.","code":"loss <- -sum(l2_norm(y_true) * l2_norm(y_pred))"},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"","code":"loss_cosine_similarity(   y_true,   y_pred,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"cosine_similarity\" )"},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"y_true Tensor true targets. y_pred Tensor predicted targets. axis axis along cosine similarity computed (features axis). Defaults -1. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"Cosine similarity tensor.","code":""},{"path":"https://keras.posit.co/reference/loss_cosine_similarity.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the cosine similarity between y_true & y_pred. — loss_cosine_similarity","text":"","code":"y_true <- rbind(c(0., 1.), c(1., 1.), c(1., 1.)) y_pred <- rbind(c(1., 0.), c(1., 1.), c(-1., -1.)) loss <- loss_cosine_similarity(y_true, y_pred, axis=-1) loss ## tf.Tensor([-0. -1.  1.], shape=(3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_ctc.html","id":null,"dir":"Reference","previous_headings":"","what":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","title":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","text":"CTC (Connectionist Temporal Classification) loss.","code":""},{"path":"https://keras.posit.co/reference/loss_ctc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","text":"","code":"loss_ctc(y_true, y_pred, ..., reduction = \"sum_over_batch_size\", name = \"ctc\")"},{"path":"https://keras.posit.co/reference/loss_ctc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","text":"y_true tensor shape (batch_size, target_max_length) containing true labels integer format. 0 always represents blank/mask index used classes. y_pred tensor shape (batch_size, output_max_length, num_classes) containing logits (output model). normalized via softmax. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name String, name object","code":""},{"path":"https://keras.posit.co/reference/loss_ctc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CTC (Connectionist Temporal Classification) loss. — loss_ctc","text":"CTC loss value.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/loss_dice.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Dice loss value between y_true and y_pred. — loss_dice","title":"Computes the Dice loss value between y_true and y_pred. — loss_dice","text":"Formula:   Formula:","code":"loss = 1 - (2 * sum(y_true * y_pred)) / (sum(y_true) + sum(y_pred)) loss = 1 - (2 * sum(y_true * y_pred)) / (sum(y_true) + sum(y_pred))"},{"path":"https://keras.posit.co/reference/loss_dice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Dice loss value between y_true and y_pred. — loss_dice","text":"","code":"loss_dice(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"dice\" )"},{"path":"https://keras.posit.co/reference/loss_dice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Dice loss value between y_true and y_pred. — loss_dice","text":"y_true tensor true targets. y_pred tensor predicted targets. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name String, name object","code":""},{"path":"https://keras.posit.co/reference/loss_dice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Dice loss value between y_true and y_pred. — loss_dice","text":"y_true y_pred provided, Dice loss value. Otherwise, Loss() instance.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the hinge loss between y_true & y_pred. — loss_hinge","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"Formula:   y_true values expected -1 1. binary (0 1) labels provided convert -1 1.","code":"loss <- mean(maximum(1 - y_true * y_pred, 0), axis=-1)"},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"","code":"loss_hinge(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"hinge\" )"},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"y_true ground truth values. y_true values expected -1 1. binary (0 1) labels provided converted -1 1 shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"Hinge loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_hinge.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the hinge loss between y_true & y_pred. — loss_hinge","text":"","code":"y_true <- array(sample(c(-1,1), 6, replace = TRUE), dim = c(2, 3)) y_pred <- random_uniform(c(2, 3)) loss <- loss_hinge(y_true, y_pred) loss ## tf.Tensor([1.0610152  0.93285507], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_huber.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Huber loss between y_true & y_pred. — loss_huber","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"Formula:   See: Huber loss.","code":"for (x in error) {   if (abs(x) <= delta){     loss <- c(loss, (0.5 * x^2))   } else if (abs(x) > delta) {     loss <- c(loss, (delta * abs(x) - 0.5 * delta^2))   } } loss <- mean(loss)"},{"path":"https://keras.posit.co/reference/loss_huber.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"","code":"loss_huber(   y_true,   y_pred,   delta = 1,   ...,   reduction = \"sum_over_batch_size\",   name = \"huber_loss\" )"},{"path":"https://keras.posit.co/reference/loss_huber.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"y_true tensor true targets. y_pred tensor predicted targets. delta float, point Huber loss function changes quadratic linear. Defaults 1.0. ... forward/backward compatability. reduction Type reduction apply loss. Options \"sum\", \"sum_over_batch_size\" NULL. Defaults \"sum_over_batch_size\". name Optional name instance.","code":""},{"path":"https://keras.posit.co/reference/loss_huber.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"Tensor one scalar loss entry per sample.","code":""},{"path":"https://keras.posit.co/reference/loss_huber.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Huber loss between y_true & y_pred. — loss_huber","text":"","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_huber(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"Formula:   y_true y_pred expected probability distributions, values 0 1. get clipped [0, 1] range.","code":"loss <- y_true * log(y_true / y_pred)"},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"","code":"loss_kl_divergence(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"kl_divergence\" )"},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"y_true Tensor true targets. y_pred Tensor predicted targets. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"KL Divergence loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_kl_divergence.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes Kullback-Leibler divergence loss between y_true & y_pred. — loss_kl_divergence","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2,3)) loss <- loss_kl_divergence(y_true, y_pred) loss ## tf.Tensor([3.5312676 0.2128672], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"Formula:   Note log(cosh(x)) approximately equal (x ** 2) / 2 small x abs(x) - log(2) large x. means 'logcosh' works mostly like mean squared error, strongly affected occasional wildly incorrect prediction.","code":"loss <- mean(log(cosh(y_pred - y_true)), axis=-1)"},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"","code":"loss_log_cosh(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"log_cosh\" )"},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. Options \"sum\", \"sum_over_batch_size\" NULL. Defaults \"sum_over_batch_size\". name Optional name instance.","code":""},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"Logcosh error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_log_cosh.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — loss_log_cosh","text":"","code":"y_true <- rbind(c(0., 1.), c(0., 0.)) y_pred <- rbind(c(1., 1.), c(0., 0.)) loss <- loss_log_cosh(y_true, y_pred) # 0.108"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"Formula:","code":"loss <- mean(abs(y_true - y_pred))"},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"","code":"loss_mean_absolute_error(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"mean_absolute_error\" )"},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"Mean absolute error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_mean_absolute_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean of absolute difference between labels and predictions. — loss_mean_absolute_error","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2, 3)) loss <- loss_mean_absolute_error(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"Formula:   Division zero prevented dividing max(y_true, epsilon) epsilon = config_epsilon() (default 1e-7).","code":"loss <- 100 * op_mean(op_abs((y_true - y_pred) / y_true),                       axis=-1)"},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"","code":"loss_mean_absolute_percentage_error(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"mean_absolute_percentage_error\" )"},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"Mean absolute percentage error values shape = [batch_size, d0, ..dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_mean_absolute_percentage_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error","text":"","code":"y_true <- random_uniform(c(2, 3)) y_pred <- random_uniform(c(2, 3)) loss <- loss_mean_absolute_percentage_error(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"Formula:","code":"loss <- mean(square(y_true - y_pred))"},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"","code":"loss_mean_squared_error(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"mean_squared_error\" )"},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"Mean squared error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_mean_squared_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean of squares of errors between labels and predictions. — loss_mean_squared_error","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2, 3)) loss <- loss_mean_squared_error(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"Note y_pred y_true less equal 0. Negative values 0 values replaced config_epsilon() (default 1e-7). Formula:","code":"loss <- mean(square(log(y_true + 1) - log(y_pred + 1)))"},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"","code":"loss_mean_squared_logarithmic_error(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"mean_squared_logarithmic_error\" )"},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"Mean squared logarithmic error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_mean_squared_logarithmic_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean squared logarithmic error between y_true and y_pred. — loss_mean_squared_logarithmic_error","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2, 3)) loss <- loss_mean_squared_logarithmic_error(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"Formula:","code":"loss <- y_pred - y_true * log(y_pred)"},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"","code":"loss_poisson(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"poisson\" )"},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"y_true Ground truth values. shape = [batch_size, d0, .. dN]. y_pred predicted values. shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"Poisson loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_poisson.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Poisson loss between y_true & y_pred. — loss_poisson","text":"","code":"y_true <- random_uniform(c(2, 3), 0, 2) y_pred <- random_uniform(c(2, 3)) loss <- loss_poisson(y_true, y_pred) loss ## tf.Tensor([2.5907533  0.66836613], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"Use crossentropy loss function two label classes.  expect labels provided integers. want provide labels using one-hot representation, please use CategoricalCrossentropy loss.  # classes floating point values per feature y_pred single floating point value per feature y_true. snippet , single floating point value per example y_true num_classes floating pointing values per example y_pred. shape y_true [batch_size] shape y_pred [batch_size, num_classes].","code":""},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"","code":"loss_sparse_categorical_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   ignore_class = NULL,   axis = -1L,   ...,   reduction = \"sum_over_batch_size\",   name = \"sparse_categorical_crossentropy\" )"},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"y_true Ground truth values. y_pred predicted values. from_logits Whether y_pred expected logits tensor. default, assume y_pred encodes probability distribution. ignore_class Optional integer. ID class ignored loss computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. axis Defaults -1. dimension along entropy computed. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"Sparse categorical crossentropy loss value.","code":""},{"path":"https://keras.posit.co/reference/loss_sparse_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy loss between the labels and predictions. — loss_sparse_categorical_crossentropy","text":"Usage compile() API:","code":"y_true <- c(1, 2) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) loss <- loss_sparse_categorical_crossentropy(y_true, y_pred) loss ## tf.Tensor([0.05129339 2.30258509], shape=(2), dtype=float64) y_true <- c(1, 2) y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)) # Using 'auto'/'sum_over_batch_size' reduction type. scce <- loss_sparse_categorical_crossentropy() scce(op_array(y_true), op_array(y_pred)) ## tf.Tensor(1.1769392, shape=(), dtype=float32) # 1.177 # Calling with 'sample_weight'. scce(op_array(y_true), op_array(y_pred), sample_weight = op_array(c(0.3, 0.7))) ## tf.Tensor(0.8135988, shape=(), dtype=float32) # Using 'sum' reduction type. scce <- loss_sparse_categorical_crossentropy(reduction=\"sum\") scce(op_array(y_true), op_array(y_pred)) ## tf.Tensor(2.3538785, shape=(), dtype=float32) # 2.354 # Using 'none' reduction type. scce <- loss_sparse_categorical_crossentropy(reduction=NULL) scce(op_array(y_true), op_array(y_pred)) ## tf.Tensor([0.05129344 2.3025851 ], shape=(2), dtype=float32) # array([0.0513, 2.303], dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = loss_sparse_categorical_crossentropy())"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"Formula:   y_true values expected -1 1. binary (0 1) labels provided convert -1 1.","code":"loss <- square(maximum(1 - y_true * y_pred, 0))"},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"","code":"loss_squared_hinge(   y_true,   y_pred,   ...,   reduction = \"sum_over_batch_size\",   name = \"squared_hinge\" )"},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"y_true ground truth values. y_true values expected -1 1. binary (0 1) labels provided convert -1 1 shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name Optional name loss instance.","code":""},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"Squared hinge loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/loss_squared_hinge.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the squared hinge loss between y_true & y_pred. — loss_squared_hinge","text":"","code":"y_true <- array(sample(c(-1,1), 6, replace = TRUE), dim = c(2, 3)) y_pred <- random_uniform(c(2, 3)) loss <- loss_squared_hinge(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"loss function weighted alpha beta coefficients penalize false positives false negatives. alpha=0.5 beta=0.5, loss value becomes equivalent Dice Loss. loss function weighted alpha beta coefficients penalize false positives false negatives. alpha=0.5 beta=0.5, loss value becomes equivalent Dice Loss.","code":""},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"","code":"loss_tversky(   y_true,   y_pred,   ...,   alpha = 0.5,   beta = 0.5,   reduction = \"sum_over_batch_size\",   name = \"tversky\" )"},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"y_true tensor true targets. y_pred tensor predicted targets. ... forward/backward compatability. alpha coefficient controlling incidence false positives. beta coefficient controlling incidence false negatives. reduction Type reduction apply loss. almost cases \"sum_over_batch_size\". Supported options \"sum\", \"sum_over_batch_size\" NULL. name String, name object","code":""},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"Tversky loss value.","code":""},{"path":"https://keras.posit.co/reference/loss_tversky.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Computes the Tversky loss value between y_true and y_pred. — loss_tversky","text":"Salehi et al., 2017","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/mark_active.html","id":null,"dir":"Reference","previous_headings":"","what":"active_property — mark_active","title":"active_property — mark_active","text":"mark_active() alias active_property(). See ?active_property() full documentation.","code":""},{"path":"https://keras.posit.co/reference/mark_active.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"active_property — mark_active","text":"","code":"mark_active(fn)"},{"path":"https://keras.posit.co/reference/mark_active.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"active_property — mark_active","text":"fn R function","code":""},{"path":"https://keras.posit.co/reference/mark_active.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"active_property — mark_active","text":"fn, additional R attribute cause fn converted active property converted method custom subclass.","code":""},{"path":"https://keras.posit.co/reference/metric_auc.html","id":null,"dir":"Reference","previous_headings":"","what":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"AUC (Area curve) ROC (Receiver operating characteristic; default) PR (Precision Recall) curves quality measures binary classifiers. Unlike accuracy, like cross-entropy losses, ROC-AUC PR-AUC evaluate operational points model. class approximates AUCs using Riemann sum. metric accumulation phrase, predictions accumulated within predefined buckets value. AUC computed interpolating per-bucket averages. buckets define evaluated operational points. metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute AUC.  discretize AUC curve, linearly spaced set thresholds used compute pairs recall precision values. area ROC-curve therefore computed using height recall values false positive rate, area PR-curve computed using height precision values recall. value ultimately returned auc, idempotent operation computes area discretized curve precision versus recall values (computed using aforementioned variables). num_thresholds variable controls degree discretization larger numbers thresholds closely approximating true AUC. quality approximation may vary dramatically depending num_thresholds. thresholds parameter can used manually specify thresholds split predictions evenly. best approximation real AUC, predictions distributed approximately uniformly range [0, 1] (from_logits=FALSE). quality AUC approximation may poor case. Setting summation_method 'minoring' 'majoring' can help quantify error approximation providing lower upper bound estimate AUC. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_auc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"","code":"metric_auc(   ...,   num_thresholds = 200L,   curve = \"ROC\",   summation_method = \"interpolation\",   name = NULL,   dtype = NULL,   thresholds = NULL,   multi_label = FALSE,   num_labels = NULL,   label_weights = NULL,   from_logits = FALSE )"},{"path":"https://keras.posit.co/reference/metric_auc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"... forward/backward compatability. num_thresholds (Optional) number thresholds use discretizing roc curve. Values must > 1. Defaults 200. curve (Optional) Specifies name curve computed, 'ROC' (default) 'PR' Precision-Recall-curve. summation_method (Optional) Specifies Riemann summation method used. 'interpolation' (default) applies mid-point summation scheme ROC.  PR-AUC, interpolates (true/false) positives ratio precision (see Davis & Goadrich 2006 details); 'minoring' applies left summation increasing intervals right summation decreasing intervals; 'majoring' opposite. name (Optional) string name metric instance. dtype (Optional) data type metric result. thresholds (Optional) list floating point values use thresholds discretizing curve. set, num_thresholds parameter ignored. Values [0, 1]. Endpoint thresholds equal {-epsilon, 1+epsilon} small positive epsilon value automatically included correctly handle predictions equal exactly 0 1. multi_label boolean indicating whether multilabel data treated , wherein AUC computed separately label averaged across labels, (FALSE) data flattened single label AUC computation. latter case, multilabel data passed AUC, label-prediction pair treated individual data point. set `FALSE`` multi-class data. num_labels (Optional) number labels, used multi_label TRUE. num_labels specified, state variables get created first call update_state. label_weights (Optional) list, array, tensor non-negative weights used compute AUCs multilabel data. multi_label TRUE, weights applied individual label AUCs averaged produce multi-label AUC. FALSE, used weight individual label predictions computing confusion matrix flattened data. Note unlike class_weights class_weights weights example depending value label, whereas label_weights depends index label flattening; therefore label_weights used multi-class data. from_logits boolean indicating whether predictions (y_pred update_state) probabilities sigmoid logits. rule thumb, using keras loss, from_logits constructor argument loss match AUC from_logits constructor argument.","code":""},{"path":"https://keras.posit.co/reference/metric_auc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_auc.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Approximates the AUC (Area under the curve) of the ROC or PR curves. — metric_auc","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_auc(num_thresholds = 3) m$update_state(c(0,   0,   1,   1),                c(0, 0.5, 0.3, 0.9)) # threshold values are [0 - 1e-7, 0.5, 1 + 1e-7] # tp = [2, 1, 0], fp = [2, 0, 0], fn = [0, 1, 2], tn = [0, 2, 2] # tp_rate = recall = [1, 0.5, 0], fp_rate = [1, 0, 0] # auc = ((((1 + 0.5) / 2) * (1 - 0)) + (((0.5 + 0) / 2) * (0 - 0))) #     = 0.75 m$result() ## tf.Tensor(0.75, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   1,   1),                c(0, 0.5, 0.3, 0.9),                sample_weight=c(1, 0, 0, 1)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32) # Reports the AUC of a model outputting a probability. model |> compile(   optimizer = 'sgd',   loss = loss_binary_crossentropy(),   metrics = list(metric_auc()) )  # Reports the AUC of a model outputting a logit. model |> compile(   optimizer = 'sgd',   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_auc(from_logits = TRUE)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates how often predictions match binary labels. — metric_binary_accuracy","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"metric creates two local variables, total count used compute frequency y_pred matches y_true. frequency ultimately returned binary accuracy: idempotent operation simply divides total count. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"","code":"metric_binary_accuracy(   y_true,   y_pred,   threshold = 0.5,   ...,   name = \"binary_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. threshold (Optional) Float representing threshold deciding whether prediction values 1 0. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match binary labels. — metric_binary_accuracy","text":"Standalone usage:             Usage compile() API:","code":"m <- metric_binary_accuracy() m$update_state(rbind(1, 1, 0, 0), rbind(0.98, 1, 0, 0.6)) m$result() ## tf.Tensor(0.75, shape=(), dtype=float32) # 0.75 m$reset_state() m$update_state(rbind(1, 1, 0, 0), rbind(0.98, 1, 0, 0.6),                sample_weight = c(1, 0, 0, 1)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) # 0.5 model %>% compile(optimizer='sgd',                   loss='binary_crossentropy',                   metrics=list(metric_binary_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"crossentropy metric class used two label classes (0 1).","code":""},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"","code":"metric_binary_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   name = \"binary_crossentropy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"y_true Ground truth values. shape = [batch_size, d0, .. dN]. y_pred predicted values. shape = [batch_size, d0, .. dN]. from_logits (Optional) Whether output expected logits tensor. default, consider output encodes probability distribution. label_smoothing (Optional) Float [0, 1]. > 0, label values smoothed, meaning confidence label values relaxed. e.g. label_smoothing=0.2 means use value 0.1 label \"0\" 0.9 label \"1\". axis axis along mean computed. Defaults -1. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy metric between the labels and predictions. — metric_binary_crossentropy","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_binary_crossentropy() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(0.8149245, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight=c(1, 0)) m$result() ## tf.Tensor(0.91629076, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_binary_crossentropy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"According Lin et al., 2018, helps apply focal factor -weight easy examples focus hard examples. default, focal tensor computed follows: focal_factor = (1 - output)^gamma class 1 focal_factor = output^gamma class 0 gamma focusing parameter. gamma = 0, focal effect binary crossentropy loss. apply_class_balancing == TRUE, function also takes account weight balancing factor binary classes 0 1 follows: weight = alpha class 1 (target == 1) weight = 1 - alpha class 0 alpha float range [0, 1].","code":""},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"","code":"metric_binary_focal_crossentropy(   y_true,   y_pred,   apply_class_balancing = FALSE,   alpha = 0.25,   gamma = 2,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"y_true Ground truth values, shape (batch_size, d0, .. dN). y_pred predicted values, shape (batch_size, d0, .. dN). apply_class_balancing bool, whether apply weight balancing binary classes 0 1. alpha weight balancing factor class 1, default 0.25 mentioned reference. weight class 0 1.0 - alpha. gamma focusing parameter, default 2.0 mentioned reference. from_logits Whether y_pred expected logits tensor. default, assume y_pred encodes probability distribution. label_smoothing Float [0, 1]. > 0 smooth labels squeezing towards 0.5, , using 1. - 0.5 * label_smoothing target class 0.5 * label_smoothing non-target class. axis axis along mean computed. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"Binary focal crossentropy loss value shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/metric_binary_focal_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the binary focal crossentropy loss. — metric_binary_focal_crossentropy","text":"","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_binary_focal_crossentropy(y_true, y_pred, gamma=2) loss ## tf.Tensor([0.32986466 0.20579838], shape=(2), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class can used compute IoUs binary classification task predictions provided logits. First threshold applied predicted values threshold converted class 0 threshold converted class 1. IoUs classes 0 1 computed, mean IoUs classes specified target_class_ids returned.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"","code":"metric_binary_iou(   ...,   target_class_ids = list(0L, 1L),   threshold = 0.5,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"... forward/backward compatability. target_class_ids list list target class ids metric returned. Options 0, 1, c(0, 1). 0 (1), IoU metric class 0 (class 1, respectively) returned. c(0, 1), mean IoUs two classes returned. threshold threshold applies prediction logits convert either predicted class 0 logit threshold predicted class 1 logit threshold. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"threshold=0, metric behavior IoU.","code":""},{"path":"https://keras.posit.co/reference/metric_binary_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Intersection-Over-Union metric for class 0 and/or 1. — metric_binary_iou","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_binary_iou(target_class_ids=c(0L, 1L), threshold = 0.3) m$update_state(c(0, 1, 0, 1), c(0.1, 0.2, 0.4, 0.7)) m$result() ## tf.Tensor(0.33333334, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 0, 1), c(0.1, 0.2, 0.4, 0.7),                sample_weight = c(0.2, 0.3, 0.4, 0.1)) m$result() ## tf.Tensor(0.17361109, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_binary_iou(         target_class_ids = 0L,         threshold = 0.5     )) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"can provide logits classes y_pred, since argmax logits probabilities . metric creates two local variables, total count used compute frequency y_pred matches y_true. frequency ultimately returned categorical accuracy: idempotent operation simply divides total count. y_pred y_true passed vectors probabilities, rather labels. necessary, use op_one_hot expand y_true vector. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"","code":"metric_categorical_accuracy(   y_true,   y_pred,   ...,   name = \"categorical_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match one-hot labels. — metric_categorical_accuracy","text":"Standalone usage:           Usage compile() API:","code":"m <- metric_categorical_accuracy() m$update_state(rbind(c(0, 0, 1), c(0, 1, 0)), rbind(c(0.1, 0.9, 0.8),                 c(0.05, 0.95, 0))) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 0, 1), c(0, 1, 0)), rbind(c(0.1, 0.9, 0.8),                c(0.05, 0.95, 0)),                sample_weight = c(0.7, 0.3)) m$result() ## tf.Tensor(0.3, shape=(), dtype=float32) # 0.3 model %>% compile(optimizer = 'sgd',                   loss = 'categorical_crossentropy',                   metrics = list(metric_categorical_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"crossentropy metric class used multiple label classes (2 ). assumes labels one-hot encoded, e.g., labels values c(2, 0, 1), y_true rbind(c([0, 0, 1), c(1, 0, 0), c(0, 1, 0)).","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"","code":"metric_categorical_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L,   ...,   name = \"categorical_crossentropy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"y_true Tensor one-hot true targets. y_pred Tensor predicted targets. from_logits (Optional) Whether output expected logits tensor. default, consider output encodes probability distribution. label_smoothing (Optional) Float [0, 1]. > 0, label values smoothed, meaning confidence label values relaxed. e.g. label_smoothing=0.2 means use value 0.1 label \"0\" 0.9 label \"1\". axis (Optional) Defaults -1. dimension along entropy computed. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy metric between the labels and predictions. — metric_categorical_crossentropy","text":"Standalone usage:           Usage compile() API:","code":"# EPSILON = 1e-7, y = y_true, y` = y_pred # y` = clip_op_clip_by_value(output, EPSILON, 1. - EPSILON) # y` = rbind(c(0.05, 0.95, EPSILON), c(0.1, 0.8, 0.1)) # xent = -sum(y * log(y'), axis = -1) #      = -((log 0.95), (log 0.1)) #      = [0.051, 2.302] # Reduced xent = (0.051 + 2.302) / 2 m <- metric_categorical_crossentropy() m$update_state(rbind(c(0, 1, 0), c(0, 0, 1)),                rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1))) m$result() ## tf.Tensor(1.1769392, shape=(), dtype=float32) # 1.1769392 m$reset_state() m$update_state(rbind(c(0, 1, 0), c(0, 0, 1)),                rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)),                sample_weight = c(0.3, 0.7)) m$result() ## tf.Tensor(1.6271976, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_categorical_crossentropy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"Computes categorical focal crossentropy loss.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"","code":"metric_categorical_focal_crossentropy(   y_true,   y_pred,   alpha = 0.25,   gamma = 2,   from_logits = FALSE,   label_smoothing = 0,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"y_true Tensor one-hot true targets. y_pred Tensor predicted targets. alpha weight balancing factor classes, default 0.25 mentioned reference. can list floats scalar. multi-class case, alpha may set inverse class frequency using compute_class_weight sklearn.utils. gamma focusing parameter, default 2.0 mentioned reference. helps gradually reduce importance given simple examples smooth manner. gamma = 0, focal effect categorical crossentropy. from_logits Whether y_pred expected logits tensor. default, assume y_pred encodes probability distribution. label_smoothing Float [0, 1]. > 0 smooth labels. example, 0.1, use 0.1 / num_classes non-target labels 0.9 + 0.1 / num_classes target labels. axis Defaults -1. dimension along entropy computed.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"Categorical focal crossentropy loss value.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_focal_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the categorical focal crossentropy loss. — metric_categorical_focal_crossentropy","text":"","code":"y_true <- rbind(c(0, 1, 0), c(0, 0, 1)) y_pred <- rbind(c(0.05, 0.9, 0.05), c(0.1, 0.85, 0.05)) loss <- loss_categorical_focal_crossentropy(y_true, y_pred) loss ## tf.Tensor([2.63401289e-04 6.75912094e-01], shape=(2), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"Formula:   neg=maximum((1-y_true)*y_pred) pos=sum(y_true*y_pred)","code":"loss <- maximum(neg - pos + 1, 0)"},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"","code":"metric_categorical_hinge(   y_true,   y_pred,   ...,   name = \"categorical_hinge\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"y_true ground truth values. y_true values expected either {-1, +1} {0, 1} (.e. one-hot-encoded tensor) shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"Categorical hinge loss values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/metric_categorical_hinge.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the categorical hinge metric between y_true and y_pred. — metric_categorical_hinge","text":"Standalone usage:","code":"m <- metric_categorical_hinge() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(1.4000001, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(1.2, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"Formula:   See: Cosine Similarity. metric keeps average cosine similarity predictions labels stream data.","code":"loss <- sum(l2_norm(y_true) * l2_norm(y_pred))"},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"","code":"metric_cosine_similarity(   ...,   name = \"cosine_similarity\",   dtype = NULL,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result. axis (Optional) Defaults -1. dimension along cosine similarity computed.","code":""},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_cosine_similarity.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the cosine similarity between the labels and predictions. — metric_cosine_similarity","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_cosine_similarity(axis=2) m$update_state(rbind(c(0., 1.), c(1., 1.)), rbind(c(1., 0.), c(1., 1.))) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0., 1.), c(1., 1.)), rbind(c(1., 0.), c(1., 1.)),                sample_weight = c(0.3, 0.7)) m$result() ## tf.Tensor(0.7, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_cosine_similarity(axis=2)))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes F-1 Score. — metric_f1_score","title":"Computes F-1 Score. — metric_f1_score","text":"Formula:   harmonic mean precision recall. output range [0, 1]. works multi-class multi-label classification.","code":"f1_score <- 2 * (precision * recall) / (precision + recall)"},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes F-1 Score. — metric_f1_score","text":"","code":"metric_f1_score(   ...,   average = NULL,   threshold = NULL,   name = \"f1_score\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes F-1 Score. — metric_f1_score","text":"... forward/backward compatability. average Type averaging performed data. Acceptable values NULL, \"micro\", \"macro\" \"weighted\". Defaults NULL. NULL, averaging performed result() return score class. \"micro\", compute metrics globally counting total true positives, false negatives false positives. \"macro\", compute metrics label, return unweighted mean. take label imbalance account. \"weighted\", compute metrics label, return average weighted support (number true instances label). alters \"macro\" account label imbalance. can result score precision recall. threshold Elements y_pred greater threshold converted 1, rest 0. threshold NULL, argmax y_pred converted 1, rest 0. name Optional. String name metric instance. dtype Optional. Data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes F-1 Score. — metric_f1_score","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes F-1 Score. — metric_f1_score","text":"","code":"metric <- metric_f1_score(threshold = 0.5) y_true <- rbind(c(1, 1, 1),                 c(1, 0, 0),                 c(1, 1, 0)) y_pred <- rbind(c(0.2, 0.6, 0.7),                 c(0.2, 0.6, 0.6),                 c(0.6, 0.8, 0.0)) metric$update_state(y_true, y_pred) result <- metric$result() result ## tf.Tensor([0.49999997 0.79999995 0.66666657], shape=(3), dtype=float32)"},{"path":"https://keras.posit.co/reference/metric_f1_score.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Computes F-1 Score. — metric_f1_score","text":"F-1 Score: float.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the number of false negatives. — metric_false_negatives","title":"Calculates the number of false negatives. — metric_false_negatives","text":"sample_weight given, calculates sum weights false negatives. metric creates one local variable, accumulator used keep track number false negatives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of false negatives. — metric_false_negatives","text":"","code":"metric_false_negatives(..., thresholds = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the number of false negatives. — metric_false_negatives","text":"... forward/backward compatability. thresholds (Optional) Defaults 0.5. float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the number of false negatives. — metric_false_negatives","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_false_negatives.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of false negatives. — metric_false_negatives","text":"Standalone usage:","code":"m <- metric_false_negatives() m$update_state(c(0, 1, 1, 1), c(0, 1, 0, 0)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 1, 1), c(0, 1, 0, 0), sample_weight=c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32) # 1.0"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the number of false positives. — metric_false_positives","title":"Calculates the number of false positives. — metric_false_positives","text":"sample_weight given, calculates sum weights false positives. metric creates one local variable, accumulator used keep track number false positives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of false positives. — metric_false_positives","text":"","code":"metric_false_positives(..., thresholds = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the number of false positives. — metric_false_positives","text":"... forward/backward compatability. thresholds (Optional) Defaults 0.5. float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the number of false positives. — metric_false_positives","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_false_positives.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of false positives. — metric_false_positives","text":"Standalone usage:","code":"m <- metric_false_positives() m$update_state(c(0, 1, 0, 0), c(0, 0, 1, 1)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 0, 0), c(0, 0, 1, 1), sample_weight = c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes F-Beta score. — metric_fbeta_score","title":"Computes F-Beta score. — metric_fbeta_score","text":"Formula:   weighted harmonic mean precision recall. output range [0, 1]. works multi-class multi-label classification.","code":"b2 <- beta^2 f_beta_score <- (1 + b2) * (precision * recall) / (precision * b2 + recall)"},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes F-Beta score. — metric_fbeta_score","text":"","code":"metric_fbeta_score(   ...,   average = NULL,   beta = 1,   threshold = NULL,   name = \"fbeta_score\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes F-Beta score. — metric_fbeta_score","text":"... forward/backward compatability. average Type averaging performed across per-class results multi-class case. Acceptable values NULL, \"micro\", \"macro\" \"weighted\". Defaults NULL. NULL, averaging performed result() return score class. \"micro\", compute metrics globally counting total true positives, false negatives false positives. \"macro\", compute metrics label, return unweighted mean. take label imbalance account. \"weighted\", compute metrics label, return average weighted support (number true instances label). alters \"macro\" account label imbalance. can result score precision recall. beta Determines weight given recall harmonic mean precision recall (see pseudocode equation ). Defaults 1. threshold Elements y_pred greater threshold converted 1, rest 0. threshold NULL, argmax y_pred converted 1, rest 0. name Optional. String name metric instance. dtype Optional. Data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes F-Beta score. — metric_fbeta_score","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes F-Beta score. — metric_fbeta_score","text":"","code":"metric <- metric_fbeta_score(beta = 2.0, threshold = 0.5) y_true <- rbind(c(1, 1, 1),                 c(1, 0, 0),                 c(1, 1, 0)) y_pred <- rbind(c(0.2, 0.6, 0.7),                 c(0.2, 0.6, 0.6),                 c(0.6, 0.8, 0.0)) metric$update_state(y_true, y_pred) metric$result() ## tf.Tensor([0.3846154  0.90909094 0.8333332 ], shape=(3), dtype=float32)"},{"path":"https://keras.posit.co/reference/metric_fbeta_score.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Computes F-Beta score. — metric_fbeta_score","text":"F-Beta Score: float.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the hinge metric between y_true and y_pred. — metric_hinge","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"Formula:   y_true values expected -1 1. binary (0 1) labels provided convert -1 1.","code":"loss <- mean(maximum(1 - y_true * y_pred, 0), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"","code":"metric_hinge(y_true, y_pred, ..., name = \"hinge\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"y_true ground truth values. y_true values expected -1 1. binary (0 1) labels provided converted -1 1 shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_hinge.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge metric between y_true and y_pred. — metric_hinge","text":"Standalone usage:","code":"m <- metric_hinge() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(1.3, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(1.1, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_huber.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes Huber loss value. — metric_huber","title":"Computes Huber loss value. — metric_huber","text":"Formula:   See: Huber loss.","code":"for (x in error) {   if (abs(x) <= delta){     loss <- c(loss, (0.5 * x^2))   } else if (abs(x) > delta) {     loss <- c(loss, (delta * abs(x) - 0.5 * delta^2))   } } loss <- mean(loss)"},{"path":"https://keras.posit.co/reference/metric_huber.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes Huber loss value. — metric_huber","text":"","code":"metric_huber(y_true, y_pred, delta = 1)"},{"path":"https://keras.posit.co/reference/metric_huber.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes Huber loss value. — metric_huber","text":"y_true tensor true targets. y_pred tensor predicted targets. delta float, point Huber loss function changes quadratic linear. Defaults 1.0.","code":""},{"path":"https://keras.posit.co/reference/metric_huber.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes Huber loss value. — metric_huber","text":"Tensor one scalar loss entry per sample.","code":""},{"path":"https://keras.posit.co/reference/metric_huber.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes Huber loss value. — metric_huber","text":"","code":"y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_huber(y_true, y_pred)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. Note, class first computes IoUs individual classes, returns mean IoUs classes specified target_class_ids. target_class_ids one id value, IoU specific class returned.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"","code":"metric_iou(   ...,   num_classes,   target_class_ids,   name = NULL,   dtype = NULL,   ignore_class = NULL,   sparse_y_true = TRUE,   sparse_y_pred = TRUE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"... forward/backward compatability. num_classes possible number labels prediction task can . target_class_ids list target class ids metric returned. compute IoU specific class, list single id value provided. name (Optional) string name metric instance. dtype (Optional) data type metric result. ignore_class Optional integer. ID class ignored metric computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. sparse_y_true Whether labels encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. sparse_y_pred Whether predictions encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. axis (Optional) -1 dimension containing logits. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Intersection-Over-Union metric for specific target classes. — metric_iou","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_iou(num_classes = 2L, target_class_ids = list(0L)) m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1)) m$result() ## tf.Tensor(0.3333333, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1),                sample_weight = c(0.3, 0.3, 0.3, 0.1)) m$result() ## tf.Tensor(0.33333325, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_iou(num_classes = 2L, target_class_ids = list(0L))))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"Formula:   y_true y_pred expected probability distributions, values 0 1. get clipped [0, 1] range.","code":"loss <- y_true * log(y_true / y_pred)"},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"","code":"metric_kl_divergence(y_true, y_pred, ..., name = \"kl_divergence\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"y_true Tensor true targets. y_pred Tensor predicted targets. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_kl_divergence.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes Kullback-Leibler divergence metric between y_true and — metric_kl_divergence","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_kl_divergence() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(0.45814303, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.91628915, shape=(), dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = 'mse',                   metrics = list(metric_kl_divergence()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"Formula:   Note log(cosh(x)) approximately equal (x ** 2) / 2 small x abs(x) - log(2) large x. means 'logcosh' works mostly like mean squared error, strongly affected occasional wildly incorrect prediction.","code":"loss <- mean(log(cosh(y_pred - y_true)), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"","code":"metric_log_cosh(y_true, y_pred)"},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN].","code":""},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"Logcosh error values shape = [batch_size, d0, .. dN-1].","code":""},{"path":"https://keras.posit.co/reference/metric_log_cosh.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh","text":"","code":"y_true <- rbind(c(0., 1.), c(0., 0.)) y_pred <- rbind(c(1., 1.), c(0., 0.)) loss <- metric_log_cosh(y_true, y_pred) loss ## tf.Tensor([ 2.16890413e-01 -1.90465432e-09], shape=(2), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"Formula:","code":"error <- y_pred - y_true logcosh <- mean(log((exp(error) + exp(-error))/2), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"","code":"metric_log_cosh_error(..., name = \"logcosh\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_log_cosh_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the logarithm of the hyperbolic cosine of the prediction error. — metric_log_cosh_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_log_cosh_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.108445205, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.21689041, shape=(), dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = 'mse',                   metrics = list(metric_log_cosh_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the (weighted) mean of the given values. — metric_mean","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"example, values c(1, 3, 5, 7) mean 4. sample_weight specified c(1, 1, 0, 0) mean 2. metric creates two variables, total count. mean value returned simply total divided count.","code":""},{"path":"https://keras.posit.co/reference/metric_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"","code":"metric_mean(..., name = \"mean\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_mean.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the (weighted) mean of the given values. — metric_mean","text":"","code":"m <- metric_mean() m$update_state(c(1, 3, 5, 7)) m$result() ## tf.Tensor(4.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(1, 3, 5, 7), sample_weight = c(1, 1, 0, 0)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"Formula:","code":"loss <- mean(abs(y_true - y_pred))"},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"","code":"metric_mean_absolute_error(   y_true,   y_pred,   ...,   name = \"mean_absolute_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_absolute_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean absolute error between the labels and predictions. — metric_mean_absolute_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_mean_absolute_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.25, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_mean_absolute_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"Formula:   Division zero prevented dividing maximum(y_true, epsilon) epsilon = keras$backend$epsilon() (default 1e-7).","code":"loss <- 100 * mean(abs((y_true - y_pred) / y_true), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"","code":"metric_mean_absolute_percentage_error(   y_true,   y_pred,   ...,   name = \"mean_absolute_percentage_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_absolute_percentage_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes mean absolute percentage error between y_true and y_pred. — metric_mean_absolute_percentage_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_mean_absolute_percentage_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(250000000.0, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(500000000.0, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_mean_absolute_percentage_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. Note class first computes IoUs individual classes, returns mean values.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"","code":"metric_mean_iou(   ...,   num_classes,   name = NULL,   dtype = NULL,   ignore_class = NULL,   sparse_y_true = TRUE,   sparse_y_pred = TRUE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"... forward/backward compatability. num_classes possible number labels prediction task can . value must provided, since confusion matrix dimension = [num_classes, num_classes] allocated. name (Optional) string name metric instance. dtype (Optional) data type metric result. ignore_class Optional integer. ID class ignored metric computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. sparse_y_true Whether labels encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. sparse_y_pred Whether predictions encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. axis (Optional) dimension containing logits. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean Intersection-Over-Union metric. — metric_mean_iou","text":"Standalone usage:         Usage compile() API:","code":"# cm = [[1, 1], #        [1, 1]] # sum_row = [2, 2], sum_col = [2, 2], true_positives = [1, 1] # iou = true_positives / (sum_row + sum_col - true_positives)) # result = (1 / (2 + 2 - 1) + 1 / (2 + 2 - 1)) / 2 = 0.33 m <- metric_mean_iou(num_classes = 2) m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1)) m$result() ## tf.Tensor(0.33333334, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1),                sample_weight=c(0.3, 0.3, 0.3, 0.1)) m$result() ## tf.Tensor(0.2380952, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_mean_iou(num_classes=2)))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"Formula:","code":"loss <- mean(square(y_true - y_pred))"},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"","code":"metric_mean_squared_error(   y_true,   y_pred,   ...,   name = \"mean_squared_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_squared_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the mean squared error between y_true and y_pred. — metric_mean_squared_error","text":"","code":"m <- metric_mean_squared_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.25, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"Formula:   Note y_pred y_true less equal 0. Negative values 0 values replaced keras$backend$epsilon() (default 1e-7).","code":"loss <- mean(square(log(y_true + 1) - log(y_pred + 1)), axis=-1)"},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"","code":"metric_mean_squared_logarithmic_error(   y_true,   y_pred,   ...,   name = \"mean_squared_logarithmic_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"y_true Ground truth values shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_squared_logarithmic_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes mean squared logarithmic error between y_true and y_pred. — metric_mean_squared_logarithmic_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_mean_squared_logarithmic_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.12011322, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.24022643, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_mean_squared_logarithmic_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_mean_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","title":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","text":"use class quickly build mean metric function. function needs signature fn(y_true, y_pred) return per-sample loss array. metric_mean_wrapper$result() return average metric value across samples seen far. example:","code":"mse <- function(y_true, y_pred) {   (y_true - y_pred)^2 }  mse_metric <- metric_mean_wrapper(fn = mse) mse_metric$update_state(c(0, 1), c(1, 1)) mse_metric$result() ## tf.Tensor(0.5, shape=(), dtype=float32)"},{"path":"https://keras.posit.co/reference/metric_mean_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","text":"","code":"metric_mean_wrapper(..., fn, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_mean_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","text":"... Keyword arguments pass fn. fn metric function wrap, signature fn(y_true, y_pred). name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_mean_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrap a stateless metric function with the Mean metric. — metric_mean_wrapper","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class can used compute IoU multi-class classification tasks labels one-hot encoded (last axis one dimension per class). Note predictions also shape. compute IoU, first labels predictions converted back integer format taking argmax class axis. computation steps base IoU class apply. Note, one channel labels predictions, class class IoU. case, use IoU instead. Also, make sure num_classes equal number classes data, avoid \"labels bound\" error confusion matrix computed.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"","code":"metric_one_hot_iou(   ...,   num_classes,   target_class_ids,   name = NULL,   dtype = NULL,   ignore_class = NULL,   sparse_y_pred = FALSE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"... forward/backward compatability. num_classes possible number labels prediction task can . target_class_ids list list target class ids metric returned. compute IoU specific class, list (list) single id value provided. name (Optional) string name metric instance. dtype (Optional) data type metric result. ignore_class Optional integer. ID class ignored metric computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. sparse_y_pred Whether predictions encoded using integers dense floating point vectors. FALSE, argmax function used determine sample's likely associated label. axis (Optional) dimension containing logits. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_one_hot_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_iou","text":"Standalone usage:     Usage compile() API:","code":"y_true <- rbind(c(0, 0, 1), c(1, 0, 0), c(0, 1, 0), c(1, 0, 0)) y_pred <- rbind(c(0.2, 0.3, 0.5), c(0.1, 0.2, 0.7), c(0.5, 0.3, 0.1),                 c(0.1, 0.4, 0.5)) sample_weight <- c(0.1, 0.2, 0.3, 0.4) m <- metric_one_hot_iou(num_classes = 3, target_class_ids = c(0, 2)) m$update_state(     y_true = y_true, y_pred = y_pred, sample_weight = sample_weight) m$result() ## tf.Tensor(0.07142855, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_one_hot_iou(     num_classes = 3L,     target_class_id = list(1L)   )) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"Formula:   Intersection--Union common evaluation metric semantic image segmentation. compute IoUs, predictions accumulated confusion matrix, weighted sample_weight metric calculated . sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class can used compute mean IoU multi-class classification tasks labels one-hot encoded (last axis one dimension per class). Note predictions also shape. compute mean IoU, first labels predictions converted back integer format taking argmax class axis. computation steps base MeanIoU class apply. Note, one channel labels predictions, class class metric_mean_iou. case, use metric_mean_iou instead. Also, make sure num_classes equal number classes data, avoid \"labels bound\" error confusion matrix computed.","code":"iou <- true_positives / (true_positives + false_positives + false_negatives)"},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"","code":"metric_one_hot_mean_iou(   ...,   num_classes,   name = NULL,   dtype = NULL,   ignore_class = NULL,   sparse_y_pred = FALSE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"... forward/backward compatability. num_classes possible number labels prediction task can . name (Optional) string name metric instance. dtype (Optional) data type metric result. ignore_class Optional integer. ID class ignored metric computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. sparse_y_pred Whether predictions encoded using natural numbers probability distribution vectors. FALSE, argmax function used determine sample's likely associated label. axis (Optional) dimension containing logits. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_one_hot_mean_iou.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes mean Intersection-Over-Union metric for one-hot encoded labels. — metric_one_hot_mean_iou","text":"Standalone usage:     Usage compile() API:","code":"y_true <- rbind(c(0, 0, 1), c(1, 0, 0), c(0, 1, 0), c(1, 0, 0)) y_pred <- rbind(c(0.2, 0.3, 0.5), c(0.1, 0.2, 0.7), c(0.5, 0.3, 0.1),                 c(0.1, 0.4, 0.5)) sample_weight <- c(0.1, 0.2, 0.3, 0.4) m <- metric_one_hot_mean_iou(num_classes = 3L) m$update_state(     y_true = y_true, y_pred = y_pred, sample_weight = sample_weight) m$result() ## tf.Tensor(0.047619034, shape=(), dtype=float32) model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_one_hot_mean_iou(num_classes = 3L)))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"Formula:","code":"metric <- y_pred - y_true * log(y_pred)"},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"","code":"metric_poisson(y_true, y_pred, ..., name = \"poisson\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"y_true Ground truth values. shape = [batch_size, d0, .. dN]. y_pred predicted values. shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_poisson.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Poisson metric between y_true and y_pred. — metric_poisson","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_poisson() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.49999997, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.99999994, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_poisson()) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_precision.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the precision of the predictions with respect to the labels. — metric_precision","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"metric creates two local variables, true_positives false_positives used compute precision. value ultimately returned precision, idempotent operation simply divides true_positives sum true_positives false_positives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. top_k set, calculate precision often average class among top-k classes highest predicted values batch entry correct can found label entry. class_id specified, calculate precision considering entries batch class_id threshold /top-k highest predictions, computing fraction class_id indeed correct label.","code":""},{"path":"https://keras.posit.co/reference/metric_precision.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"","code":"metric_precision(   ...,   thresholds = NULL,   top_k = NULL,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_precision.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"... forward/backward compatability. thresholds (Optional) float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. neither thresholds top_k set, default calculate precision thresholds=0.5. top_k (Optional) Unset default. int value specifying top-k predictions consider calculating precision. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_precision.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_precision.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the precision of the predictions with respect to the labels. — metric_precision","text":"Standalone usage:                 Usage compile() API:   Usage loss from_logits=TRUE:","code":"m <- metric_precision() m$update_state(c(0, 1, 1, 1),                c(1, 0, 1, 1)) m$result() |> as.double() |> signif() ## [1] 0.666667 m$reset_state() m$update_state(c(0, 1, 1, 1),                c(1, 0, 1, 1),                sample_weight = c(0, 0, 1, 0)) m$result() |> as.double() |> signif() ## [1] 1 # With top_k=2, it will calculate precision over y_true[1:2] # and y_pred[1:2] m <- metric_precision(top_k = 2) m$update_state(c(0, 0, 1, 1), c(1, 1, 1, 1)) m$result() ## tf.Tensor(0.0, shape=(), dtype=float32) # With top_k=4, it will calculate precision over y_true[1:4] # and y_pred[1:4] m <- metric_precision(top_k = 4) m$update_state(c(0, 0, 1, 1), c(1, 1, 1, 1)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_precision()) ) model |> compile(   optimizer = 'adam',   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_precision(thresholds = 0)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute precision given recall. threshold given recall value computed used evaluate corresponding precision. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class_id specified, calculate precision considering entries batch class_id threshold predictions, computing fraction class_id indeed correct label.","code":""},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"","code":"metric_precision_at_recall(   ...,   recall,   num_thresholds = 200L,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"... forward/backward compatability. recall scalar value range [0, 1]. num_thresholds (Optional) Defaults 200. number thresholds use matching given recall. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_precision_at_recall.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best precision where recall is >= specified value. — metric_precision_at_recall","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_precision_at_recall(recall = 0.5) m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8),                sample_weight = c(2, 2, 2, 1, 1)) m$result() ## tf.Tensor(0.33333334, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_precision_at_recall(recall = 0.8)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes R2 score. — metric_r2_score","title":"Computes R2 score. — metric_r2_score","text":"Formula:   also called coefficient determination. indicates close fitted regression line ground-truth data. highest score possible 1.0. indicates predictors perfectly accounts variation target. score 0.0 indicates predictors account variation target. can also negative model worse random. metric can also compute \"Adjusted R2\" score.","code":"sum_squares_residuals <- sum((y_true - y_pred) ** 2) sum_squares <- sum((y_true - mean(y_true)) ** 2) R2 <- 1 - sum_squares_residuals / sum_squares"},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes R2 score. — metric_r2_score","text":"","code":"metric_r2_score(   ...,   class_aggregation = \"uniform_average\",   num_regressors = 0L,   name = \"r2_score\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes R2 score. — metric_r2_score","text":"... forward/backward compatability. class_aggregation Specifies aggregate scores corresponding different output classes (target dimensions), .e. different dimensions last axis predictions. Equivalent multioutput argument Scikit-Learn. one NULL (aggregation), \"uniform_average\", \"variance_weighted_average\". num_regressors Number independent regressors used (\"Adjusted R2\" score). 0 standard R2 score. Defaults 0. name Optional. string name metric instance. dtype Optional. data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes R2 score. — metric_r2_score","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_r2_score.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes R2 score. — metric_r2_score","text":"","code":"y_true <- rbind(1, 4, 3) y_pred <- rbind(2, 4, 4) metric <- metric_r2_score() metric$update_state(y_true, y_pred) metric$result() ## tf.Tensor(0.57142854, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_recall.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the recall of the predictions with respect to the labels. — metric_recall","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"metric creates two local variables, true_positives false_negatives, used compute recall. value ultimately returned recall, idempotent operation simply divides true_positives sum true_positives false_negatives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. top_k set, recall computed often average class among labels batch entry top-k predictions. class_id specified, calculate recall considering entries batch class_id label, computing fraction class_id threshold /top-k predictions.","code":""},{"path":"https://keras.posit.co/reference/metric_recall.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"","code":"metric_recall(   ...,   thresholds = NULL,   top_k = NULL,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_recall.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"... forward/backward compatability. thresholds (Optional) float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. neither thresholds top_k set, default calculate recall thresholds=0.5. top_k (Optional) Unset default. int value specifying top-k predictions consider calculating recall. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_recall.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_recall.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the recall of the predictions with respect to the labels. — metric_recall","text":"Standalone usage:         Usage compile() API:   Usage loss from_logits=TRUE:","code":"m <- metric_recall() m$update_state(c(0, 1, 1, 1),                c(1, 0, 1, 1)) m$result() ## tf.Tensor(0.6666667, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 1, 1),                c(1, 0, 1, 1),                sample_weight = c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_recall()) ) model |> compile(   optimizer = 'adam',   loss = loss_binary_crossentropy(from_logits = TRUE),   metrics = list(metric_recall(thresholds = 0)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"given score-label-distribution required precision might achievable, case 0.0 returned recall. metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute recall given precision. threshold given precision value computed used evaluate corresponding recall. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class_id specified, calculate precision considering entries batch class_id threshold predictions, computing fraction class_id indeed correct label.","code":""},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"","code":"metric_recall_at_precision(   ...,   precision,   num_thresholds = 200L,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"... forward/backward compatability. precision scalar value range [0, 1]. num_thresholds (Optional) Defaults 200. number thresholds use matching given precision. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_recall_at_precision.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best recall where precision is >= specified value. — metric_recall_at_precision","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_recall_at_precision(precision = 0.8) m$update_state(c(0,   0,   1,   1),                c(0, 0.5, 0.3, 0.9)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   1,   1),                c(0, 0.5, 0.3, 0.9),                sample_weight = c(1, 0, 0, 1)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_recall_at_precision(precision = 0.8)) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"Formula:","code":"loss <- sqrt(mean((y_pred - y_true) ^ 2))"},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"","code":"metric_root_mean_squared_error(   ...,   name = \"root_mean_squared_error\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_root_mean_squared_error.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes root mean squared error metric between y_true and y_pred. — metric_root_mean_squared_error","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_root_mean_squared_error() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0))) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(1, 1), c(0, 0)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(0.70710677, shape=(), dtype=float32) model %>% compile(   optimizer = 'sgd',   loss = 'mse',   metrics = list(metric_root_mean_squared_error()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"Sensitivity measures proportion actual positives correctly identified (tp / (tp + fn)). Specificity measures proportion actual negatives correctly identified (tn / (tn + fp)). metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute sensitivity given specificity. threshold given specificity value computed used evaluate corresponding sensitivity. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class_id specified, calculate precision considering entries batch class_id threshold predictions, computing fraction class_id indeed correct label. additional information specificity sensitivity, see following.","code":""},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"","code":"metric_sensitivity_at_specificity(   ...,   specificity,   num_thresholds = 200L,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"... forward/backward compatability. specificity scalar value range [0, 1]. num_thresholds (Optional) Defaults 200. number thresholds use matching given specificity. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_sensitivity_at_specificity.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best sensitivity where specificity is >= specified value. — metric_sensitivity_at_specificity","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_sensitivity_at_specificity(specificity = 0.5) m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8),                sample_weight = c(1, 1, 2, 2, 1)) m$result() ## tf.Tensor(0.33333334, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_sensitivity_at_specificity()) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"can provide logits classes y_pred, since argmax logits probabilities . metric creates two local variables, total count used compute frequency y_pred matches y_true. frequency ultimately returned sparse categorical accuracy: idempotent operation simply divides total count. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":"acc <- sample_weight %*% (y_true == which.max(y_pred))"},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"","code":"metric_sparse_categorical_accuracy(   y_true,   y_pred,   ...,   name = \"sparse_categorical_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates how often predictions match integer labels. — metric_sparse_categorical_accuracy","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_sparse_categorical_accuracy() m$update_state(rbind(2L, 1L), rbind(c(0.1, 0.6, 0.3), c(0.05, 0.95, 0))) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(2L, 1L), rbind(c(0.1, 0.6, 0.3), c(0.05, 0.95, 0)),                sample_weight = c(0.7, 0.3)) m$result() ## tf.Tensor(0.3, shape=(), dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = 'sparse_categorical_crossentropy',                   metrics = list(metric_sparse_categorical_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"Use crossentropy metric two label classes. expects labels provided integers. want provide labels one-hot encoded, please use metric_categorical_crossentropy() metric instead. num_classes floating point values per feature y_pred single floating point value per feature y_true.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"","code":"metric_sparse_categorical_crossentropy(   y_true,   y_pred,   from_logits = FALSE,   ignore_class = NULL,   axis = -1L,   ...,   name = \"sparse_categorical_crossentropy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"y_true Ground truth values. y_pred predicted values. from_logits (Optional) Whether output expected logits tensor. default, consider output encodes probability distribution. ignore_class Optional integer. ID class ignored loss computation. useful, example, segmentation problems featuring \"void\" class (commonly -1 255) segmentation maps. default (ignore_class=NULL), classes considered. axis (Optional) Defaults -1. dimension along entropy computed. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the crossentropy metric between the labels and predictions. — metric_sparse_categorical_crossentropy","text":"Standalone usage:           Usage compile() API:","code":"m <- metric_sparse_categorical_crossentropy() m$update_state(c(1, 2),                rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1))) m$result() ## tf.Tensor(1.1769392, shape=(), dtype=float32) m$reset_state() m$update_state(c(1, 2),                rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)),                sample_weight = c(0.3, 0.7)) m$result() ## tf.Tensor(1.6271976, shape=(), dtype=float32) # 1.6271976 model %>% compile(     optimizer = 'sgd',     loss = 'mse',     metrics = list(metric_sparse_categorical_crossentropy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"Computes often integer targets top K predictions.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"","code":"metric_sparse_top_k_categorical_accuracy(   y_true,   y_pred,   k = 5L,   ...,   name = \"sparse_top_k_categorical_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. k (Optional) Number top elements look computing accuracy. Defaults 5. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_sparse_top_k_categorical_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes how often integer targets are in the top K predictions. — metric_sparse_top_k_categorical_accuracy","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_sparse_top_k_categorical_accuracy(k = 1L) m$update_state(   rbind(2, 1),   op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = \"float32\") ) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(   rbind(2, 1),   op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = \"float32\"),   sample_weight = c(0.7, 0.3) ) m$result() ## tf.Tensor(0.3, shape=(), dtype=float32) model %>% compile(optimizer = 'sgd',                   loss = 'sparse_categorical_crossentropy',                   metrics = list(metric_sparse_top_k_categorical_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"Sensitivity measures proportion actual positives correctly identified (tp / (tp + fn)). Specificity measures proportion actual negatives correctly identified (tn / (tn + fp)). metric creates four local variables, true_positives, true_negatives, false_positives false_negatives used compute specificity given sensitivity. threshold given sensitivity value computed used evaluate corresponding specificity. sample_weight NULL, weights default 1. Use sample_weight 0 mask values. class_id specified, calculate precision considering entries batch class_id threshold predictions, computing fraction class_id indeed correct label. additional information specificity sensitivity, see following.","code":""},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"","code":"metric_specificity_at_sensitivity(   ...,   sensitivity,   num_thresholds = 200L,   class_id = NULL,   name = NULL,   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"... forward/backward compatability. sensitivity scalar value range [0, 1]. num_thresholds (Optional) Defaults 200. number thresholds use matching given sensitivity. class_id (Optional) Integer class ID want binary metrics. must half-open interval [0, num_classes), num_classes last dimension predictions. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_specificity_at_sensitivity.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes best specificity where sensitivity is >= specified value. — metric_specificity_at_sensitivity","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_specificity_at_sensitivity(sensitivity = 0.5) m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8)) m$result() ## tf.Tensor(0.6666667, shape=(), dtype=float32) m$reset_state() m$update_state(c(0,   0,   0,   1,   1),                c(0, 0.3, 0.8, 0.3, 0.8),                sample_weight = c(1, 1, 2, 2, 2)) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) model |> compile(   optimizer = 'sgd',   loss = 'binary_crossentropy',   metrics = list(metric_sensitivity_at_specificity()) )"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"Formula:   y_true values expected -1 1. binary (0 1) labels provided convert -1 1.","code":"loss <- mean(square(maximum(1 - y_true * y_pred, 0)))"},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"","code":"metric_squared_hinge(y_true, y_pred, ..., name = \"squared_hinge\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"y_true ground truth values. y_true values expected -1 1. binary (0 1) labels provided convert -1 1 shape = [batch_size, d0, .. dN]. y_pred predicted values shape = [batch_size, d0, .. dN]. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_squared_hinge.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the hinge metric between y_true and y_pred. — metric_squared_hinge","text":"Standalone usage:","code":"m <- metric_squared_hinge() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6))) m$result() ## tf.Tensor(1.86, shape=(), dtype=float32) m$reset_state() m$update_state(rbind(c(0, 1), c(0, 0)), rbind(c(0.6, 0.4), c(0.4, 0.6)),                sample_weight = c(1, 0)) m$result() ## tf.Tensor(1.46, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the (weighted) sum of the given values. — metric_sum","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"example, values [1, 3, 5, 7] sum 16. sample_weight specified [1, 1, 0, 0] sum 4. metric creates one variable, total. ultimately returned sum value.","code":""},{"path":"https://keras.posit.co/reference/metric_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"","code":"metric_sum(..., name = \"sum\", dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_sum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_sum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the (weighted) sum of the given values. — metric_sum","text":"","code":"m <- metric_sum() m$update_state(c(1, 3, 5, 7)) m$result() ## tf.Tensor(16.0, shape=(), dtype=float32) m <- metric_sum() m$update_state(c(1, 3, 5, 7), sample_weight = c(1, 1, 0, 0)) m$result() ## tf.Tensor(4.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"Computes often targets top K predictions.","code":""},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"","code":"metric_top_k_categorical_accuracy(   y_true,   y_pred,   k = 5L,   ...,   name = \"top_k_categorical_accuracy\",   dtype = NULL )"},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"y_true Tensor true targets. y_pred Tensor predicted targets. k (Optional) Number top elements look computing accuracy. Defaults 5. ... forward/backward compatability. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"y_true y_pred missing, Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage. y_true y_pred provided, tensor computed value returned.","code":""},{"path":"https://keras.posit.co/reference/metric_top_k_categorical_accuracy.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes how often targets are in the top K predictions. — metric_top_k_categorical_accuracy","text":"Standalone usage:         Usage compile() API:","code":"m <- metric_top_k_categorical_accuracy(k = 1) m$update_state(   rbind(c(0, 0, 1), c(0, 1, 0)),   op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = \"float32\") ) m$result() ## tf.Tensor(0.5, shape=(), dtype=float32) m$reset_state() m$update_state(   rbind(c(0, 0, 1), c(0, 1, 0)),   op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = \"float32\"),   sample_weight = c(0.7, 0.3)) m$result() ## tf.Tensor(0.3, shape=(), dtype=float32) model.compile(optimizer = 'sgd',               loss = 'categorical_crossentropy',               metrics = list(metric_top_k_categorical_accuracy()))"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the number of true negatives. — metric_true_negatives","title":"Calculates the number of true negatives. — metric_true_negatives","text":"sample_weight given, calculates sum weights true negatives. metric creates one local variable, accumulator used keep track number true negatives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of true negatives. — metric_true_negatives","text":"","code":"metric_true_negatives(..., thresholds = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the number of true negatives. — metric_true_negatives","text":"... forward/backward compatability. thresholds (Optional) Defaults 0.5. float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the number of true negatives. — metric_true_negatives","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_true_negatives.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of true negatives. — metric_true_negatives","text":"Standalone usage:","code":"m <- metric_true_negatives() m$update_state(c(0, 1, 0, 0), c(1, 1, 0, 0)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 0, 0), c(1, 1, 0, 0), sample_weight = c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the number of true positives. — metric_true_positives","title":"Calculates the number of true positives. — metric_true_positives","text":"sample_weight given, calculates sum weights true positives. metric creates one local variable, true_positives used keep track number true positives. sample_weight NULL, weights default 1. Use sample_weight 0 mask values.","code":""},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of true positives. — metric_true_positives","text":"","code":"metric_true_positives(..., thresholds = NULL, name = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the number of true positives. — metric_true_positives","text":"... forward/backward compatability. thresholds (Optional) Defaults 0.5. float value, Python list float threshold values [0, 1]. threshold compared prediction values determine truth value predictions (.e., threshold TRUE, FALSE). used loss function sets from_logits=TRUE (.e. sigmoid applied predictions), thresholds set 0. One metric value generated threshold value. name (Optional) string name metric instance. dtype (Optional) data type metric result.","code":""},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the number of true positives. — metric_true_positives","text":"Metric instance returned. Metric instance can passed directly compile(metrics = ), used standalone object. See ?Metric example usage.","code":""},{"path":"https://keras.posit.co/reference/metric_true_positives.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the number of true positives. — metric_true_positives","text":"Standalone usage:","code":"m <- metric_true_positives() m$update_state(c(0, 1, 1, 1), c(1, 0, 1, 1)) m$result() ## tf.Tensor(2.0, shape=(), dtype=float32) m$reset_state() m$update_state(c(0, 1, 1, 1), c(1, 0, 1, 1), sample_weight = c(0, 0, 1, 0)) m$result() ## tf.Tensor(1.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/multi-assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Assign values to names — %<-%","title":"Assign values to names — %<-%","text":"See %<-% details.","code":""},{"path":"https://keras.posit.co/reference/multi-assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assign values to names — %<-%","text":"","code":"x %<-% value"},{"path":"https://keras.posit.co/reference/multi-assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assign values to names — %<-%","text":"right-hand-side argument, value, invisibly. called primarily side-effect assigning symbols current frame.","code":""},{"path":"https://keras.posit.co/reference/new_callback_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Callback — new_callback_class","title":"Callback — new_callback_class","text":"new_callback_class() alias Callback(). See ?Callback() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_callback_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Callback — new_callback_class","text":"","code":"new_callback_class(   classname,   on_epoch_begin = NULL,   on_epoch_end = NULL,   on_train_begin = NULL,   on_train_end = NULL,   on_train_batch_begin = NULL,   on_train_batch_end = NULL,   on_test_begin = NULL,   on_test_end = NULL,   on_test_batch_begin = NULL,   on_test_batch_end = NULL,   on_predict_begin = NULL,   on_predict_end = NULL,   on_predict_batch_begin = NULL,   on_predict_batch_end = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_callback_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Callback — new_callback_class","text":"classname String, name custom class. (Conventionally, CamelCase). on_epoch_begin   Called start epoch. Subclasses override actions run. function called TRAIN mode. Args: epoch: Integer, index epoch. logs: Named List. Currently data passed argument method may change future. on_epoch_end   Called end epoch. Subclasses override actions run. function called TRAIN mode. Args: epoch: Integer, index epoch. logs: Named List, metric results training epoch, validation epoch validation performed. Validation result keys prefixed val_. training epoch, values Model's metrics returned. Example: list(loss = 0.2, accuracy = 0.7). on_train_begin   Called beginning training. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_train_end   Called end training. Subclasses override actions run. Args: logs: Named list. Currently output last call on_epoch_end() passed argument method may change future. on_train_batch_begin   Called beginning training batch fit() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_train_batch_end   Called end training batch fit() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. on_test_begin   Called beginning evaluation validation. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_test_end   Called end evaluation validation. Subclasses override actions run. Args: logs: Named list. Currently output last call on_test_batch_end() passed argument method may change future. on_test_batch_begin   Called beginning batch evaluate() methods. Also called beginning validation batch fit() methods, validation data provided. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_test_batch_end   Called end batch evaluate() methods. Also called end validation batch fit() methods, validation data provided. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. on_predict_begin   Called beginning prediction. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_predict_end   Called end prediction. Subclasses override actions run. Args: logs: Named list. Currently data passed argument method may change future. on_predict_batch_begin   Called beginning batch predict() methods. Subclasses override actions run. Note steps_per_execution argument compile() Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Currently data passed argument method may change future. on_predict_batch_end   Called end batch predict() methods. Subclasses override actions run. Note steps_per_execution argument compile Model set N, method called every N batches. Args: batch: Integer, index batch within current epoch. logs: Named list. Aggregated metric results batch. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"\\(epoch, logs = NULL) \\(epoch, logs = NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs=NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs = NULL) \\(logs = NULL) \\(logs = NULL) \\(batch, logs = NULL) \\(batch, logs = NULL)"},{"path":"https://keras.posit.co/reference/new_callback_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Callback — new_callback_class","text":"function returns custom Callback instances, similar builtin callback functions.","code":""},{"path":"https://keras.posit.co/reference/new_layer_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer — new_layer_class","title":"Layer — new_layer_class","text":"new_layer_class() alias Layer(). See ?Layer() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_layer_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer — new_layer_class","text":"","code":"new_layer_class(   classname,   initialize = NULL,   call = NULL,   build = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_layer_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer — new_layer_class","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, call, build, get_config Recommended methods implement. See description details sections. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/new_layer_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer — new_layer_class","text":"composing layer constructor, similar behavior layer functions like layer_dense(). first argument returned function object, enabling initialize()ing call() layer one step composing layer pipe, like   initialize() layer instance call() , pass missing NULL value object, pass arguments initialize() name.","code":"layer_foo <- Layer(\"Foo\", ....) output <- inputs |> layer_foo() layer <- layer_dense(units = 2, activation = \"relu\") layer <- layer_dense(NULL, 2, activation = \"relu\") layer <- layer_dense(, 2, activation = \"relu\")  # then you can call() the layer in a separate step outputs <- inputs |> layer()"},{"path":"https://keras.posit.co/reference/new_learning_rate_schedule_class.html","id":null,"dir":"Reference","previous_headings":"","what":"LearningRateSchedule — new_learning_rate_schedule_class","title":"LearningRateSchedule — new_learning_rate_schedule_class","text":"new_learning_rate_schedule_class() alias LearningRateSchedule(). See ?LearningRateSchedule() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_learning_rate_schedule_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LearningRateSchedule — new_learning_rate_schedule_class","text":"","code":"new_learning_rate_schedule_class(   classname,   call = NULL,   initialize = NULL,   get_config = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_learning_rate_schedule_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LearningRateSchedule — new_learning_rate_schedule_class","text":"classname String, name custom class. (Conventionally, CamelCase). call, initialize, get_config Recommended methods implement. See description details sections. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/new_learning_rate_schedule_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"LearningRateSchedule — new_learning_rate_schedule_class","text":"function returns LearningRateSchedule instances, similar built-learning_rate_schedule_* family functions.","code":""},{"path":"https://keras.posit.co/reference/new_loss_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Loss — new_loss_class","title":"Loss — new_loss_class","text":"new_loss_class() alias Loss(). See ?Loss() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_loss_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loss — new_loss_class","text":"","code":"new_loss_class(   classname,   call = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_loss_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loss — new_loss_class","text":"classname String, name custom class. (Conventionally, CamelCase). call   Method implemented subclasses: Function contains logic loss calculation using y_true, y_pred. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":"function(y_true, y_pred)"},{"path":"https://keras.posit.co/reference/new_loss_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loss — new_loss_class","text":"function returns Loss instances, similar builtin loss functions.","code":""},{"path":"https://keras.posit.co/reference/new_metric_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Metric — new_metric_class","title":"Metric — new_metric_class","text":"new_metric_class() alias Metric(). See ?Metric() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_metric_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metric — new_metric_class","text":"","code":"new_metric_class(   classname,   initialize = NULL,   update_state = NULL,   result = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_metric_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metric — new_metric_class","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, update_state, result Recommended methods implement. See description section. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/new_metric_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metric — new_metric_class","text":"function returns Metric instances, similar builtin metric functions.","code":""},{"path":"https://keras.posit.co/reference/new_model_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Model — new_model_class","title":"Model — new_model_class","text":"new_model_class() alias Model(). See ?Model() full documentation.","code":""},{"path":"https://keras.posit.co/reference/new_model_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model — new_model_class","text":"","code":"new_model_class(   classname,   initialize = NULL,   call = NULL,   train_step = NULL,   predict_step = NULL,   test_step = NULL,   compute_loss = NULL,   compute_metrics = NULL,   ...,   public = list(),   private = list(),   inherit = NULL,   parent_env = parent.frame() )"},{"path":"https://keras.posit.co/reference/new_model_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model — new_model_class","text":"classname String, name custom class. (Conventionally, CamelCase). initialize, call, train_step, predict_step, test_step, compute_loss, compute_metrics Optional methods can overridden. ..., public Additional methods public members custom class. private Named list R objects (typically, functions) include instance private environments. private methods symbols scope public methods (See section \"Symbols Scope\"). instance private environment. objects private invisible Keras framework Python runtime. inherit custom class subclass. default, base keras class. parent_env R environment class methods grandparent.","code":""},{"path":"https://keras.posit.co/reference/new_model_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model — new_model_class","text":"model constructor function, can call create instance new model type.","code":""},{"path":"https://keras.posit.co/reference/normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalizes an array. — normalize","title":"Normalizes an array. — normalize","text":"input R array, R array returned. backend tensor, backend tensor returned.","code":""},{"path":"https://keras.posit.co/reference/normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalizes an array. — normalize","text":"","code":"normalize(x, axis = -1L, order = 2L)"},{"path":"https://keras.posit.co/reference/normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalizes an array. — normalize","text":"x Array normalize. axis axis along normalize. order Normalization order (e.g. order=2 L2 norm).","code":""},{"path":"https://keras.posit.co/reference/normalize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalizes an array. — normalize","text":"normalized copy array.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_abs.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the absolute value element-wise. — op_abs","title":"Compute the absolute value element-wise. — op_abs","text":"Compute absolute value element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_abs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the absolute value element-wise. — op_abs","text":"","code":"op_abs(x)"},{"path":"https://keras.posit.co/reference/op_abs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the absolute value element-wise. — op_abs","text":"x Input tensor","code":""},{"path":"https://keras.posit.co/reference/op_abs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the absolute value element-wise. — op_abs","text":"array containing absolute value element x.","code":""},{"path":"https://keras.posit.co/reference/op_abs.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Compute the absolute value element-wise. — op_abs","text":"","code":"x <- op_convert_to_tensor(c(-1.2, 1.2)) op_abs(x) ## tf.Tensor([1.2 1.2], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_add.html","id":null,"dir":"Reference","previous_headings":"","what":"Add arguments element-wise. — op_add","title":"Add arguments element-wise. — op_add","text":"Add arguments element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_add.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add arguments element-wise. — op_add","text":"","code":"op_add(x1, x2)"},{"path":"https://keras.posit.co/reference/op_add.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add arguments element-wise. — op_add","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_add.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add arguments element-wise. — op_add","text":"tensor containing element-wise sum x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_add.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add arguments element-wise. — op_add","text":"op_add also broadcasts shapes:     Note function automatically called using R operator + tensors.","code":"x1 <- op_convert_to_tensor(c(1, 4)) x2 <- op_convert_to_tensor(c(5, 6)) op_add(x1, x2) ## tf.Tensor([ 6. 10.], shape=(2), dtype=float32) # alias for x1 + x2 x1 + x2 ## tf.Tensor([ 6. 10.], shape=(2), dtype=float32) x1 <- op_convert_to_tensor(array(c(5, 5, 4, 6), dim =c(2, 2))) x2 <- op_convert_to_tensor(c(5, 6)) op_add(x1, x2) ## tf.Tensor( ## [[10. 10.] ##  [10. 12.]], shape=(2, 2), dtype=float64) x <- op_ones(c(3)) op_add(x, x) ## tf.Tensor([2. 2. 2.], shape=(3), dtype=float32) x + x ## tf.Tensor([2. 2. 2.], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_all.html","id":null,"dir":"Reference","previous_headings":"","what":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"Test whether array elements along given axis evaluate TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_all.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"","code":"op_all(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_all.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"x Input tensor. axis integer tuple integers represent axis along logical reduction performed. default (axis = NULL) perform logical dimensions input array. axis may negative, case counts last first axis. keepdims TRUE, axes reduced left result dimensions size one. option, result broadcast correctly input array. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_all.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"tensor containing logical reduction axis.","code":""},{"path":"https://keras.posit.co/reference/op_all.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test whether all array elements along a given axis evaluate to TRUE. — op_all","text":"keepdims = TRUE outputs tensor dimensions reduced one.","code":"x <- op_convert_to_tensor(c(TRUE, FALSE)) op_all(x) ## tf.Tensor(False, shape=(), dtype=bool) (x <- op_convert_to_tensor(array(c(TRUE, FALSE, TRUE, TRUE, TRUE, TRUE), dim = c(3, 2)))) ## tf.Tensor( ## [[ True  True] ##  [False  True] ##  [ True  True]], shape=(3, 2), dtype=bool) op_all(x, axis = 1) ## tf.Tensor([False  True], shape=(2), dtype=bool) op_all(x, keepdims = TRUE) ## tf.Tensor([[False]], shape=(1, 1), dtype=bool)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_any.html","id":null,"dir":"Reference","previous_headings":"","what":"Test whether any array element along a given axis evaluates to TRUE. — op_any","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"Test whether array element along given axis evaluates TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_any.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"","code":"op_any(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_any.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"x Input tensor. axis integer tuple integers represent axis along logical reduction performed. default (axis = NULL) perform logical dimensions input array. axis may negative, case counts last first axis. keepdims TRUE, axes reduced left result dimensions size one. option, result broadcast correctly input array. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_any.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"tensor containing logical reduction axis.","code":""},{"path":"https://keras.posit.co/reference/op_any.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test whether any array element along a given axis evaluates to TRUE. — op_any","text":"keepdims = TRUE outputs tensor dimensions reduced one.","code":"x <- op_array(c(TRUE, FALSE)) op_any(x) ## tf.Tensor(True, shape=(), dtype=bool) (x <- op_reshape(c(FALSE, FALSE, FALSE,                    TRUE, FALSE, FALSE),                  c(2, 3))) ## tf.Tensor( ## [[False False False] ##  [ True False False]], shape=(2, 3), dtype=bool) op_any(x, axis = 1) ## tf.Tensor([ True False False], shape=(3), dtype=bool) op_any(x, axis = 2) ## tf.Tensor([False  True], shape=(2), dtype=bool) op_any(x, axis = -1) ## tf.Tensor([False  True], shape=(2), dtype=bool) op_any(x, keepdims = TRUE) ## tf.Tensor([[ True]], shape=(1, 1), dtype=bool) op_any(x, 1, keepdims = TRUE) ## tf.Tensor([[ True False False]], shape=(1, 3), dtype=bool)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_append.html","id":null,"dir":"Reference","previous_headings":"","what":"Append tensor x2 to the end of tensor x1. — op_append","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"Append tensor x2 end tensor x1.","code":""},{"path":"https://keras.posit.co/reference/op_append.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"","code":"op_append(x1, x2, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_append.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"x1 First input tensor. x2 Second input tensor. axis Axis along tensor x2 appended tensor x1. NULL, tensors flattened use.","code":""},{"path":"https://keras.posit.co/reference/op_append.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"tensor values x2 appended x1.","code":""},{"path":"https://keras.posit.co/reference/op_append.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Append tensor x2 to the end of tensor x1. — op_append","text":"axis specified, x1 x2 must compatible shapes.","code":"x1 <- op_convert_to_tensor(c(1, 2, 3)) x2 <- op_convert_to_tensor(rbind(c(4, 5, 6), c(7, 8, 9))) op_append(x1, x2) ## tf.Tensor([1. 2. 3. 4. 5. 6. 7. 8. 9.], shape=(9), dtype=float64) x1 <- op_convert_to_tensor(rbind(c(1, 2, 3), c(4, 5, 6))) x2 <- op_convert_to_tensor(rbind(c(7, 8, 9))) op_append(x1, x2, axis = 1) ## tf.Tensor( ## [[1. 2. 3.] ##  [4. 5. 6.] ##  [7. 8. 9.]], shape=(3, 3), dtype=float64) x3 <- op_convert_to_tensor(c(7, 8, 9)) try(op_append(x1, x3, axis = 1)) ## Error in py_call_impl(callable, call_args$unnamed, call_args$named) : ##   tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__ConcatV2_N_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} ConcatOp : Ranks of all input tensors should match: shape[0] = [2,3] vs. shape[1] = [3] [Op:ConcatV2] name: concat"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arange.html","id":null,"dir":"Reference","previous_headings":"","what":"Return evenly spaced values within a given interval. — op_arange","title":"Return evenly spaced values within a given interval. — op_arange","text":"arange can called varying number positional arguments: arange(stop): Values generated within half-open interval [0, stop) (words, interval including start excluding stop). arange(start, stop): Values generated within half-open interval [start, stop). arange(start, stop, step): Values generated within half-open interval [start, stop), spacing values given step.","code":""},{"path":"https://keras.posit.co/reference/op_arange.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return evenly spaced values within a given interval. — op_arange","text":"","code":"op_arange(start, stop = NULL, step = 1L, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_arange.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return evenly spaced values within a given interval. — op_arange","text":"start Integer real, representing start interval. interval includes value. stop Integer real, representing end interval. interval include value, except cases step integer floating point round-affects length . Defaults NULL. step Integer real, represent spacing values. output , distance two adjacent values, [+1] - []. default step size 1. step specified position argument, start must also given. dtype type output array. dtype given, infer data type input arguments.","code":""},{"path":"https://keras.posit.co/reference/op_arange.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return evenly spaced values within a given interval. — op_arange","text":"Tensor evenly spaced values. floating point arguments, length result ceiling((stop - start)/step). floating point overflow, rule may result last element greater stop.","code":""},{"path":"https://keras.posit.co/reference/op_arange.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return evenly spaced values within a given interval. — op_arange","text":"","code":"op_arange(3L) ## tf.Tensor([0 1 2], shape=(3), dtype=int32) op_arange(3) # float ## tf.Tensor([0. 1. 2.], shape=(3), dtype=float32) op_arange(3, dtype = 'int32') #int ## tf.Tensor([0 1 2], shape=(3), dtype=int32) op_arange(3L, 7L) ## tf.Tensor([3 4 5 6], shape=(4), dtype=int32) op_arange(3L, 7L, 2L) ## tf.Tensor([3 5], shape=(2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arccos.html","id":null,"dir":"Reference","previous_headings":"","what":"Trigonometric inverse cosine, element-wise. — op_arccos","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"inverse cos , y = cos(x), x = arccos(y).","code":""},{"path":"https://keras.posit.co/reference/op_arccos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"","code":"op_arccos(x)"},{"path":"https://keras.posit.co/reference/op_arccos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arccos.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"Tensor angle ray intersecting unit circle given x-coordinate radians [0, pi].","code":""},{"path":"https://keras.posit.co/reference/op_arccos.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trigonometric inverse cosine, element-wise. — op_arccos","text":"","code":"x <- op_convert_to_tensor(c(1, -1)) op_arccos(x) ## tf.Tensor([0.        3.1415927], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse hyperbolic cosine, element-wise. — op_arccosh","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"Inverse hyperbolic cosine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"","code":"op_arccosh(x)"},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"Output tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_arccosh.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse hyperbolic cosine, element-wise. — op_arccosh","text":"","code":"x <- op_convert_to_tensor(c(10, 100)) op_arccosh(x) ## tf.Tensor([2.993223 5.298292], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse sine, element-wise. — op_arcsin","title":"Inverse sine, element-wise. — op_arcsin","text":"Inverse sine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse sine, element-wise. — op_arcsin","text":"","code":"op_arcsin(x)"},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse sine, element-wise. — op_arcsin","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse sine, element-wise. — op_arcsin","text":"Tensor inverse sine element x, radians closed interval [-pi/2, pi/2].","code":""},{"path":"https://keras.posit.co/reference/op_arcsin.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse sine, element-wise. — op_arcsin","text":"","code":"x <- op_convert_to_tensor(c(1, -1, 0)) op_arcsin(x) ## tf.Tensor([ 1.5707964 -1.5707964  0.       ], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse hyperbolic sine, element-wise. — op_arcsinh","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"Inverse hyperbolic sine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"","code":"op_arcsinh(x)"},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"Output tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_arcsinh.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse hyperbolic sine, element-wise. — op_arcsinh","text":"","code":"x <- op_convert_to_tensor(c(1, -1, 0)) op_arcsinh(x) ## tf.Tensor([ 0.8813736 -0.8813736  0.       ], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arctan.html","id":null,"dir":"Reference","previous_headings":"","what":"Trigonometric inverse tangent, element-wise. — op_arctan","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"Trigonometric inverse tangent, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arctan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"","code":"op_arctan(x)"},{"path":"https://keras.posit.co/reference/op_arctan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arctan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"Tensor inverse tangent element x, interval [-pi/2, pi/2].","code":""},{"path":"https://keras.posit.co/reference/op_arctan.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trigonometric inverse tangent, element-wise. — op_arctan","text":"","code":"x <- op_convert_to_tensor(c(0, 1)) op_arctan(x) ## tf.Tensor([0.        0.7853982], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":null,"dir":"Reference","previous_headings":"","what":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"quadrant (.e., branch) chosen arctan2(x1, x2) signed angle radians ray ending origin passing point (1, 0), ray ending origin passing point (x2, x1). (Note role reversal: \"y-coordinate\" first function parameter, \"x-coordinate\" second.) IEEE convention, function defined x2 = +/-0 either x1 x2 = +/-inf.","code":""},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"","code":"op_arctan2(x1, x2)"},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"Tensor angles radians, range [-pi, pi].","code":""},{"path":"https://keras.posit.co/reference/op_arctan2.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Element-wise arc tangent of x1/x2 choosing the quadrant correctly. — op_arctan2","text":"Consider four points different quadrants:     Note order parameters. arctan2 defined also x2 = 0 several points, obtaining values range [-pi, pi]:","code":"x <- op_array(c(-1, 1, 1, -1)) y <- op_array(c(-1, -1, 1, 1)) op_arctan2(y, x) * 180 / pi ## tf.Tensor([-135.  -45.   45.  135.], shape=(4), dtype=float32) op_arctan2(     op_array(c(1, -1)),     op_array(c(0, 0)) ) ## tf.Tensor([ 1.5707964 -1.5707964], shape=(2), dtype=float32) op_arctan2(     op_array(c(0, 0, Inf)),     op_array(c(+0, -0, Inf)) ) ## tf.Tensor([0.        3.1415927 0.7853982], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_arctanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse hyperbolic tangent, element-wise. — op_arctanh","title":"Inverse hyperbolic tangent, element-wise. — op_arctanh","text":"Inverse hyperbolic tangent, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_arctanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse hyperbolic tangent, element-wise. — op_arctanh","text":"","code":"op_arctanh(x)"},{"path":"https://keras.posit.co/reference/op_arctanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse hyperbolic tangent, element-wise. — op_arctanh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_arctanh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse hyperbolic tangent, element-wise. — op_arctanh","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_argmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the indices of the maximum values along an axis. — op_argmax","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"Returns indices maximum values along axis.","code":""},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"","code":"op_argmax(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"x Input tensor. axis default, index flattened tensor, otherwise along specified axis. keepdims set TRUE, axes reduced left result dimensions size one. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"Tensor indices. shape x, dimension along axis removed. Note returned integer 0-based (.e., argmax first index position, returned value 0)","code":""},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"similar R max.col(x) - 1 case 2-d array (matrix), nd-array, apply(x, axis, .max) - 1","code":""},{"path":"https://keras.posit.co/reference/op_argmax.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the indices of the maximum values along an axis. — op_argmax","text":"","code":"x <- op_arange(6L) |> op_reshape(c(2, 3)) |> op_add(10) x ## tf.Tensor( ## [[10. 11. 12.] ##  [13. 14. 15.]], shape=(2, 3), dtype=float32) op_argmax(x) ## tf.Tensor(5, shape=(), dtype=int32) op_argmax(x, axis = 1) ## tf.Tensor([1 1 1], shape=(3), dtype=int32) op_argmax(x, axis = 2) ## tf.Tensor([2 2], shape=(2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_argmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the indices of the minimum values along an axis. — op_argmin","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"Returns indices minimum values along axis.","code":""},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"","code":"op_argmin(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"x Input tensor. axis default, index flattened tensor, otherwise along specified axis. keepdims set TRUE, axes reduced left result dimensions size one. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"Tensor indices. shape x, dimension along axis removed.","code":""},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"similar R expression apply(x, axis, .min) - 1, x R array.","code":""},{"path":"https://keras.posit.co/reference/op_argmin.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the indices of the minimum values along an axis. — op_argmin","text":"","code":"x <- op_arange(6L) |> op_reshape(c(2, 3)) |> op_add(10) x ## tf.Tensor( ## [[10. 11. 12.] ##  [13. 14. 15.]], shape=(2, 3), dtype=float32) op_argmin(x) ## tf.Tensor(0, shape=(), dtype=int32) op_argmin(x, axis = 1) ## tf.Tensor([0 0 0], shape=(3), dtype=int32) op_argmin(x, axis = 2) ## tf.Tensor([0 0], shape=(2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_argsort.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the indices that would sort a tensor. — op_argsort","title":"Returns the indices that would sort a tensor. — op_argsort","text":"Returns indices sort tensor.","code":""},{"path":"https://keras.posit.co/reference/op_argsort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the indices that would sort a tensor. — op_argsort","text":"","code":"op_argsort(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_argsort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the indices that would sort a tensor. — op_argsort","text":"x Input tensor. axis Axis along sort. Defaults -1 (last axis). NULL, flattened tensor used.","code":""},{"path":"https://keras.posit.co/reference/op_argsort.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the indices that would sort a tensor. — op_argsort","text":"Tensor indices sort x along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_argsort.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the indices that would sort a tensor. — op_argsort","text":"One dimensional array:     Two-dimensional array:","code":"x <- op_array(c(3, 1, 2)) op_argsort(x) ## tf.Tensor([1 2 0], shape=(3), dtype=int32) x <- op_array(rbind(c(0, 3),                    c(3, 2),                    c(4, 5)), dtype = \"int32\") op_argsort(x, axis = 1) ## tf.Tensor( ## [[0 1] ##  [1 0] ##  [2 2]], shape=(3, 2), dtype=int32) op_argsort(x, axis = 2) ## tf.Tensor( ## [[0 1] ##  [1 0] ##  [0 1]], shape=(3, 2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a tensor. — op_array","title":"Create a tensor. — op_array","text":"Create tensor.","code":""},{"path":"https://keras.posit.co/reference/op_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a tensor. — op_array","text":"","code":"op_array(x, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a tensor. — op_array","text":"x Input tensor. dtype desired data-type tensor.","code":""},{"path":"https://keras.posit.co/reference/op_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a tensor. — op_array","text":"tensor.","code":""},{"path":"https://keras.posit.co/reference/op_array.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a tensor. — op_array","text":"","code":"op_array(c(1, 2, 3)) ## tf.Tensor([1. 2. 3.], shape=(3), dtype=float32) op_array(c(1, 2, 3), dtype = \"float32\") ## tf.Tensor([1. 2. 3.], shape=(3), dtype=float32) op_array(c(1, 2, 3), dtype = \"int32\") ## tf.Tensor([1 2 3], shape=(3), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_average.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the weighted average along the specified axis. — op_average","title":"Compute the weighted average along the specified axis. — op_average","text":"Compute weighted average along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_average.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the weighted average along the specified axis. — op_average","text":"","code":"op_average(x, axis = NULL, weights = NULL)"},{"path":"https://keras.posit.co/reference/op_average.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the weighted average along the specified axis. — op_average","text":"x Input tensor. axis Integer along average x. default, axis = NULL, average elements input tensor. axis negative counts last first axis. weights Tensor wieghts associated values x. value x contributes average according associated weight. weights array can either 1-D (case length must size along given axis) shape x. weights = NULL (default), data x assumed weight equal one. 1-D calculation : avg = sum(* weights) / sum(weights). constraint weights sum(weights) must 0.","code":""},{"path":"https://keras.posit.co/reference/op_average.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the weighted average along the specified axis. — op_average","text":"Return average along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_average.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the weighted average along the specified axis. — op_average","text":"","code":"data <- op_arange(1, 5, dtype = \"int32\") data ## tf.Tensor([1 2 3 4], shape=(4), dtype=int32) op_average(data) ## tf.Tensor(2.5, shape=(), dtype=float32) op_average(   op_arange(1, 11),   weights = op_arange(10, 0, -1) ) ## tf.Tensor(4.0, shape=(), dtype=float32) data <- op_arange(6) |> op_reshape(c(3, 2)) data ## tf.Tensor( ## [[0. 1.] ##  [2. 3.] ##  [4. 5.]], shape=(3, 2), dtype=float32) op_average(   data,   axis = 2,   weights = op_array(c(1/4, 3/4)) ) ## tf.Tensor([0.75 2.75 4.75], shape=(3), dtype=float32) # Error: Axis must be specified when shapes of x and weights differ. try(op_average(   data,   weights = op_array(c(1/4, 3/4)) )) ## Error in op_average(data, weights = op_array(c(1/4, 3/4))) : ##   Axis must be specified when shapes of x and weights differ."},{"path":[]},{"path":"https://keras.posit.co/reference/op_average_pool.html","id":null,"dir":"Reference","previous_headings":"","what":"Average pooling operation. — op_average_pool","title":"Average pooling operation. — op_average_pool","text":"Average pooling operation.","code":""},{"path":"https://keras.posit.co/reference/op_average_pool.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average pooling operation. — op_average_pool","text":"","code":"op_average_pool(   inputs,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL )"},{"path":"https://keras.posit.co/reference/op_average_pool.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average pooling operation. — op_average_pool","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". Pooling happens spatial dimensions . pool_size int tuple/list integers size len(inputs_spatial_shape), specifying size pooling window spatial dimension input tensor. pool_size int, every spatial dimension shares pool_size. strides int tuple/list integers size len(inputs_spatial_shape). stride sliding window spatial dimension input tensor. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...).","code":""},{"path":"https://keras.posit.co/reference/op_average_pool.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average pooling operation. — op_average_pool","text":"tensor rank N+2, result average pooling operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalizes x by mean and variance. — op_batch_normalization","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"op typically used batch normalization step neural network. normalizes input tensor along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"","code":"op_batch_normalization(   x,   mean,   variance,   axis,   offset = NULL,   scale = NULL,   epsilon = 0.001 )"},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"x Input tensor. mean mean vector length axis dimension input thensor. variance variance vector length axis dimension input tensor. axis Integer, axis normalized. offset offset vector length axis dimension input tensor. NULL, offset added normalized tensor. Defaults NULL. scale scale vector length axis dimension input tensor. NULL, normalized tensor multiplied scale. Defaults NULL. epsilon Small float added variance avoid dividing zero. Defaults 1e-3.","code":""},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"normalized tensor.","code":""},{"path":"https://keras.posit.co/reference/op_batch_normalization.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalizes x by mean and variance. — op_batch_normalization","text":"","code":"x <- op_convert_to_tensor(rbind(c(0.1, 0.2, 0.3),                                 c(0.4, 0.5, 0.6),                                 c(0.7, 0.8, 0.9))) op_batch_normalization(   x,   mean = c(0.4, 0.5, 0.6),   variance = c(0.67, 0.67, 0.67),   axis = -1 ) ## tf.Tensor( ## [[-0.36623513 -0.36623513 -0.36623513] ##  [ 0.          0.          0.        ] ##  [ 0.36623513  0.36623513  0.36623513]], shape=(3, 3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"binary cross-entropy loss commonly used binary classification tasks input sample belongs one two classes. measures dissimilarity target output probabilities logits.","code":""},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"","code":"op_binary_crossentropy(target, output, from_logits = FALSE)"},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"target target tensor representing true binary labels. shape match shape output tensor. output output tensor representing predicted probabilities logits. shape match shape target tensor. from_logits (optional) Whether output tensor logits probabilities. Set TRUE output represents logits; otherwise, set FALSE output represents probabilities. Defaults FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"Integer tensor: computed binary cross-entropy loss target output.","code":""},{"path":"https://keras.posit.co/reference/op_binary_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes binary cross-entropy loss between target and output tensor. — op_binary_crossentropy","text":"","code":"target <- op_array(c(0, 1, 1, 0)) output <- op_array(c(0.1, 0.9, 0.8, 0.2)) op_binary_crossentropy(target, output) ## tf.Tensor([0.10536055 0.10536055 0.22314353 0.22314353], shape=(4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_bincount.html","id":null,"dir":"Reference","previous_headings":"","what":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"Count number occurrences value tensor integers.","code":""},{"path":"https://keras.posit.co/reference/op_bincount.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"","code":"op_bincount(x, weights = NULL, minlength = 0L, sparse = FALSE)"},{"path":"https://keras.posit.co/reference/op_bincount.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"x Input tensor. must dimension 1, must contain non-negative integer(s). weights Weight tensor. must length x. default value NULL. specified, x weighted , .e. n = x[], [n] += weight[] instead default behavior [n] += 1. minlength integer. default value 0. specified, least number bins output tensor. greater max(x) + 1, value output index higher max(x) set 0. sparse Whether return sparse tensor; backends support sparse tensors.","code":""},{"path":"https://keras.posit.co/reference/op_bincount.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"1D tensor element gives number occurrence(s) index value x. length maximum max(x) + 1 minlength.","code":""},{"path":"https://keras.posit.co/reference/op_bincount.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count the number of occurrences of each value in a tensor of integers. — op_bincount","text":"","code":"(x <- op_array(c(1, 2, 2, 3), dtype = \"uint8\")) ## tf.Tensor([1 2 2 3], shape=(4), dtype=uint8) op_bincount(x) ## tf.Tensor([0 1 2 1], shape=(4), dtype=int32) (weights <- x / 2) ## tf.Tensor([0.5 1.  1.  1.5], shape=(4), dtype=float32) op_bincount(x, weights = weights) ## tf.Tensor([0.  0.5 2.  1.5], shape=(4), dtype=float32) minlength <- as.integer(op_max(x) + 1 + 2) # 6 op_bincount(x, minlength = minlength) ## tf.Tensor([0 1 2 1 0 0], shape=(6), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":null,"dir":"Reference","previous_headings":"","what":"Broadcast a tensor to a new shape. — op_broadcast_to","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"Broadcast tensor new shape.","code":""},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"","code":"op_broadcast_to(x, shape)"},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"x tensor broadcast. shape shape desired tensor.","code":""},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"tensor desired shape.","code":""},{"path":"https://keras.posit.co/reference/op_broadcast_to.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Broadcast a tensor to a new shape. — op_broadcast_to","text":"","code":"x <- op_array(c(1, 2, 3)) op_broadcast_to(x, shape = c(3, 3)) ## tf.Tensor( ## [[1. 2. 3.] ##  [1. 2. 3.] ##  [1. 2. 3.]], shape=(3, 3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_cast.html","id":null,"dir":"Reference","previous_headings":"","what":"Cast a tensor to the desired dtype. — op_cast","title":"Cast a tensor to the desired dtype. — op_cast","text":"Cast tensor desired dtype.","code":""},{"path":"https://keras.posit.co/reference/op_cast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cast a tensor to the desired dtype. — op_cast","text":"","code":"op_cast(x, dtype)"},{"path":"https://keras.posit.co/reference/op_cast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cast a tensor to the desired dtype. — op_cast","text":"x tensor variable. dtype target type.","code":""},{"path":"https://keras.posit.co/reference/op_cast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cast a tensor to the desired dtype. — op_cast","text":"tensor specified dtype.","code":""},{"path":"https://keras.posit.co/reference/op_cast.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cast a tensor to the desired dtype. — op_cast","text":"","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_cast(x, dtype = \"float16\") ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float16)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"categorical cross-entropy loss commonly used multi-class classification tasks input sample can belong one multiple classes. measures dissimilarity target output probabilities logits.","code":""},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"","code":"op_categorical_crossentropy(target, output, from_logits = FALSE, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"target target tensor representing true categorical labels. shape match shape output tensor except last dimension. output output tensor representing predicted probabilities logits. shape match shape target tensor except last dimension. from_logits (optional) Whether output tensor logits probabilities. Set TRUE output represents logits; otherwise, set FALSE output represents probabilities. Defaults FALSE. axis (optional) axis along categorical cross-entropy computed. Defaults -1, corresponds last dimension tensors.","code":""},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"Integer tensor: computed categorical cross-entropy loss target output.","code":""},{"path":"https://keras.posit.co/reference/op_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes categorical cross-entropy loss between target and output tensor. — op_categorical_crossentropy","text":"","code":"target <- op_array(rbind(c(1, 0, 0),                         c(0, 1, 0),                         c(0, 0, 1))) output <- op_array(rbind(c(0.9, 0.05, 0.05),                         c(0.1, 0.8, 0.1),                         c(0.2, 0.3, 0.5))) op_categorical_crossentropy(target, output) ## tf.Tensor([0.10536052 0.22314355 0.69314718], shape=(3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_ceil.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the ceiling of the input, element-wise. — op_ceil","title":"Return the ceiling of the input, element-wise. — op_ceil","text":"ceil scalar x smallest integer , >= x.","code":""},{"path":"https://keras.posit.co/reference/op_ceil.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the ceiling of the input, element-wise. — op_ceil","text":"","code":"op_ceil(x)"},{"path":"https://keras.posit.co/reference/op_ceil.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the ceiling of the input, element-wise. — op_ceil","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ceil.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the ceiling of the input, element-wise. — op_ceil","text":"ceiling element x, float dtype.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cholesky.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","title":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","text":"Computes Cholesky decomposition positive semi-definite matrix.","code":""},{"path":"https://keras.posit.co/reference/op_cholesky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","text":"","code":"op_cholesky(x)"},{"path":"https://keras.posit.co/reference/op_cholesky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_cholesky.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Cholesky decomposition of a positive semi-definite matrix. — op_cholesky","text":"tensor shape (..., M, M) representing lower triangular Cholesky factor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_clip.html","id":null,"dir":"Reference","previous_headings":"","what":"Clip (limit) the values in a tensor. — op_clip","title":"Clip (limit) the values in a tensor. — op_clip","text":"Given interval, values outside interval clipped interval edges. example, interval [0, 1] specified, values smaller 0 become 0, values larger 1 become 1.","code":""},{"path":"https://keras.posit.co/reference/op_clip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clip (limit) the values in a tensor. — op_clip","text":"","code":"op_clip(x, x_min, x_max)"},{"path":"https://keras.posit.co/reference/op_clip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clip (limit) the values in a tensor. — op_clip","text":"x Input tensor. x_min Minimum value. x_max Maximum value.","code":""},{"path":"https://keras.posit.co/reference/op_clip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clip (limit) the values in a tensor. — op_clip","text":"clipped tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_concatenate.html","id":null,"dir":"Reference","previous_headings":"","what":"Join a sequence of tensors along an existing axis. — op_concatenate","title":"Join a sequence of tensors along an existing axis. — op_concatenate","text":"Join sequence tensors along existing axis.","code":""},{"path":"https://keras.posit.co/reference/op_concatenate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join a sequence of tensors along an existing axis. — op_concatenate","text":"","code":"op_concatenate(xs, axis = 1L)"},{"path":"https://keras.posit.co/reference/op_concatenate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join a sequence of tensors along an existing axis. — op_concatenate","text":"xs sequence tensors concatenate. axis axis along tensors joined. Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/op_concatenate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join a sequence of tensors along an existing axis. — op_concatenate","text":"concatenated tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cond.html","id":null,"dir":"Reference","previous_headings":"","what":"Conditionally applies true_fn or false_fn. — op_cond","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"Conditionally applies true_fn false_fn.","code":""},{"path":"https://keras.posit.co/reference/op_cond.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"","code":"op_cond(pred, true_fn, false_fn)"},{"path":"https://keras.posit.co/reference/op_cond.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"pred Boolean scalar type true_fn Callable returning output pred == TRUE case. false_fn Callable returning output pred == FALSE case.","code":""},{"path":"https://keras.posit.co/reference/op_cond.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"output either true_fn false_fn depending pred.","code":""},{"path":"https://keras.posit.co/reference/op_cond.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conditionally applies true_fn or false_fn. — op_cond","text":"","code":"fn <- tensorflow::tf_function(function(x) {   op_cond(x > 0,     true_fn = \\() x + 1,     false_fn = \\() x - 1) })  fn(tensorflow::as_tensor(1)) ## tf.Tensor(2.0, shape=(), dtype=float64) fn(tensorflow::as_tensor(-1)) ## tf.Tensor(-2.0, shape=(), dtype=float64) # # Conditional side-effect (print only, no return value). file <- tempfile(fileext = \".txt\") fn <- tensorflow::tf_function(function(epochs) {   op_fori_loop(     0, epochs,     body_fun = \\(epoch, state) {       op_cond(epoch %% 20 == 0,               \\() {                 tensorflow::tf$print(                   \"epoch:\", epoch,                   output_stream = paste0(\"file://\", file))                 NULL               },               \\() {NULL})       state     },     init_val = tensorflow::as_tensor(0)) })  fn(tensorflow::as_tensor(100)) ## tf.Tensor(0.0, shape=(), dtype=float64) readLines(file) ## [1] \"epoch: 0\"  \"epoch: 20\" \"epoch: 40\" \"epoch: 60\" \"epoch: 80\" # cleanup unlink(file)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_conj.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the complex conjugate, element-wise. — op_conj","title":"Returns the complex conjugate, element-wise. — op_conj","text":"complex conjugate complex number obtained changing sign imaginary part.","code":""},{"path":"https://keras.posit.co/reference/op_conj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the complex conjugate, element-wise. — op_conj","text":"","code":"op_conj(x)"},{"path":"https://keras.posit.co/reference/op_conj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the complex conjugate, element-wise. — op_conj","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_conj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the complex conjugate, element-wise. — op_conj","text":"complex conjugate element x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_conv.html","id":null,"dir":"Reference","previous_headings":"","what":"General N-D convolution. — op_conv","title":"General N-D convolution. — op_conv","text":"ops supports 1D, 2D 3D convolution.","code":""},{"path":"https://keras.posit.co/reference/op_conv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General N-D convolution. — op_conv","text":"","code":"op_conv(   inputs,   kernel,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L )"},{"path":"https://keras.posit.co/reference/op_conv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General N-D convolution. — op_conv","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". kernel Tensor rank N+2. kernel shape (kernel_spatial_shape, num_input_channels, num_output_channels). num_input_channels match number channels inputs. strides int int tuple/list len(inputs_spatial_shape), specifying strides convolution along spatial dimension. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...). dilation_rate int int tuple/list len(inputs_spatial_shape), specifying dilation rate use dilated convolution. dilation_rate int, every spatial dimension shares dilation_rate.","code":""},{"path":"https://keras.posit.co/reference/op_conv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General N-D convolution. — op_conv","text":"tensor rank N+2, result conv operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_conv_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"General N-D convolution transpose. — op_conv_transpose","title":"General N-D convolution transpose. — op_conv_transpose","text":"Also known de-convolution. ops supports 1D, 2D 3D convolution.","code":""},{"path":"https://keras.posit.co/reference/op_conv_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General N-D convolution transpose. — op_conv_transpose","text":"","code":"op_conv_transpose(   inputs,   kernel,   strides,   padding = \"valid\",   output_padding = NULL,   data_format = NULL,   dilation_rate = 1L )"},{"path":"https://keras.posit.co/reference/op_conv_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General N-D convolution transpose. — op_conv_transpose","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". kernel Tensor rank N+2. kernel shape [kernel_spatial_shape, num_output_channels, num_input_channels], num_input_channels match number channels inputs. strides int int tuple/list len(inputs_spatial_shape), specifying strides convolution along spatial dimension. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. output_padding int int tuple/list len(inputs_spatial_shape), specifying amount padding along height width output tensor. Can single integer specify value spatial dimensions. amount output padding along given dimension must lower stride along dimension. set NULL (default), output shape inferred. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...). dilation_rate int int tuple/list len(inputs_spatial_shape), specifying dilation rate use dilated convolution. dilation_rate int, every spatial dimension shares dilation_rate.","code":""},{"path":"https://keras.posit.co/reference/op_conv_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General N-D convolution transpose. — op_conv_transpose","text":"tensor rank N+2, result conv operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_convert_to_numpy.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a tensor to a NumPy array. — op_convert_to_numpy","title":"Convert a tensor to a NumPy array. — op_convert_to_numpy","text":"Convert tensor NumPy array.","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_numpy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a tensor to a NumPy array. — op_convert_to_numpy","text":"","code":"op_convert_to_numpy(x)"},{"path":"https://keras.posit.co/reference/op_convert_to_numpy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a tensor to a NumPy array. — op_convert_to_numpy","text":"x tensor.","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_numpy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a tensor to a NumPy array. — op_convert_to_numpy","text":"NumPy array.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert an array to a tensor. — op_convert_to_tensor","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"Convert array tensor.","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"","code":"op_convert_to_tensor(x, dtype = NULL, sparse = NULL)"},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"x array. dtype target type. sparse Whether keep sparse tensors. FALSE cause sparse tensors densified. default value NULL means sparse tensors kept backend supports .","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"tensor specified dtype.","code":""},{"path":"https://keras.posit.co/reference/op_convert_to_tensor.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert an array to a tensor. — op_convert_to_tensor","text":"","code":"x <- array(c(1, 2, 3)) y <- op_convert_to_tensor(x) y ## tf.Tensor([1. 2. 3.], shape=(3), dtype=float64) op_convert_to_tensor(c(1, 3, 2, 0), \"int32\") ## tf.Tensor([1 3 2 0], shape=(4), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_copy.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a copy of x. — op_copy","title":"Returns a copy of x. — op_copy","text":"Returns copy x.","code":""},{"path":"https://keras.posit.co/reference/op_copy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a copy of x. — op_copy","text":"","code":"op_copy(x)"},{"path":"https://keras.posit.co/reference/op_copy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a copy of x. — op_copy","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_copy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a copy of x. — op_copy","text":"copy x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_correlate.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","title":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","text":"Compute cross-correlation two 1-dimensional tensors.","code":""},{"path":"https://keras.posit.co/reference/op_correlate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","text":"","code":"op_correlate(x1, x2, mode = \"valid\")"},{"path":"https://keras.posit.co/reference/op_correlate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","text":"x1 First 1-dimensional input tensor length M. x2 Second 1-dimensional input tensor length N. mode Either \"valid\", \"\" \"full\". default mode set \"valid\", returns output length max(M, N) - min(M, N) + 1. \"\" returns output length max(M, N). \"full\" mode returns convolution point overlap, output length N+M-1.","code":""},{"path":"https://keras.posit.co/reference/op_correlate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the cross-correlation of two 1-dimensional tensors. — op_correlate","text":"Output tensor, cross-correlation x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cos.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosine, element-wise. — op_cos","title":"Cosine, element-wise. — op_cos","text":"Cosine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_cos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosine, element-wise. — op_cos","text":"","code":"op_cos(x)"},{"path":"https://keras.posit.co/reference/op_cos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosine, element-wise. — op_cos","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_cos.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cosine, element-wise. — op_cos","text":"corresponding cosine values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hyperbolic cosine, element-wise. — op_cosh","title":"Hyperbolic cosine, element-wise. — op_cosh","text":"Hyperbolic cosine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_cosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hyperbolic cosine, element-wise. — op_cosh","text":"","code":"op_cosh(x)"},{"path":"https://keras.posit.co/reference/op_cosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hyperbolic cosine, element-wise. — op_cosh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_cosh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hyperbolic cosine, element-wise. — op_cosh","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"axis specified non-zeros tensor counted.","code":""},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"","code":"op_count_nonzero(x, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"x Input tensor. axis Axis tuple axes along count number non-zeros. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"integer tensor integers.","code":""},{"path":"https://keras.posit.co/reference/op_count_nonzero.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counts the number of non-zero values in x along the given axis. — op_count_nonzero","text":"","code":"x <- op_array(rbind(c(0, 1, 7, 0),                    c(3, 0, 2, 19))) op_count_nonzero(x) ## tf.Tensor(5, shape=(), dtype=int32) op_count_nonzero(x, axis = 1) ## tf.Tensor([1 1 2 1], shape=(4), dtype=int32) op_count_nonzero(x, axis = 2) ## tf.Tensor([2 3], shape=(2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_cross.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the cross product of two (arrays of) vectors. — op_cross","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"cross product x1 x2 R^3 vector perpendicular x1 x2. x1 x2 arrays vectors, vectors defined last axis x1 x2 default, axes can dimensions 2 3. dimension either x1 x2 2, third component input vector assumed zero cross product calculated accordingly. cases input vectors dimension 2, z-component cross product returned.","code":""},{"path":"https://keras.posit.co/reference/op_cross.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"","code":"op_cross(x1, x2, axisa = -1L, axisb = -1L, axisc = -1L, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_cross.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"x1 Components first vector(s). x2 Components second vector(s). axisa Axis x1 defines vector(s). Defaults -1. axisb Axis x2 defines vector(s). Defaults -1. axisc Axis result containing cross product vector(s). Ignored input vectors dimension 2, return scalar. default, last axis. axis defined, axis x1, x2 result defines vector(s) cross product(s). Overrides axisa, axisb axisc.","code":""},{"path":"https://keras.posit.co/reference/op_cross.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"Vector cross product(s).","code":""},{"path":"https://keras.posit.co/reference/op_cross.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Returns the cross product of two (arrays of) vectors. — op_cross","text":"Torch backend support two dimensional vectors, arguments axisa, axisb axisc. Use axis instead.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_ctc_decode.html","id":null,"dir":"Reference","previous_headings":"","what":"Decodes the output of a CTC model. — op_ctc_decode","title":"Decodes the output of a CTC model. — op_ctc_decode","text":"Decodes output CTC model.","code":""},{"path":"https://keras.posit.co/reference/op_ctc_decode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decodes the output of a CTC model. — op_ctc_decode","text":"","code":"op_ctc_decode(   inputs,   sequence_lengths,   strategy = \"greedy\",   beam_width = 100L,   top_paths = 1L,   merge_repeated = TRUE,   mask_index = NULL )"},{"path":"https://keras.posit.co/reference/op_ctc_decode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decodes the output of a CTC model. — op_ctc_decode","text":"inputs tensor shape (batch_size, max_length, num_classes) containing logits (output model). normalized via softmax. sequence_lengths tensor shape (batch_size) containing sequence lengths batch. strategy string decoding strategy. Supported values \"greedy\" \"beam_search\". beam_width integer scalar beam width used beam search. Defaults 100. top_paths integer scalar, number top paths return. Defaults 1. merge_repeated boolean scalar, whether merge repeated labels output. Defaults TRUE. mask_index integer scalar, index mask character vocabulary. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_ctc_decode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decodes the output of a CTC model. — op_ctc_decode","text":"list containing: tensor representing list decoded sequences. strategy=\"greedy\", shape (1, batch_size, max_length). strategy=\"beam_seatch\", shape (top_paths, batch_size, max_length). Note : -1 indicates blank label. strategy=\"greedy\", tensor shape (batch_size, 1) representing negative sum probability logits sequence. strategy=\"beam_seatch\", tensor shape (batch_size, top_paths) representing log probability sequence.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_ctc_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","title":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","text":"CTC (Connectionist Temporal Classification) loss.","code":""},{"path":"https://keras.posit.co/reference/op_ctc_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","text":"","code":"op_ctc_loss(target, output, target_length, output_length, mask_index = 0L)"},{"path":"https://keras.posit.co/reference/op_ctc_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","text":"target tensor shape (batch_size, max_length) containing true labels integer format. output tensor shape (batch_size, max_length, num_classes) containing logits (output model). target_length tensor shape (batch_size) containing true label lengths. output_length tensor shape (batch_size) containing output lengths. mask_index index mask character vocabulary. Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/op_ctc_loss.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CTC (Connectionist Temporal Classification) loss. — op_ctc_loss","text":"tensor, shape (batch_size), loss values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cumprod.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the cumulative product of elements along a given axis. — op_cumprod","title":"Return the cumulative product of elements along a given axis. — op_cumprod","text":"Return cumulative product elements along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_cumprod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the cumulative product of elements along a given axis. — op_cumprod","text":"","code":"op_cumprod(x, axis = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_cumprod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the cumulative product of elements along a given axis. — op_cumprod","text":"x Input tensor. axis Axis along cumulative product computed. default input flattened. dtype dtype returned tensor. Defaults x$dtype.","code":""},{"path":"https://keras.posit.co/reference/op_cumprod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the cumulative product of elements along a given axis. — op_cumprod","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_cumsum.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the cumulative sum of elements along a given axis. — op_cumsum","title":"Returns the cumulative sum of elements along a given axis. — op_cumsum","text":"Returns cumulative sum elements along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_cumsum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the cumulative sum of elements along a given axis. — op_cumsum","text":"","code":"op_cumsum(x, axis = NULL, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_cumsum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the cumulative sum of elements along a given axis. — op_cumsum","text":"x Input tensor. axis Axis along cumulative sum computed. default input flattened. dtype dtype returned tensor. Defaults x$dtype.","code":""},{"path":"https://keras.posit.co/reference/op_cumsum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the cumulative sum of elements along a given axis. — op_cumsum","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":null,"dir":"Reference","previous_headings":"","what":"Decorator to define a function with a custom gradient. — op_custom_gradient","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"decorator allows fine grained control gradients sequence operations. may useful multiple reasons, including providing efficient numerically stable gradient sequence operations.","code":""},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"","code":"op_custom_gradient(f)"},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"f Function f(...) returns tuple (output, grad_fn) : ... sequence unnamed arguments, tensor input nested structure tensor inputs function. output (potentially nested structure ) tensor outputs applying operations forward_fn f() .... grad_fn function signature grad_fn(..., upstream) returns list tensors size (flattened) ...: derivatives tensors output respect tensors .... upstream tensor sequence tensors holding initial value gradients tensor output.","code":""},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"function h(...) returns value f(...)[[1]] whose gradient determined f(...)[[2]].","code":""},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"Note grad function returns gradient computation requires ... well upstream named argument, depending backend set. JAX TensorFlow backends, requires one argument, whereas might use upstream argument case PyTorch backend. working TensorFlow/JAX backend, grad(upstream) sufficient. PyTorch, grad function requires ... well upstream, e.g. grad <- \\(..., upstream). Follow example use op_custom_gradient() way compatible backends.","code":""},{"path":"https://keras.posit.co/reference/op_custom_gradient.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Decorator to define a function with a custom gradient. — op_custom_gradient","text":"Backend-agnostic example.","code":"log1pexp <- op_custom_gradient(\\(x) {      e <- op_exp(x)      grad <- function(..., upstream = NULL) {       upstream <- upstream %||% ..1       op_multiply(upstream, 1.0 - 1.0 / op_add(1, e))     }      tuple(op_log(1 + e), grad)  })  if(config_backend() == \"tensorflow\") {   tf <- tensorflow::tf   x <- op_convert_to_tensor(100.0)   with(tf$GradientTape() %as% tape, {     tape$watch(x)     y <- log1pexp(x)   })   dy_dx <- tape$gradient(y, x)   stopifnot(as.numeric(dy_dx) == 1) }"},{"path":[]},{"path":"https://keras.posit.co/reference/op_depthwise_conv.html","id":null,"dir":"Reference","previous_headings":"","what":"General N-D depthwise convolution. — op_depthwise_conv","title":"General N-D depthwise convolution. — op_depthwise_conv","text":"ops supports 1D 2D depthwise convolution.","code":""},{"path":"https://keras.posit.co/reference/op_depthwise_conv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General N-D depthwise convolution. — op_depthwise_conv","text":"","code":"op_depthwise_conv(   inputs,   kernel,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L )"},{"path":"https://keras.posit.co/reference/op_depthwise_conv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General N-D depthwise convolution. — op_depthwise_conv","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". kernel Tensor rank N+2. kernel shape [kernel_spatial_shape, num_input_channels, num_channels_multiplier], num_input_channels match number channels inputs. strides int int tuple/list len(inputs_spatial_shape), specifying strides convolution along spatial dimension. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...). dilation_rate int int tuple/list len(inputs_spatial_shape), specifying dilation rate use dilated convolution. dilation_rate int, every spatial dimension shares dilation_rate.","code":""},{"path":"https://keras.posit.co/reference/op_depthwise_conv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General N-D depthwise convolution. — op_depthwise_conv","text":"tensor rank N+2, result depthwise conv operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_det.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the determinant of a square tensor. — op_det","title":"Computes the determinant of a square tensor. — op_det","text":"Computes determinant square tensor.","code":""},{"path":"https://keras.posit.co/reference/op_det.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the determinant of a square tensor. — op_det","text":"","code":"op_det(x)"},{"path":"https://keras.posit.co/reference/op_det.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the determinant of a square tensor. — op_det","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_det.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the determinant of a square tensor. — op_det","text":"tensor shape (...) represeting determinant x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_diag.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a diagonal or construct a diagonal array. — op_diag","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"Extract diagonal construct diagonal array.","code":""},{"path":"https://keras.posit.co/reference/op_diag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"","code":"op_diag(x, k = 0L)"},{"path":"https://keras.posit.co/reference/op_diag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"x Input tensor. x 2-D, returns k-th diagonal x. x 1-D, return 2-D tensor x k-th diagonal. k diagonal consider. Defaults 0. Use k > 0 diagonals main diagonal, k < 0 diagonals main diagonal.","code":""},{"path":"https://keras.posit.co/reference/op_diag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"extracted diagonal constructed diagonal tensor.","code":""},{"path":"https://keras.posit.co/reference/op_diag.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract a diagonal or construct a diagonal array. — op_diag","text":"","code":"x <- op_arange(9L) |> op_reshape(c(3, 3)) x ## tf.Tensor( ## [[0 1 2] ##  [3 4 5] ##  [6 7 8]], shape=(3, 3), dtype=int32) op_diag(x) ## tf.Tensor([0 4 8], shape=(3), dtype=int32) op_diag(x, k = 1) ## tf.Tensor([1 5], shape=(2), dtype=int32) op_diag(x, k = -1) ## tf.Tensor([3 7], shape=(2), dtype=int32) op_diag(op_diag(x)) ## tf.Tensor( ## [[0 0 0] ##  [0 4 0] ##  [0 0 8]], shape=(3, 3), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return specified diagonals. — op_diagonal","title":"Return specified diagonals. — op_diagonal","text":"x 2-D, returns diagonal x given offset, .e., collection elements form x[, +offset]. x two dimensions, axes specified axis1 axis2 used determine 2-D sub-array whose diagonal returned. shape resulting array can determined removing axis1 axis2 appending index right equal size resulting diagonals.","code":""},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return specified diagonals. — op_diagonal","text":"","code":"op_diagonal(x, offset = 0L, axis1 = 1L, axis2 = 2L)"},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return specified diagonals. — op_diagonal","text":"x Input tensor. offset Offset diagonal main diagonal. Can positive negative. Defaults 0 (main diagonal). axis1 Axis used first axis 2-D sub-arrays. Defaults 1 (first axis). axis2 Axis used second axis 2-D sub-arrays. Defaults 2 (second axis).","code":""},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return specified diagonals. — op_diagonal","text":"Tensor diagonals.","code":""},{"path":"https://keras.posit.co/reference/op_diagonal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return specified diagonals. — op_diagonal","text":"","code":"x <- op_arange(4L) |> op_reshape(c(2, 2)) x ## tf.Tensor( ## [[0 1] ##  [2 3]], shape=(2, 2), dtype=int32) op_diagonal(x) ## tf.Tensor([0 3], shape=(2), dtype=int32) op_diagonal(x, offset = 1) ## tf.Tensor([1], shape=(1), dtype=int32) x <- op_array(1:8) |> op_reshape(c(2, 2, 2)) x ## tf.Tensor( ## [[[1 2] ##   [3 4]] ## ##  [[5 6] ##   [7 8]]], shape=(2, 2, 2), dtype=int32) x |> op_diagonal(0) ## tf.Tensor( ## [[1 7] ##  [2 8]], shape=(2, 2), dtype=int32) x |> op_diagonal(0, 1, 2) # same as above, the default ## tf.Tensor( ## [[1 7] ##  [2 8]], shape=(2, 2), dtype=int32) x |> op_diagonal(0, 2, 3) ## tf.Tensor( ## [[1 4] ##  [5 8]], shape=(2, 2), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the n-th discrete difference along the given axis. — op_diff","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"first difference given [] = [+1] - [] along given axis, higher differences calculated using diff recursively.","code":""},{"path":"https://keras.posit.co/reference/op_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"","code":"op_diff(a, n = 1L, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"Input tensor. n number times values differenced. Defaults 1. axis Axis compute discrete difference(s) along. Defaults -1 (last axis).","code":""},{"path":"https://keras.posit.co/reference/op_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"Tensor diagonals.","code":""},{"path":"https://keras.posit.co/reference/op_diff.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the n-th discrete difference along the given axis. — op_diff","text":"","code":"x <- op_array(c(1, 2, 4, 7, 0)) op_diff(x) ## tf.Tensor([ 1.  2.  3. -7.], shape=(4), dtype=float32) op_diff(x, n = 2) ## tf.Tensor([  1.   1. -10.], shape=(3), dtype=float32) x <- op_array(rbind(c(1, 3, 6, 10),                   c(0, 5, 6, 8))) op_diff(x) ## tf.Tensor( ## [[2. 3. 4.] ##  [5. 1. 2.]], shape=(2, 3), dtype=float64) op_diff(x, axis = 1) ## tf.Tensor([[-1.  2.  0. -2.]], shape=(1, 4), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_digitize.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the indices of the bins to which each value in x belongs. — op_digitize","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"Returns indices bins value x belongs.","code":""},{"path":"https://keras.posit.co/reference/op_digitize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"","code":"op_digitize(x, bins)"},{"path":"https://keras.posit.co/reference/op_digitize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"x Input array binned. bins Array bins. one-dimensional monotonically increasing.","code":""},{"path":"https://keras.posit.co/reference/op_digitize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"Output array indices, shape x.","code":""},{"path":"https://keras.posit.co/reference/op_digitize.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the indices of the bins to which each value in x belongs. — op_digitize","text":"","code":"x <- op_array(c(0.0, 1.0, 3.0, 1.6)) bins <- array(c(0.0, 3.0, 4.5, 7.0)) op_digitize(x, bins) ## tf.Tensor([1 1 2 1], shape=(4), dtype=int32) # array([1, 1, 2, 1])"},{"path":[]},{"path":"https://keras.posit.co/reference/op_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"Divide arguments element-wise. — op_divide","title":"Divide arguments element-wise. — op_divide","text":"Note function automatically called using R operator * tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_divide(x, 2) ## tf.Tensor([0.  0.5 1.  1.5], shape=(4), dtype=float32) x / 2 ## tf.Tensor([0.  0.5 1.  1.5], shape=(4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Divide arguments element-wise. — op_divide","text":"","code":"op_divide(x1, x2)"},{"path":"https://keras.posit.co/reference/op_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Divide arguments element-wise. — op_divide","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_divide.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Divide arguments element-wise. — op_divide","text":"Output tensor, quotient x1/x2, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_divide.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Divide arguments element-wise. — op_divide","text":"","code":"op_divide(3, 2) ## tf.Tensor(1.5, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_divide_no_nan.html","id":null,"dir":"Reference","previous_headings":"","what":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","title":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","text":"Safe element-wise division returns 0 denominator 0.","code":""},{"path":"https://keras.posit.co/reference/op_divide_no_nan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","text":"","code":"op_divide_no_nan(x1, x2)"},{"path":"https://keras.posit.co/reference/op_divide_no_nan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_divide_no_nan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Safe element-wise division which returns 0 where the denominator is 0. — op_divide_no_nan","text":"quotient x1/x2, element-wise, zero x2 zero.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_dot.html","id":null,"dir":"Reference","previous_headings":"","what":"Dot product of two tensors. — op_dot","title":"Dot product of two tensors. — op_dot","text":"x1 x2 1-D tensors, inner product vectors (without complex conjugation). x1 x2 2-D tensors, matrix multiplication. either x1 x2 0-D (scalar), equivalent x1 * x2. x1 N-D tensor x2 1-D tensor, sum product last axis x1 x2. x1 N-D tensor x2 M-D tensor (M >= 2), sum product last axis x1 second--last axis x2: dot(x1, x2)[,j,k,m] = sum([,j,:] * b[k,:,m]).","code":""},{"path":"https://keras.posit.co/reference/op_dot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dot product of two tensors. — op_dot","text":"","code":"op_dot(x1, x2)"},{"path":"https://keras.posit.co/reference/op_dot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dot product of two tensors. — op_dot","text":"x1 First argument. x2 Second argument.","code":""},{"path":"https://keras.posit.co/reference/op_dot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dot product of two tensors. — op_dot","text":"Dot product x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_dot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Dot product of two tensors. — op_dot","text":"Torch backend accept 0-D tensors arguments.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_eig.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","title":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","text":"Computes eigenvalues eigenvectors square matrix.","code":""},{"path":"https://keras.posit.co/reference/op_eig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","text":"","code":"op_eig(x)"},{"path":"https://keras.posit.co/reference/op_eig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_eig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalues and eigenvectors of a square matrix. — op_eig","text":"list two tensors: tensor shape (..., M) containing eigenvalues tensor shape (..., M, M) containing eigenvectors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_eigh.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","title":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","text":"Computes eigenvalues eigenvectors complex Hermitian.","code":""},{"path":"https://keras.posit.co/reference/op_eigh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","text":"","code":"op_eigh(x)"},{"path":"https://keras.posit.co/reference/op_eigh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_eigh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalues and eigenvectors of a complex Hermitian. — op_eigh","text":"list two tensors: tensor shape (..., M) containing eigenvalues tensor shape (..., M, M) containing eigenvectors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_einsum.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluates the Einstein summation convention on the operands. — op_einsum","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"Evaluates Einstein summation convention operands.","code":""},{"path":"https://keras.posit.co/reference/op_einsum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"","code":"op_einsum(subscripts, ...)"},{"path":"https://keras.posit.co/reference/op_einsum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"subscripts Specifies subscripts summation comma separated list subscript labels. implicit (classical Einstein summation) calculation performed unless explicit indicator -> included well subscript labels precise output form. ... operands compute Einstein sum .","code":""},{"path":"https://keras.posit.co/reference/op_einsum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"calculation based Einstein summation convention.","code":""},{"path":"https://keras.posit.co/reference/op_einsum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluates the Einstein summation convention on the operands. — op_einsum","text":"Trace matrix:     Extract diagonal:     Sum axis:     higher dimensional tensors summing single axis can done ellipsis:     Compute matrix transpose reorder number axes:         Matrix vector multiplication:","code":"a <- op_arange(25) |> op_reshape(c(5, 5)) b <- op_arange(5) c <- op_arange(6) |> op_reshape(c(2, 3)) op_einsum(\"ii\", a) op_trace(a) ## tf.Tensor(60.0, shape=(), dtype=float32) ## tf.Tensor(60.0, shape=(), dtype=float32) op_einsum(\"ii -> i\", a) op_diag(a) ## tf.Tensor([ 0.  6. 12. 18. 24.], shape=(5), dtype=float32) ## tf.Tensor([ 0.  6. 12. 18. 24.], shape=(5), dtype=float32) op_einsum(\"ij -> i\", a) op_sum(a, axis = 2) ## tf.Tensor([ 10.  35.  60.  85. 110.], shape=(5), dtype=float32) ## tf.Tensor([ 10.  35.  60.  85. 110.], shape=(5), dtype=float32) op_einsum(\"...j -> ...\", a) op_sum(a, axis = -1) ## tf.Tensor([ 10.  35.  60.  85. 110.], shape=(5), dtype=float32) ## tf.Tensor([ 10.  35.  60.  85. 110.], shape=(5), dtype=float32) op_einsum(\"ji\", c) # return c unchanged ## tf.Tensor( ## [[0. 1. 2.] ##  [3. 4. 5.]], shape=(2, 3), dtype=float32) op_einsum(\"ij -> ji\", c) # transpose op_transpose(c)          # same as above ## tf.Tensor( ## [[0. 3.] ##  [1. 4.] ##  [2. 5.]], shape=(3, 2), dtype=float32) ## tf.Tensor( ## [[0. 3.] ##  [1. 4.] ##  [2. 5.]], shape=(3, 2), dtype=float32) op_einsum(\"ij, j\", a, b) op_einsum(\"...j, j\", a, b) a %*% b op_matmul(a, b) ## tf.Tensor([ 30.  80. 130. 180. 230.], shape=(5), dtype=float32) ## tf.Tensor([ 30.  80. 130. 180. 230.], shape=(5), dtype=float32) ## tf.Tensor([ 30.  80. 130. 180. 230.], shape=(5), dtype=float32) ## tf.Tensor([ 30.  80. 130. 180. 230.], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"Exponential Linear Unit activation function. — op_elu","title":"Exponential Linear Unit activation function. — op_elu","text":"defined : f(x) =  alpha * (exp(x) - 1.) x < 0, f(x) = x x >= 0.","code":""},{"path":"https://keras.posit.co/reference/op_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exponential Linear Unit activation function. — op_elu","text":"","code":"op_elu(x, alpha = 1)"},{"path":"https://keras.posit.co/reference/op_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exponential Linear Unit activation function. — op_elu","text":"x Input tensor. alpha scalar, slope positive section. Defaults 1.0.","code":""},{"path":"https://keras.posit.co/reference/op_elu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exponential Linear Unit activation function. — op_elu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_elu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Exponential Linear Unit activation function. — op_elu","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_elu(x) ## tf.Tensor([-0.63212055  0.          1.        ], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_empty.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","title":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","text":"Return tensor given shape type filled uninitialized data.","code":""},{"path":"https://keras.posit.co/reference/op_empty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","text":"","code":"op_empty(shape, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_empty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","text":"shape Shape empty tensor. dtype Desired data type empty tensor.","code":""},{"path":"https://keras.posit.co/reference/op_empty.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a tensor of given shape and type filled with uninitialized data. — op_empty","text":"empty tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns (x1 == x2) element-wise. — op_equal","title":"Returns (x1 == x2) element-wise. — op_equal","text":"Note function automatically called using R operator == tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_equal(x, 2) ## tf.Tensor([False False  True False], shape=(4), dtype=bool) x == 2 ## tf.Tensor([False False  True False], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns (x1 == x2) element-wise. — op_equal","text":"","code":"op_equal(x1, x2)"},{"path":"https://keras.posit.co/reference/op_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns (x1 == x2) element-wise. — op_equal","text":"x1 Tensor compare. x2 Tensor compare.","code":""},{"path":"https://keras.posit.co/reference/op_equal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns (x1 == x2) element-wise. — op_equal","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_erf.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the error function of x, element-wise. — op_erf","title":"Computes the error function of x, element-wise. — op_erf","text":"Computes error function x, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_erf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the error function of x, element-wise. — op_erf","text":"","code":"op_erf(x)"},{"path":"https://keras.posit.co/reference/op_erf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the error function of x, element-wise. — op_erf","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_erf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the error function of x, element-wise. — op_erf","text":"tensor dtype x.","code":""},{"path":"https://keras.posit.co/reference/op_erf.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the error function of x, element-wise. — op_erf","text":"","code":"x <- op_array(c(-3, -2, -1, 0, 1)) op_erf(x) ## tf.Tensor([-0.99997777 -0.9953222  -0.84270084  0.          0.84270084], shape=(5), dtype=float32) # array([-0.99998 , -0.99532, -0.842701,  0.,  0.842701], dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the inverse error function of x, element-wise. — op_erfinv","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"Computes inverse error function x, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"","code":"op_erfinv(x)"},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"tensor dtype x.","code":""},{"path":"https://keras.posit.co/reference/op_erfinv.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the inverse error function of x, element-wise. — op_erfinv","text":"","code":"x <- op_array(c(-0.5, -0.2, -0.1, 0.0, 0.3)) op_erfinv(x) ## tf.Tensor([-0.4769363  -0.17914344 -0.088856    0.          0.27246267], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the exponential of all elements in the input tensor. — op_exp","title":"Calculate the exponential of all elements in the input tensor. — op_exp","text":"Calculate exponential elements input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the exponential of all elements in the input tensor. — op_exp","text":"","code":"op_exp(x)"},{"path":"https://keras.posit.co/reference/op_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the exponential of all elements in the input tensor. — op_exp","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_exp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the exponential of all elements in the input tensor. — op_exp","text":"Output tensor, element-wise exponential x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_expand_dims.html","id":null,"dir":"Reference","previous_headings":"","what":"Expand the shape of a tensor. — op_expand_dims","title":"Expand the shape of a tensor. — op_expand_dims","text":"Insert new axis axis position expanded tensor shape.","code":""},{"path":"https://keras.posit.co/reference/op_expand_dims.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expand the shape of a tensor. — op_expand_dims","text":"","code":"op_expand_dims(x, axis)"},{"path":"https://keras.posit.co/reference/op_expand_dims.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expand the shape of a tensor. — op_expand_dims","text":"x Input tensor. axis Position expanded axes new axis (axes) placed.","code":""},{"path":"https://keras.posit.co/reference/op_expand_dims.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expand the shape of a tensor. — op_expand_dims","text":"Output tensor number dimensions increased.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_expm1.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","title":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","text":"Calculate exp(x) - 1 elements tensor.","code":""},{"path":"https://keras.posit.co/reference/op_expm1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","text":"","code":"op_expm1(x)"},{"path":"https://keras.posit.co/reference/op_expm1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","text":"x Input values.","code":""},{"path":"https://keras.posit.co/reference/op_expm1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate exp(x) - 1 for all elements in the tensor. — op_expm1","text":"Output tensor, element-wise exponential minus one.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":null,"dir":"Reference","previous_headings":"","what":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"Slides window size sequence_length last axis input stride sequence_stride, replacing last axis [num_sequences, sequence_length] sequences. dimension along last axis N, number sequences can computed : num_sequences = 1 + (N - sequence_length) // sequence_stride","code":""},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"","code":"op_extract_sequences(x, sequence_length, sequence_stride)"},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"x Input tensor. sequence_length integer representing sequences length. sequence_stride integer representing sequences hop size.","code":""},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"tensor sequences shape [..., num_sequences, sequence_length].","code":""},{"path":"https://keras.posit.co/reference/op_extract_sequences.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expands the dimension of last axis into sequences of sequence_length. — op_extract_sequences","text":"","code":"x <- op_convert_to_tensor(1:6) op_extract_sequences(x, 3, 2) ## tf.Tensor( ## [[1 2 3] ##  [3 4 5]], shape=(2, 3), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_eye.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","title":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","text":"Return 2-D tensor ones diagonal zeros elsewhere.","code":""},{"path":"https://keras.posit.co/reference/op_eye.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","text":"","code":"op_eye(N, M = NULL, k = 0L, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_eye.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","text":"N Number rows output. M Number columns output. NULL, defaults N. k Index diagonal: 0 (default) refers main diagonal, positive value refers upper diagonal, negative value lower diagonal. dtype Data type returned tensor.","code":""},{"path":"https://keras.posit.co/reference/op_eye.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a 2-D tensor with ones on the diagonal and zeros elsewhere. — op_eye","text":"Tensor ones k-th diagonal zeros elsewhere.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_fft.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Fast Fourier Transform along last axis of input. — op_fft","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"Computes Fast Fourier Transform along last axis input.","code":""},{"path":"https://keras.posit.co/reference/op_fft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"","code":"op_fft(x)"},{"path":"https://keras.posit.co/reference/op_fft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"x list real imaginary parts input tensor. tensors provided floating type.","code":""},{"path":"https://keras.posit.co/reference/op_fft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"list containing two tensors - real imaginary parts output tensor.","code":""},{"path":"https://keras.posit.co/reference/op_fft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Fast Fourier Transform along last axis of input. — op_fft","text":"","code":"x = c(op_array(c(1., 2.)),       op_array(c(0., 1.))) op_fft(x) ## [[1]] ## tf.Tensor([ 3. -1.], shape=(2), dtype=float32) ## ## [[2]] ## tf.Tensor([ 1. -1.], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_fft2.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"Computes 2D Fast Fourier Transform along last two axes input.","code":""},{"path":"https://keras.posit.co/reference/op_fft2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"","code":"op_fft2(x)"},{"path":"https://keras.posit.co/reference/op_fft2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"x list real imaginary parts input tensor. tensors provided floating type.","code":""},{"path":"https://keras.posit.co/reference/op_fft2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"list containing two tensors - real imaginary parts output.","code":""},{"path":"https://keras.posit.co/reference/op_fft2.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the 2D Fast Fourier Transform along the last two axes of input. — op_fft2","text":"","code":"x <- c(op_array(rbind(c(1, 2),                      c(2, 1))),        op_array(rbind(c(0, 1),                      c(1, 0)))) op_fft2(x) ## [[1]] ## tf.Tensor( ## [[ 6.  0.] ##  [ 0. -2.]], shape=(2, 2), dtype=float64) ## ## [[2]] ## tf.Tensor( ## [[ 2.  0.] ##  [ 0. -2.]], shape=(2, 2), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_flip.html","id":null,"dir":"Reference","previous_headings":"","what":"Reverse the order of elements in the tensor along the given axis. — op_flip","title":"Reverse the order of elements in the tensor along the given axis. — op_flip","text":"shape tensor preserved, elements reordered.","code":""},{"path":"https://keras.posit.co/reference/op_flip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reverse the order of elements in the tensor along the given axis. — op_flip","text":"","code":"op_flip(x, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_flip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reverse the order of elements in the tensor along the given axis. — op_flip","text":"x Input tensor. axis Axis axes along flip tensor. default, axis = NULL, flip axes input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_flip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reverse the order of elements in the tensor along the given axis. — op_flip","text":"Output tensor entries axis reversed.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_floor.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the floor of the input, element-wise. — op_floor","title":"Return the floor of the input, element-wise. — op_floor","text":"floor scalar x largest integer , <= x.","code":""},{"path":"https://keras.posit.co/reference/op_floor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the floor of the input, element-wise. — op_floor","text":"","code":"op_floor(x)"},{"path":"https://keras.posit.co/reference/op_floor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the floor of the input, element-wise. — op_floor","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_floor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the floor of the input, element-wise. — op_floor","text":"Output tensor, element-wise floor x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_floor_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","title":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","text":"Note function automatically called using R operator %/% tensor.","code":"(x <- op_arange(10)) ## tf.Tensor([0. 1. 2. 3. 4. 5. 6. 7. 8. 9.], shape=(10), dtype=float32) op_floor_divide(x, 2) ## tf.Tensor([0. 0. 1. 1. 2. 2. 3. 3. 4. 4.], shape=(10), dtype=float32) x %/% 2 ## tf.Tensor([0. 0. 1. 1. 2. 2. 3. 3. 4. 4.], shape=(10), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_floor_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","text":"","code":"op_floor_divide(x1, x2)"},{"path":"https://keras.posit.co/reference/op_floor_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","text":"x1 Numerator. x2 Denominator.","code":""},{"path":"https://keras.posit.co/reference/op_floor_divide.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the largest integer smaller or equal to the division of inputs. — op_floor_divide","text":"Output tensor, y <- floor(x1/x2)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":null,"dir":"Reference","previous_headings":"","what":"For loop implementation. — op_fori_loop","title":"For loop implementation. — op_fori_loop","text":"loop implementation.","code":""},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"For loop implementation. — op_fori_loop","text":"","code":"op_fori_loop(lower, upper, body_fun, init_val)"},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"For loop implementation. — op_fori_loop","text":"lower initial value loop variable. upper upper bound loop variable. body_fun callable represents loop body. Must take two arguments: loop variable loop state. loop state updated returned function. init_val initial value loop state.","code":""},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"For loop implementation. — op_fori_loop","text":"final state loop.","code":""},{"path":"https://keras.posit.co/reference/op_fori_loop.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"For loop implementation. — op_fori_loop","text":"","code":"lower <- 0L upper <- 10L body_fun <- function(i, state) state + i init_state <- 0L final_state <- op_fori_loop(lower, upper, body_fun, init_state) final_state ## tf.Tensor(45, shape=(), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_full.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a new tensor of given shape and type, filled with fill_value. — op_full","title":"Return a new tensor of given shape and type, filled with fill_value. — op_full","text":"Return new tensor given shape type, filled fill_value.","code":""},{"path":"https://keras.posit.co/reference/op_full.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a new tensor of given shape and type, filled with fill_value. — op_full","text":"","code":"op_full(shape, fill_value, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_full.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a new tensor of given shape and type, filled with fill_value. — op_full","text":"shape Shape new tensor. fill_value Fill value. dtype Desired data type tensor.","code":""},{"path":"https://keras.posit.co/reference/op_full.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a new tensor of given shape and type, filled with fill_value. — op_full","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_full_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","title":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","text":"Return full tensor shape type given tensor.","code":""},{"path":"https://keras.posit.co/reference/op_full_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","text":"","code":"op_full_like(x, fill_value, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_full_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","text":"x Input tensor. fill_value Fill value. dtype Overrides data type result.","code":""},{"path":"https://keras.posit.co/reference/op_full_like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a full tensor with the same shape and type as the given tensor. — op_full_like","text":"Tensor fill_value shape type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_gelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"approximate TRUE, defined : f(x) = 0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * x^3))) approximate FALSE, defined : f(x) = x * P(X <= x) = 0.5 * x * (1 + erf(x / sqrt(2))), P(X) ~ N(0, 1).","code":""},{"path":"https://keras.posit.co/reference/op_gelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"","code":"op_gelu(x, approximate = TRUE)"},{"path":"https://keras.posit.co/reference/op_gelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"x Input tensor. approximate Approximate version GELU activation. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_gelu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_gelu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gaussian Error Linear Unit (GELU) activation function. — op_gelu","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_gelu(x) ## tf.Tensor([-0.15880796  0.          0.841192  ], shape=(3), dtype=float32) op_gelu(x, FALSE) ## tf.Tensor([-0.15865526  0.          0.8413447 ], shape=(3), dtype=float32) x <- seq(-5, 5, .1) plot(x, op_gelu(x),      type = \"l\", #, frame.plot = FALSE,      panel.first = grid())"},{"path":[]},{"path":"https://keras.posit.co/reference/op_get_item.html","id":null,"dir":"Reference","previous_headings":"","what":"Return x[key]. — op_get_item","title":"Return x[key]. — op_get_item","text":"Return x[key].","code":""},{"path":"https://keras.posit.co/reference/op_get_item.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return x[key]. — op_get_item","text":"","code":"op_get_item(x, key)"},{"path":"https://keras.posit.co/reference/op_get_item.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return x[key]. — op_get_item","text":"x dictionary-like object key Generally, string, object __hash__ method acceptable.","code":""},{"path":"https://keras.posit.co/reference/op_get_item.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return x[key]. — op_get_item","text":"key.","code":""},{"path":"https://keras.posit.co/reference/op_get_item.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Return x[key]. — op_get_item","text":"Generally, calling x[[key]] x$key preferable.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_greater.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the truth value of x1 > x2 element-wise. — op_greater","title":"Return the truth value of x1 > x2 element-wise. — op_greater","text":"Note function automatically called using R operator > tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_greater(x, 2) ## tf.Tensor([False False False  True], shape=(4), dtype=bool) x > 2 ## tf.Tensor([False False False  True], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_greater.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the truth value of x1 > x2 element-wise. — op_greater","text":"","code":"op_greater(x1, x2)"},{"path":"https://keras.posit.co/reference/op_greater.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the truth value of x1 > x2 element-wise. — op_greater","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_greater.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the truth value of x1 > x2 element-wise. — op_greater","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_greater_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","title":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","text":"Note function automatically called using R operator >= tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_greater_equal(x, 2) ## tf.Tensor([False False  True  True], shape=(4), dtype=bool) x >= 2 ## tf.Tensor([False False  True  True], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_greater_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","text":"","code":"op_greater_equal(x1, x2)"},{"path":"https://keras.posit.co/reference/op_greater_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_greater_equal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the truth value of x1 >= x2 element-wise. — op_greater_equal","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Hard sigmoid activation function. — op_hard_sigmoid","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"defined : 0 x < -2.5, 1 x > 2.5, (0.2 * x) + 0.5 -2.5 <= x <= 2.5.","code":""},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"","code":"op_hard_sigmoid(x)"},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_hard_sigmoid.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hard sigmoid activation function. — op_hard_sigmoid","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_hard_sigmoid(x) ## tf.Tensor([0.33333334 0.5        0.6666667 ], shape=(3), dtype=float32) x <- as.array(seq(-5, 5, .1)) plot(x, op_hard_sigmoid(x),      type = 'l', panel.first = grid(), frame.plot = FALSE)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"defined : 0 x < -3 x x > 3 x * (x + 3) / 6 -3 <= x <= 3 faster, piecewise linear approximation silu activation.","code":""},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"","code":"op_hard_silu(x)  op_hard_swish(x)"},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_hard_silu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hard SiLU activation function, also known as Hard Swish. — op_hard_silu","text":"","code":"x <- op_convert_to_tensor(c(-3.0, -1.0, 0.0, 1.0, 3.0)) op_hard_silu(x) ## tf.Tensor([-0.         -0.33333334  0.          0.6666667   3.        ], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_hstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Stack tensors in sequence horizontally (column wise). — op_hstack","title":"Stack tensors in sequence horizontally (column wise). — op_hstack","text":"equivalent concatenation along first axis 1-D tensors, along second axis tensors.","code":""},{"path":"https://keras.posit.co/reference/op_hstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stack tensors in sequence horizontally (column wise). — op_hstack","text":"","code":"op_hstack(xs)"},{"path":"https://keras.posit.co/reference/op_hstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stack tensors in sequence horizontally (column wise). — op_hstack","text":"xs Sequence tensors.","code":""},{"path":"https://keras.posit.co/reference/op_hstack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stack tensors in sequence horizontally (column wise). — op_hstack","text":"tensor formed stacking given tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the identity tensor. — op_identity","title":"Return the identity tensor. — op_identity","text":"identity tensor square tensor ones main diagonal zeros elsewhere.","code":""},{"path":"https://keras.posit.co/reference/op_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the identity tensor. — op_identity","text":"","code":"op_identity(n, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_identity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the identity tensor. — op_identity","text":"n Number rows (columns) n x n output tensor. dtype Data type output tensor.","code":""},{"path":"https://keras.posit.co/reference/op_identity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the identity tensor. — op_identity","text":"identity tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_imag.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the imaginary part of the complex argument. — op_imag","title":"Return the imaginary part of the complex argument. — op_imag","text":"Return imaginary part complex argument.","code":""},{"path":"https://keras.posit.co/reference/op_imag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the imaginary part of the complex argument. — op_imag","text":"","code":"op_imag(x)"},{"path":"https://keras.posit.co/reference/op_imag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the imaginary part of the complex argument. — op_imag","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_imag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the imaginary part of the complex argument. — op_imag","text":"imaginary component complex argument.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies the given transform(s) to the image(s). — op_image_affine_transform","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"Applies given transform(s) image(s).","code":""},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"","code":"op_image_affine_transform(   image,   transform,   interpolation = \"bilinear\",   fill_mode = \"constant\",   fill_value = 0L,   data_format = \"channels_last\" )"},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"image Input image batch images. Must 3D 4D. transform Projective transform matrix/matrices. vector length 8 tensor size N x 8. one row transform [a0, a1, a2, b0, b1, b2, c0, c1], maps output point (x, y) transformed input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k), k = c0 x + c1 y + 1. transform inverted compared transform mapping input points output points. Note gradients backpropagated transformation parameters. Note c0 c1 effective using TensorFlow backend considered 0 using backends. interpolation Interpolation method. Available methods \"nearest\", \"bilinear\". Defaults \"bilinear\". fill_mode Points outside boundaries input filled according given mode. Available methods \"constant\", \"nearest\", \"wrap\" \"reflect\". Defaults \"constant\". \"reflect\": (d c b | b c d | d c b ) input extended reflecting edge last pixel. \"constant\": (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k specified fill_value. \"wrap\": (b c d | b c d | b c d) input extended wrapping around opposite edge. \"nearest\": (| b c d | d d d d) input extended nearest pixel. fill_value Value used points outside boundaries input fill_mode = \"constant\". Defaults 0. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\".","code":""},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"Applied affine transform image batch images.","code":""},{"path":"https://keras.posit.co/reference/op_image_affine_transform.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies the given transform(s) to the image(s). — op_image_affine_transform","text":"","code":"x <- random_uniform(c(2, 64, 80, 3)) # batch of 2 RGB images transform <- op_array(rbind(c(1.5, 0, -20, 0, 1.5, -16, 0, 0),  # zoom                            c(1, 0, -20, 0, 1, -16, 0, 0)))  # translation)) y <- op_image_affine_transform(x, transform) shape(y) ## shape(2, 64, 80, 3) # (2, 64, 80, 3) x <- random_uniform(c(64, 80, 3)) # single RGB image transform <- op_array(c(1.0, 0.5, -20, 0.5, 1.0, -16, 0, 0))  # shear y <- op_image_affine_transform(x, transform) shape(y) ## shape(64, 80, 3) # (64, 80, 3) x <- random_uniform(c(2, 3, 64, 80)) # batch of 2 RGB images transform <- op_array(rbind(   c(1.5, 0,-20, 0, 1.5,-16, 0, 0),  # zoom   c(1, 0,-20, 0, 1,-16, 0, 0)  # translation )) y <- op_image_affine_transform(x, transform, data_format = \"channels_first\") shape(y) ## shape(2, 3, 64, 80) # (2, 3, 64, 80)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":null,"dir":"Reference","previous_headings":"","what":"Crop images to a specified height and width. — op_image_crop","title":"Crop images to a specified height and width. — op_image_crop","text":"Crop images specified height width.","code":""},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Crop images to a specified height and width. — op_image_crop","text":"","code":"op_image_crop(   images,   top_cropping = NULL,   left_cropping = NULL,   target_height = NULL,   target_width = NULL,   bottom_cropping = NULL,   right_cropping = NULL )"},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Crop images to a specified height and width. — op_image_crop","text":"images 4-D batch images shape (batch, height, width, channels) 3-D single image shape (height, width, channels). top_cropping Number columns crop top. left_cropping Number columns crop left. target_height Height output images. target_width Width output images. bottom_cropping Number columns crop bottom. right_cropping Number columns crop right.","code":""},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Crop images to a specified height and width. — op_image_crop","text":"images 4D, 4D float Tensor shape (batch, target_height, target_width, channels) images 3D, 3D float Tensor shape (target_height, target_width, channels)","code":""},{"path":"https://keras.posit.co/reference/op_image_crop.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Crop images to a specified height and width. — op_image_crop","text":"","code":"images <- op_reshape(op_arange(1, 28, dtype=\"float32\"), c(3, 3, 3)) images[, , 1] # print the first channel of the images  cropped_images <- op_image_crop(images, 0, 0, 2, 2) cropped_images[, , 1] # print the first channel of the cropped images"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":null,"dir":"Reference","previous_headings":"","what":"Extracts patches from the image(s). — op_image_extract_patches","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"Extracts patches image(s).","code":""},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"","code":"op_image_extract_patches(   image,   size,   strides = NULL,   dilation_rate = 1L,   padding = \"valid\",   data_format = \"channels_last\" )"},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"image Input image batch images. Must 3D 4D. size Patch size int list (patch_height, patch_width) strides strides along height width. specified, NULL, defaults value size. dilation_rate input stride, specifying far two consecutive patch samples input. value 1, strides must 1. NOTE: strides > 1 supported conjunction dilation_rate > 1 padding type padding algorithm use: \"\" \"valid\". data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\".","code":""},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"Extracted patches 3D (batched) 4D (batched)","code":""},{"path":"https://keras.posit.co/reference/op_image_extract_patches.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extracts patches from the image(s). — op_image_extract_patches","text":"","code":"image <- random_uniform(c(2, 20, 20, 3), dtype = \"float32\") # batch of 2 RGB images patches <- op_image_extract_patches(image, c(5, 5)) shape(patches) ## shape(2, 4, 4, 75) # (2, 4, 4, 75) image <- random_uniform(c(20, 20, 3), dtype = \"float32\") # 1 RGB image patches <- op_image_extract_patches(image, c(3, 3), c(1, 1)) shape(patches) ## shape(18, 18, 27) # (18, 18, 27)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_map_coordinates.html","id":null,"dir":"Reference","previous_headings":"","what":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","title":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","text":"Note interpolation near boundaries differs scipy function, fixed outstanding bug scipy/issues/2640.","code":""},{"path":"https://keras.posit.co/reference/op_image_map_coordinates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","text":"","code":"op_image_map_coordinates(   input,   coordinates,   order,   fill_mode = \"constant\",   fill_value = 0L )"},{"path":"https://keras.posit.co/reference/op_image_map_coordinates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","text":"input input array. coordinates coordinates input evaluated. order order spline interpolation. order must 0 1. 0 indicates nearest neighbor 1 indicates linear interpolation. fill_mode Points outside boundaries input filled according given mode. Available methods \"constant\", \"nearest\", \"wrap\" \"mirror\" \"reflect\". Defaults \"constant\". \"constant\": (k k k k | b c d | k k k k) input extended filling values beyond edge constant value k specified fill_value. \"nearest\": (| b c d | d d d d) input extended nearest pixel. \"wrap\": (b c d | b c d | b c d) input extended wrapping around opposite edge. \"mirror\": (c d c b | b c d | c b b) input extended mirroring edge. \"reflect\": (d c b | b c d | d c b ) input extended reflecting edge last pixel. fill_value Value used points outside boundaries input fill_mode = \"constant\". Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/op_image_map_coordinates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Map the input array to new coordinates by interpolation.. — op_image_map_coordinates","text":"Output image batch images.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":null,"dir":"Reference","previous_headings":"","what":"Pad images with zeros to the specified height and width. — op_image_pad","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"Pad images zeros specified height width.","code":""},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"","code":"op_image_pad(   images,   top_padding = NULL,   left_padding = NULL,   target_height = NULL,   target_width = NULL,   bottom_padding = NULL,   right_padding = NULL )"},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"images 4D Tensor shape (batch, height, width, channels) 3D Tensor shape (height, width, channels). top_padding Number rows zeros add top. left_padding Number columns zeros add left. target_height Height output images. target_width Width output images. bottom_padding Number rows zeros add bottom. right_padding Number columns zeros add right.","code":""},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"images 4D, 4D float Tensor shape (batch, target_height, target_width, channels) images 3D, 3D float Tensor shape (target_height, target_width, channels)","code":""},{"path":"https://keras.posit.co/reference/op_image_pad.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pad images with zeros to the specified height and width. — op_image_pad","text":"","code":"images <- random_uniform(c(15, 25, 3)) padded_images <- op_image_pad(     images, 2, 3, target_height = 20, target_width = 30 ) shape(padded_images) ## shape(20, 30, 3) batch_images <- random_uniform(c(2, 15, 25, 3)) padded_batch <- op_image_pad(batch_images, 2, 3,                              target_height = 20,                              target_width = 30) shape(padded_batch) ## shape(2, 20, 30, 3)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":null,"dir":"Reference","previous_headings":"","what":"Resize images to size using the specified interpolation method. — op_image_resize","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"Resize images size using specified interpolation method.","code":""},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"","code":"op_image_resize(   image,   size,   interpolation = \"bilinear\",   antialias = FALSE,   crop_to_aspect_ratio = FALSE,   pad_to_aspect_ratio = FALSE,   fill_mode = \"constant\",   fill_value = 0,   data_format = \"channels_last\" )"},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"image Input image batch images. Must 3D 4D. size Size output image (height, width) format. interpolation Interpolation method. Available methods \"nearest\", \"bilinear\", \"bicubic\". Defaults \"bilinear\". antialias Whether use antialiasing filter downsampling image. Defaults FALSE. crop_to_aspect_ratio TRUE, resize images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image cropped return largest possible window image (size (height, width)) matches target aspect ratio. default (crop_to_aspect_ratio=FALSE), aspect ratio may preserved. pad_to_aspect_ratio TRUE, pad images without aspect ratio distortion. original aspect ratio differs target aspect ratio, output image evenly padded short side. fill_mode using pad_to_aspect_ratio=TRUE, padded areas filled according given mode. \"constant\" supported time (fill constant value, equal fill_value). fill_value Float. Padding value use pad_to_aspect_ratio=TRUE. data_format string, either \"channels_last\" \"channels_first\". ordering dimensions inputs. \"channels_last\" corresponds inputs shape (batch, height, width, channels) \"channels_first\" corresponds inputs shape (batch, channels, height, weight). defaults image_data_format value found Keras config file ~/.keras/keras.json. never set , \"channels_last\".","code":""},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"Resized image batch images.","code":""},{"path":"https://keras.posit.co/reference/op_image_resize.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Resize images to size using the specified interpolation method. — op_image_resize","text":"","code":"x <- random_uniform(c(2, 4, 4, 3)) # batch of 2 RGB images y <- op_image_resize(x, c(2, 2)) shape(y) ## shape(2, 2, 2, 3) x <- random_uniform(c(4, 4, 3)) # single RGB image y <- op_image_resize(x, c(2, 2)) shape(y) ## shape(2, 2, 3) x <- random_uniform(c(2, 3, 4, 4)) # batch of 2 RGB images y <- op_image_resize(x, c(2, 2), data_format = \"channels_first\") shape(y) ## shape(2, 3, 2, 2)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"function converts RGB images grayscale images. supports 3D 4D tensors, last dimension represents channels.","code":""},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"","code":"op_image_rgb_to_grayscale(image, data_format = \"channels_last\")"},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"image Input RGB image batch RGB images. Must 3D tensor shape (height, width, channels) 4D tensor shape (batch, height, width, channels). data_format string specifying data format input tensor. can either \"channels_last\" \"channels_first\". \"channels_last\" corresponds inputs shape (batch, height, width, channels), \"channels_first\" corresponds inputs shape (batch, channels, height, width). Defaults \"channels_last\".","code":""},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"Grayscale image batch grayscale images.","code":""},{"path":"https://keras.posit.co/reference/op_image_rgb_to_grayscale.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert RGB images to grayscale. — op_image_rgb_to_grayscale","text":"","code":"x <- random_uniform(c(2, 4, 4, 3)) y <- op_image_rgb_to_grayscale(x) shape(y) ## shape(2, 4, 4, 1) x <- random_uniform(c(4, 4, 3)) # Single RGB image y = op_image_rgb_to_grayscale(x) shape(y) ## shape(4, 4, 1) x <- random_uniform(c(2, 3, 4, 4)) y <- op_image_rgb_to_grayscale(x, data_format=\"channels_first\") shape(y) ## shape(2, 1, 4, 4)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if the targets are in the top-k predictions. — op_in_top_k","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"Checks targets top-k predictions.","code":""},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"","code":"op_in_top_k(targets, predictions, k)"},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"targets tensor true labels. predictions tensor predicted labels. k integer representing number predictions consider.","code":""},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"boolean tensor shape targets, element indicates whether corresponding target top-k predictions.","code":""},{"path":"https://keras.posit.co/reference/op_in_top_k.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Checks if the targets are in the top-k predictions. — op_in_top_k","text":"","code":"targets <- op_array(c(2, 5, 3), \"int32\") predictions <- op_array(dtype = \"float32\", rbind(   c(0.1, 0.4, 0.6, 0.9, 0.5),   c(0.1, 0.7, 0.9, 0.8, 0.3),   c(0.1, 0.6, 0.9, 0.9, 0.5) )) op_in_top_k(targets, predictions, k = 3L) ## tf.Tensor([ True False  True], shape=(3), dtype=bool)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_inv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the inverse of a square tensor. — op_inv","title":"Computes the inverse of a square tensor. — op_inv","text":"Computes inverse square tensor.","code":""},{"path":"https://keras.posit.co/reference/op_inv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the inverse of a square tensor. — op_inv","text":"","code":"op_inv(x)"},{"path":"https://keras.posit.co/reference/op_inv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the inverse of a square tensor. — op_inv","text":"x Input tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_inv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the inverse of a square tensor. — op_inv","text":"tensor shape (..., M, M) representing inverse x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_irfft.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"Computes inverse 1D Discrete Fourier Transform real-valued signal inner-dimension input. inner-dimension input assumed result RFFT: fft_length / 2 + 1 unique components DFT real-valued signal. fft_length provided, computed size inner-dimension input (fft_length = 2 * (inner - 1)). FFT length used compute odd, provided since inferred properly. Along axis IRFFT computed , fft_length / 2 + 1 smaller corresponding dimension input, dimension cropped. larger, dimension padded zeros.","code":""},{"path":"https://keras.posit.co/reference/op_irfft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"","code":"op_irfft(x, fft_length = NULL)"},{"path":"https://keras.posit.co/reference/op_irfft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"x List real imaginary parts input tensor. tensors list floating type. fft_length integer representing number fft length. specified, inferred length last axis x. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_irfft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"tensor containing inverse real-valued Fast Fourier Transform along last axis x.","code":""},{"path":"https://keras.posit.co/reference/op_irfft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse real-valued Fast Fourier transform along the last axis. — op_irfft","text":"","code":"real <- op_array(c(0, 1, 2, 3, 4)) imag <- op_array(c(0, 1, 2, 3, 4)) op_irfft(c(real, imag)) #> tf.Tensor( #> [ 2.         -2.0606601   0.5        -0.35355338  0.          0.06066012 #>  -0.5         0.35355338], shape=(8), dtype=float32) all.equal(op_irfft(op_rfft(real, 5), 5), real) #> [1] TRUE"},{"path":[]},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Check whether the given object is a tensor. — op_is_tensor","title":"Check whether the given object is a tensor. — op_is_tensor","text":"Check whether given object tensor.","code":""},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check whether the given object is a tensor. — op_is_tensor","text":"","code":"op_is_tensor(x)"},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check whether the given object is a tensor. — op_is_tensor","text":"x variable.","code":""},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check whether the given object is a tensor. — op_is_tensor","text":"TRUE x tensor, otherwise FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_is_tensor.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Check whether the given object is a tensor. — op_is_tensor","text":"checks backend specific tensors passing TensorFlow tensor return FALSE backend PyTorch JAX.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_isclose.html","id":null,"dir":"Reference","previous_headings":"","what":"Return whether two tensors are element-wise almost equal. — op_isclose","title":"Return whether two tensors are element-wise almost equal. — op_isclose","text":"Return whether two tensors element-wise almost equal.","code":""},{"path":"https://keras.posit.co/reference/op_isclose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return whether two tensors are element-wise almost equal. — op_isclose","text":"","code":"op_isclose(x1, x2)"},{"path":"https://keras.posit.co/reference/op_isclose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return whether two tensors are element-wise almost equal. — op_isclose","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isclose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return whether two tensors are element-wise almost equal. — op_isclose","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_isfinite.html","id":null,"dir":"Reference","previous_headings":"","what":"Return whether a tensor is finite, element-wise. — op_isfinite","title":"Return whether a tensor is finite, element-wise. — op_isfinite","text":"Real values finite NaN, positive infinity, negative infinity. Complex values finite real imaginary parts finite.","code":""},{"path":"https://keras.posit.co/reference/op_isfinite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return whether a tensor is finite, element-wise. — op_isfinite","text":"","code":"op_isfinite(x)"},{"path":"https://keras.posit.co/reference/op_isfinite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return whether a tensor is finite, element-wise. — op_isfinite","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isfinite.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return whether a tensor is finite, element-wise. — op_isfinite","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_isinf.html","id":null,"dir":"Reference","previous_headings":"","what":"Test element-wise for positive or negative infinity. — op_isinf","title":"Test element-wise for positive or negative infinity. — op_isinf","text":"Test element-wise positive negative infinity.","code":""},{"path":"https://keras.posit.co/reference/op_isinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test element-wise for positive or negative infinity. — op_isinf","text":"","code":"op_isinf(x)"},{"path":"https://keras.posit.co/reference/op_isinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test element-wise for positive or negative infinity. — op_isinf","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isinf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test element-wise for positive or negative infinity. — op_isinf","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_isnan.html","id":null,"dir":"Reference","previous_headings":"","what":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","title":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","text":"Test element-wise NaN return result boolean tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isnan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","text":"","code":"op_isnan(x)"},{"path":"https://keras.posit.co/reference/op_isnan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_isnan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test element-wise for NaN and return result as a boolean tensor. — op_isnan","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_istft.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"reconstruct original waveform, parameters stft.","code":""},{"path":"https://keras.posit.co/reference/op_istft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"","code":"op_istft(   x,   sequence_length,   sequence_stride,   fft_length,   length = NULL,   window = \"hann\",   center = TRUE )"},{"path":"https://keras.posit.co/reference/op_istft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"x Tuple real imaginary parts input tensor. tensors list floating type. sequence_length integer representing sequence length. sequence_stride integer representing sequence hop size. fft_length integer representing size FFT produced stft. length integer representing output clipped exactly length. specified, padding clipping take place. Defaults NULL. window string, tensor window NULL. window string, available values \"hann\" \"hamming\". window tensor, used directly window length must sequence_length. window NULL, windowing used. Defaults \"hann\". center Whether x padded sides t-th sequence centered time t * sequence_stride. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_istft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"tensor containing inverse Short-Time Fourier Transform along last axis x.","code":""},{"path":"https://keras.posit.co/reference/op_istft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse Short-Time Fourier Transform along the last axis of the input. — op_istft","text":"","code":"x <- op_convert_to_tensor(c(0, 1, 2, 3, 4)) op_istft(op_stft(x, 1, 1, 1), 1, 1, 1) ## tf.Tensor([], shape=(0), dtype=float32) # array([0.0, 1.0, 2.0, 3.0, 4.0])"},{"path":[]},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"allows small gradient unit active, defined : f(x) = alpha * x x < 0 f(x) = x x >= 0.","code":""},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"","code":"op_leaky_relu(x, negative_slope = 0.2)"},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"x Input tensor. negative_slope Slope activation function x < 0. Defaults 0.2.","code":""},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_leaky_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Leaky version of a Rectified Linear Unit activation function. — op_leaky_relu","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_leaky_relu(x) ## tf.Tensor([-0.2  0.   1. ], shape=(3), dtype=float32) # array([-0.2,  0. ,  1. ], shape=(3,), dtype=float64) x <- seq(-5, 5, .1) plot(x, op_leaky_relu(x),      type = 'l', panel.first = grid())"},{"path":[]},{"path":"https://keras.posit.co/reference/op_less.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the truth value of x1 < x2 element-wise. — op_less","title":"Return the truth value of x1 < x2 element-wise. — op_less","text":"Note function automatically called using R operator < tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_less(x, 2) ## tf.Tensor([ True  True False False], shape=(4), dtype=bool) x < 2 ## tf.Tensor([ True  True False False], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_less.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the truth value of x1 < x2 element-wise. — op_less","text":"","code":"op_less(x1, x2)"},{"path":"https://keras.posit.co/reference/op_less.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the truth value of x1 < x2 element-wise. — op_less","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_less.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the truth value of x1 < x2 element-wise. — op_less","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_less_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","title":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","text":"Note function automatically called using R operator <= tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_less_equal(x, 2) ## tf.Tensor([ True  True  True False], shape=(4), dtype=bool) x <= 2 ## tf.Tensor([ True  True  True False], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_less_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","text":"","code":"op_less_equal(x1, x2)"},{"path":"https://keras.posit.co/reference/op_less_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_less_equal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the truth value of x1 <= x2 element-wise. — op_less_equal","text":"Output tensor, element-wise comparison x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_linspace.html","id":null,"dir":"Reference","previous_headings":"","what":"Return evenly spaced numbers over a specified interval. — op_linspace","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"Returns num evenly spaced samples, calculated interval [start, stop]. endpoint interval can optionally excluded.","code":""},{"path":"https://keras.posit.co/reference/op_linspace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"","code":"op_linspace(   start,   stop,   num = 50L,   endpoint = TRUE,   retstep = FALSE,   dtype = NULL,   axis = 1L )"},{"path":"https://keras.posit.co/reference/op_linspace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"start starting value sequence. stop end value sequence, unless endpoint set FALSE. case, sequence consists last num + 1 evenly spaced samples, stop excluded. Note step size changes endpoint FALSE. num Number samples generate. Defaults 50. Must non-negative. endpoint TRUE, stop last sample. Otherwise, included. Defaults TRUE. retstep TRUE, return (samples, step), step spacing samples. dtype type output tensor. axis axis result store samples. Relevant start stop array-like. Defaults 1, first axis.","code":""},{"path":"https://keras.posit.co/reference/op_linspace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"tensor evenly spaced numbers. retstep TRUE, returns (samples, step)","code":""},{"path":"https://keras.posit.co/reference/op_linspace.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Return evenly spaced numbers over a specified interval. — op_linspace","text":"Torch backend support axis argument.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log.html","id":null,"dir":"Reference","previous_headings":"","what":"Natural logarithm, element-wise. — op_log","title":"Natural logarithm, element-wise. — op_log","text":"Natural logarithm, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_log.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Natural logarithm, element-wise. — op_log","text":"","code":"op_log(x)"},{"path":"https://keras.posit.co/reference/op_log.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Natural logarithm, element-wise. — op_log","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Natural logarithm, element-wise. — op_log","text":"Output tensor, element-wise natural logarithm x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log10.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","title":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","text":"Return base 10 logarithm input tensor, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_log10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","text":"","code":"op_log10(x)"},{"path":"https://keras.posit.co/reference/op_log10.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log10.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the base 10 logarithm of the input tensor, element-wise. — op_log10","text":"Output tensor, element-wise base 10 logarithm x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log1p.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","title":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","text":"Calculates log(1 + x).","code":""},{"path":"https://keras.posit.co/reference/op_log1p.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","text":"","code":"op_log1p(x)"},{"path":"https://keras.posit.co/reference/op_log1p.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log1p.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the natural logarithm of one plus the x, element-wise. — op_log1p","text":"Output tensor, element-wise natural logarithm 1 + x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log2.html","id":null,"dir":"Reference","previous_headings":"","what":"Base-2 logarithm of x, element-wise. — op_log2","title":"Base-2 logarithm of x, element-wise. — op_log2","text":"Base-2 logarithm x, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_log2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Base-2 logarithm of x, element-wise. — op_log2","text":"","code":"op_log2(x)"},{"path":"https://keras.posit.co/reference/op_log2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Base-2 logarithm of x, element-wise. — op_log2","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Base-2 logarithm of x, element-wise. — op_log2","text":"Output tensor, element-wise base-2 logarithm x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Logarithm of the sigmoid activation function. — op_log_sigmoid","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"defined f(x) = log(1 / (1 + exp(-x))).","code":""},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"","code":"op_log_sigmoid(x)"},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_log_sigmoid.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logarithm of the sigmoid activation function. — op_log_sigmoid","text":"","code":"x <- op_convert_to_tensor(c(-0.541391, 0.0, 0.50, 5.0)) op_log_sigmoid(x) ## tf.Tensor([-1.0000418  -0.6931472  -0.474077   -0.00671535], shape=(4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Log-softmax activation function. — op_log_softmax","title":"Log-softmax activation function. — op_log_softmax","text":"defined : f(x) = x - max(x) - log(sum(exp(x - max(x))))","code":""},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log-softmax activation function. — op_log_softmax","text":"","code":"op_log_softmax(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log-softmax activation function. — op_log_softmax","text":"x Input tensor. axis Integer, axis along log-softmax applied. Defaults -1.","code":""},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Log-softmax activation function. — op_log_softmax","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_log_softmax.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Log-softmax activation function. — op_log_softmax","text":"","code":"x <- op_array(c(-1., 0., 1.)) op_log_softmax(x) ## tf.Tensor([-2.407606   -1.4076059  -0.40760595], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_logaddexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","title":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","text":"Calculates log(exp(x1) + exp(x2)).","code":""},{"path":"https://keras.posit.co/reference/op_logaddexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","text":"","code":"op_logaddexp(x1, x2)"},{"path":"https://keras.posit.co/reference/op_logaddexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","text":"x1 Input tensor. x2 Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logaddexp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logarithm of the sum of exponentiations of the inputs. — op_logaddexp","text":"Output tensor, element-wise logarithm sum exponentiations inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"Zeros treated FALSE non-zeros treated TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"","code":"op_logical_and(x1, x2)"},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"x1 Input tensor. x2 Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"Output tensor, element-wise logical inputs.","code":""},{"path":"https://keras.posit.co/reference/op_logical_and.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the element-wise logical AND of the given input tensors. — op_logical_and","text":"Note function automatically called using R operator & tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logical_not.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the element-wise NOT of the given input tensor. — op_logical_not","title":"Computes the element-wise NOT of the given input tensor. — op_logical_not","text":"Zeros treated FALSE non-zeros treated TRUE. Note function automatically called using R operator ! tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_not.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the element-wise NOT of the given input tensor. — op_logical_not","text":"","code":"op_logical_not(x)"},{"path":"https://keras.posit.co/reference/op_logical_not.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the element-wise NOT of the given input tensor. — op_logical_not","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_not.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the element-wise NOT of the given input tensor. — op_logical_not","text":"Output tensor, element-wise logical input.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logical_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","title":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","text":"Zeros treated FALSE non-zeros treated TRUE. Note function automatically called using R operator | tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","text":"","code":"op_logical_or(x1, x2)"},{"path":"https://keras.posit.co/reference/op_logical_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","text":"x1 Input tensor. x2 Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_or.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the element-wise logical OR of the given input tensors. — op_logical_or","text":"Output tensor, element-wise logical inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logical_xor.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","title":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","text":"Compute truth value x1 XOR x2, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_logical_xor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","text":"","code":"op_logical_xor(x1, x2)"},{"path":"https://keras.posit.co/reference/op_logical_xor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logical_xor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the truth value of x1 XOR x2, element-wise. — op_logical_xor","text":"Output boolean tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logspace.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns numbers spaced evenly on a log scale. — op_logspace","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"linear space, sequence starts base ** start ends base ** stop (see endpoint ).","code":""},{"path":"https://keras.posit.co/reference/op_logspace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"","code":"op_logspace(   start,   stop,   num = 50L,   endpoint = TRUE,   base = 10L,   dtype = NULL,   axis = 1L )"},{"path":"https://keras.posit.co/reference/op_logspace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"start starting value sequence. stop final value sequence, unless endpoint FALSE. case, num + 1 values spaced interval log-space, last (sequence length num) returned. num Number samples generate. Defaults 50. endpoint TRUE, stop last sample. Otherwise, included. Defaults TRUE. base base log space. Defaults 10. dtype type output tensor. axis axis result store samples. Relevant start stop array-like.","code":""},{"path":"https://keras.posit.co/reference/op_logspace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"tensor evenly spaced samples log scale.","code":""},{"path":"https://keras.posit.co/reference/op_logspace.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Returns numbers spaced evenly on a log scale. — op_logspace","text":"Torch backend support axis argument.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"Computes logarithm sum exponentials elements tensor.","code":""},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"","code":"op_logsumexp(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"x Input tensor. axis integer list integers specifying axis/axes along compute sum. NULL, sum computed elements. Defaults toNULL. keepdims boolean indicating whether keep dimensions input tensor computing sum. Defaults toFALSE.","code":""},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"tensor containing logarithm sum exponentials elements x.","code":""},{"path":"https://keras.posit.co/reference/op_logsumexp.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the logarithm of sum of exponentials of elements in a tensor. — op_logsumexp","text":"","code":"x <- op_convert_to_tensor(c(1, 2, 3)) op_logsumexp(x) ## tf.Tensor(3.407606, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_lu_factor.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","title":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","text":"Computes lower-upper decomposition square matrix.","code":""},{"path":"https://keras.posit.co/reference/op_lu_factor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","text":"","code":"op_lu_factor(x)"},{"path":"https://keras.posit.co/reference/op_lu_factor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","text":"x tensor shape (..., M, M).","code":""},{"path":"https://keras.posit.co/reference/op_lu_factor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the lower-upper decomposition of a square matrix. — op_lu_factor","text":"tuple two tensors: tensor shape (..., M, M) containing lower upper triangular matrices tensor shape (..., M) containing pivots.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_matmul.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix product of two tensors. — op_matmul","title":"Matrix product of two tensors. — op_matmul","text":"tensors 1-dimensional, dot product (scalar) returned. either tensor N-D, N > 2, treated stack matrices residing last two indexes broadcast accordingly. first tensor 1-D, promoted matrix prepending 1 dimensions. matrix multiplication prepended 1 removed. second tensor 1-D, promoted matrix appending 1 dimensions. matrix multiplication appended 1 removed.","code":""},{"path":"https://keras.posit.co/reference/op_matmul.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix product of two tensors. — op_matmul","text":"","code":"op_matmul(x1, x2)"},{"path":"https://keras.posit.co/reference/op_matmul.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix product of two tensors. — op_matmul","text":"x1 First tensor. x2 Second tensor.","code":""},{"path":"https://keras.posit.co/reference/op_matmul.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matrix product of two tensors. — op_matmul","text":"Output tensor, matrix product inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_max.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the maximum of a tensor or maximum along an axis. — op_max","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"Return maximum tensor maximum along axis.","code":""},{"path":"https://keras.posit.co/reference/op_max.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"","code":"op_max(x, axis = NULL, keepdims = FALSE, initial = NULL)"},{"path":"https://keras.posit.co/reference/op_max.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"x Input tensor. axis Axis axes along operate. default, flattened input used. keepdims set TRUE, axes reduced left result dimensions size one. Defaults FALSE. initial minimum value output element. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_max.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"Maximum x.","code":""},{"path":"https://keras.posit.co/reference/op_max.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the maximum of a tensor or maximum along an axis. — op_max","text":"","code":"(x <- op_convert_to_tensor(rbind(c(1, 3, 5), c(1, 5, 2)))) ## tf.Tensor( ## [[1. 3. 5.] ##  [1. 5. 2.]], shape=(2, 3), dtype=float64) op_max(x) ## tf.Tensor(5.0, shape=(), dtype=float64) op_max(x, axis = 1) ## tf.Tensor([1. 5. 5.], shape=(3), dtype=float64) op_max(x, axis = 1, keepdims = TRUE) ## tf.Tensor([[1. 5. 5.]], shape=(1, 3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_max_pool.html","id":null,"dir":"Reference","previous_headings":"","what":"Max pooling operation. — op_max_pool","title":"Max pooling operation. — op_max_pool","text":"Max pooling operation.","code":""},{"path":"https://keras.posit.co/reference/op_max_pool.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max pooling operation. — op_max_pool","text":"","code":"op_max_pool(   inputs,   pool_size,   strides = NULL,   padding = \"valid\",   data_format = NULL )"},{"path":"https://keras.posit.co/reference/op_max_pool.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max pooling operation. — op_max_pool","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format = \"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format = \"channels_first\". Pooling happens spatial dimensions . pool_size int tuple/list integers size len(inputs_spatial_shape), specifying size pooling window spatial dimension input tensor. pool_size int, every spatial dimension shares pool_size. strides int tuple/list integers size len(inputs_spatial_shape). stride sliding window spatial dimension input tensor. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides = 1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format = \"channels_last\", inputs shape (batch_size, ..., channels) data_format = \"channels_first\", inputs shape (batch_size, channels, ...).","code":""},{"path":"https://keras.posit.co/reference/op_max_pool.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Max pooling operation. — op_max_pool","text":"tensor rank N+2, result max pooling operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_maximum.html","id":null,"dir":"Reference","previous_headings":"","what":"Element-wise maximum of x1 and x2. — op_maximum","title":"Element-wise maximum of x1 and x2. — op_maximum","text":"Element-wise maximum x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_maximum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Element-wise maximum of x1 and x2. — op_maximum","text":"","code":"op_maximum(x1, x2)  op_pmax(x1, x2)"},{"path":"https://keras.posit.co/reference/op_maximum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Element-wise maximum of x1 and x2. — op_maximum","text":"x1 First tensor. x2 Second tensor.","code":""},{"path":"https://keras.posit.co/reference/op_maximum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Element-wise maximum of x1 and x2. — op_maximum","text":"Output tensor, element-wise maximum x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the arithmetic mean along the specified axes. — op_mean","title":"Compute the arithmetic mean along the specified axes. — op_mean","text":"Compute arithmetic mean along specified axes.","code":""},{"path":"https://keras.posit.co/reference/op_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the arithmetic mean along the specified axes. — op_mean","text":"","code":"op_mean(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the arithmetic mean along the specified axes. — op_mean","text":"x Input tensor. axis Axis axes along means computed. default compute mean flattened tensor. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the arithmetic mean along the specified axes. — op_mean","text":"Output tensor containing mean values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_median.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the median along the specified axis. — op_median","title":"Compute the median along the specified axis. — op_median","text":"Compute median along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_median.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the median along the specified axis. — op_median","text":"","code":"op_median(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_median.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the median along the specified axis. — op_median","text":"x Input tensor. axis Axis axes along medians computed. Defaults axis = NULL compute median(s) along flattened version array. keepdims set TRUE, axes reduce left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_median.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the median along the specified axis. — op_median","text":"output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"Given N 1-D tensors T0, T1, ..., TN-1 inputs corresponding lengths S0, S1, ..., SN-1, creates N N-dimensional tensors G0, G1, ..., GN-1 shape (S0, ..., SN-1) output Gi constructed expanding Ti result shape.","code":""},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"","code":"op_meshgrid(..., indexing = \"xy\")"},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"... 1-D tensors representing coordinates grid. indexing \"xy\" \"ij\". \"xy\" cartesian; \"ij\" matrix indexing output. Defaults \"xy\".","code":""},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"Sequence N tensors.","code":""},{"path":"https://keras.posit.co/reference/op_meshgrid.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates grids of coordinates from coordinate vectors. — op_meshgrid","text":"","code":"x <- op_array(c(1, 2, 3), \"int32\") y <- op_array(c(4, 5, 6), \"int32\") c(grid_x, grid_y) %<-% op_meshgrid(x, y, indexing = \"ij\") grid_x ## tf.Tensor( ## [[1 1 1] ##  [2 2 2] ##  [3 3 3]], shape=(3, 3), dtype=int32) # array([[1, 1, 1], #        [2, 2, 2], #        [3, 3, 3])) grid_y ## tf.Tensor( ## [[4 5 6] ##  [4 5 6] ##  [4 5 6]], shape=(3, 3), dtype=int32) # array([[4, 5, 6], #        [4, 5, 6], #        [4, 5, 6]))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_min.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the minimum of a tensor or minimum along an axis. — op_min","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"Return minimum tensor minimum along axis.","code":""},{"path":"https://keras.posit.co/reference/op_min.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"","code":"op_min(x, axis = NULL, keepdims = FALSE, initial = NULL)"},{"path":"https://keras.posit.co/reference/op_min.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"x Input tensor. axis Axis axes along operate. default, flattened input used. keepdims set TRUE, axes reduced left result dimensions size one. Defaults FALSE. initial maximum value output element. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_min.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"Minimum x.","code":""},{"path":"https://keras.posit.co/reference/op_min.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the minimum of a tensor or minimum along an axis. — op_min","text":"","code":"(x <- op_convert_to_tensor(rbind(c(1, 3, 5), c(1, 5, 2)))) ## tf.Tensor( ## [[1. 3. 5.] ##  [1. 5. 2.]], shape=(2, 3), dtype=float64) op_min(x) ## tf.Tensor(1.0, shape=(), dtype=float64) op_min(x, axis = 1) ## tf.Tensor([1. 3. 2.], shape=(3), dtype=float64) op_min(x, axis = 1, keepdims = TRUE) ## tf.Tensor([[1. 3. 2.]], shape=(1, 3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_minimum.html","id":null,"dir":"Reference","previous_headings":"","what":"Element-wise minimum of x1 and x2. — op_minimum","title":"Element-wise minimum of x1 and x2. — op_minimum","text":"Element-wise minimum x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_minimum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Element-wise minimum of x1 and x2. — op_minimum","text":"","code":"op_minimum(x1, x2)  op_pmin(x1, x2)"},{"path":"https://keras.posit.co/reference/op_minimum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Element-wise minimum of x1 and x2. — op_minimum","text":"x1 First tensor. x2 Second tensor.","code":""},{"path":"https://keras.posit.co/reference/op_minimum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Element-wise minimum of x1 and x2. — op_minimum","text":"Output tensor, element-wise minimum x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_mod.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the element-wise remainder of division. — op_mod","title":"Returns the element-wise remainder of division. — op_mod","text":"Note function automatically called using R operator %% tensor.","code":"(x <- op_arange(10)) ## tf.Tensor([0. 1. 2. 3. 4. 5. 6. 7. 8. 9.], shape=(10), dtype=float32) op_mod(x, 3) ## tf.Tensor([0. 1. 2. 0. 1. 2. 0. 1. 2. 0.], shape=(10), dtype=float32) x %% 3 ## tf.Tensor([0. 1. 2. 0. 1. 2. 0. 1. 2. 0.], shape=(10), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_mod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the element-wise remainder of division. — op_mod","text":"","code":"op_mod(x1, x2)"},{"path":"https://keras.posit.co/reference/op_mod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the element-wise remainder of division. — op_mod","text":"x1 First tensor. x2 Second tensor.","code":""},{"path":"https://keras.posit.co/reference/op_mod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the element-wise remainder of division. — op_mod","text":"Output tensor, element-wise remainder division.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_moments.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the mean and variance of x. — op_moments","title":"Calculates the mean and variance of x. — op_moments","text":"mean variance calculated aggregating contents x across axes. x 1-D axes = c(1) just mean variance vector.","code":""},{"path":"https://keras.posit.co/reference/op_moments.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the mean and variance of x. — op_moments","text":"","code":"op_moments(x, axes, keepdims = FALSE, synchronized = FALSE)"},{"path":"https://keras.posit.co/reference/op_moments.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates the mean and variance of x. — op_moments","text":"x Input tensor. axes list axes compute mean variance. keepdims set TRUE, axes reduced left result dimensions size one. synchronized applicable TensorFlow backend. TRUE, synchronizes global batch statistics (mean variance) across devices training step distributed training strategy. FALSE, replica uses local batch statistics.","code":""},{"path":"https://keras.posit.co/reference/op_moments.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the mean and variance of x. — op_moments","text":"list containing two tensors - mean variance.","code":""},{"path":"https://keras.posit.co/reference/op_moments.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculates the mean and variance of x. — op_moments","text":"","code":"x <- op_convert_to_tensor(c(0, 1, 2, 3, 100), dtype = \"float32\") op_moments(x, axes = c(1)) ## [[1]] ## tf.Tensor(21.2, shape=(), dtype=float32) ## ## [[2]] ## tf.Tensor(1553.3601, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_moveaxis.html","id":null,"dir":"Reference","previous_headings":"","what":"Move axes of a tensor to new positions. — op_moveaxis","title":"Move axes of a tensor to new positions. — op_moveaxis","text":"axes remain original order.","code":""},{"path":"https://keras.posit.co/reference/op_moveaxis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Move axes of a tensor to new positions. — op_moveaxis","text":"","code":"op_moveaxis(x, source, destination)"},{"path":"https://keras.posit.co/reference/op_moveaxis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Move axes of a tensor to new positions. — op_moveaxis","text":"x Tensor whose axes reordered. source Original positions axes move. must unique. destination Destinations positions original axes. must also unique.","code":""},{"path":"https://keras.posit.co/reference/op_moveaxis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Move axes of a tensor to new positions. — op_moveaxis","text":"Tensor moved axes.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":null,"dir":"Reference","previous_headings":"","what":"Encodes integer labels as multi-hot vectors. — op_multi_hot","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"function encodes integer labels multi-hot vectors, label mapped binary value resulting vector.","code":""},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"","code":"op_multi_hot(   inputs,   num_classes,   axis = -1L,   dtype = NULL,   sparse = FALSE,   ... )"},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"inputs Tensor integer labels converted multi-hot vectors. num_classes Integer, total number unique classes. axis (optional) Axis along multi-hot encoding added. Defaults -1, corresponds last dimension. dtype (optional) data type resulting tensor. Default backend's float type. sparse Whether return sparse tensor; backends support sparse tensors. ... forward/backwards compatability","code":""},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"Tensor: multi-hot encoded tensor.","code":""},{"path":"https://keras.posit.co/reference/op_multi_hot.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Encodes integer labels as multi-hot vectors. — op_multi_hot","text":"","code":"data <- op_convert_to_tensor(c(0, 4)) op_multi_hot(data, num_classes = 5) ## tf.Tensor([1. 0. 0. 0. 1.], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_multiply.html","id":null,"dir":"Reference","previous_headings":"","what":"Multiply arguments element-wise. — op_multiply","title":"Multiply arguments element-wise. — op_multiply","text":"Note function automatically called using R operator * tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_multiply(x, x) ## tf.Tensor([0. 1. 4. 9.], shape=(4), dtype=float32) x * x ## tf.Tensor([0. 1. 4. 9.], shape=(4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_multiply.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multiply arguments element-wise. — op_multiply","text":"","code":"op_multiply(x1, x2)"},{"path":"https://keras.posit.co/reference/op_multiply.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multiply arguments element-wise. — op_multiply","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_multiply.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multiply arguments element-wise. — op_multiply","text":"Output tensor, element-wise product x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"Replace NaN zero infinity large finite numbers.","code":""},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"","code":"op_nan_to_num(x, nan = 0, posinf = NULL, neginf = NULL)"},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"x Input data. nan Optional float int. Value replace NaN entries . posinf Optional float int. Value replace positive infinity . neginf Optional float int. Value replace negative infinity .","code":""},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"x, non-finite values replaced.","code":""},{"path":"https://keras.posit.co/reference/op_nan_to_num.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Replace NaN with zero and infinity with large finite numbers. — op_nan_to_num","text":"","code":"(x <- op_convert_to_tensor(c(1, NaN, -Inf, Inf))) ## tf.Tensor([  1.  nan -inf  inf], shape=(4), dtype=float32) op_nan_to_num(x) ## tf.Tensor([ 1.0000000e+00  0.0000000e+00 -3.4028235e+38  3.4028235e+38], shape=(4), dtype=float32) op_nan_to_num(x, nan = -1, posinf = 2, neginf = -2) ## tf.Tensor([ 1. -1. -2.  2.], shape=(4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_ndim.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the number of dimensions of a tensor. — op_ndim","title":"Return the number of dimensions of a tensor. — op_ndim","text":"Return number dimensions tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ndim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the number of dimensions of a tensor. — op_ndim","text":"","code":"op_ndim(x)"},{"path":"https://keras.posit.co/reference/op_ndim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the number of dimensions of a tensor. — op_ndim","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ndim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the number of dimensions of a tensor. — op_ndim","text":"number dimensions x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_negative.html","id":null,"dir":"Reference","previous_headings":"","what":"Numerical negative, element-wise. — op_negative","title":"Numerical negative, element-wise. — op_negative","text":"Note function automatically called using unary R operator - tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_negative(x) ## tf.Tensor([-0. -1. -2. -3.], shape=(4), dtype=float32) -x ## tf.Tensor([-0. -1. -2. -3.], shape=(4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_negative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Numerical negative, element-wise. — op_negative","text":"","code":"op_negative(x)"},{"path":"https://keras.posit.co/reference/op_negative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Numerical negative, element-wise. — op_negative","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_negative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Numerical negative, element-wise. — op_negative","text":"Output tensor, y = -x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the indices of the elements that are non-zero. — op_nonzero","title":"Return the indices of the elements that are non-zero. — op_nonzero","text":"Return indices elements non-zero.","code":""},{"path":"https://keras.posit.co/reference/op_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the indices of the elements that are non-zero. — op_nonzero","text":"","code":"op_nonzero(x)"},{"path":"https://keras.posit.co/reference/op_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the indices of the elements that are non-zero. — op_nonzero","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_nonzero.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the indices of the elements that are non-zero. — op_nonzero","text":"Indices elements non-zero.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix or vector norm. — op_norm","title":"Matrix or vector norm. — op_norm","text":"function able return one eight different matrix norms, one infinite number vector norms (described ), depending value ord parameter.","code":""},{"path":"https://keras.posit.co/reference/op_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix or vector norm. — op_norm","text":"","code":"op_norm(x, ord = NULL, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix or vector norm. — op_norm","text":"x Input tensor. ord Order norm (see table Notes). default NULL. axis axis integer, specifies axis x along compute vector norms. axis length 2 vector, specifies axes hold 2-D matrices, matrix norms matrices computed. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matrix or vector norm. — op_norm","text":"Norm matrix vector(s).","code":""},{"path":"https://keras.posit.co/reference/op_norm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Matrix or vector norm. — op_norm","text":"values ord < 1, result , strictly speaking, mathematical 'norm', may still useful various numerical purposes. following norms can calculated: matrices: ord=NULL: Frobenius norm ord=\"fro\": Frobenius norm ord=\"nuc\": nuclear norm ord=Inf: max(sum(abs(x), axis=2)) ord=-Inf: min(sum(abs(x), axis=2)) ord=0: supported ord=1: max(sum(abs(x), axis=1)) ord=-1: min(sum(abs(x), axis=1)) ord=2: 2-norm (largest sing. value) ord=-2: smallest singular value : supported vectors: ord=NULL: 2-norm ord=\"fro\": supported ord=\"nuc\": supported ord=Inf: max(abs(x)) ord=-Inf: min(abs(x)) ord=0: sum(x != 0) ord=1: ord=-1: ord=2: ord=-2: : sum(abs(x)^ord)^(1/ord)","code":""},{"path":"https://keras.posit.co/reference/op_norm.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrix or vector norm. — op_norm","text":"","code":"x <- op_reshape(op_arange(9, dtype=\"float32\") - 4, c(3, 3)) op_norm(x) ## tf.Tensor(7.745967, shape=(), dtype=float32) # 7.7459664"},{"path":[]},{"path":"https://keras.posit.co/reference/op_normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalizes x over the specified axis. — op_normalize","title":"Normalizes x over the specified axis. — op_normalize","text":"defined : normalize(x) = x / max(norm(x), epsilon).","code":""},{"path":"https://keras.posit.co/reference/op_normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalizes x over the specified axis. — op_normalize","text":"","code":"op_normalize(x, axis = -1L, order = 2L)"},{"path":"https://keras.posit.co/reference/op_normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalizes x over the specified axis. — op_normalize","text":"x Input tensor. axis axis axes along perform normalization. Default -1. order exponent value norm formulation. Defaults 2.","code":""},{"path":"https://keras.posit.co/reference/op_normalize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalizes x over the specified axis. — op_normalize","text":"normalized array.","code":""},{"path":"https://keras.posit.co/reference/op_normalize.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalizes x over the specified axis. — op_normalize","text":"","code":"x <- op_convert_to_tensor(rbind(c(1, 2, 3), c(4, 5, 6))) x_norm <- op_normalize(x) x_norm ## tf.Tensor( ## [[0.26726124 0.53452248 0.80178373] ##  [0.45584231 0.56980288 0.68376346]], shape=(2, 3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_not_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return (x1 != x2) element-wise. — op_not_equal","title":"Return (x1 != x2) element-wise. — op_not_equal","text":"Note function automatically called using R operator != tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_not_equal(x, 2) ## tf.Tensor([ True  True False  True], shape=(4), dtype=bool) x != 2 ## tf.Tensor([ True  True False  True], shape=(4), dtype=bool)"},{"path":"https://keras.posit.co/reference/op_not_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return (x1 != x2) element-wise. — op_not_equal","text":"","code":"op_not_equal(x1, x2)"},{"path":"https://keras.posit.co/reference/op_not_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return (x1 != x2) element-wise. — op_not_equal","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_not_equal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return (x1 != x2) element-wise. — op_not_equal","text":"Output tensor, element-wise comparsion x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts integer tensor x into a one-hot tensor. — op_one_hot","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"one-hot encoding representation integer value converted binary vector length equal num_classes, index corresponding integer value marked 1, indices marked 0.","code":""},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"","code":"op_one_hot(x, num_classes, axis = -1L, dtype = NULL, sparse = FALSE)"},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"x Integer tensor encoded. shape can arbitrary, dtype integer. R factors coerced integer offset 0-based, .e., .integer(x) - 1L. num_classes Number classes one-hot encoding. axis Axis along encoding performed. Defaults -1, represents last axis. dtype (Optional) Data type output tensor. provided, defaults default data type backend. sparse Whether return sparse tensor; backends support sparse tensors.","code":""},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"Integer tensor: One-hot encoded tensor shape x except specified axis dimension, length num_classes. dtype output tensor determined dtype default data type backend.","code":""},{"path":"https://keras.posit.co/reference/op_one_hot.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converts integer tensor x into a one-hot tensor. — op_one_hot","text":"","code":"x <- op_array(c(1, 3, 2, 0), \"int32\") op_one_hot(x, num_classes = 4) ## tf.Tensor( ## [[0. 1. 0. 0.] ##  [0. 0. 0. 1.] ##  [0. 0. 1. 0.] ##  [1. 0. 0. 0.]], shape=(4, 4), dtype=float32) # array([[0. 1. 0. 0.] #        [0. 0. 0. 1.] #        [0. 0. 1. 0.] #        [1. 0. 0. 0.]], shape=(4, 4), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_ones.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a new tensor of given shape and type, filled with ones. — op_ones","title":"Return a new tensor of given shape and type, filled with ones. — op_ones","text":"Return new tensor given shape type, filled ones.","code":""},{"path":"https://keras.posit.co/reference/op_ones.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a new tensor of given shape and type, filled with ones. — op_ones","text":"","code":"op_ones(shape, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_ones.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a new tensor of given shape and type, filled with ones. — op_ones","text":"shape Shape new tensor. dtype Desired data type tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ones.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a new tensor of given shape and type, filled with ones. — op_ones","text":"Tensor ones given shape dtype.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_ones_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a tensor of ones with the same shape and type of x. — op_ones_like","title":"Return a tensor of ones with the same shape and type of x. — op_ones_like","text":"Return tensor ones shape type x.","code":""},{"path":"https://keras.posit.co/reference/op_ones_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a tensor of ones with the same shape and type of x. — op_ones_like","text":"","code":"op_ones_like(x, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_ones_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a tensor of ones with the same shape and type of x. — op_ones_like","text":"x Input tensor. dtype Overrides data type result.","code":""},{"path":"https://keras.posit.co/reference/op_ones_like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a tensor of ones with the same shape and type of x. — op_ones_like","text":"tensor ones shape type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_outer.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the outer product of two vectors. — op_outer","title":"Compute the outer product of two vectors. — op_outer","text":"Given two vectors x1 x2, outer product :","code":"out[i, j] = x1[i] * x2[j]"},{"path":"https://keras.posit.co/reference/op_outer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the outer product of two vectors. — op_outer","text":"","code":"op_outer(x1, x2)"},{"path":"https://keras.posit.co/reference/op_outer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the outer product of two vectors. — op_outer","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_outer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the outer product of two vectors. — op_outer","text":"Outer product x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_pad.html","id":null,"dir":"Reference","previous_headings":"","what":"Pad a tensor. — op_pad","title":"Pad a tensor. — op_pad","text":"Pad tensor.","code":""},{"path":"https://keras.posit.co/reference/op_pad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pad a tensor. — op_pad","text":"","code":"op_pad(x, pad_width, mode = \"constant\", constant_values = NULL)"},{"path":"https://keras.posit.co/reference/op_pad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pad a tensor. — op_pad","text":"x Tensor pad. pad_width Number values padded edges axis. ((before_1, after_1), ...(before_N, after_N)) unique pad widths axis. ((, ),) yields pad axis. (pad,) int shortcut = = pad width axes. mode One \"constant\", \"edge\", \"linear_ramp\", \"maximum\", \"mean\", \"median\", \"minimum\", \"reflect\", \"symmetric\", \"wrap\", \"empty\", \"circular\". Defaults \"constant\". constant_values Value pad mode == \"constant\". Defaults 0. ValueError raised NULL mode != \"constant\".","code":""},{"path":"https://keras.posit.co/reference/op_pad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pad a tensor. — op_pad","text":"Padded tensor.","code":""},{"path":"https://keras.posit.co/reference/op_pad.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Pad a tensor. — op_pad","text":"Torch backend supports modes \"constant\", \"reflect\", \"symmetric\" \"circular\". Torch backend supports \"circular\" mode. Note: Tensorflow backend supports modes \"constant\", \"reflect\" \"symmetric\".","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_power.html","id":null,"dir":"Reference","previous_headings":"","what":"First tensor elements raised to powers from second tensor, element-wise. — op_power","title":"First tensor elements raised to powers from second tensor, element-wise. — op_power","text":"Note function automatically called using R operator ^ tensor.","code":"(x <- op_arange(4)) ## tf.Tensor([0. 1. 2. 3.], shape=(4), dtype=float32) op_power(2, x) ## tf.Tensor([1. 2. 4. 8.], shape=(4), dtype=float32) 2 ^ x ## tf.Tensor([1. 2. 4. 8.], shape=(4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_power.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"First tensor elements raised to powers from second tensor, element-wise. — op_power","text":"","code":"op_power(x1, x2)"},{"path":"https://keras.posit.co/reference/op_power.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"First tensor elements raised to powers from second tensor, element-wise. — op_power","text":"x1 bases. x2 exponents.","code":""},{"path":"https://keras.posit.co/reference/op_power.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"First tensor elements raised to powers from second tensor, element-wise. — op_power","text":"Output tensor, bases x1 raised exponents x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_prod.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the product of tensor elements over a given axis. — op_prod","title":"Return the product of tensor elements over a given axis. — op_prod","text":"Return product tensor elements given axis.","code":""},{"path":"https://keras.posit.co/reference/op_prod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the product of tensor elements over a given axis. — op_prod","text":"","code":"op_prod(x, axis = NULL, keepdims = FALSE, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_prod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the product of tensor elements over a given axis. — op_prod","text":"x Input tensor. axis Axis axes along product performed. default, axis = NULL, compute product elements input tensor. keepdims set TRUE, axes reduce left result dimensions size one. dtype Data type returned tensor.","code":""},{"path":"https://keras.posit.co/reference/op_prod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the product of tensor elements over a given axis. — op_prod","text":"Product elements x given axis axes.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_psnr.html","id":null,"dir":"Reference","previous_headings":"","what":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"function computes Peak Signal--Noise Ratio two signals, x1 x2. PSNR measure quality reconstructed signal. higher PSNR, closer reconstructed signal original signal. Note can become negative signal power smaller noise power.","code":""},{"path":"https://keras.posit.co/reference/op_psnr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"","code":"op_psnr(x1, x2, max_val)"},{"path":"https://keras.posit.co/reference/op_psnr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"x1 first input signal. x2 second input signal. Must shape x1. max_val maximum possible value signals.","code":""},{"path":"https://keras.posit.co/reference/op_psnr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"float: PSNR value x1 x2.","code":""},{"path":"https://keras.posit.co/reference/op_psnr.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Peak Signal-to-Noise Ratio (PSNR) function. — op_psnr","text":"","code":"x1 <- random_normal(c(2, 4, 4, 3)) x2 <- random_normal(c(2, 4, 4, 3)) max_val <- 1.0 op_psnr(x1, x2, max_val) ## tf.Tensor(-3.433579, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_qr.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the QR decomposition of a tensor. — op_qr","title":"Computes the QR decomposition of a tensor. — op_qr","text":"Computes QR decomposition tensor.","code":""},{"path":"https://keras.posit.co/reference/op_qr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the QR decomposition of a tensor. — op_qr","text":"","code":"op_qr(x, mode = \"reduced\")"},{"path":"https://keras.posit.co/reference/op_qr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the QR decomposition of a tensor. — op_qr","text":"x Input tensor shape (..., M, N). mode string specifying mode QR decomposition. 'reduced': Returns reduced QR decomposition. (default) 'complete': Returns complete QR decomposition.","code":""},{"path":"https://keras.posit.co/reference/op_qr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the QR decomposition of a tensor. — op_qr","text":"list containing two tensors. first tensor shape (..., M, K) orthogonal matrix q second tensor shape (..., K, N)upper triangular matrixr, K = min(M, N)`.","code":""},{"path":"https://keras.posit.co/reference/op_qr.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the QR decomposition of a tensor. — op_qr","text":"","code":"x <- op_convert_to_tensor(rbind(c(1, 2), c(3, 4), c(5, 6))) op_qr(x) ## $q ## tf.Tensor( ## [[-0.16903085  0.89708523] ##  [-0.50709255  0.27602622] ##  [-0.84515425 -0.34503278]], shape=(3, 2), dtype=float64) ## ## $r ## tf.Tensor( ## [[-5.91607978 -7.43735744] ##  [ 0.          0.82807867]], shape=(2, 2), dtype=float64) c(q, r) %<-% op_qr(x)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","title":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","text":"Compute q-th quantile(s) data along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","text":"","code":"op_quantile(x, q, axis = NULL, method = \"linear\", keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","text":"x Input tensor. q Probability sequence probabilities quantiles compute. Values must 0 1 inclusive. axis Axis axes along quantiles computed. Defaults axis=NULL compute quantile(s) along flattened version array. method string specifies method use estimating quantile. Available methods \"linear\", \"lower\", \"higher\", \"midpoint\", \"nearest\". Defaults \"linear\". desired quantile lies two data points < j: \"linear\": + (j - ) * fraction, fraction fractional part index surrounded j. \"lower\": . \"higher\": j. \"midpoint\": (+ j) / 2 \"nearest\": j, whichever nearest. keepdims set TRUE, axes reduce left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the q-th quantile(s) of the data along the specified axis. — op_quantile","text":"quantile(s). q single probability axis=NULL, result scalar. multiple probabilies levels given, first axis result corresponds quantiles. axes axes remain reduction x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_ravel.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a contiguous flattened tensor. — op_ravel","title":"Return a contiguous flattened tensor. — op_ravel","text":"1-D tensor, containing elements input, returned.","code":""},{"path":"https://keras.posit.co/reference/op_ravel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a contiguous flattened tensor. — op_ravel","text":"","code":"op_ravel(x)"},{"path":"https://keras.posit.co/reference/op_ravel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a contiguous flattened tensor. — op_ravel","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_ravel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a contiguous flattened tensor. — op_ravel","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_real.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the real part of the complex argument. — op_real","title":"Return the real part of the complex argument. — op_real","text":"Return real part complex argument.","code":""},{"path":"https://keras.posit.co/reference/op_real.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the real part of the complex argument. — op_real","text":"","code":"op_real(x)"},{"path":"https://keras.posit.co/reference/op_real.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the real part of the complex argument. — op_real","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_real.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the real part of the complex argument. — op_real","text":"real component complex argument.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_reciprocal.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the reciprocal of the argument, element-wise. — op_reciprocal","title":"Return the reciprocal of the argument, element-wise. — op_reciprocal","text":"Calculates 1/x.","code":""},{"path":"https://keras.posit.co/reference/op_reciprocal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the reciprocal of the argument, element-wise. — op_reciprocal","text":"","code":"op_reciprocal(x)"},{"path":"https://keras.posit.co/reference/op_reciprocal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the reciprocal of the argument, element-wise. — op_reciprocal","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_reciprocal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the reciprocal of the argument, element-wise. — op_reciprocal","text":"Output tensor, element-wise reciprocal x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Rectified linear unit activation function. — op_relu","title":"Rectified linear unit activation function. — op_relu","text":"defined f(x) = max(0, x).","code":""},{"path":"https://keras.posit.co/reference/op_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rectified linear unit activation function. — op_relu","text":"","code":"op_relu(x)"},{"path":"https://keras.posit.co/reference/op_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rectified linear unit activation function. — op_relu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_relu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rectified linear unit activation function. — op_relu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_relu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rectified linear unit activation function. — op_relu","text":"","code":"x1 <- op_convert_to_tensor(c(-1, 0, 1, 0.2)) op_relu(x1) ## tf.Tensor([0.  0.  1.  0.2], shape=(4), dtype=float32) x <- seq(-10, 10, .1) plot(x, op_relu(x))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_relu6.html","id":null,"dir":"Reference","previous_headings":"","what":"Rectified linear unit activation function with upper bound of 6. — op_relu6","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"defined f(x) = op_clip(x, 0, 6).","code":""},{"path":"https://keras.posit.co/reference/op_relu6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"","code":"op_relu6(x)"},{"path":"https://keras.posit.co/reference/op_relu6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_relu6.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_relu6.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rectified linear unit activation function with upper bound of 6. — op_relu6","text":"","code":"x <- op_convert_to_tensor(c(-3, -2, 0.1, 0.2, 6, 8)) op_relu6(x) ## tf.Tensor([0.  0.  0.1 0.2 6.  6. ], shape=(6), dtype=float32) x <- seq(-10, 10, .1) plot(x, op_relu6(x))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_repeat.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeat each element of a tensor after themselves. — op_repeat","title":"Repeat each element of a tensor after themselves. — op_repeat","text":"Repeat element tensor .","code":""},{"path":"https://keras.posit.co/reference/op_repeat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeat each element of a tensor after themselves. — op_repeat","text":"","code":"op_repeat(x, repeats, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_repeat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeat each element of a tensor after themselves. — op_repeat","text":"x Input tensor. repeats number repetitions element. axis axis along repeat values. default, use flattened input array, return flat output array.","code":""},{"path":"https://keras.posit.co/reference/op_repeat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Repeat each element of a tensor after themselves. — op_repeat","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_reshape.html","id":null,"dir":"Reference","previous_headings":"","what":"Gives a new shape to a tensor without changing its data. — op_reshape","title":"Gives a new shape to a tensor without changing its data. — op_reshape","text":"Gives new shape tensor without changing data.","code":""},{"path":"https://keras.posit.co/reference/op_reshape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gives a new shape to a tensor without changing its data. — op_reshape","text":"","code":"op_reshape(x, newshape)"},{"path":"https://keras.posit.co/reference/op_reshape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gives a new shape to a tensor without changing its data. — op_reshape","text":"x Input tensor. newshape new shape compatible original shape. One shape dimension can -1 case value inferred length array remaining dimensions.","code":""},{"path":"https://keras.posit.co/reference/op_reshape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gives a new shape to a tensor without changing its data. — op_reshape","text":"reshaped tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_rfft.html","id":null,"dir":"Reference","previous_headings":"","what":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"Computes 1D Discrete Fourier Transform real-valued signal inner-dimension input. Since Discrete Fourier Transform real-valued signal Hermitian-symmetric, RFFT returns fft_length / 2 + 1 unique components FFT: zero-frequency term, followed fft_length / 2 positive-frequency terms. Along axis RFFT computed , fft_length smaller corresponding dimension input, dimension cropped. larger, dimension padded zeros.","code":""},{"path":"https://keras.posit.co/reference/op_rfft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"","code":"op_rfft(x, fft_length = NULL)"},{"path":"https://keras.posit.co/reference/op_rfft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"x Input tensor. fft_length integer representing number fft length. specified, inferred length last axis x. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/op_rfft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"list containing two tensors - real imaginary parts output.","code":""},{"path":"https://keras.posit.co/reference/op_rfft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Real-valued Fast Fourier Transform along the last axis of the input. — op_rfft","text":"","code":"x <- op_convert_to_tensor(c(0, 1, 2, 3, 4)) op_rfft(x) ## [[1]] ## tf.Tensor([10.  -2.5 -2.5], shape=(3), dtype=float32) ## ## [[2]] ## tf.Tensor([0.        3.440955  0.8122992], shape=(3), dtype=float32) op_rfft(x, 3) ## [[1]] ## tf.Tensor([ 3.  -1.5], shape=(2), dtype=float32) ## ## [[2]] ## tf.Tensor([0.        0.8660254], shape=(2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_roll.html","id":null,"dir":"Reference","previous_headings":"","what":"Roll tensor elements along a given axis. — op_roll","title":"Roll tensor elements along a given axis. — op_roll","text":"Elements roll beyond last position re-introduced first.","code":""},{"path":"https://keras.posit.co/reference/op_roll.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Roll tensor elements along a given axis. — op_roll","text":"","code":"op_roll(x, shift, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_roll.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Roll tensor elements along a given axis. — op_roll","text":"x Input tensor. shift number places elements shifted. axis axis along elements shifted. default, array flattened shifting, original shape restored.","code":""},{"path":"https://keras.posit.co/reference/op_roll.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Roll tensor elements along a given axis. — op_roll","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_round.html","id":null,"dir":"Reference","previous_headings":"","what":"Evenly round to the given number of decimals. — op_round","title":"Evenly round to the given number of decimals. — op_round","text":"Evenly round given number decimals.","code":""},{"path":"https://keras.posit.co/reference/op_round.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evenly round to the given number of decimals. — op_round","text":"","code":"op_round(x, decimals = 0L)"},{"path":"https://keras.posit.co/reference/op_round.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evenly round to the given number of decimals. — op_round","text":"x Input tensor. decimals Number decimal places round . Defaults 0.","code":""},{"path":"https://keras.posit.co/reference/op_round.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evenly round to the given number of decimals. — op_round","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes reciprocal of square root of x element-wise. — op_rsqrt","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"Computes reciprocal square root x element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"","code":"op_rsqrt(x)"},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"x input tensor","code":""},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"tensor dtype x.","code":""},{"path":"https://keras.posit.co/reference/op_rsqrt.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes reciprocal of square root of x element-wise. — op_rsqrt","text":"","code":"x <- op_convert_to_tensor(c(1, 10, 100)) op_rsqrt(x) ## tf.Tensor([1.         0.31622776 0.1       ], shape=(3), dtype=float32) # array([1, 0.31622776, 0.1], dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_scatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a tensor of shape shape where indices are set to values. — op_scatter","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"high level, operation zeros[indices] = updates returns output. equivalent :","code":"output <- op_scatter_update(op_zeros(shape), indices, values)"},{"path":"https://keras.posit.co/reference/op_scatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"","code":"op_scatter(indices, values, shape)"},{"path":"https://keras.posit.co/reference/op_scatter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"indices tensor list specifying indices values values. values tensor, values set indices. shape Shape output tensor.","code":""},{"path":"https://keras.posit.co/reference/op_scatter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"tensor shape shape indices set values.","code":""},{"path":"https://keras.posit.co/reference/op_scatter.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns a tensor of shape shape where indices are set to values. — op_scatter","text":"","code":"indices <- rbind(c(1, 2), c(2, 2)) values <- op_array(c(1, 1)) op_scatter(indices, values, shape= c(2, 2)) ## tf.Tensor( ## [[0. 1.] ##  [0. 1.]], shape=(2, 2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_scatter_update.html","id":null,"dir":"Reference","previous_headings":"","what":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","title":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","text":"high level, operation inputs[indices] <- updates. Assume inputs tensor shape (D1, D2, ..., Dn), 2 main usages scatter_update. indices 2D tensor shape (num_updates, n), num_updates number updates perform, updates 1D tensor shape (num_updates). example, inputs op_zeros(c(4, 4, 4)), want update inputs[2, 3, 4] inputs[1, 2, 4] 1, can use:     2 indices 2D tensor shape (num_updates, k), num_updates number updates perform, k (k <= n) size index indices. updates n - k-D tensor shape (num_updates, shape(inputs)[-(1:k)]). example, inputs <- op_zeros(c(4, 4, 4)), want update inputs[1, 2, ] inputs[2, 3, ] [1, 1, 1, 1], indices shape (num_updates, 2) (k = 2), updates shape (num_updates, 4) (shape(inputs)[3:4] == 4). See code :","code":"inputs <- op_zeros(c(4, 4, 4)) indices <- rbind(c(2, 3, 4),                  c(1, 2, 4)) updates <- op_array(c(1, 1), \"float32\") op_scatter_update(inputs, indices, updates) ## tf.Tensor( ## [[[0. 0. 0. 0.] ##   [0. 0. 0. 1.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 1.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]]], shape=(4, 4, 4), dtype=float32) inputs <- op_zeros(c(4, 4, 4)) indices <- rbind(c(2, 3),                  c(3, 4)) updates <- op_array(rbind(c(1, 1, 1, 1),                           c(1, 1, 1, 1)),                     \"float32\") op_scatter_update(inputs, indices, updates) ## tf.Tensor( ## [[[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [1. 1. 1. 1.] ##   [0. 0. 0. 0.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [1. 1. 1. 1.]] ## ##  [[0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.] ##   [0. 0. 0. 0.]]], shape=(4, 4, 4), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_scatter_update.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","text":"","code":"op_scatter_update(inputs, indices, updates)"},{"path":"https://keras.posit.co/reference/op_scatter_update.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","text":"inputs tensor, tensor updated. indices tensor list shape (N, inputs$ndim), specifying indices update. N number indices update, must equal first dimension updates. updates tensor, new values put inputs indices.","code":""},{"path":"https://keras.posit.co/reference/op_scatter_update.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update inputs via updates at scattered (sparse) indices. — op_scatter_update","text":"tensor, shape dtype inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the max of segments in a tensor. — op_segment_max","title":"Computes the max of segments in a tensor. — op_segment_max","text":"Computes max segments tensor.","code":""},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the max of segments in a tensor. — op_segment_max","text":"","code":"op_segment_max(data, segment_ids, num_segments = NULL, sorted = FALSE)"},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the max of segments in a tensor. — op_segment_max","text":"data Input tensor. segment_ids 1-D tensor containing segment indices element data. num_segments integer representing total number segments. specified, inferred maximum value segment_ids. sorted boolean indicating whether segment_ids sorted. Defaults toFALSE.","code":""},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the max of segments in a tensor. — op_segment_max","text":"tensor containing max segments, element represents max corresponding segment data.","code":""},{"path":"https://keras.posit.co/reference/op_segment_max.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the max of segments in a tensor. — op_segment_max","text":"","code":"data <- op_convert_to_tensor(c(1, 2, 10, 20, 100, 200)) segment_ids <- op_array(c(1, 1, 2, 2, 3, 3), \"int32\") num_segments <- 3 op_segment_max(data, segment_ids, num_segments) ## tf.Tensor([  2.  20. 200.], shape=(3), dtype=float32) # array([2, 20, 200], dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the sum of segments in a tensor. — op_segment_sum","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"Computes sum segments tensor.","code":""},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"","code":"op_segment_sum(data, segment_ids, num_segments = NULL, sorted = FALSE)"},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"data Input tensor. segment_ids 1-D tensor containing segment indices element data. num_segments integer representing total number segments. specified, inferred maximum value segment_ids. sorted boolean indicating whether segment_ids sorted. Defaults toFALSE.","code":""},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"tensor containing sum segments, element represents sum corresponding segment data.","code":""},{"path":"https://keras.posit.co/reference/op_segment_sum.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the sum of segments in a tensor. — op_segment_sum","text":"","code":"data <- op_array(c(1, 2, 10, 20, 100, 200)) segment_ids <- op_array(c(1, 1, 2, 2, 3, 3), \"int32\") num_segments <- 3 op_segment_sum(data, segment_ids, num_segments) ## tf.Tensor([  3.  30. 300.], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Return elements from choicelist, based on conditions in condlist. — op_select","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"Return elements choicelist, based conditions condlist.","code":""},{"path":"https://keras.posit.co/reference/op_select.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"","code":"op_select(condlist, choicelist, default = 0L)"},{"path":"https://keras.posit.co/reference/op_select.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"condlist List boolean tensors. list conditions determine array choicelist output elements taken. multiple conditions satisfied, first one encountered condlist used. choicelist List tensors. list tensors output elements taken. list length condlist. default Optional scalar value. element inserted output conditions evaluate FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_select.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"Tensor output position m m-th element tensor choicelist m-th element corresponding tensor condlist TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_select.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return elements from choicelist, based on conditions in condlist. — op_select","text":"","code":"x <- op_arange(6L) condlist <- list(x < 3, x > 3) choicelist <- list(x, x^2) op_select(condlist, choicelist, 42) ## tf.Tensor([ 0  1  2 42 16 25], shape=(6), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"defined : f(x) =  scale * alpha * (exp(x) - 1.) x < 0, f(x) = scale * x x >= 0.","code":""},{"path":"https://keras.posit.co/reference/op_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"","code":"op_selu(x)"},{"path":"https://keras.posit.co/reference/op_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_selu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_selu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scaled Exponential Linear Unit (SELU) activation function. — op_selu","text":"","code":"x <- op_array(c(-1, 0, 1)) op_selu(x) ## tf.Tensor([-1.1113307  0.         1.050701 ], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_separable_conv.html","id":null,"dir":"Reference","previous_headings":"","what":"General N-D separable convolution. — op_separable_conv","title":"General N-D separable convolution. — op_separable_conv","text":"ops supports 1D 2D separable convolution. separable_conv depthwise conv followed pointwise conv.","code":""},{"path":"https://keras.posit.co/reference/op_separable_conv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General N-D separable convolution. — op_separable_conv","text":"","code":"op_separable_conv(   inputs,   depthwise_kernel,   pointwise_kernel,   strides = 1L,   padding = \"valid\",   data_format = NULL,   dilation_rate = 1L )"},{"path":"https://keras.posit.co/reference/op_separable_conv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General N-D separable convolution. — op_separable_conv","text":"inputs Tensor rank N+2. inputs shape (batch_size,) + inputs_spatial_shape + (num_channels,) data_format=\"channels_last\", (batch_size, num_channels) + inputs_spatial_shape data_format=\"channels_first\". depthwise_kernel Tensor rank N+2. depthwise_kernel shape [kernel_spatial_shape, num_input_channels, num_channels_multiplier], num_input_channels match number channels inputs. pointwise_kernel Tensor rank N+2. pointwise_kernel shape (*ones_like(kernel_spatial_shape), num_input_channels * num_channels_multiplier, num_output_channels). strides int int tuple/list len(inputs_spatial_shape), specifying strides convolution along spatial dimension. strides int, every spatial dimension shares strides. padding string, either \"valid\" \"\". \"valid\" means padding applied, \"\" results padding evenly left/right /input output height/width dimension input strides=1. data_format string, either \"channels_last\" \"channels_first\". data_format determines ordering dimensions inputs. data_format=\"channels_last\", inputs shape (batch_size, ..., channels) data_format=\"channels_first\", inputs shape (batch_size, channels, ...). dilation_rate int int tuple/list len(inputs_spatial_shape), specifying dilation rate use dilated convolution. dilation_rate int, every spatial dimension shares dilation_rate.","code":""},{"path":"https://keras.posit.co/reference/op_separable_conv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General N-D separable convolution. — op_separable_conv","text":"tensor rank N+2, result depthwise conv operation.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_shape.html","id":null,"dir":"Reference","previous_headings":"","what":"Gets the shape of the tensor input. — op_shape","title":"Gets the shape of the tensor input. — op_shape","text":"Gets shape tensor input.","code":""},{"path":"https://keras.posit.co/reference/op_shape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gets the shape of the tensor input. — op_shape","text":"","code":"op_shape(x)"},{"path":"https://keras.posit.co/reference/op_shape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gets the shape of the tensor input. — op_shape","text":"x tensor. function try access shape attribute input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_shape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gets the shape of the tensor input. — op_shape","text":"list integers NULL values, indicating shape input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_shape.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Gets the shape of the tensor input. — op_shape","text":"TensorFlow backend, x tf.Tensor dynamic shape, dimensions dynamic context compiled function tf.Tensor value instead static integer value.","code":""},{"path":"https://keras.posit.co/reference/op_shape.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gets the shape of the tensor input. — op_shape","text":"","code":"x <- op_zeros(c(8, 12)) op_shape(x) ## shape(8, 12)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid activation function. — op_sigmoid","title":"Sigmoid activation function. — op_sigmoid","text":"defined f(x) = 1 / (1 + exp(-x)).","code":""},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid activation function. — op_sigmoid","text":"","code":"op_sigmoid(x)"},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid activation function. — op_sigmoid","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sigmoid activation function. — op_sigmoid","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_sigmoid.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sigmoid activation function. — op_sigmoid","text":"","code":"x <- op_convert_to_tensor(c(-6, 1, 0, 1, 6)) op_sigmoid(x) ## tf.Tensor([0.00247262 0.7310586  0.5        0.7310586  0.99752736], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_sign.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a tensor with the signs of the elements of x. — op_sign","title":"Returns a tensor with the signs of the elements of x. — op_sign","text":"Returns tensor signs elements x.","code":""},{"path":"https://keras.posit.co/reference/op_sign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a tensor with the signs of the elements of x. — op_sign","text":"","code":"op_sign(x)"},{"path":"https://keras.posit.co/reference/op_sign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a tensor with the signs of the elements of x. — op_sign","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a tensor with the signs of the elements of x. — op_sign","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"SiLU activation function computed sigmoid function multiplied input. defined f(x) = x * sigmoid(x).","code":""},{"path":"https://keras.posit.co/reference/op_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"","code":"op_silu(x)"},{"path":"https://keras.posit.co/reference/op_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_silu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_silu.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sigmoid Linear Unit (SiLU) activation function, also known as Swish. — op_silu","text":"","code":"x <- op_convert_to_tensor(c(-6, 1, 0, 1, 6)) op_sigmoid(x) ## tf.Tensor([0.00247262 0.7310586  0.5        0.7310586  0.99752736], shape=(5), dtype=float32) op_silu(x) ## tf.Tensor([-0.01483574  0.7310586   0.          0.7310586   5.985164  ], shape=(5), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_sin.html","id":null,"dir":"Reference","previous_headings":"","what":"Trigonometric sine, element-wise. — op_sin","title":"Trigonometric sine, element-wise. — op_sin","text":"Trigonometric sine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_sin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trigonometric sine, element-wise. — op_sin","text":"","code":"op_sin(x)"},{"path":"https://keras.posit.co/reference/op_sin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trigonometric sine, element-wise. — op_sin","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trigonometric sine, element-wise. — op_sin","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hyperbolic sine, element-wise. — op_sinh","title":"Hyperbolic sine, element-wise. — op_sinh","text":"Hyperbolic sine, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_sinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hyperbolic sine, element-wise. — op_sinh","text":"","code":"op_sinh(x)"},{"path":"https://keras.posit.co/reference/op_sinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hyperbolic sine, element-wise. — op_sinh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sinh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hyperbolic sine, element-wise. — op_sinh","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_size.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the number of elements in a tensor. — op_size","title":"Return the number of elements in a tensor. — op_size","text":"Return number elements tensor.","code":""},{"path":"https://keras.posit.co/reference/op_size.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the number of elements in a tensor. — op_size","text":"","code":"op_size(x)"},{"path":"https://keras.posit.co/reference/op_size.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the number of elements in a tensor. — op_size","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_size.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the number of elements in a tensor. — op_size","text":"Number elements x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_slice.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a slice of an input tensor. — op_slice","title":"Return a slice of an input tensor. — op_slice","text":"high level, operation explicit replacement array slicing e.g. inputs[start_indices:(start_indices + shape)]. Unlike slicing via brackets, operation accept tensor start indices backends, useful indices dynamically computed via tensor operations.","code":"(inputs <- op_arange(5*5) |> op_reshape(c(5, 5))) ## tf.Tensor( ## [[ 0.  1.  2.  3.  4.] ##  [ 5.  6.  7.  8.  9.] ##  [10. 11. 12. 13. 14.] ##  [15. 16. 17. 18. 19.] ##  [20. 21. 22. 23. 24.]], shape=(5, 5), dtype=float32) start_indices <- c(3, 3) shape <- c(2, 2) op_slice(inputs, start_indices, shape) ## tf.Tensor( ## [[12. 13.] ##  [17. 18.]], shape=(2, 2), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_slice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a slice of an input tensor. — op_slice","text":"","code":"op_slice(inputs, start_indices, shape)"},{"path":"https://keras.posit.co/reference/op_slice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a slice of an input tensor. — op_slice","text":"inputs tensor, tensor sliced. start_indices list length inputs$ndim, specifying starting indices updating. shape full shape returned slice.","code":""},{"path":"https://keras.posit.co/reference/op_slice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a slice of an input tensor. — op_slice","text":"tensor, shape dtype inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_slice_update.html","id":null,"dir":"Reference","previous_headings":"","what":"Update an input by slicing in a tensor of updated values. — op_slice_update","title":"Update an input by slicing in a tensor of updated values. — op_slice_update","text":"high level, operation inputs[start_indices: start_indices + updates.shape] = updates. Assume inputs tensor shape (D1, D2, ..., Dn), start_indices must list n integers, specifying starting indices. updates must rank inputs, size dim must exceed Di - start_indices[]. example, 2D inputs inputs = op_zeros(c(5, 5)), want update intersection last 2 rows last 2 columns 1, .e., inputs[4:5, 4:5] = op_ones(c(2, 2)), can use code :","code":"inputs <- op_zeros(c(5, 5)) start_indices <- c(3, 3) updates <- op_ones(c(2, 2)) op_slice_update(inputs, start_indices, updates) ## tf.Tensor( ## [[0. 0. 0. 0. 0.] ##  [0. 0. 0. 0. 0.] ##  [0. 0. 1. 1. 0.] ##  [0. 0. 1. 1. 0.] ##  [0. 0. 0. 0. 0.]], shape=(5, 5), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_slice_update.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update an input by slicing in a tensor of updated values. — op_slice_update","text":"","code":"op_slice_update(inputs, start_indices, updates)"},{"path":"https://keras.posit.co/reference/op_slice_update.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update an input by slicing in a tensor of updated values. — op_slice_update","text":"inputs tensor, tensor updated. start_indices list length inputs$ndim, specifying starting indices updating. updates tensor, new values put inputs indices. updates must rank inputs.","code":""},{"path":"https://keras.posit.co/reference/op_slice_update.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update an input by slicing in a tensor of updated values. — op_slice_update","text":"tensor, shape dtype inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_slogdet.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","title":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","text":"Compute sign natural logarithm determinant matrix.","code":""},{"path":"https://keras.posit.co/reference/op_slogdet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","text":"","code":"op_slogdet(x)"},{"path":"https://keras.posit.co/reference/op_slogdet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","text":"x Input matrix. must 2D square.","code":""},{"path":"https://keras.posit.co/reference/op_slogdet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the sign and natural logarithm of the determinant of a matrix. — op_slogdet","text":"list: (sign, logabsdet). sign number representing sign determinant. real matrix, 1, 0, -1. complex matrix, complex number absolute value 1 (.e., unit circle), else 0. logabsdet natural log absolute value determinant.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax activation function. — op_softmax","title":"Softmax activation function. — op_softmax","text":"elements output vector lie within range (0, 1), total sum exactly 1 (excluding floating point rounding error). vector processed independently. axis argument specifies axis along function applied within input. defined : f(x) = exp(x) / sum(exp(x))","code":""},{"path":"https://keras.posit.co/reference/op_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax activation function. — op_softmax","text":"","code":"op_softmax(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax activation function. — op_softmax","text":"x Input tensor. axis Integer, axis along softmax applied.","code":""},{"path":"https://keras.posit.co/reference/op_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax activation function. — op_softmax","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_softmax.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmax activation function. — op_softmax","text":"","code":"x <- op_array(c(-1, 0, 1)) op_softmax(x) ## tf.Tensor([0.09003057 0.24472848 0.66524094], shape=(3), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_softplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Softplus activation function. — op_softplus","title":"Softplus activation function. — op_softplus","text":"defined f(x) = log(exp(x) + 1), log natural logarithm exp exponential function.","code":""},{"path":"https://keras.posit.co/reference/op_softplus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softplus activation function. — op_softplus","text":"","code":"op_softplus(x)"},{"path":"https://keras.posit.co/reference/op_softplus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softplus activation function. — op_softplus","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_softplus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softplus activation function. — op_softplus","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_softplus.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softplus activation function. — op_softplus","text":"","code":"x <- op_convert_to_tensor(c(-0.555, 0, 0.555)) op_softplus(x) ## tf.Tensor([0.45366603 0.6931472  1.008666  ], shape=(3), dtype=float32) x <- seq(-10, 10, .1) plot(x, op_softplus(x))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_softsign.html","id":null,"dir":"Reference","previous_headings":"","what":"Softsign activation function. — op_softsign","title":"Softsign activation function. — op_softsign","text":"defined f(x) = x / (abs(x) + 1).","code":""},{"path":"https://keras.posit.co/reference/op_softsign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softsign activation function. — op_softsign","text":"","code":"op_softsign(x)"},{"path":"https://keras.posit.co/reference/op_softsign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softsign activation function. — op_softsign","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_softsign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softsign activation function. — op_softsign","text":"tensor shape x.","code":""},{"path":"https://keras.posit.co/reference/op_softsign.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softsign activation function. — op_softsign","text":"","code":"x <- op_convert_to_tensor(c(-0.100, -10.0, 1.0, 0.0, 100.0)) op_softsign(x) ## tf.Tensor([-0.09090909 -0.90909094  0.5         0.          0.990099  ], shape=(5), dtype=float32) x <- seq(-10, 10, .1) plot(x, op_softsign(x), ylim = c(-1, 1))"},{"path":[]},{"path":"https://keras.posit.co/reference/op_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Solves a linear system of equations given by a x = b. — op_solve","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"Solves x equation %*% x == b.","code":""},{"path":"https://keras.posit.co/reference/op_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"","code":"op_solve(a, b)"},{"path":"https://keras.posit.co/reference/op_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"tensor shape (..., M, M) representing coefficients matrix. b tensor shape (..., M) (..., M, N) represeting right-hand side \"dependent variable\" matrix.","code":""},{"path":"https://keras.posit.co/reference/op_solve.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"tensor shape (..., M) (..., M, N) representing solution linear system. Returned shape identical b.","code":""},{"path":"https://keras.posit.co/reference/op_solve.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Solves a linear system of equations given by a x = b. — op_solve","text":"","code":"a <- op_array(c(1, 2, 4, 5), dtype=\"float32\") |> op_reshape(c(2, 2)) b <- op_array(c(2, 4, 8, 10), dtype=\"float32\") |> op_reshape(c(2, 2)) op_solve(a, b) ## tf.Tensor( ## [[2. 0.] ##  [0. 2.]], shape=(2, 2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_solve_triangular.html","id":null,"dir":"Reference","previous_headings":"","what":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","title":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","text":"Solves linear system equations given %*% x = b.","code":""},{"path":"https://keras.posit.co/reference/op_solve_triangular.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","text":"","code":"op_solve_triangular(a, b, lower = FALSE)"},{"path":"https://keras.posit.co/reference/op_solve_triangular.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","text":"tensor shape (..., M, M) representing coefficients matrix. b tensor shape (..., M) (..., M, N) represeting right-hand side \"dependent variable\" matrix. lower logical. Use data contained lower triangle . Default use upper triangle.","code":""},{"path":"https://keras.posit.co/reference/op_solve_triangular.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Solves a linear system of equations given by a %*% x = b. — op_solve_triangular","text":"tensor shape (..., M) (..., M, N) representing solution linear system. Returned shape identical b.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sort.html","id":null,"dir":"Reference","previous_headings":"","what":"Sorts the elements of x along a given axis in ascending order. — op_sort","title":"Sorts the elements of x along a given axis in ascending order. — op_sort","text":"Sorts elements x along given axis ascending order.","code":""},{"path":"https://keras.posit.co/reference/op_sort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sorts the elements of x along a given axis in ascending order. — op_sort","text":"","code":"op_sort(x, axis = -1L)"},{"path":"https://keras.posit.co/reference/op_sort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sorts the elements of x along a given axis in ascending order. — op_sort","text":"x Input tensor. axis Axis along sort. NULL, tensor flattened sorting. Defaults -1; last axis.","code":""},{"path":"https://keras.posit.co/reference/op_sort.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sorts the elements of x along a given axis in ascending order. — op_sort","text":"Sorted tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"sparse categorical cross-entropy loss similar categorical cross-entropy, used target tensor contains integer class labels instead one-hot encoded vectors. measures dissimilarity target output probabilities logits.","code":""},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"","code":"op_sparse_categorical_crossentropy(   target,   output,   from_logits = FALSE,   axis = -1L )"},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"target target tensor representing true class labels integers. shape match shape output tensor except last dimension. output output tensor representing predicted probabilities logits. shape match shape target tensor except last dimension. from_logits (optional) Whether output tensor logits probabilities. Set TRUE output represents logits; otherwise, set FALSE output represents probabilities. Defaults toFALSE. axis (optional) axis along sparse categorical cross-entropy computed. Defaults -1, corresponds last dimension tensors.","code":""},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"Integer tensor: computed sparse categorical cross-entropy loss target output.","code":""},{"path":"https://keras.posit.co/reference/op_sparse_categorical_crossentropy.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes sparse categorical cross-entropy loss. — op_sparse_categorical_crossentropy","text":"","code":"target <- op_array(c(0, 1, 2), dtype=\"int32\") output <- op_array(rbind(c(0.9, 0.05, 0.05),                         c(0.1, 0.8,  0.1),                         c(0.2, 0.3,  0.5))) op_sparse_categorical_crossentropy(target, output) ## tf.Tensor([0.10536052 0.22314355 0.69314718], shape=(3), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Split a tensor into chunks. — op_split","title":"Split a tensor into chunks. — op_split","text":"Split tensor chunks.","code":""},{"path":"https://keras.posit.co/reference/op_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split a tensor into chunks. — op_split","text":"","code":"op_split(x, indices_or_sections, axis = 1L)"},{"path":"https://keras.posit.co/reference/op_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split a tensor into chunks. — op_split","text":"x Input tensor. indices_or_sections integer, N, tensor split N equal sections along axis. 1-D array sorted integers, entries indicate indices tensor split along axis. axis Axis along split. Defaults 1, first axis.","code":""},{"path":"https://keras.posit.co/reference/op_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split a tensor into chunks. — op_split","text":"list tensors.","code":""},{"path":"https://keras.posit.co/reference/op_split.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Split a tensor into chunks. — op_split","text":"split result equal division using Torch backend.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sqrt.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","title":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","text":"Return non-negative square root tensor, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_sqrt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","text":"","code":"op_sqrt(x)"},{"path":"https://keras.posit.co/reference/op_sqrt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_sqrt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the non-negative square root of a tensor, element-wise. — op_sqrt","text":"Output tensor, non-negative square root x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_square.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the element-wise square of the input. — op_square","title":"Return the element-wise square of the input. — op_square","text":"Return element-wise square input.","code":""},{"path":"https://keras.posit.co/reference/op_square.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the element-wise square of the input. — op_square","text":"","code":"op_square(x)"},{"path":"https://keras.posit.co/reference/op_square.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the element-wise square of the input. — op_square","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_square.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the element-wise square of the input. — op_square","text":"Output tensor, square x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_squeeze.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove axes of length one from x. — op_squeeze","title":"Remove axes of length one from x. — op_squeeze","text":"Remove axes length one x.","code":""},{"path":"https://keras.posit.co/reference/op_squeeze.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove axes of length one from x. — op_squeeze","text":"","code":"op_squeeze(x, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_squeeze.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove axes of length one from x. — op_squeeze","text":"x Input tensor. axis Select subset entries length one shape.","code":""},{"path":"https://keras.posit.co/reference/op_squeeze.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove axes of length one from x. — op_squeeze","text":"input tensor subset dimensions length 1 removed.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Join a sequence of tensors along a new axis. — op_stack","title":"Join a sequence of tensors along a new axis. — op_stack","text":"axis parameter specifies index new axis dimensions result.","code":""},{"path":"https://keras.posit.co/reference/op_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join a sequence of tensors along a new axis. — op_stack","text":"","code":"op_stack(x, axis = 1L)"},{"path":"https://keras.posit.co/reference/op_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join a sequence of tensors along a new axis. — op_stack","text":"x sequence tensors. axis Axis along stack. Defaults 1, first axis.","code":""},{"path":"https://keras.posit.co/reference/op_stack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join a sequence of tensors along a new axis. — op_stack","text":"stacked tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_std.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the standard deviation along the specified axis. — op_std","title":"Compute the standard deviation along the specified axis. — op_std","text":"Compute standard deviation along specified axis.","code":""},{"path":"https://keras.posit.co/reference/op_std.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the standard deviation along the specified axis. — op_std","text":"","code":"op_std(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_std.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the standard deviation along the specified axis. — op_std","text":"x Input tensor. axis Axis along compute standard deviation. Default compute standard deviation flattened tensor. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_std.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the standard deviation along the specified axis. — op_std","text":"Output tensor containing standard deviation values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_stft.html","id":null,"dir":"Reference","previous_headings":"","what":"Short-Time Fourier Transform along the last axis of the input. — op_stft","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"STFT computes Fourier transform short overlapping windows input. giving frequency components signal change time.","code":""},{"path":"https://keras.posit.co/reference/op_stft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"","code":"op_stft(   x,   sequence_length,   sequence_stride,   fft_length,   window = \"hann\",   center = TRUE )"},{"path":"https://keras.posit.co/reference/op_stft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"x Input tensor. sequence_length integer representing sequence length. sequence_stride integer representing sequence hop size. fft_length integer representing size FFT apply. specified, uses smallest power 2 enclosing sequence_length. window string, tensor window NULL. window string, available values \"hann\" \"hamming\". window tensor, used directly window length must sequence_length. window NULL, windowing used. Defaults \"hann\". center Whether pad x sides t-th sequence centered time t * sequence_stride. Otherwise, t-th sequence begins time t * sequence_stride. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/op_stft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"list containing two tensors - real imaginary parts STFT output.","code":""},{"path":"https://keras.posit.co/reference/op_stft.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Short-Time Fourier Transform along the last axis of the input. — op_stft","text":"","code":"x <- op_array(c(0, 1, 2, 3, 4)) op_stft(x, 3, 2, 3) ## [[1]] ## tf.Tensor( ## [[ 0.  0.] ##  [ 2. -1.] ##  [ 4. -2.]], shape=(3, 2), dtype=float32) ## ## [[2]] ## tf.Tensor( ## [[ 0.         0.       ] ##  [ 0.        -1.7320508] ##  [ 0.        -3.4641016]], shape=(3, 2), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":null,"dir":"Reference","previous_headings":"","what":"Stops gradient computation. — op_stop_gradient","title":"Stops gradient computation. — op_stop_gradient","text":"Stops gradient computation.","code":""},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stops gradient computation. — op_stop_gradient","text":"","code":"op_stop_gradient(variable)"},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stops gradient computation. — op_stop_gradient","text":"variable tensor variable gradient computation disabled.","code":""},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stops gradient computation. — op_stop_gradient","text":"variable gradient computation disabled.","code":""},{"path":"https://keras.posit.co/reference/op_stop_gradient.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stops gradient computation. — op_stop_gradient","text":"","code":"var <- op_convert_to_tensor(c(1, 2, 3), dtype=\"float32\") var <- op_stop_gradient(var)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_subtract.html","id":null,"dir":"Reference","previous_headings":"","what":"Subtract arguments element-wise. — op_subtract","title":"Subtract arguments element-wise. — op_subtract","text":"Note function automatically called using R operator - tensor.","code":"x <- op_ones(c(3)) op_subtract(x, x) ## tf.Tensor([0. 0. 0.], shape=(3), dtype=float32) x - x ## tf.Tensor([0. 0. 0.], shape=(3), dtype=float32)"},{"path":"https://keras.posit.co/reference/op_subtract.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subtract arguments element-wise. — op_subtract","text":"","code":"op_subtract(x1, x2)"},{"path":"https://keras.posit.co/reference/op_subtract.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subtract arguments element-wise. — op_subtract","text":"x1 First input tensor. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_subtract.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subtract arguments element-wise. — op_subtract","text":"Output tensor, element-wise difference x1 x2.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Sum of a tensor over the given axes. — op_sum","title":"Sum of a tensor over the given axes. — op_sum","text":"Sum tensor given axes.","code":""},{"path":"https://keras.posit.co/reference/op_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sum of a tensor over the given axes. — op_sum","text":"","code":"op_sum(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sum of a tensor over the given axes. — op_sum","text":"x Input tensor. axis Axis axes along sum computed. default compute sum flattened tensor. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_sum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sum of a tensor over the given axes. — op_sum","text":"Output tensor containing sum.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_svd.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the singular value decomposition of a matrix. — op_svd","title":"Computes the singular value decomposition of a matrix. — op_svd","text":"Computes singular value decomposition matrix.","code":""},{"path":"https://keras.posit.co/reference/op_svd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the singular value decomposition of a matrix. — op_svd","text":"","code":"op_svd(x, full_matrices = TRUE, compute_uv = TRUE)"},{"path":"https://keras.posit.co/reference/op_svd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the singular value decomposition of a matrix. — op_svd","text":"x Input tensor shape (..., M, N). full_matrices Logical compute_uv Logical","code":""},{"path":"https://keras.posit.co/reference/op_svd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the singular value decomposition of a matrix. — op_svd","text":"list three tensors: tensor shape (..., M, M) containing left singular vectors, tensor shape (..., M, N) containing singular values tensor shape (..., N, N) containing right singular vectors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_swapaxes.html","id":null,"dir":"Reference","previous_headings":"","what":"Interchange two axes of a tensor. — op_swapaxes","title":"Interchange two axes of a tensor. — op_swapaxes","text":"Interchange two axes tensor.","code":""},{"path":"https://keras.posit.co/reference/op_swapaxes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interchange two axes of a tensor. — op_swapaxes","text":"","code":"op_swapaxes(x, axis1, axis2)"},{"path":"https://keras.posit.co/reference/op_swapaxes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interchange two axes of a tensor. — op_swapaxes","text":"x Input tensor. axis1 First axis. axis2 Second axis.","code":""},{"path":"https://keras.posit.co/reference/op_swapaxes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interchange two axes of a tensor. — op_swapaxes","text":"tensor axes swapped.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_take.html","id":null,"dir":"Reference","previous_headings":"","what":"Take elements from a tensor along an axis. — op_take","title":"Take elements from a tensor along an axis. — op_take","text":"Take elements tensor along axis.","code":""},{"path":"https://keras.posit.co/reference/op_take.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Take elements from a tensor along an axis. — op_take","text":"","code":"op_take(x, indices, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_take.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Take elements from a tensor along an axis. — op_take","text":"x Source tensor. indices indices values extract. axis axis select values. default, flattened input tensor used.","code":""},{"path":"https://keras.posit.co/reference/op_take.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Take elements from a tensor along an axis. — op_take","text":"corresponding tensor values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_take_along_axis.html","id":null,"dir":"Reference","previous_headings":"","what":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","title":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","text":"Select values x 1-D indices along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_take_along_axis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","text":"","code":"op_take_along_axis(x, indices, axis = NULL)"},{"path":"https://keras.posit.co/reference/op_take_along_axis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","text":"x Source tensor. indices indices values extract. axis axis select values. default, flattened input tensor used.","code":""},{"path":"https://keras.posit.co/reference/op_take_along_axis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select values from x at the 1-D indices along the given axis. — op_take_along_axis","text":"corresponding tensor values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tan.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute tangent, element-wise. — op_tan","title":"Compute tangent, element-wise. — op_tan","text":"Compute tangent, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_tan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute tangent, element-wise. — op_tan","text":"","code":"op_tan(x)"},{"path":"https://keras.posit.co/reference/op_tan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute tangent, element-wise. — op_tan","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_tan.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute tangent, element-wise. — op_tan","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hyperbolic tangent, element-wise. — op_tanh","title":"Hyperbolic tangent, element-wise. — op_tanh","text":"Hyperbolic tangent, element-wise.","code":""},{"path":"https://keras.posit.co/reference/op_tanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hyperbolic tangent, element-wise. — op_tanh","text":"","code":"op_tanh(x)"},{"path":"https://keras.posit.co/reference/op_tanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hyperbolic tangent, element-wise. — op_tanh","text":"x Input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_tanh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hyperbolic tangent, element-wise. — op_tanh","text":"Output tensor shape x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tensordot.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the tensor dot product along specified axes. — op_tensordot","title":"Compute the tensor dot product along specified axes. — op_tensordot","text":"Compute tensor dot product along specified axes.","code":""},{"path":"https://keras.posit.co/reference/op_tensordot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the tensor dot product along specified axes. — op_tensordot","text":"","code":"op_tensordot(x1, x2, axes = 3L)"},{"path":"https://keras.posit.co/reference/op_tensordot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the tensor dot product along specified axes. — op_tensordot","text":"x1 First tensor. x2 Second tensor. axes integer, N, sum last N axes x1 first N axes x2 order. sizes corresponding axes must match. , list axes summed , first sequence applying x1, second x2. sequences must length.","code":""},{"path":"https://keras.posit.co/reference/op_tensordot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the tensor dot product along specified axes. — op_tensordot","text":"tensor dot product inputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tile.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeat x the number of times given by repeats. — op_tile","title":"Repeat x the number of times given by repeats. — op_tile","text":"repeats length d, result dimension max(d, x.ndim). x.ndim < d, x promoted d-dimensional prepending new axes. x.ndim > d, repeats promoted x.ndim prepending 1's .","code":""},{"path":"https://keras.posit.co/reference/op_tile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeat x the number of times given by repeats. — op_tile","text":"","code":"op_tile(x, repeats)"},{"path":"https://keras.posit.co/reference/op_tile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeat x the number of times given by repeats. — op_tile","text":"x Input tensor. repeats number repetitions x along axis.","code":""},{"path":"https://keras.posit.co/reference/op_tile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Repeat x the number of times given by repeats. — op_tile","text":"tiled output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_top_k.html","id":null,"dir":"Reference","previous_headings":"","what":"Finds the top-k values and their indices in a tensor. — op_top_k","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"Finds top-k values indices tensor.","code":""},{"path":"https://keras.posit.co/reference/op_top_k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"","code":"op_top_k(x, k, sorted = TRUE)"},{"path":"https://keras.posit.co/reference/op_top_k.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"x Input tensor. k integer representing number top elements retrieve. sorted boolean indicating whether sort output descending order. Defaults toTRUE.","code":""},{"path":"https://keras.posit.co/reference/op_top_k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"list containing two tensors. first tensor contains top-k values, second tensor contains indices top-k values input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_top_k.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Finds the top-k values and their indices in a tensor. — op_top_k","text":"","code":"x <- op_array(c(5, 2, 7, 1, 9, 3), \"int32\") op_top_k(x, k = 3) ## $values ## tf.Tensor([9 7 5], shape=(3), dtype=int32) ## ## $indices ## tf.Tensor([4 2 0], shape=(3), dtype=int32) c(values, indices) %<-% op_top_k(x, k = 3) values ## tf.Tensor([9 7 5], shape=(3), dtype=int32) indices ## tf.Tensor([4 2 0], shape=(3), dtype=int32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_trace.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the sum along diagonals of the tensor. — op_trace","title":"Return the sum along diagonals of the tensor. — op_trace","text":"x 2-D, sum along diagonal given offset returned, .e., sum elements x[, +offset] . two dimensions, axes specified axis1 axis2 used determine 2-D sub-arrays whose traces returned. shape resulting tensor x axis1 axis2 removed.","code":""},{"path":"https://keras.posit.co/reference/op_trace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the sum along diagonals of the tensor. — op_trace","text":"","code":"op_trace(x, offset = 0L, axis1 = 1L, axis2 = 2L)"},{"path":"https://keras.posit.co/reference/op_trace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the sum along diagonals of the tensor. — op_trace","text":"x Input tensor. offset Offset diagonal main diagonal. Can positive negative. Defaults 0. axis1 Axis used first axis 2-D sub-arrays. Defaults 1. (first axis). axis2 Axis used second axis 2-D sub-arrays. Defaults 2. (second axis).","code":""},{"path":"https://keras.posit.co/reference/op_trace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the sum along diagonals of the tensor. — op_trace","text":"x 2-D, sum diagonal returned. x larger dimensions, tensor sums along diagonals returned.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a tensor with axes transposed. — op_transpose","title":"Returns a tensor with axes transposed. — op_transpose","text":"Returns tensor axes transposed.","code":""},{"path":"https://keras.posit.co/reference/op_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a tensor with axes transposed. — op_transpose","text":"","code":"op_transpose(x, axes = NULL)"},{"path":"https://keras.posit.co/reference/op_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a tensor with axes transposed. — op_transpose","text":"x Input tensor. axes Sequence integers. Permutation dimensions x. default, order axes reversed.","code":""},{"path":"https://keras.posit.co/reference/op_transpose.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a tensor with axes transposed. — op_transpose","text":"x axes permuted.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tri.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","title":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","text":"Return tensor ones diagonal zeros elsewhere.","code":""},{"path":"https://keras.posit.co/reference/op_tri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","text":"","code":"op_tri(N, M = NULL, k = 0L, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_tri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","text":"N Number rows tensor. M Number columns tensor. k sub-diagonal array filled. k = 0 main diagonal, k < 0 , k > 0 . default 0. dtype Data type returned tensor. default \"float32\".","code":""},{"path":"https://keras.posit.co/reference/op_tri.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a tensor with ones at and below a diagonal and zeros elsewhere. — op_tri","text":"Tensor lower triangle filled ones zeros elsewhere. T[, j] == 1 j <= + k, 0 otherwise.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_tril.html","id":null,"dir":"Reference","previous_headings":"","what":"Return lower triangle of a tensor. — op_tril","title":"Return lower triangle of a tensor. — op_tril","text":"tensors ndim exceeding 2, tril apply final two axes.","code":""},{"path":"https://keras.posit.co/reference/op_tril.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return lower triangle of a tensor. — op_tril","text":"","code":"op_tril(x, k = 0L)"},{"path":"https://keras.posit.co/reference/op_tril.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return lower triangle of a tensor. — op_tril","text":"x Input tensor. k Diagonal zero elements. Defaults 0. main diagonal. k < 0 , k > 0 .","code":""},{"path":"https://keras.posit.co/reference/op_tril.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return lower triangle of a tensor. — op_tril","text":"Lower triangle x, shape data type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_triu.html","id":null,"dir":"Reference","previous_headings":"","what":"Return upper triangle of a tensor. — op_triu","title":"Return upper triangle of a tensor. — op_triu","text":"tensors ndim exceeding 2, triu apply final two axes.","code":""},{"path":"https://keras.posit.co/reference/op_triu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return upper triangle of a tensor. — op_triu","text":"","code":"op_triu(x, k = 0L)"},{"path":"https://keras.posit.co/reference/op_triu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return upper triangle of a tensor. — op_triu","text":"x Input tensor. k Diagonal zero elements. Defaults 0. main diagonal. k < 0 , k > 0 .","code":""},{"path":"https://keras.posit.co/reference/op_triu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return upper triangle of a tensor. — op_triu","text":"Upper triangle x, shape data type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_unstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"Unpacks given dimension rank-R tensor rank-(R-1) tensors.","code":""},{"path":"https://keras.posit.co/reference/op_unstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"","code":"op_unstack(x, num = NULL, axis = 1L)"},{"path":"https://keras.posit.co/reference/op_unstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"x input tensor. num length dimension axis. Automatically inferred NULL. axis axis along unpack.","code":""},{"path":"https://keras.posit.co/reference/op_unstack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"list tensors unpacked along given axis.","code":""},{"path":"https://keras.posit.co/reference/op_unstack.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors. — op_unstack","text":"[3, 4))]: R:3,%204))","code":"x <- op_array(rbind(c(1, 2),                    c(3, 4))) op_unstack(x, axis=1) ## [[1]] ## tf.Tensor([1. 2.], shape=(2), dtype=float64) ## ## [[2]] ## tf.Tensor([3. 4.], shape=(2), dtype=float64) op_unstack(x, axis=2) ## [[1]] ## tf.Tensor([1. 3.], shape=(2), dtype=float64) ## ## [[2]] ## tf.Tensor([2. 4.], shape=(2), dtype=float64) all.equal(op_unstack(x),           op_unstack(x, axis = 1)) ## [1] TRUE all.equal(op_unstack(x, axis = -1),           op_unstack(x, axis = 2)) ## [1] TRUE # [array([1, 2)), array([3, 4))]"},{"path":[]},{"path":"https://keras.posit.co/reference/op_var.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the variance along the specified axes. — op_var","title":"Compute the variance along the specified axes. — op_var","text":"Compute variance along specified axes.","code":""},{"path":"https://keras.posit.co/reference/op_var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the variance along the specified axes. — op_var","text":"","code":"op_var(x, axis = NULL, keepdims = FALSE)"},{"path":"https://keras.posit.co/reference/op_var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the variance along the specified axes. — op_var","text":"x Input tensor. axis Axis axes along variance computed. default compute variance flattened tensor. keepdims set TRUE, axes reduced left result dimensions size one.","code":""},{"path":"https://keras.posit.co/reference/op_var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the variance along the specified axes. — op_var","text":"Output tensor containing variance.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_vdot.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the dot product of two vectors. — op_vdot","title":"Return the dot product of two vectors. — op_vdot","text":"first argument complex, complex conjugate first argument used calculation dot product. Multidimensional tensors flattened dot product taken.","code":""},{"path":"https://keras.posit.co/reference/op_vdot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the dot product of two vectors. — op_vdot","text":"","code":"op_vdot(x1, x2)"},{"path":"https://keras.posit.co/reference/op_vdot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the dot product of two vectors. — op_vdot","text":"x1 First input tensor. complex, complex conjugate taken calculation dot product. x2 Second input tensor.","code":""},{"path":"https://keras.posit.co/reference/op_vdot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the dot product of two vectors. — op_vdot","text":"Output tensor.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":null,"dir":"Reference","previous_headings":"","what":"Turn a function into a vectorized function. — op_vectorize","title":"Turn a function into a vectorized function. — op_vectorize","text":"Turn function vectorized function.","code":""},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turn a function into a vectorized function. — op_vectorize","text":"","code":"op_vectorize(func, ..., excluded = NULL, signature = NULL)"},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Turn a function into a vectorized function. — op_vectorize","text":"func Callable single tensor argument. ... forward/backward compatability. excluded Optional set integers representing positional arguments function vectorized. passed directly func unmodified. signature Optional generalized universal function signature, e.g., \"(m,n),(n)->(m)\" vectorized matrix-vector multiplication. provided, func called (expected return) arrays shapes given size corresponding core dimensions. default, func assumed take scalar tensors input output.","code":""},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turn a function into a vectorized function. — op_vectorize","text":"new function applies func every element input along axis 1 (batch axis, first axis).","code":""},{"path":"https://keras.posit.co/reference/op_vectorize.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Turn a function into a vectorized function. — op_vectorize","text":"","code":"# currently does not work w/ tensorflow backend if(config_backend() != \"tensorflow\") {    myfunc <- function(a, b) a + b    vfunc <- op_vectorize(myfunc)   y <- vfunc(c(1, 2, 3, 4), 2)   print(y)   # with Jax backend, y is:   # Array([3., 4., 5., 6.], dtype=float32) }"},{"path":[]},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":null,"dir":"Reference","previous_headings":"","what":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"Schematically, op_vectorized_map() maps first dimension provided tensors. elements list tensors, tensors required size first dimension, iterated together.","code":""},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"","code":"op_vectorized_map(elements, f)"},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"elements see description f function taking either tensor, list tensors.","code":""},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"tensor list tensors, result mapping f across elements.","code":""},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"Note f may traced compiled. Meaning, R function may evaluated symbolic tensors using Jax TensorFlow backends, eager tensors. See output str() examples:","code":"(x <- op_arange(12L) |> op_reshape(c(3, 4))) ## tf.Tensor( ## [[ 0  1  2  3] ##  [ 4  5  6  7] ##  [ 8  9 10 11]], shape=(3, 4), dtype=int32) x |> op_vectorized_map(\\(row) {row + 10}) ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) list(x, x, x) |> op_vectorized_map(\\(rows) Reduce(`+`, rows)) ## tf.Tensor( ## [[ 0  3  6  9] ##  [12 15 18 21] ##  [24 27 30 33]], shape=(3, 4), dtype=int32) # simplest case, map f over rows of x, # where .x is 1 row of x input <- x output <- op_vectorized_map(input, function(.x) {   str(.x)   .x + 10 }) ## <tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> output ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) # map f over two tensors simultaneously. Here, # `.x` is a list of two # tensors. The return values from each call of `f(row)` are stacked to form the # final output input <- list(x, x) output <- op_vectorized_map(input, function(.x) {   str(.x)   .x[[1]] + 10 }) ## List of 2 ##  $ :<tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> ##  $ :<tf.Tensor 'loop_body/GatherV2_1:0' shape=(4) dtype=int32> output ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) # same as above, but now returning two tensors in the final output output <- op_vectorized_map(input, function(.x) {   str(.x)   c(.x1, .x2) %<-% .x   list(.x1+10, .x2+20) }) ## List of 2 ##  $ :<tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> ##  $ :<tf.Tensor 'loop_body/GatherV2_1:0' shape=(4) dtype=int32> output ## [[1]] ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) ## ## [[2]] ## tf.Tensor( ## [[20 21 22 23] ##  [24 25 26 27] ##  [28 29 30 31]], shape=(3, 4), dtype=int32) # passing named lists. # WARNING: if passing a named list, the order of elements of `.x` supplied # to `f` is not stable. Only retrieve elements by name. input <- list(name1 = x, name2 = x) output <- op_vectorized_map(input, function(.x) {   str(.x)   list(outname1 = .x$name1 + 10,        outname2 = .x$name2 + 20) }) ## List of 2 ##  $ name1:<tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> ##  $ name2:<tf.Tensor 'loop_body/GatherV2_1:0' shape=(4) dtype=int32> output ## $outname1 ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32) ## ## $outname2 ## tf.Tensor( ## [[20 21 22 23] ##  [24 25 26 27] ##  [28 29 30 31]], shape=(3, 4), dtype=int32) # passing a tuple() is equivalent to passing an unnamed list() input <- tuple(x, x) output <- op_vectorized_map(input, function(.x) {   str(.x)   list(.x[[1]] + 10) }) ## List of 2 ##  $ :<tf.Tensor 'loop_body/GatherV2:0' shape=(4) dtype=int32> ##  $ :<tf.Tensor 'loop_body/GatherV2_1:0' shape=(4) dtype=int32> output ## [[1]] ## tf.Tensor( ## [[10 11 12 13] ##  [14 15 16 17] ##  [18 19 20 21]], shape=(3, 4), dtype=int32)"},{"path":"https://keras.posit.co/reference/op_vectorized_map.html","id":"debugging-f","dir":"Reference","previous_headings":"","what":"Debugging f","title":"Parallel map of function f on the first axis of tensor(s) elements. — op_vectorized_map","text":"Even eager contexts, op_vectorized_map() may trace f. case, want eagerly debug f (e.g., browser()), can swap manual (slow) implementation op_vectorized_map(). Note example debug implementation handle edge cases op_vectorized_map(), particular, f returns structure multiple tensors.","code":"op_vectorized_map_debug <- function(elements, fn) {    if (!is.list(elements)) {     # `elements` is a single tensor     batch_size <- op_shape(elements)[[1]]     out <- elements |>       op_split(batch_size) |>       lapply(fn) |>       op_stack()     return(out)   }    # `elements` is a list of tensors   batch_size <- elements[[1]] |> op_shape() |> _[[1]]   elements |>     lapply(\\(e) op_split(e, batch_size)) |>     zip_lists() |>     lapply(fn) |>     op_stack()  }"},{"path":[]},{"path":"https://keras.posit.co/reference/op_vstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Stack tensors in sequence vertically (row wise). — op_vstack","title":"Stack tensors in sequence vertically (row wise). — op_vstack","text":"Stack tensors sequence vertically (row wise).","code":""},{"path":"https://keras.posit.co/reference/op_vstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stack tensors in sequence vertically (row wise). — op_vstack","text":"","code":"op_vstack(xs)"},{"path":"https://keras.posit.co/reference/op_vstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stack tensors in sequence vertically (row wise). — op_vstack","text":"xs Sequence tensors.","code":""},{"path":"https://keras.posit.co/reference/op_vstack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stack tensors in sequence vertically (row wise). — op_vstack","text":"Tensor formed stacking given tensors.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_where.html","id":null,"dir":"Reference","previous_headings":"","what":"Return elements chosen from x1 or x2 depending on condition. — op_where","title":"Return elements chosen from x1 or x2 depending on condition. — op_where","text":"Return elements chosen x1 x2 depending condition.","code":""},{"path":"https://keras.posit.co/reference/op_where.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return elements chosen from x1 or x2 depending on condition. — op_where","text":"","code":"op_where(condition, x1 = NULL, x2 = NULL)"},{"path":"https://keras.posit.co/reference/op_where.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return elements chosen from x1 or x2 depending on condition. — op_where","text":"condition TRUE, yield x1, otherwise yield x2. x1 Values choose condition TRUE. x2 Values choose condition FALSE.","code":""},{"path":"https://keras.posit.co/reference/op_where.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return elements chosen from x1 or x2 depending on condition. — op_where","text":"tensor elements x1 condition TRUE, elements x2 condition FALSE.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":null,"dir":"Reference","previous_headings":"","what":"While loop implementation. — op_while_loop","title":"While loop implementation. — op_while_loop","text":"loop implementation.","code":""},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"While loop implementation. — op_while_loop","text":"","code":"op_while_loop(cond, body, loop_vars, maximum_iterations = NULL)"},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"While loop implementation. — op_while_loop","text":"cond callable represents termination condition loop. Must accept loop_vars like structure argument. loop_vars tuple unnamed list, element loop_vars passed positionally callable. body callable represents loop body. Must accept loop_vars like structure argument, return update value structure. loop_vars tuple unnamed list, element loop_vars passed positionally callable. loop_vars arbitrary nested structure tensor state persist across loop iterations. maximum_iterations Optional maximum number iterations loop run. provided, cond output -ed additional condition ensuring number iterations executed greater maximum_iterations.","code":""},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"While loop implementation. — op_while_loop","text":"list tensors, shape dtype loop_vars.","code":""},{"path":"https://keras.posit.co/reference/op_while_loop.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"While loop implementation. — op_while_loop","text":"","code":"i <- 0 loop_vars <- list(i)  # cond() must return a scalar bool cond <- function(i) i < 10L  # body must return same shape as loop_vars body <- function(i) list(i + 1L)  op_while_loop(cond, body, loop_vars) ## [[1]] ## tf.Tensor(10.0, shape=(), dtype=float32) x <- 0; y <- 1 cond <- \\(x, y) x < 10 body <- \\(x, y) list(x+1, y+1) op_while_loop(cond, body, list(x, y)) ## [[1]] ## tf.Tensor(10.0, shape=(), dtype=float32) ## ## [[2]] ## tf.Tensor(11.0, shape=(), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/op_zeros.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","title":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","text":"Return new tensor given shape type, filled zeros.","code":""},{"path":"https://keras.posit.co/reference/op_zeros.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","text":"","code":"op_zeros(shape, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_zeros.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","text":"shape Shape new tensor. dtype Desired data type tensor.","code":""},{"path":"https://keras.posit.co/reference/op_zeros.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a new tensor of given shape and type, filled with zeros. — op_zeros","text":"Tensor zeros given shape dtype.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/op_zeros_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","title":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","text":"Return tensor zeros shape type x.","code":""},{"path":"https://keras.posit.co/reference/op_zeros_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","text":"","code":"op_zeros_like(x, dtype = NULL)"},{"path":"https://keras.posit.co/reference/op_zeros_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","text":"x Input tensor. dtype Overrides data type result.","code":""},{"path":"https://keras.posit.co/reference/op_zeros_like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return a tensor of zeros with the same shape and type as x. — op_zeros_like","text":"tensor zeros shape type x.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"Adadelta optimization stochastic gradient descent method based adaptive learning rate per dimension address two drawbacks: continual decay learning rates throughout training. need manually selected global learning rate. Adadelta robust extension Adagrad adapts learning rates based moving window gradient updates, instead accumulating past gradients. way, Adadelta continues learning even many updates done. Compared Adagrad, original version Adadelta set initial learning rate. version, initial learning rate can set, Keras optimizers.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"","code":"optimizer_adadelta(   learning_rate = 0.001,   rho = 0.95,   epsilon = 1e-07,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adadelta\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"learning_rate float, [LearningRateSchedule()] instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. Note Adadelta tends benefit higher initial learning rate values compared optimizers. match exact form original paper, use 1.0. rho floating point value. decay rate. Defaults 0.95. epsilon Small floating point value maintaining numerical stability. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema = TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema = TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adadelta.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Adadelta algorithm. — optimizer_adadelta","text":"Zeiler, 2012","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"Adafactor commonly used NLP tasks, advantage taking less memory saves partial information previous gradients. default argument setup based original paper (see reference). gradients dimension > 2, Adafactor optimizer delete last 2 dimensions separately accumulator variables.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"","code":"optimizer_adafactor(   learning_rate = 0.001,   beta_2_decay = -0.8,   epsilon_1 = 1e-30,   epsilon_2 = 0.001,   clip_threshold = 1,   relative_step = TRUE,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adafactor\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_2_decay float, defaults -0.8. decay rate beta_2. epsilon_1 float, defaults 1e-30. small offset keep denominator away 0. epsilon_2 float, defaults 1e-3. small offset avoid learning rate becoming small time. clip_threshold float, defaults 1.0. Clipping threshold. part Adafactor algorithm, independent clipnorm, clipvalue, global_clipnorm. relative_step bool, defaults TRUE. learning_rate constant relative_step=TRUE, learning rate adjusted based current iterations. default learning rate decay Adafactor. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema = TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adafactor.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Adafactor algorithm. — optimizer_adafactor","text":"Shazeer, Noam et al., 2018.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"Adagrad optimizer parameter-specific learning rates, adapted relative frequently parameter gets updated training. updates parameter receives, smaller updates.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"","code":"optimizer_adagrad(   learning_rate = 0.001,   initial_accumulator_value = 0.1,   epsilon = 1e-07,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adagrad\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. Note Adagrad tends benefit higher initial learning rate values compared optimizers. match exact form original paper, use 1.0. initial_accumulator_value Floating point value. Starting value accumulators (per-parameter momentum values). Must non-negative. epsilon Small floating point value maintaining numerical stability. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adagrad.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Adagrad algorithm. — optimizer_adagrad","text":"Duchi et al., 2011.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adam.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adam algorithm. — optimizer_adam","title":"Optimizer that implements the Adam algorithm. — optimizer_adam","text":"Adam optimization stochastic gradient descent method based adaptive estimation first-order second-order moments. According Kingma et al., 2014, method \"computationally efficient, little memory requirement, invariant diagonal rescaling gradients, well suited problems large terms data/parameters\".","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adam algorithm. — optimizer_adam","text":"","code":"optimizer_adam(   learning_rate = 0.001,   beta_1 = 0.9,   beta_2 = 0.999,   epsilon = 1e-07,   amsgrad = FALSE,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adam\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adam algorithm. — optimizer_adam","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_1 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 1st moment estimates. Defaults 0.9. beta_2 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 2nd moment estimates. Defaults 0.999. epsilon small constant numerical stability. epsilon \"epsilon hat\" Kingma Ba paper (formula just Section 2.1), epsilon Algorithm 1 paper. Defaults 1e-7. amsgrad Boolean. Whether apply AMSGrad variant algorithm paper \"Convergence Adam beyond\". Defaults FALSE. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adam algorithm. — optimizer_adam","text":"Optimizer instance","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"AdamW optimization stochastic gradient descent method based adaptive estimation first-order second-order moments added method decay weights per techniques discussed paper, 'Decoupled Weight Decay Regularization' Loshchilov, Hutter et al., 2019. According Kingma et al., 2014, underying Adam method \"computationally efficient, little memory requirement, invariant diagonal rescaling gradients, well suited problems large terms data/parameters\".","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"","code":"optimizer_adam_w(   learning_rate = 0.001,   weight_decay = 0.004,   beta_1 = 0.9,   beta_2 = 0.999,   epsilon = 1e-07,   amsgrad = FALSE,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adamw\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. weight_decay Float. set, weight decay applied. beta_1 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 1st moment estimates. Defaults 0.9. beta_2 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 2nd moment estimates. Defaults 0.999. epsilon small constant numerical stability. epsilon \"epsilon hat\" Kingma Ba paper (formula just Section 2.1), epsilon Algorithm 1 paper. Defaults 1e-7. amsgrad Boolean. Whether apply AMSGrad variant algorithm paper \"Convergence Adam beyond\". Defaults FALSE. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adam_w.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Optimizer that implements the AdamW algorithm. — optimizer_adam_w","text":"Loshchilov et al., 2019 Kingma et al., 2014 adam Reddi et al., 2018 amsgrad.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"Adamax, variant Adam based infinity norm, first-order gradient-based optimization method. Due capability adjusting learning rate based data characteristics, suited learn time-variant process, e.g., speech data dynamically changed noise conditions. Default parameters follow provided paper (see references ). Initialization:   update rule parameter w gradient g described end section 7.1 paper (see referenece section):","code":"m <- 0  # Initialize initial 1st moment vector u <- 0  # Initialize the exponentially weighted infinity norm t <- 0  # Initialize timestep t <-  t + 1 m <- beta1 * m + (1 - beta) * g u <- max(beta2 * u, abs(g)) current_lr <- learning_rate / (1 - beta1 ** t) w <- w - current_lr * m / (u + epsilon)"},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"","code":"optimizer_adamax(   learning_rate = 0.001,   beta_1 = 0.9,   beta_2 = 0.999,   epsilon = 1e-07,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"adamax\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_1 float value constant float tensor. exponential decay rate 1st moment estimates. beta_2 float value constant float tensor. exponential decay rate exponentially weighted infinity norm. epsilon small constant numerical stability. name: String. name use momentum accumulator weights created optimizer. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String, name object ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_adamax.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Adamax algorithm. — optimizer_adamax","text":"Kingma et al., 2014","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_ftrl.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","title":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","text":"\"Follow Regularized Leader\" (FTRL) optimization algorithm developed Google click-rate prediction early 2010s. suitable shallow models large sparse feature spaces. algorithm described McMahan et al., 2013. Keras version support online L2 regularization (L2 regularization described paper ) shrinkage-type L2 regularization (addition L2 penalty loss function). Initialization:   Update rule one variable w:   Notation: lr learning rate g gradient variable lambda_1 L1 regularization strength lambda_2 L2 regularization strength lr_power power scale n. Check documentation l2_shrinkage_regularization_strength parameter details shrinkage enabled, case gradient replaced gradient shrinkage.","code":"n <- 0 sigma <- 0 z <- 0 prev_n <- n n <- n + g^2 sigma <- (n^(-lr_power) - prev_n^(-lr_power)) / lr z <- z + g - sigma * w if (abs(z) < lambda_1) {   w <- 0 } else {   w <- (sgn(z) * lambda_1 - z) / ((beta + sqrt(n)) / alpha + lambda_2) }"},{"path":"https://keras.posit.co/reference/optimizer_ftrl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","text":"","code":"optimizer_ftrl(   learning_rate = 0.001,   learning_rate_power = -0.5,   initial_accumulator_value = 0.1,   l1_regularization_strength = 0,   l2_regularization_strength = 0,   l2_shrinkage_regularization_strength = 0,   beta = 0,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"ftrl\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_ftrl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. learning_rate_power float value, must less equal zero. Controls learning rate decreases training. Use zero fixed learning rate. initial_accumulator_value starting value accumulators. zero positive values allowed. l1_regularization_strength float value, must greater equal zero. Defaults 0.0. l2_regularization_strength float value, must greater equal zero. Defaults 0.0. l2_shrinkage_regularization_strength float value, must greater equal zero. differs L2 L2 stabilization penalty, whereas L2 shrinkage magnitude penalty. input sparse shrinkage happen active weights. beta float value, representing beta value paper. Defaults 0.0. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_ftrl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the FTRL algorithm. — optimizer_ftrl","text":"Optimizer instance","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Lion algorithm. — optimizer_lion","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"Lion optimizer stochastic-gradient-descent method uses sign operator control magnitude update, unlike adaptive optimizers Adam rely second-order moments. make Lion memory-efficient keeps track momentum. According authors (see reference), performance gain Adam grows batch size. update Lion produced sign operation, resulting larger norm, suitable learning rate Lion typically 3-10x smaller AdamW. weight decay Lion turn 3-10x larger AdamW maintain similar strength (lr * wd).","code":""},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"","code":"optimizer_lion(   learning_rate = 0.001,   beta_1 = 0.9,   beta_2 = 0.99,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"lion\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_1 float value constant float tensor, callable takes arguments returns actual value use. rate combine current gradient 1st moment estimate. Defaults 0.9. beta_2 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 1st moment estimate. Defaults 0.99. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_lion.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Optimizer that implements the Lion algorithm. — optimizer_lion","text":"Chen et al., 2023 Authors' implementation","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_loss_scale.html","id":null,"dir":"Reference","previous_headings":"","what":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","title":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","text":"Loss scaling technique prevent numeric underflow intermediate gradients float16 used. prevent underflow, loss multiplied (\"scaled\") certain factor called \"loss scale\", causes intermediate gradients scaled loss scale well. final gradients divided (\"unscaled\") loss scale bring back original value. LossScaleOptimizer wraps another optimizer applies dynamic loss scaling . loss scale dynamically updated time follows: train step, nonfinite gradient encountered, loss scale halved, train step skipped. dynamic_growth_steps ocurred since last time loss scale updated, nonfinite gradients occurred, loss scale doubled.","code":""},{"path":"https://keras.posit.co/reference/optimizer_loss_scale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","text":"","code":"optimizer_loss_scale(   inner_optimizer,   initial_scale = 32768,   dynamic_growth_steps = 2000L,   ...,   name = NULL,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = NULL,   ema_momentum = NULL,   ema_overwrite_frequency = NULL,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_loss_scale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","text":"inner_optimizer keras Optimizer instance wrap. initial_scale Float. initial loss scale. scale updated training. recommended high number, loss scale high gets lowered far quickly loss scale low gets raised. dynamic_growth_steps Int. often update scale upwards. every dynamic_growth_steps steps finite gradients, loss scale doubled. ... forward/backward compatability. name String. name use momentum accumulator weights created optimizer. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_loss_scale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"An optimizer that dynamically scales the loss to prevent underflow. — optimizer_loss_scale","text":"Optimizer instance","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"Much like Adam essentially RMSprop momentum, Nadam Adam Nesterov momentum.","code":""},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"","code":"optimizer_nadam(   learning_rate = 0.001,   beta_1 = 0.9,   beta_2 = 0.999,   epsilon = 1e-07,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"nadam\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"learning_rate float, LearningRateSchedule() instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. beta_1 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 1st moment estimates. Defaults 0.9. beta_2 float value constant float tensor, callable takes arguments returns actual value use. exponential decay rate 2nd moment estimates. Defaults 0.999. epsilon small constant numerical stability. epsilon \"epsilon hat\" Kingma Ba paper (formula just Section 2.1), epsilon Algorithm 1 paper. Defaults 1e-7. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_nadam.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the Nadam algorithm. — optimizer_nadam","text":"Dozat, 2015.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"gist RMSprop : Maintain moving (discounted) average square gradients Divide gradient root average implementation RMSprop uses plain momentum, Nesterov momentum. centered version additionally maintains moving average gradients, uses average estimate variance.","code":""},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"","code":"optimizer_rmsprop(   learning_rate = 0.001,   rho = 0.9,   momentum = 0,   epsilon = 1e-07,   centered = FALSE,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"rmsprop\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"learning_rate float, learning_rate_schedule_* instance, callable takes arguments returns actual value use. learning rate. Defaults 0.001. rho float, defaults 0.9. Discounting factor old gradients. momentum float, defaults 0.0. 0.0., optimizer tracks momentum value, decay rate equals 1 - momentum. epsilon small constant numerical stability. epsilon \"epsilon hat\" Kingma Ba paper (formula just Section 2.1), epsilon Algorithm 1 paper. Defaults 1e-7. centered Boolean. TRUE, gradients normalized estimated variance gradient; FALSE, uncentered second moment. Setting TRUE may help training, slightly expensive terms computation memory. Defaults FALSE. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"Optimizer instance","code":""},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"","code":"opt <- optimizer_rmsprop(learning_rate=0.1)"},{"path":"https://keras.posit.co/reference/optimizer_rmsprop.html","id":"reference","dir":"Reference","previous_headings":"","what":"Reference","title":"Optimizer that implements the RMSprop algorithm. — optimizer_rmsprop","text":"Hinton, 2012","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/optimizer_sgd.html","id":null,"dir":"Reference","previous_headings":"","what":"Gradient descent (with momentum) optimizer. — optimizer_sgd","title":"Gradient descent (with momentum) optimizer. — optimizer_sgd","text":"Update rule parameter w gradient g momentum 0:   Update rule momentum larger 0:   nesterov=TRUE, rule becomes:","code":"w <- w - learning_rate * g velocity <- momentum * velocity - learning_rate * g w <- w + velocity velocity <- momentum * velocity - learning_rate * g w <- w + momentum * velocity - learning_rate * g"},{"path":"https://keras.posit.co/reference/optimizer_sgd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gradient descent (with momentum) optimizer. — optimizer_sgd","text":"","code":"optimizer_sgd(   learning_rate = 0.01,   momentum = 0,   nesterov = FALSE,   weight_decay = NULL,   clipnorm = NULL,   clipvalue = NULL,   global_clipnorm = NULL,   use_ema = FALSE,   ema_momentum = 0.99,   ema_overwrite_frequency = NULL,   name = \"SGD\",   ...,   loss_scale_factor = NULL,   gradient_accumulation_steps = NULL )"},{"path":"https://keras.posit.co/reference/optimizer_sgd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gradient descent (with momentum) optimizer. — optimizer_sgd","text":"learning_rate float, learning_rate_schedule_* instance, callable takes arguments returns actual value use. learning rate. Defaults 0.01. momentum float hyperparameter >= 0 accelerates gradient descent relevant direction dampens oscillations. 0 vanilla gradient descent. Defaults 0.0. nesterov boolean. Whether apply Nesterov momentum. Defaults FALSE. weight_decay Float. set, weight decay applied. clipnorm Float. set, gradient weight individually clipped norm higher value. clipvalue Float. set, gradient weight clipped higher value. global_clipnorm Float. set, gradient weights clipped global norm higher value. use_ema Boolean, defaults FALSE. TRUE, exponential moving average (EMA) applied. EMA consists computing exponential moving average weights model (weight values change training batch), periodically overwriting weights moving average. ema_momentum Float, defaults 0.99. used use_ema=TRUE. momentum use computing EMA model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value. ema_overwrite_frequency Int NULL, defaults NULL. used use_ema=TRUE. Every ema_overwrite_frequency steps iterations, overwrite model variable moving average. NULL, optimizer overwrite model variables middle training, need explicitly overwrite variables end training calling optimizer$finalize_variable_values() (updates model variables -place). using built-fit() training loop, happens automatically last epoch, need anything. name String. name use momentum accumulator weights created optimizer. ... forward/backward compatability. loss_scale_factor Float NULL. float, scale factor multiplied loss computing gradients, inverse scale factor multiplied gradients updating variables. Useful preventing underflow mixed precision training. Alternately, optimizer_loss_scale() automatically set loss scale factor. gradient_accumulation_steps Int NULL. int, model & optimizer variables updated every step; instead updated every gradient_accumulation_steps steps, using average value gradients since last update. known \"gradient accumulation\". can useful batch size small, order reduce gradient noise update step.","code":""},{"path":"https://keras.posit.co/reference/optimizer_sgd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gradient descent (with momentum) optimizer. — optimizer_sgd","text":"Optimizer instance","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":null,"dir":"Reference","previous_headings":"","what":"Packs user-provided data into a list. — pack_x_y_sample_weight","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"convenience utility packing data list formats fit() uses.","code":""},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"","code":"pack_x_y_sample_weight(x, y = NULL, sample_weight = NULL)"},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"x Features pass Model. y Ground-truth targets pass Model. sample_weight Sample weight element.","code":""},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"List format used fit().","code":""},{"path":"https://keras.posit.co/reference/pack_x_y_sample_weight.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Packs user-provided data into a list. — pack_x_y_sample_weight","text":"","code":"x <- op_ones(c(10, 1)) data <- pack_x_y_sample_weight(x)   y <- op_ones(c(10, 1)) data <- pack_x_y_sample_weight(x, y)"},{"path":[]},{"path":"https://keras.posit.co/reference/pad_sequences.html","id":null,"dir":"Reference","previous_headings":"","what":"Pads sequences to the same length. — pad_sequences","title":"Pads sequences to the same length. — pad_sequences","text":"function transforms list (length num_samples) sequences (lists integers) 2D NumPy array shape (num_samples, num_timesteps). num_timesteps either maxlen argument provided, length longest sequence list. Sequences shorter num_timesteps padded value num_timesteps long. Sequences longer num_timesteps truncated fit desired length. position padding truncation happens determined arguments padding truncating, respectively. Pre-padding removing values beginning sequence default.","code":"sequence <- list(c(1), c(2, 3), c(4, 5, 6)) pad_sequences(sequence) ##      [,1] [,2] [,3] ## [1,]    0    0    1 ## [2,]    0    2    3 ## [3,]    4    5    6 pad_sequences(sequence, value=-1) ##      [,1] [,2] [,3] ## [1,]   -1   -1    1 ## [2,]   -1    2    3 ## [3,]    4    5    6 pad_sequences(sequence, padding='post') ##      [,1] [,2] [,3] ## [1,]    1    0    0 ## [2,]    2    3    0 ## [3,]    4    5    6 pad_sequences(sequence, maxlen=2) ##      [,1] [,2] ## [1,]    0    1 ## [2,]    2    3 ## [3,]    5    6"},{"path":"https://keras.posit.co/reference/pad_sequences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pads sequences to the same length. — pad_sequences","text":"","code":"pad_sequences(   sequences,   maxlen = NULL,   dtype = \"int32\",   padding = \"pre\",   truncating = \"pre\",   value = 0 )"},{"path":"https://keras.posit.co/reference/pad_sequences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pads sequences to the same length. — pad_sequences","text":"sequences List sequences (sequence list integers). maxlen Optional Int, maximum length sequences. provided, sequences padded length longest individual sequence. dtype (Optional, defaults \"int32\"). Type output sequences. pad sequences variable length strings, can use object. padding String, \"pre\" \"post\" (optional, defaults \"pre\"): pad either sequence. truncating String, \"pre\" \"post\" (optional, defaults \"pre\"): remove values sequences larger maxlen, either beginning end sequences. value Float String, padding value. (Optional, defaults 0.)","code":""},{"path":"https://keras.posit.co/reference/pad_sequences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pads sequences to the same length. — pad_sequences","text":"Array shape (len(sequences), maxlen)","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See %>% details.","code":""},{"path":"https://keras.posit.co/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://keras.posit.co/reference/pipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pipe operator — %>%","text":"commonly, result calling right hand side left hand side argument: rhs(lhs). See magritter vignette , advanced, usages.","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a Keras model — plot.keras.src.models.model.Model","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"Plot Keras model","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model plot(   x,   show_shapes = FALSE,   show_dtype = FALSE,   show_layer_names = FALSE,   ...,   rankdir = \"TB\",   expand_nested = FALSE,   dpi = 200,   layer_range = NULL,   show_layer_activations = FALSE,   show_trainable = NA,   to_file = NULL )"},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"x Keras model instance show_shapes whether display shape information. show_dtype whether display layer dtypes. show_layer_names whether display layer names. ... passed Python keras.utils.model_to_dot(). Used forward backward compatibility. rankdir string specifying format plot: 'TB' creates vertical plot; 'LR' creates horizontal plot. (argument passed PyDot) expand_nested Whether expand nested models clusters. dpi Dots per inch. Increase value image text appears excessively pixelated. layer_range list containing two character strings, starting layer name ending layer name (inclusive) indicating range layers plot generated. also accepts regex patterns instead exact name. case, start predicate first element matches layer_range[1] end predicate last element matches layer_range[2]. default NULL considers layers model. Note must pass range resultant subgraph must complete. show_layer_activations Display layer activations (layers activation property). show_trainable whether display layer trainable. to_file File name plot image. NULL (default), model drawn default graphics device. Otherwise, file saved.","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"Nothing, called side effects.","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"raises","dir":"Reference","previous_headings":"","what":"Raises","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"ValueError: plot(model) called model built, unless input_shape =  argument supplied keras_model_sequential().","code":""},{"path":"https://keras.posit.co/reference/plot.keras.src.models.model.Model.html","id":"requirements","dir":"Reference","previous_headings":"","what":"Requirements","title":"Plot a Keras model — plot.keras.src.models.model.Model","text":"function requires pydot graphviz. pydot default installed install_keras(), installed keras means, can install pydot directly :   can install graphviz directly : https://graphviz.gitlab.io/download/ Linux platforms, can install graphviz via package manager. example, Ubuntu/Debian can install   conda environment, can install graphviz :","code":"reticulate::py_install(\"pydot\", pip = TRUE) sudo apt install graphviz reticulate::conda_install(packages = \"graphviz\") # Restart the R session after install."},{"path":"https://keras.posit.co/reference/plot.keras_training_history.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot training history — plot.keras_training_history","title":"Plot training history — plot.keras_training_history","text":"Plots metrics recorded training.","code":""},{"path":"https://keras.posit.co/reference/plot.keras_training_history.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot training history — plot.keras_training_history","text":"","code":"# S3 method for keras_training_history plot(   x,   y,   metrics = NULL,   method = c(\"auto\", \"ggplot2\", \"base\"),   smooth = getOption(\"keras.plot.history.smooth\", TRUE),   theme_bw = getOption(\"keras.plot.history.theme_bw\", FALSE),   ... )"},{"path":"https://keras.posit.co/reference/plot.keras_training_history.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot training history — plot.keras_training_history","text":"x Training history object returned fit.keras.src.models.model.Model(). y Unused. metrics One metrics plot (e.g. c('loss', 'accuracy')). Defaults plotting captured metrics. method Method use plotting. default \"auto\" use ggplot2 available, otherwise use base graphics. smooth Whether loess smooth added plot, available ggplot2 method. number epochs smaller ten, forced false. theme_bw Use ggplot2::theme_bw() plot history black white. ... Additional parameters pass plot() method.","code":""},{"path":"https://keras.posit.co/reference/plot.keras_training_history.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot training history — plot.keras_training_history","text":"method == \"ggplot2\", ggplot object returned. method == \"base\", function draw graphics device return NULL, invisibly.","code":""},{"path":"https://keras.posit.co/reference/pop_layer.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove the last layer in a Sequential model — pop_layer","title":"Remove the last layer in a Sequential model — pop_layer","text":"Remove last layer Sequential model","code":""},{"path":"https://keras.posit.co/reference/pop_layer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove the last layer in a Sequential model — pop_layer","text":"","code":"pop_layer(object)"},{"path":"https://keras.posit.co/reference/pop_layer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove the last layer in a Sequential model — pop_layer","text":"object Sequential keras model object","code":""},{"path":"https://keras.posit.co/reference/pop_layer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove the last layer in a Sequential model — pop_layer","text":"input object, invisibly.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"Generates output predictions input samples.","code":""},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model predict(   object,   x,   ...,   batch_size = NULL,   verbose = getOption(\"keras.verbose\", default = \"auto\"),   steps = NULL,   callbacks = NULL )"},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"object Keras model object x Input samples. : array (array-like), list arrays (case model multiple inputs). tensor, list tensors (case model multiple inputs). TF Dataset. ... forward/backward compatability. batch_size Integer NULL. Number samples per batch. unspecified, batch_size default 32. specify batch_size data form TF Dataset generator (since generate batches). verbose \"auto\", 0, 1, 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. \"auto\" becomes 1 cases, 2 knitr render running distributed training server. Note progress bar particularly useful logged file, verbose=2 recommended running interactively (e.g., production environment). Defaults \"auto\". steps Total number steps (batches samples) declaring prediction round finished. Ignored default value NULL. x TF Dataset steps NULL, predict() run input dataset exhausted. callbacks List Callback instances. List callbacks apply prediction.","code":""},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"R array(s) predictions.","code":""},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"Computation done batches. method designed batch processing large numbers inputs. intended use inside loops iterate data process small numbers inputs time. small numbers inputs fit one batch, directly call model model$call faster execution, e.g., model(x), model(x, training = FALSE) layers BatchNormalization behave differently inference.","code":""},{"path":"https://keras.posit.co/reference/predict.keras.src.models.model.Model.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generates output predictions for the input samples. — predict.keras.src.models.model.Model","text":"See FAQ entry details difference Model methods predict() call().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/predict_on_batch.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns predictions for a single batch of samples. — predict_on_batch","title":"Returns predictions for a single batch of samples. — predict_on_batch","text":"Returns predictions single batch samples.","code":""},{"path":"https://keras.posit.co/reference/predict_on_batch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns predictions for a single batch of samples. — predict_on_batch","text":"","code":"predict_on_batch(object, x)"},{"path":"https://keras.posit.co/reference/predict_on_batch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns predictions for a single batch of samples. — predict_on_batch","text":"object Keras model object x Input data. must array-like.","code":""},{"path":"https://keras.posit.co/reference/predict_on_batch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns predictions for a single batch of samples. — predict_on_batch","text":"Array(s) predictions.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/process_utils.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocessing and postprocessing utilities — process_utils","title":"Preprocessing and postprocessing utilities — process_utils","text":"functions used preprocess postprocess inputs outputs Keras applications.","code":""},{"path":"https://keras.posit.co/reference/process_utils.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocessing and postprocessing utilities — process_utils","text":"","code":"application_preprocess_inputs(model, x, ..., data_format = NULL)  application_decode_predictions(model, preds, top = 5L, ...)"},{"path":"https://keras.posit.co/reference/process_utils.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocessing and postprocessing utilities — process_utils","text":"model Keras model initialized using application_ function. x batch inputs model. ... Additional arguments passed preprocessing decoding function. data_format Optional data format image tensor/array. NULL means global setting config_image_data_format() used (unless changed , uses \"channels_last\"). Defaults NULL. preds batch outputs model. top number top predictions return.","code":""},{"path":"https://keras.posit.co/reference/process_utils.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocessing and postprocessing utilities — process_utils","text":"list decoded predictions case application_decode_predictions(). batch preprocessed inputs case application_preprocess_inputs().","code":""},{"path":"https://keras.posit.co/reference/process_utils.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Preprocessing and postprocessing utilities — process_utils","text":"application_preprocess_inputs(): Pre-process inputs used model application_decode_predictions(): Decode predictions model","code":""},{"path":"https://keras.posit.co/reference/process_utils.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preprocessing and postprocessing utilities — process_utils","text":"","code":"if (FALSE) { model <- application_convnext_tiny()  inputs <- random_normal(c(32, 224, 224, 3)) processed_inputs <- application_preprocess_inputs(model, inputs)  preds <- random_normal(c(32, 1000)) decoded_preds <- application_decode_predictions(model, preds)  }"},{"path":"https://keras.posit.co/reference/quantize_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantize the weights of a model. — quantize_weights","title":"Quantize the weights of a model. — quantize_weights","text":"Note model must built first calling method. quantize_weights() recursively call layer$quantize(mode) layers skipped layer implement function. Currently Dense EinsumDense layers support quantization.","code":""},{"path":"https://keras.posit.co/reference/quantize_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantize the weights of a model. — quantize_weights","text":"","code":"quantize_weights(object, mode)"},{"path":"https://keras.posit.co/reference/quantize_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantize the weights of a model. — quantize_weights","text":"object Keras Model Layer. mode mode quantization. 'int8' supported time.","code":""},{"path":"https://keras.posit.co/reference/quantize_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantize the weights of a model. — quantize_weights","text":"model, invisibly. Note just convenience usage |>, model modified -place.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_beta.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from a Beta distribution. — random_beta","title":"Draw samples from a Beta distribution. — random_beta","text":"values drawm Beta distribution parametrized alpha beta.","code":""},{"path":"https://keras.posit.co/reference/random_beta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from a Beta distribution. — random_beta","text":"","code":"random_beta(shape, alpha, beta, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_beta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from a Beta distribution. — random_beta","text":"shape shape random values generate. alpha Float array floats representing first parameter alpha. Must broadcastable beta shape. beta Float array floats representing second parameter beta. Must broadcastable alpha shape. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults \"float32\" unless configured otherwise (via config_set_floatx(float_dtype)). seed integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_beta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from a Beta distribution. — random_beta","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_binomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from a Binomial distribution. — random_binomial","title":"Draw samples from a Binomial distribution. — random_binomial","text":"values drawn Binomial distribution specified trial count probability success.","code":""},{"path":"https://keras.posit.co/reference/random_binomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from a Binomial distribution. — random_binomial","text":"","code":"random_binomial(shape, counts, probabilities, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_binomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from a Binomial distribution. — random_binomial","text":"shape shape random values generate. counts number array numbers representing number trials. must broadcastable probabilities. probabilities float array floats representing probability success individual event. must broadcastable counts. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults \"float32\" unless configured otherwise (via config_set_floatx(float_dtype)). seed Python integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer None (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_binomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from a Binomial distribution. — random_binomial","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_categorical.html","id":null,"dir":"Reference","previous_headings":"","what":"Draws samples from a categorical distribution. — random_categorical","title":"Draws samples from a categorical distribution. — random_categorical","text":"function takes input logits, 2-D input tensor shape (batch_size, num_classes). row input represents categorical distribution, column index containing log-probability given class. function output 2-D tensor shape (batch_size, num_samples), row contains samples corresponding row logits. column index contains independent samples drawn input distribution.","code":""},{"path":"https://keras.posit.co/reference/random_categorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draws samples from a categorical distribution. — random_categorical","text":"","code":"random_categorical(logits, num_samples, dtype = \"int32\", seed = NULL)"},{"path":"https://keras.posit.co/reference/random_categorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draws samples from a categorical distribution. — random_categorical","text":"logits 2-D Tensor shape (batch_size, num_classes). row define categorical distibution unnormalized log-probabilities classes. num_samples Int, number independent samples draw row input. second dimension output tensor's shape. dtype Optional dtype output tensor. seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_categorical.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draws samples from a categorical distribution. — random_categorical","text":"2-D tensor (batch_size, num_samples).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomly set some values in a tensor to 0. — random_dropout","title":"Randomly set some values in a tensor to 0. — random_dropout","text":"Randomly set portion values tensor 0.","code":""},{"path":"https://keras.posit.co/reference/random_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomly set some values in a tensor to 0. — random_dropout","text":"","code":"random_dropout(inputs, rate, noise_shape = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomly set some values in a tensor to 0. — random_dropout","text":"inputs tensor rate numeric noise_shape shape() value seed Initial seed random number generator","code":""},{"path":"https://keras.posit.co/reference/random_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Randomly set some values in a tensor to 0. — random_dropout","text":"tensor copy inputs values set 0.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_gamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw random samples from the Gamma distribution. — random_gamma","title":"Draw random samples from the Gamma distribution. — random_gamma","text":"Draw random samples Gamma distribution.","code":""},{"path":"https://keras.posit.co/reference/random_gamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw random samples from the Gamma distribution. — random_gamma","text":"","code":"random_gamma(shape, alpha, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_gamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw random samples from the Gamma distribution. — random_gamma","text":"shape shape random values generate. alpha Float, parameter distribution. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults float32 unless configured otherwise (via config_set_floatx(float_dtype)). seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_gamma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw random samples from the Gamma distribution. — random_gamma","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_integer.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw random integers from a uniform distribution. — random_integer","title":"Draw random integers from a uniform distribution. — random_integer","text":"generated values follow uniform distribution range [minval, maxval). lower bound minval included range, upper bound maxval excluded. dtype must integer type.","code":""},{"path":"https://keras.posit.co/reference/random_integer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw random integers from a uniform distribution. — random_integer","text":"","code":"random_integer(shape, minval, maxval, dtype = \"int32\", seed = NULL)"},{"path":"https://keras.posit.co/reference/random_integer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw random integers from a uniform distribution. — random_integer","text":"shape shape random values generate. minval integer, lower bound range random values generate (inclusive). maxval integer, upper bound range random values generate (exclusive). dtype Optional dtype tensor. integer types supported. specified, \"int32\" used. seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_integer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw random integers from a uniform distribution. — random_integer","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw random samples from a normal (Gaussian) distribution. — random_normal","title":"Draw random samples from a normal (Gaussian) distribution. — random_normal","text":"Draw random samples normal (Gaussian) distribution.","code":""},{"path":"https://keras.posit.co/reference/random_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw random samples from a normal (Gaussian) distribution. — random_normal","text":"","code":"random_normal(shape, mean = 0, stddev = 1, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw random samples from a normal (Gaussian) distribution. — random_normal","text":"shape shape random values generate. mean Float, defaults 0. Mean random values generate. stddev Float, defaults 1. Standard deviation random values generate. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults float32 unless configured otherwise (via config_set_floatx(float_dtype)). seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw random samples from a normal (Gaussian) distribution. — random_normal","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"Keras, RNG-using methods (random_normal()) stateless, meaning pass integer seed (seed = 42), return values call. order get different values call, must use SeedGenerator instead seed argument. SeedGenerator object stateful.","code":""},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"","code":"random_seed_generator(seed = NULL, name = NULL, ...)"},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"seed Initial seed random number generator name String, name object ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"SeedGenerator instance, can passed seed = argument random tensor generators.","code":""},{"path":"https://keras.posit.co/reference/random_seed_generator.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates variable seeds upon each call to a RNG-using function. — random_seed_generator","text":"Usage layer:","code":"seed_gen <- random_seed_generator(seed = 42) values <- random_normal(shape = c(2, 3), seed = seed_gen) new_values <- random_normal(shape = c(2, 3), seed = seed_gen) layer_dropout2 <- new_layer_class(   \"dropout2\",   initialize = function(...) {     super$initialize(...)     self$seed_generator <- random_seed_generator(seed = 1337)   },   call = function(x, training = FALSE) {     if (training) {       return(random_dropout(x, rate = 0.5, seed = self$seed_generator))     }     return(x)   } )  out <- layer_dropout(rate = 0.8) out(op_ones(10), training = TRUE) ## tf.Tensor([0. 5. 5. 0. 0. 0. 0. 0. 0. 0.], shape=(10), dtype=float32)"},{"path":[]},{"path":"https://keras.posit.co/reference/random_shuffle.html","id":null,"dir":"Reference","previous_headings":"","what":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","title":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","text":"Shuffle elements tensor uniformly random along axis.","code":""},{"path":"https://keras.posit.co/reference/random_shuffle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","text":"","code":"random_shuffle(x, axis = 1L, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_shuffle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","text":"x tensor shuffled. axis integer specifying axis along shuffle. Defaults 0. seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_shuffle.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shuffle the elements of a tensor uniformly at random along an axis. — random_shuffle","text":"tensor, copy x axis axis shuffled.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_truncated_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from a truncated normal distribution. — random_truncated_normal","title":"Draw samples from a truncated normal distribution. — random_truncated_normal","text":"values drawn normal distribution specified mean standard deviation, discarding re-drawing samples two standard deviations mean.","code":""},{"path":"https://keras.posit.co/reference/random_truncated_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from a truncated normal distribution. — random_truncated_normal","text":"","code":"random_truncated_normal(shape, mean = 0, stddev = 1, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_truncated_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from a truncated normal distribution. — random_truncated_normal","text":"shape shape random values generate. mean Float, defaults 0. Mean random values generate. stddev Float, defaults 1. Standard deviation random values generate. dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults float32 unless configured otherwise (via config_set_floatx(float_dtype)) seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_truncated_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from a truncated normal distribution. — random_truncated_normal","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/random_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw samples from a uniform distribution. — random_uniform","title":"Draw samples from a uniform distribution. — random_uniform","text":"generated values follow uniform distribution range [minval, maxval). lower bound minval included range, upper bound maxval excluded. dtype must floating point type, default range [0, 1).","code":""},{"path":"https://keras.posit.co/reference/random_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw samples from a uniform distribution. — random_uniform","text":"","code":"random_uniform(shape, minval = 0, maxval = 1, dtype = NULL, seed = NULL)"},{"path":"https://keras.posit.co/reference/random_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw samples from a uniform distribution. — random_uniform","text":"shape shape random values generate. minval Float, defaults 0. Lower bound range random values generate (inclusive). maxval Float, defaults 1. Upper bound range random values generate (exclusive). dtype Optional dtype tensor. floating point types supported. specified, config_floatx() used, defaults float32 unless configured otherwise (via config_set_floatx(float_dtype)) seed R integer instance random_seed_generator(). Used make behavior initializer deterministic. Note initializer seeded integer NULL (unseeded) produce random values across multiple calls. get different random values across multiple calls, use seed instance random_seed_generator().","code":""},{"path":"https://keras.posit.co/reference/random_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw samples from a uniform distribution. — random_uniform","text":"tensor random values.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. generics compile, fit magrittr %<>% reticulate array_reshape, as_iterator, iter_next, iterate, np_array, tuple, use_python, use_virtualenv tensorflow all_dims, as_tensor, evaluate, export_savedmodel, tensorboard tfruns flag_boolean, flag_integer, flag_numeric, flag_string, flags, run_dir","code":""},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":null,"dir":"Reference","previous_headings":"","what":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"function registers custom class function Keras custom object registry, can serialized deserialized without needing entry user-provided custom_objects argument. also injects function Keras call get object's serializable string key. Note serialized deserialized, classes must implement get_config() method. Functions requirement. object registered key 'package>name' name, defaults object name passed.","code":""},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"","code":"register_keras_serializable(object, name = NULL, package = NULL)"},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"object keras object. name name serialize class package. package package class belongs . used key (\"package>name\") identify class. Defaults current package name, \"Custom\" outside package.","code":""},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"object returned invisibly, convenient piping. primarily called side effects.","code":""},{"path":"https://keras.posit.co/reference/register_keras_serializable.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Registers a custom object with the Keras serialization framework. — register_keras_serializable","text":"","code":"# Note that `'my_package'` is used as the `package` argument here, and since # the `name` argument is not provided, `'MyDense'` is used as the `name`. layer_my_dense <- Layer(\"MyDense\") register_keras_serializable(layer_my_dense, package = \"my_package\")  MyDense <- environment(layer_my_dense)$`__class__` # the python class obj stopifnot(exprs = {   get_registered_object('my_package>MyDense') == MyDense   get_registered_name(MyDense) == 'my_package>MyDense' })"},{"path":[]},{"path":"https://keras.posit.co/reference/regularizer_l1.html","id":null,"dir":"Reference","previous_headings":"","what":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","title":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","text":"L1 regularization penalty computed : loss = l1 * reduce_sum(abs(x)) L1 may passed layer string identifier:   case, default value used l1=0.01.","code":"dense <- layer_dense(units = 3, kernel_regularizer = 'l1')"},{"path":"https://keras.posit.co/reference/regularizer_l1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","text":"","code":"regularizer_l1(l1 = 0.01)"},{"path":"https://keras.posit.co/reference/regularizer_l1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","text":"l1 float, L1 regularization factor.","code":""},{"path":"https://keras.posit.co/reference/regularizer_l1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A regularizer that applies a L1 regularization penalty. — regularizer_l1","text":"Regularizer instance can passed layer constructors used standalone object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/regularizer_l1_l2.html","id":null,"dir":"Reference","previous_headings":"","what":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","title":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","text":"L1 regularization penalty computed : loss = l1 * reduce_sum(abs(x)) L2 regularization penalty computed loss = l2 * reduce_sum(square(x)) L1L2 may passed layer string identifier:   case, default values used l1=0.01 l2=0.01.","code":"dense <- layer_dense(units = 3, kernel_regularizer = 'L1L2')"},{"path":"https://keras.posit.co/reference/regularizer_l1_l2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","text":"","code":"regularizer_l1_l2(l1 = 0, l2 = 0)"},{"path":"https://keras.posit.co/reference/regularizer_l1_l2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","text":"l1 float, L1 regularization factor. l2 float, L2 regularization factor.","code":""},{"path":"https://keras.posit.co/reference/regularizer_l1_l2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A regularizer that applies both L1 and L2 regularization penalties. — regularizer_l1_l2","text":"Regularizer instance can passed layer constructors used standalone object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/regularizer_l2.html","id":null,"dir":"Reference","previous_headings":"","what":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","title":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","text":"L2 regularization penalty computed : loss = l2 * reduce_sum(square(x)) L2 may passed layer string identifier:   case, default value used l2=0.01.","code":"dense <- layer_dense(units = 3, kernel_regularizer='l2')"},{"path":"https://keras.posit.co/reference/regularizer_l2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","text":"","code":"regularizer_l2(l2 = 0.01)"},{"path":"https://keras.posit.co/reference/regularizer_l2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","text":"l2 float, L2 regularization factor.","code":""},{"path":"https://keras.posit.co/reference/regularizer_l2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A regularizer that applies a L2 regularization penalty. — regularizer_l2","text":"Regularizer instance can passed layer constructors used standalone object.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":null,"dir":"Reference","previous_headings":"","what":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"can applied either rows matrix (mode=\"rows\") columns (mode=\"columns\"). applied Dense kernel shape (input_dim, units), rows mode seek make feature vectors (.e. basis output space) orthogonal .","code":""},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"","code":"regularizer_orthogonal(factor = 0.01, mode = \"rows\")"},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"factor Float. regularization factor. regularization penalty proportional factor times mean dot products L2-normalized rows (mode=\"rows\", columns mode=\"columns\") inputs, excluding product row/column .  Defaults 0.01. mode String, one {\"rows\", \"columns\"}. Defaults \"rows\". rows mode, regularization effect seeks make rows input orthogonal . columns mode, seeks make columns input orthogonal .","code":""},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"Regularizer instance can passed layer constructors used standalone object.","code":""},{"path":"https://keras.posit.co/reference/regularizer_orthogonal.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Regularizer that encourages input vectors to be orthogonal to each other. — regularizer_orthogonal","text":"","code":"regularizer <- regularizer_orthogonal(factor=0.01) layer <- layer_dense(units=4, kernel_regularizer=regularizer)"},{"path":[]},{"path":"https://keras.posit.co/reference/reset_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Reset the state for a model, layer or metric. — reset_state","title":"Reset the state for a model, layer or metric. — reset_state","text":"Reset state model, layer metric.","code":""},{"path":"https://keras.posit.co/reference/reset_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reset the state for a model, layer or metric. — reset_state","text":"","code":"reset_state(object)"},{"path":"https://keras.posit.co/reference/reset_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reset the state for a model, layer or metric. — reset_state","text":"object Model, Layer, Metric instance Layers resettable state (E.g., adapt()-able preprocessing layers rnn layers resettable state, layer_dense() ). Calling Layer instance without resettable-state error.","code":""},{"path":"https://keras.posit.co/reference/reset_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reset the state for a model, layer or metric. — reset_state","text":"object, invisibly.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell class for the GRU layer. — rnn_cell_gru","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"class processes one step within whole time sequence input, whereas layer_gru() processes whole sequence.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"","code":"rnn_cell_gru(   units,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   reset_after = TRUE,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). recurrent_activation Activation function use recurrent step. Default: sigmoid (sigmoid). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. reset_after GRU convention (whether apply reset gate matrix multiplication). FALSE = \"\", TRUE = \"\" (default cuDNN compatible). seed Random seed dropout. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"Layer instance, intended used layer_rnn().","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"inputs: 2D tensor, shape (batch, features). states: 2D tensor shape (batch, units), state previous time step. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout used.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_gru.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cell class for the GRU layer. — rnn_cell_gru","text":"","code":"inputs <- random_uniform(c(32, 10, 8)) outputs <- inputs |> layer_rnn(rnn_cell_gru(4)) shape(outputs) ## shape(32, 4) rnn <- layer_rnn(    cell = rnn_cell_gru(4),    return_sequences=TRUE,    return_state=TRUE) c(whole_sequence_output, final_state) %<-% rnn(inputs) shape(whole_sequence_output) ## shape(32, 10, 4) shape(final_state) ## shape(32, 4)"},{"path":[]},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell class for the LSTM layer. — rnn_cell_lstm","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"class processes one step within whole time sequence input, whereas layer_lstm() processes whole sequence.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"","code":"rnn_cell_lstm(   units,   activation = \"tanh\",   recurrent_activation = \"sigmoid\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   unit_forget_bias = TRUE,   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). recurrent_activation Activation function use recurrent step. Default: sigmoid (sigmoid). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". unit_forget_bias Boolean (default TRUE). TRUE, add 1 bias forget gate initialization. Setting TRUE also force bias_initializer=\"zeros\". recommended Jozefowicz et al. kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. seed Random seed dropout. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"Layer instance, intended used layer_rnn().","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"inputs: 2D tensor, shape (batch, features). states: 2D tensor shape (batch, units), state previous time step. training: Boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout used.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_lstm.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cell class for the LSTM layer. — rnn_cell_lstm","text":"","code":"inputs <- random_uniform(c(32, 10, 8)) output <- inputs |>   layer_rnn(cell = rnn_cell_lstm(4)) shape(output) ## shape(32, 4) rnn <- layer_rnn(cell = rnn_cell_lstm(4),                  return_sequences = T,                  return_state = T) c(whole_sequence_output, ...final_state) %<-% rnn(inputs) str(whole_sequence_output) ## <tf.Tensor: shape=(32, 10, 4), dtype=float32, numpy=…> str(final_state) ## List of 2 ##  $ :<tf.Tensor: shape=(32, 4), dtype=float32, numpy=…> ##  $ :<tf.Tensor: shape=(32, 4), dtype=float32, numpy=…>"},{"path":[]},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell class for SimpleRNN. — rnn_cell_simple","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"class processes one step within whole time sequence input, whereas layer_simple_rnn() processes whole sequence.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"","code":"rnn_cell_simple(   units,   activation = \"tanh\",   use_bias = TRUE,   kernel_initializer = \"glorot_uniform\",   recurrent_initializer = \"orthogonal\",   bias_initializer = \"zeros\",   kernel_regularizer = NULL,   recurrent_regularizer = NULL,   bias_regularizer = NULL,   kernel_constraint = NULL,   recurrent_constraint = NULL,   bias_constraint = NULL,   dropout = 0,   recurrent_dropout = 0,   seed = NULL,   ... )"},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"units Positive integer, dimensionality output space. activation Activation function use. Default: hyperbolic tangent (tanh). pass NULL, activation applied (ie. \"linear\" activation: (x) = x). use_bias Boolean, (default TRUE), whether layer use bias vector. kernel_initializer Initializer kernel weights matrix, used linear transformation inputs. Default: \"glorot_uniform\". recurrent_initializer Initializer recurrent_kernel weights matrix, used linear transformation recurrent state. Default: \"orthogonal\". bias_initializer Initializer bias vector. Default: \"zeros\". kernel_regularizer Regularizer function applied kernel weights matrix. Default: NULL. recurrent_regularizer Regularizer function applied recurrent_kernel weights matrix. Default: NULL. bias_regularizer Regularizer function applied bias vector. Default: NULL. kernel_constraint Constraint function applied kernel weights matrix. Default: NULL. recurrent_constraint Constraint function applied recurrent_kernel weights matrix. Default: NULL. bias_constraint Constraint function applied bias vector. Default: NULL. dropout Float 0 1. Fraction units drop linear transformation inputs. Default: 0. recurrent_dropout Float 0 1. Fraction units drop linear transformation recurrent state. Default: 0. seed Random seed dropout. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"Layer instance, intended used layer_rnn().","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"call-arguments","dir":"Reference","previous_headings":"","what":"Call Arguments","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"sequence: 2D tensor, shape (batch, features). states: 2D tensor shape (batch, units), state previous time step. training: Python boolean indicating whether layer behave training mode inference mode. relevant dropout recurrent_dropout used.","code":""},{"path":"https://keras.posit.co/reference/rnn_cell_simple.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cell class for SimpleRNN. — rnn_cell_simple","text":"","code":"inputs <- random_uniform(c(32, 10, 8)) rnn <- layer_rnn(cell = rnn_cell_simple(units = 4)) output <- rnn(inputs)  # The output has shape `(32, 4)`. rnn <- layer_rnn(     cell = rnn_cell_simple(units = 4),     return_sequences=TRUE,     return_state=TRUE ) # whole_sequence_output has shape `(32, 10, 4)`. # final_state has shape `(32, 4)`. c(whole_sequence_output, final_state) %<-% rnn(inputs)"},{"path":[]},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"Used implement efficient stacked RNNs.","code":""},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"","code":"rnn_cells_stack(cells, ...)"},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"cells List RNN cell instances. ... Unnamed arguments treated additional cells. Named arguments passed underlying layer.","code":""},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"Layer instance, intended used layer_rnn().","code":""},{"path":"https://keras.posit.co/reference/rnn_cells_stack.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Wrapper allowing a stack of RNN cells to behave as a single cell. — rnn_cells_stack","text":"","code":"batch_size <- 3 sentence_length <- 5 num_features <- 2 new_shape <- c(batch_size, sentence_length, num_features) x <- array(1:30, dim = new_shape)  rnn_cells <- lapply(1:2, function(x) rnn_cell_lstm(units = 128)) stacked_lstm <- rnn_cells_stack(rnn_cells) lstm_layer <- layer_rnn(cell = stacked_lstm)  result <- lstm_layer(x) str(result) ## <tf.Tensor: shape=(3, 128), dtype=float32, numpy=…>"},{"path":[]},{"path":"https://keras.posit.co/reference/save_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves a model as a .keras file. — save_model","title":"Saves a model as a .keras file. — save_model","text":"Saves model .keras file.","code":""},{"path":"https://keras.posit.co/reference/save_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves a model as a .keras file. — save_model","text":"","code":"save_model(model, filepath = NULL, overwrite = FALSE, ...)"},{"path":"https://keras.posit.co/reference/save_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves a model as a .keras file. — save_model","text":"model keras model. filepath string, Path save model. Must end .keras. overwrite Whether overwrite existing model target location, instead ask user via interactive prompt. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/save_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Saves a model as a .keras file. — save_model","text":"filepath provided, function called primarily side effects, model returned invisibly. filepath provided NULL, serialized model returned R raw vector.","code":""},{"path":"https://keras.posit.co/reference/save_model.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Saves a model as a .keras file. — save_model","text":"saved .keras file contains: model's configuration (architecture) model's weights model's optimizer's state () Thus models can reinstantiated exact state.","code":"model <- keras_model_sequential(input_shape = c(3)) |>   layer_dense(5) |>   layer_activation_softmax()  model |> save_model(\"model.keras\") loaded_model <- load_model(\"model.keras\") x <- random_uniform(c(10, 3)) stopifnot(all.equal(   model |> predict(x),   loaded_model |> predict(x) )) zip::zip_list(\"model.keras\")[, \"filename\"] ## [1] \"metadata.json\"    \"config.json\"      \"model.weights.h5\""},{"path":[]},{"path":"https://keras.posit.co/reference/save_model_config.html","id":null,"dir":"Reference","previous_headings":"","what":"Save and load model configuration as JSON — save_model_config","title":"Save and load model configuration as JSON — save_model_config","text":"Save re-load models configurations JSON. Note representation include weights, architecture.","code":""},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Save and load model configuration as JSON — save_model_config","text":"","code":"save_model_config(model, filepath = NULL, overwrite = FALSE)  load_model_config(filepath, custom_objects = NULL)"},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Save and load model configuration as JSON — save_model_config","text":"model Model object save filepath path json file model config. overwrite Whether overwrite existing model configuration json filepath, instead ask user via interactive prompt. custom_objects Optional named list mapping names custom classes functions considered deserialization.","code":""},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Save and load model configuration as JSON — save_model_config","text":"called primarily side effects. model returned, invisibly, enable usage pipe.","code":""},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Save and load model configuration as JSON — save_model_config","text":"Note: save_model_config() serializes model JSON using serialize_keras_object(), get_config(). serialize_keras_object() returns superset get_config(), additional information needed create class object needed restore model. See example extract get_config() value saved model.","code":""},{"path":"https://keras.posit.co/reference/save_model_config.html","id":"example","dir":"Reference","previous_headings":"","what":"Example","title":"Save and load model configuration as JSON — save_model_config","text":"","code":"model <- keras_model_sequential(input_shape = 10) |> layer_dense(10) file <- tempfile(\"model-config-\", fileext = \".json\") save_model_config(model, file)  # load a new model instance with the same architecture but different weights model2 <- load_model_config(file)  stopifnot(exprs = {   all.equal(get_config(model), get_config(model2))    # To extract the `get_config()` value from a saved model config:   all.equal(       get_config(model),       structure(jsonlite::read_json(file)$config,                 \"__class__\" = keras_model_sequential()$`__class__`)   ) })"},{"path":[]},{"path":"https://keras.posit.co/reference/save_model_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves all layer weights to a .weights.h5 file. — save_model_weights","title":"Saves all layer weights to a .weights.h5 file. — save_model_weights","text":"Saves layer weights .weights.h5 file.","code":""},{"path":"https://keras.posit.co/reference/save_model_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves all layer weights to a .weights.h5 file. — save_model_weights","text":"","code":"save_model_weights(model, filepath, overwrite = FALSE)"},{"path":"https://keras.posit.co/reference/save_model_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves all layer weights to a .weights.h5 file. — save_model_weights","text":"model keras Model object filepath string. Path save model. Must end .weights.h5. overwrite Whether overwrite existing model target location, instead ask user via interactive prompt.","code":""},{"path":"https://keras.posit.co/reference/save_model_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Saves all layer weights to a .weights.h5 file. — save_model_weights","text":"called primarily side effects. model returned, invisibly, enable usage pipe.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/serialize_keras_object.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","title":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","text":"serialize_keras_object() serializes Keras object named list represents object, reciprocal function deserialize_keras_object(). See deserialize_keras_object() information full config format.","code":""},{"path":"https://keras.posit.co/reference/serialize_keras_object.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","text":"","code":"serialize_keras_object(obj)"},{"path":"https://keras.posit.co/reference/serialize_keras_object.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","text":"obj Keras object serialize.","code":""},{"path":"https://keras.posit.co/reference/serialize_keras_object.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve the full config by serializing the Keras object. — serialize_keras_object","text":"named list represents object config. config expected contain simple types , can saved json. object can deserialized config via deserialize_keras_object().","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/set_random_seed.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","title":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","text":"can use utility make almost Keras program fully deterministic. limitations apply cases network communications involved (e.g. parameter server distribution), creates additional sources randomness, certain non-deterministic cuDNN ops involved. sets: R session seed: set.seed() Python session seed: import random; random.seed(seed) Python NumPy seed: import numpy; numpy.random.seed(seed) TensorFlow seed: tf$random$set_seed(seed) (TF installed) Torch seed: import(\"torch\")$manual_seed(seed) (backend torch) disables Python hash randomization. Note TensorFlow seed set even using TensorFlow backend framework, since many workflows leverage tf$data pipelines (feature random shuffling). Likewise many workflows might leverage NumPy APIs.","code":""},{"path":"https://keras.posit.co/reference/set_random_seed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","text":"","code":"set_random_seed(seed)"},{"path":"https://keras.posit.co/reference/set_random_seed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","text":"seed Integer, random seed use.","code":""},{"path":"https://keras.posit.co/reference/set_random_seed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sets all random seeds (Python, NumPy, and backend framework, e.g. TF). — set_random_seed","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/shape.html","id":null,"dir":"Reference","previous_headings":"","what":"Tensor shape utility — shape","title":"Tensor shape utility — shape","text":"function can used get create tensor shape.","code":""},{"path":"https://keras.posit.co/reference/shape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tensor shape utility — shape","text":"","code":"shape(...)  # S3 method for keras_shape format(x, ..., prefix = TRUE)  # S3 method for keras_shape print(x, ...)  # S3 method for keras_shape [(x, ...)  # S3 method for keras_shape as.integer(x, ...)  # S3 method for keras_shape as.list(x, ...)"},{"path":"https://keras.posit.co/reference/shape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tensor shape utility — shape","text":"... shape specification. Numerics, NULL tensors valid. NULL, NA, -1L can used specify unspecified dim size. Tensors dispatched op_shape() extract tensor shape. Values wrapped () used asis (see examples). objects coerced via .integer(). x keras_shape object. prefix Whether format shape object prefix. Defaults \"shape\".","code":""},{"path":"https://keras.posit.co/reference/shape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tensor shape utility — shape","text":"list \"keras_shape\" class attribute. element list either ) NULL, b) R integer c) scalar integer tensor (e.g., supplied TF tensor unspecified dimension function traced).","code":""},{"path":"https://keras.posit.co/reference/shape.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tensor shape utility — shape","text":"3 ways specify unknown dimension     functions take 'shape' argument also coerce shape()     can also use shape() get shape tensor (excepting scalar integer tensors).             Combine expand shapes         Scalar integer tensors treated axis values. commonly encountered tracing function graph mode, axis size might unknown.     useful pattern unpack shape() %<-%, like :                     want resolve shape tensor can potentially scalar integer, can wrap tensor (), use op_shape().","code":"shape(1, 2, 3) ## shape(1, 2, 3) shape(NA,   2, 3) shape(NULL, 2, 3) shape(-1,   2, 3) ## shape(NA, 2, 3) ## shape(NA, 2, 3) ## shape(NA, 2, 3) layer_input(c(1, 2, 3)) layer_input(shape(1, 2, 3)) ## <KerasTensor shape=(None, 1, 2, 3), dtype=float32, sparse=None, name=keras_tensor> ## <KerasTensor shape=(None, 1, 2, 3), dtype=float32, sparse=None, name=keras_tensor_1> symbolic_tensor <- layer_input(shape(1, 2, 3)) shape(symbolic_tensor) ## shape(NA, 1, 2, 3) eager_tensor <- op_ones(c(1,2,3)) shape(eager_tensor) ## shape(1, 2, 3) op_shape(eager_tensor) ## shape(1, 2, 3) shape(symbolic_tensor, 4) ## shape(NA, 1, 2, 3, 4) shape(5, symbolic_tensor, 4) ## shape(5, NA, 1, 2, 3, 4) tfn <- tensorflow::tf_function(function(x) {   print(op_shape(x))   x }, input_signature = list(tensorflow::tf$TensorSpec(shape(1, NA, 3)))) invisible(tfn(op_ones(shape(1, 2, 3)))) ## shape(1, Tensor(\"strided_slice:0\", shape=(), dtype=int32), 3) c(batch_size, seq_len, channels) %<-% shape(x)  # `%<-%` also has support for skipping values # during unpacking with `.` and `...`. For example, # To retrieve just the first and/or last dim: c(batch_size, ...) %<-% shape(x) c(batch_size, ., .) %<-% shape(x) c(..., channels) %<-% shape(x) c(batch_size, ..., channels) %<-% shape(x) c(batch_size, ., channels) %<-% shape(x) echo_print <- function(x) {   message(\"> \", deparse(substitute(x)));   if(!is.null(x)) print(x) } tfn <- tensorflow::tf_function(function(x) {   c(axis1, axis2, axis3) %<-% shape(x)   echo_print(str(list(axis1 = axis1, axis2 = axis2, axis3 = axis3)))    echo_print(shape(axis1))               # use axis1 tensor as axis value   echo_print(shape(axis1, axis2, axis3)) # use axis1 tensor as axis value    # use shape() to compose a new shape, e.g., in multihead attention   n_heads <- 4   echo_print(shape(axis1, axis2, n_heads, axis3/n_heads))    x }, input_signature = list(tensorflow::tf$TensorSpec(shape(NA, 4, 16)))) invisible(tfn(op_ones(shape(2, 4, 16)))) ## > str(list(axis1 = axis1, axis2 = axis2, axis3 = axis3)) ## List of 3 ##  $ axis1:<tf.Tensor 'strided_slice:0' shape=() dtype=int32> ##  $ axis2: int 4 ##  $ axis3: int 16 ## > shape(axis1) ## shape(Tensor(\"strided_slice:0\", shape=(), dtype=int32)) ## > shape(axis1, axis2, axis3) ## shape(Tensor(\"strided_slice:0\", shape=(), dtype=int32), 4, 16) ## > shape(axis1, axis2, n_heads, axis3/n_heads) ## shape(Tensor(\"strided_slice:0\", shape=(), dtype=int32), 4, 4, 4) (x <- op_convert_to_tensor(2L)) ## tf.Tensor(2, shape=(), dtype=int32) # by default, shape() treats scalar integer tensors as axis values shape(x) ## shape(tf.Tensor(2, shape=(), dtype=int32)) # to access the shape of a scalar integer, # call `op_shape()`, or protect with `I()` op_shape(x) ## shape() shape(I(x)) ## shape()"},{"path":[]},{"path":"https://keras.posit.co/reference/split_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"Splits dataset left half right half (e.g. train / test).","code":""},{"path":"https://keras.posit.co/reference/split_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"","code":"split_dataset(   dataset,   left_size = NULL,   right_size = NULL,   shuffle = FALSE,   seed = NULL )"},{"path":"https://keras.posit.co/reference/split_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"dataset tf$data$Dataset, torch$utils$data$Dataset object, list arrays length. left_size float (range [0, 1]), signifies fraction data pack left dataset. integer, signifies number samples pack left dataset. NULL, defaults complement right_size. Defaults NULL. right_size float (range [0, 1]), signifies fraction data pack right dataset. integer, signifies number samples pack right dataset. NULL, defaults complement left_size. Defaults NULL. shuffle Boolean, whether shuffle data splitting . seed random seed shuffling.","code":""},{"path":"https://keras.posit.co/reference/split_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"list two tf$data$Dataset objects: left right splits.","code":""},{"path":"https://keras.posit.co/reference/split_dataset.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Splits a dataset into a left half and a right half (e.g. train / test). — split_dataset","text":"","code":"data <- random_uniform(c(1000, 4)) c(left_ds, right_ds) %<-% split_dataset(list(data$numpy()), left_size = 0.8) left_ds$cardinality() ## tf.Tensor(800, shape=(), dtype=int64) right_ds$cardinality() ## tf.Tensor(200, shape=(), dtype=int64)"},{"path":[]},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":null,"dir":"Reference","previous_headings":"","what":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"Print summary Keras Model","code":""},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"","code":"# S3 method for keras.src.models.model.Model summary(object, ...)  # S3 method for keras.src.models.model.Model format(   x,   line_length = getOption(\"width\"),   positions = NULL,   expand_nested = FALSE,   show_trainable = NA,   ...,   layer_range = NULL,   compact = TRUE )  # S3 method for keras.src.models.model.Model print(x, ...)"},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"object, x Keras model instance ... summary() print(), passed format(). format(), passed model$summary(). line_length Total length printed lines positions Relative absolute positions log elements line. provided, defaults c(0.33, 0.55, 0.67, 1.0). expand_nested Whether expand nested models. provided, defaults FALSE. show_trainable Whether show layer trainable. provided, defaults FALSE. layer_range list, tuple, vector 2 strings, starting layer name ending layer name (inclusive) indicating range layers printed summary. also accepts regex patterns instead exact name. case, start predicate first element matches layer_range[[1]] end predicate last element matches layer_range[[1]]. default NULL considers layers model. compact Whether remove white-space lines model summary. (Default TRUE)","code":""},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"format() returns length 1 character vector. print() returns model object invisibly. summary() returns output format() invisibly printing .","code":""},{"path":"https://keras.posit.co/reference/summary.keras.src.models.model.Model.html","id":"enabling-color-output-in-knitr-rmarkdown-quarto-","dir":"Reference","previous_headings":"","what":"Enabling color output in Knitr (RMarkdown, Quarto)","title":"Print a summary of a Keras Model — summary.keras.src.models.model.Model","text":"order enable color output quarto rmarkdown document html output format (include revealjs presentations), need following setup chunk:","code":"```{r setup, include = FALSE}  options(cli.num_colors = 256)  fansi::set_knit_hooks(knitr::knit_hooks)  options(width = 75) # adjust as needed for format  ```"},{"path":[]},{"path":"https://keras.posit.co/reference/test_on_batch.html","id":null,"dir":"Reference","previous_headings":"","what":"Test the model on a single batch of samples. — test_on_batch","title":"Test the model on a single batch of samples. — test_on_batch","text":"Test model single batch samples.","code":""},{"path":"https://keras.posit.co/reference/test_on_batch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test the model on a single batch of samples. — test_on_batch","text":"","code":"test_on_batch(object, x, y = NULL, sample_weight = NULL, ...)"},{"path":"https://keras.posit.co/reference/test_on_batch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test the model on a single batch of samples. — test_on_batch","text":"object Keras model object x Input data. Must array-like. y Target data. Must array-like. sample_weight Optional array length x, containing weights apply model's loss sample. case temporal data, can pass 2D array shape (samples, sequence_length), apply different weight every timestep every sample. ... forward/backward compatability","code":""},{"path":"https://keras.posit.co/reference/test_on_batch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test the model on a single batch of samples. — test_on_batch","text":"scalar loss value (metrics), named list loss metric values (metrics).","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/text_dataset_from_directory.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","title":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","text":"directory structure :   calling text_dataset_from_directory(main_directory, labels='inferred') return tf.data.Dataset yields batches texts subdirectories class_a class_b, together labels 0 1 (0 corresponding class_a 1 corresponding class_b). .txt files supported time.","code":"main_directory/ ...class_a/ ......a_text_1.txt ......a_text_2.txt ...class_b/ ......b_text_1.txt ......b_text_2.txt"},{"path":"https://keras.posit.co/reference/text_dataset_from_directory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","text":"","code":"text_dataset_from_directory(   directory,   labels = \"inferred\",   label_mode = \"int\",   class_names = NULL,   batch_size = 32L,   max_length = NULL,   shuffle = TRUE,   seed = NULL,   validation_split = NULL,   subset = NULL,   follow_links = FALSE,   verbose = TRUE )"},{"path":"https://keras.posit.co/reference/text_dataset_from_directory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","text":"directory Directory data located. labels \"inferred\", contain subdirectories, containing text files class. Otherwise, directory structure ignored. labels Either \"inferred\" (labels generated directory structure), NULL (labels), list/tuple integer labels size number text files found directory. Labels sorted according alphanumeric order text file paths (obtained via os.walk(directory) Python). label_mode String describing encoding labels. Options : \"int\": means labels encoded integers (e.g. sparse_categorical_crossentropy loss). \"categorical\" means labels encoded categorical vector (e.g. categorical_crossentropy loss). \"binary\" means labels (can 2) encoded float32 scalars values 0 1 (e.g. binary_crossentropy). NULL (labels). class_names valid \"labels\" \"inferred\". explicit list class names (must match names subdirectories). Used control order classes (otherwise alphanumerical order used). batch_size Size batches data. Defaults 32. NULL, data batched (dataset yield individual samples). max_length Maximum size text string. Texts longer truncated max_length. shuffle Whether shuffle data. Defaults TRUE. set FALSE, sorts data alphanumeric order. seed Optional random seed shuffling transformations. validation_split Optional float 0 1, fraction data reserve validation. subset Subset data return. One \"training\", \"validation\" \"\". used validation_split set. subset=\"\", utility returns tuple two datasets (training validation datasets respectively). follow_links Whether visits subdirectories pointed symlinks. Defaults FALSE. verbose Whether display number information classes number files found. Defaults TRUE.","code":""},{"path":"https://keras.posit.co/reference/text_dataset_from_directory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a tf.data.Dataset from text files in a directory. — text_dataset_from_directory","text":"tf.data.Dataset object. label_mode NULL, yields string tensors shape (batch_size,), containing contents batch text files. Otherwise, yields tuple (texts, labels), texts shape (batch_size,) labels follows format described . Rules regarding labels format: label_mode int, labels int32 tensor shape (batch_size,). label_mode binary, labels float32 tensor 1s 0s shape (batch_size, 1). label_mode categorical, labels float32 tensor shape (batch_size, num_classes), representing one-hot encoding class index.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/time_distributed.html","id":null,"dir":"Reference","previous_headings":"","what":"layer_time_distributed — time_distributed","title":"layer_time_distributed — time_distributed","text":"time_distributed() alias layer_time_distributed(). See ?layer_time_distributed() full documentation.","code":""},{"path":"https://keras.posit.co/reference/time_distributed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"layer_time_distributed — time_distributed","text":"","code":"time_distributed(object, layer, ...)"},{"path":"https://keras.posit.co/reference/time_distributed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"layer_time_distributed — time_distributed","text":"object Object compose layer . tensor, array, sequential model. layer Layer instance. ... forward/backward compatability.","code":""},{"path":"https://keras.posit.co/reference/time_distributed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"layer_time_distributed — time_distributed","text":"return value depends value provided first argument.  object : keras_model_sequential(), layer added sequential model (modified place). enable piping, sequential model also returned, invisibly. keras_input(), output tensor calling layer(input) returned. NULL missing, Layer instance returned.","code":""},{"path":"https://keras.posit.co/reference/timeseries_dataset_from_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","title":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","text":"function takes sequence data-points gathered equal intervals, along time series parameters length sequences/windows, spacing two sequence/windows, etc., produce batches timeseries inputs targets.","code":""},{"path":"https://keras.posit.co/reference/timeseries_dataset_from_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","text":"","code":"timeseries_dataset_from_array(   data,   targets,   sequence_length,   sequence_stride = 1L,   sampling_rate = 1L,   batch_size = 128L,   shuffle = FALSE,   seed = NULL,   start_index = NULL,   end_index = NULL )"},{"path":"https://keras.posit.co/reference/timeseries_dataset_from_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","text":"data array eager tensor containing consecutive data points (timesteps). first dimension expected time dimension. targets Targets corresponding timesteps data. targets[] target corresponding window starts index (see example 2 ). Pass NULL target data (case dataset yield input data). sequence_length Length output sequences (number timesteps). sequence_stride Period successive output sequences. stride s, output samples start index data[], data[+ s], data[+ 2 * s], etc. sampling_rate Period successive individual timesteps within sequences. rate r, timesteps data[], data[+ r], ... data[+ sequence_length] used creating sample sequence. batch_size Number timeseries samples batch (except maybe last one). NULL, data batched (dataset yield individual samples). shuffle Whether shuffle output samples, instead draw chronological order. seed Optional int; random seed shuffling. start_index Optional int; data points earlier (exclusive) start_index used output sequences. useful reserve part data test validation. end_index Optional int; data points later (exclusive) end_index used output sequences. useful reserve part data test validation.","code":""},{"path":"https://keras.posit.co/reference/timeseries_dataset_from_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a dataset of sliding windows over a timeseries provided as array. — timeseries_dataset_from_array","text":"tf$data$Dataset instance. targets passed, dataset yields list (batch_of_sequences, batch_of_targets). , dataset yields batch_of_sequences. Example 1: Consider indices [0, 1, ... 98]. sequence_length=10,  sampling_rate=2, sequence_stride=3, shuffle=FALSE, dataset yield batches sequences composed following indices:   case last 2 data points discarded since full sequence can generated include (next sequence started index 81, thus last step gone 98). Example 2: Temporal regression. Consider array data scalar values, shape (steps,). generate dataset uses past 10 timesteps predict next timestep, use:     Example 3: Temporal regression many--many architectures. Consider two arrays scalar values X Y, shape (100,). resulting dataset consist samples 20 timestamps . samples overlap. generate dataset uses current timestamp predict corresponding target timestep, use:","code":"First sequence:  [0  2  4  6  8 10 12 14 16 18] Second sequence: [3  5  7  9 11 13 15 17 19 21] Third sequence:  [6  8 10 12 14 16 18 20 22 24] ... Last sequence:   [78 80 82 84 86 88 90 92 94 96] data <- op_array(1:20) input_data <- data[1:10] targets <- data[11:20] dataset <- timeseries_dataset_from_array(   input_data, targets, sequence_length=10) iter <- reticulate::as_iterator(dataset) reticulate::iter_next(iter) ## [[1]] ## tf.Tensor([[ 1  2  3  4  5  6  7  8  9 10]], shape=(1, 10), dtype=int32) ## ## [[2]] ## tf.Tensor([11], shape=(1), dtype=int32) X <- op_array(1:100) Y <- X*2  sample_length <- 20 input_dataset <- timeseries_dataset_from_array(     X, NULL, sequence_length=sample_length, sequence_stride=sample_length) target_dataset <- timeseries_dataset_from_array(     Y, NULL, sequence_length=sample_length, sequence_stride=sample_length)   inputs <- reticulate::as_iterator(input_dataset) %>% reticulate::iter_next() targets <- reticulate::as_iterator(target_dataset) %>% reticulate::iter_next()"},{"path":[]},{"path":"https://keras.posit.co/reference/to_categorical.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts a class vector (integers) to binary class matrix. — to_categorical","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"E.g. use loss_categorical_crossentropy().","code":""},{"path":"https://keras.posit.co/reference/to_categorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"","code":"to_categorical(x, num_classes = NULL)"},{"path":"https://keras.posit.co/reference/to_categorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"x Array-like class values converted matrix (integers 0 num_classes - 1). R factors coerced integer offset 0-based, .e., .integer(x) - 1L. num_classes Total number classes. NULL, inferred max(x) + 1. Defaults NULL.","code":""},{"path":"https://keras.posit.co/reference/to_categorical.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"binary matrix representation input R array. class axis placed last.","code":""},{"path":"https://keras.posit.co/reference/to_categorical.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converts a class vector (integers) to binary class matrix. — to_categorical","text":"","code":"a <- to_categorical(c(0, 1, 2, 3), num_classes=4) print(a) ##      [,1] [,2] [,3] [,4] ## [1,]    1    0    0    0 ## [2,]    0    1    0    0 ## [3,]    0    0    1    0 ## [4,]    0    0    0    1 b <- array(c(.9, .04, .03, .03,               .3, .45, .15, .13,               .04, .01, .94, .05,               .12, .21, .5, .17),               dim = c(4, 4)) loss <- op_categorical_crossentropy(a, b) loss ## tf.Tensor([0.41284522 0.45601739 0.54430155 0.80437282], shape=(4), dtype=float64) loss <- op_categorical_crossentropy(a, a) loss ## tf.Tensor([1.00000005e-07 1.00000005e-07 1.00000005e-07 1.00000005e-07], shape=(4), dtype=float64)"},{"path":[]},{"path":"https://keras.posit.co/reference/train_on_batch.html","id":null,"dir":"Reference","previous_headings":"","what":"Runs a single gradient update on a single batch of data. — train_on_batch","title":"Runs a single gradient update on a single batch of data. — train_on_batch","text":"Runs single gradient update single batch data.","code":""},{"path":"https://keras.posit.co/reference/train_on_batch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Runs a single gradient update on a single batch of data. — train_on_batch","text":"","code":"train_on_batch(object, x, y = NULL, sample_weight = NULL, class_weight = NULL)"},{"path":"https://keras.posit.co/reference/train_on_batch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Runs a single gradient update on a single batch of data. — train_on_batch","text":"object Keras model object x Input data. Must array-like. y Target data. Must array-like. sample_weight Optional array length x, containing weights apply model's loss sample. case temporal data, can pass 2D array shape (samples, sequence_length), apply different weight every timestep every sample. class_weight Optional named list mapping class indices (integers, 0-based) weight (float) apply model's loss samples class training. can useful tell model \"pay attention\" samples -represented class. class_weight specified targets rank 2 greater, either y must one-hot encoded, explicit final dimension 1 must included sparse class labels.","code":""},{"path":"https://keras.posit.co/reference/train_on_batch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Runs a single gradient update on a single batch of data. — train_on_batch","text":"scalar loss value (metrics), named list loss metric values (metrics). property model$metrics_names give display labels scalar outputs.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":null,"dir":"Reference","previous_headings":"","what":"Unpacks user-provided data list. — unpack_x_y_sample_weight","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"convenience utility used overriding $train_step, $test_step, $predict_step. utility makes easy support data form (x,), (x, y), (x, y, sample_weight).","code":""},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"","code":"unpack_x_y_sample_weight(data)"},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"data list form (x), (x, y), (x, y, sample_weight).","code":""},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"unpacked list, NULLs y sample_weight provided.","code":""},{"path":"https://keras.posit.co/reference/unpack_x_y_sample_weight.html","id":"example-","dir":"Reference","previous_headings":"","what":"Example:","title":"Unpacks user-provided data list. — unpack_x_y_sample_weight","text":"can also equivalent providing default values %<-%","code":"features_batch <- op_ones(c(10, 5)) labels_batch <- op_zeros(c(10, 5)) data <- list(features_batch, labels_batch) # `y` and `sample_weight` will default to `NULL` if not provided. c(x, y, sample_weight) %<-% unpack_x_y_sample_weight(data) c(x, y = NULL, sample_weight = NULL) %<-% data"},{"path":[]},{"path":"https://keras.posit.co/reference/use_backend.html","id":null,"dir":"Reference","previous_headings":"","what":"Configure a Keras backend — use_backend","title":"Configure a Keras backend — use_backend","text":"Configure Keras backend","code":""},{"path":"https://keras.posit.co/reference/use_backend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Configure a Keras backend — use_backend","text":"","code":"use_backend(backend)"},{"path":"https://keras.posit.co/reference/use_backend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Configure a Keras backend — use_backend","text":"backend string, can \"tensorflow\", \"jax\", \"numpy\", \"torch\".","code":""},{"path":"https://keras.posit.co/reference/use_backend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Configure a Keras backend — use_backend","text":"Called primarily side effects. Returns provided backend, invisibly.","code":""},{"path":"https://keras.posit.co/reference/use_backend.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Configure a Keras backend — use_backend","text":"functions allow configuring backend keras use. Note one backend can configured time. function called library(keras3) calling functions within package (see example). experimental support changing backend keras initialized. using config_set_backend().","code":"library(keras3) use_backend(\"tensorflow\")"},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":null,"dir":"Reference","previous_headings":"","what":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"Provide scope mappings names custom objects","code":""},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"","code":"with_custom_object_scope(objects, expr)"},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"objects Named list objects expr Expression evaluate","code":""},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"result evaluating expr within custom object scope.","code":""},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"many elements Keras models can customized user objects (e.g. losses, metrics, regularizers, etc.). loading saved models use functions typically need explicitly map names user objects via custom_objects parameter. with_custom_object_scope() function provides alternative lets create named alias user object applies entire block code, automatically recognized loading saved models.","code":""},{"path":"https://keras.posit.co/reference/with_custom_object_scope.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Provide a scope with mappings of names to custom objects — with_custom_object_scope","text":"","code":"# define custom metric metric_top_3_categorical_accuracy <-   custom_metric(\"top_3_categorical_accuracy\", function(y_true, y_pred) {     metric_top_k_categorical_accuracy(y_true, y_pred, k = 3)   })  with_custom_object_scope(c(top_k_acc = sparse_top_k_cat_acc), {    # ...define model...    # compile model (refer to \"top_k_acc\" by name)   model |> compile(     loss = \"binary_crossentropy\",     optimizer = optimizer_nadam(),     metrics = c(\"top_k_acc\")   )    # save the model   model |> save_model(\"my_model.keras\")    # loading the model within the custom object scope doesn't   # require explicitly providing the custom_object   reloaded_model <- load_model(\"my_model.keras\") })"},{"path":[]},{"path":"https://keras.posit.co/reference/zip_lists.html","id":null,"dir":"Reference","previous_headings":"","what":"Zip lists — zip_lists","title":"Zip lists — zip_lists","text":"conceptually similar zip() Python, R functions purrr::transpose() data.table::transpose() (albeit, accepting elements ... instead single list), one crucial difference: provided objects named, matching done names, positions.","code":""},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zip lists — zip_lists","text":"","code":"zip_lists(...)"},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zip lists — zip_lists","text":"... R lists atomic vectors, optionally named.","code":""},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Zip lists — zip_lists","text":"inverted list","code":""},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Zip lists — zip_lists","text":"arguments supplied must length. positional matching required, arguments provided must unnamed. matching names, arguments must set names, can different orders.","code":""},{"path":[]},{"path":"https://keras.posit.co/reference/zip_lists.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Zip lists — zip_lists","text":"","code":"gradients <- list(\"grad_for_wt_1\", \"grad_for_wt_2\", \"grad_for_wt_3\") weights <- list(\"weight_1\", \"weight_2\", \"weight_3\") str(zip_lists(gradients, weights)) #> List of 3 #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_1\" #>   ..$ : chr \"weight_1\" #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_2\" #>   ..$ : chr \"weight_2\" #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_3\" #>   ..$ : chr \"weight_3\" str(zip_lists(gradient = gradients, weight = weights)) #> List of 3 #>  $ :List of 2 #>   ..$ gradient: chr \"grad_for_wt_1\" #>   ..$ weight  : chr \"weight_1\" #>  $ :List of 2 #>   ..$ gradient: chr \"grad_for_wt_2\" #>   ..$ weight  : chr \"weight_2\" #>  $ :List of 2 #>   ..$ gradient: chr \"grad_for_wt_3\" #>   ..$ weight  : chr \"weight_3\"  names(gradients) <- names(weights) <- paste0(\"layer_\", 1:3) str(zip_lists(gradients, weights[c(3, 1, 2)])) #> List of 3 #>  $ layer_1:List of 2 #>   ..$ : chr \"grad_for_wt_1\" #>   ..$ : chr \"weight_1\" #>  $ layer_2:List of 2 #>   ..$ : chr \"grad_for_wt_2\" #>   ..$ : chr \"weight_2\" #>  $ layer_3:List of 2 #>   ..$ : chr \"grad_for_wt_3\" #>   ..$ : chr \"weight_3\"  names(gradients) <- paste0(\"gradient_\", 1:3) try(zip_lists(gradients, weights)) # error, names don't match #> Error in zip_lists(gradients, weights) :  #>   All names of arguments provided to `zip_lists()` must match. Call `unname()` on each argument if you want positional matching # call unname directly for positional matching str(zip_lists(unname(gradients), unname(weights))) #> List of 3 #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_1\" #>   ..$ : chr \"weight_1\" #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_2\" #>   ..$ : chr \"weight_2\" #>  $ :List of 2 #>   ..$ : chr \"grad_for_wt_3\" #>   ..$ : chr \"weight_3\""},{"path":"https://keras.posit.co/news/index.html","id":"keras3-100","dir":"Changelog","previous_headings":"","what":"keras3 1.0.0","title":"keras3 1.0.0","text":"Chains layer_* calls |> now instantiate layers order %>% pipe chains: left-hand-side first (#1440). iterate(), iter_next() as_iterator() now reexported reticulate. User facing changes upstream Keras v3.3.3: new functions: op_slogdet(), op_psnr() clone_model() gains new args: call_function, recursive Updated example usage. op_ctc_decode() strategy argument new default: \"greedy\". Updated docs. loss_ctc() default name fixed, changed \"ctc\" User facing changes upstream Keras v3.3.2: new function: op_ctc_decode() new function: op_eigh() new function: op_select() new function: op_vectorize() new function: op_image_rgb_to_grayscale() new function: loss_tversky() new args: layer_resizing(pad_to_aspect_ratio, fill_mode, fill_value) new arg: layer_embedding(weights) providing initial weights matrix new args: op_nan_to_num(nan, posinf, neginf) new args: op_image_resize(crop_to_aspect_ratio, pad_to_aspect_ratio, fill_mode, fill_value) new args: op_argmax(keepdims) op_argmin(keepdims) new arg: clear_session(free_memory) clearing without invoking garbage collector. metric_kl_divergence() loss_kl_divergence() clip inputs (y_true y_pred) [0, 1] range. new Layer() attributes: metrics, dtype_policy Added initial support float8 training layer_conv_*d() layers now support LoRa op_digitize() now supports sparse tensors. Models layers now return owned metrics recursively. Add pickling support Keras models. (e.g., via reticulate::py_save_object()) Note pickling recommended, prefer using Keras saving APIs.","code":""},{"path":"https://keras.posit.co/news/index.html","id":"keras3-020","dir":"Changelog","previous_headings":"","what":"keras3 0.2.0","title":"keras3 0.2.0","text":"CRAN release: 2024-04-18 New functions: quantize_weights(): quantize model layer weights -place. Currently, Dense, EinsumDense, Embedding layers supported (enough cover majority transformers today) layer_mel_spectrogram() layer_flax_module_wrapper() layer_jax_model_wrapper() loss_dice() random_beta() random_binomial() config_set_backend(): change backend Keras initialized. config_dtype_policy() config_set_dtype_policy() New Ops op_custom_gradient() op_batch_normalization() op_image_crop() op_divide_no_nan() op_normalize() op_correlate() ` New family linear algebra ops op_cholesky() op_det() op_eig() op_inv() op_lu_factor() op_norm() op_erfinv() op_solve_triangular() op_svd() audio_dataset_from_directory(), image_dataset_from_directory() text_dataset_from_directory() gain verbose argument (default TRUE) image_dataset_from_directory() gains pad_to_aspect_ratio argument (default FALSE) to_categorical(), op_one_hot(), fit() can now accept R factors, offset 0-based (reported #1055). op_convert_to_numpy() now returns unconverted NumPy arrays. op_array() op_convert_to_tensor() longer error casting R doubles integer types. export_savedmodel() now works Jax backend. Metric()$add_variable() method gains arg: aggregration. Layer()$add_weight() method gains args: autocast, regularizer, aggregation. op_bincount(), op_multi_hot(), op_one_hot(), layer_category_encoding() now support sparse tensors. op_custom_gradient() now supports PyTorch backend layer_lstm() layer_gru() gain arg use_cudnn, default 'auto'. Fixed issue application_preprocess_inputs() error supplied R array input. Doc improvements.","code":""},{"path":"https://keras.posit.co/news/index.html","id":"keras3-010","dir":"Changelog","previous_headings":"","what":"keras3 0.1.0","title":"keras3 0.1.0","text":"CRAN release: 2024-02-17 package rebuilt Keras 3.0. Refer  overview https://keras.posit.co current --date documentation.","code":""}]
diff --git a/man/layer_tfsm.Rd b/man/layer_tfsm.Rd
index 44e64ae33..f336a98f8 100644
--- a/man/layer_tfsm.Rd
+++ b/man/layer_tfsm.Rd
@@ -59,8 +59,8 @@ model |> export_savedmodel("path/to/artifact")
 ## Output Type:
 ##   TensorSpec(shape=(None, 10), dtype=tf.float32, name=None)
 ## Captures:
-##   128255174032224: TensorSpec(shape=(), dtype=tf.resource, name=None)
-##   128255174017264: TensorSpec(shape=(), dtype=tf.resource, name=None)
+##   128695884597376: TensorSpec(shape=(), dtype=tf.resource, name=None)
+##   128695884598080: TensorSpec(shape=(), dtype=tf.resource, name=None)
 
 }\if{html}{\out{</div>}}
 

From 81d920ca8375f7ce9cf38e2a7dbf435d820373fe Mon Sep 17 00:00:00 2001
From: Tomasz Kalinowski <kalinowskit@gmail.com>
Date: Tue, 21 May 2024 12:16:00 -0400
Subject: [PATCH 11/11] make pkg for cran

---
 tools/make-cran-pkg.R | 1 +
 1 file changed, 1 insertion(+)
 mode change 100644 => 100755 tools/make-cran-pkg.R

diff --git a/tools/make-cran-pkg.R b/tools/make-cran-pkg.R
old mode 100644
new mode 100755
index 22ecb49e2..4c3144529
--- a/tools/make-cran-pkg.R
+++ b/tools/make-cran-pkg.R
@@ -1,3 +1,4 @@
+#!/usr/bin/env Rscript
 
 devtools::build(manual = TRUE)